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Record: 1- A Configuration Theory Assessment of Marketing Organization Fit with Business Strategy and Its Relationship with Marketing Performance. By: Vorhies, Douglas W.; Morgan, Neil A. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p100-115. 16p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.67.1.100.18588.
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A Configuration Theory Assessment of Marketing Organization Fit with Business Strategy and Its Relationship with Marketing Performance
Theory posits that organizing marketing activities in ways that fit the implementation requirements of a business's strategy enhances performance. However, conceptual and methodological problems make it difficult to empirically assess this proposition in the holistic way that it is theoretically framed. Drawing on configuration theory approaches in management, the authors address these problems by assessing marketing organization fit with business strategy as the degree to which a business's marketing organization differs from that of an empirically derived ideal profile that achieves superior performance by arranging marketing activities in a way that enables the implementation of a given strategy type. The authors suggest that marketing organization fit with strategic type is associated with marketing effectiveness in prospector, defender, and analyzer strategic types and with marketing efficiency in prospector and defender strategic types. The study demonstrates the utility of profile deviation approaches for strategic marketing theory development and testing.
Most businesses find it easier to formulate strategies that outline how they intend to achieve their goals than how to implement them (e.g., Noble and Mokwa 1999; Walker and Ruekert 1987). The literature suggests that an important cause of such strategy implementation difficulties is the way businesses organize their marketing activities (e.g., McKee, Varadarajan, and Pride 1989; Ruekert and Walker 1987). In particular, marketing theory posits that to enable strategy implementation and achieve superior performance, managers should organize marketing activities in different ways depending on their business strategy (e.g., Slater and Olson 2000; Walker and Ruekert 1987). However, organizing marketing activities in ways that successfully enable business strategy implementation is recognized as one of the most difficult challenges facing managers (e.g., Cespedes 1995; Webster 1997). Yet despite the theoretical and managerial importance of this issue, researchers know little about how marketing activities should be organized to enable business strategy implementation or how this affects performance (e.g., Walker and Ruekert 1987; Workman, Homburg, and Gruner 1998).
Investigating this complex theoretical and managerial problem presents two significant challenges. First, the organization of marketing activities and business strategy are each viewed as multidimensional phenomena consisting of many different but interconnected elements (e.g., Walker and Ruekert 1987). Yet strategic marketing theory frames relationships between these phenomena in holistic terms as marketing organization's role in implementing business strategy. Therefore, evaluating this relationship in these holistic terms requires a simultaneous assessment of the relationships between the many variables making up marketing organization and business strategy (e.g., Slater 1995; Walker and Ruekert 1987). Second, successfully organizing marketing activities to implement business strategy involves reconciling multiple and often conflicting contingencies (e.g., Ruekert, Walker, and Roering 1985). The wide range of possible contingencies makes the identification of "correct" configurations of marketing organization variables needed to implement a particular business strategy extremely difficult. Therefore, the challenge facing researchers is how to assess whether marketing activities are organized in ways that enable the implementation of a particular business strategy (e.g., Walker and Ruekert 1987).
Although assessing many strategic marketing theory propositions involves similar challenges, available research approaches in marketing are not well suited to deal with these problems. Surprisingly, the marketing literature does not address how such problems can be solved. Fortunately, research developments in organization theory (e.g., Powell 1992; Venkatraman and Prescott 1990) and strategic management (e.g., Doty, Glick, and Huber 1993; Ketchen et al. 1997) provide approaches appropriate for assessing such complex theoretical relationships. In this article, we draw on these developments to build and empirically assess a conceptual model that links the degree to which marketing activities are organized in ways that enable business strategy implementation with two different marketing performance outcomes.
Within this important domain, our study makes two contributions. First, we fill a major knowledge gap by providing empirical support for theorized links among the organization of marketing activities, business strategy, and marketing performance. This helps managers understand how to organize marketing activities to meet the implementation requirements of different business strategies and why this is important in driving performance. Second, we introduce to the marketing literature a method for testing relationships involving the simultaneous assessment of multiple interrelated variables. We demonstrate that this method provides researchers with a way to empirically assess relationships involving complex, multidimensional phenomena that is more consistent with the holistic framing of strategic marketing theory than traditional approaches are (e.g., Meyer, Tsui, and Hinings 1993).
Assessing whether a business's marketing activities are organized in ways that enable the implementation of its strategy and the impact this has on performance requires the simultaneous consideration of multiple characteristics of the business (e.g., Doty, Glick, and Huber 1993). In addressing similar research questions, scholars in organization theory and strategic management have used configuration theory- based approaches (e.g., Miller 1997; Veliyath and Srinivasan 1995). A configuration denotes a multidimensional constellation of the strategic and organizational characteristics of a business (e.g., Meyer, Tsui, and Hinings 1993; Miller and Mintzberg 1988). Configuration theory posits that for each set of strategic characteristics, there exists an ideal set of organizational characteristics that yields superior performance (e.g., Van de Ven and Drazin 1985). These configurations are ideal because they represent complex "gestalts" of multiple, interdependent, and mutually reinforcing organizational characteristics that enable businesses to achieve their strategic goals (e.g., Ketchen, Thomas, and Snow 1993; Miller 1997). Our research question pertains to the relationship between the marketing organization configuration and the business's strategy, rather than the coalignment of variables within the marketing organization configuration. Therefore, in Figure 1, we combine insights from configuration theory and the marketing literature to develop a conceptual model that links the degree to which marketing activities are organized in ways that enable business strategy implementation with marketing performance.
Defining and Assessing Marketing Organization Fit with Business Strategy
Marketing scholars have used many different terms--including "match," "alignment," "congruence," "complementary," and "consistency"--to denote holistic relationships between multidimensional phenomena such as marketing organization and business strategy. Although each of these terms can connote different meanings and technical specifications, they are often used interchangeably. To more precisely specify and assess such relationships, configuration theory-based studies draw on the well-developed literature regarding fit. In this literature, fit is recognized as a term that can be defined in several ways, each of which has specific implications for how relationships between variables are conceptualized and tested (Powell 1992; Venkatraman and Camillus 1984). Therefore, management scholars have specified the different conceptualizations and technical specifications of alternative definitions of fit and have developed guidelines for selecting the approaches that are most appropriate in studying different kinds of research questions (e.g., Venkatraman 1989). This literature specifies that when fit among multiple variables is considered simultaneously (as in the holistic study of the relationship between organization and strategy) and the impact on criterion variables (e.g., performance) is assessed, fit should be conceptualized and assessed as "profile deviation" (e.g., Doty, Glick, and Huber 1993; Venkatraman 1990).
A profile deviation approach views fit between organization and strategy in terms of the degree to which the organizational characteristics of a business differ from those of a specified profile identified as ideal for implementing a particular strategy (e.g., Venkatraman 1989; Zajac, Kraatz, and Bresser 2000). Ideal profiles are defined as configurations of organizational characteristics that fit with the implementation requirements of a particular strategy and thus produce high performance (e.g., Gresov 1989; Venkatraman and Prescott 1990). From this perspective, marketing organization fit with business strategy can be defined as the degree to which a business's marketing organization profile differs from that of an ideal marketing organization that achieves superior performance by arranging marketing activities in a way that enables the implementation of a given business strategy.
Ideal profiles against which fit can be assessed may be determined either theoretically or empirically (e.g., Venkatraman 1990; Zajac, Kraatz, and Bresser 2000). Developing theoretically derived ideal profiles requires that the relevant theoretical literature be sufficiently detailed to enable precise numerical scores to be estimated for the appropriate set of dimensions in the ideal profile (e.g., Drazin and Van de Ven 1985). However, as the configuration literature acknowledges, there are few domains in which existing theoretical knowledge is sufficiently detailed to enable researchers to objectively translate theoretical statements from the literature into precise numerical estimates across multiple dimensions (e.g., Gresov 1989; Venkatraman 1989). In the marketing domain, existing theory indicates some marketing organization characteristics that may be appropriate for firms pursuing certain types of strategy (e.g., Ruekert and Walker 1987). However, from an ideal profile perspective, the specifications provided by marketing theory are not sufficiently detailed to enable estimation of numerical scores, nor do they consider many of the organization characteristics and types of strategy identified as important in assessing marketing organization fit with business strategy.
In this common circumstance, when ideal profiles cannot be precisely specified from existing theory, the configuration literature advocates assessing fit with empirically derived ideal profiles (e.g., Gresov 1989; Ketchen, Thomas, and Snow 1993). In the context of marketing organization fit with business strategy, this approach requires the identification of high-performing businesses implementing a given strategy and a calibration of their marketing organization characteristics as an ideal profile for implementing that strategy (e.g., Drazin and Van de Ven 1985; Venkatraman and Prescott 1990). These businesses are considered to have ideal profiles because their superior performance indicates that they have configured their marketing organization in a way that enables superior implementation of their business strategy (e.g., Van de Ven and Drazin 1985).
Configurational Elements of Marketing Organization Fit with Business Strategy
As illustrated in Figure 1, configuration theory and the marketing literature suggest two major constructs that are relevant to understanding and assessing marketing organization fit with business strategy: business's strategic type and marketing's organizational characteristics. Strategic type pertains to the planned patterns of organizational adaptation to the market through which a business seeks to achieve its strategic goals (e.g., Conant, Mokwa, and Varadarajan 1990; Matsuno and Mentzer 2000). Miles and Snow (1978, p. 29) identify three viable strategic types, which differ primarily in terms of product-market strategy choices (e.g., Slater and Narver 1993; Walker and Ruekert 1987).[ 1] Prospector strategic types proactively seek and exploit new market opportunities and often experiment with responses to changing market trends. They aggressively compete on innovation, seeking first-mover advantages from developing new offerings and pioneering new markets. Defender strategic types focus more narrowly on maintaining a secure position in existing product-markets. They often compete through operations or quality-based investments that offer efficiency related advantages, rarely pioneering the development of new markets or products. Analyzer strategic types balance a focus on securing their position in existing core markets with incremental moves into new product markets. They compete by balancing investments in creating differentiation-based advantages with operating efficiency.
Marketing's organizational characteristics are the many important structural and task characteristics that together constitute the way marketing activities are organized within the business (Day 1997; Workman, Homburg, and Gruner 1998). The structural characteristics of the marketing organization pertain to how marketing activities and related decision-making authority are arranged (e.g., Doty, Glick, and Huber 1993; Ruekert, Walker, and Roering 1985). Although the literature identifies several different structural characteristics of organization, three have been viewed as particularly important in previous marketing strategy research: centralization regarding the concentration of decision-making authority at higher levels of the business's hierarchy (e.g., Jaworski and Kohli 1993; Moorman, Deshpande, and Zaltman 1993); formalization, which is the degree to which standardized rules and procedures proscribe how marketing activities are performed (Olson, Walker, and Ruekert 1995; Workman, Homburg, and Gruner 1998); and specialization, which is the extent to which marketing activities are narrowly divided into unique elements that are performed by those with specialized knowledge (e.g., Walker and Ruekert 1987). Together, these structural characteristics indicate whether marketing activities are arranged in a bureaucratic or an organic manner (Moorman and Miner 1997; Ruekert, Walker, and Roering 1985).
The task characteristics of marketing organization pertain to the nature of the marketing activities undertaken and the ways they are performed (e.g., Day 1999; Ostroff and Schmitt 1993). Among the different task characteristics identified in the literature, three have been viewed as important both in configuration theory studies in management and in previous marketing strategy studies: task complexity, which is the extent of variability in marketing activities undertaken and the degree to which they can be easily performed (Menon and Varadarajan 1992; Olson, Walker, and Ruekert 1995); marketing capabilities regarding the business's ability to perform common marketing work routines through which available resources are transformed into valuable outputs (e.g., Bharadwaj, Varadarajan, and Fahy 1993; Day 1994; Webster 1997); and work group interdependence, which is the degree to which workflows within the business require cooperation between teams in performing marketing activities (e.g., Ruekert and Walker 1987; Van de Ven, Delbecq, and Koenig 1976). Together, these task characteristics indicate the ability of the marketing organization to perform necessary marketing activities and the degree to which team-based workflows are needed to accomplish them.
Marketing Organization Fit with Strategic Type and Performance
Fit between the organizational characteristics of a business and its strategic type is viewed as a desirable state that leads to superior performance (e.g., Miles and Snow 1994; Porter 1996). Marketing theory suggests that this is also true of fit between marketing organization characteristics and strategic type. For example, the literature indicates that the marketing activities needed to implement each strategic type are different and that successfully accomplishing these marketing activities requires marketing organizations with different configurations of structural and task characteristics (e.g., Matsuno and Mentzer 2000; McKee, Varadarajan, and Pride 1989; Walker and Ruekert 1987). Therefore, marketing theory suggests that organizing marketing activities in ways that fit the business's strategic type is an important driver of marketing performance outcomes (e.g., Walker and Ruekert 1987).
Furthermore, resource-based view theory indicates that fit between marketing organization characteristics and strategic type may also exhibit the inimitability and nonsubstitutability characteristics identified as essential for sustaining competitive advantage. For example, if a firm's superior performance is driven by marketing organization fit with strategic type, it will be difficult for competitors to identify the source of the firm's performance superiority (e.g., Barney 1991). Even if identified as a driver of superior performance, the ability of competitors to distinguish precisely how this is accomplished is limited, making imitation difficult (e.g., Bharadwaj, Varadarajan, and Fahy 1993; Day 1994). In addition to being difficult to imitate, the marketing literature suggests that there may be no substitute for marketing organization fit with strategic type in driving marketing performance (e.g., Moorman and Rust 1999; Workman, Homburg, and Gruner 1998). Therefore, marketing and resource-based view theory suggest that marketing organization fit with strategic type leads to superior marketing performance and that this can be sustained over time (e.g., Powell 1992; Walker and Ruekert 1987).
However, what constitutes superior marketing performance may be different in different businesses. For example, organization theory posits that effectiveness, regarding the degree to which desired organizational goals are achieved, and efficiency, regarding the ratio of organizational resource inputs consumed to goal outcomes achieved, are two important and distinct dimensions of organizational performance (e.g., Bonoma and Clark 1988; Lewin and Minton 1986). The literature suggests that because these two dimensions of performance may not converge and can even be inversely related in the short run (e.g., Bhargava, Dubelaar, and Ramaswami 1994), firms make important trade-off decisions in emphasizing either effectiveness or efficiency in their marketing goal setting and resource allocations (e.g., Morgan, Clark, and Gooner 2002; Walker and Ruekert 1987). Therefore, configuration theory suggests that the ideal marketing organization required to fit with a particular strategic type differs depending on whether the firm seeks to maximize either effectiveness or efficiency (e.g., Tsui 1990). Assessing marketing organization fit with strategic type requires an identification of different ideal profiles against which to assess fit for firms seeking to maximize either the effectiveness or the efficiency dimension of their marketing performance (e.g., Ostroff and Schmitt 1993; Walker and Ruekert 1987).
In developing hypotheses of expected relationships between marketing organization fit with strategic type and its performance outcomes, we draw directly on existing theory and empirical evidence when possible. However, although many studies have investigated structural characteristics of marketing organization (e.g., Ruekert, Walker, and Roering 1985; Workman, Homburg, and Gruner 1998), few studies have investigated marketing organization task characteristics. Similarly, existing knowledge of analyzer strategic types is less developed in the marketing literature than knowledge regarding prospector and defender types. In addition, with a few notable exceptions (e.g., Walker and Ruekert 1987), strategic marketing theory has not considered how seeking to maximize different dimensions of marketing performance affects marketing organization. Therefore, we draw on a necessarily broad reading of the literature in developing our hypotheses.
Marketing Organization Fit with Strategic Type and Marketing Effectiveness
Marketing effectiveness pertains to the degree to which desired market-based goals are achieved (e.g., Clark 2000; Morgan, Clark, and Gooner 2002). Theory suggests that for effectiveness-maximizing businesses of each strategic type, an ideal marketing organization exists in which the configuration of structural and task characteristics enables the implementation of the business's strategy in a way that leads to superior marketing effectiveness (e.g., Cespedes 1991; Day 1997; Ruekert and Walker 1987). For example, defender strategic types focus on maintaining secure positions in established markets. Therefore, implementing this strategy requires a marketing organization configured to achieve needed market-based goals through performance of routine tactical marketing activities (e.g., Ruekert and Walker 1987; Slater and Narver 1993). Performing such routine activities calls for a marketing organization with a highly centralized, formalized, and unspecialized structure and a relatively narrow range of marketing capabilities (e.g., Walker and Ruekert 1987). By narrowly focusing the deployment of available resources, marketing organizations with these characteristics may benefit from greater depth in a few key marketing capabilities. This may be leveraged through centralized authority structures that provide control over the focus of future resource deployment and formalized work routines that minimize errors in executing required activities. Organizing marketing activities in this way should enable a business implementing a defender strategy to achieve superior marketing effectiveness.
Conversely, prospector strategic types focus on entering unfamiliar new markets and attaining differentiation-based advantages. Therefore, achieving required marketing goals in implementing a prospector strategy involves performing many complex marketing activities (e.g., McDaniel and Kolari 1987; McKee, Varadarajan, and Pride 1989). Accomplishing these activities ideally requires specialized, decentralized, and informal marketing structures with team work-flows and a range of strong marketing capabilities (e.g., Ruekert, Walker, and Roering 1985; Walker 1997). In implementing prospector strategies, such organizational characteristics should enhance marketing effectiveness because they empower marketing specialists with access to wide-ranging capabilities and provide the decision-making freedom and work routine flexibility to use these capabilities to provide timely and innovative responses in dynamic product-markets (e.g., Walker and Ruekert 1987).
Businesses pursuing analyzer strategies operate in a range of established and new markets and seek to attain both cost and differentiation-based advantages. Therefore, analyzer strategic types require marketing organizations that are able to achieve needed marketing goals by performing a particularly wide and dynamic range of marketing activities (e.g., Slater and Narver 1993). Marketing organizations ideal for the analyzer strategic type in effectiveness maximizing businesses should therefore have high levels of marketing specialization, but formalized and centralized structures with strong marketing capabilities and team workflows (e.g., Miles and Snow 1994). Such specialization, team workflow, and marketing capability characteristics enable businesses implementing an analyzer strategy to respond quickly to the complex marketing activity requirements of unfamiliar markets while continuing to service the more routine demands of established markets. At the same time, formalization minimizes error in performing required marketing activities, and centralization allows tight control over the new market opportunities pursued. Marketing organizations with such ideal characteristics should enable the implementation of analyzer strategies in a way that produces superior marketing effectiveness.
In summary, we expect a business's marketing effectiveness to be greater when its marketing organization characteristics are similar to those of the effectiveness-maximizing ideal profile in which marketing activities are arranged to fit the implementation requirements of the business's strategic type in ways that enable marketing goals to be achieved. Therefore, we hypothesize that
H1: The more similar a business's marketing organization profile is to that of the ideal marketing organization for its strategic type, the greater is its marketing effectiveness.
Marketing Organization Fit with Strategic Type and Marketing Efficiency
Marketing efficiency is the ratio of marketing performance outcomes achieved to resource inputs consumed (e.g., Bonoma and Clark 1988; Morgan, Clark, and Gooner 2002). Theory suggests that for efficiency-maximizing businesses of each strategic type, there exists an ideal marketing organization in which the configuration of structural and task characteristics enables the implementation of the business's strategy in a way that leads to superior marketing efficiency (e.g., Jennings and Seaman 1994; Milgrom and Roberts 1995; Ruekert and Walker 1987). For example, implementing defender strategies requires achieving cost-based advantages in established markets. Creating specialized structures with team workflows and developing a wide range of strong marketing capabilities are not likely to be efficient ways to achieve marketing goals when implementing this strategy (e.g., Conant, Mokwa, and Varadarajan 1990). Available resources are more productively deployed in simplifying marketing activities, increasing structural formalization and centralization, and developing a narrow range of marketing capabilities (e.g., Slater and Narver 1993). Such ideal marketing organization characteristics should maximize marketing efficiency in implementing a defender strategy by allowing more focused resource deployment in capability building, greater control of decisions involving future resource allocation, and the efficiency benefits of increased routinization (e.g., Walker and Ruekert 1987).
Conversely, efficiently implementing prospector strategies requires achieving marketing goals by entering unfamiliar new markets and delivering differentiation-based competitive advantages while minimizing the resources consumed (e.g., McKee, Varadarajan, and Pride 1989). One way to accomplish this is to narrow the scope of product-market opportunities explored and competitive advantage pursued. This simplifies required marketing activities and enables more focused investment in a narrower range of marketing capabilities. Needed flexibility to respond quickly to appropriate new opportunities can be provided by empowering marketing personnel with decentralized and informal structures. By maintaining response flexibility while consuming fewer resources through more focused investments in marketing capabilities, ideal marketing organizations that fit the prospector strategic type in this way should be more efficient in achieving marketing goals (e.g., Miles and Snow 1994).
Efficiently implementing an analyzer strategy requires minimizing the resources consumed to accomplish needed marketing goals in the different types of product-markets in which analyzers operate. Here, an ideal marketing organization requires sufficient levels of specialization, team work-flows, and marketing capabilities to perform the complex range of different marketing activities required to achieve marketing goals (e.g., Slater and Narver 1993). However, to minimize the resources consumed, such analyzer businesses may also choose to operate in product-markets and pursue competitive advantages that only require strength in a narrower range of marketing capabilities. At the same time, accomplishing needed marketing goals efficiently also requires marketing organizations with enough centralized authority to tightly control resource allocations and sufficient formalization to benefit from routinization wherever possible (see Miles and Snow 1994). Marketing organizations with such ideal characteristics in businesses that implement analyzer strategies should be more efficient.
To summarize, we expect that a business's marketing efficiency will be greater when its marketing organization characteristics are similar to those of the efficiencymaximizing ideal profile in which marketing activities are arranged to fit the implementation requirements of the business's strategic type in ways that minimize the resources consumed. We therefore hypothesize that
H2: The more similar a business's marketing organization profile is to that of the ideal marketing organization for its strategic type, the greater is its marketing efficiency.
In examining fit-performance relationships, the configuration theory literature advocates the use of single industry studies to control for industry effects and isolate more effectively the relationships of interest (e.g., Dess, Newport, and Rasheed 1993; Ketchen, et al. 1997). We selected the trucking industry as appropriate for studying marketing organization fit with strategic type and its relationship with marketing performance for several reasons. First, with more than $372 billion spent annually, accounting for some 6% of gross domestic product, and 9.5 million people directly employed, trucking is a large and important industry in the United States (e.g., American Trucking Association 1999). Second, since deregulation in 1980, trucking has become a dynamic and competitive industry (e.g., Silverman, Nickerson, and Freeman 1997) in which effective and efficient marketing has become an important driver of firm performance (e.g., Lambert, Lewis, and Stock 1993; MacLeod et al. 1999). Third, the industry contains many single business firms, which reduces the problems associated with relating business unit-level phenomena and corporate-level performance data (e.g., Ketchen et al. 1997). Fourth, the trucking industry is relatively fragmented, providing a large population of firms for sampling purposes (e.g., Boyer 1993). Fifth, because of federal reporting requirements, objective performance data for the trucking industry are available, which reduces the dangers of common method bias associated with collecting data on independent and dependent variables from the same source (e.g., Olson, Walker, and Ruekert 1995; Venkatraman and Ramanujam 1986).
We collected primary data using a key-informant survey design. We mailed questionnaires to the chief marketing executive (CME) of 677 businesses, which we randomly selected from the 2771 listed in the Transportation Technical Services (TTS) database. The TTS database is representative of the industry, listing businesses generating more than 97% of total intercity freight revenues (U.S. Bureau of the Census 1998). Of the 660 deliverable surveys, 217 were completed and returned. Of the returned surveys, 8 were unusable, resulting in an effective response rate of 31%.[ 2] To ensure comparability, we deleted observations from the data set in which complete sets of both primary questionnaire data and secondary performance data were not available. The final data set contained 186 businesses, of which 28% reported sales of less than $10 million, 25% reported sales of $10-$25 million, 23% reported sales of $26-$65 million, and 24% reported sales greater than $65 million. Of these businesses, 77 were pursuing a defender strategy, 45 were pursuing a prospector strategy, and 64 were pursuing an analyzer strategy.
Measures
Several different operationalization alternatives exist for some of the constructs in our study. In these situations, we selected the operationalization with the strongest measurement history in the literature and the greatest face validity with managers in our pretest. The items used in our scales appear in Appendix A, and we discuss them subsequently, with descriptive statistics presented in Table 1.
Strategic type. We operationalized Miles and Snow's (1978) strategic types using the self-typing paragraph descriptor approach (e.g., Doty, Glick, and Huber 1993). This measure focuses exclusively on business strategy and excludes the structural and system elements elaborated in Miles and Snow's broader descriptions of ideal organizational archetypes. This measure has been widely used as an indicator of strategic type by marketing and management researchers (e.g., Matsuno and Mentzer 2000; McDaniel and Kolari 1987; Zahra and Pearce 1990) and has been demonstrated to yield valid measures (e.g., James and Hatten 1995; Shortell and Zajac 1990).
Marketing's organizational characteristics. We measured the structural characteristics, centralization and formalization, with multi-item scales adapted from Deshpande and Zaltman (1982), Jaworski and Kohli (1993), and John and Martin (1984), based on the well-known organization theory scales developed by Aiken and Hage (1968). We operationalized specialization using a scale adapted from Doty, Glick, and Huber (1993), based on an organization theory scale developed by Inkson, Pugh, and Hickson (1970).
Task characteristics. We measured task complexity using a scale adapted from Doty, Glick, and Huber (1993), and we assessed work group interdependence using Van de Ven, Delbecq, and Koenig's (1976) measure. We developed new measures of marketing capability for this study, combining insights from the literature with interviews with trucking industry experts and senior marketing personnel. We identified and assessed two types of marketing capabilities: specialized capabilities regarding the specific marketing mix-based work routines used to transform available resources into valuable outputs (e.g., Day 1994; Grant 1996) and architectural capabilities regarding the marketing strategy formulation and execution work routines used to develop and coordinate specialized capabilities and their resource inputs (e.g., Bharadwaj, Varadarajan, and Fahy 1993; Day 1997). We measured these marketing capabilities with scales that assessed how well businesses performed five specialized marketing activities and four architectural marketing activities compared with their competitors (e.g., Conant, Mokwa, and Varadarajan 1990).
Marketing performance. We assessed marketing effectiveness using a perceptual measure with items tapping the degree to which the firm achieved its market share growth, sales growth, and market position goals (e.g., Clark 2000). We calculated marketing efficiency as the ratio of marketing and selling expenses to the firm's gross operating revenue using objective secondary financial data from TTS (e.g., Bonoma and Clark 1988).
Psychometric Analyses
We assessed the measurement properties of the constructs using confirmatory factor analyses (CFAs). Because of the relatively small number of observations, we divided the measures into three subsets of theoretically related variables in line with our conceptual model (e.g., Kohli and Jaworski 1994; Moorman and Miner 1997). This ensured that our CFAs did not exceed the five-to-one ratio of parameter estimates to observations recommended in the literature (Bentler and Chou 1987). The three measurement models fit well as indicated by the CFA results for the three structural characteristics constructs (&chi2 = 59.42, degrees of freedom [d.f.] = 41, p = .03, goodness-of-fit index [GFI] = .95, root mean square error of approximation [RMSEA] = .05), the four task characteristics (&chi 2 = 111.80, d.f. = 85, p = .03, GFI = .93, RMSEA = .04), and the two marketing performance constructs (χ2 = .32, d.f. = 2, p = .85, GFI = .99, RMSEA = .01). When significant correlations were observed between constructs (Table 1), we also conducted additional pairwise discriminant validity assessments. This involved comparing χ 2 statistics in measurement models in which the covariance coefficient between the two constructs was allowed to vary and then fixed at one (Anderson and Gerbing 1988; Bagozzi and Phillips 1982). Changes in χ2were large in each of the pairwise tests, suggesting discriminant validity in each model. Reliability analyses for the measures produced Cronbach's alpha values ranging from .72 to .85 (Table 1), suggesting acceptable reliability for all constructs.
Analysis of nonresponse bias by means of an extrapolation approach (Armstrong and Overton 1977) revealed no significant differences between first wave (early) and second wave (late) respondents on any of the constructs. This suggests that nonresponse bias is unlikely to be present in the data. To validate the data provided by the key marketing respondents, we sought additional data from a second respondent (see Slater 1995). For each of the 186 firms responding to the CME survey, we also sent the chief executive officer, president, or other general manager (GM) level executive a questionnaire containing replicated scales on the marketing organization and performance measures. A total of 88 of the GM level executives responded, producing an effective response rate of 47%. For each construct, we assessed the validity of the key informant data by examining mean scores, correlations, and paired t-tests for the GM and CME level responses (e.g., Hughes and Garrett 1990). As shown in Table 2, the significant inter-rater correlations and insignificant mean differences with no systematic bias in direction between raters support the validity of the key informant data (see Jaworski and Kohli 1993).
Testing Configuration Theory Predictions with Profile Deviation Analysis
Testing the hypothesized relationships involved several stages. First, we standardized the data to remove the effects of different measurement units (e.g., Gresov 1989). Second, we identified ideal marketing organization profiles against which marketing organization fit with strategic type could be assessed (e.g., Doty, Glick, and Huber 1993). Consistent with established configuration theory procedures, we identified the highest performing businesses of each strategic type on each of the marketing performance variables and calibrated the marketing organization characteristics of these high performers as the ideal marketing organization profiles (e.g., Doty, Glick, and Huber 1993; Drazin and Van de Ven 1985; Venkatraman 1990).
Profile deviation studies typically select the highest performing 10% or 15% of businesses in a data set to calibrate ideal profiles (e.g., Van de Ven and Drazin 1985, Venkatraman and Prescott 1990). To select the appropriate number of top performers for our study, we examined scree plots of the marketing effectiveness (in testing H1) and marketing efficiency (in testing H2) performance of the businesses in our data set. These indicated a drop-off in performance after the top five marketing effectiveness performers and the top five marketing efficiency performers for each of the three strategic types. Therefore, we selected the five highest marketing effectiveness performers of each strategic type to calibrate the ideal marketing organization profiles for effectivenessmaximizing businesses and the five highest marketing efficiency performers of each strategic type to calibrate the ideal marketing organization profiles for efficiencymaximizing businesses. Consistent with marketing (e.g., Ruekert and Walker 1987) and configuration theory predictions (e.g., Tsui 1990), we found that only one firm was identified as both a top effectiveness and efficiency performer in our sample.
In testing H1,we calculated the mean scores of the top marketing effectiveness performers for each strategic type on each of the seven marketing organization variables to form the ideal marketing organization profiles (e.g., Venkatraman 1989). For the remaining firms, we calculated the Euclidean distance of each firm from the ideal marketing organization profile for its strategic type across the seven dimensions, representing the seven marketing organization variables (e.g., Drazin and Van de Ven 1985; Venkatraman 1990), as follows:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available. where
Xsj = the score for a firm in the study sample on the jth dimension,
[Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.] Xij = the mean for the ideal profile along the jth dimension, and
j = the number of profile dimensions ( 1, 2, ..., 7).
This calculation provides a profile deviation score that represents the degree to which the marketing organization profile of each firm is similar to that of the ideal profile for its strategic type. The profile deviation score for each firm was then regressed onto marketing effectiveness to test H1. Because the trucking industry is a capital-intensive service business, in which economies of scale may be expected to affect performance (e.g., Bharadwaj, Varadarajan, and Fahy 1993; Boyer 1993; Silverman, Nickerson, and Freeman 1997), we also included firm size, indicated by the natural logarithm of the number of employees, in our regression equations as a control variable (e.g., Germain, Droge, and Daugherty 1994). We then repeated this procedure using the top marketing efficiency performers for each strategic type in calibrating ideal profiles, with the profile deviation scores of the remaining firms regressed onto marketing efficiency to test H2.
For our hypotheses to be supported, empirical results should indicate that deviation from the ideal marketing organization profile is negatively and significantly related to marketing effectiveness and positively and significantly related to marketing efficiency for each of the three strategic types (e.g., Drazin and Van de Ven 1985; Gresov 1989).[ 3] Assessing the power of these hypothesis tests requires comparing the regression models containing deviation from the ideal marketing organization profile with regression models containing deviation from an alternative "nonideal" baseline profile (e.g., Venkatraman 1989). Therefore, we randomly selected five firms of each strategic type for each of the performance dimensions, in which the level of marketing organization fit with strategic type was unknown. We used these randomly selected firms to calibrate an alternate nonideal profile from which we calculated the deviation of the remaining firms (e.g., Venkatraman and Prescott 1990). We then substituted the nonideal profile deviations into the regression models in place of the ideal profile deviations to enable comparisons.
Before testing the hypotheses, we first validated two assumptions implicit in our conceptualization. First, consistent with configuration theory predictions and prior evidence that, when implemented appropriately, any one of the strategic types can lead to superior performance (e.g., Conant, Mokwa, and Varadarajan 1990; Slater and Narver 1993), we checked that marketing performance variations between firms in our data set were not simply a result of differences in strategic type. We validated this assumption with analysis of variance tests that revealed no significant relationship between strategic type and either marketing performance outcome. Second, we checked that our ideal profiles correctly identified marketing organizations that contributed to superior performance by being configured in ways that fit the requirements of the business's strategic type--and not just those that were high performers regardless of their fit with strategic type. We compared the marketing performance outcomes of deviation from two different ideal marketing organization profiles, one developed from firms of the same strategic type and one developed irrespective of the firm's strategic type (e.g., Venkatraman 1990). This analysis (Table 3) validates our second assumption by indicating that calibrating ideal marketing organization profiles within strategic-type groups produces stronger deviation term coefficients and greater explanatory power in the regression models.
As shown in Table 4, the results of our hypothesis testing provide support for H1, which predicts that the more similar a business's marketing organization profile is to that of the ideal marketing organization for its strategic type, the greater is its marketing effectiveness. Our marketing effectiveness regression models show significant, negative coefficients for deviation from the effectiveness-maximizing ideal profile for businesses implementing a prospector strategy (β = -.42, p = .008), an analyzer strategy (β = -.64, p = .0001), and a defender strategy (β = -.28, p = .02). Confidence in the power of these tests is provided in the nonideal regression models that indicate no significant relationship between deviation from the nonideal profile and marketing effectiveness for any of the three strategic types.
In H2, we predicted that the more similar a business's marketing organization profile is to that of the ideal marketing organization for its strategic type, the greater is its marketing efficiency. This yielded mixed results. We observed significant, positive coefficients in the models that regressed deviation from the efficiency-maximizing ideal marketing organization profile against marketing efficiency in businesses implementing a prospector strategy (β = .69, p = .0002) and those pursuing a defender strategy (β = .33, p = .02). However, in analyzer strategic types, the relationship between deviation from the ideal marketing organization profile and marketing efficiency, though in the expected direction, is insignificant (β = .24, p = .17). Confidence in the power of these tests is provided in the nonideal profile regression models that indicate no significant relationships between profile deviation and marketing efficiency.
Our results indicate that organizing marketing activities in ways that fit the business's strategic type is associated with marketing effectiveness in each of the three strategic types and with marketing efficiency in firms pursuing prospector and defender strategies. This provides empirical support for strategic marketing theory predictions linking marketing organization fit with business strategy and marketing performance (e.g., Walker and Ruekert 1987). Although the total variance explained in our regression equations is moderate (ranging from 9% to 46%), these values are in line with configuration studies in the management literature (e.g., Doty, Glick, and Huber 1993; Powell 1992). The profiles of the ideal marketing organizations revealed in Appendix B are also broadly consistent with previously untested systems- structural theory propositions regarding structural differences between firms implementing different strategic types (e.g., Ruekert, Walker, and Roering 1985; Walker and Ruekert 1987).
From a strategic marketing theory perspective, these findings highlight the importance of strategy implementation as a source of competitive advantage. Our results indicate that marketing organization fit with strategic type, a key enabler of strategy implementation in marketing theory (e.g., Bonoma 1985; Walker and Ruekert 1987), is significantly associated with marketing performance. In contrast, our data suggest that a business's strategic type alone is not significantly associated with marketing performance. These findings are consistent with two central tenets of strategic marketing theory that have received only limited empirical attention: When implemented successfully, several different strategies can lead to superior performance (e.g., Day and Wensley 1988), and the way marketing activities are organized is an important enabler of strategy implementation (e.g., Walker and Ruekert 1987).
Our findings also indicate the existence of important trade-offs between the effectiveness and efficiency dimensions of marketing performance (e.g., Bonoma and Clark 1988; Walker and Ruekert 1987). For example, we observed a negative correlation between marketing efficiency and effectiveness in our data (Table 1). We found that of the 15 top performers (5 for each strategic type) used to calibrate the ideal marketing organization profiles for each dimension of marketing performance, only one firm appeared as a top performer on both marketing effectiveness and marketing efficiency performance dimensions. This highlights the need for researchers to specify and explore relationships involving different dimensions of marketing performance in empirical research (e.g., Clark 2000; Day and Wensley 1988; Slater 1995).
From a methodological perspective, our study demonstrates the utility of profile deviation approaches in assessing fit-performance relationships in strategic marketing theory. Although these approaches have been adopted in the organization theory and strategic management fields, they have not been used previously in the marketing literature. Profile deviation approaches enable researchers to assess fit in a way that is consistent with the multidimensional and holistic perspectives used in theorizing about marketing strategy. By enabling multiple variables to be assessed simultaneously, this approach also enables researchers to more closely represent the complex constructs and multiple contingencies faced by managers in the "real world" (e.g., Gresov 1989). More traditional approaches, such as moderated regression analysis, slope analysis, and subgroup analysis, can be effective in assessing fit-performance relationships involving small numbers of variables. However, these approaches are unable to effectively deal with the complex and holistic views of organization, strategy, and environment common to marketing theory (e.g., Drazin and Van de Ven 1985; Schoonhoven 1981; Venkatraman and Camillus 1984). Therefore, the profile deviation approach offers an important theory-building and theory-testing method for marketing strategy research.
From a managerial perspective, our findings highlight the need for managers to understand the multiple variables that are important characteristics of the way marketing activities are arranged and the ways they must be configured to fit the implementation requirements of the firm's business strategy. Although research on marketing organization has traditionally focused on the importance of fit between marketing organization and the served market (e.g., Achrol 1991; Day 1994), our findings indicate that to enhance performance, marketing organizations also must fit the business's strategic type (e.g., Workman, Homburg, and Gruner 1998). In designing marketing organizations to fit with business strategy, our research indicates that managers should not seek a single marketing organization template that will be both effective and efficient across different strategic types (see Aufreiter, George, and Lempres 1996; Ruekert, Walker, and Roering 1985). Rather, our findings suggest that managers should be guided by the business's strategic goals and the implementation needs of its strategic type in designing and managing their marketing organization (Walker and Ruekert 1987; Workman, Homburg, and Gruner 1998).
Furthermore, the profile deviation method used in our study and the results we obtained may be useful to managers from a benchmarking perspective. Although benchmarking has been a popular management tool in areas such as operations and quality management, its use in marketing is less common. Benchmarking involves four key stages: ( 1) identifying a firm or group of firms with superior performance, ( 2) calibrating the business processes or characteristics believed to be important in creating superior performance in the benchmark firm, ( 3) identifying gaps between the benchmark firm and the firm undertaking the benchmarking, and ( 4) developing and executing gap-closing improvement strategies to move closer to the benchmark (e.g., Camp 1989; Day 1994). The first three stages of this process are consistent with the profile deviation method outlined here. Consultants and consortia of interested firms could use this approach to undertake detailed benchmarking studies. Such studies would be able to validate the assumed link between superiority on a key business process or characteristic and superior performance and provide insights regarding the profile of the specific business processes or characteristics associated with superior performance.
For example, a trucking firm interested in benchmarking its marketing organization design could use our results as the basis of a rigorous benchmarking exercise because our study ( 1) identifies groups of firms that are particularly high performers, ( 2) calibrates their marketing organization profiles, and ( 3) demonstrates that deviation from these marketing organization profiles is associated with firm performance. Having established that marketing organization fit with business strategy is an important driver of marketing performance, managers can use the profiles of the topperforming marketing organizations in Appendix B to calibrate their own marketing organization. Managers can distinguish the appropriate benchmark for their firm by first identifying whether their firm emphasizes efficiency or effectiveness in its strategic goals and then comparing its business strategy with the strategy type descriptions in Appendix A. For example, managers in a firm that emphasizes efficiency goals with a business strategy conforming to the prospector strategy type could calibrate their marketing organization characteristics against those of similar top-performing firms in Appendix B to identify which particular marketing organization characteristics need to be changed to get closer to the benchmark profile that delivers superior marketing efficiency (see Day 1994).
As a result of trade-off decisions in research design, our study has several limitations. First, the single industry setting of our study limits the generalizability of the findings. Although such research designs are necessary to control for industry effects and isolate the fit-performance relationships of interest, studies in additional industries and multi-industry studies are needed to establish the generalizability of our findings. Second, given the novelty of the approach adopted in our study, we were conservative in our marketing organization variable selection and measurement choices to ensure that our results would be robust. Therefore, we selected only those organizational characteristics that have been highlighted as important in both configuration theory and marketing strategy studies, have well-established operationalizations to minimize measurement error, and are viewed as important by managers in our trucking industry context. Given the emergence of new virtual organizational forms (e.g., network organizations), the development of newer terms for some organizational phenomena (e.g., "empowerment"), and the need for studies in additional industries, further research may need to examine different sets of marketing organization variables.
Third, we used an empirical approach to identify ideal profiles in assessing marketing organization fit with strategic type. This is a valid and appropriate research design choice in domains in which existing knowledge is insufficient to objectively estimate theoretically derived ideal profiles. However, another intriguing possibility suggested by the management literature is the use of "experts" to derive theory-based normative ideal profiles. For example, Doty, Glick, and Huber (1993) collected questionnaire data from three of the primary researchers involved in the original development of Miles and Snow's (1978) strategic types to provide numerical estimates for theoretically derived ideal profiles of corporate characteristics appropriate for each strategic type. In exploring fit relationships in marketing strategy domains in which established theories are well accepted, future researchers may be able to similarly use data from the theory's authors as a mechanism for developing ideal profiles.
Fourth, although our study addresses the theoretically important but previously neglected question of fit between marketing organization and business strategy, we do not address the issue of the coalignment (or internal consistency) among the different marketing organization characteristics. The relationships between the multiple variables that constitute the marketing organization are a theoretically interesting and managerially difficult issue on which there has been little theoretical or empirical work. Having demonstrated the performance consequences of fit between marketing organization characteristics and business strategy, it is now important to gain an understanding of how to coalign the multiple characteristics of marketing organizations to achieve such fit. Managers need to understand how the various "levers" of marketing organization are connected to one another if they are to successfully configure marketing organizations capable of executing the firm's business strategy in ways that deliver desired strategic goals. This requires further research focused on the interrelationships between the multiple variables that are important characteristics of marketing organization.
Beyond these limitations, our results, which indicate that the relationship between marketing organization fit with strategic type and marketing performance varies across strategic types and between marketing performance dimensions, raise several questions for further research. For example, our findings suggest a particularly strong relationship between marketing organization fit with strategy type and marketing efficiency in prospector firms but weaker relationships on both dimensions of performance for defender firms. Although this could be connected with marketing having a more important role in implementing prospector strategies than defender strategies (e.g., Walker and Ruekert 1987), there is no theoretical reason that these relationships should be so varied across performance dimensions. Similarly, why does marketing organization fit with strategic type have a greater impact on marketing effectiveness in analyzers than in either prospector or defender types, but no significant impact on analyzer's marketing efficiency? The literature identifies the analyzer strategy type as difficult to execute successfully because of the conflicting demands of the simultaneous internal and external orientation required (e.g., Slater and Narver 1993). However, there is no obvious reason that this should result in such different relationships between marketing organization fit with the analyzer strategy type and the effectiveness and efficiency dimensions of marketing performance. Given the importance of these questions to managers engaged in designing and managing marketing organizations, understanding the reasons for these different relationships is a priority for further research.
Holistically framed fit-performance relationships involving strategy, organization, and environment are central to strategic marketing theory but are rarely assessed in empirical research (e.g., Day 1999; Walker and Ruekert 1987). We demonstrate that by drawing on configuration theory conceptualizations and methodological tools, many of these fit- performance relationships can be empirically assessed in ways that match their multidimensional conceptualization and holistic framing. As an example, our results indicate that organizing marketing activities in ways that fit business's strategy type can form a significant source of competitive advantage (e.g., Walker and Ruekert 1987). Given the importance of fit-performance relationships in strategic marketing theory and managers' interest in identifying such valuable sources of competitive advantage, additional studies of this type are clearly needed to enhance marketing strategy scholars' contribution to theory development and practice.
The authors gratefully acknowledge insightful comments in the development of this article from Barry Bayus, Jeff Conant, D. Harold Doty, William D. Perreault Jr., Valarie Zeithaml, and the anonymous JM reviewers.
NOTES [1] A fourth strategic type, reactors, is also identified but is deemed not to be viable in the long run as it represents firms that have no clear or consistent strategy (e.g., McKee, Varadarajan, and Pride 1989).
[2] These returns were from firms reporting a reactor strategy. Because of the small number of these respondents and the inconsistency of the reactor strategy type, we follow previous empirical studies and exclude these firms from our analysis (e.g., McDaniel and Kolari 1987; Slater and Narver 1993).
[3] The difference in the directions of the relationships is due to our marketing effectiveness measure running from low to high. Marketing efficiency is a ratio in which a smaller number represents greater efficiency.
DIAGRAM: FIGURE 1 Marketing Organization Fit with Strategic Type and Its Relationship with Marketing Performance
Legend for Chart
A = Mean
B = Standard Deviation
C = X1
D = X2
E = X3
F = X4
G = X5
H = X6
I = X7
J = X8
K = X9
L = X10
A B C D E F G H
I J K L
X1. Centralization
2.80 1.39 .84
X2. Formalization
4.85 1.27 .28** .78
X3. Specialization
4.11 1.36 -.36** .17* .72
X4. Size
1335 3231 -.01 .01 .12 N/A
X5. Task complexity
4.18 1.06 -.20** .15* .27** .01 .79
X6. Work group
interdependence
4.00 1.79 -.22** .07 .15* .01 .02 N/A
X7. Architectural
marketing
capabilities
3.49 1.31 -.46** .17* .54** .08 .29** .28**
.78
X8. Specialized
marketing
capabilities
3.34 1.11 -.41** .02 .33** .04 .26** .13[T]
.64** .72
X9. Marketing
effectiveness
3.91 1.51 -.30** .10 .30** -.07 .27** .03
.50** .60** .85
X10. Marketing
efficiency .05 .16 .06 -.07 -.06 -.03 .02 -.10
-.10 .03 -.13[T] N/A[T]p < .10.
*p < .05.
**p < .01.
Notes: Alphas are shown on the correlation matrix diagonal. N/A = not applicable.
CME GM Mean
Rater Rater Inter-Rater t-Value Inter-Rater
Mean (S.D.) Mean (S.D.) Difference[a] (significance) Correlation[b]
Centralization
2.57 (1.51) 2.42 (1.14) .15 .67 (.51) .65
Formalization
5.10 (1.37) 5.44 (.99) -.34 .95 (.35) .56
Specialization
4.58 (1.62) 4.43 (1.19) .15 .39 (.70) .56
Task complexity
4.70 (.92) 4.97 (1.28) -.27 1.10 (.29) .71
Work group interdependence
4.00 (1.60) 3.42 (2.04) .58 .86 (.40) .58
Specialized marketing capabilities
4.97 (1.05) 5.06 (1.15) -.09 .28 (.78) .55
Architectural marketing capabilities
4.77 (1.19) 5.14 (1.10) -.37 1.53 (.14) .60
Marketing effectiveness
5.26 (1.40) 5.53 (1.49) -.27 .67 (.51) .57
[a]Inter-rater difference is CME mean score less GM mean score.
[b]All correlations significant at p < .001 level.
Notes: S.D. = standard deviation.
Legend for Chart
A = Independent Variables
B = Dependent Variable Marketing Effectiveness Within Strategic-
Type Group Ideal Profile Model
C = Dependent Variable Marketing Effectiveness Across Strategic-
Type Group Ideal Profile Model
D = Dependent Variable Marketing Efficiency Within Strategic-
Type Group Ideal Profile Model
E = Dependent Variable Marketing Efficiency Across Strategic-
Type Group Ideal Profile Model
A B C D E
All Firms
Profile deviation -.44** -.39** .29** .12
Firm size (log) .09 -.02 .18* .22*
R[2] .20 .15 .13 .06
F-value 19.49** 14.09** 8.39** 3.96***p < .05.
**p < .01.
TABLE 4 Marketing Organization Fit with Strategic Type and Performance Regression Models
Legend for Chart
A = Independent Variables
B = Dependent Variable Marketing Effectiveness
Ideal Profile Models
C = Dependent Variable Marketing Effectiveness
Nonideal Models
D = Dependent Variable Marketing Efficiency
Ideal Profile Models
E = Dependent Variable Marketing Efficiency
Nonideal Models
A B C D E
Prospectors
Profile deviation -.42** -.01 .69** .22
Organization size
(log) .23* .28 -.15 -.10
R[2] .26 .08 .46 .07
F-value 6.12** 1.43 9.70** .92
Analyzers
Profile deviation -.64** .21 .24 -.19
Organization size
(log) .18 -.04 .43* .40*
R[2] .41 .04 .15 .12
F-value 18.08** 1.24 3.20* 2.78
Defenders
Profile deviation -.28* .15 .33* .16
Organization size
(log) -.11 -.16 .22 .27*
R[2] .09 .05 .15 .10
F-value 3.20* 1.55 3.98* 2.58*p < .05.
**p < .01.
Centralization (seven-point scale with "strongly disagree" and "strongly agree" as anchors)
Source: Jaworski and Kohli (1993) The following questions concern how decisions are made in your marketing organization.
How strongly do you agree or disagree with each of the following statements about your marketing organization?
- There can be little action taken in the marketing organization until a supervisor makes a decision.
- A person who wants to make his or her own decisions would be quickly discouraged in the marketing organization.
- Even small matters have to be referred to someone with more authority for a final decision.
- Any decision a person in the marketing organization makes has to have his or her boss's approval.
Formalization (seven-point scale with "strongly disagree" and "strongly agree" as anchors)
Source: Deshpande and Zaltman (1982)
The following questions concern the impact of work rules used in your marketing organization.
How strongly do you agree or disagree with each of the following statements about your marketing organization?
- Most people in the marketing organization follow written work rules for their job.
- How things are done in the marketing organization is never left up to the person doing the work.
- People in the marketing organization are allowed to do almost as they please when performing their work. (RS)
Specialization (seven-point scale with "strongly disagree" and "strongly agree" as anchors)
Source: Doty, Glick, and Huber (1993)
The following questions concern job responsibilities and skills within your marketing organization.
How strongly do you agree or disagree with each of the following statements about your marketing organization?
- Marketing personnel in this firm have very specific job responsibilities.
- Most marketing employees have jobs that require special skills.
- Standardized training procedures exist for marketing jobs. (RS)
- Written position descriptions are provided to marketing specialists.
Specialized Marketing Capabilities (seven-point scale with "not very well" and "very well" as anchors)
Source: New Scale
How well does your organization perform the following activities relative to competitors...
- advertising and promotion
- public relations
- personal selling
- new product/service development
Architectural Marketing Capabilities (seven-point scale with "not very well" and "very well" as anchors)
Source: New Scale
How well does your organization perform the following activities relative to competitors...
- environmental scanning
- market planning
- marketing skill development
- marketing implementation
Task Complexity (seven-point scale with "not at all" and "to a great extent" as anchors)
Source: Doty, Glick, and Huber (1993)
To what extent...
- is the work that people in the marketing organization do the same from day to day?
- does the work move among the marketing work groups in a sequential manner?
- is there a clearly known way to do the major types of work that marketing work groups deal with?
- do marketing employees tend to perform the same tasks in the same way?
- is there an understandable sequence of steps that can be followed to perform most marketing tasks?
Work Group Interdependence (seven-point scale with "not at all" and "to a great extent" as anchors)
Source: Van de Ven, Delbecq, and Koenig (1976)
To what extent does the flow of work in the department reflect the diagram below, in which work comes into the department and different subunits diagnose, problem solve, and work together as a group at the same time?
Materials, customer orders, and information enter the marketing
department.
(Down arrows)
Same Time Subunit 1 Subunit 2
Subunit 3 Subunit 4
(Down arrows)
Services and information leave the marketing department. Strategic Types
Source: McKee, Varadarajan, and Pride (1989)
The following descriptions characterize equally effective strategies that organizations can use to position themselves relative to their competition. Please select the description that you feel best characterizes your firm today.
Prospector Strategy: This business unit typically operates within a broad product-market domain that undergoes periodic redefinition. The business unit values being "first in" in new product and market areas even if not all these efforts prove to be highly profitable. This organization responds rapidly to early signals concerning areas of opportunity, and these responses often lead to a new round of competitive actions. However, this business unit may not maintain market strength in all areas it enters.
Analyzer Strategy: This business unit attempts to maintain a stable, limited line of products or services while moving quickly to follow a carefully selected set of the more promising new developments in the industry. This organization is seldom "first in" with new products and services. However, by carefully monitoring the actions of major competitors in areas compatible with its stable product-market base, this business unit can frequently be "second in" with a more cost-efficient product or service.
Defender Strategy: This business unit attempts to locate and maintain a secure niche in a relatively stable product or service area. The business unit tends to offer a more limited range of products or services than competitors, and it tries to protect its domain by offering higher quality, superior service, lower prices, and so forth. Often, this business unit is not at the forefront of developments in the industry. It tends to ignore industry changes that have no direct influence on current areas of operation and concentrates instead on doing the best job possible in a limited area.
Marketing Effectiveness (seven-point scale with "not very well" and "very well" as anchors) How well has your firm achieved its goals in terms of ...
- market share growth
- sales growth
- market position
Marketing Efficiency (objective data from TTS database)
Marketing and selling expenses/gross revenue
Notes: (RS) = reverse scoring.
Legend for Chart
A = Marketing Organization Characteristics
B = Prospector Firms Marketing Effectiveness
C = Prospector Firms Marketing Efficiency
D = Analyzer Firms Marketing Effectiveness
E = Analyzer Firms Marketing Efficiency
F = Defender Firms Marketing Effectiveness
G = Defender Firms Marketing Efficiency
A B C D E F G
Centralization 2.25 2.30 2.10 2.10 3.50 3.75
Formalization 4.67 4.33 5.47 5.53 6.00 5.28
Specialization 5.75 4.45 5.50 4.65 4.15 3.33
Task complexity 4.04 3.84 5.24 4.00 5.12 4.47
Work group
interdependence 3.00 4.00 4.40 3.00 4.00 3.33
Specialized
marketing
capabilities 4.24 3.70 5.62 3.53 4.00 3.62
Architectural
marketing
capabilities 4.76 4.34 5.38 3.81 4.44 3.25
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By Douglas W. Vorhies and Neil A. Morgan
Douglas W. Vorhies is Assistant Professor of Marketing, College of Business, Illinois State University. Neil A. Morgan is Assistant Professor of Marketing, Kenan-Flagler Business School, University of North Carolina at Chapel Hill.
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Record: 2- A Configurational Perspective on Key Account Management. By: Homburg, Christian; Workman Jr., John P.; Jensen, Ove. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p38-60. 23p. 1 Diagram, 7 Charts. DOI: 10.1509/jmkg.66.2.38.18471.
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A Configurational Perspective on Key Account Management
Most firms struggle with the challenge of managing their key customer accounts. There is a significant gap between the importance of this organizational design problem in practice and the research attention paid to it. Sound academic research on key account management (KAM) is limited and fragmented. Drawing on research on KAM and team selling, the authors develop an integrative conceptualization of KAM and define key constructs in four areas: ( 1) activities, ( 2) actors, ( 3) resources, and ( 4) approach formalization. Adopting a configurational perspective to organizational research, the authors then use numerical taxonomy to empirically identify eight prototypical KAM approaches on the basis of a cross-industry, cross-national study. The results show significant performance differences among the approaches. Overall, the article builds a bridge between marketing organization research and relationship marketing research.
Many companies today are faced with powerful and more demanding customers. In many industries, these powerful buyers have been shaped through corporate mergers and have been visible in many industry sectors such as retailing, automotive, computers, and pharmaceuticals. These large customers often rationalize their supply base to cooperate more closely with a limited number of preferred suppliers (e.g., Dorsch, Swanson, and Kelley 1998; Stump 1995). They may demand special value-adding activities from their suppliers, such as joint product development, financing services, or consulting services (Cardozo, Shipp, and Roering 1992). Also, many buying firms have centralized their procurement and expect a similarly coordinated selling approach from their suppliers. For example, global industrial customers may demand uniform pricing terms, logistics, and service standards on a worldwide basis from their suppliers (Montgomery and Yip 2000).
These demands from important accounts raise an organizational design problem for many suppliers. As Kempeners and van der Hart (1999, p. 312) note, "Organizational structure is perhaps the most interesting and controversial part of account management." Internal organizational structures often hamper a coordinated account management, such as when the same customer is served by decentralized product divisions or by highly independent local sales operations. In addition, the set of activities for complex customers cannot be handled by the sales function alone but requires participation from other functional groups. These developments have induced many suppliers to rethink how they manage their most important customers and how they design their internal organization in order to be responsive to these key customers. In this context, firms are increasingly organizing around customers and shifting resources from product divisions or regional divisions to customer-focused business units (Homburg, Workman, and Jensen 2000). Many firms are establishing specialized key account managers and are forming customer teams that are composed of people from sales, marketing, finance, logistics, quality, and other functional groups (Millman 1996; Wotruba and Castleberry 1993).
In a recent study, Homburg, Workman, and Jensen (2000) argue that the increasing emphasis on key account management (KAM) is one of the most fundamental changes in marketing organization. Given the relevance of designing KAM in practice, sound academic research on this topic is still surprisingly limited. Millman (1996, p. 631) notes that "Key account management is underresearched and its efficacy, therefore, is only partially understood." Although management approaches to the most important customers have received some research attention over the past 25 years (Shapiro and Moriarty 1984a; Weilbaker and Weeks 1997), the existing literature has several shortcomings. First, research has been fragmented and has not consolidated specific design aspects of KAM into a coherent framework. Second, conceptual and empirical work on KAM has primarily been based on observations of formalized key account programs in Fortune-500 companies and has hardly been extended to nonformalized KAM approaches. Third, broad-based empirical research on KAM is still scarce, as Kempeners and van der Hart (1999, p. 311) note: "Although Stevenson (1980) noted almost 20 years ago that: 'despite widespread industrial use, there has been little empirical research on national account marketing,' it seems that this is still true." The empirical work that has been done in the past has essentially been descriptive. Finally, given that conceptual work has suggested a variety of design options (Shapiro and Moriarty 1984a), there is little empirical knowledge of which types of approaches to KAM occur in practice and how successful these are.
Given these gaps in knowledge about KAM, the overall objective of this article is to study the design of approaches to KAM. More specifically, we seek to
1. Derive the core design dimensions of KAM approaches from the KAM literature and from related research areas to develop an integrative conceptualization of KAM,
- 2. Identify the key constructs within these design dimensions and develop instruments for measuring these constructs,
- 3. Identify prototypical approaches to KAM in practice on the basis of a cross-national, cross-industry taxonomy, and
- 4. Explore the outcomes of different KAM approaches.
Given that taxonomies are less frequently developed than conceptual models, a few comments on their value are in order. As Hunt (1991, p. 176) has noted, classification schemata, such as typologies or taxonomies, "play fundamental roles in the development of a discipline since they are the primary means for organizing phenomena into classes or groups that are amenable to systematic investigation and theory development." Given that the conceptual knowledge about the design of KAM is at an early stage and that our research endeavor is to expand its scope, a taxonomy is particularly useful in providing the field with new organization. By means of the taxonomy, we are studying the complex KAM phenomenon through holistic patterns of multiple variables rather than isolated variables and their bivariate relations. This research approach is consistent with the configurational perspective of organizational analysis that has been gaining increasing attention (Meyer, Tsui, and Hinings 1993). The basic premise of the configurational perspective is that "Organizational structures and management systems are best understood in terms of overall patterns rather than in terms of analyses of narrowly drawn sets of organizational properties" (Meyer, Tsui, and Hinings 1993, p. 1181). Thus, the configurational perspective complements the traditional contingency approach (Mahajan and Churchill1990). Two alternatives of identifying configurations have been distinguished: Typologies represent classifications based on a priori conceptual distinctions, whereas taxonomies are empirically derived groupings (Hunt 1991; Rich 1992; Sanchez 1993). Given our goal of identifying approaches to KAM in practice, we take a taxonomic approach. Hunt(1991) notes that grouping phenomena through taxonomies as opposed to typologies requires substantially less a priori knowledge about which specific properties are likely to be powerful for classification, because taxonomic procedures are better equipped to handle large numbers of properties
The article is organized as follows: We first summarize the literature on KAM and evaluate contributions that the personal selling and sales management literature provide for KAM. On the basis of the literature review, we develop a multidimensional conceptualization of KAM and identify outcomes of KAM. We then describe a large-scale survey of KAM approaches and develop the taxonomy. This is followed by an exploration of how the different approaches perform. We conclude by discussing implications for theory and managerial practice.
KAM Research
We subsume under KAM all approaches to managing the most important customers that have been discussed under such diverse terms as key account selling, national account management, national account selling, strategic account management, major account management, and global account management. "National account management" has become a misnomer, as business with important customers increasingly spans country borders (Colletti and Tubridy 1987). Although some research has focused on global accounts (Montgomery and Yip 2000; Yip and Madsen 1996), KAM appears to be the most accepted term in recent publications (Jolson 1997; McDonald, Millman, and Rogers 1997; Pardo 1997; Sharma 1997) and is the most widely used term in Europe.[ 1]
Table 1 presents a summary of selected KAM research. We segment this research into articles focusing on ( 1) individual key account managers, ( 2) dyadic relationships between suppliers and key accounts, and ( 3) the design of key account programs. Given our objective of understanding the design of KAM approaches, Group 3 is most relevant to our article.
Because Group 1 takes the individual key account manager as the unit of analysis, it is similar to personal selling research. Weeks and Stevens (1997) find considerable dissatisfaction of key account managers with their current training programs. Boles, Barksdale, and Johnson (1996) identify behaviors required of key account salespeople in order to build successful key account relationships.
Group 2 is closely related to relationship marketing research. Several authors describe an evolutionary path of key account relationships from lower to higher degrees of involvement and collaboration (Lambe and Spekman 1997; McDonald, Millman, and Rogers 1997). Sharma (1997) finds that customers' preference for being served by key account programs is particularly high when their buying process is long and complex. Sengupta, Krapfel, and Pusateri (1997b) study switching costs in key account relationships.
Group 3, which focuses on overall management of key accounts, is the largest group, consistent with Pardo's (1999, p. 286) conclusion that "Today, key account experts on both sides of the Atlantic agree on ... the problem of key account management as being an organizational one." Although all studies in Group 3 deal with the design of key account programs, none of these integrates the main aspects of key account program design within one study.
Four main themes emerge from the literature on key account programs. First, key account programs encompass special (interorganizational) activities for key accounts that are not offered to average accounts. These special activities pertain to such areas as pricing, products, services, distribution, and information sharing (Cardozo, Shipp, and Roering 1992; Montgomery and Yip 2000; Shapiro and Moriarty 1984b). Second, key account programs frequently involve special (intraorganizational) actors who are dedicated to key accounts. These key account managers are typically responsible for several key accounts and report high in the organization (Colletti and Tubridy 1987; Dishman and Nitse 1998; Wotruba and Castleberry 1993). They may be placed in the supplier's headquarters, in the local sales organization of the key account's country, or even in the key account's facilities (Millman 1996; Yip and Madsen 1996). It is frequently stressed that key account managers need special compensation arrangements and skills, which has implications for their selection, training, and career paths (Colletti and Tubridy 1987; Tice 1997). Third, KAM is a multifunctional effort involving, in addition to marketing and sales, functional groups such as manufacturing, research and development, and finance (Shapiro and Moriarty 1984b). Fourth, the formation of key account programs is influenced by characteristics of buyers and of the market environment, such as purchasing centralization, purchasing complexity, demand concentration, and competitive intensity (Boles, Johnston, and Gardner 1999; Stevenson 1980).
We observe several shortcomings in prior research. First, the previous design issues have mostly been studied in isolation and have not been consolidated into a coherent framework. Shapiro and Moriarty's (1984a, p. 34) assessment that "the term national account management program is fraught with ambiguity" is still valid. Second, there is a general lack of quantitative empirical studies on the design issues, particularly on the cross-functional linkages of KAM. The quantitative research that has been undertaken has essentially been descriptive and has not systematically developed and validated measures. Third, much of the empirical work that has been done (and has driven conceptual ideas) is based on observations in large Fortune-500 companies with sophisticated, formalized key account programs. This excludes small and medium-sized companies that actively manage relationships with key accounts but do not formalize the KAM approach. Quantitative empirical research has not taken up Shapiro and Moriarty's (1984a, p. 5) comment in their early conceptual work that "the simplest structural option is no program at all." Fourth, given that conceptual work has mentioned a variety of structural options (Shapiro and Moriarty 1984a), there is no broad-based empirical work that allows generalizations about how KAM is done in practice. We now position KAM research in a wider research context and evaluate the contribution of related research to the open issues in the KAM literature.
Research Related to KAM
Key account management can be subsumed under the wider context of personal selling and sales management research. From a sales management perspective, KAM represents one element within a differentiated sales force that stands next to other elements such as telemarketing, demonstration centers, and traditional personal and face-to-face selling (Cardozo, Shipp, and Roering 1987; Marshall, Moncrief, and Lassk 1999). According to Shapiro and Wyman (1981, p. 104), "National account management thus is an extension, improvement, and outgrowth of personal selling."
Most personal selling research has a different level of analysis than our work does. Although this literature has examined relationship-building activities for important customers (Jolson 1997; Weitz and Bradford 1999; Wotruba 1991) and has produced empirical classifications based on activities (Moncrief 1986), its level of analysis is the individual salesperson. Thus, although potentially enhancing knowledge about individual key account managers, this research contributes little to understanding organizational approaches to KAM.
In recent years, however, there has been a shift in the level of analysis from the individual salesperson to the selling team (Weitz and Bradford 1999). There is growing recognition that functional groups other than sales play an important role in interactions with customers (Hutt, Johnston, and Ronchetto 1985; Spekman and Johnston 1986). The team-selling literature has distinguished between "core selling teams" that are permanently assigned to customer accounts and the wider "selling center" that consists of members of all functional groups who participate on an ad hoc basis (Moon and Gupta 1997; Smith and Barclay1990). Moon and Armstrong(1994, p. 19) explicitly link team-selling literature to KAM by noting that "conceptually, national account teams can be viewed as selling teams ... that service large, complex customers."
The team-selling literature enhances our conceptual understanding of cross-functional cooperation for key accounts. One fundamental problem for sales managers is to obtain the cooperation of other organizational members without having formal authority over them (Spekman and Johnston 1986). Therefore, the achievement of selling tasks is hypothesized to be dependent on the selling center participants' commitment to the selling team and its goals (Smith and Barclay 1993) and on their connection through communication flows (Moon and Gupta 1997). However, empirical research on team selling is just as scarce as empirical research on cross-functional cooperation in KAM.
At this point, it is important to clarify how our research perspective differs from the vast body of research on relation-ship marketing and market orientation. Relationship marketing research focuses more on interorganizational issues between suppliers and their customers, such as how marketing relationships are built and maintained and what benefits accrue (Morgan and Hunt 1999).These are mostly assessed from the customer's perspective. On the contrary, our focus is more on how firms organize and cooperate internally. In addition, our level of analysis is the KAM approach (which encompasses relationships with several important customers), whereas the unit of analysis in most of the relationship marketing literature is a given dyadic relationship with an individual customer. Because most firms have the challenge to array their organizational resources at a set of strategically important customers rather than just one customer, ours is an important perspective for study. Market orientation research, in turn, studies both intraorganizational and interorganizational cooperation to create superior value for buyers. However, this research studies constructs on a high level of abstraction. Another key difference from KAM is that market orientation literature treats the customer base as a whole and does not differentiate between important customers and average customers.
Approach to the Conceptualization
In this section, we blend the insights from prior literature into an integrative conceptualization of KAM. Our conceptualization is composed of fundamental dimensions of KAM, each of which comprises several key constructs. Because we use these constructs to develop a taxonomy of KAM approaches subsequently, we give great care to their selection. As Bailey (1994, p.2) notes, "One basic secret to successful classification, then, is the ability to ascertain the key or fundamental characteristics on which the classification is to be based." The literature suggests several different, partly contradictory guidelines for the selection of input variables to a classification (for a review, see Rich 1992). There is consensus that the input variables should be derived from theory and should be meaningful for the subject under study. Therefore, given our integrative perspective on KAM, we derive theory-based constructs from the literature that are comparable across a range of industries.
The degree of admissible interdependencies among the cluster variables is more debated. Whereas Sneath and Sokal (1973) advocate to exclude variables that are logically or empirically correlated, Arabie and Hubert (1994, p. 166) note that "it is difficult to imagine empirical data arising in the behavioral sciences that would have all columns mutually independent." In addition, from a methodological vantage point, there is no assumption of uncorrelated variables in most cluster methods (Milligan 1996, p. 347). We concur with the latter viewpoint in that we accept some conceptual overlap and correlation among the constructs. However, we ensure discriminant validity in measuring these constructs.
Another debate refers to the balance between completeness and parsimony of the input variables. Whereas McKelvey (1975, p. 514) recommends that researchers "define as many organizational attributes as possible," Mayr (1969) notes that there is little point in using more dimensions than are necessary to build a sound taxonomy. From a methodological angle, the presence of spurious dimensions (i.e., dimensions that do not differentiate among clusters) has been shown to have a detrimental effect on the performance of clustering methods. Punj and Stewart (1983, p. 143) caution "to avoid 'shotgun' approaches where everything known about the observations is used as the basis for clustering." Therefore, we distinguish between two types of variables in developing our taxonomy. First, we identify parsimonious sets of theory-based key constructs that serve as "active" input variables for the cluster algorithm. Second, we complement these with several "passive," nontheoretical, descriptive variables, which we use to characterize the clusters further.
Fundamental Dimensions of KAM
We begin our conceptualization of KAM by identifying the fundamental dimensions of the KAM phenomenon. Prior research on dimensions of KAM can be summarized in terms of three basic questions: ( 1) What is done? ( 2) Who does it? and ( 3) With whom is it done? However, as we have elaborated in the literature review, the scope of prior research has been limited to formalized key account programs with designated key account managers in place. We claim that to formalize or not to formalize the key account approach represents a decision dimension of its own. Therefore, we add a fourth question to KAM research: ( 4) How formalized is it? This leads us to conceptualize four dimensions of KAM. Drawing on research on the management of collaborative relationships that has distinguished among activities, actors, and resources (Anderson, Hákansson, and Johanson 1994; Narus and Anderson 1995), we refer to the four dimensions as ( 1) activities, ( 2) actors, ( 3) resources, and ( 4) formalization. The first dimension refers to interorganizational issues, and the other three refer to intraorganizational issues in KAM. Figure 1 visualizes our conceptualization of KAM.
Previous definitions of KAM have tended to focus on specific dimensions of KAM. Some authors focus on special activities for key accounts. For example, Barrett (1986, p.64) states that "National account management simply means targeting the largest and most important customers by providing them with special treatment in the areas of marketing, administration, and service." Others emphasize the dedication of special actors to key accounts. Yip and Madsen (1996,p. 24), for example, note that "National account management approaches include having one executive or team take overall responsibility for all aspects of a customer's business." Our conceptualization is more integrative because it encompasses activities and actors, as well as resources and formalization.
We now go through each of the four fundamental dimensions of KAM to identify parsimonious sets of theoretically based key constructs, which we use as (active) input variables for the cluster algorithm leading up to the taxonomy. We then identify additional descriptive (passive) variables that help enrich our descriptions of the clusters.
Activities. As we have shown, both the KAM literature (e.g., Lambe and Spekman 1997; Montgomery and Yip 2000; Napolitano 1997; Shapiro and Moriarty 1984b) and the relationship marketing literature suggest inventories of activities that suppliers can carry out for their key accounts. Among these are special pricing, customization of products, provision of special services, customization of services, joint coordination of the workflow, information sharing, and taking over business processes the customers outsources. The first question that arises with respect to organizational activities is how intensely they should be pursued. Shapiro and Moriarty (1980, p. 5) argue that "[a] key issue here is: How will or does the servicing of national accounts differ from that of other accounts?" Therefore, we define activity intensity as the extent to which the supplier does more for key accounts than for average accounts.
In addition to the level of intensity on an activity, another important conceptual issue is the origin of that intensity. Given that powerful customers often force their suppliers into special activities, the question arises whether the supplier or the key account proposes a special activity. Millman (1999, p. 2) observes that "some ... programs are seller-initiated, some are buyer-initiated." Empirical results by Sharma (1997) and Montgomery and Yip (2000) indicate that supplier firms indeed use KAM in response to customer demand for it. According to Arnold, Birkinshaw, and Toulan (1999, p. 15), "the proactive-reactive dimension matters a great deal." Therefore, we define activity proactiveness as the extent to which activities are initiated by the supplier.
Actors. Probably the most frequently discussed topic in key account program research is which special actors participate in key account activities. These specialized actors can be viewed as a personal coordination mode in KAM. The participation of special actors has a horizontal and a vertical component. The KAM literature suggests that there are many possibilities for horizontally placing KAM actors, ranging from a line manager who devotes part of his or her time to managing key accounts to teams that are fully dedicated to key accounts (Shapiro and Moriarty 1984a). Similarly, Olson, Walker, and Ruekert (1995) present a range of coordination mechanisms with a permanent team at one end of their continuum. Marshall, Moncrief, and Lassk (1999, p. 96) note that "team work is a fairly new concept in managing accounts and that salespeople are working in a team format much more today than in the past." Cespedes, Doyle, and Freedman (1989) even argue that selling is no longer an individual activity but rather a coordinated team effort. It has been suggested that the use of teams is a reaction to the use of purchasing teams on the buyer side (Hutt, Johnston, and Ronchetto 1985). We define the use of teams as the extent to which teams are formed to coordinate activities for key accounts.
Whereas teams refer to the horizontal participation in KAM, another fundamental issue pertains to vertical participation. The KAM actors may be placed at the headquarters, at the division level, or at the regional level (Shapiro and Moriarty 1984a). The importance of senior executive involvement in KAM has frequently been underscored in the KAM literature. As Millman and Wilson (1999, p. 330) note, KAM "is a strategic issue and the process should therefore be initiated and overseen by senior management." Napolitano (1997, p. 5) points out that "Top management must also play the lead role in securing business unit management support for the program." This view is supported by writers on strategy implementation, who argue that the organization is a reflection of its top managers (Hambrick and Mason 1984). Empirical support for the importance of top management has been provided by Jaworski and Kohli (1993), who find that market orientation is positively related to top management's emphasis on it. Therefore, we define top-management involvement as the extent to which senior management participates in KAM. The top-management involvement construct, adopted from the literature on strategy implementation and market orientation, is conceptually close to the centralization construct used in organization theory, which refers to the extent of decision authority that is concentrated on higher hierarchical levels.
Resources. As Shapiro and Moriarty (1984a, p. 2) note, "Much of the [national account management] concept as both a sales and a management technique revolves around the coordination of all elements involved in dealing with the customer." The KAM literature and the team-selling literature have pointed out that support is needed for key account activities from such diverse functional groups as marketing and sales, logistics, manufacturing, information technology, and finance and accounting (Moon and Armstrong 1994; Shapiro and Moriarty 1984b). "The key question, then, is: ... how can a salesperson obtain needed resources?" (Moon and Gupta 1997, p. 32). Obtaining resources has a pull and a push component.
In some cases, key account managers have special organizational power to ensure full cooperation from other organizational members. In other cases, key account managers must rely on their informal powers and interpersonal skills (Spekman and Johnston 1986, p. 522). Because the key account manager is typically part of the sales function (Shapiro and Moriarty 1984a), this lack of authority is most obvious for functional resources outside marketing and sales. We define access to nonmarketing and nonsales resources as the extent to which a key account manager can obtain needed contributions to KAM from nonmarketing and nonsales groups.
However, even within the marketing and sales function, a key account manager may face difficulty in receiving support for his or her tasks (Homburg, Workman, and Krohmer 1999; Platzer 1984). One common problem is the lack of authority over regional sales executives who handle the local business with global key accounts (Arnold, Birkinshaw, and Toulan 1999). For example, regional sales entities often resist companywide agreements on prices or service standards. Therefore, we define access to marketing and sales resources as the extent to which a key account manager can obtain needed contributions to KAM from marketing and sales groups.
Whereas access to resources refers to pulling on resources, research on team selling has frequently emphasized that the achievement of cross-functional integration in the selling center is facilitated if the participating functions themselves push cooperation (Smith and Barclay 1993). Day (2000, p. 24) notes that to develop strong relationships with customers, "a relationship orientation must pervade the mind-sets, values, and norms of the organization." Jaworski and Kohli (1993) refer to this concept of interdepartmental culture as esprit de corps. Culture is often viewed as a resource: "Organizational resources are the assets the firm possesses that arise from the organization itself, chief among these are the corporate culture and climate" (Morgan and Hunt 1999, p. 284). Fisher, Maltz, and Jaworski (1997) note that esprit de corps fosters the exchange of customer and market information. Therefore, we define the esprit de corps of the selling center as the extent to which selling center participants feel obliged to common goals and to each other.
Formalization. As Shapiro and Moriarty (1984a, p. 4) note, one of the "major organizational decisions that must be made as a company approaches a NAM program [is,] Should there be a NAM program or no program?" We believe that the distinction between more or less programmed approaches is highly relevant. As we show in our literature review, KAM approaches that do not have a key account program in place are underresearched.
Characteristics of KAM programs are the definition of reporting lines and formal linkages between departments, the establishment of formal expense budgets, the documentation of processes, and the development of formal guidelines for how to handle the accounts (Boles, Pilling, and Goodwyn 1994). In essence, the design decision of installing a key account program revolves around the extent to which KAM should be formalized. Consistent with writers on marketing organization (Olson, Walker, and Ruekert 1995; Workman, Homburg, and Gruner 1998), we define the formalization of a KAM approach as the extent to which the treatment of the most important customers is governed by formal rules and standard procedures. Thus, formalization can be viewed as an impersonal coordination mode, as opposed to top-management involvement and use of teams, which represent personal coordination modes in KAM.
Additional Descriptive Variables
In addition to the theoretical constructs developed previously, the KAM literature also suggests several descriptive variables to characterize KAM approaches. These variables refer to concrete, mostly demographic features of KAM approaches, such as the positions of key account managers. Because these variables are not theory based, we do not use them as input to the cluster procedure. However, given that these variables have frequently been discussed in KAM publications, we use them to enrich our interpretation of different KAM approaches subsequently.
In many companies, KAM teams are led by a key account manager. We define the key account coordinator as the person who is mainly responsible for coordinating activities related to key accounts. The first descriptive variable refers to the position of the key account coordinators. One possibility is to establish dedicated full-time positions for the coordination of key accounts (Pegram 1972). A fundamental question in this context is whether key account coordinators are placed in the supplier's headquarters or locally in the country or geographic region of the key account's headquarters. An alternative to the full-time option is a part-time responsibility. As Shapiro and Moriarty (1984a, p. 5) note, "the task is often taken on by top-level managers.... In other companies top marketing and sales managers and/or field sales managers take the responsibility." The second descriptive variable connects directly to this question of part-time versus full-time responsibility. We define the key account coordinators' dedication to key accounts as the percentage of their time they spend managing key accounts versus average accounts. Another question related to the allocation of time is how much time is spent with customers compared with the time devoted to internal coordination. Colletti and Tubridy (1987) report that 40% of a major account sales representative's time is administration work. We define the internal orientation of key account coordinators as the percentage of their time they spend on internal coordination versus external interaction with customers. A final descriptive question that has frequently been raised in KAM studies is how many accounts key account coordinators are typically looking after (Dishman and Nitse 1998; Sengupta, Krapfel, and Pusateri 1997a; Wotruba and Castle-berry 1993). We define the span of accounts as the number of accounts for which key account coordinators are responsible.
Outcomes
One of our objectives is to go beyond the conceptualization of KAM approaches and the taxonomy to explore the performance effects of design decisions. We distinguish between outcomes with respect to key accounts and outcomes on the level of the overall organization. Given that KAM involves investing in special activities and actors for key accounts that are not available for average accounts, we define KAM effectiveness as the extent to which an organization achieves better relationship outcomes for its key accounts than for its average accounts. Although the benefits of KAM have often been claimed in the KAM literature, empirical evidence on the outcomes of KAM is rare and methodologically limited to t-tests or correlations of single-item ratings of performance (Platzer 1984; Sengupta, Krapfel, and Pusateri 1997a; Stevenson 1981). A much better understanding of the outcomes of collaborative relationships has been developed by relationship marketing research (e.g., Kumar, Scheer, and Steenkamp 1995). This literature suggests that firms, through building relationships, pursue such outcomes as long-term orientation and continuity (e.g., Anderson and Weitz 1989; Ganesan 1994), commitment (e.g., Anderson and Weitz 1992; Geyskens et al. 1996; Gundlach, Achrol, and Mentzer 1995), trust (e.g., Geyskens, Steenkamp, and Kumar 1998; Moorman, Deshpandé, and Zaltman 1993; Rindfleisch 2000), and conflict reduction (e.g., Frazier, Gill, and Kale 1989).
Some authors indicate that KAM has outcomes not only with respect to key accounts but also at the organization level. As Cespedes (1993, p. 47) notes, "Another benefit is the impact on business planning. Salespeople at major accounts are often first in the organization to recognize emerging market problems and opportunities." Organization-level outcomes are also affected by average accounts. Following Ruekert, Walker, and Roering's (1985) terminology, we distinguish among adaptiveness, effectiveness, and efficiency. We define them as follows:
- Adaptiveness is the ability of the organization to change marketing activities to fit different market situations better than its competitors,
- Performance in the market is the extent to which the organization achieves better market outcomes than its competitors, and
- Profitability is the organization's average return on sales before taxes over the past three years.
Data Collection and Sample
Given our research objective of identifying prototypical approaches to KAM, we collected data using a mail survey in five business-to-business sectors in the United States and Germany. The questionnaire was initially designed in English and based on an extensive literature review and on field interviews with 25 managers, consultants, and academics in Germany and 25 in the United States on major trends in marketing organization (Homburg, Workman, and Jensen 2000). To ensure equivalent questionnaires in the two countries, the English version of the questionnaire was first translated into German by one expert translator and then retranslated into English by a second; both translators were bilingual. The two expert translators reconciled differences. We pretested the resulting two versions of the questionnaire and modified them in the United States and Germany on the basis of comments from 8 marketing and sales managers who completed the entire survey.
An important issue in designing our empirical study is obtaining the appropriate informants. We reiterate that the object of our research is the overall organizational approach toward the entire portfolio of key customers. A first implication of this is that, for the intraorganizational issues, the number of potential informants is limited to higher-level managers who have an overview over the marketing and sales organization. A second implication is that, regarding the outcomes of KAM, ideally the dyadic perceptions of all key accounts would need to be combined. In light of the obvious selection problems to obtain multiple, knowledgeable, high-level respondents as well as participation from several key accounts, we opted for a key informant approach. Although the single-respondent design curbs the generalizability of the results, John and Reve's (1982, p. 522) findings "indicate that careful selection of informants in conjunction with the use of internally consistent multi-item scales can provide reliable and valid data." On the basis of the field interviews, we determined that the most appropriate respondent is the head of the sales organization. We strove to minimize the limitation imposed by the single-informant design by determining the competence of the respondent to answer the survey. We excluded answers from lower-level respondents and from respondents with less than two years' experience in the selling organization from the analysis. As the description of our sample shows, our respondents are high-level managers.
We obtained a random sample of 1000 U.S. and 1000 German firms in the five business-to-business sectors from commercial list providers and sent an initial survey to the head of the sales organization. The cover letter and directions on the survey indicated that the survey should be answered by a vice president (VP) or director of sales or should be forwarded to someone familiar with how the firm's most important set of customers is managed. Because prior research has shown that managerial practice has different labels to denote important customers, we asked respondents to fill out the survey with respect to their most important set of business customers, regardless of the label they use for these customers. We sent a reminder postcard one week after the initial mailing to encourage response. We made follow-up telephone calls starting two weeks later to verify the contact name and the appropriateness of the firm for participation in the study and to encourage response. The survey was mailed a second time to all people approximately four weeks after the initial mailing. On the basis of the telephone calls and undeliverable mail, we determined that 174 of the U.S. firms and 171 of the German firms were inappropriate for the study. We received responses from 264 German firms and 121 U.S. firms, for effective response rates of 31.8% and 14.6%, respectively, and an overall response rate of 23.3% (for the sample composition, see Table 2). These response rates are in the range reported by other surveys sent to senior-level sales and marketing managers (Harzing 1997) and are comparable to the response rates of other data collections for taxonomic purposes (Bunn 1993; Cannon and Perreault 1999).
We controlled for a possible nonresponse bias in three ways. First, we divided the data into thirds in each country on the basis of the number of days from initial mailing to response (Armstrong and Overton 1977). The t-tests within each country between mean responses of early and late respondents indicated no statistically significant differences (p < .05). Second, we compared the German and the U.S. subsamples. The distributions in the subsamples do not differ statistically by revenue and by industry on the basis of chi-square tests (p > .05). Third, we compared the resulting KAM types to approaches identified in prior literature. As we elaborate in the "Results" section, we found that our taxonomy reflects all approaches to KAM that have been discussed previously. This supports the external validity of our taxonomy. We even detect several less formalized approaches that have not been described previously.
Measure Development Procedures
General measurement approach. Given the scarcity of prior empirical research, most scales for the study were newly generated. We used three types of measures in the survey: single-item measures, reflective multi-item measures, and formative multi-item measures. A single-item measure used in the survey was profitability. If observed variables (and their variances and covariances) were manifestations of underlying constructs, we used a reflective measurement model (Bagozzi and Baumgartner 1994). In that case, we can assess the scales' psychometric properties by means of criteria based on confirmatory factor analysis (Anderson and Gerbing 1988; Fornell and Larcker 1981). If necessary, we purified the item pools. Confirmatory factor analysis is considered superior to more traditional criteria (such as Cronbach's alpha) in the context of scale validation because of its less restrictive assumptions (AndersonandGerbing1988;Bagozzi, Yi, and Phillips 1991). We applied reflective measures if not otherwise indicated.
If a construct was a summary index of observed variables, a formative measurement model (Bagozzi and Baumgartner 1994) is more appropriate. In that case, observed variables cover different facets of the construct and cannot be expected to have significant intercorrelations. We used a formative scale to measure the proactiveness of activities for key accounts because, unlike intensity, the proactiveness on one activity item is not intercorrelated with the proactiveness on another. As an example, intense coordination of manufacturing schedules (high intensity) often requires highly coordinated logistics (high intensity). However, if a key account demands that the supplier coordinate manufacturing processes (low proactiveness), it may be the supplier who comes up with the suggestion to coordinate logistics as well in order to accomplish coordinated manufacturing (high proactiveness). Thus, although high intensity on one activity goes along with high intensity on another, this cannot be expected for proactiveness. The proactiveness construct must be understood in terms of a proactiveness index across the partial activities.
Control variables. In examining the performance effects of KAM, we have controlled for the effects of two environ-mental variables. Uncertainty has been identified as a determinant of performance in much of the research on organization theory and strategy. Specifically, we control for market dynamism. If customers' structures and needs change rapidly, it becomes more difficult for suppliers to be responsive to those needs. We also control for competitive intensity, which has been argued by many strategy researchers to be one of the most important determinants of performance (e.g., Porter 1980). Both control variables have frequently been employed in the related literature on market orientation (e.g., Jaworski and Kohli 1993; Pelham 1999).
Scale assessment. The Appendix provides our scale items and scale properties. We assessed measure reliability and validity using confirmatory factor analysis. Composite reliability represents the shared variance among a set of observed variables that measures an underlying construct (Fornell and Larcker 1981). Each construct manifests a composite reliability of at least .6 (Bagozzi and Yi 1988, p. 82). In addition, coefficient alpha values suggest a reasonable degree of internal consistency among the corresponding indicators. Nunnally (1978) recommends a threshold alpha value of .70 but suggests in a previous work (1967, p. 226) that a level of .6 is acceptable for exploratory research subjects (see also Murphy and Davidshofer 1988). For each of the KAM dimensions, outcomes, and control variables, we assessed discriminant validity on the basis of the criterion suggested by Fornell and Larcker (1981), which is recognized as more rigorous than the alternative chi-square difference test.
To ensure measurement invariance across countries, we followed the procedure suggested by Steenkamp and Baumgartner (1998). Given our objective to test dependence relationships among variables, configurational invariance and metric invariance must be fulfilled. Configurational invariance implies that the factorial structure underlying a set of observed measures is the same across the two countries. Metric invariance is a stricter criterion that assesses whether the units of measurement (i.e., the scale intervals) are equivalent in the German and the U.S. subsamples. Using multiple-group confirmatory factor analysis, we found full configurational invariance and at least partial metric invariance (at least two items were metric invariant) for our constructs. Therefore, merging the two national subsamples is valid.
Taxonomic Procedures
In the previous sections, we have identified fundamental dimensions of KAM approaches and have established rigorous measures of key constructs. Next, we give a brief summary of how we technically proceeded in identifying configurations of KAM on the basis of these key constructs. Given our objective of identifying prototypical approaches, we first decided to use nonoverlapping clustering and a distance measure. We followed the procedure used by Bunn (1993) and by Cannon and Perreault (1999) and took a multistage clustering approach. The two central issues in clustering are determining the appropriate number of clusters and assigning the observations to clusters.
We used the hierarchical clustering algorithm developed by Ward (1963) in combination with Sarle's (1983) cubic clustering criterion to determine the appropriate number of clusters. The cubic clustering criterion has been among the top-performing criteria in Milligan and Cooper's (1985) comparative study of 30 methods for estimating the number of population clusters. Ward's (1963) algorithm seeks at each step to form mutually heterogeneous and internally homogeneous clusters in the sense of the least error sum of squares. Because of the method's sensitivity to outliers, we standardized the clustering variables by dividing each variable by its range. Clustering ten randomly selected subsamples from our data, each containing two-thirds of the sample, we found strong support for an eight-cluster solution.[ 2] We also evaluated the stability of the result after eliminating outliers.
We then clustered the complete sample by means of a hybrid approach combining Ward's (1963) method with the k-means approach (Punj and Stewart 1983). Simulation studies on the performance of clustering algorithms demonstrate that partitioning methods (e.g., k-means) yield excellent results if given a reasonable starting solution (for an overview, see Milligan and Cooper 1987). Using Ward's method to compute a starting solution for k-means has been shown to be a powerful combination (Helsen and Green 1991) and has been recommended by Punj and Stewart (1983). Arabie and Hubert (1994, p. 169) note that "Nearly a decade later, that recommendation still seems like a good one." Finally, we cross-validated the stability of the cluster assignment using the procedure recommended by Cannon (1992).[ 3]
Taxonomy of Approaches to KAM
Given that we obtained the clusters on the basis of a purely technical procedure, we need to ensure that different clusters are not the consequence of different understandings of what an important account is. Therefore, we controlled for the importance of the criteria companies use to define and select their most important customers. For all clusters, the current and the potential sales volume dominates other criteria, such as learning about key technologies, the international scope of the account, the possibility of using the account as a reference, demand for special treatment by the account, or internal coordination problems in catering to the account. In conclusion, our statistical tests show that the clusters are comparable.
The last step in the taxonomy is to validate the recognizability of the clusters, which verifies whether they have meaningful interpretations (Rich 1992). Table 3 shows the cluster means for each of the eight cluster variables. Following the interpretation steps suggested by Bunn (1993), we first compared the clusters on the basis of Duncan's multiple-range test and then transferred the resulting bands into verbal descriptions of a cluster's position with respect to the cluster variables (see Table 4). The results for the additional descriptive variables are shown in Tables 3 and 5.
We now interpret the clusters in turn and assign labels to the approaches. Although there are risks of oversimplification in using such labels, they serve the didactic purpose of highlighting empirically distinct aspects of different approaches and facilitate the discussion of the results.
Top-management KAM. Top-management KAM truly deserves the name "program." These companies highly formalize the management of their key accounts. More than 60% of companies in this cluster have dedicated sales managers who coordinate activities for key accounts, which is consistent with the finding that 73% of key account coordinators' time is devoted to key accounts. Of the approaches, top-management KAM manifests the highest degree of top management involvement in KAM. Therefore, it is not surprising that this approach is managed out of the company headquarters (86.1% of key account coordinators are based in the suppliers' headquarters). In addition to heavy top management involvement, these companies make extensive use of teams. Activities for key accounts are intense and are proactively initiated. An interesting finding is that selling center esprit de corps is high, whereas access to marketing and sales as well as nonmarketing and nonsales resources is low. This may suggest that access to resources is barely needed. Top management might negotiate umbrella contracts, which operative teams carry out using highly standardized procedures.
Middle-management KAM. Middle-management KAM manifests a high level of formalization, but in contrast to the first approach, top-management involvement is medium. Intensity and proactiveness with respect to activities are also on a medium level. These results may suggest that these companies have installed a formal key account program, but on a middle-management level. Our interpretation is supported by the finding that 28.8% of key account coordinators are locally based in this approach, compared with 13.8% in top-management KAM. That key account managers are often locally based may also explain the high access to marketing and sales resources. On the contrary, selling center esprit de corps and access to nonmarketing and nonsales resources are low, which gives the overall impression that KAM in these companies is mainly driven by (local) middle management in the marketing and sales function.
Operating-level KAM. Companies using operating-level KAM are doing a lot for their key accounts and have considerably standardized procedures. In these aspects, this approach is comparable to top-management KAM and middle-management KAM. However, top-management involvement is lower than in these other approaches. Not surprisingly, access to functional resources is low. Whereas the VP of sales or marketing is the key account coordinator in 27.4% of top-management KAM companies and 23.1% of middle-management KAM companies, this is only the case for 9.8% of companies in the operating-level KAM cluster. The low degree of top-management involvement, along with fairly developed activities and teams, suggests that this KAM approach is mainly borne by the operating level. None of the other approaches has such a high percentage of companies with dedicated sales managers for key accounts (70.8%), 17.1% of whom are locally based.
Cross-functional, dominant KAM. The companies using cross-functional, dominant KAM have the highest values for nearly all variables. First, activities are intense and are proactively created. Second, formal procedures and team structures are fully developed. Top management is strongly involved. Third, selling center esprit de corps and access to functional resources are high. Of cross-functional KAM companies, 65.6% have dedicated sales managers as key account coordinators. Their share of time spent externally with the customer is the highest of all approaches, as is reflected by the 46% of time spent on internal orientation. The overall picture suggests that these companies are completely focused on their key accounts. It seems that, in these companies, customer management is virtually identical with KAM.
Unstructured KAM. As shown by the low values on formalization, top-management involvement, and use of teams, companies using unstructured KAM have not created special organizational structures for key accounts and do not have a program in place. This is consistent with the observation that activities are more a reaction than a proactive initiative, as is indicated by the 3.83 mean on proactiveness. Moreover, KAM comes mainly out of the headquarters, and key account coordinators are often normal sales managers (18.5% compared with 6.3% in cross-functional KAM). We observe that 62% of key account coordinator time is spent on internal coordination, the highest percentage of all clusters. This may account for the extremely high esprit de corps for KAM among selling center members and for the ease of obtaining contributions from marketing and sales as well as other functional resources. The overall impression is that these companies are pursuing KAM on an ad hoc basis, mobilizing internal resources only when the key accounts ask for it. Of these companies, 11.1% name the general manager to be the key account coordinator, though top management involvement is the lowest of all approaches. This suggests that the general management's responsibility exists on paper only.
Isolated KAM. Intensity and proactiveness of activities as well as formalization and use of teams manifest midrange values in the isolated KAM cluster. This implies that these companies are trying to do something for key accounts, which is supported by the finding that top management is fairly involved. The most striking feature is that in 44.4% of companies in this cluster, key account coordinators are locally based. This may explain why this cluster has low values on selling center esprit de corps and on access to nonmarketing and nonsales resources. Therefore, the overall picture is that KAM is a rather isolated, local sales effort in these companies that, despite some effort from the top management, struggles for cooperation from the central business units.
Country-club KAM. The striking characteristic of the country-club KAM cluster is a high degree of top management involvement that goes along with low values on most other variables. The management of key accounts in these companies is not guided by formal procedures, and teams are hardly ever formed. Special activities are performed less intensely and less proactively than under the other approaches. Most important, there are basically no dedicated key account coordinators. The KAM coordinator is often the VP of sales, a general manager, or even the VP of marketing. The comparatively low level of activities combined with high top-management involvement and high access to sales suggests that, in these companies, KAM is little more than representation by senior managers. In 33.3% of these firms, key accounts are simply handled by normal sales managers. With the exception of the top-management involvement, this approach is fairly close to the no-KAM cluster.
No KAM. The no-KAM cluster has the lowest values on nearly all variables: Comparatively little activity is performed, but not proactively. Formalization is low, as are cross-functional cooperation and esprit de corps. Mainly VPs of marketing and sales or general managers are named as key account coordinators, though top-management involvement in this cluster is low. This suggests that the VPs have responsibility on paper but do not actually perform that role. The interpretation of this approach is straightforward: These companies do not manage their key accounts. Or some companies may only have started to manage their key accounts, given that they profess to have dedicated key account coordinators.
Comparison with Existing Research
Although prior research has never classified KAM approaches empirically, there is some discussion of options companies have in implementing KAM. McDonald, Mill-man, and Rogers (1997) suggest ideal types of KAM, assuming that KAM approaches line up along a continuum from pre-KAM to synergistic KAM. Along the continuum, the activity intensity, the use of teams, and top-management involvement are assumed to rise, which implies a correlation among these design variables. Our results do not support this ideal continuum or the correlation. As we have shown, high degrees of top-management involvement occur in combination with both high and low degrees of activity intensity and in combination with both high and low degrees of use of teams.
Shapiro and Moriarty (1984a) propose another typology of KAM programs based on qualitative interviews in 19 large manufacturing and service companies (see also the supplementary comments by Kempeners and van der Hart [1999]). These researchers distinguish among six types of KAM programs that resemble the KAM approaches we identified. More specifically, their national account division resembles cross-functional KAM, their corporate-level program is similar to top-management KAM, their operating unit program at the group level is similar to middle-management KAM, their operating unit program at the division level parallels operating-level KAM, their part-time program resembles country-club KAM, and their noprogram option is close to the no-KAM approach. However, our work goes beyond the prior work by identifying the design variables behind the approaches, providing richer descriptions of the approaches, and supplementing the descriptions with quantitative data. We also detected two additional KAM approaches, unstructured KAM and isolated KAM. These two approaches involve a considerable number of activities for key accounts but do not require formalization of the approach. In conclusion, our findings seem to indicate that we have not overlooked KAM approaches that occur in practice. This speaks for the validity of our taxonomy and for the absence of a nonresponse bias.
Outcomes
We now turn to the success of the various KAM approaches. In interpreting the results in Table 6, we must pay attention to whether the outcome variable is on the level of the key accounts or of the organization as a whole. The effectiveness of KAM can be assumed to be strongly influenced by how key accounts are managed and is therefore our main out-come variable of interest. On the contrary, variance in organization-level outcomes, such as performance in the market, adaptiveness, and profitability, can be explained by many factors other than KAM. A firm may be driving its performance, for better or worse, through the average as opposed to the key accounts.[ 4]
On both the KAM level and the organization level, the no-KAM and the isolated KAM approaches perform the worst. On the organization-level outcomes, cross-functional KAM companies stand out with respect to both performance in the market and adaptiveness. As far as profitability is concerned, top-management KAM companies perform best. That the most effective approaches are not the most profitable ones may be explained by some approaches involving higher costs in addition to generating higher revenues.
Another observation in Table 6 is that several KAM approaches are equally successful. This finding is consistent with the concept of "equifinality" emphasized in the configurational approach (Meyer, Tsui, and Hinings 1993). However, given our key informant design, it raises the issue whether a common method bias is present in the data. Two facts from our data speak against the presence of a bias. First, a possible key informant bias should affect the subjective performance measures (e.g., KAM effectiveness), but not the objective performance measure (i.e., profitability). That several configurations also manifest the same level of objective performance supports the validity of our findings on the subjective measures. Second, even in very active approaches (e.g., top-management KAM), there is much variance across the respondents regarding the performance variables. Indeed, the lack of significant differences among some approaches is due to the high variance rather than a tendency of all key informants to rate their own approach highly.
It is necessary to verify whether the performance differences hold true even when we consider environmental variables. Market dynamism and competitive intensity have been shown to influence performance in a market orientation context (Jaworski and Kohli 1993). To control for these effects, we made use of analysis of covariance (ANCOVA). Cluster membership was the (nominal) factor, and the control variables served as covariates. Table 7 shows that though market dynamism has a significant effect on performance in the market and competitive intensity has an effect on profitability, the effects of cluster membership on all performance outcomes are still significant.
Research Contribution
Despite the immense importance of KAM in managerial practice, prior research in this area has been fragmented, and sound empirical studies have been scarce. The contributions of this article come from both the conceptualization and the taxonomy.
The first contribution of this article is to provide conceptual clarity to KAM design decisions and to lay the basis for further research. In addition to synthesizing the existing literature, this article extends the conceptual scope of KAM research by drawing attention to the failure of previous research to go beyond the boundaries of formalized KAM programs and study nonformalized KAM approaches. We derive an integrative conceptualization of KAM that identifies four key dimensions: ( 1) activities, ( 2) actors, ( 3) resources, and ( 4) formalization (see Figure 1). We also develop scales for key constructs related to KAM.
A second contribution of our work consists in its being the first study to empirically classify designs of organizational approaches to selling. Although taxonomies exist for the buyer side (Bunn 1993) and for the relationship between buyer and seller (Cannon and Perreault 1999), there has been no taxonomy on the organization of the seller side. Moncrief (1986) has created a taxonomy of individual sales position designs, but the level of analysis in selling research has shifted to the selling team (Weitz and Bradford 1999). As Marshall, Moncrief, and Lassk (1999, p. 88) state, "Clearly, the operative set of sales activities representing a sales job in the mid-1980s is deficient to accurately under-stand and portray sales jobs of today." Therefore, our taxonomy closes a gap in empirical knowledge about organizational approaches to selling.
A third major contribution is the refinement of existing KAM typologies. We confirmed the types of KAM postulated by Shapiro and Moriarty (1984a), supplemented them with empirical detail, and detected two additional approaches. These two involve a considerable number of activities for key accounts but do not require formalization of the approach.
An additional contribution of our taxonomic research is to provide deeper insights into the performance aspects of KAM approaches. On a general level, it is important to note that the same level of performance can be accomplished through different approaches. Yet some approaches perform significantly worse than others. The finding that no-KAM companies are behind on all performance dimensions represents the most comprehensive empirical demonstration so far that suppliers benefit from managing their key accounts. The similar performance of isolated KAM indicates that mediocre approaches to KAM are likely to fail. These results suggest that failure to achieve access to and commitment of cross-functional resources seems to play a critical role for the success of KAM programs. This reinforces recent research on marketing organization that recognizes the cross-functional dispersion of marketing activities (Workman, Homburg, and Gruner 1998).
On a general level, our work has shown that there is value in blending relationship marketing concepts and marketing organization concepts. Within our conceptual model, the actor, resources, and formalization dimensions are inspired by marketing organization research, and the activity and the outcome dimensions draw on relationship marketing research.
Avenues for Further Research
Further research should continue building the bridge between relationship marketing concepts and marketing organization concepts. One possible avenue is to empirically link the KAM approaches identified in this article to relationship types (Cannon and Perreault 1999). In designing these empirical studies, the existence of nonformalized KAM approaches should be carefully considered.
Future empirical designs should also seek to overcome some of the limitations of this article. One limitation stems from the static design of our study. As research by Pardo, Salle, and Spencer (1995) has shown, key account approaches evolve over time. Further research should also capture the dynamic performance effects of KAM. As Kalwani and Narayandas (1995) have shown, the beneficial outcomes of customer-oriented activities appear with a certain delay. Another limitation of our article is the use of a single-informant design, which focuses on one side of the seller-buyer dyad. Future studies should also take the key accounts' perspectives into consideration. This is particularly important for analyzing the outcomes of KAM. One way to extend our examination of outcomes would be to differentiate the performance impacts of individual KAM dimensions. In this context, the effect of KAM-level outcomes on organization-level outcomes should be explored as well.
Another open issue is the effect of the environment on KAM dimensions. The literature has claimed that the formation of key account programs is influenced by characteristics of buyers and of the market environment, such as purchasing centralization, purchasing complexity, demand concentration, and competitive intensity (Boles, Johnston, and Gardner 1999; Stevenson 1980). Yet rigorous empirical research linking multiple environmental dimensions to multiple KAM dimensions is still lacking.
Managerial Implications
One of the most fundamental managerial tasks is designing the internal organization. These design decisions are typically taken on the level of the organization rather than the level of individual accounts. Therefore, the organizational perspective adopted in this research has particular appeal to top executives.
The key message to managers is not to take a laissez-faire approach to KAM. Given that the no-KAM option is markedly less successful than other approaches, our results call for managers to manage key accounts actively. That there are significant performance differences among the more actively managed approaches demonstrates that it is important to design the approach in detail. Our work also shows that KAM requires support from the whole organization. Therefore, top managers should not leave the design of the KAM approach to the sales organization alone.
The conceptualization of KAM developed in this article provides managers with a systematic way to design the KAM approach. As Day and Montgomery (1999, p. 12) note, "conceptual frameworks, typologies, and metaphors that are the precursors to actual theory building" provide valuable guidelines for managers. Managers should work through four questions: ( 1) What should be done for key accounts? ( 2) Who should do it? ( 3) With whom in the organization is cooperation needed? and ( 4) How formalized should the KAM approach be? We particularly emphasize that managing key accounts does not necessarily require setting up a formal key account program.
The taxonomy developed in this article further supports managers in designing their KAM. Managers can categorize their own companies' approach on the basis of the prototypical implementation forms identified. From the taxonomy, they can discover neglected design areas and develop alternative designs.
Key account management is a highly relevant issue for marketing and sales managers. In addition, it is an area for academic research, because it builds a bridge between marketing organization and relationship marketing. Therefore, the lack of sound academic research in this area is surprising. This article provides the basis for further research by contributing an integrative conceptualization of KAM. It also fills a gap in knowledge about how firms design their approach to key accounts. Finally, it shows that actively managing key accounts leads to significantly better performance than neglecting them does.
1 It is worth noting that some companies use different labels to denote various degrees of an account's importance within a key account program (Napolitano 1997; Shapiro and Moriarty 1982).
- 2 Seven subsamples manifested eight clusters, one manifested seven clusters, and two manifested no-cluster structure according to the cubic clustering criterion for a range of one to ten clusters.
- 3 We split the sample into three equally large subsamples (A, B, and C) and ran through the hybrid approach twice for [Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.] and [Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.] We then evaluated whether observations in Subsample B had been assigned to the same cluster in both runs.
- 4 We owe this idea to an anonymous reviewer.
Legend for chart:
A = Authors
B = Year
C = Empirical Basis
D = Dimensions Discussed
E = Main Focus/Key Statements
A Boles, Barksdale, and Johnson
B 1996
C 73 national account decision makers from NAMA list
D
E • Identifies salesperson activities, skills, and attitudes
that are appreciated by key account decision makers.
A Weeks and Stevens
B 1997
C 133 NAMA members
D
E • Key account managers are dissatisfied with sales training
programs.
• Descriptives on experience and skills of key account
managers.
Group 2 Research on Key Account Relationships
A Lambe and Spekman
B 1997
C 118 managers, mostly U.S. based
D
E • Explores differences between national account relationships
and other types of strategic alliances.
A McDonald, Millman, and Rogers
B 1997
C Interviews with 11 key account manager/purchasing manager dyads
D
E • Describes development of key account relationships from
pre-KAM, transactional phase to collaborative relationship that
goes along with increasing complexity of involvement.
A Pardo
B 1997
C 20 interviews with key accounts of electricity and telephone
companies
D
E • Suggests three ways that key accounts perceive KAM;
disenchantment, interest, and enthusiasm.
• Moderators of KAM program perception by customers are
perceived product importance and centralization of purchase
decisions.
A Sengupta, Krapfel, and Pusateri
B 1997b
C 176 NAMA members in manufacturing and service companies
D
E • Switching costs in key account relationships.
A Sharma
B 1997
C 109 interviews with buyers of telephone equipment
D
E • Customers' preference for KAM programs depends on levels
involved in purchasing, functions involved in purchasing, and time
taken for purchasing.
Group 3: Research on KAM Approaches
A Colletti and Tubridy
B 1987
C 105 NAMA members
D Actors
E • Explores reporting level, time utilization, compensation,
and required skills of account managers.
A Dishman and Nitse
B 1998
C 27 interviews with NAMA members whose key account program is older
than five years
D Actors
E • Implementation options of national account management are
cooperation with existing sales force, company executives, or a
separate sales force.
• Descriptives on number and size of customers in KAM
program.
A Montgomery and Yip
B 2000
C 191 managers in 165 manufacturing and service companies
D Activities, actors, outcomes of KAM
E • Use of global account management structures will increase.
• Use of global account management structures is driven by
customer demand.
• Customer demands encompass coordination of resources,
uniform terms of trade, and consistency in service quality and
performance.
A Napolitano
B 1997
C NAMA study among Fortune- 1000 companies, no sample size provided
D Actors, outcomes of KAM
E • The number of national account managers has tripled between
1992 and 1996.
• 53% of companies report poor effectiveness of partnering
with customers.
A Pardo, Salle, and Spencer
B 1995
C 10 interviews within one telecom company
D Activities, actors, resources
E • Case study of one key account program over 20 years.
A Pegram
B 1972
C 250 interviews with executives in manufacturing and service
companies
D Activities, actors
E Describes alternatives for assigning KAM responsibility on a
part-time or a full-time basis.
A Platzer
B 1984
C 130 interviews with national account executives
D Activities, actors, resources, outcomes of KAM
E • Describes activities for key accounts.
• Describes types of national account units.
• Describes success factors of national account programs.
A Sengupta, Krapfel, and Pusateri
B 1997a
C 176 NAMA members in manufacturing and service companies
D Actors, outcomes of KAM
E • Descriptive statistics on growth of KAM approaches and key
account manager workload.
• Identifies customer-based compensation as a success factor
of KAM.
A Shapiro and Moriarty
B 1984a
C 100+ interviews in 19 manufacturing and service companies
D Actors
E • Describes alternatives for integrating a KAM program into
the structural organization.
• Discusses issues pertaining to the internal structure of
KAM units.
A Shapiro and Moriarty
B 1984b
C 100+ interviews in 19 manufacturing and service companies
D Activities, resources
E • Describes customer need for activities in such areas as
pricing, products, service, and information.
• Describes roles of various functional groups in the
performance of activities for key accounts.
A Stevenson
B 1981
C 34 executives in 33 manufacturing companies
D Actor, outcomes of KAM
E • Explores payoffs from national account management.
A Wotruba and Castleberry
B 1993
C 107 NAMA members
D Actors, outcomes of KAM
E • Explores staffing procedures for KAM positions.
• Performance of key account managers is affected by length
of tenure, age of program, and time devoted to key accounts.
A Yip and Madsen
B 1996
C Case studies of IBM, AT&T, and Hewlett-Packard
D Actors, resources
E • Develops framework for global account management.
• Describes internal cooperation for key accounts in global
companies.
Notes: NAMA = National Account Management Association.
Total
A: Position of Respondents (n=385)
Managing director, CEO, VP of region, head of business unit 19%
VP marketing, VP sales, VP sales and marketing 49%
Head of KAM, key account manager 9%
Sales manager, product manager 19%
Other 3%
United
Germany States Total
B: Demographics of the Firms (n=264) (n=121) (n=385)
Industry* Chemical and pharmaceutical 24% 18% 22%
Machinery 22% 30% 25%
Computer and electronics 17% 14% 16%
Banks and insurances 17% 11% 15%
Food and packaged goods 20% 27% 22%
Annual Revenues* <$15 million 5% 10% 6%
$15-$30 million 14% 11% 13%
$30-$60 million 20% 15% 18%
$60-$150 million 17% 24% 19%
$150-$300 million 13% 11% 13%
$300-$600 million 11% 13% 12%
$600-$1,500 million 5% 10% 6%
>$1,500 million 14% 11% 13%*Equal structure of subsamples based on p(χ²) > .05.
Legend for chart:
A = Dimension
B = Variable
C = Cluster: Top Management KAM (n=37)
D = Cluster: Middle Management KAM (n=76)
E = Cluster: Operating Level KAM (n=57)
F = Cluster: Cross functional, dominant KAM (n=44)
G = Cluster: Unstructured KAM (n=38)
H = Cluster: Isolated KAM (n=40)
I = Cluster: Country-Club KAM (n=37)
J = Cluster: No KAM (n=46)
K = Cluster: Total (n=375)
A B C D E
F G H
I J K
Activities Activity 5.08[bc] 4.99[b] 5.15[bc]
intensity 5.44[c] 4.75[b] 5.00[b]
4.19[a] 4.11[a] 4.86
Activity 4.15[bc] 4.13[bc] 4.27[ab]
proactiveness 4.60[d] 3.83[ab] 4.16[bc]
3.54[a] 3.79[ab] 4.08
Formali- Approach 5.48[f] 5.05[e] 4.58[d]
zation formalization 5.64[f] 2.81[b] 3.64[c]
2.12[a] 2.72[b] 4.15
Actors Top management 5.66[e] 3.98[c] 3.19[b]
involvement 4.48[d] 2.52[a] 4.23[cd]
4.59[d] 3.19[b] 3.93
Use of teams 5.05[d] 3.08[b] 5.32[de]
5.62[e] 3.16[b] 4.49[c]
2.18[a] 2.53[a] 3.93
Resources Selling 5.57[c] 5.28[b] 5.52[c]
center esprit 6.14[d] 5.97[d] 3.93[a]
de corps 4.69[b] 3.82[a] 5.14
Access to 5.34[bc] 5.82[de] 5.11[b]
marketing and 6.51[f] 5.95[d] 5.50[cd]
sales 6.44[f] 4.48[a] 5.62
resources
Access to 4.37[a] 5.18[b] 4.42[a]
nonmarketing 6.05[c] 5.51[b] 4.29[a]
and nonsales 5.40[b] 4.13[a] 4.92
resources
Additional Dedication 73%[c] 66%[abc] 70%[bc]
Descriptive to key 73%[c] 57%[a] 66%[abc]
Variables accounts 62%[abc] 57%[ab] 66%
Internal 50%[ab] 49%[ab] 49%[ab]
orientation 46%[a] 62%[c] 51%[ab]
49%[ab] 58%[bc] 51%
Span of 5 5 5
accounts 5 5 5
(median) 8 10 5Notes: Reported values are mean values if not otherwise noted. In each row, cluster means that have the same superscript are not significantly different (p < .05) on the basis of Duncan's multiple-range test. Means in the lowest band are assigned "a," means in the next highest band "b," and so forth. Means in the highest band are printed in bold; means in the lowest band are in italics.
Legend for chart:
A = Variable
B = KAM Approach: Top-Management KAM (n=37)
C = KAM Approach: Middle-Management KAM (n=76)
D = KAM Approach: Operating-Level KAM (n=57)
E = KAM Approach: Cross-functional, dominant KAM (n=44)
F = KAM Approach: Unstructured KAM (n=38)
G = KAM Approach: Isolated KAM (n=40)
H = KAM Approach: Country-Club KAM (n=37)
I = KAM Approach: No KAM (n=46)
J = KAM Approach: Total (n=375)
A B C D E F G H I J
Acti- *Med- Med- *Medium *High Medium Med- ^Low ^Low
vity ium ium high ium
inten- high
sity
Acti- Medium Med- ^Low- *High ^Low- Med- ^Low ^Low
vity ium medium medium ium medium
pro-
active-
ness
App- *Very High Rather *Very Low Rath- ^Very Low
roach high high high er low
forma- low
liza-
tion
Top- *Very Med- Low High ^Very Med- High Low
man- high ium low ium
age high
ment
in-
volve-
ment
Use of Much Lit- *Much- *Very Little Med- ^Very ^Very
teams tle very much ium lit- little
much tle
Sell- Rather Rath- Rather *Strong *Strong ^Weak Rath- ^Weak
ing strong er strong er
center weak weak
esprit
de
corps
Access Rather High Low *Very Rather Med- *Very ^Very
to low high high ium high low
mar-
keting
and
sales
re-
sources
Access ^Low Med- ^Low *High Medium ^Low Med- ^Low
to non- ium ium
market-
ing
and
non-
sales
re-
sourcesNotes: Means in the highest band have *; means in the lowest band have ^.
Legend for chart:
A = Position of Key Account Coordinator
B = KAM Approach: Top-Management KAM (n=37) Headquarter
C = KAM Approach: Top-Management KAM (n=37) Local
D = KAM Approach: Middle-Management KAM (n=76) Headquarter
E = KAM Approach: Middle-Management KAM (n=76) Local
F = KAM Approach: Operating Level KAM (n=57) Headquarter
G = KAM Approach: Operating Level KAM (n=57) Local
H = KAM Approach: Cross-functional, dominant KAM (n=44) Headquarter
I = KAM Approach: Cross-functional, dominant KAM (n=44) Local
J = KAM Approach: Unstructured KAM (n=38) Headquarter
K = KAM Approach: Unstructured KAM (n=38) Local
L = KAM Approach: Isolated KAM (n=40) Headquarter
M = KAM Approach: Isolated KAM (n=40) Local
N = KAM Approach: Country-Club KAM (n=37) Headquarter
O = KAM Approach: Country-Club KAM (n=37) Local
P = KAM Approach: No KAM (n=46) Headquarter
Q = KAM Approach: No KAM (n=46) Local
R = KAM Approach: Total (n=375)
A
B C D E F G H I J
K L M N O P Q R
Normal sales manager
3.4% -- 5.8% 3.8% 2.4% 7.3% 6.3% -- 14.8%
3.7% 7.4% 11.1% 19.0% 14.3% 8.3% 8.3% 14%
Dedicated sales manager
48.3% 13.8% 44.2% 17.3% 53.7% 17.1% 40.6% 25.0% 44.4%
7.4% 18.5% 33.3% -- 4.8% 22.2% 5.6% 52%
VP of sales
24.1% -- 17.3% 5.8% 9.8% -- 15.6% -- 11.1%
3.7% 14.8% -- 28.6% 9.5% 30.6% 5.6% 22%
VP of marketing
3.4% -- -- -- -- -- 3.1% -- 3.7%
-- 3.7% -- 9.5% -- 8.3% -- 37%
General manager
-- -- 1.9% 1.9% 4.9% -- -- -- 11.1%
-- 3.7% -- 9.5% -- 8.3% -- 57%
Other
6.9% -- 1.9% -- 4.9% -- 9.4% -- --
-- 7.4% -- 4.8% -- 2.8% -- 57%
Total
86.1% 13.8% 71.1% 28.8% 75.7% 24.4% 75.0% 25.0% 85.1%
14.8% 55.5% 44.4% 71.4% 28.6% 80.5% 19.5%
Legend for chart:
A = Level
B = Variable
C = KAM Approach Top-Management KAM (n=37)
D = KAM Approach Middle-Management KAM (n=76)
E = KAM Approach Operating Level KAM (n=57)
F = KAM Approach Cross-functional, dominant KAM (n=44)
G = KAM Approach Unstructured KAM (n=38)
H = KAM Approach Isolated KAM (n=40)
I = KAM Approach Country-Club KAM (n=37)
J = KAM Approach No KAM (n=46)
K = KAM Approach Total (n=375)
A B C D E
F G H
I J K
KAM KAM 5.39[b]* 5.39[b]* 5.53[b]*
effectiveness 5.63[b]* 5.46[b]* 5.01[a]^
5.41[b]* 5.04[a]^ 5.37
Overall Performance 5.03[bc] 5.23[cd]* 5.04[bc]
organi- in the market 5.51[d]* 5.19[cd]* 4.72[ab]^
zation 5.16[cd]* 4.54[a]^ 5.07
Adaptiveness 4.75[bc] 4.87[b] 4.46[ab]^
5.43[d]* 4.85[bc] 4.25[a]^
4.50[abc]^ 4.23[a]^ 4.68
Profitability 6.38[b]* 4.98[a]^ 4.98[a]^
5.64[ab]* 5.84[ab]* 5.23[ab]*
4.82[a]^ 4.80[a]^ 5.27Notes: Reported values are mean values if not otherwise noted. In each row, cluster means that have the same [superscript] are not significantly different (p < .05) on the basis of Duncan's multiple-range test. Means in the lowest band are assigned "a," means in the next highest band "b," and so forth. Means in the highest band have *; means in the lowest band have ^.
Legend for chart:
A = Dependent Variable: Models 1 Through 4
B = ANCOVA Results Total Model Mean of Squares
C = ANCOVA Results Total Model F (p)
D = ANCOVA Results Approach Mean of Squares (d.f. = 7)
E = ANCOVA Results Approach F (p)
F = ANCOVA Results Covariates Market Dynamism Mean of Squares
(D.f. = 1)
G = ANCOVA Results Covariates Market Dynamism F (p)
H = ANCOCA Results Covariates Competitive Intensity Mean of Squares
(d.f.=1)
I = ANCOVA Results Covariates Competitive Intensity F (p)
A B C D E F G H I
1. KAM 1.76 3.72 2.20 4.65 <.01 <.01 .81 1.72
effect- (d.f.=9;370) (<.01) (<.01) (.96) (.19)
iveness
2. Perform- 3.63 5.58 4.15 6.37 2.27 3.48 .85 1.31
ance in (d.f.=9;370) (<.01) (<.01) (.06) (.25) (.25)
the
market
3. Adapt- 5.72 7.81 6.54 8.93 1.93 2.64 .47 .64
iveness (d.f.=9;369) (<.01) (<.01) (.11) (.42)
4. Profit- 22.81 3.99 12.19 2.13 .90 .16 125.97 22.05
ability (d.f.=9;322) (<.01) (.04) (.69) (<.01)Notes: d.f. = degrees of freedom.
DIAGRAM: FIGURE 1 Conceptualization of KAM
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Legend for chart:
A = Construct
B = Items
C = Composite Reliability/Coefficient Alpha
A Activity intensity (reflective scale, scored on a seven-point
scale with anchors 1 = "not more than for average accounts" and
7 = "far more than for average accounts")
B Compared to average accounts, to what extent do you do MORE in
these areas for key accounts?
• Product-related activities (e.g., product adaptation, new
product development, technology exchange)
• Service-related activities (e.g., training, advice,
troubleshooting, guarantees)
• Price-related activities (e.g., special pricing terms,
corporatewide price terms, offering of financing solutions,
revelation of own cost structure)
• Distribution and logistics activities (e.g., logistics and
production processes, quality programs, placement of own
employees in account's facilities, taking over business processes
from customer)
• Information sharing (e.g., sharing of strategy and market
research, joint production plans, adaptation of information
systems, access to top management)
• Promotion activities to final customers (e.g., joint
advertising and promotion programs to help the account sell your
products)
C .75/.71
A Activity proactiveness (formative scale, scored on a seven-point
scale with anchors 1 = "not more than for average changes" and 7
= "far more than for average changes")
B Do the activities in these areas derive more from customer
initiative or more from your own initiative? (Items equivalent to
activity intensity.)
C
A Top-management involvement (reflective scale, scored on a
seven-point scale with anchors 1 = "strongly disagree" and 7 =
"strongly agree")
B Within our organization ...
• even small matters related to key accounts have to be
referred to someone higher up for a final decision.
• very few decisions related to key accounts are made
without the involvement of senior managers.
• top management often deals with key account management.
C .64/.62
A Use of teams (reflective scale, scored on a seven-point scale
with anchors 1 = "strongly disagree" and 7 = "strongly agree")
B Within our organization ...
• when there is a problem related to our key account
relationships, a group is brought in to solve it.
• key account-related decisions are made by teams.
• we have teams that plan and coordinate activities for key
accounts.
C .85/.82
A Selling center esprit de corps (adapted from Jaworski and Kohli
1993; reflective scale, scored on a seven-point scale with
anchors 1 = "strongly disagree" and 7 = "strongly agree")
B People involved in the management of a key account ...
• are genuinely concerned about the needs and problems of
each other.
• have a team spirit which pervades all ranks involved.
• feel like they are part of a big family.
• feel they are "in it together."
• lack an "esprit de corps." (R)*
• view themselves as independent individuals who have to
tolerate others around them. (R)*
C .92/.90
A Access to marketing and sales resources (reflective scale, scored
on a seven-point scale with anchors 1 = "very difficult" and 7 =
"very easy")
B How easy is it for the key account coordinator to obtain needed
contributions for key accounts from these groups?
• Field sales
• Customer service
• Product management
C .75/.69
A Access to nonmarketing and nonsales resources (reflective scale,
scored on a seven-point scale with anchors 1 = "very difficult"
and 7 = "very easy")
B How easy is it for the key account coordinator to obtain needed
contributions for key accounts from these groups?
• Research and development
• Manufacturing
• Logistics
• Finance/accounting
• Information technology
• General management
C .85/.81
A Approach formalization (reflective scale, scored on a
seven-point scale with anchors 1 = "strongly disagree" and 7 =
"strongly agree")
B Please indicate the extent to which you agree with the following
statements:
• We have established criteria for selecting key accounts.
• Within our organization, formal internal communication
channels are followed when working on key accounts.
• To coordinate the parts of our organization working with
key accounts, standard operating procedures have been
established.
• We have put a lot of thought into developing guidelines
for working with our key accounts.
C .87/.84
A KAM effectiveness (reflective scale, scored on a seven-point
scale with anchors 1 = "very poor," 4 = "about the same," and 7 =
"excellent")
B Compared to your average accounts, how does your organization
perform with key accounts with respect to ...
• achieving mutual trust?
• achieving information sharing?
• achieving a reputation of fairness?
• achieving investments into the relationship?
• maintaining long-term relationships?
• reducing conflicts?
• meeting sales targets and objectives?
• making sales of those products with the highest margins?*
• making sales from multiple product divisions?*
C .88/.85
A Performance in the market (reflective scale, scored on a
seven-point scale with anchors 1 = "very poor," 4 = "about the
same," and 7 = "excellent")
B Relative to your competitors, how has your organization, over the
last three years, performed with respect to ...
• achieving customer satisfaction?
• providing value for customers?
• attaining desired growth?
• securing desired market share?
• successfully introducing new products?
• keeping current customers?
• attracting new customers?
C .88/.85
A Adaptiveness (reflective scale, scored on a seven-point scale
with anchors 1 = "not more than for average accounts" and 7 =
"far more than for average accounts")
B Relative to your competitors, how has your organization, over the
last three years, performed with respect to ...
• adapting to changes in the business environment of your
company?
• adapting to changes in competitors' marketing strategies?
• adapting your products quickly to the changing needs of
customers?
• reacting quickly to new market threats?
• exploiting quickly new market opportunities?
C .86/.84
A Profitability (interval item with ten levels of variable provided)
B What was your company's average pre-tax profit margin over the
last three years? 1 = negative; 2 = 0%-2%, 3 = 2%-4%, 4 = 4%-6%,
5 = 6%-8%, 6 = 8%-10%, 7 = 10%-12%, 8 = 12%-16%, 9 = 16%-20%,
10 = more than 20%
C
A Competitive intensity (adapted from Jaworski and Kohli 1993;
reflective scale, scored on a seven-point scale with anchors 1 =
"strongly disagree" and 7 = "strongly agree")
B Please indicate the extent to which you agree with the following
statements:
• Competition in our industry is cutthroat.
• There are many "promotion wars" in our industry.
• Anything that one competitor can offer, others can match
readily.
• Price competition is a hallmark of our industry.
• One hears of a new competitive move almost every day.
• Our competitors are relatively weak. (R)*
C .82/.81
A Market dynamism (adapted from Jaworski and Kohli 1993; reflective
scale, scored on a seven-point scale with anchors 1 = "strongly
disagree" and 7 = "strongly agree")
B Please indicate the extent to which you agree with the following
statements:
• In our kind of business, customers' product preferences
change quite a bit over time.
• Our customers tend to look for new products all the time.
• We are witnessing demand for our products and services
from customers who never bought them before.
• New customers tend to have product-related needs that are
different from those of our existing customers.
• We cater to many of the same customers that we used to in
the past. (R)*
C .65/.61
*Items not kept after the item purification process.
~~~~~~~~
By Christian Homburg; John P. Workman Jr. and Ove Jensen
Christian Homburg is Professor of Marketing and Director of the Institute for Market-Oriented Management, University of Mannheim. John P. Workman Jr. is Associate Professor of Marketing, College of Business Administration, Creighton University. Ove Jensen is a partner and managing director, Prof. Homburg & Partners. The research reported in this article was supported by funding from the Marketing Science Institute. The authors acknowledge the research assistance provided by Christian Johann, Jan Loewner, Andrea Model, Christine Prauschke, and Michaela Vogel in Germany and Brenda Gerhardt and Anurag Aerora in the United States.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 3- A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy. By: Venkatesan, Rajkumar; Kumar, V. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p106-125. 20p. 1 Diagram, 7 Charts. DOI: 10.1509/jmkg.68.4.106.42728.
- Database:
- Business Source Complete
A Customer Lifetime Value Framework for Customer
Selection and Resource Allocation Strategy
The authors evaluate the usefulness of customer lifetime value (CLV) as a metric for customer selection and marketing resource allocation by developing a dynamic framework that enables managers to maintain or improve customer relationships proactively through marketing contacts across various channels and to maximize CLV simultaneously. The authors show that marketing contacts across various channels influence CLV nonlinearly. Customers who are selected on the basis of their lifetime value provide higher profits in future periods than do customers selected on the basis of several other customer-based metrics. The analyses suggest that there is potential for improved profits when managers design resource allocation rules that maximize CLV. Managers can use the authors' framework to allocate marketing resources efficiently across customers and channels of communication.
Customer lifetime value (CLV) is rapidly gaining acceptance as a metric to acquire, grow, and retain the "right" customers in customer relationship management (CRM). However, many companies do not use CLV measurements judiciously. Either they work with undesirable customers to begin with, or they do not know how to customize the customer's experience to create the highest value (Thompson 2001). The challenge that most marketing managers currently face is to achieve convergence between marketing actions (e.g., contacts across various channels) and CRM. Specifically, they need to take all the data they have collected about customers and integrate them with how the firm interacts with its customers. In the academic literature, Berger and colleagues (2002) support the allocation of resources to maximize the value of the customer base, and they strongly argue that such resource allocation models are needed.
Some researchers have recommended CLV as a metric for selecting customers and designing marketing programs (Reinartz and Kumar 2003; Rust, Zeithaml, and Lemon 2004). However, there is no empirical evidence as to the usefulness of CLV compared with that of other customer-based metrics. Table 1 compares the CLV framework proposed in this study with the existing literature on CLV and database marketing. A comparison of the studies listed in Table 1 shows that most of the previous studies provide guidelines for calculating CLV and return on investment at the aggregate level. Some recent studies (Reinartz and Kumar 2003; Rust, Zeithaml, and Lemon 2004) provide empirical evidence for the existence of a relationship between marketing actions and CLV at the aggregate level. However, as Berger and colleagues (2002) state, none of the studies has proposed and tested a framework that provides rules for resource allocation across various channels of communication for each individual customer and across customers.
On the basis of the comparisons in Table 1, we summarize the significant contributions of our study as follows: We provide a framework for measuring CLV that links the influence of communications across various channels on CLV. We also evaluate the usefulness of CLV as a metric for customer selection and develop a framework for marketing resource allocation that maximizes CLV. Given the assumed link between CLV and firm profitability (Hogan et al. 2002), these are important issues.
In this study, we use customer data from a large business-to-business (B2B) manufacturer to illustrate the proposed framework empirically. The customer database of the organization focuses on B2B customers. Our analyses show that marketing communications across various channels influence CLV nonlinearly. The results from our analyses suggest that customers selected on the basis of CLV provide higher profits than do customers selected on the basis of other widely used CRM metrics. In addition, there is the potential for substantial improvement in profits when managers design resource allocation rules that maximize CLV.
In the next section, we develop the framework for the measurement and maximization of CLV. We then propose hypotheses about the influence of supplier-specific factors and customer characteristics on the various CLV components. In the subsequent section, we explain the models and data we used to estimate CLV. We then discuss the results from our analyses and explain the comparison of CLV with other metrics for customer selection. Specifically, we compare the aggregate profits provided by high-CLV customers with those of customers who score high on several other customer-based metrics. In the section "Resource Allocation Strategy," we provide details on allocating resources that maximize CLV. Our objective there is to evaluate the extent to which CLV, and thus profits, can be increased by allocating marketing resources across channels of contact for each customer so as to maximize his or her respective CLVs. Finally, we derive implications based on the results, discuss the limitations of our study, and identify areas for further research.
The various components of CLV include purchase frequency, contribution margin, and marketing costs (however, the various CLV components can vary depending on the industry). Some of the antecedents of purchase frequency and contribution margin (e.g., marketing communications) are under management's control and affect the variable costs of managing customers. We use these antecedents to maximize CLV.
Objective Function: CLV
Typically, CLV is a function of the predicted contribution margin, the propensity for a customer to continue in a relationship (customer retention), and the marketing resources allocated to the customer. In general, CLV can be calculated as follows:
[Multiple line equation(s) cannot be represented in ASCII text]
where
i = customer index, t = time index, n = forecast horizon, and r = discount rate.
In contractual settings, managers are interested in predicting customer retention, or the likelihood of a customer staying in or terminating a relationship. However, in noncontractual settings, the focus is more on predicting future customer activity because there is always a chance that the customer will purchase in the future. Therefore, managers who calculate CLV in noncontractual settings are interested in predicting future customer activity and the predicted contribution margin from each customer. Previous researchers have used the variable P(Alive), which represents the probability that a customer is alive (and thus exhibits purchase activity) given his or her previous purchase behavior (Reinartz and Kumar 2000), to predict future customer activity in noncontractual settings. However, the measure assumes that when a customer terminates a relationship, he or she does not return to the supplier. This is also called the "lost-for-good" scenario ((Rust, Zeithaml, and Lemon 2004). If a customer is won back after termination, the company treats the customer as a new customer and ignores its history with the customer.
Another method for predicting future customer activity is to predict the frequency of a customer's purchases given his or her previous purchases. The assumption underlying this framework is that customers are most likely to reduce their frequency of purchase before terminating a relationship. This assumption is in accordance with theories about the different phases in a relationship and relationship life cycles (Dwyer, Schurr, and Oh 1987; Jap 2001). In addition, such a methodology enables a customer to return to the supplier after a temporary dormancy in a relationship. Thus, in this framework, we measure CLV by predicting the purchase pattern (purchase frequency or interpurchase times) over a reasonable period. This is also called the "always-a-share" scenario.. The lost-for-good approach is questionable because it systematically understates CLV (Rust, Zeithaml, and Lemon 2004). Thus, we use the always-a-share approach in this study. Given predictions of contribution margin, purchase frequency, and variable costs, the CLV function we use can be represented as follows:
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
where
CLV[subi] = lifetime value of customer i;
CM[subi,y] = predicted contribution margin from customer i (computed from a contribution margin model) in purchase occasion y, measured in dollars;
r = discount rate for money (set at 15% annual rate in our study);
c[subi,m,l] = unit marketing cost for customer i in channel m in year l (the formulation of CLV does not change if l is used to represent periods other than one year);
x[subi,m,l] = number of contacts to customer i in channel m in year l;
frequency[subi] = predicted purchase frequency for customer i;
n = number of years to forecast; and
T[subi] = predicted number of purchases made by customer i until the end of the planning period.
In addition to accurate measurement of CLV for each customer, our objective is to allocate resources so as to maximize CLV. Thus, we model the purchase frequency and contribution margin of customers as a function of marketing resource variables such as channel contact. We then use the customer responsiveness to marketing actions, obtained from the purchase frequency and contribution margin models, to develop resource allocation strategies that maximize CLV. In summary, the objective is to identify the resource allocation rules across various channels of communication for each individual customer such that the respective CLVs (as provided in Equation 2) are maximized.( n1) Our objective function is subject to the following constraints: frequency > 0 ∀ i, t, and x[subi,m,l] ≥ 0 ∀ i, m, l.
Discounting contribution margin. We first focus on the discounting of contribution margin over a period of time. Assume that it is currently year l = 1 and that we need to forecast the contribution margin from each customer for the next n years (i.e., until l + n). It is possible that a customer makes several purchases in a given year. Berger and Nasr (1998, Equation 2) and Rust, Zeithaml, and Lemon (2004) provide guidelines for discounting contribution margin from customers when there is more than one purchase occasion (y) per year. In this approach, the discount rate from a customer is scaled according to his or her frequency of purchase (as is shown in Equation 2). For example, consider when the planning horizon is one year and the frequency of purchases is two times (frequency = 2). The first purchase occasion (y = 1) occurs after 6 months; therefore, y/frequency = .5 (in other words, we use the square root of the discount rate). The second purchase occasion (y = 2) occurs after 12 months; therefore, y/frequency = 1.
Discounting cost allocations. The discounting of cost allocations is straightforward if we assume that there is a yearly allocation of resources (as is the case in most organizations) and that the cost allocation occurs at the beginning of the year (the present period). Thus, the cost allocation in the first year need not be discounted, the cost allocation in the second year needs to be discounted for one year, and so on. Thus, we raise the denominator in the cost function calculation to current year - 1 ((i.e., l - 1)).
Discussion of model constraints. The constraints ensure the nonnegativity of the predicted purchase frequency and communication levels for each customer i during period l.
Purchase Frequency
An objective of relationship marketing is to ensure future purchase activity. Purchase frequency is also a component of our CLV calculation. Therefore, as the basis for selecting antecedents to predict purchase frequency, we use the commitment trust theory of relationship marketing (Morgan and Hunt 1994) as well as previous research in customer equity and CLV (Bolton, Lemon, and Verhoef 2004; Bowman and Narayandas 2001; Reinartz and Kumar 2003; Rust, Zeithaml, and Lemon 2004) and channel communications (Grewal, Corner, and Mehta 2001; Mohr and Nevin 1990; Morgan and Hunt 1994; Rindfleisch and Heide 1997).
The overall theoretical framework that we used is provided in Figure 1. We summarize the antecedents of purchase frequency, their operationalization, expected effects, and the rationale for our expectations in Table 2. Next, we provide a detailed discussion for a few of the hypotheses that are unique to our study.
Supplier-Specific Factors: Channel Communications
In this study, we classify channels of communication into the following contact modes: rich (e.g., face-to-face, trading event meetings), standardized (e.g., direct mail, telephone), and Web based (Mohr and Nevin 1990). Although we expect the relationships between different channels of communication and predicted customer activity to be similar, we need to analyze customer responses separately across different channels because the costs of serving customers across different channels are different, and customers might exhibit different responsiveness across the various channels. The costs of communication in each channel can influence managers frequency of communication in each channel.
Frequency of rich and standardized modes. Face-to-face communications and trading event meetings are the richest and most direct mode of communication possible among channel members (Mohr and Nevin 1990). Relational customers tend to have high commitment and trust with their suppliers, which results in less uncertainty, more cooperation, and less complexity in their relationships than in those of transactional customers (Morgan and Hunt 1994). Rich modes of communication are preferred to standardized modes when issues in the channel structure are complex and when there is a high degree of uncertainty in the relationship. Rich modes of communication are also effective in converting transactional customers to relational ones (Ganesan 1994).
Direct mail and telephone communication are the most standardized and cost-effective modes of individual-level communication available to an organization. Standardized modes are also the most cost-effective method for identifying customers who are interested in an organization's current promotion (Shepard 2001). For transactional customers, direct mail can be used in combination with telephone sales to generate interest in products while simultaneously improving the return on investment (Nash 1993). For relational customers, direct mail serves to maintain commitment and trust by communicating relationship benefits (Morgan and Hunt 1994) and to inform the best customers about new product offerings. Therefore, although the purpose of standardized communication may be different for transactional customers than for relational ones, we expect that the marginal response for increased frequency is the same across segments.
However, it has been proposed that too much communication causes a relationship to be dysfunctional (Fournier, Dobscha, and Mick 1997). In addition, the marginal response to a higher level of rich modes of communication need not always be higher; sometimes, it even can be negative. Although the utility of marketing contacts is not questioned, too much contact can overload buyers and have dysfunctional consequences (e.g., ubiquitous junk mail). Thus:
H[sub1]: An inverted U-shaped relationship exists between the frequency of rich and standardized modes of communication and a customer's predicted purchase frequency.
Intercontact time. Following the theory that leads to H1, we expect that there exists an optimal level of intercontact time between suppliers and buyers. Higher levels of previous communications lead to trust with the supplier and act as glue that holds together a communication channel (Morgan and Hunt 1994). However, too much communication may be dysfunctional. In addition, the marginal utility of an additional piece of information from a supplier firm in a short period is low. To maximize the effect of each contact, supplier firms need to pace their communication schedule to suit customer needs. Thus:
H[sub2]: An inverted U-shaped relationship exists between intercontact time and a customer's predicted purchase frequency.
Customer Characteristics: Customer Involvement
Bidirectional communication. Research on channel communications shows that highly relational channel structures are associated with large bidirectional communications among channel members (Mohr and Nevin 1990). Although customer-initiated contacts are associated primarily with complaints in business-to-consumer settings, the same is not necessarily true for B2B settings. In a B2B setting, customers can initiate contacts with suppliers for several reasons, such as if they have new needs that the supplier may be able to fulfill, if they want the supplier to conduct training programs at the customer's site (Cannon and Homburg 2001), or if the supplier invites the customer to participate in new product development sessions. On most occasions, bidirectional communication in channels strengthens a relationship, indicates customer involvement, and increases interdependence among channel members (Ganesan 1994; Mohr and Nevin 1990). Thus:
H[sub3]: The higher the level of bidirectional communication, the higher is a customer's predicted purchase frequency.
Frequency of Web-based contacts. In this study, we analyze Web-based contacts separately from other channels of communication because Web-based communication is customer initiated (i.e., a passive mode of operation for the supplier). However, there are several advantages to tracking Web-based contacts in a B2B setting. First, Web-based communication between buyers and suppliers is the most cost-effective method of communication. Second, Web-based contacts from the buyers provide some important signals to the supplier about the buyer's relationship orientation. Grewal, Corner, and Mehta (2001) find that organizations enter and actively participate in electronic markets if their motivation is to improve efficiency in transactions. In addition, participation in electronic markets (or use of Web-based initiatives) improves transaction effectiveness and efficiency (Rindfleisch and Heide 1997). Efficiency of communication and transactions among channel members is associated with a relational structure and higher customer involvement (Mohr and Nevin 1990; Sheth and Parvatiyar 1995). Thus:
H[sub4]: The higher the number of Web-based contacts from a customer, the higher is the customer's predicted frequency of purchase.
Contribution Margin
The antecedents that we adopt to predict contribution margin are based on findings from previous research on antecedents of customer revenue (Niraj, Gupta, and Narasimhan 2001) and purchase quantity (Gupta 1988; Tellis and Zufryden 1995). As with purchase frequency, we classify the antecedents of contribution margin as supplier-specific factors (total marketing efforts) and customer characteristics (lagged contribution margin and purchase quantity). We use size of an establishment and industry category as covariates in our model. In Table 2, we provide a description of the antecedents that we propose influence customer purchase frequency and contribution margin. In the database, we also provide the operationalization of the antecedents, their expected effects, and the rationale for our expectations based on previous research. Because all the antecedents we use in our contribution margin model are based on previous research and findings, we do not discuss the hypotheses in detail. In Table 2, we provide a description of the covariates we use as control variables in the purchase frequency and contribution margin models.
Model Development
To predict CLV, we need a stochastic model to predict each customer's purchase frequency and a panel-data model that predicts contribution margin. In this study, we assume that the amount a customer spends is independent of purchase timing. This is a rather restrictive assumption for frequently bought consumer goods (Tellis and Zufryden 1995). However, in our product category, we find that the correlation between purchase frequency and contribution margin is not significant.
Purchase Frequency
We model a customer's purchase frequency using the generalized gamma model of interpurchase timing that Allenby, Leone, and Jen (1999) developed. The generalized gamma model also accommodates the commonly used exponential distribution for interpurchase times (Reinartz and Kumar 2003). The likelihood function for the purchase frequency model is given as follows:
( 3) [Multiple line equation(s) cannot be represented in ASCII text]
where
f(t[subij]|α, λ[subi], γ) = the density function for the generalized gamma distribution (i.e., the probability of the jth purchase for customer i occurring at period t, given α, λ[subi]; γ);
S(t[subij]|α, λ[subi], γ) = the survival function for the generalized gamma distribution (i.e., the probability of the jth purchase for customer i occurring at a given period is greater than t, given that the jth purchase has not occurred until time t, given α λ[subi], γ);
c[subij] = the censoring indicator, where c[subij] = 1 if the jth interpurchase time for the ith customer is not right-censored, and c[subij] = 0 if the jth interpurchase time for the ith customer is right-censored;
φ[subijk] = the probability of observation j for the ith customer belonging to subgroup k; and
α λ[subi], γ = the parameters of the generalized gamma distribution.
Because we use a generalized gamma distribution to model interpurchase time and the likelihood function in Equation 3, the expected time until next purchase is given as follows:
( 4) [Multiple line equation(s) cannot be represented in ASCII text]
The ratio of 12 (because we use months as the unit of analysis) to the expected time until next purchase (which we obtain by modeling a generalized gamma distribution on the interpurchase times, as is shown in the work of Allenby, Leone, and Jen [1999]) gives the predicted purchase frequency. The parameters α and γ establish the shape of the interpurchase time distribution, and λ[sub1] is the individual-specific purchase rate parameter. We assume that the population consists of k subgroups, and φ[subik] provides the mass point (i.e., weight) for each subgroup. We model the probability of a customer belonging to each subgroup φ[subik] as a probit function of the antecedents and covariates of purchase frequency. Specifically, we represent the link function as φ[subik] = f(x[subij]β[subi]), where x[subij] represents the antecedents and covariates of purchase frequency for customer i in purchase occasion j, and β[subi] represents the customer-specific response coefficients.
Our model framework, as presented in Equation 3, resembles a hierarchical Bayes formulation of the concomitant continuous mixture model. To address the issue of endogeneity, we use the one-period lagged value for all the antecedents and covariates in our analysis (Villas-Boas and Winer 1999). However, to account for any extraneous factors, we also use the log of the lagged interpurchase.( n2) The specification of the model enables us to estimate individual customer-level coefficients for the influence of the covariates on the probability of a customer belonging to a particular subgroup and thus interpurchase times.
Contribution Margin
We model the contribution margin from a customer using panel-data regression methodologies. We needed to address endogeneity issues while using lagged contribution margin as an independent variable in our model. In panel-data models with lagged dependent variables, the endogeneity in formulation can be alleviated with a one-period difference in the dependent variable and a two-period lagged dependent variable as an independent variable (Baltagi 1998). We use the growth in contribution margin from period t - 1 to t as the dependent variable and the contribution margin in period t - 2 as an independent variable. The other independent variables we used are specific to period t - 1 ((this also accommodates the issue of endogeneity). The independent variables in the contribution margin model are thus lagged contribution margin, lagged total quantity purchased, lagged firm size, industry category, and lagged total marketing efforts. Thus, the contribution margin model is
( 5) ΔCM[subi,t] = β[sub0] + β[sub1]CM[subi,t - 2] + β[sub2]Quantity[subi,t - 1] + β[sub3]Size[subi,t - 1] + Σ[subj]β[subj]Industry[subj] + β[sub4]Totmark[subi, t - 1] + e[subi,t],
where
ΔCM[subi,t] = difference in contribution margin from period t 1 to period t for customer i, measured in dollars;
Size[subi, t = 1 = firm size for customer i in period t - 1,, measured as number of employees;
Industry = indicator variable for industry category of the customer firm;
Totmark[subi, t = 1 = total number of contacts made to customer i in period t - 1;
Quantity[subi, t = 1] = total quantity of products bought by customer i in period t - 1;
e[subi,t] = error term;
i = index for the customer; and
t = index for time.
Data
We used data from a large multinational computer hardware (servers, workstations, and personal computers) and software (integration and application) manufacturer for the empirical application of our framework. The company's database focuses on business customers. The product categories in the database represent different areas of high-technology products. In addition, for these product categories, it is the choice of the buyer and seller to develop their relationships, and there are significant benefits for both parties to maintain a long-standing relationship. The choice of vendors for the products is normally made after much deliberation by the buyer firm. Even though the firm's products are durable goods, they require constant maintenance and frequent upgrades, which provides the variance required in modeling customer response. For our analyses, we used two cohorts of customers: Cohorts 1 and 2. We assigned customers to Cohort 1 (Cohort 2) if their first purchase with the manufacturer occurred in the first quarter of 1997 (first quarter of 1998). In our samples, we removed customers who had missing values for either rich or standardized modes of communication. We also restricted our sample to customers who had made at least five purchases. Overall, we removed 20% of the original cohort of customers for our analyses, which resulted in an effective sample size of 1316 and 873 observations for customers in Cohorts 1 and 2, respectively. The interpurchase time for customers in Cohort 1 ranges from 1.5 to 23 months; for Cohort 2, it ranges from 1 month to 20 months.
Purchase frequency model. We used each observed purchase for a customer as an observation in the purchase frequency model. For Cohort 1, we selected customers who made their first purchase in the first quarter of 1997. For each customer, we omitted the first observed purchase in our analysis sample because the first observed purchase is restricted to be within three months for all customers in the cohort, and theoretically the customer retention phase begins after the first purchase. The antecedents and covariates we used can be classified as cumulative and current effects. The cumulative effects antecedents include cross-buying and upgrading, and their values represent the total number of different products (for cross-buying) or upgrades that the customer has purchased since the first purchase until the current observed purchase.
The current-effects antecedents include bidirectional communication; returns; relationship benefits; frequency of rich, standardized, and Web-based contacts; and intercontact time. The covariates in the purchase frequency model (type of product purchased) can be classified as current effects. We calculated the current-effects antecedents and covariates on the basis of the activities of the customer or the supplier (in the case of channel communications) between the previous observed purchase (j - 1)) and the current observed purchase (j). To assess the inverted U-shaped relationships, we used a quadratic conversion (including the square covariate term in Equation 3) of the respective antecedent. For all customers, we used the interpurchase times until the end of 2000 as our calibration sample. We used the 2001 data as a holdout sample and to compare strategies. All the antecedents and covariates we used in our analyses are lagged variables. Specifically, for observed purchase j, the cumulative-effects antecedents represent the customer's activity since relationship initiation until observed purchase j - 1. Similarly, for observed purchase j, the current effects antecedents and covariates represent the customer's (or supplier s) activity between observed purchases j - 2 and j - 1.
Contribution margin model. To model contribution margin from a customer, we used the annual sales from various purchases of each customer. For customers in Cohort 1, we used the annual sales from each customer from 1997 to 2000. Given our model structure in Equation 5, there are two observations per customer in our analysis sample. Specifically, for each customer, for Observation 1 the dependent variable is the difference in contribution margin between 2000 and 1999, and the independent variables include the contribution in 1998, the firm size in 1999, the industry category of the customer, the total number of contacts made to the customer in 1999, and the total quantity of products purchased in 1999. Similarly, for Observation 2, the dependent variable is the difference in contribution margin between 1999 and 1998, and the independent variables include the contribution margin in 1997. As we stated previously, we used the 2001 data as a holdout sample and to compare strategies. The descriptive statistics for the data and the correlation matrix of the antecedents are provided in Table 3.
Purchase Frequency Model
As we discussed previously, we used an effective sample size of 1316 and 873 observations that belong to Cohort 1 (first purchase in 1997) and Cohort 2 (first purchase in 1998), respectively, for our analyses. We discuss the results from Cohort 1 in detail in the text. The results from Cohort 2 are quite similar to those of Cohort 1 and are provided along with the results for Cohort 1 in the corresponding tables. We censored our data set in 2000 and used the 2001 data as our validation (or holdout) sample in both cohorts. We estimated the purchase frequency model in Equation 3 using Markov chain Monte Carlo (MCMC) methods. The results from the purchase frequency model for Cohorts 1 and 2 are provided in Table 4 (for details on the model estimation, model selection, model performance, and support for hypotheses, see Appendix A). The coefficients of the antecedents reported in Table 4 are the means from the posterior samples of β[subi], and the signs for the coefficients represent their influence on a customer's purchase frequency. There are several insights that we derive from Table 4, which we discuss subsequently.
Model fit. The results show that the generalized gamma model with two subgroups provides a good fit to the data and is better than other models for modeling purchase frequency (log-likelihood for Model 3 = -2237.82, Akaike information criterion [AIC] = -4484, and Bayesian information criterion [BIC] = -4683). We also used a hazard model with the finite mixture framework (Kamakura and Russell 1989) to model purchase frequency, and we found that our proposed model fits the data better and has better predictive capabilities.
Distribution parameters.( n3) The mean expected purchase frequency for Subgroup 1 is 4.2 purchases in a year, and the mean expected purchase frequency for Subgroup 2 is 1.01 purchases in a year. Given the variation in expected purchase frequencies in each subgroup, we term Subgroup 1 the active state and Subgroup 2 the inactive state. The component masses for Subgroup 1 (φ[sub1]) and Subgroup 2 (φ[sub2]) are .54 and .46, respectively. This implies that we expect 54% of the customers to be active in the prediction window and 46% of the customers to be inactive in the prediction window.
Supplier-specific factors. Our analyses indicate that a supplier's contact strategy and provision of relationship benefits significantly affect a customer's predicted purchase frequency. We find that the frequency of contacts affects purchase frequency nonlinearly. Specifically, we find an inverted U-shaped relationship. This leads us to believe that there is an optimal level of marketing communication for each customer. A firm's increasing communication beyond a certain threshold may result in diminishing returns in terms of customer purchase frequency. This finding also provides the reasoning to determine the optimal level of resources that needs to be allocated across channels to maximize CLV in Phase 2.
The coefficients of the marketing contacts reveal a difference in the influence of various channels on customer purchase frequency. The coefficient of the quadratic term for rich modes of communication (-1.30 for Cohort 1) is higher than the coefficient of the quadratic term for standardized modes of communication (-.28 for Cohort 1). Thus, we can infer that the rate of diminishing returns (after exceeding the threshold) is much higher for the rich mode of communications than for standardized modes. Therefore, although the rich mode of communication is interactive and effective, firms should use it with great caution.
Customer characteristics. The results indicate that upgrading and cross-buying positively influence a customer's purchase frequency. This is in line with the findings of Reinartz and Kumar (2003), who also find that breadth of purchase positively affects a customer's duration in a profitable relationship. We also find that the higher the bidirectional communication between the customer and supplier, the higher is the customer's purchase frequency. Thus, in addition to timely communication from the supplier to the customer (Morgan and Hunt 1994), communication from a customer can be a good indicator of a customer's activity.
With respect to returns from a customer, our analysis suggests that managers need to exercise caution. We find support for an inverted U-shaped relationship between purchase frequency and returns. This indicates that customers who return products within a threshold are a good asset to the firm. The results highlight the importance of firms' recognizing the customers who establish contact through the online channel in their CRM strategies.
Contribution Margin Model
We estimated the contribution margin model with annual data from 1997 (t - 4)) to 2000 (t) for Cohort 1 and from 1998 to 2000 for Cohort 2.( n4) The revenue in 2001 (t + 1) acts as a holdout sample for Cohorts 1 and 2. The coefficients of the contribution margin model are provided in Table 5. The main insight from Table 5 is that the contribution margin model provides an adjusted R2 of .68 and thus can explain significant variation in contribution margin from customers. We derive several other insights from Table 5, which we discuss subsequently.
Supplier-specific factors. Total lagged marketing efforts contribute significantly to an explanation of variation in current contribution margin, which implies that a supplier's contact strategy affects both purchase frequency and contribution margin.
Customer characteristics. The two-period lagged contribution margin provides the highest contribution to an explanation of current-period contribution margin. In addition, lagged quantity of goods is significant in explaining variation in contribution margin. Firm size and industry category explain variation in current-period contribution margin. Among the various industry categories, firms in the financial services, technology, consumer packaged goods, and government industry categories provide, on average, a higher contribution margin than do firms in other industry categories. However, firms in the education industry provide a lower contribution margin than do firms in other industries.
In this section, we compare the customer selection capabilities of the following: CLV, our proposed metric; previous-period customer revenue (PCR), a simple metric; past customer value (PCV), which is widely considered a good predictor of future customer value; and customer lifetime duration (CLD), a forward-looking metric that is also used as a proxy for loyalty. (We also compared the customer selection capabilities of CLV with other customer-based metrics, such as share of wallet and recency, frequency, and monetary value. The results were similar to the comparison with PCR, PCV, and CLD.) In general, organizations in direct marketing situations rank-order their customers on the basis of a particular metric and prioritize their resources from best customers to worst customers on the basis of the rank order (Roberts and Berger 1999). Descriptions of the various customer-based selection metrics used in our analyses are provided in Appendix B.
Performance of the Customer Selection Metrics
To compare the performance of the four metrics, we rank-ordered customers from best to worst according to each metric and then compared the sales, costs, and profits from the top 5%, 10%, and 15% of customers. We used the data from the first 30 months to score and sort the customers on each metric, as do Reinartz and Kumar (2003). We then compared the actual sales, variable costs of communication, and profits for the top 5%, 10%, and 15% of customers from the censoring period (30 months) until the end of the observation window (48 months).( n5) To select customers for contact, in general organizations choose the top 5% to 15% of their customers, rank-ordered on the basis of a scoring metric. Selection of more customers to contact may not be feasible because of limited time and resources. Thus, to reflect industry practice, we compared the performance of our metric among the top 5%, 10%, and 15% of customers. The results are provided in Table 6, and the reported values are cell medians. We subsequently summarize the results from our comparison.
Overall, Table 6 shows that the proposed metric better identifies profitable customers than do other metrics we compared in the study, such as PCR, PCV, and CLD. On the basis of the 18-month prediction window, we expect the average net profits of customers selected from the top 5% using the proposed CLV metric to be $143,295 (after accounting for cost of goods sold [70%] and variable costs of communication), whereas the average net profits are $70,929, $130,785, and $106,389 for the top 5% of the customers selected on the basis of PCR, PCV, and CLD, respectively. These findings hold across all the percentage subgroups. The results provide substantial support for incorporating the responsiveness of each individual customer across various communication channels and for the usefulness of CLV as a metric for customer scoring and customer selection. Although the difference in profits from use of PCR, PCV, CLD, and CLV is, on average, approximately $40,000 for a customer in the top 5% sample, the difference in total profit across the top 5% to 15% of the entire customer base can easily yield more than $1 million.
Having evaluated the usefulness of using CLV for customer selection, we now describe our methodology for designing resource allocation strategies that maximize CLV. The framework also provides managers a tool for assessing return on marketing investments by identifying avenues for optimal resource allocation across channels of communication for each individual customer (and possibly across customers), so as to maximize CLV. The marketing literature has provided guidelines for optimal resource allocation in acquisition and retention decisions (Blattberg and Deighton 1996; Blattberg, Getz, and Thomas 2001), promotion expenditures (Berger and Bechwati 2001; Berger and Nasr 1998), and marketing actions when future brand switching is considered (Rust, Zeithaml, and Lemon 2004). These guidelines represent a significant step toward incorporation of long-term customer profitability effects into firm-level managerial decision making. However, the models provide less insight into decisions about how to manage individual customers in a way that accounts for the heterogeneity, and they do not provide a mechanism for dynamic updating of profitability assessment (Libai, Narayandas, and Humby 2002).
Our resource allocation algorithm uses Equation 2 as the objective function, and the purpose of the optimization is to find the level of contacts across various channels with each individual customer that would maximize CLV. Equation 2 is a function of predicted purchase frequency (based on Equation 4), predicted contribution margin (based on Equation 5), and marketing costs. We first estimated the responsiveness of customers (coefficients, β[subs] to marketing contacts from the purchase frequency model and the contribution margin model. To design a CLV-based resource allocation strategy, we held the coefficients constant and identified the level of covariates (i.e., level of channel contacts) for each customer that would maximize CLV. In summary, in Equation 2, the contacts made to a customer across various channels are under the supplier's control and thus can be used to maximize CLV, depending on the cost of each mode of communication and the responsiveness of the customer (in terms of both purchase frequency and contribution margin) to each channel of communication.( n6) In other words, with respect to marketing resources for a firm, the customer contact levels across different channels appear in the revenue and cost sides of Equation 2 and thus avoid the scope of corner solutions.
We used a genetic algorithm to derive the levels of contact desired for each individual customer that maximize CLV. Genetic algorithms (Goldberg 1989) are simulation-based, parallel-search algorithms that have been used in econometrics (Dorsey and Mayer 1995; Liang and Wong 2001) to obtain optimal solutions when the complexity of the optimization function tends to be intractable and multidimensional. In our study, support for a purchase frequency distribution with two subgroups led us to believe that the optimization surface is multimodal. In addition, we intended to allocate resources for each customer on the basis of individual responsiveness. These issues made our optimization problem extremely complex and intractable with traditional analytical methods. Thus, we resorted to a search algorithm to find the optimal resource allocation levels. In addition, the multimodal nature of the optimization surface (given the support for a mixture distribution for purchase frequency) motivated us to use a parallel-search technique, which is not susceptible to local minima (common in multimodal distributions) (Venkatesan, Krishnan, and Kumar 2004). Appendix C explains how we used a parallel-search technique for resource allocation purposes.
Aggregate Results
The total net present value of future profits from a resource allocation strategy that maximizes CLV (with the predicted contribution margin) is approximately $44 million. We also computed the total net present value of future profits when the organization uses its current resource allocation strategy. Specifically, for each customer, we maintained the resource allocation levels for the most recent year and calculated CLV over a three-year period. We find that, based on this status quo resource allocation strategy, the total net present value of future profits is approximately $24 million. Therefore, we find that a resource allocation strategy that maximizes CLV results in an increase in profits by approximately 83%. The total cost of communication (over three years), based on the resource allocation strategy that maximizes CLV, is approximately $1 million. The total cost of communication in the organization's current strategy is approximately $716,188. We find that the organization improves profits by increasing costs of serving customers (rather than cost of communication in the previous year) by 48%. The return on marketing communication to the organization, based on its current strategy, is approximately $34 million ($24 million/$716,188). With a communication strategy that maximizes CLV, the return on marketing communication to the organization is approximately $44 million ($44 million/$1 million). Thus, it is possible to improve profits and return on marketing communication by appropriately identifying customers for target communications and by matching the channel of communication with customer preferences. The aggregate results suggest that given the improvement of approximately $20 million among a sample of 216 customers, there is a potential for the firm to increase its revenue by at least $1 billion across its entire customer base. However, such benefits may not be realized immediately because the firm also needs to incur costs to move toward a customer-centric view and to train its employees to manage customers on the basis of CLV.
The objective of our study was to analyze the usefulness of CLV as a metric for customer selection and resource allocation strategy. First, we developed and estimated an individual customer-level objective function, the goal of which is to measure CLV. Second, we demonstrated the superiority of selecting customers for contact on the basis of CLV compared with commonly used metrics such as PCR, PCV, and CLD. Third, we evaluated the benefits of designing marketing communications that maximize CLV. We now discuss the implications of our study and how managers can use this knowledge to design efficient marketing programs. We also provide an outline for future researchers to build on the framework proposed herein.
Implications
Antecedents of purchase frequency and contribution margin. The theoretical implications of the purchase frequency model are also related to the CUSAMS customer asset management framework (Bolton, Lemon, and Verhoef 2004). In this study, we tested parts of the CUSAMS framework and found empirical support for the parts we tested. Specifically, the CUSAMS framework proposes that marketing instruments (e.g., direct mailings, reward programs) affect a customer's price perceptions, satisfaction, and commitment. In turn, these affect the length, depth, and breadth of a relationship, which then ultimately influence CLV. We find empirical support for marketing instruments effects on purchase frequency (rich and standardized modes of communication and relationship benefits) and contribution margin (total marketing efforts), both of which ultimately influence CLV. We also find that breadth of purchases (cross-buying) and depth of buying (upgrading) affect purchase frequency, which ultimately influences CLV. In addition, we find support for a nonlinear relationship between supplier communications and purchase frequency. This supports Fournier, Dobscha, and Mick's (1997) expectations that too much communication between suppliers and customers can be disruptive. Thus, our results indicate that managers need to be cautious when designing marketing communication strategies across different channels and need to be wary of contacting customers too many times, especially through rich modes of communication.
We find an inverted U-shaped relationship between returns and purchase frequency. A possible benefit from customers who return products could be the opportunity to understand the reasons for dissatisfaction. In addition, customers who return products within a certain period may do so because they have inherent trust in the supplier and because they expect future benefits, such as improvements in the quality of the product. However, a customer's returning too many times may indicate erosion of trust with the firm or a lower level of future activity. We also find that customers that establish contact through the online channel of communication exhibit high frequency of purchases and have high involvement. Therefore, the online channel can provide an ideal setting for B2B firms to enhance their customer relationships. We find that in addition to influencing purchase frequency, marketing communications influence the expected contribution margin from a customer. Also in the B2B scenario, industry category and size seem to be important factors that influence the magnitude of contribution margin.
Enhancement of marketing productivity. Rust, Zeithaml, and Lemon (2004) propose that firm strategies and tactical marketing actions affect the marketing productivity chain. Our analyses of customer selection investigate how firms can enhance strategies, and our analysis of optimal resource allocation investigates how firms can improve tactical marketing actions.
Customer selection. A CLV metric better identifies customers that provide higher future profits than do PCR, PCV, and CLD. Our analyses indicate that CLV is preferred to incorporate the dynamics of customer purchase behavior into the customer selection process. Managers can substantially improve their return on marketing investments by using a dynamic, customer-level measure of CLV for scoring rather than using the other metrics suggested in the literature and by prioritizing contact programs.
Resource allocation strategy. The results from our study highlight the importance of firms considering individual customers responsiveness to marketing communication as well as the costs involved across various channels of communication when making resource allocation decisions. Our analyses suggest that there is a potential for substantial improvement in CLV through appropriate design of marketing contacts across various channels. When firms design resource allocation rules, they can realize the increase in profits by incorporating the differences in individual customer responsiveness to various channels of communication and the potential value provided by the customer. The proposed resource allocation strategy can be a basis for evaluating the potential benefits of CRM implementations in organizations, and it provides accountability for strategies geared toward managing customer assets.
To summarize, the major conclusions that we derive from our study are the following:
• Marketing communication across various channels affects CLV nonlinearly;
• CLV performs better than other commonly used customer-based metrics for customer selection such as PCR, PCV, and CLD; and
• Managers can improve profits by designing marketing communications that maximize CLV.
Limitations and Further Research
The study has limitations that further studies can address. The results of this study are from a customer database in the high-technology industry. Further studies need to investigate whether the results are generalizable to other industries and settings. In addition, further research needs to develop models that combine forecasts of aggregate competitive responses to marketing actions and customer brand switching with individual-level models of direct marketing. We also consider only the average levels of optimal communication strategy in a channel. However, firms can further improve the efficiency of communication strategy by appropriately sequencing their customer contacts across different channels. In addition, in our study, we provide a framework for maximizing CLV with marketing communications. However, note that we do not compare our proposed resource allocation strategy (that focuses on maximizing CLV) with a strategy that focuses on allocating resources to customers for which the increment in CLV from appropriate design of marketing communication is highest. For example, with an appropriately designed marketing strategy, it is possible that customers that previously had high CLV continue to have high CLV in the future, irrespective of the level of marketing communications, and that some customers that have had low CLV transform to high-CLV customers. Further research can investigate whether the customers selected for high levels of marketing communications are the same when the resource allocation strategy focuses on maximizing CLV or on maximizing incremental CLV. Finally, the sum of optimal CLVs for each individual customer need not lead to the optimal customer equity in the case of a budget constraint. We performed our optimization with a budget constraint, and there were no changes in the substantive results of the study. In addition, our optimization algorithm is flexible enough to allow for inclusion of the budget constraint without any substantial adjustments.
The customer- and supplier-specific antecedents used in the customer response model also can directly affect costs and thus margins. However, because we estimated the customer response model in a single step, which we then included in a net present value function (Equation 1) that includes both costs and margins, we assessed the indirect effect of the covariates on both costs and margins. Further studies can develop and test hypotheses that directly relate CRM efforts to costs and margins. In addition, it can be expected that margins change over time. In this case, the value that a customer provides to a firm is a function of both the expected time frame until the next purchase and the contribution margin at that particular period. We treated several antecedents used in our framework (e.g., upgrading, cross-buying, bidirectional communication, number of returns, number of Web-based contacts) as exogenous variables in our analysis. We used the lagged variables of these to account for potential endogeneity. Further research can investigate more sophisticated techniques that explicitly treat these variables as endogenous. Finally, a notable issue that arises from our analyses is whether the recommendations from an optimization framework pan out when implemented in the real market. Although our study is a step in the right direction to assess the accountability of marketing actions, a field experiment that tests the recommendations of such a framework on a test group against a control group that is managed according to existing norms would provide a stronger justification for CRM-based efforts.
This research study was supported partially by a grant from the Teradata Center for Customer Relationship Management at Duke University, the Marketing Science Institute, and the Institute for Study of Business Markets, and the authors owe special thanks to them. The study greatly benefited from audience discussions at the National Center for Database Marketing conference in Philadelphia and the Marketing Science Institute conference at INSEAD, as well as at Michigan State University, Curtin University, Tilburg University, State University of New York-Buffalo, and the 2003 Marketing Science Conference. The authors thank Munshi Mahfuddin and Rajendra Ladda for research assistance. Special thanks are due to Don Lehmann, Rick Staelin, Greg Allenby, and John Lynch for their comments on the proposal version of this study. The authors thank a business-to-business firm for providing the data for this study. They also thank the anonymous JM reviewers for providing suggestions to enhance the contribution of this study. They thank Renu and Andrew Peterson for copyediting the manuscript.
( n1) In this study, we do not use a budget constraint on the total resources available for contacting customers. Therefore, we are interested only in allocating resources across channels for each individual customer (i.e., within each customer across channels). However, our framework can be applied to allocate resources across channels with each individual customer and across customers in the presence of a budget constraint.
( n2) We also used lagged interpurchase time instead of the log of the lagged interpurchase time, and we did not find any difference in the substantive conclusions. We used log of the lagged interpurchase times because lagged interpurchase times can have a threshold effect on the influence of current interpurchase times (Allenby, Leone, and Jen 1999). The log of the lagged interpurchase time achieved this objective in scaling the tail of the lagged interpurchase time distribution.
( n3) The parameter estimates of the purchase frequency model are based on mean values from 50 repeats with random starting values for each repetition. We adopted such a procedure to ensure that the parameter estimates are global optimal values and are not affected by any local maxima.
( n4) We also estimated the contribution margin model at the monthly, quarterly, and semiannual levels, but we did not find any significant differences in model performance. Thus, to maintain simplicity, we used the contribution margin model with the annual data.
( n5) We also compared the metrics with a censoring time at 18 months and prediction window of 30 months. The substantive results of the study hold even for a 30-month prediction window. The results are available on request from the authors.
( n6) In Equation 2, the level of contacts in each channel for each customer is varied each year. However, to simplify our optimization routine, we assumed that the level of contacts is equal across the prediction period. Our assumption can be viewed as taking the average level of contacts in the prediction periods.
( n7) When forecasting contribution margin for three years ahead, note that we have data on marketing activities from 1997 to 2001. Our last prediction for the long-term analysis is in period 2003, for which we need marketing variables in 2002, which we do not have. For forecasting the contribution margin in 2003, we used the average of marketing-mix variables from 1997 to 2001 for imputing the values in 2002. However, for the optimization phase, the optimized marketing-mix variables are substituted for the 2002 values. We thank a reviewer for bringing this to our attention.
Legend for Chart:
A - Type of Model
B - Representative Research
C - Return on Investment Modeled and Calculated
D - CLV Calculation
E - How Marketing Communication Affects CLV
F - CLV-Based Resource Allocation
G - Resource Allocation for Each Customer
H - Resource Allocation Across Channels
I - Comparison of Customer-Based Metrics
J - Statistical Details
A B C D E F
G H I J
CLV Berger and
Nasr (1998) No Yes Yes No
No No No Yes
Berger et al.
(2002) Yes Yes Yes Yes
No Yes No No
Customer Blattberg and
equity Deighton
(1996) Yes Yes No Yes
No No No Yes
Libai,
Narayandas,
and Humby
(2002) Yes Yes Yes No
No No No No
Database Reinartz and
marketing Kumar (2000) Yes Yes No No
No No No Yes
Bolton, Lemon,
and Verhoef
(2004) Yes Yes Yes No
No No No Yes
CLV Reinartz and
antecedents Kumar (2003) Yes Yes Yes No
No No Yes Yes
Rust, Zeithaml,
and Lemon
(2004) Yes Yes Yes No
No No Yes Yes
CLV-based Berger and
resource Bechwati
allocation (2001) Yes Yes Yes Yes
No No No No
Present Study Yes Yes Yes Yes
Yes Yes Yes Yes
TABLE 2
Antecedents, Covariates, and Expected Effects
Legend for Chart:
A - Variable
B - Operationalization
C - Expected Effect
D - Rationale
A
B C
D
Purchase Frequency Model
Antecedents
Upgrading
Number of product purchase upgrades +
until an observed purchase
Customers who upgrade have higher
switching costs with each upgrade,
which can lead to lower propensity to
leave and higher recurrent needs
(Bolton, Lemon, and Verhoef 2004).
Cross-buying
Number of different product categories +
a customer has purchased
Customers who purchase across
several product categories have higher
switching costs and recurrent needs
(Bowman and Narayandas 2001;
Reinartz and Kumar 2003).
Bidirectional
communication
Ratio of number of customer-initiated +
contacts to total number of customer
contacts (customer initiated and
supplier initiated) between two observed
purchases
Two-way communication between
parties strengthens the relationship and
ensures that the focal firm is recalled
when a need arises (Morgan and Hunt
1994).
Returns
Number of products the customer ∩
returns between two observed
purchases
Returns provide an opportunity for firms
to satisfy customers and ensure repeat
purchases (Reinartz and Kumar 2003),
but too many purchases can be
detrimental to the relationship and can
indicate that the firm has not used the
return opportunities appropriately.
Frequency of
Web-based
contacts
Number of times in a month the +
customer contacts the supplier through
the Internet between two observed
purchases
Customers who use online
communication want transaction
efficiencies, and customers who want to
create efficiencies are highly relational
and have recurring needs (Grewal,
Corner, and Mehta 2001; Rindfleisch
and Heide 1997).
Relationship
benefits
Indicator variable of whether a customer +
is a premium service member (based
on revenue contribution in the previous
year)
Acknowledgment of customers with
relationship benefits reduces the
propensity of customers to quit and
increases the probability that the focal
firm is recalled when a need arises
(Morgan and Hunt 1994).
Frequency of
rich modes
of communication
Number of customer contacts by the ∩
supplier in a month (through sales
personnel) between two observed
purchases
Timely communication between parties
reduces the propensity of a customer to
quit a relationship (Mohr and Nevin
1990; Morgan and Hunt 1994), but too
much communication can be detrimental
to the relationship (Fournier, Dobscha,
and Mick 1997; Nash 1993); thus, there
is an optimal communication level.
Frequency of
standardized
modes of
communication
Number of customer contacts by the ∩
supplier in a month (through telephone
or direct mail) between two observed
purchases
Timely communication between parties
reduces the propensity of a customer to
quit a relationship (Mohr and Nevin
1990; Morgan and Hunt 1994), but too
much communication can be detrimental
to the relationship (Fournier, Dobscha,
and Mick 1997; Nash 1993); thus, there
is an optimal communication level.
Intercontact
time
Average time between two customer ∩
contacts by the supplier across all
channels of communication between
two observed purchases
Along time between contacts can lead
to forgetfulness, but contacts that are
too soon can cause dysfunction.
Covariate
Product
category
Two indicator variables: one indicates a
hardware purchase; the other indicates
a software purchase
A customer's purchase patterns may
depend on the product category
purchased.
Contribution Margin Model
Antecedents
Lagged
contribution
margin
Customer's contribution margin from the +
previous year
Previous revenue is a good predictor of
current revenue and accounts for any
model misspecification (Niraj, Gupta,
and Narasimhan 2001).
Total marketing
efforts
Total number of customer contacts +
across all channels
Previous marketing communications
and depth (quantity) of purchases
positively affect contribution margin
(Gupta 1988; Tellis and Zufryden 1995).
Total quantity
purchased
Total quantity of products the customer +
purchased across all product categories
Previous marketing communications
and depth (quantity) of purchases
positively affect contribution margin
(Gupta 1988; Tellis and Zufryden 1995).
Covariates
Size of firm
Number of employees in the customer +
firm
Control variables that accommodate for
customer heterogeneity (Niraj, Gupta,
and Narasimhan 2001).
Industry
category
Standard industrial classification-based
industry category to which the customer
firm belongs
Notes: For the purchase frequency model, the dependent
variable is purchase frequency; for the contribution
margin model, the dependent variable is contribution margin. Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
D - Purchase Frequency
E - Upgrading
F - Cross- Buying
G - Bidirectional Communication
H - Returns
I - Frequency of Web-Based Contacts
J - Relationship Benefit-Premium Service Level
K - Frequency of Rich Modes
L - Frequency of Standardized Modes
M - Intercontact Time (Days)
A B C D E
F G H I
J K L M
Purchase Frequency Model
Purchase 5 8.4 1
frequency (4.5) (6.8)
Upgrading 1.32 .89 .62(***) 1
(1.16) (.91)
Cross-buying 2.58 1.7 .53(***) .39(*)
(3.13) (1.5)
1
Bidirectional .84 2.41 .68(***) .51(*)
communication (.62) (3.52)
.48(*) 1
Returns .91 3.7 .36(***) .09
(.86) (2.81)
.13 .11(*) 1
Frequency of 3.88 25.81 .40(***) .21
Web-based contacts (4.37) (24.94)
.15 .17(**) .06 1
Relationship .09 .29 .41(***) .31(**)
benefit-premium (.12) (.25)
service
level
.36(**) .22 -.21(*) .25
1
Frequency of 1.79 5.69 .45(***) .15(**)
rich modes (1.52) (5.84)
.29(**) .32(*) .05 .34(*)
.07 1
Frequency of 20.74 47.75 .44(***) .22(*)
standardized (22.81) (45.81)
modes
.34(*) .24(**) .07 .32(*)
.19(*) .30(**) 1
Intercontact 15.3 13.8 .51(***) -.08
time (16.1) (14.2)
(days)
.06 -.01 -.01 -.04
-.03 -.11 .06 1Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
D - Growth in Contribution Margin
E - Lagged Contribution Margin
F - Total Marketing Efforts
G - Total Quantity Purchase
A B C D
E F G
Contribution Margin Model
Growth in contribution margin 4955 417,270 1
(4827) (381,297)
Lagged contribution margin 31,143 323,139 -.78(***)
(32,825) (318,867)
1
Total marketing efforts 3.49 17.26 .61(***)
(4.5) (16.24)
.08(*) 1
Total quantity purchased 1.89 13.40 .71(***)
(2.01) (14.02)
.05(**) .07 1
(*) Significant at α = 10%.
(**) Significant at α = 5%.
(***) Significant at < 1%.
Notes: The frequency of rich, standardized, and Web-based
contacts represents the frequency of the respective mode of
communication between two consecutive purchases. Values in
parentheses for the descriptive statistics represent Cohort 2.
The correlation matrix for Cohort 2 is similar to that of
Cohort 1 and is available on request from the authors. We do
not report the correlation matrix of the covariates because it
does not have any substantive interpretation. Legend for Chart:
A - Variable
B - Model 1 (Generalized Gamma Without Mixture)
C - Model 2 (Generalized Gamma with Mixture, Without Temporal
Variation)
D - Model 3 (Generalized Gamma with Mixture and Temporal
Variation)
A
B C
D
Component 1
α[sub1]
3.05 (4.12)(**) 4.1 (4.2)(**)
3.27 (3.59)(**)
ν[sub1]
26.18 (25.08)(**) 42.97 (43.82)(**)
47.05 (48.90)(**)
θ[sub1]
.007 (.002)(**) .01 (.08)(**)
.04 (.02)(**)
γ[sub1]
1.2 1.2 (1.3)
1.2 (1.5)
Mass point
.54 (.55)
.54 (.56)
Component 2
α[sub1]
.58 (1.57)(**)
1.48 (3.61)(**)
nu;[sub1]
38.21 (34.28)(**)
32.07 (49.02)(**)
θ[sub1]
.008 (.012)(**)
.005 (.001)(**)
γ[sub1]
.9 (.9)
.9 (.9)
Mass point
.46 (.45)
.46 (.44)
Coefficients
β[sub01]
.89 (-1.52)(**)
-2.05 (-2.51)(*)
Lagged log interpurchase
time
-2.09 (-2.85)(**)
Antecedents: Customer Characteristics
Upgrading
5.01 (5.62)(**)
Cross-buying
6.08 (6.92)(**)
Bidirectional communication
1.49 (1.01)(**)
Returns
10.52 (9.98)(**)
(Returns)²
-3.84 (-4.01)(*)
Frequency of Web-based
contacts
3.52 (2.38)(**)
Antecedents: Supplier-Specific Factors
Relationship benefits
9.78 (7.65)(**)
Frequency of rich modes of
communication
4.50 (5.65)(**)
(Frequency of rich modes of
communication)²
-1.30 (-1.28)(**)
Frequency of standardized
modes of communication
6.53 (7.02)(**)
(Frequency of standardized
modes of communication)²
-.28 (-.53)(**)
Intercontact time
9.64 (8.56)(**)
(Intercontact time)²
-3.21 (-4.05)(**)
Log-likelihood
-3298.91 (-3627.08) -2908.66 (-3297.04)
-2237.82 (-2437.91)
AIC
-6606 (-7262) -5825 (-6602)
-4484 (-4884)
BIC
-6639 (-7295) -5910 (-6687)
-4683 (-5083)
Relative absolute error
.95 (.91) .84 (.80)
.51 (.53)
(*) Posterior sample values between the 2.5th and 97.5th
percentile do not contain zero.
(**) Posterior sample values between the .5th percentile and
99.5th percentile do not contain zero.
Notes: Values in parentheses represent Cohort 2. The product
category variable was not significant in our analysis, and
thus we do not include it here. Relative absolute error is
with respect to a moving average model. The significance
levels apply to the coefficients of Cohorts 1 and 2. Legend for Chart:
A - Independent Variable
B - Parameter Estimate
A B
Intercept N.S. (N.S.)
Contribution in t-2 .83(***) (.85(***))
Lagged total quantity purchased .02(**) (.03(**))
Size .02(**) (.02(**))
Aerospace N.S. (N.S.)
Financial services .02(*) (.02(**))
Manufacturing N.S. (N.S.)
Technology .03(**) (.02(**))
Consumer packaged goods .03(***) (.03(***))
Education, K-12 -.03(***) (-.02(***))
Travel N.S. (N.S.)
Government .02(*) (.02(*))
Lagged total level of marketing effort .04(***) (.06(***))
(*) Significant at α = .10.
(**) Significant at α = .05.
(***) Significant at α < .01.
Notes: The reported coefficients are standardized estimates; the
values in parentheses represent Cohort 2. N.S. = not significant. Legend for Chart:
A - Percentage of Cohort (Selected from Top)
B - CLV
C - PCR
D - PCV
E - CLD
A B C D E
5%
Gross profit ($) 144,883 71,908 131,735 107,719
Variable costs ($) 1,588 979 950 790
Net profit ($) 143,295 70,929 130,785 106,389
10%
Gross profit ($) 78,401 27,981 72,686 55,837
Variable costs ($) 1,245 943 794 610
Net profit ($) 77,156 27,038 71,892 55,227
15%
Gross profit ($) 56,147 15,114 52,591 44,963
Variable costs ($) 807 944 809 738
Net profit ($) 55,340 14,170 51,782 44,225
Notes: All metrics are evaluated at 30 months, with an 18-month
prediction window. Cohort 2 provides similar results. The
reported values are cell medians. Gross profit is residual
revenue after removing cost of goods sold. In general, for the
firm that provided the database, the cost of goods sold is
approximately 70%; thus, gross profit = revenue x .3.DIAGRAM: FIGURE 1; A Conceptual Framework for Measuring and Using CLV
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Purchase Frequency Model
Model estimation. For Cohorts 1 and 2, the results are based on 50,000 samples of the MCMC algorithm (for details of the algorithm, see Allenby, Leone, and Jen 1999, Appendix). We simulated the posterior distribution using five parallel chains with overdispersed starting values. In each chain, we used the initial 40,000 iterations as burn-in and used the last 10,000 iterations to obtain posterior statistics. We used "slice-sampling" ((Neal 2000) to obtain random samples. We assessed the autocorrelations of the posterior samples to perform thinning. The autocorrelation functions revealed that every fifth sample is unrelated to every other fifth sample. Thus, the posterior statistics are based on 2000 samples (using every fifth sample from 10,000 samples). We assessed the convergence of the algorithm from the line plots of the posterior sample and by evaluating Gelman and Rubin's (1992) √R statistic. Values of √R that are closer to 1 reveal that all the chains have converged to true posterior distribution. In our analyses, we found that the value of √R ranged from 1.2 to .9 (in previous research [Cowles and Carlin 1996], these values have been found to indicate convergence of the MCMC chains). Our approach to investigating within-chain autocorrelations, computing Gelman and Rubin's statistic, and visually inspecting the sampling plots has been recommended widely for convergence diagnostics (Cowles and Carlin 1996). We implemented the estimation algorithm using the GAUSS software package, and we performed the convergence diagnostics using CODA. We also used multiple prior values for the estimates and did not find a significant impact on the posterior samples; this is because we used diffuse prior values and because of the large sample size of our data set.
Model selection. Table 4 shows that a simple generalized gamma model without the probit link function or time-varying covariates (Model 1) provides a log-likelihood of -3298.91 (we calculated the log-likelihood using Newton and Raferty's [1994] log marginal density measure)). A generalized gamma model with the probit link function (two subgroups) but no time-varying covariates (Model 2) provides a log-likelihood of 2908.66. Finally, the loglikelihood for the generalized gamma model with the probit link function (with two subgroups) and time-varying covariates (Model 3) is 2237.82. Increasing the number of subgroups in Models 2 or 3 did not result in significantly higher log-likelihoods. We also computed the AIC and BIC for model selection. For both measures, a higher value indicates a better model. We find that Model 3 has the highest value for both AIC and BIC (AIC = -4484 for Model 3, -5825 for Model 2, and -6606 for Model 1; BIC = -4683 for Model 3, 5910 for Model 2, and -6639 for Model 1). In addition, a latent-class finite mixture model with two segments provides a likelihood of -2938. Overall, the results show that the generalized gamma model with two subgroups provides a good fit to the data.
Distribution parameters. The values of γ in the generalized gamma model are γ[sub1] = 1.2 and γ[sub2] = .9 for Subgroups 1 and 2, respectively. The component masses for Subgroup 1 (φ[sub1]) and Subgroup 2 (φ[sub2]) are .54 and .46, respectively. All the distribution parameters (φ[subk], v[subk] and Θ[subk] have more than 99% of their samples different from zero. The mean expected interpurchase time for Subgroup 1 is 4.2 purchases in a year, and the mean expected frequency for Subgroup 2 is 1.01 purchases in a year. Given the variation in expected frequencies in each subgroup, we term Subgroup 1 the "active state" and Subgroup 2 the "inactive state".
Influence of antecedents and covariates. Our empirical analyses support all the proposed effects of the antecedents on purchase frequency.
Out-of-sample forecasting accuracy. We used relative absolute error (RAE) to evaluate the forecasting accuracy of the generalized gamma model compared with that of a naive moving average model (the moving average model is implemented as an updated average of every consecutive interpurchase time for a customer). We used all but the last observation for each customer (calibration sample) to estimate the parameters of each model. We then used the mean posterior values to compute the expected time until next purchase from Equation 3. We then compared the forecast time until the next purchase with the holdout sample (formed from the last observation for each customer) to assess the mean absolute deviation (MAD). The RAE is given by the ratio of the model MAD to the MAD based on the moving average measure. Based on the RAE measure, the generalized gamma model with time-varying covariates (Model 3) has an RAE of .51, compared with that of a naive moving average technique. The MAD from Model 3 is 5.21 months, compared with 10.24 months for the moving average measure. Model 3 provides the best improvement in forecasting accuracy compared with Model 2 (RAE = .84) and Model 1(RAE = .95). We also assessed the predictive capability of the purchase frequency model using hit rate. In other words, we assessed the number of purchases in the holdout sample that the model also correctly predicted as a purchase. We observe that among customers who bought within 12 months, the model currently identifies 89% of them, and among customers who did not buy within 12 months, the model currently identifies 90% of them.
Contribution Margin Model
Estimation and influence of antecedents and covariates. We estimated the contribution margin model on annual data from 1997 (t - 4)) to 2000 (t) for Cohort 1 and from 1998 to 2000 for Cohort 2. The revenue in 2001 (t + 1) is a holdout sample for Cohorts 1 and 2. The coefficients of the contribution margin model are provided in Table 5. The contribution margin model provides an adjusted R2 of .68. Overall, the results of the analyses support all the hypothesized relationships.
Out-of-sample forecasting accuracy. As with typical time-series models, we advanced the independent variables by one period to forecast the dependent variable in period t+ 1. Specifically, when the contribution margin model is used to predict period t + 3, the contribution margin in period t + 1 is an independent variable and is obtained from the prediction in period t + 1. The contribution margin model predicts the growth in revenue from period t to t + 1. We obtained the magnitude of revenue in period t + 1 by adding the predicted value from the contribution margin model to the base revenue in period t. We evaluated the performance of the contribution margin model in the holdout sample on the basis of our estimates from the calibration sample. Table A1 provides the descriptive statistics of the observed contribution margin and the predicted contribution margin in the holdout sample (period t + 1). In the holdout sample, the mean predicted contribution margin in period t+ 1 is approximately $67,729, and the mean observed contribution margin is approximately $64,396.
CLV
We entered predictions from the purchase frequency (Equation 3) and contribution margin (Equation 4) models into Equation 2 to obtain the net present value of future profits (period t + 1) from each customer. The purchase frequency model predicts the expected time in months until next purchase for each customer. We assigned a 30% margin after accounting for cost of goods sold (the managers who provided the database informed us that 30% was a nominal margin for most of their products), and we computed the variable costs using costs of communication. The mean unit cost of standardized modes of communication is approximately $3, and the mean unit cost of rich modes of communication is $60. We computed the unit cost of communication for each customer as the ratio of the total contacts for a given channel in a given year to the total cost of contact for a given mode in a given year. Finally, we used an annual discount rate of 15% for each customer, which is based on the lending rate that is appropriate for the time of the study.
PCR and PCV
We define PCR as the revenue provided by the customer in the most recent observed purchase. We define PCV as the cumulative profits obtained from a customer until the current period. The cumulative profits are calculated annually from a customer's initiation until the current period. We projected the profit in each year to current terms using a discount factor. The PCV calculation is as follows:
(B1) [Multiple line equation(s) cannot be represented in ASCII text]
where CM[subi,t] is the contribution margin for customer i in period t; MC[subi,t] denotes marketing costs for customer i in period t; t is an index for time period (t = 0 for the period of customer initiation; for example, t = 0 for 1997 for Cohort 1 customers, and t = 0 for 1998 for Cohort 2 customers); T is the current period; and r is the discount rate, which we set at 15%.
CLD
In our analysis, we evaluated the probability that a customer is alive or dead in the planning window using the P(Alive) measure that Schmittlein and Peterson (1987) and Reinartz and Kumar (2002) recommend. The P(Alive) measure uses the previous purchase pattern to predict the probability that a customer is still alive at each period in the prediction window. Higher values of P(Alive) indicate longer lifetime duration.
Researchers in marketing have only recently begun to recognize the potential benefits of using genetic algorithms (Balakrishnan and Jacob 1996; Midley, Marks, and Cooper 1997; Naik, Mantrala, and Sawyer 1998; Venkatesan, Krishnan, and Kumar 2004) in deriving optimal strategies for complex marketing problems. The genetic algorithm proceeds by searching for the optimal level of contact for each customer that maximizes CLV. The sum of the optimal CLVs from each individual customer provides the optimal customer equity of the analysis sample.( n7) Specifically, we varied the contact levels for each customer and then calculated the sum of CLV of all customers in the sample. Our objective was to calculate the maximum value for this sum of the CLVs. In this case, our optimization algorithm maximized the objective function by varying 432 parameters in Cohort 1 (216 customers and 2 parameters for each customer [levels of rich and standardized modes]). Following research in customer equity (Rust, Zeithaml, and Lemon 2004), we set the time frame for our optimization framework as three years. Our database provides information on the approximate unit cost of communication through rich modes and standardized modes to each customer. On average, the unit marketing cost through standardized modes (average of direct mail and telephone sales) is $3, and the unit marketing cost through rich modes (salesperson contacts) is $60. Thus, the cost of communication through rich modes is approximately 20 times the cost of communication through standardized modes. Such a cost index is commonly encountered. We set the parameters in the genetic algorithm as follows: population size = 200, probability of crossover = .8, probability of mutation = .25, and convergence criteria = difference in solution in the last 10,000 iterations should be less than .01%. We ran the genetic algorithm at least 50 times and used the mean of the resource levels corresponding to the maximum CLV from each run as the resource reallocation rule for each customer.
Comparison of Descriptive Statistics Between Observed Contribution Margin and Predicted Contribution Margin in the Holdout Sample
Legend for Chart:
B - Mean
C - Standard Deviation
D - Minimum
E - Maximum
A B C D E
Observed 50,199 23,850 -171 1,010,881
(49,229) (24,836) (-181) (1,420,981)
Predicted 57,729 24,538 -178 1,897,257
(74,283) (22,598) (-159) (1,938,458)
Notes: All reported values are in dollars and are rounded to
the nearest integer. Values in parentheses represent Cohort 2.~~~~~~~~
By Rajkumar Venkatesan and V. Kumar
Rajkumar Venkatesan is Assistant Professor in Marketing (e-mail: RVenkatesan@sba.uconn.edu), and V. Kumar is ING Chair Professor in Marketing and Executive Director, ING Center for Financial Services (email: vk@sba.uconn.edu), School of Business, University of Connecticut.
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Record: 4- A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to Go. By: Boulding, William; Staelin, Richard; Ehret, Michael; Johnston, Wesley J. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p155-166. 12p. DOI: 10.1509/jmkg.2005.69.4.155.
- Database:
- Business Source Complete
A Customer Relationship Management Roadmap: What Is
Known, Potential Pitfalls, and Where to Go
The goal of this preface is to describe how the special section on customer relationship management (CRM) was developed. In May 2003, Richard Staelin, Executive Director of the Teradata Center for Customer Relationship Management at Duke University, proposed that Journal of Marketing (JM) publish a special section. The proposal included activities that were designed to promote interactions among marketing academics and practitioners; the goal was to stimulate dialogue and new research on CRM. I found the proposal attractive because CRM is a broad-based topic that interests many marketers. After extensive discussion, the American Marketing Association (AMA) and the Teradata Center formally agreed to cosponsor the special section. Subsequently, there was a conference on Relationship Marketing and Customer Relationship Management (cochaired by Michael Ehret, Wesley Johnston, Michael Kleinaltenkamp, and Lou Pelton) that took place at Freie Universität Berlin in the summer of 2003;( n1) a conference on Customer Management (cosponsored by the Marketing Science Institute and the Teradata Center) that was held at Duke University in March 2004; and two special sessions on CRM that were featured at the AMA Winter Educators' Conference held in San Antonio, Tex., in February 2005. The conferences provided many opportunities for dialogue, and the response from marketers who attended these events was enthusiastic. I also invited Richard Staelin and William Boulding (Executive Codirector of the Teradata Center) to work with me as consulting editors for the special section, and they agreed. A call for papers requested that authors submit their manuscripts to JM by May 2004. The consulting editors and I evaluated every submission with the assistance of an expert panel that included Leonard Berry, John Deighton, Michael Ehret, Christian Grönroos, Sunil Gupta, Wayne Hoyer, Wagner Kamakura, Wesley Johnston, Donald R. Lehmann, Charlotte Mason, Carl Mela, Scott Neslin, Roland Rust, Michel Wedel, and Valarie Zeithaml. All submissions underwent JM 's standard double-blind review process, and members of JM's editorial review board served as reviewers. I would like to express my appreciation to everyone who participated in the development of the special section. The culmination of our work together is a set of nine articles and two essays that advance the science and practice of CRM. I hope that these articles stimulate new intellectual discoveries.
--Ruth N. Bolton
This article introduces the ten articles that appear in this special section on customer relationship management (CRM). An overarching goal of this article is to provide the reader with a roadmap that places these articles in the context of the CRM landscape. We suggest 11 propositions about what is known about CRM and the potential pitfalls and unknowns that firms face in the implementation of CRM. We also provide six recommendations for further CRM research. We organize our discussion around these themes before offering concluding comments.
What Is Known About CRM
Before assessing what is known about CRM, we begin by placing the field of CRM in the overall context of marketing thought.( n2) Many years ago, economists introduced the concept of value maximization, whereby a firm maximizes profits and consumers maximize utility. Today, we have the concept of CRM. Theorists in this area still emphasize firm performance and customer value, though they also talk about the dual creation of firm and customer value (Payne and Frow 2005; Rogers 2005; Vargo and Lusch 2004). The question we raise is whether the field's focus on CRM sheds light on the understanding of customer and firm behavior or whether it just creates more "heat." Many vendors argue that CRM requires a paradigm shift in firm behavior. If this is true, CRM is truly a really big new idea. However, others contend that the concepts of CRM are not fundamentally different from what economists put forward many years ago. If this is so, the following questions arise: Is CRM anything other than a repackaging of basic marketing ideas that have extended and built on the classic economic paradigm? Should CRM be viewed simply as one of many jargon-laden fads that have come and gone in the business world? Or is a third explanation possible? Namely, does CRM represent the evolution and integration of marketing ideas and newly feasible and cost-effective technologies? In this view, CRM is neither a fad nor a paradigm shift. After observing the development of the CRM field, we offer the following proposition:
P1: CRM is the outcome of the continuing evolution and integration of marketing ideas and newly available data, technologies, and organizational forms.
To support this proposition, we briefly document this evolution.
One of the original big ideas in marketing is that for firms to stay in existence, they should not focus on selling products but rather on fulfilling needs (Levitt 1960). Thus, a drill manufacturer is in the business of providing a customer a hole, and a railroad company is in the business of providing transportation. This is a key component of CRM because the emphasis is not on how to sell the product but rather on creating value for the customer and, in the process, creating value for the firm (staying in existence). In other words, it is a process of dual creation of value. Levitt (1969) introduced the concept of the augmented product, stressing that consumers are interested in the total buying experience, not just the core product. Again, CRM relies on this concept because it tries to find the specific elements of the exchange process that produce value to the customer.
Bagozzi (1974) refocused people's attention on the actual exchange process by reiterating the fundamental economic concept that an exchange occurs only when both parties perceive that they are receiving value. Almost ten years later, Berry (1983) shifted the emphasis to the relationship between the company and the customer. At the time, his interest was in the service sector and the need for the service organization to attract customers and then maintain and enhance these customer relationships. On the basis of his ideas and related conceptual work (Arndt 1979; MacNeil 1978; Morgan and Hunt 1994), the concept of building relationships was expanded to several different domains, such as industrial buyer-seller relationships (Dwyer, Schurr, and Oh 1987) and channels of distribution (Gaski 1984). Others adopted the idea of building relationships and extended it conceptually in various ways (Boulding et al. 1993; Grönroos 1994; Gummesson 1987; Webster 2002). This body of literature discusses concepts that are relevant to CRM, such as the influence of prior experience on future customer expectations, the different treatment of each customer, and the value of long-term relationships.
Concurrently, other marketing scholars turned their attention to the core capabilities of the firm that were necessary to develop and maintain good customer relationships. In some sense, this was a formalization of the concept and processes implied by the "three Cs" (i.e., customer, company, and competitor) analysis. As a result, concepts such as market orientation (Kohli and Jaworski 1990; Narver and Slater 1990), market focus (Day 1994), and market-based learning (Vorhies and Hunt 2005) were developed that emphasized the establishment of good information processes and capabilities within the firm to understand the needs and wants of customers, thus making firms more efficient and effective in managing customer relationships. In addition, there was an evolution from product, or brand, management to customer management (Sheth 2005) and from product portfolio management to customer portfolio management (Johnson and Selnes 2004). These transitions were due in part to work in the area of brand equity, which recognized that equity resides in the minds of consumers (Keller 1993); this shifted the locus of attention from brands and products to customers.
With these developments in marketing as a backdrop, there was an explosion of customer data in the 1980s. Although some attempts were made to organize these data for analytic purposes, many firms were overwhelmed by this onslaught of potentially useful information. In anticipation of hardware and software solutions to these data problems, Peppers and Rogers (1993) introduced the concept of one-to-one marketing, and Pine (1993) introduced the concept of mass customization. Vendors capitalized on these ideas with hardware and software solutions and began using the term CRM to refer to the collection of data and activities surrounding the management of the customer-firm interface. These CRM solutions enabled firms to acquire, warehouse, and analyze data about customer behavior and company actions more easily. Using these data and analyses, firms began to focus on acquiring new customers; retaining their current customers (i.e., building long-term relationships); and enhancing these relationships through such activities as customized communications, cross-selling, and the segmentation of customers, depending on their value to the firm (Payne and Frow 2005). Implementation of these CRM solutions also required firms to have a customer relational orientation (Jayachandran et al. 2005; Srinivasan and Moorman 2005) and to have processes in place to collect, analyze, and apply the acquired customer information (Jayachandran et al. 2005).
Thus, the question is, What is new about CRM? On the basis of our preceding discussion, it could be argued that CRM is the relabeling of a mixture of different marketing ideas in the extant marketing literature. However, we believe that CRM represents an evolution beyond a repackaging of existing ideas. Specifically, we posit that CRM goes beyond extant literature because it "requires a cross-functional integration of processes, people, operations, and marketing capabilities that is enabled through information, technology, and applications" (Payne and Frow 2005, p. 168). Indeed, CRM goes beyond a customer focus. Not only does CRM build relationships and use systems to collect and analyze data, but it also includes the integration of all these activities across the firm, linking these activities to both firm and customer value, extending this integration along the value chain, and developing the capability of integrating these activities across the network of firms that collaborate to generate customer value, while creating shareholder value for the firm.
P2: The field of CRM has begun to converge on a common definition.
Payne and Frow (2005) document numerous definitions of CRM in the literature (see their Appendix). These definitions range from CRM as the implementation of specific technology solutions to a holistic approach of managing customer relationships that simultaneously creates both customer and firm value. This plethora of definitions has caused some confusion. Parvatiyar and Sheth (2001) note that a prerequisite for an emerging field to coalesce into an established field is for the discipline to establish an acceptable definition that captures all the major aspects of the concept. Payne and Frow attempt to provide such a definition. It is possible to quibble about the specific wording, but we agree with the basic elements of their definition. Specifically, CRM relates to strategy, the management of the dual creation of value, the intelligent use of data and technology, the acquisition of customer knowledge and the diffusion of this knowledge to the appropriate stakeholders, the development of appropriate (long-term) relationships with specific customers and/or customer groups, and the integration of processes across the many areas of the firm and across the network of firms that collaborate to generate customer value.
In addition to theoretical development, a prerequisite for the applied development of CRM is that it should demonstrably enhance firm performance. This is a necessary quality in the evaluation of any firm or marketing activity (e.g., Lehmann 2004; Rust et al. 2004). With this in mind, note that it is not necessarily a widely held belief that the implementation of CRM activities leads to firm value. To this end, consider the numerous articles that appear in the business press (e.g., Rigby, Reichheld, Schefter 2002; Whiting 2001). Nonetheless, we propose the following:
P3: Companies have developed proven CRM practices that enhance firm performance.
Eight of the ten articles in this special section directly address this proposition. These articles use different measures of performance in many different contexts, and they use various research methods. However, all eight articles demonstrate that CRM activities can enhance firm performance. For a field that has come under attack for not meeting this objective, we believe that this is a powerful result.
Using a case study approach, Ryals (2005) shows that one of the business units she studied was able to achieve a 270% increase in business unit profits (above target) by implementing several straightforward CRM measures. Using a multifirm (cross-sectional) database, Srinivasan and Moorman (2005) show that firms that invest more in CRM activities and technology have greater customer satisfaction. Using another multifirm database, Mithas, Krishnan, and Fornell (2005) show that the use of CRM applications is associated with increased customer knowledge, which in turn is associated with greater customer satisfaction. Using yet another multifirm database, Jayachandran and colleagues (2005) show that firm performance measured in terms of retention and customer satisfaction is greater for firms that have good relational information processes in place.
Cao and Gruca (2005), Lewis (2005), Thomas and Sullivan (2005), and Gustafsson, Johnson, and Roos (2005) all use data collected within a single firm over time. Cao and Gruca, Lewis, and Thomas and Sullivan use data from both the firm and its customers to develop specific CRM applications to increase the firm's performance. Cao and Gruca center their attention on acquiring the "right" customers; Lewis provides a process that identifies and considers dynamic customer behavior, thus enabling a pricing scheme that increases long-term profits; and Thomas and Sullivan develop a decision support system using an enterprise database that allows the firm to modify its communication message depending on where particular customers live and how they shop. In each case, the authors show how firm profits can be increased. Gustafsson, Johnson, and Roos (2005) examine customer behavior over time and show that some of the intermediate relationship performance measures that emerge from the business-to-business literature (e.g., satisfaction, calculative commitment) directly and positively influence actual behavior in the form of retention within a business-to-consumer setting.
We must emphasize four points here. First, the eight empirical articles in this special section demonstrate the positive impact of CRM in a wide variety of industry settings. Thus, success with CRM is not contingent on being a part of a particular industry (e.g., financial services). Second, we note that all of the application articles are narrow rather than comprehensive. Thus, they find local improvements in profits. We can only speculate about what could be accomplished with a more comprehensive systems approach; we also express some concern that local solutions can sometimes be suboptimal in the long run. Third, only Ryals's (2005) study directly measures both the costs and the revenues associated with the CRM activities to assess overall profits. Lewis (2005) and Cao and Gruca (2005) examine profits, but because of data unavailability, they must make assumptions about costs to generate these numbers. All the other studies use proxies for profits. Because several of the studies use customer satisfaction for their performance measure, it is of interest that Gustafsson, Johnson, and Roos (2005) show that customer satisfaction is negatively associated with observed customer churn, thus providing strong evidence that customer satisfaction is a useful precursor of downstream outcomes. However, it is important to note that this same research indicates that satisfaction is not the only predictor of downstream performance measures.
This leads to a fourth observation about CRM activities and firm performance. Payne and Frow (2005) emphasize that one major element in any CRM system is the measurement process. Although the ultimate objective of any measurement process is to increase shareholder value, one of the real advantages of a CRM measurement process is that the firm normally also obtains measures such as customer lifetime value and acquisition and retention costs, which relate to the value dual-creation process. Thus, good CRM process measures provide the firm with the opportunity to gain deeper insights into how these intermediate process measures link to downstream firm performance. Several articles in this issue show these links. Thus, we do not believe that every article must focus its attention on the most obvious downstream measures of performance (e.g., profits, shareholder value). However, it is clear that more work must be done to establish the links between the many process measures that come from CRM systems and these downstream measures as well as the implied return on different CRM investments. The work of Gupta, Lehmann, and Stuart (2004) offers an excellent first step in this direction. This area should be of particular interest to researchers who want to demonstrate the link between marketing activities and shareholder value (Srivastava, Shervani, and Fahey 1998). In general, CRM creates the potential for firms to begin to treat as firm investments what were previously considered marketing costs (Rust, Lemon, and Zeithaml 2004). Furthermore, this implies that marketing could regain a central role in managing a key asset of the firm, namely, the customer asset.
Finally, even though we consider Payne and Frow's (2005) article conceptual rather than empirical, we note its relevance to the CRM-performance link. In particular, this conceptual framework emerges as the "best practice" from interaction research with several firms. If a firm does not achieve the desired results from its CRM activities, it might compare its practices with the best practice template that Payne and Frow provide. This comparison could reveal gaps in how the firm implements CRM relative to best practices.
Having said this, we note that Payne and Frow's (2005) framework is largely silent about how a particular context or process might interact with another process to produce differential results from CRM activities. Although the articles published in this special section show that CRM activities lead to enhanced firm performance, they also reveal situations in which CRM activities have more or less positive effects on firm performance. This leads to our next proposition:
P4: Holding fixed the level of CRM investment, the effectiveness of CRM activities depends on how CRM is integrated with the firm's (a) existing processes and (b) preexisting capabilities.
We previously noted that CRM activities need to be integrated into the fabric of the overall operations of the firm. Because different firms have different core capabilities, it is not surprising that CRM activities have a differential effect depending on the context of where and when they are implemented. This is similar to what has been observed in the context of relationship management (e.g., Coviello et al. 2002). Specifically, Jayachandran and colleagues (2005) show that the positive effects of investments in CRM technology are enhanced when the firm already has the appropriate relational information processes in place. Srinivasan and Moorman (2005) show that for online retailers, the firm's strategic commitments in terms of prior bricks-and-mortar experience and online experience affect the impact of online CRM investments on the firm's performance. Notably, they also find a few cases in which increased investments in CRM are associated with negative returns in performance.
Likewise, Mithas, Krishnan, and Fornell (2005) show that CRM activity returns are enhanced when firms share information with their supply chain members. Furthermore, Thomas and Sullivan (2005) show that an enterprise CRM system that coordinates and integrates data from different channel sources enables the firm to gain new knowledge about each customer and thus enhance firm performance.
We expect that there are many other contexts in which CRM activities are either enhanced or reduced. We discuss this issue in more depth in some of the subsequent propositions. However, before doing so, we offer another proposition that may sound somewhat contradictory to the previous proposition:
P5: Effective CRM implementation does not necessarily require sophisticated analyses, concepts, or technology.
After reading numerous submissions for this issue, we were struck by the "simplicity" of the application articles. The articles used known methodologies (e.g., latent segmentation: Lewis 2005; Thomas and Sullivan 2005), relied on known conceptual issues (e.g., adverse selection in acquiring customers: Cao and Gruca 2005), and examined small pieces of the overall set of CRM activities (e.g., use of customer lifetime value: Ryals 2005) in studying the effects of CRM on performance.
Perhaps the most striking example of this is Ryals's (2005) contribution. From a research methodology standpoint, the case study approach is technically unsophisticated. Moreover, the CRM activities implemented in these case studies are simple and straightforward. The combination of these two attributes led one reviewer of this manuscript to conclude that an important implication of this article was that even simple CRM activities yield measurable benefits for a firm.
Another surprise gleaned from the application articles is the relevance of traditional market segmentation in the context of CRM activities. Some may equate CRM with the idea that every firm offer/activity should be customized for individual consumers. However, in all of the application articles, we observed the use of basic market segmentation (Cao and Gruca 2005; Lewis 2005; Ryals 2005; Thomas and Sullivan 2005), and three of the articles identify only two segments. Admittedly, these segments were not based on standard demographics but rather on detailed analyses of prior observed behavior.
Only Ryals (2005), in one of her two case studies, shows an application in which the firm treated each customer individually, and here the firm had only ten major customers. Still, what we find most germane in considering the group of application articles published herein is that despite the simplicity in the approaches, each of these applications was able to show improvements in firm performance when the firm acted strategically in terms of using customer information to create firm value.
Jayachandran and colleagues (2005) reinforce the point about simplicity and CRM effectiveness The database they used in their analysis contained a significant number of firms that had yet to implement sophisticated CRM applications. Yet these researchers showed that as long as these firms had good relational processes in place, they were able to obtain good firm performance. Thus, although the effectiveness of CRM may vary depending on the specific context of these activities, consistent with P4, it appears that the most important element of CRM implementation is for the firm to acquire customer knowledge and then use this knowledge wisely for the dual creation of value. This leads to our next proposition.
P6: The core of CRM is the concept of dual creation of value.
Payne and Frow's (2005) Figure 1 best demonstrates "proof" of this proposition; it shows the cocreation of value as the central element in their conceptual framework. We consider the labels "cocreation" and "dual creation" of value interchangeable, but we prefer "dual creation" given instances in which firms can create value for one customer through information drawn from other customers (e.g., Amazon) rather than direct collaboration. Jayachandran and colleagues (2005) provide a deeper description of this core idea with their delineation of five subprocesses that they refer to as relational information processes. We believe that their processes, which they label "information reciprocity," "information capture," "information integration," "information access," and "information use," directly relate to the dual creation of value.
The idea that dual creation of value is at the core of CRM is also evident in all the articles that examine the firm-customer interface. For example, Cao and Gruca (2005) provide a framework whereby the firm can better limit its target market to customers who both want to hear about the firm's particular offer and qualify for that offer. As a result, the firm does not send messages to customers who are unlikely to respond, thus minimizing the disturbance to these customers. Likewise, the firm does not send messages to customers who are unlikely to qualify for the offer, thus minimizing their disappointment. The authors note that this leads to an obvious win-win situation for the firm and its customers (i.e., the dual creation of value).
In contrast, Lewis (2005) develops a pricing scheme that creates a differential value proposition for different market segments. He notes that this raises the issue of fairness and trust because the goal is for the firm to use knowledge about customers to extract more value for the firm and therefore create less value for the customer. Similarly, Ryals (2005) shows that firms reduce their attention to customers/ customer groups after they determine that they are not able to garner enough value from these groups. Thus, for certain customers, value is taken away so that firms can increase the value they receive. In a similar manner, Thomas and Sullivan (2005) examine the dual creation of value from the firm perspective. They propose a process that enables firms to migrate customers into more profitable channels. However, this article does not address the issue of whether this process creates additional customer value.
These latter studies bring to the forefront the concept of who gets the economic rents from the value creation process. Economic theory is silent on this issue, other than to maintain that neither party can be worse off. However, if CRM is implemented in a way that leads consumers to believe that they are worse off, firms can put themselves at substantial risk. Information reciprocity can break down, and consumers may ultimately choose to opt out of relationships. This leads us to consider the "dark side" of CRM or, more generally, the potential pitfalls and unknowns that firms should consider when implementing CRM activities.
Potential Pitfalls and Unknowns in
CRM Implementation
In ideal circumstances, CRM and the dual creation of value expand the pie such that both consumers and firms are better off. However, the focus in CRM applications, as some of the articles in this special section demonstrate, can be on the creation of firm value. In these cases, CRM might be considered a pie-splitting mechanism, whereby the firm can learn things about customers that enable it to take a big slice of the created value. To rephrase this in economic terms, firms may use CRM activities to attempt to extract consumer surplus. This possibility makes Wright's (1986) concept of "schemer schema" relevant in the context of CRM activities. Given customers' intuitive theories about what firms are trying to do with CRM, how will customers modify their behavior? This question leads to our first proposition about firm issues in implementing CRM activities:
P7: The successful implementation of CRM requires that firms carefully consider issues of consumer trust and privacy.
Sometimes the firm can unobtrusively collect information about the customer at the time of the transaction. Other times, the firm must rely on the customer providing this information. It is in the firm's self-interest to collect these data; as both Mithas, Krishnan, and Fornell (2005) and Jayachandran and colleagues (2005) show, firms that acquire this knowledge are more likely to have superior firm performance. However, it may not always be in the customer's self-interest to provide these data. Lewis (2005) nicely illustrates Wright's (1986) notion of schemer schema. In this application, the firm infers customer types from observed prior behavior. However, Lewis shows that some customers anticipate what a firm will do after it observes customer behavior. This leads these customers to modify their own behavior. In other words, the consumers act strategically. If consumers act strategically, this reduces the firm's share of the value creation pie, even if the firm anticipates these reactions, though anticipation on the part of the firm will reduce this reduction in share of the value pie.
In Lewis's (2005) setting, only 5% of the customers are in the "strategic behavior" segment. However, as more consumers begin to consider that firms are using CRM to act strategically, an increasing number of customers become less trusting of firm behavior and therefore act strategically in repeated interactions with the firm. The real issue from the consumers' perspective is whether they trust that firms will use their data in a way that helps the consumer. Lewis cites some fairly prominent examples of firm behavior that may reduce customer trust in those firms (e.g., attempts by Coke and Amazon to extract consumer surplus). In today's Internet environment, there is a proliferation of spyware (and spyware blockers) that has led to a distrust of the Internet shopping environment and a desire for greater consumer privacy.
Deighton (2005) suggests that issues of consumer trust could significantly undermine CRM activities. In particular, if customers lose trust in firms and believe that their data are used by firms for purposes of exploiting them, consumers will attempt to keep their data private or to distort the data. This has led, and could continue to lead, to both individually based efforts to keep data private or collectively based efforts that lead to privacy regulations. This consumer protection is certainly reasonable and appropriate. Thus, firms should think with foresight about trust and privacy implications of their CRM activities, and researchers should continue to consider these issues (e.g., Bart et al. 2005). If the firm does not adequately consider the creation of value for both their customers and themselves, they may lose access to the data required for the dual creation of value process. Stated simply, firms should not be greedy.
P8: The successful implementation of CRM requires that firms carefully consider issues of consumer fairness.
The precursor to some of these trust issues is fairness: Do customers trust that firms will be fair in splitting the value creation pie? However, there is an additional perspective of fairness to consider. In particular, CRM activities create the potential for differential treatment of customers who interact with a firm. Reitz (2005) provides examples in which customers did not become upset by being treated differentially, even when they were on the same airline flight. However, Reitz also notes that customers have norms of what is fair and what is unfair in terms of differential treatment of customers and that it is easy for firms to cross over the line in terms of what customers consider unfair.
Feinberg, Krishna, and Zhang (2002) demonstrate the risk of crossing this line by means of differential treatment. They report that there is more switching (lower retention) for the focal customer if another customer, who is perceived to be similar to the focal customer, receives better treatment from the same firm. They find the same result for the focal customer if the similar customer receives better treatment from a competitive firm. This is similar to the result of Boulding and colleagues (1993), who find that based on what customers observe other customers receiving either from the same firm or from competitive firms, customers develop expectations about what they should receive and downgrade their perceptions of the firm if they receive less than what they believe they should. Likewise, using a regret theory framework, Inman, Dyer, and Jia (1997) emphasize the importance of forgone alternatives in terms of the valuation of the chosen alternative and future purchase decisions. Both Srinivasan and Moorman's (2005) and Gustafsson, Johnson, and Roos's (2005) articles show results that are consistent with this prior research. They find that what customers experience, both within a particular firm and compared with other firms, drives what they believe they should receive or what they believe is fair and, thus, affects the firm's performance.
We believe that much is still unknown about the standards customers use to determine whether the firm is acting fairly and the connection between perceived fairness and trust. Thus, we hope that researchers continue to examine these standards (e.g., Mazumdar, Raj, and Sina 2005). Because these issues are intimately connected with customers' willingness to provide data and their satisfaction with the firm relationship, firms must take great care in monitoring and managing customer perceptions of trust and fairness.
P9: Inappropriate and incomplete use of CRM metrics can put the firm at risk of developing core rigidities, thus leading to long-term failure.
According to Payne and Frow (2005), one of the key components of CRM is a good measurement process. However, most of the most popular measures of current CRM systems (e.g., acquisition, retention, cross-selling, up-selling, customer migration, customer lifetime value) are outcome measures. These outcome measures are important and necessary. However, they may not be directly linked to the value dual-creation process, and as we previously noted, this is the core concept of CRM. Thus, it is essential that the firm also develops measures that are directly connected with this value dual-creation process, enabling the firm to understand the drivers of value and thus to ensure long-term success. The flip side of this is that failing to do so can lead to doing too much of a "good" thing, producing core rigidities (Atuahene-Gima 2005; Leonard-Barton 1992) and long-term failure.
A few examples may be useful to illustrate this point. An early insight in the development of CRM was the importance of customer retention. Indeed, it can be shown that lifetime value calculations are more sensitive to improvements in customer retention than customer acquisition (Heskett et al. 1994). Fuller (2005) describes the implications of this connection between lifetime value and retention at L.L.Bean, which focused on retention at the expense of acquisition activities. The consequence of this focus was short-term gains in profitability but, unfortunately, at the expense of new customer acquisition and a reduction in the long-term value of the firm. Subsequently, L.L.Bean adjusted the balance between acquisition and retention activities.
This example brings the importance of customer portfolio management to the forefront (Johnson and Selnes 2004). Thinking about balancing the customer portfolio in terms of customer needs, implied firm actions to create customer value, and how these customers create company value leads us to ponder one of the recommendations in the bank case study that Ryals (2005) reports. On the basis of lifetime value calculations, the bank decided to stop targeting younger, less profitable loan customers. This decision is potentially based on incomplete use of metrics in two different ways. First, it could be that the younger, less profitable customers evolve over time into older, more profitable customers. The lifetime value metric needs to be complete in the sense of capturing this potential dynamism in customer value (Du 2005). Second, it could be that dropping these smaller, less profitable customers undermines the economies of scale that enable the firm to generate profits from larger customers, as was the case for a large European financial services company (Johnson and Selnes 2005). In this case, the lifetime value metric needs to be complete in terms of considering potential externalities across customer groups. This requires the firm to allocate the costs associated with providing customer value accurately.
Another example to illustrate this proposition is what took place over several years at Xerox. Xerox was an early leader in customer satisfaction and one of the first winners of the Baldridge Award. It created excellent information systems to collect and diffuse detailed customer metrics to relevant parts of the organization, which resulted in increases in customer satisfaction and firm value. However, many of these systems focused on measuring customer satisfaction with existing products and services. It is possible that, over time, these measurement processes, which had been a source of strength for Xerox, became core rigidities, ultimately leading to subpar performance for the company. If key CRM metrics do not directly assess the value dual-creation process, there is a risk that the underlying value proposition that generates outcome metrics, such as satisfaction, retention, acquisition, and lifetime value, can slowly degrade.
Thus, although CRM enables the firm to obtain a large number of measures, it is important that at least some of these measures connect to the current and future value creation process. This should enhance the firm's innovation activities and, in general, keep the firm competitive over time. It also raises an interesting research question: What is the relationship between the level of the firm's CRM activities and its level of innovation?
P10: Successful implementation of CRM requires that firms incorporate knowledge about competition and competitive reaction into CRM processes.
It is well accepted that CRM is a strategic initiative. As such, it should be held accountable to the same standard as the evaluation of other strategic choices that a firm faces (Boulding and Staelin 1995). Does CRM provide sustainable advantage for the firm in the face of competitive reaction? A standard issue in the assessment of returns to strategic choices is that if generalizations exist about strategic relationships, firms can be expected to compete away the advantages implied by these relationships (Wensley 1982). This issue is especially salient given that P3 suggests that companies have developed proven CRM practices that enhance firm performance.
We find it surprising that the CRM literature and the articles in this special section are largely silent on this issue of competitive reaction. We note that Payne and Frow's (2005) five elements of CRM do not explicitly reflect competition, and none of the empirical articles directly considers the role of competition. We find this omission in the CRM literature especially surprising given that the evolution of CRM can be traced back to the market orientation literature. In this literature, there is a discussion of whether firms should be customer focused or competitor focused, and the conclusion is that firms should be market focused; that is, firms should focus on competitors, customers, and company capabilities (Kohli and Jaworski 1990). Thus, in addition to addressing the issue of sustainability in the face of competitive reaction, we can ask a more general question: How is competitor focus integrated into CRM?
We believe that a failure to integrate competition into a firm's CRM activities potentially puts it at serious risk. The biggest risk is related to the previous proposition (i.e., the destruction of the dual creation of value due to innovation from competition). For example, when innovation enables document copies to be made through distributed desktop printing rather than centralized copying, what does this imply for the dual creation of value for Hewlett-Packard and Xerox, respectively?
In summary, we believe that research is necessary in this area on two dimensions: First, work must be done that shows how to integrate competition into the processes underlying CRM. Second, work must be done that considers the conditions in which CRM yields sustainable advantage in the face of competitive reaction. With regard to this latter issue, it is difficult to imagine how the technology underlying CRM could create such an advantage. However, the relational information processes, the customer knowledge generated from these processes, the integration of processes, and the customer loyalty resulting from the value creation processes may be difficult to imitate.
P11: Effective CRM implementation requires coordination of channels, technologies, customers, and employees.
Again, an interesting aspect of this special section is what is not included. Little attention is given to the role of employees in the implementation of effective CRM activities. In discussing how Continental Airlines went from worst to first in customer satisfaction, Reitz (2005) stresses the importance of firms having people issues under control before investing in expensive CRM technologies. The articles in this issue that come closest to addressing the role of people in CRM are those of Jayachandran and colleagues (2005) and Srinivasan and Moorman (2005). Jayachandran and colleagues detail the processes that are needed to collect, use, and analyze customer data effectively, but they only hint at how to ensure that employees use these processes. Srinivasan and Moorman measure employee involvement in the collection and dissemination of customer data and show that this involvement is positively associated with good firm performance. However, they also do not provide insights into how to ensure that people in firms exhibit this behavior. Thomas and Sullivan (2005) demonstrate the potential value of enterprise systems that integrate customer information across channels. However, they do not indicate how to bundle these enterprise systems with people processes. For example, it would be nice for the catalog customer service representatives to have access to customers' shopping activities across all company channels.
Because employees are an integral part of the delivery of CRM activities, we believe that the organizational issues relevant to CRM are a critical area that deserves a firm's attention. Data and technology processes and systems are critical for CRM activities, but without appropriate human interaction with these processes and systems, the returns on investments in these areas are at risk. Vorhies and Morgan (2005) make a similar point in noting the interdependence of marketing capabilities. Because little is known about how people issues connect to the success of CRM activities, we believe that this is an area worthy of researcher attention. A good starting point might be the services marketing literature. This literature recognizes that services lie at the intersection of marketing, human resources, and operations. We refer the reader to the work of Zeithaml, Bitner, and Wilson (2000) for an extensive discussion of this literature.
Finally, related to the previous proposition, we also note that the integration of CRM across both people and processes may be difficult to imitate and thus provide a source of sustainable competitive advantage. Thus, we hope that others will provide deeper insights into the conditions needed to integrate people into CRM activities successfully.
Method Issues for Further CRM Research
On the basis of our discussion of the previous propositions, it is clear that there are additional substantive unknowns pertaining to the implementation of CRM and the understanding of the effects of CRM activities. We now turn to more general methodological considerations. We provide six recommendations that we believe are important for scholars interested in pursuing research in the field of CRM.
R1: CRM research should focus on the interaction among sub-processes or the interaction among processes, not total CRM systems.
We previously noted that CRM entails the integration of numerous processes; takes place across multiple areas of the value chain; and involves the confluence of technologies, data, and people. We believe that CRM is too complex and integrative to expect any one study to model all these aspects of CRM empirically. Thus, we expect that researchers will follow the lead of the eight empirical articles in this special issue and focus on specific areas within CRM, searching for insights pertaining to how particular processes or subprocesses behave and/or interact. However, we note an inherent tension in doing this. Researchers must remember that other parts of the CRM process may modify their relationships of interest. This will require researchers to either model these effects or control for them. In the following recommendations, we focus on specific methodological issues that investigators should take into account when conducting CRM research.
R2: CRM research should have the appropriate measures available for the desired insights.
Most studies on CRM are involved in one way or another with firm performance. We previously noted that the articles in this special issue use several different firm performance measures. Ryals (2005) and Cao and Gruca (2005) use profit. Lewis (2005) uses revenues. Gustafsson, Johnson, and Roos (2005) use retention, and Srinivasan and Moorman (2005) and Mithas, Krishnan, and Fornell (2005) use satisfaction. Jayachandran and colleagues (2005) use a combination of retention and satisfaction. Ultimately, CRM is about linking firm actions to the many stakeholders' values. One of the major stakeholders of relevance to CRM is the firm owners (i.e., the shareholders). A key objective for these stakeholders is profit maximization. Thus, we hope that researchers obtain cost data whenever possible, thereby enabling them to assess profits. This may require them to address the allocation of costs directly across different business units or customers groups, as Ryals (2005) does at both the customer and the segment levels. Beyond this, we hope that further work directly examines the link between CRM activities and shareholder value.
A second key stakeholder of relevance to CRM is the customer. A key objective for customers is utility/value maximization. Here, there is a great deal of research that examines how CRM connects to this objective, using the proxy of customer satisfaction. However, we reiterate our previously stated belief that more work could be done that examines how CRM affects measures of the value creation process rather than value creation outcomes. In other words, how does CRM connect to innovation and constant renewal of value creation for customers?
Other stakeholders of relevance to CRM include employees, suppliers, and collaborators. Given the integrative nature of CRM, we hope that further research explores how measures of relevance to these different stakeholders are related to CRM activities. We expect that these measures are present in a variety of other literature streams (e.g., strategy, supply chain management, organizational behavior and design).
In addition, the CRM environment provides a rich setting to test the relationship between and among various measures. Thus, Gustafsson, Johnson, and Roos (2005) examine the uniqueness of constructs that tap satisfaction, affective commitment, and calculative commitment as they are related to retention. This work emphasizes the issue that though constructs relevant to CRM may be conceptually distinct, their effects may be empirically indistinguishable, as is found with the satisfaction and affective commitment constructs. This finding suggests that careful attention to measurement issues is required when testing subtle theoretical effects relevant to the CRM domain.
R3: Research should provide conclusive evidence with respect to the causal effects of CRM activities.
Most CRM researchers are interested in assessing causality. One research strategy commonly found in the study of CRM (among other fields) is to obtain responses for several firms and then determine whether there is a tendency for firms of a particular type to display different behavior than firms of another type. Often, the researcher obtains these data by asking respondents to fill out a survey. It has long been known that if both the dependent variables and the independent variables come from the same respondent, it is likely that at least part of any association between these variables is due to common method bias. A way to avoid this bias is to use two different sources, one for the independent variables and one for the dependent variables. Two of the articles using cross-sectional data in this issue (i.e., Mithas, Krishnan, and Fornell 2005; Srinivasan and Moorman 2005) use this approach. The other article in this issue that uses cross-sectional data (Jayachandran et al. 2005) relies on survey data collected from only one source within a firm. However, Jayachandran and colleagues' interests center on the effects of an interaction term, something that could not be explained by common method bias. Moreover, they present theory that predicts the form of this interaction. Thus, we can be more confident in their interpretation of the findings.
Causality is difficult to determine if the only data available are cross-sectional. The obtained results are associational and subject to interpretations of reverse causality. Thus, it is highly desirable to have temporal separation between the independent and the dependent variables, as is shown by Mithas, Krishnan, and Fornell (2005), Srinivasan and Moorman (2005), and Gustafsson, Johnson, and Roos (2005).
In general, we note the relevance of longitudinal data or cross-sectional/longitudinal data for CRM research. This is especially true given policy changes that CRM research suggests. For example, Ryals (2005) shows an increase in business unit profits due to CRM, but she cannot document the validity of lifetime value estimates based on data from a single point in time. Therefore, Ryals must make assumptions about long-term profitability implications. Likewise, Thomas and Sullivan (2005) do not document before and after behavior, given a change in communication strategy that leads to channel migration. Instead, they must assume that model estimates made from a certain point in time continue to hold; that is, when customers migrate to a new channel, they assume the shopping behaviors of those already in that channel. We note that Lewis (2005) also does not conduct a longitudinal study. However, he is better able to assert what would happen if the firm takes a proposed action because his model reflects the interaction of the different players and is based on an underlying utility formulation. This enables him to make forward-looking policy statements that imply causality.
We believe that if the field is to advance, more emphasis must be placed on documenting causality. Further research should also reflect that most firm-customer interactions are dynamic and time varying. As such, CRM systems should help provide data for this research, because a key element of these systems is the collection over time of data that document critical aspects of the process. Perhaps the major issue for academic researchers who are interested in assessing causal relationships within CRM involves accessing these data along with the cooperation of firms.
R4: Research should acknowledge that firms do not choose CRM activities in the abstract; instead, they choose these activities on the basis of market response to these activities along with other factors, such as particular firm skills and capabilities.
Customer relationship management activities are not static. Firms and customers choose (and change) their behavior on the basis of others' actions to maximize value. Ryals (2005) shows how managers modified their behavior toward individual customers after being given access to measures of customer lifetime value. Lewis (2005) documents how customers act strategically to alter their behavior depending on the actions of the firm and that firms can alter their behavior on the basis of consumer behavior. Thomas and Sullivan (2005) show how firms can modify customers' channel choice through different communication strategies. Payne and Frow (2005) emphasize the dual creation of value between customer and firm. In all these cases, the actions of one player are chosen because of or depend on the actions of other players. In addition, Jayachandran and colleagues' (2005), Srinivasan and Moorman's (2005), and Mithas, Krishnan, and Fornell's (2005) research implies that firms should perhaps choose their CRM decisions on the basis of existing resources and capabilities.
All of this provides strong evidence that CRM activities should be considered choice variables and thus treated as endogenous in empirical models. As is well known from the strategy literature, if this is not taken into account, the result may be biased estimates of the effects of CRM activities on performance. This is because unobserved determinants of these CRM choices may also be correlated with the dependent measure of interest in the research. Thus, we hope that further CRM research uses structural modeling and estimation approaches that address this issue.
We note that this concern about endogeneity is also connected to our recommendation for increased use of longitudinal or cross-sectional/longitudinal data for CRM research. Correcting for the possibility of endogeneity bias is difficult when there is only access to cross-sectional data (Boulding and Staelin 1995). Given this limitation, we find that Mithas, Krishnan, and Fornell's (2005) approach is quite creative. Because they rely on a cross-sectional database, they cannot control for the issue of endogeneity bias. However, they present sensitivity analysis that indicates the degree to which the estimate of their theoretical effect of interest changes as a function of the (unknown) correlation of unobserved factors with the independent variable of interest. Thus, although their reported estimate is not necessarily free from bias, the reader can assess the stability of this result in the face of potential bias from unobserved factors.
R5: CRM research should suitably address potential heterogeneity in customer behavior.
An underlying premise of CRM is that customers have different needs, and thus the firm should treat them differently. By definition, this implies that researchers need to acknowledge heterogeneity in customer behavior. Given the extensive focus on heterogeneity issues in the field of marketing, there are several approaches that researchers can take to address this issue. Lewis (2005) and Thomas and Sullivan (2005) do this by constructing latent class segmentation schemes (Kamakura and Russell 1989) and then estimating different response coefficients for each segment. Cao and Gruca (2005) take a different approach by controlling for heterogeneity through observable differences in their customers. Gustafsson, Johnson, and Roos (2005) also control for heterogeneity through observable customer differences by constructing an individual-level variable that, in effect, segments customers in terms of current churn behavior based on past churn behavior. This approach is reminiscent of what Guadagni and Little (1983) did in the context of brand choice. Another approach, which none of the articles in this special issue uses, is to use Bayesian estimation (Allenby and Rossi 1999), which, at least conceptually, enables every person to have his or her own response coefficients. However, the bottom line is that CRM rests on the notion that customers may differ. Researchers need to acknowledge this potential diversity whenever they analyze customer response to firm actions.
R6: The research results should generalize rather than be idiosyncratic to the chosen research domain.
A major feature of high-impact academic research is that the findings, concepts, and ideas that result from the research can be applied to a broad set of environments and/ or domains. There are two basic approaches that a researcher can take to achieve such a goal for CRM research. The first approach is to base the research analysis on a wide range of firms (i.e., use cross-sectional analysis). Although this enables the researcher to address the issue of generalizability directly, it opens up the issue of being able to control for many environmental factors that could affect the dependent variable of interest along with the independent variables that vary across firms. One standard approach that researchers who conduct cross-sectional analyses use is to include variables that control for these differing environmental factors in their analyses. This is the approach that Srinivasan and Moorman (2005), Mithas, Krishnan, and Fornell (2005), and Jayachandran and colleagues (2005) use.
A key issue is not whether a cross-sectional analysis has adequate control variables but whether the researcher can claim that he or she has included all relevant environmental variables. Obtaining good measures for all potentially relevant environmental measures is often difficult if not impossible. Unfortunately, if one or more of the omitted environmental variables correlate with both the independent variable of theoretical interest and the dependent measure, the obtained result is biased. A way to circumvent this issue is for the researcher to obtain multiple observations on each firm (i.e., obtain a longitudinal, cross-sectional database). This enables the researcher to hold fixed the environmental factors that are unique to each firm by conducting both within-and between-firm analyses (see Boulding and Staelin 1995).
A second approach is to conduct a series of within-firm analyses over time and then look for commonalities among these studies. Here, the emphasis is on determining changes over time. Two examples of within-firm analyses over time in this special issue are those of Ryals (2005), who studies how managers modify their behavior over time in response to relevant CRM metrics, and Gustafsson, Johnson, and Roos (2005), who study how consumers change their behavior over time as a function of their prior behavior, satisfaction, and calculative commitment to the firm. When several of these single-firm analyses have been conducted, the researcher(s) can then perform a meta-analysis and look for generalized principles. In a way, this is what we tried to do by examining the ten articles in this special issue and then coming up with our propositions.
Conclusion
The field of CRM has matured over the past decade. We share Rogers's (2005) sense of excitement over this CRM maturation process. Much progress has been made, as witnessed by the number of academic centers, conferences, research papers, courses, and industry attention devoted to this topic. However, as does Rogers, we note that many unanswered questions remain to be addressed.
One question frequently asked is, What is next after CRM? Given our assertion that CRM is the outcome of the continuing evolution and integration of marketing ideas and newly available data, technologies, and organizational forms, we do not forecast a discontinuous leap after CRM. Rather, we predict a continued evolution in CRM as new ideas, technologies, and so forth, are integrated into CRM activities. We hope that this article and the other articles in this special section provide the catalyst for other researchers to continue to move the theory and practice of CRM forward in this evolutionary process.
The authors thank Ruth Bolton for her insights, guidance, and support in shaping this article and the special section and the authors of the articles in the special section for their contributions. They also thank the Teradata Center for Customer Relationship Management at Duke University, the American Marketing Association, and Journal of Marketing for their support for this special section.
( n1) This conference was cosponsored by the AMA Relationship Marketing Special Interest Group.
( n2) Space constraints force us to take a 40,000-foot perspective on the field. Thus, we do not provide an exhaustive review of the CRM literature, much less the relevant marketing literature.
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By William Boulding; Richard Staelin; Michael Ehret and Wesley J. Johnston
William Boulding is a professor and Associate Dean, Fuqua School of Business, Duke University
Richard Staelin is Edward and Rose Donnell Professor of Business Administration, Fuqua School of Business, Duke University
Michael Ehret is Assistant Professor of Marketing, Freie Universität Berlin
Wesley J. Johnston is CBIM Roundtable Professor of Marketing, Department of Marketing, Georgia State University
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Record: 5- A Longitudinal Study of Complaining Customers' Evaluations of Multiple Service Failures and Recovery Efforts. By: Maxham III, James G.; Netemeyer, Richard G. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p57-71. 15p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.66.4.57.18512.
- Database:
- Business Source Complete
A Longitudinal Study of Complaining Customers' Evaluations of Multiple Service Failures and Recovery Efforts
The authors report a repeated measures field study that captures complaining customers' perceptions of their over-all satisfaction with the firm, likelihood of word-of-mouth recommendations, and repurchase intent during a 20-month span that includes two service failures and recovery attempts. The findings suggest that though satisfactory recoveries can produce a "recovery paradox" after one failure, they do not trigger such paradoxical increases after two failures. Furthermore, "double deviations" can occur following two consecutive unsatisfactory recoveries or following an unsatisfactory recovery in response to a second failure. The findings indicate that customers reporting an unsatisfactory recovery followed by a satisfactory recovery reported significantly higher ratings at the second postrecovery period than did customers reporting the opposite recovery sequence. The outcome of the second recovery also demonstrated a significant influence on customer ratings (positively if the recovery was satisfactory, negatively if the recovery was unsatisfactory), regardless of whether the customer found the first recovery satisfactory or unsatisfactory. In addition, although the increased change in recovery expectations and failure severity ratings from the first failure to the second is more dramatic for customers who previously reported a satisfactory recovery, the increase in attributions of blame toward the firm is more pronounced for customers who previously reported an unsatisfactory recovery. Last, the results show that recovery efforts are attenuated when two similar failures occur and when two failures happen in close time proximity.
Firms can affect customer evaluations when they attempt to recover from service failures (Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998). Prior research suggests that highly effective recovery efforts can produce a "service recovery paradox" in which secondary satisfaction (i.e., satisfaction after a failure and recovery effort) is higher than prefailure levels (McCollough, Berry, and Yadav 2000; Smith and Bolton 1998). However, evidence for the paradox is sparse and mixed. Smith and Bolton (1998), employing a scenario-based experiment, report that cumulative satisfaction and patronage intentions increase above prefailure levels when respondents are very satisfied with the recovery efforts. Other studies offer contrary evidence, finding that post-recovery satisfaction levels are not restored despite effective recoveries (Bolton and Drew 1991; McCollough, Berry, and Yadav 2000). Poor service recoveries have been shown to exacerbate already low customer evaluations following a failure, producing a "double deviation" effect (Bitner, Booms, and Tetreault 1990; Hart, Heskett, and Sasser 1990). Employing a qualitative critical incident technique, Bitner, Booms, and Tetreault (1990) asked respondents to recall a dissatisfactory service experience and then explain what made them feel dissatisfied. The results indicate that poor recovery efforts intensify customer dissatisfaction. Although these studies have proved informative, they have focused only on a single failure and recovery effort. Because many service relationships are ongoing, however, customers will likely experience multiple failures over the course of a relationship. Yet it remains unclear how complainants would respond to multiple failures and recovery efforts, which suggests a need for longitudinal studies examining the dynamics of complainant perceptions over time. Such studies would help scholars and managers better understand the updating processes that complainants use in evaluating service firms. Although some longitudinal studies have examined customer satisfaction and intention (e.g., Bolton and Lemon 1999; LaBarbera and Mazursky 1983; Mittal, Kumar, and Tsiros 1999; Oliver 1980), none has explored within-subject perception changes following multiple failures and recoveries. We present a 20-month longitudinal field study that investigates within-subject evaluations of overall satisfaction with the firm, word-of-mouth (WOM) recommendations, and repurchase intent at key intervals following two customer-initiated complaints and the ensuing recovery efforts. We explore between-subject mean variations over time, depending on whether customers report satisfactory or unsatisfactory recoveries. We also consider the roles of failure severity, attributions of blame toward the firm, recovery expectations, failure similarity and type, and the time between failures in the updating process.
The Influence of Multiple Failures on Complainant Perceptions
Three extant theories suggest that multiple service failures diminish paradoxical increases in customer perceptions of firms following recovery efforts and magnify double deviation dips. Prospect theory suggests that losses are weighed more heavily than gains (Kahneman and Tversky 1979; Oliver 1997), and similarly, asymmetric disconfirmation proposes that negative performances have greater influence on satisfaction and purchase intentions than positive performances do (Mittal, Ross, and Baldasare 1998). As such, several positive experiences may be needed to overcome one negative event, and customers reporting two failures may rate the firm lower despite effective recovery efforts. Likewise, Mittal, Ross, and Baldasare (1998) find that each additional unit of positive performance has diminishing value. When a second failure occurs, complainants may focus more on the negative consequences associated with the failure, because these negative perceptions are more memorable. Thus, complainants may become desensitized to satisfactory recovery efforts, thereby mitigating their positive effects. Accordingly, satisfactory recoveries may yield paradoxical gains only in the short run. (Satisfactory recoveries in this study refer to complainant ratings above the midpoint on a three-item summated scale, and unsatisfactory recoveries refer to complainant ratings at or below the midpoint on the same three-item summated scale.)
Attribution theory also suggests diminishing complainant ratings following multiple failures. Take, for example, a situation in which bank customers complain about overcharges on their statements. Given that the bank successfully resolves the complaint, attribution theory suggests that complainants may believe that the failure was unique or due to a circumstance beyond the bank's control (i.e., an unstable attribution) (Folkes 1988). In such cases, customers may feel more positive about the firm than before the failure, triggering a recovery paradox. If another failure occurs, though, complainants may discount the circumstantial attribution and instead believe that the firm consistently makes mistakes (i.e., a stable attribution). That is, when multiple failures occur, complainants will likely reevaluate their attributions. Given that, as Weiner (2000, p. 384) has argued, "one cannot logically make unstable attributions for repeated events," customers will likely infer that multiple failures are due to problems inherent to the firm. In such cases, complainants feel heightened discontent when firms do not recover satisfactorily from two failures, generating a double deviation effect. Similarly, even consistently satisfactory recoveries may have a tempered impact following multiple failures. As such, we offer three hypotheses:[ 1]
H1: Despite perceiving two satisfactory recoveries, customers reporting two failures will rate their postrecovery overall satisfaction with the firm, repurchase intent, and favorable WOM lower than their prefailure ratings for those same variables (no paradoxical increase in perceptions of the firm).
H2: Customers perceiving two unsatisfactory recoveries following two reported failures will rate their postrecovery overall satisfaction with the firm, repurchase intent, and favorable WOM lower than their ratings after the second failure for the same variables (a double deviation effect).
H3: For customers perceiving two unsatisfactory recoveries, the magnitude of the decrease in ratings in overall satisfaction with the firm, repurchase intent, and favorable WOM from postfailure to postrecovery falls more sharply after the second failure than after the first failure (heightened discontent).
The Effects of Mixed Recoveries over Time
Although we expect diminishing service recovery returns following two satisfactory recoveries, complainants still may show a preference for when a satisfactory recovery occurs in a sequence of recoveries. Research on decision making suggests that people prefer an improving series of outcomes. For example, Ross and Simonson (1991) presented subjects with hypothetical scenarios ending with a loss (e.g., win $85 and then lose $15) versus a gain (lose $15 and then win $85). The subjects strongly preferred the scenario ending in a gain. Loewenstein and Prelec (1993) similarly argue that when a timing trade-off is involved with sequential outcomes, people become more farsighted and will prefer the sequence ending in positive rather than negative outcomes. Such effects may be explained by a recency effect (Ross and Simonson 1991) and an element of prospect theory, namely, loss aversion (Kahneman and Tversky 1979). The recency effect suggests that events occurring most recently are also most salient and are weighed more heavily when people judge the overall sequence of outcomes. Loss aversion likewise suggests that when a sequence goes from a gain to a loss, people will weigh the loss more heavily, making this sequence less attractive than a sequence going from a loss to a gain.
H4: Overall satisfaction with the firm, repurchase intent, and favorable WOM ratings after the second recovery are higher for customers perceiving an unsatisfactory/satisfactory (US) sequence than for those perceiving a satisfactory/unsatisfactory (SU) sequence (ratings for US ratings for SU at post-Recovery 2).
Similar logic suggests that an unsatisfactory recovery followed by a satisfactory recovery will result in improved customer ratings over time, despite the customer reporting a prior unsatisfactory recovery. Consistent with our previous hypotheses, low ratings are likely to occur when a first failure is followed by an unsuccessful recovery. When a second failure occurs followed by a successful recovery, however, complainants will likely focus on their most recent experience and adjust their ratings upward. Thus, ratings are likely to improve when complainants experience a US recovery sequence, which is consistent with a recency effect (Ross and Simonson 1991).
We also posit that a satisfactory recovery followed by an unsatisfactory recovery will generate a double deviation effect. When complainants perceive a satisfactory recovery, they often give the firm higher ratings. However, these complainants are also likely to update their expectations upward (Grayson and Ambler 1999). Prospect theory and asymmetric disconfirmation theory suggest that negative performances influence customer affect more than positive performances. Complainants experiencing two negative events (second failure and unsatisfactory recovery) following a satisfactory first recovery likely weigh the negative events more heavily than the satisfactory recovery, which results in significant rating dips. Such dips are also consistent with a recency effect (Ross and Simonson 1991).
H5: Despite perceiving an unsatisfactory (satisfactory) first recovery, customers perceiving a satisfactory (unsatisfactory) second recovery, that is, a US sequence (SU sequence), will report increases (decreases) in their ratings of overall satisfaction with the firm, repurchase intent, and favorable WOM from post-Failure 2 to post-Recovery 2 (a recency effect).
Expectations, Contextual Influences, and the Downside of Service Recovery
Service recovery expectations. Consistent with prior research, we conceptualize service recovery expectations as customers' predictions regarding the extent to which a firm will handle their complaint (Boulding et al. 1993; Oliver 1997). Some researchers assert that postcomplaint handling evaluations increase when expectations are met or exceeded (Tax and Brown 1998), whereas other researchers suggest that expectations increase over time (Boulding et al. 1993; Grayson and Ambler 1999). However, research has not explored how customers in ongoing service relationships update their recovery expectations after reporting multiple failures. Given that negative events are salient and easily recalled, customers previously reporting one failure will likely consider their prior experience when predicting what to expect after a second failure. Complainants are more likely to attribute one failure to bad luck or causes outside the firm's control and expect only moderate redress. When another failure occurs, however, they are likely to attribute a stable pattern of failures to the firm (Weiner 2000). To the extent that this pattern is blamed on the firm, complainants will expect more extensive redress after the second failure than after the first.
Furthermore, although complainants perceiving satisfactory recoveries may rate the firm higher for its efforts (e.g., recovery paradox), they are also likely to view the solid performance as a signal to adjust future recovery expectations upward. Such adjustments are theoretically consistent with forward assimilation, in which expectations become consistent with satisfaction (Oliver and Burke 1999). Conversely, expectations should not rise as much for complainants who previously perceived an unsatisfactory recovery, partly because the past experience offers a cue that future recoveries may also be weak. Thus, complainants perceiving a satisfactory recovery after a first failure will hold higher recovery expectations for a second failure. Accordingly, the downside of recovering well is managing higher expectations in the future.
H6: Customers reporting two failures have higher recovery expectations for the second failure than for the first failure.
H7: The magnitude of the increase in recovery expectations from the first to the second failure is greater for customers perceiving a satisfactory first recovery than for customers perceiving an unsatisfactory first recovery.
Failure severity. Customer evaluations decline as service failures become more severe (Smith, Bolton, and Wagner 1999). However, what happens to severity perceptions when two failures are reported? Because one unit of loss is more salient than one unit of gain, customers may more easily recall a failure incident than the recovery effort that followed. It seems evident that complainants experiencing a second failure will have inflated perceptions of severity when they still recall the first failure (Seiders and Berry 1998). Although the recovery paradox suggests that satisfactory recoveries enhance complainant evaluations, the service firm must then manage the higher expectations that are likely to follow. From an attribution perspective, customers reporting two failures may sense a pattern of negative performances. As such, customers may recall the first failure and combine its losses with those they perceive following the second failure. These complaining customers may begin viewing the failures as stable problems inherent to the firm. Furthermore, customers may perceive the sequential failures as one overall failure, ultimately heightening the severity of the second failure. Previously unsatisfied complainants will likely report higher severity perceptions because of the magnified discontent stemming from experiencing two failures. However, we expect that previously satisfied complainants will report greater increases in perceived failure severity than previously unsatisfied complainants, partly because of their higher expectations.
H8: Customers reporting two failures will rate the second failure as more severe than the first failure.
H9: The magnitude of the increase in the perceived severity of the failure from the first to the second failure is larger for customers perceiving a satisfactory first recovery than for customers perceiving an unsatisfactory first recovery.
Attributions of blame. Customers engage in causal thinking to ascertain why a failure occurred (Weiner 2000). Attributions of blame, which we define as the extent to which customers hold the seller responsible for a failure, can be instrumental in shaping responses to failures. Researchers conclude that some complainants blame firms for failures even when the firm is not actually responsible (Folkes and Kotsos 1986), and complainants who believe that firms are responsible for failures will be more likely to expect redress (e.g., discounts, apologies, refunds). For example, Folkes (1984) asked respondents to recall a recent restaurant experience when they were unsatisfied with the taste of their food or beverage and to explain why they were unsatisfied. The results showed that attributions of blame toward the restaurant strongly influenced whether customers believed that they deserved apologies and refunds.
Our research extends this literature by examining how blame attributions change when more than one failure is reported. When one failure is reported and the firm responds well, complainants may attribute the failure to a circumstantial cause or consider it a distinct occurrence (unstable attribution). If the firm has multiple failures, complainants may attribute the failures to causes that are consistent and stable to the firm. It follows that attributions of blame toward the firm will increase after multiple failures are reported. We also contend that attributions will be more pronounced among complaining customers who previously perceived an unsatisfactory recovery than for those who previously perceived a satisfactory recovery, partly because they now perceive multiple problems (i.e., two failures and one unsatisfactory recovery) that are no longer considered inconsistent and "unstable."
H10: Customers reporting two failures will attribute blame for the failures to the firm more strongly after the second failure than after the first failure.
H11: The magnitude of the increase in blame attributions from the first to the second failure is larger for customers who report an unsatisfactory first recovery than for customers who report a satisfactory first recovery.
Lag between failures. Failures occurring over a short period are likely to affect complainants' perceptions more negatively than failures separated by longer periods of time. Given the prospect theory view that losses are weighed more heavily than gains, it may take several positive experiences to temper the effects of a single failure. Customers reporting two failures without a considerable time frame filled with satisfactory experiences will likely perceive higher discontent and lower ratings at the time of the second complaint (Seiders and Berry 1998). Similarly, complainants reporting two failures within a relatively short time period will easily recall the first failure. These customers may view the two failures as one larger failure, likely creating more demanding customers and possibly mitigating satisfactory recovery efforts. Given that the median time interval between failures in our study was four months, we classify a "short" time interval as four months or less and a "longer" time interval as five months or more.
H12: Customers reporting two failures within a relatively short time interval have lower post-Failure 2 ratings for overall satisfaction with the firm, repurchase intent, and favorable WOM (compared with customers who report two failures over a longer time interval). H13: Customers reporting two failures within a relatively short time interval have lower post-Recovery 2 ratings for overall satisfaction with the firm, repurchase intent, and favorable WOM (compared with customers who report two failures over a longer time interval).
Failure similarity. Not only can multiple failures lead to consumer discontent, but also this discontent can be magnified when the same failure occurs (Seiders and Berry 1998). From an attribution theory perspective, similar failures may lead complainants to believe that the firm consistently makes the same errors without improving--a stable internal attribution toward the firm. In addition, consumers reporting two similar failures are more likely to hold the firm responsible for making consistent mistakes, making it more difficult for firms handling two similar complaints to recover well. Conversely, when two different failures occur, complainants may be more likely to either attribute the failures to circumstantial, nonfirm factors or view them as distinct anomalies and thus may not evaluate the firm as harshly.
H14: Customers reporting two similar failures have lower post-Failure 2 ratings for overall satisfaction with the firm, repurchase intent, and favorable WOM (compared with customers who report two different failures).
H15: Customers reporting two similar failures have lower post-Recovery 2 ratings for overall satisfaction with the firm, repurchase intent, and favorable WOM (compared with customers who report two different failures).
Sample, Procedures, and Measures
We conducted a repeated measures (RM) field study with bank complainants across a 20-month time span. We focused on customers who registered complaints about their banking experiences at one of 116 branches of an industry-leading bank. At four time periods, respondents completed surveys that assessed perceptions from six time periods: pre-Failure 1 (i.e., T1a); post-Failure 1 (i.e., T1b); post-Recovery 1, approximately two weeks after the first recovery effort (i.e., T2); pre-Failure 2 (i.e., T3a); post-Failure 2 (i.e., T3b); and post-Recovery 2, approximately two weeks after the second recovery effort (i.e., T4). As in other behavioral research involving imperfect correlations, the repeated measures aspect of our design may generate some degree of regression toward the mean. Figure 1 offers a time line of measurement for all constructs in the study, and the measurement procedures are described subsequently.
Time Period 1: pre-Failure 1 (T1a). Upon complaining for the first time to any of the branch offices, 1356 customers were asked to participate in the study at Time Period 1. Complainants completed the T1a survey in the bank shortly after registering the complaint. During Time Period 1, bank service agents informed customers that the purpose of the study was to improve the bank's service efforts and that the study consisted of several parts. When customers agreed to fully participate in the study, the service agent distributed the pre-Failure 1 survey (T1a), asking customers to think retrospectively about all their experiences with the bank up to the recent service failure (i.e., past perceptions excluding the service failure). These experiences may have included past banking service availability, support, services offered, ease of use, customer service, and so forth. Customers were then instructed to rate prefailure overall satisfaction with the firm, repurchase intent, and favorable WOM likelihood. These constructs were measured with three, four, and three items, respectively, drawn from the extant literature (e.g., Cronin and Taylor 1994).
Time Period 1: post-Failure 1 (T1b). After completing the prefailure part of the survey (T1a), the 1356 customers were then asked to think about all their experiences with the bank up until that moment. This post-Failure 1 part of the survey (T1b) asked customers to rate their perceptions of overall satisfaction with the firm, repurchase intent, and WOM after experiencing a service failure, using items identical to those used in the T1a survey. In addition, the T1b survey asked respondents to rate their perceptions regarding service recovery expectations, attributions of blame, and failure severity. A four-item recovery expectation measure, adapted from McCollough, Berry, and Yadav (2000), asked respondents to rate the extent to which they expected the firm to effectively recover from the failure. A four-item attribution measure asked respondents to indicate the extent to which the firm was responsible for the failure, and a three-item failure severity measure asked customers to indicate the severity of the failure they reported. Respondents also provided some demographic information.
Time Period 2: post-Recovery 1 (two weeks after recovery, T2). In Time Period 2, the same measures of overall satisfaction with the firm, repurchase intent, and WOM were gathered along with a three-item satisfaction with service recovery measure (Cronin and Taylor 1994). This T2 survey was administered to customers who completed both T1a and T1b and was mailed one week after the bank concluded its recovery efforts (with hopes of reaching the customer within two weeks after recovery). The bank offered incentives to participate, and research assistants telephoned customers as a reminder to respond. Of the surveys mailed, 692 usable responses were collected and matched to T1a and T1b--a 51% response rate for Failure/Recovery 1.
Second failure and recovery data collection. Customers who reported a second failure were asked to complete surveys representing perceptions and recovery efforts for the second failure. The mean time lapse between the two failures was 6.63 months, and approximately 75% of the customers who reported a second failure did so within nine months. Of the 692 respondents who completed all surveys involving the first failure, 312 complained to the bank about a second failure. Of those, 255 completed all portions of the study across four time periods. These 255 constituted the sample used in our study. The interviewing schedule; data collection procedures; and measures for pre-Failure 2 (T3a), post-Failure 2 (T3b), and post-Recovery 2 (T4) mirrored the three surveys involving the first failure and recovery effort.
Time Period 3: pre-Failure 2 (T3a). At Time Period 3, respondents who registered a second complaint completed the second prefailure survey inside the bank. The T3a survey asked customers to think retrospectively about all their banking experiences with the bank up until the most recent service failure (i.e., past perceptions). Respondents then rated their overall satisfaction with the firm, favorable WOM likelihood, and repurchase intent prior to the second failure. To validate our T3a pre-Failure 2 retrospective measures, we compared the raw mean scores of the post-Recovery 1 measures and the corresponding pre-Failure 2 measures. There were no significant differences (t-values ranged from -.281 to .864). As such, the post-Recovery 1 means, collected on average 6.63 months prior to our pre-Failure 2 measures, did not differ from our pre-Failure 2 measures.
Time Period 3: post-Failure 2 (T3b). Also in Time Period 2, after completing the prefailure survey, respondents were asked to think about all their experiences with the bank, including the most recent service failure. The post-Failure 2 survey (T3b survey) asked customers to rate their current perceptions of overall satisfaction with the firm, repurchase intent, and WOM. The T3b survey also asked customers to rate their perceptions regarding service recovery expectations, attributions of blame, and failure severity after Failure 2.
Time Period 4: post-Recovery 2 (two weeks after recovery, T4). In the fourth time period, the second postrecovery survey gathered measures of overall satisfaction with the firm, repurchase intent, WOM, and satisfaction with service recovery (identical to T2). The second postrecovery survey (T4 survey) was mailed to customers who completed all five previous portions of the study, which resulted in our sample size of 255. All items across all surveys were measured with seven-point scales and are shown in Appendix A. The raw means and standard deviations for all measures, as well as the correlations among measures, are shown in Appendix B. Across all surveys, coefficient alpha estimates for all measures ranged from .83 to .97.[ 2]
We also collected data regarding the type of failure. Consistent with other service research, bank representatives logged failures as either "core" failures or "process" failures (Gilly and Gelb 1982; Smith, Bolton, and Wagner 1999). Core failures refer to monetary-oriented complaints that involve a problem with the product offering (e.g., incorrect account postings, overcharges, faulty overdraft protection). Of the 223 core failures reported (i.e., 88 at Failure 1 and 135 at Failure 2), 43% involved nonsufficient funds over-draft fees, 27% involved incorrect account postings, and 16% involved interest or automated teller machine over-charges. Recovery strategies for these failures included waiving some or all of the questioned fees, accurately adjusting account balances, and offering conscientious customer service (e.g., listening, empathizing, apologizing). Process failures were defined as problems with the way the bank provided the service (e.g., procedures, personal inter-actions). Of the 287 process failures (167 at Failure 1 and 120 at Failure 2), 37% involved queuing/waiting times or processes, 32% involved policies and procedures that restricted on-site banking access (versus electronic access) to low-volume customers, and 26% involved poor customer service (e.g., discourteous employees). Recovery strategies for these complaints focused on offering customers flexible and accommodating options, making policy or procedural exceptions, and providing caring personal interactions.
The sample exhibited the following demographic characteristics: 56% of the respondents were women, 42% were between 35 and 42 years of age, and 65% held college degrees; 66% of the sample had used the bank's services for at least one year. In addition, the initial complaint (i.e., Failure 1) in this study represented the first complaint recorded by the bank for each respondent, creating a baseline for accurately tracking customers' perceptions regarding their first and second complaint experiences.
Checks for Respondent and Measure Bias
We employed three checks to assess respondent and measure bias. First, the bank provided us with a sample of 316 complainants who did not participate in the study. No significant differences were found among age, sex, type of complaint, account value, or length of relationship between our study respondents and this sample. Second, we collected data from a sample of 276 bank customers who had not reported a failure. The demographic profiles of these 276 noncomplainants were not significantly different from the profiles in our study's sample, not only across data collected in our survey (i.e., age, sex, education, and length of relationship) but also across data collected by the bank (e.g., account value, types of services composing the account portfolio, customer profitability). Furthermore, the noncomplainants' ratings did not differ significantly (p > .10) from our complainants' ratings of overall satisfaction with the firm, repurchase intent, and WOM at T1a (i.e., the pre- Failure 1 ratings). Our complainant sample's postfailure ratings (T1b) of overall satisfaction with the firm, repurchase intent, and WOM were lower than the noncomplainants' ratings on these variables (p < .05).
Third, given that our T1a measure of overall firm satisfaction was retrospective, we compared it with an actual pre-failure satisfaction measure collected by the bank. The bank periodically administered a customer satisfaction survey. The bank's database showed that 97 of our 255 study participants had completed a firm-derived satisfaction measure four months before our study and before they reported any failure. The satisfaction measure stated the following: "Please rate your overall experience with [firm name] bank." We measured responses using a five-point scale anchored by "unpleasant" and "completely satisfactory." The correlation of this measure with our prefailure overall firm satisfaction measure was .91. Furthermore, we calibrated our measure such that it had five scale points, making it similar to the bank's measure. The difference between our calibrated measure and the bank's measure was not significant for the n = 97 subsample (mean difference = .03, t = .52, p > .60). In summary, these data checks suggest that rating biases due to respondents or retrospective measures were minimal.
Classification Factors
Before testing our hypotheses, we constructed quantification factors as independent variables in our analyses (Neter et al. 1996). We used the satisfaction with service recovery measure (captured at T2 and T4) to form a two-level variable(i.e., satisfactory and unsatisfactory recovery). We created this between-subjects factor by summing the scores on the items in the scale and then splitting the scores at the scale midpoint into two groups: one perceiving an unsatisfactory first recovery and another perceiving a satisfactory first recovery. Scores for the unsatisfactory group ranged from 3 to 12 (on a 21-point summated scale) and from 13 to 21 for the satisfactory group. We also split the scale for satisfaction with recovery regarding the second recovery at the scale midpoint (i.e., 3 to 12 for the unsatisfactory group and 13 to 21 for the satisfactory group).[ 3]
Data Checks
Before testing the hypotheses, we examined whether the recovery paradox and the double deviation effect existed after one failure and recovery effort. We used RM MANCOVA (multivariate analysis of covariance) with one three-level within-subjects factor (time: pre-Failure 1, post-Failure 1, and post-Recovery 1), one between-subjects factor (recovery: unsatisfactory, n = 112, and satisfactory, n = 143), and three covariates (i.e., recovery expectations, attributions of blame, and severity of Failure 2 compared with Failure 1). The objective of this data check was to investigate whether the paradox and double deviation hold following one failure and satisfactory recovery.
After controlling for the variance attributed to the covariates, we used linearly independent planned comparisons, adjusting for experiment-wide error rate, to compare estimated marginal means. (The effects for all covariates were significant and are available on request.) Our results show that customers reporting a satisfactory recovery rated their postrecovery overall satisfaction (mean = 16.03), repurchase intent (mean = 21.97), and WOM (mean = 15.42) significantly higher than their prefailure ratings for these same variables (satisfaction mean = 13.41, repurchase intent mean = 20.14, WOM mean = 9.74; Wilks' λ = .555, F = 66.17, p < .01), with a large effect size (η[sup2] = .45). The univariate effects for these variables were also significant (p < .01), fully supporting the service recovery paradox for one failure and recovery. Planned comparisons also indicated that customers perceiving an unsatisfactory recovery did not rate postrecovery overall satisfaction (mean = 9.23) and repurchase intent (mean = 16.53) significantly below their postfailure ratings for these variables (satisfaction mean = 8.60, repurchase intent mean = 16.63). Indeed, these customers rated postrecovery WOM likelihood (mean = 8.21) significantly above their postfailure ratings (mean = 6.70) following an unsatisfactory recovery, and this increase drives multivariate significance (Wilks' λ = .955, F = 3.88, p < .01, η[sup2] = .05). As such, the double deviation effect did not occur after one failure and recovery. Note, however, that the postfailure ratings may be susceptible to order effects, because they were collected sequentially in the same questionnaire with prefailure measures. Nonetheless, these data check results offer robust estimates, as they accounted for the effects of attributions of blame, failure severity, and recovery expectations.
To examine H 1-H5, we incorporated the history of the first failure into the model. We conducted multiway RM MANCOVA with two within-subjects factors, ( 1) time: prefailure, postfailure, and postrecovery and ( 2) failure: Failure 1 and Failure 2. We also had two between-subjects factors, ( 1) Recovery 1: unsatisfactory and satisfactory and ( 2) Recovery 2: unsatisfactory and satisfactory, and six covariates (i.e., recovery expectations, attributions of blame, and failure severity involving Failures 1 and 2). (With the exception of failure severity at Failure 2, all covariate effects were significant.) As is shown in the top portion of Table 1, post-Recovery 2 means across the dependent variables significantly decreased below pre-Failure 2 levels for customers perceiving two satisfactory recoveries (Wilks' λ = .706, F = 33.77, p < .01, η[sup2] = .29), supporting the assertion in H1 that the recovery paradox does not occur following two failures.
The second portion of Table 1 shows the results for H2 and H3. Post-Recovery 2 means significantly decreased below post-Failure 2 levels for customers who perceived two unsatisfactory recoveries (Wilks' λ = .623, F = 49.10, p < .01, η[sup2] = .38). This supports the assertion in H2 that the double deviation effect occurs after two failures and unsatisfactory recoveries. To test H3, we computed contrasts in accordance with Vonesh and Chinchilli's (1997) recommendations to determine whether the marginal mean decrease from postfailure to postrecovery changes from Failure 1 to Failure 2. As Table 1 shows, this analysis supports H3. The mean decrease from postfailure to postrecovery was more pronounced after the Failure 2 and two sequential unsatis-factory recoveries (Wilks' λ = .730, F = 29.97, p < .01, η[sup2] = .27).
The results for H4 and H5 are shown in the third and fourth portions of Table 1. We tested H4 by comparing the post-Recovery 2 means estimated in the preceding model between the US recovery sequence and the SU sequence. As Table 1 shows, customers reporting the US sequence rated the bank significantly higher than did customers reporting the SU sequence, in support of H4. In addition, post-Recovery 2 means for customers reporting the US sequence significantly increased above post-Failure 2 levels across the dependent variables collectively (Wilks' λ = .785, F = 22.17, p < .01, η[sup2] = .22), in support of H5. However, this increase was not significant at the univariate level for repurchase intent (F = 3.55, p > .06). Post-Recovery 2 means for customers reporting the SU sequence significantly decreased below post-Failure 2 levels across the dependent variables collectively (Wilks' λ = .822, F = 17.59, p < .01, η[sup2] = .18), in further support of H5.
To test H6 through H11, we estimated an RM multivariate analysis of variance model with one within-subjects factor (failure: Failure 1 and Failure 2), one between-subjects factor (Recovery 1: satisfactory and unsatisfactory), one blocking factor (failure type: core and process failures), and three dependent variables (i.e., recovery expectations, attributions of blame, and failure severity). After controlling for the variance explained by failure type, we were able to clarify the mean differences due to satisfaction and failure levels. The top portion of Table 2 shows means and mean differences relevant to H6-H11, and the bottom portion offers univariate statistics. H6 posits that recovery expectations significantly increase from Failure 1 to Failure 2. The expectations mean in the top portion of Table 2 indicates that expectations were significantly higher following Failure 2 (F = 49.84, p < .01, η[sup2] = .17), in support of H 6. This increase was also significantly greater for customers who perceived a satisfactory recovery to Failure 1 (F = 3.97, p < .05), in support of H7. Similarly, the extent to which customers perceived their failure as severe significantly increased over failures (F = 30.40, p < .01, η[sup2] = .11), and this increase was larger for customers who perceived a satisfactory recovery to Failure 1 (F = 5.78, p < .02, η[sup2] = .02). Thus, H8 and H9 were supported.
Table 2 also shows that H10 and H11 were supported, indicating that attributions of blame significantly increase from one failure to the next (F = 47.47, p < .01, η[sup2] = .16), and the effect is larger for customers who perceived an unsatisfactory recovery after the first failure (F = 16.22, p < .01, η[sup2] = .06).
To test H12 and H13, we again constructed a quantitative classification variable (Neter et al. 1996). We asked customers to indicate the number of months they had patronized the bank. The two measures were subtracted (months[second complaint] - months[first complaint]) to form a difference score representing the interval between failures. We then verified these self-report measures using the bank's database. Next, we used a median split to divide the customers into two groups: one reporting two failures in ≤ four months (n = 128) and another reporting two failures in ≥ five months (n = 127). We then used RM MANCOVA with one within-subjects factor (time: post-Failure 2 and post- Recovery 2), one between-subjects factor (number of months between failures: &;le; four and ≥ five), three dependent variables (overall satisfaction, repurchase intent, and WOM), and one covariate (postrecovery satisfaction) to test H12. (The covariate was significantly correlated with the dependent variables and was not significantly correlated with the independent variable, so we deemed it appropriate for this analysis.) We calculated linearly independent planned comparisons to determine whether postfailure and postrecovery means were lower for complainants who reported two failures in ≤ four months.
The top portion of Table 3 shows that postfailure means for the group that perceived two failures relatively close together were not significantly lower at the multivariate (Wilks' λ = .973, F = 2.32, p > .08) or univariate (p-values for all three variables ≥ .10) levels, which does not support H12. After incorporating another covariate (i.e., post-Recovery 2 satisfaction) into the model to control for customers' satisfaction with the second recovery, we used the previous model to test H13. (The covariate was significantly correlated with the dependent variables and was not significantly correlated with the independent variable.) The second portion of Table 3 also shows that the postrecovery means for the group that perceived two failures close together were significantly lower at both the multivariate (Wilks' λ = .312, F = 183.97, p < .01, η[sup2] = .69) and univariate (p-values for all three variables < .01) levels. As such, H13 is supported.[ 4]
To test H14 and H15, we constructed another quantitative classification factor to use as the independent variable. We obtained data from bank officials that indicated whether the two failures were similar or different. (This approach included the classification of a core or process failure.) We then created a dummy variable, where 1 = different failures and 2 = similar failures. Next, we divided customers into two groups, one reporting two similar failures (n = 118) and one reporting two different failures (n = 137), and used RM MANCOVA with one within-subjects factor (time: post-Failure 2 and post-Recovery 2), one between-subjects factor (failure type: different and similar), three dependent variables (overall satisfaction, repurchase intent, and WOM), one covariate (post-Recovery 1 satisfaction), and one blocking factor (failure type: core or process). The covariate and blocking factors were significantly correlated with the dependent variables and were not significantly correlated with the independent variable. As the third portion of Table 3 shows, postfailure means for the group that reported two similar failures were not significantly lower at either the multivariate (Wilks' λ = .984, F = 1.31, p > .27) or the univariate (p-values for all three variables > .07) level, which does not support H14. However, as shown in the bottom portion of Table 3, postrecovery means were lower for customers who reported two similar failures (multivariate: Wilks' λ = .886, F = 10.67, p < .01, η[sup2] = .11; univariate p-values for all three variables < .01), in support of H15.
By including failure type as a blocking factor, we were able to reduce the sum of squares due to error, refine our estimates, and uncover some notable findings. Respondents reporting two similar core failures (CC sequence) had significantly higher ratings after the second recovery than did those reporting two similar process failures (PP sequence) (Wilks' λ = .794, F = 21.41, p < .01, η[sup2] = .21). In addition, respondents reporting a process failure followed by a core failure (PC sequence) had significantly higher ratings after the second recovery than did those reporting a core failure followed by a process failure (CP sequence) (Wilks' λ = .670, F = 40.49, p < .01, η[sup2] = .33).
The purpose of our study was to examine complaining customers' perceptions of two service failures and recovery efforts. We summarize our results and implications as follows:
Recovery paradox: For a single failure and satisfactory recovery, customers rated the firm paradoxically higher on satisfaction, WOM, and repurchase intent. However, customers reporting another failure did not rate the firm higher despite satisfactory recoveries. Thus, despite effective recovery efforts, paradoxical increases diminish after more than one failure. Although managers should strive to recover well from mistakes, they would be ill advised to use satisfactory recoveries as a crutch for poor service. Our results suggest that firms cannot merely become recovery experts and need to get it right the first time. Firms also need to learn from their mistakes when they do fail and get it right the second time.
Double deviation effect: Although ratings of satisfaction, WOM, and repurchase intent declined after one failure, the declines were not compounded after an unsatisfactory recovery; that is, there was no double deviation effect. It seems that customers discount the effects of one failure when the firm has typically provided satisfactory service. However, when two unsatisfactory recoveries occur, the double deviation effect is strong. Customers may tolerate one unsatisfactory recovery, but they likely will not tolerate two.
Mixed recovery sequences: Customers reporting a US sequence reported higher post-Recovery 2 ratings than did those reporting an SU sequence. Furthermore, ratings from post-Failure 2 to post-Recovery 2 increased for those reporting a US sequence (and decreased for those reporting an SU sequence). Our study uncovers a potential recency effect when customers report inconsistent recovery efforts, suggesting a "what have you done for me lately?" response. In ongoing relationships in which customers likely experience multiple failures and recoveries, firms may improve previously low ratings associated with an unsatisfactory recovery by subsequently providing satisfactory recoveries. Also, although customers may tolerate an unsatisfactory recovery when it occurs after they report their first failure, they are not likely to tolerate an unsatisfactory recovery when it occurs after a second failure, even if the previous recovery was satisfactory.
Preferences for recovery sequences: Our study unveils a hierarchy of postrecovery ratings when customers report various recovery sequences. The route to the highest postrecovery ratings after two complaints is an SS sequence, followed by a US, SU, and UU sequence, respectively. As such, the past seems important only when customers recall consistent recovery efforts. When inconsistent efforts occur, the past may be important only to the extent that it helped shape prefailure ratings.
Recovery expectations: Our results show that customers adjust their expectations higher from one failure to the next. This increase was greater for customers who previously reported a satisfactory recovery. These results suggest that perhaps "no good deed goes unpunished," highlighting a potential downside of recovering well. To the extent that satisfied customers rate the firm higher and correspondingly adjust their future expectations, they may be more likely to experience dissatisfaction if the supplier fails again. Therefore, managers must carefully govern these newly enhanced service expectations.
Failure severity: Our results show that customers reporting a second failure rated the second failure more severely than they rated the first. Perhaps severity ratings are stronger when customers perceive a second failure because customers consider "failure history" rather than the individual failure at hand. Our results also demonstrated that failure severity ratings increase more among customers who formerly reported a satisfactory recovery than among those who previously reported an unsatisfactory recovery, which potentially under-scores another downside of recovering well. Because severe failures require greater effort on the part of the firm, managers may need to offer additional redress accordingly.
Attributions of blame: Our results show that when multiple failures occur, customers are likely to attribute the failures in a stable, internal manner to the firm. Customers formerly reporting unsatisfactory recoveries blame firms more than do once-satisfied customers when a second failure arises. To the degree that these customers believe that multiple failures and poor recoveries represent a pattern that is stable to the firm, they may attribute failures internally to the firm and therefore require more extensive recovery efforts.
Lags between failures: Complainants reporting two failures within a short time period did not rate the firm lower after the second failure than did those reporting two failures separated by a longer time period. It appears that two failures, regardless of the time lag between them, produce unsatisfied customers. Perhaps customers experiencing longer gaps remain focused on the failure and compress the time lag (see Hornik 1984). However, complainants rate firms lower after the second recovery when two failures occur within a shorter time period. This may make it more difficult for firms to recover when two failures occur close together, partly because customers may not have time to forget about the first failure.
Failure similarity: Complainants reporting two similar failures did not rate the firm lower than did those reporting two distinct failures, which suggests that two failures, regardless of their similarity, make customers equally unsatisfied. However, failure similarity affects customer responses to recovery efforts. Customers reporting two similar failures did not rate firms as highly on recovery efforts as did those reporting distinct failures. These findings suggest a challenging implication: "Do not make the same mistake twice." Although it remains unlikely that firms will be able to avoid similar failures completely, managers can implement feedback loops into their service delivery system to reduce their occurrence.
Limitations and Research Issues
Although this study expands our knowledge of complaint handling, viable prospects for further research remain. Despite our evidence that noncomplainants are similar to complainants, it is possible that some customers chose not to complain about a failure but nonetheless expected a recovery. Although the bank encouraged complaints, it was the responsibility of customers to initiate a complaint. Therefore, further research could explore customer responses to proactive service recoveries initiated by the firm. It seems worthwhile to better understand if and how customers respond differently when firms proactively identify and successfully fix problems before customers complain (e.g., automobile recalls).
Although our results were mostly consistent across dependent variables, we found differences in univariate results between repurchase intent and WOM. For example, our double deviation data check after one failure and unsatisfactory recovery revealed different results for WOM and repurchase intent. In particular, whereas repurchase intent did not change from postfailure to postrecovery given one unsatisfactory recovery, WOM ratings increased. An unsatisfactory recovery following one failure had differential effects on types of intention, which suggests that even mildly unsatisfactory recoveries may spur increases in favorable WOM. Similarly, although post-Recovery 2 means for customers who reported a US sequence significantly increased above post-Failure 2 levels across the dependent variables collectively, this increase was not significant for repurchase intent. Perhaps complainants weigh past experiences more heavily when forming repurchase intent, which makes them less susceptible to recency effects. These results underscore the possibility that customers weigh and form various types of intentions differently. In a study of computer choice, Tsiros and Mittal (2000) find that satisfaction directly affects both purchase intent and complaint intent, but regret affects purchase intent only directly. Perhaps consumers used different processes to form complaint intentions and purchase intentions. Future studies can help clarify the cognitive and affective processes used to derive various behavioral intentions and help develop a greater understanding of the circumstances in which intentions remain stable or change over time.
Our research reinforces the notion that consumers' perceptions may change over time, signifying that perhaps what appears clear in cross-sectional studies may become complex in longitudinal studies--and vice versa. Our study joins a growing body of longitudinal research on consumer perceptions (e.g., Bolton and Lemon 1999; Mittal, Kumar, and Tsiros 1999), helping clarify and extend results found in cross-sectional studies. For example, Tax, Brown, and Chandrashekaran (1998) note that trust and commitment decrease when dissatisfaction with complaint handling increases. It seems fruitful to extend this finding by examining how trust and commitment change over time when customers report multiple failures with ongoing service providers. Similarly, extending the work by Smith, Bolton, and Wagner (1999), longitudinal studies could explore how the effects of service recovery attributes (e.g., response speed, apologies, compensation) on customer fairness perceptions change or remain stable when multiple failures occur.
Our study design reveals some potential measurement limitations that warrant examination. Although our prefailure retrospective measures of overall satisfaction were highly correlated with actual prefailure satisfaction ratings and there were no significant within-subject mean differences between our retrospective measures and actual measures, it still remains unclear when retrospective measures are accurate and when they are biased. For example, is there some time threshold (e.g., a certain number of months) within which customers can accurately recall their specific perceptions and after which their retrospective measures become biased? At what point do individual differences, environmental factors, customer involvement levels, and other factors spawn halo effects and other recall biases that cloud retrospective measures? To what extent do retrospective measures of given constructs trigger subsequent order effects when followed by a repeated measurement of the same constructs in the same questionnaire representing a different point in time (e.g., postfailure measures)? Given the challenges involved in capturing actual customer perceptions as they form over time, it seems worthwhile to investigate when retrospective measures offer reasonably accurate proxies.
Although all of our respondents received some type of redress effort in the bank's view, some or all of these efforts could have gone unnoticed or unappreciated by our respondents. A sound recovery in the bank's view may still be considered unsatisfactory or nonexistent in the customer's view. Alternatively, a customer could rate a recovery satisfactorily despite a lackluster recovery from the bank's view. As such, the same recovery effort from the bank's view could either generate paradoxical increases or spawn double deviations in customer ratings. Therefore, what one party considers a recovery may or may not be considered a recovery by the other party. Future work needs to examine if, when, and how customers and firm employees view recovery efforts differently.
Finally, the production and consumption of services are often inseparable, and customers may therefore influence the service they receive, including service recoveries. Relatively aggressive or passive customers, for example, may significantly affect the recovery process and ultimately influence their own perceptions about the experience. Does the "squeaky wheel get greased" or does the passive customer receive better recoveries? Although we captured the firm's response to complaints and how customers perceived these responses, we did not capture the extent to which customers influenced their recovery experiences. Although investigating the relationships between customer actions (as independent variables) and service experience evaluations was not the focus of this particular work, it offers a practical avenue for further research.
Notes:
1 From a between-subjects view, it is possible that satisfactory recoveries still spawn double deviations because of the dissatisfaction associated with a failure in the first place. Likewise, unsatisfactory recoveries can result in paradoxical increases just because customers are pleased that a firm at least tried to recover. Thus, the paradox and double deviation effect are not competing hypotheses.
- 2 We conducted a test of discriminant validity among constructs by comparing the average variance extracted estimates of all construct pairs with the phi correlation squared of the respective pairs (Fornell and Larcker 1981). We found discriminant validity across all pairs of constructs, time periods, and surveys. The phi correlations among constructs ranged from .83 (overall satisfaction with the firm and WOM of the first failure and recovery) and -.29 (failure severity and repurchase intent of the first failure and recovery). This information is available on request.
- 3 We also derived the unsatisfactory and satisfactory groups by conducting median splits and cluster analyses on the measures for satisfaction with recovery. For all analyses, these procedures produced results that closely resembled those of the midpoint split we employed.
- 4 We also ran the analysis by using a three-way split to create the time lag independent variable. All other aspects of our original model remained the same. We then compared the lower third to the upper third using linearly independent pairwise comparisons, and these results were relatively similar to our results using a median split. Furthermore, we also analyzed H12 and H13 through hierarchical regression. We modeled the months between failures as a continuous independent variable ranging from 1 to 20 months. The regression approach yielded the same conclusions as the RM MANCOVA approach employed to test H12, offering a multi-method reliability check of our analyses. The results are available on request.
Table 1: Linearly Independent Planned Comparisons: H1-H5
Legend for chart:
A - Dependent Variables
B - Per-Failure 2 Mean (SE)
C - Post-Recovery 2 Mean (SE)
D - Mean Difference
A
B C D
H1: Two Satisfactory Service Recoveries (N = 74)
Satisfaction
15.49 (.476) 12.89 (.450) -2.59**
Repurchase intent
22.16 (.536) 18.37 (.678) -3.79**
WOM
15.06 (.424) 10.10 (.311) -4.96**
H2 and H3: Two Unsatisfactory Service Recoveries (N = 76)
Legend for chart:
A - Dependent Variables
B - Post-Failure 2 Mean (SE)
C - Post-Recovery 2 Mean (SE)
D - H2: Mean Difference (j, T4-T3b)
E - Mean Difference (i, T2-T1b)
F - H3: Mean Difference (j - i)
A
B C D E F
Satisfaction
6.09 (.350) 2.93 (.461) -3.16** .80 -3.96**
Repurchase intent
13.17 (.599) 5.23 (.696) -7.94** -.20 -7.74**
WOM
5.06 (.290) 3.16 (.319) -1.90** 2.13 -4.03**
US Recovery Sequence (N = 69)
Legend for chart:
A - Dependent Variables
B - Post-Failure 2 Mean (SE)
C - Post-Recovery 2 Mean (SE)
D - H5: Mean Difference
A
B C D
Satisfaction
5.85 (.475) 10.82 (.627) 4.97**
Repurchase intent
12.90 (.814) 14.57 (.945) 1.67
WOM
5.61 (.394) 9.36 (.433) 3.75**
SU Recovery Sequence (N = 36)
Legend for chart:
A - Dependent Variables
B - Post-Failure 2 Mean (SE)
C - Post-Recovery 2 Mean (SE)
D - H4: Mean Difference
E - H4: Post-Recovery 2 Mean Difference (US-SU)
A
B C D E
Satisfaction
6.33 (.364) 5.03 (.479) -1.30* 5.79**
Repurchase intent
13.64 (.673) 8.88 (.723) -4.76** 5.35**
WOM
5.83 (.301) 4.01 (.331) -1.82** 5.69**
*p < .05.
**p < .01.
Notes: Estimated marginal means reported are adjusted for the effects of failure severity, attributions of blame, and recovery expectations. All variables are based on summed-item scores. SE = standardized error, which is reported for estimated marginal means.
Table 2: Linearly Independent Planned Comparisons: H6-H11
Legend for chart:
A - All Respondents (N=255)
B - Failure 1 Mean (SE)
C - Failure 2 Mean (SE)
D - Mean Difference
Recovery expectations
16.90(.370) 20.46(.354) 3.56**
Failure severity
12.81(.323) 15.21(.303) 2.40**
Attributions of blame
14.96(.270) 17.43(.246) 2.47**
Group: Unsatisfactory Service
Recovery (N=112)
Recovery expectations
18.59(.532) 21.14(.510) 2.55**
Failure severity
14.41(.465) 15.76(.436) 1.35*
Attributions of blame
13.71(.389) 17.64(.354) 3.92**
Group: Satisfactory Service
Recovery (N=143)
Recovery expectations
15.21(.513) 19.77(.492) 4.56**
Failure severity
11.21(.448) 14.65(.420) 3.45**
Attributions of blame
16.20(.375) 17.23(.341) 1.03*
Univariate Statistics
Model F Effect size (η(2))
H6: Expectations X failure 49.84 .17**
H7: Expectations X failure X recovery 3.97 .02*
H8: Severity X failure 30.40 .11**
H9: Severity X failure X recovery 5.78 .02*
H10: Attributions X failure 47.47 .16**
H11: Attributions X failure X recovery 16.22 .06**(*)p<.05 (**)p<.01. Notes: Based on estimated marginal means, controlling for the effect of failure type. All variables are based on summed-item scores. SE= standardized error.
Table 3: Linearly Independent Planned Comparisons: H12-H15
Legend for chart:
A - H12
B - Group: Shorter Gap Between Failures (Mean=2.61, N=128)
C - Group: Longer Gap Between Failures (Mean=10.69, N=127)
D - Mean Difference
A
B C D
Post-Failure 2 Means (SE)
Satisfaction
6.11 (.280) 6.24 (.281) .13
Repurchase intent
12.94 (.418) 13.35 (.419) .41
WOM
6.01 (.294) 5.31 (.295) -.70
A - H13
Post-Recovery 2 Means (SE)
Satisfaction
3.96 (.356) 11.07 (.358) 7.10*
Repurchase intent
6.52 (.490) 16.22 (.492) 9.70*
WOM
3.34 (.222) 9.24 (.223) 5.90*
Post-Failure 2 Means (SE)
Legend for chart:
A - H14
B - Group: Similar Failures (N=118)
C - Group: Different Failures (N=137)
D - Mean Difference
Satisfaction
6.05 (.299) 6.42 (.284) .38
Repurchase intent
12.65 (.444) 13.78 (.422) 1.13
WOM
5.47 (.316) 5.86 (.300) .39
Post-Recovery 2 Means (SE)
A = H15
Satisfaction
6.50 (.434) 8.05 (.412) 1.55*
Repurchase intent
9.34 (.600) 12.74 (.569) 3.40*
WOM
5.16 (.262) 6.78 (.249) 1.63*
*p < .01. Note: H12 and H13 were based on estimated marginal means, controlling for the effect of post-Recovery 1 satisfaction. H14 and H15 were based on estimated marginal means, controlling for the effects of post-Recovery 1 satisfaction and failure type. All variables are based on summed-item scores. SE = standardized error.
Figure 1: Time Line of Measurement
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Measurement Scales
Overall Firm Satisfaction[a]
1. I am satisfied with my overall experience with [firm name].[b]
- 2. As a whole, I am not satisfied with [firm name].
- 3. How satisfied are you overall with the quality of [firm name] banking service?[b]
Favorable WOM[a]
1. How likely are you to spread positive word-of-mouth about [firm name]?
- 2. I would recommend [firm name's] banking services to my friends.
- 3. If my friends were looking for a banking service, I would tell them to try [firm name].
Repurchase Intent[a]
1. In the future, I intend to use banking services from [firm name].
- 2. If you were in the market for additional banking services, how likely would you be to use those services from [firm name]?
- 3. In the near future, I will not use [firm name] as my provider.
- 4. In the future, I will continue using [firm name] for these banking services.
Service Recovery Expectations[c]
1. I have high expectations that [firm name] will fix the problem.
- 2. I expect [firm name] to do whatever it takes to guarantee my satisfaction.
- 3. I think [firm name] will quickly respond to (banking) problems.
- 4. My expectations are high that I will receive compensation when I encounter a banking service problem.
Failure Severity[c]
In my opinion, the banking problem that I experienced was a
1. Minor problem ( 1)/major problem ( 7).
- 2. Big inconvenience ( 1)/small inconvenience ( 7).
- 3. Major aggravation ( 1)/minor aggravation ( 7).
Attributions of Blame[c]
1. To what extent was [firm name] responsible for the problem that you experienced? (not at all responsible
[ 1]/totally responsible [ 7])
- 2. The problem that I encountered was all [firm name]'s fault.
- 3. To what extent do you blame [firm name] for this problem? (not at all [ 1]/completely [ 7])
Satisfaction with Service Recovery[d]
1. In my opinion, [firm name] provided a satisfactory resolution to my banking problem on this particular occasion.
- 2. I am not satisfied with [firm name]'s handling of this particular problem.
- 3. Regarding this particular event (most recent banking problem), I am satisfied with [firm name].
[a]Measured at all time periods.
[b]Indicates that the scale was anchored with "not at all satisfied" and "very satisfied."
[c]Measured once at T1b (post-Failure 1) and again at T3b (post-Failure 2).
[d]Measured once at T2 (post-Recovery 1) and again at T4 (post-Recovery 2). Notes: All items were measured on a seven-point scale. Unless noted, all items were anchored with "strongly disagree" and "strongly agree."
Construct Raw Means, Standard Deviations, and Pearson Correlations
Legend for chart:
A - Construct
B - Mean
C - S.D.
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
K - 8
L - 9
M - 10
N - 11
O - 12
P - 13
Q - 14
R - 16
S - 17
T - 18
U - 19
V - 20
W - 21
X - 22
Y - 23
Z - 24
A
B C D E F G H I J K L
M N O P Q R S T U V W
X Y Z
1. Pre-Failure 1 SAT, F1
13.38 2.95 .87
2. Pre-Failure 1 RI, F1
22.16 5.59 .55 .90
3. Pre-Failure 1 WOM, F1
9.49 3.19 .57 -.08 .83
4. Post-Failure 1 SAT, F1
8.89 3.93 .24 -.31 .75 .87
5. Post-Failure 1 RI, F1
16.31 3.74 .51 .62 .31 .15 .83
6. Post-Failure 1 WOM, F1
7.90 3.68 .09 -.34 .6 .63 .22 .86
7. Post-Recovery I SAT, F1
13.04 5.08 .39 .23 .02 -.06 .18 -.31 .93
8. Post-Recovery 1 RI, F1
19.58 6.67 .24 .37 -.05 .02 .45 -.23 .72 .96
9. Post-Recovery l WOM, F1
12.25 5.15 -.03 -.24 .02 .11 -.06 -.12 .62 .49 .93
10. Attributions of blame, F1
15.19 4.25 .26 .48 -.36 -.49 .13 -.61 .51 .40 .28
.87
11. Failure severity, F1
12.69 5.05 .21 .28 -.28 -.60 -.05 -.26 -.04 -.26 -.24
.51 .88
12. Recovery expectations, F1
16.89 5.77 .36 .18 -.01 -.42 .02 -.30 .12 -.19 -.08
.45 .69 .92
13. Pre-Failure 2 SAT, F2
12.76 5.20 .02 -.02 -.05 .03 .10 -.02 .50 .48 .48
.16 -.23 -.17 .92
14. Pre-Failure2 RI, F2
19.34 6.82 .0 -.10 -.05 .04 .03 -.06 .50 .43 .52
.22 -.18 -.14 .73 .89
15. Pre-Failure 2 WOM, F2
12.33 5.12 -.09 -.10 -.12 .06 .03 -.01 .51 .51 .58
.19 -.29 -.28 .63 .53 .89
16. Post-Failure 2 SAT, F2
6.18 3.12 -.04 .03 -.08 .00 .03 .05 .06 .12 .12
.01 -.08 -.13 .15 -.10 .51 84
17. Post-Failure 2 RI, F2
13.14 4.66 .01 .00 -.03 .02 .03 -.04 .05 .08 .07
.01 -.06 -.02 .41 .53 .30 .27 .86
18. Post-Failure 2 WOM, F2
5.66 3.30 -.05 .02 -.03 .01 .08 .07 .04 .11 .09
.01 -.06 -.11 .02 -.23 .42 .69 .09 .87
19. Post-Recovery 2 SAT, F2
7.50 5.37 .03 -.05 .09 .14 .07 .11 .15 .16 .17
.04 -.01 -.08 .33 .35 .22 -.07 .30 -.15 .97
20. Post-Recovery 2 RI, F2
11.35 7.50 .01 -.08 .03 .13 .05 .08 .20 .24 .27
.05 -.08 -.15 .34 .45 .27 -.01 .41 -.11 .81 .97
21. Post-Recovery 2 WOM, F2
6.28 3.88 .03 -.03 .03 .10 .03 .05 .20 .22 .13
.02 -.07 -.10 .11 -.01 .10 -.02 -.19 -.08 .57 .51
.96
22. Attributions of blame, F2
17.43 3.68 .08 .01 .02 -.01 .02 -.07 .03 -.02 -.05
.00 .03 .14 .26 .39 -.22 -.51 .21 -.75 .29 .24
.05 .88
23. Failure severity, F2
15.03 4.61 -.02 .00 .01 -.07 .02 -.24 -.12 -.16 -.14
-.04 .09 .08 .05 .09 -.21 -.42 -.05 -.23 .05 -.13
-.31 .42 .89
24. Recovery expectations, F2
20.31 5.34 -.01 -.02 .02 .02 .04 .01 -.10 -.12 -.11
-.10 .00 .08 .20 .14 -.13 -.39 .12 -.29 .19 .00
-.21 .47 .72 .92
Notes: F1= Failure 1 and F2= Failure 2. SAT= overall satisfaction; RI= repurchase intentions. Coefficient alphas are reported on the diagonal.
~~~~~~~~
By James G. Maxham III and Richard G. Netemeyer
James G. Maxham III is Assistant Professor of Commerce, and Richard G. Netemeyer is Professor of Commerce, McIntire School of Commerce, University of Virginia. This research was funded in part by the Bernard A. Morin Fund for Marketing Excellence at the McIntire School of Commerce. The authors thank Amanda Bower, David Mick, Bill Bearden, and the University of Virginia Statistics department for contributing insightful comments on previous versions of this article.
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Record: 6- A Marketing Perspective on Mergers and Acquisitions: How Marketing Integration Affects Postmerger Performance. By: Homburg, Christian; Bucerius, Matthias. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p95-113. 19p. 2 Diagrams, 6 Charts. DOI: 10.1509/jmkg.69.1.95.55510.
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A Marketing Perspective on Mergers and Acquisitions: How
Marketing Integration Affects Postmerger Performance
Previous research on mergers and acquisitions (M&A) has neglected marketing issues by and large. In this article, the authors examine the effects of postmerger integration in marketing (extent and speed of marketing integration) on M&A performance, as mediated by integration outcomes (magnitude of cost savings and market-related performance). Results from a survey of 232 horizontal M&A show that market-related performance after the merger or acquisition has a much stronger impact on financial performance than does cost savings. In addition, the authors find that the extent of integration is beneficial in terms of cost savings but detrimental in terms of market-related performance. Finally, they identify variables that moderate the relationships being considered.
Mergers and acquisitions (M&A) have become increasingly popular in business practice, and especially horizontal M&A (M&A that take place in the same industry, often between direct competitors) are undertaken frequently (Krishnan and Park 2002). However, there is considerable evidence that many M&A activities are unsuccessful. Estimated failure rates are typically between 60 and 80% (Marks and Mirvis 2001; Tetenbaum 1999). Thus, studying factors that influence the success of M&A is a promising field for academic research.
Because there is a growing recognition that "all value creation takes place after the acquisition" (Haspeslagh and Jemison 1991, p. 129), the topic of postmerger integration (PMI) has received increasing attention (e.g., Capron, Dussauge, and Mitchell 1998; Datta 1991; Larsson and Finkelstein 1999; Shrivastava 1986). However, marketing-related issues of PMI, such as whether and how two firms' marketing activities are integrated and how this affects the merged firms performance, have been largely neglected.
Within the marketing discipline, M&A-related research is almost totally absent. A notable exception is a study by Capron and Hulland (1999). Their findings indicate that the redeployment of marketing resources has a significant effect on firm performance after M&A.
The lack of attention given to marketing issues in the context of M&A is in sharp contrast with many statements that highlight the importance of marketing-related issues for M&A performance (e.g., Becker and Flamer 1997; Clemente and Greenspan 1997). For example, Bekier and Shelton (2002) report that there is considerable risk of losing customers in M&A. During the integration phase, managerial energy is often absorbed by internal issues, which can lead managers to neglect customer-related tasks (Hitt, Hoskisson, and Ireland 1990). This strong internal orientation is frequently accompanied by a decline in service quality (Urban and Pratt 2000). On the part of customers, this decline can result in uncertainties about their future relationship with the merging firms (e.g., prices, quality of products and services, contact persons). Restraint and defection are possible customer reactions (Chakrabarti 1990; Reichheld and Henske 1991). Competitors' actions often reinforce this effect as they take advantage of the situation and try to alienate customers. The relevance of market-related issues for the success of a PMI is also highlighted by Morall (1996, p. 19), who claims on the basis of a case study that "cost reduction to make a merger pay off is not as important as customer retention." Because such market-related issues have been neglected in previous research, we argue that the research-based understanding of the factors that lead to M&A success is still limited to date.
Against this background, we adopt a marketing perspective of M&A and explore three issues. First, we investigate how the marketing integration process (extent and speed of integration) affects integration outcomes (magnitude of cost savings and market-related performance). Second, we investigate how these relationships are affected by certain moderators (customer orientation of integration, market growth, product/service industry, relatedness of the firms market positioning, and relative size of the acquired firm). Third, we analyze the importance of market-related performance (compared with cost savings) for M&A performance.
Theoretical Background
Our study is rooted in the resource-based theory, which explains superior performance by specific resources owned by the firm (Barney 1991; Peteraf 1993; Wernerfelt 1984). Following Barney (1991, p. 101), we define resources as assets, organizational processes, capabilities, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness. According to this theory, resources must meet four requirements--value, rarity, imperfect imitability, and insubstitutability (Barney 1991)--to affect firm performance.
The resource-based view has been used frequently as a theoretical explanation for why M&A occur (e.g., Anand and Singh 1997; Karim and Mitchell 2000). Many organizational theorists argue that firms face constraints (in terms of intellectual capabilities and time) with respect to the internal development of new resources (e.g., Nelson and Winter 1982; Singh and Montgomery 1987). Therefore, many firms turn to the market to obtain new resources (Capron, Dussauge, and Mitchell 1998). Because few resources are available as single entities, firms must acquire entire businesses to extract value from the resources owned by the acquired firm (Barney 1986).
A key issue in M&A research that adopts a resource-based perspective is to what extent the merging firms integrate certain resources after a merger or acquisition to achieve a stronger competitive position and thus superior financial performance. As an example, Capron, Dussauge, and Mitchell (1998) analyze performance implications associated with the integration of research and development; manufacturing; marketing; and managerial, financial, and senior executive resources. These authors argue that among the resources with the potential to contribute to firms postmerger performance, marketing resources, such as brands and sales forces, are a highly important subset. In addition, marketing resources have been identified as resources that meet the previously mentioned requirements for relevancy for firm performance (Capron and Hulland 1999).
Thus, the resource-based perspective provides a theoretical basis for the fundamental proposition of our study, namely, that the integration of marketing resources is relevant for M&A performance. We translate this proposition into specific constructs and hypotheses.
Framework
Our unit of analysis is the total marketing activities of two merged firms. The framework of our study is a causal chain with three categories of constructs: marketing integration process, integration outcomes, and performance outcome after the merger or acquisition. Figure 1 presents an overview of the framework and the specific constructs.
Marketing integration process We define the extent of integration as the level of similarity achieved between two firms marketing systems, structures, activities, and processes. As an example, a typical question in PMI pertains to what extent the differences between two firms, in terms of their product and service offer, branding strategy, sales channels, and so forth, should be consolidated. A high level of integration means that premerger differences between the two organizations have largely disappeared. This integration can be achieved by using a system, structure, activity, or process of either company for both firms. It also can be achieved through combining the "best of both worlds" into a newly created system, structure, activity, or process. A maximal extent of integration is achieved if all differences in the firms marketing approaches are harmonized. The extent of integration has been discussed as a key driver of M&A performance (e.g., Birkinshaw, Bresman, and Hakanson 2000).( n1)
Speed of integration is defined as the shortness of the time needed to achieve the intended level of marketing integration. Although the importance of integration speed has been highlighted in many practitioner statements (e.g., Bragado 1992; Mitchell 1989), this construct has been almost totally neglected in academic research (with the exception of some qualitative work in human resources management; e.g., Buono and Bowditch 1989; Schweiger and Goulet 2000).
Integration and performance outcomes. A distinction between cost-related and market-related integration outcomes is common (Chatterjee 1986). We define magnitude of cost savings as the extent to which the merging firms reduce the amount of resources and thus costs (in terms of personnel, infrastructure, and so on) compared with the sum of their resources before the transaction. The magnitude of cost savings is minimal if the postintegration costs are identical to the sum of the two firms' premerger costs.
In addition to gains from cost reductions, M&A performance can also be influenced by the merging firms ability to increase market-related performance and thus revenues after the transaction. We define market-related performance after the merger or acquisition as the effectiveness of the combined firms' marketing activities. Positive market-related outcomes of M&A (e.g., customer loyalty, market share) may be based on phenomena such as intensified cross-selling and the use of expanded bundling opportunities (because of a broader product range), as well as improved negotiating positions toward customers.
The key performance measure in our study is financial performance after the merger or acquisition. Consistent with existing literature, we define the financial performance after the merger or acquisition as the postmerger profitability compared with the firms' premerger profitabilities (Datta 1991; Hunt 1990). This after/before comparison is typically used as an indicator of M&A success or failure (e.g., Capron and Hulland 1999).
Moderator variables. Potential moderating influences arise at the firm level and the market/industry level (see Figure 1). We based the selection of specific moderator variables on two considerations. First, previous research on M&A has identified two constructs (i.e., relatedness of the firms market positioning and relative size of the acquired firm) with potential relevance for our study. Second, we identified moderator variables (i.e., customer orientation, market growth, and product/service industry) on the basis of previous marketing research. We describe these moderator variables and justify their relevance in more detail in the following section.
Main Effects
Our first hypothesis addresses the link between the extent of integration and the magnitude of cost savings. The basic logic is that a merger or acquisition creates redundancies that can be eliminated through integration. More specifically, integration activities create the potential for a reduction in the amount of needed resources and thus costs (e.g., personnel, infrastructure) (Capron 1999; Seth 1990a). Hypothesizing a positive impact of the extent of integration on cost savings is consistent with previous findings by Capron (1999). Thus,( n2)
H1: The extent of integration is positively associated with the magnitude of cost savings.
Although we hypothesize that integration is beneficial in terms of cost savings, it is likely to be detrimental in terms of market-related performance. Extensive marketing integration, in practical terms, leads to a reduced number of brands, product variants, service offers, distribution channels, and so forth. Therefore, the merged firms' ability to adapt their offer to the needs of specific market segments is reduced through extensive integration. This reduction can be detrimental for market-related performance because some customers may perceive the value provided by the merged firm as less attractive. In addition, in the case of extensive integration, firms are likely to reassess their joint customer portfolio and eliminate unprofitable customers or segments (i.e., "controlled" loss of customers and market share) (Reichheld and Henske 1991).
Furthermore, during PMI, managerial energy is often strongly absorbed by internal issues at the expense of customer-related tasks (Hitt, Hoskisson, and Ireland 1990). This absorption is true for a high extent of integration because, in such circumstances, many internal decisions (e.g., structures, staffing) must be prepared and made. Therefore, in the case of a high extent of integration, the neglect of market-and customer-related tasks is more likely than in the case of a low extent of integration. Customer dissatisfaction, restraint, and defection are likely consequences. Thus,( n3)
H2: The extent of integration is negatively associated with market-related performance after the merger or acquisition.
Our reasoning regarding integration speed is based on the logic that an M&A activity creates uncertainty among customers, which may ultimately lead to customer defection. Uncertainty is likely to increase over time if the integration phase is long. We argue that the major beneficial effect of integration speed on M&A success is the uncertainty reduction for customers. If integration decisions are made and implemented quickly, customers will know what to expect from the merged company in terms of the product offer, pricing policy, contact persons, and so forth. Thus, customer uncertainty is reduced through a high level of integration speed. In addition, it is a common business practice for competitors to try to increase uncertainty among the merging firms' customers to promote customer switching (Clemente and Greenspan 1997). This potential source of customer uncertainty can also be eliminated through high integration speed.
Finally, a fast integration will lead to a reduced level of uncertainty among employees about their future in the merged company. In particular, the uncertainty of customer contact personnel about their future in the company can lead to increased customer uncertainty and thus be harmful for market-related performance (e.g., Singh, Goolsby, and Rhoads 1994). Thus,
H3: The speed of integration is positively associated with market-related performance after the merger or acquisition.
We further argue that market-related performance has a positive impact on the magnitude of cost savings for two reasons. First, a high level of customer loyalty after the transaction (which is a facet of market-related performance) enables companies to avoid costly activities directed toward attracting new customers. Retaining customers is much less cost intensive than is attracting new customers (e.g., Anderson, Fornell, and Rust 1997). Second, if market share (and consequently sales volume) increases, opportunities for cost savings through economies of scale emerge. Our reasoning is also supported by findings that indicate a negative impact of market performance on costs (Phillips, Chang, and Buzzell' 1983). Thus,
H4: The market-related performance after the merger or acquisition is positively associated with the magnitude of cost savings.
We finally suggest that the magnitude of cost savings and market-related performance both lead to superior postmerger financial performance. Because financial performance is defined in terms of profitability, the cost-related effect is obvious: If costs decrease, all other things being equal, profitability increases. Market-related performance after the merger or acquisition affects profitability through increased sales. Positive relationships between market share and profitability (Szymanski, Bharadwaj, and Varadarajan 1993) and between customer loyalty and profitability (Kalwani and Narayandas 1995) have been documented in previous research. In addition, hypothesizing these two positive effects is in line with previous empirical findings in M&A research (e.g., Anand and Singh 1997). This leads us to the following hypotheses:
H5: The magnitude of cost savings is positively associated with the financial performance after the merger or acquisition.
H6: The market-related performance after the merger or acquisition is positively associated with the financial performance after the merger or acquisition.
Moderating Effects
We develop hypotheses regarding the firm-level moderators (customer orientation of integration, relatedness of the firms' market positioning, relative size of the acquired firm). Subsequently, we discuss the effects of the market-and industry-level moderators (market growth before the merger or acquisition, distinction between product and service firms).
Customer orientation of integration. As we described previously, PMI is often characterized by the strongly internal orientation of managers (Hitt, Hoskisson, and Ireland 1990). A possible consequence is that decisions are made predominantly on the basis of internal criteria, such as internal structures, processes, power distribution, or individual managers' preferences. Against this background, we define customer orientation of integration as the extent to which decisions about marketing integration are driven by customer-related considerations rather than internal considerations. For example, a high level of customer orientation of integration is present if decisions are strongly influenced by the goal of creating additional customer value rather than reducing the costs of serving customers.
In the case of a high customer orientation in PMI, decisions are driven by cost reduction motives to a lesser extent.
Therefore, an increase in the extent of integration will produce less cost savings in this situation than in a context of low customer orientation. Furthermore, we predict that a high level of customer orientation can alleviate, at least to some extent, the negative market-related consequences of integration. When decisions to integrate brands, product variants, or distribution channels are made with a strong focus on customer value, the negative market-related consequences of a high level of the extent of integration should be weaker.
Our argument regarding integration speed is that speed serves as a means to reduce uncertainty among customers. We now argue that customer orientation serves as a partial substitute for speed in reducing customer uncertainty. More specifically, when customers perceive that integration decisions are driven by customer-related considerations, their trust in the newly forming firm will increase and reduce uncertainty. In other words, in the case of a high customer orientation, integration speed becomes less relevant as a means to reduce customer uncertainty and avoid detrimental effects on market-related performance. We therefore predict that the effect of integration speed on market-related performance is weaker in the case of high rather than low customer orientation. In summary, we hypothesize the following:
H7: In the case of high rather than low customer orientation of integration, the effect of the (a) extent of integration on cost savings is less positive, (b) extent of integration on market-related performance after the merger or acquisition is less negative, and (c) speed of integration on market-related performance after the merger or acquisition is less positive.
Relatedness of the firms' market positioning. We define relatedness of the firms' market positioning as the extent to which the firms offers are similar in terms of the customer needs that they satisfy, quality, and price positioning. This variable has been frequently analyzed in M&A research (e.g., Chakrabarti 1990). Unlike previous research, our research question is not related to the possible direct performance effect of relatedness but rather to how relatedness affects the way that PMI should be conducted.
Consistent with previous studies (e.g., Hagedorn and Duysters 2002), we argue that a high level of relatedness offers a great potential for cost reductions. Thus, an increase in the extent of integration may result in greater cost savings when the market positioning of the firms is highly related. However, we do not find a compelling argument for why the relatedness of market positioning should moderate the impact of the extent of integration on market-related performance.
The potential scope of changes (e.g., repositioning the strategic focus of the entire firm) is much greater in M&A between unrelated firms (Larsson 1989). Because of the reduced potential for changes in highly related M&A, a lower level of uncertainty among customers about their future relationship with the merged firm is likely. Because uncertainty reduction is the major effect of integration speed, we predict that an increase of integration speed has a smaller impact on market-related performance when relatedness is high. Thus, we hypothesize the following:
H8: In the case of high as opposed to low relatedness between the merging firms' market positioning, the effect of the (a) extent of integration on cost savings is more positive and (b) speed of integration on market-related performance after the merger or acquisition is less positive.
Relative size of the acquired firm. We define the relative size of the acquired firm as the relative annual sales volume of the acquiree compared with that of the acquirer. This variable has been frequently analyzed in previous M&A research. There is substantial evidence that the potential to create value from M&A depends on relative size (e.g., Capron 1999; Seth 1990b).
In the case of high relative size (i.e., when the acquiree is almost as big as the acquirer), there is a greater increase in scale than in the case of low relative size. Therefore, there is a greater potential for cost savings through integration in this case. In addition, because organizational size is known to be an important driver of organizational structure (e.g., Blau and Schoenherr 1971; Pugh et al. 1969), we argue that there is more structural similarity between the organizations in the case of high relative size. For example, firms with similar size should be structurally similar with respect to the presence or nonpresence of certain specialized units (e.g., market research, key account management) within the company. In other words, there will be a larger set of similar structures and therefore more redundancies in the case of high relative size so that the potential for cost reduction is greater.
With respect to market-related performance, we predict that the negative effect of the extent of integration (H2) is stronger in the case of a high relative size. If the acquiree is fairly large compared with the acquirer, PMI is likely to involve more people, and its results will not be as clear from the beginning compared with the situation in which a relatively small firm has been acquired. In these conditions, internal conflicts, interorganizational competition, holding back of information, and so forth, are likely consequences and will absorb managerial energy that is needed to serve customers. Against this background, an increase in the integration extent will produce greater damage to market-related performance when the relative size of the acquired firm is high.
With respect to integration speed, our reasoning related to relative size as a moderator variable is based on the contention that the number of customers affected by the transaction is greater when the relative size is high. When the acquired firm is relatively small in proportion to the acquirer, only a small number of customers are affected by the integration activities. With an increasing relative size, the customer base affected by the integration also broadens. In turn, the potential for rumors about possible changes, which lead to uncertainties among customers, also increases. Thus, an increase of integration speed is more likely to be beneficial for market-related performance when the relative size of the acquiree is high. Thus,
H9: In the case of high rather than low relative size of the acquired firm, the effect of the (a) extent of integration on cost savings is more positive, (b) extent of integration on market-related performance after the merger or acquisition is more negative, and (c) speed of integration on market-related performance after the merger or acquisition is more positive.
Market growth. Market growth is a frequently studied construct in marketing research. We consider market growth before the merger or acquisition and argue that the potential for cost savings through extensive integration is greater in mature markets (i.e., markets with low growth rates) than in high growth markets (e.g., Budros 1999). This reasoning is based on the proposition that in mature markets, companies have accumulated a higher level of experience with respect to business processes and routines (Davis and Thomas 1993), which facilitates cost savings. Thus, extensive integration in fast growing markets results in smaller cost savings than in low growth markets.
We also hypothesize a moderating effect of market growth on the link between the extent of integration and market-related performance. The logic behind our hypothesis is that in markets with low growth rates (which are typically in later life cycle stages), there is less dynamism than in high growth markets (e.g., Wasson 1978). For example, market shares (e.g., Arndt 1979) and the competitive environment (e.g., less entry of new competitors; Day 1981) are more stable, and switching barriers, which make it costly and risky for a customer to change its supplier, are typically higher in mature markets (e.g., Colgate and Lang 2001; Fornell 1992). Switching barriers may limit customer defection and thus reduce negative customer reactions to a high extent of integration. Moreover, the number of competitors is usually lower in low growth than in high growth markets (e.g., Buzzell 1981). Therefore, there are fewer alternative suppliers for the customer, and fewer competitors' actions to alienate customers will occur in low growth markets. As a consequence, the negative impact of the extent of integration on market-related performance is stronger in high growth than in low growth markets.
Our reasoning with respect to the impact of integration speed on market-related performance is based on a similar logic. Because customers in mature markets are less likely to defect because of high switching barriers, fewer alternative suppliers, or fewer competitors' actions, we predict that the positive effect of speed is weaker in low growth markets. This hypothesis is consistent with research that suggests that speed is less important if markets are growing slowly (Bowman and Gatignon 1995). In summary, we present the following hypothesis:
H10: In the case of high rather than low market growth before the merger or acquisition, the effect of the (a) extent of integration on cost savings is less positive, (b) extent of integration on market-related performance after the merger or acquisition is more negative, and (c) speed of integration on market-related performance after the merger or acquisition is more positive.
Manufacturing versus service firms. Services exhibit higher levels of individualization than tangible products (e.g., Zeithaml, Parasuraman, and Berry 1985). Therefore, we predict that translating a high extent of integration into significant cost savings is more difficult for firms that market services than for firms that market tangible products. In other words, we predict that the effect of the extent of integration on cost savings is stronger for product than for service firms. However, we do not find a compelling argument for why the distinction between product and service firms should moderate the impact of the extent of integration on market-related performance.
In addition, a higher level of uncertainty among customers is a key characteristic of services (Zeithaml 1981), which is relevant for the hypothesized moderator effect with respect to the link between the speed of integration and market-related performance. Avoiding customer uncertainty is particularly relevant in services industries (e.g., Murray 1991). We therefore argue that the uncertainty-reducing effect of a high speed of integration is more important for firms that market services than for those that market tangible products. Thus, we put forward the following hypothesis:
H11: For firms that market tangible products (as opposed to services), the effect of the (a) extent of integration on cost savings is more positive and (b) speed of integration on market-related performance after the merger or acquisition is less positive.
Table 1 presents an overview of the hypotheses related to moderating effects.
Sample and Data Collection Procedure
We used a survey methodology for data collection, which took place in 2002. The research was conducted in the German-speaking part of Central Europe (Germany, Austria, and Switzerland) with a strong focus on German firms. The initial sample consisted of horizontal M&A that took place between companies based in these countries during 1996-1999.( n4) We identified the M&A from several sources, including the Mergers & Acquisitions Database of the University of St. Gallen in Switzerland and several M&A-related European business magazines.
We initially identified 3360 reported horizontal M&A. On the basis of telephone calls with marketing/sales managers in the acquiring firm, we excluded those M&A in which the two firms marketing activities remained totally independent. In the same telephone call, we obtained the names of a senior executive or head of marketing and/or sales with responsibility for the PMI in the acquiring company.( n5) We identified managers responsible for the marketing integration of a total sample of 1483 M&A and sent questionnaires to them. We made follow-up telephone calls to verify the contact name and to encourage response.
On the basis of the follow-up telephone calls and undeliverable mail, we found 181 firms that were inappropriate for the study. However, 232 usable questionnaires were returned, for a response rate of 17.8%. We tested nonresponse bias by comparing early versus late respondents (Armstrong and Overton 1977). We also analyzed whether the firms we initially addressed and the responding firms differed in terms of industry. Both tests indicate that nonresponse bias is not a problem. We present respondent and M&A characteristics in Table 2.
Measure Development and Assessment
We followed standard psychometric scale development procedures (Gerbing and Anderson 1988). We were guided by a review of the literature (i.e., construct definitions and existing scales used in marketing and M&A research), as well as by the results from ten field interviews with practitioners. We provide a complete list of items in the Appendix.
We assessed the extent of integration using eight items related to the extent to which the systems, structures, activities, and processes in marketing were made similar. The specific items were partly based on items used by Datta (1991). The speed of integration was measured with the same eight items focusing on the shortness of the time period needed for the integration.
We measured the construct magnitude of cost savings with nine items, including the reduction in the products and services offered, brands, marketing, and sales personnel. The content of these items was largely generated through our field interviews. The measurement of market-related performance after the merger or acquisition was based on previous conceptualizations of market-related performance in the literature (e.g., Homburg and Pflesser 2000; Irving 1995). From these studies scales, we selected those items that are particularly relevant in the context of M&A (i.e., market share and customer loyalty). The conceptualization is also in line with previous research on M&A (e.g., Capron 1999; Datta 1991).
Consistent with our definition and previous M&A research (e.g., Anand and Singh 1997), financial performance after the merger or acquisition compares merging firms' profitability before and after the merger. In line with previous M&A-related research (e.g., Datta 1991; Hunt 1990), we use return on sales as our measure.( n6, n7) We measured customer orientation of integration from the managers perspective because customers are typically unaware of the driving forces behind company decisions. Its operationalization was influenced by Narver and Slater's (1990) customer orientation scale. Relatedness of the firms market positioning refers to the extent to which the merging firms offers are similar in terms of the customer needs they satisfy, such as quality or price positioning. The construct was measured with five items based on previous research (Achrol 1992). Relative size of the acquired firm was measured with a single item and in accordance with studies by Capron and Hulland (1999) and Datta (1991). Market growth before the merger or acquisition was also measured with a single item. Finally, we used our industry measure to categorize product and service firms (see Table 2). In Table 3, we show the summary statistics and correlations among constructs and moderator variables.
We analyzed measurement issues for each factor individually, and we provide the corresponding results in the Appendix. The results indicate acceptable psychometric properties in terms of internal consistency for all constructs. The average variances extracted and composite reliabilities are greater than the recommended threshold values of .5 and .6, respectively (Bagozzi and Yi 1988). In addition, all individual item reliabilities are greater than the required value of .4 (Bagozzi and Baumgartner 1994). Moreover, coefficient alpha values are greater than the threshold value of .7 (Nunnally 1978).
We assessed discriminant validity by performing pair-wise chi-square difference tests in which we compared a perfect correlation model with a model in which the two factors were allowed to correlate freely (Jöreskog and Sörbom 1982). We also applied the procedure suggested by Fornell and Larcker (1981). Both procedures indicated discriminant validity between all pairs of constructs.
Further Measure Validation Through Additional Data
Because performance assessments based on self-reported data can be problematic, we conducted two additional data collections to ensure the validity of the financial performance measure and the customer loyalty measure, a component of market-related performance.
For the validation of the financial performance measure, we identified all M&A in our sample for which both firms were publicly traded before the transaction. In this case, profitability information before the merger or acquisition is publicly available for both the acquiring and the target firms. These M&A represent 18.6% (i.e., 43 cases) of our total sample. We obtained sales volume and profitability information (return on sales) for these cases from a business information broker. Using this information, we calculated the following profitability development index (PDI).
The year in which the transaction took place is t = 0. For each year preceding the merger or acquisition (i.e., t = 3, t = 2, t = 1), we computed the relative magnitude of the two firms in terms of sales volume (AF = acquiring firm, TF = target firm). The specific formulas are as follows:
( 1) mA,t = SAFt/SAFt + STFt (t = -3, -2, -1), and
mT,t = 1 - mA,t,
where SAF refers to the sales volume of the acquiring firm and STF denotes the sales volume of the target firm. We then computed the weighted average profitability of these firms for each year. This weighted average profitability is
( 2) PAFt x mA,t + PTFt x mT,t
for year t, where PAFt and PTFt denote the profitability of the acquiring firm and the target firm, respectively, in year t. On the basis of these data, we computed the PDI as
( 3) [Multiple line equation(s) cannot be represented in ASCII text]
where PCFt denotes the profitability of the combined firm in year t. Thus, this index compares the average profitability of the combined firm during the three years after the transaction with the weighted average profitability of both firms during the three years before the transaction. A positive value of PDI indicates an increase in profitability. For example, if the average return on sales is 5% before the merger and 7% after the merger, PDI equals 2.
We then correlated the PDI values with managers' initial evaluations of profitability after the merger or acquisition (i.e., the perceptual item used in our study). The correlation of .704 (p < .01) indicates the high validity of the managerial assessment of postmerger financial performance.
As a basis for the validation of the customer loyalty measure, we selected two industries from the total sample: banks/insurance and machinery. Two weeks after we completed the data collection, we contacted respondents in both industries by telephone. We asked the managers to name three to five customers of the combined firm that had had a business relationship with one of the firms at the time of the merger or acquisition. Forty-eight respondents named at least three customers. We then interviewed customers of 15 firms (2 customers per firm) in both industries by telephone, resulting in a total of 60 customer interviews. We asked the customers about their loyalty toward the specific firm that had undergone the merger or acquisition and how their loyalty had changed since the merger or acquisition.( n8) We then correlated the loyalty assessments by customers with managers initial evaluations of customer loyalty after PMI. We obtained high correlations (.757 for banks/insurance and .677 for machinery; p < .01 for both industries), which indicate the high validity of the managerial assessments of customer loyalty.
After establishing the structure of the measurement model, we analyzed the overall causal model (reported in Figure 2) using LISREL 8 (Jöreskog and Sörbom 1996). The fit statistics indicate an adequate fit of the model with the data (2/degrees of freedom = 3.76, goodness-of-fit index = .94, adjusted goodness-of-fit index = .93, confirmatory fit index = .95, root mean square error of approximation = .07).
Results Related to Main Effects
In Figure 2, we summarize the results of our hypotheses testing. As we show, all six hypotheses are supported by our empirical findings. More specifically, each of the parameter estimates is significant at least at the .05 level. The strongest effects are observed with respect to the link between market-related performance and financial performance and between the extent of integration and magnitude of cost savings.( n9)
The extent of integration has differential (i.e., positive and negative) indirect effects on financial performance. To explore the impact of the extent of integration on financial performance further, we computed the magnitudes of the three indirect effects and the resulting total effect. The positive effect through cost savings (.57 x .07 = .039) is confronted with two negative effects through market-related performance (.23 x .67 = .154) and a more complex causal chain through market-related performance and cost savings (.23 x .12 x .07 = .001). Thus, the total effect (.116) is negative. In other words, the positive effect of the extent of integration on financial performance (through cost savings) is reversed to a negative total effect through the negative market-related consequences of integration.
Results Related to Moderating Effects
After we found support for the main effects, we assessed the influence of the five moderator variables. We conducted separate median splits in our sample based on the values of each individual moderator. We performed multiple group LISREL (Jöreskog and Sörbom 1996) to compare the two subsamples (low versus high values of the moderator variable) for each moderator variable individually.
In the first step, we compared two models that differ with respect to all effects we consider in our moderator analysis (γ11, γ21, γ22 for H7, H9, H11 and γ11, γ22 for H8, H10). One model restricts these parameters to be equal across subsamples, whereas the more general model allows these parameters to vary across groups. Because these are nested models, the χ² value will always be lower for the general model than for the restricted model. The question is whether the improvement in χ² when moving from the restricted to the more general model, is significant. This improvement would indicate that the moderator variable has an effect on the relationships under consideration. The χ² statistic is significant for all five moderator variables (Table 4); thus, each moderator variable has some relevance in the context of our study.
In the next step, we conducted more detailed analyses and compared, for each moderator, two models that differ only with respect to one specific effect (γ11, γ21, or γ22. In this case, one model restricts the specific parameter to be equal across subsamples, whereas the more general model allows the specific parameter to vary across groups. Again, the question is whether the improvement in χ² is significant. This improvement would indicate differential effects in the two subsamples and thus a moderator effect. We now discuss the results related to each moderator variable.
With respect to the customer orientation of integration, the χ²-difference values indicate a significant moderator effect for H7. In addition, the parameter estimates obtained in the two subsamples support our theoretical reasoning because the effect is consistently weaker for high than for low levels of customer orientation. In summary, H7 is fully supported.
H8, which pertains to the moderating effects of the relatedness of the merging firms market positioning, is also supported. The results show that the moderating effects are not strong enough to moderate the effects under consideration. In the conditions in which we predict and find a weaker effect, the effect is still significant.
With respect to the relative size of the acquired firm, the χ²-difference values indicate a significant moderator effect for H9a and H9c. Furthermore, our theoretical reasoning is supported by the parameter estimates obtained in the two subsamples; in both cases, the effect is stronger for high than for low levels of relative size. However, we fail to find support for H9b, for which the χ²-difference is not significant. A possible explanation for this nonfinding is that our hypothesized strengthening moderator effect is compensated for by other weakening moderator effects. For example, the acquisition of a relatively large target firm may yield a large number of competent managers and staff members, and gaining this resource may weaken the negative consequences in the marketplace. Thus, H9 is only partially supported.
With regard to market growth before the merger or acquisition, H10a is supported by our findings. The χ² difference is significant, and the effect of the extent of integration on cost savings (γ11) is stronger for low than for high market growth. We fail to find support for H10b because, contrary to our hypothesis, the negative link between the extent of integration and market-related performance is weaker in the case of high market growth. Our argument for our hypothesis was that higher switching barriers, fewer alternative suppliers, and fewer competitors attempts to promote customer switching in mature markets (i.e., markets with a low growth rate) would weaken negative customer reactions. A possible explanation for the finding is that the customer uncertainty created through a merger or acquisition is so strong that customers are highly receptive to competitors offers and that switching barriers cannot prevent customers from reacting negatively.
Our findings provide support for H10c. We observe a negative effect for the link between the speed of integration and market-related performance in the case of low market growth. A possible explanation is that in the case of low market growth, the (positive) uncertainty-reducing effect of speed is outweighed by other mechanisms. For example, fast integration decisions involve the risk of making wrong decisions that can result in premature solutions (Schweiger and Walsh 1990, p. 61). In summary, we obtain partial support for H10.
For the distinction between product and service firms, both χ²-difference values indicate a significant moderator effect. According to the parameter estimates obtained in the two subsamples, H11 is fully supported. The influence of the product/service distinction is not strong enough to moderate away one of the effects.
Research Issues
Previous M&A-related research has largely neglected marketing issues despite their immense importance. Against this background, we adopted a marketing perspective toward M&A. We believe that our study advances academic knowledge in our discipline in several ways.
First, in line with the study by Capron and Hulland (1999), we contribute to bridging the gap between M&A-related research and marketing research. Our findings provide support for the idea that PMI related to marketing is highly relevant for M&A performance.
Second, we find that the extent of marketing integration has positive consequences in terms of cost savings, which are outweighed, however, by negative market-related consequences. Thus, our research provides a deeper understanding of the performance implications of the postmerger extent of integration than has most previous M&A research (e.g., Larsson and Finkelstein 1999). However, it is worth emphasizing that this finding relates to marketing integration and cannot be readily generalized to integration in other areas (e.g., manufacturing, logistics, accounting).
Third, we show that the speed of integration, a construct neglected in previous M&A research, is beneficial in terms of market-related performance. This finding extends previous conceptual discussions about the outcomes of speed in PMI (e.g., Buono and Bowditch 1989; Schweiger and Walsh 1990). Whereas we agree with these purely conceptual studies that speed can have negative outcomes, our findings show that for market-related aspects of M&A performance, speed of marketing integration is beneficial. Our reasoning--that fast integration processes can limit customer uncertainty and therefore enhance market performance--is supported by our data.
Fourth, we show that there are contingency factors that systematically strengthen or weaken the relationships under consideration. To the best of our knowledge, our study is the first to address theoretically and show empirically that the links between marketing integration and integration outcomes are not equally strong in every situation. On a more general level, we draw the conclusion that marketing research and research in the strategy field should consider moderating effects to a greater extent than they have previously. Although the number of articles analyzing moderating effects is growing, many empirical studies are still restricted to main effects. This approach is likely to miss a significant part of the real structure of the relationships among the constructs under consideration.
Fifth, we find that the negative market-related outcomes of a high level of integration are much weaker in the case of high customer orientation (H7b). Together with the observation that many PMI phases are characterized by a strong internal orientation and a lack of customer orientation, this finding provides a possible explanation of why so many M&A fail. We suggest that this is the case because of a lack of customer orientation during PMI.
Sixth, our findings indicate that market-related performance is a much more important driver of financial performance after the merger or acquisition than are cost savings. This finding is potentially relevant for other areas of marketing research. Many marketing decisions involve a tradeoff between saving costs and building strong market positions. Our empirical study reveals a specific marketing approach (i.e., extensive marketing integration in the PMI phase) that enables firms to save costs but only at the expense of their market position, which is more important in driving financial performance than are the cost reductions. Although this finding cannot be readily generalized to other marketing approaches, we conclude that marketing researchers should be sensitive to the market position/ cost trade-offs of specific marketing approaches. This issue might be interesting to study with respect to the cross-country standardization of products and brands in the field of international marketing.
Limitations and Avenues for Further Research
Several limitations of our study must be mentioned. These limitations also provide avenues for further research. First, the sample of our study is restricted to a specific region in Europe. Additional research might use cross-country or cross-continent comparisons to study PMI.
Second, our findings reveal that positive market-related effects are more important in driving financial performance than is cost reduction, a finding based on the total sample. It may still be the case that for some mergers or acquisitions, cost savings are a more important driver of performance. Further research might address this issue in more detail.
Third, our study uses data obtained from customers to a limited extent. Additional studies in this field should use customer-based data to a greater extent than we did to achieve a deeper understanding of the processes that drive customer reactions to M&A. More specifically, research might measure the extent and speed of integration (with respect to the integration of customer-oriented activities such as product offers, brands, and sales forces) and market-related performance (with respect to customer loyalty) using customer survey data. Similarly, the moderator variable customer orientation of integration could be measured from the customers' perspective. This perspective would generate interesting and more general insights into the perception processes of customers during or after a merger or acquisition.
Fourth, our study uses perceptual measures for the magnitude of cost savings and extent of integration constructs. To make more precise financial predictions of how a certain reduction in costs affects return on sales, additional research might use objective measures of this construct. We also suggest that further research use objective measures of the extent of integration, which would require a content analytic approach to data collection in which researchers specifically consider the decisions made in the course of integration.
Fifth, our study merely considers the moderating effects of the customer orientation of integration. It is plausible that customer orientation of integration has a direct effect on cost savings and market-related performance. Thus, further work could analyze the additional effects of a customer orientation in PMI. In addition, it would be worthwhile to study how the customer orientation, in general, of either firm before the transaction affects the integration outcomes.
Sixth, our study considers the extent and speed of integration as independent variables. Additional studies could analyze which variables drive the extent and speed of marketing integration. As an example, researchers could analyze how the relative size of the acquired firm or relatedness of the firms market positioning affects the extent and speed of integration.
Finally, our study focuses on cost savings and market-related performance as drivers of financial performance. An interesting issue for further research would be to study additional variables that influence postmerger performance, such as how increased prices as a consequence of a merger or acquisition affect financial performance.
Managerial Implications
A first managerial implication results from the finding that marketing integration affects financial performance negatively because the negative effects of market-related performance outweigh the positive effects of cost savings. Thus, managers should be cautious about postmerger marketing integration that is driven by the goal of reducing costs. Although it is realistic to expect cost savings from a high extent of integration, our findings show that there is a price to be paid for these cost savings on the market side. Our study also shows managers how to weaken these negative market-related consequences through a high level of customer orientation of integration, because this variable is a significant moderator of this link (H7b). Thus, the specific implication for managers is that they should devote a lot of attention to customer-related issues when making decisions about PMI. For example, integration activities should be strongly guided by the needs of the merged firm's customers with respect to the product offer and customer service. Systematic customer surveys can be helpful in this context.
A second managerial implication is that, in general, managers should strive for speed in integrating their marketing operations after a merger or acquisition. Our research provides managers with information about the circumstances in which speed is particularly beneficial. For example, the effect of integration speed on market-related performance is particularly strong when the relatedness of the merging firms market positioning is low, the relative size of the acquired firm is high, and the firms are service firms.
To give managers a more concrete picture with respect to the speed of integration, in Table 5, we provide detailed information about integration speed with respect to all the issues covered in the construct. These objective numbers can be used as benchmarking information by managers.
Finally, the analysis of market-related performance and cost savings as drivers of financial M&A performance leads to straightforward managerial implications. Managers involved in PMI should strongly focus on market-related issues. For example, concrete managerial actions during the integration phase could include the implementation of customer satisfaction--based incentive systems or continuous customer feedback instruments. A predominant cost focus during PMI is likely to leave important performance potentials unexploited and lead to risks that produce negative outcomes in the market that cannibalize the success on the cost side.
Our research provides strong evidence for the importance of studying M&A from a marketing perspective. We show that the marketing integration process is highly relevant in driving ultimate M&A performance. We also show that market-related performance is far more important than are cost savings. Against this background, we conclude that the marketing discipline should devote more research attention to marketing issues in the context of M&A. It has been argued that the marketing discipline has had a limited impact on the field of strategic management (e.g., Day 1992). The discipline s influence can be increased only if key issues in corporate strategy are addressed from a marketing perspective by marketing scholars. We propose that studying drivers of M&A performance is one of these key issues that must not be neglected by marketing researchers. It is our hope that our study, following the pioneering work by Capron and Hulland (1999), contributes to the emergence of a research stream on M&A in marketing.
The authors thank the Stiftung Rheinische Hypothekenbank for supporting the research reported in this article and the University of St. Gallen for providing access to its mergers and acquisitions database.
( n1) The term "integration" has been used with different meanings in different literature streams. As an example, literature in the field of international marketing (e.g., Birkinshaw, Morrison, and Hulland 1995; Zou and Cavusgil 2002) has used integration specifically in terms of the integration of marketing activities across countries. Our use of the term was guided by previous research in the M&A field and is consistent with this work (Pablo 1994; Shrivastava 1986).
( n2) H1 is not tautological in nature because there is a clear conceptual distinction between the independent construct and the dependent construct. A high level of integration is not identical to a high level of cost savings for three reasons. First, a high level of integration does not automatically imply that something positive has been achieved. Rather, a high level of integration merely means that a high level of similarity has been achieved (for better or worse) between the two organizations with respect to a certain marketing aspect. For example, if two information systems are replaced by one information system (i.e., a high level of integration is achieved), the chosen integrated information system still may be complex to handle and therefore may increase costs. Second, a high level of integration in some areas creates the potential for cost reductions. However, this potential may or may not be exploited by managers. Third, a high level of integration may be driven by the aim to increase effectiveness rather than reduce costs. For example, if one company has better procedures in new product development or pricing, integration would primarily result in increases in effectiveness.
( n3) We do not find any compelling argument for either a positive or a negative direct impact of the extent of integration on financial performance after the merger or acquisition. Therefore, we do not put forward a hypothesis corresponding to this possible link. However, on the basis of our empirical results, we explore the magnitude of the indirect effects between extent of integration and financial performance through the magnitude of cost savings and market-related performance.
( n4) We chose this period to exclude recent M&A in which the integration process was still in an early stage and had not yet led to any significant outcomes at the time of the survey, as well as older transactions for which it is difficult to obtain detailed information about integration activities because of managerial turnover. We restricted our search to horizontal M&A (M&A that take place in the same industry, often between direct competitors) because the issue of marketing integration essentially only comes up in horizontal M&A.
( n5) We chose managers from the acquiring firm because they tend to be the most knowledgeable about the PMI. In addition, because of managerial turnover, it is often impossible to identify former executives from the target firm (Walsh 1988).
( n6) We used perceptual measures for market share and return on sales because respondents are typically reluctant to provide or do not know the precise numbers on performance constructs (Dess and Robinson 1984). Both problems may lead to respondents breaking up the response process, which reduces the achieved response rate (for similar arguments, see Datta 1991). Note, however, that we validated the perceptual profitability measure with objective data from an independent source.
( n7) Ultimate M&A success also depends on the price that was paid for the acquiree. We did not include this aspect in our framework because managers are typically reluctant to provide this information. In addition, for the majority of transactions, price information is not publicly available.
( n8) We assessed customer loyalty in the validation sample using multiple items, such as the intentions to purchase from the specific firm again, expand the relationship with the firm, and recommend the firm to others. The specific items were partly based on items used by Cannon and Homburg (2001) and Zeithaml, Berry, and Parasuraman (1996).
( n9) On the basis of a suggestion by an anonymous reviewer, we also analyzed an alternative model in which the relationship under consideration runs in the opposite direction (i.e., magnitude of cost savings is hypothesized to affect market-related performance). With respect to this alternative model, our findings indicate that the effect of the magnitude of cost savings on market-related performance is not significant (β21 = .04, p > .1). In addition, the overall fit of the model is poorer than the fit of the model we propose.
Hypotheses Related to Moderating Effects
Legend for Chart:
A - Moderator Variables
B - Main Effects Subject to Moderation Extent of integration
→ cost savings Expected Direction of Main Effects
Positive
C - Main Effects Subject to Moderation Extent of integration
→ market-related performance Expected Direction of Main
Effects Negative
D - Main Effects Subject to Moderation Speed of integration
→ market-related performance Expected Direction of Main
Effects Positive
A B
C
D
Customer orientation of Effect is less positive for
integration (COI) high values of COI (H7a)
Effect is less negative for
high values of COI (H7b)
Effect is less positive for
high values of COI (H7c)
Relatedness of the firms' Effect is more positive for
market positioning (RMP) high values of RMP (H8a)
--
Effect is less positive for
high values of RMP (H8b)
Relative size of the acquired Effect is more positive for
firm (RS) high values of RS (H9a)
Effect is more negative for
high values of RS (H9b)
Effect is more positive for
high values of RS (H9c)
Market growth before the Effect is less positive for
merger or acquisition(MG) high values of MG (H10a)
Effect is more negative for
high values of MG (H10b)
Effect is more positive for
high values of MG (H10c)
Product (PF) versus service Effect is more positive for PF
firms (SF) (H11a)
--
Effect is less positive for PF
(H11b) Sample Comparison (232 Cases)
Legend for Chart:
B - %
A B
A: Industry
Banks and insurance 38
Machinery 23
Food and packaged goods 11
Chemicals 8
Printing and publishing services 6
Automotive components 5
Pharmaceuticals 3
Other 4
Missing 2
B: Annual Revenues
(of the consolidated business)
<$25 million 23
$25-$49 million 13
$50-$99 million 15
$100-$249 million 7
$250-$499 million 11
$500-$1,000 million 14
>$1,000 million 16
Missing 1
C: Position of Respondents
Managing director, chief executive officer, vice
president of region, head of strategic business unit 67
Vice president marketing, vice president sales, vice
president marketing and sales 18
Sales manager, product manager 6
Head of M&A 5
Other 2
Missing 2
D: Relative Size of Target to Acquirer
(annual revenues)
<25% 38
25%-49% 23
50%-74% 13
75%-100% 10
>100% 14
Missing 2 Correlations and Summary Statistics
Legend for Chart:
A - Variables
B - Correlations 1
C - Correlations 2
D - Correlations 3
E - Correlations 4
F - Correlations 5
G - Correlations 6
H - Correlations 7
I - Correlations 8
J - Correlations 9
A
B C D E F
G H I J
1. Extent of integration
1.00
2. Speed of integration
.273 1.00
3. Magnitude of cost savings
.297 .209 1.00
4. Market-related performance after the merger or acquisition
-.075 .105 .121 1.00
5. Financial performance after the merger or acquisition
-.105 .226 .079 .520 1.00
6. Customer orientation of integration
.357 .304 .140 .256 .184
1.00
7. Relatedness of the firms' market positioning
.484 .261 .139 -.071 -.044
.236 1.00
8. Relative size of the acquired firm
-.132 .007 .025 -.087 .011
-.056 -.250 1.00
9. Market growth before the merger or acquisition
.128 .129 -.060 .096 -.054
.129 .094 -.103 1.00
Summary Statistics
Range
1-7 1-5 1-7 1-7 1-7
1-7 1-7 1-5 1-9
Number of items
8 8 9 2 1
3 5 1 1
Mean
4.19 3.79 2.62 4.04 4.75
4.82 4.77 3.62 5.17
Standard deviation
1.72 .98 1.25 .96 1.30
1.72 1.30 1.44 1.21 Results of Multiple Group Analysis
Legend for Chart:
A - Moderator Variable
B - Main Effect
C - Hypothesized Moderator Effect
D - Low Value of Moderator
E - High Value of Moderator
F - χ² Difference (ΔDF = 1)
A
B
C
D E
Customer
orientation of
integration
(COI)
Extent of integration → cost savings
(positive)
H7a: Effect is less positive for high values
of COI
γ11 = .64 γ11 = .47
(t = 13.10) (t = 6.50)
Δχ² = 27.83(**)
Extent of integration → market-related
performance (negative)
H7b: Effect is less negative for high values
of COI
γ21 = -.55 γ21 = .10
(t = -8.42) (t = 1.61)
Δχ² = 4.83(*)
Speed of integration → market-related
performance (positive)
H7c: Effect is less positive for high values
of COI
γ22 = .18 γ22 = -.08
(t = 4.03) (t = -1.28)
Δχ² = 9.37(**)
Δχ² for all gammas set equal across
subgroups (ΔDF = 3): 43.79(**)
Relatedness of
the firms'
market
positioning
(RMP)
Extent of integration → cost savings
(positive)
H8a: Effect is more positive for high values
of RMP
γ11 = .38 γ11 = .69
(t = 8.79) (t = 13.32)
Δχ² = 16.83(**)
Speed of integration → market-related
performance (positive)
H8b: Effect is less positive for high values
of RMP
γ22 = .24 γ22 = .18
(t = 5.26) (t = 3.02)
Δχ² = 3.80(*)
Δχ² for all gammas set equal across
subgroups (ΔDF = 2): 20.63(**)
Relative size of
the acquired
firm (RS)
Extent of integration → cost savings
(positive)
H9a: Effect is more positive for high values
of RS
γ11 = .15 γ11 = .78
(t = 4.71) (t = 13.18)
Δχ² = 46.41(**)
Extent of integration → market-related
performance (negative)
H9b: Effect is more negative for high values
of RS
γ21 = -.33 γ21 = -.14
(t = -7.31) (t = -1.32)
Δχ² = 1.80
Speed of integration → market-related
performance (positive)
H9c: Effect is more positive for high values
of RS
γ22 = .08 γ22 = .60
(t = 2.41) (t = 4.73)
Δχ² = 9.49(**)
Δχ² for all gammas set equal across
subgroups (ΔDF = 3): 46.92(**)
Market growth
before the
merger or
acquisition(MG)
Extent of integration → cost savings
(positive)
H10a: Effect is less positive for high
values of MG
γ11 = .71 γ11 = .31
(t = 14.91) (t = 10.14)
Δχ² = 25.49(**)
Extent of integration → market-related
performance (negative)
H10b: Effect is more negative for high
values of MG
γ21 = -.25 γ21 = -.17
(t = -4.82) (t = -4.89)
Δχ² = 5.85(**)
Speed of integration → market-related
performance (positive)
H10c: Effect is more positive for high
values of MG
γ22 = -.13 γ22 = .42
(t = -3.77) (t = 6.69)
Δχ² = 31.04(**)
Δχ² for all gammas set equal across
subgroups (ΔDF = 3): 57.67(**)
Product(PF)
versus service
firms(SF)
Extent of integration → cost savings
(positive)
H11a: Effect is more positive for PF
γ11 = .63 γ11 = .43
(t = 13.65) (t = 12.47)
Δχ² = 9.13(**)
Speed of integration → market-related
performance (positive)
H11b: Effect is less positive for PF
γ22 = .19 γ22 = .35
(t = 4.31) (t = 6.24)
Δχ² = 9.26(**)
Δχ² for all gammas set equal
across subgroups (ΔDF = 2): 16.23(**)
(*) p < .05.
(**) p < .01. Detailed Frequency Distributions for Speed of Integration
Legend for Chart:
A - Items
B - Duration of Integration <6 months
C - Duration of Integration 6-12 months
D - Duration of Integration 13-18 months
E - Duration of Integration 19-24 months
F - Duration of Integration >24 months
A B C D
E F
1. Products/services offered 32.0% 34.5% 14.3%
10.8% 8.4%
2. New product development 29.9% 25.9% 22.4%
10.9% 10.9%
3. Prices 54.2% 26.8% 10.5%
4.7% 3.8%
4. Communication 56.4% 22.6% 10.8%
5.1% 5.1%
5. Sales system 36.7% 27.1% 20.3%
8.7% 7.2%
6. Sales force management 38.9% 23.8% 16.1%
12.4% 8.8%
7. Information systems 37.5% 26.0% 21.2%
8.2% 7.1%
8. Internal marketing/sales support 40.8% 25.1% 16.8%
9.4% 7.9%DIAGRAM: FIGURE 1; Framework and Constructs
DIAGRAM: FIGURE 2; Results of the Hypotheses Testing (Main Effects)
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Scale Items for Construct Measure
Legend for Chart:
A - Construct
B - Items
C - Individual Item Reliability(a)
D - Average Variance Extracted(a)
E - Composite Reliability/Coefficient Alpha(b)
A
B
C D E
Extent of Integration
(seven-point rating
scale with anchors
1 = no integration,
4 = partial
integration, and 7 =
complete
integration)
To what extent were the following aspects made
similar between the two firms after the merger
or acquisition?
.74 .96/.83
• Products/services offered (e.g., harmonization
of product ranges, brand names)
.72
• New product development
.40
• Prices (e.g., harmonization of the
price positioning)
.82
• Communication (e.g., harmonization
of advertisement)
.74
• Sales system (e.g., harmonization of sales
channels, sales partners, sales offices)
.83
• Sales force management (e.g., harmonization
of the incentive and provision systems)
.85
• Information systems (e.g., harmonization of the
marketing/sales information systems)
.80
• Internal marketing/sales support
.80
Speed of Integration
(five-point rating
scale with 1 = more
than 24 months, 2 =
19-24 months, 3 =
13-18 months, 4 =
6-12 months, and
5 = less than 6
months)
How long did it take to complete the intended
integration of the following aspects?
.58 .89/.82
• Products/services offered (e.g.,
harmonization of product ranges, brand names)
.53
• New product development
.40
• Prices (e.g., harmonization of the
price positioning)
.58
• Communication (e.g., harmonization
of advertisement)
.59
• Sales system (e.g., harmonization of
sales channels, sales partners, sales offices)
.52
• Sales force management (e.g., harmonization
of the incentive and provision systems)
.66
• Information systems (e.g., harmonization
of the marketing/sales information systems)
.62
• Internal marketing/sales support
.76
Magnitude of Cost
Savings
(seven-point rating
scale with anchors
1= not reduced at
all and 7 = strongly
reduced)
To what extent were the following resources
reduced as a result of the merger or acquisition?
(Please refer to both firms together.)
.50 .89/.85
• Products offered
.43
• Services offered
.41
• Brands
.49
• Strategic business units
.54
• Sales channels
.47
• Production locations
.45
• Sales offices
.65
• Total employees in marketing
.66
• Total employees in sales
.65
Market-Related
Performance After
the Merger or
Acquisition
(seven-point rating
scale with anchors
1= significant
decline, 4 = more or
less the same, and
7 = significant
increase)
Compared to the situation before the merger or
acquisition, please indicate how have the merging
firms together performed with respect to market
share (please consider the sum of both companies).
-- -- -- /.76
(seven-point rating
scale with anchors
1= clearly worse,
4= competition
level, and 7 = clearly
better)
Relative to your competitors, how have the merging
firms together performed with respect to retaining
existing customers after the integration was completed
--
Financial
Performance After
the Merger or
Acquisition
(seven-point rating
scale with anchors
1= significant
decline, 4 = more or
less the same, and
7 = significant
increase)
Compared to the situation before the merger or
acquisition, please indicate how the return on
sales has developed (please consider the sum of
both companies).
-- -- --
Customer
Orientation of
Integration
(seven-point rating
scale with anchors
1= strongly
disagree and 7 =
strongly agree)
Please indicate the degree to which you agree with
the following statements concerning the fundamental
orientation of the integration process after the merger
or acquisition.
.85 .95/.93
• Customer needs were decisive for the realization
of integration activities.
.80
• Customer needs were the central focus during the
integration process.
.99
• Integration activities were focused on the
increase of customer benefit.
.77
Relatedness of the
Firms' Market
Positioning
(seven-point rating
scale with anchors
1 = strongly
disagree and 7 =
strongly agree)
Please indicate the degree to which you agree with
the following statements concerning the market
positioning of your company and the other company
before the M/A?
.53 .85/.81
• The products/services of both companies were
focused on the satisfaction of the same customer
needs.
.49
• The products/services of both companies
were mainly based on the same technologies.
.59
• The products/services of both companies
were mainly identical in quality.
.51
• The products/services of both companies
were mainly sold through the same distribution
channels.
.55
• The price positioning of the offers of
both companies was mainly identical.
.50
Relative Size of the
Acquired Firm
(five-point-rating
scale with 1 =
<25%, 2 =
25%-49%, 3 =
50%-74%, 4 =
75%-100%, and 5 =
>100%)
Please indicate the relative proportion of the
acquired firm's annual sales in comparison to
your firm's sales in the year before the merger
or acquisition (in the line of business concerned).
-- -- --
Market Growth
Before the Merger or
Acquisition
(nine-point-rating
scale with 1 =
decrease >15%, 2 =
decrease 5%-15%,
3 = decrease up to
5%, 4 = increase up
to 5%, 5 = increase
5%-10%, 6 =
relatively constant,
7= increase
10%-20%, 8 =
increase 20%-30%,
and 9 = increase
>30%)
Please indicate the average annual growth rate
(over the last three years before the merger or
acquisition) of the total demand in the market
concerned.
-- -- --
(a) Individual item reliability and average variance extracted
are reported when there are more than two items.
(b) Reports coefficient alpha (if more than one item) and
composite reliability (if more than two items).~~~~~~~~
By Christian Homburg and Matthias Bucerius
Christian Homburg is Professor of Business Administration and Marketing and Chair of the Marketing Department, University of Mannheim
Matthias Bucerius was a doctoral student at this department. Currently, he is a management consultant in the chemical and pharmaceutical industry (e-mail: matthias@bucerius.de).
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Record: 7- A Strategic Framework for Customer Relationship Management. By: Payne, Adrian; Frow, Pennie. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p167-176. 10p. 2 Diagrams. DOI: 10.1509/jmkg.2005.69.4.167.
- Database:
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A Strategic Framework for Customer Relationship Management
In this article, the authors develop a conceptual framework for customer relationship management (CRM) that helps broaden the understanding of CRM and its role in enhancing customer value and, as a result, shareholder value. The authors explore definitional aspects of CRM, and they identify three alternative perspectives of CRM. The authors emphasize the need for a cross-functional, process-oriented approach that positions CRM at a strategic level. They identify five key cross-functional CRM processes: a strategy development process, a value creation process, a multichannel integration process, an information management process, and a performance assessment process. They develop a new conceptual framework based on these processes and explore the role and function of each element in the framework. The synthesis of the diverse concepts within the literature on CRM and relationship marketing into a single, process-based framework should provide deeper insight into achieving success with CRM strategy and implementation.
Over the past decade, there has been an explosion of interest in customer relationship management (CRM) by both academics and executives. However, despite an increasing amount of published material, most of which is practitioner oriented, there remains a lack of agreement about what CRM is and how CRM strategy should be developed. The purpose of this article is to develop a process-oriented conceptual framework that positions CRM at a strategic level by identifying the key cross-functional processes involved in the development of CRM strategy. More specifically, the aims of this article are
• To identify alternative perspectives of CRM,
• To emphasize the importance of a strategic approach to CRM within a holistic organizational context,
• To propose five key generic cross-functional processes that organizations can use to develop and deliver an effective CRM strategy, and
• To develop a process-based conceptual framework for CRM strategy development and to review the role and components of each process.
We organize this article in three main parts. First, we explore the role of CRM and identify three alternative perspectives of CRM. Second, we consider the need for a cross-functional process-based approach to CRM. We develop criteria for process selection and identify five key CRM processes. Third, we propose a strategic conceptual framework that is constructed of these five processes and examine the components of each process.
The development of this framework is a response to a challenge by Reinartz, Krafft, and Hoyer (2004), who criticize the severe lack of CRM research that takes a broader, more strategic focus. The article does not explore people issues related to CRM implementation. Customer relationship management can fail when a limited number of employees are committed to the initiative; thus, employee engagement and change management are essential issues in CRM implementation. In our discussion, we emphasize such implementation and people issues as a priority area for further research.
CRM Perspectives and Definition
The term "customer relationship management" emerged in the information technology (IT) vendor community and practitioner community in the mid-1990s. It is often used to describe technology-based customer solutions, such as sales force automation (SFA). In the academic community, the terms "relationship marketing" and CRM are often used interchangeably (Parvatiyar and Sheth 2001). However, CRM is more commonly used in the context of technology solutions and has been described as "information-enabled relationship marketing" (Ryals and Payne 2001, p. 3). Zablah, Beuenger, and Johnston (2003, p. 116) suggest that CRM is "a philosophically-related offspring to relationship marketing which is for the most part neglected in the literature," and they conclude that "further exploration of CRM and its related phenomena is not only warranted but also desperately needed."
A significant problem that many organizations deciding to adopt CRM face stems from the great deal of confusion about what constitutes CRM. In interviews with executives, which formed part of our research process (we describe this process subsequently), we found a wide range of views about what CRM means. To some, it meant direct mail, a loyalty card scheme, or a database, whereas others envisioned it as a help desk or a call center. Some said that it was about populating a data warehouse or undertaking data mining; others considered CRM an e-commerce solution, such as the use of a personalization engine on the Internet or a relational database for SFA. This lack of a widely accepted and appropriate definition of CRM can contribute to the failure of a CRM project when an organization views CRM from a limited technology perspective or undertakes CRM on a fragmented basis.
The definitions and descriptions of CRM that different authors and authorities use vary considerably, signifying a variety of CRM viewpoints. To identify alternative perspectives of CRM, we considered definitions and descriptions of CRM from a range of sources, which we summarize in the Appendix. We excluded other, similar definitions from this list.
An important aspect of the CRM definition that we wanted to examine was its association with technology. This is important because CRM technology is often incorrectly equated with CRM (Reinartz, Krafft, and Hoyer 2004), and a key reason for CRM failure is viewing CRM as a technology initiative (Kale 2004). For this reason, we review the definitions in the Appendix with special attention to their emphasis on technology. This review suggests that CRM can be defined from at least three perspectives: narrowly and tactically as a particular technology solution, wide-ranging technology, and customer centric. These perspectives can be portrayed as a continuum (see Figure 1).
One organization we interviewed, which spent more than $30 million on IT solutions and systems integration, described CRM solely in terms of its SFA project. At this extreme, CRM is defined narrowly and tactically as a particular technology solution (e.g., Khanna 2001). We call this CRM "Perspective 1." Other definitions, such as that of Kutner and Cripps (1997), though somewhat broader, also fall into this category.
In another organization that we interviewed, the term CRM was used to refer to a wide range of customer-oriented IT and Internet solutions, reflecting Stone and Woodcock's (2001) definition. This represented CRM "Perspective 2," a point near the middle of the continuum.
"Perspective 3" reflects a more strategic and holistic approach to CRM that emphasizes the selective management of customer relationships to create shareholder value. This reflects elements of several previously noted definitions of CRM, including those of Buttle (2001), Glazer (1997), Singh and Agrawal (2003), and Swift (2000). Following this phase of our work, we identified Zablah, Beuenger, and Johnston's (2003) research, which supported our view of these perspectives.
The importance of how CRM is defined is not merely semantic. Its definition significantly affects the way an entire organization accepts and practices CRM. From a strategic viewpoint, CRM is not simply an IT solution that is used to acquire and grow a customer base; it involves a profound synthesis of strategic vision; a corporate understanding of the nature of customer value in a multichannel environment; the utilization of the appropriate information management and CRM applications; and high-quality operations, fulfillment, and service. Thus, we propose that in any organization, CRM should be positioned in the broad strategic context of Perspective 3.
Swift (2000) argues, and we concur, that organizations will benefit from adopting a relevant strategic CRM definition for their firm and ensuring its consistent use throughout their organization. Thus, we developed a definition of CRM that reflected Perspective 3. We examined the CRM literature, synthesized aspects of the various definitions into a draft definition, and then tested it with practicing managers. As our research progressed, we went through several iterations. The result is the following definition, which we use for the purposes of this study:
CRM is a strategic approach that is concerned with creating improved shareholder value through the development of appropriate relationships with key customers and customer segments. CRM unites the potential of relationship marketing strategies and IT to create profitable, long-term relationships with customers and other key stakeholders. CRM provides enhanced opportunities to use data and information to both understand customers and co-create value with them. This requires a cross-functional integration of processes, people, operations, and marketing capabilities that is enabled through information, technology, and applications.
This definition provided guidance for our subsequent research considerations and the strategic and cross-functional emphasis of the conceptual framework we developed.
Processes: A Strategic Perspective
Gartner (2001) calls for a fresh approach to business processes in CRM that involves both rethinking how these processes appear to the customer and reengineering them to be more customer centric. Kale (2004) supports this view and argues that a critical aspect of CRM involves identifying all strategic processes that take place between an enterprise and its customers. To address this challenge of adopting a fresh approach to CRM processes, we aimed to identify the key generic processes relevant to CRM.
We examined the literature to identify appropriate criteria for process selection but found little work in this area, with the exception of the contribution by Srivastava, Shervani, and Fahey (1999), who establish four process selection criteria for marketing and business processes. We chose their work as a starting point for the identification of process selection criteria for CRM. The criteria these authors propose are as follows: First, the processes should comprise a small set that addresses tasks critical to the achievement of an organization's goals. Second, each process should contribute to the value creation process. Third, each process should be at a strategic or macro level. Fourth, the processes need to manifest clear interrelationships.
As part of our research, we conducted a workshop with a panel of 34 highly experienced CRM practitioners, all of whom had extensive experience in the CRM and IT sectors. The director of a leading research and management institute specializing in the CRM and IT sectors selected the panel. Participants were selected on the basis of the following attributes to ensure that they were knowledgeable about CRM, its implementation, and its operation: substantial management and industrial experience (average of 17.2 years), maturity (average age of 40.2 years), international representation and international experience (managers from nine countries attended; most of them had international experience), and academic qualifications (degree or equivalent). In the first part of the workshop, which involved small group sessions, the panel reviewed and subsequently unanimously agreed that these four criteria were fully appropriate for selecting CRM processes. However, they also proposed two further criteria: First, each process should be cross-functional in nature, and second, each process would be considered by experienced practitioners as being both logical and beneficial to understanding and developing strategic CRM activities. We used these six criteria to select key generic CRM processes.
A Conceptual Framework for CRM
Grabner-Kraeuter and Moedritscher (2002) suggest that the absence of a strategic framework for CRM from which to define success is one reason for the disappointing results of many CRM initiatives. This view was supported both by the senior executives we interviewed during our research and by Gartner's (2001) research. Our next challenges were to identify key generic CRM processes using the previously described selection criteria and to develop them into a conceptual framework for CRM strategy development.
Our literature review found that few CRM frameworks exist; those that did were not based on a process-oriented cross-functional conceptualization of CRM. For example, Sue and Morin (2001, p. 6) outline a framework for CRM based on initiatives, expected results, and contributions, but this is not process based, and "many initiatives are not explicitly identified in the framework." Winer (2001, p. 91) develops a "basic model, which contains a set of 7 basic components: a database of customer activity; analyses of the database; given the analyses, decisions about which customers to target; tools for targeting the customers; how to build relationships with the targeted customers; privacy issues; and metrics for measuring the success of the CRM program." Again, this model, though useful, is not a cross-functional process-based conceptualization. This gap in the literature suggests that there is a need for a new systematic process-based CRM strategy framework. Synthesis of the diverse concepts in the literature on CRM and relationship marketing into a single, process-based framework should provide practical insights to help companies achieve greater success with CRM strategy development and implementation.
Conceptual frameworks and theory are typically based on combining previous literature, common sense, and experience (Eisenhardt 1989). In this research, we integrated a synthesis of the literature with learning from field-based interactions with executives to develop and refine the CRM strategy framework. In this approach, we used what Gummesson (2002a) terms "interaction research." This form of research originates from his view that "interaction and communication play a crucial role" in the stages of research and that testing concepts, ideas, and results through interaction with different target groups is "an integral part of the whole research process" (p. 345). The sources for these field-based insights, which include executives primarily from large enterprises in the business-to-business and business-to-consumer sectors, included the following:
• An expert panel of 34 highly experienced executives;
• Interviews with 20 executives working in CRM, marketing, and IT roles in companies in the financial services sector;
• Interviews with six executives from large CRM vendors and with five executives from three CRM and strategy consultancies;
• Individual and group discussions with CRM, marketing, and IT managers at workshops with 18 CRM vendors, analysts, and their clients, including Accenture, Baan, BroadVision, Chordiant, EDS, E.piphany, Hewlett-Packard, IBM, Gartner, NCR Teradata, Peoplesoft, Oracle, SAP, SAS Institute, Siebel, Sybase, and Unisys;
• Piloting the framework as a planning tool in the financial services and automotive sectors; and
• Using the framework as a planning tool in two companies: global telecommunications and global logistics. Six workshops were held in each company.
We began by identifying possible generic CRM processes from the CRM and related business literature. We then discussed these tentative processes interactively with the groups of executives. The outcome of this work was a short list of seven processes. We then used the expert panel of experienced CRM executives who had assisted in the development of the process selection schema to nominate the CRM processes that they considered important and to agree on those that were the most relevant and generic. After an initial group workshop, each panel member independently completed a list representing his or her view of the key generic processes that met the six previously agreed-on process criteria. The data were fed back to this group, and a detailed discussion followed to help confirm our understanding of the process categories.
As a result of this interactive method, five CRM processes that met the selection criteria were identified; all five were agreed on as important generic processes by more than two-thirds of the group in the first iteration. Subsequently, we received strong confirmation of these as key generic CRM processes by several of the other groups of managers. The resultant five generic processes were ( 1) the strategy development process, ( 2) the value creation process, ( 3) the multichannel integration process, ( 4) the information management process, and ( 5) the performance assessment process.
We then incorporated these five key generic CRM processes into a preliminary conceptual framework. This initial framework and the development of subsequent versions were both informed by and further refined by our interactions with two primary executive groups: mangers from the previously noted companies and executives from three CRM consulting firms. Participants at several academic conferences on CRM and relationship marketing also assisted with comments and criticisms of previous versions. With evolving versions of the framework, we combined a synthesis of relevant literature with field-based interactions involving the groups. The framework went through a considerable number of major iterations and minor revisions; the final version appears in Figure 2.
This conceptual framework illustrates the interactive set of strategic processes that commences with a detailed review of an organization's strategy (the strategy development process) and concludes with an improvement in business results and increased share value (the performance assessment process). The concept that competitive advantage stems from the creation of value for the customer and for the business and associated cocreation activities (the value creation process) is well developed in the marketing literature. For large companies, CRM activity will involve collecting and intelligently using customer and other relevant data (the information process) to build a consistently superior customer experience and enduring customer relationships (the multichannel integration process). The iterative nature of CRM strategy development is highlighted by the arrows between the processes in both directions in Figure 2; they represent interaction and feedback loops between the different processes. The circular arrows in the value creation process reflect the cocreation process. We now examine the key components we identified in each process. As with our prior work, we used the interaction research method in the identification of these process components.
Strategy Development Process
This process requires a dual focus on the organization's business strategy and its customer strategy. How well the two interrelate fundamentally affects the success of its CRM strategy.
The business strategy must be considered first to determine how the customer strategy should be developed and how it should evolve over time. The business strategy process can commence with a review or articulation of a company's vision, especially as it relates to CRM (e.g., Davidson 2002). Next, the industry and competitive environment should be reviewed. Traditional industry analysis (e.g., Porter 1980) should be augmented by more contemporary approaches (e.g., Christensen 2001; Slater and Olson 2002) to include co-opetition (Brandenburger and Nalebuff 1997), networks and deeper environmental analysis (Achrol 1997), and the impact of disruptive technologies (Christensen and Overdorf 2000).
Whereas business strategy is usually the responsibility of the chief executive officer, the board, and the strategy director, customer strategy is typically the responsibility of the marketing department. Although CRM requires a cross-functional approach, it is often vested in functionally based roles, including IT and marketing. When different departments are involved in the two areas of strategy development, special emphasis should be placed on the alignment and integration of business strategy.
Customer strategy involves examining the existing and potential customer base and identifying which forms of segmentation are most appropriate. As part of this process, the organization needs to consider the level of subdivision for customer segments, or segment granularity. This involves decisions about whether a macro, micro, or one-to-one segmentation approach is appropriate (Rubin 1997).
Several authors emphasize the potential for shifting from a mass market to an individualized, or one-to-one, marketing environment. Exploiting e-commerce opportunities and the fundamental economic characteristics of the Internet can enable a much deeper level of segmentation granularity than is affordable in most other channels (e.g., Peppers and Rogers 1993, 1997). In summary, the strategy development process involves a detailed assessment of business strategy and the development of an appropriate customer strategy. This should provide the enterprise with a clearer platform on which to develop and implement its CRM activities.
Value Creation Process
The value creation process transforms the outputs of the strategy development process into programs that both extract and deliver value. The three key elements of the value creation process are ( 1) determining what value the company can provide to its customer; ( 2) determining what value the company can receives from its customers; and ( 3) by successfully managing this value exchange, which involves a process of cocreation or coproduction, maximizing the lifetime value of desirable customer segments.
The value the customer receives from the organization draws on the concept of the benefits that enhance the customer offer (Levitt 1969; Lovelock 1995). However, there is now a logic, which has evolved from earlier thinking in business-to-business and services marketing, that views the customer as a cocreator and coproducer (Bendapudi and Leone 2003; Prahalad and Ramaswamy 2004; Vargo and Lusch 2004). These benefits can be integrated in the form of a value proposition (e.g., Lanning and Michaels 1988; Lanning and Phillips 1991) that explains the relationship among the performance of the product, the fulfillment of the customer's needs, and the total cost to the customer over the customer relationship life cycle (Lanning and Michaels 1988). Lanning's (1998) later work on value propositions reflects the cocreation perspective. However, a more detailed synthesis of work in this area is needed in further research.
To determine whether the value proposition is likely to result in a superior customer experience, a company should undertake a value assessment to quantify the relative importance that customers place on the various attributes of a product. Analytical tools such as conjoint analysis can be used to identify customers that share common preferences in terms of product attributes. Such tools may also reveal substantial market segments with service needs that are not fully catered to by the attributes of existing offers.
From this perspective, customer value is the outcome of the coproduction of value, the deployment of improved acquisition and retention strategies, and the utilization of effective channel management. Fundamental to this concept of customer value are two key elements that require further research. First, it is necessary to determine how existing and potential customer profitability varies across different customers and customer segments. Second, the economics of customer acquisition and customer retention and opportunities for cross-selling, up-selling, and building customer advocacy must be understood. How these elements contribute to increasing customer lifetime value is integral to value creation.
Customer retention represents a significant part of the research on value creation. For example, Reichheld and Sasser (1990) identify the net present value profit improvement of retaining customers, and Rust and Zahorik (1993) and Rust, Zahorik, and Keiningham (1995) outline procedures for assessing the impact of satisfaction and quality improvement efforts on customer retention and market share. More recently, research has emphasized customer equity (e.g., Blattberg and Deighton 1996; Hogan, Lemon, and Rust 2002; Rust, Lemon, and Zeithaml 2004). Calculating the customer lifetime value of different segments enables organizations to focus on the most profitable customers and customer segments. The value creation process is a crucial component of CRM because it translates business and customer strategies into specific value proposition statements that demonstrate what value is to be delivered to customers, and thus, it explains what value is to be received by the organization, including the potential for cocreation.
Multichannel Integration Process
The multichannel integration process is arguably one of the most important processes in CRM because it takes the outputs of the business strategy and value creation processes and translates them into value-adding activities with customers. However, there is only a small amount of published work on the multichannel integration in CRM (e.g., Friedman and Furey 1999; Funk 2002; Kraft 2000; Sudharshan and Sanchez 1998; Wagner 2000). The multichannel integration process focuses on decisions about what the most appropriate combinations of channels to use are; how to ensure that the customer experiences highly positive interactions within those channels; and when a customer interacts with more than one channel, how to create and present a single unified view of the customer.
Today, many companies enter the market through a hybrid channel model (Friedman and Furey 1999; Moriarty and Moran 1990) that involves multiple channels, such as field sales forces, Internet, direct mail, business partners, and telephony. There are a growing number of channels by which a company can interact with its customers. Through an iterative process, we categorized the many channel options into six categories broadly based on the balance of physical or virtual contact (see Figure 2). These include ( 1) sales force, including field account management, service, and personal representation; ( 2) outlets, including retail branches, stores, depots, and kiosks; ( 3) telephony, including traditional telephone, facsimile, telex, and call center contact; ( 4) direct marketing, including direct mail, radio, and traditional television (but excluding e-commerce); ( 5) e-commerce, including e-mail, the Internet, and interactive digital television; and ( 6) m-commerce, including mobile telephony, short message service and text messaging, wireless application protocol, and 3G mobile services. Some channels are now being used in combination to maximize commercial exposure and return; for example, there is collaborative browsing and Internet relay chat, used by companies such as Lands End, and voice over IP (Internet protocol), which integrates both telephony and the Internet.
Managing integrated channels relies on the ability to uphold the same high standards across multiple, different channels. Having established a set of standards for each channel that defines an outstanding customer experience for that channel, the organization can then work to integrate the channels. The concept of the "perfect customer experience," which must be affordable for the company in the context of the segments in which it operates and its competition, is a relatively new concept. This concept is now being embraced in industry by companies such as TNT, Toyota's Lexus, Oce, and Guinness Breweries, but it has yet to receive much attention in the academic literature. Therefore, multichannel integration is a critical process in CRM because it represents the point of cocreation of customer value. However, a company's ability to execute multichannel integration successfully is heavily dependent on the organization's ability to gather and deploy customer information from all channels and to integrate it with other relevant information.
Information Management Process
The information management process is concerned with the collection, collation, and use of customer data and information from all customer contact points to generate customer insight and appropriate marketing responses. The key material elements of the information management process are the data repository, which provides a corporate memory of customers; IT systems, which include the organization's computer hardware, software, and middleware; analysis tools; and front office and back office applications, which support the many activities involved in interfacing directly with customers and managing internal operations, administration, and supplier relationships (Greenberg 2001).
The data repository provides a powerful corporate memory of customers, an integrated enterprisewide data store that is capable of relevant data analyses. In larger organizations, it may comprise a data warehouse (Agosta 1999; Swift 2000) and related data marts and databases. There are two forms of data warehouse, the conventional data warehouse and the operational data store. The latter stores only the information necessary to provide a single identity for all customers. An enterprise data model is used to manage this data conversion process to minimize data duplication and to resolve any inconsistencies between databases.
Information technology systems refer to the computer hardware and the related software and middleware used in the organization. Often, technology integration is required before databases can be integrated into a data warehouse and user access can be provided across the company. However, the historical separation between marketing and IT sometimes presents integration issues at the organizational level (Glazer 1997). The organization's capacity to scale existing systems or to plan for the migration to larger systems without disrupting business operations is critical.
The analytical tools that enable effective use of the data warehouse can be found in general data-mining packages and in specific software application packages. Data mining enables the analysis of large quantities of data to discover meaningful patterns and relationships (e.g., Groth 2000; Peacock 1998). More specific software application packages include analytical tools that focus on such tasks as campaign management analysis, credit scoring, and customer profiling.
Front office applications are the technologies a company uses to support all those activities that involve direct interface with customers, including SFA and call center management. Back office applications support internal administration activities and supplier relationships, including human resources, procurement, warehouse management, logistics software, and some financial processes. A key concern about the front and back office systems offered by CRM vendors is that they are sufficiently connected and cocoordinated to improve customer relationships and workflow.
Gartner segments vendors of CRM applications and CRM service providers into specific categories (Radcliffe and Kirkby 2002), and Greenberg (2001) and Jacobsen (1999) provide detailed reviews of CRM vendors' products. The key segments for CRM applications are Integrated CRM and Enterprise Resource Planning Suite (e.g., Oracle, PeopleSoft, SAP), CRM Suite (e.g., E.piphany, Siebel), CRM Framework (e.g., Chordiant), CRM Best of Breed (e.g., NCR Teradata; Broadvision), and "Build it Yourself" (e.g., IBM, Oracle, Sun). The CRM service providers and consultants that offer implementation support specialize in the following areas: corporate strategy (e.g., McKinsey, Bain); CRM strategy (e.g., Peppers & Rogers, Vectia); change management, organization design, training, human resources, and so forth (e.g., Accenture); business transformation (e.g., IBM); infrastructure building and systems integration (e.g., Siemens, Unisys); infrastructure outsourcing (e.g., EDS, CSC); business insight, research, and so forth (e.g., SAS); and business process outsourcing (e.g., Acxiom). The need for comprehensive and scalable options has created scope for many new products from CRM vendors. However, despite their claim to be "complete CRM solution providers," few software vendors can provide the full range of functionality that a complete CRM business strategy requires.
The information management process provides a means of sharing relevant customer and other information throughout the enterprise and "replicating the mind of the customer." To ensure that technology solutions support CRM, it is important to conduct IT planning from a perspective of providing a seamless customer service rather than planning for functional or product-centered departments and activities. Furthermore, data analysis tools should measure business activities. This kind of analysis provides the basis for the performance assessment process.
Performance Assessment Process
The performance assessment process covers the essential task of ensuring that the organization's strategic aims in terms of CRM are being delivered to an appropriate and acceptable standard and that a basis for future improvement is established. This process can be viewed as having two main components: shareholder results, which provide a macro view of the overall relationships that drive performance, and performance monitoring, which provides a more detailed, micro view of metrics and key performance indicators.
To achieve the ultimate objective of CRM, the delivery of shareholder results, the organization should consider how to build employee value, customer value, and shareholder value and how to reduce costs. Recent research on relationships among employees, customers, and shareholders has emphasized the need to adopt a more informed and integrated approach to exploiting the linkages among them. The service profit chain model and related research focuses on establishing the relationships among employee satisfaction, customer loyalty, profitability, and shareholder value (e.g., Heskett et al. 1994; Loveman 1998). Organizations also need to focus on cost reduction opportunities. Two means of cost reduction are especially relevant to CRM: deployment of technologies ranging from automated telephony services to Web services and the use of new electronic channels such as online, self-service facilities. The development of models such as the service profit chain has been important in enabling companies to consider the effectiveness of CRM at a strategic level in terms of improving shareholder results.
Despite a growing call for companies to be more customer oriented, there is concern that, in general, the metrics used by companies to measure and monitor their CRM performance are not well developed or well communicated. Ambler's (2002) research findings raise particular concern; he finds that key aspects of CRM, such as customer satisfaction and customer retention, only reach the board in 36% and 51% of companies, respectively. Even when these metrics reach the board level, it is not clear how deeply they are understood and how much time is spent on them. Traditional performance measurement systems, which tend to be functionally driven, may be inappropriate for cross-functional CRM.
Recent efforts to provide cross-functional measures, such as the balanced scorecard (Kaplan and Norton 1996), are a useful advance. The format of the balanced scorecard enables a wide range of metrics designs. Indicators that can reveal future financial results, not just historical results, need to be considered as part of this process. Standards, metrics, and key performance indicators for CRM should reflect the performance standards necessary across the five major processes to ensure that CRM activities are planned and practiced effectively and that a feedback loop exists to maximize performance improvement and organizational learning. A consideration of "return on relationships" (Gummesson 2004) will assist in identifying further metrics that are relevant to the enterprise.
Discussion
In this article, we develop a cross-functional, process-based CRM strategy framework that aims to help companies avoid the potential problems associated with a narrow technological definition of CRM and realize strategic benefits. Our research was based on large industrial companies because the size and complexity of such enterprises is likely to present the greatest CRM challenges. We did not examine issues related to small or medium-sized companies and nonprofit organizations in this work.
This study contributes to the marketing literature in several ways. First, our work extends a managerial perspective that stresses the importance of cross-functional processes in CRM strategy and contributes to the positioning of the poorly defined CRM concept within the marketing literature. Second, it provides a process-based conceptual framework for strategic CRM and identifies key elements within each process. Third, it makes a contribution to the limited literature on interaction research. Finally, the research represents a grounded contribution that offers managers insight into the development and implementation of CRM strategies. To date, this framework has been used by companies to address several issues, including surfacing problematic CRM issues, planning the key components of a CRM strategy, identifying which process components of CRM should receive priority, creating a platform for change, and benchmarking other companies' CRM activities.
Much research remains to be done in the exploration of the multifaceted nature of CRM. Sheth (1996) notes that for an emerging management discipline, it is important to have an acceptable definition that encompasses all facets to focus understanding and growth of knowledge in the discipline. He proposes a multistage process for achieving this that begins with delimiting the domain, agreeing on a definition, developing performance measures, and developing explanatory theory. The framework we propose in this article offers a potentially useful starting point for the development of improved insight into these aspects of CRM theory. The task of delimiting the domain, agreeing on a definition for CRM, and building a research agenda will be an evolving process in this nascent area. We do not attempt to build such a research agenda in the current work; however, we emphasize the importance of CRM implementation and related people issues as an area in which further research is urgently needed. Initial work by Ebner and colleagues (2002), Gummesson (2002b, c), Henneberg (2003), Pettit (2002), and Rigby, Reichheld, and Schefter (2002) provides a useful platform from which to develop this important research area.
The authors acknowledge the financial support of BT plc and SAS with this research, and they thank the three anonymous JM reviewers and the consulting editors for their helpful comments on previous versions of this article.
CRM Defined Narrowly and Tactically
CRM Defined Broadly and Strategically
CRM is about the implementation of a specific technology solution project.
CRM is the implementation of an integrated series of customer-oriented technology solutions.
CRM is a holistic approach to managing customer relationships to create shareholder value.
DIAGRAM: FIGURE 2; A Conceptual Framework for CRM Strategy
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• CRM is an e-commerce application (Khanna 2001).
• CRM is a term for methodologies, technologies, and e-commerce capabilities used by companies to manage customer relationships (Stone and Woodcock 2001).
• CRM is an enterprisewide initiative that belongs in all areas of an organization (Singh and Agrawal 2003).
• CRM is a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer (Parvitiyar and Sheth 2001).
• CRM is about the development and maintenance of long-term, mutually beneficial relationships with strategically significant customers (Buttle 2001).
• CRM includes numerous aspects, but the basic theme is for the company to become more customer-centric. Methods are primarily Web-based tools and Internet presence (Gosney and Boehm 2000).
• CRM can be viewed as an application of one-to-one marketing and relationship marketing, responding to an individual customer on the basis of what the customer says and what else is known about that customer (Peppers, Rogers, and Dorf 1999).
• CRM is a management approach that enables organizations to identify, attract, and increase retention of profitable customers by managing relationships with them (Hobby 1999).
• CRM involves using existing customer information to improve company profitability and customer service (Couldwell 1999).
• CRM attempts to provide a strategic bridge between information technology and marketing strategies aimed at building long-term relationships and profitability. This requires "information-intensive strategies" (Glazer 1997).
• CRM is data-driven marketing (Kutner and Cripps 1997).
• CRM is an enterprise approach to understanding and influencing customer behavior through meaningful communication to improve customer acquisition, customer retention, customer loyalty, and customer profitability (Swift 2000).
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By Adrian Payne and Pennie Frow
Adrian Payne is Professor of Services and Relationship Marketing and Director of the Centre for CRM, Cranfield School of Management, Cranfield University
Pennie Frow is Visiting Fellow in Marketing, Cranfield School of Management, Cranfield University
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Record: 8- Actualizing Innovation Effort: The Impact of Market Knowledge Diffusion in a Dynamic System of Competition. By: Marinova, Detelina. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p1-20. 20p. 1 Diagram, 7 Charts. DOI: 10.1509/jmkg.68.3.1.34768.
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Actualizing Innovation Effort: The Impact of Market
Knowledge Diffusion in a Dynamic System of Competition
This study focuses on the dynamic process that governs the impact of market knowledge diffusion on innovation effort and its subsequent effect on firm performance. First, the author proposes that three aspects of market knowledge (knowledge level, knowledge change, and extent of shared knowledge about customers and competitors) influence innovation effort. In so doing, she explicitly models the dynamic process of competition, including heterogeneity in interdependence of innovation across competitors, firm-specific inertial tendency in innovation, and feedback effects reflected in satisfaction with past performance. Second, within a partial adjustment model of performance, the author studies the role of shared market knowledge and firm size in the translation of innovation effort into firm performance over time. She tests the conceptual framework with longitudinal quasi field experiments based on a Markstrat simulation exercise, including a main experiment and three validation studies. The results reveal a dynamic system in which some aspects of market knowledge diffusion propel innovation, whereas satisfaction with past performance hinders innovation effort. Furthermore, the results show that innovation effort, by itself, does not affect firm performance. In the context of the study, total shared market knowledge helps smaller firms actualize better returns from their innovation effort than larger firms.
At a fundamental level, firms act on the basis of their market knowledge: their knowledge of customers and competitors. As a result, the concepts of knowledge sharing (e.g., Hoopes and Postrel 1999; Senge 1993) and organizational learning (e.g., Cyert and March 1963; Dickson 1992) have gained substantial attention from scholars and business practitioners (e.g., Gates 1999). In general, an organization's ability to recognize the value of new information, assimilate it, and use it strategically is regarded as crucial for its ability to innovate (see Cohen and Levinthal 1990) and to gain performance advantages (Day 1997). In practice, organizations implement a team approach in developing new products to ensure organizationwide knowledge acquisition and dissemination. It is expected that information that resides in isolated pockets in the organization will become shared over time, thus leading to better decision making.
The objective of this article is to investigate the dynamic process that governs the impact of market knowledge diffusion on innovation effort and subsequent firm performance. The article also aims to close three gaps that are currently evident in research on market knowledge and innovation. First, as do related organizational processes, innovation evolves over time and thus requires the use of knowledge in a dynamic setting. However, empirical research in several disciplines--including marketing, management, and organizational behavior--has largely been guided by cross-sectional analyses of a key informant's perceptions of strategic orientation, innovation, and performance. That is, there has been no systematic conceptualization or longitudinal assessment of the dynamic process of market knowledge diffusion and its impact on innovation and subsequent performance. Lee (2003) notes that success in innovation-driven strategic divergence can be achieved by the capture of internal and external knowledge spillovers, but no conceptual or empirical work has explicated this process.
Second, market knowledge diffusion is a function of information acquisition, which is shaped over time by the dynamics of product diffusion and firms' previous innovative activities. Although prior research recognizes general time dependence in performance outcomes (e.g., Boulding 1990; Jacobson 1990), few studies have accounted for firm-specific inertial tendency in innovation (e.g., Bayus, Erickson, and Jacobson 2003). I suggest that to diagnose how a firm's own market knowledge and strategic actions uniquely drive its innovation, it is necessary to account for firm-specific inertial tendencies over time. Ignoring of firm-specific inertial tendencies can lead to erroneous conclusions regarding the impact of market knowledge diffusion on innovation and subsequent performance.
Third, prior research has failed to consider the implications of a competitive system in which the actions of a firm's competitors influence the firm's strategic actions, as the dynamic use of market knowledge. Competitors often try to emulate the performance of successful others (Dickson 1992), but not all firms are equally good at identifying the explanatory mechanisms that underlie success. Two firms that follow similar strategies in using and managing market knowledge are likely to experience greater interdependence than are two firms that follow different strategies. To understand how a firm's own market knowledge and strategic actions drive its innovation or performance over time, it is necessary to account for the interdependence in actions and outcomes across competitors.
In an important departure from the extant literature, I adopt a longitudinal perspective that examines the evolution of market knowledge, innovation, and performance over time. I conceptualize market knowledge diffusion as the dynamics of market knowledge level, change in market knowledge, and shared market knowledge among strategic decision makers, and I subsequently develop and test hypotheses about these constructs impact on innovation effort (Figure 1 depicts the tested relationships). I isolate the impact of market knowledge diffusion within the firm by accounting for innovation interdependence among firms, firm-specific proneness to inertia in innovation, and market feedback effects. I then develop a dynamic model of firm performance that reveals the unique role of decision makers shared market knowledge in translating innovation effort into performance. Finally, to test the conceptual framework, I conduct longitudinal quasi field experiments in the context of the Markstrat simulation.
In addition to extending research in marketing, this study also has managerial implications. It calls attention to the role and relative importance of different aspects of market knowledge diffusion in strategic decision-making teams. Although many organizations are attempting to maximize their market knowledge (U.S. firms spend approximately $6 billion per year on market research information, according to the American Marketing Association), others are questioning its payoffs and impact on the bottom line (Sutcliffe and Weber 2003). Sutcliffe and Weber (2003) suggest that accurate market knowledge actually hurts performance and advise against reliance on it. The present research contributes to this debate by postulating three distinct aspects of market knowledge diffusion and by demonstrating the roles they play in generating performance.
Researchers have long suggested that markets operate to disseminate information rapidly to interested parties (von Hayek 1989). However, not all new market information becomes part of the intraorganizational knowledge-diffusion process. I suggest that it is the very mechanism of intraorganizational knowledge diffusion that produces the observed stop-and-go, or interrupted, pattern of innovation generation (Chandrashekaran et al. 1999; Dickson 1996; Hunt and Morgan 1995). If markets are constantly changing (Dickson 1992; Hunt and Morgan 1995) and if innovation drives change and firm success (Schumpeter 1942), then market knowledge, change in market knowledge, and shared market knowledge should enhance innovation effort. By adopting the premise that knowledge ultimately resides at the level of the individual strategic decision maker, this research offers a microlevel perspective on market knowledge diffusion. The premise aligns with Cyert and March's (1963) theoretical perspective of organizations as comprising individual members who learn and share knowledge with others and whose knowledge drives the specific investments of strategic decision-making teams. The premise is also consistent with the notion that market knowledge is information-based knowledge (Polanyi 1967) that resides with the individual decision maker (Spender 1996).
Definitions
In defining knowledge, and specifically market knowledge, I draw on the work of Hammond and Summers (1972) and maintain that knowledge reflects the extent of a subject's accurate detection of a task's properties. A decision maker who can correctly identify customer preferences and competitors is deemed knowledgeable about customer preferences and competitors. To specify the domain of market knowledge, I build on the work of Day and Nedungadi (1994, p. 32), who note that "the two most salient features of a competitive market are customers and competitors." Market factors such as competition and demand have been recognized as crucial determinants of organizational phenomena such as innovation generation (e.g., Kessler and Chakrabarti 1996; Porter 1995). At a fundamental level, knowledge of the market is necessary to determine the needs and wants of target markets and to satisfy them better than the competition can (Kotler 2000). Therefore, in this research, the term "market knowledge" implies knowledge about customers and competitors.
Change in market knowledge refers to the magnitude of change in decision makers' knowledge about customers and competitors between two points in time. This definition focuses on the absolute change in knowledge over time rather than the direction of change, which makes it possible to separate the effects of knowledge accuracy (captured by market knowledge, as defined previously) from the effects of the magnitude of change in market knowledge. Change in market knowledge may be viewed as a consequence of adjustment to inaccuracies of knowledge compared with the "objective" reality in the market.. Furthermore, this change may be due to insights beyond current available information and based on a decision maker's anticipating the market or uniquely integrating knowledge across different market aspects. Decision makers' desire to make good decisions motivates them to update and change their knowledge about the marketplace, but inertial forces that result from the cyclical nature of the market evolution system, as well as people's bounded rationality and cognitive makeup, deter them from changing their knowledge. Thus, the magnitude of knowledge change also indicates decision makers' flexibility and willingness to change the status quo. Carlile (2002) points out that when decision makers find knowledge that proves useful, they tend to continue to rely on it and are less willing to update it to accommodate new developments. For example, although executives at GE Lighting might receive quarterly syndicated data on retail lighting sales and preferences, not all of them will examine all or even some of the data all the time, and they are likely to interpret the data differently. The executives' degree of attention to the data and their interpretation of it will be reflected in their knowledge about the market and its change over time.
Furthermore, decision makers may or may not update their knowledge on the basis of communication and information exchange with others. Thus, at any given point, they will have a different extent of shared knowledge about consumers retail lighting preferences. This research adopts the theoretical perspective of Hoopes and Postrel (1999, p. 838), who define shared knowledge as "facts, concepts, and propositions [that] are understood simultaneously by multiple agents. The word 'shared' is used as an adjective rather than a participle; that is, we mean only that the knowledge held by two or more individuals is the same, not necessarily that one person has communicated it to the other.... Common knowledge might be a more precise expression, but that term already has a specific technical definition in game theory, as well as a different colloquial meaning." Thus, shared market knowledge among a group of decision makers' is the extent of overlap in individual decision makers market knowledge.
In terms of studying innovation, research in marketing and management has focused primarily on innovation output, as measured by new product success (e.g., Ayers, Dahlstrom, and Skinner 1997) and time to market (Ittner and Larcker 1997). Research in economics has focused more on innovation input, such as research and development (R&D) investments (Miyagiwa and Ohno 1999) and innovation effort (Cheng and Tao 1999; Cohen and Klepper 1996a, b). Because product innovations arise only if a particular level of effort is exerted to bring them to market, the generative process by which innovations emerge needs to be considered. Thus, I focus on innovation effort in this investigation.
The Effect of Market Knowledge on Innovation Effort
Both researchers and practitioners recognize in broad terms that knowledge is an important factor in the creation of competitive success over time (Carlile 2002). For example, researchers studying organizational memory and absorptive capacity (Cohen and Levinthal 1990; Moorman and Miner 1997) have argued, albeit at a macro level, that accumulation of strategic information increases the capacity of organizations to interpret new information and to make strategic decisions, especially if the new information is related to the previously accumulated information. Research in cognitive psychology has shown that people have a fixed capacity to process information (Sternberg 1996), which means that accumulation of strategic information may diminish the ability to interpret new information, unless the new information is related to what has been previously learned. These perspectives together suggest that existing strategic information is more likely to facilitate the interpretation of new information if the existing information is processed, internalized, and converted to knowledge. In other words, market knowledge (rather than information) has positive effects on innovation effort.
H[sub1]: Market knowledge has a positive effect on innovation effort.
The Effect of Change in Market Knowledge on Innovation Effort
In discussing the development of new products, Webster (1997, p. 52) argues that "the product is a variable tailored to changing needs of carefully selected customers. As the customer changes, so must the product, and the organization that provides that product must have built-in flexibility and adaptiveness." Researchers who study the evolution of markets suggest that competitive advantage ensues from a focus on change rather than on static market conditions (e.g., Dickson 1996; Senge 1993). Similarly, Teece, Pisano, and Shuen (1997) argue that organizations should focus not on their current positions and market capabilities but on dynamically changing these over time. Furthermore, it has been strongly argued that the only way to achieve a sustained competitive advantage is through continuous innovation (Schumpeter 1942). These streams of work suggest that the enhancement of the organizational innovation effort requires constant monitoring of the changes in market conditions. Through the monitoring of market condition changes, decision makers enhance their market knowledge. Thus, the degree to which strategic decision makers' knowledge about key marketplace factors changes over time influences organizational innovation effort. For example, if decision makers believe that customer preferences have changed substantially, their innovation effort at that point in time will be more extensive than when they perceive customer preferences as having changed to a lesser degree. A change in knowledge about the actions of close industry competitors has a similar effect: If decision makers perceive movement on the part of their competitors, they are more likely to expend effort innovating than they would in the absence of competitors' actions.
However, how much change in market knowledge stimulates innovation is likely to depend on the original level of market knowledge. Consider two firms that evidence identical changes in market knowledge. If these firms begin at different levels of knowledge, they will have different returns from their knowledge change, and the advantage will be with the firm that has the higher original level of market knowledge, because a firm's original knowledge serves as a basis for integrating new knowledge. Teece (2001, p. 129) argues that "Knowledgeable people and organizations can frame problems and select, integrate, and augment information to create understanding and answers --[that is,] the interpretation of information and its consequent use are determined by the existing knowledge base." A firm that has little knowledge but demonstrates a change in knowledge does not possess a foundation to use this newly acquired knowledge. Therefore:
H[sub2]: A change in decision makers' market knowledge (a) results in an increase in innovation effort and (b) moderates the impact of the level of market knowledge on innovation effort.
Direct and Moderating Effects of Shared Market Knowledge
Prior conceptual work has suggested that the creation and sharing of new knowledge is essential to firms' fostering innovation (Chan and Mauborgne 1997) and building competitive advantage (Senge 1993, 1997). Hoopes and Postrel (1999) argue that unique patterns of shared knowledge are an important source of competitive advantage because they are difficult to purchase and take time to develop. Likewise, in a discussion of learning among strategic decision makers, Argyris and Schön (1978, p. 20) suggest that "[strategic decision makers ] work as learning agents is unfinished until the results of their inquiry ... are recorded in the media of organizational memory." Essentially, this will not occur until shared knowledge is in evidence. Finally, Senge (1993, p. 186) comments that "the most crucial mental models in any organization are those shared by key decision makers."
Despite the widespread belief that shared knowledge enhances innovativeness, empirical evidence is sparse. In one of the few studies that examine actual shared knowledge, Hoopes and Postrel (1999) find that shared knowledge reduces glitches in new product development. Notably, they find that shared knowledge helps significantly reduce project delays and costs associated with lost customer goodwill. Miller, Burke, and Glick (1998) also imply that shared market knowledge offers potential benefits for innovation. Although they do not explicitly focus on actual shared knowledge, they find that cognitive diversity in decision-making teams inhibits strategic long-term planning. Overall, however, researchers have not investigated effects of actual shared market knowledge on important organizational phenomena such as innovation and performance. Instead, they have referred to key respondents' perceptions of consensus about strategic issues as shared knowledge and to diversity in team composition (in terms of demographic factors and functional background) as heterogeneity in organizational knowledge.
I further propose that a high degree of shared market knowledge among decision makers intensifies the effect of the knowledge on the exerted innovation effort. Although decision makers' unified knowledge of market conditions may benefit the application of that knowledge in fostering innovation, wide acceptance of inaccurate information may hinder innovative activities. This reasoning suggests an interaction between the level (accuracy) of decision makers' market knowledge and the extent of shared knowledge among them. Finally, changes in decision makers' knowledge of customers and competitors are likely to stimulate or dampen innovation effort, depending on the extent to which the new knowledge is shared among the decision makers. In other words, changes in market knowledge are likely to intensify the need to innovate, but this relationship will be stronger or weaker depending both on whether market knowledge fosters or subdues innovation effort and on the extent of shared knowledge among decision makers.
H[sub3]: An increase in shared knowledge about customers and competitors has a positive effect on innovation effort.
H[sub4]: The extent of shared knowledge about customers and competitors moderates the impact of (a) market knowledge and (b) changes in market knowledge on innovation effort.
Several forces constitute the market landscape that shapes the coevolution of market knowledge diffusion, firm innovation, and firm performance. To isolate the effects of market knowledge dynamics on innovation effort and the subsequent effect on performance, it is necessary to consider the role of these forces.
Competitive Interdependence
Prior research has shown that a firm's own learning is not the only factor that determines which strategies (e.g., investing in innovation) it adopts and how successful it is; firms also are influenced by the evolving strategic actions and performance of their competitors (Ansoff 1984; Porter 1995). Two firms may implement similar strategies or evidence a similar level of market knowledge, change in knowledge, or knowledge sharing, but they may recover different returns on their strategies simply because they are differently embedded in the competitive landscape (see Dickson 1992). Although the literature shows that the degree of competition and imitation in a firm's market affects its decision to invest in learning, research on organizational and managerial knowledge does not incorporate this competitive aspect (Grandori and Kogut 2002).
Inertia
Against the backdrop of research that has acknowledged the ubiquity of inertia in innovation effort (Bayus, Erikson, and Jacobson 2003; Geroski, Machin, and Van Reenen 1993; Greve 1999, 2002; Miller and Chen 1994), I recognize that firms differ widely in their ability to scan the environment, to make correct cause--effect inferences in environments characterized by the delayed effects of prior strategic actions, and to take action based on these inferences (Senge 1993). Similarly, although prior learning through the adoption and replication of existing action is crucial for future actions (Hargadon and Fanelli 2002), firms differ in their abilities to leverage previous innovation effort. Therefore, I capture each firm's individual proneness to inertia, which reflects the extent to which each firm's current levels of innovation effort depend on its previous levels.
Feedback Effects: The Role of Satisfaction with Past Performance
Drawing from the conceptualization of the cyclical process of market evolution, I recognize that market performance feedback shapes decision makers' satisfaction with past performance, which continuously drives the pattern and speed of their knowledge acquisition. In turn, this influences future strategic actions, such as innovation effort and resultant performance. However, the effect of satisfaction with past performance can be quite complex. On the one hand, prior success (failure) may breed more success (failure) as a result of self-reinforcing mechanisms (see March and Sutton 1997). It is this "nothing succeeds like success" logic that underlies the Schumpeterian hypothesis: In competitive markets, firms whose products enjoy high growth in demand are likely to become more innovative because they are motivated to protect the monopoly power that success engenders (Dosi 1988). On the other hand, success may induce complacency, and failure would result in problem-solving activities aimed at reversing poor performance; these countervailing tendencies then precipitate feedback effects. Support for the latter perspective comes from research in marketing (Chandrashekaran et al. 1999) and organizational behavior (e.g., Audia, Locke, and Smith 2000; Isen and Baron 1991; Miller and Chen 1994). For example, Chandrashekaran and colleagues (1999) find that increased new product diffusion rates negatively affect future innovation generation in a firm. They offer but do not test the perspective that decision makers are prone to success-driven complacency, which arises in part from their satisfaction with the performance of existing products in the marketplace. This perspective implies that managerial knowledge of marketplace feedback results in a continually updated sense of satisfaction with firm performance, which ultimately shapes the extent of innovation effort over time. Overall, the previous two perspectives suggest a U-shaped effect of satisfaction with past performance, such that innovation effort is highest at lower and higher levels of satisfaction.
H[sub5]: Satisfaction with performance has a U-shaped effect on innovation effort.
Apart from the question of how specific aspects of market knowledge diffusion and their interplay at the level of individual decision maker influence innovation effort over time, an important issue is the following: When does innovation effort pay off? I subsequently discuss the mechanism that underlies firm performance, specify a model that tests the translation of innovation effort into performance outcomes, and propose that total shared market knowledge moderates the impact of innovation effort on performance.
What Underlies Dynamic Firm Performance?
A firm may have the market knowledge and resources to achieve a certain level of performance, but it may fail to reach its potential for various reasons, including weak deployment of resources, poor implementation, unexpected competitive actions, and stochastic factors such as random opportunities and obstacles. Furthermore, as I discussed previously, firms differ widely in their ability to make correct cause--effect inferences in environments characterized by the delayed effects of prior strategic actions. Thus, when facing a gap between potential and realized performance, firms differ in their ability to sense such shortcomings and to take action to improve their performance. Although some firms may be acutely aware that their resources should be translating into better performance, previous research indicates that most firms are "gripped by inertia, which dulls and retards responses in a dynamic market" (Chandrashekaran et al. 1999, p. 97). Indeed, the concept of inertia-induced underperformance is the basis of most of the research that attempts to explain why firms that have ample resources in one technological regime often fail to lead in another (e.g., Porter 1995).
Inertia manifests itself in dynamic firm performance by causing realized performance at time to fall continually short of a potential achievable performance at time t. That is, at any point in time, realized performance only partially adjusts to the discrepancy between previously realized performance and potential performance. Moreover, because firms are different, the rate of adjustment is firm specific. Consistent with Leeflang and colleagues (2000), I specify the following autoregressive partial-adjustment model (see the Appendix):
( 1) PERF[subit] = ρ[subi]PERF[subi,t - 1] + X[subit]β - ρ[subi]X[subi,t - 1]β + eta;[subi,t]
where PERF[subit] denotes the performance of firm i at time t, and η[subit] ∼ N(0, σ[subi, sup2]. Four issues warrant comment. First, in this specification, when ρ[subi] = 0 (the adjustment rate is large), there is no inertia. In turn, when ρ[subi] = 1, there is no adjustment in performance, and a condition of complete inertia exists. Second, each firm has a unique volatility associated with performance (note that the variance of η[subit] is firm specific). Third, cov(η[subi], η[subj] = σ[subij, sup2] This accounts for the unique interdependence in performance across firms. Fourth, although I assume a certain specification for η[subit], I explicitly test for serial correlation.
When Does Innovation Effort Pay Off?
Prior research in economics has found that innovation effort has a positive effect on performance (for a review, see Cohen and Klepper 1996a, b). However, a meta-analysis in the marketing literature (Szymanski, Bharadwaj, and Varadarajan 1993) does not find that innovation effort (in the form of R&D) has a positive impact on new product outcomes. I advance a contingency perspective to explain the effect of innovation effort on performance.
Organizational theorists have long acknowledged that knowledge resides in individual decision makers (Simon 1991). However, a person does not think in isolation but interacts with others, so that at the level of the strategic team and organization there is varied alignment of individually held schemas (Hargadon and Fanelli 2002). Reverse alignment of schemas ensures continual knowledge sharing, which enables decision makers to act with unity of purpose. In the marketing literature, scholars have built on these streams of research and have argued that "market knowledge is not fully captured in a usable form until the lessons and insights are transferred beyond those who gain the experience" (Day 1994, p. 44). Guided by this line of research, I suggest that the total shared market knowledge facilitates the translation of innovation effort into performance. A firm that has a high level of total shared market knowledge acts with a greater unity of purpose in making investment decisions than does a firm that has a low level of total shared market knowledge. Thus, I expect that an interaction of innovation effort and total shared market knowledge shapes firm performance. Prior conceptual research has recognized that the sharing of knowledge leads to performance advantages (e.g., Argote 1999; Senge 1993, 1997). However, this research has conceptualized a main effect of shared knowledge on performance and has not tested this empirically. For example, Frank and Fahrbach (1999, p. 254) maintain that processes associated with the pattern of interaction and the act of information sharing among organizational members "represent the geological forces that shape the landscape on which organizational productivity is built." Furthermore, Cannon-Bowers, Salas, and Converse (1993) suggest that shared mental models contribute to more effective teams.
H[sub6]: The impact of innovation effort on performance increases as the extent of total shared market knowledge in a firm increases.
Control Variables
Prior research has recognized the influence of firm size on innovation and competitive processes (e.g., Cohen and Klepper 1996a, b; McKee, Varadarajan, and Pride 1989). Resource-based views of the firm (e.g., Barney 1991), including the resource-advantage theory of competition (Hunt and Morgan 1995), suggest that resources possessed by organizations lead to positions of competitive advantage, which eventually translate into innovation-driven superior performance. Others suggest that small firms are more innovative than large firms because they are less prone to bureaucratic inertia (e.g., Baldridge and Burnham 1975) or that the very success that yields greater financial resources serves to create complacency, which hinders subsequent innovation (e.g., Audia, Locke, and Smith 2000). Consequently, I control for firm size in my model of innovation effort.
Prior work in economics also supports the notion that firm size influences return on innovation (Cohen and Klepper 1996a, b). Therefore, in addition to a main effect of firm size, I expect that there is an interaction between innovation effort and firm size. Inclusion of this interaction allows for an examination of the relative impact of firm size (compared with the impact of shared knowledge) in the translation of innovation effort into firm performance, and it helps empirically uncover sources of competitive advantage that prior research might have ignored. Finally, prior research in marketing has also documented that strategic orientation has an effect on firm performance (e.g., Gatignon and Xuereb 1997; Voss and Voss 2000); thus, I include strategic orientation in the study.
To address the focal research questions, a controlled setting is required that allows for the explicit capture, over time, of market knowledge and its role in actualizing innovation in a dynamic system of market evolution. I thus turned to a quasi field experiment: the Markstrat3 simulation (Larreche and Gatignon 1999), which has been widely used as an empirical setting in prior research on managerial decision making (Clark and Montgomery 1998, 1999; Glazer, Steckel, and Winer 1989, 1990, 1992; Van Bruggen, Smidts, and Wierenga 1998).
The Markstrat setting is highly suitable for this research for several reasons. First, the simulation provides objective information on all aspects of the marketplace. This is crucial for assessing decision makers' market knowledge and thus meaningfully capturing the level of accurate market knowledge, the change in knowledge, and the extent of shared knowledge. In addition, this research requires repeated measures of the various dimensions of each decision maker's knowledge over time, which precludes a cross-sectional survey method.
Second, because the simulation makes it possible to observe the vector of independent variables before performance is known to the firms, there is no problem of contemporaneous correlation between the error term and the vector of independent variables. Although in principle the research task can be accomplished by collecting longitudinal data through repeated interviews in an industrial setting, the issue of contemporaneous correlation remains, because it would be impossible to observe the multitude of relevant variables (e.g., tangible and intangible performance incentives) that simultaneously affect the outcome and the variables of theoretical interest. However, with a simulation, identical incentives are established for all participants, which is an essential requirement for the design of this study.
Third, the simulation can be set up such that all teams begin in identical positions. Consequently, variations that emerge in subsequent strategy and performance can be more easily linked to distinct capabilities across firms. Fourth, the structure of all firms is identical and simple: A team of four to six members is responsible for all decisions. This structure controls for macrostructural effects on decision making and innovation.
Participants are assigned to one of six teams, and each team is responsible for managing a firm in the Markstrat industry over a certain number of time periods (ten in the main study, which corresponds to ten years). At the start of the simulation, all firms have two products in the market. Throughout the course of the simulation, firms must make decisions about production, advertising, sales force, distribution, pricing, and product positioning for each product in each period. Firms can also purchase any or all of a set of market research reports each period. They can introduce new products (after the successful completion of R&D) and withdraw old ones from the market.
Design
I performed two pretests using different industry settings, different subjects, and different points in time to assess the feasibility of data collection in such a setting, to examine how well the models generated estimates that could serve as a basis for subsequent triangulation, and to make necessary improvements in the data collection protocols. Pretest 1 involved 17 part-time and full-time MBA students at a large Midwestern university. The participants had an average of 8.7 years of work experience and an average age of 32. The data collection process was based on nine decision periods and was divided into two phases. In the orientation phase (Periods 1-3), subjects focused on learning the various aspects of the simulation, and in the data collection phase (Periods 4-9), they completed, on an individual basis, questionnaires aimed at capturing their market knowledge at each point in time. The panel data from this pretest came from 17 decision makers, in five teams, observed over six continuous time periods. Pretest 2 helped refine the measures for knowledge on customer preferences and competition at the micro level. The design was similar to that of the first pretest. Participants were 12 full-time MBA students at a different large Midwestern university, with an average of 3.5 years of work experience and an average age of 24.4. The panel data from this pretest came from 12 decision makers, in four teams, observed over nine continuous time periods.
For the main study, six teams, each of which contained four or five MBA students from a large Midwestern university, participated in a Markstrat3 simulation in which identical starting positions were used. All teams competed in only one industry (Sonite) for the entire duration of the simulation. (Entry into a second industry [Vodite] was prevented by making conditions for entry impossible to meet.) Over two-and-a-half months, 25 participants provided data on their knowledge and perceptions of the marketplace. The average age of the participants was 27.2 (standard deviation 3.61, range 21 to 33 years), and the average work experience was 5.7 years (standard deviation 3.67, range 1 to 14 years).( n1) Before the actual simulation started, the participants were familiarized with the software and the mechanics of the simulation through a trial run of three decision periods.
Questionnaires aimed at capturing relevant aspects of each decision maker's knowledge structure were administered every period in Periods 2-9. While participants were completing the questionnaire, they were not allowed to consult their notes or their team members. To alleviate problems of diminishing interest and automatic response patterns, which are typical of repeated-measure questionnaires, participants were continually reminded that there were no right or wrong answers and that the questions aimed to uncover their perceptions of the dynamics in the market. In addition, the content of the questionnaires was varied (but contained the same key measures every decision period) to maintain participants' interest, and the questions captured the diversity of decision-making aspects well. Immediately after each team had made its decisions and before it received performance feedback, each decision maker's knowledge of the product-market factors and perceptions of the previous period's performance were measured. Therefore, the panel data for the analysis came from 25 decision makers, in six teams, observed over eight continuous time periods, which yielded 48 organization-period observations.
I also conducted three validations studies. In the first, decision makers participated in Markstrat without being surveyed at all during the simulation. This served two purposes. First, it validated the underlying generative mechanism of competitive dynamics, and second, it tested whether the process of repeated data collection influenced the dynamic system of competition. The panel data from this study were collected from 28 MBA students (not from the same university as the students who participated in the main study), with average work experience of three years, in five teams over nine time periods.
Because I also aimed to assess the robustness of the results, both by attempting to replicate them and by examining the cross-industry predictive ability of the estimates, I conducted two more validation studies in two different Markstrat industries. The second validation study involved 36 MBA students (average work experience of 7.3 years) in six teams over nine time periods, and questionnaires were administered for seven periods. The third study involved 15 MBA students (average work experience of 3.3 years) in four teams over nine time periods, and data were gathered for four periods.
Measures
Knowledge. Conceptually, knowledge is defined as the extent of the accurate detection of a task's properties; in the context of this study, it involves the accurate detection of customer preferences and competition. Specifically, in the Markstrat industry setting, critical properties are accurate identification of customer segment preferences or ideal points and competition. Because all teams ordered market research reports and spent time analyzing them, I considered the distinction between information and knowledge.
Conceptually, knowledge and information are different but related. In general, knowledge is considered interpreted information that is anchored in people's beliefs and commitment (Huber 1991). Knowledge also comprises such cognitive elements as beliefs, understanding, interpretation, and integration with previous knowledge. In the words of Bertels and Savage (1998, p. 17), "information only comes alive by our interpretation.... [W]e create meaning by distinguishing and valuing information." Although decision makers obtain market reports that list the ideal points and spend time reading reports, information needs to be integrated into their schemas and into their prior understanding of the market for it to become knowledge. If this information is not integrated with a person's existing understanding of the market and remembered for later retrieval, it is also less likely to be subsequently used. Moreover, teams (and decision makers) differ in terms of their ability to process, understand, and interpret this information. Yet the development and execution of successful strategy in Markstrat (also in general) requires the integration of many pieces of information. For example, information that addresses different elements of the competitive environment is located in different reports. Thus, decision makers need to be able to process and remember a piece of information to relate it to another when they encounter it and need to integrate it. In addition, successful performance requires marketing products that satisfy segment preferences. The greater the intensity of competition in a product market, the greater is the impact of the discrepancy between perceived characteristics of products and the "ideal" points of the target segment on sales. If decision makers understand the importance of this aspect of product marketing, target-segment ideal points will be highly salient to them, actively used, and more easily remembered.
Furthermore, teams also differ in diagnosing both the relative importance of different information that is available in the reports and the importance of the degree of accuracy of customer knowledge necessary to compete (and/or deemed important for their strategy). Given the availability of information, teams may have relatively inaccurate knowledge of the ideal points. For example, this occurs under the following three conditions: First, this occurs when teams believe that delivering a product that corresponds exactly to the ideal points of the segments is not needed and that being "close enough" is sufficient to compete in accordance with their objectives at a particular point in time (there is a variation in what "close enough" is; in the extreme case, it can mean dichotomization of the market in terms of high-and low-end products), and variation can exist across and within teams across time. Second, when teams believe that performance in the previous period suffered because they overlooked competition, they may spend more of their time analyzing competition and less time studying changes in customer preferences (decision makers also differ in their abilities to diagnose cause--effect relationships). Given fixed capacity to process information (Sternberg 1996) and limited time, their customer knowledge will likely be compromised and less accurate than knowledge of competition, especially if significant changes occurred in the market. Third, inaccurate knowledge can result from teams becoming complacent with their performance (Audia, Locke, and Smith 2000), in which case they are likely to be less sensitive and to pay less attention to changes that take place in the market. In this case, their customer knowledge will reflect previous ideal points, which are not updated and thus are not accurate.
Given the previous considerations, each decision maker's knowledge about customer preferences was assessed through a series of questions that they answered immediately after they had made decisions but before they had seen the performance outcomes. They marked the location of various customer segments' ideal points on a perceptual map, and they indicated the relative importance of each customer segment for their firm strategy and operations by dividing 100 points among the existing segments. The accuracy of their knowledge was judged by the Euclidean distance between the points they specified on the map and the objective ideal points for each customer segment (provided by the simulation), weighted by segment importance as reported by each decision maker. I incorporated segment importance to give more weight to accuracy (or inaccuracy) in knowledge for segments that are more important to the firm's operations. Thus, I measured customer knowledge as follows:
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
where k (= 1, ..., 5) denotes segment; P[subatt1,t] and P[subatt2,t] denote decision makers' perceptions of the ideal values of the two most important product attributes for each segment at time t; IP[sub1,t] and IP[sub2,t] denote the objective ideal values of the two most important product attributes for each segment at time t; and S[subk,t] is the importance of each segment k as indicated by the decision maker at time t.
I assessed knowledge about competition by asking each decision maker at each point in time t (each decision period) to list the names of all competing products in each segment and to identify the most influential competitor in each segment. I employed two measures of knowledge about competition. First, consistent with prior research on competitive monitoring (Clark and Montgomery 1999), I focused on the number of competitors identified and defined competitor knowledge as COMPK[subt] = (number of competitors identified at time t)/(total number of actual competitors at time t).( n2) Second, to account for strength or intensity of competition, I weighted each identified competing product by its distance in perceptual space from the ideal point of the segment designated by the decision maker at time t and by its importance, S[subk,t]. For each decision maker,
( 3) [Multiple line equation(s) cannot be represented in ASCII text]
where k (= 1, ..., 5) denotes segment; j (= 1, ..., m) denotes product; D is a dummy variable that equals 1 if the decision maker has identified product j as a competitor in segment k at time t and equals 0 otherwise; IP[sub1,t] and IP[sub2,t] denote the objective segment ideal values of the two most important product attributes for each segment at time t; CP[subatt1,t] and CP[subatt2,t] denote the product's position in perceptual space on the basis of the two most important product attributes at time t; and S[subkt] is the importance of each segment k as indicated by the decision maker at time t. Analyses with both measures produced the same pattern of results. Finally, I computed the average value of CUSTK and COMPK (COMPK2) across decision makers in each firm to represent, respectively, level of knowledge of customers and level of knowledge of competitors in each firm.( n3)
To validate these measures further, I estimated a panel model with market knowledge (at the level of the individual decision maker) as the dependent variable, individual as fixed (random) effect, time as fixed (random) effect (or accounted for as an autocorrelation effect, ρ = .36), and variables expected to affect market knowledge as independent variables. The results indicate that all variables are significant in the hypothesized direction and jointly explain 29% of the variance, whereas the individual effects explain 18% of the variance. In addition, within the same analysis framework, I tested the effects of involvement (measured as the percentage of effort or time respondents spent on analyzing information and preparing for decisions on their own) and found that it did not have a significant effect on market knowledge (parameter = .014, p < .78). I also examined whether differences in knowledge within a team and across team members were a result of task assignment. The average delegation of tasks within teams was 11.06% of all tasks, which accounts for 1.8% of the variance compared with between-group effects, which account for 10% of the variance. This suggests that differences in market knowledge across team members are not just a consequence of the assigned tasks within teams, nor does the amount of delegated effort fully explain the between team effects..
Change in knowledge. Given the assessment of decision makers knowledge every period, I measured individual change in knowledge about customer preferences (CUSTC) and about competition (COMPC) as |∂CUSTK[subt]/∂t| and |∂COMP[subt]/∂t|, respectively, and accounted for the rate of change in the different market segments. In the analysis, the average value of CUSTC and COMPC across strategic decision makers in each firm represented change in customer knowledge and change in competitor knowledge, respectively.
Shared knowledge. Consistent with standard measures of diversity across respondents, I measured the extent of shared knowledge about customer preferences (CUSTS) as the variation in CUSTK across decision makers in each firm. Specifically, I computed CUSTS as 1/σ[subCUSTK,t], where σ[subCUSTK,t] denotes the standard deviation in the level of customer knowledge across decision makers at time t. I measured the extent of shared knowledge about competition (COMPS) in two ways, both of which were consistent with the two measures of competitor knowledge. First, I measured shared competitor knowledge as the extent of variation in COMPK (number of competitors identified), computed as 1/σ[subCOMPK,t], where σ[subCOMPK,t] denotes the standard deviation in knowledge level of competitors across decision makers at time t. Second, I measured shared competitor knowledge per Equation 3 by including only competing products j that all decision makers in the team identified as competitors in a particular market segment.
Strategic orientation. In line with prior research (Gatignon and Xuereb 1997), each decision maker was asked to allocate 100 points to the following three categories to reflect the categories' relative importance for each time period's investment decisions: competition, consumer preferences, and nature of products. I measured the strategic orientation of the firm at time t as the average difference in the importance of the customer and competition (Day and Nedungadi 1994). Thus, positive values for this measure indicate greater emphasis on customers than on competitors, whereas negative values reveal a greater emphasis on competitors than on customers.
Total shared market knowledge. Following guidelines in prior research (Day and Nedungadi 1994), I measured total shared market knowledge as 1/σ[subORIENT,t], where σ[subORIENT,t] is the standard deviation of the firm's strategic orientation at time t, such that small values of σ[subORIENT] indicate a greater extent of total shared market knowledge. An alternative measure could be based on a combination of the measures of customer and competitor knowledge. The advantages of the measure I used are that it does not assume that there is a particular rule for combining customer and competitor knowledge, and it is an index of overall shared market knowledge, including but not limited to knowledge about customers and competitors. According to prior research (Day and Nedungadi 1994), the extent of overlap in decision makers' mental representation of their firm's strategic orientation reflects the extent of the decision makers shared market knowledge.
Satisfaction with past performance. I assessed individual decision makers' satisfaction with past performance (SAT) after each period's decisions using a seven-point Likert scale, anchored by "strongly agree" and "strongly disagree." The statements that participants rated were "I was satisfied with my team's performance" and "My team did not do as well as I thought it would." The two measures were highly correlated (.734, p < .0001); thus, I averaged scores across decision makers in a firm to represent satisfaction with past performance in that firm.
Firm size. There are many ways to measure firm size, including by number of employees, financial resources, net sales, or total expenditures (e.g., Chandy and Tellis 2000). Given the nature of this study's quasi-experimental setting and processes of interest, number of employees and net sales are not appropriate measures (see Clark and Montgomery 1999). Thus, consistent with the resource-advantage perspective, I measured firm size (SIZE) as the total financial resources (in dollars) available to each firm at each point in time t. (No loans were permitted in the simulation.) In the model of performance, following the lead of Clark and Montgomery (1999), I used total marketing expenditures as an alternative measure of firm size. The advantage of this measure is that it also controls for marketing-mix expenditures, which have been shown to drive performance. However, the pattern of results remained the same across the two measures of firm size. Therefore, I report results from the first measure.
Innovation effort. I measured innovation effort (INNOV) in two ways: by the dollar amount of each team's investment in product R&D and by the ratio of dollar investment in product R&D to total marketing expenditures (including advertising and sales force activity). The first measure captures the level of innovation effort, and the second captures its intensity. Because the two measures were highly correlated (r = .92, p < .0001) and analyses with both produced the same pattern of results, I report results from the first method.
Performance. I measured performance (PERF) in two ways: by the firm's market share, in units sold, at each point in time and by the firm's stock price index. In the context of Markstrat, the firm's stock price index is the most comprehensive measure of firm performance (Larreche and Gatignon 1999). In the context of this study, as with prior studies that used Markstrat, a significant correlation exists between unit market share and net marketing contribution (r = .82, p < .001). Descriptive statistics for all variables in the analysis appear in Table 1.
Analysis Framework
I specify the following framework for estimating the impact of market knowledge diffusion (i and t denote firm and time, respectively):
( 4) INNOV[subit] = X[subit]β + ε[subit],
( 5) ε[subit] = ρ[subi]ε[subit] - 1 + η[subit]
( 6) ρ[subi] = ρ + λ[subi], and
( 7) cov(ε[subit], ε[subjt] = σ[subij]
where X captures the independent variables that are hypothesized to affect the innovation effort (see H[sub1]-H[sub5]),( n4) η is an error term, and var(ε[subit]) = σ[subit, sup2]. Furthermore, ρ[subi] captures the extent of unique inertial tendency in innovation for firm i. Large variation in ρ[subi] (i.e., in λ[subi]) can be treated as evidence that firms are heterogeneous in their inertial tendency in innovation effort. In turn, σ[subij] in Equation 7 captures the interdependence of the innovation effort of firm i and firm j. (Note that σ[subij]/√[σ[subit, sup2] x σ[subjt, sup2] is bounded by 1 and +1 and is a correlation coefficient.) Notably, Equations 4-7 imply the following structure for innovation effort:
( 8) INNOV[subit] = X[subit]β; + ρ[subi](INNOV[subi,t] - X[subi, t - 1]β) + η[subit].
Thus, after accounting for innovation interdependence across competitors, each firm's innovation effort at every point in time is influenced by current levels of the X variables and by the extent of unique inertial tendency in its innovation effort, beyond that explained by previous levels of X.
Depending on restrictions placed on the model parameters in Equations 4-7, models that correspond to different perspectives can be obtained; these are summarized in Table 2. To estimate the models, I employed a feasible generalized least squares procedure (Greene 2002).( n5) I began with the simplest model, M00, and proceeded systematically to free parameters until I obtained the hypothesized Model M[sub12]. To assess model fit, I performed model comparison tests at each stage of the estimation. A comparison of Model M[sub11] with M[sub12] (and M[sub01] with M[sub02]), which I accomplished using χ² [sub(i degrees of freedom [d.f.])], tests for the presence of unique inertial tendency in innovation effort. A comparison of Model M[sub02] with M[sub12] (and M[sub01] with M[sub11]), which I accomplished using χ²[sub[i(i - 1)/2 d.f.]], tests for the presence of interdependence in innovation effort across competitors. I used the same approach to estimate the model of firm performance presented in Equation 1.( n6)
Market Knowledge Diffusion and Innovation Effort
Competitive interdependence and inertia Model M[sub01] (see Table 2) is not superior to model M[sub00] (ρ = .236, χ[sub1, sup2] = .41, not significant [n.s.]). Furthermore, Model M10 is superior to model M[sub00] (χ[sub15, sup2] = 54.93, p < .0001). Finally, a comparison of Models M[sub10] and M[sub12] reveals that the best-fitting model is M[sub12] (χ[sub5, sup2] = 9.80, p < .05; values of ρ across firms were .364, .047, .307, .990, -.192, and -.006). This indicates that firms exhibit a unique inertial tendency in innovation effort and that in accordance with the cyclical model of market evolution, there is significant heterogeneity in the interdependence of innovation effort across firms. Further analyses are based on Model M[sub12].
Table 3 shows all the parameter estimates of Model M12 that I used to test specific hypotheses regarding the effects of customer and competitor knowledge, change in knowledge, shared knowledge, and satisfaction on innovation effort. Because of the significant interactions in the model, I computed the net effect of the key variables at different levels of the other independent variables, and I report the results in Table 4; in each case, I used the Wald test to test significance (Greene 2002).
Effects of customer and competitor knowledge. Table 3 shows a significant interaction among shared knowledge, change in knowledge, and knowledge level. The net effect of each aspect of knowledge diffusion, which provides support for [Hsub1-H[sub4], is shown in Table 4.
Effects of shared knowledge. Overall, Panel A of Table 4 reveals that across knowledge levels and magnitude of knowledge change, increases in shared knowledge about customers and competitors enhance innovation effort, which provides strong support for H[sub3]. For example, for low customer knowledge and high knowledge change, the greater the shared knowledge, the greater is the innovation effort (net effect = 2.936, p < .001).( n7)
Effects of change in knowledge. For customer knowledge (Panel B), increases in knowledge change enhance innovation effort when shared knowledge is high. At low levels of shared knowledge, a change in knowledge has no effect on innovation effort across different knowledge levels. For competitor knowledge (Panel B), increases in knowledge change enhance innovation effort across different levels of knowledge and shared knowledge. However, these effects are weaker for low levels of shared knowledge. For example, at low levels of competitor knowledge and high levels of shared knowledge, the net effect of knowledge change is 1.131 (p < .05).( n8) In contrast, when shared knowledge is also low, the net effect of knowledge change is ten times smaller (.105; p < .1). Thus, I obtain mixed support for H[sub2a] and strong support for H[sub4b], which indicates that shared customer and competitor knowledge moderates the impact of knowledge change on innovation.
Effects of knowledge level. Panel C of Table 4 reveals that increases in the level of customer knowledge enhance innovation effort only when there are high levels of shared knowledge about customers.( n9) The effect of customer knowledge is not significantly (only directionally) enhanced by the magnitude of knowledge change, which does not support H[sub2b]. If there is little shared knowledge in the firm, an increase in customer knowledge has no impact on innovation effort. I obtained the identical result for competitor knowledge: The higher the level of competitor knowledge in the firm, the greater is innovation effort, if and only if shared knowledge about competitors in the decisionmaking team is high. If there is little shared knowledge, an increase in competitor knowledge has no impact on innovation effort. In summary, I obtained strong support for H[sub4a]: An increase in shared knowledge about customers and competitors moderates the impact of market knowledge on innovation effort.
Feedback effects: satisfaction with past performance. I obtained significant main (b[sub9] = 11.50, p < .0001) and quadratic (b10 = .205, p < .0001) effects of satisfaction in support of H[sub5] (see Table 3). Notably, because of the structure of the dynamic model, the effect of satisfaction is beyond any effects of past performance. The overall effect of satisfaction (computed as b[sub9]SAT + b[sub10]SAT²) is negative across all levels of satisfaction, which supports the hypothesis that satisfaction with performance is a complacency-producing mechanism that dampens innovation effort. The results of computation of the net effect of satisfaction on innovation effort (i.e., ∂INNOV/∂SAT = b[sub9] + 2b[sub10]SAT) reveal that at low levels of satisfaction (tenth percentile), the net effect is negative and significant (-4.676, p < .001). Thus, if decision makers are dissatisfied with their performance, an increase in their satisfaction hinders innovation effort. However, as satisfaction approaches its mean value, increases in satisfaction result in an increase in innovation effort (net effect = 1.445, p < .001). I obtained the same effect at high levels of satisfaction as well (net effect = 7.976, p < .0001). Although at the mean and high levels of satisfaction, a unit increase in satisfaction leads to an increase in innovation effort, the overall impact on innovation effort is still negative. Thus, the quadratic effect of satisfaction indicates that the perceived feedback from the market is most damaging to the innovation effort of firms that exhibit moderate levels of satisfaction. Firms that exhibit high or low levels of satisfaction witness a smaller decrease in innovation effort.
From Innovation Effort to Performance: The Role of Total Shared Market Knowledge
To ensure that the errors in Equation 1 were not autocorrelated, I extracted the residuals after estimating Equation 1 and tested them for the presence of autocorrelation. Results indicated that the errors were largely white noise (ρ = .35, χ[sub1d.f., sup2] = 1.11, n.s.). An examination of model fit revealed a significant inertial tendency in performance (ρ = .63, p < .0001) but no significant difference in the inertial tendency across firms (χ[sub5, sup2] = 3.496, n.s.), that is, no significant difference in ρ[subi] and the adjustment rates across firms. There was also support for heterogeneity in performance interdependence across firms (χ[sub15, sup2] = 39.12, p < .0001). Overall, the model explains 80% of the variation in performance.
Drivers of performance. Table 5 contains all the parameter estimates of the retained model. Observe in Table 5 that a similar pattern of effects emerges across measures of performance. For the purposes of concision, the following discussion focuses only on the results derived from the market share analysis. The results indicate that there is no significant main effect of innovation effort on performance (b[sub1] = .090, n.s.).)( n10) However, consistent with H[sub6], there is a significant interaction between innovation effort and total shared market knowledge (b[sub2] = 5.438, p < .0001) and between innovation effort and firm size (b[sub3] = .231, p < .01). Furthermore, there are significant main effects of firm size (b4 = .921, p < .0001) and strategic orientation (b[sub6] = .121, p < .0001), which corroborate prior findings in the literature.( n11) Last, it appears that total shared market knowledge per se has no direct effect on performance (b5 = .590, n.s.). On the basis of the results, the return on innovation (i.e., the net effect of innovation effort on performance) can be expressed as follows: Return on innovation = ∂PERF/∂INNOV = .090 + 5.438 x shared market knowledge -.231 x firm size.
There is strong support for H6, which indicates that the total shared market knowledge helps translate innovation effort into performance. Innovation effort results in positive returns only if it is accompanied by a high level of shared market knowledge among decision makers. Innovation effort results in negative rather than positive returns for large firms if total shared market knowledge is low (at the ninetieth and tenth percentiles for firm size and shared knowledge, respectively; ∂PERF/∂INNOV = -.026, p < .001). Notably, small firms do not witness such a negative effect: Innovation effort has no influence on performance when the level of total shared market knowledge is low; for example, at the tenth percentile for firm size and shared knowledge, ∂PERF/∂INNOV = -.003, n.s. Small firms evidence positive returns on their innovation effort when shared market knowledge reaches the fifty-fifth percentile of its distribution (at the tenth and fifty-fifth percentile for firm size and shared knowledge, respectively, ∂PERF/∂INNOV = .08, p < .06), whereas large firms show positive returns only after shared market knowledge is greater than the ninetieth percentile (∂PERF/∂INNOV = .012, p <.06). Furthermore, the results suggest that though large firms enjoy higher performance, this effect becomes weaker as innovation effort increases (observe the negative interaction between firm size and shared market knowledge). This finding implies that larger firms are less efficient in obtaining a return on resources used for innovation. Thus, although greater firm size appears to be an advantage in the generation of positive performance returns, it is also an impediment to efficient innovation effort.
Validation and assessment of theory. I conducted several validation exercises with data from three validation studies to assess the impact of repeated data collection on the generative dynamic system of competition, to attempt to replicate the substantive findings from the performance model, and to test the cross-industry predictive ability of the performance model. I then examined the nature of substantive findings that might have emerged had I ignored heterogeneity in competitor interdependence in the area of innovation effort and firm performance.
The impact of repeated data collection on the generative mechanism of competition. The focus was on validating the two aspects of dynamic competitive markets: partial adjustment of performance over time and the interdependence in competitors performance. Consistent with the validation objectives, I focused on models of firm performance without any covariates. Table 6 reports the key results. Observe in Table 6 that across three different industries at different times and involving different levels of data collection, support for the underlying generative mechanism emerges. There is partial adjustment of performance (average and standard deviation of ρs are similar across the three industries), and competitors show significant interdependence in performance. In Validation Study 1, there were no periodic intrusions on the simulation to collect data, yet the same baseline process is evident. Thus, the collection of periodic data in the main study appears not to have influenced the generative mechanism investigated.
Replication of substantive findings. Table 5 presents results of the replication of the original study. The hypothesized variables show a pattern of effects on performance that is similar to the pattern in the original study, thus replicating the previously reported findings.
Cross-industry predictions. I used the estimates of β and ρ from the original study to predict firm performance for the industries in the second and third validation studies. I was interested in determining whether I could use the obtained insights to predict performance in a different industry. The results of the cross-industry predictions are summarized in Table 7, along with the same-industry predictions. I obtained correlations of .64 and .92 between actual and predicted performances in the second and third validation studies, respectively. The results provide further evidence of the validity of the hypothesized variables impact on performance.
Impact of ignoring competitor interdependence. In terms of innovation effort, failure to account explicitly for interdependence across competitors produces a null effect for interactions that involve customer knowledge, change in customer knowledge, and shared customer knowledge, as well as for interactions that involve competitor knowledge, change in competitor knowledge, and shared competitor knowledge. In terms of firm performance, failure to account explicitly for interdependence across competitors produces a null effect for the interaction between innovation effort and total shared market knowledge as well as for the interaction between innovation effort and firm size.
This article offers a conceptualization and a longitudinal empirical assessment of the dynamic process that governs the translation of knowledge about customers and competitors into innovation effort and performance. First, I mapped the mechanism by which three aspects of knowledge (knowledge level, knowledge change, and extent of shared knowledge about customers and competitors) influence innovation effort. Second, I uncovered the role of total shared market knowledge and firm size in the translation of innovation effort to performance. The proposed model explicitly incorporates critical aspects of the dynamic competitive process, including firm-specific inertial tendency in innovation, heterogeneity in interdependence of innovation across competitors, feedback effects reflected in satisfaction with past performance, and partial adjustment of firm performance over time. Results from a longitudinal quasi field experiment and series of validation studies provide strong support for the theory.
Limitations
First, although prior studies that have used Markstrat as a research platform have typically collected data at the team level, most of the measures in this study required data from all the team members and over time. The use of frequent collection periods placed constraints on the number of participants involved in the study and on the number of variables involved. The benefits of conducting longitudinal quasi field experiments were balanced by smaller samples that provided a relatively conservative test of the theory.
Second, following Clark and Montgomery (1999), I measured firm size in terms of available financial resources rather than number of employees or net sales (see Chandy and Tellis 2000). Although I replicated the study's results using marketing expenditures as a measure of firm size, the existence of multiple methods to measure firm size warrants attention in further research. The use of other measures in different contexts might further clarify the role of firm size in innovation and performance. In addition, this study could have benefited from an investigation of additional aspects of market knowledge (e.g., it might have explored knowledge about the features of competing products), but this would have added significantly to the already substantial data collection task.
Third, I tested the theory in the context of simulated industries. As a quasi-experimental setting, Markstrat does not provide the same environment as an actual company. The simulated environment made it possible to control for organizational structural factors that might influence the studied relationship, but this required the assumption that the strategic decision-making team drives the strategic course of the organization. Thus, the simulation does not address the diffusion of market knowledge throughout the organization. However, despite the limitations inherent in the use of a simulation, the following aspects enhanced the validity of the study: ( 1) replication of the baseline generative system of competition at different points in time over two-and-a-half years, involving different levels of data collection in the context of four different industries; ( 2) replication of the study's substantive findings on firm performance; ( 3) use of an efficient feasible generalized least squares estimation, which made it possible to obtain consistent estimates of the parameters of interest (Greene 2002); ( 4) generation of solid cross-industry predictions of firm performance; ( 5) all the variables included in the analysis having a significant role in the phenomena explored, either as main effects or in interactions involving other variables, though sample size influenced the power of the estimation; and ( 6) examination of how the collection of periodic individual data might affect the behavior of decision makers, which revealed no evidence that repeated data collection adversely affects the findings. Replication of this study in nonsimulated industries would enhance the generalizability of the findings.
Fourth, the study did not directly measure decision makers expectations about their decisions; instead, it measured satisfaction with past performance. According to the expectation-disconfirmation theory of satisfaction (e.g., Oliver 1980), satisfaction is expected to mediate the effect of prior expectations on strategic decision making. According to this perspective, satisfaction with past performance should contain information about expectations associated with the decisions as well as the comparison of the expectations with performance.
Fifth, although for maximum benefit shared knowledge should comprise knowledge not only about the market but also about the company, products, financial issues, objectives, strategies, internal capabilities, processes, and so on, this article focuses strictly on shared market knowledge. It does not address other aspects of shared knowledge in general or differences in perspectives based on functional responsibility.
Innovation Effort
This study reveals that three aspects of market knowledge diffusion (market knowledge, change in market knowledge, and shared market knowledge) and their interplay over time shape the extent of innovation effort. The results indicate that mere possession of accurate knowledge about customers and competition does not lead to enhanced innovation. Instead, change in market knowledge and shared knowledge assume key roles in transforming market knowledge into innovation. The results also support prior research conjectures (e.g., Chandrashekaran et al. 1999) that satisfaction with past performance is an inertia-producing mechanism that suppresses innovation over time. The quadratic effect of satisfaction, which shows that firms that exhibit high or low levels of satisfaction demonstrate a smaller decrease in their innovation effort, is perhaps indicative of successful firms' motivation to protect their market power and of poorly performing firms' desire to change their fortune. Firms characterized by moderate levels of satisfaction are perhaps plagued by the desire merely to stay afloat; they may avoid changing their behavior because they may be more uncertain about the drivers of market success. Thus, moderate levels of satisfaction may be a manifestation of uncertainty about the market, and this uncertainty may result in failure to innovate. This constitutes a worthwhile avenue for further research.
The results also suggest that innovation effort takes shape over time under the influence of two opposing forces: market knowledge diffusion, which propels innovation, and satisfaction with past performance, which hinders it. Moreover, in the current study, the opposing forces assumed equal importance in the innovation-generation process, explaining approximately equal amounts of variation in innovation effort. Furthermore, the results offer evidence of unique inertial tendency in innovation effort and of interdependence in innovation effort across competitors. Failure to control explicitly for such strategic interdependence produces an inferior fit to the data and null effects for the impact of many variables. This points to the need for further research to consider these effects when investigating the antecedents of innovation across firms. It also raises worthwhile questions for further research. For example, how do firms learn about the innovation effort of their competitors, and what are the inference processes at work? What are the factors that determine the extent of inertial tendency in innovation effort and heterogeneity in inertia across firms? Some factors that further research might consider include individual-difference variables, such as decision makers proneness to inertia or risk taking, and aspects of the decision-making process, such as strategic conjecturing about competitors and customers, beliefs about market dynamism, and effectiveness of implementation. Bayus, Erickson, and Jacobson (2003) initiate such an inquiry by examining the number of new products introduced as a potential determinant of inertial patterns in profits across firms in the personal computer industry, but their study finds no effects.
Performance
With regard to the relationship among shared market knowledge, innovation, and performance, the results of this study suggest that shared market knowledge helps smaller firms actualize better returns for their innovation effort, which means that shared market knowledge may be a source of competitive advantage for resource-strapped firms. Specifically, large firms, which experience inertial effects of their financial resources, need to achieve higher levels of shared market knowledge to enjoy the same positive returns as small firms. Strikingly, for small firms in this study at the mean level of shared market knowledge, an increase in innovation effort by $1 million results in a return of 1.3% market share points. Large firms experienced almost the same return (1.2% market share points) only at the ninetieth percentile of shared market knowledge. Studies across different contexts should be conducted to replicate the results and to identify limiting conditions based on industry factors and dynamics. Overall, this model shows that the impact of innovation effort on performance depends on shared market knowledge and firm size. Still unknown are the mechanisms that firms can employ to facilitate the efficient and effective sharing of market knowledge. Given the cost of attaining shared market knowledge, what are the structural and cognitive impediments to achieving optimal shared knowledge? Are the patterns of inertia in innovation and firm performance also prevalent in the processes by which individual decision makers learn and share knowledge with one another? These are questions worthy of future research endeavors.
Conclusion
In a significant departure from prior work, this research adopts a dynamic perspective of the simultaneous evolution of market knowledge and innovation. It also contributes to the debate about the role of market knowledge accuracy in strategic decision making. Although I used a quasiexperimental setting to test the conceptual model (which requires replications in different industrial settings), the research clarifies the role of market knowledge and suggests that market knowledge, by itself, has no positive effects on innovation effort or performance. When market knowledge is updated (i.e., changed on the basis of individual interpretation of the market) and shared among decision makers (i.e., there is common understanding of the relevant market knowledge in the team), it leads to increased innovation effort and higher returns to innovation. Furthermore, the results reveal that though innovation effort is sensitive to shared knowledge about both customers and competitors, it is more sensitive to competitor knowledge than to customer knowledge. Although the spectrum of this response no doubt depends on the industry setting, assessment and management of the returns to market knowledge should take into account the dynamic impact of knowledge change and sharing in the strategic decision-making team over time. The results also imply that this might be more important for resource-constrained than for financially endowed firms.
Finally, this research has built on the momentum created by prior research in the marketing literature on organizational learning, strategic orientation, and organizational memory. I hope that it generates further examination of the dynamic processes that underlie market knowledge diffusion and the nature of its consequences for performance over time.
The author thanks the Institute for the Study of Business Markets for providing support for this research as a part of its Business Marketing Doctoral Support Award Competition; her dissertation chair, Murali Chandrashekaran, Bob Dwyer at the University of Cincinnati, Donald Lehmann at Columbia University, and the three anonymous JM reviewers for their helpful comments; and Jagdip Singh at Case Western Reserve University for his helpful comments and support.
( n1) The average age is based on 24 participants; 1 participant did not report her age. I also realized that study participants would be junior people in any organization. However, I do not have reason to believe that seniority of decision makers moderates or changes the relationship between dynamics in market knowledge and innovation. To check empirically whether this was the case in my data, I included the average work experience and age as main effects and interacted them with the variables in the innovation and performance model. I did not find any significant interactions; that is, the reported parameter estimates remain unchanged.
( n2) Although the ability to assess correctly the strength of competitors is an important aspect of competitor knowledge, attempts to collect data on the most influential competitor per segment yielded many missing data points and inaccuracies in recording (the demand on participants was significant). Respondents often forgot to circle the most influential competitor or, in assigning competing products to segments, noted that they were not sure in which segment the particular competitor's presence was the strongest. Because of the significant noise in the data, I chose not to include this measure in the analysis.
( n3) I also analyzed the data after weighing each team member's knowledge by distance from the team mean. This did not change the obtained substantive insights.
( n4) On the basis of H[sub1]-H[sub5], I specified X as X = [CUSTK, CUSTK x CUSTC, CUSTK x CUSTS, CUSTK x CUSTC x CUSTS, COMPK, COMPK x COMPC, COMPK x COMPS, COMPK x COMPC x COMPS, SAT, SAT², SIZE].
( n5) If the variance--covariance matrix of the disturbance term is a positive, definite matrix with unknown parameters that need to be estimated (e.g., cross-firm correlations, autocorrelations), generalized least squares is not feasible. Thus, a structure needs to be imposed on the model (i.e., obtain estimates of the unknown parameters and then proceed with the generalized least squares estimation). The resultant feasible generalized least squares estimates are consistent, unbiased, and asymptotically efficient.
( n6) I did not estimate a simultaneous system of equations because innovation takes place (and is measured) before subsequent performance, which is an important aspect of the study's longitudinal design. Empirically, correlation of the residuals from the two models (which account for contemporaneous effects and dependence on the past) is not significant (-.006, p < .588).
( n7) The effect of shared customer knowledge on innovation effort is given by b[sub3]CUSTK + b[sub4]CUSTK x CUSTC. I evaluated this at low (tenth percentile) and high (ninetieth percentile) values of CUSTK and CUSTC. Likewise, the effect of shared competitor knowledge on innovation effort is given by b[sub7]COMPK + b[sub8]COMPK x COMPC. I also evaluated this at low (tenth percentile) and high (ninetieth percentile) values of COMPK and COMPC.
( n8) The effect of change in customer knowledge on innovation effort is given by b[sub2]CUSTK + b[sub4]CUSTK x CUSTS. I evaluated this at low (tenth percentile) and high (ninetieth percentile) values of CUSTK and CUSTS. The effect of change in competitor knowledge on innovation effort is given by b[sub6]COMPK + b[sub8]COMPK x COMPS, which I also evaluated at low and high values of COMPK and COMPS.
( n9) The effect of customer knowledge level on innovation effort is given by b[sub1]+ b[sub2]CUSTC + b[sub3]CUSTS + b[sub4]CUSTK x CUSTS. I evaluated this at low (tenth percentile) and high (ninetieth percentile) values of CUSTC and CUSTS. The effect of competitor knowledge level on innovation effort is given by b5 + b[sub6]COMPC + b[sub7]COMPS + b[sub8]COMPK x COMPS, which I also evaluated at low and high values of COMPC and COMPS.
( n10) To address potential problems with multicollinearity, I created an instrument for innovation effort orthogonal to shared market knowledge, firm size, and strategic orientation.
( n11) Because size is a function of the previous period s performance, I constructed an instrumental variable estimate for size (based on a two-period lagged value) and performed the Hausman specification test. The results suggest that the estimated coefficients are not biased (χ[sub1d.f., sup2] = 1.78, n.s.).
Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
A B C D E
F G H I
J K L M
N
1. Innovation effort 1.00
2. Customer knowledge level -.30 1.00
3. Change in customer knowledge .12 -.17 1.00
4. Shared customer knowledge .30 .28 -.12 1.00
5. Competitor knowledge level -.06 .32 .33 .31
1.00
6. Change in competitor knowledge .17 -.02 .37 -.01
.27 1.00
7. Shared competitor knowledge .22 -.24 .27 -.21
.20 .33 1.00
8. Satisfaction -.17 .33 -.19 .36
-.13 -.15 -.30 1.00
9. Strategic orientation .16 -.03 -.22 .02
-.21 .01 -.05 .40
1.00
10. Total shared market knowledge -.17 .12 -.06 .01
.15 .10 .04 .05
-.20 1.00
11. Firm size .04 .30 -.15 .22
.03 -.15 -.34 .41
.21 -.18 1.00
12. Market share -.03 .23 -.14 .28
-.15 -.10 -.30 .65
.46 -.11 .57 1.00
13. Stock price index .01 .14 -.17 .13
-.13 -.12 -.30 .53
.39 -.23 .68 .92
1.00
Mean 613 .25 .63 .16
.32 .13 .16 4.42
14.57 .59 12,463 16.66
1324
Standard deviation 859 .18 .47 .15
.13 .10 .10 1.50
13.44 .32 4366 8.19
638
Notes: Means for firm size and innovation effort are reported
in thousands of dollars. Legend for Chart:
A - Assumption About Interdependence of Innovation
Effort Across Firms
B - Assumption About Inertia in Innovation Effort
No Intertia (ρ[subi] = 0 ∀ i)
C - Assumption About Inertia in Innovation Effort
Similar Inertial Tendency Across Firms
(λ[subi] = 0 ∀ i)
D - Assumption About Inertia in Innovation Effort
Unique Inertial Tendency for Each Firm
(ρ[subi] = ρ + λ[subi])
A B C
D
No interdependence M[sub00] M[sub01]
(σ[subij]= 0) ∀ i, j) M[sub02]
Interdependence σ[subij] free) M[sub10] M[sub11]
M[sub12] Legend for Chart:
A - Conceptual Focus
B - Variables (Parameter)
C - Estimate (Standard Error)
A
B C
Customer knowledge diffusion
CUSTK (β[sub1]) -.952(**)
(.437)
CUSTK x CUSTC (β[sub2]) -.229
(.366)
CUSTK x CUSTS (β[sub3]) 1.196(***)
(.306)
CUSTK x CUSTC x CUSTS (β[sub4]) 3.072(***)
(.370)
Competitor knowledge diffusion
COMPK (β[sub5]) -.569
(.316)
COMPK x COMPC (β[sub6]) .315
(.749)
COMPK x COMPS (β[sub7]) .552(*)
(.316)
COMPK x COMPC x COMPS (β[sub8]) 3.664(**)
(1.729)
Feedback from marketplace: effect of satisfaction
SAT (β[sub9]) -11.50(***)
(1.88)
SAT x SAT (β[sub10]) .205(***)
(.029)
Firm size
SIZE (β[sub11]) -.883
(1.14)
(*) p < .10.
(**) p < .05.
(***) p < .0001. A. Net Effect of Shared Customer/Competitor Knowledge
Change in Customer Knowledge
Legend for Chart:
B - Low
C - High
A B C
Customer Knowledge Level
Low .724(**) 12.31(**)
(.123) (2.088)
High 2.936(**) 49.91(**)
(.308) (5.229)
Change in Competitor Knowledge
Legend for Chart:
B - Low
C - High
A B C
Customer Knowledge Level
Low .735(**) 2.274(**)
(.254) (.598)
High 3.970(*) 12.28(**)
(1.370) (3.230)
B. Net Effect of Change in Customer/Competitor Knowledge
Shared Customer Knowledge
Legend for Chart:
B - Low
C - High
A B C
Customer Knowledge Level
Low .154 4.824(**)
(.120) (.463)
High 2.621 82.01(**)
(2.038) (7.867)
Shared Competitor Knowledge
Legend for Chart:
B - Low
C - High
A B C
Customer Knowledge Level
Low .105(*) 1.131(*)
(.059) (.475)
High .566(*) 6.105(*)
(.320) (2.564)
C. Net Effect of Customer/Competitor Knowledge Level
Shared Customer Knowledge
Legend for Chart:
B - Low
C - High
A B C
Change in Customer Knowledge
Low -.636 6.243(**)
(.416) (1.416)
High .058 27.95(**)
(.465) (2.817)
Shared Competitor Knowledge
Legend for Chart:
B - Low
C - High
A B C
Change in Competitor Knowledge
Low -.406 1.625(*)
(.354) (.8.39
High .034 6.401(**)
(.277) (1.596)
(*) p < .05.
(**) p < .0001.
Notes: All entries are estimates. Standard errors
are in parentheses. Legend for Chart:
A - Variable (Parameter)
B - Main Study Performance Measured as Market Share
C - Main Study Performance Measured as Stock Price Index
D - Validation Study 2 Performance Measured as Market Share
A
B C D
Innovation effort (β[sub1])
.090 .027(**) .008
(1.125) (.005) (.009)
Innovation effort x shared
market knowledge (β[sub2])
5.438(*) .017(**) .048(**)
(.866) (.003) (.015)
Innovation effort x firm size (β[sub3])
-.231(**) -.003(**) -.002(**)
(.082) (.0004) (.0003)
Firm size (β[sub4])
.921(**) .806(**) .247(**)
(.437) (.046) (.055)
Shared market knowledge (β[sub5])
-.590 -1.078(**) -2.350(**)
(.607) (.302) (.767)
Strategic orientation (β[sub6])
.121(**) .093(**) .044(**)
(.022) (.009) (.008)
(*) p < .05.
(**) p < .0001.
Notes: Entries are parameter estimates from the
corresponding model. Standard errors are in parentheses. Legend for Chart:
A - Aspects of Study
B - Main Study
C - Pretest 1
D - Validation Study 1
E - Validation Study 2
A
B C
D E
Microlevel data collected
Knowledge diffusion, Satisfaction with past
satisfaction with past performance, group
performance, strategic dynamics, information
orientation use issues
None Satisfaction with
past performance,
group dynamics,
strategic orientation
Number of firms (teams)
6 5
5 6
Number of time periods
8 6
9 9
Analysis data (number of
firm/time periods)
48 30
45 54
Support for the partial
adjustment model?
Yes Yes
(χ[sub1, sup2] (χ[sub1, sup2]
= 17.383, = 15.122,
p < .0001) p < .0001)
Yes Yes
(χ[sub1, sup2] (χ[sub1, sup2]
= 26.698, = 15.07,
p < .0001) p < .0001)
Average value (standard
deviation) of ρ
.732 .753
(.188) (.189)
.763 .700
(.138) (.116)
Is there performance
interdependence
across firms?
Yes Yes
(χ[sub15, sup2] (χ[sub10, sup2]
= 36.148, = 23.71,
p < .01) p < .01)
Yes Yes
(χ[sub10, sup2] (χ[sub10, sup2]
= 74.80, = 35.46,
p < .01) p < .01) Legend for Chart:
A - Criteria
B - Hypothesized Model Performance Main Study
C - Hypothesized Model Performance Validation Study 2
D - Hypothesized Model Performance Validation Study 3
A B C D
Correlation between actual .80 .64 .92
and predicted performance
Mean absolute error 2.52 2.40 2.59
Mean absolute percentage error .175 .178 .22
Mean square error 11.43 9.10 10.19
Theil's U .092 .087 .071DIAGRAM: FIGURE 1 Conceptual Framework
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The partial adjustment process can be expressed as follows (see Coleman 1968; Greve 1999):
(A1) ∂PERF[subit]/∂t = r[subi](PERF[subit, sup*] - PERF[subit],
where PERF[subit] and PERF[subit, sup*] denote the realized and potential performance for the ith firm at time t, and r[subi] captures the adjustment rate for the ith firm. When r[subi] = 0, there is no adjustment, and a condition of complete inertia is in evidence. When r[subi] is large, there is evidence of little inertia. Solving Equation A1 yields the following solution:
(A2) ln [PERF[subit, sup*] - PERF[subit]/PERF[subi,t - 1, sup*] - PERF[subi,t - 1]] = -r[subi],
which simplifies to
(A3) PERF[subit] = PERF[subit, sup*] - e[sup-r[subi]](PERF[subi,t - 1, sup*] - PERF[subi,t - 1).
Substituting ρ[subi] = e[sup-r[subi]], I obtain
(A4) PERF[subit] = PERF[subit, sup*] - ρ[subi] (PERF[subi,t - 1, sup*] - PERF[subi,t - 1).
Finally, specifying PERF[subit, sup*] = X[subit]β + ε[subit], I express the autoregressive partial-adjustment model as follows:
(A5) PERF[subit] = X[subit]β - ρ(X[subi,t-1]β - PERF[subi,t - 1] + ε[subit] - ρ[subi]ε[subi,t - 1],
which can be expressed as follows:
(A6) PERF[subit] = X[subit]β - ρ[subi](X[subi,t - 1]β - PERF[subi,t - 1] + η[subit,
where η[subit] = ε[subit] - ρ[subi]ε[subi,t - 1].
~~~~~~~~
By Detelina Marinova
Detelina Marinova is Assistant Professor of Marketing, Weatherhead School of Management, Case Western Reserve University (e-mail: detelina.marinova@case.edu).
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Record: 9- An Accidental Venture into Academics (Book). By: Wittink, Dick R.; Clark, Terry. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p124-130. 7p.
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Section: Book ReviewsAn Accidental Venture into Academics
In the summer of 2002, Terry Clark asked me to write an essay about the history and development of my professional activities. Although I was flattered, I also realized that this must be a sign that I had reached a mature, sometimes confused with wise, stage of my life cycle. I delayed informing others about my maturity by waiting a year to complete this essay. However, unless the biomedical innovations with regard to aging accelerate rapidly, I need to be realistic: At this point, I still have the time and wherewithal to ponder new options. Besides, the process of reflection might be instructive.
The title of my essay is intended to convey that seemingly random events have exerted major influences in my life. It is well known that we have little control over many aspects of our lives. Our diverse genetic makeup is fixed to a large degree, and we face extensive heterogeneity in the opportunities available to us. Nevertheless, there is a lot we can do to shape our lives. I will attempt to illustrate by reviewing my own state of affairs.
I grew up in a family and a culture dominated by a Calvinistic work ethic. Family life was dominated by my father's bakery, in part because the production facility was in the back of the house and the retail outlet in the front. All family members were expected to assist with the business when needed; therefore, demands on my time could occur any day I returned from school. To minimize the chance, I often joined friends after school to spend time at their homes or to play soccer. Broader family gatherings were important, and in the presence of extended family members, my father seemed intent on demonstrating that he had achieved at least as much financial success as two uncles with university degrees had.
Although neither of my parents had gone to high school, they seemed to believe that education would be critical to my future success. In the Netherlands, the most selective secondary school option was required for entry into universities. A second option was intended to provide the basis for low-and midlevel professional careers and a third one for skilled labor. The Department of Education mandated that all primary school children be evaluated during their last year with respect to their potential for the three options. The counselor who saw me was probably conscious of social classes, and my parents' modest education may have influenced him to recommend that I attend the school for skilled labor. However, he added that if my parents felt strongly about it, the second option might be suitable. Because neither of these options was consistent with my grades, I give my parents credit for ignoring this advice. Their decision to send me to the highest level of secondary education was especially important given that I had no interest in succeeding my father in his business. The uncertainties of a small business presented obvious pitfalls, and I vowed that my own family would enjoy a home life separate from business.
Toward the end of my secondary education, I contemplated studying economics. University study, heavily subsidized by the state, was a risky activity in the 1960s. About half the students in Holland failed to continue after the first or second year, and most graduates took longer than the norm of five to six years for the equivalent of a master's degree.( n1) I decided to pursue an unconventional option instead and applied for admission to Nyenrode, a private college created after World War II to prepare students for careers in international business, with financial support from companies such as Philips, Shell, and Unilever. Course content placed heavy emphasis on macro-and microeconomics and foreign languages including English, French, German, Spanish, and Swedish. There were also ample opportunities for participation in a wide range of sports and other activities designed to enhance social skills.
A week before graduation from Nyenrode, I was invited to return to campus for an interview related to a scholarship for study abroad. I was planning to stay in the Netherlands to pursue a career in accounting. Because accounting conventions differed widely between countries, I argued that the scholarship was irrelevant to me. However, on graduation day, the college president informed me that I had won the scholarship on the basis of my exam scores. He asked that I simply accept the prize during the ceremonies and decide later whether I would actually use it. He knew I was due for up to two years of compulsory Dutch military service and would have ample time to consider it.
During a one-year officer-training program, I discussed professional options with my fiancée and with fellow trainees who were mostly law and economics graduates. Everyone agreed that it would be a mistake for me not to accept the scholarship. My fiancíe and I decided we should complete a year of marriage before we embarked on a foreign venture. She initially favored European options, such as graduate study in economics at Sankt Gallen in Switzerland. However, America beckoned.
Although a scholarship made it easier to justify going abroad, the actual benefits were still unclear to me. Nevertheless, I accepted admission to a program at the University of Oregon. Because marketing was considered especially strong in the United States, I abandoned my interest in accounting and chose to concentrate instead in marketing. During my first quarter at Oregon, I took a course in statistics from James Reinmuth, who made complex material understandable and interesting. I ended up taking many more statistics courses and far fewer marketing courses than I had initially intended during two years of study at Oregon. My plan was to return to Holland or at least Europe, perhaps to work in market research for Philips or Unilever or to join a consulting firm. Just when I began contemplating options, two things interfered.
First, my wife Marian now preferred to stay in the United States. I might have persuaded her to return to Europe, but because she had joined me reluctantly when I chose the United States over Switzerland, it did not seem fair for me to ask her to move in both directions across the Atlantic against her preferences. She had worked full-time while I studied, and her interests deserved full consideration in our decisions. Before we married, her mother asked me whether I was sure Marian was the "right one" for me. I must have had a mind for statistics because I remember contemplating something along the lines of the following: "Given the large number of unobservables, how could I know if she is the one? Of all the eligible females, I knew just a handful well enough to make somewhat informed judgments about their suitability." I said something like that to my future mother-in-law. However, I also emphasized that I thought Marian and I were quite compatible and that I was committed to building a strong relationship. We had been childhood sweethearts and had shown a strong interest in each other at various stages in our lives. I had read a book or two about matrimony and had formed some tentative opinions on the characteristics of successful and happy marriages. Happy marriages seemed to depend especially on the compatibility of partners but also on a great deal of personal attention and flexibility by both partners. With this philosophy in mind, it seemed that it was my turn to be flexible. Thus, staying in the United States became a priority.
Second, Reinmuth suggested that I consider doctoral study. Although the thought had not crossed my mind, the idea was sufficiently intriguing for me first to try full-time teaching. I obtained one offer at a public college in Washington State, where I taught mostly statistics for two years. Research had fascinated me already. For example, I did a laboratory experiment with a fellow student on odd pricing, and Reinmuth proposed to combine a project I had completed with his own research, which several years later resulted in my first publication (Reinmuth and Wittink 1974).
I was especially intrigued by the doctoral programs at Northwestern and Purdue. I chose Purdue because it seemed to fit better with my interests. Still, during the second year of the three-year program, I had doubts about my ability to complete it. My wife convinced me to continue (or perhaps she said that quitting was unacceptable). Our daughter, who was two years old when I started the doctoral program, also played an important role. She would often accompany me during the weekends on visits to the computer center where I did batch processing of programs. She enjoyed playing with IBM punch cards and drawing on computer output.
Purdue's Krannert School had achieved a strong reputation because of Frank Bass and Mike Pessemier. In the fall of 1974, my cohort received one or more offers from a variety of schools, including Berkeley, Carnegie, Chicago, Harvard, MIT, Northwestern, Stanford, and Wharton. I accepted the offer from Stanford partly because of its research excellence across fields and the presence of marketing scholars such as Dave Montgomery, Mike Ray, Seenu Srinivasan, and Peter Wright. Interestingly, although I would join the marketing group, my job was actually to teach statistics and econometrics. Marian gave birth to our second child at about the same time I defended my thesis.
Stanford
During the doctoral program, much of my research interest focused on econometric models, including the latest methods for pooling cross-sectional and time-series data (Bass and Wittink 1975), and I pursued interaction effects between price and advertising (Wittink 1977; see also Kaul and Wittink 1995). At Stanford, Dave Montgomery and Seenu Srinivasan pursued customer preference (conjoint) measurement. This emerging research activity offered interesting opportunities. At the time, little was known about differences in performance between, for example, alternative methods (Wittink and Cattin 1981) or external validity (Montgomery and Wittink 1980). The 1981 paper I coauthored with Philippe Cattin was initially rejected by Journal of Marketing Research (JMR) in 1976. It remained on the shelf for four years until a colleague suggested that we resubmit it to the same journal!
An important mentor of mine was Richard Johnson. My first meeting with Rich was at Market Facts in Chicago, where he had developed an attribute trade-off approach that focused on two attributes at a time (Johnson 1974). He had an application that involved characteristics of life partners to interest potential users in conjoint applications.
Other research projects included a survey of commercial practices (Cattin and Wittink 1982) and an application to the marketing of arts programs (Currim, Weinberg, and Wittink 1981). The design of the latter study had three attributes with three levels and three attributes with two levels. The empirical results suggested a strong artificial effect that I focused on in various projects (e.g., Steenkamp and Wittink 1994; Wittink, Krishnamurthi, and Nutter 1982; Wittink, Krishnamurthi, and Reibstein 1990; Verlegh, Schifferstein, and Wittink 2002).
I fondly recall making a presentation at the first Advanced Research Techniques Forum on the number-of-attribute-levels effect that at that time had been shown to exist only for full-profile and trade-off matrix designs (Wittink 1991). Joel Huber asked publicly if I thought the effect would apply to adaptive conjoint analysis (ACA) (Johnson 1987). In some sense, ACA was based on Johnson's tradeoff matrix approach; it customizes trade-off questions based on self-explicated data. I suggested that the number-of attribute-levels effect might not apply to ACA. Joel suggested that it would. After my presentation, two practitioners offered to join us for a study. We designed an experimental study with four treatments, each of which had two options (see Huber et al. 1993). The empirical results for the number-of-levels effect showed that Joel was correct: ACA also showed a number-of-levels effect (Wittink et al. 1992), but its magnitude was much smaller than it was for full profile. Interestingly, we also found that the effect did not need to occur in ACA. In subsequent versions of ACA, Johnson modified the integration of self-explicated data with paired preference intensity judgments, and this modification reduced the number-of-levels effect further (Orme 1998).
An important element of my time at Stanford consisted of the development of an econometric model to aid the MBA student admission process. The idea was to relate academic performance in the MBA program to many predictors, including GMAT scores, undergraduate grade point average, and a measure of the quality of the undergraduate institution. The project spanned multiple years and included the difficulty of obtaining cooperation from successive admission directors. A working paper (never submitted for publication) discusses important issues regarding measurement, model specification, estimation, and validation (Srinivasan, Wittink, and Zweig 1981). We proposed a two-stage process for admission decisions. In the first stage, a model-based prediction of each applicant's probability of failing to satisfy minimum requirements in the quantitative and/or the managerial components in the core MBA program is obtained. Applicants with high failure likelihoods would be eliminated. In the second stage, the admission director could focus primarily on the remaining candidates' career potentials. Seenu was the primary architect, and it was a privilege for me to work with him on this project.
My Stanford period also included the Market Measurement and Analysis Conference that Dave Montgomery and I organized in 1979 on a suggestion by Frank Bass. We proposed to have select academics and practitioners interact about research. This conference is now considered the first Marketing Science conference (see Wittink 2001).
During my sixth year at Stanford, Cornell offered me a position with tenure. Marian and I decided that upstate New York is an idyllic environment in which families thrive. Another rationalization for the move was that we believed that our two children would benefit from exposure to the seasons, and Holland would be easier to visit. I am sure that the uncertainty of tenure at Stanford played no role, yet I do recall a discussion with junior-faculty colleagues at Stanford about prospects for tenure, partly as a result of the frequent failures of internal candidates to be promoted. A non-marketing colleague told me that if I just ignored my family for the remaining time, tenure would be attainable. We moved to Ithaca in 1981.
Cornell
For various reasons, including my acceptance of administrative responsibilities, and the onset of severe medical problems, my research output declined during the first part of my time at Cornell. A sabbatical leave in 1988-89, spent at Northwestern's Kellogg School and at the Faculty of Economics of the University of Groningen in the Netherlands, was critical to the reinvigoration of my research activities. My visit in Holland showed that Peter Leeflang had created a research culture that rivaled the best in the United States, and we embarked on various projects, including competitive reaction research (Leeflang and Wittink 1992, 1996, 2001). The reemergence of my interest in research was also facilitated by the availability of scanner data. In the mid-1980s, I accepted an invitation from ACNielsen to work on the development of a price-promotion model based on weekly store-level scanner data. My work at ACNielsen culminated in a paper submitted to JMR that describes the initial promotion model along with empirical results. Rejection of the SCAN*PRO paper (Wittink et al. 1988) by JMR was swift. Because there was no new estimation methodology, the reviewers argued that the paper was not suitable for the journal. Still, the model appears to satisfy Little's (1970) criteria for structure: It is simple, complete, adaptive, and robust. I regret that my coauthors and I never sent the paper elsewhere. Quite a few years later, Don Morrison, who had nothing to do with the rejection at JMR, provided some redemption when he opined that this paper should have been published (for research that uses empirical results in the working paper, see Morrison and Silva-Rossa 1995).
The SCAN*PRO model provided a basis for further research. For example, Christen and colleagues (1997) show the bias in a model of linearly aggregated data when the model of disaggregate data is nonlinear. Foekens, Leeflang, and Wittink (1999) show how promotion effects depend on the frequency and intensity of previous price discounts. The inclusion of dynamic effects is also a way to address the Lucas (1976) critique. Van Heerde, Leeflang, and Wittink (2000) show that cross-period effects can be obtained in models estimated from store data. Van Heerde, Leeflang, and Wittink (2001) use semiparametric estimation of promotion effects to show that these effects have complex main (and crossover) interaction effects. They also demonstrate superior predictive validity of the semiparametric approach relative to traditional approaches. Van Heerde, Leeflang, and Wittink (2002) describe the evolution of research related to SCAN*PRO.
I am particularly excited about two recent articles that build on the innovative and award-winning research by Sunil Gupta (1988). Much of the development in these two articles is due to the highly capable work of Harald van Heerde, a highly competent econometrician by training and a terrific research colleague. In one article, Van Heerde, Sachin Gupta, and Wittink (2003) transform the well-known household data-based elasticity decomposition result, which has been misinterpreted as reporting that the vast majority of the sales effect is due to brand switching, into a unit-sales decomposition. In the other article, Van Heerde, Leeflang, and Wittink (2005) show that substantively meaningful unit-sales decompositions can also be obtained from store data. The framework in the latter article is extremely powerful and attractive.
My association with Peter Leeflang continues to be productive. We completed a book on model building (Leeflang et al. 2001), a revision of the highly regarded book he and Philippe Naert completed when quantitative model building in marketing was in its infancy (Naert and Leeflang 1978). My research association with Peter was formalized with an honorary faculty appointment at the Department of Economics of the University of Groningen in 1993, and I received a great accolade for my research when I was elected to the esteemed Royal Dutch Academy of Sciences in 2001.
In 1990, Cornell had the privilege of attracting Pradeep Chintagunta to join the faculty. Pradeep is highly capable, enormously productive, and a superb colleague. His departure for the University of Chicago in the mid-1990s was a severe professional and personal loss for me. Both of us worked with Cornell's strong cadre of doctoral students, and I want to acknowledge especially Sachin Gupta, with whom both of us worked extensively (Gupta, Chintagunta, and Wittink 1997; Gupta et al. 1996).
In the mid-1990s, I began working with Ed McLaughlin at Cornell's Department of Agricultural Economics. This department has a strong marketing component with extensive contacts in several industries, especially the food industry. Ed and I have worked with associates on customer satisfaction research (Gomez, McLaughlin, and Wittink 2004; Sirohi, McLaughlin, and Wittink 1998), and we are now extending the research with enhanced data from the food industry. Another association that is important to me is that with my fellow doctoral student at Purdue. Dave Reibstein and I worked together on conjoint analysis (Wittink et al. 1989; Wittink, Krishnamurthi, and Reibstein 1990) and strategy (Keil, Reibstein, and Wittink 2001). In 2001, we co-organized a special-interest conference on competition, sponsored by the Marketing Science Institute, that will lead to a special issue of Marketing Science.
I want to acknowledge the role of Tom Dyckman during my Cornell period. Tom was a terrific mentor, and I am deeply grateful for his support. I take great pride in the fact that Cornell had a much stronger and larger group of marketing faculty in the late 1990s than it had in the early 1980s, when it was known primarily for accounting and finance. BusinessWeek (1998) placed Cornell in the top ten only in marketing. So why move?
Yale
Yale is attractive for many reasons, including a strong research culture at the School of Management. The transition from Cornell was amazingly smooth, and I felt immediately comfortable. Ravi Dhar and Subrata Sen helped facilitate my integration into the Yale culture. Importantly, we have been able to create a superb group of researchers with the additions of On Amir, Dina Mayzlin, Nathan Novemsky, and K. Sudhir. Another benefit is the proximity to New York City. I am a partner at Blue Flame Data Inc., a firm that I cofounded with Sev Keil (see Wittink and Keil 2000) that is located in Manhattan, and my wife and I usually have season tickets to the Metropolitan Opera.
My move to Yale in 1998 also spurred research in health care. Patients demand a greater role in the decision-making process for chronic diseases. Yet physicians do not have the time, the inclination, the ability, or the preparation to learn each patient's unique perspectives on alternative treatments that differ on multiple criteria. My personal experience illustrates the problem. I was diagnosed with type 1 (insulin-dependent) diabetes in 1983. My family physician presented multiple treatment options, such as daily injections and an insulin pump. The latter requires the insertion of a subcutaneous device attached to a visible beeperlike instrument, a method that seemed excessively obtrusive to me at the time (I had visions of being a patient walking in a hospital with an IV bottle attached to a transportable stand). I now recognize that this perception was excessively negative partly because the autoimmune disease diagnosis depressed me, and I preferred to hide the diabetic condition. However, knowledge of the severe consequences of hyperglycemia, such as cardiovascular disease and neuropathy, made it impossible for me to ignore the disease. So I chose insulin injections to cover basal requirements and the expected carbohydrate intake for 24 hours at a time. Choosing this option constrained my food intake in the sense that I risked hypoglycemic reactions if I did not eat at specified times. My interest in minimizing the chances of severe long-term effects from persistent hyperglycemia made me favor accepting the short-term risks of hypoglycemia.
I mention this for several reasons. One is that having type 1 diabetes affected my life enormously. Coming to grips with being diabetic reduced my research activity. My wife and children were greatly affected by the multiple hypoglycemic bouts I experienced. I became unconscious several times and frequently experienced reduced cognitive abilities. An obvious solution to the incidence of hypoglycemia was for me to increase the frequency of self-administered blood-sugar tests. It took me far longer than it should have to reach that conclusion, but I should have realized much faster that an alternative treatment would help too. In the mid-1990s, I finally switched from insulin injections to the insulin pump, thanks to my wife who recommended I visit a new endocrinologist. He suggested I switch to the pump for reasons of convenience. However, the switch also had a favorable influence on my research productivity and reduced the total cost of treatment.
The frustrating aspect is that I should have been able to discover my preference for this alternative treatment a lot sooner myself. The argument is that for chronic diseases such as diabetes and arthritis, patients must take a proactive role in disease management. This requires that patients become informed about the disease and about the characteristics of alternative treatments. Patients should be almost as informed as physicians. However, the health care system is still based on the traditional treatment of acute diseases. That is, a patient visits a physician when he or she experiences a problem, the physician diagnoses the problem, and the patient follows the recommended treatment. The patient is mostly passive. After the initial diagnosis of a chronic disease, the patient should take charge and be an active participant in the treatment decisions. Because patient preferences change over time and the available treatment options change as well, the patient should have the opportunity to consider the trade-offs between conflicting characteristics and to learn which treatment is the best fit, subject to medical constraints. Patients should be able to obtain information and to consider trade-offs from a kiosk that sits in the waiting room or on the Web.
By repeating a trade-off exercise periodically, the patient has a chance to discover whether changes in preferred treatment will emerge on the basis of, for example, changes in weights for relevant dimensions (as would have applied to me in the treatment of diabetes). With a continuously updated database with all relevant characteristics for alternative treatments, including new ones, patients also have the opportunity to take advantage of the latest innovations. Importantly, physicians do not have the facility to detect changes in preferences. As a result, most changes occur when patients switch physicians. Indeed, the reason I switched to the insulin pump was the new endocrinologist's argument that I would enjoy the convenience.
I am now eager to promote the idea that patients with chronic diseases, for which there are treatment alternatives that differ on multiple dimensions, have the opportunity to consider the attractiveness of characteristics on a regular basis. On the basis of each individual patient's estimated preference function for treatment characteristics relevant to a given disease, the patient and the physician can learn which treatment option provides the best fit at a given time. Greater patient involvement should result in greater patient satisfaction and enhanced compliance so that health outcomes will improve and total costs might decline. My research with Liana Fraenkel at the Yale School of Medicine is concentrated in this area (Fraenkel, Bogardus, and Wittink 2001, 2003; Fraenkel et al. 2004a, b). We are proposing to conduct experiments to demonstrate the benefits of active patient involvement. The idea of proactive involvement by patients is akin to customers employing decision aids for the selection of the best product or service when there are multiple options that differ on many dimensions.
It is well known that faculty members tend to change the relative emphasis on research, teaching, and other activities over time. Changes in activities occur because of personal preferences but also because of opportunities and institutional demands. An activity that has been fairly constant over time for me is my involvement with doctoral students (Imran Currim and Lakshman Krishnamurthi at Stanford; Naufel Vilcassim, Eric Wruck, Padmini Desikachar, Rishin Roy, John Walsh, Sachin Gupta, Alok Prasad, Rahul Guha, Anil Kaul, Seethu Seetharaman, Niren Sirohi, Sumas Wongsunopparat, and Sev Keil at Cornell; Eijte Foekens, Marco Vriens, Harald van Heerde, Csilla Horvath, and Albert van Dijk at Groningen; and Tom Steenburgh at Yale).
It is not surprising that the Cornell period has the largest number of students whose theses I supervised (the ones in Holland were joint with Peter Leeflang or Michel Wedel). This period was also enormously important personally. Ithaca is a family-friendly place that is conducive to having a happy marriage and happy offspring. It is very important to me that my wife and children realize their own personal and professional goals. The dominant professional themes in my family are health care and education. My wife, originally trained as a nurse, has worked as a childbirth educator and massage therapist, is an advocate of alternative medicines, has an extensive background in Oriental healing arts, and is a dedicated yoga teacher. Our daughter is a licensed doctor who is currently a research fellow focusing on geriatric depression at the University of Pennsylvania's School of Medicine. Our son works at the Center for Responsive Law in Washington, D.C., where one of his projects focuses on legislation related to medical malpractice. Both kids are Cornell graduates, as are their spouses. The diverse health-related professional activities of my family members happen to fit my own newly found predilection. I recently accepted the codirectorship of a proposed Center for Health Management and Policy at Yale University.
Each stage in my professional formation was based on choices I made. However, there were many events over which I had little control that have had a major effect on my career. For example, I never intended to become an academic researcher until a faculty member in statistics at the University of Oregon suggested doctoral study. And the move to the United States was prompted by a scholarship for study abroad that I initially brushed aside. Thus, it seemed appropriate to me to title this essay "An Accidental Venture into Academics." Without the move to the United States, I might not have chosen marketing as a field of study. My interest in applied statistics and econometrics is largely due to accidental exposure to James Reinmuth and Frank Bass.
My accidental venture into academics has been and continues to be a terrific experience. Although I have wondered how I might have fared in business, I cannot imagine that an alternative scenario would have generated the same satisfaction. Of course, if I had pursued a degree in economics in the Netherlands, I might still have ended up doing academic research.
Even though I may be in the mature stage of my life, I am not yet halfway through it. New activities such as the editorship of JMR present great challenges. It is my intention to continue to conduct research and to teach for a long time to come. However, I have learned to welcome seemingly accidental events that occur along the way, because they often lead to interesting developments. I hope my reflections show that having an open mind and being flexible can be very rewarding. Or, if preferred, I believe that I have been very lucky.
( n1) Higher education in the Netherlands has undergone major reorganizations during the past two decades. For example, undergraduate programs now exist, and students have the option to apply for graduate study after they receive a bachelor's degree.
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-----, Michael Addona, William Hawkes, and John C. Porter (1988), "SCAN*PRO: The Estimation, Validation, and Use of Promotional Effects Based on Scanner Data," working paper, Johnson Graduate School of Management, Cornell University.
----- and Philippe Cattin (1981), "Alternative Estimation Methods for Conjoint Analysis: A Monte Carlo Study," Journal of Marketing Research, 18 (February), 101-106.
-----, Joel C. Huber, John A. Fiedler, and Richard L. Miller (1992), "Attribute Level Effects in Conjoint Revisited: ACA Versus Full Profile," in Second Annual Advanced Research Techniques Forum Proceedings," Rene Mora, ed. Chicago: American Marketing Association, 52-61.
----- and Sev K. Keil (2000), "Continuous Conjoint Analysis," in Conjoint Measurement: Methods and Applications, Anders Gustaffson, Andreas Herrmann, and Frank Huber, eds. Berlin: Springer-Verlag, 411-34.
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-----, David J. Reibstein, William Boulding, John E.G. Bateson, and John W. Walsh (1989), "Conjoint Reliability Measures," Marketing Science, 8 (Fall), 371-74.
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By Dick R. Wittink and Terry Clark, Editor, Southern Illinois University
Dick R. Wittink is George Rogers Clark Professor of Management and Marketing, School of Management, Yale University (e-mail: dick.wittink@yale.edu), and Honorary Professor of Marketing and Marketing Research, Faculty of Economics, University of Groningen, the Netherlands. He thanks many people who made valuable contributions, in particular Marian Wittink, Marsha Wittink, and Mark Wittink.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 10- An Autobiographical Essay: When I Stop Learning, I Will Leave. By: Urban, Glen L.; Clark, Terry. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p118-124. 7p. 1 Diagram.
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Section: Book ReviewsAn Autobiographical Essay: When I Stop Learning, I Will Leave
Writing an autobiographical essay is both welcome and daunting. In putting these thoughts to paper, I reflect on the path taken and speculate on where it might yet lead me. I suspect that my experience is unique, and I am not sure that my experience will be for everyone to follow. Nevertheless, my hope is that in describing what worked for me, others may be stimulated in some way to find what works for them. As I reflect back on my career, I have no regrets and am happy with how it progressed--it has certainly been interesting, challenging, and rewarding.
In reflecting on artist Christo's sculpture "wrapping" of the Pont Neuf Bridge in Paris, the director of the De Cordova Art Museum in Lincoln, Mass., stated that Christo's work was an "art event" and not a real "sculpture" because it was only up for a few weeks. I met Christo shortly afterwards and asked him about this comment. He reaction was "nonsense," the work was "real sculpture," and the only reason it was up for such a short time was that the insurance for the installation cost him $200,000 a week. This indicates either that artists do not understand their own work or that their critics do not understand it. As a scholar, I may not be able to judge my own work. In this essay, I describe how I view my research. In doing so, I assume that I understand my own work. I hope I am not too close to it to miss the truth.
I begin by sketching out the area of my primary research--new product development. In doing this, I necessarily and incidentally touch on many of the events that influenced my development and career choices and the attributes of the research style that, in my view, have been critical to the success of my work. I close with a description of my current research plans.
I grew up in Wisconsin--the son of a man who believed that "work is a virtue." Working in Dad's business (Urban Steel Buildings) during the summers had a lasting effect on me. Working with the construction crew was hard, and the hours were long. Dad also let me try my hand in the office as a draftsman, cost estimator, and salesman. It was here that I had my first exposure to real marketing. Among other things, I learned how to sell by providing customer solutions (e.g., a roof guaranteed for 20 years against leaks) and by demonstration (e.g., a special pull rivet that forced aluminum roofing panels tightly together all the time). The product needed to reflect superior engineering and have a benefit for the consumer. We positioned our buildings as the highest quality product but also segmented the market with a "value-priced" line of steel buildings to counter low-cost competition. My intention was to go into Dad's business. I believed that engineering and marketing were the critical skills I needed, so I went to University of Wisconsin for a bachelor's degree in mechanical engineering and an MBA in marketing. I graduated in 1964. But the best laid plans sometimes do not work out.
In that same year, I enrolled in the marketing Ph.D. program at Northwestern. Phil Kotler was my advisor. After a year of core work, I needed to begin thinking about a thesis, and Phil got me involved with new product forecasting at Union Carbide Corporation. I began modeling the interaction effects of a new product on existing products and applied a simulation model to a new polyvinyl fluoride product Union Carbide was introducing. Thanks to Phil and a Union Carbide product manager, things fell into place, so that in the second year, I had a draft of my thesis. My wife and I typed it up (on mimeograph masters) and delivered it to the committee before strapping the skis on the car and heading for Aspen, Colo. On returning, we expected to spend another year on the thesis. (One committee member had asked me, "Are you going to turn your thesis in like this?" Fortunately, he was talking about the format of the tables, not the content.) But the thesis was accepted with minor revisions, and we began a quick and late job search.
Fortunately for us, Massachusetts Institute of Technology (MIT) was also late in hiring in 1966, and I was able to convince the hiring committee that there was a good fit between my management science and marketing interests and the MIT philosophy. I was warned by faculty that though MIT was not an easy place to earn tenure, "it was a good place to be from." My response has been my touchstone ever since: "Fine, when I stop learning, I will leave. Perhaps they will ask me to leave before that happens, but it is worth a try." Given the MIT opportunity, I did not go into Dad's business. Dad always said he overeducated me by funding me through graduate school. In any case, I never did stop learning, and MIT did not ask me to leave, so here I am still. After 36 years, I must say that MIT has been a wonderful environment for my entrepreneurial style of research.
The past 35 years have been an exciting time for marketing science and for modeling new product decision support. The challenges of new product design, forecasting, risk management, and launch strategy have fostered a large set of creative and useful models. No need to summarize this literature here, but interested readers will find Design and Marketing of New Products (Urban and Hauser 1993) and other summaries (e.g., Green, Krieger, and Wind 2001; Ulrich and Eppinger 1995) useful.
My new product research proper began at the University of Wisconsin with my MBA thesis "Product Planning in the Aerospace Industry," in which I described the new product processes and generalized a multistage decision sequence for the industry (see Urban 1964). I was fortunate enough to work directly with General Motors'(GM's) and 3M's aerospace divisions. My doctoral thesis at Northwestern on industrial product life cycle forecasting was a modeling effort aimed at understanding interdependencies between new and existing products (see Urban 1966). Drawing on Monte Carlo simulation and chance-constrained programming techniques, I modeled the product line substitution and complementary effects Union Carbide faced in launching and pricing a new chemical product (Urban 1968). This thesis, along with a co-authored textbook that gave a state-of-the-art view of management science in marketing in the late 1960s (Montgomery and Urban 1969) set the stage for my 35 years of research.
After arriving at MIT, my initial research focused on the launch and test market phases but then began migrating to earlier stages, including premarket testing and design and opportunity identification. This development prompted my friend and colleague, Al Silk (Professor at MIT and subsequently Harvard Business School), to quip, "Urban's research has been going backwards for many years." Although this is true, I retain a research interest in all phases of the development process. Figure 1 positions my research efforts in the new product development decision process. Here, I give a stream-of-consciousness description of my efforts and then identify the critical issues in my approach to research.
Test Marketing
In the late 1960s, major new theoretical approaches were being developed in the field of stochastic models. Growing out of a contact with a summer session student from the Noxell Corporation (which sells Noxema and Cover Girl skin care products), I learned that forecasting national sales levels based on test market results, planning the best marketing mix for launch, and tracking test market and launch for diagnosis and control were important problems that were not being adequately addressed. This led to a sponsored research project at MIT and the development of a macro flow model methodology that combined elements of stochastic models, response functions, and empirical data in a managerial tool called "SPRINTER" for managing the new product test market and launch (Urban 1970). I was interested in discovering whether this model would really help managers, so I joined John Little and Len Lodish in a firm called Management Decision Systems Inc. Applications at Noxell and Nabisco helped refine the SPRINTER model and provided great case material for my articles and teaching.
During 1970, I spent a term visiting the Indian Institute of Management in Calcutta and became interested in the management of family planning. After returning to the United States, I began working with the Atlanta Area Family Planning Program and the Centers for Disease Control in an effort to improve the efficiency and effectiveness of their programs. My approach was to elaborate and extend the SPRINTER model and apply it to the trial and adoption of family planning (Urban 1974a). At that time, I had two master's students (Ron O'Connor and Joel Lamstein) who were interested in implementing new management techniques in the public arena, so we formed a nonprofit firm called Management Science for Health. Although I am no longer involved with the company (or its spin-off, John Snow Inc.), it is gratifying to report that they now employ more than 600 people working to improve public health management worldwide.
Premarket Testing
In 1972, Cal Hodock, then market research director at Gillette, called me with an invitation to join him for lunch at Loch Ober, a premier restaurant in Boston. I was somewhat surprised because usually I bought lunch for him, in hopes of garnering MIT-sponsored research funds from Gillette. During lunch, he told me Gillette was looking for a modeling and measurement system to forecast sales of a new product in test market, based on the pretest market availability of the product, packaging, and advertising. He wanted the research to cost (on an ongoing basis) less than $25,000 and the forecast to be delivered three months after the project started. On the basis of the complexities I had seen in test market tracking and forecasting, I told him it was impossible. He was persistent. In the end, he persuaded Al Silk and me to look at his problem by dangling $40,000 for sponsored research funds at MIT. How could we refuse? Gillette's need, combined with the emerging logit modeling technology at MIT (McFadden 1970), led us to develop a convergent premarket forecasting approach based on measured changes by two independent models: ( 1) preference change and ( 2) laboratory-simulated trial and repeat purchasing. The result was the ASSESSOR model for forecasting the sales of new packaged goods (Silk and Urban 1978), and over time its validation was based on Management Decision Systems applications (Urban and Katz 1983).
In parallel with the validation work on consumer packaged goods, I was pursuing the application of pretest market forecasting to consumer durables. This grew out of discussions with a student of mine (John Dables) who worked at the Buick division of GM. He told me that the risks involved in developing a new automobile product were much greater than those involved with developing a packaged goods new product, because the investment was so much larger. The whole picture was further complicated by the lack of test marketing for automobiles. The thought occurred to me, why not apply the ASSESSOR methodology to consumer durables? Our discussion led to a five-year Buick-sponsored research project at MIT, which resulted in a durables ASSESSOR model and applications to premarket automobile forecasting based on an early production line version of the new automobile (Roberts and Urban 1988; Urban, Hauser, and Roberts 1990; Urban, Hulland, and Weinberg 1993). Good forecasting results were achieved, but top managers at GM argued that though our analysis could improve the launch, the forecast came too late in the process, because once the car existed in initial production line versions, the launch commitment was virtually assured. The costs were sunk, and on a marginal basis it was almost always profitable to go forward. This was forcefully brought home to me when we predicted in 1986 that the new down-sized Buick Riviera sales would be half of the old level rather than the hoped-for doubling of sales. Buick introduced the car anyway. Although we were glad to have the opportunity for validation (sales dropped to .4 of the old level), we were indeed too late in the process to stop the program.
In the late 1980s, Hyper Card was developed at Apple, and MIT's Media Lab had invented the basic elements of surrogate travel. In 1990, we began an effort to use interactive multimedia to create a virtual automobile market of the future, before the new car was built. We put the customer in the future environment with full information and ability to control the search, and we measured responses to predict future sales before the production commitment was made. I called this "information acceleration" (IA). The first application was to electric vehicles at GM (Urban, Weinberg, and Hauser 1996), and on the basis of application and validation experience (Urban et al. 1997), the potential of this model and measurement methodology was encouraging. Initial field testing of the GM EV-1 two-seat sports car was done at MIT with the IA model. The final forecasting was done during 1992-93 through a consulting firm called Marketing Insight Technology Inc., which I started with a few of my students to implement IA concepts. The forecasts, based on expanded proprietary surveys, were for low sales in the 1998-2000 period (fewer than 1000 units per year) and indicated that the real demand was for an economy car with a hybrid power system for reliability (an electric motor plus a small gas-powered generator). General Motors did not go into production with this vehicle but rather custom built units for sale in California and selected other locations. In 1999, the sales of the EV by GM were about 700 units, surprisingly close to the forecast, given the vagaries of this market. I was given permission to publish these results, so the results considerably strengthened the MIT initial research for publication. In 1999, I was at an American Marketing Association conference in San Diego and saw the Toyota Prius (a four-door hybrid electric vehicle) in the hallway and a sign announcing a presentation of the Prius development story. I was pleased to see that the Toyota product manager had used the IA, on the basis of my publications only, to forecast the sales of the Prius hybrid economy car and had found a real market opportunity.
Design
As the seminal work on perceptual mapping was appearing in the 1970s, there seemed to be a natural fit to new product design. The notion of a "core benefit proposition" could be represented in the positioning and in a model called PERCEPTOR. I made an early attempt to link positioning to new product sales potential and extended this model for marketing of the MIT health maintenance organization (Hauser and Urban 1977; Urban 1975). I continued research on product design in an effort to integrate Von Hippel's lead user notions with market research methods (Urban and Von Hippel 1988) and apply it to industrial product (i.e., CAD/CAM systems for electronic printed circuit boards at Computer Graphics Inc.) innovation and diffusion from lead users to other customers. This theme has carried through to current research, in which I am putting lead users on an Internet design pallet to configure their ideal pickup trucks.
Opportunity Identification
Through the 1980s, I became convinced that marketers needed not only tools to help effectively forecast and design products but also tools to help identify strategic opportunities. My first project in this area focused on market definition. This returned me to my original interest in product lines and interdependency. I tried to define a hierarchical market structure that created segments in which intra-segment competition existed but intersegment competition was limited, so that little customer switching among segments occurred. This system was called PRODEGY and addressed PRODuct stratEGY by examining the coverage and duplication of a product line (Urban, Johnson, and Hauser 1984).
The second project grew out of the empirical experience I gained from applications of ASSESSOR. Contrary to the predictions of perceptual mapping models, I noticed that second brands in a market rarely received the same share as successful first entrants, even if they had parity positioning and allocated similar resource levels to advertising and promotion. This led to a statistical cross-sectional analysis of the effects of order of entry on market share (Urban et al. 1986). My coworkers and I confirmed this order-of-entry effect in a time-series cross-sectional analysis of test market scanner data (Kalyanaram and Urban 1992) and ethical pharmaceuticals (Berndt et al. 1995). Our work in this area was contemporaneous with the PIMS data analysis and led to interesting insights as the results were integrated into the growing literature on order of entry (Kalyanaram, Robinson, and Urban 1995).
Launch and Life Cycle Management
Recently, I returned to the topic I had begun with while at Northwestern--the life cycle phase. I have developed a set of concepts for trust-based marketing over the life cycle (Urban, Sultan, and Qualls 2000), in which the use of a virtual advisor on the Internet provides customers with full and accurate information and unbiased advice in a private, secure, branded, friendly, and easy-to-use system. This project grew out of a realization that my IA ideas could be used to help customers make better decisions on existing products, as well as test new products. Inverting IA gives a system that, when supplemented by a personal advisor, provides a trust-based marketing tool. Vince Barabba, Director of Market Planning at GM, encouraged me in this work and funded an MIT-sponsored research project. After developing a prototype, we tested it in the field by application with 300 customers to pickup truck purchasing. Initial results indicated substantial increases in trust, and presumably sales can be earned through the Internet virtual advisor. General Motors is now considering implementing such an advisor on one of its Internet sites.
This chronology of my research indicates several threads I believe have been important in the success of my work. I briefly discuss some of these.
Managerial Need Input and Implementation
My research style is inductive, so I found it natural to work closely with managers making real decisions. I have always been impressed with the knowledge and insight managers accrue in facing tough decisions. As a marketer, I instinctively thought in terms of "customer needs" as I defined my customers for the analytic models I worked on as managers and tried to involve them early in the design of decision support models. In 1980, while I was reporting the results of a second PERCEPTOR study at Dow Corporation, the group product manager leaned over and said, "Tell us something we do not know this time." It was new to me, but old stuff to him. Coping with implementation problems gave me a growing awareness of decision needs, so my following projects could be better fitted to the changing managerial decision requirements. Building models and applying them should be considered an organizational change process, not an exercise in mathematical gymnastics (Urban 1974b; Urban and Karash 1971). As a result, implementation should be considered from the start of the project to beyond its academic completion if academics are to keep their research relevant and improve the practice of marketing.
Equally important, this manager orientation can help generate funds for research assistants, computers, software, and large databases. I have also found that real applications after publication are useful in assuring that models are used and that evolutionary model extensions can create a positive benefit-cost ratio for managers.
Sometimes I was involved with applications as a consultant, but more often I have worked within companies I have founded with my students. We founded these companies to implement the new technologies, and though it is nice to be economically successful, my real motivation was to change the practice of management. For example, ASSESSOR was implemented by Management Decision Systems (and subsequently by Information Resources Inc., which bought Management Decision Systems, and then by MARC, which bought the ASSESSOR business from Information Resources Inc.). But the Journal of Marketing Research publication (Silk and Urban 1978) on ASSESSOR was used by Research International Inc. and Novaction Inc. to build competitive services. My best estimate is that more than 3000 consumer packaged goods products have been tested by the ASSESSOR methodology and its derivatives. I doubt that ASSESSOR would ever have been applied without Management Decision Systems as a proving ground. Similarly, IA was implemented by Marketing Technology Inter-face, as well as by others (e.g., Toyota, Intel, various market research companies), on the basis of the Journal of Marketing Research publications (Urban et al. 1996; Urban, Weinberg, and Hauser 1997). My role in these companies was limited to less than one day per week, but this was enough to help design the implementation procedures, interact with clients on design issues, and identify new research opportunities. Overall, my intent has been to build new decision tools that reflect customer needs and result in better products and reduced risk in new product innovation.
Matching Needs to Theory
Interacting with managers has been important to me to understand their needs. However, successful research also requires matching these needs to emerging theories and methods. When the two are in sync, the ensuing research can advance the state-of-the-art of marketing science as well as affect practice. Whether it is logit modeling, multidimensional scaling, utility theory, artificial intelligence, or virtual reality, I have always looked for problems that lend themselves to analysis through the most recent management and behavioral science technologies. I view this matching of theory to practice as a creative process.
Not all problems are interesting academically. Pure application projects may be useful but lie in the consulting domain. Pure theory can be important work, but I have tended to examine problems that require application of the latest theory. I think this tendency reflects my engineering training. However, I have often found that modeling requires both theory extension and innovation in estimation. For example, ASSESSOR came from Gillette's managerial problem, but the solution was in the then-new logit analysis. When logit was first applied, McFadden (1970) was just developing the maximum likelihood algorithms. I believe that ASSESSOR was the first application in the marketing of logit analysis. This theory enabled us to develop a new convergent forecasting methodology for premarket analysis. Similarly, when modeling the problem of premarket forecasting of new automobile sales, multiattribute utility theory was used and extended as a modeling framework (Roberts and Urban 1988). My recent work to develop trust on the Internet uses artificial intelligence theory to build a trusted advisor for automobile purchasing (Urban, Sultan, and Qualls 2000).
Power of Empirical Data
I have been a heavy user of measurement and empirical data. Whether it be test market, laboratory simulation, survey, market experiments, or virtual reality data, I have felt compelled to measure customer response. I have also been diligent in testing my model predictions. This is a difficult validation process but a critical one if marketing science is to progress. Often, these empirical efforts require creativity and innovation in measurement methodology and persistence in obtaining response and validation data, but the research power gained is well worth the effort.
Research Risks
A sense of adventure, entrepreneurship, and intellectual flexibility has served me well in my efforts to match theory with managerial relevance. I have generally avoided small epsilon extensions of existing work in favor of major problems that have not been extensively studied. This is ambitious and risky, because such innovative work takes a long time--especially if it is empirically based. Reviewers may not always understand the value of the new work, or else they find many methodological problems that can only be resolved by further research. Articles may be rejected or may need to be revised and aggressively defended. But some of them may win prizes. As I see it, the low-risk way to publication is to extend previous work in a field. When this is the case, previous contributors are usually (and naturally) chosen as reviewers, and they find it easier to understand and accept the extension, even if it is not a major breakthrough. Certainly, this evolutionary research path has moved the field forward. However, it is not my style. I like to find the big problems managers face and see if I can solve them. In this effort, I have often found that the literature constrains my thinking. As a result, I often work on a problem for months before reading the existing literature and modify my efforts to profit from prior research. It is a balance between creativity and constraint. Existing theory should be used, but for me the drivers are the problem and creativity rather than the placement the new work in the structure of the existing publication stream.
Programmatic Research
I am a research planner. My method is to lay out my research intentions over one-and five-year time horizons and examine how they fit into the accomplishment of my overall research goal--improving the productivity of new product development and advancing the art of marketing science. This sometimes calls for long-term projects--most of my models have a five-year or longer development time frame. (For example, ASSESSOR was started in 1973 and published in 1978, IA was started in 1990 and published in 1996 and 1997, and trust-based marketing was started in 1996 and published in 2000.) This may not maximize the number of publications, but those that do come out the end of the pipeline can be significant. Fortunately, MIT has been patient and has tenure criteria that do not depend solely on the amount of publication.
Balance
Real academic success requires that we balance research, teaching, and administration. My annual plans have always incorporated activities from each of these areas. Most often, for me, the one that slips is research. Academics need to be disciplined to keep research priority high. The demand for high-quality teaching has continually risen over time. To help cope with this, I have tried to tie my teaching to my research to make the most efficient use of my time. For example, Dave Montgomery and I taught a marketing modeling course based on our book Management Science in Marketing (1969). In many ways, that course was the foundation for my future modeling. I taught the first new product marketing and development course at MIT. This was synergistic with my ASSESSOR research and led to the development of teaching materials such as a textbook with John Hauser titled Design and Marketing of New Products (1993). When my focus became more strategic, I found that teaching marketing strategy and co-authoring a text and case book with Steve Star, Advanced Marketing Strategy (Urban and Star 1991), helped me understand and teach the wider context of marketing and analytical support. The secret for me is balancing activities and building synergy.
In administration, I served as deputy dean (1987-91) and then dean at MIT Sloan (1993-98). These were the most difficult times for me to achieve balance. I found as dean that I could continue research work (e.g., IA, trusted advisors on the Internet) I started before taking on the dean's responsibilities, but it was difficult to start new projects. The compensating benefit was that I learned a lot about operational management, gained a wider understanding of other fields of management, and was exposed to a wide array of interesting academics and top managers. It was clear in 1998, however, that I needed to choose between becoming a full-time administrator and a researcher/teacher. I chose the latter, but I am confident that the experience in the dean's office will make me a better teacher and give me a wider perspective on research. A sabbatical between each of the two administrative positions was critical to rebalancing and energizing my research activity.
Another critical balance is between work and personal time allocations. The academic tenure system will push a professor so that it is difficult to have a life outside of work. I have found that it is critical to have a balance among work, family, and personal time. My wife Andrea recognized this problem early and signed me up for a sculpture course in 1970 at the local art museum. I enjoyed the class, and sculpture became a major hobby for me. I do mostly large steel and bronze work, but some stone and wood carving. I have more than 50 pieces in my yard and house. It is rewarding to come home from school to see the tangible results of cutting and welding steel for a couple hours, rather than the almost invisible results of daily research. Only after a long and frustrating period of research, writing, and revising is an article published. For me, sculpture provides unconstrained and immediate results. In addition, I believe sculpture has helped my research. Building a mathematical model taps the same sort of creative energy I find necessary in abstract sculpture. I have enjoyed sculpture, skiing, and sailing, but in retrospect I have put too much time into my work. If I had it to do over, I would put in fewer hours at MIT and more at home with my family. This would have been easier if the work had not been so exciting.
Great Co-authors
Good co-authors are an intellectual inspiration, and I have had some of the best. I am greatly indebted to them. I must acknowledge John Hauser (who has written more joint articles with me than anyone) for his rigor, scholarly standards, tight writing, and creative input. I have also benefited greatly from my other academic co-authors, including but not limited to Montgomery, Silk, Von Hippel, Star, Robinson, Berndt, Qualls, and Sultan (in chronological order of publication). It is also important to recognize my student co-authors, who have probably received less credit than they deserve for their input (e.g., Weinberg, Kalyanaram, Bohlman, Hulland, Roberts, Carter, Gaskin, Mucha, Johnson, Katz, and Karash, in reverse chronological order). Although I have generally not co-authored with line managers who have contributed to my work, special contributions were made by several of them (e.g., Ed Sellars of Noxell, Cal Hodock of Gillette, Tom Hatch of Miles Labs, John Dables of Buick, Roberta Chicos of MTI, and Sean McNamara and Vince Barabba of GM). Finally, John Little has been my mentor, and even though we have never co-authored a major article, he influenced every one of my works through his example, comments, and criticisms. John is a straight-thinking, rigorous scholar who believes in research paying off in practice.
My current five-year research plan calls for extensions to my new product modeling and a major thrust toward developing models to exploit the potential of the Internet for marketing managers.
My work on trust-based advisors raises a challenge for managers. If trust-based marketing involves fair comparison between alternatives, what does a firm do if its products are not the best available? One solution is to back off of trust and push what you have. A better long-term solution is to find unmet needs and build the highest quality products to fill those needs. In my trust work, we applied the virtual advisor to pickup trucks, so it was natural with GM's research support to extend the research to discover whether we could "listen in" to the dialogue between the advisor and the real customer to find unmet needs. We applied utility theory to identify the most preferred truck and the level of utility. We posited that if the utility of the most preferred truck goes down after a question in the dialogue, this indicated an unmet need. For example, if a consumer wants a small truck, the Mazda might be the most preferred. After the consumer indicates that he or she wants to tow a boat, the Chevrolet Blazer may be the most preferred, but the utility is likely to have gone down. This drop indicates the need for a small truck that can tow. This unmet need is explored by a virtual engineer who asks the customer for details about the need (e.g., How much does the boat weigh? Why do you want a small truck? For gas efficiency? Parking?). This virtual engineer provides detailed input to the platform design team. A final component in this analysis is to put the customers on a design pallet and let them specify any truck they want (e.g., a mid-sized truck that can tow 6000 pounds and still be easily parked). This system has been estimated on the basis of 1000 customer interviews; the results indicate that it can identify significant new opportunities and the need-identification algorithms are robust (Urban and Hauser 2002).
The Internet is a risky area for research, because it is so volatile and we do not have much research banked in this area, but I believe that it will be a major additional channel for marketers in the future. Currently with the support of the Inter Public Group, my colleagues (Fareena Sultan, Venky Shankar, and Iakov Bart) and I are analyzing 6800 consumer evaluations of 24 leading Internet sites to find the determinants of trust on the basis of 120 cue assessments (e.g., security, privacy, personalness, information, navigation, advice, brand). This empirical analysis will supply under-standing to enable effective experimentation of site design and consistency with other communication channels. We have begun experiments on the Internet to test the causal nature of trust-building in a site. This work is funded by Intel, and I hope in the next several years, in collaboration with others in the MIT marketing group (John D.C. Little, John Hauser, and Duncan Simester), to extend it to full adaptive marketing. We plan to draw on reinforcement learning (Sutton and Barto 1998). I also would like to study the use of the Internet as a direct manufacturer sales channel, complementary to the existing distribution system (e.g., Palm sells direct at the same price as through existing retail stores). Finally, I plan to study the implications of increasing customer power and the paradigm shift from push to trust-based marketing that this may precipitate.
The future is full of exciting marketing opportunities, and if we can effectively integrate theory, practice, empirical data, and creativity in research, we can improve the efficiency and effectiveness of marketing.
Figure 1: New Product Process, Selected Works
REFERENCES Berndt, Ernst R., Linda Bui, David R. Reile, and Glen L. Urban (1995), "Information, Marketing, and Pricing in the U.S. Antiulcer Drug Market," AEA Papers and Proceedings, 85 (May), 100-105.
Green, P.E., A.M. Krieger, and Y. Wind (2001), "Think Years of Conjoint Analysis: Reflections and Prospects," Interfaces, 31 (3), 56-73.
Hauser, John R. and Glen L. Urban (1977), "A Normative Methodology for Modeling Consumer Response to Innovation," Operations Research, 25 (July/August), 579-619.
Kalyanaram, G., W.T. Robinson, and Glen L. Urban (1995), "Order of Market Entry: Established Empirical Generalizations, Emerging Empirical Generalizations, and Future Research," Marketing Science, 14 (3), G212-G221.
------ and Glen L. Urban (1992), "Dynamic Effects of the Order of Entry on Market Sham, Trial Penetration, and Repeat Purchases for Frequently Purchased Consumer Goods," Marketing Science, 11 (Summer), 235-50.
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Record: 11- An Empirical Analysis of the Determinants of Retail Margins: The Role of Store-Brand Share. By: Ailawadi, Kusum L.; Harlam, Bari. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p147-165. 19p. 10 Charts. DOI: 10.1509/jmkg.68.1.147.24027.
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An Empirical Analysis of the Determinants of Retail
Margins: The Role of Store-Brand Share
The authors develop and test a model of the key determinants of margins that retailers earn on national brands and store brands. They particularly focus on the impact of store-brand share on percentage margin, dollar margin per unit, and total dollar margin of the retailer. The authors find not only that percentage retail margins on store brands are higher than on national brands but also that high store-brand share enables retailers to earn higher percentage margins on national brands. However, the dollar margin per unit may be smaller for store brands because of their lower retail price. Furthermore, heavy store-brand users contribute much less to the total dollar profit of the retailer than do light store-brand users. The authors conclude that it is important for retailers to retain a balance between store brands and national br ands to attract and retain the most profitable customers.
During the past decade, retail profit margins in the packaged goods industry have been the subject of substantial discussion and research. On the one hand, much has been written about the increasing power of retailers and their proclivity to negotiate lower wholesale prices and higher trade allowances from manufacturers (e.g., Alpert, Kamins, and Graham 1992; Buzzell, Quelch, and Salmon 1990; Harrison 1999a, b). On the other hand, there is growing evidence that aggregate retail profit margins do not support the purported shift in power: Retail industry margins are either shrinking or remaining steady (Farris and Ailawadi 1992; Messinger and Narasimhan 1995).
A major topic at the center of much of the discussion about retailer power and profitability relative to that of manufacturers is the growth of store brands, for which average U.S. market share was up from 15.3% in 1988 to 20% in 1998 (Dunne and Narasimhan 1999; Marketing News 1995). In a survey of retailers, Cannondale Associates (1996) found that more than 70% of retailers considered store-brand growth an important issue (Wellman 1997). In a Discount Merchandiser (1996) survey, retailers rated "better profit margins" as the most important reason for carrying store brands. No wonder that several U.S. retailers now sell store brands in many product categories. In addition, researchers have provided both theoretical and empirical insights about when store brands should be introduced (e.g., Raju, Sethuraman, and Dhar 1995) and about the determinants of store-brand share across categories and retailers (e.g., Dhar and Hoch 1997; Hoch and Banerji 1993).
Despite all the interest in retail profitability, little is known about either the determinants of retail margins across product categories or the specific relationship between store-brand share and retail margins after a store brand has been introduced. The purpose of this article is to fill the gap in the literature by conducting a systematic empirical examination of the determinants of margins retailers earn on national brands and store brands and of the impact of store-brand share on these margins.
We address three specific clusters of research questions. First, what are the key determinants of retail margins across product categories? How do factors such as manufacturer concentration, advertising, and distribution affect retail margins on national and store brands? When these factors are accounted for, do retailers earn higher percentage margins on national brands in categories in which they have strong store-brand share than in categories in which their store-brand share is weak? Second, although it is well known that retailers earn higher percentage gross margins on store brands than on national brands, how and why does this margin differential vary across product categories? Does the differential hold up when considering percentage contribution margins net of the direct product costs retailers incur and when considering dollar rather than percentage margins? Third, how does store-brand share affect the retailer's total dollar profit? Do consumers who are heavy users of store brands contribute more or less than light users or nonusers of store brands to the total profit of the retailer? To answer these questions, we use sales, share, and profit margin data from two major retail chains that are supplemented with category characteristics from other sources. The article is organized as follows: We provide the motivation for our work by reviewing prior research that is relevant to each of our research questions, and we show why the answers are not obvious. We then describe the data used in our analysis and subsequently address our research questions. We develop and estimate a model, including key determinants such as advertising, distribution, and store-brand share, of the gross and net margins that retailers earn on national brands and store brands. We then compare retail percentage and dollar margins on store brands and national brands across several product categories. We explore the relationship of store-brand share to total retail profit by comparing the average dollar profit that retailers obtain from customers who are nonusers, light users, and heavy users of store brands. We conclude with a discussion of our findings and implications for further research.
Determinants of Percentage Retail Margins
According to basic industrial organization theory, the margin that a market player can earn is a function of the player's market power. In the context of the packaged goods industry, the relative market power of retailers versus manufacturers determines how total channel profit is split between the two and the margins that retailers can earn (e.g., Kadiyali, Chintagunta, and Vilcassim 2000). In turn, the relative market power of retailers versus manufacturers is influenced by several key variables, such as national brand concentration, advertising spending, distribution, and store-brand share, all of which vary across product categories. As a result, retail margins also vary significantly across categories. The literature contains some important conceptual and analytical work on the inverse relationship between manufacturer and retail margins and on the role of advertising and distribution in the relationship (Abela and Farris 1999; Albion and Farris 1981, 1987; Lal and Narasimhan 1996; Steiner 1973, 1978, 1993). In recent years, researchers have also developed structural models of optimal retailer pricing from first principles of profit maximization (e.g., Besanko, Gupta, and Jain 1998; Chintagunta, Bonfrer, and Song 2002; Kadiyali, Chintagunta, and Vilcassim 2000). This stream of research provides a useful and flexible way to describe the type of pricing game played by a retailer and a few national brand manufacturers in a single category. However, we are not aware of any comprehensive empirical analysis of factors explaining cross-category variations in retail margins on national brands and store brands. Such a study would complement Shankar and Bolton's (2003) analysis of the variation in retailer pricing strategies across brands, categories, stores, and markets.
There is much discussion in the theoretical literature about a determinant of retail margins that is of particular interest in our research: store-brand share. In general, researchers agree that introducing a store brand can enable retailers to obtain lower wholesale prices from national brand manufacturers and therefore earn higher margins on national brands. However, there is disagreement about the relationship between store-brand share and the retailer's margin on national brands after the store brand has been introduced. On the one hand, Mills (1995) predicts that the higher the market share of the store brand, the lower is the wholesale price of national brands and therefore the higher is the margin that the retailer can obtain on national brands. On the other hand, Narasimhan and Wilcox (1998) predict a negative relationship between store-brand share and the retailer's margin on national brands. They distinguish between the exercised and unexercised threat of a store brand and argue that the retailer's ability to extract channel profit lies in the market share that the store brand can potentially attain, not in its actual market share. Morton and Zettelmeyer (2000) make a similar argument: They do not expect that store-brand market share is necessarily related to retail margins from national brands.
Despite the divergent theoretical predictions, empirical evidence of the relationship between store-brand share and retail margins is scarce. Narasimhan and Wilcox (1998) provide some empirical results, but their analysis has some important limitations that must be overcome. First, their analysis is based on aggregate data across retailers that are likely to be heterogeneous in many aspects that determine their store-brand success and their bargaining ability with national brands. Second, their analysis does not use actual margin data from specific retailers; instead, it uses estimates of average retail margins from consumer expenditure survey data and does not separate store-brand and national-brand margins. Third, it uses penetration of store brands rather than of market share, but penetration is just one component of share (Ailawadi, Lehmann, and Neslin 2001). Finally, their analysis omits several key determinants of store-brand share and margin. Thus, it is not clear whether high store-brand share increases retail margins on national brands or whether it is better to have a store brand on the shelf as an unexercised threat.
We develop an econometric model of a retailer's margin on national and store brands, which we estimate using data from individual retail chains. Because retailers may put more effort into store brands in categories with higher margins or may deliberately raise their national-brand prices and thus their national-brand margins to increase store-brand share, there may be reverse causality between retail margins and store-brand share. We control for this reverse causality.
The Store Brand--National Brand Margin Differential
In general, it is known that retailers earn higher percentage gross margins on store brands than on national brands (e.g., Handy 1985; Hoch and Banerji 1993); this primarily is because private label suppliers have little market power, in contrast with national brands. Private label suppliers operate in a competitive market with no product differentiation and thus sell to retailers at a price that is close to their marginal cost. In addition, manufacturers' advertising costs are high for national brands, and these costs are reflected in higher wholesale prices of the brands. In addition, a retailer has somewhat of a local monopoly on its store brand because competing retailers do not carry the same store brand, though they do carry the same national brands.
However, some questions about the store brand--national brand margin differential remain unanswered. First, gross and net contribution margins are often not highly correlated in practice (Borin and Farris 1990), yet there is little evidence about the difference in net margins between store brands and national brands when the retailer's direct product costs are taken into account. The single exception that we know of is Corstjens and Lal's (2000) description of a Canadian retailer's beverage category, which suggests that net percentage retail margins on store brands can be lower than on national brands. We therefore compare not only gross margins but also net contribution margins that retailers earn on national and store brands.
Second, the percentage margin differential is certainly not equal across all categories, but we are not aware of any work that examines how and why this differential varies across categories. We therefore use our cross-category determinants of retail margins on national brands and store brands to explain variations in the store brand-national brand margin differential.
Third, attention has been focused on percentage margins, probably because they can be readily compared across categories, but retailers should be concerned at least as much with dollar margins. Even if retail percentage margins are higher for store brands than for national brands, corresponding dollar margins may not be. Retailers pay lower wholesale prices for store brands, but store brands are sold at significantly lower retail prices than national brands are. If the higher percentage margin on store brands does not offset the lower retail price, dollar margin per unit may be lower for store brands than for national brands. This may be particularly true in the face of increasing store-brand competition, not just in the grocery channel but also in the mass-merchandiser and drugstore channels (Johnson 1994; Matthews 1995). Therefore, we also compare dollar margins from store brands and national brands.
The Profitability of Store-Brand Users
Retailers should also be concerned with total dollar margin in the store. How much do heavy store-brand users, compared with light or nonusers, contribute to the total dollar margin of the retailer? Even if the retailer's dollar margin per unit is higher from store brands, the total profitability of a heavy store-brand user depends on how many items and which other items he or she buys from the retailer.
Several researchers have suggested that store brands are associated with better store image and higher store loyalty (Ailawadi, Neslin, and Gedenk 2001; Corstjens and Lal 2000; Steenkamp and Dekimpe 1997), which would have a positive effect on retail sales and total dollar margin because loyal consumers buy a larger share of their total grocery requirements from the store. However, it may also be that heavy users of store brands are loyal to store brands in general, not necessarily to the store brand of a particular retailer. Furthermore, if store brands attract budget-constrained, price-conscious, or deal-prone consumers whose market baskets are small or contain other low-margin/loss-leader products, store-brand users may contribute less than nonusers do to the total dollar profit of the retailer (e.g., Quelch and Harding 1996). Although the demographic and psychographic correlates of store-brand proneness have been studied (Ailawadi, Neslin, and Gedenk 2001; Richardson, Jain, and Dick 1996), we are not aware of any research that examines store-brand users' profitability. We explore this issue by comparing the total profitability of heavy store-brand users with that of light store-brand users and nonusers.
We used data from two anonymous retail chains and conducted all our analyses for each retailer separately. This provided a valuable validation test for our results, especially because the two retailers differ in many respects: One is a grocery retail chain and the other is a drug retail chain; one practices everyday low pricing and the other practices high-low pricing. There are also some differences in the type of data from the two retailers.
The first data set (Retailer 1) consists of point-of-sale market-basket data from 20 stores that belong to a major grocery retail chain. The data include sales, price, and profit information on all items in every market basket purchased during a four-week period in 1998. The data result in records for items from 291 product categories bought in 801,821 market baskets. Of the product categories, 123 are in the produce, meats, deli, seafood, and bakery departments, in which most products are unbranded and not categorized into store and national brands. Of the remaining 168 product categories, which are in the grocery, general merchandise, dairy, specialty, frozen foods, and health/beauty/cosmetics (HBC) departments, 115 have a store brand. The average market share of the store brand is 22%, which is in line with the national average store-brand share of about 20% (Dunne and Narasimhan 1999). The profit data from Retailer 1 include both gross margin (after promotional allowances linked to a specific product are accounted for) and net contribution margin (after direct product costs incurred by the retailer, including receiving, stocking, packaging, cashiering, bagging, and so on, are subtracted) of every item in every market basket. However, the data are organized at the market-basket level, not at the panel level, so we cannot assign baskets to individual households. Because pricing and retail margins differ across the 20 stores, we conducted our analysis at the store level for Retailer 1.
To study the determinants of retail margins, we combined the sales, price, and profit variables we obtained from the market-basket data with category penetration, average purchase cycle, and promotion activity from the Marketing Fact Book (Information Resources Inc. 1998); category advertising data from Leading National Advertisers' class/ brand summary; store-brand quality data collected by Hoch and Banerji (1993) and Sethuraman and Cole (1997); and distribution data collected in our own survey of stores that belong to competing grocery, drug, and mass-merchandiser chains.
The second data set (Retailer 2) is based on purchase data from the drug retailer's customer panel for all the stores in a market during a six-month period in 2000. The data include price and gross margin for each item purchased by the panel members in every shopping trip and contain records for 41,335 customers who made a total of 347,214 shopping trips. The total number of categories is 118, and the retailer offers a store brand in 70 categories. The average store-brand market share is approximately 23%. As does Retailer 1, Retailer 2 also has a store brand in the majority of categories, and the store-brand share is consistent with levels reported in previous research. The stores in this data set are all in a single pricing zone with identical prices and margin, so we aggregated data across stores for our analysis.
The profit data from Retailer 2 include gross margin of every item in every market basket. We do not have net contribution margin in this case. However, the data are organized at the panel level, so we were able to evaluate the profitability of a customer, not just the profitability of a market basket. We also calculated category penetration, average purchase cycle, and percentage sold on promotion from the panel data. Table 1 lists all the variables we used in our study as well as their definitions and the sources from which we obtained them.
Model Development and Hypotheses
The determinants of retail margins can be grouped into three sets: variables that reflect ( 1) manufacturer market power, ( 2) retailer market power, and ( 3) consumer and category characteristics. An overarching theme that underlies our hypotheses, especially for the first two sets of variables, is the relationship of market power to margins and the inverse relationship between manufacturer and retail margins (Steiner 1993). In brief, as the manufacturer's consumer franchise and market power increase, wholesale prices and manufacturer margins increase. However, retailers cannot increase prices on the products with a strong consumer franchise because consumers may switch stores to buy their preferred brand at a better price. As a result, retail margins decrease. In contrast, if consumers are more likely to switch brands within a store than to switch stores, retail market power increases, wholesale prices are negotiated down, and manufacturer margins decrease. However, lower retail competition reduces the downward pressure on retail prices, and retail margins increase as a result.
We discuss the individual variables in our model and our expectations about their effect on retailers' gross and net contribution margins from national and store brands. Note that we expect that only the variables that influence direct product costs will have significantly different effects on gross margins than on net contribution margins. Table 2 summarizes our hypotheses about all the variables in the model, in the order that we discuss them subsequently.
Manufacturer market power. As the concentration of national-brand manufacturers increases, their market power also increases (Scherer and Ross 1990). Therefore, we expect retailers to take lower margins on national brands in categories in which the Herfindahl index, a standard measure of concentration, is high. Furthermore, because higher manufacturer power leads not only to higher wholesale prices but also to lower retail prices for national brands, retailers must reduce prices on store brands to preserve a sufficient price differential with national brands. Therefore, we expect retailers also to take lower margins on store brands as the Herfindahl index increases.
Beginning with the pioneering work of Steiner (1973, 1978), researchers have shown that national brands that manufacturers advertise heavily are sold at higher wholesale prices and lower retail margins than are less advertised brands (Albion and Farris 1981, 1987; Lal and Narasimhan 1996; Steiner 1993). This phenomenon is because advertising decreases consumers' propensity to switch to another brand within the store and increases their propensity to switch stores for their preferred brand, thus giving the manufacturer leverage over retailers. If the same phenomenon applies across product categories, we expect retailers to take lower margins on national brands in heavily advertised categories. To compete effectively in categories with high consumer pull for national brands, we expect retailers to take lower margins on store brands as well. We also expect both national brands and store brands to be sold at lower retail margins in heavily promoted categories because retailers must price more competitively in such categories (Gerstner, Hess, and Holthausen 1994; Steiner 1984).
Finally, because there is no reason to believe that any of the manufacturer power-related variables affect direct product costs, we expect them to have similar effects on gross and net contribution margins.
Retailer market power. The greater the number of retailers who distribute the category, the greater is the competition between retailers and the lower is their market power. As a result, there will be greater pressure on retailers to keep their national brand prices competitive (Shankar and Bolton 2003). Therefore, we expect retail margins on national brands to be lower for categories that are widely distributed at competing chains. As the number of competing retailers that distribute a store brand in the category increases, the competition among retailers should also increase and put downward pressure on retail prices and margins. However, there is also a counterargument that store brands engender store loyalty and therefore give retailers some monopoly power because competing retailers do not carry the same store brand. This may soften store-brand price competition among retailers and enable retailers to take higher margins on store brands. We expect that wider store-brand distribution will make retailers compete more aggressively on store-brand prices, leading to a negative relationship between store-brand distribution and retail margins on both store brands and national brands.
Because store brands provide a means of differentiation and thus market power for retailers, retailers should be able to negotiate lower prices from manufacturers and earn higher margins in categories with a strong store-brand threat (Mills 1995; Morton and Zettelmeyer 2000; Narasimhan and Wilcox 1998). As we noted previously, some researchers have argued that a store brand can be a credible threat even if its share is low. However, we hypothesize that national-brand retail margins are higher in categories in which store-brand share is high because we believe high share is definitely a threat whereas low share may not be a credible threat. We also expect that store-brand margins are higher in categories with high store-brand share because high store-brand share reflects the strength of store-brand demand compared with national-brand demand (e.g., Raju, Sethuraman, and Dhar 1995).
Basic microeconomic theory predicts an inverse relationship between optimal gross margin and price elasticity for price setters. The price elasticity of demand that retailers face varies across categories, partly as a result of varying intensity in retail competition across categories. Although we have already included distribution, which is a key variable reflecting the level of competition retailers face, we also directly included category price elasticity. We expect that retailers will take lower margins on both national brands and store brands in categories in which the price elasticity of the consumer demand they face is high. Finally, because there is no reason to believe that the retailer power variables influence direct product costs, we expect them to have similar effects on gross and net margins.
Consumer and category characteristics. Retailers may take different margins on categories, depending on how many consumers buy the category, what the typical purchase amount is, and how often it is purchased. Their direct product costs may also differ with these variables because purchase cycles and the number of transactions can influence the cost of warehousing, packaging, stocking, cashiering, and so on. There is no strong theoretical basis for a priori hypotheses about the effects of the variables on retail margins; however, some prior empirical research does suggest that retailers are willing to take lower percentage gross margins in categories in which the purchase amount or penetration is high (Lal and Narasimhan 1996; Narasimhan and Wilcox 1998). In contrast, retailers may take higher gross margins in infrequently purchased categories because there are fewer opportunities for consumers to compare and learn about prices and for retailers to make money. Thus, we expect penetration and purchase amount to be negatively associated with gross margins on both national brands and store brands, and we expect purchase cycle to be positively associated with gross margins on both national brands and store brands. Furthermore, because direct product costs such as storage, stocking, and cashiering are likely to be lower as a percentage of sales in categories with large transaction amounts, we expect purchase amount to be positively associated with net margins on national and store brands. It is difficult to predict how the different components of direct product costs net out for high penetration and long purchase cycle categories, so we do not hypothesize a specific relationship between these variables and net margins.
To control for other unobserved differences across categories, we included dummy variables for the major departments represented in our sample, though we do not have any a priori hypotheses about their coefficients. Specifically, for Retailer 1, we included dummy variables for perishable foods and HBC categories because most of the remaining categories are in the grocery department. For Retailer 2, we only needed an HBC dummy variable because most of the other categories are in the general merchandise department. Finally, to control for Retailer 1's store-level differences in pricing and consequent margins that may be due to factors such as local competition, demographics, and so on, we included 19 store-specific dummy variables. As we noted previously, Retailer 2's stores are all from a single market and pricing zone, with identical prices and margins, so we aggregated across stores and did not need store-specific dummy variables.
Store-brand share model. Retailers may expend more effort to obtain higher store-brand share in categories in which gross margins are higher, and they may raise selling prices of national brands to increase store-brand share. This suggests that there may be reverse causality in the relationship between store-brand share and retail margins from national brands. To avoid any inconsistency due to this endogeneity, we specified a model for store-brand share and estimated the resultant simultaneous system by using three-stage least squares (3SLS). We modeled store-brand market share as a function of the category characteristics that Hoch and Banerji (1993) find to be significant determinants of store-brand share: number of manufacturers, total manufacturer advertising, dollar category volume, retail margin, average quality, and distribution of store brands. We included retail margin from national brands because reverse causality may be driven primarily by margin on national brands, as we noted previously. In addition, we included both the price elasticity of the category, because we expect the store brand to do better in categories in which consumers are more sensitive to price, and the ratio of store-brand to national-brand price, because store-brand share will increase as its price advantage increases. We do not discuss the store-brand share model in detail because it is not central to our research objectives and is directly based on prior research. We simply used it to obtain consistent estimates of the coefficients in the profit margin equations.
The simultaneous model. The previous discussion leads to the following simultaneous system for retail margins on both store brand and national brands and on store-brand share:( n1)
( 1) Margin[subkij] = γ[subk0] + γ[subk1] Herfind [subij] + γ[subk2]Advtg[subi] + γ[subk3]Deal[subi]
+ γ[subk4]Depth[subi] + γ[subk5]Catdist[subi] + γ[subk6]Sbdist[subi]
+ γ[subk7]Catelast[subi] + γ[subk8]Penet[subi] + γ[subk9]Puramt[subij]
+ γ[subk10]Purcycle[subi] + gamma;[subk11]Perish[subi] + γ[subk12]HBC[subi]
+ γ[subk13]Sdum1[subj] + ... + γ[subk31]sdum19[subj]
+ β[subk1]Sbshare[sub1j] + ε[subkij], and
( 2) Sbshare[subij] = γ[sub50] + γ[sub51]Numman[subij] + γ[sub52]Advtg[subi] + γ[sub53]Catvold[subij]
+ γ[sub54]SBdist[subi] + γ[sub55]Qual[subi] + γ[sub56]Catelast[subi]
+ γ[sub57]SDum1[subij] + ... + γ[sub525]Sdum1[subij]
+ β[sub51]Margin[sub1ij] + β[sub52]Pricerat[subij] + ε[sub5ij],
where k takes values of 1 and 2 for the retailer's gross margins from national and store brands and values of 3 and 4 for corresponding net margins.( n2) All the variables are defined in Table 1.
The system is identified because the margin equation contains seven variables that do not appear in the store-brand share equation and the store-brand share equation contains three variables that do not appear in the margin equations. We focus on the three variables excluded from the margin equations. First, note that three components of category volume (i.e., penetration, purchase amount, and purchase cycle) are included in the margin equation. As we discussed previously, the three components may have different effects on margin, and thus we included them separately. In contrast, there is no reason to expect differential effects on store-brand share, so the composite category volume variable is included in that equation (Hoch and Banerji 1993). Second, the number of manufacturers is not included in the margin equation, but we included the Herfindahl index, which is a well-established measure of manufacturer concentration. Third, the quality variable is a nationwide, general measure of the perceived quality of the best store brands in the country, not the quality of the specific retailer's store brands (see Table 1). By using the store-brand share variable, which is a direct measure that is specific to the retailer, we captured the increased leverage that strong store brands provide over manufacturers. We do not expect general, nationwide perceptions of store brands to affect the margins of a particular retailer over and above their effect on that retailer's store-brand share.
Model Estimates
We estimated the model for each retailer by using categories for which we had information on all relevant variables. We have complete data on 75 of the 115 categories for Retailer 1 and 45 of the 84 categories for Retailer 2. Correlation matrices of the variables in the margin models for the two retailers are provided in Tables 3 and 4, and 3SLS estimates of the margin equations for both retailers are provided in Table 5.( n3)
Manufacturer market power. Almost all our hypotheses about the effects of the variables pertaining to manufacturer market power are supported. The relationship of margins to the Herfindahl index, deal frequency, and deal depth was as we expected. The coefficients of the Herfindahl index and deal depth are significantly negative in five of the six regressions, and the coefficient of deal frequency is significantly negative in all six regressions. Category advertising has the expected negative effect on both retailers' national-brand margins, but, contrary to expectations, Retailer 1's margin on store brands increases significantly with advertising. Perhaps national brands in heavily advertised categories not only have higher wholesale prices but also have somewhat higher retail prices, thus enabling the retailer to charge higher prices for the store brand and still maintain a price advantage over national brands.
Retailer market power. Our hypotheses about the effects of the variables pertaining to retailer market power are also well supported in both data sets. As we expected, store-brand distribution has a negative relationship to both retailers' national-brand and store-brand margins. Heavy distribution of store brands increases price competitiveness of store brands instead of providing monopoly power to retailers. In addition, store-brand market share has the expected positive coefficient in five of the six regressions. The magnitude of the standardized coefficient of store-brand share in the national-brand margin model suggests that store-brand share is one of the most important drivers of retail margins on national brands. For Retailer 1, price elasticity is negatively associated with both national-brand and store-brand margins, though its coefficient is not statistically significant for Retailer 2. Contrary to our hypothesis, category distribution is not significant in most of the regressions. This may be due to multicollinearity, because the correlation between category and store-brand distribution is .67.( n4)
Consumer and category characteristics. We had only a few a priori hypotheses about the effects of consumer and category characteristics. As we expected, purchase cycle has a positive effect on national-brand and store-brand margins for both retailers. Purchase amount also has the expected positive effect on net contribution margin and the expected negative effect on gross margin, at least for the store brand of Retailer 2. The coefficient of penetration does not support our hypothesis: It is positive for Retailer 1 and insignificant for Retailer 2.
Summary. Table 6 summarizes our results for both retailers. For each of our a priori hypotheses, it depicts whether or not the hypothesis was supported. The symbol "check" denotes that the estimated coefficient is significant and of the hypothesized sign, and the symbol "X" denotes that the estimated coefficient is significant but not of the hypothesized sign. As the large number of "checks" show, most of the variables pertaining to manufacturer, retailer, and consumer and category characteristics influence retail profit margins on national brands and store brands as we hypothesized. The pattern of coefficients is similar for both retailers, though statistical significance is weaker for Retailer 2 because of its small sample size. This similarity provides important convergent validity for our results. Particularly notable is our finding that after we controlled for other key determinants and endogeneity of store-brand share, high store-brand share enables retailers to obtain significantly higher percentage margins on national brands. We find this result for both data sets in our analysis, despite substantial differences in the type of retailers, the product categories they sell, and their pricing and promotion policy.
The Percentage Margin Differential
Having developed some important insights into the determinants of retailers' percentage margins on national brands and store brands, we now examine the differential between the two. Table 7 reports the median ratio of store-brand to national-brand margins for the two retailers across all categories and for the retailers' major departments.
The first row of Table 7 confirms that on average across all the categories, not only percentage gross margins but also percentage net margins are substantially higher for the store brand than for national brands. The numbers for both retailers are consistent with the 20%-25% gross margin advantage for store brands that is noted in the literature (Hoch and Banerji 1993). The numbers are higher than the 16% margin advantage that Albion and Farris (1987) find for unadvertised versus advertised brands, but that is not surprising because only approximately 50% of the unadvertised brands in their sample were store brands. The remaining rows of Table 7 show that the ratio varies substantially across departments. For example, the store brand--national brand margin ratio is high in the HBC department for both retailers, likely because HBC contains strong brand names with heavy advertising that can command substantially higher wholesale prices from retailers. In contrast, Retailer 1 does not have a store-brand margin advantage in its general merchandise department, which contains many commodity categories, such as pet supplies, shoe care, and stationery. The wholesale prices of national brands in this department are probably quite close to the competitive wholesale prices of store brands, thus bringing the retailers' margins on national and store brands closer together.
Cross-Category Determinants of the Percentage Margin Differential
To examine the variations in the percentage margin differential across categories more systematically, we estimated a 3SLS model with the same explanatory variables as in the previous section, but with the retailer's margin on the store brand minus the margin on national brands as the dependent variable. Note that any variables that influence the margin differential do so by influencing one or both of its components. The (unstandardized) effect of any variable on the differential will be equal to its effect on store-brand margin minus its effect on national-brand margin (Farris, Parry, and Ailawadi 1992). Thus, only variables that have a substantially different influence on the retailer's store-brand margin rather than its national-brand margin will have a significant impact on the margin differential.
Table 8 summarizes the 3SLS coefficient estimates of the margin differential model for both retailers. As can be predicted from Table 5, many of the variables that pertain to manufacturer and retailer power have a significant influence on the margin differential because their impact on store-brand margin is different from their impact on national-brand margin. We discuss the most important influences here. First, category advertising significantly increases the margin differential because retailers must take lower margins on national brands in heavily advertised categories, but their store-brand margins do not appear to suffer. Second, the depth of deals in the category also increases the differential, probably because the deep discounts on national brands hurt the retailer's national-brand margins, and store brands are not discounted heavily from their everyday low prices. Third, store-brand distribution significantly reduces the margin differential. It appears that retailers need to price their store brands even more competitively than national brands when several competing retailers also offer a store brand. They do not seem to benefit from any local monopoly on their particular store brand. Fourth, store-brand share also reduces the margin differential. The retailer is able to obtain lower wholesale prices and therefore earn higher margins on national brands as store-brand share increases. As a result, the store-brand margin advantage becomes smaller.
The Dollar Margin Differential
As we noted in the first section, that retailers earn higher percentage margins on store brands than on national brands does not guarantee that their dollar margins on store brands will also be higher. For the dollar margins on national brands and store brands to be equal, the ratio of store-brand percentage margin to national-brand percentage margin must be equal to the ratio of national-brand retail price to store-brand retail price. Given the substantially lower price at which store brands sell, the retailer's dollar margin may be lower on store brands if the percentage margin advantage is low.
Table 9 summarizes the ratio of store-brand to national-brand prices and dollar margins for both retailers.( n5) The contrast between Table 7 and Table 9 is noteworthy. Although both retailers have similar store-brand percentage margin advantages, Retailer 2 has a store-brand dollar margin advantage but Retailer 1 does not. On average, Retailer 1's gross dollar margin per unit from store brands is lower than that from national brands in all the departments except HBC. The same is true of net contribution dollar margin. As Table 9 shows, for Retailer 1, the percentage margin advantage that the store brand offers does not make up for the substantially lower retail price of the store brand. Thus, from a perspective of dollar margin per unit, store brands do not necessarily offer retailers an advantage unless they are able to keep a relatively low price differential between the store brand and national brands.
Our finding that a retailer's dollar margin per unit may be lower on store brands than on national brands suggests that, all else being equal, a customer who buys store brands contributes less to the total dollar profit of a retailer than does a customer who buys national brands. As we discussed previously, all else may not be equal. A customer's contribution to the total dollar profit of the retailer depends not only on the dollar margin per unit of purchased items but also on the total number and type of items bought. In this section, we explore how the profitability of heavy store-brand users compares with that of light users or nonusers.
Method for Profitability Comparison
To perform the analysis for the market-basket data for Retailer 1, we used 646,549 market baskets that had nonmissing profitability data and that included at least one item from the 115 categories in which a store brand was offered. For each market basket, we computed the percentage of items purchased from these 115 categories that are the store brand. This procedure accounts for the availability of store brands in the categories represented in the basket and ensures that a large basket is not characterized as having low store-brand share simply because it contains many items from categories that do not have a store brand. Approximately 40% of the baskets do not contain any store-brand items, and in slightly more than 25% of the market baskets, store-brand items constitute less than 35% of the total number of items in which a store brand could have been bought. We used cutoffs of 0% and 35% to split the baskets into zero, low, and high store-brand share baskets, and then we compared mean profitability across the three groups of baskets. As we noted previously, we did not have panel data for Retailer 1 and thus could not identify baskets from the same household. The data provide some insights at the basket level but do not speak to the total profitability of a customer.
However, for Retailer 2 we did have panel data, so we could directly examine the relationship between customer-level profit and store-brand purchasing levels. We calculated the share of each customer's purchases that were store-brand items, and using the same cutoffs as we did for Retailer 1, we compared profit across groups of customers with zero, low, and high share.
We conducted this exploratory but less constrained comparison of groups instead of estimating a continuous relationship between store-brand share and market-basket/ customer profitability because prior research does not enable us to predict a specific kind of relationship.
Results of Profitability Comparison
Table 10 provides the mean values of four profit measures across the three groups of market baskets for Retailer 1 and the three groups of customers for Retailer 2: total gross margin dollars, total contribution margin dollars, total gross margin as a percentage of sales, and total contribution margin as a percentage of sales. It also provides the mean size of market baskets and customer sales in terms of both dollar value and number of items. Given the large sample sizes in both cases, group differences are statistically significant for all the variables, so we focus on managerial importance in the insights we draw from this table.
Percentage margin differences. For Retailer 1, there is little managerially significant difference across zero, low, and high store-brand baskets in either gross or net contribution margins as a percentage of sales. Given that percentage margins are higher for store brands than for national brands, we expected that the average would be somewhat higher in market baskets with a high share of store brands. The small difference suggests that market baskets with a high share of store brands contain other products that have somewhat lower percentage gross margins than average. In contrast, for Retailer 2, the average percentage gross margin from heavy users of store brands is much higher than from nonusers. Thus, there is no evidence that heavy store-brand users disproportionately buy other low-percentage-margin products from this retailer to offset their higher-percentage-margin store-brand purchases.
Dollar margin differences. Average dollar profit varies substantially across the groups for both retailers. There is an inverted U-shaped relationship between store-brand share and profit. For Retailer 1, the dollar profit of baskets with low store-brand share is the highest; it is almost twice that of high store-brand baskets and more than three times that of zero store-brand baskets. This is because zero and high store-brand baskets are much smaller than low store-brand baskets. The inverted U-shaped relationship also holds when we examined groups of customers, not just groups of baskets, for Retailer 2. The dollar profit is highest for the light store-brand users, dropping by roughly half among the heavy store-brand users and by much more among nonusers. The difference in dollar profit across customer groups is due to heavy store-brand users making fewer trips and buying much less from the retailer than light store-brand users do; nonusers make the fewest trips and buy the least.
Why is the profitability of customers who do not buy store brands at all so small? It would appear that they are not loyal to the retailer. Not only the size of their average market basket but also their total number of trips and their total purchases from the retailer are small. This is consistent with research that shows a positive relationship between store-brand use and store loyalty (Ailawadi, Neslin, and Gedenk 2001; Corstjens and Lal 2000). Perhaps the unwillingness of the customers to buy the store brand at all is a reflection of their relatively weak perception of the retailer.
Why is the profitability of heavy store-brand users also small? They do not just make more frequent trips to the same store and buy less per trip. This is evidenced by not only the market baskets of Retailer 1 but also the total customer profit of Retailer 2 showing the same pattern. Heavy store-brand users may ( 1) operate under financial constraints and simply buy and consume less, ( 2) have smaller families and therefore fewer requirements, or ( 3) shop at multiple stores and therefore contribute less to the sales and profit of any single retailer. Prior research provides strong evidence that heavy store-brand users operate under financial constraints and are price conscious (Ailawadi, Neslin, and Gedenk 2001; Batra and Sinha 2000; Richardson, Jain, and Dick 1996), in support of the first explanation. The limited demographic data that are available on the customers of Retailer 2 also provide some support for the second explanation. The last five rows of Table 10 display the demographic profile of the three groups of Retailer 2's customers and indicate that, in general, nonusers and light users are much more similar to each other than they are to heavy users. This is consistent with our belief that the factors underlying fewer purchases by heavy store-brand users are quite different from the factors underlying fewer purchases by non-store-brand users. Nonusers of store brands are not significantly different from light users in terms of gender, household size, or age, so it is difficult to argue that they have fewer requirements. It is more likely that they are the least loyal to the retailer. Heavy users of store brands are older and have smaller households than do light users or nonusers, suggesting that they may have fewer requirements.
However, it is unlikely that differences in requirements completely explain the substantial difference in expenditure between light and heavy store-brand users. Perhaps heavy store-brand users shop and "cherry-pick" at multiple stores and therefore buy less from any one retailer. This is consistent with a Food Marketing Institute (1994) study that found heavy store-brand buyers at several different stores. Heavy store-brand buyers may buy store brands wherever they shop because they consider them a good value (i.e., they may be loyal to store brands in general, not to the store brand of a particular retailer). This appears to contradict the positive relationship between store-brand use and store loyalty that Ailawadi, Gedenk, and Neslin (2001) and Corstjens and Lal (2000) observe. However, the positive association these researchers find in their linear models may be driven by the higher loyalty of store-brand users than nonusers rather than heavy store-brand users versus light users. Our analysis suggests a nonlinear relationship between store-brand use and store loyalty.
In this article, we developed an econometric model of the percentage margins that retailers earn on national brands and store brands. We used data from two different retailers to estimate our model and to identify the key factors that explain why the margins vary across categories. This analysis provided several insights into the role of variables such as manufacturer concentration, advertising, and distribution in decreasing retail margins on national brands and store brands. The analysis also resolved a debate about the effect of store-brand share on retail margins from national brands and showed that the store-brand share effect is significantly positive. It also suggests some avenues for further research. First, although we accounted for the endogeneity of store-brand share by using 3SLS, we were limited by an essentially cross-sectional data set. Longitudinal data would allow for more flexibility in selecting instrumental variables and in examining how margins change with store-brand share. Similarly, a "before--after" quasi experiment that examines how national-brand margins change after a store brand is introduced would provide a convincing test of the direction of causality (e.g., Pauwels and Srinivasan 2003). Second, there may be interactions between some of the determinants of retail margins in our model. We tested for an interaction between advertising and distribution (Abela and Farris 1999) and between store-brand quality and store-brand share (Narasimhan and Wilcox 1998), and we found that they were not significant. However, there may be other interactions that further research should consider. Third, both self-and cross-price elasticity determine product-line pricing and margins, according to economic theory. Although we included the category price elasticity in our model, further research should also account for the self-and cross-price elasticities of national and store brands. Fourth, our interest was in distinguishing between retail margins on national brands and store brands, but margins on individual national brands also vary substantially and should be studied. It would be worthwhile to integrate such analysis with research on retail price elasticities and retailer pricing strategies (e.g., Shankar and Bolton 2003).
Next, we examined the impact of store-brand share on retail margins in more detail by answering three hitherto unaddressed questions: ( 1) Are percentage margins on store brands higher than on national brands, and why does this margin advantage vary across product categories? ( 2) Does the store-brand advantage hold when we consider dollar margin per unit? and ( 3) How much do heavy store-brand users, compared with light users and nonusers of store brands, contribute to the total dollar profit of a retailer? On average, we found that percentage margins on store brands are significantly higher than on national brands; thus, all else being equal, category percentage margins increase with store-brand share. However, there are substantial variations across categories; the store-brand margin advantage is greater in heavily advertised categories and smaller in categories with widely available store brands and high store-brand share. Furthermore, the store-brand margin advantage does not necessarily hold when we consider dollar margin, because though store brands earn higher percentage margins, they are sold at substantially lower retail prices than national brands are. Thus, retailers' rushing to increase store brands across categories simply because of their higher percentage margins may not be a good idea. Retailers should expand their store-brand offering carefully and recognize that the biggest margin rewards are in categories in which competition from other retailers is relatively small and national brands are heavily advertised. Retailers should also try to position the store brand on reasonable quality, not just on low price, because having to compete heavily on price erodes store brands' dollar margin advantage.
Our findings about the profitability of heavy store-brand users also send a cautionary message to retailers. There are two pieces of "good news" here. First, heavy users do not selectively buy other items that are significantly less profitable on a percentage basis for the retailer. Second, customers who buy at least some store-brand items contribute much more to the sales and profit of the retailer than do customers who do not buy any store-brand items. However, heavy store-brand buyers buy significantly fewer items in total and therefore contribute much less to the dollar profit of the retailer than do light store-brand buyers. This is a notable finding that deserves further examination because it sheds light on the profitability of customers attracted by store brands. Existing research and our preliminary analysis that used the panel data from Retailer 2 suggest that heavy store-brand buyers have fewer requirements either because they are financially constrained, buy less, and consume less or because they have smaller households. However, it may also be that they shop at multiple stores and are loyal to store brands in general, not to the store brand of a particular store. Further research that combines more detailed demographic and psychographic variables with panel-purchase and profit data from multiple retailers in a market is needed in order to conclusively determine which explanation is true and to study the implications of each explanation for total retailer profit. It would also be useful to conduct a cross-retailer analysis that compares the profitability of retailers with relatively weak store brands with that of strong store brands.
Some authors have noted that increasing store-brand share beyond a certain point may not be wise. They argue that there may be a downside to high store-brand share because national brand manufacturers may no longer find it worthwhile to offer better prices and trade deals (e.g., Narasimhan and Wilcox 1998; Wellman 1997). For both retailers in our analysis, there was wide variation in store-brand share across categories, but we did not find a negative relationship between store-brand share and retail margins on national brands, even at high levels of store-brand share.
However, in the final analysis, it is not just percentage margin but also dollar profit that matters; here, there seems to be a downside to high store-brand share. First, at least in some cases, the dollar margin per unit from store brands is actually smaller than that from national brands, because store brands command much lower retail prices. Second, customers with medium rather than high levels of store-brand use appear to be the most profitable from a standpoint of total dollar profit. Retailers may find it worthwhile to offer store brands despite lower dollar margins, partly as leverage over manufacturers but partly to provide a lower price alternative to price-conscious consumers whose patronage they may otherwise lose. However, our analysis suggests that pushing store brands too much at the expense of national-brand offerings is not advisable. It is important that retailers retain a balance between store brands and national brands to attract and retain profitable customers who buy some store-brand items but not too many. This supports the view that national brands continue to be traffic builders and that reducing national brand choices may make a store less attractive to profitable shoppers (e.g., Farris and Ailawadi 1992; Johnson 1994). This is also consistent with Corstjens and Lal's (2000) finding that for a quality store-brand strategy to be profitable, there should be enough customers who buy national brands.
In closing, we recognize three limitations of our data. First, we study only two retailers, and though their store-brand shares are in line with nationwide averages, it would be valuable to test our model with data from more retailers. Still, we want to reiterate that the two retailers are different from each other in significant ways, and yet most of our empirical results from both data sets are similar. We believe this establishes the convergent validity of our results rather well. Second, manufacturers sometimes provide lump-sum trade allowances to retailers that are not linked to a particular item or even to a particular product category, and these data were not available to us. However, the lump-sum payments are made across multiple categories, so it is unlikely that they would systematically bias the coefficients in our category-level margin model. Third, the store-brand quality data we use are older than the data from the two retailers. Sethuraman and Cole (1997) conducted a survey that asked consumers to rate the quality of store brands compared with that of national brands, but we could not use their data in our analysis because they were gathered only for a subset of the categories we study. However, the correlation with the Hoch and Banerji (1993) data is .70 across the categories for which both sets of data are available, suggesting that the cross-category quality differences that Hoch and Banerji (1993) report are valid for our analysis.
In conclusion, we note that the interactions among manufacturers and retailers, retailer power and profit margins, and the role that store brands play are important issues in both the business and the academic press. We hope that this article has provided convincing answers to some questions about the impact of growing store-brand share on retailer margins and has provided food for thought and specific directions for further examination of other questions.
The authors thank two anonymous retail chains, Stephen Hoch, and Raj Sethuraman for providing the data used in the article. The authors also thank Peter Vishton and Pen-Che Ho for research computing assistance; Jeannie Newton and Adam Barrer for help with data collection; and Dick Bower, Paul Farris, Praveen Kopalle, Rajiv Lal, Don Lehmann, Scott Neslin, Koen Pauwels, Al Silk, and Bob Steiner for their helpful suggestions. The authors thank participants of the Tuck Marketing Seminar Series, the 2000 INFORMS Marketing Science Conference, the Marketing Science Institute, and the University of Texas at Dallas Marketing Seminar Series for their comments. Finally, the authors are grateful for the many valuable comments and suggestions from the four anonymous JM reviewers. This research was supported by the Tuck Associates Program.
(n1) The ratio of store-brand to national-brand price (Pricerat) is also treated as endogenous in this system.
(n2) For Retailer 2, k only takes values of 1 and 2 for gross margins from national and store brands. Furthermore, we aggregated data across stores, so there is no j subscript and there are no store-specific dummy variables in the model. Finally, because the deal and depth variables are determined by Retailer 2's own decisions and are not based on nationwide retail data, as in the case of Retailer 1, we treat the two variables as endogenous for Retailer 2.
(n3) Estimates of the store-brand share equation are available from the first author on request.
(n4) In addition, category distribution may not accurately reflect retail competition in some categories, such as light bulbs, in which category distribution may be high, but retailers carry different national brands to reduce competition.
(n5) It is a challenge to compute dollar margin per unit because individual items are measured in different sizes and units, not only across categories but also within a category. Furthermore, as is typical with scanner data, the size variable is not available in a numeric form that is easy to use in computations. We did our best to compute dollar margins per unit correctly. However, to minimize the impact of outliers that may have been a result of incomparable units of measurement, we sorted items in each category according to their dollar margin per unit and deleted the top and bottom 5% from this analysis.
Legend for Chart:
A - Variable
B - Definition
C - Source for Retailer 1
D - Source for Retailer 2
A
B
C
D
Percentage gross margin
Net selling price - cost from vendor /
Net selling price
Retailer POS data
Retailer panel data
Percentage net contribution margin
Net selling price - cost from vendor - direct
product cost / Net selling price
Retailer POS data
Retailer panel data
Percentage margin for national
brands
(Multiple lines cannot be converted
in ASCII text)
Retailer POS data
Retailer panel data
Dollar gross margin
Net selling price - cost from vendor
Retailer POS data
Retailer panel data
Dollar net contribution margin
Net selling price - cost from
vendor - direct product cost
Retailer POS data
Retailer panel data
Dollar margin for national brands
Median value of dollar margin for
national brands
Retailer POS data
Retailer panel data
Herfindahl index (Herfind)
Sum of squared market shares of all
manufacturers in the category
Retailer POS data
Retailer panel data
Advertising (Advtg)
Media advertising for product
category (millions of dollars)
Leading National Advertisers' class/brand
summary
Leading National Advertisers' class/brand
summary
Deal frequency (Deal)
Percentage of all sales of the category made
on some type of deal
Information
Resources Inc.
(1998)
Retailer panel data
Deal depth (Depth)
Average percentage discount when the category
is on some type of deal
Information
Resources Inc.
(1998)
Retailer panel data
Category distribution (Catdist)
Percentage of major grocery, drug, and
mass-merchandise chains in the relevant
geographical area that carry the product
category
Primary data (2002)
Primary data (2002)
Store-brand distribution (Sbdist)
Percentage of major grocery, drug, and
mass-merchandise chains in the relevant
geographical area that carry a store brand
in the category
Primary data (2002)
Primary data (2002)
Store-brand share (Sbshare)
Dollar sales of store brand /
Total dollar sales of product category
Retailer POS data
Retailer panel data
Category elasticity (Catelast)
Average percentage increase in category sales
due to a 15% price cut
Narasimhan, Neslin, and Sen (1996); P-O-P
Times (1991)
Narasimhan, Neslin, and Sen (1996); P-O-P
Times (1991)
Penetration (Penet)
Percentage of households that buy the category
Information
Resources Inc.
(1998)
Retailer panel data
Average purchase amount (Puramt)
Average dollar amount of purchase when
category is purchased (i.e., price per
unit x number of units purchased)
Retailer POS data
Retailer panel data
Average purchase cycle (Purcycle)
Average number of days between consecutive
purchases of the category
Information
Resources Inc.
(1998)
Retailer panel data
Perishable product dummy (Perish)
Equal to 1 if the category is perishable
(i.e., frozen, refrigerated, or dairy foods);
equal to 0 otherwise
Retailer POS data
Retailer panel data
HBC dummy (HBC)
Equal to 1 if the category is in the HBC
department; equal to 0 otherwise
Retailer POS data
Retailer panel data
Number of manufacturers (Numman)
Number of manufacturers whose products are
sold in the category
Retailer POS data
Retailer panel data
Category dollar volume (Catvold)
Category sales in dollars
Retailer POS data
Retailer panel data
Average store-brand quality: 1 (Qual)
Average response by retail experts to "How does
the quality of the best private label supplier
compare to the leading national brands in the
product category?" (five-point scale)
Hoch and Banerji (1993)
Hoch and Banerji (1993)
Average store-brand quality: 2
Average response by consumers to "Please indicate
your opinion about the quality of private labels
when compared with the quality of national brands
for each product category" (seven-point scale)
Sethuraman and Cole (1997)
Sethuraman and Cole (1997)
Ratio of store-brand to national-brand
price (Pricerat)
Retail selling price of store brand /
Average retail selling price of
national brands in the category
Retailer POS data
Retailer panel data
Store dummies (Sdum 1-19)
Equal to 1 if the observation is for that store;
equal to 0 otherwise
Retailer POS data
--
Notes: POS = point of sale. Legend for Chart:
A - Independent Variables
B - Hypothesized Sign of Coefficient for Retail Margin National
Brands Percentage Gross Margin
C - Hypothesized Sign of Coefficient for Retail Margin National
Brands Percentage Contribution
D - Hypothesized Sign of Coefficient for Retail Margin Store
Brands Percentage Gross Margin
E - Hypothesized Sign of Coefficient for Retail Margin Store
Brands Percentage Contribution
F - Selected Literature Support for Hypotheses
A B C D E
F
Manufacturer Market Power
Herfindahl - - - -
index
Scherer and Ross (1990)
Advertising - - - -
Albion and Farris (1981),
Lal and Narasimhan (1996),
Steiner (1973, 1978, 1993)
Percentage - - - -
sold on deal
Chevalier and Curhan (1976),
Steiner (1984)
Deal depth - - - -
Chevalier and Curhan (1976),
Gerstner, Hess, and
Holthausen (1994), Steiner
(1984)
Retailer Market Power
Category - - - -
distribution
Abela and Farris (1999),
Scherer and Ross (1990)
Store-brand - - - -
distribution
Abela and Farris (1999),
Scherer and Ross (1990)
Store-brand + + + +
share
Mills (1995)
Category price - - - -
elasticity
Albion and Farris (1987),
Mansfield (1997)
Consumer/Category Characteristics
Category - ? - ?
penetration
Chiang and Wilcox (1997),
Lal and Narasimhan (1996)
Purchase cycle + ? + ?
Narasimhan and Wilcox (1998)
Purchase - + - +
amount
Lal and Narasimhan (1996),
Narasimhan and
Wilcox (1998) Legend for Chart:
B - Nbgm
C - Nbcm
D - Sbgm
E - Sbcm
F - Herfind
G - Advtg
H - Deal
I - Depth
J - Catdist
K - Sbdist
L - Sbshare
M - Catelast
N - Penet
O - Purcyc
P - Puramt
Q - Perish
A B C D E F G H I
J K L M N O P Q
Nbgm 1
Nbcm .91 1
Sbgm .49 .49 1
Sbcm .45 .51 .96 1
Herfind .13 .07 -.16 -.15 1
Advtg -.27 -.19 .09 .16 -.15 1
Deal -.35 -.28 -.45 -.37 .04 .06 1
Depth -.21 -.26 -.03 -.07 -.14 -.04 .14 1
Catdist -.21 -.19 .03 .01 -.12 .21 -.18 -.08
1
Sbdist -.25 -.17 -.03 .01 -.21 .32 -.07 -.13
.67 1
Sbshare .26 .23 -.01 -.02 .39 -.22 -.06 -.20
-.08 .01 1
Catelast -.20 -.07 -.16 -.07 -.15 .17 .29 -.33
.19 .31 -.05 1
Penet -.08 -.09 -.28 -.26 .12 -.08 .39 .11
-.00 -.09 .21 .13 1
Purcycle .16 .25 .37 .40 -.15 -.07 -.30 .07
.03 .11 -.13 -.04 -.56 1
Puramt -.04 .18 .25 .38 -.02 .30 -.12 -.29
.21 .29 -.16 .30 -.34 .22 1
Perish .15 .16 -.08 -.07 .15 -.13 .22 .01
-.81 -.50 .15 -.22 .07 -.28 -.14 1
HBC -.10 -.03 .42 .42 -.28 .32 -.27 .21
.32 .39 -.22 -.09 -.44 .41 .36 -.23 Legend for Chart:
B - Nbgm
C - Sbgm
D - Herfind
E - Advtg
F - Deal
G - Depth
H - Catdist
I - Sbdist
J - Sbshare
K - Catelast
L - Penet
M - Purcyc
N - Puramt
A B C D E F G H
I J K L M N
Nbgm 1
Sbgm .57 1
Herfind .27 -.32 1
Advtg -.34 .12 .07 1
Deal -.36 -.38 .11 .02 1
Depth -.19 .04 -.18 .06 .19 1
Catdist -.11 -.02 -.09 .31 -.21 .06 1
Sbdist -.28 -.05 -.16 .24 .01 -.06 .54
1
Sbshare .36 .03 .22 -.17 -.03 -.24 .03
-.03 1
Catelast -.16 -.21 -.21 .25 .34 -.25 .24
.34 -.03 1
Penet -.11 -.09 .17 -.11 .27 .04 .02
-.14 .23 .17 1
Purcycle .26 .28 -.11 -.02 -.22 .09 .05
.15 -.09 .02 -.59 1
Puramt .02 .33 .06 .27 -.17 -.34 .27
.26 -.07 .36 -.41 .26 1
HBC .04 .09 -.26 .38 -.36 .16 .29
.44 -.19 -.05 -.37 .38 .32 Legend for Chart:
A - Independent Variable
B - Standardized Coefficient in Equation for Retailer 1 National
Brands Percentage Gross Margin
C - Standardized Coefficient in Equation for Retailer 1 National
Brands Percentage Contribution
D - Standardized Coefficient in Equation for Retailer 1 Store
Brand Percentage Gross Margin
E - Standardized Coefficient in Equation for Retailer 1 Store
Brand Percentage Contribution
F - Standardized Coefficient in Equation for Retailer 2 National
Brands Percentage Gross Margin
G - Standardized Coefficient in Equation for Retailer 2 Store
Brand Percentage Gross Margin
A B C
D E
F G
Herfindahl index -.004 -.059(***)
(-.15) (-2.16)
-.159(***) -.143(***)
(-5.89) (-5.42)
-.284(***) -.417(***)
(-2.27) (-2.77)
Advertising -.059(***) -.044(***)
(-2.28) (-1.76)
.103(***) .131(***)
(4.28) (5.57)
-.315(***) .082
(-2.83) (.11)
Percentage sold on deal -.252(***) -.192(***)
(-9.57) (-7.48)
-.286(***) -.231(***)
(-11.15) (-9.18)
-.523(***) -.293(***)
(3.62) (-2.81)
Deal depth -.191(***) -.212(***)
(-7.23) (-8.15)
-.088(***) -.116(***)
(-3.30) (-4.44)
-.172(**) -.007
(-1.82) (-.08)
Category distribution -.094(***) -.010
(-2.06) (-.22)
-.026 -.121(***)
(-.57) (-2.68)
-.087 -.133
(-1.12) (1.09)
Store-brand distribution -.179(***) -.166(***)
(-5.24) (-5.03)
-.305(***) -.240(***)
(-9.44) (-7.57)
-.308(***) -.434(***)
(-3.62) (-3.97)
Store-brand share .424(***) .353(***)
(8.34) (7.21)
.233(***) .121(***)
(4.91) (2.61)
.312(***) .194
(2.74) (1.06)
Price elasticity -.064(***) -.052(**)
(-2.34) (-1.96)
-.067(***) -.079(***)
(-2.53) (-3.08)
-.118 .172
(-.77) (.73)
Category penetration .105(***) .250(***)
(3.60) (8.73)
.144(***) .243(***)
(4.87) (8.34)
-.044 -.026
(-.17) (-.31)
Purchase cycle .201(***) .377(***)
(6.98) (13.36)
.211(***) .295(***)
(7.30) (10.36)
.186(*) .341(**)
(1.61) (1.88)
Purchase amount -.034 .207(***)
(-1.34) (8.21)
.146(***) .298(***)
(5.57) (11.52)
-.042 -.231(**)
(-.64) (-1.89)
Perishable foods .048 .200(***)
(1.19) (4.96)
-.038 -.059
(-.90) (-1.42)
-- --
HBC .020 -.004
(.70) (-.15)
.367(***) .315(***)
(12.28) (10.71)
.033 -.062
(.82) (-.68)
(*) p < .15.
(**) p < .10.
(***) p < .05.
Notes: There are 19 store dummy variables in Retailer 1's model;
the coefficients are not reported here to conserve space. The
t-statistics are in parentheses. Legend for Chart:
A - Independent Variable
B - Sign of Coefficient in Equation for National-Brand Margin
Percentage Gross Margin Hypothesized
C - Sign of Coefficient in Equation for National-Brand Margin
Percentage Gross Margin Retailer 1
D - Sign of Coefficient in Equation for National-Brand Margin
Percentage Gross Margin Retailer 2
E - Sign of Coefficient in Equation for National-Brand Margin
Percentage Contribution Hypothesized
F - Sign of Coefficient in Equation for National-Brand Margin
Percentage Contribution Retailer 1
G - Sign of Coefficient in Equation for Store-Brand Margin
Percentage Gross Margin Hypothesized
H - Sign of Coefficient in Equation for Store-Brand Margin
Percentage Gross Margin Retailer 1
I - Sign of Coefficient in Equation for Store-Brand Margin
Percentage Gross Margin Retailer 2
J - Sign of Coefficient in Equation for Store-Brand Margin
Percentage Contribution Hypothesized
K - Sign of Coefficient in Equation for Store-Brand Margin
Percentage Contribution Retailer 1
A B C D E F
G H I J K
Manufacturer Market Power
Herfindahl index - N.S. Y - Y
- Y Y - Y
Advertising - Y Y - Y
- X N.S. - X
Percentage sold on deal - Y Y - Y
- Y Y - Y
Deal depth - Y Y - Y
- Y N.S. - Y
Retailer Market Power
Category distribution - Y N.S. - N.S.
- N.S. N.S. - Y
Store-brand distribution - Y Y - Y
- Y Y - Y
Store-brand share + Y Y + Y
+ Y N.S. + Y
Category elasticity - Y N.S. - Y
- Y N.S. - Y
Consumer/Category Characteristics
Category penetration - X N.S. ? +
- X N.S. ? +
Purchase cycle + Y Y ? +
+ Y Y ? +
Purchase amount - N.S. N.S. + Y
- X Y + Y
Notes: Y = statistically significant and of hypothesized sign;
X = statistically significant but not of hypothesized sign;
N.S. = not significant. Legend for Chart:
A - Group
B - Retailer 1: Median Ratio of Store-Brand Margin to National
Brand Margin Number of Observations
C - Retailer 1: Median Ratio of Store-Brand Margin to National
Brand Margin Percentage Gross Margin
D - Retailer 1: Median Ratio of Store-Brand Margin to National
Brand Margin Percentage Net Margin
E - Retailer 2: Median Ratio of Store-Brand Margin to
National-Brand Margin Number of Observations
F - Retailer 2: Median Ratio of Store-Brand Margin to
National-Brand Margin Percentage Gross Margin
A B C D E F
Full sample 2063 1.27 1.27 84 1.29
Dairy products 184 1.10 1.06 0 --
Frozen foods 254 1.18 1.16 0 --
Specialty foods 89 1.05 .99 0 --
Grocery 1026 1.30 1.30 11 1.40
General merchandise 161 .92 .74 37 1.26
HBC 349 1.73 1.75 36 1.37 Legend for Chart:
A - Independent Variable
B - Retailer 1 Percentage Gross Margin Differential
C - Retailer 1 Percentage Net Contribution Differential
D - Retailer 2 Percentage Gross Margin Differential
A B C
D
Herfindahl index -.152(***) -.087(***)
(-5.48) (-2.99)
-.141(***)
(-2.52)
Advertising .164(***) .185(***)
(6.56) (7.04)
.334(***)
(2.74)
Percentage sold on deal -.080(***) -.076(***)
(-3.06) (-2.77)
.238(***)
(3.12)
Deal depth .074(***) .062(***)
(2.74) (2.22)
.158(**)
(1.77)
Category distribution .060 -.118(***)
(1.29) (-2.42)
-.048
(-1.08)
Store-brand distribution -.165(***) -.108(***)
(-4.92) (-3.07)
-.129(**)
(-1.72)
Store-brand share -.126(***) -.186(***)
(-2.57) (-3.60)
-.118(***)
(-2.15)
Price elasticity -.016 -.042(*)
(-.59) (-1.47)
.274(**)
(1.92)
Category penetration .057(**) .036
(1.91) (1.14)
.021
(.17)
Purchase cycle .044(*) -.022
(1.49) (-.73)
.148(**)
(1.75)
Purchase amount .187(***) .141(***)
(7.08) (5.11)
-.206(*)
(-1.53)
Perishable foods -.079(**) -.239(***)
(-1.88) (-5.43)
--
HBC .369(***) .341(***)
(12.23) (10.87)
-.089
(-.73)
(*) p < .15.
(**) p < .10.
(***) p < .05.
Notes: There are also 19 store dummy variables in Retailer 1's
models; t-statistics are in parentheses. Legend for Chart:
A - Group
B - Median Ratio of Store Brand to National Brands Price per Unit
C - Median Ratio of Store Brand to National Brands Dollar Gross
Margin per Unit
D - Median Ratio of Store Brand to National Brands Dollar Net
Margin per Unit
A B C D
Retailer 1
Full sample .70 .87 .89
Dairy products .77 .87 .89
Frozen foods .69 .78 .77
Specialty foods .62 .68 .69
Grocery .70 .90 .91
General merchandise .59 .60 .57
HBC .62 1.18 1.26
Retailer 2
Full sample .91 1.18 --
Grocery .82 1.15 --
General merchandise .87 1.10 --
HBC .94 1.29 -- Legend for Chart:
A - Variable
B - Mean Value for Market Baskets Whose Store-Brand Share Is 0%
C - Mean Value for Market Baskets Whose Store-Brand Share Is <35%
D - Mean Value for Market Baskets Whose Store-Brand Share Is
≥35%
A B C D
Retailer 1
Percentage gross margin 27.6% 26.2% 27.4%
Contribution margin/sales 21.1% 19.9% 20.2%
Total gross margin dollars $3.12 $9.95 $5.48
Total contribution dollars $2.48 $7.67 $4.14
Total number of Items 5.98 23.28 13.45
Total dollar amount $11.30 $38.54 $20.78
Percentage of market baskets 40.7% 27.6% 31.7%
Retailer 2
Percentage gross margin 27.4% 32.4% 36.8%
Total gross margin dollars $15.88 $77.79 $48.35
Total number of items 16.27 80.17 46.26
Total dollar amount $54.68 $244.23 $128.53
Number of trips 3.15 10.12 7.05
Percentage of customers 11.7% 77.1% 11.2%
Percentage male 22.2% 22.5% 33.3%
Household size 2.8 2.8 2.6
Age 47.4 46.6 53.1
Percentage bought on coupons 8.3% 8.7% 6.6%
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~~~~~~~~
By Kusum L. Ailawadi and Bari Harlam
Kusum L. Ailawadi is Associate Professor of Business Administration, Tuck School of Business, Dartmouth College (e-mail: kusum.l.ailawadi@dartmouth.edu).
Bari Harlam is Vice President, Marketing Intelligence, CVS Inc. (e-mail: baharlam@cvs.com).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 12- An Exploratory Study of the Introduction of Online Reverse Auctions. By: Jap, Sandy D. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p96-107. 12p. 1 Chart. DOI: 10.1509/jmkg.67.3.96.18651.
- Database:
- Business Source Complete
An Exploratory Study of the Introduction of Online
Reverse Auctions
Buyers are increasingly turning to online reverse auctions in their negotiations with suppliers. How do these price competition mechanisms affect buyer-supplier relationships? The author considers this question in the context of a quasi experiment involving six online reverse auctions conducted in the supply base of a major industrial buyer. The results indicate that these auctions increase both new and current suppliers' beliefs that buyers act opportunistically, particularly in open-bid auctions. Current suppliers are generally more willing than new suppliers to make dedicated investments toward the buyer. Paradoxically, in sealed-bid auctions, both current and new suppliers increase their willingness to make dedicated investments toward the buyer. Although these auctions can yield cost savings, the savings are category specific and are not systematically related to an open- or sealed-bid format. The author also discusses implications for the use of online reverse auctions in industrial sourcing activities.
In recent years, Internet technologies have yielded a suite of tools to manage sourcing between industrial buyers and their suppliers. These tools aid in contract negotiation, expenditure analysis, supplier selection, risk management, and optimal supplier performance. According to one estimate (Jones 2000), technological efficiency could produce nearly $1 trillion in savings from the $7 trillion annual expenditure on components, suppliers, and services worldwide. How this will happen remains an open question; across virtually all industries, buyers and suppliers wrestle with how to use Internet technology effectively. I am particularly interested in how emerging Internet-based tools will influence the ongoing relationships between buyers and suppliers. Extensive research has already examined successful interorganizational exchanges, but almost none of it considers the interface of emerging technologies with the ongoing relationships between buyers and suppliers. This research addresses that gap.
I consider how the use of online reverse auctions affects a firm's relationship with the supply base. Such auctions have been dubbed "reverse" because sellers bid instead of buyers, and prices are bid down instead of up. These auctions are standard in e-commerce tool kits of companies such as Aruba, Commerce One, Free Markets, Oracle, Bearcats, and various vertical hubs (Scott-Lewis 2001) and have been adopted by leading firms in many industries, including aerospace, automotive, communications, consumer products, pharmaceutical, technology, and the U.S. military (Friedman et al. 2001).
Buyers tend to believe that online reverse auctions are efficient, though anecdotal evidence suggests that the online auction experience can be extremely disturbing: "I am a supplier and very recently had this experience with my major customer. To sit for five hours and watch business that you have developed, maintained, and serviced for 40 years being carved up and slowly disintegrate is a very traumatic experience. Are we seeing the demise of a purchasing staff and sales force, as we know it today?" (posted by Jack Bailey, http://www.purchasing.com, June 15, 2000). A supplier who participated in the online open-bid auctions for this research notes: "[The buyer] talks about the relationship being a partnership and [the auction] really takes that away. There is not a partnership there at all. What they do is take your existing business that you have worked very hard to achieve and maintain. You work with them to give them cost reductions over the years, and they send it out across the board for a competitive bid. I just do not think that is fair." The possibility thus remains that the process may produce grave consequences for supplier attitudes and perceptions of the buyer. This research focuses on this possibility.
I consider online reverse auctions in industrial sourcing contexts and ask questions such as the following: In an online reverse auction, how do open bids versus sealed bids affect a supplier's attitudes and strategic position or bidding performance and motivation? Are suppliers motivated to better serve the buyer? Do the effects differ for current and new suppliers? I investigate how such auctions influence (I) supplier suspicions toward the buyer, ( 2) the buyer's cost savings from the auction, and ( 3) the supplier's willingness to make idiosyncratic investments. The goal is to expose the general effects of online reverse auction processes on supplier attitudes, motivations, and performance. I do not attempt to explain how specific components of these auctions (e.g., number of rounds of bidding, nature of price visibility) affect supplier behavior. Further research can address these questions.
The research involves six quasi experiments in the supply chain of a major industrial manufacturer. Although it is too early to know the long-term effects of online reverse auctions on buyer-supplier relationships, it is possible to examine short-term effects. Drawing on existing economics, marketing, and psychology theories as well as my own experience and extensive interviews with participating sourcing managers, suppliers, and auctioneers, I consider the extent to which these theories generalize to the firm's use of an open- or sealed-bid auction across various products.
This research thus makes several contributions. The effects of Internet technology on relationships between industrial buyers and suppliers can be better understood with a systematic, empirical test in the supply base of a major industrial manufacturer. The research addresses whether interorganizational relations theories are generalizable to online contexts. It also suggests when and how a firm should use auctions for its supply base, which provides insight into ways buyers and suppliers might better organize and manage their electronic procurement activities.
In the next section, I discuss the role of online reverse auctions in the buyer's relationships with suppliers, and I then describe two types of online reverse auctions commonly used in industrial sourcing activities. This is followed by hypotheses as to how supplier attitudes, motivation, and performance might vary between open- or sealed-bid auctions and between new or current suppliers. I describe the research setting and Lest hypotheses. The article concludes with a discussion of results, limitations, and managerial implications.
Relational and Transactional Exchanges
Buyers can develop an array of purchasing arrangements with suppliers. On the one hand, they can take an arm's-length transactional approach, which explicitly defines buyer and supplier roles in preestablished contracts; buying is focused around the specific transaction, and the orientation is win-lose. On the other hand, buyers can foster more collaborative sourcing arrangements that involve specific knowledge and implicit understandings and in which parties focus on mutually beneficial activities and processes. Most purchasing arrangements fall between these two extremes. Heide (1994) further discusses the differences between these two types of exchange.
The present research context involves various sourcing relationship types that range across the spectrum from transactional to collaborative arrangements. Many supply relationships are transactional, yet it is also possible that a supplier's contract is renewed every two to five years on a long-term basis. Consequently, suppliers may have considerable experience with the buyer over the years. To enhance the generalizability of the research, I consider this mix of relationships across six product categories that vary widely in terms of their sourcing and supply management strategies. The only type of relationship not represented in this research is strategic sourcing agreements in which the buyer and supplier are highly interdependent; make mutual idiosyncratic investments; and actively create mutually beneficial, "pie-expanding" opportunities together (see Jap 1999). Suppliers in such relationships are rarely asked to participate in online reverse auctions.
Online reverse auctions have increased in popularity because they emphasize short-term price savings and can simplify and support negotiation. Such auctions have been shown to achieve gross savings (over historical cost in unit prices) from 5% to 40% (Tully 2000), with an average gross savings of 15% to 20% (Cohn 2000). The auctions also drastically reduce the average time involved in negotiation (measured as the point of mailing a request for purchase [RFP] to the compilation of a subset of viable bid offers) from six weeks to a few hours. Online technologies now enable temporal and geographical conveniences, reduced cost of contact, instant feedback, and privacy, which manual auctions cannot offer. Consequently, such auctions are ideally suited for transactional exchange contexts but may be less appropriate for relational exchanges. Some critics have argued that auctions hinder collaboration in relational contexts (Emiliani and Stec 2001); this may be because such auctions do not allow the expression of nonprice attributes, such as quality, service, and reliability. It may also be that such auctions threaten buyers' very existence and purpose in long-term exchanges. Consider the comments of Roger A. Whittier (Purchasing 2001), Director of Corporate Purchasing at Intel: "Intel has run several successful auctions to move surplus equipment and material. [However,] I haven't had that much interest out of buyers. Frankly, when you go to buyers and say we want to start reverse auctions, they feel very threatened by it. They feel they add value as negotiators and through sourcing and so forth. In any kind of business where you actually make a difference by negotiating, picking specifications and having some kind of relationship, then reverse auctions don't make a lot of sense."
Despite this doubt, some supporters maintain that online reverse auctions should play a key role in long-term sourcing arrangements because they provide the greatest payback in a direct purchase expenditure (Scott-Lewis 2001). No systematic research effort has yet examined the impact of online reverse auctions on mutually beneficial relational exchanges between industrial buyers and suppliers.
The Auction Process
I focus on one-sided (one buyer and multiple sellers) sealed-and open-bid auctions, because these two kinds differ starkly in format. Open- and sealed-bid auctions mark opposite ends of a spectrum of auction types that differ in their degree of price visibility (suppliers' ability to view their competitors' bids): Sealed-bid auctions have no price visibility, whereas open-bid auctions have full price visibility for bidders. Most online auction formats are variations of these two types (for a more complete discussion of the evolution and direction of online reverse auctions in industrial sourcing activities, see Emiliani 2000; Jap 2002).
The online reverse auction process typically begins with the buyer posting an RFP to a Web site and inviting specific suppliers to view the RFP. In a sealed-bid auction, suppliers are asked to submit their bids a few days or weeks later, and a winner is subsequently selected. Only the buyer views the bids. In this research, the sealed-bid event involves a single round of bidding. In the open-bid auction, suppliers bid sequentially through a series of product lots or subgroups and can view their competitors' bids and respond in real time. A moving end time (a "soft close") is used for each lot, which means that any bid within the last minute of the closing time automatically extends the end time for a few minutes to allow other bidders to respond.
This section begins by considering the theoretical literature on auctions and then draws on economics, marketing, and psychology research, as well as interviews with auction participants, to develop specific hypotheses. The theoretical literature illuminates the motivational and strategic concerns of participants, and the interviews identify specific organizational characteristics that constitute the sourcing context. A critical set of dependent variables emerges: opportunism suspicions, willingness to make idiosyncratic investments, and cost savings. I consider how these variables may differ across auction (open bid and sealed bid) and supplier (new or current) type.
A Perspective on the Theoretical Literature on Auctions
Many economics studies examine auctions from theoretical and empirical perspectives (for an overview, see Kagel 1995; McAfee and McMillan 1987; Milgrom 1989). Although some similarities exist between the online reverse auctions used in industrial sourcing today and the auctions of the theoretical literature (e.g., the focus on price competition in a structured negotiation format with well-defined rules for the submission and modification of bids), several significant differences make it difficult to generalize from the economics literature to industrial settings. Major differences include product type, winner determination, interdependent bidding practices, and emphasis on bidder behavior over context. First, the products in the auctions of the theoretical literature tend to be commoditized, and price determines products' complete value. In the marketplace, many online reverse auctions may involve products differentiated by price, quality, or other attributes.
Second, the majority of auctions examined in the theoretical literature specify how to determine the winner of the event, typically on a first- or second-price basis (e.g., Bulow and Klemperer 1996; Holt 1980; Milgrom 1989). In the marketplace, however, buyers have full latitude to select the winning supplier on any basis; the only explicit commitment that the buyer makes is to award the contract to one of the participants. Suppliers in the marketplace may not understand how competitive their offer is, but bidders in the auctions of the theoretical literature understand exactly where they stand relative to their competitors and are able to use this information to determine their responses to competitive bids.
A third major difference lies in the sequential bidding of interdependent product lots in online reverse auctions. Bidders must consider not only individual prices but also their capacity to accommodate the lots. This bidding problem can be complex, because the bids placed in the first lot may determine the supplier's bids for the next lot, and so forth. Although some researchers have examined multiunit purchases of homogeneous commodities (Kagel and Levin 2001; Swinkels 2001), interdependence among heterogeneous noncommodities has yet to be addressed.
Finally, the theoretical literature focuses on the processes by which individual actions translate into prices, but it does not focus on the auction context. Economists thus consider differences among bidders' valuations of auction objects, bidder characteristics (e.g., risk aversion), psychological mistakes (e.g., the winner's curse), and the effect on prices in a variety of auction formats (e.g., sealed, open, English, Dutch) that vary in allocation rules. I therefore draw on the economics literature on auctions in a limited way to develop the hypotheses; instead, I rely more heavily on interorganizational theory, particularly transaction cost economics, to determine the influence of online reverse auction processes on supplier attitudes and perceptions of the buyer.
Supplier Suspicions of Opportunism
Although both buyers and suppliers want to make a profit, their approaches differ. Buyers want to reduce the price of purchased materials so as to reduce the cost of goods sold. Suppliers want to maximize sales, particularly through long-term relationships that emphasize quality and delivery. These conditions breed discord and suspicion. The buyer's choice to use a sealed- or open-bid auction may arouse the supplier's suspicion that the buyer is using the auction opportunistically against the supplier.
Opportunism, long a hallmark of the transaction-cost economics framework, is defined as self-interest-seeking with guile. It is synonymous with misrepresentation, cheating, and deception and subsumes a range of misbehavior, such as adverse selection, moral hazard, shirking, subgoal pursuit, agency costs, and free riding (Williamson 1996); it also has received growing attention in recent years (Brown, Dev, and Lee 2000; Wathne and Heide 2000). It is worth noting that opportunism is not merely a form of distrust. Trust is a broad metaconstruct with many facets and levels; scholars across multiple disciplines do not fundamentally agree on the meaning of trust (Rousseau et al. 1998). Opportunism is more delimited and behavioral in nature; it is observable by the supplier and grounded in specific actions and should create reduced attributions of trust.
I focus on the supplier's suspicions (i.e., its perceptions) that the buyer is acting opportunistically, rather than on proven opportunism, because the supplier typically cannot verify the buyer's guile. The supplier's behavior is affected by its misgivings about the buyer's character (cf. Rusbult and Van Lange 1996); when the supplier suspects the buyer, the supplier usually holds back from the relationship to avoid vulnerability to further opportunism (Ping and Dwyer 1992; Williamson 1985, 1993). Suspicion might also motivate the supplier to use additional safeguards (contracts, incentives, or monitoring) to protect and enforce the exchange.
Sealed- versus open-bid online reverse auctions. Which auction format raises supplier suspicions of opportunism in industrial contexts? I expect that the supplier's opportunism suspicions increase in online open-bid reverse auctions more than in sealed-bid reverse auctions, because price competition is greater and more explicit in open-bid auctions. The fast-paced, dynamic bidding, along with the need to respond quickly to competitors' bids, yields tense negotiation and pressure on suppliers to cut prices vigorously. An open-bid auction increases the supplier's bargaining costs, which makes the process so disagreeable to suppliers that they will accept renegotiation rather than persist with current pricing levels (see Masten 1988). For the supplier, the open-bid format can force additional price concessions from the supplier, becoming a form of opportunistic rent seeking on the part of the buyer.
It could be argued, however, that price visibility in open-bid auctions should reduce supplier suspicions, because suppliers can gauge their bids relative to the competition and can choose whether to reveal their own value of the contract by responding to competitive bids. In contrast, the optimal bidding strategy in a sealed-bid auction demands that suppliers reveal their bottom-line bids, because there is no opportunity to change the bid, view competitors' bids, or respond to others' bids. In this sense, sealed-bid auctions appear more opportunistic.
In field interviews with suppliers, I learned that the compressed time frame of open-bid auctions creates a stressful context for the supplier. In private conversations, many suppliers complained that the format prevented them from carefully considering price bids and gave them a sense of being "out of control."
H1: The increase in supplier opportunism suspicions before and after the auction is greater in online reverse open-bid auctions than in sealed-bid auctions.
New versus current suppliers. Opportunism suspicions may also differentially increase between new and current suppliers. Current suppliers may have a rich history of exchange with the buyer. They understand the buyer's needs and constraints and may benefit from trust, implicit understandings, or relational norms. Their history could act as a switching cost, making it difficult for the buyer to choose a new supplier. But new suppliers do not have the benefit of past experience, which significantly reduces their bargaining power. A buyer's decision to use an electronic format could increase a current supplier's suspicions.
H2: In online reverse auctions, the increase in supplier opportunism suspicions before and after the auction is greater for current than for new suppliers.
Supplier Willingness to Make Idiosyncratic Investments
The dedicated investments made by buyers and/or sellers are a key aspect of the relationship. Such investments may be tangible (e.g., plant equipment, tooling, design systems) or intangible (e.g., human resources, training) and typically increase the effectiveness and efficiency of one or both parties (Dyer and Singh 1998; Heide 1994; Lusch and Brown 1996; Noordeweir, John, and Nevin 1990). The supplier's idiosyncratic investments are a risk on behalf of the buyer to produce superior returns and joint value; they represent a supplier's commitment to the buyer (Anderson and Weitz 1992) and are difficult for the supplier to redeploy if the relationship ends.
Sealed- versus open-bid auctions. I expect that the supplier's willingness to make idiosyncratic investments decreases in open-bid auctions, because the format's rapid, dynamic price competition emphasizes price reduction more strongly than in sealed-bid formats. Open-bid auctions may also signal a focus on price in the short term, which fosters a market governance structure that focuses on discrete transactions. Such a relationship does not foster mutual value or benefit in the long run. The supplier's incentive to make idiosyncratic investments is therefore reduced, because payback is unlikely to be realized over time.
H3: The decrease in a supplier's willingness to make idiosyncratic investments before and after the auction is greater in online reverse open-bid auctions than in sealed-bid auctions.
The opposite effect can be argued. Availability of information about pricing in an open-bid auction clarifies the supplier's standing relative to the competition and provides additional information about the likelihood of recouping the value of dedicated investments in the exchange. After observing competitive pricing, suppliers may be motivated to make idiosyncratic investments to attain superior value, improved coordination, or scale economies. In contrast, a sealed-bid auction does not provide feedback about a supplier's relative standing but rather encourages the supplier to minimize its bid price at all costs.
New versus current suppliers. As buyers turn to online auctions in sourcing, the motivation for current suppliers to make idiosyncratic investments may be reduced, because the current suppliers' value--their knowledge of the buyer and the buyer's specific needs--is not readily conveyed in electronic contexts. Little opportunity exists to communicate and explore potential mutually beneficial activities and processes stemming from idiosyncratic investments. As negotiation focuses on short-term price, suppliers have little incentive to create long-term investments with the buyer; accordingly, I anticipate the following:
H4: In online reverse auctions, the decrease in willingness to make idiosyncratic investments before and after the auction is greater for current suppliers than for new suppliers.
Buyer Cost Savings
A chief concern of the buyer is cost savings, which is defined as the percentage reduction in price from historical costs, and it is an important metric in industrial procurement. Does a sealed- or an open-bid auction yield higher savings? There is a significant literature in economics that considers this question for auctions with well-defined parameters (e.g., specific allocation rules, revealed number of bidders, single commodities). These results indicate that when the bidders have common values, the open-bid auction format produces greater cost savings (Milgrom and Weber 1982). When bidders' values for the contract are common, the value of the item is the same to all bidders, but bidders have different information about the underlying value. These characteristics are true of industrial sourcing auctions. A true value exists for the contract--the worth of the contract in the market--but no one knows the true value, and each bidder estimates differently as to how much the item is objectively worth. In an open-bid format, the bids partially make public each bidder's private information about the true value of the contract. Each bidder is thus able to learn from the bidding process and adjust its bid closer to the true value of the contract.
It is important to note that these results hold in auctions in which the rules of allocation are clear; in this research, the rules of allocation are more ambiguous. The buyer retains full latitude in selecting the winner, regardless of the nature of the price bids, which may influence bidders' motivation such that they do not bid as predicted by economic theory. Whether the theory is able to generalize to the open- and sealed-bid auctions of the current marketplace remains an open question. Thus, the hypothesis from the theoretical literature is as follows:
H5: The buyer's cost savings are greater in online reverse open-bid auctions than in sealed-bid auctions.
New versus current suppliers. Do new or current suppliers provide greater savings? The auction literature does not address this issue. I believe the question is empirical and yields three possible outcomes. First, it could be argued that new suppliers should be more aggressive than current suppliers, because new suppliers gain not only a purchase contract but also the potential to remain in the supply base for the long run. Second, it could be argued that current suppliers should be more aggressive than new suppliers, because the current suppliers have much to retain. These suppliers have built a history of exchange and learning with the buyer. A third possibility is that both effects operate simultaneously and produce no discernible empirical differences. I reserve prediction on the direction of this result and examine the empirical outcome in a subsequent section.
Research Setting
I conducted the research from 1999 to 2000 in the supply base of a major firm in the automotive industry that bought various components and parts. The automotive industry was an early adopter of online reverse auctions, having developed Covisint, one of the largest online markets in which online reverse auctions play a significant role in materials sourcing for major automobile manufacturers. In 2001, DaimlerChrysler held a reverse auction through Covisint, and the total order volume exceeded $2.5 billion, which made it the largest, single, Internet-based auction. The participant firm in this study rarely engaged with suppliers in long-term strategic partnerships. Instead, the buyer's mix of supplier relationships varied in product categories along the spectrum from transactional to collaborative sourcing arrangements.
In the fall of 1999, the company experimented with various types of auctions over a six-month period and evaluated the financial, relational, and strategic consequences. Sourcing managers were asked to host either an open-bid or a sealed-bid online auction. As an independent researcher, I could make recommendations to the firm but ultimately could not intervene as much as I would have liked; for example, I was unable to control the number of suppliers, lots, or product types in each event. I was only allowed to survey suppliers before and after the event, to conduct postauction interviews with suppliers, and to interview sourcing managers throughout the process.
Approximately $100 million in purchase contracts was made available for bid in three open-bid and three sealed-bid auction events in six different product categories. None of these products were pure commodities, such as maintenance, repair, and operations supplies or highly customized strategic parts. All the products were used in production or directly for parts in production. The product categories thus differed in nonprice characteristics, and supplier relationships could play a significant role in exchange. Table I provides an overview of the product categories and the number of bidders and lots in each auction.
The sourcing manager qualified (through visits, questionnaires, and research) a list of viable suppliers before each event and then invited a subset of suppliers to hid in the auction. All the auctions were conducted by a third-party auctioneer who informed the suppliers of the event rules: (I) The sourcing manager committed to select a winner from each event on any basis, but the lowest bid was not guaranteed to win the contract; ( 2) the sourcing manager was prohibited from bidding against suppliers in the auction (an unethical practice known as "shilling"); ( 3) all competitors were viable prequalified sourcing options for the buyer; and ( 4) supplier bids were legally binding. The suppliers were not told who their competitors were or how many suppliers would bid against them.
Because suppliers in the open-bid auctions had virtually no experience with the format, the auctioneer trained them to use the online interface before the auction so that they could understand cost structures and bid at, not below, their marginal costs. The auctioneer also helped the supplier imagine various scenarios to help mitigate the pressure of the real-time bidding decisions. The auctioneer's role worked against the probability of my finding significant results. By encouraging suppliers to provide high-quality, educated, and enlightened bids, the auctioneer decreases the likelihood that observed changes in the dependent variables of interest are due to confusion, misunderstanding, or other problems that might arise from suppliers being ill-prepared for the open-bidding experience.
Data Collection
The data collection was a multimethod approach that involved extensive interviews with participants and an untreated-control-group quasi experiment with a pretest and posttest around the auction events (Cook and Campbell 1979). The treatment variables were auction type (i.e., scaled/open) and supplier type (i.e., new/current), and the dependent variables were suspicions of opportunism, willingness to make idiosyncratic investments, and cost savings. The field design featured an array of controls, including ( 1) pretest and posttest survey measures from the same panel of suppliers in the treatment group. ( 2) control groups, and ( 3) replication of the design across six product categories. I initially tried to administer the control group survey twice, to correspond to the pretest and posttest surveys, but found that no noticeable change occurred over a week. In addition, suppliers resisted completing the survey twice with little change of their circumstances in between.
Procedure. In each product category, in-depth interviews were conducted with sourcing managers to obtain a better understanding of the exchange context, the composition of the supply base, and the expectations and strategic intentions for each auction. Managers provided the names and contact information of the qualified suppliers in the treatment group. Because buyers do not always involve their entire supply base in a negotiation, for the purpose of this research, the sourcing manager provided contact information for suppliers not invited to the event but equivalent to the treatment group (e.g., in production capability, price competitiveness, and product offerings). These suppliers formed the control group. The managers understood the purpose of the control group within the research design and carefully selected participants.
Suppliers in the treatment and control groups were sent an e-mail invitation one week before the event that specified that the respondent should be knowledgeable about the firm's specific relationship with the buyer. For the treatment condition, the invitation also asked that the respondent be someone who would participate in the upcoming bid process. Suppliers in the control group were not told that the buyer was hosting a competitive bid event in their product space; they were merely asked to participate in a study on buyer-supplier relationships. The invitation directed suppliers to the survey on a university Web site; it also guaranteed anonymity and reassured suppliers that the buyer would not have access to individual responses. The survey directed the supplier to complete all items in reference to the specific buying organization. One week after the auction, suppliers in the treatment group were sent an e-mail invitation to complete the posttest survey. Throughout the data collection, I monitored the buyer's activities to ensure that no major events or initiatives (e.g., retroactive charge backs) occurred to disrupt or alter supplier perceptions and attitudes.
Respondents. Respondents were typically senior executives, vice presidents, or owners of a supply business who handled large customer accounts and had the authority to determine major investment decisions and make price concessions. At the time the research was conducted, online auctions and electronic bidding had hardly permeated the marketplace, such that suppliers had virtually no experience with the formats.
Of the 154 bidders, 68 completed the surveys, which yielded a response rate of 44%. 01 the 68 bidders, 33 responded in the open-bid auctions, and 35 responded in the sealed-bid auctions. Twenty-eight of the 68 respondents were new suppliers and 40 were current suppliers. The control group was composed of 87 suppliers; 50 of these suppliers completed the survey, which yielded a response rate of 57%. Of the suppliers in both the treatment and the control groups, 33% were new suppliers and the remainder were current suppliers.
Respondent competency. Because some of the hypotheses rely completely on suppliers' perceptions, the respondents must be competent to report, and differences in respondent knowledge, position, and perceptions should be minimized. This end is accomplished by global and specific measures of respondent competency and knowledge of the phenomena. The global measure was the respondent's tenure with a firm. The respondents averaged 6.3 (standard deviation [s.d.] = 5.9) years of experience in their area and had been with their firms 103 (s.d. = 5.1) years on average.
The respondent's knowledge of the relationship with the buyer was assessed by questions at the conclusion of the pretest and the control questionnaire. The respondents were asked, "How knowledgeable are you about the following aspects?" Listed below were items such as "Your firm's willingness to work with [the buyer firm]," "The degree to which your firm trusts [the buyer firm]," and "Your firm's willingness to invest in a customer," Responses varied along a seven-point rating scale (1 = "not very knowledgeable" and 7 = "very knowledgeable"). Throughout this article, all scale scores of multi-item measures reflect the mean score of the multiple items, not a sum score. The average response to these items was 6.4 (s.d. = .57). Collectively, some confidence emerged that the selected respondents were knowledgeable, relatively involved in the survey, and unlikely to have fabricated answers to the items.
Measurement. All the scales used in this study are listed in the Appendix. The supplier's opportunism suspicions refer to bad faith in the buyer. To measure bad faith and describe it in a survey format, I conducted multiple prestudy interviews with sourcing managers and suppliers in other industrial product categories. On the basis of these interviews, I decided to measure supplier suspicion with a four-item scale that reflects the firm's specific opportunistic behaviors (e.g. reneging, lying, falsifying information) as perceived by the supplier. The supplier's suspicion of opportunism shows a mean of 2.94 (s.d. = 1.40) that ranges in value from 1 to 7; Cronbach's alpha coefficient for this scale is .79.
The mean value reflects the relationship of a powerful buyer and a lower-tier supplier. In the automotive industry, higher-tier buyers regularly exercise power over suppliers. It is thus natural that the suppliers would suspect the buyer of opportunistic behavior, though theoretical reasons also help explain this result. Williamson (1993) notes that firms tend to engage in business as usual rather than opportunism, partly because managers are well socialized and partly because governance structures mitigate opportunism. If opportunism is too high, the supplier would likely not exchange with the buyer at all.
The supplier's willingness to make idiosyncratic investments is a modified version of the scale developed by Cannon (1992) that reflects tangible and intangible investments that the supplier would find difficult to move into another relationship. The four-item scale indicates the extent to which the supplier is willing to make investments in training, production procedures, equipment, tools, and capacity to accommodate the buyer. The supplier's willingness to make idiosyncratic investments shows a mean of 4.50 (s.d. = 1.68) that ranges in value from I to 7; Cronbach's alpha coefficient for this scale is .87. The correlation between this construct and opportunism suspicions is .12 (p < .17).
I assessed the measurement properties of these two scales using a confirmatory factor analysis with maximum-likelihood estimation methods in LISREL 8.03 (Jöreskog and Sörbom 1993). The estimated measurement model of the two latent factors, each with four reflective indicators, has a chi-square fit of 38.93 with 19 degrees of freedom (p < .00). The comparative fit index is .92, the incremental fit index is .93, and the Tucker-Lewis fit index is .89. All the factor loadings and measurement errors are in acceptable ranges and are significant at α = .05, which provides evidence of convergent validity. The pair of constructs also passes the test of discriminant validity that Fornell and Larcker (1981) recommend.
Analysis of Hypotheses
To assess the impact of the open- and sealed-bid formats on supplier attitudes, I considered how the supplier scores for these measures changed from the pretest to the posttest. Because the quasi experiment is a nonequivalent design (due to its nonrandom selection process), the expected values of at least one characteristic of the groups are not equal even in the absence of a treatment effect (Cook and Campbell 1979). To obtain a reasonable estimate of the treatment effect, the analysis therefore must properly recognize and account for the effects of these initial differences; two steps accomplish this purpose. In the first step, the respondents in the pretest and control group were compared in terms of their demographics and intangible aspects along several dimensions, such as annual sales, willingness to collaborate, satisfaction with the relationship, and perceived dependence on the buyer. No significant differences were found. For reasons of space, the specific means and t-tests are not presented; the scales are listed in the Appendix.
Although the lack of differences suggests that the groups are equivalent, the groups might have shown large expected differences on other variables that could have affected the posttest scores. These differences were accounted for in the second step of the analysis, in which I used a between-subjects nested design analysis to examine the hypotheses in the treatment group. The auction type was specified as a fixed factor, and the product category and supplier responses were specified as nested random factors. From these factors, the following equation, in which I used the pretest measure as a covariate to adjust for initial differences between groups, is estimated:
( 1) Posttest = treatment + product(treatment) + pretest,
where
posttest = the posttest score on the variable of interest,
treatment = the treatment effect for open/sealed auctions or new/current suppliers,
product(treatment) = the effect of product differences nested within each treatment effect, and
pretest = the pretest score on the variable of interest.
This analysis of covariance (ANCOVA) matches the pretest value to the predicted posttest value for all levels of the treatment and examines the differences between them. A statistically significant treatment effect would thus suggest that one level of the treatment would have significantly outperformed the other level, thus controlling for differences in the pretest scores. By matching the pretest measure to each posttest measure, the ANCOVA has more power to detect true differences in the treatment effect than an elementary analysis of variance, which would consider only the posttest responses. I also assessed the extent to which differences exist across product categories (differences in the value of the purchase contract, number of bidders, response rates, and number of lots). A significant effect of product(treatment) indicates a significant difference among the product events nested within auction type.
At this point, some readers might be concerned that suppliers would always suspect buyers of being opportunistic, expressing negative attitudes toward suppliers, or overstating their willingness to make investments. Such biases could increase the scores that I observed. Even so, such biases would be operative in both the pretest and the posttest but would not explain a change in the scores over time, which is the focus of this research. It is also worth noting that at the time of the posttest, suppliers had not yet been informed of whether they had won the auction, which reduces the likelihood that their responses to the posttest measures are simply a reflection of being "sore losers."
Examination of H1. H1 posits that the increase in opportunism suspicions is greater in open-bid than in sealed-bid auctions. In the open-bid auction, the pretest mean was 2.64 (s.d. = 1.27), and the posttest mean was 4.29 (s.d. = 1,17); the difference is significant (t64 = 5.51; p < .00). Throughout this article, all t-tests are one-tailed. The pretest mean in the sealed-bid auction was 2.38 (s.d. = 1.02), and the posttest mean was 2.84 (s.d. = .99); the difference is marginal (t68 = 1.94; p < .10). The ANCOVA has an explained variance of .40. A statistical test of the treatment effect of auction type indicates that the effect is significant (F = 16.21; p < .05), which suggests that the increase in scores is significantly greater in the open-bid auction than in the sealed-bid auction. I found no significant differences (F = 1.35, not significant [n.s.]) across the various product categories. Together, the results suggest that opportunism suspicions do not change in sealed-bid auctions but increase in open-bid auctions, and the difference between the conditions is significant, in support of H1.
Does a self-selection bias explain the increase in suspicions? I considered the possibility that suppliers who responded to the posttest in the open-bid condition were potential "sore losers" (did not offer high savings) of the auction and therefore might be more likely to express opportunism suspicions. This possibility would mean that suppliers who believed they had bid poorly in the auction would be more likely to express their negative attitudes toward the buying organization in the posttest survey than would suppliers who did not bid poorly (offered high savings). In the case of such a bias, it should be observed that suppliers who responded to the posttest offered systematically lower cost savings than did the suppliers at the pretest. However, a t-test of the savings provided by suppliers who responded to the pretest and posttest survey revealed no significant differences (t64 = -.78; p < .44), which suggests that it is unlikely that a self-selection bias accounts for the results.
Does the auction process explain the increase in opportunism suspicions? It was proposed that suppliers view the open-bid process as an unfair means by which the buyer gains concessions from the supplier. This possibility is further explored in several control items included in the posttest survey to capture the supplier's direct view of the auction process. Suppliers indicated their response to these items using a scale of 1 = "strongly disagree" and 7 = "strongly agree." One item stated: "This process will reduce my chances of earning a fair margin on the business." The suppliers in the open-bid auctions averaged a response of 48 (s.d. = 1.4), and suppliers in the sealed-bid auction showed a mean response of 3.0 (sd. = 1.4). The difference between the means is significant at α = .05. Another item stated: "This process does not give a supplier a fair opportunity to bid on business." Suppliers in the open-bid auction showed a mean response of 4.1 (s.d. = 1.1), and suppliers in the sealed-bid event showed a mean response of 2.7 (s.d. = 1.6). Again, the difference in means is significant at α = .05. Both of these items reflect the sense of exploitation suppliers associate with auctions Together, they suggest that suppliers in the open-hid auctions consider the auction process more opportunistic than do suppliers in the sealed-bid process.
Additional indicators of suspicions of opportunism. After the auctions, t conducted interviews with the suppliers in the open-bid condition to assess the difference in opportunism suspicions across the treatment conditions These interviews were not intended to provide causal evidence but rather to explain the difference In the open-bid condition, the suppliers' responses show that they believe the buyer purposely selected an unfair price-competition structure. Three themes emerge The first is that suppliers believe the buyer is using the open-bid format to survey the market pricing without any intention of awarding the business As one supplier said, "All they were going to do was just feel out what the numbers were going to be. Let's say they're looking at someone in Brazil or some Korean firm out there. At this point in the junction, they weren't going to go with those guys based on what they were. So all these [people] were throwing low bids, but it had no meaning as to what was going on." The second theme indicates that the supplier believes the buyer has created false competition by including nonviable bidders, as in this example: "I didn't think the competition I was dealing with in the atmosphere I was quoting really had the wherewithal that I had to supply the parts and do the things I had to do." The third theme is the supplier's belief that the buyer is shilling its bids to push down the price artificially.
The irony is that none of the supplier perceptions correspond to reality. As an independent observer, I knew that buyers had every intention of selecting a winner and had invited only viable suppliers to bid on the purchase contract. No shilling occurred; the buyer and the auctioneer went to great pains to avoid such perceptions by clearly communicating the rules of the game. Thus, a significant gap exists between supplier perceptions of the event and reality, which should be cause for concern.
Examination of H2. H2 considers whether opportunism suspicions increase more for current than for new suppliers. The pretest mean of new suppliers was 2.36 (s.d. = 1.30), and the posttest mean was 4.19 (s.d. = 1.38); the difference between the two means is significant (t54 = 5.11; p < .00). For current suppliers, the pretest mean was 261 (s.d. = 1.03), and the posttest mean was 3.10 (s.d. = 1.03); the difference in these means is also significant (t78 = 2.11; p < .05) The ANCOVA has an explained variance of .48. A statistical test indicates that the overall effect between new and current suppliers is not significant (F = 2.19, n.s.), and the differences among product categories are not significant (F = 2.60, ms.) Consequently, although opportunism suspicions increase, this increase does not differ between new and current suppliers.
Examination of H3. H3 considers whether the supplier's willingness to make idiosyncratic investments decreases more in open-bid auctions than in sealed-bid auctions. In open-bid auctions, the pretest mean was 4.89 (s.d. = 1.34), and the posttest mean was 5.32 (s.d. = .90); these means do not differ significantly (t64 = 1.48; n.s.). In sealed-bid auctions, the pretest mean of the supplier's willingness to make investments was 4.13 (s.d. = 1.67) and the posttest mean was 4.86 (s.d. = 1.23); this difference is significant (t68 = 2.10; p < .05). The ANCOVA has an explained variance of .32. A statistical test of the treatment effect of auction type indicates that the effect is not significant (F = 1.95, n.s.), which suggests that the mean increase does not differ across auction type, nor does any significant (F = .03, n.s.) effect of differences in means exist across the product categories. Collectively, these results suggest that in the sealed-bid condition, suppliers increase their willingness to make idiosyncratic investments whereas suppliers in the open-bid condition do not change.
Why does willingness to make investments increase in the sealed-bid condition? Posttest interviews provide some speculative answers to this question. In the sealed-bid auctions, suppliers view the process as an improvement over the manual processes of the past. They interpret the electronic bidding process as a signal that the buyer is informed about recent technological developments, which suggests a mutual orientation and potential benefit for both exchange parties. A buyer's technological stance toward mutual benefit increases the supplier's willingness to make dedicated investments.
Examination of H4. H4 considers whether the decrease in willingness to make idiosyncratic investments is greater for current than for new suppliers. For current suppliers, the pretest mean was 4.03 (s.d. = 1.64), and the posttest mean was 4.96 (s.d. = 1.18); the difference is significant (t78 = 2.91; p < .00). The pretest mean of new suppliers was 5.18 (s.d. = 1.18), and the posttest mean was 5.26 (s.d. = .95); the difference is not significant (t54 = .30; n.s.). The ANCOVA has an explained variance of .21. A statistical test of the treatment effect of supplier type is not significant (F = 1.16, n.s.), and the differences across product categories are also not significant (F = 1.19, n.s.). These results indicate that current suppliers increase their willingness to make idiosyncratic investments to match the level of new suppliers such that no significant difference exists after the auction in willingness across supplier type.
Examination of H5. H5 examines whether the difference in cost savings is greater in open-bid than in sealed-bid auctions. Because this information is extremely sensitive to the host firm, I do not reveal the absolute magnitude here. Instead, I provide standardized indicators to give readers a sense of the relative magnitude of the savings across auction type. The unit of analysis for testing this hypothesis is the lowest bid of each lot. This number is then divided by the historical price and subtracted from unity to generate a cost-savings percentage from the auction. Because the supplier bids are not normally distributed, I used the logarithms of the cost-savings percentage offered in each lot to make the data less nonnormal. This logarithm is then averaged with the savings of the other lots within the same auction type to produce a mean savings for the auction type. As a result, 21 lots produced varying cost-savings percentages in the open-bid auctions, and I averaged these together to produce the average cost savings in the open-bid auctions (XO); similarly, I computed a mean for the sealed-bid condition (XS). I indexed the mean of these two numbers (XOS) to 100. The mean sealed-bid auction savings can then be reflected as 92, and the open-bid auction savings can be reflected as 108. The ANCOVA has an explained variance of .16. A test of the treatment effect indicates that it is insignificant (F = 1.29, n.s.); however, a statistical test of the effect across product categories is significant (F = 13.28; p < .00). Collectively, these results indicate that though the savings across auction types does not differ, significant variation exists across product categories.
I also examined the differences in savings offered by new and current suppliers. In this case, the unit of analysis is the individual supplier's bid. I averaged the cost-savings percentage offered by new suppliers and the percentage offered by current suppliers; they differ by less than one percentage point. The ANCOVA has an explained variance of .18. A statistical test of the treatment effect indicates that it is insignificant (F = .53, n.s.); however, the effect of product categories is significant (F = 5.75; p < .001). These results indicate that differences in savings occur because of differences across product categories and do not vary systematically across auction types.
The results of the quasi experiment indicate that online reverse auctions increase both new and current suppliers' beliefs that the buyer acts opportunistically toward the supplier, particularly in open-bid auctions. Paradoxically, the supplier responds to online auctions by increasing its willingness to make dedicated investments when the buyer uses a sealed-bid auction format. The results also show that current suppliers increase their willingness to make idiosyncratic investments toward the buyer. Although online reverse auctions can yield cost savings, this savings is category specific and is not systematically related to an open- or sealed-bid format. Together, these results indicate that online reverse auctions can exert complex relational effects on the supply base.
Supplier Suspicions of Opportunism
The results show that suppliers' suspicions of opportunism increase after an open-bid auction. Suppliers consider the open-bid process exploitative and unfair. After the event, suppliers voiced their resolution to avoid such events and condemned the process: "In the future, I would never play this game again. We'll play it, but what will happen is we'll be even more adamant as far as to what our prices are. It wasn't a very professional way to handle the business." The supplier's inference of the buyer's opportunism is critical, because it affects suppliers' view of exchange governance and their subsequent actions. In the future, suppliers might demand more explicit contractual assurances or contingency agreements to safeguard their returns. To maintain the lower pricing scheme, they might be forced to reduce quality, value-added services, or overall responsiveness to the buyer, all of which are features that might also be withdrawn to retaliate against the buyer. Consequently, the supplier's suspicion of opportunism might motivate it to respond in kind (Axelrod 1984).
Suppliers in the automotive industry are already beginning to organize themselves against online reverse auctions (Kisiel 2002), claiming that buyers abuse the auctions and calling for the creation of a formal code of conduct to discourage what they perceive as opportunism. These suppliers contend that online reverse auctions have far outnumbered the actual contracts awarded and that buyers use these auctions to determine how low suppliers are willing to bid and then wring additional price concessions from current suppliers. This scenario illustrates how the mere perception of opportunism surrounding these online reverse auctions can poison relationships between buyers and suppliers.
The results also indicate that opportunism suspicions are heightened for both new and current suppliers, contrary to my predictions. For current suppliers, online reverse auctions may inhibit their ability to sell intangible aspects to the buyer. More than 50% of the suppliers in the postevent interviews considered this inability to express their full capabilities a disadvantage to electronic bidding mechanisms. It is notable that new suppliers also display heightened suspicions. They may consider electronic bidding a poor medium for sales, or perhaps they consider such suspicions a cost of doing business with a powerful supplier. Although the evidence and possibilities are intriguing, further research must determine the appropriate explanation.
Supplier Willingness to Make Idiosyncratic Investments
One notable result of this research is suppliers' increased willingness in the sealed-bid condition to make idiosyncratic investments in transactional exchanges. Suppliers evidently consider the buyer's choice of an online sealed-bid auction an attempt to improve the transaction, which is taken as a signal of mutual orientation and encourages suppliers to make dedicated investments. This, in turn, increases the likelihood that suppliers will recoup the value of their investments in the long run.
The data also suggest that online reverse auctions may provide a useful wake-up call to current suppliers, which are as willing to make idiosyncratic investments in the buyer as are new suppliers. Current suppliers may also be signaling their commitment to the buyer in the long run.
Buyer Cost Savings
Another result of this research is that cost savings do not differ systematically across the online reverse auction type or supplier type but may be affected by different characteristics and conditions of the auction event. Perhaps suppliers have not yet developed a long-term strategy to handle such auctions. Because this research involves only six online reverse auctions, I cannot investigate more fully how differences in the number of bidders, lots, size of purchase contracts, or other characteristics might have been systematically related to the level of savings for each event. The decision to use online reverse auctions for direct sourcing activities is complex, and its success depends on various conditions yet to be identified and understood.
Collectively, the results paint an intriguing picture of supplier reactions and financial implications in transactional exchanges. The prevailing theory predicts that economic actors are shortsighted, self-interested profit maximizers that respond to the signals and behaviors of their exchange partners. In reality, firms are not as shortsighted and non-strategic as might be believed; rather, they make complex decisions about their exchanges. Perhaps this strategy is rational for firms that must make both short- and long-term Choices.
Limitations
The research exhibits some limitations. It is specific in scope because it considers only two types of one-sided online reverse auctions in transactional exchanges. The observed effects may not generalize to alternative online reverse auction formats, to procurement of indirect materials, or to close partnerships. In addition, the supplier training may have decreased the amount of variation that was observed in the bids. It is also not clear whether the observed effects will last, because measurements were taken after only one online reverse auction. However, the extent to which I witnessed any noticeable changes in supplier attitudes and perceptions is notable, given that these constructs are typically quite stable over time. The online reverse auction thus exerts a fairly significant impact on suppliers in the short run. Because this was a field experiment, I had limited discretion as to its design and level of control. This research is not a pure test of the theoretical literature on auction theory; rather, it has the advantage over laboratory experiments of examining experienced professionals with larger, real sums of money at stake.
Implications for Management
The results demonstrate that open-bid, online reverse auctions can raise supplier suspicions of buyer opportunism. Buyers should therefore be selective in their use of these auctions, perhaps limiting them to purchases involving less important supplier relationships, such as the purchase of indirect materials. Because the purchase of indirect materials can constitute anywhere from 40% to 60% of a firm's total purchasing volume, a sizable opportunity remains for auctions to exert a significant impact on sourcing costs.
Online reverse auctions could also be used as a screening mechanism for long-term sourcing arrangements, which would capitalize on a current supplier's willingness to make the necessary investments, to assure the buyer that it receives competitive pricing, and to mitigate opportunism suspicions in the long run. Some firms already use online reverse auctions in this way. Emily Andren of the Gartner Group notes that, "Initially, most reverse auctions were used for spot buying, but companies are increasingly using them to select suppliers for long-term contracts."
Another implication is that the supply base may not be able to provide constant price reductions over the long run. Over time, suppliers may need to leave the industry because of their inability to compete, or they may consolidate to reach the scale economies to support lower prices. Both possibilities reduce the number of alternative suppliers for the buyer and shift power to suppliers. Buyers should therefore carefully consider using online reverse auctions repeatedly in the long run.
Directions for Further Research
Further research might consider whether online reverse auction formats that reveal less information, perhaps only the lowest market bid or a rank ordering of the bids, would produce different effects from what this research indicates. More work is needed on the circumstances that create cost savings. Do specific supplier characteristics, conditions in the supply base, or buyer-sourcing strategies yield significant savings? What role do the numbers of bidders, lots, rounds of bidding, and size of the purchase contract play in motivating how suppliers bid in various types of online reverse auctions? Further research should consider these questions across many more auctions.
The results also raise the question of how online reverse auctions affect supplier behavior over time. Do buyers observe a reduction in quality and service? What other effects exist on supplier motivation and attitudes toward the buyer? Experimental evidence from common value auctions suggests that "market learning" occurs as bankruptcies drive out the aggressive bidders and as more aggressive bidders earn lower than average profits. Garvin and Kagel (1994) observe that over time bidders begin to self-select out of future auctions. They also respond to repeated losses by bidding less. Further inquiry must determine whether this result generalizes to the marketplace.
Finally, this research is the first field experiment in the marketing literature on interorganizational relations. Although such quasi experiments are plentiful in other areas of marketing (e.g., advertising), interorganizational researchers have not used them. The firm's need to leverage Internet technologies offers a prime opportunity for researchers to find and test unique predictions and to conduct longitudinal tests and quasi experiments that will better enhance the understanding of the role and value of emerging technologies in marketing strategy.
Legend for Chart:
A - Auction
B - Product
C - Number of Bidders
D - Number of Lots
A B C D
Sealed Transportation 72 81
Nonproduction services 8 2
Semiconductors 7 3
Open Plastics 12 1
Electrical parts 35 5
Metal parts 20 6
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The Supplier's Suspicions of Opportunism (α = .79)
(mean = 2.94; s.d. = 1.40; minimum = 1, maximum = 7)
How likely is it that [the buyer firm] would do the following? (1 = "very unlikely"; 7 = "very likely")
Make false accusations.
Provide false information.
Be unwilling to accept responsibility.
Expect my firm to pay for more than their fair share of the costs to correct a problem.
The Supplier's Willingness to Make Idiosyncratic Investments (α = .87)
(mean = 4.50; s.d. = 1.68; minimum = 1, maximum = 7)
In working with [the buyer firm], my firm may have opportunity to make investments in time, energy, and/or money specifically to accommodate it. These investments would be lost if my firm switched to another customer.
Just for [the buyer firm], we would be willing to provide dedicated... (1 = "strongly disagree"; 7 = "strongly agree")
Training for buyers.
Production procedures.
Capital equipment and tools.
Plant capacity.
The correlation between this construct and the supplier's suspicions of opportunism is .12 (p < .17).
Willingness to Collaborate (α = .81)
(mean = 5.08; s.d. = 1.48; minimum 1, maximum = 7)
How willing is your firm to do the following for the buyer? (1 = "very unwilling"; 7 = "very willing")
Participate in product design efforts.
Work together to exploit unique opportunities.
Work on joint projects tailored to their needs.
Look for synergistic ways of doing business.
Satisfaction with the Relationship (α = .87)
(mean = 4.40; s.d. = 1.46; minimum = I, maximum = 7)
(1 = "strongly disagree"; 7 = "strongly agree")
Our relationship with [the buyer] has more than fulfilled our expectations.
We are satisfied with the outcomes of our relationship.
Our relationship with [the buyer] has been a successful one.
The Supplier's Perceived Dependence on the Buyer (α = .90)
(mean = 3.34; s.d. = 1.73; minimum = 1, maximum = 7)
(1 = "strongly disagree"; 7 = "strongly agree")
If our relationship were discontinued with [the buyer], we would have difficulty making up sales volume.
It would be difficult for us to replace [the buyer].
We are quite dependent on (the buyer].
~~~~~~~~
By Sandy D. Jap
Sandy D. Jap is Associate Professor of Marketing, Goizueta Business School, Emory University. This research was supported by grants from the Center for EBusiness at MIT, the Leaders for Manufacturing Program, and the MIT-Ford Alliance. The author thanks the buying organization, sourcing managers, suppliers, and auctioneers for their cooperation throughout the data collection process and John Lynch, Prasad Naik, Nader Tavassoli, Joe Urbany, and the anonymous JM reviewers for helpful comments.
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Record: 13- An Investigation into the Antecedents of Organizational Participation in Business-to-Business Electronic Markets. By: Grewal, Rajdeep; Comer, James M.; Mehta, Raj. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p17-33. 17p. 5 Charts, 2 Graphs. DOI: 10.1509/jmkg.65.3.17.18331.
- Database:
- Business Source Complete
An Investigation into the Antecedents of Organizational
Participation in Business-to-Business
Electronic Markets
Business-to-business electronic markets have a profound influence on the manner in which organizational buyers and sellers interact. As a result, it is important to develop an understanding of the behaviors of firms that participate in these markets. The authors develop a typology for the nature of organizational participation to explain the behaviors of user firms in business-to-business electronic markets. The proposed model hypothesizes that the nature of participation depends on organizational motivation and ability. The authors conceptualize motivational factors in terms of efficiency and legitimacy motivations and theorize that ability results from the influence of organizational learning and information technology capabilities. They test the model using organizational-level survey data from jewelry traders that conduct business in an electronic market. The results indicate that both motivation and ability are important in determining the nature of participation; however, the level of influence of motivation and ability varies with the nature of participation.
Electronic commerce is a growing reality and provides viable alternatives to traditional forms of commerce. The major impact of electronic commerce is expected to occur in the business-to-business sector, which is estimated to be approximately six times larger than the business-to-consumer electronic commerce sector, and to reach $1.3 trillion by 2003 (BusinessWeek 2000). Moreover, many large firms recently have announced their ventures into the realm of business-to-business electronic commerce using Internet protocols (communication standards). For example, the recent alliance between the big three auto manufacturers (General Motors, Daimler Chrysler, and Ford) to establish Covisint (an electronic market for automotive parts suppliers), the launch of RetailLink by Wal-Mart, and the Transfer Process Network by General Electric all fall in this category. In addition, in fragmented industries, a host of third-party-operated electronic markets are emerging; more than 750 were in existence at the beginning of the year 2000 (The Economist 2000). Examples include e-STEEL for the steel industry, IMX for home mortgage, and Paper Exchange for the paper industry. The economic significance of electronic markets makes it imperative to study them. Our research takes important steps in this direction by Otudying participant organization behavior in a third-party electronic market.
Despite the interest in third-party electronic markets of practitioners (BusinessWeek 1998) and academicians (Kaplan and Sawhney 2000), concerted efforts to understand these markets have been lacking. Previous research emphasizes the effect of new technologies on organizational processes (Glazer 1991; Heide and Weiss 1995) but does not discuss the influences of electronic markets, and the extant case-based research focusing on electronic markets limits itself to the characteristics of the market maker, the firm that manages and administers the market (e.g., Bakos and Brynjolfsson 1993; Hess and Kemerer 1994). The success of electronic markets, however, depends not only on the characteristics of the market maker but also on the value the market provides for the participant organizations. These participant organizations derive value by isteracting with other participant organizations in the market. Market makers build such positive network externalities by making it attractive for participant organizations to (1) initially adopt the market, (2) subsequently elect to stay on, and (3) be satisfied with the market.
Building positive network externalities, however, depends on the nature of organizational participation, that is, whether the adopting organizations actively participate in the market or are mere passive observers. If a firm is a passive observer, for example, its presence in the electronic market is of no value to other firms in the market because it is unlikely to engage in business transactions with these other firms. The primary purpose of this research is to investigate the factors that influence the nature of organizational participation in electronic markets, which is important because (1) electronic markets are becoming viable alternatives to traditional markets and hierarchies (Bakos 1998); (2) the commercial potential of electronic commerce is immense, and across the globe business-to-business electronic markets are the fastest growing electronic commerce phenomenon (BusinessWeek 1999); and (3) little, if anything, is known about the factors that influence the nature of organizational participation in electronic markets (Alba et al. 1997). Developing an understanding of the nature of organizational participation also would enable market makers to make their markets more attractive to participant organizations.
We rely on the motivation-ability framework (Merton 1957) to develop our conceptual model and make four important contributions. First, we develop and find empirical support for a typology for the nature of organizational participation. We believe that the nature of firm participation is pivotal in developing an understanding of organizational behaviors in electronic markets. Second, we demarcate the two primary organizational motivations for adopting electronic markets; in addition to the traditional efficiency motive, we survey the literature in institutional theory (DiMaggio and Powell 1983) to develop the legitimacy motive. As well as providing realism for the rational models of organizations, the legitimacy motive, together with the efficiency motive, renders a comprehensive conceptualization of organizational motivations. Third, we move beyond case-based research that typically has been used to study electronic markets and use organizational-level survey data to examine our research hypotheses. Fourth, our study tests the motivation-ability theory in the new context of electronic markets. Testing a theory in diverse contexts helps gauge the strengths of the theory and aids in empirical generalizations (Bass 1995).
Time and again, technology has had major influences on marketing practice and scholarship. Recent examples include the influence of integrated information and communication technology (Buzzell 1985). These technologies have revolutionized retailing by the use of scanner data, which also has resulted in improved marketing research models (Blattberg, Glazer, and Little 1994; Curry 1993). In terms of facilitating buyer-seller interactions, electronic data interchange (EDI) helps firms build close relationships and facilitate logistics (Stern and Kaufmann 1985). The EDI systems were generally proprietary to the initiating firm (Frazier 1983) and required significant investments on the part of both firms in a relationship (O'Callaghan, Kaufmann, and Konsynski 1992). Nonproprietary EDI systems were often industry created and facilitated logistic and operational management of interfirm relationships (McGee and Konsynski 1989). In this string of technological advances, the most recent phenomenon is the emergence of the Internet. Internet technology has immense implications for various branches of marketing, including consumer (Hoffman and Novak 1996; Peterson, Balasubramanian, and Bronnenberg 1997), business (Kaplan and Sawhney 2000; Klein and Quelch 1997), and international (Mehta, Grewal, and Sivadas 1996; Quelch and Klein 1996) marketing.
Internet-based business-to-business electronic markets represent an interorganizational information system that facilitates electronic interactions among multiple buyers and sellers (Bakos 1991; Choudhury, Hartzel, and Konsynski 1998). At a basic level, electronic markets can be viewed as information technology (IT)-facilitated markets (Bakos 1998). In electronic markets, buyers and sellers come together in a marketspace and exchange information related to price, product specifications, and terms of the trade, and a dynamic price-making mechanism (such as the bid-and-ask system) facilitates transactions between the firms (Kaplan and Sawhney 2000). Depending on the configurations of buyers and sellers and the price-making mechanism used, such electronic markets have been referred to as catalog aggregators, auctions, reverse auctions, or exchanges. Unlike EDI, which helps maintain existing interfirm relationships, electronic markets help buyers search for sellers and sellers search for buyers to engage in transactions. Thus, although EDI helps firms achieve strategic objectives by facilitating operational management of buyer-seller relationships, electronic markets are more strategic and assist firms to interact with other firms in a market setting (as opposed to a relational setting).
Usually an electronic market is sponsored or maintained by a market maker. The primary function of market makers is to gather buyers and sellers in a marketspace (Klein and Quelch 1997). Market makers also may perform other supplementary functions, such as providing credit, warranties, and logistics, and sometimes even may help in negotiations among potential business partners. The business models for market makers tend to vary with the functions they perform in addition to providing the marketspace (Kaplan and Sawhney 2000).
Depending on the position of the market maker in the value chain, an electronic market may be either hierarchical (biased) or market-driven (third-party) (Malone, Yates, and Benjamin 1994). In a hierarchical electronic market, the market maker is also a buyer or a seller. An example of such an environment is a seller-sponsored market such as Sabre (initially sponsored by American Airlines). Hierarchical markets are inherently biased toward the sponsor (i.e., the market maker), and as a result the market maker has advantages over competitors that conduct business in that market. Copeland and McKenney's (1988) detailed study of airline reservation systems (Sabre) points to these disadvantages of biased electronic markets, which eventually limit the scope and the survival likelihood of such markets.
A third party (neither a buyer nor a seller) sponsors an unbiased market-driven electronic market; thus, the market maker does not carry out transactions in the market. Examples include PaperExchange for paper and related products and SportsNet, which links more than 3500 dealers of sports cards. It is these third-party markets that have experienced the most success and are forecasted to dominate fragmented industries (BusinessWeek 1999; Krantz 1999). In our research, we study buyers, sellers, retailers, pawnbrokers, appraisers, and other intermediaries that trade in jewelry and related products in a third-party, unbiased, arket-driven electronic market, Polygon, as we discuss in greater detail subsequently (henceforth, we use "organization" and "firm" to refer to a user firm that participates in an electronic market).
In this section, we provide a rationale for a firm's need to participate in electronic markets and then present background on the nature of organizational participation, the motivation and ability variables, and environmental dynamism, the dominant characteristic of electronic markets. Strategic considerations motivate organizations to build capabilities and preempt competition and thereby to serve customers better (Day and Wensley 1988; Slater and Narver 1995). Considering the prominent role of technology in modern society (Blattberg, Glazer, and Little 1994; Buzzell 1985), it comes as no surprise that organizations view technology as a means of building sustainable competitive advantage (Day and Glazer 1994; Glazer and Weiss 1993). We contend that strategic considerations, such as providing better customer service (Parasuraman and Grewal 2000) and competitive hedging and/or preemption (Dickson 1992), gain significance in the organizational decision to participate in electronic markets.
We theorize that the nature of organizational participation in an electronic market depends on a firm's motivation and ability (Figure 1). Literature in diverse fields such as consumer behavior (MacInnis, Moorman, and Jaworski 1991), organizational behavior (O'Reilly and Chatman 1994), and marketing strategy (Boulding and Staelin 1995) emphasizes the criticality of both motivation and ability. However, we broaden the theorizing on motivations to encompass (1) the traditional emphasis on efficiency (Rindfleisch and Heide 1997) and (2) the institutional motivations of attaining legitimacy, which we refer to as the legitimacy motive (DiMaggio and Powell 1983). Furthermore, we rely on the organizational learning literature (Sinkula 1994) to conceptualize a firm's ability as its (1) learning about the electronic market and (2) IT capabilities. We also study the influence of environmental dynamism that is inherent in electronic markets (Lee and Clark 1997).
Nature of Organizational Participation
Organizations participate in electronic markets in several ways. At one extreme, organizations spend concerted effort and resources to streamline their electronic market operations. At the other extreme, they adopt electronic markets on an experimental basis (Dickson 1992). We develop a istinct-states conceptualization for the nature of organizational participation to illustrate diverse organizational behaviors in electronic markets. In addition to the literature on innovation assimilation (Meyer and Goes 1988) and electronic markets (Choudhury, Hartzel, and Konsynski 1998), we rely on exploratory interviews to develop our typology for the nature of organizational participation. Several times during the research process, we interviewed the chief executive officer (CEO) and marketing manager of the market maker. The CEO noted that customers were "a mixed lot with many different activity levels." Managers of participant firms also suggested that participants tend to have varying levels of activities in the electronic market.
To capture the varying levels of firm activity in electronic markets, we conceptualize the nature of firm participation in terms of the exploration state, the expert state, and the passive state. In the exploration state, firms do not know the requirements to conduct operations in an electronic market effectively but are expending substantial efforts to learn the particulars of doing business in the marketplace. Firms in the exploration state are "testing the waters" to understand the new medium better and in the process are using cognitive, physical, and financial resources. These organizations are trying to sense which business practices they need to reengineer, how they can reengineer, and whether it will be in their best interest to reengineer.
In the expert state, firms believe they have been successful in reengineering their business processes to function effectively in the electronic market. Similar to experts, they possess the know-how to perform market-related tasks successfully (Alba and Hutchinson 1987). Expert firms have substantive knowledge about their electronic market and procedural knowledge pertaining to the way of doing business in the market. These firms also understand the cause-and-effect relationships for their activities in the electronic market (i.e., axiomatic knowledge) and are thereby in a position to make high-quality decisions regarding their market operations (Sinkula 1994; Slater and Narver 1995). As the electronic market evolves, the expert firms regularly update their knowledge base about the market to remain current and thereby create episodic knowledge (Sinkula 1994). In a manner, expert firms have made an implicit or explicit pledge to continue conducting business over the electronic medium.
In the passive state, organizations carry out virtually no business in the electronic market but continue to maintain a presence. Our exploratory research suggests that the passive state is (1) propagated by firms entering electronic markets on an experimental basis, perhaps to supplement their traditional markets at some time in the future; (2) due to competitive hedging wherein a firm does not believe that electronic markets are viable but considers them to be a future opportunity or threat and therefore wants to observe and learn; (3) perpetuated by low entry barriers, in that entering an electronic market involves buying a computer to access the Internet; and (4) reinforced because maintaining a presence in business-to-business electronic markets is not expensive, requiring a firm simply to pay its monthly subscription fee. Organizations in this state are unwilling to expend the cognitive, physical, or financial resources that are needed to develop human capital and reengineer business practices to conduct business over the electronic medium actively.
Motivation
The literature on organizational founding suggests that the motives, processes, and structures that firms stress at the time of their inception have a long-lasting and perpetual influence on their behaviors (Baum and Oliver 1992; Schulz 1998). Accordingly, we expect the motives a firm emphasizes when entering an electronic market to influence the firm's operations in the market for a substantial period. We suggest that organizational motivations for entering electronic markets include an economic expectation of enhancing efficiency and a normative objective of attaining legitimacy.<SUP>4</SUP> We rely on transaction cost economics to develop the efficiency motive (Rindfleisch and Heide 1997), and the literature on institutional theory provides the bases for the legitimacy motive (Meyer and Rowan 1977).
Efficiency motive. An economist would argue that electronic markets improve transaction effectiveness and efficiency (Rindfleisch and Heide 1997). Improving efficiency is likely to be driven by the organizational strategic consideration of serving customers better (Day and Nedungadi 1994; Day and Wensley 1988). As Dickson (1992) observes, to serve their customers effectively and efficiently, firms often are motivated to experiment with new ways and innovations. Malone, Yates, and Benjamin (1994) suggest that electronic commerce leads to greater use of markets, rather than hierarchies, because these markets have relatively lower transaction costs. Research in the management of information systems and the economics of electronic markets supports this assertion (Bakos and Brynjolfsson 1993; Gurbaxani and Whang 1991; Hess and Kemerer 1994).
Reducing the cost of doing business is consistent with an organization's attempt to improve efficiency. Recent theoretical developments in transaction cost economics (TCE), which emphasize minimizing transaction costs, explicitly recognize this efficiency orientation and conceptualize TCE as a constrained-efficiency framework (Roberts and Greenwood 1997; Williamson 1992). This perspective regards organizations as efficiency seeking (Nelson and Winter 1982) under cognitive constraints (e.g., bounded rationality; Williamson 1987) and/or institutional restraints (Scott 1987).
Efficiency considerations are usually internal to an organization, but often attempts to enhance efficiency are externally oriented (Oliver 1990). The efficiency motive of reducing the costs of transacting with vendors, for example, might prompt an organization to use automated systems. In addition, the efficiency motive can be used to gauge the emphasis an organization puts on reducing costs and enhancing productivity (internal consideration) when entering an electronic market (external orientation). Over time, this emphasis on efficiency should become embedded in the organizational culture and influence the formal and informal functioning of firms (Deshpande and Webster 1989). This goal-directed orientation also should increase organizational commitment toward IT and organizational effectiveness in electronic markets (Ginzberg 1981; Newman and Sabherwal 1996).
Legitimacy motive. Institutional theory suggests that organizations must justify their actions and perform in accordance with existing societal norms and institutional expectations (DiMaggio and Powell 1983; Scott 1987). Adhering to societal norms enhances an organization's legitimacy and increases the likelihood of organizational survival. One way for organizations to attain legitimacy is to carry out activities that are deemed suitable by institutional constituents, including the government, consumer bodies, trade associations, and the public.
Electronic markets by their very nature are technologically intensive, and organizations dealing in these environments are likely to be perceived as having technological savvy. Being perceived as technologically knowledgeable is a definite advantage (Glazer and Weiss 1993). Thus, a possible organizational motive to enter electronic markets is to portray an image of technological sophistication. In other words, organizational stakeholders view technologically sophisticated firms more favorably in comparison with technologically naive firms; therefore, the organizational legitimacy of technologically sophisticated firms is higher because they provide a better fit with the modern-day organizational profile.
Organizations also mimic behaviors of a successful benchmarked group (Deephouse 1996; DiMaggio and Powell 1983). Institutional theorists argue that imitation is an uncertainty reduction mechanism in the sense that when a firm successfully adopts a structural change, other organizations mimic the change while attributing their success to the nature of the structural transformation (Haunschild and Miner 1997; Haverman 1993). Firms usually mimic the structures and the processes of other firms that are perceived to be legitimate (Haunschild 1993; Suchman 1995). Diverse research streams, including those on strategic alliances (Pangarkar and Klein 1998) and outsourcing (Lacity and Hirschheim 1993), have studied this mimicking behavior under the rubric of the bandwagon effect. The bandwagon effect suggests that sometimes organizations engage in activities simply because other firms do and provides a possible reason for the prominence of strategic alliances in some industries and their absence from others (Osborn and Hagedoorn 1997). In this study, organizational attempts to appear technologically sophisticated and mimic the behavior of successful organizations are classified as the legitimacy motives.
Ability
Knowledge acquisition and utilization processes help firms build capabilities and sustain strategic competence (Fiol and Lyles 1985). Knowledge developmental processes also provide organizations with resources, assets, and skills to compete effectively (Sinkula 1994; Slater and Narver 1995). Our research studies (1) organizational learning, a major source for building a knowledge base, and (2) a firm's IT capabilities, an avenue for extracting rents from the knowledge base (March 1991). By virtue of the newness and novelty associated with electronic markets, organizations have either not developed business models for this medium or tested existing business models in the medium. Learning therefore becomes pivotal for developing and adopting business models. Organizational capabilities reflect the extent of knowledge utilization in a firm and are critical for building sustainable competitive advantage (Day 1994). In electronic markets, a dominant organizational resource is a firm's IT capabilities (Guha et al. 1997).
Learning. Huber (1991, p. 90) describes knowledge acquisition as "the process by which knowledge is obtained," and this learning by an organization is a function of its age and effort (Garvin 1993). In organizations, experience is a prime source of learning and captures the incessant trial-and-error process by which organizations acquire information (Sinkula 1994). This acquisition of information leads to richer and proprietary knowledge bases. Eventually, the distribution, interpretation, and utilization of knowledge bases result in sustainable competitive advantages.
We examine two important learning constructs. The first is age-based or experiential learning, which we define as the learning an organization obtains from the extent of its experience and operationalize as the age of a firm's electronic market operations. Experiential learning has been shown to predict organizational survival, even after size and resource-enhancing linkages are controlled for (Brittain 1989), and the literature on operations management documents the positive effect of age on performance (Mody 1989). Although age is an important indicator of learning, it does not capture the effort spent in learning. To overcome this weakness, our second learning construct taps directly into the effort a firm devotes to developing skills to manage an electronic market. We refer to this construct as effort-based learning and conceptualize it as the effort, in terms of organizational resources and human capital, used to develop knowledge about an electronic market (Simon 1991).
IT capabilities. Firm capability hinges on the efficient development and use of resources (Sinkula 1994). Information use has been the focus of marketing research since the American Marketing Association/Marketing Science Institute-sponsored workshop on knowledge development (Myers, Massy, and Greyser 1980). This research stream indicates that knowledge development enhances the value of firms' resources and organizational capabilities (Moorman, Zaltman, and Deshpande 1992). In electronic markets, a participant's IT capability is an important organizational resource, should play a vital role in building sustainable competitive advantages, and should increase the firm's capacity to manage electronic markets (Auer and Reponen 1997; Mata, Fuerst, and Barney 1995).
Organization information processing research suggests that it is not organizational capabilities per se but the fit between organizational information processing needs and capacity that is critical (Galbraith 1973; Tushman and Nadler 1978). Information serves as a means of reducing uncertainty and thereby attaining the desired objective. In turn, information requirements depend on uncertainty, and by extension the desired information processing capacity depends on the information processing needs.
Environmental Dynamism
Environmental dynamism is a dominant characteristic of electronic markets (Klein and Quelch 1997). Rapidly increasing subscription bases, along with ever-changing technologies, characterize electronic markets (Lee and Clark 1997). Environmental dynamism captures these changes in demand and technology (Weiss and Heide 1993), induces uncertainty (Achrol and Stern 1988), and influences organizational structures and processes (Achrol 1991).
Because we have conceptualized the nature of organizational participation in terms of three distinct states (exploration, expert, and passive), we need to use one of the states as a base state and compare it with the other states. That is, because a firm can be in only one state, the three states compete against one another in some fashion. In formulating our hypotheses, we use the exploration state as the base state because initially almost every firm should be in this state.
Efficiency Motive and IT Capabilities
According to the motivation-ability framework, both firm motivation (efficiency motive) and ability (IT capabilities) should be present to affect organization participation (Merton 1957). Research shows that information systems projects often fail when either motivation or ability is lacking (see Ewusi-Mensah and Przasnyski 1991; Reich and Benbasat 1990). We therefore expect both motivation and ability to influence the nature of organizational participation. Emphasis on the efficiency motive and IT capabilities should enhance the likelihood of a firm being in the expert state and lower the likelihood of the firm being in the passive state.
H1: The greater the emphasis on the efficiency motive and IT capabilities, (a) the higher is the likelihood of a firm being in the expert state and (b) the lower is the likelihood of a firm being in the passive state.
Organization information processing research suggests that the influence of IT capabilities is likely to be nonlinear (Daft and Lengel 1986). As Day (1994) observes, the market dictates the extent and nature of capabilities that an organization develops, and the information processing needs determine the extent of information processing capacity required (Day and Glazer 1994). As organizational information processing capacity increases, the need to develop more of this capability declines (March 1991). In other words, to a specific level, IT capabilities should help a firm reach the expert state and reduce the chances of the firm being in the passive state, but eventually the influence of IT capabilities should level off. We therefore hypothesize that as the level of IT capabilities increases, the likelihood of a firm being in the expert state increases, but at a declining rate. We also propose that as the level of IT capabilities increases, the likelihood of a firm being in the passive state decreases, but at a declining rate.
H2: As the level of IT capabilities increases, IT capabilities (a) positively affect the likelihood of a firm being in the expert state, but at a declining rate, and (b) negatively affect the likelihood of a firm being in the passive state, but at a declining rate.
Legitimacy Motive
Legitimacy motives represent organizational attempts to adopt electronic markets either to portray a specific image to stakeholders or to mimic benchmarked organizations. Legitimacy is construed as "a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially construed system of norms, values, beliefs, and definitions" (Suchman 1995, p. 574). It seems that if enhancing organizational legitimacy is the motive for joining an electronic market, an organization achieves its objective just by entering the market. In other words, by virtue of a firm's entry into an electronic market, it is in a position to assert to its stakeholders that it is technologically advanced and ready for the challenges of the information age. Organizations that embrace electronic markets to mimic a successful benchmark firm believe that the benchmarked organization succeeded primarily because of its participation in electronic markets. In summary, some organizations ceremoniously adopt electronic markets as a mere pretense to attain legitimacy (DiMaggio and Powell 1983; Scott 1987).
For firms that emphasize the legitimacy motive, attempts to acquire knowledge or build capabilities are likely to be minimal. Reaching the expert stature without knowledge development is unlikely (Alba and Hutchinson 1987). In other words, simply entering electronic markets may not be sufficient to attain the expert state, which requires a firm to learn new routines, rules, and strategies. Attaining legitimacy in the eyes of stakeholders and adopting electronic markets on an experimental basis are likely to motivate a firm to maintain presence in the electronic market, even if the presence does not yield direct economic gains. Therefore, emphasis on legitimacy motives should lower the likelihood of a firm being in the expert state and increase the likelihood of the firm being in the passive state.
H3: The more an organization subscribes to legitimacy motives for entering an electronic market, (a) the lower is the likelihood of a firm being in the expert state and (b) the higher is the likelihood of a firm being in the passive state.
Learning
Research on organizational learning suggests that age-based learning helps capability development (Sinkula 1994). However, such learning has been shown to have limitations related to unintentional and unsystematic learning. As Levinthal and March (1993, p. 96) observe, "Experience is a poor teacher, being typically quite meager relative to the complex and changing nature of the world in which learning is taking place." Frequently, employees are the sensors that gather information and draw inferences. The cognitive constraints of rationality that inhibit human activity are also likely to impede age-based learning (Dickson 1992). Experience also tends to be ambiguous, with multiple and conflicting interpretations, making it difficult to decipher the "complex worlds" (Levinthal and March 1993, p. 97). Research on experience curves attests to this supposition and demonstrates that learning increases with age, but at a decreasing rate (Bass 1995). We expect age-based learning to increase the likelihood of a firm being in the expert state, but the rate of increase should lessen with age. Firms that are not effective learners, however, should sooner or later move to the passive state, and the probability that these firms will move into the passive state should vary positively with the age of their electronic market operations.
H4: As the level of age-based learning increases, age-based learning (a) positively affects the likelihood of a firm being in the expert state, but at a declining rate, and (b) negatively affects the likelihood of a firm being in the passive state at an increasing rate.
In addition to age-based learning, the effort that an organization spends in developing its knowledge bases is an important aspect of organizational learning. However, effort does not imply automatic success; organizations often spend considerable efforts in new ventures and fail (Golder and Tellis 1993). In such cases, firms fail to develop the skills and knowledge bases needed to manage the new ventures (Tushman and Anderson 1997). If effort-based learning is successful, it helps firms build their skill levels and move on to the expert state. In contrast, if effort fails to develop knowledge, the commitment to a failing course of action is likely to be minimal, and the level of effort should decline. Therefore, the probability of the firm moving to the passive state should increase.
H5: As the emphasis on effort-based learning increases, (a) the likelihood of a firm being in the expert state increases and (b) the likelihood of a firm being in the passive state increases.
Environmental Dynamism
Dynamic environments are characterized by high variability in demand and unpredictability regarding the actions of competitors (Achrol and Stern 1988). Research suggests that environmental dynamism makes it difficult for organizations to assimilate and anticipate environmental conditions and has an adverse influence on performance (March 1991). These challenges make it hard for firms to identify and develop the skills needed to succeed. As a result, learning is likely to be a longer and a more deliberate process. We therefore expect environmental dynamism to reduce a firm's likelihood of being in the expert state. Environmental dynamism is also likely to frustrate firms and therefore increase the likelihood that the firms give up in dynamic markets, thereby enhancing the probability of the firms being in the passive state.
H6: The higher the environmental dynamism in an electronic market, (a) the lower is the likelihood of a firm being in the expert state and (b) the higher is the likelihood of a firm being in the passive state.
Firm-specific factors typically moderate the influence of environmental variables (Dess and Beard 1984). In electronic markets, IT capability is one such firm-specific variable that should help organizations manage environmental dynamism; it is an organizational resource and reflects a firm's ability to exploit accumulated information. Organization information processing theory (Daft and Lengel 1986) also suggests that IT capabilities help manage environmental dynamism and therefore should moderate its effect.
H7: Information technology capabilities will moderate (a) the negative influence of environmental dynamism on the likelihood of a firm being in the expert state and (b) the positive influence of environmental dynamism on the probability of a firm being in the passive state.
Research Context
In our research, we study the nature of participation of firms that adopt Polygon, a market-driven electronic market for jewelry and related products (www.polygon.net). Polygon Network Inc. was founded in 1983, primarily to provide an electronic market for jewelry pawnbrokers, appraisers, manufacturers, wholesalers, retailers, buyers, and sellers. It is a subscription-based service for which members pay a monthly access fee, which enables them to buy and/or sell such jewelry items as cut diamonds, watches, and rings, as well as acquire other benefits such as gemological and related technical information. Polygon migrated in the late 1980s to an exchange format and facilitated interfirm transactions by having user firms call the Colorado server to retrieve their e-mail and information from the buyer-seller bulletin board. In 1995, Polygon acquired the domain name polygon.net and began its migration to Web-based operations. Although the transition to the Web has eased access for user firms, the nature of the information exchange has remained the same. Polygon represents an "open bazaar" in which buyers and sellers meet in an electronic environment and that makes it efficient for firms to exchange information related to price, product specifications, and terms of trade. Although Polygon does not enable participating firms to make payments electronically, it provides ratings for all participating firms based on their payment history.
Independent Measures
Following standard psychometric procedures (Churchill 1979), we created a pool of items for each of the constructs (all items were measured on a five-point semantic differential scale, where 1 = "disagree" and 5 = "agree"), with the exception of age-based learning. We extensively field tested the items by means of personal interviews with Polygon subscribers.
The efficiency motivation construct gauged the degree to which a firm stressed cost reduction and output enhancement when joining Polygon. We used four items that measured organizational emphasis on (1) increasing efficiency, (2) reducing costs of doing business, (3) streamlining operations, and (4) reducing the costs of transacting with exchange partners. The first two items for the legitimacy motivation construct measured the extent to which the firm mimicked competitors and other jewelers, respectively; the third item emphasized legitimacy attainment as a motive; and the final item gauged the extent to which the organization wanted to portray the image of being high tech. We measured a subscriber's IT capabilities by adapting six items from King and Teo's (1996) study of facilitators and inhibitors for the strategic use of IT. To assess effort-based learning, we used a two-item measure. The first item gauged the time and effort expended by employees to learn about Polygon. The second item measured the overall organizational effort. Finally, we adopted Klein, Frazier, and Roth's (1990) environmental dynamism scale.
We used the age of an organization's Polygon operations to assess age-based learning (Brittain 1989). Another instrument for age-based learning could be the number of years in business, not the tenure of involvement with an electronic market. We believe that the tenure of involvement is a more suitable instrument for at least two reasons. First, electronic markets are quite different from traditional forms of commerce, and therefore learning about them is likely to be critical. Second, years in business induces other effects, such as creating inertia (Chandrashekaran et al. 1999), thereby making it difficult to decipher the effect of learning about the electronic market. Bricks-and-mortar retailers, for example, were slower to establish their electronic retail outlets than were new start-ups. However, years in business is an important variable, and further research should examine the dynamics propagated by it.
Dependent Measure: The Nature of Organizational Participation
We used a polychotomous dependent variable to measure the nature of organizational participation. While developing this measure, we assessed the finest possible classification for the nature of organizational participation. This measurement serves two purposes: (1) It helps us rule out the possibility of a fourth state, and (2) it provides flexibility because it is easier to aggregate data than to disaggregate them. The dependent measure asked respondents to choose one of six categories that best described their present Polygon operations. Table 1 details the specific statements for each category. We used two statements to measure the exploration state, one to categorizes the expert state, and three to measure the passive state.
Pretest
To obtain a preliminary assessment of the internal validity of our measurement instrument, we mailed 300 questionnaires to Polygon subscribers and received 34 complete and usable responses. We used item-to-total correlation to assess the validity of the measurement instruments. These correlation coefficients were greater than .78 for all items, and therefore we retained all items. In the final study, we mailed the questionnaire to the remaining 1846 Polygon members and followed the same procedure as in the pretest.
Sample and Nonresponse Bias
We mailed the questionnaire to all 2146 Polygon subscribers in our sample frame and received 306 responses. The survey consisted of the scale items under investigation and a cover letter stating the purpose of the study and that Polygon Network Inc. endorsed the study. The cover letter noted that the purpose of the study was to research organizations at the frontier of adopting the Internet and carrying out electronic commerce. Polygon Network Inc. supplied the name of the key respondent for each firm, and typically the key respondent interacted with the Polygon Network Inc. on a regular basis.
We used the c2statistic to compare the sample (both pretest and final study combined) characteristics with those of the population (all Polygon subscribers) (Table 2). The results showed no significant differences between the characteristics of the sample and the population (c2 = .875, degrees of freedom [d.f.] = 3, p > .83). We compared the responses to our independent variables for the early respondents with those for the late respondents and the pretest respondents with those for the respondents in the final study and found no statistical differences (Armstrong and Overton 1977).
Measure Validation
We carried out measure validation in two phases. First, we personally interviewed three Polygon members and the CEO and marketing manager of Polygon Network Inc. to determine what it meant to do business in this electronic market. On the basis of our discussions, we sought to refine our conceptual model and develop a sample list of items. Then we conducted a second round of interviews to verify our conceptual model and refine our measures. Second, we used confirmatory factor analysis (CFA) to establish the convergent validity for the efficiency motive, legitimacy motive, effort-based learning, IT capabilities, and environmental dynamism scales (Table 3).
We retained all items for the efficiency motive, effort-based learning, and dynamism scales. The item deleted from the legitimacy motive scale emphasized mimicking competitors. Two items were deleted from King and Teo's (1996) scale. The four remaining items measured a firm's (1) IT planning capabilities, (2) strength of technical support staff, (3) understanding of benefits from the application of IT, and (4) knowledge about IT. Our CFA results show that all factor loadings were greater than the recommended .4 cut-off and were statistically significant (Nunnally and Bernstein 1994). The c2 statistic was not significant, which implies that the sample and estimated covariance matrix were alike. The goodness-of-fit index, adjusted goodness-of-fit index, nonnormed fit index, and comparative fit index were greater than the recommended .9; the parsimony normed fit index was greater than the recommended .6; and the root mean square error of approximation, as recommended, was less than .08 and not statistically different from .05 (Hair et al. 1995). To assess the validity of our measure for age-based learning, we used its correlation with (1) the number of transactions made per week on the Polygon and (2) the annual dollars transacted through Polygon. As we expected, these correlation coefficients were positive and statistically significant (r = .296, p < .01, n = 171; r = .191, p < .02, n =171, respectively)
To establish the internal consistency of our measurement model, we examined the reliabilities and average variance extracted (Fornell and Larcker 1981). All the reliabilities were greater than the recommended .7 (Nunnally and Bernstein 1994). The average variance extracted for each measure was greater than the recommended .5, with the exception of the scale for legitimacy motivation, which extracted 46% of variance (Bagozzi and Yi 1988). Additional research could refine our measure of the legitimacy motive. We used two methods to assess discriminant validity. First, the 95% confidence bands around the fs did not contain 1 (Anderson and Gerbing 1988). Second, we compared the average variance extracted with the fs (Fornell and Larcker 1981). The average variance extracted was greater than the respective fs for all measures. Table 4 displays the descriptive statistics.
We used a series of t-tests to examine the differences across the three states for (1) the number of transactions per week, (2) the percentage of business transacted through Polygon, and (3) the time since the adoption of Polygon. We expected the number of weekly transactions to be higher for both the exploration and the expert state in comparison with the passive state, which was the pattern of results we obtained. For the exploration versus passive comparison, we obtained b = 2.30, p < .01, and for the expert versus passive comparison, we obtained b = 2.59, p < .01. A comparison of the exploration and expert states gave us b = -.29, p > .78 (although this test was not significant, in absolute terms, the number of weekly transactions was higher for the expert state). Thus, in terms of number of weekly transactions, the exploration and expert states were statistically equal to each other but higher than the passive state.
As with the number of transactions, the percentage of business that firms undertook through Polygon in the passive state (2.58%) was statistically lower than that of firms in either the exploration (18.31%; b = 15.73, p < .01) or the expert (19.95%; b = 17.37, p < .01) state. Again, although firms in the expert state transacted more business over Polygon than did firms in the exploration state, statistically the two values were equal (b = -1.65, p > .70).
Typically, firms enter the exploration state and then move to either the expert or the passive state. In terms of average time since the adoption of Polygon, our results supported this assertion. Specifically, the age of Polygon operations of firms in the exploration state was statistically lower than that of firms in either the expert (b = -9.03, p < .07) or the passive (b = -11.50, p < .05) state. In addition, there was no statistical difference between firms in the expert state and those in the passive state (b = -2.46, p > .77).
Taken together, the results suggest that the three states differ from one another in expected manners. The exploration state differed from the expert state in terms of time since adoption of Polygon, and both of these differed from the passive state in terms of the number of weekly transactions and the percentage of business transacted over Polygon.
Estimation Model
Because we have a discrete dependent variable with three states (i.e., exploration, expert, and passive), we used a multinomial logit (MNL) model to test our hypotheses.
Where ORG-PAR is the polychotomous dependent variable with the exploration state as the base, EFF stands for efficiency motive, LEGIT represents the legitimacy motive, AGE denotes age-based learning, EFFORT represents effort-based learning, IT-CAP depicts IT capabilities, and ENV-DYN designates environmental dynamism. The maximum likelihood estimate for this model yields two sets of estimates, one for the expert state and the other for the passive state.
Overall Model Test
In Table 5, we display the results from our MNL model with the exploration state as the base. The likelihood ratio test for the overall fit of the model indicates that the independent variables explain statistically significant variance in the dependent measure (c<SUP>2</SUP> = 58.06, d.f. = 20, p < .01). In addition to establishing descriptive validity for the structure of the model, it is also important to evaluate the predictive validity of the model. We used estimates from the MNL model to carry out a discriminant exercise and tabulate the proportion of correct predictions (Table 5). The proportion of correct classifications for the model is 54.0%, which is greater than the two benchmarks recommended by Morrison (1969): maximum chance criterion (45.6%) and proportional chance criterion (35.8%)
Because we have interaction terms in our model, we interpret the main effects as contingent on the appropriate interaction term (Jaccard, Turrisi, and Wan 1990). The main effect of efficiency, for example, represents the impact of efficiency when IT capabilities equal zero. Mathematically then, y = b0 + b1 EFF + b9 EFF IT-CAP, and when IT-CAP = 0, y = b0 + b1 EFF. Therefore, in a way, b1 by itself is meaningless, because the influence of efficiency varies with IT capabilities. To aid in interpreting these coefficients, we carried out Wald tests for the significance of the efficiency motive and environmental dynamism at various levels of IT capabilities and for the significance of IT capabilities at various levels of the efficiency motive and environmental dynamism (Table 6).
Antecedents of the Expert State
The first hypothesis suggests that both IT capabilities and an efficiency motive would be needed for a firm to be in the expert state. We find support for this assertion (b9 = .879, p < .01). To provide a better understanding of this interaction effect, we examined the efficiency motive at three levels of IT capabilities. As Table 6 shows, the influence of the efficiency motive on the likelihood of a firm being in the expert state increases from -1.178 (p < .01) at low levels of IT capabilities to .266 (p > .46) at high levels of IT capabilities. H2 posits that, as IT capabilities increase, the positive effect of IT capabilities on the likelihood of a firm being in the expert state will decline. We do not find support for this hypothesis (b7 = -.108, p > .33). Our hypothesis on the legitimacy motive, which proposes that an emphasis on the legitimacy motive will decrease the likelihood of a firm being in the expert state, is marginally supported (H3 : b2 = -.355, p < .10). H4 suggests that age-based learning tends to increase the likelihood of a firm being in the expert state and that this positive effect diminishes as the level of age-based learning increases. Our data support this hypothesis, with significance of both the linear (b3 = .473, p < .01) and square (b4 = -.024, p < .05) terms for age-based learning. As Figure 2 shows, the influence of age-based learning increases at a decreasing rate. H5 posits a positive relationship between effort-based learning and the likelihood of a firm being in the expert state. We find marginal support for this hypothesis (b5 = .389, p < .10). We hypothesized in H6 that environmental dynamism would have a negative effect on the probability of a firm being in the expert state and in H7 that IT capabilities would moderate this negative effect. Our results support both these hypotheses (H6 : b8 = -2.294, p < .05; H7 : b10 = .757, p < .05). As Table 6 shows, the effect of environmental dynamism varies from a low of -.197 (p > .62) at low levels of IT capabilities to a high of 1.012 (p < .05) at high levels of IT capabilities.
Antecedents of the Passive State
H1 posits that emphasis on both IT capabilities and efficiency motives should decrease the likelihood of a firm being in the passive state. Our results do not support this hypothesis (b9 = .294, p > .12). The main effect for the efficiency motive (b1 = -1.858, p < .05) shows that such an emphasis reduces the likelihood of a firm moving to the passive state. H2 proposes that the IT capabilities will negatively influence the probability of a firm being in the passive state and that this negative influence will decrease as the level of IT capabilities increases. Our results support this hypothesis because both the linear (b6 = -3.836, p < .05) and square (b7 = .247, p < .10) terms are significant. H3 suggests that the legitimacy motive will have a negative effect on the probability of a firm being in the passive state. Our data do not support this hypothesis (b2 = -.203, p > .17). We expected the negative effect of age-based learning on the likelihood of a firm being in the passive state to increase with the level of age-based learning. Our results do not support this hypothesis, because neither the linear (b3 = .074, p > .16) nor the square (b4 = .001, p > .31) terms are significant. We hypothesized in H5 that effort-based learning would move a firm away from the exploration state, and we find support for this hypothesis (b5 = .293, p < .10).<SUP>6</SUP> Environmental dynamism was hypothesized to influence the probability of a firm being in the passive state positively, and we expected this positive effect to be moderated by IT capabilities. We do not find support for either the main effect hypothesis (H6 : b8 = -.794, p > .22) or the moderating role of IT capabilities (H7 : b10 = .371, p > .11).
Our findings suggest that participating firms must emphasize the right motives and should have the requisite capabilities to participate actively in electronic markets. An emphasis on the efficiency motive and IT capabilities is critical for firms to attain the expert state and avoid the passive state. There is, however, a critical difference between the results for the expert state and those for the passive state. For the expert state, the interaction term between the efficiency motive and IT capabilities is significant, whereas this interaction term is not significant for the passive state. In other words, the influences of the efficiency motive and IT capability on the likelihood of a firm being in the passive state are independent of each other, whereas in the case of the expert state, the two variables act as catalysts in attenuating each other's influence. The finding that IT capabilities help firms manage the dynamism inherent in electronic markets further strengthens the criticality of this organizational ability. However, our results do not support the hypothesized nonlinear effect of IT capabilities, perhaps because of the context of our study. The jewelry business is not traditionally high tech; consequently, our sample may have firms that have not reached saturation in terms of IT skills. A replication of our study in a high-tech industry or with a multi-industry sample may provide for a more thorough testing of the nonlinear effect of IT capabilities.
In terms of motivation, developing a proper mindset by stressing efficiency motives and de-emphasizing legitimacy motives is critical for firms to attain the expert state. To become experts, firms also must emphasize organizational learning (age- and effort-based). However, the positive influence of age-based learning tends to decline with age. Although effort-based learning is critical for attaining the expert state, it also tends to increase the likelihood of a firm becoming a passive participant. Overall, we find marginal support for our assertion that effort-based learning will move firms out of the exploration state.
Managerial Implications
Firms that intend to participate in electronic markets should be aware that their nature of participation is dependent not only on their IT capabilities but also on their motivation. Entering the market simply to jump on the bandwagon or establish an image of being technologically proficient does not seem to enhance activity levels in the market. Moreover, to become experts, firms should strive to achieve efficiency and work to build their IT capabilities. Allocating time and effort to learn and understand the environment also can yield substantial benefits. Firms that enter electronic markets on an experimental basis or with the desire to mimic others will most likely become passive participants. If a firm joins an electronic market to experiment, it must expend resources to achieve the objectives of its experiment.
Market makers are more likely to succeed if they provide positive network externalities for participating firms. A market maker must understand the reasons for a user firm's participation and thereby be able to provide the right incentives for these firms to adopt the market. Understanding the goals of their participating firms enables market makers to design programs that facilitate goal achievement and enhance member retention. Polygon, for example, could identify active firms and encourage them to develop their IT capabilities, as well as facilitate their learning about the environment. Passive participants might be encouraged to retain their membership for reasons that reinforce their objectives. However, convincing the passive participants to expend resources to become experts may be in the best interest of a market maker in the long run. A market maker's strategic orientation also influences the type of companies that join the market. If a market maker wants to create a market primarily with expert firms, for example, its strategic orientation and tactical actions should identify IT-capable firms whose objectives are to make the market their major sales channel.
Research Implications
Our primary contribution to theory lies in developing and substantiating the existence of a typology for the nature of organizational participation. The typology helps qualify the network externality argument for the efficacy of electronic markets. This argument emphasizes that the attractiveness of a market to a user firm depends on the number of other firms (potential business partners) in the market. We find, however, that in the case of electronic markets, network externality does not depend on the total number of firms participating in the market but rather on the number of expert (and to a lesser extent, exploration) firms in the market. Thus, the passive participants must be discounted.
We also develop the construct of legitimacy motives, which, together with efficiency motives, provides a holistic theorization of organizational motivations. In addition, we contribute to the generalizability of previous case-based research on electronic markets (e.g., Hess and Kemerer 1994) by using organizational-level survey data to test our model and a representative electronic market. Similar to most electronic markets, Polygon facilitates negotiations on product specifications and terms of exchange. Polygon strives to foster a community atmosphere and is contemplating providing ratings of participating firms that would be based on their participation history. Thus, on the basis of the characteristics and scope of Polygon, we contend that our results are generalizable to other similar electronic markets.
Limitations and Further Research
Consistent with most survey research, our results are constrained by issues related to common method variance, though we tried to minimize it in two ways: (1) We assessed nonresponse bias by comparing sample characteristics with those of the population, and (2) we used secondary data to operationalize age-based learning. To our knowledge, this is the first attempt to examine organizational participation in electronic markets empirically; as a result, readers should be cautious in generalizing these results. Replications, especially in markets in which market makers adopt business models different from those of Polygon, are needed. Additional research, for example, might study firm participation in electronic markets in which a transaction fee is the primary revenue source for the market maker (e.g., e-STEEL). In electronic markets that rely on transaction fees, participation firms that do not engage in transactions do not incur any costs (other than the initial setup costs). In such cases, we believe that participating firms are even more likely to move into the passive state, and the challenges for market makers to induce active participation would be even greater. Other business models of market makers (e.g., those based on transaction or subscription fees), other characteristics of market makers (e.g., the ownership structure-consortia-led versus third-party), strategy of the market maker (e.g., exclusive markets in which entry is contingent on some qualification versus open markets), and the structure of the industry (e.g., degree of fragmentation) also should be studied.
Further research on business-to-business electronic markets should proceed in at least two directions. First, research should study user firms in electronic markets further. Researchers need to understand the role of user firm capabilities, such as market orientation and strategic adaptability, and how these important strategic variables might change in the context of electronic markets. Researchers should examine the consequences of the nature of organizational participation, such as its influence on building interfirm relationships and firm performance. In addition, researchers should develop an understanding of the drivers of firm performance and typologies of firm strategies in electronic markets. Finally, all electronic markets are international, and understanding the role of country-specific institutional environments and the implications of omnipresent global competition among user firms is critical in developing a full appreciation of the impact of electronic markets.
Second, we investigate only one electronic market and therefore cannot contribute with regard to the role of market makers in other contexts. Further research should work toward developing a taxonomy of business-to-business electronic markets, competition among electronic markets, role of market makers and their influence on the type and characteristics of user firms, the scope and context of electronic markets (e.g., generic versus industry-specific electronic markets), and international issues such as the country of origin of market makers, among others. For example, an interesting research avenue would be to investigate the factors behind the emergence and initial success of consortia-led electronic markets.
Measures for the Nature of Organizational Participation
State Measures
Exploration State We have recently initiated the Polygon
service and are beginning to learn how
to do business through them.
We have learned a lot about the way to
do business on the Polygon, but there
is still much more to learn. Our comfort
level with doing business on Polygon is
improving with every passing day.
Expert State We are comfortable with our Polygon
operations and are aware of the ins and
outs of these operations. Our dealings
on the Polygon are a regular part of our
business, and we think that there is not
much new to learn.
Passive State We carry virtually no business through
Polygon but still are listed as a member
of Polygon Network and will continue to
be listed with Polygon.
We are seriously considering terminating
our Polygon operations.
We have terminated our relationship with
Polygon.
Sample Classification
Population
Characteristics
Type of Sample (Supplied by
Business (%) Characteristics Polygon)
Retailers 59.1% 60.0%
Wholesalers 23.2% 20.0%
Pawnbrokers 9.5% 10.0%
Miscellaneous
(e.g., manufacturers,
designers, appraisers) 8.2% 10.0%
Legend for Chart:
A - Constructs and Items
B - Factor Loading (t-Value)
[a] One item was deleted after confirmatory analysis. It read,
"Our competitors joined Polygon."
[b] Two items, "Is experienced with IT" and "Gives high
importance to strategic use of IT," were deleted.
A B
Efficiency: We decided to subscribe to
Polygon because
We thought it would increase our .66 (11.0)
efficiency.
We expected it to reduce our costs .85 (15.4)
associated with running our business.
We thought it would streamline our .79 (13.9)
operations.
We believed that it would reduce the .73 (12.3)
cost associated with transacting
business with our exchange partners.
Legitimacy: We decided to subscribe to
Polygon because[a]
It would provide legitimacy to our .76 (11.3)
organization.
It portrays us as a high-tech organization. .74 (11.0)
The best in the business at the time .50 (7.4)
were doing so.
Effort-Based Learning: We are interested
in your experiences with using the
Polygon system. To what extent do you
agree with the following statements?
Our personnel have spent a lot of time .52 (5.8)
and effort learning specific techniques
used in the Polygon.
Our organization has expended a lot of .90 (7.0)
time and effort to develop our Polygon
operations.
IT Capabilities: The following questions
pertain to information technology (IT)
capabilities for your organization.
Your firm currently[b]
Has strong IT planning capabilities. .83 (14.7)
Has strong technical support staff. .82 (14.5)
Has an understanding of possible benefits .63 (10.2)
of IT applications.
Has adequate knowledge about information .68 (11.4)
technology.
Environmental Dynamism: Please focus
on the BUYING/SELLING environment
through the Polygon. To what extent do
you agree with the following statements?
We are often puzzled by actions of .78 (11.5)
retailers and wholesalers.
We are often astonished by actions of .71 (10.6)
our competitors.
We are often surprised by our customers' .62 (9.2)
actions.
χ² (d.f., p-value) 96.48 (94, .41)
Root mean square error of approximation .010 (.99)
(p-value)
Goodness-of-fit index .96
Adjusted goodness-of-fit index .94
Nonnormed fit index .99
Parsimony normed fit index .73
Comparative fit index .99 Descriptive Statistics, Reliabilities, Average Variance Extracted, and Intercorrelations Among the Refined Measures
A= Construct
B= Mean
C= Standard Deviation
D= Reliability
E= Average Variance Extracted
F= LEGIT
G= IT-CAP
H= AGE
I= EFFORT
J= ENV-DYN
A B C D E F
G H I J
EFF 3.400 .867 .85 .58 .343*
.026 .069 -.166* .092
LEGIT 2.768 .868 .71 .46
.034 .043 -.288* .084
IT-CAP 3.568 .798 .83
.56 .095 -.088 -.205*
AGE 4.223 6.465
-.006 .084
EFFORT 3.342 .801 .69 .
54 .023
55
ENV-DYN 3.106 .718 .75 .50
*p < .01
Estimation Results from the Multinomial Logit Model
Coefficient Coefficient
Independent Variable (Expert State)a (Passive State)a
Constant(B0) 15.674*** 10.528**
(6.530) (5.872)
EFF (b1) -3.612*** -1.858**
(1.267) (.967)
LEGIT (b2) -.355* -.203
(.221) (.208)
AGE (b3) .473*** .074
(.191) (.074)
AGE AGE (b4) -.024** .001
(.013) (.002)
EFFORT(b5) .389* .293*
(.239) (.213)
IT-CAP (b6) -4.508** -3.836**
(2.252) (2.040)
IT-CAP IT-CAP (b7) -.108 .247*
(.253) (.182)
ENV-DYN (b8) -2.294** -.794
(1.371) (1.048)
EFF IT-CAP (b9) .879*** .294
(.330) (.244)
ENV-DYN IT-CAP (b10) .757** .371
(.379) (.292)
Log-likelihood -234.400
Restricted (slope = 0)
log-likelihood -263.425
X2 58.06*** (d.f. = 20, p < .01)
Predictive Validity (n = 248)
Maximum chance criteria b .456
Proportional chance criteria b .358
Proportion correctly classified (model) .540
A= Standard errors are in parentheses (one-tailed tests).
B= We extended Morrison's (1969) proportional chance criteria and
maximum chance criteria from a two-category discriminant problem
to a three-category problem. If a, b, and g are the proportion of
respondents in each of the three categories, maximum chance
criteria would be the maximum of these three proportions, and
proportional chance criteria would be a2 + b2 + g2.
*p < .10.
**p < .05.
***p < .01
Results for Interaction Effectsa Hypothesis Lowb Mediumb Highb Effect on the Expert State Coefficient of efficiency as a Function of IT capabilities. -1.178 -.477** .226 (.407) (.245) (.305) Coefficient of environmental dynamism as a function of IT capabilities. -.197 .408* 1.012*** (.395) (.260) (.402) Effect on the Passive State Coefficient of efficiency as a function of IT capabilities. -1.043 -.808 -.573*** (.339) (.219) (.240) Coefficient of environmental dynamism as a function of IT capabilities. .232 .529*** .824*** (.318) (.239) (.348) a Standard errors are in parentheses (two-tailed tests). b We define high as m (mean) + s (standard deviation), medium as m, and low as m - s. We now illustrate one case of the Wald test. Consider the influence of efficiency at a high level of IT capabilities. The mean for IT capabilities is 3.568, and the standard deviation is .798, which implies that high IT capabilities equal 4.366 (see Table 4). The effect of efficiency on a firm in the expert state is given as ORG - PAR = b1 + b9 IT-CAP, and at a high level of IT capabilities this effect equals ORG - PAR = b1 + b9< 4.366. Because b1= -3.612 and b = .879 (see Table 5), ORG-PAR = .226 (Table 6). Similarly, we can calculate the standard error as b12 + b9 2 4.366 2 + 2 4.366 b1 bb9 Cov(b1, b9). *p < .12 **p < .10 ***p < .05 +p < .01
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GRAPH: FIGURE 1: Conceptual Framework
GRAPH: FIGURE 2: Influence of Age-Based Learning
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By Rajdeep Grewal; James M. Comer and Raj Mehta
Rajdeep Grewal is Assistant Professor of Marketing, Smeal College of Business, Pennsylvania State University.
James M. Comer is Professor of Marketing
Raj Mehta is Associate Professor of Marketing, University of Cincinnati
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Record: 14- Antecedents and Consequences of Merit Pay Fairness for Industrial Salespeople. By: Ramaswami, Sridhar N.; Singh, Jagdip. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p46-66. 21p. 2 Diagrams, 3 Charts. DOI: 10.1509/jmkg.67.4.46.18690.
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Antecedents and Consequences of Merit Pay Fairness for
Industrial Salespeople
How do salespeople make judgments of merit pay fairness? By what mechanisms do fairness judgments influence the performance and commitment of salespeople? Using equity and social exchange theories, the authors examine these questions for industrial salespeople who work in a Fortune 500 firm and provide four key findings. First, of the three dimensions of fairness judgments, they find the interactional fairness dimension to be relatively more important than procedural or distributive fairness in influencing job outcomes of salespeople. Second, supervisory behaviors have significant influence in shaping salespeople's fairness judgments, particularly judgments of distributive and interactional fairness. Third, the results underscore the contrasting mediating role of trust in supervisor and job satisfaction. Although trust in the supervisor is important in reducing salespeople's opportunistic behaviors, the authors find job satisfaction to be important in enhancing their loyalty to the organization. Fourth, salespeople's job performance is influenced directly by extrinsic factors such as fairness of current rewards and potential for rewards. In addition, the authors outline implications for theory and practice.
Of the various decisions made by sales managers, merit pay-raise decisions (i.e., decisions about incremental pay awarded to employees on the basis of their performance) are arguably one of the most important. Merit pay raises are instrumental in directing salespeople's behaviors toward organizational goals and in facilitating retention of high-performing salespeople (Bartol 1999; Lawler 1990). Retention is facilitated by organizations differentially recognizing salespeople whose performance is exemplary. Merit pay raises also have a compounding effect on a salesperson's financial well-being. A gap of a few percentage points in merit pay-raise amounts can accumulate over a salesperson's career to produce a significant financial impact on that person's time-value of employment income. Studies consistently show that salespeople value pay raises more than any other performance reward, including promotion opportunities, fringe benefits, and recognition awards (Chonko, Tanner, and Weeks 1992; Churchill, Ford, and Walker 1979; Cron, Dubinsky, and Michaels 1988; Ford, Churchill, and Walker 1985; Ingram and Bellenger 1983; Money and Graham 1999).
Despite the recognition of pay valence for employees in general and salespeople in particular, dissatisfaction with pay and compensation plans remains prominent in employee surveys (Denton 1991; Farnham 1989; Leonard 2001). When pay expectations are not met, employees may believe that the organization has violated its obligations and disregarded its commitments (Lester et al. 2002). However, this does not mean that salespeople expect to receive the highest monetary reward; rather, they expect a fair level of reward relative to their performance (Denton 1991; Livingstone, Roberts, and Chonko 1995). Thus, if every salesperson received the same reward regardless of performance, it not only would raise issues of inequity and distress but also would likely undermine salespeople's motivation to raise their effort and performance level (Denton 1991).
In contrast, fair treatment of employees sets into motion a social exchange process by which the supervisory and organizational efforts to make fair decisions engender an obligation to reciprocate on the part of employees. Although previous research has provided general support for this reciprocity effect (Aryee, Budhwar, and Chen 2002), our specific focus is on both the antecedents and the consequences of fairness in salesperson pay decisions. In particular, we examine how perceptions of fair pay decisions not only strengthen salespeople's long-term relationship and attachment to their organization but also encourage reciprocity with functional behaviors, including less opportunism (Ramaswami, Srinivasan, and Gorton 1997) and greater job performance, which enables enhanced organizational effectiveness (Netemeyer et al. 1997). Moreover, we examine how salespeople formulate pay fairness perceptions as a result of supervisor and management actions.
Although previous research has explicated the social exchange basis of employee attitudes and behaviors, notable gaps remain in the application of the equity and social exchange theory frameworks to an understanding of the antecedents and consequences of salespeople's perceptions of merit pay-raise fairness. First, although research has identified several facets of fairness (namely, distributive, procedural, and interactional), little work has been done to examine the differential effect of these dimensions empirically in a single study (Konovsky and Pugh 1994; Kumar, Scheer, and Steenkamp 1995; Livingstone, Roberts, and Chonko 1995; Netemeyer et al. 1997; Organ 1988). Some researchers argue that the distributive dimension is most important as it relates to the magnitude of reward (e.g., Organ 1988). In contrast, in nonsales contexts, Konovsky and Pugh (1994) and others suggest that procedural fairness is more significant as it pertains to processes that determine rewards. Most studies ignore interactional fairness, that is, how salespeople are treated during the rewards process. The lack of attention to interactional fairness is notable, because issues pertaining to human dignity and respect are assuming greater importance in the workplace. Thus, current knowledge about the relative significance of distributive, procedural, and interactional fairness judgments in the context of merit pay-raise decisions is largely deficient.
Second, recent studies in social exchange research suggest that job satisfaction (Netemeyer et al. 1997) and supervisory trust (Aryee, Budhwar, and Chen 2002; Konovsky and Pugh 1994) mediate the influence of fairness dimensions on outcomes. Unfortunately, empirical studies in sales management have largely failed to study the presence and importance of such mediating processes. We were unsuccessful in finding a single study that examines different, potentially competing mediating processes. Netemeyer and colleagues (1997, p. 95) echo this gap in their observation that "sales-oriented ... research ... should consider several mediators simultaneously to determine which ones have stronger effects." Addressing this gap will enable integration of processes that heretofore have been examined independently.
Third, in studies of fairness consequences, sales researchers have focused their attention primarily on social exchange concepts, such as commitment and trust. Little research exists on the effects of fairness on job performance or opportunistic behaviors of salespeople. Performance on assigned tasks and, in the context of salespeople, opportunistic behaviors (e.g., "managing" data and effort to create favorable impressions and evaluations) likely have a significant effect on the bottom line. Understanding how fairness in merit pay raises can influence such bottom-line consequences holds implications for managers and researchers alike.
Finally, in general, sales researchers have neglected the antecedents of fairness. This neglect is notable because studies of fairness consequences do not tell a complete story. They establish that fairness perceptions matter, but they provide little insight into what managers can do to promote fairness. As such, the focus on fairness consequences in prior studies is likely to be less useful in drawing managerial implications.
We aim to address the preceding gaps by ( 1) examining three distinct fairness dimensions, ( 2) exploring the mediating mechanisms that govern fairness effects by using a nomological model rooted in social exchange and equity theories, ( 3) examining outcomes (commitment, performance, and opportunism) that are important for organizational effectiveness, and ( 4) drawing on diverse literature to propose and test an initial set of fairness antecedents. Overall, inclusion of all three fairness dimensions avoids misspecification bias due to omitted constructs, and an understanding of the key, competing mediating pathways is likely to yield theoretical payoffs and more concrete managerial guidelines for enhancing fairness-outcome relationships.
Merit Pay Fairness: Conceptualization and Framework
Fairness issues in organizations span several facets of work, including job design, performance evaluation, monetary rewards, and resource allocation. The focus of this study is on fairness of merit pay-raise decisions. Although the literature on pay fairness for salespeople is sparse, three points underscore the significance of research in this area. First, merit pay systems serve an instrumental function by directing the individual salesperson's behaviors toward fulfilling organizationally mediated sales goals and toward linking rewards received to the achievement of sales goals. Second, merit pay systems facilitate greater work motivation by differentially rewarding top performers over marginal performers. Studies have shown that a discriminating pay system can increase employees' motivation to perform by as much as 40% (Lawler 1990). Third, merit pay systems play a crucial role in retaining more "effective" salespeople. Opportunities (or lack thereof) for enhanced compensation levels influence salespeople's decisions to quit and their commitment to the organization (Lawler 1990; Singh, Verbeke, and Rhoads 1996).
Figure 1 presents the framework we used to study the antecedents and consequences of fairness dimensions based on equity and social exchange theories. Our focus is on fairness dimensions (i.e., understanding how employees determine whether they have been treated fairly in their merit pay-raise decisions) and fairness processes (i.e., examining the nature and strength of relationships involving antecedents and consequences). We begin our discussion with fairness dimensions.
Distributive, Procedural, and Interactional Dimensions of Merit Pay Fairness
Distributive fairness involves magnitude of rewards. Salespeople gauge the reward magnitude relative to their input and then compare this ratio with reward-to-input ratios of similar employees (Livingstone, Roberts, and Chonko 1995). People perceive inequity if their reward-to-input ratios do not compare favorably with such ratios of others. However, because employees may not have complete information about the reward-to-input ratio of others, they sometimes derive distributive fairness evaluations by comparing the rewards received with their own expectations (Netemeyer et al. 1997).
Procedural fairness deals with how decisions are made. The degree of individual control over the decision process shapes employees' views about procedural fairness (Lind and Tyler 1988). Researchers have identified two types of controls: ( 1) process control, or the degree of influence afforded to employees over the procedures that supervisors use to arrive at pay raise decisions, and ( 2) decision control, or the amount of direct control employees have in determining pay-raise outcomes.
Finally, interactional fairness involves how decisions are communicated. As such, it focuses on the social enactment of procedures and the quality of supervisor-salesperson interaction during the pay decision process (Goodwin and Ross 1992). Often, noninstrumental values, such as dignity, respect, and social standing, are evoked in such evaluations (Folger and Konovsky 1989).
Fairness Consequences: Direct and Mediated Pathways
Following Katz and Kahn (1978), we focus on three organizational outcomes: attachment, performance, and opportunism. Katz and Kahn prescribe that ( 1) when employees enter the organization, they should be engaged with it (organizational commitment); ( 2) as employees, they should carry out their roles in a dependable, superior fashion (job performance); and ( 3) employees should not engage in activities that thwart or undermine organizational effectiveness (opportunism). As we noted previously, fairness studies have provided greater attention to the organizational commitment outcome and less attention to the other two outcomes.
Two distinct pathways, direct and mediated, have been proposed for the influence of fairness dimensions on outcomes. The direct pathway hypothesizes that distributive, procedural, and interactional fairness have a direct impact on outcomes (George 1991; Konovsky and Cropanzano 1991). Notably, the theoretical mechanism for the direct hypothesis is the notion of reciprocity in a balance theory framework. For example, Greenberg (1990) finds that employees who experience pay inequity respond with acts of deviance or opportunism to address the inequity. In contrast, fair employer actions are reciprocated with favorable employee responses, including greater performance and commitment.
The mediating pathway proposes that the influence of fairness dimensions on employee outcomes is mediated by two social exchange variables: supervisor trust and job satisfaction. Perceptions of fairness are likely to promote enhanced feelings of job satisfaction (because of the attainment of valued rewards) and trust in the supervisor (for making a good faith effort to be fair) (Konovsky and Pugh 1994; Roberson, Moye, and Locke 1999). In turn, job satisfaction and trust in supervisors are posited to influence employees' commitment, performance, and opportunism. Although prior studies have examined the mediating role of either job satisfaction (Netemeyer et al. 1997) or supervisor trust (Konovsky and Pugh 1994), few studies have examined the simultaneous and possibly differential mediating roles of job satisfaction and trust in fairness decisions. To the extent that these competing and differential mediating effects are supported, our study integrates prior studies and advances the understanding of mechanisms that underpin the relationships between fairness and critical work outcomes. Next, we develop hypotheses separately for each chain in the mediation pathway.
Influence of Fairness Dimensions on Supervisor Trust and Job Satisfaction
Because trust reflects salespeople's willingness to rely on their supervisors to protect their interests, it is a key element in the development and maintenance of social exchange relationships (Morgan and Hunt 1994). Why would salespeople trust their supervisors? Existing research indicates that an important driver of trust is employees' perceptions of procedural fairness (Dwyer, Schurr, and Oh 1987; Flaherty and Pappas 2000; Konovsky and Pugh 1994; Lind and Tyler 1988). Konovsky and Cropanzano (1991) suggest that the use of fair procedures generates expectations of fair treatment in the long run. In turn, these expectations lead to a generalized sense of positive regard for (and trust in) the supervisor who uses fair procedures. In contrast, supervisors who have a reputation for unfair, unilateral action signal that they are only interested in their own welfare and organizational welfare. Such a negative reputation is likely to reduce employees' trust in the supervisor (Ganesan 1994). Furthermore, when supervisors demonstrate respect for the rights and dignity of salespeople through communication and high-quality interactions, they signal that salespeople are valued members of the group (Folger and Konovsky 1989). Treatment of salespeople in a manner that reinforces their self-worth helps enhance their trust in the supervisor (Lind and Tyler 1988)
Two opposing arguments can be posited for the relationship between distributive fairness and supervisor trust. Konovsky and Pugh (1994, p. 658) suggest that "a norm of distributive fairness implies that the parties to an exchange give benefits with the expectation of receiving comparable benefits in the short run." In other words, fair compensation is expected and therefore will not contribute to increased trust. In support of this, Konovsky and Pugh find no empirical association between distributive fairness and supervisor trust. Alternatively, taking a long-term view of salespeople's relationship with the organization, salespeople's distributive fairness perceptions could exhibit social exchange effects if the merit pay awards even out in the long run. We posit that the effect of distributive fairness is in line with Konovsky and Pugh's proposition, but we note the potential for an alternative explanation.
In addition, each fairness dimension is hypothesized to have an unequivocal, positive influence on salespeople's job satisfaction. Job satisfaction is defined as a pleasurable or positive emotional state that results from self-appraisal of a job or job experiences (Livingstone, Roberts, and Chonko 1995). In a merit pay context, employees who experience distributive fairness are likely to exhibit greater satisfaction (Moorman 1991; Netemeyer et al. 1997). According to equity theory, the greater the discrepancy between the amount employees believe they should receive and the actual amount they receive, the greater is their tension or dissatisfaction (Lawler 1990; Livingstone, Roberts, and Chonko 1995). Moreover, job satisfaction is likely to be positively associated with the degree to which the merit pay system adheres to salespeople's sense of procedural fairness (Roberson, Moye, and Locke 1999). For example, employees who perceive that procedures are unfair may entertain feelings that they would have obtained a higher merit pay under a procedure that was "fairer" and consequently might feel angry and dissatisfied (Folger 1986). Furthermore, employees' perceptions of interactional fairness may be associated with how salespeople perceive management's valuation of their contribution, thereby affecting job satisfaction (Moorman 1991). Although similar value judgments can be communicated through formal procedures, the quality interactions with the supervisor in pay decisions provide compelling evidence of an individual employee's worth on the job. Thus, we hypothesize the following:
H1: The greater the procedural or interactional fairness perceptions, the greater is salespeople's trust in the supervisor.
H2: Distributive fairness perceptions are unrelated to trust in the supervisor.
H3: The greater the distributive, procedural, or interactional fairness perceptions, the greater is employees' job satisfaction.
The Consequences of Supervisor Trust and Job Satisfaction
It has been hypothesized that salespeople with high levels of supervisor trust perform relatively better, evidence greater commitment, and are more restrained in opportunistic behaviors than are salespeople with low levels of supervisor trust. Specifically, in terms of job performance, a salesperson with a high level of trust in the supervisor is more likely to cooperate with the latter because of a desire to make the relationship work and to add value to it (Morgan and Hunt 1994). A way to add value is for employees to enhance their job performance, because this has the effect of enhancing the supervisor's performance (Costa, Roe, and Tallieu 1991; Rich 1997; Tyagi 1985).
Trust in the supervisor also enhances employees' perceptions of the value of staying in a long-term relationship with the organization, resulting in increased commitment (Moorman, Zaltman, and Deshpandé 1992). Morgan and Hunt (1994) note that employees will commit themselves to organizations that provide trustful work relationships. Likewise, McDonald (1981, p. 834) notes that "mistrust breeds mistrust and ... would also serve to decrease commitment in the relationship." Moorman, Zaltman, and Desphandé (1992) and Morgan and Hunt provide empirical support for this linkage.
Finally, employees often behave politically and engage in intentional acts of influence to enhance or protect their self-interest (Crant and Bateman 1993). Political behaviors are opportunistic and may include selectively presenting or intentionally distorting information given to supervisors and others (Ramaswami 1996). In sales settings, opportunistic behaviors arise when salespeople fail to engage in behaviors that are warranted but not measured by the performance system and/or when they engage in irrelevant behaviors because they are measured by the performance system (Jaworski and MacInnis 1989). They may also manipulate the information processed by supervisors for setting sales goals (Ramaswami, Srinivasan, and Gorton 1997), shirk from making the required number of sales calls, or fail to fulfill promises (Ganesan, Weitz, and John 1993). However, when salespeople trust their supervisors, they are more likely to believe that they can achieve better long-term rewards by reciprocating with cooperative rather than self-interested behaviors (Anderson and Narus 1990).
In contrast, we expect job satisfaction to influence commitment and opportunistic behaviors but not job performance. Specifically, regarding job performance, a strong argument can be made that a satisfied employee is also a productive employee (Organ 1977; Petty, McGee, and Cavender 1984). However, researchers have failed to find evidence of strong association between satisfaction and performance, despite the many studies that have been conducted to uncover this relationship (Brown and Peterson 1993). In a meta-analysis, Iaffaldano and Muchinsky (1985, p. 270) suggest that satisfaction and performance form only "an illusory correlation ... between two variables that we logically think should interrelate ... [but] in fact do not." In a more recent meta-analysis, Judge and colleagues (2001) update previous findings and note that though the satisfaction-performance relationship is weak (correlation ∼ .30), it is positive and significant. Nevertheless, given this mixed evidence, we do not hypothesize a relationship between job satisfaction and performance.
When an organization provides employees with satisfying jobs, commitment levels should increase. Existing empirical models seem to provide support for job satisfaction as an antecedent to commitment (Brown and Peterson 1993; Dailey and Kirk 1992; Johnson, Barksdale, and Boles 2001; Johnston et al. 1990; Summers and Hendrix 1991). Williams and Hazer (1986, p. 230) state that "through a process of the evaluation of costs and benefits, individual needs and desires are satisfied, and the resulting affective state becomes associated with the organization.... Commitment results from this association." Martin and Bennett (1996) suggest that whereas job satisfaction is an affective response to specific work-related facets (e.g., pay), organizational commitment represents an affective response to the entire organization. They state (p. 85), "as individual needs are satisfied, the resulting satiated state becomes associated with the focal organization." For salespeople, Singh, Verbeke, and Rhoads (1996) provide empirical support for this association. Likewise, if salespeople are satisfied with their jobs, they are less likely to be opportunistic out of a sense of debt, obligation, or even decency. On the basis of the preceding discussion, we hypothesize the following:
H4: The greater the salesperson's trust in the supervisor, the greater is his or her performance and organizational commitment and the lower is the tendency to engage in opportunistic behavior.
H5: The greater the salesperson's job satisfaction, the greater is his or her organizational commitment and the lower is the tendency to engage in opportunistic behavior.
H6: The salesperson's job satisfaction is unrelated to his or her job performance.
Antecedents of Distributive, Procedural, and Interactional Fairness Judgments
Previous fairness research has focused primarily on fairness consequences in an effort to explain various organizational behaviors and outcomes and has paid little attention to the antecedents of fairness perceptions. The few studies that do examine fairness antecedents have invariably grounded their work in a managerial perspective for selecting antecedent factors. We are not surprised by this, given that the study of fairness antecedents is apparently of greater interest for managerial practice than for theory-driven researchers. However, this apparent difference in focus does not pass careful scrutiny, because the study of fairness mechanisms must try to tie together its antecedents and consequences (Greenberg 1990).
Toward this end, we identify antecedent factors for each fairness dimension that ( 1) originate in either procedural justice or equity theory, ( 2) can be easily acted on by managers, and ( 3) have received empirical attention in the academic literature. However, there might be other managerially relevant antecedent factors that are not included in our study in order to keep the empirical model manageable and reasonable. Because our intent is to lay the foundation for theory building in the area of fairness antecedents, we provide this work as an initial attempt in this direction. As such, we draw propositions rather than hypotheses to capture the relationships involving fairness antecedents.
Before developing specific propositions, we elaborate on the broad theoretical mechanisms underlying the effects of modeled antecedents. Notably, distributive fairness perceptions will be influenced by factors that are related to supervisory evaluation of outcomes relative to inputs. With regard to inputs, employees want supervisors to use performance measures that are relevant and important to their jobs. With regard to outcomes, employees would like supervisors to maintain correspondence between their inputs (i.e., performance) and their outcomes (i.e., rewards). In addition, salespeople expect supervisors to apply standards consistently for everyone. Finally, salespeople may be concerned as much about future input-outcome relationships as they are about current ones. Thus, the supervisor's attempt to improve future performance-reward linkages may be important.
Procedural fairness perceptions will be influenced by process factors that directly or indirectly contribute to employees' sense of influence on the merit reward decision. The specific process factors may include ( 1) the presence of mechanisms that enable employees to provide their views and ( 2) the absence of mechanisms that signal undue influence of other employees. The first category includes factors associated with employee voice mechanisms, including participation, or the extent to which the employee has voice in setting work goals and the means to achieve them (Goodwin and Ross 1992; Korsgaard, Schweiger, and Sapienza 1995), and improvement plan, or the extent to which the supervisor works with the employee in developing a plan to improve future outcomes or rewards. The second category includes factors that level the playing field, such as the use of appropriate performance measures (Leventhal 1980) and their consistent application across all employees (Greenberg 1986).
Interactional fairness is based not on the ability to shape outcomes but on satisfaction of noninstrumental values such as social standing, dignity, and respect. When supervisors help salespeople develop a plan to improve future performance and communicate clearly that the organization is concerned for their well-being, interactional fairness is likely enhanced. In addition, supervisors promote interactional fairness when they allow the employees to participate in setting work goals.
Performance-reward linkage. We argue that only when supervisors use performance information that is reliably related to outcomes will employees be favorably disposed to the distributive fairness of their outcomes. Linking pay and performance has been a cornerstone of employee compensation (Lawler 1990). However, this ideal often is not achieved, because organizational rewards may be based on several factors beyond performance, including budget availability, political behavior, seniority, supervisor-employee dependence, and other extrarole behaviors (Bartol and Martin 1989; Podsakoff and MacKenzie 1994; Turban and Jones 1988). In addition, managers may have other reasons (e.g., equality versus equity) for not linking pay to performance (Miceli 1993; Sarin and Mahajan 2001). In a study of managers, Markham (1988) finds a weak significant correlation (r = .19) between supervisor's performance rating and the merit raise received by employees. When organizations fall short of fulfilling the core principle of linking pay to performance, distributive fairness of merit pay decisions is likely to be compromised. Thus:
P1: The greater the perceived linkage between job performance and pay outcomes, the greater are a salesperson's perceptions of distributive fairness.
Consistent/unbiased application of performance standards. When employees observe that reasonable standards are not applied consistently across all employees, both distributive and procedural fairness judgments are likely to be affected (Bartol 1999; Kumar, Scheer, and Steenkamp 1995). In a limited budget scenario, employees know that inconsistent application of standards could upset the input-outcome relationship by providing greater allocation to some and less allocation to others. In Sashkin and Williams's (1990) study, "playing favorites" was a factor employees often mentioned when asked to describe managers' unfair actions. Moreover, when performance standards are applied inconsistently, the implication is that the process of determining pay allocations is subverted. Tyler (1989) suggests that salespeople are particular that their management creates a neutral arena (i.e., a level playing field) in which to resolve their problem or conflict. Thus:
P2: The greater the consistent/unbiased application of standards, the greater are a salesperson's perceptions of (a) distributive fairness and (b) procedural fairness.
Performance improvement plans. Performance appraisal processes often serve two basic purposes: ( 1) They provide information for managerial decisions such as pay and promotion, and ( 2) they enable supervisors to counsel employees on ways to improve future job performance (Dubinsky and Barry 1982; Muczyk and Gable 1987). Because of the significance of the latter in improving long-term pay returns, performance improvement plans are likely to influence distributive, procedural, and interactional fairness judgments. The focus on performance improvement informs the employee that the supervisor is interested in the employee's receiving greater future rewards, and it engenders feelings of reduced unfairness about rewards received in the current period. Furthermore, a performance improvement plan enables the supervisor to provide advance notice to the employee of future expectations (Martin and Bartol 1998) and consequently can result in greater role clarity and more favorable perceptions of procedures (Gilliland 1993; Ramaswami 1996). Finally, when using performance improvement plans, supervisors provide useful how-to information about progress toward desired outcomes (Bartol 1999). This feedback signals that supervisors are concerned about employees' personal development and that they care enough to spend time and effort to improve employees' performance, thus enhancing perceptions of interactional fairness. Thus:
P3: The greater the supervisory focus on developing plans for performance improvement, the greater are a salesperson's perceptions of (a) distributive fairness, (b) procedural fairness, and (c) interactional fairness.
Performance measure appropriateness. Fundamental to a fair merit pay system is the use of credible, appropriate measures of performance (Lawler 1990). If input measures are problematic, the input-outcome ratio will likely be compromised. When employees consider measures inappropriate, the implication is that supervisors either are not evaluating certain job facets that are important for a salesperson's success or are not using measures that capture critical job facets well (Pettijohn, Pettijohn, and Taylor 2000). For example, although customer orientation and satisfaction may be relevant measures of performance, organizations may focus exclusively on sales volume when making pay-raise decisions (Brown and Peterson 1993; Lawler 1990; Mowen et al. 1985). Another negative consequence of inappropriate measures is performance incongruence, whereby employees' evaluations of their performance are not congruent with their supervisor's evaluations (Ramaswami 1996), which results in lower procedural fairness perceptions. Thus:
P4: The more appropriate the performance measures that are used in merit pay rewards, the greater are a salesperson's perceptions of (a) procedural fairness and (b) distributive fairness.
Participation. Recent studies have provided mounting evidence that the information generated through participatory goal-setting may provide employees with a clearer understanding of their tasks, goals, and expectations (Goodson and McGhee 1991; Sashkin and Williams 1990). As such, employees who participate in the merit pay process improve their attitudes toward the process, making the procedures appear more fair (Cascardi, Poythress, and Hall 2000). Moreover, participation fulfills a salesperson's desire to be heard regardless of whether the expression influences the supervisor. Voice is important to salespeople, because it signals that the supervisor values their input (Kumar, Steenkamp, and Scheer 1995). Using group affiliation arguments, Tyler (1989) suggests that because participation supports employees' beliefs that the supervisor is interested in enhancing their sense of self-respect, participation contributes to greater perceptions of interactional fairness. Thus:
P5: The greater the participation in decision making, the greater are a salesperson's perceptions of (a) procedural fairness and (b) interactional fairness.
Sample
We obtained data from salespeople employed by a Fortune 500 organization that uses a regional sales structure in which each salesperson reports to an area sales manager. Salespeople sell directly to large customers and use a distributor structure to reach small customers. Although salespeople are involved in account servicing, their primary responsibility is selling. The company requires an annual performance review for input into a merit pay-raise decision. Salespeople are also eligible for sales commission based on their relative performance in their market region. The salary component of the overall compensation is approximately 80%-90%. As such, the magnitude and fairness of annual merit raises is a significant matter. In informal interviews, it was suggested that the merit pay raises range from 3% to 20% of the base salary; however, because of the sensitivity of the information and for reasons of confidentiality, the company refused to allow collection of quantitative pay information through the survey instrument.
At the time of the study, the organization employed 167 salespeople. All salespeople were mailed the survey instrument with a letter that guaranteed confidentiality. Given the study's focus on merit pay decisions, maintaining the confidentiality of the individual responses was a major issue in order to ensure participation and to obtain high-quality data. The respondents were instructed to mail the completed survey in a sealed envelope directly to the researchers.
We obtained responses from 165 salespeople; however, we could not include 11 responses because of missing values, yielding a usable response rate of 92.2%. Because of the relatively small sales force, our pretest discussions with salespeople alerted us to the concern that obtaining customary demographic data (e.g., age, years of experience, gender) might compromise respondent confidentiality. Consequently, in accord with our confidentiality agreement with the sponsoring company, we did not obtain demographic and background data. However, according to company management, the sales force was predominantly male, 30 to 45 years of age, and college educated.
Measurement of Study Variables
For all study constructs, we directly borrowed or adapted the scale items from the literature. In addition, we considered minor wording changes on the basis of comments made by the organization's senior management. The Appendix lists the operational items we used for each construct, and Table 1 provides the univariate statistics for the constructs and the intercorrelations among them.
Fairness dimensions. We measured distributive fairness using a four-item scale that captured the degree to which employees perceive the pay outcomes they received as fair. These items are based on those of Folger and Konovsky (1989) and Greenberg (1986) and pertain to the degree to which employees believe that their pay raise is fair and provides the full amount that they deserved or expected. Procedural fairness pertains to the degree of control or influence afforded to the employee by the process the supervisor uses in arriving at the outcome decision. We measured this concept using a four-item scale that captured both process control, or the degree to which employees have the opportunity to provide input for the decision, and decision control, or the degree to which employees are able to influence the pay decision directly. Interactional fairness items focus on the supervisor's interpersonal behavior. Specific items we used included the degree to which the supervisor was sensitive to employees' needs, considered employees' rights, and dealt with employees in an honest and dignified manner. We drew these items from the work of Folger and Konovsky (1989) and Moorman (1991). A confirmatory factor analysis of fairness items with an a priori hypothesized three-factor model and loading structure produced acceptable fit statistics: χ² = 50.8, degrees of freedom (d.f.) = 74, p > .98, comparative fit index (CFI) = 1.0, normed fit index (NFI) = .97, standardized root mean square residual (SRMR) = .042, and root mean square error of approximation (RMSEA) = 0 (90% confidence interval [CI] of .0 to .028). All estimated loadings were substantively and statistically significant (values > .6 and p < .01, respectively), indicating convergent validity. Consistent with this, we estimated the reliabilities for the distributive, procedural, and interactional fairness dimensions at .89, .78, and .92, respectively. The estimated intercorrelations among the three fairness dimensions range from .50 (distributive-procedural) to .42 (distributive-interactional), indicating that less than 25% variance is shared among them and thus providing initial evidence of discriminant validity. In a recent meta-analysis, Colquitt and colleagues (2001, pp. 437-38) report that the corrected intercorrelations among the fairness dimensions range from .38 to .56; moreover, they note, "our review showed that ... justice [dimensions] have distinct correlates, and measuring the three separately allows for further differences among the dimensions to be examined." Our measures of fairness dimensions yield psychometric evidence that coheres with Colquitt and colleagues' meta-analysis.
Mediators. We measured supervisory trust using a three-item scale developed by O'Reilly and Roberts (1974) and used extensively in previous research. This scale showed a high level of internal consistency, with reliability of .88. We measured job satisfaction using a four-item scale developed by Lucas and colleagues (1987) with some modifications to enhance its contextual relevance. This scale has a reliability value of .94.
Outcomes. We used Mowday, Steers, and Porter's (1979) nine-item organizational commitment scale. We dropped three items because of low loadings. The reliability for the six-item reduced scale is .75. We obtained sales performance evaluations from the supervisor. Because self-reported measures of performance are often subject to biases (e.g., self-presentation), supervisor evaluations are useful. The response rate from supervisors was 100%. Supervisors rated salespeople on three goals set by the organization: sales target performance, business growth, and professional growth. Supervisors also provided overall evaluations of each salesperson. The internal consistency estimate for this performance construct was .93. We measured opportunistic behavior using Jaworski and MacInnis's (1989) six-item scale. The dysfunctional actions captured in this scale include smoothing, focusing, and invalid data reporting. We dropped two items because of low construct loadings. Overall, the four-item measure has an acceptable level of reliability at .75.
Fairness antecedents. We measured linkage to rewards using a three-item scale that assesses whether employees perceive pay raises as directly linked to sales performance and how performance compares to the goals. However, we dropped one of the items, because it did not converge with the other two items. The reliability for the two-item scale was .75. We measured consistent/unbiased application of performance standards using two items that measure the extent to which the merit increases are based on organizational politicking or on the quantity and quality of work performed. We drew these two items from Tyler's (1989) work, and their reliability was .83. We drew the three items that measure performance improvement plan from the work of Folger and Konovsky (1989). These items refer to supervisors' efforts to improve employees' future performance, including discussions of ways to improve performance and use of that information in developing future plans. The alpha reliability for this scale was .70. For a measure to be appropriate, it should track performance for all relevant activities of an employee's job, be based on a thorough analysis of each activity, have a norm or standard for comparison, and be precise. We specifically developed four items to capture these aspects of measure appropriateness. The reliability for this scale was .83. Pay raises are typically determined on the basis of the extent to which an employee meets and exceeds goals. On-the-job goal determination is usually a joint decision between the employee and the supervisor and involves bargaining in a give-and-take environment. Participation refers to the employee's involvement in the goal-setting process. We based these measures on the work of Vroom (1964) and Teas (1981); the reliability for this scale was .83.
Method of Analyses
In testing the proposed hypotheses, we used an analytical method that was sensitive to three issues: ( 1) confounding effects of measurement error, ( 2) potential for misspecification bias, and ( 3) test for mediation effects. In regard to measurement error, we were concerned that the presence of random error would bias the estimation of structural paths unpredictably (Bollen 1989). Although this favors the use of structural equations modeling, we were concerned about its stability and power in light of our sample size (N = 154). To strike a balance between these concerns, we used an approach based on Bagozzi and Edwards's (1998) suggestion of partial disaggregated models. Specifically, we used two composites formed by combining the odd-and even-numbered items wherever possible as indicators for each latent construct. Bagozzi and Edwards show that partial disaggregated models are likely to have better statistical properties than approaches that use individual measures as indicators. Such models perform reasonably well, compared with fully disaggregated models, in terms of control over measurement error. As in structural equations models, the estimated coefficients reflect relationships among underlying theoretical constructs and are "adjusted" for measurement error. In addition, they provide a systematic basis for evaluating the "fit" of the hypothesized model to data (e.g., χ² statistic, incremental fit indexes, RMSEA; Bentler 1995).
Misspecification bias can occur if some of the unhypothesized effects are significant and not included in the empirical analysis. Specifically, the proposed theoretical model includes a system of effects wherein the fairness antecedents (Level I) influence fairness evaluations (Level II), which in turn affect posited mediators (Level III), and finally the mediators influence outcomes (Level IV). Hypotheses that link Level I and Level III, for example, are not included. To examine the significance of such nonhypothesized direct effects systematically without forsaking parsimony, we used the proposed model as the baseline model and tested for the significance of incremental increases in model fit due to nonhypothesized direct effects. This involved computing a change in the χ² statistic and the corresponding change in degrees of freedom. To streamline this procedure, we used sets of effects that represented different levels in the model. For example, we included direct effects from all fairness antecedents (Level I) to job satisfaction and supervisor trust (Level III) and the change in χ² tested with 10 degrees of freedom. We examined individual coefficients to retain the significant effects for the next step of analysis. We systematically implemented this procedure to test all potential unhypothesized effects and mitigate misspecification bias.
Finally, we were concerned about providing a test for the mediation hypotheses. Our model hypothesizes that supervisor trust and job satisfaction mediate the effect of fairness evaluations on job outcomes. To test mediation effects, we followed the procedures Baron and Kenny (1986) suggest. Specifically, we estimated a "direct" model in which we eliminated mediation variables and estimated direct effects. We then compared the direct effects with the corresponding coefficients from a model that included the mediating variables. A full mediation was indicated if ( 1) the "direct" effects model produced a significant effect on a given outcome, ( 2) the corresponding direct effect was reduced to insignificance after inclusion of the mediating variable, and ( 3) the mediator had a significant effect on the focal outcome. Mediation was not indicated when the direct effect remains virtually unchanged in Step 2. Finally, partial mediation was indicated when the direct effect in Step 2 is reduced but does not become nonsignificant. In addition, given the sample size of 154 and a complex model that involved interrelationships among 13 distinct constructs, we were concerned about the power of statistical tests at the customary level of significance (.05). Consequently, we use a 10% level of significance for statistical testing.
Measurement Model Analysis
Before testing the hypothesized model, we estimated a fully disaggregated measurement model with all observed indicators to ensure that the measures corresponded only to their hypothesized constructs and evidenced acceptable reliability as well as convergent and discriminant validity. Using the confirmatory factor analysis procedures available in EQS, we estimated a measurement model that included all 51 items that we hypothesized to measure the 13 study constructs. We proposed the individual measures to load only on a single factor, in accord with conceptual definitions. This measurement model produced the following fit statistics: χ² = 1307, d.f. = 1147, p < .01, NFI = .90, NNFI = .99, CFI = .99, SRMR = .062, and RMSEA (90% CI) = .032 (.021 to .039). Although the χ² statistic is significant, the other indicators of relative and absolute fit (e.g., NFI, CFI, RMSEA) and the indicator for parsimonious fit (e.g., NNFI) unequivocally suggest that the hypothesized measurement model is a reasonably good representation of the variance-covariance matrix of study measures. The estimated parameter estimates in Table 2 reveal that the standardized factor loadings, without exception, are statistically significant (t-values > 2, p < .05) and substantively large (>.30). In addition, the composite reliability estimates exceed .70, and variance extracted exceeds .50, with two exceptions that involve improvement plan and commitment. Note that the composite reliability estimates differ slightly from alpha reliability estimates we provided previously; the former are based on maximum likelihood estimates. Finally, the average factor correlation is .33, indicating that, on average, less than 11% of the variance is shared among the constructs. Overall, our results suggest that the modeled constructs have reasonable psychometric properties and appear suitable for substantive analysis and interpretation.( n1)
Overall Fit of the Hypothesized Structural Model
Initially, we estimated the hypothesized model of Figure 1. We encountered no particular problems in estimation and achieved convergence without any boundary conditions. The estimated coefficients from this model are listed in Table 3 as "initial" coefficients. In accord with our analytical plan, we then examined the potential for misspecification bias by estimating three less restrictive models in sequential steps that systematically allowed for direct effects between nonadjacent levels of variables. After each step, we retained significant coefficients for the next step of analysis. In all, we included one path in Step 1 that involved measure appropriateness (effect on interactional fairness), four paths in Step 2 that involved distributive fairness (effect on performance and opportunistic behaviors) and participation (significant effect on satisfaction and trust), and one path in Step 3 (improvement plan → performance). The final model yielded fit statistics as follows: χ² = 276.7, d.f. = 258, p = .20, NFI = .94, NNFI = .99, CFI = .99, SRMR = .051, RMSEA (90% CI) = .023 (.001 to .040), and Akaike information criterion (AIC) = -239.3 (independence AIC = 3699.2). Given the nonsignificant χ² goodness-of-fit test and other fit indicators, it appears that the final model provides an acceptable representation of the data.
Structural Coefficients and Hypotheses Tests
Table 3 provides the estimated coefficients from the final model, and Figure 2 displays these results graphically. Overall, it appears that the final model provides a reasonable explanation for distributive and interaction fairness (R²= .48 and .58, respectively) but not procedural fairness (R² = .12). Likewise, we achieved meaningful explanation levels for performance and commitment (R² = .40 and .42, respectively) but not opportunistic behaviors (R² = .17). Finally, the explanation levels for supervisor trust and job satisfaction are also reasonable (R² = .69 and .38, respectively).
Regarding H1, Table 3 reveals that interactional fairness has a strong positive effect on supervisor trust (β = .46, p < .01); however, the effect of procedural fairness is nonsignificant. Moreover, contrary to H2, distributive fairness is significantly associated with supervisor trust (β = .18, p < .01). Although not posited, participation is positively associated with supervisor trust (β = .34, p < .01). Moreover, as we note in Table 3, the initial coefficient for interactional fairness is significantly higher, indicating that its effect is overestimated if the model is not respecified to account for participation.
Regarding job satisfaction, we found interactional fairness to enhance a salesperson's job satisfaction significantly, in accord with H3 (β = .19, p < .05). Neither distributive nor procedural fairness yielded a significant effect; however, participation produced a significant influence on job satisfaction (β = .54, p < .01). As in the case of supervisor trust, the initial coefficient for interactional fairness is more than twice as great (β = .54 versus .19), underscoring the potential misspecification as a result of the direct effects of participation on job satisfaction.
In partial support of H4, supervisor trust significantly enhances the commitment of salespeople (β = .16, p < .05) and diminishes their tendency to engage in opportunistic behaviors (β = -.52, p < .01). However, the performance of salespeople is unaffected by supervisor trust. Notably, the initial estimated coefficients indicate that the influence of supervisor trust is significant (β = .50); however, this effect reduces to nonsignificance (β = .13) when the model is respecified to account for the direct effects of distributive fairness and performance improvement plans. Consistent with H5, salespeople's job satisfaction positively affects their commitment (β = .57, p < .01), but it fails to curb their opportunistically oriented behaviors. In addition, in accord with H6, we found that job satisfaction does not have a significant affect on performance; however, distributive fairness and improvement plan significantly and positively influence salesperson's performance (β = .28 and .35, p < .01). In addition, distributive fairness has a positive, significant effect on opportunistic behaviors (β = .29, p < .01).
Finally, to test for mediation, we estimated a direct effects model that excluded the hypothesized mediating variables of supervisor trust and job satisfaction. The model yielded the following fit statistics: χ² = 401.2, d.f. = 265, p < .001, NFI = .91, NNFI = .96, CFI = .97, SRMR = .17, RMSEA (90% CI) = .06 (.047 to .07), and AIC = -128.6. Compared with the mediated model, the direct effects model indicates a significant and substantive deterioration in model fit (χ²difference = 125.2, d.f. = 7, p < .001), indicating that the proposed mediators play a significant role in fairness mechanisms. In addition, for organizational commitment, a significant direct effect emerges for interactional fairness (β = .34, p < .01). Because this direct effect becomes nonsignificant for the mediated model and other conditions for mediation are met (significance of interactional fairness → job satisfaction, and job satisfaction → commitment paths; see Table 3), we can conclude that the effect of fairness judgments on commitment is fully mediated. In contrast, for performance, significant direct effects emerge for distributive fairness and improvement plan (β = .31 and .38, respectively, p < .01) that remain significant in the mediated model (see Table 3, corresponding values of .28 and .35). Furthermore, because none of the mediation conditions is satisfied, the effects of fairness judgments on job performance are unmediated. We obtained partial mediation for opportunistic behaviors, because though the direct effects of interactional and distributive fairness are significant (β = -.20 and .16, respectively, p < .05), only the latter remains significant in the mediated model. Combined with the significant effects obtained in the mediated model for the interactional fairness → trust and the trust → opportunistic behavior paths, we can conclude that the effect of interactional fairness on opportunistic behaviors is fully mediated, but the effect of distributive fairness is not. Overall, the effect of fairness judgments on opportunistic behaviors is partially mediated.
In terms of propositions, Table 3 indicates that the antecedents significantly influence distributive fairness. Consistent with P1, the linkage to rewards is positively and significantly associated with distributive fairness (β = .25, p < .01). Likewise, the distributive fairness perceived by salespeople is significantly enhanced when they view the merit pay decision-making procedures as unbiased and when supervisors work with them to develop plans for performance improvement (β = .33 and .20, respectively, p < .05). These results support P2 and P3. Consistent with P4, measure appropriateness is positively associated with distributive fairness (β = .18, p < .10).
In contrast, the included antecedents are less effective in accounting for the variability in procedural fairness. Of the hypothesized antecedents, the use of consistent, unbiased decision-making procedures and participation have a significant influence on procedural fairness (β = .19 and .18, respectively, p < .10). These results support P2 and P5 but not P3 or P4.
For interactional fairness, when supervisors help develop performance improvement plans, salespeople's perceptions of interactional fairness are enhanced (β = .34, p < .01). Participation in decision making is also associated with greater interactional fairness (β = .32, p < .01). These results support P3 and P5. Finally, measure appropriateness enhances interactional fairness (β = .25, p < .01).
This study was motivated by three objectives: ( 1) to use a broader, more complete conceptualization of the merit pay fairness construct that included dimensions of distributive, procedural, and interactional fairness; ( 2) to study the simultaneous mediating mechanisms of supervisory trust and job satisfaction in fairness processes; and ( 3) to explore the supervisory behaviors during the merit pay decision process that increase fairness perceptions. Our findings offer initial insights into these issues and provide concrete directions for further research and managerial guidelines. Before discussing these findings, we address the limitations of our study.
Limitations
Several limitations of our study are noteworthy. First, the study is based on cross-sectional survey data, and we advise caution in drawing cause-effect inferences. In addition, the association among constructs may be inflated as a result of common method variance. To reduce this inflation, we followed Heneman's (1974) suggestions for enhancing the accuracy and validity of self-ratings by guaranteeing confidentiality. Furthermore, we collected data for salespeople's performance from a different source (i.e., supervisors). The use of multiple source data likely reduces the influence of common method bias. Nevertheless, because we focus on the differential effects of constructs (e.g., interactional and distributive fairness), we recognize that the common method variance would act to obscure such differential effects. In this sense, our findings of differential patterns are likely conservative. Second, the study is based on a sample of salespeople employed by a single organization. Studies in other contexts, such as across organizations and over time, are needed to establish the generalizability of our findings. Third, two of the study constructs (commitment and improvement plan) have less-than-ideal psychometric properties, with several low-loading items. Low loadings result in low reliability that introduces random noise in the results, thereby making it difficult to detect significant effects. Nevertheless, the confirmatory and exploratory factor analysis results confirm that the measures are not confounded. Fourth, the actual merit pay-raise data for the sample were not available. It is possible that salespeople who receive higher-than-average raises evidence a positive bias. However, we were able to test for this bias indirectly by using perceived favorability of outcomes as a covariate in the analysis; we found that our results were virtually unaltered.
Despite these limitations, our results offer useful insights into the differential effects of fairness dimensions by considering the simultaneous influences of all three fairness judgments, direct and mediated pathways to satisfaction and supervisor trust that link fairness judgments and outcomes, and potential relationships among antecedents and fairness judgments. We discuss each of these contributions. Thereafter, we draw managerial implications.
Differential Effects of Fairness Dimensions
The results of our study reveal a differential pattern of effects for the three fairness dimensions. Although interactional fairness significantly influences supervisor trust and job satisfaction, distributive fairness has a significant effect on supervisor trust but not on job satisfaction. It appears that when the effect of interaction quality between salespeople and their supervisors is accounted for, presence or absence of distributive fairness is unimportant for generating job satisfaction.( n2) This result is counter to the findings of a study by Netemeyer and colleagues (1997). However, because Netemeyer and colleagues did not consider interactional fairness, it is difficult to predict whether the impact of distributive fairness that they observed could potentially be weakened with a complete accounting of fairness dimensions.
More important, procedural fairness is associated with neither supervisor trust nor job satisfaction. This nonsignificance of procedural fairness in our study is at variance with results from previous studies (Konovsky and Pugh 1994; Kumar, Scheer, and Steenkamp 1995). However, most previous studies have used a composite view of procedural fairness, including decision-making structures and supervisors' enactment of these structures (Aryee, Budhwar, and Chen 2002). Our findings suggest that when procedures and their enactment are separated, it is interactional fairness that affects work outcomes. The inference that can be drawn from this finding is that the effects observed for procedural fairness in (some) previous studies may be attributable to the interactional fairness component: Why should interactional fairness be more important than procedural fairness? It is possible that salespeople have reasonably complete information about how the supervisor interacted with them (because of the information's relative transparency), but they do not have complete information on the quality of procedures used by the supervisor to arrive at merit pay decisions (because of its relative confidentiality). In addition, interactional fairness may be more potent not only because of its intrinsic value (e.g., treating salespeople with dignity) but also because of its signaling value (e.g., as a "signal" for the procedures used). The validity of these conjectural explanations needs to be established by further replication and extension of our work, yet we appear to have sufficient evidence to suggest that studies that fail to include all three fairness dimensions risk misspecification bias.
Mediated and Direct Pathways of Fairness: Outcomes Relationships
Although we posited that the influence of fairness judgments on salesperson outcomes is mediated by supervisor trust and job satisfaction, our results offer a complex pattern of evidence. Overall, fairness effects on commitment are indirect and fully mediated, whereas opportunistic behaviors are influenced by partially mediated pathways that involve a combination of direct and indirect effects. In contrast, fairness effects on performance are direct and unmediated. We discuss each in turn.
In terms of salespeople's commitment to their organizations, the direct influence of fairness perceptions is marginal; rather, commitment is driven primarily by salespeople's job satisfaction and, to a lesser degree, by supervisor trust.( n3) This suggests that the way employees feel about their job experiences is more important to organizational loyalty than the level of trust they have with their supervisors. Because satisfaction and trust, in turn, are mostly influenced by interactional fairness, it appears that organizational loyalty is influenced more by whether an organization treats its employees with respect and dignity and less by whether it provides fair distribution of monetary rewards.
A salesperson's propensity to engage in opportunistic behaviors is influenced directly by supervisor trust and distributive fairness.( n4) When trust is established through dignified and respectful treatment of the employee (e.g., interactional fairness and participation), salespeople may view the supervisor as a partner rather than an adversary and may work actively to make their job less difficult. It appears that employees do this by avoiding opportunistic behaviors that may otherwise demand valuable supervisory time for close monitoring of employee activities (Bateman and Organ 1983). As a result, the sales manager is left to perform strategic functions rather than become distracted in time-consuming, nonproductive monitoring behaviors. The results indicate a direct, positive effect of distributive fairness, suggesting that higher fairness of merit pay awards increases the propensity of opportunistic behaviors. This is counterintuitive. However, distributive fairness has an indirect, negative effect on opportunistic behaviors through its effect on supervisor trust. The direct and indirect effects are in opposition such that the net effect of distributive fairness is a weak, marginally positive effect (.15). A possible explanation for this positive net effect is that salespeople may not be satisfied with the absolute merit pay distributions, though they remain satisfied with the relative distributions. In turn, this dissatisfaction may prompt salespeople to engage in self-interest-seeking opportunistic behaviors. Because of restrictions on the measurement process imposed by the organization, it was not feasible to collect information on the actual pay-raise amount, and thus we cannot evaluate this explanation in the present setting. Further research needs to examine fairness of both relative and absolute distributions and their relative effects.
Finally, in terms of job performance, although neither satisfaction nor trust has a significant, direct influence, both distributive fairness and performance improvement plans yield direct, unmediated effects. The lack of a significant satisfaction-performance association is consistent with previous research; however, the lack of trust effect is somewhat surprising. There are two possible explanations for this result. The first, and probably less viable, explanation is that salesperson performance is under the direct regulation of both merit pay awards (i.e., distributive fairness) and performance enhancement plans and is less sensitive to social exchange mechanisms. The second, and probably more viable, explanation is that when the supervisor acts in an equitable manner and provides employees with feedback that can improve their future performance, trust may have a lesser role in encouraging reciprocal actions. In this sense, the social exchange variable, trust, has a significant effect, but only in a fairness deficient environment. Further research is needed to test these speculations by examining potential moderators of the trust → performance relationship.
In addition, our study sheds new light on an outstanding issue in the literature related to the (lack of) relationship between salesperson job performance and satisfaction. Our results reveal that though satisfaction and performance are weakly correlated, the explanation levels are relatively high for both at 38% and 40%, respectively. Participation and interactional fairness have a strong effect on job satisfaction, and performance improvement plan and distributive fairness have a strong effect on job performance. As such, it appears that satisfaction and performance are linked to different fairness mechanisms. Salespeople's job satisfaction is sensitive to the aspects of their relationship with the supervisor and work that "touches their heart," such as when the supervisor engages in behaviors that signal respect, dignity, and participatory decision making. In contrast, salesperson performance is apparently more sensitive to "head" factors, including the supervisor's attempts to aid in developing a plan for individual performance improvement and the hard calculus of distributive fairness. The notion that job satisfaction and performance may be related to "heart" and "head" mechanisms that are rooted in different aspects of fairness perceptions offers new insight and directions for further research.
Antecedents of Fairness Perceptions
Our objective was to use this study's empirical results to provide a theoretical foundation for the study of fairness antecedents. Using theoretical and pragmatic considerations, we included five antecedents for our initial examination. Our results demonstrate that the included antecedent factors are meaningful in understanding the formulation of distributive and interactional fairness judgments in merit pay decisions (R² = 48% and 58%, respectively). However, in regard to procedural fairness, our results indicate a more tentative stance (R² = 12%). We discuss each result in turn.
Distributive fairness. Results show that distributive fairness perceptions are greater when ( 1) the merit pay rewards that salespeople receive are linked to job performance, ( 2) the supervisor is unbiased and consistent in applying appropriate performance standards, and ( 3) the supervisor aids in designing a plan for improving a salesperson's future performance. Taken together, these results imply that after the impact of the linkage between current input and outcomes is accounted for, the prospect of improved future outcomes influences salespeople's beliefs about the fairness of their current reward decision. This indicates that unfairness perceptions arising from current distributions could be mitigated if supervisors were to begin discussing performance details post hoc and outlining how performance can be improved in the future. As such, our results contradict conventional wisdom that salespeople are concerned solely about short-term rewards. Evidently, even though salespeople respond to current merit rewards, they are responsive to strategies for enhancing future rewards.
Procedural fairness. Two of the four proposed antecedents for procedural fairness (use of unbiased procedures and participation) were statistically significant. Neither use of appropriate data nor supervisory focus on future performance was significant. Consequently, it appears critical that sales managers engage in behaviors and practices that emphasize an unbiased approach in merit pay decisions. An option is to produce and share hard evidence that supports that performance-pay linkages are not compromised by individual manager biases. In cases in which performance can be reasonably quantified, it may be useful to compute and monitor performance-pay correlation with the notion that attenuation of this correlation beyond a cutoff value (e.g., .80) is probably on account of process errors. Although contextual factors must be considered in designing such strategies, our findings argue for management behaviors and practices that increase transparency of merit pay decisions and promote neutrality by curbing individual biases. The significance of participation rests on it being a means of influencing the merit pay decision-making process. Employees who participate may believe that they were able to manage their supervisor's expectations when making pay decisions. It is inevitable that employees will sometimes receive unfavorable outcomes, and participation can temper the potential backlash from employees when this occurs.
The nonfindings for performance measure appropriateness and improvement plan are intriguing. Use of appropriate measures enables supervisors to focus on job activities that are important. Apparently, although they may use appropriate measures, supervisors can still show bias in translating individual measurements into rewards. Likewise, it was believed that when supervisors discuss improvement plans with employees, the latter would be presented with the opportunity to provide input and thereby influence the performance evaluation process. Then, why is it that the design of improvement plans would influence distributive fairness but not procedural fairness? Is it possible that salespeople consider these plans instrumental in achieving greater performance but not necessarily in increasing their influence over the reward decision? More research is needed to unravel these intriguing effects.
Interactional fairness. Three antecedents (improvement plans, measure appropriateness, and participation) are strongly related to interactional fairness. Developing performance improvement plans not only helps in aligning perceptions and expectations between supervisors and employees (Morgan and Hunt 1994) but also signals that the supervisor cares. Using appropriate measures indicates that the supervisor understands that the salesperson has the right to be evaluated correctly. Finally, participation reinforces the social standing of salespeople in their respective groups and informs that they are valued members of the sales team.
Overall, the supported propositions in regard to fairness antecedents suggest that these linkages are meaningful, and despite the limited number of antecedents that we could include in our study, they together explain a significant proportion of the variance in distributive and interactional fairness. Although the explanation level for procedural fairness is more modest, the pattern of results appears reasonable and offers a fertile ground for future theorizing.
Managerial Implications
A major finding of potential interest to managers is the dominant role of interactional fairness in achieving two of the three outcomes that govern organizational effectiveness. It was suggested previously that supervisory interactions not only might be more transparent than procedures but also might signal to employees that the supervisor cares for their well-being. This is good news for managers, because the economic costs of interacting in a manner that raises the dignity and standing of employees are not likely to be as high as the costs associated with satisfying either procedural or distributive fairness. This does not imply that the distribution of rewards or the procedures used to determine them are not important in their own right. Our point is merely that interactional fairness is critical in fairness mechanisms and one that can be easily achieved by managers.
Another major finding of the study is the importance of distributive fairness in influencing employees' job performance. The sense of equity that arises from being rewarded fairly provides the incentive to salespeople to work harder and to improve their job performance. In a way, this result argues for unveiling the shroud of secrecy that typically exists in pay-raise situations. Previous research has noted that when employees are not provided comparative pay information, they tend to believe that they obtained a raise that is lower than others, which in turn typically results in lower fairness perceptions (Futrell and Jenkins 1978).
From a managerial perspective, it would be especially unfortunate to interpret our results to imply that procedural fairness may be safely ignored: The results are more complex. Often, in the analysis of the sort used herein, the relative importance of a variable is determined by the principle of discrimination, that is, the variable along which the respondents can be differentiated the most. As such, the appropriate interpretation is that given the situational context of our sample, further increases in procedural fairness may be less potent than similar increases in interactional fairness. Furthermore, varying interactional fairness holds more promise given the current situation and pay practices for the specific sample of salespeople used. Managers are cautioned to develop such understanding in the light of their specific context and sales force practices.
What can managers do to shape employee perceptions of the three fairness dimensions? They can put into place a performance improvement plan for employees. Such plans are opportunities for supervisors to provide feedback to salespeople about what they are doing "right" or "wrong" and to explicate what they should do to improve performance. The positive effect of such planning suggests that salespeople do not view it as a performance impediment resulting from supervisors stepping on their turf. A clear message from the results is that managers need to spend more time with salespeople with an aim to improve their future performance ability.
Managers could also allow salespeople to participate in goal setting and process determination, because this has a favorable influence on not only interactional and procedural fairness but also the two mediators. Participation is the backbone behind the concept of "quality circles" in total quality management. As our results suggest, it may also be the backbone behind the "loyalty" of salespeople to their supervisors and organizations. It appears that employees expect to be asked to participate (based on influence on procedural fairness) and derive great value from the symbolism this represents (based on its influence on interactional fairness). Employees are being asked to do more with less, and participation may be a way by which employees can be made to believe that they are being given more. Finally, managers need to ensure that pay is linked to performance and to make that linkage known to employees. They may be able to do this not only by using appropriate measures of performance but also by ensuring that no employee has undue influence over the rewards process.
The study of fairness judgments and its consequences in sales force settings dates to the mid-1980s, and a considerable body of work exists that establishes the relevance and significance of such judgments. Our study advances this body of work by posing different questions and shifting the direction of inquiry. Instead of asking whether fairness judgments matter for sales force outcomes, we ask, How do fairness judgments work to influence critical outcomes and what factors influence the formation of fairness judgments? This shift has important theoretical and managerial implications.
Theoretically, by highlighting the contrasting mechanisms by which distributive, procedural, and interactional fairness judgments influence job outcomes, our findings implore future researchers to eschew studies that fail to capture the breadth of the fairness construct and/or complexity of its mechanisms. Significantly, our work opens new dialogues on several unresolved issues in sales force management. We presented arguments about why performance and job satisfaction, two seemingly related concepts, may be driven by different fairness mechanisms. Likewise, although our study clarifies that pay-to-performance linkages work, it also highlights the performance-commitment dilemma; that is, although distributive fairness and appropriate pay-for-performance linkages may propel salespeople to perform better, they do little to gain their commitment. Organizational commitment is influenced by job satisfaction; in our data, job satisfaction is shaped mainly by interactional fairness. Consequently, to motivate salespeople toward higher performance and retain them requires that two separate mechanisms be activated simultaneously, one involving interactional fairness and job satisfaction and the other involving distributive fairness and improvement plans. Activation of just one mechanism produces obvious pitfalls such as losing the high performers or retaining the low and average performers. This requires managers to focus simultaneously on multiple dimensions of fairness and activate multiple mechanisms. Human agency and judgments are complex, dynamic phenomena that defy simple answers, yet this complexity and dynamism can be understood and captured with the tools at the disposal of marketing science today. Our study has provided an initial attempt in the context of merit pay-raise decisions. Future researchers and managers will find our proposed fairness model a fertile ground for further refinement and development to understand how to motivate and retain a high-performance sales force.
The author names are listed in alphabetical order; both authors contributed equally to the research effort.
The authors thank Stephen A. Gorton, an independent consultant, for his help with data collection.
(n1) Based on the criterion of comparing average variance extracted with the highest variance shared, two subsets of constructs involving (1) commitment, satisfaction, and improvement plan and (2) participation, interactional fairness, and trust failed to provide clear evidence of discriminant validity. To check this further, we performed exploratory factor analyses of these subsets to ensure that the measures are not confounded and that interfactor correlations are far from unity. In each case, we obtained supporting and unequivocal evidence. Specifically, the items have a dominant loading on the hypothesized factor, and cross-loadings, when present, are relatively smaller in magnitude. Moreover, interfactor correlations are all less than .65.
(n2) To further test for this differential effect, we tested a constrained model in which the three fairness dimensions were constrained to have equal effect on job satisfaction. This analysis revealed that though the invariance hypothesis cannot be rejected for distributive and procedural fairness (χ² = .47, p = .7), the influence of interactional fairness is indeed distinct (χ² = 3.80, p = .05). Likewise, when the effects of procedural and interactional fairness on supervisor trust were constrained to be equal, we obtained a significant χ² indicating rejection of this hypothesis (χ² = 13.1, p < .01).
(n3) To test for this differential effect, we constrained the effects of supervisor trust and satisfaction on commitment to be equal. This yielded a significant statistic, indicating a rejection of this proposal (χ² = 4.74, p < .05).
(n4) Likewise, a differential effects test produced a significant statistic, suggesting that opportunistic behaviors are differently related to satisfaction, trust, and distributive fairness (χ² = 10.25, p < .01).
Legend for Chart:
B - PF
C - IF
D - DF
E - ST
F - JS
G - JP
H - OC
I - OB
J - RL
K - CA
L - IP
M - MA
N - PT
O - Mean
P - Standard Deviation
A B C D E
F G H I
J K L M
N O P
Procedural fairness(PF) 1.00
2.94(a) .88
Interactional fairness(IF) .46 1.00
4.23(b) .75
Distributive fairness(DF) .50 .42 1.00
3.01(b) 1.06
Trust(ST) .48 .74 .44 1.00
5.29(c) 1.45
Job satisfaction(JS) .19 .38 .11 .34
1.00
4.47(b) .59
Job performance(JP) .18 .29 .27 .28
.04 1.00
3.57(b) .98
Organizational
commitment(OC) .14 .29 .16 .28
.54 .08 1.00
4.48(b) .44
Opportunistic behavior(OB) .07 -.13 .03 -.23
-.04 -.03 .02 1.00
2.83(b) .83
Linkage to rewards(RL) .36 .36 .42 .35
.23 .12 .19 -.11
1.00
3.53(b) 1.02
Consistent/ unbiased
application(CA) .08 .10 .04 -.04
.14 -.05 .14 -.14
.56 1.00
2.84(d) .71
Improvement plan(IP) .58 .54 .44 .39
.27 .02 .24 .04
.39 .16 1.00
3.82(b) .84
Appropriateness of
measures(MA) .45 .54 .44 .45
.29 .20 .22 .04
.36 .05 .54 1.00
3.61(b) .82
Participation(PT) .44 .52 .28 .55
.51 .24 .31 -.17
.25 .08 .37 .37
1.00 3.80(b) .78
(a) Four-point Likert scale.
(b) Five-point Likert scale.
(c) Seven-point Likert scale.
(d) Three-point scale.
Notes: Correlations less than .15 are nonsignificant (p > .05). Legend for Chart:
A - Construct/Item
B - Loading
C - t-Value
D - Variance Extracted
E - Highest R²
F - Average R²
G - Reliability
A B C D E F G
Job Performance
JP1 .81 11.52 .74 .15 .07 .93
JP2 .76 10.52
JP3 .82 11.84
JP4 .93 14.51
JP5 .89 13.42
Opportunistic Behaviors
OB1 .40 4.55 .50 .08 .02 .75
OB2 .62 7.49
OB3 .87 11.09
OB4 .71 8.75
Organizational Commitment
OC1 .40 4.61 .45 .40 .11 .75
OC2 .54 6.44
OC3 .38 4.23
OC4 .74 9.48
OC5 .78 10.16
OC6 .59 7.09
Supervisor Trust
ST1 .76 10.54 .72 .65 .32 .88
ST2 .83 12.06
ST3 .94 14.54
Job Satisfaction
JS1 .85 12.63 .78 .40 .14 .94
JS2 .91 14.02
JS3 .92 14.19
JS4 .86 12.76
Distributive Fairness
DF1 .93 14.44 .66 .28 .18 .89
DF2 .92 14.12
DF3 .74 10.10
DF4 .64 8.34
Interactional Fairness
IF1 .83 12.04 .68 .65 .29 .92
IF2 .74 10.08
IF3 .87 12.99
IF4 .85 12.39
IF5 .80 11.35
IF6 .81 11.51
Procedural Fairness
PF1 .65 8.08 .58 .36 .17 .78
PF2 .68 8.58
PF3 .57 6.87
PF4 .82 10.91
Linkage to Rewards
RL1 .52 6.65 .62 .24 .13 .75
RL2 .98 16.30
Consistent/Unbiased Application
CA1 .91 11.43 .72 .26 .12 .83
CA2 .78 9.64
Improvement Plan
IP1 .62 7.46 .44 .47 .24 .70
IP2 .72 8.93
IP3 .64 7.73
Measure Appropriateness
MA1 .77 10.34 .56 .42 .21 .83
MA2 .83 11.49
MA3 .73 9.56
MA4 .66 8.34
Participation
PT1 .82 11.34 .55 .50 .33 .83
PT2 .66 8.39
PT3 .68 8.76
PT4 .81 11.14
Notes: Loading = standardized coefficient estimate by the
elliptical reweighted least squares method using EQS software.
The t-values greater than 1.645 indicate significant effects at
p = .05 for a one-tailed test. Variance extracted is based on
Fornell and Larcker's (1981) formula. Highest R² is the
highest variance shared between this construct and any other
construct in the model; it is computed as the square of highest
R (correlation). Average R² is the average variance shared
between this construct and all other constructs; it is computed
as the mean of squared correlations. Composite reliability is
based on Fornell and Larcker's formula. Legend for Chart:
A - Dependent Variable
B - Initial Coefficient
C - Final Coefficient
D - t-Value
E - R²
A
B C D E
Distributive Fairness (DF)
.48(x)
Linkage to rewards (RL) → DF
.27(x) .25(x) 2.9(x)
Consistent/unbiased application (CA) → DF
.31(x) .33(x) 4.0(x)
Measure appropriateness (MA) → DF
.14 .18 1.6
Improvement plan (IP) → DF
.23(x) .20(x) 2.1(x)
Procedural Fairness (PF)
.12(x)
Consistent/unbiased application (CA) → PF
.20(x) .19(x) 2.1(x)
Improvement plan (IP) → PF
.03 .06 .51
Measure appropriateness (MA) → PF
.01 .03 .04
Participation (PT) → PF
.22(x) .19 1.6
Interactional Fairness (IF)
.58(x)
Improvement plan (IP) → IF
.45(x) .34(x) 3.7(x)
Measure appropriateness (MA) → IF
-- .25(x) 2.7(x)
Participation (PT) → IF
.43(x) .32(x) 3.4(x)
Supervisor Trust (ST)
.69(x)
Distributive fairness (DF) → ST
-- .18(x) 2.4(x)
Procedural fairness (PF) → ST
.06 .02 .10
Interactional fairness (IF) → ST
.77(x) .46(x) 5.1(x)
Participation (PT) → ST
-- .34(x) 3.7(x)
Job Satisfaction (JS)
.38(x)
Distributive fairness (DF) → JS
-.11 -.14 -1.4
Procedural fairness (PF) → JS
.06 -.09 -1.1
Interactional fairness (IF) → JS
.54(x) .19(x) 1.7(x)
Participation (PT) → JS
-- .54(x) 4.7(x)
Job Performance (JP)
.40(x)
Supervisor trust (ST) → JP
.50(x) .13 1.1
Job satisfaction (JS) → JP
-- -.03 -.40
Distributive fairness (DF) → JP
-- .28(x) 2.6(x)
Improvement plan (IP) → JP
-- .35(x) 3.0(x)
Organizational Commitment (OC)
.42(x)
Supervisor trust (ST) → OC
.16(x) .16(x) 1.7(x)
Job satisfaction (JS) → OC
.58(x) .57(x) 4.4(x)
Opportunistic Behaviors (OB)
.17(x)
Supervisor trust (ST) → OB
-.33(x) -.52(x) -3.2(x)
Job satisfaction (JS) → OB
.09 .11 1.1
Distributive fairness (DF) → OB
-- .29(x) 2.2(x)
Notes: Initial coefficient is the estimated standardized
coefficient before we respecified the model to account for
misspecification bias. Paths not hypothesized and not tested
for respecification are indicated by a dash. Final coefficient
is the estimated standardized coefficient after we respecified
the model and accounted for misspecification bias. The
corresponding t-values are in the adjacent column; t-values
greater than 1.645 indicate significant effects at p = .05
for a one-tailed test. Coefficients in (x) bold are significant
at p = .05, and coefficients in italics are significant
at p = .10.DIAGRAM: FIGURE 1 The Conceptual Model for Understanding Fairness Mechanisms and Consequences
DIAGRAM: FIGURE 2 The Estimated Model for Understanding Fairness Mechanisms and Consequences
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Unless otherwise noted, we measured the following items on a five-point Likert scale where 1 = "strongly disagree" and 5 = "strongly agree." The items marked with [O] were removed from the analyses because of poor internal consistency with their respective scales.
Linkage to Rewards (Folger and Konovsky 1989)
1. My pay increases are based upon how my performance compares with my goals.
2. My merit increases are directly tied to my performance.
Consistent/Unbiased Application of Performance Standards (Tyler 1989)
1. Do you think that you received a better or worse merit increase than others because of your race, sex, age, nationality, or some other characteristic of you as a person? (Three-point scale: 1 = "yes"; -1 = "no"; 0 = "don't know")
2. Do you think that your supervisor treated you worse than others because of your race, sex, age, nationality, or some other characteristic of you as a person? (Three-point scale: 1 = "yes"; -1 = "no"; 0 = "don't know")
Performance Improvement Plan (Folger and Konovsky 1989)
Indicate the extent to which you believe your supervisor did each of the following during the last performance cycle:
- Discussed plans or objectives to improve your performance.
- Asked for your ideas on what you could do to improve your performance.
- Developed an action plan for future performance.
Measure Appropriateness (New Scale)
1. My performance is evaluated on all relevant and important skill areas of my job.
- 2. Standards or performance targets are set for each skill area of my job.
- 3. The targets set for my job, I believe, are appropriate.
- 4. The standards used in performance review are all based on a thorough analysis of the job I perform.
Participation (Teas 1981; Vroom 1964)
1. I am allowed a high degree of influence in the determination of my work goals.
- 2. I really have little voice in the formulation of my work goals. [reverse scored]
- 3. The setting of my work goals is pretty much within my control.
- 4. My supervisor usually asks for my opinions and thoughts when determining my work goals.
Distributive Fairness (Folger and Konovsky 1989)
1. I consider the size of my last merit increase to be fair.
- 2. My last merit increase gave me the full amount I deserved.
- 3. The size of my last merit increase was more than what I expected.
- 4. The level of merit increase I received was (1 = "very unfair"; 4 = "very fair").
Procedural Fairness (Folger and Konovsky 1989; Moorman 1991)
1. How much of a chance or opportunity did your supervisor give you to describe your achievements and contributions to him/her before making your merit increase decision? (Four-point Likert scale: 1 = "a great deal of opportunity"; 4 = "not much opportunity at all")
- 2. How much influence did you have over the merit decision made by your supervisor? (Four-point scale: 1 = "a great deal of influence"; 4 = "not much influence at all)
- 3. How much consideration did your supervisor give to what you said when making merit increase decisions? (Four-point scale: 1 = "a great deal of consideration"; "4 = "not much consideration at all)
- 4. Overall, how fair were the methods used by your supervisor to make your merit increase decision? (Four-point scale: 1 = "very fair"; 4 = "very unfair")
Interactional Fairness (Folger and Konovsky 1989; Moorman 1991)
Indicate the extent to which you believe your supervisor did each of the following during the last performance cycle:
- Was honest and ethical in dealing with you.
- Showed a real interest in trying to be fair.
- Treated you with respect and dignity.
- Was sensitive to your personal needs.
- Showed concerns for your rights as an employee.
Supervisory Trust (O'Reilly and Roberts 1974)
1. How free do you feel to discuss with your immediate supervisor the problems and difficulties in your job without jeopardizing your position or having it held against you later? (Seven-point scale: 1 = "completely free"; 7 = "very cautiously")
- 2. Immediate supervisors at times must make decisions which seem to be against the interest of employees. When this happens to you as a employee, how much trust do you have that your immediate supervisor's decision was justified by other considerations? (Seven-point scale: 1 = "trust completely"; 7 = "feel very distrustful")
- 3. To what extent do you have trust and confidence in your immediate supervisor regarding his or her general fairness? (Seven-point scale: 1 = "completely"; 7 = "very little")
Job Satisfaction (Lucas et al. 1987)
1. My job is satisfying.
- 2. My job is exciting.
- 3. I'm really doing something worthwhile in my job.
- 4. The work I perform gives me a sense of accomplishment.
Job Performance (New Scale)
This employee
1. Performed above average on annual sales objective.
- 2. Performed above average on business growth objective.
- 3. Performed above average on professional growth objective.
- 4. Is one of our best managers.
- 5. Is outstanding.
Opportunistic Behaviors (Jaworski and MacInnis 1989)
1. I sometimes tend to ignore certain job-related activities simply because they are not monitored by our supervisors.
- 2. I sometimes work on unimportant activities simply because they are evaluated by our supervisors. [O]
- 3. When my performance is inconsistent, I have tried to make it appear consistent.
- 4. When presenting data to upper management, I generally try to emphasize data that reflects favorably upon me.
- 5. Most employees underreport sales potential in their territory to obtain lower sales targets. [O]
- 6. When presenting data to upper management, I generally try to avoid being the bearer of bad news.
Organizational Commitment (Mowday, Steers, and Porter 1979)
1. I am willing to put in a great deal of effort beyond that normally expected to help this organization be successful.
- 2. I talk about this organization to my friends as a great place to work.
- 3. I would accept almost any type of job assignment in order to keep working for this organization.
- 4. I find that my values and the organization's values are similar. [O]
- 5. I am proud to tell others that I am part of this organization.
- 6. I am extremely glad that I chose this organization to work over others I was considering at the time I joined.
- 7. This organization really inspires the very best in me in the way of job performance. [O]
- 8. I really care about the fate of this organization.
- 9. For me, this is the best of all organizations for which to work. [O]
~~~~~~~~
By Sridhar N. Ramaswami and Jagdip Singh
Sridhar N. Ramaswami is Professor of Marketing, Iowa State University.
Jagdip Singh is Professor of Marketing, Weatherhead School of Management, Case Western Reserve University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 15- Antecedents and Consequences of the Service Climate in Boundary-Spanning Self-Managing Service Teams. By: Jong, Ad de; Ruyter, Ko de; Lemmink, Jos. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p18-35. 18p. 1 Diagram, 7 Charts. DOI: 10.1509/jmkg.68.2.18.27790.
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Antecedents and Consequences of the Service Climate in
Boundary-Spanning Self-Managing Service Teams
In this article, the authors examine antecedents and consequences of the service climate in boundary-spanning self-managing teams (SMTs) that deliver financial services. Using data from members of 61 SMTs and their customers, the authors show a differential impact of the SMT service climate on various marketing performance measures. Furthermore, they obtain support for independent group-level effects of intrateam support and team member flexibility on employee perceptions of the SMT service climate. Both effects are persistent over time and demonstrate that collective perceptions in the SMT have incremental value in the explanation of the service climate.
Research on boundary-spanning service employees has shown that delegation of authority to the front line allows for greater flexibility and adaptability in the performance of service activities through better problem solving, closer employee cooperation, and more efficient knowledge transfer (Hartline and Ferrell 1996). In apparent recognition of this, some companies have organized their front-office operations around self-managing teams (SMTs), or groups of interdependent employees that have the collective authority and responsibility to manage and perform relatively whole tasks. Team members are typically cross-trained in various skills, including developing work routines, planning, and monitoring team performance (Yeatts and Hyten 1998). Companies such as Charles Schwab, Taco Bell, Prudential, Pacific Bell (now part of SBC Communications), CIGNA, Welch Foods, and Xerox have implemented boundary-spanning SMTs (Batt 1999; Cameron and Boise 1995; Wageman 1997).
It has been argued that self-management is an excellent mechanism for improving the performance of the employee-customer interface (Gilson, Shalley, and Blum 2001). However, this claim is not substantiated by the empirical evidence. Whereas Batt (1999) shows that front-office service SMTs perform significantly better in terms of service quality and sales volume than do teams under management control, Chaston (1998) reports an adverse impact on service quality and productivity. Whereas Wageman (1997, p. 32) states that the performance of field-service SMTs at Xerox is "critical to the company's ultimate success," other reports from the business press contest the validity of this assumption (e.g., Zemke 1993). Empirical inconsistencies, conflicting anecdotes, and a lack of theoretical development regarding SMTs in service settings emphasize the need for research that addresses four important theoretical and empirical issues that have been unresolved in previous studies.
First, the research to date has virtually ignored the development of a mediating construct to account convincingly for the apparent inconsistencies in SMT performance. At the firm level, Schneider, White, and Paul (1998) demonstrate that a service climate is a key mediating factor in the prediction of marketing performance. Accordingly, we advance a construct of the SMT service climate that is proximal to perceptions of work-group practices to explain performance variability among teams.
Second, previous research on customer-contact SMTs has focused predominantly on processes within the team (e.g., Cohen, Chang, and Ledford 1997). However, as Hackman (1992) argues, teams do not operate in isolation. Therefore, climate perceptions may also be the result of organizational context characteristics. We develop a conceptual framework that takes into account both team-and company-related predictors of the SMT service climate.
A third issue that has not been explored is whether climate-defining team characteristics have an impact at the work-group level that is beyond the perception of individual employees. Each SMT may develop a unique set of shared perceptions of desirable behavior (e.g., the level of support to other team members), and between-group differences may be contingent on these perceptions (Mathieu and Kohler 1990). Therefore, we examine whether group-level aggregations of team member perceptions incrementally determine employee perceptions of the SMT service climate. The group-level effects may be contingent on the type of service delivery. Stewart and Barrick (2000) demonstrate that task type moderates variability in the magnitude of reported predictor-criterion relationships in explaining manufacturing SMT performance. We extend this finding by considering distinct types of service delivery in our analysis of group-level antecedents and consequences of the SMT service climate.
Fourth, the impact of boundary-spanning teams on business performance measures (Batt 2002) is unknown. Part of the gap in the knowledge about SMT effectiveness stems from the complexity in considering various types of performance measures (e.g., customer evaluations versus productivity measures) across different types of service delivery (Batt 2002). Another reason is that extant studies may not have been able to provide definitive conclusions on frontline SMT effectiveness because they employed cross-sectional data on relationships that might have needed to be separated in time (Griffin 1991). Therefore, we examine the lagged effects of the SMT service climate on customer perceived quality, share of customer, and sales productivity for two types of service delivery.
The increasing importance and ubiquity of boundary-spanning SMTs, despite inconsistent and sometimes contradictory findings, create the need for more definitive research that describes and defines the nature and scope of SMTs and their possible effects. We propose four additions to SMT research and test the theoretical and empirical advantages and implications of these additions.
SMT Service Climate
Although various employee-based measures (e.g., job satisfaction) have been advanced as drivers of service performance, it also has been argued that service climate has superior predictive power (Schneider, Wheeler, and Cox 1992). We extend previous research by adopting a team-level focus on service climate. Our conceptual point of departure for developing several definitional assumptions is Katz and Kahn's (1978) description of climate being the result of a distinct pattern of individual team members' collective beliefs developed through members' interaction with their social environments. First, this description theoretically relates climate to antecedent variables in the organizational and team contexts (Lindell and Brandt 2000). Second, Katz and Kahn (1978) posit that perceptions of climate are essentially a property of the individual member that can be aggregated to reflect a group-level construct (see James and James 1989). Third, as climate is related to various environments, different climates may exist for organizational goals and structural levels. Proximal measures that conceptualize climate in terms of both goals (e.g., customer service) and levels (e.g., the team) produce strong relationships with targeted performance parameters (Tesluk et al. 1995). Finally, because climate involves the construction of shared meaning through the process of interaction, the process is dynamic and in line with Hackman's (1987) process criterion of effectiveness, which relates to team members' effort, knowledge, skill, and performance in achieving team goals. Thus, we define SMT service climate as the collective beliefs of SMT members on effort, knowledge, skills, and performance with regard to effective service delivery.
Antecedents of SMT Service Climate: An Individual-Level Perspective
Our framework, which relates SMT service climate to its antecedents, has two distinct conceptual roots: ( 1) Hackman's (1987) normative model of team effectiveness, which distinguishes intra-and extrateam factors, and ( 2) the involvement approach (Bowen and Lawler 1992), according to which employees are given the authority and resources to coordinate, plan, and control the service delivery process. Three main characteristics that differentiate service SMTs from other traditional work groups govern our choice of predictor variables: ( 1) higher levels of autonomy, ( 2) functional flexibility, and ( 3) interdependency within and between teams (Campion, Medsker, and Higgs 1993). In general, SMTs are designed with a certain degree of role-related diversity (Yeatts and Hyten 1998). Researchers have posited that perceptions of collective phenomena (i.e., service climate) represent cognitive interpretations of proximal structures and processes based on a person's experience, values, knowledge, and expertise (Brown and Leigh 1996). Thus, members of the same SMT may have different perceptions of the SMT service climate defining antecedents. Prior research on work groups has demonstrated that within-group perceptual deviation reflects systematic (not random) variance that may represent differential cognitive appraisals of the team environment (Van Yperen and Snijders 2000). Thus, we postulate predictor-criterion relationships at the individual level.
Hackman (1987) posits that a supportive organizational context is a major determinant of group effectiveness. A central component of the involvement approach is empowerment, which refers to the notion that service employees must be given a certain degree of autonomy and be able to perform job-related activities with skill (Hartline and Ferrell 1996). Recent research on production teams demonstrates that senior management's lack of tolerance for self-management may be an important barrier to SMT effectiveness (Balkema and Molleman 1999). Furthermore, in the case of boundary-spanning SMTs, it has been consistently argued that perceived autonomy is critical to the attitude and behavior of customer-contact personnel (Batt 1999; Van Mierlo et al. 2001; Wageman 1995). Thus:
H<sub>1</sub>: Tolerance for self-management positively affects employees' perceptions of the SMT service climate.
In addition, SMT members need to be able to use delegated authority optimally; this ability has been associated with several synergistic processes in teams (Hackman 1987). The SMT members should be capable of performing various team tasks, whether operational, managerial, or administrative (Spreitzer, Cohen, and Ledford 1999). As the spectrum of SMTs' tasks grows, job assignments rapidly evolve, SMTs need to become highly interdependent, and there is a need for flexible and multiskilled members (Batt 1999; Sundstrom, de Meuse, and Futrell 1990). Recent research (Marks, Mathieu, and Zaccaro 2001; Mathieu et al. 2000) has demonstrated that the ability to perform interrole behaviors facilitates attainment of team goals. We hypothesize that perceptions of team member flexibility form another relevant foundation for the establishment of service climate:
H<sub>2</sub>: Flexibility of team members positively affects employees' perceptions of the SMT service climate.
The literature on employee involvement has demonstrated that coworker involvement reduces perceptions of boundary-spanner role stress and increases service performance (Bettencourt and Brown 1997). The implication is that when service employees experience peer-based learning and coworkers' service-driven attitude, they will be motivated to carry over this attitude to their customer encounters. Frequently, cooperative interaction within and between SMTs is required to address customer service requests successfully and to create a service-oriented work environment (Horwitz and Neville 1996). The services marketing literature posits that mutual support among employees is essential to the implementation of service-quality improvements (Berry, Parasuraman, and Zeithaml 1994). Batt (1999) has shown that collaborative endeavors are a key success factor of SMT effectiveness in boundary positions. Conceptually, we distinguish between inter-and intrateam support. Interteam support refers to team member perceptions of the internal service and communication between teams and other units in the organization, whereas intrateam support pertains to team members' willingness to offer help and to deliver service to other members of the group in order to attain work-group goals (Campion, Medsker, and Higgs 1993). We hypothesize the following:
H<sub>3</sub>: Employee perceptions of the SMT service climate are positively affected by (a) interteam support and (b) intrateam support.
Although some researchers claim that the individual perspective constitutes the only proper unit of analysis, others argue that the study of group phenomena can be analyzed in a meaningful way only at the group level (Lindell and Brandt 2000). Therefore, in the next section, we discuss the conceptualization of the aforementioned predictor variables at the group level.
Antecedents of SMT Service Climate: A Group-Level Perspective
It has been advanced that by evoking the notion of climate, each team can develop a unique set of norms and mental models regarding desirable behavior (e.g., the level of support to other team members), thus reflecting between-group differences (Mathieu and Kohler 1990). Studies on shared mental models (Mathieu et al. 2000) and transactive memory in teams (Liang, Moreland, and Argote 1995) demonstrate that team members develop shared beliefs about their team that instigate team members to develop interrelated knowledge and norm structures to facilitate group processes. Lindell and Brandt (2000, p. 332) state that shared beliefs reflect that individual members "are socialized to act in similar ways, are exposed to similar features within contexts, and come to share their interpretations with others in the setting." Beliefs are conceptually distinct from constructs that exist at the group level only (e.g., team size) (Gully et al. 2002). Aggregate-level constructs reflect psychosocial traits that are not captured by the individual-level measurement (Hackman 1992). These constructs may differentially influence individual members' perceptions of the SMT service climate. Group-level constructs strongly reflect the basic assumption of synergistic processes within the SMT (Hackman 1987). Poole and McPhee (1983, p. 213) view sources of work-unit climate as collective attitudes that are "continually produced and reproduced by members' interactions." Particularly in service SMTs, there is a relatively high level of interdependence and interactions (Batt 1999), in which shared perceptions, unique to the work unit, are formed.
To better understand the similarities and differences inherent in multiple-level constructs, scholars have used the typology of elemental composition (Bliese 2000; Chan 1998). Elemental composition takes place when a higher-level construct consists of collective lower-level measures. The composition model for the SMT antecedents in our study is the direct consensus model, which characterizes climate-defining antecedents as properties of the individual team member. At the same time, when there is consensus between individual perceptions (e.g., on intrateam support), the aggregate composes a construct at the work-group level that represents shared perceptions of a collective belief. Prior research on teams' withdrawal behavior (absenteeism and lateness) in boundary-spanning service settings provides evidence of the influence of group-level predictors on individual employee behavior beyond individual-level antecedents. Mathieu and Kohler (1990) and Blau (1995) report significant effects of aggregated group-level variables on individual employee behavior in auto repair, hospital, and banking service teams. Their results seem to support Bryk and Raudenbush's (1992) contention that group-level aggregations of contextual properties represent a distinct perspective that may not be captured by individual-level measures. Therefore, we hypothesize the following:
H<sub>4</sub>: At the group level of analysis, the positive effects of (a) tolerance for self-management, (b) team member flexibility, (c) interteam support, and (d) intrateam support account for a significant amount of additional variance in individual employees' perceptions of the SMT service climate.
Antecedent-SMT Service Climate Relationships Across Service Types
Positive linear relationships between SMT characteristics and member-related outcomes (e.g., employee satisfaction, organizational commitment) within and across various frontline service settings have not yielded a consistent pattern (Batt 1999; Cohen, Chang, and Ledford 1997; Gilson, Shalley, and Blum 2001; Wageman 1997). Variability in the magnitude of reported predictor-criterion relationships may indicate the presence of moderator variables, such as task characteristics. In a manufacturing setting, Stewart and Barrick (2000) show that SMTs that are responsible for routine tasks are less likely to be affected by flexibility and interdependency than are SMTs that perform nonroutine activities. Recent research by Marks and colleagues (2002) demonstrates that the development of shared beliefs regarding team processes fosters coordinated task behavior and the ability to adapt dynamically in uncertain environments. Particularly in the case of nonroutine tasks, norm congruence and shared understanding on how to function as an SMT affect climate perceptions. Because services have also been classified in terms of a routine-nonroutine continuum (Davis 1999), we expect service type to moderate the impact of group-level predictor variables. We posit the following:
H<sub>5</sub>: Service type moderates the positive effects of (a) tolerance for self-management, (b) team member flexibility, (c) interteam support, and (d) intrateam support on employees' perceptions of the SMT service climate, such that effects are significantly stronger at the group level for nonroutine services than for routine ones.
Consequences of SMT Service Climate
There is ample evidence that employee perceptions of service climate at the firm level have a positive influence on customer perceptions of service quality (Schneider et al. 1996; Schneider and Bowen 1985; Schneider, White, and Paul 1998). In addition to psychological outcome parameters, it has been argued that the behavioral outcome "share of customer," or the number of services purchased from a specific service provider, is a key marketing-performance indicator (Babin and Attaway 2000). The overall premise is that the impact of policies and practices aimed to serve the customer should also be observable in customer behavior, because both perceived service quality and purchase behavior are closely related and should be evaluated simultaneously when pursuing profitability (Soteriou and Zenios 1999). Service firms may also focus on productivity as a performance parameter, specifically on quantifiable behavioral standards of employee behavior, such as volume of services sold (Singh 2000). However, recent evidence suggests that, especially in the area of services, firms should make trade-offs between establishing a focus on delivering quality services and sales ratios per employee (Anderson, Fornell, and Rust 1997; Singh 2000). Thus, we expect that a strong SMT service climate with an emphasis on providing high-standard services will result in decreased productivity. It has been suggested in both the service marketing and the team literature that inconsistencies in performance assessment are contingent on the time frame used in the analysis (Griffin 1991). Empirical evidence from the services marketing literature shows that customers' assessments of service quality are relatively constant and subject to slow change (Bolton and Drew 1991). Moreover, Bernhardt, Donthu, and Kennett (2000) argue that the true impact of service changes in terms of observable customer behavior (e.g., actual services purchased) can be assessed only longitudinally. Finally, Griffin (1991) reports a delay between the implementation of frontline service SMTs and performance improvements. Therefore, we propose the following:
H<sub>6</sub>: At the group level of analysis, SMT service climate at T1 positively affects (a) customer perceived service quality at T2 and (b) share of customer at T2.
H<sub>7</sub>: At the group level of analysis, SMT service climate at T1 negatively affects sales productivity at T2.
Assessment of Consequences Across Service Types
Finally, we posit that the impact of SMT service climate on the aforementioned parameters may depend on the type of service. Nonroutine service delivery frequently involves dealing with complex problems and equivocal situations that may benefit from an extended dialogue within the team and between team members and customers. We expect that a climate that fosters service excellence is more influential in such circumstances:
H<sub>8</sub>: At the group level of analysis, service type moderates the positive effects of SMT service climate at T1 (a) on customer perceived service quality at T2 and (b) on share of customer at T2 and moderates (c) the negative effect on sales productivity at T2, such that effects are significantly stronger at the group level for nonroutine services than for routine ones.
Figure 1 reflects our conceptual framework and provides an overview of the issues discussed thus far.
Research Setting
Members of a large Dutch bank's SMTs and customers were surveyed. The bank employs approximately 48,000 people and has 424 branch offices. It operates in both business and consumer markets and promotes service excellence as a key to marketing success. In each branch, separate routine and nonroutine SMTs are responsible for servicing consumers, and other SMTs deal with business customers or are responsible for internal operations (e.g., human resources management, general and technical services). In our study, we focused on SMTs that deliver consumer services. Each branch offers a wide range of services to consumers: nonroutine, knowledge-intensive services (e.g., investment consulting, trust services, estate planning) and routine, transaction-intensive services (e.g., checks and deposits, currency exchanges, credit application accounts). At each branch, separate front-office SMTs are responsible for the different service types (e.g., financial versus client advisory teams). The bank considered implementation of team-based self-management an important organizational change process. Therefore, the practical rationale for conducting our study at the initiation phase (T1) and seven months later (T2) was to evaluate the central role of SMT service climate and examine its impact on service performance.
Sampling and Surveying
Of 848 boundary-spanning SMTs, we randomly selected a sample of 100. Unwillingness to cooperate and the lack of sales productivity and customer data resulted in a total set of 61 SMTs. We collected data from individual employees organized in SMTs using self-report questionnaires (at T1 and at T2) and from their customers (at T2) using mail questionnaires. For the employee survey, we invited all members of the SMT to participate. In total, 939 questionnaires were returned at T1 (76.4%) and 730 questionnaires at T2 (62.1%). For the customer survey, we drew a random sample of 150 customers per SMT at T2. In total, 1884 questionnaires were returned (20.8%). For the employee survey, we used 568 questionnaires from 36 nonroutine service teams (316 from 36 teams at T1 and 252 from 33 teams at T2) and 917 questionnaires from 25 routine service teams (509 from 25 teams at T1 and 408 from 23 teams at T2) for further analysis. For the customer survey, we analyzed 957 questionnaires of customers of 36 nonroutine service teams and 577 questionnaires of 25 routine service teams. With respect to the customer survey, we acquired the following sample profile: Most respondents were male (63.8%) and older than 44 years of age (58.5%). Virtually all respondents had a long-lasting relationship with the bank (93.1% more than five years) and half of them visited the bank at least once a month (49.8%). Detailed background data on participants at T1 as well as functional areas and responsibilities of the SMTs are included in Table 1.
Measurement Issues
The assessment of the SMT service climate involved items we specifically developed for this study; in-depth interviews with frontline employees; and scales developed by Schneider, Wheeler, and Cox (1992) and Peccei and Rosenthal (1997). We based the operationalization of tolerance for self-management (seven items) largely on the tolerance-of-freedom instrument of the Leader Behavior Description Questionnaire, which assesses the degree of autonomy given to employees to manage their task responsibilities themselves (Cook et al. 1981). We measured team member flexibility and inter-and intrateam support with scales developed by Campion, Medsker, and Higgs (1993). We measured all scale items for the employee survey on a seven-point scale that ranged from 1 = "strongly disagree" to 7 = "strongly agree." A qualified translator translated items into Dutch using the double back-translation procedure (Brislin 1980).
We employed two techniques to test the factor structure and the item loadings of the data collected at T1. We initially examined coefficient alphas and the factor structure (using principal components analysis) for all the scale items simultaneously. We achieved a five-factor structure in which items loaded on a priori dimensions. In addition, we conducted a confirmatory factor analysis (CFA) using LISREL to assess the measurement properties of the items. The fit indexes of the proposed factor model, construct reliabilities of the scales, and confirmatory factor loadings with t-values for each item are reported in Table 2. The indexes of the proposed factor model provide a good fit and show unidimensionality of the scales.( n1) We tested construct reliabilities of the scales by means of Cronbach's alpha. Coefficients of all measures were greater than .65, which implies that reliability is acceptable (Nunnally and Bernstein 1994).
Next, we examined within-method convergent validity by investigating the significance and magnitude of the item loadings. All items loaded significantly on their respective construct (minimum t-value = 12.50), and 95% of all items had a standardized loading of at least .50. In addition, we evaluated discriminant validity. All chi-square difference tests (1 degree of freedom [d.f.]) were significant (p < .05), which indicates that all pairs of constructs correlated at less than one. Subsequently, we included as control variables the demographic variables education, organizational tenure, and age, as well as the work-group-design variables team size and percentage of front-office activities (i.e., direct customer-contact responsibilities) and nonroutine services, which is a dummy for service type (we coded nonroutine services as one and routine services as zero).
We measured two performance measures, customer perceived service quality and share of customer, using the customer survey. We based the scale for service quality on the SERVQUAL instrument developed by Parasuraman, Zeithaml, and Berry (1988). Our goal was to measure the quality of the services delivered by the service employees. Therefore, we restricted our measure to eight items that specifically addressed employee-related aspects of quality (Hartline and Ferrell 1996). We measured items on a five-point scale that ranged from 1 = "very dissatisfied" to 5 = "very satisfied." Principal components analysis showed construct validity (we extracted a single factor; loadings for all items were greater than .70). In addition, we conducted CFA to assess measurement properties. The fit indexes are reported in Table 2; they demonstrate unidimensionality, construct reliability, and convergent validity of the construct. To obtain a measure that we call "share of customer," customers were asked to indicate which types of services they used. The share-of-customer score for an SMT pertains to a group mean based on the average number of different service categories used by the customers of a specific team (see Table 2). Finally, we used data from the bank's internal database on the two service types (i.e., life insurance policies and investment portfolios for nonroutine services and number of checking and savings accounts for routine services). These parameters reflect the average amount of services sold per team member per year; we used them as a measure of sales productivity.
Means, standard deviations, and individual-and grouplevel correlations between the employee variables are presented in Table 3, which reveals that nonroutine service delivery is associated with higher-educated employees, smaller team sizes, higher percentages of front-office workers, and higher levels of tolerance for self-management than is routine service delivery. In Table 4, group-level means, standard deviations, and (partial) correlations of employee variables and external outcomes are presented. The SMT service climate appears to have the highest correlations with customers' perceived service quality. Furthermore, the correlations between antecedents and perceived service quality are noticeably weaker when the effect of SMT service climate is accounted for, which implies that the SMT climate mediates the relationships between antecedents and perceived service quality (see Baron and Kenny 1986). Compared with share of customer and sales productivity, the mediating role of SMT service climate is less obvious.
Analysis
To justify data aggregation, we examined tolerance for self-management, flexibility of team members, inter-and intrateam support, and SMT service climate on within-group agreement and interdependence. First, the average rWG(j) coefficients (ranging from .84 to .95 at T1 and at T2) are high for all variables and show high ratings consistency among employees within groups (James 1982). Second, the intraclass correlation (ICC) ( 1) coefficients (ranging from .06 to .18 at T1 and from .05 to .14 at T2) indicate that for all variables, a small to moderate part concerns between-group variance. To determine interdependence appropriately, it is also relevant to consider group size (Bliese 2000). Therefore, we calculated ICC ( 2) coefficients, because this measure of interdependence accounts for group size. Of the ICC ( 2) values, 90% are greater than .50, which provides evidence for reliable group means and enables the detection of group-level relationships, even in the case of relatively small ICC ( 1) values (Bliese 2000).
We tested H<sub>1</sub>-H<sub>5</sub> with employee data at T1 and estimated multilevel models using MLwiN software (Rasbash et al. 2000). We first included the control variables and the antecedents (Model A1). We then specified interactions between the dummy variable nonroutine services and the antecedents (Model A2).( n2) Model specification is provided in the Appendix.
To compare individual-and group-level effects of the antecedents on SMT service climate, we split the antecedent variables into the group mean and within-group deviation score (i.e., individual score - group mean).( n3) The coefficient of the group means reflects the group-level effect, whereas the coefficient of the within-group deviation scores reflects the individual-level effect (Bryk and Raudenbush 1992; Snijders and Bosker 1999). To interpret the coefficients appropriately, we needed to know whether the individual-and group-level coefficients were equal. First, when both coefficients are significant and equal, the variable functions principally at the individual level, and there is no separate main effect at the group level. Second, when only the individual-level coefficient is significant and the group-level coefficient is not significant, the effect is solely based on social comparison within the group. Finally, when both the group-and the individual-level coefficients are significant but differ in magnitude, there is both an individual-level effect and an independent group-level effect (Van Yperen and Snijders 2000).
Table 5 contains the results of the multilevel analyses. There are positive individual-level effects of tolerance for self-management, flexibility, and inter-and intrateam support on the SMT service climate, in support of H<sub>1</sub>-H<sub>3</sub>. In addition, we tested the cross-level hypothesis (H4). A comparison of the individual-and group-level coefficients shows that tolerance for self-management and flexibility have significant, positive group-level effects on the SMT service climate. However, these do not significantly differ from the individual-level effects. Thus, the results indicate no support for H<sub>4a</sub> and H<sub>4b</sub>. The group-level effect of interteam support is nonsignificant and significantly weaker than its individual-level counterpart. Therefore, H<sub>4c</sub> is rejected. Only intrateam support shows a significant, positive group-level effect on SMT service climate that is significantly stronger than its individual-level effect, in support of H<sub>4d</sub>.
With respect to the control variables, our findings show a significant, positive group-level effect of age on SMT service-climate perceptions that is significantly stronger than its individual-level counterpart. Similarly, we find a significant, negative group-level effect of organizational tenure on the outcome variable. At the same time, however, although the group-level predictors are highly positively correlated, each has a negligible correlation with the SMT service climate. Yet the inclusion of group-level tenure as a second predictor of the SMT service climate in addition to group-level age significantly increases the model fit (Χ²(l) = 5.68; p < .05) and results in significant effects of both predictors on the SMT service climate. In contrast, inclusion of age as a second predictor beyond tenure results in a better model fit (Χ²(l) = 5.56; p < .05) and significant effects for the two predictors. Therefore, it appears that both tenure and age can be considered suppressor variables that mutually suppress each other's irrelevant variance for the prediction of the SMT service climate. Essentially, suppressors partial out the variance in the predictor variable that is due to measurement artifacts (Hinkle, Wiersma, and Jurs 1994). Therefore, to determine the effects of tenure and age on SMT service climate adequately, it is necessary to include both variables in the model. Finally, the results show that nonroutine service tasks have a positive impact on climate perceptions.
In addition, we tested whether the relationships between antecedents and the SMT service climate differ across service settings by specifying interactions between nonroutine services and antecedents. The inclusion of the interaction terms does not significantly improve the fit of Model A2 compared with Model A1. We found one significant, positive interaction of nonroutine services x team member flexibility at the group level. The addition of this interaction term to the model leads to a significant increase in model fit (Χ²(l) = 5.92; p < .05), indicating support for H<sub>5b</sub>. Specifically, the magnitude of this group-level interaction effect is significantly stronger than its individual-level counterpart.
The specified group-level interactions of nonroutine services with tolerance for self-management and inter-and intrateam support appear to be nonsignificant. This implies that there are no differences in group-level effects of the antecedents across service types. Thus, H<sub>5a</sub>, H<sub>5c</sub>, and H<sub>5d</sub> are rejected. Finally, the percentage of explained variance at the grouplevel is higher than that at the individual level, indicating that between-group differences of SMT service climate are more effectively explained than within-group differences.
To substantiate our results, we performed additional analyses. The results of the analyses are presented in Table 6. First, we conducted a lagged analysis and investigated the delayed effects of the antecedent variables at T1 on SMT service climate at T2 (Model B1). To control for an employee's previous evaluation of SMT service climate at T1, we included this variable as an additional predictor (see Bolton and Drew 1991). The reported estimates reveal a somewhat weaker but largely consistent pattern as compared with the cross-sectional analysis (Model A2). Specifically, these findings empirically demonstrate that the antecedents have a positive impact on the development of the SMT service climate over time. Second, to test the accuracy and stability of parameter estimates (Model B2), we conducted the simulation procedure using Gibbs sampling (Gilks, Richardson, and Spiegelhalter 1996).( n4) The parameter values we obtained through this simulation were fairly similar to the Model A2 estimates. Third, we performed the Chow F<sub>c</sub> test to determine whether there had been a structural change in the antecedent-SMT service climate relationships between T1 and T2. The Chow F<sub>c</sub> test reported no significant F value (F<sub>25, 1435</sub> = 1.00; p = .46), which indicates that there were no structural alterations over time. Overall, this model validation indicates insensitivity of the results to various time frames and different estimation methods, and it confirms consistency in findings and provides additional support for their managerial relevance.
Subsequently, we investigated the effect of SMT service climate at T1 on several team outcomes at T2. To assess the linkage between SMT service climate and its consequences empirically, we aggregated employee and customer perceptions to the group level. It has been argued that customers primarily observe the outcome of integrative working relationships among multiple employees (Allen and Grisaffe 2001). Furthermore, outgroup-homogeneity theory postulates that people tend to perceive other groups as more uniform than their own group (Quattrone and Jones 1980). This means that whereas employees may have a relatively detailed view of their own team's performance, external customers are likely to generalize the quality of service offered by one or multiple team members as a common characteristic of a homogeneous out-group. Moreover, it was not possible to match empirically employee and customer evaluations and productivity criteria at the individual level of analysis.
We tested H<sub>6</sub>-H<sub>8</sub> using a multivariate regression model formulated as a two-level hierarchical linear model. Level 1 refers to the dependent variables indexed by h = 1, ..., m, and Level 2 reflects the teams j = 1, ..., N. Thus, each measurement of a dependent variable for some team is indicated by a separate line in the data matrix, which contains the values j, h, Y<sub>hj</sub>, x<sub>1j</sub> and those of other explanatory variables. To formulate the multivariate regression model as a hierarchical linear model, we used the dummy variables d<sub>1</sub> to d<sub>m</sub> to indicate the dependent variables (i.e., perceived service quality<sub>T2</sub>, share of customer<sub>T2</sub>, and sales productivity<sub>T2</sub>). Dummy variable d<sub>h</sub> is equal to one or zero, depending on whether the data line refers to the dependent variable Y<sub>h</sub> or to another dependent variable. With the dummy variables, the regression models for the in dependent variables can be integrated into one two-level hierarchical model with the following expression:
( 1) [Multiple line equation(s) cannot be represented in ASCII text]
We multiplied all variables (including the constant) by the dummy variables. Note that in the sums over s = 1, ..., m, only the term for s = h renders a contribution; all other terms disappear. The following equation involves a simplified representation of Equation 1:
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
where Y<sub>hj</sub> is the measurement on the hth variable for group j, SERVCLIM<sub>T1</sub> is the team's SMT service-climate average at T1, and NROUT reflects the type of service.
Our results in Table 7 reveal significant, positive effects of SMT service climate<sub>T1</sub> on perceived service quality<sub>T2</sub> and share of customer<sub>T2</sub> and a significant, negative effect on sales productivity<sub>T2</sub>. The findings support H<sub>6</sub> and H<sub>7</sub>. Furthermore, the service-type dummy "nonroutine services" is positively related to customer perceived service quality but is negatively related to share of customer and sales productivity. In addition, we tested interactions between service type and the SMT service climate. There is a positive interaction effect of nonroutine services x SMT service climate on perceived service quality, but no significant interactions appear for share of customer and sales productivity. Thus, H<sub>8a</sub> is supported, and H<sub>8b</sub> and H<sub>8c</sub> are rejected.
The key objective of our study was to analyze the chain of events between perceptions of boundary-spanning service employees organized in SMTs and critical marketing performance criteria. We developed the construct of SMT service climate and empirically demonstrated that it plays a pivotal role in the prediction of SMT performance dimensions. We have empirical verification that tolerance for self-management, flexibility, and inter-and intrateam support have a direct, positive impact on individual employees' service-climate perceptions. In addition, we found that the group-level measure of intrateam support adds uniquely to the explanation of SMT service-climate perceptions. Moreover, we found that service type moderates the group-level effect of flexibility. Both effects persist over time, which nuances Mohammed, Mathieu, and Bartlett's (2002) general observation that perceptual agreement among team members with respect to interpersonally oriented behaviors is crucial to the performance of the team's core tasks. For boundary-spanning service delivery, shared perceptions of support and team flexibility are conducive to the creation of a climate related to the SMT's core task. Our results suggest that the dynamics of supportive behavior in the SMT's providing service to customers have crystallized into a coherent, collective perception and that beliefs of any team member are likely to be influenced by the attitudes and behavior of other members. This seems important because service delivery is heterogeneous across employees, and delivery of consistent service quality is a constant challenge. Further more, because nonroutine services are relatively more complex and require in-depth know-how, there is a greater need to integrate mechanisms and group-embedded expertise when addressing heterogeneous, unpredictable customer demands on the front line. The group-level interaction effect signifies that a collective understanding of one another's roles and shared beliefs about the capability to perform various roles are particularly important for service-climate perceptions in SMTs that deliver nonroutine services. This extends Druskat and Pescosolido's (2002) finding that in complex task conditions, there is a higher need for information exchange from manufacturing SMTs to the setting of nonroutine service delivery. In contrast, no additional cross-level effects (i.e., beyond individual perceptions) were reported for tolerance for self-management and interteam support. This implies that boundary-spanning service employees' perceptions of the impact of climate-defining determinants are based primarily on personal cognitions and interpretations of the team environment rather than on a property of the team.
This study extends previous research by taking into account a comprehensive set of SMTs' marketing performance measures. Our results demonstrate that SMT service-climate perceptions have a positive impact on customer perceived service quality and share of customer and a negative effect on sales productivity. We also find that the positive effect of customer perceived quality is significantly stronger for nonroutine services than for routine services. Thus, the direct relationship between organizational service climate and perceived service quality in financial services reported by Schneider, White, and Paul (1998) holds at the team level, but the strength of the relationship seems to be contingent on the service type. Particularly in the delivery of nonroutine services, which is often aimed at fulfilling specific customer needs, a climate focusing on service excellence appears to provide added value from customers' perspectives. In contrast, the relatively high within-group variability of SMT service climate for the routine service setting may have weakened the relationship between SMT service climate and perceived service quality.
Although SMT service climate contributes to a higher share of customer, it is associated with lower sales productivity levels. These results and the low correlations between the effectiveness measures may indicate what has been labeled the "performance paradox," a phenomenon that has been encountered in previous studies on firm performance (Meyer and Gupta 1995) and team performance (Spreitzer, Cohen, and Ledford 1999). An SMT climate for service does not necessarily improve all aspects of frontline service performance. This confirms Anderson, Fornell, and Rust's (1997) contention that trade-offs between different performance parameters are particularly applicable to services, and it emphasizes the necessity to set off customer parameters against productivity parameters to create an optimal balance.
Our study contributes to the theoretical literature on SMTs in four ways. First, our findings illustrate the relevance of the SMT service climate in explaining the divergent findings encountered in the SMT literature. Although service climate has been studied extensively at the organizational level, the concept has been virtually unexplored in relation to SMTs. We conducted our study in the context of financial consumer services. Further research should assess the generalizability of our findings to other service settings (e.g., technical support, business-to-business services). Although we unequivocally demonstrate the impact of service climate on important performance parameters, the body of work on this construct in the marketing and organizational behavior areas suggests that there is still much to be learned about the nature and scope of the SMT service climate as it relates to team performance. Following Schneider, Salvaggio, and Subirats (2002), we modeled within-team variability by taking the standard deviation of the SMT service climate as a measure of cohesiveness. Notably, significant, positive interaction effects of SMT service climate. SMT climate cohesiveness on customer perceived service quality and sales productivity emerged. These findings demonstrate the relevance of perceptual agreement on SMT climate as a moderator of the relationship between SMT service climate and the outcome variables. However, the effect of cohesiveness did not hold when we specified conditions for service type, possibly because of relatively strong differences in within-group variability between routine and nonroutine service delivery. Additional insight is needed into the contingencies that influence the relationship between SMT service climate and effective customer service.
Second, our research contributes to a better understanding of which factors shape a climate for service within boundary-spanning SMTs. Both team-and company-related characteristics influence team members' perceptions of the SMT service climate. Our set of predictor variables pertains predominantly to interpersonally oriented behaviors. In further research, the focus might be more on the impact of technical-administrative or task-related behavior in SMTs.
Third, our study advances the understanding of the impact of predictor-criterion relationships across levels of analysis. Although recent multilevel research has recognized the importance of comparing relationships across levels (Ostroff, Kinicki, and Clark 2002), studies have focused almost exclusively on methodological issues. Additional investigation is needed to address the underlying theoretical mechanisms that cause these differences. For example, our findings on interteam support suggest the relevance of considering distortions in employee perceptions during comparison of relationships across levels. The purpose of ratings, performance norms, and social dynamics may affect ratings of situational elements (Ostroff 1993). It makes a difference whether respondents evaluate their own team or other (rival) teams within the organization. In other words, the team members' various motives and interests influence their workplace evaluation. Thus, conceptual development is required on implicit and explicit processes among individual service employees, their colleagues, and the broader organizational context.
Fourth, our findings present a balanced perspective of service climate as a success factor related to SMTs. Although the growth and importance of SMTs has been studied, research has neglected how SMTs are related to important business-performance measures. The study empirically demonstrates that all benefits of this factor may not be realized simultaneously and that inherent trade-offs are made. The divergent effects of the SMT service climate on customers' perceived service quality and share of customer, rather than sales productivity, may motivate researchers to investigate complementary mediator variables that explain the performance paradox between customer and productivity-based measures. Future conceptual models need to include more productivity-oriented performance mediators (e.g., cost consciousness) that are related to SMTs' planning and control activities (Singh 2000).
Our findings suggest several managerial implications. Given the group-level impact of intrateam processes, competency development and measurement with regard to intrateam support and flexibility should be aligned at the team level. Team-level interventions that are effective in creating a collective sense of support in the team include group exercises aimed at consensus building on, for example, service delivery standards, joint problem identification and analysis of customer complaints, the provision of assistance to colleagues in dealing with customers, and groupware and shared databases that facilitate information exchange. In addition, performance assessments should be related to the team's collective capabilities in providing internal and external service. Cross-training programs that focus on the ability of specialist employees to promote interchangeable expertise within nonroutine SMTs may be offered. Particularly for teams delivering complex and extended services, group meetings should be devoted to increasing team members' understanding of one another's tasks through role-play exercises. Cases on handling complex customer requests may be used to demonstrate problem solving and decision-making skills.
Our findings show that it is important to create a context that is supportive to self-management on the firm's front line, one that goes beyond formally loosening the managerial reigns. Tolerance for self-management entails creating a sense of personal freedom and control with respect to, for example, meeting customer expectations (see Bowen and Lawler 1992). However, the authority to perform in the interest of the customer as a team should be accompanied by the knowledge and skills as well as the resources to analyze team performance data from a business perspective, because this is one of the essential self-managing responsibilities of the team. In addition to the dissemination of power, timely feedback and strategic information on, for example, the impact of SMT decisions on customer perceptions and company profitability should be distributed and discussed within the team. Management should play a leading role in coaching SMTs in their role as empowered customer-company interfaces. This calls for active engagement in plenary discussions on the firm's service strategy; involvement in training programs for skills in problem solving and customer complaint handling, process analysis techniques, and performance assessments; and embedding of the role of SMTs in the social structure of the organization. Support from other teams is also an important driver of service-climate perceptions. Interteam support may be encouraged through job-rotation schemes, cross-training between SMTs on customer issues, and joint customer visits. Kirkman and Rosen (2000) refer to an insurance company that uses so-called bridge teams that are responsible for facilitating the communication and cooperation between service teams. Because the impact of extrateam factors occurs predominantly at the individual level, managerial strategies need not focus exclusively on team-level interventions. Personalized training in conjunction with plenary meetings and internal media may be used to reach employees and to strengthen the impact of climate-shaping antecedents for teams of frontline service providers.
A potentially counterintuitive and important finding suggests that firms should be aware of the SMT service climate's potential to have an adverse effect on sales productivity. Thus, SMTs may need both a productivity and a service orientation if management expects them to affect different outcomes positively. If the costs of service operations need to be reduced (a likely reality today), SMTs need to be involved in developing efficiency improvements, such as designing "smarter" service-delivery routines or identifying opportunities for cross-or up-selling during service encounters. When there is a need to work simultaneously on multiple drivers of different performance parameters, the results may vary across service types. Moreover, it is unlikely that all desirable outcomes will manifest at the same time. Our findings confirm that the impact of SMT climate can be observed over an extended period of time and can provide evidence of the potential to incorporate time frames in diagnostic assessments of the effectiveness of SMTs.
The authors thank Richard Feinberg, Roberta Levy, Christine Moorman, Tor Wallin Andreassen, Pratibha Dabholkar, and the four anonymous JM reviewers for their valuable comments on previous drafts of this article.
(n1) To assess the construct validity of our focal variable, we conducted an additional CFA in which we included both SMT service climate and its conceptual counterpart "overall service climate" (Schneider, White, and Paul 1998). The results (Χ² = 236.74, 43 d.f.; goodness-of-fit index = .97; adjusted goodness-of-fit index = .95; root mean square error of approximation = .057; normed fit index = .98; and comparative fit index = .98) indicate the unidimensionality of both constructs. To determine discriminant validity, we used a chi-square difference test (1 d.f.) to test for unity between the constructs. The test showed significance at p < .05.
(n2) To control for multicollinearity, we inspected the variance inflation factors of the variables. The control variables and the antecedents yielded values less than 3.9 and 2.0, respectively, indicating the absence of serious multicollinearity problems (Kleinbaum, Kupper, and Muller 1988).
(n3) Within-group deviation is similar to group mean-centering. In all analyses, we group mean-centered individual-level variables and grand mean-centered group-level variables to (1) distinguish within-and between-group variance, (2) reduce multicollinearity, and (3) facilitate model estimation (see Bryk and Raudenbush 1992).
(n4) Gibbs sampling works by simulating a new value for each parameter from its conditional distribution and by assuming that the current values for the other parameters are the true values.
Legend for Chart:
B - Employees of SMTs in Nonroutine Services (N = 316) Frequency
C - Employees of SMTs in Nonroutine Services (N = 316) Percentage
D - Employees of SMTs in Routine Services (N = 509) Frequency
E - Employees of SMTs in Routine Services (N = 509) Percentage
A B C D E
Sex
Male 187 59.2 86 16.9
Female 129 40.8 423 83.1
Age (Years)
Younger than 40 149 47.2 303 59.5
Older than 40 167 52.8 206 40.5
Education Level
Secondary 59 18.7 131 25.7
Tertiary 257 81.3 378 74.3
Organizational Tenure (Years)
Less than 5 123 38.9 242 47.5
More than 5 193 61.1 267 52.5
Team Experience (Years)
Less than 2 146 46.2 166 32.6
More than 2 170 53.8 343 67.4
Job
Full time 217 68.7 217 42.6
Part time 99 31.3 292 57.4
Legend for Chart:
B - SMT Characteristics Nonroutine Services
C - SMT Characteristics Routine Services
A B
C
Team size (mean [s.d.]) 13.98 (5.85)
36.76 (12.70)
Functional areas General financial advisers
Mortgage advisers
Portfolio advisers
Financial planners
Insurance advisers
Clerical staff
Risk analysts
Client services employees
Cashiers
Receptionists
Travel services
Clerical staff
Customer services (customer
complaint management)
Responsibilities Division and allocation of work
Planning and budgeting
Recruitment and training
Developing new work routines
Monitoring member and team performance
Service recovery
Internal and external coordination
Division and allocation of work
Planning and budgeting
Recruitment and training
Developing new work routines
Monitoring member and team performance
Service recovery
Internal and external coordination
Notes: In essence, the responsibilities of the SMTs do not differ
between routine and nonroutine services. Legend for Chart:
A - Measures
C - Item Mean
D - Loading
E - t-Value
A C D E
Employee Data(a)
SMT Service Climate (n = 6; α =
.86 at T1; α = .88 at T2)
1. Our team is continually working to 5.81 .78 25.54
improve the quality of service we
provide to our customers.
2. Our team has specific ideas about 5.32 .67 20.94
how to improve the quality of service
we provide to customers.
3. Our team often makes suggestions 5.01 .51 14.85
about how to improve the service quality
of our organization.
4. In our team we put a lot of effort 5.68 .83 28.22
in attempting to satisfy customer
expectations.
5. No matter how we feel, we always 5.45 .71 22.55
put ourselves out for every customer
we serve.
6. Within our team, employees often 5.30 .73 23.23
go out of their way to help customers.
Tolerance for Self-Management (n = 6;
α = .89 at T1; α = .90 at
T2)
1. In our team we are permitted to use 5.27 .71 22.50
our own judgment in solving problems.
2. In our team we are encouraged to 6.09 .64 19.48
take initiative.
3. Our team is allowed a high degree 5.83 .81 27.28
of initiative.
4. In our team we are allowed complete 5.54 .86 29.94
freedom in our work.
5. In our team we are allowed to do 5.25 .76 24.73
our work the way we think best.
6. As a team we are able to handle all 5.22 .72 23.09
tasks assigned to us.
Flexibility of Team Members (n = 4;
α = .68 at T1; α = .74 at
T2)
1. In our team it is easy to stand in 5.47 .66 18.03
for each other.
2. Most team members know each other's 5.97 .58 15.49
tasks.
3. I have much confidence that my team 5.69 .66 17.94
members would be able to take over my
activities.
4. Exchanging team roles and 4.68 .48 12.50
responsibilities causes few problems.
Interteam Support (n = 7; α =
.83 at T1; α = .83 at T2)(One
Reversed Item)
1. Other teams act in a responsive 4.44 .55 16.03
manner when we forward customer
complaints.
2. The knowledge of other teams assists 4.93 .56 16.56
us in serving customers.
3. The quality of service delivered 4.64 .75 23.75
by other teams to our team is good.
4. Because of insufficient feedback 3.46 .53 15.28
from other teams our service to customers
is substandard. (reversed item)
5. Other teams provide good feedback 4.66 .54 15.80
on how to serve customers.
6. The cooperation between teams within 4.32 .85 28.56
the bank is good.
7. The employees of other teams are 4.71 .75 24.09
helpful in solving problems of customers.
Intrateam Support (n = 4; α =
.66 at T1; α = .68 at T2)
1. In our team we help each other in 5.06 .68 19.88
serving the customer.
2. The mutual support of team members 5.18 .79 23.57
is highly valued.
3. Each team member is personally 4.98 .51 15.03
responsible for the assistance of other
members in serving the customer.
4. In our team members need not formally 5.04 .52 14.67
be monitored with regard to the
assistance of colleagues.
Customer Data
Legend for Chart:
A - Measures
B - Item Mean
C - Loading
D - t-Value
A B C D
Customer Perceived Service Quality (n = 8;
α = .92)
1. The extent to which employees make clear
appointments 4.10 .62 26.17
2. Speed at which the promised information
is provided 4.04 .65 27.46
3. The friendliness and politeness of
employees 4.38 .72 31.71
4. The competence of the service by employees 4.07 .82 37.20
5. The time taken by employees to serve you 4.34 .75 33.12
6. The attention employees pay to you 4.25 .82 37.79
7. The extent to which employees show empathy 4.03 .81 37.83
8. The readiness of the employees to help you 4.25 .86 40.41
Legend for Chart:
A - Share of Customer
B - Nonroutine Services
C - Routine Services
A B
C
Average customer usage Mortgages, loans (53.6%)
rates (%) of the different Investment funds (52.7%)
services offered Stocks (43.9%)
Insurance (46.5%)
Checking account (95.2%)
Savings account (79.1%)
Electronic/telephone banking (30.3%)
Currency exchange (20.1%)
Credit application accounts (44.9%)
Travel services (18.7%)
(a) The CFA is based on employee data collected at T1.
Notes: All t-values are significant at p<.05. For employee data,
fit indexes are χ² = 925.40, 314 d.f.; goodness-of-fit
index (GFI) = .92; adjusted goodness-of-fit index (AGFI) = .91;
root mean square error of approximation (RMSEA) = .049; normed
fit index (NFI) = .90; and comparative fit index (CFI) = .93.
For customer data, fit indexes are χ² = 44.64, 20 d.f.;
GFI = .99; AGFI = .98; RMSEA = .036; NFI = .99; and CFI = 1.00. Legend for Chart:
A - Variables
B - Mean (s.d.)
C - 1
D - 2
E - 3
F - 4
G - 5
H - 6
I - 7
J - 8
K - 9
L - 10
M - 11
A
B C D E
F G H
I J K
L M
1. Education
4.23 (1.60)(a) -- -.07 .01
-.57(***) .35(***) .68(***)
.19 .42(***) -.06
-.07 .01
2. Tenure
3.41 (1.67)(b) -.28(***) -- .71(***)
-.18 .19 .22(*)
-.08 .01 .00
-.21 -.10
3. Age
2.62 (.85)(c) -.16(***) .70(***) --
-.15 .24(*) .27(**)
.18 .08 .15
-.31(**) -.01
4. Team size
20.44 (13.54)(d) -- -- --
-- -.40(***) -.69(***)
-.06 -.34(***) .09
-.05 .08
5. Front office (%)
47.03 (14.21)(d) -- -- --
-- -- .46(***)
.19 .27(**) -.04
-.16 .01
6. Nonroutine services
.59 (.50)(d) -- -- --
-- -- --
.13 .34(***) -.04
-.15 -.10
7. SMT service climate
5.43 (.82) .02 -.00 .02
-- -- --
-- .56(***) .48(***)
.08 .62(***)
8. Tolerance for self-management
5.54 (.89) .09(**) .04 .02
-- -- --
.37(***) -- .25(*)
.10 .34(***)
9. Flexibility of members
5.45 (.89) -.00 -.01 .01
-- -- --
.38(***) .24(***) --
.18 .53(***)
10. Interteam support
4.45 (.93) -.07(**) -.07(**) -.10(***)
-- -- --
.28(***) .17(***) .26(***)
-- .33(**)
11. Intrateam support
5.07 (.94) -.03 .03 .06
-- -- --
.49(***) .32(***) .46(***)
.33(***) --
(*) p < .10.
(**) p < .05.
(***) p < .001.
(a) Education consists of seven categories that include various
forms of secondary and tertiary education.
(b) Tenure consists of six categories that range from "<1 year"
to ">5 years."
(c) Age consists of six categories that range from "<21 years
old" to ">60 years old."
(d) Means (s.d.) of team size, front-office work, and nonroutine
services are based on the group averages.
Notes: N = 825 respondents of 61 groups. Individual-level
correlations are in the lower triangle, and group-level
correlations are in the upper triangle. Correlations in the
upper triangle are the correlations between the group averages. Legend for Chart:
A - Variables
B - Mean (s.d.)
C - N = 61 1
D - N = 61 2
E - N = 61 3
F - N = 61 4
G - N = 61 5
H - N = 61 6
I - N = 61 7
J - N = 61 8
K - N = 61 9
L - N = 61 10
M - N = 61 11
N - N = 61 12
O - N = 61 13
P - N = 61 14
A
B C D E
F G H
I J K
L M N
O P
1. Perceived service quality<sub>T2</sub>
4.17 (.16) --
.20
.11 -.02 .07
-.07(i) .14(i)
-.13(i) -.05(i)
2. Share of customer<sub>T2</sub>
2.34 (.65)(a) -.14 --
-.23(*)
.08 .33(**) .30(**)
-.14(i) -.14(i)
.14(i) .19(i)
3. Sales productivity<sub>T2</sub>
525.73 (477.92)(a) -.25(*) .63(***) --
-.33(**)
.07 .13 .19
-.26(*)(i) -.05(i)
.11(i) .27(**)(i)
4. Nonroutine services
.59 (.50) .24(*) -.69(***) -.97(***)
--
5. SMT service climate<sub>T1</sub>
5.42 (.35) .40(***) .12 -.21
.13 --
6. Tolerance for self-management<sub>T1</sub>
5.52 (.43) .37(***) -.12 -.38(***)
.34(***) .56(***) --
7. Flexibility of members<sub>T1</sub>
5.41 (.40) .28(**) .13 -.04
-.04 .48(***) .25(*)
--
8. Interteam support<sub>T1</sub>
4.44 (.48) .02 .33(***) .11
-.15 .08 .10
.18 --
9. Intrateam support<sub>T1</sub>
5.03 (.38) .30(**) .31(**) .02
-.10 .62(***) .34(***)
.53(***) .33(**) --
10. SMT service climate<sub>T2</sub>
5.44 (.35) .25(*)(i) -.11(i) -.32(**)(i)
.29(**)(i) .63(***)(i) .50(***)(i)
.42(***)(i) .07(i) .36(***)(i)
--
11. Tolerance for self-management<sub>T2</sub>
5.61 (.41) .06(i) -.18(i) -.37(***)(i)
.34(**)(i) .32(**)(i) .62(***)(i)
.16(i) .06(i) .14(i)
.47(***)(i) --
12. Flexibility of members<sub>T2</sub>
5.51 (.33) .23(*)(i) -.18(i) -.19(i)
.16(i) .42(***)(i) .36(***)(i)
.38(***)(i) -.08(i) .30(**)(i)
.46(***)(i) .40(***)(i) --
13. Interteam support<sub>T2</sub>
4.54 (.50) -.08(i) .12(i) .04(i)
-.04(i) -.06(i) .03(i)
.19(i) .57(***)(i) .20(i)
.19(i) .39(***)(i) .32(**)(i)
--
14. Intrateam support<sub>T2</sub>
5.17 (.52) .06(i) .12(i) .10(i)
-.11(i) .40(***)(i) .14(i)
.11(i) .24(i) .36(***)(i)
.41(***)(i) .32(**)(i) .50(***)(i)
.40(***)(i) --
(*) p < .10.
(**) p < .05.
(***) p < .001.
(a) There are large differences between routine and nonroutine
services with respect to share of customer (routine services, mean
[s.d] = 1.97 [.43]; nonroutine services, mean [s.d.] = 2.88 [.54])
and sales productivity (routine services, mean [s.d] = 1076.26
[171.32]; nonroutine services, mean [s.d] = 143.37 [67.77]).
Notes: (i) For italicized correlations, N = 56. Correlations
among variables are represented in the lower triangle.
Coefficients in the upper triangle are the partial correlations
between antecedents and outcomes. For the partial correlations
with antecedents at T1, we partialled out the effect of SMT
service climate<sub>T1</sub>; for antecedents at T2, we
partialled out the effect of SMT service climate<sub>T2</sub>. Legend for Chart:
B - Model A1 SMT Service Climate<sub>T1</sub> Coefficient
(Standard Errors)(a)
C - Model A1 SMT Service Climate<sub>T1</sub> Magnitude
Difference(b)
D - Model A2 SMT Service Climate<sub>T1</sub> Coefficient
(Standard Errors)(a)
E - Model A2 SMT Service Climate<sub>T1</sub> Magnitude
Difference(b)
F - Hypothesis
A
B C F
D E
Intercept
.573 (.713)
.827 (.766)
Individual-Level Control Variables
Education
-.017 (.017)
-.018 (.017)
Tenure
-.027 (.021)
-.021 (.021)
Age
.040 (.039)
.036 (.040)
Group-Level Control Variables
Education
-.015 (.063) .003 (.065)
-.011 (.067) .006 (.070)
Tenure
-.225 (.082)(**) -.198 (.085)(**)
-.219 (.084)(**) -.198 (.087)(*)
Age
.361 (.153)(**) .322 (.159)(*)
.317 (.160)(*) .277 (.167)(*)
Team size
.004 (.003)
.004 (.003)
Front office (%)
-.082 (.199)
-.085 (.208)
Nonroutine services
.179 (.101)(*)
.182 (.106)(*)
Individual-Level Antecedents
Tolerance for self-management
.220 (.032)(**) H<sub>1</sub>
.224 (.031)(**)
Flexibility of team members
.163 (.033)(**) H<sub>2</sub>
.170 (.033)(**)
Interteam support
.133 (.034)(**) H<sub>3a</sub>
.134 (.034)(**)
Intrateam support
.238 (.031)(**) H<sub>3b</sub>
.231 (.032)(**)
Group-Level Antecedents
Tolerance for self-management
.219 (.080)(**) .000 (.086) H<sub>4a</sub>
.193 (.082)(**) -.021 (.088)
Flexibility of team members
.160 (.094)(*) -.008 (.099) H<sub>4b</sub>
.217 (.099)(*) .055 (.105)
Interteam support
-.006 (.071) -.141 (.077)(*) H<sub>4c</sub>
-.039 (.074) -.172 (.080)(*)
Intrateam support
.497 (.100)(**) .262 (.105)(**) H<sub>4d</sub>
.460 (.102)(**) .224 (.107)(*)
Cross-Level Interactions: Individual-Level
Antecedents x Nonroutine Service Type
Tolerance
-.035 (.025)
Flexibility
-.042 (.028)
Interteam
-.021 (.027)
Intrateam
.034 (.027)
Group-Level Interactions: Group-Level
Antecedents x Nonroutine Service Type
Tolerance
H<sub>5a</sub>
-.007 (.031) -.003 (.031)
Flexibility
H<sub>5b</sub>
.074 (.033)(*) .086 (.034)(**)
Interteam
H<sub>5c</sub>
-.002 (.031) .002 (.031)
Intrateam
H<sub>5d</sub>
-.047 (.036) -.054 (.037)
Increase in Model Fit
χ² = 362.72 (21 d.f.)(**)
χ² = 11.41 (8 d.f.)
Explained Variance (%)
Individual level
37.9%
38.2%
Group level
58.9%
60.0%
(*) p < .05.
(**) p < .01.
(a) Unstandardized regression coefficients.
(b) We tested differences in magnitude between individual- and
group-level coefficients by means of raw-score analyses, and
these are reflected in the presented group-level coefficients.
Notes: Significance is based on one-tailed tests. Legend for Chart:
B - Model B1 (Lagged Analysis) SMT Service Climate<sub>T1</sub>
Coefficient (Standard Errors)(a)
C - Model B1 (Lagged Analysis) SMT Service Climate<sub>T1</sub>
Magnitude Difference(b)
D - Model B2 (Gibbs Sampling)(c) SMT Service Climate<sub>T2</sub>
Coefficient (Standard Errors)(a)
E - Model B2 (Gibbs Sampling)(c) SMT Service Climate<sub>T2</sub>
Magnitude Difference(b)
A
B C
D E
Intercept
-.255 (.967)
.842 (.773)
SMT Service Climate<sub>T1</sub>
.334 (.051)(**)
Individual-Level Control Variables
Education
-.001 (.024)
-.018 (.017)
Tenure
.000 (.030)
-.021 (.021)
Age
.141 (.060)(**)
.035 (.040)
Group-Level Control Variables
Education
-.064 (.086) -.045 (.094)
-.012 (.069) .006 (.071)
Tenure
-.189 (.092)(*) -.192 (.102)(*)
-.219 (.085)(**) -.196 (.088)(*)
Age
.496 (.188)(**) .363 (.207)(*)
.316 (.163)(*) .278 (.169)(*)
Team size
.004 (.004)
.004 (.003)
Front office (%)
-.308 (.280)
-.088 (.214)
Nonroutine services
.265 (.145)(*)
.182 (.107)(*)
Individual-Level Antecedents
Tolerance for self-management
.138 (.058)(**)
.223 (.032)(**)
Flexibility of team members
.164 (.073)(*)
.169 (.034)(**)
Interteam support
.089 (.049)(*)
.132 (.031)(**)
Intrateam support
.100 (.051)(*)
.232 (.031)(**)
Group-Level Antecedents
Tolerance for self-management
.183 (.099)(*) .019 (.118)
.194 (.084)(*) -.021 (.090)
Flexibility of team members
.121 (.119) .040 (.136)
.214 (.101)(*) .064 (.106)
Interteam support
.009 (.087) -.076 (.106)
-.039 (.075) -.168 (.083)(*)
Intrateam support
.335 (.121)(**) .242 (.138)(*)
.459 (.105)(**) .214 (.109)(*)
Cross-Level Interactions: Individual-Level
Antecedents x Nonroutine Service Type
Tolerance
-.009 (.038)
-.035 (.025)
Flexibility
.052 (.053)
-.041 (.027)
Interteam
.001 (.037)
-.023 (.025)
Intrateam
-.054 (.039)
.035 (.027)
Group-Level Interactions: Group-Level
Antecedents x Nonroutine Service Type
Tolerance
-.014 (.046) -.003 (.050)
-.007 (.031) -.002 (.032)
Flexibility
.078 (.044)(*) .083 (.048)(*)
.074 (.033)(*) .082 (.034)(**)
Interteam
.066 (.038)(*) .060 (.041)
-.002 (.032) .002 (.032)
Intrateam
-.082 (.052) -.058 (.057)
-.046 (.037) -.054 (.038)
Explained Variance (%)
Individual level
42.3%
37.7%
Group level
59.0%
57.9%
(*) p < .05.
(**) p < .01.
(a) Unstandardized regression coefficients.
(b) We tested differences in magnitude between individual- and
group-level coefficients by means of raw-score analyses, and
these are reflected in the presented group-level coefficients.
(c) Model B2: Gibbs sampling (10,000 iterations).
Notes: Significance is based on one-tailed tests. Legend for Chart:
B - Model C1 Coefficient (Standard Errors)(a)
C - Model C2 Coefficient (Standard Errors)(a)
D - Hypothesis
A
B
C D
SERVCLIMT1 → QUAL<sub>T2</sub>(b)
.941 (.308)(**)
.969 (.295)(**) H<sub>6a</sub>
NROUT → QUAL<sub>T2</sub>
.501 (.313)
.510 (.299) (*)
SERVCLIM<sub>T1</sub> x NROUT → QUAL<sub>T2</sub>
.336 (.134)(**) H<sub>8a</sub>
SERVCLIM<sub>T1</sub> → SHARE<sub>T2</sub>
.275 (.119)(**)
.268 (.118)(**) H<sub>6b</sub>
NROUT → SHARE<sub>T2</sub>
-.953 (.121)(**)
-.955 (.119)(**)
SERVCLIM<sub>T1</sub> x NROUT → SHARE<sub>T2</sub>
-.082 (.053) H<sub>8b</sub>
SERVCLIM<sub>T1</sub> → SPROD<sub>T2</sub>
-73.862 (30.076)(**)
-72.430 (29.961)(**) H<sub>7</sub>
NROUT → SPROD<sub>T2</sub>
-922.734 (30.539)(**)
-922.305 (30.403)(**)
SERVCLIM<sub>T1</sub> x NROUT → SPROD<sub>T2</sub>
16.811 (13.570) H<sub>8c</sub>
Residual Between-Groups
Covariance Terms
σ²<sub>h</sub> = var(e<sub>hj</sub>), (h = 1)
1.417 (.256)
1.298 (.235)
σ²<sub>h</sub> = var(e<sub>hj</sub>), (h = 2)
.211 (.039)
.206 (.037)
σ²<sub>h</sub> = var(e<sub>hj</sub>), (h = 3)
13,507.870 (2440.253)
13,385.760 (2423.142)
σ<sub>12</sub> = cov(e<sub>1j</sub>, e<sub>2j</sub>)
-.041 (.070)
-.006 (.066)
σ<sub>13</sub> = cov(e<sub>1j</sub>, e<sub>3j</sub>)
6.225 (17.704)
-.871 (16.872)
σ<sub>23</sub> = cov(e<sub>2j</sub>, e<sub>3j</sub>)
-7.755 (6.927)
-6.144 (6.773)
Increase in model fit:(c)
χ² = 232.45(6 d.f.)(**)
χ² = 10.32 (3 d.f.)(*)
Increase in model fit:(d)
χ² = 1.71(3 d.f.)
χ² = .85 (3 d.f.)
(*) p < .05.
(**) p < .01.
(a) Unstandardized regression coefficients.
(b) As we aggregated the scale of perceived service quality to
the group level, we calculated r<sub>WG(J)</sub>-, ICC
(1)-, and ICC (2)- coefficients (respectively .96, .04, and .51)
to determine within-group agreement and interdependence.
(c) Increase in model fit with inclusion of the predictor
variables.
(d) Increase in model fit with inclusion of the covariance terms
among the outcome variables.
Notes: Significance is based on one-tailed tests.DIAGRAM: FIGURE 1; Conceptual Framework
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~~~~~~~~
By Ad de Jong; Ko de Ruyter and Jos Lemmink
Ad de Jong is a postdoctoral researcher (e-mail: a.dejong@mw.unimaas.nl), Maastricht Academic Center for Research in Services
Ko de Ruyter is Professor of International Service Research and Director of the Maastricht Academic Center for Research in Services (e-mail: k.deruyter@mw.unimaas.nl)
Jos Lemmink is Professor of Marketing and Market Research and Department Chair (e-mail: j.lemmink@ms.unimaas.nl), Department of Marketing, Faculty of Economics and Business Administration, Maastricht University, The Netherlands.
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Record: 16- Antecedents and Outcomes of Marketing Strategy Comprehensiveness. By: Atuahene-Gima, Kwaku; Murray, Janet Y. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p33-46. 14p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.68.4.33.42732.
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Antecedents and Outcomes of Marketing Strategy
Comprehensiveness
Comprehensiveness has long been recognized as a key feature of marketing strategy decision making. However, few studies have examined its antecedents and the conditions under which it influences performance. This study attempts to contribute to a better understanding of marketing strategy by investigating project-level antecedents and outcomes of marketing strategy comprehensiveness (MSC). Drawing on contingency and institutional theories perspectives, the authors develop and test the effects of output and process rewards, task conflict, and project members' intra-and extra-industry relationships on MSC. They find that whereas process reward and extra-industry relationships are positively related to MSC, task conflict (when combined with conflict avoidance) hinders its development. Furthermore, the results indicate that MSC has a more positive effect on performance when implementation speed is higher. Finally, the authors discover that technology and market uncertainties differentially moderate the relationship between MSC and performance; the former has a positive effect, and the latter has a negative effect. The authors also discuss the theoretical and practical implications of their findings.
For several years, marketing scholars have dedicated considerable efforts to understanding the process of marketing strategy development and implementation (Bonoma 1984; Burke 1984; Glazer and Weiss 1993; Menon, Bharadwaj, and Howell 1996; Menon et al. 1999; Noble and Mokwa 1999). A key feature of this process is marketing strategy comprehensiveness (MSC), defined as the extent to which project members are extensive and exhaustive in the search for market information, the generation of many alternative courses of action, the examination of multiple explanations, and the use of specific criteria in making decisions in marketing strategy development and implementation. Comprehensiveness reflects the structure, rigor, and thoroughness of information search and analysis in marketing strategy decision making (Menon et al. 1999). It forces managers into a hypothesis-testing mode (Eisenhardt 1989, p. 558), thereby creating new insights that ensure a better understanding of marketing strategy and an increase in managerial confidence in decision making (Day 1994). Therefore, comprehensiveness is a key component of a quality marketing strategy (Menon, Bharadwaj, and Howell 1996). Despite its importance, the antecedents and conditions under which MSC influences performance have yet to be addressed in extant theoretical or empirical research. There is a good reason they should be: Increasingly, marketing managers go to great lengths and employ several techniques (e.g., consideration of obligatory alternatives, fishbowl reviews) (Menon et al. 1999) in an attempt to be comprehensive in making a marketing strategy.
The purpose of this study is to address this oversight. The first research gap concerns the variability in the extent to which firms develop comprehensive marketing strategies. Andrews and Smith (1996) study marketing strategy creativity at the individual level and find that the manager's risk-taking propensity, diversity of education, knowledge of the environment, intrinsic motivation, and interaction with others are key antecedents. Menon and colleagues (1999) find that innovative culture is positively related to MSC measured at the firm level. Although these studies acknowledge that most marketing strategies are formulated and implemented for specific products, they do not focus on the project level. Thus, knowledge of the internal and external determinants of MSC at the project level is limited.
A second research gap pertains to the relationship between MSC and performance. Several studies have examined the direct effects of strategy comprehensiveness on performance and the moderating role of environmental uncertainty at the firm level. However, empirical findings have been mixed on both fronts (e.g., Fredrickson 1984; Fredrickson and Mitchell 1984; Goll and Rasheed 1997; Priem, Rasheed, and Kotulic 1995). The mixed findings may stem from two causes. First, by using the firm as a unit of analysis, previous studies ignore the array of internal and external factors that may influence the effect of a specific strategic decision process on performance (Hough and White 2003). Second, previous studies examine environmental uncertainty as a unidimensional construct, thereby ignoring the different sources of uncertainty and their potential differential implications for the efficacy of strategy comprehensiveness.
Against this backdrop, in this study, we focus specifically on a project level in an attempt to contribute to the marketing literature in three ways. First, drawing on contingency theory, we offer explanations for some of the variance in MSC. A key argument in contingency theory is that a better understanding of the nature of organizational strategies is gained by examining its antecedents in the form of internal organizational and external environmental factors (Hofer 1975). Given that MSC involves complex information-processing tasks, we focus specifically on factors such as reward systems and task conflict, which affect the information-processing capacity of project members as internal determinants of MSC.
Second, we draw on institutional theory to explain that pressures for conformity may influence how internal factors such as task conflict affect MSC and how project members' external relationships act as means by which they acquire external market information in developing MSC. Empirical investigation of these issues is extremely important because most previous studies focus on which internal and environmental factors affect marketing strategies (e.g., Glazer and Weiss 1993) but pay little attention to the processes by which such factors influence managers in developing or adapting their strategies (Zeithaml, Varadarajan, and Zeithaml 1988, p. 40).
Third, we examine the conditions under which MSC influences performance, defined as the extent to which a product, which is the focus of a marketing strategy, has achieved planned sales, market share, and profit objectives. Contingency theory also informs this issue by arguing that firms that can adapt their strategies effectively to both internal and external factors are likely to perform better (Hofer 1975; Zeithaml, Varadarajan, and Zeithaml 1988). We argue that given the costs involved in achieving a high level of MSC and possible diminishing returns to its benefits, it is likely that a level of MSC that is either too low or too high may hurt performance, but a moderate level may be optimal. This implies that managerial ability to develop a level of MSC that is consistent with organizational resources may determine its success. We examine the nonlinear relationship between MSC and performance that is implied by this contingency argument.
Scholars have long recognized that strategy implementation plays a potentially important role in the linkage between a marketing strategy and performance (Bonoma 1984; Noble and Mokwa 1999). We contribute to this tradition by investigating the moderating effect of implementation speed. Finally, Song and Montoya-Weiss (2001, p. 61) argue that "uncertainty should be studied in relation to specific components of the environment in order to properly attribute its effects." Following this insight, we advance the literature by investigating two sources of environmental uncertainty (technology and market uncertainties) in an attempt to uncover their differential moderating effects between MSC and performance. Through this richer framework and empirical assessment, we attempt to respond to Menon and colleagues' (1999, p. 36) call for research that "examines more complex models of antecedents and outcomes of [marketing strategy making]." Figure 1 presents the conceptual model.
Internal Antecedents of MSC
Drawing on contingency theory (Hofer 1975) and extant marketing strategy research (Andrews and Smith 1996; Burke 1984; Menon et al. 1999), we identify internal conditions that influence effective information processing in strategy making as determinants of MSC. Market information for strategy making tends to contain tacit components that retard collective interpretation and information sharing. Even when market information is made explicit in a codified routine and communicated to project members, some of them may not understand it because they interpret and apply the knowledge in a different functional and experiential context. Consequently, market information collection, analysis, and interpretation in marketing strategy making for a specific product is a complex process that involves the understanding of multiple functions (Menon, Bharadwaj, and Howell 1996; Menon et al. 1999). For this reason, development of a comprehensive marketing strategy requires internal processes that focus project members' attention on and commitment to effective market information processing (Noble and Mokwa 1999). Two internal processes that are capable of engendering effective generation and sharing of market information in marketing strategy development are reward systems (Burke 1984; Jaworski and Kohli 1993; Ruekert and Walker 1987) and task conflict among project members (Menon, Bharadwaj, and Howell 1996). Noting the potential importance of the former, Menon and colleagues (1999, p. 35) call for further research to explore how output-and process-based reward systems influence marketing strategy development. We respond to their call in this study.
Output reward refers to a process of monitoring and compensating project members for achieving desired performance targets, such as meeting deadlines, budgets, and target market success (e.g., sales volume, market share). Output reward provides incentives and responsibilities for results, thereby ensuring that project members eschew politics and commit to the strategy-making process. Reduced politics occurs with an increased focus on the content and objectives of the market strategy, whereas commitment occurs with the collective efforts of project members directed toward diverse information collection and in-depth analysis of strategic options (Noble and Mokwa 1999). In addition, output reward shifts performance risk from the organization to the project members because environmental and company factors that may affect the outcome of the marketing strategy are beyond the project members' control (Oliver and Anderson 1994). A key means of attenuating the risks is to increase the quality of strategy with thorough information search and evaluation. By allowing autonomy over the means of achieving outcomes, output reward offers an incentive for project members to be comprehensive in attenuating performance risk (Atuahene-Gima and Li 2002).
Process reward is a means of monitoring and compensating project members for completing specified procedures and activities that are critical to achieving desired objectives in marketing strategy development. A key objective of process reward is to ensure thorough market information search and analyses, particularly about customers and competitors at specific stages of the strategy-making process. When rewards are tied to the completion of such process activities, project members believe that they will be rewarded for the quality of their strategies. Thus, process reward increases members' propensity to find and discuss a wider range of marketing strategy options (Menon, Bharadwaj, and Howell 1996, p. 308). A characteristic of MSC is the diversity and extensiveness of search for alternative courses of action, which requires project members to explore beyond the firm's boundary. Because such exploration increases the risk of errors in strategy making, there must be some level of protection for project members. We argue that a process reward establishes a norm of risk sharing between the firm and project members, thereby encouraging them to be expansive and rigorous in marketing strategy making (Atuahene-Gima and Li 2002; Oliver and Anderson 1994). Therefore:
H[sub1]: The greater the (a) output reward and (b) process reward, the greater is the MSC.
The task conflict construct refers to the debate and disagreements among group members about the content, goals, and processes of marketing strategy development (Jehn 1995). Task conflict is a key resource in group decision making because the vigorous debate and disagreements among project members encourage them to gather new data, to delve into issues more deeply, and to gain a more complete and expansive understanding of problems to develop alternative solutions (Jehn 1995; Ruekert and Walker 1987). Despite these benefits, task conflict can be misinterpreted as personal criticism or misconstrued as a challenge to the competence of the project members, particularly by people who are perceived as experts on the issues under discussion (Pelled, Eisenhardt, and Xin 1999). This may limit the expansive and barrier-breaking thinking that is required for comprehensiveness. Institutional theory argues that organizational culture and political processes tend to perpetuate conformity within a firm or group. This suggests that given the tacitness of knowledge and the perceived power of experts, pressures to conform to dominant views and interpretations may render task conflict ineffective in enhancing MSC. On balance, then, the effect of task conflict on MSC is unclear.
Consistent with contingency theory, we posit that the impact of task conflict on MSC may depend on the conflict resolution strategy adopted (Ruekert and Walker 1987). Although there are several conflict resolution methods, Song, Xie, and Dyer (2000) suggest that collaboration and avoidance are the ideal and worst types, respectively, and are the types most often adopted by marketing managers. In this study, we focus on collaboration and avoidance for the sake of parsimony. Collaborative conflict resolution refers to the extent to which project members confront conflicts by openly exploring areas of differences and commonality to find integrative solutions that are in the best interests of the strategy-making process. Collaborative conflict resolution is likely to enhance the positive effect of task conflict on MSC for two reasons. First, it reduces the uncertainties and misattribution associated with task conflict because it ensures that people understand the concerns and perspectives of others. This enables project members to concentrate on the content of discussions rather than on personality issues (Song, Xie, and Dyer 2000). Second, collaborative conflict resolution creates a sense of common dependence, thereby enhancing the willingness of project members to share information and to explore strategic options, which in turn enhances MSC.
Conflict avoidance refers to the extent to which project members avoid, ignore, or smooth over conflicts. Thus, conflict avoidance describes behaviors that minimize conflicts explicitly. Conflict avoidance reduces productive interactions among project members and therefore severely limits the timely collection and use of accurate and quality information (Song, Xie, and Dyer 2000). In addition, conflict avoidance inhibits open communication and exchange of quality information because project members focus less on the content of dissenting information and more on the intentions and motivations of the members who provide the information. Consequently, project members tend to pursue self-interested motives that limit the scope of exploration of market information and strategic options (Jehn 1995).
H[sub2]: Task conflict has a more positive effect on MSC when collaborative conflict resolution is higher than when it is lower.
H[sub3]: Task conflict has a more negative effect on MSC when conflict avoidance is higher than when it is lower.
External Antecedents of MSC
Contingency theory also recognizes the importance of external environmental determinants of firm strategy (Hofer 1975). Given their cognitive and resource limitations, decision makers tend to rely on external referents for information and insight into plausible strategic alternatives (Cyert and March 1963). In this respect, institutional theory implies that managers' external ties serve as conduits for information that shape managerial views about the environment and the strategic choices they make. The key idea of this theory is that firm strategies and practices are embedded in social relationships and may have a social meaning. Consequently, managers are affected by conformity and legitimacy pressures to adopt prevailing strategies (DiMaggio and Powell 1983; Meyer and Rowan 1977). In support of this theory, Geletkanycz and Hambrick (1997) find that managers' external relationships with other managers within and outside their industry influence their propensity to conform to prevailing organizational strategies. Given the increasing focus on MSC among marketing managers (Menon et al. 1999), this suggests that project members' ties with managers within and outside the firm's industry are antecedents of MSC.
Intraindustry relationships are project members' ties with managers in the same industry as the focal firm. Through such relationships, project members gain more comprehensive knowledge of industry strategic norms and recipes and more insight into the nature and context of the marketing strategies of other firms. This allows for greater diversity of perspectives that enhance members' search and analysis of strategic alternatives. Extraindustry relationships are project members' ties with managers outside the focal industry. Extraindustry relationships provide project members with an even broader range of information about strategies of firms outside the focal industry (Geletkanycz and Hambrick 1997). Such relationships increase the strategic options considered for selection in marketing strategy making. Thus:
H[sub4]: The greater the (a) intraindustry relationships and (b) extraindustry relationships of the project members, the greater is the MSC.
Performance Effect of MSC
Nonlinear effect. A key argument in the extant literature is that strategy comprehensiveness enhances performance because by generating diverse information about the market environment and by identifying the strengths and weaknesses of several strategic options, the firm is in a better position to implement a strategy more effectively (Eisenhardt 1989, p. 558; Menon, Bharadwaj, and Howell 1996, p. 308; Menon et al. 1999, p. 26). Although there may be good reasons for this argument, there are also significant costs associated with MSC. Indeed, some scholars have argued that given the cognitive limitations and bounded rationality of decision makers, strategy comprehensiveness is nearly impossible because of the high cost and time-consuming processes of information acquisition and analysis (Bahaee 1992). These arguments imply that too much or too little MSC may diminish performance, thus suggesting that it has a positive effect on performance only at a moderate level. From a contingency theory perspective, this suggests that managers who are able to determine the level of MSC that reflects their cognitive and other internal resources will derive higher performance from MSC. Thus:
H[sub5]: There is an inverted U-shaped relationship between MSC and performance, such that at extremely high and low levels its effect on performance is negative, but at moderate levels its effect on performance is positive.
Moderating effect of implementation speed. Extant research argues that a marketing strategy is more likely to result in better performance when it is implemented successfully (Bonoma 1984; Noble and Mokwa 1999). Although strategy formulation cannot be completely disassociated from its implementation, such a conceptual distinction enables researchers to better identify the discrete, albeit overlapping, aspects of strategy making that in combination may affect performance (Eisenhardt 1989). A key aspect of successful strategy implementation is implementation speed, defined as the pace of activities between the time project members formulate a marketing strategy and the time they fully deploy it in the marketplace. Implementation speed captures the acceleration of the decision-making activities from their conception to their implementation. Implementation speed may enhance performance by itself because it implies an ability to deal with potential hindrances to the efficacy of a marketing strategy and to ensure first-to-market benefits for a product (Noble and Mokwa 1999). In addition to its potential direct effect on performance, we posit that implementation speed enhances the positive effect of MSC on performance. The logic is that the formulation of a comprehensive marketing strategy is inherently a slow process because it takes a lot of time to consider many alternatives; to obtain input from many sources; and to engage in extensive, in-depth analysis. Thus, MSC is likely to achieve positive performance effects if project members can implement it speedily. Speedy implementation enables the firm to tap quickly into the window of opportunities uncovered by the process of strategy making. For example, Eisenhardt (1989) finds that decision making in the most successful companies is simultaneously fast and comprehensive. In contrast, slow implementation may exacerbate the costs associated with being comprehensive, thereby diminishing the impact of MSC on performance.
H[sub6]: The effect of MSC on performance is more positive when implementation speed is higher than when it is lower.
Moderating effect of technology and market sources of uncertainty. Technology uncertainty is the speed of change and instability of the technological environment. The conventional wisdom is that technology information is highly time sensitive; that is, it becomes obsolete quickly (Weiss and Heide 1993). Such information is believed to be of a dense variety, reflecting a high frequency of unexpected and novel changes, which thus makes it difficult for firms to respond with objective and formal procedures. Technology information is also perceived as highly equivocal, which means that it has multiple and ambiguous underlying meanings and causes that defy specific analysis and uniform interpretation (Daft and Macintosh 1981). In addition, technology uncertainty tends to disrupt the balance between project resource needs and available firm resources and skills. As Song and Montoya-Weiss (2001) find, technology uncertainty disrupts synergies among project members' resources and skills and synergies needed for effective strategy making. This suggests that with the existence of technology uncertainty, MSC is likely to diminish performance.
A counterargument is that whereas technological uncertainty is time sensitive, it is nevertheless amenable to effective comprehensive strategic processes because it leads managers to increase their information search efforts (Weiss and Heide 1993). For example, Bourgeois and Eisenhardt (1988) find that successful firms adapt to rapid technological changes by adopting a strategic decision process that involves comprehensive information search and thorough analysis of strategic alternatives. The key argument in their findings is that the perceived time sensitivity of technology information is low because the direction of technological change tends to be predictable. As Pavitt (1998) shows, technological change tends to progress along defined trajectories, such that firms can recognize and understand the directions of change. In this respect, Glazer and Weiss (1993, p. 510) also argue that "higher levels of interperiod change that are predictable may not be troublesome" in marketing strategy making. This suggests that MSC becomes more important for performance when technology is uncertain, because project members can identify critical decision variables to allow for a more expansive and effective analysis of strategic options. These equivocal arguments suggest the following competing hypotheses:
H[sub7a]: Marketing strategy comprehensiveness has a more positive effect on performance when technology uncertainty is higher than when it is lower.
H[sub7b]: Marketing strategy comprehensiveness has a more negative effect on performance when technology uncertainty is higher than when it is lower.
Market uncertainty refers to the speed of change in competitor actions and customer needs and preferences (Jaworski and Kohli 1993). Market uncertainty involves significant pace of change, heterogeneity, and unpredictability of customer needs and competitor actions, all of which tend to curtail deliberate and expansive information search efforts and to defy precise and comprehensive analysis (Glazer and Weiss 1993). For this reason, project members require rapid and flexible strategic processes to enhance performance in such an environment. Yet MSC is time consuming and less flexible, which suggests that it is of little value to performance when market uncertainty is high, because strategic decisions quickly become irrelevant (Bahaee 1992, p. 210). In addition, firms typically collect customer and competitor information by analyzing customers' choice criteria and attribute comparisons of the firm's products with competitors' products (Day and Wensley 1988). Such practices tend to result in institutionalized analyses and responses in strategy making in highly uncertain market environments that diminish performance. In support of these arguments, Glazer and Weiss (1993) find that market information is highly time sensitive and is not conducive for obtaining effective MSC outcomes.
Although the preceding arguments are persuasive, there is a contrary viewpoint to them. Market uncertainty prompts firms to reach out to customers (Li and Calantone 1998), which leads to an enhanced understanding of emerging customer needs and competitor actions (Jaworski and Kohli 1993). This means that market information may be less equivocal to project members and therefore is amenable to specific analysis and interpretation. As Daft and Macintosh (1981, p. 208) argue, when information is analyzable, use of an objective analytical process in strategy making enhances performance because correct responses can usually be identified. In light of these divergent arguments, we posit the following competing hypotheses:
H[sub8a]: Marketing strategy comprehensiveness has a more positive effect on performance when market uncertainty is higher than when it is lower.
H[sub8b]: Marketing strategy comprehensiveness has a more negative effect on performance when market uncertainty is higher than when it is lower.
Sample and Data Collection
We drew our sample from a mailing list of U.S. manufacturing firms that we obtained from Thomson Directory. We made telephones calls to identify project managers who met two selection criteria: ( 1) were involved in and ( 2) were knowledgeable about marketing strategy decision making for the most recent product introduced to market by the firm. We identified 393 company informants who met our selection criteria. We ensured that the informants were professionally interested, conscientious, and committed to providing accurate data by assuring them of confidentiality and by offering them a summary of the results. Subsequent to two follow-up reminders, we received 149 usable questionnaires, for a response rate of 38%. Of the sample, 70% of respondents worked in the high-technology industry: information technology, computers, and software (20%); electronics and electrical and scientific equipment (20%); pharmaceutical and biotechnology (12%); and automotive components (18%). The rest worked in low-technology industries: food (8%); forest, paper, and building products (13%); and other (e.g., footwear, clothing) (9%). Following the work of Menon and colleagues (1999), we pooled the data because the analysis of variance test showed that the constructs did not differ significantly (p > .10) among the industry groupings. The average project size in the sample was 5.66 people (standard deviation [s.d.] = 3.95). The average market duration for the product (defined as the number of months the product has been offered for sale in the market) was 17.5 months (s.d. = 15.09). T-test analysis showed no significant differences (p > .10) in the study variables between early and late respondents, which suggests that nonresponse bias is not a major concern.
Previous studies (e.g., Li and Calantone 1998; Menon, Bharadwaj, and Howell 1996; Menon et al. 1999) have found that project managers in senior positions, such as chief executive officer, vice president, and marketing manager, are reliable sources of information about marketing strategies. Of our project manager informants, 55% listed their job titles as marketing manager, 9% as vice president of marketing, 11% as product manager, 17% as chief executive officer, and 8% as engineering manager. The average work experience of informants in their firms was 9.95 years. Their degree of involvement and knowledge about the marketing strategy on a ten-point scale (see Conant, Mokwa, and Varadarajan 1990) was a high 8.89. These characteristics of the informants imply that they had the knowledge and confidence to respond to the issues under study.
Measures of Constructs and Validity
Table 1 presents the measures and their sources. We pretested the instrument in interviews with 35 part-time MBA students who had a minimum of three years of business experience. We obtained feedback that pertained mainly to ambiguities or difficulties in responding to the items and suggestions for adaptations to ensure the clarity and appropriateness of items. We revised the instrument accordingly. We defined marketing strategy development as involving the determination of decisions (e.g., product design, development, promotion, pricing, distribution) that require large resource commitments and long time horizons and are difficult to reverse in the short run.
We measured performance (α = .84) with three items by asking respondents to indicate the extent to which the product has achieved its sales, market share, and profit objectives since its launch. We also asked respondents to indicate on a single item the degree to which the overall performance of the product has met management expectations (1 = "well below expectations," and 10 = "well above expectations"). These two measures have a high correlation of .72 (p < .001), which suggests that there is convergent validity. We measured MSC (α = .91) with five items by asking informants to rate, for example, the extent and depth of the search for strategic alternatives. We measured output reward (α = .89) with four items by asking informants to indicate the degree to which rewards for project members were based, for example, entirely on performance outcomes. We measured process reward (α = .87) with four items by asking informants, for example, the degree to which rewards were based on the quality of strategic decisions.
We measured task conflict (α = .88) with five items that tapped the degree of disagreements among project members about ideas, goals, and processes adopted in the strategy-making process. We measured collaborative conflict resolution (α = .81) with four items that examined the degree to which the project members confronted and collaborated to resolve conflicts in the strategy-making process. Similarly, we measured conflict avoidance (α = .72) with four items that reflected the extent to which the project members refrained from confronting the conflicts. We captured intraindustry relationships (α = .88) and extraindustry relationships (α = .96) with four new items based on Geletkanycz and Hambrick's (1997) conceptual descriptions and project members' contacts with managers within and outside the industry, respectively.
We measured implementation speed (α = .78) with four new items that tapped the degree to which the strategy implementation was timely and faster than the planned schedule. We measured technology uncertainty (α = .90) with four items that pertained to the unpredictability of changes in technology and the rate of product introductions. The five items measuring market uncertainty (α = .75) reflected the speed of change of customer demand and competitor actions.
We controlled for several variables. We used firm size, measured by the number of employees, to control for greater complexity and economies of scale in large firms in strategy making. We used project size, measured by the number of people who have significant influence in marketing strategy decision making, to control for interaction dynamics that affect performance of groups. We coded industry as high technology ( 1) and low technology (0). We included product advantage (α = .73) (measured with three items that reflected the quality of the product, compared with competitors' products and previous products of the firm) and market duration of the product (measured by the number of months the product has been on the market) because each is a likely antecedent of performance.
We sought to control for common method bias by encouraging respondents to seek multiple responses to the questionnaire. A t-test analysis indicated no significant differences for all the study variables between the 20% of questionnaires completed by multiple informants and the 80% of questionnaires completed by single informants. We conducted a statistical check for common method variance with the Harman one-factor method (Podsakoff and Organ 1986). If common method bias is a serious problem, a single factor should emerge or one general factor should account for most of the variance. A principal components factor analysis of all measures yielded 13 factors with eigenvalues greater than 1.0, with total explained variance of 73%. Because several factors were uncovered and the first factor accounted for only 15% of the variance, common method bias may not be a serious problem (Menon et al. 1999, p. 31). Finally, we tested several interaction effects that could not be explained by common method bias because informants could not have guessed the complex relationships involved (Aiken and West 1991; Evans 1985).
In a confirmatory factor analysis, each measure loaded significantly on the expected constructs, which demonstrates convergent validity. Together, the factor loadings and model fit indexes (goodness-of-fit index = .89, root mean square error of approximation = .06, comparative fit index = .91, and nonnormed fit index = .93) presented in Table 1 suggest that the model fit is acceptable. Table 2 reveals that the diagonal elements representing the square roots of the average variance extracted (AVE) for each of the constructs are greater than the off-diagonal elements, which satisfies the criterion of discriminant validity (Fornell and Larcker 1981; Sakar, Echambadi, and Harrison 2001). Finally, the constructs' previously reported alpha and the composite reliabilities (CRs) presented in Table 2 indicate that each exceeded the accepted reliability threshold of .70. Table 2 presents the correlations and descriptive statistics of the constructs. We used the average score of measures of each construct for further analysis.
Model Specification and Estimation
( 1) MSC = α[sub0] + α[sub1] (OUTR) + α[sub2](PROP) + α[sub3](TASKC) + α[sub4](COLLA) + α5(AVOID) + α[sub6](INTRA) + α[sub7](EXTRA) + α[sub8](TASKC X COLLA) + α[sub9](TASKC X AVOID) + α[sub10](CON[sub1-4]) + ε[sub1], and
( 2) PDPERF = β[sub0] + β[sub1](MSC) + β[sub2](IMPSD) + β[sub3](MKTUN) + β[sub4](TEKUN) + β[sub5](MSCSQ) + β[sub6](MSC X IMSPD) + β[sub7](MSC X MKTUN) + β[sub8](MSC X TEKUN) + β[sub9](CON[sub1-12]) + ε[sub2],
where
MSCSQ = squared term for marketing strategy comprehensiveness, OUTR = output reward, PROR = process reward, TASKC = task conflict, COLLA = collaborative conflict resolution, AVOID = conflict avoidance, INTRA = intraindustry relationships, EXTRA = extraindustry relationships, PDPERF = performance, IMPSD = implementation speed, MKTUN = market uncertainty, TEKUN = technology uncertainty, and CON = control variables.
As Aiken and West (1991) recommend, we meancentered relevant variables before we created the interaction terms. The variance inflation factors in the regression models were all less than 2, which indicates that multicollinearity is not a serious problem. Table 3 contains the results.
Model 1 in Table 3 tests the effects of the control variables on MSC. Model 2 adds the main effects of the antecedent variables, which contribute 42% (ΔF = 12.82, p < .001) more than the variance explained by the control variables. The addition of the interaction terms in Model 3 added 3% (ΔF = 2.60, p < .01) to the explained variance obtained in Model 2. H[sub1a], which predicts that output reward is positively related to MSC, is not supported. Process reward is positively related to MSC, in support of H[sub1b] (α = .24, p < .01). H[sub2], which suggests that the relationship between task conflict and MSC is more positive when collaborative conflict resolution is higher than when it is lower, is not supported. Rather, the results show that collaborative conflict resolution is a positive predictor of MSC (α = .15, p < .05). H[sub3] is supported because the relationship between task conflict and MSC is more negative when conflict avoidance is higher than when it is lower (α = -.16, p < .01). [H[sub4a], which posits a positive link between intraindustry relationship and MSC, is not supported. The link between extraindustry relationship and MSC is positive (α = .36, p < .001), in support of H[sub4b].
Model 4 in Table 3 reports the main effects of the control variables on performance. Note that in this model, we also controlled for process and output rewards (Atuahene-Gima and Li 2002), intra-and extraindustry relationships (Geletkanycz and Hambrick 1997), and task conflict and conflict resolution methods (Jehn 1995) because previous studies suggest that they can influence performance. Model 5 adds the main effects of the antecedent variables, which contribute 5% (ΔF = 1.73, p < .10) more than the variance explained by the control variables. Model 6 adds the squared term for MSC and the interaction terms. These variables increased explained variance by 8% (ΔF = 2.92, p < .01) more than the explained variance we obtained in Model 5. The data do not support H[sub5], which predicts a nonlinear relationship between MSC and performance (β = .07, not significant). In support of H[sub6], the data in Model 6 show that MSC has a more positive effect on performance when implementation speed is higher than when it is lower (β = .24, p < .001). H[sub7a] is supported because the relationship between MSC and performance is positively moderated by technology uncertainty (β = .18, p < .05). H[sub8b] is supported because market uncertainty negatively moderates the effect of MSC on performance (β = -.23, p < .01). Regarding the control variables, product advantage (β = .38, p < .001) and task conflict (β = .16, p < .10) are positively related to performance.
This study examines the antecedents and conditions under which MSC affects performance at the project level. The results suggest that by rewarding process activities in marketing strategy development, managers provide significant incentives for project members to broaden the search for strategic alternatives and to deepen their analysis in marketing strategy development. This finding builds on Menon, Bharadwaj, and Howell's (1996) finding that formalization is important for developing quality marketing strategy. It also provides evidence that formalized reward systems enhance strategy comprehensiveness (Fredrickson 1986). A key finding is that the positive effect of task conflict on MSC is completely buffered by conflict avoidance, such that it becomes negative. It appears that open debate, diversity of opinions, and interpretations of market information can often generate adverse reactions from people whose views are criticized (Jehn 1995). The avoidance of such conflicts stifles the positive effect of task conflict on MSC. It is surprising that the collaborative conflict resolution method does not moderate the link between task conflict and MSC, as we predicted. These findings suggest that conflict avoidance and collaborative conflict resolution methods are related phenomena but are not opposite ends of the same spectrum.
Extraindustry relationships, but not intraindustry relationships, significantly enhance MSC. By having extraindustry relationships, project members gain exposure to diverse information and perspectives, which help in the discovery and analysis of diverse alternative strategic options. Contrary to our expectations, intraindustry relationships have no relationship with MSC. A possible reason for this finding is that such relationships tend to limit project members' capacity to envision multiple and different strategic alternatives in marketing strategy making. Together, the findings illuminate the enhanced insights that can accrue from combining both contingency and institutional theories as an explanatory platform for MSC.
The results on the contingent relationship between MSC and performance advance the literature on three fronts. First, we bring some clarity into the literature by showing that the relationship between MSC and performance is not concave. Indeed, MSC has no significant, direct effect on performance in this sample. Second, MSC has a positive effect on performance when it is combined with implementation speed. Implementation speed, by itself, is negatively related to performance. This finding could suggest that because it is a slow process, MSC and implementation speed are contradictory, such that extreme comprehensiveness may have a strong dilutive effect on the benefits of speedy implementation. It is also possible that a firm has a relatively slow strategy development process but speeds up its implementation process to market a product quickly, thereby enhancing performance (Eisenhardt 1989).
We explored these arguments in a post hoc analysis. We created a typology of four groups of firms using the median split method and compared them using analysis of variance. Firms with high MSC and implementation speed (49 firms) had significantly higher performance mean scores (3.97) than did firms with low MSC and high speed (3.41) (38 firms), which in turn had higher mean scores than firms with high MSC and low speed (2.98) (32 firms), which in turn did not differ from the 18 firms that had low MSC and low speed (2.56) (F = 7.79, p < .01). The results suggest that though MSC is important, it must be implemented speedily to achieve positive performance effects, which reinforces the importance of strategy implementation in extant marketing strategy research (Noble and Mokwa 1999).
Third, on the premise that technology and market uncertainties may have different implications for the effect of marketing strategy making on performance, we examined their differential moderating effects. Technology uncertainty positively moderates the effect of MSC on performance, in support of the argument that it provides a more conducive environment for MSC because the direction of change may be recognizable. That is, the time sensitivity of technology information may be low and therefore amenable to systematic analysis and comprehension (Pavitt 1998). The argument that technology information is unsuited for effective MSC is therefore not supported by our data. In contrast, MSC diminishes performance when market uncertainty is high. This lends support to the view that market information is highly time sensitive and that strategy making requires more real-time information flow than can be provided through a formal, comprehensive process (Glazer and Weiss 1993).
In summary, our study provides empirical support for the argument that components of the environment differ in their perceived time sensitivity and information-processing demands on project members in marketing strategy making. This extends the work of Glazer and Weiss (1993) and indicates the perils of a simple categorization of the environment into certain and uncertain. Our study suggests that if distinctions are not made among sources of uncertainty, insights into the complexity of the moderating effect of environmental uncertainty on the relationship between MSC and performance may be obscured.
Managerial Implications
The findings of our study suggest that to engender MSC, managers must reward project members for adherence to specific processes that ensure rigor and thoroughness in information collection and analysis. In addition, managers should encourage project members to cultivate relationships with people outside their own industries to gain both new insights and an expanded perspective in marketing strategy development. This could involve a provision of specific training and resources that enhance the external relationships of project members. Finally, managers must note that avoiding conflicts that result from task disagreements may hurt the level of MSC.
The results of this study also caution managers that an unquestionable positive view may be too simplistic, because its impact on performance is moderated by both internal and external factors. Specifically, the study suggests the need for managers to pay more attention to enhancing their firm's strategy implementation capability, because speedy implementation appears to alleviate the costs of the inherent slowness of MSC. The differential moderating effects of technology and market uncertainties suggest to managers that these sources of uncertainties create different information-processing demands in strategic decision making. Therefore, managers must understand the nature of the information-processing expertise required under each environmental condition in undertaking MSC to increase its performance effects. These findings underscore the need for managers to be more proactive in training project members to acquire the appropriate information-processing skills for marketing strategy making.
Limitations and Directions for Further Research
This study has some limitations. First, although we followed previous research (Li and Calantone 1998; Menon, Bharadwaj, and Howell 1996; Menon et al. 1999) in using a single, knowledgeable project manager who held a senior-level position as our informant, the results may be subject to single-informant bias. A reasonable argument can be made that such senior managers' knowledge about a marketing strategy may be of a summary, top-line nature and likely reflects a positive bias. Further research that uses middle managers as informants would help clarify whether the results reported herein are sensitive to key informants' level of seniority.
Second, because the focus of our study was MSC at the project level, it cannot speak directly to the contradictory findings reported by studies of MSC at the firm level. However, we believe that future studies that test the thrust of our theoretical model at the firm level can provide a means for resolving the previous discordant findings. Third, although objective performance measures would have been more desirable, they are usually unavailable at the project level. However, we note that subjective measures of performance continue to be useful in studies of marketing strategy development (e.g., Menon, Bharadwaj, and Howell 1996; Menon et al. 1999) and may provide the best option given differences in the nature of industries, time horizons, economic conditions, and goals of the sample firms. Finally, the generalizability of the findings is limited because our sample is not representative of U.S. firms.
In addition to alleviating the limitations of this study, there are other fertile avenues for further research in this domain. First, further research should examine other project-level antecedents because we explained only 46% of the variance in MSC. Second, we find that collaborative conflict resolution does not moderate the effect of task conflict on MSC. Xie, Song, and Stringfellow (1998) identify several other conflict resolution methods, such as competition, accommodation, compromise, and hierarchical methods. The extent to which such methods moderate the effect of task conflict on MSC should be examined in further research. Third, our study examined intra-and extraindustry relationships with a specific focus on managers. However, consultants, suppliers, and customers may be sources of input into marketing strategy development. Studying project members' relationships with these external constituents will enrich the understanding of how MSC emerges.
Fourth, future researchers should further explore the internal and external conditions under which MSC affects other performance outcomes. In particular, implementation factors such as strategic and role commitment (Noble and Mokwa 1999) and cross-functional integration (Menon et al. 1999) require investigation. Fifth, although several previous studies have reported differential moderating effects of sources of uncertainty in the relationship between marketing strategy and performance (e.g., Atuahene-Gima 1995; Glazer and Weiss 1993; Jaworski and Kohli 1993; Weiss and Heide 1993), few have offered a theoretical rationale for the observed differences. Given the importance of environmental uncertainty to the understanding of marketing phenomena, our distinction between the differential moderating roles of technology and market uncertainties is a valuable addition to the literature. The theoretical differences advanced herein hold promise for further research that examines how marketing strategy and other marketing capabilities are influenced differentially by these and other sources of uncertainty.
Finally, some scholars would argue that our description of MSC is an incomplete view of the reality of strategic decision making because it fails to recognize the cognitive limitations of managers and their resource limitations in searching for and interpreting information (Bahaee 1992; Cyert and March 1963). Such scholars would point to incremental decision making that involves experienced-based mental routines that produce answers automatically without apparent formal information search and evaluation. Further research should examine the determinants and outcomes of such decision-making processes in marketing strategy making.
The authors thank the three anonymous JM reviewers for their support and constructive comments on previous versions of this article. This article also benefited from comments and suggestions by seminar participants at the Center of Innovation Management and Organizational Change, Department of Management, City University of Hong Kong. The work described in this article was fully supported by Strategic Research Grant No. 7000750 from City University of Hong Kong.
Legend for Chart:
A - Measures and Sources
B - Description
C - Standardized Factor Loading
D - t-Value
A
B C D
Performance
CR = .80
AVE = .68
(Atuahene-Gima 1995)
To what extent have the objectives for marketing
the product been achieved with respect to
• Sales .85 12.34
• Market share .78 10.53
• Profit .87 13.01
MSC
CR = 89
AVE = .79
(Menon et al. 1999)
During the marketing strategy development
process (product and associated marketing
strategies), to what extent did the project
members
• Develop many alternative courses of
action to achieve the intended objectives? .88 12.80
• Conduct multiple examinations of any
suggested course of action the project members
wanted to take? .82 11.04
• Thoroughly examine multiple explanations
for the problems faced and for the
opportunities available? .81 10.96
• Search extensively for possible
alternative courses of action to take advantage
of the opportunities? .78 9.50
• Consider many different criteria before
deciding on which possible courses of
action to take to achieve your intended
objectives? .79 9.88
Output reward
CR = .89
AVE = .68
(Atuahene-Gima and
Li 2002)
To what extent do you agree with the following
statements about the process of rewarding
project members?
• Rewards to project members were entirely
related to achievement of performance
objectives for project activities. .84 10.44
• Rewards for project members were entirely
based on final outputs achieved. .81 9.56
• The project members' rewards depended
upon the market performance of the product. .79 8.45
• In rewarding the project members, primary
weight was placed on objective criteria such as
results achieved. .76 7.86
Process reward
CR = .83
AVE = .59
(Atuahene-Gima and
Li 2002)
To what extent do you agree with the following
statements about the process of rewarding team
members?
• Rewards to project members were based on
subjective criteria such as attributes of the
product. .76 7.89
• Rewards to project members were based on
effectiveness of implementation of the .75 7.70
strategy rather than results.
• The rewards depended entirely on the
quality of strategic decisions made rather than
results. .66 6.66
• Project members were rewarded for
completing major stages in the
marketing strategy development process. .65 6.06
Task conflict
CR = .89
AVE = .69
(Menon, Bharadwaj,
and Howell 1996;
Pelled, Eisenhardt,
and Xin 1999)
To what extent did project members in the
marketing strategy development disagree with
each other about
• Ideas concerning the best way to maximize
the effectiveness of the marketing strategy. .65 7.60
• Ideas concerning the different goal
priorities for the marketing strategy. .75 8.86
• The best way to ensure the success of the
strategy. .73 8.82
• Which marketing objectives should be .62 7.25
considered more important.
• Different approaches for developing and .85 11.18
implementing the strategy.
Collaborative conflict
behavior
CR = .82
AVE = .55
(Jehn 1995; Song,
Xie, and Dyer 2000)
When conflicts arose among project members
during the marketing strategy development
process:
• We tried to exchange complete and
accurate information to resolve them. .77 10.12
• We played down our differences and
emphasized our common interests. .47 5.69
• We engaged in genuine collaborative
effort to resolve them. .82 11.29
• We discussed them, focusing on the
common goals of the strategy. .85 12.15
Conflict avoidance
behavior
CR = .73
AVE = .41
(Jehn 1995; Song,
Xie, and Dyer 2000)
When conflict arose among project members during
the marketing strategy development process:
• We refrained from arguments about
the issues. .42 4.63
• We avoided the issues altogether. .65 7.60
• We tried to stay away from any
disagreements. .75 8.86
• Our disagreements were swept under
the carpet. .72 8.47
Implementation speed
CR = .75
AVE = .44
(New scale)
To what extent do you agree with the following
statements about implementing the market
strategy?
• The implementation was faster than in
other previous strategies. .75 8.73
• The implementation was much faster than
our planned schedule required. .69 7.70
• The strategy was implemented in a
shorter time than expected. .57 6.08
• Speed of implementation of the strategy
was a critical concern of the project members. .66 7.00
Product advantage
CR = .74
AVE = .50
(Atuahene-Gima
1995)
To what extent do you agree or disagree with
the following statements about the product?
• Quality of the product compared well
with competitor products. .78 9.55
• The product was of higher quality than
competing products. .59 6.17
• Quality of the product compares well
with others we have developed in the past. .83 10.21
Market uncertainty
CR = .72
AVE = .37
(Jaworski and Kohli
1993)
Indicate your degree of agreement about how
well these statements describe the market
environment for the product.
• Competitor activities in the market
are quite uncertain. .40 4.80
• The product-market has many new
competitors. .45 5.26
• The product-market competitive
conditions are highly unpredictable. .52 5.88
• Customers' preferences change quite
rapidly. .77 7.22
• Customers' needs in our industry are
changing quite rapidly. .85 7.98
Technological
uncertainty
CR = .88
AVE = .65
(Jaworski and Kohli
1993)
Indicate your degree of agreement about how
well these statements describe the
technological environment for the product.
• The technology in the market
environment was changing rapidly. .78 10.55
• Technological changes provide big
opportunities in the industry. .63 7.87
• A large number of new product ideas
have been made possible through technological
breakthroughs in the industry. .81 11.11
• There are major technological
developments in the industry. .94 14.53
Intraindustry
relationships
CR = .84
AVE = .62
(New scale)
To what extent do you agree with the
following statements about your project
members during the market strategy
development?
• Project members communicated
frequently with knowledgeable executives
within our industry. .59 8.33
• Project members had close interactions
with knowledgeable people about conditions in
our industry. .64 8.55
• Project members received a lot of
information from other executives within
our industry. .84 12.46
• Project members received advice about
the project from knowledgeable people within
our industry. .74 10.37
Extraindustry
relationships
CR = .92
AVE = .71
(New scale)
To what extent do you agree with the following
statements about your project members during the
marketing strategy development?
• Project members put a lot of effort into
communicating with knowledgeable people outside
our industry. .70 9.23
• Project members maintained close contacts
with knowledgeable people in firms outside our
industry. .84 11.18
• Project members learned a lot from
knowledgeable people in firms not operating in
our industry. .71 9.30
• Project members received useful
information from knowledgeable people outside
our industry. .81 10.89
Notes: We measured all items on a five-point scale (1 = "No
extent," and 5 = "To a great extent"), except for performance,
which we measured on a five-point scale where 1 = "Strongly
disagree," and 5 = "Strongly agree." Legend for Chart:
A - Variables
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
O - 14
P - 15
Q - 16
R - 17
A
B C D E F
G H I J K
L M N O P
Q R
1. Performance
.82(a)
2. MSC
.21(*) .89
3. Process reward
.05 .34(**) .77
4. Output reward
.18(*) .18(*) .24(**) .82
5. Intraindustry relationships
.03 .21(*) .15 -.01 .84
6. Extraindustry relationships
.11 .44(**) .09 .04 .22(**)
.78
7. Task conflict
.11 .36(**) .26(**) .02 .11
.28(**) .69
8. Collaborative conflict resolution
.16 .23(**) .16(*) .14 .04
.04 -.00 .74
9. Conflict avoidance behavior
-.11 -.20(*) -.01 -.24(**) -.04
.01 -.01 -.18(*) .64
10. Implementation speed
-.10 .14 .18(*) .45(**) .05
.03 .22(**) .31(**) -.08 .66
11. Technology uncertainty
.01 .00 .20(*) .07 .02
.09 .22(**) -.08 .06 .01
.81
12. Market uncertainty
.14 .05 .08 .14 -.03
.06 .35(**) -.11 .12 .16
.45(**) .61
13. Product advantage
.43(**) .24(**) .22(**) .34(**) .13
.11 -.07 .38(**) -.18(*) .31(**)
-.02 -.03 .71
14. Market duration of product
-.06 -.12 -.11 -.02 .00
-.06 -.10 .13 -.12 -.04
-.13 -.23(**) -.04 N.A.
15. Project size
.07 .05 .07 -.01 -.03
.01 .12 .13(*) -.12 .20(*)
.06 -.02 .01 .20(*) N.A.
16. Firm size
.08 .04 .05 .02 -.02
.08 .16 .06 -.02 .10
.14 .13 .08 -.02 .01
N.A.
17. Industry dummy
.07 -.02 -.13 .15 .03
.11 -.09 .11 -.04 .10
.25(**) -.00 .16(*) -.03 -.10
.06 N.A.
Mean
3.45 2.92 2.34 2.81 2.88
2.24 2.84 3.71 2.26 2.97
3.32 2.85 3.68 17.54 5.66
401.15 .65
S.D.
.91 .88 .86 1.03 1.05
1.06 .85 .75 .85 .86
1.10 .79 .77 15.09 3.95
223.64 .35
(*) p < .05.
(**) p < .01.
(a) Figures on the diagonal are square roots of AVE.
N.A. = not applicable. Legend for Chart:
A - Variables
B - Hypotheses
C - MSC Model 1
D - MSC Model 2
E - MSC Model 3
F - Performance Model 4
G - Performance Model 5
H - Performance Model 6
A
B C D
E F
G H
Controls and Direct Effects
Project size
- .04 .01
(.46) (.19)
.01 .05
(.13) (.59)
.10 .08
(1.08) (.87)
Firm size
- -.02 -.05
(-.32) (-.70)
-.04 .03
(-.54) (-.33)
.03 .05
(.35) (.52)
Industry dummy
- -.04 -.08
(-.49) (-1.06)
-.06 .05
(-.85) (.60)
.05 .05
(.57) (.59)
Product advantage
- - -
- .42
(4.12)(***)
.44 .38
(4.28)(***) (3.67)(***)
Market duration of product
- - -
- -.09
(-1.07)
-.09 -.10
(-1.03) (-1.17)
Technology uncertainty
- -.06 -.11
(-.57) (-1.44)
-.10 -
(-1.20)
-.09 -.05
(-.89) (-.47)
Market uncertainty
- .09 .06
(.92) (.68)
.06 -
(.77)
.18 .10
(1.74)(*) (.93)
Output reward
H[sub1a] - .05
(.64)
.04 .01
(.47) (.13)
.07 .13
(.71) (1.22)
Process reward
H[sub1b] - .26
(3.53)(***)
.24 -.07
(3.24)(**) (-.82)
-.07 -.14
(-.72) (-1.39)
Task conflict
- - .19
(2.35)(**)
.17 .15
(2.08)(**) (1.53)(†)
.13 .16
(1.27) (1.55)(†)
Collaborative conflict
resolution behavior
- - .16
(2.22)(**)
.15 .04
(2.05)(*) (.39)
.10 .14
(.99) (1.39)
Conflict avoidance behavior
- - -.16
(-2.16)(**)
-.18 -.02
(-2.43)(**) (-.17)
-.01 -.02
(-.13) (-.20)
Intraindustry relationships
H[sub4a] - .05
(.72)
.06 -.06
(.86) (-.66)
-.05 -.03
(-.56) (-.29)
Extraindustry relationships
H[sub4b] - .39
(5.21)(***)
.36 .02
(4.82)(***) (.19)
.01 .02
(.14) (.17)
Implementation speed
- - -
- -
-.21 -.20
(-1.99)(**) (-1.94)(*)
MSC
- - -
- -
.01 .00
(.05) (.03)
Relevant Interaction Effects
Task conflict x collaborative
conflict resolution behavior
H[sub2] - -
.02 -
(.24)
- -
Task conflict x conflict
avoidance behavior
H[sub3] - -
-.16 -
(-2.26)(**)
- -
MSC²
H[sub5] - -
- -
- .07
(.71)
MSC x implementation speed
H[sub6] - -
- -
- .24
(2.77)(***)
MSC x technology uncertainty
H[sub7a] - -
H[sub7b]
- -
- .18
(1.81)(*)
MSC x market uncertainty
H[sub8a] - -
H[sub8b]
- -
- -.23
(-2.11)(**)
R²
- .01 .43
.46 .22
.27 .34
Adjusted R²
- .00 .38
.39 .13
.16 .21
F-value
- .20 7.70(***)
7.14(***) 2.58(**)
2.42(**) 2.66(***)
ΔR²
- - .42
.03 -
.05 .08
Partial F-value
- - 12.82(***)
2.60(**) -
1.73(†) 2.92(**)
N
- 135 135
135 131
131 131
(†) p < .10.
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: We report standardized regression coefficients (t-values
are in parentheses). Reduced sample size for Models 4-6 is the
result of the deletion of outlier cases.DIAGRAM: FIGURE 1; Antecedents and Outcomes of MSC
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By Kwaku Atuahene-Gima and Janet Y. Murray
Kwaku Atuahene-Gima is Professor of Innovation Management and Marketing, Department of Management, City University of Hong Kong (e-mail: mgkwaku@cityu.edu.hk). Janet Y. Murray is Associate Professor of International Business, Boeing Institute of International Business, John Cook School of Business, Saint Louis University (e-mail: murrayjy@slu.edu).
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Record: 17- Antecedents of Export Venture Performance: A Theoretical Model and Empirical Assessment. By: Morgan, Neil A.; Kaleka, Anna; Katsikeas, Constantine S. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p90-108. 19p. 1 Diagram, 6 Charts. DOI: 10.1509/jmkg.68.1.90.24028.
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Antecedents of Export Venture Performance: A Theoretical
Model and Empirical Assessment
Both the size and the rapid growth of global exporting have focused the attention of marketing researchers on the factors associated with firms' export performance. However, knowledge of this increasingly important domain of marketing activity remains limited. To address this knowledge gap, the authors draw on the strategy and marketing literature to develop an integrative theory of the antecedents of export venture performance. The interplay among available resources and capabilities, competitive strategy decisions, and competitive intensity determines export venture positional advantages and performance outcomes in the theoretical model. The authors empirically assess predicted relationships using survey data from 287 export ventures. Results broadly support the theoretical model, indicating that resources and capabilities affect export venture competitive strategy choices and the positional advantages achieved in the export market, which in turn affect export venture performance outcomes. In contrast to structure-conduct-performance theory predictions, the data indicate that the competitive intensity of the export marketplace does not have a direct effect on export venture positional advantages or performance. However, competitive intensity moderates the relationship between export venture competitive strategy choices and the positional advantages realized.
Worldwide exporting has grown to exceed five trillion dollars annually and accounts for more than 10% of global economic activity (e.g., International Monetary Fund 2001; World Bank 2001). Because of increasing globalization, exporting is also a means of foreign market entry and sales expansion for firms; thus, it is a significant area of research interest in marketing (e.g., Cavusgil and Kirpalani 1993; Samiee and Anckar 1998). Researchers have responded to managers' and policymakers' interests by focusing attention on the internal (e.g., international experience, standardization of marketing programs) and external (e.g., industry technology, export market characteristics) antecedents of firms' export performance (e.g., Aaby and Slater 1989; Cavusgil and Zou 1994; Szymanski, Bharadwaj, and Varadarajan 1993). However, despite increased attention, theoretical and empirical knowledge of exporting remains limited and offers few insights for managers who are responsible for export performance and policymakers who are concerned with export trade development (Czinkota 2000; Katsikeas, Leonidou, and Morgan 2000).
The literature highlights three particular problems that limit existing research. First, the majority of studies either are descriptive and largely atheoretic (Axinn 1994; Katsikeas, Leonidou, and Morgan 2000) or draw on a wide range of divergent theoretical perspectives (Aaby and Slater 1989; Zou and Stan 1998). The resulting lack of a comprehensive theory base for explaining firms' export performance makes it difficult to integrate findings from different studies into a coherent body of knowledge (e.g., Aulakh, Kotabe, and Teegen 2000; Zou and Stan 1998). Second, the export venture (i.e., the firm's efforts in a single product or product line exported to a specific foreign market) has been identified as the primary unit of analysis in understanding export performance (Ambler, Styles, and Xiucum 1999; Myers 1999). Despite this, most studies adopt a firm-level unit of analysis and aggregate firms' various product-market export ventures (Katsikeas, Leonidou, and Morgan 2000; Madsen 1987), which makes it difficult to identify and isolate specific antecedents of export performance because firm-level analyses fail to capture differences in the strategies executed by export ventures that face various marketplace requirements (Ambler, Styles, and Xiucum 1999; Cavusgil and Zou 1994). Third, export performance is multidimensional, incorporating both economic and strategic dimensions (Bello and Gilliland 1997; Zou, Taylor, and Osland 1998). However, most studies use individual performance measures, such as the firm's export ratio, which may not represent important economic and strategic aspects of export performance (e.g., Cavusgil and Zou 1994; Shoham 1998). In addition, the many unrelated performance indicators used in different studies also make integration of empirical findings problematic (Aaby and Slater 1989; Aulakh, Kotabe, and Teegen 2000; Diamantopoulos 1998).
In this article, we address these three problems. We develop and empirically assess a comprehensive theory of export venture performance. Our study makes three contributions to knowledge in this increasingly important domain of marketing activity. First, we integrate the structure-conduct-performance (SCP) paradigm and the resource-based view (RBV), two rival theories that often have been viewed as incongruent, into a cohesive theoretical model of the antecedents of export venture performance. We present empirical evidence of the interplay between the resources and capabilities available to export ventures, competitive strategy decisions, and the competitive intensity of the export market served in determining export venture positional advantages and performance outcomes that support key relationships in our theoretical model. Our study provides an important new theory base on which to build further export research and to integrate findings, and it offers new insights for managers and policymakers. Second, our study provides new evidence as to how competitive intensity affects export venture performance, which has important implications for theory development. In contrast to SCP predictions, our results indicate that competitive intensity does not directly influence export venture positional advantage and performance outcomes. Rather, we show that competitive intensity moderates the relationship between the export venture's intended competitive strategy and its realized positional advantage. Third, our study provides empirical support for previously untested marketing theory propositions regarding the effects of resources and capabilities on business performance (e.g., Bharadwaj, Varadarajan, and Fahy 1993; Day 1994; Hunt and Morgan 1995). We also extend RBV theory in marketing by distinguishing between firms' resource endowments and the capabilities with which they are developed and deployed as sources of positional advantage (Teece, Pisano, and Shuen 1997).
We begin by integrating insights from established theory in economics and resource-based strategy with emerging theories of dynamic capabilities to develop a comprehensive theoretical model of key antecedents of export venture performance. Next, we draw on qualitative fieldwork and literature-based insights to specify relevant constructs. After describing our research design, we validate our measures and estimate a structural model that represents key relationships predicted by our theory. We then present results of the analysis and explore their theoretical, managerial, and policymaking implications. Finally, we assess the limitations of our study and suggest areas for further research.
Antecedents of Export Venture Performance
Two broad theoretical approaches, the SCP paradigm and the RBV, dominate explanations of firm performance. Of the few theory-based exporting studies that have been conducted, most have examined the antecedents of export performance from an SCP viewpoint (e.g., Aaby and Slater 1989; Axinn 1994; Cavusgil and Zou 1994; Zou and Stan 1998). The SCP paradigm posits that firm performance is determined primarily by two fundamental sets of antecedents. First is the structural characteristics of the firm's markets that determine the competitive intensity (or rivalry) the firm faces. In the export venture context, competitive intensity concerns the degree to which rivals in the target export marketplace are able and willing to respond to the actions of the firm's export venture (e.g., Jaworski and Kohli 1993; Porter 1980). The second antecedent is the firm's ability to achieve and sustain positional advantages through the efficient and effective execution of planned competitive strategy (Porter 1980, 1985; Scherer and Ross 1990). In this context, positional advantage pertains to the relative superiority of the export venture's value offering to customers in the target export market and the cost of delivering this realized value (Day and Wensley 1988; Porter 1985). Export venture competitive strategies are planned patterns of resource and capability deployments that support choices about how the venture will compete for target customers and achieve its desired goals (e.g., Aulakh, Kotabe, and Teegen 2000; Bharadwaj, Varadarajan, and Fahy 1993).
In contrast, the RBV emphasizes resources as central to understanding firm performance (e.g., Amit and Shoemaker 1993; Peteraf 1993). In this domain, recent theoretical contributions regarding dynamic capabilities distinguish between capabilities and other types of resources available to the firm (e.g., Makadok 2001; Teece, Pisano, and Shuen 1997). In the export venture context, resources are the firm-controlled asset stocks that constitute the raw materials available to the firm's export venture business units (e.g., Black and Boal 1994; Peteraf 1993). Capabilities are the organizational processes by which available resources are developed, combined, and transformed into value offerings for the export market (e.g., Amit and Shoemaker 1993; Day 1994). The RBV characterizes firms as idiosyncratic bundles of resources and capabilities that are available for deployment by the firm's business units (e.g., Conner 1991; Hamel and Prahalad 1994). Heterogeneity in the resources and capabilities explains variations in firm performance (Makadok 2001; Teece, Pisano, and Shuen 1997). From this perspective, export venture managers deploy available firm-specific resources and capabilities that result in positional advantage in the export market (Barney 1991; Grant 1991). Firms sustain an advantage if rivals are unable to acquire and deploy a similar or substitute mix of resources and capabilities (Dierickx and Cool 1989; Mahoney and Pandian 1992).
The SCP and RBV approaches historically have been positioned as competing theories that offer incongruent explanations of firm performance (e.g., Porter 1991; Spanos and Lioukas 2001). However, as we suggest in Figure 1, a dynamic view of business performance as a process (March and Sutton 1997; Van de Ven 1992), with identifiable stages and linkages between them, enables the two different viewpoints to be synthesized into a robust theoretical model of the antecedents of export venture performance.
Resources and Capabilities: Insights from RBV Theory
Consistent with RBV and dynamic capabilities theory, our theoretical model indicates that both the resources and the capabilities available to the export venture have a direct effect on the venture's positional advantage in its target export market (e.g., Collis 1995; Day and Wensley 1988). For example, reputational assets may translate directly into image-related positional advantages, and relationship-building capabilities may directly create relationship-based advantages (e.g., Day 1994; Srivastava, Shervani, and Fahey 1998). Resources, as inputs to the complementary capabilities with which available resources are combined and transformed to create value offerings, also have an indirect effect on positional advantage (e.g., Oliver 1997; Teece, Pisano, and Shuen 1997). For example, export-market-knowledge resources can be leveraged with complementary product development capabilities to create superior value offerings for the export market (e.g., Calantone, Schmidt, and Song 1996).
Competitive Strategy: Insights from SCP Theory
In addition to the direct resource and capability-positional advantage linkages, our theoretical model draws on SCP theory to posit that these relationships are mediated by the competitive strategy that the export venture pursues (e.g., Hunt 2000; Spanos and Lioukas 2001). Competitive strategy mediates the relationship between an export venture's available resources and capabilities and its positional advantage by determining ( 1) how well available resources and capabilities are matched with market requirements (Collis 1995; Teece, Pisano, and Shuen 1997), ( 2) the appropriateness of planned resource and capability allocations (Castanias and Helfat 1991; Oliver 1997), and ( 3) the quality of strategy implementation (Day and Wensley 1988; Dickson 1992). Thus, our theoretical model posits that both the strategic choices about how the venture will compete for target customers and the combinations of available resources and capabilities to be deployed in the export market mediate linkages among available resources and capabilities and the positional advantages achieved by the export venture (Conner 1991; Grant 1991).
Competitive Intensity: Insights from SCP and RBV Theories
A fundamental premise in SCP theory is that the structural forces that determine competitive intensity in a market have a strong impact on firm performance (McGahan and Porter 1997; Scherer and Ross 1990). Thus, SCP theory posits that the level of competitive intensity is an essential determinant of market attractiveness (e.g., Porter 1980, 1985), whereas RBV theory treats competitive intensity as a less significant issue. Nonetheless, RBV theory posits that rivals' willingness and ability to imitate a firm's strategy or to use substitute resources and capabilities to deliver an equal value proposition determine the extent to which a firm's positional advantage may be "competed away" (Barney 1991; Conner 1991; Dierickx and Cool 1989). Our theoretical model posits three ways that competitive intensity in the target export market affects export venture performance. First, rivals' ability to take independent strategic actions and to respond to the export venture's competitive strategy moves moderates the venture's success in translating its planned competitive strategy into realized positional advantages (cf. Jaworski and Kohli 1993). This may occur because the export venture fails to anticipate correctly rivals' independent strategic actions in planning its competitive strategy and because of rivals' responses to the venture's own competitive strategy moves. Second, because the export venture's positional advantage is relative to the positions of its rivals, competitive intensity also has a direct, negative impact on a venture's positional advantage (Cavusgil and Zou 1994). Third, competitive intensity affects the likelihood of price competition, the cost of achieving realized positional advantages (Porter 1980, 1985), and distributor and customer choices (Day and Wensley 1988). Thus, competitive intensity also directly affects export venture performance.
Positional Advantage and Export Venture Performance
Positional advantages are direct antecedents of export venture performance because the relative superiority of a venture's value offering determines target customers' buying behavior (Anderson, Fornell, and Lehmann 1994; Piercy, Kaleka, and Katsikeas 1998) and the outcomes of this behavior for the export venture (Cavusgil and Zou 1994; Karnani 1984). Our theoretical model considers both economic and strategic dimensions of export venture performance (Bello and Gilliland 1997; Zou, Taylor, and Osland 1998). Economic elements include the achievement of economic goals (e.g., sales and market share) and the resources consumed in doing so (cf. Peng and York 2001). Strategic performance elements include the achievement of other goals, such as the establishment and maintenance of relationships with key channel members in the target export market (Cavusgil and Zou 1994). Although both economic and strategic elements are important dimensions of export venture performance, there may be trade-offs between them in the short run (Aaby and Slater 1989). Therefore, our theoretical model posits that an export venture's positional advantage affects the venture's economic and strategic performance (Katsikeas, Leonidou, and Morgan 2000).
Dynamic Considerations: Insights from RBV and Dynamic Capabilities Theories
For any theory of business performance to be useful, it must be dynamic (Porter 1991). Our theoretical model is explicitly dynamic. We view the relationships predicted among resources, capabilities, competitive strategy, positional advantage, and performance as stages in the export performance process (March and Sutton 1997; Ven de Ven 1992). In addition, we posit two particular dynamic relationships in our theoretical model. First, RBV theory indicates that some of the economic outcomes of positional advantages will be reinvested to acquire or to develop available resources and capabilities (Grant 1991; Hamel and Prahalad 1994). In the same vein, marketing theory indicates that strategic outcomes, such as relationships with customers and channel members, often become "market-based assets" that add to the firm's existing resource stock (Srivastava, Shervani, and Fahey 1998). Second, RBV and dynamic capabilities theories indicate that because of learning effects, many resources and most capabilities are enhanced by use (Grant 1996). For example, export market knowledge and relation-shipbuilding capabilities are likely enhanced as a result of the experiential learning that is associated with their use in planning, executing, and monitoring the outcomes of competitive strategy decisions (Day 1994; Morgan et al. 2003).
Theoretical Model Summary
By integrating RBV and SCP predictions in a dynamic model of export performance, the main premise of our theoretical model (Figure 1) is that export ventures can achieve positional advantages in foreign markets and, in turn, superior performance by deploying available resources and capabilities while pursuing appropriate export venture competitive strategies. Furthermore, we theorize that competitive intensity in the export market directly affects the export venture's positional advantages and performance outcomes and limits its ability to execute competitive strategy decisions. Our theoretical model indicates that an export venture's performance is sustained over time by reinvestment, the creation of market-based assets, and learning effects that build and enhance the resources and capabilities available to the export venture.
As an integrative general theory of export venture performance, we conceptualized our theoretical model at the same level as the SCP and RBV theories on which it draws. Assessing relationships at this level of analysis required us to treat the variables in our model as higher-order constructs (e.g., Matsuno and Mentzer 2000; Zou and Cavusgil 2002). This necessitates the identification of relevant dimensions of the constructs in our model (Bagozzi 1994; Heide and John 1992). In addition, because the absence of relevant secondary data sources requires primary data collection to assess our theoretical model, we needed to identify or develop valid and reliable measures of each of the dimensions of our theoretical constructs. The difficulties of longitudinal data collection in export ventures preclude a time-series assessment of predicted dynamic relationships (Katsikeas, Leonidou, and Morgan 2000). However, cross-sectional primary data enable us to assess key relationships in our theoretical model. To this end, to identify specific dimensions of each of the higher-order constructs in our model and to guide the development of appropriate measures, we synthesized insights from exploratory fieldwork interviews and existing literature.
The fieldwork involved 17 in-depth interviews, each of which lasted one and a half to two hours, with marketing managers, international business development managers, chief executive officers, and account development managers in different firms from a cross-section of industries, including textiles, carpets, and rugs (Standard Industrial Classification [SIC] code 22); finished apparel (SIC code 23); chemicals, plastics, paints, and cosmetics (SIC code 28); rubber and plastic products (SIC code 30); engines, machinery, and computing and office equipment (SIC code 35); and electronic and electrical components and appliances (SIC code 36). Overall, the interviewed managers were responsible for 29 export ventures. In addition to providing insights into the identification and measurement of dimensions that represent our theoretical constructs, the fieldwork interviews also provided important support for the face validity of our theoretical model.
Resources Available to the Export Venture
Although many different kinds of resources may be available, four emerged as particularly important in our fieldwork. First, experiential resources, such as market and process knowledge gained from the firm's overseas market operations experience, enable the venture's marketing programs to match the needs of channel members and customers (Daily, Certo, and Dalton 2000; Morgan et al. 2003). Second, scale resources, which pertain to the size and scope of the firm's operations, significantly affect cost structures and influence competitive strategy and performance (e.g., Cavusgil and Zou 1994; Cooper and Kleinschmidt 1985). Third, the working capital and financial liquidity requirements of export operations mean that access to financial resources is essential (Gomez-Mejia 1988; Tseng and Yu 1991). Fourth, physical resources, such as modern equipment and access to valuable supply sources that facilitate process efficiency and product effectiveness, can also be important sources of advantage in export markets (Cavusgil and Naor 1987; Leonidou, Katsikeas, and Piercy 1998).
Capabilities Available to the Export Venture
The literature and fieldwork interviews suggest three particularly important types of capabilities. First, informational capabilities, which pertain to the acquisition and dissemination of information about customers, competitors, channels, and the broader export market environment, help reduce uncertainty in export marketing (Katsikeas and Morgan 1994; Souchon and Diamantopoulos 1996). Second, relationship-building capabilities (with suppliers, customers, and other channel members) enable better understanding of and response to export market requirements (Bello, Urban, and Verhage 1991; Rosenbloom and Larsen 1992). Third, product development capabilities, which include existing product modification and new product development, affect the venture's effectiveness and efficiency in delivering superior value to the target market (Calantone, Schmidt, and Song 1996; Cooper and Kleinschmidt 1985).
Export Venture Competitive Strategy
We identified three key areas of planned resource and capability deployment that support a venture's strategic choices in competing for target customers. First, cost leadership, such as investments in new manufacturing technologies, enhances efficiency in the delivery of value offerings to customers (Aulakh, Kotabe, and Teegen 2000; Hill 1988; Sullivan and Bauerschmidt 1991). Second, marketing differentiation, such as investments in promotional and brand development activities, enables the delivery of a distinctive value offering to customers (Samiee and Roth 1992; Styles and Ambler 1994). Third, service differentiation, such as the implementation of customer-service programs that offer higher levels of customer support than do competitors' programs, enhance customer value (Cavusgil and Zou 1994; Roth and Morrison 1992).
Positional Advantage in the Export Market
We identified three types of positional advantage that have particular relevance to export venture performance: ( 1) cost advantage, which involves the resources consumed in producing and marketing the venture's value offering and affects price and perceived value in the export market (cf. Kotha and Nair 1995); ( 2) product advantage, which denotes quality, design, and other product attributes that differentiate the venture's value offering from those of competitors (Kim and Lim 1988; Song and Parry 1997); and ( 3) service advantage, which includes service-related components of the value offering, such as delivery speed and reliability and after-sales service quality (cf. Li and Dant 1999).
Export Venture Performance
Theory indicates that important aspects of economic performance are effectiveness (i.e., the extent to which desired goals are achieved), efficiency (i.e., the ratio of performance outcomes achieved to the resources consumed), and adaptability (i.e., the export venture's ability to respond to environmental changes) (Walker and Ruekert 1987). The literature, along with our fieldwork, suggests that strategic elements of export venture performance center primarily on two different stakeholders: distributors and end-user customers (Cavusgil and Zou 1994; Peng and York 2001). In particular, export ventures often monitor their performance with respect to desired customer attitudes and behaviors (e.g., customer satisfaction and retention) and those of channel intermediaries (e.g., distributor loyalty) (Katsikeas, Leonidou, and Morgan 2000).
Export Market Competitive Intensity
Our fieldwork supports previous research that has identified the willingness and ability of rivals to respond to competitive moves in the export market as an important antecedent of export venture performance (Cavusgil and Zou 1994).
Manufactured exports account for the bulk of total world export trade (World Bank 2001). Therefore, we empirically assessed our theoretical model in a field study of manufacturing firms that operate in the same SICs as firms in our previous fieldwork. We excluded service firms and firms engaged in primary industries because of their idiosyncratic international expansion patterns, regulatory requirements, and performance characteristics (Zou and Cavusgil 2002). We used a multi-industry sample to increase observed variance and to strengthen the generalizability of the findings (e.g., Bello and Gilliland 1997; Samiee and Anckar 1998). Most firms in the SICs in our sample export through foreign distributors because this provides relatively easy and low-cost export market access (Bello and Gilliland 1997; Peng and York 2001). To facilitate the development of valid measures and provide greater control over extraneous sources of variation, we therefore focused on only firms that exported through foreign distributors (cf. Dutta, Heide, and Bergen 1999).
Measures
We began by combining fieldwork and literature-based insights to specify the domain of each of the 17 construct dimensions we identified and to develop items that could serve as indicators of each construct. A preliminary survey instrument was developed and then evaluated by nine academic researchers in international marketing and competitive strategy who served as expert judges to assess face validity. Next, to evaluate individual item content, clarity of instructions, and response format, we pretested the revised survey in a series of face-to-face settings with 12 export venture managers. The survey was further refined by means of the feedback and was then pretested by mail. No particular problems were detected with the survey instrument. The final questionnaire used multi-item measures with seven-point scales to measure all constructs. Appendix A provides the complete set of items as well as their scale anchors and reliability coefficients.
Sample and Data Collection
We drew a random sample from the Dun & Bradstreet database of 1000 manufacturing firms that were involved in exporting and that employed between 50 and 1000 full-time personnel. We contacted each firm by telephone to identify the firms that had export venture activities through overseas distributors for at least five years, to identify an appropriate key informant for the study, and to prenotify the firm of the research project. After multiple telephone calls and successive snowballing in many cases, we identified 601 people who were responsible for specific export ventures, who met the key informant knowledgeability requirements, and who were willing to complete our survey. Of the 399 firms excluded in this process, 11 could not be contacted because of incorrect contact details, 68 traded directly with only a few export customers, 96 had been engaged in export venture activities for less than five years, 11 were local export intermediaries, 22 had discontinued exporting or employed less than 50 personnel, 8 had ceased operations entirely, and 183 reported a corporate policy of nonparticipation in external studies.
A survey packet was mailed to each of the 601 key informants. Respondents were asked to provide information for a specific export venture of the firm in which only one foreign distributor had been employed to sell the focal product in the venture market for at least five years. This enabled us to control for potential confounding factors associated with the use of multiple foreign distributors in a particular export venture market (Bello and Gilliland 1997) and to collect data on established export venture activities, which is essential in studying export venture performance (Cavusgil and Zou 1994).( n1) To ensure variation in export venture performance, we developed three versions of the survey. One version asked informants to respond with regard to one of their more successful export ventures; the other two focused respectively on averagely successful or less successful export ventures (cf. Weiss, Anderson, and MacInnis 1999).( n2) The initial mailing, a follow-up postcard, and two further waves of surveys produced 332 responses. Of these, 21 failed our informant competency tests (discussed subsequently), 15 had excessive missing data (missing responses on three or more items on any single scale; see Fitzgerald et al. 1997), and 9 were from export ventures that used multiple foreign distributors. These responses all were dropped from subsequent analysis, leaving a data set that comprised observations from 287 export ventures, for a response rate of 48%. Key demographic characteristics of the 287 export ventures in our data set are presented in Appendix B.
To assess potential nonresponse bias, we compared early and late respondents with respect to various firm characteristics, including number of full-time employees, years of exporting, annual sales volume, age of the venture, number of export markets, key informant self-reported competency evaluation indicators, and the construct measures (Armstrong and Overton 1977). We detected no significant differences between early and late respondents. In addition, using secondary information on employee numbers and annual sales volume, we also compared the respondent firms and a group of 87 randomly selected nonparticipant firms. We found no differences between respondents and nonrespondents. We concluded that nonresponse bias was not a significant problem in our data.
In addition to the presurvey telephone screening to identify appropriate informants, we also conducted a post hoc check for respondent competency. We collected data that tapped each respondent's knowledge of the activities, strategies, and performance of his or her export venture and those of its main export market competitors (cf. Jap 1999), involvement with the export venture's foreign distributor (cf. Heide and John 1992), responsibility for export venture strategy decisions (cf. Weiss, Anderson, and MacInnis 1999), and confidence in answering the survey questions (Cannon and Perreault 1999). We eliminated from further analysis the 21 respondents who reported a score of less than 4 on the seven-point scales for any one of these items. In the final data set (n = 287), the mean informant scores were greater than 6.0 on seven-point scales for all items except knowledge of the venture's main competitors, which had a mean score of 5.43. This indicates a high level of competency among our key informants.
We validated the data collected from our key informants in several ways. First, we attempted to gather data from a second informant in each respondent venture. In export ventures, usually only one manager is responsible for and knowledgeable about the full range of each venture's plans and activities. Even so, in 34 cases we were able to collect data from a knowledgeable second informant. Interrater reports were positively correlated, at levels ranging from .34 (p < .05) for export venture positional advantage to .74 (p < .01) for export venture competitive strategy. Second, we gathered data about the distributor and customer dimensions of export venture performance from 22 overseas distributors used by export ventures in our sample.( n3) Correlations between export venture manager and distributor responses for these export ventures were .60 (p < .01) and .40 (p < .10) for the distributor and end-user customer dimensions of venture performance, respectively. In the absence of secondary data sources to validate the economic dimension of export venture performance, we subsequently contacted the companies in our sample and requested their cooperation in providing us with objective financial performance data. We were able to collect primary objective economic performance data on sales volume, market share, relative profit margins, and revenue from new products for 31 of the export ventures in our sample. We correlated these objective data with the relevant indicators of economic performance that we used in our export venture performance measure (ECON1-4, Appendix A) at the level of .89 (p < .01), .89 (p < .01), .88 (p < .01), and .81 (p < .01), respectively. Collectively, the three sets of additional data provide strong support for the validity of our key informant data.( n4)
Measure Validation
We purified our measures using exploratory factor analysis and reliability analysis. We retained items with high item-to-total correlations, high loadings on the intended factors, and no substantial cross-loadings. We then subjected the set of items to confirmatory factor analysis (CFA) to verify the hypothesized factor structure. With the exception of competitive intensity, we considered each construct in our theoretical model as representing a higher-order factor, with the observed items originating from first-order factors that in turn arise from a second-order factor (cf. Heide and John 1992). Given the number of parameters to be estimated, sample-size constraints (Bentler and Chou 1987) led us to divide our measures into three subsets of the most theoretically related variables (e.g., Kohli and Jaworski 1994; Moorman and Miner 1997). In each measurement model, we used the elliptical reweighted least squares estimation procedure, which yields unbiased parameter estimates for both multivariate normal and nonnormal data (Sharma, Durvasula, and Dillon 1989; Zou and Cavusgil 2002).
Measurement Model 1 in Table 1 estimates resources available to the export venture as a second-order factor that comprises experiential, scale, financial, and physical resources, and it estimates capabilities available to the export venture as a second-order factor comprising informational, relationship-building, and product development capabilities. Although the chi-square statistic of Measurement Model 1 is significant (χ²(222) = 463.66, p < .001), as might be expected given the sensitivity of the test statistic to sample size (Bagozzi and Yi 1988), the other fit indexes (comparative fit index [CFI] = .97; nonnormed fit index [NNFI] = .96; root mean square error of approximation [RMSEA] = .062; and average off-diagonal standardized residual [AOSR] = .064) suggest good model fit. In Measurement Model 2, Table 1, we estimated export venture competitive strategy as a second-order factor comprising cost leadership, marketing differentiation, and service differentiation, and we estimated positional advantage in the export market as a second-order factor comprising cost, product, and service advantage. This model also represents a close fit to the data (χ²(163) = 264.12, p < .001; CFI = .96; NNFI = .96; RMSEA = .047; and AOSR = .048). Measurement Model 3, Table 1, estimates export venture performance as a second-order factor comprising economic, distributor, and end-user customer performance, and it estimates competitive intensity as a separate first-order construct. The results also suggest good fit for this model (χ²(131) = 248.66, p < .001; CFI = .97; NNFI = .97; RMSEA = .056; and AOSR = .041).
All three measurement models support our conceptualization of the resources and capabilities available to export ventures, competitive strategy, positional advantage, and performance as separate second-order constructs and competitive intensity as a separate first-order construct.( n5) As is shown in Table 1, all factors and items load significantly on designated constructs, and there is no evidence of any cross-loading. Factor and item loadings all exceed .52, and all t-values are greater than 8.55, which provides evidence of convergent validity among our measures (Fornell and Larcker 1981). We assessed discriminant validity among all of our measures by using two-factor CFA models that involved each possible pair of constructs; we freely estimated and then constrained to one the correlation between the two constructs. In all cases, the chi-square value of the unconstrained model was significantly less than that of the constrained model, providing evidence of discriminant validity between all of our constructs (Bagozzi, Yi, and Phillips 1991).( n6) The descriptive statistics and correlations in Table 2 provide a general picture of the constructs and measures. All measures exhibit satisfactory levels of reliability, and coefficient alphas range from .71 to .90 (see Appendix A). Overall, we conclude that our constructs exhibit good measurement properties.
Structural Model Estimation
To attain the ratio of sample size to parameter of greater than 5 to 1 that is suggested for reliable parameter estimates, we adopted a parsimonious approach to estimate our structural model (Bentler 1995; Bollen 1989). We used weighted composite scales, based on the first-order factor loadings of the measurement models in Table 1, to represent the first-order factors, and, apart from competitive intensity that is viewed as a first-order construct, we then employed these as indicators of the corresponding higher-order latent construct (e.g., Fitzgerald et al. 1997; Hart 1999). Standardized parameter estimates, t-values, and significance levels for the structural paths are shown in Table 3. Overall, the fit indexes for the structural model (χ²(111) = 234.56, p < .001; CFI = .94; NNFI = .93; RMSEA = .062; and AOSR = .056) suggest good fit to the data. The results indicate that except for three paths (linking competitive strategy with positional advantage and competitive intensity with positional advantage and performance) that we found to be insignificant, all remaining paths proposed in our theoretical model are significant and in the expected direction. Furthermore, the structural model exhibits good explanatory power; squared multiple correlations are .31 for the capabilities available to the export venture, .21 for export venture competitive strategy, .64 for positional advantage in export market, and .76 for export venture performance.
Although our structural model assesses two of the relationships involving competitive intensity in the export market that we predict in our theoretical model, testing the third prediction (that competitive intensity moderates the relationship between competitive strategy and positional advantage) required an additional analysis. To assess this relationship, we split our sample into two groups at the median level of competitive intensity and reestimated our structural model (e.g., Hewett and Bearden 2001; Osterhaus 1997). We estimated two models: one in which we constrained the path between competitive strategy and positional advantage to be equal across the two groups and one in which we allowed the path coefficients to vary freely. A highly significant chi-square difference (Δχ²( 1) = 8.57, p < .001) signifies much better fit for the unconstrained model, thus indicating that the relationship between competitive strategy and positional advantage is different in the two groups. As is shown in Table 3, the two-group moderator test supports the prediction of our theoretical model. In the low-competitive-intensity group, the competitive strategy-positional advantage relationship is positive and significant (path coefficient = .43, t-value = 2.16), whereas in the high-competitive-intensity group, the relationship is not significant (path coefficient = -.01, t-value = -.09).
Adopting a dynamic process conceptualization of export venture performance, our theoretical model integrates RBV and SCP perspectives to explain how the resources and capabilities available to export ventures, competitive strategy choices, and competitive intensity in the export market interact to determine export venture positional advantage and performance. Our empirical assessment of key relationships predicted in our theoretical model indicates support for seven of the ten relationships we examined, and it explains 76% of the variance in export venture performance in our sample. Our findings indicate that export venture performance is strongly related to its positional advantage in the marketplace. Positional advantage, in turn, is directly connected with the availability of key resources and capabilities. Furthermore, our research reveals that the key resources and capabilities are linked with each other and are directly connected with the export venture's competitive strategy choices.
Our data do not support the predicted relationship between export venture competitive strategy and positional advantage (path coefficient = .12, t-value = 1.30). However, our two-group moderator analysis indicates that this relationship is positive and significant when the level of competitive intensity in the export market is low. Our findings are consistent with suggestions that gaps between "intended" and "realized" strategy are common and are often caused by rivals' actions and reactions (e.g., Day and Wensley 1988; Roth 1995). This can result both from rivals making unanticipated independent competitive moves and from rivals reacting to the venture's strategy implementation moves in ways that reduce their impact on positional advantage (Mintzberg and Waters 1985; Song and Parry 1997). The two remaining unsupported relationships predicted in our theoretical model are those that link competitive intensity in the export market with the export venture's positional advantage (path coefficient = .03, t-value = .32) and performance (path coefficient = -.11, t-value = -1.45).
Overall, the empirical results provide broad support for our theoretical model. From an RBV theory perspective, support is particularly strong for the resource and capability antecedents of export venture positional advantage and performance that we identify. Our findings indicate less support for two related SCP-based aspects of our theoretical model. First, our findings suggest that competitive intensity is less important in directly determining export venture positional advantage and performance than SCP theory suggests (e.g., Porter 1985; Scherer and Ross 1990). Second, in contrast to the key SCP premise that appropriate competitive strategy choices lead to positional advantage, our data indicate that competitive strategy choices are only related to positional advantage outcomes in less competitively intense export markets. These results support several studies that report that firm-specific resources and capabilities are more important than industry or market characteristics in determining interfirm performance variations (e.g., McGahan and Porter 1997; Rumelt 1991).
Implications for Theory Development
Our research has three important implications for marketing theory development in export performance and, more broadly, firm performance. First, in linking resource and capability heterogeneity with export venture performance, our research provides empirical support for the RBV explanations of firm performance that have been adopted by an increasing number of marketing researchers (e.g., Hunt and Morgan 1995). However, our research also extends traditional RBV explanations by supporting the emerging dynamic capabilities paradigm that links the organizational processes by which firms develop and deploy resources with business performance. Distinguishing between the firm's resource endowments and the capabilities with which it develops and deploys its resources as explanations of interfirm performance variations is an important theoretical distinction (Makadok 2001) that is rarely applied in marketing theory. Our theoretical model and empirical results indicate that researchers should pay particular attention to delineating and assessing marketing capabilities in order to build on traditional RBV theory approaches to explaining export venture and firm performance.
Second, our research has important implications for SCP-based approaches that center on the role of industry/ market characteristics and competitive strategy choices in determining firm performance. Our findings suggest that the direct effect of competitive intensity on export venture positional advantages and performance is less important than was previously believed, but the indirect effect on positional advantages through strategy implementation is significant. This suggests that researchers who draw on SCP theory regarding the effect of industry characteristics should not simply examine the direct effect of structural characteristics (e.g., rivalry between players in a market) on firm performance but should also focus on the indirect effect of such industry or market characteristics on firms' ability to implement competitive strategy decisions to achieve positional advantage. Our study also indicates that researchers who investigate strategy--performance linkages should not assume that competitive strategy decisions are subsequently realized but should consider the important role of competitive intensity in determining the effective implementation of planned competitive strategy decisions.
Third, given the growing importance of understanding the role of marketing in determining firm performance, our research highlights the utility of integrating divergent theoretical perspectives. Viewing RBV and SCP perspectives as competing rather than complementary has limited researchers' ability to explain interfirm performance variations. Our theoretical model and the substantial proportion of variance in export venture performance accounted for in our empirical study indicate that the two theoretical approaches can be integrated in a way that allows for a more complete explanation of firm performance over time. For example, RBV theory identifies relationships between resources and capabilities as contributing to isolating mechanisms that inhibit competitive imitation, such as asset interconnectedness and social complexity (Barney 1991; Bharadwaj, Varadarajan, and Fahy 1993). By specifying relationships between export venture resources and capabilities and competitive strategy choices in our integrated theory, the potential for such isolating mechanisms increases even further. Thus, although we were unable to empirically assess this prediction with our data, integrating RBV and SCP theories provides a stronger theoretical rationale for explaining export venture performance over time than either theory would alone.
Implications for Managers and Policymakers
In the past, managers have been offered competing theory prescriptions on how to improve performance. The SCP-based prescriptions lead export venture managers to focus their efforts on formulating and implementing competitive strategies that are appropriate for their export market (e.g., Porter 1980, 1985). The RBV prescriptions lead managers to focus their efforts on acquiring, assessing, and deploying available resources (e.g., Grant 1991). However, our theoretical model and empirical findings indicate that managers should attend to the interrelationships between both types of activity. Specifically, our research indicates that in attempting to enhance export venture performance, managers should focus their efforts on the key areas of resource acquisition and capability building and on matching competitive strategy choices with available resources and capabilities and the needs and requirements of channel partners and customers in the target export market. Our data point to the importance of managers' close monitoring and forecasting of competitors' independent strategy moves and rivals' responses to competitive strategy decisions as key decision-making input that may strengthen the link between competitive strategy decisions and the achievement of positional advantages.
In discussing our results with export venture managers, they requested additional insight into individual positional advantages, resources, and capabilities associated with export venture performance in our data. Sample-size limits precluded a comprehensive structural equation modeling analysis that involved each individual dimension of our higher-order constructs. However, a post hoc analysis provides insight into the issue. We split our sample at the median level of export venture performance and examined the levels of individual positional advantages, resources, and capabilities observed in the high-and low-performance group (Table 4). The results indicate that investments in all four types of resources may lead to export venture performance payoffs. Given the nature of experiential and scale resources, the payoffs may increase over time as the level of these resources increases. From a capabilities perspective, significant differences exist between the high-and low-performing export venture groups for all three capabilities we examined. The larger differences in relationship-building and informational capabilities available to export ventures in each group imply that enhancing these capabilities may be a priority area for investment consideration. Finally, in terms of positional advantage, our results indicate that export venture managers (at least in developed countries) might be wise to emphasize strategies that deliver superior service-and product-based positional advantages rather than cost-based advantages.
Given the economic impact of exporting, export performance is also a significant area of interest for policymakers whose major objective is to stimulate sustainable export activity among indigenous firms (Czinkota 2000). Traditional approaches emphasize the provision of foreign market data to help current and potential exporters develop more effective competitive strategies. Our results indicate that policymakers should focus more attention on increasing the resources available to export ventures. Although the provision of direct financial, scale, and physical resources is beyond the scope of most policymakers, our results indicate that experiential resources may be a useful area of focus. Traditional export-trade promotion activities may indirectly aid the development of some aspects of the experiential resources available to export ventures. However, policymakers should also consider ways to directly help firms gain experience in export markets. For example, organizing field-research trips for managers to particular foreign markets may help managers learn from experience, thereby more directly raising levels of available experiential resources. Similarly, creating networks of noncompeting firms that are involved in selling in individual export markets and enabling cross-firm information sharing may also facilitate the development of relevant experiential resources by providing opportunities for firms to learn from one another.
In addition, our study reveals the importance of available capabilities in strengthening export venture performance, which suggests that policymakers should seek to assist firms in acquiring and enhancing relevant capabilities. To aid the development of stronger informational capabilities, rather than just responding to specific export market information requests, appropriate export trade development assistance should also provide training for managers in export market research and analysis. Our results also indicate that policymakers should consider ways they can aid the development of firms' relationship-building and product development capabilities. For example, design and funding of benchmarking studies to bolster relationship-building and product development capabilities in current and potential exporter firms may be a worthwhile export trade development investment.
Our research focuses on the antecedents of export venture performance as an area of key managerial and theoretical interest. This focus somewhat limits our theory's applicability at the firm level, which requires consideration of the factors that lead firms to select target export markets and to create export ventures. Although such choices will be influenced by available resources and capabilities, they are likely to be affected by the characteristics of the various export marketplaces that are open to the firm. Further research that examines the internal resource and capability characteristics and external market characteristics that drive export market selection choices would help extend our theory to the firm level. In addition, our findings raise the question of the extent to which the sharing of resources and capabilities between export ventures contributes to firm-level export performance.( n7) In theory, firms that share resources and capabilities across a greater number of export ventures (and other business units in the firm) than competitors should be able to invest to create superior resource and capability stocks (e.g., Hamel and Prahalad 1994). Further research that examines resource and capability sharing across export ventures within the firm will allow for further adaptation of our theory, thereby leading to a better understanding of firm-level export performance.
The empirical assessment of our theoretical model should be interpreted in light of several limitations resulting from trade-off choices in our research design. First, absence of secondary data and logistical constraints in primary data collection required us to assess our theoretical model empirically using cross-sectional data, which precluded assessments of both the investment and learning effects on the resources and capabilities available to export ventures and the sustainability of export venture performance we observed. Although longitudinal research designs are time consuming and logistically difficult, they would enable time-series data analysis, which more fully reflects the dynamic relationships in our theoretical model of export venture performance. Second, because the use of a single distributor in an export market is the most popular export market entry and sales expansion mode, and to provide greater control over sources of extraneous variance, we focused on export ventures that only use a single distributor. To enhance the generalizability of our findings, additional studies should assess our theory in the less common contexts of export ventures that use multiple distributors and ventures that sell direct to export customers.
Third, we rely on fieldwork insights to guide the selection of the dimensions we used to indicate each of the higher-order constructs in our theoretical model. Further research should examine the extent to which additional and/ or different dimensions of each construct enhance understanding of the antecedents of export performance. Our focus on developing and testing a general theoretical model and the size of our sample also precluded a detailed examination of the effect of individual-level resources and capabilities and their interrelationships on the competitive strategy, positional advantage, and performance of export ventures. Further research that examines the role of individual resources and capabilities, as well as configurations of different resources and capabilities, would provide additional theoretical and managerial insights into the determinants of export venture performance.
Beyond these limitations, and our discussion of implications for theory development, an additional direction for further research is the role of competitive intensity in determining strategy implementation. Although marketing researchers have long recognized that successful implementation of strategy decisions is key to explaining firm performance (e.g., Day and Wensley 1988; Walker and Ruekert 1987), the theoretical and empirical understanding of this issue remains limited. Our study indicates that in addition to internal factors, such as organization design (e.g., Vorhies and Morgan 2003) and management and employee buy-in (e.g., Noble and Mokwa 1999), external factors, such as the abilities and behaviors of marketplace rivals, also have an important effect on strategy implementation success. Further research that identifies additional external factors that affect the implementation of strategic decisions and examines the relative importance of different internal and external factors under various conditions would contribute greatly to the understanding of marketing's role in determining export venture and, more broadly, firm performance.
Despite the size and importance of exporting and the keen interest of both managers and policymakers, the absence of a general theory that explains export venture performance has resulted in significant gaps in knowledge. Viewing performance as a dynamic process, we propose an integrative new theory of the antecedents of export venture performance and provide initial empirical support for many of the predicted relationships. Given the increasing importance of export ventures in determining firm and national economic performance, additional studies are needed to promote further understanding of export venture performance. With roots in established economics, strategy, and marketing theories, and sufficient scope to incorporate disparate empirical findings into a cohesive body of knowledge, our theoretical model provides a strong foundation for knowledge development in this increasingly important domain of marketing activity.
The authors thank Rick Bagozzi, Nigel Piercy, Bill Perreault, Saeed Samiee, Doug Vorhies, and the three anonymous JM reviewers for their helpful comments and suggestions.
(n1) The field interviews suggested that the use of a single foreign distributor in the export venture market is a common approach, particularly among more experienced exporting firms for which building close relationships with a prime distributor is considered essential in penetrating overseas markets (cf. Kalwani and Narayandas 1995).
(n2) Paired t-tests on managers responding for more successful, averagely successful, and less successful export ventures revealed significant differences in performance in the expected direction among each of the three groups.
(n3) In line with previous research (e.g., Bello and Gilliland 1997; Cavusgil and Zou 1994), we used self-reported performance measures because (1) our interviews indicated that managers are often unwilling to disclose objective performance data, (2) such export venture-specific information is not provided in company financial statements (Katsikeas, Leonidou, and Morgan 2000), (3) managerial decisions and actions are driven by perceptions of export performance (cf. Day and Nedungadi 1994), and (4) perceptual measures have been shown to yield reliable and valid performance indicators (Dess and Robinson 1984; Venkatraman and Ramanujam 1987).
(n4) In addition, we followed Podsakoff and Organ's (1986) approach to assess the degree to which common method variance may be present in our data. If this is the case, a CFA containing all constructs should yield a single method factor. The fit indexes for a single-factor model (CFI = .67; NNFI = .69; RMSEA = .13; and AOSR = .13) suggest a poor model fit, indicating that common method bias alone is not likely to explain any observed relationships between our model variables.
(n5) We also compared each measurement model with an alternative single-factor model, and in each case, chi-square difference evaluations strongly supported the hypothesized measurement model.
(n6) Discriminant validity is evident, as the results indicate that the critical value (Δχ²(1) = 3.84) was comfortably exceeded in all pair-wise comparison tests: resources with capabilities (Δχ²(1) = 26.73), strategy (Δχ²(1) = 10.30), positional advantage (Δχ²(1) = 28.30), and performance (Δχ²(1) = 30.78); capabilities with strategy (Δχ²(1) = 19.87), positional advantage (Δχ²(1) = 15.23), and performance (Δχ²(1) = 73.53); strategy with positional advantage (Δχ²(1) = 16.83) and performance (Δχ²(1) = 19.53); and positional advantage with performance (Δχ²(1) = 52.80).
(n7) We thank a reviewer for pointing out the potential impact of this issue on firm-level export performance.
Legend for Chart:
A - Available Resources First-Order Factors
B - Available Resources Standardized Loadings(a)
C - Available Capabilities First-Order Factors
D - Available Capabilities Standardized Loadings(a)
A B C D
Measurement Model 1
Experiential Informational
EXP1 .83(b) INF1 .71(b)
EXP2 .52 (8.55) INF2 .73 (10.68)
EXP3 .92 (18.28) INF3 .85 (12.32)
EXP4 .88 (17.28) INF4 .84 (12.30)
INF5 .67 (9.89)
Scale
SCL1 .86(b) Relationship Building
SCL2 .92 (17.34) REL1 .78(b)
SCL3 .69 (12.42) REL2 .76 (12.29)
REL3 .91 (14.03)
Financial
FIN1 .89(b) Product Development
FIN2 .93 (14.38) PRD1 .87(b)
PRD2 .87 (13.15)
PRD3 .66 (10.80)
Physical
PHY1 .78(b)
PHY2 .77 (11.58)
PHY3 .81 (11.98)
Goodness-of-Fit Statistics
χ²[sub(222)] = 463.66, p < .001
CFI = .97
NNFI = .96
RMSEA = .062
AOSR = .064
Legend for Chart:
A - Competitive Strategy First-Order Factors
B - Competitive Strategy Standardized Loadings(a)
C - Positional Advantage First-Order Factors
D - Positional Advantage Standardized Loadings(a)
A B C D
Measurement Model 2
Cost Leadership Cost Advantage
COS1 .62(b) ACOS1 .94(b)
COS2 .66 (7.08) ACOS2 .70 (14.78)
COS3 .77 (7.21) ACOS3 .96 (30.09)
ACOS4 .79 (18.32)
Marketing Differentiation
MAR1 .78(b) Product Advantage
MAR2 .76 (9.84) APRD1 .81(b)
MAR3 .74 (9.76) APRD2 .73 (10.27)
APRD3 .65 (9.47)
Service Differentiation
SER1 .61(b) Service Advantage
SER2 .76 (8.63) ASER1 .68(b)
SER3 .86 (8.57) ASER2 .79 (9.71)
ASER3 .60 (8.14)
ASER4 .61 (8.30)
Goodness-of-Fit Statistics
χ²[sub(163)] = 264.12, p < .001
CFI = .96
NNFI = .96
RMSEA = .047
AOSR = .048
Legend for Chart:
A - Export Performance First-Order Factors
B - Export Performance Standardized Loadings(a)
C - Competitive Intensity First-Order Factors
D - Competitive Intensity Standardized Loadings(a)
A B D E
Measurement Model 3
Economic Competitive Intensity
ECON1 .90(b) COMP1 .68(b)
ECON2 .90 (19.18) COMP2 .73 (9.38)
ECON3 .73 (13.95) COMP3 .62 (8.22)
ECON4 .74 (14.17) COMP4 .70 (9.09)
COMP5 .65 (8.57)
Distributor
DIS1 .67(b)
DIS2 .76 (10.58)
DIS3 .84 (11.40)
DIS4 .71 (9.95)
DIS5 .80 (11.03)
End User
END1 .64(b)
END2 .78 (9.97)
END3 .72 (9.33)
END4 .83 (10.32)
Goodness-of-Fit Statistics
χ²[sub(131)] = 248.66, p < .001
CFI = .97
NNFI = .97
RMSEA = .056
AOSR = .041
(a) The t-values from the unstandardized solution are in
parentheses.
(b) Fixed parameter. Legend for Chart:
A - Constructs and Measures
B - Mean
C - S.D.
D - RES1
E - RES
F - 2RES3
G - RES4
H - CAP
I - CAP1
J - CAP2
K - CAP3
L - STR
M - STR1
N - STR2
O - STR3
P - ADV
Q - ADV1
R - ADV2
S - ADV3
T - PRF
U - PRF1
V - PRF2
W - PRF3
X - CIN
A B C D E F
G H I J K
L M N O P
Q R S T U
V W X
Resources
Available
(RES) 4.29 .98
.49
.28 .44
.49
.01
RES1
Experiential 4.90 1.31 1.0
RES2
Scale 3.85 1.43 .46 1.0
RES3
Financial 4.14 1.44 .31 .52 1.0
RES4
Physical 4.45 .92 .26 .50 .47
1.0
Capabilities
Available
(CAP) 4.97 .79
1.0
.32 .44
.58
-.09
CAP1
Informational 4.55 1.02 .40 .34 .20
.26 1.0
CAP2
Relationship
Building 5.41 .92 .42 .25 .20
.24 .59 1.0
CAP3
Product
Development 5.02 1.10 .17 .22 .20
.29 .23 .24 1.0
Competitive
Strategy
(STR) 5.04 .89
1.0 .26
.29
.01
STR1
Cost
Leadership 5.29 1.14 .15 .15 .17
.18 .20 .13 .22
1.0
STR2
Marketing
Differentiation 4.31 1.35 .16 .07 .11
.11 .23 .18 .13
.22 1.0
STR3
Service
Differentiation 5.22 1.25 .10 .19 .14
.23 .22 .13 .04
.39 .17 1.0
Positional
Advantage
(ADV) 4.94 .72
1.0
.48
-.02
ADV1
Cost 4.20 1.23 .02 .13 .18
.19 .14 .10 .14
.03 -.01 .09
1.0
ADV2
Product 5.14 .95 .18 .21 .07
.19 .17 .26 .23
.11 .18 .08
-.18 1.0
ADV3
Service 4.98 .97 .33 .34 .23
.26 .26 .35 .23
.20 .19 .19
-.17 .48 1.0
Venture
Performance
(PRF) 5.14 .76
1.0
-.11
PRF1
Economic 4.68 1.19 .30 .31 .19
.18 .31 .35 .28
.18 .14 .12
.10 .17 .23 1.0
PRF2
Distributor 5.39 .90 .36 .35 .21
.27 .41 .51 .25
.20 .20 .14
.08 .37 .47 .34
1.0
PRF3
End-user 5.08 .86 .39 .29 .20
.25 .37 .39 .24
.15 .17 .14
.13 .31 .36 .29
.66 1.0
Competitive
Intensity
(CIN) 4.26 1.21 .02 -.01 .02
.00 -.11 -.05 -.03
.05 -.03 .01
-.01 -.01 -.01 -.08
-.07 -.13 1.0
Notes: Correlation coefficients greater than .12 are significant
at p < .05, and correlations greater than .15 are significant at
p < .01. S.D. = standard deviation. Legend for Chart:
A - Paths in Theoretical Model
B - Standardized Coefficient
C - t-Value
D - Probability ≤
A
B C D
Available resources → available capabilities
.56 6.30 .001
Available resources → export venture competitive
strategy
.25 2.03 .021
Available capabilities → export venture competitive
strategy
.28 2.21 .014
Available resources → positional advantage in
export market
.26 2.69 .043
Available capabilities → positional advantage in
export market
.56 4.63 .001
Export venture competitive strategy → positional
advantage in export market
.12 1.30 .097
Positional advantage in export market → export
venture performance
.87 5.04 .001
Export market competitive intensity → positional
advantage in export market
.03 .32 .374
Export market competitive intensity → export
venture performance
-.11 -1.45 .074
Fit Indexes
χ²[sub(111)] = 234.56, p < .001
CFI = .94; NNFI = .93
RMSEA= .062; AOSR = .056
Split Group Moderator Test(a)
Low-Competitive-Intensity Group
Export venture competitive strategy → positional
advantage in export market
.43 2.16 .019
High-Competitive-Intensity Group
Export venture competitive strategy → positional
advantage in export market
-.01 -.09 .540
(a) Groups split at median level of competitive intensity. Legend for Chart:
A - Resources, Capabilities, and Positional Advantages
B - Above-Median Performer Mean (S.D.)
C - Below-Median Performer Mean (S.D.)
D - t-Value (Probability ≤)
A B C
D
Experiential resources 5.36 (1.30) 4.45 (1.15)
6.27 (.001)
Financial resources 3.84 (1.34) 4.44 (1.48)
3.60 (.001)
Scale resources 4.25 (1.44) 3.44 (1.30)
4.91 (.001)
Physical resources 4.67 (1.01) 4.24 (.78)
4.01 (.001)
Product development capabilities 5.29 (1.07) 4.73 (1.07)
4.47 (.001)
Relationship-building capabilities 5.80 (.79) 5.01 (.88)
7.95 (.001)
Informational capabilities 4.89 (1.06) 4.20 (.86)
6.04 (.001)
Service-based advantage 5.44 (.87) 4.52 (.84)
9.14 (.001)
Product-based advantage 5.45 (.97) 4.81 (.83)
5.92 (.001)
Cost-based advantage 4.27 (1.24) 4.10 (1.21)
1.21 (.227)
Notes: S.D. = standard deviation.DIAGRAM: FIGURE 1 A Theoretical Model of the Antecedents of Export Venture Performance
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Legend for Chart:
A - Construct and Measurement Items
B - Reliability
A B
Resources Available to Export Ventures
("Much worse" and "Much better" compared with main
competitors are scale anchors)
A. Experiential .87
EXP1: Knowledge of export venture market
EXP2: Length of firm's export experience (years)
EXP3: Number of export ventures in which the firm
has been involved
EXP4: Past venture performance
B. Scale .86
SCL1: Annual turnover
SCL2: Number of full-time employees
SCL3: Percentage of employees mainly involved in
the export function
C. Financial .90
FIN1: Availability of financial resources to be devoted
to export activities
FIN2: Availability of financial resources to be devoted
to this export venture
D. Physical .82
PHY1: Use of modern technology and equipment
PHY2: Preferential access to valuable sources of supply
PHY3: Production capacity availability
Capabilities Available to Export Ventures
("Much worse" and "Much better" compared with main
competitors are scale anchors)
A. Informational .87
INF1: Identification of prospective customers
INF2: Capturing important market information
INF3: Acquiring export market-related information
INF4: Making contacts in the export market
INF5: Monitoring competitive products in the export
market
B. Relationship Building .85
REL1: Understanding overseas customer requirements
REL2: Establishing and maintaining close supplier
relationships
REL3: Establishing and maintaining close overseas
distributor relationships
C. Product Development .84
PRD1: Development of new products for our
export customers
PRD2: Building of the product to designated or
revised specifications
PRD3: Adoption of new methods and ideas in the
manufacturing process
Competitive Strategy
("No emphasis at all" and "Great emphasis" are
scale anchors)
A. Cost Leadership .71
COS1: Improving production/operating efficiency
COS2: Maintaining experienced and trained personnel
COS3: Adopting innovative manufacturing methods
and/or technologies
B. Marketing Differentiation .81
MAR1: Improving/maintaining advertising and promotion
MAR2: Building brand identification in the export
venture market
MAR3: Adopting new/innovative marketing techniques
and methods
C. Service Differentiation
SERV1: Achieving/maintaining quick product delivery .76
SERV2: Achieving/maintaining prompt response to
customer orders
SERV3: Offering extensive customer service
Positional Advantage
("Much worse" and "Much better" compared with main
competitors are scale anchors)
A. Cost .90
ACOS1: Cost of raw materials
ACOS2: Production cost per unit
ACOS3: Cost of goods sold
ACOS4: Selling price to end-user customers
B. Product .77
APRD1: Product quality
APRD2: Packaging
APRD3: Design and style
C. Service .76
ASERV1: Product accessibility
ASERV2: Technical support and after-sales service
ASERV3: Delivery speed and reliability
ASERV4: Product line breadth
Export Venture Performance
("Much worse" and "Much better" compared with
main competitors over past 12 months are scale anchors)
A. Economic .89
ECON1: Export sales volume
ECON2: Export market share
ECON3: Profitability
ECON4: Percentage of sales revenue derived from
products introduced in this market during
the past three years
B. Distributor .87
DIS1: Service quality
DIS2: Quality of your company's relationship
with distributor
DIS3: Reputation of your company
DIS4: Distributor loyalty to your company
DIS5: Overall satisfaction with your total
product/service offering
C. End-user .83
END1: Quality of your company's end-user customer
relationships
END2: Reputation of your company
END3: End-user customer loyalty to your firm
END4: End-user customer satisfaction
Competitive Intensity .81
("Strongly agree" and "Strongly disagree"
are scale anchors)
COMP1: Competition in our export market is cut-throat.
COMP2: There are many promotion wars in our
export market.
COMP3: Anything that one competitor can offer
others can match easily.
COMP4: Price competition is a hallmark
of our export market.
COMP5: One hears of a new competitive move
almost every day.
Notes: Full references for sources of individual
measurement items are available from authors. Legend for Chart:
B - Mean (S.D.)
C - Median
D - Mode
E - Range
A
B C D E
Firm employee size
203 (214) 140 100 28 to 2200
Firm sales revenue
$30M ($81M) $15M $15M $400,000 to $1.2B
Years firm has been engaged in exporting operations
24 (23) 20 20 5 to 198 years
Number of export markets in which the firm operates
28 (26) 20 20 1 to 150
Years of operation of the export venture reported on
9 (8) 6 5 5 to 100 years
Notes: S.D. = standard deviation.~~~~~~~~
By Neil A. Morgan; Anna Kaleka and Constantine S. Katsikeas
Neil A. Morgan is Assistant Professor of Marketing, Kenan-Flagler Business School, University of North Carolina, Chapel Hill (e-mail: Neil_Morgan@unc.edu).
Anna Kaleka is Lecturer in Marketing and Strategy (email: kalekaA@cardiff.ac.uk)
Constantine S. Katsikeas is Sir Julian Hodge Professor of Marketing (e-mail: Katsikeas@cardiff.ac.uk), Cardiff Business School, Cardiff University.
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Record: 18- Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study. By: Bart, Yakov; Shankar, Venkatesh; Sultan, Fareena; Urban, Glen L. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p133-152. 20p. 10 Charts. DOI: 10.1509/jmkg.2005.69.4.133.
- Database:
- Business Source Complete
Are the Drivers and Role of Online Trust the Same
for All Web Sites and Consumers? A Large-Scale
Exploratory Empirical Study
The authors develop a conceptual model that links Web site and consumer characteristics, online trust, and behavioral intent. They estimate this model on data from 6831 consumers across 25 sites from eight Web site categories, using structural equation analysis with a priori and post hoc segmentation. The results show that the influences of the determinants of online trust are different across site categories and consumers. Privacy and order fulfillment are the most influential determinants of trust for sites in which both information risk and involvement are high, such as travel sites. Navigation is strongest for information-intensive sites, such as sports, portal, and community sites. Brand strength is critical for high-involvement categories, such as automobile and financial services sites. Online trust partially mediates the relationships between Web site and consumer characteristics and behavioral intent, and this mediation is strongest (weakest) for sites oriented toward infrequently (frequently) purchased, high-involvement items, such as computers (financial services).
The Internet has evolved into an important marketing medium and channel and is now an integral part of a multichannel strategy for firms. E-business has risen strongly since the collapse of the Internet bubble. For example, the USA Today Internet 50 index was up by 8.8% in 2004 from 2003 (www.usatoday.com). The Dow Jones Internet index was up by 24% in 2004 from 2003, compared with an increase of only 9% in the Standard & Poor's 500-stock index (www.spglobal.com). Under the current challenging economic conditions, however, managers must allocate scarce marketing resources efficiently across all channels and within the Internet channel to develop sustainable customer relationships.
To create long-term customer relationships, firms need to build customer trust (e.g., Doney and Cannon 1997; Dwyer, Schurr, and Oh 1987; Ganesan 1994). Customer trust is particularly important in the online context because customers increasingly rely on the Internet for information and purchases and can be more loyal online (Shankar, Smith, and Rangaswamy 2003). To formulate a successful e-business or Internet marketing strategy, companies need a deeper understanding of how trust is developed and how it affects consumer behavioral intent in the online context.
Web site design is a critical part of Internet marketing strategy and an important element in building trust (e.g., Hoffman, Novak, and Peralta 1999; Shankar, Urban, and Sultan 2002; Urban, Sultan, and Qualls 2000). The design strategies of different Web site categories emphasize different site characteristics, such as privacy, navigation, and advice to build trust. For example, consider the different Web site design characteristics used by Autochoiceadvisor (automobile category), Orbitz (travel category), Intel (computers category), and Dell (computer and electronics category) to build trust. Autochoiceadvisor and Orbitz stress advice, Intel emphasizes navigation and presentation, and Dell focuses on customization. Do some Web site characteristics build trust more effectively for some categories of Web sites or some consumer segments than others? How should managers of different Web site categories and those targeting particular segments allocate site design resources to improve trust and positively influence behavioral intent? We address these critical Internet strategy issues.
Although previous academic studies have emphasized the significance of trust in Internet strategy (e.g., Hoffman, Novak, and Peralta 1999; Urban, Sultan, and Qualls 2000) and have suggested potential determinants and consequences of online trust (e.g., Belanger, Hiller, and Smith 2002; Shankar, Urban, and Sultan 2002; Yoon 2002), there has been no systematic, large-scale empirical investigation of the differences in the drivers (Web site characteristics) and role of trust in e-business across different categories and consumer segments. The primary purpose of this study is to examine differences across Web site categories. The secondary goal is to investigate consumer heterogeneity in drivers of online trust to provide some generalizable insights into these issues.
Specifically, we examine the following research questions: What Web site and consumer characteristics influence consumer trust in a Web site and to what extent? Does trust mediate the relationships between the factors that influence Web site trust and behavioral intent? Most important, how do the role of antecedents and the role of trust vary by Web site category and by consumer segment? To address these questions, we propose a conceptual framework and perform an empirical analysis of responses from 6831 consumers to 25 Web sites across eight categories using structural equation modeling (SEM) with a priori and post hoc segmentation.
There is a significant body of related prior research (e.g., Belanger, Hiller, and Smith 2002; Fogg et al. 2001; Shankar, Urban, and Sultan 2002; Yoon 2002). Shankar, Urban, and Sultan (2002) provide a broad conceptual overview and framework of antecedents and consequences of online trust from multiple stakeholder perspectives. They identify a wide range of Web site characteristics (e.g., navigation, community features) as potential drivers of online trust. Yoon (2002) studies antecedents of online trust based on surveys of Korean college students and finds that company awareness and reputation are significantly associated with Web site trust. Belanger, Hiller, and Smith (2002) examine privacy and security as antecedents of online trust and find that consumers value security features more than privacy seals or statements. Fogg and colleagues (2001) study Web site characteristics that constitute online credibility based on a large-scale survey, and they conclude that real-world feel, ease of use, and expertise are among the most influential Web site elements in boosting the credibility of a site. However, to our knowledge, our study is the first to offer insights into the differences among online trust determinants across Web site categories and consumers.
Our study complements that of Shankar, Urban, and Sultan (2002) in three ways. First, our effort is empirical, whereas their work is conceptual. Second, we examine variations in relationships across Web site categories and consumers. Third, we examine trust from a consumer standpoint, whereas they focus on the perspectives of all stakeholders.
Our study also adds to that of Yoon (2002). First, we develop a more comprehensive framework that includes a broader set of Web site and consumer antecedents. Second, our study is a large-scale empirical study of real consumer perceptions of known U.S. Web sites. In contrast, Yoon's work is a study of college students' perceptions of Korean online shopping-mall sites. Finally, we examine differences across Web site categories and consumers in the drivers of online trust.
Our work also extends that of Belanger, Hiller, and Smith (2002) in four ways. First, whereas Belanger, Hiller, and Smith consider only privacy and security, we examine a more comprehensive set of antecedents. Second, their measures of Web site characteristics comprise only five items, whereas we have measures of more than 100 items. Third, their analysis is based mainly on partial correlations and relative ranks, whereas our analysis involves SEM. Finally, we examine variations in drivers of online trust across Web site categories and consumers.
Online Trust: Its Drivers and Its Mediating Role
For the purpose of this study, we adopt the following well-accepted definition of trust: "Trust is a psychological state comprising the intention to accept vulnerability based on positive expectations of the intentions or behaviors of another" (Rousseau et al. 1998, p. 395). Intrinsically, trust implies a party's willingness to accept vulnerability but with an expectation or confidence that it can rely on the other party (Lewicki, McAllister, and Bies 1998; Moorman, Zaltman, and Deshpandé 1992; Morgan and Hunt 1994). In the marketing literature, trust has been studied primarily in the context of relationship marketing (Doney and Cannon 1997; Dwyer, Schurr, and Oh 1987; Ganesan 1994; Ganesan and Hess 1997; Morgan and Hunt 1994). In studies of buyer--seller relationships, trust in a sales agent evolves over time and is based on a buyer's observation of a sales representative's honesty, reliability, consistency, and trustworthiness (Anderson and Narus 1990; Doney and Cannon 1997; Ganesan 1994).( n1) This view of trust is consistent with Schlosser, White, and Lloyd's (2003) conceptualization of behavioral trust.
We focus on online trust, or Web site trust, which differs from offline trust in important ways. Unlike offline trust, the object of online trust is the Web site, the Internet, or the technology. A firm's Web site could be viewed as a store from the standpoint of building customer trust, extending Jarvenpaa, Tractinsky, and Vitale's (2000) salesperson metaphor. A customer's interaction with a store is somewhat similar to his or her interaction with a Web site, and consumers develop perceptions of trust in a Web site based on their interactions with the site. To the extent that a consumer has positive impressions of a site and accepts vulnerability, he or she develops trust with that site. A consumer's perception of a site's competence to perform the required functions and his or her perception of the firm's good intention behind the online storefront contribute to the perception of trust in that site. Thus, online trust includes consumer perceptions of how the site would deliver on expectations, how believable the site's information is, and how much confidence the site commands. Many antecedents may drive these perceptions.
Although online trust has several possible antecedents and consequences (for a detailed review, see Shankar, Urban, and Sultan 2002), we focus on Web site and consumer characteristics as the antecedents and on behavioral intent as the key consequence because of the potential managerial implications we previously outlined. On the basis of pilot studies, we chose privacy, security, navigation and presentation, brand strength, advice, order fulfillment, community features, and absence of errors as the Web site characteristics. In addition, we chose familiarity with the Web site, online savvy/expertise, Internet shopping experience, and entertainment or chat experience as the consumer characteristics. We propose a conceptual framework in which the effects of Web site and consumer characteristics on Web site trust and of trust on behavioral intent are positive. We argue that the strength of the positive relationships between Web site characteristics and online trust varies across Web site categories, depending on the following underlying Web site factors:
• Financial risk : This refers to the uncertainty of incurring monetary losses while interacting on a Web site (Betman 1973; Biswas and Biswas 2004; Grewal, Gotlieb, and Marmorstein 1994).
• Information risk : This refers to the uncertainty associated with providing information on a Web site and is related to the risk of personal information being exposed. This is similar to the transaction risk construct in Biswas and Biswas's (2004) study of online shopping signals.
• Involvement with or ticket price of the product or service on the Web site : This refers to the level of the consumer's engagement with the product or service offered on the Web site. Moorthy, Ratchford, and Talukdar (1997) also treat price level and involvement similarly.
• Information on the Web site : This refers to the depth of information content on a Web site. This factor is consistent with the usage of this construct by Pan, Ratchford, and Shankar (2002, 2003) and Shankar, Rangaswamy, and Pusateri (2001) in their studies of e-tailer price dispersion and online price sensitivity, respectively.
• Search for the product or service on the Web site : This refers to the degree of information search typically required for the product or service on the Web site. This factor is consistent with those of Moorthy, Ratchford, and Talukdar (1997) and Ratchford, Pan, and Shankar (2003) in their studies of consumer search behavior and online price dispersion, respectively.
The expected influences of these underlying Web site factors on the strength of relationships between different drivers and online trust appear in Table 1. Subsequently, we discuss the expected effects of the antecedents of trust on trust and their differences across Web site categories based on these underlying factors. We also expect that the effects of the drivers of online trust vary across consumer segments, which is consistent with Mittal and Kamakura's (2001) findings that the attribute drivers of repeat purchase intent differ systematically by consumer demographics. Because we have no a priori expectations of how the effects of antecedents of online trust might vary by consumer groups, we treat this variation as an empirical issue in this article.
Privacy. Privacy refers to the protection of individually identifiable information on the Internet, and it involves the adoption and implementation of a privacy policy, notice, disclosure, and choice/consent of the Web site visitors (www.privacyalliance.org). Privacy is a key driver of online trust (Hoffman, Novak, and Peralta 1999), and its influence on trust may differ across Web site categories. It is likely to be higher for categories that involve high information risk. Thus, when determining whether a travel or community Web site is trustworthy, a consumer may consider privacy more important than he or she would for a computer site. This is because a travel purchase may require and contain more personal information, such as the whereabouts and activities of a person, than would a computer purchase. Similarly, users of a community Web site often share high levels of personal information. Therefore, we expect that the importance of privacy in determining Web site trust is greater for Web site categories with personal information at risk than it is for other Web site categories.
Security. Security on a Web site refers to the safety of the computer and credit card or financial information. Consumers consider security important in purchasing goods or services on the Internet (Belanger, Hiller, and Smith 2002). Seals of approval, such as Better Business Bureau, Verisign, and TRUSTe, are considered indicators of security by consumers, have been adopted by many Web sites, and have a positive effect on trustworthiness (Cheskin/Sapient and Studio Archetype/Sapient 1999). However, the relationship could be different for different Web site categories. Security is related to financial risk on Web sites (Biswas and Biswas 2004). Some Web site categories, such as transaction-oriented financial services, computer and travel Web sites, and those with high involvement or ticket prices, entail greater financial risk than other categories. When consumers purchase from Web sites that have products or services that are high-involvement items, they are typically concerned about the exposure of financial information. For such Web sites, we expect that the impact of security on online trust is greater than it is for other Web sites.
Navigation and presentation. Navigation and presentation refer to the appearance, layout, and possible sequence of clicks, images, and paths on a Web site. Navigation and presentation are directly related to the flow construct (Hoffman and Novak 1996) and to the Web site's perceived ease of use. Factors such as navigation and presentation, convenience, and ease of use drive trustworthiness (Belanger, Hiller, and Smith 2002; Cheskin/Sapient and Studio Archetype/Sapient 1999). The positive association of navigation and presentation is likely to be different across Web sites. Navigation and presentation are particularly important for Web sites with high information content, such as community, e-tailer, portal, and sports Web sites. When consumers visit Web sites with high information content, they may perceive that the Web sites that have a good appearance and layout and that are capable of taking visitors to their desired destination with a minimum number of clicks are trustworthy. Thus, we expect that the relationship between navigation and presentation and online trust is stronger for Web site categories with high information than it is for other categories.
Brand strength. A brand is a trust mark for all intangible trust-generating activity, and absent human touch, it can be a symbol of quality and assurance in building trust (Keller 1993). In the absence of all relevant information for comparison, brands can provide greater comfort online than offline in customer choice (Degeratu, Rangaswamy, and Wu 2000; Yoon 2002). For example, Amazon.com has high brand strength and enjoys a greater level of trust than rival book e-tailers (Pan, Ratchford, and Shankar 2003). The importance of brand strength in building trust may vary by Web site category. We expect that the effect of brand strength on Web site trust is greater for categories for which consumer involvement or the ticket price of the product or service purchased is high. For sites dealing with high-involvement items, such as automobiles, financial services, and computers, brand is an important attribute in that brand association with the item and the Web site may be quite strong. Thus, brand strength may be a more effective driver of online trust for such categories than for other categories. Brand strength is also expected to be a more influential determinant of online trust for high-search goods or services Web sites than for other Web sites. When consumers undertake a high degree of search for an item on a Web site, they may rely more on the brand behind the Web site to be able to trust the information, item quality, and performance.
Advice. Advice is a Web site feature that informs and guides a consumer toward appropriate solutions for problems and issues on a Web site. Urban, Sultan, and Qualls (2000) demonstrate that the presence of "virtual advisors" can enhance trust in a Web site in the situation of purchasing pickup trucks. We expect that the effect of advice on Web site trust differs across Web site categories and customer groups. For Web sites marked by high financial risk and information risk, such as automobile, e-tailer, financial services, and computer Web sites, the existence of an advisory mechanism could assuage a consumer's concerns about that site and increase consumer perceptions of trust. Suggestions and assistance that a Web site offers to its visitors to narrow the choices or to arrive at the desired location faster may be taken more seriously for products with high financial risk. Advice can also enhance credibility on a Web site when consumers believe that sharing information with that site could be at risk. Thus, advice is expected to be a stronger determinant of online trust for Web site categories that are characterized by a high level of Web site information and high search efforts than it is for other categories.
Order fulfillment. Order fulfillment refers to the delivery of a product or service relative to orders placed by consumers, and it is an essential aspect of Web sites with transactional ability. Order fulfillment reliability is related to prices on a Web site (Pan, Ratchford, and Shankar 2002, 2003). The importance of order fulfillment as a builder of online trust is likely to vary across Web sites; we expect it to be greater for sites with high involvement or high ticket prices (e.g., travel, financial services, computer, e-tailer sites) than for other Web sites. When consumers deeply care about the products they buy on a Web site and are unsure about trusting that Web site, they may rely on the order fulfillment track record of that Web site. Thus, order fulfillment may be an important determinant of online trust for high-involvement items.
Community features. This construct refers to the opportunities available to visitors to a Web site to interact with other visitors to the same Web site by participating in a bulletin board, chat group, or similar online forum. A brand community in a computer-mediated environment has a structured set of social interactions based on a shared consciousness, rituals and traditions, and a sense of moral responsibility (Muniz and O'Guinn 2001). These community features promote information exchange and knowledge sharing and offer a supportive environment for the consumer, thus increasing consumer trust in the site. The effect of community features on consumer trust may be different for different categories of Web sites. Community features are particularly useful for trust formation in situations in which the expected uncertainty about sharing and gathering of information on a Web site is high. In such situations, the shared consciousness and sense of moral responsibility and affinity enhance the consumer's level of trust in a Web site. Therefore, we expect that the dominance of community features' impact on online trust is greater for Web sites characterized by greater information risk and information on the Web site, such as community Web sites.
Absence of errors. This construct refers to the lack of mistakes on a site in response to consumers' actions on that site. Consumers expect a site not to have errors, such as wrong information or incorrect processing of inputs and orders. To the extent that a site is devoid of such errors, we expect that its trust among consumers is high. Because errors may not be acceptable to consumers on any site, we do not expect the impact of absence of errors on online trust to differ across sites.
Consumer characteristics likely have significant effects on Web site trust. We do not have any a priori theoretical expectations for the variation of these effects by Web site category, so we view this variation as an empirical issue.
Familiarity with the Web site. Some consumers are more familiar than others with a given Web site. This familiarity could result from prior visits to that site and satisfactory experiences with either the site or the provider of the product or service on the site. Yoon (2002) shows that Web site trust is influenced by consumer familiarity and prior satisfaction with e-commerce. Familiarity builds consistent expectations of a Web site that may positively affect trust for that Web site.
Online savvy/expertise. Consumer expertise with the Internet may influence Web site trust. An expert user of the Internet is more likely to have greater confidence on the Internet than a novice user. Therefore, online trust may be greater for an expert or Internet-savvy consumer.
Internet shopping experience. Customer experience in the online environment is important in determining customer behavior on a Web site (Novak, Hoffman, and Yung 2000). Prior experience affects individual trust propensity (Lee and Turban 2001) and drives customer satisfaction (Boulding, Kalra, and Staelin 1999; Shankar, Smith, and Rangaswamy 2003), and satisfaction is related to trust (Singh and Sirdeshmukh 2000). Consumers may use shopping experience as an inoculation against potential feelings of regret that might arise from a negative outcome of behavioral intent on the Web site to justify their intent on a Web site, thus implicitly building Web site trust (Inman and Zeelenberg 2002). Thus, a consumer's Internet shopping experience may be positively related to online trust.
Online entertainment or chat experience. Many consumers use the Internet for online entertainment, and many use online chat rooms to share their experiences, obtain information from other consumers on products and services, and increase their confidence in Web sites. Greater confidence is associated with reduced uncertainty and greater trust (Ganesan 1994). Therefore, the greater entertainment and chat experience on the Internet may lead to greater trust in a Web site.
The presence or significance of the underlying Web site factors for the Web site categories in our study appears in Table 2. Automobile, financial services, and computer Web sites involve high-search goods with financial risk and involvement. Community Web sites are characterized by high information risk and deep information on the site. Portals and sports sites carry a high degree of information. Travel sites involve high information risk, and e-tail Web sites have high information risk and are associated with high financial risk.
Prior studies suggest that trust affects behavioral intent (e.g., Shankar, Urban, and Sultan 2002; Yoon 2002). Behavioral intent may include willingness to conduct tasks, such as clicking through further on a Web site, abandoning or returning to the site, sending e-mail messages, downloading files, and ordering from the site. Trust affects the consumer's attitude and risk perception, which in turn influences the willingness to buy in an electronic store (Jarvenpaa, Tractinsky, and Vitale 2000). Pan, Shankar, and Ratchford (2002) find that online trust has a positive impact on Web site traffic and visits to Web site categories, such as gifts, flowers, and computer hardware. Trust may also have a significant effect on prices that consumers pay (Ratchford, Pan, and Shankar 2003).
Geyskens, Steenkamp, and Kumar (1998) and Singh and Sirdeshmukh (2000) examine the role of trust as a moderator or mediator in relationship situations, though not in the context of Web sites. Schlosser, White, and Lloyd (2003) find that the effect of Web site investments on consumer purchase intentions may be moderated by consumer trust in the company's competence, not in the Web site. However, prior studies have not examined whether trust mediates the relationships between trust antecedents such as Web site and consumer characteristics and behavioral intent related to the Web site. Little is known about whether such mediation is stronger for certain Web site categories than for others. If it is, managers of those Web site categories can formulate strategies aimed at directly influencing consumers' intentions to act on the Web site.
The mediating relationship (if it exists) may be stronger for some categories (e.g., infrequently purchased, high-involvement/high-ticket-price items) than for others. Consumers typically go through a longer buying process for infrequently purchased, high-involvement items, and consumers in these Web site categories are typically engaged in a problem-solving task of moderate to high complexity. For such tasks and buying processes, trust formation is more likely to be an intermediate event that precedes the formation of behavioral intent, such as a decision to purchase. For example, products such as computers and electronic items are high-involvement/high-ticket-price items, whereas banking products and services are transaction-oriented products that require more frequent use by consumers. Therefore, we expect that the mediating role of trust is stronger for computers and electronic items than for financial services.
Methodology, Data, and Model
We developed measures of trust determinants, trust, and behavioral intent based on an initial exploratory study and a qualitative study. We conducted a pilot study of MBA students in the spring of 2000 to help identify specific Web site characteristics that could affect respondents' perceptions of trust in a site. With the assistance of a market research firm, we conducted a qualitative study comprising 24 one-on-one, in-depth interviews (each lasting 45 minutes) over a three-day period in the fall of 2000 in Boston.
Respondents were asked to examine a particular Web site, after which the moderator asked general questions about their experience (e.g., likes, dislikes, overall impressions, fulfillment of expectations) and specific questions about site layout, navigation, and content. The questions also covered other issues such as security, privacy, and trust. Respondents were asked to circle words or phrases in the questions or items they found confusing, reword statements in their own words, and make any other general comments about the statements. To control for expert bias and to ensure closer representation of an average consumer, respondents whose immediate family worked in public relations, marketing, or Web site design/production were eliminated from the sample. On the basis of this process of qualitative research, we decided on the measures of the antecedents and dimensions of trust and finalized the questionnaire.
The final questionnaire has 126 close-ended measures of the constructs (for a full list of the questions, see the Appendix). Unlike previous studies, we used a comprehensive set of items to cover Web site and consumer characteristics and other measures articulated by consumers. For example, Fogg and colleagues (2001) predefined their site factors and designed the scale items a priori, not empirically as we do in our study. Although our measures were driven exclusively by research conducted before 2000, they are consistent with existing theory. In particular, the measures of trust somewhat reflect the dimensions of credibility and benevolence used in prior research on trust. They are also consistent with, though not the same as, those of Lynch, Kent, and Srinivasan (2001), particularly with regard to delivering on promises and confidence in the site. The items on delivery against promise and believability of information reflect the credibility dimension, and the items on confidence and overall trust indicate the benevolence dimension. The behavioral intent measures include purchase, recommendation, information sharing, bookmarking, and registration.
We constructed a large sample from National Family Opinion's online panel and administered a survey on this sample during March 2001. At that time, the National Family Opinion online panel comprised 550,000 U.S. households, or 1.4 million people, representing a cross-section of the U.S. population, including men and women, old and young, urban and rural, and affluent and low-income households. We administered the survey in two stages. In the first stage, we sent out 92,726 prescreener invitations, and in the second stage, we sent out 575-855 panelist invitations per Web site. We obtained 6831 usable responses, of which we randomly selected 4554 for model estimation and retained the remaining 2277 for model prediction and validation.
We investigated 27 Web sites that we chose from eight categories of industries, but two of the Web sites went out of business during the study period. The remaining 25 sites belonging to eight categories appear in Table 3. We chose industry categories from the list of the 18 most popular categories among household consumers, as reported by Nielsen//NetRatings during 2001, on the basis of their having a business-to-consumer focus or including shopping/ order fulfillment features. In each category, we chose the two most popular sites. We also chose other, lesser-known sites or those with characteristics such as advisors and decision-making aids so that we could test the predictions on the proposed drivers of trust.
Each respondent was assigned one Web site that he or she evaluated using the questionnaire after a browsing "tour" of that Web site. Respondents examined their assigned Web site according to this tour and were given time to examine the Web site further as they chose before completing the online survey questionnaire.
Our analysis consists of four parts: ( 1) SEM analysis of an overall model linking Web site and consumer characteristics to trust and behavioral intent, ( 2) mediation analysis of trust, ( 3) analysis of differences across Web sites, and ( 4) analysis of consumer heterogeneity. To estimate the relationships between site and consumer characteristics and trust, we estimated a measurement model that involves the antecedents and consequences of trust and an SEM that links Web site and consumer factors to trust and behavioral intent. For the mediation analysis, we performed partial and full mediation tests, as Baron and Kenny (1986) propose. We analyzed the differences across site categories using multigroup SEM analysis. We estimated a separate model for each site category and examined the mediating role of online trust separately for each category.
We performed the consumer heterogeneity analyses using a priori and post hoc segmentation methods. According to Wedel and Kamakura (2000), SEMs estimated on an aggregate sample may lead to serious biases if there are significant differences in model parameters across unobserved segments of population. There are two basic approaches to address this problem: a priori segmentation (in which consumers can be assigned to segments a priori on the basis of some demographic and psychographic variables) and post hoc segmentation. We performed the a priori segmentation using multigroup SEM analysis. A priori segmentation is typically useful, but it does not address unobserved heterogeneity. A finite mixture SEM analysis may be a more appropriate post hoc segmentation method for uncovering unobserved heterogeneity (Jedidi, Jagpal, and DeSarbo 1997).
In the finite mixture model framework, heterogeneous consumer groups are identified simultaneously with the estimation of the SEM in which all the observed variables are measured with error. This approach extends the classic multigroup SEM to the case in which group membership is unknown and cannot be determined a priori. The method enables us simultaneously to uncover customer segments and estimate segment-specific path coefficients in our main model. After the sample was partitioned into a finite number of groups, we performed a follow-up analysis to relate segment membership to observed demographic variables to identify marketing recommendations for various customer segments.
The finite mixture SEM is identified as long as the multigroup model for known groups is identified and the data for the unknown groups follow multivariate normal distributions. After establishing identification, we estimated the model using a modified EM algorithm (Dempster, Laird, and Rubin 1977). We then obtained converged estimates of model parameters with their asymptotic covariances. We can use the estimates to assign each consumer to one of the segments identified by the results. We used the MPlus 3.01 software to implement the EM algorithm estimation for the finite mixture modeling approach. For greater details on this methodology and its application, see Jedidi, Jagpal, and DeSarbo (1997) and Titterington, Smith, and Markov (1985). To estimate our model, we used two-thirds (4554) of our sample (6831).( n2)
Results and Discussion
For the measurement model, in line with the work of Anderson and Gerbing (1988), we conducted a confirmatory factor analysis (see Table 4). The model fit is good, and the convergent validity and reliabilities for the scale items of the constructs are high.( n3) We assessed the discriminant validity of the constructs using two different procedures, one proposed by Bagozzi, Yi, and Phillips (1991) and the other by Fornell and Larcker (1981). Both procedures yielded similar results.( n4) Because at least one procedure supports strong discriminant validity, we conclude that, in general, our scales measure distinct model constructs. The correlation matrix of these 14 constructs appears in Table 5.
The results of the structural model for partial mediation, no mediation, and full mediation in the overall sample appear in Table 6. The partial mediation model fit metrics (e.g., root mean square error of approximation, comparative fit index) are superior to those of the no mediation and full mediation models.( n5) Three coefficients (absence of errors, privacy, and entertainment experience) are insignificant in the behavioral intent equation. Although brand strength has a small but negative and significant impact on behavioral intent in the behavioral intent equation, this result could be due to heterogeneity among consumers and across Web site categories, which we subsequently discuss. The effects of consumer characteristics are also consistent with our expectations. However, our interest is in exploring differences in these effects across Web site categories and consumer groups.
We used multigroup SEM analysis on the total (calibration and validation) sample to examine whether there are significant differences in the factor loadings and path coefficients across the eight different categories of Web sites used in our study. We used a testing procedure similar to the one we described in the previous section, estimating chi-square difference tests for three cases: ( 1) Every parameter is restricted to be equal in all eight categories (χ² = 81,176.86, degrees of freedom [d.f.] = 6069), ( 2) every parameter except path coefficients is restricted to be equal in all eight categories (χ² = 80,450.78, d.f. = 5894), and ( 3) every parameter except path coefficients and factor loadings is restricted to be equal in all eight categories (χ² = 74,645.54, d.f. = 5621).
Our data reject all null hypotheses of no significant differences in the effects of the drivers of online trust among the categories. We estimated the model separately for each category and analyzed the relative sizes of path coefficients in relation to our expectations. The maximum likelihood (ML) method typically used to report SEM results requires the sample covariance matrix to be positive and definite, which was not the case when we analyzed our data by separate categories. Therefore, following Wothke's (1993) suggestion, we also analyzed the data by the unweighted least squares method, which does not provide efficient estimates but offers consistent point estimates of the model parameters. The results show that the differences between these estimates are rather small, so we report only the ML estimates for the total sample in Table 7.
We discuss the results against the backdrop of our expectations and offer plausible explanations. First, privacy is highly influential for travel, and it is also important for e-tail and community Web sites. The findings for travel and community sites are consistent with our expectation that privacy is important for categories with high information risk. The frequent practice of providing personal information required for travel reservations and the common appearance of intrusive pop-up advertisements on travel Web sites exacerbate information risk for customers. Similarly, members of community Web sites share information freely with one another, so such Web sites are also susceptible to information risk. Furthermore, it appears that information risk may be high for some e-tail sites, making privacy important in this category.
Second, navigation is important for most categories of Web sites, but it is critical for sports, portal, and e-tail Web sites. Web sites in these three categories carry extensive information, and we expected navigation to be a more influential driver for such Web sites than for other Web sites. Therefore, this result supports our expectation. Consumers typically surf a sports-related Web site for quick information on their favorite event, sportsperson, or product, so navigation and presentation are critical. Because portals are information intensive, navigation is important for this category as well. Because e-tailers carry an array of items with deep information on their Web sites, good presentation of items and a quick path to desired items are important elements for building trust. Web sites with more easy-to-use features and greater ability to take the visitor quickly to his or her desired destination are in a better position to build trust than others.
Third, brand strength is a significant determinant of online trust for all categories except portals, but it is most important for automobiles, financial services, computers, and community sites. We hypothesized that brand strength is an important driver of online trust for categories with high involvement/high ticket price and for those involving high search effort. Automobiles, financial services, and computers are examples of such high-involvement/high-ticket-price items that need high consumer search. Therefore, the differences in the effect of brand strength on online trust across Web sites are consistent with our hypothesis. Brand strength is also high for sports sites, most likely because Nike, a powerful brand, was included in this category in our data.
Fourth, advice is an influential driver of online trust for automobile, computer, and travel-related products and e-tailers. We expected advice to be a powerful driver of trust for information-intensive Web sites whose product categories require a high degree of consumer search. Depending on the needs of the individual, automobiles, computers, and travel products can be complex to purchase and may require information assistance from the Web site. Web sites with the right suggestions and recommendations build confidence and trust with prospective buyers. Likewise, search goods and services on e-tail Web sites comprise intensive information. For such categories, advice is a dominant determinant of online trust.
Fifth, order fulfillment is most influential for travel products and e-tailers. We expected order fulfillment to be a dominant driver of online trust for Web site categories with high involvement/high ticket price. Travel is one such category. Because confirmation of a reservation immediately becomes available to consumers for most travel services, fulfillment is particularly important for travel services. Because e-tail sites are purchase-oriented sites, fulfillment is an essential aspect for trust formation in the e-tail category as well. Although categories such as automobile and computers are also typically high-involvement categories, order fulfillment is not an influential driver of Web site trust in these categories. Furthermore, the influence of order fulfillment on trust is somewhat small for e-tailers. A possible reason for these findings is that our data did not include actual consumer purchases. Thus, the measure of order fulfillment may not reflect an experience-based rating.
Sixth, the absence of errors is consistently important for all Web site categories. We did not expect the effect of this Web site characteristic to differ much across Web sites. Consumers expect any Web site to be free of errors, so absence of errors could be a minimum expectation on the part of consumers, regardless of the Web site category.
Although we did not have any formal hypotheses for differences in the effects of consumer characteristics on Web site trust, our results show some differences. Familiarity with the Web site is a particularly important driver for automobile, travel, and e-tailer sites. Online expertise seems to matter for trust building only in financial Web site categories. Shopping experience is a strong determinant of trust for portal sites. Notably, entertainment or chat experience is strongly associated with trust for computers. Because we did not have a priori expectations of these effects, we treat these as notable empirical findings.
A summary of the expected and actual effects appears in Table 8. In most cases, the actual effects are consistent with the expected effects. However, some relationships are not in the expected directions. We can speculate about the reasons for these findings, but further research is necessary to address these issues. Community features are negatively associated with trust for travel and computer Web sites. Community features, such as bulletin boards and chat rooms, enable visitors to share and receive tips on travel and computer purchases or usage from other users. Sometimes, if negative comments and information dominate the Web sites, these features may be negatively associated with trust, thus possibly explaining the negative relationship. Surprisingly, security is not a significant determinant of trust for any Web site category. It is likely that the security level offered by each Web site in our study is above a threshold level for the consumers, so it is not a significant determinant of consumers' overall trust. Brand strength is a dominant driver of trust for community Web sites. We did not hypothesize this effect. We expected brand strength to be important driver of trust at Web sites with high-involvement products and services. The community Web sites we used the study were ancestry.com and foodtv.com. Consumer involvement tends to be high when consumers visit and interact with these particular sites. Therefore, it is not surprising that brand strength is a dominant driver for these two sites.
The mediating role of trust on the effects of drivers of online trust on behavioral intent is different for different Web site categories. The mediating effect of trust on behavioral intent is strongest for computer sites and weakest for financial services sites. However, even in the case of financial services Web sites, the mediating role of online trust on behavioral intent is stronger than any other direct effect of the drivers on behavioral intent. We hypothesized that the mediating role of online trust would be stronger for categories in which the involvement/ticket price is high and the product is infrequently purchased. Most of the activities at financial services Web sites involve frequent transactions in which consumers directly click on action buttons. Trust has less of a mediating role for such a situation than for infrequently purchased high-ticket items, such as computers, for which consumers may need to go through a longer intermediate phase involving trust formation.
We used multigroup SEM analysis on the total sample to examine whether there were significant differences in the path coefficients across different demographic groups. The chi-square tests revealed that there were significant differences mainly across the education and income demographic splits. Brand strength is more influential for consumers with high education than for those with low education (p < .01). These results are somewhat surprising because we would expect consumers with low education or low income to rely more on navigation and those with high education or high income to be critical and not rely on the brand when developing trust in a Web site. A possible explanation is that high-income or high-education consumers spend less time on the Internet, relying on factors such as brand and advice to shape their trust levels and behavioral intent in relation to the site. Furthermore, it may be that more educated people are well aware of brands on the Internet and their value and are willing to attribute trust to the brand behind a site. In contrast, less educated people may not have much experience with brands on the Internet and may be somewhat skeptical of relying on them when building their trust in a site.
The results of the post hoc latent class mixture model segmentation appear in Table 9. To obtain convergent solutions by this method, we dropped the dichotomous variables without loss of generality as both the methodology and the software recommend. A six-segment solution was the best fitting and most interpretable solution. The demographic profiles of these segments are not dramatically different, so we do not discuss differences among them.
Segment 1 consumers reveal a balanced influence of all the drivers of online trust. It is the largest segment, constituting approximately 60% of the sample. Brand strength has the largest effect, but other major Web site characteristics also have significant effects on trust. Thus, for a majority of consumers, most Web site characteristics are important drivers of trust in relation to that Web site.
For consumers in Segment 2 (5% of the sample), the primary drivers of online trust are advice and brand strength. Advice is of paramount importance to this segment. Internet expertise, privacy, and navigation and presentation are not significant determinants of trust for this group.
Segment 3 consumers are somewhat similar to Segment 2 consumers in that they are predominantly concerned with advice. Segment 3 also constitutes a small portion (approximately 3%) of the sample. There are also some differences between Segments 2 and 3 with respect to other determinants of trust. For example, although brand strength has a significant influence on trust for Segment 2, it is insignificant for Segment 3. Surprisingly, the navigation and presentation parameter is negative, significant, and large for this segment. Although this anomaly could be due to statistical chance, it deserves further exploration in future studies.
The perceptions of trust for Segment 4 consumers are driven by brand strength, privacy, and advice. Segment 4 has approximately 6% of all consumers in the sample. Surprisingly, navigation and presentation and online expertise are not important drivers of trust for this segment. The concern for privacy differentiates this segment from the others.
Segment 5 consumers are mainly driven by brand strength in their perceptions of Web site trust. As with Segments 2, 3, and 4, Segment 5 is small, constituting approximately 4% of the sample. Although brand strength seems to be the most dominant driver for this segment, navigation and presentation and consumer online expertise or Internet savvy are also significant drivers. Advice, privacy, and absence of errors are not significant.
The trust perceptions of consumers in Segment 6 are driven by advice, brand strength, navigation and presentation, and privacy. Segment 6 is the second-largest segment, containing approximately 20% of the sample. As with Segments 1, 4, and 5, brand strength has the largest effect. However, unlike Segments 1, 4, and 5, advice has a sizable effect on online trust. Online expertise is also a significant and important determinant of trust for this segment. Absence of errors, however, is not a significant determinant of trust for this segment.
In summary, for the majority of consumers, online trust is driven by the Web site's advice, navigation and presentation, and brand strength. Brand strength has a greater influence on online trust levels for people with higher education than for those with lower education.
We performed some robustness checks on the model results. First, we checked whether the partially mediated model of trust was true for a randomly chosen validation sample. We estimated the model separately on the calibration and validation samples (assuming invariant factor structure) and analyzed the differences between the path coefficients we obtained from the two samples overall and for each category. The factor correlations are fairly close, providing strong evidence for the predictive validity of our model.
Second, we used multigroup SEM analysis to perform a series of nested models estimations and respective chi-square difference tests for three cases: ( 1) Every parameter is restricted to be equal in both samples (χ² = 16,523.91, d.f. = 1389), ( 2) every parameter except path coefficients is restricted to be equal in both samples (χ² = 16,490.67, d.f. = 1364), and ( 3) every parameter except path coefficients and factor loadings is restricted to be equal in both samples (χ² = 16,487.17, d.f. = 1325). Relaxing the restrictions on the path coefficients and factor loadings did not result in a significant improvement in model fit (p > .10), so we are confident about the predictive validity of the proposed model.
Managerial Implications
The key implications of our study are related to Web site differentiation strategy by category and customer segment. A company could allocate greater resources to the drivers of trust that are most influential for its category of Web sites. For example, automobile sites could focus on brand, advice, and navigation; community, financial services, and sports sites could focus on navigation and brand; computer sites could focus on brand and advice; portals could focus on navigation, privacy, and advice; and travel sites could focus on privacy, advice, and fulfillment.
Although we studied only eight categories, we can reasonably generalize the implications to a wide array of Web site categories based on the underlying Web site factors. A summary of the expected dominant drivers of online trust for 18 broad Web site categories appears in Table 10. We also list examples of subcategories under each broad Web site category together with the primary underlying Web site factors that influence the effects of the drivers of online trust. If these expected differences among Web site categories can be supported by further research, a Web site manager can use this summary to identify the key drivers that he or she should focus on to improve consumer trust in the Web site. For example, the manager of a Web site for children may want to emphasize navigation and presentation, whereas the manager of a car rental Web site may want to focus on privacy and order fulfillment features to build trust.
Companies can also build trust by differentiating and personalizing the site for different consumers by identifying customer groups on the basis of survey data. Our results suggest that the influence of different trust drivers, such as advice, brand, navigation, and absence of errors, differs across customers and that companies can personalize their Web sites for these different customer groups. If companies cannot obtain these data because of resource or time constraints, they can personalize their sites by the income or education level of the visitors. Although for the majority of consumers the influences of different drivers on trust are balanced, there is a sizable segment of consumers for whom brand and advice are the primary determinants of trust. The influential drivers of trust are different for consumers with different levels of education, and a company can emphasize the right trust drivers for the right consumer segment. Emphasizing the brand could be an effective trust-building initiative for highly educated, high-income consumers. Improving order fulfillment and privacy could also be the appropriate trust-generating effort for other groups of consumers. Companies can also personalize navigation and advice to suit the user's needs. For example, companies can enable the user to increase screen space for a personal advisor while reducing complex menu bars or enable a user to choose from a range of navigation styles (e.g., fast and direct versus personal and advisor driven).
The results have implications for the examples we discussed in the beginning of the article. For example, General Motors' Autochoiceadvisor Web site is characterized by the underlying Web site factors of financial risk and involvement. It correctly uses high brand strength and advice to gain trust and positive behavioral intent. However, it may want to reexamine its resource distribution and allocate more resources to navigation and presentation, which are also influential drivers of online trust for the automobile category. Similarly, Orbitz's use of Orbot to find and compare prices is an example of trust building through advice, but Orbitz could enhance trust by building its brand strength, emphasizing that it is co-owned by leading airline brands, such as United Airlines, American Airlines, and Delta Airlines. It could also differentiate itself and boost trust by emphasizing privacy and fulfillment, the factors that are the most influential drivers of trust for travel Web sites. Intel's Download Web site's decision assistance tips have positive effects on building trust through better navigation. Nevertheless, it could build stronger trust by emphasizing its brand and focusing more efforts on an advisor because these are two factors that are significant in building trust for computer-related Web sites. Finally, although Dell's online trust is enhanced by its strong brand, it may want to allocate more resources to advice because its current site is cluttered with promotions. In general, managers should emphasize navigation, advice, and brand in their site design but also extend this to the more creative presentation aspects.
The findings also have some broad implications. Managers must go beyond privacy and security and focus on factors such as navigation and presentation, advice, and brand strength to enhance trust for their Web sites. Collectively, navigation and presentation, advice, and brand strength are more influential predictors of online trust than are privacy and security.
Another important finding of our work is that trust partially mediates the relationship between Web site characteristics and behavioral intent more strongly for some Web site categories than for others. Therefore, incorporating Web site cues that enhance trust can result in a long-term favorable consumer relationship with the firm, and trust cues need to be explicitly incorporated in Web site design strategies. Managers should think not only of direct effects on behavioral intent (e.g., sales effects from promotions at the Dell Web site) but also of the relationship effects of trust building, especially because the mediating effect of trust is strongest for computer-related products. Dell's promotions may have a positive short-term effect of increasing behavioral intent of buying, but the long-term effects of enhanced Web site trust may be more important. Managers of such Web sites should consider trust an intervening state that consumers must move through and design their Web sites to build consumer trust through all the previously cited elements.
A final implication of our results is for multichannel trust building. Multichannel shopping and marketing are growing trends. We examined the Internet, but many of the same factors are present in other channels, such as e-mail, telephone, direct mail, and physical store formats. Navigation and layout of the physical store are analogous to site navigation and presentation. Advice can be given by sales personnel or telemarketing operators. Brand strength is relevant in all channels. Privacy and security are relevant in the store, on the telephone, and on the Internet. Presentation is evident in store design, telephone conversations, and channel layout. Furthermore, each channel has its association with some product categories and its own geodemographics. Channel--category associations interact with customer geodemographics to explain a sizable portion of the share of volume of different channels (Inman, Shankar, and Ferraro 2004). Managers should maintain a high level of coherence across the channels so that trust-building efforts are reinforced throughout the multichannel consumer experience.
Limitations and Further Research
Our study has several limitations that further research could address. First, because our study is exploratory in nature, it could be replicated with other Web site categories and consumer groups, and some of the anomalies could be reexamined. Second, whereas online trust has an implicit dynamic nature, our study presents a cross-sectional view. Third, our study does not actually measure consumer action on the Web site in terms of actual purchase, so the effects of order fulfillment might be understated. Fourth, potential interactions among the drivers of Web site trust, such as that between brand strength and security, could be explored. Fifth, additional data on multidimensional measures of online trust and variables, such as number of years in business, reputation, offline presence, service quality, and length of relationships, could also help explore more potential antecedents of online trust. However, some of these variables are likely to be correlated among themselves and with the consumer and Web site characteristics in our study. Sixth, our research could be extended through behavioral and market experiments by sequentially altering specific Web site trust drivers we identified in our study to build an "Internet trust generator." Seventh, the indicators of the Web site characteristic constructs we used in the analysis are primarily reflective rather than formative, but formative indicators may provide a more comprehensive and richer representation of the constructs and potentially lead to fewer model misspecification errors (Jarvis, Mackenzie, and Podsakoff 2003).
Conclusion
This study empirically shows that the influences of Web site and consumer characteristics on trust and the role of trust in the relationships between trust drivers and behavioral intent are significantly different for different Web site categories and customer groups. Privacy and order fulfillment are the most influential determinants of trust for Web sites for which both information risk and involvement are high, such as travel sites. Navigation is strongest for information-intensive sites, such as sports sites, portals, and community sites. Brand strength is critical for categories with high involvement, such as automobile and financial services sites, and advice is the most powerful determinant for search good categories with high financial risk, such as computer sites. Online trust partially mediates the relationships between Web site and consumer characteristics and behavioral intent, and this mediation is strongest for sites with infrequently purchased, high-involvement items, such as computers. Conversely, it is weakest for sites that are oriented toward frequent transactions, such as financial services. The influences of different drivers on online trust are balanced for most customers, but there is a sizable segment of consumers for whom brand strength and advice are the primary determinants of online trust. Brand strength influences the online trust levels of people with higher education more than it does those of people with lower education. The results offer important implications for Web site design strategies.
The authors acknowledge the support of the Center for eBusiness@MIT and McCann Erickson and National Family Opinion Inc. for their intellectual and financial support of this research. They also thank the three anonymous JM reviewers; participants at the marketing seminars at American University and Texas A&M University; and Su Chiang, Shun Yin Lam, P. Rajan Varadarajan, and Manjit Yadav for helpful comments.
( n1) For a detailed review of trust in different disciplines, see Shankar, Urban, and Sultan (2002).
( n2) Location of Internet usage is a potential control variable because it is possible that consumers have different degrees of trust in a Web site if they log in primarily from home or business, depending on their perceptions of the levels of security, firewall function, and how the information is exchanged on a Web site. This construct lacked the necessary validity and reliability in our data, so we do not include it in our final model. Therefore, as part of the measurement purification process, we dropped three variables (Q101, Q103, and Q104 in the questionnaire in the Appendix, relating to whether the consumer purchased on the Web site and the primary location from which the Internet is accessed--business or home) from the analysis.
( n3) The chi-square statistic is significant (p < .01). The model fit is fairly good (e.g., root mean square error of approximation = .06, comparative fit index = .92). Moreover, all loadings on hypothesized factors are highly significant (p < .001) and substantively large (35 of 39 items have loadings greater than .70), which establishes convergent validity. Almost all the reliabilities of the individual scales we report in Table 4 are above recommended levels, ranging from .61 to .92 for Cronbach's alpha (Bagozzi and Yi 1988) and for composite reliability (Baumgartner and Homburg 1996) (for 12 of 14 constructs, greater than .83).
( n4) In the first procedure that Bagozzi, Yi, and Phillips (1991) propose, each pair of constructs is analyzed through a pair of measurement models with and without the correlation between the constructs fixed to unity. We found that the chi-square statistic for the unconstrained model is significantly lower than that of the constrained model for each of 91 pairs in our model (difference in χ² ranges from 1505.24 to 9000.66, degree of freedom = 1, p < .001). In the second procedure, consistent with Fornell and Larcker's (1981) test for discriminant validity, the average variance extracted is greater than .5 for 12 of 14 constructs, and the average extracted variances were greater than the squared correlations for all but one pair of constructs (trust and intent).
( n5) Consistent with the work of Anderson and Gerbing (1988), our results reject the null hypothesis for both nested model pairs (comparison with the fully mediated model: Δχ² = 646.99, d.f. = 12, p < .01; comparison with the nonmediated model: Δχ² = 750.74, d.f. = 1, p < .01). Independent latent variables explain a considerable portion of the variance in the endogenous constructs (64% for trust and 81% for behavioral intent).
Legend for Chart:
A - Driver of Online Trust
B - Underlying Web Site Factors Financial Risk
C - Underlying Web Site Factors Information Risk
D - Underlying Web Site Factors Involvement/Ticket Price
E - Underlying Web Site Factors Information on the Site
F - Underlying Web Site Factors Search Good/Service
A B C D E F
Privacy +
Security +
Navigation and presentation +
Brand strength + +
Advice + + + +
Order fulfillment +
Community features + +
Absence of errors + + + + +
Notes: The "+" sign indicates that the effect of a driver of
online trust (e.g., privacy) on Web site trust is greater for
Web site categories that are dominant with this Web site
characteristic (e.g., information risk). Legend for Chart:
A - Category
B - Underlying Web Site Factors Financial Risk
C - Underlying Web Site Factors Information Risk
D - Underlying Web Site Factors Involvement/Ticket Price
E - Underlying Web Site Factors Information on the Site
F - Underlying Web Site Factors Search Good/Service
A B C D E F
Automobile X X X
Community X X
Financial services X X X
Computer X X X
Portal X
Sports X
Travel X
E-tailer X X
Notes: X indicates the presence or significance of the
underlying Web site factor for the Web site category. Automobile
•Carpoint.com
•gmbuypower.com
•kbb.com
•carsdirect.com
Finance
•etrade.com
•marketwatch.com
•schwab.com
Computer
•dell.com
•microsoft.com
Sports
•nba.com
•sportsline.com
•nike.com
Travel
•aa.com
•travelocity.com
•cheaptickets.com
E-Tailer
•amazon.com
•cdnow.com
•proflowers.com
•ebay.com
Community
•ancestry.com
•foodtv.com
Portal, Search Engine,
and Shopbot
•aol.com
•lycos.com
•Webmd.com
•mysimon.com
Legend for Chart:
A - Item
B - Mean
C - Standard Deviation
D - Navigation and Presentation
E - Brand Strength
F - Privacy
G - Security
H - Advice
I - Order Fulfillment
J - Absence of Errors
K - Community Features
L - Shopping Experience
M - Entertainment Experience
N - Online Familiarity
O - Expertise
P - Behavioral Intent
Q - Trust
A B C D E F G H
I J K L M N O
P Q
Q2 5.23 1.39 .89
Q4 5.30 1.42 .86
Q10 5.12 1.45 .71
Q19 4.99 2.13 .75
Q20 5.27 1.45 .95
Q23 5.18 1.31 .73
Q27 5.29 1.52 .88
Q28 5.31 1.48 .95
Q32 4.93 1.54 .77
Q35 .55 .50 .82
Q36 .54 .50 .94
Q37 .57 .49 .87
Q52 4.02 1.70 .68
Q54 4.40 1.58 .91
Q55 4.33 1.57 .90
Q71 .82 .39
.90
Q72 .76 .42
.89
Q74 .87 .34
.87
Q77 5.63 1.82
.84
Q78 5.97 1.51
.92
Q79 6.13 1.32
.86
Q89 .52 .50
.84
Q90 .27 .45
.73
Q91 .41 .49
.86
Q96 .91 .29
.95
Q102 .86 .34
.74
Q98 .87 .33
.68
Q99 .38 .48
.65
Q100 .40 .49
.83
Q109 6.05 16.81
.54
Q105 5.12 1.48
.78
Q106 4.85 1.50
.86
Q107 5.42 1.34
.86
Q118 4.67 1.78
.83
Q119 4.99 1.66
.89
Q122 4.22 1.97
.82
Q124 5.11 1.28
.88
Q125 5.40 1.23
.87
Q126 5.10 1.27
.90
Variance extracted .68 .66 .76 .77 .69
.79 .76 .66 .73 .44 .49 .70
.72 .78
Reliability .86 .85 .90 .91 .87
.92 .91 .85 .84 .61 .65 .87
.88 .91
Cronbach's alpha .85 .84 .90 .91 .86
.92 .91 .85 .83 .61 .62 .87
.88 .91 Legend for Chart:
A - Construct
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
O - 14
A
B C D E F
G H I J K
L M N O
1. Privacy
1.00
2. Security
.14 1.00
3. Navigation and presentation
.64 .14 1.00
4. Brand strength
.46 .07 .50 1.00
5. Advice
.48 .41 .49 .30 1.00
6. Order fulfillment
.16 .51 .17 .10 .40
1.00
7. Community features
.13 .52 .11 .08 .34
.53 1.00
8. Absence of errors
.48 .00(n.s.) .52 .44 .33
.10 -.03 1.00
9. Familiarity
.09 .14 .16 .45 .04
.12 .05 .14 1.00
10. Online expertise
.28 .03 .26 .30 .19
-.02(n.s.) .02(n.s.) .32 .17 1.00
11. Shopping experience
.08 -.05 .12 .16 .03(n.s.)
.06 .14 -.08 .22 .32
1.00
12. Entertainment experience
.09 .13 .12 .14 .13
.10 .21 .09 .06 .41
.15 1.00
13. Behavioral intent
.53 .21 .61 .56 .50
.28 .18 .49 .47 .30
.30 .17 1.00
14. Trust
.59 .16 .63 .64 .49
.20 .13 .57 .33 .39
.25 .20 .86 1.00
Notes: All correlations, except those with (n.s.) (not
significant), are significant (p < .05). Legend for Chart:
A - Relationship
B - Partial Mediation
C - No Mediation
D - Full Mediation
A
B C D
Web Site/Consumer Characteristics Impact on Trust
Privacy → trust
.15 (.01)(***) .28 (.03)(***) .14 (.02)(***)
Security → trust
.00 (.01) -.17 (.03)(***) .00 (.01)
Navigation and presentation → trust
.17 (.02)(***) .15 (.03)(***) .19 (.02)(***)
Brand strength → trust
.26 (.02)(***) -.30 (.04)(***) .25 (.02)(***)
Advice → trust
.16 (.02)(***) .35 (.03)(***) .17 (.01)(***)
Order fulfillment → trust
.02 (.01) -.07 (.03)(**) .03 (.01)(*)
Community features → trust
.00 (.02) .03 (.03) .00 (.01)
Absence of errors → trust
.18 (.01)(***) .24 (.02)(***) .17 (.01)(***)
Familiarity → trust
.11 (.02)(***) .72 (.03)(***) .13 (.01)(***)
Online expertise → trust
.09 (.01)(***) .01 (.03) .07 (.01)(***)
Shopping experience → trust
.08 (.01)(***) .05 (.02)(*) .09 (.01)(***)
Entertainment experience → trust
.04 (.02)(*) .10 (.03)(***) .04 (.02)(*)
Web Site/Consumer Characteristics Impact on Behavioral
Intent, Mediated by Trust
Trust → behavioral intent
.70 (.02)(***) -- .87 (.01)(***)
Privacy → behavioral intent
.02 (.01) .36 (.05)(***) --
Security → behavioral intent
-.01 (.01) -.32 (.05)(***) --
Navigation → behavioral intent
.12 (.02)(***) .20 (.04)(***) --
Brand strength → behavioral intent
-.08 (.02)(***) -.94 (.10)(***) --
Advice → behavioral intent
.11 (.01)(***) .56 (.05)(***) --
Order fulfillment → behavioral intent
.04 (.01)(**) -.11 (.05)(*) --
Community features → behavioral intent
.02 (.01) .08 (.05) --
Absence of errors → behavioral intent
-.01 (.01) .24 (.04)(***) --
Familiarity behavioral intent
.24 (.02)(***) 1.42 (.10)(***) --
Online expertise → behavioral intent
-.08 (.01)(***) -.16 (.04)(***) --
Shopping experience → behavioral intent
.09 (.01)(***) -.09 (.04)(*) --
Entertainment experience → behavioral intent
.01 (.01) .16 (.05)(***) --
χ² (d.f.)
10,295.03 (611) 11,045.76 (612) 10,942.02 (623)
RMSEA
.059 .061 .060
NFI
.918 .913 .913
NNFI
.906 .900 .902
CFI
.922 .917 .917
GFI
.896 .889 .890
RMR
.048 .049 .051
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: Standard errors are in parentheses. Chi-squared difference
tests are significant at the .001 level. RMSEA = root mean square
error of approximation, NFI = normed fit index, NNFI = nonnormed
fit index, CFI = comparative fit index, GFI = goodness-of-fit
index, and RMR = root mean residual. Legend for Chart:
A - Relationship
B - ML
C - Automobile
D - Community
E - E-Tailer
F - Finance
G - Computer
H - Portal
I - Sports
J - Travel
A
B C D
E F G
H I J
Privacy → trust
.14(***) (.01) .16(***) (.03) .25(***) (.06)
.22 (.08)(***) .09 (.05) .14(*) (.07)
.12(***) (.04) .13(***) (.04) .34(***) (.07)
Security → trust
.01 (.01) .01 (.03) .01 (.05)
.03 (.03) .05 (.04) .03 (.04)
.02 (.03) -.02 (.04) .03 (.04)
Navigation and presentation → trust
.18(***) (.01) .11(***) (.04) .12 (.08)
.27 (.04)(***) .20(***) (.05) -.06 (.09)
.29(***) (.04) .29(***) (.04) -.09 (.09)
Brand strength → trust
.27(***) (.01) .23(***) (.03) .42(***) (.11)
-.12 (.21) .35(***) (.05) .41(***) (.07)
.06 (.05) .28(***) (.03) .17(*) (.07)
Advice → trust
.16(***) (.01) .17(***) (.04) .04 (.08)
.23 (.06)(***) .07 (.05) .26(***) (.06)
.18(***) (.04) .02 (.05) .22(***) (.04)
Order fulfillment → trust
.02 (.01) -.07 (.04) .09 (.07)
.14 (.04)(***) -.07 (.04) .08 (.05)
.01 (.02) .04 (.03) .44(*) (.20)
Community features → trust
.00 (.01) .06 (.05) -.04 (.05)
-.11 (.05)(**) .11(**) (.04) -.23(***) (.05)
.04 (.03) .05 (.04) -.47(*) (.21)
Absence of errors → trust
.18(***) (.01) .22(***) (.03) .15(**) (.05)
.13 (.04)(***) .15(***) (.03) .20(***) (.05)
.19(***) (.03) .13(***) (.03) .21(***) (.03)
Familiarity → trust
.12(***) (.01) .19(***) (.03) -.04 (.07)
.45 (.20)(*) .09(**) (.04) .01 (.03)
.14(***) (.04) .14(***) (.03) .18(**) (.06)
Online expertise → trust
.08(***) (.01) .02 (.02) .07 (.04)
.06 (.03)(*) .14(***) (.03) -.05 (.05)
-.05 (.03) .06(*) (.03) .02 (.06)
Shopping experience → trust
.05(***) (.01) .04(*) (.02) .02 (.04)
.07 (.05) .05(*) (.02) -.03 (.03)
.11(***) (.02) -.02 (.03) .04 (.05)
Entertainment experience → trust
.03(**) (.01) .00 (.03) -.01 (.04)
-.01 (.04) .03 (.04) .20(**) (.06)
.03 (.03) .09(**) (.03) -.01 (.04)
Trust → behavioral intent
.73(***) (.02) .71(***) (.05) .58(***) (.09)
.67 (.12)(***) .44(***) (.08) .85(***) (.09)
.72(***) (.06) .61(***) (.06) .71(***) (.14)
Sample size
1087 513
1144 745 570
1105 848 819
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: Standard errors are in parentheses. Legend for Chart:
A - Driver
B - Predicted Categories
C - Actual Categories
D - Possible Explanation
A B
C
D
Privacy Community, travel
Travel
--
Security Financial services, computer,
travel
None
Perhaps security is so basic
for all sites that it does not
explain any variance in the
presence of other drivers.
Navigation and presentation Community, e-tailer, portal,
sports
E-tailer, portal, sports
--
Brand strength Automobile, financial
services, computer
Automobile, financial
services, computer,
community
--
Advice Automobile, e-tailer, financial
services, computer
Automobile, e-tailer,
computer
--
Order fulfillment Travel, financial services,
computer, e-tailer
Travel, e-tailer
The effect of order fulfillment
could be understated for
financial services and
computer categories
because the measures of
behavior did not include any
purchase or orders.
Community features Community
Computers (-), travel(-)
For computer and travel
categories, community
features such as user groups
and bulletin boards may give
rise to complaining behavior
such as venting, leading to a
snowballing negative effect
on trust.
Absence of errors All
All
-- Legend for Chart:
A - Web Site Characteristic
B - Segment 1 (60%)
C - Segment 2 (5%)
D - Segment 3 (3%)
E - Segment 4 (6%)
F - Segment 5 (6%)
G - Segment 6 (20%)
A
B C D
E F G
Privacy
.14(***) (.02) .25 (.11) -.01 (.33)
.35(**) (.11) .08 (.07) .11(*) (.04)
Navigation and presentation
.21(***) (.02) .07 (.19) -.87(***) (.29)
-.07 (.17) .33(***)(.10) .16(**) (.05)
Brand strength
.29(***) (.01) .26(*) (.09) .22 (.13)
.50(***) (.07) .50(***)(.08) .37(***) (.03)
Advice
.11(*) (.02) .45(**) (.18) .56(**) (.22)
.20(*) (.12) -.25 (.27) .25(***) (.05)
Absence of errors
.12(***) (.04) -.07 (.56) .40 (.61)
-.07 (.35) .07 (.22) .02 (.09)
Online expertise
.20(***) (.02) -.10 (.08) -.18 (.17)
.09 (.09) .23(***)(.06) .13(**) (.04)
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: Figures in parentheses of segments are relative sizes of
the segments in the sample. Figures in parentheses of parameters
are standard errors. The large significant coefficients are not
absolute but reflect the relative differences in the influence
of these variables across the different segments. Legend for Chart:
A - Category
B - Examples of Subcategories
C - Primary Underlying Web Site Factor
D - Expected Dominant Drivers of Online Trust
A B
C
D
Arts Movies, television programs,
writing, photography, painting
Information on the site
Navigation and presentation,
advice, community features
Automobile Finished vehicles, parts
Financial risk, involvement,
search good
Security, absence of errors,
brand strength, advice
Business Marketing, e-commerce,
entrepreneurship
Information on the site,
financial risk
Security, absence of errors,
navigation and presentation
Education High schools, graduate
schools, training, kids
education
Information on the site,
involvement
Brand strength, navigation
and presentation
Electronics and computer Computers,
telecommunications,
television sets, DVD players,
camcorders
Financial risk, involvement,
search good
Security, absence of errors,
brand strength, advice
Finance Banking, insurance, financial
services, taxes
Financial risk, involvement,
search good
Security, absence of errors,
brand strength, advice
Family and community Parenting, babies, kids,
teens, genealogy, pets
Information on the site
Navigation and presentation
Fashion Apparel, models, designs
Involvement, search good
Brand strength, advice,
absence of errors
Health Beauty, medicine, fitness
Information on the site
Navigation and presentation,
advice, absence of errors,
community features
Home Real estate, gardening,
moving
Financial risk, involvement,
search good
Security, absence of errors,
brand strength, advice
News and portal Newspapers, magazines,
auctions, search engines,
shopbots
Information on the site
Navigation and presentation,
advice, absence of errors,
community features
Recreation Humor, outdoors, games,
toys
Involvement, information on
the site
Brand strength, absence of
errors, navigation and
presentation, advice
Reference Libraries, maps
Information on the site
Navigation and presentation,
advice
Science Space, biology, physics,
chemistry
Information on the site
Navigation and presentation,
advice
Shopping and e-tailer Retail categories (grocery,
drug, durables)
Financial risk
Security, absence of errors,
order fulfillment
Society and community Government, religion
Information risk, information
on the site
Privacy, absence of errors,
community features
Sports Specific sports, athletics,
sports news, sports apparel
Information on the site
Navigation and presentation
Travel Airlines, hotels, car rentals,
cruises
Information risk
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Geyskens, Inge, Jan-Benedict E.M. Steenkamp, and Nirmalya Kumar (1998), "Generalizations About Trust in Marketing Channel Relationships Using Meta-Analysis," International Journal of Research in Marketing, 15 (3), 223-48.
Grewal, D., J. Gotlieb, and H. Marmorstein (1994), "The Moderating Effects of Message Framing and Source Credibility on the Price-Perceived Risk Relationship," Journal of Consumer Research, 21 (June), 145-53.
Hoffman, Donna L. and Tom P. Novak (1996), "Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations," Journal of Marketing, 60 (July), 50-68.
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Inman, J. Jeffrey, Venkatesh Shankar, and Rosellina Ferraro (2004), "The Roles of Channel-Category Associations and Geodemographics in Channel Patronage," Journal of Marketing, 68 (April), 51-71.
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Jarvenpaa, Sirkka L., Joam Tractinsky, and Michael Vitale (2000), "Consumer Trust in an Internet Store," Information Technology and Management, 1 (1-2), 45-71.
Jarvis, Cheryl Burke, Scott B. Mackenzie, and Philip M. Podsakoff (2003), "A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, 30 (2), 199-218.
Jedidi, Kamel, Harsharanjeet S. Jagpal, and Wayne S. DeSarbo (1997), "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, 16 (1), 39-59.
Keller, Kevin (1993), "Conceptualizing, Measuring, and Managing Customer-Based Brand Equity," Journal of Marketing, 57 (January), 1-22.
Lee, Matthew K.O. and Efraim Turban (2001), "A Trust Model for Consumer Internet Shopping," International Journal of Electronic Commerce, 6 (1), 75-91.
Lewicki, R.J., D.J. McAllister, and R.J. Bies (1998), "Trust and Distrust: New Relationships and Realities," The Academy of Management Review, 23 (3), 438-58.
Lynch, P.D., R.J. Kent, and S. Srinivasan (2001), "The Global Internet Shopper: Evidence from Shopping Tasks in Twelve Countries," Journal of Advertising Research, 41 (3), 15-23.
Mittal, Vikas and Wagner A. Kamakura (2001), "Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics," Journal of Marketing Research, 38 (February), 131-42.
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Web Site Characteristics
1. The site is easy to use. (Navigation)
- 2. Overall layout of the site is clear. (Navigation)
- 3. The site layout is consistent across all pages. (Navigation)
- 4. The process for browsing is clear. (Navigation)
- 5. The site has legible images, colors, and text. (Navigation)
- 6. The site uses simple language. (Navigation)
- 7. The site uses a layout that is familiar. (Navigation)
- 8. There is a readily available site map (a summary of site links), which allows you to figure out where to go and what you can do at the site. (Navigation)
- 9. There are useful links to other sites that aid the primary purpose of coming to this site. (Navigation)
- 10. The site is visually appealing. (Navigation)
- 11. The visual appearance and manner of the site is professional (not amateur looking). (Navigation)
- 12. The site displays a high level of artistic sophistication/creativity. (Navigation)
- 13. This site features are state-of-the-art, better than most sites in this industry. (Navigation)
- 14. The site visually conveys a sense of honesty. (Navigation)
- 15. The site feels warm and comforting. (Navigation)
- 16. The site is engaging and captures attention. (Navigation)
- 17. The site is entertaining. (Navigation)
- 18. Information on the site can be obtained quickly. (Navigation)
- 19. I am familiar with the company whose site this is. (Brand)
- 20. The site represents a quality company or organization. (Brand)
- 21. The site carries products and services with reputable brand names. (Brand)
- 22. I am generally familiar with other brands (products and services) being advertised on the site. (Brand)
- 23. The quality of the brands being advertised on this site is consistent with the quality of the site's sponsoring company. (Brand)
- 24. The site is consistent with my image of the company whose site this is. (Brand)
- 25. The site enhanced how I feel about the company whose site this is. (Navigation)
- 26. The general privacy policy is easy to find on the site. (Privacy)
- 27. The text of the privacy policy is easy to understand. (Privacy)
- 28. The site clearly explains how user information is used. (Privacy)
- 29. Information regarding security of payments is clearly presented. (Privacy)
- 30. Informational text regarding the site's use of cookies is clearly presented. (Privacy)
- 31. I believe the company sponsoring this site will not use cookies to invade my privacy in any way. (Privacy)
- 32. The site explains clearly how my information will be shared with other companies. (Privacy)
- 33. I would be comfortable giving personal information on this site. (Privacy)
- 34. I would be comfortable shopping at this site. (Privacy)
- 35. There were signs or symbols on the site placed there by third-party companies indicating that the site had been reviewed or audited for sound business practices. (Security)
- 36. There were trust seals present (e.g., TRUSTe). (Security)
- 37. There were seals of companies stating that my information on this site is secure (e.g., Verisign). (Security)
- 38. Information is present indicating that this site has received a best site award. (Security)
- 39. Endorsement by celebrities is present. (Community)
- 40. Testimonial/endorsement by past users is present. (Community)
- 41. The site content is easy for me to understand. (Navigation)
- 42. The content appears to be up-to-date. (Navigation)
- 43. The site provides accurate and relevant information. (Navigation)
- 44. The site provides me with sufficient information to make a purchase decision on all products being offered. (Advice)
- 45. The illustrations for the products and services at the site are helpful in making a purchase decision. (Navigation)
- 46. The site has useful shopping support tools (such as a calculator or planner). (Advice)
- 47. The site provides an explanation of services and products being offered. (Advice)
- 48. The site set up can be personalized to my needs. (Advice)
- 49. The site can recommend products based on previous purchase. (Advice)
- 50. The site allows me to create products or services to exactly fit my needs. (Advice)
- 51. Products can easily be compared. (Advice)
- 52. Comparisons of all competing brands are presented. (Advice)
- 53. Good shopping tips are provided. (Advice)
- 54. To recommend products, easy to answer questions are asked about my preferences. (Advice)
- 55. Useful shopping recommendations are made based on my personal information and preferences. (Advice)
- 56. The site is helpful to me in reaching my buying decisions. (Advice)
- 57. The site presents both benefits and drawbacks of products and services. (Advice)
- 58. A toll free number is easily found for live help. (Advice)
- 59. Informative magazine articles or editorial content are present. (Community)
- 60. The site asks questions to determine needs and preferences. (Advice)
- 61. There is a search tool to help find information on the site. (Order fulfillment)
- 62. It is possible to interact on the screen with a shopping advisor. (Community)
- 63. It is possible to contact a shopping assistant through e-mail. (Order fulfillment)
- 64. It is possible to communicate via fax to an expert advisor. (Community)
- 65. The site appears to offer secure payment methods. (Order fulfillment)
- 66. The site accepts a variety of payment methods. (Order fulfillment)
- 67. Easy ordering and payment mechanisms exist. (Order fulfillment)
- 68. Service and product guarantees are clearly explained. (Order fulfillment)
- 69. Shipping and handling costs are listed up front. (Order fulfillment)
- 70. The site tells me immediately if something is out of stock, so time is not wasted going through the checkout process and finding this out later. (Order fulfillment)
- 71. Delivery options are available. (Order fulfillment)
- 72. Return policies or other measures of accountability are present. (Order fulfillment)
- 73. Once an order is placed, it can be tracked to see where it is in the shipping process. (Order fulfillment)
- 74. Order confirmation is given via e-mail. (Order fulfillment)
- 75. The items I looked at were in stock. (Order fulfillment)
- 76. The Internet links were in working order. (Absence of errors)
- 77. There were no errors or crashing. (Absence of errors)
- 78. There were no busy server messages. (Absence of errors)
- 79. There were no pages "under construction." (Absence of errors)
- 80. The download time was acceptable. (Absence of errors)
- 81. All text and menus displayed properly. (Absence of errors)
- 82. The site and its contents could be accessed without requiring too much personal information. (Absence of errors)
- 83. All features of the site could be used without the requirement to download programs (such as downloading a "flash" program to watch a video or to hear music). (Absence of errors)
- 84. It is easy to interact with other users of this site who may have bought things at the site before or who use the site frequently. (Community)
- 85. I enjoyed the overall experience of the site. (Navigation)
- 86. I found games/puzzles/freebies or gifts on the site. (Community)
- 87. I found photos of people/family/kids on the site. (Community)
- 88. I found bios of executives on the site. (Community)
- 89. The site allows user direct input or posting to site (e.g., bulletin board, e-mail, personals). (Community)
- 90. Evidence of the site participating in philanthropy/ charity is present. (Community)
- 91. A chat room is available where consumers can discuss their experience with the site and/or its products. (Community)
Customer/Consumer Characteristics
- 94. I use the Internet as an information tool.(a)
- 95. I use the Internet for e-mail.(a)
- 96. I use the Internet for shopping. (Shopping experience)
- 97. I use the Internet for banking/investing. (Shopping experience)
- 98. I use the Internet for entertainment. (Entertainment or chat experience)
- 99. I have used the Internet to take part in chat rooms. (Entertainment or chat experience)
- 100. Before this survey, I was familiar with the site I have just evaluated. (Familiarity)
- 101. I have made a purchase on this site in the past. (Familiarity)(a)
- 102. I have purchased products or services at other sites by completing the transaction online. (Shopping experience)
- 103. I use the Internet primarily for business/work related activities.(a)
- 104. I use the Internet primarily for household related activities.(a)
- 105. I consider myself to be quite knowledgeable about Internet sites in general. (Online savvy/Expertise)
- 106. I am confident in my ability to assess trustworthiness of web sites. (Online savvy/Expertise)
- 107. I am confident in my ability to assess the quality of a site. (Online savvy/Expertise)
- 108. The number of hours I spend per week on the Internet are: (Entertainment or chat experience)
- 109. Before today, approximately how many times had you visited this site? (Familiarity)
Demographics
- 110. What is your gender?
- 111. What is your age?
- 112. What is your employment status?
- 113. What is the highest level of education you have completed?
- 114. Including yourself, how many people live in your household? (Select one)
- 115. What is your household's combined yearly income? Be sure to combine the total income for all household members living with you such as wages or salaries, income from self-employment, rents, dividends, etc.--BEFORE tax deductions. (Select one)
- 116. Where do you live? (Select one)
Trust Items
- 117. This site appears to be more trustworthy than other sites I have visited. (Trust)
- 123. The site represents a company or organization that will deliver on promises made. (Trust)
- 124. My overall trust in this site is. (Trust)
- 125. My overall believability of the information on this site is. (Trust)
- 126. My overall confidence in the recommendations on this site is. (Trust)
Behavioral Intent Items
- 118. I would purchase an item at this site. (Intent)
- 119. I would recommend this site to a friend. (Intent)
- 120. I am comfortable providing financial and personal information on this site. (Intent)
- 121. I would book mark this site. (Intent)
- 122. I would register at this site. (Intent)
(a) This item does not represent any particular construct in the SEM.
Notes: The construct onto which the corresponding item loads highest is in parentheses after that item. Corresponding constructs appear in italics for items not included in the SEM.
~~~~~~~~
By Yakov Bart; Venkatesh Shankar; Fareena Sultan and Glen L. Urban
Yakov Bart is a doctoral student, Haas School of Business, University of California at Berkeley.
Venkatesh Shankar is Professor of Marketing and Coleman Chair in Marketing, Mays Business School, Texas A&M University.
Fareena Sultan is an associate professor, College of Business Administration, Northeastern University.
Glen L. Urban is David Austin Professor of Marketing, Sloan School of Management, Massachusetts Institute of Technology.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 19- Attention Capture and Transfer in Advertising: Brand, Pictorial, and Text-Size Effects. By: Pieters, Rik; Wedel, Michel. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p36-50. 15p. 1 Black and White Photograph, 1 Diagram, 4 Charts, 3 Graphs. DOI: 10.1509/jmkg.68.2.36.27794.
- Database:
- Business Source Complete
Attention Capture and Transfer in Advertising: Brand,
Pictorial, and Text-Size Effects
The three key ad elements (brand, pictorial, and text) each have unique superiority effects on attention to advertisements, which are on par with many commonly held ideas in marketing practice. This is the main conclusion of an analysis of 1363 print advertisements tested with infrared eye-tracking methodology on more than 3600 consumers. The pictorial is superior in capturing attention, independent of its size. The text element best captures attention in direct proportion to its surface size. The brand element most effectively transfers attention to the other elements. Only increments in the text element's surface size produce a net gain in attention to the advertisement as a whole. The authors discuss how their findings can be used to render more effective decisions in advertising.
Magazines are an important advertising medium, as illustrated by their projected 13% share of ad spending in 2003 in the United States and the even greater shares in countries such as France (32%), Germany (24%), Italy (15%), the Netherlands (27%), and the United Kingdom (16%) (International Federation of the Periodical Press 2003). To reach consumers effectively and to communicate with them, print advertisements need to cut through the clutter of competing advertisements and editorial messages. Because the typical magazine contains more than 50% advertising, consumers cannot fully absorb all the advertising and editorial content. Failures to capture consumers' attention (i.e., to attract and retain it) reduce the effective reach of print advertising, thereby increasing the cost-per-thousand and jeopardizing the attainment of longterm communication and marketing goals. Because competitive clutter is on the rise, some industry experts argue that "the power of marketing is eroding ... from lack of attention" (Sacharin 2001, p. 3). Attention has been referred to as the scarcest resource in today's business (Adler and Firestone 1997; Davenport and Beck 2001). This makes the capture of consumers' attention an increasingly important aim for print advertising.
The general belief underlying print advertising tactics is that size matters: larger advertisements attract and retain more attention, and the larger an advertisement's brand, pictorial, and text elements, the more attention they should capture. However, the precise attention effects of brand, pictorial, and text sizes have been vigorously debated (Aitchinson 1999; Maloney 1994; Moriarty 1986; Rossiter and Percy 1997) but rarely empirically studied. Much is known about the influence of the size of the entire advertisement on consumers' memory (Diamond 1968; Finn 1988; Hanssens and Weitz 1980; Twedt 1952), but attention to advertisements cannot be directly inferred from consumers' memory for them, because different psychological processes are involved with distinct antecedents. There is no research on the simultaneous effects of the size of the brand, pictorial, and text elements on consumers' attention patterns. We believe that this is surprising, because the element sizes are key variables that are manipulated jointly in advertising design, and changes in each of them may affect attention to the others and to the entire advertisement. Moreover, we are not aware of research that has systematically examined the influence of the brand-element size in advertising, despite the element's central role in the communication process and the surge of interest in branding and visual brand-identity issues.
Our research aims to enhance knowledge about attention to advertising as follows: First, we examine the contribution of the surface size of the brand, pictorial, and text elements of advertisements in capturing consumers' attention to the entire advertisement. Second, we identify the extent to which consumers' attention to the brand, pictorial, and text elements of advertisements increases with the surface size devoted to them. Third, we assess potential carryover effects of attention to ad elements. Specifically, because consumers' attentional resources are limited, their increasing attention to one ad element may be at the expense of other ad elements, which reveals attention competition. In contrast, attention to a particular ad element may also spill over to other ad elements. The possibility of such positive and negative attention effects among ad elements has been suggested in the advertising literature (e.g., Poffenberger 1925; Wells, Burnett, and Moriarty 2000) but has not yet been examined empirically. Finally, we examine the extent to which the surface size effects of ad elements are homogeneous across three important marketing variables: product involvement, product motivation, and brand familiarity.
To accomplish this, we propose a conceptual model of attention capture and transfer by elements of advertisements (AC-TEA), and we estimate its statistical formalization on eye-tracking data for more than 1300 print advertisements and 3600 regular consumers. In the next section, we begin to summarize the debate in the advertising literature about the role of the brand, text, and pictorial in capturing consumers' attention to advertising. Then, we present the ACTEA model and hypotheses about the influence of the brand, pictorial, and text elements of print advertising. We then describe the methodology, data, and results. The findings document the unique effects that the space devoted to the three ad elements has in capturing and transferring attention, and they demonstrate the superiority of each ad element for a specific attention function.
Most print advertisements contain a brand, pictorial, and text element. The brand element covers the visual brand-identity cues in print advertisements, such as the brand name, trademark, and logo of the source (Keller 2003). The text element comprises all textual information of the advertisement, excluding all incidences of the brand name. The pictorial element comprises all nontextual information of the advertisement, excluding all incidences of the brand trademark and logo. There is a long-standing debate in advertising about the attention effects of these three ad elements and their surface size and about the management of the elements to maximize attention capture.
Influence of the Brand Element
Some scholars have recommended maximization and others minimization of the brand element's size in advertising. Proponents of the first position argue that the brand should be prominently featured in print advertising, as one step in the brand value chain (see Burton and Purvis 1987; Higgins 1986; Kapferer 1992; Keller 2003; Moran 1990). As a case in point, Moriarty (1986, p. 291) argues, "the most important thing to remember in national advertising is to focus on identification of the brand. ... It sounds simple; it isn't. Play the brand front and center." Likewise, Smith (1973, p. 66) recommends to advertisers: "In any case, be sure that your product or company name appears clearly and loud." The reasoning is that a prominent brand element, reflected among others in its size, captures more attention to the brand, which is a necessary condition for obtaining the desired brand-communication effects. The weight that industry places on the size of the brand element is illustrated by the detailed corporate identity guidelines of firms such as AT&T, J.D. Edwards, Dow Chemical, and Sun Microsystems. For example, AT&T (2003) specifies that its "globe" brand logo should never be reproduced smaller than 3/8 inch and the the brand name never smaller than attention to them.
In contrast, some advertising practitioners claim that brand presence should be curtailed in advertising because the brand element signals that the message is an advertisement in which consumers purportedly are not interested. Taking an extreme position, Aitchinson (1999, p. 61) argues that the advertisement should be so good that consumers know what the brand is without it being present in the advertisement: "Because consumers hate advertising, once they see a page with a logo in the corner, it doesn't look like editorial[;] it doesn't look like the reasons why they bought the magazine in the first place. It's a trigger that makes them turn the page faster." The International Newspapers Limited (2003) media company expresses this more moderately as follows: "A logo is not a benefit. It serves the ego of the client, not the customer. It also flags to the reader, Warning: this is an advertisement." Such viewpoints may fuel tendencies to minimize brand presence in advertising (Kover 1995).
Influence of Pictorial and Text Elements
The debate about the pictorial and text element centers on which of the two commands the most attention and what the influence of their size is in the process. The pictorial illustration is commonly supposed to be the chief element in capturing consumers' attention (Assael, Kofron, and Burgi 1967; Poffenberger 1925; Rossiter 1981; Singh, Lessig, and Kim 2000). For example, Rossiter and Percy (1997, p. 295) assert that "the picture is the most important structural element in magazine advertising, for both consumer and business audiences," and they recommend that "the straightforward rule for magazine ads, therefore is: the bigger the picture, the better." Likewise, Wells, Burnett, and Moriarty (2000, p. 295) affirm that "the bigger the illustration, the higher [is] the attention-getting power of the advertisement." Similar beliefs are held in advertising practice, as is illustrated by Canada's magazine association (Magazines Canada 2001): "A strong visual is perhaps the single most important weapon a magazine has in gaining and capturing reader attention. In this case, it appears that picture size does matter. Ads using visuals that are 2/3 of a page perform best."
The text element is also believed to be key in capturing consumers' attention. For example, Ogilvy (1963, p. 104) argues that the headline, the largest text, is the vital part of print advertisements and that "[t]he wickedest of all sins is to run an advertisement without a headline." Belch and Belch (2001, p. 290) mention that most advertisers consider the headline the most important ad element.
This ongoing debate about the influence of ad elements and their size is further complicated if attention to the ad elements is interdependent. Then, attention devoted to a particular ad element promotes attention to or detracts attention from the other ad elements. In the first case, there is attention cooperation; in the second case, there is attention competition, which is reflected in positive and negative covariance, respectively, of the attention to the relevant elements. Such cases are examples of positive or negative attention transfer.
Ideas about attention transfer have been postulated since the early days of magazine advertising. Most often, positive attention transfer from the pictorial to the other ad elements is expected. For example, Nixon (1924, p. 18) believes that pictures in print advertisements may serve to direct attention to the text. Others note that attention may be relocated from the pictorial to the brand in advertisements, because the former evokes interest in the latter (Poffenberger 1925, pp. 156-61). Such attention transfer has even been formulated as a goal of advertising. Wells, Moriarty, and Burnett (2000, p. 331) point out that print advertisements try to "guide the eye" over their surface to induce attention carryover.
Notably, although there are advocates for each of the ad elements being of key importance to capture and transfer consumers' attention to advertising, these ideas have remained largely untested. Rayner and colleagues (2001, p. 220) emphasize that "remarkably little is known about the extent to which viewers look at the picture versus the text" of advertisements. This study aims to contribute to such knowledge.
Advertisements that capture attention attract consumers so that they select the advertisement from its environment, and they retain consumers so that they pay more attention to the advertisement and its elements than to other advertisements. Attention capture enables higher-order cognitive functions to operate on more parsimonious and salient input (LaBerge 1995). This function of attention is central in our AC-TEA model. Its background is summarized in Figure 1, and we describe it in the next sections. In short, the AC-TEA model describes bottom-up (stimulus) and top-down (person and process) mechanisms of visual attention to advertising. It differentiates two forms of attention capture by ad elements, one being independent (baseline) and the other being dependent (incremental) on its size, and it distinguishes two forms of attention transfer from one ad element to the others, one being independent (endogenous) and the other being dependent (exogenous) on its size.
Determinants of Visual Attention to Advertising: Bottom-Up and Top-Down
There are two broad determinants of selective visual attention (indicated in Figure 1): bottom-up factors in the stimulus and top-down factors in the person and in the attentional process itself (Chun and Wolfe 2001; Posner 1980; Theeuwes 1994; Yantis 2000). The attentional processes driven by the bottom-up and top-down determinants reside in distinct but connected areas of the brain (Itti and Koch 2001).
Bottom-up factors are features of advertisements that determine their perceptual salience (Janiszewski 1998), such as size and shape. These features capture attention to ad elements rapidly and almost automatically, even when the consumer is not actively searching for them (Wolfe 1998; Yantis and Jonides 1984). Arrows 1 and 2 in Figure 1 represent these effects. Top-down factors reside in the person and in his or her attentional process. Person factors, such as involvement with products or familiarity with brands (Rayner et al. 2001; Rosbergen, Pieters, and Wedel 1997), encourage subjects to voluntarily pay more or less attention to advertisements and their elements. The dotted arrows in Figure 1 signify these effects.
Part of this study focuses on process effects on visual attention. Process-related factors manifest themselves when attention to a particular ad element, regardless of its surface size, is observed to depend on the amount of attention paid to one or more other ad elements. We conjecture that such dependence is caused by voluntary (top-down) shifts of visual attention. Because advertisements are composed of complex scenes and texts, the visual system and knowledge operate jointly to guide attention. This occurs because memory and expectations are required to locate and recognize elements with particular visual features in the scene. Semantic representations of previously attended elements are stored in memory and provide cues that allow for voluntary redirection of attention based on what was already attended to (Yantis 2000; Yarbus 1967). Arrow 3 in Figure 1 indicates these effects.
Attention Capture: Baseline and Incremental
It is useful to distinguish two forms of attention capture, baseline and incremental, as in theories of visual attention in search tasks (Bundesen 1990; Folk, Remington, and Johnston 1992; Logan 1996) and reading (Reichle et al. 1998).
Baseline attention is the attention devoted to an ad element, independent of its surface size and other factors, and is at least partially caused by the visual pop-out of the element. The higher the baseline attention, the higher is the information-mode priority of consumers for that specific ad element. Thus, if consumers in general paid more attention to the pictorial than to the text, independent of the size of these two ad elements, the baseline attention of the former would be higher.
Incremental attention is the extra amount of attention that an ad element captures beyond baseline attention because of increases in its surface size. The higher the incremental attention, the higher is the surface-size elasticity of attention for that specific ad element. Some early work based on observational data found that attention increased with the square root of surface size, which implies an elasticity of .5 (Nixon 1924; Poffenberger 1925).
Attention Transfer: Exogenous and Endogenous
Attention transfer occurs when attention to a particular ad element depends on other ad elements, which can occur through exogenous and endogenous processes (Posner 1980).
Exogenous attention transfer occurs when the surface size of an ad element affects attention to one or more other ad elements. This takes place when, for example, an increase in the size of the pictorial element directly increases or decreases attention to the text or brand element (see Arrow 2 in Figure 1). Endogenous attention transfer occurs when attention to an ad element depends on attention to another ad element, independent of their surface sizes. Such attention transfer is endogenous, because the attentional process itself provides the cue for redirection of attention instead of it being directly driven by stimulus or person factors. Endogenous attention transfer manifests itself when, for example, attention to the pictorial element promotes attention to the text or brand elements in the advertisement, independent of the factors that stimulated attention to the pictorial element in the first place (see Arrow 3 in Figure 1). Not much is known about endogenous attention transfer in complex scenes (Henderson and Hollingworth 1998, 1999) such as advertisements.
What is the influence of ad elements and their size on capturing consumers' attention? We expect that the pictorial captures most baseline attention, independent of its size, and that the text captures most incremental attention because of its size. We base these predictions on the reasoning that follows.
Scene (picture) perception is genotypically much older than text perception. It relies more on peripheral and preattentive processes that are automatic, parallel, fast, and less effortful (Loftus 1983; Öhman, Flykt, and Esteves 2001; Stolk, Boon, and Smulders 1993), and this may have established an attentional priority for it. In addition, pictures are often perceptually more distinct than words (Childers and Houston 1984), which draws bottom-up attention. We expect that this jointly contributes to a picture superiority effect on baseline attention, independent of the size of the pictorial.
Text perception is genotypically more recent. It relies more on focal attentive processes, which are voluntary, serial, slow, and effortful (Loftus 1983; Rayner 1998; Reichle et al. 1998). Thus, although the gist of a scene can often be comprehended in a few glances (Henderson and Hollingworth 1998, 1999), text requires more eye fixations to be comprehended. In addition, text is usually heavily packed in the visual field. Thus, more attention per unit surface is required for text than for scene perception, and an increase in the ad size devoted to text further increases its attentional demand. We expect that this jointly contributes to a text superiority effect on incremental attention.
Specific predictions about effects of the brand-element size on attention would be conjectural given the current lack of knowledge, so we chose to explore these effects. We test the following:
H<sub>1</sub>: Pictorial superiority effect on baseline attention: Independent of the surface sizes, more attention is devoted to the pictorial than to the other elements in print advertisements.
H<sub>2</sub>: Text superiority effect on incremental attention: Increases in the surface size of the text element have a greater effect on attention to this element than do increases in the surface size of the other ad elements on attention to them.
Which predictions can be made about the transfer of attention between the elements of print advertisements? There is reason to expect substantial competition for attention between ad elements based on their surface size. An increase in the surface size of an ad element increases its perceptual salience and its attentional demand, which is likely to detract attention from other ad elements (Janiszewski 1998), because salience is a relative phenomenon (Itti and Koch 2001; Wolfe 1998). Such competition for attention is likely when attentional resources are limited but heavily taxed, such as with advertisements in cluttered magazines. Thus, we predict a negative exogenous attention transfer effect (i.e., competition for attention between ad elements because of their surface sizes).
Conversely, we predict positive endogenous attention transfer effects (i.e., cooperation for attention between ad elements after we control for their surface sizes). To the extent that advertisers succeed in integrating the three ad elements to convey a joint message, attention for one of the ad elements is likely to covary positively with that for other ad elements (Rayner et al. 2001). Attention to one element will raise the level of interest in the others, eliciting voluntary redirection of attention, similar to what has been shown in target search tasks (Yantis 2000). At the current level of knowledge, we cannot offer more specific predictions about attention-transfer effects. Therefore, we test the following hypotheses:
H<sub>3</sub>: Negative exogenous attention transfer: Increases in the surface size of a particular ad element decrease attention to the other ad elements.
H<sub>4</sub>: Positive endogenous attention transfer: Attention to a particular ad element, independent of its size, is positively associated with attention to other ad elements.
Finally, the question is, What is the net effect of adelement capture and transfer on attention to the advertisement as a whole? That is, will enlargement of the brand size eventually increase or decrease attention to the entire advertisement, and what will the overall effects of the size of the pictorial and text element be? Because of the lack of theory, we abstain from formulating hypotheses, but if advertising practioners' previously reported recommendations hold, larger pictorial sizes should result in a net positive effect on attention to the entire advertisement. If the fears of some advertising practitioners are confirmed, larger brand sizes should results in a net negative effect on attention to the entire advertisement. The data will reveal whether this is the case.
Tests of Print Advertisements
Verify International, a company that specializes in eye-tracking market research, made available data from 33 independent eye-tracking tests of print advertisements conducted in 1998. Tests were conducted for the present research, to fill the company's database of tested advertisements, and for use in communication to prospective clients. On average, 110 randomly selected adult consumers (male/ female, ages 18 to 55) from across the Netherlands participated in each test (the sample selection procedure was the same in all tests). Thus, the current data are based on a subject sample of slightly more than 3600 consumers.
The 33 tests contained 1363 full-page advertisements (1/1), with an average of 41 advertisements per test. Advertisements came from 65 consumer magazines published in the Dutch market, including popular national magazines, such as Libelle, Panorama, and Tip Culinair, and national versions of popular international magazines, such as Cosmopolitan, Elle, and Esquire. Magazine page sizes were homogeneous, and there were no nonstandard-sized magazines, such as Reader's Digest or National Geographic. Verify selected the magazines, which covered a wide range of consumer advertisements, brands, and product categories in the Dutch market. For each test, advertisements from the most recent issues of the magazines were sampled. There were advertisements for 812 national and international brands in 71 product categories, such as airlines, alcoholic and nonalcoholic beverages, cars, cleansing products, clothing, financial products, fragrances, home entertainment, personal care, pet foods, photo equipment, real estate, restaurants, and retail stores.
Ad Exposure and Eye Tracking
On entering the market research firm, participants provided sociodemographic information and engaged in a visual exploration task of print advertisements (Janiszewski 1998; Wedel and Pieters 2000); the protocol was the same for each test. Participants were instructed as follows: "Page through several magazines that are presented on the monitor. You can do this at your own pace, as you would do at home or in a waiting room." Next, the advertisements were shown each with the editorial counter page (i.e., the page that contains regular magazine content and is opposite an advertising page), preceded by the front cover and trailed by the back cover of the relevant magazine, and in the sequence in which they appeared in the magazines. Instructions and stimuli were presented on NEC 21-inch LCD monitors in full-color bitmaps with a 1280 x 1024 pixel resolution. Participants continued to the subsequent page by touching the lower right-hand corner of the (touch-sensitive) screen, as when paging. After completion, participants engaged in other unrelated studies. We believe that the visual exploration task that we used mimics real-life situations closely and allows for maximal bottom-up effects of the ad layout itself.
Eye tracking was done by means of infrared corneal reflection methodology (Duchovski 2003; Ober 1994): The cornea is a clear, dome-shaped surface that covers the front of the eye. It is the first lens in the eye's optical system. In corneal reflection, an infrared light is pointed at the cornea and reflected off it. When the eye moves across a spatial stimulus, the difference between the incoming and outgoing angle of the infrared light beam changes. After calibration, this is related to the specific position on the stimulus to which the eye moves and at which the fovea in the retina is directed. Infrared light is applied because it is "invisible" to the eye and does not distract participants. The specific eye-tracking equipment used leaves participants free to move their heads in a virtual box of approximately 30 centimeters. Cameras track the position of the eye and head and allow for continuous correction of position shifts. Measurement precision of the eye-tracking equipment is better than .5 degree of visual angle. The eye-tracking procedure is summarized in Figure 2.
The top-left part of Figure 2 shows the room in which data collection took place; participants at the eye trackers are on the right-hand side, and the operator is on the lefthand side. The top-right part of Figure 2 shows a closer view of a participant at an eye tracker. The bottom-right part of Figure 2 illustrates how a glass sheet between the participant and monitor reflects the infrared beam from the top (where the light beam comes from) to the eye and back and is transparent for all other light. The bottom-left part of Figure 2 illustrates infrared corneal reflection.
Three indicators of visual attention employed in previous research (Chandon 2002; Fox et al. 1998; Krugman et al. 1994; Lohse 1997; Rayner et al. 2001; Rosbergen, Pieters, and Wedel 1997) were used: ad selection, ad gaze duration, and ad-element gaze duration. Ad selection is the percentage of participants who fixated on a target advertisement at least once. It measures how many consumers an advertisement can attract in its editorial environment. Ad gaze duration is the total time that consumers who selected the advertisement, on average, spent on it. It measures how well an advertisement can retain consumers in its editorial environment. Ad-element gaze duration measures the time spent on each of the ad elements. Because of the demands on data storage and analysis, individual-level fixations could not be retained, so their sequence is unknown, and we analyzed the data as a cross-section.
Surface Size of Ad Elements and Covariates
We established surface sizes of ad elements with specialized software by drawing the appropriate boxes and polygons around them, which enabled us to isolate cases of one element being embedded in another (e.g., brand logo in a pictorial). We defined ad elements as we described previously. Table 1 presents summary information about the surface size of the three ad elements, the entire advertisement, and attention to them.
We added the following (stimulus and person) covariates as control variables: overall ad size, serial position of test advertisements, magazine type in which the advertisements were placed, product involvement and motivation, and brand familiarity. We assessed the variables in a content analysis of the 1363 advertisements and 65 magazines. We included overall ad size because of its influence on attention to advertising (e.g., Lohse 1997). Although all the advertisements are full-page, their surface sizes vary because of differences in magazine formats and the use of bleed or trim. The addition of overall ad size as a covariate ensures proper estimation of the effects of ad-element surface sizes across advertisements. We included information about the serialposition of advertisements (from 1 to n: first to last) in the 33 tests, because advertisements seen earlier on typically capture more attention than later ones (Lohse 1997). Magazine type may influence visual attention to advertising through technical reproduction quality and editorial context, particularly glossy magazines, which provide significantly better reproduction quality. To control for this potential effect, two judges categorized each of the 65 magazines into leisure/glossy versus other magazines (92% agreement). Of the advertisements, 62% appeared in leisure/glossy magazines (n = 843), such as Cosmopolitan and Elle, and 38% (n = 520) appeared in other magazines, such as Margriet and Top Sante.
In addition, we included product involvement, motivation, and brand familiarity because of prior evidence that these factors may influence attention to print advertising (Hanssens and Weitz 1980; Pratkanis and Greenwald 1993; Ratchford 1987; Rayner et al. 2001; Rosbergen, Pieters, and Wedel 1997; Vaughn 1980). An independent sample of ten trained judges categorized product involvement and brand familiarity of the advertised products and brands, and another independent sample of ten trained judges (MBA students, five males and five females, in both cases) categorized product motivation to minimize possible learning across tasks. To assess product involvement, judges individually sorted the advertised products (names typed on cards) into two categories: ( 1) involved, an important decision ("I'm very motivated when I buy a product from this category, a decision about this is [very] important") and (0) uninvolved, not an important decision ("I'm not [at all] motivated when I buy a product from this category, a decision about this is [very] unimportant"). Cronbach's alpha across the ten judges was .916, and each product was assigned its modal involvement. Of the advertisements, 46% were for low-involvement products (n = 624), and 54% were for high-involvement products (n = 739).
To assess product motivation, judges individually sorted the advertised products (names typed on cards) into the following two categories: ( 1) think, functional ("I buy this product because it solves or prevents a problem and/or because I need it") and ( 2) feel, hedonic ("I buy this product because it makes me feel good and/or because it helps my personal growth"). Cronbach's alpha across the ten judges was .866, and each product was assigned the modal motivation category. Of the advertisements, 47% were for think products (n = 643), and 53% were for feel products (n = 720).
To assess brand familiarity, judges sorted the advertised brands (names typed on cards) in the following three categories: ( 1) "unknown brand, I do not know this brand name," ( 2) "known brand, I know this brand name, but know little more," and ( 3) "well-known brand, this brand is familiar to me; I know more than just the brand name." Cronbach's alpha across the ten judges was .951, and the modal brand familiarity was assigned to each brand name. Of the advertisements, 30% were for unknown brands (n = 407), 25% were for known brands (n = 347), and 45% were for well-known brands (n = 609).
The AC-TEA model can be expressed as a multilevel multivariate regression model (Goldstein 1995; Zellner 1972). We begin by describing ad-element gazes. Suppose that i = 1, ..., I indicates the 1363 advertisements, j = 1, ..., J (and k = 1, ..., K) indicates the three ad elements, and t = 1, ... , T indicates the 33 tests. The vector q<sub>i,t</sub> = [q<sub>i,t,j</sub>] denotes gaze duration on the J elements of advertisement i in test t, and the vector s<sub>i,t</sub> = [s<sub>i,t,j</sub>] denotes the surface sizes of the J elements of advertisement i in test t. In addition, we have a vector, x<sub>i,t</sub> = [x<sub>i,t</sub>], of covariates:
( 1) [Multiple line equation(s) cannot be represented in ASCII text]
The model has a double log specification to accommodate the nonnegativity and right skewness of gaze durations and ad-element sizes. The log-log formulation accounts for nonlinear and multiplicative effects, which is desirable (Bundesen 1990; Reichle et al. 1998), and it renders interpretation easier, because coefficients are scale free and can be interpreted as elasticities (i.e., percentage change in attention for a 1% change in a variable).
Baseline attention effects are represented by μ = [μ<sub>j</sub>, vector of ad element-specific constants. Incremental attention effects due to the size of the ad elements are represented by the diagonal of Γ = [γ<sub>j,k</sub>] a matrix of ad element-specific coefficients. This diagonal taps the influence of the surface size of ad-element j on attention to element j itself. Arrow 1 in Figure 1 indicates this. Exogenous attention transfer is represented by the off-diagonal part of Γ. It taps the influence of the surface size of ad-element j on attention to other ad elements k ≠ j. Arrow 2 in Figure 1 indicates these effects. Endogenous attention transfer is represented by the matrix A = [α<sub>j,k</sub>]. Because Equation 1 controls for all other effects, Matrix A represents influences that are endogenous to the attentional process itself. The vector [q<sub>r,i,t,-j</sub>] contains J - 1 elements, but the jth element itself is missing because attention to element j cannot affect itself. Arrow 3 in Figure 1 indicates these effects. Attention transfer effects can be asymmetrical (e.g., attention to the pictorial and text may transfer [endogenously or exogenously] more to the brand than the other way around).
Matrices B and E in Equation 1 represent the effects of covariates on attention to the ad elements. The covariate vector x<sub>r,i,t</sub> = [x<sub>r,i,t,p</sub>] contains stimulus and person factors that may directly influence attention to the ad elements. Matrix E represents the influence on attention that product involvement, motivation, and brand familiarity have in interaction with ad-element sizes. Dotted arrows in Figure 1 represent the effects of these covariates. We specify two (J x J) vectors of random effects to assess differences between tests and between advertisements within tests, with Φ<sub>t</sub> ∼ N(0, V<sub>Φ</sub>), and N(0, V<sub>ω</sub>), where V<sub>Φ</sub> and V<sub>ω</sub>are (J x J) diagonal matrices to be estimated.
To examine the effect of ad-element sizes on attention capture to the entire advertisement, we estimated the model in Equation 1 simultaneously for the ad selection and ad gaze duration measures (i.e., j = 1,2 indicates these two measures in this case). This reduced version of the AC-TEA model does not include endogenous attention transfer effects (i.e., A = 0). We used a logit specification of ad selection to accommodate this measure being a proportion and to facilitate comparison with the log gaze durations.
Model Estimation and Evaluation
Because the model is a multivariate multilevel regression model, we employed Markov chain Monte Carlo methods to estimate it, which facilitates the evaluation of the multiple integrals occurring in the likelihood function (Gelman et al. 1995). In each Markov chain Monte Carlo iteration, we draw from the full conditional distribution of parameters, conditional on the values of the other parameters obtained from the last draw. All prior distributions are standard noninformative distributions, for a fixed generic parameter θ, p(θ) ∞ 1, and for a generic scalar variance σ², p(1/σ²) ∼ gamma;(.001, .001). We use 10,000 draws, with a burn-in of 2000, and we retain every target draw. We start the algorithm from iterative generalized least squares estimates and monitor convergence through plots of key parameters against iterations, which indicate convergence well before the end of the burn-in. We present the posterior mean and posterior standard deviation (S.D.) of coefficients. A parameter is considered significant if the posterior mean is at least twice as great as the posterior S.D. To assess incremental model fit, we use the deviance, deviance information criterion (DIC; Spiegelhalter et al. 2002), and pseudo R² statistics.
Table 1 presents descriptive statistics of the ad elements and visual attention. The pictorial is the largest ad element (2.74 dm² = 42.47 inch²), followed by the text (1.33 dm² = 20.62 inch2) and the brand (.53 dm² = 8.22 inch²). On average, advertisements were selected by 95.7% of the participants (who fixated at least once), and the lowest-scoring advertisement was skipped by 39% of the participants. Participants who selected the advertisements, on average, attended 1.73 seconds to the advertisement as a whole (text .7 seconds, pictorial .6 seconds, and brand .4 seconds), which is typical when consumers explore regular magazines at their own pace (e.g., Rosbergen, Pieters, and Wedel 1997) but lower than when advertisements are tested in laboratory conditions (e.g., Fox et al. 1998).[ 1]
Attention to the Entire Advertisement
We examine what the net effect of brand, pictorial, and text size is on attention to the entire advertisement. Table 2 presents the results of the analysis. For parsimony, we omit the interactive effects of the covariates with size, because none of these was significant and the overall fit of the model with all effects did not improve (deviance = 1683; DIC = 1778).
Neither the surface size of the brand nor the pictorial affects attention to the entire advertisement for attention selection and duration. However, a 1% increase in the text surface size increases attention significantly: Selection improves by .05% (coefficient = .048) and duration by approximately .16% (coefficient = .156). In particular, the text-size effect on duration of attention to the advertisement is noteworthy; it accounts for approximately 17% of the variation. The patterns of the results for attention selection and duration are similar, indicating that during visual exploration, bottom-up factors that influence attention selection also influence attention duration.
The observed positive text surface-size effect is not caused by differences in the overall size of advertisements from different magazines or by (unobserved) features of the magazines in which the advertisements were placed. In a follow-up analysis, we added dummy variables to the model, representing each of the magazines that contributed 20 or more advertisements to the sample. Estimates of the surface-size effects remained virtually unchanged: The brand and pictorial surface-size effects were insignificant, and the text surface-size effect was positive and significant. Several covariates influence attention to the entire advertisement systematically, notably serial position (advertisements that are placed later in the page sequence are less attended), product involvement (advertisements for high-involvement products are more attended), and brand familiarity (advertisements for familiar brands are less attended).
Thus, an increase in the size of the pictorial does not increase attention to the entire advertisement, but an increase in the surface size devoted to the text does. We obtained these effects while controlling for relevant stimulus and person factors that could potentially bias the findings. Next, we examine how these net effects on attention to the entire advertisement came about from attention to the three ad elements, and we test the hypotheses.
Attention to the Elements of Advertisements
We estimated the AC-TEA model in Equation 1 for gaze duration to the three ad elements. The overall fit of this model is R2 = 32.2%, accounting for considerable portions of variance in attention to the brand (46.4%), pictorial (15.3%), and particularly the text (69.8%) in advertisements.
Attention capture: baseline and incremental. In support of H<sub>1</sub>, a notable pictorial superiority effect on baseline attention emerges. Table 3 reveals that the constant for pictorial attention is high and significant (coefficient = 3.395) and that the constants for brand attention and text attention both are small and insignificant. This indicates that the pictorial has an intrinsic tendency to capture a substantial amount of attention, independent of its surface size and of all other factors in the model, whereas the other two ad elements lack this tendency.
In support of H<sub>2</sub>, a strong text superiority effect on incremental attention is evident: The mean surface-size elasticity for the text element is .85, which is more than two times greater than the mean coefficients for the brand (.32) and pictorial surface-size effects (.32). Thus, a 1% increase in the surface size of the text leads to a .85% increase in gaze duration, which is substantial. The text size effect is significantly larger than the brand and pictorial size effects.
Table 3 also demonstrates the extent to which the effects of the surface-size element are stable across product involvement, product motivation, and brand familiarity. In particular, the surface size of the text element interacts with the three covariates; that is, advertisements for high-involvement products gain more from increasing the surface size of their text element than do advertisements for low-involvement products. Advertisements for think products gain less from increasing the surface size of their text element. Finally, at smaller surface sizes, advertisements for well-known brands capture more text attention that do advertisements for unknown brands, though the effect is fairly small.[ 2]
The findings support our hypotheses that the pictorial in advertisements captures superior baseline attention independent of its size and that the text in advertisements captures superior incremental attention because of its size, when we control for the effects of relevant covariates.
Attention transfer: exogenous and endogenous. In support of H<sub>3</sub>, the effects of an ad element's surface size on attention to other ad elements all have a negative sign, and three of them are significant (in Table 3, under "Size Effects of Ad Elements"). The most notable case of this exogenous attention competition between ad elements due to their surface size occurs for the brand and text. An increase in the surface size of the brand (by 1%) detracts a large amount of attention from the text (.41%), and an increase in the surface size of the text (by 1%) detracts attention from the brand element (.23%). Finally, an increase in the pictorial surface size (by 1%) withdraws attention from the brand (.11%).
Figure 3 summarizes the surface-size effects. For each ad element, the estimated effect of an increase in its surface size on attention to itself and to the other ad elements is shown for when all other variables are held constant (estimated attention on the y-axis, observed surface sizes of the elements on the x-axis). Figure 3 illustrates the strong incremental attention effect of text surface size and the attention competition between brand and text surface size.
H<sub>4</sub> predicts positive endogenous attention transfer between the three ad elements, when we account for attention effects due to the surface sizes. In support of this, four of the six effects are positive and substantial; the other two are not different from zero. Notably, brand attention plays a major role. That is, increased attention to the brand (1%) is associated with more attention to the pictorial (.22%) and to the text (.51%). Although increased attention to the pictorial or text in advertisements (1%) is associated with more attention to the brand (.03% and .16%, respectively), the latter effects are quite small; they are smaller than the positive attention-transfer effects of the brand.
These findings suggest the presence of an unexpected brand-superiority effect in endogenous attention transfer. They suggest that when attention has been drawn to the brand, the attention is linked more strongly to the pictorial and text elements than it is when it has been drawn to the pictorial and text elements.
Figure 4 summarizes the influence that ad elements have in endogenous transfer of attention and illustrates the potential superiority effect for the brand. A cautionary note on the interpretation of these effects is important. Because we used cross-sectional data and no sequential information is available, strictly speaking we cannot draw conclusions on causality; we can only conclude that attention to ad elements covaries. However, note that the endogenous transfer effects are unlikely to be caused, for example, by a mere total attention effect. If overall attention to the advertisement were to confound the effects of attention paid to a particular element, it would bias all attention transfer effects homogeneously. However, correcting for ad-element surface sizes, we find quite different effects across the elements.
Benchmark model comparisons. To gain more insight into the relative magnitudes of the attention effects under study, we estimated a sequence of benchmark models. Table 4 gives the analysis of deviance as well as the DIC and pseudo R² statistics of the models. First, we estimated the null model (Model 0), which contained constants only. Second, we added the following effects at each subsequent stage: covariate effects on attention (Model 1), attention capture (own effects of element surface sizes; Model 2), exogenous attention transfer (cross-effects of element sizes; Model 3), interaction effects of element sizes with covariates (Model 4), and endogenous transfer effects (Model 5). A comparison of this sequence of models reveals the notable finding that the three attention-capture effects present 21.5% of the total attention process, whereas exogenous and endogenous transfer jointly account for only 6.4%. This finding points to the importance of bottom-up processes in visual exploration of print advertisements.
The brand, pictorial, and text elements of print advertisements have significant effects on attention capture and transfer that are on par with common ideas in advertising practice and literature. Figure 5 summarizes the relative magnitudes of effects by combining the attention source (ad element versus surface size) and attention function (capture versus transfer), which we believe has important implications for advertising management and for theory.
Managerial Implications
The surface size of the pictorial element has no demonstrable effect on attention to print advertisements as a whole. Moreover, it has only a small effect on attention to the pictorial itself. However, the pictorial draws significant amounts of baseline attention during ad exploration, regardless of its size. This picture superiority in baseline attention is important in view of recommendations such as "make the illustration relatively large" (Armstrong 2000, p. 6), make it at least larger than half the size of the advertisement (Assael, Kofron, and Burgi 1967), make it two-thirds of the advertisement, or "the bigger the picture, the better" (Rossiter and Percy 1997, p. 295). On the basis of the current findings, advertisers and agencies would be ill advised to maximize the surface size of the pictorial, regardless of its content, in an effort to maximize attention to the entire advertisement.
However, there is evidence for text superiority in incremental attention, with the surface size devoted to the text having a substantial positive effect on attention to the entire advertisement. This is because an increase in text surface size raises attention to this element much more than it simultaneously reduces attention to the brand and pictorial elements. We obtained these results while controlling for product involvement and motivation, brand familiarity, media context, and other possible confounders, which points to their generality. From our study, we cannot conclude the extent to which the surface-size effect is caused by text layout (e.g., increases in font type or size, such as in the headline), text amount (e.g., number of words), or increases in the amount of information provided by it. However, in view of our findings and the large amount of variability explained in attention to the text element (70%) by the surface size alone across the large sample of advertisements, advertisers aiming to maximize attention to the entire advertisement should seriously consider devoting more space to text. Follow-up research is needed to examine the effects of increased text attention on downstream communication effects such as brand awareness and attitude.
Increases in the surface size of the brand element do not have a net negative effect on attention to the entire advertisement. This finding should relieve advertisers and agencies that fear that a prominent brand would trigger consumers to turn the page faster. Increases in an advertisement's surface size promote more attention to the brand element, but this is offset to some extent by reduced attention to the text. Thus, strong branding practices that involve more prominent placement of the brand's visual identity symbols in print advertisements favor rather than harm attention to the brand; in addition, they have only small negative effects on attention to the other ad elements and no negative net effect. Attention to the brand and pictorial increases with an increase in surface size at the same rate; that is, with somewhat less (surface.<sup>32</sup>) than the square root of the surface size, as has been suggested (Nixon 1924; Poffenberger 1925).
Unexpectedly, we find sizable brand superiority in endogenous attention transfer. That is, our results suggest that attention captured by the brand element transfers more readily to the pictorial and text than to the brand. This has not been previously described and runs counter to common beliefs in advertising practice. Instead of attention carrying over easily from the pictorial, attention captured by the brand appears to play a key role in routing attention through advertisements. Although the net effect of (exogenous and endogenous) attention transfer is small compared with attention capture, and we should be cautious in interpreting the effects as causal, the findings point to a possible crucial role of the brand in the integration of the information contained in the advertisement. Advertisements that succeed in capturing attention to their source (i.e., the brand) may thus also succeed in relocating attention to the message of the advertisements, as contained in the pictorial and text elements; the size of the brand element helps achieve this, as our results indicate.
We showed that brand familiarity reduces attention to the brand element but simultaneously increases attention to the text element, rather than having a global attention reduction effect across all ad elements. Because the decrease in brand attention surpassed the increase in text attention, the net effect on attention to the entire advertisement was a reduction. Advertisements for well-known brands may invite consumers to inspect the text more, which presumably contains further information about the brand. Even for small surface sizes, we observed the text-pull effect by brand familiarity. This suggests that particularly familiar brands gain attention from increasing the text surface in their advertisements, which may also be desirable in view of the demonstrated text-brand attention transfer effects. Although it becomes more difficult to attract consumers' attention in clustered advertising media, the good news from this study is that advertisements' bottom-up influences account for more than three times the variance than topdown influences, which may inhibit the intrinsic attention capture effects caused by advertising design with respect to its elements.
Limitations and Directions for Further Research
Limitations of this study offer opportunities for further research. First, this research has focused on how the attention of consumers is captured and transferred by elements of print advertisements, leaving the following question unanswered: How much attention is required for communication effects such as brand awareness and attitude? There is increasing evidence that even small attentional effects of advertisements and other commercial stimuli, such as catalogs and packaging designs, can have substantial effects on brand memory, attitudes, and sales (e.g., Chandon 2002; Janiszewski 1998; Pieters and Warlop 1999; Wedel and Pieters 2000), but further research into the link between attention and its downstream effects is desirable.
A second limitation is that our data were cross-sectional. The danger of inferring causality from cross-sectional data is well known, and our approach is no exception. Although the time order is of no concern for attention capture or exogenous transfer, endogeneity caused by ad content may hinder causal interpretations of endogenous transfer effects.³ We have attempted to include as controls the effects of several important covariates related to the content and context of the advertisements. Currently, we have no indication that this potential biasing effect of other content-related variables is present and systematic across the large number of advertisements under study. We investigated the effect of the size variables across a large representative sample of advertisements and consumers, accounting for random differences between the advertisements. The revealed effects were motivated from prior theory in visual perception, which provides additional support for a causal interpretation. Still, only moment-to-moment analyses of attention scanpaths and detailed content analyses of advertisements can provide more definite answers.
Conclusion
Advertising-attention research has come a long way since Nixon (1924) hid behind a curtain and painstakingly observed eye movements of consumers who were paging through a magazine with print advertisements. Eye-movement data of large samples of consumers attending to large samples of advertisements gathered using infrared eye tracking are currently being produced on an industrial scale and used by companies to optimize decisions on the design of advertisements, packages, Web pages, and other carriers of their visual brand-equity symbols. The application of theories and models of visual attention, such as the AC-TEA model presented in this study, is likely to render such research and the decisions based on it more effective. The availability of direct measures and detailed models of attention enables tests of long-standing beliefs in the advertising industry and substantial improvements in the validity of findings and recommendations. The first and foremost finding derived from this study is that size clearly matters in capturing attention to advertising, but it matters in ways that are quite different from what was commonly assumed.
The authors thank Dominique Claessens and Chris Huijnen of Verify International for the eye-tracking data.
[1] Average gaze duration on editorial pages was 2.79 seconds, which is significantly higher than on the advertisements.
[2] The observed superiority effect of text surface size on incremental attention is not due to these interaction effects. In a model without the interaction effects, the coefficient of text surface size was .927 (S.D. = .022), which again was more than two times greater than the effects of brand (.376, S.D. = .013) and pictorial (.411, S.D. = .038) surface size on attention to those ad elements.
[3] We thank David Schmittlein and an anonymous reviewer for pointing this out.
Legend for Chart:
A - Variables
B - Mean
C - S.D.
D - Minimum
E - Maximum
A B C D E
Surface Size (dm²)
Advertisement 4.72 .177 4.14 5.43
Brand .53 .384 .05 3.53
Pictorial 2.74 1.279 .04 5.02
Text 1.33 .863 .03 4.27
Attention
Selection (percentage) 95.70 4.78 61 100
Duration (gaze in .1 seconds)
Advertisement 17.26 5.68 3.71 52.96
Brand 4.31 1.80 .84 14.03
Pictorial 5.75 2.80 .05 22.85
Text 7.21 4.88 .05 30.50
Tests (n) 33
Advertisements (n) 1363
Notes: Selection is the percentage of participants in a test who
fixated at least once on an advertisement. We calculated duration
across participants who selected an advertisement. Legend for Chart:
A - Parameter
B - Advertisement Attention Selection Coefficient
C - Advertisement Attention Selection S.D.
D - Advertisement Attention Duration Coefficient
E - Advertisement Attention Duration S.D.
A B C
D E
Constant -.786 .575
1.671(a) .350
Covariates
Ad size (log dm²) 2.086(a) .374
.808(a) .227
Ad serial position: first to last (1 - x) -.003(a) .001
-.003(a) .000
Magazine type: leisure/glossy-other (1 - 0) -.021 .033
-.029 .020
Product involvement: high-low (1 - 0) .117(a) .027
.071(a) .016
Product motivation: think-feel (1 - 0) -.033 .027
.005 .015
Brand familiarity: high-low (2 - 0) -.030(a) .014
-.029(a) .009
Size Effects of Ad Elements
Brand (log dm²) .001 .019
-.011 .011
Pictorial (log dm²) .021 .020
-.008 .012
Text (log dm²) .048(a) .017
.156(a) .010
Variance at test level .021 .007
.022 .006
Variance at ad level .188 .007
.063 .002
Deviance 1682
DIC 1759
Pseudo R² 22.2%
Notes: Selection = logit (proportion of participants fixating at
least once on an advertisement). Duration = log (average time
spent on advertisement, if fixated). We report posterior mean
coefficients and standard deviations. (a) Posterior means twice
as great as the posterior standard deviations are boldface; the
95% credible interval of boldface coefficients does not cover
zero. Legend for Chart:
A - Parameter
B - Brand Attention Coefficient
C - Brand Attention S.D.
D - Pictorial Attention Coefficient
E - Pictorial Attention S.D.
F - Text Attention Coefficient
G - Text Attention S.D.
A B C D E
F G
Constant -.014 .392 3.395 1.030
.687 .669
Covariates
Total ad size (log
dm²) 1.060(a) .253 -1.542(a) .676
.031 .435
Ad serial position: first
to last (1 - x) -.003(a) .000 -.001 .001
-.003(a) .001
Magazine type: leisure/
glossy-other (1 - 0) -.054(a) .022 .103 .058
.026 .038
Product involvement:
high-low (1 - 0) .019 .028 -.039 .077
.002 .031
Product motivation:
think-feel (1 - 0) .057(a) .027 -.125 .075
.059(a) .032
Brand familiarity:
high-low (2 - 0) -.045(a) .013 -.032 .038
.039(a) .017
Size Effects of Ad
Elements
Brand (log dm²) .322(b) .026 -.061 .046
-.409(a) .026
Pictorial (log dm²) -.111(a) .014 .315(b) .080
-.028 .025
Text (log dm²) -.230(a) .017 -.087 .051
.852(b) .044
Heterogeneity of Size
Effects
Ad element<sub>j</sub>
x product involvement -.018 .024 .070 .067
.273(a) .039
Ad element<sub>j</sub>
x product motivation .046 .024 .071 .068
-.205(a) .040
Ad element<sub>j</sub>
x brand familiarity .050(a) .023 .032 .066
-.131(a) .034
Endogenous Transfer
Brand attention (log .1
second) -- -- .217(a) .075
.512(a) .047
Pictorial attention (log
.1 second) .026(a) .010 -- --
-.012 .017
Text attention (log .1
second) .159(a) .015 -.027 .042
-- --
Variance at test level .014 .004 .026 .012
.012 .005
Variance at ad level .084 .003 .629 .024
.258 .010
Deviance 5712
DIC 5828
Pseudo R² 32.2%
Notes: We report posterior mean coefficients and standard
deviations. (a) Posterior means twice as great as the posterior
standard deviations are boldface; (b) coefficients for
surface-size effects are boldface and underlined; the 95%
credible interval of boldface coefficients does not cover zero. Legend for Chart:
A - Model
B - Predictors
C - Deviance
D - DIC
E - R²
A B C D E
0 Constant 8425 8507 --
1 +Covariates 8063 8161 4.3
2 +Size effects: capture 6253 6355 25.8
3 +Size effects: exogenous transfer 6020 6128 28.5
4 +Heterogeneity of size effects 5915 6032 29.8
5 +Endogenous transfer 5712 5828 32.2
DIAGRAM: FIGURE 1; Determinants of Attention Capture and Transfer to Elements of Print Advertisements
PHOTO (BLACK & WHITE): FIGURE 2; The Eye-Tracking Procedure
GRAPH: FIGURE 3; Surface-Size Effects of Brand, Pictorial, and Text on Attention: Capture and Exogenous Transfer
GRAPH: FIGURE 4; Effects of Attention to Brand, Pictorial, and Text on Attention to the Other Ad Elements: Endogenous Transfer
GRAPH: FIGURE 5; Relative Magnitudes (%) of Brand, Pictorial, and Text Superiority Effects on Attention to Print Advertisements
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~~~~~~~~
By Rik Pieters and Michel Wedel
Rik Pieters is Professor of Marketing, Marketing Department, Tilburg University (e-mail: pieters@uvt.nl). Michel Wedel is Professor of Marketing, University of Michigan Business School (e-mail: wedel@bus.umich.edu).
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Record: 20- Balancing Acquisition and Retention Resources to Maximize Customer Profitability. By: Reinartz, Werner; Thomas, Jacquelyn S.; Kumar, V. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p63-79. 17p. 2 Diagrams, 7 Charts. DOI: 10.1509/jmkg.69.1.63.55511.
- Database:
- Business Source Complete
Balancing Acquisition and Retention Resources to Maximize
Customer Profitability
In this research, the authors present a modeling framework for balancing resources between customer acquisition efforts and customer retention efforts. The key question that the framework addresses is, "What is the customer profitability maximizing balance?" In addition, they answer questions about how much marketing spending to allocate to customer acquisition and retention and how to distribute those allocations across communication channels.
Measuring, managing, and maximizing customer profitability is not an easy task. It requires that in resource allocation decisions, both the benefits and the costs of marketing, sales, and customer interactions are considered. In this research, we conceptualize the marketing resource allocation problem in terms of determining how much to spend on customer acquisition and customer retention and how those expenditures are allocated. Given this conceptualization, the fundamental marketing resource allocation question is, What is the right balance of resources that optimizes customer profitability?
Prior research has examined parts of these issues, but to date, there has not been a comprehensive examination of marketing resource allocation that focuses on all three following questions: How much? How? and What is the profit-optimizing balance? For example, Blattberg and Deighton (1996) address the question of how much to spend on customer acquisition and customer retention. However, they stop short of simultaneously considering acquisition and retention spending, which is critical to address the issue of balancing resources.
Using Blattberg and Deighton's (1996) framework, Berger and Nasr-Bechwati (2001) assume a budget amount and then suggest a model to address how that budget should be allocated between acquisition and retention. However, their model is not tested empirically. In contrast, in this research, we propose an integrated approach that sheds statistical insight on this issue and thus goes beyond the deterministic approach that Berger and Nasr-Bechwati provide.
Mantrala (2002) points out that Blattberg and Deighton's(1996) approach is only a first step and that there is great scope for more research into customer profitability based decision modeling of the marketing resource allocation problem. However, this problem is not straightforward, as Hanssens (2003, p. 16) highlights:
The more challenging task is to assess long run marketing effectiveness and to allocate the overall marketing budget across the key activities that generate customer equity....
For any given set of business and customer response parameters, there is an optimal level of customer acquisition and retention which translates into optimal acquisition and retention spending levels.
Prior models have begun to investigate these issues. For example, Blattberg, Getz, and Thomas (2001) incorporate acquisition, retention, and cross-buying into a model of customer lifetime value and customer equity but do not identify the specific impact of marketing expenditures on customer profitability. Thomas (2001) examines the link between customer acquisition and customer duration. Reinartz and Kumar (2000, 2003) examine the link between customer duration and customer profitability. Rust, Lemon, and Zeithaml (2004) address both acquisition and retention aspects, but their model does not provide for separate or distinct investments in the acquisition of new customers and the retention of existing customers. Although Rust, Lemon, and Zeithaml's approach enables a trade-off analysis between different aspects of the marketing mix, it does not provide for an understanding of how to trade off specific investments at different points in the customer firm relationship. Bolton, Lemon, and Verhoef (2004) provide a conceptual model for linking marketing actions and expenditures to customer retention and profitability but do not provide empirical results. Focusing only on existing customers, Venkatesan and Kumar (2004) develop a resource allocation model that provides guidance on how much to invest in distinct communication channels. By estimating the frequency of buying and the change in the contribution margin from one period to the next, they compute and seek to maximize the future value of the firm's existing customer base. In terms of the data similarities and the discussion of spending across communication channels, substantively our research is similar to that of Venkatesan and Kumar. However, our research takes a more longitudinal perspective and examines resource allocations more comprehensively because it begins before the successful acquisition of a customer. We depict a conceptual view of our perspective in Figure 1.
A natural extension to the contributions made by the previous studies is a conceptual framework and model that can be used to balance resource allocations between customer acquisition and retention and whose objective is to maximize the firm's long-term profitability. It is important that the model be flexible enough to address simultaneously how much and how to invest. This study is that extension. Specifically, Figure 1 conceptually depicts the key processes in the evolution of the customer firm relationship. In addition, our study presents a comprehensive system of equations that links the acquisition and retention processes to customer profitability and can be used for resource allocation decisions. Because of the linkage, the system can be used to assess the trade-offs that occur in resource allocation decisions. It is important to acknowledge that our approach necessitates that resources can be split between customer acquisition and retention, which is the case in many business-to-business (B-to-B) or direct marketing contexts.
Based on the existing research, our specific objectives in this article are to
1. Present a resource allocation model that addresses the questions of how much to invest in customer relationships and how to invest at different points of the customer firm relationship;
- 2. Illustrate the application of the statistical model with an empirical example; and
- 3. Show by a simulation how varying different inputs to the model (e.g., expenditures, number of communication contacts) affects acquisition rates, retention rates, customer profitability, and the magnitude of the firm's return on investment.
The context in which we address the issue of balancing resource allocations is customer contact strategies. However, the framework that we provide can extend beyond the customer contact strategy. With the rise of the Internet and electronic technology, the question of how firms should interact with their customers is gaining in importance, especially as firms consider the cost differences between traditional communications media, such as television and sales forces, and electronic media, such as the Web and e-mail.
To answer the questions of how much to spend on customers and how to allocate expenditures, we must understand the key drivers of customer profitability (for a review, see Berger et al. 2002).( n1) Given our data, the specific drivers that we focus on relate to customer contact channels. The allocation of a budget to customers and across different contact channels is a classic problem that has gained heightened attention in today's multichannel environment. However, typical media planning investigations have been conducted at the firm level (see, e.g., Aaker 1975; Rust 1986). According to Tellis (2003, p. 45), the use of the most disaggregate measure the individual customer is probably most appropriate for allocating media expenditures because persuasion is created at the individual level and because media increasingly can be targeted at the individual level. Therefore, an individual-level investigation is a contribution of our study.
At the most simple level, different contact channels may have independent effects on a specific dependent variable. Contact channels (e.g., personal selling, telephone, direct mail, e-mail) have been characterized as more or less interpersonal. Personal selling, at one extreme of the communications continuum, is dyadic in nature, offers the ability for message customization, enables rich interaction, and allows for personal relationship building (Moriarty and Spekman 1984; Stewart and Kamins 2002). Venkatesan and Kumar (2004) find that higher-level bidirectional communication is associated with higher purchase frequencies. Prior research also asserts that if the buying environment can be described as high involvement decision making (such as a B-to-B purchase), a more involving and interpersonal contact channel, such as a personal sales call, will have a much higher conversion rate on average than will a less involving contact channel, such as e-mail or telesales (Anderson and Narus 1999, p. 302). Therefore, the ability to customize the message easily and build personal bonds with customers will eventually lead to greater retention through personal selling, especially in B-to-B settings. In addition, research has shown that buyers and sellers that have strong personal relationships are more committed to maintaining their relationships than are less socially bonded partners (Mummalaneni and Wilson 1991).
Extrapolating prior findings to this context suggests that more interpersonal channels will have a greater positive impact on customer acquisition (Mohr and Nevin 1990; Moriarty and Spekman 1984). Similarly, more interpersonal contact channels will be associated with greater customer retention compared with less interpersonal contact channels. However, there is little theory or rigorous testing to focus our understanding about the efficacy of contact channels with regard to customer profitability. Thus, a unique aspect of this research is that it addresses the issue of the marginal efficiency of contact channels with respect to customer acquisition and two longitudinal performance measures: customer retention and long-term customer profitability.
An important consideration when investigating the impact of communication modes is the potential interaction effect between contact channels. The investigation of interaction effects between different promotional vehicles is complex and rarely addressed by researchers (Sethuraman and Tellis 1991). According to Farris (2003), there is a need to develop resource allocation models that reflect media synergies and interactions. For example, Jagpal (1981) studied radio and print advertising for a commercial bank and was the first to present empirical evidence of synergy in multimedia advertising. More recently, Naik and Raman (2003) find empirical evidence for the existence of synergistic effects between television and print media. In a hypothetical scenario, Berger and Nasr-Bechwati (2001) account for the possibility of media interaction effects in their deterministic model of customer equity. Yet so far, no customer profitability approach has modeled empirically the interaction between the marketing-mix variables. In addition, the empirical tests have all been conducted at the aggregate level.
In addition to the marginal effects, this study empirically tests for the synergistic effect of multiple communications media on individual consumers acquisition, retention, and profitability. Investigating the impact of media interaction on the allocation decision for each of the dimensions is critical because it demonstrates whether it is necessary to change the communications strategy at different stages of the customer life cycle. For example, customer acquisition might be optimized by means of more (highly involving) personal sales calls, but when customers have been acquired, the retention strategy may be most effectively managed by less obtrusive or less interpersonal communication, such as e-mail or Internet-based interactions. The idea that different types of communication channels play varying roles in the acquisition and retention processes has only been discussed conceptually so far (Dwyer, Schurr, and Oh 1987). We investigate this assertion empirically in a B-to-B setting. Thus, the testing of the effect of media synergies on individual customers behavior is another unique aspect of this research.
Data for the study come from a large, multinational, B-to-B high-tech manufacturer. The company's database includes firms that function in B-to-B and business-to-consumer (B-to-C) markets. The product categories in the database represent different spectra among high-technology products. Even though the products are durable goods, they require constant maintenance and frequent upgrades; this characteristic provides the variance required to model the customer response. The choice of vendors for the products is normally made after much deliberation by the buyer firm. For the product categories, the buyer and seller choose whether to develop their relationships, and there are significant benefits to maintaining a long-standing relationship for both buyers and sellers.
The data used in the study cover a four-year period from the beginning of 1998 to the end of 2001. All of the customers are new to the firms and made their first purchase from the manufacturer in the first quarter of 1998. A total of 12,024 prospects were contacted for potential acquisition, and of those, 2908 made at least one purchase in the first quarter of 1998. The average interpurchase time for an individual customer ranged between 1.5 and 21 months.
To help the manager make an allocation decision, he or she has at his or her disposal information about each prospect before acquisition and each customer after acquisition, as follows( n2): date of each purchase, number of proactive manufacturer-initiated marketing campaigns before that date, type of campaign (face-to-face, telephone, e-mail), and the number of customer-initiated contacts with the supplier firm (through the Web). From this information, we constructed the variables FACE-TO-FACE, TELEPHONE, EMAIL, and WEB to measure the number of contacts that the firm had with the customer through the specific contact mode. In the acquisition equation, the variables represent the total number of preacquisition contacts in each channel before the first purchase. In the duration equation, the variables measure the total number of contacts in each channel after the first purchase. In the profitability equation, the variables are operationalized as every contact (pre-and postacquisition) that the customer has with the firm. If any two modes of contact with a customer or prospect occurred in a given month, we formulated an interaction term between those two contacts. Specifically, we operationalized the interaction terms as the number of times any two communication modes occurred in the same month,( n3) which helped us assess whether the use of two different contact modes (e.g., telephone and e-mail) in a given period provides added effectiveness.( n4)
Additional decision variables under the firm's control are the amount of acquisition dollars spent for each prospect (ACQUISITION DOLLARS) and the amount of retention dollars spent for each customer (RETENTION DOLLARS). These dollar expenditures are allocated to the four different communication channels. Thus, the expenditure amounts cannot change without adjustments in the allocation of effort to the communication channels. In addition to linear terms for the expenditures, we also included quadratic terms for acquisition dollars (ACQUISITION DOLLARS2) and retention dollars (RETENTION DOLLARS2) in the model. As firms increase their acquisition and retention budget, the associated acquisition rate, retention rate, and customer profitability will be less responsive (concavity). So far, this effect has not been demonstrated in empirical customer lifetime value literature, except for the deterministic (not statistical) approach taken by Blattberg and Deighton (1996). The quadratic terms do not impose a curvature but help uncover nonlinear effects (e.g., diminishing marginal effects) of the relationship between the expenditure and the dependent variables.
We calculated customer profitability (PROFIT) by subtracting direct (product-related) cost, total retention costs, and acquisition cost from the total revenues the customer generates for the firm during the observation period.
Control Variables
We introduce several covariates to control for exchange and customer characteristics. Exchange characteristics that may have important bearings on the different dependent variables include customer-initiated contacts, the degree of cross-buying, the frequency of transactions (e.g., Reinartz and Kumar 2003), the customer's share-of-wallet with the focal firm (e.g., Verhoef 2003), and the relationship duration (Bolton 1998; Bolton and Lemon 1999; Reinartz and Kumar 2000).
In this context, the firm records the number of customer-initiated contacts that is executed through the Internet. From a utility perspective, customers who have greater expected benefits and utility from an ongoing relationship are more likely to commit to it. Customer-initiated contacts are a way to signal this commitment, and there is ample evidence that frequency of communication is positively associated with a partner's commitment (Anderson and Narus 1990). We introduce this count measure as a covariate for all three dependent variables.
Cross-buying, which is an indicator of stronger relationships (Kamakura et al. 2003), should have a potential impact on both relationship duration and customer profitability. We operationalize the variable CROSS-BUY as the number of different categories from which the customer buys.
Frequency of transactions is also a sign of the quality of a relationship (Anderson and Weitz 1992; Kalwani and Narayandas 1995) and therefore should have an impact on both relationship duration and customer profitability. We operationalize the variable FREQUENCY as the number of purchase occasions for each customer.
The firm's share-of-wallet with a particular customer captures the competitive aspect. As a customer allocates relatively more category purchases to a focal vendor, competitors have less access to the customer. Firms that own a greater share-of-wallet of their customers have a strategic advantage over their competitors. A larger share-of-wallet allows for (and requires) greater learning about customer requirements, allows for (and requires) more communication between the parties, and justifies greater relationship-specific investments (Anderson and Narus 2003). Thus, a larger share-of-wallet should have an impact on relationship duration and customer profitability. We operationalize the variable SOW as the percentage of the customer's information technology budget that is spent with the focal firm.
Finally, we introduce the LENGTH OF RELATIONSHIP as a covariate for modeling customer profitability. The expectation with respect to relationship duration and customer profitability is that as the length of the customer tenure rises, it allows for more transactions (volume and frequency). If the transactions are profitable, there should be overall greater relationship profitability (Kamakura et al. 2003; Reinartz and Kumar 2000).
To control for observed heterogeneity across customers, we include additional determinants in the specification. The three available variables represent the following characteristics of the potential targets: type of industry, annual revenues, and number of employees. The variable INDUSTRY TYPE classifies customers as either B-to-B or B-to-C firms. In addition, we use ANNUAL SALES REVENUE ($ millions) and SIZE OF FIRM (number of employees) in this analysis.
Based on these data, the model underlying this research and the potential drivers of acquisition, duration, and customer profits appears in Figure 2. Figure 2 is a conceptual representation of the three equations that make up our statistical model. In terms of the statistical specification, all three equations are similar with respect to firm (e.g., expenditure amounts, communication channels) and customer (e.g., Web-based communications) action variables. In addition, customer characteristics enter the acquisition equation because the information is available for each prospect. Through the correlation structure, the duration and profit equations also capture the influence of these customer characteristics. Finally, the control variables for customer behavior (e.g., FREQUENCY, SOW, CROSS-BUY) are applicable only to acquired prospects and thus only appear in the duration and profit equations.
Right Censoring
Because of the noncontractual nature of the relationship, customers are subject to silent attrition. Our model includes an estimate of the customers relationship duration, and we therefore must account for the possibility of right censoring. This possibility was established with the use of Allenby, Leone, and Jen's(1999) approach to compute the expected time until the next purchase. If the expected time until the next purchase exceeds the time elapsed since the last purchase, the account is considered active and the duration is considered right censored. If this is not the case, the relationship is assumed to have been terminated at the last purchase.
We provide descriptive statistics in Table 1. The unique strength of the data set lies in the availability of individual-level marketing-mix contacts/communications, costs associated with the channel contacts, and profile data. These data enable us to use individual-level models and derive optimal marketing guidelines for each individual customer or at the segment level.
Statistical Model
To link customer acquisition, relationship duration, and profitability, we use a system of equations known as a probit two-stage least squares model. We provide mathematical representations of the model in Equations 1, 2, and 3.
( 1) yLi = β'LsxLi + γ'syDi + εLis if zi = 1
(Cumulative profitability equation)
= 0 otherwise.
( 2) yDi = β'DsxDi + εDis if zi = 1
(Duration equation)
= 0 otherwise.
( 3) z*i = α'svi + μi (Acquisition equation)
zi = 1 if z*i > 0
zi = 0 if z*i ≤ 0,
where
z*i = a latent variable indicating customer i's utility to engage in a relationship with the firm,
zi = an indicator variable showing whether customer i is acquired (zi = 1) or not (zi = 0),
vi = a vector of covariates affecting the acquisition of customer i,
yDi = the duration of customer i's relationship with the firm,
xDi = a vector of covariates affecting the duration of customer i's relationship with the firm,
yLi = the cumulative profitability of customer i,
xLi = a vector of covariates affecting customer i's lifetime value,
αs, βLs, βDs = segment-specific parameters, and μis, εLis and = error terms.
In this recursive simultaneous equation model, a probit model determines the selection or acquisition process, and two distinct regression equations (in this context, they are censored regressions) characterize duration and long-term customer profitability. Logically, the duration and customer profitability are observed only if the customer is acquired. Thus, the duration and profitability equations are conditional regressions determined partly by the acquisition likelihood of a customer.( n5)
The linkages among the three equations in a probit two-stage least squares model are captured by the error structure of the model. Specifically, this model assumes that the error terms (εLis, εDis and μis) are multivariate normal with a mean vector zero and a covariance matrix as specified in Equation 4 (Lee, Maddala, and Trost 1980; Roberts, Maddala, and Enholm 1978):
( 4) [Multiple line equation(s) cannot be represented in ASCII text].
Because of the recursive structure of the system of equations (Equations 1 3), this model can be estimated in stages (Roberts, Maddala, and Enholm 1978). Amemiya (1974) and Heckman (1976) have established precedence for multistage estimation methods for these types of models. The first step is to estimate the probit model on all the data (i.e., acquired and nonacquired prospects). With the use of the estimated parameters from the probit, a selectivity variable, lambda (λis), is constructed for the acquired customers and included as an independent variable in the duration and cumulative profitability equations. In managerial terms, the lambda captures the interaction between customer acquisition and retention and customer acquisition and profitability. Mathematically, the lambda variable is an artifact of the correlation between the error term in the acquisition equation (Equation 3) and each of the errors in the conditional regression equations (Equations 1 and 2). Given this correlation, unbiased parameter estimates are obtainable only by taking conditional expectations of the error terms. The result of this process is the specific functional form of the selectivity variable represented in Equation 5.
( 5) λis = π(αsvi)/Π(αsvi).
We refer to Equation 5 as the inverse mills ratio, where π(⋅) is the standard normal density function, and Π(⋅) is the cumulative standard normal function. This ratio is a monotonically decreasing function of the probability that a customer is acquired or selected into the sample (Heckman 1979; Roberts, Maddala, and Enholm 1978). Although this method for estimation and bias correction in selection models has its basis in econometrics literature, similar bias correction approaches have been applied in marketing contexts (Krishnamurthi and Raj 1988; Winer 1983).
The second step of the process is to estimate the duration model with regressors, including the estimated lambda in Equation 5 and the relevant covariates that affect duration. Estimation in the second step distinguishes between the noncensored and the right-censored observations. It is performed using a standard right-censored Tobit model. With the estimated parameters and the data from the acquired sample, a forecast is made about the expected relationship duration for each customer. This forecast is used as a covariate in the third step.
In the third step, customer profitability is estimated with regressors, such as a vector of exogenous variables that affect the long-term profitability of a customer, the forecasted relationship duration from the second step, and the estimated lambda from Equation 5. The cumulative profitability model specified in Equation 1 is also estimated with a standard right-censored Tobit model.
Covariance Correction
Although it is computationally simpler to estimate it in stages, proper inference requires that the standard errors of the estimates account for the additional sources of variance introduced by using estimated parameters in the second and third steps. Lee, Maddala, and Trost (1980) provide a detailed account of the correct variance covariance matrix for the parameters of this model. Consistent with their research, we correct the covariances of our estimates to obtain consistent parameter estimates.
Unobserved Heterogeneity
In addition to right censoring, our system of simultaneous equations also accounts for unobserved heterogeneity among customers. In this research, we apply a latent-class segmentation approach (Kamakura and Russell 1989) to account for unobserved heterogeneity at the segment level. There can be a different number of segments for the selection/ acquisition process and a different number that best characterizes the duration and/or customer value models. By estimating the model in steps, we allow for this flexibility. Consequently, we can choose the appropriate heterogeneity specification for each step in the model.
Model Selection
Consistent with the latent-class segmentation approach, we estimated the model assuming a fixed number of segments. On the basis of changes in model fit statistics (e.g., Akaike information criterion [AIC], Bayesian information criterion), we determined the appropriate model specification. Beginning with the probability of acquisition, we determined that one segment best characterized the data (AIC is .502 for one segment and .508 for two segments). Thus, we argue that the additional sources of variance in the acquisition likelihood that are captured by firmographic variables explain the negligible unobserved heterogeneity. Table 2 shows the likelihoods and AIC statistics for one and two segments.
After assessing the level of unobserved heterogeneity in the preselected sample, we estimated the duration model, followed by the cumulative profitability model. At each phase of the estimation, we considered the possibility that there could be multiple segments in the population. However, the statistics in Table 2 indicate that there is little unobserved heterogeneity. This outcome suggests that the response profiles are likely to be similar for customers who purchase at approximately the same time. We report the results from the model estimation in Table 3 (standardized parameter estimates).
Marginal Effects of Communication Modes
Our model yields largely consistent results regarding the association between the number of contacts through the different contact channels and the three dependent variables (acquisition, duration, and profitability). All communication modes have a positive impact on acquisition, but specifically, our model indicates that face-to-face interactions have the greatest impact (β = .452), followed by telephone (β = .298) and then e-mail (β = .271) contacts.
When the question is addressed of which mode of contact is most effective for increasing relationship duration, our results remain unchanged relative to the acquisition model. Our model indicates that face-to-face interactions have the greatest impact (β = .381), followed by telephone (β = .328) and then e-mail (β = .152) contacts.
Finally, with respect to customer profitability, we find that the most interpersonal contact mode (i.e., face-to-face) is the strongest driver (β = .396) of customer profitability, followed by telephone (β = .356) and then e-mail (β = .255) contacts. Thus, the effects are similar to acquisition and duration.
Although contacts through more personalized channels have a stronger association with the three dependent variables in comparison with less personal contact channels, this finding pertains only to the number of contacts through the respective channel. It is also important to note that these modes of contact are distinctive in terms of their costs. The cost aspect is captured through acquisition and retention spending, which we include in the model.
Interactions Between Communication Modes
With regard to contact channel interactions in this research, we find evidence of positive synergies between telephone and e-mail and face-to-face and e-mail with respect to acquisition, relationship duration, and profitability (see Table 3). The face-to-face x telephone interaction is not significant in any of the three cases. This finding is important because prior research has found evidence of media synergies only at the aggregate level. To compare the relative effectiveness of acquisition versus retention expenditures, it is not sufficient to consider only the parameter estimates or the marginal effects of the variables, because the way the expenditures are allocated across the communication channels will influence the effectiveness of the expenditures. We therefore make this assessment in the next section by considering simultaneously how much is allocated and how the expenditures are allocated.
Control Variables
The control variables that we introduced in the model are, for the most part, significant at p < .05, and they show no counterintuitive signs. Acquisition expenditures and retention expenditures have diminishing marginal associations with the likelihood of customer acquisition, lifetime duration, and customer profitability. This finding empirically verifies Blattberg and Deighton's (1996) proposition, in which they assert that there are decreasing returns to acquisition and retention expenditures. Thus, there are optimal expenditure levels that result in optimal acquisition and retention rates.
A key aspect of our selection model approach is the ability to assess the impact of the acquisition stage on the later duration and profitability stages. Consistent with prior research (Thomas 2001), the data reveal that the duration of a relationship is correlated with the likelihood of acquiring a customer (βλ = .299). In addition, we find a marginally significant, positive association of acquisition likelihood and profitability (βλ = .096). This important finding posits that a customer who is more likely to be acquired is also more likely to generate higher returns for the company, which underscores the importance of targeting the correct prospects as opposed to all potential prospects. It also underscores the need to model the constructs in a simultaneous fashion, as is done here. Of the total variance explained across the three equations, the distribution of the relative weights, on average, is approximately 25%, 27%, 23%, and 25% for the acquisition expenditure, retention expenditure, firm-initiated contacts, and remaining control variables, respectively.
According to our analysis of the parameter estimates, there are several key issues that must be explored further:
- Given budget constraints, how does the profit-maximizing strategy allocate resources between the contact modes that vary in their degree of interpersonal interaction and costs?
- Which is more critical for profitability, acquisition or retention expenditures?
- Does the contact strategy that maximizes customer profitability also maximize acquisition or retention rates?
To explore these issues more concretely, we performed several simulations based on the parameter estimates and the equations for acquisition, duration, and customer profitability (i.e., Equations 1-3).
The Profit-Maximizing Resource Allocation Strategy
Maximizing customer profitability for the duration of the customer firm relationship is the goal of this simulation. In Scenario 1 (see Table 4), we determined the levels of acquisition and retention expenditures, the number of contacts in each channel, and the degree to which the channels should be used at the same time to maximize cumulative profits. In our model, the intermediate steps in this profit maximization are the estimation of ( 1) the λ parameter given the expenditure and contact levels and ( 2) the relationship duration given the expenditure and contact levels. We assume that all other variables in the model (i.e., share-of-wallet, cross-buying, frequency, and firmographics) are at their mean.
To be consistent with the model variables, it is important for this analysis (and all other scenarios) that the expenditures are directly linked to the number and type of contacts. Thus, in each simulation, the total expenditure (acquisition plus retention) equals the total cost of contacting the customer through the various modes of communication. This necessary condition still allows the allocation across channels to be different for the same levels of expenditure.
As a comparison to Scenario 1, we simulated Scenario 2, in which we maximized customer profitability but allowed the number of contacts in each channel and the degree of simultaneous usage to vary. In Scenario 2, the expenditures and all the other variables are fixed at their means. We present the results for the two scenarios in Table 4.
Scenario 1 shows that the profit-maximizing strategy for the firm is to invest 78.9% of its budget in retention and 21.1% in acquisition. This allocation is slightly different from the firm's current mean allocation of 74.8% for retention and 25.2% for acquisition (Scenario 2). However, the dramatic difference between the profit-maximizing solution (Scenario 1) and the mean level of spending (Scenario 2) is in the amount spent. Specifically, the analysis shows that, assuming an optimal allocation of contacts across channels in both scenarios, a 68.31% decrease in spending from the mean level increases the cumulative profitability of a customer by 41.52%. Thus, currently the firm is overspending on its customers. This overspending apparently increases the customer acquisition rate (.2234 in Scenario 1 versus .2579 in Scenario 2) and the expected relationship duration (1490 days in Scenario 1 versus 1514 days in Scenario 2). However, the long-term profitability of a customer is declining, which suggests that overspending can result in inefficient resource allocation and, more important, a negative return on investment (ROI). We provide a more detailed discussion of ROI subsequently. From a measurement perspective, this result supports the claim that firms should not use a single measure to assess their performance. Measuring acquisition rates, retention rates, and long-term profitability is important for understanding customer behavior and for accurately identifying potential problems in the firm's customer management practices.
In terms of the effort allocation and number of contacts, the profit-maximizing solution (Scenario 1) suggests that the dominant form of communication should be e-mail (80.4% of the total effort). Telephone communication is the second most impactful contact and should receive 11.0% of the total effort allocation. From the parameter estimates alone, this finding may appear erroneous because e-mail communications are the least effective in terms of acquisition, duration, and profitability. A rationale for this effect may be that budget constraints, a reality for almost all firms, factor into the simulation but are not taken into account when the parameters are determined from the estimation. In the simulation, we allow expenditures and/or the number of contacts to vary within the range of the data used to estimate the model. With budget constraints and in these conditions, cost effectiveness, not just efficiency, must be a consideration and compared across alternatives.
From the cost effectiveness considerations of the e-mail communication channel, an obvious question that arises is, To what extent should e-mail be used in conjunction with other modes of communication? We address this question in our analysis through the interaction terms. The interaction terms measure the number of times that any two communication modes should occur in the same month but do not represent an increase in the total number of communication contacts. Thus, in the optimal scenario, we find that when telephone communications are used, an e-mail should accompany it during that same period on 1.6 occasions. In other words, 37% of the telephone contacts should be accompanied by an e-mail. With regard to face-to-face communication, the optimal scenario is to accompany the face-to-face interaction with an e-mail interaction in the same period 67% of the time.
Although the firm cannot control the degree of customer-initiated interactions, it can create opportunities to encourage such communications, and the Web is a tool for doing that. The simulation suggests that 7.1% of the firm's resources should be directed toward communicating with customers through the Web channel.
As a contrast to the profit-maximizing effort and expenditure allocation (Scenario 3), we also examine the profit-maximizing communication strategy given a mean level of expenditures (Scenario 2). At the mean level of expenditure, which is higher, the profit-maximizing contact strategy is to increase the total number of contacts by nearly 60% (from 40 to 63). The distribution of the contacts is split more evenly, with 50.3% directed toward e-mail communication and 43.9% directed toward telephone communication. Compared with the e-mail and telephone channels, the emphasis on Web and face-to-face channels is minimal.
Comparing the differences in budget and channel emphasis across Scenarios 1 and 2, we conclude that overspending tends to lead to greater investments in more costly and interpersonal communications that do not pay off in terms of customer profitability. Firms can use this key practical insight when faced with the need for cutbacks on expenditures.
Which Is More Critical: Acquisition Expenditures or Retention Expenditures?
The goal of this analysis is to demonstrate the impact of not optimizing acquisition expenditures versus not optimizing retention expenditures. To demonstrate this impact, we focus on customer profitability. We predict profits using unstandardized estimates and the model represented by Equations 1-3. As in the previous part of the simulation, before predicting profits, we estimate and use the lambda and relationship duration in the profit prediction, and we fix all other necessary but nonrelevant variables at their means.
To mimic the economic environment in which firms reevaluate their budgets, we allow the expenditure change to be either 10% or 25% and be driven by a change in either the acquisition or the retention budgets. In Table 5, we show how the per-customer profitability changes at different expenditure levels. For reference, in Table 6 we report the total expenditure change as acquisition and retention expenditure deviations from their optimal levels.( n6) When interpreting Table 5, it is important to recall that acquisition and retention expenditures represent different proportions of the total budget (Table 4). Thus, a 10% deviation in the acquisition budget (which is a significantly smaller portion of the total budget) from its optimal level represents only a 2.11% deviation from the optimal total budget.
The profit projection in Table 5 shows that the percentage changes in profit are small, particularly at the level of a 10% change in the total budget. This projection suggests that relatively substantial deviations from optimal expenditures are associated with relatively modest changes in the dependent variable (i.e., a flat maximum). However, despite the modest percentage changes in profits for a single customer, on an absolute basis and for multiple customers, the loss in profitability is significant. For example, the smallest difference occurs when the acquisition budget is .90 of the optimal and the retention budget is optimal.( n7) At this expenditure level, a firm with a portfolio of 200,000 customers( n8) will save approximately $2.69 million from reduced marketing expenditures but lose approximately $39.3 million in long-term customer profitability because of suboptimal budget allocations. These figures only become more magnified as the budgets deviate further from the optimal level.
There are several important insights regarding the ROI when budgets deviate from the optimal level. To compute the ROI, we use the approach described by Rust, Lemon, and Zeithaml (2004), which focuses on the size of the investment and the change in customer equity that results from the investment. In this context, we measure the change in customer equity as the deviation in the long-term profitability from the optimal level. Thus, the change in customer equity and the resultant ROI calculations are negative. We report the exact numbers in Table 7. The first insight from the ROI analysis pertains to acquisition spending.
Acquisition Spending Under the assumption that retention spending is optimal and contacts are optimally allocated, the misallocation (i.e., deviation from the optimal acquisition expenditure) is asymmetric; underspending on acquisition is worse than overspending on acquisition by the same amount. For example, overspending the optimal acquisition budget by 25% results in an ROI of -2.83, whereas underspending by 25% results in an ROI of -3.03. A similar insight can be drawn from retention spending.
Retention Spending Under the assumption that acquisition spending is optimal and contacts are optimally allocated, overspending on retention is better than underspending on retention by the same amount. This result is most significant at the 25% level. At this level, underspending on retention results in an ROI of 55.29, whereas overspending by 25% results in an ROI of only 4.27.
The results on the diagonal of Table 7 also provide insight into the acquisition/retention trade-off. Specifically, the diagonals help answer the following question: When faced with the need to make a budget change, how should firms make that change? In most cases, firms consider this question not as either/or but rather as how much to pull from or add to each budget. The diagonals provide insight into this question.
Diagonal 1. Diagonal 1 describes the situation in which neither the acquisition nor the retention budget is at its respective optima, and instead, one type of expenditure is increased while the other is decreased. At comparable absolute total budget changes (see Table 6), the results in Table 7 show that increases in acquisition expenditures and decreases in retention expenditures (from the optimal) result in lower ROIs than do decreases in acquisition and increases in retention. This important outcome is not obvious from the parameter estimates because the ROI metric we used in this analysis taps further into a firm's performance than just profitability. Here, the ROI measure focuses on the incremental change in customer value and compares the size of that change for different levels of investment. Thus, consistent with our prior assertion, multiple metrics are useful for assessing firm performance.
Diagonal 2. The highlighted Diagonal 2 provides insight into the impact of jointly increasing or decreasing the acquisition and retention budgets. At comparable absolute changes in the total budget, the results indicate that underspending decreases the ROI more than does overspending. This effect becomes significantly more magnified at greater deviations from the optimal expenditure levels. This result has important implications for customer portfolio management because it suggests that firms must invest sufficiently to acquire and maintain relationships. Although the investment in the customer may become less efficient beyond the optimal expenditure level, the decreased efficiency does not outweigh the lost value that the firm would incur if the customer's potential was not fully realized.
Does the Optimal Contact Strategy Maximize Customer Profits, Acquisition Likelihood, and Relationship Duration?
Intuitively, an optimal strategy should maximize the customer's profitability, the acquisition likelihood, and the relationship duration. However, the relative magnitude of the parameter estimates across the three equations suggests that this is not the case. To investigate this proposition, we vary the number of contacts and the interaction level between the contacts but fix the other variables (including expenditures) at their means. Then, we use the estimates from each equation of the model to determine the number and distribution of contacts that would maximize customer profitability (Scenario 2), the probability of acquisition (Scenario 3), and the length of the customer firm relationship (Scenario 4). We show this comparison in Table 4.
In Table 4, note that the contact strategy, which maximizes customer profitability, results in a .26 probability of acquisition and a relationship duration of 1514 days. In contrast, the contact strategy that maximizes the acquisition likelihood results in a probability of acquisition of .30. The contact strategy that maximizes relationship duration results in a duration of 1587 days. Thus, the profit-maximizing contact strategy maximizes neither acquisition likelihood nor relationship duration. Because the communication budget is fixed in this scenario, it is appropriate to consider it in terms of maximization, not in terms of optimization. The maximization of acquisition and retention rates results from various channel usage scenarios within the budget restriction.
A reason for this outcome could be that the profit equation parameters reflect the cost effectiveness (i.e., cost benefit) of each contact mode, whereas the acquisition and duration equation parameters reflect only the effectiveness of each contact mode. This distinction leads to a difference in the relative magnitude of the parameters when profit is the response objective versus when acquisition likelihood or relationship duration is the response objective. Therefore, the optimal solution for acquisition or duration will not necessarily be the optimal solution for a profit objective.
Another interesting finding from this analysis pertains to the strategy for allocating resources across the contact channels. Note that in Scenarios 3 and 4, it is not the total number of contacts that is determined but rather the acquisition or retention contacts. Thus, when we compare Scenario 2 with Scenarios 3 or 4, we consider the percentage of either the acquisition or retention effort, not the percentage of total effort.
Comparison of Scenario 2 (maximizing profits) and Scenario 3 (maximizing acquisition rates) reveals a similar but reverse emphasis. In both scenarios, telephone and email are the dominant forms of communications. Specifically, the results suggest that more than 93% of the acquisition effort should be directed toward these two channels. However, when cumulative profits are the objective, telephone is more dominant than e-mail, whereas the reverse is true when acquisition is the objective. In contrast to the acquisition scenario, the comparison between Scenario 2 (maximizing profits) and Scenario 4 (maximizing duration) suggests a similar optimal distribution of communications across the channels. Specifically, the relative ordering of retention communication efforts across the channels is the same for cumulative profit maximization and duration maximization. In combination, Scenarios 2, 3, and 4 suggest that when the goal is short term, such as customer acquisition, interpersonal interactions are not as critical. However, for objectives managed over a longer time horizon (e.g., cumulative profitability), interpersonal interactions aid in the achievement of the objective.
In summary, this simulation addresses three general questions that a practicing manager may confront:
- If marketing budgets deviate from their optimal level, what is the impact on customer profitability (see Table 5)?
- If managers are mandated to cut their budgets by, say, a fixed percentage, from which "bucket" should they make the cuts--acquisition, retention, or both (see Tables 5 and 6)?
- Increasingly, marketing managers are assessed on the basis of not only their profits but also their ROI. Thus, under the assumption of a suboptimal allocation of the budget, in which allocation scenario is the ROI more attractive (see Table 7)?
In this study, we investigated several drivers of customer profitability and derived some implications for resource allocations to customers and prospects. A key assertion demonstrated in this analysis is that both the amount of investment and how it is invested in a customer relate directly to the acquisition, retention, and profitability of that customer. Expanding on this basic principle, we arrive at several substantive conclusions for our empirical setting.
How Much to Invest
We find that underspending is more detrimental and results in smaller ROIs than does overspending. When firms trade off between expenditures for acquisition and those for retention, a suboptimal allocation of retention expenditures will have a greater impact on long-term customer profitability than will suboptimal acquisition expenditures. Consistent with prior research (Chintagunta 1993; Tull et al. 1986), there appears to be a flat maximum with respect to acquisition and retention expenditures. Specifically, we find that a 10% deviation in either acquisition or retention expenditures from their respective optimal levels results in less than a 1% change in the long-term profitability of a customer.
How to Invest
If the firm initiates the contact, the relative effectiveness of highly interpersonal and interactive communication channels is greater than that of less interpersonal and interactive communication channels. However, this generalization does not hold for all response variables if the customer initiates the interaction. The customer communications strategy that maximizes long-term customer profitability maximizes neither the acquisition rate nor the relationship duration. Instead, developing a communications strategy to manage long-term customer profitability generally requires a long-term and holistic perspective toward the relationship. This perspective tends to give more emphasis to more interpersonal and interactive communications than does a limited focus on acquisition.
Each of these conclusions can be used for the strategic advantage of a firm and can have a potentially large impact on the cost and/or revenue aspect of customer profitability. We acknowledge that generalizability of some of the results can be achieved only with further testing on multiple data sets.( n9) However, many of the conclusions would not have been revealed if our model did not integrate acquisition, retention, and customer profitability into a single framework that accounts for the natural linkages and endogeneity of the relationship dimensions, a critical contribution of our research. Specifically, we present a decision framework for managing multiple dimensions of a customer firm relationship that is based on established statistical models and econometric methods. In line with much research in customer relationship management, individual customer profitability should be the objective function. A key conclusion of this research is that firms can manage their customer base profitably, but it requires resource allocation decisions that involve trade-offs.
Limitations and Suggestions for Further Research
Despite the usefulness of this decision-modeling framework, we realize that it has certain limitations. For example, a competitor's action may have an impact on the focal firm's customer behavior. However, because we lack more specific data, in this research we account for competition by including only a customer's share-of-wallet with the focal firm.
Another caveat of this research is our ability to separate marketing expenditures between customer acquisition and retention. This separation is more straightforward in a B-to-B context but can become quite complex in B-to-C settings and when mass communications are a major part of the marketing expenditure.
Finally, to arrive at empirical generalizations, additional research should investigate the relationships between the key constructs in other industries and with regard to other resource decisions. The key limiting factor seems to be the availability of appropriate data that enable these kinds of tests. However, this research has taken a first step toward a more complete understanding of the drivers of customer profitability by using a database that tracks information from before acquisition to termination by the customer or to the right-censoring period.
The authors thank the JM reviewers and the participants of the 2003 Marketing Science Conference for their valuable comments on a previous version of the article. They also thank the high-tech firm for providing access to the data, without which this study would not have been possible. All authors contributed equally.
( n1) We use the terms customer profitability and customer value (to the firm) interchangeably in this research. Both expressions represent a multiperiod measure of the economic value of a customer to the firm, expressed in contribution margin terms. Although the term customer lifetime value has been used abundantly in that context, we refrain from doing so. Conceptually, there may be reservations about using customer lifetime value because it implies complete knowledge (i.e., past and future) about a customer's value to the firm. We do not take such a viewpoint.
( n2) The manager here refers to the manager at the firm who supplied the data.
( n3) A limitation of the data is that we cannot distinguish an interaction between media from a media pulsing strategy. Therefore, we may underestimate the true level of media interactions in the data.
( n4) In these data, there are no incidences of more than two contact modes used in the same month.
( n5) Although at first glance, Equation 3 does not seem to be part of the system of equations, a selectivity correction term will be specified for the estimation of Equations 1 and 2 to correct for selection bias due to nonacquired prospects. Thereby, Equation 3 will become part of the system.
( n6) In Tables 5 and 6, we round all dollar amounts to the nearest whole number and all percentages to two decimal points.
( n7) As a result of rounding, increasing and decreasing the acquisition budget by 10% appears to give the same result. However, through our analysis, we find that reducing the budget results in profits that are slightly lower than those that result from increasing the acquisition budget by the same amount. The absolute difference between a 10% increase and a 10% decrease is $.42.
( n8) This number is representative of the size of this firm's customer base.
( n9) We also estimated the model presented in this research on a data set for a B-to-C firm that used similar customer contact modes. Although this empirical context was not as rich, the effects that are of focal interest (decreasing returns to acquisition and retention, flat maximum, asymmetric response to acquisition, and retention spending changes) were consistent with our original findings.
Descriptive Statistics
Legend for Chart:
B - Preacquisition Sample(*) Mean
C - Preacquisition Sample(*) Standard Deviation
D - Acquired/Selected Sample(**) Mean
E - Acquired/Selected Sample(**) Standard Deviation
A B C
D E
Independent Variables
Acquisition dollars per person 581 101
508 77
Acquisition dollars² 342,118 10,180
269,496 5,829
Retention dollars per person
1506 209
Retention dollars²
2,281,741 43,693
Contact Channel(**)
Telephone 3.1 1.8
13.4 3.6
Face-to-face .1 .1
2.4 .6
Web .2 .1
1.2 .4
E-mail 2.7 2.1
15.1 4.2
Telephone x e-mail .6 .2
3.1 .4
Face-to-face x e-mail .02 .005
.6 .1
Cross-buying
4.2 1.8
Frequency
8.8 3.1
Frequency²
79.87 72.8
Share-of-wallet
41.6 8.2
Lambda
.48-.71 range .2
Firmographics
Industry type (B-to-B) 56% .5
Annual sales revenue 40.2 39.6
Size of firm (employees) 212 96
Dependent Variables
Duration (days)
1380 141
Predicted duration (days)
1339 133
Lifetime values($)
356,280 28,712
Percentage right censored
41
(*) The data reported refer to the entire pool of acquired and
nonacquired people before acquisition.
(**) In the preacquisition sample, these figures only represent
acquisition contacts. In the acquired sample, the numbers
represent the total number of contacts throughout the
customer-firm relationship. Model Selection Accounting for Unobserved
Heterogeneity
A C
Probit Acquisition Model
Sample size 12,024
1 Segment Solution
Log-likelihood -704.16
AIC .502
2 Segment Solution
Log-likelihood -703.98
AIC .508
Tobit Duration Model
Sample size 2908
1 Segment Solution
Log-likelihood -73.21
AIC .093
2 Segment Solution
Log-likelihood -73.13
AIC .098
Tobit Lifetime Value Model
Sample size 2908
1 Segment Solution
Log-likelihood -55.11
AIC .069
2 Segment Solution
Log-likelihood -55.05
AIC .073 Standardized Parameter Estimates
Legend for Chart:
A - Acquisition Equation
B - Tests of Significance
A B
Acquisition dollars -.559(**)
Acquisition dollars(2) -.012(*)
Contact Channel
Firm-initiated:
Telephone .298(**)
Face-to-face .452(**)(a)
E-mail .271(*)(a,b)
Telephone x e-mail .086(**)(a,b,c)
Face-to-face x e-mail .052(**)(a,b,c)
Customer-initiated:
Web .376(**)
Firmographics
Industry type .306(*)
Annual revenue .414(**)
Size of firm .370(**)
Duration Equation
Retention dollars .501(**)
Retention dollars² -.101(*)
Contact Channel
Firm-initiated:
Telephone .328(**)
Face-to-face .381(**)(a)
E-mail .152(*)(a,b)
Telephone x e-mail .093(**)(a,b,c)
Face-to-face x e-mail .077(**)(a,b,c)
Customer-initiated:
Web .386(**)
Frequency .417(**)
Frequency² -.079(*)
Cross-buying .288(**)
Share-of-wallet .335(**)
Lambda .299(**)
Legend for Chart:
A - Profitability Equation
B - Tests of Significance
A B
Acquisition dollars .581(**)
Acquisition dollars² -.219(**)
Retention dollars .458(**)
Retention dollars² -.203(**)
Contact Channel
Firm-initiated:
Telephone .356(**)
Face-to-face .396(**)(a)
E-mail .255(*)(a,b)
Telephone x e-mail .063(**)(a,b,c)
Face-to-face x e-mail .057(**)(a,b,c)
Customer-initiated:
Web .301(**)
Frequency .322(**)
Frequency² -.074(*)
Estimated duration .301(**)
Cross-buying .338(**)
Share-of-wallet .296(**)
Lambda .096(*)
(*) p < .10.
(**) p < .05.
(a) The coefficient is significantly different (at p < .05)
from that of the telephone contact channel.
(b) The coefficient is significantly different (at p < .05)
from that of the face-to-face contact channel.
(c) The coefficient is significantly different (at p < .05)
from that of the e-mail contact channel. Simulation Results
Legend for Chart:
A - Objective Constraints
B - Scenario 1 Maximize Customer Profitability by
Optimizing Spending
C - Scenario 1 Maximize Customer Profitability by
Optimizing Spending
D - Scenario 2 Maximize Customer Profitability by Optimizing
Total Contacts Spending Fixed at Means
E - Scenario 2 Maximize Customer Profitability by Optimizing
Total Contacts
F - Scenario 3 Maximize Acquisition Rate by Optimizing
Acquisition Contacts Spending Fixed at Means
G - Scenario 4 Maximize Relationship Duration by Optimizing
Retention Contacts Spending Fixed at Means
A B C D
E F G
Acquisition spending $134.55 21.1% $508.00
25.2% $508.00
Retention spending $503.77 78.9% $1,506.00
74.8% $1,506.00
Total spending $638.32 $2,014.00
Cumulative profits $754,087.56 $441,015.03
Percentage change
in profitability from
optimal -41.52%
Percentage deviation
in budget from optimal 215.51%
-68.31%
Probability of
acquisition .2234 .2579
.2996
Expected relationship
duration 1490.51 1514.76
1587.55
Percentage change in
relationship duration
4.8%
Legend for Chart:
A - Number of Contacts
B - Total Contacts
C - Total Effort
D - Total Contacts
E - Total Effort
F - Acquisition Effort
G - Retention Effort
H - Acquisition Contacts
I - Acquisition Effort
J - Retention Contacts
K - Retention Effort
A B C D E
F G H I
J K
Telephone 4.38 11.0% 27.80 43.9%
50.9% 42.1% 4.05 24.9%
21.37 41.5%
Face-to-face .60 1.5% .87 1.4%
.3% 1.6% .50 3.1%
1.54 3.0%
Web 2.80 7.1% 2.80 4.4%
3.7% 4.6% .60 3.7%
2.33 4.5%
E-mail 31.90 80.4% 31.90 50.3%
45.1% 51.6% 11.10 68.3%
26.20 50.9%
Telephone x e-mail 1.60 1.60
1.40
1.29
Face-to-face x e-mail .40 .40
.04
.39
Total contacts 39.68 63.37
16.25
51.44
Notes: All numbers are per customer. Long-Term Customer Profit Predictions (Percentage Change from
Optimal Profits)
Legend for Chart:
B - Retention Expenditures .75 x Optimal
C - Retention Expenditures .9 x Optimal
D - Retention Expenditures Optimal
E - Retention Expenditures 1.1 x Optimal
F - Retention Expenditures 1.25 x Optimal
A B C
D E
F G
Acquisition Expenditures
.75 x optimal $698,676 (-7.35%) $752,459 (-.22%)
$752,859 (-.16%) $752,459 (-.22%)
$750,359 (-.49%) Diagonal 1
.9 x optimal $727,163 (-3.57%) $753,491 (-.08%)
$753,891 (-.03%) $753,491 (-.08%)
$751,392 (-.36%)
Optimal $726,273 (-3.69%) $753,688 (-.05%)
$754,088 $753,688 (-.05%)
$752,588 (-.33%)
1.1 x optimal $736,623 (-2.32%) $753,491 (-.08%)
$753,891 (-.03%) $753,491 (-.08%)
$751,391 (-.36%)
1.25 x optimal $750,358 (-.49%) $752,458 (-.22%)
$752,858 (-.16%) $752,458 (-.22%)
$750,358 (-.49%) Diagonal 2
Notes: Predictions assume an optimal communication allocation.
All numbers are per customer. Percentage Change in Total Budget from Optimal Total Budget
Legend for Chart:
B - Retention Expenditures .75 x Optimal
C - Retention Expenditures .9 x Optimal
D - Retention Expenditures Optimal
E - Retention Expenditures 1.1 x Optimal
F - Retention Expenditures 1.25 x Optimal
A B C D
E F G
Acquisition Expenditures
.75 x optimal -25.00% -13.16% -5.27%
2.62% 14.46% Diagonal 1
.9 x optimal -21.84% -10.00% -2.11%
5.78% 17.62%
Optimal -19.73% -7.89% .00%
7.89% 19.73%
1.1 x optimal -17.62% -5.78% 2.11%
10.00% 21.84%
1.25 x optimal -14.46% -2.62% 5.27%
13.16% 25.00% Diagonal 2 Effect of Marketing Expenditures on ROI
Legend for Chart:
B - Retention Expenditures .75 x Optimal
C - Retention Expenditures .9 x Optimal
D - Retention Expenditures Optimal
E - Retention Expenditures 1.1 x Optimal
F - Retention Expenditures 1.25 x Optimal
A B C D
E F G
Acquisition Expenditures
.75 x optimal -116.74 -3.94 -3.03
-3.49 -6.10 Diagonal 1
.9 x optimal -54.97 -2.04 -1.31
-1.88 -4.59
Optimal -55.29 -1.68 n/a
-1.58 -4.27
1.1 x optimal -34.21 -1.99 -1.30
-1.85 -4.47
1.25 x optimal -7.83 -3.62 -2.83
-3.26 -5.67 Diagonal 2
Notes: n/a = not applicable.DIAGRAM: FIGURE 1; Linking Customer Acquisition, Relationship Duration, and Customer Profitability
DIAGRAM: FIGURE 2; Determinants of Focal Constructs
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~~~~~~~~
By Werner Reinartz; Jacquelyn S. Thomas and V. Kumar
Werner J. Reinartz is Associate Professor of Marketing, INSEAD
Jacquelyn S. Thomas is Associate Professor of Integrated Marketing Communications, Northwestern University
V. Kumar is ING Chair Professor and Executive Director, ING Center for Financial Services, School of Business, University of Connecticut
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Record: 21- Benchmarking Marketing Capabilities for Sustainable Competitive Advantage. By: Vorhies, Douglas W.; Morgan, Neil A. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p80-94. 15p. 1 Diagram, 5 Charts. DOI: 10.1509/jmkg.69.1.80.55505.
- Database:
- Business Source Complete
Benchmarking Marketing Capabilities for Sustainable
Competitive Advantage
Market-based organizational learning has been identified as an important source of sustainable competitive advantage. One particular learning mechanism, benchmarking, is a widely used management tool that has been recognized as appropriate for identifying and enhancing valuable marketing capabilities. However, despite widespread admonitions to managers, the benchmarking of marketing capabilities as a route to sustainable competitive advantage has received scant empirical attention. The authors empirically examine the potential business performance benefits available from benchmarking the marketing capabilities of top-performing firms. The results suggest that benchmarking has the potential to become a key learning mechanism for identifying, building, and enhancing marketing capabilities to deliver sustainable competitive advantage.
Market-based learning has been recognized as an important source of sustainable competitive advantage (e.g., Hult 1998; Slater and Narver 1995). A widely adopted market-based learning approach is benchmarking, a structured process by which a firm seeks to identify and replicate "best practices" to enhance its business performance (Camp 1995; Zairi 1998). One of the most popular management tools in the world, benchmarking has become a primary instrument in firms' total quality management, knowledge management, and process improvement efforts (e.g., Anderson 1999; Garvin 1993; Rigby 2001). It has also been recommended as a marketing capability improvement tool (e.g., Brownlie 2000; Day 1994; Dickson 1992; Woodburn 1999), with firms having undertaken benchmarking projects in areas such as customer satisfaction monitoring and brand management (e.g., Andriopoulos and Gotsi 2000; Hiebeler, Kelly, and Ketteman 1998). Yet despite the popularity of benchmarking and the theoretical importance of market-based learning, there is almost no empirical evidence either to support admonitions to benchmark marketing capabilities as a route to sustainable competitive advantage or to guide managers' benchmarking efforts if they follow this advice (e.g., Ettlie and Johnson 1994).
This article addresses three important gaps in knowledge regarding the benchmarking of marketing capabilities. First, we examine the key normative benchmarking theory premise that marketing capabilities associated with superior firm performance can be identified and that the marketing capability gap between a firm and top-performing benchmarks explains significant variance in business performance. This provides the first calibration of the performance benefits potentially available through benchmarking marketing capabilities. Second, we present the first empirical assessment of important benchmarking process design questions regarding what the appropriate number of benchmark sites is, whether to search for benchmark sites within or across industries, which marketing capabilities may be appropriate for benchmarking, and how they should be examined. Third, we demonstrate how profile deviation can be used as a sophisticated and robust tool for benchmarking marketing capabilities, and we extend this method by using models that incorporate weightings and interdependence among capabilities and sensitivity analyses using multiple different benchmark profiles. We also provide practical guidance for managers regarding how to implement benchmarking processes to identify and improve marketing capabilities as a route to sustainable competitive advantage.
We begin by describing benchmarking and the theoretical rationale linking it with sustainable competitive advantage. Next, we identify and develop indicators of relevant marketing capabilities. We then describe our data collection and measures and examine the relationship between marketing capabilities and business performance. Using profile deviation analysis to operationalize key stages of the benchmarking process, we then identify top-performing firms and calibrate their marketing capability profiles as benchmarks. Next, we assess the business performance impact of deviation from these benchmarks and the effect of weighted versus unweighted marketing capability models, different numbers of benchmark firms, and benchmarks from the same industry versus across industries on this relationship. Finally, we discuss the implications and limitations of our study and identify important areas for further research.
Benchmarking is a market-based learning process by which a firm seeks to identify best practices that produce superior results in other firms and to replicate these to enhance its own competitive advantage (Camp 1995; Mittelstaedt 1992). Over time, the primary focus of benchmarking has moved from the content of the product or services produced, the strategy pursued, and performance outcomes achieved by top-performing firms to a process focus on the capabilities believed to have produced the superior performance outcomes observed (e.g., Anderson 1999; Ralston, Wright, and Kumar 2001). Although this process/content dichotomy is widely used in the literature, in practice benchmarking organizational capabilities involves both content and process issues (e.g., Fawcett and Cooper 2001; Zairi 1998). Benchmarking organizational capabilities is a structured learning process comprising ( 1) a search stage in which managers search for firms exhibiting superior performance and identify the capability drivers of observed performance superiority, ( 2) a gap-assessment stage in which the capability differences between the firm and the benchmark sites are assessed, and ( 3) a capability improvement stage in which the firm plans and executes gap-closing capability improvements (e.g., Camp 1995; Garvin 1993).
Three major theoretical perspectives support normative suggestions that benchmarking marketing capabilities can provide a source of sustainable competitive advantage. First, resource-based view (RBV) theory identifies heterogeneity in the levels, value, inimitability, and nonsubstitutability of firms' resources and capabilities as the fundamental cause of interfirm performance variations (Amit and Shoemaker 1993; Barney 1991; Wernerfelt 1984). To the extent that benchmarking can enable a firm to enhance the level and value of its stock of marketing capabilities, it should therefore lead to competitive advantage (Teece, Pisano, and Shuen 1997). Furthermore, to the extent that benchmarking as a continuous higher-order learning capability is itself valuable, inimitable, and nonsubstitutable, benchmarking-based improvements in a firm's stock of marketing capabilities can be sustained (Dickson 1992).
Second, strategic marketing scholars have identified a firm's market orientation--its ability to learn about its market environment and to use this knowledge to guide its actions appropriately--as a key driver of business performance (e.g., Hunt and Morgan 1995; Jaworski and Kohli 1993; Narver and Slater 1990). Market orientation researchers have specified benchmarking as an important market-based learning tool that can enable firms to build and deploy resources and capabilities in ways that are appropriate for their market environment (Slater and Narver 1995). The literature indicates that benchmarking provides an operational mechanism for directing manager and employee attention to the external market environment (e.g., Hiebeler, Kelly, and Ketteman 1998; Teece, Pisano, and Shuen 1997), for reaching a shared interpretation of the capabilities required to achieve superior performance (e.g., Camp 1995; Zairi 1998), and for appropriately directing investments in capability improvement (e.g., Brockett et al. 2001; Camp 1989). Therefore, market orientation researchers have posited benchmarking as a learning tool that can help create market-driven firms (Day 1994; Slater and Narver 1995).
Third, organizational learning theory indicates that for market-based learning to form a source of sustainable competitive advantage, a firm's market surveillance must be more alert, timely, and accurate than that of its rivals (e.g., Dickson 1992; Teece, Pisano, and Shuen 1997). Benchmarking has been identified as a structured and continuous process that helps reduce perceptual bias (e.g., Dickson 1992), core rigidity (e.g., Leonard-Barton 1995), and satisficing problems (e.g., Winter 2000) that constrain a firm's motivation and ability to learn from market surveillance (e.g., Levinthal and Myatt 1994). The literature also posits that organizational learning may be accomplished by both imitation and experimentation (e.g., March 1991). Benchmarking has been identified as an important mechanism for imitative learning (Mittelstaedt 1992; Voss, Ahlstrom, and Blackmon 1997). However, the effect of different organizational and capability contexts on imitative capability improvement efforts inevitably results in the creation of a unique stock of capabilities in the benchmarking firm (Collis 1994; Grant 1996). Therefore, benchmarking also provides an important opportunity for learning by experimentation (Dickson 1992; Haunschild and Miner 1997).
Despite this theoretical support, the benchmarking literature makes important normative theory assumptions and poses many benchmarking process design implementation questions to which little or no empirical attention has been paid. We focus on theoretical assumptions and process design questions associated with the search and gap-assessment stages of benchmarking for three reasons. First, unless the assumptions underpinning normative benchmarking theory can be validated in the first two stages, deploying resources on the final capability improvement stage of benchmarking is likely to be unproductive. Second, ceteris paribus, firms that successfully accomplish the search and gap-assessment stages will have an advantage over rivals in the alertness, accuracy, speed, and efficiency of their benchmarking efforts (e.g., Dickson 1992). Third, the market-based identification and monitoring of valuable sources of competitive advantage such as marketing capabilities can provide fact-based evidence to help managers recognize the need for capability improvements (e.g., Day and Wensley 1988). The search and gap-assessment stages of benchmarking marketing capabilities are also required to select the most appropriate benchmarks, calibrate the potential value of alternative capability improvements options, and trigger the appropriate detailed investigations of the benchmark site required to plan and execute capability improvement actions (Camp 1995; Day 1994). Therefore, the first two stages of benchmarking both constitute a source of competitive advantage in their own right and are required for the success of the final capability improvement stage.
In assessing these stages of the benchmarking process, we first examine the theoretical assumption that distinct marketing capabilities can be identified and linked with superior business performance. We then examine four important but unresolved questions regarding how benchmarking marketing capabilities should be accomplished: ( 1) Do valuable interdependencies exist that require benchmarking the entire set of marketing capabilities associated with superior performance? ( 2) If they do, can individual marketing capabilities be treated as equally important in assessing capability gaps? ( 3) Should firms search for benchmarks across industries or only in their own industries? and ( 4) What is an appropriate number of top-performing firms that should be used as benchmark sites?
Identifying Marketing Capabilities for Benchmarking
The search stage of process benchmarking involves identifying the capabilities contributing to superior performance that should be isolated for further study (Camp 1989). Because the notion of benchmarking marketing capabilities is relatively new, relevant marketing capabilities have yet to be comprehensively catalogued (e.g., Menon et al. 1999; Moorman and Slotegraaf 1999). As a starting point, however, the literature identifies specific capabilities used to transform resources into valuable outputs based on the classic marketing mix (e.g., Day 1994; Vorhies and Morgan 2003) and the capabilities used to orchestrate marketing-mix capabilities and their resource inputs involving market information management and marketing strategy development and execution (e.g., Capron and Hulland 1999; Day 1994; Morgan et al. 2003). To gain insights into relevant marketing capabilities in practice, we conducted in-depth field interviews with 30 managers involved in senior marketing roles in a wide range of firms. This was supplemented with four focus groups, three involving 24 marketing managers from different firms and one involving the 9 managers on the senior marketing management team of a major division of a Fortune -500 high-technology company. We used open-ended questions, asking these 63 managers to identify and describe the marketing capabilities of their firms that they believed contributed most to creating value for customers and for the firm.
Synthesizing insights from our fieldwork with those in the literature, we identified eight distinct marketing capabilities that are viewed as contributing to business performance and therefore suitable for benchmarking:( n1) ( 1) product development, the processes by which firms develop and manage product and service offerings (e.g., Dutta, Narasimhan, and Rajiv 1999); ( 2) pricing, the ability to extract the optimal revenue from the firm's customers (e.g., Dutta, Zbaracki, and Bergen 2003); ( 3) channel management, the firm's ability to establish and maintain channels of distribution that effectively and efficiently deliver value to end-user customers (e.g., Weitz and Jap 1995); ( 4) marketing communications, the firm's ability to manage customer value perceptions (e.g., McKee et al. 1992); ( 5) selling, the processes by which the firm acquires customer orders (e.g., Shapiro, Slywotzky, and Doyle 1997); ( 6) market information management, the processes by which firms learn about their markets and use market knowledge (Day 1994; Menon and Varadarajan 1992); ( 7) marketing planning, the firm's ability to conceive marketing strategies that optimize the match between the firm's resources and its marketplace (Morgan et al. 2003); and ( 8) marketing implementation, the processes by which intended marketing strategy is transformed into realized resource deployments (e.g., Noble and Mokwa 1999).
Linking Marketing Capabilities and Business Performance
Normative benchmarking theory assumes that managers not only can isolate distinct marketing capabilities they believe to be valuable but also can empirically link these capabilities with superior business performance. In doing so, the literature highlights two key benchmarking search process design alternatives: functional benchmarking, in which individual capabilities are assessed separately, and integrative benchmarking, in which a set of related capabilities is assessed collectively (e.g., Fawcett and Cooper 2001). The theoretical literature indicates that interdependencies between individual capabilities often exist and can be a valuable source of competitive advantage (e.g., Srivastava, Shervani, and Fahey 1999; Teece, Pisano, and Shuen 1997). Empirically, the extent to which such valuable interdependencies exist between marketing capabilities should determine whether marketing capabilities require functional or integrative benchmarking process designs.
In the absence of relevant secondary data sources, we collected primary data on the eight marketing capabilities identified and firm performance through a mail survey of the top marketing executives of 748 U.S. firms in six industry types: consumer durables, consumer nondurables, consumer services, business durables, business nondurables, and business services. Within each of these industry types, we randomly selected two three-digit Standard Industrial Classification codes. The 12 industries in the sample were audio and video appliances; household appliances; canned and frozen foods; soaps and toiletries; insurance; hospitals; process equipment; machine tools and patterns; chemicals, gases, and pigments; packaging; trucking; and business software services. We generated a mailing list of firms in each industry from business directories and mailed a survey packet to the top marketing executive at each firm. In all, 230 usable surveys were returned, representing a 31% response rate.
We assessed the eight marketing capabilities using new multi-item measures developed by means of insights from our fieldwork and the literature (for item sets, see the Appendix). We had pretested and modified these measures through two smaller-scale surveys before using them in this project. We measured business performance through respondents' subjective assessments of their customers' satisfaction, using a synthesis of previous measures (e.g., Fornell et al. 1996); profitability, using perceptual scales related to performance over the past 12 months and expectations for the following year (e.g., Morgan, Clark, and Gooner 2002); and market effectiveness, using a scale that tapped the degree to which the firms' market-based goals had been achieved (e.g., Vorhies and Morgan 2003). In addition, for a subset of 109 respondent firms, we were able to collect the objective data necessary to calculate return on assets (ROA) from secondary sources. To minimize the impact of any short-term unobserved events and to allow for lagged effects, we calculated the average ROA for the two-year period immediately following our primary data collection (e.g., Boulding, Lee, and Staelin 1994). Finally, we also collected data using Jaworski and Kohli's (1993) scales to control for the environmental effects of competitive intensity, market dynamism, and technological turbulence on firm performance (e.g., Menon et al. 1999).
We assessed the measurement properties of the constructs using confirmatory factor analyses (CFAs). To ensure acceptable parameter estimate-to-observation ratios, we divided the measures into three subsets of theoretically related variables (e.g., Bentler and Chou 1987). The measurement models fit well with the data as indicated by the CFA results for the eight marketing capability constructs (χ² = 761.91, 499 degrees of freedom [d.f.], p < .001; comparative fit index [CFI] = .942; root mean square error of approximation [RMSEA] = .048), the four performance constructs (χ² = 142.19, 94 d.f., p < .001; CFI = .990; RMSEA = .051), and the three environmental constructs (χ² = 63.91, 55 d.f., p < .02; CFI = .982; RMSEA = .049). We also conducted additional pairwise discriminant validity assessments by comparing CFA models in which we allowed the covariance coefficient between each possible pair of constructs to vary and then fixed it at one (Anderson and Gerbing 1988; Bagozzi and Phillips 1982). Changes in χ² were large in each case, suggesting discriminant validity in each model. Reliability analyses (Table 1) produced Cronbach's alpha values ranging from .80 to .91 for the marketing capability measures, .89 to .95 for the business performance measures, and .71 to .91 for the environmental control measures. Overall, we conclude that our measures demonstrate good measurement properties.
Tests revealed no significant differences between firstwave (early) and second-wave (late) respondents on any of the constructs, indicating that nonresponse bias is unlikely to be present in the data (Armstrong and Overton 1977). To assess whether our results are likely to be significantly affected by common method bias, we used the objective ROA data from secondary sources to validate all analyses using the perceptual measures from the primary survey data and obtained similar results. In addition, Harmon's single-factor post hoc test for common methods variance indicated no "same-source" factor in our data.( n2) Therefore, there are no indications of common method problems in our data.
Using full-information structural equation modeling (SEM), which estimates the loading from each indicant to the latent construct, we simultaneously examined ( 1) the benchmarking premise that the marketing capabilities we identified are linked with business performance and ( 2) the benchmarking process design question whether valuable interdependencies among these capabilities exist that would require that they be benchmarked as an integrated set. We estimated the eight individual marketing capabilities as first-order constructs using the relevant indicants from our survey data and estimated marketing capability interdependence as a second-order construct capturing the covariance among the eight marketing capabilities. Likewise, we estimated overall firm performance as a second-order factor comprising the three first-order latent performance factors (customer satisfaction, market effectiveness, and profitability) that we estimated using the relevant indicants from our survey data (e.g., Venkatraman 1990).
As we show in Figure 1, each marketing capability is positively and directly related to firm performance, indicating that these marketing capabilities are sources of competitive advantage and are therefore appropriate targets for benchmarking. The data also support the second-order factor representing interdependence among the eight marketing capabilities, and we find that this marketing capability interdependency factor is strongly and positively linked with firm performance. Furthermore, the indirect paths linking each marketing capability with firm performance by way of marketing capability interdependence are stronger than the direct paths from each marketing capability to firm performance. This indicates that in designing benchmarking processes for the firms in our sample, these marketing capabilities should be benchmarked as a set.
The Potential Business Performance Impact of Different Benchmarking Approaches
Having identified a set of marketing capabilities that are appropriate for benchmarking, we now turn our attention to calibrating the potential performance benefits of successfully benchmarking these marketing capabilities. In addition, we consider the impact of different benchmarking process design alternatives for searching for benchmarks within or across industries and the impact of the number of benchmark sites used in conducting capability gap assessments. The literature suggests that these process design alternatives contribute to important trade-offs in benchmarking efficiency and effectiveness. For example, for efficiency reasons some analysts advocate limiting the total number of firms included in the benchmark search, focusing only on industries closely related to that of the benchmarking firm, and minimizing the number of high-performing benchmark sites used in capability gap assessments (e.g., Spendolini, Friedel, and Workman 1999). However, given the relatively small number of major firms in many industries and the mimetic isomorphism among them, other analysts argue that limiting benchmarking searches to the firm's own industry reduces benchmarking effectiveness (Camp 1989). To maximize the probability of correctly identifying a firm exhibiting superior performance and to enhance the likelihood of gaining generative rather than adaptive insights, benchmarking analysts advocate searching for larger numbers of top-performing benchmarks across industries (Benner and Tushman 2002; Camp 1995).
We assess the potential business performance impact of benchmarking marketing capabilities and the effect of these benchmarking process design alternatives using profile deviation analysis. Profile deviation analysis involves identifying top-performing firms, calibrating the characteristics of the firms that are believed to be important in determining their superior performance as an ideal profile, and assessing the relationship between deviation from this profile and the performance outcome of interest (e.g., Drazin and Van de Ven 1985; Venkatraman 1990). Profile deviation analysis is particularly appropriate here because it is directly analogous to the search and gap-assessment stages of the benchmarking process. Therefore, it not only offers a method for assessing the performance potential of benchmarking but also, as we illustrate, may be used by managers as a sophisticated and robust methodology for conducting benchmarking in practice (e.g., Vorhies and Morgan 2003).
In the selection of benchmark sites, the literature indicates that firms that have superior both market and financial performance should be targeted (e.g., Camp 1995; Spendolini 1992). Therefore, we selected top-performing firms in our sample to serve as benchmarks on the basis of two criteria: ( 1) the firms needed to report the highest performance scores on both customer satisfaction and current profitability, and ( 2) the firms needed to be anticipating superior financial performance for the following year. We found only one firm in the data set reporting the highest possible scores on all items composing the customer satisfaction, current, and anticipated profitability scales, and therefore we selected this firm as the primary benchmark site. We were also able to track this benchmark firm's stock performance for the 18-month period immediately following our data collection (at which point it merged with another firm). Rising 202%, the stock price of our benchmark firm outperformed the index of the stock market on which it is listed (which rose by 36%) by a wide margin, providing confidence in our primary benchmark site selection.
The next step in the benchmarking process is to determine the gap between the benchmarking firm's marketing capabilities and those in the benchmark site (e.g., Camp 1995; Spendolini 1992). This requires calibrating the set of marketing capabilities of the top-performing firms as the benchmark and comparing the marketing capabilities of the remaining firms in the data set to this benchmark (e.g., Gresov 1989; Venkatraman and Prescott 1990). We accomplished this by calculating the Euclidean distance from the benchmark of all other firms in the sample across the eight marketing capability dimensions (e.g., Venkatraman 1990; Vorhies and Morgan 2003),( n3) using the following formula:
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where Xsj = the score for a firm in the study sample on the jth dimension, Xij = the mean for the ideal profile along the jth dimension, and j = the number of profile dimensions ( 1, 2, ..., 8). This provides a "profile deviation" score representing the gap between the marketing capabilities of the benchmark firm and each of the remaining firms in our sample.
To calibrate the potential performance impact of benchmarking these marketing capabilities, we regressed each firms' marketing capability profile deviation score onto its business performance. To control for the possible effects of scale economies, industry, and environmental conditions on firm performance, we also included firm size; a dummy industry variable; and the competitive intensity, market dynamism, and technological turbulence constructs in our regression analyses (e.g., Menon et al. 1999). We regressed the profile deviation score for each firm, along with the control variables, in turn onto its overall firm performance; the three individual perceptual performance measures of customer satisfaction, market effectiveness, and profitability; and the objective ROA performance data (Table 2). If benchmarking marketing capabilities has the potential to significantly improve business performance, then our results should indicate that deviation from the benchmark marketing capabilities profile is negatively and significantly related to business performance (e.g., Drazin and Van de Ven 1985; Venkatraman and Prescott 1990).
As we show in Table 2, the significant, negative coefficient for deviation from the benchmark marketing capability profile in explaining firms' overall performance (β = -.56, p < .001) clearly indicates the potential business performance benefits of benchmarking marketing capabilities. We also assessed the impact of treating each of the eight marketing capabilities as equally important by comparing the regression containing the unweighted benchmark profile deviation term with one in which each marketing capability deviation score was weighted by that marketing capability's contribution to overall business performance (calculated from the SEM results in Table 2) (Venkatraman and Prescott 1990).( n4) As we show in Table 2, the regression model containing the weighted marketing capability profile deviation term performs no better than the unweighted regression model in our data.
To assess the potential performance impact of firms identifying top performers and benchmarking marketing capabilities across industries rather than limiting their search to their own industries, we also separately identified the single top-performing firm for each of the six industry types in our sample using the same selection criteria described previously. We then used the marketing capability profile of each top performer as the benchmark for the rest of the firms within that industry type. As we show in Table 2, our regression analyses revealed a significant, negative coefficient on firms' deviation from the within-industry marketing capability benchmark profile onto their overall performance. However, the smaller profile deviation term coefficient (-.47 versus -.56) and lower R² value (.25 versus .35) indicate a greater potential performance impact of cross-rather than within-industry benchmarking of marketing capabilities.
We also assessed the impact of deviation from the cross-industry marketing capability benchmark on each performance dimension. With significant, negative profile deviation coefficients ranging from -.49 to -.44 and R2 values ranging between .21 and .27, the performance impact potential of benchmarking marketing capabilities appears to be consistent across the three dimensions of business performance. Confidence in the findings is enhanced by the regression results using the objective ROA data obtained for 109 firms in our sample as the performance dependent. The coefficient for the deviation term (-.43) and R² value (.22) in the ROA regression are very much in line with the values we observed when using the perceptual indicators of business performance as dependents.
To provide empirical insights regarding appropriate numbers of top-performing benchmark sites, we also examined the impact of deviation from the marketing capability profile of single-versus multiple-benchmark sites. We accomplished this through a sensitivity analysis in which we gradually relaxed the selection criteria for top-performers and used breakpoints observed in the performance data to identify benchmark groups containing different numbers of top-performing firms (e.g., Venkatraman 1990). For each benchmark group, we calibrated the mean value of each of the eight marketing capabilities as the benchmark marketing capability profile. The regressions in Table 3 show the impact on the profitability performance dependent of using larger numbers of top-performing firms (N = 5, 8, and 16) as marketing capability benchmark sites compared with using the single top-performer benchmark (N = 1).( n5) The results for the single versus top-five and top-eight performing firms are very similar in terms of R2 (.21 versus .20 versus .19) and the impact of marketing capability profile deviation (β = -.44 versus -.43 versus -.42). However, as more firms are added to the benchmark group (N = 16), overall model fit (R2 = .16) and the impact of marketing capability profile deviation (β = -.39, p < .001) decline.
For comparison purposes, we also randomly selected five firms in which the level of business performance was unknown and used these firms to calibrate a "nonbenchmark baseline" marketing capability profile (e.g., Venkatraman and Prescott 1990; Vorhies and Morgan 2003). The low R² (.03) and insignificant coefficient for deviation term in the random baseline profile regression shown in Table 3 provide additional confidence in our profile deviation results (e.g., Venkatraman and Prescott 1990).
Finally, we also examined the marketing capability profiles of the top-performing firms used as the benchmarks in our study (Table 4). The single top-performing benchmark firm rated six of its eight marketing capabilities with the highest possible score (the pricing and market information management capabilities both scored six on the seven-point scale). In line with the results of our sensitivity analysis, the profiles indicate that the marketing capability mean scores tend to trend downward in a linear fashion as more firms are added to the benchmark group. The rate of decline ranges from selling capabilities, which appear to decline least quickly, to marketing communications capabilities, which appear to decline the most quickly in our sample. Overall, consistent with the predictions of RBV theory, each of the top-performing benchmark groups exhibit marketing capability scores that are much higher than the mean for either the sample as a whole or the random baseline group (Table 4). In addition, the high marketing capability scores for each of the benchmark groups and the general trend downward as lower-performing firms are added to the benchmark groups also provide additional confidence in the validity of the approach we used to select appropriate benchmark sites.
Our findings support the previously untested central premise of normative benchmarking theory--that marketing capabilities associated with superior business performance can be identified and that the marketing capability gap between top-performing benchmarks and other firms explains significant variance in business performance. Our SEM results indicate that the eight marketing capabilities we identify are associated with business performance and are therefore appropriate for benchmarking. Furthermore, the firms most closely matching the benchmark marketing capability profile in our sample significantly outperformed firms that were less similar to the benchmark in customer satisfaction, market effectiveness, profitability, ROA, and overall firm performance (Table 2). Sensitivity analyses using different benchmarks and the insignificant results using a random baseline benchmark (Table 3) indicate that these relationships are robust.
The significant, negative coefficients for deviation from the benchmark marketing capability profile and the variance accounted for in each of the business performance dependents in our regressions provide a calibration of the potential business performance benefits of successfully benchmarking marketing capabilities. Our results indicate the value of a benchmarking process in which managers search among competitors and peers in other industries to identify the marketing capability drivers of superior performance and assess and monitor these capabilities within their own firms. From a "what gets measured gets done" perspective, successfully completing these search and gap-assessment stages of the benchmarking process can help bring about successful marketing capability improvement (e.g., Day 1994; Teece, Pisano, and Shuen 1997). By focusing on the capability sources of competitive advantage, rather than just observed outcomes, and using competitors and peers as referents, such benchmarking provides an important component of a comprehensive marketing control system (e.g., Day and Wensley 1988; Morgan, Clark, and Gooner 2002). When this benchmarking is an ongoing process, it helps managers plan and monitor the outcomes of their capability improvement efforts and thereby aids continuous improvements in the firm's marketing capabilities (Camp 1995).
This approach to benchmarking can also enable transformative marketing capability changes. By providing a continuous and structured process for directing managerial attention externally to competitors and peers and by reaching a shared interpretation of the marketing capabilities required to achieve superior performance, benchmarking can deliver important generative organizational learning insights (e.g., Camp 1989; Day 1994; Slater and Narver 1995). It can also trigger and guide more detailed investigations of specific marketing capabilities in particular benchmark sites (Camp 1995). This is not purely imitative learning, because it is difficult to replicate the capabilities of a benchmark firm exactly (Dickson 1992). Furthermore, the marketing capability interdependency we identify means that even if capabilities are perfectly replicated, improvements in one marketing capability will likely have an impact on a firm's remaining capabilities. Benchmarking can therefore lead to novel changes in a firm's marketing capability stock, which both enables experimentation and innovation and increases heterogeneity between firms (e.g., Haunschild and Miner 1997). Therefore, it is likely to be beneficial not only for the benchmarking firm and its customers but also for the economy (Dickson 1992).
Implications for Marketing Theory
Our study has three primary implications for marketing theory. First, from an RBV perspective, we provide new insights by identifying and directly measuring eight distinct marketing capabilities and linking these with business performance in a cross-industry sample. More important, our results offer the first empirical support for the existence and performance impact of interdependency among individual marketing capabilities. This indicates that the firms in our sample have not established superiority in only one or a small number of marketing capabilities. Theoretically, such interdependency may make marketing capabilities a more inimitable resource and therefore a greater potential source of competitive advantage (Barney 1991). Interdependency among marketing capabilities also suggests that in allocating scarce capability improvement resources, managers should be careful not only to consider individual marketing capabilities as separate investment options but also to assess the implications of such investments for the firm's overall set of marketing capabilities. Therefore, our findings indicate that strategic marketing theory explanations of firm performance should more explicitly consider the interdependence among multiple marketing capabilities.
Second, in calibrating the significant potential business performance benefits available from successfully benchmarking marketing capabilities, our study contributes to the market orientation literature by empirically supporting propositions that market-based learning should include learning from competitors and peers (e.g., Day 1994; Slater and Narver 1995). Because we benchmark the firms in our sample with the highest customer satisfaction performance, our benchmark marketing capability profiles also provide new empirical insights into the capabilities required by market-driven firms. These profiles support the proposition that market-oriented firms require strong marketing capabilities (Day 1994); the profiles also reveal that strength across a range of marketing capabilities--and not just in market information management--is required to deliver superior customer satisfaction and business performance. Overall, our findings indicate that benchmarking should be an important tool in managers' efforts to create market-oriented firms (Day 1994; Slater and Narver 1995).
Third, our study also contributes to the literature on organizational learning. Although benchmarking has been posited as a valuable tool for market-based learning (DiBella, Nevis, and Gould 1996; Leonard-Barton 1995; Teece, Pisano, and Shuen 1997), ours is among the first studies to calibrate its potential performance benefits empirically. In addition, the larger profile deviation coefficients and greater variance explained in business performance when using across-industry versus within-industry top performers as benchmarks indicate that where an organization learns from affects the potential value of what it may learn. In line with Nelson and Winter's (1982) evolutionary perspective of viewing routines (the subprocesses on which capabilities are built) as the "genes" of an organization, our results suggest the intriguing possibility that learning from peers in other industries represents the potentially transformative value of "gene splicing" (Dickson 1992).
Implications for Managers
In addition to verifying the potential value of benchmarking marketing capabilities, our study reveals new insights into how managers can benchmark marketing capabilities to achieve sustainable competitive advantage. First, we demonstrate how profile deviation can be used as a tool for undertaking the search and gap-assessment stages of benchmarking. Profile deviation enables managers to calibrate the value potential of improving individual marketing capabilities or sets of them. It is also flexible enough to allow the benchmarking of more content-focused areas, such as marketing organization design (e.g., Vorhies and Morgan 2003), or hybrid content-and process-related phenomena, such as the marketing capabilities of top-performing firms in specific industries or strategy-type groups or even those of highly market-oriented firms. In practice, benchmarking consortia such as those organized by the American Productivity and Quality Center and others may provide members with samples that may be suitable for using profile deviation to benchmark marketing capabilities. For example, using standardized measures of various marketing processes, the United Kingdom's Chartered Institute of Marketing is developing a database of the marketing capabilities of medium-sized firms (Woodburn 1999).
Second, in benchmarking marketing capabilities in practice, our study provides new insights relevant to each of the three stages of the benchmarking process:
- Search stage. Our benchmark marketing capability profiles support the use of customer satisfaction and profitability criteria to select top-performing firms to serve as benchmarks. Our marketing capability measures also provide a starting point for benchmarking search efforts that may be useful in firms' monitoring of their capabilities compared with those of competitors and peers and with their own prior performance (Day and Wensley 1988; Morgan, Clark, and Gooner 2002). In addition, our results indicate the potential value of cross-industry benchmark searches and the use of single or small groups of top-performing firms as marketing capability benchmarks. This supports the practice of benchmarking cross-industry "best in class" capabilities in single-benchmark sites such as Wal-Mart's logistics capabilities and Toyota's new product development process (e.g., Camp 1995). Although these findings are specific to our sample, by using the SEM and profile deviation approaches we illustrate that managers can assess for themselves the likely impact of such benchmarking search alternatives in the context of their own industries, strategies, and capability focuses.
- Gap-assessment stage. Profile deviation also provides a tool for measuring marketing capability gaps between the benchmark and other firms and for linking the size (deviation) and composition (capability interdependence and weighting) of the capability gap to business performance. The value of marketing capability interdependence revealed in our data suggests that among the firms in our study, the eight marketing capabilities we examine should be benchmarked as a set. Our results further indicate that for these firms, weighting the individual capabilities by their impact on business performance may not add much insight beyond that provided by an unweighted model. However, managers using profile deviation analysis can assess for themselves the extent to which this is true for the capabilities on which they are focused and the set of firms in their benchmark search set. In an example of this in practice, Touchstone Energy, guided by the insights provided in our study, is currently using profile deviation as a benchmarking tool to assess the size, composition, and performance implications of customer satisfaction monitoring capability gaps between top customer satisfaction performers and others among its more than 600 electric co-op members.
- Capability enhancement stage. Firms often allocate scarce marketing capability improvement resources to individual capabilities that are internally perceived to be weak and benchmark firms in which this capability is believed to be strong (e.g., Andriopoulos and Gotsi 2000; Woodburn 1999). In contrast, our study indicates that managers should attempt first to identify the marketing capability drivers of superior business performance and then to assess the relevant capability gap between themselves and top-performing benchmarks. Assessments of the relative performance of weighted and unweighted profile deviation models can then indicate where managers should allocate their capability improvement resources. For example, our results show that in our data set, firms should prioritize marketing capabilities where their current profile is the weakest relative to the benchmark in allocating their capability improvement resources.
When managers have determined which capability improvements will likely yield the greatest return, the literature suggests that they should then communicate and discuss benchmarking findings within the firm to develop a common understanding, use process mapping tools to conduct more detailed investigations of the target capabilities in the benchmark sites, agree on specific capability improvement goals, develop and execute detailed capability improvement action plans, and monitor outcomes using market and cost feedback to enhance initial capability improvements further (e.g., Camp 1995; Day 1994; Dickson 1992; Zairi 1998). This approach was used by IBM to improve its "market management" capability after a cross-industry benchmarking study in the early 1990s. This resulted in a radically new market management process design that IBM believed was superior to those of its competitors. Consistent with our capability interdependency finding, the improved market management capability had important ripple effects in changing and enhancing IBM's selling, pricing, and product management capabilities. In combination, these capability improvements transformed the way IBM addressed its markets and helped create a more market-driven organization.
Several limitations in our study result from trade-off decisions in our research design. First, guided by our fieldwork, the literature, and our SEM results, we benchmark eight specific mid-level marketing capabilities. This precluded any assessment of higher-level integrative marketing capabilities such as brand management and customer relationship management that might usefully be examined by future researchers (e.g., Day 1994; Grant 1996). Second, although we control for several factors in our analyses, we were unable to collect data to control for firms' other organizational capabilities (e.g., research and development). As more parsimonious capability measures are developed, researchers may be better able to control for such differences among firms in further research. Third, because we examine eight different marketing capabilities, our capability measures are relatively broad and use standard activity performance-level indicants. Inevitably, this results in a relative lack of depth of understanding of any single marketing capability. Future researchers should focus on developing more fine-grained measures of individual marketing capabilities (e.g., Dutta, Narasimhan, and Rajiv 2003) and examine the potential of more novel process measurement approaches such as process mapping (e.g., Day 1994; Keen 1997).
Beyond these limitations, our study indicates three important new areas for further research. First, having calibrated the potential value of the search and gap-assessment stages of benchmarking, we believe that additional research is required on the capability improvement stage. A particularly useful area for such research is enhancing the understanding of the specific stages of the subprocesses underlying individual marketing capabilities and illuminating how movement between these stages is successfully accomplished. This may provide insights that would enable managers to diagnose better the capabilities of a benchmark site and plan detailed capability improvement actions required in their own firm. A related issue arising from our findings is the potential for trade-offs between the novel capability insights that may result from benchmarking top-performing peers in other industries versus managers' ability to translate these novel insights into valuable capability improvements within the benchmarking firm. The relationship between different search and gap-assessment marketing capability insights available from competitors versus peers in other industries and the resultant capability improvements and outcomes under different conditions should be a priority for further research.
Second, whereas our study indicates the potential value of using competitor-or peer-focused benchmarking of marketing capabilities to enhance firms' customer satisfaction performance, Ettlie and Johnson (1994) report that in a new product context there may be trade-offs between using such competitor-or peer-focused benchmarking tools and using more directly customer-focused tools such as Quality Function Deployment. Furthermore, Nilsson, Johnson, and Gustafsson (2001) report that orientations toward both satisfying employees and process management are required for firms to achieve a customer orientation and superior performance. This raises the important question whether in a marketing capabilities context there are trade-offs between the competitor-or peer-focused benchmarking supported in our research and the customer-and employee-oriented approaches and tools supported in prior research. If there are, what balance of customer, competitor or peer, and internal orientations is required to maximize business performance under different conditions?
Third, although our study provides insights into the benchmarking of marketing capabilities, it does not address how firms should develop, deploy, and enhance their higher-order benchmarking capability. Other than Nilsson, Johnson, and Gustafsson's (2001) work on process orientation, we know little about how firms deal with these issues (DiBella, Nevis, and Gould 1996). For example, organizational learning theory indicates that the embedded learning represented in a firm's capabilities can constrain both the motivation and the ability to generate and respond to benchmarking insights (Dickson, Farris, and Verbeke 2001; Tripsas and Gavetti 2000) and that firms need to ensure that they balance their exploration knowledge development and exploitation knowledge deployment efforts (March 1991). However, we have little understanding of the types of organizational culture and benchmarking process designs that prevent marketing capability inertia and help firms balance continuous marketing process improvement with "bedding down" and routinization. Similarly, although theory indicates that benchmarking is an important higher-order "learning to learn" capability, we have no empirical insights into how firms can best develop and enhance such capabilities (e.g., Dickson 1992; Winter 2000).
Although benchmarking has been identified as a key market-based learning tool, there has been little empirical evidence either to support normative calls to benchmark marketing capabilities or to guide managers' benchmarking efforts. Our findings support normative benchmarking theory, indicating that marketing capabilities associated with superior business performance can be identified and that the marketing capability gap between a firm and top performing benchmarks explains significant variance in business performance. Our study also illustrates the utility of SEM and profile deviation as benchmarking tools and offers new insights into how managers can benchmark marketing capabilities to achieve sustainable competitive advantage.
The authors gratefully acknowledge insightful comments and suggestions in the development of this article from William D. Perreault Jr. and Paul Root.
( n1) We do not suggest that these are the only marketing capabilities worth benchmarking--merely that these capabilities are both easily distinguished and identifiable by managers and have support in the literature as being potentially valuable determinants of business performance.
( n2) Comparison of a single-factor confirmatory model (CFI = .42, RMSEA = .11) with a 14-factor confirmatory model (CFI = .92, RMSEA = .04) yields a χ² difference equal to 3885.81, 91 d.f., p < .001.
( n3) We use the averages of each firm's score on the items composing each marketing capability. A comparison using item-factor coefficients indicated that the profile deviation results obtained using the simple averages are robust.
( n4) We are grateful to an anonymous reviewer for this suggestion.
( n5) Analyses using the other perceptual performance measures and objective ROA produced similar results.
Construct Means, Alphas, and Correlations
Legend for Chart:
B - Mean (S.D.)
C - X1
D - X2
E - X3
F - X4
G - X5
H - X6
I - X7
J - X8
K - X9
L - X10
M - X11
N - X12
O - X13
P - X14
Q - X15
R - X16
A B C D
E F G
H I J
K L M
N O P
Q R
X1 Product development
capabilities 4.74 (1.13) .80
X2 Pricing capabilities 4.72 (.84) .29(***) .83
X3 Channel management
capabilities 4.89 (1.21) .31(***) .29(***)
.90
X4 Marketing communication
capabilities 4.25 (1.15) .37(***) .18(**)
.25(***) .84
X5 Selling capabilities 4.71 (1.09) .38(***) .42(***)
.52(***) .35(***) .90
X6 Market information
management capabilities 4.47 (1.14) .55(***) .40(***)
.38(***) .54(***) .49(***)
.86
X7 Marketing planning
capabilities 4.56 (1.16) .46(***) .36(***)
.37(***) .63(***) .63(***)
.38(***) .91
X8 Marketing implementation
capabilities 4.61 (1.15) .45(***) .40(***)
.40(***) .41(***) .57(***)
.56(***) .68(***) .91
X9 Profitability 4.86 (1.29) .31(***) .07
.26(***) .11(*) .39(***)
.26(***) .43(***) .37(***)
.95
X10 Customer satisfaction 5.45 (1.06) .41(***) .04
.37(***) .20(***) .42(***)
.37(***) .38(***) .47(***)
.38(***) .91
X11 Market effectiveness 5.15 (1.11) .34(***) .06
.32(***) .19(***) .37(***)
.32(***) .37(***) .32(***)
.58(***) .49(***) .89
X12 ROA .09 (.17) .25(***) .23(**)
.14 .04 .14
.25(***) .23(**) .21(**)
.35(***) .20(**) .22(**)
N/A
X13 Firm size 391(2045) .01 -.05
-.07 .08 -.07
.04 .02 -.03
-.08 -.16(**) -.10
-.10 N/A
X14 Competitive intensity 4.22 (1.36) -.08 .15(**)
.12(*) -.10 .10
-.07 -.01 .04
.01 -.01 -.01
.23(**) -.04 .83
X15 Market dynamism 3.76 (1.19) .06 .01
.06 -.08 -.04
-.07 .02 .05
.07 .04 .06
.17(*) -.14(**) .21(***)
.71
X16 Technological turbulence 4.63 (1.51) .02 -.11
-.03 .02 -.16(**)
.01 .04 -.04
.12(*) .03 .07
-.01 -.02 -.15(**)
.30(***) .91
(*) p< .10.
(**) p< .05.
(***) p< .01.
Notes: Alphas are shown in bold on the correlation matrix
diagonal. S.D. = standard deviation, N/A = not applicable. Regression Results for Deviation from Single Top Performer
Benchmark (N = 1)
Legend for Chart:
B - Cross-Industry Overall Performance (Unweighted)
C - Cross-Industry Overall Performance (Weighted)
D - Within-Industry Overall Performance (Unweighted)
E - Customer Satisfaction
F - Market Effectiveness
G - Profitability
H - Two-Year Average ROA
A B C D
E F G
H
Deviation from benchmark -.56(**) -.56(**) -.47(**)
-.49(**) -.44(**) -.44(**)
-.43(**)
Competitive intensity -.02 -.01 -.04
-.03 -.02 -.01
-.02
Market dynamism .01 .02 .05
.01 .02 .01
.12
Technological turbulence .12(*) .13(*) .05
.05 .10 .16(*)
-.05
Firm size (log) -.13(*) -.13(*) -.18(**)
-.15(**) -.09 -.08
-.08
Industry .01 .01 .03
.01 -.01 -.01
.12
R² .35 .34 .25
.27 .21 .22
.22
F-value 19.56 19.21 11.89
13.66 9.78 10.06
4.82
Number of firms(a) 229 229 224
229 229 229
108
(*) p< .05.
(**) p< .01.
(a) Total less benchmark firm. Profitability Regressions for Deviation from Different
Top-Performing Benchmark Groups
Legend for Chart:
A - Number of Benchmark Firms
B - N = 1
C - N = 5
D - N = 8
E - N = 16
F - Random Baseline(a)
A B C D
E F
Deviation from benchmark -.44(*) -.43(*) -.42(*)
-.39(*) -.11
Competitive intensity .01 -.01 .01
-.01 -.02
Market dynamism .01 -.02 .01
.02 .02
Technological turbulence .16(*) .17(*) .17(*)
.16(*) .11
Firm size (log) -.08 -.03 -.02
-.01 -.02
Industry -.01 -.02 -.01
-.02 -.01
R² .21 .20 .19
.16 .03
F-value 9.89(*) 8.93(*) 8.20(*)
6.49(*) 1.13
Number of firms(b) 229 225 222
214 225
(*) p< .01.
(a) Profile of N = 5 randomly selected firms; we obtained
similar results for alternative N = 8 and N = 16 random
profiles.
(b) Total less benchmark firms. Marketing Capability Profiles of Top-Performing Benchmark Groups
Legend for Chart:
A - Marketing Capability Area
B - Number of Firms in Top-Performing Group N = 1
C - Number of Firms in Top-Performing Group N = 5
D - Number of Firms in Top-Performing Group N = 8
E - Number of Firms in Top-Performing Group N = 16
F - Profile of Entire Sample N = 230
G - Random Baseline Profile N = 5
A B C D
E F G
Pricing capabilities 6.00 6.25 6.13
5.78 4.72 4.90
Product development capabilities 7.00 6.15 6.16
5.95 4.75 4.30
Distribution capabilities 7.00 6.40 6.28
5.98 4.89 4.80
Marketing communication
capabilities 7.00 5.40 5.03
4.95 4.25 4.65
Selling capabilities 7.00 6.52 6.40
6.04 4.71 5.12
Market information
management capabilities 6.00 5.84 5.75
5.40 4.47 4.12
Marketing planning capabilities 7.00 6.45 6.06
5.84 4.56 4.25
Marketing implementation
capabilities 7.00 6.15 6.09
5.83 4.61 4.63 Second-Order SEM and Results
Legend for Chart:
A - Paths modeled:
D - Coefficient
E - t-Value
A
B C D E
Product development
← Overall firm performance .27 2.55
Pricing
← Overall firm performance .33 3.50
Channel management
← Overall firm performance .23 2.76
Marketing communications
← Overall firm performance .34 3.18
Selling
← Overall firm performance .35 2.80
Market information management
← Overall firm performance .34 1.71
Marketing planning
← Overall firm performance .43 2.87
Marketing implementation
← Overall firm performance .48 2.92
Product development
← Capability interdependence .62 8.27
Pricing
← Capability interdependence .51 6.52
Channel management
← Capability interdependence .50 7.13
Marketing communications
← Capability interdependence .72 9.37
Selling
← Capability interdependence .73 10.64
Market information management
← Capability interdependence .84 10.92
Marketing planning
← Capability interdependence .90 14.84
Marketing implementation
← Capability interdependence .82 12.51
Market effectiveness
← Overall firm performance .78 8.33
Profitability
← Overall firm performance .68 7.93
Customer satisfaction
← Overall firm performance .67 7.34
Capability interdependence
← Overall firm performance .67 7.41
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Marketing Capabilities and Performance Scales
Please rate your business unit relative to your major competitors
in terms of its marketing capabilities in the following areas.
Seven-point scale running -3 ("much worse than competitors") to
+3 ("much better than competitors").
Pricing Using pricing skills and
systems to respond quickly
to market changes
Knowledge of competitors'
pricing tactics
Doing an effective job of
pricing products/services
Monitoring competitors' prices
and price changes
Product development Ability to develop new
products/services
Developing new products/services
to exploit R&D investment
Test marketing of new
products/services(a)
Successfully launching new
products/services
Insuring that product/service
development efforts are
responsive to customer needs
Channel management Strength of relationships
with distributors
Attracting and retaining the
best distributors
Closeness in working with
distributors and retailers(a)
Adding value to our distributors'
businesses
Providing high levels of
service support to distributors
Marketing communication Developing and executing
advertising programs
Advertising management and
creative skills
Public relations skills
Brand image management skills
and processes
Managing corporate image and
reputation
Selling Giving salespeople the training
they need to be effective
Sales management planning and
control systems
Selling skills of salespeople
Sales management skills
Providing effective sales
support to the sales force
Market information Gathering information about
management customers and competitors
Using market research skills
to develop effective marketing
programs
Tracking customer wants and
needs
Making full use of marketing
research information
Analyzing our market information
Marketing planning Marketing planning skills
Ability to effectively segment
and target market
Marketing management skills
and processes
Developing creative marketing
strategies(a)
Thoroughness of marketing
planning processes
Marketing implementation Allocating marketing resources
effectively
Organizing to deliver marketing
programs effectively
Translating marketing strategies
into action
Executing marketing strategies
quickly
Monitoring marketing
performance(a)
Customer satisfaction Customer satisfaction
Delivering value to your
customers
Delivering what your customers
want
Retaining valued customers
Market effectiveness Market share growth relative
to competitors
Growth in sales revenue
Acquiring new customers
Increasing sales to existing
customers
Performance: Please evaluate the performance of your business
over the past year (the next twelve months) relative to your
major competitors. Seven-point scale running -3 ("much worse
than competitors") to +3 ("much better than competitors").
Current (anticipated) Business unit profitability
profitability
Return on investment (ROI)
Return on sales (ROS)
Reaching financial goals
(a) Items deleted during scale purification.~~~~~~~~
By Douglas W. Vorhies and Neil A. Morgan
Douglas W. Vorhies is Assistant Professor of Marketing, School of Business Administration, University of Mississippi
Neil A. Morgan is Assistant Professor of Marketing, Kenan-Flagler Business School, University of North Carolina, Chapel Hill
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Record: 22- Beyond Adoption: Development and Application of a Use-Diffusion Model. By: Chuan-Fong Shih, Alladi; Venkatesh. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p59-72. 14p. 1 Diagram, 8 Charts. DOI: 10.1509/jmkg.68.1.59.24029.
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Beyond Adoption: Development and Application of a
Use-Diffusion Model
The study tests a use-diffusion model in the context of home technology use. The authors combine two constructs, variety and rate of use, to yield four user segments. The results show that user segments vary on the basis of social context and technological makeup of the household as well as personal factors and external influences. Furthermore, user segments differ with regard to users' satisfaction with technology and interest in acquiring future technologies.
In the field of marketing, research on new product diffusion has traditionally focused on the adoption perspective (Dickerson and Gentry 1983; Mahajan and Muller 1979; Midgley and Dowling 1978; Rogers 1995). We label this the adoption-diffusion (AD) paradigm, which examines the process by which an innovation reaches a critical mass of adopters, the diffusion is accelerated, and innovation is considered successful (Mahajan, Muller, and Bass 1990). Recently, researchers have pointed out the limitations of the AD model, stating that though diffusion processes cannot be understood without studying the nature of adoption, to complete the diffusion story, use-diffusion (UD) processes also need to be examined (Anderson and Ortinau 1988; Golder and Tellis 1998; Lewis and Seibold 1993; Roberston and Gatignon 1986).( n1) This is especially true of some consumer technologies for which their complexity and evolving nature indicate that the trajectory and time scale of diffusion can be quite prolonged. Accordingly, our current research extends the diffusion concept further with a systematic study of postadoption UD. We believe that the UD framework offers some motivating theoretical and managerial implications for ongoing research in this area and for market segmentation and product development. The specific research questions in this study are as follows: ( 1) What are the characteristics of UD? ( 2) What are the determinants and outcomes of UD? ( 3) Given that users might display different use patterns in terms of variety and rate of use, how can users be classified into meaningful categories? and ( 4) How can the results of our study of UD provide insights or input into market segmentation and new product strategies? To address these research questions, we propose and test a model of UD that can be applied to a range of consumer durables with multiple applications. Examples include, but are not limited to, computer hardware and software, wireless telephones, personal data assistants, home entertainment systems, videogame consoles, and "smart" home appliances.
Before we present the theoretical UD model, we briefly discuss the underlying structural differences between AD and UD (see Table 1). The variable of interest in the AD model is rate or time of adoption; in the UD model, the variable of interest is use or, more specifically, rate of use and variety of use.
The theoretical considerations for the AD model include an S-shaped diffusion curve, speed of penetration and critical mass, and a two-step model of diffusion. The corresponding theoretical elements for the UD model are the evolving nature of use (rate and variety), sustained continuous use (or disadoption), and technology outcome considerations (technology integration, perceived essentialness of technology, impact of technology, and user proneness to adopt new technologies).
The standard diffusion theory states that the diffusion curve is divided into three stages: ( 1) introduction, ( 2) growth, and ( 3) maturity. The theory further states that along the diffusion curve lies a typology of adopters (innovators, early adopters, early majority, late majority, and laggards). In the UD model, the proposed categories (which we elaborate on in the next section) are intense users, specialized users, nonspecialized users, and limited users.
The models share some common constructs (innovativeness, social communication, complexity, media influence, and relative advantage); however, these constructs may not be identical in their content. For example, adopter innovativeness is not the same as use innovativeness (Price and Ridgeway 1983). There are also criteria that differentiate the models. Unique to the AD model are observability, compatibility, and trialability (Rogers 1995), whereas the UD model includes experience with technology (positive and negative), competition for use (among multiple users), sophistication of technology, and satisfaction from use.
A Conceptual UD Model
The conceptual model that guides our work is presented in Figure 1. The model has three key components: ( 1) UD determinants, ( 2) UD patterns, and ( 3) UD outcomes. Each of these components has some subcomponents. Although all three components are integral to our model, UD patterns (i.e., the typology of uses or, derivatively, typology of users) occupy a special place in the model because of the key role they play in UD. We therefore begin with use patterns.
UD Patterns
We conceptualize usage as comprising two distinct dimensions: variety of use and rate of use. Variety of use refers to the different ways the product is used. Usage rate refers to the time a person spends using the product during a designated period. Although it is conceivable that variety and use are correlated (i.e., the higher the variety, the higher the rate is), their exact relationship has not been empirically examined. However, Ram and Jung (1990) and Ridgeway and Price (1994) note that a higher rate without a higher variety of use may signal routinized needs. Furthermore, different antecedent factors may influence rate and variety of use, and thus they should be treated as distinct.
The combination of variety (low/high) and rate (low/ high) yields a fourfold typology of use or users (Figure 1): intense, specialized, nonspecialized, and limited.( n2) Intense use describes situations in which an innovation is used to a significant degree in terms of both variety of use (time spent per week) and rate of use (number of applications). With specialized use, the focus shifts to increasing rate of use. Such usage behavior essentially treats the innovation as a specialized tool (Tinnell 1985). Nonspecialized use refers to a pattern of use in which variety of use is more critical than rate of use. Such a pattern best (though not exclusively) describes usage based on trial and error. Finally, limited use refers to low variety of use and low rate of use; that is, users find little, if any, worthwhile application potential and therefore relegate the product to a relatively minor role, even to the point of "disadoption" (Lindolf 1992).
Note that the fourfold typology we have put forward has face validity because it distinguishes four types of users on the basis of use characteristics, which is the focus of our study. Note also that at any given time, the typology represents mutually exclusive sets of user segments (i.e., a user can belong to only one category); however, user identities need not be fixed over time (i.e., users can move from one category to another given their needs and other situational factors). For example, an intense user may settle down to a routine of limited use over time and thus be reclassified as a specialized user. Such movements between segments are not uncommon in a marketing context, as is noted in psychographic research in which people undergo lifestyle changes and move from one category to another.
We present four dimensions that may affect the patterns of use we have described: ( 1) the household social context in which the user operates, ( 2) the technological dimension (characteristics associated with the innovation), ( 3) the personal dimension (e.g., use innovativeness), and ( 4) external factors (external communication and media exposure).
Rate of use represents temporal patterns of use and depends on the user's specific needs at a given point in time and is less influenced by interactions with others. If a person needs to do more of the same thing that he or she has been doing, that person knows the basis of his or her activity and has less reason to communicate with others. However, if the person is engaged in different activities in connection with technology, he or she is probably exploring different possibilities and thus may want to communicate with others because of limits to his or her own knowledge. Variety of behavior implies a process of discovery and thus offers a greater scope for innovativeness. We place variety of use on a slightly higher plane than rate of use because it represents a more complex behavior and involves a higher cognitive effort. This distinction has motivated the way we postulate different hypotheses in our study.
Household Social Context
The marketing literature recognizes the importance of the social context as a key aspect of diffusion (Fisher and Price 1992). The household social context consists of three variables: ( 1) household communication, ( 2) competition for limited resources, and ( 3) prior experience with technology.
Household communication. Literature on diffusion of innovation has always stressed the importance of interpersonal communication (e.g., Kraut et al. 1998; Valente 1995); Dodson and Muller (1978) have shown the key role of word-of-mouth communication in the diffusion process. Communication can be quite intensive in close-knit groups (e.g., families) (Reingen and Kernan 1986). When the user can discuss questions with others, particularly with more knowledgeable users, information can be quickly exchanged to overcome difficulties in using the technology (Kiesler et al. 2001). In contrast, when users are unable to resolve a situation alone, they may be discouraged and either limit the amount of time spent on the technology or abandon it altogether. In addition, it is not the mere existence of communication in the social network that is influential; the level of interaction or intensity of communication also plays a significant role (Blonski 1999; Wasserman and Faust 1994). Although communication is central to usage behavior, its role as applied to rate of use is limited because people are less likely to want to communicate when they are doing the same thing over and over again. When people attempt different things in connection with the technology, there may be a greater need to communicate or consult with others for assistance. Thus:
H[sub1]: Higher intensity of communication with other users about the product leads to higher variety of use.
Competition for limited resources. Tensions arise because of possible claims to resources that are not available to all members of a social network at all times. Daly (2001, p. 290) calls this "the presence of negative valence." Salazar (2000) has found that family members negotiate social boundaries in sharing technological space and that electronic and space boundaries are instrumental in determining who uses the computers and when. In Lee's (2000) work on WebTV, he found that family members, especially children, compete among themselves to watch WebTV. By implication, we argue that limited technological resources have a negative impact on collective use of the technology. However, competition for resources implies access to technology, and access is limited by the amount of time a person can spend with the technology. People do not necessarily compete for how to use the technology (variety), but for how much time to allocate in using the technology (rate). Thus, competition affects rate of use, not variety of use.
H[sub2]: Competition for technology in the household results in lower rate of use.
Prior experience. In addition to interpersonal dynamics, the updating of users' knowledge may be a relevant variable in predicting the direction of UD. The complexity of technology suggests that user knowledge plays a critical role in shaping the UD pattern (Alba and Hutchinson 1985; Kiesler et al. 1997, 2001; Norman 1999). Technologies change rapidly; thus, users need to update their knowledge to reach a more advanced level of use. User knowledge can come from accumulated experience, which provides users with the skills they need to recognize situations in which the technology can be applied and how to apply it. Hahn and colleagues (1994) refer to positive effects of "direct product experience" as a key precondition for triers (adopters) to become repeaters. Thus, when people gain greater experience with technology, they have developed some efficiencies and perceive themselves as dependent on the technology for their continued use. At the same time, people's experience teaches them to become more familiar with the technology and its different possibilities, which positively affects both rate and variety of use. Therefore, we hypothesize the following:
H[sub3]: Higher accumulated product experience results in higher variety and rate of use.
Technological Dimension
The technological dimension consists of two variables (technological sophistication and use of complementary technologies) and refers to the overall technological environment.
Technological sophistication. Technological sophistication includes the inherent characteristics of the technology, that is, its versatility and capabilities. Technology can be sophisticated without being difficult to use. For example, compared with automobiles of 25 years ago, automobiles today are more technologically sophisticated, but they are also easier to drive. The capabilities of the system define the boundaries of what users can do with it. In general, we expect users with access to more-advanced systems to exhibit a greater variety of use. At the same time, we believe those users are comfortable with the level of technological sophistication and will want to spend more time with the technology, leading to higher rate of use. Thus, we hypothesize the following:
H[sub4]: Use of more-advanced technology (i.e., technological sophistication) results in higher variety and rate of use.
Complementary technologies (household technological density). Use of any technology must take into consideration the use of other technologies in the home; we refer to this as the "technological density of the home." Rogers (1995, p. 224) originally proposed this idea, though he refers to it as "technology clusters." According to Rogers, the existence of technology clusters influences the rate of adoption of new innovations. Vitalari, Venkatesh, and Gronhaug (1985) proposed and tested a similar idea under the rubric of cognate technologies, which share similar characteristics and can be either competing or complementary. In general, competing technologies act as substitutes, and a new technology can displace an existing technology if its performance is superior or more acceptable. This idea is similar to the notion of relative advantage, which is commonly cited in the diffusion literature. Complementary technologies create synergistic effects; that is, they increase the level of use of all the technologies in the cluster. Technology clusters introduce new possibilities for technology use and the ability to exploit the technology in different ways (variety).
H[sub5]: Use of other complementary technologies results in higher variety of use of the specific technology in question.
Personal Dimension
Use innovativeness. The effect of personal variables on usage behavior is an area that is much investigated in previous research. Hirschman (1980) proposes inherent novelty seeking as an antecedent of usage variety, and Price and Ridgeway (1983) develop the use innovativeness scale, which taps into the concept of inherent novelty seeking. Price and Ridgeway hypothesize that for consumers to use existing products in multiple novel ways, they must have the ability (creativity) and the incentive (curiosity) to do so. Ram and Jung (1990) proceed along the same lines and further suggest that involvement and innovativeness have a positive relationship with usage variety, though the effect of involvement is lower in comparison. Consumers' being innovative means being experimental and having an inclination to try different things. Thus, innovativeness has a direct link to variety of use. On the basis of previous theory and empirical work, we hypothesize the following:
H[sub6]: Higher use innovativeness results in higher variety of use.
Frustration with technology. Complex technology often frustrates users (Mick and Fournier 1998). Similarly, Mukherjee and Hoyer (2000, p. 470) observe that the reasons for frustration include "associated learning cost and comprehension difficulty ... and lack of control." For this same reason, Lupton and Miller (1992, p. 11) argue that designers have expended significant time "domesticating and humanizing" home-based technologies. An outcome of such arguments is that though a technology may be useful, difficulties in making it perform intended tasks often cause reactions ranging from aggravation to disappointment. Frustration arises because the technology fails to perform reliably or to meet the user's expectations. As a result, the product is used less frequently (rate of use) and is put to fewer uses than originally intended. This leads us to the following hypothesis:
H[sub7]: Higher frustration with the technology leads to lower variety and rate of use.
An alternative hypothesis about the level of frustration and use of technology can also be put forward. As Davis (1989) and Mick and Fournier (1998) point out, consumers may continue to use a product despite frustration and difficulty because the product provides certain utility. Thus, if we observe a lack of effect for user frustration, it may be because concerns of utility outweigh levels of frustration.
H[sub8]: Level of frustration with technology has no impact on variety or rate of use.
External Factors
Factors that are external to the adopting unit may influence usage behaviors. A supportive social environment increases use potential. If a person speaks to friends and coworkers about products, such communication reinforces user belief systems and, consequently, behaviors. Similarly, use of technologies outside of the home (e.g., office, school) also influences the use of technology at home by increasing variety, but it may result in a lower rate of variety because some of the usage time at home is taken up by use outside the home. In addition, we argue that higher exposure to media may stimulate involvement with the technology, which in turn may account for higher levels of use. Therefore, we hypothesize the following:
H[sub9]: External communication intensity pertaining to the innovation leads to higher variety of use.
H[sub10]: Access to the innovation outside of the home environment leads to higher variety of use but lower rate of use in the home.
H[sub11]: Exposure to a more target-related media results in higher variety and rate of use.
UD Outcomes
Satisfaction with technology and perceived impact of technology. It has been suggested that a person's ability to use a product successfully results in higher satisfaction (Anderson and Ortinau 1988; Downing 1999; Kekre, Krishnan, and Srinivasan 1995). It follows that users who exhibit an intense UD pattern are more satisfied with the technology than are users who exhibit limited use. Using the expectation confirmation/disconfirmation paradigm, Bolton and Lemon (1999) find that customer satisfaction and service usage are highly correlated. Furthermore, the relation is dynamic; that is, customers tend to compare actual usage and normative expectations, and when the comparison is favorable, satisfaction increases. Similarly, in product usage, customers may have some normative expectations about product performance. When usage behavior approaches intense use, the actual usage is likely to exceed prior expectations and thus lead to higher product satisfaction. Over time, satisfaction with the technology may spur more usage (as shown by the dotted line in Figure 1) in a dynamic process.
The effects of specialized and nonspecialized use on satisfaction are less clear. However, according to Oliver (1980, 1995), consumers' satisfaction with a product is a function of the product's ability to fulfill their expectations. As a result, we expect that the adopting unit will exhibit higher satisfaction with specialized use than with nonspecialized use. If we take intense use as the highest end of the technology-satisfaction continuum, we can argue that it is followed respectively by specialized, nonspecialized, and limited use. The degree of use also directly results in the perceived impact of the technology on daily lives. On the one hand, intense use can result in "cultural anchoring," in which the technology becomes so inextricably part of a user's life that it modifies how the consumer operates on a daily basis. On the other hand, limited use will not have the same perceived impact, because it is a less integral part of the user's activities. Specialized and nonspecialized use result in moderate perceived impact for reasons we stated previously.
H[sub12]: Intense UD is associated with the highest level of (a) satisfaction with the technology, (b) perception of its essentialness in the home, and (c) impact on daily life, followed by specialized, nonspecialized, and limited use.
Interest in new (futuristic) technologies. Ellen, Bearden, and Sharma (1991) find that the level of satisfaction with an existing technology increases resistance to and reduces the likelihood of adopting an alternative. A natural extension of their finding is interest in acquiring related new technologies. Users who have successfully integrated the technology into their lives should be least resistant to acquiring similar technologies, because past successful experiences reduce the level of perceived risk involved and heighten the possible benefits that can be realized (Venkatesh, Kruse, and Shih 2003). Rogers (1995) presents a similar argument in his discussion of the adoption of technology clusters. In this regard, we expect that the highest interest in future technology acquisition is in the intense use category, followed by the nonspecialized use category. In contrast, specialized use may not result in heightened interest in future technology acquisition, because users have invested time and effort in developing expertise in applying the existing technology to repeated use or to a specialized set of tasks.
H[sub13]: Intense UD results in the highest level of interest in acquiring new related technologies, followed by nonspecialized use. Specialized use and limited use have the lowest interest in acquiring new related technologies.
A national telephone survey of 910 U.S households that owned computers was conducted by means of the random-digit dialing method, a well-established procedure in survey sampling. The respondent from each household was selected on the basis of who had the most information about computer use in the household. At the time of data collection (1999 and 2000), the U.S. Census Bureau (1997) estimated that computer penetration in the United States was approximately 60% of the total population, slightly skewed toward higher-income households. Therefore, to maximize the probability of representing the computer-owning households, we oversampled higher-income households. A comparison of our final sample of computer households with the Census report indicated a close match (see Table 2). Before the full-scale study was launched, the questionnaire for the study was pretested among 25 households for accuracy, validity, and ease of administration.
Dependent Variable: Variety of Use and Rate of Use
We determined variety of use with a checklist of possible uses of the computer (Table 3). We based the checklist items on household activities performed by household members with the help of the computer (Robinson and Godbey 1997; Venkatesh 1996). We randomized the list to eliminate any ordering effects. In this study, we identified 17 different uses for computers (Table 3), which we grouped into seven major categories that define activity spaces in the home. In terms of UD, variety of use shows how computer use diffuses across different household activities. For each household, we summed the responses (1 = "yes," 0 = "no") to the 17 items (i.e., the uses); the mean uses are 8.28, and the median uses are 8.
We measured rate of use as the number of hours of computer use at home by the whole family in a typical week. The primary respondent who reported for other household members gathered rate-of-use information on every computer-using member of the household. We dichotomized rate of use from the median high and low rate-of-use behavior. The median use and the mean use for each household were 14.0 and 20.26 hours per week, respectively. To avoid household-size bias, we normalized the measure by number of users in each household, yielding 6.63 hours for the median and 8.81 hours for the mean. We divided the normalized rate of use by the median into high and low rate of use.
Together, variety of use and rate of use formed the 2 x 2 matrix (see Figure 1) of UD patterns. Table 4 gives the percentage of each use pattern in our sample.
Determinants
Respondents were asked the frequency of computer-related communication with other users in the home, and we constructed a measure as follows:
( 1) [Multiple line equation(s) cannot be represented in ASCII text].
where
HCI[subh] = household communication intensity,
λ[subij] frequency of communication between users i and j (2 = "frequently," 1 = "sometimes"),
κ[subij] = knowledge of the users i and j (2 = "at least one is an expert," 1 = "none are experts") and
H[subh] = number of users in the household.
This index makes an upward adjustment when communication about computers between two users is more frequent and when at least one of the users is more knowledgeable about computers. We made these adjustments because we suspect that more frequent communications, particularly among or with expert users in the home, can contribute more to solving usage difficulties.( n3) To avoid biases toward larger household sizes, we normalized household communication intensity by the number of computer users in the household.
We operationalized competition for computers (USERSPC) as the ratio of the number of computers available at home to the number of users. We operationalized experience with computers (YEARSPC) by considering how long (in years) the households had used computers at home. We operationalized technological sophistication of computers (PCAGE) as the age of the newest computer in the home. We reverse-coded the variable so that a newer computer would have higher value (i.e., more sophisticated) than an older computer. Although this operationalization ignores variations of different computer configurations, it is still a fair measure of computing capabilities because, in general, computers purchased in a particular year have more memory (RAM) and faster CPU speed than computers purchased in a previous year.
We operationalized complementary technologies (TECHOWN) as the number of complementary information and media technologies other than computers that were used in the household (for a list of technologies, see the Appendix).
We measured use innovativeness (INNOVATE) by modifying the scale developed by Price and Ridgeway (1983). We selected the items from each of the four factors reported and reworded them to fit the context of the study. We randomized the items and measured them on a five-point scale (1 = "not at all," 5 = "very much") of how well the statements describe the respondents themselves (see the Appendix). Reliability analysis indicated that the five items have a Cronbach's alpha of .81, and we took the average of the items as an overall measure of use innovativeness.
We measured frustration with technology (FRUST) with a two-item scale in the context of home use ("Computers are difficult to use" and "Often feel frustrated using a computer"); the two items had a Cronbach's alpha of .70.
External communication intensity (ECI) measures the extent to which members of the household communicate with friends, coworkers, or a company help desk for assistance with computer usage. For each of these, we assigned a score of 2 = "frequently," 1 = "sometimes," and 0 = "never," and we summed the scores to form an index. Use of the computer and the Internet outside of the home (OUTACC) is a dummy variable that is equal to 1 if the answer is yes and equal to 0 otherwise. We operationalized media exposure of the household (HHMEDIA) as the proportion of users in the household who are exposed to computer-related magazines.
Finally, we included three demographic variables in our analysis: highest education in the household, household income, and age (oldest user in the household). These variables have been discussed in prior research (Dutton, Kovaric, and Steinfield 1985; Venkatesh 1996). We included them as control variables to test whether our main hypotheses would hold in the presence of demographic influences. Summaries of independent variables are reported in Table 5.
Outcome Variables
We determined perceived impact of computers by a randomized ten-item scale (1 = "strongly disagree," 5 = "strongly agree") that was meant to tap into effects of using computers on household activities (see the Appendix). A factor analysis with varimax rotation resulted in two underlying factors that explain 52.38% of the total variance. The first factor (eigenvalue = 4.13, Cronbach's α =.79) is essentialness of the computer at home, and the second factor (eigenvalue = 1.11, Cronbach's α =.66) is the impact of the computer on daily life. We averaged the items for each factor. We measured satisfaction by using a scale with two items ("experience with computers in general" and "computer's overall performance"; 1 = "not at all satisfied," 5 = "very satisfied"). We averaged the two items for overall satisfaction (Cronbach's α =.75).
We assessed interest in future technologies by asking respondents what their level of interest (1 = "not at all," 4 = "very") was in technologies that were under development (but not in the market at the time of the study). These technologies are sometimes referred to as "smart-home technologies," and we selected them on the basis of ongoing industry research and development of the next wave of technologies for the home.
We believe that variety and rate of use are simultaneously determined. A reason for this is that variety and rate of use can be empirically correlated, such that increases in variety of use often result in increases in rate of use because more time may be needed to perform more uses. All else being equal, less variety of use can lead to lower rate of use because users simply have less to do. In addition, we believe that variety and rate of use depend on the exogenous variables described in the preceding hypotheses. Thus, we specified a two-equation model of variety and rate of use and estimated it with two-stage least squares (2SLS).
Although we do not present a complete model of the consequences of variety and rate of use, we investigated the influences of the four diffusion patterns on perceived impact of technology, satisfaction with technology, and interest in future technologies by using a multinomial logit model (MNL). We used the MNL model because of our interest in analyzing the likelihood of the adopting unit experiencing one of the four different nominal, discrete UD patterns and in identifying the variables that are likely to produce this result. In the MNL model, it is customary to use a reference category among the categories that constitute the dependent variable. Limited use is a logical choice for a reference category because it represents the lack of unit UD, whereas intense, specialized, and nonspecialized uses capture UD through rate of use, variety of use, or both. Consequently, we interpreted the coefficients relative to limited use.
To test for the UD outcomes, as reflected in H12 and H13, we used a one-way analysis of variance with least significant multiple comparison t-test to test the differences among the four UD categories on the following variables: satisfaction with the computer, perceived impact of the computer on home life, and interest in acquiring future technologies.
2SLS Regression Analysis
Results for the 2SLS are shown in Table 6. In general, the determinant variables performed better in explaining variety of use (R² =.326), and seven hypothesized relationships tested significant. In addition, we found one demographic variable to be significant. For rate of use, we found five hypothesized relationships to be significant (R² =.212) and none to be significant in the demographic category. On the basis of the regression analysis, we were able to find empirical support for H[sub1]-H[sub6]. As for the competing hypotheses, H[sub7] and H[sub8], our empirical result is consistent with H[sub8] but not H[sub7]. H[sub9]-H[sub11] were also supported. Because our independent variables use different scales, we subsequently present the standardized coefficients for ease of comparison.
In terms of relative magnitude of the explanatory variables, the top three variables for variety of use were use innovativeness, sophistication of technology, and complementary technology. For rate of use, the top three variables were competition for technology, household media exposure, and sophistication of technology. However, without further theoretical justification, at this time we do not attach any significant interpretation to the relative rankings of these determinants.
The effect of social dimension on UD received good empirical support. Intensity of household communication was positively related to variety of use (β =.166, p <.001), which confirms that household members, particularly those who have more computer expertise, assist other household members in using technologies for novel consumption situations. The observed interaction among users appears to have introduced and reinforced higher usage behavior as well as new usage behavior, thereby broadening the potential applicability of the technology and diffusion in the home.
The effect of competition among users for computing resources in the home is negatively related to rate of use, as we hypothesized (β = -.224, p <.001). Experience with computers had a significant effect on variety and rate of use, as we hypothesized (β =.165,.098, p <.01). The two variables within the technological factor significantly affect UD. Sophistication of the computer system is positively related to variety of use and rate of use (β =.207,.099, p <.001), in confirmation of our hypothesis. Complementary technology ownership affected variety of use (β =.172, p <.001). For the personal dimension, use innovativeness is positively related to variety of use (β =.212, p <.001), which is in agreement with the results that Ridgeway and Price (1994) and Ram and Jung (1990) report. The null effect of frustration with using the computer indicates that adopters are not deterred in their use of the technology despite frustration and difficulties in using it, consistent with H[sub8].
For external dimension, external communication index was positively related to variety of use (β =.123, p <.001), and using the computer outside the household had no impact on variety of use but was negatively related to rate of use (β = -.071, p <.05). This indicates a substitution effect. Similarly, media exposure was significant in predicting variety of use and rate of use (β =.132,.107, p <.01).
As for the sociodemographic variables we tested, the principal effect we found was for highest education in the household, which was positively related to variety of use. Although we did not make explicit hypotheses about these variables, but used them only as control variables, the results are not substantial.
MNL Analysis
We performed an MNL analysis on the four UD patterns: intense, specialized, nonspecialized, and limited use. Limited use was the control category. Results of the MNL analysis are reported in Table 7. The model -2 log-likelihood is 2008.612 with χ² = 404.544 (p <.001). Except for frustration, all independent variables were significant in predicting UD patterns.
Among the household social dimensionality variables, household communication intensity was significantly related to intense use (β =.201, p <.01) and nonspecialized use (β =.487, p <.01). In contrast, competition for computing resources reduces the likelihood of UD, particularly intense and specialized use (β = -.242, -.462, p <.05), and this confirms the notion that when people compete for limited resources, the synergy of collective usage is affected. Years of experience was significant in predicting intense use (β =.092, p <.001), specialized use (β =.051, p <.05), and nonspecialized use (β =.053, p <.05). This finding suggests that users who interact frequently with other household users tend to develop broader variety of use than do users with limited usage behavior.
For technological dimension, the sophistication of the computer increases the probability of intense, specialized, and nonspecialized uses but not limited use. It seems essential to update technology before it fails to keep up with usage requirements. In contrast, the complementary nature of household technologies is significant in relation to intense use (β =.178, p <.01) but not other uses. It stands to reason that use of related technologies is correlated with variety of uses. By the same token, because we did not observe the impact of extended computer use for a single purpose on specialized use, it is reasonable to suggest that it does not translate into a need for related technologies. For personal dimension, use innovativeness was significant in predicting intense use (β =.710 p <.001) and nonspecialized use (β =.348, p <.01). The findings and interpretation here are similar to those of the regression analysis and consistent with those of Ridgeway and Price (1984). Although we found none of the coefficients associated with frustration to be significant, all were negative as might be expected. External communication index had a positive effect on intense and nonspecialized use (β =.195,.324, p <.05), and outside access had a negative impact on specialized use (β = -.524, p <.05). In effect, communication with people outside of the household has a similar effect as communication with household users, and access to computers outside of the home reduces some of the time spent on computers at home. Media exposure was positively related to intense, specialized, and nonspecialized uses (β = 1.704, 1.098,.826, p < .05).
Finally, the only sociodemographic (control) variables we found to have any impact were the effect of education on specialized use (positive) and the effect of income on nonspecialized use (positive). Absent any theory, it is rather difficult to interpret these two results meaningfully.
UD Outcomes: Multiple Comparison Test
We found significant differences among diffusion categories on all the outcome variables we examined. The means of each outcome variables investigated are reported in Table 8. Consistent with the predictions, users who experienced an intense UD pattern rated the computer highest with regard to its essentialness in the home, and users who experienced a limited UD pattern rated it lowest (3.65 versus 2.35, p < .05). The remaining two groups, in the categories of specialized and nonspecialized use, were not significantly different from each other (3.01 versus 2.99, p >.10), but both were significantly lower than intense users and significantly higher than limited users. For impact on daily life, all comparisons were significant; intense use was the highest (3.37), followed by specialized, nonspecialized, and limited (2.84 versus 2.65 versus 2.09). Similar patterns of ranking hold for overall satisfaction with the computer at home (4.22 versus 3.95 versus 4.06 versus 3.70, p <.05, except between specialized and nonspecialized use). In general, we found that the more extensive the UD of technology in the home, the more impact it has on daily life, the more it is perceived as essential, and the more satisfied adopters are.
We also hypothesized that UD patterns are related to adopters' interest in acquiring new technologies. In particular, we expect that adopters who exhibit higher UD are more interested in futuristic technologies than are adopters with a lower level of UD. In general, this hypothesis was supported. In every case, intense UD pattern ranks the highest with regard to interest in acquiring futuristic technologies, but this interest is weakest in limited UD. For specialized UD, interest in these technologies is the second highest in all cases, and nonspecialized use is not significantly different from limited use. When viewed as a whole, the data suggest that different UD patterns result in differential levels of interest in future technology acquisition and that UD patterns that are higher on variety of use (intense) exhibit a higher level of interest than the others.
We began by stating that the existing AD paradigm has some gaps. Prior research in marketing supports our position and provides some extensions to the diffusion framework (Hahn et al. 1994; Ram and Jung 1990; Ridgeway and Price 1994; von Hippel 1986). Our main contribution to previous research is the identification of a fourfold typology of users (intense, specialized, nonspecialized, and limited) based on two distinct elements: variety of use (high and low) and rate of use (high and low). Between the two elements, variety of use assumes a slightly more central position because it is one of the key elements of use innovativeness (Ridgeway and Price 1994). It also plays a significant role in identifying intense users. Many new technologies are capable of performing multiple functions and are therefore candidates for variety of use. However, producer-initiated multifunctionality is a necessary condition, but it is the consumer's actual variety of use that matters in the final analysis. We find that variety of use is not only an intuitive concept but a theoretically rich construct for application in new product development and design.
The results also support our typology, which indicates that intense and limited users are at the two extremes and specialized and nonspecialized users are in the middle on many independent measures (Table 8). These categories are roughly analogous to the adopter categories that have been proposed in the diffusion literature. The different users have some distinct characteristics that can be further explored from a marketing standpoint to develop a segmentation strategy and to position new products. Although our empirical investigation is focused on personal computers, the conceptual model can easily be adopted to a wide range of innovations that have multiuse potential.
Use/User Typology and Market Segmentation
Because product-use patterns determine the formation of segments, the fourfold typology of users we have developed is a constructive way to visualize the market. In terms of the proposed segments, intense users seem to dominate other users, given the number of variables we found to be significant (Table 7). This shows that a critical factor in UD is how involved consumers are in the use of the product in terms of both variety of use and rate of use. In the UD context, intense users may be considered use innovators par excellence because they score high on both variety and rate.
We also propose that the other two key segments worthy of market attention are nonspecialized users and specialized users, because they can be considered potential intense users. Limited users may appear less appealing because their product use is not significant; however, they are good candidates for upgrading to a higher level of use if the reasons for their limited use are known in advance.
From our study, we were able to show that intense users constitute 30% of the sample, followed by nonspecialized users (20%) and specialized users (19%). Together, these segments constitute approximately 70% of the user population and thus represent a substantial portion of consumers who might be of interest to marketers of key technologies. Although we do not claim that our figures constitute a population estimate, given that our sample is a national one, we believe that the values are plausible. In terms of Hahn and colleagues' (1994) study, we classify these three segments collectively as "repeaters" in the sense that they have used the product and are ready for repurchase because of their use experience and satisfaction. In addition, intense users represent the highest level in terms of use innovativeness. In many respects, they can be likened to the lead users that von Hippel (1986) identifies in his work on industrial diffusion.
We found that use of related technologies is positively related to higher UD levels and therefore leads to higher satisfaction with the technology itself. However, to determine how technology functions can be optimally integrated, we believe the marketer must first understand the nature of use of the technology and its UD path.
We also found that adopters with higher UD levels not only are more satisfied with the current innovation but also are more interested in adopting future innovations. In general, we did not find major demographic differences between the groups, though income and education seem to have a marginal impact. A reason for this may be that whereas income and education may differentiate adopters and nonadopters, they may have less significance in determining actual use patterns.
Key Variables and Their Implications
For the independent variables, the results show (Table 6) that the four dimensions of the UD model (household social, technology, personal, and external factors) explain a large measure of the variance. Among the three major use categories, intense use is the most highly correlated with the variables in the model, followed by nonspecialized use and somewhat distantly by specialized use (Table 7). A possible conclusion is that nonspecialized users can be considered potential intense users, whereas specialized users are less likely to become intense users because they appear to be set in their ways. However, specialized users may be concerned about improvements in existing products that guarantee better performance in their specialized product-use category.
In terms of the specific variables, the role of experience is critical in UD because experience leads to cumulative knowledge and learning (Hoch and Deighton 1989). Hoch and Deighton (1989) show that motivated learners exhibit more versatile new product behaviors than do unmotivated learners. We believe that intense users and nonspecialized users are more similar to motivated learners and more likely to take advantage of experiential learning.
Competition for resources reduces access potential, which in turn negatively affects a person's ability to use the technology. Thus, access and use are related. The greater the access, the greater are the opportunities for use (Hoffman, Novak, and Venkatesh 1998). If several users need to share the technology, each is limited in his or her use. A solution is to increase the number of units in the home; another solution is to schedule technology use in a way that maximizes opportunities for everyone. The current examples include television sets, telephones, and automobiles.
Our results also show that the social contexts of communication and interactions (internal and external) are central to the UD process, as they are to the AD process. Communication among users increases UD, especially with respect to variety of use. Our research examines not only the presence or absence of communication but also the intensity of it, including media exposure, which plays an influential role among different sets of users.
Because communication with other users can play a significant role in UD, establishing user groups, either virtual or physical, may encourage users to educate and support one another openly in the UD process. We also found that, in general, exposure to an external source of information, such as other users, fosters a higher level of UD. Additional emphasis should be placed on targeting existing adopters and educating them about potential applications of technology that may not be readily apparent.
In terms of technology variables, the presence of complementary technologies increases variety of use, as does ownership of the latest technology, which provides further support to the notion of technology clusters in new product development. However, acceptance of new technologies is not automatic; they must be marketed strategically with different appeals to different segments.
For the other key variables, use innovativeness emerges as a key factor. Our results show that use innovativeness affects variety of use, but not rate of use, in a significant way. A question not examined in our study is whether adoption innovativeness and use innovativeness are the same. We know from other studies that children display higher levels of use innovativeness than do adults, though children are not mentioned in the context of adoption. Although we did not specifically investigate this in our study, we use it to illustrate the point made here.
That users are frustrated with technology because of its complexity does not seem to have much bearing, presumably because this problem is neutralized by other factors (e.g., perceived benefits of technology). At this point, we speculate on the relationship between two key types of use: intense use and nonspecialized use. Although both types of use are distinct, they are related; that is, nonspecialized use may eventually lead to intense use because the key aspect of nonspecialized use is to explore various technological possibilities. In terms of diffusion outcomes (Table 8), consumers in the intense-use category show greater satisfaction, believe there is a greater impact of technology on family life, and are more likely to regard computers as an essential technology than are other groups.
To conclude, when a new product that is capable of fulfilling multiple tasks is introduced into the market, conditions must be created that enable higher variety of use and higher rate of use. For example, the goal of marketing communications can be extended beyond traditional promotional strategies that focus on product adoption to include creative uses of the product as well. Our research suggests that to encourage such usage behavior, it is important to disseminate use knowledge and to nurture use-based learning. In practical terms, marketers of these technological products must be creative in presenting new usage scenarios to the adopters and in fostering direct communications among adopters. This suggests that some formal mechanisms should be established for gathering use data on a regular basis.
Conclusions and Directions for Further Research
Our study uses a national probability sample of households, and therefore the results can be considered reasonably robust. However, the study has some limitations that can be addressed in future studies. First, the study is not longitudinal and therefore does not capture the full dynamism of UD. Thus, our empirical findings are correlational rather than causal. Further research should adopt a longitudinal approach to gain full causal insights. Second, our empirical focus is on a single product. Studies that involve multiple products would yield some richer insights on how UD varies across different product categories. For example, the complexity of a product category with respect to UD can be tested across different segments. The impact of different levels of multifunctionality on UD can also be tested. We urge researchers to address these issues.
This study was funded by the U.S. National Science Foundation Grant Nos. IRI-9619695 and SES-0121232. The authors gratefully acknowledge the helpful comments of the anonymous JM reviewers. The authors are listed alphabetically.
(n1) To quote Robertson and Gatignon's (1986, p. 3, emphasis in original) seminal article, "[T]he speed of diffusion of technological innovation depends on the consumer's ability to develop new knowledge and new patterns of experience.... Because the emphasis is on technological innovation, adoption is not the only relevant concern of diffusion research. The degree of use of that technology is an important variable that describes the extent of diffusion of that innovation."
(n2) Previous researchers, including von Hippel (1986, 1995) and Hahn and colleagues (1994), have attempted to extend the diffusion framework by proposing similar typologies specific to their research concerns. Although both authors' studies are relevant to our current research and provide the right motivation, there are some key differences in terms of both the theoretical basis and the empirical questions. Von Hippel's basic interest is identifying lead users in the industrial context and differentiating them from non-lead users. Our focus is on consumer households in the context of their use of complex technologies. In addition, our fourfold typology of users is based on different criteria. Hahn and colleagues focus on a repeat trial model of nondurables (e.g., medical products), whereas our focus is on use-diffusion patterns among users (not repeat users) of durables.
(n3) If no communication exists between users i and j, then κ[subij] + Λ[subij] is set equal to zero.
Legend for Chart:
B - Variable of Interest
C - Typology of Population
D - Relevant Criteria
E - Elements Unique to Each Model
F - Elements Common to Both Models
A B C D
E F
AD Adoption Innovators Timing or
Early adopters rate of
Early majority adoption
Late majority
Conservatives
Observability Innovativeness
Compatibility Social communication
Trialability Complexity
Influence of media
Relative advantage
UD Use Intense users Rate of use
Specialized users Variety of use
Nonspecialized users
Limited users
Product experience Innovativeness
Competition for use Social communication
Sophistication of Complexity
technology Influence of media
Satisfaction Relative advantage Legend for Chart:
B - Survey Sample
C - U.S. Census (1997)
A B C
Sample Size
(Number of
Households) 910 17,814
Household Income
$15,000 or less 2.6% 8.1%
15,001-30,000 12.1% 14.8%
30,001-50,000 23.2% 26.3%
50,001-75,000 22.1% 25.4%
75,001 or greater 29.5% 25.4%
Race(a)
White 88.1% 86.3%
Black 3.6% 5.3%
Hispanic 3.0% 3.9%
Asian 2.2% 3.8%
Other 1.5% .7%
Children in Household
Yes 44.1% 47.1%
No 55.9% 52.9%
(a) Race taken as the self-reported race of the
head of the household. Legend for Chart:
A - Activity Space
B - Activities
A B
Work/employment related 1. Job related
2. E-mail (work-related/
school-related)
Family communication 3. E-mail (personal)
4. Writing letters/
correspondence other
than e-mail
Family recreation 5. Games/entertainment
Home management 6. Home management
(recipes, family records)
7. Health information
8. Travel
information/vacation
planning
9. Financial management
10. Online banking
Home shopping 11. Shopping (frequently
purchased goods)
12. Shopping (large-ticket
Items)
13. Shopping (other)
Education/learning 14. School-related
Information center 15. News
16. Sports information
17. Community information Legend for Chart:
A - UD
B - Variety of Use
C - Rate of Use
D - Percentage of Sample
A B C D
Intense use High High 29.9
Specialized
use Low High 20.4
Nonspecialized
use High Low 19.4
Limited use Low Low 30.2 Legend for Chart:
A - Variable Name
B - Variable Description
C - Measure
D - Mean
E - Standard Deviation
A B
C
D E
HCI Household communication
intensity
(Σλ[subij])/(number of users)
2.843 1.049
USERSPC Competition for technology
(Number of personal computers in
use)/(number of users)
2.049 1.186
YEARSPC Experience with technology
Years since adoption
6.953 5.374
PCAGE Sophistication of technology
Age of newest personal computer (reversed)
12.096 1.768
TECHOWN Complementary technology
ownership
Number of complementary technologies owned
6.263 1.974
INNOVATE Use innovativeness
Five-item scale, α = .81
3.162 1.053
FRUST Frustration with use of technology
Two-item scale, α = .70
2.391 .961
ECI External communication intensity
Sum of degree of communication with friends,
coworkers, and other sources (e.g., help
lines, online chat groups) for advice about
computer use (2 = "frequently," 1 =
"sometimes," and 0 = "never")
3.171 1.394
OUTACC Access to technology outside of
home
Dummy variable: 1 = if anyone in household
has access, 0 = otherwise
.654 .476
HHMEDIA Household media exposure
(Number of users who read computer
magazine)/(number of users)
.236 .344
HHEDU Highest education level in the
home
Years of education for the most educated
member of the household (ten-point scale)
6.582 2.038
INCOME Household income
Household income (six-point scale)
3.882 1.287
AGE Age of the oldest user in the
household
Age of the user
41.770 13.688 Legend for Chart:
B - Variety of Use Coefficients
C - Variety of Use β
D - Variety of Use Standard Error
E - Rate of Use Coefficients
F - Rate of Use β
G - Rate of Use Standard Error
A B C D E
F G
α 5.254(***) -- 1.257 .948
-- 2.439
HCI .373(***) .166 .067 --
-- --
USERSPC -- -- -- -1.507(***)
-.224 .232
YEARSPC .113(***) .165 .028 .146(**)
.098 .060
PCAGE .444(***) .207 .089 .457(**)
.099 .175
TECHOWN .322(***) .172 .066 --
-- --
INNOVATE .745(***) .212 .143 --
-- --
FRUST -.158 -.041 .114 -.016
-.002 .267
ECI .330(***) .123 .088 --
-- --
OUTACC .185 .024 .256 -1.185(*)
-.071 .590
HHMEDIA 1.428(**) .132 .436 2.490(**)
.107 .82
HHEDU .167(**) .092 .056 .281
.065 .166
INCOME .200 .070 .107 -.419
-.018 .237
AGE .008 .030 .010 .045
.066 .025
VARIETY -- -- -- .927(***)
.244 .219
RATE -.103 -.110 .087 --
-- --
R² .326 .212
Adjusted R² .317 .203
F 34.860(***) 23.146(***)
(*) p < .05.
(**) p < .01.
(***) p < .001. Legend for Chart:
B - Intense
C - Standard Error
D - Specialized
E - Standard Error
F - Nonspecialized
G - Standard Error
A B C D
E F G
α -10.037(***) 1.207 -3.076(**)
1.105 -8.089(***) 1.177
HCI .201(**) .074 .106
.216 .487(*) .221
USERSPC -.242(*) .113 -.462(***)
.127 .093 .105
YEARSPC .092(***) .023 .051(*)
.024 .053(*) .024
PCAGE .408(***) .069 .263(***)
.064 .219(**) .064
TECHOWN .178(**) .060 -.013
.061 .060 .063
INNOVATE .710(***) .116 .204
.118 .348(**) .118
FRUST -.081 .113 -.115
.112 -.074 .117
ECI .195(*) .080 -.062
.082 .324(***) .082
OUTACC -.160 .235 -.524(*)
.230 .186 .251
HHMEDIA 1.704(***) .344 1.098(**)
.347 .826(*) .396
HHEDU .109 .057 -.123(*)
.057 .062 .058
INCOME .028 .092 .006
.091 .303(**) .097
AGE -.077 .255 .333
.009 .335 .264
-2 LL 2008.612
Model
χ² 404.544(***)
Pearson
χ² 2633.947
Pseudo-R² .367
(*) p < .05.
(**) p < .01.
(***) p < .001. Legend for Chart:
B - F
C - Intense
D - Specialized
E - Nonspecialized
F - Limited
A
B C D
E F
Essentialness
85.35(***) 3.65(c) 3.01(b)
2.99(b) 2.35(a)
Impact on daily life
112.51(***) 3.37(d) 2.84(c)
2.65(b) 2.09(a)
Satisfaction with personal computer
14.08(***) 4.22(c) 3.95(b)
4.06(b) 3.70(a)
Interest in Future Technologies
Overall
22.59(***) 2.58(c) 2.45(b)
2.12(a) 2.13(a)
Shopping
4.69(**) 1.81(b) 1.84(b)
1.58(a) 1.60(a)
Home communication
9.38(***) 2.95(b) 2.90(b)
2.42(a) 2.44(a)
Home entertainment
15.09(***) 2.67(c) 2.47(b)
2.18(a) 2.23(a)
Security/home management
10.52(***) 2.97(b) 2.90(b)
2.49(a) 2.55(a)
Home local area network
18.01(***) 2.49(c) 2.14(b)
1.97(b) 1.81(a)
(a b c d) Row comparison is significantly
different at p < .05.
(*) p < .05.
(**) p < .01.
(***) p < .001.DIAGRAM: FIGURE 1; Use-Diffusion Model
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Venkatesh, Alladi (1996), "Computers and Other Interactive Technologies for the Home," Communications of the ACM, 39 (12), 47-54.
-----, Erik Kruse, and Chuan-Fong Shih (2003), "The Networked Home: An Analysis of Current Developments and Future Trends," Cognition, Technology, and Work, 5 (April), 23-32.
Vitalari, Nicholas P., Alladi Venkatesh, and Kjell Gronhaug (1985), "Computing in the Home: Shifts in the Time Allocation Patterns of the Households," Communications of the ACM, 28 (5), 512-22.
von Hippel, Eric (1986), "Lead Users: A Source of Novel Product Concepts," Management Science, 32 (7), 791-805.
----- (1995), The Sources of Innovation. New York: Oxford University Press.
Wasserman, Stanley and Katherine Faust (1994), Social Network Analysis. Cambridge, UK: Cambridge University Press.
Technologies in the Home
• Electronic organizer or handheld computer
• Fax or telex machine
• Pager
• Voice mail or answering machine
• Video-game console
• Digital videodisc, DivX, or laser disc player
• Stereo system or compact disc player
• Satellite television
• Cable television
• Video camera
• VCR
• Digital camera
Use Innovativeness Scale (Adopted from Price and Ridgeway [1983])
• I am creative with computers.
• I am very curious about how computers work.
• I am comfortable working on computer projects that are different from what I am used to.
• I often try to do projects on my computer without exact directions.
• I use a computer in more ways than most people do.
Perceived Impact of Computer (Two Factors)
1. Factor 1: Essentialness of Computer at Home
• The computer is as essential in my home as is any other household appliance.
• It would be difficult to imagine life without a computer in my home.
• Households with a computer are run more efficiently than those without a computer.
• The computer has saved me time at home.
• The computer has become part of the daily routine in my home.
2. Factor 2: Impact of Computer on Daily Life
• The computer has changed the way I do things at home.
• The computer has replaced the telephone as the major communication device in my home.
• I have more contact with friends as relatives now that I have e-mail.
• My family watches less television as a result of using the computer or the Internet.
• The computer has increased the amount of job-related work I do at home.
Interest in Future-Oriented Technologies
Cost aside, how interested would you be in having this for your home. Would you be "not at all interested," "slightly interested," "somewhat interested," or "very interested" in having this product, or do you already have something like this in your home? (List read in random order)
• A refrigerator with a computer screen on the door that would allow you to keep track of food inventories and would be linked to the Internet so you could order food and other supplies online.
• An audiovideo system linked to units in each room that would allow you to share home theater events, share VCR programs, and share stereo sound to each room.
• A communication system that combines your telephone, computer, Internet access, and television in one single unit.
• A home computer control system that manages lighting, temperature, home security, and appliances in any and all rooms of your home.
• A local area network for the home that connects multiple computers, printers, and data sources so that you can share information between computers or send information to any printer in your home.
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By Chuan-Fong Shih and Alladi Venkatesh
Chuan-Fong Shih is Assistant Professor of Marketing, Babcock Graduate School of Management, Wake Forest University (e-mail: eric.shih@mba.wfu.edu).
Alladi Venkatesh is Professor of Management and Computer Science and Associate Director of the Center for Research on Information Technology and Organizations, University of California, Irvine (e-mail: avenkate@uci.edu).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 23- Book Reviews. By: McKee, Daryl; Clark, Terry. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p264-265. 2p. DOI: 10.1509/jmkg.2005.69.4.264.
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- Business Source Complete
Book Reviews
Consuming Religion: Christian Faith and Practice
in a Consumer Culture
by Vincent J. Miller (New York: Continuum, 2003,
208 pp., $16.47).
Vincent J. Miller's Consuming Religion is a densely packed examination of the possibilities for religion in a commercially saturated postmodern world. Miller's central argument that the relationship between religious belief and religious practice is moderated by consumer culture makes his book immediately relevant to consumer behavior and marketing scholars. A person might be forgiven for assuming that such a book would be in the form of a condemnation of consumer culture. However, it is not that kind of book. Rather, Miller is concerned with how religious belief can survive in an advertising-saturated world.
The book is relevant for marketers on several levels. First, it addresses the issue of how people "consume" religious belief and practice. This discussion alone should make the book a compelling read for people, churches, and the industry that supplies the faith "industry" (McKee 2003). Second, and perhaps more important, the book explores the complex interactions between market values and religious values in postmodern culture.
In his critique of the market system, Yale economist Charles Lindblom (2001, p. 206) queries whether "it is prudent and not unethical for the executive to misrepresent what the enterprise offers, to play cynically on customers' emotions, to disparage what other enterprises offer, or to rattle customers' minds with irrelevancies that will attract them." In a similar vein, University of Michigan economist Rebecca Blank (Blank and McGurn 2004) points to an apparent conflict between the market system ethic of self-interest and the Christian ethic of community, other-interest, and concern for the poor.
This point of conflict between the market ethic and Christian ethics has led "a number of influential voices in Christian ethics … [to] view contemporary economic life as essentially foreign to the faith, an exercise in uncontrolled greed" (Stackhouse 2001, p. 229). Miller's answer is not that work and commerce conflict with Christian ethics but that commercial work is a form of service to others and folds neatly into Christianity and other major religious belief systems.
Miller's argument is that postmodern society "commodifies" culture in a "process in which the habits and dispositions learned in the consumption of literal commodities spread into our relationship to culture" (p. 32). In this postmodern consumer culture, practices that at one time had their own rules and standards (e.g., religion, friendship, art) are instead subsumed under the rules and standards of consumption. This consumption approach to all practices assumes a seed of dissatisfaction with the present and a continuing search for something better.
Miller attributes the consumer culture to several influences, including a capitalist-induced acquisitiveness, advertising, a self-centered therapeutic culture, alienation from work, and isolation of single-family housing. Aggregated supply requires corresponding aggregate demand. Sales and advertising that was initially developed to provide reliable demand and to reduce risk to capital has become institutionalized and accentuated, fostering a cultural trait of acquisitiveness.
Indeed, Miller suggests that consumption of advertising has become a practice in itself. In this view, perusing advertisements involves a regular meditative review in terms of their potential meaning for a person's own life and situational shortcomings. The "'practice of advertising' is every bit as formative of our desires as more traditional religious disciplines and practices such as saying the rosary or sitting in meditation," notes Miller (p. 125). He goes on to argue that this "practice of advertising," with its emphasis on materiality and possession, contributes to social insecurity. Furthermore, by co-opting cultural symbols (e.g., the cross of Christianity, the yin-yang symbol of Taoism), commercial use diminishes the depth of meaning these forms have in their original contexts.
According to Miller, a self-centered therapeutic culture has been created from a variety of movements that fed into consumerism. Outsourcing of production rather than home-crafting products and services reduces consumers to "passive spectators" (p. 60). Drawing on ethicist Peter Sedgwick, Miller argues that consumerism grounds its moral justification in eighteenth-century Romanticism, which emphasizes self-creation and the importance of display for the maintenance of social identity. The result, he claims (p. 85), is "a fragmenting narcissism that transforms everything, including religion, into a self-centered, therapeutic exercise."
Alienation from work, a vestige of Marxist theory, is also rooted in the move from home-crafting products to specialization of labor. The argument is that by engaging in mind-numbing work for hire, people are alienated from their creative capabilities and, instead, seek a sense of identity in consumption. Moreover, isolation of people into single-family housing could diminish intergenerational influences. Instead of receiving traditions and values handed down from grandparents in the home, children receive their ethical training from television.
According to Miller, this collective ensemble of relatively new cultural factors encourages a shallow engagement with reality. For example, a purchased piece of furniture is not appreciated in terms of its origins. The materials (e.g., the type of trees that produced the wood) and labor (e.g., the skills and time of the worker) receive little consideration. More tellingly, this same shallow engagement is extended in postmodern culture to include people's approach to friendships, civic involvement, and religion.
Instead of being morally committed to a spiritual belief system and community of believers, Miller suggests that "consumers" of religion pick and choose from a menu of spiritual offerings and that a "society where we are so free to choose our adult identities inevitably produces seekers who move from tradition to tradition and lift elements willy-nilly without commitment to the overarching goals of any one tradition" (p. 139). Moreover, after the "buzz of novelty wears off, and we are left with the daily monotony of saying the rosary and sitting in meditation, we move on; … we are trained to seek, search, and choose, but not to follow through" (pp. 141-42).
Miller offers a variety of potential remedies. For example, he suggests developing awareness of the origins of products and the tactics used to sell them, purchasing farm produce grown locally, developing a practice of handcrafts, "sacramentalizing" the profane as a way to experience the sacred in the everyday, setting limits on personal consumption, treating cultural objects seriously (e.g., deciding not to use religious or sacred symbols from other cultures ornamentally), locating in small communities that share the same religious beliefs, and so on.
This book makes a thoughtful contribution to marketing scholars and practitioners. For scholars, it may broaden their perspectives on the discipline, perhaps contributing to improved theory and teaching. Although most people recognize that marketing practice contributes to development and distribution of an ever-expanding array of helpful goods and services, marketing is a professional practice that is responsible for its own improvement. Those who articulate the theory behind the practice and those who also teach (future) practitioners have an ethical responsibility to develop and disseminate information on culturally responsible practice. The book should have particular importance for marketing scholars who are interested in the emerging area of religion and marketing. Best and Kellner's (1991) Postmodern Theory is a useful complement to reading Miller's book. Although not necessary to understanding Miller's book, this critically acclaimed book helps organize postmodern theory and clarify its limitations.
For practitioners, Consuming Religion may help develop sensitivity to the potential for offending broad communities of believers through misuse of symbols that others hold sacred. Although the book is not light reading, it should offer any reader insights into how consumer culture affects religious faith and practice.
REFERENCES Best, Steven and Douglas Kellner (1991), Postmodern Theory: Critical Interrogations: New York: Guilford Press.
Blank, Rebecca and William McGurn (2004), Is the Market Moral? A Dialogue on Religion, Economics, and Justice. Washington, DC: Brookings Institution Press.
Lindblom, Charles E. (2001), The Market System: What It Is, How It Works, and What to Make of It. New Haven, CT: Yale University Press.
McKee, Daryl (2003), "Spirituality and Marketing: An Overview of the Literature," in Handbook of Workplace Spirituality and Organizational Performance, Robert A. Giacalone and Carole L. Jurkiewicz, eds. Armonk, NY: M.E. Sharpe, 57-75.
Stackhouse, Max L. (2001), "Business, Economics, and Christian Ethics," in The Cambridge Companion to Christian Ethics. Robin Gill, ed. Cambridge, UK: Cambridge University Press, 228-42.
~~~~~~~~
By Daryl McKee, Louisiana State University and Terry Clark, Editor, Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 24- Building and Sustaining Buyer-Seller Relationships in Mature Industrial Markets. By: Narayandas, Das; Rangan, V. Kasturi. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p63-77. 15p. 3 Charts. DOI: 10.1509/jmkg.68.3.63.34772.
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Building and Sustaining Buyer--Seller Relationships in
Mature Industrial Markets
Empirical research in relationship management has tended to take a snapshot of a relationship at a given time and attempt to project its trajectory, despite agreement among researchers that a longitudinal perspective focused on process models advances the implications for practice. The authors use a field investigative approach to study, over time, the evolution of three industrial buyer-seller relationships in mature industrial markets. The relationships are characterized by various degrees of initial asymmetry and have evolved in dramatically different ways over time. Their findings suggest that weaker firms can structure and thrive in long-term relationships with powerful partners because initial asymmetries are subsequently redressed through the development of high levels of interpersonal trust across the dyad, which in turn leads to increased levels of interorganizational commitment.
A move in industrial markets from adversarial relationships focused on a single exchange to collaborative partnerships focused on building long-term relationships through nonmarket modes of governance has been documented in business publications since the late 1980s (e.g., Davis 1989; Emshwiller 1991). Among the benefits advanced for such relationships are reduced transaction costs, enhanced productivity, and higher economic returns for customers and suppliers (Kalwani and Narayandas 1995; Noordewier, John, and Nevin 1990). However, not all relationships yield mutually beneficial outcomes. Firms at times have behaved opportunistically in such situations to accrue greater benefits for themselves at the expense of their partners. For example, not long ago, the vice president of worldwide purchasing for General Motors, José Ignacio Lopez de Arriortua, used the company's power to void written contracts with suppliers and to reopen bidding with demands for price cuts of an additional 20% (Webster 1995).
In the academic literature, relationship marketing has been characterized as a fundamental reshaping of the marketing field (Webster 1992) and as a paradigm shift that deserves new theory and language (e.g., Anderson, Håkansson, and Johanson 1994; Nevin 1995; Sheth and Parvatiyar 1995). Despite the enthusiastic attention to the importance of relationship marketing, other than the pioneering field-based research of relationship evolution by the International Marketing and Purchasing (IMP) group (e.g., Ford 1990; Håkansson 1982; Håkansson and Wootz 1979), there is little empirical research in business marketing that informs academics and practitioners of the process through which industrial buyer-seller relationships evolve over time (Wilson 1995). This gap has prompted researchers such as Anderson (1995), Jap and Ganesan (2000), and Lambe, Wittmann, and Spekman (2001) to call for more field-based research that involves longitudinal case studies that draw on material from the multiple exchange episodes that constitute relationships and that offers insights into the process of relationship initiation and maintenance.
In this article, we attempt to fill this important void by investigating how buyer-seller relationships are initiated, built, and nurtured in mature industrial markets and why and how they succeed or fail. Using field research from three industrial buyer-seller settings, we derive a set of propositions that explains and integrates existing knowledge about buyer-seller relationship formation and advances the understanding of how the relationships evolve over time. Our contribution is in the spirit of theory construction from grounded field observations.
In the next section, we offer some background on gaps that we perceive in the relevant literature. We then describe our methodology, discuss the field research data, and distill our inferences in the form of four propositions. In the closing section, we elaborate on five processes that explain how industrial buyer-seller relationships are initiated, maintained, and established in mature markets.
The marketing literature is replete with perspectives that shed light on the underlying characteristics of buyer-seller relationships. For example, the theory of power (Emerson 1962) has been adapted by several marketing scholars to explain relationship initiation and maintenance (e.g., Frazier 1983; Reve and Stern 1979). Transaction cost theory (Williamson 1975, 1985), with its emphasis on efficiency, has been invoked to explain the nature of governance in interfirm relationships (Heide 1994; Heide and John 1992); relationships (Heide 1994; Heide and John 1992); relational contract theory (MacNeil 1980) to interpret the spectrum of governance structures that characterize industrial buyer-seller relationships (e.g., Dwyer, Schurr, and Oh 1987; Lusch and Brown 1996); and social exchange theory (Blau 1968; Ekeh 1974; Homans 1958; Thibaut and Kelley 1959) to explain the process of relationship development and maintenance (e.g., Lambe, Wittmann, and Spekman 2001). Although each of these theoretical perspectives has spawned impressive research streams in marketing that are variously focused on the input and desired output of relationships, marketing researchers have been frustrated in their attempts to use any single theory to explain the evolution of interfirm relationships in industrial markets from initiation to maintenance. Consequently, scholars have been forced to crisscross paradigms, particularly scholars who invoke the power paradigm to acknowledge its consequences for trust and commitment among relationship partners and scholars who work with social exchange theory concepts to acknowledge the role of power in creating environments in which relationships flourish or perish. Such efforts notwithstanding, a set of curious "process" gaps persists in the literature. Many important pieces of research reported in the marketing literature have identified the different constructs that characterize the relationship at its different stages but offer limited insight into the transition from one stage to another.
For example, Jap and Ganesan (2000) investigate supplier-retailer relationships in each of five stages, as defined by Dwyer, Schurr, and Oh (1987). Using a cross-sectional survey design, Jap and Ganesan find systematic variance in the interrelationships among state variables across the different relationship phases, as proposed by Wilson (1995). For example, they find that the use of relational norms moderates the effect of a retailer's investments on supplier commitment in the relationship buildup and decline stages but not in the maturity stage. However, because of the cross-sectional nature of their design, Jap and Ganesan refrain from offering inferences on longitudinal transition.
The literature is also mixed on how initial power positions play out in long-term relationships. Several researchers have concluded that asymmetrical power and dependence structures result in dysfunctional relationships (Gundlach and Cadotte 1994; McAlister, Bazerman, and Fader 1986). Others have suggested economic (Bucklin 1966), political (Stern and Reve 1980), and sociopsychological (Anand and Stern 1985) reasons that channel members with "authority" might relinquish channel control. More recent research has highlighted conditions in which mutually trusting and committed relationships might be developed in the absence of a balance of power between two parties. For example, Kumar, Scheer, and Steenkamp (1995b) find that even in the presence of power and dependence asymmetries, firms engage in relational exchange as long as the weaker firm perceives the more powerful firm as fair. Similarly, Kumar, Scheer, and Steenkamp (1995a) find that interdependence asymmetry leads to conflict but that the effect is countered by an increase in total interdependence. These are useful inferences, and they provide justification for the weaker partner to seek a business relationship. However, still missing is the process through which transformation can occur, that is, the rationale for the powerful party behaving fairly in an asymmetrical relationship. It would also be useful to identify the process whereby interdependence is increased in relationships characterized by initial asymmetries.
Transaction cost theory's premise that universal partner opportunism limits the effectiveness of relational governance has been shown to be untrue by researchers who have found that relational controls are effective governance mechanisms (Anderson and Narus 1984, 1990; Heide and John 1992; Morgan and Hunt 1994). Yet there is no empirical evidence of a process that explains the formation of such relational controls in industrial markets over time, especially if they did not exist at the outset. Relational contract theory, though helpful in categorizing relationships, provides little insight into the transformation elements that facilitate migration from a transactional to a relationship mode over time.
Notwithstanding a few pioneering attempts to focus on the nature of relationship building and the changing dynamics of relationships over time, important voids persist, which is a consequence of the cross-sectional designs used in most empirical work in relationship management to date. Such studies can only suggest preconditions for the formation of interfirm relationships and the underlying factors responsible for a healthy/unhealthy dynamic. Because industrial buyer-seller relationships develop episodically, deeper insights into the evolution of such relationships must rely on a longitudinal approach and concomitant time and effort to collect field data. This is why Lambe, Wittmann, and Spekman (2001), even as they call for such investigations, acknowledge (as do Anderson [1995] and Wilson [1995]) that this is a daunting task for researchers who live in a "publish-or-perish" world.
Given our objective of understanding the evolution of relationships, and consistent with a call for studies that explore the temporal dimension of relationships, we used a clinical field research methodology to observe the phenomenon and to collect data (Bonoma 1985; Van Maanen 1979). Such a methodology is appropriate for the exploration of new phenomena or little-understood aspects of known phenomena (Bonoma 1985). We recognize that the small number of observations, and therefore the lack of statistical validity, makes such a methodology more credible in theory construction than in theory testing (Eisenhardt 1989; Rangan, Corey, and Cespedes 1993).
Clinical field research calls for a purposive rather than random sampling design (Argyris 1990). Our interest in understanding processes that support relationship development prompted our focus on the component parts and industrial supplies (rather than capital equipment) value chain. Relationships in this value chain exhibit several unique characteristics that inform our study. First, because such markets are mature with competitors that are able to offer equivalent products at competitive prices (Spekman 1988), suppliers tend to focus on relationship management rather than product technology (Rangan, Moriarty, and Swartz 1992). Second, customers use the products and services repetitively in production or maintenance processes, which prompts continuous rather than limited and infrequent interaction (Noordewier, John, and Nevin 1990) and thus affords attendant opportunities for participants to initiate and manage relationship changes. Finally, operating risk, or the risk that a supplier will fail to deliver (Wilson 1995), is reduced for buyers that can potentially source the same component from multiple suppliers, which allows for the existence of a spectrum of buyer-seller dyad types, from purely transactional to long-term relationships.
The Appendix describes three dyads involved in the selling and buying of frequently purchased intermediate components in mature markets. Each dyad not only fits the sample selection criteria laid out but also exhibits variations in important constructs that are critical to our inquiry. For example, in terms of size, in the first dyad, the vendor (General Electric [GE]) seemed to be much more powerful than its master distributor (RCI), whereas in the second dyad, the vendor (Peak Electronics) seemed to be much weaker than the original equipment manufacturer (OEM) buyer (Ford). In the third dyad, Alpha Tires and Delta Mines (disguised names) seemed to be equally powerful. Across the dyads, products varied from maintenance, repair, and operations components (GE-RCI: refrigeration and air-conditioning components) to production components (Ford-Peak: printed circuit boards [PCBs]; Alpha-Delta: earthmoving tires) and from low-ticket items (GE-RCI and Ford-Peak) to big-ticket purchases (Alpha-Delta).
Theory-construction research requires that researchers "trap" the phenomena of most interest for closer scrutiny (Argyris and Schön 1974; Glaser and Strauss 1967; Zaltman, LeMasters, and Heffring 1982) while still providing openings for alternative causal variables to emerge (McCracken 1988; Zaltman 1997). We approached the study from the perspective of the gaps we had identified in the literature. Recognizing that relationships have beginnings, we were interested in the nature of interfirm interactions at the outset and the process of relationship initiation. Next, we focused on individual episodes and their impact on the evolution of a relationship. Finally, we sought to identify key change points that might have affected the course of a relationship over time. On average, we spent six interview days at each dyad (roughly three on each side) and talked to anywhere from 5 managers (at GE-RCI) to 18 managers (at Alpha-Delta). This number does not include the managers we observed at in-company meetings as part of the research process. Because of scheduling considerations and our need to absorb and understand the data, we staged the site visits and the research at each dyad over approximately one year. Because we did not investigate the three dyads simultaneously, the overall data collection process lasted more than two and a half years. Our study covered five broad areas of inquiry:
- Conditions in which the relationship was initiated;
- Initial terms and conditions of the contract or agreement;
- Changing nature of interaction between the two firms (i.e., the actions taken by each and evaluation of the actions by the other);
- Changes over time in each side's orientation to the relationship, associated expectations, and perceptions of outcomes; and
- Subsequent changes to the initial terms and conditions of the contract or agreement.
The interview sequence began with a focus on the existing agreement between the two parties and an attempt to identify its scope and antecedents. We posed the five broad areas of inquiry in the reverse order as shown previously. Because each question was a broad entrée to an in-depth inquiry, not all interviews provided complete coverage of all questions. That some questions were answered more elaborately than others reflected the knowledge of the relevant manager. To minimize interviewer bias and to broaden the inquiry beyond what was suggested by our preconceived frame, we asked for open-ended responses to questions about causal linkages and key transition points in the relationship (McCracken 1988; Zaltman 1997). It can be argued that such an approach emphasizes managers' subjective interpretations of events over the underlying objective data, but the managers we interviewed also were key decision makers, and thus it was their perceptions that ultimately counted. Moreover, industrial marketing researchers have used such interpretive data analysis (Flint, Woodruff, and Gardial 2002; Workman 1993).
We present our field data and interpretations thereof along a longitudinal dimension that captures the highlights of each dyad's evolution. We conclude our discussion by reconciling our field data with potential theoretical explanations and by considering how it extends extant theory.
Relationship Initiation and Initial Governance Structure
Table 1 provides a detailed description of the initial power positions of the two firms in each dyad. Because few suppliers in the 1960s were capable of manufacturing the quality and range of electrical components used in products manufactured by GE's various divisions, the prevailing channel pattern was for component manufacturers to route products to OEMs for original equipment and for subsequent repair. For example, if a Carrier air-conditioning system failed, repair and replacement of any GE parts and components would be exclusively accommodated by Carrier's distribution channel arrangements. In the prevailing system, independent contractors could not source GE repair parts. However, RCI was attempting to bridge this gap by selling repair parts to non-Carrier distribution outlets that would then make them available to independent contractors. Thus, GE brought technology, products, and brand power to the relationship, whereas RCI, having not yet developed the aftermarket networks, could promise only market access. At the outset, no formal contract was established between the two parties. Danny Schwartz, the chief executive officer of RCI, summarized the situation as follows.
My father knew that setting up the aftermarket channel would mean a lot of investments that RCI had to make. It was clear to him that the deal was a nonstarter if GE did not let him sell their products in this channel. Realizing that he was not in a position to demand any concessions, he went ahead based on the verbal commitment he received from GE managers. As I look back, at that point, there was no basis for him to expect that GE would keep its end of the bargain. Yet he plunged into the relationship with literally no safety net; he had no choice.
In the Ford-Peak relationship, when encouraged by Ford managers to select and prequalify a manufacturing site in anticipation of becoming a Ford supplier, Peak's founder, Early Yancy, refused to move forward without several concessions from the automaker:
Ford wanted me to acquire a plant for manufacturing PCBs, but they were already buying such components from other suppliers. I therefore asked Ford to give us subsidy payments to achieve parity in production efficiency with their incumbent suppliers. We also needed some commitment on purchase volumes before we could reasonably make an investment.
Ford managers, believing that their supplier sponsorship program was clear and fairly generous, had expected a quick, relatively simple negotiation process. However, Yancy, who believed that he needed to achieve parity in production efficiency with incumbent suppliers, had entered the negotiations with a focus on Peak's expected competition and risks. The final agreement required Ford managers to provide detailed specifications and volume commitments for parts made by Peak over a period of three years. In return, Peak was obligated to meet a time line and quality and cost targets established by Ford. To help Peak offset setup costs and achieve profitability as quickly as possible, Ford agreed to provide a $2.9 million subsidy over three years. For its part, Peak was expected ( 1) to gear up capacity to make and deliver parts at Ford's Q1 quality rating within a year of its plant becoming operational and ( 2) to develop (and review with the Ford supplier quality assurance engineer) a quality operating system within 120 days of the signing of the agreement. The agreement also included a rider that stipulated that the promised volumes were preliminary and would change as Ford acquired more information about the market for its vehicles. The concluding paragraph of the agreement laid out Ford's options to terminate if Peak did not comply with its terms.
Before initiating its current relationship with Alpha, Delta had had two other viable options: Beta Tires and Gamma Tires. Its relationship with the former had soured in 1995, when Beta exploited a shortage in the tire market to increase prices opportunistically in the middle of a contract. At the time, Alpha had tended to ration the limited volume of tires sourced from its European factory, typically wanting and being content to receive only 10% volume share of Delta's tire requirements. Things changed dramatically when Alpha commissioned its U.S. manufacturing capacity in 1996. An Alpha manager explained the following:
With a tire purchase volume three times that of any other North American customer, Delta was obviously a very important customer for us. We needed to fill our plant capacity. We decided to target a much larger share of their requirements than the approximately 10% we were currently getting. But knowing that our competitors would be thinking the same thing, we sought a long-term relationship. Fortunately, Delta managers were thinking of a longterm relationship as well. We were both on the same page, and after a series of particularly productive meetings between our two management teams, we wrote up this rather informal one-page document. It simply said that Delta would purchase at least 51% share of their earthmoving tires from us, and we assured them that we would give them the lowest prevailing price.
Reconciling with Previous Research
Extant research provides insights into the structure of arrangements and modes of governance. The various theories advanced suggest different approaches to structure. For example, the various bases of power (French and Raven 1968) engender asymmetrical relationship structures whereby the powerful parties dictate to the weaker parties and exact returns in proportion to their influence (McAlister, Bazerman, and Fader 1986; Porter 1980). Because social exchange theory assumes that parties expect that entering into and maintaining relationships is rewarding in the long run, it reaches quite different conclusions about relationship initiation (Blau 1968; Homans 1958). Specifically, social exchange theory implies that relationships begin small, with the parties interested more in gauging each other than in articulating formal expectations about the nature of relationship outcomes (Lambe, Wittmann, and Spekman 2001), but it stops short of inferring formal governance structures.
Transaction cost theory predicts that firms that invest in relationship-specific assets are likely to invoke formalized governance structures at the outset to prevent opportunistic exploitation (Heide 1994; Heide and John 1992). Governance modes in such relationships are hypothesized to be unilateral; that is, the powerful partner has the leverage to formulate rules and instructions and to impose decisions, and the weaker firm is concerned primarily with protecting itself from being exploited (Heide 1994). Along the same lines, absence of relationship history leads relationship contract theory to predict that governance at the outset will be by formal, legal agreements (MacNeil 1980). When power asymmetries prevail, the powerful party is likely to seek a safeguard: an explicit contract that enables it to coordinate activities, protect its reputation, exact concessions, and insist on formalizing its advantage in a written legal document (Lusch and Brown 1996). In contrast, because there are no well-accepted incomplete contracting frameworks (Maskin and Tirole 1999), the theory of incomplete contracts suggests that firms are content to sign contracts that do not detail contingencies but attempt to maximize contract value (Hart and Moore 1999; Tirole 1999).
Comparisons of the three field sites reveal clear patterns in the structure and governance of relationships from the outset. Consistent with theory, the powerful party dictated the terms of each arrangement and exerted influence in defining terms that established its advantage. Using French and Raven's (1968) widely adopted bases of power (reward, coercion, expert, information, and legitimacy), we can interpret GE's reward and expert power as deriving from its product range and brand equity. However, RCI's access to information through market reach, though untested, did not carry the same leverage as GE's power. Using the same framework, we can determine that Ford had the upper hand in its relationship with Peak. With respect to the Alpha-Delta relationship, Delta had more alternatives on paper (recall that its relationship with alternate supplier Beta had soured), but this advantage was offset by Alpha's size and technology expertise.
However, somewhat at odds with the extant literature, we find that the powerful partner did not always seek a formal agreement to protect its position. Research on the use of power indicates that the powerful party protects its interests by securing formal acknowledgment of its valuable assets. Yet GE avoided a written contract with RCI, and GE was under no obligation to offer RCI anything except to make its product range available. It was also unclear to GE what RCI's founder, Mark Schwartz, would bring to the relationship. We suspect that because GE had a strong branded product line and could have withheld supply to RCI at any time, it did not need a formal document to protect itself.
Ford's relationship with Peak began along the same lines: At the outset, Ford was interested in a handshake agreement that would enable it to hedge, if necessary, its future options in case Peak failed to deliver. When Yancy insisted on subsidies and volume commitments, Ford reverted to the prescriptions of extant theory, that is, the use of a formal agreement that addressed issues of technology transfer, quality standards, capacity reservation, and a market contingency to protect Ford's interests. That Ford submitted to a more well-specified arrangement than the one GE granted RCI was perhaps due to Yancy's astute exercise of power relative to Ford's minority supplier development program, whereby corporate headquarters directed operating divisions to seek sourcing relationships with and to increase purchases from minority suppliers. The corporate group's action can be explained by its desire to preserve a reputation for fair dealing that would enable Ford to continue to exchange transaction-specific investments under conditions of high uncertainty. Yancy leveraged Peak's status as a sought-after minority vendor to Ford's corporate purchasing group to negotiate what appeared to him to be a generous written agreement.
Although both partners were formidable in terms of size and resources, whereas Delta Mines had two alternatives, Alpha Tires' next best customer was far smaller in scale. Because Alpha's U.S. factory investments necessitated tire sales volumes and Delta needed quality tires at low prices, especially in the wake of its poor experience with Beta Tires, there was little jockeying for advantage in the contract terms. Sufficiently broad to align the two sides' goals reasonably well, the agreement was perhaps consistent with the prescriptions of the theory of incomplete contracts, whereby both sides are willing to enter into a contract that specifies neither all the contingencies nor specific performance criteria. The partners' intentions were mutually transparent; both sides knew they had to deliver whatever it would take to realize their objectives. Their mutual interdependence was sufficient to keep the two parties honest.
Is it because it is difficult to write contracts that cover all contingencies that the powerful parties did not seek formal contracts? Analyzing our field findings in the light of the various theoretical perspectives, we believe that this might be a partial explanation. In mature and commoditized markets with a wide range of available alternatives, the powerful party prefers an informal agreement that reduces investment to a minimum and accommodates easy exit in the event that things do not work out (in each of the three relationships we studied, the powerful party did not have much at stake at the outset, and the associated risk of relationship failure was minimal). Were a relationship to evolve favorably, it would always be possible for the powerful party to leverage its position and revisit the terms and conditions at a later date. An informal agreement might be viewed as an option that a powerful firm can buy at the outset of a relationship and subsequently cash in if the relationship succeeds and if the weaker party delivers added value. A weaker party that has and exercises the power to demand a formal agreement is likely to be countered by a powerful partner that seeks not only to protect its position but also explicitly to write the terms of the agreement in its own favor.
Ring and Van de Ven (1994) suggest that long-term cooperative relationships require that individual choices made in the present and realized in the future be congruent. Because the development of interfirm relationships is grounded in individuals' motivational and cognitive predispositions, managers need to engage in sense making and bonding processes (Weick 1979) that permit parties with initially different views of the potential purposes and expectations of a relationship to achieve congruency. This sensemaking process gives rise to psychological contracts (Argyris 1960; Levinson et al. 1962) that, different from most legal contracts, consist of unwritten and largely nonverbalized sets of congruent expectations and assumptions that transacting parties have about each other's prerogatives and obligations. The reluctance of the powerful party forces the weaker firm to take the lead in achieving congruence and in developing an informal (psychological) contract in lieu of a formal one. The weaker party will need to accept that the other side might be only marginally aware of mutual expectations from the relationship.
Our inferences from field research suggest the following propositions.
P[sub1]: In mature industrial markets, the more powerful party will prefer an informal agreement at the outset of a buyer-seller relationship.
P[sub2]: In such a relationship, the weaker party (which has the power neither to structure a formal agreement nor to set up formal safeguards to protect its investments) will initially attempt to construct a psychological agreement that paves the way for subsequent formalization.
Assessing the Partner's Performance in a Relationship
Table 2 describes the actions taken by the two firms in each dyad as the relationship moved beyond the initial stages. Moving beyond initiation, absent a formal contract and the onus being entirely on RCI, Mark Schwartz went out of his way to demonstrate his firm's value to GE. Danny Schwartz explained the following:
My father was able to bring a lot of value to GE from the start. For example, he personally devised and implemented a number of innovations, such as a universal mount option that enabled GE engineers to rationalize dozens of models of potential relays to six that significantly reduced GE's manufacturing costs. On the sales front, in addition to significant sales volumes, we realized significant price premiums [and thus margins] for GE on their product sales in the aftermarket. This was another unanticipated bonus for GE.
General Electric rewarded RCI's efforts by granting it product exclusivity in the aftermarket. These terms were not firmed up in the absence of a formal written contract, yet notwithstanding GE's ability to change the nature of the agreement, both parties accepted and honored the conditions. In the face of RCI's superb execution and performance, which far exceeded expectations, GE had no reason to be dissatisfied. Indeed, it was GE's confidence in RCI that led it to offer its partner exclusivity in the aftermarket. Meanwhile RCI continued to add value by breaking bulk and by offering small, appropriately packaged and labeled assortments. Moreover, it provided credit to wholesalers that frequently purchased GE products in small quantities.
Schwartz continued to develop new markets and introduce product innovations that further reduced GE's manufacturing costs, and GE managers responded by making institutional resources available to Schwartz (an action they had previously been reluctant to take) and by consciously eliminating their alternate distribution options. Therewith, arrangements ceased to be of the informal handshake variety. Volume quotas and targets by part number were discussed and agreed on, and agreements, albeit informal, began to emerge. For example, RCI was not to distribute competing products from other suppliers, and GE was to provide RCI exclusivity in the aftermarket distribution of its components. Although informal, the rules became etched into both sides' expectations and assumed the force of a written contract.
With key parameters set forth in its formal contract with Ford, Peak performed admirably by achieving the required quality rating before the projected 12 months. Yancy pointed out the following:
I think we impressed the Ford operating managers with the quality discipline we were able to quickly institute in our factory. We ensured that we did better than expected on all the points that Ford had specified in the contract.
Things began to change when, in the face of an unexpected downturn in car sales, Ford ordered only $700,000 instead of the promised $1.8 million worth of parts for the first six months. This deviation caused considerable operating difficulty for Peak, which was soon short of working capital. Ford corporate managers (not the electronics division operating managers) compensated Peak by advancing in the first year the entire $2.9 million in subsidy payments. Despite a solid beginning, Peak experienced financial difficulties during its first year of operations, incurring operating expenses that were twice what Yancy had anticipated. Ford perceived Peak's subsequent working-capital shortfalls as due to accounting errors. Ford further discovered that Peak's expenses were understated. Although Ford's operating managers believed that they had gone beyond their contractual obligations to shore up their partner, Yancy continued to perceive the origin of Peak's problems as Ford's failure to deliver the promised volumes. Moreover, Peak was unable to sell its spare capacity to other buyers, despite Ford managers' assertions that they had introduced the company to several potential buyers. Yancy's view was clear:
All our problems were due to Ford's failure to deliver the promised volumes. Quality is related to having a steady flow of orders. Without scale it is impossible to maintain quality. The entire operation suffered when the volumes did not come. We were back to square one with no apparent recognition of this fundamental operating principle at Ford.
Good intentions combined with performance achievement fostered a more durable relationship between Alpha Tires and Delta Mines. The account manager Steve Tyrone explained Alpha's approach after signing the initial agreement:
Having received Delta's commitment to purchase 51% of its earthmoving tires from us, we began to make serious investments in the relationship. First, we made sure that our tires were delivered on time and in the right quantities. A mine can come to a standstill if the trucks are not operational to move the ore. We also developed a comprehensive tire management system designed to increase tire life and reduce Delta's overall tire costs, and [we] established at our own expense, at each of the mines, local warehouses to speed tire replacement and repair and thereby significantly reduce Delta's operating costs as well. We also began to develop a new, low profile, low-pressure tire tailored specifically to Delta's operating conditions and worked with Delta's equipment vendor to design new hardware that would enable Delta to use the new tires without overhauling its equipment at considerable cost. These are just a few of the many actions we took that were not even discussed when we got into the new relationship with Delta.
A Delta manager explained his firm's response as follows:
Impressed with their performance, we offered Alpha a share of tires on our mine maintenance equipment as well [the original agreement was for earthmoving tires only]. Moreover, when the mining service contract at one of the mine sites came up for renewal, Alpha's initial bid was uncompetitive. Under normal circumstances, we would have rejected Alpha's bid and awarded the contract to [the] lowest-cost vendor. Instead, at the behest of several managers from the mines, we offered Alpha a chance to revise its original bid. Such an approach was virtually unheard of in our firm. Kudos to Alpha that they not only came back with very attractive pricing but [also] followed up with the highest service levels.
By 1998, Alpha's share of Delta's earthmoving tire requirements had reached 75%. In addition, it had a share of tires for mine maintenance equipment and an after-service contract for one mine.
Reconciling with Previous Research
As both sides moved beyond the initiation stage, their performance relative to expectations influenced the strength of the relationships. The research stream pursued by the IMP group conceptualizes an exchange relationship as a series of discrete episodes or interactions that yield economic and social outcomes (Håkansson 1982; Håkansson and Wootz 1979). It has been determined that evaluations of such interactions that provide opportunities for firms to interpret each other's actions (Anderson and Narus 1990; Doney and Cannon 1997; Morgan and Hunt 1994) are a factor in firms' decisions to continue or terminate a relationship (Friedman 1991) and to influence the nature of dispute settlement (Pruitt and Rubin 1986). Consistent with the aforementioned research, several conceptual process models of relationship development in marketing (e.g., Anderson 1995; Ford 1990; Håkansson 1982; Håkansson and Wootz 1979; Nevin 1995; Wilson 1995) have implied that firms evaluate each other's performance episodically (or periodically) and have posited that parties remain in a relationship as long as its rewards are satisfactory relative to its costs.
Research in relationship management has tended to focus on events that occur within the frame of the contract (formal or informal). As we discuss next, our findings suggest that two sets of actions determine performance in a relationship. One is the set of activities that is mutually agreed on formally or informally by the parties; the second involves activities that are outside of the "letter" of the agreement. For example, in the absence of a formal contractual arrangement, the GE-RCI relationship was driven almost entirely by the latter's unilateral efforts to perform beyond the informally agreed-on terms. RCI's extracontractual performance, on which RCI delivered superbly in terms of sales volumes and premium prices, soon became an informal expectation on GE's part. This "above-and-beyond" performance led to more structured expectations, and the virtuous cycle kicked in: GE formally awarded RCI exclusive aftermarket distribution rights for a variety of its products.
In contrast, despite heavy investments on both sides, the Ford-Peak relationship never got going either within or outside of the parameters of the agreement. Neither party met the other's expectations within the contractual terms; Peak believed that Ford reneged on its order volume commitments, and Ford believed that Peak was unable to achieve production viability despite having been compensated for its sales deficits.
The Alpha-Delta relationship illustrates complementary performance both within and outside of a contract. When Alpha provided a range of extracontractual technical assistance, Delta Mines responded by granting its partner greater-than-promised market share and additional business in unexpected areas. These extracontractual actions were mutually beneficial, and their immediate and direct impact on order quantities and service levels was a healthy shot in the arm for the relationship.
Our findings are consistent with research in services marketing that suggests that firms do not necessarily restrict their evaluations of each other to performance per agreement terms but often include actions outside of the agreed-on frame (Bitner 1995; Grönroos 1994). For example, from a customer's perspective, relationships are built not only on encounters that test a seller's ability to keep its promises but also on encounters that exceed expectations. It is during such encounters that customers glimpse sellers' efforts; each encounter has the potential to contribute to the customers' overall satisfaction and willingness to continue to do business with the seller (Bitner 1995; Bitner, Booms, and Tetreault 1990; Woodside, Frey, and Daly 1989).
A parallel can be found in the sales management literature. Traditionally, research on performance of sales representatives has focused on in-role aspects such as sales volume, evaluation of sales effectiveness, and so forth (Brown and Peterson 1993). More recently, the definition of performance has been broadened to include such extrarole aspects of performance as organizational citizenship behaviors, which Organ (1988) defines as discretionary behaviors on the part of a salesperson that directly promote an organization's effective functioning.
In the context of our field research, extracontractual actions such as the ones performed by RCI for GE and by Alpha Tires for Delta Mines significantly fortified a developing relationship. Such behaviors enhanced the perception of a partner firm's performance and thereby the focal firm's long-term orientation to the relationship. When firms perceive each other as having failed to deliver on their promises, such as in the Ford-Peak dyad, nothing can resurrect the faltering relationship. However, when extracontractual behaviors reinforce those within the agreement, as with the other two dyads, the relationship is advanced.
In summary, consistent with social exchange theory, we find that firms are more interested in gauging each other in the initial stages than in articulating formal expectations about the nature of relationship outcomes (Lambe, Wittmann, and Spekman 2001). It is in this context that the two sides evaluate each other's actions in the early stages. Consistent with previous research, we also find that at any time that partners fail to perform to agreed-on terms, the relationship is in jeopardy. Finally, our field investigations suggest that to be able to move a relationship to the next level, firms need to go beyond the terms of the contract. It is favorable performance in the extracontractual venue that influences and redefines the other party's expectations over time.
The following proposition on the role of actions outside of the agreement terms extends the prescriptions of extant research:
P[sub3]: Because the two parties' expectations about the nature of relationship outcomes are only loosely formed in the initial stages of a buyer-seller relationship, it is actions that go beyond the initial terms that help the two sides formalize and broaden their relationship over time.
This does not preclude actions outside of the terms of a contract that have unfavorable outcomes. Although our field investigations found no such cases, extracontractual behavior can have a destabilizing effect in such situations and can even lead to relationship dissolution.
Relationship Development and Maintenance
Table 3 describes how the three dyadic relationships evolved over time. Continuing growth in trust and commitment was interrupted for the first time when Mark Schwartz transferred control of RCI to his son, Danny Schwartz. This phase was characterized by reduced commitment on GE's part and the younger Schwartz's subsequent attempts to establish parity. Danny Schwartz explained this:
It was not long after I took over from my father that GE began to withdraw the exclusivity arrangement with RCI for one product after another. Being young and hotheaded, at first I threatened to terminate the relationship, but this had zero impact on GE. It took me a while to accept the new economic reality that the aftermarket had become sufficiently large and important for GE to serve through alternate distributors. It didn't matter that we had actually made that reality happen. It is only then that I started to reduce the disparity in our relationship. For example, I successfully established an alternate source of supply of "contactors" by helping Component Manufacturing, a small supplier, to tool up and manufacture devices at a cost lower than GE's. The entrepreneur who set up this operation was a GE engineer who was well known to my father. I also negotiated with GE's competitor, A.O. Smith, to develop a line of private label motors to be sold under the RCI brand. It did not take GE too long to realize our value and importance to them, but by then our relationship had entered a new phase.
Faced with Schwartz's success and with RCI as its de facto exclusive distributor, GE was forced to initiate a new phase in the relationship that was characterized by pragmatic acknowledgment that RCI possessed strengths and alternatives as well. The GE senior sales manager John Elliot was assigned to work with RCI to bridge the "misunderstanding." Elliot explained:
I meet everyone in the market, including those who request direct sales. I have informed Danny Schwartz of every such meeting in advance so that there is no suspicion of going behind his back. In every case, it has so worked that we have ended up convincing the party that going through RCI is the most efficient and effective way for us to service that customer. Our understanding goes beyond that. Every so often we will have excess in production that comes our way, and with only a phone call we will ship products to RCI's warehouse. Usually we have been able to work out a pricing that is profitable for both.
Reflecting on the changed mode at GE, Schwartz offered this complementary view:
John [Elliot] is part of all our planning meetings. We do not hide anything from him. He knows our customers, what they buy, our profits, everything. He knows the value we bring to the relationship. John has responded by becoming RCI's spokesperson in GE. He is always making sure that RCI is not treated unfairly. GE is a large firm and, sometimes, corporate directives can hurt individual relationships. John knows the market reality and his local presence and our mutual trust have helped educate GE. Our rapport with him has put the relationship back on an even keel.
Whereas cooperative behavior prevailed eventually in the evolution of the GE-RCI relationship, excessive distrust impaired and ultimately brought about the demise of the Peak-Ford relationship. At a time when neither side had any reason to trust the other, Peak's early demand for increased financial assistance was a clear effort to increase Ford's commitment. Yancy's subsequent achievement of quality, productivity, and other targets specified in the contract built his credibility with the electronics division managers and enhanced Peak's commitment level. When reasonable commitment levels had been established on both sides, the relationship appeared set to make gains, but a downturn in its markets prevented Ford from ordering the promised volume from Peak. Yancy explained his actions in response to this turn of events:
When Ford's operating managers were unrelenting of our request for additional subsidies generated by their shortfall in orders, we took up the matter with corporate purchasing[, which has] known me as a reliable supplier of coal for a long time.
Disinclined to view the volume shortfall as a failure on their part and resenting Peak's dependence balancing efforts, the operating managers developed distrust for Yancy. Trust was further eroded and Yancy's capability and credibility questioned when Ford discovered several accounting and forecasting errors by Peak. The trust pendulum had swung dangerously to the other side. Lack of trust between parties began to affect the firms' commitment. Ford management had decided that unless Peak was able to demonstrate its long-term viability on its own, the relationship would be terminated. Ford's willingness to consider Peak a PCB supplier had been a function of the trust that had developed between Yancy and Ford's corporate managers in the coal contract relationship. However, this trust had not transferred automatically to Yancy's relationship with the operation managers who were responsible for the Peak relationship. The fledgling trust that was built through Yancy's performance within the contract was insufficient to weather an economic downturn and dissolved altogether when both sides failed to live up to the terms of the agreement. More fundamentally, it was lack of trust that impeded the building of commitment.
Despite historically high levels of potential interdependence, the Alpha Tires-Delta Mines dyad was initially a marriage of convenience. The starting point for a renewed relationship between the two firms was the personal and professional relationship between Mike Copper, the head of corporate purchasing for Delta, and Steve Tyrone, account manager for Alpha. Copper recalled the following:
Having done an "ABC" analysis, it became apparent that there were about half a dozen items at the mine that accounted for 80% of our purchase dollars. It made sense to develop a relationship with a few selected vendors for each of these items. We had transitioned the top two items to that mode with impressive results in the last year, and I found in Steve [Tyrone] a willing ally in working with us on the No. 3 item: earthmoving tires.
Tyrone corroborated Copper's view:
Copper was interested in building strategic alliances with Delta's key suppliers. With our North American plant commissioned, we were wedded to the idea of moving from competitive transactional exchanges to more collaborative long-term relationships. It was central to our strategy of gaining production economies at the new plant. Our increased dependence on Delta's purchase volumes to support the new U.S. manufacturing facilities and Delta's disappointing relationship with Beta Tires had provided the necessary impetus for Copper and [me] to work together.
The development of the relationship between Tyrone and Copper brings to mind observations by Ring and Rands (1989) of situations in which transacting parties reached informal understandings before their organizations negotiated and committed to legal contracts. The two men engaged in intense sense-making activities over an extended period that yielded a strong psychological contract regarding the need for cooperation between their respective organizations. They trusted each other's ability to commit their respective principals to the venture and had long-range plans that anticipated the success of the initial cooperative effort. As the two men experienced each other's ability to deliver the terms of their psychological contract, their reliance on trust deepened. Consequently, the men increasingly worked outside of the terms of the formal agreements between their organizations. In summary, although specific actions to be taken with respect to certain provisions of legal contracts were not yet spelled out, informal norms and understandings of acceptable behavior that stemmed from trust enabled the parties to proceed to executing the informal commitments implicit in their psychological contract, which then set the stage for the establishment of formal commitments.
In contrast to Mark Schwartz's initial emphasis on building personal trust with the GE managers, Alpha's approach involved not just personal trust but also extensive commitment by investing people, processes, and the organization in the relationship. Copper explained this as follows:
As the mine personnel [came] to see what Alpha was willing to do, they responded by both increasing the percentage of earthmoving tires purchased from Alpha and expanding the scope of the relationship to other tire categories as well. The key to the two sides growing closer to [each other] were all the actions that the Alpha executives and tire service personnel took on their own initiative. I think if these individuals on Alpha's account and service teams had attempted only to execute the letter of the contract, it would have been unlikely that our people would have raised our firm's commitment.
Even as the relationship developed, personnel changes occurred on both sides. When Tyrone was promoted within Alpha and moved to Asia, a veteran Alpha salesperson assumed management of the Delta account. At about the same time, the relationship having passed several designated milestones, Copper handed off management responsibility to another Delta materials manager. The transitions affected the trust levels in the relationship. As we observed in the other relationships, trust did not transfer seamlessly, and the chemistry between Tyrone's and Copper's successors was not the same. Copper explained, "Although the two managers were working constructively, they had not gone through what Tyrone and I had. The relationship between the two was not as intense as my relationship with Tyrone."
Reconciling with Previous Research
Our findings from the three field sites reveal a process whereby trust and commitment are formed (or not) and contribute to the development (or destruction) of a relationship. Our research indicates that trust invariably forms between individual people in an organization, whereas commitment is an interorganizational phenomenon. Thus, we infer that trust and commitment operate at different levels in a relationship: Trust is an interpersonal construct, and commitment is an interorganizational one. We believe that this distinction is a nuanced departure from extant wisdom. Much of the literature on relationship marketing fails to distinguish interpersonal from interorganizational effects. Some scholars have assumed that both trust and commitment operate at individual levels (e.g., Ganesan 1994; Morgan and Hunt 1994); others have assumed that the trust and commitment variables operate across individual and organizational levels (e.g., Doney and Cannon 1997). Focusing on the constructs themselves, most scholars are indifferent to the levels at which they operate. To some extent, our conclusions illuminate previous findings of the IMP group, which has advocated the use of interpersonal and interorganizational variables to study buyer-seller relationships. In addition, Wilson's (1995) conceptualization of structural bonds that arise from investments made by firms and social bonds that grow out of mutual personal relationships perhaps gains new meaning in light of our inference.
We believe that our finding that trust forms between individuals and not organizations is a consequence of the context of our study, that is, relationships in commoditized industrial markets. Such markets are characterized by equivalent products and technologies across vendors, and absent any significant uncertainty, customers expect vendors to deliver automatically on product quality and performance. Trust in a firm is consequently a given, that is, a hygiene factor. Relationship management, service, and support become the main differentiators and the basis for judging vendor performance. Promises made and kept by account executives become the cornerstones of such relationships, and trust between individual people in the firm gains prominence (and possibly dominates other forms of trust). Expressed as investments made by firms, commitment begins to emerge at the organizational level. In summary, our research provides a fine-grained interpretation of the trust and commitment constructs and the levels at which they operate:
P[sub4]: In buyer-seller relationships in mature industrial markets, trust is mainly built between individuals across the dyad, and commitment is formed and formalized at the firm level.
Another conclusion that emerges from our observations is the relationship between trust and commitment. Whereas trust between Mark Schwartz and GE managers engendered a higher level of commitment between the respective firms, distrust between Yancy and Ford managers was responsible for weakening Ford's commitment to Peak. That trust drives commitment is consistent with the work of Morgan and Hunt (1994). However, when we pin the development of trust at the individual level, our causal flow also has hitherto unexplained implications for how organizations build commitment based on their managers' perceptions and actions.
It is possible that the causality goes both ways (Morgan and Hunt 1994). For example, in the face of Alpha Tires' decision to site a North American factory and Delta Mines' promise to grant Alpha at least a 51% share of its tire purchases, it might be argued that an organizational commitment preceded the trust bonds that Tyrone and Copper developed. If this were true (i.e., if commitment causes trust), the Ford-Peak partnership should have prospered as well, because both parties entered the relationship with strong commitments. That Ford's commitment deteriorated consequent to poor performance by Peak leads us to believe that the directionality is from trust to commitment rather than the other way around. However, many times a commitment platform is prerequisite to individuals' engagement in trust-inducing interactions. There is no doubt that such a platform preceded individual interactions in the Ford-Peak and Alpha-Delta dyads. However, whether that platform is strengthened or weakened depends on how people on the two sides of a dyad engage one another. Tyrone and his colleagues at Alpha and Copper and his colleagues at Delta reinforced their firms' commitment platform by engaging in mutually trustworthy actions.
In conclusion, our findings build on research that has established a causal link between trust and commitment. They suggest that interpersonal dynamics affect interorganizational orientations, whereas the opposite is not true. Our results also have implications for how firms in mature industrial markets manage interfirm relationships when trust between individual employees breaks down as a result of personnel turnover. The following propositions summarize our findings:
P[sub5]: The presence (or absence) of interpersonal trust in buyer-seller relationships in mature industrial markets facilitates the development (or destruction) of interorganizational commitment.
P[sub6]: The existence of interorganizational commitment facilitates only the formation of new interpersonal relationships, not the subsequent development of trust.
The fresh insights teased out of the four propositions developed herein have informed the identification of five processes that explain the evolution of buyer-seller relationships in mature industrial markets.
Leveraging Relative Position and Power to Define Initial Agreement Terms
Asymmetry in the resources that each party controls typically results in the two sides of a dyad not being equally dependent on each other. Thus, power dependence is usually skewed in favor of a firm at the outset. Given this condition, theory suggests that the powerful party will attempt to extract concessions. We suggest that the powerful party will not formalize its position because it has other levers to protect itself. The two sides will arrive at an agreement that guides the exchange of products or services, defines the broader infrastructure that supports the relationship, and provides both sides with a clear understanding of what to expect from each other. The weaker party, which usually lacks the leverage to force a protective written agreement, needs to recognize and accept that a powerful firm will control a disproportionate amount of the initial available surplus.
Evaluating Performance and Converting It to Interpersonal Trust and Interorganizational Commitment
As a relationship moves into the execution phase, the parties will attempt to implement the letter and spirit of the agreement. At this stage, each side's performance is noted and monitored by the other side and measured against a benchmark, which typically is expectations. Conceptually, we distinguish between performance within and outside of the terms of an agreement. It is often the case that the two sides agree to specified performance levels and construe anything less to be a shortfall. To understand the impact of performance outside of the contract terms, it is helpful to examine unanticipated deviations from conditions outlined in a contract. Contract execution occasionally encounters difficulties. For example, a buyer might request an immediate shipment or midstream enhancement in quality standards, or a seller might develop a pressing need for cash flow and thus advance payments. Accommodation of such requests entails adjustments that significantly increase the immediate cost of executing the extracontractual request. The cost increases will far outweigh any short-term benefits to the firm that accommodates the requests. Although refusal to take action in response to such requests would, in theory, not violate the agreement, dyad members view fulfillment of such requests in a highly favorable light.
The development of trust and commitment is built one interaction (or episode) at a time. We suggest that trust is built and maintained at the individual level and that commitment is a broader organizational phenomenon. Actions within and outside of the terms of an agreement have a differential impact on trust and commitment. Performance within the contract terms engenders mutual respect for a cross-section of two organizations and is extremely important for building both trust between individual members of the firm and commitment between firms. Performance outside of the terms of a contract is more important for jumpstarting the trust-building process between individuals.
Transferring Interpersonal Trust to Interorganizational Commitment
Trust between individuals spurs the development of commitment between firms. Individuals who build trust in each other will transfer this bond to the firm level. The cumulative effect of several such transfers is an increase in interfirm commitment. Our formulation further suggests that the core of relationship development is the personnel who manage the dyad. When trust levels increase sufficiently, the managers will encourage their firms to invest in and thereby increase commitment to the relationship. We do not expect the reverse effect. Our conclusion that interorganizational commitment does not foster interpersonal trust has implications for relationship management. Managers will not gain from their firms' commitment if they do not create an environment that fosters trust building between contact personnel on both sides of the dyad.
Increasing Interpersonal Trust to Balance Initial Contract Terms
Interpersonal trust affects not only interorganizational commitment but also performance. Individuals who begin to trust each other become motivated not only to meet their partner's expectations within the agreement terms but also to step outside of the terms to help the other side in times of need. Such actions elevate a partner's evaluation of both performance and trust. The flip side of this process is that individuals who have reason to mistrust a partner's contact personnel are less motivated to uphold their end of the bargain and are unwilling to take any actions outside of the contract terms. This reinforces the reasons for mistrust and precipitates a rapid breakdown of trust and, in turn, commitment in the relationship.
Increasing Interorganizational Commitment to Balance Initial Power Asymmetries
A powerful party that has previously extracted a disproportionate share of the surplus will relent somewhat. In economic terms, this is an "adjustment" for the lower risk that the powerful party now perceives in the relationship. The contract, though still not fully equitable, will likely be more so than previously. These exchanges and subsequent negotiations will continue in a virtuous cycle. In a vicious cycle, the opposite is the case. With reduced commitment, the two firms will exploit their positions and leverage any asymmetries to their respective advantage. Negotiations will become increasingly difficult because the firms will perceive each other's demands as unreasonable. The relationship will become competitive, turn sour, and eventually collapse.
Summary
We have demonstrated that through these five processes, healthy relationships can be built and sustained regardless of initial power asymmetries. By the same token, a balanced power situation at the beginning of a relationship does not guarantee that a virtuous cycle of commitment and trust will prevail.
Drawing on our field research findings as well as theories of power, dependence, contracts, performance, commitment, and trust, we developed six propositions that capture the process of relationship development and identified five processes that illustrate how the propositions play out in the evolution of a relationship. Our findings suggest that relationships are built on the intentions and interactions of firms and individuals. More specifically, they emphasize the role of initial power-dependence asymmetries in the development of contracts and their subsequent reduced impact in relationships that are characterized by high degrees of commitment and trust. We hypothesize that interpersonal trust enhances interorganizational commitment over time and that high levels of trust and commitment can, in turn, neutralize the impact of initial power-dependence asymmetries. We do not preclude the simultaneous existence of these various mechanisms; rather, we believe that their relative effects on relationship development change over time, thus enabling weaker firms to thrive in equitable relationships with powerful partners. Our field findings also lead us to speculate that initial symmetries in power and dependence at the beginning of a relationship do not guarantee its longterm viability and success. Even in this situation, we believe that satisfactory performance and the subsequent development of trust and commitment are critical to successful relationship management.
The five processes offer practitioners and academics alike several guidelines for managing industrial buyer-seller relationships. Management of relationships in commodity markets is much more than trying to "tie" the other side to a long-term contract. Customers are seldom interested in writing well-specified contracts at the outset in such situations. A weaker party might benefit from working to develop an informal contract rather than demanding a formal written one. As the relationship moves to the execution stage, it is important that a firm keep its promises. This not only creates a favorable perception of performance but also crystallizes and shapes the other side's expectations over time. Having performed satisfactorily within the agreement terms, a firm's willingness to step outside of the agreement and take positive actions that benefit the other party will dispose the other side to reciprocate in kind and, more important, influence how the relationship evolves over time.
We also find that relationship development is not smooth and monotonic through the various stages. Internal and external changes can derail even a well-set relationship. The five processes we have described offer account managers guidance in weathering rough seas in a relationship. For example, firms that are interested in pursuing long-term relationships should appoint boundary-role individuals who are capable of engendering trust in the relationship. Because our research indicates that interpersonal trust can build interorganizational commitment, it is just as important to pick and empower the right people at the interface as it is to support a relationship with appropriate institutional commitment. Moreover, at times of organizational change, firms should convert trust into institutional commitment before effecting sensitive personnel changes.
Limitations and Future Directions
Our contributions are directed at building a theory of relationship management in mature, industrial markets. As prescribed by grounded theory development, our data are based on field investigations that combine multiple participant interviews, observation, and documents related to three buyer-seller dyads. As is typical in such studies, the limited number of firms included in our study constrains the generalizability of our interpretations. Nevertheless, we believe that our careful selection of the three dyads mitigates this circumstance and that our study has implications for the relationship management literature and for further research.
Buyer-seller relationships in industrial markets are complex. It is clear that the field is neither neatly bifurcated into "relationship" and "transactional" dyads nor elegantly explained by any unifying theory of managing relationships. Our study contributes to a growing body of process models of relationship evolution in industrial markets. However, the proof must come from an empirical verification of the processes we have described. Such empirical work will require longitudinal studies of larger samples. In all three relationships, we found that firms evaluated partner firms' performance on the basis of the outcome of an action taken, not the action itself. A worthwhile area for further research is to identify whether and when performance evaluation based on outcomes or actions is more critical to the development of a relationship.( n1)
Our objective for this research has been to develop a deeper understanding of the processes that affect relationship dynamics. We believe that this is crucial to improving the state of the art of relationship management strategies in industrial markets, and we encourage research in this area that expands on the IMP group's pioneering work. This study is an early step in that direction.
Both authors contributed equally, and their names are listed in alphabetical order. The authors acknowledge the financial support provided by the Division of Research at Harvard Business School. The authors also thank Professors Rajiv Lal, Al Silk, Ben Shapiro, and Roy Shapiro as well as the three anonymous JM reviewers and the participants at Babson College's Research Symposium for their many useful comments on previous versions of this article.
( n1) We thank a reviewer for bringing this to our attention.
Legend for Chart:
A - Dyad
B - Description
A B
GE-RCI RCI's founder, Mark Schwartz, sought the
relationship with GE. He offered to open the
aftermarket channel route for GE, which had
until then focused on OEM sales. Absent the
support of GE, on which it was totally
dependent for products, RCI would have
been in no position to execute its distribution
plan. Because it was quite busy supplying
large OEM customers, GE did not need RCI;
moreover, at the time, RCI was only a small
entrepreneurial start-up with no significant
revenue. GE was the more powerful partner.
Ford-Peak Wanting to outsource its PCB requirements,
Ford's electronics division, in keeping with
the company's policy of expanding support
for minority suppliers, approached the
minority business owner Earl Yancy, a highly
rated supplier of coal to the company. Yancy
was seeking an opportunity to offset the
cyclical nature of his commodity coal business
with an entry into value-added manufacturing,
but he lacked both technical
expertise and market access. Ford provided
both and was more powerful than Yancy's
PCB supply firm (Peak Electronics).
Alpha Tires- Alpha Tires was one of three suppliers to
Delta Delta Mines. The largest mining customer in
Mines North America, Delta accounted for nearly
30% of industry purchases of earthmoving
tires, with an estimated value of $100 million.
Whereas both Alpha and Delta possessed
size and wherewithal, it was the former
that sought a strategic supplier
relationship with the latter. Alpha needed
Delta's tire volume to enhance use of its
new $500 million U.S. manufacturing facility.
Rebounding from a recent poor experience
with its preferred supplier Beta Tires, Delta
viewed Alpha's willingness to develop
customized solutions as a favorable, positive
step. The power structure seemed to be
slightly skewed in Delta's favor. Legend for Chart:
A - Dyad
B - Description
A B
GE-RCI Soon after initiating the relationship with GE,
Mark Schwartz worked with its managers to
develop new products through a cooperative
problem-solving approach. GE reciprocated
by granting RCI exclusivity for its entire line
of contactors, capacitors, and relays. In the
first ten years of the relationship, sales
volume grew to $20 million. GE realized
significant gains in a market that (at that time)
it hardly knew existed, and its products and
brand name helped RCI establish itself as a
master distributor.
Ford-Peak Exceeding the expectations of the agreement
signed with Ford, Yancy achieved
Ford's Q1 quality disciplines in his factory in
half the required time. Ford was unable to
deliver the $1.8 million production volume
promised in the first year but still made good
on its assurance of providing Peak a $2.9
million start-up subsidy. However, Peak
experienced financial difficulties, which
Yancy attributed to Ford's lack of commitment
to the promised volumes.
Alpha Tires- Armed with the pledge that it would be
Delta granted in excess of 51% of Delta's
Mines earth-moving tire purchases, Alpha made
significant investments in the relationship.
Delta responded by increasing Alpha's share of its
purchase volume to nearly 75% over an 18-month
period and by offering Alpha a significant
share of other tire requirements not
covered in the agreement. Delta not only
mitigated the setback with Beta Tires but
also gained the cooperation of a leading
technological provider of mining tires at
attractive prices. Legend for Chart:
A - Dyad
B - Description
A B
GE-RCI Mark Schwartz and GE managers developed
a high level of trust in one another.
GE granted RCI exclusivity and a private
label option for a few selected product
lines. When Danny Schwartz succeeded
his father, GE began to withdraw RCI's
product exclusivity. The younger Schwartz
responded by developing alternative suppliers.
GE subsequently appointed a new
account manager, John Elliot, who brought a
sense of pragmatism to the relationship and
steadily built bonds of trust with Danny
Schwartz.
Ford-Peak Peak's cost overruns and Ford's reduced
purchase volumes soon steered the relationship
off course. The electronics division
managers resented that Yancy had
bypassed them to seek assistance directly
from Ford's corporate managers. Accounting
irregularities further clouded their attitude
toward Yancy. For his part, when promised
volumes did not materialize, Yancy believed
that he was justified in seeking what he
considered a reasonable level of compensation
directly from Ford's corporate managers.
With such divergent views of the same set
of events, it was not long before the two
sides lost trust in each other. Ford managers
subsequently terminated the relationship.
Alpha Tires- Delta's one-page agreement to deliver Alpha
Delta more than 51% share of its tire volume was
Mines broadened beyond Tyrone and Copper, the
respective architects of the relationship.
Alpha continued to expand its service and
support, and Delta signaled its satisfaction
by increasing Alpha's share of its purchases
of earthmoving tires and by granting Alpha a
larger share of its purchases in other tire
categories. With a change in key personnel
on both sides, Delta managers revised their
perception of Alpha's service and its share
of tire purchases accordingly. Under the
guidance of new managers on both sides of
the dyad, the relationship slipped somewhat,
though Alpha continued to receive a
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GE-RCI
RCI was founded in 1946 as a GE motor-repair franchise. In its subsequent role of master distributor, which it assumed in 1962, RCI sold GE parts to wholesalers, which in turn sold them to thousands of electrical contractors that installed and repaired commercial air-conditioning and refrigeration equipment. Because RCI's founder, Mark Schwartz, had initiated aftermarket distribution, RCI was able to secure "exclusive" distribution rights; that is, GE would not appoint any other master distributor or wholesaler to supply parts to the aftermarket. Previously, GE had sold parts only to OEMs that assembled air-conditioning and refrigeration systems and maintained them through equipment distributors. In 1975, RCI obtained (nonexclusive) distribution rights for GE's fractional horsepower motor, thereby becoming a distributor for a wide variety of GE electrical components and parts.
Family circumstances forced an unwilling Danny Schwartz, the 23-year-old son of the founder, to begin to assume ownership responsibilities in 1974. Beginning in the mid-1970s, GE began to strip away RCI's exclusive rights to distribute various GE products. With the demise of Mark Schwartz in 1986, RCI's ownership transition was complete; the younger Schwartz was in charge of all business matters. By then, with annual sales of approximately $60 million, RCI was among the largest electrical master distributors. With approximately 30% of RCI's share, GE was the dominant supplier.
From the mid-1970s to the 1990s, against the backdrop of GE's efforts to bypass RCI and restrict its role, RCI had continued to be a valuable distribution partner for GE. What had been a strong relationship during the 1960s and early 1970s suffered during the 1980s, seemed to steady in the 1990s, and then stabilized.
Ford-Peak
Earl Yancy founded Peak Electronics in 1989 to supply PCBs to the $80 billion Ford Motor Company under the automobile giant's minority supplier vendor program. Peak's PCB products formed the interconnection in electronic circuits in the antilock brakes on rear wheels of selected Ford cars. Peak's appointment came in the wake of Ford's initiative to broaden its engagement with minority-owned suppliers of component parts.
The idea was to build Peak into a $20 million-a-year PCB supplier. The relationship, carefully structured and negotiated over 18 months, came to fruition in January 1990, when Peak began initial operations. By November 1990, Peak had made major strides in achieving business plan objectives that included quality targets set by Ford and the institution of manufacturing discipline. Sales volume increased slowly but steadily in the first few months of 1991. Soon thereafter, the relationship began to founder. Peak, unable to achieve a state of financial viability without extracontractual assistance from Ford, attributed the problem to Ford's inability to provide the agreed-on steady stream of orders. Both Peak and Ford tried to resolve matters, and for a brief period, it appeared that their differences might be patched up. The relationship began to encounter frequent difficulties again, though, and in 1994, Ford attempted to dissolve the relationship. Not unexpectedly, Yancy fought vigorously to maintain the relationship and continued to assert that Ford was primarily responsible for its deterioration. In the end, the misunderstanding and distrust were far too gaping to bridge, and the relationship collapsed.
Alpha Tires-Delta Mines
Alpha Tires, a leading tire manufacturer, had 1996 sales in excess of $10 billion. Earthmoving tires accounted for roughly $1 billion, which made Alpha the No. 2 player (behind Beta Tires) in earthmoving tires. Earthmoving tires were typically sold to mining operations that fitted them to earthmoving equipment, such as haulage trucks. Because of the demanding conditions and around-the-clock equipment operation, earthmoving tires were a top-four supply item purchased by mines.
Delta Mines, a $2 billion conglomerate that operated several mines in North and South America, was the largest buyer of earthmoving tires in the United States. Until 1996, the relationship between Alpha Tires and Delta Mines was lukewarm, and Alpha maintained a No. 2 or No. 3 supplier position. Delta managers viewed Alpha as a good-quality, though high-priced, tire supplier and as not particularly customer responsive.
Alpha's worldwide market share of 20%-25% compares with Beta Tires' 70%-75% and Gamma Tires' 5%-10%. In 1996, Alpha, which had previously produced all its earthmoving tire requirements at its factory abroad, made a strong strategic statement by building a state-of-the-art tire facility in the United States. Over the next four years, Alpha's market share at Delta Mines increased steadily from 10% to nearly 75%, at the expense of Beta Tires, before adjusting to approximately 55% by the end of 2000.
~~~~~~~~
By Das Narayandas and V. Kasturi Rangan
Das Narayandas is Associate Professor of Business Administration, Harvard Business School (e-mail: nnarayandas@hbs.edu). V. Kasturi Rangan is Malcolm P. McNair Professor of Marketing, Harvard Business School (e-mail: vrangan@hbs.edu).
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Record: 25- Building Brand Community. By: McAlexander, James H.; Schouten, John W.; Koenig, Harold F. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p38-54. 17p. 7 Diagrams, 5 Charts. DOI: 10.1509/jmkg.66.1.38.18451.
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Building Brand Community
A brand community from a customer-experiential perspective is a fabric of relationships in which the customer is situated. Crucial relationships include those between the customer and the brand, between the customer and the firm, between the customer and the product in use, and among fellow customers. The authors delve ethnographically into a brand community and test key findings through quantitative methods. Conceptually, the study reveals insights that differ from prior research in four important ways: First, it expands the definition of a brand community to entities and relationships neglected by previous research. Second, it treats vital characteristics of brand communities, such as geotemporal concentrations and the richness of social context, as dynamic rather than static phenomena. Third, it demonstrates that marketers can strengthen brand communities by facilitating shared customer experiences in ways that alter those dynamic characteristics. Fourth, it yields a new and richer conceptualization of customer loyalty as integration in a brand community.
For decades, marketers have sought the Holy Grail of brand loyalty. Just as the legendary grail of Arthurian quest held the promise of extended life and renewal, marketers attribute to brand loyalty and its sister icon, customer retention, the promise of long-term profitability and market share (Bhattacharya, Rao, and Glynn 1995; Reicheld and Sasser 1990). Unfortunately, marketing's knights-errant face a daunting problem: They have not fully understood what the grail looks like or where it can be found. As a result, marketers have devised strategies and designed programs to build loyalty with limited information about their real impact or ultimate consequences (Dowling and Uncles 1997; Fournier, Dobscha, and Mick 1998).
To address this problem, we sought out places where we could find loyal customers, and we studied the processes that led to their loyalty. What we found were consumers forging and strengthening a variety of relationships. Many recent quests for the loyalty grail have ventured into the area of relationship marketing (see Garbarino and Johnson 1999; Gruen, Summers, and Acito 2000; Price and Arnould 1999). As a new imperative in marketing practice (Berry 1995; Deighton 1996; Gundlach, Achrol, and Mentzer 1995; Webster 1992), a focus on customer relationships is presented as an avenue to competitive advantage (Berry 1983, 1995; Kalwani and Naryandas 1995; Peppers and Rogers 1993). The relationships we observed were in many ways different from those that have been the focus of most prior research (see Fournier 1998). We found consumers and marketers jointly building communities. In exploring those communities, we discovered new ways of understanding loyalty.
A community is made up of its member entities and the relationships among them. Communities tend to be identified on the basis of commonality or identification among their members, whether a neighborhood, an occupation, a leisure pursuit, or devotion to a brand. What seems relatively self-evident about communities is the extent to which they are instrumental to human well-being. Through communities, people share essential resources that may be cognitive, emotional, or material in nature. Among all the things that may or may not be shared within any given community-things such as food and drink, useful information, and moral support-one thing seems always to be shared: the creation and negotiation of meaning.
With no more than a cursory look at contemporary society, we can identify communities whose primary bases of identification are either brands or consumption activities, that is, whose meaningfulness is negotiated through the symbolism of the marketplace. Scholars have grappled conceptually and empirically with such communities and have examined some of the dimensions that shape them (see Arnould and Price 1993; Celsi, Rose, and Leigh 1993; Fischer, Bristor, and Gainer 1996; Granitz and Ward 1996; Holt 1995; Moore, Mazvancheryl, and Rego 1996; Muniz and O'Guinn 1996, 2001; O'Guinn 1991; Schouten and McAlexander 1995).
Our understanding of marketplace communities begins with what Boorstin (1974, p. 89) describes as consumption communities, which he characterizes as "invisible new communities ... created and preserved by how and what men consumed." He observes that in the emerging consumer culture that followed the industrial revolution, the sense of community in the United States shifted away from the tight interpersonal bonds of geographically bounded collectives and into the direction of common but tenuous bonds of brand use and affiliation:
The modern American, then, was tied, if only by the thinnest of threads and by the most volatile, switchable loyalties, to thousands of other Americans in nearly everything he ate or drank or drove or read or used. Old-fashioned political and religious communities now became only two among many new, once unimagined fellowships. Americans were increasingly held to others not by a few iron bonds, but by countless gossamer webs knitting together the trivia of their lives. (Boorstin 1974, p. 148)
Visit Camp Jeep or a HOG (Harley-Davidson) rally. Participate in a Saturn Homecoming. Go to a DeWalt contractors night at the local lumberyard. In each of these settings, and others, the so-called invisible consumption communities described by Boorstin (1974) suddenly become visible. Although we found Boorstin's concept of consumption communities attractive, in our own field research we discovered phenomena, such as subcultures of consumption (Schouten and McAlexander 1995), that more closely resembled his "iron bonds" than his "gossamer webs." We apparently were seeing a different kind of community.
Another kind of collective, a brand community, is defined by Muniz and O'Guinn (2001, p. 412) as "a specialized, on-geographically bound community, based on a structured set of social relationships among users of a brand." The communities they describe in their insightful work are a better fit for the types of relationships we encountered in the field than were Boorstin's consumption communities. Muniz and O'Guinn's study of brand community, along with other work in the realm of consumer collectives (Holt 1995; Schouten and McAlexander 1995), indicates that intercustomer relationships figure importantly in the loyalty equation. In our research, we have found the emphasis on social relationships among customers to be correct but not entirely complete. Other entities and relationships weave through the fabric of community.
Muniz and O'Guinn (2001) envision a brand community as a customer-customer-brand triad. We suggest an extension of their model as well as a shift of perspective. Construing brand community as a social aggregation of brand users and their relationships to the brand itself as a repository of meaning (see Aaker 1996; Aaker 1997; Gardner and Levy 1955; Grubb and Grathwohl 1967) overlooks other relationships that supply brand community members with their commonality and cultural capital (Holt 1998). Customers also value their relationships with their branded possessions (see Belk 1988; Holbrook and Hirschman 1982; Wallendorf and Arnould 1988) and with marketing agents (see Doney and Cannon 1997; Dwyer, Schurr, and Oh 1987) and institutions (see Arnould and Price 1993; Belk 1988; Bhattacharya, Rao, and Glynn 1995; Brown and Dacin 1997; Gruen, Summers, and Acito 2000; Morgan and Hunt 1994; Price and Arnould 1999) that own and manage the brand. Granting community-member status to the branded product and to the marketer situates both the customer-brand dyad (the traditional focus of brand loyalty scholars) and the customer-customer-brand triad (Muniz and O'Guinn's [2001] elemental brand community relationship) within a more complex web of relationships (see Figure 1). We take the perspective that brand community is customer-centric, that the existence and meaningfulness of the community inhere in customer experience rather than in the brand around which that experience revolves.
The Dynamic Nature of Brand Community
Research on consumption and brand communities identifies several dimensions on which they differ, including geographic concentration, social context, and temporality. Typically, these dimensions are treated as static identifiers in typological discussions (see Fischer, Bristor, and Gainer 1996; Granitz and Ward 1996; Tambyah 1996). Scholars have yet to fully examine these dimensions of brand community as dynamic continua or shifting mosaics, yet this is necessary if they are to understand how the amorphous consumption communities described by Boorstin (1974) somehow coalesced into the visible, vibrant, and multifaceted brand communities that we encountered in the field.
Geography is one dimension on which communities differ. Although brand communities have been defined as nongeographically bounded (Muniz and O'Guinn 2001), they may be either geographically concentrated (Holt 1995) or scattered (Boorstin 1974). They may even exist in the entirely nongeographical space of the Internet (Granitz and Ward 1996; Kozinets 1997; Tambyah 1996). Studies have tended to be situated statically on the dimension of geographic concentration, even if they consider multiple geographies. What can be learned if this dimension is treated dynamically? For example, how does a normally scattered brand community respond to temporary geographic concentrations, such as in the case of a brandfest (McAlexander and Schouten 1998)?
Related to, but not dependent on, geographical concentration is the dimension of social context. Interactions within a brand community may be rich in social context or nearly devoid of it (Fischer, Bristor, and Gainer 1996). Communication may be predominantly face to face, mediated by electronic devices, or a function of corporate mass media (Boorstin 1974). Community members may have a great deal of information about one another, including such data as age, sex, attractiveness, and personal history, or they may know nothing of one another but pseudonymous "handles" and openly demonstrated topic knowledge (Granitz and Ward 1996). Studies of brand communities tend to be situated statically with respect to social context. There is little understanding of movement along this dimension. What happens, for example, when mass-mediated brand communities have the opportunity for context-rich relationships?
Yet another dimension of communities is their temporality. Some are stable or enduring (Schouten and McAlexander 1995). Others are temporary or periodic (Arnould and Price 1993; Holt 1995; McGrath, Sherry, and Heisley 1993). The temporal stability of a community can be an asset to marketers inasmuch as longevity equates with a long-term, stable market. Still, even situational communities have been observed to share meaningful consumption experiences (Arnould and Price 1993; McGrath, Sherry, and Heisley 1993). What happens to a temporary brand community after its situational relevance ends? Is a brand community's temporality affected by changes in other dimensions, such as increased social context?
Another dimension that adds complexity to the study of communities is their basis of identification. Communities may be based on such wide-ranging commonalities as kinship ties, occupational connections, religious beliefs, or leisure pursuits. In the context of any person's life, some communities may overlap and interlock significantly. Others may represent separate arenas of activity. Scholars of brand community often neglect the effects of multiple community memberships. Interesting questions arise when the possibility of interlocking community ties is recognized. For example, does commitment to a brand community increase (or decrease) if other members of a person's extended family, neighborhood, or work community also belong?
This multiple-method research program began with ethnography through which findings or themes emerged and developed in a nonlinear fashion. We added quantitative support through a pretest/posttest design and structural equations analysis of a key conceptual model. Finally, we returned to ethnographic work for longitudinal and integrative perspectives (for a research time line, see Figure 2<. Taken together, these methods and findings harmonized in a way that may best be described in terms of Price and Arnould's (1998) "conducting a choir" metaphor.
Ethnography
A focus on two brands, Jeep and Harley-Davidson, and two ethnographic research programs served as the foundation of this research. An account of the methods used in the work with Harley-Davidson owners already exists (Schouten and McAlexander 1995). Except where noted, the following description pertains to the study of Jeep owners.
This study conforms in many ways to the technique of situated or autoethnography (Denzin 1997; Denzin and Lincoln 1994), in that the ethnographers became fully situated as members of the group being studied (see Schouten and McAlexander 1995). With corporate access and support, we began as naïve, neophyte off-road drivers with rented Jeep vehicles. By the third year of our involvement, two authors had acquired Jeep vehicles. As suggested by Stewart (1998), this research employed prolonged fieldwork in two brand communities (Jeep and Harley-Davidson), which gave us the opportunity to experience brand community from multiple perspectives and observe ways in which ownership experiences and motives evolved.
Unlike pure autoethnography, this study was influenced by accountability to corporate marketing decision making. Sponsorship of the Jeep-related research by DaimlerChrysler and Bozell (Jeep Division's principal advertising agency) forced us periodically to withdraw from the situated-consumer perspective and engage in analysis of the owner as "other" in order to consider marketing implications of our emergent findings. Consistent with the current psychological notion of fluid, dynamic, and contextual self structures (Markus and Wurf 1987), shifting our roles as researchers turned out to be almost as easy as shifting context.
Ethnographic Fieldwork
We began our ethnographic research at brandfests (McAlexander and Schouten 1998) and then broadened the field context to include sites that were not event related. Eventually, through product adoption we became fully situated in the experience of Jeep ownership and gained personal points of reference on which to reflect our data and analyses. The brandfests, including Jeep Jamborees, Camp Jeep, and Jeep 101, hosted significant numbers of brand owners and potential owners engaging in brand consumption any the celebration thereof. Briefly, Jamborees are regional rallies with a focus on off-road trail driving; Camp Jeep is a national rally that, in addition to off-road driving opportunities, offers lifestyle and product-related activities; and Jeep 101yis a touring off-road driving course coupled with product-related activities and displays. Although the participant mix varied somewhat by event, all the brandfests attracted a wide range of owners (and friends), from veteran off-roaders to neophytes and from first-time owners to those with family heritages of Jeep ownership. These events are described more completely in McAlexander and Schouten's (1998) work.
Fieldwork at brandfests consisted mainly of participant observation augmented by photography, videotaping, and informal interviews. Near the end of an event, it frequently became convenient to engage in more formalized depth interviews. Our data-recording methods mirrored the activities of many other participants who sought to capture their experiences to share with "the folks back home." We gathered data throughout the events, including at orientations, at meals, on the trails, and at social gatherings. Typically, for the first day or two of an event, we maintained an unobtrusive presence as participants. Accurately representing ourselves as relative newcomers to the off-road experience, we placed ourselves in positions to empathize with other first-timers and to receive instruction and socialization from experienced owners. Our conversations covered such topics as ownership history; off-road experience; current thoughts (e.g., anxieties, excitement, periodic boredom, opinions about the event); plans for Jeep usage; and personal data, including names, occupations, family situations, and occasionally addresses. On-trail relationships developed a familiarity that enabled us to observe changes in attitudes and social relationships over the course of an event.
To challenge our emergent findings from event ethnography, we broadened our scope to include informants who did not choose to attend brandfests. We interviewed six informant households in a western city (selected for diversity in ownership of the basic Jeep platforms). This research is also informed by ongoing ethnographic involvement with Harley-Davidson owners beyond that reported previously. Relationships with informants established eight years earlier provide us longitudinal perspectives of these consumers' experiences in brand communities. We also conducted depth interviews with marketing managers and consumers of the brands DeWalt (a marketer of power tools) and Mentor Graphics (a producer of software used in high-technology design). These interviews helped us determine whether the findings are resonant across diverse industries and markets rather than idiosyncratic to the industries we studied.
Interpretive Analysis
Analysis of the ethnographic data occurred at several levels. We conducted a first level of analysis individually throughout the process of data collection, continually reflecting them against previous data and emerging themes. We conducted a second level of analysis at intervals throughout each day in group analysis and synthesis sessions. These sessions involved the principal researchers and frequently included research assistants and/or agency personnel who attended and monitored the events. At these sessions, each person discussed his or her activities, findings, and insights from the foregoing period. We used previous findings to elaborate, test, and delimit emerging themes as well as to suggest directions for further inquiry. The third level of a analysis involved intensive study of recorded data. The goal of this analysis was to scrutinize themes more thoroughly and elaborate those that stood up to scrutiny.
Brandfests, in essence, provide for geotemporal distillations of a brand community that afford normally dispersed member entities the opportunity for high-context interaction. These conditions prevail for all the types of customer-centric relationships that make up a brand community. In this section, we present findings that both support and extend Muniz and O'Guinn's (2001) brand community work, especially with respect to their central issues of consciousness of kind, shared rituals and traditions, and sense of moral responsibility. We then expand on the dynamics of geography, social context, temporality, and interlocking communities. Last, we offer a new conceptualization of customer loyalty as integration in a brand community.
Brand Community Emerges
During fieldwork at Jamborees and Camp Jeep, we observed the building of brand community. The Jeep owners who came to these events were quite diverse. Our observations, supported by event registration data, revealed that roughly half of event participants had never taken their vehicles off the highway. They belonged to no jeep-related clubs, felt no subcultural affiliation, and had few interactions with other Jeep owners beyond incidental encounters on the roads or parking lots of their daily living environments. Even among the veteran off-roaders, important differences existed. For example, certain drivers espoused the "tread lightly" ethos promoted by conservation groups and Jeep corporate communications, whereas others demonstrated with mud-covered vehicles a more cavalier stance toward the environment and an ethos of conquest over nature. Notwithstanding their differences, these diverse Jeep owners created meaningful temporary communities within the broader brand community.
All the characteristics of brand community discussed by Muniz and O'Guinn (2001) soon manifested themselves: consciousness of kind, shared rituals and traditions, and a sense of moral responsibility. In our observation, however, these characteristics of brand community did not all and equally exist before the brandfest events.
Consciousness of kind, for many community members, was tempered by a fear of not belonging. Interviews at the inaugural Camp Jeep revealed that one potential barrier to participation among some owners was a fear of not fitting in. Put simply, the images they held of other customers in this particular consumption context were inaccurately based on prejudicial stereotypes that often emphasize ways in which these "others" are different from themselves. Jake, a new Grand Cherokee owner, said that he "almost didn't come" to Camp Jeep because he expected the event to attract a predominance of "barbarians" and Shard-core four-wheelers." He spoke of his fear of feeling "like some geeky yuppy on the sidelines." That fear was quelled somewhat upon his arrival as he observed other Grand Cherokees, which he presumed belonged to people similar to himself. Similarly, Amanda, the upscale wife of a retired surgeon, attended the 1996 Camp Jeep reluctantly. On the first day of the three-day event, she explained, "I just don't see myself as a 'Jeep Person,'" in a pejorative tone. At the close of the event, we spoke with her again, and she reported having experienced a quantum shift in attitude. She lauded Chrysler's efforts in creating an enjoyable event. Her experience gave her added appreciation for her own Jeep and its capabilities. Moreover, having interacted pleasurably with many people, she no longer maintained a me-versus-them attitude about "Jeep people."
Anxieties about belonging were dispelled in part by the outreach of experienced participants acting out of a sense of moral responsibility. For example, on-trail instruction for neophytes at Jamborees was provided by knowledgeable participants who spontaneously stepped forward to assist them through difficult stretches of trail. In Colorado, a long-time Jeep owner spent time at an intimidating stream crossing, loudly guiding drivers along the "correct" route through the rough water. He encouraged inexperienced drivers and reassured them about the capabilities of their vehicles. He seemed to relish the recognition and status that came with his superior knowledge and skills. The benefits of brand socialization between more and less experienced owners are symbiotic. New owners benefit from the expertise and social approval of veterans. At the same time, veterans benefit from the status accorded them in their assumed leadership roles. Moreover, the community as a whole benefits as exchanges of knowledge[cement relationships through reciprocal exchanges of value (Gouldner 1960; Sahlins 1972).
As community members, marketers also contribute to the process of community building by creating the context in which owner interaction occurs. For example, participants shared their driving experiences through ritual storytelling facilitated by activities, such as barbecues and roundtable discussions, hosted by the marketers. Topics ranged from trails and adventures to aftermarket equipment. One relatively mundane conversation centered on the ritual "Wrangler wave" given by Jeep Wrangler owners when they pass each other on the road. The wave is a tradition that many owners learn in the course of everyday driving, but we saw this in-group knowledge verified and reinforced through discussion at a Jeep 101 gathering.
Marketers may also take an active role in establishing the shared rituals, traditions, and meanings that foster consciousness of kind. Tools such as a "History of Jeep" exhibit at Camp Jeep and Jeep 101 helped underscore a sense of similarity, authenticity, and exclusivity among participants. Promotional materials that depict the product in use may help establish shared aspirations. One informant at Camp Jeep told us that he had come to the Colorado event from his midwestern home largely because of inspiring advertisements that depicted a Jeep like his in a pristine mountain environment. His excitement was heightened by the registration packet, which provided guidelines for enjoyment of the event. Certain techniques of off-road driving, such as starting the vehicle in gear to avoid tire slippage, were repeated by staff driving instructors many times over the course of a Jeep Jamboree and were codified in a booklet of off-road driving tips.
Marketers, as well as owners, have incentives to exercise moral responsibility to brand and community. For example, the ethic of nondestructive driving was a matter of intense indoctrination at Jamborees. The event sponsors heavily promote the "tread lightly" program as a cornerstone value of Jeep and Jeep ownership, and the message is passed along by owners. To the owners and marketers alike, "treading lightly" serves the dual purposes of protecting the environment and preserving access to off-highway trails.
The community-building activities of event participants, including marketers, appeared to be remarkably efficient. Even owners who came to events dwelling on how different they felt from others often left after two or three days believing they belonged to a broader community that understands and supports them in realizing their consumption goals. In this discussion of event-intensified brand communities, we have dwelt primarily on human-to-human interactions. In these geotemporally concentrated situations, the relationships between owners and their vehicles and between customers and the brand also benefit from enriched context. Through ritual consumption, participants routinely reported newfound intimacy with and understanding of their vehicles. Likewise, their many positive experiences under the auspices of the Jeep brand conveyed its potency as a symbol of their values and lifestyle preferences. To better understand how these transformations occur, we turn again to the dynamic dimensions of brand community.
Dynamic Dimensions of Brand Communities
At an Ouray, Colo., Jeep Jamboree, we interviewed participants from many parts of the country, including California, Utah, Iowa, and Illinois. Some had come with friends or members of local clubs, but many had only passing acquaintanceships, if that, with other Jeep owners. One couple from Illinois was illustrative. Their prior connections to brand community were limited to the purchase transaction with their dealer, direct mail from Jeep, and advertising. They learned about the Jamboree from a brochure. They had been to Colorado for winter ski trips but had never experienced the Rockies during the summer. The Jamboree gave them an excuse to do so and to become familiar with the four-wheel drive capabilities of their Grand Cherokee. This was their first off-pavement excursion. At the beginning of an all-day trail ride, we found ourselves lined up behind them. We were intrigued by their enthusiasm, especially that of the wife, who carried a large camcorder as though it were an appendage of her body. By midway through the day, we were calling each other by first names, sharing personal stories, and exchanging addresses with the expectation of future interaction. An unforgettable part of the experience was watching their confidence in their Jeep grow as they surmounted the challenges of the rough and sometimes precipitous road. Their appreciation for their vehicle was contagious. This was not an isolated case. Similar bonds were formed among strangers at every event we attended, and at every event people were vocal with their enthusiasm for Jeep and their Jeep vehicles.
Just as Jeep owners got to know one another by virtue of sharing the same geotemporal space, they also got to know, or at least interact with, agents of DaimlerChrysler and the Jeep brand. At Camp Jeep they were able to converse with Jeep engineers in roundtable discussions. At Camp Jeep and Jeep 101, uniformed "camp counselors" were on hand to deliver hospitality that ranged from free beverages to Jeep product information to off-highway trail recommendations. The message in this: Behind the product and the normal corporate communications are real people who understand and care about their customers.
Compared with the normally diffuse nature of a brand community, the temporary geographic concentrations provide a rich social context for communication. In close proximity, people got to know one another in ways that would be difficult or impossible through electronic or mass media. Face-to-face contact reduces opportunities for personal misrepresentation. A novice driver would have difficulty boasting of off-road driving prowess while nervously hanging on to every word of a trail coach or spotter. More to the point, high-context interactions speed up the processes of socialization. They enable consumers to see, feel, and hear demonstrations of product use. They provide more information on which to base judgments about people's credibility, sincerity, and concern for one another. In some cases, sustained interpersonal interactions can lead to relationships that transcend mere common interest in a brand and its applications.
By the end of a Jamboree or Camp Jeep, weekend participants may have formed friendships necessitating good-byes. Inherent in the good-byes there may be a desire to reconnect with people or form similar friendships at future times in similar situations. At our first Camp Jeep, we were struck by the enthusiasm that we shared upon encountering a couple whom we initially met at our first Jamboree. At the second Camp Jeep, we found participants seeking friends whom they had met the previous year at the inaugural Camp Jeep event. This included us, as we reestablished ties with informants we had met at both the previous Camp Jeep and the earlier Jamborees. What began as a temporary or situational community had developed an apparent momentum toward greater temporal stability. This increased sense of community longevity appeared to be a direct result of the qualities of relationships facilitated by the temporary geographic concentration and the contextual richness of the vents.
The Jeep brand community is not the primary community affiliation for most, if any, of our informants, nor does it exist independently of other social groups. Consider the phenomenon described by Pamela and Greg, Cherokee owners, in this West Coast in-hme interview excerpt:
Pam: A number of people have bought 'em (Jeep Cherokees) since we've bought....
Greg: Yeah, it's sort of like it's just grown in a circle of people. The people that we initially-before children-we used to hang out a lot with, three or four of them have Jeeps of some kind or....
Pam: They all lived in this little cul-de-sac, and they all owned Jeep Cherokees! I mean you didn't move there unless you [laughing] had a Cherokee!
Other informants also spoke of friends and family who owned Jeeps. Waiting in line to drive at Jeep 101 in Boston, we struck up a conversation with Tom and Cindy, who were approximately 35 years of age, and asked if any of their friends or family also owned Jeeps. Tom described a three-year chain reaction of Jeep purchases that began with his own and ended with three of his five siblings as well as his father also owning Jeep vehicles of one model or another. Tom's experience is a vivid illustration of what Olsen (1995) calls the "lineage factor" in brand loyalty.
By encouraging participants to bring friends or family to Jeep 101, the corporate sponsors provide opportunities for sharing brand enthusiasm and experiences. Among Jeep owners, we have observed many cases of conversion to the brand among family members, including siblings, spouses, children, and parents. Larry, a Boston optometrist we interviewed at Jeep 101, called to his 15-year-old son during our interview and introduced him as "the next generation of Wrangler owner" to the son's enthusiastic agreement. The Goldsmiths, a New York family and owners of their third Grand Cherokee, came to Jeep 101 to assess the Wrangler as a possible car for their daughter, who was about to turn 16; by the end of the event, they were planning the purchase right down to the choice of color. In an interview with a father-son pair in New York, we learned that the father, approximately 45 years of age, had traded in his luxury sedan for a Jeep Grand Cherokee on the urging of his son, a college freshman who had owned ' used Jeep Cherokee for about a year.
A particularly fervent expression of missionary zeal comes from an interview with Barbara, a woman approximately 50 years of age who now owns her second Jeep Cherokee:
I really annoy a lot of my friends because I am constantly talking about Jeep. I absolutely love my Jeep and I will continue to love my Jeep. To me there's just no substitute for Jeep. I could walk around with a sign: SI - love - my - Jeep!" [in a chanting cadence, then laughter] I do this to my friends. I really do. All the time.
Integration in the Brand Community
An ethnographic perspective helps us understand how and why community grows through and in the form of customer-centered relationships. Events like Jeep Jamborees, Camp Jeep, and HOG rallies bring together people, or parties of people, who often share no other connection than an interest in a brand and its consumption. Given the opportunity for context-rich interaction, in which previous communication was either nonexistent or limited to mass or electronic media, participants share extraordinary consumption experiences (see Arnould and Price 1993; McAlexander and Schouten 1998). Sharing meaningful consumption experiences strengthens interpersonal ties and enhances mutual appreciation for the product, the brand, and the facilitating marketers. Virtualities become real ties. Weak ties become stronger. Strong ties develop additional points of attachment. Our analysis suggested that consumer-centric relationships with different entities in the brand community might be cumulative or even synergistic in forming a single construct akin to customer loyalty. Put another way, more and stronger points of attachment should lead to greater integration in a brand community (IBC). Similar to the construct of brand loyalty in that it conveys an emotional and behavioral attachment to a brand (Ehrenberg 1988; Jacoby and Chestnut 1978), IBC is a more comprehensive concept grounded in consumers' total-life experience with a brand as most broadly construed.
As compelling as we found our ethnographic evidence to be, we nevertheless desired to triangulate our data. Quantitative testing enabled us to confirm qualitative interpretations and answer several empirical questions.
Ethnographic work revealed four customer-centered relationships that appeared to be integral at the macro level to the brand community and at the micro level to individual IBC. We observed that the brandfests, which provided much of the context for our study, have an impact on each of those relationships. Our desire for quantitative triangulation and additional empirical exploration led us to examine the following hypotheses in the context of Camp Jeep:
H<SUB>1</SUB>: Integration in the Jeep brand community (IBC) is a function of the customers' perceived relationships with their own vehicles, the brand, the company, and other owners.
H<SUB>2</SUB>: Customers will report more positive relationships with their own vehicles after participating in the brandfest.
H<SUB>3</SUB>: Customers will report more positive relationships with the Jeep brand after participating in the brandfest.
H<SUB>4</SUB>: Customers will report more positive relationships with Jeep as a company after participating in the brandfest.
H<SUB>5</SUB>: Customers will report more positive relationships with other Jeep owners after participating in the brandfest.
H<SUB>6</SUB>: The overall level of integration in the Jeep brand community will increase as a result of participation in the brandfest.
Quantitative Methods
This field study was constrained in some ways by corporate requirements. The construction of the questionnaire was influenced by the need to address a broad assortment of topics for different corporate audiences, the desire to maximize response rate, the need to manage a budget, and other conflicting objectives. The intent of this portion of the study was to triangulate the qualitative findings and to some extent quantify effects of the marketing program. The scales we used were derived largely from ethnographic data. On the basis of informant interaction, they have face validity.
We collected quantitative data in connection with two consecutive national Camp Jeep events. The first year, we conducted a post-event survey of participants. It included questions related to the participants' experience at the event and measured variables related to our model of brand community. Analysis of this data served as a pretest for the design and execution of the survey at the second event. The assessment of the second Camp Jeep was structured as a one-group pretest/posttest quasi-experimental design (Cook and Campbell 1979) of the impact of the event. In this study, the experimental treatment was participation in the event.
Because no control group was associated with this design, the interpretation of our results must acknowledge the possibility of several different biases (Cook and Campbell 1979). We designed the measurement to minimize the potential impact of these biases. For example, we timed the survey and devised the instrument to reduce the impact of testing bias. The pretest survey had a cutoff return date approximately five weeks prior to the event and asked a wide range of questions that were not specific to the research topic (e.g., proprietary segmentation questions). The post-event questionnaire was similarly constructed to make it difficult for the respondent to glean our research purpose (e.g., including a series of questions evaluating specific event activities) and was sent approximately three weeks after the event. Consequently, the repeated measures occurred about eight weeks apart, a time period we believed was sufficiently long that respondents could not readily recall the first set of questions or their responses to them. Moreover, the quantitative findings need not stand alone; they are triangulated with ethnographic fieldwork conducted at the sites of these and other brand community events.
Sampling Plan
Camp Jeep was national in scope. It was promoted through a national print campaign. In addition, recent Jeep purchasers were sent direct-mail invitations to the three-day event. Before the event, all registrants were sent a package of materials, which included information about activities, suggestions for items to bring to the event (e.g., sunscreen, appropriate clothing), and an identification-coded pre-event questionnaire. They were directed to return the questionnaire with other registration information in a prepaid return envelope. Participants were encouraged to submit this information by a date approximately five weeks prior to the event to reserve space in desired activities. Approximately 40% (n = 453) of preregistered participants returnedthe questionnaire on time.
Three weeks after the event, an identification-coded post-event questionnaire was sent with a cover letter and other materials to all respondents who had returned the pre-event questionnaire. Respondents were also sent a reminder card. A total of 259 usable questionnaires were returned, for a response rate of 57% of initial respondents. Because we were able to match returned post-event questionnaires with the pre-event questionnaires, it was possible to provide comparisons between consumers who did and those who did not return the second questionnaire. We found no meaningful differences between these groups.
Measurement
On the basis of the model developed from our ethnographic research, we measured four customer-centric relationships: owner-to-product, owner-to-brand, owner-to-company, and owner-to-other owners. All items were measured with a five point Likert-type scale, anchored by ( 1) "strongly disagree" and ( 5) "strongly agree."
The customer-product relationship was measured with four items that attempt to capture owners' feelings about the product they own (see Table 1y. The selection of these items was informed by our ethnography, Belk's (1988) work on extended self, and the earlier pretest. The customer-brand relationship was measured with two scales. One represented important brand-related values or associations that have been promoted by the marketer.[ 1] The other scale consisted of measures that are typically associated with brand loyalty. The brand association scale consisted of three items. The brand loyalty scale consisted of four items (Table 1). The customer-company relationship was measured with two items that attempt to capture the feelings owners have about the organization that sponsored the event. This scale reflects the company's concern for customers, a theme that came up consistently in our on-site ethnography and interviews among participants (Table 1). The customer-customer relationship was measured with three items that attempt to capture the feelings owners of the product have about other owners (Table 1).
The survey items that correspond to the four customer-centered relationships were tested in a four-factor confirmatory model (details are described in the Appendix). An acceptable fit was achieved after three items were eliminated; this change had a negligible impact on the substantive content of the affected dimensions, and reliability and average variance extracted (AVE) for each dimension were good (Table 2, Appendix). The next step was to assess whether these four constructs were an adequate reflection of a single higher-order construct. For this purpose, a second-order factor structure was tested (see Figure 3), and an acceptable fit for this model was found (Table 3). The confirmatory factor analysis (CFA) and the second-order model provide evidence of two important points: First, each dimension has good measurement properties and is distinct from the other dimensions, and second, the dimensions can be combined to form one higher-order construct. From this point, the focus shifted to examining the brandfest's impact on participants' attitudes.
We performed two tests to begin to understand the relationship between the pre- and post-event scores. The first test was an overall assessment that modeled pre-event IBC and post-event IBC as unobservables in a structural equation model (see Figure 4p. The structural parameter between the pre- and post-event brand community was left as a free parameter to be estimated in one model and fixed at zero in a second model. When the parameter was estimated, the coefficient was positive and significant, and the fit of the model was significantly better than the model estimated without the parameter (see Table 4).
The second test was a repeated measures analysis that used the four components as well as the composite measure of IBC as dependent variables. Separate analyses were run for each variable. On the basis of an examination of pre-event frequencies, it was clear that some owners already had strong positive feelings. Because these owners had nearly "pegged the scale" before attending the brandfest, it would be difficult for the brandfest to have a significant, positive impact on their attitudes. To account for this ceiling effect, IBC and the four component relationships were sorted by the magnitude of the pre-event scores, split at the median, and analyzed separately as "high" and "low" groups.
The majority of the analysis of variance (ANOVA) results showed violations of the homogeneity of variance assumption (Levene 1960). We estimated the data sets that exhibited homogeneity of variance violations using the generalized least squares (GLS) procedure (Judge et al. 1985) in the SHAZAM econometric package (White 1977), and we estimated the remainder using ordinary least squares (OLS). Using the approach suggested by Cohen and Cohen (1983), we calculated the percentage of within-subjects variance explained by time for each regression.
For owners who were less positive before the event, all five repeated measures analyses showed significant changes between pre- and post-event scores, with R² values ranging from .25 to .57. Three of the five upper-half analyses were significant, with R² values ranging from .04 to .08 (Table 5J. Pre- and post-event means for each group are also included in Table 5.
Both the quantitative and qualitative analyses offer support for hypotheses regarding the multifaceted nature of brand community and the way in which brandfests may build it. With respect to hypotheses H<SUB>2</SUB>- H<SUB>6</SUB>, we also found that the strength of prior owner bonds wields significant moderating influence in the experience of the brandfest. A discussion of the hypotheses is followed by additional ethnographic findings.
H<SUB>1</SUB>: The Construct of Brand Community
H<SUB>1</SUB> was confirmed strongly. Quantitative analysis supports the integrity of a construct of IBC as the cumulative impact of four types of customer-centered relationships. In addition, although the individual relationships are depicted as dyadic, our ethnographic data reveal that they do not function entirely independently of each other. Rather, they develop interdependently in ways that are mutually reinforcing.
The following excerpt from an interview at Jeep 101 provides an example of how brand community may begin to form in the early stages of purchase and ownership. The informant, Susan, is a first-time Jeep owner attending Jeep 101 with her fiancé, George. Throughout the purchase process, Susan referred to her interpersonal relationships for assistance and input. Interaction with friends, car dealers, and the Internet (with the assistance of her brother) all helped her arrive at her purchase decision. Subsequently, postpurchase communication with her dealer and from the company further strengthened her connection to the brand:
I've been very happy. I get a lot of communications from Jeep, which I've been so impressed with. Usually you buy a car and then you're a forgotten soul. Its kinda like they want you to be part of the family. As soon as I got the invitation for Jeep 101, I registered. I was very excited. But I was nervous. I didn't think I would end up driving. I was very relieved to see someone in the car with you, 'cause it gave you the confidence to do what you're supposed to. Otherwise, I had visions of abandoning toe truck on the hill and saying, "I can't do it!" [laughs]. I thought I might wimp out, but I didn't [smiles].
Susan viewed "Jeep," a corporate entity, as a caring institution, a family, that provided her a sense of belonging and importance, not feelings of being a "forgotten soul." In a situation that inspired fear as well as excitement, "Jeep" was there in the form of a real person to support her in achieving a meaningful personal triumph. Her experience left her wanting more of the same kind of interaction in the future. She feels and values a sense of community among Jeep owners:
I would love to do a Jamboree.... If we had something locally I would definitely go. Or even somewhere on the East Coast, you know, Virginia, Florida, I'd go. Definitely. And it really makes you feel like a part of a family.
Susan's relationship with the Jeep brand means that she is not alone in the important and sometimes daunting arena of automobile ownership. Moreover, she appeared to be blind to the lines between the corporate and dealer organizations, seeing instead a unified entity that she can trust to take care of her and her Jeep vehicle. A combination of attentive dealer service, periodic marketing communications, and face-to-face interaction with company agents at Jeep 101 have strengthened Susan's perceived relationships with the entity of "Jeep." She has learned to understand and appreciate her own Jeep vehicle better. She feels like she belongs to a benevolent family of Jeep that includes both owners and marketers. This community gives her confidence and inspires her to seek out additional opportunities, such as Jamborees, to join in shared consumption of the brand.
Susan's story is far from unique among our informants. Repeatedly in our ethnographic work, we have encountered similarly synergistic development of customer relationships. Products are purchased and consumed in the context of social and business relationships, which in turn influence feelings about the products specifically and brands more generally. Each relationship connects to all the others through the central nexus of consumer experience, creating the holistic sense of a surrounding community. Moreover, each relationship acts as a personal linkage to the brand community. The more each relationship is internalized as part of the customer's life experience, the more the customer is integrated into the brand community and the more loyal the customer is in consuming the brand.
H<SUB>2</SUB>: The Brandfest and the Customer-Product Relationship
For Jeep owners who felt less of a bond to their vehicles before the brandfest, H<SUB>2</SUB> was confirmed strongly; brandfest participation led to more positive relationships with their Jeep vehicles. However, for the owners who felt more positive toward their vehicles, we found a statistically significant reduction in their enthusiasm for their Jeep vehicles.
The increase in positive feelings toward their vehicles of owners with weaker product bonds (and who usually have less product-related experience) can be explained by the nature of their brandfest experience. Many of them have their first off-road experiences, learn new skills, and, more important, learn about previously unknown or unappreciated capabilities of their Jeep vehicles. At the conclusion of a Colorado Jeep Jamboree, Vivian, a single woman approximately 50 years of age, offered these remarks that indicated the development of an interdependent (Fournier 1998) customer-product relationship:
Researcher: Did your experience change the way you think about your Jeep?
Vivian: I love my Jeep now. I liked it before, but now I feel like we're a team.
Researcher: You've formed a relationship with the vehicle that didn't exist before?
Vivian: That's right [continues nodding her head].
Researcher: Could you explain how this happened?
Vivian: Well, mine sits in the garage a lot because I have a second vehicle. I've had the Grand Cherokee for a year, and it's just kind of my luxury car and so I didn't know it as well as I have since this weekend. [Now] I just feel like it's very dependable. And I feel like we could probably go anywhere and I'm not going to be fearful.
In contrast to Vivian and other relative neophytes, participants who were already more highly engaged with their own vehicles tend already to be very familiar with them. Vigilant for information about engineering improvements in new models or newly available performance modifications, the more experienced owners may feel some dissatisfaction from comparing their current vehicles with others, which they regard as even marginally superior. For example, we asked Jody, a 24-year-old Wrangler owner, what hp thought of the new Wrangler model (on display and in use at Jeep 101). He described at length the advantages of its new fully independent coil-spring suspension over the leaf-spring suspension of his own vehicle. When we asked if he thought he would trade up to the new model, his response was that he would if he could afford it. Exposure to new and improved products within the context of a brand community at a brandfest may be instrumental in motivating trade-ups and the purchase of new vehicles and accessories.
H<SUB>3</SUB>: The Brandfest and the Customer-Brand Relationship
For Jeep owners who exhibited weaker connections to the brand before the event, H<SUB>3</SUB> was confirmed strongly; brandfest participation led to more positive relationships with the Jeep brand. Those who felt stronger brand connections before the event also showed strengthening in the customer-brand relationship, but to a lesser degree. The main difference in attitude shifts seemed to be a simple matter of room for improvement; those in the upper measurement category of the customer-brand relationship had already pretested at such high levels that there was less room left in the measurement scale to accommodate upward change.
Changes in the customer-brand relationship among customers who previously had weaker bonds to the brand were sometimes dramatic. For example, David, who was approximately 25 years of age and was the owner of a Ford Explorer, had this to say after his Jeep 101 driving experience:
Dave: I just had the most amazing time. My dad's friend is the dealership, you know, the Ford guy? So I don't want to mock him or anything, but (the Wrangler) kills my Explorer. My Explorer's a Limited. It has air suspension, all this special crap. And -t just destroys my Explorer.
Researcher: Would you feel comfortable on this course with your car?
Dave: I don't think so. I think it might tip, actually.
David left the Jeep 101 resolved to buy a new Jeep, despite a long-standing family friendship with the Ford dealer who had made him a deal on a top-of-the-line Explorer sport-utility vehicle. Among on-Jeep owners who attended Jeep 101, mostly as guests of Jeep owners, we interviewed several who appeared to have become converts to Jeep.
Other evidence of improved customer-brand relationships manifested in participants' interactions with brand displays and in purchases of branded products. Camp Jeep and Jeep 101 participants report deriving considerable benefit from Jeep heritage displays (which include Jeep advertisements through the decades, a video about Jeep's military beginnings, and antique Jeep vehicles), engineering displays (including life-size cutaway models of new Jeep vehicles), and product displays (which include new Jeep models and accessories). At these and other such events, we observed brisk sales of branded accessories. Informants proudly wear branded apparel from the events when they return home and for long afterward (see Cornwell 1990).
H<SUB>4</SUB>: The Brandfest and the Customer-Company Relationship
For those who felt less tied to the company before the event, H<SUB>4</SUB> was confirmed strongly; brandfest participation led to more positive relationships with the Jeep corporate entity. The owners who scored higher on this scale before the event showed no significant change in this relationship. As for H<SUB>3</SUB> the main difference in attitude shifts between these owner groups appears to be relative room for improvement.
Corporate image can play an important part in customer reactions to a company's products (Brown and Dacin 1997). Our analysis, which examines perceptions of the company as a caring and approachable entity, demonstrates that a brandfest can effectively influence that image. Participants report how impressed and respectful they are that Jeep would go to the expense of hosting Jeep 101 or Camp Jeep without introducing hard-sell (or even soft-sell) sales tactics. Consistent with gift-giving theory (Sherry 1983), the company has given customers a gift, the brandfest, without pushing for direct reciprocity, thereby creating a sense of indebtedness or generalized goodwill on the part of the customer.
More important, our research emphasizes the value of going beyond image-building endeavors to establishing real relationships between the company and customers. Customers crave audience with the people behind the brands. The ethnographic evidence for this observation is staggering. At Camp Jeep, the engineering roundtables consistently drew maximum-capacity participation. At Harley-Davidson brandfests, where intense corporate interaction with customers is made policy, some corporate officers have achieved celebrity status and occasionally are swamped by requests for photographs or signatures. Brandon J., an employee who routinely travels to attend the company's events, has developed friendships with people from coast to coast who seek him out at events to chat or for after-hours socializing. Employees can provide customers a human manifestation of the company at a time when many corporations are perceived as impersonal and unfeeling bureaucracies.
H<SUB>5</SUB>: The Brandfest and the Customer-Customer Relationship
For customers who felt less connected to other owners, H<SUB>5</SUB> was confirmed strongly; brandfest participation led to more positive relationships with other Jeep owners. Owners who pretested higher on this scale showed no significant difference in their feelings toward fellow owners. The latter result can be explained, as for previous hypotheses, in terms of high base-level responses. These typically more experienced owners likely have had more interaction with other owners. Less experienced owners probably have had less cause to form brand-related interpersonal relationships, and their overall image of other users is more likely to suffer from stereotypes or misconceptions before the brandfest.
For an example of how a brandfest can facilitate lasting customer-customer relationships, consider the case of Katie, a 22-year-old Michigan woman and first-time Jeep Wrangler owner. She had attended her first Jamboree with her sister-in-law Kim in Michigan the previous year. At the Michigan event, she met two young men and their father who also owned a Wrangler. The two parties got acquainted during the event, and afterward they corresponded to share home videos of the Jamboree experience. By prearrangement they met again at the Jamboree in French Lick, Ind., where the friendship continued. When we encountered Katie during the second day of activities she told us in animated tones how the young men had just taught her how to remove the doors from her Jeep for a more open feeling. When asked about her intentions for the future of the relationship she quickly stated that there were no romantic considerations but that they all intended to keep in touch and meet at other Jamborees.
H<SUB>6</SUB>: The Brandfest and the Construct of Brand Community
Analysis strongly confirms H<SUB>6</SUB> for the entire sample; brandfest participation led to significant increases in overall feelings of integration in the Jeep brand community (IBC). Not evident from an analysis of the quantitative data is that the reasons and the processes behind the strengthening of community differ between the two groups. From the ethnographic experience, we learned that novice owners at a brandfest begin to feel more a part of the community as they learn to consume the brand in ways that provide greater benefits to them, whether those benefits are utilitarian, self-expressive, social, and/or hedonic. For the more experienced owners, the brandfest provides opportunities to demonstrate and reaffirm their community ties while both mentoring and performing for neophyte owners.
Long-Term Impact of Temporary Communities
The longevity of relationships, especially those of an interpersonal nature, that have their genesis in a brandfest or other brand-specific context should be considered. The quantitative data provide only inferential evidence of long-term impact (i.e., attitudinal measures related to future intentions: "I would buy another Jeep"). Additional evidence is provided by proprietary tracking research conducted by Chrysler that suggests that its community-building efforts through brandfests result in significantly increased repurchase rates among participants (Duffy 1999). Our ethnographic data strongly indicate a long-term, lasting impact of relationship-building efforts. We have witnessed many participants, like Katie and Kim, who formed interpersonal relationships at a brandfest that were maintained over a distance and reaffirmed at a subsequent event. As researchers, we have maintained long-term (some as long as eight years) connections with Harley owners, many of whom initially built relationships with the community at a brandfest and still value their connections to the brand community.
We have noted that customers who purchase a branded product often do so with the support of other users, which leads to the possibility of brand-focused interpersonal bonds. Social support from such relationships may, in turn, influence increased personal investment in a customer's consumption of the product and the brand. To the extent that the company behind the brand facilitates such interactions, the customer base is likely to reciprocate with increased appreciation for the company and a sense of being an important part of a larger set of social phenomena.
Part of the reason for the longevity of customer-centered relationships may be their role as exit barriers. Customers value the relationships available to them as a result of brand ownership. For some customers, the expectation of developing these types of relationships motivates initial product acquisition; they are looking for a sense of community. Other customers acquire the brand for more strictly utilitarian or self-expressive reasons and discover the benefits of brand community in the course of consumption. Community ties become exit barriers as owners realize that valued interpersonal relationships would be altered or lost if they were to defect to another brand. Positive relationships with marketers and bonding with personal possessions also create exit barriers. The same elements that lend longevity to successful interpersonal and community relationships, such as reciprocity (Gouldner 1960; Sahlins 1972), investment, commitment, interdependence, and integration in social networks (Lund 1985E, exist in the community of brand, product, company, and customers.
Marketing Implications
In today's marketing environment, sustaining a competitive advantage on the basis of product differentiation often is an exhausting race to a constantly shifting finish line. Any lead in the race is eroded quickly by imitation or even by superior technology from competitors. One way to sidestep this treadmill is to redefine the terms of competitive advantage. Part of the success of brands like Jeep lies in their focus not merely on the product or its positioning but also on the experience of ownership and consumption. Differentiating on the basis of ownership experience can be achieved through programs strategically designed to enhance customer-centered relationships. The events examined here provide opportunities for consumers to experience anticipated but unrealized product benefits, share those experiences with others, meet with the previously faceless and nameless people behind the brand, and learn more about the brand's heritage and values. Our research demonstrates that such marketing programs can have a measurable impact on the full range of customer-centered relationships. By proactively providing the context for relationships to develop, marketers can cultivate community in ways that enhance IBC and thereby increase customer loyalty.
The benefits to a firm of cultivating brand community are many and diverse. community-integrated customers serve as brand missionaries, carrying the marketing message into other communities. They are more forgiving than others of product failures or lapses of service quality (Berry 1995). They are less apt to switch brands, even when confronted with superior performance by competing products. They are motivated to provide feedback to corporate ears. They constitute a strong market for licensed products and brand extensions. In many cases, we even find loyal customers making long-term investments in a company's stock. Customers who are highly integrated in the brand community are emotionally invested in the welfare of the company and desire to contribute to its success.
Limitations and Directions for Further Research
Although our research extols the virtues of cultivating customer relationships, recent research suggests that customers are becoming overwhelmed by attempts of marketers to engage them in relationship marketing (Fournier, Dobscha, and Mick 1998). As marketers consider cultivating relationships with customers, it is important to recognize that relationships take on many forms (Fournier 1998). In addition, marketers should recognize that relationships are reciprocal: Both parties give and receive. For example, the brandfests discussed in our work were events in which managers made conscious decisions not to view the events as strictly short-term investments. The marketers provided experiences, entertainment, and education that customers perceived to be in excess of the costs they incurred to participate. The anticipation that customers would reciprocate with increased loyalty and trust (see Morgan and Hunt 1994) was confirmed by this research.
Our study identifies many issues that merit further research. It would be valuable, for example, to examine how Fournier's (1998) work regarding the character of relationships applies to the different relationships formed within the brand community. Further research might consider exploring the characteristics of products and services that make them amenable to the types of relationships we have identified. Our experience in the business-to-business context (e.g., De Walt and Mentor Graphics), for example, indicates that our findings may have applicability beyond the consumer market. In addition, in the brand communities we have studied, the focal products are rich in expressive, experiential, or hedonic qualities. How might IBC be relevant to less flamboyantly experiential brands? The overlapping and interlocking nature of communities suggests that the real importance of some brand communities may lie in strengthening ties within other communities, such as extended families or towns, which may be more important from the consumers' point of view. Research has shown that the ownership of brands of more mundane products such as bath soap, tools, and toys provide valued bridges to family, friends, or neighbors (see Fournier 1998; Gainer and Fischer 1991; Olsen 1993), which suggests the potential relevance of our current findings to these types of product categories.
Our largely exploratory research has inherent methodological limitations. The practical demands of the field study constrained our ability to examine quantitatively many of the relevant issues on which this research touches. We have, however, identified directions for the development of measurement scales. There is a need to further develop and refine instruments that measure the relationships of brand community and integration within it. Within any product category, it should also be possible to create an index of IBC to facilitate the diagnosis of opportunities for building brand community. We posit that any brand community, weak or strong, exists somewhere on the continua of geotemporal concentration, richness of social context, and overlap with other communities and that the brand's position on these continua can be influenced.
Our research also touches on issues that would especially benefit from the types of insights marketers have sought from qualitative research (see Levy 1981; Winick 1961). It would be valuable to examine, for example, what characteristics lead consumers to value brand community and participate in communal activities (cross-cultural research might be of particular interest). Further research might also be directed toward the executional elements of brandfests that are most effective in cultivating a given brand community. For example, our research indicates the importance of designing events with a focus on socializing neophytes while offering special recognition to those who are already the most integrated in the community. It would also be valuable to examine circumstances that may lead to defection from the community or conflict within it and explore the implications of such issues to community vitality and potential impacts on brand positioning or brand equity.
1 We also measured a customer's identification with the brand (as reflected by a desire to wear branded clothing or purchase branded accessories). Additional analysis substituting these measures of the customer-brand relationship produced similarly supportive results.
Year(s)
1990-98 1994 1995 1996 1997 1998
Method(s) Reflexive Event Event Event Event Depth
consumer ethno- ethno- Ethno- ethno- interviews
ethno- graphy, graphy, graphy, graphy
graphy depth post- pre- and
inter- event post-
views survey event
surveys
Focus Harley Jeep Jeep Jeep Jeep DeWalt and
Davidson owners owners owners owners Mentor
owners and Graphics
(nonowner) Marketing
guests managers
Context Events, Jeep Camp Camp Jeep 101 Workplace
meetings, Jambo- Jeep Jeep
organized rees, (inaug-
activities, in-home ural)
everyday
life Dimension Items
Product 1. I love my Jeep vehicle.
2. I am proud of my Jeep vehicle. a
3. My Jeep vehicle is one of my favorite
possessions.
4. My Jeep vehicle is fun to drive.
Brand 1. I value the Jeep heritage.[a]
2. <proprietary>
3. <proprietary>
4. I would recommend Jeep to my friends.[a]
5. If I were to replace my Jeep vehicle, I would buy another
Jeep.
6. Jeep is of the highest quality.
7. Jeep is the ultimate sport-utility vehicle.
Company 1. The Jeep division understands my needs.
2. The Jeep division cares about my opinions.
Other 1. I have met wonderful people because of my Jeep.
owners 2. I feel a sense of kinship with other Jeep owners.
3. I have an interest in a club for Jeep owners.
[a]Removed from final scale.
Notes: Items identified as proprietary reflect specific brand-related
values. To protect the competitive position of participating
organizations, these individual items are not reported. Number
Construct of Items Reliability AVE
Owner-product 3 .90 .74
Owner-brand 5 .88 .58
Owner-company 2 .88 .79
Owner-other owners 3 .70 .61
Legend for chart:
A = Owner-Product
B = Owner-Brand
C = Owner-Company
D = Owner-Other Owners
Indicator A B C D
First-Order Loadings (λ<SUB>y</SUB>)
Own-Pr1 .894[b]
Own-Pr2 .830 (15.29)
Own-Pr3 .863 (16.40)
Own-Brd1 .761 (9.89)
Own-Brd2 .753 (8.78)
Own-Brd3 .706[b]
Own-Brd4 .759 (9.86)
Own-Brd5 .757 (9.84)
Own-Org1 .795 (9.98)
Own-Org2 .986[b]
Own-Own1 .713 (10.82)
Own-Own2 .900[b]
Own-Own3 .712 (10.81)
Second-Order Loadings (&gamm;<SUB>jk</SUB>)
First-Order Construct Community Integration
Product .925 (13.41)
Brand .939 (10.13)
Organization .595 (8.53)
Other owners .802 (11.19)
Goodness-of-Fit Statistics
χ² (61 d.f.) = 223.61 (p = .0)
Goodness-of-fit index = .867
RMSR = .060
Normed fit index = .872
CFI = .903
[a]The t-values are in parentheses.
[b]Fixed parameter.
Legend for chart:
A = Fit Statistics for the Model Connecting Pre- and Post- event IBC
Measures
B = Fit Statistics for a Model of Independence of Pre- and Post-event
IBC Measures
C = Change in Value
A B C
IBC
Correlated error 4 of 4 possible 4 of 4 possible
terms used
χ² 25.763, 15 d.f. 92.852, 16 d.f. 67.089,1 d.f.
β(2,1)[b] .634 (7.34)[a] .0
RMSR .0142 .0800 .0658
Goodness-of-fit .971 .902 .069
index
Normed fit index .965 .872 .093
CFI .985 .890 .095
[a]The t-value is in parentheses.
[b]Standardized parameter estimate. Lower Half[a] Upper Half
IBC Pre-event 43.42 55.73
mean
Post-event 49.64 57.23
mean
F<SUB>(1,98)</SUB> F<SUB>(1,97)</SUB>
= 80.28 = 6.37[c]
p < .001 p < .025
R² = .450[b] R² = .062
Product Pre-event 11.22 14.67
mean
Post-event 12.51 14.22
mean
F<SUB>(1,98)</SUB> F<SUB>(1,97)</SUB>
= 33.44 = 8.55[c]
p < .001 p < .005
R² = .254 R² = .081
Brand Pre-event 17.20 22.00
mean
Post-event 19.38 22.51
mean
F<SUB>(1,98)</SUB> F<SUB>(1,97)</SUB>
= 49.27[c] = 4.54[c]
p < .001 p < .05
R² = .335 R² = .045
Company Pre-event 5.68 8.16
mean
Post-event 6.94 8.28
mean
F<SUB>(1,98)</SUB> F<SUB>(1,97)</SUB>
= 55.23[c] = .70[c]
p < .001 p = n.s.
R² = .360 R² = .007
Other Pre-event 8.07 12.16
owners mean
Post-event 10.57 12.46
mean
F<SUB>(1,98)</SUB> F<SUB>(1,97)</SUB>
= 132.13[c] = 2.19[c]
p < .001 p = n.s.
R² = .574 R² = .022
[a]Based on a median split of the pre-event scores.
[a]R² presented is the percentage of within-subjects variance
explained by time.
[c]We used GLS estimation to account for differences in variance
between the pre-event and post-event scores.
Notes: n.s. = not significant.DIAGRAM: FIGURE 1 Key Relationships of Brand Community
DIAGRAM: FIGURE 2 Research Time Line
DIAGRAM: FIGURE 3 Two-Stage Model of IBC
DIAGRAM: FIGURE 4 Model to Assess the Association Between Pre-event and Post-event Measures
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Scale Development
We used LISREL 8 (Jöreskog and Sörbom 1993a) to perform a CFA of the survey items based on the dimensions of community integration observed in the qualitative data collection. The fit of the data to this model was marginal (χ² with 98 degrees of freedom [d.f.] 520.79, root mean square residual [RMSR] = .0647, comparative fit index [CFI] = .829), and we refined the set of items using the approach suggested by Gerbing and Anderson (1988). The process concluded after we dropped three items: two from the owner-brand construct and one from the owner-product construct. Although we deleted two items from the owner-brand construct, we judged the substantive content to have changed minimally: The final set of items contained two brand association items and three brand loyalty/quality items. At this point, we judged the fit of the data to the model to be acceptable (χ² with 59 d.f. = 197.485, RMSR = .0503, CFI = .917).
The reliabilities of three of the hypothesized constructs (Gerbing and Anderson 1988) were strong (see Table 2). In addition, we calculated the AVE for each construct. The AVE is a more conservative measure than reliability, which examines the amount of variance captured by the construct (Fornell and Larcker 1981). The minimum acceptable value for AVE is .50; all four of these constructs easily exceed the .50 value. By using Gerbing and Anderson's (1988) approach, we created unidimensional measures of each construct based on the internal consistency and external consistency criteria implied by the multiple-indicator measurement model.
Second-Order Model
To assess whether these constructs were an adequate reflection of a higher-order construct, the analysis proceeded to test a second-order factor structure (see Figure 3). This model fits the data reasonably well (χ² with 61 d.f. = 223.61, RMSR = .060, CFI = .903). Compared with the CFA, the second-order solution has two more degrees of freedom and a chi-square value 26 points higher. This chi-square difference is statistically significant, but the marginal decrease in the CFI (from .917 to .903) indicates that the change has little practical impact. The second-order loadings (γ<SUB>jk</SUB> are significant and reasonably uniform across the four first-order constructs (Table 3). In addition, the reliability of the community-integration construct is strong (.89), and the AVE is good (.68).
The fit of the model and the consistency of the second-order loadings provide solid evidence that these constructs are a reflection of the second-order factor, community integration.
Associations Between Pre- and Post-event Measures
After the structural equation analyses, we directed our attention to determining whether a significant connection existed between the pre- and post-event scores for each component of community integration, as well as the composite measure of community integration.
We performed two tests to better understand the relationship between the pre- and post-event measures. The first test involved an assessment of the importance of a structural parameter linking pre- and post-event community integration in a structural equations model. The second test used a repeated measures analysis to determine if significant differences in level existed between the pre- and post-event constructs.
Structural equation test. We modeled both pre- and post-event community integration as unobservables (η). We assessed the importance of estimating a structural connection between the constructs in the structural model by observing the difference in the fit between the model that estimated β ( 2, 1) and the model in which β ( 2, 1) was fixed at zero (see Figure 4).
Given the longitudinal nature of the data collection, which used the same items as in the pre- and post-event questionnaires, it is not unexpected that measurement errors (θε) for these items would be correlated (Jöreskog and Sörbom 1993b). Anticipating that correlated measurement error would be added to the model, we deemed it advantageous to have fewer input variables in the analysis. As we determined the final set of items for each of the four dimensions to be unidimensional on the basis of the criteria for internal consistency and external consistency (Gerbing and Anderson 1988), we summed the individual items to create a single score for each dimension. The input to LISREL was then eight variables: a pre-event and post-event score for each of the four dimensions of IBC.
Correlated error terms were added one at a time, until all re-item error variances were correlated with the corresponding post-item error variances. Adding each correlated error term resulted in a statistically significant drop in the chi-square value and a corresponding increase in the fit indices. It was noted in the process of adding correlated error terms that the modification indices indicated that estimating the structural coefficient between the pre- and post-event community integration would produce a greater decrease in the hi-square value than allowing correlated error between any of the pre- and post-event community integration factors.
The standardized estimate for the structural parameter (β 2,1) and the fit statistics for the final models, which both included four correlated error terms, are presented in Table 4. As correlated error terms were added to the model, the structural parameter connecting the pre- and post-event measures was consistently positive and significant, and fixing this parameter at zero resulted in a significant decrease in the fit of the model (Table 4). The comparison of these models indicates a strong positive association between pre- and post-event community integration.
Repeated measures analysis. on the basis of an examination of the pre-event frequencies, it was clear that a subset of the product owners came to the brandfest with high levels of enthusiasm for the product, brand, sponsoring organization, and other owners. As a result of these owners' strong positive feelings, it would be difficult for the brandfest to have added significantly to these owners' enthusiasm levels.
Taking this into account, we sorted each of the four components and overall community integration by the magnitude of their pre-event scores. Each set was split at the median of the pre-event score, and we performed a repeated-measures ANOVA on the upper and lower half of each component and overall community integration.
When we examined the ANOVA results, eight of the ten data sets exhibited significant differences in variance between the pre-event and post-event scores on the basis of Levene's (1960) test. When a more variable group is compared with a less variable group, GLS regression can be used to conduct a repeated measures analysis (Judge et al. 1985). An additional benefit of the regression approach is an easily interpretable measure of effect size, R². We adapted all data sets to a regression format and estimated those that had significant pre-event/post-event differences in variance using the GLS procedure in the SHAZAM econometric package (White 1997). We estimated the data sets that satisfied the homogeneity of variance assumption in a regression format using OLS estimation.
Using the approach suggested by Cohen and Cohen (1983), we estimated the percentage of between-subjects variance and calculated the amount of within-subjects variance. We began this process by regressing the attitude scores on a dummy variable for time (pre-event, post-event) using OLS for the data sets that satisfied the homogeneity of variance assumption and GLS for the remainder. The R² from this regression is the proportion of the total variance in event scores accounted for by the difference in time (Cohen and Cohen 1983). The proportion of total variance was used to calculate the percentage of within-subjects variance explained by time (Table 5).
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By James H. McAlexander; John W. Schouten and Harold F. Koenig
James H. McAlexander and Harold F. Koenig are Associate Professors of Marketing, College of Business Administration, Oregon State University. John W. Schouten is Associate Professor of Marketing, School of Business Administration, University of Portland. The authors thank the people of Harley-Davidson Inc., Bozell Worldwide, and DaimlerChrysler for their time and support in this project. The authors also thank the associates and colleagues of Ethos Market Research and Diagnostic Research International for their contributions to this research. The authors thank James C. Anderson, James R. Brown, Kent Eskridge, Key-Suk Kim, and the four anonymous JM reviewers for comments and suggestions on previous drafts of this article.
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Record: 26- Building Organizational Capabilities for Managing Economic Crisis: The Role of Market Orientation and Strategic Flexibility. By: Grewal, Rajdeep; Tansuhaj, Patriya. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p67-80. 14p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.65.2.67.18259.
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BUILDING ORGANIZATIONAL CAPABILITIES FOR MANAGING ECONOMIC
CRISIS: THE ROLE OF MARKET ORIENTATION AND
STRATEGIC FLEXIBILITY
Firms around the world often must manage and survive economic crises. Recent cases in Asia, Eastern Europe, and South America bear testimony to this point. As economic weak spots are integrated into the global economy, it is timely to develop an understanding of organizational capabilities that can help firms manage their way through such crises. The authors investigate the role of market orientation and strategic flexibility in helping Thai firms manage the recent Asian crisis. The results demonstrate the contingent nature of the influence of market orientation and strategic flexibility on firm performance after a crisis has occurred. As hypothesized, market orientation has an adverse effect on firm performance after a crisis. This effect is moderated by demand and technological uncertainty and is enhanced by competitive intensity. In contrast, strategic flexibility has a positive influence on firm performance after a crisis, which is enhanced by competitive intensity and moderated by demand and technological uncertainty. It seems that market orientation and strategic flexibility complement each other in their efficacy to help firms manage varying environmental conditions.
Organizations frequently must cope with anomalous events, referred to as crises, that create high levels of uncertainty and are potential threats to the viability of an organization. The past decade, for example, has witnessed tremendous economic upheavals that have manifested in economic crises, such as the crashes of the Mexican peso, the Russian ruble, and the Brazilian real. Organizational crises have been extensively researched from divergent perspectives, including those of psychology (Halpern 1989), social polity (Weick 1988), and technological structure (Pauchant and Douville 1994). We add to this body of research by studying the relevance of market orientation and strategic flexibility in determining firm performance in developing economies and during periods of economic crisis; we investigate these relationships in the context of the recent Asian economic crisis.
Literature on the Asian crisis (see Champion 1999; Goad 1999) emphasizes, in general, the need to "better manage" but does not underscore the specifics of this better management. We adopt a resource-based perspective to identify organizational capabilities that would help firms manage their way out of an economic crisis (see Barney 1991; Dickson 1992; Hunt and Morgan 1995). Resources embody "stocks of knowledge, physical assets, human capital, and other tangible and intangible factors that a business owns or controls, which enable a firm to produce, efficiently and/or effectively, market offerings that have value for some market segments" (Capron and Hulland 1999, p. 42). In turn, the firm uses the capabilities developed by resource utilization to manage its environment and perform (Day 1994). Two such capabilities are market orientation and strategic flexibility.
Central to the development of high-caliber marketing practice is the construct of market orientation (Day 1994; Kohli and Jaworski 1990). Being market oriented implies delivering products and services valued by consumers, usually accomplished through (1) ongoing monitoring of market conditions and (2) adaptation of organizational responses (Narver and Slater 1990; Shapiro 1988). Top management plays a critical role in fostering market orientation (Webster 1992), and market orientation influences organizational performance, commitment, and motivation (Jaworski and Kohli 1993). Given the importance of market orientation, it comes as no surprise that this construct has received scrutiny from marketing scholars.
The past decade has witnessed an increase of interest in strategic flexibility, which bestows on a firm the ability to respond promptly to market opportunities and changing technologies (Sanchez 1995). Technological advances in diverse fields such as communication and transportation have endowed organizations with the ability to carry out real-time market research, reduce new product development time and costs, offer a wider product line, mass customize products, and upgrade products at a faster pace than ever before (Kotha 1995). Again, the development of capabilities to be flexible rests on the mandate of top management, helps firms manage environmental uncertainty, and tends to enhance firm performance (Evans 1991).
However, there are at least two limitations of current research on both market orientation and strategic flexibility that preclude researchers from claiming their centrality to the field of marketing. First, researchers primarily have examined the two constructs in the context of organizations in either the United States or Western Europe. As the number of emerging economies in Asia, Eastern Europe, and South America grows, generalizability of market orientation and strategic flexibility rests on the constructs' applicability to the developing world. Our research takes a step in this direction by examining the performance consequences of these constructs for firms in Thailand. Second, research on market orientation and strategic flexibility has concentrated on the normal course of a firm's business and as a result has ignored the constructs' impact on the firm's ability to manage crises. Because of increasing globalization and the emergence of the network economy (Achrol and Kotler 1999), sooner or later economic crises are going to have a direct or indirect effect on almost every firm. Thus, it is essential to develop an understanding of organizational capabilities that will help firms manage an economic crisis. Our research examines the role of market orientation and strategic flexibility in helping Thai firms manage the recent Asian economic crisis. By studying both market orientation and strategic flexibility, we hope to shed light on the resource allocation decision between these two organizational capabilities. The practical implications from our theoretical model and its empirical examination should provide managers with concrete lessons for devising strategies in crisis situations.
In this section, we review literature on (1) economic crises, (2) market orientation, and (3) strategic flexibility to develop our hypotheses. The literature on economic crises helps us crystallize the challenges that organizations face in managing the critical event of an economic crisis. In contrast, literature on market orientation and strategic flexibility provides a means for these organizations to manage this critical event.
Economic Crisis
A crisis represents "a low probability, high impact situation that is perceived by critical stakeholders to threaten the viability of the organization" (Pearson and Clair 1998, p. 66). The significant impact of crises, which may be manifested in the firm's demise, makes it critical for managers to understand and effectively manage these events. Crises come in many forms, including natural disasters such as earthquakes and meteor showers, technological disasters such as the fervor regarding the Y2K computer bug, firm-level crises such as labor strikes, and economic crises such as the one in Asia in 1997. Our research focuses on economic crises and firm-level strategies for managing them (henceforth, we use "crisis" to refer to "economic crisis").
Economic crises are inexorably linked to the concept of business cycles (sometimes referred to as crisis cycles; Mattick 1981), which have continued to befuddle scholars since the beginning of the nineteenth century. Macroeconomics giants, including Keynes (1936), Mathews (1959), and Schumpeter (1939), expended considerable effort to understand these elusive cycles and the ensuing crises. Indeed, the primary criticism of capitalism in Marx's Das Kapital and by subsequent proponents of Marxist thinking (see Mandel 1980) is centered on the contraction phase of business cycles.
Even though much research has been carried out to understand the advent of business cycles and the ensuing periods of expansion and contraction, they remain an enigma (Sharma 1999). The complications stem from the existence of many different cycles, including those with 50-60-year waves, 15-25-year waves, 6-10-year waves, and 40-60-month waves (Mullineux 1984). After adding these cycles, economists must take general trends (for example, an upward trend for a growing economy), along with interdependencies among national economies (which may have different general trends and/or cyclical waves) and external shocks (such as natural disasters), into consideration to get a measure of the complexity involved in predicting and understanding business cycles. However, not all periods of contraction (or troughs in a cycle) are classified as crises. Crises refer to contractions in which real output decreases, not to periods of slow growth. Therefore, it comes as no surprise that it is difficult to predict and gauge the influence of these economic crises.
Furthermore, there is little consensus as to the reasons for the manifestation of economic crises. Whereas the Great Depression of the 1930s was characterized as a Keynesian crisis (i.e., chronic insufficiency of demand) and the oil shock of 1970s was attributed to an external shock, the Brazilian crisis of the 1980s was blamed on governmental failures (excessive and distorted growth of the state), and the recent Asian crisis was considered a culmination of antiquated banking practices and idiosyncratic cultural elements, such as lack of transparency (Aggarwal 1999; Alon and Kellerman 1999; Pereira 1996). However, crises are characterized by the co-movement of many macroeconomic indicators, including decreases in real output (measured by real gross domestic product [GDP]), high levels of inflation and unemployment, and an unstable currency.
The organizational crisis literature focuses on myriad factors that influence strategies for crisis management, including the psyche of managers, the nature of crisis-triggering events, organizational structures and processes, and environmental variables (Pearson and Clair 1998). Research on the organizational response, however, has primarily focused on industrial crises (Smith 1990). Industrial crises, such as those related to negative consequences of product consumption (e.g., the silicon breast implants of Dow Coming) and industrial accidents (e.g., the 1984 Union Carbide gas leak incident in Bhopal, India), usually influence a single firm at a time. Unlike industrial crises, which influence a firm or an industry, economic crises affect a country (e.g., Mexico in 1994) or a region (e.g., Asia in 1997). Furthermore, industrial crises usually involve a struggle for legitimacy, in which organizational moral and ethical standards are subject to public scrutiny (Pauchant and Douville 1994). In contrast, economic crises alter demand patterns, thereby testing organizational marketing skills (Block 1979). In addition, organizational research has not examined the significance of market orientation and strategic flexibility, both of which are considered important organizational capabilities and critical for competing effectively in the marketplace. Research on organizational crises (D'Aveni and MacMillan 1990) shows that surviving firms, in comparison with failing firms, focus on both external and internal environments, which is a critical feature of market orientation (Kohli and Jaworski 1990), and the attainment of a balance between the two environments, which is an important aspect of strategic flexibility (Volberda 1996).
Scholars assert that the environmental context interacts with organizational capabilities to influence firm performance (Houston 1986; Lusch and Laczniak 1987). Research on market orientation has examined the interactional effects of the facets of the environment and market orientation on firm performance (e.g., Jaworski and Kohli 1993; Slater and Narver 1994). In an ordinary course of events (without a crisis), firms develop capabilities to manage their environment. Organizational investments in these capabilities should reflect the firm's environmental needs (Clark, Varadarajan, and Pride 1994). In environments characterized by high uncertainty, for example, a firm will face many diverse situations and should invest more in being flexible (Harrigan 1985).
Thus, a firm develops its capabilities to maximize performance (we refer to this as performance before crisis) during the normal course of its activities. The firm uses these capabilities to manage crises (i.e., performance after the crisis has occurred, henceforth referred to as performance after crisis). Therefore, drawing from contemporary research on market orientation, we examine three facets of the environment: competitive intensity, demand uncertainty, and technological uncertainty (Kohli and Jaworski 1990). These three facets provide a comprehensive theorizing of organizational environments (Clark, Varadarajan, and Pride 1994).
It is important to emphasize that an economic crisis does not influence all firms in a similar manner. If a firm has foreign customers, for example, it may benefit from a crisis. However, if the firm has foreign suppliers, it might suffer and may need to look for alternative sources of supply. Likewise, as a crisis influences the currency exchange rates, the nature of a firm's debt becomes important. In a similar vein, a firm's performance before crisis should affect its performance after crisis (Kuran 1988). Therefore, we cannot apply the macroenvironmental phenomenon of an economic crisis homogeneously at the firm level. To conceptualize crises at the firm level, we control for a firm's performance before crisis and reliance on international suppliers, international demand, and international financial institutions. By controlling the organizational context, we customize a crisis for a firm and thereby conceptualize it at the firm level. We present our theoretical model in Figure 1, which summarizes the hypotheses pertaining to market orientation and strategic flexibility. Next, we develop these hypotheses.
Market Orientation
Market orientation represents the implementation of the marketing concept, an important cornerstone of the marketing discipline (Barksdale and Darden 1971; Felton 1959; McNamara 1972). A "market oriented organization is one whose actions are consistent with the marketing concept" (Kohli and Jaworski 1990, p. 1). Contemporary research on market orientation focuses on (l) its definition and conceptualization (Jaworski and Kohli 1993; Narver and Slater 1990), (2) its antecedents and consequences (Jaworski and Kohli 1993; Slater and Narver 1994), (3) its influence on employee attitudes (Siguaw, Brown, and Widing 1994), and (4) its measurement (Deshpande and Farley 1998; Kohli, Jaworski, and Kumar 1993).
Following the work of Jaworski and Kohli (1993; Kohli and Jaworski 1990; Kohli, Jaworski, and Kumar 1993), we conceptualize market orientation in terms of the activities of information generation, information dissemination, response design, and response implementation. Information generation captures the organizational emphasis on gathering information on current and future customer needs, information dissemination is the degree of sharing of information across departments, and response design (the use of market intelligence in planning) and implementation (execution of the plans) assess organization-wide responsiveness.
A standard argument in the market orientation literature suggests that market-oriented firms are in a better position to satisfy the needs of their customers (Narver and Slater 1990). Empirical research in the U.S. context supports this assertion (e.g., Jaworski and Kohli 1993; Lusch and Laczniak 1987; Slater and Narver 1994). Therefore, researchers expect market orientation to be manifested in enhanced firm performance (i.e., under the normal course of events), at least in the U.S. context.
According to Hofstede's (1980) cultural dimensions, Thailand is similar to its Asian neighbors and clearly different from Western countries, where most market orientation research has been undertaken. Yet a recent empirical study of Thai managers' attitudes toward market orientation supports the centrality of this construct for Thai firms (Powpaka 1998). Managers of Thai firms and those in other Asian countries have adopted U.S. business practices in recent years. The widespread acknowledgment of U.S. business school models is homogenizing managerial thinking and market-based practices (e.g., the use of a market orientation) across nations (see Doremus et al. 1998). The role of world bodies, such as the World Bank and International Monetary Fund, reinforces this thinking, because the United States is the primary contributor to these bodies and therefore exerts a high level of control over them. The preeminent position of U.S. consulting firms in Thailand further strengthens this line of reasoning (see Mertens and Hayashibara 1998). Therefore, market orientation should have a positive influence on firm performance in noncrisis situations for Thai firms.
Meanwhile, we expect market orientation to have a negative influence on firm performance after crisis. Research on market orientation also shows that excessive customer orientation, an important aspect of market orientation, can be harmful for organizations (see Bennett and Cooper 1979; Frosch 1996; Macdonald 1995). For example, Christensen and Bower (1996, p. 198) conclude from their analysis of the hard disk drive industry that "firms lose their position of industry leadership ... because they listen too carefully to their customers." Similarly, Hamel and Prahalad (1994, p. 99) view this customer orientation as the "tyranny of the served market" and think of customers as "notoriously lacking in foresight." In defense of market orientation, Slater and Narver (1998, p. 1003; also see Connor 1999; Slater and Narver 1999) point out that in comparison with customer-oriented firms, market-oriented firms "scan the market broadly, have a longer term focus, and are more likely to be generative learners." In a similar vein, Jaworski, Kohli, and Sahay (2000) theorize market orientation as both market driven and market driving. The focus of market orientation is on both expressed and latent customer needs, unlike customer orientation, which focuses only on expressed customer needs (Slater and Narver 1998). Market orientation also stresses learning from and monitoring competitors' capabilities and plans, as opposed to customer orientation, which neglects competitors.
Market orientation is indeed a learning process in which organizations learn from all aspects of their environment, including customers and competitors, and take both short-and long-term organizational goals into consideration (Kohli and Jaworski 1990). Market orientation captures organizational learning from the environment, and organizations derive benefits from this learning (Slater and Narver 1995). However, we do not expect this learning to be useful in crisis situations for at least two reasons. First, because crises are unique, low-probability situations, firms do not encounter them frequently and therefore cannot learn about them in advance. Second, learning from nonunique crisis situations is less likely to prove useful because firms rarely encounter these situations, do not have ample opportunity to use their learning about crises, and therefore should be less motivated to learn.
Crises also "defy interpretations and impose severe demands on sensemaking" (Weick 1988, p. 305). It is possible that even an organizational capability as powerful as market orientation may not be able to capture the rare circumstances that organizations can face in a crisis. Highly attuned market orientation would cause firms to lock into a standard mode of cognition and response, thereby building inertia instead of the creative thinking needed to manage crises (Day 1994; Scott 1987). In the context of reactions to competitive threats, Chandrashekaran and colleagues (1999) show that it is fairly easy and common for firms to steer into such inertia. At least three factors contribute to creating inertia. First, managerial bias toward the status quo creates inertia by enhancing the preferences for tested and institutionalized business models (Ritov and Baron 1992). Second, research on bounded rationality recognizes the cognitive limitations of managers and organizations and the difficulties those limitations create in evaluating new business models, specifically in high-turbulence situations such as crises (Dickson 1992). Third, sunk cost fallacy, driven by the human tendency to be more averse to losses than gains, contributes toward creating barriers to change time-tested techniques and procedures (Kahneman and Lavallo 1993). Market orientation contributes to organizational success (Jaworski and Kohli 1993; Slater and Narver 1994) and entrenches business models, thereby creating inertia. Thus, we expect market orientation to have an adverse effect on firm performance in the face of a crisis.
H1: The greater a firm's market orientation, the lower will be the level of firm performance after crisis.
Interactions Between Market Orientation and Facets of the Environment
Competitive intensity. Competitive intensity, the degree of competition that a firm faces, has been purported to moderate the influence of market orientation on firm performance. As competitive intensity increases, so does a firm's need to be market oriented (Houston 1986). Therefore, in highly competitive environments, greater emphasis on market orientation is required for better performance (Kohli and Jaworski 1990).
Firms in highly competitive environments focus considerable attention on competitors. In these markets, firms often assume that competitors' actions are optimal and mimic them (Day and Nedungadi 1994; Day and Wensley 1988). Such mimicking should not pay off in a crisis situation, because the idiosyncratic challenges of a crisis should also befuddle competitors. In addition, a crisis represents an anomaly and has the potential to change the very basis of competition. Firms that get locked into precrisis assumptions of competition are likely to be at a disadvantage. Arthur (1989), for example, discusses the way small, chance events result in nonoptimal decisions (e.g., the "QWERTY" typewriter keyboard) and have a lingering, long-term influence on organizational activities. Likewise, DiMaggio and Powell (1983) note how the pressures of professionalization are manifested in similar thinking across firms, which leads to institutionalized business models. Similarly, firms in highly competitive environments focus more on learning about competitors, which is a key aspect of market orientation (Han, Kim, and Srivastava 1998), and over time this learning becomes institutionalized. Organizations that are market oriented are more likely to be locked into institutionalized thinking about competitive behaviors. This type of thinking becomes a greater burden as competitive intensity increases, because the need for an appropriate response to competitors is greater in highly competitive environments (Jaworski and Kohli 1993). Thus, as competitive intensity increases, we expect the negative relationship between market orientation and firm performance to become stronger.
H2: The greater the competitive intensity, the stronger will be the negative relationship between market orientation and firm performance after crisis.
Demand uncertainty. Demand uncertainty captures the variability in customer populations and preferences, which requires organizations to adapt their product offerings, plans, and strategies to the changing demand conditions. Market orientation helps firms track these changes in the consumer environment and should aid in managing this uncertainty. As the demand uncertainty increases, so does a firm's need to be market oriented. Therefore, researchers posit that the positive relationship between market orientation and firm performance should become stronger as demand uncertainty increases (Jaworski and Kohli 1993; Slater and Narver 1994).
In the long run, an economic crisis may change the nature of consumer demand. Usually, economic crises manifest themselves in high inflation and tend to make consumers more price-sensitive (Block 1979). As a result, consumers (1) resort to greater information search, (2) postpone their purchase decisions, or (3) switch brands. Congruently, a major decline in the sales of consumer durable products, such as automobiles and household appliances, occurred during the recent Asian economic crisis, perhaps because of postponement of purchase (Hla 1999) and/or high rates of brand switching (see Siam Commerce 2000). Similar consumer behaviors were reported in South Korea. Korean students, for example, switched from a U.S. educational institution to a Korean university for their undergraduate studies (Woodard 1998). In the short run, economic crises may cause consumers to move downward on the demand curve and buy at a lower price or to purchase less quantity at the same price. Research on consumer behavior shows that consumers learn from experience, and this learning affects their future behavior (Hoch and Deighton 1989). Therefore, in addition to the temporary effects of crises on consumer behavior, the changes in consumer behavior, such as increased price sensitivity of consumers, postponement of purchase decisions, increased consumer information search, and brand switching, can have far-reaching, long-term implications and perhaps even alter the nature of the demand.
Market-oriented firms in high-demand uncertainty environments are more accustomed to monitoring consumers and therefore, with their focus on the consumer, should be in a better position to make the adjustments necessary to tap into the new demand curves (Slater and Narver 1995). The nature of demand is inherently complex in high-demand uncertainty markets. A crisis is likely to complicate these markets further, because it will directly affect the demand pattern (e.g., a rise in inflation makes some consumers more price sensitive; they therefore resort to greater information search). The market orientation skills of a firm are critical and are subjected to a Herculean examination in crisis-torn, high-demand uncertainty markets. After an economic crisis, market orientation is even more important in markets characterized by high levels of demand uncertainty as opposed to low-demand uncertainty markets. Therefore, we expect demand uncertainty to moderate the negative effect of market orientation on firm performance after crisis.
H3: The greater the demand uncertainty, the weaker will be the negative relationship between market orientation and firm performance after crisis.
Technological uncertainty. Both the pace and degree of innovations and changes in technology induce technological uncertainty. Often organizations use technological orientation as an alternative means to market orientation to build sustainable competitive advantage (Kohli and Jaworski 1990). Even though a balance between an emphasis on technological orientation and one on market orientation is possible, firms in high-technology markets tend to allocate greater resources to technology to manage the uncertainty created by technological changes (Glazer 1991; Slater and Narver 1994). Emphasis on technological orientation as a means of competing should reduce the importance of market orientation. The positive relationship between firm performance and market orientation should weaken as technological uncertainty increases (Jaworski and Kohli 1993).
The effect of an economic crisis on reducing consumers' buying power and altering the basic demand pattern makes market orientation even more critical for two reasons. First, consumers become more price sensitive, which thereby reduces the importance of relatively expensive, technologically advanced products (Bass 1995). Second, the increased price sensitivity makes organizational ability to satisfy consumer needs even more critical. Furthermore, firms in markets characterized by high technological uncertainty, compared with firms in markets characterized by low technological uncertainty, compete more on the basis of technology than on the basis of market orientation (Hayes and Wheelwright 1984). The increased importance of market orientation due to the crisis and the dearth of market orientation capabilities should make market orientation a valued capability. Therefore, we expect technological uncertainty to moderate the negative influence of market orientation on performance after crisis.
H4: The greater the technological uncertainty, the weaker will be the negative relationship between market orientation and firm performance after crisis.
Strategic Flexibility
Strategic flexibility represents the organizational ability to manage economic and political risks by promptly responding in a proactive or reactive manner to market threats and opportunities, thereby making it possible for firms to resort to what Ansoff (1980) terms "surprise management." Usually built by means of a flexible resource pool and a diverse portfolio of strategic options, strategic flexibility enables firms to manage uncertain and "fast-occurring" markets effectively (Aaker and Mascarenhas 1984). Strategic flexibility is expected to increase the effectiveness of communications, plans, and strategies, which, coupled with adapted product offering and other aspects of marketing mix, should enhance firm performance (see Miles and Snow 1978).
It is best to consider strategic flexibility a polymorphous construct; that is, the exact meaning and conceptualization of strategic flexibility varies from one context to another (Evans 1991; Young-Ybarra and Wiersema 1999). To study strategies for exiting markets, for example, Harrigan (1980)theorizes strategic flexibility as a firm's ability to redeploy its assets without friction and discusses how this flexibility helps firms overcome exit barriers in declining industries. Similarly, Sanchez (1995) conceptualizes strategic flexibility in the context of product competition as comprising (l) the flexibility inherent in product-creating resources (resource flexibility) and (2) flexibility in using these available resources (coordination flexibility). Likewise, Evans (1991) proposes the offensive/defensive dichotomy for strategic flexibility, in which offensive strategic flexibility aims to create and seize an initiative and defensive strategic flexibility guards against unforeseen competitive moves and environmental eventualities.
In the case of economic crises, the appropriate form of strategic flexibility is reactive. Because the extent, nature, and timing of a crisis are difficult to predict, proactive offensive action to manage the crisis is unlikely, but reactive strategic flexibility capability should be useful. Organizations develop reactive strategic flexibility (henceforth, we use the term "strategic flexibility" to refer to "reactive strategic flexibility") by building excess and liquid resources (Cyert and March 1963) and creating the capacity to be agile and versatile (Evans 1991). One way for a company to build excess resources is to hedge its options, which is related to organizational slack (the buffer for managing environmental uncertainty) and should mitigate the loss potential of a crisis (Eppink 1978). Liquid assets involve minimal switching costs to convert them to alternative forms and are reflected in the overall organizational emphasis on managing political, economic, and financial risks (Jones and Ostroy 1984). To achieve agility and versatility, organizations instill capabilities for responding to diverse scenarios. Such capabilities are built by placing emphasis on the management of environmental diversity and variability (Evans 1991).
Similar to most resource allocation decisions, opportunity costs are associated with the resources used in building strategic flexibility. Organizations building these resources foreclose other opportunities and means of making profits, such as deriving benefits from scale economies. Therefore, in the normal course of events, when a firm does not need to respond reactively to environmental eventualities, we expect strategic flexibility to have an adverse influence on firm performance (Levitt 1983; McKee, Varadarajan, and Pride 1989).
However, when the benefits of adapting outweigh the gains from standardized strategy, as in crisis situations, strategic flexibility capabilities are likely to be useful. Crises offer greater contingencies and uncertainties to organizations by altering most aspects of competition. A firm's ability to alter and adapt its programs and strategies is likely to come in handy. (Indeed, the economists who study organizational management of business cycles have laid the foundation for work on strategic flexibility; see Hart 1937; Kindleberger 1937; Stigler 1939.)Therefore, we expect strategic flexibility to be manifested in enhanced firm performance after crisis:
H5: The greater a firm's strategic flexibility, the higher will be the level of firm performance after crisis.
Interactions Between Strategic Flexibility and Facets of the Environment
Competitive intensity. Competitive intensity, the degree of competition a firm faces, requires firms to take a flexible approach so that they can adapt and improvise to put their best foot forward (Moorman and Miner 1998). In conditions of low competitive intensity, investments in flexible resources and strategic options are not useful, because an organization is less likely to face circumstances that require the use of these resources. In contrast, in highly competitive environments, strategic flexibility is a valuable asset (Aaker and Mascarenhas 1984).
A crisis represents an anomaly and has the potential to change the very basis of competition. Firms that have the flexibility to respond to new competitive behaviors are at a definite advantage; they can easily redeploy critical resources and use the diversity of strategic options available to them to compete effectively. Thus, as competitive intensity increases, we hypothesize that the positive relationship between strategic flexibility and firm performance after crisis should be strengthened.
H6: The greater the competitive intensity, the stronger will be the positive relationship between strategic flexibility and firm performance after crisis.
Demand uncertainty. Demand uncertainty creates difficulty in assimilating information and devising strategic plans. Managing uncertain environments requires concerted deployment of resources devoted to the product-market operations and response to demand idiosyncrasies. Strategic flexibility, by definition, emphasizes answering to the unique needs of consumers, business partners, and institutional constituents (Allen and Pantzalis 1996). Because firms are more likely to face challenging and unique situations in uncertain markets than in stable markets, strategic flexibility should be more useful in these uncertain markets.
Nonetheless, an economic crisis alters the demand characteristics. A firm may be unaware of the new nature of demand or may never have faced the new demand conditions. Even a flexible portfolio of options is unlikely to contain a remedy for the crisis, because it is a low-probability anomaly (Bowman and Hurry 1993). As a result, firms must learn (as manifested in market orientation), not just respond in a flexible manner with an existing toolkit. Therefore, we expect demand uncertainty to moderate the influence of strategic flexibility on firm performance.
H7: The greater the demand uncertainty, the weaker will be the positive relationship between strategic flexibility and firm performance after crisis.
Technological uncertainty. Variability in technology stemming from innovations contributes to technological uncertainty. Strategic flexibility involves capability building to respond quickly to changing market conditions. Such capability building usually involves investing in diverse resources and possessing a wide array of strategic options (Bowman and Hurry 1993). Because technologically uncertain markets are likely to offer a greater number and range of threats and opportunities for firms to adapt and improvise, we expect strategic flexibility to be of higher importance in markets characterized by high levels of technological uncertainty than in low-technological uncertainty markets.
In contrast, an economic crisis diminishes the importance of technologically advanced products and increases the importance of demand management. Even a flexible portfolio of options is unlikely to be useful in crisis, because the prime need of that moment is to learn and not just respond in a flexible manner. Therefore, we expect technological uncertainty to moderate the positive influence of strategic flexibility on firm performance after crisis.
H8: The greater the technological uncertainty, the weaker will be the positive relationship between strategic flexibility and firm performance after crisis.
Thailand: The Center of the Economic Crisis
The Asian economic collapse began in Thailand in July 1997 with a sudden fall of the Thai baht, which could no longer be pegged to a basket of major currencies. The government spent all its reserves to try to keep the baht close to the pegged rate, but without success. In a few months, the baht devalued from approximately 25 baht per U.S. dollar to more than 50 baht. Quickly, the crisis spread to other Asian and then Latin American countries and has had lingering global effects. Therefore, we believe that Thailand is an appropriate context in which to study this crisis. Our data collection exercise was carried out from November 1998 to March 1999, which coincides with signals related to the bottom of the crisis and the recovery of the Thai economy. Since then, the baht has revalued to a floating rate of approximately 35 baht per U.S. dollar, and the short-term interest rates (20%-25% at the height of the crisis) began to decline to approximately 12% in June 1999. Economists have declared Thailand and Korea as frontrunners in managing their way out of the crisis (Aghevli 1999).
Generalizability of Context
We argue that Thailand provides an appropriate context for testing the generalizability of our research on market orientation and strategic flexibility. It is a non-Western nation with a clearly different set of cultural values in comparison with the United States and Western European countries, where most of the research on market orientation and strategic flexibility has been carried out (Hofstede 1980; McGill 1995). Thai managers and business owners are representative of a non-U.S, sample for Asia, because many are Chinese in origin and thereby similar to their counterparts in other Southeast Asian countries (Powpaka 1998). Thailand has also been the regional headquarters of many multinational companies in Southeast Asia, and Thai managers have been employed to run subsidiaries throughout the region.
We further established the generalizability of the Asian crisis and its impact on Thailand in two ways. First, we compared the influence of the Asian economic crisis on Thailand, South Korea, and Japan. Thailand saw a drop in GDP growth from 5.5% to -10%, whereas the drop was not so adverse for South Korea (from 5.8% to -6.8%) and Japan (from 2.9% to -5.2%). The three countries also witnessed negative growth rates, as pointed out in our definition of an economic crisis. The crisis resulted in rising consumer inflation and unemployment, along with currency devaluation in the three countries. The current account deficits also dramatically declined, which signals a substitution of foreign goods for those produced within the country. Second, we compared the influence of the Asian crisis with those for Mexico and Russia. In terms of real GDP growth, consumer price inflation, unemployment rates, and changes in currency exchange rates, the influence of the Asian crisis on Thailand was similar to economic crises in Mexico (1994) and Russia (1997).
Control Variables
We must control for both the historic levels of firm performance and international dependencies that may influence performance after crisis. Aptly described as the "tenacious past" by Kuran (1988) and "path dependence" by Arthur, Ermoliev, and Kaniovski (1987), higher performance before crisis generally should be manifested in higher performance after crisis. Furthermore, we viewed international dependencies in terms of linkages with suppliers outside Thailand, the extent to which the product/service is exported, and dependence on international financial agencies. Reliance on suppliers from countries not affected by the Asian crisis is likely to have an adverse influence on performance after crisis, because raw materials and other products used in manufacturing become more costly. Demand dependence captures the extent to which a firm relies on international demand. An economic crisis usually results in currency devaluation that makes exported products cheaper. Demand dependence should therefore enhance performance after crisis. Finally, we controlled for financial dependence, which indicates the extent of reliance on borrowing in foreign currencies. The higher the reliance on international financial institutions, the more severe should be the adverse effects of a crisis.
Sample and Data Collection Procedure
We focused on small and midsized Thai firms, which were relatively more vulnerable to the crisis because organizational slack (buffer) directly varies with firm size (see Clark, Varadarajan, and Pride 1994). Data were collected from these firms in three waves. First, consistent with recent research on Thai firms (Powpaka 1998), the data were collected during November 1998 from 49 middle managers and owners participating in an executive MBA program at a large university in northeastern Thailand. A subsequent group of respondents who participated in the program in March 1999 provided the second set of 61 responses. Third, during March 1999, a senior manager in a prominent Thai conglomerate in Bangkok agreed to the conglomerate's participation in the study. We distributed the survey to the 30 firms affiliated with the conglomerate and obtained 22 responses. Thus, we received 132 responses, of which 120 were complete and usable. Furthermore, we compared the three groups in terms of the number of employees before crisis (BEMP) and number of employees after crisis (AEMP) and found no differences. We also compared the change in the number of employees (CEMP = BEMP - AEMP) for the three groups and found that the mean number of employees increased for the three groups and that there were no statistical differences in the change in these means. Finally, we translated the questionnaire from the original English version to Thai and used the back-translation technique to ensure that the original meaning was maintained.
Measures
We operationalized market orientation with four subconstructs: information generation, information dissemination, response design, and response implementation. Specifically, we adopted Jaworski and Kohli's (1993) 31-item measure with 10 items for information generation and 7 items for each of the remaining three subconstructs. We carried out a measure purification exercise similar to that used by Kohli, Jaworski, and Kumar (1993, p. 475), who note that "As globalization issues assume the forefront of marketing practice, it is important to consider whether (I) the scale 'makes sense' in other languages and (2) subsequent measure assessment would produce similar results." However, after the development of this market orientation measure, advances in psychometric research on instrument development provided evidence of two potential issues with this measure. First, Bagozzi and Baumgartner (1994) recommend using 5 or fewer items to measure a unidimensional construct. Because all the subconstructs of market orientation have more than 5 items, it is possible that assessing the unidimensionality of these constructs will pose problems. Second, Herche and Engelland (1996) demonstrate that reverse-scored items need not be the opposite of positively worded items and therefore should be avoided. In the 31item measure of market orientation, 10 items are reverse-scored. Therefore, cognizant that the market orientation measure may pose challenges, we sought to assess the psychometric properties of this measure as a peripheral objective in the Thai context.
We used four items to measure strategic flexibility. The first item captures the organizational objective of building excess resources by hedging (Eppink 1978) and likewise stresses sharing investments across business activities. Such investment sharing buffers an organization from external shocks, because the organization can find alternative uses for its resources. The next two items gauge organizational attempts to build agility and versatility by instilling capabilities to respond to disparate situations. Specifically, the second item appraises a firm's emphasis on deriving benefits from diversity in the environment, and the third item measures the importance the firm puts on benefiting from opportunities that arise from variability in the environment. These emphases on actively managing the diversity and variability help organizations become agile and versatile (Jones and Ostroy 1984). The final item appraises strategic flexibility in terms of a firm's strategic emphasis on managing macroenvironmental risk (i.e., political, economic, and financial risks). Firms placing such an emphasis attempt to gain a competitive edge by developing superior abilities in responding to environmental uncertainties. In operational terms, these firms may possess liquid resources or options to enhance the speed and extent of their maneuvering capabilities.
To measure the three components of the environment (i.e., competitive intensity, demand uncertainty, and technological uncertainty), we adopted items from Jaworski and Kohli's (1993) work. The four items for competitive intensity assessed the extent of competition in general, promotional wars, price competition, and new competitive moves. The four items for demand uncertainty measured the uncertainty created by variability in consumer demand, product and brand features, price/quality demanded by customers, and competitive moves. The three-item technological uncertainty scale appraised changes in technology, opportunities created by technology, and manifestation of new products as a result of technology.
We measured performance (both before and after crisis) by assessing satisfaction with respect to return-on-investment goals, sales goals, profit goals, and growth goals. We appraised international interdependencies with three three-item measures. The items for international supplier dependence measured relying on international suppliers, buying raw materials and other supporting materials from abroad, and relying on multinational corporations for raw material. The scale for international demand dependence assessed selling products to foreign customers, relying on overseas demand, and being able to satisfy multinational and foreign customers. The measure for international financial dependence appraised financing from abroad, the criticality of funding from abroad, and financing from international monetary agencies.
Measure Validation
We used confirmatory factor analysis to assess the convergent and discriminant validity for our measurement models (Gerbing and Anderson 1988). Specifically, we estimated four measurement models: the first for the three environmental variables (competitive intensity, demand uncertainty, and technological uncertainty), the second for the three control variables (supplier dependence, demand dependence, and financial dependence), the third for the two performance variables (performance before and after crisis) and strategic flexibility, and the fourth for market orientation. We summarize the results from these models in Table 1. Overall, the results demonstrate adequate levels of fit, and all factor loadings are greater than the .4 cutoff (Nunnally and Bernstein 1994). In addition, discriminant validity is established, in that all the [phi]s are statistically different from 1 (Anderson and Gerbing 1982).
We also used low factor loadings, high standardized residuals, and high modification indices from our confirmatory factor analysis results to purify our measures. As we suspected, the majority of the problems pertaining to unidimensionality were related to either long scales (Bagozzi and Baumgartner 1994) or reverse-scored items (Herche and Engelland 1996). We encountered problems in the market orientation subconstructs, especially for response design, which had four of seven items reverse-coded. There is a need for a more reliable measure for market orientation. Finally, all reliabilities are greater than .7, with the exception of the response design subconstruct (Nunnally and Bernstein 1994). The descriptive statistics for the constructs, along with their correlations, appear in Table 2.
In Table 3, we summarize the regression results. Typically, multiplying the appropriate independent variables creates indicators for the interaction terms. Because this approach is prone to collinearity (Jaccard, Turrisi, and Wan 1990), we took an instrumental variable approach to capture the interaction effects. Specifically, we ran a regression in which the product of the two variables in question was the dependent measure and the two variables used to obtain the product term were independent variables. We used the residual of this estimation as the instrument for the interaction hypothesis (for statistical details, see Hansen 1982; White 1983). Conceptually, these residuals are orthogonal to the two variables used to obtain them; in terms of hypothesis testing, they explain variance in addition to that explained by the main effects.
For the control variables, our assertions regarding path dependencies and international demand dependence were supported. Firms with high levels of performance before crisis tended to perform better after crisis (b = .319, p < .01), and international demand dependence leads to higher levels of performance after crisis as exports become cheaper in the world market (b = .214, p < .01). However, international supplier dependence (b = .029, p < .67) and international financial dependence (b = -.012, p < .88) do not seem to influence firm performance after crisis. Our informal discussions with the respondents reveal a possible explanation for these results. The suppliers for the firms in our sample often were from neighboring countries that were equally influenced by the crisis. In addition, the financial institutions provided the funds in local currencies, which thereby insulated the firms from the vagaries of international currency fluctuations. Although we had conjectured along these lines for international supplier dependence and international financial dependence, by measuring these variables we controlled for the biases that might have been induced had we not incorporated these variables in our analysis.
Does market orientation help in managing market crisis situations? Our results show that it does only in certain conditions. In general, market orientation has a negative influence on firm performance after crisis (H1: b = -.734, p < .05), which is aggravated in conditions of high competitive intensity (H2: b = -.230, p < .01). However, market orientation helps firms manage conditions of high demand uncertainty (H3: b = .301, p < .01) and high technological uncertainty (H4: b = .158, p < .10).
Unlike market orientation, strategic flexibility is useful when firms must navigate their way out of crises (H5: b = .603, p < .01) and becomes even more important as competitive intensity increases (H6: b =. 186, p < .05). However, demand uncertainty (H7: b = -.362, p < .01) and technological uncertainty (H8: b = -. 140, p < .05) moderate the positive influence of strategic flexibility on firm performance after crisis.
We estimated a model with performance before crisis as a dependent measure and market orientation, strategic flexibility, and their interactions with the facets of the environment as independent measures. We recognize that such a model is not theoretically sound, because we are trying to explain the 1996 performance with organizational variables measured in 1998. Nonetheless, we found that market orientation positively influences firm performance before crisis and that this effect is moderated by technological uncertainty. In addition, reactive strategic flexibility has an adverse effect on firm performance before crisis, which is moderated by demand uncertainty.
Using the Asian economic crisis in Thailand as our research context, we studied the importance of market orientation and strategic flexibility in helping firms manage the chaos and challenges an economic crisis poses. Reasoning that crises "defy interpretations and impose severe demands on sensemaking" (Weick 1988, p. 305), we suggested that learning firms would be locked into set modes of cognition and response because crises are low-probability events and preclude creative sensemaking. The inertia created by market orientation often hampers learning pertaining to the changes in the environment after a crisis, thereby resulting in a negative link between market orientation and firm performance after crisis.
Our results indicate that market orientation is useful for managing crises only in conditions of high demand uncertainty or high technological uncertainty, and it might not be emphasized when competitive intensity is high. When firms have an emphasis on market orientation, they get locked into institutionalized thinking about competitors. However, precrisis assumptions of competitive behavior are no longer valid after a crisis, and as a result market orientation tends to hurt market-oriented firms. Conversely, an emphasis on market orientation enables firms to learn the new demand patterns quickly and effectively, because their primary focus in high-demand uncertainty environments is consumers (Day and Wensley 1988). An economic crisis shifts competition away from innovative new products, which tend to be expensive, and toward other market factors such as demand management. Again, market orientation comes in handy here.
In contrast, the tools and skills developed by posturing strategic flexibility are useful in crisis situations. Our results recommend flexibility in managing environments with high competitive intensity. However, flexibility is not a cure for environments with either high demand uncertainty or high technological uncertainty. Readers are advised to observe that in markets characterized by high competitive intensity, strategic flexibility should be emphasized and market orientation should be deemphasized. In markets with high demand uncertainty or high technological uncertainty, market orientation should be emphasized and strategic flexibility should not be stressed. The complementarity of market orientation and strategic flexibility in managing varying environmental conditions suggests that top management should develop both of these capabilities in tandem. This complementarity is further reinforced by the finding that market orientation and strategic flexibility capabilities can be simultaneously pursued, as is indicated by the high correlation of .48 between the two constructs (see Table 2). Firms can simultaneously build these two capabilities and thereby, to an extent, make the resource allocation decision between these two capabilities moot.
Limitations
The main limitation of our research pertains to the nature of our sample. Two of the three sample sources are executive MBAs, which indicates that caution is necessary in drawing inferences. Firms that participate in executive MBA programs are likely to be somewhat different from firms that do not; they are more likely to succumb to the pressures of professionalization (DiMaggio and Powell 1983) and as a result are more likely to adopt the models propagated by business schools, such as the importance of market orientation.
Three more limitations require caution as we draw implications from and generalize our results. First, we are limited by our context, and replications with other economic crises are needed. Second, there is a need to develop a better measure of strategic flexibility that would give a better sampling of the domain of the construct. Third, similar to most survey research, our results suffer from survival bias. Firms that did not survive the crisis are missing from our sample.
Theoretical Contributions and Implications
We believe that our research makes important contributions to the literature on economic crisis, market orientation, and strategic flexibility. By using organization-level data with a large number of respondents, we move beyond the theoretical (see Pearson and Clair 1998) and case-based (Abolafia and Kilduff 1988) research that dominates the crisis literature. We also show that the organizational capability (market orientation or strategic flexibility) that would aid organizations in managing a crisis is contingent on the facets of the environment.
We also contribute to the literature on market orientation. Time and again, scholars have expressed the need to study market orientation in a non-U.S, context (e.g., Kohli, Jaworski, and Kumar 1993). We take an important step in this direction and highlight three issues. First, our research examines the psychometric properties of Kohli, Jaworski, and Kumar's (1993) MARKOR measure, and our results suggest further refinement of this measure. Second, we demonstrate that market orientation influences performance after crisis but find that it is only useful for managing economic crises in environments characterized by high levels of either demand or technological uncertainty. Third, we study the boundary conditions for the influence of market orientation. Several studies have shown that customer orientation can be detrimental (Christensen and Bower 1996). Slater and Narver (1998, 1999) rightly argue that market orientation goes beyond customer orientation and should help overcome the weakness inherent in customer orientation. In the case of economic crises, our research shows that market orientation does not help firms effectively manage all environmental conditions and demonstrates the need to refine the construct further. The emergence of the network economy is increasing the interconnectedness among countries (Achrol and Kotler 1999), and regional economic crises therefore may have riveting effects around the world. It therefore becomes important for organizations to build capabilities to manage crises and for marketing researchers to be attuned to market orientation for crisis situations. We also demonstrate the importance of strategic flexibility in crisis situations, in that strategic flexibility helps firms manage crises in markets characterized by either high levels of competitive intensity or low levels of demand uncertainty and technological uncertainty.
In addition to demonstrating the limitations of a market orientation in crisis situations, our research hints at the manner in which this important construct could be refined. Market orientation primarily reflects a firm's learning about its environment; that is, a firm learns from its environment and learns to manage its environment. However, a firm may face a situation it has never encountered. Crises are obvious examples, but we could also put breakthrough technological advances, such as the emergence of electronic commerce, in this category. If a firm has not been schooled in managing rare situations, it is at odds for its response. The lethargy with which bricks-and-mortar retailers adopted the Internet is an apt example (see Brooker 1999). Our study suggests that a market-oriented firm or a generative learner (see Sinkula 1994) should build a buffer to manage unique, unpredictable challenges reactively. Slater and Narver (1995) discuss buffering but in the context of proactive rather than reactive management. We believe that reactive actions are necessary though not desirable. We recognize that we provide only preliminary evidence for the refinement of market orientation in the direction of incorporating reactive resources, but we have taken an important step in this direction.
Managerial Contributions and Implications
What capabilities do firms build to manage crises? This is an important question that today's practitioners are asking as organizations around the world try to cope with the growing pains of economic prosperity. Our research helps provide a partial answer to this question. Managers should stress building the skills of market orientation and strategic flexibility while recognizing their usefulness in managing different facets of the environment.
Market orientation aids in enhancing performance before crisis and, consistent with the "tenacious past" (Kuran 1988) argument, indirectly enhances performance after crisis (through firm performance before crisis). Market orientation should also be stressed in environments characterized by high demand or technological uncertainty, whereas strategic flexibility should be sought after in markets characterized by high levels of competitive intensity.
Conclusion
Economic crises are complex phenomena from both a theoretical and a practical perspective. Our study is among the few attempts to unravel how organizational capabilities may be used to manage these situations effectively. We touch on only two capabilities, and many questions remain to be answered. We hope our research stimulates interest and motivates more organization-level research on economic crises.
Legend for chart:
A = Range of Standardized Factor Loadings
B = NNFI
C = CFI
D = SRMR
E = RMSEA
F = X2(d.f., p-Value)
Measurement A B C D E F
Model
Environment[a] .60-.92 .90 .93 .08 .09 81.2(41, p < .01)
Dependence[b] .68-.98 .94 .96 .06 .11 56.9(24, p < .01)
Performance .41-.94 .95 .96 .04 .07 82.3(51, p < .01)
and strategic
flexibility[c]
Market .43-.80 .81 .84 .10 .09 224.5(113, p < .01)
orientation[d]
Market .62-.85 .91 .97 .04 .14 6.7(2, p < .03)
orientation-
second order[e]
[a]The reliabilities for the environmental variables were
competitive intensity = .92, demand uncertainty = .87, and
technological uncertainty = .86.
[b]The reliabilities for the international dependence variables
were supply dependence = .95, demand dependence = .91, and
financial dependence = .95.
[c]The reliability for strategic flexibility was .77. The
reliabilities for the performance variables were performance
before crisis = .91 and performance after crisis = .95.
[d]The reliabilities for the facets of market orientation were
information generation = .81, information dissemination = .85,
response design = .61, and response implementation = .82. During
the item-purification exercise, we deleted the following items
from Jaworski and Kohli's (1993) scale: information generation:
4, 7, 8, 9, 10; information dissemination: 6, 7; response design:
1, 3, 5, 7; and response implementation: 2, 6, 7.
[e]Reliability for a second-order factor structure with an
average of four subconstructs as items. We also calculated it
using the method of linear combinations (see Nunnally and
Bernstein 1994, pp. 266-73). Specifically, we calculated
reliability as
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available. where
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available. is the variance for subconstruct i, rii is the reliability of subconstruct i,
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available. is the variance of the construct (i.e., market orientation in our case), and p is the reliability. This method gave us the reliability value of .91.
Notes: NNFI = nonnormed fit index, CFI = comparative fit index, SRMR = standardized root mean square error, RMSEA = root mean square error of approximation, and d.f. = degrees of freedom.
Legend for chart:
A = ISD
B = IDD
C = IFD
D = CI
E = DU
F = TU
G = MO
H = SF
I = PBC
J = PAC
A B C D E
F G H I J
International supplier .37** .40** .17 .33**
dependence (ISD) .23* .22* .21* -.01 .10
International demand .55** -.05 .13
dependence (IDD) .10 .21* .12 .01 .33**
International financial -.01 .14
dependence (IFD) -.06 .23* .26** -.12 .16
Competitive intensity .54**
(CI) .41** .33** .30** .03 -.07
Demand uncertainty (DU)
.44** .48** .41** .11 .11
Technological uncertainty
(TU) .45** .41** .19* -.04
Market orientation (MO)
.48** -.07 .11
Strategic flexibility
(SF) -.06 -.06
Performance before crisis
(PBC) .20*
Performance after crisis
(PAC)
Mean 2.74 2.91 2.34 4.15 4.53
4.67 4.93 4.32 3.92 4.82
Standard deviation 1.64 1.96 1.51 1.48 1.30
1.35 0.99 1.11 1.51 1.14
*p < .05.
**p < .01.
Dependent
Measure:
Independent Performance
Variable After Crisis
Constant 1.195
(1.604)
Performance before crisis .319***
(.087)
International supplier dependence .029
(.068)
International demand dependence .214***
(.060)
International financial dependence -.012
(.083)
Competitive intensity (CI) -.211**
(.116)
Demand uncertainty (DU) .561***
(.153)
Technological uncertainty (TU) -.050
(.138)
Market Orientation (MO)
MO -.734**
(.356)
MO x CI -.230***
(.090)
MO x DU .301***
(.106)
MO x TU .158*
(.101)
Strategic Flexibility (SF)
SF .603***
(.220)
SF x CI .186**
(.087)
SF x DU -.362***
(.094)
SF x TU -.140**
(.083)
*p < .10.
**p < .05.
***p < .01.
[a]Standard error is in parentheses (one-tail rests).
R2 = .27.
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DIAGRAM: FIGURE 1 Conceptual Model
~~~~~~~~
By Rajdeep Grewal and Patriya Tansuhaj n B. Heide d Peter H. Reingen
Rajdeep Grewal is Assistant Professor of Marketing, and Patriya Tansuhaj is Professor of Marketing, Washington State University, Pullman. The authors appreciate helpful comments from Li-Ming Han, Bill Hallagan, Jim McCullough, and Jerman Rose and acknowledge the financial support from the International Business Institute at Washington State University. The article benefited from the thoughtful and pertinent feedback of the three anonymous JM reviewers.
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Record: 27- Buyer-Supplier Relationships and Customer Firm Costs. By: Cannon, Joseph P.; Homburg, Christian. Journal of Marketing. Jan2001, Vol. 65 Issue 1, p29-43. 15p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.65.1.29.18136.
- Database:
- Business Source Complete
Record: 28- Cherry-Picking. By: Fox, Edward J.; Hoch, Stephen J. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p46-62. 17p. 6 Charts, 4 Graphs. DOI: 10.1509/jmkg.69.1.46.55506.
- Database:
- Business Source Complete
Cherry-Picking
The authors analyze cherry-picking in the context of grocery shopping, comparing the behavior of consumers who visit two grocery stores on the same day (8% of shopping trips) with single-store shoppers. The authors find cherry-picking to be consistent with rational economic behavior. Cherry pickers benefit by saving 5% more per item while buying systematically (67%) larger market baskets.
The Merriam-Webster Dictionary defines cherry-picking as "selecting the best or most desirable" or, describing one idiom with another, "taking the pick of the litter." The term is used to describe both buyer and seller behavior. Sometimes the phrase describes sellers that are selective about which customers they serve. For example, Southwest cherry-picks price-sensitive travelers who place little premium on amenities, and Dell cherry-picks customers who are savvy enough to buy computers on the Internet and to make the necessary customization choices without much hand-holding. Both firms choose not to serve customers with a higher willingness to pay because it would require significant changes to efficient operating models. The Cambridge International Dictionary of Idioms defines cherry-picking as choosing "only the best people or things in a way that is not fair," as do financial institutions that refuse to serve high-risk populations. The term also describes the behavior of buyers who are selective about which products or services they purchase at what locations and prices. In both seller and buyer contexts, cherry pickers opportunistically take the best and leave the rest.
This article focuses on buyer-side cherry-picking. In a retail context, cherry-picking can occur within a single-store visit, as when consumers literally stand at an end-cap display of Mt. Rainier cherries and pick through every one, choosing only the largest and plumpest. Cherry-picking can also occur across stores. Levy and Weitz (2004) define cherry pickers as customers who visit a store and buy only merchandise sold at deep discounts. Consumer Reports (1988, p. 158) advises consumers to "scrutinize the food-day ads and cherry-pick the specials."
Imagine a market with two grocery chains, Jewel and Dominick's Finer Foods, for example. The preceding comments imply that consumers can cherry-pick in at least two related ways. First, each week they can buy their entire market basket at the retailer that they believe offers the best deals. To the extent that competing retailers promote non-identical items, consumers who switch stores across weeks are afforded more cherry-picking opportunities than are consumers who are store loyal. Moreover, the transaction costs associated with switching stores across weeks do not seem to be much greater than those incurred by store-loyal shoppers, under the assumption that travel costs to the stores from which they choose are similar and that consumers switch often enough to be adequately familiar with store layouts.
Second, shoppers can engage in a more extreme form of cherry-picking in which store switching occurs within weeks. In this case, customers split their market basket across stores within a week (potentially on the same day) to benefit from deals offered by different stores. Grocery retailers typically offer new specials every week, including uncoordinated promotions on the same brands (Dréze 1999; Lal 1990), so this second form of shopping behavior offers many more cherry-picking opportunities. The nature of these opportunities is illustrated in Figure 1, a histogram of the differences in weekly prices at Jewel and Dominick's for approximately 21,000 stockkeeping units over a period of 104 weeks. It shows the savings available from buying each item at the lower-priced versus the higher-priced retailer, savings that are more accessible to consumers who switch stores within weeks. The average difference in prices is approximately 10%, even though the two chains match prices on nearly one-third of the items and average prices are almost identical. Moreover, there is substantial surplus available to cherry pickers who are selective about which cherries they pick. For example, the top decile of price differences averages 39%, the second 21%, and the third 15%. But unlike cherry-picking across weeks, within-week (or -day) cherry-picking is likely to increase transaction costs substantially because it requires two store visits.
We view cherry-picking as one end of the price sensitivity and deal proneness continuum. In terms of models of horizontal competition in the Hotelling tradition, cherry pickers have low travel and search costs (Narasimhan 1988; Raju, Srinivasan, and Lal 1988) and are therefore willing to search for information on deals and visit more than one store to benefit from them (Lal and Rao 1997). The goal of this article is to gain an in-depth understanding of the shopping behavior of consumers who cherry-pick by visiting two or more grocery stores on the same day. Using two years of household panel purchase data, we identify the individual characteristics (traits), both demographic and geographic, that are most closely associated with frequent across-store, within-day cherry-picking. We compare how people shop when they cherry-pick two stores versus when they engage in more common single-store visits, and then we examine how experienced cherry pickers exploit a cherry-picking occasion compared with those who are less experienced.
We also focus on the question: Is cherry-picking worth it? The answer boils down to a cost/benefit trade-off between the extra money saved and the extra shopping costs incurred when customers visit an additional store. We calculate the money saved as a result of cherry-picking and compare it with a reasonable estimate of shoppers' opportunity cost of time. We find that the savings on cherry-picking days average over $14 more than the savings on single-store days. This economic benefit exceeds the opportunity costs of the extra store visit for most households. Cherry-picking savings arise from two sources: ( 1) higher percentage savings on items purchased (approximately 5% higher) and, equally important, ( 2) much larger market baskets (approximately 67% larger in both dollars and units). Both of these effects are consistent with economic theories of search.
This article contributes to the marketing literature in several ways. Despite its ubiquity as a surrogate for price sensitivity in economic models of retail competition, cherry-picking has seldom been a subject of empirical research. Analysis of this behavior broadens our understanding of consumer response to price and promotion. We examine long-term shopping decisions and patterns (e.g., propensity to cherry-pick) and derive insights into shopping strategies that would be less discernible if we had focused on single-purchase occasions. Moreover, the multioutlet panel data we use facilitate the analysis of shopping across stores in a depth not previously possible. Our analysis shows why and when shoppers cherry-pick and identifies the traits of frequent cherry pickers. We find that cherry-picking is consistent with economic rationality, specifically with models of price search across stores. We also document systematic differences in shopping behavior that are associated with cherry-picking and that bring predictable consequences for retailer sales and profits. Finally, we show which retailers are most severely affected by cherry-picking behavior.
The remainder of the article is organized as follows: In the next section, we briefly review the relevant literature and develop propositions about cherry-picking. Next, we describe the data to be analyzed. The analysis proceeds as follows: We examine how demographic and geographic characteristics are related to cherry-picking; then we determine how time-varying factors influence the probability of cherry-picking; next, we measure the savings that shoppers generate by cherry-picking; and finally we investigate buying behavior in shoppers primary (most frequently visited) and secondary (less frequently visited) stores. In the concluding section, we discuss the results of our study and further research.
There is an extensive literature in economics and marketing on price search that is relevant to cherry-picking. This literature focuses on two distinctly different problems: ( 1) sequential search across stores for a single costly durable good (e.g., Lippman and McCardle 1991) and ( 2) price search across grocery stores for information about frequently purchased goods (e.g., Stigler 1961). Studies of the first type find that the extent of search is negatively related to the opportunity cost of time, usually operationalized as wage rate or household income (Marmorstein, Grewal, and Fishe 1992; Ratchford and Srinivasan 1993), but positively related to self-reported benefits from search such as enjoyment of shopping (Doti and Sharir 1980; Marmorstein, Grewal, and Fishe 1992) or social returns to market knowledge (Feick and Price 1987). Note that these findings pertain to individual characteristics, or traits, that are predictive of search behavior. Studies of the second type relate similar individual traits to price search among grocery stores. Petrevu and Ratchford (1997) and Urbany, Dickson, and Kalapurakal (1996) find that economic benefits from searching, which are dependent on price dispersion and per capita income, are positively related to search. They also find that the costs of search, including opportunity cost of time and perceived costs such as time pressure, difficulty in comparing stores, and lack of physical energy, are negatively related to search.
Burdett and Malueg (1981) and Carlson and McAfee (1984) model a consumer visiting one or more grocery stores to minimize the cost of shopping for a predetermined list of items. Burdett and Malueg (1981, p. 362) detail their assumptions:
Before starting out to buy groceries for the week, suppose an individual makes a list of the n goods required (n ≥ 2). The list also states the amount required of each good. There are many grocery stores the individual can visit. Each of these stores sells all the goods desired. The individual can visit any store at a cost and observe the n vector of prices offered, one price for each of the n goods. The individual can purchase any number of the goods required from this store and then continue to search for the remaining goods.
In other words, the consumer may buy all required items at the first store visited or buy a subset of the required items there and visit additional stores to buy the remaining items. Burdett and Malueg (1981) develop a mathematical model of this decision for n = 2 products; Carlson and McAfee (1984) extend it for any positive integer n. Together, these models represent a theory of multistore shopping for a predetermined list of goods. Because shopping lists and retailer prices vary between trips, we expect trip-specific variables that we term "state" (as opposed to trait) to affect multistore shopping.
Carlson and Gieseke (1983) test some predictions of this theory. They find that prices paid are negatively related to the extent of search, which they operationalize as the number of grocery store visits a shopper made during a given period (1 week for perishable goods, 13 weeks for storable goods). Carlson and Gieseke also find that the number of store visits is positively related to quantities purchased and to the shopper's age.
We apply Burdett and Malueg's (1981) and Carlson and McAfee's (1984) multistore shopping theory to cherry-picking. Three propositions about cherry-picking behavior are implied by the theory:
P1: There is a negative relationship between the probability of cherry-picking on a given shopping trip and the consumer's cost of visiting an additional store.
P2: There is a positive relationship between the probability of cherry-picking on a given shopping trip and the size of the consumer's shopping list.
P3: Prices paid while cherry-picking are less than the prices paid on single-store visits.
P1 pertains to the costs of search, whereas the other two propositions involve the benefits of search. We examine what this cost/benefit trade-off implies about who is likely to cherry-pick, when they are likely to cherry-pick, how cherry-picking affects shopping behavior, and what benefits accrue from cherry-picking.
P1 considers the cost of an incremental store visit, including extra planning and travel and time spent shopping and checking out. It is well established that the cost of a shopping trip increases with the consumer's opportunity cost of time. This leads us to hypothesize that there is a negative relationship between the probability of cherry-picking on a given shopping trip and the consumer's opportunity cost of time. Because opportunity cost is not directly observable, our empirical analysis of this hypothesis will include the following proxy measures: ( 1) household earned income [-], ( 2) the presence of a working adult female in the household [+], ( 3) a senior citizen ≥ 65 years of age [-], ( 4) college education [+], and ( 5) weekend versus weekday shopping time [-]. (The sign in brackets following the proxy measure of opportunity cost reflects the expected relationship, positive or negative, between the probability of cherry-picking and that specific measure.) The first four measures are shopper traits that commonly serve as proxies for opportunity cost of time (e.g., Becker 1965; Blattberg et al. 1978; Hoch et al. 1995), whereas the last is a state, or trip-specific, variable. The cost of shopping also increases with the distance the shopper must travel to the store, reflecting the time and direct cost of transportation. We cannot measure travel distance for specific shopping trips, however, because we do not know with certainty where each trip originated and what route the shopper took. Therefore, we must treat travel distance as a household trait rather than a state variable. If we make the simplifying assumptions that ( 1) all trips originate from home and ( 2) the shopper travels from the first store visited directly to subsequent stores without intermediate stops, then the distance between the closest and next-closest stores reflects the cost of an incremental store visit.( n1) Therefore, we hypothesize that there is a negative relationship between the probability of cherry-picking on a given shopping trip and the distance between the closest and next-closest stores to the consumer's home.
Together, P2 and P3 relate the benefits from cherry-picking to the size of the consumer's shopping list and the price dispersion of individual items across stores. Visiting two stores on the same day basically doubles the number of savings opportunities that shoppers can exploit if they so choose (see Figure 1). If a shopper plans to purchase a larger-than-average market basket, the benefits of search will be greater because the total savings from cherry-picking equals the number of items multiplied by the savings per item. Basket size is increased by buying larger quantities, so we hypothesize that there is a positive relationship between the probability of cherry-picking on a given shopping trip and a household's purchase quantities. Large families in particular tend to buy in larger quantities (Blattberg and Neslin 1990) and are more price sensitive (Hoch et al. 1995). Note that Carlson and Gieseke (1983) find that the low prices shoppers obtain by searching across stores induce them to buy larger quantities, which suggests a healthy dose of endogeneity in the purchase quantity and cherry-picking decisions. The size of the shopping list also depends on the cost of holding inventory, so we hypothesize that there is a negative relationship between the probability of cherry-picking on a given shopping trip and a household's inventory-holding cost. This suggests that household traits that reduce holding cost--for example, home ownership--have a positive impact on the likelihood of cherry-picking. Inventory-holding cost is also positively related to current inventory. We expect that current inventory (and therefore holding cost) is lower and the likelihood of cherry-picking is higher ( 1) when more time has elapsed since the last shopping trip and ( 2) when spending on the last trip was smaller. We summarize the propositions and associated hypotheses in Table 1. Note that we offer no additional hypotheses for P3 we test it directly subsequently.
Data and Variable Construction
The data come from a multioutlet Information Resources Inc. household panel in Chicago covering a two-year period (104 weeks) between October 1995 and October 1997. This data set is different from the majority of panel data sets used in academic research because panelists record their purchases using in-home scanning equipment, so their purchase histories are not limited to the usual small sample of stores. All purchases, including the Universal Product Codes of packaged goods purchases, are captured on all trips to a wide variety of retailers. Our analysis focuses on grocery retailers. We find that the two largest grocery chains, Jewel and Dominick's, together account for 74% of panelists' purchases at known grocery stores; the eight largest grocery chains together account for 99% of purchases. Each packaged goods purchase in the panel data set is accompanied by the purchase price and indicators of whether the item was sold on deal or feature advertised.
The purchase data set is supplemented by a merchandise file containing all item prices at Jewel and Dominick's for the following ten packaged goods categories: chocolate candy, carbonated beverages, coffee, diapers, dog food, household cleaners, laundry detergent, salty snacks, sanitary napkins, and shampoo. The availability of item prices at the two largest retailers enables us to compare them directly and quantify the savings due to cherry-picking. The data set also includes demographic information for each household, enabling us to test the relationships between household characteristics (traits) and cherry-picking. The data set is further augmented by locations of panel households and grocery stores.( n2) These locations enable us to compute travel distances both from shoppers homes to stores and between stores in order to assess the relationships between geographic variables and cherry-picking. We limit our analysis to store visits on which the shopper makes a significant purchase, as opposed to, for example, buying only a bag of ice. Accordingly, we analyze visits with purchases of at least $5 (including smaller trips has no material influence on the results).
We present a broad set of analyses. For most of the analyses, we report shopping trips, purchases, spending, and so forth, across the eight largest grocery store chains in the market. This makes our investigation as comprehensive as possible. However, calculating the economic benefits of cherry-picking requires us to measure savings in a relative rather than an absolute context. Because this market is essentially a duopoly, we have chosen to compute savings using a direct comparison of Jewel and Dominick's prices in the ten categories for which we have item-level price information. We then extrapolate from these sample categories to the entire market basket to assess the magnitude of savings. Using this approach, our analysis of the economic benefits of cherry-picking includes a sample of 22,913 individual purchases made during 9562 shopping trips.
Because preliminary analyses suggested that some of the panel households were not faithfully recording all of their shopping trips and purchases, we developed criteria to screen households that did not appear to be recording diligently. Panel households were included in our data set only if, over the 24-month duration of the panel, ( 1) they recorded grocery purchases in each month, ( 2) they averaged less than 14 days between shopping trips, and ( 3) they spent at least $25 per week at grocery stores. This resulted in a total of 201 households with complete data. Table 2 reports available demographic and geographic variables for these households, along with their monthly grocery spending and shopping trips. The households shopped on a total of 27,978 days (5.8 shopping days per household per month) over the two-year period. Note that the supercenter format had very low penetration in the market during the period in which data were gathered (October 1995--October 1997), so households eliminated by our screening process were unlikely to have been shopping at supercenters as an alternative to grocery stores. Aware that our screening criteria may have resulted in the omission of households that were accurately recording sparse grocery purchases or that less vigilant recorders may behave differently from those who are more diligent, we ran the same analyses after relaxing the screening criteria. The inferences were virtually the same as those reported here.
Cherry-Picking Propensity: Demographic and Geographic Variables
As mentioned previously, we view cherry-picking as a continuum much like a shopper's degree of price sensitivity or deal proneness. For the purpose at hand, a shopping trip is classified as cherry-picking if two or more grocery stores are visited on the same day, that is, without intervening consumption. We use the term "trip," though we do not actually observe whether the shopper goes from one store directly to the next or makes intervening stops, perhaps at home. Moreover, this operationalization misses some cherry-picking occasions, in which shoppers with foresight visit two or more stores on different days during the same week to acquire that week's provisions. At the same time, our operationalization avoids confusing a fill-in trip, when the consumer runs out of items or forgets to get everything on the list, with a preplanned and purposeful attempt to split the market basket in order to save money.
Figure 2 shows the distribution across households of cherry-picking trips as a percentage of all the household's shopping trips. Two important characteristics are evident. First, nearly one-fifth of the sample does not cherry-pick at all. Second, the distribution is heavily skewed with a long right tail. Therefore, most households are on the low end of the cherry-picking continuum. The mean and median of the percent cherry-picking distribution are 7.7% and 4.2%, respectively, whereas households in the top decile cherry-pick on 32.0% of shopping trips. Although they may not seem compellingly large, these statistics belie the fact that each cherry-picking trip is made up of multiple store visits. If we consider the distribution of store visits, which reflect retailer traffic counts, we find that the mean and median are 13.2% and 8.2% respectively, whereas the top decile cherry-picks on 49.3% of visits.
The dependent variable in our first analysis is the percentage of trips on which the household cherry-picked (visited ≥ grocery stores) out of all shopping trip days. By aggregating cherry-picking behavior across time at the household level, we can focus on household-specific traits, both demographic and geographic, rather than time-varying states. As mentioned previously, nearly 20% of the households never cherry-picked. To accommodate this "spike at zero," we model cherry--picking across households using a censored regression methodology (Tobin 1958). We log-transform the dependent variable to approximate more closely the assumption of normally distributed residuals. Because the demographic and geographic predictors are in some cases interrelated, we specify the model using a forward stepwise procedure, sequentially adding whichever predictor maximizes the likelihood of the data, conditioned on the current set of predictors. The resultant specification therefore avoids multicollinearity, so the standard errors may be used for inference. The stepwise procedure stops when addition of another variable fails to improve the Akaike information criterion.
The resulting model is shown in Table 3. Along with the overall fit of the model, Table 3 shows the regression coefficient, associated standard error, and p-value for each independent variable. The last column in Table 3 displays the expected percentage of cherry-picking if that parameter took on the value of zero while all other parameters were at their estimated values; to give an indication of the substantive impact of each variable, the numbers can be compared with the expected value of percent cherry-picking when all parameters take on their estimated values; that is, E(y)|Β = Β* = 4.18%. The results are straightforward and are consistent with an opportunity cost perspective. Specifically,
• Households are less likely to cherry-pick when there is a working adult female in the family, presumably because it is more costly for them to spend time shopping for groceries. If none of the households in our sample contained working adult females, the expected cherry-picking probability would be approximately 16% higher ([4.83% - 4.18%%]/4.18%).
• Senior citizens (≥ 65 years of age) are less likely to be employed outside the home and thus have more time to invest in shopping. The presence of a senior citizen in the household increases percent cherry-picking by approximately 15%.
• Home ownership implies greater inventory-holding capability. This further implies that home-owning households can take advantage of the greater number of discounts available through cherry-picking because they have more opportunity to accelerate purchases by forward buying. Home ownership increases the propensity to cherry-pick by approximately 43%.
• Wealthy households are assumed to have higher opportunity costs and be less price sensitive. Indeed, household income has a negative impact on percent cherry-picking.
• Larger households are assumed to be more price sensitive because they need to spend a greater proportion of their income on groceries (budget constraint) and they have greater returns to price search by virtue of purchasing scale. Each additional person in the household increases the propensity to cherry-pick by approximately 4%.
• Travel distances and times also exert an influence on cherry-picking propensity. Although we find no effect for how close the nearest store is to the home, there is a negative effect for the distance from the nearest to the next-nearest store. We take this as evidence that the household considers the incremental cost of the extra store visit (which can be very low if nearby stores are close together or more costly if not) against the expected benefit of the additional discounts available if that extra store visit is made. Each extra mile between stores decreases the percentage of cherry-picking by approximately 5%.
• Finally, note that though college education (another surrogate for opportunity costs) entered the final model through the stepwise procedure, the positive parameter estimate is not significant.
Given the large number of household-specific factors that significantly affect the propensity to cherry-pick, we now consider whether cherry pickers are heterogeneous. To investigate heterogeneity we perform a k-means cluster analysis on the 50 households that compose the top quartile of cherry pickers using the demographic and geographic factors selected by the stepwise procedure as predictors. The two-cluster solution explains more than 43% of the variance in these predictors and offers a clear interpretation. Solutions with higher numbers of clusters offered relatively small increases in R², generated small cluster sizes, and were difficult to interpret. The larger cluster (60% of top quartile households) is composed of 43% senior citizens with an average of 2.16 members per household; none of these households have five or more members. The smaller cluster (40% of the top quartile) has only 10% senior citizens but averages 4.75 members per household, almost half of which have five or more members. Therefore, there appear to be two distinct types of cherry-picking households: ones with older heads of household and ones with many members.
Probability of Cherry-Picking: Trip-Specific Variables
Multistore shopping theory focuses on the household's list for a particular shopping trip. Because the shopping list differs from trip to trip, our empirical analysis must include trip-specific factors. Having already considered consumer traits as antecedents of cherry-picking, we now turn our attention to state, or trip-specific, factors.
Consider the day of the week when consumers shop. The standard workweek is Monday through Friday; on weekends consumers bear the opportunity cost of time for nonworking days. We find that both single-store and cherry-picking trips are made at a higher rate on weekends, particularly if we include Friday. The most popular shopping day is Sunday, followed by Friday and Saturday; 58% of cherry-picking trips and 46% of single-store trips are made on those three days. This suggests that opportunity costs on weekends are lower than those on weekdays.
Consumption, by its definition, implies a negative change in inventory over time. Because higher inventory levels mean lower purchase requirements, we expect the number of items on the consumer's shopping list to increase with the time since the last shopping trip, the last opportunity to replenish household inventory. On that last trip, the shopper's inventory was incremented by the amount purchased. By the same reasoning, then, we expect the number of items on the shopping list to decrease with the amount purchased on the last shopping trip. Therefore, the probability of cherry-picking should increase with the time since the most recent trip but decrease with the amount purchased on that trip.
Table 4 shows the results of a mixed-effects logistic regression of shopping trips, with cherry-picking (1 = yes, 0 = no) as the dependent variable. We model only households that make at least one cherry-picking trip, resulting in a total of 23,796 trips, 2416 (10.15%) of which involved cherry-picking. To control for consumer traits, we model the intercept parameter as a random effect that varies across households according to a normal distribution. We model other predictors as fixed effects. These predictors are ( 1) an indicator variable for whether the trip was made on a weekend (1 = yes, 0 = no), ( 2) the number of days since the last shopping trip, ( 3) dollar spending on the last shopping trip, and ( 4) the interaction between the previous two. We subtract one from the number of days since the last trip so that the main effect of spending on the last trip can be interpreted as applying to the day after that trip. If we do not "relocate" this variable by one day, the main effect of spending on the last trip would apply to the day of that trip (i.e., zero days since the last trip), which makes interpretation difficult. Spending on the last trip is median centered. The main effect of time since the last trip therefore assumes the median value of spending on that trip. We use the median rather than the mean for centering because the distribution of spending on the last trip is highly skewed.
Estimated coefficients for all predictors are statistically different from zero and consistent with a priori reasoning:
• The longer the time since the last shopping occasion, the more likely it is that people engage in a cherry-picking trip. Presumably this is because inventory is lower, so the household has greater inventory-holding capacity and can better exploit the extra deals afforded by shopping two stores rather than just one. Increasing the time since the last trip by one day increases the probability of cherry-picking by an average of nearly 2%.
• Analogously, the amount spent on the last trip is a significant negative predictor. This effect is also consistent with the inventory-holding story just presented. Increasing the amount spent on the last trip by $10 decreases the probability of cherry-picking by an average of 3%.
• The interaction between time since the last shopping trip and amount spent is positive and significant, which suggests that the negative effect of the last trip's spending on the probability of cherry-picking dissipates as the time since that trip increases.
• The indicator variable for weekend is a positive predictor of cherry-picking trips. The effect size is substantial--the expected probability of cherry-picking would be reduced by 7.9% ([9.35% - 10.15%%]/10.15%) if no trips were made on weekends. Marmorstein, Grewal, and Fishe (1992) counterintuitively argue that the marginal cost of nonworking time should reflect a higher overtime wage rate, but our result suggests that paid overtime is not often an alternative to time spent shopping or at leisure on the weekend. To the contrary, it suggests that the opportunity cost of time during the weekend is lower than during the workweek.
Behavioral Consequences of Cherry-Picking
We have shown that household traits, including consumer and household characteristics and distances between stores, affect the probability that a household will cherry-pick. We have also shown that trip-specific state variables, including previous shopping decisions and day of the week, affect the probability of cherry-picking. Next, we examine how shopping behaviors differ between cherry-picking and single-store trips. We model these shopping behaviors as a function of the household's propensity to cherry-pick (trait), whether the trip is single store or cherry-picking (state), and the interaction of trait and state. Our intent with these models is to investigate whether the economic returns to cherry-picking are influenced by consumers experience using this shopping strategy.
Table 5 summarizes differences in a variety of measures of shopping behaviors (left-hand panel) on cherry-picking versus single-store trips. The center panel compares cherry-picking shopping trip days with single-store shopping trip days across all panel members. The right-hand panel of Table 5 reports the parameters and their associated standard errors for multiple regressions of each dependent variable onto
1. HH% CP: household percentage of cherry-picking trips, a measure of household-specific differences in cherry-picking propensity;
- 2. CP Trip: a cherry-picking indicator for the trip of interest (1 = yes, 0 = no) that captures trip-specific differences; and
- 3. HH% CP x CP Trip: the interaction of the two.
Applying appropriate techniques for moderated regression (Irwin and McClelland 2001), we center HH% CP on its median value. The main effect of CP Trip therefore assumes the median-level HH% CP (5%). We code CP Trip (1 = yes, 0 = no) so that the main effect of HH% CP is based on the more common non-cherry-picking (i.e., single-store) trip. Depending on the distribution of the dependent variable, we estimate an ordinary least squares (OLS), weighted least squares (WLS), logistic, or censored regression.
For eight of nine of the dependent variables in Table 5, there are obvious and statistically significant differences between cherry-picking and single-store trips (the state variable), the exception being quantity purchased. We discuss each modeled shopping behavior in turn and consider how HH% CP, the household trait reflecting cherry-picking experience, influences differences between single-store and multistore shopping occasions. Unless noted, the main effects and interactions we discuss are statistically significant (see Table 5).
Trip size. There are substantial differences in the amount of merchandise purchased depending on whether the household visits one or multiple stores on a particular shopping occasion. Whether measured in total dollar expenditures or number of units, people buy over two-thirds more when cherry-picking. As shown in Table 5, this cherry-picking effect is large and statistically reliable. In addition, the negative main effect of cherry-picking propensity and the large positive interaction indicates that the trip size differences between cherry-picking and single-store occasions are substantially greater for households that are more practiced at cherry-picking. To get a handle on the magnitude of this effect, we compare the top quartile of HH% CP households with the other three-quarters. For the top quartile of cherry pickers, the difference between single-store and multistore visits is more than 105% ($119 versus $58) compared with less than 60% ($119 versus $75) for the remaining households. This suggests that more experienced cherry pickers may plan on being opportunistic. By buying less on single-store trips, they put themselves in a position to benefit more from cherry-picking trips, in which they can take greater advantage of the additional price deals afforded by shopping two stores. These findings are related to the results in Table 4, where the size of the last trip and time since the last trip are shown to have negative and positive influences, respectively, on the likelihood of cherry-picking on the current trip.
Items on deal or feature advertised. The pattern of results for both the percentage of items bought at discounted prices and feature advertised is quite similar. Because consumers have more potential deals to exploit at two stores than at just one, we find that people purchase 25% more items on deal. They also buy over one-third more items that are feature advertised. Although we have no direct evidence of this, it is possible that shoppers are more likely to read the best food day advertisements when they cherry-pick, or perhaps particularly attractive advertised specials provide some motivation to cherry-pick. In addition to the significant main effects of CP Trip, the price deal and advertising variables also have positive main effects for HH% CP, coupled with negative interactions. Irrespective of whether the shopping occasion is single-store or multistore, heavy cherry-picking households (i.e., the top quartile) are more likely to buy items on deal (23%) or advertised (25%) than are other households (deals, 17%; advertised items, 23%). The negative interaction coefficients indicate that the differences in buying items on deal or advertised between cherry-picking and cherry-picking days are smaller for heavy cherry pickers (deals, 12%; advertised items, 21%) than they are for the other three-quarters of the sample (deals, 33%; advertised items, 50%). Overall, these results suggest that households take advantage of the increased number of price savings opportunities afforded by cherry-picking. The results also suggest that heavy cherry pickers are more vigilant shoppers irrespective of type of shopping occasion, though there are diminishing returns to this vigilance.
Quantities purchased. Another way that households can take advantage of the additional price savings opportunities afforded by cherry-picking is to buy larger package sizes. The quantity variable used in this analysis is volumetric units purchased (e.g., ounces, liters, count, number of rolls) divided by average volumetric units purchased in that category, thus enabling comparison across categories. Like an index, the quantity variable is centered at one. As Table 5 shows, people buy 3% larger package sizes when cherry-picking, though this difference is not significant. The only statistically significant effect is for the HH% CP variable. The top quartile of cherry pickers buy 5% larger package sizes than do the rest of the population. This difference might be related to the lower inventory-holding costs of these heavy cherry pickers that we observed previously, which may make it easier for them to increase the overall size of cherry-picking trips through more opportunistic planning of big purchases.
Prices paid. To get a sense of differences in price sensitivity between shopping occasions, we construct a standardized measure of price per unit. We divide the item price by the number of volumetric units (e.g., liters, ounces, count, number of rolls) of the item and then standardize it by dividing by the average price per unit for the category. This controls for noncomparability in package sizes and for the fact that most products are priced to provide a quantity discount for large package sizes. We observe that people pay 5% less on cherry-picking occasions than on single-store trips, a difference that is economically significant. We also find a significant, negative main effect of HH% CP along with a significant, positive interaction. Overall, heavy cherry pickers pay more than 5% less per unit than does the rest of the sample. The interaction reduces this difference on cherry-picking occasions (declining from 6% versus 4%).
Economic Benefits from Cherry-Picking
The next set of analyses focuses on the economic benefits customers realize when shopping at a single store compared with cherry-picking multiple stores. We have already shown that there are substantial differences in the size of cherry-picking ($119) and single-store ($71) shopping trips. To estimate the total savings from cherry-picking two stores, we first determine how much money shoppers save compared with if they had purchased all items at the higher-priced store. Although cherry-picking affords twice as many potential savings opportunities as shopping at a single store, important questions are whether and how well shoppers exploit those opportunities. Recall that the Chicago grocery market is effectively a duopoly, in which the majority (65%) of cherry-picking at known stores is done at Jewel or Dominick's Finer Foods.( n3) Also recall that Figure 1 shows how most items can be purchased less expensively, sometimes far less, at one retailer or the other even though Jewel and Dominick's have many common prices. To determine whether shoppers pay lower prices when cherry-picking, we focus exclusively on item purchases at Jewel and Dominick's in the ten categories for which we have Universal Product Code--level price information. We include only items that are available concurrently at both Jewel and Dominick s. Although this is an imperfect measure because people shop at other retailers, we see no reason to believe that there is any systematic bias. The resultant item-purchase data set contains 22,913 individual item purchases, 2450 of which are made while cherry-picking.
Buying at the lower price. For each household, we compute the percentage of times that items are bought at the lower price (between Jewel and Dominick's) both when customers cherry-pick and when they shop at a single store. Note that if Jewel and Dominick's offered the same price, we record that the shopper received the lower price. As Table 5 shows, households are able to exploit the additional savings opportunities afforded by cherry-picking. The probability of buying at the lower price is 8% greater when customers cherry-pick versus when they shop at a single store. Consistent with other results in Table 5, the positive main effect of HH% CP suggests that heavy cherry pickers tend to be more vigilant shoppers (trait) irrespective of the type of trip (state).
Percent off the higher price. Having established that consumers buy items at the lower price most of the time, we now turn to the question how much they save. Figure 3 displays separate histograms of item-level savings off the higher price for cherry-picking and single-store purchases. The histograms clearly show that cherry-picking shifts the distribution of percent savings to the right, reducing the number of zero and small savings and increasing the number of larger savings. Because we have complete pricing data at both Jewel and Dominick's for only ten categories and there are many trips on which households did not buy anything in those categories, we cannot compute savings on a trip-by-trip basis. Consequently, we have elected to aggregate all purchases in the ten key categories for each household into two bins--cherry-picking trips and single-store trips--and then compute percent savings off the higher price using the two bins as household observations. This aggregation enables us to avoid modeling the large proportion of missing observations and to analyze the data with a linear (WLS) model. The resultant data set includes every household with at least three item-purchase observations at Jewel or Dominick's in a given state: 113 households when cherry-picking and 187 households when shopping at a single store, for a total of 300 observations. Note that we tested alternative screening criteria to include a household's single-store or cherry-picking observation in our data set, anywhere from a minimum of one to five item purchases. Although the total number of household cherry-picking observations changed with the screening criterion, our findings did not.
Similar to the results we obtained for the probability of buying at the lower price, Table 5 shows positive main effects of both HH% CP and CP Trip when percent off the higher price is modeled. Shoppers save 46% more on each item when cherry-picking (15%) than when shopping at a single store (10%). This is consistent with our previous observation that the price per unit is 5% less when customers cherry-pick. Moreover, the top quartile of cherry pickers saves substantially more on all shopping occasions (14%) than other shoppers (11%).
Savings generated by cherry-picking. The previous analyses showed that people are on a shopping mission when they cherry-pick two stores; that is, the act of cherry-picking is premeditated and thought out. For example, shoppers spend far more on cherry-picking days, nearly twice what they spend on single-store days. A similar finding applies to the number of items bought. This is not surprising because the shopper is visiting twice as many stores, but it suggests that the shopper planned on spending more than normal to take advantage of the extra store visit. Thus, cherry-picking appears to be a case of planned opportunism (Hayes-Roth and Hayes-Roth 1979). Cherry-picking trips are associated with greater sensitivity to temporary price discounts and feature advertising, and customers generally pay lower prices than they do on single-store shopping occasions. Moreover, these tendencies are magnified by the household's propensity to cherry-pick. The overall portrait suggests that cherry pickers reside in the upper tail of the price sensitivity and deal proneness distributions.
An important issue is whether there is any rational economic justification for cherry-picking. Undoubtedly, shoppers may experience some transaction utility from being a market maven (Feick and Price 1987) or a smart shopper (Schindler 1992). Beyond these psychological benefits, however, it is interesting to find whether cherry pickers save enough money to justify the extra store visit. Our priors were that the savings would be modest at best; it turns out that we were wrong.
To quantify the savings that consumers reap from cherry-picking, we extrapolate from the average percent savings in the ten product categories for which we have detailed price information. We calculate the savings per shopping trip as follows:
[Multiple line equation(s) cannot be represented in ASCII text]
where h indexes the household, Khi is the number of item-purchase observations for household h when customers either cherry-pick (i = 1) or shop at a single store (i = 0), price+ is the larger price available on the item at Jewel or Dominick's, and price is the actual price paid. Separating cherry-picking and single-store trips, this calculation scales the household's average percentage off the higher price (the quantity in parentheses in the denominator) by its average spending per trip.
Table 5 shows that shoppers save over 160% more on cherry-picking versus single-store trips. This occurs because trip savings is essentially the size of the shopping trip (cherry-picking trips are 68% larger) multiplied (as in the preceding equation) by the percentage off the higher price (45% greater for cherry-picking trips). By cherry-picking two stores rather than just one, shoppers save more than $14 ($23.56--$$8.90) on average. Using average trip savings as the dependent variable, we estimate a WLS regression with the same predictor variables as in the other analyses in Table 5. Weights are proportional to the square root of the number of item purchase observations, Khi. Although the main effect of HH% CP is not significant, both the CP Trip main effect and the interaction are statistically different from zero. The results are graphed in Figure 4. For households that seldom cherry-pick, we find that the savings when they cherry-pick is roughly twice the savings when they shop at a single store. However, as cherry-picking experience increases, cherry-picking savings increase rapidly because of the interaction. The difference between the two curves gives us incremental savings due to cherry-picking. For the median cherry-picking household, which cherry-picks on 4.3% of shopping trips, this difference is $11.93. For a household in the 75th percentile of cherry-picking (10.1% of trips), the expected incremental savings is $13.34. For a household in the 90th percentile of cherry-picking (20.0% of trips), the expected incremental savings goes up to $15.76. These findings are insensitive to the weighting scheme, as a standard regression produces similar results.
There appear to be two reasons that cherry pickers benefit more from the extra store visit. First, they are more experienced and consequently more accomplished at taking advantage of the extra savings opportunities afforded by the extra store visit. Second, their greater marginal benefit results from the opportunistic planning of shopping trips into two types: much smaller single-store visits that may be fill-in trips and larger two-store trips in which they buy significantly more.
Cost/Benefit Analysis of Cherry-Picking
Is the decision to make an extra store visit on the same day economically justified? Across all shopper types, the incremental savings for cherry-picking versus single-store trips is $14.66. Habitual cherry pickers, by virtue of extended practice, enjoy greater incremental savings. For example, a household in the 90th percentile of cherry-picking propensity realizes $3.83 more in incremental savings per trip than a median cherry-picking household. If we assume InsightExpress.com's (2003) estimate of 47 minutes to make a shopping trip, the ratio of incremental savings per additional hour spent shopping is $18.49 = $14.66/.78 hours. Although this savings does not include time spent planning or direct transportation costs (e.g., gasoline, vehicle depreciation), it nonetheless compares favorably with prevailing after-tax wage rates.
Consider the following analysis: First, we compute the average wage rates of households in our data set. Dividing annual household income by the number of hours worked per year (2000 for a full-time, 1000 for a part-time employed adult), we find the average household wage rate to be $22.09 per hour for the 176 (of 201 total) households in which either a male or female head of household worked at least part time. We then compare each household's average wage rate with its expected incremental savings from cherry-picking.( n4) For 33.5% (59 of the 176) of households, the incremental savings from an extra 47-minute store visit exceeded the average wage rate. In addition, the 25 households in which no adult worked (201 - 176)) need not have forgone any wages to make an extra store visit when cherry-picking. Yet this analysis is conservative, overstating the opportunity cost of cherry-picking in several ways: ( 1) the incremental store visit while cherry-picking is likely to consume less time than the typical 47-minute visit because the shopper spends only $45 (see Table 6) compared with $71 (see Table 5) when single-store shopping; ( 2) cherry-picking trips are made more often on weekends, when opportunity costs are systematically lower (see Table 4); ( 3) if two adults in a household work, their average wage rate will almost certainly be higher than the minimum of the two; and ( 4) only 39% of households in our sample had working women and 18% had senior citizens. Building on the last point, 44 households in the sample included a nonworking adult along with one that worked. Given that these households also did not need to forgo wages to make an extra store visit, we find that for 63.5% ([59 + 25 + 44]/201) of households in our data set, the opportunity cost in wages of making an additional shopping trip was less than the expected savings due to cherry-picking. Irrespective of the assumptions made in computing the incremental costs and savings due to cherry-picking, there appears to be a strong economic justification for most households.
Cherry-Picking at Primary Versus Secondary Stores
The final issue of interest is how cherry-picking differentially affects retailers. Specifically, we focus on how cherry-picking behavior varies depending on whether the store is the shopper's primary grocery outlet or a secondary grocery outlet. The reasoning for this analysis is that it is one thing to be a shopper's primary store, where every so often the shopper cherry-picks the competition and thus spends a bit less money than on a single-store trip. It is another thing to be a secondary store, the one that is being cherry-picked. The shopper not only fails to spend as much there as in the primary store but also opportunistically buys more sale items. For each household, we designated as its primary store the grocery chain at which the household spent the most money over the two-year period. All other stores were designated secondary outlets.
We estimate regressions using the same dependent variables as in Table 5. However, this analysis involves store visits at primary and secondary outlets rather than store trips. Note that these store visits are all made on cherry-picking days. The predictors for these regressions are ( 1) the trait variable HH% CP; ( 2) Secondary Store, an indicator for whether the visit was made to a secondary outlet that captures trip-specific (i.e., state) differences; and ( 3) the interaction of the two. We again centered HH% CP on its median value, so the main effect of Secondary Store assumes a household with a median-level propensity to cherry-pick. We coded Secondary Store (1 = yes, 0 = no) so that the main effect of HH% CP assumes a visit to the primary rather than a secondary outlet.
Table 6 shows the results. Cherry-picking visits at secondary outlets are systematically smaller than those at primary outlets, 37% in dollars and 40% in units. This is not surprising given that shoppers are more familiar with their primary store. We also observe that shoppers are more likely to buy discounted (12% more) and feature-advertised (32% more) items at the secondary store. However, the negative main effect of HH% CP and the positive interaction in the two promotional analyses suggest a slightly more complicated picture. Heavy cherry pickers show a much larger increase in the probability of buying discounted or feature-advertised items when shopping in secondary stores compared with their primary shopping outlet than do lighter cherry pickers. The large difference in the feature-advertising variable (32%) suggests that a sizable group of consumers may plan their secondary store visits on the basis of a perusal of that week's best food day advertising circular. The rest of the shopping behaviors analyzed in Table 6 show no systematic differences between visits to primary and secondary stores. And though shoppers save 18% more in their primary outlet, the savings is proportionally greater in secondary stores because spending on visits to primary stores is 38% larger.
In summary, we find that secondary stores are hurt more (proportionately) by cherry-picking than is a household's primary outlet. The pain is compounded by the shopper's propensity to cherry-pick, which has a significant moderating effect. The primary driver of secondary stores' disadvantage seems to be smaller purchase amounts, which are substantially smaller, rather than systematically greater price sensitivity or deal proneness. Moreover, differences in price sensitivity and deal proneness seem to be limited to experienced cherry pickers.
We also note that a visit to the primary store in many ways resembles a single-store shopping trip. Comparing column 3 in Table 5 to column 3 in Table 6, we find strong similarities in the values for single-store trips and primary outlet visits during cherry-picking. The only differences are that shoppers are more sensitive to deals and advertising and consequently are more likely to save money off the higher price.
In this article, we examine buyer-side cherry-picking, asking and answering three previously open questions. First, which household-specific and trip-specific factors lead to cherry-picking? Second, is cherry-picking worthwhile for consumers in the sense that the savings realized from visiting two grocery stores on the same day is economically justified? Third, how badly is the retailer hurt by this extreme manifestation of price sensitivity? Our research strategy was to examine cherry-picking as both a state (whether the consumer chooses to cherry-pick on a given shopping trip) and a consumer trait (the household's overall propensity to cherry-pick). We found that several characteristics are predictive of a household's propensity to cherry-pick: a working woman in the household [-], a senior citizen head of household [+], a larger family [+], household income [-], and home ownership [+]. We also found that the proximity of local stores to each other facilitates cherry-picking, though the proximity of the store to the shopper's home does not. Analysis of trip-specific factors showed that shoppers are more likely to cherry-pick on the weekend than on weekdays; cherry-picking also increases when household inventories are depleted because shoppers have not shopped recently or their most recent basket purchase was small.
We uncovered substantial differences in shopping behaviors due to cherry-picking, involving both the trait (household propensity to cherry-pick) and the state (cherry-picking on a given trip). Shoppers buy much more when cherry-picking than when shopping at a single store--in the case of frequent cherry pickers, twice as much. Not surprisingly, cherry-picking trips involve the purchase of a higher percentage of discounted items than do single-store trips, at least partly because cherry-picking trips evidence greater attention to retailers' weekly feature advertising. Inveterate cherry pickers buy more feature-advertised and discounted items whether or not they are cherry-picking. In the same vein, shoppers pay lower prices per unit when they cherry-pick and are more likely to select items at the lowest price available in the market, as multistore shopping theory predicts. In addition, frequent cherry pickers buy in larger quantities than do less frequent cherry pickers, suggesting that they are more willing to incur inventory-holding costs to reduce their per-unit prices, which we also found to be significantly lower. These systematic differences in shopping behavior suggest that cherry-picking is a case of planned opportunism, in which shoppers exploit the additional deals available from visiting multiple stores across a larger basket of goods. Moreover, the portrait of the frequent cherry picker that emerges is a shopper who is opportunistic on all trips, exploiting price deals and buying larger sizes regardless of whether they cherry-pick on a particular day.
Our analysis of the economic benefits of cherry-picking revealed two related but separate ways consumers save money when visiting two grocery stores on the same day. First, two same-day store visits afford shoppers more opportunities to save money, especially when they can plan their shopping with information from feature advertisements. Accordingly, we found that cherry-picking affords shoppers approximately 5% extra savings per item across the total shopping basket. This is only one aspect of cherry-picking, and arguably not the most important one, from a consumer welfare perspective. The second way that cherry-picking generates consumer surplus is that shoppers plan their cherry-picking days and purchase more (over two-thirds more on average) on such occasions, so shoppers apply the 5% savings to a much larger expenditure than they would normally make on a single-store shopping day. We recognize that the size of the cherry-picking basket is endogenous; it both affects (Carlson and Gieseke 1983) and is affected by the decision to cherry-pick.
Finally, we considered the impact of cherry-picking on the retailers being cherry-picked. This is difficult to assess in the absolute, though comparing the extra consumer surplus extracted while cherry-picking to supermarket gross margins of roughly 25% and net margins of 1.5%-2% suggests that cherry-picking has a material effect on customer profitability. Cherry-picking gives shoppers access to more of the surplus, 5% more off the premium (higher) market price per item. Yet much of this savings on cherry-picking trips is due to the purchase of more promoted items, for which the consumer surplus is subsidized by manufacturer discounting. Thus, the burden of cherry-picking is borne by both retailer and manufacturer, and manufacturers sell more on deal as a result. Also of interest to manufacturers is the negative correlation between cherry-picking and brand loyalty. Returning to the retailer's perspective, we note that households that cherry-pick more often also have more family members and thus consume more goods, which implies that cherry-picking households may generate more retailer revenues. This is true for our sample; we find that the top quartile of cherry-picking households spends $576 per month, whereas households that cherry-pick less frequently spend only $498 per month. The additional spending of frequent cherry pickers increases their attractiveness to retailers, but their lower loyalty--they spend only 47% of their grocery budget at their primary store versus 68% for other households--suggests that the extra revenues are spread more equally across retailers. It is clear that cherry-picking shoppers are far more likely to be unprofitable for their secondary stores than for their primary store. Secondary stores not only sell less per shopper ($45 versus $71) but also make lower margins on what they sell to cherry pickers. The predicament of secondary stores suggests that it may be more important for retailers to become the preferred store for their customers who frequently cherry-pick than to increase the loyalty of single-store shoppers. Cherry-picking is an economically justifiable form of price search that is employed by a substantial percentage of the population, and it is here to stay. Whether by implementing effective loyalty programs or by modifying pricing and promotion strategies, retailers would profit more by serving than by lamenting these shoppers, who really do "pick the best and leave the rest."
This study focuses on identifying and describing buyer-side cherry-picking behavior. In light of our empirical findings, several questions remain. Is cherry-picking a sequential search process as our theory assumes and thus dependent on the prices observed at the first store, or is it truly premeditated on the basis of shoppers' expectations about prices at the two stores? Answering this question would require testing alternative models of the behavior. In light of our findings linking promotional response to cherry-picking trips and a household's propensity to cherry-pick, it would also be useful to explore the relationship between promotions and cherry-picking. By exploring this relationship, further research might address how a retailer's promotional decisions influence whether customers cherry-pick its stores and whether frequent cherry pickers shop there. Another empirical finding related the propensity to cherry-pick to the distance between stores near the shopper's home. The cost of an additional shopping trip likely depends on other factors, including the size of the store (smaller stores are easier to shop) and the time of day (off hours mean shorter checkout lines). Exploring these factors represents another interesting research opportunity, though it would require additional data.
We held cherry-picking behavior up to the lens of a theory of multistore shopping that is grounded in economic rationality (Burdett and Malueg 1981; Carlson and McAfee 1984), but we did not consider the psychological benefits of cherry-picking. A study that focuses on the psychological benefits, or better still incorporates them along with the economics of cherry-picking, would be compelling. Another topic of interest is the importance of cherry-picking from the point of view of the retailers and manufacturers sales and profits. The trade press claims that cherry-picking has a material effect on retailer performance (e.g., MMR 2002); assessing such claims could be quite useful. An additional potentially important topic for study is the effect of cherry-picking on retail customer loyalty and lifetime value. With customer "share of wallet" and customer relationship management being increasingly of interest to retailers, studying the effect of cherry-picking on store selection could also make a meaningful contribution.
( n1) In our data set, on 99% of cherry-picking days only two stores are visited. Therefore, the distance from the nearest store to the store closest to it provides a reasonable approximation of the cost of an incremental store visit.
( n2) The household location provided is the centroid of the panelist's zip code +4. To preserve panelists' privacy, we do not provide street addresses. Travel distances are Euclidean distances between stores and households (or for one analysis, between stores and other stores).
( n3) Jewel and Dominick's together account for 65% of cherry-picking visits but 75% of all store visits. The difference is explained by the number of possible store combinations. If stores were chosen randomly, Jewel and Dominick's would have a 2/S (S = the number of grocery store chains in Chicago) probability of being chosen. The random probability of both Jewel and Dominick's being chosen for cherry-picking would be ([S - 2]!H2!)/S!, however, which is much smaller given the number of grocers in the Chicago market.
( n4) We compute the household's incremental savings using parameter estimates from Table 5 as follows: CP Savings = 11.4 + (24.4 H [HH% CP - median% CP]). Recall that we centered HH% CP on its median value for the original estimation.
Propositions and Associated Hypotheses
Legend for Chart:
A - Proposition
B - Hypothesis
A B
P[sub1] There is a negative relationship between
the probability of cherry-picking on a
given shopping trip and the consumer's
cost of visiting an additional store.
• There is a negative relationship between
the probability of cherry-picking on a given
shopping trip and the consumer's opportunity
cost of time, operationalized as (a)
household earned income [-], (b) presence
of a working adult female in the household
[+], (c) a senior citizen *65 years of age [-],
(d) college education [+], and (e) weekend
versus weekday shopping time [-].(a)
• There is a negative relationship between
the probability of cherry-picking on a given
shopping trip and the distance between the
closest and next-closest stores to the
consumer's home.
P[sub2] There is a positive relationship between
the probability of cherry-picking on a
given shopping trip and the size of the
consumer's shopping list.
• There is a positive relationship between the
probability of cherry-picking on a given
shopping trip and the household's purchase
quantities, operationalized (a) directly and
(b) indirectly as family size.
• There is a negative relationship between
the probability of cherry-picking on a given
shopping trip and the household's
inventory-holding cost, operationalized as (a) the
time elapsed since the last shopping trip [+],
(b) the amount spent on the last trip [-], and
(c) home ownership [+].(a)
P[sub3] Prices paid during cherry-picking are less
than the prices paid on single-store visits.
(a) The sign in brackets [+/-] reflects the expected sign of the
relation ship between the probability of cherry-picking and the
specific measure of opportunity cost of time or inventory-holding
cost. Descriptive Statistics and Intercorrelations of Household
Characteristics and Behaviors
Legend for Chart:
A - Measure
B - Mean
C - S.D.
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
K - 8
L - 9
M - 10
N - 11
A
B C D E F
G H I J
K L M N
Demographics
1. Working adult female(a)
.39 .49
2. Senior citizen (≥65 years)(a)
.18 .38 -.24(**)
3. Home owner(a)
.88 .33 .08 -.03
4. Family size(a)
2.07 1.48 -.12 -.29(**) .15(*)
5. Household income (x $1000)
54.6 26.6 .16(*) -.19(**) .21(**)
.16(*)
6. College educated(a)
.25 .43 .10 -.03 .00
.04 .24(**)
7. Married(a)
.75 .44 -.16(*) -.09 .28(**)
.53(**) .22(**) .07
8. Young children (≤6 years)(a)
.18 .38 -.11 -.22(**) .05
.46(**) .06 .09 .24(**)
Geographic Variables
9. Distance between nearest store and home (miles)
1.17 1.11 -.04 -.10 .06
.10 .11 .10 .15(*)
.15(*)
10. Distance between nearest and next-nearest stores (miles)
1.34 1.10 .09 -.02 .05
.12 -.02 .13 .19(**)
.06 .32(**)
Shopping Behaviors
11. Grocery spending per month (in dollars)
518 220 -.11 -.17(*) .14
.46(**) .16(*) -.04 .36(**)
.21(**) .13 .01
12. Grocery trips per month
5.84 2.63 -.10 .02 -.02
.17(*) -.04 -.03 .23(**)
.07 -.07 -.08 .56(**)
(*) α = .05.
(**) α = .01.
(a) Percentage of shoppers reporting the demographic
characteristic.
Notes: S.D. = standard deviation. Stepwise Censored Regression for Household Percentage of
Cherry-Picking Trips
Observations 201
Percent cherry-picking > 0 162
Percent cherry-picking = 0 39
Log-likelihood -340.2943
U² .0435
E(y)|β = β(*) 4.18%
Legend for Chart:
A - Parameter
B - β(*)
C - Standard Error
D - p-Value
E - E(y)|β = 0
A B C D E
Intercept -4.112 .394 <.0001
Working adult female -.599 .248 .0159 4.83%
Senior citizen (≥65 years) .739 .318 .0200 3.55%
Home owner .738 .359 .0399 2.37%
Distance between nearest and
next-nearest stores (miles) -.226 .106 .0334 5.36%
Family size .154 .083 .0613 3.20%
Household income (x $1000) -.010 .005 .0295 6.56%
College educated .406 .268 .1304 3.85% Mixed-Effects Logistic Regression for Cherry-Picking Trips
Observations 23,796
Cherry-picking = 1 2416
Cherry-picking = 0 21,380
Log-likelihood -6747.96
U(2) .591
E(y)|β = β(*) 10.15%
Legend for Chart:
A - Parameter
B - β(*)
C - Standard Error
D - p-Value
E - E(y)|β = 0
A B C D E
Intercept(a) -2.91 .10 <.00001
Weekend .259 .028 <.00001 9.35%
Spending on last trip -.00335 .00047 <.00001 10.57%
Time since last trip .0206 .0048 .00002 9.48%
Spending on last trip x
time since last trip .00023 .00009 .00726 10.01%
(a) Standard deviation of the estimated intercept across
households is 1.124 with a standard error of .056 and associated
p-value of <.00001. Cherry-Picking Trait Versus State Analyses
Legend for Chart:
A - Variable
B - Marginal Means Cherry-Picking Trip
C - Marginal Means Single-Store Trip
D - Marginal Means Percent Difference
E - Parameter Estimates Analysis(a)
F - Parameter Estimates N
G - Parameter Estimates R²(b)
H - Parameter Estimates HH% CP
I - Parameter Estimates CP Trip
J - Parameter Estimates HH% CP x CP Trip
A B C D
E F G
H I J
Trip size (in dollars) $118.93 $70.71 68.2
OLS 27,978 .076
-98.7(**) 45.5(**) 99.6(**)
(3.9) (1.7) (8.0)
Trip size units 54.5 32.4 68.1
OLS 27,978 .054
-49.8(**) 20.2(**) 53.2(**)
(2.2) (1.0) (4.5)
Percentage of items 37.4% 29.9% 24.8
bought at
discounted prices
Tobit 25,879 .015
.275(**) .108(**) -.365(**)
(.022) (.009) (.043)
Percentage of items 24.4% 18.2% 34.3
bought that are
advertised
Tobit 25,879 .017
.278(**) .121(**) -.347(**)
(.023) (.009) (.045)
Price per unit .952 1.006 -5.3
(indexed
measure)
OLS 22,913 .003
-.295(**) -.0348(**) .152(*)
(.043) (.0157) (.079)
Quantity 1.028 .997 3.2
(indexed
measure)
OLS 22,913 .001
.272(**) .0220 -.184
(.062) (.0399) (.114)
Probability of buying 78.0% 72.4% 7.8
at the lower price
Logistic 22,913 .003
1.09(**) .125(*) -.055
(.21) (.069) (.406)
Percent off higher 14.9% 10.2% 45.9
price
WLS 300 .160
.168(**) .0280(**) -.0509
(.036) (.0088) (.0587)
Savings per trip $23.56 $8.90 164.6
WLS 300 .385
5.92 11.4(**) 24.4(**)
(5.75) (1.4) (9.3)
(*) α = .05.
(**) α = .01.
(a) For most continuous dependent variables, OLS is used. For
continuous dependent variables that are the means of unequal
numbers of observations (e.g., savings per trip for each
household), WLS is used; the weight assigned to each observation
is proportional to the square root of the number of observations
that compose the dependent variable (e.g., the square root of
the number of savings observations for each house hold). Logistic
regression is used when the dependent variable is binary.
Censored regression is used for continuous dependent variables
that are bounded below at zero and where there are several zero
observations (e.g., the proportion of feature-advertised products
purchased on a given trip).
(b) For tobit regressions, a pseudo R (2) that approximates the
percentage of variance explained is reported; for logistic
regressions, U² is reported (U² and R² are not
directly comparable). Primary Versus Secondary Store Visit Analyses for Cherry-Picking
Trips
Legend for Chart:
A - Variable
B - Marginal Means Secondary Store
C - Marginal Means Primary Store
D - Marginal Means Percent Difference
E - Parameter Estimates Analysis(a)
F - Parameter Estimates N
G - Parameter Estimates R²(b)
H - Parameter Estimates HH% CP
I - Parameter Estimates Secondary Store
J - Parameter Estimates HH% CP x Secondary Store
A B C D
E F G
H I J
Visit size (in dollars) $45.01 $71.44 -37.0
OLS 5206 .107
17.0(**) -15.8(**) -59.0(**)
(5.4) (1.7) (6.9)
Visit size units 20.2 33.4 -39.5
OLS 5206 .082
9.50(**) -8.24(**) -27.6(**)
(3.08) (.95) (3.9)
Percentage of items 40.4% 36.2% 11.6
bought at
discounted prices
Tobit 4818 .004
-.167(**) -.0136 .244(**)
(.037) (.0126) (.055)
Percentage of items 29.3% 22.2% 31.8
bought that
are advertised
Tobit 4818 .010
-.119(**) .0262(*) .180(**)
(.040) (.0135) (.059)
Price per unit .936 .972 -3.6
(indexed measure)
OLS 2450 .003
-.137 -.0332 -.001
(.089) (.0301) (.130)
Quantity 1.009 1.052 -4.1
(indexed measure)
OLS 2450 .003
.218 .0024 -.265
(.118) (.0399) (.173)
Probability of buying 79.4% 76.3% 4.1
at the lower price
Logistic 2450 .006
.418 -.049 1.30
(.451) (.149) (.68)
Percent off higher
price 15.7% 13.5% 16.4
WLS 152 .074
.0212 .0040 .185(*)
(.0654) (.0145) (.094)
Savings per store
visit $9.72 $11.92 -18.5
WLS 152 .042
6.35 -2.68 4.73
(6.63) (1.47) (9.49)
(*) α = .05.
(**) α = .01.
(a) For most continuous dependent variables, OLS is used. For
continuous dependent variables that are the means of unequal
numbers of observations (e.g., savings per trip for each
household), WLS is used; the weight assigned to each observation
is proportional to the square root of the number of observations
that compose the dependent variable (e.g., the square root of
the number of savings observations for each house hold). Logistic
regression is used when the dependent variable is binary.
Censored regression is used for continuous dependent variables
that are bounded below at zero and where there are several zero
observations (e.g., the proportion of feature-advertised products
purchased on a given trip).
(b) For tobit regressions, a pseudo R² that approximates the
percentage of variance explained is reported; for logistic
regressions, U² is reported (U² and R² are not
directly comparable).GRAPH: FIGURE 1; Histogram of the Difference in Prices Across Grocery Chains
GRAPH: FIGURE 2; Distribution of Household Percent Cherry-Picking Trips
GRAPH: FIGURE 3; Percent Off the Higher Price by Purchase Occasion Type
GRAPH: FIGURE 4; Interaction Model of Cherry-Picking on Savings per Shopping Trip
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~~~~~~~~
By Edward J. Fox and Stephen J. Hoch
Edward J. Fox is W.R. and Judy Howell Director of the JCPenney Center for Retail Excellence and Assistant Professor of Marketing, Edwin L. Cox School of Business, Southern Methodist University
Stephen J. Hoch is John J. Pomerantz Professor and Chair of the Marketing Department, Wharton School, University of Pennsylvania
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Record: 29- Choice of Supplier in Embedded Markets: Relationship and Marketing Program Effects. By: Wathne, Kenneth H.; Biong, Harald; Heide, Jan B. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p54-66. 13p. 2 Charts. DOI: 10.1509/jmkg.65.2.54.18254.
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CHOICE OF SUPPLIER IN EMBEDDED MARKETS: RELATIONSHIP
AND MARKETING PROGRAM EFFECTS
Recent research has documented how exchanges between buyers and sellers are frequently embedded in social relationships. An unresolved question, however, is the extent to which such relationships protect incumbent suppliers from new competitors and their marketing programs. The authors develop a conceptual framework of how relationship and marketing variables influence choice of supplier and test the framework empirically in the context of business-to-business services. The results show that interpersonal relationships between buyers and suppliers serve as a switching barrier but are considerably less important than both firm-level switching costs and marketing variables. Moreover, unlike switching costs, interpersonal relationships do not play the frequently mentioned role of a buffer against price and product competition. Finally, the authors show that buyers and suppliers hold systematically different views of the determinants of switching.
Since the publication of Arndt's (1979) seminal article on "domesticated markets," there has been an explosion of research on various aspects of interorganizational relationships. Collectively, these studies have made a strong case for the importance of creating close, or relational, exchanges with key customers. They have also generated important insights into the properties of such relationships (e.g., Crosby and Stephens 1987; Dwyer, Schurr, and Oh 1987; Heide and John 1990; Moorman, Zaltman, and Deshpande 1992; Noordewier, John, and Nevin 1990).
From a strategic standpoint, the implicit assumption in much of the relationship literature is that certain relationship properties serve defensive purposes by acting as switching barriers (e.g., Crosby, Evans, and Cowles 1990). In certain respects, this represents a different perspective from the one that underlies much of the marketing strategy literature. As Fornell and Wernerfelt (1987) note, this literature has emphasized offensive strategies designed to encourage customer switching on the basis of deliberate deployment of marketing-mix variables such as product and price.
Both intuition and anecdotal evidence suggest that economic actors are influenced by both marketing variables and relationship properties. Theoretically, this is consistent with Granovetter's (1985) thesis that economic transactions are "embedded" in social relationships. Unfortunately, although this thesis is difficult to refute, it is also conceptually vague (Uzzi 1996). An intriguing yet unanswered question is whether marketing and relationship variables differ in relative importance in the choice of exchange partner.
Some economists have shown skepticism toward the embeddedness thesis and downplayed the role of social relations (Baron and Hannan 1994). Similarly, Crosby and Stephens (1987, p. 411) caution against the implicit assumption in the relationship marketing literature that "relationships are entirely social" and instead emphasize the role that marketing variables play in a customer relationship. In contrast, others have downplayed the role of marketing variables and argued that focusing on them is "simplistic and restrictive" (Christopher, Payne, and Ballantyne 1991, p. 8). Given that both relationship-building and marketing variables involve investments of various kinds, knowledge about their relative effectiveness would represent important input of a firm's resource allocation decision.
In a similar vein, the extant literature does not offer much insight into whether these variables have interactive effects on buyer behavior. From a theoretical perspective, a case could be made that each set of variables represents a different source of utility and that the effect of one could be moderated by another (e.g., Burt 1992; Granovetter 1992). For example, the effect of a superior price offered by a new entrant may depend on whether the target customer has developed a strong relationship with an incumbent supplier. If the target customer has developed close personal relationships with the incumbent, switching means giving up utility from the preexisting relationship. Consequently, the positive effect of an entrant's superior price on customer switching might decrease as interpersonal relationships tied to the incumbent supplier increase. Unfortunately, such hypotheses are rarely found in the marketing literature, and empirical evidence is virtually nonexistent.
Finally, a largely unexplored question is whether buyers and suppliers within an ongoing relationship have convergent perceptions regarding the importance of marketing and relationship variables. Much of the prior research on relationships has been limited to exploring one party's perspective, typically that of a manufacturer or supplier. Moreover, to the extent that perceptual differences in a relationship have been recognized, they have often been viewed somewhat narrowly, as a source of measurement error (e.g., Phillips 1981; for an exception, see Steinman, Deshpande, and Farley 2000). We argue, however, that such differences have important strategic implications. For example, a supplier that overestimates the strength of the relationship with a particular buyer and its effect on the buyer's decisions may be vulnerable to competitive moves. Moreover, a supplier that assumes that relationship building is more important than developing new products or services may be misallocating marketing resources and over time may become locked in with customers that do not take a long-term perspective on the relationship. Therefore, we seek to shed light on the implicit assumption in much of the extant literature about the importance of close customer relationships.
In summary, we seek to make two main contributions to the literature. First, we examine whether and how buyers' choices of suppliers in business-to-business services markets are influenced by the categories of variables mentioned previously, namely, a new supplier's marketing program elements and aspects of the relationship between the buyer and an incumbent supplier. In addition to examining the main effect of each set of variables, we assess the relative importance of the different variables in influencing supplier choice and examine whether interrelationships exist between the different variables, in the sense that the effect of one depends on the level of another. Second, by virtue of collecting data from both buyers and suppliers, we document whether systematic differences exist between parties regarding the effects of the focal variables.
The article is organized as follows: In the next section, we present our conceptual framework, including our research hypotheses. Then we describe our research design and the empirical tests. Finally, we discuss the implications of our findings, the study's limitations, and possible topics for further research.
The general focus of this study is the determinants of supplier choice. Specifically, we examine whether a buyer that has a preexisting supplier relationship but is being approached by a new competitor will decide to remain with the incumbent supplier or switch to the new one. We explore whether (1) certain aspects of an incumbent relationship protect that particular supplier and (2) the tools that are available to a new supplier can undermine an existing relationship. Our focus is consistent with Keaveney's (1995, p. 71) call for "a theory of customer switching behavior, from the customer's perspective."
Prior research provides considerable insight into the conditions that promote continuity in a given relationship, such as social and structural bonds (Berry 1995), relationship quality (Crosby, Evans, and Cowles 1990), satisfaction (Fornell 1992), and service quality (Ostrom and Iacobucci 1995). By virtue of promoting continuity, certain relationship properties are also assumed to constitute impediments to switching to a competing relationship. However, it has not always been clearly articulated why particular relationship characteristics represent switching barriers.
In the following sections, we focus on two particular aspects of a buyer's relationship with an incumbent supplier that we expect to be intimately linked with switching decisions. Specifically, we focus on the effects of interpersonal relationships (e.g., Granovetter 1992; Seabright, Levinthal, and Fichman 1992) and switching costs (e.g., Heide and Weiss 1995; Weiss and Anderson 1992). Both of these derive from previous investments in the supplier relationship. However, the specific nature of the investments differs. Whereas interpersonal relationships derive from individuals' investments in social capital (e.g., Coleman 1990), switching costs arise from organizational-level investments in transaction-specific assets (e.g., Williamson 1985). Thus, each dimension exists at a different level, that is, interpersonal and interorganizational, respectively.
In the next sections, we specify how interpersonal relationships and switching costs protect an incumbent supplier. As we discuss, each one serves as a switching barrier due to the potential loss of an investment in case of relationship termination. Next, we predict how a potential new supplier may use price and product strategies to induce switching. Finally, we discuss the likely interactions among these variables.
Interpersonal Relationships
One of the cornerstones of the economic sociology literature on embedded markets (e.g., Granovetter 1985, 1992) is that economic transactions take place within the context of interpersonal relationships. Interpersonal relationships are conceptualized as the degree to which a close and personal relationship exists between boundary personnel in the transacting organizations (Baker 1990; Marsden and Campbell 1984; Uzzi 1997). Wilson (1995) notes that within the context of buyer-supplier relationships, interpersonal relationships evolve through social interaction between buyers and account managers. The extant literature has identified such relationships in a variety of industries, including apparel (Uzzi 1997), publishing (Coser, Kadushin, and Powell 1982), construction (Eccles 1981 ), advertising (Baker, Faulkner, and Fisher 1998), auditing (Seabright, Levinthal, and Fichman 1992), computer hardware (Larson 1992), life insurance (Crosby and Stephens 1987), and grocery retailing (Murry and Heide 1998).
The existing marketing literature acknowledges the importance of interpersonal relationships. For example, researchers in the relationship marketing area discuss the emotional bonding that transcends economic exchange (Sheth and Parvatiyar 1995). Implicitly or explicitly, the assumption is made in this literature that the presence of a close personal relationship protects an existing relationship from competition. The stronger the relationship, the lower is the likelihood of switching. For example, Juttner and Wehrli (1995, p. 230) have argued that "the focal points for facilitating and maintaining relationships are the psychological and social factors of the individual actors" and that "affinity is the first consideration...[;] the ability to produce services is secondary."
Although we acknowledge the potential role of interpersonal relationships, we believe that prior research has paid insufficient attention to the specific reasons that personal relationships prevent switching. The economic sociology literature mentioned previously provides one possible explanation. Specifically, within an exchange relationship, a party derives utility both from the attributes of a focal product or service and from interpersonal relationships (Frenzen and Davis 1990; Granovetter 1992).[1] Regarding the latter, Burt (1992) describes how close interpersonal relationships reflect social bonds that hold relationships together. These bonds arise from prior investments in social capital, whose return depends on the relationship's longevity. Social capital is broadly defined as an asset that inheres in social relationships (Coleman 1988), which over time accumulates in the form of a series of relationship-specific obligations and reciprocity expectations. Butt (1997) and Coleman (1990) describe this capital in terms of "credit slips" that a person can draw on in time of need.
Thus, the structure of a person's social relationship with an exchange partner will determine how potential new partners are viewed. The closer the preexisting interpersonal relationship, the greater are the prior investment in social capital and the likelihood that the relationship in question will be maintained. In Dwyer, Schurr, and Oh's (1987) terminology, a strong interpersonal relationship serves as a form of mobility barrier. A parallel argument can be derived from social exchange theory (Anderson and Narus 1990; Gassenheimer, Houston, and Davis 1998; Thibaut and Kelley 1959): The better the outcome from the interactions with a focal exchange partner, the less attractive other partners will be perceived to be. On the basis of this theoretical discussion, we propose the following hypothesis:
H1: The closer the interpersonal relationships between the boundary personnel in the supplier and customer firms, the lower is the likelihood of customer switching.
Switching Costs
Switching costs refer to the buyer's perceived costs of switching from the existing to a new supplier (Heide and Weiss 1995; Weiss and Anderson 1992). Buyer switching costs arise as a result of prior partner-specific investments in physical assets, organizational procedures, and/or employee training. For example, buyers may develop procedures for dealing with a specific supplier that will need to be modified if a new relationship is established (Heide and John 1990).
Similar to interpersonal relationships, switching costs represent a disincentive to explore new suppliers (Anderson and Narus 1990; Morgan and Hunt 1994). To the extent that a buyer's investments are idiosyncratic to an individual supplier, switching means giving up future returns. The buyer may also incur direct search and evaluation costs, as well as opportunity costs due to lost synergies (Zajac and Olsen 1993). Thus, as with interpersonal relationships, the history of a particular buyer-supplier relationship has implications for its future course (Ford 1990; HS. kansson 1982). We propose the following hypothesis:
H2: The higher the level of supplier-related switching costs, the lower is the likelihood of customer switching.
Although the conditions mentioned in both H1 and H2 may serve as impediments to switching, two important differences between them should be pointed out. First, whereas the impediment to switching in the case of personal relationships originates from investments in social capital (e.g., Coleman 1990), the source of switching costs are investments in other forms of capital (including specific assets; Williamson 1985). Second, switching costs exist at the firm level, whereas interpersonal relationships exist at the individual level. In Wilson's (1995) terminology, these two factors are examples of structural and social bonds, respectively.
Our discussion so far has focused on the conditions that protect an incumbent supplier from new competitors. We consider next the tools available to a new supplier for penetrating an existing relationship.
Price
A new potential supplier that offers economic terms superior to those of the incumbent enables a buyer to realize immediate cost savings. Over time, these savings may become substantial (Kranton 1996). Therefore, intuition suggests that the lower the entrant's price relative to the incumbent, the greater is the economic payoff from switching and the higher is the likelihood that the customer will switch.
This intuition does not reflect the scope of extant theory on buyers' responses to price. Two different streams of research are relevant here. First, prior research in the information-processing tradition (Monroe and Dodds 1988) suggests that buyers may associate a low price with low quality. As such, a lower price by a competing supplier need not represent an incentive to switch.
Second, offering a contrasting prediction, information economics suggests that a low price may serve as a signal of high quality. In Kirmani and Rao's (2000) terminology, a low introductory price is an example of a "sale-contingent, default-independent" signal. Specifically, high-quality suppliers may attempt to induce trial through a low price (Schmalensee 1978), because a high-quality offering will generate more repeat purchases and the focal supplier may be willing to sacrifice current profits for future revenues. In contrast, a low-quality supplier that will not enjoy repeat purchases would not be motivated to send such a signal. Therefore, a low price may convey credible quality information about some aspect of the supplier and provide an incentive for customers to switch.
As Kirmani and Rao (2000) note, the information economics perspective differs in important ways from the conventional information processing perspective. Whereas the latter perspective considers "lazy" consumers who use pricing information to make cognitive shortcuts, the information economics perspective assumes rational consumers who evaluate the implicit commitments that underlie various signals. We subscribe to the information economics perspective and offer the following prediction:
H3: The lower the price offered by the new potential supplier relative to the incumbent, the higher is the likelihood of customer switching.
Product Breadth
Another means by which a new potential supplier can combat an incumbent is through a product strategy. For example, an entrant could try to attract a customer by differentiating the quality of its product and service offering from the incumbent supplier (Porter 1980). Note, however, that from the perspective of a new competitor that is trying to induce switching from an incumbent supplier, product quality is not always the most obvious candidate. In many industries (e.g., gasoline retailing, personal computers, retail banking), the core product is approaching commodity status and offers limited room for differentiation (Ovans 1997).
Increasingly, new competitors rely instead on product line breadth (or bundling) as an entry strategy. Specifically, new suppliers offer customers enhanced value though a product or service portfolio whose breadth (1) exceeds what is currently provided and (2) is capable of meeting future needs. There are at least four reasons for this trend.
First, from a buyer's perspective, there are search economies from having all required products or services available from the same source (Hoch, Bradlow, and Wansink 1999; Soyster 1997). For example, a single point-of-purchase and after-sale service ("one-stop shopping") reduces the cost of qualifying suppliers. Moreover, because business customers' purchase decisions are often made at a system rather than product level (Wilson, Weiss, and John 1990), savings in both search and operating costs can be substantial.
Second, bundling may offer buyers enhanced performance to the extent that a given supplier's products incorporate proprietary interfaces. For example, by designing each part with the other pans in mind, a supplier can build a "turnkey" system that enhances performance (Wilson, Weiss, and John 1990).
Third, many markets are characterized by considerable turbulence, as indicated by changing buyer preferences (Fisher 1997; Richardson 1996) and competitive moves (D'Aveni and Gunther 1995; Dickson 1992). From a buyer's standpoint, market turbulence makes it difficult to specify in advance the exact requirements from a supplier (Hakansson 1979; Ward and Webster 1991). Everything else being equal, market turbulence increases the value to customers of suppliers that possess a broad product and/or service portfolio. Specifically, from a customer's viewpoint, a decision to remain in a relationship with a supplier whose product line is more restricted than a competitor's involves substantial risk. The risk involves the current opportunity costs of being in a relationship with an inferior product offering and the future maladaptation costs if the firm's needs change (Balakrishnan and Wernerfelt 1986; Seabright, Levinthal, and Fichman 1992). For example, if the incumbent supplier's line is more restricted than the new entrant's, it may indicate that (1) it is misreading the customers' (future) needs or (2) it is incapable of adapting to new market conditions. In any case, a failure to switch in the short run may require the buyer to incur search and renegotiation costs in the future in order to locate an appropriate supplier.
Fourth, given that product breadth is easily visible to a buyer and can be assessed without preexisting experience with the vendor in question (Guiltinan 1987), it constitutes an attractive entry strategy. Therefore, we propose the following hypothesis:
H4: The broader the product range offered by the new potential supplier relative to the incumbent, the higher is the likelihood of customer switching.
Moderating Effects
In the preceding sections, we discuss how a buyer's decision to remain with an incumbent supplier or switch to a new one is influenced by (1) certain aspects of the preexisting relationship with the incumbent and (2) the new competitor's marketing program. We suggest that all these variables have potential effects on a buyer's decision, though the specific nature of the effects is hypothesized to vary.
So far, we have limited our focus to the independent effects of the focal variables. It may be useful to consider, however, whether some of these variables have modifying effects, in the sense that the effect of one depends on the level of another. The extant literature contains some accounts of such effects. For example, the literature on relationship marketing suggests that strong interpersonal relationships should serve as buffers against product and price competition. Thus, we would expect to see a diminishing effect of a competing supplier's marketing variables as interpersonal relationships and switching costs tied to an incumbent supplier increase.
Unfortunately, the extant relationship marketing literature generally has failed to specify the theoretical reasons such effects should be expected. We draw on economic sociology to propose two moderator effects. Granovetter (1992, p. 35, italics added) suggests the general contingency hypothesis that "the mere fact of attachment to others may modify economic action" and that attachments may cause a party to remain in a relationship "despite economic advantages elsewhere." Why would this be the case? Theoretically, this may happen because parties recognize different forms of utility. Recall from our previous discussion that buyers receive utility from both attributes of a supplier's marketing program and aspects of the supplier relationship itself(e.g., Burt 1992; Frenzen and Davis 1990). A new potential supplier that offers a superior price may increase the buyer's utility if switching occurs. However, if the buyer in question has developed close personal relationships with the incumbent and/or faces high levels of partner-specific commitments, switching means giving up utility from the preexisting relationship. In turn, this makes switching to a new supplier less likely. If such assessments are made, the hypothesized positive effects of the entrant's superior price on customer switching should decrease as interpersonal relationships and switching costs tied to an incumbent supplier increase.[2] Stated formally,
H5: The positive effect of a new potential supplier's superior price on the likelihood of customer switching will be negatively moderated by the presence of (a) close personal relationships and (b) high supplier-related switching costs.
A parallel effect is expected for the competing supplier's product line breadth. On the one hand, a broader product portfolio has the potential to increase the buyer's utility and therefore increase the probability of customer switching. On the other hand, if the buyer's representative has developed a close personal relationship with the incumbent and/or faces high switching costs, switching means giving up utility. Thus, the presence of close interpersonal relationships and high switching costs will decrease the positive effect of product breadth on customer switching. Stated formally,
H6: The positive effect of a new potential supplier's superior product breadth on the likelihood of customer switching will be negatively moderated by the presence of (a) close personal relationships and (b) high supplier-related switching costs.
A conjoint design was used to address the research questions presented in the preceding section. This particular design was chosen for several reasons. The nature of our research questions required a design that enabled us to examine how buyers develop preferences for alternatives (i.e., suppliers) that differed systematically on certain attributes (i.e., marketing and relationship variables). Specifically, a conjoint design enabled us to examine buyers' evaluations of both an incumbent and a potential new supplier.
In our situation, a conjoint design had the additional benefit of requiring respondents to perform a realistic task. Customers in many markets are approached on a regular basis by new suppliers (e.g., Achrol 1997). Our conjoint task was designed to describe the situation faced by a buyer that has an existing relationship but is being approached by a potential new supplier and must make a choice on the basis of a joint consideration of relationship and marketing program attributes.
Before settling on the conjoint design, we also considered a retrospective survey approach. Although survey designs have been used frequently in prior studies of buyer-supplier relationships, we believed that a conjoint design approach possessed several distinct advantages, given the specific nature of our research questions. With a survey design, we would be required to obtain responses from customers that either had been approached by a competing supplier firm (and was in the process of evaluating the offer) or had recently completed such a process. As such, we were concerned that asking questions ex post about their behaviors might introduce retrospective biases because they did not remember the relevant factors and considerations clearly. More seriously, social desirability biases may be introduced (Mick 1996), to the extent that respondents would rationalize their actual choices. Conjoint analysis, which uses hypothetical scenarios, minimizes this problem.
The conjoint design also had an important advantage for estimating the relative importance of the marketing and relationship variables in influencing buyer behavior. Relying on a survey design to assess importance weights would require the respondents to make trade-offs on each of the independent variables directly. When asked directly about relative importance, respondents typically have difficulty making trade-offs (Fornell 1992). In our particular case, social desirability biases may again have been a concern, to the extent that respondents may have failed to give a truthful measure of the impact of interpersonal relationships. The "decompositional" nature of a conjoint task is highly beneficial in this respect, because it does not require the respondents to evaluate any given attribute directly (Murry and Heide 1998). Rather, this information is derived from the respondents' global judgments of the different scenarios.
Research Context
The context for the study is relationships between commercial banks and corporate customers. These exchanges cover credit (e.g., line of credit) and noncredit (e.g., cash management) products. We chose this particular context for several reasons. First, deregulation, technological innovation, and mergers have increased competition both among traditional banks and between banks and nonbank suppliers (Mehra 1996). Thus, customers are frequently required to evaluate new supplier offerings and by implication their incumbent relationships.
Second, initial discussions with managers and reviews of both the academic and the trade literature suggested that our focal theoretical variables all manifest themselves in this setting to varying degrees. With respect to the marketing program variables, the recent mergers have enhanced many suppliers' ability to compete on price. In addition, the merged firms frequently provide a broad range of financial services that are capable of meeting all the needs of individual customers (Hanes 1998). With respect to the relationship variables, some suppliers have implemented formal account management programs that facilitate the development of interpersonal relationships (Perrien, Paradis, and Banting 1995). Finally, switching costs arise because of buyer and supplier investments of various kinds. For example, suppliers sometimes adapt their cash management systems to meet the unique requirements of particular customers. In turn, customers may invest in training their personnel to use the systems and may purchase computer software and supplies that are supplier-specific in nature. Together, these observations convinced us that our focal theoretical variables could be studied in this context.
Development of Conjoint Scenarios
To develop the materials for our study, we initially consulted both the existing literature on buyer-supplier relationships and the trade press. Subsequently, we conducted several personal interviews with managers from both sides of the relationship dyad (two bank and key account managers from the supplier side and two business managers from the customer side). The interviews focused on the key decision criteria used in choosing a financial services supplier. We asked the managers to consider both positive and negative factors influencing choice. These interviews helped us (1) develop the conjoint scenarios, (2) identify key decision makers on both sides of the dyad, and (3) acquire the necessary industry terminology.
From these exploratory investigations, we developed 16 conjoint scenarios (four factors each with two levels), which we subsequently showed to one bank manager, one account manager, and two business managers. On the basis of the feedback received, we modified and revised the conjoint scenarios. Subsequently, we tested them in interviews with another business manager. This pretest revealed no major problems with any of the scenarios or the response format. In total, we devoted approximately 45 hours of personal interviews to developing the conjoint scenarios.
Measures. The four factors that constitute the conjoint task and the levels of each are as follows:
- Interpersonal relationships
- Terminate a relationship in which the account manager is unknown.
- Terminate a relationship in which a close and personal relationship has been established with the account manager.
- Switching costs
- Minimal costs incurred by switching to a new bank.
- Substantial costs incurred by switching to a new bank.
- Price
- Same economic terms as those provided by the existing bank.
- 15% better economic terms.
- Product breadth
- Access to the same set of services the firm is currently receiving.
- Access to a wide range of financial services covering both current and possible future needs.
Our first factor, interpersonal relationships, refers to the existence of a close and personal relationship between the boundary personnel in a buyer and a seller firm (Seabright, Levinthal, and Fichman 1992) and varied according to whether the focal account manager was unknown to the buyer or a close and personal relationship existed. The second factor, switching costs, was defined as the buyer's perceived costs of switching from the existing to a new supplier (Heide and Weiss 1995; Weiss and Anderson 1992). The respondents were instructed to consider all the different switching costs that may apply. The factor levels were minimal and substantial switching costs, respectively.
The third factor, price, described the economic terms offered (e.g., interest and fees on the financial services provided by the supplier). For the first level of this factor, the terms offered by the rival supplier were the same as those received from the incumbent supplier. The second level described a rival supplier that offered a 15% premium. The 15% was decided on from our field interviews with bank and account managers. The fourth factor, product breadth, refers to the breadth of the portfolio of services offered by the supplier. This factor varied in terms of whether the rival supplier only offered the services currently provided by the incumbent or whether a wider range of services was offered that was capable of meeting the customer's future needs.
Sampling and Data Collection
Sampling frame. The initial sampling frame for the study was a list of buyers of corporate financial services provided by a commercial bank. We contacted all midsize customers in one region (a total of 443) personally by telephone to locate an appropriate key informant within each firm.
Key informant selection. Campbell's (1955) criteria of being (1) knowledgeable about the phenomenon under study and (2) able and willing to communicate with the researcher constituted our criteria for informant selection. From the telephone contacts, we identified 114 informants who met these criteria and who verbally agreed to participate in the study (25.7% of 443). The formal titles of the informants within the customer firms were either business manager or general manager. In the remainder of the 443 companies contacted, either an appropriate key informant could not be found within the time constraints imposed by the administration of the study or the relevant person refused to participate in the study. As an additional step toward minimizing informant bias, we included the following question in the survey materials: "What influence do you personally have on your company's decisions regarding choice of bank?" On a seven-point scale, the mean response was 5.8 (standard deviation = 1.4), providing evidence of the quality of our key informants.
To test whether systematic differences existed between buyers and the supplier regarding the hypothesized relationships, we contacted all (39) key account managers at corporate headquarters personally by telephone and asked them to participate in the survey. In total, these account managers were responsible for the 443 customer accounts. Of the 39 key account managers (95%), 37 agreed to participate in the study.
Nonresponse bias. To assess the possibility of nonresponse bias in our data, we compared the final sample with the other firms in the sampling frame with respect to sales volume. We found no significant differences, which suggested that nonresponse bias was not a problem.
Conjoint administration. For each informant who agreed to participate in the study, an appointment was made to conduct a personal interview. A professional marketing research company administered the conjoint experiment. All 114 customers and 37 key account managers who agreed to participate in the study completed the conjoint task.
A full-profile presentation method was chosen to add realism to the conjoint task (Carroll and Green 1995). The managers were asked to envision a situation in which their company had a preexisting banking relationship but was being approached by a new potential supplier. Each scenario described two aspects of an incumbent relationship that may protect the existing supplier and two marketing tools available to the new potential supplier for penetrating the existing relationship. For example, one scenario described a situation in which the buyer would (1) receive the same economic terms as those provided by the incumbent, (2) gain access to a wide range of bank and insurance services, (3) incur minimal switching costs, and (4) terminate a relationship with an unknown account manager. A full-factorial design was used to administer the scenarios.
Each of the 16 scenarios was presented to the managers with verbal descriptions written on cards. In addition to the 16 cards, the managers were given a short description of the decision-making situation and the four factors. The scenarios involved shifting the entire business volume to a new supplier, a so-called lost-for-good scenario (Jackson 1985). The managers first were asked to rank-order the conjoint scenarios by preference. Specifically, they were asked to rank each scenario from I to 16, where I was the scenario in which it would be most likely that their firm would decide to switch from the incumbent to a new supplier. Next, the managers were asked to rate each hypothetical relationship on a seven-point scale that indicated the likelihood of switching to a new suppler. The scale was anchored by "very unlikely to switch supplier" and "very likely to switch supplier." The rating measure was used in the final analysis. The ranking task was used to facilitate and ensure variation on the rating measure (Alwin and Krosnick 1985; Murry and Heide 1998).
A parallel task was administered to the account managers on the supplier side. Managers were asked to evaluate the hypothetical conjoint scenarios as if they themselves represented a customer. As noted previously, our final sample from the supplier side consisted of 37 managers.
Test of Hypotheses
We tested the hypotheses by estimating two ordinary least squares regression models. We used an effects coding scheme (Cohen and Cohen 1983) to represent the different levels of the factors. Under such a scheme, the first level of each factor (e.g., low switching costs) is coded as -l, and the other (e.g., high switching costs) as +1. Interactions were defined by multiplicative cross-product terms between the relevant factors (Green and DeSarbo 1979). The statistical models involved estimating a buyer's tendency to switch from an incumbent to a new supplier (SWITCH) as a function of interpersonal relationships, switching costs, price, and product breadth. With one exception (discussed subsequently), we tested the same model in both the buyer and supplier samples. Table 1 shows the estimated coefficients (standardized and unstandardized) and associated t-statistics.
Both models explain a sufficient amount of variance to justify examining the individual coefficients (adjusted R2 = .35 and .39, respectively). The results show that interpersonal relationships between buyers and suppliers have a significant and negative effect on a buyer's tendency to switch (tbuyer = -3.64, tsupplier = -9.03). This result provides support for H1. Similarly, firm-level switching costs have a significant and negative effect on a buyer's tendency to switch in both samples (tbuyer = -11.70, tsupplier = -6.99), which provides support for H2.
Price has a significant and positive effect in both samples (tbuyer = 24.14, tsupplier = 14.77), consistent with H3. Also, as we predict in H4, product breadth has a significant and positive effect on the tendency to switch in both samples (tbuyer = 7.85, tsupplier = 5.18).
Consistent with H5b, the interaction between switching costs and price is significant and negative for the buyer sample (tbuyer = -4.42). A similar result was found for the interaction between switching costs and product breadth in this sample (tbuyer = -1.80), consistent with H6b. These findings provide support for our hypotheses that the positive effect of an entrant's price and product advantage on switching is attenuated by high levels of supplier-related switching costs. The parallel effects in the supplier sample were both negative, as we predicted, but not significant (tsupplier = -1.10 and -.67, respectively). None of the interactions involving interpersonal relationships was significant, in contrast with H5a and H6a.
As shown in Table 1, the model for the buyer sample includes an additional term, namely, firm size (operationalized by buyer sales volume). This variable, which was administered only to the buyers, was included in the model for control purposes. Note that because the respondents are evaluating hypothetical buying scenarios, it is impossible to introduce control variables that get at actual aspects of suppliers, markets, or buying situations. The only appropriate controls are ones that may influence the respondents' completion of the conjoint task. Sales volume is a reasonable candidate in this respect, because firm size may be a proxy for professionalism and/or expertise and thus may affect the completion of the task. Here, however, the purpose of the control variable is to account for certain respondent-level characteristics that may influence the dependent variable. As is evident from Table 1, size has a significant and positive effect on a buyer's tendency to switch (tbuyer = 3.67).
Relative Importance of Factors
From a managerial standpoint, the relative importance of the different factors studied would be of considerable interest. From the perspective of an incumbent supplier, knowledge about the extent to which an interpersonal relationship serves as a switching barrier, or is perceived by a buyer as more important than a new competitor's marketing program, would be of substantial value. Conversely, a new competitor that must decide whether to allocate resources to product development or relationship building would have an inherent interest in knowing which tool has the greater impact on the buyer.
Unfortunately, to the best of our knowledge, existing theory does not enable us to develop strong a priori theoretical predictions about relative effectiveness. It is noteworthy, however, that much competing conjecture exists. The literature on relationship marketing (e.g., Gronros 1987; Gummeson 1987) typically has put much less weight on marketing variables than on relationship features. In contrast, industry evidence often presents a different scenario. For example, a study presented in Forbes (Levine 1993, p. 232) reports that "banking customers couldn't care less about relationships. Just tell them what the loan will cost them or what the interest is."
Although we do not offer a priori hypotheses about relative importance, the particular research design used (i.e., a conjoint task) enables us to undertake such comparisons ex post. Specifically, importance weights are computed by dividing each factor's part-worth range by the sum of all the part-worth ranges. The aggregated importance weights for the buyer and supplier sample are shown in Table 2. The importance weights show that systematically different views exist between buyers and suppliers regarding the determinants of switching. Both buyers and suppliers consider price the most important factor influencing the decision to switch (Wbuyer = .50, Wsupplier = .40). However, divergence exists regarding the second most important factor. Buyers place somewhat more weight on the existence of switching costs than suppliers do (Wbuyer = .25, Wsupplier = .20) and view this factor as the second most important one overall. In contrast, suppliers believe that buyers will place more weight on interpersonal relationships when making switching decisions. The derived weight for the interpersonal relationships variable in the buyer sample is only .08, in contrast with .26 in the supplier sample. As in all conjoint studies, these importance weights should be interpreted with some caution, because they depend on the specific factor levels included in the study.
We report one final set of analyses because of the relatively small size of the supplier sample. Unfortunately, an agreement with a sponsoring organization that endorsed the survey gave us a limited window for data collection, which made it impossible to generate a larger supplier sample. Although the relative importance measures are unaffected by sample size, careful attention should be paid to the various significance tests, because a lack of hypothesis support may be attributable to a lack of statistical power. In this case, however, this may not be an issue because of our reliance on a conjoint design in which each respondent evaluated multiple supplier scenarios. Nevertheless, to explore whether the discrepancies between the buyer and supplier samples were likely to be caused by sample size differences, we drew a random sample of 37 from the buyer sample and reestimated the regression model in that subsample. The results of that model were almost identical to those for the full sample. Specifically, the two interactions involving switching costs, price x switching costs and product breadth x switching costs, remained significant (t = -2.83 and t = -1.79, respectively). This suggests that (1) the observed differences cannot be explained easily by sample size differences and (2) there are substantive differences between buyers and suppliers.
Theoretical Implications
The main objective of this study was to examine the effects of marketing and relationship variables on customer behavior. Although the marketing strategy literature historically has emphasized offensive strategies based on deploying marketing-mix variables, some of the emergent literature on relationship marketing has downplayed the importance of marketing variables compared with dimensions of the relationship itself. To the best of our knowledge, this study represents the first attempt to compare these variables directly and document the effects of competition on an existing relationship.
Consistent with the relationship marketing literature, our results show that customers are influenced by the nature of their relationships with incumbent vendors. As such, relationship history matters, and new vendors do not start with a clean slate. However, our overall pattern of results paints a more complex picture than is currently expressed in the literature.
The customers in our study generally perceived the marketing variables to be more important determinants of switching intentions than the relationship dimensions (interpersonal relationship and switching costs). Specifically, price dominated all of the other factors, and the combined importance weight for price and product exceeded the total weight of the relationship dimensions.
Furthermore, our results showed that important differences existed between types of relationship properties. Specifically, customers attached considerably more weight to firm-level switching costs in deciding to remain with an incumbent vendor. Relatively speaking, the presence of interpersonal relationships did not seem to be an important disincentive to switch suppliers. This result is interesting, particularly in light of the emphasis placed on this factor in the relationship marketing literature.
Similarly, the presence of interpersonal relationships did not diminish the effect of a new competitor's price or product strategy, as often is assumed. Apparently, customers were not willing to make trade-offs between the utility from a marketing program and prior investments in social capital. However, we observed such an interaction for switching costs. Specifically, we showed that the positive effect of a new supplier's superior price or product breadth on switching decreased for higher levels of switching costs. These results suggest that buyers do make joint assessments of different sources of utility (e.g., marketing and relationship variables). They also confirm Dwyer, Schurr, and Oh's (1987) hypothesis that an incumbent relationship can be organized in such a way that it forecloses competing ones. However, our results also show that some relationship characteristics are more effective than others in protecting an incumbent supplier.
The limited effect of interpersonal relationships raises some interesting theoretical questions. Specifically, it may be useful to consider the specific reasons some customers were willing to switch to a new supplier and sacrifice existing social capital. We believe that one possible explanation is the different roles played by an individual within a relationship.
In Montgomery's (1998) terminology, the managers in the customer firms studied are both (1) businesspeople who are asked to maximize profits for their employer and (2) friends of the supplier's representative who may feel an obligation to cooperate by maintaining the relationship. Historically, the relationship marketing literature has emphasized the latter and implicitly assumed that the utility derived from interpersonal relationships is universally important. Recently, Montgomery (1998) challenged the common assumption of a "unitary actor" and argued that different situations evoke different roles. In our context, a customer's role as friend may assume less importance in the presence of explicit competitive offers. In general, our results suggest that claims about the importance of relationships should be made cautiously and on the basis of the specific roles and contextual factors at hand.
Finally, our study documents whether buyers and suppliers within an ongoing relationship have convergent perceptions regarding marketing and relationship variables. By administering the conjoint task to both sides of the buyer-supplier dyad, we were able to document that systematic differences existed between the two parties. Most important, suppliers seem to have inflated perceptions of the importance of interpersonal relationships compared with buyers.
Managerial Implications
Despite the general acceptance of the relationship marketing concept, there is considerable evidence that shows that efforts to establish long-term customer relationships often fail. In highly competitive markets, knowledge of why customers decide to dissolve a relationship is key to achieving a strategic advantage. By understanding the determinants of dissolution, a firm can identify the issues that must be addressed in order to prevent future defections. Our study identified large discrepancies between buyers and suppliers regarding the determinants of switching. This suggests that buyers and suppliers have different perceptions regarding the factors needed to maintain successful interfirm relationships.
Our results suggest that both existing and new suppliers must offer competitive terms to be considered possible partners. Although managers frequently believe that long-lasting relationships will reduce customers' price sensitivity (Perrien, Paradis, and Banting 1995), it is also plausible that dealing with the same supplier for a long time may increase customers' price expectations. Specifically, Kalwani and Narayandas (1995, p. 5) argue that "in long-term relationships, customers expect the few selected suppliers to pass on the benefits of lower costs in the form of price reductions over time." Thus, when new competitors accommodate these expectations by offering lower prices, a customer's incentive to switch may be even larger than what the objective price difference between the competing suppliers should indicate.
We also showed that firm-level switching costs have a key influence on switching behavior. However, we add a few words of caution regarding strategies designed to increase such costs. A fear of dependence may discourage some customers from establishing a close relationship in the first place. For example, customers that need to make investments in supplier-specific assets face the risk of subsequent supplier opportunism in the form of price increases (e.g., Williamson 1996).
Deploying strategies based on customer switching costs may also create risk for a supplier, to the extent that it underestimates the actual switching barrier faced by a customer. For example, suppliers that use price cuts to attract customers in anticipation of future lock-in must have a clear picture of the ease with which the customer in question can locate alternative suppliers in the future (Shapiro and Varian 1999).
From a practical viewpoint, it may be surprising that customers attached the least importance to interpersonal relationships. Historically, both academicians and practicing managers have considered social rewards the glue that holds relationships together. However, from a resource allocation viewpoint, our results suggest that a supplier's best option may be to create switching costs at the firm level. At the same time, our results do not suggest that an incumbent supplier can refrain from providing competitive terms. Customers attach considerable weight to immediate price advantages. As such, price may be the strongest competitive tool available to a new entrant that wishes to undermine an existing relationship.
Limitations and Further Research
Some limitations of the current research should be noted. First, for theory-testing purposes, we decided to test our hypotheses in one particular (and homogeneous) context, namely, business-to-business services. Restricting our sample in this fashion served the dual purposes of (1) controlling extraneous sources of variation and (2) developing grounded measures. However, while this industry is both large and growing (Berry 1999), caution should be used in extrapolating our results to other contexts. For example, although recent studies (Petersen and Raghuram 1994; Uzzi 1997) indicate that interpersonal relationships play an important role in banking relationships, a promising avenue for further research would be to test the relative effect of these relationships in other nonservice industries.
Second, although the conjoint design used possesses several distinct advantages, it simplifies some of the focal phenomena. For example, we implicitly treated interpersonal relationships as a unidimensional concept, but other dimensions may also influence the tendency to switch suppliers, such as those the sociology literature identifies: sociability, approval, prestige, trust, reciprocity and power (e.g., Granovetter 1992). On a related note, it is possible that fundamentally different categories of relationships exist, which may have implications for customer switching. Recall that our current focus was the two extreme modes that are identified in the embeddedness literature, namely, relationships with no social content and relationships that are close and personal in nature. Conceivably, however, other constellations may also be observed, such as professional relationships that are not personal in nature. Further research could be directed usefully toward exploring how switching tendencies manifest themselves in such a scenario.
Furthermore, the nature of the conjoint task required us to limit our focus to one particular dependent variable, namely, switching behavior. A promising avenue for further research is to expand the current framework to include other aspects of buyer behavior. For example, concerns about switching may induce a buyer to renegotiate terms with the incumbent before making the final decision. As such, an interesting question is whether some of the variables in the present study influence a buyer's desire to renegotiate with an incumbent supplier or whether a given variable has similar effects on renegotiation and switching.
As an example, consider the possible effects of switching costs. Our results suggest that the existence of high firm-level switching costs represents a disincentive for supplier switching. Conceivably, in lieu of switching, buyers in such relationships may use a competing offer to renegotiate with the incumbent. Notice, however, that the presence of high switching costs places the buyer at a considerable bargaining disadvantage (Weiss and Anderson 1992; Williamson 1975). Under such conditions, switching may be a noncredible threat, and a buyer's renegotiation efforts may be constrained.[3]
Finally, the observed differences between buyers and suppliers raise some intriguing questions. Although our present data do not enable us to identify unambiguously the specific sources of these differences, we believe that they are due in part to the parties' different positions in the value chain. For example, Ring and Van de Ven (1994) describe how a firm's perception of a particular situation and/or exchange partner is a function of its own role in the relationship. Furthermore, because firms act on these perceptions, they ultimately determine the functioning of the relationship itself (Schuman 1982). An important question for further research is how firms' role perceptions become aligned. Embeddedness (e.g., Granovetter 1985) and game theory (e.g., Axelrod 1984) suggest that the time dimension of a particular relationship may have an impact because of the ability of socialization processes and patterns of interaction to promote convergence. At the same time, participation in other relationships may have the opposite effect, to the extent that these relationships promote other roles. Documenting the specific sources of perceptual differences across a dyad is a promising area for further research.
1 In the economic sociology literature (e.g., Butt 1992), the utility a party derives from interpersonal relationships is of a noneconomic nature. However, as Williamson (1985, p. 62) notes, it is also possible that such relationships produce economic benefits-for example, in the form of communication economies.
21n principle, it could be hypothesized that price and product breadth attenuate the effect of interpersonal relationships and switching costs. However, we treat switching costs and interpersonal relationships as moderator variables for two reasons. First, treating them as moderator variables is the test of Granovetter's (1992, p. 35) hypothesis that "attachments modify economic action." Second, this treatment is consistent with our perspective of testing whether the efforts of an entrant (that deploys the marketing variables) are modified (technically, moderated) by a preexisting relationship.
3Interpersonal relationships may also constitute a renegotiation barrier, though for different reasons. In Bonoma's (1976) terminology, a strong bond between boundary personnel reflects the presence of a so-called bilateral power system, in which the individual parties' utility functions are replaced by a global utility function for the relationship as a whole. Such systems tend to be governed by particular norms, which promote relationship-oriented behaviors and discourage actions that advance the interests of the individual parties. Depending on the history of a particular relationship, or the manner in which past exchange "episodes" (Ford 1990) have been conducted, renegotiation may be an unlikely strategy.
Legend for chart
A = Switch: Buyer Sample: Unstandardized Coefficient
B = Switch: Buyer Sample: Standardized Coefficient
C = Switch: Buyer Sample: t-Value
D = Switch: Supplier Sample: Unstandardized Coefficient
E = Switch: Supplier Sample: Standardized Coefficient
F = Switch: Supplier Sample: t-Value
Independent
Variable A B C D E F
Interpersonal -.14 -.07 -3.64** -.49 -.29 -9.03**
relationships
Switching costs -.44 -.24 -11.70** -.38 -.23 -6.99**
Price .91 .51 24.14** .80 .48 14.77**
Product breadth .30 .16 7.85** .28 .17 5.18**
Price x
interpersonal
relationships -.002 -.01 -.54 -.004 -.03 -.79
Price x switching
costs -.17 -.09 -4.42** -.006 -.04 -1.10
Product breadth x
interpersonal
relationships -.0004 -.00 -.13 .001 .00 .25
Product breadth x
switching costs -.007 -.04 -1.80* -.003 -.02 -.67
Size .00003 .08 3.67**
R[2] adjusted = .35 R[2] adjusted = .39
*p < .05.
**p < .01.Notes: One-tailed tests are used because of our directional hypotheses.
Conjoint Factor Buyer Supplier
Interpersonal
relationships .08 .26
Switching costs .25 .20
Price .50 .40
Product breadth .17 .14
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~~~~~~~~
By Kenneth H. Wathne; Harald Biong and Jan B. Heide d Peter H. Reingen
Kenneth H. Wathne is a visiting scholar, Marketing Department, School of Business, University of Wisconsin-Madison; when this article was initiated, Wathne was a doctoral candidate, Department of Marketing, Norwegian School of Management. Harald Biong is Associate Professor of Marketing, Norwegian School of Management. Jan B. Heide is Churchill Professor of Marketing, School of Business, University of Wisconsin-Madison. The authors acknowledge the contribution of John Murry, University of Iowa, to the development of the conjoint design and materials used in this study and the assistance of Inge Brechan, Norwegian School of Management, with the field interviews and data collection. The authors thank the Fund for the Promotion of Studies in Banking and Financing Services at the Norwegian School of Management and Union Bank of Norway for financial assistance. The authors also thank the three anonymous JM reviewers for their helpful comments on previous versions of this article. The authors are listed in random order. They all contributed equally to the article.
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Record: 30- Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies. By: Meuter, Matthew L.; Bitner, Mary Jo; Ostrom, Amy L.; Brown, Stephen W. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p61-83. 23p. 2 Diagrams, 6 Charts. DOI: 10.1509/jmkg.69.2.61.60759.
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Choosing Among Alternative Service Delivery Modes: An
Investigation of Customer Trial of Self-Service Technologies
Electronic commerce is an increasingly popular business model with a wide range of tools available to firms. An application that is becoming more common is the use of self-service technologies (SSTs), such as telephone banking, automated hotel checkout, and online investment trading, whereby customers produce services for themselves without assistance from firm employees. Widespread introduction of SSTs is apparent across industries, yet relatively little is known about why customers decide to try SSTs and why some SSTs are more widely accepted than others. In this research, the authors explore key factors that influence the initial SST trial decision, specifically focusing on actual behavior in situations in which the consumer has a choice among delivery modes. The authors show that the consumer readiness variables of role clarity, motivation, and ability are key mediators between established adoption constructs (innovation characteristics and individual differences) and the likelihood of trial.
It is widely recognized that the use of information technology has transformed business processes over the past ten years. With the explosion of the Internet and other tools, many firms are incorporating technology into their marketing and operations. The impact has been especially profound in the services arena, which has traditionally relied on close, personal contact between customers and employees. Technology is dramatically changing how services are conceived, developed, and delivered. Indeed, not only has technology infiltrated back-office processes, but it has also been established prominently within the firm-customer interface through self-service technologies (SSTs), such as automated teller machines, pay-at-the-pump, automated hotel checkout, telephone banking, and Internet transactions (Meuter et al. 2000).
The lure of incorporating technology into the service interaction can be tremendous. For example, IBM shifted 99 million service telephone calls to an online service provision, which resulted in cost savings of $2 billion (Burrows 2001). Although the potential financial benefits of successful technology incorporation are enticing, the savings cannot be realized unless customers embrace and use the new technologies. For example, McKinsey & Company reports that one firm projected a $40 million savings from moving its billing and service calls to the Web. However, it suffered a $16 million loss, partially as a result of lower customer use than was projected. Thus, despite the proliferation of SSTs, firms are increasingly aware that there are barriers to customer adoption and substantial implementation obstacles to be overcome.
The most prominent obstacle is getting customers to try new SSTs for the first time, which often involves a significant behavior change in which patterns that are ingrained must be altered. Not only must customers change their behaviors, but in a self-service situation, they must also become coproducers of the service, with responsibility for delivery of the service and for their own satisfaction ( Bendapudi and Leone 2003; Meuter and Bitner 1997). Across industries, firms are trying to develop stronger partnerships with their customers and to help them be better coproducers (Vargo and Lusch 2004).
This article explores the underlying factors that influence customer trial of new SST options and, by extension, trial of innovations that require significant behavior change or coproduction activity. We begin by examining the literature on innovation adoption for clues to the causes of customer adoption behavior. The traditional view of innovation adoption focuses on demographic characteristics and characteristics of the innovation itself as the primary predictors of adoption (Rogers 1995). A review of the research on consumer use of SSTs reveals a primary focus on individual differences (e.g., Parasuraman and Colby 2001) and on attitude models to predict intended behaviors (e.g., Curran, Meuter, and Surprenant 2003; Dabholkar and Bagozzi 2002).
We build on this literature by exploring a set of variables labeled "consumer readiness," which are positioned as mediators between established adoption variables and trial. Consumer readiness encompasses role clarity, motivation (intrinsic and extrinsic), and ability. We expect that exploring consumer readiness and its underlying constructs will broaden our understanding of the antecedents of SST trial and provide greater depth of knowledge with respect to why consumers try innovative services. Another contribution of this study is to provide marketing managers with a concise and actionable set of factors for directly influencing SST trial behavior. The consumer readiness constructs can be actively managed before an SST is introduced as well as after it is fully operational. This is important because the innovation characteristics and demographic factors that have been previously shown to influence trial either are not readily manipulated (e.g., age, sex) or are easily managed only before the SST is introduced (e.g., complexity, trialability).
Next, we develop a conceptual model and hypotheses to predict SST trial behavior. Then, we test our hypotheses across two different technologies with actual trial behavior as the dependent variable. We include both users and nonusers of the services in the study. The underlying theory as well as the practical lessons learned can be extended to other coproduction situations in which consumer readiness for a significant behavior change may be equally important.
Conceptual Foundations
Diffusion and adoption research has a rich history and has been studied in a wide range of fields (for a comprehensive review, see Rogers 1995). Within the adoption literature, several constructs have received widespread attention. Perceptions of innovation characteristics (Eastlick 1996; Labay and Kinnear 1981; Rogers 1995; Venkatraman 1991) and individual differences (Dickerson and Gentry 1983; Eastlick 1996; Greco and Fields 1991) have been shown to predict adoption behaviors, such as trial or commitment.
Although there is support in the literature for such factors influencing adoption behavior, the results have often been inconclusive or contradictory. For example, a study found significant relationships between adoption and the perceived relative advantage, complexity, and compatibility of the innovation (Labay and Kinnear 1981), whereas another study found only relative advantage to be significantly linked to adoption behavior (Venkatraman 1991). However, the contradictory findings within studies are even more troubling. In one study, a significant link was found between adoption behavior and relative advantage in two different contexts; however, the relationship was positive in one context and negative in the other (Venkatraman 1991). A meta-analysis exploring the directionality of links between adoption behavior and perceptions of innovation characteristics (with no regard to the magnitude or statistical significance of the findings) found that only three of ten characteristics consistently related to adoption in the same direction (Tornatzky and Klein 1982, p. 41): "If it could be demonstrated that a finite number of perceived characteristics seem to be consistently related to innovation adoption across settings and technologies this would serve to focus both policy and research." We begin to address this concern with our mediating variables.
In addition, individual differences, such as demographics, have generated largely inconsistent findings. For example, although adopters are usually predicted to be younger (Eastlick 1996; Venkatraman 1991), a comprehensive review of the relationship between age and innovation adoption concludes that only half of the 228 studies reviewed showed a significant relationship between age and adoption behavior (Rogers 1995). It is also problematic that of the studies with significant links between age and adoption behavior, some conclude that adopters are younger, and others conclude that adopters are older (Rogers 1995).
It is important to understand why certain innovation characteristics or individual differences vary in direction and significance across different contexts. A way to clarify the inconsistencies is through the use of mediating variables that are included specifically to explain relationships between variables. To date, the question of why individual differences or innovation characteristics influence adoption behavior has been left largely unexplored. The model we develop herein includes key mediators and is designed to explore why the relationships exist.
In response to the increasing role of technology in services, researchers have begun to explore customer perceptions and usage of service delivery technologies. For example, a critical incident study describes the key factors that lead to (dis)satisfaction related to customer use of SSTs (Meuter et al. 2000). In addition, Parasuraman (2000, p. 308) proposes a "technology readiness" construct, which refers to the "propensity to embrace and use new technologies for accomplishing goals in home life and at work." Technology readiness is a generalized individual difference concept that balances contributors (optimism and innovativeness) and inhibitors (discomfort and insecurity). Similarly, some researchers have explored the capacity and willingness of customers as predictors of adoption (Walker et al. 2002), and others have investigated customers' attitudes toward the technology as a means to predict behavioral intentions (Curran, Meuter, and Surprenant 2003; Dabholkar and Bagozzi 2002; Plouffe, Vandenbosch, and Hulland 2001). The technology acceptance model posits that ease of use and the perceived usefulness of a new technology influence customers' attitude toward using the technology, which in turn directly influences intentions to use the technology (Adams, Nelson, and Todd 1992; Davis 1989). Furthermore, Taylor and Todd (1995) compare the effectiveness of the technology acceptance model and the theory of planned behavior, both of which focus on behavioral intentions of the user. Dabholkar (1994) explores competing models to understand the attitudinal forces that influence the choice between interpersonal and technology-based service experiences.
Going beyond the emphasis on attitudes and behavioral intentions in the services technology literature, our study is designed to extend the literature by focusing on actual trial behavior. We designed this study to introduce new factors for the prediction of trial of technology and to embed the new factors within well-established innovation and adoption models.
Customer use of a new SST implies coproduction of the service, which frequently requires customers to engage in new behaviors. For example, in the grocery industry, customers are increasingly given the option of scanning their own grocery items and paying for and bagging their food without assistance from a sales clerk. This option has revolutionized the typical interface between the customer and the service provider as well as the behaviors required of customers in the grocery industry. Lovelock and Young (1979) were among the first to discuss customer coproduction and to indicate that customers are important contributors to a firm's productivity (e.g., by using automated teller machines or by pumping their own gas). Other researchers describe a shift in which both practitioners and academics recognize that a "separation of production and consumption is not a normative goal, and toward a recognition of the advantages, if not the necessity, of viewing the consumer as a coproducer" (Vargo and Lusch 2004, p. 11). Defining the nature of the customer's role requires conducting a "job analysis" of the customer's responsibilities as is traditionally done for a firm's employees (Schneider and Bowen 1995). Therefore, we propose that successful SST coproduction relies on customers knowing what is expected of them (role clarity), being motivated to engage in desired behaviors (motivation), and having the necessary knowledge and skills (ability) to fulfill their responsibilities (Dellande, Gilly, and Graham 2004; Schneider and Bowen 1995).
However, a paucity of empirical research exists on customer coproduction (for a recent exception, see Dellande, Gilly, and Graham 2004). A review of the customer-coproduction literature from 1979 to 2000 finds that of the 23 studies, only 3 are empirical (Bendapudi and Leone 2003). Furthermore, no research has examined SST coproduction. Our study is designed to build on and extend the three streams of research that we previously reviewed.
Conceptual Model and Hypotheses
A wide range of SST options is available, yet most consumers use only a few of them (Barczak, Ellen, and Pilling 1997, p. 131). This study investigates the critical factors that influence trial of SSTs in situations in which consumers have a choice among service delivery alternatives. We developed the conceptual model (see Figure 1) using both qualitative depth interviews of consumers and insights from related research streams. We used the qualitative interviews (n = 22) to focus on variables that are relevant and important to consumers and to ensure that key variables were not overlooked.
On the far right-hand side of the conceptual model, we show a traditional six-step adoption process that begins with awareness and leads to commitment to illustrate how our research relates to the process of innovation adoption and commitment (Rogers 1995). However, we focus our empirical research specifically on actual trial behavior as the focal dependent variable. The focus on trial is motivated by companies' experiences, indicating that a key barrier in consumer adoption of new technologies is getting customers to actually try the SST for the first time. Consider the airline self-check-in SST as an illustration. Such SSTs provide significant time savings for customers, yet many travelers who are unsure of the system or its potential benefits often do not use them. Similar outcomes have been observed across industries.
In the model, we divide predictors of trial into mediating variables (consumer readiness) and antecedent predictors (innovation characteristics and individual differences). A contribution of the model is the establishment of the set of consumer readiness variables as mediators between the antecedent variables and trial. To conclude mediation, several sets of relationships must be present (Baron and Kenny 1986). First, there must be a direct effect of the consumer readiness variables on trial. Second, there must be a direct effect of the antecedent predictors on consumer readiness. Third, there must be a direct effect of the antecedent predictors on trial that is lessened by inclusion of the consumer readiness variables. We discuss the literature and theory that supports each of the requisite sets in the following section.
Consumer readiness is a condition or state in which a consumer is prepared and likely to use an innovation for the first time. We conceptualize consumer readiness as role clarity, motivation, and ability. Role clarity reflects the consumer's knowledge and understanding of what to do, motivation refers to a desire to receive the rewards associated with using the SST, and ability relates to possessing the required skills and confidence to complete the task. We adapted the constructs from a framework to improve employees' performance from the human resource and industrial psychology literature streams (Bowen 1986; Schneider and Bowen 1995; Vroom 1964).
Role clarity. Because services are traditionally provided by an employee, using an SST requires a set of new coproduction behaviors for the consumer. A study found that 89% of firms reported problems of either staff or customer confusion (reduced role clarity) in relation to new services or products (Easingwood 1986). Participation can be constrained by insufficient clarity in terms of a consumer's understanding of his or her role in the service process ( Larsson and Bowen 1989). Potential users of an SST who do not understand what to do are unlikely to try the SST. Thus, we expect to find a significant, direct relationship between role clarity and trial.
Motivation. Because consumers may have a choice between interpersonal and SST delivery options, they must be sufficiently motivated to produce a service independently. Motivation as a key predictor of usage of technology-based products and services is theoretically well supported in the literature (Barczak, Ellen, and Pilling 1997). The willingness to perform has been shown to be dependent on motivational levels for both employees and customers in the production of services (Larsson and Bowen 1989; Vroom 1964).
We expect both intrinsic and extrinsic rewards to be important in influencing the likelihood of SST trial. Some consumers may prefer an active role in the production of a service because they find participation to be intrinsically attractive (Bateson 1985; Dabholkar 1996; Schneider and Bowen 1995). Feelings of accomplishment, prestige, personal growth, or mere pleasure from engaging in the activity are intrinsic motivational factors that are related to the use of SSTs (Becker 1970; Rogers 1995). Consumers are also motivated by their own self-interests, emphasizing the role of extrinsic motivation (Schneider and Bowen 1995). Users have been found to be motivated by a price discount, time savings, or other extrinsic advantages (Dabholkar 1996). Without motivation to perform, it is unlikely that a customer will use an SST. Thus, we expect that both intrinsic and extrinsic motivation have a significant, direct effect on trial.
Ability. Ability relates to having the necessary skills and confidence required to perform a task (Ellen, Bearden, and Sharma 1991; Jayanti and Burns 1998; Jones 1986). Ability refers to what a person "can do" rather than what he or she "wants to do" or "knows how to do." Self-efficacy research has shown that competent behavior in a situation requires both specific skills and beliefs of self-efficacy. Low self-efficacy is more likely with complex tasks, but even relatively simple tasks have been shown to create feelings of inability (Ellen, Bearden, and Sharma 1991). It has also been proposed that perceived confidence in the ability to engage in a task influences behavior within computer-mediated environments (Hoffman and Novak 1996). In general, self-efficacy has been shown to be a strong predictor of behavior (Maddux, Norton, and Stoltenbert 1986). When people believe that they are incapable of performing a task, they will not engage in the behavior, even if they acknowledge that it is a better alternative (Seltzer 1983). Thus, we anticipate that there is a significant, positive relationship between ability and trial.
Another important set of relationships illustrated in the conceptual model is the influence of the antecedent predictors on consumer readiness. We explore two sets of antecedent variables: innovation characteristics and individual differences. To conclude that consumer readiness mediates their effect on trial, there must be a direct relationship between the antecedent predictors and the consumer readiness variables.
Innovation characteristics. The innovation characteristics that we explore are compatibility, relative advantage, complexity, observability, trialability, and perceived risk. They are commonly tested in the adoption literature (Rogers 1995) and thus have well-developed measures from which to draw (Moore and Benbasat 1991). Theoretical and empirical justification for the direct influence of each of the antecedent variables on consumer readiness appears in Table 1. Overall, we expect that relative advantage, observability, trialability, and compatibility have a positive effect on the consumer readiness variables. We expect that complexity and perceived risk have a negative effect on consumer readiness variables.
Individual differences. The individual differences that we include are inertia, technology anxiety, need for interaction, previous experience with related SSTs, and demographic characteristics. We include demographic characteristics and previous experience because of their pervasive nature in previous adoption studies (Rogers 1995). We include need for interaction, inertia, and technology anxiety because of their presence in recent services technology research (Dabholkar 1996, 2000; Parasuraman 2000) and their relevance to the technology-based service delivery context. Theoretical and empirical justification relating each of the individual differences to the consumer readiness variables appears in Table 1. Overall, we expect a positive relationship between previous experience and the consumer readiness variables. However, we expect that need for interaction, inertia, and technology anxiety have negative effects on consumer readiness. We consider demographic variables such as age, income, education level, and sex. We expect that higher-income, highly educated consumers have greater consumer readiness. The effects of age and sex are less clear, though there is a belief that younger people and males are more likely to have higher levels of role clarity, motivation, and ability with respect to technology innovations than are older people and females.
Without a direct effect of an antecedent predictor on trial, mediation by the consumer readiness variables is not possible. Justification for the direct influence of each of the antecedent variables on trial appears in Table 1. Because the variables have been tested in prior studies (albeit with mixed results), we do not discuss the relationships at length here. We expect that relative advantage, observability, trialability, and compatibility are positively related to trial, whereas complexity and perceived risk have a negative effect on trial. Consistent with previous research, we expect that inertia, technology anxiety, and need for interaction have a negative effect on trial, whereas previous experience with related technologies has a positive effect on trial. We also expect that consumers who are more educated, have a higher income, are younger, and are male are more likely to try the SST.
As we conceptualize in Figure 1 and have developed in the previous discussion of important relationships, the model indicates that consumer readiness variables mediate the relationships among the innovation characteristics, individual differences, and the likelihood of trial. That is, we believe that the consumer readiness variables provide a concise set of variables that can explain why the direct effects of the antecedent predictors occur and why we may observe inconsistent directionality and magnitude of effects for the antecedent predictor variables. On the basis of the conceptualization of the model, the literature we reviewed, and the previous discussion, we propose the following mediating hypothesis:
H1: Role clarity, extrinsic motivation, intrinsic motivation, and ability mediate the relationship (a) between the individual difference variables and the likelihood of trial and (b) between the innovation characteristic variables and the likelihood of trial.
In addition to the mediating effects of the consumer readiness variables, it is important to explore the relative strength of the various sets of predictors of trial. Although we propose the consumer readiness variables as mediators, it is also important to know whether the variables are more effective than traditional antecedents in the direct prediction of trial. We expect that the small set of consumer readiness variables may be more stable across contexts, because previous research shows that these factors consistently drive human behavior (Bowen 1986; Schneider and Bowen 1995; Vroom 1964). A key value of the constructs is to provide managers with a consistent and concise set of actionable variables that influence trial. It is our belief that, overall, the consumer readiness variables are more robust predictors. Thus, we provide the following hypothesis:
H2: The consumer readiness variables are better predictors of trial than are the innovation characteristic or individual difference variables.
Methodology, Procedure, and Analysis
To test the conceptual model empirically, we conducted two studies, each of which focused on a new SST. The replication across two SSTs provides a strong test of the model and hypotheses.
We selected a context with three traits. First, it was important to allow consumers to have a choice between the SST and non-technology-based delivery options. Second, it was necessary to identify a group of consumers that had used the SST and a group that had never tried it. Third, it was important for the SST to be a newly implemented delivery option to maintain recency in the consumer decision of whether to try the SST. A national company that satisfies the three criteria was selected as a partner. The context is consumers' prescription refill ordering through a mail-order pharmacy. Customers typically order a three-month supply of their prescription medication (insurance industry restrictions allow orders only every 90 days). All respondents are customers who have ordered a prescription refill through the mail-order pharmacy and who were confronted with the choice of using an SST or other ordering options.
Most customers use the mail-order pharmacy to order prescription refills for illnesses that require continual medication (e.g., diabetes). Therefore, customers are regularly (every 90 days) faced with the decision of ordering through the SST or not. Prescription refill requests can be filled through non-SST alternatives (speaking with a live customer service representative or mailing a refill request) or one of the SST alternatives (an interactive voice response [IVR] telephone system or an Internet-based system). Study 1 explores the IVR telephone system, which is fully automated so that the customer does not talk to a company representative. Study 2 explores the Internet-based SST, which is also fully automated through the company's Web site and has no live support from employees. There is no financial cost difference for customers between the ordering options, and they can select either option. Company representatives estimated that at the times of data collection, a majority of the firm's customers had not yet tried the SST. At the time of the IVR study, the Internet ordering option was not yet available.
We developed a self-administered cross-sectional survey to explore the variables in the conceptual model. We used a multistep process to develop the survey instrument. We used existing scales for all measures except previous experience and perceived risk. The survey was reviewed by 14 employees of the sponsoring firm, and after necessary changes were made, we pretested the survey instrument with a convenience sample of 21 participants to assess its clarity. The instrument was then administered to a small group of mail-order pharmacy customers for further insight. During this process, wording was adapted as needed, and ambiguous questions were clarified or deleted. Final items included in the survey and their sources appear in the Appendix.
We assessed all multiple-item measures on seven-point Likert scales with the endpoints "strongly disagree" and "strongly agree." Trial, the key dependent variable, was assessed with a single-item question that indicated whether the customer had used the SST before. To verify the validity of the measures, we created a measurement model and tested it with the CALIS procedure in SAS using data from Study 1. We tested all four consumer readiness variables, six innovation characteristic variables, and four individual difference variables. As a result of this process, we dropped one item from the original measure for ability, observability, trialability, perceived risk, and need for interaction. The measurement model fit was acceptable: chi-square = 2286, degrees of freedom = 690 (chi-square/degrees of freedom = 3.3); comparative fit index = .9452; nonnormed fit index = .9349; and root mean square error of approximation = .0555 (Hair et al. 1998).
We also conducted tests to assess the reliability and validity of the factors and their indicators. The alpha values for the latent constructs were sufficiently high (see the Appendix), and most of the variance extracted values are greater than .70 (Hair et al. 1998). We established convergent validity by examining the t-tests for factor loadings, and all are significant (p < .0001). We established discriminant validity using the confidence interval test and the variance extracted test (Hatcher 1994). In addition, a correlation matrix with means, standard deviations, reliabilities, and correlations among variables appears in Table 2. On the basis of the overall pattern of positive results, we are confident in the revised measurement model and the measures. To maintain consistency in Study 2, we used purified measures from Study 1.( n1)
To assess the hypotheses empirically, we analyzed a series of multiple regression and logistic regression models. We did not use simultaneous path analysis, because the key dependent variable, trial, is a discrete variable with no continuous latent variable underlying the construct. Path analysis assumes multivariate normality in the dependent variable, which is not the case here. In addition, the large number of constructs that we explore exceeds the recommended ratio of number of indicators to sample size for path analysis (Hair et al. 1998; Hatcher 1994). Therefore, a series of regressions and logistic regression models provide a more effective analysis approach to test the hypotheses.
To test for mediation, we used a four-step process (Baron and Kenny 1986). The first step is to ensure that the selected mediator has a significant influence on trial. The second step is to assess the impact of the antecedent predictors on trial. The third step is to regress the antecedent predictors on the selected mediator variable. The fourth step is to assess the influence of the selected mediator with the antecedent predictors on trial. In the fourth step, the influence of the antecedent predictors (established in Step 2) must be lessened when they are modeled with the selected mediator variable (in Step 4). We used logistic regression for Steps 1, 2, and 4 in which trial is the dependent variable, and we used multiple regression for Step 3.
Complete mediation is rarely observed with behavioral data; therefore, partial mediation is a more realistic expectation. Complete mediation occurs when the inclusion of the selected mediator variable (in Step 4) eliminates any significant influence of the antecedent predictor on trial. Partial mediation occurs when the inclusion of a mediator variable (in Step 4) reduces the significance of the influence of the antecedent predictors from Step 2 (Baron and Kenny 1986). To determine a reduction of the influence of the antecedent predictors between Steps 2 and 4, we examined changes in the beta coefficients and p -values. An implication of this data analysis approach is that comparisons across the steps in the process require the testing of individual mediators (Baron and Kenny 1986; Ocker and Morand 2002). Thus, we conducted this four-step process eight separate times to test for all possible mediating effects in Study 1. We tested each of the consumer readiness variables individually as a mediator between the individual differences variables and trial, followed by an assessment of each readiness variable as a mediator between the innovation characteristics and trial.
Study 1 Results
Study 1 explores customer trial of the IVR telephone-based SST. At the time of data collection, the Internet-based SST was not available, and the IVR system had been in place for less than one year. More than 60,000 customers who had recently placed orders using the IVR system were identified, and 800 were randomly selected for the study. At the same time, more than 60,000 customers who had recently placed orders but did not use the IVR system were identified, and 1200 were randomly selected for the study.( n2) A total of 2000 surveys were mailed, and 406 users and 499 nonusers returned the survey. Of these, 77 surveys were unusable, resulting in a total of 828 usable responses, for an overall response rate of 41% (828/2000). More women (57%) responded to the survey than men (43%), and ages ranged from 21 to 94 years, with an average age of 56 years. Approximately 75% of the respondents were between 40 and 69 years of age. The most common educational category was "some college education" (28%); however, a significant percentage (20%) had graduate degrees. Income was distributed normally, except for a large group (25%) that had an income of more than $90,000 per year.
The first step in the test for mediation determines whether the proposed mediator (role clarity, motivation, or ability) has a significant, direct effect on trial. We tested each of the mediating variables individually using logistic regression, and all had a significant, positive influence on trial. All were significant (p < .0001), with strong classification-accuracy statistics. The classification accuracy for the role clarity mediator is 86%, indicating that 86% of the sample was correctly classified into the trial (or no-trial) group on the basis of the role clarity score. Classification-accuracy measures for extrinsic and intrinsic motivation and ability were 73%, 67%, and 78%, respectively.
We also analyzed results using an independent-samples t-test to determine whether the means for each of the consumer readiness variables are significantly different between the trial and no-trial groups. The mean scores for role clarity were 6.4 and 3.6 for triers and nontriers, respectively (t-value = 25.8, p < .0001). Mean scores for ability were 6.7 and 4.8 for triers and nontriers, respectively (t value = 15.5, p < .0001). The trial group mean score for extrinsic motivation was 3.2, and the nontrial group mean score was 1.9 (t-value = 16.1, p < .0001).( n3) Mean scores for intrinsic motivation were 2.3 and 1.1 for triers and nontriers, respectively (t-value = 11.1, p < .0001). The values show that participants in the trial group had significantly higher levels of role clarity, motivation (both intrinsic and extrinsic), and ability. The high p -values, strong classification-accuracy scores, and significant t-tests satisfy the first step in the test for mediation.
To assess the mediating power of the consumer readiness variables, we completed the remaining three steps in the test for mediation. Because of space limitations, it is impractical to discuss in detail the results from all eight multistep tests for mediation in Study 1.( n4) Table 3 provides a summary of the findings for the eight tests for mediation in Study 1, showing the key comparisons between Step 2 and Step 4. For example, in the first test (role clarity as a mediator between the individual difference variables and trial), technology anxiety, need for interaction, and previous experience all had a significant, direct effect on trial in Step 2. However, in Step 4 (when the individual difference variables were modeled with role clarity), the effect became nonsignificant, indicating that role clarity completely mediates the main effects of technology anxiety, need for interaction, and previous experience on trial. In addition, the influence of inertia and age on trial went from highly significant (both at p < .0001) to a much less powerful influence (p = .04 for inertia, and p = .003 for age). The results indicate that role clarity partially mediates the relationship between inertia and age with trial.
The test for mediation for each antecedent predictor could fail in Steps 2, 3, or 4. Antecedent predictors that failed to have a significant influence in either Step 2 or Step 3 were discontinued from the mediation analysis (shown as a dash in the Step 4 column of Table 3). Failures in Step 2 indicate that the antecedent predictor did not have a direct effect on trial and thus could not be mediated by the consumer readiness variables. The failures to mediate are not due to limitations of the consumer readiness variables but rather to the weaknesses of the antecedent predictors. Sex and education were two individual difference variables that failed in Step 2.
The failures to mediate that are due to limitations in the consumer readiness variables are failures to mediate in either Step 3 or Step 4. Failures in Step 3 indicate that the antecedent predictor did not have a direct effect on the consumer readiness variable, and thus the consumer readiness variable cannot be a mediator. Failures in Step 4 indicate that when the antecedent predictor is modeled with the mediator, the influence of the antecedent predictor is not lessened. This indicates that the consumer readiness variable is not a mediator for that particular variable.
Overall, the pattern of results supports the conclusion that the consumer readiness variables mediate the effects of individual differences on trial. Of the eight individual difference antecedent predictors, only two were not mediated by the consumer readiness variables, and the failures were due to the inability of sex and education to show a direct link to trial. Of the six remaining variables, all were mediated by at least one of the consumer readiness variables. Need for interaction was mediated by all four readiness variables, whereas technology anxiety and age were mediated by three of the four readiness variables. In summary, all individual difference antecedent predictors that effect trial were mediated by the set of consumer readiness variables. The results provide support for H1a.
With the innovation characteristics, a similar pattern emerges. Two of the antecedent predictors (complexity and observability) did not have a direct effect on trial and thus could not be mediated. Of the remaining four variables, three are mediated by at least two of the readiness variables. Relative advantage was not mediated by any of the consumer readiness variables. Compatibility was mediated by three of the four readiness variables, and perceived risk and trialability were mediated by two of the readiness variables. Therefore, the only innovation characteristic not mediated by the readiness variables was relative advantage. The overall pattern of results also provides support for H1b.
It is also insightful to explore the relative mediating power of the consumer readiness variables. In Study 1, it appears that role clarity and ability were the strongest mediators, both mediating five of the six individual difference antecedent predictors that were eligible for mediation. Extrinsic and intrinsic motivation are important mediators, though they mediated only three and two (respectively) of the six individual difference antecedent predictors that were eligible for mediation. The same pattern held when we evaluated the relative mediating power of the consumer readiness variables on the innovation characteristic antecedent predictors. Role clarity and ability mediated three of the four eligible antecedent predictors, and extrinsic and intrinsic motivation mediated one and none of the antecedent predictors, respectively.
Study 2 Results
Replicating Study 1 with a different SST helps determine the strength of the results and provides a further test of the model. In the replication study, the SST of interest was an Internet ordering system for prescription refills that was offered by the same company. The company implemented the Internet ordering system several months after the completion of Study 1, and the system had been in place less than six months at the time of data collection. We collected data from current mail-order pharmacy customers who had the option of using the Internet SST or placing an order through other means (i.e., mail, IVR SST, or a customer service representative on the telephone).
For Study 2, we used the same sampling procedure as that in Study 1. Of the 2000 surveys mailed, 401 usable surveys were returned from SST users, and 333 usable surveys were returned from SST nonusers. The overall response rate in the Study 2 was 37% (734/2000), and sample demographics closely matched those in Study 1.
The first step in the test for mediation shows that role clarity, motivation (both extrinsic and intrinsic), and ability all have significant, direct effects on trial. All were tested individually with logistic regression and were highly significant (p < .0001), with strong classification-accuracy scores. Classification-accuracy scores were 92% for role clarity, 78% for extrinsic motivation, 72% for intrinsic motivation, and 83% for ability.
We also analyzed results using an independent-samples t-test to determine whether the means for each of the consumer readiness variables are significantly different between the trial and no-trial groups, as they were in Study 1. Mean scores for role clarity are 6.4 and 2.9 for triers and nontriers, respectively (t-value = 33.6, p < .0001). Mean scores for ability are 6.6 and 4.1 for triers and nontriers, respectively (t-value = 19.8, p < .0001). The trial group mean score for extrinsic motivation is 3.3, and the nontrial group mean score is 1.7 (t-value = 19.4, p < .0001). Mean scores for intrinsic motivation are 2.5 and 1.2 for triers and nontriers, respectively (t-value = 11.4, p < .0001). The high p -values, strong classification-accuracy scores, and significant t-tests verify the link between the consumer readiness variables and trial that we established in Study 1.
To confirm the mediating power of the consumer readiness variables that we established in Study 1, we completed the remaining three steps in the test for mediation. Table 4 provides a summary of the findings for the eight tests for mediation in Study 2, showing the key comparison between Steps 2 and 4. The individual difference variables of technology anxiety and education did not have a direct effect on trial. The variables could not be mediated because of their failure in Step 2. As with Study 1, there were a handful of mediation failures in Steps 3 and 4.
As in Study 1, the positive pattern of results supports the mediating role of the consumer readiness variables. Of the eight individual difference antecedent predictors, only two (technology anxiety and education) were not mediated by at least one consumer readiness variable, and the failures were due to the inability of the antecedent predictor to significantly influence trial. Of the six remaining variables that were eligible for mediation, all were mediated by at least one of the readiness variables. Need for interaction, previous experience, age, and income were mediated by all four readiness variables, and sex was mediated by three of the four. Therefore, as in Study 1, all individual difference variables that had a direct effect on trial were meditated by the set of consumer readiness variables. The results provide support for H1a.
We found similar results in the replication of the mediation of the innovation characteristics. Trialability and observability did not have a direct effect on trial and thus could not be mediated. Of the remaining four variables, all were mediated by at least one of the readiness variables. Therefore, all innovation characteristic variables that had a direct effect on trial were mediated by the set of consumer readiness variables. However, as in Study 1, intrinsic motivation failed to mediate any of the innovation characteristic variables. The overall pattern of results provides support for H1b.
It is also insightful to explore the relative mediating power of the consumer readiness variables. In Study 2, role clarity was again the strongest mediator, mediating all six of the eligible individual difference variables. The other three readiness variables were also strong mediators; intrinsic motivation and ability mediated five of the six individual difference variables, and extrinsic motivation mediated four. With respect to innovation characteristics, role clarity again mediated the most antecedent predictors, mediating three of the remaining four innovation characteristic antecedent predictors. Extrinsic motivation mediated two, and ability mediated one innovation characteristic.
Relative Importance of Consumer Readiness
With the consumer readiness variables established as mediators across two studies, it is valuable to explore the overall effectiveness and relative strength of the consumer readiness variables as a group. Although we conducted the test for mediation one consumer readiness mediator at a time (because of methodological considerations), the group of predictors can be regressed on trial when they are separated from the test for mediation. Across both studies, the consumer readiness variables, when taken as a group, generated high-classification-accuracy statistics. The classification scores were 86% and 93% for IVR and Internet studies, respectively (see Table 5). The high scores indicate that 86% (93%) of the respondents in the IVR (Internet) study were correctly classified into the trial or no-trial groups on the basis of their consumer readiness variable scores. The regression results also help determine the relative strength of the consumer readiness variables. In both studies, the other consumer readiness variables in the model overwhelmed the impact of ability (see Table 5). Intrinsic motivation was only marginally significant (p = .09) in the IVR study, and it was nonsignificant in the Internet-based study. Thus, role clarity and extrinsic motivation were the two strongest consumer readiness predictors.
In addition to exploring the relative strength of the consumer readiness variables, it is illuminating to compare the three sets of predictors that we have explored: consumer readiness, innovation characteristics, and individual differences. As Table 5 shows, the consumer readiness set of predictors generated a higher classification-accuracy score than did either of the other sets of variables across both studies. Based on the classification-accuracy scores, the consumer readiness variables are the best set of predictors, followed by the innovation characteristics, and finally the individual difference variables. The consistency of results across the two studies further strengthens the findings. Overall, this comparison of predictor models provides support for H2.
Discussion
The findings from both studies support the proposed model. The direct effects of the consumer readiness variables on trial were significant across both studies. Most important, the mediating effects (H1) of the consumer readiness variables were replicated across two studies that focused on different SSTs. In Study 1, of the ten antecedent predictors that had a direct effect on trial, only one (relative advantage) was not mediated by any of the consumer readiness variables. In Study 2, all ten antecedent predictors that had a direct effect on trial were mediated by at least one of the consumer readiness variables. Figure 2 summarizes the mediating effects found in Studies 1 and 2. The findings support the central role of the consumer readiness variables as key mediators to better understand when and why consumer trial occurs. Examining the strength of consumer readiness variables as predictors of trial in comparison with that of the antecedent predictors provides further reinforcement for the importance of the consumer readiness variables. In Studies 1 and 2, the consumer readiness variables were stronger predictors of trial than were either the set of innovation characteristics or individual differences, thus providing support for H2.
Although all consumer readiness variables are important, the mediation results (H1) and the predictive comparisons (H2) suggest that role clarity and extrinsic motivation are the dominant consumer readiness variables in the prediction of trial for this context. Indeed, although ability mediated several antecedent predictors in each study, its direct influence on trial was overwhelmed by the stronger effects of role clarity and extrinsic motivation when all the factors were modeled together to predict trial. Similarly, intrinsic motivation was only marginally significant in the prediction of trial when all consumer readiness variables were tested.
The model and results contribute significantly to our theoretical understanding of the factors that influence consumer trial. The traditional adoption model variables and attitudes explored in previous research are not disputed. However, the added explanatory power of the consumer readiness variables and their role as mediators are significant. In practice, consumers may evaluate a new product or service positively, yet they may choose not to try it. Our results suggest that lack of "consumer readiness" can explain much of this failure to try. That is, even customers who have a positive evaluation of an innovative service may choose not to use it if they do not understand their role (role clarity), if they perceive no clear benefit to using it (motivation), or if they believe that they are not able to use it (ability).
Our findings suggest that the consumer readiness variables are not only additional predictors but also key factors with strong mediating properties. The mediating role of the consumer readiness variables also provides a partial answer to the "why" question with respect to several innovation characteristics and individual difference variables that have been tested in previous research. For example, extensive literature concludes that as experience with related technologies increases, the chance of adopting a new technology also increases. The consumer readiness mediators help explain why this relationship exists. In Study 2, it is not merely that increased experience with Internet-based tools leads to a greater likelihood of trial but also that increased experience leads to higher levels of role clarity, motivation (both extrinsic and intrinsic), and ability relative to the Internet ordering system, which increases the likelihood of trial.
Even more important are situations in which data might be misinterpreted without including the mediating variables. For example, in Study 1, complexity did not have a direct effect on trial. If the mediators are not included, the natural conclusion is that complexity does not influence trial behavior. However, we found that complexity has a significant, negative influence on role clarity (-.23, p < .0001), extrinsic motivation (-.10, p < .004), and ability (-.28, p < .0001), which in turn decreases the likelihood of trial. Although this is not technically a mediated relationship, complexity is nonetheless an important factor to consider because of its influence on the consumer readiness variables. We found a similar result with observability in Study 1 and with trialability, observability, and technology anxiety in Study 2
Another contribution of this study is that it establishes a more concise set of constructs as better predictors of trial. The key consumer readiness variables show more consistency in their influence on trial across two different technologies as well as higher classification-accuracy scores than do traditional innovation characteristics and individual difference variables. Our model and results suggest that the relevant variables for increasing trial are those that increase consumer readiness.
For many firms, often the challenge is not managing the technology but rather getting consumers to try the technology. The findings should be useful to firms that are considering SST implementation as well as those that are struggling with the management of existing SSTs. This is especially relevant given a study that Forrester Research conducted, which shows that 41% of the firms surveyed observe no return on their self-service investments (Zurek et al. 2001). By establishing the consumer readiness variables as key mediators, we provide an actionable set of factors to help firms understand and influence SST trial behavior, a key driver of SST success. Managers can use tactical strategies to influence role clarity, motivation, and ability either before or after an SST has been introduced.
Management can take several steps to influence the actionable consumer readiness variables directly (Bitner, Ostrom, and Meuter 2002). For example, education and training, in the form of detailed, customer-friendly instructions or aids, are important in influencing role clarity. Contextually relevant education aids, such as wallet cards, magnets, and mouse pads with instructions (for SSTs used from home) or posters showing the steps to use the SST (for SSTs in remote locations), could be used to build role clarity and perceptions of ability. In addition, considerable "hand holding" should be readily available in accessing and using the SST. For example, if the SST is available on the Internet, management could consider a robust "first-time user" area and provide detailed instructions and frequently asked questions, a toll-free telephone number, and online help such as live text chat.
Motivation is another actionable consumer readiness variable that drives SST trial. To encourage trial, firms must clearly communicate valued customer benefits of an SST. For example, some consumers find appeal in SSTs that save them time or money, whereas others are attracted to the extended availability or easier access. To provide added motivation for potential first-time users, firms should give consumers the opportunity to try the SST with no obligations. When technologically feasible, offering the opportunity to interact with and learn from other consumers may also be appealing to consumers and increase their motivation to try.
This research also contributes to the understanding of variables that underlie effective customer coproduction. To be truly customer centric, firms need to strengthen the effectiveness of their customers as coproducers and cocreators of value (Vargo and Lusch 2004). Applying employee-management practices to customers can lead to effective coproduction by increasing role clarity, motivation, and ability of customers. The coproduction framework provides a lens through which firms can develop, adjust, and evaluate their operational procedures, technology friendliness, human resource practices, and performance criteria (Bettencourt et al. 2002). Effective coproduction can increase the likelihood of product or service success and customer satisfaction and can present a competitive opportunity for firms.
Limitations and Future Research Directions
As with any research, there are limitations associated with the studies. First, we use cross-sectional data rather than a longitudinal study. Time and cost constraints limited the feasibility of such an approach. Second, there is limited generalizability to other contexts, because we conducted this research within one organizational context. Additional studies in more diverse industries with other consumer groups should be conducted to provide additional support and increase the generalizability of the findings.
Beyond addressing the limitations, this research suggests opportunities for further research. A central question that remains unanswered is, What are the key drivers of role clarity, the most influential mediator in the set of consumer readiness variables? The antecedent predictors-need for interaction, previous experience, perceived risk, and complexity--appear to be the most consistent predictors of role clarity, so further research can explore the antecedents in more detail. In addition, prior research investigating the role clarity of employees in a work setting suggests that the nature of socialization activities (i.e.., their content, context, and social aspects) and the feedback provided can affect role clarity perceptions (Anakwe and Greenhaus 1999; Bettencourt and Brown 2003). In the context of health care compliance, it was shown that provider characteristics influence role clarity (Dellande, Gilly, and Graham 2004). Thus, further research could investigate provider characteristics, the socialization of consumers, and the role of feedback for various customer groups and SSTs.
The conceptual model provides a framework for additional research. Although trial was the central dependent variable in this study, any of the other steps in the adoption process could be explored in detail. For example, the critical factors that influence commitment to SSTs or those that influence the investigation or evaluation steps that precede trial could be developed and tested. Along these lines, research could assess the differential influence of the consumer readiness variables across the stages in the adoption process.
Further research could also explore how SST usage influences consumer loyalty and, ultimately, revenue and profitability. Despite the increase in SSTs that firms are offering, scholars are just beginning to learn about how the absence of human interaction affects the bond between consumers and firms (Selnes and Hansen 2001). It is important to understand the long-term implications of shifting customers away from interpersonal interactions, which are traditionally viewed as important elements for establishing trust and loyalty in service contexts. Finally, additional research could extend the study of coproduction beyond SSTs to other contexts. This rich area of inquiry would benefit from studies in multiple contexts to determine what relevant antecedents increase consumer readiness and the differential influence of role clarity, motivation, and ability on trial in other high-customer-participation settings.
The authors thank the Center for Services Leadership at Arizona State University for its support in securing a research partner for the study. The authors extend their gratitude to the sponsoring firm for its involvement in the project and to the three anonymous JM reviewers for their insights throughout the review process.
( n1) Full details on the measurement model are available in a technical appendix, which can be acquired from the lead author.
( n2) We selected more IVR nonusers to compensate for the possibility that some participants who identified with the nonuser group had actually used the IVR SST between the time they identified as a nonuser and the completion of the survey. We did this in an attempt to generate roughly equal sample sizes for the SST user and nonuser groups.
( n3) Whereas all Likert scale measures used a 1-7 scale, extrinsic motivation uses a 0-4 scale, and intrinsic motivation uses a 0-5 scale. The expectancy theory's conceptualization of motivation uses a multiplicative function to generate a motivation score, which was significantly out of alignment with other measures and was recalibrated (by dividing the motivation scores by 100). This has no impact on the data analysis other than bringing the motivation measures into alignment with the other scales.
( n4) Full details are provided in a technical appendix, which is available from the lead author.
Legend for Chart:
A - Antecedent Predictors
B - Dependent Variable: Consumer Readiness Variables
Justification
C - Dependent Variable: Consumer Readiness Variables Supporting
Literature
D - Dependent Variable: Trial Justification
E - Dependent Variable: Trial Supporting Literature
A B
C
D
E
Compatibility Compatibility will increase motivation
because the SST will be consistent
with values and lifestyle.
This may also influence the willingness
to learn about the SST, thus
increasing role clarity.
Eastlick (1996),
Gatignon and
Robertson
(1991)
Increased compatibility
with personal
values and lifestyle
increases the odds
of a customer trying
the SST.
Eastlick (1996),
Gatignon and Robertson
(1991), Labay and
Kinnear (1981), Moore
and Benbasat (1991),
Rogers (1995)
Relative Relative advantage will encourage
advantage customers to learn about the SST,
positively influencing both role
clarity and ability. The advantages
also provide a motivational force by
providing incentives or perceived
rewards.
Eastlick (1996),
Gatignon and
Robertson
(1991)
Because the SST is
perceived as better
than an alternative,
it is more likely to be
tried.
Eastlick (1996),
Gatignon and Robertson
(1991), Labay and
Kinnear (1981), Moore
and Benbasat (1991),
Rogers (1995)
Complexity A complicated, confusing SST will
hinder role clarity and ability
because it will be more difficult to
operate and understand and may
also make the benefits (motivation)
less apparent to the user.
Eastlick (1996),
Gatignon and
Robertson
(1991)
If an SST is perceived
as more
complicated or confusing,
a customer
will be less likely to
try the SST.
Eastlick (1996),
Gatignon and Robertson
(1991), Labay and
Kinnear (1981), Moore
and Benbasat (1991),
Rogers (1995)
Observability Observability helps clarify the role
of the consumer, increase feelings
of confidence, and show positive
outputs to increase motivation.
Eastlick (1996),
Gatignon and
Robertson
(1991)
The ability to
observe and communicate
with others
about the SST
increases the
chances that it will
be tried.
Eastlick (1996),
Gatignon and Robertson
(1991), Labay and
Kinnear (1981), Moore
and Benbasat (1991),
Rogers (1995)
Trialability Trialability enables users to
observe how the SST works, allowing
them to recognize the benefits,
understand their role, and have
confidence in their abilities.
Eastlick (1996),
Gatignon and
Robertson
(1991)
The ability to test
the SST increases
chances that it will
be tried.
Eastlick (1996),
Gatignon and Robertson
(1991), Labay and
Kinnear (1981), Moore
and Benbasat (1991),
Rogers (1995)
Perceived risk As perceived risk increases, the
likelihood of rewards decreases,
reducing motivation to use an SST
and hindering feelings of ability and
desire to learn about the SST.
Ellen, Bearden,
and Sharma
(1991)
As perceived risk
increases, the
likelihood of trial
decreases.
Aaker (1991), Gwinner,
Gremler, and Bitner
(1998), Ostlund (1974),
Venkatraman (1991)
Inertia Inertia may limit efforts to learn
about SSTs (role clarity and ability).
Using a new SST also requires an
investment in time and energy,
thus reducing motivation.
Gremler
(1995),
Olshavsky and
Spreng (1996)
Inertia inhibits
changes in behavior
and thus results in
hesitancy to try new
service delivery
options.
Aaker (1991), Gremler
(1995), Heskett,
Sasser, and Hart
(1990)
Technology Technology anxiety may lead to
anxiety confusion regarding the task to be
performed (role clarity), decreased
motivation levels, and a reduced
perception of ability.
Meuter and
Bitner (1997),
Parasuraman
(2000), Raub
(1981), Ray
and Minch
(1990)
High levels of technology
anxiety may
lead to the avoidance
of technological
tools, in this
case SSTs.
Igbaria and Parasuraman
(1989), Meuter
and Bitner (1997),
Parasuraman (2000),
Parasuraman and
Colby (2001), Raub
(1981), Ray and Minch
(1990)
Need for A high need for personal interaction
interaction may lead to decreased interest
in learning how SSTs work (role
clarity and ability) and reduced
motivation to try it.
Dabholkar
(1996),
Langeard et al.
(1981)
A high level of need
for personal interaction
decreases the
desire to try an SST.
Bateson (1985),
Dabholkar (2000),
Langeard et al. (1981),
Meuter et al. (2000)
Previous The previous use of related technology
experience will increase perceptions of
self-confidence and ability and may
also allow for the recognition of
rewards (motivation) and guide
behavior (role clarity).
Bowen (1986),
Gardner,
Dukes, and
Discenza
(1993), Mahajan,
Muller, and
Bass (1990),
Mohr and Bitner
(1991)
Heavy users of
related technologies
are more likely to try
SSTs.
Danko and MacLachlan
(1983), Dickerson and
Gentry (1983), Eastlick
(1996), Gatignon and
Robertson (1991),
Rogers (1995)
Demographics Higher education may lead to confidence
(age, sex, (ability) and the perception
education, of the SST as more understandable
and income) (role clarity) and rewarding
(motivation). Higher income may
increase the chances of access to
the required tools (ability) and the
motivation (time savings, or other)
to use SSTs. Age and sex may
also have similar effects.
Breakwell et al.
(1986), Gist
(1987), Igbaria
and Parasuraman
(1989)
People who adopt
new technologies
tend to be younger,
male, and more educated
and have a
greater income than
those who do not
adopt it.
Danko and MacLachlan
(1983), Darian (1987),
Dickerson and Gentry
(1983), Gatignon and
Robertson (1991),
Greco and Fields
(1991), Labay and
Kinnear (1981), Rogers
(1995), Sim and Koi
(2002), Venkatraman
(1991), Zeithaml and
Gilly (1987) Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
K - 8
L - 9
M - 10
N - 11
O - 12
P - 13
Q - 14
A B C D
E F G H
I J K L
M N O P
Q
1. Role clarity 5.22 1.99 .96
2. Ability 5.99 1.56 .69(**)
.94
3. Intrinsic motivation 1.81 1.61 .28(**)
.30(**) N/A
4. Extrinsic motivation 2.72 1.22 .41(**)
.53(**) .63(**) N/A
5. Inertia 4.18 1.90 -.05
-.07 -.09(*) -.03 .90
6. Technology anxiety 2.32 1.55 -.29(**)
-.33(**) -.10(**) -.29(**) .18(**)
.93
7. Need for interaction 3.90 1.99 -.26(**)
-.28(**) -.15(**) -.40(**) .09(**)
.33(**) .88
8. Previous experience 5.18 1.53 .32(**)
.35(**) .09(*) .29(**) -.13(**)
-.54(**) -.31(**) .72
9. Relative advantage 4.99 1.83 .47(**)
.44(**) .53(**) .74(**) -.00
-.30(**) -.43(**) .29(**) .95
10. Compatibility 5.25 1.92 .50(**)
.54(**) .50(**) .74(**) -.09(**)
-.36(**) -.43(**) .34(**) .81(**)
.95
11. Complexity 2.48 1.47 -.51(**)
-.53(**) -.42(**) -.62(**) .08(*)
.40(**) .45(**) -.36(**) -.70(**)
-.70(**) .83
12. Observability 5.64 1.72 .43(**)
.49(**) .36(**) .55(**) -.13(**)
-.37(**) -.32(**) .37(**) .58(**)
.68(**) -.53(**) .92
13. Trialability 5.27 1.74 .43(**)
.49(**) .43(**) .54(**) -.15(**)
-.28(**) -.30 .27(**) .54(**)
.65(**) -.48(**) .55(**) .81
14. Perceived risk 2.59 1.50 -.49(**)
-.43(**) -.33(**) -.52(**) .12(**)
.44(**) .46(**) -.30(**) -.60(**)
-.59(**) .64(**) -.44(**) -.43(**)
.85
(*) p < .05.
(**) p < .01.
Notes: The figures are based on data from Study 1. Sample sizes
for the table range from 791 to 827 because of missing data. We
report means and standard deviations on the basis of a
seven-point scale (except for intrinsic and extrinsic motivation,
which we recalibrated [dividing by 100] to be in alignment with
the other measures; intrinsic motivation uses a 0-5 scale, and
extrinsic motivation uses a 0-4 scale). Correlation alphas are
reported along the diagonal. The expectancy theory
conceptualization of motivation results in a single motivation
score, thus we do not calculate an alpha. N/A= not applicable. Legend for Chart:
A - Description of Test
B - Significance of Mediator: Step 1
C - Change in Effects of Antecedent Predictors Between Steps
2 and 4 Predictor
D - Change in Effects of Antecedent Predictors Between Steps
2 and 4 Step 2
E - Change in Effects of Antecedent Predictors Between Steps
2 and 4 Step 4
F - Conclusion
A
B
C D
E F
Role clarity as a
mediator of the
relationship
between individual
difference variables
and trial
Role clarity:
1.3 (.0001)
Inertia -.19 (.0001)
-.12 (.04) Partial mediation
Technology anxiety -.13 (.01)
n.s. Completely mediates main effects(a)
Need for interaction -.18 (.0001)
n.s. Completely mediates main effects(a)
Previous experience .15 (.002)
n.s. Completely mediates main effects(a)
Age -.19 (.0001)
-.03 (.003) Partial mediation(a)
Income .12 (.02)
-- No mediation, failed in Step 3
Sex n.s.
-- No direct effect on trial
Education n.s.
-- No direct effect on trial
Extrinsic motivation as
a mediator of the
relationship
between individual
difference variables
and trial
Extrinsic
motivation:
1.0 (.0001)
Inertia -.19 (.0001)
-- No mediation, failed in Step 3
Technology anxiety -.13 (.01)
n.s. Completely mediates main effects(a)
Need for interaction -.18 (.0001)
n.s. Completely mediates main effects(a)
Previous experience .15 (.002)
.19 (.001) No mediation, failed in Step 4
Age -.19 (.0001)
n.s. Completely mediates main effects(a)
Income .12 (.02)
-- No mediation, failed in Step 3
Sex n.s.
-- No direct effect on trial
Education n.s.
-- No direct effect on trial
Intrinsic motivation as
a mediator of the
relationship
between individual
difference variables
and trial
Intrinsic
motivation:
.53 (.0001)
Inertia -.19 (.0001)
-.18 (.0002) Partial mediation(a)
Technology anxiety -.13 (.01)
-- No mediation, failed in Step 3
Need for interaction -.18 (.0001)
-.14 (.002) Partial mediation(a)
Previous experience .15 (.002)
.21 (.001) No mediation, failed in Step 4
Age -.19 (.0001)
-- No mediation, failed in Step 3
Income .12 (.02)
.13 (.0004) No mediation, failed in Step 4
Sex n.s.
-- No direct effect on trial
Education n.s.
-- No direct effect on trial
Ability as a mediator
of the relationship
between individual
difference variables
and trial
Ability:
1.3 (.0001)
Inertia -.19 (.0001)
-- No mediation, failed in Step 3
Technology anxiety -.13 (.01)
n.s. Completely mediates main effects(a)
Need for interaction -.18 (.0001)
n.s. Completely mediates main effects(a)
Previous experience .15 (.002)
n.s. Completely mediates main effects(a)
Age -.19 (.0001)
-.02 (.004) Partial mediation(a)
Income .12 (.02)
n.s. Completely mediates main effects(a)
Sex n.s.
-- No direct effect on trial
Education n.s.
-- No direct effect on trial
Role clarity as a
mediator of the
relationship
between innovation
characteristic
variables and trial
Role clarity:
1.3 (.0001)
Perceived risk -.28 (.0001)
n.s. Completely mediates main effects(a)
Relative advantage .32 (.0002)
-- No mediation, failed in Step 3
Complexity n.s.
-- No direct effect on trial
Compatibility .34 (.0002)
.31 (.002) Partial mediation(a)
Trialability .17 (.01)
n.s. Completely mediates main effects(a)
Observability n.s.
-- No direct effect on trial
Extrinsic motivation as
a mediator of the
relationship
between innovation
characteristic
variables and trial
Extrinsic
motivation:
1.0 (.0001)
Perceived risk -.28 (.0001)
-- No mediation, failed in Step 3
Relative advantage .32 (.0002)
.36 (.0001) No mediation, failed in Step 4
Complexity n.s.
-- No direct effect on trial
Compatibility .34 (.0002)
.32 (.0003) Partial mediation(a)
Trialability .17 (.01)
.18 (.01) No mediation, failed in Step 4
Observability n.s.
-- No direct effect on trial
Intrinsic motivation as
a mediator of the
relationship between
innovation
characteristic
variables and trial
Intrinsic
motivation:
.53 (.0001)
Perceived risk -.28 (.0001)
-- No mediation, failed in Step 3
Relative advantage .32 (.0002)
.41 (.0001) No mediation, failed in Step 4
Complexity n.s.
-- No direct effect on trial
Compatibility .34 (.0002)
.37 (.0001) No mediation, failed in Step 4
Trialability .17 (.01)
.20 (.003) No mediation, failed in Step 4
Observability n.s.
-- No direct effect on trial
Ability as a mediator
of the relationship
between innovation
characteristic
variables and trial
Ability:
1.3 (.0001)
Perceived risk -.28 (.0001)
-.22 (.01) Partial mediation(a)
Relative advantage .32 (.0002)
.33 (.0003) No mediation, failed in Step 4
Complexity n.s.
-- No direct effect on trial
Compatibility .34 (.0002)
.22 (.01) Partial mediation(a)
Trialability .17 (.01)
n.s. Completely mediates main effects(a)
Observability n.s.
-- No direct effect on trial
Notes: The numbers shown are maximum likelihood parameter
estimates, and p-values (based on Wald chi-square significance
test) are shown in parentheses. All nonsignificant (n.s.)
relationships indicate a p-value greater than .05. (a) Text
shown in bold indicates a relationship in which the mediator
either completely or partially mediates the relationship
between the antecedent variable and trial. Legend for Chart:
A - Description of Test
B - Significance of Mediator: Step 1
C - Change in Effects of Antecedent Predictors Between Steps
2 and 4 Predictor
D - Change in Effects of Antecedent Predictors Between Steps
2 and 4 Step 2
E - Change in Effects of Antecedent Predictors Between Steps
2 and 4 Step 4
F - Conclusion
A
B
C D
E F
Role clarity as a
mediator of the
relationship between
individual difference
variables and trial
Role clarity:
1.4 (.0001)
Inertia -.20 (.0002)
-.21 (.01) Partial mediation(a)
Technology anxiety n.s.
-- No direct effect on trial
Need for interaction -.28 (.0001)
-.24 (.01) Partial mediation(a)
Previous experience .61 (.0001)
.23 (.02) Partial mediation(a)
Age -.03 (.0002)
n.s. Completely mediates main effects(a)
Income .12 (.0003)
n.s. Completely mediates main effects(a)
Sex -.47 (.007)
n.s. Completely mediates main effects(a)
Education n.s.
-- No direct effect on trial
Extrinsic motivation as
a mediator of the
relationship between
individual difference
variables and trial
Extrinsic
motivation:
1.3 (.0001)
Inertia -.20 (.0002)
-- No mediation, failed in Step 3
Technology anxiety n.s.
-- No direct effect on trial
Need for interaction -.28 (.0001)
n.s. Completely mediates main effects(a)
Previous experience .61 (.0001)
.46 (.0001) Partial mediation(a)
Age -.03 (.0002)
n.s. Completely mediates main effects(a)
Income .12 (.0003)
n.s. Completely mediates main effects(a)
Sex -.47 (.007)
-- No mediation, failed in Step 3
Education n.s.
-- No direct effect on trial
Intrinsic motivation as
a mediator of the
relationship between
individual difference
variables and trial
Intrinsic
motivation:
.58 (.0001)
Inertia -.20 (.0002)
-- No mediation, failed in Step 3
Technology anxiety n.s.
-- No direct effect on trial
Need for interaction -.28 (.0001)
-.23 (.0001) Partial mediation(a)
Previous experience .61 (.0001)
.53 (.0001) Partial mediation(a)
Age -.03 (.0002)
n.s. Completely mediates main effects(a)
Income .12 (.0003)
.10 (.02) Partial mediation(a)
Sex -.47 (.007)
n.s. Completely mediates main effects(a)
Education n.s.
-- No direct effect on trial
Ability as a mediator
of the relationship
between individual
difference variables
and trial
Ability:
1.2 (.0001)
Inertia -.20 (.0002)
-- No mediation, failed in Step 3
Technology anxiety n.s.
-- No direct effect on trial
Need for interaction -.28 (.0001)
-.16 (.01) Partial mediation(a)
Previous experience .61 (.0001)
.33 (.0001) Partial mediation(a)
Age -.03 (.0002)
n.s. Completely mediates main effects(a)
Income .12 (.0003)
n.s. Completely mediates main effects(a)
Sex -.47 (.007)
n.s. Completely mediates main effects(a)
Education n.s.
-- No direct effect on trial
Role clarity as a
mediator of the
relationship between
innovation
characteristic
variables and trial
Role clarity:
1.4 (.0001)
Perceived risk -.34 (.0004)
-.23 (.04) Partial mediation
Relative advantage .42 (.0002)
.49 (.001) No mediation, failed in Step 4
Complexity -.46 (.0001)
n.s. Completely mediates main effects
Compatibility .43 (.0001)
.27 (.07) Partial mediation
Trialability n.s.
-- No direct effect on trial
Observability n.s.
-- No direct effect on trial
Extrinsic motivation as
a mediator of the
relationship between
innovation
characteristic
variables and trial
Extrinsic
motivation:
1.3 (.0001)
Perceived risk -.34 (.0004)
-- No mediation, failed in Step 3
Relative advantage .42 (.0002)
.38 (.001) Partial mediation
Complexity -.46 (.0001)
-.56 (.0001) No mediation, failed in Step 4
Compatibility .43 (.0001)
.39 (.001) Partial mediation
Trialability n.s.
-- No direct effect on trial
Observability n.s.
-- No direct effect on trial
Intrinsic motivation as
a mediator of the
relationship between
innovation
characteristic
variables and trial
Intrinsic
motivation:
.58 (.0001)
Perceived risk -.34 (.0004)
-- No mediation, failed in Step 3
Relative advantage .42 (.0002)
1.1 (.0001) No mediation, failed in Step 4
Complexity -.46 (.0001)
-- No mediation, failed in Step 3
Compatibility .43 (.0001)
-- No mediation, failed in Step 3
Trialability n.s.
-- No direct effect on trial
Observability n.s.
-- No direct effect on trial
Ability as a mediator
of the relationship
between innovation
characteristic
variables and trial
Ability:
1.2 (.0001)
Perceived risk -.34 (.0004)
-- No mediation, failed in Step 3
Relative advantage .42 (.0002)
-- No mediation, failed in Step 3
Complexity -.46 (.0001)
-.41 (.001) Partial mediation
Compatibility .43 (.0001)
.63 (.0001) No mediation, failed in Step 4
Trialability n.s.
-- No direct effect on trial
Observability n.s.
-- No direct effect on trial
Notes: The numbers shown are maximum likelihood parameter
estimates, and p-values (based on Wald chi-square significance
test) are shown in parentheses. All nonsignificant (n.s.)
relationships indicate a p-value greater than .05. (a) Text
shown in bold indicates a relationship in which the mediator
either completely or partially mediates the relationship
between the antecedent variable and trial. Legend for Chart:
A - Predictors of Trial
B - Study 1: IVR Consumer Readiness Model
C - Study 1: IVR Innovation Characteristics Model
D - Study 1: IVR Individual Differences Model
E - Study 2: Internet Consumer Readiness Model
F - Study 2: Internet Innovation Characteristics Model
G - Study 2: Internet Individual Differences Model
A B C D
E F G
Role clarity .23 -- --
(.0001)
1.2244 -- --
(.0001)
Ability n.s. -- --
n.s. -- --
Extrinsic motivation .11 -- --
(.0001)
.2289 -- --
(.0025)
Intrinsic motivation .0262 -- --
(.0945)
n.s. -- --
Perceived risk -- -.28 --
(.0001)
-- -.34 --
(.0003)
Relative advantage -- .32 --
(.0002)
-- .42 --
(.0002)
Complexity -- n.s. --
-- -.46 --
(.0001)
Compatibility -- .34 --
(.0002)
-- .43 --
(.0001)
Trialability -- .17 --
(.01)
-- n.s. --
Observability -- n.s. --
-- n.s. --
Inertia -- -- -.19
(.0001)
-- -- -.20
(.0002)
Technology anxiety -- -- -.13
(.01)
-- -- n.s.
Need for interaction -- -- -.18
(.0001)
-- -- -.28
(.0001)
Previous experience -- -- .15
(.002)
-- -- .61
(.0001)
Age -- -- -.19
(.0001)
-- -- -.03
(.0002)
Income -- -- .12
(.02)
-- -- .12
(.0002)
Sex -- -- n.s.
-- -- -.47
(.007)
Education -- -- n.s.
-- -- n.s.
Classification 86% 79% 65%
accuracy
93% 86% 74%
Notes: n.s. = not significant.DIAGRAM: FIGURE 1 Key Predictors of Consumer Trial of Self-Service Technologies
DIAGRAM: FIGURE 2 Significant Mediated Effects
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Legend for Chart:
B - Coefficient Alpha Study 1
C - Coefficient Alpha Study 2
A
B C
Consumer Readiness
Role Clarity: (five items adapted from Rizzo,
House, and Lirtzman [1970]) .96 .94
• I feel certain about how to effectively
use the SST.(a)
• I am NOT sure how to use the SST properly.
• I know what is expected of me if I use
the SST.
• The steps in the process of using the SST
are clear to me.
• I believe there are only vague directions
regarding how to use the SST.
Ability: (six items adapted from Jones [1986] and
Oliver and Bearden [1985]) .94 .96
• I am fully capable of using the SST.
• I am confident in my ability to use the
SST.
• Using the SST is well within the scope of
my abilities.
• I do NOT feel I am qualified for the task
of ordering a prescription refill with the SST.
• My past experiences increase my confidence
that I will be able to successfully use the SST.
• In total, using the SST sometimes involves
things that are more difficult than I am
capable.(b)
Extrinsic Motivation: (three expectancy items
adapted from Tyagi [1985]; four instrumentality
and four valence items created for the context) N/A(c) N/A
Expectancy
• If I put forth the effort, I could
successfully order a refill prescription with
the SST.
• If I tried to use the SST, my prescription
would be ordered successfully.
• Making the effort to use the SST would
result in the refill being ordered successfully.
Instrumentality
• Using the SST would provide me with added
convenience.
• Using the SST would allow me to order a
refill more quickly.
• Using the SST would allow me to order a
refill whenever I want.
• Using the SST would provide me more control
over the refill ordering process.
Valence
• When I order a prescription refill,
convenience is desirable.
• When I order a prescription refill, being
able to order a refill quickly is desirable.
• When I order a prescription refill, being
able to order a refill whenever I want is
desirable.
• When I order a prescription refill, having
control over the refill ordering process is
desirable.
Intrinsic Motivation: (three expectancy items
adapted from Tyagi [1985]; five instrumentality
and five valence items created for the context) N/A N/A
Expectancy
• If I put forth the effort, I could
successfully order a refill prescription with
the SST.
• If I tried to use the SST, my prescription
would be ordered successfully.
• Making the effort to use the SST would
result in the refill being ordered successfully.
Instrumentality
• Using the SST would provide me with
personal feelings of worthwhile accomplishment.
• Using the SST would provide me with
feelings of enjoyment from using the technology.
• Using the SST would provide me with
feelings of independence.
• Using the SST would allow me to feel
innovative in how I interact with a service
provider.
• Using the SST would allow me to have
increased confidence in my skills.
Valence
• When I order a refill, a personal feeling
of worthwhile accomplishment is desirable.
• When I order a prescription refill, a
personal feeling of enjoyment is desirable.
• When I order a prescription refill, a
feeling of independence is desirable.
• When I order a refill, feeling innovative
in how I interact with a service provider is
desirable.
• When I order a prescription refill,
increased confidence in my skills is desirable.
Individual Differences
Inertia: (three items adapted from Gremler [1995]) .90 .91
• Changing refill ordering methods would be
a bother.
• For me, the cost in time, effort, and grief
to switch prescription refill ordering methods is
high.
• It's just not worth the hassle for me to
switch prescription refill ordering methods.
Technology Anxiety: (four items adapted from
Raub [1981]) .93 .93
• I feel apprehensive about using technology.
• Technical terms sound like confusing
jargon to me.
• I have avoided technology because it is
unfamiliar to me.
• I hesitate to use most forms of technology
for fear of making mistakes I cannot correct.
Need for Interaction: (three items adapted from
Dabholkar [1996]) .88 .87
• Personal contact with an employee makes
ordering a prescription refill enjoyable for me.
• Personal attention by a customer service
employee is important to me.
• It bothers me to use a machine when I could
talk to a live person instead.(b)
Previous Experience: (three items created for the
context of interest) .72 .81
• I commonly use lots of automated systems
when dealing with other businesses.
• I do not have much experience using the
Internet.
• I use a lot of technologically based
products and services.
Innovation Characteristics
Compatibility: (three items adapted from Moore and
Benbasat [1991]) .95 .97
• Using the SST is compatible with my
lifestyle.
• Using the SST is completely compatible with
my needs.
• The SST fits well with the way I like to
get things done.
Relative Advantage: (three items adapted from
Moore and Benbasat [1991]) .95 .95
• Using the SST improves the prescription
refill process.
• Overall, I believe using the SST is
advantageous.
• I believe the SST, in general, is the best
way to order a prescription refill.
Complexity: (three items adapted from Moore and
Benbasat [1991]) .83 .88
• I believe that the SST is cumbersome to use.
• It is difficult to use the SST.
• I believe that the SST is easy to use.
Observability: (three items adapted from Moore
and Benbasat [1991]) .92 .94
• I would have no difficulty telling others
about the results of using the SST.
• I believe I could communicate to others the
outcomes of using the SST.
• The results of using the SST are apparent
to me.(b)
Trialability: (three items adapted from Moore and
Benbasat [1991]) .81 .85
• I can use the SST on a trial basis to see
what it can do.
• It is easy to try out the SST without a big
commitment.
• I've had opportunities to try out the
SST.(b)
Perceived Risk: (five items created for the
context of interest) .85 .87
• I fear using the SST reduces the
confidentiality of my medical history.
• I am unsure if the SST performs
satisfactorily.
• Using the SST infringes on my medical
privacy.
• Overall, using the SST is risky.
• I am sure the SST performs as well as the
other prescription refill ordering methods.(b)
Trial
Trial: (single-item measure created for the
context of interest) N/A N/A
• Have you successfully completed a
prescription refill request using the SST?
(a) On the survey, "automated telephone refill system" and
"Internet refill ordering system" were used instead of "SST."
(b) We dropped this item from the analysis during the measure
purification process.
(c) The expectancy theory conceptualization of motivation
results in a single motivation score, thus we did not calculate
an alpha score.
N/A = not applicable.~~~~~~~~
By Matthew L. Meuter; Mary Jo Bitner; Amy L. Ostrom and Stephen W. Brown
Matthew L. Meuter is Professor of Marketing, Department of Finance and Marketing, California State University, Chico.
Mary Jo Bitner is PETsMART Chair in Services Leadership and Professor of Marketing, Department of Marketing, Arizona State University.
Amy L. Ostrom is Associate Professor of Marketing, Department of Marketing, Arizona State University.
Stephen W. Brown is Edward M. Carson Chair of Services Marketing and Professor of Marketing, Department of Marketing, Arizona State University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 31- Cognitive Lock-In and the Power Law of Practice. By: Johnson, Eric J.; Bellman, Steven; Lohse, Gerald L. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p62-75. 14p. 4 Charts, 4 Graphs. DOI: 10.1509/jmkg.67.2.62.18615.
- Database:
- Business Source Complete
Cognitive Lock-In and the Power Law of Practice
The authors suggest that learning is an important factor in electronic environments and that efficiency resulting from learning can be modeled with the power law of practice. They show that most Web sites can be characterized by decreasing visit times and that generally those sites with the fastest learning curves show the highest rates of purchasing.
The widespread use of information technology by buyers and sellers is thought to increase competition by lowering search costs. "The competition is only a click away" is a common phrase in the popular press and an oft-cited reason for the failure of Internet ventures to achieve profitability. A potential result of reduced search costs is a decrease in brand loyalty and an increase in price sensitivity. At the extreme, there is the fear of a price-cutting spiral that drives out profits--labeled in the popular press as "perfect competition" or "frictionless capitalism," but more correctly called Bertrand competition (Bakos 1997; for a discussion, see Brynjolfsson and Smith 2000; for dissenting reviews, see Alba et al. 1997; Lal and Sarvary 1999; Lynch and Ariely 2000).
As a result, there has been interest in how to retain customers in electronic environments. The most commonly discussed solution is creating loyalty to Web sites, so research attempts to identify which sites exhibit greater loyalty or "stickiness" and speculates about what causes repeat visits. The most common loyalty metric is the frequency and cumulative duration of visits. For example, eBay is listed on the New York Times top ten stickiest sites because, though it has relatively few users, visitors spend approximately 90 minutes a month there, according to Web rating services such as Media Metrix. Consequently, eBay is thought to be highly successful. Other loyalty metrics relate visiting loyalty and purchasing loyalty, such as the number of visits per purchase, which is termed the "browse to buy" or "look to book" ratio (Schonberg et al. 2000), to the percentage of customers who become repeat customers (Win 2001).
In this article, we describe a mechanism and model for understanding the development of loyalty in electronic environments and an accompanying metric based on an empirical generalization from cognitive science, the power law of practice (Newell and Rosenbloom 1981). For an intuitive understanding of the mechanism, imagine a user visiting a Web site to purchase a compact disc (CD). This user must first learn how to use the Web site to accomplish this goal. We believe that after the CD has been purchased, having learned to use this site raises its attractiveness relative to competing sites for the consumer, and all other things being equal (e.g., fulfillment), the site will be more likely to be used in the future than a competitor. Further use reinforces this difference because practice makes the first site more efficient to use and increases the difference in effort between using any other site and simply returning to the first site, where browsing and buying can be executed at the fastest rate. This reinforcement generates an increasing advantage for the initial site. Sites can actively encourage this learning by implementing a navigation scheme that can be rapidly apprehended by visitors and using various forms of customization, including personalization, recommendations, or easy checkout. Learning how to navigate a site and customization together can increase the relative attractiveness of the site, generating a type of "cognitive loyalty program" that adds another, more cognitive explanation of loyalty to the existing rich set of definitions (Oliver 1999).
Two analogies may reinforce this idea and suggest that our analysis of learning is applicable to nonelectronic environments as well. On a first visit to a new supermarket, some learning takes place. The aisle location of some favorite product classes, the shelf location of some favorite brands, and a preferred shopping pattern through the store may be acquired (Kahn and McAlister 1997). This knowledge of the layout of a physical store, which increases with subsequent visits, makes the store more attractive relative to the competition. We argue that the same process happens with virtual stores. A similar argument has been commonly made about learning software such as word processors. Experience with one system raises the cost of switching to another, which explains, for example, the slow conversion from WordPerfect to Word (Shapiro and Varian 1999).
In this article, we examine learning in electronic environments by studying the time spent visiting individual Web sites. We focus on the cognitive costs of using a site and how they decrease with experience. We argue that this decrease can be modeled with a simple functional form that is used often in cognitive psychology to study learning--the power law of practice. We then investigate the relationship between the phenomenon of decreasing visit times and repeat visit loyalty and online purchasing using data from a panel of consumers from the World Wide Web.
The article proceeds as follows: We first review the literature that describes learning as a power law function, and discuss its underlying causes. We then discuss why this type of learning might apply to use of the Web. Using panel data that capture the in situ Web surfing of a large consumer panel, we examine the fit of the power law function, and alternatives, to the observed visit times. We then attempt to determine whether such learning is related to purchases. Finally, we discuss the implications of these results for managers of firms competing in electronic environments and for further research in this area.
When information about sellers and their prices is not available completely or free of cost to buyers, sellers are able to charge prices in excess of marginal costs (Bakos 1997; Salop 1979; Stiglitz 1989). Such search costs have two components: physical search costs, which represent the time required to find the information needed to make a decision, and cognitive costs, which represent the costs of making sense of information sources and thinking about the information that has been gathered (Payne, Bettman, and Johnson 1993; Shugan 1980).
Electronic environments may produce a shift in the relative importance of cognitive and physical search costs. Although the widespread diffusion of information technology markedly lowers physical search costs, it has had less impact on cognitive costs. As West and colleagues (1999) observe, whereas Moore's law has reduced the cost of computing, it has not affected the cost or speed of the human information processor. More important, because the number of stores and products that can be searched online has increased because of low entry costs, electronic commerce potentially increases the relative importance level of cognitive search costs.
Cognitive costs are dynamic and change with experience. With practice, the time required to accomplish a task decreases. For example, it should be much more efficient to search a favorite site--following, we hypothesize, a power relationship with amount of use--than to learn the layout of a novel site. This would imply that perceived switching costs increase the more times a favorite site is visited, which creates a cognitive "lock-in" to that site over time, just as firms can lock in customers with high physical switching costs (Klemperer 1995; Williamson 1975).
The power law of practice is an empirical generalization of the ubiquitous finding that skill at any task increases rapidly at first, but later, even minor improvements take considerable effort (Newell and Rosenbloom 1981). At the beginning of the twentieth century, task performance was found to improve exponentially with practice, for example, when using a typewriter (Bair 1902; Swift 1904). The exponential learning curve was one of the first proposed laws of human psychology (Thurstone 1937). Groups, organizations, and people can exhibit learning curves (Argote 1993; Epple, Argote, and Devadas 1991), and since World War II, learning curves have been used to forecast the increasing efficiency over time of industrial manufacturing (Hirsch 1952). Newell and Rosenbloom (1981) review the empirical evidence and show that improvement with practice is not exponential but instead is linear in log-log space; that is, it follows a power function. The power law of practice function and its equivalent log-log form is
( 1) T = BN-alpha, and
( 2) log(T) = log(B) - alpha log(N),
where T is the time required to complete the task, the most commonly used dependent measure of performance efficiency, though any dependent measure of efficiency can be used; N is the number of trials; and B is the baseline, an intercept term reflecting the performance time on the first trial (N = 1). The rate of improvement, α is the slope of the learning curve, which forms a straight line when the function is graphed in log-log space.[ 1]
Explanations for the Power Law of Practice
Two explanations have been proposed for the form of the power law of practice, though in most tasks, a combination of both is more likely responsible for log-log improvement over time. According to the method selection explanation (Crossman 1959), when a task is repeated, less efficient methods of accomplishing the task are abandoned in favor of more efficient methods as more efficient methods are discovered. In effect, the person performing the task is learning by trial and error the most efficient combination of methods, which could be revealed more systematically by a time and motion analysis (e.g., Niebel 1972). Over time, it becomes increasingly harder to distinguish minor differences among methods, and this accounts for the gradual slowing down of improvement. Card, Moran, and Newell (1983) demonstrate that improvement in the task of text editing could be modeled by the selection of the most efficient combination of task components.
The other explanation of practice law effects focuses on the cognitive processing of the input and output of the task rather than on the methods used in its performance. Rosenbloom and Newell (1987) explain log-log improvement as due to the "chunking" of patterns in the task environment, in much the same way that complex patterns can be memorized as a limited number of higher-order chunks (Miller 1958; Servan-Schreiber and Anderson 1990). Input-output patterns that occur often are readily learned in the first few trials, but rarer input patterns that occur maybe once in a thousand times require thousands of trials to chunk.
Applying the Power Law to Electronic Markets
Although the power law of practice has been found to operate in such diverse areas as perceptual motor skills (Snoddy 1926), perception (Kolers 1975; Neisser, Novick, and Lazar 1963), motor behavior (Card, English, and Burr 1978), elementary decisions (Seibel 1963), memory retrieval (Anderson 1983), and human-computer interaction (Card, Moran, and Newell 1983), there are many reasons to be skeptical of its applicability to consumer behavior on the Web and in other electronic environments.
First, there are theoretical reasons that the power law may not apply. Time spent at a site is routinely used as a measure of interest in the site (Novak and Hoffman 1997), which would seem to predict increasing, not decreasing, visit duration. Similarly, consumers spend more time looking at stimuli describing the alternatives they eventually choose (Payne 1976). In addition, purchasing usually requires at least one more page view than browsing (to enter data on the purchase form page), so any correlation between visit time and purchasing should work against the power law.
Second, there are several pragmatic concerns. If the content of a Web site changes regularly or, as is the case with dynamically generated Web pages, is different for every visit or when new navigation features are introduced to the site, each visit will involve a mixture of old (practiced) tasks and new (unpracticed) tasks, which attenuates any learning process. Thus, visits potentially consist of many aggregated tasks. Some tasks, such as site registration, are only performed on the first visit. Similarly, many classic power law studies observe hundreds or thousands of repetitions of a task. In contrast, the subjects in our Web data set have made many fewer visits to individual sites. The time between visits, which may be seconds in laboratory studies, is much greater in our data and varies significantly. The median time between visits to the same site is more than four days.
Third, if there are unobserved visits to Web sites, before panel membership or at another location such as at work, we will have underestimated the number of visits, which leads to underestimates of both learning parameters and reduces our ability to observe a power law. Fourth, our data are likely to be much noisier than those from a typical power law study. Our data come from panelists surfing in their living rooms, not in tightly controlled lab conditions. Their goals for visiting sites and the tasks they perform probably vary widely across visits.[ 2] These reasons suggest that though the power law might be, in theory, a useful metric for understanding real-world learning, it is not obvious that it is either applicable or detectable in data collected from real-life Internet users.
Data
The data we used came from the Media Metrix panel database, which records all the Web pages seen by a sample of personal computer (PC)-owning households in the 48 contiguous United States (Media Metrix is now a division of comScore Networks; www.comscore.com). During the period of analysis, Media Metrix maintained an average of 10,000 households in its panel every month. During the 12 months, from July 1997 to June 1998, examined in this study, the number of participants in the panel averaged 19,466 per month, roughly 2 per household. On each PC in the household, Media Metrix installs a software application that monitors all Web-browsing activity. Members of the household must log in to this monitoring software when they start the computer or take over the computer from another member of the household, as well as at half-hour intervals. This ensures that PC activity is assigned to the unique user who performed it. Media Metrix surveys more than 150 variables for each panelist, detailing among other things each person's age, sex, income, and education. The URL of each Web page viewed by members of the household, the date and time at which it was accessed, and the number of seconds for which the Web page was the active window on the computer screen are routinely logged by the software. Media Metrix records all the page views made by a household, even if these page files have come from a cache on the local computer. Although the Media Metrix panel contains participants of all ages, we restricted our analysis to a database of page views from panelists between 18 and 70 years of age, thus eliminating younger users who were unlikely to be purchasing on the Web.
Site Selection
We selected the books, music, and travel categories because they register the highest numbers for repeat visits and repeat online purchasing among online merchants (see also Bryn-jolfsson and Smith 2000; Clemons, Hann, and Hitt 2002; Johnson et al. 2002). Sites in each category were chosen from lists of leading online retailers from Media Metrix, BizRate (www.bizrate.com), and Netscape's "What's Related" feature, a service provided by Alexa (www.alexa.com) that defines related sites by observing which sites are visited by users. Table 1 shows the sites considered from each of the three categories.[ 3] Although there are certainly more sites on the Web in each category, the number of users from the Media Metrix panel who visited other sites was too low for us to conduct meaningful analyses.
During the period we examined, July 1997-February 1999, the two largest online booksellers, Amazon.com and Barnes and Noble, also started to sell music and other categories. Although we could identify the category being browsed on these sites from the URL, we could not easily assign the time spent on the site to the different categories. We ended our analysis of data from the books and music categories after June 1998, when Amazon opened its music store (Amazon.com 1998).
For a subset of the sites in each category, noted by an asterisk in Table 1, we were able to determine whether a purchase had been made from the site with a reasonably high degree of certainty. These were sites that confirmed purchases with a "thank you" page that has the same text in the URL for every purchase made on the site. We used this subset of sites to examine the relationship between the parameters of the power law and whether a purchase had been made. Although this measure confirms a purchase, it does not provide the size of the purchase.
Defining Visits
Each row of the Media Metrix data contains a URL, a household identifier, the date and time the page became active (became the window on the desktop with "focus" attached to it), and the number of seconds it remained active.[ 4] For our purposes, we defined a visit to a site as an unbroken sequence of URLs related to the same storefront. Our goal was to ( 1) eliminate visits that were accidental (e.g., typing the wrong URL, clicking on the wrong link, being misdirected from a search engine); ( 2) identify a series of page views of a site that should be considered one visit, despite a brief side trip to another site; and ( 3) eliminate visits that were artificially lengthened because the user walked away from the computer, minimized the browser and did something else on the machine, and so forth.
To define visits, we first examined the distribution of the time between page views for individual panelists visiting the same site. These gap times, or interpage times, were the number of seconds between the time when the panelist stopped actively viewing one page from the site and the time when another page from the same site became active. Most gaps between page views were instantaneous (0 seconds duration), as is expected if pages are viewed consecutively. Approximately two-thirds of interpage gaps were less than a minute in duration, and beyond one minute, the distribution flattened out rapidly, with 95% of all gaps less than 15 minutes long. We therefore used 15 minutes between page views as the cut-off to distinguish one visit to the same Web site from a repeat visit. With this definition of a repeat visit, the median time between repeat visits across all three product classes is 4.5 days (books 6.2 days, music and travel both 4.2 days). In addition, we eliminated any visits that had a total duration of less than 5 seconds (a typical page load time) or exceeded 3 hours (which we assumed reflected an unattended browser). These numbers are similar to the definitions used by Media Metrix and other firms to define visits, and a sensitivity analysis showed that our conclusions were robust to these assumptions. To provide enough data points to allow at least one degree of freedom for testing a power law relationship, only the panelists who made three or more visits to a site in one of the three categories were retained in the data set (N = 7034). To provide stable estimates, we examined all sites that had at least 30 visitors (providing at least ten observations per parameter).
Analysis
From the 20-month database of page views, we extracted a separate data set for each site, sorting these data sets by date and time for each panelist. The active viewing time for each page during a visit was summed to yield total visit duration in seconds. After using the natural log function to transform visit number and visit duration, we estimated the power law using two approaches. The first is an individual-level linear regression,
( 3) log(T) = β + alpha log(N),
where T is the visit duration, N is the number of that visit, β is the intercept (which can be interpreted as an estimate of the log of B, the initial visit baseline time), and alpha is the learning rate. This approach makes no assumptions about the sign of alpha, though the power law posits a negative estimate. These individual linear regressions avoid many of the problems associated with the analysis of aggregate practice law data (Delaney et al. 1998). The mean of the individual-level estimates of α for each site provides an unbiased indicator of the mean power law slope for that site (Lorch and Myers 1990), and we conducted a series of one-tailed t-tests to compare the value of alpha with 0.[ 5]
Although these individual-level estimates are unbiased, they are a conservative measure and limit the number of predictor variables, which provides limited flexibility in testing alternative models. Our second estimation approach therefore was to use a hierarchical (random effects) linear model that allows heterogeneity in β and alpha and provides empirical Bayes estimates for each panelist:
( 4) Log(T)ij = (βj + lambda1i + (alphaj + lambda2i)log(Nij) + epsilonij,
where βj is the intercept for site j, and alphaj is the slope of the learning curve for site j. In addition, we estimated lambda1i and lambda2i which represent individual-level heterogeneity in estimates of β and alpha respectively. We assumed that lambda1 and lambda2 were distributed normally and independently and that epsilonij had mean 0 and was independent.
The Power Law and Repeat Visits to Web Sites
Table 2 shows the mean individual-level estimates for(the intercept) and α (the learning rate), as well as the mean of the empirical Bayes estimates including heterogeneity, for the 36 sites. The sample-weighted average learning rate for the individual-level estimates, is -.19 (95% confidence interval = -.21 to -.18; Hunter and Schmidt 1990). With two exceptions, Delta-Air.com and HotelDiscount.com, the individual-level means are negative, so visit duration declines as more visits are made, as we would expect if the power law of practice applied to Web site visits. Of the 36 sites, 28 (78%) had significantly more negative than positive individual-level estimates of alpha and the number of negative estimates was significantly more than would be expected by chance (50%). There were no significant positive slopes.
The empirical Bayes estimates generally agreed with the individual-level regression estimates. All but 3 sites had negative empirical Bayes mean slopes, and 30 of the 36 sites (83%) had negative slopes and a mean that was significantly negative, p <.05. The empirical Bayes model enabled us to test the estimates for the fixed components of the slope and the intercept across all the sites in a product category. In all three categories, the negative slope (alpha) and positive intercept (β) were significant, p < .001, and the majority (77.8%) of the learning coefficients (alpha) for specific sites were both significant and negative.
Figure 1 illustrates the estimated learning functions for both book sites, the four music sites, and some of the most frequently visited travel sites. As can be seen in Figure 1, there are significant differences in the learning rates across the sites in all three categories. In the case of books, the learning rate for Amazon is much faster that that for Barnes and Noble. These learning curves conform to the conventional wisdom that, initially at least, Barnes and Noble's online store lagged Amazon in the quality of its interface design. Nielsen (1999), for example, said "the best major site was probably amazon.com as of late 1998," and many commentators accused Barnes and Noble of playing "catchup" in its approach to online design.
We should note, however, that there are several reasons that differences in slopes and intercepts must be interpreted with some caution. Across categories, the nature of the task may change. Finding books may involve different decisions than finding an appropriate airline ticket. Across sites, the set of users attracted to the site, their online experience, network connection speed, and other variables may also differ. The major point to be drawn from Figure 1 and Table 2, therefore, is that for most sites, the power law of practice provides a good account of visit times. The dynamic nature of Web content makes it difficult to relate specific characteristics of these particular Web sites to their power law parameters. Without an archive of server images for these Web sites collected at regular intervals, it is practically impossible to ascertain all the changes in content and design made on these sites during the time of observation. However, such research is possible to conduct prospectively, as are studies that explore these issues in experimental contexts.
Alternative Models and Tests
Although theory and evidence from other studies of practice suggest that a decrease in task duration is best modeled by a power law, we compared the results from the power law regression analysis with a likely alternative, a simple linear model, similar to the one used in Equation 3 but with a simple linear representation of the number of visits. The natural log of visit time T remains the dependent variable, because this transformation normalizes the distribution of visit times.[ 6] To compare models, we used the Bayesian information criterion (BIC). All models had the same number of parameters. As can be seen in Table 2, the power law model was a superior model to the linear model of learning in all three product classes.[ 7]
In addition to comparing the two functional forms, we can construct an ordinal test of the differences in visit duration (untransformed) for the first three visits made by each panelist. If the data follow an exponential trend, the difference in duration between Trial 1 (t1) and Trial 2 (t2) will be greater than the difference in duration between Trial 2 (t2) and Trial 3 (t3). That is,
( 5) (t1 - t2) > (t2 - t3).
If, however, these differences follow a linear trend, the probability of observing a first difference greater than the second difference will not differ from chance (p = .5). In other words, with a linear slope, only approximately 50% of subjects will have a first difference (t1 - t2) greater than the second difference (t2 - t3), whereas for an exponentially decreasing slope, this number should exceed 50%. Table 3 shows the results of a series of binomial tests for each site with more than 30 visitors. At each of these sites, more than 50% of users had a first difference (t1 - t2) greater than the second (t2 - t3), and for 30 of the sites (83.3%), this difference was significant. We also examined the differences in duration of the second, third, and fourth visits, though fewer panelists recorded this many visits. Again, for the majority of the sites (63.9%), the percentage of visitors with a second difference (t2 - t3) greater than the third (t3 - t4) was significantly greater than 50%. If the signs of these differences are considered independent trials, the overall percentage for (t1 - t2) > (t2 - t3) is 57.7% and for (t2 - t3) > (t3 - t4) is 56.8%. Both are significantly different from the 50% that would result if a linear model was the best description of the data. These results strengthen our claim that the decline in visit duration with successive visits is exponential and better modeled with a power function than a simple linear function.
A major difference between laboratory applications of the power law and the real-world task that we observe is the variability in the periods between trials. In laboratory studies, one task occurs right after another with little intervening time. However, in our naturalistic application, trials may occur on the same day or months apart.[ 8] We examined whether we could improve the fit of the power law by including the interval between repeat visits as a covariate in the following empirical Bayes estimation:
( 6) Log(T)ij = (βj + lambda1i) + (alphaj + lambda2i)log(Nij) + (gammaj + lambda3i)log(G[sub ijN) + epsilon[sub ij,
where GijN is the interval time (or gap) preceding the Nth visit (N > 1) by user i to site j (log transformed to normalize the distribution of G), gammaj is the fixed effect of the gap in time between visits to site j, and lambda3 is a normally distributed random variable accounting for individual-level heterogeneity in gamma. These intervals were significant and positive (travel = .045, p < .0001; books = .056, p < .0001; music = .031, p < .001), and the inclusion of a gap parameter improved the fit of the model, which indicates that the longer the time between visits, the longer the visit takes. Yet the power law still described the data;remained significantly negative in two of the three categories (travel p < .0001; books p = .273, not significant [n.s.]; music p = .006). This alternative model represents an important modification of the power law when applied to nonexperimental Web data. Whereas traditional applications of the power law emphasize the amount of practice and ignore its timing, this modified power law suggests that the density of practice matters in these data.
Alternative Explanations
An alternative explanation for this power law function is that it does not reflect learning on the part of the user but rather adaptation on the part of the network to the user's needs. Specifically, many Internet service providers and browsers cache copies of popular pages, that is, keep local copies of Web pages so they can be retrieved faster after the initial access.
To control for caching, we reran the power law model and added a variable that distinguished the first (and presumably uncached) visit to the site from all subsequent visits. If the decrease in visit times we observed was due to caching, we would expect this variable to be significant and the power law relationship to disappear or be greatly diminished. Although the inclusion of this control variable diminished the size of the slope coefficient, oo most remained negative and significant. The first trial dummy variable was significant for travel sites (F( 1, 65000) = 61.69, p < .0001) and book sites (F( 1, 7504) = 4.32, p = .038) but not for music sites (F( 1, 2962) = 1.29, n.s.). However, for all three categories--travel (F(30, 65000) = 7.40, p < .0001), books (F( 2, 7504) = 2.97, p = .026), and music (F( 4, 2962) = 2.42, p = .023)—α remained significantly negative. Similar results were found at the individual-site level. For example, 16 (53.3%) of the 30 travel sites possessed a significant, negative slope coefficient, and 23 (76.7%) of 30 remained negative. In addition, we compared the power law and linear models with the cache term included in both models. This enabled us to test whether the apparent increase in fit of the power law compared with a linear learning function was due to lengthy first visits followed by subsequent caching. However, for all three categories, the power law model had a lower BIC than the linear model.
We also examined the possibility that the slope coefficient, α might reflect not learning, but rather a decrease in interest in the site. We examined the correlation between a panelist's individual-level α for a site and the number of observations (visits) used to estimate that α. These correlations showed no systematic pattern across product classes (r = -.07, -.002, and .04 for books, music, and travel, respectively) but are statistically significant given the large sample sizes. This analysis, along with our subsequent demonstration that faster learning leads to increased probability of buying, suggests that a decrease in interest does not account for our observed results.
Another reasonable alternative explanation for the observed decrease in visit duration is that people allocated a certain amount of time to Web surfing per session, but with the number of Web sites increasing over the period spanned by our data set from 646,000 in January 1997 to 4.06 million in January 1999 (www.iconocast.com), less time could be devoted to any one site. If this hypothesis is correct, the number of sites in any product class visited per month by a household should constantly increase, and each should receive a decreasing share of session time. However, the number of sites visited per month appears to be constant within a product class over time (Johnson et al. 2002).
Although our results and the power law model were consistent with a learning account, our results also parallel survey evidence that new Internet users navigate the Web in a more exploratory, experiential mode compared with experienced users (Novak, Hoffman, and Yung 2000). This transition from initial exploration to more efficient, goal-directed navigation may be another factor in diminishing visit times at specific sites, may apply to overall Web surfing behavior, and may explain connections with purchasing, but it does not rule out the underlying operation of the power law of practice.
Does Learning Lead to Buying?
Although we have found strong evidence at the individual level for the power law of practice in Web browsing behavior, is the power law consistently related to the buying behavior of Web site visitors? Are visitors more likely to buy from the sites they know best and can navigate more efficiently? If this is true, we should find a relationship between the two learning parameters, alpha and β and the probability of making a purchase on any particular visit. We expect a negative relationship with purchasing for both parameters, in that faster initial visits (lower β) and faster slopes (lower alpha) may produce a greater likelihood of buying. To test this, we included the individual empirical Bayes estimates of alpha and β as predictors, as well as a variable N - 1, where N is the number of visits to the product category. Prior analysis suggests that buying probability increases as more visits to a category are made (Moe and Fader 2001). In addition, although we had no a priori theory of how the effect of the power law parameters might change over time, we included the interactions between N and the power law coefficients. We use N - 1 rather than N because this enables meaningful interpretations of these interaction terms (Irwin and McClel-land 2001). When N - 1 = 0, the model predicts purchase probability for the first visit (N = 1) using only the intercept and the two learning parameters.
We estimated the following logit model for each product class:
( 7) BuyN = gamma0 + gamma1alpha + gamma2β + gamma3(N -1) + gamma4alpha(N - 1) + gamma5β(N - 1) + epsilonij,
where BuyN is 1 if category visit number N by a visitor to a site results in a purchase, 0 otherwise; alpha is that visitor's learning rate for this site; βis the visitor's power law function intercept (i.e., the estimated log of first visit time); and N is the category visit number. In addition, alpha(N - 1) is the interaction of α and the category visit number N; similarly, β(N - 1) is the corresponding interaction, and gamma0 the intercept, and gamma1, gamma2, gamma3, gamma4 and gamma5 are all parameters to be estimated. The results are shown in Table 4. The logit model explained a significant amount of variance in buying (versus not buying) during specific visits.
For all three product classes, the main effect of alpha was negative and significant, as we predicted. The effect of β for two of the three product classes, music and travel, was significant in the predicted direction. As we expected, there was a significant tendency in both product classes for the probability of a purchase to increase with an increase in category visits.
The next two columns of Table 4 show that the number of visits to the site moderated some of these effects. For music, both interactions suggest that the effect of learning, alpha decreases over time, whereas for travel, the effect of β seems to increase over time. However, they are very small effects compared with the simple effect of alpha and, within the range of learning we observed, slightly attenuate but do not reverse the beneficial effects of learning.
To illustrate the entire pattern, Figure 2 plots the variation in purchase probability for music sites over a range of alpha and N that is observed in the data we used to estimate the model. In Figure 2, alpha ranges ±1.5 standard deviations from its mean (Jaccard, Turrisi, and Wan 1990), and the number of visits to the category N increases from one to ten, when β is held constant at the sample mean. Visitors with the fastest learning rates (alpha) had the highest probability of purchase at all trials. For example, changing the learning rate from -.1 to -.2 doubles the probability of purchase from .01 to .02 on the fourth visit. The effect of N can be seen as the entire plane tilts upward, but this effect is small compared with the increase due to the learning rate. Finally, the interaction between alpha and N produces a slight flattening of the slope of the effect of learning as N increases, but this effect is obscured by the effect of the logit transform and only noticeable outside the range of visits we typically observed.
The plots for the significant interaction effects of first visit duration, β and N for the music and travel sites were similar to Figure 2. Visitors with faster first visit times had a higher probability of purchasing at all trials, though there was a slight tendency in travel for this effect to decrease with more visits.
Limitations
The data from the time period we examined are rather sparse, because the frequency of online buying was relatively limited compared with subsequent periods. Similarly, the number of stores visited is limited, which makes the analysis of visit patterns difficult. Analysis of more recent data may not only replicate our current results but also be able to test new hypotheses in data sets that observe more frequent purchase visits. Another significant limitation is that these data lack several covariates that would increase our ability to predict visit times. We lack information about connection speed; details about the contents of each Web page and product offering; and details about caching, network delays, and so forth. Finally, unobserved prior visits not only flatten the learning curve, making it harder to detect learning improvement, but also introduce selection effects that may be alternative explanations for the relationships we observe. For example, the increasing number of visits may in reality have no effect on purchase probability but appears to do so because our data omit early visit purchases made before panel membership. Such information is becoming increasingly available, especially in controlled lab studies, and we believe our current work is a first step toward more sophisticated models that will provide excellent accounts of viewing time and purchase behavior.
Implications for Web Competition
We have shown that visit duration declines the more often a site is visited. This decrease in visit time follows the same power law that describes learning rates in other domains of individual, group, and organizational behavior. Just as practice improves proficiency with other tasks, visitors to a Web site appear to learn to be more efficient at using that Web site the more often they use it. This is consistent with the small amount of competitive search observed in similar analyses of the Media Metrix data set, in which most panelists are loyal to just one store in the books, music, and travel categories (Johnson et al. 2002).[ 9] Consistent with this view, we find a relationship between the ease of learning a Web site and the probability of purchasing.
The major implication of the power law of practice is that a navigation design that can be learned rapidly is one of a Web site's strongest assets. Although it is inconceivable that a Web site would be designed to be difficult to use, our results show considerable variation in ease of learning across sites and, perhaps most important, indicate that easier learning of a Web site leads to an increased probability of purchase. This suggests that the layout of a site can be an important strategic tool for online stores. Our advice for managers of Web sites with rapid learning rates is to maintain the current navigation design if possible. Altering the navigation design of a site reduces the cognitive lock-in effect of practiced efficiency and reduces an important competitive strength. If customers must learn a site design all over again, they might decide to learn someone else's instead. Customers come back on repeat visits to find new content, and the more varied the content, the more they will be encouraged to return. Whereas content should be refreshed often, changes in site design should be reviewed carefully.
Interface design can be exploited by both incumbents and competitors. An existing firm with a large customer base can extend to new product categories by using its familiar navigation design to encourage purchasing. This seems to be the heart of what might be termed Amazon's "tabbing" strategy, which introduces additional product classes (e.g., CDs) using the same navigational structure as previous categories (e.g., books) use and adds these new product classes as tabs along the top of a page. Such techniques lower the cost of using the site for new categories; some tasks will be new, but others are already completed (such as registering) or more easily accomplished. Site designers can take advantage of the power law to sequentially space the introduction of new features, allowing sufficient time between changes for the previous feature to be fully mastered so that cognitive resources can be devoted to mastering the new feature.[10]
Within legal limits, competitors can copy many design features of a familiar user interface. Most Web sites have already recognized the value of intuitive navigation design, and sites that have made successful innovations in site design have had many imitators. Some elements of site navigation, such as the ubiquitous use of tabs, quick search boxes, cookie-set preferences, and sometimes the whole look and feel of a competitor, are easily copied. Other navigation elements are harder to reproduce; for example, Amazon.com applied for a process patent for its 1-Click feature and, since an out-of-court settlement with major rival Barnes and Noble (Cox 2002), has licensed its use. An additional competitive advantage can be elements that customize the site in ways that make it easier to use. For example, the accuracy of purchase recommendations based on previous purchases at one store cannot easily be duplicated by that store's competitors, so it represents a difficult-to-imitate source of learning.
Another example from the short history of Web retail competition of how information can provide lock-in is eBay's seller ratings, which lock sellers into the service and diminish the risk for buyers. This feature enabled eBay to maintain an 80% market share when well-known competitors such as Yahoo! were offering similar auction services.
Managers of Web sites with customers locked in by the ease of using the site may be able to take advantage of cognitive switching costs and charge price premiums. Smith and Brynjolfsson (2001) provide evidence that Amazon and Barnes and Noble charge a price premium over less well-known and, therefore, more risky sites. Sites that have easy-to-learn but difficult-to-imitate interfaces may also realize premiums in valuation. In the absence of other switching costs or loyalty schemes, cognitive lock-in implies an installed base of loyal customers whose lifetime value will provide a steady stream of earnings in the future (Shapiro and Varian 1999).
Further Research and Extensions
We used the analogy of the familiarity of a supermarket's layout as a form of cognitive lock-in, and we believe our results may be applicable far beyond the Web. For a broad range of products, ranging from video cassette recorders and personal digital assistants to services such as electronic organizers or voice mail menus, ease of learning relative to the competition is a relevant competitive attribute, not just because ease of use is itself good but also because it increases switching costs. Although this observation is not new, our work proposes a framework for modeling and metrics for assessing ease of learning that might be helpful. This framework could be used to study learning and loyalty in many environments in which cognitive costs are a newly important factor because technological advances have minimized physical costs.
Focusing on the Web, many new metrics have been proposed for measuring the attractiveness of Web sites, such as stickiness and interactivity (Novak and Hoffman 1997). Many of these measures assume a positive correlation between a visitor's involvement with the site and the duration of his or her visit or the number of pages viewed. We suggest that this relationship between visit length and interest is typical of a visitor's initial online behavior after adoption of the Web, but it is important, especially with regard to experienced Web surfers, to distinguish between utilitarian transactional and informational sites and hedonic media and entertainment sites (for a similar classification, see Hoffman and Novak 1996; Zeff and Aronson 1999). When a site's primary purpose is to encourage transactions, a decreasing pattern of visit times may be a good outcome. However, for a media site likely supported by advertising revenues, we expect the opposite pattern, or perhaps a constant mean duration, to characterize a successful site.[11]
An area of further research of much interest for online retailers is identifying what makes a site easy to learn. What are the determinants of low initial visit times? What features of a Web site determine subsequent learning? Additional research could characterize the attributes of various Web sites, in terms of both infrastructure (servers, caching) and page design (limited graphics, useful search capabilities), and relate them to observed visit times. Such empirical research would help the development of a better cognitive science of online shopping (Nielsen 2000). Experimental work in this direction recently has been reported by Zauberman (2002) and Murray and Häubl (2002).
Economic theory suggests that the low physical costs of information search on the Web should encourage extensive search (e.g., Bakos 1997). However, when the data are examined, Web information search is fairly limited (Johnson et al. 2002), and this, coupled with our finding about cognitive switching costs, argues for the development of a behavioral search theory that extends economic theory beyond its concentration on physical costs. Cognitive switching costs are difficult to value in monetary terms, at least for the consumer evaluating the decision to search multiple sites versus staying with one familiar site. It would be worthwhile to examine whether this observed loyalty is a rational adaptation to search costs or if there are systematic deviations that can be predicted from an alternative theoretical framework.
The reaction of markets to cognitive lock-in is another interesting topic for additional research. Just as they consider other sources of switching costs, customers who anticipate that adopting a site as a favorite will lock them in should adopt the standard strategies for minimizing the effects of lock-in (Shapiro and Varian 1999). First, they should sell their loyalty dearly, choosing the site that pays the most for their lifetime value or offers the most support for relearning another site's navigation. Second, they should always have an escape strategy. For example, consumers should choose sites or tools that minimize switching costs. One example that has not been widely adopted is a nonproprietary shopping wallet that can be used for quick buying from multiple sites.
Conclusion
We suggest that the power law of practice, an empirical generalization from cognitive science, applies to visits to Web sites. Our results show that visits to Web sites are best characterized by decreasing visit times and that this rate of learning is related to the probability of purchasing.
We suggest that cognitive rather than physical costs are important in online competition and that this has several implications for Web site managers. Cognitive lock-in also has welfare implications for consumers, and we suggest some strategies they can adopt to reduce its effects. The phenomenon of cognitive lock-in due to the power law of practice is an important area for further research. Although we have empirically examined the applicability of this idea using Web sites, we believe such cognitive lock-in is an increasingly important factor for a broad range of products.
The authors thank Media Metrix, Inc. for providing the data used here and the supporting firms of the Wharton Forum on Electronic Commerce for their financial support.
This research has benefited from the comments of participants in seminars at Ohio State University, Columbia University, University of Rochester, and the University of Texas and helpful comments by Peter Fader, Asim Ansari, and John Zhang.
1 Systematic deviations from a straight-line power law function have often been observed in previous studies. Improvement in the performance of a task, such as cigar rolling, ultimately reaches an asymptote imposed by the physical limitations of the tools used to perform the task, such as a cigar-rolling machine (Crossman 1959), and the observed data curve upward from a straight line as N increases. When the baseline time is not observed for a person, the empirically estimated power law curve shifts horizontally and appears flatter than curves estimated from subjects for whom the first observed trial is the baseline. Newell and Rosenbloom (1981) augment the simple power function form to derive a general power law of practice:
T = A + B(N + E)-alpha,
where A is the asymptote, the minimum possible time in which the task can be performed, and E, prior experience, is the number of trials in which the person learned to perform the task before observation.
We could not take advantage of the general form of the power law function to model any systematic deviations that might be present in the data because of the low number of visits made by the majority of panelists. Very few would have made enough trials to hit up against their personal asymptotic performance. It is unlikely that a constant asymptote exists for physical performance of the site navigation task, because of typical variance in network delays across Web sessions experienced by most Web visitors. Because we have data from in-home Web surfing only, we may be missing many observations that occurred when the panelists visited these sites from other locations. In addition, many of our subjects may have visited these sites before they joined the panel, so the number of trials is underestimated. The number of prior trials, E, can be estimated by means of a grid search for an E0 that minimizes a loss function (Newell and Rosenbloom 1981). However, stable estimates of the number of prior visits require solid estimates of the power law function itself based on a large number of observed visits, and that is precisely what we do not have for most of our subjects.
3 Many of these Web companies have several different Web sites or pseudonyms that Media Metrix identifies with a single domain name. For example, Barnes and Noble has seven Web addresses for its site, six of which are hosted on America Online servers. Because it is important for our analysis that we identify all the related sites at which a visitor could learn a particular interface, we independently checked Media Metrix's roll-up definitions of domain names for the sites we considered. We searched for sites that had similar words in their URLs for one month, June 1998, and checked whether these sites belonged to companies on our list and were pseudonyms for identical storefronts. We verified the number of page views for our roll-up definitions with the Media Metrix counts for the same domain names.
4 Our data are superior to typical Web server log file data in this respect. Web server log files record only the date and time a requested file was sent to the requesting Internet provider address. If the file was sent successfully, it can be assumed that the receiver at least began to read the file. The time spent reading the file is unobserved, but it can be assumed to equal the time between the first request and a second request for a page from the same site. If no further request is made, it is typically assumed that the page was read for 30 minutes and then the session with that site ended. There can be many problems with these assumptions. The Media Metrix data show that active viewing often ceases before the next request is made from a site; for example, a visitor may focus on another application (e.g., sending an e-mail), which makes this second application the active window instead of the browser. See Novak and Hoffman (1997) and Drèze and Zufryden (1998) for further discussion of these issues.
5 We also examined aggregate patterns for the power law, a method that is inferior because of heterogeneity across consumers. The power law results are qualitatively similar. For example, an analysis of Amazon.com shows an α of -.31 with an R2 of .45, a result that does not change much if we alter the number of visits used in estimation from 3 to 5 to 20.
6 Similar analyses with an untransformed dependent measure show a weaker pattern of results than the log-transformed visit times.
7 We performed similar tests using individual-level regressions with similar results: The fit of the linear model is worse, overall, than the fit of the power law model, and only five sites (13.9%) have more significant estimates offrom the linear model than from the power law model.
8 We thank an anonymous reviewer for this insightful suggestion.
9 A possible explanation for the low level of comparison shopping is that people use one site to comparison shop, that is, a price-bot. We found little usage of pricebots (e.g., Acses) in the Media Metrix data.
We thank an anonymous reviewer for this recommendation.
The drop in visit times we observe and model as exponential learning could represent the failure of Web shopping sites to generate a "compelling online experience" (Novak, Hoffman, and Yung 2000, p. 23) that keeps visitors browsing for long periods on successive visits. Instead, visitors are returning only to buy, which takes much less time than browsing. The more rapidly visitors switch from an experiential, exploratory mode of visiting to a goal-directed purchasing mode, the more purchasing occurs. Again, we thank an anonymous reviewer for these observations.
Travel Sites (July 1997-February 1999)
AAA.com ETN.nl PreviewTravel.com*
AlaskaAir.com Expedia.com Priceline.com*
AA.com HotelDiscount.com Southwest.com*
Amtrak.com 1096HOTEL.com TheTrip.com
Avis.com* ITN.net TravelWeb.com
BestFares.com LVRS.com TravelZoo.com
CheapTickets.com LowestFare.com Travelocity.com
City.Net MapBlast.com TWA.com
Continental.com MapQuest.com UAL.com
Delta-Air.com NWA.com USAirways.com
Book Sites (July 1997-June 1998)
Acses.com Books.com Kingbooks.com*
AltBookStore.com BooksaMillion.com Powells.com*
Amazon.com* BooksNow.com* Superlibrary.com
BarnesandNoble.com Borders.com* Wordsworth.com*
BookZone.com*
Music Sites (July 1997-June 1998)
BestBuy.com* CDWorld.com* MusicCentral.com
CDConnection.com eMusic.com* MusicSpot.com
CDEurope.com Ktel.com Newbury.com*
CDNow.com* MassMusic.com TowerRecords.com*
CDUniverse.com* MusicBoulevard.com Tunes.com*
CdUSA.com
*Purchases can be identified from Media Metrix data (URL) with a high level of confidence.
Legend for the Chart
A Site
B Individual-Level
Ordinary Least Squares Power Law Estimates - N
C Individual-Level
Ordinary Least Squares Power Law Estimates - β
D Individual-Level
Ordinary Least Squares Power Law Estimates - alpha
E Empirical Bayes Power Law Estimates - β
F Empirical Bayes Power Law Estimates - alpha
G Empirical Bayes Linear Model Estimates - β
H Empirical Bayes Linear Model Estimates - alpha
A
B C D E F G H
Travel Sites
6146
Map Quest.com
1482 5.37 -.118*** 5.39 -.053** 5.31 -.007*
Travelocity.com
1394 5.52 -.176*** 5.59 -.081*** 5.45 -.009***
Expedia.com
1227 5.41 -.102*** 5.42 -.032* 5.37 -.003
PreviewTravel.com
1167 5.13 -.164*** 5.13 -.053** 5.04 -.002
City.net
1005 4.87 -.215*** 5.00 -.149*** 4.81 -.029***
Southwest.com
620 5.56 -.279*** 5.69 -.138*** 5.46 -.015***
AA.com
595 5.34 -.167*** 5.36 -.073** 5.22 -.002
Delta-Air.com
425 5.03 .009 5.06 .010 5.05 .005
NWA.com
402 5.67 -.321*** 5.78 -.228*** 5.37 -.020***
Continental.com
331 5.27 -.236*** 5.36 -.143*** 5.12 -.016**
UAL.com
326 5.19 -.141* 5.32 -.127** 5.14 -.022**
ITN.net
326 5.03 -.298*** 5.58 -.090** 5.42 -.007
Priceline.com
292 5.35 -.230** 5.89 -.344*** 5.53 -.098***
USAirways.com
284 5.05 -.423*** 5.33 -.335*** 4.83 -.049***
TravelWeb.com
261 5.14 -.359*** 5.16 -.035 5.12 -.009
TheTrip.com
213 5.21 -.287*** 5.30 -.116** 5.08 -.006
BestFares.com
203 5.53 -.379*** 5.55 -.158*** 5.27 -.014**
Amtrak.com
198 5.38 -.602*** 5.68 -.414*** 5.04 -.048***
MapBlast.com
181 5.35 -.083 5.35 -.023 5.32 -.004
TWA.com
151 5.43 -.388*** 5.39 -.098* 5.22 -.008
TravelZoo.com
150 5.12 -.301*** 5.27 -.197** 4.97 -.025*
AAA.com
104 5.13 -.159 5.53 -.302*** 5.07 -.039**
LowestFare.com
99 4.26 -.082 4.77 .042 4.79 .019
CheapTickets.com
95 5.29 -.509*** 5.81 -.513*** 5.21 -.122***
Avis.com
79 5.44 -.167 5.50 -.076 5.36 .002
HOTEL.com
77 5.06 -.243* 5.30 -.210* 4.96 -.021
AlaskaAir.com
49 5.13 -.286* 5.30 -.175* 5.02 -.022
ETN.nl
43 5.03 -.534** 5.41 -.427** 5.01 -.142*
LVRS.com
43 5.14 -.329 5.42 -.242* 4.99 -.015
HotelDiscount.com
39 4.56 .028 4.96 -.031 4.88 .009
BIC 257,471 257,708
Book Sites
1282
Amazon.com
1044 5.17 -.175*** 5.27 -.077*** 5.13 -.006*
BarnesandNoble.com
370 4.78 -.044 4.76 .013 4.76 .007
BIC 30,796 30,816
Music Sites
534
CDNow.com
256 5.29 -.169** 5.24 -.022 5.18 .004
MusicBoulevard.com
206 5.11 -.189* 5.15 -.078* 5.00 -.003
BestBuy.com
75 4.92 -.286* 5.15 -.230** 4.76 -.019*
CDUniverse.com
42 4.89 -.343* 5.00 -.197* 4.70 -.027
BIC 11,706 11,730
*p < .05 (one-tailed).
**p < .01 (one-tailed).
***p < .001 (one-tailed).
Notes: All β significantly > 0, p < .001. BIC = Bayesian
information criterion.
Legend for the Chart
A Site
B N1[a]
C %(t1 - t2) > (t2 - t3)
D N2[b]
E %(t2 - t3) > (t3 - t4)
A B C D E
Travel Sites
MapQuest.com 1482 55.7*** 970 57.5***
Travelocity.com 1354 55.8*** 932 53.2*
Expedia.com 1204 56.0*** 837 58.1***
PreviewTravel.com 1156 56.1*** 712 55.8***
City.net 1003 55.9*** 602 58.3***
AA.com 583 56.1** 371 58.0***
Southwest.com 575 53.6* 394 57.4**
Delta-Air.com 417 60.4*** 272 59.2***
NWA.com 337 58.5*** 237 62.9***
Continental.com 331 58.9*** 225 55.6*
UAL.com 325 62.2*** 190 53.2
USAirways.com 284 60.9*** 175 59.4**
Priceline.com 255 57.6** 128 59.4*
BestFares.com 201 62.7*** 143 62.2**
Amtrak.com 198 57.6* 109 66.1***
TravelWeb.com 190 61.1*** 107 57.0
MapBlast.com 181 58.0* 107 59.8*
TheTrip.com 166 60.2** 127 62.2**
ITN.net 153 66.0*** 91 58.2*
TWA.com 150 64.0*** 94 48.9
TravelZoo.com 150 67.3*** 89 58.4*
AAA.com 101 60.4* 53 56.6
CheapTickets.com 95 64.2** 61 55.7
1096HOTEL.com 73 53.4 35 57.1
Avis.com 65 61.5* 35 45.7
AlaskaAir.com 46 58.7 28 50.0
ETN.nl 43 58.1 24 70.8*
LVRS.com 35 62.9* 21 33.3
LowestFare.com 33 75.8*** 17 52.9
HotelDiscount.com 25 80.0*** 12 58.3
Book Sites
Amazon.com 962 61.0*** 603 53.9*
BarnesandNoble.com 360 56.9** 204 50.0
Music Sites
CDNow.com 250 58.8** 152 57.2*
MusicBoulevard.com 176 53.4 99 61.6**
BestBuy.com 75 56.0 50 52.0
CDUniverse.com 42 52.4 23 65.2*
Overall 13076 57.7*** 8329 56.8***
*p < .05 (one-tailed).
**p < .01 (one-tailed).
***p < .001 (one-tailed).
a Number of visitors with three valid trials.
b Number of visitors with four valid trials.
Legend for the Chart
A n[a]
B Intercept, Parameter, gamma0
C Predictors: alpha, Parameter gamma1
D Predictors: β, Parameter gamma2
E Predictors: N-1, Parameter gamma3
F Predictors: alpha x(N-1), Parameter gamma4
G Predictors: βx(N - 1), Parameter gamma5
H Likelihood (5 Degrees of Freedom)
A B C D E F G H
Books
2824 -3.42*** -5.54* .035 -.044 .192 .005 14.23
Music
1526 -2.50*** -5.52** -.80*** .010*** .068* .009*** 13.02*
Travel
57639 -3.03*** -2.19*** -.45*** .001*** .000 -.002*** 516.08***
*p ≤ .05 (one-tailed).
**p ≤ .01 (one-tailed).
***p ≤ .001 (one-tailed).
a Number of observations (visits). Number of valid visits per
individual ranges from 3 to 678
GRAPHS: FIGURE 1: Power Law Learning Curves for Sites from the Travel, Music, and Books Categories
GRAPH: FIGURE 2: Probability of Purchase: Variation Over the Observed Range for Learning Rate (α) and Number of Visits to the Product Category (N) for Music Sites
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By Eric J. Johnson; Steven Bellman and Gerald L. Lohse
Eric J. Johnson is Professor of Marketing, Columbia Business School, Columbia University. Steven Bellman is Senior Lecturer, Graduate School of Management, University of Western Australia. Gerald L. Lohse is with the Customer Decision Analytics Practice at Accenture.
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Record: 32- Communicating the Consequences of Early Detection: The Role of Evidence and Framing. By: Cox, Dena; Cox, Anthony D. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p91-103. 13p. 1 Chart. DOI: 10.1509/jmkg.65.3.91.18336.
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COMMUNICATING THE CONSEQUENCES OF EARLY DETECTION:
THE ROLE OF EVIDENCE AND FRAMING
Despite the enormous benefits of early-detection products, consumers are reluctant to use them. The authors explore this reluctance, testing alternative approaches to communicating the consequences of detection behaviors. The results suggest that anecdotal messages are more involving than statistical messages and that positive anecdotes (about gains from screening) are less persuasive than negative anecdotes (about the losses from failing to get screened); positive anecdotes appear to cause a "boomerang" effect. The authors discuss implications for promoting consumer risk-reduction behaviors.
In recent years, there has been a proliferation of new products that enable the early detection of disease, including new scanning devices for detecting osteoporosis, genetic tests for inherited breast cancer risk, and home testing kits for HIV and colon cancer (see, e.g., Ameghino 1998; Farhan 1996; Gavaghan 1998). Such products not only represent potential revenue streams for the firms that develop them but also provide a potential means for achieving two important but often incompatible societal goals: improving public health and simultaneously reducing health care costs (see, e.g., Elder et al. 1994; Reagan 1992). Many of the major killers in an industrialized society (e.g., high blood pressure, diabetes, cancer) are insidious diseases, doing much of their damage before the patient experiences symptoms. If such diseases can be detected early in their development, they can be treated much more effectively, saving both lives and money.
However, the success of early detection programs requires more than new technology. It also requires widespread consumer adoption of screening products, and gaining consumer adoption of these products is a major marketing challenge. Despite the many benefits of screening, consumers are often reluctant to participate in it (see, e.g., Andreasen 1995; Elder et al. 1994).
One source of this reluctance appears to be a general consumer ambivalence toward problem-detection products, ranging from home radon detection services (Weinstein and Lyon 1999) to drug testing kits marketed to worried parents (Snyder 1996). Consumers who do not believe that they are susceptible to a given problem may question the benefit of being tested for something they "know" they do not have. In contrast, consumers who are concerned about having an underlying problem may have anxiety about the test's outcome, making them reluctant to find out whether their fears are justified (see McCaul et al. 1996). Although the long-term benefits of early problem detection are often great, the short-term outcome may be bad news. And many people are ambivalent about seeking out bad news. While proverbial wisdom advises that "Forewarned is forearmed" and "Knowledge is power," it also advises that "Where ignorance is bliss, 'tis folly to be wise" and "So long as I know it not, it hurteth me not" (Simpson 1982).
Consumers' ambivalence about screening outcomes raises an important question for advocates of problem-detection products: In designing messages to persuade consumers to adopt such products, how should potential testing outcomes be portrayed? In this study, we examine how consumers' beliefs and attitudes toward screening are affected by two specific message-design factors: (1) whether screening consequences are communicated with anecdotal evidence or statistical evidence and (2) whether these consequences are framed in terms of potential losses or potential gains. Each of these two factors is discussed subsequently.
The Effects of Anecdotal Versus Statistical Evidence
When communicating the potential consequences of a behavior, promoters can employ either a specific illustrative anecdote or more general population statistics. For example, in communicating the benefits of wearing a seat belt, promoters could either tell the story of a specific person whose life was saved by wearing a seat belt or cite statistics on the lower accident fatality rates among passengers wearing seat belts. From an objective standpoint, statistics are generally more informative than anecdotes (because an isolated anecdote can be found to support almost any point of view). However, research suggests that audiences tend to be more interested in and influenced by anecdotal than statistical evidence (see e.g., Brosius and Bathelt 1994; Hogarth 1980; Taylor and Thompson 1982). In interpreting these findings, Hogarth (1980, p. 98) posits that "specific case data are ... encoded and remembered on several dimensions with a correspondingly rich set of meaningful associations" (see also Shedler and Manis 1986). In contrast, many subjects seem to "tune out" abstract generalizations, especially statistical generalizations. Brosius and Bathelt (1994, p. 50) note the "difficulties people have in processing ... percentages, probability, and so forth," and Taylor and Thompson (1982, p. 162) note subjects' tendency to "underuse ... statistical information."
Although anecdotal messages may be more involving than statistical messages, this does not necessarily mean that they are more persuasive. Petty and Cacioppo (1981) argue that involving messages can be either more or less persuasive than noninvolving messages, depending on the perceived strength of the arguments contained in these messages (see also Eagly and Chaiken 1993).
Framing the Consequences of Consumer Health Decisions
In promotions for any course of action, either the potential gains from pursuing it or the potential losses from not pursuing it can be emphasized. For example, an advertisement could emphasize either the money gained by mailing in a rebate or the money lost by not mailing it in. In a series of experiments involving hypothetical choice situations, Tversky and Kahneman (1981) find that people are more likely to pursue an action when it is framed as a means to avoid a loss rather than to achieve a gain. According to Tversky and Kahneman's "prospect theory," decision makers are motivated by both losses and gains but tend to give greater decision weight to potential losses (see also Kanouse 1984). This is especially true when the consequences of an action are delayed. Behavioral economists have found that the decision weight given to a consequence is "discounted as a function of the delay of its delivery" (Madden 2000, p. 16) and that future gains tend to be discounted more than future losses (see, e.g., Simpson and Vuchinich 2000).
Tversky and Kahneman's (1981) experiments show the effects of framing on hypothetical choices, typically between monetary bets or public policy options. However, Meyerowitz and Chaiken (1987) extend this research to the study of consumers' personal health behavior, specifically breast self-examination (BSE) behavior among female college students. Consistent with Tversky and Kahneman's hypothesis, these researchers find that subjects are more motivated to participate in BSE when messages stress the potential losses from not performing BSE rather than the potential gains from performing BSE.
However, when subsequent researchers have examined the effects of gain versus loss framing in the context of real consumer health decisions, the results have been mixed. Some studies have found loss framing more persuasive (e.g., Banks et al. 1995; Meyerowitz and Chaiken 1987), some have found gain framing more persuasive (e.g., Rothman et al. 1993), and still others have found no framing effects at all (e.g., Lauver and Rubin 1990). These mixed findings have prompted researchers to search for potential moderators of framing effects.
Message Involvement as a Moderator of Framing
Maheswaran and Meyers-Levy (1990, p. 361) suggest that framing effects are moderated by subjects' involvement with the framed message. Specifically, they posit that "negatively framed messages should be more persuasive than positively framed ones when issue involvement is high." To derive this hypothesis, the authors integrate Tversky and Kahneman's prospect theory with the cognitive response theory of persuasion (e.g., Petty and Cacioppo 1981): Prospect theory implies that loss-framed arguments will be perceived as stronger or more compelling than gain-framed messages. However, cognitive response theory posits that for subjects to be influenced by argument strength, they must first be sufficiently involved with message content to evaluate argument strength. Consistent with this hypothesis, Wright and Weitz (1977) find that women show a greater aversion to negative features of birth control devices when purchase is imminent (which increases motivation to process the information) than when purchase will take place in the distant future. In interpreting these and other findings, Wright (1981, pp. 279) argues that "overweighting [of negative information] may only occur when an audience member is sufficiently concerned over message content to bother generating reactions and integrating those into an overall impression, and to worry about making errors in this."
To test their involvement hypothesis, Maheswaran and Meyers-Levy (1990) conducted a 2 2 experiment in which college students were given information on cholesterol screening. Involvement was manipulated by telling subjects either (1) that heart disease can affect people 20 to 29 years of age (high involvement) or (2) that heart disease affects only the elderly (low involvement). Next, subjects were given cholesterol-screening messages, which were either gain framed (stressing the health benefits of screening) or loss framed (stressing the health risks of not being screened). As hypothesized, loss frames were more persuasive among high-involvement subjects. Maheswaran and Meyers-Levy (1990, p. 366) concluded "that negatively framed appeals can be highly persuasive ... only if individuals who receive the appeal are sufficiently involved with the issue." In addition, the experiment indicated that among low-involvement subjects, gain framing was more persuasive than loss framing. In interpreting this finding, the authors posited that, though subjects who scrutinize gain-framed messages judge them to be weak arguments, less involved subjects simply view them as positive peripheral cues, which enhances persuasion among these subjects (see Petty, Cacioppo, and Schumann 1983).
Subsequent experiments have replicated the finding that loss-framed messages are more persuasive among message-involved subjects, though they have tended to find no effect of framing under low-involvement conditions. Rothman and colleagues (1993) examine how the effectiveness of alternatively framed messages to promote skin cancer screening is moderated by involvement. However, instead of manipulating involvement, the authors use subjects' sex as a surrogate for involvement. Citing evidence that "women as compared to men were more concerned about sun tanning and skin cancer and, therefore, were considered more involved with the health issue," they conclude that sex "was a reasonable proxy for degree of issue involvement" (Rothman et al. 1993, p. 421). As predicted, loss-framed messages were more effective among women, whereas framing had little effect on men. Rothman and colleagues (1993, p. 420) conclude that "exposure to negatively framed information led women to be even more likely ... to intend to obtain a skin exam."
Finally, Block and Keller (1995) examined the raming-involvement interaction, manipulating involvement by altering the perceived effectiveness of the target behavior. Gleicher and Petty (1992) found that when subjects were assured that a protective behavior was effective, they were less likely to scrutinize the subsequent arguments that support this behavior (perhaps fearing that such scrutiny would weaken their confidence in the protective behavior) than when they were told that the behavior's effectiveness was doubtful. On the basis of this research, Block and Keller (1995) hypothesized that messages advocating low-efficacy health behaviors would elicit greater message involvement than messages advocating high-efficacy behaviors, and therefore loss-framed messages would be more persuasive for low-efficacy behaviors. They tested these hypotheses in two experiments, the first involving a sexually transmitted disease and the second involving skin cancer. In both experiments, subjects in the low-efficacy conditions exhibited greater message involvement/processing, and among such subjects loss framing induced more favorable attitudes and intentions than did gain framing. However, among subjects exposed to the less involving (high-efficacy) message, framing had no effect. On the basis of these experimental results, Block and Keller (1995, p. 192) concluded that "when subjects process [messages] in-depth, negative frames are more persuasive than positive ones."
Behavior Type as a Moderator of Framing
In a recent review article on framing effects and consumer health behavior, Rothman and Salovey (1997) acknowledge that message involvement might play a role in moderating such effects. However, they suggest that an additional moderator might be at least as important as involvement: the type of health behavior being promoted. Rothman and Salovey note that in studies involving prevention behaviors (such as using sunscreen or infant car seats), gain-framed advocacies were often more persuasive than loss-framed behaviors. However, in studies involving disease-detection behaviors (such as screening for cancer or heart disease), loss framing is typically more persuasive.<SUP>1</SUP>
In interpreting this difference, Rothman and Salovey (1997) argue the following:
- The relative effectiveness of gain and loss frames depends on how well each frame matches subjects' prior perceptions about the target behavior; in other words, "we must consider how framed information is integrated into prior perceptions" (p. 9).
- Consumers tend to view detection behaviors in a negative light and thus are less receptive to messages that stress the benefits of these behaviors than to messages that stress the even greater risks/costs of failing to engage in these behaviors. Rothman and Salovey argue persuasively that consumers tend to perceive disease-screening behaviors as inherently unpleasant; some of the unpleasantness is fairly certain (e.g., cost, discomfort, embarrassment, inconvenience), whereas other aspects are less certain (e.g., the possibility of bad news about the patient's health).
- Therefore, the best way to position screening is as the lesser of two evils-that is, to point out the even greater unpleasantness that can result from not screening and hope that in this context, consumers will view getting screened as the "least bad" option. This is what a loss-framed message does. A gain-framed message, in contrast, tries to motivate screening by stressing its benefits. According to Rothman and Salovey, this positioning runs counter to consumers' predominantly negative attitudes toward disease-detection behaviors. Therefore, a gain-framed screening advocacy is likely not to be persuasive.
The Potential for Boomerang Effects
Thus, Rothman and Salovey (1997) conclude that among essage-involved subjects, gain-framed messages will be less persuasive than loss-framed messages. However, they do not pursue another implication of their reasoning, which has not been investigated in any previous study either: If gain-framed messages conflict with consumer perceptions of isease-detection behaviors, perhaps such messages are not only unpersuasive but counterpersuasive-that is, perhaps gain-framed messages create a boomerang effect, shifting consumer attitudes in the opposite direction from that intended by the advocacy. Similar to Rothman and Salovey (1997), Petty and Cacioppo (1981, p. 225) observe that when audiences "relate information in the message [to] re-existing knowledge about the topic, ... they may either agree or disagree with the message." However, Petty and Cacioppo (1981, p. 225) go on to note that subjects' "antagonistic ... responses may be so much more persuasive than the arguments contained in the message that a position opposite to that advocated might be adopted."
Such boomerang effects have been discovered in a variety of persuasive contexts, including exposure to messages that are highly discrepant from subjects' prior attitudes (Dignan et al. 1985; Whittaker 1968), reactance against perceived strong persuasive intent (Snyder and Wicklund 1976), statements of facts already presumed to be true (Gruenfeld and Wyer 1992), and presentation of extremely counterstereotypical examples (Kunda and Oleson 1997). In addition, Sutton, Balch, and Lefebvre (1995) have speculated that efforts to educate consumers about cancer risk factors can sometimes decrease consumers' motivation to undergo cancer screening. However, no previous study has examined the potential role of a boomerang in message framing effects.
The present study is explicitly designed to search for boomerang effects. In addition to the treatment groups exposed to framed messages, the experiment includes a control group that was not exposed to any message. This enables us to determine whether gain-framed messages are merely less persuasive than loss-framed messages or whether gain-framed messages produce more negative attitudes toward the advocated behavior. As noted previously, several studies have found that involved subjects who are exposed to a loss-framed message have more positive attitudes toward the target behavior than involved subjects who are exposed to a gain-framed message, and researchers have attributed this difference to a proadvocacy attitude change created by the loss-framed message. Of the framing experiments discussed previously, only Meyerowitz and Chaiken's (1987) included a control group. These authors did not evaluate the possibility of a boomerang effect for gain-framed messages. However, an examination of their cell means reveals that compared with control subjects who saw no framed message, subjects exposed to a gain-framed message had more negative responses to the target behavior on three of the five dependent measures. Meyerowitz and Chaiken do not provide sufficient information to test for the statistical significance of these differences (and because n = 79, the power of such tests would be low). Nonetheless, their findings suggest the possibility of a boomerang effect for gain-framed messages.
Hypotheses
On the basis of the preceding discussion, we hypothesize the following consumer responses to messages advocating screening behaviors:
H1 : Among subjects exposed to (low-involvement) statistical messages, framing will have little effect on attitudes and beliefs toward the target behavior.
H2 : Among subjects exposed to the (high-involvement) anecdotal messages, gain framing will be less persuasive than loss framing. Compared with loss-framed messages, gain-framed messages will (a) be perceived as having less informational value, (b) be perceived as less persuasive (i.e., less likely to influence subjects' future behavior), (c) elicit more negative beliefs toward the detection behavior, and (d) elicit more negative attitudes toward the detection behavior.
H3 : Anecdotal/gain messages will create a boomerang effect. Compared with control subjects who are not exposed to any advocacy, subjects who are exposed to anecdotal/gain messages will (a) have more negative beliefs toward the detection behavior and (b) have more negative attitudes toward the detection behavior.
Target Behavior and Population
We tested our hypotheses in the context of a specific screening behavior: mammography. Breast cancer is one of the leading causes of death among women, causing about 44,000 deaths annually in the United States alone (Roberts 1996). One of the most effective ways to reduce breast cancer deaths is to encourage women to have regular screening mammograms. Among women older than 50 years of age, clinical trials indicate that screening mammography can reduce the mortality rate of this disease by 30%-50% (Reynolds and Jackson 1991). However, despite the procedure's benefits, many women either do not have mammograms at all or have them less often than is recommended by the medical experts. For example, only about 25% of Medicare recipients follow the National Cancer Institute recommendation to have annual mammograms (Centers for Disease Control and Prevention 1995a). Research indicates that attitudinal factors, including ambivalence about potential test outcomes, play a central role in women's failure to get mammograms (for a review, see Fuller et al. 1992).
Experimental Design
The study employed a 2 (statistical or anecdotal evidence) 2 (gain versus loss framing) between-subjects experimental design with a control group. Control subjects were given the same procedure and questionnaire as treatment subjects but saw no experimental advertisement. Each of 174 subjects was assigned randomly to one of the five experimental conditions: 117 to one of the four treatment conditions, and 57 to the no-advertisement control condition.
Subjects
A total of 174 women over the age of 50 years were recruited from social and volunteer organizations in a Midwestern metropolitan area. As noted previously, women over 50 are the primary target for annual mammograms. To encourage participation, organizations received $1 per subject and a chance to win $250 in a lottery. Subjects' ages ranged from 51 to 89 years, with a mean of 70. Subjects were 67% white, 27% married (23% divorced, 39% widowed, 10% never married, 1% other), and 90% high school graduates, and 81% reported "good" or "excellent" health. Thirty-six subjects reported that a family member had had breast cancer.
Compared with U.S. population averages for women over age 50, women in our sample had slightly greater levels of self-reported health and education and were considerably less likely to be currently married (see Centers for Disease Control and Prevention 1999). Therefore, caution should be used in generalizing our findings to all women over age 50. However, analysis of our data indicated that subjects' attitudes toward mammography and breast cancer and their responses to the experimental messages did not differ according to their self-reported health status, education, or marital status.
Experimental Stimuli
The stimuli were four black-and-white advertisements. Each had the headline "Why Should You Get a Mammogram?" and contained the same basic information on breast cancer and mammography, which was drawn from health education materials produced by the National Cancer Institute and the American Cancer Society. All advertisements provided a telephone number "For information on where to obtain a screening mammogram near you" and a picture of a telephone.
However, the advertisements differed in the type of evidence used (statistical or anecdotal) and whether the consequences of mammography were framed in terms of gains or losses. Specific statistics were based on the clinical research finding that annual screening mammograms reduce a woman's chance of breast cancer death by 30%-50%. The statistical gain message used the more conservative figure: a 30% reduction in risk if a woman has a mammogram. The statistical loss message stated the equivalent risk increase: a 43% increase in risk if a woman fails to have a mammogram.<SUP>2</SUP> Initial versions of the messages were developed, pretested for ease of comprehension with ten women over 50, and revised. This resulted in the following four messages:
- Statistical, gain: "Many women have no family history of breast cancer and have never felt any lump in their breast. But they follow the advice of the American Cancer Society and start having annual screening mammograms when they turn fifty. Because of this, doctors are able to detect their tumors at an early, treatable stage, and they are 30% less likely to die of breast cancer."
- Statistical, loss: "Many women have no family history of breast cancer and have never felt any lump in their breast. So they don't follow the advice of the American Cancer Society to start having annual screening mammograms when they turn fifty. Because of this, doctors are not able to detect tumors at an early, treatable stage, and they are 43% more likely to die of breast cancer."
- Anecdotal, gain: "No one in Sara Johnson's family had ever gotten breast cancer, and she had never felt any lump in her breast. But she followed the advice of the American Cancer Society and started having annual screening mammograms when she turned fifty. Because of this, doctors were able to detect her breast tumor at an early, treatable stage, and now Sara can look forward to a long life, watching her grandson, Jeffrey, grow up."
- Anecdotal, loss: "No one in Sara Johnson's family had ever gotten breast cancer, and she had never felt any lump in her breast. So she didn't follow the advice of the American Cancer Society to start having annual screening mammograms when she turned fifty. Because of this, doctors were not able to detect her breast tumor at an early, treatable stage, and now Sara may miss out on a long life, watching her grandson, Jeffrey, grow up."
Subjects received a booklet containing four black-and-white advertisements: a randomly assigned mammography advertisement and three dummy advertisements (for vitamins, soap, and insurance); the latter were the same for all groups. The treatment advertisement was always the second advertisement in the booklet. The control subjects did not see a mammography advertisement; in its place was a fourth dummy advertisement.
Manipulation and Confounding Checks
To verify that the anecdotal messages were more involving than the statistical messages, we conducted a pretest. We recruited 96 women over the age of 50 years (none of whom participated in the main experiment) from local organizations. Each subject viewed one (randomly assigned) message for 30 seconds and then reported agreement with six statements: "I got involved in what the ad had to say," "The ad's message seemed relevant to me," "This ad really made me think," "This ad was thought-provoking," "The mammogram ad was very interesting," and "I felt strong emotions while reading this ad." These six items formed a summed scale with a coefficient alpha of .96. Analysis confirmed that the anecdotal messages were significantly more involving (mean = 33.52) than the statistical messages (mean = 25.11; t = 6.81, p < .001).
Rothman and Salovey (1997) suggest that framing effects are moderated not only by involvement but also by the perceived risk of the behavior that is being promoted. Therefore, it was important to establish that subjects in the two evidence conditions did not differ in their perceptions of the risk in getting a mammogram. Toward this end, we conducted a second pretest. We recruited 65 women over the age of 50 years (none of whom participated in either the previous pretest or the main study) from local organizations. Each subject viewed one of the (randomly assigned) target messages for 30 seconds and then reported her agreement (on a seven-point scale) with five statements: "Getting a mammogram is risky," "Mammograms can lead to bad results," "Mammograms have uncertain outcomes," "Getting a mammogram makes me feel anxious," and "Getting a mammogram would cause me to worry." (These statements were based on Rothman and Salovey's [1997] and Dowling and Staelin's [1994] conceptualizations of perceived risk.) The five items formed a summed scale with coefficient alpha of .77. Perceptions of behavioral risk did not differ between subjects exposed to anecdotal messages (mean = 12.5) and subjects exposed to statistical messages (mean = 11.97; t = .33, p = .75).
Procedure
We randomly distributed booklets among groups of women at each participating organization. (Such organizations are frequently the setting for the presentation of health-related information to older adults; see, e.g., List et al. 1999.) While pretesting the materials, we had found that some women in this age group had difficulty completing scales. We made several modifications to the questionnaire to help facilitate this process. First, we used a 13-point type size to increase readability. Second, the experimental procedure began with a warm-up exercise in which subjects completed Likert scales for statements about a local retailer, which were unrelated to the study topic. When the warm-up exercise was completed and subjects indicated that they understood how to complete numerical scales, the experiment began. All subjects (both treatment and control) received identical instructions:
We are interested in your thoughts about some health topics and about some advertisements for mature women focusing on those health topics. In a moment, you will be asked to view four proposed advertisements. We will give you about 30 seconds to look at each ad carefully. Please do NOT look back at any ad after the examiner says STOP. Directly behind these ads is a questionnaire.... Due to the limited time, we will be asking different questions to different people.
No mention of breast cancer or mammograms was made before ad exposure. After ad exposure, subjects completed the questionnaire. Consistent with the cover story (that the study focused on "health topics," and "we will be asking different questions to different people"), all questionnaires stated, "You have been chosen to answer questions about mammograms (breast cancer screening x-rays) and other health behaviors concerning breast cancer."
Measures
Beliefs about target behavior. Subjects reported agreement (on a seven-point Likert scale) with 16 belief statements about breast cancer and mammography. These health belief items were adapted from prior studies of mammography behavior (Stein et al. 1992). The 16 items were entered into a principal components analysis by means of a varimax rotation, which produced four interpretable, orthogonal factors with eigenvalues greater than 1.0. The first factor was labeled "mammography benefits" (e.g., mammography can detect a tumor when your doctor can't, mammography is effective in early detection of breast cancer, breast cancer can be cured if detected early). The second factor represented "mammography barriers" (e.g., mammograms are embarrassing, inconvenient, painful, cost too much). The third factor represented "perceived susceptibility to breast cancer" (e.g., more likely than average to get breast cancer, get breast cancer sometime in life). The fourth factor was labeled "risk factor knowledge" (e.g., can develop breast cancer without symptoms, can develop breast cancer without a family history of breast cancer).
Evaluation of the advertisement. Subjects rated the informational value of the mammogram advertisement on eight semantic differential scales (believable/not believable, realistic/not realistic, factual/not factual, good/bad, useful/not useful, appropriate/not appropriate, helpful/not helpful, and educational/not educational). These items were combined to form a summed scale with a coefficient alpha of .74.
In addition, each subject assessed the likelihood that the advertisement would influence her behavior. Subjects were asked, "If you saw this advertisement in a magazine, how likely would you be to go and get a screening breast mammogram?" Finally, subjects' overall attitude toward screening mammography was assessed by asking them to express their agreement or disagreement with the statement, "I think women my age should have a yearly mammogram."
Effects of Framing Within Anecdotal Versus Statistical Messages
Our first analyses examined whether the impact of framing on subjects' beliefs and attitudes varied depending on the type of evidence (statistical versus anecdotal) in the message. We conducted 2 2 analyses of variance (ANOVAs), in which the factors were evidence (statistical versus anecdotal) and framing (gain versus loss). The dependent variables included subjects' evaluations of the advertisement's informational value, perceived behavioral influence of the advertisement, beliefs about the target behavior and disease (benefits, barriers, susceptibility, and risk factor knowledge), and overall attitude toward the target behavior. Cell means from these ANOVAs are presented in Table 1.
Effects on advertisement evaluations. Neither framing nor evidence had a main effect on subjects' evaluations of the advertisement's informational value. However, there was a significant interaction between framing and evidence (F(1,87) = 10.08, p = .002). To test our hypotheses regarding this interaction, we analyzed the simple effects of framing within each of the two evidence conditions (see, e.g., Keppel 1982, pp. 214-19). As hypothesized, loss-framed information was evaluated as having significantly more informational value among subjects who were exposed to the (more involving) anecdotal messages (F(1,87) = 10.45, p < .01), whereas framing had no significant effect among subjects who were exposed to the statistical messages (F(1,88) = 2.51, N.S.).
Effects on predicted behavior. Framing and evidence also had an interactive effect on subjects' predictions of their own mammography behavior (F(1,103) = 10.87, p = .001). As hypothesized, loss framing was more persuasive among subjects who were exposed to the anecdotal presentation <BR> (F(1,103) = 7.57, p < .01). Within a statistical presentation, the subjects who were exposed to the gain message had a slightly higher mean, but this effect was not significant at p < .05 (F(1,103) = 3.77, p = .06).
Effects on overall attitude toward screening mammography. The analysis also revealed a significant interaction of framing and evidence type on subjects' overall attitude toward the target behavior (F(1,108) = 7.19, p = .008). Within anecdotal messages, loss framing produced more positive attitudes toward the target behavior (F(1,108) = 11.14, p < .01). Within statistical messages, framing had no effect on mammogram attitudes (F(1,108) = .23, N.S.).
Effects on mammogram beliefs. There were no effects of evidence or framing on subjects' beliefs about their susceptibility to breast cancer, the perceived benefits of mammography, or risk factor knowledge. However, there was an interactive effect of framing and evidence on perceptions of the mammogram barriers (F(1,104) = 8.92, p = .004). In anecdotal messages, loss framing produced lower perceived barriers to mammography (F(1,104) = 8.65, p < .01). In statistical messages, framing had no significant effect (F(1,104) = 1.69, N.S.).
Mediation Analysis
Thus, among subjects exposed to the high-involvement anecdotal messages, loss frames were significantly more effective than gain frames. To help interpret these findings, we conducted mediation analysis. This analysis followed James and Brett's (1984): If the effects of an antecedent (A) on an outcome (O) are completely mediated by a third variable (M), the three simple correlations (rao , ram , and rmo ) should be statistically significant, but the correlation between the antecedent and the outcome should become nonsignificant when the mediator is controlled (i.e., rao.m = N.S.).
We tested several mediational models, including one in which the impact of framing on predicted behavior was mediated by beliefs and attitudes toward the behavior:
framing --perceived barriers-attitude-predicted--behavioral response
However, although each of the successive simple correlations implied by this model is statistically significant (e.g., framing.barriers , rbarriers.attitude , rattitude.intention ), the model does not stand up to mediational analysis. For example, the correlation between framing and predicted behavior remains significant, even after perceived barriers and attitude are controlled for. This indicates that the impact of framing on predicted behavior is not mediated by attitudes or perceived barriers.
In addition, when framing is controlled for, the correlation between attitude and predicted behavior becomes nonsignificant. According to Pedhazur (1982), this result suggests that framing either (1) mediates a causal relationship between attitude and predicted behavior or (2) jointly (and independently) influences both attitude and predicted behavior. Since framing was randomly manipulated, it is exogenous and cannot possibly mediate the relationship between two measured variables. Therefore, the remaining plausible explanation is the second: Framing jointly and independently influences both attitude toward the target behavior and predicted behavioral response to the advertisement; that is,
framing-attitude--predicted behavioral response
Further analysis indicates that the impact of framing on attitudes is completely mediated by beliefs about barriers to the target behavior. Consistent with James and Brett's (1984) criteria, all three simple correlations are statistically significant (rframe,attitude = .37, p <FONT SIZE=4><</FONT> .01; rframe,barriers = -.38, p <FONT SIZE=4><</FONT> .01; rbarriers,attitude = -.41, p <FONT SIZE=4><</FONT> .01), but the impact of framing on mammogram attitudes becomes nonsignificant when perceived mammogram barriers are controlled (rframe,attitude.barriers = .22, p = .117).
The mediation analysis also suggests that the effect of framing on predicted behavioral response is mediated by evaluations of the advertisement's informational value. All three simple correlations are significant (rframe,behav = .34, p = .011; rframe,adeval = .44, p = .004; radeval,behav = .58, p < .001), but the effect of framing on predicted behavior becomes nonsignificant when advertisement evaluation is controlled (rframe,behav.adeval = .23, p = .131).
Comparing the Treatment Groups with the Control Group
As noted previously, when prior studies have found more positive attitudes among subjects who were exposed to loss-framed (versus gain-framed) messages, they have tended to conclude that the loss-framed messages elicited proadvocacy attitude change. Howener, in the absence of a no-message control group, such findings are open to another interpretation: that gain-framed messages cause counteradvocacy attitude change. To examine this issue, our experiment included a no-message control group.
We compared the attitudes and beliefs of subjects who were exposed to the four framed advocacy messages with the control subjects, who saw no advocacy at all. Table 1 shows the control and treatment groups' beliefs and attitudes toward mammography and breast cancer. Measures that referred specifically to the mammogram advertisement (e.g., advertisement evaluation) obviously were not applicable to the control group. We made significance tests using Dunnett's table of critical values for comparing treatment and control groups, where k (the total number of groups) equals 5 (see Winer, Brown, and Michels 1991, pp. 462, 977-78).
The control group comparisons produced several interesting findings. They revealed that the mammography attitudes and beliefs of subjects who were exposed to the low-involvement statistical advertisements were not significantly different from those of subjects who saw no mammogram message at all (all t-statistics less than 1). Thus, our findings not only suggest that framing has little effect on low-involvement subjects (as Block and Keller [1995] and Rothman et al. [1993] find) but also support Taylor and Thompson's (1982) finding that subjects tend not to rely on statistical information when making judgments (see also Brosius and Bathelt 1994; Hogarth 1980).
Consistent with prior theory (e.g., Maheswaran and Meyers-Levy 1990; Wright and Weitz 1977), control-group comparisons suggest that highly involving framed information influenced subjects' attitudes and beliefs about the advocated behavior. However, the pattern of this influence departed somewhat from that predicted in prior framing research. As we hypothesized, subjects who were exposed to a highly involving gain-framed message had substantially more negative attitudes and beliefs toward mammography than subjects who were exposed to no message at all. Indeed, the counteradvocacy effects of the gain-framed message appear to be stronger than any proadvocacy effects of the loss-framed message. Women exposed to the anecdotal/gain message had significantly less favorable overall attitudes toward mammography than the control subjects did (t = 2.26, p < .05) and were significantly more likely to deny their susceptibility to breast cancer than the no-advertisement controls (t = 2.48, p < .025). In addition, the anecdotal/gain subjects seemed to perceive greater mammogram barriers than the no-advertisement controls, though this difference (t = 1.88) did not reach statistical significance at the .05 level, according to the fairly conservative Dunnett test.
Although subjects exposed to the anecdotal/loss message had higher mean attitudes toward mammography (t = -1.48) and lower perceived barriers to getting a mammogram (t = 1.43), these effects were weaker than the counteradvocacy effects of the gain message, and none of the loss/control comparisons reached statistical significance. Therefore, it is not clear that the loss message created roadvocacy attitude change, as prior framing researchers suggest. However, the gain messages created counteradvocacy attitude change.
Depth Interviews
To aid further in the interpretation of the experimental findings, we conducted depth interviews among members of the target audience. The use of depth interviews to interpret experimental findings dates back at least to Merton and Kendall's (1946) classic article on the focused interview. These authors note (p. 542) that "The primary purpose of the focused interview was to provide some basis for interpreting statistically significant effects of mass communication" in experimental studies. In particular, they advocate the use of depth interviews "to locate the source of ... 'boomerang effects' in film, radio, pamphlet and cartoon propaganda" (see also Gorden 1980).
Fourteen women, ranging in age from about 50 to 80 years, were recruited to participate in individual depth interviews. None of these women had participated in the original experiment. Each subject was asked to examine each advertisement (anecdotal gain-framed and anecdotal loss-framed) for approximately 30 seconds and then report any "thoughts or feelings" she had while reading that advertisement (see Merton and Kendall 1946, p. 550). The order of advertisement presentation was varied from one interview to the next. After subjects had reported their reactions to each advertisement, they were asked to report their feelings and experiences regarding mammography. Subjects' comments were audiotaped, professionally transcribed, and then organized according to recurring themes (see Leydon et al. 2000; Ritchie and Spencer 1994).
Responses to the gain-framed message. As might be expected, many subjects reported positive emotions after reading the upbeat, gain-framed messages. For example,
"[I feel] happy. You felt positive after reading it, ... just a positive message."
"It is positive and hopeful ... because she can look forward to a long life."
"[I]t has a happy ending."
"[I]t is giving her hope."
"[I]t's warm inside and everything is wonderful."
"It makes me feel better already."
"[It] leads you to hope that things work out better. A positive outlook."
For many products, such positive feelings would be likely to enhance the persuasive power of an advertisement (see Monahan 1995). However, the elicitation of positive feelings is likely to have two effects that reduce subjects' motivation to use a disease-screening product. First, the elicitation of positive feelings may give subjects a false confidence that they are not vulnerable to the disease. Forest and colleagues (1979, p. 161) find that "People who are feeling good may exaggerate their sense of control over the environment and may feel less vulnerable." Consistent with this, the subjects in our experiment who were exposed to the anecdotal, gain-framed message perceived themselves to be significantly less susceptible to breast cancer (p < .025) than did the control subjects. In addition, several interviewees reported that the gain-framed message, though pleasant, was almost too reassuring-that it did not create a sense of urgency to get a mammogram and even fostered a sense of complacency:
"It doesn't do anything to create a sense of urgency.... Again, going back to the sense of urgency. Unless I have something that I'm concerned about, why go through it? ... Squashing your breasts...."
"[I]t's not a threat. And if you are not concerned about it, you are not going to accept that threat as your own."
"[T]here might be some women that the positive one would not have touched."
"[S]he never felt a lump, so she didn't see any reason to do anything. And so in that case, you know, if it's not broke, don't fix it."
"Well, I would probably think, well you know that is really something that I need to do, but maybe I'll do it next week."
"I would probably never stop and look at this message because I would think 'oh, I know.'"
"It didn't have the same emotional impact as [the loss-framed message]."
Second, the elicitation of positive feelings can make subjects less willing to engage in a task they perceive to be unpleasant. For example, both Forest and colleagues (1979) and Isen and Simmonds (1978) find that though elicitation of positive feelings generally makes subjects more likely to engage in helping tasks, there is a notable exception: The elicitation of positive feelings makes subjects less likely to engage in a helping task that they perceive to be unpleasant. Forest and colleagues (1979, p. 168) conclude that "persons who are feeling good are concerned about preserving their mood and will avoid activities that they expect would destroy their good feelings." Citing this research, Monahan (1995, p. 93) speculates that "If people are generally negatively disposed to an idea (say, wearing a condom), inducing positive affect ... may cause recipients of the message to denigrate the idea even further." Women clearly perceive mammography to be an "unpleasant task." When asked to discuss the procedure, subjects gave long and eloquent litanies of its short-term costs, including pain, expense, inconvenience, embarrassment, and fear.
"The fear of it maybe being painful.... [Y]ou get caught up in everyday life and you just don't do the things you should."
"[I]t is uncomfortable.... [I]f you've never done it before it is real scary.... [Y]ou don't know what is going to happen in there.... [T]hat's real scary, ... just the idea that a stranger is going to have you lay your [breast] up [on] a plate and squash it.... [B]efore I had my first one, I was really frightened of what it was going to be like, ... that and the expense if [you] don't have insurance."
"A lot of insurance doesn't pay for them.... If I didn't understand the reason for them, I wouldn't get them either because they hurt.... A man had to have designed that machine.... [J]ust the denial kind of thing. Some women just don't want to know. If I pretend it's not there, it will go away."
"They are uncomfortable.... And there are so many jokes about it [i.e., the pain].... I think, ... am I somehow increasing my chances, you know, x-rays, radiation? ... [Y]ou can never do it, at least not through my health plan. You can't do it at the same time you make your doctor's appointment. And so they give you a sheet and tell you to come back any time, but then there is like a line. I'm busy and I don't do it."
"[Women] are fearful of it,... especially some women who don't go to the doctor regularly are fearful of just what it entails, the procedure itself. And [some] people who are afraid because they think they do have something and so it's better not to know you have something."
"I think [women] hear that it hurts. They think it costs a lot of money for people who don't have access to it easily.... [I]ts not easy to get a mammogram actually.... I just think it's denial and inaccessibility and cost. And convenience. And it does hurt."
"I think in general women don't [take] care of their health for themselves as much as for their family members. They are busy with work and looking after everybody else, and they put themselves last. That's the reason. Lack of information about what goes on in a mammogram. And there is some fear that it might be painful."
"The pain.... You have to take time out of your day to go to the out clinic and get it all taken care of. Insurance doesn't cover it like it does other things."
"They hurt, ... [and] it is a scheduling issue.... [J]ust working it into your time and schedule, and you are looking at a time in women's lives when they are raising kids and working.... You don't have time to breathe. Much less, schedule a pancake squeeze."
"I think the cost. Just other stories that they have heard about, that they hurt, ... and some could even be embarrassed to have to go in to get a mammogram.... [Y]ou're supposed to go in yearly, and you get too busy and forget."
Responses to the loss-framed message. Subjects' reactions to the loss-framed message were, in many ways, the reverse of their responses to the gain-framed message. Whereas the gain-framed advertisement elicited positive feelings, the loss-framed message tended to elicit negative emotions, especially fear.
"This one is more scary.... I guess this one would be like if you wanted to scare somebody, this one would probably do it."
"Ooh, ... it's depressing. You know, they were not able to detect and Sara may miss out on a long life, watching her grandson. And that's just depressing. I was thinking, you know, she could. If you don't catch breast cancer, the chances are it gets into the bone."
"I'm scared when I read this one."
"That one is scary and I don't want to deal with it."
However, despite (or perhaps because of) the negative emotions elicited by the loss-framed message, many women found it compelling, especially in motivating an otherwise reluctant woman to take action:
"Some people would have to have something ... hit them in the face before they do anything about it."
"[T]he fear of something happening I think maybe gets your attention and makes you start thinking that maybe this is something I need to do."
"This one is like, wow, should they do it and look at all the negative things that are happening because she didn't do it."
"[H]ow important it is to follow the advice to get a mammogram because she didn't and now she's not going to see her grandson grow up, and telling you ... the risk patients are not following the advice.... I would take it just a little more to heart because it's telling that she did not see her grandson grow up and telling you, you know, well when the other one said she did so things worked OK, then this one shows things are not OK so you might tend to go along with it."
"That hit home a little more because ... she didn't get to see him grow up."
"My reaction is how sad that she is not going to see her grandson.... How important mammograms are."
"The person would miss out on a lot of her life because she didn't go get a mammogram.... They're fearful. And this kind of plays along with that."
"[T]his could happen to you too. You know, if you think about it this could happen to you if you don't get your mammogram."
"This [would affect people] because it seems more likely that Sara will die. And I think we all are afraid of death.... I guess I'll take care of it now."
"[I]t has a little more shock value to it. I guess it maybe draws you up a little shorter. You know, if you figure you are not going to watch your grandson grow up, that kind of thing might catch my attention more than something that says everything is fine."
"I think it's a fear that a woman has of having breast cancer and having this example where she is missing out on family life. It just strikes you more than a happy life that turned out OK."
"[S]omething could have been prevented that wasn't because she didn't have the checkup. And the consequence is terrible. So, it just seems the consequence from that ... is more striking than the good consequence of getting it.... I think the fear factor is working.... But I've known women who died of breast cancer, so maybe I can relate to that one."
"Because it's scary. It gives the idea that this can happen to you."
"That one hurts more. That one strikes home. You know, the thought of missing things in life.... That's what really struck me on that one.... Because that is more of a real threat. The idea that, OK, I have a lump, maybe I can get it treated. There is no sense of urgency in that. But ... if you are going to miss something ... that makes it much more real."
Although early-detection behaviors offer great potential benefits, consumers are often reluctant to engage in them. As a consequence, communication campaigns promoting these behaviors often meet with limited success. Our research examines how the persuasiveness of early-detection advocacies is affected by two message design factors: whether the consequences of screening are communicated through anecdotal or statistical evidence and whether these consequences are framed in terms of potential losses or potential gains.
As hypothesized, the effects of framing were moderated by the type of evidence employed in the persuasive messages. Among subjects exposed to the low-involvement statistical messages, framing had no significant attitudinal effect. However, among subjects exposed to high-involvement anecdotal messages, gain framing (compared with loss framing) elicited significantly more negative evaluations of both the advertisement and the advocated behavior. To this point, our findings are consistent with those of prior studies (e.g., Block and Keller 1995; Maheswaran and Meyers-Levy 1990; Wright 1981).
However, our experiment also produced some novel findings that may shed new light on the interpretation of message framing effects. When prior researchers have found that subjects exposed to loss-framed messages had more positive attitudes than subjects exposed to gain-framed messages, they have attributed this difference to proadvocacy attitude change created by the loss-framed messages. However, our experiment, employing a no-message control group, suggests a different interpretation. When we compared the attitudes and beliefs of our treatment groups with those of our control group, the results indicated that at least some of the effect of framing occurred because the anecdotal, gain-framed message had a negative effect on women's attitudes toward mammography. These data suggest that some framing effects may be due to a boomerang effect of the gain-framed message.
Why does this boomerang effect occur? Our data (from both the experiment and the depth interviews) show that consumers perceive screening to have many short-term negative consequences, and their perceptions of these negative consequences mediate the effects of framing on consumers' attitudes toward screening. Gain-framed messages are viewed as providing relatively weak arguments for getting screened, and these arguments do not provide consumers with a sufficient justification for enduring the short-term costs of the target behavior. In contrast, loss-framed messages are viewed as providing a more powerful argument for why consumers should endure the short-term discomfort of being screened.
Many health communicators favor gain-framed messages because they believe that such messages create a positive emotional response among the target audience (Backer, Rogers, and Sapory 1992; Monahan 1995). Consistent with this view, our interview subjects reported that the gain-framed message evoked pleasant emotions, including happiness and hopefulness. Ironically, however, the pleasantness of gain-framed messages may be one source of their weakness in promoting early detection. Previous research (e.g., Forest et al. 1979; Isen and Simmonds 1978) has found that after exposure to pleasant stimuli, subjects tend to perceive themselves to be less vulnerable and are less willing to engage in behaviors they perceive to be unpleasant. Consistent with this, subjects in our experiment who were exposed to the upbeat, gain-framed anecdotal message had both significantly more optimistic perceptions of their chances of getting breast cancer and significantly more negative attitudes toward getting a mammogram than did control subjects exposed to no mammogram message.
How do our findings compare with the "conventional wisdom" among designers of health communication campaigns? Backer, Rogers, and Sapory (1992) find that the majority of health communicators strongly favor the use of positive (gain-framed) messages. In summarizing these views, Backer, Rogers, and Sapory (1992, p. 30) state that health campaigns "are more effective if they emphasize positive behavior change rather than the negative consequences of current behavior. Arousing fear is rarely successful as a campaign strategy.... Campaigns are more effective if they emphasize current rewards rather than the avoidance of distant negative consequences."
Several of Backer, Rogers, and Sapory's (1992) respondents expressed concern that negatively framed appeals create a boomerang effect, reducing compliance with the message. For example, one respondent stated, "Unless fear appeals are done very cleverly, the evidence suggests that a negative reaction will be produced. The audience tends to discount the message or to behave counter to the message or simply to deny the message" (Backer, Rogers, and Sapory 1992, p. 54). In contrast, none of Backer, Rogers, and Sapory's respondents discussed circumstances under which gain-framed messages might be ineffective, much less cause a boomerang.
Further evidence regarding health marketers' attitudes toward gain versus loss framing can be obtained by examining the promotional materials they produce. We collected and content-analyzed 15 widely distributed promotional pieces that were designed to promote disease screening. These pieces encompassed several behaviors (including screening for breast cancer, colon cancer, and cervical cancer), sponsors (National Cancer Institute, American Cancer Society, a state health department, and a drug industry group), and media (print advertisements, pamphlets, media releases, Web sites, and slide shows). Of the 15 pieces, 14 contained only gain-framed arguments, and 1 included a combination of gain- and loss-framed arguments. Of 39 total framed statements, 37 were gain-framed, and 2 were loss-framed. Four of the messages were anecdotes about specific women who had gotten breast cancer, and all were gain-framed: Because of early detection, each of these women can look forward to healthy, productive lives.
Why do gain-framed arguments tend to dominate such campaigns? According to Latour and colleagues (Latour and Rotfeld 1997; Latour, Snipes, and Bliss 1996), there is a widespread wariness of negatively framed messages among marketing communication professionals, stemming, at least in part, from a misreading of the "fear appeal" literature. Although the modal finding of 50 years of research is that fear appeals are generally effective (see Hale and Dillard 1995; Latour and Rotfeld 1997; Petty and Cacioppo 1981), the one experiment every communication professional seems to remember (the "mainstay of textbooks," according to Latour and Rotfield 1997) is Janis and Feshbach's 1953 experiment, in which the ineffectiveness of a message that showed the dire consequences of poor dental care was attributed to "defensive avoidance." Among campaign designers afraid of such potential backlash to negative appeals, gain-framed appeals are apparently viewed as the safe alternative.
Areas for Further Research
Further research could build on the present study in several ways. First, it would be interesting to examine the long-term effects of evidence type and framing on consumer attitudes toward early detection, as well as their effects on actual screening behavior. Such behavioral follow-up would be somewhat challenging for mammograms, which are generally given (at most) annually or semiannually; however, it might be more feasible for screening behaviors that are performed more frequently (see Meyerowitz and Chaiken 1987).
Second, further research should examine the effects of message framing in increasingly naturalistic settings. For example, rather than provide subjects with a single forced exposure to a framed message, future experimenters should consider embedding framed messages unobtrusively among other media content to which consumers are exposed (e.g., a magazine, television program) as well as examining how repeated exposure may moderate the effects of framing (see Eagly and Chaiken 1993, pp. 286-87).
In addition, further research should continue to explore how framing effects are influenced by consumers' prior perceptions of the behavior being promoted. Of particular interest is the distinction, stressed by Rothman and Salovey (1997), between health behaviors aimed at detection versus prevention. Rothman and Salovey argue that consumers view detection behaviors in terms of their negative short-term consequences; therefore, they are especially receptive to loss-framed messages that advocate such behaviors. Our findings support that position. Our subjects associated mammography with a variety of unpleasant consequences (discomfort, inconvenience, embarrassment, and fear) that are shared by many screening behaviors, including tests for cancers of the colon, cervix, and prostate and HIV infection. In addition, our data indicated that the effect of message framing on attitudes toward the target behavior was mediated by consumers' perceptions of these negative consequences.
However, Rothman and Salovey's (1997) statements regarding prevention behaviors seem more debatable. They argue that consumers perceive prevention behaviors as having few negative consequences, and therefore consumers are more receptive to gain-framed appeals for these behaviors. For example, Rothman and Salovey (1997, p. 9) state that "In contrast to detection behaviors, the salient function of a preventive behavior is to provide a relatively certain, desirable outcome." But is this statement valid? The specific prevention studies Rothman and Salovey review focus on innocuous behaviors with few negative short-term consequences (e.g., using sunscreen or infant car seats). However, there are other prevention behaviors that consumers perceive as having substantial short-term costs. For example, many elderly people avoid having annual flu shots because they are afraid that the shot will hurt or make them sick (Centers for Disease Control and Prevention 1995b). Similarly, many women at risk of HIV infection fear the short-term consequences (especially the reactions of their partners) of insisting on the use of a condom (see, e.g., Darroch and Frost 1999). Further research should examine the relative effectiveness of loss- versus gain-framed messages in motivating prevention behaviors (e.g., vaccination, condom use, smoking cessation) that consumers perceive to entail significant short-term sacrifice.
Finally, further research should examine the extent to which the phenomena observed in this study apply not only to health-related behaviors but also to a much broader range of consumer behaviors. There are many situations in which consumers must decide whether to endure relatively certain immediate costs to avoid uncertain (but much more severe) future costs. For example, consumers may face this type of avoidance-avoidance conflict when deciding whether to purchase life or disability insurance or whether to perform expensive preventive home maintenance. In promoting such behaviors, should marketers employ the kind of gain-framed messages (e.g., "Thank God, Bill had life insurance") generally favored by marketing communicators (see LaTour and Rotfeld 1997), or is it possible that such messages are too reassuring to be effective? This would be a worthwhile avenue for further research.
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Experimental Cell Means
A= Dependent Measures a
B= Statistical Advertisements/Gain-Framed
C= Statistical Advertisements/Loss-Framed
D= Anecdotal Advertisements/Gain-Framed
E= Anecdotal Advertisements/Loss-Framed
F= No-Advertisement Control
Perceived informational value
of advertisement Perceived
likelihood of having a
mammogram, after seeing
advertisement 5.48 4.37 4.07b 5.54 N.A.
Overall attitude toward
mammography for women
older than 50 years 6.38 6.17 5.28b 6.71 6.14
Perceived barriers to
mammography -.17 .18 .34b -.44 -.11
Perceived susceptibility
to breast cancer .10 -.10 -.34c -.11 .23
Perceived benefits of
mammography -.28 -.08 -.10 -.11 .03
Risk factor knowledge -.20 -.10 .09 .01 .10
a Higher numbers indicate higher perceived informational value of
advertisement, greater perceived likelihood of getting a
mammogram, more positive attitude toward mammography, higher
perceived barriers and benefits to mammography, greater perceived
susceptibility to breast cancer, and greater risk factor
knowledge.
b The difference between anecdotal, gain-framed and anecdotal,
loss-framed messages is statistically significant at p < .01
c The difference between anecdotal, gain-framed message and
no-message control is statistically significant at p < .05
~~~~~~~~
By Dena Cox and Anthony D. Cox
Dena Cox and Anthony D. Cox are Associate Professors of Marketing, Kelley School of Business, Indiana University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 33- Conflicts in the Work-Family Interface: Links to Job Stress, Customer Service Employee Performance, and Customer Purchase Intent. By: Netemeyer, Richard G.; Maxham III, James G.; Pullig, Chris. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p130-143. 14p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.69.2.130.60758.
- Database:
- Business Source Complete
Conflicts in the Work-Family Interface: Links to Job
Stress, Customer Service Employee Performance, and Customer
Purchase Intent
Because customer service employees often represent the sole contact a customer has with a firm, it is important to examine job-related factors that affect customer service employee performance and customer evaluations. In two diverse customer settings, the authors capture matched responses from service employees, supervisors, and customers. The authors use the data to examine the potential chain of effects from customer service employee work-family conflict and family-work conflict, to job stress and job performance, to customer purchase intent (CPI). The results show direct (and indirect) effects of work-family conflict and family-work conflict on service employee customer-directed extra-role performance (CDERP). The results also show direct effects of job stress on service employee in-role performance (IRP) and CDERP and on CPI. Furthermore, the findings show that job stress has a more pronounced effect on IRP than on CDERP and that CDERP has a greater effect on CPI than does IRP. The authors conclude with a discussion of managerial and theoretical implications.
In recent years, the changing workforce (e.g., more dual career couples) and the nature of work itself (e.g., an increase in the number of service sector jobs) have given rise to conflicts in the work-family interface that may affect the work performance of employees and organizations (De Jonge and Dormann 2003). The conflicts may have their most pronounced effects in the stressful environment of customer service jobs. Customer service employees may take their jobs home with them, creating work-family conflicts (WFCs) that lead to additional stress at work, thus affecting their performance and customers' evaluations. Indeed, recent reports suggest that conflicts in the work-family interface lead to job stress, which in turn seriously impairs service employee performance (Molpus 2003; National Institute for Occupational Safety and Health 2002). Two stressors in this interface are WFC and family-work conflict (FWC); the former is a form of inter-role conflict in which the demands created by the job interfere with the performance of family-related responsibilities, and the latter is a form of interrole conflict in which the demands created by the family interfere with the performance of work-related responsibilities (Netemeyer, Boles, and McMurrian 1996). As with most WFC and FWC conceptualizations, our concepts view family as a larger, nonwork entity that includes responsibilities toward spouses, children, nonmarried partners, and home life in general (Adams, King, and King 1996; Frone 2000; Frone, Russell, and Cooper 1992).
To date, there is primarily anecdotal evidence of the effects of WFC and FWC on employee performance and customer outcomes. For example, USA Today reports that 32% of employees indicated that balancing work and family demands was their leading job-related concern (Armour 2002). Likewise, a Financial Times article notes that despite the best managerial support, the home demands of customer service employees may permeate their work lives and negatively affect their job performance (Furnham 2002). Given that customer service employees are highly stressed and struggle to balance the demands of several parties, it is important to examine the potential chain of effects from WFC and FWC, to job stress, to performance, and, ultimately, to customer purchase intent (CPI). Furthermore, because service employees often need to go beyond their inrole performance (IRP) to help customers, research must explore how the work-family interface affects customer-directed extra-role performance (CDERP) and customer evaluations.
Our research examines three questions pertaining to the potential chain of effects from WFC and FWC to CPI. First, how do WFC and FWC affect customer service employee performances? Are their effects mediated by job stress, or are they direct? Second, what is the effect of job stress on customer purchase intent? Third, what are the relative effects of WFC, FWC, and job stress on service employee performances? We collect multisource data in two settings (one business-to-consumer and one business-to-business) to address these questions.
Overview and Hypotheses
Figure 1 presents two models of the relationships among WFC, FWC, job stress, service employee IRP, service employee CDERP, and CPI. Although many frameworks have been proposed for studying the work-family interface, our models are derived from three mostly complementary theoretical perspectives: ( 1) interrole conflict theory (Kahn and Byosiere 1992), ( 2) identity theory (Thoits 1991), and ( 3) conservation of resources (COR)/resource drain theory (Hobfoll 2002). The three theories share the common premise that WFC, FWC, and job stress compete for or tax the employee resources of time, cognitive effort, and emotional and psychological energy.
The mediated model in Figure 1, Panel A, posits that the effects of WFC and FWC on CDERP are mediated by job stress, and the effect of job stress on CPI is mediated by the two performance constructs (i.e., IRP and CDERP). This model is consistent with the more traditional approaches of how job stressors (i.e., WFC and FWC) and job stress affect performance and customer outcomes (Beehr 2000; Kahn and Byosiere 1992); that is, job stress is an affective mediator of the effects of WFC and FWC on performance, and performance is a behavioral mediator of the effect of job stress on CPI. The incremental effects model in Figure 1, Panel B, posits that FWC and WFC directly affect CDERP beyond their effects on job stress, and job stress directly affects CPI beyond the effects of the performance variables. We compare the mediated and incremental effects models with the premise that the incremental direct effects of WFC and FWC on CDERP and of job stress on CPI are tenable.
Job stress and the effects of WFC and FWC. We define job stress as nervousness/anxiety associated with the job, affecting an employee's emotional and/or physical health (Cox, Griffiths, and Rial-Gonzalez 2000). Interrole conflict theory contends that WFC should affect job stress because of the competing demands that WFC places on time, cognitive, and emotional resources that are necessary to fulfill both work and family roles (Frone, Russell, and Cooper 1992). Similarly, identity theory contends that people have multiple role identities and devote resources to maintain identities that are salient to them. People experience job stress when they encounter impediments to these identities. Thus, WFC represents an impediment to successfully meeting the family role, which in turn increases job stress (Thoits 1995). In addition, the relationship between WFC and job stress is consistent with the COR/resource drain approach, which specifies that energy, cognitive effort, and so forth, are finite resources that people apply to both work and family. Thus, WFC leads to job stress because resources are lost in the process of juggling both work and family, creating elevated levels of stress at work (Hobfoll 2002).
Interrole conflict theory suggests that job stress is experienced if a person is struggling to meet the demands of a role because of interferences from the family role. As such, those who perceive high FWC may be overwhelmed by their home duties and experience job stress (Frone, Russell, and Cooper 1992). Consistent with identity theory and the COR/resource drain approach, FWC may represent an impediment to completing work duties, because the family role is competing for resources that are necessary to fulfill the work role. This undermines a person's work-role identity, increasing job stress. Scholars suggest that such an effect represents a spillover in which the demands of one role exacerbate the stress in another role (Edwards and Rothbard 2000).
H1: Both (a) WFC and (b) FWC positively affect (produce) job stress.
Service employees and job performance. Before offering rationale for the effects of job stress on customer service employee performances, we provide an overview of service employees' customer-linking behaviors, and we distinguish between IRP and CDERP. At least three customer-linking behaviors of service employees have been identified (Bettencourt and Brown 2003). First, service employees can enhance their firm's image by advocating the firm's products and services to third parties (i.e., customers). Second, service employees are in a position to unveil customer wants, needs, and expectations that can be communicated to the firm to serve customers better. Third, customer evaluations are contingent on the service employees with whom the customer interacts. Such high-quality interaction enhances a firm's customer evaluations. These customer-linking behaviors can help retain current customers and attract new customers through favorable word of mouth from current customers. Embedded in these behaviors are IRP and CDERP.
Our conceptualization of IRP is consistent with three works. First, Motowidlo and Van Scotter (1994) suggest that IRP reflects officially required performances that serve the goals of or support the technical core of the firm. Second, Singh, Verbeke, and Rhoads (1996) operationalize IRP in terms of performance with regard to knowledge of the firm's products, knowledge of customer needs, management of time, company resources, and expenses, as well as the number (quantity) of customers served. Third, Singh (2000) views IRP in terms of productivity and quality. Productivity reflects quantifiable output in terms of customer contacts/ calls and backroom functions (e.g., paperwork), and quality reflects formally mandated behaviors during the employee-customer interface. Consistent with these views, we define (and operationalize) IRP as ( 1) the quality of performance with regard to the employee's knowledge of the company, competitor products, and customers; ( 2) the quality of performance with regard to the accurate management of records, time, and expenses; and ( 3) the quantity of work achieved.
We focus on the most relevant type of extra-role performance in the customer service setting, CDERP. Borman and Motowidlo (1993) suggest that favorably representing the organization to customers is part of "contextual" customer performance that goes beyond an employee's in-role job requirements. Bettencourt, Gwinner, and Meuter (2001) and Bettencourt and Brown (1997) suggest that CDERP reflects employees' extra efforts to take initiatives that improve service when they communicate with customers, as well as conscientious extra efforts to respond to customer concerns. Consistent with these views, we define CDERP as the degree to which the service employee "goes the extra mile" in serving customers during the employee-customer interface.
Effects of job stress on performance. It is widely accepted that the customer service job is stressful (De Jonge and Dormann 2003), and it follows that job stress can hinder an employee's IRP. First, customer service employees typically believe that they lack control over their jobs and that they have the onerous task of satisfying conflicting demands of customers and managers. To magnify these issues, technology improvements have enabled service employees to handle more customer inquiries per hour, and employees rarely know the pace or nature of their work ahead of time (Schneider and Bowen 1995; Slepicka 2000). These factors contribute to job stress, detracting from the time that is necessary for such IRP tasks as record keeping and staying abreast of product knowledge. Second, job stress disrupts employees' ability to perform prescribed tasks, because it also strips them of the energy resources that are necessary to perform (Hobfoll 2002). Thus, we expect job stress to negatively affect IRP.
We also expect job stress to affect CDERP. Because IRP requires formal reporting that is expected by management, some portion of a service employee's resources is likely devoted to IRP, leaving fewer resources to engage in CDERP. This notion is consistent with the COR/resource drain view, and job stress theorists note that an indication of elevated job stress is when employees stop engaging in CDERPs (Jex 1998). In addition, frontline employees may "disengage" from performing CDERPs, because job stress drains the types of emotional and cognitive resources (patience, empathy) that are necessary to offer stirring performances during the employee-customer interface (De Jonge and Dormann 2003; Kahn 1990).
H2: Job stress negatively affects (a) IRP and (b) CDERP.
Effects of performance on CPI. Perhaps no other customer outcome is a better indicator of loyalty than the intent to purchase again from a firm (Oliver 1997). Service employee performance may play a key role in influencing such intent. For example, service employee role-prescribed behavior has been found to affect customer satisfaction and loyalty (Bettencourt and Brown 1997). Furthermore, CDERPs, as rated by customers, have been found to positively affect customer perceptions of justice in a complaint-handling setting (Maxham and Netemeyer 2003). Conceptual writings also suggest that CDERP "delights" customers and enhances their intent to repurchase (Schneider and Bowen 1999).
H3: Both (a) IRP and (b) CDERP positively affect CPI.
FWC and WFC effects on CDERP. Although we expect that job stress is the primary predictor of IRP and CDERP, we also expect that FWC and WFC explain incremental variance in CDERP beyond that which is explained by job stress. Boundary spanners, such as customer service employees, must balance the demands of groups internal (supervisor, customers) and external (family) to the job. Compartmentalizing the cognitive and emotional resources that are necessary to fulfill the demands is difficult, negatively affecting certain aspects of performance, namely, CDERP. Furthermore, effective CDERP requires employees to engage customers, patiently listen to their concerns, and quickly develop suitable resolutions. Employees might not be able to perform these critical work roles effectively when they are expending considerable cognitive and emotional resources to resolve conflicts with their lives outside of their jobs (Kahn 1990). As such, employees may disengage from performing, and we posit that FWC negatively (and incrementally) affects CDERP.
We also contend that WFC has an incremental direct effect on CDERP. Edwards and Rothbard (2000) suggest that conflict in one domain reduces the personal capability to meet demands in the other domain, thereby inhibiting role performance in the other domain. They suggest that such an effect is direct, as well as indirect, through a mediating mechanism such as mental/physical health (i.e., job stress). Identity theory further contends that competing salient roles (family and work) potentially have a spillover effect, implying that work that interferes with family may affect CDERP. Finally, in his conceptualization of being personally disengaged from work, Kahn (1990) suggests that conflict in the work-family interface directly affects performance, particularly in service-related jobs. He suggests that employees may withdraw from a work role because resources that are necessary for that role (i.e., patience, understanding, and cognitive effort endemic to CDERP) are the same resources that are devoted to an outside role (family).
H4: Beyond the effect of job stress on CDERP, (a) FWC and (b) WFC negatively affect CDERP.
Effect of job stress on CPI. We posit that beyond the effects of employee performances, job stress incrementally affects CPI. Our rationale is as follows: First, although employees are believed to self-regulate by trying to conserve resources that are necessary to perform their jobs, the resources are quickly depleted when the job situation is stressful (Muraven, Tice, and Baumeister 1998). In customer service settings, there is a heightened potential for contentiousness between service employees and customers, as customers impose a variety of cognitive, emotional, and behavioral demands on service employees that may lower overall customer evaluations (De Jonge and Dormann 2003).
Second, customers are likely to detect the manifestations of job stress in the forms of poor decision making, lack of self-control, carelessness, or inhospitable looks. In effect, this lowers the ability to generate CPI, because service employees who experience job stress may not provide quality interactions with customers. Such a process concurs with Kahn's (1990) view of disengagement in critical moments of the work role. Specifically, job stress may directly affect CPI by inhibiting the service employee from offering a stirring role performance when interacting with customers. Customers may sense this and give low CPI ratings. Finally, there are anecdotal writings that suggest that when employees experience less job stress, customers rate the service they receive more favorably, creating positive customer evaluations (Furnham 2002; Schneider and Bowen 1999).
H5: Beyond the positive effects of the performance constructs on CPI, job stress has an incremental negative effect on CPI.
WFC versus FWC and their effects on CDERP. It is important to understand whether FWC or WFC has a more detrimental effect on CDERP. Conventional wisdom and interrole conflict theory suggest that FWC should have a greater effect. That is, FWC reflects the degree to which the family role interferes with performing work-related tasks, whereas WFC reflects the degree to which the job role interferes with performing family-related tasks. Interrole conflict theory further suggests that negative behavioral outcomes are more likely experienced in the domain to which the source of the conflict is directed (i.e., family interfering with work) (Netemeyer, Boles, and McMurrian 1996). Finally, the employee resources that are necessary to fulfill the family role (patience, understanding, and empathy) are the same resources that are endemic to CDERP. As the family role depletes the resources, less of the resource is available for CDERP. Thus, we expect that FWC has a stronger effect on CDERP than does WFC.
H6: The effect of FWC on CDERP is stronger than the effect of WFC on CDERP.
Job stress and its effect on IRP versus CDERP. Does job stress have a stronger effect on IRP or on CDERP? Considering the type of extra-role performance that we examine, we believe that job stress has a stronger effect on IRP. Identity theory suggests that the strength of identification with a role dictates the resources that are applied to the role. This tenet also holds for a role within a given role (IRP versus CDERP within the overall work role) (Rothbard and Edwards 2003). Specifically, the goals of the firm are at the core of an employee's work-role investment. In a customer service context, a firm's goal is to maximize customer loyalty, and employees may allow job stress to affect IRP more than CDERP because they identify more strongly with the CDERP aspect of the work. In addition, consistent with the attraction-selection-attrition framework (Schneider, Goldstein, and Smith 1995) and Bitner, Booms, and Mohr's (1994) work, boundary spanners are attracted to their positions because they possess the characteristics and the desire to provide good service. Finally, in our samples, the service employees went through extensive selection, training, and socialization processes. These processes emphasized the goal of customer retention by "going the extra mile" and "taking ownership" of a customer's problem. On the basis of this rationale, we expect that IRP suffers more at the hands of job stress than does CDERP.
H7: Job stress has a stronger effect on IRP than on CDERP.
CDERP versus IRP and their effects on CDERP. We posit that CDERP has a stronger effect on CPI than on IRP for three reasons. First, customers are more likely to witness extra-role behaviors. Given that many IRPs are not seen or do not resonate with customers unless they are exceptionally good or poor, it seems reasonable that the effect of IRP is not as strong as that of CDERP. Furthermore, because extra-role behaviors go "beyond the call of duty," these behaviors are more likely to strike a chord with customers and resonate with them over time. This notion is consistent with the writings of Bitner, Booms, and Tetreault (1990). In their analysis of critical customer service incidents, they report that "extraordinary" service employee behaviors, such as courtesy and thoughtfulness, translated into customers' experiencing high levels of satisfaction and remembering the service encounter.
Second, customers tend to recall encounters that are vivid or distinct, such as those involved in our customer complaint settings (Price, Arnould, and Tierney 1995). Furthermore, given that CDERPs are discretionary, customers may view the behaviors as internal to the customer service employee with whom they dealt. Similar to the cognitive processes that underlie managers' performance ratings of employees, customers may attribute CDERPs as stable and internal to the employee (i.e., "this person is giving great service because he or she wants to, not because he or she has to"). Such CDERPs are likely to be retained in memory and recalled in deriving final evaluations (i.e., CPI) (e.g., MacKenzie, Podsakoff, and Fetter 1993). Thus, CDERP should have a greater effect on CPI than does IRP.
Finally, service employees are hired to handle customer issues, and in managers' communication to employees (as in our samples that follow), they reiterate that in-role job duties are primarily tools that may help employees solve customer problems. It is also strongly stressed to employees that they must "see the big picture" of the organizational goal of retaining customers by solving customer problems. It would seem that CDERP is more instrumental to that goal than is IRP.
H8: CDERP has a stronger effect on CPI than does IRP.
Study Methods
Procedures. With our first sample, we gathered data from customers, customer service employees, and supervisors of an online electronics retailer (i.e., a business-to-consumer setting). Customers initiated contact with the firm by telephoning the customer service center, which routed them to a service employee. (The primary responsibility of each employee was to handle customer complaints.) The employee logged a "job problem report" into the customer database and then attempted to resolve the complaint. All customer queries appeared in a complaint register, which was closely monitored by service employee supervisors. After exhausting all efforts to resolve a complaint, customer service employees "closed out" the job problem report in the customer database and noted the event history in the buyer's profile.
Customer measures. Our data-collection process began with customers. A total of 700 surveys were e-mailed to customers after they experienced a customer service interaction. Of the 700 e-mailed surveys, 346 were returned, of which 320 had complete responses across all study variables, yielding a 45.7% customer response rate. Within the six-month study period, each of the 320 customers dealt exclusively with only one service employee, and in all cases, the complaint that the customers registered was their first ever with the firm.
After the customer's most recent service experience with the firm and its employee, customers rated three CPI items on a seven-point scale, ranging from "not likely" to "very likely." Given that customer satisfaction is commonly viewed as an antecedent of CPI, we created a measure of satisfaction based on the complaint/service request we described previously. We used the measure as a control variable to estimate all models that follow. In total, 60% of the sample was male, 69% had been customers for two to four years, 51% reported incomes from $40,000 to $60,000, and 74% held college degrees.
Employee measures. After a customer service inquiry was closed out, an e-mail containing a survey was sent to the service employee who handled the complaint. Surveys were sent to 700 service employees who specifically handled the 700 customer-initiated complaints. Of the surveys sent to customer service employees, 621 usable responses were returned, yielding an 88.7% response rate. In total, 320 surveys were matched to the fully completed customer survey, yielding a 45.7% response rate. That is, 320 service employee surveys were matched to the specific customer survey that represented the customer whom they had served. Thus, all analyses involving Sample 1 use a sample of 320 customer-employee response pairs.
The survey asked employees about their perceptions of the focal constructs (i.e., WFC, FWC, and job stress) over the past six months. We measured WFC and FWC with three items from the scales that Netemeyer, Boles, and McMurrian (1996) developed. We measured job stress with three items from the anxiety-stress scale that House and Rizzo (1972) developed and with one item that we generated. We also gathered two other measures from service employees. Job satisfaction and affective organizational commitment (OC) have been shown to be potential outcomes of WFC, FWC, and job stress and to be potential antecedents and/or outcomes of IRP and CDERP (MacKenzie, Podsakoff, and Ahearne 1998; Podsakoff et al. 2000). We used a three-item measure of job satisfaction and a four-item measure of OC (Mowday, Steers, and Porter 1979), and we used these measures as control variables to estimate all models that follow. All items were rated on a seven-point scale, ranging from "strongly disagree" to "strongly agree." All employees were full time, 53% were female, 82% had been employees for five years or fewer, 81% reported incomes from $40,000 to $70,000, and 67% held college degrees.
Supervisor measures. The 53 supervisors who managed the 320 employees were also sent e-mailed surveys, and all 53 participated. The supervisors managed an average of six service employees and rated their performance for the six-month study period. The supervisor surveys were returned and were matched to the respective employees whom they rated. Given that supervisors rated more than one employee, we created a partially nested design between employees and supervisors. In addition to estimating our structural equation models (see Figure 1), we estimated a set of linear mixed models that control for any random effects of the supervisors' repeated measurements on the parameter estimates that involve the supervisor-employee pairs (H2-H3). For both samples, there was no significant across-or within-supervisor rating biases due to evaluating multiple employees within a workgroup.
We measured employees' IRP with three items that we adapted from Singh Verbeke, and Rhoads's (1996) work; the items were rated on a seven-point scale, ranging from "among the worst in the company" to "among the best in the company." We measured CDERP with four items that we adapted from Bettencourt and Brown's (1997) work; the items were rated on a seven-point scale, ranging from "never" to "as often as possible." We used supervisor-rated measures of employee performance for several reasons. First, employees may overrate their performance (Murphy and Cleveland 1995). Second, evidence suggests that as predictors of a criterion variable other than that rated by employees or supervisors, supervisor-rated measures of employee performance have the stronger predictive validity (Atkins and Wood 2002). Other scholars also suggest that supervisor-rated measures of employee performance are more valid than are employee self-ratings (Scullen, Mount, and Goff 2000). Finally, we used a supervisor-rated measure of employee performance because customers rated the prime dependent variable of interest, CPI. Thus, the links between the performance constructs and CPI are free of same-source bias (Podsakoff et al. 2003). Of the supervisors, 60% were female, 80% had been with the firm for eight years or more, 77% reported incomes from $55,000 to $70,000, and 57% held college degrees (the Appendix shows the measures for the focal constructs of our studies [i.e., CPI, WFC, FWC, job stress, IRP, and CDERP]).( n1)
Procedures. Our second sample used 132 customers, service employees, and supervisors of a manufacturer/seller of technology-related equipment for retailers and financial service institutions (i.e., business-to-business setting). Customers initiated a call to the service employee, who personally visited the customer's site and tried to resolve a problem that the customer was having with equipment purchased from the manufacturer. The majority of complaints involved software/hardware malfunctions, and supervisors often accompanied service employees. We used the exact same data-collection procedures as we did for Sample 1.
Customer measures. Surveys were e-mailed to customers after they experienced a customer service interaction. Of the 200 surveys that were sent, 132 were returned with complete responses across the same customer measures that we used in Sample 1 (a 66% customer response rate). The 132 customer respondents dealt specifically and exclusively with one of the 132 service employees over the six-month study period. These customers were responsible for the purchasing and day-to-day functioning of the technical equipment used in their firms. In total, 67% of the customers were female, and 82% held college degrees.
Employee measures. After the customer service inquiries were resolved, a survey was e-mailed to the 200 service employees who specifically handled the 200 customer-initiated complaints. Of the surveys sent, 185 responses were returned, yielding a 92.5% employee response rate. Of these, 132 surveys were matched to the completed customer survey, yielding a 66% matched response rate. Thus, all analyses for Sample 2 use a sample of 132 customer-employee response pairs. The employees completed the same measures that we reported in Sample 1. All employees were full time, 67% were female, 82% had been employees for three years or fewer, 72% reported incomes from $50,000 to $70,000, and 87% held college degrees.
Supervisor measures. The 27 supervisors who managed the 132 employees were also sent e-mailed surveys. Each supervisor managed an average of five employees and rated the employees for the six-month study period over the same performance measures that we used in Sample 1. The supervisor surveys were matched to the respective service employee survey. In total, 42% of the supervisors were female, 60% had been employees for six years or more, 78% reported incomes from $50,000 to $70,000, and 65% held college degrees.( n2)
Discriminant validity and reliability. We examined the psychometric properties of our measures by estimating a 30-item, 9-factor (WFC, FWC, job stress, job satisfaction, OC, IRP, CDERP, CPI, and customer satisfaction) measurement model. This model fit the data well for Sample 1 (Χ² = 882.35, degrees of freedom [d.f.] = 369; comparative fit index [CFI] = .94; nonnormed fit index [NNFI] = .93; and root mean square error of approximation [RMSEA] = .06) and for Sample 2 (Χ² = 592.29, d.f. = 369; CFI = .93; NNFI = .92; and RMSEA = .06) (Hu and Bentler 1995). Tables 1 and 2 show summary statistics, internal consistency estimates, and correlations for Samples 1 and 2, respectively. Coefficient alpha estimates of internal consistency ranged from .61 to .95, and average variance estimates (AVE) ranged from .30 to .87. The disattenuated correlations among the constructs (φ estimates) ranged from -.79 (job stress and IRP) to .72 (IRP and CDERP). We examined discriminant validity among the constructs by comparing φ to AVE. If the square of the parameter estimate between two constructs (φ²) is less than the average AVE between the two constructs, discriminant validity is supported (Fornell and Larcker 1981). This criterion was met for all possible pairs of constructs in Tables 1 and 2.
Multigroup measurement invariance. We also tested whether the measures were invariant across samples by estimating a hierarchy of multigroup measurement invariance models (Steenkamp and Baumgartner 1998). The first model that we estimated was a baseline measurement model of the same pattern of fixed and free parameters. This model fit the data well (Χ² = 1474.64, d.f. = 738; CFI = .94; NNFI = .93; and RMSEA = .06). Next, we estimated a model that constrained the factor loadings to be invariant across groups. The fit indices for this model were acceptable (Χ² = 1491.11, d.f. = 759; CFI = .94; NNFI = .93; and RMSEA = .06), and the difference in fit between this model and the baseline measurement model was not significant (Χ²diff = 16.47, d.f.diff = 21, p > 10). Thus, the loadings for all measurement items were invariant across samples. The third model that we estimated specified invariant factor variances and covariances in addition to invariant factor loadings. It had acceptable fit indices (Χ² = 1510.29, d.f. = 804; CFI = .94; NNFI = .93; and RMSEA = .06), and the difference in fit between this model and the baseline measurement model was not significant (Χ²diff = 35.65, d.f.diff = 66, p > 10). Thus, all correlational (covariance) relations among model constructs, as well as construct (factor) variances, were equal across samples. Finally, we estimated a fully invariant measurement model (i.e., invariant factor loadings, invariant factor variances and covariances, and invariant item error loadings). Although the indices showed adequate fit (Χ² = 1659.54, d.f. = 834; CFI = .93; NNFI = .93; and RMSEA = .07), this model's fit differed from the fit of the baseline measurement model (Χ²diff = 184.90, d.f.diff = 96, p < .01). Still, the invariance of the factor loadings, factor variances, and covariances suggests that comparing model paths across samples is appropriate.
Analyses and Results
To test the structural models, we used a multiple-group approach akin to the method that Singh (2000) uses. With this approach, we estimated two multigroup models. The first model freely estimates all paths across samples (i.e., the baseline structural model). The model is then compared with a model in which all paths are constrained to be equal across samples (i.e., a full structural invariance model). If the difference in fit between the two models is not significant, there is evidence that all structural paths are invariant across samples. If structural invariance exists, hypothesized paths can be tested with the multigroup approach.
Mediated model. We first used this approach for the mediated model that appears in Figure 1. As we previously noted, we measured three control variables: service employee job satisfaction, OC, and customer satisfaction. As is the recent trend in structural equation models to control for variables that are not central to study hypotheses (Baltes and Heydens-Gahir 2003; Rothbard and Edwards 2003), we also estimated paths among the control variables and the focal constructs (prior research suggests 14 paths among the focal constructs and control variables; Podsakoff et al. 2000; Oliver 1997). Thus, for all structural models that follow, the paths among the focal constructs ("Hypothesized Paths" in Table 3) reflect the effects of simultaneously estimating paths among the focal constructs and control variables ("Control Variable Paths" in Table 3).
The baseline structural model of freely estimated paths across samples demonstrated fit levels approaching adequacy (Χ² = 2082.60, d.f. = 768; CFI = .89; NNFI = .88; and RMSEA = .08), as did the full structural invariance model (Χ² = 2089.83, d.f. = 788; CFI = .89; NNFI = .88; and RMSEA = .08). Given that the difference in fit between the two models was not significant (Χ²diff = 7.23, d.f.diff = 20, p > .10), all paths are equal across groups, and we offer the results for the mediated model based on full structural invariance.
We predicted that WFC and FWC would affect (produce) job stress (H1). As we show in Table 3, the WFC → job stress path was significant and positive, in support of H1a. Although correlated with job stress (φ = .41 and .46 from Tables 1 and 2), the FWC → job stress path was not significant, likely because of the shared variance between WFC and FWC. Still, the WFC and FWC paths explained 45% of the variance in job stress. H2 posits direct effects of job stress on IRP and CDERP. As we show in Table 3, both hypotheses were supported, explaining large portions of the variance in IRP (R² = .64) and CDERP (R² = .57). Finally, H3 of the mediated model predicts that both IRP and CDERP positively affect CPI. Both paths were supported, explaining 53% of the variance in CPI.
Incremental effects model. H4 posits that both FWC and WFC incrementally affect CDERP (i.e., job stress does not fully mediate the WFC/FWC effects on CDERP). H5 posits a direct effect of job stress on CPI-that is, job stress explains incremental variance in CPI beyond that which IRP and CDERP explain. To test the hypotheses, we added paths from FWC and WFC to CDERP and a path from job stress, to CPI, to the mediated model to create the incremental effects model. Again, we used a multigroup model approach in which the baseline structural model of freely estimated paths across samples showed adequate fit (Χ² = 1875.85, d.f. = 762; CFI = .90; NNFI = .89; and RMSEA = .07). The full structural invariance model also fit well (Χ² = 1884.80, d.f. = 785; CFI = .91; NNFI = .90; and RMSEA = .07), and its difference in fit from the baseline structural model was not significant (Χ²diff = 8.95, d.f.diff = 23, p >.10), again suggesting that all paths are equal across groups. Thus, in Table 3, we offer the results for the incremental effects model based on full structural invariance.
Consistent with the writings on mediation (Iacobucci and Duhachek 2003), we first compared the incremental effects model to the mediated model. The difference in fit between the two models was significant (Χ²diff = 205.03, d.f.diff = 3, p < .01), suggesting that job stress does not totally mediate the effects of FWC and WFC on CDERP and that IRP and CDERP do not totally mediate the effect of job stress on CPI. The path estimates in Table 3 reinforce this conclusion. The FWC → CDERP and WFC → CDERP paths were significant, and they explained an additional 12% of the variance in CDERP beyond that explained by job stress in the mediated model. The job stress CPI path was also strong, and it explained an additional 16% of the variance in CPI beyond that explained by IRP and CDERP in the mediated model.
By employing multigroup analyses with the full structural invariance incremental effects model, we tested our relative effects hypotheses. We tested each hypothesis with a chi-square difference test at 1 d.f. in which the two paths are constrained to be equal in one model but are freely estimated in another. We show the results in Table 4. Although H6 posits that the direct effect of FWC on CDERP is stronger than the direct effect of WFC, the hypothesis was not supported. Both FWC and WFC equally affected CDERP. We tested H7 by comparing the job stress → IRP path to the job stress → CDERP path, and we found that job stress had a greater effect on IRP than on CDERP. Finally, H8 was supported because CDERP had a stronger direct effect on CPI than did IRP.
As we noted previously, WFC had strong direct effects on job stress and CDERP, and FWC had a direct effect on CDERP. Although not hypothesized, WFC and FWC showed a series of important indirect and total effects as well. Indirect effects are those that are mediated by at least one other variable, and total effects are the sum of the direct (e.g., WFC → CDERP) and indirect (e.g., WFC → job stress → CDERP) effects (Bollen 1989). The indirect effect of WFC on CDERP through job stress was significant (unstandardized indirect path = -.31, standardized indirect path = -.29; t = 6.97, p < .01), as was the total effect of WFC on CDERP (unstandardized total effect = -.65, standardized total effect = -.69; t = 11.41, p < .01). The indirect effects of WFC on IRP (unstandardized indirect path = -.59, standardized indirect path = -.49; t = 9.67, p < .01) and CPI (unstandardized indirect path = -.48, standardized indirect path = -.51; t = 10.18, p < .01) were also significant. Although FWC did not show a direct effect on job stress, it showed a direct effect on CDERP and had one total effect. The FWC total effect on CDERP was significant (unstandardized total effect = -.18, standardized total effect = -.13; t = 2.62, p < .01).
Discussion
Effects of WFC and FWC. Both FWC and WFC had negative incremental effects on CDERP. Thus, their effects on CDERP were not totally mediated by job stress. Furthermore, WFC showed strong indirect effects on IRP, CDERP, and CPI.
Effects of job stress and performance. Job stress affected both aspects of performance, and job stress was more strongly related to IRP than to CDERP. Both IRP and CDERP affected CPI in the mediated model. However, job stress had a strong negative incremental effect on CPI that was not totally mediated by the positive effects of performance. Finally, CDERP had a stronger effect on CPI than did IRP.
Our results have important managerial implications. However, to be beneficial, all the initiatives and programs that we subsequently discuss require a real cultural shift in the way an organization views its employees. First, managers should be trained to regard their employees as family members, with all the issues and responsibilities that go with it, and they must understand that service employees do not leave family problems at the workplace doorstep (Rifkin 1996). Thus, just as service employees are asked to "take ownership" of customer problems, supervisors and managers may need to take at least some ownership of employee problems that stem from the work-family interface. Managers who take the attitude that "it's not my problem" may effectively be hurting the organizational goal of customer retention. Although training programs to teach managers these skills may seem like an obvious solution, many firms do not implement them because of the costs involved. One study reports that fewer than half (43%) of firms currently train managers to respond to the work-family needs of employees (Galinsky and Bond 1998). Our results provide some evidence that not training managers to deal with service employee job stress and its WFC and FWC antecedents may also carry a high cost.
Second, managers can structure the workplace so that employees are encouraged to balance work and family. Managers can implement family-friendly programs that send signals that management supports family roles. Some popular programs that members of Fortune 's "100 Best Companies to Work For" have implemented include mentoring, child care, family leave, flextime, maternity/ paternity leave, telecommuting, and on-site job counseling for work-family matters (Tkaczyk et al. 2003). Research shows that such programs can reduce work and family conflicts (Lobel 1999), enhance employee performance (Konrad and Mangel 2000), and positively correlate with a firm's share price (Arthur 2003). However, such programs are not a panacea in addressing WFC, FWC, and job stress effects. Management must be careful to design work-life initiatives with the specific needs of employees in mind. For example, work-life initiatives have been shown to be more effective for companies with an educated and professional workforce (such as our samples) (Konrad and Mangel 2000).
Therefore, we recommend that managers assess sources of WFC, FWC, and job stress and design programs that include the most appropriate elements for their workforce. Not to consider each workforce individually may result in such policies being ineffective (Rau and Hyland 2002).
Third, managers can offer stress management workshops that, instead of offering employees advice on how to live life, ask for help in understanding how family roles affect work and what management can do about it (Osland, Kolb, and Rubin 2001). Workshops can create a welcoming forum in which employees freely provide suggestions for the better support of family roles. There is some evidence that workshops can reduce stress. Kossek, Colquitt, and Noe (2001) show that employee anxiety, irritability, and depression are reduced when managers encourage employees to openly discuss family concerns with supervisors and peers. Thus, even the simple notion of seeking employee feedback can help employees feel more valued and less stressed.
Fourth, properly trained managers can institute changes to the work itself. For example, employee compensation packages could be restructured to reward customer satisfaction in addition to rewarding prescribed in-role outcomes (e.g., call-time quotas). Indeed, some call centers have lowered expectations for the number of customer problems handled per shift and instead have empowered employees to take as long as is necessary to satisfy customer needs (Molpus 2003). Furthermore, managers could offer service employees a "voice" in determining aspects of their jobs. For example, managers could restructure systems for scheduled employee breaks (in their busy complaint-handling days) that allow employees to participate in the decision as to when the breaks are needed. Thus, managers and supervisors can create a culture that empowers employees, which may have the effect of helping employees balance the work-family interface and make the work itself less stressful.
Fifth, firms may benefit from bolstering the mental health benefits for employees who cope with stress and conflict that stem from the work-family interface. An initiative that seems particularly relevant is to assist employees in role and boundary management. Qualified mental health professionals can help employees manage personal relationships both at work and at home. There is evidence that the relationship between stressor and stress is largely the outcome of a person believing that he or she is unable to cope adequately with an identified threat (i.e., dissatisfied family or superiors) (Lazarus and Folkman 1984). Thus, firms should consider assisting employees in developing relationship skills to successfully juggle competing roles.
Incremental effects of WFC, FWC, and job stress. Traditional models view the effects of WFC and FWC (job stressors) on performance as likely being mediated by job stress and the effects of job stress on customer outcomes as likely being mediated by performance. However, our results suggest that this is not the case. In the high-stress customer service job, outside-role conflicts can directly affect performance, and within-domain stress can directly affect customer outcomes. Thus, future models may want to consider the context of the job when specifying relationships among WFC, FWC, job stress, performance, and customer outcomes. When conflicts in the work-family interface and job stress are high, these constructs may directly affect important behavioral outcomes for employees and customers.
Relative effects of WFC, FWC, and job stress. Both theory and intuition suggest that FWC has a stronger effect on CDERP than does WFC. However, the overall pattern of results suggests that this is not the case. Our results show a direct "spillover effect" of WFC that affects CDERP. Moreover, our results show that indirect and total effects of WFC on CDERP and CPI are stronger than are the indirect and total effects of FWC on CDERP and CPI. We offer two speculative explanations as to why we obtained such results.
First, perhaps employees are cognizant of the potential for FWC to affect their job performance, and thus they engage in processes or behaviors that partially compensate for the effect. However, the opposite may not be true. Employees may not be aware of the potential for WFC to affect job performance, and thus they may not engage in cognitive processes or behaviors that could reduce the effect. Second, it has recently been demonstrated that FWC is likely to have a strong effect on work performance when the pressure to participate in the family domain (pressure from the family) is strong and the pressure to participate in the work domain (pressure from the firm or supervisors) is concurrently weak (Greenhaus and Powell 2003). However, in customer service settings, the opposite may be more likely. Given the importance of employee performances in affecting customer evaluations, the pressure to participate in the work role could be stronger than the pressure to participate in the family role, resulting in a weaker FWC → CDERP path. Further studies could examine why the WFC → job performance spillover effect is so strong. What are other potential reasons for it? Is it as likely in less stressful marketing jobs?
With respect to the relative effects of job stress, we found that it affected IRP more than it did CDERP. In stressful customer service job situations, when employees believe that they are unable to perform all roles, they may perceive doing extra things that help affect customer evaluations as more salient to their job-role identity. Further studies might consider investigating different dimensions of performance and the relative effects of job stress on each dimension.
Limitations and further research. Our results are tempered with certain limitations. First, given that we collected WFC, FWC, and job stress measures cross-sectionally, establishing a temporal ordering among these constructs is not possible. Although our posited flow of WFC and FWC as correlated constructs that affect job stress is consistent with most research, other specifications are tenable (Williams and Alliger 1994). Although we estimated such alternative models and found a pattern of results similar to that which we report in Table 3, further studies that use experiments may better detect the causality among WFC, FWC, and job stress. For example, Greenhaus and Powell (2003) manipulated family-and work-role pressures to determine their effects on the decision to participate in a work activity versus a family activity. Such experiments may be useful to better show the causal flow among WFC, FWC, and job stress.
Second, we did not collect two critical measures of role salience: ( 1) the importance of the family role versus the job role for employees and ( 2) the importance of CDERP versus IRP. Such measures could have helped solidify the findings and bolster the use of identity theory as a basis for some of the hypotheses. Third, the WFC measure was more strongly related to job stress than was the FWC measure. Although this finding is consistent with the theoretical rationale for the WFC and FWC relationships with job stress, wording of the WFC items may suggest more of a content similarity to job stress than does the wording of the FWC items. Further studies might elect to use a job stress measure that assesses symptomatic health aspects related to work (e.g., high blood pressure, loss of appetite). Fourth, we examined only one customer per service employee, and we related customer-specific purchase intent to employee performance measures, encompassing a six-month period. Although extensive procedures suggest that our customer samples were not biased and the literature suggests that employee performance is relatively stable over time, further studies that examine multiple customers per employee are warranted.
This research was funded in part by the Bernard A. Morin Fund for Marketing Excellence at the McIntire School of Commerce. The authors thank Tom Bateman and members of the marketing faculty at the Leeds School of Business at the University of Colorado for their helpful comments on previous drafts of this article.
( n1) Three measurement issues are of note. First, we began with 23 employee-rated items. Using confirmatory factor analyses and our own judgment, we trimmed this pool to 17 items (3 WFC, 3 FWC, 4 job stress, 3 job satisfaction, and 4 OC). We began with 9 supervisor-and 6 customer-rated items. Using confirmatory factor analyses, we retained 7 supervisor items and 6 customer items. Second, because we are linking performance measures that span a six-month period to a specific customer's purchase intents, we assume that performance is stable over time. There is some evidence to support this assumption; Viswesvaran, Ones, and Schmidt (1996) report a meta-analytic correlation of .81 for supervisor ratings of employee performance over two time periods. The strength of the correlation is indicative of stable performance over time. Third, although the performance-CPI links are free of same source bias, WFC, FWC, and job stress items were rated by the same source. We compared the path estimates of a same-source methods factor model to the path estimates from a model without the same-source factor (Williams and Anderson 1994). We found that the path estimates were somewhat attenuated when the same-source factor was present, but the path estimates were not significantly affected.
( n2) We conducted several sample bias checks. For both samples, we compared the demographic characteristics of our employee, supervisor, and customer samples with several other samples that did not participate in our study. We found no significant differences between our samples and the other samples across demographics. We also assessed potential effects of employee gender, age, and education. As predictors or moderators of WFC and FWC, these variables had no effects. For Sample 2 (n = 132), we assessed whether marital status and having child dependents affected the results. These variables had no effects as either predictors or moderators of WFC and FWC.
Legend for Chart:
B - Mean
C - Standard Deviation
D - α
E - AVE
F - 1
G - 2
H - 3
I - 4
J - 5
K - 6
L - 7
M - 8
N - 9
A B C D E F
G H I J K
L M N
Focal Constructs
1. WFC 5.86 1.17 .91 .78 1.00
2. FWC 5.44 .82 .84 .70 .69
1.00
3. Job Stress 5.47 .86 .89 .73 .64
.41 1.00
4. IRP 3.83 1.39 .94 .85 -.36
-.28 -.79 1.00
5. CDERP 3.01 1.22 .95 .87 -.69
-.52 -.68 .72 1.00
6. CPI 3.24 1.03 .90 .75 -.75
-.47 -.76 .63 .62 1.00
Control Variables
7. Job satisfaction 4.69 .88 .77 .60 -.32
-.17 -.52 .42 .24 .54
1.00
8. OC 4.44 .60 .63 .31 -.29
-.27 -.51 .50 .27 .61
.52 1.00
9. Customer satisfaction 5.48 1.00 .83 .65 -.51
-.45 -.52 .55 .61 .40
.17 .19 1.00
Notes: Means and standard deviations are based on summated scale
averages. Correlations are disattenuated estimates from a
confirmatory-factor measurement model. Legend for Chart:
B - Mean
C - Standard Deviation
D - α
E - AVE
F - 1
G - 2
H - 3
I - 4
J - 5
K - 6
L - 7
M - 8
N - 9
A B C D E F
G H I J K
L M N
Focal Constructs
1. WFC 5.81 1.22 .94 .84 1.00
2. FWC 5.36 .85 .80 .64 .63
1.00
3. Job Stress 5.55 .84 .87 .66 .60
.46 1.00
4. IRP 3.76 1.39 .93 .83 -.37
-.36 -.76 1.00
5. CDERP 3.11 1.22 .92 .82 -.73
-.60 -.65 .69 1.00
6. CPI 3.25 1.05 .89 .72 -.72
-.50 -.73 .66 .66 1.00
Control Variables
7. Job satisfaction 4.65 .90 .75 .59 -.29
-.22 -.52 .38 .27 .53
1.00
8. OC 4.50 .57 .61 .30 -.24
-.33 -.43 .49 .27 .66
.53 1.00
9. Customer satisfaction 5.50 1.00 .87 .71 -.47
-.45 -.50 .51 .58 .40
.15 .10 1.00
Notes: Means and standard deviations are based on summated scale
averages. Correlations are disattenuated estimates from a
confirmatory-factor measurement model. A: Model Fit
Legend for Chart:
B - χ²
C - d.f
D - χ2diff(3)
E - CFI
F - NNFI
G - RMSEA
A B C D E F G
Mediated model 2089.83 788 -- .89 .88 .08
Incremental effects
model 1884.80 785 205.03 .91 .90 .07
B: Path Estimates
Legend for Chart:
A - Hypothesized Paths
B - Mediated Model Unstandardized
C - Mediated Model Standardized
D - Mediated Model t-Value
E - Mediated Model Supported
F - Incremental Effects Model Unstandardized
G - Incremental Effects Model Standardized
H - Incremental Effects Model t-Value
I - Incremental Effects Model Supported
A B C D E
F G H I
H1a: WFC → job stress .51 .62 10.44 Yes
.55 .62 10.45 Yes
H1b: FWC → job stress .05 .04 .79 No
.02 .02 .33 No
H2a: Job stress → IRP -1.15 -.80 19.30 Yes
-1.14 -.80 19.14 Yes
H2b: Job stress → CDERP -1.10 -.87 15.38 Yes
-.72 -.56 9.19 Yes
H3a: IRP → CPI .32 .42 7.85 Yes
-.05 -.06 .92 No
H3b: CDERP → CPI .33 .38 6.63 Yes
.18 .21 3.73 Yes
H4a: FWC → CDERP -- --
-.18 -.14 2.94 Yes
H4b: WFC → CDERP -- --
-.34 -.32 5.73 Yes
H5: Job stress → CPI -- --
-.82 -.74 9.02 Yes
R²: Job stress .45
.44
R²: IRP .64
.65
R²: CDERP .57
.69
R²: CPI .53
.69
Control Variable Paths
WFC → job satisfaction .05 .05 .61
.08 .08 1.12
WFC → OC .09 .33 2.48
.08 .21 2.32
FWC → job satisfaction .08 .07 1.08
.05 .04 .60
FWC → OC -.09 -.18 2.36
-.10 -.20 2.61
IRP → job satisfaction -.06 -.07 .79
-.13 -.16 1.90
Job satisfaction → OC .13 .32 4.09
.12 .30 3.85
Job satisfaction → CDERP -.16 -.15 3.13
-.15 -.14 3.14
OC → CDERP -.27 -.10 1.68
-.25 -.09 1.60
Job stress → job satisfaction -.78 -.64 6.40
-.90 -.74 7.57
Job stress → OC -.21 -.44 4.35
-.22 -.45 4.40
IRP → customer satisfaction .11 .16 1.99
.11 .16 2.06
CDERP → customer satisfaction .32 .42 6.08
.33 .43 6.53
Job stress → customer
satisfaction -.10 -.11 1.09
-.09 -.10 1.02
Customer satisfaction → CPI -.06 -.05 .98
-.10 -.09 1.89 Legend for Chart:
B - Path Comparison Hypotheses
C - χ2diff
A B C
H6: FWC → CDERP > 2.15 (n.s.) No
WFC → CDERP
H7: Job stress → 18.28 Yes
IRP > job stress → CDERP
H8: CDERP → CPI > 8.46 Yes
IRP → CPI
Notes: We conducted all χ2diff tests at a
difference of 1 d.f. by comparing the model that constrained
the paths of interest to be equal to the model in which the
paths were freely estimated--that is, the incremental effects
model with a (2) of 1884.80 and d.f. of 785. Except where
noted as "n.s." (not significant), all (2) differences were
significant at the .05 level or better.DIAGRAM: FIGURE 1; Mediated and Incremental Effects Models of WFC and FWC
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WFC
1. Due to work-related duties, I have to make changes to my plans for family activities.
- 2. The amount of time my job takes up makes it difficult to fulfill family responsibilities.
- 3. The demands of my work interfere with my home and family life.
FWC
1. The demands of my family or spouse/partner interfere with work-related activities.
- 2. Things I want to do at work do not get done because of the demands of my family or spouse/partner.
- 3. My home life interferes with my responsibilities at work, such as getting to work on time, accomplishing daily tasks, and working overtime.
Job Stress
1. My job tends to directly affect my health.
- 2. At the end of the day, my job leaves me "stressed-out."
- 3. Problems associated with work have kept me awake at night.
- 4. I feel fidgety or nervous because of my job.
IRP
1. How do you rate this employee in terms of quality of performance in regard to knowledge of your products, company, competitor products, and customer needs?
- 2. How do you rate this employee in terms of quality of performance in regard to management of time, planning, and expenses?
- 3. How do you rate this employee in terms of quantity of work achieved?
CDERP
1. How often did this employee go above and beyond the "call of duty" when serving customers?
- 2. How often did this employee willingly go out of his or her way to make a customer satisfied?
- 3. How often did this employee voluntarily assist customers even if it meant going beyond job requirements?
- 4. How often did this employee help customers with problems beyond what was expected or required?
CPI
1. If you (YOUR FIRM) were in the market for (products), how likely would you be to buy from (FIRM)?
- 2. In the future, I (MY FIRM) will use (FIRM) as a provider.
- 3. In the future, I (MY FIRM) intend(s) to use (products) from (FIRM).
~~~~~~~~
By Richard G. Netemeyer; James G. Maxham III and Chris Pullig
Richard G. Netemeyer is Ralph A. Beeton Professor of Free Enterprise, McIntire School of Commerce, University of Virginia
James G. Maxham III is Assistant Professor of Commerce, McIntire School of Commerce, University of Virginia.
Chris Pullig is Assistant Professor of Marketing, Hankamer School of Business, Baylor University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 34- Consumer Response to Retailers' Use of Partially Comparative Pricing. By: Barone, Michael J.; Manning, Kenneth C.; Miniard, Paul W. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p37-47. 11p. 1 Black and White Photograph, 3 Charts. DOI: 10.1509/jmkg.68.3.37.34769.
- Database:
- Business Source Complete
Consumer Response to Retailers' Use of Partially
Comparative Pricing
Consumers sometimes encounter a combination of comparative and noncomparative prices in the marketplace. For example, a grocer may employ signage that provides favorable price comparisons with those of a competitor for a portion of its products, a practice that the authors refer to as "partially comparative pricing." The authors examine the effects of partially comparative pricing on consumer response and find that it has both desirable and undesirable effects. On the one hand, such pricing enhances consumers' beliefs about the relative prices of comparatively priced products and about the retailer's relative prices in general. On the other hand, such pricing also reduces consumers' relative price beliefs about noncomparatively priced products and their intentions to purchase such products. The authors further show that the adverse influence of partially comparative pricing stems from consumers' suspicions about why price comparisons exist for some, but not all, products. They also document how these effects depend on store patronage. They discuss implications of their research and provide suggestions for future empirical efforts.
When walking down the aisles of a major grocery store chain, consumers encounter point-of-purchase (POP) signage for some products that indicates the store's lower price relative to that of a competing grocery chain (e.g., Miller Lite 12-pack: our price, $6.99; Cub Foods price, $8.69). For this subset of comparatively priced products, consumers have price comparisons available to consider during their decision-making process. Elsewhere in the store are noncomparatively priced products for which the grocer has provided only its prices (e.g., Clorox Bleach, 96-ounce container: our price, $1.43). Partially comparative pricing (i.e., the combination of comparative and noncomparative prices) is not limited to the grocery industry. Kmart recently undertook a "Dare to Compare" campaign in which in-store signs indicated its lower prices compared with those of key competitors (e.g., Target) on certain items (Merrick 2001a, b). Although partially comparative pricing is common at the point of purchase, it can also be presented on Web sites and in advertisements.
Previous research (e.g., Compeau and Grewal 1994; Grewal, Marmorstein, and Sharma 1996; Lichtenstein, Burton, and Karson 1991) has examined the effects of comparative price claims on consumers' perceptions of a comparatively priced product's pricing and value. However, unexplored is the potential impact of price comparisons that detail a retailer's superiority for some products on consumers' perceptions about other products carried by the retailer that are priced noncomparatively. As we elaborate in the following section, there are reasons to suspect that partially comparative pricing influences consumers' relative price beliefs about noncomparatively priced products. An understanding of the existence of such effects is important because these beliefs can influence consumer search (e.g., Urbany 1986) and choice behavior (e.g., Briesch et al. 1997; Winer 1986).
Although the influence of comparative prices on consumers' perceptions of the pricing of subsequently encountered noncomparatively priced products has yet to receive empirical attention, previous research suggests several competing hypotheses regarding the potential nature of such effects. In this regard, Pechmann (1996) finds that consumer processing of an advertisement that contains a price superiority claim for a featured service (e.g., an overnight mail service with morning delivery) results in beliefs of price superiority for a nonfeatured service (e.g., overnight mail with afternoon delivery) that the advertisement never presents.
Pechmann's (1996) results may have arisen from inferential processes. For example, relative price beliefs for the nonfeatured service can stem from probabilistic inferences if consumers use beliefs about the company's price superiority for the comparatively priced service to form relative price beliefs about the nonfeatured service (Alba and Hutchinson 1987; Dick, Biehal, and Chakravarti 1990). In contrast, under evaluative-consistency inferencing (Alba and Hutchinson 1987; Dick, Biehal, and Chakravarti 1990), consumers may infer relative prices for the nonfeatured service from more global input (e.g., an overall perception of price superiority for the featured company that is based on comparative price information that indicates this superiority). By means of similar inferential processes, in the context of partially comparative pricing, consumers may employ comparative price claims (which reflect the price superiority of the featured retailer) to infer that the retailer also offers lower prices on noncomparatively priced products.
Although beliefs that a featured retailer offers lower prices than a competitor for noncomparatively priced products are inapplicable to Pechmann's (1996) research setting (which focuses on price beliefs about a nonfeatured service), such beliefs can also arise through priming. Under priming, cognitions activated during the processing of comparative price information may prompt interpretation of noncomparative prices in a manner consistent with the implications of the price comparison (see Herr 1989). Priming has been shown to explain why superiority claims about nonprice attributes may lead to perceptions of a competitive advantage for other product features that are described noncomparatively in the same advertisement (Barone and Miniard 1999).
In the present context, the price advantages associated with comparatively priced products may lead consumers to interpret noncomparative prices more favorably in terms of how they fare compared with the prices of the competitor referenced in the comparisons. In this regard, both inferencing and priming suggest a beneficial impact of partially comparative pricing:
H[sub1]: Partially comparative pricing enhances relative price beliefs about noncomparatively priced products.
When consumers encounter partially comparative pricing, they may make other, less favorable inferences about noncomparatively priced products. Kalra and Goodstein (1998) report that price comparisons lead to greater price sensitivity. The enhanced price sensitivity following the processing of comparative price claims should lead consumers to pay more attention to price when they encounter noncomparatively priced products. In so doing, the lack of price comparisons should be rather conspicuous. Recognition of the "missing" comparisons for the noncomparatively priced products may evoke what Wright (1986) refers to as a "secrecy" schema, which prompts consumers to become suspicious about why certain information is presented (e.g., price comparisons for comparatively priced products) and other information is not (e.g., price comparisons for noncomparatively priced products). Suspicion has been defined in the literature as a psychological state that occurs in the presence of ulterior motives (Campbell and Kirmani 2000; Fein 1996; Fein, Hilton, and Miller 1990). In the present context, consumers who are suspicious of partially comparative pricing may infer that price comparisons are not provided for noncomparatively priced products because the retailer charges more than its competitor for such products. Such inferences may be especially prevalent among consumers who perceive partially comparative pricing as a way to manipulate their pricing beliefs (Campbell 1995). This line of reasoning suggests that partially comparative pricing may actually exert certain detrimental effects:
H[sub2]: Partially comparative pricing undermines relative price beliefs about noncomparatively priced products.
Although the preceding discussion has considered why partially comparative pricing might enhance or undermine relative price beliefs about noncomparatively priced products, it is also possible that partially comparative pricing does not affect these beliefs. Perhaps consumers are too preoccupied with the shopping tasks at hand to pay much attention to either price comparisons or the lack thereof (Dickson and Sawyer 1990). Even if they notice the missing comparisons, they may have more important things to think about than why the retailer provides comparisons for only some of its products. However, both an awareness of partially comparative pricing and consequent thinking about the reason for the absence of price comparisons are necessary to arouse the type of suspicion that underlies H[sub2].
Similarly, neither inferencing nor priming may occur as H[sub1] presumes when consumers encounter partially comparative pricing. Simmons and Lynch (1991) observe that inferences may be infrequent even when the absence of information is conspicuous. In the present context, consumers may not believe that a retailer's price advantages in some product categories are a sufficient basis for inferring similar advantages in other categories. Nor can the favorable priming effect of comparative price claims take place if consumers do not attend sufficiently to price comparisons at the point of purchase. These various possibilities suggest that relative price beliefs about a retailer's noncomparatively priced products will be unaffected by partially comparative pricing.
H[sub3]: Partially comparative pricing does not influence relative price beliefs about noncomparatively priced products.
Method
A total of 124 undergraduate business students at a Western university participated in Study 1. The students were instructed that they would encounter some of the products available from a grocery retailer called Johnston's that was planning to expand into the local market. In addition, they were told "When looking at these products, please imagine that you are making a small grocery-shopping trip and are considering whether or not you should buy them." Participants were assigned randomly to two POP price-information conditions (partially comparative pricing and noncomparative pricing). As in previous retail pricing studies (e.g., Miyazaki, Sprott, and Manning 2000), the POP stimuli included pictures of four food products, each centered above illustrated shelves that contained tags that included the price of each item. We manipulated the format of the price claim for two of the products between the two conditions. The partially comparative pricing condition included competitive reference prices for the first two products (cranberry juice and cup-of-noodles), such that Johnston's price was followed by the product's (higher) price at an actual competitor store in the market in which we conducted the study. Only Johnston's price was shown next to the remaining two products (pickles and potato chips). The noncomparative pricing condition featured only Johnston's price for all four products. Figure 1 (Panel A) includes a shelf price tag for a comparatively priced product from the partially comparative pricing condition; we omitted reference to the comparison-store price in the noncomparative price-information condition (see Figure 1, Panel B).
To determine which prices to use in the POP stimuli, we gathered price information from four major grocery chains. We adjusted Johnston's prices, which were held constant across conditions, to approximately 15% less than the average price for each of the four products ($2.88 for cranberry juice, $.40 for cup-of-noodles, $2.24 for pickles, and $1.19 for potato chips), and we set the competitor store prices (presented only in the partially comparative pricing condition) at approximately the average price ($3.39 for cranberry juice and $.51 for cup-of-noodles). After examining the price information, participants provided relative price beliefs for each product without returning to any of the price information they had previously encountered.
We used relative measures (see Grewal et al. 1997; Miniard et al. 1993; Rose et al. 1993) to gauge price beliefs for Johnston's compared with those of the competitor referenced in the partially comparative pricing condition. For each product, participants indicated the likelihood that Johnston's price was lower than the comparison store on a scale that ranged from 1 = "very unlikely" to 9 = "very likely."
Results
We first considered evidence germane to the success of the price manipulation, which involved varying the presence of price comparisons for the initial two products (cranberry juice and cup-of-noodles) presented in the POP stimuli. We submitted responses to the relative price-belief measures for these products to a 2 (price information: partially comparative pricing versus noncomparative pricing) x 2 (product: cranberry juice versus cup-of-noodles) mixed analysis of variance (ANOVA) in which price information served as a between-subjects factor and product served as a within-subjects factor. Cell means are presented in Table 1.
The analysis yielded a significant main effect for the price manipulation (F[sub1,123] = 35.94, p < .001, η² = .226). As we expected, participants in the partially comparative pricing condition were more likely than participants in the noncomparative pricing condition to believe that Johnston's had lower prices than the comparison store for the comparatively priced products. Although the main effect for the product factor was not significant (p > .8), a marginally significant (F[sub1,123] = 3.57, p < .1, η² = .029) interaction emerged, indicating a stronger effect of the price manipulation for cranberry juice (F[sub1,123] = 47.5, p < .001, η² = .279) than for cup-of-noodles (F[sub1,123] = 13.08, p < .001, η² = .096). Apparently, participants interpreted the $.51 difference for juice (featured-store price: $2.88; comparison-store price: $3.39) as representing a stronger price advantage than the $.11 difference for noodles (featured-store price: $.40; comparison-store price, $.51).
Participants' relative price beliefs about the two products for which noncomparative prices were presented in both conditions (pickles and potato chips) provided evidence relevant to our competing hypotheses. The results of a 2 (price information) x 2 (product) mixed ANOVA show a significant effect for only the price-information manipulation (F[sub1,123] = 5.59, p < .05, η² = .044; for all other effects, p > .2). Consistent with H[sub2], compared with participants in the noncomparative pricing condition, participants in the partially comparative pricing condition believed that it was less likely that Johnston's had lower prices than the comparison store. Although the product type and the priceinformation manipulation did not interact (p > .2), contrasts revealed that the effect of the price-information manipulation was significant for potato chips (F[sub1,122] = 6.43, p = .01, η² = .05) but not for pickles (F[sub1,122] = 2.31, p = .13, η² = .019).
Discussion
The results from Study 1 support the potential for consumers' relative price beliefs about noncomparatively priced products to be affected by exposure to comparative pricing for other products. In accordance with H[sub2], partially comparative pricing significantly undermined relative price beliefs about one of the two noncomparatively priced products tested in the study. Although the cell means for the remaining product also suggested an adverse influence of partially comparative pricing, the difference did not reach statistical significance. Accordingly, we executed another study using a different set of noncomparatively priced products to assess the robustness of any undesirable effects that arise from partially comparative pricing.
Presumably, the undermining of consumers' relative price beliefs should lessen the odds of their buying noncomparatively priced products. Unfortunately, Study 1 did not employ the type of product-specific purchase-intention measures needed to assess this possibility, which is a shortcoming that we remedied in Study 2. Nor did Study 1 examine whether partially comparative pricing influences consumers' general (i.e., not product specific) price beliefs about the retailer providing such comparisons. Although the featured retailer's lower prices for comparatively priced products should drive consumer opinions and evaluations in one direction, the effects observed for noncomparatively priced products should exert an opposite influence. We explore the net impact of the two countervailing influences on general price perceptions of the retailer in Study 2. We also address the potential impact on more general perceptions by measuring consumers' attitudes toward the retailer and their intentions to shop at the retailer. These additions should enable us to better evaluate the advantages and disadvantages of partially comparative pricing.
Beyond these measurement limitations, Study 1 did not examine the process that is presumed to be responsible for the observed effects. As we noted previously, when consumers encounter partially comparative pricing, they may speculate about the reasons for the absence of comparisons for noncomparatively priced products. Consumers who are suspicious of the missing price comparisons may attribute their absence to the featured store's charging more than its competitor for the noncomparatively priced products, thereby undermining the relative price beliefs about these products. From this perspective, suspicion plays a central role in shaping consumers' response to partially comparative pricing. Study 2 explores the influence of suspicion by testing the following hypothesis:
H[sub4]: Suspicion mediates the effects of partially comparative pricing on relative beliefs formed about noncomparatively priced products.
Method
A total of 88 undergraduate students at a Midwestern university were assigned randomly to either the partially comparative pricing or the noncomparative pricing condition. The stimuli and processing conditions in Study 2 paralleled those from Study 1, with two exceptions. First, for purposes of generalizability, we used two different noncomparatively priced products (shampoo and cereal) in this study. Second, we conducted the study in a different market than Study 1, which necessitated a change in the competitor store referenced in the price comparisons.
We replaced the relative price-belief measures used in Study 1 with measures designed to provide a clearer indication of participants' perceptions of the retailers' relative prices. The measures from the initial study are ambiguous about whether the less favorable relative price beliefs observed for noncomparatively priced products reflect the opinion that ( 1) the featured retailer offered the products at higher prices than the comparison store or ( 2) both stores offered the products at the same price levels. Accordingly, participants in Study 2 reported their relative price beliefs on a response scale anchored by 1 = "[the competitor] charges a much lower price than [the featured store]" and 9 = "[the featured store] charges a much lower price than [the competitor]." The midpoint of the scale was labeled to reflect that the "[featured store] and [competitor] charge the same price," so that participants would understand which part of the scale they should mark if they believed that the stores were equal in their prices for the product.( n1)
To assess the possible presence and influence of suspicion, participants were asked to explain their response to the price-belief measure presented for each product ("In the space provided in the box below, please explain why you answered the above question the way you did"). An initial examination of the data led to the formation of the following coding categories:
- Suspicion regarding the lack of a price comparison (e.g., "There wasn't a comparison ... so that leads me to believe that Johnston's was probably more expensive, or they would have compared themselves to [comparison store]"),
- Uncertainty due to the lack of a price comparison ("Not sure since they don't compare the stores' prices"),
- Perceived equality due to the lack of a price comparison ("Because I don't know the price at [comparison store], I would assume the prices are the same"),
- Price comparisons for other products that reflect lower prices (e.g., "Since Johnston's has some lower prices, they are probably lower than [comparison store]"), and
- Other (e.g., "I believe the price was $3.59 for this product," "The price seems high but all cereal is high priced").
Two judges who were blind to the study's purpose coded responses. Interjudge agreement was 84%, and they resolved all disagreements through discussion. Following Campbell and Kirmani (2000), we computed a suspicion index based on the proportion of suspicious thoughts to total thoughts provided by each participant. We used this index to test the mediating role of suspicion (H[sub4]).
After responding to the relative price-belief measure and explaining the reason for their response, participants indicated their relative intentions to purchase the product ("Assume that Johnston' groceries operated in the [city name] area and that you needed to purchase this product. Given this, where would you be more likely to buy it?") on a nine-point scale (1 = "More likely to buy it at [the competitor store]," 5 = "Equally likely to buy it at [either store]," 9 = "More likely to buy it at [the featured store]"). After the product-specific measures, participants encountered measures that pertained to the featured store. We assessed general (i.e., store-level) relative price beliefs using a nine-point scale that employed endpoints that mirrored the product-specific measures (1 = "In general, [the competitor] charges lower prices than [the featured store]," 5 = "In general, [the featured store] and [the competitor] have about the same prices," 9 = "In general, [the featured store] charges lower prices than [the competitor]").( n2) We computed relative store attitudes as the average response (r = .80) to two nine-point measures ("Compared to [the competitor], my opinion of [the featured store] is more unfavorable/more favorable; more negative/more positive"). We also assessed relative shopping intentions on a nine-point scale (1 = "More likely to shop at [competitor]," 9 = "More likely to shop at [featured store]").
Results
Manipulation check results. To gauge the success of the price-information manipulation, we analyzed relative price beliefs for the two comparatively priced products (cranberry juice and cup-of-noodles) using a 2 (price information: partially comparative pricing versus noncomparative pricing) x 2 (product: juice versus cup-of-noodles) mixed ANOVA (cell means are presented in Table 2). We observed a significant (F[sub1,87] = 20.87, p < .001, η² = .195) main effect associated with the price-information manipulation (for the product main effect, p > .9). In support of the manipulation, compared with participants in the noncomparative pricing condition, participants exposed to partially comparative pricing believed that Johnston's had a lower price than the comparison store for the two products for which a price advantage was conveyed. A price information x product interaction also emerged (F[sub1,87] = 8.45, p < .01, η² = .089). As in Study 1, the price-information manipulation exerted a stronger effect on relative price beliefs for cranberry juice (F[sub1,87] = 64.58, p < .001, η² = .242) than for cup-of-noodles (F[sub1,87] = 16.38, p < .01, η² = .083).
Findings involving relative price beliefs for noncomparatively priced products. We submitted the relative price beliefs for the two noncomparatively priced products to a 2 (price information) x 2 (product) mixed ANOVA (for cell means, see Table 2). Only the main effect of the priceinformation manipulation achieved significance (F[sub1,87] = 6.30, p = .001, η² = .128; for all other effects, p > .6). Consistent with H[sub2], participants exposed to partially comparative pricing held less favorable relative price beliefs than did participants in the noncomparative pricing condition, which is an effect that held for both products (shampoo: F[sub1,88] = 10.97, p < .001, η² = .113; cereal: F[sub1,88] = 8.11, p < .01, η² = .086). Moreover, tests of the mean responses in the partially comparative pricing condition against the scale midpoint (representing perceived price equivalence between the featured store and its competitor) were significant for both shampoo (M = -.57; t[sub67] = -3.82, p < .001) and cereal (M = -.56; t[sub67] = -3.76, p < .001). The results show that participants who were exposed to the partially comparative pricing perceived the featured store as charging higher prices than the competitor for the noncomparatively priced products.
Findings involving the mediating role of suspicion . Next, we examined whether suspicion mediates the influence of partially comparative pricing on relative price beliefs about noncomparatively priced products. Based on Baron and Kenney's (1986) recommended procedure, an initial regression showed that the independent variable (partially comparative pricing versus noncomparative pricing) had a significant (t = 3.55, p < .001, R² = .128) effect on the dependent variable (average relative price belief for the two noncomparatively priced products; M[subpartial comparative] = -.57, M[subnoncomparative] = .43). A second regression demonstrated that the independent variable significantly (t = 3.47, p = .001, R² = .123) influenced the mediator variable (suspicion), such that there was a higher proportion of suspicious thoughts reported by participants in the partially comparative pricing condition (M = .44) than in the noncomparative pricing condition (M = .01). The final analysis involved regressing the dependent variable on both the independent and the mediating variables. Although the effect of suspicion was significant (t = 7.53, p < .001), the effect of the price manipulation was eliminated (t = 1.63, p > .1), in support of the mediating role of suspicion and H[sub4].
We obtained further evidence about the importance of suspicion in determining participants' responses to partially comparative pricing by reanalyzing their relative price beliefs for noncomparatively priced products. Previously, we reported the results of a 2 (price information) x 2 (product) mixed ANOVA for these beliefs. We replicated this analysis after splitting participants in the partially comparative pricing condition into two groups on the basis of whether they expressed suspicion (n = 29) or did not express suspicion (n = 39) in their protocols for the two noncomparatively priced products. The new analysis entailed a 3 (group: partially comparative pricing and suspicious; partially comparative pricing and unsuspicious; noncomparative pricing) x 2 (product: shampoo versus cereal) mixed ANOVA. Only the group main effect was significant (F[sub1,85] = 32.55, p < .001, η² = .434; for all other effects, p > .7). Pairwise comparisons revealed that participants in the partially comparative pricing and suspicious group provided relative price beliefs for the shampoo product (M = -1.41) that were significantly less favorable than those provided by participants in the partially comparative pricing and unsuspicious (M = .05, p < .000, η² = .348) and the noncomparative pricing (M = .50, p < .000, η² = .346) groups. Similarly, for the cereal product, we observed less favorable relative price beliefs in the partially comparative pricing and suspicious group (M = -1.40) than in either the partially comparative pricing and unsuspicious (M = .08, p < .000, η² = .367) or the noncomparative pricing (M = .35, p < .000, η² = .316) group. Beliefs in the partially comparative pricing and unsuspicious and the noncomparative pricing groups did not differ for either product (p > .14).
Thus, two different response patterns emerged when we partitioned participants in the partially comparative pricing condition on the basis of their suspiciousness. From this analysis, it is evident that the support for H[sub2] we observed in the previous aggregate analyses was driven by suspicious participants. Unsuspicious participants' beliefs about the noncomparatively priced products were unaffected by partially comparative pricing, a result that supports the null effects predicted by H[sub3]. Beyond documenting the role played by suspicion, these results illustrate the value of dividing participants in the partially comparative pricing condition into suspicion groups, a procedure that we adopt in subsequent analyses when appropriate.
Findings involving relative purchase intentions for noncomparatively priced products. The practical significance of the adverse effects of partially comparative pricing on beliefs about noncomparatively priced products would be substantially undermined if they did not carry over to participants' intentions to purchase the products. Accordingly, we submitted their responses to the product-specific intention measures to a 3 (group) x 2 (product) mixed ANOVA. The only significant effect to emerge from the analysis was the main effect of the group factor (F[sub1,85] = 4.48, p < .05, η² = .095; for all other effects, p > .3). For the shampoo product, follow-up contrasts revealed that participants in the partially comparative pricing and suspicious group indicated less favorable purchase intentions (M = -1.13) than did participants in the partially comparative pricing and unsuspicious (M = -.20, p < .05, η² = .072) and the noncomparative pricing (M = .05, p < .05, η² = .092) groups. Analogously, for the cereal product, we found less favorable intentions for the partially comparative pricing and suspicious group (M = -1.24) than for the partially comparative pricing and unsuspicious (M = -.38, p < .05, η² = .076) or the noncomparative pricing (M = -.15, p < .05, η² = .085) group. No difference in intentions existed between the partially comparative pricing and unsuspicious and the noncomparative pricing groups for either product (p > .58). Thus, the detrimental effect of partially comparative pricing on suspicious participants' beliefs about the relative price of noncomparatively priced products also existed for their intentions to purchase the products.
Findings involving the featured store. We next consider whether partially comparative pricing affected participants' responses to measures that involved the featured store by conducting separate one-way ANOVAs with general relative price beliefs, store attitudes, and store shopping intentions as the dependent variables and the three groups (partially comparative pricing and suspicious, partially comparative pricing and unsuspicious, and noncomparative pricing) as the independent variable. We observed a significant effect for only general price beliefs (F[sub2,84] = 4.08, p < .05, η² = .089; for store attitudes and intentions, p > .18). Follow-up contrasts revealed less favorable beliefs about the prices charged, in general, by the featured store compared with the comparison store for the noncomparative pricing group (M = .30) and the partially comparative pricing and suspicious group (M = .46) than for the partially comparative pricing and unsuspicious group (M = 1.33, for both comparisons, p < .05). However, relative price beliefs provided by participants in the partially comparative pricing and suspicious group did not differ (p > .7) from those provided by participants in the noncomparative pricing group.
Discussion
Study 2 extends our understanding of consumers' reactions to partially comparative pricing in several ways. It implicates suspicion as an important determinant of the relative price beliefs formed for noncomparatively priced products; suspicion fully mediated the effects of the price-information manipulation on such beliefs. Moreover, participants who were suspicious of partially comparative pricing believed that the featured store's prices for noncomparatively priced products were more expensive than the competitor's prices. In contrast, participants who were not suspicious of the absence of price comparisons for some products gave no indication that partially comparative pricing affected their relative price beliefs.
Study 2 further shows that the adverse influence of partially comparative pricing on noncomparatively priced products is not limited to relative price beliefs about such products. The likelihood of consumers buying these products is also reduced, as is evidenced by the results for the purchase-intention measures. The findings underscore the potential risks faced by retailers pursuing this pricing strategy.
Nonetheless, the adverse effects did not extend to participants' attitudes toward the retailer or to their intentions to patronize the retailer. Notably, unsuspicious participants' relative beliefs about the retailer's prices in general actually benefited from partially comparative pricing. Apparently, the highly favorable relative price beliefs formed after participants processed the price comparisons for the comparatively priced products carried significant weight in their impressions of the retailer's overall price position.
A limitation of our studies is their use of a fictitious store that uses partially comparative pricing. Consequently, we were unable to assess the reactions of shoppers who are most germane to a particular retailer, namely, its own patrons. Indeed, retailers should be most interested in how their customers respond to partially comparative pricing. Accordingly, we executed another study using existing retailers (Target and Wal-Mart) for the featured and comparison stores, which enabled us to understand how consumers who shop primarily at the featured store versus the comparison store respond to the use of partially comparative pricing.
There are at least two reasons to believe that consumer response to partially comparative pricing depends on store patronage. Prior research indicates that users of the competitive brand referenced in a comparative advertisement perceive comparisons as less informative (McDougall 1978), less credible (Prasad 1976), and ultimately less persuasive (Barone and Miniard 1999, Study 3; Wu and Shaffer 1987, Study 1) than do nonusers. These findings highlight the potential for comparisons to evoke particularly negative reactions from comparison-brand users who find their brand "under attack." As such, partially comparative pricing may engender greater suspicion in the minds of comparisonstore patrons, thus causing them to respond more negatively than other consumers when forming their relative beliefs about noncomparatively priced products.
Consumers who shop primarily at the featured store may experience less suspicion than other consumers about the missing price comparisons for noncomparatively priced products. Campbell (1999) shows how consumers are lenient in their inferences about a retailer's pricing motives when the retailer possesses a positive rather than a negative reputation. Presumably, a store enjoys a good reputation among consumers who choose to shop primarily at that store. If so, featured-store shoppers should be less likely to infer that the retailer is withholding price comparisons for the noncomparatively priced products because it charges more than its competitor for the products. For these reasons, we propose the following hypothesis:
H[sub5]: Comparison-store shoppers respond more negatively than featured-store patrons to partially comparative pricing in terms of their relative price beliefs about noncomparatively priced products.
Beyond testing H5, Study 3 also speaks to the robustness of our prior results. It does so by examining partially comparative pricing in a nongrocery retail environment and by using a real rather than a fictitious featured retailer. Moreover, Study 3 alters the ratio of comparatively to noncomparatively priced products in the partially comparative pricing condition. Products that are comparatively priced represent a minority of products encountered during the study, just as they typically constitute a minority of the products encountered in retail environments.
Method
A total of 117 undergraduate business students were assigned randomly to either the partially comparative pricing or the noncomparative pricing condition. In both conditions, participants were exposed to prices charged by a major retailer (i.e., Target) for a set of ten products (cordless telephone, calculator, CD, personal stereo, batteries, backpack, printer, CD player, digital camera, and DVD). In the noncomparative pricing condition, participants were presented only with the featured retailer's prices, whereas in the partially comparative pricing condition, the prices of a major competitor (i.e., Wal-Mart) were also provided for the first two products.
After processing this price information, participants responded to measures of product-specific relative price beliefs and more general store perception that were similar to the measures in Study 2. Finally, participants indicated how many times in the past month they had shopped at the featured and comparison stores. We then categorized participants as either featured-or comparison-store patrons on the basis of whether they shopped at one of the two stores at least four times in the past month. We employed this criterion to ensure that participants categorized as featured-store (comparison-store) shoppers would perceive a level of association with the store that was consistent with the theoretical rationale presented previously. The categorization resulted in 38 featured-store patrons and 38 comparisonstore patrons. We removed 41 participants who did not meet the criterion from the analysis. Thus, the study's research design involved a 2 (price information: partially comparative pricing versus noncomparative pricing) x 2 (store patronage: featured-store versus comparison-store patrons) between-subjects factorial design.
Results
Manipulation check results. Table 3 presents the cell means for the product-specific and store measures. We begin by examining relative price beliefs associated with the initial two products (telephone and calculator) for which we manipulated the presence of price comparisons. We analyzed the beliefs using a 2 (price information) x 2 (store patronage) x 2 (product) mixed ANOVA. We found a significant main effect (F[sub1,75] = 130.48, p < .001, η² = .644) for the price-information factor, such that we observed more favorable relative price beliefs in the partially comparative pricing condition (in which the price information for the products reflected the superiority of the featured store; M = 2.65) than in the noncomparative pricing condition (M = -.93). This result supports the success of the price-information manipulation. The price information x product interaction was also significant (F[sub1,75] = 19.00, p < .001, η² = .181) and indicated a stronger effect of the price-information manipulation for the calculator (F[sub1,75] = 154.84, p < .001, η² = .677) than for the telephone (F[sub1,75] = 84.85, p < .001, η² = .534). No other effect achieved significance (p > .13).
Findings involving relative price beliefs for noncomparatively priced products. Next, we consider the relative price beliefs provided for the eight products that were noncomparatively priced in both conditions (for cell means, see Table 3). We submitted the relative price beliefs to a 2 (price information) x 2 (store patronage) x 8 (product) mixed ANOVA. Significant main effects emerged for the price-information (F[sub1,75] = 38.04, p < .001, η² = .252) and store-patronage (F[sub1,75] = 25.75, p < .001, η² = .103) factors. The price-information main effect indicated more favorable relative price beliefs for the noncomparative pricing condition (M = .13) than for the partially comparative pricing condition (M = -.39). The main effect of store patronage reflected more favorable relative price beliefs for featured-store patrons (M = .22) than for comparison-store patrons (M = -.44).
Contrary to H[sub5], the price information x store patronage interaction was not significant (p > .5). This null effect suggests that featured-and comparison-store patrons did not differ in their responses to partially comparative pricing. However, on the basis of our a priori expectations of the potential for comparison-store patrons to respond less favorably than featured-store users to partially comparative prices, we conducted a series of planned contrasts (Jaccard 1998; Winer, Brown, and Michels 1991). One-way ANOVAs that assessed the effect of the price-information manipulation in each shopper group uncovered considerable differences between the shoppers. Test significance and explained variance (η²) are shown in Table 3.
Comparison-store patrons were highly responsive to the price-information manipulation. For this group, relative price beliefs regarding seven of the eight noncomparatively priced products were affected by the price-information manipulation at the .05 alpha level, and the remaining product was significant at the .1 alpha level. Moreover, the average explained variance was nearly 15%. In contrast, similar ANOVAs for featured-store patrons revealed that their relative price beliefs regarding four of the eight noncomparatively priced products were unaffected by the priceinformation manipulation at the .1 level; only three attained significance at the .05 level. The average explained variance was less than 9%. Collectively, the additional analyses indicate that comparison-store patrons respond more negatively to partially comparative pricing than do patrons of the featured store, as H[sub5] predicted.
Findings involving the featured store. We submitted responses to the measures of general relative price beliefs, attitudes, and intentions regarding the featured store to separate 2 (price information) x 2 (store patronage) ANOVAs (for cell means, see Table 3). In support of the validity of the patronage classification procedures used in this study, we found a significant main effect of store patronage on all three measures (p < .01), such that we detected more favorable responses for featured-store patrons than for comparison-store patrons. The price-information manipulation also exerted a significant main effect on participants' general relative price beliefs (F[sub1,75] = 10.08, p < .01, η² = .180) but not on their store attitudes or shopping intentions (p > .67). Participants in the partially comparative pricing condition held more favorable beliefs about the featured store's relative prices in general than did participants in the noncomparative pricing condition. Finally, the price information x store patronage interaction was not significant for any of these measures (p > .56).
Discussion
Despite changes in the retail category, the ratio of comparatively to noncomparatively priced products, and the use of an existing rather than a fictitious featured retailer, Study 3 again shows that partially comparative pricing ( 1) enhances consumers' relative price beliefs about comparatively priced products and the retailers' relative prices in general, ( 2) undermines relative price beliefs about noncomparatively priced products, and ( 3) has no influence on attitudes toward or intentions to shop at the retailer that uses the comparisons. Study 3 further reveals that the negative influence of partially comparative pricing on relative price beliefs about noncomparatively priced products holds for a retailer's own customers, though to a lesser extent than for comparison-store patrons.
A general finding from research on reference pricing is that price comparisons enhance consumers' perceptions of a product's value (Biswas, Wilson, and Licata 1993). However, this literature has not considered the potential impact of such comparisons on consumers' perceptions of a retailer's noncomparatively priced products. As such, existing research is unable to address the full range of effects that can arise when retailers adopt a partially comparative pricing strategy. Our research takes an initial step toward alleviating this gap in the literature. Specifically, we examine the impact of partially comparative pricing on relative price beliefs about comparatively priced products, relative price beliefs about noncomparatively priced products, intentions to purchase noncomparatively priced products, beliefs about a retailer's relative prices in general, attitudes toward the retailer, and intentions to shop at the retailer.
We find that partially comparative pricing has both desirable and undesirable consequences. A positive finding is that the pricing strategy enhanced participants' relative price beliefs about the comparatively priced products and about the retailer's prices in general.( n3) Nonetheless, partially comparative pricing caused participants to hold less favorable relative price beliefs for noncomparatively priced products than those that existed in the absence of comparative price information. They also reduced participants' intentions to purchase the products. Finally, we observed no effect of partially comparative pricing on participants' attitudes toward the retailer or on their shopping intentions.( n4) Accordingly, retailers should consider these trade-offs before using comparative price claims. Such comparisons should be employed only if their perceptual and behavioral benefits (e.g., beliefs that the featured retailer has lower prices that promote the purchase of comparatively priced products) exceed their related costs (e.g., beliefs that the featured retailer has higher prices that reduce the purchase of noncomparatively priced products).( n5)
Beyond examining the various types of effects produced by partially comparative pricing, our research also explores whether suspicion is responsible for the effects' adverse influence on relative price beliefs about noncomparatively priced products. Suspicion prompted by the presence of partially comparative pricing is shown to mediate fully the effects observed for these beliefs. Moreover, participants exposed to partially comparative pricing who did not become suspicious held relative price beliefs about the noncomparatively priced products that were equivalent to those of participants who received only noncomparative prices. Thus, only participants who expressed suspicion about the lack of a price comparison held less favorable beliefs about the relative price of noncomparatively priced products.
The findings indicate that it is important for retailers that employ partially comparative pricing to manage consumers' perceptions of this tactic. Successful management of the level of suspicion associated with partially comparative pricing should help reduce its adverse effects. Development of low-price positioning for the retailer may help reduce suspicion that a retailer presents price comparisons for some but not all of its products because the noncomparatively priced products are more expensive. Store signage that claims that the retailer offers lower prices than a competitor for many items, including the featured set of comparatively priced products, might also help reduce suspicion. Conversely, retailers' use of price comparisons in a way that promotes suspicion should undermine relative price beliefs. For example, Kmart's "Dare to Compare" campaign triggered accusations from competitors that Kmart presented erroneous information in price comparisons (Merrick 2001a). Media coverage of a subsequent lawsuit filed by a Kmart competitor may have been sufficient to increase consumer suspicion toward the campaign. The heightened suspicion may have prompted beliefs that Kmart charged higher prices than did the referenced competitors.
Our research also explores the impact of store patronage on consumer response to partially comparative pricing. Comparison-store shoppers displayed a more adverse reaction in their beliefs about noncomparatively priced products than did shoppers of the store that used partially comparative pricing. Even so, the latter group still evidenced a detrimental influence of partially comparative pricing on their beliefs about noncomparatively priced products, thereby underscoring the relevance of our findings to retailers that implement or are contemplating this pricing strategy.
Beyond the practical implications of our findings, they also bear on the persuasion knowledge literature. An important research task that consumer researchers face involves improving the field's understanding of how consumers' knowledge about the marketplace can influence their responsiveness to persuasion tactics (Wright 2002). Consistent with this recommendation, researchers have examined consumers' interpretations of marketers' actions in advertising, personal selling, and pricing contexts (e.g., Campbell 1995, 1999; Campbell and Kirmani 2000). The current inquiry complements existing work by providing additional evidence that suspicion is a key cognitive reaction that shapes consumer response to retailers' persuasion attempts. Our findings further demonstrate that the existing relationship between consumer and retailer (i.e., whether consumers shop at the retailer or its competitor) can be an important determinant of the favorability of consumers' responsiveness to persuasion tactics (e.g., partially comparative pricing).
Limitations and Further Research
Although we believe that our research provides an important contribution by offering an initial exploration of consumer response to partially comparative pricing, there are particular limitations that warrant acknowledgment and discussion. The strength of our findings' pragmatic implications hinges on the degree to which the effects observed in our studies occur in actual shopping environments. Our experimental setting represents a processing environment devoid of the distractions encountered in stores. The stimulus materials given to participants made the existence of price comparisons for some products and their absence for others plainly evident. Consequently, it is possible that partially comparative pricing was more noticeable and thus received greater attention in our studies than would be the case in actual stores. In turn, this would facilitate the emergence of effects.
Only further research that examines consumer response to partially comparative pricing in more natural shopping environments can determine our findings' dependency on the methodology used in our studies. In this regard, subsequent inquiries could examine the impact of partially comparative pricing in computer-simulated retail environments (Burke et al. 1992). The assessment of consumer response in the marketplace itself offers the ultimate test. Ideally, field experiments that examine whether the presence of partially comparative pricing reduces the sales of the retailer's noncomparatively priced products would be undertaken. Statistical models (e.g., logit) might also be able to estimate the effects of partially comparative pricing on a retailer's sales in a manner analogous to the use of such models in prior research that examines consumer response to same-store external reference prices (e.g., Mayhew and Winer 1992) and price promotions (e.g., Mela, Gupta, and Lehmann 1997).
Another feature of our experimental materials that limits their external validity is that they presented only a single brand in a product category. Typically, retailers offer multiple brands in a product category. If a comparative price is provided, it is usually limited to a single brand. Note that retailers' practice of providing comparative prices for only one brand should enhance the chances of consumers noticing the existence of partially comparative pricing. This observation somewhat diminishes reservations that partially comparative pricing is more noticeable in our studies than in actual shopping environments. However, it points to our studies' silence on the potential influence of a comparatively priced brand on relative price beliefs about the remaining brands in the product category that are noncomparatively priced. Would the same type of effects reported herein be found? Is it possible that consumers infer that the existence of a price advantage for one brand implies price superiority for other brands in the category, especially if the comparatively priced brand is the most expensive brand or a market leader? At this point, with the exception of Pechmann's (1996) research, little is understood about when a known price advantage for one product or brand is used to infer a price advantage for another product or brand.
In addition, our research does not address several factors that may affect consumer response to partially comparative pricing. For example, although our findings show adverse effects of partially comparative pricing for noncomparatively priced products among the retailer's own shoppers, we did not explore whether these effects might be lessened, perhaps to the point of disappearing, for a store's most loyal patrons. As such, further research might consider the role of store loyalty in moderating the influence of partially comparative pricing. Similarly, consumers' perceptions of a retailer's relative price positioning (e.g., cheap versus expensive) in the marketplace and their knowledge about competitors' prices for noncomparatively priced products might also be important. For example, if the featured retailer is perceived as an inexpensive place to shop, consumers might be more likely to generalize the price advantage of comparatively priced products to noncomparatively priced products. If consumers know that the competitor referenced in the price comparisons charges the same or an even higher price than the featured retailer for a noncomparatively priced product, they should be unlikely to form unfavorable relative price beliefs.
Finally, we should acknowledge that the adverse influence of partially comparative pricing was not present for all the noncomparatively priced products tested in our studies. Although for some products the lack of statistical significance might reflect inadequate statistical power, this would not seem to explain all the null effects (e.g., CDs and batteries for featured-store patrons in Study 3). The reason for such variations is unclear and awaits future investigation.
( n1) To communicate our findings more easily, we recoded responses to the measures using endpoints of -4 ("competitor charges a much lower price than the featured store") and +4 ("featured store charges a much lower price than the competitor") and a midpoint of 0 ("featured store and competitor charge the same price"). Thus, negative responses that differ significantly from the midpoint reflect beliefs that the competitor store charges lower prices than the featured store, and positive responses that are significantly different from the midpoint indicate beliefs that the featured store has lower relative prices.
( n2) We recoded responses to the global measures in the manner described in Note 1 for relative price beliefs.
( n3) This finding is consistent with the many studies that have concluded that external reference prices enhance price perceptions of the focal product. However, previous reference price research has typically employed nonrelative measures of perceived value of the offer, perceived savings, and shopping intentions (e.g., Berkowitz and Walton 1980; Lichtenstein and Bearden 1989; Urbany, Bearden, and Weilbaker 1988) rather than measures of relative price beliefs, as in the present research.
( n4) A reviewer asked us to discuss why store attitudes and shopping intentions are not adversely affected by partially comparative pricing, given the suspicions it aroused and its negative effects on relative price beliefs about noncomparatively priced products. It should be recognized that partially comparative pricing produced countervailing effects. Beyond undermining beliefs about noncomparatively priced products, partially comparative pricing enhanced beliefs about the relative prices of comparatively priced products. Apparently, participants had more confidence in their relative price beliefs about the comparatively priced products than about the noncomparatively priced products, as is evidenced by the fact that beliefs about the retailer's relative prices in general were enhanced by its use of partially comparative pricing. This is to be expected, given that the former beliefs were based on actual price comparisons, and the latter beliefs needed to be inferred because of the absence of price comparisons. Even in the absence of such countervailing influences, changes in the price beliefs about a few of the thousands of products carried by the type of retailers examined in our research may not be sufficient to cause significant shifts either in how much consumers like a retailer or in their shopping intentions. Finally, although price is important, it is but one of a set of salient attributes that determine the attitudes and shopping intentions of many consumers. There appear to be several reasons why null effects occurred for store attitudes and intentions.
( n5) We extend our appreciation to a reviewer for this insight.
Legend for Chart:
A - Product
B - Noncomparative Pricing
C - Partially Comparative Pricing
A B C
Cranberry juice(a) 5.44 (1.95) 7.73 (1.77)
Cup-of-noodles(a) 5.85 (2.60) 7.37 (2.03)
Pickles(b) 5.15 (1.75) 4.60 (2.20)
Potato chips(b) 5.48 (2.10) 4.48 (2.78)
(a) Comparatively priced product in the partially comparative
pricing condition.
(b) Noncomparatively priced product in the partially
comparative pricing condition.
Notes: Standard deviations are in parentheses. Product-belief
measures (how likely it is that Johnston's has a lower price
than comparison store) are 1 = "unlikely" and 9 = "likely." Legend for Chart:
A - Product
B - Noncomparative Pricing
C - Partially Comparative Pricing
A B C
Cranberry juice(a) .50 (1.82) 2.54 (1.44)
Cup-of-noodles(a) 1.00 (1.78) 2.03 (1.34)
Shampoo(b) .50 (1.39) -.57 (1.24)
Cereal(b) .35 (1.35) -.56 (1.23)
(a) Comparatively priced product in the partially comparative
pricing condition.
(b) Noncomparatively priced product in the partially comparative
pricing condition.
Notes: Standard deviations are in parentheses. Responses to
the relative price-belief measures were recoded such that
-4 = "competitor charges a much lower price than the featured
store," 0 = "featured store and competitor charge the same
price," and +4 = "featured store charges a much lower price
than the competitor." Legend for Chart:
A - Measures
B - Featured-Store Patrons Noncomparative Pricing
C - Featured-Store Patrons Partially Comparative Pricing
D - Featured-Store Patrons p
E - Featured-Store Patrons η²
F - Comparison-Store Patrons Noncomparative Pricing
G - Comparison-Store Patrons Partially Comparative Pricing
H - Comparison-Store Patrons p
I - Comparison-Store Patrons η²
A B C D E
F G H I
Relative Price Belief
Telephone(a) -.71 (.85) 2.45 (1.28) .000 .683
-.90 (1.36) 2.06 (2.18) .000 .418
Calculator(a) -.77 (1.30) 3.00 (1.08) .000 .726
-1.70 (.97) 2.56 (2.12) .000 .642
CD(b) .82 (1.63) .75 (1.12) .872 .001
.75 (1.10) -.06 (.75) .019 .152
Personal stereo(b) 1.12 (1.41) .25 (1.16) .048 .107
.50 (1.29) -.44 (1.06) .029 .133
Batteries(b) .53 (1.12) .35 (1.09) .626 .007
-.60 (.74) -1.44 (.62) .001 .271
Backpack(b) .24 (1.09) -.45 (1.43) .116 .069
.10 (1.20) -.94 (.70) .004 .216
Printer(b) .58 (1.58) -.40 (1.39) .051 .104
.20 (1.41) -.88 (.81) .012 .173
Radio(b) .58 (1.33) -.65 (.99) .003 .232
.20 (1.00) -.94 (.70) .000 .342
Digital camera(b) .18 (1.78) -.30 (1.26) .348 .025
-.10 (1.58) -.94 (.93) .075 .090
DVD(b) .41 (2.24) -.25 (1.02) .243 .039
.25 (1.57) -.94 (1.17) .018 .154
Featured Store
Relative price -1.24 (.95) .29 (1.56) .005 .196
-2.45 (.96) -1.13 (1.56) .003 .235
Relative attitude 2.23 (2.35) 2.33 (1.92) .985 .000
.54 (1.89) .92 (1.73) .411 .020
Shopping intention 3.00 (1.34) 2.86 (1.47) .774 .002
-1.55 (1.49) -1.44 (1.93) .847 .001
(a) Comparatively priced product in the partially comparative
pricing condition.
(b) Noncomparatively priced product in the partially comparative
pricing condition.
Notes: Responses to the relative price-belief measures were
recoded such that -4 = "competitor charges a much lower price
than the featured store," 0 = "featured store and competitor
charge the same price," and +4 = "featured store charges a much
lower price than the competitor." A similar recoding occurred
for the relative price, shopping intentions (-4= "more likely
to shop at competitor" and +4 = "more likely to shop at featured
store"), and relative attitude (-4 = "more favorable attitudes
for competitor than featured store" and +4 = "more favorable
attitudes for featured store than competitor") measures. A. Comparative Pricing
B. Noncomparative Pricing
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~~~~~~~~
By Michael J. Barone; Kenneth C. Manning and Paul W. Miniard
Michael J. Barone is Associate Professor of Marketing, Iowa State University (e-mail: mbarone@iastate.edu). Kenneth C. Manning is Associate Professor of Marketing, Colorado State University (e-mail: ken.manning@ colostate.edu). Paul W. Miniard is BMI Professor of Marketing, Florida International University (e-mail: miniardp@fiu.edu). Preparation of this manuscript was partially supported by an Iowa State University Faculty Development Grant awarded to the first author. The third author dedicates this article to his father, Ernest Paul Miniard. The authors also wish to acknowledge the guidance provided by the anonymous JM reviewers.
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Record: 35- Consumer Trust, Value, and Loyalty in Relational Exchanges. By: Sirdeshmukh, Deepak; Singh, Jagdip; Sabol, Barry. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p15-37. 23p. 2 Diagrams, 4 Charts, 3 Graphs. DOI: 10.1509/jmkg.66.1.15.18449.
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Consumer Trust, Value, and Loyalty in Relational Exchanges
The authors develop a framework for understanding the behaviors and practices of service providers that build or deplete consumer trust and the mechanisms that convert consumer trust into value and loyalty in relational exchanges. The proposed framework ( 1) uses a multidimensional conceptualization for the trustworthiness construct; ( 2) incorporates two distinct facets of consumer trust, namely, frontline employees and management policies and practices; and ( 3) specifies value as a key mediator of the trust-loyalty relationship. The authors test the proposed model using data from two service contexts-retail clothing (N = 264) and nonbusiness airline travel (N = 113). The results support a tripartite view of trustworthiness evaluations along operational competence, operational benevolence, and problem-solving orientation dimensions. Moreover, the authors find evidence of contingent asymmetric relationships between trustworthiness dimensions and consumer trust. For frontline employees, benevolent behaviors demonstrate a dominant "negativity" effect (i.e., a unit negative performance has a stronger effect than a unit positive performance), whereas problem-solving orientation has a dominant "positivity" effect (i.e., a unit positive performance has a stronger effect than a unit negative performance). Value completely mediates the effect of frontline employee trust on loyalty in the retailing context and partially mediates the effect of management policies and practices trust on loyalty in the airlines context. The role of frontline employees is more critical in the retailing context, whereas management practices and policies play the dominant role in the airlines context. Overall, the proposed framework successfully models trust and loyalty mechanisms across the two industries examined in the study, while remaining sensitive to essential contextual differences.
The growing importance of relationship marketing has heightened interest in the role of trust in fostering strong relationships. As Berry (1996, p. 42) asserts, "the inherent nature of services, coupled with abundant mistrust in America, positions trust as perhaps toe single most powerful relationship marketing tool available to a company." Likewise, Spekman (1988, p. 79) has observed that trust is the "cornerstone" of long-term relationships. Not surprisingly, several conceptual (Gundlach and Murphy 1993; Nooteboom, Berger, and Noorderhaven 1997) and empirical (Garbarino and Johnson 1999; Tax, Brown, and Chandrashekaran 1998) studies have posited trust as a key determinant of relational commitment. For example, Urban, Sultan, and Qualls (2000) propose customer trust as an essential element in building strong customer relationships and sustainable market share. More directly, Reichheld and Schefter (2000, p. 107) observe that "[t]o gain the loyalty of customers, you must first gain their trust."
Despite the well-recognized significance of trust building in consumer-firm relationships, few studies have examined company behaviors and practices that build or deplete consumer trust or the mechanisms by which these behaviors/practices contribute to trust enhancement and/or depletion. Instead, most studies have focused on the consequences of perceived trust for outcomes such as loyalty and cooperation (Garbarino and Johnson 1999; Tax, Brown, and Chandrashekaran 1998). Therefore, although sufficient evidence exists to suggest that trust matters for critical relational outcomes, fundamental gaps remain in the understanding of the factors that build or deplete consumer trust and the mechanisms that might explain the process of trust enhancement or depletion in consumer-firm relationships.
This research aims to fill the preceding gap in the literature. Specifically, four aspects of our study are noteworthy. First, we distinguish between trustworthiness and trust; develop a multifaceted, multidimensional model of the behavioral components of trustworthiness; and examine their differential effects on consumer trust. The focus on specific behavioral dimensions for two key facets of relational exchanges-frontline employee (FLE) behaviors and management policies and practices (MPPs)-is conceptually appealing because these dimensions and facets are rooted in strong theoretical frameworks and facilitate a fine-grained understanding of their differential effects on consumer trust. Moreover, this focus is managerially useful because it pinpoints those frontline behaviors and management practices that likely are the key drivers of consumer trust. Second, in mapping the mechanisms that link trustworthy behaviors and practices to consumer trust, we do not limit our conceptualizations to simple, linear relationships. Instead, on the basis of emerging theoretical ideas in social psychology and decision-making research, we postulate contingent asymmetric relationships. Specifically, we allow for the possibility that the trust-building effect of a unit positive change in performance on any factor of trustworthy behaviors/practices may not be equivalent to the trust depletion effect produced by a unit negative change in performance. Managerially, this implies that for some dimensions, negative performance may not deplete consumer trust significantly, and positive performance on other dimensions may not build consumer trust. Linear conceptualizations fail to reveal such theoretically and managerially interesting asymmetries. Third, we do not study consumer trust in isolation. Rather, we test a nomological model that proposes interrelationships among consumer trust and loyalty, in which value serves as a critical mediating variable. This approach provides severa l advantages, including ( 1) a direct confrontation of the thesis that consumer trust matters in relational exchanges, ( 2) understanding the differential effects of trust facets on value and loyalty, and ( 3) insights into mechanisms that link consumer trust and loyalty. To enhance the validity of our nomological model, we control for recency effects by partialling out the effect of satisfaction, a transactional variable capturing customers' experiences during the most recent episode. Fourth, to examine the sensitivity of the proposed model, we use data from two different relational service contexts for empirical testing. In particular, we use data from retail (i.e., major clothing purchase from a frequently visited department store) and service (i.e., nonbusiness travel on a frequently used airline) industries. We begin our discussion with the proposed conceptual model.
The conceptual model guiding this research is depicted in Figure 1. The proposed model draws from the diverse research on trust in social relationships (Deutsch 1958; Sorrentino et al. 1995) and interorganizational relationships (Moorman, Deshpande, and Zaltman 1993; Morgan and Hunt 1994). However, we recognize that the distinct characteristics of consumer-firm exchanges, including unique structural aspects (Fournier, Dobscha, and Mick 1998), asymmetric relationship motivations (Deighton and Grayson 1995), and desired end states (Gwinner, Gremler, and Bitner 1998), make the direct translation of constructs from other contexts difficult at best and inappropriate at worst. Therefore, we used caution in translating constructs and adapting conceptualizations based on related literature in consumer behavior. We begin our discussion of the proposed model by conceptualizing consumer trust and distinguishing it from trustworthy behaviors and practices.
Facets of Consumer Trust and Trustworthy Behaviors and Practices
As in Figure 1, we conceptualize consumer trust as a multifaceted construct, involving FLE behaviors and MPPs as distinct facets. In the literature, some authors have conceptualized trust in conative or behavioral terms (Ganesan 1994; Mayer, Davis, and Schoorman 1995). Emphasizing behavioral intent, Moorman, Zaltman, and Deshpande (1992, p. 315) define trust as "a willingness to rely on an exchange partner in whom one has confidence." Other researchers use cognitive or evaluative definitions of trust, arguing that the link between trust evaluations and behavioral response should be open to empirical investigation and likely subject to the influence of other contextual factors (Doney and Cannon 1997; Morgan and Hunt 1994). Adopting this approach, Morgan and Hunt (1994, p. 23) define trust "as existing when one party has confidence in the exchange partner's reliability and integrity." Therefore, we define consumer trust as the expectations held by the consumer that the service provider is dependable and-can be relied on to deliver on its promises.[ 1]
Consumers' trust in the service provider is hypothesized to develop around two distinct facets, FLEs and MPPs.[ 2] In most service contexts, these facets are structurally distinct nodes around which the customer is likely to make independent judgments during the course of a service exchange. For example, it is plausible for a consumer to trust a retail clothing store's management but view its salespeople with less trust or, perhaps, with distrust. These differences may occur because the inferential basis of evaluations is different; FLE evaluations are based on observed behaviors that are demonstrated during the service encounter, whereas MPP judgments are based on the policies and practices governing the exchange. The inclusion of multiple facets in consumer evaluations of services has been supported by several authors (Crosby and Stephens 1987; Doney and Cannon 1997; Singh 1991). Crosby and Stephens (1987) conceptualize consumers' overall satisfaction with a service as having three distinct facets, including satisfaction with ( 1) the contact person, ( 2) the core service, and ( 3) the organization. Likewise, in a medical service context, Singh (1991) demonstrates that the consumer's judgments of satisfaction at three distinct nodes, including the physician, hospital, and insurance provider, achieve discriminant validity.
More important, the preceding studies demonstrate that a multifaceted conceptualization is not only consistent with data on consumer/buyer judgments but also more likely to reveal the differential effects of the facets. For example, in Crosby and Stephens's (1987) study, each facet of satisfaction relates to different sets of antecedents (e.g., contact person satisfaction is mostly sensitive to interactional factors) and contributes uniquely to overall satisfaction. Likewise, Macintosh and Lockshin (1997) find that for customers with strong interpersonal ties with a retail salesperson, store loyalty and purchase intentions are influenced more strongly by salesperson trust than by store trust. In contrast, trust in the store was a critical determinant of store loyalty for consumers without such interpersonal ties.
Consequently, trustworthy behaviors and practices are conceptualized distinctly for FLEs (i.e., trustworthy behaviors) and management (i.e., trustworthy practices). We define trustworthiness to include FLE behaviors and MPPs that indicate a motivation to safeguard customer interest. Recognizing that only a subset of the complete domain of observed behaviors and practices is likely to be relevant for the trustworthiness construct, prior research has sought to identify valid and relevant dimensions (Ganeson 1994; Smith and Barclay 1997). Invariably, a multidimensional conceptualization is suggested that includes notions of ( 1) competence and ( 2) benevolence. Next, we develop and extend this conceptualization by including problem-solving orientation as the third dimension of trustworthiness. We initially propose hypotheses for direct, linear, symmetric effects of trustworthy behaviors and practices on their corresponding trust facets. Thereafter, we discuss the potential for asymmetries and propose hypotheses for empirical testing. This coheres with our methodological approach, in which we examine the asymmetrical hypotheses for their incremental contribution to a baseline model of symmetrical effects.
Readers will note that our discussion of the development of trustworthiness cognitions in the following sections is in the context of "experience" services, in which consumers have the ability to make judgments by processing experience information. In contrast, judgments of trustworthiness and development of trust in "credence" contexts are more likely to approximate bonding and signaling processes, because consumers are unable to obtain experience-based information veridical to the judgment at hand. W5 allude to this alternative mechanism subsequently.
Dimensions of Trustworthy Behaviors and Practices and Their Effects on Trust
Operational competence. The expectation of consistently competent performance from an exchange partner has been noted as a precursor to the development of trust in a variety of business relationship contexts. For example, Mayer, Davis, and Schoorman's (1995, p. 717) conceptual model includes ability, or "that group of skills, competencies, and characteristics that enable a party to have influence within some specific domain," as a key element of trustworthiness. Likewise, Smith and Barclay (1997) define role competence as the degree to which partners perceive each other as having the skills, abilities, and knowledge necessary for effective task performance. Sako (1992, p. 43) goes as far as to say that "competence trust is a prerequisite for the viability of any repeated transaction." Empirically, competence-related dimensions have been found to exert a strong influence on trust in diverse contexts. For selling alliances in the computer industry, Smith and Barclay (1997) find that perceptions of role competence have a significant effect on the partner's willingness to invest in the relationship. Doney and Cannon (1997) find that salesperson expertise is a significant predictor of the buyer's trust in the salesperson.
We extend the preceding discussion by focusing on the notion of operational competence in service exchanges. By operational competence, we imply the competent execution of visible behaviors as an indication of "service in action" (e.g., response speed) and distinguish it from the inherent competence (e.g., knowledge) of FLEs and MPPs. In consumer-service provider exchanges, this operational focus is appropriate because competence judgments are typically based on observation of FLE behaviors and/or MPPs. For example, a retail salesperson may possess the knowledge or ability required to perform his or her role, but unless this knowledge is translated into observable behaviors (e.g., helping the consumer in finding a desired style of clothing), it is less likely to be processed as an indication of trustworthiness. Likewise, although management may be technically competent, consumers would likely lack information to make competency judgments unless it is indicated by visible practices (e.g., providing enough check-out counters to reduce wait times). Therefore, we propose that consumer judgments of operational competence are a critical determinant of trust and are drawn from the relevant domains of FLE behaviors and MPPs.
H<SUB>1</SUB>: The consumer's perception of the operational competence evident in FLE behaviors is positively related to FLE trust.
H<SUB>2</SUB>: The consumer's perception of the operational competence evident in MPPs is positively related to MPP trust.
Operational benevolence. Operational benevolence is defined as behaviors that reflect an underlying motivation to place the consumer's interest ahead of self-interest. Our notion of operational benevolence recognizes that simply having a benevolent motivation is not sufficient; rather, this motivation needs to be operationalized in visible FLE behaviors and MPPs that unambiguously favor the consumer's interest, even if a cost is incurred in the process. Sako (1992, p. 39) refers to this dimension as "goodwill trust" and notes that, unlike competence trust, a benevolent partner "can be trusted to take initiatives [favoring the customer] while refraining from unfair advantage taking." Benevolent behaviors provide diagnostic evidence of trust because by going beyond the terms of the explicit "contract," the service provider indicates proconsumer motivations, restraint on self-serving opportunism, and a willingness to assume fiduciary responsibility (Barber 1983; Ganesan and Hess 1997; Morgan and Hunt 1994). Consequently, benevolent behaviors and practices are often regarded as "extra-role" actions that are performed at a cost to the service provider with or without commensurate benefits. Empirical findings generally corroborate the influence of operational benevolence in the development of trust (Hess 1995; Smith and Barclay 1997). In a study of consumer trust in a brand, Hess (1995) demonstrates that altruism, or the perception that the brand has the consumer's best interests at heart, explains the greatest proportion (40%) of variance in trust. Smith and Barclay (1997) report that character (including operational benevolence) has a significant impact on investment in buyer-seller relationships. Likewise, McAllister (1995) finds that the manager's affective trust in a peer is positively affected by the citizenship or extra-role behaviors.
Extending the preceding research to consumer-service provider exchanges, we propose that consumers formulate perceptions of operational benevolence separately for FLEs and management on the basis of corresponding behaviors and practices. For example, airline management might provide evidence of operational benevolence by instituting practices that indicate respect for the customers and favor their best interests (e.g., upgrading passengers, providing more leg room). In turn, because operational benevolence is associated with restrained opportunism and building "goodwill," consumers are thought to reciprocate benevolent FLE behaviors (MPPs) by placing greater trust in the FLE (management).
H<SUB>3</SUB>: The consumer's perception of the operational benevolence evident in FLE behaviors is positively related to FLE rust.
<SUB>4</SUB>: The consumer's perception of the operational benevolence evident in MPPs is positively related to MPP trust.
Problem-solving orientation. Finally, problem-solving orientation is defined as the consumer's evaluation of FLE and management motivations to anticipate and satisfactorily resolve problems that may arise during and after a service exchange. It is recognized that ( 1) problems often arise during the course of service delivery (Bitner, Booms, and Tetreault 1990; Zeithaml and Bitner 1990) and/or in the postexchange phase (Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998) because of service heterogeneity (e.g., large variance in service delivery) and intangibility (e.g., quality can be reliably judged only after experience), and ( 2) the manner in which service providers approach such problems are critical incidents that provide insight into the character of the service provider (Kelley and Davis 1994; Smith, Bolton, and Wagner 1999). Interest in the problem-solving orientation of service providers can be traced to prior work on the role of service recovery in consumer evaluations. For example, Goodwin and Rosso(1992) suggest that problem-solving perceptions are affected by the nature and promptness of company effort. Likewise, Smith, Bolton, and Wagner (1999) find that failures in the process of service delivery (attributed to the FLE) are a greater cause of dissatisfaction than are tangible problems such as stock-outs. Hart, Heskett, and Sasser (1990, p. 151) note that "every customer's problem is an opportunity for the company to prove its commitment to service [and build trust]-even when the company is not to blame."
The services literature offers conceptual and empirical evidence to suggest that problem-solving orientation is a distinct factor in consumer judgments. Zeithaml and Bitner (2000, p. 179) note that "for service employees, there is a specific need for [problem-solving] training.... [E]ffective recovery skills include hearing the customer's problems, taking initiative, identifying solutions, and improvising." Calantone, Graham, and Mintu-Wimsatt (1998, p. 21) emphasize the unique aspects of problem solving, noting that it is "characterized by behaviors that are cooperative, integrative, needs-focused, and information-exchange oriented." Levesque and McDougall (2000) go so far as to suggest that problem-solving contexts involve unique "exchanges" that occur within the context of the larger consumer-firm relationship.
As such, behaviors that demonstrate a problem-solving orientation are related to but distinct from those demonstrated during routine contexts. Specifically, such behaviors demonstrate the firm's ability and motivation to sense and resolve customer problems during and after exchange episodes. Although operational competence and operational benevolence are likely to be implicated during problem solving, they are not likely to capture the unique cognitive judgments that arise during and after problem resolution.[ 3] Consequently, we argue that they cannot be subsumed under the other two dimensions, and we propose problem-solving orientation as a distinct dimension of trustworthiness. Surprisingly, the role of problem-solving orientation has not been examined in most trust research to date. One exception is a study by Tax, Brown, and Chandrashekaran (1998) that uses the justice literature to propose that fairness in problem solving is crucial to consumer evaluations of satisfaction and trust in a range of service industries (e.g., bank, telecommunications firm, health care insurer). Their results indicate that first-time customers' dissatisfaction with problem handling was strongly and directly related to trust in the service organization (β = -.73). This was also evident for existing customers (β = -.70), though favorable prior experiences dampened this effect.
Drawing on the preceding literature, we posit that in service contexts, consumers garner evidence from FLE behaviors and MPPs that facilitates evaluation of the problem-solving orientation of each facet (i.e., FLEs and MPPs). However, this evidence is not limited to postconsumption service failures and may include problems that the customer faces during the actual service encounter. For example, during the course of a flight, a distressed airline passenger may require assistance from a flight steward in contacting family on the ground. Similarly, airline policies and practices for locating and retrieving lost baggage may provide critical evidence of trustworthiness. Consequently, we posit that consumers are alert to evidence of problem-solving orientation throughout the process of service consumption and use this evidence to formulate trust judgments. Therefore,
H<SUB>5</SUB>: The consumer's perception of the problem-solving orientation evident in FLE behaviors is positively related to FLE trust.
H<SUB>6</SUB>: The consumer's perception of the problem-solving orientation evident in MPPs is positively related to MPP trust.
Thus far, we have proposed that ( 1) consumers use evidence from three critical domains of FLE behaviors and MPPs, including operational competence, operational benevolence, and problem-solving orientation, and ( 2) judgment of trust in the FLE and management is directly affected by consumers' perceptions of trustworthy behaviors and practices. In developing the hypotheses for asymmetric effects of trustworthy behaviors and practices on trust, we view hypotheses H<SUB>1</SUB> to H<SUB>6</SUB> as the baseline model of linear effects and examine the potential for asymmetries.
Asymmetric Effects of Trustworthy Behaviors and Practices on Consumer Trust
Although trust research has mostly focused on linear effects, we propose that trustworthy behaviors and practices may exert asymmetric effects on trust. That is, for any dimension of trustworthy behaviors an practices, negative versus positive performance may have a differential impact on consumer trust. The limited research in marketing proposing asymmetric effects has primarily argued for negativity, or the dominance of negative over positive information in judgments (Anderson and Sullivan 1993; Mittal, Ross, and Baldasare 1998). Theoretical support for these predictions has been primarily drawn from Kahneman and Tversky's (1979) loss-aversion hypotheses and from Wyer and Gordon's (1982) notion of distinctive coding of negative events in memory. Empirical support for these theoretical predictions has been found in several streams, including multiattribute judgments (Kahn and Meyer 1991), effects of performance on disconfirmation (Mittal, Ross, and Baldasare 1998), effects of disconfirmation on customer satisfaction (Anderson and Sullivan 1993), and effects of service quality on behavioral consequences (Zeithaml, Berry, and Parasuraman 1996).
We extend this work by drawing on research in norm theory (Herzberg 1966) and cue diagnosticity in social judgments (Oliver 1997; Skowronski and Carlston 1987; Taylor 1991) to propose "contingent" asymmetric effects where either negativity or positivity effects may be observed. In accord with the classic need satisfaction theories, such as Herzberg's (1966) dual-factor theory, researchers distinguish between "hygienes" (the dissatisfaction-avoidance factors) and "motivators" (the satisfaction-producing factors). Negative performance on hygienes has a stronger effect on satisfaction than does positive performance, in accord with the negativity effect. In the case of motivators, however, stronger effects are expected for positive performance than for negative performance. Drawing from cue diagnosticity theory, Skowronski and Carlston (1987) note that the perceptual interpretation of performance on an attribute is affected by the person's neutral point (anchor) for that attribute compared with other attributes. If past performance indicates that positive (negative) performance is the norm, then negative (positive) performance on that attribute may carry a greater weight in subsequent judgments. As such, this view rejects the notion that negativity effects are pervasive and argues that both negativity and positivity effects are plausible "contingent" on the nature of the attribute. Several authors have found support for this contingency hypothesis (Maddox 1981; Swan and Combs 1976). In the context of clothing purchases, Swan and Combs (1976) identify "instrumental" (hygiene) factors-including durability and construction-that are expected to contribute to maintaining satisfaction or to lead to dissatisfaction when performance is poor. Another set of factors, identified as "expressive" (motivators)-including styling and color-is expected to enhance or maintain satisfaction. However, dissatisfaction is not expected to result from poor "expressive" performance. The results of the study support the predictions based on theory. Even in Mittal, Ross, and Baldasare's (1998) study that proposes hypotheses solely based on negativity arguments, some evidence of contingent effects is obtained. In their analysis of automobile satisfaction, Mittal, Ross, and Baldasare report that for the attribute of "interior roominess," the regression coefficient for positive performance is about threefold larger than for negative performance (.49 versus .17), suggesting a positivity effect.
Although we draw from the preceding literature to propose asymmetric relationships between trustworthy dimensions and trust facets, it is difficult to predict directional hypotheses because of three limitations of prior research. First, to our knowledge, extant trust research (Lewicki, McAllister, and Bies 1998; Singh and Sirdeshmukh 2000) has not empirically examined propositions regarding the proposed asymmetry in the underlying mechanisms. As a result, confidence in the conceptual arguments remains tentative until a base of empirical support is built. Second, these studies primarily discuss asymmetry in the consequences of trust versus distrust (rather than the determinants of trust). For example, Singh and Sirdeshmukh (2000) propose that the absolute magnitude of the influence of competence distrust on prepurchase expectations would be greater than competence-based trust. Asymmetric influences of trust determinants have not been proposed or empirically tested to date. Third, this stream of work has focused on loss aversion-based hypotheses, ignoring the possibility of contingent effects. For FLE operational benevolence, it is possible that consumers expect FLEs to work for the customers' best interests (e.g., "after all, that is what they are hired for") so that a negativity effect may be more plausible. Alternatively, the FLE may be so closely associated with self-serving or profit-making interests (e.g., in the case of automobile retailing) that when an FLE behaves benevolently, a positivity effect is evident. These asymmetrical relationships may be contingent not only on the dimension of trustworthiness but also on the service context. Therefore, we adopt an exploratory perspective and posit nondirectional asymmetrical hypotheses.
H<SUB>7</SUB>: FLE (MPP) trust will be affected asymmetrically by a unit positive change in FLE (management) operational competence versus a unit negative change.
H<SUB>8</SUB>: FLE (MPP) trust will be affected asymmetrically by a unit positive change in FLE (management) operational benevolence versus a unit negative change.
H<SUB>9</SUB>: FLE (MPP) trust will be affected asymmetrically by a unit positive change in FLE (management) problem-solving orientation versus a unit negative change.
Reciprocal Relationship Between FLE Trust and MPP Trust
Consumer trust in FLEs is proposed to influence MPP trust directly, consistent with agency theory (Bergen, Dutta, and Walker 1992) and research on the role of causal attributions in judgments (Folkes 1988). According to agency theory, FLEs interact with a customer as agents of the firm, presumably acting within the roles prescribed by management rather than as completely independent entities. Therefore, greater consumer trust in FLEs is likely to generate a higher level of consumer trust in the management-the principal that apparently controls and determines the behaviors of the agent. Likewise, attribution theory proposes a related mechanism whereby consumers attribute FLE trust in part to management involvement in FLE hiring, training, service culture, and other practices (Heskett, Sasser, and Schlesinger 1997). Although FLE behaviors are directly observable, the reasons underlying the behaviors must be inferred by consumers. To the extent that the consumer attributes the locus and controllability of the causes underlying FLE behaviors to MPP, FLE trust is likely to influence MPP trust (Folkes 1988). Empirical support is forthcoming from the services literature (Bitner, Booms, and Tetreault 1990; Crosby and Stephens 1987). For example, Crosby and Stephens (1987) demonstrate that satisfaction with the contact employee contributes to the customers' judgment of the core service.
The literature also offers support for a reciprocal relationship such that consumers' judgments of MPP trust are likely to enhance trust in the FLE.[ 4] Doney and Cannon (1997) argue that when customers have limited knowledge of the salesperson, their trust in the firm is likely to have a direct impact on trust in the salesperson through a process of affect transfer. The authors find support for the proposed reciprocal effects, though salesperson trust had a stronger effect on trust in the firm (b = .77) than the reverse effect did (b = .52). In our research, consumers are evaluating providers with which they are in a relational exchange (i.e., they have experience and familiarity with the provider and its employees). In such contexts, the process of affect transfer is less likely to determine FLE trust; rather, judgments based on observed behaviors are likely to dominate, as proposed previously. Therefore, in the present research context, we posit the following:
H<SUB>10</SUB>: FLE trust will have a reciprocal influence on MPP trust such that the direct effect of FLE trust on MPP trust is larger than the reciprocal influence.
Consumer Trust and Loyalty
Consistent with prior research, consumer trust in the FLE and MPPs is posited to affect consumer loyalty toward the service provider directly. Consumer loyalty is indicated by an intention to perform a diverse set of behaviors that signal a motivation to maintain a relationship with the focal firm, including allocating a higher share of the category wallet to the specific service provider, engaging in positive word of mouth (WOM), and repeat purchasing (Zeithaml, Berry, and Parasuraman 1996).
The proposed relationship between consumer trust and loyalty is supported by reciprocity arguments. When providers act in a way that builds consumer trust, the perceived risk with the specific service provider is likely reduced, enabling the consumer to make confident predictions about the provider's future behaviors (Mayer, Davis, and Schoorman 1995; Morgan and Hunt 1994). Here, we distinguish between relational risk (i.e., perceived risk within the relational exchange context) and industry risk (i.e., perceived risk in a specific industry such as medical, airline, or hair styling). The mechanisms involving these two types of risk may be different in nature and independent. For example, industry risk is likely to moderate rather than mediate the trust-loyalty relationship within an exchange. While recognizing the potential role of industry risk, we focus on relational risk for the purposes of our study. When service providers' behaviors and practices reduce relational risk, the reciprocity literature argues that consumers are likely to act "cooperatively" toward such a trustworthy service provider to maintain trust, by demonstrating behavioral evidence of their loyalty (Gassenheimer, Houston, and Davis 1998). Thus, with increasing trust in FLE and MPPs, consumers' loyalty is likely enhanced.
Trust also influences loyalty by affecting the consumer's perception of congruence in values with the provider (Gwinner, Gremler, and Bitner 1998). When there is perceived similarity in values between the firm and the consumer, the consumer's embeddedness in a relationship is enhanced, promoting reciprocity and contributing to relational commitment. Gwinner, Gremler, and Bitner (1998) demonstrate that such value congruence is significantly related to the consumer's loyalty and satisfaction. For this reason, we propose the following:
H<SUB>11</SUB>: The consumer's loyalty toward the focal firm will be positively influenced by FLE trust.
H<SUB>12</SUB>: The consumer's loyalty toward the focal firm will be positively influenced by MPP trust.
The Mediating Role of Value in the Trust-Loyalty Relationship
We posit an alternative mechanism for the trust-loyalty relationship whereby value mediates the effect of trust on loyalty. Following Zeithaml (1988), we define value as the consumer's perception of the benefits minus the costs of maintaining an ongoing relationship with a service provider. Relational benefits include the intrinsic and extrinsic utility provided by the ongoing relationship (Gwinner, Gremler, and Bitner 1998; Neal and Bathe 1997), and associated costs include monetary and nonmonetary sacrifices (e.g., time, effort) that are needed to maintain the relationship (Houston and Gassenheimer 1987; Zeithaml 1988).
Goal and action identification theories provide a conceptual framework for hypothesizing the mediating role of value in relational exchanges (Carver and Scheier 1990; Vallacher and Wegner 1987). Together, these theories posit that ( 1) consumer actions are guided or "identified" by the underlying goal they are expected to help attain; ( 2) multiple and sometimes conflicting goals may be operative at any instance; ( 3) goals are organized hierarchically, with superordinate goals at the highest level and subordinate goals at the lowest level; and ( 4) consumers regulate their actions to ensure the attainment of goals at the highest level. As such, superordinate goals are desired end states, whereas focal and subordinate goals serve instrumental roles. Bagozzi and Dholakia (1999) and Bagozzi (1992) have recently discussed the significance of goal and action identification theories for consumer behavior. We supplement and extend this work to the study of relational exchanges.
Using the perspective of goal and action identity theories, we posit value as the superordinate consumer goal in relational exchanges.[ 5] The central role of consumer value has been conceptualized (Houston and Gassenheimer 1987; Neal 1999; Woodruff 1997) and empirically demonstrated (Bolton and Drew 1991; Grisaffe and Kumar 1998) in the marketing literature. As "value-maximizers" (Kotlerp2000, p. 32), consumers are thought to consummate exchanges with providers that provide maximal value. The key role of value is also notable in calls for building "consumer-value-centric" organizational processes and competencies (Heskett, Sasser, and Schlesinger 1997; Srivastava, Shervani, and Fahey 1999). For example, Srivastava, Shervani, and Fahey (1999, p. 172) assert that "the value ... experienced by end customers is the driving obsession [of organizations]." Holbrook (1994, p. 22, emphasis in original) goes as far as to note that "customer value is the fundamental basisfor all marketing activity."
Value, in turn, is hypothesized to be affected by judgments of FLE and MPP trust. Specifically, trust creates value by ( 1) providing relational benefits derived from interacting with a service provider that is operationally competent, benevolent toward the consumer, and committed to solving exchange problems and ( 2) reducing exchange uncertainty and helping the consumer form consistent and reliable expectations of the service provider in ongoing relationships. Although no empirical study has examined this hypothesis, indirect support is forthcoming from the service quality literature. For example, in the context of telephone services, Bolton and Drew (1991) find a positive association between global service assessment ("easy to do business with") and value. Kerin, Jain, and Howard (1992) report a similar effect on value in a retail context using a composite measure of FLE friendliness and store MPPs (e.g., variety, check cashing policy).
On the basis of self-regulation processes, we posit that value, a superordinate goal, regulates consumer actions at the lower level, including behavioral intentions of loyalty toward the service provider (Carver and Scheier 1990). Consumers are expected to regulate their actions-that is, engage, maintain, or disengage behavioral motivation-to the extent that these actions lead to attainment of superordinate goals. Accordingly, consumers are hypothesized to indicate behavioral intentions of loyalty toward the service provider as long as such relational exchanges provide superior value. Otherwise, the consumer is motivated to disengage, demonstrating lack of loyalty. focusing on behavioral motivation, we recognize that in some circumstances, individual choice may be constrained by switching costs, market constraints, or other impediments such that while the behavioral motivation exists, the consumer is unable to disengage. The notion that value drives loyalty, albeit imperfectly, has substantial support among marketing practitioners (Neal 1999) and scholars alike (Chang and Wildt 1994). For example, Bolton and Drew (1991) report that value is a significant determinant of consumers' behavior intentions to remain loyal to a telephone service by continuing the relationship and engaging in positive WOM. Empirical support for this linkage is also established in different contextual settings by Chang and Wildt (1994) and Grisaffe and Kumar (1998).
Because loyalty is regulated by the consumer's superordinate goal of value, we posit that trust will affect loyalty through its influence in creating value. This parallels the mediational role of value hypothesized and tested in service quality-loyalty relationships in prior research (Chang and Wildt 1994; Grisaffe and Kumar 1998). For example, Chang and Wildt (1994) report that value mediates the perceived quality-loyalty link in the context of personal computers and apartments. However, Grisaffe and Kumar's (1998) research indicates that though value may be a significant mediator of the service quality-loyalty relationship, it does not imply that value fully mediates the effect of quality. In their study of office products and financial services, the authors find that though value mostly mediates the effect of quality on positive WOM, quality continues to have residual direct effects on positive WOM that are borderline significant. Similarly, we hypothesize that value partially mediates the relationship between trust and loyalty. Direct effects of trust on loyalty may achieve significance, consistent with H<SUB>11</SUB> and H<SUB>12</SUB>, in addition to the mediated effect through value. Therefore,
H<SUB>13</SUB>: Consumer loyalty toward the service provider will be positively influenced by value.
H<SUB>14</SUB>: Value will be positively influenced by FLE trust.
H<SUB>15</SUB>: Value will be positively influenced by MPP trust.
Overall Considerations
Two industries, retail (clothing purchases) and services (nonbusiness airline travel), were selected as the exchange context for this research. The use of multiple service categories provides a robust test of model relationships by allowing greater variability in study constructs. By means of multiple-group path analysis procedures, the modeled relationships can be examined simultaneously and compared for equivalence across the two service contexts. This procedure allows for a systematic examination of salient similarities and differences across the service contexts.
The service contexts selected for the study possessed multiple desired characteristics, including ( 1) experience properties, ( 2) distinct role of the FLE, ( 3) consequentiality, and ( 4) variability in the significance of MPP and FLE. We preferred experience service contexts because such contexts enable consumers to observe and evaluate behaviors of service providers and are consistent with the behavioral focus of the trustworthiness construct. In contrast, in credence contexts, trust development is likely affected by signals that convey credibility and bonding, given the consumer's inability to interpret and process behavioral evidence (Bergen, Dutta, and Walker 1992; Singh and Sirdeshmukh 2000). We preferred consequential service contexts because we reasoned that less consequential and relatively risk-free exchanges were more likely to evidence transactional characteristics and therefore, a priori, were less relevant to trust development. On the basis of some evidence from the qualitative work and our judgments, we asked consumers to focus on exchanges with a retail store that involved at least a $50 purchase in the last visit and at least two visits over the past six months. If consumers could not come up with exchanges that satisfied the preceding qualifying criteria, they were excluded. Likewise, for airline travel, we asked consumers to focus on exchanges with an airline company for which they have a frequent flyer account and made at least one nonbusiness trip during the past six months. Finally, we preferred service contexts that indicated a distinct role for the FLE and variability in the relative effects of FLE and MPP trust. We reasoned that relationships with the FLE could range from "close" to "distant," and this might influence the relative effect of FLE trust. Recently, Gupta (1999) reported that reliability was more frequently mentioned as a key factor in the airline context, whereas process customization was more frequently mentioned in the re tail context. The latter is likely to heighten the role of FLEs, just as the former is likely to diminish it.
Because of the nascent stage of the consumer trust literature, we used a mix of qualitative and quantitative approaches for data collection. Initially, we employed focus groups and personal interviews to identify salient behavioral domains that underlie consumer judgments of trustworthiness and to generate and refine items for the quantitative phase. Next, we administered cross-sectional surveys with structured questions in two waves. We asked respondents to identify a specific, recent service exchange encounter with a provider that met qualifying criteria and to complete the survey with that relational exchange in mind. Although the unit of analysis is the relational exchange between a consumer and service provider maintained across multiple episodes, we reasoned that cuing a specific encounter would facilitate recall of exchange characteristics and relational judgments. Similar approaches have been used in services research (Bitner, Booms, and Tetreault 1990; Tax, Brown, and Chandrashekaran 1998).
Sample
The sample was randomly drawn from the population of consumers with household annual incomes of $35,000 or higher, who reside within the metropolitan area of a large city in the Midwest. Questionnaires containing the measures, accompanied by a cover letter and a stamped, return envelope, were mailed to 1230 respondents for each service category. The cover letter explained the purpose of the study, assured confidentiality of data, and thanked the participant. After the initial section, respondents completed measures pertaining to FLE behaviors, FLE trust, MPPs, MPP trust, value, and loyalty, and finally, respondents answered demographic questions. Four weeks after the initial mailing, a second wave of questionnaires was mailed to all respondents along with a cover letter with a reminder.
Because a random sample includes consumers who may lie anywhere on the transactional-relational continuum, we excluded respondents who did not fall within the relational domain, using the frequency (e.g., number of visits/flights) and level of commitment (e.g., amount spent/frequent flier account). We used data from respondents who did not meet these criteria and extrapolation methods to estimate the number of disqualified respondents and compute reasonable response rates. In the retail category, the first wave resulted in 182 returned surveys, of which 153 (84%) customers met prequalifying criteria, and the second wave led to 143 responses, of which 93 (65%) customers qualified. Extrapolating to a third mailing and averaging across waves, we imputed a usable response rate of 29% for the retail category (Armstrong and Overton 1977).[ 6] In the airline travel category, the first wave produced 160 responses, of which 72 (45%) met the prequalifying criteria. Likewise, of the 141 responses in the second wave, 41 (29%) met the prequalifying criteria. Extrapolating to the third wave and averaging across the three waves yielded a qualification rate of 30%, or 378 consumers. With this qualification rate, the 113 usable responses give a usable response rate of 29% (see n. 6).
Sample characteristics are reported in Table 1. A majority of respondents had a college degree or higher, were white, and were married. In the aggregate sample, 45% of respondents were men and 55% were women. However, there was a significant sex imbalance in each service category: Approximately 70% of respondents in the retail sample but only approximately 30% in the airline sample were women. A wave analysis was conducted to examine for profile differences of early and late respondents in each service category. Except for one exception, results indicated no significant demographic differences between the two waves in the retail sample (χ² ranging from .53 to 7.9, p > .1) or the airline sample (χ² ranging from .16 to 10.10, p > .1). In the airline sample, the education level of Wave 1 respondents was significantly higher than for Wave 2 respondents (χ² = 12.75, p < .01).
Measurements
Table 2 provides descriptive statistics, intercorrelations, and reliabilities of study constructs, and the Appendix provides the scale items used.
Trustworthy practices and behaviors. Although previous studies have operationalized the construct of trustworthy behaviors along multidimensional facets, they are exclusively limited to interorganizational contexts (Kumar, Scheer, and Steenkamp 1995; McAllister 1995). To extend this work to the consumer context and obtain contextually meaningful operational items, we initially used four focus groups made up of specific combinations of sex (male, female) and household income level (<$35,000, >$35,000). Thereafter, we conducted in-depth interviews lasting 90 minutes each with 12 consumers who met prespecified criteria to refine the operational items. We developed a card-sorting exercise in which each card contained an operational item of trustworthy behavior or practice retained from focus group analysis. "Think aloud" data provided by consumers yielded insight into interpretations of operational items and guided their refinement. On the basis of the results of in-depth interviews, we developed a set of operational measures for trustworthy FLE behaviors and MPPs along three dimensions-operational competence, operational benevolence, and problem-solving orientation-and retained them for the subsequent pretesting phase. Items generated were pretested by five judges, who evaluated them for wording/meaning and consistency with corresponding definitions of the dimensions. On the basis of this feedback, items were either modified or dropped. The resulting instrument included 16 items each for MPPs and FLE behaviors.
We performed two further analyses on the pooled retailing and airline data to ensure that the operational items for trustworthy behaviors and practices had acceptable reliability as well as convergent and discriminant validity. First, we used exploratory factor analysis (EFA) to analyze items separately for each facet. For the MPP items, EFA yielded a three-factor solution based on the "breaks in eigenvalues" criterion. Together, the three factors accounted for 76% of the variance extracted, corresponding closely with the hypothesized dimensions of competence, operational benevolence, and problem solving. However, the results showed that 7 of 16 items were inadequate. These measures did not demonstrate a dominant loading on the hypothesized factor (<.3) and/or had significant cross-loadings (>.3), and they were dropped from further analysis. Likewise, EFA of the FLE behavior items yielded a three-factor solution that accounted for 73% of the variance extracted. This coheres with our hypothesis of three dimensions of employee trustworthiness-operational competence, operational benevolence, and problem-solving orientation. We retained the 9 items that demonstrated acceptable loading on their hypothesized factor (>.3) and no significant cross-loading for further analysis.
Before proceeding to the next step of analyses, we conducted additional procedures to further establish the robustness of the three-factor solution. In particular, our procedures focused on the problem-solving dimension. We reasoned that if problem-solving orientation was not a distinct dimension, forcing a two-factor solution should show that problem solving collapses into one or the other dimension. Conversely, if the other two dimensions collapse into each other and problem solving retains its distinction, this would support our contention that problem-solving orientation is a distinct aspect of consumer judgments. Results supported the latter; problem-solving orientation maintained its distinctiveness, and the remaining factors collapsed into(one for the FLE as well as MPP facets.
Second, we estimated a restricted factor analysis (RFA) model simultaneously for the MPPs and FLE behavior items wherein the items were allowed to load on their hypothesized factor and the cross-loadings were restricted to zero. In addition, we allowed the latent factors to correlate freely. We reasoned that our hypotheses for the validity of trustworthiness facets and dimensions would be supported if ( 1) the measurement model fitted the data reasonably well, ( 2) the loadings on hypothesized factors were significant and large, ( 3) each factor yielded reliabilities exceeding .70, and ( 4) the intercorrelation among the factors (dimensions) produced evidence of discriminant validity. This measurement model (displayed in Figure 2) produced the following fit statistics: χ² = 216.2, degrees of freedom (d.f.) = 120, comparative fit index (CFI) = .99, normed fit index (NFI) = .98, nonnormed fit index (NNFI) = .99, root mean square residual (RMSR) = .04, and root mean square error of approximation (RMSEA) = .047 (90% confidence interval [CI] of .037 to .057).[ 7] Moreover, the loadings on hypothesized factors are significant and substantively large (see Table 3). Each factor yielded composite reliability exceeding .70 (Fornell and Larcker 1981). The intercorrelation among the management and employee dimensions ranges from .89 to .54, and constraining this correlation to unity invariably produced a significant change in the goodness-of-fit statistic (δχ² ranges from 46.5 to 376.2, d.f. = 1, p < .01).[ 8] This suggests that the hypothesized measurement model of Figure 2 fits the data reasonably well, and the posited dimensions and facets evidence acceptable reliability and convergent and discriminant validity. The Cronbach reliabilities of the management dimensions of operational competence (three items), operational benevolence (three items), and problem-solving orientation (three items) were .77, .90, and .87, respectively, for the retail con text and .73, .86, and .74, respectively, for the airline context. Likewise, the employee dimensions produced corresponding αs of .91, .84, and .72, respectively, for the retailing context and .87, .81, and .82, respectively, for the airline context.
Notwithstanding the adequate measurement properties of the three-dimensional operationalization and the correspondence between our conceptual definitions and operational items, we note the need to conduct further psychometric work in developing the trustworthiness construct. In particular, the items capturing problem-solving orientation bear further refinement and cross-validation across service contexts.
MPP and FLE trust. Measures of MPP and FLE trust were adapted from extant research (Ganesan 1994; Morgan and Hunt 1994). Both measures were operationalized by four items assessed by ten-point semantic differential scales ("very undependable"/"very dependable," "very incompetent"/"very competent," "very low integrity"/"very high integrity," "very unresponsive to customers"/"very responsive to customers"). Alpha reliabilities of the MPP trust and FLE trust scales were .96 or higher for both retail and airline contexts (Table 2).
Value. We adapted the measure of value from existing value research (Dodds, Monroe, and Grewal 1991; Grisaffe and Kumar 1998). We measured the value construct using four items that included the benefits obtained from the relational exchange given the prices paid, the time spent, and the effort involved in maintaining a relationship with the focal provider (a = .92 for both contexts).
Loyalty. The loyalty measure was drawn from extant services literature (Zeithaml, Berry, and Parasuraman 1996) and included four items measuring the share of category wallet, intention to recommend, and likelihood of repeat purchase (α ≥ .90 in both contexts).
Satisfaction. Three items were included to measure episode-specific consumer satisfaction with the last experience ("highly unsatisfactory"/"highly satisfactory," "very unpleasant"/"very pleasant," "terrible"/"delightful"). These measures, intended to capture a transactional evaluation, were adapted from satisfaction research (Spreng, MacKenzie, and Olshavsky 1996). The scale demonstrated satisfactory interitem reliability in both contexts (α ≥ 94).
Method of Analysis
We examined the proposed hypotheses by introducing dummy variable terms in a regression-like equation for each dependent variable. Because of multiple dependent variables, the analytical method was based on simultaneous estimation of the following system of equations:
Y<SUB>1</SUB> = β<SUB>o1</SUB> + β<SUB>1</SUB>Y<SUB>2</SUB> + β<SUB>11</SUB>X<SUB>1</SUB> + β<SUB>21</SUB>X<SUB>2</SUB> + β<SUB>31</SUB>X<SUB>3</SUB> + β<SUB>41</SUB>DX<SUB>1</SUB> + β<SUB>51</SUB>DX<SUB>2</SUB> + β<SUB>61</SUB>DX<SUB>3</SUB> + ε<SUB>1</SUB>,
Y<SUB>2</SUB> = β<SUB>o2</SUB> + β<SUB>2</SUB>Y<SUB>1</SUB> + β<SUB>12</SUB>Z<SUB>1</SUB> + β<SUB>22</SUB>Z<SUB>2</SUB> + β<SUB>32</SUB>Z<SUB>3</SUB> + β<SUB>42</SUB>DZ<SUB>1</SUB> + β<SUB>52</SUB>DZ<SUB>2</SUB> + β<SUB>62</SUB>DZ<SUB>3</SUB> + ε<SUB>2</SUB>,
Y<SUB>3</SUB> = β<SUB>o3</SUB> + β<SUB>13</SUB>Y<SUB>1</SUB> + β<SUB>23</SUB>Y<SUB>2</SUB> + ε<SUB>3</SUB>, and
Y<SUB>4</SUB> = β<SUB>o4</SUB> + β<SUB>14</SUB>Y<SUB>1</SUB> + β<SUB>24</SUB>Y<SUB>2</SUB> + β<SUB>34</SUB>Y<SUB>3</SUB> + ε<SUB>4</SUB>,
where Y is a vector of dependent variables, and Y<SUB>1</SUB>, Y<SUB>2</SUB>, Y<SUB>3</SUB>, and Y<SUB>4</SUB> correspond to FLE trust, MPP trust, value, and loyalty, respectively. The vectors X and Z represent independent variables; X<SUB>1</SUB>, X<SUB>2</SUB>, and X<SUB>3</SUB> correspond to the operational competence, operational benevolence, and problem-solving orientation dimensions of FLE trust; and Z<SUB>1</SUB>, Z<SUB>2</SUB>, and Z<SUB>3</SUB> are the corresponding trustworthy dimensions for MPP trust. Note that the asymmetric effects are examined by the use of the dummy variable indicated by D in the equations. The dummy variable (D) is coded so that it takes on a value of zero for all nonpositive values of the corresponding trustworthy dimension; otherwise, it is coded as unity. As such, the estimated coefficients for expressions with dummy variables (e.g., β<SUB>41</SUB> in the Y<SUB>1</SUB> equation for FLE competence) indicate the incremental effect of the respective trustworthy dimension over and above its linear effect (e.g., β<SUB>11</SUB> in the Y<SUB>1</SUB> equation for FLE competence). The asymmetric hypothesis would be rejected if the corresponding coefficient estimated for the dummy variable is not significantly different from zero (Cohen and Cohen 1983). Finally, the reciprocal relationship between FLE and MPP trust is captured by the coefficients β<SUB>1</SUB> and β<SUB>2</SUB> in the Y<SUB>1</SUB> and Y<SUB>2</SUB> equations, respectively. These coefficients are identified because the three trustworthiness dimensions of FLE trust serve as its instrumental variables and likewise for MPP trust.
In estimating the preceding equations, we were sensitive to three methodological concerns that could interfere in drawing valid inferences: ( 1) simultaneity, ( 2) cutoff points, and ( 3) recency effects. Because the modeled equations have common variables (e.g., the dependent variable in one equation appears as an independent variable in another), we reasoned that the use of standard multiple regression analysis would risk a misspecification bias. This may occur because multiple regression analysis estimates the coefficients for each equation independently (of other equations), assuming that the error terms are uncorrelated. When multiple equations share common variables, this assumption is not warranted. Instead, a simultaneous analysis of the modeled equations is necessary to account for correlated error terms and produce unbiased coefficients. To do so, we used path analysis with the software EQS. This approach allows a simultaneous estimation of all hypothesized relationships, including multiple-group analysis across service contexts (to be discussed). Although we considered the use of latent-variable structural equation modeling, the inclusion of asymmetric terms made this choice less reasonable given the sample sizes involved. Nevertheless, the use of path analysis with EQS has several advantages, including modeling for "restricted" models with systematic constraints on proposed relationships. These restricted models can be evaluated for their fit to the data based on a χ² statistic and fit indices including NNFI, CFI, and RMSEA (Marsh, Balla, and Hau 1996).
Determining appropriate cutoff points is a relevant concern in defining the asymmetric terms. In developing the dummy variables, it is necessary to define a point on the trustworthy response scale that would separate the positive and negative domains. Although some researchers have used an absolute cutoff point regardless of the dimension considered (e.g., midpoint of scale provided), this approach is problematic for several reasons. First, the data obtained on most response scales have at best interval properties such that absolute points do not have identical interpretation across different dimensions. Second, consistent with Zeithaml, Berry, and Parasuraman (1996), the notion of "positive" and "negative" evaluations is conceptually defined relative to certain norms. That is, a positive evaluation on a given dimension occurs when the provider is judged to exceed the norm for that dimension; otherwise, consumers are likely to make a negative evaluation. Such norms are likely to vary with the trustworthy dimension considered. To account for this, we obtained the cutoff points by ( 1) standardizing the scores for each dimension and ( 2) coding the dummy variable as 1 for evaluations greater than zero and as a 0 otherwise. Note that because the mean of a standardized score is zero, the preceding dummy coding approach ensures that cutoff points are based on the distribution of scores for each dimension. Moreover, we derived the cutoff points separately for each service context to avoid confounding between asymmetric and industry effects.
Finally, we were sensitive to the possibility of recency effects. One particular recency effect of interest is encounter-specific satisfaction. Responses from consumers who are very satisfied with a specific recent exchange with the service provider might inflate the observed correlations and overemphasize the influence of trust factors on value and loyalty. To the extent that more satisfied consumers tend to be overrepresented in surveys (Peterson and Wilson 1992), the recency effects due to satisfaction may be significant. To reduce this bias, we modeled this effect by including satisfaction as an independent variable in each of the four hypothesized equations. Because path coefficients are partial effects, this procedure ensures that the coefficients are estimated after partialling the effect of satisfaction. This procedure has precedence in the literature (Crosby and Stephens 1987).
We fitted the proposed model simultaneously to the airline travel and retail samples using multiple-group path analysis. Initially, we held all paths invariant across the two data sets and estimated a fully restricted model. Subsequently, on the basis of the Lagrange-multiplier test, we sequentially released paths with significant test statistics until further freeing up of constraints failed to enhance model fit. The resultant coefficients and fit statistics are presented in Table 4. On the basis of the statistical test for the goodness of fit, the hypothesized model fits the data adequately (χ² = 97.3, d.f. = 87, p > .21). Consistent with this, other indicators of fit, including the relative indices (e.g., NFI = .99, CFI = .99) and absolute indicators of fit (e.g., RMSEA = .02, 90% CI = .00-.037; standardized root mean square residual = .03), indicate that the proposed model is a reasonable explanation of observed covariances among the study constructs. In addition, the NNFI, which is thought to provide an indicator of balance between explanation and parsimony, exceeds .99, indicating that the hypothesized model strikes an appropriate balance between these competing goals. Likewise, the proposed model explains a reasonable proportion of the variances in the dependent variables, including FLE trust (R² = .75, .77), MPP trust (R² = .75, .83), value (R² = .40, .63), and loyalty (R² = .40, .48).[ 9] Taken together, this suggests that the hypothesized model is a reasonable fit to the aggregate data, and the estimated coefficients can be validly examined to reveal interrelationships among the modeled constructs.
Table 4 provides the estimated coefficients from the multiple-group path analysis. Consistent with H<SUB>1</SUB>, H<SUB>3</SUB>, and H<SUB>5</SUB>, each dimension of FLE trustworthy behaviors, including operational competence (β<SUB>OpComp</SUB> = .22), operational benevolence (β<SUB>OpBen</SUB> = .43), and problem-solving orientation (β<SUB>ProbSolv</SUB> = .11), has a significant, direct effect on FLE trust (all with p < .05). In addition, these effects are invariant across retailing and airline contexts. In contrast, for the MPP facet, trustworthy practices and policies neither are uniformly significant nor achieve invariance across contexts. For the retailing context, operational competence (β<SUB>OpComp</SUB> = .10) and problem-solving orientation (β<SUB>ProbSolv</SUB> = .25) significantly influence MPP trust (all with p < .05), but operational benevolence does not (β<SUB>OpBen</SUB> = .02). For the airline context, however, operational competence (β<SUB>OpComp</SUB> = .10) and operational benevolence (β<SUB>OpBen</SUB> = .29) have a significant effect on MPP trust, but problem-solving orientation does not (β<SUB>ProbSolv</SUB> = .12). Thus, across both contexts, only the effect of operational competence is invariant. This provides mixed support for H<SUB>2</SUB>, H<SUB>4</SUB> and H<SUB>6</SUB>.
Moreover, the results in Table 4 provide some support for H<SUB>7</SUB> to H<SUB>9</SUB> wherein we had hypothesized asymmetric effects of trustworthy behaviors and practices on their corresponding trust facets. For FLE trust, operational benevolence (δβ<SUB>OpBen</SUB> = -.26, p < .01) produced a significant change coefficient for positive evaluations. In addition, a borderline effect was obtained for positive evaluations of FLE problem-solving orientation (δβ<SUB>ProbSolv</SUB> = .17, p < .10). These asymmetric effects for FLE behaviors were invariant across retailing and airline contexts. For MPP trust, a different pattern of asymmetric effects emerged. For the retail context, only the change coefficient for operational competence was borderline significant (&deltaβ<SUB>OpComp</SUB> = -.18), whereas for airlines, none of the MPP dimensions achieved significance for asymmetrical effects. Taken together, this offers partial support for H<SUB>8</SUB> and H<SUB>9</SUB> for FLE trust and H<SUB>7</SUB> for MPP trust.
In accord with H<SUB>10</SUB>, FLE trust positively influences MPP trust regardless of context, though the influence is substantially stronger for the retail context (β<SUB>FLE</SUB> = .56, p < .01) than for the airline context (β<SUB>FLE</SUB> = .40, p < .01). The reciprocal relationship is also supported, as the effect of MPP trust on FLE trust is significant and invariant across contexts (Β<SUB>MPP</SUB> = .16, p < .05). However, as hypothesized, the direct effect of FLE trust is at least twofold stronger than the reciprocal effect of MPP trust (β<SUB>FLE</SUB> versus β<SUB>MPP</SUB> = .40 versus .16, p < .01).
In addition, the two facets--FLE and MPP trust--were posited to directly affect consumer loyalty after we controlled for the mediating influence of value (H<SUB>11</SUB> and H<SUB>12</SUB>). Our findings in Table 4 provide support for H<SUB>12</SUB> but not H<SUB>11</SUB>. That is, regardless of context, FLE trust has a minimal effect (β<SUB>FLETrust</SUB> = .04) and MPP trust has a significant effect on loyalty (β<SUB>MPPTrust</SUB> = .22 p < .05). These trust facets significantly influence value as well, in accord with H<SUB>14</SUB> and H<SUB>15</SUB>. However, these relationships vary by context. For the retailing context, value is strongly and positively affected by perceptions of FLE trust (β<SUB>FLETrust</SUB> = .38, p < .01) but minimally influenced by MPP trust (β<SUB>MPPTrust</SUB> = .07). In contrast, in the airlines context, value is strongly influenced by MPP trust (β<SUB>MPPTrust</SUB> = .50, p < .01) but unaffected by FLE trust perceptions (β<SUB>FLETrust</SUB> = .08). This provides mixed support for H<SUB>14</SUB> and H<SUB>15</SUB>.
Finally, regardless of context, value significantly affects loyalty (β<SUB>Val</SUB> = .40, p < .01), in support of H<SUB>13</SUB>. Taken together, this supports the hypothesized partial mediating role of value, as the trust facets have significant influence on value and value in turn significantly affects loyalty. Specifically, for the retailing context, value appears to mediate the effect of FLE trust on loyalty, whereas for the airlines context, the effect of MPP trust on loyalty is partially mediated by value.
To test this partial mediation hypothesis further, we estimated a model that excluded the value construct. We reasoned that partial mediation by value was supported if ( 1) FLE and MPP trust had a significant and substantial effect on loyalty in the retail and airlines context, respectively, and ( 2) this effect declined significantly when value was introduced into the model. In the model that excluded value, FLE trust yielded a significant effect on loyalty in the retail context (β = .32, p < .05), and MPP trust produced a similar significant effect on loyalty in the airlines context (β = .66, p < .01). When value is introduced as a partial mediator, the corresponding effects for FLE and MPP trust are β = .04, p > .50, and β = .22, p < .05, respectively, in support of the partial mediation hypothesis.
In this study, we aimed to ( 1) use a multidimensional and multifaceted model for the behavioral components of trustworthiness in consumer-firm exchange relationships, ( 2) examine the asymmetric influence of trustworthiness dimensions on facets of consumer trust, ( 3) empirically test the linkage between consumer trust and loyalty with value as a partial mediator, and ( 4) explore variations in these relationships across industry contexts. Previous studies have examined neither the antecedents of consumer trust nor the mediated influence of trust on loyalty. Consequently, our study can directly address many questions that have remained largely untested but hold significant interest for theory and practice. What FLE behaviors and MPPs contribute to trust building and, conversely, trust depletion? Is the depletion effect (reduction in consumer trust due to a unit drop in trustworthiness behavior and practices) symmetrically equivalent to the building effect (the gain due to a unit increase in trustworthiness behavior and practices)? Does consumer trust translate into loyalty? If so, what is the magnitude of this conversion effect, and what role does value play in this conversion? Are these effects robust to varying satisfaction levels in individual encounters? Do the results depict variability across service contexts? Our study offers clear and compelling answers to these questions. Nevertheless, we recognize that a single, cross-sectional study can offer only initial insights. In this light, we first]discuss the limitations of our work and follow it up with a discussion of the key findings.
Limitations
This study is subject to several limitations. First, the study may have limited generalizability because of the regional sampling plan used. Note that we randomly sampled from a list of households residing in Zip codes within the selected standard metropolitan statistical area. We selected this statistical area because of the location of our affiliated university, presuming that respondents were more likely to comply with a request from a recognized institution. This might have biased the responses in an unspecified manner. In addition, the size of the airline sample is relatively small, mainly because of a lower qualifying rate. This is consistent with the expectation that in a random sample, consumers are more likely to have shopped at least twice at a retail clothing store in the last six months than to have traveled on an airline for a nonbusiness trip. Nevertheless, replication studies in different service contexts and with varying sampling procedures would provide greater confidence in our results.
Second, because this was a cross-sectional study, the findings may be biased by common method variance and spurious cause/effect inferences. Common method variance is known to inflate correlations, resulting in overestimations of the influence of hypothesized predictors. However, our focus is the differential pattern of results-in terms of asymmetric effects and mediation pathways. Because method variance is "common," affecting all relationships equally, it is likely to work against detection of differential effects. Moreover, we provided a partial control over common variance by partialling out the effect of satisfaction on all constructs of this study. This reduces the bias due to at least one source of common variance. We recognize that drawing cause/effect inferences from cross-sectional data is essentially tenuous, and we agree that longitudinal studies are needed to establish the hypothesized sequence of effects.
Third, although we employed several procedures to refine and adapt operational measures for the trustworthiness constructs, more work is needed to establish their psychometric properties. Our qualitative and quantitative procedures inform us that operationalizations from interorganizational contexts cannot be easily adapted to the consumer-firm contexts. Future researchers should regard our operationalizations as starting points for further conceptualizations of the trustworthiness constructs. In particular, it is useful to explore the role of corporate reputation and responsibility in defining the trustworthiness construct and the formation of trust judgments. Yet given the acceptable evidence of reliability and convergent and discriminant validity of the reported measures, it appears that the procedures used in the present study were successful.
Fourth, we recognize that the hypothesized model does not include individual dispositional variables that are likely to moderate the specified relationships. One such dispositional variable that is worthy of pursuit in further research involves individual sensitivity to trust judgments. For some people, a high level of trust is necessary for consummating exchanges, but others may not regard relational trust as highly important.
Fifth, alternative procedures for examining asymmetric effects may be examined. Our approach is based on using cutoff points and estimating the incremental coefficients for the positive domain of the asymmetrical relationship. Alternatively, cubic polynomials can be used to assess asymmetries without relying on cutoff points. Finally, because of the small sample size and inclusion of asymmetric effects, we used a path model with simultaneous estimation of modeled equations but without control over measurement error. Measurement error is known to bias path coefficients. Although procedures for incorporating measurement error in complex nonlinear equations have become available recently, they demand large sample sizes. In addition, data about the performance of these procedures are lacking. Future researchers attempting to replicate or extend the present work may find it useful to examine the potential of these procedures.
Trustworthiness Dimensions and Facets
This study offers support for the proposed multifaceted, multidimensional model of consumer trustworthiness. This support is based on several converging pieces of empirical evidence. First, the dimensions evidence acceptable psychometric properties of reliability and convergent and discriminant validity. Without exception, the operational items load significantly on their posited dimensions. Moreover, a constrained model that restricted all cross-loadings to zero reproduced the observed variance-covariances reasonably well, thereby supporting the validity of the trustworthiness dimensions. Conversely, a model that constrained intercorrelations between the facets or among the dimensions to unity produced an ill-fitting model that significantly deteriorated the correspondence between the data and model. This enhances our confidence in the discriminant validity of the trustworthiness facets and dimensions.
Second, the trustworthiness dimensions and facets demonstrate nomological validity through a differential pattern of effects. For example, the management facet of trust had a significant effect on loyalty in both contexts (β<SUB>MPPTrust</SUB> = .22), but the effect of the FLE facet was nonsignificant (β<SUB>FLETrust</SUB> = .04). The MPP facet has a significant effect on value in the airline industry (β<SUB>MPPTrust</SUB> = .50) but not in the retailing context (β<SUB>MPPTrust</SUB> = .07). The opposite pattern emerges for the FLE facet (β<SUB>FLETrust</SUB> = .08 and .38 for airline and retailing, respectively). This differential pattern of effects would likely be obfuscated by an aggregate construct of company trust.
Third, because separate antecedents of FLE and MPP are modeled, we are able to examine the reciprocal relationships among the two trust facets. Evidently, MPP trust spills over to affect trust in the FLE, in accord with the transfer hypothesis. However, this transfer effect is relatively weak compared with the strong and robust influence of consumers' FLE trust on their trust in the management, regardless of context. These dynamic, reciprocal relationships are also obfuscated in an aggregated trust construct. Likewise, the trustworthiness dimensions depict a clear pattern of differential asymmetric effects on their respective facets (to be discussed subsequently). Taking these findings together, we appear to have sufficient evidence to conclude that operational competence, operational benevolence, and problem-solving orientation are distinct dimensions of perceived trustworthiness that are evaluated separately by the consumer for the MPP and FLE facets in relational service exchanges. Several advantages accrue from a well-specified and fine-grained conceptualization of trustworthiness. It addresses a clear gap in the literature on developing the consumer trustworthiness construct and responds to calls by several researchers who have argued for the centrality of this construct in understanding consumer loyalty (Hart and Johnson 1999). In addition, the inclusion of and support obtained for the problem-solving orientation dimension coheres with findings from recent research in service relationships that has underscored its critical role in building lasting relationships (Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998). Finally, our approach can provide managerial insights for targeted intervention efforts because of its focus on specific FLE behaviors and MPPs.
Nevertheless, fruitful areas for further examination of the trustworthiness construct can be identified. First, the psychometric validity of the trustworthiness facets and dimensions across other nonconventional contexts needs to be established. It is conceivable that in certain contexts (e.g., a dentist engaged in private practice), the FLE may be virtually indistinguishable from management and therefore a single facet may suffice. Alternatively, with the rapid growth of e-commerce, technology may emerge as an additional facet of evaluation (see Reichheld and Schefter 2000). Second, the robustness of the three trustworthiness dimensions should be evaluated by further replications and extensions. In particular, although we posit problem-solving orientation as another dimension of trust, further analysis of problem solving versus routine episodes may be pursued for a better understanding of the process by which trustworthiness cognitions develop and are stored. Finally, more work is needed to establish the distinct influence of trustworthiness dimensions and facets. As an initial step, we partialled out the effect of satisfaction. Other constructs may be similarly considered in order to reveal the distinctive influence of trustworthiness dimensions and facets.
Asymmetric Effects of Trustworthiness Dimensions
In extending the current trust literature, we hypothesized asymmetric effects for the trustworthiness dimensions and tested these hypotheses by estimating a baseline effect coefficient and evaluating the statistical significance of the incremental coefficient for positive trustworthiness perceptions (see the δPositive columns in Table 4). The coefficient for positive change is derived by adding it to the baseline coefficient, and the latter serves as the estimated effect for a negative change. On the basis of these derivations, we plotted the effects separately for each trust facet and industry in Figure 3. These plots help clarify our numerical results and guide our discussion.
Overall, a clear and compelling pattern of asymmetric effects for FLE trust is evident in Figure 3 (Panel A) that is invariant to contextual factors. In particular, the effect of operational competence on FLE trust perceptions is significant but invariant across the positive and negative performance domains (β<SUB>OpCom</SUB> = .22). This suggests that FLE competence contributes equally to trust building and depletion. As such, FLE operational competence is both a motivator and a hygiene factor, because losses and gains matter equally. In contrast, FLE operational benevolence depicts negativity effects whereby its trust-depletion effect is significant and large (β<SUB>OpBen</SUB> = .43) but its trust-enhancing effect is relatively weak but significant (β<SUB>OpBen</SUB> = .17). As such, FLE operational benevolence is more of a hygiene factor than a motivator. This result supports current speculation that though subordinating self-interest to consumers' best interest may help build trust, marketers' actions driven by self-interest that perceptibly subordinate consumer interest are surely going to deplete trust. To the extent that trust depletion in turn reduces loyalty (to be discussed subsequently), this depletion effect can have significant bottom-line consequences. Finally, in accord with cue diagnosticity theory and counter to loss-aversion arguments, positivity effects emerged for the FLE problem-solving orientation. Although the depletion effect due to a unit negative change is significant (β<SUB>ProbSolv</SUB> = .11), the trust-building effect is more substantial (δβ<SUB>ProbSolv</SUB> = .28). As such, a unit positive change in FLE problem-solving orientation boosts FLE trust strongly. Thus, problem-solving orientation is a motivator, because its motivating effects significantly exceed its hygiene effects. This coheres with the growing recognition that problem solving is instrumental in shaping trust judgments (Tax, Brown, and Chandrashekaran 1998) and supports Hart and Johnson's (1999) speculation that this dimension holds significant managerial relevance for building consumer trust.
A weaker pattern of asymmetric effects emerges for MPP trust that is disparate across the two contexts (see Figure 3, Panels B and C). For the retailing context, weak effects are obtained for MPP operational benevolence (β<SUB>OpBen</SUB> ≈ .02). In contrast, for the airline context, MPP operational benevolence has equivalent and significant depletion and enhancing effects (β<SUB>OpBen</SUB> = .29). As such, MPP operational benevolence is both a hygiene factor and a motivator in the airline context but is largely impotent in the retailing context. However, operational competence has a significant depleting effect for MPP trust such that a unit negative change produces substantial declines in MPP trust in both contexts (β<SUB>OpCom</SUB> = .10). In contrast, a unit positive change yields a substantially lower and nonsignificant effect on MPP trust for the retailing context (β<SUB>OpCom</SUB> = -.08), but it yields a significant effect for the airlines context that is equivalent to the negativity effect (β<SUB>OpCom</SUB> = .10). As such, operational competence is a hygiene factor for the retailing context but serves a motivator role as well in the airlines context. Finally, MPP problem-solving orientation has significant and equivalent trust-building and trust-depletion effects for the retailing context (β<SUB>ProbSolv</SUB> = .25), but its effects in the airlines context are nonsignificant. Thus, problem-solving orientation is both a hygiene factor and a motivator for the retailing context but is largely impotent in the airlines context.
Overall, two broad conclusions can be drawn from the pattern of results obtained. First, it appears theoretically meaningful and pragmatically useful to examine the antecedents of consumer trust. Specific FLE behaviors and MPPs can be conceptualized and psychometrically measured for investigation of their differential effects on consumer trust. Managerial initiatives and interventions for enhancing consumer trust can also be developed. Second, we appear to have sufficient evidence to conclude that further research should reconsider employing linear formulations of the effects of trustworthiness dimensions on trust. Fine-grained insights into the asymmetric mechanisms of trust building and depletion and the way these mechanisms vary across industry contexts are more likely to emerge if researchers adopt approaches along the lines of those employed in this study. At the same time, this study must be viewed as an initial step that encourages future researchers to explore the broad scope and diverse nature of asymmetric mechanisms that involve trust and its dimensions, as proposed by Singh and Sirdeshmukh (2000), Lewicki, McAllister, and Bies (1998), and others. Concurrently, our results suggest that trust judgments are not bound by the rule of negativity effects rooted in loss-aversion arguments. Rather, either positivity or negativity effects may emerge depending on the consumer norms for a given dimension. Further research into the formation and stability of trustworthiness norms and their role in trust mechanisms is warranted.
The Mediating Role of Value in Trust-Loyalty Relationships
Unlike much prior research, we proposed that the effect of trust on loyalty is partially mediated by value. Our conceptual rationale was based on two arguments. First, we posited that though the direct effect of trust on loyalty presumes that trust is intrinsically beneficial, the mediated effect assumes that trust benefits are conditional on producing value. Second, we had noted that value is a superordinate goal in market exchanges, exerting a dominant effect on loyalty and serving as a key mediator of the trust-loyalty relationship.
Our results provide initial empirical evidence to sort through the preceding propositions. Value emerges as the consistent, significant, and dominant determinant of consumer loyalty, regardless of the service category (β<SUB>Val</SUB> = .40). Specifically, although trust in MPPs has a significant direct effect on loyalty, this influence is relatively weak compared with the effect of value (β<SUB>MPPTrust</SUB> = .22 versus β<SUB>Val</SUB> = .40). The direct effect of FLE trust is nonsignificant (β<SUB>FLETrust</SUB>= .04). This suggests that consumers' evaluation of value in relational exchanges appears to carry greater weight in loyalty judgments, though consumers find it inherently preferable to maintain long-term relationships with service providers whose policies and practices they can trust.
Our results also establish that value partially mediates the effect of trust on loyalty judgments. This is because, in the retailing context, FLE trust has a significant effect on value, and value in turn influences loyalty. Because the direct effect of FLE trust on loyalty is minimal after we controlled for value in the retailing context, it is clear that value completely mediates the effect of FLE trust. This is also substantiated by the results from a model that excludes value (see n. 8). Likewise, f-r the airlines context, value appears to partially mediate the influence of MPP trust because ( 1) MPP trust has a significant, direct effect on value (β<SUB>MPPTrust</SUB> = .50); ( 2) MPP trust has a significant, direct effect on loyalty (β<SUB>MPPTrust</SUB> = .22); and ( 3) the direct effect on loyalty is significantly smaller than its effect when value is omitted (β<SUB>MPPTrust</SUB> = .66). However, value does not mediate the influence of MPP and FLE trust in the retailing and airline contexts, respectively. To the extent the mediated effects are significant (e.g., for MPP trust in airlines) or dominant (e.g., for FLE trust in retailing), these results suggest that the effect of trust on loyalty is conditional on its ability to enhance value. Without net increments in value, consumer trust is good to create but apparently does little good for the bottom line.
Taken together, these results suggest caution against blanket assertions that are common in popular press about the purported power of "total" trust in creating consumer loyalty (Hart and Johnson 1999). Our results provide compelling data to counter conventional beliefs that consumer trust converts directly into loyalty and indicate that such beliefs are overly simplistic and probably misleading. As such, managers are well advised to forsake "blind" investments in trust-building activities, in hopes that trust in and of itself produces loyalty. Instead, a careful assessment is needed that provides a full accounting of rust-conversion mechanisms. Our results reveal that the conversion of trust to loyalty involves complex, multiple-loop processes that require an understanding of ( 1) how specific trustworthiness dimensions can build greater consumer trust in MPPs, the FLE, or both; ( 2) how increased consumer trust can enhance value for the consumers; and ( 3) how value translates into loyalty. Our results also suggest that such understandings are sensitive to contextual and industry factors and are likely to involve asymmetric influences. In summary, although there are significant payoffs from building consumer trust in relational exchanges, realizing them is neither straightforward nor inevitable.
Theoretically, the construct of value needs further development. Although the services literature has primarily directed attention toward relational benefits, the nature and role of relational costs have until recently remained largely unexplored (Cannon and Homburg 2001). Because consumers attempt to "manage" their relationships with marketers, they also experience significant and diverse relational costs (Fournier, Dobscha, and Mick 1998). Ironically, these costs are not constant and may decrease or even increase over a relationship with a given provider (e.g., more direct solicitations, information use and privacy concerns). A more complete accounting of value would require a balanced study of the costs and benefits of relationships.
Industry Variability in Trust Mechanisms
The inclusion of multiple service contexts makes possible the testing of the generalizability of our hypothesized model. By generalizability, we do not imply that the estimated path coefficients are necessarily invariant across the two service contexts but that a single conceptual model is an adequate representation of trust mechanisms in both service contexts. However, by imposing parameter constraints, we can examine the sensitivity of path coefficients to contextual variability.
Our results support the generalizability of the conceptual model as indicated by its goodness of fit to the data from two different service contexts (see Table 4). In addition, several of the estimated path coefficients achieve invariance across the service contexts, which suggests that underlying processes are stable and consistent. In all, 15 of the 22 hypothesized paths are estimated to be invariant. More significantly, several critical mechanisms appear to be robust to the service context, including determinants of ( 1) loyalty and ( 2) FLE trust. That is, the linkages between loyalty determinants (i.e., MPP trust, FLE trust, and value) and loyalty are consistent across service contexts. Likewise for the asymmetric mechanisms that link FLE trustworthiness and trust. Finally, the proposed model explains a significant amount of variance in dependent variables, ranging from .40 to .83. Overall, this suggests that the conceptual model provides a generalizable, meaningful, and reasonable foundation for the study of consumer trust and loyalty mechanisms across different service settings.
At the same time, the proposed model helps pinpoint important differences across the two service contexts. Specifically, our results suggest that MPPs are more critical to trust and loyalty mechanisms in airlines, whereas FLE behaviors play a more central role in a retail clothing context. This is because, compared with the retailing context, for airlines ( 1) MPP trust has a stronger, dominant effect on value (.50 versus .07); ( 2) the effect of FLE trust is minimal and nonsignificant (.08 versus .38); ( 3) whereas MPP trust has a significant effect on loyalty in both contexts, FLE trust has a weaker, less dominant effect on MPP trust (.40 versus .56); and ( 4) management operational benevolence holds greater potential in consumer trust building (.29 versus .02). This is consistent with some work in the popular literature that underscores the significance of frontline functions such as personalization and prompt attention in retail business (e.g., Whittemore 1993) and of MPPs such as overbooking and schedule convenience in airline travel (Ostrowski, O'Brien, and Gordon 1993). Therefore, within the context and limitations of our study, we recommend that to provide value to consumers and win their loyalty, retailers should focus strategically on FLE effectiveness and trustworthiness. For airlines, the strategic thrust must keep MPPs and policies in focus as consumers rely heavily on judgments of airline management trustworthiness to determine value in relational exchanges and reciprocate with loyalty accordingly. Overall, we appear to have converging evidence to suggest that we are unlikely to find simple and profound insights into trust and loyalty mechanisms that remain unperturbed by contextual variability.
Contemporary thought in marketing recognizes that trust is a critical factor in relational exchanges between consumers and service providers. Although our findings cohere with this basic thought, we refine and extend the literature in several important ways. By modeling trust-building and trust-depletion processes, our approach rejects static notions of trust and embraces a dynamic, asymmetric view in which all good behaviors and practices do not always build trust and the potential for trust depletion is imminent. By including multiple dimensions of trustworthiness, including operational competence, operational benevolence, and problem-solving orientation, along two distinct facets of trust judgments, our modeling offers fine-grained insights into trust-building and trust-depletion processes. This refines and extends contemporary understanding of trust dynamics to provide theoretical and managerial insights. Moreover, by including value as a mediator of the trust-loyalty effect, our study identifies mechanisms that mediate the conversion of trust into loyalty. This rejects simplistic views that payoffs from efforts to build trust are inevitable and enables us to empirically test theory-driven hypotheses about the mechanisms that govern these payoffs. Consequently, our study calls for a shift in the kind of questions that managers and researchers should entertain about the role of trust in relational exchanges. Instead of asking if trust is important to have or whether trust matters, our study argues for questions such as "How can firms build trust?" "What actions will deplete trust?" and "What factors mediate and/or moderate the influence of trust on loyalty?" Although our study only begins to scratch the surface of these inquiries, the insights obtained indicate several fruitful avenues for further research. By pursuing these avenues, future researchers can shed further light on the effect of trust in consumer-firm relationships and the mechanisms that und erlie its influence on key consequences, including value and loyalty. These efforts, in turn, have the potential to help managers unlock the payoffs from trust and win consumer loyalty while alerting managers to behaviors and practices that will likely deplete consumer trust and erode consumer loyalty.
1 Unless otherwise specified, the term "service provider" is used in this article to refer to the service organization as an entity. When appropriate, distinct facets including company management and FLEs are identified and referred to separately.
- 2 The specific facet of interest in this research is "management policies and practices," or MPPs, rather than management. The focus is on the specific domain of policies and practices that consumers experience rather than consumers' overall perceptions of the company's management.
- 3 As we discuss in the "Methods" section, we collected qualitative data (through focus groups and depth interviews) to substantiate inductively the key dimensions of trustworthiness in consumer-firm relationships. Independent judges who were provided with definitions for each dimension coded and sorted data into prespecified dimensions. The notion that problem-solving orientation may be a salient and distinct factor in consumers' trust judgments was evident in these codings. Specifically, judges coded a significant number of the total responses into problem-solving orientation for FLEs (23%) and MPPs (23%).
- 4 We thank a reviewer for suggesting that we investigate this reciprocal relationship.
- 5 In a broader context, the consumer's life values (e.g., happiness, love, security) are the "super-superordinate" goals, and obtaining value in market exchanges is a lower-level goal. Our point is that within a market exchange context, the superordinate goal for most consumers is to obtain maximal value, or more aptly "market value."
- 6 Nonqualifiers are expected to be represented by late respondents rather than early respondents, and therefore an extrapolation to a third mailing is recommended (Armstrong and Overton 1977). A linear extrapolation of Wave 1 and Wave 2 results leads to an estimate of 50% qualified respondents in Wave 3. The average qualification rate was thus estimated at 66%, or 811 customers. Thus, the 246 usable responses translate to a usable response rate of 30%. In the airline category, the qualified respondents in the first two waves were 45% and 29%. Extrapolating to a third wave estimate of 15%, the average qualification rate was 30%, or 378 customers. The 113 responses translate to a usable response rate of 29%.
- 7 Reasonable models that effectively reproduce the observed variance-covariance matrix are characterized by CFI, NFI, and NNFI values exceeding .95; RMSR values less than .05; and RMSEA of .08 or lower with the upper CI not exceeding .10 (Marsh, Balla, and Hau 1996).
- 8 We also estimated the measurement model separately for the retailing and airline data. The overall pattern of results was similar, with no violation of the conditions for convergent and discriminant validity.
- 9 For each construct, the R2 values for the retail sample are followed by values for the airline sample. <><>
Legend for chart
A = Age (in Years): Retail
B = Age (in Years): Airline
C = Sex: Retail
D = Sex: Airline
E = Level of Education: Retail
F = Level of Education: Airline
G = Ethnicity: Retail
H = Ethnicity: Airline
A B C D E F G H
18-24 1.2 1.7 Male 30.2 71.2 High 17.0 6.0 White 93.4 94.0
school
25-34 15.1 9.3 Female 69.8 28.8 Some 28.2 19.7 African 5.4 4.2
college American
35-44 26.5 26.3 College 35.5 46.2 Other 1.2 1.8
degree
45-54 28.6 25.4 Graduate 19.3 28.1
school
55+ 28.6 37.3
Annual
Household Size Household
Marital Status (Number of People) Income
Retail Airline Retail Airline Retail Airline
Married 77.2 80.5 1 10.5 11.1 Less than $35,000 8.7 2.5
Single 9.7 6.8 2 32.5 41.0 $35,000-$44,999 15.3 11.4
Divorced/ 8.9 10.2 3 17.1 15.4 $45,000-$54,999 16.9 12.3
separated
Widow/ 4.2 2.5 4 23.3 21.4 $55,000-$64,999 12.8 15.8
widower
5 12.3 7.7 $65,000-$94,999 27.3 25.5
>6 4.3 3.4 $95,000 and over 19.0 32.5
Notes: All numbers are in percentages. Legend for chart:
A = Management: MOC
B = Management: MOB
C = Management: MPS
D = Employee: EOC
E = Employee: EOB
F = Employee: EPS
G = MPP Trust
H = FLE Trust
I = Value
J = Loyalty
K = Satisfaction
Intercorrelations[a], [b]
A B C D E F
G H I J K
Management
Operational .77/.73 .62 .61 .69 .65 .45
competence .68 .61 .61 .49 .46
(MOC)
Operational .54 .90/.86 .70 .69 .79 .62
benevolence .78 .67 .61 .58 .56
(MOB)
Problem- .46 .74 .87/.74 .64 .75 .66
solving .69 .63 .59 .60 .46
orientation
(MPS)
Employee
Operational .61 .62 .54 .91/.87 .76 .67
competence .77 .75 .63 .64 .54
(EOC)
Operational .51 .68 .56 .70 .84/.81 .77
benevolence .78 .76 .65 .62 .52
(EOB)
Problem- .37 .63 .63 .51 .59 .72/.82
solving .62 .73 .54 .50 .40
orientation
(EPS)
MPP trust .49 .66 .63 .57 .67 .54
.96/.96 .85 .72 .65 .63
FLE trust .49 .68 .59 .69 .76 .60
.84 .96/.97 .65 .54 .56
Value .39 .49 .33 .40 .51 .40
.53 .55 .92/.92 .66 .55
Loyalty .19 .42 .39 .38 .40 .44
.51 .52 .56 .90/.94 .43
Satisfaction .42 .46 .38 .41 .51 .43
.61 .59 .52 .48 .94/.96
[a]The alpha reliabilities are on the diagonal, and estimates for the
retail context are presented first.
[b]The intercorrelations for the retail context are below the
diagonal, and the corresponding correlations for the airline context
are above the diagonal. All values are significant at p = 05. Construct/ MPPs FLE Behaviors
Item Loading[b] t-Value[c] Loading[b] t-Value[c]
Operational Competence
OpComp<SUB>1</SUB> .74 13.2 .76 17.4
OpComp<SUB>2</SUB> .67 12.2 .72 17.7
OpComp<SUB>3</SUB> .86 13.0 .74 18.5
Operational Benevolence
OpBen<SUB>1</SUB> .75 17.7 .68 17.0
OpBen<SUB>2</SUB> .81 18.4 .85 18.8
OpBen<SUB>3</SUB> .77 16.6 .70 13.0
Problem-Solving Orientation
ProbSolv<SUB>1</SUB> .70 13.3 .57 11.9
ProbSolv<SUB>2</SUB> .81 17.8 .79 18.4
ProbSolv<SUB>3</SUB> .81 14.0 .52 10.2
Goodness-of-Fit Statistics
χ² 216.2
d.f. 120
NFI .98
NNFI .99
CFI .99
RMSR .04
RMSEA .047
(90% CI) .037-.057
[a]The estimates reported are from the ERLS (iteratively reweighted
generalized least squares) procedure using EQS.
[b]This is the standardized loading estimate from the ERLS procedure.
[c]Based on one-tailed tests: for t-values greater than 1.65, p <
.05; for t-values greater than 2.33, p < .01.
Legend for chart:
A = Dependent Variable: R²/Independent Variable
B = Retail: Coefficient (t-Value)[c]
C = Retail: Δ for Positive Performance[d]
D = Airline: Coefficient (t-Value)[c]
E = Airline: Δ for Positive Performance[d]
A B C D E
Dependent Variable: Trust in FLEs
R² .75 .77
MPP Trust .16 (1.9) .16 (1.9)
Operational .22 (3.2) -.01 (-.1) .22 (3.2) -.01 (-.1)
competence
Operational .43 (5.7) -.26 (-2.1) .43 (5.7) -.26 (-2.1)
benevolence
Problem-solving .11 (1.6) .17 (1.5) .11 (1.6) .17 (1.5)
orientation
Satisfaction .14 (3.1) .14 (3.1)
Dependent Variable: Trust in MPPs
R² .75 .83
FLE Trust .56 (7.3) .40 (5.0)
Operational .10 (1.8) -.18 (-1.5) .10 (1.8) .03 (.3)
competence
Operational .02 (.2) .04 (.4) .29 (3.5) .04 (.4)
benevolence
Problem-solving .25 (3.2) -.12 (-1.1) .12 (1.4) -.12 (-1.1)
orientation
Satisfaction .17 (4.3) .17 (4.3)
Dependent Variable: Value
R² .40 .63
FLE trust .38 (3.3) .08 (.6)
MPP trust .07 (.6) .50 (3.9)
Satisfaction .27 (4.7) .27 (4.7)
Dependent Variable: Loyalty
R² .40 .48
FLE trust .04 (.09) .04 (.09)
MPP trust .22 (2.3) .22 (2.3)
Value .40 (6.1) .40 (6.1)
Satisfaction .09 (1.4) .09 (1.4)
Goodness-of-Fit Statistics
Chi-square (p-value) 97.3 (.21)
d.f. 87
NFI .99
NNFI .99
CFI .99
RMSR .03
RMSEA .02
(90% CI) (.000-.037)
[a]The estimates reported are from the ERLS (iteratively reweighted
generalized least squares) procedure using EQS.
[b]The results are based on multiple-group analyses in which the
nomological model was estimated simultaneously in the airline and
retail samples. Coefficients that differed significantly (p < .05)
across the groups are italicized.
[c]t-Values are in parentheses. Based on one-tailed tests: for
t-values greater than 1.65, p < .05; for t-values greater than
2.33, p < .01. Significant coefficients are in bold.
[d]t-Values are in parentheses. Based on two-tailed tests: for
t-values greater than 1.96, p < .05. Significant coefficients are
in bold.DIAGRAM: FIGURE 1 The Empirical Model Tested for Estimating the Interrelationships Among Trustworthiness, Trust, Value, and Loyalty
DIAGRAM: FIGURE 2 The Measurement Model Used for the Consumer Trustworthiness Construct
GRAPHS: FIGURE 3 The Effects of Trustworthiness Dimensions on Consumer Trust in Retail and Airline Contexts: A: Effects of FLE Trustworthiness on FLE Trust[a] B: Effects of MPP Trustworthiness on MPP Trust in the Retailing Context C: Effects of MPP Trustworthiness on MPP Trust in the Airline Context [a] The coefficients for positive and negative performance are displayed for each dimension. Findings are invariant for retailing and airline contexts.
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FLE Behaviors (five-point scale, "strongly disagree"/"strongly agree")
The (store) employees ...
Operational Competence (μ<SUB>R</SUB> = 3.67, σ<SUB>R</SUB> = .8)
Work quickly and efficiently.
Can competently handle most customer requests.
Can be relied upon to know what they are doing.
Operational Benevolence (μ<SUB>R</SUB> = 3.79, σR = .8)
Act as if they value you as a customer.
Can be relied upon to give honest advice even if they won't make a sale.
Treat you with respect.
Problem-Solving Orientation (μ<SUB>R</SUB> = 3.28, σ<SUB>R</SUB> = .7)
Don't hesitate to take care of any problems you might have with clothing items purchased at the store.
Go out of their way to solve customer problems.
Are willing to bend company policies to help address customer needs.
MPPs (five-point scale, "strongly disagree"/"strongly agree")
The store ...
Operational Competence (μ<SUB>R</SUB> = 3.61, σ<SUB>R</SUB> = .9)
Is organized so as to make it easy to pick your clothing selection.
Is generally clean and free of clutter.
Keeps checkouts staffed and moving so you don't have to wait.
Operational Benevolence (μ<SUB>R</SUB> = 3.49, σ<SUB>R</SUB> = .8)
Has policies that indicate respect for the customer.
Has policies that favor the customer's best interest.
Acts as if the customer is always right.
Problem-Solving Orientation (μ<SUB>R</SUB> = 3.56, σ<SUB>R</SUB> = .8)
Has practices that make returning items quick and easy.
Goes out of the way to solve customer problems.
Shows as much concern for customers returning items as for those shopping for new ones.
Satisfaction (ten-point scale, μ<SUB>R</SUB> = 7.29, σ<SUB>R</SUB> = 1.8)
How satisfying was your last shopping experience at this store?
"Highly unsatisfactory"/"highly satisfactory."
"Very unpleasant"/"very pleasant."
"Terrible"/"delightful."
Trust in MPPs (ten-point scale, μ<SUB>R</SUB> = 7.84, σ<SUB>R</SUB> = 1.6)
I feel that this store is ...
"Very undependable"/"very dependable."
"Very incompetent"/"very competent."
"Of very low integrity"/"of very high integrity."
"Very unresponsive to customers"/"very responsive to customers."
Trust in FLEs (ten-point scale, μ<SUB>R</SUB> = 7.38, σ<SUB>R</SUB> = 1.7)
I feel that the employees of this store are ...
"Very undependable"/"very dependable."
"Very incompetent"/"very competent."
"Of very low integrity"/"of very high integrity."
"Very unresponsive to customers"/"very responsive to customers."
Value (ten-point scale, μ<SUB>R</SUB> = 7.28, σ<SUB>R</SUB> = 1.5)
Please evaluate the store on the following factors.
For the prices you pay for clothing items at this store, would you say shopping at this store is a ["very poor deal"/"very good deal," ten-point scale]?
For the time you spent in order to shop at this store, would you say shopping at this store is ["highly unreasonable"/"highly reasonable," ten-point scale]?
For the effort involved in shopping at this store, would you say shopping at this store is ["not at all worthwhile"/"very worthwhile," ten-point scale]?
How you would rate your overall shopping experience at this store? ["extremely poor value"/"extremely good value," ten-point scale].
Loyalty (ten-point scale, "very unlikely/"very likely," μ<SUB>R</SUB> = 6.98, σ<SUB>R</SUB>= 2.1)
How likely are you to ...
Do most of your future shopping at this store?
Recommend this store to friends, neighbors, and relatives?
Use this store the very next time you need to shop for a clothing item?
Spend more than 50% of your clothing budget at this store?
FLE Behaviors (five-point scale, "strongly disagree"/"strongly agree")
The (airline) employees ...
Operational Competence (μ<SUB>A</SUB> = 3.76, σ<SUB>A</SUB> = .7)
Work quickly and efficiently.
Can competently handle most customer requests.
Can be relied upon to know what they are doing.
Operational Benevolence (μ<SUB>A</SUB> = 3.58, σ<SUB>A</SUB> = .8)
Act as if they value you as a customer.
Can be relied upon to give accurate information in the event of flight delays or cancellations.
Treat you with respect.
Problem-Solving Orientation (μ<SUB>A</SUB> = 3.31, σ<SUB>A</SUB> = .8)
Don't hesitate to take care of any problems that might arise during flight.
Go out of their way to solve customer problems.
Are willing to bend company policies to help address customer needs.
MPPs (five-point scale, "strongly disagree"/"strongly agree")
The airline ...
Operational Competence (μ<SUB>A</SUB> = 3.51, σ<SUB>A</SUB> = .8)
Has fast, efficient check-in procedures.
Keeps its airplanes clean and free of clutter.
Has fast, efficient baggage claim service.
Operational Benevolence (μ<SUB>A</SUB> = 3.23, σ<SUB>A</SUB> = .8)
Has practices that indicate respect for the customer.
Favors the customer's best interest.
Acts as if the customer is always right.
Problem-Solving Orientation (μ<SUB>A</SUB> = 3.14, σ<SUB>A</SUB> = .9)
Makes every effort to get you to your final destination as quickly as possible when there are delays or cancellations.
Goes out of the way to solve customer problems.
Shows as much concern for customers in economy class as it does for customers in first/business class.
Satisfaction (ten-point scale, μ<SUB>A</SUB> = 6.83, σ<SUB>A</SUB> = 1.8)
How satisfying was your last experience with this airline?
"Highly unsatisfactory"/"highly satisfactory."
"Very unpleasant"/"very pleasant."
"Terrible"/"delightful."
Trust in MPPs (ten-point scale, μ<SUB>A</SUB> = 7.24, σ <SUB>A</SUB> = 1.7)
I feel that this airline is ...
"Very undependable"/"very dependable."
"Very incompetent"/"very competent."
"Of very low integrity"/"of very high integrity."
"Very unresponsive to customers"/"very responsive to customers."
Trust in FLEs (ten-point scale, μ<SUB>A</SUB> = 7.44, σ<SUB>A</SUB> = 1.8)
I feel that the employees of this airline are ...
"Very undependable"/"very dependable."
"Very incompetent"/"very competent."
"Of very low integrity"/"of very high integrity."
"Very unresponsive to customers"/"very responsive to customers."
Value (ten-point scale, μ<SUB>A</SUB> = 6.54, σ<SUB>A</SUB> = 1.8)
Please evaluate the airline on the following factors...
For the prices you pay for traveling with this airline, would you say travelling on this airline is a ["very poor deal"/"very good deal," ten-point scale]?
For the time you spent in order to travel with this airline, would you say travelling on this airline is ["highly unreasonable"/"highly reasonable," ten-point scale]?
For the effort involved in traveling with this airline, would you say travelling on this airline is ["not at all worthwhile"/"very worthwhile," ten-point scale]?
How you would rate your overall experience with this airline? ["extremely poor value"/"extremely good value," ten-point scale].
Loyalty (ten-point scale, "very unlikely"/"very likely," μ<SUB>A</SUB> = 7.30, σ<SUB>A</SUB> = 2.1)
How likely are you to ...
Do most of your future travel on this airline?
Recommend this airline to friends, neighbors, and relatives?
Use this airline the very next time you need to travel?
Take more than 50% of your flights on this airline?
~~~~~~~~
By Deepak Sirdeshmukh; Jagdip Singh and Barry Sabol
Deepak Sirdeshmukh is Visiting Assistant Professor of Marketing, Case Western Reserve University, and President, Enterprise Loyalty Group. Jagdip Singh is Professor of Marketing, Case Western Reserve University. Barry Sabol is President, Strategic Consumer Research. The authors acknowledge the financial support provided by the Marketing Science Institute, the Weatherhead School of Management Research Support Office, and Strategic Consumer Research for conducting the study and collecting data. Helpful and constructive comments provided by the three anonymous JM reviewers are deeply appreciated. The authors also benefited from the comments provided by the participants of the MAPS research seminar series at the Weatherhead School of Management, Case Western Reserve University.
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Record: 36- Consumer--Company Identification: A Framework for Understanding Consumers' Relationships with Companies. By: Bhattacharya, C. B.; Sen, Sankar. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p76-88. 13p. 2 Diagrams, 1 Chart. DOI: 10.1509/jmkg.67.2.76.18609.
- Database:
- Business Source Complete
Consumer-Company Identification: A Framework for Understanding
Consumers' Relationships with Companies
In this article, the authors try to determine why and under what conditions consumers enter into strong, committed, and meaningful relationships with certain companies, becoming champions of these companies and their products. Drawing on theories of social identity and organizational identification, the authors propose that strong consumer-company relationships often result from consumers' identification with those companies, which helps them satisfy one or more important self-definitional needs. The authors elaborate on the nature of consumer-company identification, including the company identity, and articulate a consumer-level conceptual framework that offers propositions regarding the key determinants and consequences of such identification in the marketplace.
Customer intimacy, customer equity, and customer relationship management (CRM) are the marketing mantras of today. In their quest for sustained success in a marketplace characterized by product proliferation, communication clutter, and buyer disenchantment, more and more companies are attempting to build deep, meaningful, long-term relationships with their customers.[ 1] Yet if the business press is any indication, only a few companies (e.g., Harley-Davidson, The Body Shop, Patagonia, Southwest Airlines) seem to have realized the ultimate promise of such relationship-building efforts: the consumer champion or advocate, who not only is utterly loyal but also enthusiastically promotes the company and its products to others.
What distinguishes the companies that have struck relationship-gold from the rest? What is the nature of the relationships they have with their customers? When and why are such relationships likely to occur? A large body of research, in domains ranging from customer satisfaction (e.g., Fournier and Mick 1999), relationship marketing (e.g., De Wulf, Odekerken-Schroder, and Iacobucci 2001), and loyalty (e.g., Reichheld 1996) to, more recently, CRM (e.g., Winer 2001), has tried to understand and delineate how firms, or the "people behind the brands" (McAlexander, Schouten, and Koenig 2002, p. 50), can build deeper, more committed relationships with customers and turn them into champions. However, as Fournier, Dobscha, and Mick (1998) point out, such relationships are likely to remain elusive for most marketers without a more precise understanding of when and why consumers respond favorably and strongly to companies' relationship-building efforts, entering volitionally into the kinds of consumer-company relationships that transform them into fervent supporters of the companies and their products (see Malaviya and Spargo, in press).
This article contributes to the growing research on consumer-company relationships by proffering the notion of consumer-company identification (C-C identification) as the primary psychological substrate for the kind of deep, committed, and meaningful relationships that marketers are increasingly seeking to build with their customers. Moreover, it draws on theories of social identity (Brewer 1991; Tajfel and Turner 1985) and organizational identification (Bergami and Bagozzi 2000; Dutton, Dukerich, and Harquail 1994; Mael and Ashforth 1992; Whetten and Godfrey 1998) to provide a coherent, comprehensive articulation of both the conditions in which consumers are likely to identify, or feel a sense of belonging (Mael and Ashforth 1992), with a company and the bases and consequences of such identification.
To date, identification research has focused primarily on elucidating employees' relationships with their employer (e.g., Dutton, Dukerich, and Harquail 1994) and members' relationships with nonprofit organizations, such as museums, theaters, and universities (e.g., Bhattacharya, Rao, and Glynn 1995; Mael and Ashforth 1992). Central to this article is the notion that identification with organizations can also occur in the absence of formal membership (Pratt 1998; Scott and Lane 2000), as with the case of consumers and companies, both for-and nonprofit. More specifically, on the basis of the well-documented, strong, positive consequences of identification (e.g., Bergami and Bagozzi 2000; Mael and Ashforth 1992), we assert that consumers become champions of the companies with whom they identify (e.g., Apple, Greenpeace).
We draw on prior research (Dutton, Dukerich, and Har-quail 1994; Pratt 1998) to conceptualize consumers' identification with a company as an active, selective, and volitional act motivated by the satisfaction of one or more self-definitional (i.e., "Who am I?") needs. In doing so, we bring a consumercentric perspective to CRM rhetoric and suggest that identification-based consumer-company relationships cannot be unilaterally imposed by companies; they must be sought out by consumers in their quest for self-definitional need fulfillment. In other words, in addition to the array of typically utilitarian values (e.g., high product value, consistency, convenience) that accrue to consumers from their relationship with a company (Malaviya and Spargo, in press), we propose a higher-order and thus far unarticulated source of company-based value that consumers receive when they identify with the company. This value enhances the importance of the relationship and results in certain company-directed behaviors that are qualitatively distinct from those typically obtained in the marketplace.
In the following sections, we draw on extant research in both individual and organizational psychology to elaborate on the nature of C-C identification and articulate our consumer-level conceptual framework, which offers propositions regarding the key determinants and consequences of such identification in the marketplace. We then offer possible approaches to test these propositions. We conclude with a discussion of the theoretical significance of C-C identification and its implications for companies seeking consumer champions.
Our central assertion is that some of the strongest consumer-company relationships are based on consumers' identification with the companies that help them satisfy one or more key self-definitional needs. Such C-C identification is active, selective, and volitional on consumers' part and causes them to engage in favorable as well as potentially unfavorable company-related behaviors. Support for this assertion comes from research implicating organizations as key components of people's social identity. Social identity theory (Brewer 1991; Tajfel and Turner 1985) posits that in articulating their sense of self, people typically go beyond their personal identity to develop a social identity. They do so by identifying with or categorizing themselves in a contextual manner (Kramer 1991) as members of various social categories (e.g., gender, ethnicity, occupation, sports teams as well as other, more short-lived and transient groups).
Ashforth and Mael (1989, p. 23) were the first to examine explicitly the role of organizations in people's social identities, conceptualizing the person-organization relation-ship as organizational identification, or a person's perception of "oneness or belongingness" with an organization. Drawing on social identity theory, they argue that organizational identification occurs when a person's beliefs about a relevant organization becomes self-referential or self-defining (Pratt 1998). More recently, Bergami and Bagozzi (2000, p. 557) review extant research on organizational identification to isolate it from not only its evaluative and emotional consequences but also the processes underlying it as "a cognitive state of self-categorization," the definition we adopt here. Such self-categorization into organization-ally defined categories is thought to be fundamental to the process of identity construction (i.e., "Who am I?") and occurs through consumers' comparison--ranging from an atomistic attribute-by-attribute process to a holistic, gestalt match--of their own defining characteristics (e.g., personality traits, values, demographics) with those that define the category (Ashforth and Mael 1989; Dutton, Dukerich, and Harquail 1994).
Most research has examined such self-categorization in formal membership contexts. Yet according to social identity theory (Brewer 1991), people need not interact or even feel strong interpersonal ties to perceive themselves as members of a group. Recent organizational identification research (Pratt 1998; Scott and Lane 2000) suggests that in line with social identity theory, people seek out organizations for identification purposes even when they are not formal organizational members. We argue that in today's era of unprecedented corporate influence and consumerism, certain companies represent and offer attractive, meaningful social identities to consumers that help them satisfy important self-definitional needs. As a result, such companies constitute valid targets for identification among relevant consumers, even though they are not formal organizational members. Notably, the notion of C-C identification is conceptually distinct from consumers' identification with a company's brands (e.g., Aaker 1997), its target markets, or, more specifically, its prototypical consumer. For example, whereas brands are often emblematic of their producing organizations, a brand's identity (e.g., Marlboro cigarettes) is often distinct from that of the company (e.g., Philip Morris).
Constituents of Company Identity
What constitutes a company's identity?[ 2] Consumers' knowledge structures about a company, conceptualized alternatively as corporate image, corporate reputation, or, more broadly, corporate associations (Brown and Dacin 1997; Fombrun and Shanley 1990), include consumers' perceptions and beliefs about relevant company characteristics (e.g., culture, climate, skills, values, competitive position, product offerings), as well as their reactions to the company, including company-related moods, emotions, and evaluations (e.g., Dowling 1986). Not all of these associations constitute the informational bases for consumers' identification with a company. Research suggests that people's identification with an organization is based on their perceptions of its core or defining characteristics, that is, its perceived identity (Dutton, Dukerich, and Harquail 1994). This identity is shaped by the organization's mission, structure, processes, and climate and, as do individual identities, represents possibly hierarchical constellations of characteristics or traits (Kunda 1999; Scott and Lane 2000) that are central to the organization, distinctive from other organizations, and relatively enduring over time (Albert and Whetten 1985).
We propose that consumers identify with the subset of company associations that constitutes the company's identity. This identity is likely to comprise traits that reflect (Figure 1) the company's ( 1) core values, as embodied in its operating principles, organizational mission, and leadership (Whetten and Godfrey 1998), and ( 2) demographic characteristics (Pelled, Cummings, and Kizilos 2000; Pfeffer 1983), such as industry, size, age, market position, country of origin, geographic location, and the prototypical profile of its leadership and/or employees.
Communicators of Company Identity
Prior research suggests that a company's identity is conveyed to consumers through a variety of communicators (Whetten and Godfrey 1998). For example, Albert and Whetten (1985) note that though identity is often disseminated through official documents, such as annual reports and press releases, it is also conveyed through signs and symbols (e.g., logos, appearance of corporate headquarters). A counterpoint to such company-controlled internal communicators of identity (e.g., product offerings, corporate communications, corporate social initiatives, company-sponsored forums) is the large and perhaps increasing numbers of external communicators of identity (e.g., media, customers, monitoring groups, channel members) that are not entirely controlled by the company. For example, a company can control the portrayal of its identity through its own corporate communication efforts (e.g., Microsoft as a champion of innovation), but it usually has little control over how its identity is communicated in the media (e.g., Microsoft as predatory). Similarly, a company can exert greater control over the identity communicated by members of its value chain (e.g., employees, channel members) than by those who are not part of the value chain (e.g., shareholders, customers). In summary, there are many communicators of company identity, which are likely to vary in the extent to which they are controllable by the company (Figure 1).
Next, we examine the specific conditions under which C-C identification is likely to occur. A key premise under-lying our consumer-level model of C-C identification is that though identification can be relatively pervasive and direct in the formal membership domain, in consumer contexts it will occur only under a specific set of contingencies.
Overview
This framework articulates the individual-level dynamics of C-C identification in terms of two sequential relationships (Figure 2).[ 3] The first focuses on the link between perceived company identity and identity attractiveness, the key antecedent of C-C identification. We suggest that in the marketplace, as in other contexts, consumers' evaluations of a company's identity attractiveness are based on their perceptions of that identity. More important, we suggest that consumers are likely to be attracted to a company identity that helps satisfy at least one of their three basic self-definitional needs: self-continuity, self-distinctiveness, and self-enhancement. A company's identity attractiveness is likely to depend on how similar it is to consumers' own identity (i.e., identity similarity), its distinctiveness in traits consumers value (i.e., identity distinctiveness), and its prestige (i.e., identity prestige).
We also propose that the link between consumers' perceptions of a company identity and their reactions to it depends on the extent to which they know and trust the identity. Specifically, consumers are more likely to use their perceptions of company identity to make similarity, distinctiveness, and prestige judgments when they believe they know the company's identity well (i.e., identity knowledge and identity coherence are key moderators). Similarly, we suggest that consumers are more likely to make identity attractiveness evaluations on the basis of their identity-related judgments when they perceive the identity to be trustworthy (i.e., identity trustworthiness is a key moderator).
The second relationship focuses on the link between identity attractiveness and C-C identification, including its key consequences. Although this link is likely to be direct and strong among formal members (e.g., employees) of a company (Dutton, Dukerich, and Harquail 1994), we expect consumers to identify with an attractive identity only when their interactions with the company embed them in its organizational folds (i.e., embeddedness). Such interactions not only draw consumers into the center of vital company-related networks (Rao, Davis, and Ward 2000) but also increase the salience of the company identity (i.e., identity salience) in their minds, increasing the likelihood of identification (Pratt 1998). Finally, we offer several positive consequences of C-C identification (e.g., company loyalty, company promotion, customer recruitment, resilience to negative information) and one that can potentially hurt the company (i.e., greater claim on the company).
Company Identity -> Identity Attractiveness
Consumers' attractiveness evaluations of a company's identity are based on their perceptions of that identity as derived through the various communicators discussed previously. In other words, a company's perceived identity is the primary antecedent of consumers' identity attractiveness evaluations. Moreover, in consumers' efforts to satisfy their fundamental needs for self-continuity, self-distinctiveness, and self-enhancement, the attractiveness of a company's identity will depend, cumulatively, on the extent to which consumers perceive it to be similar to their own, distinctive on dimensions they value, and prestigious.
Identity similarity. In their efforts to understand themselves and their social worlds, consumers are motivated to maintain a stable and consistent sense of self, both over time and across situations (for a review, see Kunda 1999). Prior organizational research (Pratt 1998) suggests that this need for self-continuity is a key driver of people's choice of organizations to identify with as they attempt to construct viable, cognitively consistent social identities (Heider 1958). In other words, consumers are likely to find a company's identity more attractive when it matches their own sense of who they are. The link between identity similarity and perceived identity attractiveness is likely to occur (Dutton, Dukerich, and Harquail 1994) not only because consumers find the self-relevant information inherent to company identities that are similar to their own easier to focus on, process, and retrieve (Markus and Wurf 1987) but also because such identities enable them to maintain and express more fully and authentically (Pratt 1998) their sense of who they are (i.e., their traits and values). For example, a consumer who cares about animal rights will be more attracted to a company that has distinguished itself in this regard (e.g., a company that does not engage in animal testing or the nonprofit organization People for the Ethical Treatment of Animals) than to another that focuses, say, on the arts.
P1: The more similar consumers perceive a company identity to be to their own, the more attractive that identity is to them. In other words, the relationship between consumers' perceptions of a company identity and their evaluation of its attractiveness is mediated by the identity's perceived similarity to their own.
Identity distinctiveness. Social identity research contends that people need to distinguish themselves from others in social contexts (Tajfel and Turner 1985). Specifically, Brewer's (1991) theory of optimal distinctiveness suggests that people attempt to resolve the fundamental tension between their need to be similar to others and their need to be unique by identifying with groups that satisfy both needs. Therefore, distinctiveness is an important organizational characteristic from an identity attractiveness perspective. Thus, while consumers' need for distinctiveness is likely to vary with cultural norms, individual socialization, and recent experience (Brewer 1991), it is likely to make the (self-relevant) distinctiveness of a company's identity a key determinant of attractiveness. Because distinctiveness is likely to be articulated relative to other companies, it in turn will depend not only on the company's own identity but also on its competitive landscape (e.g., the number of competitors; their identities, particularly the similarities among them; the company's perceived positioning relative to competition).
P2: The more distinctive consumers perceive a company's identity to be on dimensions they value, the more attractive that identity is to them. In other words, the relationship between consumers' perceptions of a company identity and their evaluation of its attractiveness is mediated by the identity's perceived distinctiveness on dimensions they value.
Identity prestige. People also like to perceive themselves in a positive light; self-concept research (Kunda 1999) suggests that people's need for self-continuity goes hand in hand with their need for self-enhancement, or the maintenance and affirmation of positive self-views that result in greater self-esteem. Moreover, as with self-continuity, organizational identification research (Ashforth and Mael 1989; Dutton, Dukerich, and Harquail 1994) suggests that a key way consumers seek to satisfy their self-enhancement need is by identifying with organizations that have prestigious identities. Prestige here refers to organizational stakeholders' perceptions that other people, whose opinions they value, believe that the organization is well regarded (Bergami and Bagozzi 2000). That is, consumers' identification with a company that has a prestigious identity enables them to view themselves in the reflected glory of the company, which enhances their sense of self-worth. Thus, the attractiveness of a company's identity is likely to be determined in part by its perceived prestige (Cheney 1983; Pratt 1998).
P3: The more prestigious consumers perceive a company's identity to be, the more attractive that identity is to them. In other words, the relationship between consumers' perceptions of a company identity and their evaluation of its attractiveness is mediated by the identity's perceived prestige.
When people are formal members of an organization and interact with it on a frequent, intimate basis (e.g., employees), their levels of understanding and trust of the organizational identity are likely to be high. Thus, if they perceive their company's identity to be distinctive, prestigious, and/ or similar to their own, its attractiveness to them is virtually ensured. In contrast, consumers perceive a company's identity through cognitive and evaluative filters that often distort, fragment, or obscure its identity (Figure 1). Therefore, consumers' knowledge of a company's identity, as well as their more specific appraisals of its clarity and veracity, is likely to be more dispersed than is that of their formal counterparts, resulting in reduced veridicality in both their identity-related judgments and their attractiveness evaluations. Specifically, consumers' similarity, distinctiveness, and prestige judgments are likely to vary with two related but conceptually distinct factors: their perceptions of how much they know about the company's identity (identity knowledge) and, more specifically, the extent to which they perceive what they do know as a consistent, coherent whole (identity coherence). Similarly, consumers' perceptions of the identity's trustworthiness are likely to moderate their willingness to use these judgments as viable input into their identity attractiveness evaluations.
Identity knowledge. Compared with that among formal members, the level of company identity knowledge among consumers is likely to be more varied and lower. Research on the effects of knowledge on information use and decision making (Alba and Hutchinson 2000; Raju, Lonial, and Man-gold 1995) suggests that consumers' use of their identity perceptions as input into their familiarity, prestige, and distinctiveness judgments varies with their sense of how knowledgeable they are about the identity. Specifically, lower subjective knowledge (i.e., people's perceptions of how knowledgeable they are) about a company identity is likely to decrease consumers' confidence in their ability to make identity-based judgments, weakening the link between their identity perceptions and such judgments. This moderating effect is underscored by the positive correlation between subjective knowledge and objective or actual knowledge (Raju, Lonial, and Mangold 1995), which diminishes consumers' ability to make meaningful identity-related judgments. Consumers' knowledge, in turn, is determined by the extent to which they learn about a company's identity through the communicators depicted in Figure 1. For example, they are more likely to be familiar with the identities of companies that actively engage in identity communication (e.g., corporate advertising) or are the subject of identity-revealing media coverage or word of mouth.
P4: Consumers' perceived knowledge about a company identity moderates the extent to which they use their identity perceptions to make identity-related (i.e., similarity, prestige, and distinctiveness) judgments.
Identity coherence. Coherence, or "the organization and patterning of attributes of personality within an individual" (Beisanz and West 2000, p. 425), is believed to be the stable behavioral signature of personality, conveying the gestalt that is commonly understood as personality by laypersons. According to recent personality research (see Shoda and Mischel 2000), coherence emerges from the distinct and stable patterns of behavior variability that people display over time and plays a key role in how they perceive and under-stand others. Similarly, the coherence of a company identity, or how constituent traits relate to one another, is likely to play a key role in consumers' comprehension of that identity, given that it is likely to be large, complex, and unwieldy (Albert and Whetten 1985). Specifically, consumers' under-standing of a company's identity, including their ability to make identity-related judgments, is likely to be greater when the company's actions in disparate domains coalesce into stable, distinctive, and meaningful connections among its defining characteristics than when no such underlying coherence is apparent (i.e., the actions seem inconsistent). In other words, when consumers comprehend a company's identity to be an internally consistent, coherent whole, they are better able to discern its distinctiveness, prestige, and similarity to their own identity (see Kristof 1996, p. 86).
Several factors determine an identity's perceived coherence. Some identities are inherently more coherent than others, such as those achieved through distinctive corporate positioning strategies that are consistent over time (e.g., Wainwright Bank's support of an array of related social issues) or for companies that have spent considerable effort articulating their own identities (e.g., Ben & Jerry's). Coherence is also likely to be affected by a range of marketplace activities, such as mergers and acquisitions (e.g., Hewlett Packard and Compaq). Finally, the greater the number of identity communicators and the lower a company's ability to control them, the more likely it is that people will receive incoherent, even contradictory identity information.
P5: The perceived coherence of a company identity moderates the extent to which consumers use their identity perceptions to make identity-related (i.e., similarity, prestige, and distinctiveness) judgments.
Identity trustworthiness. Much research (e.g., Chaudhari and Holbrook 2001; Gottleib and Sarel 1992) suggests that consumers' trustworthiness perceptions of company communications and actions moderate the extent to which their product perceptions lead to positive product evaluations. Similarly, organizational research (e.g., Kramer 1999) points to organizational trustworthiness as a key determinant of various positive organizational citizenship behaviors. Drawing on this, we suggest that the perceived trustworthiness of a company identity is likely to moderate the effect of consumers' identity-related judgments on their identity attractiveness evaluations.[ 4] Specifically, the relationship between an identity's distinctiveness, prestige, and similarity judgments and its attractiveness to consumers is likely to depend on the extent to which consumers trust their identity-related judgments (i.e., the identity), including the company's motives in defining itself thus. In other words, if consumers trust the company's identity, they are likely to perceive lower risk (Grewal, Gottleib, and Marmorstein 1994) in using their identity-related judgments to gauge identity attractiveness.
Consumers' perceptions of identity trustworthiness are likely to vary across companies and depend, in general, on their historical experience with that company, its reputation, the type or category of company (e.g., tobacco), and, in particular, the attributions they make about the company's intentions and actions from available data. In turn, the nature of these attributions is likely to depend, at least in part, on the sources of information; consumers are less likely to trust identity information from company-controlled sources, such as corporate advertising.
P6: The perceived trustworthiness of a company identity moderates the relationship between consumers' identity-related judgments and their evaluation of its overall attractiveness.
Identity Attractiveness -> C-C Identification
Most organizational identification research has focused primarily on membership contexts, in which membership in the relevant organization is not only formal but also central (i.e., plays an encompassing, defining role) to the lives of the identifying individuals (e.g., employees with employer organizations, students with colleges). Thus, it is not surprising that this research has drawn a strong, direct connection between identity attractiveness and organizational identification: "[T]he greater the attractiveness of the perceived identity of an organization, the stronger [is] a person's identification with it" (Dutton, Dukerich, and Harquail 1994, p. 244). However, companies do not typically play such central roles in consumers' lives. As a result, identity attractiveness in the consumer-company context is likely to be a necessary but not sufficient condition for identification. In particular, we suggest that consumers will identify with an attractive company identity only when their interactions with that company are significant, sustained, and meaningful enough to embed them in the organizational network. Such embeddedness[ 5] (Rao, Davis, and Ward 2000) not only establishes the company as a viable social category in consumers' minds but also, more specifically, makes this category salient relative to those borne of other organizational affiliations.
Embeddedness. In recent years, consumers' interactions with companies have evolved from impersonal economic exchanges to participation in long-term relationships with both key internal stakeholders (e.g., senior management) and other consumers. These interactions vary in the extent to which they embed the consumer in the organizational network (Scott and Lane 2000). Research on embeddedness, or the ongoing contextualization of economic exchange activity in social structures (Granovetter 1985; Rao, Davis, and Ward 2000; for a review, see Dacin, Ventresca, and Beal 1999), suggests that in contrast to arm's-length relationships, consumers' embedded relationships with companies are likely to be strong, intricate, and trusting, resulting in consumers feeling more like insiders than outsiders.
Embeddedness places consumers closer to the center of the social network embodied by the company, making them feel more integrated in the network (O'Hara, Beehr, and Colarelli 1994). Embedded consumers are active in the organization, have easy access to other organizational members, can mediate the flow of resources or information in the organization, and have connections to central organizational members (Faust 1997). Consumers' embeddedness in a company-derived social network is therefore likely to be key to their designation of it as a viable social category capable of shaping their social identity (Rao, Davis, and Ward 2000). In other words, consumers will not identify with every company whose identity they find attractive; identification is likely to occur only when their embeddedness makes it both easier and more important for them to categorize themselves socially in terms of the company.
Embedded relationships arise when consumers engage in company-related rites, rituals, and routines (i.e., a variety of institutionalized socialization tactics) that cast them in legitimate membership roles (Kristof 1996; Pratt 2000). Such behaviors are often enacted in a "local, tribal context" (Ashforth 1998, p. 219), including "member conferences" (e.g., Holiday Inn; Cross 1992) and other such company-sponsored forums (e.g., Camp Jeep, Harley-Davidson Brandfest; McAlexander, Schouten, and Koenig 2002), in which consumers meet company insiders (e.g., management). Embeddedness also increases when consumers network with other company stakeholders through on-and offline communities (e.g., the discussion forums hosted by American Cancer Society) or get involved in company decision making (e.g., Southwest Airlines invites frequent fliers to interview prospective flight attendants; Heskett et al. 1994). Finally, embedded relationships are more likely to occur when the company and its products contribute to the satisfaction of idiosyncratic, important interests (e.g., hobbies) and provide opportunities for self-expression than when they satisfy less important needs.
P7: The embeddedness of consumers' relationship with a company moderates the relationship between identity attractiveness and C-C identification. Consumers are more likely to identify with an attractive company identity when they are embedded in the company-defined social network.
Identity salience. The organizational identification literature (Hogg and Terry 2000; Pratt 2000; Scott and Lane 2000) implicates identity salience, or the extent to which specific identity information dominates a person's working memory, as a key determinant of identification. In particular, research suggests that when an organizational identity is salient, it is likely to be evoked across a wider range of situations and increase consumers' propensity to focus and elaborate on its implications for their social identity over other, possibly competing identities. When consumers can easily access attractive, self-relevant identity information from memory, the likelihood of their identifying with it is higher. In the employee-employer context, the salience of the employer's identity is likely to be uniformly high, driven by daily interaction, continual following of organizational routines, and a myriad of other socialization tactics (Scott and Lane 2000). In the consumer-company context, however, there is likely to be significant variation among consumers in the extent to which the company identity is salient, thereby making it a key moderator of the identity attractivenessC-C identification relationship.
Although embeddedness is likely to increase identity salience, it is not the only determinant; salience is also heightened by factors such as the intensity of the company's corporate image communication efforts. Specifically, initiatives such as corporate advertising and public relations not only educate consumers about the company's identity but also make it more salient relative to other competing organizational identities. Moreover, salience is likely to be particularly high when in-group/out-group differences are heightened (Pratt 1998), such as in Apple's "Computers for Everyone Else" and "Think Different" campaigns. In general, corporate branding is likely to increase identity salience, because some of the strongest associations of corporate brands (Keller 1998) are intangible and identity related (e.g., innovative, market leader, environmentally conscious). Also, some identities are likely to be inherently more salient, particularly if they are more distinctive or novel than their competition's (Pratt 1998). Over time, these factors increase an identity's share of mind and help consumers internalize its relevance to their social identity (Scott and Lane 2000), making it more salient and accessible.
P8: Identity salience moderates the relationship between identity attractiveness and C-C identification. Consumers are more likely to identify with an attractive company identity when it is more salient.
C-C Identification: Consequences
A central question guiding our perspective on consumer- company relationships is: What benefits accrue to a company when consumers identify with it? Virtually all organizational identification research points to the multitude of positive consequences that stem from people's self-categorization into organization-based social categories (Bagozzi and Bergami 2002; Mael and Ashforth 1992; Scott and Lane 2000). Identification causes people to become psychologically attached to and care about the organization, which motivates them to commit to the achievement of its goals, expend more voluntary effort on its behalf, and inter-act positively and cooperatively with organizational members. We discuss the implications of these consequences for the consumer-company domain next.
Company loyalty. Researchers (e.g., Bagozzi and Bergami 2002) have established a strong link between identification and identifier commitment in terms of reduced turnover in the employer-employee context and greater financial and membership-related support in the context of educational and cultural institutions (Bhattacharya, Rao, and Glynn 1995; Wan-Huggins, Riordan, and Griffeth 1998). In addition, research (e.g., Bergami and Bagozzi 2000; Dutton, Dukerich, and Harquail 1994) has suggested that among organizational members, identification leads to increased competition with and even derogation of nonmembers. Because consumption is the primary currency of consumer- company relationships, such identification-based commitment is likely to be expressed through a sustained, long-term preference for the identified-with company's products over those of its competitors. In other words, company loyalty is a key consequence of C-C identification. Because the consumer identifies with the company rather than its products, this loyalty is likely to be somewhat immune to minor variations in product formulation and extend, ceteris paribus, to all the products produced by the company. Moreover, consumers' commitment and desire to increase the welfare of the company (e.g., Dutton, Dukerich, and Harquail 1994) are likely to manifest in their more specific efforts to support the company in its inherently risky endeavor of new product introduction. The consumption of new products gives identified consumers yet another opportunity to support the company and enables them to feel like they are bearing some of its risk.
P9: The higher the level of C-C identification, the more likely consumers are to be loyal to the company's existing products and try its new products.
Company promotion. Research (Ashforth and Mael 1989; Dutton and Dukerich 1991) suggests that identifiers have a vested interest in the success of the company and, because of their self-distinctiveness and enhancement drives, want to ensure that their affiliation with it is communicated to relevant audiences in the most positive light possible. Such communication also helps consumers socially validate their identity claims so that they can be internalized (Ashforth 1998). Thus, consumers' support of the company is likely to be expressed through avenues other than just consumption (Scott and Lane 2000). In other words, consumers are likely to promote the company to significant others. Conversely, in their efforts to manage "outsider" impressions of the company (Dutton, Dukerich, and Har-quail 1994), they are likely to defend the company and its actions, should either come under adverse scrutiny in the media or among relevant publics.
Such promotion can be both social and physical. In the social domain, consumers are likely to initiate positive word of mouth about the company and its products (e.g., First Direct, the U.K. retail bank, is recommended by its customers every four seconds, gaining more than one-third of its new business from referrals; Smith 2001). They will also defend the company when it is attacked by others, particularly nonmembers. When identification is especially strong, consumers may adopt visible, more chronic proxies or "markers" of their internal psychological state (Schlenker 1986). Such physical promotion takes the form of consumers bearing markers of the company identity (e.g., logo, name) through the collection of memorabilia, apparel choices, and even tattoos (Katz 1994). It is not surprising, then, that companies such as Ben & Jerry's and Harley-Davidson have a variety of collectibles that consumers can purchase (Allen 1993). Such physical promotion is most likely when identification is driven by the needs of self-enhancement or distinctiveness, for which need fulfillment is particularly contingent on the social validation of identification.
P10: The higher the level of C-C identification, the more likely consumers are to promote the company, both socially (i.e., talk positively about it and its products) and physically (i.e., adopt company markers).
Customer recruitment. Identified consumers have a clear stake in the company's success (Ashforth and Mael 1989). From the identifier's perspective, an effective path to long-term success, beyond consumption of the company's product, lies in recruiting new consumers for the company. Thus, customer recruitment is likely to be a key manifestation of identified consumers' voluntary efforts (O'Reilly and Chat-man 1986) to contribute to the company's long-term welfare. Such recruitment efforts are also likely to be informed by the heightened in-group/out-group distinctions that result from identification (Scott and Lane 2000; Tajfel and Turner 1985), driving consumers to strengthen the in-group with more like-minded people (e.g., friends, family, coworkers), as in the case of Virgin Atlantic (Smith 2001). In addition to helping the company, a large in-group helps legitimize and reaffirm each member's company-based social identity while bringing the recruiter closer to the center of the organizational network. Finally, because identified consumers are motivated to engage in helpful and supportive behaviors toward in-group members (Scott and Lane 2000), which results in intragroup cohesion, cooperation, and altruism (Ashforth and Mael 1989), recruiting people they like or care about to be part of the company-based in-group enables consumers to maintain an overall coherence among the different social domains in which they operate. In summary, we expect consumers to be actively involved in recruiting customers for the company they identify with, but such recruitment is likely to be enacted primarily among extant social networks of family, friends, and colleagues.
P11: The higher the level of C-C identification, the more likely consumers are to recruit people from their extant social networks to be new customers of the company.
Resilience to negative information. We expect identified consumers to overlook and downplay any negative information they may receive about a company (or its products) they identify with, particularly when the magnitude of such information is relatively minor (Alsop 2002). For example, focus groups of Tom's of Maine customers (Chappell 1993) suggest that when customers share a company's values, their relationship with it is not tarnished by their disappointment over the performance of a single product. The likelihood of such resilience to negative information is underscored by Bergami and Bagozzi's (2000) finding that identification with an organization causes people's interactions with it to be characterized by courtesy, altruism, and sportsmanship. In the consumer context, these characteristics are likely to cause consumers to make more charitable attributions regarding the company's intentions and responsibility when things go wrong and to be more forgiving of the company's mistakes if its culpability is established. In other words, just as consumers are likely to forgive themselves for minor mistakes, they will forgive the companies they identify with, particularly because identification leads them to trust the company and its intentions (Hibbard et al. 2001; Kramer 1991).
P12: Within a zone of tolerance, the higher the level of C-C identification, the greater is consumers' resilience to negative information about the company.
However, such resilience is likely to be nonlinear. When the negative information is of a relatively major magnitude and is identity related (e.g., a socially responsible company is exposed for using sweatshops), identified consumers are likely to react more strongly and more permanently than nonidentified consumers (Bagozzi and Bergami 2002), perhaps by boycotting the company's products and engaging in negative word of mouth. Such a heightened response is particularly likely in the face of a perceived betrayal of identity authenticity and trust, as when the domain of the company's "failure" is perceived to be controllable by the company and is the central basis for identification (e.g., an environmen-tally conscious consumer's reaction to Ford's alleged failure to live up to its own "green" principles; Hakim 2002).
P13: Beyond a zone of tolerance, the higher the level of C-C identification, the stronger and more permanent are consumers' reactions to negative information about the company, particularly when such information is identity related.
Stronger claim on company. Although identification is likely to benefit companies in many ways, prior research (Dukerich, Kramer, and Parks 1998; Hibbard et al. 2001; Kristof 1996) suggests that from the company's perspective, there is a potential risk to identification as well. When consumers identify with a company, their company-borne social identity becomes more important to them (Boldero and Francis 2000). With this importance, consumers perceive their claim on the organization as more legitimate and urgent (Mitchell, Agle, and Wood 1997), and they are likely to press this claim more actively and consistently. This is particularly key when a company's efforts at long-term success result in identity-related changes. Dutton and Dukerich (1991) suggest that when such changes threaten identified consumers' sense of self, they are likely to resist them and lobby the company to be consistent with its original, less viable identity. More generally, in trying to induce identification, companies may unwittingly boost consumers' power by embedding them too deeply into the organization. In other cases, consumers may identify more with one another than with the organization and act collectively to further their own agendas. Overall, these actions may result in consumers having greater power over the company, which reduces its autonomy in relevant spheres of endeavor.
P14: The higher the level of C-C identification, the stronger are consumers' claims on the company.
Empirical testing is the logical next step in establishing the validity of our model and its propositions. Such testing must be based on multiple companies, with methods ranging from laboratory experiments to field surveys. Because of the number of constructs in the model and the complex relationships among them, it is best to test it in two or more parts before testing the entire model. Moreover, regardless of the method employed, extensive qualitative research (e.g., focus groups, depth interviews) is a key first step. Such research would not only help generate a list of companies with which consumers are likely to identify but also help develop new or refine existing measures of the model's key constructs. Examples of such measures are presented in Table 1. Some measures (e.g., C-C identification, identity coherence) can be obtained or adapted from prior work in the domain. Of those that must be developed, several (e.g., identity knowledge, trustworthiness) are subjective in nature and can be measured by means of multi-item Likert scales (for examples, see Table 1). Others (e.g., the consequences of C-C identification) can be operationalized by means of either subjective or objective measures or a combination thereof.
After the measures are finalized, we propose separate tests of the two submodels that constitute our conceptual framework (i.e., company identity -> identity attractiveness and identity attractiveness -> C-C identification, including the consequences of the latter). Because of the relatively time-independent nature of the company identityidentity attractiveness submodel, it is particularly amenable to experimental tests involving the manipulation of company identity information and the measurement of the dependent variable, the mediators, and the moderators. The two submodels can also be tested by means of surveys administered to relevant populations regarding their relationships with one or more focal companies from first the same, but eventually different, industries. Possible approaches to estimating these submodels include path analysis or structural equation modeling (Mullen 1995).
This article contributes to several different research streams. By implicating consumer-company similarity perceptions as a key driver of identification, this research complements that on consumer-brand congruity (Aaker 1997; Fournier 1998; Kleine, Kleine, and Allen 1995). Specifically, we argue that akin to consumers' brand congruity perceptions on self-relevant dimensions, their perceptions of congruence between their own identity and that of relevant companies can be a source of self-definition. Of course, consumers' relationships with a company are also likely to be influenced by their relationships with its brands. However, a theoretical contribution of this research lies in highlighting the role of the nonproduct aspects of a company (Brown and Dacin 1997), such as its values and demographics, its social responsibility efforts, and the networking opportunities it provides in building the consumer- company bond. In general, by positing that consumers can express themselves vicariously through their identification with select companies, this research also adds to the notion of the extended self (Belk 1988). The extended-self literature has thus far focused primarily on the role of material possessions in identity formation. We suggest that consumers' identification with companies can also contribute to such self-extension.
By contrasting the dynamics of identification in the consumer-company realm to that in formal membership contexts (e.g., employees), this research adds to the organizational identification literature. Whereas organizational identification research has focused on the antecedents, mediators, and moderators of identification within formal membership, our framework underscores the likelihood of identification in non-or pseudomembership situations and pinpoints the roles of several individual-specific factors in driving such identification. An understanding of these dynamics may be particularly important for organizational researchers in an era of flexible location and work schedules, in which the lines between organizational insiders and outsiders are increasingly blurred. More important, we suggest that, differences apart, encouraging identification may be not only a good employee retention strategy but also, under certain conditions, a good customer retention one.
By clarifying the consumer-based contingencies under which advocacy or championing behavior is likely to occur, our research adds an important new dimension to managers' understanding of the possibilities and limits of their customer relationship-building strategies. In particular, our framework suggests that in harnessing the power of identification in their own company-consumer contexts, managers must ask themselves the following questions: ( 1) Do we want consumers to identify with our company? and ( 2) If so, how can we get consumers to identify with our company? Specifically, what do managers need to do in terms of identity articulation, identity communication, and identification management?
Before formulating and implementing an identificationbuilding strategy, managers must ascertain whether they actually want their consumers to identify with their company. As Malaviya and Spargo (in press) point out, not all companies will benefit from going beyond satisfying consumers' basic utilitarian needs to fulfilling their higher-order self-definitional needs; the rewards are likely to depend, among other things, on a company's industry, its customer base, its competitive positioning, and its overall strategy. For example, for companies with a broad consumer base, identification among one consumer segment might lead to disidentification among others (Elsbach and Bhattacharya 2001). In general, facilitating identification is not only costly in terms of resources but also potentially risky in terms of limiting a company's strategic degrees of freedom with regard to future business decisions, which makes a clear cost-benefit analysis an essential precursor to the pursuit of C-C identification.
What types of companies are likely to benefit from identification? Compared with business-to-business companies, business-to-consumer companies may benefit more because they are better known to the general public and provide opportunities for direct consumption, with concomitant opportunities for self-expression. Also, because company attributes are likely to play a greater role in contexts of low product differentiation, companies in such product categories may benefit more from identification. In light of the role played by consumer-company interactions in facilitating embeddedness and thus identification, service companies are perhaps more likely to benefit from identification than are those that sell products. Finally, because identification is expected to engender loyalty to the company rather than to a specific brand, providers of multiple products targeted to the same segment are likely to benefit to a greater extent.
If C-C identification is deemed desirable, companies must articulate and communicate their identities clearly, coherently, and in a persuasive manner. A thoughtfully designed and executed communications strategy is essential: Marketers must communicate clearly, through controllable channels, the identity dimensions that their consumers are likely to perceive as distinctive, prestigious, and similar to their own, as well as continually monitor identity information that is disseminated through uncontrollable channels, to address discrepancies in a prompt and persuasive manner.
Finally, companies must devote significant resources to identification management. This task is likely to be easier for companies with small, relatively homogeneous target markets (e.g., niche markets) or those that have targeted specific consumer subgroups for identification. In either case, companies must devise strategies for sustained, deep, and meaningful consumer-company interactions that embed consumers in the organization and make them feel like insiders. Notably, such interactions should not necessarily be mediated through the product (e.g., brand communities), because such efforts might highlight the instrumentality that characterizes most consumer-company relationships. Instead, these interactions should focus on bringing consumers face to face with the organizational identity, while drawing them closer to the center of the organization through co-creation activities (Malaviya and Spargo, in press; Sheth and Parvatiyar 1995) that are focused on the organization (e.g., company policies, personnel recruitment) rather than on its output (e.g., product design, advertising).
Further Research
Of the many research issues that can be pursued in this area, the most pressing is the need for empirical testing. It is important to articulate the longitudinal, higher-order effects, including feedback loops, that are likely to occur in our proposed model. For example, embeddedness is likely to facilitate the identity attractiveness -> C-C identification link and lead to greater identity knowledge. In other words, the mechanisms underlying identity attractiveness and identification are likely to be iterative, and over time, identification itself may affect some of the independent variables in our model. For example, identifi-cation may alter consumers' perceptions of identity similarity and prestige, as well as their desire for greater embeddedness. Similarly, consumers engaging in company promotion may intensify their identification over time. Relatedly, it is important to delineate the interactions among the independent and mediator variables in the model. For example, the three identity-related judgments and the three moderators (identity knowledge, coherence, and trustworthiness) may interact positively in their cumulative effect on identity attractiveness.
Both authors contributed equally to the article.
The authors thank Andrew Hoffman, Patrick J. Kaufmann, Hayagreeva Rao, and the three anonymous JM reviewers for their help with the article.
1 We use the term company in its broadest sense to refer to any organization (both for-profit and nonprofit) that operates in the marketplace and makes product offerings (e.g., goods, services, experiences, information, ideas) to satisfy consumers' needs and wants.
2 Hatch and Schultz (1997) make a conceptual distinction between organizational identity, which is the internal stakeholders' perceptions of the company (i.e., the company's view of itself), and corporate identity, company image, or reputation, which is the external stakeholders' perceptions of it. However, the pertinence of this distinction, borne largely of variations in the communication of company actions to these two sets of stakeholders, is likely to diminish with increasing levels of interaction between "insiders" and "outsiders" and the media-driven transparency of company behavior. Given our focus on consumers' perceptions, we only use the term "company identity" for the sake of consistency.
3 Although we expect feedback loops from several of the downstream constructs (e.g., consequences of identification) to certain upstream ones (e.g., identity trustworthiness), we leave an explicit articulation of such second-order effects to further research.
4 We expect identity trustworthiness, unlike identity knowledge and coherence, to be a moderator of consumers' evaluative responses to a company's identity (i.e., attractiveness evaluations) rather than of their purely cognitive ones (i.e., identity-related judgments). For example, consumers' ability to make identity-related judgments, particularly those of similarity and distinctiveness, is unlikely to be affected by identity trustworthiness.
5 Embeddedness may also influence consumers' perceptions of identity knowledge, coherence, and trustworthiness. However, as with feedback loops, we leave an articulation of these second-order effects to further research.
Construct Measure Type
Company Identity->Identity Attractiveness
Company identity Trait adjective ratings (see O'Reilly,
Chatman, and Caldwell 1991; Sen and
Bhattacharya 2001)
Identity attractiveness Likert-type multi-item scale
(e.g., "I like what Company X stands
for"; "Company X has an attractive
identity") Identity similarity
Likert-type multi-item scale
(e.g., "I recognize myself in
Company X"; "My sense of who I am
matches my sense of Company X")
Identity distinctiveness Likert-type multi-item scale
(e.g., "Company X has a distinctive
identity"; "Company X stands out
from its competitors")
Identity prestige Likert-type multi-item scale
(e.g., "Company X is a first-class,
high-quality company")
Identity knowledge Likert-type multi-item scale
(e.g., "I feel like I know very well
what this company stands for")
Objective knowledge test about
company traits
Identity coherence Likert-type multi-item scale
(e.g., "It's difficult to get a
clear sense of what this company
stands for from its actions")
Objective coherence measure
(e.g., idiographic analysis of
company identity; see Shoda,
Mischel, and Wright 1994)
Identity trustworthiness Likert-type multi-item scale
(e.g., "I don't trust this company")
Identity Attractiveness->C-C Identification
C-C identification Two-item identification measure
(Bergami and Bagozzi 2000)
Implicit association measures (see
Brunel et al. 2002)
Salience Likert-type multi-item scale
(e.g., "I think about company X
often")
Objective salience measures
(e.g., free recall, recognition and
recall reaction times)
Embeddedness Likert-type multi-item scale
(e.g., "My interactions with Company
X make me an important player in the
organization")
Objective centrality measure
(e.g., degree centrality or
eigenvector centrality; see Faust
1997)
Product loyalty Likert-type multi-item scale
Existing products (e.g., "I am loyal to the products
Company X makes")
New products (e.g., "I like to try every new
product Company X introduces")
Product purchase behavior
Existing products (e.g., number of Company X product
purchases in a specified time period)
New products (e.g., number of new product
purchases from Company X in a
specified time period)
Company promotion Likert-type multi-item scale
Social (e.g., "I often talk favorably about
Company X and its products to my
friends and colleagues")
Physical (e.g., "I often wear clothing with
the Company X logo")
Promotion behavior
Social (e.g., number of times in a specific
time period respondent generated
positive word of mouth about
Company X)
Physical (e.g., number of Company X souvenirs,
memorabilia, tattoos)
Customer recruitment Likert-type multi-item scale
(e.g., "I try to get my friends and
family to buy Company X's products")
Recruitment behavior
(e.g., number of people recruited by
respondent to buy Company X's
products)
Resilience to negative Likert-type multi-item scale
information (e.g., "I forgive Company X when
it makes mistakes"; "I will forgive
company X for [specific negative
information]")
Resilience behavior
(e.g., reaction to specific
unfavorable media coverage)
Stronger claim Likert-type multi-item scale
on company (e.g., "I feel I have a right to
tell Company X what it should do")
Claim behavior
(e.g., number of demands made on
Company X in a specified time period)DIAGRAM: FIGURE 1: The Constituents and Communicators of Company Identity
DIAGRAM: FIGURE 2: Conceptual Framework
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By C.B. Bhattacharya and Sankar Sen
C.B. Bhattacharya is Associate Professor of Marketing, School of Management, Boston University. Sankar Sen is Associate Professor of Marketing, Zicklin School of Business, Baruch College/CUNY.
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Record: 37- Consumers' Search and Use of Nutrition Information: The Challenge and Promise of the Nutrition Labeling and Education Act. By: Balasubramanian, Siva K.; Cole, Catherine. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p112-127. 16p. 3 Diagrams, 4 Charts, 1 Graph. DOI: 10.1509/jmkg.66.3.112.18502.
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Consumers' Search and Use of Nutrition Information: The Challenge and Promise of the Nutrition Labeling and Education Act
Four studies investigate the Nutrition Labeling and Education Act's (NLEA's) impact on how consumers use nutrition information. Field and laboratory studies compare, but do not detect any changes in, consumers' search for nutrition information or their recall of this information in the pre-and post-NLEAperiods. However, the search activities of a select group (highly motivated and less knowledgeable consumers) benefited more from the NLEA than did other groups. Additional results from the field and lab studies indicate that the NLEA changed attention to negative nutrition attributes (such as fat and sodium, of which less is better) more than it changed attention to positive attributes such as calcium and vitamins. Analyses of scanner databases confirm this trend (with the exception of calories). Focus group results also reflect these findings. The authors discuss implications for public policy, management, academic research, and consumer welfare.
The 1990 Nutrition Labeling and Education Act (NLEA) dramatically changed nutrition labels on packaged foods in supermarkets, thereby increasing the amount of nutrition information available at the point of purchase. This law requires packaged foods to display nutrition information prominently in a new label format, namely, the Nutrition Facts panel. It also regulates serving size (to reflect what people really eat), health claims (that link a nutrient to a specific disease), and descriptor terms (e.g., "low fat") on food packages.
This legislation's primary goal is to improve consumer welfare by providing nutrition information that will "assist consumers in maintaining healthy dietary practices" (NLEA 1990, § 2). The underlying hope is that if consumers have reliable nutrition information available at the point of purchase and if they understand how their diet affects their risk of different diseases, they will make risk-reducing food choices. Ultimately, this change in behavior could reduce the costs to society of treating conditions such as heart dis-ease and some cancers.
The food industry incurred significant costs, estimated at more than$2 billion, to comply with the law (Andrews, Nete-meyer, and Burton 1998; Silverglade 1996). Because of the NLEA's potential benefits and actual costs, we decided to evaluate its impact on consumers with three queries: First, did the NLEA succeed in promoting greater search and attention to into renutrition information? Second, given the large amount of point-of-purchase information the NLEA made available, are consumers selectively attending to some information, such as specific nutrition attributes or package descriptors like "low-sodium"? Third, because public policy aims to promote everyone's health, did the NLEA legislation help consumers who are least likely to help themselves? Potentially, consumers who know less about nutrition may benefit more from the onset of the NLEA than those who know more. If the NLEA homogenized the population so that less knowledgeable consumers behave more like knowledgeable consumers when making nutrition-related choices, the NLEA will have enhanced social welfare. Motivation may also play a vital role here. Whereas highly motivated consumers are likely to find the nutrition information-rich environment in the post-NLEA phase especially attractive, it is useful to examine the NLEA's impact on less motivated consumers.
We organize the article as follows: The next section delineates research issues and hypotheses. Subsequent sections present a series of complementary research studies. In a field study, we analyze how grocery shoppers used nutrition information for three food categories, both before and after the NLEA took effect. Next, in a lab experiment, we create different levels of knowledge and motivation in consumers and then ask them to shop for cereal with either the old or the new labels. In our third study, we analyze longitudinal data extracted from scanner databases to assess the NLEA's impact across a longer period of time than the previous two studies. Our fourth study uses focus groups to examine consumers' views about food labels. Finally, we discuss implications for public policy, management practice, academic research, and consumer welfare.
Search, Recall, and Choice
We compare three aspects of in-store behavior by studying three different variables: search intensity, recall efficiency, and food choices. Search intensity refers to the degree of attention and effort consumers direct toward obtaining information about the specific purchase under consideration. We examine whether the NLEA's introduction increased the intensity of consumers' overall search for nutrition information. We also measure recall efficiency for several reasons: First, when assessed immediately after brand selection, recall accuracy is a good measure of how well point-of-purchase information has been evaluated and integrated into memory (Dickson and Sawyer 1990). Second, recall performance on a specific nutrition attribute indicates how important consumers perceive that attribute to be. Third, by adjusting recall performance for search intensity, we can determine if the new label increases the efficiency with which people acquire information. In addition, we assess whether the abundant post-NLEA nutrition information has prompted consumers to choose brands that have desirable nutritional characteristics. By studying sales patterns, we can detect whether consumers' preferences for foods with nutritionally desirable characteristics have changed with the enforcement of the NLEA.
The Impact of the NLEA
The NLEA may not only stimulate consumers to spend more time acquiring nutrition information but also increase the efficiency with which they process it. The post-NLEA labels provide much information in an easy-to-use format (Levy, Fein, and Schucker 1996). For example, the percent Daily Values (%DV) in these labels facilitate comparisons across and within brands. In addition, the NLEA increased the proportion of packaged foods that display nutrition information from approximately 60% to almost 100%. By encouraging nutrition education, the NLEA may also increase the perceived benefits of using nutrition information. Thus, the NLEA may increase consumers' performance on our research variables.
Conversely, it is possible that the NLEA will produce an opposite effect on these variables, because it allows manufacturers to make health claims about diet-disease linkages on food packages. Although consumers can inspect health claim information and the Nutrition Facts panel and integrate the two, recent evidence indicates that they may rely on easily visible nutrition claims and ignore the Nutrition Facts panel (Roe, Levy, and Derby 1999). More important, the strict nutrition regulations in the post-NLEA regime may reduce consumers' urge to verify claims by inspecting the Nutrition Facts panel. Thus, if consumers neglect to gather information from the new food labels, their performance on our research variables may decline.
Both sets of arguments advanced previously appear plausible, which makes it difficult to hypothesize the general direction of the NLEA's impact. Nevertheless, this empirical issue is important for researchers, public policymakers, and practitioners, so we frame it as a research question (RQ):
RQ: Compared with the pre-NLEA period, did the following change in the post-NLEA era: (a) search intensity for nutrition information, (b) recall efficiency for specific nutrition attributes, and (c) choice?
The NLEA, Consumer Characteristics, and Nutrition Information Use
The current model (see Figure 1, Panel A) depicts our hypotheses. It features four consumer characteristics (micro factors) that affect how much consumers search for and how efficiently they process nutrition information. Specifically, we incorporate motivation and knowledge because they emerge as important facilitators in most models of informa --tion search and processing. In addition, we include brand loyalty and perceived similarity of brands (along nutrition dimensions) because they serve as perceptual screens that limit search. Although these last variables previously have been identified as important (Putrevu and Ratchford 1997; Urbany, Dickson, and Kalapurakal 1996), they have yet to be incorporated into a model of nutrition-related behavior. The model indicates that the likelihood that consumers will make nutritionally desirable food choices reflects not only how much information they seek out but also the environment (macro factors such as the NLEA legislation) that directly affects the information made available to them. In this model, the NLEA factor interacts with the micro factors (depicted schematically by the curved arrow in Figure 1, Panel A); the dashed arrows represent links that are not addressed in our study.
Attribute-level impact. Logically, consumers might expect to be interested in foods that have ( 1) lower or no negative nutrition attributes and ( 2) higher positive nutrition attributes. A negative attribute is a nutritional characteristic that should be reduced, such as fat or sodium, whereas a positive nutrition attribute is a nutritional characteristic that should be increased,suchascalciumorvitamins.Severalreasonsunderlieconsumers'tendency to focus more attention on negative attributes than positive ones. They may ascribe greater information diagnosticity to negative than positive attributes (Burton, Garretson, and Velliquette 1999; Garretson and Burton 2000). Also, consumers may realize that a dietary supplement can supply positive nutrition attributes that are missing from their diet, but no pill can effectively subtract negative attributes (Russo etal. 1986).The emphasis on negative attributes is also compatible with prospect theory: People overweigh attributes associated with losses rather than those associated with gains (Tversky and Kahneman 1981).
The NLEA is likely to accelerate this bias toward negative attributes. First, health claims allowed under NLEA guidelines (that associate specific nutrients with reduced risk of specific diseases) reinforce the bias. Of the seven health claims approved by the Food and Drug Administration (FDA) at the NLEA's onset, three link negative attributes exclusively with deadly diseases (i.e., dietary fat and cancer, sodium and hypertension, and dietary saturated fat and high cholesterol and heart disease), and only one claim features a positive attribute (calcium and osteoporosis). The remaining claims showcase the combined role of several positive and negative attributes (21 C.F.R. 101.72 to 101.78). Second, NLEA regulations on nutrient content claims focus more on negative attributes (calories, sugar, sodium, fat, fatty acid, and cholesterol) than on positive attributes such as fiber (21 C.F.R. 101.54 to 101.62; 105.56). More important, because they are regulated, both health and content claims appear more credible to post-NLEA consumers. The preceding reasons, when combined with consumers' predisposition to attend more to negative attributes, may steer food manufacturers toward increasing their emphasis on negative attributes as they market their products. Under the circumstances, consumers could absorb more information about negative attributes and therefore be more likely to select brands that tout attractive levels of negative attributes in the post-NLEA era than in the pre-NLEA era.
H1: Compared with the pre-NLEA era, consumers in the post-NLEA era will increase search intensity, recall efficiency, and choice for negative nutrition attributes more than they change behavior related to positive nutrition attributes.
Motivation to process nutrition information. Motivation, defined as consumers'goal-directed arousal to process nutrition information, increases the effort devoted to search and encode such information (Moorman 1996). Highly motivated consumers will search more intensely and attain greater recall efficiency than will less motivated consumers. However, an intervention such as the NLEA may weaken this relationship in socially beneficial ways.
Specifically, the NLEA may increase how much less motivated consumers search by lowering consumers' search costs and prompting manufacturers to introduce interesting foods with desirable nutritional characteristics. According to Rothschild (1999), offering low-motivation consumers a broad set of interesting alternatives decreases motivation's influence on behavior. Thus, when food manufacturers responded to the NLEA by improving the nutrition quality of ( 1) existing brands by adding positive nutrients and ( 2) brand extensions by deleting negative nutrients (Moorman 1998), even low-motivation consumers may have discovered that it was relatively easy to eat healthfully. In other words, we predict that motivation will have a less important influence on both search intensity and recall efficiency in the post-NLEA era. This is a desirable outcome of the NLEA because it implies that the search and recall performance of less motivated consumers has become more similar to those who are highly motivated.
H2: Compared with the pre-NLEA era, consumers' motivation in the post-NLEA era will be a less important determinant of (a) search intensity and (b) recall efficiency.
Nutrition knowledge. Consistent with Moorman (1996), we propose that nutrition knowledge affects recall performance rather than search intensity. For a given level of search effort, high-knowledge consumers will recall nutrition attributes better because they can better interpret, retain, and access domain-related information. However, the easy-to-comprehend nutrition information available on post-NLEA food labels may have weakened the relationship between nutrition knowledge and recall efficiency in socially desirable ways. For example, consumers with low nutrition knowledge are unlikely to absorb unit-specific attribute information readily (e.g., two grams of fiber/serving) from the old food label. In contrast, the unit-free %DV value in the new food label may help them grasp such information quickly by placing it in the larger and more meaningful context of standard daily fiber intake (see the supporting evidence by Levy, Fein, and Schucker[1996]). Recall efficiency of low-knowledge consumers could therefore increase when they use the new label. This increase is less likely to occur for high-knowledge consumers, because they interpret label information easily regardless of how it is presented.
H3: Compared with the pre-NLEA era, consumers' knowledge in the post-NLEA era is a less important determinant of recall efficiency.
Perceived similarity of brands (on nutrition content). When evaluating alternatives on several dimensions, consumers may ignore a dimension if all alternatives appear equivalent on that dimension. As rival brands become more similar on key attributes, the perceived benefits from search diminish progressively (Urbany, Dickson, and Kalapurakal 1996). Thus, the greater the perceived similarity across alternatives, the lower is the effort invested in information search.
Intuitively, if a shopper is confident that brands in a given food category are nutritionally similar, he or she is less likely to search and compare brands. Because the NLEA standardized serving sizes and introduced %DV information, post-NLEA shoppers can compare brands more easily and form judgments about nutritional similarity with greater confidence. Therefore, the relationship between perceived similarity of nutrition content(across brands in a category) and search effort will be stronger and more negative in the post-NLEA period.
H4: Compared with the pre-NLEA era, in the post-NLEA era the relationship between perceived similarity across brands and search intensity will be more negative.
Brand loyalty. Szykman, Bloom, and Levy (1997) suggest that experiential prior knowledge may discourage preventive health behavior. In the pre-NLEA era, brand loyalty may act as experiential prior knowledge that reduces consumers' in-store search for nutrition information. People behave similarly for price information: Brand-loyal shop-pers who know a lot about their preferred product engage in little in-store price search (Putrevu and Ratchford 1997). In contrast, in the post-NLEA environment, the relatively information-insensitive brand-loyal consumers could initially seek out new information for their preferred brands. When the novelty of the new information wears off, brand-loyal consumers may again stop searching. Initially, though, the NLEA may attenuate the negative relationship between brand loyalty and search intensity.
H5: Compared with the pre-NLEA era, in the post-NLEA era the negative relationship between brand loyalty and search intensity will be weaker.
Table 1 summarizes the research question and five hypotheses that guided our research.
Overview
To assess the impact of NLEA-mandated labels on consumers' search for nutrition information, we observed shoppers in grocery stores both before (early 1993) and after (late 1994) the onset of the new food labels. Participants included 337 randomly selected shoppers in three chain grocery stores in a town located in the U.S. Midwest. We distributed data-gathering occasions evenly across stores, time of day, and day of the week. Trained observers watched, inter-viewed, and paid all participants $1.00 each.
Measures
Consistent with prior in-store research (Cole and Balasubramanian 1993; Dickson and Sawyer 1990), we positioned trained observers in grocery store aisles for three product categories: breakfast cereal, crackers, and packaged bread. Unobtrusively, the observer recorded search intensity (PAN-TIME), measured as the time in seconds subjects devoted to the Nutrition Facts panel of the first brand chosen. Given the in-store stacking arrangement for packaged foods, this panel, which is located on the side of package, is not observable unless the product is removed from the shelf. By restricting our focus to packages that were picked up by consumers, PANTIME excluded search activities unrelated to the nutrition panel. PANTIME possessed high reliability (interobserver agreement: 92%) and validity (correlated negatively with recall error).
The observer then solicited the consumer's participation in a survey. Most consumers contacted (80.4%) agreed. They initially responded to scales that measured independent variables: motivation (MOTIVPRO), a four-item scale to measure motivation to process nutrition information (e.g., "Today, I was interested in looking at the nutrition information on the cereal package"; alpha = .82); knowledge (KNOWLEDGE), a two-item scale to measure nutrition knowledge (e.g., "I am knowledgeable about the nutrition aspect of cereal"; r = .86); brand loyalty (BRANDLOY), a two-item scale (Cole and Balasubramanian 1993; r = .78) to assess loyalty toward the chosen brand; and perceived nutritional similarity of brands (NUTRISIM), a single-item scale (e.g., "All breakfast cereals are similar on nutritional content."). Data collection also included several background variables: category familiarity (CATEGFAM), purchase frequency (PURCHREG), health status (HLTHSTAT), consumption frequency (OFTENEAT), age (AGE), and education (EDUC).
For the brand they had just chosen, subjects were asked to recall the content value per serving for each of several nutrition attributes; the observer later recorded actual values for these attributes from a package of the selected brand. Following prior research (Dickson and Sawyer 1990), we derived the respondent's attribute-specific relative recall error (RRER) for sodium, potassium, protein, calories, cholesterol, fat, and fiber as the absolute of [(actual attribute value - recalled attribute value) ### 100/(actual attribute value)]. Finally, we computed a respondent-specific recall error index (REI) to capture the overall difference between recall errors for key negative and positive attributes as follows: [(RRERCalories + RRERFat) - (RRERProtein + RRERFiber)]/(RRERCalories + RRERFat + RRERProtein + RRERFiber). We excluded sodium, cholesterol, and potassium from REI because some respondents confused these milligram-denominated attributes as gram-denominated ones, thereby inflating recall errors unreasonably. The REI ranges between -1 and +1, where a value of -1 (+1) indicates that positive (negative) nutrition attributes mainly contribute to the consumer's total recall error.
Analyses and Results
RQa and RQ b. Within each food category, we first conducted t-tests that compared the pre-and post-NLEA respondent groups on background variables. To investigate RQa, we conducted analyses of covariance in each food category with PANTIME, our measure of search intensity as the dependent variable, and an NLEA dummy (pre-NLEA = 0, post-NLEA = 1) as the independent factor. Background variables that reflected significant differences between preand post-NLEA respondents were entered as covariates. For all three categories, the analysis of covariance results indicated that the NLEA had no statistical effect on PANTIME (breakfast cereal: pre-versus post-NLEA: .50 versus .44, F1,112 = .01, p < .93; crackers: pre-versus post-NLEA: 1.75 versus .94, F1,102 = .26, p < .61; packaged bread: pre-versus post-NLEA: .00 versus .77, F 1,106 = 2.49, p < .12).
To study RQ b, we estimated regression models for each product category, with RRER for various nutrition attributes as dependent variables and with the NLEA dummy, PAN-TIME, and other background variables as independent variables. Standard regression diagnostics pointed to a variancestabilizing transformation or weighted least squares (Neter and Wasserman 1974), with all models estimated after weighting by 1/[CATEGFAM]2. Although we attempted to collect recall data on several nutrition attributes in each category, data were unavailable for all attributes in both the preand the post-NLEA era. Given this constraint, only eight weighted regressions could be estimated. The NLEA dummy was not significant in seven of these eight regression equations. That is, only one regression (with RRERCalories as a dependent variable for the crackers data) indicated a significant increase in recall efficiency (p < .00) after the onset of the NLEA. Overall, therefore, we did not detect much evidence that the new food labels significantly changed either the intensity of consumers'search for nutrition information or their recall efficiency.
H[SUB 1]. We tested H1 using a weighted regression model (weight = 1/[NUTRISIM] 2) on data pooled across the three product categories. The dependent variable was REI, and the NLEA dummy, NUTRISIM, and HLTHSTAT were independent variables (see the left-hand part of Table 2). The negative and significant coefficient for the NLEA dummy reflects a greater post-NLEA decrease in recall errors for negative than for positive nutrition attributes, thus supporting H1. A different operationalization of REI (using only calories and protein, the top negative and positive attributes, respectively) yielded more observations for analysis but did not materially alter the results.
H[SUB 2] through H[SUB 5]. To test the hypotheses predicting inter-action effects between the NLEA dummy and motivation, perceived similarity, and brand loyalty, we first ran a regression using PANTIME as the dependent variable (see the right-hand part of Table 2). Contrary to H2a, the NLEA variable did not interact with motivation. Consistent with H4, there was a significant interaction between nutrition similarity (NUTRISIM) and the NLEA dummy on PANTIME (b = -.71, p < .05). Using Cohen and Cohen's (1983) approach to analyze this interaction, we found that as the hypothesis predicted, the impact of NUTRISIM on PANTIME was more negative in the post-NLEA era (b = -.096) than in the pre-NLEA era (b = .279). Consistent with H5, the interaction between brand loyalty (BRANDLOY) and the NLEA dummy on PANTIME was also significant (b = .55, p < .05). As predicted by this hypothesis, the impact of BRANDLOY on PANTIME was more negative in the pre-NLEA era (b = -.310) than in the post-NLEA era (b = .009).
To be consistent with Moorman (1996), and because fat consistently ranks high among consumers' nutrition concerns, we used RRERFat as the dependent variable in a second weighted regression analysis (weight = 1/[CATEGFAM]2). Contrary to H2b and H3, the NLEA dummy did not significantly interact with motivation or knowledge.
The evidence did not support the view that the NLEA changed search intensity, defined as the time devoted to the Nutrition Facts panel. This finding supplements results from a prior study (Moorman 1996), which reported that the NLEA increased nutrition information acquisition, measured as elapsed time between the consumer accessing the first brand in the category and making the final brand choice divided by the number of brands purchased. Given the different measures of information search in the two studies, a plausible integrative insight emerges: The NLEA may have increased attention to nutrition information found outside the Nutrition Facts panel on food packages, such as nutrition claims or descriptor terms such as "low fat." This is consistent with the findings of Roe, Levy, and Derby (1999) who observe that respondents who truncate their search or view claims allocate greater weight to information in claims than to the information in the Nutrition Facts panel.
Our work also shows that the NLEA did not change recall efficiency for most nutrients. Such attribute-specific information is available only in the Nutrition Facts panel, so the poor recall results suggest that people do not consult this panel much. When they do, evidence indicates that they are influenced more by negative than positive nutrients.
We found interactions involving the NLEA and two consumer characteristics: perceived nutritional similarity and brand loyalty. In the post-NLEA era, there is a stronger negative relationship between the perceived nutritional similarity of brands and consumers' search for nutrition information. The post-NLEA era provides better access to nutrition information than ever before, so consumers are likely more confident about judgments of nutrition similarity across brands. Our findings also show that brand loyalty is less negatively related to search intensity in the post-NLEA period, though this pattern may hold only as long as the information about the preferred brand remains new.
Our remaining studies overcome several limitations. First, we may not have detected changes in consumer behavior because of a lack of awareness about NLEA-mandated changes in food labels. In line with other surveys (e.g., Silverglade 1996), only 65% of our subjects in the post-NLEA sample were aware of the new food labels. Our longitudinal scanner data analysis (reported subsequently) overcomes this limitation by studying purchase behaviors over extended periods. Second, in the field setting, we were unable to track search intensity for positive and negative attributes. Third, the lack of interaction effects for motivation and knowledge with the NLEA are tempered by the finding that many field study respondents had low scores on our motivation and knowledge measures. These limitations motivated our laboratory experiment, described next.
Overview
In this experiment, we manipulate three factors: knowledge about nutrition information, motivation to process this information, and the nutrition label format (pre-NLEA versus post-NLEA) to investigate the RQ, H1, H2, and H3. Because the computer recorded the specific information that consumers inspected during a shopping task, we could examine whether the post-NLEA labels changed the type of information our subjects used.
Design, Procedure, and Independent Variables
We used a 2 (knowledge) x 2 (motivation) x 2 (label format) between-subjects design. We randomly assigned 190 students at a major university to one of several computers in a research lab (and thereby to one of the experimental treatments) equipped to run the Search Monitor program (Brucks 1988). Subjects completed a short questionnaire assessing their familiarity with a practice product and breakfast cereals. They read a handout describing the information accessible on the computer for each product. To make the shopping task realistic, we announced that 20% of the participants would receive a free sample of the selected practice product and selected breakfast cereal. Then they completed the shopping task for each product and, in a postchoice survey, recalled the fat content per serving of the cereal they selected.
We manipulated knowledge about nutrition information through education. One-half of the subjects (high-knowledge condition) studied an informative brochure on the topic, which was adapted from FDA (1994) publications. To manipulate motivation to process nutrition information, we instructed high-motivation subjects to follow a physician's recommendation: Select a cereal that is low in fat, sodium, and cholesterol. By using the adjective "low" (instead of, say, a ceiling criterion such as less than three grams of fat per serving), we encouraged information search rather than satisficing behavior. Low-motivation subjects did not receive this instruction. We manipulated label format by making information about each of 12 cereal brands accessible on the computer to subjects in the format of either the old or the new nutrition label. The attribute values, obtained from actual brands, were the same in both label conditions. We also used brand names because they provide important information.
Dependent Variables
All measures were derived from computer records of subjects' search activities. As in our field study, we measured RRERFat from the postchoice survey and PANTIME as the time spent to inspect the nutrition panel for the brand selected. In addition, we obtained usage measures for specific attributes, calculated as the percentage of search requests devoted to brand name (BRND%), nutrition claims (NC%), serving size (SS%), and information from the nutrition panel (PAN%). The use of percentages facilitates relative comparisons in search effort across nutrition attributes and helps control for individual differences in the extent of search. Finally, we calculated the CAPRO% index using one negative attribute (calories) and one positive attribute (protein) as follows: (number of search requests about calories - number of requests about proteins) x 100/(total number of search requests). We excluded other attributes from the numerator because the motivation manipulation focused on those attributes.
Results
Manipulation checks. We assessed knowledge with a 12-item multiple-choice test about nutrition. High-knowledge subjects obtained significantly higher test scores than low-knowledge subjects (low versus high knowledge: 4.52 versus 7.18, F1,145 = 108.2, p < .01). Consistent with others who manipulated knowledge in experimental settings, we excluded from our remaining analyses high-knowledge participants who scored below the median on the knowledge test as well as low-knowledge participants who scored above the median on the same test.
To check the effectiveness of the motivation manipulation, we compared high-and low-motivation subjects on the percentage of information accessed about the three physician-specified attributes. Consistent with the manipulation, high-motivation subjects accessed a higher percentage of the three physician-specified attributes than did low-motivation subjects (F1,138 = 25.53, p < .01).
Effects of new labels. With respect to RQ, we found that the new food label format neither increased the amount of time spent inspecting information contained in nutrition panels nor affected recall efficiency (PANTIME: F1,132 = .58, p < .45; RRERFat controlled for PANTIME: F1,132 = .93, p < .33). Both these findings are consistent with the field study. For PANTIME, a significant knowledge ### motivation interaction (F1,132 = 3.74, p < .05) indicated that high-knowledge subjects spent the same amount of time inspecting information contained in nutrition panels (approximately 33 seconds) regardless of motivation levels, whereas low-knowledge subjects spent more time when their motivation levels increased (from 27 to 55 seconds).
H1, H2, and H3. Consistent with H 1, we found a significant label effect: With the new label, there was a larger relative difference between search activity devoted to negative and positive attributes than with the old label (CAPRO% index: new versus old label 2.15% versus .68%, F1,136 = 6.36, p < .02). Contrary to H2 and H3, but again consistent with the field study, we did not find any significant interactions between label format and motivation on PANTIME or RRERFat, nor was the interaction between the label and knowledge on RRERFat statistically significant.
However, other interactions suggest that the new label changed the relationships between motivation and the type of information used. First, the significant label x motivation interaction (F1,136 = 6.0, p < .02) on NC% occurred because in the old label condition, motivation influenced nutrition claim usage: High-motivation consumers used nutrition claim information more than low-motivation consumers (HM 16% versus LM 3%, t = 2.87, p < .01). In the new label condition, motivation did not affect nutrition claim usage, which averaged approximately 4% for both motivation groups (t = .90, p < .30). Second, we found a significant three-way interaction among motivation, knowledge, and label format on SS% (F1,136 = 4.21, p < .04). This interaction occurred, in part, because of a significant effect of motivation on SS% under the old label (HM 2.1% versus LM .3%, t = 2.78, p < .05) and a nonsignificant effect of motivation on SS% under the new label (HM 1.4% versus LM .2%, t = 1.41, p < .16). Third, a marginally significant label motivation interaction on PAN% (F1,136 = 2.8, p < .1) indicates that the impact of motivation on PAN% is stronger with the new label than with the old label (new label: HM 55% versus LM 25%, t = 3.9, p < .01; old label: HM 38% versus LM 28%, t = 1.54, p < .12).
Summary
In both our field study and lab experiment, we did not detect any effect of the NLEA-mandated change in the food label on ( 1) the relationships between motivation and search intensity and between motivation and recall efficiency or ( 2) the relationship between knowledge and recall efficiency. However, we did find that consumers rely more on negative than positive attributes. These consistencies emerged regardless of whether motivation was measured or manipulated and whether knowledge was assessed through self-report or quiz scores.
Our findings offer new insights about how the new food label altered the relationship between motivation and the type of information inspected. On the one hand, high-motivation consumers using the new label (compared with those using the old label) shifted focus away from nutrition claims and serving sizes toward the nutrition panel. This behavior was particularly pronounced for low-knowledge consumers. On the other hand, low-motivation consumers, who were relatively insensitive to label format, relied mostly on brand names. From a social welfare perspective, we conclude that in the post-NLEA era, at least one group (high-motivation, low-knowledge consumers) depended less on nutrition claims, benefited more from standardized serving sizes, and devoted a larger proportion of search effort to the nutrition panel.
Overview
To develop additional insights about the NLEA, we analyze changes in longitudinal sales data. Such a study has several advantages over the previous ones. First, people's food choices may be a more useful gauge of their nutrition-related concerns than is search or recall. Second, analyzing sales trends across a relatively long time horizon may provide insights about NLEA that are unavailable from comparisons of data collected immediately before and after the effective date of NLEA. Third, instead of relying on the Nutrition Facts panel, post-NLEA consumers may use descriptor nutrition information, such as "low fat," "low sodium," and "calcium added," that may appear on food packages. As discussed during the development of H1, products featuring descriptor terms in the post-NLEA period needed to conform to prescribed NLEA guidelines. Such products often represent line extensions, and the corresponding descriptor is effectively integrated into the brand name for display purposes (e.g., Keebler Hydrox Reduced Fat Cookies). For such foods, the descriptor is prominently featured on the front package panel to aid prepurchase exposure (in contrast, the Nutrition Facts information appears on the side panel). Within a given category, comparing the relative sales performance of products with and without a specific set of descriptors across pre-and post-NLEA periods may shed new light on the influence of nutrition information outside the Nutrition Facts panel. Finally, by studying descriptors across multiple food categories, we can assess the generalizability of results.
Data and Model Characteristics
To address the preceding research issues, we analyzed longitudinal data on variables derived from scanner databases for several packaged food categories. The database in any given category comprised sales transactions at each of several store locations in a large city's major grocery chain over an extended period (September 14, 1989, through May 14, 1997).
Within each category, we initially examined universal product code (UPC)-level product descriptions to identify descriptors of potential research interest. Note that NLEA regulations (21 C.F.R.) allow a choice of several descriptors for a given nutrient. With respect to fat, the set of available descriptors includes "low fat," "reduced fat," and "fat free"; for calories, applicable descriptors range from "light," "lite," and "diet" to "low calorie." To qualify for the use of a descriptor, a food must satisfy stringent content criteria on the nutrient associated with that descriptor. Given our interest in studying the NLEA's impact on consumers' choice for foods with healthy characteristics (i.e., more [less] of a positive [negative] nutrient), we compiled a list of UPCs representing healthful levels of each nutrient in each food category in the database. For sodium-healthy canned soups, this included soups associated with one of two descriptors ("low sodium" and "lower salt") that represent nutritionally attractive sodium levels.
Sales transactions were aggregated across stores to derive the weekly category share for the healthy UPCs associated with a given nutrient. We excluded from analysis any descriptor/category combination in which ( 1) classification problems stemming from incomplete product descriptions at the UPC level could not be resolved after consultations with food manufacturers and/or retailers, ( 2) data were unavailable in both the pre-and post-NLEA eras, or ( 3) any string of missing data exceeded ten contiguous weeks. We then estimated the following regression model for each descriptor/category combination available for analysis:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Results
Table 3 summarizes regression results for eight descriptor set/category combinations, organized by the valence of the nutrient in the descriptor (positive or negative). It is useful to compare results for descriptors featuring positive nutrition attributes (vitamin C and calcium; see the first two rows in Table 3) with the next three rows, which highlight negative nutrients (sodium-healthy and fat-healthy). The R-squared values indicate a much better model fit for the latter. For descriptors with positive nutrition attributes, the estimate for the NLEA dummy indicates either a decline in post-NLEA category share (vitamin C-added bottled juices) or no impact on post-NLEA category share (calcium-added refrigerated juices). In contrast, the corresponding estimates for models in the next three rows reflect an increase in post-NLEA category share. Interpretively, post-NLEA consumers increased purchases of descriptor sets featuring negative nutrients (for a visual overview of the category share data for sodium-healthy UPCs, see Figure 2); however, their purchases of descriptor sets featuring positive nutrients either decreased or remained unchanged after the onset of the NLEA. This pattern of results lends support to H1 from a choice perspective.
Nevertheless, an important difference emerges when we compare the preceding results for fat-healthy and sodium-healthy descriptors with the next three rows, which feature calorie-healthy foods. Although all six regression models involve a negative nutrient, those featuring calorie-healthy foods consistently show a negative estimate for the NLEA dummy; that is, the relative sales performance of such items decreased after the onset of NLEA. The longitudinal time-series analysis detected this post-NLEA decline in the appeal of calorie-healthy descriptors compared with other descriptors. Post-NLEA consumers may find it more attractive to consume fat-healthy foods, which afford control over both fat and calorie intake, than low-calorie foods, which afford control over calorie intake only. In addition, consumers may have increased attention to fat and lowered attention to calories because NLEA mandated that, where applicable, calorie information on the label must be presented in the context of fat (i.e., calories from fat). The NLEA also allows this calorie information to be linked to saturated fat (i.e., calories from saturated fat). Thus, consumers can manage calories efficiently by attending to fat content and without pursuing low-calorie foods. Moreover, although the NLEA allows health claims on several negative attributes, calories represent a negative attribute that is not featured in an allowable health claim under NLEA. In conclusion, with regard to RQc, our results indicate a post-NLEA increase in category share of fat-healthy cheese and cookies and sodium-healthy canned soups.
Summary
Our analyses of scanner data show that the onset of the NLEA is associated with a change in consumer food choices, unlike the results involving search and recall variables. The nature of this change depended on the valence of the nutrient involved (with the exception of calories).
Some methodological and data limitations remain. Our models do not incorporate explanatory variables such as price or other types of deals. Although our models were estimated on data aggregated across all stores, these variables are more appropriately modeled at a disaggregate level of analysis. We tried to analyze the data within the constraints imposed by the pervasive problem of missing data. Moreover, the data analyzed pertain to stores within one grocery chain in a major city. So insights about the total market (i.e., including competing store chains) are precluded by the nature of the data.
Our research efforts thus far broadly focused on what nutrition attributes consumers explore; the analyses supporting our hypotheses highlighted several reasons they might do this. Because these reasons are largely inferential (they were deduced from quantitative analyses), it is useful to seek qualitative insights from consumers directly about when and why they use (or do not use) nutrition information. To do this, we conducted a series of focus groups.
This section integrates findings from six focus groups. Each focus group was limited to approximately eight primary food shoppers. To facilitate the comparison of comments across focus groups, the moderators used a common set of discussion questions for each group but allowed minor variations to accommodate unique member characteristics. In addition, the moderators structured the discussion to ensure active involvement of participants and a clear topic emphasis on nutrition labels. Each participant received $10.00. Five focus groups were conducted in a medium-sized university town (identified in protocols as City A). Participants included 35 women and 5 men, representing university staff members or spouses of students. Another focus group, in a different university town, included 6 women and 2 men (identified in protocols as City B) who responded to a newspaper advertisement soliciting participation from community members.
Method and Results
Two coders classified statements in focus group transcripts into several categories (intercoder reliability was 83%). Our discussion organizes participants' comments around three questions: ( 1) When/why do consumers use food labels? ( 2) When/why do they not use labels? and ( 3) What tradeoffs do they perceive between using and not using nutrition information?
When/whydo consumers use food labels? Several participants reported that they began using nutrition information after becoming more health conscious. A representative observation follows: "Lately I've been watching what I eat. So I've been fixing more, watching fat gram intake, and try to stay away from fatty high-calorie foods" (woman, City A). When asked what prompted them to become health conscious, many participants reported a concern about weight gain: "I became health conscious when I gained extra weight. It was part of a diet-exercise routine" (woman, City B).
Because of this weight consciousness, shoppers limit attention to a few negative nutrients: "The only thing I really notice is the total fat grams and the calories.... I'm just starting to pay attention to sodium" (woman, City A). The attention bias toward negative nutrition attributes in these statements about post-NLEA shopping behavior is consistent with H1.
When/why do consumers not use food labels? Consumers do not consider nutrition when shopping for foods described as "fun" or "bad." Typical comments follow: "If I think it's 'fun,' I don't look at nutrition" (woman, City A). "The only time I probably don't care is when I've decided that I'm going to have something really 'bad.' I don't give a hoot what it's got in it or how much it costs" (woman, City A). "If I want a candy bar anyway, a couple grams of fat isn't going to make a difference and I'll just get the kind that I like. But, if I were looking at cereal, I would look for something that was lower in fat" (woman, City A). Consumers avoid search on a nutrition attribute if the alternatives considered do not vary on that dimension: "[The search] depends on what you're buying. Soup is soup. Even healthy soup is salty" (woman, City B).
Consumers may not use the information in food labels because of skepticism toward several aspects of the new label: claims that are hard to verify, serving size information, and disclosure of information that is not meaningful. Regarding the claims that were hard to verify, "If they say 'no' cholesterol, 'no' fat, it's pretty easy to tell (from the label) whether it is or not. But with 'lower' or 'light,' it's hard to tell" (woman, City A). Several participants conveyed their distrust of serving size: "Serving sizes are a joke" (woman, City B). Others noted healthful claims on products they considered unhealthy (disclosure of nonmeaningful information): "Like salad dressing. It may say 'low fat,' but it's not especially good for you anyway" (woman, City A). Some focus group members complained about nonpackaged foods without labels: "My husband likes the store's low-fat potato salad; it's easy for me, but there isn't any information" (woman, City A).
What are the trade-offs? Nutrition versus budget versus taste constraints. Grocery shoppers act as purchasing agents for their households. This role imposes significant budget and taste constraints on food choices that potentially under-mine nutrition value. Some participants mentioned a price/nutrition trade-off: "But I don't think I would pay extra for something that was more nutritious" (woman, City A). Other participants described a taste/nutrition trade-off: "[Nutrition content] doesn't matter because if it doesn't taste good, you're not going to buy it again" (woman, City A). "I look for the low-sodium stuff too, as well as low fat.... Of course, [my husband and children] are so finicky, it's hard to get anything in them sometimes" (woman, City A).
Summary
Four themes emerged from the focus groups: First, consumers'search for nutrition information in a given food category depends on how they perceive that category. Consumers may ignore nutrition information for fun foods such as candy because these foods meet hedonistic (as opposed to health-related) needs. Shoppers may use a variation of the "psychophysics of price" heuristic (Grewal and Marmorstein 1994) in matters related to nutrition. That is, they may be willing to search more for some foods because of a nutritionally desirable payoff (e.g., compare cereal brands to save three grams of fat per serving). But for other fun foods--say, cheesecake--consumers may be unwilling to undertake a similar search effort for a larger payoff (e.g., to save ten grams of fat per serving). In such cases, participants believed that time and other costs (such as loss of taste) do not justify a search for better nutrition value. Second, consumers who are motivated by a desire to lose weight may limit attention to a few negative nutrition attributes. Third, we found a general distrust for information such as serving sizes and hard-to-verify nutrition claims that appeared on food packages. Remarkably, the distrust was not attenuated even after the moderator noted that the NLEA had standardized serving sizes and nutrition claims. Fourth, food shoppers view themselves as purchasing agents responsible for managing constraints imposed by the budget, taste preferences, and nutrition needs of their families.
Have the benefits of the NLEA outweighed the costs incurred by the government, food manufacturers, and consumers? Although the implementation costs were substantial, our research indicates that only a few benefits have been realized. In response to the first query raised in the introductory section of the article, we did not detect any general effect by the NLEA on ( 1) consumers' search for information from the Nutrition Facts panel or ( 2) their efficiency in processing that information. But we found evidence that the onset of the NLEA has increased the sensitivity of consumers' search, recall, and choice activities to negative nutrients compared with positive nutrients. More specifically, in response to the second query asked previously, inthe post-NLEA era, we found an increase in consumers' sensitivity to some negative nutrition attributes, such as sodium and fat, but not to calories. Regarding calories, post-NLEA consumers may consume low-fat foods to manage both fat and calorie intake rather than consume low-calorie foods to manage just calorie intake. Finally, regarding the third query, in the laboratory study we found that one group (low motivation, low-knowledge consumers) benefited in socially desirable ways under the new food labels.
We organize our discussion around ways that public policy and management practice can help achieve the key normative nutrition goals listed in Table 4. Goal 1 in Table 4 represents the key objective of the NLEA: to promote healthy dietary practice through nutritionally wise food choices. Goals 2, 3, and 4 stem from the widespread recognition that unless a consumer's attention to nutrition includes all foods, all eating occasions, and all nutrients, efforts to control dietary intake will remain ineffective (Scarbrough 1995). Goal 5 emphasizes the importance of having all consumers focus on the benefits of nutrition information processing. Unfortunately, our research suggests that progress toward these goals is slow: Consumers possess a greater predisposition to attend to negative nutrition attributes over positive ones, and they attend more to nutrition in certain food categories. Moreover, it is unlikely that access to NLEA-mandated information is available over all eating occasions (consumers may not have the same degree of nutrition information access when eating at home and when dining in a restaurant), so consumers may attend to nutrition on limited occasions. Finally, from the NLEA perspective, results appear mixed with regard to Goal 5. Our lab study indicates that only some consumers benefited from the new labels. Nevertheless, our analyses of people's food choices in selected categories over several years before and after the onset of NLEA suggest a growing realization that ( 1) some nutrients (fat and sodium) are more important than others (calories) and ( 2) desirable outcomes associated with one nutrient (calorie reduction) can be by-products of managing another nutrient (fat). Many of our proposed remedies rely on a simple message from cost/benefit models of information search; that is, search outcomes can be improved by decreasing search costs or by enhancing search benefits.
Public Policy Challenges and Remedies
To improve progress toward these goals, public policy officials can increase education and increase availability of information.
Education. Our focus group participants reported that their attention to nutrition is not uniform for all foods consumed. New education initiatives should emphasize the dysfunctional consequences of not focusing on all the foods consumed. For example, Ippolito and Mathios (1994) describe an impressive decline in fat consumption between 1977 and 1985 in the meat category, based on national food consumption surveys. However, a large part of this reduction in fat intake was lost because of increased fat consumption in other categories. A converging result across our studies is that the onset of the NLEA has increased consumers' sensitivity to negative nutrients. The analysis of category share of healthy foods from our scanner database indicates that in the post-NLEA era, sales of brands touting descriptor terms for positive nutrients decreased or remained unaffected; at the same time, post-NLEA sales of brands featuring descriptors for negative nutrients (except calories) increased significantly. It is plausible that the NLEA increased consumers' awareness and knowledge about diet-disease links involving a dis-proportionate number of negative nutrients. Other literature suggests that consumers who possess knowledge about diet-disease links use packaged goods nutrition information (Andrews, Netemeyer, and Burton 1998; Szykman, Bloom, and Levy 1997). As a result, to encourage consumers to direct their attention to all nutrients, more information should be made available about diet-disease relationships involving other nutrients. In addition, education efforts could encourage consumers to integrate information contained in brand names and claims with Nutrition Facts panel information, a task that prior research shows consumers can perform but often fail to (Ford et al. 1996; Garretson and Burton 2000; Roe, Levy, and Derby 1999).
Three constraints impinge on this recommendation: First, as our focus groups indicate, distrust of hard-to-verify diet-disease claims could present a communications barrier. Second, the NLEA may unwittingly constrain the educational value of diet-disease claims by placing limits on what types of claims are allowed. More specifically, restricting the scope of claims significantly undermines the roles of knowledge and motivation in dietary management tasks (Moorman 1996). For example, the NLEA prohibits health claims for cooking oils because their fat content exceeds a threshold value. Despite evidence from a heart disease perspective that cooking oils lower in saturated fats are superior to other oils, Mathios (1998) concludes after analyzing scanner data that the elimination of health claims in the post-NLEA phase has led consumers to shift purchases to cooking oils with higher saturated fats.
Third, education efforts need to reach a variety of consumers, including those with low motivation. Our lab study shows that after the onset of NLEA, low-motivation consumers continued to rely heavily on qualitative signals such as brand names and nutrition claims instead of the data presented in the Nutrition Facts panel. In contrast, high-motivation (and low-knowledge) consumers made a successful transition to increased reliance on the Nutrition Facts panel. These and other results reveal the difficulty faced by certain groups of consumers (nonwhite, less educated, and over 55 years of age) in using nutrition information. Taken together, they suggest that the ultimate success of the NLEA rests on reaching different groups of consumers with different needs and abilities.
Availability. In some cases, consumers do not search because nutrition information is simply unavailable. For example, restaurants are exempted from the NLEA mandate unless they make a specific nutrition claim, but almost a third of consumers' meals are at restaurants (Shapiro 1995). Similarly, nutrition labels are not required for traditionally nonpackaged foods (e.g., vegetables). Finally, only limited nutrition information appears in advertising (Andrews, Burton, and Nete-meyer 2000; Andrews, Netemeyer, and Burton 1998). Remedies include increasing the amount of nutrition information on nonpackaged goods, on menu items, and in advertising.
It is helpful to recognize the constraints on implementing these suggested remedies. The restaurant industry has "opposed the requirement of nutrition labeling, citing the variability of recipes and portion sizes from day to day" (Scarbrough 1995, p. 38). Furthermore, unless education efforts accompany any increase in information, consumers are unlikely to use it effectively.
Management Practice
Grocery retailers. From the retailer's perspective, an effective way to attack search costs associated with using food labels is to shift the entire burden of search and processing tasks from consumers to computers. Internet-based technologies such as electronic search agents enable computers to handle complex information search tasks accurately with little or no human effort. For example, online grocers (e.g., Peapod.com) allow a single, unified, Webbased search on nutrition attributes in each of several food categories. A logical extension of this technology to in-store nutrition information search may feature computer terminals that enable grocery shoppers to generate individually tailored lists of brands available in the store that satisfy prespecified nutrition criteria.
We capture this idea in Figure 1. Whereas the current model of human nutrition information processing (Figure 1, Panel A) reflects the conceptual underpinnings of our research, Figure 1, Panel B, offers a futuristic model that relegates the bulk of nutrition information processing to computers. Although the current and futuristic models share the same end goal (desirable nutrition behavior), consumer motivation and knowledge play different roles within each model. Whereas motivation and knowledge are critical in Figure 1, Panel A, they are less critical in Figure 1, Panel B. In the latter case, consumers only need to recognize the importance of good nutrition behavior and demonstrate a willingness to use computers to achieve this goal.
Although computers effectively perform search and information processing tasks at which consumers are remarkably inefficient, several concerns emerge. Using computers of any kind for even routine nutrition-related tasks is not costless for consumers or retailers. For consumers, the learning curve may be steep, and they may be as susceptible to misinterpreting information here as in the supermarket aisles. Also, retailers and manufacturers can deliberately or inadvertently enter deceptive information. Finally, retailers may find a technology-based system expensive.
Therefore, in the short run, retailers could develop simpler nutrition information management tools. For example, they could launch special programs to broaden consumers' nutrition focus beyond packaged foods. Examples include quizzes about general nutrition, simple point-of-purchase reminders in the fresh vegetable and fruit sections to "eat five a day," and free recipes showing consumers how to combine foods in nutritionally balanced ways.
Food manufacturers. Manufacturers of packaged foods can contribute to consumers' awareness of nutrition through product development (Moorman 1998), repositioning, or promotion efforts. Regarding repositioning, new market opportunities may arise by recasting "fun" foods in a nutritious light. From a promotion perspective, food manufacturers can rely on brand-specific shelf markers and interactive point-of-purchase materials to deliver tailored information about changes in the nutrition content of their products to health-aware segments.
However, both our field study and focus groups suggest caveats. Given the concerns in the focus groups about nutrition/taste trade-offs, food manufacturers need market research to explore whether these strategies enhance the appeal of their brands to chosen target markets. In addition, our field study shows that in the post-NLEA era, any perceived nutritional similarity across brands discourages consumers' search for nutrition information. To break through the competitive clutter, a food manufacturer wishing to position a brand on a nutrition attribute should first change any perception among consumers that all brands are equivalent in terms of nutrition content.
Implications for Further Research and Consumer Welfare
Further research in this area can focus attention on how and why consumers process nutrition information from different package locations in different ways. Consumer characteristics and situations that motivate the use of relatively accessible descriptors and nutrition claims on food packages may differ markedly from those that motivate consumers to use the Nutrition Facts panel. Also, future studies could employ multiple methods and multiple outcome variables. A key strength of our study is its focus on discovering convergent findings across multiple research methodologies. In general, pursuing multiple research methods helps balance the strengths and weaknesses of specific methods and better enables researchers to highlight the complexities that direct and constrain the use of nutrition information. We also gained unique insights by studying outcome variables such as search, recall, choice, and unstructured comments.
Our studies collectively enhance understanding about the NLEA's overall impact. If we focus only on the Nutrition Facts panel, the NLEA's onset did not change in-store search and recall of nutrition information. Although the new food labels successfully improved the availability of relevant nutrition information, they failed to stimulate consumers' search and use of this information. However, if we focus beyond the Nutrition Facts panel (e.g., NLEA-sanctioned descriptors such as "low sodium"), our scanner results show that the NLEA stimulated desirable food choices by encour-aging consumers to minimize their intake of negative nutrients. Combined, these results provoke questions about the NLEA and its net effect on social welfare. On the one hand, consumers' failure to use the Nutrition Facts panel information as intended undermines the benefits of the NLEA; on the other hand, consumers' willingness to increase purchases of foods without undesirable nutritional characteristics has positive welfare benefits. More important, if the results pertaining to calories in our longitudinal time-series analyses signal the widespread emergence of consumers' ability to discriminate reasonably among negative nutrients in nutrition management tasks, the onset of the NLEA holds considerable promise for the future. Obvious next steps for enhancing consumer welfare will be to build greater consumer sensitivity for health benefits that stem from positive nutrients.
However, if we restrict the interpretive time horizon for our findings to the immediate past by integrating them with results from other recent work (e.g., Roe, Levy, and Derby 1999), the following insight emerges: Consumers care about nutrition information, but with two important nuances: First, they appear to rely on simple heuristics to collect nutrition information, that is, using the easy-to-digest information in descriptor terms or nutrition claims rather than the more comprehensive information in the Nutrition Facts panel. Because the former are regulated in the post-NLEA era, this approach may be defensible. Second, they appear to care more about certain types of nutrition information (negative nutrients). Both nuances may yield suboptimal nutrition choices and reflect new and complex challenges unleashed by the onset of the NLEA. Optimally, the richer information in the Nutrition Facts panel will guide most consumer food choices when the transition from Panel A to Panel B of Figure 1 is complete.
[ 1] We also estimated this model equation without the lagged dependent variable. The results for this model version were consistent (in terms of the sign and statistical significance of the NLEA dummy) with the results reported in Table 3.
Summary of Research Question/Hypotheses and Findings
Legend for Chart:
A - Research Question or Hypothesis
B - Field Study
C - Lab Experiment
D - Scanner Data Analyses
E - Focus Groups
A
B C
D E
RQ: Compared with the pre-NLEA
period, did the following change in
the post-NLEA era:
(a)Search intensity for nutrition
information?
No change No change
Some consumers
(sensitive to weight/health)
noticed the new label
information.
(b)Recall efficiency for specific
nutrition attributes?
No change No change
(c)Choice (for foods that highlight
desirable levels of specific
nutrition attributes)?
Yes Participants reported more
purchase of such foods
within taste and budget
constraints.
H1: Compared with the pre-NLEA era,
consumers in the post-NLEA era will
increase search intensity, recall
efficiency, and choice for negative
nutrition attributes more than they
change behavior related to positive
nutrition attributes.
Supported Supported
for recall for search
error intensity
Supported by In the post-NLEA period,
changes in participants noted frequent
category shares checks on negative
of brands that attributes but did not
highlight negative mention positive attributes.
nutrition attributes
(except calories)
H2a: Compared with the pre-NLEA era,
consumers-motivation in the post-
NLEA era will be a less important
determinant of search intensity.
No support Supported
for regulated
attributes
(nutrition
claims,
serving size)
In the post-NLEA period,
some motivated
consumers remain
skeptical about serving-
size information and hard-
to-verify claims.
H2b: Compared with the pre-NLEA era,
consumers-motivation in the post-
NLEA era will be a less important
determinant of recall efficiency.
No support No support
H3: Compared with the pre-NLEA era,
consumers-knowledge in the post-
NLEA era is a less important
determinant of recall efficiency.
No support No support
H4: Compared with the pre-NLEA era, in
the post-NLEA era the relationship
between perceived similarity across
brands and search intensity will be
more negative.
Supported
In the post-NLEA period,
search diminishes as
competing foods are
similar in nutrition content.
H5: Compared with the pre-NLEA era, in
the post-NLEA era the negative
relationship between brand loyalty
and search intensity will be weaker.
Supported Field Study: Regression Results
Legend for Chart:
A - Test for H1, Independent Variables
B - Test for H1, Dependent Variables, REI[a]
A B
Intercept 2.03 (.61)
NLEA dummy -.51 (.15)***
NUTRISIM -.10 (.05)*
HLTHSTAT -.12 (.05)**
R2 .49
N 28
Legend for Chart:
A - Tests for RQ[a], RQ[b], H2, H3, H4, and H5, Independent Variables
B - Tests for RQ[a], RQ[b], H2, H3, H4, and H5, Dependent Variables,
PANTIME
C - Tests for RQ[a], RQ[b], H2, H3, H4, and H5, Dependent Variables,
RRER(Fat)[b]
A B C
Intercept -2.75 (2.03) 260.65 (150.77)*
NLEA dummy -.53 (2.27) 162.62 (160.67)
KNOWLEDGE -1.65 (16.11)
KNOWLEDGE x NLEA dummy -.16 (18.07)
MOTIVPRO .23 (.08)*** 7.54 (9.71)
MOTIVPRO x NLEA dummy -.00 (.10) -16.89 (11.34)
NUTRISIM .57 (.25)** 16.03 (22.58)
NUTRISIM x NLEA dummy -.71 (.36)** 13.75 (26.04)
BRANDLOY -.43 (.17)** -5.83 (18.78)
BRANDLOY x NLEA dummy .55 (.24)** 5.10 (20.30)
CATEGFAM -.03 .10 -2.57 (7.91)
PURCHREG .05 (.21) -52.54 (12.07)***
OFTENEAT .13 (.27) -54.65 (16.20)***
AGE .15 (.23) 46.73 (14.05)***
PANTIME -1.59 (4.06)
R2 .08 .30
N 321 190*p < .10.
**p < .05.
***p < .001.
[a]Weighted least squares: weighted by 1/[NUTRISIM]( 2).
[b]Weighted least squares: weighted by 1/[CATEGFAM]( 2).
Notes: Standard error is in parentheses.
Longitudinal Analyses of Scanner Data
Legend for Chart:
A - Attribute Valence
B - Common Descriptors
C - Category
D - Regression Results, Dependent Variable (Category % Share of)
E - Regression Results, Intercept
F - Regression Results, NLEA Dummy
G - Regression Results, Lagged Dependent Variable
H - Regression Results, R2
I - Regression Results, N
A
B C
D E
F G
H I
Positive
Vitamin C fortified Bottled juices
Vitamin C-added UPCs 3.071***
(.302)
-.685* .156**
(.310) (.059)
.05 284
Plus calcium/calcium added Refrigerated juices
Calcium-added UPCs 3.323***
(.228)
.428 4.01E-3
(.259) (.051)
.01 396
Negative
Low sodium/lower salt Canned soup
Sodium-healthy UPCs .652***
(.090)
.544*** .702***
(.100) (.037)
.72 378
Low fat/reduced fat/fat free Cheese
Fat-healthy UPCs 1.216***
(.172)
.512*** .705***
(.141) (.038)
.63 340
Low fat/reduced fat/fat free Cookies
Fat-healthy UPCs 2.402***
(.372)
3.037*** .597***
(.529) (.051)
.73 266
Low calorie/diet/light/lite Bottled juices
Calorie-healthy UPCs 4.167***
(.302)
-1.538*** .368***
(.191) (.047)
.44 392
Light/lite Frozen entrees
Calorie-healthy UPCs 5.291***
(.341)
-2.601*** 4.74E-4
(.373) (.051 )
.12 396
Light/lite Frozen dinners
Calorie-healthy UPCs 9.092***
(.890)
-4.507*** -7.75E-3
(.963) (.066)
.10 232*p < .05.
**p < .01.
***p < .000.
Notes: Standard error is in parentheses.
Normative Goals for Nutrition Related Behavior, Findings, Implications, and Remedies
Normative Goals
1. Consumers should make nutritionally desirable food choices in the store.
Research Findings
- Amount of in-store nutrition search low.
- Poor recall accuracy for nutrition information.
- Food choices influenced more by descriptors/claims than by Nutrition Facts panel.
Managerial Implications
- Decrease search costs (e.g., in-store computer terminals).
- Promote benefits of healthy diet in-store (e.g., "eat 5 a day" program).
- Include diet-disease links on packages.
- Use claims to leverage competitive advantage.
Public Policy Remedies
- Enhance education about different diet-disease links.
- Link descriptors/ claims to Nutrition Facts panel.
Normative Goals
2. Nutrition focus should include all food products.
Research Findings
- Little search done for "fun" foods compared with "non-fun" foods.
Managerial Implications
- Attack perceptions about nutrition/taste trade-offs with promotion and new products.
- Increase healthy brands in all food categories.
- Increase the amount of nutrition information in nonpackaged foods, menu items, and advertising messages.
Public Policy Remedies
- Label restaurant menus with nutrition information.
Normative Goals
3. Nutrition focus should include all purchase/eating occasions.
Research Findings
- Low search when brands are perceived as similar in nutrition content.
- Consumers may not consider nutrition information for nonpackaged foods.
Managerial Implications
- Explore opportunities to market foods that are differentiated on nutrition attributes.
- Educate consumers to combine packaged and nonpackaged foods to enhance nutritional value.
Public Policy Remedies
- Educate consumers to use %DV information.
- Educate consumers about nutritional balance.
Normative Goals
4. Nutrition focus should include all nutrition attributes.
Research Findings
- Consumers rely on a few negative nutrition attributes.
- Consumers are attracted to descriptor terms featuring negative nutrients.
Managerial Implications
- Educate consumers about links between different attributes and disease.
- Explore opportunities for brands with multiple descriptor terms.
Public Policy Remedies
- Expand consumers' focus from diet/weight toward nutrition/ health issues.
- Educate consumers about benefits of positive nutrients.
Normative Goals
5. All consumers should focus on nutrition information.
Research Findings
- Some distrust of claims and information on food packages.
- Brand loyalty is negatively correlated with search.
- Highly motivated low-knowledge consumers benefited from new label.
Managerial Implications
- Assure consumers of reliability of nutrition information.
- Educate loyal consumers about nutrition value of brand.
- Improve motivation and knowledge of all consumers through education.
Public Policy Remedies
- Educate consumers that nutrition claims are regulated.
- Develop simple tests of nutrition knowledge.
- Educate all consumers about benefit of label.
DIAGRAM: FIGURE 1: Models of Nutrition Information Processing: A: Current Model
DIAGRAM: B: Futuristic Model
DIAGRAM: FIGURE 2: Category Share of Sodium-Healthy Canned Soup (with Ten-Week Moving Average)
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By Siva K. Balasubramanian and Catherine Cole
Siva K.Balasubramanian is Henry J. Rehn Professor of Marketing, College of Business Administration, Southern Illinois University at Carbondale. Catherine Cole is Associate Professor of Marketing, College of Business Administration, University of Iowa.The authors appreciate insightful comments from the three anonymous JM reviewers and from colleagues during research presentations at Hong Kong University for Science and Technology, National University of Singapore, Oklahoma State University, Southern Illinois University, and Tilburg University (The Netherlands).They are grateful for financial support from the Marketing Science Institute and thank the following companies and individuals for assistance in data collection: Country Foods, Kroger Food Stores, General Mills Inc., Kellogg Company, Mandeep Singh, Ron Socha, Ashwini Vaidya, and Lara Wilbur. Finally, they acknowledge the Marketing Department at University of Chicago as the source of scanner data analyzed in this study.
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Record: 38- Corporate Associations and Consumer Product Responses: The Moderating Role of Corporate Brand Dominance. By: Berens, Guido; van Riel, Cees B. M.; van Bruggen, Gerrit H. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p35-18. 14p. 1 Black and White Photograph, 2 Diagrams, 4 Charts. DOI: 10.1509/jmkg.69.3.35.66357.
- Database:
- Business Source Complete
Corporate Associations and Consumer Product Responses: The
Moderating Role of Corporate Brand Dominance
This study investigates the effect of corporate brand dominance--that is, the visibility of a company's corporate brand in product communications--on the relationship between corporate associations and product evaluations. The results show that corporate brand dominance determines the degree to which associations with the company's corporate ability and corporate social responsibility influence product attitudes, as well as the nature of the moderating effects of fit and involvement.
When communicating with customers, multibusiness companies can choose whether to label an individual product by a separate brand name ("stand-alone"), by only the corporate brand name ("monolithic"), or by the two names together ("endorsed" or "dual") (Laforet and Saunders 1994; Olins 1989). For example, in general, Procter & Gamble uses separate brand names without reference to the corporate brand, Philips uses its corporate brand prominently on most of its products, and Nestlé uses its corporate name as an "endorser" behind many of its products. An important managerial question is, Which of these strategies should a company use, and under what conditions? Although some empirical studies (Milberg, Park, and McCarthy 1997; Rao, Agarwal, and Dahlhoff 2004) have examined the effects of using different corporate branding strategies on people's reactions, it is not clear how a company's corporate branding strategy influences the effects of the different types of associations that people have with the corporate brand. Several academic studies have shown that customers' different types of associations with a company have different influences on their product evaluations (e.g., Brown and Dacin 1997; Sen and Bhattacharya 2001). Particularly, these studies have found that associations with a company's corporate ability (CA) and its corporate social responsibility (CSR) both influence product evaluations but that CA associations have a stronger effect than CSR associations. Thus, an important question is, How do the effects of corporate associations on consumer product responses differ for different corporate branding strategies? Answering this question can provide organizations with insights into which branding strategy is most effective when a company wants to leverage a specific type of corporate association.
To address this question, we present the results of a study that examines the effects of the corporate branding strategy of a financial services company on the transfer of CA and CSR associations to customer product evaluations. The results show that CA associations are most effective when organizations use a monolithic branding strategy, and CSR associations are most effective when organizations use an endorsed strategy. In addition, corporate brand dominance (CBD) influences the way that the effects of CA and CSR are moderated by the fit between the company and the product and by consumer involvement with the product.
Literature Review
Several studies have explicitly investigated the roles of CA and/or CSR associations in consumer reactions to products (for an overview, see Brown 1998). However, researchers have begun only recently to address the psychological mechanisms through which these types of corporate associations influence people's product responses (Brown and Dacin 1997; Gürhan-Canli and Batra 2004; Keller and Aaker 1998; Sen and Bhattacharya 2001). In a pioneering study, Brown and Dacin (1997) found that CA associations influence product attitudes through their influence on the evaluation of specific product attributes ("product sophistication") as well as through their influence on the overall evaluation of the company. In contrast, CSR associations influence product attitudes only through their influence on the overall company evaluation. Keller and Aaker (1998) report similar findings, and Madrigal (2000) reports that CSR also affects judgments of specific product attributes.
More recently, researchers have extended Brown and Dacin's (1997) study by investigating the conditions in which CA and CSR influence product responses. Sen and Bhattacharya (2001) find that the type of CSR a company adopts moderates the effect of CSR on product preferences. Gürhan-Canli and Batra (2004) show that a high risk associated with a product increases the effect of CA but not the effect of CSR. Madrigal (2000) finds that the perceived fit between the product and the corporate brand positively influences both the effect of CA associations and the effect of CSR associations. The latter result is consistent with findings in the literature on consumer evaluations of product brand extensions (for a review, see Czellar 2003). This literature examines the transfer of CA-type associations with a product brand (e.g., brand quality) to evaluations of new products that are marketed under the brand name, and it has often reported that perceived fit positively moderates this transfer. In addition, the literature has shown that brand associations have more influence on consumer judgments when people have a low involvement with the type of product and/or with the judgment itself (e.g., Maheswaran, Mackie, and Chaiken 1992) or when people have a low expertise with the product class (e.g., Broniarczyk and Alba 1994).
To date, few studies have investigated the effects of companies' branding strategies as a moderating variable in the transfer of corporate associations. Rao, Agarwal, and Dahlhoff (2004) find that a monolithic strategy leads to more favorable stock market results than a stand-alone strategy. Sheinin and Biehal (1999) report that corporate associations influence product attitudes when only the corporate brand is shown on the product advertisement but not when a subsidiary brand is also shown. Furthermore, Milberg, Park, and McCarthy (1997) show that the (main) effect of fit on the evaluation of product brand extensions is smaller when both a parent brand and a subbrand are shown than when only the parent brand is shown. This result suggests that the process of brand image transfer is different when the (corporate) parent brand is dominantly visible than when the parent brand is not dominantly visible. However, Milberg, Park, and McCarthy do not test this proposition. In addition, it is not clear how brand strategies affect the influence of other moderating variables, such as product involvement. Finally, the results from studies in the area of product brands may not be completely applicable in a corporate branding context. This is because ( 1) corporate brands often evoke associations with CSR, whereas in general, product brands do not and ( 2) even CA-type associations may be qualitatively different for corporate brands than for product brands (Aaker 1996). For corporate brands, in general, these associations are based on more than one category of products and on more than one source of information. This variety in sources can lead to a more elaborate and confidently held impression than that which is obtained from knowledge about individual products. In this article, we investigate the influence of a company's branding strategy on the effects of corporate associations with CA and CSR and on the moderating effects of fit and involvement.
Hypotheses Development
Figure 1 graphically displays the research model underlying our study. We propose four sets of relationships. First, we propose that there is a relationship between associations with a company's CA and CSR and people's product attitudes (labeled "I" in Figure 1) (Brown and Dacin 1997; Madrigal 2000). Second, we expect that these relationships are moderated by the degree of perceived fit between the product and the brand and by the degree of involvement that people have with the product (labeled "II" in Figure 1). Third, we expect that these moderating effects are in turn moderated by the degree to which the corporate brand is dominantly visible in product communications (labeled "III" in Figure 1). Fourth, we expect that the associations with CA and CSR are influenced by the amount of knowledge that people have about the company (Laroche, Kim, and Zhou 1996) and that product attitudes are influenced by information that people receive about the product itself (labeled "IV" in Figure 1).( n1) However, we do not test these latter two relationships in our empirical study.
To predict how CBD, fit, and involvement moderate the effects of CA and CSR, we use the accessibility-diagnosticity framework (Feldman and Lynch 1988; Lynch, Marmorstein, and Weigold 1988), which explains the influence of any piece of information stored in a person's memory on any evaluation that person makes. It states that the likelihood that information is used is a function of ( 1) the accessibility of the information in the person's memory, ( 2) the accessibility of other pieces of information, and ( 3) the perceived diagnosticity of the information. Information is more likely to be used for a certain evaluation when it is easily recalled, when other "competing" pieces of information are less easily recalled, and when the information is perceived as useful for the evaluation. Furthermore, a person uses only enough information for a certain evaluation to satisfy a "diagnosticity threshold"--that is, a minimum level of certainty about the evaluation (Lynch, Marmorstein, and Weigold 1988). In this study, we assume that CBD influences the (relative) accessibility of CA and CSR associations, that perceived fit influences the diagnosticity of the associations, and that product involvement influences a person's diagnosticity threshold. We explain this reasoning, which we illustrate in Table 1, and its consequences for the effects of CA and CSR in the subsequent paragraphs.
We use the term "CBD" to indicate the degree of visibility of the corporate brand compared with the visibility of a subsidiary brand in product communications. This dominance is a direct consequence of a company's corporate branding strategy. When a company uses a monolithic branding strategy, CBD is high. In contrast, when a company uses an endorsed branding strategy, CBD is low.
It seems likely that when the corporate brand is dominantly visible, CA and CSR associations have more of an impact on product evaluations than when the corporate brand is less dominantly visible (Sheinin and Biehal 1999). In line with the logic of the accessibility-diagnosticity framework, we posit that when the dominance of the corporate brand decreases, CA and CSR associations with the corporate brand become less accessible than associations with the subsidiary brand (see Table 1), and thus the corporate brand associations have less influence on product evaluations. More important, we also expect that the dominance of the corporate brand in product communications determines the degree to which perceived fit and product involvement are important in the transfer of CA and CSR associations.
CBD and the moderating effect of fit. "Fit" can be defined as the similarity between a (extension) product and a brand (e.g., Bhat and Reddy 2001). This similarity consists of two aspects: ( 1) that between the product category (or categories) of the brand and the category of the product and ( 2) that between the associations evoked by the brand and the associations evoked by the product. In this article, we focus on the latter aspect, which encompasses the former (Bhat and Reddy 2001). Previous research has shown that the effects of CA and CSR associations on consumer product evaluations are stronger when people perceive a high fit between the product and the brand (e.g., Madrigal 2000). In terms of the accessibility-diagnosticity framework, perceived fit influences the diagnosticity of corporate associations for the evaluation of a new product (Ahluwalia and Gürhan-Canli 2000) and thus the likelihood that the associations will be used. When consumers perceive the product as similar to the brand's image, they will reason that what they know about the brand can be used to predict the product's attributes. However, we expect that this reasoning holds for the effect of CA associations but not for the effect of CSR associations.
Furthermore, we predict that the moderating influence of fit on the effect of CA associations depends on the dominance of the corporate brand. In line with the logic of the accessibility-diagnosticity framework, we posit that when the corporate brand is not dominantly visible, subsidiary brand associations are more accessible than corporate associations (see the entries under "Accessibility" in Table 1) and are likely to be diagnostic enough to satisfy the diagnosticity threshold. Therefore, we expect that these associations alone influence product evaluations and that increasing the diagnosticity of CA associations with the corporate brand (through a higher degree of fit) does not enhance their influence. Thus, it is likely that the moderating effect of fit on the influence of CA associations is absent or weaker when the corporate brand is not dominantly visible. This expectation is consistent with the results of Milberg, Park, and McCarthy's (1997) study, which shows that the main effect of fit on the evaluation of products diminishes when the dominance of the parent brand decreases.
Whether the fit between a product and a brand influences diagnosticity of a certain type of corporate associations is likely to depend on whether these associations can be translated directly into product attributes (Batra and Homer 2004). Madrigal (2000) finds that the moderating effect of fit on the influence of (environmental) CSR associations is even stronger than the effect of fit on the influence of CA associations. However, he explicitly chose an environmentally responsible product so that in his study, CSR associations could be directly translated into attributes of the product. When the product is not positioned explicitly as socially responsible, we expect that this direct translation into product attributes cannot occur and therefore that fit does not determine the influence of CSR associations. In the industry that we focus on herein, the financial services industry, only so-called socially responsible investment funds are positioned as socially responsible, whereas other products and services are not. Therefore, we expect that for most products in this industry, fit does not influence the diagnosticity of CSR associations, and CBD does not influence the moderating effect of fit. Rather, we expect that the diagnosticity of CSR is low to moderate, independent of the degree of perceived fit (see Table 1). Thus, we hypothesize a three-way interaction among CA associations, fit, and CBD but not among CSR associations, fit, and CBD.
H1: When CBD is high, CA associations have a stronger effect on product attitudes when fit is high than when fit is low. When CBD is low, the effect of CA associations is not moderated by fit.
H2: The effect of CSR associations on product attitudes is not moderated by fit, independent of whether CBD is high or low.
CBD and the moderating effect of involvement. We also expect that CBD influences the effect of product involvement. Involvement has been defined as "an unobservable state of motivation, arousal, or interest evoked by a particular stimulus" (Jain and Srinivasan 1990, p. 594). In this study, we focus on consumer involvement with a product category. Maheswaran, Mackie, and Chaiken (1992) find that when people have a low involvement with a product or task, CA associations have more influence on product evaluations than when people are highly involved. In line with the accessibility-diagnosticity framework, we posit that this is because a person's threshold for the diagnosticity of information decreases when involvement decreases (see Table 1). Thus, he or she is more easily satisfied with information that is less diagnostic but more accessible than actual product attribute information (Lynch, Marmorstein, and Weigold 1988). Alternatively, in such a case, it could be that people use the high accessibility of the CA associations as a heuristic cue to infer a high diagnosticity because a low involvement reduces people's motivation to assess diagnosticity at all (Menon and Raghubir 2003). We assume that product information is less accessible in general than are people's associations with the company, because it takes some effort to process product information, whereas corporate associations only have to be recalled from memory (Maheswaran, Mackie, and Chaiken 1992). Therefore, we expect that involvement negatively moderates the effects of corporate associations on product attitudes. Although we assume that the diagnosticity of CSR associations for evaluating a product is lower than the diagnosticity of CA associations (Brown and Dacin 1997), we expect that the effect of involvement occurs for both CA and CSR associations. When people have a low diagnosticity threshold, it is likely that even associations that have a relatively low degree of diagnosticity can have an effect on product evaluations.
Furthermore, we expect that the moderating influence of involvement depends on the CBD. When the corporate brand is dominantly visible, associations with this brand are more easily accessible. When people have a low involvement with the product and therefore have a low diagnosticity threshold, they tend to use only the most accessible information. Therefore, corporate associations are especially likely to be used as a cue to evaluate the product. Conversely, when corporate associations are less accessible because the corporate brand is not dominantly visible, it may be that they influence product evaluations only when people have a high diagnosticity threshold (i.e., when they are highly involved with the product). In such a case, information that is less accessible can also influence people's judgments. However, because this is the case only for information that has a relatively high diagnostic value for evaluating the product (Alba, Hutchinson, and Lynch 1991), we expect that this positive moderating influence holds for CA associations but not for CSR associations. Therefore, we formulate the following hypotheses:
H3: When CBD is high, CA associations have a stronger effect on product attitudes when involvement is low than when involvement is high. When CBD is low, CA associations have a stronger effect on product attitudes when involvement is high than when involvement is low.
H4: When CBD is high, CSR associations have a stronger effect on product attitudes when involvement is low than when involvement is high. When CBD is low, the effect of CSR associations is not moderated by involvement.
Method
To test our hypotheses, we conducted a field experiment. Our respondents were potential customers of a large financial services provider, who were asked to evaluate products that were marketed by subsidiaries of this company. These products were shown on advertisements in which we manipulated the dominance of the corporate brand as a between-subjects variable.
In our study, the financial services provider consists of a large number of subsidiary banks and insurance companies, most of which operate under their own name (without explicitly referring to the parent company). We investigated the evaluation of eight different products that are marketed by four subsidiaries. From each subsidiary, we chose one product from the retail banking market and one product from the wholesale banking market (see Table 2). Each respondent evaluated one of these products after being confronted with the product advertisement. To ensure sufficient realism of the materials, we based these on existing print advertisements.
To manipulate CBD, two versions of each advertisement were developed. On the first of these advertisements (low CBD), the name and logo of the parent company were added in a small font below the name and logo of the subsidiary company after the words "part of." On the second (high CBD) advertisement, the name and logo of the subsidiary were replaced completely by the corporate name and logo. In addition, the background color of the advertisement was modified to the corporate color (orange). Examples of the advertisements used, including the manipulation of CBD, appear in Figure 2.
A total of 273 respondents participated in the study, with a roughly equal number of retail and wholesale prospects (139 and 134, respectively). All respondents were responsible for financial matters in their families and companies, respectively. To ensure that questions about specific associations with the corporate brand would be meaningful to the respondents, we asked them about their familiarity with the corporate brand (and its subsidiaries) on seven-point semantic differential scales. For respondents who indicated that they were completely unfamiliar with the corporate brand (i.e., those who obtained a score of "1"), the interview was completed, but we excluded their responses from the analyses. We randomly assigned respondents to one of the two CBD conditions.
The wholesale prospects were prerecruited by telephone and interviewed at their offices. Retail prospects were interviewed at their homes. We used a face-to-face interview procedure in which the interviewer posed questions and filled out the respondent's answers. After asking questions about demographics and familiarity with the different brands, the interviewer showed the product advertisement. When the respondent had studied the advertisement, it was removed from view, and the respondent was asked to evaluate the product shown in the advertisement and then to indicate his or her purchase intentions to the product. Next, he or she answered questions about perceived fit between the product and the corporate brand; this was followed by questions about involvement with the product. Finally, the questionnaire was given to the respondent, who filled out the remaining questions about CA and CSR associations (in that order). On average, the interviews lasted for approximately 50 minutes.
For all measures, we used multiple-item scales that consisted of seven-point Likert or semantic differential scales. All measures and their reliabilities, as well as descriptive statistics and correlations, appear in the Appendix.
Independent measures. Our independent variables are CA and CSR associations. To measure these constructs, we adapted Fombrun, Gardberg, and Sever's (2000) reputation quotient scale, which captures several aspects of corporate reputation. This 20-item scale distinguishes the following six dimensions: emotional appeal, products and services, vision and leadership, workplace environment, social and environmental responsibility, and financial performance. In line with Brown and Dacin's (1997, p. 68) definition of CA as "the company's expertise" and their operationalization of it as the quality of the processes in a company that deal with product development and manufacturing, we chose the products and services and the workplace environment subscales to operationalize CA associations.( n2) Vision and leadership is also relevant to CA, but the items in this scale could be equally interpreted as pertaining to leadership regarding CSR.( n3) Financial performance may be conceptualized better as a (perceived) consequence of CA than as an aspect of CA.
Moderator measures. We operationalized perceived fit as the perceived similarity between the image of the corporate brand and the image of the product. We measured this construct with two items adapted from previous literature on brand extensions (e.g., Bhat and Reddy 2001). We measured involvement with the product category with two of the three items of the relevance subscale from the new involvement profile (Jain and Srinivasan 1990). These items capture cognitive (rather than affective) involvement (i.e., the product's perceived relevance and importance rather than its perceived pleasure or sign value). We made this choice because, in general, these latter dimensions are not applicable to financial products and services (Aldlaigan and Buttle 2001).
Dependent measures. We measured people's attitudes toward the products on three subscales: quality, appeal (feelings about the product), and reliability (cf. Petroshius and Monroe 1987). In addition, we measured respondents' product purchase intentions with three items (cf. Petroshius and Monroe 1987).
To purify our measures, we computed item-to-total correlations for each scale, followed by a confirmatory factor analysis of all variables. We began with a total of 27 items and ultimately retained 23. We dropped two items from the product attitude scale and one item from the involvement scale because they had a low correlation (<.4) with the total scale. We also dropped one item from the CSR scale because it seemed to measure an overall evaluation rather than CSR.
Because some of the constructs we used (i.e., CA and product attitude) were composed of different subscales, our measurement model was a second-order factor model (see Rindskopf and Rose 1988). This model showed adequate fit (χ²233 = 315.54, p = .00; standardized root mean square residual = .05; comparative fit index = .97).( n4) However, one of the items in the CSR scale ("Do you think that [parent company] behaves in an ethically responsible manner?") had high positive residuals (>2.58) with items from the involvement and purchase intention scales, suggesting that this item is related more to an overall evaluation than to the specific CSR concept (see Steenkamp and van Trijp 1991). Therefore, we removed this item. The fit of the resulting model was also adequate (χ²211 = 284.43, p = .00; standardized root mean square residual = .05; comparative fit index = .98). However, the decrease in fit caused by imposing the second-order factors was significant (χ²16 = 30.30, p = .02), suggesting that the better parsimony of the second-order model did not quite weigh up to the loss of fit that resulted from "forcing" the subscales under the second-order factors (see Rindskopf and Rose 1988). However, because the fit of the second-order factor model was adequate and because it corresponded to the constructs we identified, we proceeded to use the previously defined scales.
Results
We analyzed the data using hierarchical moderated regression models. The independent variables are CA, CSR, CBD, fit, involvement, and the interactions between these variables; the dependent variables are product attitude and purchase intention. However, because product attitude is our main dependent variable, we focus our discussion on the results for this variable and report the results for purchase intention only when they diverge from those for product attitude. To account for the different types of products that we used, we also included dummy variables that represent the different products as independent variables. Although moderated regression models are especially sensitive to measurement error, the biasing effects of such errors are minimal when the reliability of all the scales used is high (i.e., approximately .8 or .9; see Ping 1996). This is the case with our measures, so we assumed that using regression analyses rather than structural equation models would not substantially bias our results.
To improve the interpretability of the main effects in the presence of interaction variables, we mean-centered the continuous variables before computing the interaction variables (see Jaccard, Turrisi, and Wan 1990). The main effects can be interpreted as conditional effects, or effects that hold when the other continuous variables in the model are at their mean (i.e., zero). To estimate the "real" (unconditional) main effects, we examined the models lower in the hierarchy that do not include the interaction terms under consideration. To interpret the significant three-way interactions, we examined the conditional two-way interactions that constitute each of them as well as the conditional main effects of CA and CSR, which in turn constitute the significant conditional two-way interactions (see Jaccard, Turrisi, and Wan 1990). We also tested the significance of the conditional effects. In doing so, we used modified levels of the significance level alpha to correct for performing multiple statistical tests on the same interaction effect (Holm 1979). We present these alpha values in the discussion of our results.
The results of the hierarchical regression model for product attitude appear in Table 3. We observe that CA associations have a significant, positive effect on product attitudes, but CSR associations do not. In addition, there is a significant, negative interaction between CSR and CBD, implying that CSR associations especially influence product attitudes when the corporate brand is not dominantly visible on the product advertisement (i.e., when the corporate brand is used as an endorser). Regarding our hypotheses, we note that the results for CA are largely as we predicted, whereas the results for CSR are not.
We hypothesized that the dominance of the corporate brand would influence the moderating effect of fit for CA associations but not for CSR associations (H1 and H2). Conforming to this expectation, there was a significant, positive three-way interaction among CA, fit, and CBD. Conversely, and contrary to our expectation, there was a significant, negative three-way interaction among CSR, fit, and CBD.( n5) To interpret these interactions, we next discuss the conditional effects that underlie them.
The interaction between CA and fit for different levels of CBD. Although the three-way interaction among CA, fit, and CBD is significant, neither of the two conditional two-way interactions between CA and fit are significant (in the case of high CBD: b = .11, t = 1.64, p = .10, α = .05; in the case of low CBD: b = -.12, t = 1.59, p = .11, α = .10). Although the pattern of these results is consistent with our hypothesis (i.e., there is a positive interaction between CA and fit for high CBD but not for low CBD), the lack of significance implies that we do not have sufficient evidence to accept the hypothesis. Conversely, we find a significant two-way interaction between fit and CBD. Consistent with previous research (Milberg, Park, and McCarthy 1997), this implies that the main effect of fit is significantly stronger when CBD is high than when CBD is low.
The interaction between CSR and fit for different levels of CBD. For high CBD, there is no significant interaction between CSR and fit (b = -.05, t = -.81, p = .42, α = .10). For low CBD, the interaction between CSR and fit is positive and significant (b = .24, t = 3.68, p = .00, α = .05). When we examine the conditional main effects that constitute the latter interaction, we observe that CSR has a significant, positive effect when fit is high (b = .61, t = 4.63, p = .00, α = .05) but not when fit is low (b = -.04, t = -.33, p = .74, α = .10). Figure 3 graphically displays this pattern of results. Contrary to our hypothesis (H2), the fit between the product and the corporate brand affects the influence of CSR associations on product attitudes but only when the corporate brand is not dominantly visible (i.e., when it is used as an endorser).
We also expected that CBD would influence the moderating effects of involvement (H3 and H4). The results show that there is a significant three-way interaction among CA, involvement, and CBD and among CSR, involvement, and CBD.( n6) Next, we analyze the conditional effects that constitute these interactions.
The interaction between CA and involvement for different levels of CBD. Contrary to our expectation in H3, for high CBD, there is no significant interaction between CA and involvement (b = -.10, t = -1.47, p = .14, α = .10). However, as we predicted, there is a significant, positive interaction between CA and involvement when CBD is low, which indicates that CA has a stronger influence on product attitudes when involvement is high than when involvement is low (b = .15, t = 2.37, p = .02 α = .05). When we examine the conditional effects that constitute this significant interaction, we observe that CA has a significant influence when involvement is high (b = .45, t = 3.29, p = .00, α = .05) but not when involvement is low (b = -.02, t = .12, p = .91, α = .10).
The interaction between CSR and involvement for different levels of CBD. Contrary to our predictions in H4, there is no significant interaction between CSR and involvement when CBD is high (b = .01, t = .11, p = .91, α = .10). Conversely, when CBD is low, there is a significant, negative interaction between CSR and involvement (b = -.15, t = -3.10, p = .00, α = .05). An examination of the conditional main effects that constitute this interaction shows that CSR has a significant effect when involvement is low (b = .52, t = 4.89, p = .00, α = .05) but not when involvement is high (b = .05, t = .39, p = .70, α = .10). Contrary to our hypothesis, this suggests that involvement has a significant effect on the influence of CSR associations only when the corporate brand is not dominantly visible on the advertisement (i.e., when it is used as an endorser).
Discussion
The results of our study show that a company's corporate branding strategy affects the relationship between corporate associations and customer product attitudes. When firms use a monolithic branding strategy (i.e., when the corporate brand is dominantly visible), associations with CA have a strong influence, independent of fit and involvement. In contrast, associations with CSR do not have a significant influence, again independent of fit and involvement. When firms use an endorsed strategy (i.e., when the corporate brand is not dominantly visible), the influence of CA associations is positively moderated by product involvement. Conversely, in such a case, the influence of CSR associations is negatively moderated by involvement and positively moderated by fit.
Our results suggest that a company's corporate branding strategy is an important determinant of the mechanism through which CA and CSR associations influence customer product evaluations. When the corporate brand is dominantly visible, CA associations appear to be highly salient cues that influence product evaluations, independent of perceived fit and product involvement. In contrast, when the corporate brand is not dominantly visible, consumers appear to use CA associations only as a means to increase the reliability of their product evaluation. In this case, CA associations influence product evaluations only when involvement is high but not when involvement is low. In terms of the accessibility-diagnosticity framework (Alba, Hutchinson, and Lynch 1991; Feldman and Lynch 1988; Lynch, Marmorstein, and Weigold 1988), low CBD presumably decreases the accessibility of CA associations so that they are less likely to be used. However, because the associations have a relatively high diagnosticity, they may still be used when people have a high diagnosticity threshold. In such a case, CA associations can be used as a "backup" that may enhance consumer confidence in product judgments.
The finding that the influence of CA is not moderated by involvement when the corporate brand is dominantly visible can be explained by assuming that in our study, CA associations have a relatively high diagnostic value for evaluating the products. In such a case, these associations would have a high probability to be used in people's product evaluations, independent of the height of their diagnosticity threshold (i.e., independent of their level of involvement). Similarly, the insignificance of the effect of perceived fit when CBD is high can be explained by a ceiling effect. Because the mean level of perceived fit in our study was relatively high (5.45 on a seven-point scale), the diagnosticity of CA associations perhaps did not vary enough.( n7) An alternative explanation may be a lack of statistical power. The three-way interaction among CA, fit, and CBD was significant and in the expected direction, whereas none of the conditional two-way interactions that constituted this interaction was significant. This issue deserves attention in future empirical studies.
However, with respect to the effect of CSR associations, the story is different. When the corporate brand is dominantly visible, CSR does not appear to have any effect on product evaluations. When the corporate brand is used as an endorser and therefore is not dominantly visible, CSR has an effect but only when fit is high or involvement is low. That the influence of CSR increases in the case of low rather than of high involvement is consistent with our reasoning that CSR associations have only limited diagnostic value for product evaluations. The significant, positive interaction between CSR and perceived fit is not consistent with our hypothesis and suggests that to some degree, our respondents observed the associations as directly applicable to product attributes. However, this explanation must be substantiated with further research.
An interesting but puzzling finding is that the moderating effects of fit and involvement on the influence of CSR associations occur when CBD is low rather than high. A possible explanation for this finding is that when people evaluate a product's quality, the dominance of the corporate brand selectively increases the accessibility of CA associations while decreasing the accessibility of CSR associations. Making the subsidiary brand dominant in product communications changes the role of the corporate brand from that of the driver of a product purchase to that of the endorser of the product (Aaker 1996). In the case of high CBD, the corporate brand acts as the driver, and CSR associations may become relatively inaccessible because the task of evaluating a product's quality induces people to focus on the brand's CA rather than its CSR. Conversely, the endorser role does not primarily involve providing product information. Therefore, when the corporate brand assumes the endorser role, it may induce people to focus on the parent company's other roles, such as its contributions to the community and its efforts to limit environmental damage. The accessibility of CSR associations may then increase, which in turn induces people to use these associations when the associations are diagnostic or when people have a low diagnosticity threshold.
To test the validity of this reasoning, we conducted a small additional empirical study. In this study, we investigated the effect of the CBD manipulation on the accessibility of CSR associations. We asked 74 undergraduate business administration students to list their spontaneous associations with the corporate brand after being exposed to either an advertisement with high CBD or an advertisement with low CBD.( n8) Three independent judges coded the respondents' associations as CSR, CA, or other attributes. We operationalized the accessibility of CSR associations as the presence or absence of CSR associations in a respondent's list of associations. Most of the respondents' CSR associations addressed the quality of the company as an employer (e.g., "good employer"), the recent publicity surrounding an increase in the parent company's top management compensation (e.g., "scandals regarding top salaries"), and the organization's sponsorship of events (e.g., "sport sponsorship"). A log-linear model showed that significantly more people mentioned CSR associations when CBD was low than when CBD was high (λ = -1.24, Z = -1.81, p = .07). This result supports our reasoning. In contrast, CBD had no significant effect on the number of people who mentioned CA associations (λ = -.15, Z = -.32, p = .75). Finally, an analysis of variance with CBD as a between-subjects factor and the type of associations judged (CA or CSR) as a within-subjects factor showed that there was no significant difference between the CBD conditions in terms of the degree to which respondents viewed the company's CSR or CA as diagnostic for evaluating the product (F = .52, p = .47). However, consistent with our assumption, CA was perceived as significantly more diagnostic than CSR (F = 102.21, p = .00). There was no significant interaction between CBD and the type of associations (F = .54, p = .47).
In conclusion, our results suggest that people use CA associations as a salient cue when the corporate brand is dominantly visible in product communications and only as a means to increase confidence when the corporate brand is not dominantly visible. In contrast, people seem to use CSR associations as a salient cue primarily when the corporate brand is not dominantly visible.
Our results have implications for managerial choices for the use of the corporate brand in product communications. Specifically, the findings suggest that when a company wants to leverage its CA associations, a monolithic branding strategy (i.e., a dominant visibility of the corporate brand) is most effective. An endorsed strategy (i.e., a low CBD) seems to be effective only when products are perceived as high-involvement products. When a company wants to leverage associations with its CSR, an endorsed strategy seems to be the most effective. This is especially the case when the product is perceived as fitting well with the corporate brand and is perceived as a low-involvement product.
Although this study reports several important findings, it is not without limitations. First, we assessed people's associations with a single parent company, which likely induced truncation of the measures of these variables. This implies that care must be taken when generalizing the results in this study to situations in which people's corporate associations are extremely favorable or extremely unfavorable. Further research should corroborate our findings by studying multiple organizations or by using experimental manipulations of corporate associations. Second, we did not include measures of people's CA and CSR associations with the subsidiary brands in our study. The reason for this choice was that the emphasis of the study was on associations with the corporate brand. In addition, some of the subsidiary brands we used were relatively unknown to the public; thus, it seemed unlikely that most respondents would be able to answer questions about specific cognitions regarding these brands. Third, we measured the independent and dependent variables in the same questionnaire, which potentially could inflate the reported relationships (e.g., Feldman and Lynch 1988). We tried to address this by measuring the independent variables after measuring the dependents. Still, it could be possible that respondents' answers on the dependent measures (i.e., product responses) influenced their responses on the independent measures (i.e., corporate associations).
In this study, we examined the role of CA and CSR associations on customer reactions. A worthwhile issue for further research is the generalizability of our results to judgments of stakeholders other than customers. For example, corporate brands are also used on the job market and the stock market. To what degree do CA and CSR associations play a role in these contexts? What would be the influence of corporate branding strategies in these contexts? A priori, we expect that CSR associations play a larger role in the context of evaluating jobs and stocks because they are likely to be perceived as more diagnostic. Therefore, in these cases, the role of CSR associations may be similar to the role of CA associations in our study.
In addition, it would be worthwhile to replicate our findings in the context of other types of companies, industries, and products. Both the diagnosticity and the accessibility of CA and CSR associations may be different in other contexts. For example, recent research has shown that the diagnosticity of CA associations may be lower for products that involve a low degree of risk (Gürhan-Canli and Batra 2004). Similarly, CSR associations may be more diagnostic for products that are positioned as socially responsible, such as social/green investment funds or environmentally friendly products. Furthermore, the accessibility of CA and CSR associations may depend on the familiarity of the parent company (which was moderate in our case) and its positioning strategy. For highly familiar companies that position themselves on CA- and/or CSR-related attributes, it is likely that these types of associations will have larger overall effects on product evaluations but also that the moderating effects of CBD will be weaker because of the higher accessibility of these associations.
The authors thank the anonymous JM reviewers for their many helpful comments and suggestions.
( n1) We thank an anonymous reviewer for suggesting the latter two effects.
( n2) Although the name of this variable suggests a focus on employee treatment, it addresses the expertise of employees and management.
( n3) We thank an anonymous reviewer for this suggestion.
( n4) Note that the model yielded an inappropriate solution (a negative estimate of the error variance or "Heywood case") for one of the items on the involvement scale. We remedied this by forcing the estimate of the error variance for this item to be positive. This strategy is justifiable in our case, given the relatively large size of our sample (see Dijkstra 1992).
( n5) For purchase intention, the interaction among CSR, fit, and CBD is also significant (b = -.31, t = -2.11), but the interaction among CA, fit, and CBD is not (b = .26, t = 1.58). The pattern of conditional effects that constitutes the significant interaction is similar to that for product attitude.
( n6) For purchase intentions, neither of these two interactions were significant (for CA x involvement x CBD, b = -.05, t = -.33; for CSR x involvement x CBD, b = -.08, t = -.76).
( n7) We thank an anonymous reviewer for this suggestion.
( n8) The product they viewed was that of Subsidiary B for the retail banking market.
Legend for Chart:
C - Low CBD Low Fit Low Involvement
D - Low CBD Low Fit High Involvement
E - Low CBD High Fit Low Involvement
F - Low CBD High Fit High Involvement
G - High CBD Low Fit Low Involvement
H - High CBD Low Fit High Involvement
I - High CBD High Fit Low Involvement
J - High CBD High Fit High Involvement
A B
C D E F
G H I J
CA Accessibility
Medium Medium Medium Medium
High High High High
Diagnosticity
Low Low High High
Low Low High High
Diagnosticity threshold
Low High Low High
Low High Low High
CSR Accessibility
Medium Medium Medium Medium
High High High High
Diagnosticity
Low/medium Low/medium Low/medium Low/medium
Low/medium Low/medium Low/medium Low/medium
Diagnosticity threshold
Low High Low High
Low High Low High Legend for Chart:
A - Subsidiary
B - Retail Banking Market
C - Wholesale Banking Market
A B
C
A Industrial disability insurance (n = 39)
Employee benefit plan (n = 37)
B Ordering stocks and bonds through the Internet (n = 32)
Payment service within Europe (n = 30)
C Investment fund mortgage (n = 44)
Real estate finance for entrepreneurs (n = 36)
D Financial consultancy for prospective lawyers (n = 24)
Consultancy for succession problems in
family-owned businesses (n = 31)
Notes: For reasons of confidentiality, the brands are labeled
A-D. Legend for Chart:
B - Main Effects Only B
C - Main Effects Only t
D - Main Effects + Two-Way Interactions B
E - Main Effects + Two-Way Interactions t
F - Full Model B
G - Full Model t
A B C D E
F G
(Constant) -.15 -.91 -.14 -.87
-.16 -1.04
Product A: retail .21 1.03 .16 .81
.15 .76
Product B: retail -.06 -.26 -.11 -.55
-.08 -.39
Product C: retail -.05 -.26 -.10 -.51
-.09 -.47
Product D: retail -.22 -.96 -.15 -.66
-.09 -.38
Product A: wholesale .43 2.08 .39 1.94
.39 1.99
Product B: wholesale .25 1.15 .20 .93
.20 .96
Product C: wholesale .20 .95 .09 .44
.10 .51
CA .34 4.64 .24 2.35
.22 2.13
CSR .06 1.00 .27 2.79
.28 2.99
CBD .07 .70 .09 .89
.09 .84
Fit .27 6.55 .16 2.70
.19 3.17
Involvement .12 3.63 .20 4.25
.18 3.84
CA x fit .01 .21
-.12 -1.59
CA x involvement .03 .62
.15 2.37
CA x CBD .14 .98
.14 1.03
CSR x fit .09 1.97
.24 3.68
CSR x involvement -.07 -2.06
-.15 -3.10
CSR x CBD -.33 -2.75
-.31 -2.61
Fit x CBD .19 2.36
.16 1.98
Involvement x CBD -.13 -1.96
-.08 -1.29
CA x fit x CBD
.24 2.29
CA x involvement x CBD
-.24 -2.69
CSR x fit x CBD
-.30 -3.18
CSR x involvement x CBD
.15 2.20
Adjusted R² .36 .40
.43
Notes: All coefficients are unstandardized regression
coefficients.DIAGRAM: FIGURE 1 Research Model: The Effect of CBD, Fit, and Involvement on the Degree to Which CA and CSR Associations Influence Product Attitudes
PHOTO (BLACK & WHITE): FIGURE 2 Sample Advertisements with Low (Left) and High (Right) CBD
GRAPH: FIGURE 3 Effect of CSR on Product Attitude for Different Levels of CBD and Fit
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A: Measures
Legend for Chart:
B - Scale
D - Alpha
A B D
CA associations Products and services .88
• Do you think that [parent
company] develops innovative
products and services?
• Do you think that [parent
company] offers high-quality
products?
• Do you think that [parent
company] offers products with
a good price-quality ratio?
Workplace environment
• Do you think [parent
company] is well managed?
• Do you think that [parent
company] employs talented
people in comparison with
competitors?
CSR associations • Do you think that [parent .85
company] supports good causes?
• Do you think that [parent
company] behaves responsibly
regarding the environment?
Fit • Do you think that this .84
product fits the image of
[parent company]?
• Do you think that this
is a logical product for
[parent company] to market?
Involvement • How essential do you .85
find this type of products?
(endpoints: "essential" and
"not essential")
• How useful do you find
this type of products?
(endpoints: "useful" and
"useless")
Product attitude Quality (endpoints: "very low" .90
and "very high")
• How favorable is your
judgment of this product?
• What do you think about
the quality of this product?
• What do you think about
the quality of this product
in comparison with similar
products?
• How high do you think
the returns of this product
are for the customer?
Appeal
• Do you find this product
sympathetic?
• Do you find this product
attractive?
• Does this product give
you a pleasant feeling?
Reliability
• Do you find this product
reliable?
• Does this product give
you a safe feeling?
Purchase intention • If you were planning .81
to buy a product of this type,
would you choose this product?
• Would you purchase this
product?
• If a friend were looking
for a product of this type,
would you advise him or her
to purchase this product?
B: Descriptive Statistics and Correlations
Legend for Chart:
B - Descriptive Statistics Mean
C - Descriptive Statistics Standard Deviation
D - Correlations CA
E - Correlations CSR
F - Correlations Fit
G - Correlations Involvement
H - Correlations Product Attitude
A B C D E
F G H
Total Sample
CA 5.12 .93
CSR 4.58 1.09 .61(**)
Fit 5.45 1.32 .24(**) .15(*)
Involvement 4.45 1.58 .01 .04
.13(*)
Product attitude 4.60 1.05 .42(**) .33(**)
.44(**) .26(**)
Purchase intention 3.67 1.52 .39(**) .33(**)
.38(**) .27(**) .67(**)
High CBD Condition
CA 5.18 .94
CSR 4.63 1.14 .62(**)
Fit 5.44 1.32 .30(**) .12
Involvement 4.37 1.54 .04 .09
.04
Product attitude 4.64 1.04 .45(**) .24(**)
.54(**) .17(*)
Purchase intention 3.79 1.58 .47(**) .31(**)
.41(**) .26(**) .64(**)
Low CBD Condition
CA 5.05 .92
CSR 4.53 1.03 .60(**)
Fit 5.45 1.33 .16 .19(*)
Involvement 4.54 1.63 -.02 -.03
.22(*)
Product attitude 4.54 1.07 .38(**) .44(**)
.33(**) .37(**)
Purchase intention 3.53 1.44 .27(**) .36(**)
.34(**) .30(**) .72(**)
(*) p < .05.
(**) p < .01.~~~~~~~~
By Guido Berens; Cees B. M. van Riel and Gerrit H. van Bruggen
Guido Berens is an assistant professor, Department of Business-Society Management, Rotterdam School of Management, Erasmus University.
Cees B.M. van Riel is Professor of Corporate Communication, Department of Business-Society Management, Rotterdam School of Management, Erasmus University.
Gerrit H. van Bruggen is Professor of Marketing, Department of Marketing Management, Rotterdam School of Management, Erasmus University.
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Record: 39- Corporate Environmentalism: Antecedents and Influence of Industry Type. By: Banerjee, Subhabrata Bobby; Iyer, Easwar S.; Kashyap, Rajiv K. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p106-122. 17p. 2 Diagrams, 5 Charts. DOI: 10.1509/jmkg.67.2.106.18604.
- Database:
- Business Source Complete
Corporate Environmentalism: Antecedents
and Influence of Industry Type
How does a business firm manage its relationship with the natural environment? What are the factors that influence the choice of such strategies? Does industry type matter? The authors introduce and operationalize the concept of corporate environmentalism in an effort to answer these questions. Using stakeholder theory, the authors identify four important antecedents to corporate environmentalism, namely, public concern, regulatory forces, competitive advantage, and top management commitment. The authors then use a political-economic framework to develop testable hypotheses. To test the hypotheses, the authors perform multigroup path analysis on data gathered from more than 240 firms. They find that corporate environmentalism is related to all hypothesized antecedents and that industry type moderates several of those relationships. In the high environmental impact sector, public concern has the greatest impact on corporate environmentalism, followed by regulatory forces. In the moderate environmental impact sector, competitive advantage has the greatest impact on corporate environmentalism, followed by regulatory forces. There are strong direct and mediating influences from top management commitment, which is the antecedent with the greatest impact on both industry groups. The influences of regulatory forces, public concern, and competitive advantage are all significantly mediated by top management commitment and moderated by industry type. The empirical findings and the ensuing discussion will be of interest to managers and public policy officials.
Serious efforts to study the extent and nature of inter-action between business activities and the natural environment are of recent vintage.[ 1] These research efforts span many disciplines, including ecology (Carson 1962), law (Calvo y Gonzales 1981), history (Adler 1995), business strategy (Menon and Menon 1997; Walley and Whitehead 1994), and organizational analysis (Shrivastava 1995). Many academic journals, such as Academy of Management Review, Academy of Management Journal, Journal of Advertising, Psychology and Marketing, Long Range Planning, and Journal of Marketing Management, have published special issues on corporate environmentalism. Two streams of research on the firm-environment relation-ship have emerged. In the first stream, the focus is on changes required in the managerial and policy mindset to bring about the required paradigm shift in an organization (Gladwin, Kennelly, and Krause 1995; Hart 1997; Starik and Rands 1995). The second stream of research is case study oriented with emphasis on business economics or competitiveness (Porter and van der Linde 1995; Sharma and Vredenburg 1998; Shrivastava 1995). Our work is situated in the latter stream but departs from a case orientation and has a strong empirical base.
There are two facets to corporate environmentalism: orientation and strategies. Inthe marketing strategy literature, Menon and Menon (1997) have introduced the concept of enviropreneurial marketing--one that reflects a company's orientation and commitment to the environment. Merely emphasizing orientation without concern for strategic implementation might lead to a charge of "greenwashing" (Entine 1995). Therefore, we propose that corporate environmentalism include two dimensions: environmental orientation and environmental strategy. Environmental orientation is the recognition by managers of the importance of environmental issues facing their firms, and environmental strategy is the extent to which environmental issues are integrated with a firm's strategic plans. Thus, we define corporate environmentalism as the recognition of the importance of environmental issues facing the firm and the integration of those issues into the firm's strategic plans.
A firm's environmental orientation, usually expressed in mission statements, may be focused internally or externally (Banerjee 2002). Internal environmental orientation reflects a company's internal values, standards of ethical behavior, and commitment to environmental protection. External environmental orientation refers to the aspects of a firm's environmental orientation that affect its relationships with external constituencies, such as financial or community stake-holders. To frame our discussion on translating orientation into strategies, we use the four levels of strategy, namely, enterprise, corporate, business, and functional strategies, proposed by Schendel and Hofer (1979).[ 2] Enterprise strategy speaks to the fundamental mission of the firm and its role in society, and few businesses show evidence of having integrated environmental concerns at this level. Corporate strategy pertains to the kinds of businesses a firm should enter to meet its enterprise strategy goals. Strategies leading to product differentiation or targeting niche markets exemplify business-level environmental strategies, whereas modifying operating procedures within different functions, such as advertising or sales, is typical of environmental strategies at the functional level.
For our purposes we focus only on corporate and business-or functional-level strategies. Strategies regarding entering new businesses, choice of technology, plant locations, and research and development investments are generally decided at higher levels within a firm (Banerjee 1999). We call these environmental corporate strategies. Integration is the key issue, and environmental concerns are not treated as ex post issues after corporate strategic plans are made but as ex ante concerns to be integrated with the corporate strategic planning process. Environmental concerns can also influence business-and functional-level strategies. Targeting the environmentally conscious consumer segment--estimated at more than $200 billion annually (U.S. Environmental Protection Agency [EPA] 1990)--and developing new environmentally friendly products are examples of marketing strategies that could result from such concerns. We call these business-and functional-level strategies environmental marketing strategies.
What factors lead a firm to embrace environmentalism? Is it a response to regulation or part of a strategy to gain competitive advantage in the marketplace? Is it a reaction to public outcry or part of a proactive business strategy? We combine stakeholder theory with a political-economic framework in developing our theoretical model. We use stakeholder theory (Harrison and Freeman 1999; Henriques and Sadorsky 1999) to identify environmental stakeholders and a political-economic framework to specify the relationships.
Stakeholder Theory
Variations in corporate performance arise from differences in strategies designed to satisfy multiple stakeholders (Harrison and Freeman 1999; Henriques and Sadorsky 1999). We define environmental stakeholders as individuals or groups that can affect or be affected by the achievement of a firm's environmental goals (Freeman 1984). According to this definition, the following are environmental stakeholders: regulators, organizational members, community members, and the media (Henriques and Sadorsky 1999). Regulators mandate compliance to environmental standards and are an important antecedent to environmentalism. Organizational stakeholders represent an assortment including shareholders, customers, and employees. Community stakeholders include many nongovernmental organizations and other potential lobbies that have an interest in the environment; they have the ability to mobilize public opinion. The manner in which a firm responds to these stakeholders depends on its environmental orientation, so we identify public concern as an important antecedent to corporate environmentalism. For stakeholders to be successful in influencing a firm's strategy, they need access to and the attention of top management (Agle, Mitchell, and Sonnenfeld 1999). Therefore, we identify top management as a key antecedent that not only influences corporate environmentalism directly but also helps modify the influence of other stakeholders. Whereas the media have been proposed as stakeholders, we do not identify any specific antecedent representing them for two reasons. First, it is hard to isolate the influence of the media because of their pervasive nature. Second, other stakeholders use the media to advance their agendas, and in that sense, the media need not be separately identified as a stakeholder.
Political-Economic Framework
A firm's choice of strategies in complex social environments is driven by consideration of political and economic forces within and external to the firm (Stern and Reve 1980); this is a useful framework to explain how each of these antecedents influences corporate environmentalism. Crossing these dimensions results in four forces: external political, external economic, internal political, and internal economic.
Public concern for the environment is growing deeper every year (Stisser 1994) and is a vivid exemplar of external political force. Regulations represent another external political force and have kept pace with the growing public concern (Buchholz 1993). Compliance with regulations may also impose added costs and in that sense could be viewed as an external economic force. A strategy of being a "green" company can be the result of an external economic impetus that offers superior performance through strategic and competitive advantages (Hart 1997; Porter and van der Linde 1995). New green products, introduced in response to this trend, accounted for more than 13% of all new product introductions in 1991 (Ottman 1993). Strategies perceived to enhance the economic value of the firm, such as material substitution and cost reduction, will likely unleash internal economic forces and be of interest to various organizational units. Such forces tend to originate in boundary-spanning units such as marketing and sales before permeating into other units such as manufacturing, design, or procurement. Finally, top management, a key stakeholder, wields internal political force and mediates the support it provides corporate environmentalism (Shrivastava 1995; Shrivastava and Hart 1995). In summary, our conceptual model of corporate environmentalism (see Figure 1) includes public concern, regulatory forces, competitive advantage, and top management commitment as antecedents to corporate environmentalism.
Industry as a Moderator
Do these antecedents affect all industries equally? The emerging view, that different sectors of the economy need to be dealt with differently (Fiorino 1996), is based on the following logic: Command-and-control regulations have assumed that a uniform policy will work for all industries and all firms within an industry and have superseded judgments of managers who are closer to the problem (Fiorino 1996). The alternative view, recommended by the President's Council on Sustainable Development (1996), suggests a sector-based approach. This approach was endorsed by the EPA, which identified six sectors for immediate implementation (U.S. EPA 1994). These policy initiatives, not necessarily empirically supported, suggest that industry will be a moderator. There are many ways to operationalize industry, such as competitive intensity, concentration, and barriers to entry and exit, but we dichotomize industries on the basis of environmental impact. (To test this hypothesis empirically, we dichotomized industry type into high environmental impact [HEI] and moderate environmental impact [MEI] sectors. We provide details subsequently.) Our decision was based on significant differences that we documented along four dimensions: amount of pollution, level of public concern, stringency of environmental regulations, and environmental liability risks. First, there is empirical evidence that amount of pollution and its toxicity vary from industry to industry. The EPA, on the basis of the Toxic Chemical Release Inventory, has consistently rated the chemical industry as one of the biggest polluters (Hoffman 1999; Ochsner 1998) and labeled utilities and manufacturing industries "dirty" (Levy 1995; Ochsner 1998). Second, level of public concern for the environment varies with industry, and its impact will be more acute on dirty industries, such as chemicals, than on "clean" ones, such as consulting. Third, firms in dirty industries are more severely regulated than their clean counterparts, so the cost of compliance is significantly higher (Jaffe and Palmer 1997; Lanjouw and Mody 1996). Fourth and last, environmental litigation disproportionately affects smokestack industries that face greater environmental liability risks. Hoffman (1999) has documented a 5400% increase in environmental cases filed in the courts between 1970 and 1993. These dimensions justify our operationalization, enabling us to test for the moderating role of industry.
H1: Industry type will moderate the influence of public concern, regulatory forces, competitive advantage, and top management commitment on environmental orientation and environmental strategy.
Public Concern for the Environment
Several surveys of the North American public indicate that concern for the environment remains high on the public agenda and has been so since the late 1980s (Roper Organization Inc. 1990, 1992). Public concern for the environment is partly an external political force exerted by community stakeholders, such as environmental activists, and partly an external economic force represented by customers demanding environmentally friendly products. It can influence corporate environmentalism in two ways: First, firms may present a green image to indicate their responsiveness to public concern, and second, firms could develop environmental strategies to target green consumers. Peretz, Bohm, and Jasienczyk (1997) conclude that manufacturing industries such as metal products, rubber, and plastics produce greater amounts of hazardous waste than other industries, such as food manufacturing or packaged consumer goods. Therefore, we can expect public concern for the environment to vary with people's perceptions of environmental problems prevalent in that industry (Stisser 1994). We can also expect the increased public concern to influence environmental orientation and strategy.
H2: Public concern will be positively related to environmental orientation.
H3: Public concern will be positively related to environmental strategy.
H4: The effect of public concern on environmental orientation will be greater in the HEI sector than in the MEI sector.
H5: The effect of public concern on environmental strategy will be greater in the HEI sector than in the MEI sector.
Regulatory Forces
Regulators are important stakeholders that exert external political and economic forces on the firm; regulations on packaging content (McCrea 1993), product formulation (Ottman 1993), and distribution channels (Green Market Alert 1993) have influenced business strategies. Moreover, in the United States alone, more than one-half of the $100 billion per year spent on environmental protection is borne by industry (Piasecki 1995). Environmental regulations and the associated compliance costs vary from industry to industry. Smokestack industries attract greater legislation than others because their environmental risks and liabilities are greater (Hoffman 1999; Ochsner 1998). On the basis of a survey of 20 chemical firms, Ochsner (1998) finds that environmental legislation is the most important incentive for developing pollution prevention strategies. In general, strategies of pollution prevention or source reduction, as opposed to pollution control and waste cleanup, are more common in industries that are subject to strict environmental legislation. These same industries are more likely to have comprehensive environmental management systems, such as total quality environmental management, or comprehensive environmental standards, such as ISO 14000. Therefore, we expect type of industry to moderate the influence of regulations on corporate environmentalism.
H6: Regulatory forces will be positively related to environ-mental orientation.
H7: Regulatory forces will be positively related to environ-mental strategy.
H8: The effect of regulatory forces on environmental orientation will be greater in the HEI sector than in the MEI sector.
H9: The effect of regulatory forces on environmental strategy will be greater in the HEI sector than in the MEI sector.
Competitive Advantage
Competitive advantage is a powerful economic force, internal and external to a firm, that influences corporate environ-mentalism (Lee and Green 1994; Taylor and Welford 1993). Corporate environmentalism offers competitive advantage by significantly lowering costs in the long run or helping differentiate products and services (Porter and van der Linde 1995). On the basis of a study of 16 firms, Roy (1999) concludes that designing a "greener" product helps create new markets and increase or maintain market share. Many firms, including 3M, AT&T, Carrier, DuPont, and IBM, have experienced cost reductions resulting from environmentalism. Typically, firms achieve cost savings by using cheaper recycled raw materials or through energy savings and process improvements (Smith 1991). For example, Procter & Gamble used a dual source reduction strategy--concentrating the product and designing a refillable pouch--when introducing Downy, a product that was environmentally friendly and cost effective (Simon 1992). The 3M Company saved more than $1 billion and improved its competitive position with a corporate program--3P Plus--that emphasized source reduction over pollution control measures (Shrivastava 1995).
Targeting environmentally conscious consumers can also garner competitive advantage. Of today's consumers, 25% are environmentally conscious (Roper Organization Inc. 1990, 1992). Two programs adopted by the Boston Park Plaza Hotel gained it competitive advantage by reducing costs and increasing its customer base (NBC Nightly News 1993). The Body Shop and Patagonia Inc. derive competitive advantage and enjoy unique market positions from their green strategic market positioning strategies (Kearins and Klÿn 1999; Sweeney 1997). However, these advantages will vary across industries. In the MEI sector, because regulations are few and strategic options many, firms such as Body Shop or Patagonia can derive competitive advantage through corporate environmentalism. In contrast, firms in the HEI sector may experience cost savings initially but cannot sustain the competitive advantage because regulations tend to level the playing field very soon (Sharma and Vredenburg 1998). Given the relatively high visibility of environmental issues in the HEI sector, responding to environmental concerns is an imperative and not a matter of strategic choice.
H10: Competitive advantage will be positively related to environmental orientation.
H11: Competitive advantage will be positively related to environmental strategy
H12: The effect of competitive advantage on environmental orientation will be greater in the MEI sector than in the HEI sector.
H13: The effect of competitive advantage on environmental strategy will be greater in the MEI sector than in the HEI sector.
Top Management Commitment
Top management commitment is a strong internal political force that can foster corporate environmentalism (Drumwright 1994; Starik and Rands 1995; Taylor and Welford 1993). Top management's direct involvement in environmental issues is more prevalent in firms that perceive regulations as a major threat or whose customers come from the environmentally friendly segment (Banerjee 1998; Coddington 1993). Top management demonstrates its commitment to environmentalism by appointing senior managers responsible for overseeing the firm's environmental orientation and strategies. In other cases, such as DuPont, Digital Equipment Corporation, Kodak, and 3M, members of the top management are directly involved in environmental issues facing their firms (Coddington 1993). Such direct involvement promotes a corporate atmosphere conducive to policy implementation and, when backed with adequate incentives for employees, leads to improved environmental performance.
In many cases, top management will lobby or form alliances with governmental agencies in writing regulations that will eventually affect the business. The Super-Efficient Refrigerator Program, an alliance between consortia of major appliance manufacturers on the one hand and the U.S. EPA and the U.S. Department of Energy on the other hand, is a case in point (Iyer and Gooding-Williams 1999). This alliance, formed with full cooperation from top management at all participating firms, was asked to draft regulation that would increase the production and sale of energy-efficient refrigerators and is credited with the creation of a new generation of energy-efficient appliances.
In other cases, firms partner with other businesses or nonprofit organizations to modify their industry in a manner beneficial to their own business. The most celebrated business-nonprofit alliance that led to modified packaging is the one between McDonald's and the Environmental Defense Fund. Coca-Cola and Hoechst Celanese formed an alliance to develop a new bottle based on postconsumer plastic; this resulted in enhancing Coca-Cola's image and market share in the competitive soft drink market (Biddle 1993).
H14: Top management commitment will be positively related to environmental orientation.
H15: Top management commitment will be positively related to environmental strategy.
H16: The effect of top management commitment on environ-mental orientation will be greater in the HEI sector than in the MEI sector.
H17: The effect of top management commitment on environ-mental strategy will be greater in the HEI sector than in the MEI sector.
Top management influences corporate environmentalism directly and, by recognizing only the more salient, legitimate, or powerful stakeholders (Mitchell, Agle, and Wood 1997), also mediates the effects of all other factors. For example, a marketing manager might change packaging to include more recycled content, but if the level of public concern is high and receives the attention of top management, the result might be a modification of corporate environmental strategy, such as developing and marketing environmen-tally friendly products. Top management was responsible when McDonald's modified its packaging (Simon 1992) and when Du Pont phased out chlorofluorocarbons (Business and the Environment 1993). Several case studies indicate that public concern was instrumental in chief executive officers' (CEOs') development of policy guidelines on environ-mentally friendly purchasing practices (Drumwright 1994). Environmental regulations also get top management's attention (Agle, Mitchell, and Sonnenfeld 1999), shifting the company's focus from mere compliance to pollution prevention (Buchholz 1993).
H18: Top management commitment will mediate the impact of public concern on environmental orientation.
H19: Top management commitment will mediate the impact of public concern on environmental strategy.
H20: Top management commitment will mediate the impact of regulatory forces on environmental orientation.
H21: Top management commitment will mediate the impact of regulatory forces on environmental strategy.
H22: Top management commitment will mediate the impact of competitive advantage on environmental orientation.
H23: Top management commitment will mediate the impact of competitive advantage on environmental strategy.
The CEOs of dirty industries will be under greater pressure because public concern for the environment will vary with the perceived amount of environmental problems prevalent in an industry (Peretz, Bohm, and Jasienczyk 1997). Therefore, we hypothesize that the effect of public concern on top management will be greater in the HEI sector. In general, smokestack industries spend more on regulatory compliance and are more likely to be subject to legislation (Hoffman 1999; Ochsner 1998). As a result, firms in the HEI sector are selectively exposed to risks and liabilities that arise from environmental litigation. Thus, the effect of regulatory forces on top management will be greater in the HEI sector than in the MEI sector. The impact of competitive advantage on corporate environmentalism will be mediated by top management and will vary with industry. The Body Shop and Patagonia seek unique market positions from their greenness (Kearins and Klyn 1999; Sweeney 1997). Firms in the HEI sector are more regulated, which leaves managers fewer options than their counterparts in the MEI sector. Thus, the effect of competitive advantage on top management will be greater in the MEI sector.
H24: The effect of public concern on top management commitment will be greater in the HEI sector than in the MEI sector.
H25: The effect of regulatory forces on top management commitment will be greater in the HEI sector than in the MEI sector.
H26: The effect of competitive advantage on top management commitment will be greater in the MEI sector than in the HEI sector. Environmental Orientation and Environmental Strategy
Organizational learning about environmentalism occurs in the collective consciousness of a firm, and over time, the resultant knowledge is fused and internalized within the corporate values and beliefs. After encoded within a firm's standard operating procedures, environmental values eventually influence a firm's strategies (Mintzberg 1994a, b). For example, in Eastman Kodak Company, the CEO chairs a committee on environmental responsibility, an influential group that promotes education and training and sets standards used to audit performance (Poduska, Forbes, and Barber 1992). We hypothesize that environmental orientation will positively influence environmental strategies, though its influence will vary with industry. The effect of all antecedents, that is, public concern, regulations, and top management commitment, will be more acutely felt by firms in the HEI sector, and therefore the effect of environmental orientation on environmental strategy will be greater in the HEI sector than in the MEI sector.
H27: Environmental orientation will be positively related to environmental strategy.
H28: The effect of environmental orientation on environmental strategy will be greater in the HEI sector than in the MEI sector.
Our data were from surveys distributed to a large listing of managers in North America from a diverse range of firms and industries. The survey items were adopted from published research (Banerjee 2002), pretested on a convenience sample of regional managers, and modified before the survey was mailed out to our sample.
Sampling Frame and Mailing Procedure
We used the American Marketing Association's directory listing names, titles, and addresses of managers as the sampling frame. We used a systematic random sampling method to draw a sample of 944 managers from this directory. Following the procedure recommended by Dilman (1978), we communicated three times with our potential respondents. The first mailing included a cover letter, a questionnaire, and a business reply envelope. The cover letter described the purpose of our survey and included a sample of the format in which survey results would eventually be mailed to interested respondents. The second mailing, sent ten days later, was ahandwritten reminder on a postcard. The third and final mailing was sent yet another ten days after and was essentially identical to the first mailing. Our survey included an item that assessed the respondent's self-reported knowledge of environmental issues; on the basis of this item, we discarded some responses and used only those from knowledgeable respondents in our subsequent analyses. Of the 944 surveys mailed, 42 were returned because of address changes, and 14 were incomplete for a variety of reasons, leaving 888 successful contacts. We received 291 responses (32.8% response rate) but discarded 48 responses because of the respondents' poor knowledge of environmental issues, which left us with 243 usable questionnaires (effective response rate of 27.4%). Moreover, we checked for potential biases--and there were none--by randomly selecting and contacting 10% of nonrespondents by telephone.
Operationalization
There were between three and eight items measuring any one construct, all of which used seven-point Likert scales: 1 = "strongly disagree," 7 = "strongly agree." The first section had items measuring environmental orientation, the second contained items measuring environmental strategy, the third included items measuring all the antecedents, and the last assessed the demographic profile of the respondent and that of the firm.
We based public concern (PC) on the responding manager's perceptions of importance assigned by the general public to protecting the environment and the potential customer demand for environmentally friendly products and services. We used items dealing with managerial perceptions of the influence of government regulation on strategy and on the level of environmental regulation faced by the industry to measure regulatory forces (RF). We measured competitive advantage (CA) using items that focused on investment in research and development, cost savings, and growth opportunities in new markets. We measured top management commitment (TM) as the respondent's perception of top management's environmental commitment to and support for environmental initiatives.
To measure internal environmental orientation (IEO), we used items that referred to the importance of preserving the environment and diffusing such values companywide. The external environmental orientation (EEO) scale had items measuring managerial perceptions on the relation between environmental issues and the firm's financial health. To measure environmental corporate strategies (ECS), we included items that assessed the degree to which natural environment was integrated with the firm's strategic planning processes. Last, we measured environmental marketing strategies (EMS) using items that pertained to the degree to which the firms' product-market decisions were influenced by environmental concerns.
Sample Profile
Companies in the sample had annual sales ranging from $1 million to $60 billion, with a median value of $442 million. The number of employees ranged from 7 to 200,000, with a median figure of 1350 employees. Of respondents, 88% claimed to be knowledgeable about their firms' environmental activities, and only their responses were included in the analysis.
Two independent judges categorized firms into seven industries: manufacturing (28%), chemicals (18%), consumer products (16%), foods (14%), services (12%), pharmaceuticals (6%), and utilities (6%). On the basis of the same coders' judgment of environmental impact, we dichotomized firms into the HEI sector (i.e., manufacturing, chemicals, pharmaceuticals, and utilities) or the MEI sector (i.e., services, consumer products, and foods). Our categorization was consistent with that proposed by the U.S. Bureau of the Census (1993).
Comparison of Means
Differences between groups were statistically significant (F = 4.47, p < .0001). Mean scores on EO and ES were significantly higher for firms in the HEI sector than for those in the MEI sector (see Table 1). The summed scores (of 28) on IEO and EEO were 18.87 and 19.85 in the HEI group and
15.52 and 17.71 in the MEI group. Summed scores for ECS (of 35) and EMS (of 21) were 18.06 and 12.17 for firms in the HEI group and 14.38 and 9.64 for firms in the MEI group. Firms in the MEI sector had significantly lower scores on all antecedents. Pharmaceuticals and chemicals had the highest summed scores on all constructs, and service industries had the lowest scores, which lent face validity to our measures.
We assessed the quality of our measurements with confirmatory factor analysis and followed accepted norms (Hart-line, Maxham, and McKee 2000) in including only items that loaded in excess of .5 (see the Appendix). The overall fit supports the measurement model given the large number of indicators used (chi2436d.f. = 859.9, p < .001). The chi2/degrees of freedom (d.f.) ratios in the range 1.50-3.0 are acceptable, and lower values indicate a good fit (Byrne 1989); we had a ratio of 1.97, which indicated a good fit. The root mean square error of approximation (RMSEA) was .06, the comparative fit index was .92, and the parsimony normed fit index was .75 (see Table 2). Scale reliabilities far exceed the recommended threshold of 70% (Hair et al. 1992), and the variance extracted exceeded the recommended threshold of 50% for all constructs except EEO. Thus, our measures are reliable and cover at least one-half of a construct's domain.
Measurement Invariance
To establish similarity in meanings of all constructs in both groups, we conducted a multigroup confirmatory factor analysis (Steenkamp and Baumgartner 1998). We estimated a series of models in which we progressively relaxed constraints on factor loadings, factor variances and covariances, and error variances in the two groups. The overall chi2 was 1696.4 with 964 d.f. for the multigroup constrained model and 1661.2 with 940 d.f. for the unconstrained model. The difference in chi2 statistics was 35.2 with 24 d.f. (p > .06). When the factor loadings and factor variances and covariances were set equal, partial measurement invariance, a necessary and sufficient condition for structural comparisons across groups, was established (Steenkamp and Baumgartner 1998). This validated the measurement model shown in Table 2 for both groups.
Multigroup Path Analysis
Because of the large number of indicators, we summed all scales to represent relevant constructs and tested our hypotheses with path analysis using LISREL 8 for Windows (Jöreskog and Sörbom 1993). We report the four models estimated in Table 3. First, we estimated a multigroup path model with all hypothesized paths in which path coefficients were restricted to be equal in both groups; we refer to this as the overall model (chi236d.f. = 53.6, p < .03, goodness-of-fit index [GFI] = .93, RMSEA = .05). Second, we eliminated 7 of 23 paths because of their insignificance and estimated a multigroup restricted model, in which path coefficients were restricted to be equal. In general, the restricted model fit the data better than the overall model (chi243d.f. = 55.3, p < .09, GFI = .93, RMSEA = .05) and provided support for some of our hypotheses. Third, we estimated a multigroup unrestricted model, in which all path coefficients were allowed to vary freely. Comparison of the fits between the restricted and unrestricted models helped us test for the moderating effects of industry type. Finally, we estimated a mixed model in which results of previous analysis guided our decisions to free or restrict path coefficients. Consistent wit prior research (Ganesan 1994; Siguaw, Simpson, and Baker 1998), we fixed the error variances of all constructs using composite reliabilities and allowed measurement errors for both factors of the dependent variables to correlate. This analysis enabled us to test the hypotheses (see the ensuing discussion and the summary in Table 4) and compute and compare indirect and total effects of each antecedent in both groups (see Table 5). Figure 2 displays the final path analytical model showing significant paths in both HEI and MEI groups.
Overall Hypothesis
To test our overall hypothesis (H1) that predicted a moderating role for industry, we compared the fits of the restricted and unrestricted models. The unrestricted model improved the fit (chi227d.f. = 27.6, p < .43, GFI = .96, RMSEA = .01); this difference in chi216d.f. = 27.7 was significant, in support of the hypothesized moderating effect of industry type (p < .04). Because the omnibus test was significant, we used the unrestricted model and proceeded to test for the moderating effects of each antecedent in three stages: First, we imposed equality restrictions on the path coefficients; second, we allowed the path coefficients to freely vary; and third, we compared the two fits, which resulted in a series of nested tests. Differences in chi2 between the constrained and unrestricted models enabled us to infer the relative impact of each antecedent in either group. The ensuing discussion is based on this comparative analysis and follows the order in which the hypotheses were originally proposed.
Public Concern: H2-H5
We predicted that PC would be positively related to EO and ES and that its impact in the HEI sector would be greater than in the MEI sector. We found that the PC -> IEO and PC -> EEO paths were not significant in either group. However, PC -> EMS was significant in both groups, whereas PC -> ECS was not in either industry. We conducted a nested chi2 test to examine the moderating effect of industry type. In one model, we constrained the path from PC to EMS to be equal and then compared the fit with that of a model in which these paths were not constrained. There was no deterioration in fit, but the added power resulted in improving the significance of the path coefficients. The difference in chi2 was 0 with 1 d.f., implying that industry did not moderate this relationship between PC and EMS.
Regulatory Forces: H6-H9
Our prediction regarding RF as an antecedent received mixed support. We found that RF had a positive effect on EEO and ECS but no impact on IEO or EMS. Furthermore, one path was significant in the HEI group (RF -> EEO), whereas the other was significant in both groups (RF -> ECS). The predicted moderating role for industry also received mixed support: RF had a significant effect on EEO only in the HEI sector, in support of the moderating role for industry type. However, when we set the path from RF to ECS to be equal in both groups, the resulting model showed no deterioration in fit (chi21d.f. = .72), suggesting that in the case of ECS, there was no moderating role for industry type.
Competitive Advantage: H10-H13
As an antecedent, CA had significant influence on all environmental orientation and strategy constructs. The CA -> EMS path was significant in both groups. For the other constructs, its influence was as follows: The CA -> IEO path was significant in the HEI group, whereas the CA -> ECS and CA -> EEO paths were significant in the MEI group. Our prediction of CA as an antecedent received mixed support. To test for the moderating effect of industry, we constrained each set of path coefficients leading to IEO and EEO to be equal in both groups. The difference in oo was significant (chi22d.f. = 6.77), indicating that industry moderated the influence of CA on EO. In the case of strategy constructs, a similar test was not significant (chi22d.f. = 3.26), suggesting that industry did not moderate the relationship between CA and ES.
Top Management Commitment (Direct Effects): H14-H17
There was mixed support for the hypotheses relating the role of top management commitment as an antecedent to environmental orientation and environmental strategy. One path coefficient, TM -> IEO, was significant in both groups, and three others, TM -> EEO, TM -> ECS, and TM -> EMS, were significant only in the HEI group. It seems that TM was an antecedent in many, but not all, cases. A nested chi2 test provided no support for the moderating effect of industry in the case of EO (chi22d.f. = 1.94) or ES (chi22d.f. = 4.28), implying that H15 was not supported.
Top Management Commitment (Mediating Effects): H18-H23
We used common metric standardized coefficients from the mixed model (see Table 3), which had an excellent fit (chi237d.f. = 35.96, p > .52, GFI = .96, RMSEA = .00), to calculate the indirect effects of the antecedents on EO and ES (see Table 5). Except for three paths, CA -> IEO, EMS in the HEI sector, and PC -> EMS in the MEI sector, we found indirect effects for all significant paths from the antecedents to the EO and ES constructs. These findings provide strong support for the mediating role played by TM. We found support for H17 regarding the moderating effect of industry in regard to the effect of PC on TM. The difference in chi2 was highly significant (chi21d.f. = 5.31), indicating that, as expected, the effect of PC on TM was higher in the HEI group than in the MEI group.
We report the direct, indirect, and total effect of all antecedents on the constructs of ES in Table 5. We found that TM significantly mediated the effect of PC on IEO and EEO in the HEI group. Although there was no direct effect of PC on EEO or IEO in this group, the indirect effects (.23 and .36 for EEO and IEO, respectively) were significant. In contrast, PC did not have any direct or indirect effects on the EO constructs in the MEI group. Regulatory forces had a significant direct (.27) and indirect (.11) effect on EEO in the HEI group. In comparison, there was no direct effect on EEO in the MEI group, but an equal indirect effect (.11) was evident in the MEI group. In both groups, RF had no direct effect and an equal indirect effect (.17) on IEO, which provides support for the hypothesis of the mediating role of TM. There was no direct or indirect effect of CA on EEO in the HEI group. In sharp contrast, we found a significant direct (.37) and indirect (.21) effect in the MEI group. With respect to IEO, we found that CA had a significant direct effect (.22) only for the HEI group but a larger significant indirect effect (.32) for the MEI group, which indicates that TM mediated this relationship. In summary, we conclude that TM mediates the relationship between the following EO constructs:
- PC and IEO in the HEI sector,
- PC and EEO in the HEI sector,
- RF and IEO in both sectors,
- RF and EEO in both sectors,
- CA and IEO in the MEI sector, and
- CA and EEO in the MEI sector.
Public concern had no direct effect on ECS in either group. However, there was an indirect effect (.16) in the HEI group, suggesting that TM mediated its effect on ECS. In the case of EMS, we found equal direct effects in both groups (.24), but a strong indirect effect (.15) only in the HEI group. Similarly, we found that RF had equal direct (.13) and indirect (.08) effects on ECS in both groups. In both groups, RF did not have a direct effect on EMS, and the indirect effect (.07) was the same for both groups. Finally, CA did not have a direct or indirect effect on ECS in the HEI group, but in the MEI group, there was a strong direct (.17) as well as indirect (.14) effect. In the case of EMS, though there was an equal direct effect (.27) in both groups, we found an indirect effect (.13) only in the MEI group. Therefore, we conclude that TM mediates the relationship between the following ES constructs:
- PC and ECS in the HEI sector only,
- PC and EMS in the HEI sector only,
- RF and ECS in both sectors,
- RF and EMS in both sectors,
- CA and ECS in the MEI sector only, and
- CA and EMS in the MEI sector only.
Top Management Commitment (Industry Effects): H24-H26
A nested chi2 test revealed that the stronger effect of PC on TM in the HEI sector was statistically significant (chi21d.f. = 5.29, p < .025). However, a similar test for the effect of RF on TM found no differences between the HEI and MEI sectors (chi21d.f. = .64, p < .4). The stronger effect of CA on TM in the MEI sector was also statistically significant (chi21d.f. = 9.18, p < .005), providing support for the hypothesized role of industry type as a moderator.
Environmental Orientation and Environmental Strategy: H27 and H28
We found one significant path in the HEI sector (IEO -> ECS) and two in the MEI sector (IEO -> ECS, EEO -> EMS), which implies mixed support for H27.With respect to H28, industry type did not moderate the relationship between EEO and EMS (chi21d.f. = .83) or between IEO and ECS (chi21d.f. =2.10).
We begin our summary with a caveat: Our research goal was to understand environmental strategy and not to distinguish "good" from "bad" companies. Environmental strategy is an emerging area of research dominated by exploratory single-industry case studies with little empirical evidence; our study is an attempt to fill that void. Because the proposed model fits the data well,[ 3] we were able to present the much-needed empirical support for the relationships between various antecedents and corporate environmentalism, as well as the moderating role for industry. Taken collectively, our results have many implications for strategy and public policy, which we discuss after itemizing our main findings.
Industry type
--was overall significant and moderated various relationships.
Public concern for the environment
--influenced environmental marketing strategy.
--had greater effect on top management commitment
in the HEI sector.
Regulatory forces
--influenced environmental corporate strategy.
--influenced top management commitment.
--had greater effect on external environmental orientation
in the HEI sector.
Competitive advantage
--influenced environmental marketing strategy.
--had greater effect on external environmental orientation
in the MEI sector.
--had greater effect on internal environmental orientation
in the HEI sector.
--had greater effect on environmental corporate strategy
in the MEI sector.
Top management commitment
--influenced external environmental orientation.
--influenced internal environmental orientation.
--influenced environmental strategy.
In the HEI sector, the antecedents ranked in order of decreasing
importance were top management commitment, public concern for
the environment, regulatory forces, and competitive advantage.
In the MEI sector, the antecedents ranked in order of decreasing
importance were top management commitment/competitive advantage,
regulatory forces, and public concern.Public concern was positively related only to environ-mental marketing strategy in both the HEI and the MEI sector. The impact of public concern on environmental marketing strategy was higher than its impact on environmental corporate strategy. This bias toward environmental marketing strategy may be based on the firm's ability to obtain immediate and quick benefits by implementing environmental marketing strategy as opposed to environmental corporate strategy. This is consistent with prior findings that environ-mental marketing strategies, such as green niche marketing strategies (Porter and van der Linde 1995; Shrivastava 1995) and consumer-oriented green advertising strategies (Banerjee, Gulas, and Iyer 1995), are lucrative and easier to implement. Our results also showed that public concern was indirectly related to environmental corporate strategy through top management commitment in the HEI sector only. Consistent with stakeholder theory, this signifies the importance accorded public opinion by top management trying to foster corporate environmentalism in the HEI sector. Its overall influence on environmental strategy was greater in the HEI sector. In this group, top management commitment partially mediated the relationship between public concern and environmental marketing strategy but completely mediated the relationship between public concern and environmental corporate strategy. This suggests that in the HEI sector, public concern only influences environmental marketing strategies, and top management involvement would be vital to influence environmental corporate strategy.
Regulatory forces had a significant direct influence on environmental corporate strategy but none on environmental marketing strategies. However, the indirect influence, mediated by top management commitment, was comparable at both levels of strategy and in both sectors, which suggests that regulations readily attract the attention of top management across all industries. Regulatory forces were positively related to external environmental orientation; this relation-ship was moderated by industry type. The impact of regulatory forces on external environmental orientation was higher in the HEI sector. This is probably because managers in HEI industries are more aware of noncompliance costs. Competitive advantage positively influenced internal environmental orientation in both sectors but influenced external environmental orientation only in the MEI sector. Internal environmental orientation involves developing corporate value and vision statements, efforts typically directed by top management. External environmental orientation pertains to aspects of a firm's relationship with its external stake-holders. In the HEI sector, regulations may disproportionately influence external environmental orientation, thereby diminishing the effect of competitive advantage on external environmental orientation. Because regulations are less important in the MEI sector, the effect of competitive advantage on external environmental orientation becomes significant. In the case of environmental strategies, our results are consistent with prior research findings that any competitive advantage derived from adopting environmental strategies varies across industries and not just from firm to firm within industries (Christmann 2000). External environmental orientation's impact on environmental marketing strategy was greater than its impact on environmental corporate strategy, and its impact was present in the MEI sector but absent in the HEI sector. Regulatory forces and public concern probably made corporate environmentalism an imperative in the HEI sector, thus diminishing the effect of competitive advantage. In the MEI sector, firms could gain competitive advantage (Sharma and Vredenburg 1998) from relatively low-cost environmental marketing strategies, such as changed packaging. Conversely, in the regulated HEI sector, high investments would be needed to realize competitive advantages. These explain the differential influence of competitive advantage on both levels of strategy and type of industry.
Top management commitment was positively related to all antecedents and, with its direct and mediating effects, emerged as the single most influential antecedent to corporate environmentalism. Its relationships with public concern and competitive advantage were moderated by industry type. Public concern was related to top management commitment in the HEI sector only. This is consistent with prior qualitative research describing how senior managers in HEI industries, such as chemicals and utilities, communicated their company's environmental goals to customers and the general public (Coddington 1993; Ottman 1993). Top management's vision of competitive advantage was moderated by industry type. Its influence was more apparent in the MEI than the HEI sector, because regulations were less prevalent in the former than in the latter sector. Top management commitment was positively related to external environmental orientation in all industries, probably because management frequently interacts with external stakeholders.
The relationship between environmental orientation and environmental strategy is complex, mostly because of their multidimensional nature. In both sectors, external environ-mental orientation was not related to environmental corporate strategy but only to environmental marketing strategy. Among other things, external environmental orientation involves balancing environmental protection and the interests of various external stakeholders, such as shareholders, with financial interests and community organizations with environmental interests. Our conclusion that these decisions are made at the functional level could have been the result of focusing only on the marketing function, in which such trade-offs are common. Internal environmental orientation, in contrast, was related to environmental corporate strategy in both sectors. Internal environmental orientation, in general, pertains to corporate values regarding the firm- environment relationship and thus is more likely to have an impact at the corporate level regardless of industry type.
Recommendations for Business and Public Policy Managers
The scores on corporate environmentalism are not particularly high, suggesting that there is room for considerable improvement. Whereas our study sheds light on the "what" and "why" of corporate environmentalism, the "how" remains elusive. Inducing firms to adopt corporate environ-mentalism requires the use of different agents of influence, and that choice depends on industry type. Our recommendations in this regard are speculative and follow the two themes of our research: the impact of antecedents and the moderating effect of industry.
Any efforts to move firms toward environmentalism must recognize the significant role of top management commitment. Other forces, most notably public concern, regulations, and competitive advantage, influence top management, though these influences are moderated by industry type. Recall that according to stakeholder theory, the choice of corporate strategy and, as a result, the recognition accorded various stakeholders are driven by a firm's desire to balance its and shareholders' fiscal welfare with other stakeholder interests. In that sense, gaining the attention and concurrence of top management--so vital because of its strong influence--must vary with industry type. Public concern in the HEI sector, regulatory forces in both sectors, and competitive advantage in the MEI sector significantly influence top management. The obvious implication is that in the HEI sector, regulatory forces and public concern should be maintained at high levels, ensuring that, in the process, environmental issues receive the attention of top management. In the MEI sector, in addition to establishing regulations, it will be important to create an appropriate market environment that enables firms to gain competitive advantage by adopting proenvironmental strategies.
Whereas all antecedents significantly affected environ-mental strategy, the pattern was different depending on the industry. Public concern was the most significant in the HEI sector, but competitive advantage was the most significant in the MEI sector; in both cases, top management commitment was a significant mediator. Public concern influenced the lower-level environmental marketing strategy, whereas regulatory forces influenced the higher-level environmental corporate strategy. A clear implication is that raising environmental strategies to the corporate level will require high internal environmental orientation, high levels of public concern, strong regulatory forces, and vigorous top management commitment.
We address public policy managers with the following recommendations: First, to successfully influence firm strategies, regulators must work with top management (Milne, Iyer, and Gooding-Williams 1996). However, appealing on emotional or moral grounds will not suffice; it will need to be based on strong strategic grounds (Porter and van der Linde 1995). When top management perceives added competitive advantage and economic value to the firm, it becomes an invaluable change agent (Iyer and Gooding-Williams 1999). Second, especially in the MEI sector, regulators must also attempt to modify market characteristics that stimulate proenvironmental strategies. In the long run, modifying market characteristics will weed out less efficient firms and reward environmental innovation. Third, regulators can sponsor nonprofit organizations and other community groups as part of a program geared toward maintaining public pressure on firms. These might include public funding for informational programs sponsored by environmental nongovernmental organizations or for alliances formed by various nonprofits to promote corporate environmentalism.
Limitations and Further Research
Our study has some limitations that we must acknowledge. First, we used cross-sectional data to derive causation. Second, we recruited only one respondent per firm, though our survey had an item assessing the self-reported knowledge on environmental issues and only surveys completed by knowledgeable respondents were included in the analyses. Third, we measured only managers' perceptions; we did not measure environmental investments made by firms. These may be better indicators of firms' environmental strategy and need to be included in further studies. Fourth, although our study focused on environmental orientation and environ-mental strategy as components of corporate environmentalism, we did not measure the actual environmental performance of firms. Developing appropriate indicators of environmental performance remains a challenging task for researchers. Fifth, we focused solely on the marketing function when conceptualizing business-and functional-level strategies. Whereas our own interests were behind this limited focus, expanding the scope to include other functions is necessary to develop fully the understanding of the relation-ship between functional strategy and corporate environmentalism.
Further research, in addition to addressing these limitations, can follow other fruitful avenues. One would focus on important organizational characteristics, which we deliberately kept outside the scope of our current research. If corporate environmentalism can be conceptualized as a firm-specific strategic capability, then we should expect differences in environmental orientation and environmental strategy among firms within the same industry. How are environmental orientation and environmental strategies developed and disseminated throughout the organization? How do firms integrate environmentalism at higher levels of strategy? What are the barriers to implementing an environ-mental strategy at the corporate level? How do firms develop, implement, and monitor their environmental strategies? Various firm-specific factors, such as organizational context, differences in managerial perceptions of environ-mental issues, asset complementarity, and competitiveness, might explain how environmental strategies are differentially implemented. The goal of such further research would be to identify barriers to and enablers of corporate environmentalism.
Focusing on interactions among the antecedents, which we had precluded to keep our model simple, would be useful as well. For example, regulatory forces and public concern might interact. Often, environmental legislation, enacted as a result of public concern or pressure from environmental organizations (Hoffman 1999), will in turn influence corporate environmental strategies (McCrea 1993; Reinhardt 1999). In one case, top management, on realizing the extent of the new investment required to comply with a proposed legislation, lobbied to have the legislation modified (Banerjee 2001).
Another important and interesting future research avenue would be to examine the consequences of corporate environmentalism as opposed to what we did, that is, identify the antecedents to and measure their relative impact on corporate environmentalism. Say a firm decides to adopt corporate environmentalism. What impact would this have on its profitability? This research could explore the relation-ship between corporate environmentalism and a comprehensive set of performance criteria, for example, profitability, market share, customer loyalty, and customer retention. The rising costs of environmental compliance will make such studies of great value to corporations.
The authors thank Tom Brashear, George Milne, and the three anonymous JM reviewers for their valuable suggestions.
1 Henceforth, the term environment is used instead of the term natural environment.
2 We focus only on corporate and functional strategies in this study.
3 This is evidenced by the nonsignificant chi-square statistics and further confirmed by the GFIs. Note that 30 of 46 originally spec-ified paths were significant, and the final model contained 5 non-significant paths that provided support for directional hypotheses and 8 combined paths that showed lack of support for the proposed hypotheses.
Legend for the Chart
A Industry
B (N)
C IEO[b]
D EEO[b]
E ECS[c]
F EMS[a]
G PC[c]
H RF[d]
I CA[c]
J TM[a]
A
B C D E F G H I J
Services
28 11.21 14.68[e] 8.21 5.61 11.46 10.25 9.68 9.96
6.95 4.80[f] 4.57 3.95 4.85 6.13 6.28 5.83
Consumer products
37 17.05 17.78 16.49 11.27 14.24 18.37 15.95 14.57
6.44 5.10 6.83 4.63 3.93 5.04 5.59 4.09
Foods
35 17.34 20.06 17.09 11.14 14.20 17.71 16.29 15.09
5.66 4.38 5.32 4.54 4.10 4.12 4.66 3.78
Moderate Group
100 15.52 17.71 14.38 9.64 13.45 15.87 14.31 13.46
6.82 5.18 6.88 5.05 4.40 6.15 6.17 5.01
Pharmaceuticals
15 20.93 20.73 20.40 15.73 17.20 21.70 17.73 15.67
4.96 3.99 5.03 3.97 2.43 3.64 4.45 4.01
Utilities
17 20.24 19.47 18.12 9.76 15.18 19.59 16.59 16.00
5.14 4.00 4.91 4.74 4.16 4.23 5.81 3.98
Manufacturing
66 16.97 18.68 16.71 11.52 14.17 18.55 16.79 14.39
6.02 4.72 6.05 4.99 4.16 5.36 6.34 4.34
Chemicals
45 20.47 21.40 19.24 12.84 15.96 21.91 19.00 17.07
5.70 4.87 5.81 4.68 3.13 4.05 4.59 3.39
High Group
143 18.87 19.85 18.06 12.17 15.17 20.06 17.56 15.56
5.94 4.74 5.86 4.96 3.82 4.90 5.63 4.11
a Summed scores on a three-item seven-point scale.
b Summed scores on a four-item seven-point scale.
c Summed scores on a five-item seven-point scale.
d Summed scores on a six-item seven-point scale.
e Mean score.
f Standard deviation.
Item IEO EEO ECS EMS PC RF CA TM
IEO1 .74
IEO2 .79
IEO3 .86
IEO4 .93
EEO1 .65
EEO2 .54
EEO3 .84
EEO4 .51
ECS1 .84
ECS2 .75
ECS3 .86
ECS4 .83
EMS1 .71
EMS2 .88
EMS3 .87
PC1 .69
PC2 .89
PC3 .76
RF1 .77
RF2 .64
RF3 .66
RF4 .69
RF5 .79
CA1 .59
CA2 .57
CA3 .76
CA4 .69
CA5 .85
CA6 .84
TM1 .92
TM2 .82
TM3 .78
Reliability .90 .74 .89 .86 .83 .84 .87 .88
Variance .69 .42 .67 .68 .62 .51 .53 .71
extracted
Notes: chi2 = 859.9, 436 d.f., p < .001, GFI = .82, comparative fit index = .92, parsimony normed fit index = .75, RMSEA = .06, chi2/d.f. ratio = 1.9.
Legend for the Chart
A Structural Path
B Overall Model
C Restricted Model
D Unrestricted Model: HEI Industries
E Unrestricted Model: MEI Industries
F Final Mixed Model: HEI Industries
G Final Mixed Model: MEI Industries
A
B C D E F G
PC->TM
.37 (2.99) .37 (3.05) .61 (4.00) .04 (.18) .54 (3.69) .12 (.64)
PC->IEO
.02 (.17)
PC->EEO
.13 (.85)
PC->ECS
-.02 (-.25)
PC->EMS
.23 (1.97) .23 (2.26) .23 (1.59) .22 (1.44) .17 (2.12) .17 (2.12)
RF->TM
.26 (2.43) .25 (2.39) .10 (.77) .41 (2.43) .26 (2.48) .26 (2.48)
RF->IEO
-.06 (-.45)
RF->EEO
.07 (.58) .12 (1.12) .23 (1.72) .08 (.43) .27 (1.94) -.02 (-.11)
RF->ECS
.12 (1.62) .19 (3.58) .14 (1.09) .20 (1.89) .13 (2.03) .13 (2.03)
RF->EMS
-.06 (-.60)
CA->TM
.10 (.81) .10 (.86) -.13 (-.85) .46 (2.41) -.19 (-1.31) .49 (2.71)
CA->IEO
.18 (1.61) .18 (2.59) .23 (2.87) .02 (.15) .22 (2.93) .05 (.43)
CA->EEO
.15 (1.10) .28 (3.22) .06 (.41) .42 (2.28) .06 (.44) .37 (2.14)
CA->ECS
.10 (1.31) .21 (1.41) .07 (.84) .21 (1.95) .03 (.44) .20 (2.08)
CA->EMS
.33 (3.06) .26 (2.67) .30 (2.43) .30 (1.67) .31 (3.11) .31 (3.11)
TM->IEO
.65 (8.03) .63 (9.20) .65 (7.61) .69 (5.54) .66 (9.28) .66 (9.28)
TM->EEO
.41 (4.09) .47 (5.47) .49 (4.41) .26 (1.59) .42 (4.42) .42 (4.42)
TM->ECS
.32 (4.23) .32 (4.54) .40 (4.50) .09 (.73) .29 (3.94) .29 (3.92)
TM->EMS
.28 (2.80) .28 (3.26) .36 (3.10) .16 (1.31) .27 (3.16) .27 (3.16)
EEO->ECS
.06 (.66)
EEO->EMS
.19 (1.58) .17 (2.14) .11 (1.04) .25 (1.97) .22 (2.16) .22 (2.16)
IEO->ECS
.49 (5.23) .55 (8.92) .48 (5.89) .66 (6.45) .55 (8.64) .55 (8.64)
IEO->EMS
-.02 (-.14)
chi2(d.f.)
53.36 (36) 55.3 (43) 27.6 (27) 35.96 (37)
p-Value
(.03) .09 .43 (.52)Notes: Boldface numbers indicate significant paths. Numbers in parentheses represent t-values. We report standardized coefficients to facilitate model comparisons.
Hypothesis Finding
Industry
H1: HEI (not equal to) MEI
PC, RF, CA, TM->EO, ES
Industry moderates effects of PC,
RF, TM, and CA on EO and ES.
Public Concern
H2: PC->EO
PC not antecedent to IEO. PC not
antecedent to EEO.
H3: PC->ES
PC not antecedent to ECS. PC
antecedent to EMS.
H4: PC->EO: HEI > MEI
Not tested, as PC->EO was
not significant.
H5: PC->ES: HEI > MEI
Industry does not moderate effect of
PC on EMS.
Regulatory Forces
H6: RF->EO
RF not antecedent to IEO. RF antecedent
to EEO in HEI.
H7: RF->ES
RF antecedent to ECS. RF not antecedent
to EMS.
H8: RF->EO: HEI > MEI
Industry moderates effect of RF on EEO.
H9: RF->ES: HEI > MEI
Industry does not moderate effect of
RF on ECS.
Competitive Advantage
H10: CA->EO
CA antecedent to IEO in MEI. CA
antecedent to EEO in HEI.
H11: CA->ES
CA antecedent to ECS in MEI. CA
antecedent to EMS.
H12: CA->EO: HEI < MEI
Industry moderates effects of CA
on EEO but not IEO.
H13: CA->ES: HEI < MEI
Industry moderates effects of CA
on ECS but not EMS.
Top Management Commitment
H14: TM->EO
TM antecedent to IEO. TM antecedent
to EEO.
H15: TM->ES
TM antecedent to ECS. TM antecedent
to EMS.
H16: TM->EO: HEI > MEI
Industry does not moderate effects
of TM on EEO or IEO.
H17: TM->ES: HEI > MEI
Industry does not moderate effects
of TM on ECS or EMS.
H18: PC->TM->EO
TM mediates effects of PC on EEO and
PC on IEO in HEI.
H19: PC->TM->ES
TM mediates effects of PC on ECS and
PC on EMS in HEI.
H20: RF->TM->EO
TM mediates effects of RF on EEO and
PC on IEO.
H21: RF->TM->ES
TM mediates effects of RF on ECS and
TM on EMS.
H22: CA->TM->EO
TM mediates effects of CA on EEO and
CA on IEO in MEI.
H23: CA->TM->ES
TM mediates effects of CA on ECS and
CA on EMS in MEI.
H24: PC->TM: HEI > MEI
Industry moderates effect of PC on TM.
H25: RF->TM: HEI > MEI
Industry does not moderate effect of
RF on TM.
H26: CA->TM: HEI < MEI
Industry moderates effect of CA on TM.
Environmental Orientation
H27: EO->ES
EEO antecedent to EMS. IEO antecedent
to ECS.
H28: EO->ES: HEI > MEI
Industry does not moderate effect
of IEO on ECS.
Industry does not moderate effect
of EEO on EMS. Legend for the Chart
A Antecedent
B Environmental Construct
C Effects in HEI Industries: Direct
D Effects in HEI Industries: Indirect
E Effects in HEI Industries: Total
F Effects in MEI Industries: Direct
G Effects in MEI Industries: Indirect
H Effects in MEI Industries: Total
A B C D E F G H
PC[a] IEO .00 .36 .36 .00 .00 .00
EEO .00 .23 .23 .00 .00 .00
ECS .00 .16 .16 .00 .00 .00
EMS .24 .15 .39 .24 .00 .24
PC[b] ECS .00 .36 .36 .00 .00 .00
EMS .24 .19 .43 .24 .00 .24
RF[a] IEO .00 .17 .17 .00 .17 .17
EEO .27 .11 .38 .00 .11 .11
ECS .13 .08 .21 .13 .08 .21
EMS .00 .07 .07 .00 .07 .07
RF[b] ECS .13 .17 .30 .13 .17 .30
EMS .00 .13 .13 .00 .09 .09
CA[a] IEO .22 .00 .22 .00 .32 .32
EEO .00 .00 .00 .37 .21 .58
ECS .00 .00 .00 .17 .14 .31
EMS .27 .00 .27 .27 .13 .40
CA[b] ECS .00 .12 .12 .17 .32 .49
EMS .27 .00 .27 .27 .23 .50
TM[c] IEO .66 -- .66 .66 -- .66
EEO .42 -- .42 .42 -- .42
ECS .29 .36 .65 .29 .36 .65
EMS .27 .07 .34 .27 .07 .34a Indirect effects mediated by TM.
b Indirect effects mediated by TM and environmental orientation constructs.
c Indirect effects mediated by environmental orientation constructs.
Notes: We report common metric standardized coefficients to facilitate group comparisons.
DIAGRAM: FIGURE 1: Corporate Environmentalism: Antecedents and Influence of Industry Type
DIAGRAM: FIGURE 2: Significant Paths in Final Model
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Public Concern
Our customers feel that environmental protection is a critically important issue facing the world today.
The North American Public is very concerned about environmental destruction.
Our customers are increasingly demanding environmentally friendly products and services.
The public is more worried about the economy than about environmental protection. (R, D)
Our customers expect our firm to be environmentally friendly. (D)
Regulatory Forces
Regulation by government agencies has greatly influenced our firm's environmental strategy.
Environmental legislation can affect the continued growth of our firm.
Stricter environmental regulation is a major reason why our firm is concerned about its impact on the natural environment.
Tougher environmental legislation is required so that only firms that are environmentally responsible will survive and grow. (D)
Our firm's environmental efforts can help shape future environmental legislation in our industry.
Our industry is faced with strict environmental regulation.
Competitive Advantage
Being environmentally conscious can lead to substantial cost advantages for our firm.
Our firm has realized significant cost savings by experimenting with ways to improve the environmental quality of our products and processes.
By regularly investing in research and development on cleaner products and processes, our firm can be a leader in the market.
Our firm can enter lucrative new markets by adopting environmental strategies.
Our firm can increase market share by making our current products more environmentally friendly.
Reducing the environmental impact of our firm's activities will lead to a quality improvement in our products and processes.
Top Management Commitment
The top management team in our firm is committed to environmental preservation.
Our firm's environmental efforts receive full support from our top management.
Our firm's environmental strategies are driven by the top management team.
Internal Environmental Orientation
Environmental issues are not very relevant to the major function of our firm. (R, D)
At our firm, we make a concerted effort to make every employee understand the importance of environmental preservation.
We try to promote environmental preservation as major goal across all departments. (D)
Our firm has a clear policy statement urging environmental awareness in every area of operations.
Environmental preservation is high priority activity in our firm.
Preserving the environment is a central corporate value in our firm.
External Environmental Orientation
The natural environment does not currently affect our firm's business activity. (R, D)
The financial well being of our firm does not depend on the state of the natural environment. (R)
In our firm, environmental preservation is largely an issue of maintaining a good public image. (D)
Our firm's responsibility to its customers, stockholders, and employees is more important than our responsibility toward environmental preservation. (R)
Environmental preservation is vital to our firm's survival.
Our firm has a responsibility to preserve the environment.
Our firm strives for an image of environmental responsibility. (D)
Environmental Corporate Strategy
Our firm has integrated environmental issues into our strategic planning process.
In our firm, quality includes reducing the environmental impact of products and processes.
At our firm we make every effort to link environmental objectives with our other corporate goals.
Our firm is engaged in developing products and processes that minimize environmental impact. (D)
Environmental protection is the driving force behind our firm's strategies. (D)
Environmental issues are always considered when we develop new products.
Our firm develops products and processes that minimize environmental impact. (D)
Environmental Marketing Strategy
We emphasize the environmental aspects of our products and services in our ads.
Our marketing strategies for our products and services have been considerably influenced by environmental concerns.
In our firm, product-market decisions are always influenced by environmental concerns.
We highlight our commitment to environmental preservation in our corporate ads. (D)
Notes: For reliabilities, see Table 2. R represents scale reversals; D represents items dropped.
~~~~~~~~
By Subhabrata Bobby Banerjee; Easwar S. Iyer and Rajiv K. Kashyap
Subhabrata Bobby Banerjee is Professor of Strategic Management, International Graduate School of Management, University of South Australia. Easwar S. Iyer is Associate Professor of Marketing, Isenberg School of Management, University of Massachusetts, Amherst. Rajiv Kashyap is Associate Professor of Marketing, Department of Management and Marketing, Cotsakos College of Business, William Paterson University. The authors' names are in alphabetical order.
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Record: 40- Cross-Unit Competition for a Market Charter: The Enduring Influence of Structure. By: Houston, Mark B.; Walker, Beth A.; Hutt, Michael D.; Reingen, Peter H. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p19-34. 16p. 4 Charts. DOI: 10.1509/jmkg.65.2.19.18256.
- Database:
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CROSS-UNIT COMPETITION FOR A MARKET CHARTER:
THE ENDURING INFLUENCE OF STRUCTURE
Marketing strategists who operate in turbulent markets face a competitive landscape marked by volatility and evolving market structures. As customer requirements change, an organization that stays in alignment with its markets will form new business units or alter the market charters of existing business units. In a longitudinal study, the authors traced the structural realignments that accompanied a Fortune-500 firm's entry into the Internet market. As the charter moved from a freshly created unit to an established business unit, the authors found support for the prediction that the former organizational structure will continue to shape the identity, beliefs, and social ties of managers. The study highlights the structural, social, and cognitive factors that must be managed as corporate decision makers search for the best strategy-structure fit for an emerging market opportunity.
Aligning the structure of the modern corporation to capture emerging market opportunities is a continuing challenge for firms that compete in turbulent markets (Day 1997). In the multidivisional organization, various interdependent divisions are "chartered" to monitor one or more businesses and the associated market domains. However, fast-paced changes in customer requirements expose gaps in a firm's market alignment that top management attempts to fill by creating new business units or adjusting the market charters of existing business units. A charter is defined as the product and market arenas in which a business unit actively participates and for which it has been assigned responsibility within the firm (Galunic and Eisenhardt 1996). The product markets that constitute the domain of a business unit can be defined in terms of the customer benefits provided, the technologies applied, the customer segments served, and the level of integration represented in the value creation process (Abell 1980; Day 1984). Charter change, then, involves the assignment of responsibility for a particular product-market domain to a new or existing business unit. Charter changes can involve the creation of a new business unit, the addition of a product-market domain to an existing business unit, or the transfer of responsibility for a product market from one business unit to another. When markets are relatively stable, charter change is less critical, but when markets are turbulent, charter adjustments enable firms to focus on the most promising opportunities (Eisenhardt and Brown 1999). For example, to capitalize on its competencies in ink-jet printing and scanning technologies, Hewlett-Packard surprised competitors by forming a new business unit and assigning it responsibility for a promising new market--digital photography (Kaplan 1999). In line with Webster's (1997) and Cravens's (1997) work, charter change ignites a set of strategic processes that spotlights marketing's strategic role in the firm at both the corporate and business unit levels.
By guiding the strategic process of matching the firm's core competencies to customer needs, marketing assumes an active role in the charter change process. Central to the strategic dialogue at the corporate level is the question of which business unit is best equipped to deliver superior customer value and compete in this newly defined market domain. Instead of having a unitary voice from marketing, managers representing various business units may hold different views regarding the preferred path for marketing strategy and the desired form that the value proposition should take. These divergent positions are grounded in the distinct identities that various business units possess (Albert and Whetten 1985). A charter carves out the product and market arena that a business unit serves, defines its turf and relative status in the organization, and shapes the pattern of reward allocation observed (Kramer 1991). When a charter is won, a business unit may subsequently lose it, relinquishing responsibility to another business unit if performance lags or the expectations of top management are not met.
The context for our study was provided by the creation of a new business unit (referred to as INFOSERVE) at a Fortune-500 high-technology firm (referred to as COMMCO). The INFOSERVE unit was chartered by top management to lead the firm's entry into the Internet market. Rapidly changing perceptions of customer needs, combined with the recently announced entry of a formidable new competitor to INFOSERVE, provided added appeal to the market context. Because the Internet market constituted an attractive domain, rival business units had actively lobbied top management for the initial charter assignment. By failing to articulate a clear Internet strategy, INFOSERVE subsequently lost the charter, and senior management at COMMCO reassigned responsibility for the Internet market to a rival business unit. The purpose of this article is to examine the impact of charter change on the strategy beliefs, social ties, and identity of the affected managers. The study provides a rare opportunity to isolate the structural, social, and cognitive factors that hamper the development of marketing strategy when a charter is transferred from one business unit to another. Combining qualitative and quantitative methodologies, we first examined the beliefs of INFOSERVE executives and those of executives (including presidents) of competing business units regarding the strategic significance of the online venture and the critical strategic issues that confronted the initiative. Next, after top management reassigned the Internet charter to COMMCO's largest business unit, we again examined the beliefs of key participants and isolated managers' organizational identities and patterns of social ties that endured in the new organizational structure.
Our study differs from previous research on several counts. First, although a rich research tradition has centered on cross-functional working relationships in new product development (e.g., Griffin and Hauser 1996), scant attention has been given to the interplay among business units as established charters are altered to meet changing customer requirements or capture new market opportunities. By adopting the business unit as the unit of analysis, our study moves beyond cross-functional comparisons to reveal the strategy beliefs that divide senior executives and marketing managers who represent one business unit versus another. Although marketers are encouraged to play an integrative role in keeping the organization focused on the customer (Anderson 1982; Day 1992; Webster 1997), our study suggests that such integration efforts can be derailed by rigidities in managers' beliefs and communication patterns that are shaped by the organizational structure. Research by Homburg, Workman, and Krohmer (1999) reveals the influential role that marketing managers assume in shaping the strategic direction of business units. Our study contributes to the marketing literature by illuminating the politics of charter change and the special challenges that market strategists face in developing a value proposition for customers that will win the support of surrounding business units.
Second, charter change has received little attention in the marketing literature but assumes a prominent role in the strategies of leading firms, "where the new competitive reality includes volatility and evolving market structures" (Prahalad 1995, p. v). Rosa and colleagues (1999) demonstrate that producers and consumers possess shared knowledge structures that define product markets and that their understanding of markets evolves as these knowledge structures change. By contrast, our study illustrates how managers who represent different business units share markedly different beliefs regarding how a particular product market should be defined and addressed.
Third, Day (1997) argues that organizational design issues are rising to the top of the agenda for the future of marketing as organizations seek to continually adapt the alignment of strategies, activities, and distinctive capabilities to shifting market requirements (for a complete review of marketing organization, see Workman, Homburg, and Gruner 1998). Although a strong research tradition encircles strategic change (e.g., Pettigrew and Whipp 1991; Quinn 1980) and the strategy-structure-performance paradigm (for a comprehensive review, see Galunic and Eisenhardt 1994), there are no prior studies, to our knowledge, that have systematically examined the impact of a structural realignment on the identities, beliefs, and patterns of social ties of managers across business units. By addressing these issues, the present study contributes to this research stream and isolates important inertial barriers that emerge as an organization searches for the proper strategy-structure fit in a new market domain. Moreover, the study responds to the call of strategy researchers (e.g., Galunic and Eisenhardt 1994; Varadarajan 1992; Varadarajan and Clark 1994) for research that moves beyond a static view of product-market boundaries and considers the evolving nature of markets and the corresponding structural changes that are spawned in the organizations that serve them.
Our discussion is divided into three parts. First, we provide a synthesis of the collective action theory of strategic decision processes--a conceptual perspective particularly appropriate to our study--and explore the structural forces that shape the beliefs of various interest groups when a charter change is implemented. Second, we report research results from the examination of managers' strategy beliefs, both before and after a major change in organizational structure, and isolate the social ties and patterns of identification that emerged in the new structural home for the market initiative. Third, we conclude by discussing key managerial and research implications.
Corporate strategy, business strategy, and marketing strategy interact to shape the competitive advantage of individual business units within a firm's portfolio of businesses (Varadarajan and Jayachandran 1999). By centering on issues that relate to the organization's domain and the allocation of resources across business units, charter change involves a strategic decision process that is best captured by theories of collective action (Walker, Ruekert, and Roering 1987). The process seeks to improve organizational performance by matching the firm's capabilities to a newly defined market space for the organization. Possible structural outcomes of the process include the creation of a new business unit or a change in the market charters of existing units. During strategic decision processes, interest groups form around formal objectives and the goals of business units; they also form around differences among groups at varying levels of the organizational hierarchy (Dickson 1992; Pettigrew and Whipp 1991). Drawing on Anderson's (1982) work, we define charter change as the outcome of a bargaining process among business unit coalitions.
Rapidly changing technologies and customer requirements create business opportunities that an organization might profitably serve, thereby creating a market for charters among business units (Galunic and Eisenhardt 1996). In this situation, the business units that constitute an organization are competing within an "economy of charters" for the opportunity to lead the firm's strategy in a choice market domain. Rather than compete only for financial resources within the organizational hierarchy, business units also actively compete for the information, power, support, and legitimacy that a new or expanded charter provides (Dutton 1993). The nature of competition among business units varies by organization.
Cooperative Versus Competitive Structures
Firms that emphasize cooperation versus competition have different internal configurations with regard to centralization, integration, control practices, and incentive systems (Hill, Hitt, and Hoskisson 1992). In cooperative organizations, cooperation between business units is fostered by senior corporate executives who exercise some degree of centralized control to achieve coordination across business units (Mintzberg 1983). Moreover, the corporate office also uses integrating mechanisms to achieve lateral communication among strategic business units, evaluates business unit performance on a range of subjective (e.g., extent of cooperation among interdependent units) and objective (e.g., market share, growth) criteria, and uses incentive systems for business unit managers that are linked to corporate rather than business unit profitability (Gupta and Govindarajan 1986; Keating 1997). In contrast, in competitive organizations, competition among business units is fostered by organizational arrangements that feature a decentralized structure and an arm's-length relationship between the corporate office and business units. Business unit managers are responsible for operating decisions, objective financial criteria are used to measure unit performance, and incentive systems are tied directly to business unit profitability. Because a newly chartered business unit requires an exchange of information, knowledge, and resources with other business units, cooperative structures are more conducive to charter development than competitive structures are. Cooperative behavior is also enhanced when organizational members have a common identity.
The Influence of Structure on Identity and Beliefs
Social identity theory (Tajfel and Turner 1985) suggests that people define their self-concepts through their connections with social groups. Although the self-concept may be composed of a variety of identities (e.g., 'gender, race, personality traits), organizations also offer an important source of identification (Ashforth and Mael 1989). Because identification assumes an important role in defining and enhancing the self-concept (Dutton, Dukerich, and Harquail 1994), people tend to identify most strongly with groups that are distinctive and prestigious and that compete with a salient set of out-groups. For example, a person's identity may be derived from organizational membership, department, function, or work group.
In a given context, organizational members can invoke higher-order identities (division or organization) or lower-level identities (function or work group) (Ashforth and Johnson 2001). In some firms, such as Hewlett-Packard, company-wide socialization programs are used to persuade employees to define themselves in terms of a higher-order rather than a lower-order identity (Tsui 1994). The more salient a higher-order identity, the more likely it is that an organizational member will pursue organizational goals ahead of narrow lower-order goals, interpret issues and events from a higher-order perspective, cooperate with other organizational members across units, and engage in organizational citizenship behaviors (Ashforth and Mael 1996; Dutton, Dukerich, and Harquail 1994). Although many organizations strive to create a shared identity among their members, scarce resources and reward systems that typically focus on subunit performance spawn heterogeneous identities (business unit, department, work group) that tend to conflict and compete (Ashforth and Mael 1989).
The structural categories of an organization--such as business units---determine the contours of social comparison (us versus them) while also shaping the pattern of reward allocation observed (Kramer 1991). Structural categories also define the pattern of interaction and tell organizational members who they are, what their role in the organization is, and where they fit in the formal and informal hierarchies that constitute the organization. Managers derive a sense of identity from the affiliation with an organization or their connection to social groups within the organization, such as functional area or business unit (Bhattacharya, Rao, and Glynn 1995; Fisher, Maltz, and Jaworski 1997). In large and heterogeneous organizations, managers tend to identify more strongly with their immediate work groups than with the organization as a whole (Ashforth and Mael 1989; Kramer 1991).
Organizational members who strongly identify with a particular unit engage in a pattern of in-group and out-group dynamics. Strong identification prompts increased cooperation with organizational members who are part of the group and increased competition with nonmembers (Dutton, Dukerich, and Harquail 1994). Social identity theory provides insights into the meaning that organizational members ascribe to strategic change. First, to the extent that the business unit domain defines the identity of organizational members and provides a base of power, members will be reluctant to see those boundaries altered. A threat to a group's domain tends to strengthen members' identification with the group. Cross-unit conflict intensifies as "group lines are drawn more sharply, values and norms are underscored, and we/they differences are accentuated'' (Ashforth and Mael 1989, p. 25). To illustrate, Fisher, Maltz, and Jaworski (1997) find that marketing managers who identify more strongly with the marketing function than with the organization are more likely to use coercive influence strategies when dealing with people from other functions. Second, identification strongly influences cognition (by priming attention), affect (by defining what is valued), and behavior (by promoting identity-consistent acts) (Ashforth and Mael 1996). Identification helps organizational members direct interpretation by providing a reference point for gauging the importance of strategic issues, influencing perceptions of their legitimacy, and shaping their meaning (Dutton and Dukerich 1991). When that reference point is the organization as a whole (higher-order identity), organizational members are more likely to think, feel, and act in ways consistent with broader organization goals. Alternatively, when the reference point is the business unit, a strong identity can also promote what Dougherty (1990, 1992) describes as distinct "thought worlds," in which one unit focuses on different environmental contingencies and reflects different values, beliefs, and goals than another unit (Daft and Weick 1984; Frankwick et al. 1994).
To recapitulate, these structural-cognitive perspectives suggest that cooperative structures, such as reward systems tied to organizational goals and senior management's success in making higher-order identities more salient to organizational members, will contribute to shared organizational beliefs regarding a strategic initiative for a new market. In contrast, for organizations that emphasize competitive structures and those in which lower-level identities (business unit) are more salient to organizational members, we predict that managers from different business units will form different interpretations regarding a strategic initiative for a new market: a charter change. By posing a threat to the boundaries and activity domain of some units and an opportunity for others, a change of charters spawns rivalry among units that wish to defend or expand their base of power within the organization. Specifically, we propose the following:
H1: Regarding a strategic initiative for a new market, (a) the beliefs of managers will vary by business unit, and (b) the beliefs that are shared among managers will vary by business unit.
Organizational Inertia
The importance of achieving a fit or consistency between organizational states and environmental demands is evident in conceptual perspectives from the strategy literature (e.g., Porter 1980). In a theory of organizational evolution, however, Tushman and Romanelli (1985) argue that the very forces that enhance success and create consistencies in a firm's operations become an impediment to change when environmental conditions change. They (p. 190) suggest that "internal requirements for coordinated activities and flows result in increased structural elaboration and social complexity.'' Organizational members build elaborate routines to gain greater control over their work, and these routines focus attention and filter information in support of the status quo. Moreover, through joint decision making, they develop shared commitments and beliefs that justify previous actions (Weick 1979).
Within organizations, these interdependent structural and social linkages increase individual and group commitments to the current strategy course but reduce the probability that fundamental change can be successfully introduced (Miller and Friesen 1980). Research suggests that cognitive inertia prevents managers from modifying their cognitive structure in response to new information from the environment (Reger and Palmer 1996). For example, Hodgkinson (1997) finds that managers' cognitions of competitive conditions in a volatile market are highly stable over time, despite significant changes in the market. Organization-environment consistencies also contribute to the development of a structurally and socially anchored inertia (Hannan and Freeman 1984; Tushman and Romanelli 1985). By competing in a particular industry or market domain, an organization develops webs of interdependent relationships with customers, suppliers, channel members, alliance partners, and other external constituents. As these relationships develop and become institutionalized, the organization develops inertia--a resistance to all but incremental change. As Ashforth and Mael (1996, p. 53) note, "Over time, identity and strategy tend to become more tightly coupled as the latter comes to symbolize the former." In support, Rosa and colleagues (1999) view product markets as socially constructed knowledge structures that are shared among producers and consumers. Neither orchestrated nor imposed by producers or consumers, product markets evolve from producer--consumer interaction feedback effects.
Drawing on these conceptual perspectives, we argue that the structural forces that advance performance and define a firm's activities in a particular market domain also create rigid cognitive and social boundaries that are difficult to erase as technologies and customer needs evolve. Although the formal organizational structure can be redesigned to meet changing market requirements, these inertial forces may continue to tie organizational members to the old structure, which provides an enduring source of identification. Such inertial forces may be especially strong in organizations characterized by multiple business unit identities rather than a salient higher-order identity and those that emphasize competitive versus cooperative structures. Special challenges confront newly chartered business units in forging ties with established business units that have deeply embedded systems, processes, and strategies. On the basis of this discussion, we propose the following:
H2: After a structural realignment, the former organizational structure will continue to shape the (a) business unit identity of affected managers, (b) strategy beliefs of managers, and (c) social ties of managers.
We contend that it is a manager's strength of identification with the unit, rather than mere membership in a business unit, that shapes strategy beliefs and social ties (Ashforth and Mael 1996). A market charter is created to combine organizational resources and competencies in a new way to capture a market opportunity. To succeed in the new market domain, members of the newly chartered unit depend on competency-related knowledge flows from other business units (Galunic and Rodan 1998). Such knowledge flows may include the exchange of information, know-how, and histories regarding competencies. However, competencies can become institutionalized, thereby creating rigidities that impede the flow of knowledge across areas (Leonard-Barton 1992). The more strongly a manager identifies with a particular competency, the higher is the resistance to new knowledge from other competency areas (Galunic and Rodan 1998). Boundaries between competencies pose a special problem for new market charters. As people interact within a competency area (e.g., Internet technology), they develop a common language and interpretive systems that facilitate internal information flows but restrict the dissemination of knowledge across competency areas. On the basis of this discussion, we propose the following:
H3: The strength of a manager's business unit identity will mediate the relationship between business unit membership and (a) the strategy beliefs of affected managers and (b) the social ties of managers.
Context
We sought to identify a high-technology firm that featured multiple business units that were competing for a coveted market charter. Consistent with these criteria, the entrance of COMMCO, a Fortune-500 communications firm, into the Internet market provided an ideal context for our research. The senior leadership team at COMMCO believed that the development of an online service was critical to the firm's long-term strategic position. Initially, top management created a new business unit, INFOSERVE; staffed it with managers from other units within COMMCO; and assigned the unit the market charter for the Internet.
To draw on the collective strengths of COMMCO, top management also enlisted executives from other business units to provide the information, resources, and technical expertise that the INFOSERVE unit might require. From the outset, however, many executives from surrounding business units strongly opposed the INFOSERVE position on fundamental strategic issues (e.g., Should the online service be targeted to the consumer or business market?). Moreover, these executives argued that their respective business units were better equipped to serve online customers.
Executives from two focal business units at COMMCO were central players in the debate: one business unit that develops offerings targeted to the business market (the rival business unit) and a second business unit that is the firm's largest revenue and profit producer and serves the consumer market (the dominant business unit). For executives of a third business unit (the neutral business unit), the Internet initiative had little direct bearing on the core mission of the group. The result of the active internal debate regarding the strategic course for the Internet initiative was that the Internet charter and INFOSERVE's personnel were reassigned to the dominant business unit. In terms of physical location, former INFOSERVE managers were dispersed throughout the dominant business unit. This structural realignment occurred seven months after the INFOSERVE unit was created.
To test the hypotheses, we collected data at two points in time. In the first phase, we conducted depth interviews midway through INFOSERVE's seven-month existence as an independent unit to examine the impact of business unit membership on the valence, sharing, and content of managers' beliefs (Frankwick et al. 1994). In the second phase, a questionnaire was administered four months after INFOSERVE lost the charter. The goal in this phase was to capture the lingering effects of initial business unit membership on a manager's identity, beliefs, and patterns of social ties relevant to the online venture. In a turbulent environment, the managers who are directing a new market initiative face immediate pressure to perform from both the corporate level and surrounding business units (Galunic and Eisenhardt 1996). We believed that four months was an appropriate interval for the INFOSERVE members to transition fully into the dominant business unit. Therefore, seven and one-half months intervened between the first and second round of data collection. Our research is consistent with that of Van de Ven (1992), who admonishes researchers to study organizational phenomena in "real time," before participants know the final outcomes of decisions and actions.
Phase 1
A snowball sampling technique was used to identify the set of key managers that was most involved in shaping and competing for the Internet initiative within COMMCO. The technique isolates the relevant set of participants by asking each actor to identify others with whom he or she communicates about a specific issue. In this case, we asked managers the following questions: "Whom, within COMMCO, do you talk with about Internet initiatives?" and "Are there other influential people in or outside of your unit with whom we should talk about the Internet initiative?" In this study, our set of influential managers included executives who were mentioned by at least two other managers. This research approach isolated the executives who were most directly involved in the venture. Following this procedure, we identified 39 high-level executives (INFOSERVE, n = 12; dominant business unit, n = 11; rival business unit, n = 8; neutral business unit, n = 8). The INFOSERVE team included members who were formerly from the rival (n = 7), neutral (n = 3), and dominant (n = 2) business units. Our sample included the president of each participating business unit, corporate vice presidents, and important senior-level managers who represented marketing as well as other functional domains.
In Phase 1, a personal (n = 17) or telephone (n = 22) interview was conducted with each of the key participants. The interviews were conducted individually and were tape-recorded with the permission of the respondent. Respondents were assured that their replies would be kept confidential. Interviews averaged approximately one hour. Because the set of beliefs surrounding the Internet charter was not well defined, we used semi-structured interviews (versus more structured elicitation techniques) to elicit the potentially wide range of issues that divided the business units (Dougherty 1992).
At first, respondents were asked to describe their personal role and the role of their business unit in the INFOSERVE initiative. Executive participants were then asked to describe the strategic significance of the INFOSERVE initiative to their business unit and to COMMCO as a whole. In addition, respondents outlined the factors they believed would drive the success (failure) of the INFOSERVE initiative. With probing, they provided support for their positions. Participants were also asked to detail other sensitive issues or areas of disagreement that surrounded the launch of the online service. Finally, to isolate the set of executives involved in shaping the INFOSERVE initiative, respondents were asked to identify other executives with whom they had communicated regularly about INFOSERVE as well as others involved in directing the online venture.
To capture differences in the beliefs that members of the focal business units held regarding INFOSERVE's Internet strategy, we systematically coded and analyzed the transcribed interviews. Using exploratory interviews, written communications from INFOSERVE's management, internal documents supplied by INFOSERVE's leadership, and several transcripts, we developed a dictionary for coding positive, neutral, and negative beliefs related to the Internet strategy. In addition, we identified five general categories or themes that reflected the content of the beliefs regarding the strategic significance of INFOSERVE and ten categories that captured the factors that executives perceived would facilitate or hinder its success. Each category reflected a set of beliefs that was positive, neutral, or negative. The dictionary included a definition of each category and sample phrases that reflected each theme.
Following a procedure similar to that used by Frankwick and colleagues (1994), two judges, working independently, coded a subset of the transcripts. The judges then discussed their decisions, fine-tuned category definitions, and considered adding new categories that emerged during the coding process. After reaching a satisfactory level of agreement, the judges completed the coding of the transcripts. Individual beliefs were counted only once, even if the belief was repeated several times during the interview. In completing the task, each judge coded all the transcripts. Calculating Perreault and Leigh's (1989) index of reliability (Ir), intercoder reliability was .847, exceeding the established benchmark (Ir = .80) for satisfactory reliability. All disagreements were resolved by discussion. Examples of the belief categories are reported in Table 1.
Phase 2
The purpose of Phase 2 was to examine the impact of the former organizational structure on the business unit identity of affected managers, beliefs about the Internet strategy, and the patterns of social ties among study participants. Four months after the Internet charter and INFOSERVE personnel were transferred to the dominant business unit, a mail survey was administered to the 39 respondents who had participated in Phase 1. Twenty-eight usable questionnaires (72%) were returned.
Identity. Following Bhattacharya, Rao, and Glynn (1995), we adapted a scale from Mael and Ashforth (1992) to assess each respondent's strength of identification with the INFOSERVE initiative, the current home business unit (former INFOSERVE members now belong to the dominant business unit), and COMMCO as a whole. To minimize the length of the questionnaire, we examined the items and correlations reported by Mael and Ashforth (1992) and selected four of the six original items for use in this study: (1) When someone criticizes [the INFOSERVE initiative, the home business unit, COMMCO], it feels like a personal insult; (2) I am very interested in what others think about [the INFOSERVE initiative, the home business unit, COMMCO]; (3) The successes of [the INFOSERVE initiative, the home business unit, COMMCO] are my successes; and (4) When someone praises [the INFOSERVE initiative, the home business unit, COMMCO], it feels like a personal compliment. Each item was rated using a five-point scale, where I = "strongly disagree" and 5 = "strongly agree." Thus, for each of the three levels (initiative, business unit, firm), a higher score on the scale indicated stronger identification. The coefficient alphas for identification with the INFOSERVE initiative, the home business unit, and COMMCO were .86, .91, and .87, respectively.
Means-end beliefs. Consistent with a belief measurement approach developed by Walker (1985), we assessed the means-end beliefs of key participants regarding various aspects of INFOSERVE's online strategy. According to Walker (1985), a means-end belief links a particular activity or action (e.g., "the planned Internet services") to a specific outcome (e.g., "reach younger, high-tech consumers"). Drawing on the interview data from Phase 1, we constructed 18 statements that reflected a relationship between INFOSERVE's online initiative (means) and a consequence or outcome of that initiative (end). Eleven of the statements depicted a positive means-end association (e.g., "The planned Internet services will help reduce customer turnover"), three of the statements were negatively valenced (e.g., "Development of complex service offerings by INFOSERVE unit increased time to market"), and four means-end beliefs were neutral (e.g., "Innovativeness of Internet strategy depends on COMMCO's financial support"). Using a five-point scale (where I = "very weak relationship'' and 5 = "very strong relationship"), we asked respondents to rate the strength of the relationship between the means (listed on the left-hand side of the page) and the end (listed on the right-hand side of the page).
Social tie strength. Following the procedures and measures used in other studies of social networks within organizations (Brass and Burkhardt 1993), we provided respondents with a list identifying the key participants in the study. Using two seven-point scales, they rated one another on frequency of interaction (where I = "semiannually" and 7 = "daily") and personal closeness (where I = "very distant acquaintance" and 7 = "good friend"). A higher scale score indicated a stronger tie. The coefficient alpha for the two scales was .92.
Influence beliefs. Finally, respondents from the list of key participants were asked, "How influential do you feel each manager is to the direction of COMMCO's Internet and online strategies as a whole?" Beliefs about each manager's degree of influence were assessed on a seven-point scale, where I = "low influence" and 7 = "high influence."
In general, at Phase 1, the results reveal sharp differences across business units on beliefs about INFOSERVE and its Internet strategy. Especially pronounced are differences between INFOSERVE and the dominant and rival business units. Despite the change in charter ownership and resulting realignment that folded INFOSERVE into the dominant business unit several months later, a similar pattern of results emerges at Phase 2. These results suggest that the original business unit assignments continued to shape the identities, beliefs, and social ties of organizational members.
Phase 1 Results: Effects of Initial Business Unit Assignments on Beliefs
We identified four business units as the key players in the internal market for the Internet charter: INFOSERVE, the dominant business unit, the rival business unit, and the neutral business unit. To study the effects of initial business unit assignments on beliefs, we analyzed managers' responses to two questions: (1) What is the strategic significance of INFOSERVE's Internet initiative to your business unit and to COMMCO as a whole? and (2) What are the factors that will drive the success (failure) of INFOSERVE's Internet strategy? For each dependent measure, the responses to each question were analyzed in turn.
Belief valence. Consistent with Hl, the valence of managers' beliefs about the strategic significance of INFOSERVE's Internet initiative differed across business units (see Table 2). To test this hypothesis, we first computed the proportion of positive and negative beliefs elicited by each manager by dividing the number of positive and negative beliefs, respectively, by the total number of beliefs. For beliefs regarding the strategic significance of the Internet initiative, a one-way analysis of variance (ANOVA) revealed a significant relationship between business unit membership and the proportion of positive (and therefore negative) beliefs (F = 13.66, degrees of freedom [d.f.] = 3,35; p < .001 ). As reflected by the post hoc comparisons in Table 2, Part A, INFOSERVE managers have a greater proportion of positive beliefs and therefore a smaller proportion of negative beliefs about the strategic significance of the INFOSERVE initiative than managers from the dominant and especially the rival business units.
Similarly, for beliefs regarding the factors that would drive the success or failure of the Internet initiative, a repeated-measures ANOVA revealed a significant belief valence x business unit interaction (F = 4.56, d.f. = 3,35; p < .008). In line with beliefs about the strategic significance of the Internet initiative, note in Table 2, Part B, that INFOSERVE managers have a smaller proportion of negative beliefs compared with managers in the rival business unit. In contrast, managers from the dominant business unit have a greater proportion of positive beliefs than managers from INFOSERVE. Analysis of the content of the belief categories presented subsequently shows that whereas the dominant business unit focused on how the online service could bolster its business, INFOSERVE managers centered on the implementation challenges that were delaying their progress. Regardless of business unit membership, the proportion of negative beliefs (critical issues) far outweighed the proportion of positive thoughts (success factors) that surrounded the initiative (F = 74.07, d.f. = 1,35; p < .001). Thus, although INFOSERVE managers were enthusiastic about the strategic significance of the online initiative to COMMCO, they were concerned about the competitive and operational challenges the Internet market presented.
Belief sharing. Further support for H1 was found for shared beliefs. Individually held beliefs may or may not be shared with other managers in a business unit. Sharing among business unit members was assessed through operations on a respondent (i) x belief (j) matrix (A). If respondent i has belief j, then cell ij contains a 1; otherwise, cell ij contains a 0. The number of beliefs shared among group members is given by AAT. The average number of beliefs a respondent shares with other group members is determined by summing the off-diagonal elements in the matrix AAT across rows (or columns) and dividing the sum by n - 1 (where n = group size). Similar to our analysis of belief valence, we analyzed the proportion of shared beliefs that were positive and negative.
A one-way ANOVA yielded a significant relationship between business unit membership and the proportion of shared positive beliefs (F = 9.99, d.f. = 3,34; p < .001) for beliefs related to the strategic significance of the initiative. In Table 2, Part C, we report the results of the post hoc comparisons of belief sharing between INFOSERVE and each of the other business units. These comparisons reveal that INFOSERVE members shared a significantly higher proportion of positive beliefs than the members of the dominant business unit and the rival business unit did. Note again that the members of INFOSERVE were the most positive, whereas managers from the rival business unit tended to be the least positive.
A similar pattern emerged for factors that would impede or facilitate the success of INFOSERVE's Internet strategy. A repeated-measures ANOVA revealed a significant shared belief valence x business unit interaction (F = 8.54, d.f. = 3,33; p < .001). In line with the analysis of strategic significance-related beliefs, observe in Table 2, Part D, that the INFOSERVE managers shared a greater proportion of positive beliefs and a smaller proportion of negative beliefs than those in the rival business unit. In contrast to the strategic significance results, executives in the dominant business unit shared a greater proportion of positive beliefs than INFOSERVE managers did. Overall, managers, including INFOSERVE executives, shared a greater proportion of negative (versus positive) beliefs (F = 62.25, d.f. = i,35; p < .001).
Belief category. We now turn our attention to the contrasting perspectives held by business unit executives regarding the strategic significance of INFOSERVE's online initiative as well as the factors that would shape the initiative's success (failure) when INFOSERVE owned the market charter. Table 3 presents the one-way (business unit) ANOVA results and post hoc comparisons that analyze the average number of beliefs managers elicited for each of the belief categories related to the strategic significance of the initiative (Part A) and the critical issues and success factors (Part B), respectively.
Strategic significance-related beliefs. Overall, when executives considered the strategic significance of developing an Internet strategy, they viewed the Internet as an attractive new market for COMMCO and generally applauded the creation of a dedicated Internet market charter. Disagreement centered on INFOSERVE's particular approach to the Internet market. Note in Table 3, Part A, that significant differences across business units emerged regarding the goal of the online initiative ("strengthen relationships in the consumer market"), INFOSERVE's exclusive focus on the consumer versus the business market ("units divided over target market"), and the need to develop alternative Internet solutions within COMMCO ("opposing unit pursues separate initiative"). Analyses contrasting the beliefs of INFOSERVE with each of the other business units reveal the strongest differences with the rival business unit.
To INFOSERVE managers, the strategic significance of the online initiative is to strengthen the loyalty bond that consumers feel toward COMMCO. One INFOSERVE executive stated, "I think we will capture customers for the online INFOSERVE business. My hope is that customers signed up with [the dominant business unit service offering] would have less reason to leave COMMCO and go to a competitor's firm." INFOSERVE executives viewed the online venture as enhancing COMMCO's presence in the consumer (versus business) market.
In contrast, rival business unit managers contended that INFOSERVE's exclusive focus on the consumer market was "all wrong." Noting the growth of the work-at-home market segment, the rivals suggested that the distinction between the consumer market and business market had become "a very fuzzy boundary for people." As one rival business unit executive stated, "The model is so broken. Chat service is very consumer oriented. Going online for information is very business oriented. Paying for it is a business proposition. People may want to do both." Frustrated with the exclusion of all business market considerations from INFOSERVE's online initiative, the rival business unit, more than any other unit, was a strong advocate for developing alternative online offerings within COMMCO.
Critical issues and success factors. Despite some agreement on the forces that would facilitate (hinder) the success of the INFOSERVE initiative (e.g., rigid boundaries within COMMCO), managers were largely divided on the issues they considered most critical to the future of the Internet strategy. Observe in Table 3, Part B, that significant differences across business units emerged regarding the link to the core business ("lever for growth in the consumer business"), the competition ("formidable competitors"), resources ("level of resource commitment"), the target market ("units divided over target market"), communication across units ("cross-unit collaboration"), and INFOSERVE's internal operations ("implementation by INFOSERVE unit").
Compared with other managers, INFOSERVE executives centered on three issues: formidable competitors, the level of resource commitment, and implementation by INFOSERVE unit. As an illustration, INFOSERVE, more than the dominant and rival business units, perceived the late entrance into an already crowded Internet market space as an important barrier to its success. For example, an INFOSERVE executive noted, "I am afraid of XYZ Co. [a pseudonym]. They are very, very rich; a very, very successful organization .... When that organization decides that they want to capture a business, they will invest quite heavily in that business." Although INFOSERVE managers clearly wanted their initiative to succeed, the daily challenges that confronted them dampened the prospects for their own success.
In contrast to INFOSERVE executives, managers from the dominant business unit identified lever for growth in consumer business and cross-unit collaboration as the key issues that determine the success of the online initiative. To dominant business unit executives, the strategic connection between INFOSERVE's initiative and COMMCO's core business would support and facilitate the success of the Internet strategy. One dominant business unit manager noted, "What's going to make [INFOSERVE] ultimately successful is the extent that they really find a way to be of value to the consumer franchise--draw on our expertise and combine it with their knowledge of the market .... That's what will make them and us more effective." However, the dominant business unit managers were surprised that the INFOSERVE unit was developing-the Internet strategy in isolation and failed to tap their well-honed marketing skills. Overall, however, given their enthusiasm for the link between INFOSERVE's Internet strategy and the core business, managers from the dominant business unit were most optimistic about the initiative's success.
Consistent with our findings on strategic significance-related beliefs, managers from the rival business unit isolated one issue--units divided over target market--as their key concern. Except for implementation by the INFOSERVE unit, the beliefs of neutral business unit managers did not differ significantly from the critical issues and success factors that were raised by INFOSERVE managers (see Table 3, Part B).
Phase 2 Results: Effects of Initial Business Unit Assignments on Identification, Beliefs, and Tie Strength
In Phase 2, data were collected four months after the Internet charter and INFOSERVE managers were transferred to the dominant business unit. This provided an opportunity to examine the impact of the former organizational structure on managers' business unit identity, beliefs, and social ties. In H2, we propose that despite charter change and the resulting structural realignment, the former organizational structure will continue to guide the identity, beliefs, and pattern of social ties among the relevant set of managers identified in Phase 1.
Strength of identification. In line with H2a, managers' pattern of identification reflects the original (versus realigned) organizational structure. To examine this hypothesis, we measured how strongly each manager identified with (1) the INFOSERVE initiative, (2) their current business unit, and (3) COMMCO as a whole. Using the strength of identification as a within-subjects factor, an ANOVA revealed a significant strength of identification x business unit interaction (F= 19.23, d.f. = 3,24; p < .001). In Table 4, Part A, we report the post hoc comparisons between the former INFOSERVE executives and members from each of the other business units. Observe in Table 4, Part A, that there are no significant differences between the members of the former INFOSERVE unit and the other business unit members on strength of identification with COMMCO as a whole. However, although the former INFOSERVE members identify less strongly with their (new) home business unit (dominant business unit) than the other members of the dominant business unit, they also identify more strongly with the former INFOSERVE initiative than other business unit members, especially those in the rival business unit. Thus, even four months after joining the dominant business unit, the identification patterns of INFOSERVE executives are consistent with the former (versus realigned) organizational structure.
Belief valence. Analysis of the valence of managers' means-end beliefs confirmed our expectations defined in H2b. To test this hypothesis, we examined managers' average ratings of 11 positive means--end belief statements and 3 negative means-end belief statements, respectively. Each means--end belief linked an INFOSERVE activity or action (means) to a specific outcome (end). A repeated-measures ANOVA, using belief valence as the repeated factor, uncovered a significant belief valence x business unit interaction (F = 5.33, d.f. = 3,23; p < .007). Note in Table 4, Part B, that the former INFOSERVE managers agreed more strongly with positive means-end beliefs about the Internet initiative than their counterparts in the dominant business unit and (especially) the rival business unit and less strongly with the negative means--end beliefs about the initiative than all other business unit members.
Belief sharing. Additional support for H2b was found when the pattern of shared beliefs was analyzed. To test this hypothesis, we considered respondents' agreement with all 18 means-end belief statements (11 positive, 3 negative, 4 neutral). We first dichotomized the ratings of relationship strength or agreement with each means-end dyad. We assigned a I to ratings above the scale midpoint to indicate that a respondent held the particular means-end belief; a 0 indicated otherwise. Next, we constructed a respondent (i) by belief (j) matrix (A) and computed belief sharing using the same procedures outlined in the Phase 1 analysis of shared beliefs. As in the previous analysis, we examined the proportion of shared positive and negative beliefs to the total number of beliefs (including neutral means-end beliefs).
A repeated-measures ANOVA, in which the proportion of shared beliefs (positive, negative) was the repeated factor, revealed a significant shared belief valence x business unit interaction (F = 5.59, d.f. = 3.22; p < .006). Note in Table 4, Part C, that the former INFOSERVE managers share significantly more positive and fewer negative beliefs than their new colleagues in the dominant business unit and their counterparts in the rival business unit. Thus, despite the structural realignment, this pattern of shared beliefs reflects the old (versus new) organizational structure.
Pattern of social ties. Consistent with the effects of old structural lines on patterns of identification and belief sharing, we also expected the pattern of social ties within the organization to reflect the former (versus realigned) organizational structure. To begin, we dichotomized executives' ratings of every other manager on interaction frequency and personal closeness. A social tie between two managers was designated as strong (weak) if the average rating across the two seven-point scales was five or greater (less than five). Because the sample size varied across business units, we then computed the proportion (versus number) of strong and weak ties that were held by each manager. Specifically, we calculated the proportion of strong and weak ties by dividing the number of strong and weak ties that a manager had with the other members of the same business unit by the total number of other members within that unit.
Using a repeated-measures ANOVA with tie strength (strong, weak) as the repeated factor, we found a significant tie strength x business unit interaction (F = 4.17, d.f. = 3,23; p < .02). Table 4, Part D, presents the post hoc comparisons. Observe that the former managers of INFOSERVE have significantly more strong ties and fewer weak ties than members of the dominant business unit. Because the initiative relied on the contributions of managers across the dominant business unit, the online strategy could not be effectively implemented if INFOSERVE remained a self-contained group. Despite the reorganization, the pattern of social relationships among former INFOSERVE managers reflects the old (versus new) structural lines.
Influence beliefs. Consistent with H2b, we expected that the former organizational structure would continue to shape the managers' beliefs regarding where influence resides in setting the course for COMMCO's Internet strategy. To test this hypothesis, we divided the managers into two groups: former INFOSERVE executives and non-INFOSERVE executives. Then, we examined the influence ratings of each ingroup (e.g., INFOSERVE and non-INFOSERVE managers' ratings of themselves) and compared these ratings with the influence ratings of each out-group (e.g., INFOSERVE and non-INFOSERVE managers' ratings of each other). Higher ratings of the in-group and lower ratings of the out-group would indicate the lingering effect of the old (versus new) organizational structure on perceptions of influence.
A repeated-measures ANOVA, in which group type (ingroup, out-group) was the repeated factor, revealed a significant group x business unit interaction (F = 6.20, d.f. = l, 26; p < .02). Consistent with a pattern of in-group/out-group bias, both groups of managers rated themselves (the ingroup) as significantly more influential in directing COMMCO's online strategies than their counterparts (the out-group) rated them. Specifically, INFOSERVE executives rated themselves as significantly more influential than the non-INFOSERVE managers rated them (M = 5.31 and M = 4.64, respectively). In contrast, non-INFOSERVE managers rated themselves as more influential than the INFOSERVE managers rated them (M = 3.04 and M = 2.75, respectively).
Mediating role of identity. Finally, in H3, we predicted that the relationships between business unit membership and the key dependent variables (beliefs, pattern of social ties) would be mediated by a manager's strength of identification with the INFOSERVE initiative. To test this hypothesis, we used analysis of covariance in which a manager's strength of identification with the INFOSERVE initiative served as the covariate. Support for mediation would be provided if the significant relationship between the independent (e.g., business unit membership) and dependent (e.g., beliefs, pattern of social ties) variables is reduced through the introduction of the covariate (Batra and Ray 1986).
We found partial support for H3. Specifically, identity partially mediated business unit membership's relationships with means-end beliefs (F = 5.33, p < .007, eta2 = .41 was reduced to F = 3.05, p < .05, eta2 = .29) and belief sharing (F = 5.59, p < .006, eta2 = .43 was reduced to F = 3.53, p < .04, eta2 = .33). Identity completely mediated the link between business unit membership and influence beliefs (F = 6.20, p < .02, eta2 = .19 was reduced to F = 2.51, p >. 10, eta2 = .10). The mediating effect of identity on the relationship between business unit membership and the pattern of social ties was not significant. Identification with the current home business unit and with COMMCO as a whole did not mediate the key relationships of interest.
Epilogue
Information derived from a poststudy interview with a key member of the original INFOSERVE team and corporate press releases provide some closure to the Internet story at COMMCO. Shortly after Phase 2 data collection, the charter for the Internet market and the personnel who constituted the original INFOSERVE unit were reassigned again--this time to the rival unit. The dominant unit was locked in a heated competitive battle in COMMCO's core business, and the Internet market was not a vital concern. Meanwhile, the president of the rival unit, who was moving ahead with a separate Internet initiative for the business market, actively sought the market charter and won control of it. Two months later, the rival unit narrowed the Internet strategy focus and scrapped the original strategy plans that had been forged by the INFOSERVE unit. INFOSERVE's former president was reassigned and subsequently left COMMCO, as did several other members of the original INFOSERVE unit. Although certain proprietary services planned by INFOSERVE were dropped, COMMCO became an Internet service provider.
Although prior studies have identified boundaries that divide managers who represent different functions (Dougherty 1992; Frankwick et al. 1994; Hutt, Walker, and Frankwick 1995), our results reveal the divergent pattern of strategy beliefs across business units. More important, our results suggest that the identity, beliefs, and social ties of managers may endure after a structural alignment, thereby hampering the development and implementation of marketing strategy.
Consistent with H1, the beliefs of executives involved in the Internet strategy differed markedly across business units, and a distinct pattern of belief sharing was evident in each of the focal units. The greatest differences emerged between INFOSERVE and the rival and dominant business units. Our findings provide a vivid portrait of the thought-world differences that can divide the business unit leaders and therefore even the marketing managers who serve one prescribed market domain in the firm versus another. These results support Rosa and colleagues' (1999) conceptualization of product markets as knowledge structures that are shared by managers. Indeed, the executives who guided strategy for the established business units held opposing views regarding the course of the Internet strategy, and those beliefs were closely aligned with the traditional product markets they addressed for COMMCO: The rival business unit conceptualized the strategy in a business market context, whereas the dominant business unit envisioned a consumer market initiative. In dynamic markets, Rosa and colleagues (1999) argue that mature and rigid knowledge structures can hinder a firm's ability to adapt when environmental contingencies shift.
Central to our contribution are the findings that demonstrate the inertial forces that develop around the product markets served by business units. After the INFOSERVE unit was disbanded and its personnel and charter were reassigned to the dominant business unit, the social and cognitive linkages that united INFOSERVE managers in the old structure continued to endure. In support of H2, the former organizational structure continued to guide the business unit identity, strategy beliefs, and social ties of managers. Though remaining a cohesive group--even after being physically dispersed--in the new structure, the former INFOSERVE executives failed to forge the ties that would be needed to integrate the Internet strategy into the dominant business unit's ongoing operations. Although interpersonal proximity tends to facilitate interaction and strengthen identification (Ashforth and Mael 1989), the bond that united INFOSERVE managers endured despite the physical separation of the team members. To our knowledge, this is the first study to isolate empirically the inertial forces that an established organizational structure can continue to impose on the identity, beliefs, and social ties of managers after a structural realignment. Moreover, partial support was found for H3, which isolated identity as the primary contributor to the social and cognitive inertia within the firm. Fisher, Maltz, and Jaworski (1997) find that a strong relative functional identity inhibits cross-functional communications, and our study illustrates that a strong business unit identity can impede the knowledge flows that a freshly .chartered business unit was created to capture.
Limitations
To put our findings in a proper perspective, we must consider some limitations of the study. First, exploring the charter for a strategic market initiative in one organization limits the generalizability of the results. For example, COMMCO possesses an organizational structure that could be characterized as competitive and rewards business unit leaders on the basis of unit versus company performance. Further research might examine the way different reward systems influence the nature of charter change and the performance of firms entering newly defined product markets. Moreover, further research might examine the structural and strategic processes that organizations employ to promote cross-business unit cooperation as the organization is realigned to capture emerging market opportunities. The challenge of securing access to multiple organizations, especially to proprietary decision processes regarding market entry, led us to a single-firm focus. However, our design incorporated a full complement of decision makers across units, including business unit presidents, marketing managers, and others. Although these business unit presidents were members of the senior leadership team, we were unsuccessful in securing the participation of the chief executive officer and chief financial officer.
Second, a more comprehensive portrait of charter change might be secured by exploring the different ways in which charters are assigned within organizations. COMMCO awarded the Internet charter to a newly created business unit, and top management made subsequent transfers of that charter to other business units. Some firms let internal competition for a charter flourish across business units, especially when there is uncertainty about how the market will evolve (Eisenhardt and Galunic 2000).
Third, our insights into the evolution of the Internet charter at COMMCO are based on depth interviews with managers and an associated questionnaire that explored means-end relationships and patterns of social ties. We did not have complete access to their thoughts or the patterns of influence that may have been operative during the study period. However, field studies of a major strategic market initiative that incorporate a longitudinal component and include a cadre of executive decision makers are rare, both in the marketing and general strategy literature. Above all, the study responds to the criticism lodged at marketing by strategic management researchers (e.g., Biggadike 1981; Prahalad 1995), who question the degree of prominence accorded to the served market construct in the discipline, and addresses the recommendations of marketing scholars for research that more fully explores the realities of strategic market decision making in the firm (Day 1992; Varadarajan and Jayachandran 1999; Workman 1993).
Implications for Managers
First, in creating a charter for a new product market, the fundamental task for strategists at the corporate level is to develop a strategy map for the newly chartered market with which organizational members can strongly identify. Providing a sense of direction to organizational members, the strategy map identifies a projected future goal as an impetus and guide for achieving some desired changes in structure, process, and performance. For example, Gioia and Thomas (1996) describe how the future goal of becoming a top-ten research university provided a powerful stimulus for corresponding changes in the structures, processes, and performance of an institution. Marketing assumes a focal role in the strategic dialogue that surrounds the creation of charters for new product markets by analyzing customer needs and the capabilities of existing and potential competitors and by developing the firm's overall value proposition (Webster 1997). For charter change, this is a challenging task, because organizational members (including marketing managers) residing in different business units may hold opposing views of the preferred strategy course and how it fits into the value proposition of the firm. In line with managers' structural positions and associated identity, the transcripts of interviews from our research indicate that managers from some business units, for example, were strong advocates for addressing the business market first and then using this as a pathway into the Internet market at the consumer level. Meanwhile, the INFOSERVE unit was devoting primary attention to developing a value proposition for the consumer market. These divergent views demonstrate the challenging task that corporate-level strategists confront when creating a charter for an important new market: Link or meld the identities of the various business units into the organization's emerging identity and new concept of strategy (Fiol 1991).
Second, the data from our study revealed a pattern of social ties that united members of the INFOSERVE unit but included few strong links to managers outside the unit. Because a rigid internal focus was adopted, progress with the initiative was hampered and the legitimacy of INFOSERVE as an autonomous unit was damaged. Members of a newly chartered business unit must forge important links to other units and actively communicate the value proposition and proposed strategy to internal constituents. Marketing managers assume a central role in these boundary-spanning activities that center on melding the views of top management; coordinating cross-unit work flows and obtaining feedback; and scanning the organization and the environment for ideas and information about the competition, the market, and the technology (Ancona and Caldwell 1992). By building a shared understanding of the strategic market initiative within the organization, the legitimacy of a new venture is established (Dutton 1993).
Third, our results demonstrate how the forces of structural inertia pose a threat to a firm's efforts to realign the organization to capture new product markets. Established business units, communication flows, and organizational routines shape an organization's activities in each of the product markets. A new unit, such as INFOSERVE, faces a formidable challenge in breaking the established order, drawing on the collective strength of the enterprise, and linking its strategy to overall corporate strategy. The study also provides a rare demonstration of the enduring impact of structural boundaries on the beliefs of managers. When a restructuring initiative moved the INFOSERVE unit to COMMCO's most influential business unit, the identity and communication problems that were evident in the old structure continued to persist in the new structure. Research suggests that firms can break the stranglehold of structural inertia only by creating cultures marked by extensive cross-unit communication and placing collaborative decisions with the managers of businesses or product lines who understand both short-term tactics and long-term vision (Brown and Eisenhardt 1998).
Fourth, at a more fundamental level, Webster (1997) argues that instead of centering on strategy-structure fit, organizations must instead center on developing a capability for responding to the changing environment. The executive leadership at COMMCO developed a vision for the Internet market, but rapidly changing customer demands, quickly shifting technologies, and unexpected moves by competitors immediately challenged that vision of the future. Research on successful and rapidly changing firms in the computer industry signals an alternative path that COMMCO might have followed (Brown and Eisenhardt 1997). Instead of investing in one version of the future, managers at these firms rely on experimentation, practice improvisation in new product development (Moorman and Minor 1998), and launch a series of low-cost probes across markets. These probes take the form of experimental products and services, strategic alliances with leading providers of complementary products, and regular meetings among managers that look to the future. These low-cost probes are valuable because they can be used to create options for the future, uncover threats, reveal the unexpected, and speed learning about new markets.
Implications for Researchers
By exploring the evolution of a charter for a new product market and the belief patterns that united and divided managers, we offer several implications for strategy research. First, fast-paced changes in customer needs and technologies, combined with a host of other environmental forces, spawn frequent structural changes in organizations. However, strategy research has given only limited attention to the question of "how firms move from one strategy-structure position to another" (Galunic and Eisenhardt 1994, p. 246). Research is needed on the alternative approaches that firms use in implementing charter changes and the performance consequences of those approaches. Moreover, some firms demonstrate a record of success in responding to rapidly changing market conditions, whereas others in the same industry do not. A comparison of firms that fit these contrasting profiles may yield valuable insights into market responsiveness.
Second, restructuring initiatives by large, diversified firms represent a clear move toward a greater degree of focus with respect to product-market-technology scope (Varadarajan 1992). For example, research suggests that the performance of firms that diversified into businesses requiring a highly skilled workforce was enhanced by a centralized multidivisional structure (Markides and Williamson 1996). In this structure, the corporate center exercises strategic and financial control over the divisions and intervenes in their operating decisions. Further research is needed to explore how alternative structural configurations inhibit or support the creation of new charters and the transfer of knowledge across business units.
Third, research might also examine the strategic decision processes that underlie the creation of market charters that fall outside the scope of a firm's current strategy. On the basis of a field study of the evolution of Intel's corporate strategy, Burgelman (1991) argues that such initiatives are most likely to emerge at a level in the organization at which managers are directly in contact with new technological developments and changes in market conditions. Through the interaction of various types of champions and top management, these autonomous initiatives may become part of the organization's strategy. A valuable and interesting line of inquiry might examine the relative performance of new market charters that emerged from top-down versus bottom-up processes. Alternatively, Christensen and Bower (1996) study the global disk drive industry and find that successful disruptive technologies were more likely to emerge when developed by an independent unit rather than within the mainstream organization. Projects tended to stall within the mainstream organization because "programs addressing the needs of the firms' most powerful customers always preempted resources from the disruption technologies" (Christensen and Bower 1996, p. 207). Research is needed to assist firms in choosing the appropriate structural fit for a new charter. Under what conditions should a charter for a new product market be located in a separate organizational unit or added to the market domain that an existing unit serves? Likewise, what steps can marketing managers take to integrate new market charters and personnel into an established business unit?
A: The Strategic Significance of INFOSERVE
Belief Category (Valence[a]) Illustrative Beliefs
1. Entry into an attractive Future of communication industry
new market. (+) is the online market.
COMMCO should be seen as a
technology leader
2. A dedicated market One unit should coordinate
charter. (+) online effort.
A separate business unit can
grow online profit.
2. Strengthen relationships Add value to current customer's
in consumer market. (+) service experience.
Build core business in consumer
market.
3. Units divided over target Cannot separate business
market. (-) offering from consumer offering.
Develop singular offer for home
and work.
4. Opposing unit pursues INFOSERVE interferes with other
separate initiative. (-) desirable initiatives.
Other business units should also
pursue the online market.
B: Critical Issues and Success Factors
Belief Category (Valence) Illustrative Beliefs
1. Lever for growth in INFOSERVE will create market
consumer business. (+) share.
Strategically compatible with
COMMCO's core business.
2. Formidable competitors. (-) Many competitors already in
market space.
Announced entry by powerful
competitor poses a challenge.
2. Level of resource Must invest in technology to
commitment. (=) win.
Success of INFOSERVE tied to the
level of funding provided by
COMMCO.
3. Units divided over target Business-consumer market
market. (-) distinctions are wrong for
online customers.
4. Relative size of INFOSERVE Difficult to be entrepreneurial
unit. (-) in a large organization.
A small unit among giants.
5. Cross-unit collaboration. INFOSERVE refuses help from
(-) other units.
INFOSERVE is a closed group.
6. Implementation by INFOSERVE Executing the strategy is
unit. (-) difficult.
Internal priorities are unclear.
7. Credibility of INFOSERVE's No track record.
leadership. (-) Little marketing experience.
8. Rigid boundaries at COMMCO. Vertical smokestacks provide a
(-) barrier for new initiatives.
COMMCO struggles with cross-unit
ventures.
10. Dynamic new industry. (=) Marketing research needed to
understand customers.
This is a young industry, and
the demand for online services
is uncertain.a(+) positive beliefs, (-) negative beliefs, (=) neutral beliefs.
A: Beliefs Regarding the Strategic Significance of INFOSERVE
Business Units
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Valence of Beliefs (n = 12) (n = 11) (n = 8) (n = 8)
Proportion of beliefs .98 .69** .87 .29***
that are positive
Proportion of beliefs .02 .31** .13 .71***
that are negative
B: Beliefs Regarding the Critical Issues and Success
Factors Facing INFOSERVE
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Valence of Beliefs (n = 12) (n = 11) (n = 8) (n = 8)
Proportion of beliefs .11 .28** .05 .05
that are positive
Proportion of beliefs .54 .51 .38 .79*
that are negative
C: Shared Beliefs Regarding the Strategic
Significance of INFOSERVE
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Valence of Beliefs (n = 12) (n = 11) (n = 7) (n = 8)
Proportion of shared 1.00 .68** .84 .34***
beliefs--positive
Proportion of shared .00 .32** .16 .66***
beliefs--negative
D: Shared Beliefs Regarding the Critical Issues and
Success Factors Facing INFOSERVE
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Valence of Beliefs (n = 12) (n = 11) (n = 7) (n = 8)
Proportion of shared .15 .25* .05 .05*
beliefs--positive
Proportion of shared .41 .39 .29 .74***
beliefs--negative
*p [is less than or equal to] .05.
**p [is less than or equal to] .01.
***p [is less than or equal to] .001.Notes: Data entries reflect the total number of times a positive (negative) belief was mentioned by managers within a particular business unit divided by the total number of beliefs (including neutral beliefs, where relevant) elicited by managers who were members of that unit. The shared belief data entries reflect the total number of positive beliefs that was shared by managers within a business unit divided by the total number of beliefs that was shared by managers within that unit. Post hoc analysis (least significant difference test) reflects comparisons of each business unit to INFOSERVE only.
A: The Strategic Significance of INFOSERVE
Business Units
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Belief Categories F (n = 12) (n = 11) (n = 8) (n = 8)
1. Entry into an 1.25 .33 .73 .62 .25
attractive new
market (+)
2. A dedicated 1.59 1.17 .91 1.12 .37*
market charter.
(+)
3. Strengthen 2.74* 2.67 2.09 1.37 .62**
relationships
in the consumer
market. (+)
4. Units divided 7.02** .00 .18 .00 1.00***
over target
market. (-)
5. Opposing unit 3.35* .25 1.09 .87 2.00**
pursues separate
initiative. (-)
B: Critical Issues and Success Factors
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Belief Categories F (n = 12) (n = 11) (n = 8) (n = 8)
1. Lever for growth 5.41** 1.33 2.64* .57 .50
in consumer
business. (+)
2. Formidable
competitors. (-) 4.06** 1.17 .09** .43 .37*
3. Level of 4.71** 2.08 .54** 2.00 .25**
resource
commitment. (=)
4. Units divided 6.21** .92 .27 .62 3.12**
over target
market. (-)
5. Relative size 1.50 1.08 .27 .43 .25
of INFOSERVE
unit. (-)
6. Cross-unit 5.93** .17 2.00*** .14 .50
collaboration.
(-)
7. Implementation 3.95* 2.42 .45** .00** .37*
by INFOSERVE
unit. (-)
8. Credibility of 1.36 .33 1.81* .86 1.00
INFOSERVE's
leadership. (-)
9. Rigid boundaries .09 .92 1.00 1.29 1.00
at COMMCO. (-)
10. Dynamic new .30 2.00 1.64 2.12 1.50
industry. (=)
*p [is less than or equal to] .05.
**p [is less than or equal to] .01.
***p [is less than or equal to] .001.Notes: Data entries reflect the total number of times a belief in a specific category was mentioned by managers within a particular business unit, divided by the total number of managers who were members of that business unit. Post hoc analysis (least significant difference test) reflects comparisons of each business unit to INFOSERVE only.
A: Identity[a]
Business Units
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Strength of Identification (n = 11) (n = 7) (n = 7) (n = 3)
with
INFOSERVE initiative 3.82 3.18 2.75** 1.83***
Home business unit 3.05 4.32** 4.54*** 3.92
COMMCO 3.07 3.86 3.36 4.00
B: Means-End Beliefs[b]
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Valence of Beliefs (n = 11) (n = 7) (n = 6) (n = 3)
Mean rating--positive 3.75 3.18* 3.57 2.45***
beliefs
Mean rating--negative 2.55 3.48** 3.33* 3.78**
beliefs
C: Shared Means-End Beliefs[c]
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Valence of Beliefs (n = 11) (n = 7) (n = 6) (n = 3)
Proportion of shared .69 .46** .66 .30***
beliefs---positive
Proportion of shared .03 .22* .02 .40*
beliefs--negative
D. Tie Strength[d]
Dominant Neutral Rival
INFOSERVE Unit Unit Unit
Tie Strength (n = 11) (n = 7) (n = 6) (n = 3)
Proportion of ties--strong .66 .24** .56 .53
Proportion of ties--weak .26 .60** .33 .47
*p [is less than or equal to] .05.
**p [is less than or equal to] .01.
***p [is less than or equal to] .001.[a]Data entries reflect the average scale rating across four five-point scales, where 1 = "strongly disagree" and 5 = "strongly agree."
[b]Data entries reflect the average scale rating across 11 positive and 3 negative means-end beliefs, respectively.
[c]Data entries reflect the total number of positive (negative) means-end beliefs divided by the total number of means-end beliefs (including neutral beliefs).
[d]Data entries reflect the number of strong (weak) ties that a manager had with the other members of the business unit divided by the total number of other members within the unit.
Notes: Post hoc comparisons (least significant difference test) involve INFOSERVE only.
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~~~~~~~~
By Mark B. Houston; Beth A. Walker; Michael D. Hutt and Peter H. Reingen
Mark B. Houston is Assistant Professor of Marketing, University of Missouri-Columbia. Beth A. Walker is Associate Professor of Marketing, Michael D. Hutt is Ford Motor Company Professor of Marketing, and Peter H. Reingen is Davis Distinguished Research Professor of Marketing, Arizona State University. The authors thank Steve Brown and Blake Ashforth for their assistance and the three anonymous JM reviewers for their insightful comments on previous drafts of the article. The authors also gratefully acknowledge the support of the Center for Services Marketing and Management at Arizona State University. The first author acknowledges the research support provided by the John Cook School of Business, Saint Louis University.
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Record: 41- Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations, 2d ed. By: Clark, Terry. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p151-153. 3p. DOI: 10.1509/jmkg.67.2.151.18611.
- Database:
- Business Source Complete
Section: Book ReviewsCulture's Consequences (Book)
Culture's Consequences: Comparing Values, Behaviors,
Institutions and Organizations Across Nations,
2d ed. by Geert Hofstede (Thousand Oaks, CA: Sage
Publications, 2001, 596 pp., $97.95)
The publication of this second edition of Culture's Consequences marks an important moment in the field of cross-cultural studies. The first edition of the book, published in 1980, launched what some have called a revolution in the social sciences. Researchers who previously had not questioned whether theories of human behavior were applicable to all people were suddenly confronted with a systematic framework of cultural dimensions suggesting that not only behavior but also the processes and mechanisms governing behavior could be captured in four cultural dimensions. Although the first edition brought the field of cross-cultural studies to the forefront of social science research, Hofstede's framework has since come under intense scrutiny. Although this second edition makes many needed changes and additions, it will be interesting to discover whether the intellectual community still finds simple frameworks, even those as influential as Hofstede's, to be relevant to cross-cultural research.
The second edition is a good place for interested marketing scholars to get an introduction to cross-cultural issues. The book includes both a (limited) survey of relevant research during the past 25 years and an introduction to Hofstede's framework. However, if new researchers are inspired to do cross-cultural research, they must go beyond Hofstede's framework to adequately represent the dynamic and complex effect of culture on psychological processes and behavior. Hofstede's approach is perhaps most appropriate as a teaching aid for introducing the notion of cultural differences to marketing students, because it provides an easy-to-understand framework with many examples of practical business applicability. I studied under Hofstede during my undergraduate, study-abroad days at Maastricht University, and I have successfully followed his lead many times with my global marketing students in teaching such topics as intercultural marketing communications.
Although Hofstede's framework for understanding national differences has been one of the most influential and widely used frameworks in cross-cultural marketing studies, in the past ten years or so, it has also become one of the most widely criticized. Detractors contend that his dichotomized way of representing cultural differences leads to unjustifi- able generalizations and ignores the subtleties and frequent contradictions inherent in many national cultures. Many social scientists contend that there is no such thing as national culture, because subcultures in a country can vary greatly in their values and beliefs. Moreover, the original data presented by Hofstede in the first edition of this book, from which he derives his framework, have been misunder-stood and applied in inappropriate ways, leading to criticisms of the framework from a methodological stance.
No new data are presented in the second edition; the data presented are the same original data collected from IBM employees in the late 1960s and early 1970s. What is new, however, is that the findings from various studies that used the framework are discussed, some additional countries are included, dated material is removed, calculations are redone, and, most interesting, Hofstede responds to his critics head on.
Although Hofstede's work has fallen out of favor with many marketing scholars (for reasons discussed subsequently), it is nevertheless still widely used in marketing research. There is no denying the impact this work has had on marketing thinking in the past 20 years. Hofstede himself describes his impact as paradigm shifting (p. 73), and his work was significant in sensitizing many marketing scholars to the question of the universal applicability of many common marketing models--models that have since been shown to be culturally variable, such as the diffusion of innovation model and the advertising effects model.
The data published in the first edition of Culture's Consequences were collected using IBM employees (because it was believed they would be similar on almost all dimensions except culture) in a 53-country project aimed at systematically understanding cultural similarities and differences around the world by measuring cultural values related to spheres such as interpersonal relationships and hierarchies. The second edition supplements the first with an additional 19 countries, using data collected subsequently by Hofstede and others. Four dimensions of culture emerged that help explain the differences among respondents: ( 1) uncertainty avoidance, a society's tolerance of the unpredictable; ( 2) power distance, a society's acceptance of the unequal distribution of power; ( 3) individualism/collectivism, the extent to which the interests of an individual prevail over the interests of the group within a society; and ( 4) masculinity/femininity, the relative strength of masculine versus feminine values in a society.
Since these dimensions were initially published in 1980, researchers have "confirmed" them in various cultures around the world and have used them to analyze a variety of marketing issues, such as variations in symbolic consumption behavior, consumer responses to advertising, marketing management practices in various cultural settings, and so forth.
In 1987, researchers based in Hong Kong, headed by Michael Bond, published results from a Chinese Value Survey they had administered in 23 countries (Chinese Culture Connection 1987). This survey replicated three of the original four of Hofstede's cultural dimensions. However, the fourth dimension was different, ostensibly because the survey was prepared on the basis of Eastern values rather than Western values. They termed this new dimension long-term/short-term orientation. When Hofstede republished his original results in a textbook version of the first edition of Culture's Consequence, titled Cultures and Organizations (1991), he included this dimension as a fifth dimension of culture. It is also included as an added chapter in this second edition of Culture's Consequences. This second edition includes chapters on all five of the dimensions, explaining at length what they mean and represent, and incorporates findings from the 1980s and 1990s into the discussions of each dimension.
The second edition emphasizes the reporting of the many validations of Hofstede's framework since the first edition was published. However, he may be a bit disingenuous, in that he includes only the studies that confirm his original framework. Results that challenge his framework are ignored. Moreover, the minor empirical variations that he reports have appeared in the literature are simply brushed aside. That said, Hofstede openly acknowledges a considerable Western bias in the entire research project. He even outlines his own history, beliefs, values, and scores on all dimensions, so readers can take them into account when evaluating and interpreting the results he presents.
In a widely expanded introductory chapter on the nature of values and culture, Hofstede discusses the so-called ecological and reverse ecological fallacies. The ecological fallacy occurs when data collected at a countrywide level (e.g., Hofstede's data) are used to predict individual behavior. Hofstede's data have been used in this manner many times in marketing research--a highly inappropriate use of his indices. For example, a diversity of people who score higher (or lower) on the masculinity scale can be expected in any given country. When a country scores high on the masculinity index, it implies there are more people in that country who subscribe to masculine values, but it does not indicate how to determine whether any given individual will score high on these values. Without confirming at the individual level whether a person indeed subscribes to the values indicated at a countrywide level, a researcher cannot assume a person will act in the way ascribed to his or her country in general. Because his data have been misused so often in this way, Hofstede is careful to explain "unit of analysis" in detail. This is a key point on which his data and framework seem to be misunderstood by marketing scholars. Individual behavior cannot be predicted with his framework. Rather, the unit of analysis is the nation. Thus, predictions can be made only at this generalized level. Although Hofstede acknowledges index variation within countries, he makes no attempt to account for it.
The reverse ecological fallacy occurs when variables correlated at the individual level are used to explain countrywide data. Although this error occurs less frequently than the ecological fallacy, Hofstede cautions that it is also highly inappropriate. Most important, he points out that these issues are not merely measurement issues but paradigm issues. A recent special issue of Journal of Cross-Cultural Psychology explores how to conceptualize and measure differences cross-culturally, as advocated by Hofstede (for a summary, see Singelis 2000).
In response to those critics who have contended that his data are stale, Hofstede notes (p. 66) that no one set of measures or items on a measure will always account for national differences throughout time. He explains, for example, that the original questionnaire used to measure masculinity in the 1970s has been modified in more recent studies, because the meaning of this construct in various societies has changed since then. Having acknowledged this, Hofstede has a tendency to brush aside variations in the replication studies rather than openly discuss whether his five cultural dimensions are still meaningful in the way they were originally envisioned.
With reference to the long-term/short-term orientation dimension, the other four dimensions tend to divide in an East-West division, but this dimension does not. Hofstede claims (p. 368) in this second edition, however, that the existence of this value "proves" that there is a cultural basis for the economic growth of the so-called Asian dragon countries, because there is a correlation between countries scoring high on this dimension and growth during the 1990s. Growth of the dragon countries has dramatically slowed since the recent Asian economic crisis, and Hofstede admits the relationship could be temporary. Many scholars have pointed out, however, that attributing the dragon countries' growth to culture is spurious, in that the same cultural traits were often used in the past to justify why they were economically "backwards."
In the final applications chapter (intercultural encounters), Hofstede recognizes that his results suggest a relativism with respect to such macro-level concepts as democracy (he argues it is not necessarily the correct political system for all countries), capitalism (same as preceding), and human rights (e.g., the United Nations goal of universal human rights would not be supported by his data). Whereas arguing for an increased relativism with respect to political, economic, and moral systems may be a laudable goal, it also serves to illustrate one of the primary drawbacks of this second edition: making sweeping statements when they are not justified by the data and relating the framework to every aspect of human society and behavior. For example, Hofstede cites (p. 117) archaeological evidence that 4000 years ago there were centralized governments in the Middle East and democracies in Scandinavia to support the view that the power distance variable has been at work for millennia. It would appear that Hofstede subscribes to the view that all
historical, political, economic, and social (or any other) events that have ever happened throughout history, in the present or in the future, are related to and can be explained by his national culture dimensions. Although his framework may be appropriate for accounting for certain observable behaviors (i.e., the differences in rates of eating out in various countries), most business and cultural researchers would hardly buy into the argument that his framework of values can account for all world occurrences.
Hofstede outlines (p. 73) what he believes are the five primary criticisms his original work received: that surveys are not a suitable way to measure cultural differences, that nations are not the best units for studying culture, that studying the subsidiaries of one company is not representative of national cultures, that the data are old and obsolete, and that five dimensions are not enough to represent the complexity of culture. He answers each of these criticisms with a reply along the lines of, "things could be improved and changed, and I hope they are, but this research is a valid and important start." He insists his work was a paradigm shift and argues throughout this second edition that the cultural tendencies he has tapped into are centuries old, and thus his data are not outdated. Hofstede firmly maintains throughout that cultural tendencies and values are inherently stable, a stance at which many anthropologists would take umbrage. Most social science researchers, including those in the business disciplines, use a dynamic constructivist model of culture when engaging in cultural analyses, and this development in cultural studies seems to have passed by Hofstede.
A major flaw in this second edition is Hofstede's tendency to review only literature that supports his framework. The book would have been stronger with a representative sample (pro and con) of the literature that has employed his framework. In discussing the marketing applications of his framework in Chapter 9, for example, he seems to have a particularly limited horizon, citing primarily the work of De Mooij (1998), a former associate of his, when his work has been used by multiple marketing researchers. A more balanced review would illustrate both the impact his work has had on the field and the inappropriate uses of his framework in the field.
Overall, the main problem with Hofstede's research program, including its representation in this second edition, is that he holds a static vision of culture, a view no longer tenable in light of decades of social science research suggesting otherwise. He uses historical occurrences to tautologically justify his five dimension scores, which implies that almost all behavior and historical conflicts are value based, another contention argued against in many business and social science disciplines.
In the end, the results of Hofstede's research program can be useful to marketing researchers if their goal is to compare actual behavior (not processes or motives) at the countrywide level. For example, Houston and Eckhardt (2001) use Hofstede's categorizations to cross-culturally investigate a wide variety of observable behaviors related to food consumption, such as brand choice and brand loyalty, in several Asian countries. Such observable measures are descriptive accounts of phenomena that reveal themselves in a similar manner across cultures. Thus, using a framework such as this is appropriate and can identify patterns of similarity in a global context. If a researcher is going to use a framework like this, Hofstede's is the most comprehensive and validated. Even so, the results of studies using his framework typically suggest new areas of inquiry, and they are not conclusive. Hofstede himself acknowledges that one of the primary contributions of his research is to stimulate researchers to think in a different way and come up with more sophisticated cultural models. In that spirit, I encourage marketing researchers interested in cross-cultural issues to use this second edition of Culture's Consequences as a starting point for thinking about and categorizing cultural differences (and similarities). But by all means, they should go beyond Hofstede's model and investigate and represent the dynamism and complexity of culture in their own research.
REFERENCES Chinese Culture Connection (1987), "Chinese Values and the Search for Culture-Free Dimensions of Culture," Journal of Cross-Cultural Psychology, 18 (2), 143-67.
De Mooij, Marieke (1998), Global Marketing and Advertising: Understanding Cultural Paradoxes. Thousand Oaks, CA: Sage Publications.
Hofstede, Geert (1980), Culture's Consequences: International Differences in Work Related Values. Beverly Hills, CA: Sage Publications.
------ (1991), Cultures and Organizations: Software of the Mind. London: McGraw-Hill.
Houston, Michael J. and Giana M. Eckhardt (2001), "Culture's Consequences on Consumer Behavior Toward Food in Asia," Asian Journal of Marketing, 8 (2), 33-54.
Singelis, Theodore M. (2000), "Some Thoughts on the Future of Cross-Cultural Social Psychology," Journal of Cross-Cultural Psychology, 31 (1), 76-91.
GIANA ECKHARDT
Australian Graduate School of Management
~~~~~~~~
By Terry Clark, Editor, Southern Illinois University
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Record: 42- Customer Knowledge Development: Antecedents and Impact on New Product Performance. By: Joshi, Ashwin W.; Sharma, Sanjay. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p47-59. 13p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.68.4.47.42722.
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Customer Knowledge Development: Antecedents and Impact on
New Product Performance
By enhancing the fit between new product features and customer preferences, the customer knowledge development process fosters new product success. Despite this significant benefit, there is considerable variance in the extent to which firms engage in this process in their new product development projects. This is because not all firms can meet the resource, strategic flexibility, and motivational requirements of the process. In this research, the authors develop a nomological network wherein they identify ( 1) the organizational actions that enable effective implementation of the customer knowledge development process, ( 2) the characteristics of new product development projects that moderate the effects of these actions, and ( 3) the outcomes that are generated by the process. The results from a survey of 165 marketing managers who had recently participated in new product development projects provide substantial support for the nomological network. The authors explore the theoretical and managerial implications that arise from their results and provide future research directions.
The failure rate of new products is somewhere between 40 and 75% (Stevens and Burley 2003). Given the high costs associated with new product development, minimization of the high failure rate is a topic of considerable theoretical and managerial interest. Customer knowledge development-that is, the development of an understanding of customer preferences-has been identified as a key prerequisite for new product success (Cooper and Kleinschmidt 1995, 1996). Despite the acknowledged importance of customer knowledge development in fostering new product success (e.g., Nonaka and Takeuchi 1995), there is considerable variance in the extent to which firms engage in this process in their new product development projects (Cooper 1998). In this research, we identify the underlying reasons for this variance.
We observed three key limitations in prior literature on customer knowledge development in new product development projects. First, although there is a general understanding that customer knowledge development occurs through a bilateral process of firm-customer interaction across the various stages of new product development (Leonard 1998; Prahalad and Ramaswamy 2004), this understanding has not been formalized in terms of a theoretical construct. It is essential to develop a formal conceptualization of the process of customer knowledge development to build the systematic body of empirical research that is necessary to advance the understanding of this process.
The process of customer knowledge development unfolds in an organizational context and, more immediately, in the context of a specific new product development project. As such, these contexts have an important role to play in determining the effectiveness of the process (Dougherty 1992; Dougherty and Hardy 1996; Dougherty and Heller 1994). The second limitation of prior research on customer knowledge development in new product development projects is that specific organizational actions that can foster customer knowledge development have not been identified, to the best of our knowledge. Furthermore, the extent to which project characteristics can enhance or mitigate (i.e., moderate) the impact of organizational actions on customer knowledge development is not known. This lack of attention to the interrelated impact of different organizational factors on new product development activities is also a general limitation in the new product development literature (Henard and Szymanski 2001).
Third, because much of the knowledge about customer knowledge development is grounded in case studies, large-scale empirical evidence for its antecedents, moderators, and effects on new product performance is lacking.
Accordingly, our objectives in this research are to
1. Develop a formal construct to reflect customer knowledge development as a process that occurs across the new product development stages;
- 2. Develop a nomological network that comprises the antecedents, moderators, and outcomes of customer knowledge development; and
- 3. Provide an empirical test for this nomological network in a large-scale survey-based setting. We begin by defining customer knowledge development and by articulating our conceptual framework. We then elucidate the methods and measures we used to test the framework. Following presentation of the results, we close with a discussion of the results in terms of their theoretical, managerial, and further research implications.
Customer Knowledge Development
New product development projects comprise two main phases: prelaunch and postlaunch. Learning about customer preferences (i.e., customer knowledge development) occurs in both phases but entails lower costs and lower strategic risk in the prelaunch phase (Cooper 1998). Accordingly, we focus on customer knowledge development in the prelaunch phase of new product development. The prelaunch phase includes the following stages: idea generation, concept refinement, product development, and product testing (Troy, Szymanski, and Varadarajan 1982).
As we noted previously, an understanding of customer preferences is essential to the creation of successful new products. Recent research on customer new product preference formation suggests that rather than being a preexisting state that directs the course of the new product development project, such preferences evolve through customer engagement with specific new product ideas, concepts, and prototypes across the stages of new product development (Hamel and Prahalad 1991, 1994). Accordingly, to develop an understanding of evolving customer new product preferences, it is necessary for customer knowledge development to be an evolutionary process.
As an evolutionary process, customer knowledge development has three defining characteristics: ( 1) It is ongoing in the prelaunch stages of new product development; ( 2) it is emergent in that novelty at each stage (e.g., idea generation, concept refinement) arises from customer interaction; and ( 3) it entails an action-based, trial-and-error mode of organizational learning about customer preferences (Huber 1991; Lynn, Morone, and Paulson 1996; Miner, Bassoff, and Moorman 2001; Van de Ven and Polley 1992).
On the basis of the preceding, we formally define customer knowledge development as a process of developing an understanding of customer new product preferences that unfolds through the iteration of probing and learning activities (Lynn, Morone, and Paulson 1996) across stages of the prelaunch phase of new product development. Probing activities include the deployment of new product ideas, concepts, and prototypes among target customers, and learning activities entail the analysis of customer feedback and the development of subsequent probes based on the analysis (Hargadon and Sutton 2000; Leonard 1998).
Given that customer knowledge development occurs in an organizational context in general and in the context of a specific new product development project in particular, we expect that these contexts have a significant influence on this activity. In the following discussion, we identify factors in each context that explain the variance in the extent to which customer knowledge development is practiced.
Antecedents of Customer Knowledge Development
Each of the three defining characteristics of the customer knowledge development process described previously creates requirements that need to be fulfilled for the process to unfold effectively across the prelaunch stages of new product development. We turn now to an examination of these requirements.
Organizations allocate resources among activities by assigning budgets to them. However, the accuracy of the budget depends on knowledge of the scope and extent of the activity (Eisenhardt 1988, 1989; Jaworski 1988; Merchant 1985). Given the ongoing nature of customer knowledge development activities both across stages and within each stage of new product development, it is difficult to develop precise estimates of the scope and extent of such activities (Cooper 1998; Damanpour 1991). Accordingly, access to resources other than original budget allocations is the first requirement to be fulfilled for customer knowledge development to be effective (i.e., to result in new products that accurately reflect customer preferences).
Customer knowledge development is described as an emergent process because novelty emerges in each stage of new product development on the basis of responsiveness to customer feedback (Huber 1991; Lynn, Morone, and Paulson 1996). Thus, rather than being predetermined, novelty is generated organically in the customer knowledge development process. However, for this organic growth to occur, it is essential that the customer knowledge development process learns from and is responsive to customer feedback. Therefore, development of the requisite "strategic flexibility" (Eisenhardt and Tabrizi 1995; Grewal and Tansuhaj 2001; Johnson et al. 2003) in the customer knowledge development process is the second requirement to be fulfilled for customer knowledge development to be effective.
As a process that evolves through trial and error, customer knowledge development entails many failed experiments in the form of ideas, concepts, and prototypes that do not accurately reflect customer preferences. Failure in a course of action reduces the motivation to persist with that particular course of action (Leonard 1998; Sitkin 1992). For the people who are directly involved in and responsible for new product development activities, repeated failures in developing ideas, concepts, and prototypes that meet customer preferences can reduce their motivation to persist in the pursuit of successful outcomes. Accordingly, the final requirement to be fulfilled for customer knowledge development to be effective pertains to the management of employee motivation to persist with customer knowledge development activities despite repeated failures.
We draw on prior research to identify specific actions that organizations can undertake to fulfill each of these requirements for customer knowledge development to occur. The provision of resource slack, the creation of cross-functional new product development teams, and the championing of the organizational goal of product leadership are the antecedent constructs in our model that refer to organizational actions that enable the customer knowledge development process to meet the resource, strategic flexibility, and motivation requirements, respectively.
Each of these organizational actions creates the necessary conditions for customer knowledge development to occur. However, we argue that not all organizations that undertake such actions are equally successful at fostering customer knowledge development in their new product development projects. The effectiveness of these organizational actions depends on project characteristics that can moderate the impact of these actions on customer knowledge development. In our conceptual framework, we identify an intelligent-failure reward system, the integration model of conflict resolution, and project members' goal of product leadership as project characteristics that respectively moderate the organizational actions that we listed previously.
Hypotheses
Organizational action: provision of resource slack . Resource slack refers to a situation in which resources in addition to the original budgeted allocation are made available to support the activity (Nohria and Gulati 1996; Sharma 2000). Because the customer knowledge development process is ongoing, it is difficult to forecast resource requirements accurately for the requisite activities in the process. Consequently, a resource cushion is required for the customer knowledge development process to unfold effectively in each stage and across stages. Therefore, the organizational decision to provide resource slack for customer knowledge development activities enables the customer knowledge development process to unfold effectively.
Consistent with this reasoning, prior empirical research has shown that resource availability enhances organizations' customer-related learning in their new product development activities (Dougherty and Hardy 1996; see also the meta-analysis by Henard and Szymanski 2001). On the basis of the preceding theoretical rationale and empirical evidence, we hypothesize the following:
H[sub1]: The provision of resource slack is positively related to customer knowledge development.
Project characteristic: intelligent-failure reward system. The conventional practice in new product development projects is to provide rewards to employees whose activities yield successful outcomes and to punish directly or indirectly employees whose activities result in failure (Dougherty and Hardy 1996; Leonard 1998). In contrast to this practice, an intelligent-failure system rewards employees who are engaged in new product development projects on the basis of the extent to which they undertake creative and learning-oriented activities, without regard to the immediate success or failure of the activities (Kanter 1988; Sarin and Mahajan 2001; Sitkin 1992).
Resource slack provides the customer knowledge development process with access to additional resources, thereby enabling it to be ongoing. However, the underlying assumption in this argument is that the slack is used in service of customer knowledge development activities. Given the propensity for failure in customer knowledge development activities, employees' willingness to use these resources to further such activities is likely to be directly affected by the presence of disincentives against failure in the new product development project. By removing the disincentives and by actually providing incentives for engaging in learning-oriented activities, an intelligent-failure reward system can enhance employees' willingness to use resources in support of customer knowledge development activities.
Consequently, when such an incentive system exists in the new product development project, we expect that the use of slack resources toward customer knowledge development activities is high; conversely, when such an incentive system does not exist, we expect that resource use in support of customer knowledge development activities is low. Accordingly, we expect the following:
H[sub2]: The greater the development of an intelligent-failure reward system, the stronger is the positive relationship between provision of resource slack and customer knowledge development.
Organizational action: creation of cross-functional new product development teams. A cross-functional team exists when responsibility for a specific business activity (e.g., new product development) is assigned to a formalized team of personnel from multiple functional areas (Sethi 2000a, b; Sethi, Smith, and Park 2001). For novelty to emerge in the customer knowledge development process, it is necessary for the process to be responsive to customer feedback. Such responsiveness or "strategic flexibility" requires both the rapid dissemination of customer feedback information to the different functional units in the organization and a synergistic coordinated response by the units to this information (Brown and Eisenhardt 1995, 1997; Sheremata 2000). By bringing together people from different functional areas and assigning them responsibility for new product development, cross-functional teams foster both information flow and synergistic coordination, thereby enhancing strategic flexibility in the customer knowledge development process (Nonaka and Takeuchi 1995). Thus, we argue that by creating cross-functional new product development teams, organizations foster the strategic flexibility that is necessary for the effective unfolding of the customer knowledge development process.
Consistent with this argument, field studies (Clark and Fujimoto 1991; Clark and Wheelwright 1992; Griffin 1997) show that adoption of a cross-functional team structure enhanced the pace of learning in new product development projects. Accordingly, we propose the following:
H[sub3]: The creation of cross-functional new product development teams is positively related to customer knowledge development.
Project characteristic: integration mode of conflict resolution. The integration mode of conflict resolution involves the creation of novel solutions that satisfy the underlying needs of the conflicting parties (Follett 1982). Consider a frequent conflict between the marketing function, which prefers to offer customers customized solutions (thereby enhancing customer satisfaction), and the operations function, which prefers to create standardized products (thereby lowering manufacturing costs). Integration involves the creation of a new solution (e.g., mass customization through form postponement; Pine, Victor, and Boynton 1993) that satisfies the underlying needs of both parties.
Cross-functional new product development teams create the strategic flexibility that is necessary to enable customer knowledge development to be an emergent process. However, the underlying assumption in this argument is that such teams actually generate novelty. Although cross-functional teams create appropriate conditions for the emergence of novelty, they are also breeding grounds for conflict given the diverse "thought worlds" (Dougherty 1992, p. 182) and interests that exist within them (Dyer and Song 1997; Pinto, Pinto, and Prescott 1993; Song, Xie, and Dyer 2000). If improperly managed, conflict can easily undermine the production of novelty. Thus, to harness the potential of cross-functional teams to generate novelty, it is necessary to ensure that team conflicts are managed productively (Lovelace, Shapiro, and Weingart 2001).
The integration mode of conflict resolution fosters the development of creative solutions to conflicts, thereby generating novelty in the customer knowledge development process. Consequently, we expect that when new product development projects are characterized by the integration mode of conflict resolution, the novelty-generating potential of cross-functional teams is harnessed and the destructive potential of conflicts is contained. Therefore, we hypothesize the following:
H[sub4]: The greater the development of the integration mode of conflict resolution, the stronger is the positive relationship between the creation of cross-functional new product development teams and customer knowledge development.
Organizational action: championing the organizational goal of product leadership. A firm with a "prospector" type of strategic orientation (Lukas 1999; Matsuno and Mentzer 2000; McKee, Varadarajan, and Pride 1989; Miles and Snow 1978)-that is, a firm that wants to create "leading-edge products and services that consistently enhance the customer's use or application of the product, thereby making rivals' goods obsolete" (Treacy and Wiersema 1993, p. 84)--manifests the organizational goal of product leadership.
As a trial-and-error process, customer knowledge development generates several failed experiments. For new product development project members who are involved in the experiments, repeated failures are likely to reduce their motivation to engage in continued experimentation (Sitkin 1992), thereby curtailing the customer knowledge development process.
Employees can be motivated to engage in particular activities either because of the outcomes they derive from the activities or because they regard the activities as intrinsically significant (Schein 1992). We argue that by championing product leadership as the organizational goal, the organization infuses the activity of experimentation with "meaning" or intrinsic significance (Bartlett and Ghoshal 1993; Ghoshal and Bartlett 1994). Consequently, we expect that by enhancing employee motivation to persist with experimentation, this organizational action facilitates the effective unfolding of the customer knowledge development process. Accordingly, we hypothesize the following:
H[sub5]: The championing of the organizational goal of product leadership is positively related to customer knowledge development.
Project characteristic: project members' goal of product leadership. The organization's goal for itself can be distinguished from the organizational goal of employees (i.e., project members). Project members' goal of product leadership refers to the extent to which project members regard product leadership as a desirable organizational goal.
The championing of the organizational goal of product leadership infuses customer knowledge development activities with meaning, thereby enhancing project members' motivation to engage in these activities. The underlying assumption in this argument is that project members accept this organizational message. However, message acceptance by project members depends on their prior beliefs (Areni 2002; Slocum, Cron, and Brown 2002). Specifically, we argue that project members who view product leadership as a desirable organizational goal are more receptive to the organization's message about the importance of product leadership than are project members who do not view product leadership as a desirable organizational goal. Consequently, we expect that the motivation to persist with customer knowledge development activities is higher among the former than the latter.
Accordingly, we expect that when project members' goal of product leadership is high, the positive effect of championing the organizational goal of product leadership on customer knowledge development is enhanced. Consistent with this argument, prior research has shown that congruence between employee and organizational values reduces role conflict and role ambiguity, thereby enhancing employees' motivation to perform (Flaherty, Dahlstrom, and Skinner 1999; Siguaw, Brown, and Widing 1994). On the basis of the preceding theoretical rationale and empirical evidence, we hypothesize the following:
H[sub6]: The greater the project members' goal of product leadership, the stronger is the positive effect of championing the organizational goal of product leadership on customer knowledge development.
Consequences of Customer Knowledge Development
Profitability, market share, and market growth rate are commonly used metrics for measuring performance (Deshpandé, Farley, and Webster 1993; Li and Calantone 1998; Matsuno, Mentzer, and Özsomer 2002; Moorman 1995; Sethi 2000b). As we previously noted, a common reason for new product failure is that the products do not accurately reflect customer preferences. There are two reasons customer preference knowledge accuracy can be undermined: ( 1) because the knowledge has been insufficiently verified and ( 2) because customer preferences have evolved.
As an ongoing activity across the stages of new product development, customer knowledge development creates many opportunities for verification of customer knowledge, and it keeps pace with evolving customer preferences to ensure that the new products reflect customer preferences as they exist at the time of product launch. Accordingly, by enhancing customer preference knowledge accuracy, we expect that customer knowledge development enhances new product performance. Thus, we hypothesize the following:
H[sub7]: Customer knowledge development is positively related to new product performance.
Controls
In addition to organizational factors, firms may be motivated to engage in customer knowledge development as a strategic response to environmental turbulence. Prior research has identified three sources of environmental turbulence: ( 1) competitive intensity, which refers to the extent of competitive activity in the industry; ( 2) customer dynamism, which refers to the rate of change of customer preferences; and ( 3) technological turbulence, which describes an environment characterized by continually evolving technological standards (Jaworski and Kohli 1993). We control for the effects of each source on customer knowledge development. Furthermore, given that customer knowledge development creates emergent new product designs, it may be the preferred choice in projects for which the intent is to create new-to-the-world innovations (Heany 1983). Accordingly, we control for the extent of innovation that is achieved in the specific new product development project.
Sampling Frame and Sample
The sampling frame in this study included marketing managers of divisions (strategic business units) of the top 1000 manufacturing firms (in terms of sales revenue) in Canada. The Dun & Bradstreet database contained the name, mailing address, and telephone numbers of people in the sampling frame. To enhance the response rate, we telephoned each person in the sampling frame to inform them of our research objectives and to solicit their participation. Our survey required respondents to serve as key informants on a recent new product development project in which they were active participants and that was typical of the new product development projects that exist in their firms. Thus, an additional objective of the telephone call was to ensure that the respondent had the necessary experience to qualify as a key informant. Of the 1000 people in the database, we made contact with 831.( n1) Of these, 717 expressed an interest in the study and agreed to participate. In the following week, we mailed 717 copies of the questionnaire along with a cover letter and a postage-paid return envelope. Two weeks later, we sent out reminder and thank-you postcards. Over the subsequent week, we received 16 requests for fresh copies of the questionnaire, which we promptly dispatched. We closed the survey five weeks after the telephone contact week, having received 169 completed responses from 831 potential respondents (response rate = 20.3%).
Data Characteristics and Evaluation
Prior research (Cooper 1998; Sethi, Smith, and Park 2001) has shown that marketing managers play a critical role in new product development projects. Accordingly, we identified marketing managers as key informants on new product development projects. Manufacturing firms in high-technology (24%), transportation equipment (33%), and food processing (18%) industries constituted more than 70% of our sample. The average sales revenue in the sample was $138 million, ranging from $24.3 million to $1.5 billion.
To test for nonresponse bias, we compared the early respondents (the first one-third of respondents) with late respondents (the last one-third of respondents) on firm characteristics such as sales revenue, number of employees, and sector (i.e., manufacturing or service), as well as on the model constructs. We found no statistically significant differences. In other words, we did not discern a threat of nonresponse bias in the data. To ensure data quality, we established checks on respondent knowledge of survey issues. In addition to the initial check in the telephone interview, the survey included an item that asked respondents to provide a self-report of their level of knowledge of survey issues. The mean score on this item was 4.6 on a five-point scale (where 1 = "not at all knowledgeable," and 5 = "highly knowledgeable"). Furthermore, the mean number of years of work experience in the division was 12.7 years, and on average the respondents had worked on seven new product development projects in this period. Four respondents had worked in the division for less than two years. We deleted these respondents from our sample, which brought our effective sample size to 165.
Validation of Respondent Data
We requested that study respondents provide the name of one nonmarketing manager who participated in the new product development project; 96 respondents complied with our request. We sent copies of the questionnaire to these 96 people and received responses from 25 nonmarketing managers. Correlations between the marketing and nonmarketing respondents on the model constructs were statistically significant in all cases, ranging from r = .41 (p < .05) to r = .88 (p < .01), with an average correlation across constructs of r = .73. The results provide additional evidence of the validity of the data that we obtained from our respondents.
Measures
Scale development involved mapping out the domain of each construct and establishing scale items to represent the domain. Both activities were predicated on our analysis of prior literature and field interviews (Churchill 1979).( n2) Following this, we submitted the preliminary scales to qualitative pretesting in interviews with four marketing executives, who proposed several refinements, modifications, and new items. We then pretested the scales through a small sample survey (n = 41) of marketing executives. Having modified two items on the basis of the survey results, we believed that the questionnaire was ready for large-scale administration. We discuss the measures subsequently. Note that the scale items are listed in full in the Appendix and that the descriptive statistics of the individual scales are contained in Table 1.
Customer knowledge development. Drawing on prior conceptual accounts (Hamel and Prahalad 1994; Sitkin 1992) and case studies, we developed a five-item scale to measure the three defining characteristics of this construct; namely, that it is an ongoing, emergent, and trial-and-error process.
Provision of resource slack. This scale measures the extent to which additional resources were made available for the new product development project. We developed three items to represent the existence of resources in addition to those formally budgeted to support new product development. We adapted items from prior research (Sharma 2000) to construct this scale.
Intelligent-failure reward system. We used the conceptual writings of Sitkin (1992) and Kanter (1988) as the bases for the creation of a three-item scale to measure an incentive system that rewards learning from failures.
Creation of cross-functional new product development teams. This construct represents the existence of formally sanctioned multifunctional groups that are assigned specific business process responsibilities, such as new product development. Drawing on prior research (Menon et al. 1999; Sethi, Smith, and Park 2001), we developed a four-item measure of this construct.
Integration mode of conflict resolution. This mode of conflict resolution focuses on the generation of creative solutions that satisfy the underlying interests of the different parties. We drew on the conceptual writings of Follett (1982) and on prior conflict resolution scales (Song, Xie, and Dyer 2000) to construct this three-item scale.
Championing the organizational goal of product leadership. This construct captures the extent to which the organization promotes the goal of being a product leader in the industry among employees. Because senior management typically defines organizational goals, our measure focuses explicitly on its role in championing the organizational goal of product leadership. We drew on Treacy and Wiersema's (1995) conceptual discussion and on the scale of the "prospector" strategy type, developed by Matsuno and Mentzer (2000), to create a three-item measure of this construct.
Project members' goal of product leadership. This construct captures the belief among members of the new product development project that their firm should be regarded as a product innovator in the industry. We drew on our field interviews to construct this three-item scale.
New product performance. Following Matsuno, Mentzer, and Özsomer's (2002, p. 21) recommendation to view performance outcomes in "competitive terms," we used the main competitor's new product performance as a reference point with which to compare the firm's new product performance on profitability, market share, and market growth rate. We measured performance by the new product's profitability, market share, and growth compared with that of its main competitor. We adopted this scale from prior research (see Deshpandé, Farley, and Webster 1993, p. 35).
Controls: turbulence in the customer, competitor, and technological environment sectors. We adapted items from Jaworski and Kohli (1993) to measure these constructs. The scales capture both the variety (e.g., number of different customer types) and the dynamism (e.g., rate of change in customer preferences) aspects of turbulence.
Control: innovation range. We developed a three-item scale to measure the extent to which innovation that was developed by the focal new product development project was revolutionary or new to the world rather than incremental and imitative (or me-too) (Heany 1983).
Validation of Measures
We deleted items with low item-to-(scale) total correlations from subsequent analyses, as well as items that exhibited significant cross-loadings and/or did not load significantly on their assigned factor in the exploratory factor analysis. Having performed these preliminary procedures, we then submitted the data to confirmatory factor analysis (CFA).
The first CFA model examined the validity of the antecedent constructs in the research model (i.e., Figure 1). The results show that despite a statistically significant chisquare (Χ² = 212.65, degrees of freedom [d.f.] = 137, p < .05), the other fit indexes (average off-diagonal standardized residual [AOSR] = .04; normed fit index [NFI] = .90; nonnormed fit index [NNFI] = .91; and comparative fit index [CFI] = .93) provide evidence of a good fit of the model to the data, thereby confirming the validity of the measures of the antecedent constructs. The second CFA model examined the validity of the customer knowledge development construct along with the outcome construct (i.e., new product performance) in the research model. The results again show that despite a statistically significant chisquare (Χ² = 29.31, d.f. = 19, p < .05), the other fit indexes (AOSR = .02; NFI = .97; NNFI = .95; CFI = .97) provide evidence of a good fit of the model to the data, thereby confirming the validity of the measures of customer knowledge development and new product performance. Furthermore, in both CFA models, the item loadings on their respective constructs were statistically significant (t = 12.11, p < .01 was the weakest loading) (Anderson and Gerbing 1988). Note that all factor loadings are shown in the Appendix.
The construct reliability of each model construct was greater than .70, which, coupled with the statistically significant item loadings, provides evidence for the convergent validity of the measures. To assess discriminant validity of the measures, we sequentially (and independently) constrained all the construct correlations in both CFA models to unity. We then compared the chi-square statistic of the constrained model with the chi-square statistic of the free model. In each case, the chi-square of the constrained model was greater than that of the free model (difference in Χ² = 35.44, d.f. = 1, p < .01 was the smallest difference), thereby indicating that the free model provides a better representation of the data. The results provide evidence for the discriminant validity of the measures (Gerbing and Anderson 1988). In addition, the results from the exploratory factor analysis also provide evidence for the discriminant validity of the measures examined as a whole (i.e., all the items being examined simultaneously).
Given that we collected the data on both the independent and the dependent variables from the same respondent with the same questionnaire format, the potential for common methods bias exists. To assess the manifestation of this problem, we performed the Harman's one-factor test on the items (Podsakoff and Organ 1986). The results of a principal components factor analysis show ten factors with an eigenvalue greater than 1. These factors account for 78% of the total variance. Because many factors emerged from the factor analysis and because the first factor accounted for only 23% of the total variance, common methods bias does not appear to exist in the data (Menon et al. 1999, p. 31). The correlation matrix and descriptive statistics for the study variables are presented in Table 1.
Hypothesis Testing
We used multiple regression analysis to test the effects of the controls, the antecedents, and their interactions on customer knowledge development (see Table 2). Given that we estimated both main and constituent interaction terms in this regression equation, the potential for multicollinearity was high. Consequently, we used the mean-centering approach (Aiken and West 1991), which involves using mean-centered values of the independent variables and their interactions. Examination of the variance inflation factors for the regression equation showed that the highest variance inflation factor was 2.17, which is well below the cutoff value of 10 that indicates multicollinearity (Neter, Wasserman, and Kutner 1990). We tested the effect of customer knowledge development on new product performance using ordinary least squares regression (see Table 3).
With the exception of the main effect of the provision of resource slack (H1) and the moderating effect of project members' goal of product leadership (H6), all the hypotheses were supported by our research results (see Tables 2 and 3).
With respect to the moderating effect of intelligent-failure reward systems on the main effect of the provision of resource slack on customer knowledge development (H[sub2]), we performed a follow-up analysis on the significant interaction effect reported in Table 2 by splitting the moderator variable (i.e., intelligent-failure reward system) on its mean and by comparing the effect of the provision of resource slack on customer knowledge development in the two groups (i.e., low and high intelligent-failure reward system). Consistent with our expectation, the strength of the positive relationship between provision of resource slack and customer knowledge development was greater in the high-mean group (b = .43, t = 8.14, p < .01) than in the low-mean group (b = .11, t = 1.41, p < .10). The results provide further support for H[sub2]. A similar follow-up analysis on the moderating effect of integration mode of conflict resolution on the relationship between the creation of cross-functional new product development teams and customer knowledge development (H[sub4]) showed that the main effect of this independent variable on customer knowledge development was stronger in the high-mean group (b = .67, t = 9.90, p < .01) than in the low-mean group (b = .18, t = 1.70, p < .10), thereby providing additional evidence in support of H[sub4].
Evaluating the Direct Effects of the Antecedents on New Product Performance
Our model (see Figure 1) posits that customer knowledge development is a perfect mediator between the antecedents and outcomes. To validate this claim, we used Baron and Kenny's (1986, p. 1177) tests of mediation. Of the independent variables that had a significant effect on customer knowledge development (see Table 2), only the creation of cross-functional new product development teams (b = .23, t = 2.41, p < .01), the integration mode of conflict resolution (b = .16, t = 1.67, p < .05), and the championing of the organizational goal of product leadership (b = .18, t = 1.97, p < .05) had statistically significant, direct effects on new product performance. Having controlled for the effect of customer knowledge development on new product performance, we found that only the creation of cross-functional new product development teams had a statistically significant effect (b = .16, t = 1.67, p < .05) on new product performance, though the effect was weaker than it was when we did not include customer knowledge development as a predictor. Thus, the results from the tests for mediation show that customer knowledge development completely mediates the effects of the integration mode of conflict resolution and the championing of the organizational goal of product leadership, and it partially mediates the effect of creation of cross-functional new product development teams. Accordingly, we conclude that the contention that customer knowledge development is a mediator is supported by our results to a limited extent.
Our research contributes to the literature on customer knowledge development in new product development projects in three ways: ( 1) by developing a formal construct that captures the defining characteristics of the customer knowledge development process; ( 2) by identifying the antecedents, moderators, and outcomes of this process; and ( 3) by providing a large-scale empirical test of our proposed nomological network.
Our finding that customer knowledge development enhances new product performance emphasizes the strategic significance of the customer knowledge development process in new product development projects. We identified three requirements that must be fulfilled for the customer knowledge development process to unfold effectively: The process requires ( 1) access to resources, ( 2) strategic flexibility, and ( 3) management of project members' motivation. On the basis of these requirements, we identified the provision of resource slack, the creation of cross-functional new product development teams, and the championing of the organizational goal of product leadership as antecedents of customer knowledge development. The results show that both the creation of cross-functional new product development teams and the championing of the organizational goal of product leadership foster customer knowledge development, with the effect being that the provision of resource slack is not significant.
In addition to the main effects of organizational actions, we identified project characteristics as moderators of the main effects. The results show that intelligent-failure reward systems enhance the positive effect of the provision of resource slack on customer knowledge development and that the constructive conflict mode of conflict resolution enhances the positive effect of cross-functional new product development teams on customer knowledge development. The moderating effect of project members' goal of product leadership on the relationship between championing the organizational goal of product leadership and customer knowledge development is not significant.
Because we previously discussed the rationale for the hypotheses that were supported, we focus our attention here on the hypotheses that were not supported, namely, the main effect of provision of resource slack (H[sub1]) and the moderating effect of project members' goal of product leadership (H[sub6]).
The nonsignificant main effect of the provision of resource slack on customer knowledge development, coupled with the significant moderating effect of an intelligent-failure reward system on the relationship, suggests that it is not the existence of slack per se, but the extent to which project members receive incentives for using slack in service of customer knowledge development activities that determines the extent to which customer knowledge development occurs. Prior literature on the relationship between organizational slack and innovation-related activities has identified two conditions--namely, the nature of slack (i.e., the extent to which it is dedicated to innovation-related activities) (Singh 1986) and the level of slack (i.e., a moderate level as opposed to the extremes) (Nohria and Gulati 1996)--under which organizational slack is positively related to innovation. The results from our research imply a third condition under which organizational slack is positively related to innovation, namely, the extent to which incentives are provided to use the slack.
Given the lack of support for the moderating effect of project members' goal of product leadership, we speculate that rather than being a moderator, this construct may mediate the effect of championing the organizational goal of product leadership on customer knowledge development. Thus, rather than being a preexisting belief, project members' goal of product leadership may be fostered by championing the organizational goal, with project members' goal of product leadership in turn motivating project members' engagement in goal-fulfilling behavior (Hartline, Maxham, and McKee 2000; Slocum, Cron, and Brown 2002). We tested this argument following Baron and Kenny's (1986) procedures and found that championing the organizational goal of product leadership has a statistically significant effect on customer knowledge development (b = .20, t = 2.09, p < .05) and project members' goals of product leadership (b = .35, t = 5.89, p < .01) and that its effect on customer knowledge development is not significant when the effect of project members' goal of product leadership is estimated (b = .07, t = 1.14, p = not significant). This pattern of results provides empirical support for our post hoc argument that project members' goal of product leadership mediates the relationship between championing the organizational goal of product leadership and customer knowledge development.
Managerial Implications
On the basis of our research results, we identify specific actions that both senior management and managers of new product development projects must implement for customer knowledge development to occur. For senior management, our results underscore the importance of creating and assigning the responsibility of new product development to cross-functional teams. In addition, our results point to the importance of senior management championing the organizational goal of product leadership, because this has the effect of infusing customer knowledge development activities with meaning and significance, thereby enhancing the motivation of project members to engage in such activities.
For new product development project managers, our results emphasize the importance of designing incentive systems that reward innovation-related activities instead of outcomes. Furthermore, our results point to the importance of cultivating a culture of constructive conflict resolution in cross-functional new product development teams.
Limitation and Future Research Directions
The cross-sectional nature of our data does not facilitate testing of the causal sequence that is implied in Figure 1. A longitudinal research design is necessary to validate these claims of causality. Furthermore, because respondents provided data on both the independent and the dependent variables, there is the possibility that the correlations are inflated as a result of single-source bias. The results of Harman's one-factor test (Podsakoff and Organ 1986) enabled us to rule out single-source bias. However, the fact remains that data collected from multiple sources (e.g., senior management, organizational employees) would have provided a stronger test of the model.
Our model of customer knowledge development focuses on intrafirm characteristics that enhance this process. However, because customer knowledge development occurs at the firm-customer interface, it is also necessary to examine the role of customer-related factors in fostering this process. Further research should ( 1) identify the factors that foster customers' effective participation in the process of customer knowledge development and ( 2) study how customer-related factors moderate the main effects of organizational actions on customer knowledge development.
The authors thank Eileen Fischer and the three anonymous JM reviewers for their constructive comments.
( n1) We made three attempts in one week to contact each person.
( n2) We interviewed 15 people who were not part of the sampling frame. In these interviews, we "talked through" our conceptual model with them and used their comments to refine our study constructs and to build measures.
Legend for Chart:
A - Variables
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
A
B C D E F
G H I J K
L M
1. Provision of resource slack
1.00
2. Intelligent-failure reward system
.12 1.00
3. Creation of cross-functional new
product development teams
.07 .03 1.00
4. Integration mode of conflict
.18(*) .14 .21(**) 1.00
5. Championing the organizational
goal of product leadership
-.05 .15 .29(**) .07 1.00
6. Project members' goal of product leadership
.03 .12 .31(**) .01 .33(**)
1.00
7. Customer knowledge development
.11 .33(**) .56(**) .29(**) .36(**)
.10 1.00
8. Performance
.03 .13(*) .26(**) .16(*) .21(**)
.07 .31(**) 1.00
9. Innovation range
.09 .11 .26(**) .14(*) .33(**)
.14 .34(**) -.04 1.00
10. Competitive intensity
-.17(*) -.03 .13(*) -.18(*) .19(*)
.02 .18(*) .18(*) .12 1.00
11. Customer dynamism
-.07 -.00 .09 -.20(**) .14(*)
.06 .15 .34(**) .09 .21(**)
1.00
12. Technological turbulence
-.12 -.09 -.01 -.09 -.06
.00 -.07 -.17(*) -.06 .30(**)
.17(*) 1.00
Mean
2.97 3.12 3.91 3.06 3.82
2.77 2.89 1.94 1.69 3.11
3.73 2.90
Standard deviation
1.10 1.73 .68 1.81 1.9
.98 1.39 .46 1.12 .96
1.41 1.53
Number of items
3 3 4 3 3
3 5 3 3 3
3 3
Construct reliability
.78 .83 .86 .82 .82
.90 .89 .74 .77 .79
.81 .76
(*) p < .05.
(**) p < .01.
Notes: With the exception of performance (which we measured on
a three-point scale), we measured all variables on five-point
scales. All significance tests are one-tailed. Legend for Chart:
A - Independent Variables
B - Standardized Coefficients
C - t-Value
D - Results
A
B C D
Innovation range
.21 (2.11)(**)
Customer turbulence
.10 (1.38)(*)
Competitive turbulence
.16 (1.65)(**)
Technological turbulence
-.01 (-.21)
Provision of resource slack
.06 (1.11) H[sub1]: not supported
Intelligent-failure reward system
.21 (2.11)(**)
Creation of cross-functional new
product development teams
.27 (3.01)(***) H[sub3]: supported
Integration mode of conflict resolution
.18 (1.98)(**)
Championing the organizational goal
of product leadership
.20 (2.09)(**) H[sub5]: supported
Project members' goal of product leadership
.07 (1.29)(*)
Resource slack x reward system
.19 (2.03)(**) H[sub2]: supported
Cross-functional team x integration mode of
conflict resolution
.46 (5.92)(***) H[sub4]: supported
Organizational goal x project members' goal
.11 (1.48)(*) H[sub6]: not supported
Adjusted R²
.47
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: All significance tests are one-tailed. Legend for Chart:
A - Independent Variables
B - Standardized Coefficients
C - t-Value
D - Results
A B C
D
Innovation range -.01 (-.11)
Customer turbulence .12 (1.48)(*)
Competitive turbulence .08 (1.18)
Technological turbulence -.01 (-.18)
Customer knowledge development .28 (3.11)(**)
H[sub7]: supported
Adjusted R² .20
(*) p < .10.
(**) p < .01.DIAGRAM: FIGURE 1; Factors That Affect Customer Knowledge Development and Its Effect on New Product Performance
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Singh, Jitendra V. (1986), "Performance, Slack, and Risk-Taking in Organizational Decision Making," Academy of Management Journal, 29 (3), 562-85.
Sitkin, Sim B. (1992), "Learning Through Failure: The Strategy of Small Losses," in Research in Organizational Behavior, Vol. 14, Barry M. Staw and Larry Cummings, eds. Greenwich, CT: JAI Press, 231-36.
Slocum, John W., Jr., William L. Cron, and Steven P. Brown (2002), "The Effect of Goal Conflict on Performance," Journal of Leadership and Organizational Studies, 9 (1), 77-90.
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Stevens, Greg A. and James Burley (2003), "Piloting the Rocket of Radical Innovation," Research Technology Management, 46 (2), 16-26.
Treacy, Michael and Fred Wiersema (1995), The Discipline of Market Leaders. Reading, MA: Addison-Wesley.
Troy, Lisa C., David M. Szymanski, and P. Rajan Varadarajan (2001), "Generating New Product Ideas: An Initial Investigation of the Role of Market Information and Organizational Characteristics," Journal of the Academy of Marketing Science, 29 (Winter), 89-101.
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Independent Variables (CFA Model 1: Χ² = 212.65, d.f. = 137, p < .05; AOSR = .04; NFI = .90; NNFI = .91; CFI = .93)
Provision of resource slack (construct reliability = .78). In this project, additional resources were made available when:
- The cost of new product development exceeded original estimates. (.88, t = 14.88)
- The cost of developing new product ideas exceeded original estimates. (.74, t = 13.49)
- The cost of testing new product ideas exceeded original estimates. (.72, t = 13.12)
Intelligent-failure reward systems (construct reliability = .83)
1. In this project, individuals were rewarded for developing new ideas, regardless of the eventual success/failure of these ideas. (.81, t = 14.09)
- 2. In this project, individuals were rewarded for testing new ideas, regardless of the eventual success/failure of these tests. (.88, t = 14.89)
- 3. In this project, individuals were rewarded for codifying the knowledge that was created from idea development and testing, regardless of the success or failure of these ideas. (.80, t = 13.99)
Creation of cross-functional new product development teams (construct reliability = .86)
1. This project comprised individuals drawn from a number of different functional areas. (.87, t = 14.83)
- 2. In our organization, functional areas are viewed as resource pools from which to draw personnel for cross-functional teams. (.83, t = 14.31)
- 3. This project team was given a budget and had specific responsibilities in terms of new product development. (.85, t = 14.59)
- 4. In our organization, budgets and responsibilities are allocated only to functional areas. (.86, t = 14.67) (reverse coded)
Integration mode of conflict resolution (construct reliability = .71)
1. In this project, conflicts were resolved by developing creative solutions that meet the underlying needs of the parties. (.78, t = 13.90)
- 2. In this project, conflicts were resolved by addressing their root causes. (.68, t = 12.86)
- 3. In this project, the resolution of conflicts left both parties with increased knowledge and understanding of the total system and of their role in this system. (.61, t = 12.11)
Championing the organizational goal of product leadership (construct reliability = .82)
1. Senior management has repeatedly stated that innovations in product and strategy are key to the firm's success. (.66, t = 12.70)
- 2. Senior management has made it clear that the firm differentiates itself from competitors in the industry by being a superior innovator. (.87, t = 14.74)
- 3. Senior management believes that innovation is what this company is fundamentally about. (.81, t = 14.08)
Project members' goal of product leadership (construct reliability = .90)
1. The members of this project team understood that innovations in product and strategy are key to the firm's success. (.83, t = 14.37)
- 2. The members of this project team believed that firm differentiates itself from competitors in the industry by being a superior innovator. (.94, t = 15.49)
- 3. The members of this project team were convinced that innovation is what this company is fundamentally about. (.85, t = 14.49)
Focal and Outcome Constructs (CFA Model 2: Χ² = 29.31, d.f. = 19, p < .05; AOSR = .02; NFI = .97; NNFI = .95; CFI = .97)
Customer knowledge development (construct reliability = .89). Please rate these to the extent that they characterize the new product development (NPD) process in this project.
1. We went through lots of iterations based on customer feedback prior to launching the product in the market. (.92, t = 15.14)
- 2. We developed and tested lots of new ideas over the course of this NPD process. (.90, t = 14.97)
- 3. The NPD process in this project involved numerous failed experiments. (.91, t = 15.05)
- 4. We learned about customer preferences as we worked with them through the new product iterations. (.79, t = 13.84)
- 5. The actual new product that we took to market was very different from our initial expectation. (.78, t = 13.90)
New product performance (construct reliability = .74) . Relative to our main competitor's new product, the performance of the new product developed by this project is:
- (a) Less profitable, (b) about equally profitable, (c) more profitable. (.82, t = 14.29)
- (a) Has a lower market share, (b) has about the same market share, (c) has a greater market share. (.69, t = 12.90)
- (a) Has a slower growth rate, (b) has about the same growth rate, (c) has a faster growth rate. (.73, t = 13.36)
Control Variables
Innovation range (construct reliability = .78)
1. Please evaluate the "newness" of the product on the following scale (.69, t = 12.94): a = "line extension," b = "new to the company," and c = "new to the industry."
- 2. Please evaluate the "distinctiveness" of the value proposition on the following scale (.81, t = 14.15): a = "me-too value proposition," b = "somewhat distinct from the competition," and c = "significantly different from the main competitor."
- 3. Please evaluate the "newness" of product features on the following scale (.77, t = 13.73): a = "minor modifications to existing features," b = "new features to this company," and c = "new features to this industry."
Customer turbulence (construct reliability = .79)
1. Customers' preferences for product features have changed quite a bit over time.
- 2. We are witnessing demand for our products from customers who never bought them before.
- 3. New customers tend to have product-related needs that are different from those of our existing customers.
Competitor turbulence (construct reliability = .81)
1. Our competitors are constantly changing their product features.
- 2. Our competitors are constantly changing their sales strategies.
- 3. New competitors are entering our industry. (new item)
Technological turbulence (construct reliability = .76)
1. The technology in our industry is changing rapidly.
- 2. It is unlikely that today's technological standard will still be dominant five years from now.
- 3. Technological breakthroughs contribute to the development of new product ideas in our industry.
~~~~~~~~
By Ashwin W. Joshi and Sanjay Sharma
Ashwin W. Joshi is Associate Professor of Marketing, Schulich School of Business, York University, Toronto (e-mail: ajoshi@schulich.yorku.ca). Sanjay Sharma is a professor, School of Business and Economics, Wilfrid Laurier University, Waterloo (e-mail: ssharma@wlu.ca). The Social Sciences and Humanities Research Council of Canada and the Schulich School of Business provided funding for this research.
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Record: 43- Customer Portfolio Management: Toward a Dynamic Theory of Exchange Relationships. By: Johnson, Michael D.; Selnes, Fred. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p1-17. 17p. 1 Diagram, 2 Charts, 4 Graphs. DOI: 10.1509/jmkg.68.2.1.27786.
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Customer Portfolio Management: Toward a Dynamic Theory of
Exchange Relationships
Management of an entire portfolio of customers who are at different relationship stages requires a dynamic theory of exchange relationships that captures the trade-offs between scale economies and lifetime customer value. This article contributes to the understanding of relationship management by developing a typology of exchange relationship mechanisms and a model of relationship dynamics and by simulating the model to provide guidelines for customer portfolio management. An important insight from the research is that a key to the creation of value through closer relationships lies in bringing weaker relationships into a portfolio in the first place. Another insight is that firms that position themselves toward offerings with low economies of scale, such as personal services, must build closer relationships to create value.
The past half century has brought dramatic shifts in the underlying goals of marketing and strategic market planning. In the decades of growth following World War II, economies of scale and scope were central, because profits were primarily a reflection of market share (Buzzell and Gale 1987). As markets throughout North America, Europe, and parts of Asia matured in the 1980s and into the 1990s, the lifetime value of customers became a central marketing goal (Fornell 1992). The increasingly global economy is now marked by large technology shocks, from such sources as the Internet and e-commerce, to the fields of biotechnology, information technology, and energy technology. The result is a mix of mature and growth markets and their associated risks and returns, where the goal of a given firm's marketing strategy is not so clear. These changes underscore the need for a theory of exchange relationships that encompasses the basic trade-offs between increasing market share to achieve economies of scale and the lifetime value of customers.
"Offensive marketing" typically refers to activities aimed at increasing the size of a firm's customer base, and "defensive marketing" refers to activities aimed at existing customers, such as customer retention (Fornell and Wernerfelt 1987) and service recovery efforts (Smith, Bolton, and Wagner 1999). Current thought in marketing theory and practice is that defensive marketing has become more profitable, and the implication is that companies should allocate resources to build more cooperative and long-lasting relationships with their customers (Day 2000; Rust, Zeithaml, and Lemon 2000; Storbacka, Strandvik, and Grönroos 1994). We couch this argument in the larger question of how a firm should invest in an entire portfolio of relationships at different levels of cooperation to create value (Christopher, Payne, and Ballantyne 2002; Gummesson 2000; Hunt 2002). The relationship marketing literature recognizes the need to build portfolios of relationships or relational resources (Hunt 1977) to increase a firm's return on relationships (Gummesson 1994). However, as Hunt (2002) points out, there is significant ambiguity surrounding relationship portfolio decisions, because the portfolios are not selected at a particular point in time; rather, they take time to develop. Therefore, investments in a customer portfolio should be a function of underlying firm, customer, and industry characteristics.
In particular, customer relationship theories must grow to include the accumulated effect that the number of customers (i.e., relationships) has on economies of scale and the resultant cost structure of competing firms. This underscores the need to differentiate relationships on the basis of how value is created and to link value creation within individual relationships to overall value creation for a firm. An important consequence is that offensive and defensive marketing strategies become more comprehensive concepts than has been previously considered. As we argue subsequently, offensive marketing not only increases a customer base but also provides a basis for relationship development, whereas defensive marketing works not only to retain current customers but to create additional value with those customers through relationship development.
The purpose of this article is to move relationship management research toward a dynamic theory of how to build value for a firm across an entire portfolio of customer relationships. We first develop a typology of exchange relationship mechanisms that captures fundamentally different forms of value creation. The different forms are a function of customers' capabilities and problem-solving behavior as well as suppliers' capabilities and resource-allocation decisions. Because the different types of relationships imply different profit mechanisms, sellers' marketing strategies should encompass an entire portfolio of customers at different relationship levels. We refer to this process as "customer portfolio management." We then introduce a model of customer portfolio lifetime value (CPLV) that links value creation within individual customer relationships with overall value creation for a firm. The model provides a basis for understanding when and when not to grow relationships. Finally, we use the framework, model, and simulation to develop a series of postulates and propositions regarding the dynamics of customer portfolios and portfolio management. We begin by developing a theoretical framework that connects customer-supplier relationships to industry factors and societal institutions. The framework takes a dynamic approach that extends existing theories of competition in marketing (Alderson 1965; Dickson 1992; Hunt and Morgan 1995, 1996) and provides the basis for our relationship typology and CPLV model.
In general, marketing scholars agree that the fundamental phenomenon to be explained, predicted, and controlled in the dynamics of the marketplace is the exchange relationship (Bagozzi 1975; Hunt 1976, 1983; Kotler 1972). The purpose of an exchange relationship is to connect a customer's needs with a supplier's resources and offerings. We use the terms "customer" and "supplier" herein to apply to both business-to-consumer and business-to-business relationships. From a supplier's perspective, value creation is a process of understanding the heterogeneity of customer needs, developing products (goods and services) to fill those needs, and matching customers to products through marketing activities in competition with other suppliers (Alderson 1957, 1965; Reekie and Savitt 1982). From the customer's perspective, the customer chooses the supplier or suppliers that provide the highest expected benefits less any associated costs and risk, where benefits encompass a bundle of qualities, processes, and/or capabilities (Murphy and Enis 1986).
At a basic level, an exchange relationship serves its purpose when the customer has received the product and paid for it. However, in some relationships, the customer and the supplier collaborate, share information, socialize, integrate or link activities, and even commit future resources to the relationship. Relationships are formed to create economic value more effectively than what may be achieved through the market (price) mechanism. This phenomenon is by no means new, either in the marketplace or in economic theory. In 1937, Coase had already referred to forming relationships in those cases in which short-term contracts would be unsatisfactory (e.g., with services and labor). Richardson (1972) argued that relationships in the form of ex ante cooperation are more likely, even necessary, when the activities of the buyer and the seller are complementary but dissimilar.
Following these arguments, we define an exchange relationship as a mechanism for creating value through the coordination of production, consumption, and related economic activities between a customer and a supplier. There are various exogenous factors that influence how value is created in exchange relationships. In this article, we explore in detail how firm, customer, and industry factors are likely to affect economic activity organized in relationships. Our exchange relationship framework is presented in Figure 1. The central concept in the framework is the nature of the exchange relationship mechanism. The value created in an exchange relationship is a direct function of both the customer's and the supplier's capabilities and strategies.
The strategy of the customer in the market-matching process is to know when and where to solve problems (Howard 1977, 1983; Murphy and Enis 1986) to identify the supplier that is perceived as the best in terms of overall benefits less the costs and risks involved. This problem solving has historically been linked to discrete product decisions (transactions). We propose that customer problem solving be conceptualized more broadly to include implicit or explicit relationship decisions; that is, a customer evaluates both the expected benefits versus the costs of a given relationship (versus the same relationship with other suppliers) and the benefits versus the costs of alternative forms of that relationship. A customer's capability to evaluate these tradeoffs affects his or her ability either to convert to a closer relationship or to change suppliers. Customers' motivation to solve problems is a direct function of the homogeneity versus the heterogeneity of demand (Alderson 1965). The more heterogeneous the demand, the greater is the benefit of finding an alternative that better fits customer needs relative to the costs and risks incurred.
The strategy of the supplier in this exchange relationship is to provide an offering and to organize resources to match the needs of the market to create the best return on investment and a sustainable competitive advantage (Day 1997; Dyer and Singh 1998). We propose and subsequently describe three different strategies for how customer value is created to accomplish these goals: parity value, differential value, and customized value.
A firm's efforts to evaluate the relative value of different exchange relationships occur within a dynamic competitive environment in a state of constant change (Day and Wensley 1988; Dickson 1992; Hayek 1978; Hunt and Morgan 1995). Industry factors naturally directly affect customer capabilities and problem solving, supplier capabilities and resource allocation, and the exchange relationship mechanism. The direct effects of industry factors in Figure 1 capture the influence of an industry's norms and opportunities as exogenous variables, or "rules of competition," that come with competing in a particular industry (Porter 1985), as when economies of scale are high or low. Similarly, societal institutions capture the customs, laws, and institutions of society in general (North 1990) that influence exchange relationships. For example, public service agencies and utilities are often required by law to serve segments of customers that are not profitable. The indirect effects of industry factors and societal institutions on exchange relationships capture the factors that influence customers' or suppliers' benefits, costs, and risks of entering or maintaining a relationship.
The development of an exchange relationship also depends directly on the ability and motivation of both the customer and the supplier to participate. For example, when a supplier wants to move a relationship to a higher degree of commitment, the move is contingent on the customer's willingness and ability to contribute. Therefore, in an understanding of exchange relationships, both sides must be understood simultaneously. At the same time, the exchange relationships that exist or evolve in the marketplace have some effect on what customers and suppliers do. Thus, we posit a reciprocal relationship between both customer and supplier decisions and the exchange relationship mechanism in Figure 1.
Several researchers in marketing have addressed differences among exchange relationship mechanisms and have proposed typologies based on, for example, the differences between products and services (Berry 1980; Shostack 1977), the degree and form of collaboration (Day 2000; Heide 1994), industrial versus consumer markets (Webster 1978), and the potential value of a firm's customer portfolio (Dickson 1983; Fiocca 1982; Krapfel, Salmond, and Spekman 1989). In the context of our framework and discussion, it is more parsimonious to focus on differences in how value is created (i.e., the mechanism for coordination of production, consumption, and related economic activities between a customer and a supplier). We subsequently present three types of relationships: acquaintances, friends, and partners. The labels in this typology are similar to those in the work of Krapfel, Salmond, and Spekman (1989), but the definitions are different.
Our goal is to link value creation at the level of the individual relationships with value creation for the firm as a whole. The individual relationships are building blocks for understanding the value created across an entire customer portfolio. Although individual exchange relationships are primarily economic relationships in our typology, they retain social meaning. As relationships evolve from acquaintances, to friends, to partners, the trust and commitment in these relationships suggests that the social mechanism for creating economic value changes. However, unlike purely social relationships, customers and suppliers are likely to dissolve any form of exchange relationship if the economics of the relationship thus dictate.
From Strangers to Acquaintances
Strangers are customers and suppliers in a preawareness and/or pretransaction period. At the industry level, strangers may be conceptualized as customers that have not yet entered the market. At the firm level, they may include customers of competing suppliers. As soon as there has been a transaction in which awareness and trial are achieved (Ehrenberg 1972), a minimum of familiarity is established, the supplier and customer become acquaintances, and an exchange relationship is established. In this case, suppliers need only provide a value proposition to customers that is on par with competitors, or parity value. Through replication of processes and accumulated volume, suppliers gain experience, learn to improve production efficiencies, and gain a cost advantage relative to those with less experience and volume (Ettlie 2000; Porter 1985).
For a customer, an acquaintanceship is effective as long as the supplier provides the product in a satisfactory way at a price that is perceived as fair. With repetitive interaction, the customer gains experience and becomes familiar with the supplier's products and services. This reduces uncertainty about expected benefits and costs of continuing the relationship and thus enhances the relative attractiveness of the supplier relative to other suppliers (Hoch and Deighton 1989). Repetitive interaction creates familiarity with the customer, which facilitates marketing, sales, and service. Thus, an acquaintance relationship facilitates transactions primarily through the reduction of a customer's perceived risk and a supplier's costs.
Given the nature of acquaintanceships and their focus on parity value, standardization, and repetition, the potential for a firm to develop a sustainable competitive advantage through relationship activities is limited. However, competitors can create value through acquaintances, which is difficult to imitate, by learning from the transactions as a whole. For example, Amazon.com has created value even with acquaintances through a highly developed order and sales system. By processing and organizing historical idiosyncratic transaction data from a customer and comparing it with data from other customers, the system is able to generate cross-selling opportunities.
From Acquaintances to Friends
A major tenant of dynamic theories of competition is that competitors are constantly trying to adapt to changing customer needs (Alderson 1965; Dickson 1992; Hunt and Morgan 1995, 1996). As a result, suppliers diversify their product portfolios to target both old and new customer segments with differentiated offerings (Buzzell 1966) and superior value propositions (Best 2000). The key to the creation of value and profit is no longer just parity, standardization, and repetitive operations, it is also convincing customers to pay a premium for superior performance.
A firm's potential to develop sustainable competitive advantage through friendships should be higher than for acquaintances. Because the offering is more unique and the customer comes to trust that uniqueness, a competitive advantage is created. The activities that link customer and supplier are also more unique and more difficult to imitate, especially as they grow into a series of linked activities (Gustafsson and Johnson 2003). For example, IKEA is an international furniture retailer that creates value for both customers and suppliers through a series of linked activities that include low-cost manufacturing; in-house design; and direct involvement of customers in the service, selection, and assembly activities.
As suppliers move from supplying parity value to differential value, the exchange relationship transforms from acquaintance to friendship. Psychologically, the transition from acquaintanceship to friendship requires the development of trust in the relationship (Morgan and Hunt 1994), be it to a brand, an individual (e.g., a service provider), or an entire organization (e.g., industrial buying). Customers not only become familiar with a particular supplier or suppliers but also come to trust that the supplier provides superior value. Suppliers provide a diversified product or products that meet the needs of a particular market segment better than competitors. A natural consequence is that customers require more information, as through advertising and word of mouth. The customer also provides more information to the supplier (e.g., in the form of market research) to enable suppliers to identify changes in customers needs, communicate them through the organization, and use the information to improve products and services (Kohli and Jaworski 1990; Narver and Slater 1990).
From Friends to Partners
Not captured thus far in our typology is a partnership that reaches the level of ex ante cooperation and coordination of production and supply. Research on industrial buyer behavior provides the psychological foundation for understanding the transition from friends to partners. In their commitment-trust theory of relationships, Morgan and Hunt (1994) argue that the longevity, level of cooperation, and acquiescence in an exchange relationship are predicated on not just trust but also relationship commitment. Drawing on research both inside and outside of marketing (Cook and Emerson 1978; Moorman, Zaltman, and Deshpandé 1992), Morgan and Hunt define commitment as "an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it" (p. 23).
The collaborative coordination of complementary activities (Coase 1937; Richardson 1972) requires suppliers to provide more customized value, which has several implications for how the relationship should be managed (Cannon and Perrault 1999; Heide 1994). Suppliers must use customer knowledge and information systems to deliver highly personalized and customized offerings to create the highest level of congruence between the heterogeneity of demand and supply (Edvardsson et al. 2000). The key to profitability becomes a supplier's ability to organize and use information about individual customers more effectively than competitors. Customers benefit from suppliers whose customer knowledge and information systems enable them to deliver highly personalized and customized offerings (Huffman and Kahn 1998; Pine and Gilmore 1998; Pine, Peppers, and Rodgers 1995). However, customers must be willing to pay a price premium or to commit themselves to the supplier for an extended period of time (Van de Ven 1976; Williamson 1983). Over time, the relationship may evolve through continuous adaptation and commitment (Dwyer, Schurr, and Oh 1987; Johanson, Hallén, and Seyed-Mohamed 1991), and the parties may become increasingly interdependent. The relationship may advance from having only a matching purpose to becoming a source of sustainable competitive advantage (Dyer and Singh 1998; Hunt 1997, 2002; Selnes and Sallis 2003).
That trust is a necessary but not sufficient condition for partnerships is consistent with Spekman's (1988) argument that trust provides a platform or "cornerstone" from which more long-term commitments are built. Similarly, Morgan and Hunt (1994) theorize that the creation of trust leads to the creation of commitment. This follows from social exchange theory and its concept of generalized reciprocity (McDonald 1981), which holds that trust breeds trust, which ultimately increases commitment and results in a shift from short-term exchanges to long-term relationships. In support of the theory, Morgan and Hunt (1994) and Moorman, Zaltman, and Deshpandé (1992) find that trust has a significant effect on relationship commitment and resultant loyalty among industrial customers. Commitment is also a necessary condition for customers to extend the time perspective of a relationship (Spekman 1988). Just as a customer's trust in brand concepts enables friends to purchase products routinely yet in an arm's-length way, the deepening of trust and the establishment of commitment further reduce the customer's need to solve problems in the traditional sense of "finding a better alternative." This trust and commitment are not limited to business-to-business contexts. Johnson and colleagues (2001) find that relationship commitment plays a direct role in building end-user loyalty as well.
Table 1 summarizes our exchange relationship typology and the basic arguments about how value is created in the different types. From a customer's problem-solving perspective, the formation of satisfaction, trust, and commitment corresponds to the customer's willingness to engage in an exchange relationship as an acquaintance, friend, and partner, respectively. From a supplier's resource-allocation perspective, the delivery of parity value, differential value, and customized value corresponds to the supplier's ability and motivation to create an acquaintance, friend, or partner. The implication is that as customers make the transition from satisfaction-based acquaintanceships to trust-based friendships to commitment-based partnerships, we expect that both the value and the length of cooperation increase.
Relationship Development and Switching Behavior
Our framework and typology suggest that customer relationships progress over time to closer and closer forms of value creation, though there are individual cases in which the progression of relationships is different. A relationship between a supplier and a customer may be established directly at a friendship level without progressing through the acquaintance stage. Customers may move from being acquaintances with one supplier to friends with another supplier because the competitor's offering has a superior value proposition. Here, the trust required of friendships may be acquired not through direct experience but through brand building, personal selling, and so forth. Customers may also move directly to the partnership level, such as when an industrial customer decides to outsource its information technology or customer service activities completely to a partner with which it has no previous experience.
However, at an aggregate level, we assume that a supplier's customers move progressively from strangers, to acquaintances, to friends, to partners. This is consistent with the evolution of market growth as it relates to the diffusion of innovation within a product life cycle (Mahajan, Muller, and Bass 1995). A natural consequence of market growth is that customer demand becomes more heterogeneous as a wider variety of customers (and customer needs) emerges (Dickson 1992). In response to this heterogeneity, firms constantly innovate, putting forward alternative systems, service processes, and products to create value for and to attract customers (Day and Wensley 1988). Thus, as markets grow, we expect to observe the emergence of differentiated and customized products with a mix of relationships that include an increasing number of friends and partners over time.
The likelihood of observing this progression of relationship levels should vary by firm, customer, and industry. The greater the lifetime values of customers, the more incentive the suppliers have to pursue closer relationships. The more that sellers segment the market to meet particular customer needs, the more incentive customers have to pursue closer relationships. The more intense the competition in any particular segment, the more customers are likely to switch because of the abundance of substitutes. For example, consider cases in which both the costs of market entry and the value of building close relationships are high. This might describe technologically intensive industries in which customers and suppliers benefit significantly from close cooperation. Although the probability of any given firm being able to create acquaintances is low, as a result of entry barriers, the probability of moving the acquaintances to become partners may be higher. In contrast, consider cases in which both the costs of entry and the benefits of partnership are both low, such as in Internet retailing. In this case, it may be easier to create acquaintances than to move customers to a friendship or partnership stage.
On the basis of our typology, we believe that this relationship progression should systematically affect both the propensity for customers to switch and the costs incurred either to gain customers from competitors or to move them to an even closer relationship. The strength of a closer relationship creates a stickiness that lowers the probability of customer switching (Bendapudi and Berry 1997; Johnson et al. 2001). Customers simply have a diminishing need to solve problems or to shop around. Closer relationships also increase the costs that a competitor incurs to induce switching at a given relationship level (Fornell 1992; Fornell and Wernerfelt 1987). The costs of converting a customer from level to level should similarly increase as customers move from being strangers to being acquaintances, friends, and partners. The cost increases are directly related to the creation of parity value, differential value, and customized value (as is shown in Table 1) and, at a general level, the relationship-specific investments incurred when a firm seeks closer customer relationships (Bendapudi and Berry 1997; Williamson 1981). Thus, the costs of converting customers to a higher-level relationship or gaining customers from competitors at a given level should increase, and the probability of switching should decrease as customers progress from strangers to acquaintances, to friends, to partners. Closer relationships also create premium value for firms, which is a basic tenant of the customer lifetime value (CLV) argument (Fornell et al. 1996; Gummesson 2002; Rust, Zeithaml, and Lemon 2000). It has been argued that customers that are closer to the firm tend to buy more (because they are better acquainted with the firm's offerings), cost less to serve (because the firm knows them better), and are less price sensitive (because they have higher switching costs; Libai, Narayandas, and Humby 2002).
We state our arguments about the dynamics of the relationship typology as a series of postulates. Our arguments about switching probabilities and relationship costs and premiums are stated formally as follows:
Postulate 1: As customers progress toward a closer relationship with suppliers, (a) the probability of customers switching to competitors decreases, (b) the costs of gaining customers from competitors at a given relationship level increase, (c) the costs of converting customers to an even closer relationship increase, and (d) revenue premiums increase.
Firms differ in their resources and resulting marketing capability to learn and to create a competitive advantage with respect to the costs and probabilities of attracting customers and developing relationships (Baker and Sinkula 1999; Day 1984, 2000; Hunt and Morgan 1995, 1996). Some suppliers are simply better at adapting to the heterogeneity of customer demand over time by learning from customers and competitors and by implementing what they learn (Dickson 1992). The relational view of developing competitive advantage identifies relationship learning (e.g., interfirm knowledge sharing) as an important avenue for the creation of differential relationships and "supernormal" profits in relationships (Dyer and Singh 1998; Pine, Peppers, and Rogers 1995; Selnes and Sallis 2003). A firm's capability to learn from and about its market and customers has several direct implications for relationship development and how value is created in its customer relationships, which we state formally as follows:
Postulate 2: The stronger the dynamic marketing capabilities of a firm, (a) the lower is the probability that a customer will switch to competitors, (b) the higher is the probability that a customer will switch from competitors, (c) the higher is the probability of converting customers to closer relationships, (d) the lower is the cost of converting customers to closer relationships, and (e) the higher is the revenue premium from closer relationships.
Aside from market growth and the heterogeneity of demand and supply that follow, one of the most important industry factors that affects value creation is economies of scale (Porter 1985, 1995). Economies of scale relate to the generation of market-level efficiencies through increased volume (Best 2000; Milgrom and Roberts 1992). The process and production efficiencies gained through accumulated production and resultant knowledge lead to a drop in cost per transaction or unit sold, which decreases at the margin over time. From a customer portfolio standpoint, this creates a fundamental resource-allocation trade-off for managers. Should resources be allocated to create simple transactions and growth as a means of leveraging systemwide economies of scale, or should resources be allocated to build relationships through cooperation to increase the lifetime value of customers?
These economies of scale should directly affect the attractiveness of different types of customers in a customer portfolio. In industries in which economies of scale are large (e.g., a manufacturer of commodity goods), there should be greater value created by just having customers in the portfolio, even if they are on-again/off-again acquaintances. In industries in which economies of scale are much smaller, the creation of closer relationships that leverage the lifetime value of customers should be more important. For example, the larger the service component of a product offering, the lower are the economies of scale. Because services are more customized to individual needs and involve the human resources of the firm, it is more difficult to increase quality without lowering productivity (Anderson, Fornell, and Rust 1997; Huff, Fornell, and Anderson 1996). However, even service industries vary in their degree of scale effects, such as when self-service technologies bring some efficiency to particular service activities (Edvardsson et al. 2000). Another straightforward implication of increased economies of scale is that the overall value of a customer portfolio (i.e., CPLV) should increase as a result of cost reduction. We state the dynamics regarding economies of scale as follows:
Postulate 3: As the scale advantages of growth in a market increase, (a) overall CPLV increases, and (b) the contribution of distant relationships increases relative to closer relationships.
How should firms allocate resources to attract customers and move relationships to higher forms of value creation to maximize profits and develop a sustainable competitive advantage? The answer to this question requires an understanding of the dynamics of value creation and the profitability of the different types of relationships over time. In every market period, firms must decide whether to incur the costs required to create acquaintances through parity value, friends through differential value, and partners through customized value. We do not consider discounted cash flows here for several reasons. The first is to keep the defined time period open to accommodate both relatively short and relatively long product life cycles (e.g., high-technology versus low-technology products; Ettlie 2000). The second reason is to keep the model and discussion as parsimonious as possible. Finally, the impact on our model is straightforward. As relationships take time to develop, a discount factor simply decreases the value of closer relationships with respect to distant relationships.
In the following sections, we develop a model of CPLV and a set of propositions that capture how the market dynamics and firm capabilities in Postulates 1-3 affect the evolution and value of a supplier's customer portfolio of relationships over time. We use a series of logical experiments, or simulations, to operationalize the CPLV model under various conditions. We produced values of the different parameters used in the simulation; we did not take them from any particular market or markets. In accordance with Moorthy (1993), the focus in theoretical modeling is on internal validity. The purpose is to construct an environment, or model, in which actions take place. In this sense, our simulations do not test the theory per se. Rather, they operationalize the theoretical model, or "produce the effects by logical argument," and thus help the researcher "construct cause-effect explanations of marketing phenomena" (Moorthy 1993, p. 94). Although the testing of our postulates or assumptions and resulting propositions is beyond the scope of this research, they are ultimately tested by their predictions in different areas of applicability.
The CPLV Model
Marketing scholars have devoted considerable attention in recent years to operationalizing and estimating CLV models, which examine expected profits over time for individual customers (Blattberg, Getz, and Thomas 2001; Gupta and Lehmann 2003), new versus existing customers (Hogan, Lemon, and Rust 2002), and market segments (Libai, Narayandas, and Humby 2002). The overriding emphasis in these models is how to increase the lifetime value of individual customers as assets (Hogan, Lemon, and Rust 2002; Hunt 1997). Although we use this as a foundation, our point of departure is that we focus on value created through an entire portfolio of customers at different relationship stages over time, or CPLV.
The problem for any given supplier's is to maximize CPLV as the sum of the value of acquaintances (A), friends (F), and partners (P). To specify CPLV more explicitly for a given firm over time, we first specify the total number of acquaintances (A<sub>st</sub>), friends (F<sub>st</sub>), and partners (P<sub>st</sub>) for supplier's at time t as follows:
( 1) A<sub>st</sub> = A<sub>st - 1</sub> + A<sub>st Converted</sub> + A<sub>st Gained</sub> - A<sub>st Lost</sub> - F<sub>st Converted</sub>,
( 2) F<sub>st</sub> = F<sub>st - 1</sub> + F<sub>st Converted</sub> + F<sub>st Gained</sub> - F<sub>st Lost</sub> - P<sub>st Converted</sub>, and
( 3) P<sub>st</sub> = P<sub>st - 1</sub> + P<sub>st Converted</sub> + P<sub>st Gained</sub> - P<sub>st Lost</sub>,
where the subscripts "Converted," "Gained," and "Lost" denote relationships converted from one level to another, relationships gained from the competition at a given level, and relationships lost to the competition at a given level for supplier s time t, respectively. Thus, A<sub>st Converted</sub> is the number of strangers new to the market that are converted to acquaintances, and A<sub>st Gained</sub> is the number of competitors' customers gained as acquaintances by supplier s at time t. Equations 1-3 take into account a supplier's ability to create closer relationships, the gain and loss of customers at a given level (i.e., switching behavior), and the attrition of customers to higher-level relationships. To estimate the value of overall CPLV for supplier s at time t, we must also define parameters to capture the relative costs and revenues involved, where
UC<sub>st</sub> = unit cost for supplier s at time t;
CR<sub>st</sub> = base customer revenue for supplier s at time t;
FP<sub>st</sub> = friendship premium for supplier s at time t;
PP<sub>st</sub> = partner premium for supplier s at time t;
AC<sub>st Conversion</sub>, FC<sub>st Conversion</sub>, and PC<sub>st Conversion</sub> = acquaintance, friend, and partner cost of conversion, respectively, for supplier s in time t; and
AC<sub>st Gaining</sub>, FC<sub>st Gaining</sub>, and PC<sub>st Gaining</sub> = acquaintance, friend, and partner cost of gaining, respectively, for supplier s in time t.
The unit cost figure is a percentage of the economies of scale for the industry, based on a firm's market share. The costs apply to every customer in the portfolio. The base customer revenue similarly reflects the revenues that apply to every customer in the portfolio, and the premiums for converting customers to friendships and partnerships are the marginal profit (additional ongoing revenue - additional ongoing cost) captured as a result of moving customers to closer relationships. Finally, the costs of conversion and gaining represent the costs of moving a customer to a closer relationship and enticing customers to switch from a competitor, respectively.
The overall value of CPLV is the number of customers at each relationship stage multiplied by the total revenues less costs for each customer, less the costs attributed to converting and gaining customers, all aggregated over a defined time period n. Specifically:
( 4) [Multiple line equation(s) cannot be represented in ASCII text]
Certain implications for portfolio management follow directly from the CPLV model per se. For example, the costs associated with inducing a customer either to switch from a competitor or to move to a higher-level relationship should have little impact on the relative contribution of acquaintances, friends, and partners over time. Although changes in the costs affect short-term contributions during periods of significant relationship growth, the costs occur only when a given customer converts to a closer relationship or switches from a competitor. In contrast, base customer revenues and relationship premiums are captured for every period that customers are retained. Thus, the cost of either converting customers to higher-level relationships or gaining customers from competitors at a given level have little long-term effect on CPLV, unless the costs are categorically higher than the revenues and premiums. This is consistent with the argument that the costs incurred to gain customers pale in comparison to the revenues that customers generate over time (Fornell et al. 1996; Reichheld 1996).
Operationalizing a Baseline Scenario
How do the dynamics of the CPLV model actually play out? To gain insight into the model's implications, we conducted a series of simulations to operationalize the model. For brevity, we report only a few of the simulations here so as to highlight the more interesting insights. We develop a baseline scenario (Scenario 1) against which we simulate variations in firm, customer, and industry characteristics through assigning a set of parameters different values. This yields a series of propositions as to how particular firm, customer, and industry characteristics affect CPLV. Our simulations operationalize CPLV for supplier s using Equation 4. On the basis of our postulates and the CPLV model, we operationalize the following parameters: ( 1) market growth over time, ( 2) changes in unit costs over time, ( 3) the cost of converting customers from one relationship level to another, ( 4) the cost of gaining customers from competitors at a given relationship level, ( 5) the relationship premium or value created at different relationship levels, ( 6) the probability of converting customers from one relationship level to another, and ( 7) the relative probability of gaining versus losing customers from competitors at a given relationship level. The relationship conversion and switching probabilities enable us to determine the total number of acquaintances (A<sub>st</sub>), friends (F<sub>st</sub>), and partners (P<sub>st</sub>) for supplier s at time t (i.e., Equations 1-3) as input to Equation 4.
We examined differences in market growth over time within a product life cycle that moves toward one million units sold over 100 market periods. We assume that there is a straightforward diffusion curve for each scenario in our simulation. The quantity sold in period t or Q<sub>t</sub> is
( 5) Q<sub>t</sub> = Q<sub>max</sub>/(1 + e<sup>-(a + bt)</sup>),
where Q<sub>max</sub> is one million units and a and b are constants, which we set at -5 and .15. This yields sales levels over time that follow a logarithmic S-shaped diffusion process (Mahajan, Muller, and Bass 1995) in which there is no extended "time to take off" and some period of market maturity. This enables us to observe differences as the market evolves from introduction, to growth, to maturity. We keep this market growth constant across all scenarios.
Economies of scale represent industry learning related to the reduction in unit costs with market growth. Unit cost at time T (UCt) varies as follows:
( 6) UC<sub>st</sub> = UC<sub>minimum</sub> + UC<sub>decrease</sub>/(c + dQ<sub>st</sub>),
where UC<sub>minimum</sub> is the minimum possible unit cost, UC<sub>decrease</sub> is the asymptotic decrease in unit cost over time, Q<sub>t</sub> is the industry unit sales volume (from Equation 5), and c and d are constants. In each learning curve, the unit cost is set at a value of $100 (UC<sub>minimum</sub> + UC<sub>decrease</sub> = $100). In the baseline scenario, unit costs decrease asymptotically from $100 to $50 over the course of the product life cycle (UC<sub>minimum</sub> = $50, UC<sub>decrease</sub> = $50). Note that the unit costs apply to the industry (product life cycle) as a whole. We operationalize an individual supplier's unit cost savings as a percentage of industry unit cost savings based on the supplier's market share at time t.
Marketing dollars are required to build relationships and associated internal resources to move customers from strangers, to acquaintances, to friends, to partners (Fornell and Wernerfelt 1987). Recall that customer problem-solving decisions include the decision of whether to move the relationship with a supplier to a higher level. Because customer risks and resource sharing increase as a relationship grows, it should be more expensive to convince a customer to become a partner than to become a friend or acquaintance. In the baseline scenario, the acquisition cost of converting a customer to an acquaintance, friend, and partner is $100, $150, and $200, respectively. Suppliers also incur some cost associated with gaining customers from competitors at a given relationship level, that is AC<sub>st Gaining</sub>, FC<sub>st Gaining</sub>, and PC<sub>st Gaining</sub> (acquaintance cost of gaining, friend cost of gaining, and partner cost of gaining for supplier s at time t) in Equation 4. In the baseline scenario, we set this cost equal to $100, $200, and $300, respectively, positing that it should be more expensive to switch a customer from competitor to competitor than it is to create a closer relationship with a customer already in the portfolio.
When a customer moves from being a stranger to an acquaintance, supplier's receives a base customer revenue (CR<sub>st</sub> in Equation 4) in each time period that the customer is retained. Following the relationship typology logic in Table 1, when customer demand is met through differentiation, friendships are formed, which yields a friendship premium for the supplier (FP<sub>st</sub>). When customer demand is met through an even greater investment in customization, partnerships are formed, which yields a partnership premium for the supplier (PP<sub>st</sub>). We held the base revenue for acquaintances, friends, and partners constant at $100 across scenarios. In the baseline scenario, moving a customer to become a friend and partner adds a premium of $25, such that the overall customer revenue (not including unit cost) equals $100, $125, and $150 for acquaintances, friends, and partners, respectively.
To operationalize the CPLV model, we needed to make an assumption about the probability that a supplier is able to convert a customer to a closer relationship. We set this probability to .25 in the baseline scenario. We make other simplifying assumptions at this point. Our market growth curve in Equation 5 defines how many customers are in the market at a given point. Using a spreadsheet database, the simulation multiplies the number of new customers entering the market in a period by a supplier's probability of creating acquaintances. This probability determines the share of new customers entering the market that are acquaintances of supplier s at time t. Competitors acquire all remaining acquaintances that are entering the market at time t. We then take into account that the market-matching process requires some minimum time period before customers are in a position to move to a higher relationship. For example, it takes time for customers to solve problems and for suppliers to allocate resources to create differential and customized value. The simulation assumes that it takes a minimum of 10 market periods for any customer either to transition to a closer relationship with the same supplier or to switch to the same type of relationship with a different supplier. In our preliminary tests of the simulation, we also set the minimum time equal to 20 market periods, and we found that the pattern of results was robust.
We also made an assumption about competitors. After being an acquaintance for ten periods, there is some probability that a customer is converted to a higher-level relationship, is lost to the competition, or remains at the current relationship level (where the sum of the probabilities of converting and losing customers must be less than or equal to one). To calculate the total number of customers at any given relationship stage for supplier s, the simulation first determines the number of customers that competitors have at each relationship stage at time t. To make this determination, we assume that the competitors' probabilities of creating friends and partners are equal to .25, which is the baseline level of the probabilities for our target supplier s. This enables us to simulate situations in which the target supplier's probabilities and the competitors' probabilities are equal (the baseline), the supplier is better than competitors at creating relationships, and the supplier is worse than competitors at creating relationships.
Following Postulate 1a, the deeper the relationship that customers form with a particular supplier, the more difficult it is to move customers from one competitor to another. The baseline scenario reduces the probability of gaining one of the competitor's acquaintance customers versus gaining one of the friend customers from .25 to .125, and the probability of gaining one of the partner customers is reduced to .0625. This represents a 50% decrease in the switching probabilities for each relationship level. We set the probabilities of losing one of the focal supplier's customers to a competitor in the same way and with the same values in the baseline model.
In summary, the baseline simulation scenario uses the diffusion curve defined in Equation 5 as a base. We applied the conversion and switching probabilities to this customer base to determine the number of acquaintances, friends, and partners for both the focal supplier's Equations 1-3) and competitors as a whole in each of 100 time periods as the market evolves. We then used these relationships as well as the cost and margin parameters to determine the contribution of each type of relationship in the supplier's portfolio over time as well as overall CPLV using Equation 4. The aggregated CPLV results for the simulations are shown in Table 2.
Figure 2 shows the contributions by market period and relationship type for the baseline scenario. Figure 2 illustrates how acquaintances, friends, and partners make their greatest contribution to overall CPLV at different points in time. Although the baseline scenario suggests that acquaintances contribute the most to CPLV over time (friends and partners add value as the market matures), note that this is a direct function of the input used. It is more important to focus on relative changes as we vary the parameters.
In the following sections, we first examine how variation in economies of scale affects the development of the portfolio. We then examine how differences in competitive capabilities reflected in probabilities of converting customers to closer relationships and gaining or losing customers at a given relationship level affect the portfolio over time. Finally, we illustrate the dynamics of industry shocks on CPLV, especially as they relate to sudden changes in unit costs and relationship premiums.
Economies of Scale
To determine the effect of differences in economies of scale, we developed scenarios that deviate from the baseline (both greater and lesser economies of scale). Scenario 2 illustrates a situation in which the economies of scale are lower than the baseline. This is typical of many service firms and industries in which the more service that is added to the overall product offering, the more involved are the human resources of the firm and customers themselves, which lowers productivity (Huff, Fornell, and Anderson 1996). In this scenario, industrywide unit costs decrease asymptotically from $100 to $80 over the course of the product life cycle (UC<sub>minimum</sub> = $80, UC<sub>decrease</sub> = $20), which compares with the lower limit of $50 in the baseline scenario. We operationalize an individual supplier's actual share of the cost savings as the percentage of industry unit cost savings based on the supplier's market share at time t. For example, the target supplier's market share reaches 36% by Period 100 in the baseline scenario. This translates into a unit cost of approximately $82 when the industry maximum is 50% (baseline scenario) and $93 when the industry maximum is 20% (Scenario 2).
The results for Scenario 2 are shown in Figure 3. A comparison of Figures 2 and 3 (and the results in Table 2) illustrates two important predictions of the typology and CPLV model. First, as economies of scale decrease, the overall value of CPLV decreases rather dramatically. The CPLV drops from $448 million in the baseline scenario to just $218 million when economies of scale are small (see Table 2). Second, the relative contribution of acquaintances is much more susceptible to changes in economies of scale than are the contributions of friends and partners. In Figure 3, acquaintances are no longer the most profitable relationship category early in a market's growth; acquaintances are a losing proposition over the early market periods because the scale advantages of growth have yet to compensate for the cost of creating the relationships. The contribution of friendships toward overall CPLV increases from 29% in the baseline to 43% in Scenario 2, and the contribution of partnerships increases from 11% to 18%. In contrast, the contribution of acquaintances falls from 61% in the baseline to 38% in Scenario 2. When economies of scale are even higher than in the baseline scenario (80% maximum unit cost decrease for the industry), the contribution of acquaintances increases relative to both friends and partners such that arm's-length relationships dominate the market over time. The simulations illustrate the inherent challenge with acquaintances. Because the margins on acquaintances are much lower than the margins on friends and partners, their contribution to CPLV is more susceptible to changes in economies of scale.
The results help explain the basic shift in marketing thinking as economies have matured over the past 50 years. The loss of economies of scale has resulted in a shift in strategy that once focused on market share and arm's-length relationships to a focus on closer relationships with customers. Not only are friendships and partnerships more profitable as a market matures, but also their attractiveness increases in a customer portfolio when scale economies are limited.
Competitive Capability in Converting Relationships
A firm's particular competitive capability to convert customers to closer relationships affects the attractiveness of different relationships in its portfolio. We conducted various simulations of the CPLV model to understand these implications. Scenario 3 illustrates a situation in which firms have the requisite skills to increase the probability of converting relationships to a level of .50 for each type of relationship, compared with a level of .25 in the baseline scenario. That is, the absolute level of conversion probabilities increases compared with the baseline, but it remains constant across relationships. Figure 4 shows the contribution over time by relationship type for Scenario 3. Note that there is a systematic transition over time where acquaintances and friends make the greatest contributions early in the market, but by Period 61, partners come to dominate the portfolio. Moreover, the CPLV for Scenario 3 (see Table 2) grows to more than twice the value of the baseline CPLV ($1.083 billion versus $448 million). We formalize these findings in the following proposition:
P<sub>1</sub>: As the competitive capability of converting customers to closer relationships increases relative to competition, (a) CPLV increases, and (b) closer relationships make a greater contribution to CPLV over a longer period of time.
Put simply, P<sub>1</sub> captures how a firm that is superior at building closer relationships creates more value through those relationships over time.
Competitive Capability in Creating Acquaintances
More notable are firm-level differences with respect to the ability to create a particular type of relationship. To illustrate, Scenario 4 operationalizes a set of conversion probabilities that vary by relationship level, in which it is easier for the supplier to create acquaintances (p = .50) than friends (p = .25) or partners (p = .125) in any given time period. Again, the probability that competitors are able to create either friends or partners is held constant at .25 (the baseline level). Variation of the probabilities by relationship level offers important insights into the dynamics of a customer portfolio. Differences in the contributions over time are not as revealing as the change in the ultimate value of acquaintances, friends, and partners, as reported in Table 2. For firms that are better at creating acquaintances than friends and partners, the CPLV equals $898 million, which is more than double the baseline CPLV of $448 million. Although partnerships make up a larger proportion of CPLV in the baseline scenario, the contribution of each relationship type declines. These findings lead to the following proposition:
P<sub>2</sub>: As the capability of creating distant relationships increases relative to the capability of converting customers to closer relationships, (a) CPLV increases, and (b) distant relationships make a greater relative contribution to CPLV over a longer period of time.
The important insight from P2 is that a key to creating value through friendships and partnerships is a firm's ability to bring customers into the portfolio as acquaintances in the first place. If a firm specializes in creating close relationships at the expense of being poor at converting strangers into acquaintances, profitability decreases dramatically. Although closer relationships make greater relative contributions to the customer portfolio, there is a much smaller customer base to draw on, such that all relationship types are less profitable. Put simply, it is better to generate a larger bucket of distant relationships than a smaller bucket of close relationships. Without the larger, leakier bucket, the number of close relationships the supplier can form is inherently restricted.
Competitive Capability of Gaining and Keeping Relationships
Our relationship typology suggests that the deeper the relationship customers form with a particular supplier, the more difficult it is to move customers from one competitor to another. That is why probabilities of gaining and losing customers decrease from acquaintances, to friends, to partners in each scenario. However, the level of the probabilities should again vary with a supplier's marketing capabilities. In Scenario 5, we assume that the supplier is twice as likely to gain customers from competitors at a given relationship level (probabilities of gaining customers are .5, .25, and .125 for acquaintances, friends, and partners, respectively) as they are to lose customers to competitors (probabilities of losing customers are .25, .125, and .0625 for acquaintances, friends, and partners). This is a situation in which the supplier has relatively strong marketing capabilities of gaining versus losing customers.
The results for Scenario 5 vary from the baseline scenario in two important ways. First, the overall value of CPLV increases by 63% (from $448 million to $731 million). Second, and more important from a portfolio management standpoint, the relative contribution of acquaintances in the overall CPLV increases from 61% to 69% of the portfolio. The relative contributions of friendships and partnerships decrease from 29% and 11%, respectively, in the baseline scenario to 23% and 8% in Scenario 5. These findings provide the basis for the following proposition:
P<sub>3</sub>: As the competitive capability of gaining and keeping customers increases, (a) CPLV increases, and (b) distant relationships make a greater contribution to CPLV than do closer relationships over time.
Particularly notable is the flip side of P<sub>3</sub>. If a supplier is relatively poor at retaining customers, it must rely more on friendships and partnerships to survive. The weaker a firm's relative switching probabilities, the more it benefits from closer relationships. Such a firm is simply less likely to lose customers that manage to become friends and partners. This result is consistent with research by Ghosh and John (2002), who find that firms operating from a position of strength are less likely to create close alliances with suppliers. This result is also consistent with the research of Rust, Moorman, and Dickson (2002), who examine whether it is more profitable for firms to pursue a strategy of quality improvements to enhance customer satisfaction, which subsequently will attract and keep customers, or to pursue a strategy of quality improvements to enhance cost efficiency to improve margins. On the basis of a sample of established companies, they find that firms that emphasize building a larger customer base perform best.
Shocks in the System
Finally, we use the CPLV model to examine the effects of shocks in the system on the value of different relationships in a portfolio. We explored two types of shocks that directly affect contributions: sudden changes in unit costs and sudden changes in revenue premiums. Periodically, companies incur costs associated with, for example, point-in-time investments in technology upgrades, expansion of a physical plant, or the purchase of new equipment. To illustrate this situation, Scenario 6 illustrates the baseline scenario while adding unit costs that amount to one-third of the Period 0 unit cost base of $100 (i.e., $33.33) in Period 34 and again in Period 67. For simplicity, we reduced the additional unit costs over time at the same rate as the original unit costs.
The results are reported in Table 2 and Figure 5, which illustrates how the cost shocks result in greater swings in the contribution of acquaintances in relation to friends and partners. Even though the contributions from acquaintances remain high, the contribution risks are also high. Partners and friends show less fluctuation in their contributions over time. Because the margins are relatively thin for acquaintance customers, the sudden increases in costs have more dramatic effects on their contributions. Another notable result from Scenario 6 is that the relative contribution of friends and partners increases to a point at which friends provide the greatest overall contribution because they effectively balance contribution risk and return.
We also explored shocks or sudden reductions in the revenue premiums, as might occur when economic conditions are particularly weak (customers spend less and are more price sensitive). As we expected, the revenue premium shocks reduce the relative contribution of partners and friends and make the contributions less predictable. These results provide the basis for the following proposition:
P<sub>4</sub>: When a market is subject to sudden increases in unit costs (reduced revenue premiums), (a) closer (distant) relationships make a greater contribution to CPLV than do distant (closer) relationships, and (b) closer relationships have less (more) variance or risk associated with their contributions than do distant relationships.
In our discussion, we explore the firm-level implications of this risk-return trade-off.
Sensitivity Analyses
Recall that we simulated the CPLV model over various parameter values that we have not reported. We summarize these results here to evaluate the sensitivity of our propositions to wider variation in the parameters. Reducing the relationship premiums from closer relationships to relatively low values (an additional $5 for friends and $10 for partners versus $25 and $50 for the baseline scenario) or increasing the premiums to double the baseline scenario ($50 for friends and $100 for partners) only affects the magnitude of the differences across relationships and does not change the relative predictions. In general, as the relationship premiums from closer relationships increase, the positive contributions from the relationships increase and occur earlier in a market's growth. Similar results occur when ( 1) the probability of converting customers to closer relationships continues to increase relative to competitors and ( 2) the probability of converting friends to partners increases relative to the probability of converting stranger to acquaintances. The various simulations show that P<sub>1</sub>-P<sub>4</sub> are not sensitive to alternative starting values in the baseline scenario.
We also simulated what happens when the base revenue per customer for supplier's at time t (CR<sub>st</sub> in Equation 4) gradually erodes over time, as often occurs when competition reduces base margins. Similar to our findings for cost shocks, acquaintances are much more sensitive to these changes because their margins are lower. One scenario decreased base customer revenues by $.25 each period. The contribution of friends dominated the scenario (CPLV of $128 million) and partners were profitable (CPLV of $48 million), but acquaintances became rather unprofitable (CPLV of -$40 million), though the acquaintances provided the base from which the more profitable friends and partners evolved.
Marketing has evolved over time from an emphasis on market share and size to an emphasis on developing relationships with customers as the key to profitability. An understanding of this evolution and of what drives profitability over time requires a dynamic theory of exchange relationships that encompasses the trade-offs between the two sources of profitability. Scale economies of growth emphasize the need to move customers "in the system" to lower costs. In contrast, CLV emphasizes the need to build relationships and to keep customers over time to capture the "back loading" of customer revenues. We have contributed to this theory by developing a typology of exchange relationship mechanisms that capture fundamentally different forms of value creation. Our typology suggests that marketers take a more comprehensive view of offensive and defensive marketing strategies than currently exists. The purpose of offensive marketing is not only to increase a market but also to provide a basis for customer portfolio development. The purpose of defensive marketing is not simply to prevent customer defections but also to create value through relationship development.
The overall value of a firm's customer portfolio is an aggregation of the contribution from individual exchange relationships over time. At a basic level, suppliers create acquaintances by providing parity value, friends by providing differential value, and partners by providing customized value. Prevailing theories and strategies in marketing argue for building friendships (e.g., brand relationships) and partnerships. Brand building has been the primary means of building a purchase-consumption-repurchase relationship with suppliers (Aaker 1996). Theories of customer behavior have similarly focused on brand concepts (Howard 1977, 1983) and loyalty, defined as repeat purchase and related behaviors (e.g., repurchase intentions, word of mouth, price sensitivity) (Bloemer, Ruyter, and Wetzels 1998). Partnering is usually connected to business-to-business marketing, in which commitment-based resource sharing is widely recommended (Moorman, Zaltman, and Deshpandé 1992; Morgan and Hunt 1995). The theoretical framework presented here should open the eyes of the marketing community to focus more on accumulated value creation of a customer portfolio, not on the value created in single relationships. Although in some situations the accumulated value created in a customer portfolio may be dominated by friends and partners, acquaintances are more likely to be the cornerstone of a firm's portfolio and the primary source of economies of scale. Thus, managers should not necessarily stop doing business with customers that are less profitable on an individual basis.
We illustrated the complexity of how value is created in a portfolio of relationships in several scenarios. One of the more interesting scenarios shows that a leaky bucket of customers may create more value than a tight bucket. If a firm specializes in creating close relationships at the expense of acquiring acquaintances, profitability decreases dramatically. The important implication is that the key to creating value through closer relationships is rooted in a firm's ability to bring weaker relationships into a portfolio in the first place. Another noteworthy and important insight is how sensitive portfolio value is to variations in economies of scale. If economies of scale are low, as they are for many types of services, closer relationships tend to generate more value than distant relationships. Thus, the more a firm is positioned toward product offerings with low economies of scale (i.e., high personal-service component), the stronger it must be in developing and keeping closer relationships.
Another scenario illustrates the risk-return trade-offs due to unexpected market changes. When the uncertainty is related to cost increases, closer relationships are better buffers toward unwanted variance in contributions. The implication is that intermediate-level relationships (i.e., the friends in our typology) provide a balance of risk and return. Friendships created through the development of strong brands should be particularly attractive when firms face considerable economies of scale (where distant relationships are relatively more profitable) but costs are uncertain (where closer relationships lower contribution risk). When uncertainty is related to relationship premiums, as when consumer spending varies over time, more distant relationships are better buffers against contribution variance.
A natural next step is to begin empirical investigations of our underlying postulates and resulting propositions. There is also a need to explore various dynamic factors that are certain to affect CPLV. These include demand shocks due, for example, to changes in technology or major events in society. In the current model, we have assumed a rather simplistic construction of the market in terms of segments (customers are only differentiated as acquaintances, friends, and partners). The validity of our theory and model will continue to improve as we model other types of segments in combination with the typology presented here. An example is segments based on purchasing volume (i.e., light versus heavy users). For example, what are the implications if the heavy users in the market are partners or acquaintances? Another segment-related issue is whether there are customers in the market that do not move through the progression from acquaintances, to friends, and to partners, but may begin directly as a friend or a partner customer. An implication of this is that the importance of acquaintances observed in the presented scenarios would be less. Thus, we require a deeper understanding of how different types of segments affect the value of the customer portfolio over time. We need to explore firm-level dynamics and their influence on customer portfolios as well. If first movers focus more on building acquaintances, should subsequent entrants invest more exclusively in the brands required to build friendships or in the firm-level capabilities required to build partnerships? Another issue is the effect of improvements in marketing capabilities over time. If a firm can improve either its acquisition skill or its relationship development skill, which would have the greatest impact in different circumstances?
Our intent has been to introduce a conceptual framework for how companies can better manage their customer portfolios. A natural question, then, is, How can we relate this conceptualization to a practical or applied level? A natural starting point for a company is its current portfolio of customers and, if available, its customer relationship management databases. A premise of our CPLV model is that customers should be classified according to how relationship value is created both for the customer and for the company. Thus, a challenge would be to develop measurement instruments or classification schemes that sort customers according to their value creation (e.g., relationship premiums, conversion probabilities, switching probabilities). Instruments are also needed to assess relative firm capabilities and the costs of customer acquisition and development. Finally, methods are required to analyze the dynamics of exchange relationships in a firm's portfolio as markets evolve that capture the interplay among customers, competitors, technology, and other factors. Moving the conceptual framework to an empirical and applied level is a challenge and an opportunity for both academics and practicing managers.
We provide a foundation for answering the questions raised by viewing a firm's customer base as a portfolio of exchange relationships that create value in fundamentally different ways over time. Our framework, relationship typology, postulates, and propositions provide scholars working in marketing and customer relationship management with a more dynamic approach to building a sustainable competitive advantage. A firm's portfolio contains a combination of acquaintances, friends, and partners that is constantly changing. This requires decisions as to when to invest in relationships based on their attractiveness over time. Our approach provides a starting point for thinking about the conditions in which different relationship types should be the focus of a firm's customer portfolio.
The authors thank Dave Stewart and the three anonymous JM reviewers for their constructive comments on previous drafts of the article. The authors' names are in alphabetical order, and they have contributed equally to the article.
Legend for Chart:
B - Acquaintances
C - Friends
D - Partners
A B
C
D
Product offering Parity product as a form of
industry standard
Differentiated product
adapted to specific market
segments
Customized product and
dedicated resources
adapted to an individual
customer or organization
Source of competitive Satisfaction
advantage
Satisfaction + trust
Satisfaction + trust +
commitment
Buying activity Satisfaction facilitates and
reinforces buying activity
and reduces need to
search for market
information.
Trust in supplier is needed
to continue the buying
activity without perfect
information.
Commitment in the form of
information sharing and
idiosyncratic investments is
needed to achieve
customized product and to
adjust product continuously
to changing needs and
situations.
Selling and servicing Familiarity and general
activities knowledge of customer
(identification) facilitates
selling and serving.
Specific knowledge of
customer's connection to
segment need and situation
facilitates selling and
serving.
Specific knowledge of
customer's need and
situation and idiosyncratic
investments facilitates
selling and serving.
Acquisition costs Low: Generally low but
depends on industry factors
such as market growth,
satisfaction with competing
alternatives, distribution,
and media availability.
Medium: Acquisition and/or
conversion costs increase
with degree of
differentiation in product
(perceived risk), established
preferences for competing
alternatives, and availability
of segment specific
channels and media.
High: Acquisition and/or
conversion costs increase
with degree of
customization and level of
idiosyncratic investments
from one or both sides.
Time horizon Short: Generally short
because the buyer can shift
supplier without much effort
or cost.
Medium: Generally longer
than acquaintance
relationships because trust
in a differentiated position
takes a longer time to build
and imitate.
Long: Generally long
because it takes time to
build and replace
interconnected activities
and to develop a detailed
knowledge of a customer's
need and the unique
resources of a supplier to
commit resources to the
relationship.
Sustainability of competitive Low: Generally low, but
advantage competitors can vary in
how they build unique value
into selling and serving
even if the product is a
form of industry standard.
Medium: Generally medium
but depends on
competitors' ability to
understand heterogeneity
of customer needs and
situations and the ability to
transform this knowledge
into meaningful,
differentiated products.
High: Generally high but
depends on how unique
and effective
interconnected activities
between customer and
supplier are organized. Legend for Chart:
A - Scenario
B - Parameter Values Varied
C - Acquaintances' Dollar Contribution (Percentage of CPLV)
D - Friends' Dollar Contribution (Percentage of CPLV)
E - Partners' Dollar Contribution (Percentage of CPLV)
F - CPLV in Dollars (Percentage of CPLV)
A
B
C D
E F
1. Baseline scenario
Economies of scale = 50% industry maximum
Probability of converting relationships = .25 for all
relationships
Probability of gaining relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
Probability of losing relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
$271,604,881 $128,384,427
(60.6%) (28.7%)
$48,000,757 $447,990,064
(10.7%) (100%)
2. Decreased economies of scale
Economies of scale = 20% industry maximum
Probability of converting relationships = .25 for all
relationships
Probability of gaining relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
Probability of losing relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
$83,894,091 $94,337,516
(38.4%) (43.2%)
$40,099,941 $218,331,548
(18.4%) (100%)
3. Increased probability of
relationship conversion
Economies of scale = 50% industry maximum
Probability of converting relationships = .50 for all
relationships
Probability of gaining relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
Probability of losing relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
$374,213,019 $328,771,857
(34.5%) (30.4%)
$380,446,304 $1,083,431,180
(35.1%) (100%)
4. Increased probability of converting
acquaintances, decreased probability of
converting partners
Economies of scale = 50% industry maximum
Probability of converting relationships = .50
(acquaintances), .25 (friends), and .125 (partners)
Probability of gaining relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
Probability of losing relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
$556,162,600 $289,965,158
(61.9%) (32.3%)
$51,992,103 $898,119,861
(5.8%) (100%)
5. Increased probability of
gaining customers from competitors
Economies of scale = 50% industry maximum
Probability of converting relationships = .25 for all
relationships
Probability of gaining relationships = .50
(acquaintances), .25 (friends), and .125 (partners)
Probability of losing relationships = .25
(acquaintances), .125 (friends), and .0625 (partners)
$505,739,692 $170,023,053
(69.2%) (23.2%)
$55,387,093 $731,149,838
(7.6%) (100%)
6. Cost shocks over time
Cost shocks of $33.33 introduced in periods 34 and 67.
Other parameters same as baseline scenario.
$62,931,022 $88,863,027
(33.1%) (46.8%)
$38,172,167 $189,966,216
(20.1%) (100%)DIAGRAM: FIGURE 1; Exchange Relationship Framework
GRAPH: FIGURE 2; Contributions over Time for Baseline Scenario (Scenario 1)
GRAPH: FIGURE 3; Contributions over Time When Economies of Scale Decrease (Scenario 2)
GRAPH: FIGURE 4; Contributions over Time When Probability of Relationship Conversion Increases (Scenario 3)
GRAPH: FIGURE 5; Contributions over Time When Cost Shocks Occur (Scenario 6)
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~~~~~~~~
By Michael D. Johnson and Fred Selnes
Michael D. Johnson is D. Maynard Phelps Professor of Business Administration and Professor of Marketing, University of Michigan Business School, University of Michigan (e-mail: mdjohn@umich.edu).
Fred Selnes is Professor of Marketing, Norwegian School of Management BI (e-mail: f.selnes@bi.no).
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Record: 44- Customer Profitability in a Supply Chain. By: Niraj, Rakesh. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p1-16. 16p. 5 Charts, 5 Graphs. DOI: 10.1509/jmkg.65.3.1.18332.
- Database:
- Business Source Complete
CUSTOMER PROFITABILITY IN A SUPPLY CHAIN
Estimating current profitability at the individual customer level is important to distinguish the more profitable customers from the less profitable ones. This is also a first step in developing estimates of customers' lifetime values. This exercise, however, takes on additional complexities when applied to an intermediary in a supply chain, such as a distributor, because the costs of servicing a retail customer include not only those incurred directly in servicing this customer but also those incurred by the distributor in dealing with its own vendors for goods supplied to this customer. The authors develop a general model and measurement methodology to relate customer profitability to customer characteristics in a supply chain. The authors show how heterogeneity in customer purchasing characteristics leads to important profit implications and illustrate the implementation of the methodology using data from a large distributor that supplies to grocery and other retail businesses.
In recent years, marketing practitioners and academicians alike have embraced such concepts as one-to-one marketing, relationship marketing, targeted marketing, interactive marketing, and so on and have increasingly talked about the importance of building relationships with customers. The basic premise behind these concepts is that if a firm can address and target individual customers, it can improve its profitability by serving these customers differently. Several authors have advanced conceptual frameworks and have argued that targeted marketing, or marketing different programs to different customers, is a winning strategy. Popular books by Peppers and Rogers (1993) and Hallberg (1995) stressing customer heterogeneity are prime examples. This emphasis on relationship has given a new meaning to the age-old marketing concept of being customer driven, especially as the falling cost and increasing capabilities of information technology make it possible for more and more firms to practice targeted marketing. However, to be customer driven under this new paradigm, firms increasingly need-and should develop the capability-to evaluate profitability at the customer level to formulate their marketing strategies better. In this article, we develop a model of single-period customer profitability in the context of a supply chain. Applying the model to data from a large wholesaler/distributor, we demonstrate that individual customer purchasing behavior affects not only customer revenues but also customer service costs and profits.
In the marketing literature, attempts to build customer profitability models have usually been in a direct marketing context (e.g., Berger and Nasr 1998; Mulhern 1999), in which customer profitability is evaluated solely on the transactions between the direct marketer and the customer. The focus of these studies has been primarily on direct marketing costs such as direct mailing costs, promotions, free samples, and so forth. In a supply chain, firms deal with customers, supplying products and services to them, and with vendors, acquiring products and services from them. The logistic and operation costs of buying, storing, and selling are typically as important as, if not more important than, direct marketing costs. We show that explicitly recognizing all these types of transactions from the perspective of an intermediary in a supply chain raises several interesting issues in modeling customer profitability as well as in the managerial uses of such a model.
There are several dimensions of differences between direct marketing and supply chain contexts of customer profitability models. First, the purchasing, carrying, and shipment costs borne by the distributor generally vary across different products in a typical supply chain. Second, customer characteristics such as size, decentralization of purchasing, inventory holding and reorder policy, number of delivery locations, payment habits, and so forth have a significant effect on customer servicing costs. Third, the execution of orders and related costs are different depending on the type and size of order (i.e., normal or direct delivery [DD] shipment) in the supply chain. In summary, the costs of purchasing, inventory, order processing, delivery, and services are important and vary widely across customers in most supply chains, but these are usually not recognized in more traditional (i.e., direct marketing) contexts. In this article, we build a detailed activity-based cost model to understand the behavior of costs and profits associated with individual customers in a supply chain.
In Figure 1, we sketch the flow of products and orders from the perspective of an intermediary in a supply chain for a typical packaged consumer good. The intermediary we focus on is a distributor in this chain (e.g., A in Figure 1) selling to other downstream intermediary firms (e.g., B or C in Figure 1, which are customers of A). Sales are made to the customers and purchases are made from upstream suppliers (manufacturers in Figure 1). Products are delivered to Customers B or C either by Distributor A (termed normal shipment) or directly by an upstream supplier (termed DD shipment). Using activity-based costing (ABC) methodology (Kaplan and Cooper 1997), we develop cost pools and identify cost drivers for the flow of orders and products related to Distributor A.<SUP>1 </SUP>We then combine a general revenue model with a general cost model to arrive at a profitability model at the individual customer level for the distributor. We analyze the impact of customer purchasing characteristics on service costs and subsequently on the profitability of Distributor A's customers in the supply chain.
Not surprisingly, we find evidence that all customer dollars are not equal in their effect on the firm's net profits. Although sales volume remains an important indicator of profitability, differences in the cost of serving different customers play an equally important role in determining which customer is profitable and which is not. We find that many customers (32% of total) in our data, including some of the largest customers, are unprofitable. Our results also suggest that new distribution practices such as efficient consumer response (ECR; Kurt Salmon Associates Inc. 1993) could affect service costs and their distribution to various channel members in such a way that many profitable customers of a distributor may become unprofitable.
Our contributions to the marketing literature are the following: We integrate the customer profitability literature in marketing with the latest developments in management accounting and develop a customer profitability model that includes a carefully developed cost model using ABC analysis. We do this in the context of a supply chain, taking into account both direct costs (those affected by customer behavior) and indirect costs (those incurred as a result of customers affecting the firm's pattern of interaction with its own suppliers). We also provide a blueprint for implementing customer profitability analysis in an important distribution channel setting that is prevalent in many industries. This blueprint could be adapted to a variety of business-to-business situations. We provide evidence of relationships among customer transaction characteristics and revenue, components of service cost, and measures of profitability. We also discuss the implications of our customer profitability model and findings for marketing strategies such as customer selection, menu-based pricing, and evaluating business practices. Finally, we discuss the managerial and broader business policy implications of more and more firms obtaining the capability of ascertaining the profitability at the individual customer level.
The remainder of the article is organized as follows: In the next section, we present a brief review of the literature and position our article therein. Following this, we describe interactions in multiechelon supply chains and then build a model of customer profitability. In the following section, we present the research site and illustrate the process of implementing our conceptual model. We then present both our analysis of factors that affect customer profitability and our empirical testing of the relationships posited previously. We conclude with implications, limitations, and a brief discussion of the future research directions.
Blattberg and Deighton (1996) suggest that in this customer-centric era, firms should focus on building and managing customer equity and not just brand equity. Customer equity is the sum of lifetime values (LTVs) of customers, where each customer's LTV is the sum of the properly discounted stream of net profits from the customer over the lifetime of the customer-firm relationship. When each customer's LTV can be computed, interesting possibilities open up. The firm can identify its best prospects and target the most profitable ones differently from the less profitable ones. Marketing, service, and other strategies aimed at customer acquisition or retention can be reevaluated on the basis of which strategy yields the richest pool of customers with high potential LTVs.
Customers generally interact with a firm over multiple periods. Figure 2 (adapted from Wayland and Cole 1997) illustrates that to calculate the LTV of a customer, models of current period costs and revenues as well as future revenues and costs are required. We believe and subsequently illustrate that obtaining the current period costs and revenues at the customer level is not always straightforward. It is even more complex to estimate future revenues and costs, because metrics and models would be needed to help forecast how customers' current revenue streams and associated costs will evolve in the future in addition to forecasting how long these relationships are likely to last. We provide a description of the two streams of literature related to the LTV concept next.
Customer Profitability Models
The issue of customer profitability has attracted interest in both the management accounting and marketing literature. With the advent of activity-based costing in the 1990s, management accounting researchers have been interested in understanding the processes and factors that drive customer service costs and profitability and using this information for better management and control of customer services and related operations (Shields 1997). More recently, customer profitability measures have been used to investigate the impact of nonfinancial performance measures, such as customer satisfaction, on financial performance measures, such as revenues and customer profits (Foster and Gupta 1999) and firm value (Ittner and Larcker 1998).
In the marketing literature, Berger and Nasr (1998) advance a series of models along with numerical examples that illustrate how to compute LTV in some typical situations. They focus on providing a "systematic theoretical taxonomy" of such models. However, they do not provide any empirical application of their customer profitability models, nor do they consider multiple dimensions of consumer heterogeneity. Using data from the pharmaceuticals industry, Mulhern (1999) provides an application for evaluating current customer profitability as an input to computing LTV. Our work, though similar in spirit, is different from his in important ways. First, Mulhern's model is set in a specialized, direct-marketing-like context, whereas ours is set in a supply chain context. As explained previously, modeling the profitability of a customer in a supply chain involves carefully accounting for both upstream and downstream costs in the supply chain. Second, Mulhern's main focus is on showing heterogeneity in the effectiveness of a marketing program (what he calls "concentration"), whereas we focus on heterogeneity in customer characteristics in evaluating customer profitability. Finally, a major difference between Mulhern's study and ours lies in the details of revenue and costs we each consider. On the revenue side, Mulhern implicitly assumes constant gross margins by treating revenues from all products and all customers as having the same impact on the "top line." In contrast, we allow for the possibility of different gross margins across customers due to product as well as price differences (including price discounts). On the cost side, Mulhern considers only direct marketing costs such as salespeople's time, free samples, and direct mail expenditure. In contrast, we include not only the marketing costs but also the operations costs associated with servicing the customers. In addition, we take into account the indirect upstream costs induced by customers in the firm's dealings with its own suppliers.
Forecasting Models
In the marketing literature, stochastic choice models have been advanced to predict the likelihood of future events based on past history. These models can be used to identify the likelihood of current customers being active in the future and to predict future revenue. Schmittlein, Morrison, and Colombo (1987) propose a purchase event-duration model to predict the probability that a customer will remain active and use this model to predict the future level of transactions. Schmittlein and Peterson (1994) apply the framework developed by Schmittlein, Morrison, and Colombo to an industrial purchasing context and extend it by incorporating the dollar volume of transactions in the forecasting model. Schmittlein and Peterson's model can answer questions such as how likely a customer is to be active, how much longer he or she is likely to continue being active, and how much he or she is expected to purchase. Reinartz and Kumar (1999) also build on Schmittlein, Morrison, and Colombo's methodology and, using data from a direct marketing context, forecast customer lifetime duration for noncontractual customer-firm relationships. They also provide an analysis of factors that affect the likelihood of a continued relationship using a proportional hazard model. This stream of articles provides us with methodologies and illustrations to forecast the future revenue potential of a customer (i.e., Period [t + 1] and beyond in Figure 2).
Forecasting the profit streams for customers' lifetime duration will be difficult if revenue or cost components cannot be correctly identified or measured even at their current levels. Also, different revenue and cost elements often interact with one another, and their future values can be affected differently by the firm's actions (e.g., adoption of new technology, targeted advertising and promotional policies). We believe that carefully identifying and quantifying the components of the revenues and costs for a given period will improve the forecasting of these elements over future periods or over the lifetime of customers. Our focus in this article is to advance a model to measure the current-period profitability of a customer.
In this section, we first describe the activities in a multiechelon supply chain, and then we develop a general model of current period customer profitability. Finally, we discuss how customer characteristics affect customer profitability.
Activities of the Firms in a Supply Chain
To construct a model of current-period customer profitability for a firm that operates in a multiechelon supply chain, we start by describing the general supply chain shown in Figure 1. Three generic echelons of firms are included in Figure 1. Our focus in developing the model is a distributor such as A in Figure 1. The model can be generalized to any other intermediary firm in a supply chain with any number of echelons. Although each echelon may consist of several (indeed, hundreds of) firms, in Figure 1, we show only the focal distributor among the distributors and representatives of other firms in the supply chain, provided that they have some buying or selling relationship with the distributor. Also, although each echelon may consist of many distinct firms, it is possible for a firm to own facilities in multiple echelons; for example, a firm may own a redistribution facility to serve several of its own retail outlets exclusively. We conceptualize the supply chain interactions as consisting of two flows, the flow of orders and the flow of physical goods to fulfill these orders. As retail outlets such as C deplete their stocks by selling to final consumers, they initiate the ordering process. In general, these outlets place orders to a redistributor such as B (which may be owned by the retailers themselves), which in turn places orders to the distributor. The flows involving two firms in two adjacent echelons of the supply chain are termed "normal" in the model. However, in search of logistic efficiencies, it is not uncommon for firms to bypass one or more echelons in the supply chain. This is indicated in the figure as DD orders or shipments. For example, a retail outlet, C, may place orders directly to the distributor. Our model includes both normal and DD transactions insofar as Distributor A gets involved in at least one of the flows. The orders may be consolidated (across items and/or customers) at each level and passed on to a higher level in the supply chain. The physical flow virtually mirrors the flow of orders, except in certain DD shipments in which a shipment might skip one or more echelons and be sent directly to a customer's customer.
The activities involved in maintaining the two flows are sustained by certain support activities. These include warehousing, accounting, and administrative services such as those associated with selecting vendors, matching physical flows with orders, performing routine customer maintenance services, and so on. Finally, firms in each echelon also engage in varying degrees of marketing and sales activities.
Assigning Costs to Customers
In building a detailed customer-specific cost model based on the principles of ABC, we must determine the portion of the distributor's activities that are attributable to its individual customers. For each customer, these levels of activities are multiplied by appropriate cost rates and then summed over all activities to arrive at the customer-level transaction or service costs. Activity levels are measured in terms of cost drivers. A cost rate is the cost of effort or resources needed to meet the demand of a specific activity (e.g., order processing) measured for the associated cost driver (e.g., purchase order). To construct the cost model for the distributor, we start by briefly describing the set of activities for period t; in doing so, we use the following notations. In the model building, we focus on only one period, and therefore the subscript for period t is dropped for simplicity in the subsequent discussions:
I = number of customers indexed by i;
S = total number of items handled by the distributor, indexed by s;
Ni = number of retail outlets for Customer i;
mis = demand in number of units for Item s for each of the retail outlets of Customer i;
ki = number of orders placed by Customer i;
Di= number of delivery locations for Customer i;
SiN = the set of items Customer i receives as normal shipment from the distributor, A;
SiD = the set of items Customer i receives as DD shipment from A's suppliers;
SiT = the set of all items bought by Customer i through the distributor;
Pis = net price per unit charged to Customer i for Item s;
ks = number of orders placed by the distributor for Item s on its supplier;
bs = fraction of total normal demand the distributor maintains as inventory for Item s;
Cs = cost of Item s to the distributor; and
Vols = physical volume per unit of Item s (e.g., in cubic feet).
Sales and direct marketing costs. The field sales force of the distributor spends time and other resources to carry out routine customer visits, generate and take orders, resolve order discrepancies, and so forth. All these are recurring tasks that are necessary to sustain the current business of the firm. Costs incurred in generating new accounts are not included here. The sales force's time allocation to customers is modeled as a function of the customer's size (total revenue) and the complexity of transactions (e.g., number of delivery locations, number of different items, order frequency). Let SF1 through SF4 be the cost rates corresponding to each unit of these cost drivers-revenue, delivery locations, number of items, and order frequency, respectively. Allocation of these costs to Customer i can then be expressed as
Order processing and order fulfillment costs. To compute the costs for processing orders received from customers, assume that Customer i raises ki purchase orders to the distributor in the period. Let the cost rate per purchase order be OR. Therefore, the order processing cost attributable to Customer i is given by
(2) OR ki.
The distributor also incurs costs for shipping orders to customers and accounts receivable. The total ki customer orders translate into ki normal shipments made to Customer i in a period. Let HS1 be the transaction-level handling cost rate corresponding to activities such as locating the order, scanning the items, checking for accuracy, and so forth. Furthermore, let HS2 be the unit-level handling cost rate, representing activities such as moving and loading ordered units. In addition, there are paperwork costs (e.g., accounts receivable, shipment papers) for each order. We denote this order-processing paperwork cost rase by AR. Another order-processing cost is incurred when the distributor fills a part of a customer's order by arranging a DD shipment directly from its suppliers to the customer. If this order-processing paperwork cost rate is denoted by DR, the expression for this portion of the transaction costs is
(3) HS1 x ki + HS2 x mis x Ni+ AR x ki+ DR x ki
Purchase and warehousing costs. Costs for vendor maintenance arise because the distributor must maintain suppliers for all the items it sells. For each item, it incurs costs for identifying suppliers, negotiating rates for the items, and maintaining supplier accounts. We denote the vendor maintenance cost rate per item by VM. Each customer i is allocated a share of this item-specific cost in proportion to its unit share for the item.
Raising purchase orders to suppliers is another cost to the distributor. It purchases items from its suppliers (manufacturers) on the basis of a consolidated pattern of its customers' purchase orders. We assume that there is an item-specific order frequency (ks ) for normal orders, that is, orders that the distributor places to its suppliers for delivery to its own warehouse. Let OI be the cost rate for each purchase order. A share of purchasing costs for normal orders is allocated to the customer in proportion to its unit share of the normally shipped units of the item. Also note that each item being filled as DD shipment results in a separate order being placed to the relevant supplier for delivery to the relevant customer. Combining these two components, purchasing costs allocated to Customer i are given as
The distributor incurs costs for receiving shipments from manufacturers and accounts payable. Goods supplied through normal orders are received and stored at the distributor's warehouse first and then shipped at the customers' demand. Let HR1 be the transaction-level handling cost rate for activities such as receiving the order and checking for accuracy. Furthermore, let HR2 be the unit-level handling cost rate, incorporating activities such as physically moving and unloading items. In addition, each shipment received by the distributor (for normal shipments) or directly by its customers (for DD shipments) generates paperwork costs (e.g., accounts payable) at the rate of, say, AP per shipment.
To compute warehousing space and capital cost, let ST be the per-unit volume cost of space and let HO be the cost of working capital per dollar of inventory for the distributor.
These cost components together result in the current-period service cost for Customer i of the distributor.
Investments. Each period, the distributor also incurs costs that have long-term implications, such as new customer acquisition costs and the cost of certain advertising and other marketing activities aimed at building the brand name or awareness among current and future consumers. Because long-term costs are essentially investments and cannot be attributed to a single-period cost or profit of a customer, they are excluded from our single-period profitability model and subsequent empirical analysis. To the extent that these long-term costs also provide current-period benefits, our model underestimates customer-specific service costs and overestimates profitability for the period.<U>
Current Profitability of Customers
To complete the customer profitability model, we need to specify the total revenue and cost of goods sold components at the individual customer level.
In this general formulation for revenue, the price (net of discounts or premia) for the same item is allowed to differ across customers. Discounts/premia include any volume discounts, credit discounts, special charges, and special markdowns.
Explicitly incorporating various upstream and downstream transaction costs, as discussed in the previous subsection in computing customer profits, we now can
Customer service costs and therefore profitability of customers differ in a complicated manner depending on characteristics such as order frequency, number of items, volume and degree of customization of purchase mix, and so forth. In general, customer service costs will neither be the same across customers nor be merely proportional to units sold or to sales revenue. To put customer profitability in the context of the overall LTV model.
Where r is the discount rate, CPit is the customer profits associated with Customer i for Period t, and T is the relevant time horizon.
Trade-Offs and Interaction Among Customer Characteristics
Various factors influencing the customer profitability in our model can be categorized as volume, price/gross margin, complexity factors, and efficiency factors (see Figure 3). We discuss these factors next.
Volume. As mentioned in our model, some costs vary with unit volumes, others vary with transactions (e.g., number of shipments), and yet others vary with transaction entity (e.g., customer or supplier). Transaction- or entity-level costs do not vary directly with volume; however, ceteris paribus, higher volume implies spreading these costs over more units. Therefore, customer sales volume usually has a positive association with total service costs as well as with customer profit margins.
Price/gross margins. Our revenue expression in Equation 9 allows for different prices to be charged for the same item to different customers. A common reason for this price differential is the traditional practice of offering volume-based discounts, which results in a lower gross margin for high volume customers. Firms may also try to compensate for high service costs imposed by certain customers by charging a higher price to them than to other customers. To the extent that price differentials successfully adjust for service cost differentials across customers, gross margins are likely to be positively (negatively) associated with factors that increase (decrease) service costs. Finally, price differentials may be offered for competitive reasons, long-term growth prospects, and so forth. Analysis of such strategic pricing schemes is beyond the scope of this article. Product mix also plays a role in determining customer-level gross margins, because certain items may inherently command a higher gross margin than others. Overall, higher gross margins are expected to be positively related to customer profits but may be moderated by higher service costs.
Complexity factors. Complexity factors refer to customer characteristics other than sales volume (e.g., number of orders, number of items, degree of product mix customization, number of delivery locations) that drive resources to fulfill customers' orders. Ceteris paribus, these complexity factors are expected to result in higher customer service costs and lower customer profits.
Efficiency factors. Efficiency factors refer to customer-specific factors that lead to cost savings, such as a larger proportion of orders filled as DD shipments from manufacturers. Recall that each DD shipment may require more effort in terms of order processing and paperwork but less physical handling and storage, because the goods need not come to the distributor's warehouse. Quite opposite to high complexity factors, a higher level of efficiency factors is expected to generate lower customer service costs and higher customer profits.
In addition to the direct effects of these four factors on customer profitability, there are indirect effects due to interactions among these characteristics. For example, higher volume generates scale economies but also may result in higher price discounts. Higher complexity factors increase costs but may also provide opportunities for premium pricing. We provide a more extended discussion of such interactions in our empirical analysis.
In this section, we apply the general customer profitability model developed previously to the data from our field site. The data for our empirical study were made available by a wholesaler/distributor (name withheld for confidentiality) with more than 50 distribution centers that supply to several thousand grocery and other retailer customers nationwide. The data pertain to one of its regional distribution center operations. Each distribution center is a decentralized unit responsible for its purchase and selling operations and associated costs and profits. Our research site buys from hundreds of suppliers and serves more than 650 customers of various sizes scattered throughout a multistate region surrounding the warehouse. The activities and issues of this site are broadly representative of the supply chain interactions of many packaged consumer goods distributors.
We use monthly transaction data that contain the distributor's purchasing, selling, and warehousing activities for a period of one year. Our customer profitability model allocates all costs associated with these activities to customers each period. Discussions with management determined that no unused capacities existed at our site; therefore, we chose to allocate all local costs, except the allocated head office costs, to customers, because all of these costs are spent each period to serve the existing customers. We did not include any one-time-only costs, such as customer acquisition costs, because (1) data on these costs were not available and (2) these costs provide benefits over the lifetime of customer and not merely in the current period. We also did not include any corporate-level overhead and general marketing and advertising costs, because these cannot be associated with individual customers. The costs of relevant activities or cost pools were obtained from the company's internal accounting documents. Extensive interviews with activity managers and facility managers were used to establish the validity and accuracy of cost rates. Finally, the allocated costs were reconciled with the overall accounting statements of the distributor. The supplier- and customer-specific activity information was obtained from data files detailing the distributor's purchase and sales activities. We used information from these complementary sources to identify revenues and costs to be allocated to each customer, according to the model detailed in the previous section, for a period of 12 months. A schematic describing the process of arriving at customer profitability from the information sources is given in Figure 4.
Distribution statistics for the 6764 different items the distributor purchases and sells are presented in Part A of Table 1. There is substantial heterogeneity in quantities, the number of purchases, and the average size of purchase orders across items (stockkeeping units [SKUs]), resulting in wide variations in the per-unit rates for the upstream supply chain costs (purchasing and warehousing). Upstream costs vary from $.005 to $102 per unit, with a median of $1.77 across SKUs. These large differences in upstream cost per item unit also translate into wide variations in upstream costs for individual customers. This can be seen from Part C in Table 1, in which purchasing and warehousing costs vary between .56% and 241% of sales dollars across customers, with a mean of 2.62%.
In Parts B and C of Table 1, we present a descriptive profile of the distributor's customer base. In Part B, we present the mean, the median, and the range for some of the customer characteristics used in our profitability model. There is substantial heterogeneity among customers along each of these characteristics. For example, the number of items ordered varies between 1 and 1652, and the dollar value per order varies between $2.5 and $7,945. Part C of the table provides the distribution of a revenue dollar across various cost and profitability measures. As a percentage of total sales revenue, purchase and warehousing costs total approximately 2.62%, order fulfillment and other sales-end costs total another 2.23%, and sales and direct marketing costs add up to approximately 2.52%. Individually and together (approximately 7.37%), these three components of service costs are substantial relative to the net profit margin of approximately 8.46% for the distributor. At the individual customer level, the service costs vary from 3.6% to 306.5% of revenues, and customer profitability varies from -251.7% to 59.6% of sales revenue. In addition to the distribution for the entire customer base, we present the distribution for two median customers (customers ranked 329 and 330 on sales revenue). The dramatically different cost and margin numbers for these two essentially similar-sized customers highlight the importance of not relying solely on sales revenue.
Volume and Price/Margin
Customer size, or total revenue, is an important determinant of profitability. In our data, the top 2% of customers by sales revenue account for approximately 80% of revenues and net profits and 70% of total service costs. This ratio can be considered an extreme form of the celebrated 80/20 rule. At the same time, as many as 214 (32% of total) customers are found to be unprofitable, according to our model. The Schultz coefficient of 86.5 and modified Gini coefficient of .49 also represent the same extremely concentrated distribution of profits across customers.<SUP>3</SUP> Substantial variation in profitability exists at all levels of revenue, including at the very top.
In Table 2, we present summary statistics for customers, which are organized into 12 groups by revenue. The first group consists of 13 very large (top 2%) customers with revenue in excess of $1 million each. The last group consists of 82 very small customers with annual revenues less than $100. This group consists of atypical, occasional walk-up customers and will be excluded from further analysis because it is insignificant by any criteria. The top group is important enough to warrant a closer examination, which we provide subsequently. The remaining 563 customers are arranged by their sales revenue into ten portfolios (marked 1 through 10 in Table 2) of approximately 56 customers each.
As is shown in Table 2, the average gross margins and service costs percentages monotonically increase with size. The monotonic behavior of costs is primarily due to the volume effect. At lower total sales, there are fewer units to absorb transaction-level and transaction entity-level costs, which makes the costs higher as a percentage of revenue. The distributor appears to compensate partially for higher service costs through higher prices at a lower volume level, as reflected in higher gross margins from smaller customers. The small customers are likely to be price takers, and typically in this situation a high profit margin net of service costs could be expected from these customers. Management at the field site, though intuitively aware of the high service costs of these customers, nevertheless appears to underestimate these costs and fails to recover them in price, which results in many unprofitable small customers. The existence of unprofitable customers in each of the ten portfolios indicates that management does not have a good appreciation of the nature or the magnitude of costs associated with individual service activities or customers.
In Table 3, we present detailed descriptive profiles for each of the 13 very large customers to illustrate that large variation in profitability exists even among this elite group of customers. The 13 customers in this group are ranked in decreasing order of sales revenue. The gross margin varies between 10 and 18.7%. Service cost percentage varies between 4.1 and 15.5%, and the net profit margin varies between -1.5 and 12.5%. There does not appear to be any specific relation among sales revenue, gross margin, and service costs in this group. Some large customers (e.g., Customer 13) have a relatively high gross margin and low service costs, whereas some others (e.g., Customer 6) have a relatively high gross margin and high service costs. Although other reasons, including competitive and strategic factors or a short-term revenue focus of the sales force, cannot be ruled out, to the extent that the pricing policy is distorted by misestimation of service costs, there is a need for a better understanding of costs. For example, our discussions with company management revealed that the less profitable among these large customers (Customers 6 and 11) have recently shifted to ECR. And even though management tried to mark the effective prices for these customers up, it failed to compensate sufficiently for the true increase in the cost of servicing these customers because of incomplete and inaccurate knowledge of customer-specific service costs.
Complexity and Efficiency Factors
We use the following three multiple regression models to analyze the effects of the customer characteristics that represent volume, complexity, and efficiency factors:
(14) [gross profits/service costs/customer profits] = (sales revenue, number of orders, number of delivery locations, DD shipment units, number of different items, special item units).
Because customers differ in size by several orders of magnitude, heteroskedasticity is a concern in these regression models, and a White's general test confirms that. To correct for heteroskedasticity, we adopt a weighted least square procedure and divide both sides of our regression equations by the sales revenue variable (Greene 1999). This essentially converts the left-hand measures to respective margins (percentage of sales revenue). The intercept now represents the effect of sales revenue (volume factor) on these margins. Regression results are reported in Table 4.
The gross profit results in Table 4 demonstrate the distributor's success in adjusting prices (or margins) in response to customer characteristics. The service cost results highlight the impact of customer-specific complexity and efficiency factors on service costs. Note that an increase in the magnitude of complexity factors or a drop in the efficiency factor is not necessarily bad for the distributor if it is counterbalanced by increased sales or increased margins. The net customer profit results provide this net effect of customer characteristics on the distributor's bottom line.
Except for the number of delivery locations, none of the other complexity or efficiency factors has any significant impact on gross margins. As noted previously, there are not any significant systematic and proportional adjustments in prices in response to most of the customer-specific complexity and efficiency factors. However, we find that these factors have a significant effect on service costs and consequently on net profit margins. Factors resulting in higher complexity (number of orders, number of delivery locations, and special item units) are significantly more costly and have a negative profit impact, whereas factors promoting efficiency (direct delivery units) significantly reduce costs and improve profit. An exception is the number of different items ordered (SKUs). It is not simply more SKUs (which could be common across customers) but a customer-specific (special) SKU that imposes significant additional costs on the distributor. The net cost effect of complexity and efficiency factors in our data dominates their revenue or price effect.
Note that an increase in the number of orders and delivery locations, as well as a decrease in direct delivery units, has a negative impact on net profits. Such changes in the complexity and efficiency factors are typical under an ECR program. Our results show that unless there is a sufficient recovery of increased service costs through volume or prices, such marketing and supply chain innovations may not be as beneficial as is predicted in much of the practitioner literature. Similarly, special items, often considered a source of additional revenues and higher margins, are not so in our data. Salespeople under pressure to bring in revenues often pick up special item orders at prices that do not fully reflect increased service costs. Our results convey that the management and sales force at our field site, and in general, need to be selective in providing services and careful in encouraging specific customer behavior, because many such behaviors could be money-losing propositions in the absence of a proper understanding of their benefits and costs.
Sensitivity Analysis
In this subsection, we compare results from our model with alternative models of customer profitability. Our first benchmark model (BASE) is the traditional method of evaluating customer profitability, which assumes that all sales, service, and supply chain costs are proportional to revenues and allocates these costs to customers as a constant percentage of sales revenue. Under this approach, a customer is profitable as long as its gross margin is greater than the distributor's overall service cost margin. The second benchmark model (MULH) is based on the customer profitability model proposed by Mulhern (1999). This model identifies direct marketing and promotion costs specific to individual customers. However, this model does not identify or allocate costs related to procurement, warehousing, order processing, and other supply chain operations to individual customers. Notice that in our data, these supply chain costs account for approximately two-thirds of the total customer service costs that would be excluded under the MULH model. In Table 5, we present the average net customer profit margin for each of the ten customer portfolios discussed previously. To facilitate comparison, we also provide the gross and net profit margin under our model in Table 5.
The portfolio means for net profit margins for both the BASE and the MULH models are all higher compared with those under our model. Indeed, smaller customers (in Portfolio 10) appear to be quite profitable under both these models. Only 6 customers are unprofitable under the BASE model, and 102 customers are unprofitable under the MULH model, compared with 214 in our proposed model. The higher profitability under the MULH model is primarily due to supply chain costs being excluded. Although the BASE model allocates all costs to customers, it does so at a rate proportional to revenue instead of using any ustomer-specific allocations. A small customer with few revenue dollars receives a proportionately small allocation of service costs and therefore appears to be more profitable than it does in our proposed model. The overall average service cost margin is 7.37% in our data, and that is exactly the difference between gross margins and net margin under the BASE model for each of the ten customer portfolios.
To test the sensitivity of our results to the variable or fixed nature of costs, we constructed another model (REDC) by excluding all costs that are relatively fixed, those that may represent long-term commitment, and those that may have long-term benefits. These costs include warehousing costs, facility-level management costs, customer service costs, and approximately 30% of sales force costs. Taken together, these excluded costs represent approximately 26% of our original total costs. Recall that in our model we have already excluded fixed costs and costs that cannot be identified to individual customers, namely, corporate overhead costs, corporate-level marketing costs, and any customer acquisition costs. Results from this reduced-cost model are given in Table 5. Under the REDC model, 180 customers remain unprofitable. The net profit margins across portfolios follow largely the same patterns as in our proposed model, albeit at higher profit levels.
In Part B of Table 5, we present net profit margin regression results for these three alternative profitability models. For the sake of comparison, the net profit regression results from Table 4 are also replicated in Table 5, Part B. The results for the REDC model are qualitatively similar to those for our model. The results for the BASE model are the same as those for the gross profit margin regression in Table 4, because the net profit margin under this model is simply gross profit margin minus a constant service cost margin for each customer. The net profit margin results under the MULH model are similar to those for the BASE model. Both show no significant association with customer characteristics except in the number of delivery locations (also a driver of sales costs) because of the exclusion of most of the supply chain costs in this model. Overall, our customer profitability analysis results are significantly different from the existing customer profitability models in the literature and clearly show the linkages between customer characteristics and profitability. Moreover, our results do not appear to be qualitatively sensitive to including relatively fixed costs in the computation of customer profitability.
In this article, we examine the drivers of current customer profitability in a supply chain for a large distributor with a heterogeneous client base. We integrate the marketing literature with the latest developments from the management accounting literature and provide evidence on the drivers of customer profitability. We find that, on the one hand, a small percentage of customers contributes to a large percentage of total profits, and on the other hand, a substantial percentage of customers is unprofitable, which further supports prior claims of a similar nature (Hallberg 1995; Mulhern 1999; Peppers and Rogers 1993). We document that many customer purchase characteristics can have opposing effects on the gross margin and service costs, which leads us to conclude that simply focusing on customer revenue as a driver of profitability could be misleading.
A natural follow-up to our study is the question, "How should a firm better manage its customers' profitability?" A customer profitability analysis is primarily aimed at determining what a firm is currently doing and does not indicate per se what a firm should do with respect to managing its customers for better profitability. The ability to conduct the profitability analysis at the individual level, however, makes it possible to make many decisions in a more informed way with a better awareness of the trade-offs involved. Next, we provide some guidelines to managers for how to use the information generated by a customer profitability analysis. We also discuss the implications of industrywide adoption of customer profitability models.
Implications
Customer selection strategies. A blanket approach to drop customers on the basis of current-period profitability is not appropriate. One argument for keeping some unprofitable customers is based on treating them as marginal customers. The marginal cost of doing business for such customers would be less than the full cost we arrive at using ABC analysis. The firm serves these marginal customers, so the argument goes, by using surplus capacity. However, if there is indeed surplus capacity for all customer service activities, then to the extent it is possible, profits could be increased more by shedding some of the surplus capacity than by using it to serve customers that are not justified under full costing. A customer profitability analysis, as advocated in this article, could serve as a first step in identifying where to maintain/divest capacity to manage the profitability of the marginal customers.
A proper decision to acquire or retain customers requires the linking of the two elements of the LTV analysis-present profitability and future profit potential. The future potential should be calculated by a joint assessment of the possibility to induce favorable behavior in terms of service characteristics, forecasted revenue, and expected lifetime duration. Consider the customer classification scheme in Figure 5. Customers in Cell 4 are probably the ones ripe to be divested as a group, whereas customers in Cell 3 are the best and need to be nurtured the most. Customers in Cells 1, 2, and 5 should be steered to more favorable characteristics and greater profits. Customers in Cell 6 are the cash cows, the backbone of a firm's profitability, if not its growth.
Customer relationship management. Adoption of information technology-based initiatives and methodologies such as customer profitability analysis enables firms to collect much more customer-specific data, both on their preferences and their transaction patterns. This extensive customer-specific information could offer firms not only better targeting of products and prices but also a better understanding of customer service costs and therefore customer profitability. This ability has important strategic implications as well as broader business policy implications. Among the strategic implications, an interesting issue is the strategic effect of competition on firms' incentives to build customer-specific profitability models. Assume, for example, that firms are asymmetric in their sizes and therefore in their sales volumes or market shares, and further assume that market share is correlated with the share of the loyal customers. It then follows that large firms typically have higher absolute incentives to analyze their customer base to identify the most profitable customers and implement customer relationship management strategies to retain these. The reason is that if these customers are profitable and can be retained through nonprice initiatives, then the firm has effectively isolated the most profitable ones from ruinous price competition (Brooks 1999). Because a larger firm faces a higher opportunity cost in a price competition, it has a higher incentive to take customer relationship management initiatives.
Another example of a strategic effect follows from the possibility of "mistargeting." In a recent article, Chen, Narasimhan, and Zhang (2001) show that when individual targeting is feasible but imperfect, improvement in targeting can often lead to a win-win situation. As a firm becomes better at identifying its own customers, it can achieve a benevolent advantage on its competitors. Because such targeting efforts are predicated on analysis of customer-level data and the profitability of customers, we speculate that understanding customer-level profitability can lead to better targeting and therefore create win-win situations.
Understanding customer-specific service costs in detail would help a distributor understand how the same transactions may also affect costs at the customer's end. This creates an opportunity to eliminate transactions that may not add value to a supply chain and to reengineer the process for symbiotic gains among the supply chain partners rather than continue the typical adversarial relationships between a supplier and its customers. As a manager of Owens & Minor Inc. noted after implementing a pricing scheme based on customer profitability analysis,
Our relationship has changed from adversarial to a close partnership with parties working together to identify, reduce and eliminate non-value-added activities. Both our firm and its customers have a vested interest in reducing expenses. When customers understand this, they are no longer focused on negotiating a lower fee. (Brem and Narayanan 2000, p. 3)
Among the broader issues, an important one involves how to ensure that the information about customers is used effectively. For example, a firm must design control mechanism and incentive systems that restrict managers, who may take a myopic view of short-term gains, from dropping customers that are unprofitable in the short run. Firms considering dropping certain customers may also face the problems of how to explain the rationale behind their actions to the customers being dropped and whether it is thsir responsibility to refer the customers to an alternative supplier (Bruns and Harmeling 1998). From an ethical point of view, how to use the information fairly is another business policy issue. For example, should companies be allowed to cherrypick only profitable customers and drop services to those who need it most? The issue is especially sensitive and has serious welfare implications in industries such as health care, insurance, and even banking and financial services (Gasparino 1999).
Pricing policies. In many situations, a preferred way to deal with unprofitable customers is to change their incentives and prices by adopting a policy of service-based or menu-based pricing so that they become profitable. In a survey of managers in the United Kingdom, the most important use of customer profitability analysis was found to be for pricing policies (Innes and Mitchell 1995). Explicit discounts/surcharges linked to service requirements and costs can be used as tools to implement such pricing. Several financial institutions such as banks now regularly price their services in great detail. Knowing the costs better is not a sufficient reason for a firm to adopt service-based pricing, especially in the presence of strategic or competitive forces. Besides, a price discrimination policy, even if it is based on services provided, could make a firm vulnerable to price-gouging charges. Also, charges of a more serious nature may arise if a demographic or otherwise sensitive group perceives such policies to be selective discrimination.
Evaluating business practices. Analysis of customer service costs and profitability permits a reevaluation of business processes and practices. For example, the distributor in our study engaged in many business initiatives, such as implementing ECR for selected clients, servicing small walk-up customers, adopting electronic data interchange technology, and so on. The managers of our research site believed that imposing an additional constant markup would be sufficient to compensate for the increased costs of implementing ECR for certain customers. As discussed in the previous section, our analysis demonstrated that the profitability of these customers deteriorated under ECR and they were being subsidized by the non-ECR customers. The firm has since revised its pricing and marketing program for ECR. In case of very small customers, it could be beneficial for the distributor to encourage them to become its customers' customers. Our analysis also demonstrates that rationalizing the product line and the assortment carried could lead to cost savings more than enough to compensate for the forgone revenue. Another aspect of streamlining business practices relates to aligning the internal incentive structure of the firm, particularly the sales force incentives. When the sales force is partly compensated on the basis of sales revenue, it might adopt practices such as excessive price discounts or customized products or services (special items) for small revenue increases. A better understanding of costs and profits could help management and salespeople be more selective in pursuing additional revenue or offering special services.
Implications in an e-commerce world. As the recent turmoil in the business-to-consumer dot-com world indicates, selecting and retaining the right customer base is critical to the survival of firms, and wrong investments in this regard can lead to dire consequences. Indeed, capital markets have started to take keen interest in the viability and economics of the customer base of companies, and it is not uncommon to have major market reactions in response to news related to changes in customer bases (Schonfeld 2000). Still, there are no specific regulations that require companies to disclose much about their customer base. To the extent that customer equity has become a large part of firm value, it might be important for regulators to require companies to disclose changes in the distribution of their customer profitability profiles over time. This is a potential opportunity for disclosure policy researchers.
New Internet- and e-commerce-based business models are geared to alter the nature of transactions in existing models of supply chains. Although the economics of these models remain untested, they promise to reduce costs and change customer profits in supply chains. One interesting avenue for further research would be to investigate how the nature of customer service costs changes under the new technology and the implications such changes have for the profitability and LTV of customers.
Limitations and Directions for Further Research
Our model and analysis in this article are based on three simplifying assumptions that need to be considered in general applications of our results. Although these assumptions may be perceived as limiting the generalizability of our results, they also provide rich opportunities for further research in this area. First, following the general accounting convention of allocating costs, we assumed linearity of the cost components with respect to their respective cost drivers. It is conceivable that this relationship may not be linear for certain cost drivers, for example, fulfillment costs that increase at an increasing rate with the number of customized products. Warehousing and storage costs might increase in a stepwise fashion or show an increase at a decreasing rate. To determine the exact nature of the relationship, a researcher can either undertake more detailed industrial Engineering-type time and motion studies or elicit and rely on expert opinions about each cost driver (Horngren, Foster, and Datar 1997). Even though we did not go into that level of detail, we point out that by breaking up the total service cost in more than a dozen different activity-based components, the costs are already nonlinear in both total quantity purchased and revenue dollars. Opportunities exist for future researchers to improve the understanding of the behavior of customer service costs and other drivers.
The second simplifying assumption involves taking all the demand and service parameters to be deterministic. Put differently, our analysis is based on total behavior only and ignores the variances in activities and their drivers over time for any given customer. Future researchers could develop richer models to explore how stochasticity in the level of transactions affects customer service costs to maintain the same service level (e.g., by affecting safety inventory).
The third assumption pertains to the existence of demand-side factors that are outside our analysis. These include selecting customers for reasons such as externalities among customers, possible strategic issues in dealing with competitors, and the firm's different bargaining strengths with different customers. Further research in this area could explicitly incorporate the joint nature of these characteristics, because all these factors could be important in evaluating the LTV of these customers.
Most firms should be able to generate the information required for estimating customer profitability from their existing data resources. Despite increasing interest in ABC analysis, most companies are still not routinely tracking the costs associated with activities. We hope that the measurements and methodologies we propose here will encourage future researchers and practitioners to better track customer specific costs and profits as a precursor to developing customer-centric strategies.
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Shields, Michael D. (1997), "Research in Management Accounting by North Americans in 1990s," Journal of Management Accounting Research, 9, 3-61. Wayland, Robert E. and Paul Michael Cole (1997), Customer Connections: New Strategies for Growth. Boston: Harvard Business School Press.
A: Item Activity Profile (6764 Items)
Item Mean Median Range
Quantity of units 678.9 50 1-134,000
Number of total purchases 11.6 3 1-651
Quantity (units) per
Purchase 120.5 10.5 1-44,667
Percentage of units DD
Shipped 24.0 0 0-100
Unit upstream costs in
Dollars .5 1.8 .005-102
B: Customer Activity Profile (658 Customers)
Total units purchases 5990.4 147 1-757,252
Number of orders placed 683.1 17 1-107,158
Number of different
Delivery locations 6.3 1 1-374
DD shipments as percentage
Of total units 24.5 7.9 1-100
Number of different items
Ordered 49.0 8 1-1652
Special item units as
Percentage of total 24.4 8.0 0-100
Dollar value per order 249.8 97.6 2.5-7945
C: Customer Profitability Profile (658 Customers)
Net revenue 100 100 100-100
Cost of goods sold 84.17 67.09 26.2-97.6
Gross profit 15.83 32.91 2.4-73.8
Purchase and
warehousing
costs 2.62 2.73 .56-241.2
Order processing and
Fulfillment costs 2.23 .70 .3-89.2
Sales and direct marketing
Costs 2.52 3.45 1.8-231.4
Total service costs 7.37 6.88 3.6-306.5
Net customer profits 8.46 26.03 -251.7-59.6
A= Group
B= Share of Total Sales (%)
C= Mean Value/Gross Profit Margin Percentage of Sales
D= Mean Value/Service Cost as Percentage of Sales
E= Mean Value/Net Profit Margin Percentage
F= Range/Gross Profit Margin Percentage
G= Range/Service Cost as Percentage of Sales
H= Range/Net Profit Margin Percentage
A B C D E F
G H
Very Large 78.54 15.12 7.02 8.10 10.03-18.70
4.10-15.48 -1.53-12.45
1 14.28 17.63 9.80 7.83 5.45-33.31
4.75-24.71 -2.19-26.32
2 3.54 21.10 12.25 8.86 9.00-54.07
3.61-37.12 -10.59-50.46
3 1.78 20.47 13.69 6.78 11.44-64.14
4.54-56.39 -25.58-69.59
4 .91 21.57 15.52 6.06 2.36-48.85
4.46-71.95 -53.87-33.44
5 .47 25.61 16.28 9.34 9.97-52.31
5.28-72.03 -35.68-38.95
6 .24 25.54 15.99 9.55 2.35-61.20
5.10-51.53 -31.60-53.02
7 .13 29.60 17.69 11.91 10.00-62.14
7.76-52.43 -30.50-54.39
8 .06 31.77 29.85 1.92 3.54-70.14
10.04-264.94 -243.77-58.87
9 .03 38.82 37.13 1.69 11.52-68.12
16.84-111.97 -79.76-46.73
10 .01 40.44 61.14 -20.70 5.52-71.04
29.20-193.80 -161.92-39.10
Very small .006 42.21 138.73 -96.54 5.00-73.81
53.12-306.54 -251.70-3.92
A= Customer Number
B= Share of Total Sales (%)
C= Gross Profit Margin Percentage
D= Service Cost as Percentage of Sales
E= Net Profit Margin Percentage
F= Number of Orders
G= Number of Delivery Locations
H= Drop Shipments as Percentage of Total Units
I= Number of Different Items Ordered
J= Special Item Units as Percentage of Total
A B C D E F
G H I J
1 18.7 12.05 4.28 7.77 16,449
181 67.0 894 44.6
2 9.0 18.70 7.10 11.59 44,760
374 43.7 1652 12.9
3 8.8 14.60 4.10 10.49 6019
4 57.4 344 39.5
4 7.3 15.16 5.53 9.62 12,651
180 50.0 1012 37.2
5 6.0 18.08 5.63 12.45 8177
195 40.9 935 18.6
6 5.9 16.09 15.48 .61 107,159
275 .0 547 1.6
7 5.3 14.91 5.84 9.07 11,247
163 44.5 888 19.2
8 4.9 15.06 5.46 9.60 10,179
67 53.8 713 31.6
9 4.7 18.50 8.52 9.98 53,689
134 .4 567 10.7
10 2.9 10.03 4.61 5.43 3126
131 80.5 374 26.4
11 2.1 11.56 13.19 -1.53 41,832
29 .0 420 2.5
12 1.5 14.56 5.94 8.62 5159
16 59.3 455 29.3
13 1.4 17.25 5.65 11.61 3655
3 41.6 380 16.1
A= Intercept
B= Number of Orders
C= Number of Delivery Locations
D= DD Shipment Units
E= Number of Different Items Ordered
F= Special Item Units
Gross profit .22* .05 27.70* .40 .96 -.39
(Adjusted R2= .22) (.01) (.85) (3.15) (.47) (1.21) (.47)
Total service cost .05* 6.66* 5.87* -2.33* .82 2.50*
(Adjusted R2= .66) (.01) (.89) (3.30) (.49) (1.26) (.49)
Net customer profit .17* -6.62* -23.17* 2.74* .14 -2.90*
(Adjusted R2= .29) (.01) (1.29) (4.80) (.72) (1.84) (.72)
*Significant at the p < .01 level (two-tailed test).
Notes: Number of observations = 576; standard errors for the
estimated parameters are given in parentheses.
A: Portfolio Means for Net Profit Margins Under Alternative Models
A= Portfolio Number
B= Gross Profit Margin Percentage
C= Our Model
D= BASE
E= MULH
F= REDC
A B C D E F
1 17.63 7.83 10.26 15.07 10.48
2 21.10 8.86 13.73 17.90 11.80
3 20.47 6.78 13.10 17.04 9.70
4 21.57 6.06 14.20 17.14 9.39
5 25.61 9.34 18.24 21.17 12.71
6 25.54 9.55 18.17 20.88 12.46
7 29.60 11.91 22.23 24.04 15.00
8 31.77 1.92 24.40 23.15 9.99
9 38.82 1.69 31.45 22.50 6.36
10 40.44 -20.70 33.07 11.29 -16.40
Number of unprofitable customers 214 6 102 180
B: Customer Net Profitability Regressions A= Intercept B= Number of Orders C= Number of Delivery Locations D= DD Shipment Units E= Number of Different Items Ordered F= Special Item Units Our Model .17* -6.62* -23.17* 2.74* .14 -2.90* (Adjusted R2= .29) (.01) (1.29) (4.80) (.72) (1.84) (.72) BASE .22* .05 27.70* .40 .96 -.39 (Adjusted R2= .22) (.01) (.85) (3.15) (.47) (1.21) (.47) MULH .20* -.50 -9.08* .46 .98 -.54 (Adjusted R2= .03) (.01) (.86) (3.19) (.48) (1.22) (.48) REDC .18* -4.49 -28.31* 2.72* .86 -2.81* (Adjusted R2= .27) (.01) (1.14) (4.23) (.63) (1.61) (.62) *Significant at the p < .01 level (two-tailed test). Notes: Number of observations = 576; standard errors for the estimated parameters are given in parentheses.
GRAPH: FIGURE 1: Transaction Flows in a Multichelon Supply Chain
GRAPH: FIGURE 2: Customer Profitability Models and Customer LTV
GRAPH: FIGURE 3: Factors Influencing Customer Profitability
GRAPH: FIGURE 4: Linking Information Sources to Arrive at Customer Profitability
GRAPH: FIGURE 5: A Scheme of Customer Classification
~~~~~~~~
By Rakesh Niraj
Rakesh Niraj is a doctoral candidate in marketin
Mahendra Gupta is Associate Professor of Accountin
Chakravarthi Narasimhan is Philip L. Siteman Professor of Marketing
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Record: 45- Customer Satisfaction and Shareholder Value. By: Anderson, Eugene W.; Fornell, Claes; Mazvancheryl, Sanal K. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p172-185. 14p. 6 Charts, 1 Graph. DOI: 10.1509/jmkg.68.4.172.42723.
- Database:
- Business Source Complete
Customer Satisfaction and Shareholder Value
In this article, the authors develop a theoretical framework that specifies how customer satisfaction affects future customer behavior and, in turn, the level, timing, and risk of future cash flows. Empirically, they find a positive association between customer satisfaction and shareholder value. They also find significant variation in the association across industries and firms.
Does satisfying customers go hand in hand with satisfying shareholders? For customers and customer satisfaction to become an important topic for senior management and shareholders, there must be rigorous theoretical and empirical support for a positive association between customer satisfaction and long-term financial performance. In the absence of such findings, people involved in corporate governance are likely to remain ambivalent toward customer satisfaction as a business performance metric (Ambler 2000; Barwise, Marsh, and Wensley 1989; Day and Fahey 1988; Srivastava, Shervani, and Fahey 1998).
A growing body of research links customer satisfaction and accounting-based measures of financial performance, such as operating margin (Bolton 1998; Rust, Zahorik, and Keiningham 1994, 1995), return on investment (ROI; Anderson, Fornell, and Lehmann 1994; Anderson, Fornell, and Rust 1997; for a review, see Zeithaml 2000), and accounting returns (Ittner and Larcker 1998). However, empirical work based on accounting measures does not measure shareholder value reliably (Rappaport 1986). Although accounting approaches provide valuable insight, they cannot be viewed as a substitute for direct examination of the association between customer satisfaction and long-term financial performance, as measured by data from capital markets.
The objective of this article is to provide the first extensive theoretical and empirical examination of the association between customer satisfaction and shareholder value. We begin by discussing why a positive association between customer satisfaction and long-term financial performance should be expected. We develop a framework that specifies how customer satisfaction affects current and future customer behavior and how, in turn, the behavior of satisfied customers influences the level, timing, and risk of future cash flows and, consequently, shareholder value. We also posit why and how this association varies systematically across industries.
The study builds on research that relates customer satisfaction to accounting-based measures of financial performance and to the one previous study that examines the association between customer satisfaction and shareholder value. Ittner and Larcker (1996) examine the correlation between customer satisfaction and a firm's raw market value, but they find mixed and inconclusive results. This article builds on Ittner and Larcker's pioneering work in two ways. First, our conceptual framework explicates the underlying theoretical mechanisms that link satisfaction and shareholder value. Second, although their work uses an event study approach and examines only one year of data, we have access to a longer time series, and we employ correspondingly sophisticated techniques of analysis to better control for potential sources of estimation bias.
To test our hypotheses, we employ the American Customer Satisfaction Index (ACSI) database of nearly 200 publicly traded Fortune 500 firms from 1994 to 1997. We review several widely used measures of shareholder value and select Tobin's q, a measure that is forward looking, risk adjusted, comparable across firms, and well-grounded in economic theory. The findings indicate that the association between ACSI and Tobin's q is positive and significant, even after we control for unobservable factors. We also observe that ACSI is positively associated with two measures: equity prices and ratios of price to book value. Finally, we find significant systematic variation in the association between ACSI and Tobin's q across industries. A test for underlying industry characteristics that moderate the association indicates that it is weaker in more fragmented industries that are characterized by a high degree of rivalry.
The fundamental logic that underlies our theoretical framework is that ( 1) customers are the primary source of all future positive cash flows, and ( 2) customer satisfaction is a significant indicator of the strength of the firm's customer relationships (and thus the timing, level, and stability of cash flows).
In this section, we propose a new conceptual framework that combines three distinct theoretical components: ( 1) traditional arguments about how customer satisfaction influences customer behavior, ( 2) a new linkage between customer satisfaction and the relative bargaining power of the firm, and ( 3) how both customer behavior and the firm's bargaining power derived thereof influence shareholder value.
To begin, we discuss how customer satisfaction influences shareholder value through customer behaviors that determine the timing, level, and stability of cash flows. Next, we address the role of competitive forces and how these influence the association between customer satisfaction and shareholder value.
Customer Satisfaction and Shareholder Value
A growing body of empirical work supports the fundamental logic that customer satisfaction should positively influence customer retention (Anderson and Sullivan 1993; Bearden and Teel 1983; Bolton 1998; Bolton and Drew 1991; Boulding et al. 1993; Mittal and Kamakura 2001; Oliver 1980; Oliver and Swan 1989; Yi 1991). It is argued that by increasing retention, customer satisfaction secures future revenues (Fornell 1992; Rust and Zahorik 1993; Rust, Zahorik, and Keiningham 1994, 1995) and reduces the cost of future customer transactions, such as ones associated with communications, sales, and service (Reichheld and Sasser 1996; Srivastava, Shervani, and Fahey 1998). As a consequence, net cash flows should be higher. At the same time, greater customer retention indicates a more stable customer base that provides a relatively predictable source of future revenue as customers return to buy again, one that is less vulnerable to competition and environmental shocks (Anderson and Sullivan 1993; Narayandas 1998). Thus, customer retention also should positively affect shareholder value by reducing the volatility and risk associated with anticipated future cash flows.
Additional satisfaction-driven customer behaviors that are likely to influence shareholder value include buying more of a particular product or service from a given supplier, buying additional products and/or services, making recommendations to others, and increasing price tolerance. Although not all these behaviors are expected to play a role in every industry, a significant subset is likely to provide the underlying mechanism with which customer satisfaction influences shareholder value in any industry.
First, customer satisfaction should lead customers to purchase more requirements from the supplying firm (e.g., Bolton 1998; Bolton, Kannan, and Bramlett 2000; Verhoef, Franses, and Hoekstra 2001). When this happens, acquisition and transaction costs decline and revenues increase, thus enhancing the size of net cash flows.
Second, greater cross-buying also may result from higher levels of customer satisfaction (Reichheld and Sasser 1996). Cross-selling not only enhances net cash flows in a manner similar to supplying a greater share of customer requirements but also accelerates the timing of new cash flows. A loyal and satisfied customer base provides a ready market for new add-on services or product-line extensions. Thus, we should expect customer satisfaction to lead to faster market penetration and, in turn, to accelerated cash flows (Srivastava, Shervani, and Fahey 1999).
Third, recommendations and positive word of mouth from satisfied customers also are expected to influence shareholder value. Positive word of mouth should lead to lower acquisition costs and thus to greater net cash flow (Anderson 1998; Fornell 1992). Positive word of mouth should also help a firm penetrate new and existing markets and thus should lead to accelerated cash flows.
Finally, greater customer satisfaction may enable the firm to charge higher prices or at least to better resist downward pressure on prices (Anderson 1996; Narayandas 1998). Thus, greater customer satisfaction may lead to greater shareholder value through increased price tolerance.
In addition to the classic linkages between customer satisfaction and customer behavior that we have discussed, we posit that customer satisfaction influences shareholder value through its impact on competitive forces. A loyal and satisfied customer base increases the organization's relative bargaining power with respect to suppliers, partners, and channels. Such a customer asset provides a source of leverage with respect to other members of the firm's value chain that want to serve the same customer base. Suppliers and other partners are likely to value and to seek to maintain favorable relationships with a firm that "owns" such a valuable asset. Channel members should be more likely to provide favorable treatment to firms whose offerings will help them attract and retain customers. Thus, a firm that creates superior satisfaction should be able to extract greater value from the networks of suppliers, partners, and channels that serve a market in the form of lower costs, higher volumes and prices, and faster market penetration. The combined effect should be to raise the level of net cash flow and to lower the risk of anticipated future cash flows for firms with relatively high satisfaction.
Following this reasoning, we state the following main hypothesis:
H[sub1]: There is a positive association between customer satisfaction and shareholder value.
Cross-Industry Variation
We expect significant variation in the association between customer satisfaction and shareholder value across industries. Both industry and customer factors are likely to dampen or amplify the effect. For example, we posit that the degree of competition affects the link between market orientation and firm performance (Kohli and Jaworski 1990; Slater and Narver 1994). A similar logic should hold for customer satisfaction. In terms of customer characteristics, customer satisfaction may have a greater or lesser affect on customer behaviors from industry to industry and, consequently, on shareholder value.
Empirically, Anderson, Fornell, and Rust (1997) find the association between customer satisfaction and ROI to be greater for goods than for services. Ittner and Larcker (1998) find the correlation between customer satisfaction and market value to be negative for retailers.
We follow the literature on market orientation in arguing that the degree of competition, as measured by the widely used proxy of degree of concentration, in an industry should affect the association between customer satisfaction and shareholder value (Kohli and Jaworski 1990; Slater and Narver 1994). The degree of concentration should affect both customer behavior and the firm's relative bargaining power and thus the degree to which customer satisfaction affects shareholder value. When an industry is fragmented and concentration is low, the degree of rivalry in the industry is likely to be more intense. Even satisfied customers are likely to be more difficult to retain and more price sensitive and to find other supply sources. At the same time, the firm's ties to its customers will be weaker and the relative bargaining power of the firm reduced. Thus, the association between customer satisfaction and shareholder value should be attenuated in more fragmented markets that are characterized by low concentration.
Following this reasoning, we state the following moderating hypothesis:
H[sub2]: As the degree of concentration in an industry declines, the impact of customer satisfaction on shareholder value is less positive.
Summary
We expect that the association between customer satisfaction and shareholder value is positive because customer satisfaction provides a valuable, forward-looking indicator of future net cash flows. In addition, when the degree of concentration is low, we expect customer satisfaction to have less of an impact on shareholder value. In the remainder of this article, we discuss the measurement and testing of ( 1) the direct association between customer satisfaction and shareholder value and ( 2) the moderating role of concentration on this association.
This conceptual framework rests in a broader nomological network that includes other company-and context-specific factors that may affect customer satisfaction, shareholder value, and the association between the two. For our modeling efforts, we first focus on an appropriate reduced-form specification to estimate the association between customer satisfaction and shareholder value, employing appropriate controls for the company-and context-specific factors that are "unobservable" and not explicitly included in the model. Next, we examine cross-industry variation and test whether the degree of industry concentration plays a role, as we hypothesize.
The testing of our hypotheses requires appropriate measures of customer satisfaction and shareholder value. The ACSI (Fornell et al. 1996) measures overall customer satisfaction.
An individual firm's ACSI represents its served market's (i.e., its customers) overall evaluation of total purchase and consumption experience (Anderson, Fornell, and Lehmann 1994; Fornell 1992; Johnson and Fornell 1991).
Fornell and colleagues (1996) provide a detailed description of the nature and purpose of ACSI. Here, we briefly summarize its key features. First, ACSI measures customer satisfaction as experienced by customers, rather than by expert ratings (e.g., Consumer Reports) or managers perceptions (e.g., PIMS). Second, it is designed to provide a comprehensive picture of customer satisfaction for each of seven major economic sectors. In each sector, the largest industries and the largest firms in each industry are measured. The total sales of the 200 Fortune 500 firms included in ACSI amount to more than 40% of the U.S. economy's annual gross domestic product. Third, ACSI provides a uniform set of comparable customer-based firm performance measures for a broad range of manufacturing and service firms that can be matched with traditional accounting-based performance measures and capital market data. Thus, ACSI offers a unique and powerful database to test the study's hypotheses.
We require a long-term measure of firm economic value, one that is forward looking and cumulative. The measure should also be generalizable and comparable across firms in many different industries. Finally, the measure needs to be such that we can develop a theoretically sound econometric model for our empirical test.
Most research on firm performance has relied on accounting-based ratio measures, such as ROI (Buzzell and Gale 1987; Jacobson 1988, 1990a) and return on assets (ROA). Other studies use direct measures such as sales (Dekimpe and Hanssens 1995), price (Boulding and Staelin 1995), and cost (Boulding and Staelin 1993). However, such measures typically contain little or no information about the future value of a firm (Geyskens, Gielens, and Dekimpe 2002). Because of industry and firm differences in accounting practices, a comparison of these measures across industries and firms is problematic.
Ratio measures such as ROI and ROA have the advantage of greater comparability across firms, at least across firms within an industry. However, these measures represent accounting profit, whereas our focus is on economic profit. The ROI measure assumes that previous investments affect only current-period earnings, but in reality they can affect future earnings as well. Thus, ROI reports previous profitability, but it is not a forward-looking measure. It is also sensitive to accounting conventions and tax laws. Moreover, ROI may be easier to manipulate than capital market data.
With respect to capital market based measures of firm performance, a parsimonious candidate is a firm's stock price, which traditionally is considered a key benchmark of its future financial performance. According to the efficient market theory, stock prices incorporate all information about expected future earnings (Fama 1970). Thus, the price of a stock can be viewed as a measure of long-term performance and value that is forward looking and cumulative. However, by itself, stock price represents an arbitrary division of shareholder or market value by however many shares are offered. Therefore, stock prices lack a natural common baseline that allows for comparison across firms or industries.
Stock returns are used to convert stock prices to a common baseline, yet stock returns are not risk adjusted. One firm may have a high stock return and high market risk, whereas another firm may have a low stock return and low market risk, yet both would be appropriate.
Examination of "abnormal" stock return (i.e., the degree to which a stock return exceeds a normal baseline return) has become a common means for evaluating whether a particular event provides unanticipated information to the capital market that is subsequently incorporated into stock price. If the current study were to focus on whether the announcing of ACSI scores has an impact on stock price, it would be appropriate to use an event study approach to determine whether subsequent returns indicated that such announcements contained information unanticipated by the stock market. However, rather than being concerned with an announcement effect, in our main hypothesis, we are concerned with whether firms that achieve higher levels of customer satisfaction also exhibit greater shareholder value and cannot be easily tested using stock price changes (Montgomery and Wernerfelt 1988). In addition, the use of abnormal returns can be problematic, because there remains controversy over how to establish a normal return as a baseline (Fama 1998).
A forward-looking, capital market based measure of the value of a firm is Tobin's q. A firm's q value is the ratio of its market value to the current replacement cost of its assets (Tobin 1969). The intuition is that replacement cost (the denominator of q) is a logical measure of alternative uses of a firm's assets. A firm that creates a market value that is greater than the replacement cost of its assets is perceived as using its resources more effectively and thus as creating increased shareholder value (Lewellen and Badrinath 1997). A firm that does not create incremental value has a Tobin's q equal to 1. The gap between a firm's Tobin's q and 1 indicates the degree of anticipated future abnormal returns.
Tobin's q (or simply q) has gained wide acceptance as a measure of a firm's economic performance. It is based on the supposition that the securities market efficiently evaluates the firm's expected future revenue stream in determining the firm's value. Because the q value is based on the stock price of a firm, it is a more forward-looking measure (i.e., it is based on the anticipated future performance of the firm) than measures such as ROI, which measure historical financial performance. Tobin's q is also adjusted for expected market risk and is less affected by accounting conventions, which makes it comparable across firms in different industries. As Montgomery and Wernerfelt (1988, p. 627) point out, "By combining capital market data with accounting data, q implicitly uses the correct risk-adjusted discount rate, imputes equilibrium returns, and minimizes distortion."
McFarland (1988) uses a Monte Carlo simulation to compare actual constructed values of q and ROI with their respective estimated values. The findings indicate that whereas both are useful measures of profitability, estimates of q have smaller average errors than the accounting rate of return. McFarland also finds that estimates of q tend both to have a much higher average correlation with their true measures than do estimates of ROI and to outperform ROI measures in econometric models of performance. The implication of this work is that Tobin's q has superior measurement properties than ROI.
A review of the empirical literature in industrial organization and financial economics shows an increasing use of q. Important applications include analysis of industry structure and economic rents (Hirschey 1985; Smirlock, Gilligan, and Marshall 1984), the effects of diversification on a firm's performance (Lang and Stultz 1994; Montgomery and Wernerfelt 1988), the role of multinationality in shareholder value creation and destruction (Reid and Harrison 2000), and the contributions of information technology assets to firms' future growth potential (Bharadwaj, Bharadwaj, and Konsynski 1999). In marketing, Tobin's q has been applied in measuring the value of brand equity (Simon and Sullivan 1993).
There are several notable challenges and limitations in the use of Tobin's q. First, the numerator of q uses stock market--based information to measure the long-term value of a firm. This is an indirect and external method of measuring something that is, in a sense, intrinsic to the firm (Sheperd 1986) and is subject to fluctuation on the basis of sudden changes in overall market factors and other extraneous influences. Second, the denominator of q excludes intangible assets from its calculations. The intangible assets contribute to the value of a firm, but estimates of replacement costs for such assets are not a part of the denominator. This results in an "overestimation" of a firm's true q value. Third, estimation of the replacement value of a firm's tangible assets is complex and can be quite difficult to compute (Hall et al. 1988). In the "Methodology" and "Data" sections that follow, we use prescribed methods for calculating q and for controlling for potential factors that have been shown to minimize these limitations as much as possible.
In summary, Tobin's q appears to be the best measurement option, given its strengths of being forward looking, comparable across firms, and based on economic theory. In the following section, the latter characteristic plays an important role as we turn our attention to specifying an appropriate econometric model for our empirical test.
In addition to providing a measure of the value of a firm, the usefulness of Tobin's q is in its ability to trace the sources of this value. Following the work of Lindenberg and Ross (1981), we decompose Tobin's q as a function of the firm's market value, M[subt], normalized with respect to the replacement cost of the firm's physical assets:
( 1) q = M[subt]/M[subk] = f(M[subk], M[subc], M[subn], M[subd])/M[subk],
where M[subk] is the replacement cost of the firm's assets (e.g., plant, equipment) and is equal to the value of the firm that is attributable to its tangible assets, M[subn] represents monopoly rents attributable either to a monopoly position or to entry barriers, M[subc] is the portion of company-specific rents that are attributed to a firm's cost-reducing factors, and M[subd] is the part of the total value of the firm that is attributed to company-specific factors (including market-based assets) that contribute to firm value. Accordingly, we specify the following:
( 2) q[subit] = SAT[subit, supβ[sub1]] AS[subit, supβ[sub2]] MS[subit, supβ[sub3]] CONC[subit, supβ[sub4]] e[supα0 + ε[subit],
q[subit] = Tobin's q value of firm i at time t,
SAT[subit] = firm i's satisfaction level at time t,
AS[subit] = advertising-to-sales ratio of firm i at time t,
MS[subit] = market share of firm i at time t,
CONC[subit] = concentration level of firm i's industry,
α[sub0] = constant,
β[subi] = corresponding slope coefficients, and
ε[subit] = stochastic error term.
Market-based assets that lead to shareholder value are captured by the firm's satisfaction levels as determined by the ACSI (SAT). To focus on the relationship between customer satisfaction and shareholder value, we use conventional controls to account for rents that are due to monopoly factors, M[subn], and to firm-specific factors, M[subc] (Simon and Sullivan 1993). To control for monopoly factors, we employ industry concentration (CONC) as a control variable (Gale 1972; Smirlock, Gilligan, and Marshall 1984). To capture the effect of firm-specific factors, we include the firm's market share (MS) and advertising-to-sales ratio (AS) (Montgomery and Wernerfelt 1988). Taken together, these covariates control for firm-and industry-specific sources of economic rents (Scherer 1990), as well as sources of value creation other than satisfaction (Montgomery and Wernerfelt 1988). Linearization of Equation 2 yields
( 3) ln q[subit] = α[sub0] + β[sub1]ln SAT[subit] + β[sub2]ln AS[subit] + β[sub3]ln MS[subit] + β[sub4]ln CONC[subit] + ε[subit].
In estimating Equation 3, we must control for potential bias that is due to omitted fixed, random, and time-varying effects. As we discussed previously, there are many company-and context-specific factors for which we cannot directly control, such as managerial expertise, firm efficiencies, regulatory environment, and luck.( n1)
A series of methodological approaches for addressing omitted variables has gained wide acceptance in marketing (Boulding 1990; Boulding and Staelin 1993; Erickson and Jacobson 1992; Jacobson 1990a, b). In our empirical work, we apply these recommended methods. First, to control for the possible presence of random effects among the financial measures, we follow the standard practice of employing instruments that are lagged one period beyond the error term for each specification, M0 (Boulding and Staelin 1995). As we discuss subsequently, using a theoretically specified set of related variables as instruments, we estimate ACSI as a latent variable in a separate analysis that accounts for measurement error.
Second, to control explicitly for time-invariant unobserved firm-and industry-level fixed effects, we follow standard practice and transform the model through first-differencing We label the resultant specification, M1, the "instrumental variable/fixed-effect" (IV/FE) specification. Once again, we use instrument variables to estimate M1. In this case, the instruments are independent variables that are lagged one period beyond any information contained in the error, or t -- 2.
Another potential source of bias in Equation 3 is serial correlation (Boulding and Staelin 1993). Serial correlation in our context may be due to factors such as consumer tastes, research and development efforts, and industry trends. To address this potential source of bias, we follow standard practice and employ quasi differencing, or ρ differencing. We label this model, M2, the "IV/FE/serial correlation" (IV/FE/SC) specification. We estimate this model using the iterative procedure of Hildreth-Lu, which selects the value of ρ that minimizes the sum of squared error (Greene 1993). For instruments, we use lagged values of t -- 2 or t -- 3 as appropriate.
For customer satisfaction, we used annual ACSIs from 1994 to 1997 that were made available to us by the National Quality Research Center at the University of Michigan. The ACSI methodology provides a uniform, independent, customer-based, cumulative, firm-level satisfaction measure for nearly 200 companies in 40 industries and in 7 sectors of the U.S. economy. It covers more than 40% of the gross domestic product of the United States and includes both the private sector and the public sector. The raw data for the ACSI are collected from random telephone surveys of customers (at least 200 customers per firm) who have recently consumed a specific brand of a firm's product or service. Respondents are asked questions on 15 measurement variables, which are then used as indicators of 6 latent variables or constructs, including the overall customer satisfaction index, ACSI, which can range from 0 to 100.
The ACSI methodology uses a multiple indicator approach to measure ACSI as a latent variable. A version of partial least squares (PLS) is used to estimate the model; PLS is an iterative procedure for estimating causal models that does not impose distributional assumptions on the data and that accommodates continuous and categorical variables. Using the 15 survey response variables as instruments, PLS estimates weights for each instrument that maximize the ability to explain customer loyalty as the ultimate endogenous or dependent variable. The estimated weights are used to construct index values (transformed to a 100-point scale) for ACSI and the other model constructs. The resultant indexes have been shown to have high reliability (Fornell et al. 1996), that is, the extent to which the variation of the measure is due to the "true" underlying phenomenon rather than random effects.
To calculate the dependent variable, Tobin's q, we estimated the components of the denominator and the numerator in the following equation:
( 4) q = Total market value of all outstanding securities/ Total replacement costs of assets.
There are several possible methods to measure q (Hall et al. 1988; Lindenberg and Ross 1981). We used an approach that is gaining wide acceptance in the economics and finance literature: Chung and Pruitt's (1995) method (Berger, Ofek, and Swary 1996). The details of this calculation are provided in the Appendix.
To obtain an accurate and appropriate measure of market share and to be consistent with our dependent variable, we departed from conventional techniques of measuring market share, which use either industry reports or overall sales figures from databases (Szymanski, Bharadwaj, and Varadarajan 1993). We determined division-level U.S. annual sales for each firm in our samples using the detailed data available in the company's 10-K reports. We then used these figures to calculate the relative market shares of the firms in the ACSI. We measured industry concentration using the Herfindahl Hirschman index (HHI), which is the sum of the squared market shares of the firms in the industry (Schmalensee 1977).
To calculate advertising intensity, we used brand-level domestic advertising expenses from Competitive Media Reporting (1993-1997). This method provides an advertising measure that is not only suitable but also more accurate than the measures of advertising intensity that currently are used in marketing research (Ailawadi, Farris, and Parry 1999).
We took special care with the diversified corporations in the sample. We collated U.S. annual sales figures for each division using the disaggregated segment-and division-level data in the company's 10-K reports. On the basis of the sales data, we calculated market share and the concentration measure (HHI) for each division (range: .10 to .61). For each division of the company for which division-level ACSI data were available, we used the division-level market share and concentration values as independent variables in our regressions. For example, Sara Lee has two divisions, foods and apparel, for which separate ACSI data were collected (at the division level) for the period under analysis. Therefore, we calculated sales, market share, and concentration index levels for both divisions and used them in the analysis along with the appropriate ACSI score. For Procter & Gamble, ACSI data were available only for the personal products division. We matched these data with sales, market share, and concentration indexes for the personal products division. For diversified companies in which only company-level ACSI data, not division-level ACSI data, were available, we also aggregated the sales level, shares, and concentration indexes to the company level.
After we deleted companies because of missing data and mergers, we had a total of 330 usable observations over four years for M1, which we reduced to 216 observations for M2. The means and standard deviations for the full data set are shown in Table 1. Correlations among the variables used in the analysis are given in Table 2.
The findings for H[sub1] are summarized in Table 3. As Table 3 shows, the association between customer satisfaction and shareholder value (as measured by Tobin's q) is positive and significant in both specifications (albeit marginally in M2). The estimates are also close in value, given the standard errors.
For our data set, the Hausman test suggests that the IV/FE specification (M1) is appropriate. It indicates that our controlling for both random and fixed effects offers an improvement over simply controlling for random effects, yet the test also indicates that the IV/FE/SC (M2) specification provides no further improvement (m = .3017). Thus, in the following discussion, we focus on the estimates for M1.( n2)
The findings for the remaining independent variables are notable. We found the coefficient for industry concentration to be positive and significant at .29 in M1. This indicates that increased concentration levels enable a firm to capture increased Ricardian rents (Ricardo 1817; Scherer 1990) and to create shareholder value, which is consistent with other studies that apply Tobin's q (Lindenberg and Ross 1981; Montgomery and Wernerfelt 1988; Simon and Sullivan 1993).
We found the coefficient for market share to be insignificant in both M1 and M2. Thus, after we controlled for omitted fixed effects and the effects of satisfaction, advertising, and market concentration, there is little support for the argument that high market share is a result of a firm's ability to perform value-adding activities better than its competitors. This finding is consistent with theory and with findings in economics (Smirlock, Gilligan, and Marshall 1984, 1986) and marketing (Fornell 1995; Jacobson and Aaker 1985). The finding implies that rents that often are posited to be associated with market shares might be due to concentration levels in an industry. When the association between concentration and firm value is taken into account, market share does not have an incremental association with value in our data set.
The coefficient for advertising intensity is positive in M1, but its impact is relatively small, at .03. This finding suggests that the intensity of advertising has, at best, a small role in building long-term value when we control for the impact of both customer satisfaction efforts (which may or may not be advertising related) and other firm-and market-level variables.
As a check on our findings, it is worthwhile to examine the association between ACSI and other measures of long-term financial performance. To this end, Table 4 summarizes correlations between year-to-year changes in ACSI and contemporaneous changes in two alternative measures of firm value: equity price and the ratio of price to book value. We calculated these accounting measures using data from the Center for Research in Securities Prices and COMPUSTAT for 114 publicly held firms in the ACSI sample. As Table 4 shows, there is a positive correlation between changes in ACSI and contemporaneous changes in both alternative measures, which indicates further support for a positive association between customer satisfaction and shareholder value.( n3)
To test the moderating influence of competition on the association between ACSI and Tobin's q, we used our measure of concentration. The findings appear in Table 5. Hausman's test (m = .538) again suggests that the IV/FE specification, M1, is appropriate.
The interaction between satisfaction and industry concentration is positive and significant in the IV/FE estimation (p = .08), which indicates that the positive association between customer satisfaction and shareholder value is weaker when concentration levels are low and when the industry is fragmented. The association is stronger in less fragmented markets with medium levels of concentration (HHI ranges from .10 to .61).
The preceding analysis focuses on the overall average association between customer satisfaction and shareholder value by taking steps to control for industry-and firm-level differences. However, we believe that heterogeneity within and across industries is interesting, in and of itself. In this section, we explore the nature and degree of such heterogeneity.
A possible approach is to perform a classic pooled versus unpooled test of Equation 3. However, given the limited number of time periods available per firm (four), this standard approach is not a viable option. Rather, we require a method that provides the benefits of pooling without giving up the requirement of parameter estimates for each industry and firm. An approach that meets these criteria is hierarchical Bayesian regression. From an intuitive standpoint, hierarchical Bayesian regression operates by "borrowing" information from across firms to improve firm-level estimates. In this process, it provides a way both to separate variance associated with industry-level differences from variance within industries and to introduce covariates to explain variance across firms and industries. We subsequently specify our hierarchical model of Tobin's q. For within firms,
( 5) ln q[subijt] = π[sub0ij] + π[sub1ij] ln SAT[subijt] + e[subijt], where e[subijt] ∼ N(0, σ²).
For within industries,
(6a) π[sub0ij] = π[sub00i] + π[sub01i] ln AS[subij] + π[sub02i]= ln MS[subij] + r[sub0ij], where r[sub0ij] ∼ N(0, τ[sub0]), and
(6b) π[sub1ij] = π[sub10i] + r[sub1ij], where r[sub1ij] ∼ N(0, τ[sub1]).
For between industries,
(7a) π[sub00i] = π[sub000] + π[sub001] ln CONC[subi] + u[sub00i], where u[sub0i] ∼ N(0, τ π[sub00]);
(7b) π[sub01i] = π[sub010] + u[sub01i], where u[sub1i] ∼ N(0, τ[sub01]);
(7c) π[sub02i] = π[sub020] + u[sub02i], where u[sub1i] ∼ N(0, τ[sub02]); and
(7d) π[sub10i] = π[sub100] + u[sub10i], where u[sub1i] ∼ N(0, τ[sub10]).
Equation 5, the Level 1 or "within-firm" equation, estimates the association between q and satisfaction for a given firm over repeated periods. The Level 1 dependent variable, q[subijt], represents Tobin's q for firm j in industry i during period t. The first term on the right-hand side of Equation 5, π[sub0ij], represents the firm-specific constant or fixed effect. Satisfaction, SAT[subijt], is the independent variable. The association between satisfaction and shareholder value for firm j in industry i is estimated by the coefficient, π[sub1ij]).
Equation 6, Level 2 of the model, represents variation between firms within each industry. For Equation 6a, the first within-industry equation, we modeled the firm-specific effect for firm j in industry i, π[sub0ij], as a function of the industry-specific fixed effect, π[sub00i], and firm-specific controls: advertising-to-sales ratio and market share (Montgomery and Wernerfelt 1988). The error term, r[sub0ij], captures the unique firm-specific effect for firm j. Equation 6b models within-industry heterogeneity in the association between customer satisfaction and shareholder value, π[sub1ij]. The first term, π[sub10j], represents the mean industry coefficient for the association, and the second term, r[sub1ij], represents the unique firm-specific effect that moderates the association.
Equation 7a (Level 3) captures heterogeneity across industries. Here, we incorporate cross-industry variation in fixed effects and the association between satisfaction and shareholder value into the model. We model the industry-specific fixed effect for industry i, π[sub00i], as a common fixed effect, π[sub00] and the concentration control variable (CONC) is suggested by application of Tobin's theory (Lindenberg and Ross 1981). Equations 7b and 7c model industry variation in the effect of the advertising-to-sales ratio and market share control variables. We model industry differences in the association between customer satisfaction and shareholder value in Equation 7d as a common fixed effect, π[sub10], and an industry-specific fixed effect, π[sub10i]. To test for the moderating role of concentration, we rewrite Equation 7d as follows:
( 8) π[sub10i] = π[sub100] + π[sub101] ln CONC[subi] + u[sub10i], where u[sub1i] ∼ N(0, τ[sub10]).
A summary of the estimation results appears in Table 6. We find the association between ACSI and Tobin's q to be 1.63, compared with 1.62 in the preceding analysis (M1). The coefficients for the advertising-to-sales ratio and concentration remain positive and significant. Notably, when we separate out industry-level variance, we again find that market share has a significant effect within industries. Although the coefficient is only marginally significant (p = .08), that the coefficient emerges as positive suggests a firm-specific effect of market share. When industry heterogeneity is controlled, market share appears to be positively associated with shareholder value across firms that compete in the same industry.
Table 6 also reports the sources of variation in the association between shareholder value and customer satisfaction. The variance components represent the degree of heterogeneity in the estimates apportioned by level of analysis. The variance components for the association between shareholder value and customer satisfaction are 1.22943 across industries and 7.26775 within industries. These figures imply that 14.5% of the variance in the association between ACSI and Tobin's q is due to industry differences, and 85.5% is due to within-industry differences across firms. The within-firm, within-industry, and across-industry variance components for Tobin's q are .16377, .07677, and .09620, respectively. Therefore, 48.6% of the conditional variance in Tobin's q is at the firm level, 22.8% is due to within-industry firm-specific effects, and the remaining 28.6% is across industries. Thus, in our sample, firm-level differences in shareholder value and the link between satisfaction and shareholder value dominate industry-level differences. The findings are consistent with those of Rumelt (1991), who finds that nearly 50% of the variance in the rate of return is at the firm level, and approximately 10% is due to industry membership.
Industry-level empirical Bayes estimates of the association between customer satisfaction and shareholder value are shown in Figure 1. The estimates are greatest for department stores, supermarkets, and appliances, which implies a stronger association between satisfaction and shareholder value for this group than for industries such as automobiles, apparel, and personal computers. Notably, goods and services appear to be spread throughout the distribution.
As is shown in Table 6, we also estimated the model using concentration as a moderator of the association between ACSI and Tobin's q. The coefficient for the interaction term is positive and significant (3.24). This finding indicates further support for our hypothesis that competitive intensity weakens the association between customer satisfaction and shareholder value.( n4)
This study develops a new conceptual framework that links customer satisfaction to shareholder value and that brings the role of market structure into the nomological network that links the two key constructs. Empirically, we document a positive association between customer satisfaction and shareholder value. Given the overall estimate of the association between ACSI and Tobin's q of 1.62, a 1% change in customer satisfaction (as measured by ACSI) is associated with an expected 1.016% change in shareholder value (as measured by Tobin's q). The average level of Tobin's q for our overall data set is 1.73, which translates into an increase in q of approximately .027 for a 1% increase in satisfaction. For a BusinessWeek 1000 firm with average assets of approximately $10 billion, a 1% improvement in satisfaction implies an increase in the firm's value of approximately $275 million. This effect would be much greater for larger firms or for firms with a stronger association between satisfaction and shareholder value.
The finding of a positive association between ACSI and shareholder value has important implications. Although this study does not provide diagnostic guidance for managers who are exploring ways to improve customer satisfaction or for specific guidelines to implement customer satisfaction programs, it provides a strong rationale: Firms that achieve higher customer satisfaction also create more shareholder wealth.
The study also indicates that the association between customer satisfaction and shareholder value varies significantly across industries and across firms. The link between the two is considerably stronger in some industries than in others, and further research should seek to understand the underlying industry characteristics that account for this finding. Indeed, the degree of heterogeneity in the satisfaction shareholder value link is even greater across firms in a given industry. This latter finding strongly suggests that firm conduct, both strategy and implementation, matters more than industry context in determining whether the pursuit of customer satisfaction is consistent with the creation of shareholder value. An understanding of the firm characteristics and actions that influence the association between customer satisfaction and shareholder value seems to be a particularly promising area for further research in terms of developing useful implications for managers.
We investigate the influence of the most logical moderator of the association between customer satisfaction and shareholder value: an industry's market structure. We find that the association between ACSI and Tobin's q is weaker in more fragmented industries in which rivalry should be, on average, more intense. Our findings for market share and Tobin's q are also notable. Market share does not appear to be a significant factor in enhancing financial performance, unless industry differences are controlled for and the within-industry association between market share and Tobin's q is isolated. Under such conditions, there is a weak positive association between the two.
Investors should note our findings. The long-term nature of economic returns from improved customer satisfaction has important implications for the capital market valuation of the firm. Loyal and satisfied customers represent a revenue-generating asset for the firm that is costly to develop and maintain. Such an asset should figure prominently in assessments of a firm's future financial health. If each firm provides a standardized customer satisfaction index score as part of its financial reporting, capital markets will be better informed.
Further research in this domain should identify and test additional potential moderators of the association between satisfaction and shareholder value. Another important direction for further research is to replicate the findings of the current study in other parts of the world in which national customer satisfaction measurement systems and active capital markets coexist. In addition, future researchers could investigate the information content of ACSI and the role of such measures in conveying information in efficient markets. It may be that such measures aggregate information that is otherwise difficult to observe, costly to obtain, and/or noisy, such as private information that individual customers possess about their consumption experiences and is therefore "unanticipated" by the market. Because our data set consists of industries that range from "very fragmented" to "mildly oligopolistic", further research should also include more "strongly oligopolistic" or "monopolistic" industries. It may be that the moderating effect of concentration is nonlinear and that the association between satisfaction and shareholder value is also attenuated at higher levels of concentration than are present in the current data set. Future researchers also could determine whether our findings hold for other measures of shareholder value, including other approximations of Tobin's q. Finally, we propose but do not test a new underlying logic for the association between customer satisfaction and shareholder value. Further investigations should develop and test these arguments for how satisfaction influences customer loyalty and firm bargaining power and, in turn, shareholder value.
In summary, the findings presented herein represent the strongest support to date for a positive association between customer satisfaction and shareholder value. Although no single study can be regarded as definitive, the current investigation has several important strengths that readers might keep in mind as they weigh the findings. First, it conceptually links customer satisfaction and shareholder value on the basis of the framework proposed by Srivastava, Shervani, and Fahey (1999). Second, the sample is based on ACSI. As such, our measure of customer satisfaction is based on a nationally representative sample of actual customer experience with a particular firm's offerings; it is not self-reported by managers. Furthermore, the ACSI database includes customer satisfaction measures for nearly 200 Fortune 500 firms, selected to represent fully the largest industries in the seven major sectors of the U.S. economy that end users directly experience. Third, our measure of shareholder value, Tobin's q, is strongly grounded in the economic theory of firm long-term profit maximization (Lindenberg and Ross 1981; Tobin 1969). It provides a measure of firm value that is long term, risk adjusted, forward looking, and cumulative. Tobin's q is generalizable and comparable across firms in different industries. Finally, unlike existing research, this study employs a methodological approach to control for fixed, random, and time-varying "unobservable" factors that may bias empirical estimates of the association (Boulding 1990; Boulding and Staelin 1995; Jacobson 1990a).
Together, the findings of this study empirically affirm a fundamental principle of capitalistic free markets: Sellers that do well by their customers are rewarded with more business from buyers and with more capital from investors. Likewise, if businesses fail to satisfy customers as effectively and efficiently as competitors, customers and investors turn elsewhere. The flow of investors and their capital moves hand in hand with the flow of customers and their business. In theory, this is how free economies should operate, with the market allocating capital and other resources to create the greatest possible customer satisfaction in the most efficient manner possible. It is also the reason utility maximization and the creation of a satisfied customer is a central strategic concern of any business. Yet this study is the first to demonstrate this empirically. By implication, the significance of marketing knowledge and marketing activity is accentuated. For customer satisfaction is determined not only by quality but also by market segmentation and customer selection executed though product and service offerings, distribution channels, communications, and pricing. Herein lies the immense contribution of marketing thought and practice that is yet to be fully realized.
The order of authorship is alphabetical and does not reflect the relative contribution of each author. The authors gratefully acknowledge the data provided by the National Quality Research Center at the University of Michigan Business School. This work has benefited substantially from the comments of Carl Simon, Rich Frankel, Joe Priester, Rajeev Batra, Tirthankar Roy, Mrinal Ghosh, Lopo Rego, and participants in seminars at State University of New York-Buffalo, Hong Kong University of Science and Technology, University of California at Davis, University of North Carolina, and Southern Methodist University.
( n1) It is worth noting that some studies of Tobin's q have used one or more additional control variables, including firm size, leverage, ROI, ROA, and market growth.
( n2) Previous research has shown that q is autocorrelated even after accounting for fixed effects. The relatively low autocorrelation may be due to our small sample. We also estimated models using both a state-dependent formulation (lagged q) and one that included the unanticipated effects of ROI. In both cases, the association between ACSI and q remained significant and of similar magnitude.
( n3) We checked the stability of the findings by splitting our sample in two ways: ( 1) first two years versus last two years and ( 2) low-customer-satisfaction firms versus high-customer-satisfaction firms. Chow tests show that the estimates are stable in both cases. Because the 50,000 data points used to calculate firm-level ACSI scores are collected over the course of a year, we also tested whether the association between ACSI and q is stable across collection periods. We found Chow tests for differences to be insignificant.
( n4) We also tested other possible firm-and industry-level characteristics that we expected to moderate the association, including industry growth rates, capital intensity, advertising-to-sales and research and development ratios, and the type of industry in which the firm operated (i.e., goods or services). However, we found no other factor to play a significant moderating role in our sample.
Legend for Chart:
B - q
C - SAT
D - SHARE
E - CONC
F - AS
A B C D E F
Mean 1.35 77.71 .17 .23 1.69
Standard deviation .93 6.02 .15 .11 2.34 Legend for Chart:
B - ΔlnQ
C - ΔlnSAT
D - ΔlnSHARE
E - ΔlnCONC
F - ΔlnAS
A B C D
E F
ΔlnQ 1.000 .162(***) .029
-.086 .084(*)
ΔlnSAT .162(***) 1.000 .086
.165(***) .042
ΔlnSHARE .029 .086 1.000
.112(**) -.104(*)
ΔlnCONC -.086 .165(***) .112(**)
1.000 .107(**)
ΔlnAS .084(*) .042 -.104(*)
.107(**) 1.000
(*) p < .10.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Model
B - Intercept
C - β[subSAT,t]
D - β[subAS]
E - β[subSHARE]
F - β[subCONC]
G - ρ
H - R²
I - Number of Observations
A B C D E
F G H I
M0 -6.82(***) 1.74(***) .03(**) .08(***)
(ordinary (1.72) (.39) (.01) (.02)
least
squares)
.30(***) -- .18 456
(.07)
M1 .69 1.62(**) .03(***) .00
(IV/FE) (.45) (.71) (.01) (.03)
.29(**)
(.11) -- .16 330
M2 .89 1.28(*) .02 .01
(IV/FE/SC) (.95) (.80) (.05) (.03)
.29(*)
(.15) .05 .07 216
(*) p < .10.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Change in ACSI
B - Contemporaneous Change in Equity Price
C - Contemporaneous Change in Price-to-Book Ratio
A B C
From 1994 to 1995 .06(**) .09(**)
From 1995 to 1996 .23(***) .12(***)
From 1996 to 1997 .09(**) .04(*)
(*) p < .10.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Model
B - Intercept
C - β[subSAT,t]
D - β[subAS]
E - β[subSHARE]
F - β[subCONC]
G - β[subCONC x SAT]
H - ρ
I - R²
J - Number of Observations
A B C D E
F G H I
J
M0 (ordinary -2.43(***) 1.56(***) .03(*) .16(***)
least squares) (2.465) (.59) (.02) (.06)
.22(***) 1.54(**) -- .25
(.06) (.92)
456
M1 (IV/FE) 1.33(**) 1.48(*) .02 .00
(.52) (.81) (.02) (.02)
.22 .88(*) -- .14
(.23) (.51)
330
M2 (IV/FE/SC) 1.58(*) 1.10 .00 .01
(.89) (.91) (.02) (.02)
.15(*) 1.18(*) -.05 .03
(.09) (.68)
216
(*) p < .10.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Independent Variable
B - Coefficient
C - Model 1
D - Model 2
A B C D
Intercept π[sub000] .03 .03
(.07) (.08)
ACSI π[sub100] 1.63(***) 1.51(***)
(.55) (.52)
Advertising to sales π[sub010] .14(**) .14(**)
(.06) (.06)
Market share π[sub020] .07(*) .06
(.04) (.04)
Concentration π[sub001] .70(***) .83(***)
(.21) (.24)
Concentration x ACSI π[sub101] 3.24(*)
(2.06)
Sources of variation
Within firm σ² .16377 .16359
Within industry τ[sub0] .07677 .07433
τ[sub1] 7.26775 7.06366
Between industries τ[sub00] .09620 .09971
τ[sub01] .04618 .04864
τ[sub02] .00211 .00328
τ[sub10] 1.22943 .83474
(*) p < .10.
(**) p < .05.
(***) p < .01.GRAPH: FIGURE 1; Industry-Level Empirical Bayes Estimates of the Association Between ACSI and Tobin's q
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(A1) q = (Market value of equity + book value of debt)/Total assets = (Share price x number of shares outstanding + total value of preferred stock + long-term debt + short-term debt)/Total assets
First, we obtained firm-specific identification numbers (Committee for Uniform Securities Identification Procedures numbers) of the publicly traded firms in the ACSI sample (both foreign and domestic). Second, we obtained share price and preferred stock value from the Center for Research in Securities Prices databases. Third, we obtained long-term debt, short-term debt, and total assets from the COMPUSTAT databases.
~~~~~~~~
By Eugene W. Anderson; Claes Fornell and Sanal K. Mazvancheryl
Eugene W. Anderson is Associate Dean for Degree Programs and Professor of Marketing (e-mail: genea@umich.edu), and Claes Fornell is Donald C. Cook Professor of Business Administration (e-mail: cfornell@umich.edu), University of Michigan Business School. Sanal K. Mazvancheryl is Visiting Assistant Professor of Marketing, McDonough School of Business, Georgetown University (e-mail: sm438@georgetown.edu).
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Record: 46- Customer Satisfaction, Cash Flow, and Shareholder Value. By: Gruca, Thomas S.; Rego, Lopo L. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p115-130. 16p. 8 Charts, 2 Graphs. DOI: 10.1509/jmkg.69.3.115.66364.
- Database:
- Business Source Complete
Customer Satisfaction, Cash Flow, and Shareholder Value
In this article, the authors strengthen the chain of effects that link customer satisfaction to shareholder value by establishing the link between satisfaction and two characteristics of future cash flows that determine the value of the firm to shareholders: growth and stability. Using longitudinal American Customer Satisfaction Index and COMPUSTAT data and hierarchical Bayesian estimation, the authors find that satisfaction creates shareholder value by increasing future cash flow growth and reducing its variability. They test the stability of findings across several firm and industry characteristics, and they assess the robustness of the results using multimeasure and multimethod estimation.
How does customer satisfaction create shareholder value? This is an important question for both top executives and marketing managers. Traditionally, the success of marketing actions has been evaluated by marketplace outcomes, such as sales or market share (Lehmann 2004). Today, however, top managers insist that every functional area has as its ultimate goal the creation of shareholder value (Day and Fahey 1988; Hunt and Morgan 1995).
Indeed, there is a great deal of empirical research showing that by satisfying customers, firms elicit desirable behaviors, such as increased loyalty, greater receptivity to cross-selling efforts, and positive word-of-mouth advertising (e.g., Anderson 1996; Fornell 1992; Fornell et al. 1996). Such behaviors translate into superior performance as measured by traditional metrics. Recently, a study by Anderson, Fornell, and Mazvancheryl (2004) found a positive association between a firm's current level of customer satisfaction and contemporaneous financial market measures, such as Tobin's q, stock, and market-to-book ratio. Although promising, the empirical research supporting the claim that customer satisfaction is a driver of shareholder value is incomplete. We must complete the chain of effects by forging a link between customer satisfaction and the characteristics of future cash flow that determine the value of the firm to shareholders (Rust et al. 2004).
Srivastava, Shervani, and Fahey (1998) develop a framework that links the market-based assets of customer and partner relationships to shareholder value using the discounted cash flow model of firm valuation (Kerin, Mahajan, and Varadarajan 1990; Rappaport 1986). They argue that beneficial effects of satisfaction on customer behavior result in increased cash flow growth and acceleration as well as reduction in cash flow volatility. These particular characteristics of future cash flows determine the value of a firm to its shareholders (Kerin, Mahajan, and Varadarajan 1990; Rappaport 1986). Therefore, to understand fully how customer satisfaction leads to increased shareholder value, it is necessary to understand how customer satisfaction translates into cash flows with desirable attributes (Rust et al. 2004). This is critical given the significant potential for trade-offs between increasing and stable cash flows. Our study details how customer satisfaction affects a firm's financial risk. To our knowledge, this is the first attempt to examine this important effect empirically.
In this article, we examine the relationship between customer satisfaction and future cash flows. By focusing on the effect of this relationship, we gain a more comprehensive understanding of how customer satisfaction increases shareholder value. The discounted cash flow model is among the most widely accepted valuation techniques in finance (Fernandez 2002; Rapapport 1986). Because chief financial officers (CFOs) use discounted cash flow methods in their own decision making (Ryan and Ryan 2002), our model of future cash flows provides marketing managers a common "language" with which to communicate the importance and impact of customer satisfaction to finance-oriented executives. Fostering such understanding has been a major concern of the Marketing Science Institute (2002, 2004) for several years.
Our focus on the impact of satisfaction on future cash flows is consistent with empirical research on its effect on consumer behavior. In satisfying a customer, a firm generates benefits for itself beyond the present transaction and current time period. In other words, the firm benefits from customer satisfaction primarily in the future, during the next buying opportunity (because of increased loyalty) or company-initiated contact (through an increased receptivity to cross-selling). Understanding how these benefits shape future cash flows provides managers with insights into the potential trade-offs between cash flow growth and stability. Furthermore, our results are highly actionable given the streams of research on how to measure (Fornell 1992; Fornell et al. 1996) and influence (Ambler et al. 2002; Syzmanski and Henard 2001) customer satisfaction.
This is an important topic for academics, practitioners, and investors. For academics, this study examines the suitability of customer satisfaction as a measure of the market-based asset of customer relationships. For managers, recognizing how customer satisfaction affects an organization's future cash flows can provide guidance for valuing alternative efforts that are intended to improve customer satisfaction. Finally, for investors, identifying the drivers of a firm's future cash flow is crucial for the accurate assessment of a firm's value to shareholders.
We organize this article as follows: First, we briefly review the customer satisfaction and shareholder value literature and summarize how these two constructs are linked. Second, we develop a set of models to examine the influence of customer satisfaction on cash flow growth and variability. Third, we describe the methodology we used and present the results. Finally, we discuss the implications of our findings and explore directions for further research.
Linking Customer Satisfaction to Shareholder Value
As the formal owners of the firm, shareholders are an important constituency whose interests should be included in a manager's evaluation of decision alternatives (Day and Fahey 1988). To align the interests of managers with those of the shareholders, boards of directors often link a large proportion of a top executive's pay to the firm's stock price through options or other stock price-related incentives (Guay 1999; Morgan and Poulsen 2001). Although increasing a firm's stock price is a seemingly obvious way to increase shareholder value, it has important limitations. The most obvious is the existence of a wide range of factors, many beyond the control of executives, that might influence the price of a stock (e.g., Lambert 2000; Lehmann 2004).
Despite the multiplicity of factors that influence day-today changes in a firm's stock price, its value can be more readily assessed by means of the discounted cash flow model (Fernandez 2002; Rappaport 1986). According to this valuation method, managers should aim to maximize future cash flows and to minimize the risks associated with those cash flows to maximize shareholder value (Rappaport 1986; Srivastava, Shervani, and Fahey 1998, 1999). A reduction in the variability of cash flows over time reduces the firm's cost of capital and creates shareholder value. Because future cash flows are discounted, a given level of current cash flow is preferred to that same amount at a future date. Thus, cash flow acceleration is a further source of shareholder value.
However, there are alternative measures of shareholder value. The most widely cited include Tobin's q (Anderson, Fornell, and Mazvancheryl 2004), price-to-book ratios (Erickson and Whited 2001), and economic value added (Ohlson 1995) as well as traditional financial metrics, such as return on assets (for an excellent review, see Rust, Lemon, and Zeithaml 2004). Our choice to measure shareholder value using future cash flows is motivated by its favorable characteristics. Future cash flow is a forward-looking measure that is comparable across firms and widely available, and its value can be determined unambiguously (Fernandez 2002).
Srivastava, Shervani, and Fahey (1998) suggest that market-based assets such as customer relationships create value for customers and thus result in improved marketplace performance and increased shareholder value. An important measure of the quality of a firm's relationship with its customers is embodied in assessments of customer satisfaction. As Fornell (2002, p. 41) notes, "satisfied customers can be viewed as economic assets that yield future cash flows." Next, we discuss the path from satisfaction to future cash flows.
By satisfying a customer, a firm generates benefits for itself beyond the present transaction and the current moment. These benefits arise from the positive shaping of the satisfied customer's future behavior. For example, satisfied customers are more loyal and increase their level of purchasing from the firm over time (Anderson and Sullivan 1993; Reichheld 1996; Reichheld and Sasser 1990). Some of this increased purchase level is due to satisfied customers being more receptive to cross-selling efforts (Fornell 1992). During future purchase occasions, satisfied customers are less likely to defect to competing products as a result of lower prices (Fornell et al. 1996). In addition, the positive word of mouth that satisfied customers generate influences other consumers' future purchases (Anderson 1996).
These positive behavioral consequences of satisfaction result in increased cash flows over time. Furthermore, customer satisfaction insulates customers from competitors' efforts and from external environmental shocks, leading to a reduction in the variability of future cash flows. The combination of these effects on future cash flows increases shareholder value (Rappaport 1986).
Measuring Customer Satisfaction and Shareholder Value
In this study, we measure customer satisfaction using the American Customer Satisfaction Index (ACSI) database. The ACSI is the first comprehensive U.S. customer satisfaction database (Fornell et al. 1996). Data are collected at the consumer level; more than 50,000 respondents are surveyed by computer-aided telephone interviews every year since 1994. Respondents are questioned on 15 indicator variables, which are then used to compute six latent constructs, including an overall customer satisfaction index. All measures are based on customers' experiences with the particular service or product.
The survey includes more than 200 members of the Fortune 500 (both private and public firms) in more than 40 industries, providing a nationally representative sample of goods and services. In some respects, the ACSI is similar to a macroeconomic indicator, because the sales volume of the surveyed firms represents more than 40% of the U.S. gross domestic product (Fornell et al. 1996).
Since 1994, there have been several studies examining the relationship between customer satisfaction and firm performance using the data from Sweden (Anderson, Fornell, and Lehmann 1994) and the newer ACSI (Anderson, Fornell, and Rust 1997; Fornell et al. 1996; Ittner and Larker 1998). Despite the significance of these efforts, theoretical and operational limitations of the dependent variables limit the studies' ability to understand how customer satisfaction affects shareholder value (Rust et al. 2004; Srivastava, Shervani, and Fahey 1998).
Modeling future cash flow as the source of shareholder value is consistent with the current theory (and practice) of firm valuation. Finance researchers have long proposed that the value of a firm to shareholders equals the net present value of all future cash flows (Rappaport 1986). Marketing scholars have also supported future cash flows as an appropriate measure of shareholder value (Day and Fahey 1988; Hunt and Morgan 1995; Srivastava, Shervani, and Fahey 1998, 1999). Furthermore, firm valuation determined by various discounted cash flow methods (differing with respect to how they treat taxes and the definition of cash flow) yields the same value of the company to shareholders (Fernandez 2002). In addition, modeling the effect of customer satisfaction on the characteristics of future cash flows provides actionable insights for managers (Ambler et al. 2002). Finally, by tracking the influence of marketing actions to characteristics of metrics that CFOs already monitor (i.e., cash flows), the discounted cash flow model provides a "road map" for CFOs and marketing managers to coordinate and translate their decisions into shareholder value.
Model Formulation
Because of the importance of future cash flows in models of firm valuation, there has been a great deal of research on this topic in the accounting and finance literature. Several findings from these studies are germane to our task of understanding how customer satisfaction affects the growth and variability of future cash flows.( n1) First, Lorek, Schaefer, and Willinger (1993) find that time-series models provide better predictions of future cash flow than do cross-sectional models. Second, the prediction of future cash flow using a time-series model is enhanced by the inclusion of both current earnings and current cash flow (Dechow, Kothari, and Watts 1998).( n2) Third, there are significant cross-industry differences in the processes that generate future cash flows (e.g., Barth, Cram, and Nelson 2001) and govern the variations in cash flow over time (Ismail and Choi 1996). Accordingly, the accounting and financial literature streams have traditionally modeled cash flows as follows:
( 1) Cash flow(t + 1) = f1 Cash flow(t); Earnings[sub (t)).
The primary contribution of this study is to extend this formulation and examine the incremental ability of customer satisfaction to explain variations in future cash flows. Thus, we include current levels of customer satisfaction as an explanatory variable:( n3)
( 2) Cash flow(t + 1) = f2 (Cash flow(t); Earnings(t); Satisfaction(t)).
In addition to being consistent with current accrual-based time-series models, this formulation can be extended to include firm and industry covariates. This is particularly relevant because prior research linking customer satisfaction to firm performance suggests that the association varies significantly across industries (Anderson, Fornell, and Rust 1997; Fornell et al. 1996) and across firms (Cadogan et al. 2002; Gray, Matear, and Matheson 2002). However, these sources of heterogeneity have not been modeled in a systematic and comprehensive way in any prior research on the determinants of two important characteristics of future cash flows: growth and variability (Srivastava, Shervani, and Fahey 1998, 1999).
To model the effects of customer satisfaction on cash flow growth, we develop an empirical model that summarizes the current research on predicting future cash flows (e.g., Dechow, Kothari, and Watts 1998):
(3a) Cash flow(t + 1)ij = π0ij + π1ij x Cash flow(t)ij + π2ij x Earnings(t)ij + π3ij x Satisfaction(t)ij + etij.
Cash flow for firm i in industry j at time (t + 1)--future cash flows-is a function of cash flow in the current period (t), current earnings, and current level of customer satisfaction. The term etij is a random error and is normally distributed.
Prior research (e.g., Buzzell and Gale 1987; Cadogan et al. 2002) indicates that firms can follow different strategies to achieve their performance goals. Controlling for such differences is necessary to understand the linkages between customer satisfaction and shareholder value. Furthermore, research on financial performance (Capon, Farley, and Hoenig 1990; Syzmanski, Bharadwaj, and Vadararajan 1993) identifies several firm-level characteristics that can influence the association between customer satisfaction and future cash flow. These include advertising and research and development (R&D) intensities (Bharadwaj, Bharadwaj, and Konsynski 1999) and levels of diversification (Varadarajan 1986).
We add to this well-established list of measures an indicator for the number of brands that a firm offers. Some researchers suggest that having a large stable of brands enhances firm performance. By offering many brands to customers (the "house-of-brands" strategy), a firm is more likely to match disparate needs in the market by establishing separate identities and target markets for each brand. The brand proliferation strategy can be used to signal a product breakthrough, reduce channel conflict (Aaker and Joachimsthaler 2000), and block the entry of new competitors by controlling retail shelf space (Schmalensee 1978). Conversely, the potential cost implications of supporting a large number of brands have motivated some researchers to suggest that brand pruning rather than brand proliferation should be the focus of many consumer goods companies (Kumar 2003).
Finally, the market share that a firm controls has implications for its strategic goals and behaviors, including segment coverage, channel selection, and choice of marketing partners (Houston et al. 2001). This is a particularly interesting issue, and Fornell (1995) suggests that pursuing market share is incompatible with increasing customer satisfaction.
Although not an exhaustive list, these measures represent a range of firm-level strategic differences. We examine how these strategic differences influence ( 1) the discounted cash flow model (impact on π0ij) and ( 2) the strength of the association between customer satisfaction and cash flow growth by allowing the customer satisfaction base-level estimate (π3ij) to be dependent on these firm-level controls.( n4) The firm-level model reflects these influences, which we summarize in Equation 3b:
(3b) [Multiple line equation(s) cannot be represented in ASCII text]
where Market shareij is firm's i share of total demand in industry j, Advij is a firm-level measure of advertising intensity (advertising-to-sales ratio), RDij is a firm-level measure of R&D intensity (R&D spending-to-sales ratio), Brandsij is the total number of different brands that a firm offers, and Segmentsij is the number of distinct business segments in which the firm competes (Carter 1977). The two firm-level spending-intensity variables (Advij, RDij) are expressed as the ratio of firm i's level divided by industry j's mean. This yields a positive index indicating whether a firm's capital intensity is above (>1) or below (<1) the industry mean. The variables Brandsij and Segmentsij are unstandardized measures. Finally, the terms r0-3ij are normally distributed random errors that are specific to firm i in industry j.
These firm-level strategic differences are not expected to influence the accruals (π1ij and π2ij), because this process has been traditionally modeled as a simple random walk (Dechow, Kothari, and Watts 1998; Finger 1994). Structural characteristics (i.e., industry differences) rather than firm characteristics are more likely to influence the accruals process (Barth, Cram, and Nelson 2001). Nonetheless, we tested for the influence of firm covariates on the process. As expected, we found mostly estimates that were not significant. Thus, we concentrate on how the firm differences affect cash flow growth directly and the strength of the association between customer satisfaction and cash flow growth.
Research on the determinants of future cash flow (e.g., Barth, Cram, and Nelson 2001) suggests that a firm's cash flows are influenced in part by the industry in which it competes. Therefore, we expect that the main effect firm-level parameters in Equation 3b (i.e., β00j, β10j, β20j, and β30j) are likely to vary by industry. We model these effects by including a set of industry-level controls, which we summarize in Equation 3c:
(3c) [Multiple line equation(s) cannot be represented in ASCII text]
where HHIj (i.e., the Hirschmann-Herfindahl index) is a measure of market concentration in industry j, Demand growthj is the average five-year sales growth for industry j, and Demand instabilityj is the standard deviation of the five-year sales growth for industry j (Finkelstein and Boyd 1998). The terms u00-30-35j are normally distributed random errors that are specific to industry j.
The sign and magnitude of π3ij test the hypothesis that customer satisfaction contributes to growth in future cash flows. This parameter estimate is the primary focus of this study because it measures the influence of customer satisfaction on future cash flows beyond that of current cash flows and current earnings. The role of firm and industry differences on the association between current customer satisfaction and future cash flow is indicated by estimates of the relevant β and γ parameters.
To test the impact of customer satisfaction on the variability of future cash flows, we must construct a measure of cash flow variability. For this purpose, we compute a coefficient of cash flow variability for each year using quarterly cash flow data (Rajgopal and Shevlin 2002) by dividing each firm's quarterly cash flow coefficient of variation (i.e., standard deviation divided by the mean) by the market's quarterly cash flow coefficient of variation (which we obtained from the Wilshire 5000 index for our market definition). The coefficient of cash flow variability measures how variable an individual firm's cash flows are compared with the variability in the market's cash flows. For ease of exposition, we model cash flow variability in the same way as the previously described cash flow growth model. This yields the following formulation:
(4a) CF variability(t + 1)ij = π0ij + π1ij x CF variability(t)ij + π2ij x Earnings(t)ij + π3ij x Satisfaction(t)ij + etij.
We included earnings in the model because, all else being equal, larger firms are more likely to have more stable cash flows (Sloan 1996). The remainder of the cash flow variability model replicates Equations 3b and 3c, generating equivalent Equations 4b and 4c, which we do not present in the interest of conserving space.
Again, we test the hypothesis that customer satisfaction reduces future cash flow variability using the sign and magnitude of the coefficient in the firm-level model (π3ij). Firm and industry differences can be determined by analyzing the corresponding β and γ coefficients.
To estimate the previous models, we use a multilevel hierarchical Bayes formulation known as a hierarchical linear model (HLM). The HLM approach builds on familiar concepts from regression and analysis of variance to estimate multilevel or nested models (Bryk and Raudenbush 1992; Goldstein 1987). This is particularly relevant for this study given the hierarchical nature of the data (i.e., firms nested within industries). In addition, given the potential significance of firm and industry covariates, it is important to be able to estimate which proportions of future cash flow variance are attributable to the industry-versus firm-level differences. To this end, HLM enables us to separate the different variance components by modeling an individual firm's future cash flow as a distribution around the firm's average over time. In turn, this firm average is distributed around the industry mean. Ultimately, the industry means are distributed around the grand mean for the entire sample. Operationally, the HLM approach uses an empirical Bayes method to estimate within-industry coefficients, generalized least squares to estimate between-industry coefficients, and a maximum likelihood-expected maximization algorithm to estimate variance and covariance components (for a marketing application of this methodology, see Brown 1999).
In addition to the HLM methodology, we estimate the growth (Models 3a-3c) and variability (Models 4a-4c) models using a random coefficients formulation to test the robustness of our results with respect to the estimation method that we use. We present these results in the same tables as the HLM results.
Data
To estimate the empirical models presented in the previous section, we used ACSI data for the years 1994 through 2002, which the National Quality Research Center at the University of Michigan Business School made available. Fornell and colleagues (1996) provide extensive details on the methodology used to collect the information contained in the ACSI database. These data were complemented with COMPUSTAT data from 1994 to 2003 to estimate the impact of customer satisfaction on cash flow growth and cash flow variability. Specifically, net cash flow from operations (COMPUSTAT Data Item #308) and net income before extraordinary items (COMPUSTAT Data Item #18 [i.e., earnings]) were collected for the periods and firms in our sample.
Market share data were collected for all the companies in the data set for the entire period being analyzed. We also used the COMPUSTAT database to compute firm-level advertising and R&D intensities. As we noted previously, deviations from the industry mean were calculated as a ratio and used in the firm-level portion of the models (Equations 3b and 4b). We used the business segment portion of the COMPUSTAT database to compute the firm-level diversification variable (Segmentsij), which is defined as the number of "unique business a specific company" operates in, and each business segment is "identified with a unique NAICS code" (Standard & Poor's 2003, pp. 14-16).5
Finally, we examined 10-K filings with the Securities and Exchange Commission for all publicly traded companies in the data set for every year to determine the total number of brands offered by each firm (Brandsij). We cross-validated this measure for a sample of 15 firms using the database "Brands and Their Companies." The correlation between these two measures of the number of brands (r = .89) is considered acceptably high (James, Demaree, and Wolf 1984).
Because the five firm-level controls (i.e., market share, advertising, R&D, number of brands, and number of segments) do not vary significantly over time, they are included as firm-level averages and therefore are time invariant. This enables us to calculate directly the percentage of variance accounted for by this set of firm-level controls.( n6)
We used the COMPUSTAT database to compute industry average demand growth and demand instability, using five-year average sales growth for demand growth and the associated standard deviation for demand instability (Finkelstein and Boyd 1998). Finally, we used the market share data collected at the firm level to compute the HHI of market concentration.( n7) We used these data items at the industry-level portion of our formulation (Equations 3c and 4c).
Because cash flows are strongly correlated with firm size, normalization of the data was required. In line with prior studies on the prediction of future cash flows (e.g., Cipriano, Collins, and Revsine 2000; Sloan 1996), we used the firm's total assets to standardize cash flows. We standardized the earnings data in the same way.( n8) Because ACSI is already an index (Fornell et al. 1996), no normalization was required.
To estimate Equations 4a-4c, we computed firm-level coefficients of cash flow variability on a yearly basis using quarterly cash flow data. In line with the method that Rajgopal and Shevlin (2002) describe, we compared variability of a firm's cash flow during a given year with the variability of cash flows of the broader market. In this case, we used the Wilshire 5000 Index as the proxy for the broader market. A coefficient of variability equal to one indicates that the firm's cash flows are as variable as those of the overall market. Likewise, a coefficient of variability greater than (less than) one indicates that the firm's cash flows are more (less) variable than are those of the market. Because the resultant relative coefficients of variability exhibited skewness, we used a logarithm transformation to normalize the data. This transformation has implications for the interpretation of the parameters we obtained from estimating Equations 4a-4c because they are semilog models.
The overlap between the ACSI and COMPUSTAT data sets resulted in 840 firm-year observations in 23 industries over the eight-year period under analysis.( n9) Tables 1 and 2 summarize descriptive statistics for the two data sets. Table 3 lists the 23 industries, the total number of firm years included in the analyses, the industry's average satisfaction score, and the net operational cash flows in millions of dollars.
Results
Tables 4 and 5 summarize our analyses. For both models, each column reports increasingly complex formulations beginning with the finance formulation, which includes the current level of cash flow (or variability) and current level of earnings (Equation 1). We then added the current level of customer satisfaction to the model (Equations 3a and 4a). We continued by adding firm covariates (Equations 3b and 4b) and industry covariates (Equations 3c and 4c). For multimethod comparison purposes, the last column contains estimates we obtained using the equivalent random coefficients model formulation. We report variance partitioning and fit measures at the bottom of both tables. In addition, Table 6 reports the main effects of industry covariates on the cash flow process by current cash flows (cash flow variability) and earnings on future cash flow (future cash flow variability).
Overall, there is a positive, significant association between customer satisfaction and cash flow growth. In addition, there is a negative, significant impact of satisfaction on future cash flow variability.( n10) These results indicate that higher levels of customer satisfaction contribute to the creation of shareholder value. Next, we discuss in detail how satisfaction influences future cash flow growth and future cash flow variability.
Main effects. The financial model (Equation 1) estimations indicate that 47% of the variance in future cash flow is attributable to industry characteristics, 47% is attributable to firm differences, and 5% is attributable to effects over time. These percentages change as we add customer satisfaction, firm covariates, and industry controls: approximately 12% of total variance is attributable to effects over time, 53% is attributable to firm differences, and 35% is attributable to industry differences (column 5 of Table 4). The likelihood function and the Akaike information criteria (AIC) improve with the addition of the firm covariates. The addition of industry covariates improves the likelihood function but not the AIC because of the large number of additional parameters that are estimated.
Overall, firm differences explain the majority of the variance in the dependent variable, suggesting that these differences (presumably a consequence of management decisions) are the most important determinants of future cash flows. However, a large percentage of variance accounted for at the industry level (35%) indicates that a manager's ability to influence cash flow growth may be bound by structural characteristics of the industry in which the firm competes.
Closer inspection of the full model estimates (Equation 3c) indicates that the current cash flows and current customer satisfaction (at time t) jointly determine future cash flows (at time t + 1). A 49% carryover of current cash flow to future cash flow is consistent with results of prior studies of future cash flows (e.g., Finger 1994). Surprisingly, current earnings do not have a significant impact on future cash flows. These estimates differ from existing accounting and finance research (Dechow, Kothari, and Watts 1998) and suggest that the accruals process for the firms in our data set (i.e., consumer products firms in the Fortune 500) is different from other studies that included smaller public companies and those from firms that primarily serve business-to-business customers.
The positive and significant estimate for π3 (.00101) confirms that customer satisfaction is a substantive determinant of future cash flow growth. The apparently small size of the estimate must be interpreted with the knowledge that we standardized cash flows by asset size. This estimate is indicative of the large influence of customer satisfaction in creating shareholder value. The economic interpretation of this estimate suggests that a one-point increase in customer satisfaction generates an additional $1.01 in net operating cash flows in the following year for every $1,000 in assets. To put this amount in perspective, consider that the average firm in our data set has $54 billion in assets. A one-point increase in customer satisfaction translates into an increase in future cash flows of $55 million, a substantial figure by any measure.
Firm covariates. The parameter estimates for the firm-specific covariates suggests that market share, advertising intensity, and the number of brands moderate the strength of the relationship between customer satisfaction and future cash flows and change the baseline cash flows (by the main effect on π0). Firms exhibiting higher market shares, above (industry) average advertising intensities, or larger brand portfolios also exhibit lower baseline cash flows per assets, perhaps because of the increased expenses associated with such firm characteristics. However, higher market share and advertising intensity increase the positive association between customer satisfaction and future cash flow growth. In contrast, firms possessing larger brand portfolios decrease the efficacy of converting satisfied customers into future cash flows. The intensity of R&D and diversification do not have any significant effect on the future cash flow growth, either directly or through interaction effects.
Industry covariates. Analysis of the industry-level estimates indicates that market concentration and demand instability do not influence future cash flow growth. However, industry demand growth increases the baseline future cash flows but reduces the strength of the association between satisfaction and cash flow growth. Table 6 reveals a similar pattern for the moderated effect of demand growth on future cash flows from current cash flows but no significant effect from current earnings. The last column in Table 4 summarizes estimates we obtained by running the equivalent full model formulation using a random coefficients formulation. Overall, these estimates are comparable to those obtained using HLM, and they confirm the robustness of the results to different estimation methods.
Main effects. The financial model (Equation 1) indicates that 75% of the variance in future cash flow variability (coefficient of variation) is attributable to industry differences. With the sequential addition of covariates, this percentage decreases to 54% for the full model (Equation 4c). These results strongly suggest that structural characteristics (i.e., industry differences) are important determinants of cash flow variability. Firm differences account for 33% of the variance in cash flow variability, whereas temporal trends explain 13% of such variance. Finally, the likelihood function and AIC improve with the addition of the firm and industry covariates.
Analysis of the main effects (Equation 4c) indicates the existence of a small carryover effect from year to year (10%) that is consistent with prior research (Ismail and Choi 1996). In addition, current earnings have no direct influence on future cash flow variability. This is also consistent with prior research (Hertzel and Rees 1998). The estimate for π3 indicates that customer satisfaction reduces the coefficient of variation associated with a firm's cash flows by more than .04. Because of the log transformation of the dependent variable, this result suggests that a one-point increase in customer satisfaction reduces the variability of a firm's cash flows by more than 4%. This is an important result because it indicates that firms can rely on satisfied customers to generate more stable cash flow patterns, which has obvious consequences on decreasing the cost of capital for the firm (Bae and Jo 2002).
Firm covariates. Inspection of the time invariant firm controls indicates that as market share increases, baseline future cash flow variability decreases. In addition, increased market share strengthens the effect of customer satisfaction in reducing future cash flow variability. Conversely, advertising expenditures increase baseline cash flow variability, yet they have no significant effect on the strength of the satisfaction-variability association. Finally, R&D expense, the number of brands, and the number of segments do not significantly affect, directly or indirectly, the association between satisfaction and cash flow variability.
Industry covariates. Increased market concentration decreases the baseline cash flow variability and strengthens the effect of satisfaction in reducing such variability (i.e., cash flow will be less variable). However, demand patterns (i.e., growth and instability) have no significant impact on the mentioned associations. Finally, it is important to note that these results are robust to methodology choice because the overall nature and magnitude of the associations between the variables remains almost unchanged when estimated by the random coefficients methodology (column 6 in Table 5).
Because industry characteristics account for a sizable percentage of the variance in both cash flow growth (35%) and variability (54%), we examined how other structural differences may influence the relationships. The HLM methodology is particularly apt for handling this task. We obtained firm-and industry-level empirical Bayes residuals for the π3 estimates for both models. We then used these to compute industry averages of π3, which we report in Figures 1 and 2. These figures reveal interesting patterns in the associations between satisfaction and cash flow growth and variability.
Figure 1 summarizes differences in firm-level estimates for the association between customer satisfaction and future cash flows. The influence of customer satisfaction on cash flow growth is greatest for low-involvement, routinized, and frequently purchased products (e.g., supermarkets, beer, fast food) and smallest for big-ticket, high-involvement, and less frequently purchased products (e.g., property insurance, household appliances). In addition, these results indicate that higher levels of customer satisfaction seem to have a negative effect on cash flow growth in two industries: long-distance telephone service and automobiles.
Figure 2 summarizes the cash flow variability analyses. Overall, customer satisfaction reduces the variability of future cash flows because all industry average estimates and the vast majority of firm-level estimates are less than 100%. However, when comparing industry averages, we observe that customer satisfaction can be relatively more or less effective in reducing the variability of future cash flows for products in the same category (e.g., life insurance versus personal property insurance). Note that customer satisfaction has a beneficial effect on reducing future cash flow variation in the fashion-driven apparel and athletic shoe industries.
To assess the predictive ability of the cash flow growth and variability models, we created two different types of holdout samples from our data. One consisted of the last three years of data and the other consisted of a random sample of 15% of the firms across all years. For each type of holdout sample, we estimated each of the models using the remaining data. We then predicted Cash flow(t + 1)ij and CF variability(t + 1)ij for all holdout observations and compared these results with the actual observations. We computed the mean absolute percentage error for each model formulation and type of holdout sample. The results appear in Table 7. Overall, Table 7 reveals two important conclusions: First, the addition of customer satisfaction increases the predictive accuracy of the model whether we use a time-based or randomly selected holdout sample. Second, our results are robust because they do not depend on a specific time period or set of firms.
A central argument of this study is that satisfaction positively influences consumer behavior such that it increases the value of the firm to shareholders. In the previously discussed models, we focused on the impact of customer satisfaction on the nature of future cash flows. If the positive effects of customer satisfaction on shareholder value are generalizable, we should find similar effects using alternative measures of firm value and return volatility. We tested this conjecture using Tobin's q, price-to-book ratio, and stock price as alternative measures of firm value. We also examined whether customer satisfaction influences the stability of returns for a firm's stock as represented by its beta coefficient. We report these analyses in Table 8.
The results confirm our finding that customer satisfaction has a positive, significant impact on the future value of the firm, regardless of the measure of firm value we use. In addition, customer satisfaction significantly reduces the volatility of a firm's returns compared with that of the overall market. Therefore, we conclude that the positive influence of current satisfaction on future firm value does not depend on the measure of shareholder value or return volatility used, which further confirms the robustness of our results. Finally, to control for possible model misspecification and omitted variables, we ran first differences growth and variability equivalent specifications. These estimates also replicate our results.
Discussion
Our study shows that customer satisfaction increases future cash flows and reduces their variability. We show that these results are robust under different estimation methods (HLM versus random coefficients) and under various measures of firm value (Tobin's q, market-to-book, stock price) or return volatility (beta). The positive effects of customer satisfaction on future cash flows are both statistically significant and managerially relevant. For the average firm in our sample, a one-point increase in customer satisfaction translates into a $55 million increase in net operating cash flow in the next year. That same one-point increase in customer satisfaction results in a reduction in the variance of future cash flows of more than 4%. Such outcomes boost the value of a firm to its shareholders.
Equally important are the findings that firm and industry differences moderate the relationships between the characteristics of future cash flows (growth and stability) and customer satisfaction. Firms commanding higher market shares are more efficient in converting customer satisfaction into future cash flow growth and reduced variability. Larger advertising expenditures enable firms to translate satisfaction into future cash flows more efficiently, but they do not seem to increase cash flow stability. Notably, the larger the brand portfolio, the less efficient are firms in increasing their cash flows. At the industry level, firms facing overall industry demand growth seem to be less efficient in using satisfaction to increase future cash flows, whereas firms operating in more concentrated industries can convert satisfaction into reduced cash flow variability more effectively.
In addition to the preceding moderating effects on the satisfaction-cash flow association, Figures 1 and 2 reveal additional industry-level effects. It appears that the nature of the product itself can have an important influence on the impact of customer satisfaction on future cash flows. Comparing the results for the durable and nondurable industry sectors, we observe that, in general, satisfaction has a greater positive effect on the cash flow growth and stability of most nondurables. This makes sense when the comparatively short interpurchase cycle of nondurable products is considered. Some of these products are purchased seasonally (e.g., apparel, athletic shoes), and others may be purchased weekly (e.g., food, beverages, cigarettes). Firms selling in a market with a short interpurchase cycle have more opportunities in a given period to reap the benefits from satisfying a customer. For the fashion-oriented categories of apparel and athletic shoes, high levels of satisfaction keep consumers from switching to other brands, which boosts cash flow growth and provides stability in traditionally volatile markets. For products such as computers, automobiles, and appliances, firms may need to wait from 18 months to many years for the opportunity to reap the rewards of satisfaction during the next buying cycle.
The widely varying experiences of firms in the services sector are partly the result of differences in the nature of the services. It might be expected that services with more of a relationship-like nature, such as local or long-distance telephone service, would benefit greatly from customer satisfaction. However, firms in these industries have worked so hard to reduce switching costs that customers can pursue the best combination of price and quality with little effort. This result is reflected in the corresponding detrimental effects on the future cash flows of firms in these industries. Paradoxically, customer satisfaction seems to transform the nature of cash flows for services that are traditionally considered transaction oriented (hotels and airlines) into those that are consistent with a relationship-type service. Hotels and airlines with high levels of customer service benefit from increased loyalty and a reduced focus on price that is usually associated with services that require a long-term commitment.
The preceding discussion suggests that there are tradeoffs between cash flow growth and cash flow variability. Such insights can be useful in guiding CFOs and marketing managers in determining which goal might be more efficient in the creation of shareholder value, thus completing the links of the marketing productivity chain (Rust et al. 2004). Customer satisfaction has a significant impact on building shareholder value because of its influence on cash flow growth and stability. Our results show that these benefits are based on the context in which the firm competes and how the firm chooses to compete within that context.
Limitations and Further Research
By using the ACSI database, our results are limited to the sample of large consumer product and service firms that are surveyed in this program. We expect that the same marketplace advantages documented in other research on the effects of customer satisfaction on cross-selling, positive word of mouth, price sensitivity, and so forth, hold for smaller firms serving consumers as well as firms primarily serving business-to-business customers. However, we show that context effects have a strong influence on the linkages between customer satisfaction and future cash flows. Thus, there is a need for more research on this topic using a broader range of industries and settings.
In addition, our study did not analyze all sources of shareholder value (Srivastava, Shrevani, and Fahey 1998). For example, we did not ascertain whether customer satisfaction results in a larger base of customers, which can boost the residual value of a firm to shareholders. This is an important issue in the proper valuation of firms whose primary market-based assets reside in enduring customer relationships. Furthermore, we restricted our study to a measure of the market-based asset of the firm's relationship with its customers. The impact of the quality of a firm's relationships with its partners, including suppliers, distributors, and complementors (e.g., Intel, Microsoft), on the nature of future cash flows remains an important area for further research. This stream of research is valuable for the marketing discipline because it strengthens the marketing-finance interface and contributes to marketing's voice at the higher levels of the corporation.
The authors thank the Lloyd J. and Thelma W. Palmer Research Fellowship for financial support and the National Quality Research Center at the University of Michigan for the American Customer Satisfaction Index data. This work has benefited substantially from the comments of Sri Deepak, Don Lehmann, Sanal Mazvencheryl, Neil Morgan, Doug Vorhies, and participants in seminars at the University of Illinois-Urbana and at the MSI conference Linking Marketing to Financial Performance (Dallas). The authors gratefully acknowledge the three anonymous JM reviewers for their insights and constructive suggestions.
( n1) Srivastava, Shervani, and Fahey (1998) include acceleration as a characteristic of cash flows that contributes to shareholder value. Although we found that customer satisfaction significantly influences cash flow acceleration, this result can be inferred from the cash flow growth model, and in the interest of conserving space, we do not reported it.
( n2) Beginning with a random walk model of sales, Dechow, Kothari, and Watts (1998) explore the relationships among future cash flow, current earnings, and current cash flow as governed by the accounting accrual process.
( n3) This is consistent with our preceding discussion on the effect of satisfaction on future consumer behavior.
( n4) Note that these firm-level strategic differences do not have a time subscript, because for the period under analysis, they are modeled to be time invariant. We provide additional details on this procedure in the "Data" section.
( n5) The acronym NAICS stands for North American Industry Classification System, which replaces the Standard Industrial Classification.
( n6) Note that the results remain the same when we use time-varying data. However, the ability to measure directly the portion of variance explained in cash flow growth and variability motivated our use of the time invariant data.
( n7) The HHI is the best measure of market concentration (Curry and George 1983).
( n8) The results and significance did not change in models that used unstandardized data. However, the interpretations of the coefficients are different.
( n9) One year of data is lost to compute the cash flow variability analysis, resulting in 735 data points over seven years. We also estimated the cash flow growth model using this trimmed data set, and the substantive results remain consistent.
( n10) Despite the strength of some of the associations between variables in our data set (e.g., lagged terms), autocorrelation is not a significant problem based on the traditional Durbin Watson test and the more robust modified Box-Pierce Q statistic (Lobato, Nankervis, and Savin 2001).
Legend for Chart:
B - N
C - Mean
D - Standard Deviation
E - Q1
F - Median
G - Q3
A B C D
E F G
Time Variant Firm Level
Cash flowt+1 1694 1306.87 2952.19
67.70 334.11 1134.26
Cash flowt 1593 1300.59 2949.65
60.47 331.52 1131.96
Earningst 1322 4733.03 8192.83
894.75 2041.60 4820.59
Satisfactiont 928 76.37 6.17
71.89 76.83 81.17
Valid N (listwise) 840
ln (Coefficient
variationt+1) 764 -.51 .98
-1.11 -.57 .05
ln (Coefficient
variationt) 778 -.50 .98
-1.10 -.58 .06
Satisfactiont 928 76.37 6.17
71.89 76.83 81.17
Valid N (listwise) 735
Time Invariant Firm Level
Market share 143 9.36 15.70
.61 4.81 9.29
Advertising intensity 167 .82 .77
.49 .77 .93
R&D intensity 126 .99 .92
.60 .92 1.11
Number of brands 129 15.28 12.00
4.00 12.00 15.28
Number of segments 124 2.81 2.81
2.00 2.81 3.00
Valid N (listwise) 105
Industry Level
HHI 23 .05 .07
.01 .03 .05
Demand growth 23 18.01 7.57
11.50 17.99 24.40
Demand instability 23 2.21 .34
1.97 2.21 2.43
Valid N (listwise) 23 Legend for Chart:
B - CFt+1
C - CFt
D - CVt+1
E - CVt
F - Earnt
G - Satt
H - MSt
I - Advt
J - RDt
K - Brndt
L - Segst
M - HHIt
N - Growt
O - Instbt
A B C D E
F G H I
J K L M
N O
Cash flow[sub t + 1 1.000
Cash flowt .747 1.000
ln (Coefficient
variationt + 1) .050 .035 1.000
ln (Coefficient
variationt) .007 .051 .596 1.000
Earningst .114 .143 .053 .039
1.000
Satisfactiont .399 .369 -.174 -.108
.165 1.000
Market share .007 .007 -.073 -.078
.089 .084 1.000
Advertising
intensity -.055 -.051 .053 .059
-.159 .163 -.145 1.000
R&D intensity -.011 -.011 -.004 .003
-.006 -.017 .086 .106
1.000
Number of brands .054 .053 .007 .009
.158 .110 .082 -.001
-.018 1.000
Number of segments .043 .040 -.059 -.052
-.183 .025 .149 .093
.048 .095 1.000
HHI .059 .058 -.023 -.024
-.022 .228 .354 -.031
-.126 .105 .126 1.000
Demand growth -.084 -.080 -.011 -.012
-.244 -.367 -.258 -.046
-.014 -.133 .035 -.423
1.000
Demand instability -.117 -.112 .021 .002
-.356 -.398 -.216 .030
.113 -.170 .194 -.246
.676 1.000 Legend for Chart:
A - Sector
B - Industry
C - Observations: Firm Year
D - Satisfaction: Average Over Time and Firms (100 index)
E - Yearly Cash Flows: Average Over Time and Firms (in Millions
of Dollars)
A B C D E
Nondurables Apparel 29 79 1,284
Athletic shoes 16 77 1,542
Beer 18 81 1,973
Cigarettes 16 77 1,153
Food processing 89 83 1,542
Personal care 30 83 1,749
products
Soft drinks 24 85 2,245
Durables Automobiles 73 80 1,029
Consumer electronics 40 81 495
Household appliances 24 83 1,175
Personal computers 30 73 1,878
and printers
Services Airlines 45 67 1,687
Hotels 23 73 1,340
Local telephone 40 73 1,999
service
Long-distance 22 76 1,765
telephone service
Parcel delivery/ 16 79 1,829
express mail
Utilities 127 74 885
Retail Department and 56 74 1,030
discount stores
Fast food, pizza, 31 69 1,750
carryout
Supermarkets 53 75 1,671
Financial Banks 16 69 1,342
Life insurance 11 75 99
Personal property 11 76 392
Insurance
Overall average 76 1,306 Legend for Chart:
B - Equation 1 (Financial)
C - Equation 3a (Satisfaction)
D - Equation 3b (Firm)
E - Equation 3c (Industry)
F - Random Coefficients
A B C
D E F
π0(Intercept) .05295 -.07084
p-value .00 .00
-.03960 .01586 -.01958
.27 .36 .42
π1(Cash flow(t)ij) .55976 .58389
p-value .00 .00
.60579 .49883 .53407
.00 .00 .05
π2(Earnings(t)ij) .00210 .00167
p-value .35 .40
.00267 .02169 .01666
.40 .18 .21
π3(Satisfaction(t)ij) .00155
p-value .00
.00111 .00101 .00124
.00 .02 .03
β01(Market shareij)
p-value (Main effect on intercept)
-.00238 -.00121 -.00193
.04 .04 .02
β02(Advertising
intensityij)
p-value
-.99212 -.61262 -.84102
.02 .03 .06
β03(R&D
intensityij)
p-value
.815871 .42279 .20082
.21 .15 .19
β04(Number
of brandsij)
p-value
-.00067 -.00055 -.00070
.05 .06 .04
β05(Number
of segmentsij)
p-value
.00401 -.01289 .01476
.78 .67 .68
β31(Market
shareij)
p-value (Interaction with satisfaction)
.00004 .00002 .00001
.01 .06 .10
β32(Advertising
intensityij)
p-value
.01017 .00451 .00278
.03 .05 .04
β33(R&D
intensityij)
p-value
-.05434 -.06534 -.04617
.17 .19 .29
β34(Number
of brandsij)
p-value
-.00002 -.00001 -.00012
.05 .06 .02
β35(Number
of segmentsij)
p-value
-.00079 .00045 .00016
.48 .72 .74
γ001(HHIj)
p-value (Main effect on intercept)
.16421 .55227
.80 .75
γ002(Demand
growthj)
p-value
.02512 -.02605
.09 .14
γ003(Demand
instabilityj)
p-value
-.04081 -.04663
.42 .61
γ301(HHIj)
p-value (Interaction with satisfaction)
-.00422 .01924
.59 .21
γ302(Demand
growthj)
p-value
-.00036 -.00051
.07 .06
γ303(demand
instabilityj)
p-value
.00032 .00016
.91 .86
Variance Partitioning
Across industry(%) 47.26 34.11
33.89 34.98 31.90
Across firms/within industry(%) 47.48 6.35
57.48 52.73 58.40
Within firms/over time(%) 5.25 5.55
8.62 12.35 9.71
Likelihood Function -488.34 -449.88
-407.21 -403.77 --
AIC 1008.68 948.91
943.10 952.33
Notes: Variance partitioning percentages may not add up to 100%
because of rounding. Legend for Chart:
B - Equation 1 (Financial)
C - Equation 4a (Satisfaction)
D - Equation 4b (Firm)
E - Equation 4c (Industry)
F - Random Coefficients
A B C
D E F
π0(Intercept) .73645 .68751
p-value .00 .05
.79978 .86091 .46059
.02 .01 .03
π1(CV[Cash
Flow](t)ij) -.03311 .03858
p-value .05 .05
.02801 .10302 .25021
.07 .03 .04
π2(Earnings(t)ij) -.50748 .18721
p-value .86 .72
-.08085 .28629 .17865
.73 .66 .69
π3(Satisfaction(t)ij) -.06041
p-value .03
-.07455 -.04129 -.08274
.04 .03 .01
β01(Market shareij)
p-value (Main effect on intercept)
-.14396 -.11495 -.04601
.06 .04 .12
β02(Advertising
intensityij)
p-value
.22438 .21037 .14082
.13 .07 .08
β03(R&D
intensityij)
p-value
-.45160 -.42977 -.15101
.26 .19 .47
β04(Number
of brandsij)
p-value
.04508 .03461 .03964
.70 .72 .68
05(Number of segmentsij)
p-value
-.60191 -.56964 -.35931
.39 .29 .55
β31(Market shareij)
p-value (Interaction with satisfaction)
-.00209 -.00717 -.01384
.04 .03 .00
β32(Advertising
intensityij)
p-value
-.83443 -.70900 -.55498
.48 .33 .37
β33(R&D
intensityij)
p-value
.54224 .23155 .29955
.58 .49 .42
β34(Number
of brandsij)
p-value
-.00061 -.00046 -.00010
.70 .77 .81
β35(Number
of segmentsij)
p-value
.02650 .03412 .01822
.08 .09 .25
γ001(HHIj)
p-value (Main effect on intercept)
-.52307 -.23902
.08 .20
γ002(Demand
growthj)
p-value
.63000 .87020
.59 .21
γ003(Demand
instabilityj)
p-value
.01335 -.00936
.51 .49
γ301(HHI[sub j)
p-value (Interaction with satisfaction)
-.06997 -.09598
.06 .03
γ302(Demand
growthj)
p-value
.00911 .01282
.57 .40
γ303(Demand
instabilityj)
p-value
-.18221 .35164
.39 .22
Variance Partitioning
Across industry(%) 74.96 67.45
6.79 53.92 48.78
Across firms/within industry(%) 18.03 25.64
31.03 33.41 39.91
Within firms/over time(%) 7.01 6.88
8.20 12.67 11.30
Likelihood Function -892.20 -869.81
-824.44 -815.19 --
AIC 1816.40 1790.99
1783.76 1782.53
Notes: Variance partitioning percentages may not add up to 100%
because of rounding. Legend for Chart:
A - Growth Model
B - Interaction Cash Flow(t) ij
C - Interaction Earnings(t) ij
A B C
γ101(HHIj) -.23823 .23037
p-value .66 .17
γ102(Demand growthj) -.03514 .00160
p-value .02 .40
γ103(Demand
instabilityj) .43928 -.02843
p-value .09 .61
Legend for Chart:
A - Variability Model
B - CF Variability(t)ij
C - Earnings(t)ij
A B C
γ201(HHIj) .37573 .50691
p-value .07 .06
γ202(Demand growthj) -.00234 -.09290
p-value .31 .51
γ203(Demand
instabilityj) -.11140 .43732
p-value .72 .19 Legend for Chart:
B - Mean Absolute Percentage Error Growth Model Three-Year
Holdout(%)
C - Mean Absolute Percentage Error Growth Model 15% Holdout(%)
D - Mean Absolute Percentage Error Variability Model Three-Year
Holdout(%)
E - Mean Absolute Percentage Error Variability Model 15%
Holdout(%)
A B C D E
Intercepts model 29.31 27.24 82.47 79.78
Financial model 25.58 21.48 44.59 43.56
Customer satisfaction model 19.33 16.31 37.74 34.81
Firm covariates model 18.14 14.57 35.99 32.12
Industry covariates model 17.09 15.96 31.03 29.02 Legend for Chart:
B - Q(t +1)
C - PB(t +1)
D - Price(t +1)
E - beta(t +1)
A B C D
E
Log-likelihood
Intercepts only -632.96 -1306.65 -1753.79
-195.33
Financial model -548.33 -1268.84 -1683.43
-40.05
Satisfaction model -531.84 -1250.59 -1674.22
-31.74
AIC
Intercepts only 1273.91 2621.30 3515.59
398.66
Financial model 1114.66 2555.67 3384.87
98.10
Satisfaction model 1095.68 2533.18 3380.45
95.49
Estimates
Intercept .14599 -5.39429 -41.22636
.68570
t-value 2.432 -8.332 -6.254
4.147
Lagged dependent(t) .66926 .70070 .59095
.66601
t-value 3.427 4.277 4.545
2.032
Satisfaction(t) .00585 .08671 .74897
-.00703
t-value 3.410 3.884 4.153
-2.024
Notes: Estimates are for the satisfaction model (Equations
3a and 4a).GRAPH: FIGURE 1 Industry Differences and Cash Flow Growth
GRAPH: FIGURE 2 Industry Differences: Cash Flow Variability
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Cadogan, John W., Sanna Sundqvist, Risto T. Salminen, and Kaisu Puumalainen (2002), "Market-Oriented Behavior: Comparing Service with Product Exporters," European Journal of Marketing, 36 (9-10), 1076-1102.
Capon, Noel, John U. Farley, and Scott Hoenig (1990), "Determinants of Financial Performance: A Meta-Analysis," Management Science, 36 (10), 1143-59.
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Curry, B. and K.D. George (1983), "Industrial Concentration: A Survey," Journal of Industrial Economics, 31 (3), 203-255.
Day, George and Liam Fahey (1988), "Valuing Market Strategies," Journal of Marketing, 52 (July), 45-57.
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~~~~~~~~
By Thomas S. Gruca and Lopo L. Rego
Thomas S. Gruca is an associate professor, Tippie College of Business, University of Iowa.
Lopo L. Rego is an assistant professor, Tippie College of Business, University of Iowa.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 47- Customer Strategy: Observations from the Trenches. By: Rogers, Martha. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p262-263. 2p. DOI: 10.1509/jmkg.2005.69.4.262.
- Database:
- Business Source Complete
Customer Strategy: Observations from the Trenches
This short article traces the evolution of customer relationship management from the perspective of an author who has written, researched, and worked in the field since the beginning of the relationship revolution in the early 1990s. Additional topics for research are suggested, especially in the area of understanding how companies can increase enterprise value by managing the rate of increase in customer equity.
This issue of Journal of Marketing is a landmark. It is an issue with a group of articles on one topic: customer strategy and building customer value. It has been a long time coming. I met my coauthor and partner, Don Peppers, in 1990, and after five minutes, we decided to write a book together about how technology would change the way companies conduct business. The first of the books in our "one-to-one" customer strategy series appeared in 1993. The One to One Future proposed a strategic aftermath to mass marketing based on the technological upheaval of the information age.
Since then, customer relationship management (CRM) has become a requisite fixture at many companies around the world. Chief marketing officers now coordinate direct relationships with mass media brand building, and companies have created chief customer officers, chief relationship officers, directors of customer experience, chief privacy officers (who may be marketers or may be lawyers), and even customer value officers. It is also apparent that managing customer relationships, coupled with building the value of the customer base, is no longer a business management fad based on the latest consultantspeak but rather a fruitful new avenue of business competition that has been rendered necessary by permanent innovations in the technological landscape.
In addition to markets, segments, and niches, today's breed of marketing executives engaged in building customer relationships and value must use technology to pay attention to individual customers, one customer at a time. The reward for such efforts can be significant: "If you're my customer and I get you to talk to me and remember what you say, I can know something about you that my competitor does not know and do something for you that my competitor cannot do." Because it is now possible to track and remember interactions with customers, it is competitively essential to do so. It is no longer a question of whether a company can be organized to focus on increasing the value of each current and future customer but rather how soon it can do so and how effectively.
Since The One to One Future in 1993, Don Peppers and I have seen a proliferation of hardware systems and software applications designed to facilitate the management of customer relationships. We have observed technology-facilitated customer initiatives launched by companies in every vertical industry, by organizations both large and small, public and private, profit and nonprofit, some with dizzying success and some with disappointing failure. Companies recognize that customer relationships are the underlying tool for building customer value, and they are finally realizing that growing customer value is the key to increasing enterprise value. Teradata funded the Teradata Center for Customer Relationship Management Research and Learning at the Fuqua School of Business at Duke University in 2000 (www.teradataduke.org), and other centers have also appeared at Rutgers, Yale, and Baylor. Media departments at universities have embraced e-everything; information technology and computer science professors teach database management and its business implications; and business schools have slowly begun to include customer strategy, CRM, one-to-one, and customer value building as a regular part of the curriculum. There is now a customer relationship textbook, there are courses at dozens of campuses, and doctoral students are doing work in the field.
When we wrote The One to One Future in 1993, our ideas were hypothetical. What if…? Good grief, the Internet did not even exist yet. We cited very few academic studies, and none were related to anything CRM-ish. Yet when we put our newest book, Return on Customer, to bed in the spring of 2005, we included 35 single-spaced pages of endnotes and one appendix with five pages of suggestions for further research, and at least half of the references are to the work of academics. Unlike the proprietary studies underway at a feverish rate in companies struggling to be the leaders in their category to understand the winning formula, the academic work is available for all to learn. We have come a long way in only a dozen years. The best academic work in this burgeoning new field is already beginning to appear in "business translation" (professionalspeak for broader consumption).
However, there is still much to be done. There are hundreds of questions that require immediate answers, such as,
• Under what circumstances can traditional or direct marketing actually destroy enterprise value?
• How can the interconnection between a culture of customer trust and the growth of customer value be quantified?
• What do we need to know to build multichannel capabilities?
• How do different touchpoints with customers result in differential equity change?
• How do companies that view privacy as a compliance issue differ in shareholder value from companies that view privacy as a relationship issue?
• How do companies successfully change their organizations, processes, culture, training, metrics, and compensation to increase customer equity?
• What are the demonstrable ingredients for successful management of customer portfolios?
• How can increased switching costs be compatible with customer trust?
• Why has CRM been so frustrating for some companies, and why do some companies succeed in strategy, information management, and adoption when others fail?
• How do we convince Wall Street that an increase in customer equity and growth in shareholder value are intertwined?
Mostly, decision makers must be held accountable today for the impact in the future of the decisions they make today on companies' only true source of revenue: the customers they have today and the customers they will have tomorrow. We need to learn more about the leading indicators of customer value tomorrow (measurable today) and to understand better the strong tie between customer equity and enterprise and shareholder value, and we must learn about how the elements of a relationship are measurable and executable.
There are 6000 "large" companies in the world, tens of thousands of medium-sized companies, and countless small businesses. They all need customer relationship and value expertise. Currently, there are likely only about 1000 truly qualified people who can help these companies, though there are thousands more who will claim this ability. What is known is still inadequate to answer all the questions that arise every day that affect the lives of managers, customers, employees, and shareholders. This is the challenge we face.
Understanding how to increase the value of the customer base, how to measure and manage on the basis of the rate of change of that growth, and how the resulting decisions affect shareholder value is at the forefront of the field of marketing in the early twenty-first century. No matter what the readers of this issue are currently working on, they should consider getting into this game. These concepts must be better understood and better articulated. Learning must be used to make better decisions, to bolster the success of companies, and to bring together marketing and finance in an unprecedented way. The authors of the CRM articles in this issue are among the pioneers, the academics who are asking and testing the earliest questions. We all need the answers.
REFERENCES Peppers, Don and Martha Rogers (1993), The One to One Future. New York: Currency Doubleday.
----- and ----- (2005), Return on Customer: Creating Maximum Value from Your Scarcest Resource. New York: Currency Doubleday.
~~~~~~~~
By Martha Rogers
Martha Rogers is Founding Partner of Peppers & Rogers Group, a Carlson Marketing Group company, and Adjunct Professor, Fuqua School of Business, Duke University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 48- Decision Making and Coping of Functionally Illiterate Consumers and Some Implications for Marketing Management. By: Viswanathan, Madhubalan; Rosa, José Antonio; Harris, James Edwin. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p15-31. 17p. 2 Diagrams, 3 Charts. DOI: 10.1509/jmkg.69.1.15.55507.
- Database:
- Business Source Complete
Decision Making and Coping of Functionally Illiterate
Consumers and Some Implications for Marketing Management
A study of the decision making and coping of functionally illiterate consumers reveals cognitive predilections, decision heuristics and trade-offs, and coping behaviors that distinguish them from literate consumers. English-as-a-second-language and poor, literate consumers are used as comparison groups. The strong predilection for concrete reasoning and overreliance on pictographic information of functionally illiterate consumers suggest that companies should reconsider how they highlight the added benefits of new products or the differentiating aspects of existing product offerings across channels such as advertising, in-store displays, and positioning. Concrete reasoning also has strong implications for the execution and presentation of price promotions through coupons and in-store discounts, because many consumers are unable to process the information and thus avoid discounted products. Finally, the elaborate coping mechanisms identified and the loyalty that functionally illiterate consumers display toward companies that are sensitive to their literacy and numeracy deficiencies reveal a potential for loyalty programs aimed at this population that do not involve price discounts.
To be functionally literate, people must have the language and numeracy competencies required to function adequately as adults in day-to-day life (e.g., Kirsch and Guthrie 1977). Because of significant differences in what adult life demands across economies and cultures, researchers have argued for a plurality of literacies, and they emphasize that the skills needed to grasp written and verbal meaning successfully are dependent on context (Scribner and Cole 1981). For example, the language and numeracy skills required of consumers in Central American rural villages are different from those required of consumers in New York City. Moreover, it is possible that New York consumers are less functionally literate in rural shopping contexts than are Central American villagers on Fifth Avenue. However, in economies in which the typical consumer experience involves choosing among differentially priced offerings at self-service displays, the literacy and numeracy competencies required are relatively clear-cut. They include the ability to read labels for information that differentiates product offerings, to navigate complex shopping environments by using signage, to calculate or estimate unit prices as a way to ascertain value, and to keep a running total to avoid being short on funds at the checkout counter. The functional literacy demands in most modern economies are substantial, and the absence of such skills has significant implications for consumers and marketers. How do functionally illiterate consumers navigate shopping environments, choose among the products available, assess value, and cope with the outcomes?( n1) Should marketing managers be concerned with these consumers? If they should be, what are the implications of having customers whose literacy and numeracy skills do not match what current marketing practices take for granted?
The question whether marketers should be concerned is addressed by some revealing statistics. In 1992, the National Adult Literacy Survey revealed that between 21 and 23% of U.S. consumers lack many of the rudimentary language and numeracy skills required to navigate the typical retail environment. Moreover, between 46 and 51% of U.S. consumers lack the skills required to master specific aspects of shopping, such as credit applications and sales agreements (Kirsch et al. 1993). Estimates of functional illiteracy are equally sizable for other industrialized countries and even higher for developing countries (UNESCO 2000). Whereas poverty and low educational levels are associated with functional illiteracy, most functionally illiterate consumers have discretionary income and together represent a significant market. In the United States, most functionally illiterate consumers are employed and, on average, have 40% as much purchasing power as literate consumers (e.g., Kirsch, Jungeblut, and Campbell 1992). Taken with government studies (Bureau of Labor Statistics 2003), this suggests that functionally illiterate U.S. consumers may control as much as $380 billion in spending. Moreover, given the high incidence of functional illiteracy in emerging economies, in which standards of living and consumer spending are increasing, the global purchasing power of functionally illiterate consumers is significant and likely to increase.
Research on functional illiteracy spans basic and applied disciplines and covers a wide range of contexts and perspectives. It has been argued that literacy influences societies because literate thought is abstract compared with nonliterate thought, and its absence influences the level at which societies respond to their surroundings (Kintgen, Kroll, and Rose 1988). For example, the widespread use of mnemonic and word-syllable communication systems has been credited with new modes of logical thinking and context-independent abstraction (Goody and Watt 1968; Greenfield 1972). In contrast, the oral communication on which low-literate consumers rely is predominantly pictorial, dependent on context, and detrimental to abstraction (Havelock 1963; Luria 1976). Despite what is known about how functional illiteracy influences other domains of human experience, the understanding of its influence on consumers is limited. There has been relatively little research into marketing to functionally illiterate consumers, despite the segment's size and purchasing power (Wallendorf 2001). Therefore, the attaining of a more detailed understanding of how functionally illiterate consumers think and behave can help marketing research and practice better meet the needs and demands of all consumers.
A study of functionally illiterate consumers can take many directions, but we focus on how functionally illiterate consumers navigate shopping environments, assess value, make decisions, and cope with the outcomes of their decisions. We find that functionally illiterate consumers exhibit cognitive predilections, decision heuristics and trade-offs, and coping behaviors. We also find that functionally illiterate consumers struggle considerably with elements of the shopping environment (e.g., product labels, store signs, prices) that most consumers take for granted, and they spend considerable energy and cognitive resources assessing value and making decisions from information that literate consumers process tacitly and automatically. Moreover, functionally illiterate consumers incur distinct emotional and behavioral costs from shopping while displaying ingenuity in coping with such costs. Finally, functionally illiterate consumers respond positively and in sophisticated ways to marketers' efforts to accommodate their needs, and in many cases this leads to consumer loyalty.
We begin with a discussion of our methods. Next, we discuss our findings, highlighting various aspects of functionally illiterate consumers, and compare them with English-as-a-second language (ESL) and poor, literate consumers. We conclude with theoretical and practical implications and limitations of our research.
Research into functionally illiterate consumers poses two challenges. First, illiteracy poses significant hurdles to the use of standard instruments, such as experiments and surveys (Wallendorf 2001). Even if respondents receive assistance, it is difficult to ascertain the measurement error that is likely to enter a standard instrument study of functionally illiterate consumers because of the range of abilities found in the low-literate population (Kirsch, Jungeblut, and Campbell 1992). Second, functional illiteracy can be a source of emotional stress and a topic that many respondents will not discuss openly. Because of the limitations, we adopted interviews and observation for our data collection. Our methods are best characterized as part way along the "continuum ranging from positivism to idealism" (Deshpandé 1983, p. 102) and can be furthered characterized as hermeneutic (Spiggle 1994), in that the breadth of data-gathering methods, the categories used to code the data, and the abstraction and comparison of data elements developed iteratively. Our purposive sampling (Lincoln and Guba 1985) was also iterative because we added ESL and poor, literate consumers to our sample as we learned more about functionally illiterate consumers.
However, ours is not an interpretive study in the classic hermeneutic tradition (e.g., Thompson, Pollio, and Locander 1994). We are not attempting to articulate the culturally informed meanings that functionally illiterate consumers attach to product labels, store signage, price information, or shopping experiences. Our objective is to better understand the processes by which functionally illiterate consumers assimilate information and make decisions and to distinguish them from other types of consumers. Even when we explore their coping mechanisms and their antecedents (e.g., anxiety, negative emotion), which by necessity involves meaning nuances, we tried to remain as close as possible to the labels the informants used. Our research is a "discovery-oriented approach" (Bendapudi and Leone 2002, p. 83), in which methods are adapted to the problems being explored and to the unique characteristics of the population under study. Data collection and analysis occurred in three phases, which we depict in Figure 1. We first describe the informants and then different elements of the methodology.
Informants
The informants were enrolled at adult-education centers in a Midwestern market. They ranged in age from 16 to older than 90 years and were divided into two groups (zero through fourth grade level and fifth through twelfth grade level) on the basis of standardized test scores in math and reading. We provide a list of disguised key informants and their characteristics in Table 1. Details about informant scores are available on request. We added ESL and poor, literate consumers to the study to sharpen our understanding of functionally illiterate consumers and to distinguish the influence of functional illiteracy from related factors such as language proficiency and poverty. We recruited ESL students from classes offered at one of the adult-education centers. Their English skills ranged from second to sixth grade level, and all had one or more university degrees. We interviewed functionally literate, poor adults, whose education ranged from high school to postgraduate studies, at a homeless shelter.
Methodology
We used interviews and observations. At the start of the process, two of the authors attended volunteer tutor training and served as tutors at an adult-education center, one for 150 hours over 18 months and the other for 15 hours over 2 months. The tutoring served a twofold purpose: ( 1) to identify the best approach for gathering data from the respondents and ( 2) to establish the trust required to breach the sensitive subject of illiteracy. All interviews were unstructured, but recurring themes from early phases (e.g., Phases 1 and 2) were interwoven, as appropriate, into later phases (e.g., Phases 2 and 3). Interviews ranged from 20 minutes to 2.5 hours (averaging 1 hour), and most interviews were tape-recorded and transcribed. Observation took place during classroom activities, one-on-one tutoring sessions, and shopping trips designed as learning exercises. Teaching at adult-education centers is adapted to student needs and focuses on everyday-life skills. Notes and conversations were recorded during and immediately after observation sessions and were transcribed and analyzed. The zero through fourth grade reading level students were observed during classroom activities and on two shopping field trips in which shopping tasks were assigned. Students chose to complete shopping tasks either by themselves or in groups.
One-on-one shopping observations of 15 students who had been tutored at the center were conducted during Phase 3 of the study. These informants were asked to complete their typical shopping at a large chain store, and their personal funds were supplemented with $10 gift cards and two coupons. Consumers were observed from a distance and occasionally approached to ask clarification questions. In-depth interviews followed the observations. Data collection extended over 55 months and included interviews with 14 functionally illiterate consumers at the zero through fourth grade level; 21 functionally illiterate consumers at the fifth through twelfth grade level; 9 ESL students; and 10 poor, literate consumers. It also included one-on-one shopping observations, with 4 respondents of the zero through fourth grade level respondents and 11 of the fifth through twelfth grade level, and two shopping field trip observations, each of which involved 10 respondents of the zero through fourth grade level. The observed respondents were among those interviewed.
Table 1 provides information about respondents who are specifically mentioned or quoted in the findings section. Of the 14 zero through fourth grade level respondents, 9 are listed, along with 9 of the 21 respondents of the fifth through twelfth grade level. Table 1 also lists 4 of the 9 ESL respondents and 2 of the 10 poor, literate respondents. The proportion of listed respondents to total respondents, by category, reflects the level of variance in how literate consumers make decisions and deal with their outcomes for each group, not the level of intensity with which we studied the groups. The behavior of zero through twelfth grade level respondents differed dramatically from that of ESL and poor, literate consumers and thus receives more attention in our discussion.
Data Analysis
All authors analyzed interview and observation data independently, focusing attention on statements and behaviors that shed light on how functionally illiterate; ESL; and poor, literate consumers process information and evaluate alternatives, make decisions, and cope with outcomes and the environment. We analyzed data following established guidelines for qualitative inquiry (Glaser and Strauss 1967; McCracken 1988; Strauss and Corbin 1990), by which we identified commonalities and differences among respondents. We resolved all discrepancies through discussion, and several interrelated themes developed iteratively. The themes were further validated by ten university students who were not familiar with the analysis and who were each asked to read a subset of transcripts for insights into how the interviewed consumers process information and evaluate alternatives, make decisions, and cope with situations that arise during shopping. As a group, the student readers identified the same themes that we identified. In addition, postinferential checks were performed by teachers at the adult-education center, who agreed that our findings characterize the functionally illiterate students that they know. Our findings are elaborated in the next section with quotes from the data.
In general, we found that functionally illiterate consumers display cognitive predilections, decision rules and tradeoffs, and coping behaviors distinct from those of literate consumers. The findings are illustrated in Figure 2, arrayed as a hierarchy, with cognitive predilections as basic or foundational for decision heuristics and trade-offs and ultimately for coping strategies. The hierarchical layout is intended to represent what we perceive as differences in conscious complexity, where cognitive predilections are primitive thought mechanisms or approaches that respondents adopt by necessity, but of which few are aware; decision heuristics and emotional trade-offs are implemented deliberately, but are not always based on sound reasoning; and coping strategies are carefully considered and orchestrated. Although our discussion follows the classificatory scheme in Figure 2, some of the quotes serve to illustrate multiple phenomena (e.g., concrete reasoning and decision heuristics). Their concurrent manifestation in the common language conversation of functionally illiterate consumers serves as evidence of the interrelatedness of the hierarchical levels we impose on the data and illustrate in Figure 2.
Cognitive Predilections
Concrete reasoning. Functionally illiterate consumers display a predilection for what we call "concrete reasoning," or the basing of decisions and behaviors on the literal or concrete meaning of single pieces of information (e.g., price, single ingredient content, size) and without regard to the product attributes that are represented by the isolated bits of information. Concrete reasoning was manifest often when consumers struggled with trade-offs. For example, when considering price and size, many functionally illiterate consumers focused exclusively on only one dimension, as illustrated next:
Interviewer: Let's say you have a big bag that costs $2.50 versus a small bag that costs, say, $.90. How do you consider sizes? Do you look for that at all?
Rita: Yeah, I look and see if they've the big ones or do they have any smaller size. Just like in cereal. I buy like the ... [pause]. They have the big kinds of cereals, then they have, like, the smaller size. Just like the Raisin Bran; I look to see which costs the most and which costs the less, and so I just get the smaller one because they cost the less.
Furthermore, concrete reasoning was evident even when follow-up questions were more pointed.
Interviewer: Let's say you buy a packet of bread that's half the size. You are getting less bread for the money. How do you try to make sure it's cheapest in terms of how much you are getting also?
Naomi: I just look at the tag and see what's cheapest. I don't look by their sizes.
The price fixation is often caused by concrete reasoning predilections that emerge even when respondents try to be careful shoppers. The most common shopping approach reported by respondents before they enter adult education is to shop indiscriminately until their money is gone. This approach often results in running short on food and other necessities late in the pay period. To avoid such outcomes, adult-education courses reinforce the wisdom of looking for the lowest-priced products and budgeting weekly, which takes for granted that students are able to calculate or estimate unit costs and shop accordingly. However, because many respondents find it difficult to calculate unit values, they focus exclusively on the price dimension.
Putting aside respondents who report buying the cheapest or smallest product because of financial or storage constraints, there emerge some distinctions among consumers with respect to their concrete reasoning foci. Whereas consumers such as Rita and Naomi focus on lowest price or smallest size, others compare the physical package sizes (e.g., height, width) of products to derive intuitive size-to-price ratios on which they base their decision, but they do not use the standard volume or unit information printed on labels for their decision (e.g., a tall 16 oz. bottle may be perceived as containing more than a short 16 oz. bottle). When asked, respondents who relied on physical package characteristics for their decision reported that they checked sizes and received the best deals, but their claims were not borne out when unit prices were checked.
Not all concrete reasoning focused on price. We also found respondents who fixated on single attributes, such as sugar or sodium content.
Teresa: Anything sweet, I eat it.
Interviewer: And how do you tell what the sugar is?
Teresa: I go down, and we've got, like,... Some of them say 14 grams; the other might say 46 grams [of sugar]. I get the one that got the most.
Interviewer: Is there anything you look for on the package when you are buying canned food, other than price?
Megan: The sodium. It tells you like 30% and stuff like that.
Interviewer: What would be high?
Megan: About 40% would be high. I can't have too much sodium.
Other consumers focus on fat content and calories, usually without regard to serving sizes or how much of a particular ingredient is appropriate given the product type being considered. Respondents did not report trading off between arrays of product attributes.
In contrast, and without exception, ESL consumers are able to make and articulate complex multiattribute and price-size trade-offs in the same way that literate consumers are expected and advised to do.
Interviewer: OK, how do you decide whether to buy large or small? Do you look at price? Do you look at size?
Mei Kim: If it's more cheaper, then I buy the large size.... For example, small size, two times the large size, but price is 1.5 times or something; I buy it large size.
Poor, literate consumers also reported multiattribute and price-size trade-offs without exception.
Henry: I look at price. If I can get it in economy size for cheaper on a per-unit basis, I'll get the economy size.
We also found similar trade-offs for product attributes such as nutritional or caloric content on a standard serving basis. Unlike functionally illiterate respondents who did not seem to grasp the notion of attribute or price-size trade-offs or could not articulate how they considered these issues, ESL and poor, literate respondents displayed a clear grasp of the concepts involved. As an aside, they perceive price-size and attribute trade-offs as essential and taken-for-granted aspects of shopping. Some were surprised when we asked if they performed trade-offs, as if they found it difficult to envision who would not.
Another manifestation of concrete reasoning by functionally illiterate consumers is their difficulty in transferring knowledge across domains of experience, in this case shopping venues. Poor, literate and ESL consumers did not report anxiety or confusion when visiting new stores. Both groups welcomed opportunities to shop in novel environments. In contrast, nearly all functionally illiterate respondents became anxious when they shopped in new stores. They had difficulty transferring the shopping skills gained from adult-education classes, such as comparing prices and considering private label products, when shopping in new stores. Some even exhibited difficulty transferring basic arithmetic skills across different domains.
Interviewer: OK, now, before you went to adult education, would you check prices like you're checking now?
Otto: No, I'd just go in and get stuff and throw it in the basket and keep going.
Interviewer: Even though you could count very well?
Otto: Yeah, I'd just throw it in there and go, not even worry about it. But now, you see, you gotta look, be careful, you know.
Otto is someone who needed to be taught to apply arithmetic skills when shopping, despite having proved his numeracy skills in other contexts. Before going to prison, he sold illegal drugs for several years, a profession in which counting errors can result in beatings or worse. In that occupation, Otto handled transactions worth hundreds of dollars and made proper change on street corners. However, he could not process prices and keep running counts in grocery stores. In other words, his numeracy skills were context dependent. Another example of context dependent numeracy comes from Esther, who entered the adult-education center in her eighties. Although Esther has difficulty with simple arithmetic tasks in the abstract (e.g., adding columns of numbers), she can keep a running total of what she has put in her cart and compare it with whatever she has available to spend on the basis of magnitude relationships between currency types that she learned as a youth. If Esther can hold or envision dollar amounts in currency (e.g., bills and coins), she can perform simple addition and subtraction and maintain a relatively accurate running total of her purchases. She cannot make the same calculations in classroom exercises.
Our findings are consistent with previous research that shows that low-literate people can perform concrete operations on specific units such as time and engage in concrete context-sensitive thinking based on practical necessity, but they have difficulty with trade-offs that require abstraction (Greenfield 1972; Luria 1976). Among functionally illiterate consumers, price appears to be a central unit on which concrete operations are performed. The necessities of handling money, transacting on the basis of price, having relatively available price information, and identifying the lower price (i.e., the lower number) relatively easily are likely factors that influence concrete reasoning using price. However, combining attribute information to generate value abstractions seems to be beyond the common practice of many functionally illiterate consumers.
Pictographic thinking. A related predilection revealed by functionally illiterate consumers is for pictographic thinking, or the attachment of literal and concrete meaning to pictorial elements, such as color, font, package illustrations, and even words, instead of the abstract and metaphorical meaning often intended. Pictographic thinking is not simply the use of pictorial information as representative of products or attribute arrays when choosing brands or viewing advertisements. This is something that literate consumers also do (Scott 1994). Pictographic thinking extends beyond a high reliance on pictures and includes the treatment of symbolic information (e.g., brand names, dollar amounts) as images, or visualizing amounts to buy rather than using the more symbolic weight or volume information. In the case of functionally illiterate consumers, we found inordinate and sometimes exclusive reliance on the pictographic characteristics of encountered stimuli and an accompanying face value interpretation of whatever was being attended to. Use of physical package sizes (e.g., height, width) to perform price-to-size value assessments is one example of the pictographic thinking we encountered.
Many functionally illiterate consumers reveal an almost complete reliance on context-based pictorial representations. We also found that many functionally illiterate consumers treat product category nomenclature on store signs (e.g., canned soups, paper products), brand names, and even frequently encountered numbers as objects in a scene or photograph and dismiss much of the symbolic meaning behind the bits of information. In turn, such mental handling of information leads to confusion when the graphical characteristics (e.g., font style, color) of familiar words and brands are altered. A pointed example comes from Garvey, who spent almost 30 minutes looking for ice cream during a shopping excursion at an unfamiliar store. Garvey was made nervous by the store layout, which interfered with his ability to apply the rudimentary reading skills he had acquired in class. He wanted to rely on pictographic thinking, but because the graphic characteristics of the store signage (e.g., color, font, background) were different, he was not able to navigate the store by relying on pictographic thinking either. His fallback strategy was to walk up and down the aisles until he spotted the logo for his favorite brand of ice cream, relying on pictographic thinking at the package level.
The most common use of pictographic thinking was memorizing brands as combinations of letters in specific fonts and colors without processing the brand name as a word. Otto, who could count but not read, made most of his purchase decisions that way. He seemed to have a pictographic image of brands, irrespective of his liking for them after previous purchases. He relies almost exclusively on those images when making decisions. It is not surprising that he made mistakes when almost identical packaging was used for different products (e.g., Domino's brown sugar and white sugar). Along with several other respondents, Otto also reported using pictographic thinking as a surrogate for shopping lists. He visualizes cooking situations (e.g., making stew) to determine how much of each ingredient to buy, and he shops by picturing himself going through the act of cooking different dishes, picking up products as his cooking mental episodes unfold.
Another manifestation of pictographic thinking is the way many functionally illiterate consumers relate to currency. Functionally illiterate consumers such as Esther recognize coins and paper currency because of the faces on them. Many consumers have memorized the relative order of value between currency denominations (i.e., $5 is less than $10) in the same less-than or greater-than way that they recognize which product has the lowest price in a particular category. As does Esther, many students display rudimentary arithmetic skills when exercises are framed in the context of money, but they have difficulty applying the same skills in the abstract. Luria (1976) reports a similar reliance on pictographic thinking and the manipulation of images in the estimation of travel times between cities among low-literate peasants.
Some respondents recognized pictographic thinking as a strategy they use. Ricardo exemplifies consumers who recognize their predilection for pictographic thinking.
Ricardo: Sometimes I still have trouble with words. I am more sight reading. If I see something or a word that I don't know, and you show it to me and tell me what that word is, a lot of times, the next day or the day after, I am still going to know what that word is. I call it sight reading....
Interviewer: When you're buying groceries have you ever been confused in the store because of reading?
Ricardo: No.
Interviewer: How do you do it?
Ricardo: If I want a can of Spam, I know it when I see it.
Interviewer: If I gave you a bunch of cans on the wall how would you see it?
Ricardo: It has the name of it on it.
Interviewer: New words?
Ricardo: Then I might have to ask somebody and I am not ... [pause]. The majority of food products, I know. I know Kellogg's cereal and a lot of that food is still on the shelf and I haul a lot of it. It's not a problem with me, you see.
Ricardo recognizes that he is not reading, but in effect he recognizes brand logos in the same way he might recognize people he knows in a photograph. This method is what he calls "sight reading." Ricardo's approach to buying Spam is the same as Garvey's searching for ice cream in a new store. Ricardo is also notable in that he is an interstate truck driver who relies on pictorial representations of street names or parts of names to navigate between cities.
Poor, literate consumers did not exhibit reliance on pictographic thinking, other than the imagery shortcuts common to literate consumers (Scott 1994). The ESL consumers exhibited some pictographic thinking, such as dependence on brand logos and pictorial menus, but it did not extend to treating numbers as surface-level pictorial representations or visualizing usage situations (e.g., cooking) to guide their shopping. The ESL consumers reported that they treated some information at the pictorially based surface level, and they recognized why they were doing it.
Interviewer: Why don't you ask the waitress?
Onuki: When we just got here, we can't really understand what he says.
Interviewer: Then what did you do?
Onuki: The senior schoolmate would communicate with him in English, but we couldn't understand what they're saying. So actually till this day I still don't know how to order food, since they [the seniors] are all gone. We can only remember where the approximate position of the food we order was on the menu.
The ESL consumers also reported that their reliance on pictographic thinking was a tactic to overcome language difficulties. Many expressed that they had overcome their need for pictographic thinking the longer they lived in the United States or that they expected to overcome their need over time.
The distinction between how ESL respondents and functionally illiterate respondents manage number information is also important because it has implications for the differences in the value assessments we noted previously. All ESL respondents in our study had a university education and a familiarity with numbers and relations between numbers as abstract conveyors of information. It is not surprising that they were able to use number information as intended and to make value assessments between products, even if they could not read all the information offered on the package. However, functionally illiterate consumers treat both words and numbers as pictorial elements, and they engage in surface-level processing of both types of information. Thus, they are more limited than ESL consumers in assessing value.
Pictographic thinking is a predilection consistent with findings that illiteracy leads to graphic thinking anchored in the here and now (Luria 1976). Moreover, the interrelationship between concrete reasoning and pictographic thinking is noteworthy. Pictographic thinking reflects a primitive ability to process information with a one-on-one correspondence to the physical world that is available to the senses rather than to the symbolic world that develops with literacy (Havelock 1963). This is the mode of processing that functionally illiterate consumers favor. When functionally illiterate consumers are confronted with the practical necessity of completing transactions that involve price or choosing products by using symbolic information, they concretize the decision task by focusing on single attributes, such as price. In general, functionally illiterate consumers function primarily in the visual and concrete realm rather than in the symbolic and abstract realm.
Decision Making by Functionally Illiterate Consumers
Decision heuristics. Some of the data discussed in the preceding section also reveal examples of single-attribute, habitual, and random product choices among functionally illiterate consumers. For example, Teresa focuses on sugar and consistently chooses products high in sugar, whereas Megan focuses on sodium. We also find decisions based on small size as a surrogate for low price. Naomi applies the same "buy the small one" rule to everything she buys, from potato chips to laundry detergent. This is in contrast to literate consumers, who consider multiple attributes (e.g., price, size, content, performance characteristics) and perform mental trade-offs among such attributes. Moreover, we found that Otto used to apply a random choice model in his shopping but more recently switched to a habitual choice model that processes brand names as pictorial elements.
The data also provide clear distinctions between the context-based single attribute decisions of functionally illiterate consumers and consumers who, on the surface, appear to focus on single attributes but actually base their decisions on abstraction and inference processing that we did not find among functionally illiterate consumers.
Interviewer: Imagine you have [a product package] in your hand. What kind of information do you look at? What's the first thing you pay attention to?
Chen: The first I'd pay attention to is what it's selling, whether it attracts me or not, does it look tasty on the picture.
Interviewer: So pictures are important?
Chen: Yeah. If the picture attracts me, I'd look at its price secondly. For price, I'd see if I can afford it, is it too expensive. If the price is OK, then calories. If a small thing has high calories, then I won't buy it.
Interviewer: How about ice cream?
Chen: If it's ice cream, then I don't look at the calories.
Interviewer: So it's selective.
Chen: If I look at the calories, then I can't eat it. It'll be too painful [smiles]. For ordinary stuff, I'd look at the calories.
Chen uses an elimination-by-aspects rule (Russo and Dosher 1983) when purchasing products, holding different decision criteria for different product categories and moving between focal attributes and decision standards with relative ease. Ice cream is held to a price constraint but not a calories constraint, and it appears that few products are allowed such an exception. Chen relies on stored category knowledge, possibly derived from some form of compensatory processing during previous shopping trips, to make inferences and abstractions (in much the same way literate consumers apply noncompensatory rules). However, in the case of functionally illiterate consumers, the rules are basic, applied without adjustment across most if not all product categories, and do not seem to involve abstraction or inferences. Although functionally illiterate consumers apply noncompensatory, single-attribute decision rules, they apply the rules at a more concrete level than that used by literate consumers. We believe that the applications of single attribute decision rules by functionally illiterate consumers are better classified as a coping mechanism implemented as a result of social context (Luce, Bettman, and Payne 2001) than as the use of noncompensatory decision rules (Russo and Dosher 1983).
Megan's shopping behavior further illustrates the idea that functionally illiterate consumers apply single-attribute decision rules as coping mechanisms. In addition to the "avoid sodium" rule, Megan applies a "buy the cheapest" rule to most purchases, where "cheapest" is assessed in terms of package price. We observed Megan ponder for several minutes the choice between a small $3.38 box of Honey Nut Cheerios and a "two boxes for $6.00" deal on the larger size of the same cereal. She visually moved back and forth between the choices several times and, at one point, handled the larger box for several seconds to assess its weight. Abruptly, however, she placed the small box in her shopping cart, and when we asked why she chose the smaller box, she answered that "it was cheaper." On the basis of her indecision and apparent stress over the decision, we concluded that she recognized that applying a compensatory rule would be desirable, but her difficulty in performing the calculation pushed her to decide on surface characteristics. Megan is anchored in the concrete world of the senses (Luria 1976), and she applies single-attribute decision rules at the same concrete level of thinking, a level that is different from what we found that ESL and poor, literate consumers used.
Emotions and decision trade-offs. Megan's struggle over the Cheerios purchase highlights another distinguishing characteristic of functionally illiterate consumers: the experience of adverse emotions associated with purchase decisions. Stress and anxiety over purchase decisions does not affect only functionally illiterate consumers. In some circumstances, such as choosing a family health plan, for which trade-offs between coverage and affordability are required, decisions can provoke anxiety for any consumer because of the potential losses that the different alternatives entail (Luce, Bettman, and Payne 2001). However, among functionally illiterate consumers, we found recurring and acute anxiety even in circumstances that, on the surface, did not seem to merit such emotional reactions (e.g., shopping at a new store). Because of their difficulty in performing calculations or in reading store signs and labels, and because of the reactions of store personnel and other consumers to such difficulties, functionally illiterate consumers experience negative emotions and often find their self-esteem undermined. They make significant trade-offs to avoid negative emotions and to protect their self-esteem in marketplace encounters.
Interviewer: If something is 30% off and the price is $19.98, would you go up to a person and ask how much it really is?
Dee: No.
Interviewer: Why not?
Dee: I'd be embarrassed.
Dee is not alone in refusing to ask for assistance in stores or in avoiding products with fraction-off and percentage-off labels to avoid dealing with price uncertainties. Julie, another respondent, only considers products that are 50% off because she can estimate the cost as half of what is posted. She avoids all other fraction-off or percentage-off discounts. In addition, we find that many respondents will not shop alone and expend considerable effort coordinating with other parties and planning shopping events in advance to avoid negative emotions and stress. In effect, they avoid the possible negative emotions and stress of shopping by delegating their shopping to family members and other trusted persons.
Naomi: No, I don't do too much stuff, my daughter do all the shopping....
Interviewer: You used to do a lot of shopping?
Naomi: No, not really. You know, when I am a daughter, my mom did a lot of shopping.
Delegating shopping to others involves trading away convenience and time for the sake of avoiding emotional costs, because it places functionally illiterate consumers at the mercy of other people's schedules. An extreme case of such delegation that we found is a consumer who shops only when her brother comes to visit every few weeks. In many of the cases we found, the delegation of shopping responsibilities is a coping strategy driven by the avoidance of emotional costs. Moreover, it is not difficult to imagine that some of the consumers probably suffer privations when their supply of important products is exhausted before their next planned shopping excursion, which adds welfare to the time and convenience that they trade away.
Another strategy to shopping delegation is to schedule shopping trips around the availability of store personnel who help the consumers. Garvey plans his shopping trips around the work schedule of a cashier who is aware of his literacy and numeracy deficiencies and who is willing to work with Garvey when items in the shopping basket exceed the funds he has on hand.
Interviewer: Are you ever afraid that you don't have enough money?
Garvey: Yeah. I try to add up in my head. It is just right sometimes, and sometimes it goes over.
Interviewer: How does that make you feel?
Garvey: Not too good.
Interviewer: If you go over, what happens?
Garvey: Nothin' really. You just say, "I can't buy that and have to take it off." I know the woman at County Market.
Garvey's case is a good example of relationships between store personnel and customers contributing to store loyalty (e.g., Macintosh and Lockshin 1997), which in this case involves a sensitive issue. An additional observation about Garvey is the unmistakable sense of comfort that he drew from knowing that nothing would happen if he went over at the checkout counter. Not all functionally illiterate consumers were that fortunate. We interviewed several who attached great significance to seemingly trivial occurrences, even celebrating when they had enough money at the checkout counter or despairing when they were short of money.
In general, we find that functionally illiterate consumers invest substantial effort in non-product-related aspects of shopping to reduce negative emotions. Moreover, we find that many undertake time, value, and welfare trade-offs to reduce stress and emotional costs. Similar trade-offs were made by some ESL informants, but not by any of the poor, literate consumers we interviewed. For example, Onuki appears to give up dining variety by always choosing from the same section of the menu so as not to interact with the waiter or waitress. We also encountered several ESL consumers who had recently arrived in the United States and relied on friends to help them navigate shopping environments. At least for a short time, such consumers gave up time and convenience. Other ESL consumers shopped primarily at a single store because it was within walking distance of their home and they did not have autos. The same applied to poor, literate consumers who were limited in their choice of shopping venues because of transportation problems. In general, however, ESL and poor, literate consumers exhibited little stress over shopping and were comfortable shopping in different stores as circumstances dictated or as opportunities emerged. If friends invited them to other stores, ESL and poor, literate consumers went enthusiastically. They did not report feeling anxious about changes in shopping venue despite the challenges that different signage and product locations represent, and they shopped as needed without undue reliance on friends and family. The ESL and poor, literate consumers we interviewed exhibited independence and self-determination similar to what we expect from literate consumers and around which many marketing practices are based.
Coping Strategies of Functionally Illiterate Consumers
Given the anxiety and emotional costs that many functionally illiterate consumers associate with shopping, we were not surprised to find various avoidance and confrontative coping strategies, some problem-focused and others emotion-focused and some implemented before product choice decisions and others implemented after the decision. Some of the coping strategies are also used by ESL and poor, literate consumers, but we noted differences in the frequency with which the different groups use different strategies. The observed coping strategies are summarized in Table 2. We classified them as avoidance and confrontative strategies (Luce, Bettman, and Payne 2001; Mick and Fournier 1998) and problem-or emotion-focused (Luce, Bettman, and Payne 2001) for illustrative purposes, based on the primary reason that respondents gave for using that strategy (typically in response to a "Why did you do that?" question). We also categorized the strategies as being implemented prepurchase decision or postpurchase decision. The categorization scheme is not meant to be exhaustive or definitive. In addition, some of the coping strategies can overlap categories, particularly with respect to emotional and problem-solving motivations. We also find that functionally illiterate consumers often implement coping strategies simultaneously, which reinforces the previous observation that shopping behaviors can involve advanced and intricate planning. Our discussion intermingles some avoidance and confrontative strategies.
Some of the commonly used coping strategies are evident in examples we have already presented. Same-store and same-brand strategies are examples of accepting the status quo (Luce, Bettman, and Payne 2001). The strategies are also common among ESL informants, though predominantly among those who had been in the United States for a short time. As the tenure of ESL informants in the United States increases, their brand and store loyalties decrease.
Interviewer: Can you describe to me what it is like when you go shopping when you just arrived here, and then after one year, two years, and now?
Kwon: The first year I just stick to my habit from Taiwan. So most of the cases I just choose what used like the stuff in Taiwan. I was willing to try something, but I should say that the habit is a little different from what I am right now. I kind of understand the stuff I want to buy and kind of try out the weird things I have. So I'm a little more Americanized now.
Other coping strategies are to avoid fraction-off and percentage-off prices and to base decisions on single attributes, both of which are problem-focused strategies in situations in which information-processing capabilities are limited. As we observed and listened to informants, we also detected an underlying need for them to perceive themselves as in control and competent as consumers, which comes across as an emotion-focused motivation.
We already mentioned shopping with family members and relying on other trusted helpers. A related strategy is carrying limited amounts of cash so as not to overspend. Victoria implements all three. She primarily relies on her mother but will turn to others if needed, and she carries a limited amount of cash ($5) when shopping alone.
Victoria: Well, if I'm, like, at the store with my mom and stuff, I go around, put all the stuff in my cart, I wait until I find her, and then I go, "Mom, do I have enough money for this"? and if she tells me no, then I put back most of the stuff.
Interviewer: What about when you are shopping on your own?
Victoria: When I'm shopping on my own, I mostly stay in my budget.
Interviewer: OK.
Victoria: If I, like, had $5 on me, I would just get one thing. That would be something that is, like, $3 or cheaper than $5.... Yeah, I don't, like, add very well and stuff, so it's kind of confusing to me. I would need someone to help me with it. Like, I would call somebody in the store or an adult--whoever is with me--and I would ask them.
Another common strategy is to give cashiers all the money they are carrying, trusting cashiers to give them the correct change.
Interviewer: What is the most you have spent?
Larry: $200 or $300. I don't count it out. I give [the checkout clerk] the money I have in my pocket. I can't read.
We found that many informants used dissimulation as part of their coping strategies. Some stand in store aisles and pretend to study product labels and compare prices so other shoppers cannot guess their literacy and numeracy deficiencies. Otto used to cover up his deficiencies by claiming to have vision problems.
Interviewer: So, you mentioned before that if you needed somebody's help and you would ... [interrupted].
Otto: I would be embarrassed, I would lie, tell them that I need this, help me with this, but now I don't have to lie no more. If I can't read, I just can't read. Could you please help me if you don't mind? I'm more comfortable....
Interviewer: Now, you mentioned that sometimes you would say, "Well, I can't read, can you help out?" What else would you do to get by before, when you were shopping?
Otto: I tell people I got problems with my eyes. "I can't see." "I don't have my glasses with me." "Could you help me please?"
Some coping strategies are implemented to offset outcomes from other strategies. Sam does not know how to make change and is one of many informants who gives all their money to cashiers and expects the right change back. To avoid being cheated and remain within budget, Sam also implements a "one-item-at-a-time" strategy at fast-food restaurants.
Interviewer: How much money does it normally take?
Sam: $10 or $20.
Interviewer: How do you know what you want to buy?
Sam: I look at the menu and look to what you want and you got to figure out what it's going to come to.
Interviewer: How do you do that?
Sam: First you buy one thing at a time, so it come to a different price.
Sam also reports buying in small quantities and visiting stores more often to reduce the risk of being cheated.
The coping strategies discussed thus far are predecision strategies. Functionally illiterate consumers spend a lot of time planning and developing ways of getting around the challenges and negative emotions involved with shopping. They also implement postdecision coping strategies, such as retrospectively rationalizing outcomes so as to shift responsibility away from the self. For example, Victoria had a car repossessed for missing payments. She took responsibility for not staying in school and acquiring literacy skills but shifted responsibility for losing her car to unscrupulous lenders. Another example comes from Valencio.
Valencio: I bought chicken plenty of times, and it was spoiled. Because the workers that work in the back that's making the chicken, they be taking the old chicken and selling it. They don't sell the new chicken. It's just crazy. But like I said, I bought chicken before and it was spoiled. I took it to the store, but I couldn't get my money back.
Interviewer: This only happened to you one time?
Valencio: It happened to me a couple of times. Not at the same store, but at different store. You know, it was spoiled.
Interviewer: Why wouldn't they let you take it back?
Valencio: I don't know. I don't know if they was prejudiced or what. I don't know. I came to them the correct way. I said, "I bought some chicken. Here's my receipt, and the chicken is spoiled. And you can smell it if you want to." "Well, we can't take it, sir, because it's opened." So I said, "Well, when I took it to the house I had to open it to put it in some bags, and it was spoiled." So, what am I supposed to do? For me not to argue with him I said, "No problem." I just gave him the chicken and I just went to the house. I didn't provoke anything, you know. I'm not going to make myself look bad for arguing with the man over the chicken.
Valencio was convinced that backroom clerks relabeled past-dated chicken and placed it back on store shelves. That other people do not report similar problems after shopping at the same stores does not affect his thinking. Sam used similar rationalizations to account for why he sometimes falls short of cash, attributing shortfalls to being cheated by retailers.
Another postdecision coping strategy is to remain passive and personable even when negative outcomes arise from being functionally illiterate. Garvey put on a "no-big-deal" air whenever his groceries exceeded how much he had available. Xenia responds more proactively but remains friendly and courteous throughout the situation.
Interviewer: Have you been embarrassed going over and not having enough money? Has that happened?
Xenia: Yeah. Like, I went to IGA to buy some things and the money that I pick up off the table at home was not the money I supposed to pick up so when I finish buying everything I just take out the money and hand it her and she says, "No, this is $5 you gave to me," and it [the total] was over $10, almost $20. Then I'm like, "Oh my God, I feel so bad." I tell her, I say, "OK, don't put it back. I'll just walk home and get the money and come back." I says, "OK?" She says, "Yes." So I went home and get the money.
Being friendly and courteous was common among functionally illiterate consumers, who also tend to be more passive and accepting than literate consumers.
In contrast, ESL and poor, literate consumers displayed more combative tendencies and less willingness to forgive retailers for negative experiences and the emotional or tangible costs incurred. Ben (a poor, literate consumer) exemplifies the general response of poor, literate and ESL consumers when they are stigmatized by retailers, aggressively confronting store employees who he believed were not treating him properly.
Ben: I was at the grocery store not too long ago. We were pricing Christmas gifts, because our money was tight. We are doing some price comparison shopping. This lady from the front ... immediately just looks funny at us. We are walking through the store and she starts to follow us. "Is there a problem?"... "Do you think we are shoplifting?" She goes, "You do look kind of suspicious." We filed a grievance with the store and I have asked my friends not to shop at the store until the grievance is taken care of.
In general, we found ESL and poor, literate consumers to be more willing to be confrontative and to demand different treatment from store personnel, and ESL consumers only hold back because of social norms. In contrast, functionally illiterate consumers were consistently more passive; most of the functionally illiterate respondents accepted the status quo of being treated differently as a metalevel coping strategy (Luce, Bettman, and Payne 2001).
Our research sheds light on functionally illiterate consumers, an understudied population with measurable and significant purchasing power. We identify cognitive predilections, decision heuristics and trade-offs, and coping strategies that influence many of their behaviors. The factors distinguish functionally illiterate consumers both from literate consumers and from consumers who, because of poverty or limited language skills, may be considered similar. Our research provides valuable insights and has important implications for marketing research and practice.
Implications for Marketing Research and Management
A fundamental theoretical issue that this research raises is whether existing models in marketing are adequate in capturing the decision making of functionally illiterate consumers. The model of the "cognitive miser," who seeks to economize on cognitive resources, for example, has been prevalent in marketing and psychology. However, the view that arises from our research is more of a "cognitive survivor," who, despite spending considerable resources, cannot readily engage in the abstract and analogical thinking that makes cognitive miserliness possible.
Our findings emphasize that several extant theories and models of consumer behavior (e.g., Luce, Bettman, and Payne 2001) should be expanded to take into account functionally illiterate consumers. Despite being employed and having considerable spending power, functionally illiterate consumers process information and make decisions in ways that do not match commonly held beliefs about the influence of brand information, pricing, and product attributes on consumer judgments and choices. Functionally illiterate consumers also violate assumptions about the importance of value-producing rather than non-value-producing aspects of decision contexts. In many situations, they base decisions almost exclusively on non-value-producing factors, such as familiarity with the shopping environment or the personalities of sales personnel, instead of on product attributes and price. It seems reasonable that the incorporation of a deeper understanding of functionally illiterate consumers into marketing theories will result in theories that have greater explanatory and predictive power and in more effective marketing practice. Similar arguments can be made in the realm of public policy, in which regulation that pertains to product labeling, pricing, and branding has been implemented without apparent consideration of the needs of functionally illiterate consumers.
The broad implication of our findings for marketers is that careful consideration of and research into the functionally illiterate segment with an aim toward enhancing all elements of the marketing mix can be beneficial. Marketers can use the cognitive predilections, decision heuristics and trade-offs, and coping strategies that we identify to examine and design their interactions with consumers in the areas of product, price, promotion, distribution, and customer service. Even more poignant are some of the managerial implications of having such a large segment of the population unlikely to process word and number information as managers intend. We believe that the incidence of functional illiteracy has strategic implications in areas such as product differentiation and new product introduction, avoidance of product liability, management of price promotions, and customer loyalty programs. Functional illiteracy also has significant tactical implications for retailers and product managers. Because of space limitations, we discuss some of the strategic implications next and include, without extensive discussion, an illustrative list of tactical considerations in Table 3. We link the presented implications to cognitive predilections, decision heuristics and trade-offs, and coping strategies.
Product differentiation and new product introduction. It is difficult to imagine a consumer goods industry in which producers do not seek a competitive advantage through new products or improvements to existing products and in which the effective communication of product offerings is not a significant challenge. Billions of dollars are spent on advertising, brand management, point-of-purchase displays, and packaging to communicate what defines a product and the array of benefits such that it delivers a good value even at premium prices. However, for many consumers, most of the efforts are wasted or may be misunderstood and misrepresented as a result of an overreliance on words and numbers as carriers of information to a population that processes them differently from other consumers. Many functionally illiterate consumers do not process words and numbers by incorporating them into abstracted representations of the products and their categories. They focus instead on a limited set of words that they process concretely, and they often base their decisions on heuristics that overemphasize single attributes and deemphasize or even ignore how new product attributes and unique characteristics deliver value at a more inclusive level. For functionally illiterate consumers, strategies that add features to existing brands (e.g., bleach alternatives in laundry detergents, whitening agents in toothpaste, vitamins in soft drinks) are unlikely to result in additions of the new attributes to the brands' preexisting quality and performance images. A more likely outcome is that these consumers will perceive the new product as a new product category (e.g., Tide Bleach, Colgate White Paste, Pepsi Vitamins) that may or may not be compatible with the brands' cultivated image. In contrast, brands may be associated with attributes that are not central to their value propositions, which could lead to brand confusion instead of the intended enhancement of a brand's core characteristics. Similar unintended outcomes are also possible when new brands are introduced or when brands are revitalized with changes to the fonts or colors used to represent them. Our research suggests that functionally illiterate consumers are more likely to ignore new brands. Moreover, if the new or revitalized brand undermines or replaces the preexisting brand as a pictorial element in its typical context and thus generates confusion, the resultant anxiety may be enough to trigger brand switching. Many functionally illiterate consumers are brand loyal because of pictographic recognition coupled with adequate brand performance. When company actions compromise pictographic recognition, functionally illiterate consumers may not search the shelf for new renditions of the same brand or for product information that signals that the brand has been recast for their benefit. They are more likely to seek out and purchase a competing pictorially recognizable brand.
These are not insurmountable problems, but they require that producers take into account that functionally illiterate consumers do not process word and number information in the same way as literate consumers do. For example, new attributes, such as bleaching agents in detergent or whitening agents in toothpaste, would benefit from pictorial representations of the benefits (e.g., before-and-after laundry depictions, a smile with dazzling white teeth), along with a simple-to-process word (or words) that represent the benefit. Culturally sensitive metaphors (e.g., pearls for white) can also be used pictorially to take advantage of well-learned associations that may be part of an oral tradition and transcend literacy and numeracy skills. Needless to say, the use of audiovisual media is important, because information processed as conversations can be used to generate a rich set of associations to which simple-to-process words can be linked.
An additional tendency of functionally illiterate consumers that should be managed in the introduction of product improvements and new products is consumers' reliance on assistance from family, friends, and store personnel in making purchase decisions. Without calling attention to consumer deficiencies, it may be possible to remind functionally literate helpers to recommend new and improved products to friends as a way of being helpful to others who may not be aware of the product's benefits. Assuming that people who help functionally illiterate consumers do so because they care, it seems plausible that emphasizing ways they can be even more helpful will be considered compatible with their motivations and thus likely to generate positive responses.
Finally, it is important for marketers to note functionally illiterate consumers' high level of advance planning and, therefore, to seek a place on their shopping list before they arrive at the store. Even in highly familiar environments, it is reasonable to expect that functionally illiterate consumers are somewhat more anxious than are literate consumers, because they focus their attention on recognizing preferred products and brands pictographically amidst the tens of thousands of stockkeeping units carried by the typical store. The cultivation of advance familiarity through television and radio advertising, pictorially rich billboards and posters, and even easy-to-comprehend direct mail pieces can enhance the likelihood of purchase.
Avoiding product liability risks. Consumer misuse of products is an ongoing concern among producers (at least in the litigious U.S. market), and emphasis is placed on both explaining the proper use of products to consumers and using warnings about the potential consequences of product misuse. However, our research suggests that many of companies' liability avoidance practices, such as detailed warnings and instructions in multiple languages on package labels, will not be effective with functionally illiterate consumers. At best, functionally illiterate consumers may ignore such warnings because they cannot process them. Moreover, given functionally illiterate consumers' tendencies to fixate on single pieces of information and process them at a concrete level, it is not difficult to imagine circumstances in which liability avoidance practices may result in some consumers using the product in precisely the way that producers seek to avoid. For example, bottles of dishwashing liquid may contain claims such as "antistress" and "aromatherapy" in large, bold print, with descriptors and warnings such as "concentrated dish liquid" and "do not use with chlorine bleach" in small print. Furthermore, such bottles often have pictorial representations of flowers and other aromatic ingredients on the label but no equally visible and interpretable pictorial representation of the product's intended use. A functionally illiterate consumer could misinterpret a product label such as the one we have described such that he or she perceives the product as a freshener that can be used in settings that include doing laundry. If combined with chlorine bleach, such a product could produce potentially harmful fumes. The same could be said for over-the-counter medications that contain sweeteners (e.g., throat lozenges) and a printed warning "this is not candy" on the label. A functionally illiterate consumer who focuses on the sweetener content and the word "candy" could easily use the product inappropriately.
Such scenarios can be replicated in the thousands, if not millions, of households and business settings around the world in which functionally illiterate consumers live and work. If producers were to argue that their not knowing that such a high percentage of the population is low-literate should exonerate them, widespread availability of the National Adult Literacy Survey (Kirsch et al. 1993) has weakened that argument. Marketing managers need to be sensitive to functionally illiterate consumer predilections for concrete reasoning and pictographic thinking, and they should develop instructions and warnings that clearly communicate the products' intended uses and potential dangers from misuse. If functionally illiterate consumers can be counted on to make decisions based on concrete reasoning and pictographic thinking, it is good business to reach consumers through those information-processing mechanisms and to avoid product liability complications by doing so.
Managing price promotions. In 2002, consumer promotions in the United States totaled more than $233 billion (Promo 2004), much of it devoted to coupons and other forms of price discounts designed to induce action at the point of purchase. However, our data suggest that for many functionally illiterate consumers, such promotions are confusing and may even cause them to switch away from the promoted brands. Among the functionally illiterate consumers we interviewed and observed, we found recurring and significant deficiencies in the ability to calculate accurate prices when coupons and percentage-off or fraction-off deals are used. In turn, these deficiencies engender anxiety and trigger avoidance strategies in some consumers. For the most part, consumer promotions are intended to enhance value to the consumer by reducing price and have been shown to result in category expansion and enhanced market share for promoted brands (Neslin 2002). They have also been linked to increased sales for competing brands, a phenomenon that is not always well understood. Our research provides a possible factor that contributes to increased competitor sales, given that in some cases consumer promotions scare functionally illiterate consumers away from promoted brands and to competing brands. Our research also emphasizes that many consumers have difficulties in calculating or even accurately estimating the value enhancement that consumer promotions are intended to provide.
As with product liability concerns, careful attention to consumer predilections for concrete reasoning and pictographic thinking can lead to modes of presenting consumer promotions that will make them attractive instead of confusing and repulsive to functionally illiterate consumers. Our data suggest that functionally illiterate consumers are receptive to price deals but lack the numeracy skills to estimate value from price-discount calculations. However, if price and discounts are represented pictorially (e.g., pie charts), it seems reasonable to expect that more functionally illiterate consumers will grasp discount magnitudes quickly and accurately. Moreover, given the relatively low cost of printing customized labels, the adoption of graphical representations of price promotion information across markets and product categories is not an onerous task. Our data do not enable us to estimate the dollar value of increased effectiveness from such tactics, but if a large group of consumers respond to consumer promotions adversely because of literacy and numeracy deficiencies, the returns from aligning the presentation of consumer promotions to the skills of these consumers should be substantial.
Consumer loyalty programs. Research into the efficacy of consumer loyalty programs has shown that because of the expense of continually satisfying customers and concurrently meeting their expectations for preferential prices, such programs are often not profitable (e.g., Reinartz and Kumar 2000, 2002). Companies spend large amounts of money to keep track of customer purchases, to stock the products they favor, and to design consumer promotions to keep regular consumers satisfied; it is far from clear that such investments generate positive returns. This may be caused by consumer expectations that loyalty should be rewarded with lower prices, given that most other aspects of shopping environments are indistinguishable from those offered elsewhere. However, when it comes to functionally illiterate consumers, our research suggests that differentiation is possible through actions such as store signage designed to have pictorial information alongside word information, limited changes to store layout whenever possible, employee training in sensitivity to consumers who display literacy and numeracy deficiencies, and implementation of safeguards against functionally illiterate consumers' exploitation by unscrupulous employees. For example, the understanding revealed by Garvey's favorite cashier should not be left to chance but should be part of employee training in markets in which one in four customers may be functionally illiterate. Our research further suggests that functionally literate consumers who find safe and amenable shopping environments are loyal and are not likely to expect discounted prices for their patronage. Among functionally illiterate consumers, there are many who greatly appreciate shopping environments that respond to their needs for pictorial information, help protect their interests and self-esteem, and seek to reduce the stress of shopping. Consequently, such consumers represent an untapped opportunity for at least some retailers to generate loyalty from a consumer group with documented spending power and whose behaviors suggest that they can be profitable.
The solutions that marketers devise for functionally illiterate consumers may sometimes coincide with solutions for various other groups, such as novice consumers, time-constrained consumers, consumers in developing countries, and consumers shopping in unfamiliar environments (e.g., foreign countries). There appears to be a strategic overlap between catering to functionally illiterate consumers and catering to other significant consumer segments in the global marketplace. However, the surface-level similarities between groups may hide deeper differences that marketers need to address, as our comparison groups illustrated.
Further Research and Limitations
Our findings also point to some specific directions for further research. For example, pictographic thinking has implications for visual information processing. If attribute information that is presented pictorially is processed differently, it is likely to have unexplored effects on decision making and memory. In addition, the conditions under which functionally illiterate consumers use different types of decision heuristics and coping strategies need to be identified. Similar arguments can be made for concrete reasoning. In every area depicted in Figure 2, functionally illiterate consumers respond differently than do literate consumers, and their thinking and behavior needs to be better understood.
Cross-cultural research on functionally illiterate consumers would also be beneficial, given the large segments of immigrant and illegal-alien functionally illiterate consumers in the Canadian, European, and U.S. markets and the even greater numbers of functionally illiterate consumers in the global marketplace. Such research should be conducted in cultures that vary in overall levels of literacy and in their marketing infrastructure, given that functional illiteracy is context determined. Other notable cross-cultural research questions include: How do both low-literate buyers and sellers, not uncommon in developing contexts, negotiate the marketing environment? Are the emotional and coping phenomena observed in industrialized economies replicated; if not, what takes their place? Finally, the combined and overlapping effects of functional illiteracy and language deficiencies, such as those with ESL consumers, must be studied in order to better understand consumer behaviors in ethnically diverse markets.
At a managerial level, the effects of levels of functional literacy on several phenomena also merit additional research. For example, the effects of illiteracy on the processing of nutritional and health claims should be clearly understood. Equally important is the influence of functional illiteracy on consumer relationships with store personnel and on the efficacy of customer service. Functional literacy is taken for granted throughout much of the marketing management literature. The prescriptions of most extant research need to be examined for their applicability to functionally illiterate consumers, with the objective of developing an enhanced understanding and more appropriate theories and practices.
Our study has several limitations that temper its implications. First, our functionally illiterate informants are likely to be different in level and type of motivation from ones who are not enrolled in adult-education programs. Respondents in our study are trying to overcome literacy and numeracy deficiencies, which suggests that they are highly motivated and possibly quite different from functionally illiterate consumers who are not seeking to become literate. This is another topic on which additional research is needed, though our experiences with accessing students enrolled in adult-education centers suggest that even greater difficulties are possible in trying to reach nonstudent samples.
Second, although we combined multiple research methods, their scope was limited by the inherent difficulty of functionally illiterate consumers to deal with objective methodologies such as surveys and experiments. There is certainly more going on in the minds of functionally illiterate consumers than our interviews and observations can reveal, though we deliberately focus on behaviors and outcomes that we repeatedly observed. Whereas large-scale self-administered surveys remain unrealistic in studies of functionally illiterate consumers, the careful use of well-designed experiments and personally administered surveys may be worth exploring.
A final limitation pertains to the authors' tacit biases. Even with training and self-monitoring, we remain functionally literate consumers examining a world with which we cannot cognitively and emotionally relate. There is probably much that we missed about how functionally illiterate consumers navigate modern shopping environments that must be explored by further research.
( n1) We use the more appropriate term "low-literate" when we discuss the broad spectrum of literacy conditions. However, when we discuss functional literacy, we use the term "functionally illiterate" rather than "functionally low-literate" for improved readability.
The authors thank Ellen Garbarino, Abbie Griffin, Curtis Haugtvedt, Cele Otnes, Bill Qualls, Mark Ritson, John Sherry, and participants at seminars at the University of Illinois, Nanyang Technological University and National University in Singapore, and Ohio State University for their helpful comments. They especially thank John Muirhead, Debbie McDermott, Deborah Schlomann, JoAnne Eizinger, Diane Joy, Martha Bailey-Gaydos, Carol Belber, Nina Heckman, Ellen McDowell, and Greg Vankoevering for their valuable support of the data collection and Reggie Gaither, Kevin Sanford, and Roland Gau for assistance with the data collection. This research was supported by grants to the first author from the National Science Foundation and from the Campus Research Board and the College of Business, University of Illinois. This material is based on work supported by National Science Foundation Grant No. 0214615.
Informant Name, Age, and Reading/Math Level Information
Legend for Chart:
A - Name
B - Age
C - Educational and Skill Level in Reading and Math
A B C
Ben (poor, literate) 21 Not applicable, grade 12
education
Chen (ESL) 27 University graduate, grade
2-6 reading (English)
Dee 16 Grade 5-12
Esther 93 Grade 0-4
Fiona 38 Grade 0-4
Garvey 40 Grade 0-4
Ivan 29 Grade 0-4
Julie 16 Grade 5-12
Kee 38 University graduate, grade
2-6 reading (English)
Kwon (ESL) 28 University graduate, grade
2-6 reading (English)
Larry 57 Grade 0-4
Megan 58 Grade 0-4
Mei Kim (ESL) 26 University graduate, grade
2-6 reading (English)
Naomi 60 Grade 5-12
Onuki (ESL) 26 University graduate, grade
2-6 reading (English)
Otto 38 Grade 0-4
Ricardo 50 Grade 0-4
Rita 22 Grade 5-12
Sam 45 Grade 0-4
Teresa 17 Grade 5-12
Tina 24 Grade 5-12
Henry (poor, literate) 21 Not applicable, some college
education
Valencio 21 Grade 5-12
Victoria 25 Grade 5-12
Xenia 46 Grade 5-12 Coping Strategies
Legend for Chart:
A - Coping Strategies
B - Classifications
A
B
Avoidance
Shop at the same store: avoids stress of unfamiliar
environment
Problem focused: shops effectively
Predecision: habitual choice about store helps
with choices about products
Shop at smaller stores: avoids cognitive demands from
product variety
Emotion focused: reduces stress
Predecision: requires advance planning
Single-attribute decisions: avoids stressful and complex
product comparisons
Problem focused: makes decisions manageable
Emotion focused: preserves image of competence
Predecision: requires advance planning
Avoid percentage- and fraction-off discounted items:
avoids difficult numerical tasks
Emotion focused: reduces stress
Problem focused: less chance of mistakes
Predecision: implements habitually
Buy only known brands (loyalty): avoids risks from
unknown brands
Problem focused: facilitates shopping
Predecision: implements habitually
Rationalize outcomes to shift responsibility: avoids
responsibility for outcomes
Emotion focused: protects self esteem
Postdecision: implements after outcome is clear
Carry limited amounts of cash: avoids risks of
overspending and being cheated
Problem focused: controls transactions
Predecision: requires advance planning
Buy small amounts more often: avoids risk of large scale
cheating
Problem focused: controls transactions
Predecision: requires advance planning
Pretend disability: avoids revealing deficiencies and
embarrassment
Problem focused: obtains assistance
Emotion focused: preserves public image
Predecision: requires advance planning
Pretend to evaluate products and prices: avoids revealing
deficiencies indirectly
Emotion focused: preserves public image
Predecision: requires advance planning
Confrontative
Shop with family members and friends: enables others to
know deficiencies
Problem focused: helps shop on a budget
Predecision: involves advance planning
Establish relationships with store personnel: enables
others to know deficiencies
Emotion focused: avoids embarrassment and stress
Predecision: involves advance planning
Seek help in the store: enables others to know
deficiencies
Problem focused: facilitates final decision
Predecision: leads to a purchase decision
Give all money in pockets to cashier: admits deficiencies,
plays on honesty standards
Problem focused: avoids not being able to count
Predecision: implements habitually
Buy one item at a time: addresses the problem of loss of
control when turning over cash
Problem focused: controls pace of transactions
and flow of funds
Predecision: requires advance planning
Confront store personnel and demand different treatment:
focuses on responses and behaviors of others
Emotion focused: seeks to minimize or eliminate
embarrassment and to preserve or restore public image
Postdecision: implements in response to others
Plan expenditures with assistance from others: enables
others to know deficiencies
Problem focused: facilitates a budget
Predecision: involves advance planning Illustrative Tactical Responses for Retailers and Producers
Legend for Chart:
B - Concrete Reasoning Value Trade-Off; Concretizing Tasks
C - Concrete Reasoning Magnitude Estimation and Computing
Difficulties
D - Pictographic Thinking Sight Reading; Pictorial Recognition
E - Pictographic Thinking Visualizes Attributes and Usage
Situations
F - Pictographic Thinking Visual Counting
G - Decision Heuristics Habitual Choice; Random Choice;
Single-Attribute Decision Making
H - Coping Strategies Dependence on Others; Self-Esteem
Maintenance; Dissimulation
A B C
D E
F G
H
Retailers Actual price Computation aids
presented on shelf for total basket-price
clearly; shopping management
aids on retail shelf (e.g., shopping
such as size carts with
graphics; visual scanners); dollar
depiction of and cents
price-size ratio on discounts rather
shelf. than fraction-off
discounts.
Maintain familiar Pictorial dollar and
store layout or cents displays;
ease transitions to shelf displays
new layout over matched to use
time; familiar brand (e.g., Sunday
logos on store dinner); carts that
signage. suggest other
ingredients and
locations.
Fractions in Pictorial
pictorial form; explanations of
dollar and cents brand logo
discounting and changes if
displays of savings producers do not
to replace fraction- provide them;
and percentage-off pictorial depictions
displays. of value deals.
Family-friendly
shopping
environments; train
personnel to
recognize
consumer needs
and assist while
preserving
consumers' dignity.
Producers Pictorial depiction Pictorial depiction
of attribute of volume content
information; in forms that do not
pictorial depiction change as does
of expiration date. packaging.
Retain familiar Pictorial depictions
logos or implement of unique attribute
smooth transitions information on
to new logos. packages.
Value propositions Consumer
in visual form; education aids
price-size savings (through adult-education
in visual form. programs) that
highlight brands
and assist in
teaching of safe
food handling and
balanced-diet
needs.
Family-focused
brand promotion
programs using
"stealth" and
similar promotional
programs based on
word of mouth.DIAGRAM: FIGURE 1; Data Gathering and Analysis Process
DIAGRAM: FIGURE 2; Conceptual Hierarchy of Findings
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~~~~~~~~
By Madhubalan Viswanathan; José Antonio Rosa and James Edwin Harris
Madhubalan Viswanathan is Associate Professor of Business Administration, College of Business, University of Illinois at Urbana-Champaign
José Antonio Rosa is Assistant Professor of Marketing, Weatherhead School of Management, Case Western Reserve University
James Edwin Harris is Assistant Professor of Business Administration, Saint Norbert College
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 49- Decomposing Influence Strategies: Argument Structure and Dependence as Determinants of the Effectiveness of Influence Strategies in Gaining Channel Member Compliance. By: Payan, Janice M.; McFarland, Richard G. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p66-79. 14p. 1 Diagram, 8 Charts, 2 Graphs. DOI: 10.1509/jmkg.69.3.66.66368.
- Database:
- Business Source Complete
Decomposing Influence Strategies: Argument Structure and
Dependence as Determinants of the Effectiveness of Influence
Strategies in Gaining Channel Member Compliance
Although there is considerable research examining the effects of influence strategies on relational outcomes, research has been silent on the effectiveness of influence strategies in achieving the primary objective: channel member compliance. The authors develop a theoretical model that predicts that noncoercive influence strategies (Rationality, Recommendations, Information Exchange, and Requests) with an argument structure that contains more thorough content result in relatively greater levels of compliance. The model further predicts that coercive influence strategies (Promises and Threats) result in compliance only when target dependence levels are high. The authors develop a new influence strategy, Rationality, which represents a noncoercive strategy with a full argument structure. In general, empirical findings support the theoretical model. However, in contrast to expectations, the use of Recommendations had a negative effect on compliance. Post hoc analysis revealed a significant interaction between trust and Recommendations on compliance, thus providing an explanation for this unexpected result. When trust is low, Recommendation strategies are counterproductive. The authors discuss implications of the findings and directions for further research.
Influence strategies are compliance-gaining tactics that channel members use to achieve their desired actions from channel partners (Frazier and Summers 1984). Previous research on influence strategies has demonstrated their importance within channels of distribution. This research has examined many important issues, such as the effect of influence strategies on channel conflict (Frazier and Rody 1991), interfirm agreements (Frazier and Summers 1984), satisfaction (Frazier, Gill, and Kale 1989; Scheer and Stern 1992), relationalism (Boyle et al. 1992), and solidarity (Kim 2000). Researchers have also examined antecedents of influence strategies, including power (Boyle and Dwyer 1995; Venkatesh, Kohli, and Zaltman 1995) and dependence (Frazier, Gill, and Kale 1989; Frazier and Summers 1984; Gundlach and Cadotte 1994). This research is valuable; nevertheless, there is little, if any, existing research that examines the effectiveness of influence strategies in gaining channel member compliance. Given that the purpose of influence strategies is to gain compliance, this is a critical gap in the literature. As a result, there is little advice for channels managers about the effectiveness of the different influence strategies.
This surprising gap in the literature may be explained by the common categorization of influence strategies as either coercive or noncoercive and a corresponding focus on the effects of coercion on relational outcomes. With the recent focus on the importance of relationships in meeting channels objectives (Jap 1999; Morgan and Hunt 1994), it is understandable that channel researchers have focused on relational outcomes. Unfortunately, the impact of these strategies in gaining compliance remains unknown. For example, it is known that, in general, the use of noncoercive influence strategies results in positive relational outcomes and that the use of coercive influence strategies undermines relationships (e.g., Boyle et al. 1992; Frazier and Rody 1991); however, it is not adequately understood whether either achieves its primary objective, namely, compliance with the influence attempt.
The purpose of this article is to develop a comprehensive theory that predicts the effectiveness of influence strategies in gaining channel member compliance. We complement previous research on influence strategies and channels relationships and fill the void regarding influence strategies and channel member compliance. We do this by drawing on argument structure theory from the consumer behavior literature and on dependence theory from the marketing channels literature; from these, we develop our own theoretical framework on the compliance-gaining effectiveness of influence strategy.
Our theoretical model takes an expanded view of influence strategies and predicts that the effectiveness of noncoercive influence strategies (i.e., those that are meant to be persuasive) is based on the completeness of their argument structure and that coercive influence strategies (i.e., those based on sanctions or rewards) are effective only when the target is highly dependent on the source of the influence. Argument structure theory posits that the structural content of a persuasive argument is critically related to its effectiveness (Areni 2002; Munch, Boller, and Swasy 1993), whereas dependence theory suggests that the effectiveness of coercive strategies is based on the level of dependence of the target on the source.
In the next section, we review the literature on channels influence strategies, argument structure theory, and dependence theory as the basis for our theoretical model. Following that section, we develop hypotheses on the impact of these influence strategies on compliance. Then, we discuss the research methods and findings and provide implications of these findings. Finally, we note the limitations of the study and suggest future directions for research.
Literature Review
Influence strategies are the communicated portion of influence attempts that one channel member uses to gain the compliance of another channel member. Such strategies can be used for simple coordination purposes or for more serious matters such as major interfirm initiatives. Influence strategies are classified as either coercive or noncoercive. Coercive influence strategies motivate compliance on the basis of the influence mechanism of source-controlled rewards and punishments, whereas noncoercive influence strategies operate by changing the attitude of the target about the desirability of the intended behavior (Frazier and Summers 1984, 1986). The predicted outcomes of our conceptual model are based on these two influence mechanisms (which have been confirmed by two meta-analyses conducted on influence behavior in channels of distribution; Johnson, Koenig, and Brown 1985; Johnson et al. 1993).
The underlying phenomenon of source-controlled consequences for desired behavior has been given different labels in channels research, such as contingent (John 1984; Scheer and Stern 1992), economic (Etgar 1978), direct outcome control (Kaulis, Spekman, and Bagozzi 1978), and mediated (Johnson, Koenig, and Brown 1985) influence mechanisms. With this influence mechanism, the underlying action may or may not be desirable to the target, yet the action is undertaken to gain an additional reward or to avoid being punished. With the noncoercive influence strategy, because attitude change must occur for compliance to take place, the more convincing the influence strategy is, the more likely compliance is to occur.
Frazier and Summers (1984) were the first to specify the six most commonly studied influence strategies in marketing channels research. Noncoercive influence strategies consist of Requests,( n1) Information Exchange, and Recommendations, whereas coercive strategies consist of Promises, Threats, and Legalistic Pleas. (Note that throughout the article, we capitalize influence strategies to distinguish them from their structural components. For example, an influence strategy may or may not contain a request for a specific action; however, this structural component should not be confused with the influence strategy, which is conventionally referred to as Requests.) Subsequent research has demonstrated that Legalistic Pleas are a special case of Threats (e.g., Johnson et al. 1993). Prestudy interviews with owners and managers of distribution firms confirmed that Legalistic Pleas are perceived as Threats but are rarely used (for definitions of the influence strategies we use in this study, see Table 1). As we subsequently discuss in more detail, on the basis of argument structure theory, it was necessary to add a new noncoercive strategy to this taxonomy. We call this strategy Rationality.( n2)
Argument structure theory from consumer behavior research has demonstrated that advertising messages with more thorough argument structure have a stronger positive impact on consumer beliefs and message acceptance than do those with less thorough argument structure (Areni 2002; Areni and Lutz 1988; Munch, Boller, and Swasy 1993). Aristotle originally described the structure of an ideal argument as a syllogism, and Toulmin (1958) subsequently referred to it as argument structure. Argumentation structure theory and rhetorical discourse present the ideal argument as a complete argument. A complete argument is ideal because it is predicted to have the highest level of influence. The three structural elements of a complete argument are claim, data, and warrant. The claim is an assertion, a request, or a demand put forth for acceptance. The data are information or facts that when linked to the claim offer evidentiary support for the claim. The warrant is the conclusion or linkage between the data and the claim. An example of a complete argument in a compliance context would be as follows: "I'd like you to promote the product only in these specific sales territories [claim]. This five-year forecast indicates that the target market will continue to grow in these territories [evidence]. Therefore, you would realize more profit if you promoted the product only in these territories [concluding statement or linkage between the evidence and the request]."
Frazier and Sheth (1985) suggest that influence strategies can be defined by whether or not two structural components are present. Thus, their work is useful in aiding the theoretical application of argument structure theory to influence strategies. They state that influence strategies can be weighted or unweighted. Weighted strategies include a discussion about the benefits of the intended action beyond its potential for a future reward or a future punishment, whereas unweighted strategies do not discuss the benefits of the intended actions. An example of an unweighted strategy might be a simple Request: "We would like you to start shipping in full rather than partial truckloads." A weighted strategy would discuss the logical benefits to the target of the intended action: "Your savings associated with shipping in full truckloads will more than offset any potential higher carry costs." Frazier and Sheth also state that strategies can be direct or indirect. Direct strategies include an explicit request for the actions that the source of influence wants the target to take, whereas indirect strategies do not directly discuss the request. (Again, note the difference between the Requests influence strategy and the request structural component.) An example of an indirect strategy would be Information Exchange, in which the source might simply say, "Many of our dealers have had great success with our new just-in-time system." The intention of the strategy is to convince the channel member to implement the new just-in-time system, yet this is not explicitly stated. Table 2 applies Frazier and Sheth's categories to each of Frazier and Summers' (1984) six influence strategies.
By decomposing influence strategies into their structural components, we can map argument structure theory onto the structure of influence strategies. The direct request of a direct influence strategy is analogous to the claim in a complete argument, because a claim is an assertion, a request, or a demand put forth for acceptance. In the context of channel compliance, a source issues a request in the hope that the target will agree that compliance with the request is acceptable. In the language of Kelman and Hamilton (1989), the source communicates the specific response desired of the target (the request) in the hope that the target will perceive compliance as "prepotent" or the strongest alternative relative to other possible responses (including noncompliance). Thus, a request corresponds to a claim. Further refinement is required when we map weighted strategies onto the structural elements of data and warrant. A weighted strategy may contain the element of data/ evidence (e.g., in the case of Information Exchange), the element of a concluding statement/warrant (e.g., in the case of Recommendations), or both the evidence and a concluding statement (e.g., in the case of Rationality). Table 3 depicts each of the noncoercive influence strategies and denotes which of the three components of a complete argument are present in each strategy. This table shows that none of the noncoercive influence strategies (i.e., Requests, Information Exchange, and Recommendations) examined in prior studies have a complete argument structure. Thus, it is necessary to add the Rationality strategy, which has complete argument structure.
Most research on dependence in a marketing channels context draws on the theoretical work of Emerson (1962), who conceptualized dependence as the level of value that one firm can garner from another firm compared with the value it can garner from alternative firms in achieving its goals. Typically, channel studies include dependence as a precursor to the use of influence strategies (Boyle et al. 1992; Frazier and Rody 1991; Frazier and Summers 1986; Gundlach and Cadotte 1994). However, these studies do not elucidate how dependence and the use of influence strategies operate together to achieve compliance. Kelman and Hamilton (1989) provide some theoretical direction. They define dependence as the extent to which the target perceives the source as instrumental to the achievement of its goals, and they suggest that the target is dependent on the source to the extent that the target perceives that the source can facilitate or impede the target's goals. This perception is formed by ( 1) the target's perception that the source has the capacity to affect the target's goal achievement, which might take the form of controlling resources or having the ability to apply certain sanctions, and ( 2) the target's perception that the source will indeed use the capacities at its disposal.
We suggest that if the target perceives itself to be highly dependent on a source firm and the source attempts to communicate that it will apply sanctions in an influence attempt (i.e., Threats or Promises), the effect on compliance will be amplified. Keith, Jackson, and Crosby (1990, p. 31) explain, "When [the target] has a large stake in a relationship (e.g., a significant proportion of sales and profits accrue from the relationship), [the target] is more dependent on [the source] and is more likely to be tolerant of demands [e.g., Threats] made by [the source]." Conversely, it is our position that with the use of noncoercive influence strategies (i.e., Rationality, Recommendations, Information Exchange, and Requests), which are based on persuasion and not on coercion, dependence is not likely to be a major target concern (the use of persuasion gives the target room not to be persuaded).
Hypotheses
In the following section, we develop hypotheses based on a theoretical framework that suggests that noncoercive influence strategies are more or less effective on the basis of the thoroughness of their content and that coercive influence strategies result in compliance only when the target is highly dependent on the source. We depict this theory in Figure 1.
Frazier and Rody (1991) and Frazier and Summers (1984) state that noncoercive influence strategies rely primarily on changing the target's perception. In other words, the source attempts to convince the target that a certain course of action is warranted, and in the process, the target's attitude about the attractiveness of taking that action changes. Presumably, and consistent with attitude theory, when a target's attitude about the attractiveness of taking the action becomes positive, the target is more likely to act. Raven and Kruglanski (1970) theorize that influence behavior that changes perceptions results in positive influence, and presumably compliance, because the accepted logic would become a part of the target's cognitive structure. Together, this implies that noncoercive influence strategies, which are persuasive in nature, are likely to be positively associated with compliance. However, drawing on argument structure theory, we maintain that the thoroughness of the argument associated with the influence strategy is related to its relative effect on compliance. For example, an influence strategy that contains arguments that support a request may be more likely to result in compliance than an influence strategy that contains only a request without any communicated logical support for the request.
Consistent with argument structure theory, we posit that the Rationality influence strategy, which has a more thorough argument structure than the other three noncoercive influence strategies (i.e., Recommendations, Requests, and Information Exchange), has the strongest positive effect on compliance. In other words, we posit that Recommendations, Requests, and Information Exchange have incomplete argument structure, and thus they may lead to a target's faulty inference about the missing portions. Following this logic, Recommendations should have the next strongest impact on compliance because two of the three structural components are present, and therefore fewer inferences are necessary with Recommendations than with Requests and Information Exchange influence strategies.
Argument structure theory provides rationale for the relative effectiveness of the noncoercive influence strategies; additional support can be found in the concept of the quality of strong versus weak arguments that is included in the elaboration likelihood model that Petty and Cacioppo (1981) propose. Empirical studies have demonstrated that the central processing of strong arguments results in higher levels of persuasion and compliance than that of weak arguments. Although the specification of argument quality is not always precise across studies, Petty, Cacioppo, and Goldman (1981) suggest that a strong argument provides persuasive evidence (e.g., statistics, data) in support of a claim. Some researchers have suggested that argument quality resides primarily in the structure of the argument; that is, the elaboration likelihood model's strong argument quality consists of a complete argument structure, and weak argument quality consists of incomplete argument structure (Areni and Lutz 1988; Boller, Swasy, and Munch 1990). Thus, the elaboration likelihood model provides additional support for our contention that Rationality is a more effective influence strategy than Recommendations.
Argument structure theory supports our prediction that Rationality and Recommendations are more effective than Requests or Information Exchange strategies because Rationality and Recommendations have more thorough argument structure than do Requests or Information Exchange, and more thorough or complete arguments are more effective. That Recommendations is more effective than Requests is supported by several interpersonal empirical studies that demonstrate that a request in and of itself (whether processed centrally or peripherally) is less effective than if it is accompanied with supportive information (e.g., Folkes 1985; Langer, Blank, and Chanowitz 1978). However, argument structure theory cannot guide us in determining whether Requests is more, less, or equally as effective as the Information Exchange strategy.
Munch, Boller, and Swasy (1993) suggest that an argument devoid of both a claim and a warrant (analogous to Information Exchange) is even more likely to be interpreted inaccurately than an argument that consists solely of a claim (analogous to the Requests influence strategy). A lower likelihood of compliance with the use of Information Exchange versus Requests is due to Information Exchange being the most unfocused of the influence strategies. The Information Exchange influence strategy lacks specificity as to what the source wants the target to do. The specific action that the target wants the source to undertake may remain clouded, whereas with the Requests influence strategy, the desired action the source wants the target to take is explicitly noted. Thus, Information Exchange is likely to result in more faulty inferences than Requests. Therefore, we posit that Information Exchange is likely to be less effective than Requests and thus the least effective noncoercive influence strategy overall. Stated formally:
H1: (a) The Rationality influence strategy has a stronger positive influence on compliance than do the other noncoercive influence strategies. (b) Recommendations has the next strongest influence on compliance, followed by (c) Requests. (d) Information Exchange is the least effective noncoercive influence strategy.
Influence strategies based on the influence mechanism of source-controlled consequence (i.e., Threats and Promises) offer threats of future penalties for lack of compliance or promises of future incentives for compliance (Frazier and Summers 1986). A target of influence in a dyadic channels relationship is likely to comply with these coercive influence strategies only if the attainment of the rewards or the avoidance of penalties can be administered by the source of influence and only if the rewards or penalties are important to the target in achieving its desired goals. "Dependence refers to a firm's need to maintain the business relationship in order to achieve desired goals" (Frazier 1984, p. 69). If the target can leave the relationship easily or with few consequences (and obtain equivalent rewards or avoid equivalent penalties more cheaply elsewhere), dependence is low, and the impact of coercion is dulled (Dwyer, Schurr, and Oh 1987).
When the importance of a relationship is low, the value of complying with coercive influence strategies should logically be lower as well. Firms stay in poor relationships only if there are no viable alternatives, that is, when dependence is high. This suggests that at lower levels of dependence, the use of coercive influence strategies is ineffective. The target of influence is likely to comply with coercive influence strategies only when it is forced to do so because of its dependence on the source of coercive influence strategies. Thus:
H2: There is a positive interaction between dependence and the coercive influence strategies of (a) Threats and (b) Promises on compliance.
Method
We obtained the names and addresses of 6049 owners and managers of distribution firms of specialty tools and fasteners in the United States (Standard Industrial Classification codes 5072-05 and 5072-13). Because it was likely that many of the people included in this list were not directly involved in channel management, it was necessary to screen them to ensure that the final sample included only key informants who were the primary decision makers in their firms and who were the most knowledgeable about their firm's interactions with suppliers. We mailed prestamped return postcards that asked the owners and managers about their knowledge regarding the topics covered in the study. A total of 1038 people, who constituted our sample of key informants, indicated that they were knowledgeable about the topics in the study. These respondents were mailed a questionnaire and a return envelope. In line with the survey methods that Dillman (2000) suggests, we sent a reminder postcard seven working days after the initial questionnaire mailings. A total of 363 usable surveys were returned.
As an additional competency check to ensure that our respondents were indeed key informants, we included two items in the survey as informant competency checks. The two items asked ( 1) how much the informant knew about his or her firm's perspective on the study topics and ( 2) how much the informant knew about specific experiences with the source firm. Of the informants, 99% had knowledge about their firm's perspective, and 98.2% had knowledge about experiences with the source firm. Thus, we eliminated seven cases from the database for the purpose of testing the hypotheses, which left 356 usable survey responses for analysis.
To check for nonresponse bias, we mailed 150 letters and prestamped return postcards to a random list of nonrespondents. Of these postcards, 32% were returned. We did not find any significant differences between respondents and nonrespondents. In addition, in line with Armstrong and Overton's (1977) guidelines, we found no significant differences between early and late responders.
For details of all scale items, see the Appendix. Participants responded to five-point Likert-type scales for all variables. The reliability for all scales exceeds the recommended cutoff criteria: Cronbach's alpha > .70 (Nunnally 1978), composite reliability > .70 (Fornell and Larcker 1981), and variance extracted > .50 (Hair et al. 1998). For summary statistics and the correlation matrix for all scales, see Table 4. We measured the scales for the influence strategies on the basis of the frequency of usage for each influence strategy over the past year. The scales were anchored by 1 ("never") and 5 ("very often"). We drew measures for Threats, Promises, Information Exchange, and Recommendations from Venkatesh, Kohli, and Zaltman's (1995) work and for Requests from Boyle and colleagues' (1992) work. We based the Rationality items on Gundlach and Cadotte's (1994) information persuasion scale. We made minor adjustments to the scales to conform to the context of interest.
On the basis of current research and consistent with our hypotheses, we used a measure of dependence that was designed to reflect the criticality/scarcity of the source of supply (Andaleeb 1996; Kumar, Scheer, and Steenkamp 1998). We used the target's perception of his or her dependence on the source because it is this perception, rather than the source's perception, that affects the target's compliance decision. Kelman and Hamilton (1989) suggest that a target makes a quick "visceral" assessment of its dependence on the source as a precondition for compliance. We adapted dependence items from Kumar, Scheer, and Steenkamp's (1998) work as a reflective measure. This five-point Likert-type scale was anchored by 1 ("very strongly disagree") and 5 ("very strongly agree").
Compliance refers to the target acting in accordance with an influence attempt from the source. We asked respondents to assess how frequently they complied with the influence strategies that the source used over the past year (i.e., the same period of time that respondents provided information on influence strategy usage). These scales were anchored by 1 ("never") and 5 ("very often"). We borrowed and modified the items from Hunt, Mentzer, and Danes's (1987) multi-item measure of compliance probability. Whereas their scale measures the probability or intention to comply, we adapted the scales to reflect actual compliance rather than an estimate of the probability of future compliance. (This distinction is important and is analogous to the distinction between a behavioral intention and actual behavior, which are often not highly correlated.)
Before developing the pretest questionnaire, we conducted interviews with dealer principals and domain experts to ensure the development of appropriate measures. We paid particular attention to the development of the compliance and Rationality measures. Next, we pretested the questionnaire with 460 owners and managers of distribution firms of specialty tools and fasteners who we did not include in the final study. We asked each owner or manager to respond to the questionnaire and to indicate any instructions or questions that they believed were confusing. We received 126 responses, of which 107 were usable (a response rate of 23%). On the basis of these responses, we shortened the questionnaire and eliminated several open-ended questions. At this stage, we determined that the questionnaire was ready for the final study.
Because we are exploring the relative effectiveness of each influence strategy, it is imperative that our measures demonstrate discriminant validity and appropriate factor structure. Therefore, we initially ran an exploratory factor analysis on the influence strategy measures. On the basis of a baseline eigenvalue of 1.0, we arrived at a six-factor solution using oblique rotation. As we show in Table 5, the six influence strategies have good factor structure.
Next, we analyzed all measures in a single confirmatory factor analysis model using LISREL 8.54 (Jöreskog and Sörbom 1996). Model fit exceeded the standard cutoffs for acceptable fit: χ² = 336, degrees of freedom = 224; root mean square error of approximation = .038; nonnormed fit index = .98; and comparative fit index = .98. Convergent validity is indicated when the path coefficients (loadings) for each latent-trait factor to their manifest indicators are statistically significant. All items loaded significantly on their corresponding latent factors. Using the procedure that Fornell and Larker (1981) recommend, we obtained discriminant validity for all pairs of measures. To test for unidimensionality, we analyzed each construct as a one-factor scale, using confirmatory factor analysis (Gerbing and Anderson 1988). In each case, the single-factor model had an acceptable fit (i.e., root mean square error of approximation < .08, comparative fit index > .95), which indicates that the constructs are unidimensional.
Results
We used multiple regression analysis to test the hypotheses (Cohen et al. 2003). To avoid problems with multicollinearity, we mean-centered the exogenous variables, as Cohen and colleagues (2003) recommend. The variance inflation factors indicate that multicollinearity is not a threat to the conclusions of the study. To test the interaction hypotheses, we conducted an interaction regression. However, there is some controversy in the literature regarding the interpretation of main effects in an interaction regression equation because the beta coefficients represent conditional relationships for the independent variables in the estimated model, whereas with a main effects-only model, the coefficients represent the effects of the independent variables on the dependent variable across all levels of the other independent variables (Jaccard, Turrisi, and Wan 1990). Thus, to test H1, we conducted a main effects-only model, which we specify in Table 6. To test H2, we specified a second interaction regression model, following Cohen and colleagues' (2003) guidelines. The results of the two regression models appear in Table 6, and we specify them in the following equations. The main effects model explains 24% of the variation in the dependent variable (F(7, 348) = 15.9, p < .000), and the interaction model explains 28% of the variation in the dependent variable (F(9, 346) = 17.2, p < .000). We specified the main effects model as follows:
(1) Compliance =
α0 + β1X1 + β1X2
+ β3X3 + β4X4
+ β5X5
+ βX6 + β7X7
+ ε1,
where
X1 = Rationality,
X2 = Recommendations,
X3 = Requests,
X4 = Information Exchange,
X5 = Dependence,
X6 = Threats, and
X7 = Promises.
The interaction model was specified as follows:
(2) Compliance =
α0 + β1X1 + β2X2
+ β3X3 + β4X4
+ β5X5 + β6X6
+ β7X7 + β8X6X5
+ β8X7X5 + ε1.
The student t-test for each beta coefficient in the estimated regression model represents the probability that the coefficient is statistically different from zero and thus meaningful. To test for the relative magnitude of the regression coefficients, we conducted statistical comparisons between each pair of beta coefficients in accordance with the test procedure for multiple regression analysis using SAS 8.02 (for a summary of the results of these tests, see Table 7).( n3) Following this approach, H1a is supported because Rationality has the largest positive impact on compliance (standardized coefficient = .224, t-value = 3.80, p < .01) among the four noncoercive influence strategies, and we reject the null test hypotheses that the beta coefficient for Rationality is equal to the beta coefficients for Recommendations, Requests, and Information Exchange. Contrary to expectations, Recommendations has a negative effect on compliance (standardized coefficient = -.166, t-value = -2.81, p < .01), and not surprisingly given this negative value, the tests results we present in Table 7 indicate that the beta coefficient for Recommendations is significantly less than the other three (positive) beta coefficients. Thus, H1b is not supported. Given this result, we conducted further analysis to determine whether any intervening factors could explain the negative relationship between Recommendations and compliance. We discuss this analysis in the "Post Hoc Analysis" section. Requests has the next strongest influence on compliance (standardized coefficient = .09, t-value = 1.76, p < .05), followed by Information Exchange (standardized coefficient = .013, t-value = not significant). Although we have concluded that the effects of Requests and Information Exchange are significantly less than the effect of Rationality on compliance, on the basis of the test results, we cannot conclude that the difference between the beta coefficients of Requests and Information Exchange is significant (see Table 7). Thus, there is mixed support for H1c and H1d. With the exception of Recommendations, there appears to be evidence that the argument completeness of noncoercive influence strategies is related to their effectiveness in gaining compliance. At a minimum, we can conclude that the Rationality strategy, which has a complete argument structure, is significantly more effective than noncoercive influence strategies that have less complete argument structure.
In line with Cohen and colleagues' (2003) guidelines, we estimated the interaction effects between the coercive influence strategies and dependence on compliance using multiple regression analysis. The interaction between Threats and dependence is significant (standardized coefficient = .10, t-value = 2.19, p < .01), indicating that the effect of Threats on compliance varied across levels of dependence. We conducted simple slope tests to explore the form of the interaction (see Cohen et al. 2003). This test involves estimating the slope of the relationship between Threats on compliance at high and low levels of dependence (i.e., one standard deviation above for high and one standard deviation below for low). We plot the results using the unstandardized estimates and intercepts in Figure 2 (Cohen et al. 2003). This analysis indicates that Threats has a positive impact on compliance when dependence is high (β = .08, t-value = 3.71, p < .01) but is not significantly related to compliance when dependence levels are low (β = .05). Thus, H2a is supported. The interaction between Promises and dependence on compliance is not significant; thus, H2b is not supported.
Generally accepted theory holds that the use of Recommendations should be positively related to relational outcomes, and most authors would assume that the use of Recommendations is also positively associated with compliance. Yet we found that the use of Recommendations has a significant, negative impact on compliance. On reviewing the literature further, we found that such results may not be as unexpected as most researchers in the field would traditionally assume. Many published studies that have conducted empirical analyses with the Recommendations influence strategy have had unexpected results. The first study largely responsible for the subsequent examination of influence strategies also had unexpected results with Recommendations. Frazier and Summers (1984) found that Recommendations correlated negatively with Information Exchange and positively with Promises, Threats, and Legalistic Pleas. Furthermore, they found that Recommendations negatively correlated with interfirm agreements. Frazier and Summers (1986) found that the use of Recommendations was negatively related to accommodative intentions. Boyle and colleagues (1992) found that Recommendations had a negative correlation with relationalism. Frazier and Rody (1991) found that Recommendations fell into the same group ( factor) as Promises, Threats, and Legalistic Pleas for both supplier and dealer samples. These findings indicate that an additional intervening variable may be causing these unexpected results.
Our application of argument structure theory to influence strategies, the addition of the Rationality influence strategy to the influence strategy taxonomy, and Rationality's distinction from the Recommendations strategy provide a promising explanation for both our findings and prior unexpected empirical findings regarding the Recommendations influence strategy. Unlike the Rationality influence strategy, in which a complete argument is given in support of the desired behavior, Recommendations provides a request and a concluding statement without evidence to support it. Thus, it is incumbent on the target to trust that this evidence exists and that there is truth in the source's concluding statement, or the target must trust that the source is behaving benevolently toward the target in making its Recommendations. Thus, there is a theoretical rationale that trust is potentially important for the successful use of Recommendations.
To test this post hoc hypothesis, we added trust and the interaction between Recommendations and trust as exogenous variables to the interaction model we specified in Equation 2. The main effect of trust (standardized coefficient = .115, t-value = 2.14, p < .01) and the interaction term (standardized coefficient = .089, t-value = 1.73, p < .05) were significant. We conducted simple slope tests to explore the form of the interaction, following the procedure that we discussed previously. We plot the results using the unstandardized estimates and intercepts in Figure 3. This analysis indicates that the use of Recommendations is not significantly related to compliance when trust levels are high (β = -.02) but that there is a negative impact on compliance when trust is low (β = -.21, t-value = -3.56, p < .01). Thus, our post hoc analysis suggests that the use of Recommendations is counterproductive when trust is low.
Although additional interactions were not predicted or indicated because of mixed findings (as in the case of Recommendations), we conducted further exploratory analyses for possible interactions to provide a richer examination of the data. We did not find any significant interactions between dependence and the remaining influence strategies (other than the significant interaction reported between dependence and Threats) or between dependence and trust. We found a significant interaction between trust and Recommendations (as we discussed previously) and between trust and Requests; however, trust did not significantly interact with the other influence strategies. Given no a priori expectations for an interaction between trust and Requests, this finding should be viewed with caution.
Discussion
This study advances Frazier and Sheth's (1985) and Frazier and Summers's (1984, 1986) work on influence strategies. Argument structure theory holds that the more complete the argument structure of the communication that occurs in an influence attempt, the more effective is the communication. A complete argument structure in rhetorical discourse (Toulmin 1958) consists of ( 1) a request, ( 2) evidence, and ( 3) a concluding statement that links the evidence with the request. We categorize noncoercive influence strategies (i.e., Recommendations, Information Exchange, and Requests) on the basis of the number of elements of a complete argument that the strategy contains. Specifically, we note that Recommendations includes two elements, the request and a concluding statement that links the evidence and the request; Information Exchange contains only one element, evidence; and Requests contains only one element, the request. As a result of the application of this theory, we add a fourth noncoercive influence strategy, Rationality, because it contains all three elements of a complete argument structure.
In support of our theory, we find that use of the Rationality influence strategy is more likely to result in compliance than is the use of influence strategies that contain only one or two of these elements. However, contrary to our expectations, we find that Recommendations has a negative effect on compliance (post hoc analysis found an interaction between Recommendations and trust). As we expected, Requests, which includes only one element of a complete argument, has a small yet positive impact on compliance. Finally, we find that Information Exchange has the least (i.e., not significant) association with compliance. In summary, there is some support for the contention that the completeness of noncoercive influence strategies is related to their effectiveness in gaining compliance. However, the pattern of results suggests that compliance effectiveness depends more on whether all three elements of a complete argument structure are present or not than on the number of elements (i.e., two of three elements versus one of three elements) that make up a complete argument structure. In other words, compliance effectiveness may be enhanced if all three elements of a complete structure are present, but if any of the three elements are not present, a firm may make inferences about the missing elements of the argument structure before deciding to comply or not to comply with another influence attempt. Any inferences about the missing elements of a complete argument structure may be realistic or faulty inferences that are affected by other relationship variables (e.g., lack of trust).
This study finds no main effects for Threats and Promises on compliance. A stream of prior research that suggests that the use of Threats and Promises is damaging to interfirm relationships and our findings that Threats and Promises are ineffective might lead managers to avoid the use of these influence strategies altogether. However, at least two conditions suggest that the use of these strategies are appropriate. First, this study suggests the use of Threats is defensible when the target of influence is highly dependent on the source. Second, Scheer and Stern (1992) suggest that the use of Threats and Promises is not damaging to interfirm relationships if compliance leads to positive target performance.
Finally, we found that the use of Recommendations is negatively related to compliance, and we provided a post hoc explanation for this finding based on the significant interaction between Recommendations and trust. As we discussed previously, a review of the literature found many studies in which the use of Recommendations resulted in unanticipated outcomes (often significant in the nonhypothesized direction). The application of argument structure theory to influence strategies has demonstrated that Recommendations has only two of the three structural components that constitute a complete argument. Only with this theoretical advance can we explain these many unexpected previous findings. Recommendations is, in essence, advice without evidentiary support. Thus, when trust is low, it is likely that the target will view that advice as one-sided in the source's favor. We believe that this finding is a major contribution, particularly in light of the many unexplained and unexpected results across prior studies.
Several points regarding the role of dependence and the effectiveness of influence strategies in gaining compliance should be noted. First, Gundlach and Cadotte (1994, p. 525) suggest that "increasing dependence between exchange partners promotes cooperation rather than conflict" (and presumably compliance), and Frazier (1984) argues that dependence should be a direct antecedent of outcome variables. We find that dependence has a significant main effect on compliance. Indeed, our results indicate that dependence had a larger main effect on compliance than did any of the influence strategies. The size of the direct, positive effect of dependence on compliance in this study and the significant dependence moderation of the effect of one influence strategy (Threats) on compliance (discussed subsequently) show that dependence is a strong determinant of compliance. This provides further evidence that increasing dependence between channel members can be beneficial under some circumstances.
Second, channel studies have found that the frequency of coercive strategy usage is inversely related to target dependence (e.g., Frazier and Rody 1991; Frazier and Summers 1986). This may be due to the perception that the use of noncoercive influence strategies is ineffective under low target dependence. Thus, the source might rely on coercive strategies more frequently as a perceived "last resort." However, in conditions of low target dependence, a relatively frequent use of coercive strategies may not lead to compliance. Indeed, our results indicate that the use of coercive strategies under low dependence levels is ineffective. Thus, both the literature and our findings indicate that when target dependence is low, the source is more likely to use coercive influence strategies but that these strategies are also more likely to be ineffective. This issue has important managerial implications.
In summary, this study makes several important contributions. We emphasize that the effectiveness of channels' influence strategies in gaining compliance (their fundamental purpose) has not been the focus of prior published research. For that reason, this study provides fruitful avenues for further research and useful guidelines for channel managers. We presented a comprehensive theory that decomposes noncoercive influence strategies into three structural components and that argues that the relative effectiveness of a noncoercive influence strategy depends on the completeness of the influence strategy's argument structure. There is evidence that influence strategies with more complete argument structure are more effective than influence strategies with less complete argument structure. This theory, which is confirmed by our empirical findings, demonstrates the necessity for the inclusion of the Rationality influence strategy to the existing influence strategies taxonomy. Our theory suggests and finds support for an interaction effect between coercive influence strategies and dependence. Finally, the many counterintuitive empirical findings of the Recommendations influence strategy (including our preliminary findings of a negative direct effect for Recommendations on compliance) can be explained by a previously unidentified interaction between trust and Recommendations. This result and our findings of the importance of dependence on compliance outcomes provide additional support for the importance of the cooperative-based governance of channel relationships.
Because our sample consists of a single industry, the results may have limited generalizability. However, this limitation should be somewhat tempered because every respondent represented a unique firm. In addition, our study is based on cross-sectional data. Several researchers have noted that there is a need for longitudinal studies to advance the understanding of the impact of cumulative interactions between firms in forming long-term relationships (e.g., Geyskens, Steenkamp, and Kumar 1998; Jap 1999). Future longitudinal studies should also examine the sequential use of influence strategies. It is possible that certain sequences of influence strategies have differential effects. Furthermore, it is logical to assume that in at least some cases, the use of coercive strategies is a desperate attempt used only after other influence attempts have failed. To the extent that this is true, cross-sectional studies may underrepresent the effectiveness of coercive influence strategies.
As we have noted, the target must go through an inference-making process when a source uses Requests. We suggest that the target infers that the source has a logical rationale for the request. This point of view is consistent with the many studies that label Requests as noncoercive (see n. 1). However, the target might also infer that sanctions would result for noncompliance. Indeed, the latter assumption was the basis for Frazier and Summers's (1984) original conception of Requests. Further research should attempt to operationalize Requests that are associated with assumed logical rationales and those that are based on coercion.
Individual influence strategies should be examined in more detail. For example, we examine the nature of some influence strategies on the basis of the thoroughness of their information. The nature of the request is also likely to have an effect on compliance. The more the source asks for (i.e., the greater the imposition of the request), the less likely the target is to comply (Bagozzi, Yi, and Baumgartner 1990). In other words, if a manufacturer asks a dealer to double inventory levels, the dealer is less likely to comply than if the manufacturer makes a minor request, such as a temporary change in delivery schedules. Mohr, Fisher, and Nevin (1996) note that the media by which an influence attempt is communicated (e.g., face-to-face, telephone, e-mail, written) and other communications facets (e.g., frequency, bidirectionality, formality) can have an effect on influence strategy outcomes. Therefore, we suggest, as does Kim (2000), that there is a need to examine more thoroughly the specific details of influence strategies and the nature of communication in which they take place. An episodic study design would be helpful for examining these topics.
As one reviewer suggested, because our dependent measure for compliance is based on a global level and because influence strategies are measured according to the frequency of their use, it is possible that the dependent variable is underaccounting for the relative effectiveness of effective strategies that are used infrequently. Similarly, strategies that are used frequently may be overaccounted for in terms of their relative effectiveness on the dependent measure. We offer two solutions for addressing this issue in further research. First, an episodic study design that examines a single influence attempt and a single compliance outcome would unambiguously and directly determine the effectiveness of each influence strategy on a strategy-by-strategy basis. Second, more precise measures of compliance could be developed by asking respondents both how frequently an influence strategy has been used and how likely they would be to comply with that influence strategy if it were used.
Note that as with prior channel studies, we examine the use of influence strategies on an individual basis rather than in conjunction with one another. As Frazier (1999) suggests, examining the use of influence strategies in conjunction with one another is an important avenue for further research. Certain combinations or mixes of influence strategies may yield different levels of effectiveness. For example, an extension of our argument structure rationale for the effectiveness of influence strategies might suggest that a Threat or Promise used in conjunction with Rationality is more effective. Threats and Promises do not include a rational argument on their own, and thus whether they are more effective when used with a rational argument should be investigated.
Several final recommendations can be made in light of our findings. First, the Rationality influence strategy should be included in further influence strategy research, given its unique factor structure and differential impact on compliance. Second, we found that Recommendations has a negative impact on compliance when trust is low; thus, the relationship between noncoercive influence strategies with incomplete argument structure and trust should be investigated further. Third, we found that Threats were effective only at high levels of dependence and that Promises were completely ineffective. Because coercive influence strategies may be used more frequently in certain situations than in others (i.e., when all other tactics have failed or when a target has initially agreed to comply and then has second thoughts) and because using coercive strategies more frequently may render them less effective, further research should examine the use of coercive influence strategies on a longitudinal basis.
The authors contributed equally to this research. The authors thank Goutam Challagalla, James Reardon, Ed Rigdon, and the three anonymous JM reviewers for their helpful comments on previous drafts of this article.
( n1) Because Requests is devoid of any additional information, such as a discussion about the desirability of the requested action or a discussion of rewards or punishments, the actual influence mechanism of Requests is based on the target's inference. As one reviewer suggested, the target of influence may infer that there is a rational basis for Requests. Alternatively, the target may infer that sanctions would be applied for noncompliance. In this study, we accept the most common classification of requests as noncoercive and assume that the basis for compliance is on an inferred argument rather than on inferred sanctions. Our assumption is supported by our prestudy interviews with owners and managers of distribution firms.
( n2) We developed the influence strategy of Rationality on the basis of an application and extension of argument structure theory. However, it should be noted that Gundlach and Cadotte (1994) examine an equivalent influence strategy in their research, which they call "information persuasion."
( n3) In accordance with the suggestion of a reviewer, we tested for the equivalence of the distributions for each measure. The chi squared test for differences between all measure distributions was 14.65 with 8 degrees of freedom, which is not significant at a p-value of .05, indicating that the distributions are equivalent.
Legend for Chart:
A - Influence Strategy
B - Definition
C - Underlying Influence Mechanism
A B
C
Rationality The source presents reasons accompanied
with supportive information for a target
to comply with a request.
Changes in the perception of
the desirability of compliance
Recommendations The source predicts that the target will be
more profitable if the target follows the
source's suggestions.
Changes in the perception of
the desirability of compliance
Requests The source simply states the actions it
would like the target to take.
Changes in the perception of
the desirability of compliance
Information The source discusses general issues and
exchange procedures to try to alter the target's
general perceptions without stating
a request.
Changes in the perception of
the desirability of compliance
Threats The source threatens the target with
a future penalty if the target does not
comply with a request.
Source-controlled
consequences
Promises The source promises the target a reward
if the target complies with a request.
Source-controlled
consequences Legend for Chart:
A - Influence Strategies
B - Weighted
C - Direct
A B C
Noncoercive
Requests No Yes
Information exchange Yes No
Recommendations Yes Yes
Coercive
Threats/legalistic pleas No Yes
Promises No Yes Legend for Chart:
A - Influence Strategy
B - Direct (Request explicitly stated)
C - Weighted Evidence (Data)
D - Weighted Concluding Statement (Warrant)
A B C D
Requests Yes No No
Information exchange No Yes No
Recommendation Yes No Yes
Rationality Yes Yes Yes
Legend for Chart:
A - Variable
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
A B C D E F
G H I
1. Rationality 1.00
2. Recommendations .45 1.00
3. Requests .23 .00 1.00
4. Information exchange .33 .19 .33 1.00
5. Threats -.03 -.09 .12 .03 1.00
6. Promises .37 .47 -.04 .17 .06
1.00
7. Dependence .18 -.05 .17 .18 .00
.04 1.00
8. Compliance .21 -.07 .20 .13 .09
.04 .43 1.00
Mean 2.73 2.96 2.52 2.21 2.01
2.82 3.25 3.27
Standard deviation .85 1.07 .93 .84 1.21
1.10 1.01 .77
Composite trait
reliability .80 .91 .83 .79 .95
.90 .78 .88
Variance extracted .58 .78 .63 .56 .87
.75 .55 .71
Notes: Correlations greater than ± .11 are significant
at p < .05. Legend for Chart:
A - Items
B - Factors 1
C - Factors 2
D - Factors 3
E - Factors 4
F - Factors 5
G - Factors 6
A B C D E F
G
Rationality 1 .710
Rationality 2 .869
Rationality 3 .630
.113
Recommendations 1 .732
.101
Recommendations 2 .927
Recommendations 3 .908
Requests 1 -.119 .689
Requests 2 .837
Requests 3 .822
Information exchange 1 .160 .615
Information exchange 2 .853
Information exchange 3 .756
Threats 1 .920
Threats 2 .985
Threats 3 .904
Promises 1
.829
Promises 2
.838
Promises 3
.902
Notes: For clarity, we include only factor loading greater than
± .100. Legend for Chart:
A - Independent Variable
B - Hypotheses
C - Standardized Coefficient
D - t-Value
E - Hypotheses Supported?
A B C
D E
Main Effects Model
Rationality H1a .224
3.80(**) Yes
Recommendations H1b -.166
-2.81(**) No
Requests H1c .090
1.76(*) Mixed support
Information exchange H1d .013
.25 Mixed support
Dependence .391
8.18(**)
Threats .045
.95
Promises -.029
-.52
Model Fit Adjusted
R² = 24%
F(7, 348)
= 15.9
Interaction
Effects Model
Rationality .232
4.04(**)
Recommendations -.170
-2.94(**)
Requests .083
1.66(*)
Information exchange .000
.00
Dependence .418
8.92(**)
Threats .083
1.79(*)
Promises -.019
-.34
Threats x dependence H2a .100
2.19(**) Yes
Promises x dependence H2b .028
.61 No
Model Fit Adjusted
R² = 28%
F(9, 346)
= 17.2
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Null Test Hypotheses
B - F-Statistic (degrees of freedom = 1,346)
C - p-Value
D - Reject Null?
E - Conclusion
F - H1 Supported?
A B C D
E F
βRationality =
βRecommendations 18.24 .0001 Yes
βRationality > βRecommendations Yes
βRationality = βRequests 4.05 .0451 Yes
βRationality > βRequests Yes
βRationality = βInformation
exchange 7.75 .0056 Yes
βRationality > βInformation exchange Yes
βRecommendations = 11.57 .0007 Yes
βRequests
βRecommendations < βRequests No
βRecommendations =
βInformation exchange 18.24 .0001 Yes
βRecommendations <
βInformation exchange No
βRequests = βInformation
exchange .93 .3366 No
βRequests = βInformation exchange NoDIAGRAM: FIGURE 1 Theoretical Framework of Influence Strategy Effectiveness
GRAPH: FIGURE 2 Threats x Dependence on Compliance Interaction
GRAPH: FIGURE 3 Recommendations x Trust on Compliance Interaction
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Rationality (α = .80)(a)
1. Made a case based on sharing specific information or data that your firm should comply. (.79)
- 2. Made a case based on sharing market research related to their request that you should comply. (.74)
- 3. Made a case based on past experience with similar issues that you should comply. (.75)
Recommendations (α = .91)(a)
1. Provided a picture of the anticipated positive impact to your firm that his or her recommended course of action will have. (.83)
- 2. Predicted positive consequences from the environment (e.g., that your firm would be more profitable) if you complied with their request. (.95)
- 3. Suggested you would be more successful financially if you followed their advice. (.86)
Requests (α = .83)(a)
1. Asked you to accept new ideas without specifying rewards or penalties. (.71)
- 2. Inquired if you would be willing to comply with a request without mention of rewards or penalties. (.81)
- 3. Shared a desire for your firm to make specific changes without incentives. (.85)
Information Exchange (α = .79)(a)
1. Provided you with market information without indicating what your firm should do. (.63)
- 2. Presented competitive information without indicating any action that needed to be taken. (.82)
- 3. Shared information about his or her company without explanation about his or her objective(s) in sharing this information. (.78)
Threats (α = .95)(a)
1. Indicated that there would be a penalty for noncompliance. (.92)
- 2. Threatened to discontinue specific benefits for noncompliance. (.97)
- 3. Stated that your firm would lose preferential status for noncompliance. (.91)
Promises (α = .90)(a)
1. Offered an incentive for compliance with their request. (.85)
- 2. Promised your firm a reward for your firm's cooperation. (.85)
- 3. Indicated how they would reward your firm's conformance with a request. (.90)
Dependence (α = .78)(b)
1. The work we do with this supplier is very important to our success. (.76)
- 2. There are few firms that could provide us with comparable output to what we obtain from this supplier. (.75)
- 3. Our total costs of switching from this supplier to a competing firm would be costly. (.71)
Compliance (α = .84)(a)
1. We accommodate what this supplier would like for us to do. (.82)
- 2. When this supplier asks us to change, we adjust accordingly. (.80)
- 3. My firm accommodates the desires of this supplier. (.91)
(a) We used a five-point Likert-type scale for these measures, anchored by 1 = "never" and 5 = "very often."
(b) We used a five-point Likert-type scale, anchored by 1 = "very strongly disagree" and 5 = "very strongly agree."
Notes: α = Cronbach's alpha scale reliability. We report standardized item loadings in parentheses following each item.
~~~~~~~~
By Janice M. Payan and Richard G. McFarland
Janice M. Payan is an assistant professor, Department of Marketing, Kenneth W. Monfort College of Business, University of Northern Colorado
Richard G. McFarland is an assistant professor, Department of Marketing, Kansas State University
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Record: 50- Dependence, Trust, and Relational Behavior on the Part of Foreign Subsidiary Marketing Operations: Implications for Managing Global Marketing Operations. By: Hewett, Kelly; Bearden, William O. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p51-66. 16p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.65.4.51.18380.
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Dependence, Trust, and Relational Behavior on the Part of Foreign Subsidiary Marketing Operations: Implications for Managing Global Marketing Operations
The authors explore how a global firm's ability to foster successful relationships between its foreign subsidiaries' and headquarters' marketing operations can enhance the performance of products across markets. The results show that cooperative behaviors are positively associated with product performance in the subsidiaries' markets. National culture in the foreign markets is also found to moderate the effect of trust on relational behaviors. In addition, the subsidiaries' acquiescence becomes increasingly important as the firm attempts to standardize marketing programs.
In today's global marketplace, it is increasingly likely that firms have a presence in more than one national market. Achieving success in the different markets in which the firm operates is largely dependent on the firm's ability to manage its marketing activities on a global basis. In particular, the abilities of the managers at the marketing operations in the individual foreign markets, as well as their willingness to work in conjunction with managers at the headquarters' marketing operation to achieve established objectives, may determine whether the firm can achieve marketing success.
Whether or not multinational corporations (MNCs) have a tendency toward standardizing or adapting global marketing strategies across foreign markets, cultivating effective relationships with the managers in the marketing operations at those locations is critical (Jain 1989; Quelch and Hoff 1986). According to Bartlett and Ghoshal (1991), a key change in strategy in MNCs is the building of multinational flexibility by relinquishing strategic roles to individual subsidiaries. As subsidiaries take on different strategic marketing roles, there is a greater need for effective management of the relationships between the headquarters' and subsidiary's marketing operations. From a relationship perspective, key success factors in cultivating successful relationships include the subsidiary's trust in and dependence on the headquarters (Blau 1964; La Valle 1994; Makoba 1993; Morgan and Hunt 1994).
Research examining the interface between the headquarters' and foreign subsidiary's marketing operations continues to suggest the importance of these relationships. Recent studies have focused more generally on the issues of power and control over foreign subsidiaries (see Nobel and Birkinshaw 1998; Nohria and Ghoshal 1994; O'Donnell 2000; Roth and Nigh 1992) and the management of knowledge flows across markets (Gupta and Govindarajan 2000). In addition, research focusing on other intrafirm relationships, such as those between functional units (see Ruekert and Walker 1987; Song, Montoya-Weiss, and Schmidt 1997; Song and Parry 1997) has tended to focus more directly on how the units differ in their perceptions of their roles and/or positions within the firm. Last, the literature on marketing relationships has largely addressed relationships in one national setting and has not considered the influence of national culture or the multinational firm context. We extend the findings from these different branches of literature by focusing more directly on the global marketing operation and by employing a relationship marketing framework to understand the factors driving successful relationships between the headquarters' and subsidiary's marketing operations. Finally, by examining the influence of national culture on these relationships, we provide additional insights into managing relationships in an international context.
In this article, we explore the factors that contribute to firms' abilities to successfully manage the marketing function globally, focusing on the perceptions of the headquarters'subsidiary relationships by the subsidiaries' marketing managers. We argue that cooperative and acquiescent behaviors on the part of the marketing operations at the foreign subsidiaries should enhance the likelihood that performance objectives for individual products are achieved in the subsidiaries' individual markets. As such, we approach the present research from a relationship marketing perspective (see Anderson and Narus 1990; Ganesan 1994; Morgan and Hunt 1994). Specifically, we link predictions based on the relationship marketing literature with findings regarding standardization/adaptation of marketing strategy and the influence of national culture on managerial behavior. Subsequently, we develop a framework that provides a lens through which to examine headquarters'subsidiary marketing function relationships and to investigate their importance in managing global marketing programs.
The importance of cultivating brands on a global basis is well recognized. When properly managed, a brand can contribute to the MNC's reputation and performance worldwide, acting as a symbol of the company's global image (Morris 1996). Also, global cultivation of a brand can yield economies of scale in the marketing and management of those brands (Aaker and Joachimsthaler 1999; Buzzell 1968; Levitt 1983; Quelch and Hoff 1986). Recent research in global marketing has focused largely on standardization/adaptation (see Jain 1989; Picard, Boddewyn, and Grosse 1998; Roth 1995a, b; Samiee and Roth 1992; Szymanski, Bharadwaj, and Varadarajan 1993; Walters and Toyne 1989), issues related to culture (see Money, Gilly, and Graham 1998), cross-national comparisons (see Samiee and Ancker 1998; Song and Parry 1997), and country-of-origin effects (see Hong and Wyer 1989; Maheswaran 1994). To date, limited empirical work has been done to explore the global management of the marketing function.
An example of the importance of headquarters-subsidiary communication in an MNC dedicated to global marketing is Colgate-Palmolive. In a recent study of consumer branding by global firms, Boze and Patton (1995) found that more than one-third (37%) of the Colgate brands included in their study were marketed in multiple countries and six of the brands are marketed in more than 33 countries. Cooperation from managers in those foreign markets with Colgate's headquarters is key to success in implementing global marketing programs for brands and achieving the firm's goal of superior performance in those markets (Kindel 1994).
The headquarters-subsidiary relationship has been suggested to be one of increasing importance for the MNC (Roth and Nigh 1992). Moreover, there is at least some evidence that headquarters-subsidiary marketing operation relationships may vary significantly in effectiveness. Managers, for example, may be reluctant to accept ideas communicated to them because they may not want to acknowledge the value of others' ideas in a competitive corporate environment (Goodman and Darr 1996). Similarly, Picard, Boddewyn, and Grosse (1998) have discovered that the subsidiary's autonomy is an important factor influencing an MNC's international marketing decisions.
The present research draws on the relationship marketing literature to conceptualize the interface between the headquarters' and subsidiary's marketing operations. Social exchange theory, which we use in developing our conceptual framework, is often used as a foundation for understanding factors that influence relationship quality. One central idea underlying relationship marketing is that the goal of marketers is to nurture lasting relationships by means of a structure of mutual benefits for the parties involved (Achrol 1997). The importance of fostering successful relationships, such that both parties achieve long-term benefits, is highlighted in relationship marketing studies (see Morgan and Hunt 1994). An important question for the MNC is the extent to which enhancing the relationship between the headquarters' and subsidiary's marketing operations is associated with successful implementation of marketing programs in the subsidiaries' markets.
Largely on the basis of the predictions of social exchange theory, certain features have repeatedly been found to be important to building quality relationships. Specifically, trust and dependence between parties have been suggested to be central factors in motivating each party to participate or engage in successful and mutually beneficial exchange relationships (see Blau 1964; La Valle 1994; Makoba 1993; Morgan and Hunt 1994). Based largely on social exchange theory, the conceptual framework depicted in Figure 1 suggests the importance of both trust and dependence for forming successful relationships. These antecedent factors not only have consistently been found to be important factors in studies across a variety of relationship contexts (see Anderson and Narus 1990; Ganesan 1994; Garbarino and Johnson 1999; Joshi and Arnold 1998; Morgan and Hunt 1994) but also are particularly relevant to the context of this study.
Relational Behaviors in the Context of the MNC
Studies of relationship marketing typically examine different characteristics of exchange relationships (e.g., trust, dependence) in terms of their influence on some desired outcome. More specifically, these outcomes generally represent desired behaviors on the part of one or more of the partners in the exchange. The specific outcomes that are examined tend to differ on the basis of the context of the study. For example, studies of buyer'supplier relationships focus on behaviors such as acquiescence, a decreased propensity to leave a relationship (Morgan and Hunt 1994), or a long-term orientation in the relationship (Ganesan 1994), whereas studies in the marketing channels context focus on behaviors such as cooperation (Anderson and Narus 1990) or flexibility (Lusch and Brown 1996). Across these different contexts, acquiescence and cooperation are consistently highlighted as representing desirable behavioral outcomes from successful relationships (Anderson and Narus 1990; Bendapudi and Berry 1997; Ganesan 1994; Kumar, Stern, and Achrol 1992; Morgan and Hunt 1994). These two behaviors not only are consistent across the relationship marketing literature but also are particularly appropriate in the context of headquarters-subsidiary marketing function relationships in terms of the implementation of marketing program elements in individual foreign markets.
On the basis of findings from previous studies of marketing relationships, these two behaviors are suggested in our conceptual framework to result from perceptions of dependence and trust on the part of managers at the subsidiary's marketing operations. Specifically, successful relationships between the headquarters' and subsidiary's marketing operations should result in the subsidiary's ( 1) acquiescence to the headquarters in terms of marketing procedures, directives, and programs implemented in the local market and ( 2) cooperation with the headquarters' marketing operation to achieve mutual goals with respect to the marketing procedures, directives, and programs for a particular product. An important distinction between these two constructs, highlighted by Morgan and Hunt (1994), is that cooperation is proactive whereas acquiescence is reactive.
- Acquiescence. Acquiescence is defined as the extent to which one party in an exchange situation accepts or adheres to another's specific requests (Bendapudi and Berry 1997; Kumar, Stern, and Achrol 1992; Morgan and Hunt 1994; Steers 1977). Because different subsidiaries in dispersed geographic locations will have different responsibilities and will operate in various market conditions, each subsidiary will possess unique knowledge and experience and may have interests that diverge from those of the headquarters (Nohria and Ghoshal 1994). Therefore, the headquarters faces a need to utilize its unique knowledge in decision making, while somehow influencing the subsidiaries to act in line with its interests.1 Kumar, Stern, and Achrol (1992), in their assessment of reseller performance in supplier"reseller relationships, suggest that reseller compliance, or the reseller's reception of the supplier's channel policies and programs, is important for the supplier's ability to present its products to end users in the manner it wishes. We similarly view subsidiary acquiescence as important for the headquarters' ability to present its products to end users in accordance with its proposed marketing plans. In the present study, we perceive the extent to which the foreign subsidiaries act in line with the headquarters' interests as reflecting the quality of the relationship. Acquiescence then reflects the subsidiary marketing managers' participation in marketing procedures, directives, and programs that the marketing function at the headquarters attempts to implement, as well as their performance of headquarters' marketing operation requests.
- Cooperation. Cooperation is defined as complementary coordinated actions taken by the headquarters' and subsidiary's marketing functions to achieve mutual outcomes (see Anderson and Narus 1990, p. 45). Morgan and Hunt (1994) suggest that cooperation requires the two parties in a relationship to participate actively to achieve mutual benefits and that cooperation promotes success in relationships. Similarly, Roth and Nigh (1992) define coordination by foreign subsidiaries as collaborative actions to achieve unity of effort with the MNC and suggest that collaboration is characteristic of effective headquarters-subsidiary relationships. We view cooperation from the perspective of the subsidiary's managers, in terms of their interactions, communications, and goals with respect to marketing procedures and programs for a product.
Dependence-Based Path to Relational Behavior
The dependence of one party on another can be defined as the extent to which the first party relies on the relationship for the fulfillment of important needs (Rusbult and Van Lange 1996). In the present study, dependence reflects the extent to which the subsidiary depends in general on the effective functioning of the headquarters in order to perform its own tasks related to the implementation of a marketing program for a product. In the context of marketing operations at MNC headquarters and subsidiaries, some form of a dependence relationship is likely.
According to social exchange theory, the existence of an imbalance of power due to one party's dependence on the other makes it possible for one party to direct the activities of another (Blau 1964; Molm 1994). The level of perceived dependence of one partner on another is thought to be an important feature of the relationship (Anderson and Narus 1990; Berry and Parasuraman 1991; Blau 1964; Gundlach and Cadotte 1994; Parsons 1964; Smith, Ross, and Smith 1997). The dependence of one party on another is also suggested to be positively associated with acquiescence to that party (Bendapudi and Berry 1997; Blau 1964; Morgan and Hunt 1994). The greater the perceived dependence of the subsidiary's marketing operation on the headquarters, the less powerful the subsidiary's marketing managers will feel, and the more likely the subsidiary will be to acquiesce to the marketing function at headquarters. Although some research in the channels literature has concluded that dependence is not related to control (see Gaski 1984), our view is that a subsidiary's managers should feel compelled to follow directives on the basis of its reliance on headquarters for important resources.
Support for these notions can be found in several related studies. For example, Prahalad and Doz (1981) study the influence of dependence on the strategic control a headquarters has over its subsidiaries. Similar to the view of dependence and acquiescence taken here, Prahalad and Doz's definition of strategic control is the "extent of influence that a head office has over a subsidiary concerning decisions that affect subsidiary strategy" (Prahalad and Doz 1981, p. 5). Anderson and Narus (1990) also examine unilateral dependence as a determinant of the extent to which one firm has influence over its partner, and these authors find a link between unilateral dependence and the use of influence by a supplier over its distributors. In addition, Joshi and Arnold (1998) find that the dependence of a buyer on a supplier in an industrial setting leads to buyer compliance. Thus, the perceived dependence of the subsidiary's marketing function on that at headquarters should result in higher levels of acquiescence. In summary,
H1: The subsidiary's perceived dependence on the MNC headquarters'
marketing function is positively related to the subsidiary's
acquiescence to the headquarters.
Trust-Based Paths to Relational Behavior
The trust-based paths reflect the importance of trust in relationships (Bendapudi and Berry 1997; Blau 1964; Ganesan 1994; Garbarino and Johnson 1999; Kozak and Cohen 1997). The literature dealing with trust is vast, and definitions range from those viewing trust as a personality trait (Dwyer and LaGace 1986) to those encompassing beliefs about another's behavior or behavior that reflects the truster's vulnerability to the other (Moorman, Zaltman, and Deshpand' 1992). In Doney and Cannon's (1997, p. 36) study of trust in buyer-seller relations, the authors define trust as "the perceived credibility and benevolence of a target of trust." Doney and Cannon's definition captures the motives and/or intentions of the other party and is adopted here. Although many studies in an organizational context focus solely on the credibility aspect (see Moorman, Zaltman, and Deshpand" 1992; Morgan and Hunt 1994), without specific regard for the notion of benevolence, the emphasis on benevolence may be particularly important in this context, because the subsidiaries in our sample were all wholly owned subunits of the parent corporations. From the subsidiary's perspective, a concern for its welfare may be particularly important in motivating relational behavior, because the headquarters may be seen as "going beyond the call of duty."
Trust is positioned here as having a direct influence on acquiescence and cooperation (Bendapudi and Berry 1997; Morgan and Hunt 1994). From a relational perspective, trust is important as a mechanism both for persuasion and for encouraging future exchanges. Exchange partners often heed each other's suggestions by virtue of the trust placed in the partner (La Valle 1994, p. 596). Morgan and Hunt's (1994) findings regarding the influence of trust on acquiescence support this expectation. Doney and Cannon (1997) likewise find that trust enhances the likelihood of future interactions among parties. Relatedly, Moorman, Zaltman, and Deshpand' (1992) find that trust enhances the quality of user-researcher interactions and commitment to those relationships. Similarly, the trust the subsidiary's marketing function has in the headquarters should enhance acquiescence. Multiple aspects of trust embodied in our definition can also be seen as leading to a willingness to follow directives. First, a subsidiary may be more willing to acquiesce to the extent that it perceives the headquarters as likely to keep promises and provide reliable information (i.e., credibility). Likewise, if the subsidiary perceives the headquarters as concerned about its welfare (i.e., benevolence), it may be more likely to perceive directives to be in its best interest and more likely to follow them. More formally,
H2: The subsidiary's trust in the headquarters' marketing function is
positively related to its acquiescence to the MNC's headquarters.
Morgan and Hunt (1994) also find that trust leads to cooperative behaviors and to a decrease in uncertainty. Arguments similar to those provided for the effects of trust on acquiescence can be made for the influence of trust on cooperation. If the headquarters is perceived as credible and as concerned with the subsidiary's welfare (benevolence), the subsidiary may be more likely to perceive objectives as mutually beneficial and may be more likely to cooperate. Relatedly, Ganesan (1994) finds that trust positively influences a retailer's long-term orientation toward a relationship, reflecting a perception that joint outcomes will benefit both organizations in the long run. Thus, trust enhances perceptions that outcomes will be mutually beneficial for both partners. Likewise, Anderson and Narus (1990) argue that cooperative behaviors lead to outcomes that exceed what one partner could achieve if it acted alone and focused only on its interests. Trust, then, is expected to lead to cooperation. Therefore,
H3: The subsidiary's trust in the headquarters' marketing function is
positively related to the subsidiary's cooperation with the MNC's
headquarters.
Cultural Influences on Relational Behavior
The behavior and attitudes of managers at foreign subsidiaries are likely to differ from those of managers at the MNC's headquarters if the cultures in those markets differ significantly. Hofstede (1980) found that cultural differences vary along four dimensions: uncertainty avoidance, individualism/collectivism, tolerance of power distance, and masculinity/femininity.2 On the basis of a study of more than 88,000 employees at subsidiaries of a U.S.-based MNC, Hofstede (1980) created indices for more than 40 countries for each dimension. These indices have been used in more than 60 applications (Sondergaard 1994). The underlying values and attitudes of different cultural groups can influence the behavior of those groups, as well as the nature of decisions they make (Hofstede 1980; Schneider and DeMeyer 1991; Shane 1994; Tayeb 1994). As examples, cultural values can affect organizational processes (Hofstede 1983; Stephens and Greer 1996) and leadership styles of managers (Tayeb 1994). In addition, Tse and colleagues (1988) find that culture influences decisiveness and choice of decision strategies used in marketing situations.
In terms of cultural influences on relational behavior, the present study focuses on the influence of the individualism/collectivism dimension. Individualism/collectivism reflects the way people in a society interact and has been suggested to be the most pervasive difference associated with national culture (Williams, Han, and Qualls 1998). This dimension of culture also appears most relevant to the study of relationships, given its focus on interactions among people. In more individualistic cultures, unilateral or individual goals take on greater importance than group goals, whereas in collectivistic societies, interpersonal ties take on greater importance, and people are expected to focus more on the needs of the collective group than on their own needs (Doney, Cannon, and Mullen 1998; Hofstede 1984). Our interest in examining the effect of culture, and individualism/collectivism specifically, is to understand conditions under which a relationship based on trust might be more effective in leading to cooperative behaviors.
Recently, the level of individualism/collectivism has been suggested to influence the likelihood of cooperative behaviors in multinational organizations directly (Chen, Chen, and Meindl 1998). Empirical evidence also points to a link between this dimension and relational behaviors. Chatman and Barsade (1995), for example, find that members of collectivistic cultures are more likely to reciprocate in cooperative behaviors. In addition, Williams, Han, and Qualls (1998), in their study of cross-cultural business relationships, find that managers in highly collectivistic countries are more receptive to social bonding, which focuses on personal factors such as trust, than structural bonding, which focuses on strategic objectives. More specifically, managers in collectivistic cultures reacted more strongly to interpersonal factors such as trust than monetary incentives for motivating relational behaviors. Therefore, trust should be more effective in motivating relational behavior among managers in collectivistic cultures than in individualistic cultures. Because cooperation generally reflects proactive behaviors on the part of managers at the subsidiary to achieve mutual outcomes with the headquarters (see Anderson and Narus 1990), the level of individualism/collectivism should influence the extent of cooperative behaviors. Specifically, we expect individualism/collectivism to moderate the relationship between trust and cooperation such that trust will take on greater importance in motivating cooperative behaviors in more collectivistic cultures. The following hypothesis summarizes this expectation:
H4: Trust will have more of an effect on cooperation in highly
collectivistic cultures than in highly individualistic cultures.
Performance as an Outcome of Relational Behaviors
In an effective relationship, both partners are expected to receive long-term benefits from the relationship so that both parties can achieve their respective goals (see Morgan and Hunt 1994). Although many factors can be linked to performance, we view the possible performance implications of strong relationships as an important question that is worthy of study. To the extent that the subsidiary's marketing function displays cooperation and acquiescence, we expect the ability of a product to meet the objectives established for the individual subsidiary's market to be enhanced. Similar to Moorman and Miner (1997), we are concerned here with how well the product achieves profitability, sales, and share goals.
The ability of an MNC's headquarters to motivate and control the subsidiary's actions in executing its global strategies is described as a critical aspect of the firm's ability to compete successfully (Doz, Prahalad, and Hamel 1990). Intuitively, the willingness of subsidiaries to follow directives in implementing marketing programs for a particular product should be positively associated with the ability of that product to achieve its established performance objectives. In addition, international cooperative behaviors have been suggested to be key to the success of global companies (Adler 1991). Relatedly, Jap (1999) finds support for the notion that coordination efforts on the part of partners in a dyadic relationship, defined as their pattern of complementary actions and activities, can lead to enhanced performance in terms of profits resulting from dyadic collaboration efforts. From a relationship marketing perspective, relational behaviors should result in both parties in an exchange relationship achieving their respective goals. In our context, achieving the product's goals, as established in the global marketing program, can be seen as a mutual objective. Consistent with these arguments, we propose the following:
H5: The subsidiary's marketing function cooperation is
positively related to brand performance in the market in which
that subsidiary operates.
Moderating Effect of Global Marketing Program Orientation
Last, as Simpson and Wren (1997) suggest, several nonrelational factors may influence the effect of the nature of an exchange on the outcome of that exchange. Notably, the influence of such nonrelational factors has not been studied extensively. An important aspect of the MNC's marketing strategy is the extent to which the marketing program for the product is standardized across markets or adapted to each market (Douglas and Wind 1987; Jain 1989; Picard, Boddewyn, and Grosse 1998; Quelch and Hoff 1986). This choice of marketing program orientation has implications for both relationship quality and product performance. For example, Jain (1989) hypothesizes that conflict and poor relationships between the marketing functions at an MNC's headquarters and its foreign subsidiaries discourage the transfer of global marketing program elements to individual markets. We focus on standardization versus adaptation in terms of the content of the marketing program, in accordance with Jain's (1989) description of this variable.
The greater the extent to which the headquarters' marketing function has an orientation toward standardizing the marketing program for a product, the more important the relationship with the marketing function at headquarters would be to an individual subsidiary's ability to implement that program successfully in its market. In situations in which the marketing program for a particular product is customized, such that the subsidiary's marketing function is more autonomous in its developmental efforts or strategy implementation, acquiescence to the headquarters may be less important for the product's ability to meet its objectives. In addition, when the headquarters is attempting to standardize the marketing program, the likelihood of goal congruity between the headquarters and subsidiaries will be greater than when the subsidiaries are more autonomous. Thus, the importance of relational behaviors on the part of the subsidiary for achieving those goals should be less. On the basis of these arguments, we offer the following:
H6: When global marketing program standardization is high,
acquiescence will have a greater effect on market brand performance
than when standardization is low.
In this section, we describe the procedures used to gather the data in detail. First, we discuss the survey instrument and selection of key informants. Next, we describe the procedures used to assess nonresponse bias. Finally, we present the measures and steps followed in validating the multiple-item scales included in our questionnaire.
Survey Procedures and Sample
We conducted the present study using a mail survey. Where possible, we used existing measures for operationalizing constructs. We administered the questionnaire in English, because target respondents were senior managers at U.S.-based firms. We pretested the questionnaire among 16 academic experts and marketing managers to assess clarity of instructions and scale items. We sent draft questionnaires to participants and made telephone appointments for debriefing after comments were received. Participant comments focused primarily on response format instructions; we made modifications on the basis of their feedback.
Key informants were subsidiary-based marketing managers who were responsible for the marketing activities for one or more products/brands sold in their respective markets. Selecting key informants on the basis of their formal roles in the subsidiary, such that they are knowledgeable regarding the phenomenon under study, is critical in organizational research (Kumar, Stern, and Anderson 1993). Therefore, the sampling procedure required the identification of foreign subsidiaries of U.S.-based global firms and people with the appropriate responsibilities at those subsidiaries.
We developed the sample using the following steps: First, using the International Directory of Corporate Affiliations: Corporate Affiliations Plus (1997), we accessed hierarchies of U.S.-based firms and identified firms with foreign subsidiaries. Using this list of firms and subsidiaries, we attempted to identify target respondents and gain cooperation of the key informant in the survey (see Hartline and Ferrell 1996). It was necessary that there be a company-owned marketing presence in each foreign market. Second, we placed telephone calls to corporate headquarters in an effort to identify marketing managers with the appropriate responsibilities in foreign subsidiaries. When the appropriate contact at headquarters was not available, we called the subsidiaries. This procedure resulted in the identification of 435 subsidiaries from 135 U.S.-based corporations. Third, subsequent correspondence and telephone calls indicated that in a few cases, the foreign office was not appropriate. Elimination of such locations resulted in a final sample of 406 foreign subsidiaries, which represented 133 U.S.-based firms.
A mail questionnaire was sent to each of the identified respondents. As in Dillman's (1978) work, follow-up reminder postcards were sent to nonrespondents after three weeks, and follow-up questionnaires were sent after six and ten weeks.3 Cover letters outlined the nature of the study and emphasized the confidentiality of the respondents. Respondents indicated on the questionnaires the name of the product or brand, its product category, and the geographic region for which they responded. As an incentive for participation, respondents were also given the opportunity to request a summary report of findings from the completed study (see Robertson, Eliashberg, and Rymon 1995). Initial and follow-up mailings resulted in responses from 143 subsidiaries of 66 different MNCs, for a 35% response rate. These 143 responses represent 36 different country markets of the 49 included in the original mailing. The respondents represented more than 30 industries. The median number of employees at the subsidiaries was 150; the average sales for the subsidiaries in U.S. dollars was 533 million. The respondents averaged 3.33 years of experience in their particular positions and 7.90 years of experience with the subsidiary.
As an additional measure to increase the speed of response and the overall response rate, participants were offered the options of either faxing their responses (Vazzana and Bachman 1994) or using a return envelope. Of the respondents, 17% faxed their returns, and the remaining 83% mailed them or used a courier service such as DHL.
Estimating Nonresponse Bias
We first examined nonresponse bias using the procedures recommended by Armstrong and Overton (1977). As such, we compared the responses from the first mailing with the responses from the third mailing by testing for mean differences on all the variables in the study, including subsidiaries' characteristics. The results of this comparison showed no significant differences across the waves of mailings on responses to any multiple-item scales or indices or to any questions regarding subsidiaries' characteristics. (The p-values for these comparisons ranged from .20 to .87.) The same analyses using the first versus the second and third waves combined also yielded no significant differences. In addition, we gathered secondary data on subsidiaries' characteristics for both responding and nonresponding firms (AmericA's Corporate Families and International Affiliates: Corporate Affiliations Plus 1998). Comparisons across the numbers of employees and total sales of subsidiaries also yielded no significant differences (p ' .77 for employees, and p ' .43 for sales). Finally, we compared response rates across industry groups (nondurables, durables, and services) and found that they did not differ significantly (p ' .40).
Measurement
Preexisting measures were identified where possible and adapted on the basis of the nature of the phenomena under study. Dependence was assessed on a scale adapted from a measure used by Astley and Zajac (1990) in their study applying exchange theory to study subunit power within MNCs. This measure included items intended to assess the extent to which the managers perceive that their subsidiary depends in general on the effective functioning of the headquarters in order to perform its own tasks, and the scale was originally developed with the objective of applying social exchange theory to relationships between subunits of MNCs. Doney and Cannon's (1997) measure of trust was adapted for the study to assess subsidiaries' marketing managers' trust in the headquarters. Kumar, Stern, and Achrol (1992, p. 240) develop a measure of reseller compliance with suppliers, which they define as the extent to which a reseller complies with the supplier's channel policies and programs. This conceptualization parallels the concept of acquiescence proposed previously and therefore was adapted for this study. Cooperation was measured on a scale adapted from Song, Montoya-Weiss, and Schmidt's (1997) study of cross-functional cooperation.
In measuring performance, we employed procedures similar to those used by Moorman and Miner (1997), in that respondents were asked to rate the extent to which a particular product achieved various outcomes related to profitability, sales, and market share. We conducted several additional analyses to assess the validity of the performance index. First, an examination of the corrected item-to-total correlations revealed that the estimates for the five indicators ranged from .66 to .86. As such, each of these values exceeds the recommended cutoff value of .50 for item retention (Zaichkowsky 1985). Second, we correlated the averaged index of relative performance (i.e., we operationalized each indicator using subjective measures that reflected performance relative to objectives) (see Moorman and Miner 1997, p. 102) with an absolute measure of market share for the product and market being investigated, controlling for the brand's length of time in the market. This correlation was .38 (p ' .01). More important, though modest in strength, this estimate was significant and positive. The absence of a stronger relationship is due, on the one hand, to differences between the relative measure used in testing our hypotheses and the absolute measure of performance and, on the other hand, to the inherent fallibility of self-reported data (see Shimp and Kavas 1984, p. 800).
The culture index values developed by Hofstede (1980) were used to reflect individualism/collectivism, and an index of marketing program elements based on the work of Jain (1989) was used for the marketing program orientation construct. The standardization/customization index included items reflecting various elements of the marketing program for a product, each of which can have varying degrees of standardization or customization. For these latter two measures, positive responses indicate higher levels of individualism and customization, respectively.
We subjected all scaled multiple-item measures that were assessed with reflective indicators (i.e., all measures except the individualism/collectivism and marketing program orientation indices) to purification procedures designed to evaluate dimensionality, reliability, and discriminant validity (see Anderson 1987; Gerbing and Anderson 1988). Across all the scales, we identified five items with low factor loadings (l < .50) and subsequently dropped them from further analyses. The final measurement scales, as well as Cronbach's a values, are presented in Appendix A. Using the PROC CALIS procedure in SAS, we first assessed the psychometric properties of our final measures using confirmatory factor analysis. Given the limitations of our sample size, we divided the constructs into two subsets: exogenous (e.g., trust and dependence) and endogenous (e.g., acquiescence, cooperation, and performance) variables to form measurement models (see Doney and Cannon 1997). For the second model, the performance construct was estimated as a higher-order factor with two sets of indicators representing sales volume (sales and market share) and profitability (return on investment, return on assets, and profit margin). Although the chi-square statistics for both models were statistically significant (c2 = 88.88, degrees of freedom [d.f.] = 26 for Model 1 and c2 = 144.99, d.f. = 72 for Model 2), these estimates are sensitive to sample size and should not be considered without examinations of other fit indices (Sharma 1996). Other fit indices (goodness-of-fit index = .88 for Model 1 and .86 for Model 2, Tucker-Lewis index = .82 for Model 1 and .92 for Model 2, relative noncentrality index = .87 for Model 1 and .94 for Model 2) suggest that the measures provide a reasonable fit to the data. In addition, loadings for all indicators were significant (t-values all -4.60).
With one exception (acquiescence), Cronbach's a for the final scales exceeded .70, providing evidence of generally acceptable reliability (see Peter 1979). The coefficient a estimate of internal consistency for acquiescence was .67. In addition, composite reliability scores based on the item loadings from confirmatory factor models ranged from .68 to .89 (for these same variables). Before performing more formal tests of discriminant validity, we performed exploratory factor analysis on the two subsets of measures used in the measurement model analyses described previously. No substantial cross-loadings were observed among items across the different constructs. We assessed discriminant validity using the procedures recommended by Gerbing and Anderson (1988). First, we ran confirmatory factor analysis models with two factors involving each possible pair of constructs. In the first model, we constrained the f coefficient to 1.0 and then estimated it freely in the second model. In all cases, we found the model with the free f coefficient to be superior to the model with the fixed f coefficient. Second, we constructed confidence intervals around the f coefficient estimates using two times the standard error of the f coefficient for each pair of constructs. In none of the cases did the confidence interval contain 1.0, which provided additional evidence of discriminant validity. The results of these analyses are summarized in Appendix B.
In an additional effort to assess the content validity of the measurement scales, we conducted a smaller-scale study in which we reevaluated the measures using procedures similar to those recommended by Zaichkowsky (1985) and employed in several studies in the marketing literature (e.g., Netemeyer et al. 1997; Netemeyer, Burton, and Lichtenstein 1995; Saxe and Weitz 1985). Specifically, we administered a questionnaire to expert judges with experience doing key informant research, asking them to rate the degree to which each item represented the constructs in our model. Using the procedure recommended by Zaichkowsky (1985), the panel of 26 expert judges rated each item as "clearly representative," "somewhat representative," or "not representative" of the construct of interest. Across all items, mean responses were greater than 2.0, and in no case did fewer than 80% of judges indicate their perception that the item was at least somewhat representative. This 80% level of agreement is consistent with that used by Zaichkowsky (1985) in determining representativeness of items.
The summary statistics and intercorrelations for all variables included in the study are shown in Table 1. Moderated regression analysis was used to assess support for individual hypotheses, including the hypothesized moderation effects for individualism/collectivism and marketing program orientation (see Arnold 1982; Barron and Kenny 1986).4 In an initial series of analyses, multicollinearity was found between the interaction terms and their underlying components in tests of H4 and H6. To address this problem, variables were mean-centered before forming the interaction terms, a procedure recommended to reduce the problems associated with multicollinearity (see Aiken and West 1991; Jaccard, Turrisi, and Wan 1990). As a check on the effect of this procedure, the variance inflation factors (VIFs) for all variables were computed. The largest of the resulting VIFs was 1.35, well below the maximum level of 10.0 suggested by Mason and Perreault (1991; see also Neter, Wasserman, and Kutner 1990, p. 409).
Separate regression analyses were performed for each of the three dependent variables (i.e., acquiescence, cooperation, and performance). Three control variables representing the subsidiary's size (sales in dollars) and industry (two dummy variables representing durables, nondurables, and services) were also included in all three regression models (see Moorman 1995). Product terms using composite indices that represented the moderator variables and appropriate main effect variables were also included. Details of the results of these tests are discussed subsequently. Note that plots of residuals for all three equations indicated no outlying observations, and a normal probability plot suggested no violation of the normality assumption (Neter, Wasserman, and Kutner 1990). The results for all three models are presented in Table 2.
Effects on Acquiescence
H1 and H2 predict relationships with acquiescence as an outcome. Table 2, Part A, presents the results of the regression equations used to test these hypotheses. Standardized parameter estimates are provided; the associated t-values are shown in parentheses. As shown, the coefficient for dependence is not significant, indicating a lack of support for H1. However, the coefficient for trust is significant (t = 6.53, p ' .01) and in the hypothesized direction, providing support for H2. Thus, it appears that trust has a significant effect on acquiescence, at least for our sample of marketing managers.
Effects on Cooperation
H3 and H4 predict relationships with the subsidiary's marketing function cooperation as an outcome. The results of the regression equations used to test these hypotheses are presented in Table 2, Part B. Regarding H3, trust is shown to be significantly associated with the subsidiary's marketing function's level of cooperation (t = 7.56, p ' .01). For H4, the coefficient for the interaction term is also significant (t = '2.04) and in the direction hypothesized.
In an effort to understand the interaction effect, we followed the slope analysis procedure described by Aiken and West (1991). According to Aiken and West, the presence of the interaction term is evidenced by the significance of its coefficient. By means of the slope analysis procedure, the relationship between trust and cooperation can be understood at different levels of individualism/collectivism. The equation is calculated using values of individualism/collectivism one standard deviation below the mean, at the mean, and then one standard deviation above the mean. We substituted high, moderate, and low values of individualism/collectivism in a model that also included main effects of trust and individualism/collectivism. This analysis revealed that at high levels of individualism, the relationship between trust and cooperation is weakest (b = .439), and at low levels, the relationship is strongest (b = .730). These results support H4.
Performance as an Outcome
H5 and H6 predict relationships with product performance in the subsidiary's market. The regression results using performance as the dependent variable are shown in Table 2, Part C. As indicated in Table 2, the data support H5 and H6. A significant coefficient was found for the main effect of cooperation (t = 2.62, p ' .01), and a marginally significant coefficient was found for the interaction of marketing program orientation and acquiescence (t = '1.93, p ' .056). That is, in support of H5, cooperation had a direct effect on performance, as predicted. The negative coefficient for the marketing program orientation ' acquiescence interaction term suggests that the greater the extent to which the marketing program elements for a given product are customized by the individual subsidiary for its market, the weaker is the relationship between acquiescence and performance of that product in the subsidiary's market. Again, the nature of this significant interaction was examined. The slope analysis revealed that at high levels of standardization (low levels of customization), the relationship between acquiescence and performance is strongest (b = .24, t = 1.92), and at low levels of standardization, it is weakest (b = '.092, t = '.77). Thus, and as predicted, it appears that acquiescence becomes increasingly important for performance as the elements of the marketing program for a product are standardized across geographic markets.
A key objective of this study was to understand the antecedents of relational behaviors on the part of the marketing operations at a global firm's individual foreign subsidiaries. The conceptual model, derived largely from the relationship marketing literature, suggested two such antecedents: the dependence of one party on the other and the trust that one party has in the other (see Anderson and Narus 1990; Bendapudi and Berry 1997; Makoba 1993; Morgan and Hunt 1994). The findings reported here emphasize the importance of dedication-based relationships. That is, trust was found to be significantly associated with both acquiescence and cooperation, whereas dependence was not found to be significantly associated with acquiescence.
The influence of national culture on headquarters-subsidiary marketing function relationships was also explored. On the basis of prior findings (see Williams, Han, and Qualls 1998) regarding the receptiveness of managers in collectivistic cultures to forms of bonding that focus more on personal factors such as trust than on more economic-type rewards, we predicted a moderating effect of individualism/collectivism (Hofstede 1980) on the trust'cooperation relationship (Williams, Han, and Qualls 1998). Our findings suggest that in more collectivistic cultures, trust takes on greater importance in motivating cooperative behaviors.
Finally, the research extends traditional relationship marketing frameworks to explore the influence of relational behaviors on self-reports of product performance in individual foreign markets. Specifically, our findings indicate that there is a positive association between cooperative behaviors on the part of the subsidiary's marketing managers surveyed and the ability of a product to achieve its objectives in the subsidiary's individual market. In addition, we found that acquiescent behaviors take on greater importance to the extent that marketing program policies and procedures are standardized. Following a brief description of limitations of this research, we discuss the implications of our findings for MNC strategy and for further research.
Limitations
Although the survey was conducted among marketing managers who were knowledgeable about the brand or product management activities of the foreign subsidiaries and who interacted regularly with the marketing operation at corporate headquarters, this research suffers from some of the limitations associated with mail surveys. For example, although steps were taken to ensure that the correct person was identified as the key informant, the potential for single-respondent bias still exists. Related to this issue of single-respondent bias is the use of perceptual measures for operationalizing the constructs. Other concerns related to the measures are that subjective performance measures were obtained and that these performance measures were obtained at the same time as other construct indicators.5 Additional objective firm performance measures and/or external secondary data would have been helpful in validating performance. However, brand performance in foreign markets is not reported widely, and therefore extensive secondary data are not available, particularly when needed for such a large and varied group of individual products and markets. It is also possible that our measures do not fully capture the concepts under study, and caution should be used in interpreting our findings. However, as discussed in our methods section, standard analyses designed to assess reliability and discriminant validity were employed, and our measures were found to be adequate.
Given the correlational design, it is not appropriate to make causal statements regarding the relationships observed among variables. Although we observed associations among certain variables, it is impossible to draw conclusions of causality. Moreover, the potential exists for common methods variance as an explanation for the relationships observed. We investigated the effects of common methods bias using the procedures described by Netemeyer and colleagues (1997, p. 96). In these analyses, we formed two structural equation models using constructs with multiple indicators on the basis of the order of hypotheses (i.e., H1'H3 and H5). A breakdown using two models was needed because of the limited sample size. For both models, we added a "same-source" factor to the indicators of all constructs. We compared an unconstrained model, in which the same-source factor loadings are estimated freely, with a constrained model, in which the loadings are constrained to zero. These analyses resulted in significant differences between the constrained and unconstrained models for both sets of constructs, which suggests the presence of some methods bias (c2diff = 269.18, d.f.diff = 18 for the first model and c2diff = 193.11, d.f.diff = 14 for the second model). However, the strength of the hypothesized paths remained consistent with our findings, even in the presence of this bias.
An additional limitation is the constraint placed on the nationality of the headquarters organization. The sample was limited to foreign subsidiaries of U.S.-based MNCs only, which made it difficult to generalize results across firms based in other countries. However, the study was limited to U.S.-based firms to control for the effects of parent-firm nationality, which might affect management styles, as well as the structure of product management activities. For example, parent-dominated relationships as opposed to more balanced or subsidiary-dominated relationships may be more common among firms based outside of the U.S. Future studies that expand their scope across firms based in other countries should attempt to assess possible influences on firm structure, as well as the organization of marketing activities.
Importance of Relational Behaviors for Performance
The importance of relational behaviors on the part of the subsidiary's marketing operation for product performance in the subsidiary's market was hypothesized in the conceptual framework tested here. Although cooperation was found to be positively associated with the self-reported measure of product performance, acquiescence was not. Acquiescence was, however, found to become increasingly important as the firm attempts to standardize a product's marketing program across geographic markets. Although prior applications of the relationship marketing perspective have assessed behavioral outcomes, such as a reduced propensity to leave a relationship (Morgan and Hunt 1994) and expectations of continuity in the relationship (Garbarino and Johnson 1999; Heide and John 1990), performance implications resulting from those behaviors have not been widely assessed. One exception is the study reported by Lusch and Brown (1996), who examine performance in terms of the efficiency and productivity of wholesale-distributor relationships and find that it is not significantly associated with relational behavior. In addition, supplier performance has been found to be enhanced when suppliers pursue long-term as opposed to short-term relationships with customers (Cannon and Perreault 1999; Kalwani and Narayandas 1995; Naidu et al. 1999). Finally, Jap (1999) reports that coordination efforts on the part of a dyad, rather than one partner, can result in higher levels of profitability. In the study reported here, the emphasis is more specifically on performance outcomes resulting from relational behaviors on the part of the subsidiary's marketing operations. As such, our findings provide evidence of benefits from fostering successful relationships among a firm's subunits.
The finding that acquiescence is not significantly associated with performance, except in situations in which the headquarters is attempting to standardize the marketing program for the product, led us to consider factors that might explain this result. The arguments of Janis (1972) regarding the effects of "groupthink" could be relevant. Given that the subsidiaries operate as part of a system of subunits, acquiescence among all subsidiaries could characterize the overall MNC. According to Janis (1972), acquiescence among a group could lead to groupthink, such that creative processes may shut down; this has, in turn, a detrimental influence on performance. Given that our sample is primarily composed of only one subsidiary for each MNC, this is an unlikely explanation. Future studies involving multiple subunits for a set of MNCs would allow for an assessment of multiple subsidiaries, so that performance outcomes based on group-level characteristics could be investigated.
Effects of Trust Versus Dependence
One possible explanation for the significance of trust compared with dependence in influencing relational behavior may lie in the subsidiary's marketing operation's perception of uncertainty in its environment as a result of a perceived power imbalance. It has been suggested that a more dependent party may feel uncertain (Etgar and Valency 1983). In addition, in relationships in which one party is more dependent, the perceived individual payoffs for relational behaviors might be lower for the weaker party (Lusch and Brown 1996).
In prior research on relationships in channels, similar evidence has been found regarding stronger effects of trust than of dependence. Specifically, Lusch and Brown (1996) find that the unilateral dependence of a wholesaler on a supplier has no impact on relational behavior. In addition, Lusch and Brown (1996) find that even when a wholesaler is dependent on a supplier, normative contracting rather than explicit and contractual governing mechanisms leads to relational behaviors. Moreover, Gundlach, Achrol, and Mentzer (1995) suggest that governance based on social norms rather than "law" may be more effective in long-term relationships. Bucklin and Sengupta (1992) also find that power imbalance in an alliance reduces the effectiveness of the alliance. Finally, Anderson and Narus (1990) find that dependence, through its effect on the use of influence by a partner, leads to conflict in relationships. Moreover, the literature on power in channels of distribution from nearly two decades ago also seems to indicate that dependence may not lead to perceptions of power and thus may not have the predicted positive effect on acquiescence. In particular, Gaski (1984, p. 23), in his review of studies on power and conflict in marketing channels, concludes that there is "little evidence to support a strong relationship between power and dependence in marketing channels."
A distinction between dependence and trust, which could be key to understanding our findings, is the observation that dependence leads to an increase in uncertainty whereas trust may lead to a decrease in uncertainty. Morgan and Hunt (1994) find a negative relationship between trust and uncertainty in marketing relationships. Similarly, Beckett-Camarata, Camarata, and Barker (1998) suggest that alliances, in which firms partner for a long-term strategic purpose, are characterized by greater uncertainty than short-term relationships. This uncertainty is due to the greater likelihood in the long run than in the short run that market conditions will change and the weaker party's perception that it may be unable to control its position in the future. Further research could be undertaken in which the degree of ownership or contractual ties between parties in different contexts is measured and the role of dependence in successful relationships is explored. In addition, the distinction between unilateral and bilateral dependence (see Lusch and Brown 1996) provides a direction for further research, and the focus on only unilateral dependence here represents a potential limitation. Variation in bilateral dependence might be expected in relationships between headquarters and subsidiaries, and future studies could similarly explore the effects of bilateral dependence on both the subsidiary's and the headquarters' behavior.6
It is also possible that acquiescence may not have resulted from high levels of dependence because of contingencies not considered here. For example, the nature of marketing program requests being given to the subsidiary might be responsible for the levels of acquiescence. If subsidiaries perceive requests to be potentially detrimental, the subsidiaries may be less likely to comply with the requests. However, our belief is that if the headquarters has consistently handed down requests that are viewed as potentially detrimental to the subsidiary, the trust in the headquarters will be low. Research in marketing relationships suggests that past experiences are related to trust (Anderson and Narus 1990; Morgan and Hunt 1994).
Finally, we performed additional analyses to explore the possibility that dependence has a different relationship with acquiescence than predicted. We considered the possibility that cooperation might moderate the relationship between dependence and acquiescence, given the possibility that a more cooperative environment might enhance the likelihood that dependence will lead to acquiescence. Using regression analysis, we found the interaction between cooperation and dependence to be nonsignificant (t = '.50, p ' .62).
Effects of National Culture
The present findings support the premise that cultural differences affect the relationship between trust and relational behaviors. As such, a multinational firm may take different approaches to managing relationships among marketing operations in highly individualistic cultures versus in collectivistic cultures. Given the findings regarding differences among people in individualistic versus collectivistic cultures, differences in attitudes toward relationships and relationship marketing efforts in different cultures might be expected (Williams, Han, and Qualls 1998). Additional research is also needed to investigate how different approaches to managing marketing relationships in these different cultures may influence performance in different markets.
Global Marketing Program Orientation
One additional result from our effort was the finding that a subsidiary's marketing operation's acquiescence to the marketing operation at a global firm's headquarters becomes increasingly important as the firm attempts to standardize the elements of the marketing program across geographic markets (H6). As firms pursue global standardization of the marketing activities, our results suggest that performance is even more dependent on outlying unit acquiescence to programs and directives. Further research should explore more explicitly the influence of a firm's global marketing strategy on its relationships with its foreign subsidiaries.
The authors appreciate the support of the Center for International Business Education and Research at the University of South Carolina and the helpful comments and suggestions of the three anonymous JM reviewers. This project also benefited from contributions of various colleagues, especially Thomas J. Madden, Robert M. Morgan, S. Ratti Ratneshwar, Kendall Roth, and Martin S. Roth. [ 1]In addition to the research reported in this article, we conducted depth interviews with global marketing managers at MNC headquarters (n = 5). These interview responses revealed that global managers considered poor relationships to be detrimental to the success of marketing programs to the extent that the poor relationships influenced the subsidiary's marketing operations not to adhere to or accept policies or procedures handed down by the headquarters (i.e., acquiescence). [ 2] In subsequent studies, a fifth dimension, Confucian dynamism, or long-term orientation, was found (Hofstede 1984). [ 3] Dillman (1978) recommends reminders after one week and replacement questionnaires after three weeks and seven weeks for domestic surveys. Because international mailings are hampered by the lengthy delivery process, two weeks were added for the reminder postcard, and three weeks were added for the follow-up mailings with replacement questionnaires. [ 4] We replicated the moderated regression analyses using path analysis that employed composite scores as indicators of all constructs (setting error terms at 1 " square root of the reliability for constructs with reflective indicators). We estimated interaction effects in the path analysis using composite score product terms (see Grewal, Monroe, and Krishnan 1998).
Legend for Chart:
A - Variable
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
(a) These variables were included in analyses only when
combined with other variables as interaction terms.
* p ≤ .05.
** p ≤ .01.
A B C D
E F G
H
1. Vertical dependence 1
2. Trust .26** 1
3. Individualism/collectivism(a) -.16 -.23* 1
4. Acquiescence .15 .53** -.13
1
5. Cooperation .44** .65** -.18*
.38** 1
6. Performance .31 .29** -.06
.16 .25** 1
7. Market program orientation(a) -.28** -.05 .13
-.06 -.11 -.05
1
Mean 4.51 4.87 42.90
4.90 4.75 4.66
3.20
Standard deviation 1.28 1.09 27.78
1.00 1.21 1.37
.71 Legend for Chart:
A - Independent Variables
B - Prediction
C - Parameter Estimate
D - t-Value
* p < .05.
** p < .01.
A B C D
A: DEPENDENT VARIABLE: ACQUIESCENCE
Dependence (H1, +) -.051 -.62
Trust (H2, +) .553** 6.53
Control Variables
Subsidiary's sales -.091 -1.11
Industry dummy variable 1 .186 2.27
Industry dummy variable 2 .010 .13
F-statistic 9.472**
Adjusted R2 .27
B: DEPENDENT VARIABLE: COOPERATION
Trust (H3, +) .584** 7.56
Individualism -.091 -1.20
Trust x individualism (H4, -) -.152** -2.04
Control Variables
Subsidiary's sales .122 1.61
Industry dummy variable 1 -.105 -1.42
Industry dummy variable 2 .018 .25
F-statistic 13.71**
Adjusted R2 .40
C: DEPENDENT VARIABLE: PERFORMANCE
Cooperation (H5, +) .260** 2.62
Acquiescence -.002 -.02
Marketing program orientation .013 .14
Acquiescence x marketing
program orientation (H6, -) -.175 -1.93
Control Variables
Subsidiary's sales .160 1.73
Industry dummy variable 1 .141 1.52
Industry dummy variable 2 .072 .80
F-statistic 3.01**
Adjusted R2 .11DIAGRAM: Figure 1: A Model of Headquarters-Subsidiary Relationships
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Final Scale Items Vertical Dependence (Adapted from Astley and Zajac 1990; Cronbach"s a = .78)
Please indicate the extent to which each of the following statements describes your subsidiary's marketing operation using a seven-point scale, where 1 = "to a very little extent" and 7 = "to a very great extent."
- To perform its own tasks effectively, the marketing operation at your subsidiary relies on the effective functioning of the marketing operation at headquarters.
- Knowledge gained in the marketing operation at headquarters is transferred to the marketing operation at your subsidiary.
- Work in the marketing operation at your subsidiary is connected to the work of the marketing operation at headquarters.
Trust (Adapted from Doney and Cannon 1997; Cronbach's a = .84)
Please rate your agreement with each of the following statements regarding the marketing operation at headquarters using a seven-point scale, where 1 = "to a very little extent" and 7 = "to a very great extent."
- The marketing operation at headquarters keeps promises it makes to our marketing operation.
- We believe the information that the marketing operation at headquarters provides to us.
- The marketing operation at headquarters is genuinely concerned with the success of the marketing operation at this subsidiary.
- The marketing operation at headquarters considers our welfare when making marketing decisions regarding this market.
- Individuals in the marketing operation at headquarters are trustworthy.
- Individuals in the marketing operation at headquarters are not always honest with us. (r)
Acquiescence (Adapted from Kumar, Stern, and Achrol 1992; Cronbach's a = .67)
Please rate your agreement with each of the following statements regarding your subsidiary's brand or product management activities using a seven-point scale, where 1 = "to a very little extent" and 7 = "to a very great extent."
- Generally, your marketing operation conforms to headquarters' accepted procedures.
- Your marketing operation has had trouble implementing marketing programs that headquarters recommends. (r)
- Your marketing operation has frequently gone against the terms contained in headquarters' marketing operation directives. (r)
- Your marketing operation accurately performs requests of the marketing operation at headquarters in a timely fashion.
Cooperation (Adapted from Song, Montoya-Weiss, and Schmidt 1997; Cronbach's a = .86)
Please rate your agreement with each of the following statements regarding your subsidiary's brand or product management activities using a seven-point scale, where 1 = "to a very little extent" and 7 = "to a very great extent."
- People from the marketing operations at both headquarters and your subsidiary regularly interact.
- There is open communication between the marketing operations at headquarters and your subsidiary.
- The marketing operations at headquarters and your subsidiary have similar goals.
- Overall, your subsidiary's marketing operation is satisfied with its interaction with the marketing operation at headquarters.
- There is a give-and-take relationship between the marketing operations at headquarters and your subsidiary.
Performance (Adapted from Moorman and Miner 1997; Cronbach's a = .90)
Now, rate the extent to which the brand/product you indicated on page 1 has achieved the following outcomes relative to its original objectives for the most recent annual fiscal period using a seven-point scale, where 1 = "to a very little extent" and 7 = "to a very great extent."
• Sales
- Return on assets
- Profit margin
- Return on investment
Global Marketing Program Orientation
Think about the brand/product you indicated on page 1. For each of the following marketing program elements, please approximate the extent to which headquarters has developed standardized processes that it requires you to use in your market versus allowing your subsidiary's marketing operation to develop and implement market- or country-specific marketing processes. (Check "N/A" if an item does not apply.) Scale: 1 = 100% standardized by headquarters' marketing operation, 2 = 75% standardized/25% customized, 3 = 50% standardized/50% customized, 4 = 25% standardized/75% customized, 5 = 100% customized by subsidiary marketing operation. a. Product design h. Sales promotion b. Product positioning i. Media allocation c. Brand name used j. Salesforce responsibilities d. Packaging k. Management of salesforce e. Price l. Use of middlemen f. Basic advertising message m. Type of retail outlet n. Customer service g. Creative expression
Notes: (r) = reverse-coded
Discriminant Validity Analysis for Multiple-Item Scales
Legend for Chart:
A - Trust
B - Vertical Dependence
C - Acquiescence
D - Cooperation
E - Performance
A B C D E
Trust .50
Vertical dependence .30 .55
99.71
88.88
Acquiescence .62 .22 .42
22.88 73.26
70.60 36.14
Cooperation .79 .37 .36 .56
46.94 33.14 33.14
157.78 62.10 62.10
Performance .29 .37 .20 .24 .64
263.05 112.30 80.81 285.58
281.83 192.43 208.94 230.06Notes: Entries below the diagonal show ( 1) φ coefficients (reflecting correlations among constructs), ( 2) difference in chi-square from fixed (φ = 1.00) model and free (φ estimated) model, and ( 3) chi-square for free model. Shared variance values are provided on the diagonal.
~~~~~~~~
By Kelly Hewett and William O. Bearden
Kelly Hewett is Assistant Professor of Marketing, College of Business Administration, Winthrop University.
William O. Bearden is Professor of Marketing, Darla Moore School of Business, University of South Carolina.
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Record: 51- Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions. By: Simonson, Itamar. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p32-45. 14p. 5 Diagrams. DOI: 10.1509/jmkg.69.1.32.55512.
- Database:
- Business Source Complete
Determinants of Customers' Responses to Customized
Offers: Conceptual Framework and Research Propositions
Marketers have been challenged by proponents of individual (one-to-one) marketing to shift from focusing on market segments to making individually customized offers. Building on current knowledge regarding the construction of customers' preferences, the author examines the basic assumptions underlying individual marketing and presents a process model of customers' responses to customized offers. The model addresses ( 1) preference development, ( 2) evaluation of customized offers, ( 3) likelihood of purchasing the customized offers, and ( 4) maintenance of relationships with one-to-one marketers. The analysis leads to specific propositions regarding determinants of customers' responses to customized offers. The author discusses future research directions and managerial implications.
Over the past decade, marketers have been challenged to change their old, inefficient ways and take advantage of new technologies to offer individual customers exactly what they want. In their best-selling Harvard Business Review article titled "Do You Want to Keep Your Customers Forever?" Pine, Peppers, and Rogers (1995, p. 103) argue that "Customers, whether consumers or businesses, do not want more choices. They want exactly what they want--when, where, and how they want it--and technology now makes it possible for companies to give it to them." Similarly, marketing textbooks (e.g., Kotler 2000) and instructors have emphasized the importance of individual marketing, and new tools and management approaches have been introduced to enable marketers to serve the wants of individual customers better. A prominent example of such approaches is one-to-one marketing (e.g., Peppers and Rogers 1993, 1997; Peppers, Rogers, and Dorf 1999), and the emphasis on customizing offers to individual customer preferences is also a key component of mass customization (Gilmore and Pine 1997; Pine 1993), customer relationship management (e.g., Freeland 2003; Lemon, White, and Winer 2002; Winer 2001), personalization, and smart agents (e.g., Alba et al. 1997; West 1996; West et al. 1999).
Although each of these management approaches has its unique aspects, a common underlying assumption is that customers have hidden or overt preferences that marketers can reveal by building a learning relationship (Peppers and Rogers 1997). After uncovering customers' preferences, marketers can offer them what they want, often even before customers know they want it (e.g., anticipate which shoes, eyeglasses, or greeting cards the customer will prefer). If successful, marketers will be rewarded for the superior value they provide with higher customer loyalty, which will create a "literally insurmountable barrier to competition, for one individual customer at a time" (Peppers and Rogers 1997, p. 177). Indeed, in theory at least, serving "segments of one" cannot be less effective than serving larger segments, unless the additional benefits are outweighed by the additional costs. Learning individual customer preferences and tailoring offers to those preferences is not a new concept and has been the standard practice in many service, business-to-business, and other markets. However, new technologies now enable marketers to apply individual marketing using mass customization in a much wider range of markets.
Offers that are customized to individual customers' preferences may provide superior value if customers have preferences that marketers can uncover and if customers can recognize offers that provide a superior fit to their preferences. However, recent research on the construction of preferences (e.g., Bettman, Luce, and Payne 1998) suggests that customer preferences are often ill defined and susceptible to influence by seemingly irrelevant factors. Therefore, the fundamental assumptions underlying the new approaches for satisfying individual customer preferences often may not hold--customers may not have well-defined preferences to be revealed, and they may fail to appreciate customized offers that fit their measured preferences. Furthermore, when customers do not have well-defined preferences, they may need to rely on various proxies or cues to assess whether an individual offer indeed fits their preferences.
Therefore, a question that arises is whether and, more specifically, under what conditions preferences can be uncovered and translated into superior customized offers that are appreciated and rewarded by the customer. The present research addresses this question and examines the factors that determine customers' evaluation and acceptance of customized offers and one-to-one relationships with marketers. The focus is on situations in which an individual customer's preferences for the focal product or service are first measured or tracked and the information is then used to design offers tailored for that customer. Although the distinction between what has been referred to as individual marketing and market segmentation is subtle (e.g., many examples of one-to-one marketing could also be classified as usage-based segmentation), this research does not focus on approaches that can be regarded as traditional customer segmentation.
The discussion follows the process model summarized in Figure 1. The first component, preference development and stability, underlies customers' evaluations and acceptance of and responses to customized offers. The next component, evaluation of customized offers, refers to factors that affect the perceived fit and attractiveness of the offer. However, the likelihood of customers actually accepting a recommendation or purchasing the offer (holding fit and attractiveness constant) may depend on the specific characteristics of the customized offer and the category. Finally, this research examines influences on the customer's decision to maintain one-to-one relationships and to continue to purchase previously customized offers. The theoretical and managerial implications of this research are discussed.
The characteristics of customers' preferences are the antecedents to and main drivers of the response to marketers' offers, including individually customized offers. The emerging consensus among researchers of consumer decision making is that buyers often do not have well-defined preferences that can be retrieved, and they construct their preferences when faced with the need to make decisions (for a review, see, e.g., Bettman, Luce, and Payne 1998; Fischhoff 1991; Slovic 1995). The notion that preferences are constructed rather than retrieved is supported by a great deal of evidence indicating that preferences are contingent on the framing of options (e.g., Levin and Gaeth 1988), the characteristics of the decision task (e.g., Tversky, Sattath, and Slovic 1988), and the choice context (e.g., Huber, Payne, and Puto 1982). For example, Simonson and Tversky (1992) demonstrate that consumers are more likely to exchange $6 for an elegant Cross pen when they also have the option of exchanging $6 for a less attractive pen.
It is noteworthy that the conclusion that preferences tend to be unstable and susceptible to various influences does not apply equally to all preference levels. In particular, much of the research supporting the notion that preferences are constructed and susceptible to a variety of seemingly irrelevant influences has involved options with different attribute values that were in the same product or service category. In contrast, although choices between options in different categories can be difficult and susceptible to influence, preferences for product or service categories or types tend to be more stable and well defined. For example, consumers' likings or dislikings for smoking, plain yogurt, or gambling are likely to be rather stable over time, and consumers are likely to be well aware of their preferences for such product types.
The notion that customers' preferences are often constructed rather than revealed has potentially important implications for the effectiveness of customizing offers to individual tastes. Such approaches would offer the greatest value to the customer and, correspondingly, the greatest advantage to the customizing marketer if the following conditions were to hold: ( 1) customers have well-defined and reasonably stable preferences; ( 2) the customers themselves cannot easily define their precise preferences or identify the available options that offer them the best fit; ( 3) by gathering information about individual customers, marketers can reveal preferences and use the information to customize their offers given those preferences; and ( 4) customers can recognize and respond favorably to offers that fit their revealed preferences.
The first condition is straightforward. It is easier to satisfy well-defined, stable preferences than fuzzy preferences that are susceptible to influence by contextual, framing, and task factors. That is, if preferences are stable and well defined, a technique that effectively reveals those preferences will enable a marketer to generate a customized offer that accurately matches the customers' wants. The second condition is less straightforward. Consider a typical market in which there is more than one supplier that can potentially provide the same service or product. In that case, if customers have well-defined and stable preferences, the more insight they have into their preferences, the less dependent they are on the private information of a particular marketer, and the easier it is for them to select the most attractive competitive offer. That is, knowledge of their own preferences gives customers greater independence, though even customers with good preference insight can benefit from assistance in identifying suitable options.
The third condition for effective customized offers is again more straightforward--a one-to-one marketer should be able to use information about the customer's preferences (e.g., based on prior purchases) to generate an offer that fits future preferences. However, as is discussed further subsequently, this is a challenging task. Finally, suppose that the consumer has well-defined preferences but does not have good insight into those preferences. In that case, even if the marketer or agent can learn the true preferences of the customer and translate them into a customized offer, the customer may fail to recognize the attractive offer. That is, because customers often do not have good insight into their preferences (e.g., Nisbett and Wilson 1977), they may fail to recognize a good match to their measured preferences.
This analysis suggests that there are two dimensions of consumer preferences that play key roles in the response to customized offers: ( 1) the degree to which consumers have stable, well-developed preferences and ( 2) the consumers' self-insight into those preferences, including their perception of the stability and clarity of their preferences. Although both dimensions reflect continuums rather than dichotomies, to simplify the analysis it can be assumed that consumer preferences in a particular category fall into one of four cells, as shown in Figure 2.
The degree to which preferences are stable and well developed and the self-insight into those preferences are likely to be positively correlated. For example, consumers are likely to have stable preferences for product types (e.g., plain yogurt, strawberry jam) and good insight into those preferences. In addition, more knowledgeable and experienced customers are likely to have both better-developed preferences and better insight into those preferences than are less knowledgeable and experienced customers. However, the two factors may diverge in many cases. In particular, customers might believe that they have preferences, but such beliefs are incorrect. Combining the two dimensions of consumer preferences, four basic groups can be defined (see Figure 2):
Group 1. If customers have unstable and fuzzy preferences, it is impossible to offer them a solution that provides a satisfactory fit to their (unstable, weak) preferences. However, because these consumers have poor insight into their preferences, they are susceptible to influence and could be convinced that a customized offer is satisfactory and fits with what they like. If the customized offer does not fail, these consumers may later define their preferences on the basis of the option that they mistakenly believed fit their prior preferences. For example, a consumer may be unable to distinguish between merlot and cabernet sauvignon wines in a blind taste test yet believe that merlot is superior. In that case, a customized offer of a merlot wine may create a more stable preference for merlot wines.
Group 2. Customers who know that they do not have stable, well-defined preferences are likely to evaluate offers on the basis of their apparent attractiveness rather than their fit with (weak) preferences. Furthermore, these customers are likely to be most receptive to advice and assistance in defining their preferences. For example, motivated wine drinkers who recognize their ignorance in wines are likely to be receptive to education and suggestions.
Group 3. This group of customers is probably the smallest. It represents customers who have stable preferences that guide their choices, but they have poor insight into the drivers of their preferences. For example, consumers might believe that their choices are based on rational, objective criteria, even though their decisions are based primarily on affective or aesthetic considerations. Consequently, these customers may mistakenly accept customized offers or choice criteria that do not really fit their preferences, which leads to dissatisfaction. Alternatively, they might reject customized offers and choice criteria that actually do fit their preferences.
Group 4. Customers in this group have both well-defined preferences and good preference insight, which enable them to judge correctly whether a customized offer fits their preferences. Thus, such customers might be good candidates for customized offers, and they may be more satisfied with marketers that make an effort to learn their preferences. However, given their insight into their preferences, they are likely to be less dependent on marketers' recommendations, and if needed, they can teach other marketers how to satisfy their preferences.
Note that the same customer may fall into different preference classification groups, even within a category. In particular, preferences for product types are more likely to fall in Group 4 (well-developed preferences and good insight) than are preferences for specific options within a category. Thus, usage information at the individual customer level is likely to be a better predictor of future preferences for product types than for specific options.
Given that consumers' preferences are often undeveloped and unstable to at least some significant degree and customers tend to have limited insight into their own preferences, the main focus of the present analysis is customers in Groups 1 and 2 in Figure 2. In addition, the factors influencing the response to and impact of customized offers among consumers with well-developed preferences and a good insight into those preferences (i.e., Group 4) are examined.
Customers whose preferences are (at least somewhat) fuzzy and unstable but who believe that they have preferences (i.e., Group 1 in Figure 2) are likely to rely on cues for assessing the fit of a customized offer with their preferences. Thus, under preference uncertainty, customers' evaluations of the attractiveness of a customized offer are likely to be influenced significantly by the manner in which it is presented. Figure 3 depicts key drivers of the evaluation of customized offers, which are discussed next.
Influences on the Perceived Preference Fit of Customized Offers
The "customized" label effect. Perhaps the most basic fit cue is the presentation of an offer as customized to the individual customer's tastes. Specifically, given that customers often do not have well-defined preferences or good insights into their preferences, the "customized" label, by itself, can positively affect perceived fit, assuming that the customer trusts the one-to-one marketer or agent. Prior research has identified several key antecedents of trust, including the supplier's expertise, reliability, and motives (e.g., Moorman, Zaltman, and Deshpandé 1992). Accordingly, the evaluation of an offer by a marketer or agent that is presented as customized to the individual customer's tastes is likely to depend on the degree to which the marketer is regarded as a competent and reliable customizer with access to attractive solutions (e.g., has the technology to identify the best deals on the Internet).
Furthermore, the impact of a "customized" label on perceived fit is likely to be mediated by a confirmation bias (e.g., Lord, Ross, and Lepper 1979). Specifically, if customers trust the marketer or agent and believe that it has information about their true preferences, they will be inclined to interpret even ambiguous evidence as consistent with the "customized to your preferences" label. For example, when evaluating a wine presented as based on their previous selections, trusting consumers may find evidence that the "customized offer" indeed fits their preferences. This leads to the first proposition:
P1: An offer presented as customized to the customer's preferences will be perceived as providing a superior fit, compared with the same offer without the customization label, assuming that ( 1) the customer trusts the marketer and ( 2) there is ambiguity about preference fit.
Customer participation in the design of customized offers as a moderator of perceived fit. In some cases, a marketer informs the customer that a particular offer is individually customized, whereas in other cases a customer is unaware that an offer was derived from his or her preferences and profile (e.g., the marketer tracks and uses the customer's prior preferences to generate an offer but does not reveal that practice to the customer). In other situations, customers both are aware that an offer was customized to their tastes and actively participated in the offer's design. Such participation is likely to be a particularly powerful cue that the offer fits the customer's preferences. Thus, for example, the success of mass customization as applied by Dell Computer, in which customers select the product attribute values they want, can be partly due to the customers' participation in designing the provided products.
Recent research by Kramer (2003) provides indirect evidence in support of the proposition that, other things being equal, an offer that was produced with the active participation of the customer would be perceived as offering a better fit to the customer's preferences. Kramer argues that the effect of preference measurement on a customer's perceptions of the fit of an offer is likely to depend on whether the customer can detect the correspondence between the preferences expressed in the measurement process and the customized offer. Consistent with this proposition, Kramer shows that respondents who provide direct measures of attribute importance weights and partworth values (Srinivasan and Park 1997) evaluate an individually customized option more favorably than do respondents whose preferences have been measured with full-profile conjoint analysis, a decompositional approach that is less transparent to respondents.
In a similar fashion, active participation of customers in the production of offers customized to their tastes is expected to make the fit between the offer and the customers' preferences more transparent. However, the degree to which active customer participation affects perceived offer fit is likely to depend on customers' perceptions of their own preferences and their familiarity with the category and the options available on the market. Specifically, customers who believe that they have strong, well-defined, and informed preferences are likely to place greater value on their participation in the process than are customers who are less sure that they know what they want. However, stronger preferences and greater category familiarity also enable customers to evaluate more accurately the true fit of the customized offer with their preferences. Thus, the role of the strength and clarity of preferences in moderating the impact of active participation in the offer's production on its perceived fit depends on the relative weights of two conflicting factors: the strength of belief that the customer's input to the offer design is important and the customer's ability to evaluate the offer's true fit with his or her preferences.
P2: Customized offers produced with the customer's participation in the customization process (holding the offer's specifications constant) will be perceived as providing a better fit to the customer's preferences than will customized offers produced without the customer's participation. The magnitude of this effect depends on ( 1) the degree to which customers perceive their input to the offer production process to be well informed and thus important and ( 2) customers' ability to evaluate the true fit of the offers with their preferences.
Key preference fit indicators. Customers' preferences can be divided into those that are shared by many other customers and those that are perceived as idiosyncratic to the customer or to a small segment. Kivetz and Simonson (2003) show that idiosyncratic preferences play a key role and tend to be overweighted in consumers' decisions. For example, Kivetz and Simonson demonstrate that students who like sushi more than most other students do are more likely to join a loyalty program that offers a certain reward (movie tickets) for purchasing both 12 sandwiches and 12 orders of sushi than to join a loyalty program that offers the same reward for purchasing just 12 sandwiches. Similarly, when evaluating a customized offer, customers are likely to overweight the offer's fit on their "signature" preferences, which they believe separate them from most other customers. That is, a match or mismatch on an attribute that is idiosyncratically important to the customer can often determine the offer's perceived attractiveness.
P3: A fit (or misfit) of a customized offer on customers' signature (idiosyncratic) attributes will be overweighted in the evaluation of the offer, compared with equal or more important preference dimensions that are perceived to be as important to many other customers.
Moderators of the Perceived Attractiveness of Customized Offers
As discussed previously, many customers may be aware that their preferences are not well defined (i.e., Group 2 in Figure 2). Accordingly, when customers are presented with offers that are (presumably) customized for them, they are likely to focus on the attractiveness of the offer rather than on its fit with their (ill-defined) preferences. Furthermore, the perceived attractiveness of an offer is likely to affect its perceived preference fit among consumers with unstable, poorly developed preferences, who may mistakenly believe that they have well-defined preferences (Group 1 in Figure 2). Primary influences on perceived offer attractiveness (aside from the obvious effect of superior attribute values, such as lower price or higher reliability) are expected to include the set configuration and format of the presented customized offers.
The customized offers' presentation context. A great deal of research has shown that customers tend to evaluate options relative to the (local) choice set presented to them, giving surprisingly little consideration to the other options available on the market (e.g., Huber, Payne, and Puto 1982; Simonson 1989). Accordingly, assuming that marketers present customers with more than one option that is customized to their tastes (e.g., three "recommended" options), they can present a set of options that will make a particular option appear attractive compared with the other presented options. Particularly when customers do not have clear, strong preferences, one option appearing superior to other presented alternatives can convince customers that this target option offers a good fit and is a good buy. However, if customers perceive a recommended, less attractive option as a mere decoy that is designed to make another option appear like a bargain, this strategy will backfire and diminish customers' trust in the one-to-one marketer.
P4: Customized offers that seem attractive in the context in which they are evaluated will be perceived as offering a superior fit to the customer's preferences. This effect will be strong for customers with less developed preferences and those who perceive the context to be credible.
The customized offers' presentation format. Customized offers may take different forms. The marketer might offer the customer just one option that best fits that customer's preferences. Alternatively, the marketer might provide the customer with several suitable options, rank-ordering them in terms of fit or value or just presenting them as a set of options that fit the customer's preferences or profile. Prior research suggests that the presentation format used and the number of options presented can have a significant effect on customers' response, including both whether a choice will be made and which option will be selected (for a review, see Bettman, Luce, and Payne 1998). In particular, presenting the customer with two about equally attractive alternatives, as opposed to just one recommended option, may often produce conflict and make it difficult for the customer to identify one offer as clearly attractive, leading to indecision (e.g., Dhar 1996, 1997; Tversky and Shafir 1992). Furthermore, Iyengar and Lepper (2000) demonstrate that presenting consumers with a large set of options decreases the likelihood that any single option will be perceived as sufficiently attractive to justify a purchase.
However, a limitation of presenting just one customized option is that the customer may have difficulty evaluating whether the recommendation represents a good value, because customers tend to rely on comparisons among the options shown to them to assess value (e.g., Huber, Payne, and Puto 1982; Simonson and Tversky 1992). Therefore, there is a higher likelihood that customers will purchase a customized option if they are presented with two or more options and they perceive one recommendation as superior to another (legitimate) option.
Presenting a set of options that are rank-ordered in terms of fit to the customers' tastes has the advantage that it offers customers several customized options to choose from, while reducing the conflict associated with choice among multiple alternatives. However, presentation of rank-ordered recommendations can backfire if the customer has a prior unfavorable opinion of the option ranked first. For example, a customer may already be familiar with and dislike the recommended book or wine ranked first, in which case the customer may become skeptical of the value and accuracy of the customization process. Furthermore, customers may be reluctant to select an option that is ranked lower than one they do not like.
P5: Customers are more likely to perceive an offer as attractive and as offering a good fit if they are presented with a set of rank-ordered recommended options, as opposed to just the top-ranked alternative or a set of unranked alternatives. If, however, they have a prior negative opinion of the top-ranked alternative, they are more likely to accept an offer if recommended options are unranked.
Beyond the evaluation of an offer's fit and attractiveness, a critical question is whether customers will actually accept or purchase the offer. In particular, are customers inherently more receptive to recommendations of certain option types? Although this question has not received much attention, recent research suggests that, if the attractiveness of an offer is held constant, the impact of recommendations on customer action (or purchase decisions) depends on the characteristics of the recommended options (e.g., their price, quality, and risk).
Customized recommendations may also be more effective for certain product categories and types of purchases. For example, recommendations may be made for categories such as books or movies, in which consumers make frequent selections of product variations, or for infrequently purchased products, such as cameras and laptop computers. Again, customers might be more receptive to recommendations in some categories than in others. Figure 4 presents key influences on the likelihood of customers acting on or purchasing customized offers.
The Effect of the Customized Option's Characteristics on Purchase Likelihood
Customers' receptivity to high-price, high-quality versus low-price, low-quality recommendations. A basic dimension on which products and services often differ is price and quality (broadly defined), such that customers might be presented with a customized high-price, high-quality brand or a low-price, low-quality brand. Prior research suggests that it is easier to cause consumers to switch from a low-price, low-quality to a high-price, high-quality option (e.g., in response to a sale) than to switch in the opposite direction (e.g., Heath et al. 2000; Nowlis and Simonson 2000). Simonson, Kramer, and Young (2004) demonstrate that this generalization is not limited to the effect of sales and that across a wide range of conceptually distinct conditions, consumers are more likely to switch to high-price, high-quality options. For example, consumers who observe another consumer choose a high-price, high-quality option are more likely to select a high price, high-quality option, whereas observing another consumer choose a low-price, low-quality alternative often has little effect on purchase decisions. These findings suggest that consumers are more likely to accept or act on customized offers and recommendations of high-price, high-quality alternatives. Conversely, customized offers to choose low-price, low-quality products are less likely to cause customers to change their purchase decisions.
P6: Other things being equal, customers are more likely to accept recommendations to choose a high-price, high-quality option than a low-price, low-quality option.
Although P6 suggests a main effect whereby customers are more likely to accept a customized recommendation to choose a high-price, high-quality option, the magnitude of this effect is expected to interact with two factors: the level of trust in the marketer making the customized offer and the manner in which the recommended options are presented. It is reasonable to expect that acceptance of a marketer's recommendation to purchase a more expensive item requires a higher level of trust than adoption of a customized recommendation to purchase a less expensive product. Specifically, because of the inherent ambiguity about quality and the typically greater profitability (for the seller) of high-price options, accepting a recommendation to pay more for a high-quality option requires that the customer trusts the marketer. In contrast, a low price is less ambiguous and appears to go against the seller's profit incentive, which is likely to make the recommendation more credible and therefore less dependent on trust. Accordingly, the main-effect prediction that customers are more receptive to customized recommendations to choose a high-price, high-quality option is qualified by an interaction with the level of trust.
P7: The tendency to accept recommendations to choose a high-price, high-quality option is positively correlated with the level of the customer's trust in the marketer.
Prior research also suggests that when customers evaluate each alternative separately compared with two or more options jointly, they are more likely to prefer high-price, high-quality brands (e.g., Nowlis and Simonson 1997). Furthermore, when customers are presented with three or more options compared with just two, they tend to avoid the lowest-price, lowest-quality alternative (e.g., Simonson, Nowlis, and Lemon 1993). These results suggest that the number of customized options shown to customers is likely to influence their choices. Specifically, ( 1) customers are more likely to purchase a high-price, high-quality alternative presented as a separate recommendation ("the best option for you"), and ( 2) customers are least likely to choose the cheapest alternative when it is presented simultaneously with two or more other recommended options.
P8: Customers are more likely to accept a recommendation for a high-price, high-quality product if that option is presented separately; they are least likely to accept a recommendation to choose a low-price, low-quality option when it is the lowest-price, lowest-quality alternative in a set of three or more recommended options.
Customers' receptivity to high-risk, high-return versus low-risk, low-return customized recommendations. Recent research (Simonson, Kramer, and Young 2004) suggests that in choices between low-risk, low-return and high-risk, high-return options, customers tend to choose the safe option by default. Furthermore, various manipulations, such as high involvement and observation of others' choices, often cause consumers to switch from a low-risk, low-return alternative (e.g., using a proven but more limited technology) to a high-risk, high-return alternative (e.g., the next-generation technology); conversely, it is often more difficult to change customer choices from a high-risk, high-return option to a low-risk, low-return alternative. These findings, consistent with the notion that customized recommendations decrease the perceived uncertainty about the riskier option, suggest that customized recommendations that endorse high-risk, high-return alternatives are more likely to influence customers' purchase decisions. However, this prediction is also expected to depend on the level of trust in the customizing marketer. Specifically, the lower the customer's trust in the marketer making the recommendation, the lower is the likelihood that the customer will accept a risky recommendation, and vice versa.
P9: In response to a customized offer, customers are more likely to switch from a low-risk, low-return to a high-risk, high-return option than to switch in the opposite direction. This prediction interacts with the level of a customer's trust in the marketer: Lower trust is associated with a lower willingness to accept a risky recommendation.
Customers' receptivity to recommendations of luxuries versus necessities. Consumers often need to choose between spending money on necessities (e.g., savings, education, food) or buying luxuries (e.g., fancy food, a cruise, an expensive bottle of wine) that go beyond the indispensable minimum but add to their quality of life. Kivetz and Simonson (2002) show that many consumers have difficulty choosing a luxury over a necessity and consequently feel that they tend to underconsume hedonic luxuries (see also Scitovsky 1992). To remedy the tendency to reject luxuries in favor of easy-to-justify necessities, consumers may precommit to luxury consumption. For example, approximately 30% of women who were offered a reward for their participation in a study chose to receive a day spa coupon worth $70 over $75 in cash. These findings suggest that consumers often need reinforcement to allow themselves to splurge and choose luxuries over necessities. In particular, compared with the default choices that consumers would have made on their own, customized recommendations to select a luxury over a necessity are likely to have greater impact than recommendations to prefer the necessity. This effect is expected to be more pronounced if the recommended luxury options are offered at a discount (e.g., on sale), which makes it easier for consumers to justify such choices.
P10: Recommendations to choose hedonic luxuries, compared with utilitarian necessities, are expected to have greater impact on the choices consumers make. This effect is stronger if the recommended option (luxury or necessity) is offered at a discount.
The Effect of Purchase Type and Variety Seeking on Response to Customized Offers
The propensity to accept or reject customized offers is also likely to depend on the nature and degree of variety seeking in the product or service category. In some cases, customers seek no variety at all and habitually purchase the same item without considering other options (e.g., Bettman 1979). In such cases, customization means that the marketer provides the customer with the usual choice without waiting for the customer to ask for it, and the customer accepts the offer.
In other categories, customers do seek variety, but the set of considered options is bounded. For example, customers may have a set of several flavors of yogurt or types of cereal that they purchase from time to time. Because the selection of a particular item depends on the customer's current state of mind (e.g., mood), customization is unlikely to be effective. That is, given that no algorithm or model of the customer's prior preferences or preferences of similar customers can predict transient, state-dependent preferences, the likelihood that the customer will accept a customized offer corresponds to the probability that the customer happens to be in the state of mind that fits the offer.
In yet other situations, customers seek variety, and the set of options is effectively unbounded. For example, in categories such as books, movies, and wine, consumers often seek variety from a large and ever-expanding set of options. In this type of purchase decision, the role of customized offers is to identify and suggest attractive options of the type preferred by the customer. For example, if a consumer likes autobiographies or California cabernet sauvignons that cost around $20, the customizer may suggest attractive new options in these categories. The main focus of the seller's message needs to be indicators or evidence that, in addition to being in the relevant category, the proposed option is indeed attractive. For example, informing a customer that many other customers purchased a particular autobiography and rated it highly is likely to enhance the probability that the customer will accept the (customized) recommendation.
Finally, in infrequently purchased categories in which specifications and features evolve over time (e.g., durable products), the customer's prior preferences are unlikely to provide much guidance to the marketer (and the customer) regarding the best offer. Accordingly, a key role of the customizer is to find out, in as much detail as possible (given preference insight limitations), the features desired by the customer and use that information to identify the most attractive options available on the market. However, persuading the customer to purchase a recommended, customized offer in an infrequently purchased product category can be challenging for at least three reasons.
First, given the financial and other sources of risk, the customer is likely to seek further evidence (e.g., expert reviews) in support of the recommended option. Second, regardless of whether customers have well-developed preferences and good insight into those preferences, they are more likely to accept an offer that is customized to their tastes if they trust the customization process. Specifically, when a product is purchased infrequently, it is clear that the customizer cannot rely on the revealed preferences of the customer. Instead, the marketer or agent must elicit the current preferences of the customer and use that information to identify the best available options. However, if customers believe that they do not have sufficient knowledge or clear preferences, they are likely to be skeptical of recommendations derived from those preferences. Third, because infrequently purchased goods are often associated with high prices and profits for the seller, the risk that a one-to-one marketer has ulterior motives when making customized recommendations is high. For all these reasons, customers are less likely simply to accept a customized offer in a high-price, infrequently purchased category. Instead, they might use the recommendations as one source of information and further examine the product using other sources. To the extent that these customers are susceptible to a confirmation bias (e.g., Lord, Ross, and Lepper 1979), the customized recommendations could still exert an impact on the final purchase decision. This discussion leads to the following general proposition:
P11: The purchase type and the degree of variety seeking affect customers' acceptance of recommended customized offers. Specifically, (a) higher variety seeking decreases receptivity to customized offers, and this effect is more pronounced if variety seeking is driven by transient states and tastes, and (b) for infrequently purchased high-price items, the willingness to accept customized recommendations is positively correlated with the strength of independent evidence supporting the customized recommendation.
The determinants of customers' interest in and commitment to relationship marketing have been examined extensively in prior research (see, e.g., Bhattacharya and Bolton 2000; Dowling 2002; Garbarino and Johnson 1999; Gundlach, Achrol, and Mentzer 1995; Morgan and Hunt 1994; Sheth and Parvatiyar 1995). In the context of the present research, the question of interest is related to a narrower issue: the effect of a marketer's investment in learning the customer's preferences and designing a customized offer on the likelihood that the customer will continue to purchase from the marketer. As indicated previously, proponents of one-to-one marketing and related approaches have argued that developing learning relationships with customers and using information about customers' preferences to generate customized offers promote customer loyalty and create an almost insurmountable competitive advantage. However, when customers do not have well-defined preferences or good insight into those preferences, the significance of the advantage produced by learning relationships with customers may come into question. Furthermore, customers whose preferences are somewhat fuzzy and unstable may be less willing to commit to continuing to purchase from a one-to-one marketer if the perceived costs of such a commitment outweigh its perceived benefits. The following discussion addresses factors that are expected to influence the tendency of customers with well-developed, stable preferences and those without such preferences to continue purchasing from one-to-one marketers that have customized their offers (see Figure 5).
Commitment of Customers with Well-Defined Preferences to One-to-One Marketers
Most of the examples used by proponents of one-to-one marketing and mass customization to illustrate the value of one-to-one relationships (e.g., Pine, Peppers, and Rogers 1995) refer to situations in which the customer presumably wants the same product or variations of a product repeatedly, such as the same coffee, the same eyeglasses style, and the same shoe or greeting card design. However, in most cases, customers are unlikely to purchase the same item habitually, without considering any other options. Indeed, for such habitual decisions to occur, the following conditions must hold: ( 1) The product category is not characterized by significant variety seeking or satiation, ( 2) the product category is characterized by little change in preferences over time, ( 3) the set of options available in the product category does not change significantly over time, and ( 4) customers do not feel the need to reevaluate options before each decision. Because one or more of the conditions for habitual purchases usually do not hold, most customers are unlikely to repeat purchase decisions automatically without considering alternatives.
In terms of the customer's costs and benefits of maintaining one-to-one learning relationships, customers with well-defined, stable preferences are likely to prefer marketers that remember their preferences, particularly when the convenience of not needing to respecify wants, switching costs, or inertia play a key role. Furthermore, the marketer's effort to remember and act on the customer's prior preferences can create a favorable attitude and enhance customer loyalty to that marketer. However, customers who have well-established and well-defined preferences and know those preferences are likely to be less dependent on any particular marketer and therefore less willing to accept suboptimal offers. Specifically, the added convenience of not needing to respecify (well-defined) preferences on each purchase occasion often may not be a significant decision factor. More important, when customers know their preferences, they can usually transfer this knowledge easily to other suppliers if they have a reason to do so. These arguments suggest that customers with stable, well-defined preferences may be more price sensitive and less loyal to any particular marketer, including one-to-one marketers that have invested in learning and storing their preferences.
P12: When faced with competing customized offers, customers with more stable, well-defined preferences are less loyal and more price sensitive than are customers with weaker preferences.
Commitment of Customers with Less Well-Defined Preferences to One-to-One Marketers
As explained previously, in most purchase decisions, customers' preferences are not fixed and predetermined, and customers are likely at least to consider the possibility of selecting other options. In such cases, customers may not respond favorably to marketers' presumptions that they know what the customers want even without asking. Consistent with the notion of reactance (e.g., Brehm 1966), customers may believe that their freedom of choice is restricted as marketers try to invade the domain of their personal preferences by suggesting to them what they want. Customers may also perceive marketers' claims of customization to their tastes as attempts to persuade and manipulate (e.g., Friestad and Wright 1994). Moreover, customers may object to marketing practices that involve keeping track and storing their preferences and then using that information to tell them what they are expected to choose.
Therefore, despite the benefits of individual marketing to customers, entering into customized relationships with marketers may represent a commitment that many customers do not wish to make. Consider the business concept of the Custom Foot chain of footwear stores. As Peppers and Rogers describe (1997, pp. 153-59), the customer first spends seven to ten minutes with each foot placed on a scanner device that makes a perfect measurement of the foot's contours. Next, the salesperson manually takes additional measurements.( n1) The customer then sits at a computer console, answering specific questions about the type of wear that he or she has experienced on the current shoes, other questions about his or her prior shoe experiences, and various marketing questions. Custom Foot then uses this information to manufacture shoes that are customized to the customer's profile. However, even if the customized shoes fit fine, the customer may be reluctant to return to the store for the next pair because of the implied obligation to rely on the prior measurements and preference knowledge of the store. Indeed, customers are likely to feel uncomfortable going back to the same store and telling the salesperson that they have changed their minds and tastes and are now looking for a different type of shoe and a different design. Thus, entering into a "learning relationship" creates a commitment that many customers may not wish to make or maintain. This analysis is likely to apply in situations in which the purchase process involves human contact, such as the relationship between a customer and a salesperson. It is less likely to apply if the customized offers are designed and delivered by machines (e.g., a computer). Therefore, such concerns about the response to customized offers are not expected to apply to smart agents.
P13: A learning relationship used to customize offers may be perceived by customers as an indicator of good service but also as a restriction to their freedom of choice and can be a source of discomfort. The stronger the weight of the latter compared with the former factor, the lower is the likelihood that a customer will sustain a one-to-one relationship with the marketer.
P14: The level of discomfort associated with a one-to-one learning relationship will be greater if the customization procedure involves human rather than machine (e.g., computer) contact.
Giving customers what they want (at a profit) is perhaps the most basic principle of marketing. Accordingly, the idea that marketers can obtain an insurmountable competitive advantage by learning and satisfying the wants of one customer at a time, as opposed to focusing on market segments, seems compelling. Similar approaches have been employed by marketers in many business-to-business markets, by catalogs, by the local barber and butcher, and by other sellers, which have used customer familiarity and personal service as key selling propositions. However, the new approaches to marketing suggest that learning individual customer preferences and satisfying those segments of one, including anticipating future wants, can now be applied using mass customization. It has thus been assumed in recent years that the age-old practice of targeting market segments is dominated and will be displaced by individual marketing (e.g., Kotler 2000). That is, in the future, customers in most markets may expect and will receive offers customized to their individual preferences. The present research examines the conditions that moderate customers' responses to customized offers and the effectiveness of individual marketing methods.
The process model presented in Figure 1, combined with the component models in Figures 2, 3, 4, and 5, capture key antecedents and phases of customers' responses to customized offers in the short and long run. An essential implication of the proposed framework and research propositions is that the benefits and costs of addressing individual customer preferences are much more complex and less deterministic than has typically been assumed. Furthermore, even when customers have well-defined preferences and receive offers that fit those preferences, it is far from certain that the response to such offers will be consistently and materially more favorable than the response to offers that are based on an analysis of the preferences and characteristics of market segments. Indeed, a great deal of research over the past two decades has shown that customer preferences are often ill defined and susceptible to various influences, and in many cases, customers have poor insight into their preferences (e.g., Bettman, Luce, and Payne 1998; Simonson 1993). The "noise" level created by these characteristics of customer preferences suggests that the true fit between the preferences and the offer is just one determinant of customers' responses to marketing offers. Thus, the response to customized offers depends more on cues regarding the offer's fit and value, customers' receptivity to certain types of offers, and customers' perceptions of one-to-one relationships than on marketers' ability to provide the perfect match to the customers' preferences. This analysis raises important questions that deserve further research, as discussed next.
Directions for Further Research on Individual Marketing
New managerial concepts and techniques that promise dramatic performance improvements have been introduced over the years. Although many of them were supported mainly by examples or anecdotes and were not subjected to rigorous testing, they enjoyed great popularity, usually for a short period. Given the short life cycle of such managerial innovations relative to the time required to publish an article in a major journal, researchers have been less inclined to study such techniques despite their initial popularity. Although this limitation may also apply to specific approaches such as one-to-one marketing and personalization, individual marketing and customer response to customized offers are fundamental to the marketing concept and deserve to be rigorously studied.
The present research identifies several areas for future study and outlines specific propositions. In addition to basic questions about the construction of preferences, further research should investigate the cues that customers use for determining whether the marketer made a genuine attempt to customize the offer or recommendation to their individual preferences and whether the offer fits their preferences. As discussed previously, such an investigation must consider the impact of relevant moderating variables, such as the degree to which customers have stable preferences and good insight into those preferences, the type of purchase at issue, the customer's active participation in the customization process, offer presentation format, and key fit indicators.
Beyond the evaluation of customized offers, further research might examine the factors that influence customers' acceptance of or tendency to act on customized recommendations. As suggested previously, if the perceived fit and attractiveness of an offer are held constant, prior research suggests that certain recommendations (e.g., to purchase a high-price, high-quality alternative or a luxury) are likely to affect actual purchase decisions.
In addition to studying the response to individual customized offers, it is important to examine the impact of practicing customization on the customer's commitment to and long-term relationships with marketers. As argued previously, customers with well-defined preferences and good insight into those preferences, in theory the bread and butter of one-to-one marketing, may be less committed and less loyal to customized relationships and one-to-one marketers. Furthermore, although one-to-one relationships can certainly offer significant benefits to customers, they may also be regarded as a source of discomfort and as restricting the customer's freedom of choice. Again, investigation of these issues must consider the moderating variables (e.g., the degree of variety seeking, the type of purchase and buyer-seller interaction) instead of just searching for main effects.
Managerial Lessons
The present framework and research propositions suggest four key lessons for managers. The first lesson is that the promise of individual marketing-based approaches and their advantage over segmentation have been greatly exaggerated, and in many cases, implementation of such approaches will not produce the expected benefits. Therefore, considering that adoption of individual marketing often involves large investments and profound organizational and marketing strategy changes, managers should carefully evaluate the costs and likely benefits of such a strategy in their particular conditions. Consistent with segmentation based on usage, offering customers what they have consistently purchased in the past (e.g., offering red wine options to consumers who regularly buy red rather than white wines) will be more effective than offering the same options to all consumers. Furthermore, because preferences for generic product types (e.g., frozen orange juice, liquid detergent) tend to be more stable and predictable than preferences for specific options within a category, tracking usage at the individual customer level can improve the effectiveness of promotional tactics. For example, mailing coupons or advertisements for a new frozen orange juice to prior frozen orange juice buyers is likely to produce a higher redemption rate (as practiced, for example, by Catalina Marketing; see www.catmktg.com).
However, because consumer preferences are often unstable and susceptible to influence and consumers often have poor insight into their own preferences, the value-added and impact of individually customized offers, as opposed to simple usage/benefit-based segmentation, will often be rather limited. Therefore, contrary to the claims of the proponents of individual marketing, attempts to customize to individual tastes are unlikely to guarantee customer loyalty or prevent customers from being just as responsive to similar offers, coupons, and advertisements of competing brands.
One qualification to this lesson involves situations in which customers habitually purchase the same item without considering other options. Although such habitual purchases are unlikely to represent most purchase decisions, habitual purchases are likely to be associated with clear and known preferences (to both the marketer and the customers), which make it possible to provide these customers with exactly what they want, when and where they want it. However, even under this scenario, customers may not attach much value to the marketer's knowledge of what they like and may not hesitate to switch to another supplier that offers superior value (e.g., a better price), even if such an action requires transferring the knowledge of their preferences to the new supplier. Thus, although good customer service can be an important purchase factor in many cases, even when individual marketing is effectively applied, it is doubtful that it will be sufficient to create an insurmountable competitive advantage (Peppers and Rogers 1997).
The second lesson from the present research is that managers should not consider individual marketing a technique to match individual customers' preferences. Instead, individual marketing means that a marketer provides the customer with cues that the provided offers fit the customer's preferences. Whereas attempts to learn the customer's true preferences and use that information to formulate the offer might affect the response to these offers, other cues can often have a greater impact on the customer's perceptions. Such cues include, for example, framing the offer as individually tailored, encouraging customer participation in the offer's design, and paying special attention to the customer's signature preferences. Furthermore, because customers often do not have well-defined preferences, presenting a customized offer in a context that makes it appear attractive compared with other presented options (e.g., it is clearly superior to another presented option) is likely to enhance its perceived fit.
The third lesson to managers is that in addition to conveying to customers that individual offers designed for them fit their preferences, it is important to consider the likelihood that the recommended offer will be accepted by the customer. That is, if the perceived preference fit of an offer is held constant, customers are likely to be more receptive to some types of offers than to others. For example, as explained previously, customers are more likely to change a default choice and accept a customized offer if the recommended option is associated with a relatively high price and high quality, a more risky option, or a hedonic luxury option.
The fourth lesson is related to the nature of the one-to-one relationship between the marketer and the customer. Given that the actual value of individual marketing to the customer is constrained when preferences are unstable and poorly defined, a marketer should not overestimate the willingness of the customer to make a long-term commitment to the relationship. Furthermore, despite their investment in developing learning relationships with customers, marketers must make it clear that customers are free to change their preferences at any time, and the marketer's knowledge of what the customers want, or wanted at some point, in no way restricts their freedom of choice and the freedom to consider new options.
Furthermore, individual marketers should be sensitive to the risk of irritating customers and damaging the relationship between the company and the customers by making incorrect assumptions about customers' preferences. For example, The Wall Street Journal (Zaslow 2002) reports on a variety of (sometimes entertaining) false assumptions made by TiVo and Amazon about their users. As reported in that article, when Mr. Bezos, Amazon's chief executive officer, performed a live demonstration in front of a large audience of Amazon.com's ability to cater to its customers' interests, the top recommendation the system gave him was the DVD for Slave Girls from Beyond Infinity. A spokesman for Amazon later explained that this recommendation appeared because Mr. Bezos had previously ordered the movie Barbarella.
Although preference-matching technologies can be improved, the underlying limitations and instability of customers' preferences are unlikely to change. Therefore, it is unwise to adopt indiscriminately the simplified assumption that individual marketing dominates segment-based marketing and will become the standard practice in the future. Instead, marketing researchers should study the implications of the characteristics of customers' preferences and the determinants of customers' responses to marketing offers with respect to the conditions that moderate the effectiveness of one-to-one marketing and ways to enhance the effectiveness of individual marketing.
The article has benefited from the comments of Dan Ariely, John Deighton, and Thomas Kramer.
( n1) As an aside, Peppers and Rogers (1997) quote the company's chief executive officer, who indicated that the manual measurements are never used, but they increase the customers' confidence in the product's fit.
DIAGRAM: FIGURE 1; Constructed Response to Customized Offers: An Overview of the Process Model
DIAGRAM: FIGURE 2; Preference Development: Customer Classification Based on Preference Stability and Insight
DIAGRAM: FIGURE 3; Influences on Customers' Evaluations of Customized Offers
DIAGRAM: FIGURE 4; Influences on Purchases of Customized Offers
DIAGRAM: FIGURE 5; Maintaining Relationships with Customizing Marketers
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~~~~~~~~
By Itamar Simonson
Itamar Simonson is Sebastian S. Kresge Professor of Marketing, Graduate School of Business, Stanford University
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Record: 52- Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research. By: Chandon, Pierre; Morwitz, Vicki G.; Reinartz, Werner J. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p1-14. 14p. 1 Diagram, 3 Charts, 4 Graphs. DOI: 10.1509/jmkg.69.2.1.60755.
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Do Intentions Really Predict Behavior? Self-Generated
Validity Effects in Survey Research
Studies of the relationship between purchase intentions and purchase behavior have ignored the possibility that the very act of measurement may inflate the association between intentions and behavior, a phenomenon called "self-generated validity." In this research, the authors develop a latent model of the reactive effects of measurement that is applicable to intentions, attitude, or satisfaction data, and they show that this model can be estimated with a two-stage procedure. In the first stage, the authors use data from surveyed consumers to predict the presurvey latent purchase intentions of both surveyed and nonsurveyed consumers. In the second stage, they compare the strength of the association between the presurvey latent intentions and the postsurvey behavior across both groups. The authors find large and reliable self-generated validity effects across three diverse large-scale field studies. On average, the correlation between latent intentions and purchase behavior is 58% greater among surveyed consumers than it is among similar nonsurveyed consumers. One study also shows that the reactive effect of the measurement of purchase intentions is entirely mediated by self-generated validity and not by social norms, intention modification, or other measurement effects that are independent of presurvey latent intentions.
Consumers' self-reported intentions have been used widely in academic and commercial research because they represent easy-to-collect proxies of behavior. For example, most academic studies of satisfaction use consumers' intentions to repurchase as the criterion variable (for an exception, see Bolton 1998), and most companies rely on consumers' purchase intentions to forecast their adoption of new products or the repeat purchase of existing ones (Jamieson and Bass 1989). However, it is well known that consumers' self-reported purchase intentions do not perfectly predict their future purchase behavior, nor do these differences cancel each other out when intentions and behavior are aggregated across consumers. In a meta-analysis of 87 behaviors, Sheppard, Hartwick, and Warshaw (1988) find a frequency-weighted average correlation between intentions and behavior of.53, with wide variations across measures of intentions and types of behavior (for a review, see Morwitz 2001).
To improve the ability to forecast behavior from intentions, researchers have tested alternative scales (Reichheld 2003; Wansink and Ray 2000) and have developed models that account for biases in the measurement and reporting of intentions, the heterogeneity across customers, changes in true intentions between the time of the survey and the time of the behavior, and the stochastic and nonlinear nature of the relationship between intentions and behavior (Bemmaor 1995; Hsiao, Sun, and Morwitz 2002; Juster 1966; Kalwani and Silk 1982; Manski 1990; Mittal and Kamakura 2001; Morrison 1979). In practice, the studies adjust the intention scores by analyzing the actual purchase behavior of consumers whose purchase intentions have been measured previously. For example, the popular ACNielsen BASES model forecasts aggregate purchase rates by applying conversion rates to measured purchase intentions (e.g., it assumes that 75% of consumers who checked the top purchase-intentions box will actually purchase the product). To obtain these conversion rates, BASES uses previous studies that measured the purchase intentions of consumers and then tracked their actual purchases.
However, a limitation of these studies is that they focus on the internal rather than the external accuracy of purchase-intention measures. That is, the studies measure the improvement in the ability to forecast the behavior of consumers whose intentions they previously measured, not the behavior of consumers whose intentions they did not measure. Therefore, the studies assume that they can extrapolate the intention-behavior relationship of nonsurveyed consumers on the basis of the relationship that surveyed consumers exhibit. In doing so, the studies ignore the potentially important problem that the measurement of intentions itself might self-generate some of the association between the intentions and the behavior of a particular consumer (Feldman and Lynch 1988).
Finding that part of the predictive power of purchase intentions is an artifact of the measurement would have serious implications for researchers and managers. It would suggest that studies that measure the strength of the association between intentions and behavior on the same sample of consumers overstate the external predictive accuracy of purchase intentions. This would explain why so many new products fail even after they perform well in purchase-intention tests. In general, researchers who are interested in measuring the true association between two constructs (in this case, for consumers whose behavior was not influenced by the measurement) would need a method that detects and corrects for the effects of measurement.
In this research, we develop a comprehensive latent framework to conceptualize the reactive effects of the measurement of purchase intentions. This framework distinguishes between two sources of measurement reactivity. The first is self-generated validity effects, which we define as a strengthened relationship between latent intentions and behavior due to the measurement of intentions. The second source includes all measurement effects that are independent of latent intentions, such as those that social norms or postsurvey intention modifications create.
We also describe a two-stage procedure to detect whether the act of measurement alters the strength of the relationship between a latent construct that is measured through surveys, experiments, or observations and its consequence (e.g., intentions-behavior, attitudes-intentions, attitudes-behavior, satisfaction-behavior) and to determine the true magnitude of the relationship in the absence of measurement. We demonstrate three empirical applications of this method using large-scale data sets that contain purchase or profitability data from both consumers whose purchase intentions were measured and similar consumers whose purchase intentions were not measured. In the three applications (groceries, automobiles, and personal computers [PCs]), we show that the strength of the relationship between latent intentions and behavior is stronger for surveyed consumers than for similar nonsurveyed consumers. In the final section, we discuss the managerial and research implications of our results.
Self-Generated Validity and Other Sources of Measurement
Reactivity
Ample evidence indicates that measurement can influence both the intensity of a measured construct and its association with other constructs. In intentions research, the reactive effects of measurement have been called the "mere measurement effect," "the self-erasing error of prediction," and "self-prophecy." We refer to the behavioral differences between surveyed and nonsurveyed consumers as the " reactive effects of measurement" or simply as "measurement reactivity."
In competitive markets in which most existing customers have positive attitudes toward a product category, the measurement of purchase intentions increases purchasing in the category of accessible and preferred brands. Research has shown these effects for both hypothetical and real brands, for financially important and relatively inconsequential behaviors, and for short (a few minutes) and long (six months) delays between the measurement and the behavior (Chandon, Morwitz, and Reinartz 2004; Dholakia and Morwitz 2002b; Fitzsimons and Morwitz 1996; Morwitz and Fitzsimons 2004).
In a related stream of research, studies show that asking consumers to predict their future behavior influences the likelihood that they will engage in that behavior (Sherman 1980; Spangenberg 1997; Spangenberg and Greenwald 1999; Sprott et al. 2003). Focusing on socially normative behavior, these studies demonstrate that if respondents are asked to predict the likelihood that they will perform a behavior in the future, they are more likely to engage in socially desirable behaviors, such as voting or recycling, and less likely to engage in socially undesirable behaviors, such as singing "The Star-Spangled Banner" over the telephone.
The self-generated validity theory (Feldman and Lynch 1988), the most popular explanation of the reactive effects of measurement, uses two lines of argument. First, preexisting intentions may become more accessible in memory when the researcher asks the question. (It is also possible that consumers have no preexisting intentions and form them only in response to the researcher's question.) The measurement process thereby leads survey respondents to form judgments that they otherwise would not access in their memory or that they otherwise would not form. Second, higher relative accessibility and diagnosticity of intentions, compared with other inputs for purchase decisions (e.g., tastes, mood, competitive environment), may make subsequent purchase behavior more consistent with prior intentions.
Several studies provide indirect evidence in support of the self-generated validity theory for public opinion ( Simmons, Bickart, and Lynch 1993) and marketing research (Fitzsimons and Morwitz 1996; Morwitz and Fitzsimons 2004; Morwitz, Johnson, and Schmittlein 1993). However, none has examined the core prediction of self-generated validity theory directly, namely, that the association between prior intentions and behavior is stronger among surveyed consumers than among similar nonsurveyed consumers. The studies have been unable to test this prediction because they have not estimated the purchase intentions of consumers who were not surveyed.
Consistent with Feldman and Lynch's (1988) predictions, Fitzsimons and Morwitz (1996) find that the measurement of general intentions to purchase automobiles increases the likelihood that buyers will repurchase the automobile brand that they previously owned and that first-time buyers will purchase brands with large market shares. Under the assumption that prior purchase rates or market shares are proxies for latent, brand-specific purchase intentions, Fitzsimons and Morwitz's results suggest that the measurement of general intentions increases the association between latent, brand-specific intent and brand choice.
Similarly, in a series of laboratory studies, Morwitz and Fitzsimons (2004) find that the measurement of general purchase intentions for candy bars makes consumers more likely to choose brands that they like and less likely to choose those that they dislike. If we consider prior brand preference a proxy for intention to purchase the brand, this study suggests that the measurement of consumers' intentions to buy from the category increases the association between their latent intentions to buy the brand and the likelihood of subsequently choosing this brand. Finally, Morwitz, Johnson, and Schmittlein (1993) examine the effects of repeated measurements of intentions (and behavior) on people with high and low initial measured purchase intentions. They find that the repeated measurement of intentions and behavior increases the association between behavior and the initial measure of intentions. However, their analysis is restricted to consumers whose purchase intentions have been measured at least once.
Ignoring obvious alternative explanations, such as selection biases, that violate our definitional assumption that surveyed and nonsurveyed consumers are identical, we consider at least two other explanations for the reactive effects of measurement in purchase-intentions surveys: social norms and intention modification. Both differ from self-generated validity in that they operate independently of consumers' intentions at the time of the survey.
In the context of socially normative behaviors, Sherman (1980) shows that asking people to predict their future behavior biases their reported intentions toward a social norm (e.g., donating to charities, not singing over the telephone). Consumers then act according to their newly reported intentions, not according to their prior unreported intentions, to reduce the cognitive dissonance between their reported intentions and their behavior (Spangenberg and Greenwald 1999; Sprott et al. 2003).
With regard to intention modification, consumers tend to evaluate market research surveys positively because they either find the survey informative or enjoy being asked their opinion (Dholakia and Morwitz 2002a; Sudman and Wansink 2002). In a subsequent stage, this positive evaluation of the survey carries over to the evaluation of the company and its products. Consumers also regard the survey as a signal of the firm's customer orientation, which directly improves their evaluation of the company and its products. In both cases, the positive attitude triggered by the survey leads to greater purchasing by surveyed consumers.
Both explanations share the view that the measurement of purchase intentions modifies consumers' purchase intentions rather than makes prior intentions more accessible in memory or more diagnostic of future purchase decisions. In the context of purchase-intention surveys for common products and services, the measurement effects make consumers more likely to report positive purchase intentions and then actually purchase the product, regardless of their purchase intentions at the time of the survey.
To better understand the differences between the possible sources of measurement reactivity, in Figure 1 we plot hypothetical purchase behavior (e.g., purchase quantity) as a function of presurvey, latent (i.e., unmeasured) purchase intentions for both consumers whose intentions were not measured (control group) and those whose intentions were measured.
In Figure 1, we show that the different sources of measurement reactivity have markedly different effects on purchase behavior and on the link between intentions and behavior. Intention modification leads to a consistent upward shift in purchase behavior but leaves the slope of the relationship between presurvey intentions and behavior unchanged. In contrast, self-generated validity effects do not lead to a general increase in purchase behavior but strengthen the association between intentions and behavior. If measurement reactivity is due to self-generated validity, intention measurement makes consumers with positive purchase intentions more likely to purchase but also makes consumers with negative purchase intentions less likely to purchase, which increases the steepness of the slope between intentions and behavior.
In Figure 1, we also show that in contrast to intention modification, self-generated validity effects do not necessarily lead to measurement reactivity. For example, the measurement of intentions does not change the purchase behavior of consumers who have neutral purchase intentions, that is, those who are undecided about purchasing and not purchasing. Similarly, self-generated validity effects cancel out if there are as many positively inclined consumers as there are negatively inclined ones (i.e., if the distribution of purchase intentions is symmetric around the neutral point). In this case, the average purchase behavior of surveyed consumers may be the same as the average purchase behavior of similar nonsurveyed consumers, though the purchase behavior of each consumer is more extreme. However, self-generated validity effects are a sufficient condition for measurement reactivity when the majority of consumers have positive purchase intentions the most common case in field studies of actual products in competitive markets because the measurement of purchase intentions makes these consumers more likely to follow their intentions (i.e., more likely to purchase).
Conceptualizing and Estimating the Reactive Effects of
Measurement
The framework we present in Figure 2 relates purchase behavior (B) to measured (self-reported) purchase intentions (MI), prior latent (unmeasured) purchase intentions (LI), and the survey that measures purchase intentions (S). In line with conventional representations of structural equation models, we use rectangles to represent observed variables, ovals for latent variables, arrows between constructs for causal relations, an arrow pointing to another arrow for an interaction effect, and a double arrow for a correlation.
We consider LI an unobserved hypothetical construct that captures, without error, consumers' determination to purchase just before the time of the survey. Thus, B is a function of LI (with regression coefficient β1) and random error (ε). In the model, we assume that all consumers, both surveyed and nonsurveyed, have some latent purchase intentions at the time of the survey. However, this assumption does not imply that consumers have decided whether to buy before the survey, because prior latent intentions can be neutral; rather, it implies that consumers do not form intentions only when they are surveyed (we explore the implications of this assumption in the "General Discussion" section). By definition, these prior latent intentions are independent of whether the consumers' intentions are surveyed or not. If S is randomly administered, LI are identical for surveyed and nonsurveyed consumers, as we show in Figure 2 by excluding a link between S and LI.
We present the observed measures of LI on the left-hand side of Figure 2. Purchase intentions measured by the survey constitute one such measure, but this is not the only one. We can also measure latent intentions by other reflective indicators (denoted RI1, RI2, ..., RIn), including indirect measures, such as physiological measures or implicit tests, and behavioral measures, such as information search or the purchase of complementary products. Both LI and the measurement error (δRI) influence these reflective indicators. Other indicators of LI may be formative (e.g., prior purchase behavior, demographics), in which case LI is a function of the m formative indicators (denoted FI1, FI2, ..., FIm) and a random disturbance term (ζFI). We assume that these other indicators are independent of intention measurement (no correlation with S), whereas MI exist only for surveyed consumers (the correlation between MI and S is one). To identify the latent model, we must scale it by choosing one indicator for which the factor loading is set to one and the intercept is zero. Choosing MI as the scaling indicator enables us to scale the LI to the familiar units of MI. In doing so, we assume that there are no systematic reporting biases and that surveyed consumers retrieve their prior LI from memory. (We subsequently report simulation studies in which we examine what happens if MI are systematically biased upward because of social norms or intention modification.)
With the latent model, we can define self-generated validity effects more broadly. Originally, Feldman and Lynch (1988) studied the effects of measurement on the observed correlations among constructs. For example, Simmons, Bickart, and Lynch (1993) asked specific questions about the strength of election candidates before or after they measured general voting intentions. They then measured the impact of question order on the observed correlation between answers to specific questions and general voting intentions. We argue that the measurement of intentions makes presurvey latent intentions relatively more accessible and diagnostic than it does other antecedents of behavior, which strengthens the relationship between presurvey latent intentions and postsurvey behavior. Therefore, we represent self-generated validity in Figure 2 by the β3 parameter, or the effect that S has on the link between LI and B.
This broader definition enables us to test for self-generated validity effects among latent (nonmeasured) and observed (measured) constructs and not only between observed constructs, as in Simmons, Bickart, and Lynch's (1993) study. It also excludes social norms and intention-modification effects, both of which imply that the surveying of intentions increases purchase behavior independent of prior latent intentions and that the relationship between prior latent intentions and behavior remains the same. However, these other sources of measurement reactivity lead to an increase in purchase behavior, regardless of prior latent intentions. Therefore, in Figure 2, we represent their effects by the β2 parameter, which captures the effect of S on B and is not mediated by the strengthening of the relationship between LI and B.
The right-hand side of the latent model in Figure 2 can be expressed as the following latent equation:
( 1) B = α1 + β1(LI) + β2(S) + β3(LI)(S) + ε,
where B is the future purchase behavior of interest; LI is latent purchase intentions; S is a binary variable that indicates whether intentions are surveyed; ε is the error term that captures random disturbance; and α1, β1, β2, and β3 are parameters to be estimated.
Parameter interpretation. In Equation 1, the β3 parameter of the interaction between S and LI on B captures self-generated validity effects. When S is coded as.5 for surveyed consumers and -.5 for nonsurveyed consumers, we expect β3 to be positive, which indicates a higher association between LI and B for surveyed consumers than for similar nonsurveyed consumers.
As Irwin and McClelland (2001) explain, the β1 and β2 coefficients of the LI and S variables capture the simple effects of each variable when the other variable involved in the interaction is zero. Therefore, the β1 parameter captures the mean effect of LI on B across both surveyed and nonsurveyed consumers. Because LI are scaled according to MI, the interpretation of the β2 parameter depends on whether MI are measured on a bipolar or a unipolar scale.
When purchase intentions are mean-centered and measured on a unipolar scale (e.g., a timed intent scale ranging from "intend to buy immediately" to "will never buy"), the β2 parameter captures the effects that measurement has for consumers with average LI. However, when purchase intentions are measured on a bipolar interval scale with a neutral point (e.g., 3 on a five-point scale, where 1 = "completely disagree" and 5 = "completely agree") and are centered on this neutral midpoint, β2captures the effect of measurement on the purchase behavior of consumers with neutral purchase intentions. In other words, β2 measures the sources of measurement reactivity that are due not to self-generated validity but rather to social norms or intention modification. (This is because making a neutral purchase intention more accessible or more diagnostic should not influence behavior.) Any differences in purchase behavior between surveyed and nonsurveyed consumers with a neutral intent to buy cannot be explained by self-generated validity effects and therefore must be attributable to these other explanations.
This interpretation of β2 requires a set of assumptions. First, the construct of interest is valenced (i.e., can be positive, negative, or neutral), which is not problematic if the construct of interest is attitude or satisfaction, both of which are valenced constructs. Many studies of purchase intentions also assume that intentions are valenced, at least implicitly (e.g., when the studies measure intentions on a bipolar Likert scale). This assumption is inconsistent with Fishbein and Ajzen's (1975) definition of behavioral intentions as a probability, or a unipolar concept. Second, all consumers view answering at the midpoint of a valenced scale (e.g., "neither agree nor disagree") as a neutral, nonvalenced intention (i.e., consumer heterogeneity with respect to this perception is evenly distributed). This assumption is problematic, for example, in cross-national research in which there should be strong differences in response styles across countries. However, note that none of these assumptions is required to interpret β3, the main coefficient of interest, which captures self-generated validity effects regardless of whether the intentions are measured on a bipolar or a unipolar scale.
Two-stage estimation. The difficulty of estimating Equation 1 is that LI is an unobserved latent variable, but fortunately we can estimate such a model using a two-stage approach (Bollen 1996; Bollen and Paxton 1998). As we detail in the Appendix, we can substitute LI in Equation 1 with MI - δMI to obtain an equation with only observed variables. Because MI is correlated with the new composite disturbance term (μ), which now includes δMI, ordinary least squares (OLS) cannot estimate the modified Equation 1. In addition, MI is missing for the control group of consumers who did not answer the survey.
To overcome these obstacles, we regress MI on the other indicators of LI (RI1, RI2, ..., RIn; FI1, FI2, ..., FIm) using data from the survey group. These other indicators serve as instrument variables for MI because they are correlated with LI but not with δMI (and therefore not with the new composite disturbance term in Equation 1). We then use the fitted parameters of this regression to substitute MI into Equation 1 with its predicted value MI in both the survey and the control groups. Because MI is a linear combination of variables that are not correlated with μ MI is not correlated with μ. Thus, we can use an OLS regression to estimate the modified equation, including MI.
Simulation analyses. To estimate the model in Figure 2 and Equation 1, we must assume that ( 1) multiple indicators of LI are available, ( 2) MI are unbiased indicators of LI that are unaffected by social norms or intention modification, and ( 3) surveyed and nonsurveyed consumers are identical (i.e., there are no selection biases). We tested the importance of each assumption by conducting extensive simulations, which also enabled us to estimate the ability of the two-stage procedure to recover the true effects of LI on B for surveyed and nonsurveyed consumers when these assumptions were not satisfied. Specifically, we manipulated the quality of the other indicators of LI (factor loadings ranging from.3 to.9), the presence of positive reporting biases in MI (e.g., those caused by social norms or intentions modification), and the presence of selection biases (only positively inclined consumers agree to answer the survey). We find that the β3 coefficients estimated with the two-stage procedure are stable even in the extreme scenario of reporting or selection biases combined with poor indicators of LI. In addition, these problems inflate the standard errors of the coefficients and work against our hypotheses. Overall, the simulation analyses show that the two-stage procedure can estimate self-generated validity effects reliably even in imperfect measurement and experimental conditions. (The complete results of the analyses are available from the authors on request.)
The latent model enables us to broaden our definition of self-generated validity effects to include the effects that measurement has on the relationship between latent and measured constructs. We show that with a two-stage procedure, we can estimate ( 1) the latent purchase intentions of nonsurveyed consumers, ( 2) the impact of the measurement of intentions on the relationship between latent intentions and behavior (self-generated validity effects), and ( 3) the impact of the measurement of intentions on the behavior of consumers with neutral intentions (social norms and intention-modification effects).
Quantifying Self-Generated Validity and Other Sources of
Measurement Reactivity: Three Field Studies
In this section, we quantify self-generated validity and other sources of measurement reactivity through three large-scale studies of intended and actual purchases of groceries, automobiles, and PCs. The three field studies differ significantly in terms of the sampling frame, the type of purchase behavior studied, and the measurement of intentions and behavior, but they all contain information about purchasing from two groups of consumers: those whose purchase intentions were measured and a control group of similar consumers whose purchased intentions were not measured. Therefore, we describe the three studies and their results collectively.
Grocery study. In this study, we measured consumers' intentions to repurchase from an online grocer. The data (for a detailed description, see Chandon, Morwitz, and Reinartz 2004) were gathered from a field study conducted in collaboration with a leading French Web-based grocer that offers online an assortment that is typical of a large supermarket (50,000 stockkeeping units of food and some durable products) and nationwide delivery. During the last week in May and the first week in June 2002, 251 customers were contacted by telephone and asked about their intent to repurchase from the online grocer in the future. The respondents were chosen at random from customers who had made their first purchase with the online grocer in October or November 2001. The data set contained demographic information about the age, number of children, and number of pets of each customer, as well as detailed transaction data for all their purchases between January 2001 and April 2002 (i.e., nine months before and nine months after the survey). Transaction data included the date of the order, the quantity and price of each ordered product, and the total profit that the online grocer made. The same data were available for a control group of 140 consumers who were randomly selected from the same cohort but whose purchase intentions were not measured.
To obtain reliable indicators of purchase intentions, we measured consumers' agreement with two statements (translated from French): "I am thinking of using [name of grocery company] for my next online purchases," and "I am thinking of remaining a customer of [name of grocery company] for a long time." We measured the statements on a five-point Likert scale, where 1 = "completely disagree" and 5 = "completely agree." We averaged the answers to produce a reliable intention scale (Cronbach's α =.86). Because we surveyed existing buyers, in general the intention scores were above the midpoint (mean = 3.86, standard error [S.E.] = 1.19, p <.01) and somewhat negatively skewed (skewness = -1.13). However, there was enough variance in the measures to test for self-generated validity effects (26% of consumers had negative to neutral [i.e., below 3] intentions to purchase).
For the grocery study, we examined two dependent variables: purchase incidence and customer profitability. We chose purchase incidence to facilitate comparisons with our other two studies and with previous measurement-reactivity research and to examine the effects of the measurement of intentions on the consumers' first repurchase after the survey. Using a binary variable, we determined whether the consumer placed at least one order halfway through the postsurvey period (just more than four months after the survey). We chose this time horizon because self-generated validity theory predicts, and prior research demonstrates, that the effects of the measurement of purchase intentions decay over time (Chandon, Morwitz, and Reinartz 2004).
We studied cumulative customer profitability because it includes information about the first and subsequent repurchases that the customer made. In addition, it is the most important measure for managers, and prior research has shown that one-time transactional gains do not necessarily lead to improved customer profitability for the company, especially for commonly purchased consumer goods (Reinartz and Kumar 2000). We measured total customer profitability as the cumulative net profit attributable to the customer (i.e., the sum of the contribution of all orders placed in the nine-month postsurvey period less coupons and delivery costs). Because the company routinely surveys its customers and would continue to do so even in the absence of measurement-induced purchases, we treated the cost of administering the survey as a fixed cost and did not subtract it from the cumulative contribution of surveyed customers. We used the full postsurvey period because we measured cumulative profits, which include the first and subsequent purchases. Although the effects of measurement decay over time, the positive impact of the first purchase carries through to subsequent purchases; therefore, measurement-reactivity effects persist over time on cumulative customer profitability (Chandon, Morwitz, and Reinartz 2004).
Automobile and PC studies. The other two data sets refer to the automobile and PC data that Morwitz, Johnson, and Schmittlein (1993) use and describe. Intentions to buy and ownership of home PCs and automobiles were measured using two different but similar U.S. consumer mail panels. Both panels were designed to be representative of U.S. households, according to census data, and each panel comprised approximately 100,000 households. Intentions and behavior were measured during seven survey waves, each approximately six months apart. The surveys requested that the person in the household who was most involved in the purchase decision complete the survey. In each survey wave, panel households were asked to provide their timed intentions for buying an automobile or PC in the future. Extensive demographic information about the panel households also was collected, including the size of the household, annual household income, age of the head of the household, marital status, home ownership, household stage of life, occupation, education of the head of the household, race, number of cars owned, and regional dummy variables.
In contrast to the grocery data, for which consumers were randomly selected to be surveyed, Morwitz, Johnson, and Schmittlein's (1993) data for both products reflect the results of naturally occurring or quasi experiments. Because of panel dynamics (members entering and exiting a panel over time), panel members varied in whether and how often their intentions were measured. For both products, we compared the behavior of panel members who entered the panel only in time to receive the intentions question in the sixth survey wave with the behavior of those who joined the panel after the sixth but before the seventh wave and thus whose intentions were not measured in the sixth wave. As in Morwitz, Johnson, and Schmittlein's research, to control for any differences in the experimental and control groups due to factors other than the experiment, we weighted the data by two different criteria: stage in the life cycle and age of the head of the household. Because the results for both weighting schemes are similar, we report only the results for weighting by life cycle.
For the automobile data, the intention question asked, "When will the next new (not used) car (not truck or van) be purchased by someone in your household?" The following response alternatives were provided: 1 = "6 months or less," 2 = "7-12 months," 3 = "13-24 months," 4 = "25-36 months," 5 = "over 36 months," and 6 = "never." We reverse coded the responses so that higher numbers represent a higher intention to repurchase, which is consistent with the grocery data (mean = 2.36, S.E. =.027, p <.01, skewness = -.74). During each survey wave, respondents also were asked whether they had purchased a new automobile during the previous period. For the automobile study, we analyzed data from 8347 households, 3571 whose intentions were measured and 4776 whose intentions were not measured.
The PC data are similar in format to the automobile data. The intention question asked, "Do you or does anyone in your household plan to acquire a (another) personal computer in the future for use at home?" The following response alternatives were provided: 1 = "yes, in the next 6 months"; 2 = "yes, in 7 to 12 months"; 3 = "yes, in 13 to 24 months"; 4 = "yes, sometime, but not within 24 months"; 5 = "no, but have considered acquiring one"; and 6 = "no, will not acquire one." We also reverse coded these responses (mean = 2.03, S.E. =.028, p <.01, skewness = -1.47). In each wave, respondents indicated whether they had purchased a computer in a given time period. As do Morwitz, Johnson, and Schmittlein (1993), we restricted our analysis to households that initially did not own a PC, and we assumed that a household bought a PC if it switched from being a nonowner to being an owner from one wave to the next. There were 7772 households in the data, 2138 whose intentions were measured and 5634 whose intentions were not measured.
For both the PC and the automobile data, we examined only purchase incidence (i.e., whether a purchase occurred in the six-month period following the intent measurement). Because intentions were measured during every survey wave, a longer-term analysis would confound duration with the number of times intentions were measured.
Predicting purchase intentions in the control group. In all three studies, we used demographic and behavioral indicators of LI as the instrument variables to predict MI in both the survey and the control groups. To select the instrument variables, we measured their predictive power by splitting the survey group into two random samples, regressing MI on the instrument variables, and then using the regression to predict intent in the second sample. As Armstrong and Collopy (1992) recommend, for both random samples, we selected the combination of variables with the best predictive accuracy and measured it with the median average percentage error (MdAPE) and the median relative absolute error (MdRAE). We obtained the MdRAE by dividing the median of the absolute forecast error by the corresponding error for the naive model, so we assigned the average MI of the survey group to all consumers in the control group. We then reestimated the best model of the MI for the full sample of consumers in the survey group and used the parameters from the regression to predict purchase intentions for both the survey and the control samples. To check the robustness of the final results for the choice and quality of the instrument variables, we tried several different predictions of intent that provided similar to significantly worse predictive power. The results were virtually unchanged.( n1)
Across the three studies, we can predict purchase intentions moderately well. For the grocery data, MdAPE =.16 and MdRAE =.95; for the PC data, MdAPE =.087 and MdRAE =.78; and for the automobile data, MdAPE =.17 and MdRAE =.54. For both the PC and the automobile data, the error rates were similar when we used weighting methods to ensure equivalence between the survey and the control groups.
Method checks and descriptive results. As we expected, predicted purchase intentions were similar for surveyed and nonsurveyed consumers across all three studies (see Table 1). The difference between the groups was not statistically significant for the grocery study but was statistically significant for the automobile and PC studies; this is probably due to the larger number of observations in the latter studies (n = 8306 for the automobile study, n = 7772 for the PC study). The finding that predicted that purchase intentions are lower in the survey group than in the control group (as in two of three cases) helps rule out selection biases, which would cause consumers with higher LI to be more likely to appear in the survey group. Overall, the results indicate that surveyed and nonsurveyed consumers are similar and that the measurement of their purchase intentions causes the differences between their purchase behavior.
As Table 1 and previous studies with these data (Chandon, Morwitz, and Reinartz 2004; Morwitz, Johnson, and Schmittlein 1993) show, in general the measurement of purchase intentions increases the purchase incidence for grocery, automobile, and PC products. (Note that the PC data are marginally significant on the basis of a one-tailed test, as Morwitz, Johnson, and Schmittlein [1993] report.) In Table 1, we show that the variance in future behavior is lower in the control groups than in the survey groups for all studies and dependent variables (though the difference is not statistically significant in the PC study). This new result, which previous measurement-reactivity studies have not explored, is consistent with the self-generated validity theory, which argues that the measurement of a person's intention to perform a behavior, not other factors (e.g., mood, price promotions), increases the likelihood that he or she will act on this behavior. If we assume that the other factors cancel out, self-generated validity theory predicts that the purchase behavior of people in the control group will regress to the mean, which explains the lower variance in the control group. Alternatively, purchase intentions may cause more variance in behavior when they are made salient by measurement because they magnify the differences between people with high and low intentions. As we report subsequently, the variance differences have implications for the selection of the correct method to test for self-generated validity effects.
Variable coding. We used the predicted purchase intentions that we estimated in the first stage to estimate the regression represented in Equation 1 in accordance with the procedure we detail in the Appendix. For the automobile and PC studies, whose timed measures of purchase intentions have no neutral point, we mean-centered the predicted purchase intentions (MI) to zero when they measure 2.36 (automobile) and 2.04 (PC). However, for the grocery study, we measured purchase intentions on a five-point Likert scale (1 = "completely disagree" and 5 = "completely agree"), and therefore the neutral midpoint is 3 ("neither agree nor disagree"). To estimate the effects of the measurement of purchase intentions for consumers with neutral purchase intentions, we centered MI on the midpoint so that it equals zero when predicted purchase intentions measure 3. Finally, we coded the binary variable S, which captures the effect of intentions measurement, as.5 for consumers in the survey group and -.5 for consumers in the control group. We report the results (parameter estimates and standard error) of the second-stage regression in Table 2.
Model results. As we show in Table 2, consumers with higher LI are more likely to purchase in all three studies and are more profitable for the firm in the grocery study (the β1 coefficients are all positive and statistically significant). Therefore, we replicate prior studies' findings that purchase intentions are a strong but imperfect predictor of purchasing. In addition, we find the expected interaction between latent intentions and intention measurement for all three studies and all dependent variables (the β3 coefficients are all positive and statistically significant). Thus, LI are stronger predictors of the behavior of surveyed consumers than of nonsurveyed consumers. In other words, the measurement of purchase intentions strengthens the associations between latent purchase intentions and purchase behavior or customer profitability; this is a self-generated validity effect.
Finally, the β2 coefficients, which capture the simple effects of the purchase intentions survey, are positive and statistically significant for the automobile and PC studies; thus, the measurement of purchase intentions increases future purchasing by consumers with average latent purchase intentions. Our two-stage method replicates previous findings from the same data that were obtained using different methods. However, for the grocery study, the β2 coefficients for repeat purchase and customer profitability are not statistically different from zero, which demonstrates that the measurement of purchase intentions does not increase the purchases or profitability of consumers who have neutral latent purchase intentions.( n2) Therefore, measurement reactivity in the grocery study is entirely mediated by self-generated validity effects.
Separate analyses for surveyed and nonsurveyed consumers. We performed the following analyses to obtain a more intuitive grasp of the magnitude of self-generated validity effects. We computed the correlation between predicted intentions and behavior in each group, for each study, and for each dependent variable. As we report in Figure 3, the results show that self-generated validity effects are great. On average, the correlation between intentions and behavior is 58% greater in the surveyed groups than in the control groups. In addition, the magnitude of the self-generated validity effects is approximately constant across all studies and dependent variables, regardless of the intensity of the true association between intentions and behavior (which varies between.07 in the automobile study and.26 in the grocery study).
To further illustrate the magnitude of self-generated validity effects, we regressed purchase behavior on predicted purchase intentions separately in the survey and control groups. As we show in Table 3, unstandardized regression coefficients for predicted purchase intentions are 76% greater in the survey groups than in the control groups. For example, a one-point difference in predicted purchase intentions (measured on a five-point scale) in the grocery study leads to a €52.71 gain in customer profitability when intentions are measured but only €23.95 when intentions are not measured. Similarly, although predicted purchase intentions are reliable predictors of purchase behaviors in all studies, the t-values are, on average, 70% greater in the survey groups than in the control groups. Taken together, the results show that the external accuracy of purchase intentions is significantly weaker and less reliable than is their internal accuracy and that we cannot extrapolate one from the other.
In Figures 4 and 5, we provide another perspective on the results by reporting the purchase behavior of three equal groups of consumers with low (<32nd percentile), moderate (33-66th percentile), and high (>67th percentile) predicted purchase intentions. In the grocery study (Figure 4), we find that the measurement of purchase intentions increases the repeat purchase incidence and customer profitability for high and moderate (positive) intenders but decreases both behaviors for low intenders, who have mostly negative intentions. The purchase-incidence findings from the automobile and PC studies (see Figure 5) replicate the same pattern. Overall, the pattern of results in Figures 4 and 5 matches the hypothetical self-generated validity effects shown in Figure 1 for all three studies and all dependent variables.
General Discussion
Because purchase intentions are widely used but are imperfect indicators of actual purchasing, a large body of research is devoted to improving their internal accuracy (the ability to predict the behavior of consumers from their previously measured intentions). However, we contribute to this literature by studying the external accuracy of measured intentions (i.e., their ability to predict the behavior of consumers whose intentions are not measured). We develop a comprehensive latent model of the reactive effects of the measurement of purchase intention in which we distinguish between two sources of measurement reactivity. The first is self-generated validity effects, which we define as a strengthened relationship between the measured latent construct and its behavioral consequences. Thus, self-generated validity effects increase the likelihood that consumers will follow their intentions. The theory behind these effects predicts that the measurement of intentions makes high intenders more likely to purchase and low intenders less likely to purchase but does not change the behavior of consumers with neutral intentions. The second source includes measurement effects that are independent of intentions, such as those created by social norms or intention modification. Unlike self-generated validity effects, the effect of social norms and intention modification influence the behavior of all consumers, regardless of their prior intentions.
We provide a two-stage procedure, which enables us to quantify the magnitude of the self-generated validity effects and other sources of measurement reactivity. In the first stage, we estimated the relationship between measured intentions and other indicators of latent intentions, using data from surveyed consumers. We then used the fitted parameters from our analysis to predict the latent purchase intentions of both surveyed and nonsurveyed consumers. In the second stage, we compared the strength of the association between our predicted intentions and actual behavior across both groups. Using data from three large-scale field studies with control groups, we find that the measurement of purchase intentions increases the association between latent intentions and purchase behavior. The effects are significant and robust across a variety of purchase behaviors, sampling frames, and ways to measure intentions and behavior. In addition, one study shows that the measurement of purchase intentions does not influence the purchases of consumers who have neutral purchase intentions, which suggests that self-generated validity effects cause all the reactive effects of measurement. The results have implications for both applied and academic research.
The obvious implication of our results is that commonplace procedures and models (e.g., ACNielsen's BASES model) that measure the intentions and behavior of the same sample of consumers overestimate the strength of their association. For most tested concepts, which elicit positive intentions in general, the models overstate aggregate purchase probabilities. Therefore, our results strongly call into question the common practice of extrapolating to the general population the conclusions of studies that measure the intentions and behaviors of the same consumers. When choosing the best measure of purchase intentions or the best method to predict behavior from intentions, marketers should focus on the external, not internal, validity of the measure and the method.
For example, there has been a recent debate in the Harvard Business Review about the merits of different measures of customer feedback. Reichheld (2003) argues in favor of measuring consumers' willingness to recommend the product, because he claims that it is the best predictor of future purchasing. However, this conclusion is based on a comparison of the predictive accuracy of different measures of customer feedback that are tested on surveyed consumers only. Our results suggest that marketers who are interested in selecting the measure that best predicts future purchasing should use our method to determine whether it predicts the behavior of nonsurveyed consumers.
Our results also emphasize the importance of investigating the sources of measurement-reactivity effects. Knowing whether self-generated validity or other measurement effects drive the behavioral differences between surveyed and nonsurveyed consumers has implications for the improvement of forecasting and targeting. For example, Jamieson and Bass (1989) describe multiple conversion schemes that marketers use to forecast purchase behavior from intentions. These conversion schemes are obtained by analyzing the behavior of consumers whose intentions have been measured. A scheme that Jamieson and Bass describe is 75%-25%-10%-5%-2% for each purchase-intention box (e.g., 75% of consumers who state that they would " definitely buy" actually do so, 25% of consumers who state that they would "probably buy" actually do so). If social norms or intention modification causes the reactive effects of measurement, these weights are inflated and should be reduced by a constant (e.g., the correct weighting scheme might be 60%-10%-0%-0%-0%). In this case, marketers should consider narrowing their target to focus only on consumers who have strong positive purchase intentions, because they are the only ones likely to purchase. However, if self-generated validity causes measurement reactivity, conversion rates should be regressed toward their means (e.g., the correct weighting scheme might be 60%-20%-15%-10%-8%). This flatter purchasing profile implies that marketers should broaden their target to encompass consumers who have negative purchase intentions because they are more likely to purchase than the conversion rates, which are determined by the measurement of surveyed consumers, suggest.
An area for further research is to relax the assumption that all consumers have some form of prior latent intention before the survey. For our research, it is reasonable to assume that an existing customer of a Web grocer has formed a repurchase intention or that a U.S. consumer would have an intention to buy an automobile or PC. However, as Feldman and Lynch (1988) argue, a segment of the population may form an intention only when asked about it. Although it would violate a main assumption of the model, we expect that our procedure would still be able to detect self-generated validity effects even if a sizable segment of consumers did not have a latent intention. Suppose the sample consists of a probability mixture of two groups, one lacking prior latent intention and one with latent intention. Then suppose that intention measurement causes a latent intention among respondents who did not previously have one and makes them more likely to follow this new intention. Finally, suppose that there is no change in the strength of the relationship between latent intentions and behavior for people in the group with preexisting latent intentions. The first stage of our two-stage procedure would incorrectly assign a purchase intention to the segment of consumers in the control group who have none. However, because the purchase behavior of these consumers remains independent of this predicted intention, in the second stage of the procedure, we would find that the association between the control group's latent intentions and behavior is small and different from the association between the intentions and behavior in the survey group. Thus, we believe that our estimation procedure is capable of detecting self-generated validity effects even in cases in which latent intentions do not exist before measurement.( n3)
Our study relates to previous studies that have demonstrated that measurement-related biases can lead to incorrect inferences about the strength of the relationship between two measured marketing constructs and therefore have developed corrective techniques (Baumgartner and Steenkamp 1992; Rossi, Gilula, and Allenby 2001). For example, Greenleaf (1992) investigates how different response biases affect the relationship between self-reported attitudes and behavioral frequencies. For a large battery of behaviors, he develops a method to detect whether response styles reflect true attitude differences, in which case researchers should not adjust for them, or are biased, in which case researchers should adjust for them. A fruitful area for additional research would be to integrate the methods and findings from that stream of research with our method for the estimation of latent constructs among respondents for whom the constructs were not measured.( n4)
The method we offer helps measure and correct for self-generated validity effects in many research contexts, including laboratory experiments and field observations, and for many constructs, including beliefs, attitudes, and satisfaction. When self-generated validity effects are possible, researchers should collect data about the criterion (e.g., behavior) of a control sample of consumers who did not answer the survey as well as multiple indirect measures of the target explanatory constructs (e.g., intentions, attitudes) that the survey does not influence (e.g., behavioral or demographic data that is measured with a different method than that used to measure the explanatory and criterion variables). With this information, researchers should be able to predict the level of the explanatory construct for a control group of consumers who did not answer the survey and to measure the link between the predicted and the criterion variables.
For example, this method could clarify inconsistencies between survey results and the behavior of the general population, such as in contingent valuation surveys for environmental policies or products (Irwin 1999). It also could examine the consequences and antecedents of latent, as opposed to measured, satisfaction. In particular, the estimation of the true association between latent satisfaction and customer lifetime value could contribute to the debate about the value of improving customer satisfaction (Bolton 1998; Kamakura et al. 2002). In general, we believe that any research that uses a survey that goes beyond description and examines the association between constructs can benefit from our method for testing for self-generated validity and other sources of measurement reactivity.
The authors acknowledge the helpful input of the anonymous JM reviewers, John Lynch, Gilles Laurent, and Albert Bemmaor, as well as those who participated when the authors presented this research at INSEAD; at the Association for Consumer Research Conference, Portland; at the Marketing Science Conference, College Park, Md.; and at the ESSEC-HEC-INSEAD conference. In addition, the authors thank the French online grocer Prodigy Services Company and Allison Fisher for providing data and INSEAD for its financial assistance.
( n1) We computed predicted purchase intentions for the grocery study as follows: MI = 5.467 - .00321(REC) -.03067(AGE) + .34831(PET) -.40852(BABY), where REC is purchase recency (number of days since the latest purchase), AGE is the customer's age, and BABY and PET are dummy variables that indicate that the household has at least one child or one pet. For the automobile study, MI = 5.56050 1.38433(EDUCATION1) - 1.70868 (EDUCATION2) - 1.38598(EDUCATION3) - 1.37272(EDUCATION4) - 1.20131(EDUCATION5) - 1.22879(EDUCATION6) - .00828(INCOME) - .46495(MANAGER) - .28947(TECH) + .09822(SERVICE) - .06099(FARM) +.06992(CRAFT) + .08894(OPERATOR), where EDUCATION1-6 are dummy variables that represent different levels of the education of the head of the household; INCOME is total household income; and MANAGER, TECH, SERVICE, FARM, CRAFT, and OPERATOR are dummy variables that represent different occupations of the head of the household. For the personal computer study, MI = 4.42389 +.01317(HH_AGE) -.28590(NEWHH) -.12844 (NEWBB) -.17102(LOWMIDF) -.62929(UPSCALE) -.04337(MIDAGE) +.29444(LOWMIDE), where HH_AGE is the age of the head of the household, and NEWHH, NEWBB, LOWMIDF, UPSCALE, MIDAGE, and LOWMIDE represent mutually exclusive levels of the customer life-cycle variable that the survey company created.
( n2) If we mean-center predicted purchase intentions instead of centering them on the midpoint of the scale ( 3), the β2 coefficients become positive and statistically significant for repeat purchase (β2 =.12, S.E. =.04, p <.01) and, to a lesser extent, for customer profitability (β2 = 9.73, S.E. = 5.16, p <.10). Using this coding, we replicate the descriptive results that we present in Table 1. The measurement of purchase intentions increases the purchasing and profitability of consumers who have average or, in this case, positive (mean = 3.86) purchase intentions, which is fully consistent with the predictions of self-generated validity theory.
( n3) We thank an anonymous reviewer for noting that our method would also work in this circumstance.
( n4) We thank the editor for noting the relationship between our work and previous research on calibration.
Legend for Chart:
A - Study
B - Variable
C - Control Group
D - Survey Group
A B
C D
Grocery Number of observations
140 251
Predicted purchase intentions (1-5)
3.91 (.43) 3.86 (.41)
Repeat purchase incidence
- .229 (.421) .331(*) (.471)(*)
Customer profitability(€)
19.53 (42.27) 27.43 (57.08)(**)
Automobile Number of observations
4776 3530
Predicted purchase intentions (1-6)
-
2.25 (.768) 2.52(**) (.798)(**)
Purchase incidence
.024 (.153) .033(*) (.178)(**)
PC Number of observations
5634 2138
Predicted purchase intentions (1-6)
2.05 (.456) 2.02(*) (.468)(**)
Purchase incidence
.038 (.191) .045 (.207)
(*) p < .05.
(**) p < .01 (all tests two-tailed).
Notes: Standard deviations (in parentheses) are compared
according to Levene's F-test. Legend for Chart:
A - Study
B - Purchase Behavior
C - Prior Latent Intentions (β1)
D - Survey (β2)
E - Interaction (β3)
A B C
D E
Grocery Repeat purchase incidence .38(**) (.05)
-.11 (.10) .26(**) (.11)
Customer profitability 39.32(**) (6.20)
-13.70 (12.17) 26.78(*) (12.39)
Automobile Purchase incidence .02(**) (.00)
.06(**) (.02) .01(**) (.00)
PC Purchase incidence .06(**) (.01)
.01(*) (.01) .02(*) (.01)
(*) p < .05.
(**) p < .01 (all tests two-tailed). Legend for Chart:
A - Study
B - Purchase Behavior
C - Control Group
D - Survey Group
A B C
D
Grocery Repeat purchase incidence .25 (3.11)
.51(**) (7.81)
Customer profitability 25.93 (3.18)
52.71(*) (6.40)
Automobile Purchase incidence .015 (5.13)
.028(**) (7.39)
PC Purchase incidence .048 (8.71)
.069(*) (7.35)
(*) p < .05.
(**) p < .01 (all tests two-tailed).
Notes: Regression coefficients are compared with a Chow test
(grocery: F1, 389; automobile: F1, 8302; PCs:
F1, 7768).GRAPH: FIGURE 1; Self-Generated Validity and Other Sources of Measurement Reactivity
DIAGRAM: FIGURE 2; A Latent Model of the Reactive Effects of the Measurement of Purchase Intentions
Legend for Chart:
B - Survey group
C - Control group
A B C
Grocery Study (repeat purchase incidence) .44 .26
Grocery Study (customer profitability) .38 .26
Automobile Study (purchase incidence) .12 .07
PC Study (purchase incidence) .16 .11 A: Repeat Purchase Probability
Legend for Chart:
B - Survey group
C - Control group
A B C
Low 12.5% 14.4%
Medium 31.5% 35.1%
High 49.0% 66.7%
Predicted Purchase Intentions
B: Customer Profitability
Low €3.5 €5.4
Medium €18.0 €21.7
High €32.2 €59.4
Predicted Purchase Intentions
A: Automobile
Legend for Chart:
B - Survey group
C - Control group
A B C
Low .5% 1.4%
Medium 2.0% 3.5%
High 4.5% 5.4%
Predicted Purchase Intentions
B: PC
Low 1.7% 2.3%
Medium 4.5% 6.5%
High 7.3% 10.6%
Predicted Purchase Intentions
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We briefly describe the two-stage least squares estimator that Bollen (1996) introduces. The two-stage least squares estimator is consistent, allows nonnormal observed and latent variables, enables easy estimation of interaction effects, and has been used in many applications (for a review, see Schumacker and Marcoulides 1998).
The structural equation model represented in Figure 1 consists of a latent variable model (described in the text) and a measurement model, which can be expressed as follows:
(A1) MI = LI + δMI, and RI = αRI + λRI(LI) + δRI,
where MI is measured intent as provided by the survey, LI is a latent variable that measures intent just before the time of the survey without error, RI is another reflective indicator of prior latent intent (the model can easily accommodate more indicators), αRI is an intercept, and δMI and δRI are the two disturbance variables. Equation A1 shows that MI is set to have the same metric and origin as LI (by setting the intercept to zero and the factor loading to one) to provide a scale for the latent variable.
It is likely that some of the other indicators of prior latent intent are formative. We can express the relationship between a formative indicator (FI) and LI as follows:
(A2) LI = αFI + λFI(FI) + ζFI,
where ζFI is another disturbance term. We can extend Equation A2 to multiple formative indicators. Following the traditional assumptions of structural equation modeling, we assume that δMI and δRI are independent of LI and of each other and that LI, δMI, δRI, ζFI, and ε are each i.i.d. random variables. We also assume that δMI, δRI, ζFI, and FI are independent of S (a binary variable that measures whether consumers were surveyed).
Equation A1 shows that LI = MI - δMI. Substituting LI with MI - δMI, we obtain the following:
(A3) B = α + β1(MI) + β2(S) + β3(MI)(S) + μ,
where B is the future behavior of interest (purchase incidence, customer profitability), and μ is a composite disturbance:
(A4) μ = ε - β1(δMI) - β3(S)(δMI).
Equation A3 shows that the original latent variable model in Equation 1 can be rewritten as a model with only observed variables and a disturbance term μ. Because of measurement error, MI and δMI are correlated, and thus MI is correlated with the composite disturbance term μ, which violates the assumptions of the OLS estimator. Therefore, we must replace MI with an instrument variable that is correlated with MI but not with δMI. Other indicators of prior latent intent, whether reflective or formative, can be used as instrument variables in a two-stage procedure because they are not correlated with δMI and because, as other measures of latent intent, they are correlated with LI.
In the first stage, we regress MI on n other reflective indicators of LI (RI1, RI2, ..., RIn) and on the m other formative indicators of LI (FI1, FI2, ..., FIm), using data from the survey sample. In the second stage, we replace MI with its predicted value (MI) in both samples. We then obtain the following equation:
(A5) B = α1 + β1(MI) + β2(S) + β3(MI)(S) + μ.
Because MI is a linear combination of instrument variables (RI1, RI2, ..., RIn; FI1, FI2, ..., FIm), it is uncorrelated with δMI and ε and thus with μ. Therefore, Equation A5 can be estimated through a regular OLS regression to obtain β1, β2, and β3, the parameters of interest.
~~~~~~~~
By Pierre Chandon; Vicki G. Morwitz and Werner J. Reinartz
Pierre Chandon is Assistant Professor of Marketing, INSEAD, and currently he is Visiting Assistant Professor of Marketing, Kellogg School of Management, Northwestern University
Vicki G. Morwitz is Associate Professor of Marketing and Robert Stansky Faculty Research Fellow, Stern School of Business, New York University.
Werner J. Reinartz is Associate Professor of Marketing, INSEAD.
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Record: 53- Do Satisfied Customers Buy More? Examining Moderating Influences in a Retailing Context. By: Seiders, Kathleen; Voss, Glenn B.; Grewal, Dhruv; Godfrey, Andrea L. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p26-43. 18p. 1 Diagram, 5 Charts, 1 Graph. DOI: 10.1509/jmkg.2005.69.4.26.
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- Business Source Complete
Do Satisfied Customers Buy More? Examining Moderating
Influences in a Retailing Context
In this research, the authors propose that the relationship between satisfaction and repurchase behavior is moderated by customer, relational, and marketplace characteristics. They further hypothesize that the moderating effects emerge if repurchase is measured as objective behavior but not if it is measured as repurchase intentions. To test for systematic differences in effects, the authors estimate identical models using both longitudinal repurchase measures and survey measures as the dependent variable. The results suggest that the relationship between customer satisfaction and repurchase behavior is contingent on the moderating effects of convenience, competitive intensity, customer involvement, and household income. As the authors predicted, the results are significantly different for self-reported repurchase intentions and objective repurchase behavior. The conceptual framework and empirical findings reinforce the importance of moderating influences and offer new insights that enhance the understanding of what drives repurchase behavior.
Marketing literature consistently identifies customer satisfaction as a key antecedent to loyalty and repurchase, but current knowledge fails to explain fully the prevalence of satisfied customers who defect and dissatisfied customers who do not (Bendapudi and Berry 1997; Ganesh, Arnold, and Reynolds 2000; Jones and Sasser 1995; Keaveney 1995). Although prior research points to several variables that may moderate the satisfaction--repurchase relationship, empirical results are equivocal and difficult to reconcile.
Many empirical studies examining direct and moderated satisfaction--repurchase effects measure repurchase intentions rather than objective repurchase behavior. Studies can produce erroneous inferences if there are significant differences between intentions and subsequent behavior (Bolton 1998; Kamakura et al. 2002; Mittal and Kamakura 2001; Morwitz, Steckel, and Gupta 1997) or if common method variance inflates estimates of the association between self-reported satisfaction and intentions (Bolton 1998; Gruen, Summers, and Acito 2000; Morwitz and Schmittlein 1992). Satisfaction levels at which customers report a positive intent can differ considerably from those at which customers engage in the corresponding behavior (Mittal and Kamakura 2001). Therefore, additional research is necessary that explicitly examines the extent to which results converge when using repurchase intentions versus objective repurchase behavior as the dependent measure.
In response to calls for deeper insight into factors that may moderate the satisfaction--repurchase relationship (e.g., Bolton, Lemon, and Verhoef 2004), we propose a conceptual framework that explains why two customers with the same (different) levels of satisfaction engage in different (the same) patterns of repurchase behavior. We use consumer resource allocation theory to support our prediction that, after we control for main effects established in prior research (Anderson and Sullivan 1993; Bolton 1998; Boulding et al. 1993; Rust, Zahorik, and Keiningham 1995), customer, relational, and marketplace characteristics moderate the relationship between satisfaction and repurchase behavior but do not moderate the relationship between satisfaction and repurchase intentions. For example, convenience (a marketplace characteristic) conserves customers' time and effort and thereby facilitates a satisfied customer's ability to fulfill his or her intent.
We test the conceptual framework in an understudied retail context that is characterized by low switching costs and comparison shopping behavior. This context is noteworthy because no known research has examined differences in intentions and objective repatronage behavior in a retail shopping category marked by moderate repurchase frequency. Research suggests that the predictive validity of repurchase intentions varies widely from frequently purchased convenience goods to infrequently purchased durables (e.g., Chandon, Morwitz, and Reinartz 2005). In addition, the satisfaction--repurchase relationship can differ significantly between contractual services and discrete, recurring purchases (Lemon, White, and Winer 2002; Reinartz and Kumar 2003), for which switching costs are lower and customers typically are not obligated to give all their business to any one firm (e.g., Rust, Lemon, and Zeithaml 2004). Thus, our research extends current knowledge by capturing the complexity of the satisfaction-repurchase relationship in a context marked by discrete recurring transactions.
Conceptualizing a Moderated Satisfaction--Repurchase
Behavior Relationship
In Figure 1, we present a conceptual framework that proposes satisfaction and customer, relational, and marketplace characteristics as antecedents to repurchase intentions and behavior. We conceptualize customer satisfaction as a cumulative, global evaluation based on experience with a firm over time (Homburg, Koschate, and Hoyer 2005). Repurchase intentions represent the customer's self-reported likelihood of engaging in future repurchase behavior, whereas repurchase behavior is the objectively observed level of repurchase activity. The default expectation is that satisfaction positively influences both repurchase intentions and behavior, and we offer no formal hypothesis for this well-established relationship.
The dotted lines in Figure 1 capture direct relationships that have been previously established in the literature (e.g., Beatty and Smith 1987; Bolton, Kannan, and Bramlett 2000; Rust, Lemon, and Zeithaml 2004; Soberon-Ferrer and Dardis 1991). In the sections that follow, we provide brief reviews of the relevant literature for these direct effects, but we do not offer formal hypotheses for them. Instead, we focus on the moderating effects depicted by the solid lines in Figure 1. Specifically, we predict that customer, relational, and marketplace characteristics moderate the relationship between satisfaction and objective repurchase behavior after we explicitly control for their direct (i.e., main) effects. Moreover, we believe that these variables do not moderate the relationship between satisfaction and repurchase intentions after we control for direct effects.
For conceptual, methodological, and empirical reasons, we believe that customer, relational, and marketplace characteristics moderate the effect of satisfaction on objective repurchase behavior but not on repurchase intentions. First, consumer resource allocation theory suggests that repurchase behavior reflects intervening contingencies that measures of repurchase intentions do not. Consumers allocate a variety of resources to purchase decisions (Batshell 1980; Roberts and Dant 1991; Zeithaml 1988), including money (Marmorstein, Grewal, and Fishe 1992), time and effort (Becker 1965; Feldman and Hornik 1981; Jacoby, Szybillo, and Berning 1976), motivation, opportunity, and cognitive ability (e.g., MacInnis and Jaworski 1989; MacInnis, Moorman, and Jaworski 1991; Peracchio and Meyers-Levy 1997). One stream of research depicts consumers as cognitive misers (e.g., Shugan 1980) who lack the motivation and cognitive ability to incorporate intervening contingencies into their predicted repurchase probabilities. Because consumers are not motivated to consider simple intervening characteristics (e.g., how different levels of income might facilitate or constrain future repurchase activity) or capable of foreseeing complex intervening factors (e.g., competitive interactions among firms), they routinely provide inaccurate predictions of their future behavior (Kahneman and Snell 1992; Morwitz 1997; Morwitz, Steckel, and Gupta 1997). Thus, consumer resource allocation theory explains why people fail to consider intervening contingency effects in predicting their future behavior and predicts subsequent differences in their motivation and capability to engage in repurchase behavior.
Second, from a methodological perspective, we expect systematic differences in the measurement properties of repurchase intentions and behavior. Because intentions measures typically use five-or seven-point scales, information lost as a result of range restrictions and coarseness can attenuate researchers' ability to detect significant interaction effects that truly exist in the population (Russell and Bobko 1992). Range restriction occurs when information is lost because the highest or lowest point on the scale does not accurately capture extreme variations in the construct of interest. Similarly, coarseness refers to information that is lost when one-point scale variations do not accurately capture within-range variation in the construct of interest. Range-restricted and coarse scales may capture direct linear relationships with other constructs, especially if the two measures share common method variance and response bias (Bolton 1998; Morwitz and Schmittlein 1992). Measurement theory suggests that intentions measures do not capture the nuanced, complex variations that are provided by objective repurchase behavior measures, even if respondents could make accurate predictions.
Finally, prior empirical research demonstrates that the conversion of intent into repurchase is moderated by various factors, including the type of product (Jamieson and Bass 1989; Kalwani and Silk 1982; Young, DeSarbo, and Morwitz 1998), demographics (Morwitz and Schmittlein 1992), experience (Bentler and Speckart 1979; Morwitz and Schmittlein 1992), and time lapse (Chandon, Morwitz, and Reinartz 2005; Mittal and Kamakura 2001; Young, DeSarbo, and Morwitz 1998). Studies that Chandon, Morwitz, and Reinartz (2005) conducted suggest that consumers provide relatively more accurate predictions of frequent, routine purchase decisions, such as those involving grocery items, than of infrequent, complex purchase decisions, such as those involving computers or automobiles. We attribute this lower accuracy in the prediction of infrequent, complex purchase decisions to unforeseen contingency effects that emerge between intentions measurement and subsequent repurchase (Kalwani and Silk 1982).
In the following section, we rely on this conceptual, methodological, and empirical evidence to develop specific hypotheses that build on prior research that has examined moderators of the satisfaction-repurchase relationship. In Table 1, we summarize the studies that support moderating effects of various customer, relational, and marketplace characteristics. We report the results only for moderating effects; that is, we do not include results for main effects. A review of Table 1 shows that our study makes unique contributions by testing formerly unexamined moderating variables; linking survey data to self-reported intentions and objective, longitudinal repurchase behavior; and investigating a previously understudied context marked by low exit barriers.
Development of Hypotheses
The conceptual framework we present in Figure 1 proposes three categories of moderators that operate at different levels. Customer characteristics explain variations in the satisfaction-repurchase relationship due to individual differences, relational characteristics capture customers' investments in building or formalizing relationships with a specific firm, and marketplace characteristics account for variations related to market-level competition. For each category of moderator, we propose and subsequently test two specific moderating variables. In each case, we predict an interaction effect after we control for main effects.
Customer characteristics explain variations in peoples' purchase levels for an entire purchase category. We expect that customer-level variables have a direct influence on repurchase intentions and behavior and moderate the relationship between satisfaction and repurchase behavior. We examine involvement, a motivational resource, and household income, a monetary resource. Because both moderators are closely linked to key resources, they are likely to be among the most significant customer-level influences.
Involvement. Involvement is the importance of the purchase category to the consumer and is based on the consumer's inherent needs, values, and interests (Mittal 1995). From a resource perspective, highly involved customers allocate more time and effort to search (Beatty and Smith 1987; Bloch, Sherrell, and Ridgway 1986; Maheswaran and Meyers-Levy 1990) and report higher levels of repatronage intentions (Wakefield and Baker 1998), which suggests a positive direct link between involvement and repurchase intentions and behavior. We acknowledge an alternative view that involvement could negatively affect repurchase intentions. More involved consumers may be more likely to search and potentially identify more preferred alternatives in the market, regardless of their level of satisfaction.
We also expect that involvement enhances the positive effect of satisfaction on actual repurchase behavior but not on repurchase intentions. Involved shoppers should allocate more time, effort, and money to retailers that provide exceptional satisfaction. They should also be more discriminating among offerings and more responsive and committed to superior offerings (Beatty, Kahle, and Homer 1988). This positive moderating effect would extend to repurchase intentions if involved customers accurately incorporated these complex effects into their predictions, but because we do not expect such incorporation to occur, we formally hypothesize the following:
H1: Involvement (a) moderates (enhances) the positive relationship between customer satisfaction and objective repurchase behavior but (b) does not moderate the positive relationship between customer satisfaction and repurchase intentions.
Household income. Household income is positively related to consumers' routine expenditures for multiple types of services (Nichols and Fox 1983; Soberon-Ferrer and Dardis 1991), loyalty among online shoppers (Keaveney and Parthasarathy 2001), and profitable lifetime customer duration (Reinartz and Kumar 2000). On the basis of these findings, we expect that household income has a positive influence on repurchase intentions and behavior.
Household income should also intensify the relationship between satisfaction and repurchase behavior. The conversion of intent into purchase varies across groups that differ in their ability to fulfill that intent (Morwitz and Schmittlein 1992), and lower-income customers may be constrained in their purchases. Because higher-income customers place a higher value on time and are more discriminating in how they allocate their time (Marmorstein, Grewal, and Fishe 1992), they should visit and spend less at retailers that offer low satisfaction and more at retailers that offer high satisfaction. This positive moderating effect would extend to intentions only if higher-and lower-income customers accurately incorporated the enabling and constraining effect of income. Because we do not expect such incorporation to occur, we formally hypothesize the following:
H2: Household income (a) moderates (enhances) the positive relationship between customer satisfaction and objective repurchase behavior but (b) does not moderate the positive relationship between customer satisfaction and repurchase intentions.
Relational characteristics represent formal and informal bonds between the firm and its customers; relational bonds can create social and financial switching barriers that provide firms with an advantage insulated from competitor actions. Although relational moderators have been examined primarily in the context of contractual services, relational strategies designed to encourage discrete, ongoing repurchase are widespread. Proposed relational moderators include relationship age with the focal firm and participation in the firm's relationship program.
Relationship age. Prior experience influences intent, repurchase behavior (Anderson, Fornell, and Lehmann 1994; Morwitz and Schmittlein 1992), and loyalty (Ganesh, Arnold, and Reynolds 2000). Relationship age is positively related to customer profitability (Reinartz and Kumar 2000, 2003), retention (Bolton 1998), number of services purchased (Verhoef, Franses, and Hoekstra 2002), continued museum membership (Bhattacharya 1998; Bhattacharya, Rao, and Glynn 1995), and (we expect) repurchase intentions and behavior.
Empirical results indicate that length of prior experience enhances the positive association between satisfaction and subsequent relationship duration (Bolton 1998) and that relationship age enhances the link between satisfaction and retention and the number of services purchased (Verhoef 2003; Verhoef, Franses, and Hoekstra 2002). This effect would extend to intentions only if relational customers accurately incorporated the moderating effect of prior relational investments, but because we do not expect such incorporation to occur, we hypothesize the following:
H3: Relationship age (a) moderates (enhances) the positive relationship between customer satisfaction and objective repurchase behavior but (b) does not moderate the positive relationship between customer satisfaction and repurchase intentions.
Relationship program participation. Relationship programs represent company initiatives that target individual customers who agree to exchanges that may be complementary or ancillary to their purchase transactions. These programs promote retention by enhancing customers' perceptions of the relationship investment and increasing their trust and commitment (De Wulf, Odekerken-Schroder, and Iacobucci 2001; Rust, Lemon, and Zeithaml 2004). Participants may receive personalized communications that keep them informed of new offerings or preferential treatment and rewards for past loyalty. Empirical findings indicate that relationship program participation has positive direct effects on intentions, usage levels, retention, and customer share development (Bolton, Kannan, and Bramlett 2000; Garbarino and Johnson 1999; Verhoef 2003).
We also expect that relationship program participation enhances the positive effect of satisfaction on repurchase behavior. Customers enter relationships in part to reduce the time and effort required for purchase decisions (Bhattacharya and Bolton 2000; Sheth and Parvatiyar 1995), which suggests that relationship program participants should be less inclined to shop around and more inclined to allocate purchases to relational providers that offer superior satisfaction. This positive moderating effect would extend to intentions only if customers accurately incorporated the moderating effect of relational program participation. Because we do not expect such incorporation to occur, we hypothesize the following:
H4: Relationship program participation (a) moderates (enhances) the positive relationship between customer satisfaction and objective repurchase behavior but (b) does not moderate the positive relationship between customer satisfaction and repurchase intentions.
Marketplace moderators feature interactions among customers, the focal firm, and competing firms. For example, intense competition that spurs price promotions may increase switching behavior and overall purchase volume, or new firms entering the marketplace may steal customers and market share from entrenched competitors. We examine the convenience of the focal firm's offering and its interaction with competitive intensity in the marketplace.
Convenience. Overall convenience is a second-order construct that consists of five types of time and effort costs involved in service experiences (Berry, Seiders, and Grewal 2002). Empirical findings indicate that convenience is significantly related to customer satisfaction and behavioral intentions (Andaleeb and Basu 1994), consumer switching behavior (Keaveney 1995), and customer perceptions and retention (Rust, Lemon, and Zeithaml 2004).
In addition to its direct effects, we propose that convenience enhances the positive effect of satisfaction on repurchase behavior but not on intentions. From a resource allocation perspective, a convenient offering conserves customers' time and effort and thereby facilitates a satisfied customer's ability to fulfill his or her intent. In this capacity, convenience functions less as an input to evaluation and more as an ongoing barrier that encourages or discourages repurchase behavior. This is likely to be particularly relevant for repatronage behavior, for which access to geographically based retailers or other service firms is a major decision factor, and can produce both planned and unplanned trade-offs between degree of convenience and level of satisfaction. Thus:
H5: Convenience (a) moderates (enhances) the positive relationship between customer satisfaction and objective repurchase behavior but (b) does not moderate the positive relationship between customer satisfaction and repurchase intentions.
Competitive intensity. We define competitive intensity as the level of direct competition that the focal firm faces within its immediate business domain. Competitive intensity can attenuate competitive advantage and influence repurchase behavior over time because competition erodes customers' perceptions of differential advantage along unsustainable dimensions. For example, convenience represents a characteristic that can be readily replicated in many marketplaces; thus, the relative advantage it offers when competition is low is eroded as competition intensifies.
We illustrate the expected interaction using an anecdote about gas station competition and repurchase. A consumer routinely travels three distinct routes along which he or she makes repurchase decisions. On the first route, there is only one gas station; the convenience of the offering may be paramount, so the traveler repurchases at this gas station, especially if he or she is satisfied with the service station but, when necessary, even if he or she is not. On the second route, there are two gas stations on opposite sides of the road, both of which are open with no waiting line; convenience may lead the traveler to repurchase at whichever station is on the side of the road in the direction he or she is traveling. Alternatively, one of the competitors may deliver higher satisfaction on another dimension, which would lead the traveler to cross the road if necessary to repurchase from the same gas station. On the third route, there are four gas stations located on the four corners of an intersection; each is open without a waiting line. Convenience may continue to play a key role (e.g., stop at the first one on the same side of the road that does not have a line), but an alternative decision rule could lead to convenience becoming irrelevant.
This anecdote suggests a three-way interaction among satisfaction, convenience, and competitive intensity. When competitive intensity is low, convenience prevents defection and facilitates repurchase behavior, thus exerting both a direct and a moderating influence on repurchase. However, as competitive intensity increases, convenience plays a less important role in the repurchase decision. It is not clear whether competitive intensity will have a significant direct effect, which would depend on whether customers perceive shopping synergies associated with a large number of competitors in a single destination, such as at a regional shopping mall. We do not expect that customers will incorporate these complex interactions into repurchase intentions, which suggests the following hypothesis:
H6: Competitive intensity (a) moderates the relationships among customer satisfaction, convenience, and repurchase behavior such that convenience enhances the relationship between satisfaction and repurchase behavior when competitive intensity is low but not when competitive intensity is high but (b) does not moderate the relationship among customer satisfaction, convenience, and repurchase intentions.
Research Design and Empirical Results
To examine our hypotheses, we worked with a national specialty retail chain that sells its own brand of upscale women's apparel and home furnishings in approximately 100 North American locations. The company provided contact information for 3117 customers and offered a $20 coupon to customers who responded to the four-page questionnaire. The customer list included randomly selected names of customers who had purchased merchandise from any store during the 12 weeks before the generation of the list. Thus, the sampling frame represents current customers.
Contact information included names and addresses for all 3117 customers and e-mail addresses for 1150 customers who had joined the relationship program, which featured frequent e-mails announcing newly arrived merchandise and promotions. We sent e-mail messages to all 1150 e-mail addresses, inviting potential respondents to click through to an online survey. Of these 1150 addresses, 264 e-mails were returned as undeliverable, leaving an effective sampling frame of 886. After two weeks, we sent an additional e-mail to nonrespondents, offering them another chance to participate. We ultimately received 285 surveys, for an effective response rate of 32%. We eliminated 12 respondents who provided incomplete information from subsequent analyses, leaving a total of 276 usable responses.
We sent postal mail to the other 1967 names on the customer list. Of these, 28 were returned as undeliverable, leaving an effective sampling frame of 1939. After four weeks, we sent a follow-up letter and survey to the nonrespondents, offering them another chance to participate. A total of 721 people responded, for an effective response rate of 37%. Of these, 52 incomplete surveys were unusable, leaving a total of 669 usable responses. The 945 respondents to both surveys were primarily women (99%) between the ages of 35 and 54 years (66%) with at least some college education (96%) and an average household income exceeding $58,000.
We operationalized repurchase behavior using two measures from the company's records: the number of repurchase visits and the amount of repurchase spending during the 52 weeks after completion of the survey. The use of objective repurchase data for the year following the survey eliminates concerns of common method variance, simultaneity, or endogeneity. We log transformed the repurchase behavior measures to improve distribution normality.
Several independent measures were objective secondary data or single-item, self-reported measures. We measured household income as the median household income reported in the 2000 census for the respondent's zip code. Relationship age was a single-item measure (i.e., "How long have you been a … customer?"). Relationship program participation was a dichotomous variable indicating whether the customer had opted in to the company's e-mail program. To operationalize competitive intensity, we used Census Bureau Zip Code Business Patterns data that report the number of establishments competing in each North American Industry Classification System (NAIC; http:// censtats.census.gov/cbpnaic/cbpnaic.shtml); using the respondent's zip code, we included the total number of competitors in women's clothing (NAIC code 448120) and other home furnishings (NAIC code 442299).
We adapted multi-item scales to measure repurchase intentions (Parasuraman, Zeithaml, and Berry 1994), satisfaction (Voss, Parasuraman, and Grewal 1998), and involvement (Beatty and Talpade 1994). Because no comprehensive convenience scale existed, we followed standard procedures to develop scale items for each of the five convenience types (Berry, Seiders, and Grewal 2002). The multigroup confirmatory factor analysis that we report in the Appendix supports the reliability and consistency of the scales (Voss and Parasuraman 2003). We used mean scores for the latent constructs in subsequent regression analyses.
In Table 2, we present descriptive statistics and construct correlations for the variables of interest. Comparison of the means for the postal mail and e-mail samples indicates that relationship program participants are more involved, have lower relationship ages, and engage in more repurchase visits and spending. These mean differences raise questions as to whether there are differences in the structural relationships of interest across the two samples. We conducted an exploratory analysis to address this. Of the 15 possible structural differences across the three models (five for each model: repurchase visits, spending, and intentions), only one was significant at the p < .05 level; moreover, there was no increase in the adjusted R² for any of the models. These results reinforce the generalizability of the findings across the two samples.
To test the hypotheses, we ran a series of regression analyses to estimate identical models for repurchase intentions, visits, and spending. Preliminary analyses indicated that the hypotheses were supported by 12 of the 18 tests and that the interaction effects of relationship age (H3) and relationship program participation (H4) were not significant in any model. For reasons of parsimony, we reestimated the three models without these variables. We present the nonstandardized coefficients and t-values for the reduced models in Table 3, in which we group the hypothesized interaction terms in the lower half to facilitate inferences about hypothesized effects. Each model is significant (p < .01), but the explanatory power of the model with repurchase intentions as the dependent variable is much higher than those with repurchase visits or spending as the dependent variable. This finding suggests that some of the explanatory power of the repurchase intentions model is due to shared method variance.
The relatively low explanatory power of the equations with objective dependent measures raises some concern about omitted variable bias. To address this concern, we reran the analyses and included lagged dependent measures (i.e., purchase visits and spending for the previous year), which capture unobserved, systematic variation across respondents. This lagged analysis produced no changes in the results for customer or market characteristics and a minor attenuation of the direct effects of relational characteristics, thus indicating that the lagged dependent variables partially mediate the effects of relationship age and relationship program participation. These results suggest that omitted variable bias is not a significant concern.
Repurchase behavior. In the repurchase visits model, three of the four hypothesized interactions (income x satisfaction, convenience x satisfaction, and convenience x competition x satisfaction) are significant and in the expected direction. These three results are replicated in the repurchase spending model, in which the fourth interaction term (involvement x satisfaction) is also significant and in the expected direction. Thus, H1a receives partial support, and H2a, H5a, and H6a are fully supported in both analyses. The graphs in Figure 2 facilitate interpretations of these results.
With regard to H1a, involvement moderates the satisfaction-repurchase spending link (t = 2.22, p < .05, effect size [ES] = .07) but not the satisfaction-repurchase visits link (Cohen 1988). As we show in Figure 2, Panel A, the relationship between satisfaction and repurchase spending is positive only when involvement is high; it is flat when involvement is low. For H2a, household income moderates the link between satisfaction and repurchase visits (t = 3.12, p < .01, ES = .10) and between satisfaction and repurchase spending (t = 2.53, p < .01, ES = .08). As we show in Figure 2, Panel B, the relationship between customer satisfaction and repurchase spending is not significant when household income is low, but it is significantly positive when household income is high. Consistent with resource allocation theory, this result shows that highly satisfied, lower-income customers are constrained in their repurchase spending.
In support of H5a, the convenience x satisfaction term is significantly positive in both the repurchase visits (t = 1.85, p < .05, ES = .06) and the repurchase spending (t = 1.73, p < .05, ES = .06) models. In support of H6a, the three-way convenience x competition x satisfaction term is significantly negative in the two objective repurchase behavior analyses (repurchase visits: t = -2.17, p < .05, ES = .07; repurchase spending: t = -2.44, p < .05, ES = .08). To examine the nature of the interaction effect, we divided the overall sample into three (low, medium, and high) subgroups based on competitive intensity and reran the analysis. The results indicate that the convenience x satisfaction term is significant only in the low-competition subgroup. As we show in Figure 2, Panel C, the relationship between customer satisfaction and repurchase spending is not significant when convenience is low, but it is significantly positive when convenience is high.
Repurchase intentions. The significant, negative coefficient for the involvement x satisfaction interaction term is the only unexpected result for the repurchase intentions model. As we show in Figure 2, Panel D, the relationship between satisfaction and repurchase intentions is more positive when involvement is low than when involvement is high, and repurchase intentions are nearly as strong for highly satisfied, low-involvement customers as for highly satisfied, high-involvement customers. Combining the results from Figure 2, Panels A and D, offers additional insight: Low-involvement customers overestimate the impact of increasing satisfaction on their subsequent repurchase behavior.
Although we did not hypothesize the baseline direct effects captured by the dotted lines in Figure 1, the results offer some inferences worth noting. For the repurchase visits model, five of the six antecedents--involvement, relationship age, relationship program participation, convenience, and competition--have significant main effects. Although the main effects of satisfaction and income are not significant, significant higher-order terms indicate that the direct effects are contingent. The results for the repurchase spending model are largely similar to the repurchase visits model; although the main effects of convenience and competition are not significant, significant higher-order terms indicate that the direct effects are contingent. These results offer general support for the baseline model depicted in Figure 1, in that all antecedents have a significant effect on repurchase visits and spending. All main effect sizes are small (ES ≤ .11), with the exception of relationship program participation, which has the strongest effect size (ES = .29) with repurchase visits as the dependent variable.
Only three antecedents have significant effects on repurchase intentions. The main effect sizes are moderately large for involvement (ES = .40) and satisfaction (ES = .26) and are smaller for convenience (ES = .14). Household income, relationship age, relationship program participation, and competitive intensity have no significant effects. Common method variance offers a plausible explanation for this pattern of results; all the independent variables that are self-reported measures using Likert scales are positively related to repurchase intentions, but the other measures are not. In general, our findings, with some interesting exceptions, confirm the results of prior studies that report significant direct effects of the model's antecedents on both repurchase intentions and behavior.
Discussion
The marketing concept, which proposes that customer satisfaction should be the focal point of business activities, is based on the explicit assumption that satisfied customers repurchase more and therefore are more profitable. In questioning this fundamental assumption, we predicted that customer, relational, and marketplace characteristics would moderate the relationship between satisfaction and repurchase behavior but not repurchase intentions. In a specialty retailing context, we find that satisfaction has a strong positive effect on repurchase intentions but no direct effect on repurchase behavior; customer and marketplace characteristics play significant moderating roles; and relational factors have a positive direct influence on repurchase behavior but not intentions.
Consistent with our prediction, we find that inferences related to moderating effects vary dramatically across self-reported repurchase intentions and objective measures of repurchase behavior. Our results show the absence of significant moderating effects on the satisfaction--repurchase intentions relationship for five of the six interactions, but they show the presence of significant moderating effects on the satisfaction-repurchase behavior (visits and spending) relationship for 7 of the 12 interactions. This pattern of results supports our argument--drawn from consumer resource allocation theory--that customers often fail to consider intervening contingency effects when they predict their own future behavior.
These divergent findings, consistent with the modest correlations between intentions and objective visits and spending (see Table 2), again raise questions about the reliability of customers' self-reported repurchase intentions for testing conceptual models of repurchase behavior, including those that examine the role of moderating variables. We summarize the hypotheses test results in Table 4.
Customer characteristics. Our results add to a growing body of research that offers strong support for the moderating role of customer characteristics on repurchase behavior across a variety of contexts (see Table 1). As we expected, involved customers shop and spend more than do less involved customers. The significant moderating effect on repurchase spending indicates that involved customers spend even more when their satisfaction is high (Figure 2, Panel A). The interaction effect is not significant for repurchase visits, which suggests that satisfaction has no linear effect on involved customers' repurchase frequency. These active shoppers likely patronize a variety of competing stores within their evoked set, and delivering satisfaction to this customer group simply establishes a presence in that set. Delivering superior satisfaction does not lead to increased repurchase frequency (i.e., customers continue to shop around), but it does lead to significantly higher spending. Unexpectedly, involvement has a negative moderating effect on repurchase intentions. Figure 2, Panel D, suggests that when low-involvement customers perceive superior satisfaction, they register significantly higher repurchase intentions; however, Panel A indicates that low-involvement customers fail to follow through on those intentions.
Consistent with Becker's (1965) theory of time allocation, household income enhances the effect of satisfaction on repurchase visits and spending (Figure 2, Panel B). This result confirms the role of household income as a constraint that attenuates the influence of satisfaction on repurchase behavior for lower-income customers. As we expected, household income does not moderate the link between satisfaction and repurchase intentions. In contrast, household income has no significant direct effects, a finding we did not anticipate. This result may reflect the highly focused merchandising strategy and lifestyle orientation of the specialty retailer we studied.
Relational characteristics. Our results add to equivocal findings with respect to relational characteristics, which seem to play a moderating role in contractual or industrial purchase contexts or when specific types of repurchase behavior are examined (see Table 1). Relational variables may have weaker moderating effects in contexts marked by discrete purchase events and low exit barriers than in contractual relationships (e.g., Bolton 1998; Reinartz and Kumar 2000; Verhoef 2003). The predicted nonsignificant moderating effects of relationship age and relationship program participation on repurchase intentions and the positive direct effects on repurchase visits and spending but not on intentions are consistent with our belief that it is difficult for customers to incorporate background factors such as relationship age and program participation into future purchase predictions.
The results indicate that habit plays a major role in determining behavior in this context, and we speculate that relationship programs that feature direct communications may act as a personal shopper by providing updates on merchandise, sales, and promotions that simplify the shopping process. These programs conserve participants' resources and provide them with more frequent incentives to visit the retailer's stores. Participants may also perceive a greater relationship investment by the retailer and respond with higher behavioral commitment, even if their intentions are unaffected.
Marketplace characteristics. Our results add to the small number of studies that have demonstrated moderating effects of marketplace characteristics, and the results highlight the importance of considering firm x competitor interactions that distinguish retail competition across geographic marketplaces. The significant results for convenience as a positive moderator of satisfaction's effects on repurchase visits and spending highlight the importance of this relatively unexamined construct. Collectively, the findings suggest that convenience directly encourages repurchase visits but that repurchase spending occurs only if satisfaction also is high.
Convenience has been conceptualized as a multidimensional construct that has particular importance for retail patronage behavior (Seiders, Berry, and Gresham 2000). We contribute to the emerging literature in this area by developing and testing a scale that captures the multiple dimensions of shopping convenience. As a threshold variable, convenience assumes a different role from switching costs, which have been examined in other studies (e.g., Burnham, Frels, and Mahajan 2003; Jones, Mothersbaugh, and Beatty 2000). Whereas switching costs represent a one-time penalty for customers that is directly associated with moving from one firm to another, convenience is a strategically used marketing variable and a relatively stable attribute of the offering. The lack of convenience can be a motive to defect, whereas the presence of convenience can motivate trial or discourage defection.
Our study is one of the first to examine the moderating effect of competitive intensity on the satisfaction--repurchase relationship directly. The results support our expectation that increasing competition attenuates the positive effect of convenience, which is a relatively easy-to-copy source of advantage. It would also be instructive to explore the extent to which competitive intensity erodes other sources of competitive advantage. Furthermore, competition exerts a positive main effect on repurchase visits, which suggests that competitors in this category benefit from locating next to one another to create a shopping destination.
Satisfaction scores by themselves may not predict repurchase behavior accurately and may create false security if managers assume that higher satisfaction scores necessarily lead to stronger repurchase behavior. That someone is an ongoing customer suggests that he or she is at least somewhat or very satisfied (if not delighted). However, greater value may be gleaned by tracking defecting customers to determine the cause of their defection or by developing customer relationship management systems that track actual repurchase decisions. Such behavioral data are more accurate in evaluating the effectiveness of firms' marketing strategies and therefore represent an important complement to customer self-reported data.
Managers also would benefit from a better understanding of moderating variables, such as involvement and household income, that can be used to segment customers into lower or higher repurchase groups. Firms can identify customers with higher levels of involvement and then attempt to foster long-term relationships with members of that group. Managers can invest resources in offering programs (e.g., by using initiatives such as in-store events, experiential classes, and charitable campaigns) to increase customer involvement. For example, Whole Foods Market regularly promotes a program in which it matches customers' contributions to featured national environmental organizations through the highly visible sale of coupons offered for purchase at the stores' checkout terminals. Our results support the assumption that these carefully focused initiatives can lead to more repurchase visits and spending by increasing involvement among customers.
Our results also suggest that managers should encourage repurchase behavior through deliberately multifaceted strategies that conserve customers' time and effort. For example, innovative and comprehensive approaches to site location analysis should be a priority for retailers. Retail firms can develop strategies that promote convenience and reduce uncertainty by communicating specific and detailed information about merchandise online and by focusing on coordination to ensure consistency across channels. Related to this is the importance of encouraging customers to opt in to permission-based communications and then delivering tangible value to those who participate in relationship programs. In our study, respondents from the e-mail sample visited and spent approximately twice as much in the store as did the postal mail respondents (see Table 2). Managers should consider offering incentives to motivate customers to join these programs. Moreover, firms should not only allocate resources to attract and retain customers who elect to join permission-based communications programs but also use this channel as a means for creative differentiation. These types of initiatives construct effective exit barriers and contribute to competitive strength and viability.
As with all research, our study is constrained by limitations that suggest areas for further research. Although prior research suggests that satisfaction is a partial mediator of the effect of convenience on repurchase, we do not explicitly examine the direct effect of convenience on satisfaction. In terms of our sample, 99% was female; because prior research has demonstrated shopping differences between men and women, caution should be exercised when extending our findings to a general population. The sample also included only current customers; thus, our findings may not extend to noncustomers who have no experience with the firm or to customers who have defected. We encourage additional research that examines defection to illuminate the differences between customers who defect and those who do not.
We especially encourage additional studies that investigate direct and moderating effects of relational characteristics. We collected objective repurchase measures one year after we measured the relationship program participation, but endogeneity cannot be completely ruled out as an alternative explanation for the robust direct effects, because customers who elect to participate in relationship programs may be particularly enthusiastic or loyal. If this is true, the causal ordering between relationship program participation and repurchase behavior is ambiguous. We believe that the involvement construct included as an independent measure effectively controls for purchase category enthusiasm, but we did not control for store loyalty. Further research could attempt to disentangle relationship program participation effects from the effects of other, related constructs.
The dynamism of fashion, which encourages variety-seeking shopping behavior, might explain the lack of significant moderating effects of relational characteristics in the current study. Significant moderating effects of relational characteristics might be found in discrete repurchase contexts that are less dynamic and less hedonic. The type of relationship program that the retailer implements might also affect whether moderating effects manifest. For example, relationship programs that are multilevel (e.g., with different levels of benefits) rather than dichotomous (as was the case with the retailer in our study) might elicit significant moderating effects.
Our results indicate key moderating roles of customer characteristics, such as involvement and income, and marketplace characteristics, such as perceived convenience and competitive intensity. Further research could provide a deeper understanding of how these variables and relational characteristics influence repurchase behavior across a variety of conditions. We suspect that convenience is important in explaining behavior for discrete, recurring purchase decisions and likely becomes even more important as the frequency of repurchase increases (e.g., supermarket shopping). The multidimensional convenience scale that we present in the Appendix may be useful in exploring the role of convenience in other purchase contexts.
The study of additional customer and marketplace characteristics that may moderate the satisfaction--repurchase relationship is an important next step. Customer characteristics that warrant examination for moderating effects (see Table 1) include the propensity to engage in relationships and variety seeking. Additional marketplace characteristics, such as switching barriers and the attractiveness of alternatives for customers, should also be investigated further (Jones, Mothersbaugh, and Beatty 2000). For example, if attractive alternatives exist, less-satisfied customers would be more likely to register regret (in passing up the alternative); thus, they should be less likely to repurchase from the focal retailer (Inman, Dyer, and Jia 1997; Inman and Zeelenberg 2002; Lemon, White, and Winer 2002). This suggests that the link between satisfaction and repurchase would be more positive when attractive alternatives exist.
We examine the impact of customer, relational, and marketplace factors in a specialty retailing context in which repurchase behavior equals repatronage. We propose that these three categories of moderators likely generalize across repurchase situations; thus, the conceptual framework in Figure 1 can be applied, for example, to brand repurchase and to retail repatronage behavior. More specifically, we expect that the categories of moderators are generalizable and that the specific variables in each category that are salient may vary across purchase situations. Therefore, the conceptual framework should be tested in additional repurchase contexts to confirm that it can generalize across products and services.
Finally, to our knowledge, no research has examined the role of situational factors in moderating the satisfaction--repurchase association. Decisions influenced by transitory needs, such as those driven by emergency, point-of-purchase, or time pressure factors, often lead customers to engage in isolated unsought, impulse, or suboptimal purchase behavior. Such situational moderating influences warrant better understanding in terms of how they affect specific, stand-alone transactions and ongoing customer--firm relationships.
Despite these limitations and opportunities for additional research, the current study introduces new insights into the moderated relationship between satisfaction and repurchase behavior in a context marked by discrete, recurring purchases. The conceptual framework and empirical results improve the understanding of the complex and contingent relationship between customer satisfaction and repurchase behavior and suggest that habit, convenience, task simplification, and individual differences in involvement and household income play important roles. The findings also serve to identify new directions for further research that ultimately will enhance the understanding of what drives repurchase behavior.
The authors thank the anonymous JM reviewers, the Marketing Science Institute for supporting this research, and Katherine Lemon for helpful comments on this article.
Legend for Chart:
A - Study
B - Dependent Variable(s) Context and Design
C - Customer Characteristics
D - Relational Characteristics
E - Marketplace Characteristics
A B C
D E
Bolton (1998) Relationship Duration
(OM)
Telecommunications
Longitudinal
Contractual service
Length of
experience (+)
Bowman and Share of Category Heavy user (+)
Narayandas Requirements (SR)
(2001) Consumer package goods
Cross-sectional
Noncontractual goods
Loyalty (+)
Bowman and Share of Customer Size (-)
Narayandas Wallet (SR)
(2004) Processed metal
Longitudinal
Noncontractual
industrial goods
Account Satisfaction with
management tenure competitor (+)
(+)
Burnham, Frels, Intention to Stay with
and Mahajan Provider (SR)
(2003) Credit card and
telephone service
Cross-sectional
Contractual service
Relational switching Procedural
costs (n.s.) switching costs
(n.s.)
Financial switching
costs (n.s.)
Capraro, Defection/Repurchase Objective
Broniarczyk, (SR) knowledge (n.s.)
and Srivastava Health insurance Subjective
(2003) Longitudinal knowledge (n.s.)
Contractual service
Garbarino and Future Intentions(SR)
Johnson (1999) Professional theater
Cross-sectional
Contractual and
noncontractual service
Relational
orientation (-)
Homburg and a. Recommendation Income
Giering (2001) Intentions (SR) SP (-: a, b, c)
b. Brand Repurchase SSP (+: a, b)
Intentions (SR) Involvement:
c. Dealer Repurchase SSP (-: b)
Intentions (SR) Gender:
Auto manufacturer/dealer SP (+m: c)
Cross-sectional SSP (+f: b)
Contractual goods and Age
services SP (+: a, b, c)
SSP (-: b)
Variety seeking
SP (-: a, b, c)
Jones, Repurchase Intentions
Mothersbaugh, (SR)
and Beatty Banking and hair salon
(2000) Cross-sectional
Contractual and
noncontractual services
Interpersonal Switching costs (-)
relationships (-) Attractiveness of
alternatives (+)
Magi (2003) a. Share of Purchase Economic
(SR) orientation
b. Share of Visits (SR) (-: a; n.s.: b)
Grocery stores Personalizing
Longitudinal orientation
Noncontractual (-: a, b)
consumption goods Apathetic shopping
orientation
(n.s.: a, b)
Age (n.s.: a, b)
Purchase volume
(+: a; n.s.: b)
Mittal and Repurchase Behavior (OM) Sex (+)
Kamakura Automobile manufacturer Education (+)
(2001) Longitudinal Marital status
Contractual durable (n.s.)
goods Age (+)
Children (+)
Urban versus
suburban (n.s.)
Verhoef (2003) a. Customer Retention
(OM)
b. Customer Share
Development (OM)
Insurance
Longitudinal
Contractual service
Relationship age
(+: a; n.s.: b.)
Verhoef, Franses, a. Customer Referrals
and Hoekstra (SR)
(2002) b. Number of Services
Purchased (OM)
Insurance
Cross-sectional and
longitudinal
Contractual service
Relationship age
(n.s.: a; +: b)
Current Study Repurchase Intentions Involvement
(SR), Household income
Repurchase Visits (OM),
and Spending (OM)
Apparel and home
furnishings
Cross-sectional and
longitudinal
Noncontractual
fashion goods
Relationship age Competitive
Relationship intensity
program Convenience of
participation offering
Notes: +/- indicates that the effect of satisfaction on the
dependent variable increases/decreases as the moderating variable
increases/decreases; n.s.= not significant; SR = self-reported
measure provided by respondent; OM = objective measure taken from
secondary source; SP = satisfaction with the product; and
SSP = satisfaction with the sales process. Legend for Chart:
B - Overall Mean (Standard Deviation): N = 945
C - Postal Mail Mean (Standard Deviation): N = 669
D - E-Mail Mean (Standard Deviation): N = 276
E - 1
F - 2
G - 3
H - 4
I - 5
J - 6
K - 7
L - 8
M - 9
A B C D
E F G
H I K
J L M
1. Satisfaction 4.34 4.36 4.29
(.72) (.71) (.74)
1.0
2. Involvement 4.03 3.99(*) 4.14(*)
(.73) (.74) (.70)
.27 1.0
3. Household income 58,776 59,941(*) 55,952(*)
(20,254) (20,500) (19,394)
-.06 -.05 1.0
4. Relationship age 3.13 3.39(*) 2.50(*)
(2.44) (2.68) (1.56)
.01 .10 .06
1.0
5. Relationship program .29 0 1
participation (.45) (0) (0)
-.04 .10 -.09
-.17 1.0
6. Convenience 3.89 3.88 3.92
(.54) (.53) (.55)
.66 .28 .02
.09 .03
1.0
7. Competitive intensity 7.45 7.59 7.12
(10.39) (10.55) (10.03)
-.01 -.03 .03
.01 -.02 1.0
.00
8. Repurchase intentions 4.31 4.29 4.36
(.70) (.71) (.68)
.53 .48 -.06
.03 .04 .00
.47 1.0
9. Repurchase visits 4.13 3.27(*) 6.22(*)
(9.62) (9.03) (10.65)
.07 .10 .01
.03 .14 -.01
.11 .11 1.0
10. Repurchase spending 326.68 237.97(*) 541.72(*)
(1083.00) (644.33) (1718.80)
.07 .11 .00
.00 .13 -.03
.10 .10 .74
(*) Means are significantly different across groups (p <. 01);
correlations ≥ |.07| are significant at p < .05 (two-tailed
test). Legend for Chart:
A - Independent Variables
B - Dependent Variables Repurchase Visits Coefficient
C - Dependent Variables Repurchase Visits t-Value
D - Dependent Variables Repurchase Spending Coefficient
E - Dependent Variables Repurchase Spending t-Value
F - Dependent Variables Repurchase Intentions Coefficient
G - Dependent Variables Repurchase Intentions t-Value
A B
C D E F
G H
Intercept
1.11(**) (29.80) 5.88(**) (202.99)
4.34(**) (202.98)
Satisfaction
.04 (.57) .06 (1.11)
.30(**) (8.24)
Involvement
.17(**) (3.80) .12(**) (3.55)
.33(**) (13.19)
Household Income
.01 (.83) -.00 (-.18)
-.01 (-1.02)
Relationship age
.04(**) (3.22) .02(*) (1.91)
-.00 (-.64)
Relationship program participation
.31(**) (9.13) .21(**) (7.79)
.01 (.63)
Convenience
.14(*) (1.76) .08 (1.40)
.20(**) (4.42)
Competition
.01(*) (1.79) .00 (1.23)
.00 (.64)
Competition x satisfaction
-.00 (-.71) -.00 (-.75)
-.00 (-.77)
Competition x convenience
.01 (1.06) .00 (.57)
-.00 (-.56)
H1 Involvement x satisfaction
.06 (1.17) .09(*)(a) (2.22)(a)
-.07(**)(a) (-2.40)(a)
H2 Income x satisfaction
.07(**)(a) (3.12)(a) .04(**)(a) (2.53)(a)
.01 (.73)
H5 Convenience x satisfaction
.12(*)(a) (1.85)(a) .09(*)(a) (1.73)(a)
-.06 (-1.59)
H6 Convenience x competition x satisfaction
-.01(*)(a) (-2.17)(a) -.01(**)(a) (-2.44)(a)
-.00 (-.85)
Model F value (degrees of freedom = 13/931)
11.53(**) 9.14(**)
53.92(**)
Adjusted R²
.13(**) .10(**)
.42(**)
(*) p < .05.
(**) p < .01.
Notes: We report nonstandardized regression coefficients with
t-values in parentheses for each effect. (a) Significant
hypothesized interaction effects are bolded for visual clarity. Legend for Chart:
A - Hypothesized Moderators of the Effect of Satisfaction on:
B - Repurchase Behavior Repurchase Visits Hypothesis
C - Repurchase Behavior Repurchase Visits Supported
D - Repurchase Behavior Repurchase Spending Hypothesis
E - Repurchase Behavior Repurchase Spending Supported
F - Repurchase Intentions Hypothesis
G - Repurchase Intentions Supported
A B C D E F G
H1: Involvement x + No + Yes 0 No
satisfaction
H2: Income x satisfaction + Yes + Yes 0 Yes
H3: Relationship age x + No + No 0 Yes
satisfaction
H4: Relationship program + No + No 0 Yes
participation x satisfaction
H5: Convenience x + Yes + Yes 0 Yes
satisfaction
H6: Convenience x competition - Yes - Yes 0 Yes
x satisfaction
Notes: + = variable moderates (enhances) the positive
relationship between customer satisfaction and repurchase
behavior, - = convenience moderates (enhances) the relationship
between satisfaction and repurchase behavior when competitive
intensity is low but not when competitive intensity is high,
and 0 = variable does not moderate the relationship between
customer satisfaction and repurchase intentions.DIAGRAM: FIGURE 1 A Framework for Examining Moderators of the Relationship Between Customer Satisfaction and Repurchase
GRAPH: FIGURE 2 Significant Interaction Plots
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Legend for Chart:
A - Item Descriptions
B - Lambda Loading
C - Construct Reliability
D - Average Variance Extracted
A B C D
Decision Convenience .75 .52
I can easily determine prior .82
to shopping whether SR will
offer what I need.
Deciding to shop at SR is quick and easy. .50
I can quickly find information .79
before I shop to decide if
SR has what I'm looking for.
Access Convenience .82 .54
I am able to get to SR quickly and easily. .79
SR offers convenient parking. .59
SR offers convenient locations. .87
SR offers convenient store hours. .67
Transaction Convenience .89 .73
I am able to complete my .84
purchase quickly at SR.
SR makes it easy for me to .93
conclude my transaction.
It takes little time to pay .78
for my purchase at SR.
Benefit Convenience .84 .57
It is easy to find the products .80
I am looking for at SR.
I can easily get product advice at SR. .59
The merchandise I want at SR .85
can be located quickly.
It is easy to evaluate the .75
merchandise at SR.
Post benefit Convenience .80 .61
SR takes care of product .74
exchanges and returns promptly.
Any after-purchase problems I .73
experience are quickly resolved at SR.
It is easy to take care of .76
returns and exchanges at SR.
Satisfaction .90 .74
I am pleased with the overall .82
service at SR.
Shopping at SR is a delightful experience. .87
I am completely satisfied with .88
the SR shopping experience.
Involvement .89 .73
I have a strong personal .81
interest in stores like SR.
Stores like SR are very important to me. .92
The kinds of products SR sells .84
are important to me.
Repurchase Intentions .81 .68
How likely are you to shop .80
more often at SR in the future?
How likely are you to continue .84
shopping at SR?
Fit Statistics
Chi-square (degrees of freedom = 1363) 2351.60
Nonnormed fit index .93
Comparative fit index .94
Notes: SR = the specialty retailer brand name.~~~~~~~~
By Kathleen Seiders; Glenn B. Voss; Dhruv Grewal and Andrea L. Godfrey
Kathleen Seiders is Associate Professor of Marketing, Carroll School of Management, Boston College.
Glenn B. Voss is Associate Professor of Marketing, Department of Business Management, North Carolina State University.
Dhruv Grewal is Toyota Chair of e-Commerce and Electronic Business and Professor of Marketing, Babson College.
Andrea L. Godfrey is a doctoral student, University of Texas, Austin.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 54- Do Satisfied Customers Really Pay More? A Study of the Relationship Between Customer Satisfaction and Willingness to Pay. By: Homburg, Christian; Koschate, Nicole; Hoyer, Wayne D. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p84-96. 13p. 6 Charts, 2 Graphs. DOI: 10.1509/jmkg.69.2.84.60760.
- Database:
- Business Source Complete
Do Satisfied Customers Really Pay More? A Study of the
Relationship Between Customer Satisfaction and Willingness to Pay
Two experimental studies (a lab experiment and a study involving a real usage experience over time) reveal the existence of a strong, positive impact of customer satisfaction on willingness to pay, and they provide support for a nonlinear, functional structure based on disappointment theory (i.e., an inverse S-shaped form). In addition, the second study examines dynamic aspects of the relationship and provides evidence for the stronger impact of cumulative satisfaction rather than of transaction-specific satisfaction on willingness to pay.
Customer satisfaction (CS) has become an important focus of corporate strategy. In the past, many executives trusted their intuitive sense that higher CS would lead to improved company performance. Thus, many companies have implemented programs for measuring and improving CS.
Recent research supports the notion that there is a positive relationship between CS and financial performance (e.g., Anderson, Fornell, and Rust 1997; Reichheld and Sasser 1990; Rust and Zahorik 1993). In an important study, Anderson, Fornell, and Lehmann (1994, p. 63) analyze this link on data obtained from the Swedish Customer Satisfaction Index, and they find that "firms that actually achieve high customer satisfaction also enjoy superior economic returns."
However, the understanding of the constructs that mediate the link between CS and firm profitability is still limited (Szymanski and Henard 2001). The studies that exist find that higher levels of CS lead to greater customer loyalty (e.g., Anderson and Sullivan 1993; Bearden and Teel 1983; Bolton and Drew 1991a, b; Fornell 1992; LaBarbera and Mazursky 1983; Oliver 1980; Oliver and Swan 1989a, b), which in turn has a positive impact on profitability (Reichheld and Teal 1996). Other studies find that satisfied customers can increase profitability by providing new referrals through positive word of mouth (e.g., Mooradian and Olver 1997).
A notable question is whether CS also affects the customer's willingness to pay (WTP) for the product or service. This relationship is important because price is a key element in the profit equation and therefore is directly linked to profitability. Furthermore, the general belief that satisfied customers are willing to pay higher prices is typically based on anecdotal evidence (e.g., Finkelman 1993; Reichheld and Sasser 1990).
Despite the importance of this issue, price-related outcomes of CS (and WTP) have often been neglected in previous research (Anderson 1996). To our knowledge, only one study (Anderson 1996) focuses on the link between CS and price tolerance (the maximum price customers are willing to pay or tolerate before switching), and it reports mixed results with respect to the assumed linear link between the two variables at the company level.
Our research follows Anderson's (1996) and Gotlieb, Grewal, and Brown's (1994) suggestions that research needs to test links between CS and price-related constructs in controlled settings in which the variables are manipulated. In this study, we explore the link between CS and WTP in two experimental studies, and we focus on three research questions.
First, we examine whether there is a (positive) relationship between CS and WTP at the individual level. The WTP concept has not been investigated in this context in previous research. In the theoretical domain, answering this research question provides an improved understanding of the link between CS and profitability. From a manager's perspective, providing an answer to this question can have important implications for pricing practices.
Second, we study the functional structure of the relationship between CS and WTP. In this context, it is worthwhile to determine whether the relationship (if it exists) is essentially linear or whether there are significant nonlinear effects. Understanding the functional structure of this relationship is important for managers to determine an aspired level of CS. This research question is in line with the increasing interest in more complex functional structures of the links in the satisfaction-profit chain (Anderson and Mittal 2000). However, as Ngobo (1999) notes, there has been a lack of a theoretical foundation in the examination of nonlinear effects between CS and behavioral outcome variables. Therefore, in this article, we provide theoretical developments and reasoning for two alternative functional structures for the relationship between CS and WTP as well as a strong empirical test of these notions.
Third, current research indicates the importance of studying dynamic aspects in the link between CS and outcome variables (Bolton 1998; Bolton and Lemon 1999). Thus, we investigate how the relationship between CS and WTP changes over time. To our knowledge, this has not been examined in prior research.
Hypotheses Development
Because a key focus of this article is to examine how CS affects the customer's WTP, it is important first to define the terms. "Satisfaction" is the result of a postconsumption or postusage evaluation, containing both cognitive and affective elements (Oliver 1997). According to the expectancy--disconfirmation paradigm (Oliver 1980), customers judge satisfaction by comparing previously held expectations with perceived product or service performance. In addition, affect (positive or negative), which arises from the cognitive process of confirmation/disconfirmation, contributes to (dis)satisfaction (Oliver 1993; Oliver, Rust, and Varki 1997).
In this research, we concentrate on satisfaction with "performance," which is a postconsumption evaluation of perceived quality relative to prepurchase performance expectations about quality (e.g., Anderson 1994; Anderson and Sullivan 1993; Bitner 1990; Churchill and Surprenant 1982; Oliver 1980; Oliver and DeSarbo 1988; Tse and Wilton 1988). Under this conceptualization, price is not included as part of the satisfaction judgment.
With respect to dynamic aspects, the literature differentiates between "transaction-specific satisfaction" and "cumulative satisfaction." Transaction-specific satisfaction is a customer's evaluation of his or her experience with and reactions to a particular product transaction, episode, or service encounter (Olsen and Johnson 2003), and cumulative satisfaction refers to the customer's overall evaluation of a product or service provider to date (Johnson, Anderson, and Fornell 1995).
The WTP is the maximum amount of money a customer is willing to spend for a product or service (Cameron and James 1987; Krishna 1991). Economists refer to WTP as the reservation price (Monroe 1990). Thus, WTP is a measure of the value that a person assigns to a consumption or usage experience in monetary units. It has been studied in the marketing literature, including such areas as advertising (Kalra and Goodstein 1998), consumer dealing patterns (Krishna 1991), and pre-test markets (Cameron and James 1987).
To justify theoretically the nature of the relationship between CS and WTP, we turn to equity theory, which focuses on fairness in social exchange (Adams 1965; Homans 1961; Oliver and Swan 1989a, b). In the context of the current study, the exchange involves the customer receiving a specific level of satisfaction and the seller receiving an agreed-on payment (Lind and Tyler 1988). Equity theory suggests that parties to an exchange perceive equitable treatment if the ratio of their outcomes to inputs is in some sense fair (distributive justice). Both positive and negative inequity produce negative affective states that motivate people to change parameters of the exchange to reestablish equity. For example, Bolton and Lemon (1999) find that customers try to maintain payment equity over time by adjusting items under their control (in this case, usage levels) in response to changes made by the company (e.g., price changes, changes in service quality).
When customers experience elevated states of satisfaction, they perceive a high outcome of an exchange and therefore are willing to pay more (i.e., more than less satisfied customers) because this still results in an equitable ratio of outcome to input. This is one way to maintain payment equity (Bolton and Lemon 1999). Similarly, when satisfaction is low, customers perceive a low payment as adequate to establish a fair exchange. Thus, WTP should be lower in cases of low satisfaction than in cases of high satisfaction. This leads to the following hypothesis:
H1: The price that customers are willing to pay increases with the level of CS.
Most current research addresses nonlinear effects of antecedents on CS rather than on outcomes of satisfaction (which is the focus of this study) (Anderson and Mittal 2000; Mittal, Ross, and Baldasare 1998; Oliver 1995). For example, Mittal, Ross, and Baldasare (1998) examine nonlinear effects of attribute performance on CS and find support for an S-shaped function, which is steep in the middle and flat at the extremes.
There are only a few studies that find empirical evidence for nonlinear effects in the satisfaction-outcome link (with dependent variables such as customer loyalty and complaining behavior). Among the studies that examine CS and customer loyalty, there is no consensus about the functional structure for this specific relationship. For example, for the satisfaction-retention link, Mittal and Kamakura (2001) find nonlinear effects in the form of increasing returns. On the basis of anecdotal evidence, Coyne (1989) and Finkelman (1993) argue for an inverse S-shaped function, which is flat in the middle and steep at the extremes. Oliva, Oliver, and MacMillan (1992) find a similar functional structure in their study based on a catastrophe model. Ngobo (1999) predicts an opposite functional structure, which is steep in the middle and flat at the extremes, and finds partial empirical support for this function. Singh and Pandya (1991) investigate the link between dissatisfaction and various dimensions of complaining behavior and find different nonlinear patterns for the dimensions. However, all the aforementioned studies investigate other behavioral outcomes of CS and not the WTP construct, which is the focus of this study.
In hypothesizing about the functional structure, two viewpoints are the most relevant: The first focuses on disappointment theory (Loomes and Sudgen 1986) and on emotions in the CS experience (Oliver 1993; Oliver, Rust, and Varki 1997), and the second draws on prospect theory. With respect to disappointment theory, there is empirical evidence in the literature that high positive and high negative disconfirmation are much more emotionally charged than is confirmation. Although positive disconfirmation results in emotions such as delight and elation (Oliver, Rust, and Varki 1997; Rust and Oliver 2000), negative disconfirmation leads to the emotion of disappointment (Oliver and DeSarbo 1988; Oliver and Westbrook 1993; Westbrook and Oliver 1991). In contrast, mere confirmation adds almost no emotional content to a consumption or usage experience (Oliver 1997). This state has also been described as "cool satisfaction" (Woodruff, Cadotte, and Jenkins 1983).
Disappointment theory, which is rooted in the field of behavioral decision theory, incorporates the emotions of disappointment and elation into the utility formula (Bell 1985; Inman, Dyer, and Jia 1997; Loomes and Sudgen 1986). This theory suggests that disappointment occurs when the outcome of a choice is below prior expectations, whereas elation arises when the outcome of a choice exceeds prior expectations. The greater the disparity between outcome and expectations, the greater is a person's disappointment or elation. The theory assumes that both emotions generate additional value (negative or positive) to the basic value of the consumption or usage experience from the process of confirmation/disconfirmation. More specifically, elation (disappointment) should generate an increment (decrement) of value. A crucial aspect of this theory is that both emotion values should increase to a greater degree at the margins, which leads to a convex shape for elation values and a concave shape for disappointment values (Loomes and Sudgen 1986).
Building on these research areas, we hypothesize about the structural relationship between CS and WTP. In the following discussion, CS0 denotes the satisfaction level that is achieved if customer expectations are exactly met; WTP0 denotes the WTP that is present if CS equals CS0. As we argued in the justification of H1, satisfaction influences a customer's WTP positively. However, as we mentioned previously, simple confirmation does not add much emotion to the consumption or usage experience. Therefore, near CS0, the functional structure will be relatively flat. Moving away from CS0, the two research streams mentioned previously suggest that the magnitude of changes in WTP produced by changes in satisfaction level increases substantially as the result of elation or disappointment. In other words, the function relating CS to WTP is convex for satisfaction levels above CS0 and concave for satisfaction levels below CS0 (see Figure 1, Panel A). It is possible that diminishing returns to delight/disappointment occurred at some point (i.e., it is unlikely that there is an infinite WTP for extreme levels of delight). However, it seems that extreme levels of delight are unlikely to be reached for most products or services. This reasoning leads to the following hypothesis:
H2: The relationship between CS and the price that customers are willing to pay follows an inverse S-shaped function, which is first concave and then convex.
The second viewpoint draws on prospect theory (Kahneman and Tversky 1979) and proposes an opposite functional structure, which is steep in the middle and flat at the extremes. In the application of this theory, there are two important aspects. First, the judgment of satisfaction would be reference dependent. In this case, the reference point is the expected satisfaction level (CS0 in Figure 1, Panel B). Satisfaction above the reference point (CS > CS0) would be considered a gain, whereas satisfaction below this standard of comparison would be perceived as a loss (CS < CS0). Second, evaluations of satisfaction would display diminishing sensitivity. That is, marginal values of gains and losses decrease in size with increasing levels of satisfaction or dissatisfaction.(n1) This functional structure is steep in the middle and flat at the margins (see Figure 1, Panel B); Ngobo (1999) suggests this for the CS[sub 0 loyalty link. This leads to the following alternative hypothesis:
H2alt: The relationship between CS and the price that customers are willing to pay follows an S-shaped function, which is first convex and then concave.
This study also examines how the nature of the relationship between CS and outcome variables can evolve over time (Bolton 1998; Bolton and Lemon 1999). More specifically, satisfaction, which is based on repeated experiences (i.e., cumulative satisfaction), should have a stronger impact on outcome variables than should satisfaction with a single consumption experience (Anderson, Fornell, and Rust 1997; Olsen and Johnson 2003; Rust, Zahorik, and Keiningham 1995). This study extends previous work that compares cumulative satisfaction with transaction-specific satisfaction by investigating a new dependent variable (i.e., WTP) and by considering nonlinear effects.
To support the notion that the relationship between CS and WTP changes over time, we draw on research on the attitude-behavior link. A meta-analysis on this link indicates that attitude certainty moderates the relationship between the two variables: The higher the attitude certainty, the stronger is the relationship (Kraus 1995). With respect to the current study, we propose that attitude certainty (i.e., certainty with the satisfaction judgment) is stronger for cumulative satisfaction than for transaction-specific satisfaction because customers have had more opportunities to validate their judgment. Additional theoretical support is provided by the Bayesian information updating approach, which Boulding, Kalra, and Staelin (1999) and Rust et al. (1999) use to justify dynamic effects. This leads to the following hypothesis:
H3: The more the CS judgment moves from transaction specific to cumulative, the stronger is the relationship between CS and WTP.
We now present two experimental studies. Study 1 examines how different levels of CS increase the WTP (H1, H2, and H2alt) in a lab experiment, and Study 2 extends the research to a real consumption experience and captures the dynamic aspects (i.e., cumulative satisfaction) of the situation (H1 H3).
Study 1: Methodology
In the first experimental study, participants evaluated written scenarios that were set in a restaurant context. To induce different levels of CS, we established expectations about the restaurant (which we held constant) and then manipulated the actual experience with the restaurant. The expectations were set up in the introductory section. The restaurant was described as an upscale Italian restaurant that offered one three-course menu. To enhance the realism of the experiment, participants needed to choose among three options for each course; the price of the menu was independent from the actual choice of the participant. Furthermore, participants were told to imagine that they were going out for dinner with a friend.
The manipulation of the actual experience was analogous to a conjoint design (similar to the approach that Smith, Bolton, and Wagner [1999] adopt). We selected three key attributes of the restaurant: quality of food, ambience, and service (Bernhardt, Donthu, and Kennett 2000). Each attribute was varied at two levels (for the complete wording, see Table 1), resulting in eight different scenarios, which we applied as a within-subjects design. We randomized the order of the attributes across the scenarios.
The sample comprised 80 students from various majors at a major German university. The experiment consisted of ten sections. The first section included the introduction in which the expectations were set. Each of eight subsequent sections contained a different satisfaction condition that manipulated the experience with the restaurant. The order of the satisfaction scenarios was completely randomized across participants. After reading a scenario, participants responded to measures of their WTP. Note that we measured WTP after the restaurant experience, not before. Then, there was an intervening story, which we designed to distract the participants from thinking about price to thinking about the original restaurant experience. We then measured satisfaction with the restaurant experience. After all eight scenarios had been evaluated, there was a final set of general questions.
We assessed CS using a four-item measure that closely parallels previous approaches to measuring CS (e.g., Anderson and Sullivan 1993; Bearden and Teel 1983; Churchill and Surprenant 1982; Fornell et al. 1996).( n2) The satisfaction scale had excellent internal consistency, with a composite reliability (Fornell and Larcker 1981) of .98, which exceeds Bagozzi and Yi's (1988) suggested threshold value of .6. For further analysis, we calculated the satisfaction score as the average of the four satisfaction scale items. Table 2 shows the means of the satisfaction measures for all scenarios.
We measured WTP with an open-ended question. Participants were asked the price that they would be willing to pay for the restaurant visit. This type of measure has been widely used in other studies in this area (e.g., Cameron and James 1987; Krishna 1991). Table 2 provides the means of the WTP measures for the eight scenarios.
Study 1: Results
First, H1 predicts a positive relationship between CS and WTP. We tested the hypothesis with the following random coefficient regression model, which controls for participants' effects:( n3)
( 1) WTPij = b0 + b1CSij + uj + rij,
where WTPij is the WTP of the jth individual on the ith scenario, and CSij is the CS of the jth individual on the ith scenario. The individual intercepts are expressed as the sum of an overall mean (b0) and a series of random deviations from that mean (uj). The slope is modeled as a constant (b1) across all people, and rij is the random error associated with the ith scenario of the jth individual. The model has two fixed effects (an intercept [b0] and a slope [b1] effect for CS) and two random effects (one for the intercepts [registered by the uj with variance τ²] and one for the observations of individuals [registered by the rij with variance σ²]).( n4)
We estimated the model with the maximum likelihood method using the procedure MIXED in SAS 8.02.( n5) The estimation results appear on the left-hand side of Table 3. Note that b1 is positive and significantly different from zero (b1 = 2.839; p < .0001). This indicates a statistically significant and positive relationship between CS and WTP and confirms H1 that satisfied customers are willing to pay more for the product or service.
Because the same manipulation drives both satisfaction and WTP, it is important to demonstrate that satisfaction mediates the relationship between service quality and WTP. The results of a mediation analysis (Baron and Kenny 1986) provide support for the mediating role of satisfaction.( n6)
Our second analysis pertained to the functional structure of the relationship between CS and WTP. H2 proposes an inverse S-shaped function, whereas H2alt suggests an S-shaped function. We tested the hypotheses with the following cubic random coefficient regression model:
( 2) WTPij = b0 + b1CSij + b2CSij, sup 2 + b2CSij, sup 3 + uj + rij,
The model has four fixed effects (an intercept [b0] and three slope parameters [b1, b2, b3] for CS) and two random effects (one for the intercepts [registered by the uj with variance τ²] and one for the observations of individuals [registered by the rij with variance σ²]).
To control for multicollinearity associated with a cubic regression model, we used orthogonal polynomial variables as predictor variables (Kleinbaum et al. 1998, p. 293).( n7) The right-hand side of Table 3 shows the estimation results. Most important, the coefficient b3 is positive and significant (b3 = 42.868; p < .0001), which implies that the effect of CS on WTP increases at the margins. This supports H2, which states that the relationship between CS and WTP is best described with an inverse S-shaped function. However, the results contradict the prediction of H2alt, which proposes an S-shaped function.
Furthermore, the cubic model contributes significantly more to the explanation of WTP than does the linear model, which is indicated by a hierarchical likelihood ratio chi-square test.( n8) The null hypothesis--that the additional predictors of the cubic model do not exceed the contribution of the linear model can be rejected (p < .001). Therefore, the cubic model significantly improves prediction. In addition, we compared the fit of the models using Akaike's information criterion (AIC statistic) of model evaluation (Akaike 1974; Homburg 1991). The results support the cubic model because the corresponding AIC value (4491.7) is smaller than that of the linear model (4527.8). Using the Schwarz Bayesian information criterion (BIC; Burnham and Anderson 2002; Schwarz 1978) instead of the AIC leads to similar conclusions (the BIC cubic model is 4506.0, which is less than the BIC linear model value of 4537.3). This supports the results we obtained with the likelihood ratio test statistic.
Overall, the findings support H2 (see Figure 2). The function is concave for low satisfaction levels and convex for high satisfaction levels; there is an inflection point at which the function switches from concave to convex.
We now turn to Study 2, which extends the investigation in three important aspects. First, Study 2 uses the context of a real consumption/usage experience, and it uses a behavioral outcome variable. Second, it captures the dynamic aspects of the relationship between CS and WTP (H3). Third, it investigates the hypotheses in a product setting, whereas Study 1 investigates the hypotheses in a service setting.
Study 2: Methodology
We designed Study 2 around the evaluation of a newly created product--a CD-ROM tutorial--which could be used to provide academic assistance in a difficult pricing class and which the customers (students who were taking the pricing class) could actually buy. We gave participants three sample chapters (trials) of the CD-ROM tutorial over time in a computer-based format, and we asked them to solve a sample pricing problem that was related to the sample chapter. We then provided performance feedback on the pricing problem and obtained key measures.
Study 2 used an 8 (levels of satisfaction) x 3 (trial) full factorial design. Satisfaction was a between-subjects factor, and trial was a within-subjects factor. First, to manipulate CS, we established expectations about the CD-ROM tutorial (which we held constant across the experimental conditions). In the introductory section of Study 2, the purpose of the study guide was described (i.e., to help course participants understand difficult material in the class), and an overview of the content was provided. Participants were informed that the CD-ROM tutorial contained 73 chapters, which would be similar to the ones they received in the testing phase but would cover different pricing topics.
Second, we manipulated the actual consumption experience. Participants were given a sample chapter and were asked to solve a related pricing problem. To manipulate a high satisfaction evaluation, the content of the CD-ROM sample chapter made it easy to understand and to solve the pricing problem. Furthermore, participants received positive feedback on the pricing task after a team of the instructor's assistants checked their solutions. To manipulate a low satisfaction evaluation, the content of the CD-ROM chapter was difficult to read and provided almost no information related to the pricing problem. In addition, the participants received negative performance feedback.
To create different degrees of cumulative satisfaction, which makes it possible to examine the dynamic relationship between satisfaction and WTP (H3), we manipulated satisfaction across three different trials. This involved presenting participants with three different chapters from the CD-ROM tutorial and solving three different pricing problems over time. Table 4 outlines the eight different conditions.
The sample consisted of 157 marketing students who were enrolled in a graduate-level pricing class at a large German university. This is an appropriate sample given the nature of the product that was being evaluated. Participants were aware that previous students had experienced difficulties in this pricing class. To address this problem, they were told that a CD-ROM study guide had been developed to assist participants in solving difficult pricing problems in the course. Furthermore, they would have the chance to test the CD-ROM tutorial before deciding whether they wanted to buy it.
Before the first trial, participants were given the introductory section of the CD-ROM, which set up the expectations (purpose and content of the CD-ROM). Then, they received one sample chapter of the CD-ROM study guide (first trial), after which they solved a problem related to the material. We manipulated satisfaction in the manner previously described and then measured the key variables (WTP and then CS). We obtained WTP as a behavioral outcome variable by way of Becker, DeGroot, and Marschak's (1964) mechanism (hereinafter BDM; see also Wertenbroch and Skiera 2002), which we describe in the next section. In addition, participants were committed to pay their own money. Furthermore, participants were asked intervening questions to distract them from the evaluation situation, after which we measured CS. This was followed by an additional intervening task in which participants read a newspaper article about a recent pricing problem in practice and were asked to answer some open-ended questions about the content of the article. The procedure for the second and third trial was analogous to that of the first trial.
We measured CS with the four items from Study 1 and with two additional emotion items (elation and disappointment). The internal consistency of the satisfaction scale was excellent across the three trials (Cronbach's α = .94 [first trial], .96 [second trial], and .96 [third trial]). Thus, for further analyses, we calculated the satisfaction scores as the means of the satisfaction scale items.
We obtained the key dependent variable, WTP, using the BDM method as Wertenbroch and Skiera (2002) suggest. The advantages of the BDM method are that it is incentive compatible (i.e., customers have an incentive to reveal their WTP truthfully), realistic, transparent to respondents, and operationally efficient. In our study, participants were told that they would have a chance to purchase the CD-ROM tutorial without needing to invest more money than they were willing to pay. After using the CD-ROM tutorial and receiving feedback, participants were asked to indicate a price for the CD-ROM, which should equal the highest price they were willing to pay for the CD-ROM in each trial. They were told that the price for the CD-ROM tutorial was not yet set and that it would be determined randomly from a prespecified distribution after the testing phase of the CD-ROM tutorial. If the randomly determined price was less than or equal to the participant's bid, the participant was obligated to buy the CD-ROM tutorial at the randomly determined price. However, if the randomly determined price was higher than their bid, they would not have a chance to buy the product. This mechanism ensures that participants had no incentive to indicate a price that is higher or lower than their true WTP.
In addition, we collected several variables as possible covariates: age, sex, income, budget for studying material, perceived pressure to buy the CD-ROM tutorial, price consciousness, value consciousness, and self-confidence. Analyses indicate that none of the variables had any effect as covariates. Thus, we dropped them from further analysis.
Study 2: Results
We tested H1, H2, and H]2alt with random coefficient regression models that were analogous to those in Study 1. The analyses were based on the data from the third trial (in which satisfaction is the most cumulative). Thus, we did not consider a random intercepts effect (uj). The results provide strong support for H1 and H2 (see the right-hand side of Table 5). First, in a linear model, there is a positive and statistically significant relationship between CS and WTP (b1 = 1.201; p < .0001). Second, in the cubic model, the coefficient b3 was positive and significant (b3 = 11.706; p < .05), in support of H2.( n9) However, the results contradict H2alt, which proposes an S-shaped function with decreasing returns at the margins.
Furthermore, the cubic model contributes significantly more to the explanation of WTP than does the linear model, which is evidenced by a hierarchical likelihood ratio chisquare test (p < .05). In line with this is the result of the AIC, which is smaller for the cubic model (918.3) than for the linear model (921.4). In summary, the cubic model is significantly stronger in the prediction of WTP than is the linear model. The results provide strong support for the predicted inverse S-shaped function, which we propose in H2. It is only the BIC that does not provide support for the superiority of the cubic model (938.6) over the linear model (935.6). Overall, however, we believe that there is reasonable support for the inverse S-shaped function, because the rigid statistical likelihood ratio test indicates that the cubic model should be favored over the linear model.( n10)
As in Study 1, we conducted a mediation analysis, which indicated that CS completely mediated the relationship between quality and WTP.( n11) H3 predicts that as the CS judgment moves from transaction-specific to cumulative, the impact on WTP is strengthened. Satisfaction becomes more cumulative across the three trials as participants gain more experience with the CD-ROM tutorial. As we show in Table 5, the parameter for the cubic effect becomes stronger over the three trials. However, a statistical test of H3 involves an examination of the interaction between CS and trial in the pooled model. We tested H3 with two random coefficient regression models: one for the linear and one for the cubic case. The first model tests H3 on the basis of a linear relationship between CS and WTP:
( 3) WTPij = b0 + b1CSij + b2TRIALi + b3CSijTRIALi + uj + rij.
The model has four fixed (b0, b1, b2, and b3) and two random (uj and rij) effects. The significant and positive interaction between CS and TRIAL (b3 = .076; p < .05) provides empirical evidence that the slopes for CS increase substantially across trials. We then tested H3 on the basis of a cubic relationship between CS and WTP:
( 4) WTPij = b0 + b1CSij + b2TRIALi + b3CSijTRIALi + b4 CSij, sup 2 + b5CSij, sup 2TRIALi + b6CSij, sup 3 + b7CSij, sup 3TRIALi + uj + rij.
The analysis was based on orthogonal polynomials and provides additional support for H3. Most important, the interaction between the cubic term of CS and trial is positive and significant (b7 = 2.491; p < .05). This provides empirical evidence that the nonlinear effect of CS on WTP is more pronounced in later trials. Figure 3 shows the graphs of the estimated cubic regression models in the second and the third trial (results for the first trial were not significant). The results support H3.
Discussion
The first objective of our study was to examine whether there is a (positive) relationship between CS and WTP. The findings reveal strong support for such an effect. It is worthwhile to compare the results of the present study with those from Bolton and Lemon's (1999) research, which focuses on the concept of "payment equity." Their findings suggest that customers try to maintain payment equity over time by adjusting items under their control (in this case, usage levels) in response to changes made by the company (e.g., price changes, changes in service quality). Our study extends Bolton and Lemon's work by identifying another aspect that the customer can use to restore equity--namely, WTP. Both of the studies illustrate the usefulness of equity theory in understanding the relationship between pricing issues and satisfaction.
The second objective of our study was to investigate the functional structure of the relationship between CS and WTP. The findings provide support for the function that research on emotions in the CS experience and disappointment theory predicts (Bell 1985; Loomes and Sudgen 1986; Oliver, Rust, and Varki 1997; Rust and Oliver 2000), which suggests that the functional structure should have an inverse S-shaped form that is concave for low satisfaction levels, convex for high satisfaction levels, and relatively flat for medium satisfaction levels. The first experimental study found empirical evidence for the inverse S-shaped function on the basis of a within-subjects design, and the second study replicated the results using a between-subjects design.
From an academic perspective, we found it interesting that the strongest impact of CS on WTP is at the extremes of the satisfaction distribution. This finding is important because most of the previous research implicitly or explicitly assumes a linear relationship between CS and behavioral outcomes. More specifically, the results offer an advanced analytical understanding of the relationships in the satisfaction-profit chain and provide additional insights into the positive impact of CS on profitability.
Such insight is important for further research, which might examine optimal levels of CS (Kamakura et al. 2002) and develop analytical models related to this issue (i.e., develop a "CS calculus"). Such modeling approaches would need to integrate the effects of CS on loyalty and WTP as well as the cost implications of increasing CS. The focal point of such models would then be to identify optimal satisfaction levels in terms of the cost-benefit relationship. A precise understanding of the functional form of the relationship between CS and its outcomes is crucial for developing such models. In addition, it would be worthwhile to examine the possibility that at an upper-level threshold or point, WTP could level off. However, in most situations, it seems unlikely that products and services reach the very extreme levels of delight or disappointment that are needed to produce this effect.
The third objective of our study was to investigate the impact of transaction-specific and cumulative satisfaction on WTP. The results indicate that the relationship between CS and WTP becomes stronger as CS judgment moves from transaction-specific to cumulative. The findings show the importance of increasing cumulative CS (Olsen and Johnson 2003).
A potential limitation of Study 1 is that of common method bias (i.e., when all measures are acquired with the same instrument). However, in Study 2, we reduced this problem substantially by using the BDM method to assess WTP (Wertenbroch and Skiera 2002). The advantage of the BDM method is that it measures WTP as a behavioral outcome variable and that customers have an incentive to reveal their WTP truthfully. This should substantially decrease the potential of common method bias.
Our study represents only a first step in the study of relationships between CS and price-related constructs. This initial study suggests several possibilities for further research. For example, research could examine whether there are potential moderators that strengthen or weaken the relationship between CS and WTP. It could be hypothesized that the relationship is weaker in highly competitive markets than in low competitive markets.
In addition, further research should explore the relationship between CS and other price-related constructs. For example, it would be worthwhile to study the impact of CS on customers' reactions to price changes. It could be hypothesized that negative reactions to price increases are weaker for highly satisfied customers than for moderately satisfied customers. It would also be worthwhile to examine customers' perceptions of price changes. For example, customers may infer different types of motives (both positive and negative) when they encounter a price change ( Campbell 1999). Research could investigate whether the level of CS influences the degree to which customers infer positive or negative motives.
Further research could also analyze the nature of the flat part of the functional relationship between satisfaction and WTP in more detail. It is plausible to argue that this area is centered on a point of zero disconfirmation. Further research could test this assumption by conducting a study that specifically has a no disconfirmation condition.
Our research supports the managerial belief that "satisfied customers--those receiving higher quality service or who feel better about the product--are, in fact, willing to pay more for it" (Finkelman 1993, p. 25) and that this relationship is nonlinear. The findings have important implications for setting prices and for investing in CS.
Our findings suggest that the customer's satisfaction level could influence a company's pricing strategy. Specifically, companies could potentially charge a premium price for their product or service if they have a high level of customer satisfaction. Note that this does not mean that a firm should selectively charge more-satisfied customers a higher price but rather that having a large segment of highly satisfied customers may enable the firm to charge higher prices in general.
Moreover, there are situations in which companies could charge higher prices to highly satisfied customers. Although this is not typically applicable in consumer goods marketing, it constitutes an option in markets in which prices are not standardized but rather are negotiated with individual customers. For example, this is the case in the marketing of customized products or professional services. In such environments, our findings inform managers that high levels of CS give them a stronger position to negotiate prices with their clients.
Moreover, the specific functional structure found in our study is also relevant for managers. More specifically, the finding that marginal payoffs from increasing CS increase if satisfaction is above the inflection point implies that (unlike in situations in which there are decreasing marginal returns) it may be suitable to aim for very high levels of CS.
However, it is worth emphasizing that generation of high levels of CS often involves significant costs. Therefore, managers need to consider whether it is financially viable to strive for very high levels of CS for certain customers or customer segments. A possible consequence of such considerations is that firms differentiate with respect to the aspired level of CS. More specifically, companies might strive for very high levels of CS (i.e., satisfaction levels in the steep part of the curve) among their highly valued customers but accept a lower level of satisfaction (possibly in the left part of the flat area of the curve) for their less valued customers.
Finally, our results suggest that approaches to the measurement and enhancement of CS should focus on cumulative satisfaction rather than on transaction-specific satisfaction. In business practice, many companies measure CS on the basis of specific transactions (i.e., the most recent purchase or service encounter). Our findings suggest that longterm, cumulative satisfaction is more relevant because it is the stronger driver of customer behavior (which, in this case, was WTP).
The authors thank the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 504, for financial support.
( n1) Prospect theory also emphasizes loss aversion, which results in an asymmetric functional structure. More specifically, the theory assumes a function that is steeper for losses than for gains. Such asymmetric effects are beyond the scope of this study, and therefore we do not consider them.
( n2) We measured satisfaction with the following items: "All in all, I would be satisfied with this restaurant"; "The restaurant would meet my expectations"; "The earlier scenario compares to an ideal restaurant experience"; and "Overall, how satisfied would you be with the restaurant visit just described?" We measured the items on an 11-point Likert-type scale. For the first three items, the scale ranged from "strongly agree" to "strongly disagree," and for the last item, the scale ranged from "very satisfied" to "very dissatisfied."
( n3) For this type of regression, see Cohen and colleagues (2003) and Snijders and Bosker (1999).
( n4) Both error terms are assumed to be uncorrelated, normally distributed, and constant and to have a mean zero.
( n5) We used the maximum likelihood method because the focus of the analysis is on deviance tests and not on the random part parameters for which the restricted maximum likelihood method is preferable (Snijders and Bosker 1999).
( n6) To test the mediation, we estimated three regression equations. First, we regressed the mediator, CS, on quality; this showed a significant effect (b]qual = .821; t = 36.366; p < .0001). Second, we regressed the dependent variable, WTP, on quality; this also showed a significant effect (bqual = .489; t = 14.155; p < .0001). Third, we regressed WTP on both quality and CS. The third equation demonstrated that when CS was included with quality in the regression analysis, CS was highly significant (bsat = .346; t = 5.860; p < .0001). The effect of quality remained significant (bqual = .205; t = 3.470; p < .001), but the effect was much smaller than it was in the second equation. Thus, CS partially mediated the effect of quality on WTP.
( n7) Orthogonal polynomial variables are linear combinations of the simple polynomials and are pairwise uncorrelated, which completely eliminates any collinearity. We calculated the orthogonal polynomial variables with the ORPOL function, using the interactive matrix language in SAS 8.02.
( n8) The hierarchical likelihood ratio chi-square test is performed analogously to the multiple-partial F test in ordinary least squares regression (Kleinbaum et al. 1998, p. 650). This test compares two nested models. The null hypothesis states that the contribution of the additional predictors of the more complex model (i.e., the cubic model) does not exceed the contribution of the predictors of the simpler model (i.e., the linear model). Here, the hierarchical likelihood ratio test with 2 degrees of freedom is 4519.8 - 4479.7 = 40.1.
( n9) The estimation was based on orthogonal polynomial variables, which eliminate the problem of multicollinearity in the cubic regression model.
( n10) Many researchers have argued that in the case of nested models, model comparison should be based on the likelihood ratio test and that information criteria should be used in the case of nonnested models (Cohen et al. 2003; Kleinbaum et al. 1998). Because the two models we consider are nested, the result of the likelihood ratio test should be given the strongest emphasis.
( n11) The first regression analysis showed a significant effect of quality on WTP (bqual = .485; t = 6.855; p < .0001). The second regression indicated a significant effect of quality on CS (bqual = .843; t = 19.511; p < .0001). The third equation, in which WTP was regressed on CS in addition to quality, showed that CS was significant (bsat = .279; t = 2.145; p < .05), whereas the initially highly significant predictive ability of quality was eliminated (bqual = .249; t = 1.911; p = .058). Thus, CS mediated the link between quality and WTP.
Legend for Chart:
A - Attributes
B - Dimensions
C - Favorable
D - Unfavorable
A B
C
D
Quality of food Taste, freshness,
preparation
The food is excellent. All
ingredients are fresh. The
combination of the food is
creative, and the preparation is
exquisite.
Several ingredients are not that
fresh. The combination of the
food/dishes is interesting, but
some of them are too spicy. The
food's quality is medium.
Ambience Interior design,
loudness, temperature
The interior design is neat and
elegant. The noise level is low,
and you are able to talk in
peace. The temperature is
pleasant.
The interior design is simple.
The noise level is high, and it is
sometimes quite turbulent. It is
too cool in the restaurant, and
that is why you are freezing.
Service Timing, friendliness,
competence
The servers give you competent
advice about the available food
and beverages. The period of
time between the courses is just
right. The service is very friendly
and courteous throughout the
evening.
The period of time between the
courses is too long. The service
is somewhat rude throughout
the evening. Moreover, the
servers give you insufficient
advice about the available food
and beverages. Legend for Chart:
A - Scenario
B - Attributes Quality of Food
C - Attributes Ambience
D - Attributes Service
E - CS
F - WTP(a)
A B C D E F
1 - - - 1.35 (.67) 22.67 (12.87)
2 - - + 3.36 (1.55) 31.40 (12.96)
3 - + - 3.37 (1.88) 32.91 (14.28)
4 + - - 4.75 (2.05) 35.20 (14.75)
5 - + + 5.89 (2.28) 37.64 (14.09)
6 + - + 7.05 (2.20) 41.90 (16.83)
7 + + - 7.72 (1.95) 43.90 (16.18)
8 + + + 10.77 (.53) 54.71 (19.92)
(a) WTP is measured in German marks.
Notes: + = favorable attribute; - = unfavorable attribute.
Study 1: Results of Random Coefficient Regression Models
Legend for Chart:
B - Linear Model
C - Cubic Model(a)
A B C
-2 Log Likelihood 4519.8 4479.7
Legend for Chart:
A - Parameter
B - Effect
C - Solutions for Fixed Effects Linear Model Estimate
D - Solutions for Fixed Effects Linear Model t-value
E - Solutions for Fixed Effects Linear Model p
F - Solutions for Fixed Effects Cubic Model(a) Estimate
G - Solutions for Fixed Effects Cubic Model(a) t-value
H - Solutions for Fixed Effects Cubic Model(a) p
A B C D E
F G H
b0 Intercept 37.541 24.190 .000
37.541 24.250 .000
b1 CS 2.839 34.530 .000
234.590 35.470 .000
b2 CS²
-1.827 -.270 .785
b3 CS³
42.868 6.440 .000
Legend for Chart:
A - Parameter
B - Solutions for Random Effects Linear Model Estimate
C - Solutions for Random Effects Linear Model Z-value
D - Solutions for Random Effects Linear Model p
E - Solutions for Random Effects Cubic Model(a) Estimate
F - Solutions for Random Effects Cubic Model(a) Z-value
G - Solutions for Random Effects Cubic Model(a) p
A B C D
E F G
τ² (variance of uj) 187.220 6.140 .000
186.580 6.160 .000
σ²(variance of rij) 43.780 16.730 .000
40.787 16.730 .000
(a) The results are based on orthogonal polynomials. Legend for Chart:
A - Experimental Condition
B - 1
C - 2
D - 3
A B C D
1 High High High
2 High High Low
3 High Low High
4 High Low Low
5 Low High High
6 Low High Low
7 Low Low High
8 Low Low Low
A: Linear Model
Legend for Chart:
B - WTP Trial 1
C - WTP Trial 2
D - WTP Trial 3
A B C D
-2 Log Likelihood 940.9 916.2 915.4
Legend for Chart:
A - Parameter
B - Effect
C - Solutions for Fixed Effects WTP Trial 1 Estimate
D - Solutions for Fixed Effects WTP Trial 1 t-value
E - Solutions for Fixed Effects WTP Trial 1 p
F - Solutions for Fixed Effects WTP Trial 2 Estimate
G - Solutions for Fixed Effects WTP Trial 2 t-value
H - Solutions for Fixed Effects WTP Trial 2 p
I - Solutions for Fixed Effects WTP Trial 3 Estimate
J - Solutions for Fixed Effects WTP Trial 3 t-value
K - Solutions for Fixed Effects WTP Trial 3 p
A B C D E F G
H I J K
b0 Intercept 2.902 2.320 .022 2.326 2.120
.036 1.426 1.360 .177
b1 CS .999 5.190 .000 1.030 6.020
.000 1.201 6.990 .000
Legend for Chart:
A - Parameter
B - Solutions for Random Effects WTP Trial 1 Estimate
C - Solutions for Random Effects WTP Trial 1 Z-value
D - Solutions for Random Effects WTP Trial 1 p
E - Solutions for Random Effects WTP Trial 2 Estimate
F - Solutions for Random Effects WTP Trial 2 Z-value
G - Solutions for Random Effects WTP Trial 2 p
H - Solutions for Random Effects WTP Trial 3 Estimate
I - Solutions for Random Effects WTP Trial 3 Z-value
J - Solutions for Random Effects WTP Trial 3 p
A B C D E F
G H I J
σ² (variance
of rij) 25.323 8.800 .000 22.454 8.770
.000 21.490 8.800 .000
B: Cubic Model(a)
Legend for Chart:
B - WTP Trial 1
C - WTP Trial 2
D - WTP Trial 3
A B C D
-2 Log Likelihood 939.1 910.5 908.5
Legend for Chart:
A - Parameter
B - Effect
C - Solutions for Fixed Effects WTP Trial 1 Estimate
D - Solutions for Fixed Effects WTP Trial 1 t-value
E - Solutions for Fixed Effects WTP Trial 1 p
F - Solutions for Fixed Effects WTP Trial 2 Estimate
G - Solutions for Fixed Effects WTP Trial 2 t-value
H - Solutions for Fixed Effects WTP Trial 2 p
I - Solutions for Fixed Effects WTP Trial 3 Estimate
J - Solutions for Fixed Effects WTP Trial 3 t-value
K - Solutions for Fixed Effects WTP Trial 3 p
A B C D E F G
H I J K
b0 Intercept 9.044 22.500 .000 8.515 22.720
.000 8.296 22.790 .000
b1 CS 26.144 5.220 .000 28.538 6.120
.000 32.412 7.140 .000
b2 CS² -3.045 -.610 .545 6.375 1.360
.175 3.533 .780 .438
b3 CS³ 5.873 1.170 .244 9.335 2.000
.047 11.706 2.580 .011
Legend for Chart:
A - Parameter
B - Solutions for Random Effects WTP Trial 1 Estimate
C - Solutions for Random Effects WTP Trial 1 Z-value
D - Solutions for Random Effects WTP Trial 1 p
E - Solutions for Random Effects WTP Trial 2 Estimate
F - Solutions for Random Effects WTP Trial 2 Z-value
G - Solutions for Random Effects WTP Trial 2 p
H - Solutions for Random Effects WTP Trial 3 Estimate
I - Solutions for Random Effects WTP Trial 3 Z-value
J - Solutions for Random Effects WTP Trial 3 p
A B C D E F
G H I J
σ² (variance
of rij) 25.042 8.800 .000 21.634 8.770
.000 20.532 8.800 .000
(a) The results are based on orthogonal polynomials.DIAGRAM: FIGURE 1; Alternative Functional Structures for the Relationship Between CS and WTP
GRAPH: FIGURE 2; Study 1: Empirical Relationship Between CS and WTP
GRAPH: FIGURE 3; Study 2: Empirical Relationship Between CS and WTP
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By Christian Homburg; Nicole Koschate and Wayne D. Hoyer
Christian Homburg is Professor of Business Administration and Marketing and Chairman of the Department of Marketing, University of Mannheim, Germany
Nicole Koschate is Assistant Professor of Marketing, University of Mannheim, Germany.
Wayne D. Hoyer is James L. Bayless/William S. Farish Fund Chair for Free Enterprise and Chairman of the Department of Marketing, McCombs School of Business, University of Texas at Austin
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Record: 55- Do Suppliers Benefit from Collaborative Relationships with Large Retailers? An Empirical Investigation of Efficient Consumer Response Adoption. By: Corsten, Daniel; Kumar, Nirmalya. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p80-94. 15p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.69.3.80.66360.
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Do Suppliers Benefit from Collaborative Relationships
with Large Retailers? An Empirical Investigation of Efficient
Consumer Response Adoption
Collaborative manufacturer-retailer relationships based on efficient consumer response (ECR) have become ubiquitous over the past decade. Yet academic studies of ECR adoption and its impact on marketing relationships are relatively scarce. Inspired by the relational view of competitive advantage, the authors empirically investigate whether the extent to which suppliers of a major retailer adopt ECR has a beneficial impact on their outcomes. The results demonstrate that whereas ECR adoption has a positive impact on supplier economic performance and capability development, it also generates greater perceptions of negative inequity on the part of the supplier. However, retailer capabilities and supplier trust moderate some of these main effects. The overall results are robust with respect to differences in supplier size as well as between branded and private-label suppliers.
Over the past two decades, marketing theory and practice has embraced the idea of relationship marketing (e.g., Dwyer, Schurr, and Oh 1987; Morgan and Hunt 1994). In contrast to the traditional transaction-based focus of market governance, the literature now exhorts firms to develop collaborative partnerships and relational governance (e.g., Anderson and Weitz 1992; Ganesan 1994). Compared with the typical adversarial transactions that involve bidding procedures in which multiple suppliers compete against one another in an effort to drive down prices, collaborative relationships adopt a long-term perspective and include an ongoing process to lower acquisition and operating costs (Cannon and Homburg 2001; Kalwani and Narayandras 1995). Although collaborative relationships through joint efforts of the partners create unique value that neither partner can create independently, there is tension between maximizing such value and distributing it between the partners (Zajac and Olsen 1993). This makes collaborative relationships challenging to implement in practice, particularly with powerful parties.
The challenge of developing collaborative marketing relationships is perhaps most apparent in the fast-moving consumer goods industry. Although there are differences of opinion in the academic literature as to whether power has shifted from manufacturers to retailers (Ailawadi 2001), there is little doubt about the consolidation in the retail sector. For example, in the United States, the ten largest retailers now account for 80% of the average manufacturer's business compared with approximately 30% a decade ago (Boyle 2003). Besides the resultant price pressure from large retailers, suppliers are finding it increasingly difficult to develop their marketing strategy in isolation of the particular retailer's strategy. This has encouraged suppliers to develop closer relationships with major retailers in an attempt to change the latter's focus from purely price to reducing the total cost in the marketing channel and increasing value; this is a fundamental change in marketing strategy (Kumar 1996). The major industry initiative to help achieve this is called "efficient consumer response" (ECR).
ECR
The U.S. grocery retailers and branded manufacturers in the fast-moving consumer goods industry launched the ECR initiative in 1992. A study by Kurt Salmon Associates (1993), a retail management consultancy firm, estimated that streamlining the supply chain through the adoption of ECR would lead to a total savings of 10.8% of retail price, or $30 billion. It was anticipated that manufacturers would receive 54% of these savings, and distributors and retailers would receive the remaining 46% (ECR Europe 1997).
Over time, ECR has become a comprehensive initiative comprising a dozen different ECR practices that are organized within three major areas of manufacturer-retailer collaboration: ( 1) demand side management, or collaborative practices to stimulate consumer demand by promoting joint marketing and sales activities; ( 2) supply side management, or collaborative practices to optimize supply, with a focus on joint logistics and supply chain activities; and ( 3) enablers and integrators, or collaborative information technology and process improvement tools to support joint relational activities. Although collaboration in each of these areas could be pursued independently, in practice a comprehensive approach is promoted. Thus, we define ECR as a cooperative value-creation strategy whereby retailers and suppliers jointly implement collaborative business practices with the ultimate objective of fulfilling consumer wishes together, better, faster, and at less cost.
Despite the initial enthusiasm, a decade later, signs of skepticism seem to be gathering steam. In particular, suppliers believe that retailers have been the prime beneficiaries of ECR. There is a widespread belief among suppliers that ECR is just a convenient label for large and powerful retailers to continue doing what they have always been perceived as doing, namely, finding ways to pass costs back to the suppliers. To investigate this issue rigorously, we empirically assess whether and under what conditions suppliers benefit from collaborative ECR relationships with major retailers.
A review of the literature indicates surprisingly few empirical investigations of ECR adoption on performance. Related empirical studies of manufacturer-retailer relationships tend to fall into two categories. First, several investigations assess the impact of tighter manufacturer-retailer relationships on performance, as reflected in relational constructs such as interfirm coordination, trust, or mutual dependence (e.g., Heide and John 1992; Lusch and Brown 1996). Although greater trust, mutual interdependence, or interfirm coordination may be associated with ECR, these constructs are conceptually distinct from ECR, which promotes the joint implementation of collaborative processes and routines. Furthermore, these studies have typically examined supplier relationships with relatively small retailers (e.g., automobile, tire dealers) rather than with the large retailers that populate and often dominate suppliers in the packaged goods industry.
Second, three studies (Dhar, Hoch, and Kumar 2001; Gruen and Shah 2000; Stank, Crum, and Arango 1999) have examined the effects of the adoption of specific aspects of ECR, such as category management, on performance within the grocery industry. However, these studies do not assess the specific benefits for suppliers from ECR participation. In the face of large retailers that have the potential ability to dominate suppliers and thus appropriate any gains, whether ECR provides any benefits to suppliers remains an open question.
There are three objectives to this study. First, we propose a comprehensive scale to measure collaborative ECR relationships between suppliers and retailers. Second, we examine the effects of ECR adoption on supplier outcomes and the conditions under which such relationships with large retailers are likely to be beneficial to suppliers. Third, we attempt to determine whether the effects of collaborative ECR relationships are similar for large versus small manufacturers and branded versus private-label suppliers. Currently, we do not know what collaborative ECR relationships are, who they benefit, and under which conditions.
Theory and Research Hypotheses
Recently, Dyer and Singh (1998) proposed a "relational view of competitive advantage" based on the observation that a firm's critical resources may span firm boundaries. Dyer and Singh argue that such a relational view differs fundamentally from the two prominent perspectives that currently explain the sources of competitive advantage--industry structure (Porter 1980) and the resource-based view of the firm (Barney 1991)--and they propose four key sources of relational rents. According to Dyer and Singh, relational rents flow when alliance partners ( 1) invest in relation-specific assets, ( 2) develop interfirm knowledge-sharing routines, ( 3) use effective governance mechanisms, and ( 4) exploit complementary capabilities. Our conceptual model of the antecedents and consequences of suppliers' ECR adoption (see Figure 1) is inspired by this framework. In developing the model, we selected constructs that were both relevant to the practice of ECR and of theoretical interest to the relationship marketing literature, while ensuring coverage of all four sources of relational advantage.
Relation-specific assets help lower total value chain costs, enhance product differentiation, reduce operational problems, and accelerate product development cycles. Transaction-specific investments (e.g., Anderson and Weitz 1992; Heide and John 1988), cross-functional teams, and tailored incentive systems (e.g., Procter & Gamble's customer development team for Wal-Mart based in Bentonville) are most frequently mentioned as critical antecedents of supplier ECR adoption, and therefore we included these in our framework.
Interfirm knowledge-sharing routines facilitate information sharing and help alliance partners increase their partner-specific absorptive capacity (Cohen and Levinthal 1990; Lane and Lubatkin 1998). In its essence, ECR adoption is about sharing information and designing interfirm routines that facilitate interorganizational learning to enhance customer value.
Effective governance influences the willingness of alliance partners to engage in value-creation initiatives (Zajac and Olsen 1993). Self-enforcing safeguards (e.g., trust, economic hostages) are preferable to third-party safeguards (e.g., legal contracts) because they lower transaction costs and create incentives for value-creation initiatives (Telser 1980).
Complementary capabilities are the justification for interorganizational marketing relationships because they help partners create value that they cannot generate independently (Zajac and Olsen 1993). Thus, we included retailer capabilities in our framework as a moderator because ECR relationships with smarter retailers should result in more beneficial outcomes for suppliers.
In summary, given our focus on investigating supplier outcomes from ECR adoption, our model comprises four sets of factors: ( 1) the antecedent supplier factors that foster or discourage ECR adoption, ( 2) the focal ECR adoption construct, ( 3) the outcomes for the supplier from ECR adoption, and ( 4) the moderating factors of trust and retailer capabilities that either strengthen or weaken the relationships between ECR adoption and performance. However, we note that to some extent, the relationships in Figure 1 between the antecedent constructs (i.e., transaction-specific assets, cross-functional teams, and incentive systems) and ECR are reciprocal. The logic for ordering the antecedents of ECR was based on three factors. First, we conceptualize ECR as a process that gains over time, whereas incentive systems, investments, and cross-functional teams have more of an "on/ off" character to them. Second, our framework follows the structure-conduct-performance approach (Bain 1956) in which, as a process for managing the relationship, ECR is viewed as "conduct," whereas the antecedent variables are viewed more as "structure." Third, interviews with managers indicated that they believed that unless the three antecedents were in place, any ECR initiative was bound to fail.
Transaction-specific investments are investments in a relationship that are of lower value when used in an alternative relationship (Heide and John 1988). Close relationships often emerge as a response to safeguard transaction-specific investments (Williamson 1985). Historically, and before the advent of ECR, suppliers of large and powerful retailers in particular have been forced to commit to physical, process, and human assets for dedicated production capacity, logistics capabilities, and market research to adapt to a retailer's assortment and replenishment concepts (Bloom and Perry 2001; Johnson 1999). Suppliers that have made investments in relation-specific structures with a retailer increase their collaborative conduct in relation to that of their partner to safeguard their previously unprotected dedicated assets (Bain 1956).
Effective ECR adoption requires that suppliers implement supportive organizational systems, such as cross-functional teams and ECR friendly incentive systems, before ECR adoption. Because ECR requires tight coordination between supply side and demand side practices between partners, manufacturers such as Unilever assign multilevel, multifunctional, customer business development teams to their major retail accounts. Such teams replace the traditional supplier-retailer interfaces, which were characterized by lower-level sales representatives who called on buyers and emphasized prices, quantities, and deals. In addition, companies such as Procter & Gamble have adapted their incentive systems to support ECR adoption. An interview partner mentioned that "if internal performance measurement and reward systems do not capture true costs and profits, then the ECR effort will not result in significant and lasting progress." Thus:
H1: The greater the transaction-specific investments by the supplier, the higher is the level of ECR adoption.
H2: The greater the implementation of (a) cross-functional teams and (b) a supportive incentive system in the supplier's organization, the higher is the level of ECR adoption.
Given our objective to assess whether suppliers benefit from adopting ECR, we pursue a comprehensive assessment of supplier performance from three different perspectives: economic, relational, and strategic.
Economic performance. Growth, profits, and sales are the most frequently used measures of economic performance (Kumar, Stern, and Achrol 1992; Venkatraman and Ramanujam 1986). To be close to the concept of relational rents, we define supplier economic performance as the sales, profits, and growth that a supplier generates in the product category with the focal retailer compared with its performance at other retailers and other categories.
Suppliers that adopt ECR incur lower transaction costs because, contrary to traditional adversarial relationships, trading partners that adopt ECR do not need to specify every detail of the agreement in a contract. Monitoring costs are also lowered as self-enforcement replaces the more expensive external or third-party monitoring. Suppliers proactively engage in value-creation initiatives, such as sharing valuable knowledge (e.g., consumer and shopper knowledge) or combining complementary resources (e.g., to develop categories or new solutions for consumers), if they are credibly assured that this knowledge will not be readily shared with competitors (Dyer and Singh 1998). Parties in ECR relationships are more likely to engage in such value-creating activities because the joint processes serve as economic hostages, and there are credible assurances that they will be rewarded for their efforts.
Perceived equity. Relational performance can be examined through supplier perceptions of equity in the relationship with the retailer. Equity is related to the division of benefits and burdens. A supplier experiences equity when it perceives that the outcomes it and the retailer receive are proportional to their respective inputs to the relationship (Scheer, Kumar, and Steenkamp 2003).
Jap (2001, p. 88) notes that "how the sharing process affects the relationship also carries long-term ramifications. In many industries, organizations need to work with each other on a repeated basis. If organizations act opportunistically in the short run, they may develop a negative reputation that will inhibit other organizations from working with them in the future." Because ECR relationships are long-term relationships with significant specific investments, partners are more likely to monitor and address any temporary inequities over time to prevent dissolution resulting from inequity.
Capability development. Finally, we conceptualized supplier strategic performance using the dynamic capability lens that emphasizes the importance of capability development through organizational learning (Teece, Pisano, and Shuen 1997). Empirical attempts to measure capability improvements are scarce. Herein, we conceptualized a supplier's capability development as improvements in ECR-related processes resulting from collaboration with the focal retailer.
Interorganizational learning is critical to competitive success because organizations often learn by collaborating with other organizations (e.g., Inkpen 1996). It is through collaborative experiences that both explicit and tacit knowledge are shared and new knowledge is created (Inkpen 1996). Efficient consumer response helps create interfirm knowledge-sharing routines that permit the transfer, recombination, or creation of specialized knowledge. Furthermore, the ability to exploit outside sources of knowledge is largely a function of prior related knowledge or the "absorptive capacity" of the recipient (Cohen and Levinthal 1990). Efficient consumer response is likely to help the supplier develop partner-specific absorptive capacity to assimilate valuable knowledge from a particular retailer (Lane and Lubatkin 1998).
H3: The higher the level of ECR adoption, the greater is the supplier's (a) economic performance, (b) perceived equity, and (c) capability development.
In adversarial relationships, suppliers devote significant resources to detect, estimate, and counteract retailer opportunism (e.g., diverting, forward buying), thus increasing transaction costs and lowering economic performance. Trust results in greater openness between suppliers and retailers and thus greater knowledge and appreciation for each other's contribution to the relationship. Consistent with this reasoning, several studies find positive associations between trust and economic performance (e.g., Geyskens, Steenkamp, and Kumar 1998; Zaheer, McEvily, and Perrone 1998) as well as between trust and distributive justice (e.g., Kumar, Scheer, and Steenkamp 1995).
Although it is widely acknowledged that openness and transparency have a positive effect on learning (Doz 1996; Hamel 1991; Nonaka and Takeuchi 1995), few studies discuss the effects of trust on capability development. From a transaction-cost perspective, self-enforcing safeguards such as trust contribute to a freer and greater exchange of information between committed exchange partners because decision makers do not believe that it is necessary to protect themselves from the other's opportunistic behavior.
More important than the quantity of information exchanged is the ability to absorb tacit and "sticky" know-how. Unlike information, knowledge is about beliefs, commitment, action, and meaning. Thus, it is often defined as "justified true belief" (Nonaka and Takeuchi 1995). Information and know-how are also context specific and relational (i.e., they depend on the situation and are created dynamically in social interaction among people). Information in adversarial relationships may be suspected of being false, misleading, or manipulative and therefore may not be internalized. Trust increases the perceived truthfulness of knowledge, enhances the absorption of tacit and sticky know-how from an exchange partner, and thus improves the capability development of the supplier.
H4: The higher the level of trust, the greater is the supplier's (a) economic performance, (b) perceived equity, and (c) capability development.
Although suppliers may be forced to adopt collaborative ECR practices by dominant retailers, in the absence of trust, it is unlikely that suppliers will proactively initiate many of the value-creating initiatives that would benefit both parties. There is always the fear in nonexclusive relationships that the other party may share the acquired knowledge with others. In the presence of trust, ECR adoption leads to an even freer and greater exchange of information and know-how between retailers and suppliers because of the reduced fear of opportunistic behavior. Specifically, ECR provides the ability to work with the partner using more effective and efficient routines, but trust motivates parties to exploit its potential benefits fully. Furthermore, in addition to obtaining greater benefits, companies that have ECR relationships with high levels of trust are likely to invest more in the relationship. The ensuing positive spiral of investments and benefits for each party should make it more difficult to keep track of strict proportionality, thus leading to greater feelings of equity.
H5: The higher the level of trust, the greater are the effects of ECR adoption on the supplier's (a) economic performance, (b) perceived equity, and (c) capability development.
Although congruent competencies help a company understand the limitations, processes, and nature of the other party's competencies, they impede the ability to create returns beyond that which is individually obtainable to each firm. In contrast, complementary retailer resources supply critical capabilities and generate greater performance benefits for the supplier. For example, in an ECR relationship, suppliers may contribute to category management with a distinct know-how of managing products and understanding consumers, whereas retailers may supply their knowledge about categories and the shoppers. For efficient replenishment processes, manufacturers possess unique know-how about competitive downstream promotions, and retailers possess unique knowledge about proprietary downstream sales patterns. The synergistic potential of such complementary assets may vary and create differential potentials for interpartner learning and thus for increasing relational rents (Dyer and Singh 1998). In addition, the greater the retailer capability, the more valued are the inputs of the retailer, thus leading to more favorable perceptions of equity.
H6: The higher the level of retailer capabilities, the greater is the supplier's (a) economic performance, (b) perceived equity, and (c) capability development.
Although in principle the retailer's capabilities are equally available for all of its suppliers, not all suppliers are equally capable of exploiting them (Lane and Lubatkin 1998). Recent research indicates that the ability of alliance partners to realize the benefits from a partner's strategic resources is conditioned on compatibility in decision processes, information and control systems, and culture (Doz 1996). By default, ECR adoption leads to greater compatibility of organizational systems because the creation of joint processes and the sharing of data and know-how increases interoperability of processes and systems, which in turn reduces transaction cost and increases economic and operational performance.
Firms vary in their ability to identify potential partners and to value their resources as a result of differences in both prior collaborative experiences and internal search and evaluation capabilities (Dyer and Singh 1998). We argue that suppliers that engage in close and transparent ECR relationships have a superior judgment of the retailer's capabilities, which favorably influences their perceptions of equity.
The effect of ECR adoption on capability development is enhanced in the presence of superior retailer capabilities because such capabilities create additional synergistic resources that can be leveraged more effectively by the supplier in intense collaborations. In ECR relationships, partner-specific absorptive capacities enable better sharing, absorption, and transformation of sticky and tacit knowledge. Superior retailer capabilities reflect a larger reservoir of knowledge that is available for absorption by the supplier.
H7: The higher the level of retailer capabilities, the greater is the effect of ECR adoption on the supplier's (a) economic performance, (b) perceived equity, and (c) capability development.
Research Setting and Data Collection Procedure
A supermarket chain that is among the world's top 40 retailers was the empirical setting. The retailer had asked all suppliers to adopt ECR, but the response was mixed. Thus, it volunteered to support our study. Selecting the suppliers of a single retailer allowed the degree of control necessary to enable us to tie any performance benefits for suppliers to the effects of the constructs of interest (e.g., ECR adoption) rather than to extraneous factors (e.g., differences between retailer strategies or competitive environment) (cf. Hibbard, Kumar, and Stern 2001). We collected data from two sources: ( 1) survey data from suppliers of the retailer on the extent of ECR adoption, perceived outcomes, antecedents, and moderating constructs and ( 2) archival data from the retailer's records on supplier economic performance.
For the survey data collection, the retailer provided email addresses of active suppliers and the name of the main supplier contact (typically the key account manager) for the relationship. In total, 996 questionnaires, which promised confidentiality of responses, were sent by e-mail. We asked suppliers to select one of the three largest categories that they supplied to the focal retailer and to answer all questions with respect to this category. A total of 216 e-mails failed because of invalid e-mail addresses, four suppliers were listed twice, and four suppliers had merged with other suppliers on the list; this resulted in 772 questionnaires that effectively reached their destination. Nonrespondents were sent reminders by e-mail and were later telephoned. We received 266 completed questionnaires, for an effective response rate of 34.5%.
We evaluated nonresponse bias using Armstrong and Overton's (1977) procedure. Using two-tailed t-tests, we compared early with late respondents on four important demographic variables--supplier size, category share of supplier's sales, retailer's share of supplier category sales, and supplier's share of category in the overall grocery industry--and seven outcome measures--perceived economic performance, archival sales value, archival sales volume, archival supplier service, archival invoice accuracy, perceived equity, and capability development. Because we observed no significant differences (p < .05), nonresponse bias did not appear to be a problem, though a more stringent test would have been to compare respondents with nonrespondents (Mentzer, Flint, and Hult 2001).
Measure Validation for ECR Adoption
Adapting Anderson and Gerbing's (1988) two-step approach, we developed separate measurement models before conducting tests of the hypothesized relationships between constructs. There is no ECR adoption scale in the academic literature. Exploratory field research indicated that the Global ECR Scorecard, developed by a team of practitioners, consultants, and academics, provides a comprehensive framework for structuring ECR activities between retailers and suppliers. The Global ECR Scorecard, which is conceptualized as an index, is widely used and is linked to a Web site on which online comparisons with "best practices" are possible (www.globalscorecard.net). The Global ECR Scorecard comprises 37 questions, each related to a specific ECR practice, spanning the three ECR dimensions: ( 1) demand side, which covers demand strategy and capabilities, consumer value creation, and optimizing assortments, promotions, and new product introductions; ( 2) supply side, which covers supply strategy and capabilities, responsive replenishment, integrated demand-driven supply, and operational excellence; and ( 3) enablers and integrators, which covers common data and communication standards, cost/profit and value measurement, collaborative planning, forecasting and replenishment, and e-business.
The Global ECR Scorecard has not been submitted to any psychometric validation, and it has not been used in academic studies. Many of the items are complex and difficult to understand. In addition, the practice of ECR has evolved since 1998 when several national ECR scorecards were consolidated into the Global ECR Scorecard questionnaire. To ensure that we covered the entire scope of ECR as currently practiced, we reformulated the original items into simpler questions. Then, using managers of the retailer's supplier relations department and several suppliers, we reworded the questions so that their intended meaning would be accessible without additional explanation. This entailed splitting items into distinct questions, adding questions to convey the nuances of the concepts, or dropping redundant items and those practices not currently used under the scope of ECR. Consistent with the Global ECR Scorecard, we used a five-point scale, anchored by "nothing planned" and "fully implemented." As a stem we formulated, "For the product range you have chosen, to what extent have the retailer and your company jointly implemented a process to," followed by the specific item.
Because ECR is conceptualized as a formative construct, it is assumed that the items cause the latent variable rather than the construct being reflected in its items (Jarvis, Mackenzie, and Podsakoff 2003). Because formative constructs require a census of all concepts that form the construct, we presented the resulting items to panels of retailers and suppliers with extensive ECR experience to ensure that they covered the entire domain of the concept (Jarvis, Mackenzie, and Podsakoff 2003). We modified problematic items for greater clarity. We then submitted all items to a panel of three academics to assess face validity. On the basis of this, 33 items constituted the ECR index; they reflect three facets: ( 1) the demand side items, which encompass four factors (each with three items) that we labeled collaborative category development, collaborative new product introduction, collaborative consumer value creation, and collaborative channels development; ( 2) the supply side items, which encompass two factors that we labeled collaborative planning, forecasting, and replenishment (which consists of five items) and collaborative transport optimization (which consists of two items); and ( 3) the enablers and integrators, which encompass three factors that we labeled common data standards, collaborative operational problem solving, and collaborative process improvement tools.
Because ECR is an index, we did not estimate a confirmatory measurement model (Jarvis, Mackenzie, and Podsakoff 2003). To form the index, we averaged the items to obtain a score for each subfactor. Then, we averaged these subfactor scores to obtain the scores for each of the three facets, which we combined to obtain the ECR score for each relationship. The Appendix contains the items, means, and standard deviations.
Measure Validation for Other Constructs
We conceptualized transaction-specific investments in line with Williamson's (1985) distinction among physical, process, human, and site-specific assets. However, because our interviews indicated that site-specific investments were a minor issue, we concentrated on the first three categories of specific investments. We tailored items that Anderson and Weitz (1992) and Heide and John (1988) use to the specific situation in the fast-moving consumer goods industry on the basis of supplier interviews. We estimated a measurement model that specified transaction-specific investments as a second-order factor and physical, process, and human assets as first-order factors. As Table 1 indicates, although the chi-square (125.593; degrees of freedom [d.f.] = 52) was significant (p < .001), the comparative fit index (CFI) (.960) and the Tucker-Lewis index (TLI) (.950) were above the benchmark of .90. For the three first-order factors, both composite reliabilities were between .84 and .89, and the overall reliability for the second-order factor was estimated at .78.
We developed new scales for cross-functional teams and incentive systems. On the basis of supplier interviews, it appeared that the three key supplier areas to help implement ECR across retailers were category management, key account management, and supply chain management. Therefore, three items assessed whether cross-functional teams and supportive incentive systems were implemented in relation to category management, key account management, and supply chain management across retailers. We estimated a measurement model that specified cross-functional teams and incentive systems as two first-order factors. The chi-square (53.031; d.f. = 8) was significant (p < .001), but the overall fit was acceptable because the CFI (.956) and the TLI (.918) were above the recommended level of .90. The composite reliabilities were acceptable at .87 for cross-functional teams and at .91 for incentive systems.
We measured the two moderators of trust and retailer capabilities using five and seven items, respectively. We used Kumar, Scheer, and Steenkamp's (1995) five items of trust, which assess the extent to which the retailer is honest and benevolent. There was no existing scale to measure retailer capabilities. On the basis of supplier interviews, we identified seven key retailer capabilities with respect to ECR, and the scale assessed the relative capabilities of the focal retailer compared with other retailers. We estimated a measurement model that specified the 12 items loading on to the two constructs of trust and retailer capabilities. Although the chi-square (214.238; d.f. = 53) was significant (p < .001), the overall fit was reasonable because the CFI (.912) and the TLI (.891) were close to or above the recommended level of .90. The composite reliabilities were .83 and .92 for trust and retailer capabilities, respectively.
We assessed economic performance in three ways. We measured ( 1) the supplier's perception of its economic performance, which encompasses turnover, profitability, and growth, compared with other product categories and other retailers. From the retailer's archival records, for 206 of the 266 suppliers and for a period of 63 weeks (roughly 30 weeks before and 33 weeks after the initial mailing of the questionnaire), we obtained data on each supplier's ( 2) weekly sales performance (i.e., sales value, or retail sales value to the retailer per week) and sales volume (i.e., the number of cases sold per week) and ( 3) weekly service performance (i.e., supplier service, or percentage of cases supplied against what the retailer ordered) and invoice accuracy (i.e., the percentage of supplier invoices that match the goods received in depot). Because data for each measure were highly correlated, for each supplier, we averaged the archival performance data across the 63 weeks and then reduced the results to single standardized scores, one for archival sales performance and one for archival service performance. After a logarithmic transformation of each measure, we performed a principal components analysis for each pair of two measures to extract two single-factor scores for the subsequent analyses.
We used Scheer, Kumar, and Steenkamp's (2003) adaptation of Walster, Walster and Berscheid's (1978) global equity measure in which equity is calculated as the quotient of the perceived outputs and inputs of the supplier less the quotient of the perceived outputs and inputs of the retailer. Equity values less than zero indicate that the supplier perceives negative inequity, whereas an equity index greater than zero indicates that the supplier perceives positive inequity. Because this is an index, we did not estimate a confirmatory measurement model for equity.
Kale, Singh, and Perlmutter's (2000) work inspired our notion of capability development, which assessed whether the supplier had significantly improved seven ECR-related capabilities through working with the retailer. The measurement model for perceived economic performance and capability development showed a significant chi-square (421.51; d.f. = 64, p < .001), but the overall fit was marginal because the CFI (.833) and the TLI (.796) were close to or above the marginal threshold of .80. The composite reliabilities were .81 for economic performance and .93 for supplier capability development, and both exceeded the preferred level of .70.
For all the confirmatory measurement models, we assessed discriminant validity between pairs of constructs using Anderson and Gerbing's (1988) procedure as well as Fornell and Larcker's (1981) more stringent procedure, which requires that the average variance extracted for any two constructs is greater than their shared variance. All constructs demonstrated discriminant validity. For example, the shared variance between cross-functional teams and incentive systems was .245, whereas the average variance extracted for the two constructs was .553 and .625, respectively. In addition, we compared the average within-construct item correlation with the average between-constructs item correlation for the eight multi-item constructs. Of the 21 such comparisons, all demonstrated lower between-construct correlations. The previously discussed measure validation procedures demonstrate that all the measures possess adequate unidimensionality, reliability, and convergent and discriminant validity.
Results
To test our main and moderating effects hypotheses, we used generalized least squares (GLS) analysis, which is preferred to ordinary least squares (OLS) regressions when the residuals of the regression equations are correlated and the system is recursive, because it fully accounts for correlation of the diagonal sigma matrix and leads to estimates that are unbiased and consistent (Greene 2003). In the face of a triangular beta matrix of endogenous variables, seemingly unrelated regression is equivalent to GLS (Lahiri and Schmidt 1978); thus, we estimated our equations using seemingly unrelated regression in SAS; however, for our archival-based dependent variables, we used OLS because their residuals were not correlated with ECR adoption as a result of different measurement approaches. For all scales, with the exception of the ECR adoption index, we used factor scores to combine the items into a construct score ( Lastovicka and Thamodaran 1991). The construct level correlation matrix appears in Table 2.
Table 3 provides an overview of the results. Consistent with H1 and H2, transaction-specific investments (.416, p < .001), cross-functional teams (.128, p < .001), and incentive systems (.075, p < .05) have a positive impact on ECR adoption. Consistent with H3a and H3c, ECR adoption has a positive effect on the supplier's perceptual economic performance (.343, p < .001), archival sales (.391, p < .001), and capability development (.845, p < .001). However, contrary to H3b, ECR adoption has a significant, negative effect on perceived equity (-.305, p < .001). We did not observe a significant effect on archival service.
Consistent with H4, trust has a positive effect on the supplier's archival sales performance (.462, p < .05) and perceived equity (.689, p < .001) but not on perceptual economic performance, archival service performance, or capability development. Consistent with H5a, trust enhances the relationship between ECR adoption and perceived economic performance (.221, p < .05), but it has no significant moderating effects on the two archival measures and capability development. Thus, H5a is partially supported, but H5c is not. Contrary to H5b, trust negatively influences the relationship between ECR adoption and perceived equity (-.158, p < .05).
Retailer capabilities have a positive effect on supplier perceptual economic performance (.711, p < .01), archival service performance (.586, p < .05), and capability development (.455, p < .01). Thus, H6c is fully supported, but H6a is only partially supported because the effects on archival sales performance are not significant. Contrary to what we expected, the effects of retailer capabilities on perceived equity (H6b) are negative (-.446, p < .05).
The moderating effects of retailer capabilities are complex. Contrary to H7a, retailer capabilities have a significant, negative moderating effect on the relationship between ECR adoption and supplier perceptual economic performance (-.229, p < .05). The effects on archival sales and service performance are not significant. However, consistent with H7b, retailer capabilities have a positive moderating effect on the relationship between ECR adoption and perceived equity (.283, p < .001). We did not observe moderating effects of retailer capabilities on capability development; thus, H7c is not supported.
Effects of Supplier Size and Brand Type
Practitioners often have diverse opinions on what type of suppliers--large versus small and private label versus branded--benefit more from ECR. Lacking any theoretical reasons for such differences, we decided not to develop hypotheses but rather to explore this issue post hoc. We conducted two sets of analyses. First, to assess the robustness of the results with respect to supplier size, we divided the overall sample into large versus small suppliers. Second, to assess the robustness of the results with respect to branded versus private-label suppliers, we divided the sample into those primarily supplying supplier brands and those primarily supplying private-label products. We initially conducted paired differences tests to examine whether the split samples differed significantly on the key antecedents and outcome constructs. Because we did not observe significant differences in the case of either split, we concluded that the subsamples were representative of the overall sample. We then tested the effects of supplier size and brand type by including corresponding dummy variables into each of the five regression equations that we estimated previously. All ten coefficients related to the dummies were insignificant, indicating that neither supplier size nor supplier brand type had any significant effects (p < .10) on our results. Therefore, our findings are robust with respect to differences between supplier size and brand type.
In addition, we explored whether certain types of suppliers were more likely to adopt ECR. Regression results with ECR adoption as the dependent variable indicate that the supplier's total company sales (.166, p < .05), the product range's share of the supplier's total sales (.213, p < .01), and the retailer's share of the supplier's total sales in this product group (.224, p < .01) had significant, positive effects on ECR adoption. In contrast, the proportion of the supplier's turnover with the retailer in the product range that is private label (.061, p > .10) and the share of this product range in the total grocery market (.031, p > .10) did not have significant effects. In summary, the larger the supplier is and the more important the category and the retailer are to the supplier, the more likely it is that the supplier adopts ECR.( n1) Conversely, the proportion of branded versus private-label supplies or the size of the category does not seem to make a difference.
General Discussion
Although "win-win" partnerships, such as that between Wal-Mart and Procter & Gamble, are frequently documented in the popular press, academic studies of collaborative ECR relationships are scarce, probably because of the sensitivity of the parties involved in providing the necessary data. Compared with small dealers that frequently constitute the sample of relationship marketing research, it is more difficult to persuade large retailers to cooperate with academic studies. We created the conditions for supplier cooperation with this study by obtaining a retailer's cooperation and by promising an independent, confidential investigation. This is unique because, in general, suppliers are unwilling to disclose particulars of their relationships with dominant retailers. In addition, archival performance data from the focal retailer complemented the performance perceptions of the suppliers, thus providing a comprehensive view of the effects of ECR adoption.
To the question, "Do collaborative relationships with large retailers benefit suppliers?" the answer is a qualified yes, because suppliers perceived significant economic and learning payoffs. However, contrary to our expectation, ECR adoption led to greater feelings of inequity in the relationship on the part of suppliers. Perhaps this explains why many suppliers complain that they do not observe any benefits from the adoption of ECR (Corsten and Kumar 2003). Although suppliers gain from ECR adoption in absolute terms, as demonstrated by the positive effects on perceived economic performance, archival sales, and capability development, their perception of the inequitable sharing of the benefits and burdens of ECR adoption leads them to believe that they are relatively deprived. In other words, although suppliers gain more in ECR relationships than in other relationships, they still believe that they receive less than they deserve. In addition, the negative interaction between ECR adoption and trust on equity indicates that as suppliers' trust in the retailer increases, greater ECR adoption makes suppliers believe that they are even more inequitably treated.
The negative impact of ECR adoption on equity, even in the presence of high trust, raises the question whether the suppliers' feelings of greater inequity in ECR relationships are accurate and justified or merely a perceptual problem. Perhaps, and anecdotal evidence suggests this, smart retailers use their power advantage to extract proportionately greater benefits from ECR adoption while cajoling suppliers into making the necessary investments for ECR. Indeed, our findings suggest that suppliers believe that they are particularly exploited by retailers that they consider to possess superior know-how in the market. That large and powerful retailers such as Metro, Tesco, and Wal-Mart are demanding that their suppliers adopt ECR may be considered further evidence in support of this point of view. Given retailer power, suppliers have little choice but to comply with retailer demands and learn to live with inequitable returns from ECR adoption.
The alternative view is that suppliers' perceptions of greater inequity in ECR relationships are inaccurate and that suppliers receive equitable benefits from ECR. Ailawadi's (2001) findings that, despite 20 years of increasing retail power, supplier rents have actually risen compared with retailer rents may be cited as evidence in support of this view. However, it is possible that suppliers are simply "paranoid" when they claim that retailers always receive the lion's share (Farris and Ailawadi 1992).
Unfortunately, we cannot make definitive judgments about the accuracy of suppliers' perceptions of inequity associated with ECR adoption or the process underlying any misperceptions with our data. We must leave the ultimate resolution to further research. Furthermore, from an economic welfare perspective, it would be interesting to understand whether ECR benefits are indeed competed away at the retail level, resulting in tangible benefits for the consumer, such as lower prices, more choice, and more service, or whether it simply leads to greater economic performance for suppliers and retailers.
That trust increases archival sales and that retailer capabilities enhance perceived economic and archival service performance partially support our reasoning. It pays to develop trust in relationships and to work with smarter retailers. In addition, ECR relationships with trusted retailers enhance some of the economic benefits for the supplier. The moderating effects of retailer capabilities are more complex. Unexpectedly, retailer capabilities have a negative interaction with ECR adoption on supplier economic performance. This finding seems somewhat at odds with the observed and expected positive interaction between retailer capabilities and ECR adoption on perceived equity. It implies that smart retailers take a bite out of the supplier's economic performance, yet suppliers are happier with what is left.
We speculate that gradually, suppliers become dependent on the capabilities of smarter retailers. After all, ECR is a knowledge-and data-intensive process, and as suppliers become increasingly locked in on consumer data and knowledge of smarter retailers, such retailers may be able to extract some of the additional rents generated through ECR adoption and appropriate them for themselves. Still, why do suppliers perceive greater equity in ECR relationships with smarter retailers, especially in light of the lowered supplier economic benefits that they receive from ECR adoption with these capable retailers?
Bargaining and negotiation theory predicts that asymmetric capabilities lead to a perception of relative inferiority, which in turn leads to the passive acceptance of lower performance outcomes. Dwyer and Walker (1981) examine bargaining between pairs of negotiators when there are power imbalances. They show that weaker parties appear to expect and to receive a smaller share of the benefits to divide between them, whereas the stronger parties enjoy the reverse situation. In addition, suppliers may be more willing to make concessions to powerful and smarter retailers in the hope that such relationships may help expand market share. Bloom and Perry (2001) find that suppliers of Wal-Mart, certainly a powerful and smart retailer, with a large market share perform better than do large-share suppliers that report retailers other than Wal-Mart as their primary customers. Finally, suppliers learn from smart retailers, which may explain why they believe that it is fair that such retailers take a bigger bite out of the ECR returns. However, these are conjectures that require more rigorous research to resolve.
Limitations
We must note some limitations of this study. First, we conducted our research in a particular setting--that is, with the suppliers of a single retailer--which raises questions of generalizability with respect to both other retailers and other countries. Second, although we obtained archival data on the performance of the suppliers, it would have been useful to examine the retailer's perceptions of ECR adoption and the outcomes from ECR. Adding category or brand development indexes as performance measures to better understand the competitive effects of ECR adoption would also be valuable. The archival service performance measure would benefit from further investigation to better understand what other variables explain the remaining variance. Third, a more stringent test of respondents versus nonrespondents should have been conducted. Fourth, for the first time in the literature, we propose a scale to assess ECR adoption. This scale requires subsequent replication and refinement. Finally, the role of antecedents, focal construct, and moderating variables (trust and retailer capabilities) and their impact on outcomes would benefit from more stringent longitudinal studies.
Conclusions
Notwithstanding the new relationship paradigm, there is considerable cynicism among suppliers about deeper collaborative relationships with large and powerful retailers. This has led some observers to note that in practice, "the days of power play between retailers and manufacturers are far from over" (Kuipers 2001, p. 25). Despite this, our study demonstrates that suppliers achieve greater economic performance and develop their capabilities in collaborative ECR relationships. These findings apply regardless of whether a supplier is small or large or whether it supplies private-label or branded goods. However, considering the high cost of ECR adoption, suppliers should be prudent and safeguard their investments. If a supplier has a choice, our study provides some guidelines as to which type of retailers it should favor in establishing ECR relationships. As a supplier, it is preferable to partner with trusted and smart retailers.
Our findings on perceived equity indicate that suppliers should be realistic about the sharing of benefits from ECR adoption if they want to avoid negative feelings associated with inequity. Negative inequity can lead to frustration and hostility, and eventually, this can threaten a relationship. It may be wise for suppliers to "manage" equity by adapting their perceptions of contributions and benefits and by accepting some inequity as the "cost of doing business," particularly when, as we demonstrate herein, there are substantial economic and learning benefits from ECR relationships.
The authors thank Kurt Edel, Inge Geyskens, Bruce Hardie, Joerg S. Hofstetter, Jan-Benedict Steenkamp, Lisa Scheer, and the anonymous JM reviewers for their helpful comments. They also acknowledge the assistance of Elisabeth Honka for her dedicated support with the data collection and analysis.
( n1) Note that size of the supplier is a different construct from the archival sales measure that we use as one of our dependent variables. Size of the supplier reflects sales to all retailers across all categories, whereas our archival sales measure reflects sales to this retailer in the focal category. The correlation between the two indicators is .133.
Legend for Chart:
A - Antecedents
B - Items
C - Composite Reliabilities
D - Fit Measures
A B C D
Measurement Model 1
Transaction-Specific Investments
Physical assets 3 .84 χ2(52)
Process assets 5 .89 = 125.593
Human assets 4 .88 CFI = .960
12 .78 TLI = .950
RMSEA = .073
Measurement Model 2
Organizational Enablers
Cross-functional teams 3 .87 χ2(8)
Incentive systems 3 .91 = 53.031
CFI = .956
TLI = .918
RMSEA = .146
Measurement Model 3
Moderators
Trust 5 .83 χ2(53)
Retailer capabilities 7 .92 = 214.238
CFI = .912
TLI = .891
RMSEA = .107
Measurement Model 4
Supplier Outcomes
Perceived economic performance 6 .81 χ2(64)
Capability development 7 .93 = 421.510
CFI = .833
TLI = .796
RMSEA = .145
Notes: RMSEA = root mean square error of approximation. Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
A
B C D E
F G H I
J K L
Mean
2.29 3.85 3.27 2.22
4.11 3.82 4.02 (a)
(a) -.44 3.66
Standard deviation
.69 1.22 1.29 1.33
1.2 1.34 1.03 (a)
(a) .84 1.47
1. ECR adoption
1
2. Transaction-specific investments
.592(***) 1
3. Cross-functional teams
.326(***) .178(**) 1
4. Incentive systems
.372(***) .374(***) .328(***) 1
5. Trust
.482(***) .334(***) .088 .181(**)
1
6. Retailer capabilities
.546(***) .424(***) .131(*) .273(***)
.545(***) 1
7. Perceived economic performance
.387(***) .309(***) .082 .212(***)
.399(***) .416(***) 1
8. Archival sales performance
.275(***) .402(***) .153(*) .126
.224(**) .223(**) .245(***) 1
9. Archival service performance
.071 .260(***) .069 -.005
.031 .183(**) .088 .273(***)
1
10. Perceived equity
.149(*) -.027 -.025 .024
.415(***) .347(***) .269(***) -.002
-.028 1
11. Capability development
.663(***) .686(***) .236(***) .37(***)
.52(***) .613(***) .418(***) .420(***)
.225(**) .151(*) 1
(*) p < .05.
(**) p < .01.
(***) p < .001.
(a) Denotes confidential data. We have standardized these
measures at the request of the focal retailer. Legend for Chart:
A - Independent Variables
B - ECR Adoption
C - Economic Performance Perceived Economic Performance
D - Economic Performance Archival Sales Performance(a)
E - Economic Performance Archival Service Performance(a)
F - Perceived Equity
G - Capability Development
A
B C D
E F G
Constant
2.29(***) -.784(***) -.845(***)
(.032) (.217) (.226)
-.076 .209 -1.929(***)
(.235) (.193) (.172)
Transaction-specific investments
.416(***)
(.034)
Cross-functional teams
.128(***)
(.033)
Incentive systems
.075(*)
(.035)
ECR adoption
.343(***) .391(***)
(.094) (.098)
.030 -.305(***) .845(***)
(.102) (.083) (.074)
Trust
-.333 .462(*)
(.219) (.228)
-.457 .689(***) -.041
(.237) (.194) (.171)
Retailer capabilities
.711(**) .129
(.217) (.225)
.586(*) -.446(*) .455(**)
(.234) (.192) (.170)
ECR x trust
.221(*) -.156
(.089) (.092)
.150 -.158(*) .051
(.096) (.079) (.069)
ECR x retailer capabilities
-.229(*) -.054
(.092) (.095)
-.152 .283(***) -.084
(.099) (.081) (.072)
R²
.431 .281 .149
.061 .233 .532
(*) p < .05.
(**) p < .01.
(***) p < .001.
(a) OLS estimation.
Notes: Approximate standard error is in parentheses.DIAGRAM: FIGURE 1 Model of ECR Adoption
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Demand Side Collaboration (1.98, .04)(g)
Collaborative Category Development(a) (1.98, .06)(g)
For the product range that you have chosen, to what extent have the retailer and your company jointly implemented a process to
• share and discuss consumer and shopper wants and needs;
• share and discuss shopping, buying, and consumption patterns (e.g., loyalty cards); and
• evaluate promotions jointly against a common set of objectives?
Collaborative New Product Introduction(a) (2.27, .06)(g)
For the product range that you have chosen, to what extent have the retailer and your company jointly implemented a process to
• discuss new product ideas and prototypes;
• jointly test prototypes (trial launches), formulas, and recipes; and
• evaluate new product introductions jointly against a common set of objectives?
Collaborative Consumer Value Creation(a) (2.38, .06)(g)
To what extent have the retailer and your company jointly implemented a process to
• display products in combination with complementary products or services;
• implement innovative point-of-sales displays, shelves, or services; and
• derived all plans and strategies from the principles of creating value for the consumer?
Collaborative Channel Development(a) (1.35, .04)(g)
To what extent have the retailer and your company jointly implemented a process to
• sell products over the internet;
• deliver products directly to customers' homes; and
• establish other alternative, nontraditional channels to consumers?
Supply Side Collaboration (2.55, .06)(g)
Collaborative Planning, Forecasting, and Replenishment(a) (2.38, .06)(g)
For the product range that you have chosen, to what extent have the retailer and your company jointly implemented a process to
• share and discuss planning information,
• share and discuss forecasting information,
• plan production and replenishment along the whole supply chain,
• plan and schedule production processes based on the retailer's sales data, and
• optimize product flow while balancing service level and costs along the whole supply chain?
Collaborative Transport Optimization(a) (2.74, .08)(g)
For the product range that you have chosen, to what extent have the retailer and your company jointly implemented a process to
• optimize transport utilization without compromising the required service level and
• integrate hauliers, logistics, and/or information service providers into operational processes?
Enablers and Integrators (2.28, .05)(g)
Common Data Standards(a) (2.68, .08)(g)
To what extent do the retailer and your company use international standards
• to track and trace products, shipping containers, pallets, and/or locations (e.g., European article numbering);
• to exchange master data (e.g., European article numbering);
• to share information by the Internet (e.g., global messaging protocols);
• for electronic data interchange (e.g., EDIFACT); and
• for barcode scanning?
Collaborative Operational Problem Solving(a) (2.34, .07)(g)
To what extent have the retailer and your company jointly implemented a process to solve problems concerning
• product availability at point of sales;
• delivery accuracy;
• production effectiveness; and
• upstream supply of ingredients, raw material, and packaging?
Collaborative Process Improvement Tools(a) (1.81, .05)(g)
For the product range that you have chosen, to what extent have the retailer and your company jointly implemented a process to
• regularly map and analyze joint processes and
• continuously improve processes into more integrated joint processes?
To what extent do the retailer and your company use
• profit/cost modeling (e.g., activity based costing) to analyze the supply chain cost and identify joint savings;
• scorecards and templates to analyze, assess, and monitor each other's relational capabilities; and
• checklists, templates, or guidelines to assist decision making?
Transaction-Specific Investments
Physical Assets(b)
Our company has made significant investments dedicated to the retailer that cannot be deployed elsewhere in
• production systems (e.g., dedicated lines),
• logistics and distribution systems, and
• information systems.
Process Assets(b)
To meet the requirements of dealing with the retailer, our company has specifically tailored the
• category management process,
• product development process,
• promotion process,
• replenishment process, and
• product launch process.
Human Assets(b)
Substantial time and effort is spent meeting face-to-face with the retailer's representatives by our
• customer business or key account managers,
• supply chain managers, and
• product development managers.
Cross-Functional Teams(c)
To what extent has your company implemented cross-functional teams for
• category management,
• key account management, and
• supply chain management?
Incentive Systems(c)
To what extent has your company implemented incentive systems and remuneration policies to support
• category management,
• key account management, and
• supply chain management?
Trust(b)
• The retailer usually keeps the promises it makes to our company.
• The retailer gives sound advice on our business, and our company knows it is sharing its best judgment.
• The retailer is concerned about our company's welfare, particularly when making major decisions.
• The retailer responds with understanding when we inform it of problems.
• Our company can depend on the retailer's support in matters of importance to us.
Retailer Capabilities(d)
Compared with other retailers that you work with, to what extent has the retailer superior know-how with respect to
• category management,
• supply chain management,
• consumer/customer understanding,
• pricing management,
• promotion management,
• new product launch, and
• new product development?
Perceived Economic Performance(e)
For this product range, and compared with others, how high is your
• profitability,
• turnover, and
• growth?
At the retailer, and compared with other retailers that you work with, for the chosen product range, how high is your
• profitability,
• growth, and
• turnover?
Perceived Equity(f)
All things considered, evaluate your company's and the retailer's relative participation in this relationship
• your company's contributions to the relationship,
• the retailer's contributions to the relationship,
• the outcomes your company receives from the relationship, and
• the outcomes the retailer receives from the relationship.
Capability Development(e)
Through working with the retailer, to what extent has your company improved capabilities in
• category management,
• supply chain management,
• consumer understanding,
• pricing management,
• promotions management,
• new product launch, and
• new product development?
(a) This is measured on a five-point scale with "nothing planned" and "fully implemented" as the anchors.
(b) This is measured on a seven-point scale with "strongly disagree" and "strongly agree" as the anchors.
(c) This is measured on a five-point scale with "minimal use" and "standard practice" as the anchors.
(d) This is measured on a seven-point scale with "very small extent" and "very large extent" as the anchors.
(e) This is measured on a seven-point scale with "significantly below average" and "significantly above average" as the anchors.
(f) This is measured on a six-point scale with "extremely low" and "extremely high" as the anchors.
(g) The mean and standard deviation for ECR adoption subdimensions are in parentheses.
~~~~~~~~
By Daniel Corsten and Nirmalya Kumar
Daniel Corsten is Associate Professor of Supply Chain Management and Vice-Director of the Kuehne-Institute for Logistics, University of St. Gallen, Switzerland
Nirmalya Kumar is Professor of Marketing, Director of Centre for Marketing, and Codirector of Aditya Birla India Centre, London Business School, United Kingdom
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 56- Does Distance Still Matter? Geographic Proximity and New Product Development. By: Ganesan, Shankar; Malter, Alan J.; Rindfleisch, Aric. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p44-60. 17p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.2005.69.4.44.
- Database:
- Business Source Complete
Does Distance Still Matter? Geographic Proximity and
New Product Development
Many firms rely on external organizations to acquire knowledge that is useful for developing creative new products and reducing the time needed to bring these products to market. Cluster theory suggests that this knowledge is often obtained from organizations located in close geographic proximity. Specifically, proximity is assumed to foster heightened face-to-face communication, strengthened relational ties, increased knowledge acquisition, and enhanced new product outcomes. The authors identify the limitations of these assumptions and offer an enriched model of the influence of geographic proximity on new product development, which they test using both a cross-sectional survey of 155 firms in the U.S. optics industry and a longitudinal follow-up survey of 73 of these firms. They find that firms located in close proximity engage in increased face-to-face communication, but this communication has little effect on the acquisition of the types of knowledge that lead to enhanced new product outcomes. In contrast, they find that e-mail communication leads to both enhanced new product creativity and development speed. In addition, they find that relational ties moderate rather than mediate the path connecting geographic proximity and new product outcomes. These findings imply that the new product development outcomes typically ascribed to close geographic proximity may actually be attributed to strong relational ties.
In the continual search for competitive advantage, firms try to develop innovative new products as quickly as possible. The importance of new product development for long-term competitive success is widely recognized by the marketing community (for a review, see Hennart and Szymanski 2001). In general, marketing has viewed the development of new products from the perspective of an isolated firm engaged in solo activity. Thus, the major thrust of extant new product development research has been on internal processes, such as the formation and coordination of new product development teams (e.g., Olson, Walker, and Reukert 1995; Sarin and Mahajan 2001) and the role of organizational culture in new product development success (e.g., Deshpandé, Farley, and Webster 1993; Moorman 1995).
Because of the growth of global competition, rapid technological advances, and increasing demands from customers, many firms realize that they need help from external organizations, such as customers, suppliers, and even competitors, to develop innovative and timely new products (Wind and Mahajan 1997). In response, an increasing number of marketing studies have begun to examine new product development alliances (Rindfleisch and Moorman 2001, 2003; Sivadas and Dwyer 2000). However, research suggests that formal alliances represent only a small fraction of interorganizational influence on new product development because much of this influence comes in the form of informal information sharing rather than formal agreements (Allen 1983; Von Hippel 1987). To date, the marketing literature has little to contribute to the nature or the impact of this informal information sharing on new product development activities.
Outside of marketing, however, the connection between informal interorganizational information sharing and new product development has received considerable attention from scholars who study industry clusters. These scholars argue that informal information sharing is vital for new product development and that this sharing is facilitated by geographic proximity, which serves to enhance face-to-face communication and the development of strong relational ties (Gordon and McCann 2000; Porter 1998a, b; Sternberg 1999). As evidence for these claims, the cluster literature has generated many case studies that document the innovation-related benefits associated with such industry clusters as Silicon Valley (Saxenian 1994), the Formula 1 race car cluster near London (Henry and Pinch 2000), and the knitwear and clothing clusters in northern Italy (Rosenfeld 1997). Although these case studies suggest a wide range of benefits for firms located in such clusters, the evidence is mostly anecdotal and does not directly examine the process by which geographic proximity affects new product outcomes.
In this article, we offer a new conceptual model of how geographic proximity affects new product development outcomes. However, rather than taking cluster theory's assumed linkages at face value, we offer an enriched theoretical explanation that integrates cluster theory with research on electronic communication, interorganizational relationships, organizational learning, and new product development. This alternative model accounts for important subtleties that cluster theorists have not considered. For example, we question the widely held assumption that geographic proximity leads to close relational ties (e.g., Porter 1998a, b). Similarly, our model recognizes that electronic communication may rival face-to-face contact as a means to acquire key knowledge. We test the proposed model through both a cross-sectional survey of 155 optics manufacturing firms and a longitudinal survey of 73 of these firms.
The Role of Distance in Marketing and New Product
Development
The role of distance (or location) has a long history in the marketing literature and has been examined across a wide range of contexts, including marketing-mix decisions (e.g., Howard 1957), retail structure (e.g., Cox 1959; Ingene and Brown 1987), distribution channel design (e.g., Bucklin 1966), and manufacturing investment (e.g., Alderson and Green 1964; Greenhut 1956). In general, this earlier literature focused on spatial distribution of buyers and sellers and physical distribution costs. More recent marketing studies examine the role of geographic proximity in interfirm relations, finding that firms in close geographic proximity face lower costs (Cannon and Homburg 2001), display a weaker competitor focus (McEvily and Zaheer 1999), and draw on each other's knowledge base when developing new products (Rosenkopf and Almeida 2001). Thus, the marketing literature has shown an enduring interest in geographic location; however, it has given relatively little attention to the role of location in interorganizational new product development.
Geographic proximity has also been the subject of considerable inquiry among economists (e.g., Krugman 1991; Marshall 1920). For example, the concept of "agglomerative economies" argues that geographically concentrated firms in the same industry benefit from externalities, such as access to skilled labor, existing channels of distribution, and knowledge spillovers (e.g., Ciccone and Hall 1996; Goldstein and Gronberg 1984). This view has given rise to the concept of industry clusters (also known as "industrial districts") among scholars in economic geography, regional development, and business strategy (e.g., Porter 1990, 1998a, b; Rosenfeld 1997; Saxenian 1994; Sternberg 1991). According to Porter (1998b, p. 199), clusters are "a geographically proximate group of interconnected companies and associated institutions in a particular field." An essential difference between the older concept of agglomerative economies and the newer study of industry clusters is the notion that in addition to enjoying a common set of externalities, the members of a cluster also share close relational ties (i.e., norms of trust and reciprocity) that foster knowledge exchange (Harrison 1992; Rosenfeld 1997). In contrast to new product alliances, cluster-based knowledge sharing largely reflects informal mechanisms rather than formalized cooperative arrangements (Enright 1991).
Cluster theorists argue that clusters represent a new way to view competitiveness and strategy in general (e.g., Porter 1990, 1998a, b). Specifically, cluster theory offers a novel, institutional perspective on marketing strategy by suggesting that individual firm-level outcomes are influenced by a manufacturer's location in a broader, geographically concentrated learning community. Thus, cluster theory points to the important role of external players, such as nearby suppliers, customers, competitors, and research institutes. A firm's relationships (both geographic and social) with these broader constituents are believed to play a key role in its learning ability, innovation outcomes, and ultimate success (Porter 1998a, b; Saxenian 1994). As a result, cluster theory emphasizes the importance of external agents in providing firms with the information and know-how necessary for innovative activities.
To date, cluster research concentrates on either developing theoretical treatises of the benefits of geographic proximity or demonstrating industry clusters in action through case studies of prominent clusters. In general, cluster advocates seem to suggest that geographic proximity provides an almost magical effect on new product innovation. The purported benefits of geographic proximity for new product development hinge on three critical but relatively untested assumptions: ( 1) proximity enhances face-to-face communication and the development of strong relational ties (e.g., Enright 1991; Rosenfeld 1997; Saxenian 1994), ( 2) face-to-face communication is the optimum way to acquire knowledge (e.g., Porter 1998b; Sternberg 1991), and ( 3) the most valuable knowledge comes in tacit (i.e., noncodified) form (e.g., Porter 1998b; Rosenfeld 1997). However, as we note in the following section, research on interorganizational relationships, electronic communication, organizational learning, and new product development indicates that each of these assumptions may be either inaccurate or incomplete. Our next section integrates the key findings from these other literature bases into cluster theory to develop an enriched model of how geographic proximity influences new product development.
Conceptual Framework
In line with both cluster theory (e.g., Saxenian 1994; Sternberg 1999) and research on new product development (e.g., Moorman 1995), our conceptualization of how geographic proximity influences new product development focuses on the acquisition and utilization of new product -related knowledge. We define this knowledge as "technical information directly relevant to new product development" (Rindfleisch and Moorman 2001, p. 4) and recognize that this knowledge has properties of both form and content. The remainder of this section elucidates the theoretical basis of our model and its hypothesized effects, which we graphically depict in Figure 1.
The fundamental tenet of cluster theory is that close geographic proximity enables frequent face-to-face contact with key knowledge providers, including suppliers, buyers, research institutes, alliance members, and even competitors (Audretsch 1998; Enright 1991; Rosenfeld 1997). For example, Sternberg (1991) describes how shared geographic proximity among optics firms in Rochester, N.Y., enables them to engage in frequent face-to-face communication through such forums as local engineering association meetings. Likewise, Audretsch and Stephan (1996) suggest that close geographic proximity facilitates a firm's face-to-face contact with scientists from research institutes through participation in local workshops and seminars and through informal social interactions. This purported link is also supported by research on interpersonal communication, which has found that physical proximity is positively related to greater amounts of face-to-face communication (Conrath 1973; Gullahorn 1952).
In addition to communicating face-to-face, cluster members can also communicate with one another through several alternative communication channels, such as telephone, fax, mail, e-mail, and electronic discussion groups. To date, the cluster literature is largely silent on the relationship between geographic proximity and these alternative modes of communication. Intuitively, there is no reason to expect that physical closeness should enhance these other forms of communication, because most of them have been developed to overcome physical distance (Audretsch and Stephan 1996). Indeed, because new product development personnel in organizations located further away (i.e., beyond a short driving distance) should have less opportunity to meet face-to-face, they may favor the increased usage of distance-spanning communication such as e-mail as a partial substitute for face-to-face meetings. However, the lack of prior research on this topic does not provide a solid basis to hypothesize that proximity will be either positively or negatively related to e-mail communication. Thus:
H1: Geographic proximity is (a) positively related to the frequency of face-to-face communication but (b) unrelated to the frequency of e-mail communication.
Geographic proximity is also believed to help firms develop strong relational ties with their knowledge providers (Audretsch 1998; Harrison 1992; Henry and Pinch 2000; Porter 1998a). For example, Harrison (1992) notes that the repeated interaction (both planned and unplanned) afforded by close geographic proximity helps firms develop mutual trust. As Rosenfeld (1997, p. 20) suggests, "Trust is established through the kind of informal business and social exchanges that take place at barbecues and golf events, not videoconferences." This view is also echoed by sociologists, who argue that close physical proximity enhances the development of trust and reciprocity among community members (Etzioni and Etzioni 1999). Thus, close geographic proximity is assumed to enhance the formation of "strong ties" between knowledge providers and receivers (see Granovetter 1973).
Although geographic proximity may foster the development of strong ties among some cluster members, the generalizability of this relationship remains an open question. The literature does not provide empirical evidence of such a relationship. As Granovetter (1973) notes, strong ties take a considerable amount of time and effort to build and maintain. Thus, social communities are likely to be composed of a few strong ties and many weak ones. According to Van der Linde (2003), geographically concentrated industry clusters often consist of hundreds of firms. Thus, it seems untenable that a firm would develop strong relational ties with all the suppliers, buyers, competitors, and research institutes within close geographic proximity. Moreover, in the marketing relationship literature, geographic proximity is not a central component of any existing conceptualizations of relational norms, commitment, or trust (e.g., Ganesan 1994; Heide 1994; Moorman, Zaltman, and Deshpandé 1992). Therefore, we diverge from cluster theory by suggesting that relational closeness is not synonymous with or an automatic consequence of geographic proximity; we consider it an exogenous construct in our conceptual model. Thus:
H2: Geographic proximity is unrelated to relational tie strength with knowledge providers.
As we previously noted, cluster theorists emphasize the importance of interpersonal, face-to-face communication for knowledge acquisition. Our conceptual model attempts to enrich this view by adopting a broader perspective of both constructs. Specifically, as our first hypothesis suggests, e-mail communication is an important means of knowledge acquisition. Moreover, we recognize that knowledge has properties of both form and content. In this section, we specify the relationship between communication mode and knowledge type in greater detail, and we consider the moderating role of relational ties.
Communication and knowledge form. The cluster literature emphasizes tacit knowledge acquisition as a key outcome of face-to-face communication (Enright 1991; Porter 1998a, b; Rosenfeld 1997). However, tacit knowledge is inherently difficult to articulate (Polanyi 1966); it is difficult to codify in writing, tends to be hands-on and informal in nature, and is thus difficult to transfer to others (Sternberg et al. 2000). Such noncodified knowledge is viewed as best delivered through individual, face-to-face contact in an apprentice-like manner. This view of tacit knowledge focuses on the form (i.e., codification in writing) of knowledge as a key property that affects its ease of transfer (Kogut and Zander 1992). Everyday examples of noncodified knowledge include such practical know-how as tying shoes or riding a bicycle. In an industrial context, noncodified knowledge often includes the embodied know-how of a skilled technician, which can be essential to the development of innovative routines to manufacture new products.
Given its embodied nature, knowledge in noncodified form is assumed to be best transmitted through the intimate, high-context, and hands-on setting of face-to-face interaction rather than through less personal, sensory-poor, distance-spanning communication vehicles, such as telephone conversations or e-mail messages (Baptista 2001; Zaheer and Manrakhan 2001). In addition to being intimate and informal, face-to-face communication is also considered richer and more capable of conveying more nuanced understandings because of its use of nonverbal cues and the ability to provide synchronous feedback (Daft and Lengel 1986). Thus, the rich modality of face-to-face communication should enhance noncodified knowledge acquisition (Porter 1998a, b; Sternberg 1991). Thus:
H3: Face-to-face communication is more strongly related to noncodified knowledge acquisition than is e-mail communication.
Communication and knowledge content (product and process). Research on organizational learning suggests that in addition to form, content is another important aspect of knowledge. This literature distinguishes between product knowledge and process knowledge (Kogut and Zander 1992; Rindfleisch and Moorman 2001). Product knowledge encompasses facts, whereas process knowledge encompasses procedures (Kogut and Zander 1992; Zander and Kogut 1995). Because we expect that relational ties have a moderating influence on the effect of mode of interpersonal communication on the acquisition of each type of knowledge content, we do not offer a hypothesis about the direct effects between these constructs; instead, we focus on the more nuanced moderating role of relational ties.
The moderating role of relational ties. Although relational ties are unlikely to covary with geographic distance, we suggest that these ties are important in terms of the acquisition of knowledge content from knowledge providers. Specifically, we propose that relationship tie strength moderates the relationship between the communication mode and both product and process knowledge acquisition. This premise is based on findings from the strength-of-ties literature, which suggests that valuable knowledge is much more likely to be transmitted through strong ties than through weak ones. For example, Frenzen and Nakamoto (1993) show that consumers are significantly more willing to transmit knowledge about an important sale to a close friend than to a casual acquaintance. In an organizational context, Rindfleisch and Moorman (2001) find that tie strength is positively associated with knowledge acquisition in new product alliances.
Specifically, we propose that strong relational ties enhance the transfer of each type of knowledge content through its primary communication mode (i.e., face-to-face or e-mail). An important dimension of knowledge that affects its ease of transfer is complexity. Zander and Kogut (1995) argue that more complex knowledge (i.e., knowledge that involves a larger number of critical and interacting elements) is more difficult to communicate and transfer to another organization. According to this view, product-related knowledge tends to be relatively simple and straightforward; thus, product knowledge should be highly amenable to e-mail communication. In contrast, knowledge about processes tends to be more complex; thus, it is more difficult to communicate, rendering it more amenable to face-to-face communication. Unlike e-mail, face-to-face communication affords the opportunity to explain highly detailed specifications, monitor a recipient's understanding, and clarify misunderstandings in real time. Strong relational ties should enhance the transfer of both types of knowledge because the closeness and mutual reciprocity that characterize such ties (e.g., Granovetter 1973) will enhance the ability and motivation of knowledge providers to better understand how best to convey and clarify both product and process knowledge. Thus:
H4a: Relational tie strength moderates the effect of e-mail communication on product knowledge acquisition such that its acquisition is greater among firms with strong ties to their knowledge providers.
H4b: Relational tie strength moderates the effect of face-to-face communication on process knowledge acquisition such that its acquisition is greater among firms with strong ties to their knowledge providers.
As several new product development scholars note, knowledge is the foundation for new product innovation (Kotabe and Swan 1995; Madhavan and Grover 1998; Moorman and Miner 1998). Both the form and the content of this knowledge appear to be important inputs to successful new product development outcomes. For example, noncodified (tacit) knowledge is viewed as providing firms with the embodied know-how necessary to develop innovative products (Nonaka, Toyama, and Konno 2000). Conversely, product and process knowledge provide the important facts, specifications, and procedural details that enable a firm to control the innovation process (Nonaka and Takeuchi 1995).
For many firms, two of the most critical outcomes of new product development are ( 1) the creativity of new products and ( 2) the speed with which these products are developed (Griffin 1993; Moorman 1995). Although these outcomes are often positively related (Rindfleisch and Moorman 2001), they appear to have different antecedents in terms of knowledge inputs. Specifically, new product creativity is most often determined in the early stages of the product development process (Urban and Hauser 1980). Research indicates that the initial stages of idea generation and concept testing are highly reliant on the development or acquisition of novel concepts and findings (i.e., product knowledge) (Andrews and Smith 1996). In contrast, the speed of new product development is more highly dependent on later stages of the product development process (Urban and Hauser 1980). Research suggests that late-stage activities, such as prototype development and manufacturing design, rely heavily on the development or acquisition and application of efficient processes and procedures (i.e., process knowledge) (Millson, Raj, and Wilemon 1992).
An examination of the different knowledge needs of new product creativity and development speed suggests that creativity mostly depends on product knowledge acquisition, whereas speed mostly depends on process knowledge acquisition (Miner, Bassoff, and Moorman 2001; Rindfleisch and Moorman 2001). Furthermore, noncodified knowledge appears to influence creativity and speed differently. Specifically, the informal, unstructured, and dynamic nature of tacit knowledge should enhance new product creativity (Cooke and Morgan 1998; Leamer and Storper 2001) but hamper the speed of new product development (Hansen 1999; Zander and Kogut 1995). Thus:
H5: New product creativity is more strongly influenced by product knowledge than by process knowledge.
H6: New product development speed is more strongly influenced by process knowledge than by product knowledge.
H7: (a) New product creativity is positively related to noncodified knowledge, whereas (b) new product development speed is negatively related to noncodified knowledge.
Organizational learning scholars often characterize the acquisition and utilization of knowledge as a dynamic process that unfolds over time (Moorman 1995; Nonaka, Toyama, and Konno 2000). To use acquired knowledge fully, organizations must engage in assimilation, sense-making, and dissemination activities. The dynamic nature of organizational learning is reflected in the concept of absorptive capacity, which posits that over time, firms can develop their ability to assimilate and apply knowledge effectively (Cohen and Levinthal 1990). Although absorptive capacity has traditionally been conceptualized as a byproduct of internal research and development (R&D), recent research suggests that it can also be fostered by the acquisition of information from external knowledge providers (Scott 2003; Zahra and George 2002). Thus, in addition to enhancing short-term effects on new product outcomes by providing information that helps a firm solve an immediate new product development dilemma, product and process knowledge acquisition can also have a longer-term payoff for new product development by enhancing a firm's basic ability to develop new products in a more creative and timely manner.
Tacit knowledge is also a dynamic entity that can take considerable time to convey and acquire (Polanyi 1966). For example, in Germany, the making of optical instruments has been called the "technology of the golden hands," requiring specialized skills that are traditionally passed from an experienced craftsman to an apprentice over a period of several years (Enright 1991). Thus, cluster theory's assumed beneficial effects of tacit knowledge on new product development outcomes may occur over a lengthy period. Over time, the positive effects of acquiring noncodified knowledge on new product creativity should be enhanced, whereas its negative effects on new product development speed should be attenuated as the recipient firm acquires a deeper level of understanding and learns heuristic shortcuts to speed new products to market. Thus, we expect the following effects of knowledge acquisition (both content and form) on new product outcomes over time:
H8: The positive effect of acquiring product knowledge on new product creativity is strengthened over time.
H9: The positive effect of acquiring process knowledge on new product development speed is strengthened over time.
H10: (a) The positive effect of noncodified knowledge on new product creativity is strengthened over time, and (b) the negative effect of noncodified knowledge on new product development speed is weakened over time.
Method
We selected the U.S. optics industry as the context for our inquiry. This industry is particularly appropriate because optics manufacturers place considerable importance on knowledge acquisition and new product development (Committee on Optical Science and Engineering 1998). In addition, most U.S. optics firms and research institutions are located in a few geographically concentrated clusters (e.g., Boston, Boulder, Orlando, Rochester, Tucson).
Although all optical products share a common basis in the science of light and light transmission, the industry includes a diverse range of products and applications (e.g., fiber optics for telecommunications, imaging systems for medical and office equipment, lenses for microscopes and telescopes) and does not fall neatly into existing industry classification systems, such as Standard Industrial Classification codes. Therefore, we constructed our own database of optics manufacturers from membership directories of professional societies and regional industry associations. From these sources, we identified 655 U.S. optics firms for possible inclusion in our study.
In line with prior studies of new product development (e.g., Rindfleisch and Moorman 2001; Robertson and Gatignon 1998), we identified specific key informants (Campbell 1955), targeting vice presidents of R&D or people in similar high-level positions with intimate knowledge of their firms' new product development activities. Precontacting these people for verification eliminated 219 firms that were either not manufacturers or not engaged in any recent new product development or for which we were not able to identify a knowledgeable informant. This screening process yielded a sampling frame of 436 firms.
Initial survey. We mailed each firm a cover letter, an endorsement letter from the head of a leading university-based optical sciences center, a survey, a postage-paid reply envelope, and $5 as an incentive. Three weeks later, we sent a reminder postcard. We mailed a second set of survey materials (sans the $5) to firms that did not respond within six weeks. Twelve surveys were returned as undeliverable, and another 36 firms replied that they were not currently involved in any new product activities. This left a final sampling frame of 388 firms, 169 of which returned the survey (155 were usable), for an effective response rate of 44%. The response rate and sample size compare favorably with recent studies of new product development (e.g., Sivadas and Dwyer 2000). We received the 155 usable responses from firms in 25 states, including each of the eight U.S. optics clusters. Of the 155 responding firms, 124 (80%) were located in an optics cluster, and the rest were located in states without a large concentration of optics firms.
We assessed potential nonresponse bias through an extrapolation method that compared early respondents with late respondents (Armstrong and Overton 1977). We found no significant differences in means or variances for any key constructs between early (i.e., before the second mailing) and late (i.e., after the second mailing) respondents, suggesting that nonresponse bias was not a problem in this study. As a validity check, respondents reported that they were highly knowledgeable about (mean = 6.62 on a seven-point scale) and involved in (mean = 6.38 on a seven-point scale) the focal new product development project and had worked for their firm for an average of ten years. Seventy-one percent were chief executive officers, presidents, vice presidents, or directors, and most respondents (72%) had advanced degrees. These results suggest that our sampling approach was successful in identifying knowledgeable key informants.
Follow-up survey. Approximately 30 months following the mailing of our initial survey, we conducted a follow-up survey to test our three longitudinal hypotheses (H8, H9, and H10). We sent surveys to 152 of our original respondents (three respondents did not provide contact information). Surveys for 27 firms were undeliverable because of a combination of factors, leaving a final sampling frame of 125 firms. Of the surveys, 73 were returned for an effective response rate of 58%. Of the 73 responding firms, 56 (77%) were located in an optics cluster. The firms that responded to the follow-up survey were statistically similar to the nonresponding firms.
Measure development began with field interviews and pretests of the survey among several people who were connected to the U.S. optics industry. These efforts helped develop and refine our measurement scales and general survey design. Subsequently, we detail the measures we used to assess our key constructs and control variables, and we provide their intercorrelations, reliability indexes, and descriptive statistics in Table 1. The specific wording and scaling used for each measure appear in the Appendix. To ground our measurement assessment, we instructed all respondents to "focus on one specific new product project that either has recently concluded or has been active over the past six months."
Key knowledge provider. We asked respondents to select the most important optics firm or research institution their firm had been in contact with during the focal project and to classify the nature of their relationship with this organization (e.g., supplier, customer, competitor). They were told that formal or contractual relations with this organization were not necessary. We refer to this organization as the key knowledge provider. The majority of key knowledge providers were channel members (suppliers = 40%, customers = 22%). The rest were research institutions (19%), alliance partners (12%), competitors (2%), and others (5%).
Geographic proximity. We assessed geographic proximity by asking respondents to report the locations and distances (in miles) of the optics firm or research institute they identified as a key knowledge provider. We confirmed (and adjusted when necessary) this self-reported distance by calculating the actual geographic distance between the respondent's firm and the key knowledge provider using distance calculation applications (e.g., MapQuest).
As Table 1 shows, on average, the firms in our sample were located more than 1000 miles from their key knowledge provider. Of the firms, 33% were located less than 100 miles from the key knowledge provider (i.e., within a two-hour drive), whereas 20% were located more than 2000 miles from the key knowledge provider. To control for this distributional skewness, we transformed geographic distance using a log transformation.
Relational tie strength. We measured strength of the relational tie between the respondent's firm and the focal knowledge provider with a five-item version of the relational embeddedness scale that Rindfleisch and Moorman (2001) developed. As Granovetter (1985) notes, highly embedded relations are composed of firms that share strong ties with one another. We assessed relationship embeddedness in both our initial and follow-up surveys (r = .74). This scale displayed strong reliability (αinitial = .91, αfollow-up = .91).
Interpersonal communication mode (face-to-face and email). In line with previous research (e.g., Cannon and Homburg 2001; Hansen 1999; Mohr, Fisher, and Nevin 1996), we asked key informants how many times during the average workweek (over the previous six months) they personally communicated with scientists, engineers, or technical workers from the focal knowledge provider using each communication mode (i.e., face-to-face and e-mail). We chose to phrase this measure at an interpersonal level because cluster theory focuses on the role of interpersonal, face-to-face contact in knowledge transfer activity (Porter 1998a, b). The majority of our respondents (70%) were from small firms (i.e., 100 employees or less) and were often the founder, primary scientist, and principal communicator with external knowledge providers. Thus, we believe that our measures of communication mode tap the communication patterns of the person most centrally connected to the focal new product development project.
Knowledge form. The embodied and noncodified nature of tacit knowledge makes knowledge form an inherently difficult construct to measure. Nevertheless, prior research has successfully developed several measures of various aspects of this construct (e.g., Hansen 1999; Sternberg et al. 2000; Zander and Kogut 1995). We used a slightly adapted version of Hansen's (1999) three-item degree-of-knowledge-codification scale, which taps the form of acquired technical knowledge by asking respondents to rate the degree to which the knowledge received by their firm was tacit (cf. codified, written, and documented). Thus, higher scores indicate higher degrees of tacitness, and lower scores indicate higher degrees of codification. The alpha for this measure was .69.
Knowledge acquisition (product and process). To assess the amount of knowledge acquired from the firm's key knowledge provider, we adapted scales that Rindfleisch and Moorman (2001) developed to measure process knowledge acquisition (e.g., new manufacturing processes) and product knowledge (e.g., key product specifications). Note that knowledge content acquisition is assessed independently from the form (tacit or codified) of acquired knowledge because either process or product knowledge can be codified or noncodified (Kogut and Zander 1992). Both measures displayed good reliability (αprocess = .85, αproduct = .88).
New product outcomes (creativity and speed). To assess new product creativity and development speed, we used slightly adapted versions of scales that Rindfleisch and Moorman (2001) developed, and we assessed both outcomes in our initial and follow-up surveys. These measures displayed strong reliability in both surveys (initial survey: αcreativity = .89, αspeed = .85; follow-up survey: αcreativity = .88, αspeed = .88) and were significantly correlated over time (rcreativity = .41, p < .001; rspeed = .31, p < .001).
Control variables. We also asked respondents to report the number of years they (length of personal interaction) and their organization (length of organizational interaction) had interacted with the knowledge provider and the size of their firm in terms of annual sales revenue and number of employees.
Examination of dimensionality and discriminant validity. We assessed the unidimensionality of the measures we used in our initial survey with a confirmatory factor analysis model using LISREL 8.3. As Table 2 shows, all factor loadings were significant, and all fit indexes met or exceeded recommended levels (comparative fit index [CFI] = .90, nonnormed fit index [NNFI] = .90, root mean square error of approximation [RMSEA] = .07, and standardized root mean square residual [SRMR] = .08). Next, we calculated the composite reliability using the procedures that Fornell and Larcker (1981) suggest. We also examined the parameter estimates and their associated t-values and calculated the average variance extracted (AVE) for each construct (Gerbing and Anderson 1988). As Table 2 shows, composite reliabilities ranged from .78 to .90, indicating acceptable levels of reliability for the constructs (Fornell and Larcker 1981). Finally, the AVEs ranged from 51% to 61%, which are greater than the recommended level of 50% (Bagozzi and Yi 1988). We assessed discriminant validity by calculating the shared variance between all possible pairs of constructs and verified that they were less than the AVE for all individual constructs, thus satisfying Fornell and Larcker's (1981) test and indicating that our multi-item scales display adequate discriminant validity.
Analysis and Results
We analyzed the data from our initial survey using structural equations modeling (LISREL 8.3). Specifically, we specified a model that examined our a priori hypothesized relationships (Model 1), a model that examined a post hoc set of expanded relationships (Model 2), and a model that investigated a competing perspective (Model 3). The standardized parameter estimates and standard errors for Model 1 and Model 2 appear in Table 3. We analyzed the data from our follow-up survey using regression analysis because the size of our follow-up sample (n = 73) did not allow us to employ structural equations modeling techniques (see Table 4).
In line with Bagozzi and Heatherton's (1994) suggestions, we created two composite items for each latent factor to serve as its indicators. We followed this approach for our five latent constructs (i.e., noncodified knowledge, process and product knowledge, and creativity and speed of new product development). We also included our three single-item measure constructs (i.e., geographic proximity and face-to-face and e-mail communication) and fixed their error variance on the basis of the reliabilities of each measure (Hayduk 1987). Finally, we included four control variables (annual sales, number of employees, length of personal interaction, and length of organizational interaction), which we treated as independent variables along with geographic proximity.
For efficiency purposes, we focus on the results from Model 1 and discuss only the paths in Model 2 that are substantively different from Model 1. With the exception of H2, we describe our results in the order they are listed in our conceptualization and as portrayed from left to right in Figure 1. We save our discussion of the findings for H2 until the competing model section (Model 3) because it entails the specification and testing of an alternative model. The fit statistics associated with Model 1 are reasonable (CFI = .88, IFI = .87, RMSEA = .08, and SRMR = .09), the overall R² is .34, and the explained variance of dependent variables ranged from .11 (creativity) to .56 (e-mail). However, model fit could still be improved. Therefore, we estimated a second model (Model 2) that duplicated the paths shown in Figure 1, adding relational tie strength as a moderator of the paths between geographic proximity and communication mode and the paths between knowledge acquisition and new product outcomes. The fit for this expanded model (CFI = .90, IFI = .91, RMSEA = .06, and SRMR = .09) is superior to Model 1.
Our conceptual model suggests that the effect of communication mode (face-to-face and e-mail) on knowledge content (process and product) is moderated by the strength of the relationship between the focal organization and its knowledge provider. We tested these moderating effects through multigroup analyses (Stone and Hollenbeck 1989) by partitioning our sample on the basis of a median split of relational tie strength (median = 5.0). We then assessed the invariance of the parameter estimates between the strong and the weak relational tie subgroups by comparing the chi-square from a model that constrained the paths between communication mode and knowledge content to equality with that of a model that allowed these paths to vary freely. This analysis shows that the chi-square difference between the constrained and unconstrained models is statistically significant (χ²( 9) = 17.02, p < .05), suggesting the presence of a moderating effect of relational ties.
Our results indicate that geographic distance (i.e., the inverse of proximity) is negatively related to face-to-face communication (b = -.19, p < .05). In effect, as the distance (proximity) between optics organizations increases, their frequency of face-to-face communication decreases (increases). Thus, H1a is supported. We also find that geographic distance does not affect the frequency of e-mail communication between optics organizations (b = .07, not significant [n.s.]), in support of H1b. This suggests that more proximal organizations have a greater tendency to exchange knowledge through face-to-face (but not electronic) communication.
The results from Model 2, however, reveal that the relationship between geographic proximity and face-to-face communication found in Model 1 is limited to firms that have strong ties to their key knowledge providers (strong ties: b = -.32, p < .01; weak ties: b = .20, n.s.). In other words, being in close proximity to key knowledge providers appears to enhance face-to-face contact only among relationally close firms. Model 2 also shows that though geographic proximity may be unrelated to e-mail communication at an overall level, the presence of strong ties appears to encourage physically distant firms to maintain contact through electronic means (strong ties: b = .29, p < .05; weak ties: b = -.15, n.s.).
Next, we examined the effects of communication mode on the form of knowledge acquired. As cluster theorists predict, we find that face-to-face communication is positively related to noncodified (tacit) knowledge acquisition (b = .21, p < .05). We further find that e-mail communication is negatively related to noncodified knowledge acquisition (b = -.15, p < .10). Because our hypothesis (H3) investigates the relative effects of communication mode on noncodified knowledge acquisition, we conducted a chi-square difference test. The difference between the two models is significant (χ²( 1) = 6.21, p < .001), suggesting that the effect of face-to-face communication is significantly larger than the effect of e-mail communication on noncodified knowledge acquisition. These results provide support for H3. The results from Model 2 further show that the effects of face-to-face and e-mail communication are magnified in the presence of strong ties (b = .32, p < .01; b = -.19, p < .05).
In H4a, we propose that e-mail communication is associated with a greater amount of product knowledge acquisition for firms with strong ties to their knowledge provider. Model 1 indicates that e-mail communication is positively related to product knowledge acquisition for both strong and weak ties (b = .60, p < .01; b = .31, p < .01). A chi-square test comparing a constrained with an unconstrained model shows a significant difference (χ²( 1) = 7.45, p < .01), in support of H4a. In addition, we find that e-mail communication is related to the process knowledge acquisition for strong ties (b = .34, p < .01) but not for weak ties (b = .09, n.s.). These results show that electronic modes of communication, such as e-mail, are positively associated with process and product knowledge acquisition when firms have strong relationships with their knowledge providers.
In H4b, we propose that face-to-face communication is associated with a greater amount of process knowledge acquisition for firms with strong ties to their knowledge provider. This hypothesis is not supported; face-to-face communication has no effect on process knowledge for either weak or strong ties (b = .01, n.s.; b = .03, n.s.). Moreover, we find that face-to-face communication has a negative effect on the product knowledge acquisition for both strong and weak ties (b = -.39, p < .01; b = -.15, p < .10).
Next, we examined the impact of knowledge content and form on new product outcomes. As we predicted, product knowledge acquisition is more strongly associated with greater new product creativity (b = .33, p < .01) than is process knowledge acquisition (b = .16, p < .05). A chi-square test in which we allowed the effect of product knowledge on new product creativity to vary compared with one in which we constrained it reveals a significant difference (χ²( 1) = 6.31, p < .01), in support of H5. We also find that process knowledge acquisition is more strongly associated with accelerated speed of new product development (b = .20, p < .05) than is product knowledge acquisition (b = -.03, n.s.), in support of H6. However, the results do not support H7, which predicts that noncodified (tacit) knowledge enhances creativity (b = .05, n.s.) and slows down development speed (b = -.12, n.s.). This suggests that the type of content acquired from knowledge providers is more strongly associated with new product outcomes than is the form of knowledge acquired.
Finally, Model 2 shows that the positive linkages between both product knowledge (strong ties: b = .46, p < .01; weak ties: b = .04, n.s.) and process knowledge (strong ties: b = .21, p < .01; weak ties: b = .12, n.s.) and new product creativity are limited to firms with strong ties to their knowledge providers. Thus, it appears that firms that acquire knowledge from weakly tied providers are unable or unwilling to use this knowledge to develop more creative new products.
Our specification of relational ties as ( 1) unrelated to geographic proximity and ( 2) a moderator of the effect of communication frequency on knowledge acquisition represents a dramatic departure from the way that cluster theory depicts this construct. Specifically, cluster theorists argue that relational ties are a direct outcome of geographic proximity and have a direct influence on knowledge acquisition (Porter 1998b; Rosenfeld 1997).
To test this alternative theoretical perspective explicitly, we specified a competing model that was identical to Model 1 except that ( 1) we specified a path between geographic proximity and relational tie strength and ( 2) we removed the moderating paths of relational tie strength between communication mode and knowledge type. In effect, this competing model specifies relational tie strength as a mediator rather than a moderator of the effect of geographic proximity on higher-level cluster outcomes. The results indicate that this alternative model is largely inferior to our hypothesized model; its fit statistics (CFI = .87, NNFI = .86, RMSEA = .12, and SRMR = .15) are weaker than those of Model 1. Moreover, this model revealed that the path between geographic distance and relationship tie strength is not significant (b = -.04, n.s.). These results not only provide strong support for H2 but also offer significant validity to our moderating model of relational tie strength.
We examined the longitudinal effect of knowledge form and content on new product development outcomes by specifying two regression models. In both models, the measures of noncodified knowledge, process knowledge, and product knowledge from our initial survey were the independent variables. The dependent variables were our follow-up measures of new product creativity and development speed that we collected 30 months later. As a baseline comparison, we also specified two models that used the same independent variables, but we used the measures of creativity and speed from our initial survey as the dependent variables. As Table 4 shows, the effect of product knowledge on new product creativity is stronger at Time 2 (b = .42, p < .01) than Time 1 (b = .30, p < .05), and this difference is significant (p < .05). In contrast, process knowledge is unrelated to new product development speed at both Time 1 (b = .22, n.s.) and Time 2 (b = .14, n.s.). Finally, noncodified knowledge is unrelated to creativity at both Time 1 (b = .01, n.s.) and Time 2 (b = -.14, n.s.) and is negatively related to new product development speed at both Time 1 (b = -.33, p < .05) and Time 2 (b = -.27, p < .10). This difference is not significant. Collectively, these results support H8 but not H9 or H10.
Discussion
Our research paints a portrait of the role of geographic distance in new product development that is markedly different from that offered by the traditional views of industry clusters and marketing strategy. Specifically, our results show that ( 1) geographic proximity is related to face-to-face communication but is unrelated to relational ties, ( 2) relational ties moderate several linkages in the path between geographic proximity and new product development, ( 3) face-to-face communication is less effective than electronic communication as a means of knowledge acquisition, and ( 4) knowledge content has a greater effect on new product development than knowledge form. In combination, this set of results is rather surprising when it is juxtaposed with extant theory on industry clusters and marketing strategy, and it offers several insights into the relationship between geographic proximity and new product development.
At first glance, our results appear to suggest that distance still matters. The results of both Model 1 and Model 2 show that firms located in closer physical proximity engage in more frequent face-to-face contact. However, our analysis also reveals that geographic proximity is unrelated to the presence of strong relational ties between knowledge providers and recipients. According to cluster scholars such as Saxenian (1994, p. 104), "there is little doubt that geographic proximity fosters the frequent interaction and personal trust needed to maintain these relationships." However, our results question the generalizability of such assertions.
It appears that even for firms located in close physical proximity, relational ties must be nurtured and cannot be taken for granted. As organizational scholars observe, firms need to consider both geographic closeness and relational closeness in understanding interfirm behavior (Ghemawat 2001). Our results support and enrich this observation by showing that relational ties are a key moderator for nearly every path in the chain that links geographic proximity to new product development. In summary, nearly all the effects of geographic proximity depend on strong relational ties.
Another widely held assumption among both cluster theorists and knowledge scholars is the necessity of face-to-face contact for the transfer of noncodified (tacit) knowledge. Although our results do not wholly refute such claims, they add some necessary refinement by showing that though face-to-face communication may facilitate the transfer of tacit forms of knowledge, its value in transferring the content of knowledge may be matched or exceeded by other forms of communication, such as e-mail. As Table 3 shows, both process and product knowledge content are critical to new product development because product knowledge enhances new product creativity and process knowledge enhances new product development speed. Our longitudinal data show that knowledge in noncodified form has no effect on new product creativity and hampers new product development speed. Thus, our findings suggest that e-mail and other means of electronic communication are more critical to new product development than is frequent face-to-face contact with external knowledge providers.
Recent research on virtual teams indicates that people can work effectively together without ever meeting in person (Cummings 2004; Majchrzak et al. 2004). Thus, e-mail and other forms of electronic communication may be attractive from an efficiency standpoint, regardless of geographic distance, because knowledge seekers may be willing to trade off the richness of face-to-face contact for the timeliness and low cost of an e-mail message. The knowledge benefits of e-mail provide further support for those heralding the "death of distance" and advocates of virtual technologies in general.
Two of our most surprising findings are that face-to-face communication is unrelated to process knowledge acquisition, even when there are strong relational ties with the knowledge provider (contrary to H4b), and that it is negatively related to product knowledge acquisition (see Table 3). How can this be? In an attempt to answer this question, we conducted follow-up interviews with a few of our respondents. These interviews revealed three possible explanations. First, it appears that face-to-face communication with knowledge providers occurs mainly at the start and end of a project. Thus, the bulk of communication during the active R&D and knowledge acquisition phase of a project takes place at a distance. Second, respondents noted the generally unproductive nature of face-to-face meetings in a new product development context, which can apparently even be counterproductive. Third, they suggested that a high level of face-to-face contact could be a sign of a troubled relationship (i.e., one in which little exchange of product knowledge takes place). These possibilities lend further support to the primacy of e-mail as a means to acquire relevant new product-related knowledge.
High-tech firms that want to enhance their new product development outcomes can draw several insights from our research. First, in contrast with the recommendations of cluster advocates about the importance of geographic proximity, our results suggest that there is no magic that stems automatically from being located near other firms or research institutions in the same industry. Instead, a firm must first develop strong relationships with key knowledge providers to gain access to knowledge, regardless of whether these organizations are near or far. In the absence of close relationships, simply being located in close physical proximity to a knowledge provider does not lead to enhanced communication, improved knowledge acquisition, or better new product outcomes. Geographic proximity may offer an opportunity for relationship development, but this opportunity must be acted on to provide benefits. Therefore, a key managerial priority should be to develop and nurture relationships with potential knowledge providers regardless of their physical location.
Second, after firms establish close relations (at any distance), e-mail can be an effective and efficient means for acquiring both product and process knowledge. As we previously noted, some of our respondents revealed that the socially laden nature of face-to-face meetings can actually be counterproductive. In contrast, e-mail appears to help focus communication on the business at hand, thus resulting in more effective transfer of knowledge that is useful for new product development. The relatively impersonal nature of e-mail may provide added efficiency and clarity by avoiding the symbolic and social barriers that often accompany face-to-face interactions (Durrance 1998; Trevino, Lengel, and Daft 1987). This efficiency advantage of electronic communication should further increase as instant messaging and other interactive technologies become more widely used.
Our conceptualization and measurement assume that face-to-face communication enables a rich and interactive exchange of information in real time, whereas e-mail communication is leaner, less interactive, and off-line in nature. This represents an important limitation of our current study; richness is not only inherent in a communication medium but also dependent on contextual factors such as the nature of interactions between the sender and the receiver and the meanings ascribed to them (Lee 1994). Further research could add value by exploring the nuances of these modes of communication in the context of new product development.
In this study, we focused on one important aspect of tacit knowledge: the degree of tacitness (i.e., noncodification) of information acquired from the knowledge provider; however, this study did not directly assess other aspects, such as the absolute quantity of information acquired in tacit form. Because e-mail communication in our sample was more frequent than face-to-face meetings, the (unmeasured) amount of tacit information acquired may have been small compared with the amount of codified information received. Different respondent interpretations of this measure may have led to the underreporting of the tacitness of information acquired and perhaps may have contributed to the nonsignificant effects of tacit knowledge on new product creativity and development speed, contrary to our predictions in H7 and H10.
Another limitation of our work is its focus on optics-related firms. Although our sample covers applications in diverse product subcategories, a study that covers a broader spectrum of industries would enable researchers to test the generalizability of our findings. Perhaps geographic proximity plays a more important role in the establishment of relational ties and the development of new products in lower-technology industries, such as furniture or textile manufacturing.
Further research could also add value by expanding and enriching our measures of new product outcomes. Our use of survey self-reports of new product creativity and speed provides snapshots of these processes at individual times. It would be valuable to supplement these snapshots with either ethnographic accounts of the role of geographic proximity in new product development as these processes unfold in real time or more precise accounts of proximity's impact on actual (rather than perceived) new product outcomes. Our examination of the benefits of geographic proximity is also limited by our focus on knowledge acquisition. Further research should examine other potential benefits of being located near other organizations in the same industry, such as access to a ready supply of qualified workers and the prestige of being a member of a well-known cluster.
The authors thank the Marketing Science Institute for generously funding this research and Jan Heide, Kyriakos Kyriakopolous, Don Lehmann, Chris Moorman, Brian Ratchford, Fred Webster, and seminar participants at the 2002 Arizona Marketing Consortium, the 2003 MSI Young Scholars Conference, the 2005 University of Southern California Winter Research Camp, Emory University, Texas Christian University, and the University of Houston for their helpful comments on this research.
Legend for Chart:
A - Measure
B - Mean
C - Standard Deviation
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
K - 8
L - 9
M - 10
N - 11
O - 12
A
B C D E
F G H I
J K L M
N O
1. Geographic distance
1119 1420 N.A.
2. Face-to-face
communication
1.30 .89 -.02 N.A.
3. E-mail communication
2.00 1.17 .05 .09
N.A.
4. Tacit knowledge form
4.34 1.45 -.14(*) .14(*)
-.08 .69
5. Process knowledge
2.95 1.45 -.06 .03
.31(***) -.14(*) .85
6. Product knowledge
3.21 1.26 .05 -.06
.41(***) -.19(**) .43(***) .88
7. New product creativity
5.35 1.13 -.03 -.17(**)
.11 -.04 .26(***) .33(***)
.89
8. New product
development speed
4.10 1.09 .02 -.07
.02 -.27(***) .21(***) .06
.24(***) .85
9. Relational tie strength
4.97 1.07 -.10 .05
.24(***) -.19(**) .35(***) .37(***)
.15(*) .15(*) .91
10. Annual sales
(in millions of dollars)
94.6 617 -.05 -.03
-.05 -.03 -.10 .01
.11 .08 -.01 N.A.
11. Number of employees
327 1593 -.06 -.05
-.06 -.05 -.09 .01
.10 .10 .00 .94(***)
N.A.
12. Personal interaction
6.30 6.87 -.16(**) -.03
.04 -.16(**) -.07 -.07
.12 -.02 .16(**) .00
-.01 N.A.
13. Organizational
interaction
6.42 6.03 -.12 .06
-.02 .02 .03 .03
.08 -.11 .18(**) .08
.07 .58(***)
(*) p< .10.
(**) p< .05.
(***) p< .01.
Notes: The coefficient alpha for each measure is on the diagonal,
and the intercorrelations among the measures are on the
off-diagonal. N.A.= not applicable. The correlations reported for
geographic distance are based on actual distances. Our analyses
are based on log-transformed distance. Legend for Chart:
A - Items
B - Creativity
C - Speed
D - Product Knowledge
E - Process Knowledge
F - Tacit Knowledge
A B C
D E F
Create1 .66 (8.91)
Create2 .81 (11.94)
Create3 .86 (12.98)
Create4 .81 (11.80)
Create5 .73 (10.28)
Create6 .78 (11.11)
Speed1 .76 (10.62)
Speed2 .74 (10.07)
Speed3 .88 (12.94)
Speed4 .72 (9.86)
Product1
.75 (9.74)
Product2
.68 (8.69)
Product3
.63 (7.78)
Product4
.71 (9.10)
Process1
.78 (11.02)
Process2
.67 (9.02)
Process3
.83 (12.16)
Process4
.82 (11.93)
Process5
.80 (11.61)
Tacit1
.78 (9.83)
Tacit2
.89 (11.22)
Tacit3
.51 (6.28)
Composite reliability .90 .86
.79 .89 .78
Variance extracted (%) 60 60
51 61 55
Highest shared variance (%) 12 12
20 20 11
Notes: The numbers indicate standardized factor loading. The
t-values are in parentheses. Legend for Chart:
B - Paths
C - Model 1 All Firms
D - Model 2 Weak Ties
E - Model 2 Strong Ties
A B
C D E
H1a Geographic distance → face-to-face communication
-.19(**) .20 -.32(***)
(-2.07) (n.s.) (-2.82)
H1b Geographic distance → e-mail communication
.07 -.15 .29(**)
(n.s.) (n.s.) (2.15)
H3 Face-to-face communication → noncodified
knowledge
.21(**) -.10 .32(***)
(2.31) (n.s.) (3.70)
H3 E-mail communication → noncodified knowledge
-.15(*) .14 -.19(**)
(1.77) (n.s.) (-2.53)
Legend for Chart:
B - Paths
C - Model 1 Weak Ties
D - Model 1 Strong Ties
E - Model 2 Weak Ties
F - Model 2 Strong Ties
A B
C D E F
H4a E-mail communication → product knowledge
.31(***) .60(***) .33(***) .53(***)
(2.71) (5.80) (3.23) (5.30)
E-mail communication → process knowledge
.09 .34(***) .07 .35(***)
(n.s.) (4.45) (n.s.) (4.05)
H4b Face-to-face communication → process knowledge
.01 .03 .00 .07
(n.s.) (n.s.) (n.s.) (n.s)
Face-to-face communication → product knowledge
-.15(*) -.39(***) -.26(**) -.32(**)
(-1.72) (-3.05) (-2.92) (-2.78)
H5 Product knowledge → creativity
.33(***) .04 .46(**)
(3.70) (n.s.) (3.91)
H5 Process knowledge → creativity
.16(**) .12 .21(**)
(1.99) (n.s.) (2.09)
H6 Product knowledge → speed
-.03 -.01 -.01
(n.s.) (n.s.) (n.s.)
H6 Process knowledge → speed
.20(**) .21(*) .21(**)
(2.16) (2.37) (2.37)
H7a Noncodified knowledge → creativity
.05 .02 .02
(n.s.) (n.s.) (n.s.)
H7b Noncodified knowledge → speed
-.12 -.17(*) -.17(*)
(n.s.) (-1.74) (-1.74)
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: We report the common metric completely standardized
solution. The t-values are in parentheses. n.s.= not significant. Legend for Chart:
A - Hypotheses
B - Variables
C - New Product Creativity at Time 1
D - New Product Creativity at Time 2
E - New Product Speed at Time 1
F - New Product Speed at Time 2
A B
C D E F
H8 Product knowledge
.30(**) .42(***) .05 .02
(2.08) (2.84) (.75) (.14)
H9 Process knowledge
.24(*) -.17 .22 .14
(1.67) (-1.18) (1.55) (.92)
H10a, b Noncodified knowledge
.01 -.14 -.33(**) -.27(*)
(.09) (-.99) (-2.37) (-1.89)
Length of personal interaction
-.01 -.08 -.08 .09
(-.07) (-.48) (-.52) (.52)
Length of organizational interaction
.06 .22 -.14 .24
(.37) (1.30) (-.86) (1.4)
Annual sales
-.44(*) .22 -.39(*) -.23
(1.85) (.90) (1.65) (-.93)
Number of employees
.28 -.33 .35 .08
(1.15) (-1.32) (1.43) (.32)
R²
.27 .23 .26 .21
Adjusted R²
.16 .12 .15 .09
F value (degrees of freedom:
numerator, degrees of freedom:
denominator)
2.52(**) 2.05(*) 2.43(**) 1.80(*)
(7, 48) (7, 48) (7, 48) (7, 47)
(*) p< .10.
(**) p< .05.
(***) p< .01.
Notes: We collected the follow-up (Time 2) survey data 30 months
after the initial (Time 1) survey data. We ran both the Time 1
and Time 2 regressions using the sample size of 73 respondents
that responded to the Time 2 survey. The results using the entire
sample (n = 155) are similar.DIAGRAM: FIGURE 1; The Effect of Geographic Proximity and Relational Ties on New Product Outcomes
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Mode of Communication (Frequency)
We adapted the measure for mode of communication from the work of Hansen (1999) and Mohr, Fisher, and Nevin (1996). We measured it on a five-point scale ranging from 1 ("less than once a week") to 5 ("more than once a day").
1. Face-to-face: Over the past six months, how many times during the average workweek did you personally communicate directly with scientists, engineers, or technical workers from this organization in person ?
2. E-mail: Over the past six months, how many times during the average workweek did you personally communicate directly with scientists, engineers, or technical workers from this organization by e-mail?
Tacit Knowledge Form
We adapted the measure for tacit knowledge from the work of Hansen (1999). We measured it on a seven-point scale.
1. Considering all the types of technical information that you received from this organization (as indicated on the previous page), how well documented was this information? (reversed) (1 = "it was not well documented," 4 = "it was somewhat well documented," and 7 = "it was very well documented")
- 2. How much of this technical information was thoroughly explained to your firm in writing (i.e., written reports, manuals, faxes, e-mails, etc.)? (reversed) (1 = "none of it was," 4 = "half of it was," and 7 = "all of it was")
- 3. Overall, how would you describe the type of technical information that you acquired from this organization? (1 = "mainly formal reports, manuals, documents, and so forth," 4 = "half know-how and half reports and documents," and 7 = "mainly informal practical know-how, tricks of the trade")
Process Knowledge
We adapted the measure for process knowledge from the work of Rindfleisch and Moorman (2001). We measured it on a seven-point scale ranging from 1 ("low amount") to 7 ("high amount").
Please rate the amount of new product-related information that your firm has acquired from this organization over the past six months in the following areas:
- Information about new manufacturing processes
- Insights into new ways to approach product development
- Information about new ways of combining manufacturing activities
- Insights about key tasks involved in the production process
- Insights into new ways to streamline existing manufacturing processes
Product Knowledge
We adapted the measure for product knowledge from the work of Rindfleisch and Moorman (2001). We measured it on a seven-point scale ranging from 1 ("low amount") to 7 ("high amount").
Please rate the amount of new product-related information that your firm has acquired from this organization over the past six months in the following areas:
- Information about R&D projects conducted outside your firm
- Research findings related to new product development
- Information about end-user requirements
- Information about competitors' technology
New Product Creativity
We adapted the measure for new product creativity from the work of Rindfleisch and Moorman (2001). We measured it on a seven-point semantic differential scale.
Please circle the degree to which each of the following items provides an accurate description of this new product development project over the past six months:
- Very ordinary for our industry/very novel for our industry
- Not offering new ideas to our industry/offering new ideas to our industry
- Not creative/creative
- Uninteresting/interesting
- Not capable of generating ideas for other products/capable of generating ideas for other products
- Not promoting fresh thinking/promoting fresh thinking
Speed of New Product Development
We adapted the measure for speed of new product development from the work of Rindfleisch and Moorman (2001). We measured it on a seven-point semantic differential scale.
Please circle the degree to which each of the following items provides an accurate description of this new product development project over the past six months:
- Far behind our project timeline/far ahead of our project timeline
- Slower than the industry norm/faster than the industry norm
- Much slower than we expected/much faster than we expected
- Slower than our typical product development time/faster than our typical product development time
Relational Tie Strength
We adapted the measure for relational tie strength from the work of Rindfleisch and Moorman (2001). We measured it on a seven-point scale ranging from 1 ("strongly disagree") to 7 ("strongly agree").
Please rate the degree to which the following statements describe your firm's relation with this organization:
- We feel indebted to this organization for what they have done for us.
- Our interactions with this organization can be defined as "mutually gratifying."
- Maintaining a long-term relationship with this organization is important to us.
- Our business relationship with this organization could be described as "cooperative" rather than an "arm's-length" relationship.
- We expect to be interacting with this organization far into the future.
~~~~~~~~
By Shankar Ganesan; Alan J. Malter and Aric Rindfleisch
Shankar Ganesan is Associate Professor and Payne Fellow of Marketing, Eller College of Management, University of Arizona.
Alan J. Malter is Assistant Professor of Marketing, Eller College of Management, University of Arizona.
Aric Rindfleisch is Visiting Professor of Marketing, Tilburg University, the Netherlands, and Associate Professor of Marketing, School of Business, University of Wisconsin-Madison
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Record: 57- Driving Customer Equity: How Customer Lifetime Value Is Reshaping Corporate Strategy. By: Mittal, Vikas; Clark, Terry. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p107-109. 3p.
- Database:
- Business Source Complete
DRIVING CUSTOMER EQUITY (BOOK REVIEW)
Driving Customer Equity: How Customer Lifetime
Value Is Reshaping Corporate Strategy
by Roland T. Rust, Valarie Zeithaml, and Katherine
N. Lemon (New York: The Free Press, 2000, 292
pp., $28.00, hardback)
A historical review of customer satisfaction and service quality suggests that the literature is entering its third generation. In the first generation (emerging during the 1980s), scholars were concerned mainly with specifying and measuring satisfaction antecedents, and firms were obsessed with maximizing customer satisfaction (at any cost!). During the second generation (early to mid-1990s), the major concern was to link satisfaction to key customer behaviors (e.g., repurchase, word of mouth). The third generation (emerging only recently) seems concerned mainly with how to link satisfaction and service quality to profitability. We might summarize the literature by saying there has been a shift from maximizing satisfaction to maximizing retention to optimizing the bottom line. Driving Customer Equity by Rust, Zeithaml, and Lemon represents a major step in articulating and implementing the philosophy of the third generation.
The central thesis of Driving Customer Equity is that firms should work to maximize customer equity by addressing its three key drivers: value equity, brand equity, and retention equity. Thus, although the book represents a milestone in the third generation of customer satisfaction and service quality research, it goes beyond that. The customer satisfaction and service approach is really encapsulated in one of the three key equity drivers: value equity. Conceptually, Driving Customer Equity expands on the ideas proposed by Rust, Zahorik, and Keiningham (1995), suggesting that every quality-related expenditure should be made financially accountable.
The book argues that a firm should manage its customer equity--the total of the discounted lifetime value of all its customers. This customer equity is made up of three subcomponents: value equity--the customer's objective assessment of the utility of a brand, based on perceptions of what is given up for what is received; brand equity--the customer's subjective and intangible assessment of the brand above and beyond its objectively perceived value; and retention equity--the tendency of the customer to stick with the brand above and beyond the customer's objective and subjective assessments of the brand. Rust, Zeithaml, and Lemon explain why these types of equity are important, how firms can measure them, and how the types of equity can be strategically deployed.
The book argues that firms should move from a brand to a customer equity perspective. The problem with a brand equity perspective as the authors see it is that it is limiting in that it promotes focus on product-level profitability. Rather, they argue, in the context of the new economy, more and more firms are selling services rather than products. This means that the focus is moving away from a transactional and toward a relational approach to customers. Thus, firms are formulating goals aimed at retaining rather than attracting customers, with the result that they now focus on customers per se rather than brands. This argument suggests that the value of a firm lies in the value of its customers rather than in its brands alone. Hence the move toward customer equity.
Rust, Zeithaml, and Lemon develop many points and examples to bolster their arguments. For example, most customers buy an assortment of products from a firm. As the firm successively eliminates unprofitable items from its portfolio, it may also successively alienate profitable customers who become dissatisfied if they do not find the full assortment. If these customers leave, the firm has lost the customer's entire lifetime worth of revenues across all of its products, even though short-term, product-level profitability may be maximized. Similarly, the authors emphasize that calculating lifetime value on the basis of retention probabilities alone may lead to an underestimation, because customers who switch away can also switch back. As such, brand switching patterns over time may be needed to calculate customer lifetime value accurately.
The book discusses the interaction of the three components of customer equity and the shifting relative importance of each across different strategic domains. For example, brand equity may be the dominant concern during customer acquisition, but retention equity comes to the forefront in retaining customers for a mature brand. Therefore, firms are advised to determine the drivers and contextual importance of each type of equity, benchmark its position against competitors, and invest in areas in which the payback to customer equity is highest.
The discussion on the drivers of the various subcomponents of customer equity will be of great interest to academics and managers alike. For example, firms might increase value equity by giving customers more of what they want (e.g., expanded offering) or by reducing what customers do not want (e.g., price reduction). Value equity seems to matter most when products are differentiated, when prepurchase decision making is complex, and in most business-to-business situations. Because the authors propose that value equity itself is driven by factors such as service delivery, service product, physical product, service environment, and so forth, a multi-attribute approach to conceptualizing products and services (see Rust, Zahorik, and Keiningham 1995) might prove to be a useful approach to identifying specific value equity drivers.
The book argues that because brand equity building has received so much attention, managers understand it better than value equity or retention equity. The authors argue that brand equity performs the important functions of building awareness and attracting customers, building emotional ties with customers, and reminding customers to repurchase. Specific drivers of brand equity include customers' awareness of a brand, their attitude toward the brand, and their perceptions of brand ethics.
The book's coverage of retention equity includes a discussion of how simple loyalty programs often degenerate into price wars and how firms can avoid the loyalty trap. The authors identify loyalty programs (special discounts to customers who have a loyalty card), special recognition programs (e.g., frequent flyer miles), affinity programs, community programs, and knowledge-building programs as key drivers of retention equity.
The book also discusses implementation issues, including an outline of the market research needed to identify key drivers of customer equity and benchmark the firm's performance on those key drivers. Also included in this discussion is a detailed illustration, drawn from survey data from four different industries (grocery stores, airlines, rental cars, and facial tissue), of how to evaluate the financial impact of each action taken to improve customer equity. Readers who are already familiar with customer satisfaction and service quality-type research will no doubt find this section repetitive.
The authors also argue that variability in customer profitability provides a basis for segmenting and classifying customers into tiers (least profitable to most profitable). Typically, customers in different profit tiers place different weights on the various equity drivers and have different volume and incidence of new business. The profitability impact of improving customer service and satisfaction varies enormously among the tiers. Building on this argument, the authors go on to show how companies can build customer pyramids by classifying customers as platinum (most profitable) through iron (least profitable but still desirable) to lead (unprofitable and undesirable). This implies several practical problems with which managers must deal. For example, managers must fight simply to implement the approach--finding out what customers spend on the company's services, recording and storing costs associated with each customer in the customer information file, developing profiles of different customer tiers, and determining ways to access customers in each tier individually or as a group.
The chapter, "Customer Alchemy," discusses the art of turning less profitable customers into more profitable customers. The central idea in customer alchemy is that firms should drop customers into the lead tier when they are unprofitable and undesirable. Conversely, iron customers can be moved to the gold tier, and gold customers can be moved to the platinum tier. The trick is to identify the specific and unique equity drivers for each customer tier and to fulfill those needs and requirements better.
The book also discusses the role of the Internet in the firm's customer equity strategy. The Internet can be useful, it is argued, in implementing the customer equity approach--for example, in capturing and disseminating customer-level data to calculate profitability, increasing value equity by increasing customer convenience, saving customer's time, providing customers with information, and so forth. Naturally, the role of the Internet is going to be different for different customer tiers.
The authors conclude with an argument that to become truly customer-centered, a traditional firm must be reconfigured. Instead of a functional approach, an equity-based approach to organizing a firm is proposed. Such a firm would have a value equity officer, a brand equity officer, and so forth. The reconfigured firm would need to pay attention to capturing all relevant data needed for the analysis. By implication, the information processing department, accounting department, and so forth would need to go beyond their traditional functional roles.
Managers at customer-oriented firms will find great value in this book. Such managers are often exhorted to be "customer-centric," and their firms are asked to have a customer orientation. What are the costs and benefits of being customer-centric or customer oriented? More important, how can a firm be customer oriented? Should it build better products to offer more value? Should it create brand equity? This book offers a framework to help managers arrive at specific answers to such general questions. Stated differently, the book tackles the important task of illustrating how the customer equity framework can be implemented. The book provides specific examples of the three types of equity and shows how a firm can measure these through surveys. The book clearly outlines how customer equity calculations can be done to assess the profitability impact of marketing actions.
Most books aimed at the practicing manager fall in one of two categories. The first type is typically intended to be inspirational. Such books are filled with vague exhortations and typically rely on isolated case studies to illustrate best-in-class practices. Also, although these sorts of books usually suggest what proper goals are (e.g., be customer oriented, delight the customer, deliver excellent service), they usually fail to provide systematic guidance on how to achieve them. The second type is "how-to-do" books (e.g., conducting a satisfaction survey, producing a balanced scorecard). These types of books are useful as implementation guides only. Rust, Zeithaml, and Lemon have produced a book that is a blend of both approaches and balances the strategic with the tactical. Managers who like the strategic approach advanced in this book do not have to look elsewhere to implement it. The book provides them with the road map.
Academic researchers will also find the book useful, primarily because it integrates current thinking on customer equity. By bringing together areas of satisfaction/quality (value equity), brand management (brand equity), and relationship marketing (retention equity), Driving Customer Equity provides the groundwork for more integrative research.
Academic researchers might best measure Driving Customer Equity's contribution not simply by asking, "Does it answer any questions?" but rather by asking, "Does it raise new and interesting questions?" Indeed, the book raises several questions that should claim the attention of the research community. For example,
- Which factors (industry-, firm-, and customer-related) determine the relative importance of equity drives? The authors find that the importance varies for the industries investigated. However, a more theoretical understanding is needed. The level of product/service differentiation, the nature of competition in an industry, and the life cycle stage of an industry are a few among the many factors in need of research. Cultural factors may also come into play, because the importance of each equity driver varies systematically with cultures.
- How is customer equity related to other measures of firm value? How do these linkages vary across industries and which factors explain variation in the links?
- How do the dynamics of customer equity work? For example, a customer life cycle approach could be developed to model customer lifetime value and customer equity.
- In the context of the new economy, what are the mediators and moderators of the various equity relationships? Similarly, it is important to understand the interactive effect of the three types of equity drivers.
Is this a good book, and should marketers spend their time reading it? My recommendation? Yes and yes! The book's style is direct and simple; its illustrative style ensures that abstract concepts are made clear by the examples provided. Thankfully, this is one of those books that illuminates rather than inundates.
A few caveats. My sense is that in most firms it is still difficult to implement the customer equity approach because of the lack of data availability and integration. Specifically, implementing this approach involves merging survey data with operational data and internal metrics. Even in this day and age of information technology, many firms are unable to produce such integrated databases. Reasons for this may include functional and departmental turf battles, database incompatibilities, and top management's wanting proof that the customer equity approach works before it spends resources on data integration (the chicken-and-egg problem). The book will have served a valuable purpose if it can convince managers of the importance, viability, and applicability of the approach.
Rust, Roland T., Anthony J. Zahorik, and Timothy L. Keiningham (1995), "Return on Quality (ROQ): Making Service Quality Financially Accountable," Journal of Marketing, 59 (April), 58-70.
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By Vikas Mittal, University of Pittsburgh and Terry Clark, Editor, Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 58- Dynamic Customer Relationship Management: Incorporating Future Considerations into the Service Retention Decision. By: Lemon, Katherine N.; White, Tiffany Barnett; Winer, Russell S. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p1-14. 14p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.66.1.1.18447.
- Database:
- Business Source Complete
Dynamic Customer Relationship Management: Incorporating Future
Considerations into the Service Retention Decision
The authors examine the influence of customer future-focused considerations, over and above the effects of satisfaction, on the customer's decision to discontinue a service relationship. The authors find that expected future use and anticipated regret influence this decision. Understanding and managing these future-focused considerations is critical to successful dynamic customer relationship management.
The trend in marketing toward building relationships with customers continues to grow, and marketers have become increasingly interested in retaining customers over the long run. Not surprisingly, many practical and theoretical models of customer retention have explored satisfaction as a key determinant in customers' decisions to keep or drop (i.e., discontinue) a given product or service relationship (Bolton 1998; Boulding et al. 1993; Rust and Zahorik 1993; Rust, Zahorik, and Keiningham 1995; Zeithaml and Parasuraman 1996). Indeed, satisfaction measures have accounted for up to 40% of the variance in models of customer retention (Reichheld 1996). In this research, we seek to understand what other factors may influence the customer's decision to keep or drop a 1roduct or service, over and above satisfaction.
Satisfaction has been defined in many ways by many researchers over the years. Most recently, Oliver (1996, p. 12) brings together these definitions in the following overall view of satisfaction: "Satisfaction is the consumer's fulfillment response. It is a judgment that a product or service feature, or the product or service itself, provided (or is providing) a pleasurable level of consumption-related fulfillment, including levels of under- or overfulfillment." Another traditional definition of satisfaction that is often used comes from Tse and Wilton (1988, p. 204): "The consumer's response to the evaluation of the perceived discrepancy between prior expectations [or some other norm of performance] and the actual performance of the product as perceived after its consumption." Similarly, Anderson and Sullivan (1993, p. 126) suggest that satisfaction can be "broadly characterized as a postpurchase evaluation of product quality given prepurchase expectations." These definitions suggest that customer satisfaction (as traditionally measured and incorporated in such models) has primarily focused on the customer's past and current evaluations of the product or service (see also Oliver 1977, 1980, 1981; Oliver and Swan 1989; Oliver and Winer 1987).
We propose that, though robust, current models of customer retention that focus on the influence of customer satisfaction on the decision can be enhanced by incorporating the effects of the customer's future considerations as well. Specifically, we advance the notion that when deciding whether to continue a product or service relationship, consumers not only consider current and past evaluations of the firm's performance (e.g., overall satisfaction, service quality, perceived quality) but also incorporate future considerations regarding the service. Accordingly, we examine two consumer-anticipated future states-the consumer's anticipation of future benefits (modeled as expected future use) and the consumer's anticipation of future regret-and demonstrate the impact of these factors, over and above perceptions of satisfaction, on consumers' keep/drop decisions. We also examine whether these future considerations influence the consumer's keep/drop decision differently depending on whether the consumer's decision is in an ongoing (relational) or transactional context.
Current models of customer retention have not incorporated a customer's future orientation. For example, Bolton (1998) examines the effects of satisfaction on customer retention but does not incorporate the customer's future considerations. Similarly, Rust, Zahorik, and Keiningham (1995) examine the effects of service quality and satisfaction on customer retention rate, but they also do not incorporate customer's future considerations. Anderson and Sullivan (1993) find a strong link between satisfaction and repurchase intentions but do not take customer future considerations (of their own behavior) into account. Although some models have examined the effects of customers' future expectations of firm performance (e.g., expected service quality) on customer retention, there has been little research examining customers' expectations of their own future considerations (e.g., How much do I think I will benefit from this service in the future? How much might I regret it if I drop this service or stop consuming this product?). We propose that this omission leads to incorrectly specified models of customer retention and, more important, less-than-optimal marketing decisions aimed at maximizing the likelihood of customer retention. Understanding that consumers take future considerations into account when making decisions about the firm should influence customer acquisition and retention strategies and all elements of the traditional marketing mix.
In the sections that follow, we review literature that motivates the inclusion of future considerations into models that predict consumers' keep/drop decisions and present the overall theoretical approach. We then focus on the relationship of expected future use and the consumer's decision, and we describe a study designed to test the hypotheses. We place particular emphasis on the manner in which the anticipation of future use moderates the impact of satisfaction on this decision. In addition, we explore the antecedents of customer perceptions of expected future use and show how the omission of this construct may lead marketing managers to incorrect assessments and decisions. Next, we discuss the proposed effects of a second forward-looking component, anticipated regret, on consumers' decisions to keep or drop a given service. We describe a second study that is designed to test the hypotheses. In addition, we conceptually distinguish the decision to continue or discontinue an ongoing service relationship from the decision to repurchase (or revisit) a given service or establishment (i.e., a more transaction-based service), highlighting the differential impact of anticipated regret on these disparate service types. Finally, we discuss the findings from both studies and the implications of our findings for marketing theory, marketing practice, and further research.
Traditional models of the keep/drop decision have assumed that past- and present-focused measures (such as overall satisfaction or perceived value) capture all aspects of the customer's underlying utility that factor into this decision. We propose a model that examines the effects of two future-focused measures, expected future use and anticipated regret, on this decision. Before we discuss either of these factors in depth, we provide an overview of the proposed model and briefly discuss its contribution to the marketing field. The model we propose is shown in Figure 1. Traditional models have focused on the unshaded portion of Figure 1. The focus of this research is the shaded portions of the figure-expected future use (and its antecedents) and anticipated egret.
Given that the focus of this article is the additional explanatory power these future-oriented factors add to the model, we do not examine the antecedents of overall satisfaction in this research. Rather, we make the simplifying assumption that satisfaction arises and is updated through some underlying consumer behavior process-a process that has been examined extensively in the literature (for reviews of this literature, see Giese and Cote 2000; Hunt 1977; Oliver 1996). As is shown in Figure 1, we examine the effects of expected future use and anticipated regret directly on the keep/drop decision. In addition, we postulate an updating mechanism for customer perceptions of expected future use and examine potential antecedents of this construct. Finally, as we discuss subsequently, we postulate that the customer's anticipation of regret may affect the keep/drop decision differently for exchange versus relational environments.
We focus on the extent to which consumers take future considerations into account when deciding whether to maintain or end a given service relationship. Several literature streams suggest that future considerations should affect customer decision making. Research in marketing and organizational behavior has examined the impact of mental simulation (of future and past events) on people's decision making and behavior. Mental simulation has been described by Taylor and Schneider (1989, p. 175) as "the cognitive construction of hypothetical scenarios or the reconstruction of real scenarios." Taylor and Schneider (1989) note that mental simulation serves many functions; such simulation can serve a planning function, help set expectations, and potentially lead to behavioral confirmation. Kahneman and Miller (1986) suggest that mental simulation may also serve a "norm-setting" function, making expectations explicit such that the norms or expectations imagined in the simulation may be accessed when making future decisions.
Of particular relevance is the general finding that future-oriented mental simulation can substantially affect people's current behaviors (e.g., Sherman and Anderson 1987; Taylor and Pham 1996; Taylor et al. 1998). Taylor and Pham (1996) find, for example, that when subjects engage in future-oriented mental simulation, the likelihood of action in the current time frame that is consistent with the futuristic mental simulation is substantially increased. Similar results have been found by Taylor and colleagues (1998), who demonstrate that subjects' task performance is affected by the extent to which they mentally simulate possible future outcomes. Finally, in a study more closely related to the keep/drop decision, Sherman and Anderson (1987) find that outpatients at a psychiatric clinic are less likely to terminate therapy if they imagine engaging in future therapy sessions.
In addition to these mental simulation findings, there is also literature in marketing supporting the notion that consumers incorporate future expectations into their purchase decisions (Boulding et al. 1993; Bridges, Kim, and Breisch 1995; Holak, Lehmann, and Sultan 1987; Jacobsen and Obermiller 1990; Winer 1985). For example, Holak, Lehmann, and Sultan (1987) find that consumers, when considering the purchase of high technology products, incorporate their expectations of the timing of the next-generation technology into their current purchase and upgrade decisions (see also Boone, Lemon, and Staelin 2001; Bridges, Kim, and Breisch 1995). With respect to price, Jacobsen and Obermiller (1990) find that consumers incorporate future price expectations into their current time period purchase decisions. Winer (1985) also finds that consumer expectations of future price play a key role in the purchase decision for durables. More broadly, research in the area of economics has long supported the idea that expectations should be incorporated into micro models of consumer behavior (e.g., Shaw 1984). In general, these studies suggest that consumers do take future considerations into account in current-period decisions.
Taken together, this discussion suggests that incorporating a forward-looking component into the keep/drop decision can yield richer insights into the consumer's keep/drop decision than can models that assume that consumers take only past and current experiences into account. For example, according to the traditional expectancy disconfirmation model of satisfaction, consumers compare current product or service experiences to current expectations (which are a function of previous product/service experiences) to form satisfaction judgments, which are a function of the difference between these expectations and the consumer's current experiences (e.g., Tse and Wilton 1988). In addition to satisfaction, however, to what extent do future-oriented factors influence the keep/drop decision? In addressing this question, we examine what on the surface may appear to be paradoxical consumer decision making. For example, consider the highly satisfied customer who chooses to discontinue the service relationship. Alternatively, consider a customer who appears to be highly dissatisfied, yet remains in the service relationship. Traditional retention models, which assume a positive association between satisfaction and the decision to continue or discontinue a service4relationship, would predict the opposite outcomes.
We propose that this prediction may stem from such models' omission of key situational variables. Situational variables may influence the keep/drop decision in a manner that is conceptually distinct from customer assessments of their current levels of satisfaction (for a discussion of other situational effects on buyer behavior, see Belk 1974). We propose that it is important to consider the process by which and the extent to which consumers' evaluations of the future benefits (or anticipated losses) they expect to receive from the product or service influence their current decisions. Specifically, we propose that when consumers decide whether to keep or drop a product or service, they take two future-focused considerations into account: ( 1) the extent to which they expect to use (and derive benefits from) the product or service and ( 2) anticipated regret. In this research, we examine the influence of these future-focused considerations, over and above the effects of customer satisfaction, on the consumer keep/drop decision. We also examine the extent to which the context of the customer decision (ongoing relationship or transaction context) may influence the effects of these future-focused considerations on the customer's decision. Overall, we propose a model that acknowledges the importance of consumer satisfaction on this decision but also helps account for apparent inconsistencies in this link between satisfaction and retention by incorporating future-oriented situational aspects into the model of the customer retention decision.
We first examine the relationship between customer expectations of future use and the decision to keep or drop a product or service. As noted previously, prior research suggests that mental simulation of future events affects consumer decision making (Kahneman and Miller 1986; Sherman and Anderson 1987; Taylor and Pham 1996; Taylor and Schneider 1989). Similarly, research suggests that consumers incorporate future expectations into their current decision making (e.g., Bridges, Kim, and Breisch 1995; Holak, Lehmann, and Sultan 1987; Jacobsen and Obermiller 1990). Building on this research, we propose that expected future use influences the customer's underlying expected utility from the service. Specifically, when making the keep/drop decision, consumers will take such expectations into account, in addition to past and current experiences.
We examine whether a customer expects to use a service more or less frequently in the future and predict that greater expectations of future use will be positively associated with the customer's utility and, therefore, customer retention. In addition, we examine potential antecedents of customer perceptions of expected future use and propose an updating mechanism for the construct. We first examine the effects of expected future use on the keep/drop decision. On the basis of this discussion, we propose that customers who expect to use the service more will be more likely to stay.
H<SUB>1</SUB>: Higher levels of expected future use will be associated with a higher probability of remaining in the service relationship.
For the purposes of this model and research, we make several assumptions about the customer's keep/drop decision. We assume that the customer has already adopted the service in a previous period. We also assume that he or she has have the opportunity to use or consume the service in the present time frame. We propose that the customer decides to continue or drop the service on the basis of past experience, experience in the current time period, and expectations of future experiences. In this research, we are examining the customer's keep/drop decision for an individual service. Finally, we assume that the customer decides to drop a service if the perceived utility of continuing the service is below some customer-specific, yet unobserved, threshold value.
Modeling the Keep/Drop Decision
The customer's decision to keep or drop the service is proposed to be a function of the underlying utility the customer receives (or expects to receive) from the service (akin to Winer's [1985] notion of subjective expected value). This utility-based framework has been widely used. In the new product adoption literature, it has been used to characterize the process by which a consumer decides to adopt a new product (e.g., Roberts and Urban 1988). It has also been used to characterize customer brand choice decisions (e.g., Guadagni and Little 1983; Gupta 1988). Using this approach, we model the probability of the customer continuing his or her relationship with the service. We propose that customer expectations of future use and customer perceptions of overall satisfaction will affect the customer's unobserved utility from the service and therefore the customer's probability of continuing the service.
Specifically, the consumer will remain in the service relationship if
Utility from keeping the service > utility from dropping the service,
where
Utility = f(OVSAT, EFU),
Where
EFU<SUB>nt</SUB> = expected future use of the service (elicited at time t, for interval t that follows) and
OVSAT<SUB>nt</SUB> = consumers' overall satisfaction with the service (elicited at time t).
Because utility is a latent variable, we only observe the customer's keep/drop decision in a given time period (keep = 1, drop = 0). This decision is modeled as a logit model in which we assume that the consumer chooses the option with the highest utility. In particular, the probability that the consumer keeps the service is the probability that the utility from keeping is greater than the utility from dropping.
The keep/drop decision model will be estimated using a recursive system of two equations: the keep/drop equation and the expected future use integration (updating) equation. We propose that customers update their assessment of expected future use in each time period and that this updating provides further rationale for the use of expected future use as a key element associated with continuing (or dropping) the service. How do consumers update expected future use of the service? Prior research (Boulding et al. 1993; Hamer, Liu, and Sudharshan 1999; Jacobson and Obermiller 1990) suggests that customers use an integration or averaging model to update their expectations. This approach has been used extensively in the choice modeling literature to model the customer's brand loyalty updating process (e.g., Guadagni and Little 1983; Gupta 1988). Adopting this model, which is consistent with Nerlove's (1958, 1983) model of expectations formation (for a discussion, see Winer 1985), we propose that customers will update their future use expectations in the following way. Following Boulding and colleagues (1993), we expect customers to update expected future use (measured at t for the three-month interval that follows) on the basis of the prior period's expected future use (measured at t - 1 for the three-month interval that follows) and a determination of actual usage in the current time period (measured at t).
H<SUB>2</SUB>: Customers' expectations of future use from the prior time period (EFU<SUB>t-1</SUB>) will be positively related to customers' expectations of future use in the current time period (EFU<SUB>t</SUB>).
H<SUB>3</SUB>: Customers' actual usage in time t (Usage<SUB>t</SUB>) will be positively related to customers' expected future use in time t (EFU<SUB>t</SUB>).
To recap, we propose that customers' expectations of future use, over and above the effects of satisfaction, will influence their decisions of whether to remain in or leave an ongoing service relationship. In addition, we postulate a relatively simple updating mechanism to gain insight into the antecedents of expected future use. Because our primary interest in this research is to understand the role of forward-looking aspects in the customer's keep/drop decision, this simple approach seems appropriate.
Overview
The goal of Study 1 is to establish the basic notion that future considerations affect keep/drop decisions. In this study, we examine the relationship between consumers' future expectations of their own behavior and the consumers' keep/drop decision in an actual consumer service context. Specifically, we examine the differential impact of expected future use and overall satisfaction on this decision, testing H<SUB>1</SUB>. We also examine the factors that influence the consumers' perceptions of expected future use through the expected future use integration model (H<SUB>2</SUB> and H<SUB>3</SUB>. In Study 1, we use attitudinal and behavioral data from a consumer subscription service. The longitudinal nature of the data enables us to test the proposed relationships.
The Data
The empirical analysis was conducted on data collected from consumers of an interactive television entertainment service. Consumers chose to subscribe to this service and have the opportunity to keep or to drop the service monthly. They also choose levels of usage and specific service aspects that suit their needs, and the firm can monitor these choices. There is a monthly fee for this service that does not vary by usage level. Upon subscription, consumers also pay an initial fee to purchase necessary hardware.
We assembled the data using a panel design so that changes in consumers' opinions and behavior over time could be measured. We selected a random sample of 490 households from a sampling frame of current subscribers. We mailed the first wave of questionnaires to customers in the sample, who were informed that the firm had authorized the study but that their responses would remain confidential. We offered them a small gift as a participation incentive. We mailed the second survey to respondents of the first survey five months after the initial wave. A total of 191 households completed both waves of the survey, for a two-wave response rate of 39%. Of these 191 households, 47 households decided to drop the service by the end of the observation period. We used these 191 households (144 continue, 47 drop) for this analysis.[ 1]
Construct Operationalization
The constructs and their associated measures are displayed in Table 1. The measurement descriptions indicate the specific point or time interval when (or over which) each variable was measured. As an initial test of the effect of future considerations on the keep/drop decision, we examine consumers' expectations of their own future use of the service.
The questionnaire elicited measures of overall consumer satisfaction, expectations of future use, and current usage levels and ratings of various service aspects. We measured consumers' expectations of future use of the service by asking respondents directly about their future usage expectations. We measured overall satisfaction on a seven-point scale, ranging from "very dissatisfied" to "very satisfied." Therefore, higher numbers were indicative of greater levels of satisfaction.
Estimation
The keep/drop decision is estimated as a logit model, in which the decision of whether to remain in the service relationship is modeled as a function of expected future use and overall satisfaction with the service. The results reflect the effect of the independent variables on the consumer's decision to continue the service relationship. The logistic regression procedure from SAS 6.12 was used for the estimation. The expected future use equation is estimated two ways: with and without the restriction that the coefficients on EFU<SUB>t</SUB> and Usage<SUB>t</SUB> must sum to 1. The resulting parameter estimates provide insights regarding the effect of each of the variables in the model on the customer's evaluation of expected future use and the overall decision to continue or drop.
Results
We first tested the effects of the model with overall satisfaction and expected future use in a single model (Model 1, Table 2). Although the expected future use parameter is significant (β<SUB>EFU</SUB> = .597, p < .0001, χ&SUP2; = 59.88, p < .0001), the satisfaction parameter is not significant in this model (β<SUB>ovsat</SUB> = .188, p = .17). This suggests, in line with our prediction (H<SUB>1</SUB>), that, over and above the effects of satisfaction, consumers will be more likely to remain in a given service relationship when they anticipate high expected future use. It is important to note that the simple correlation between the expected future use and the satisfaction variables is not high (r = .4), and therefore multicollinearity does not appear to be a major concern in this study.
It was surprising to find that overall satisfaction was not significant in the proposed model. To examine this finding further, we compared the full model to models that include only the individual effects of overall satisfaction or expected future use on the keep/drop decision. The results from these models are presented in Table 2 (Models 2 and 3). In a model estimating the effects of satisfaction on the keep/drop decision, we find that this variable contributes significantly to the variance in subjects' keep/drop likelihood (β<SUB>OVSAT</SUB> = .473, χ&SUP2; = 17.42, p < .0001, Model 1). As expected, higher satisfaction was associated with a greater likelihood of remaining in the service relationship. Similarly, in a model testing only the effects of expected future use, we find evidence that consumers are forward-looking with respect to their continuation decisions. Specifically, the effect of expected future use was positive and significant (β<SUB>EFU</SUB> = .641, χ&SUP2; = 57.97, p < .0001, Model 2), suggesting that consumers who anticipated high expected future use were indeed more likely to continue.
Together, these results suggest that expected future use mediates the effect of satisfaction on the keep/drop decision, contributing to the keep/drop decision over and above satisfaction perceptions. To examine this, we use (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) a measure suggested by Ben-Akiva and Lerman (1985): (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) = [1 - AIC/ (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) (0)], where AIC is the Akaike information criterion that corrects (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.)(β) for the number of estimated parameters.[ 2] Using this statistic, we find that when only the satisfaction variable appears in the equation, the model explains a moderate amount of variance, as (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) = .063 (Model 2, Table 2). When we added expected future use, however, the explanatory power of the model increases substantially, (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) = .253 (Model 1, Table 2).[ 3] When adjusting for the additional parameters, we find that the model in which expected future use is the sole explanatory variable does as well as the model with both expected future use and satisfaction: (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) = .253 (Model 3, Table 2).[ 4]
Finally, to examine the extent to which expected future use might have a differential effect on satisfied and dissatisfied consumers, we examine the interaction of satisfaction and expected future use. We test the traditional interaction term (OVSAT × EFU) and find that this interaction is not significant and does not add explanatory power to the model (β = .008, p = .91, χ² = .011). To investigate this effect further, we constructed a dummy variable for satisfied/dissatisfied from the respondents' overall satisfaction scores. Respondents whose answer to this question was greater than the median (4 on a seven-point scale) were coded as "satisfied." Although dissatisfied customers had lower expected future use, on average, than satisfied customers, we find no significant difference between dissatisfied and satisfied customers (β = .213, p = .25; EFU means for dissatisfied customers: EFU if keep = 3.00, EFU if drop = 1.00; EFU means for satisfied customers: EFU if keep = 4.58, EFU if drop = 3.60). These results provide additional support for H<SUB>1</SUB>, suggesting that regardless of level of satisfaction, expected future use influences the keep/drop decision.
Expected future use updating/integration model. The results shown in Table 3 strongly support the expected future use updating model. Customers appear to place more weight on current usage experience than prior measures of expected future use when updating their assessments of expected future use (EFU<SUB>t-1</SUB> = .267, Usage<SUB>t</SUB> = .733 (restricted model, see Table 3), but both factors are significant. The restriction that the parameters for EFU<SUB>t-1</SUB>1 and Usage<SUB>t</SUB> must sum to 1 is not binding, which provides additional evidence for the updating model. For completeness, we also included two marketing communications covariates in the model to examine whether marketing communications influenced expected future use. Neither of the communications variables is significant in this equation. Results for this model are presented in Table 3.
Study 1 Discussion
Study 1 provides evidence to support the proposed effects of expected future use and satisfaction (H<SUB>1</SUB>) on the consumers' keep/drop decision. In general, the results suggest that consumers factor expectations of future use as well as their current evaluations of the service when deciding whether to remain in a service relationship. The results of this study also suggest that expected future use mediates the effect of satisfaction on the keep/drop decision. In other words, although the level of satisfaction clearly affects this decision, we find evidence that high expectations of future use may override low satisfaction and that low expectations of future use may override high levels of satisfaction. Another way of thinking about this is as follows: Satisfaction with the current experience may interact with specific aspects of the consumer's situation to influence the consumer's perception of expected future use.
The results of Study 1 suggest that it is important to include this future temporal component in a model of the keep/drop decision. However, it is important to consider alternative explanations for our results. One alternative explanation for these results may be that expected future use (as operationalized) is an equivalent measure to keep/drop, because a consumer who plans to drop the service (in the future) will also plan not to use the service (in the future). The results support our conceptual model in that expected future use and keep/drop are not equivalent. We find that even for customers who drop the service in the following three months, their planned use of the service is significantly greater than zero (EFU<SUB>Drop</SUB> = 1.24 times/week). This suggests that consumers forecast use (however poorly) and that these forecasts should be incorporated into models of the customer keep/drop decision. Incorporating a consumer's perceptions of expected future use as an antecedent to the keep/drop decision provides a more complete model of the customer's decision. We find that incorporating a step-ahead forecast of expected future use greatly enhances the retention model and has significant managerial implications, which suggests that consumers' expectations of future use could even be managed by the firm.
Alternatively, an explanation for our results could be that consumer usage of the service is significantly different from satisfaction and therefore that expected future use perceptions are just capturing this "usage component" and not a future-focused aspect of the keep/drop decision. To rule out this explanation, we compare a model with current (i.e., actual) usage and satisfaction with our proposed model. We find that the proposed model explains significantly more variance (current usage and satisfaction: (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) = .073; EFU and OVSAT: (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) = .253).
Finally, note that this model implicitly assumes that all customers have the same threshold for the keep/drop decision. Consider the following: Customer A is a light cellular telephone user, with typical use of 100 minutes per month. Customer B is a heavy user, with typical use of 1000 minutes per month. A light user (Customer A) might drop the service if his or her expected use falls to less than 50 minutes per month; a heavy user (Customer B) might only drop if his or her expected use falls below 500 minutes per month. We tested a model in which we allowed for heterogeneity across consumers by examining the effect of a customer's future use expectations, compared with that customer's prior use, on the decision. The results for this relative expected future use model were consistent with results reportedpreviously.
The results of Study 1 suggest that customers' estimations of how much they will use the service in the future period is a much better predictor of customer retention than traditional models that focus on overall evaluations of the service. Given this robust result, it is important to understand factors that affect customers' expectations of future use. Consistent with an adaptive expectations model, customers appear to be updating their future use expectations, using both their most recent usage patterns and prior expectations as input into their current expectations of future use. Given the apparent importance of expected future use as a determinant of the keep/drop decision, understanding its antecedents will be key for marketers interested in retailing customers.
In Study 1, we explored the impact of one future-oriented consideration, expected future use, on consumers' decisions to remain in or leave an ongoing service relationship. We now turn to an examination of the manner in which the keep/drop decision is influenced by the anticipation of regret over erroneously dropping a given service. We also examine the degree to which the impact of anticipated regret may be moderated by the type of service in question.
Just as consumers may be motivated in their keep/drop decisions to take into account expectations of future benefits, they may be similarly motivated to make decisions that minimize negative future outcomes. In this research, we focus on one type of negative future outcome or state, namely, postoutcome regret. Zeelenberg (1999) defines regret as a negative, cognitively based emotion people experience when they realize or imagine that their present situation could have been more positive if they had behaved differently. Anticipated regret, then, is considered to occur when before or in the process of making a given decision, a person considers the possibility of postoutcome (i.e., future) regret. Recent research suggests not only that people anticipate postbehavioral affective consequences of their actions and take these consequences into account when making decisions (Bell 1982, 1985; Kahneman and Tversky 1982; Loomes 1982; Miller and Taylor 1995; Van Dijk, van der Pligt, and Zeelenberg 1999) but also that people are motivated to make decisions that avoid potential regret (Connolly, Ordonez, and Coughlan 1997; Josephs et al. 1992; Simonson 1992). Zeelenberg and colleagues (1996) show, for example, that even in situations in which people exhibit a clear preference for a course of action (or inaction), anticipation of regret can influence this preference.
In light of this research, we explore the impact of anticipated regret on consumers' decisions to keep or drop a given service relationship. In our previous theorizing on the impact of future anticipated states, we argued that consumers take future considerations into account when making the current keep/drop decision. The preceding literature suggests that the avoidance of regret may be one type of positive future outcome that consumers seek in their current keep/drop decisions. Accordingly, we test the notion that regardless of their current level of satisfaction, consumers will be less willing to drop a given service relationship if they expect to experience regret over doing so. The predicted negative relationship between the amount of anticipated regret and the likelihood of discontinuing can be stated formally as follows:
H<SUB>4</SUB>: All else being equal, consumers who anticipate regret associated with discontinuing a service relationship will be less likely to drop than consumers who do not anticipate regret.
Of additional relevance to regret research is Kahneman and Miller's (1986) norm theory, which advances the notion that choices of conventional or default options are associated with lower regret. According to norm theory, people feel greater regret for actions that deviate from the norm or default option, "because it is easy to imagine doing the conventional thing" (Simonson 1992, p. 105). In support of this theory, Simonson (1992) demonstrates that the consumers' choice probabilities for a given default option could be increased by asking them to anticipate the regret that might be associated with deviating from that default and experiencing a negative outcome as a result of that action. This "omission bias," the tendency for people to regret negative outcomes that stem from actions (commissions) more than equivalent outcomes that stem from inactions (omissions), has been empirically demonstrated by several researchers (Gilovich and Medvec 1994; Gleicher et al. 1990; Kahneman and Tversky 1982; Landman 1987; Spranca, Minsk, and Baron 1991).
When applied to the keep/drop decision, norm theory research suggests that consumers who anticipate regret associated with keeping or dropping a given service "in error" may attempt to alleviate that regret by choosing the safer option. However, how the safe option is determined remains unclear. In resolving this uncertainty, it is important to distinguish conceptually the decision to keep a service to which a consumer currently subscribes on an ongoing basis (ongoing services) from the decision to repurchase a service that is consumed on a transaction-by-transaction basis (transaction-based services). Ongoing service relationships are conceptualized here as entailing a series of exchanges between a consumer and a service provider in which an implicit or explicit, formal or informal agreement has been reached between parties to continue exchanges for some period (e.g., health club, video rental, health maintenance organization membership). In contrast, more transaction-based relationships (e.g., restaurants, hotels) are those in which no such agreement exists. In these cases, a service provider may be chosen multiple times, but it is chosen among actively considered alternatives and on the basis of its superiority on key dimensions (e.g., convenience, price) in a given purchase situation. With the exception of Bolton (1998), most customer retention researchers focus on transaction-based services, in which the keep/drop decision is modeled as the consumer's "repurchase intent."
The conceptual distinction between these two service types suggests that the choice of the "safe" option in the keep/drop decision may vary by service type. In particular, the distinction suggests that though ongoing service relationships are more likely to continue unless they are ended, transaction-based services are more likely to end unless continued. In other words, the keep/drop decision for consumers in transaction-based relationships represents one in which an action is necessary to keep or repurchase the service, and the inactive option is to fail to repurchase the product or service. The converse is true of ongoing service relationships. Here, the decision to dispose of the service relationship requires an action, and the default or inactive option is to keep the service. Thus, continuing the relationship represents the inactive, safe option for ongoing services, but the active (and therefore less safe) option for more transaction-based services.
In light of this distinction, we explore the differential impact of anticipated regret on consumers' decisions to remain in or leave ongoing service relationships versus their decisions to repurchase a transaction-based service. We predict that the influence of anticipated regret will vary significantly between the two types of service relationships. To the extent that discontinuing an ongoing service relationship requires an action on consumers' parts, we expect both the anticipation of regret and the effect of that anticipated regret to be greater for these keep/drop decisions versus the transaction-based repurchase decision. Specifically, we hypothesize the following:
H<SUB>5</SUB>: Consumers who consider decision-related regret will anticipate greater regret for dropping an ongoing service relationship in error than for discontinuing a transaction-based relationship in error.
H<SUB>6</SUB>: All else being equal, consumers who anticipate regret associated with dropping an ongoing service relationship in error will be less likely to discontinue the service relationship than those making transaction-based repurchase decisions.[ 5]
Anticipated Regret and Satisfaction
Of additional interest is an understanding of the manner in which the anticipation of regret might interact with consumers' satisfaction perceptions to influence customer retention. In particular, we seek to understand the extent to which the effect of anticipated regret may vary for satisfied versus dissatisfied consumers. We have argued that consumers who anticipate regret associated with discontinuing a service relationship in error will be less likely to drop than consumers who do not anticipate such regret. In addition, much of the research studying the link between satisfaction and retention has demonstrated that consumers who are dissatisfied are significantly more likely to drop a service than those who are satisfied (Cronin and Taylor 1992; LaBarbera and Mazursky 1983; Taylor and Baker 1994). In other research, Tsiros and Mittal (2000) demonstrate that experienced regret has a negative influence on satisfaction. Research by Inman, Dyer, and Jia (1997) provides additional evidence of the differential impact of regret on outcome satisfaction versus dissatisfaction. These researchers examine the effects of regret on postchoice valuation and demonstrate that the effect of experienced regret is larger for subjects who are disappointed (or dissatisfied) than for those who are elated (or satisfied). Similarly, the anticipation of regret may be a more effective deterrent to dropping for consumers who are dissatisfied than for satisfied consumers. In line with this reasoning, we argue that the effect of anticipated regret will be greater for consumers who are dissatisfied (than for those who are relatively satisfied) with a given product or service provider. Specifically, we hypothesize the following:
H<SUB>7</SUB>: The effect of regret (versus no regret) on dissatisfied consumers' likelihood of remaining in a given service relationship will be greater than the effect on satisfied consumers.
In addition, following H<SUB>6</SUB>, we hypothesize that the type of service in question will further moderate the proposed interaction between regret and satisfaction. Specifically,
H<SUB>8</SUB>: Compared with satisfied customers, dissatisfied consumers asked to anticipate regret associated with dropping an ongoing service relationship in error will be less likely to discontinue (i.e., drop) the service relationship than will dissatisfied cons5mers making transaction-based repurchase decisions.
Study 2 was designed to test the individual and interactive effects of satisfaction and anticipated regret on consumers' decisions to continue or discontinue exchanges with ongoing as well as transactional service providers (<SUB>4</SUB>, H<SUB>5</SUB>, H<SUB>6</SUB>, H<SUB>7</SUB>, H<SUB>8</SUB>). Through a controlled laboratory experiment, we independently manipulate satisfaction, anticipated regret, and the nature of the service in question (i.e., ongoing versus transactional).
Method
We constructed the experiment using a 2 (anticipated regret: regret, no regret) 2 (satisfaction: satisfied, dissatisfied) × 2 (service type: ongoing, transactional) completely between-subjects design. We randomly assigned subjects, 160 upper-class undergraduates at a Midwestern university, to one of eight experimental conditions. Subjects completed the experiment in exchange for extra course credit in a marketing management course. The experiment was embedded within a larger project related to students' online purchase behaviors.
Procedure
All subjects read a hypothetical service description that described either an ongoing relationship or a transaction-based service of a similar nature. Both services were described as online (i.e., Internet) grocery store delivery services named either QuickRuns (ongoing) or Deliver_me.como(transaction-based). The service descriptions were pretested by a group of subjects (n = 25) from the population studied and were found to be conceptually similar in concept and attractiveness. The scenarios chosen were also determined to be distinct in that, as expected, the ongoing service description was indeed considered more relational than the transaction-based service. To test these perceptions, we asked pretest subjects to answer the following questions: "To what extent is the relationship you read likely to be long term?" "How likely is it that, the next time you need to grocery shop, you would use the QuickRuns service?" "How committed would you be to this company?" "How likely are you to frequent this company on a regular basis?" "How much of an obligation would you feel to do business with this company (relative to its competitors)?" We used responses to these questions to form a "relationship" composite (α = .91). We tested six scenarios. We used scenarios with the highest (X = 6.4) and lowest (X = 2.5) scores on these scales in the experiment.
Manipulated Variables
We asked all subjects to imagine that they began using the service 18 months ago. Both service types (i.e., ongoing and transactional) were described as online grocery stores that deliver grocery, personal care items, books, and so forth directly to customers' homes. To manipulate the type of service in questions, half the subjects read a description of a transaction-based service relationship. We asked these subjects to imagine that they use the service in question on a pay-as-you-go basis, paying only a small delivery fee, plus the cost of groceries, each time they use the service. The description stated explicitly that if subjects did not use the service, they pay nothing (i.e., they acquire the goods from some other source). The other half of the subjects read ongoing service relationship descriptions. These participants read the same general description of the online grocery store, but we asked them to imagine that they were members of a "Home Delivery Club." We told these participants that they pay a small monthly membership fee, instead of a delivery fee, in addition to the cost of goods purchased. We stated explicitly to these subjects that they paid no initiation fee for joining the club and that they could discontinue their membership at any time without financial penalty. In this sense, the scenarios varied only in the presence or absence of an ongoing, nonfinancial membership; there was no greater economic loss or disadvantage associated with keeping or dropping either type of service. We also included the satisfaction manipulation in the description. We manipulated this variable by informing half the subjects that "lately you have been particularly satisfied [dissatisfied] with the quality of service provided by this company."
In this experiment, we manipulated anticipated regret by explicitly priming subjects to think about the regret they might feel if they either stopped using the transaction-based service or discontinued the ongoing service relationship in error. All subjects, regardless of condition, were asked to imagine either that they were in need of groceries this month (transaction-based conditions) or that they were in need of groceries and had just received their monthly membership renewal notice in the mail (ongoing conditions). In both conditions, it was clear that the keep/drop decision was imminent. To induce the anticipation of regret, half the subjects were also asked to imagine that as they are making their decision, they see an advertisement for the firm in question that makes them consider the regret they would feel if they either did not use the service this month (transaction-based) or discontinued the (ongoing) membership "and found out later that [they] shouldn't have."[ 6] The remaining subjects (no-regret condition) were given no information relevant to regret or the anticipation of regret. This control group of subjects served as both a baseline for assessing the extent to which people consider regret in the absence of experimental priming and a comparison group (i.e., to those in the primed regret conditions), which allowed a test of the effect as well as the extent of anticipated regret experienced by subjects across the various experimental conditions.
Dependent Measures
The dependent measure of interest, consumers' assessments of how likely they would be to use (continue using) the service again this month, was measured immediately afterward. These intentions were measured on a seven-point scale ranging from 1 = "not very likely to use [continue using] the service again this month" to 7 = "very likely to continue."
In addition, subjects were asked to assess how much regret they would anticipate over dropping or discontinuing their hypothetical service relationship in error. These responses were also measured on a seven-point scale ranging from 1 = "not much" to 7 = "a great deal." Next, subjects were asked to rate both how favorably they would evaluate the service and how much they like the idea of a service such as QuickRuns.com (or Deliver_me.com).[ 7] However, because (as expected) no significant effects were found between conditions on this latter variable, it is excluded from further discussion. Finally, as part of the debriefing procedure, subjects were asked to report their conjectures about the purpose and intent of the experiment. One hundred fifty-three subjects reported no knowledge about the purpose of the experiment, whereas seven subjects indicated that some, albeit small, level of hypothesis guessing might have taken place. These subjects were excluded from further analysis.
Results
We tested the effects reported subsequently using a multifactor analysis of variance (ANOVA). Unless otherwise specified, we used two-tailed significance tests to estimate all simple effects. In Table 4 we report all effects estimated regarding the key dependant variable. In addition, we provide the means of the key dependent measure, namely, willingness to remain in the service in question, across experimental conditions.
H<SUB>4</SUB> predicts that, all else being equal, consumers asked to anticipate regret associated with dropping a service in error would be less likely to drop the service than would those who were not asked to make this consideration. The hypothesis was supported by a significant main effect for regret condition (F( 1,152) = 17.48, p < .0001). Subjects in the regret condition were significantly more likely (than those in the control) to remain in the service relationship (X<SUB>regret</SUB> = 5.0, X<SUB>control</SUB> = 4.2).
Our next prediction, H<SUB>5</SUB>, was that the extent to which consumers who are actively considering anticipated regret expect to anticipate regret over dropping a given service would be affected by the type of service in question. Specifically, we hypothesized that subjects would anticipate greater regret over dropping ongoing versus transaction-based services in error. Our findings support this prediction. We find a significant interaction effect between the regret condition and the service type variable (F( 1,152) = 4.91, p < .05). As expected, among subjects in the anticipated regret conditions, subjects in the ongoing service relationships reported that they would anticipate feeling more regret over dropping their service in error than did those in the transaction-based services (X<SUB>ongoing</SUB> = 5.1, X<SUB>trans-based</SUB> = 4.2; t(152) = 2.16, p < .05). Also as expected, the difference between anticipated regret for subjects in ongoing versus transaction-based service relationships was not statistically significant for those in the control (or no-regret) groups (X<SUB>ongoing</SUB> = 3.9, X<SUB>trans-based</SUB> = 4.2; t(152), p > .5).
In H<SUB>6</SUB>, we predicted that the effect of regret would be moderated by the type of service considered, such that the effect of regret on the keep/drop decision would be greater for ongoing than transaction-based services. Therefore, we sought a significant regret × service type interaction. Our results also show support for this hypothesis. Specifically, the predicted interaction was significant (F( 1,152) = 4.77, p < .05). In line with the theory developed, subjects were more greatly affected by concerns for regret in the ongoing service conditions (X<SUB>regret</SUB> = 5.3 versus X<SUB>control</SUB> = 4.1) than in transaction-based service conditions (X<SUB>regret</SUB> = 4.7 versus X<SUB>control</SUB> = 4.3). Additional analysis revealed that, as expected, the simple effect of regret was significant within levels of both service types (t(152) = 4.52, p < .001 and t(152) = 1.41, p<SUB>1-tailed</SUB> < .05, respectively).[ 8]
With respect to the predicted regret × satisfaction interaction, our hypothesis (H<SUB>7</SUB>) was supported. The regret × satisfaction interaction was significant (F( 1,152) = 14.52, p < .001), revealing that the effect of regret (versus no regret) on dissatisfied subjects (X<SUB>disat, regret</SUB> = 3.9 versus X<SUB>disat, control</SUB> = 2.3) was greater than on subjects in the satisfaction conditions (X<SUB>sat, regret</SUB> = 6.10 versus X<SUB>sat, control</SUB> = 6.05). Additional analysis revealed that the simple effect of regret was significant within the dissatisfied conditions (i.e., dissatisfied with regret prime versus dissatisfied without regret prime) (t(152) = 5.67, p < .0001). However, thinking about regret did not significantly alter satisfied subjects' responses (versus the control group) (t(152), p > .5).
Finally, in H<SUB>8</SUB>, we predicted that the differential effect of regret on dissatisfied compared with satisfied consumers (i.e., the interaction predicted in H<SUB>7</SUB>) would be even greater for consumers in ongoing services than for those in transaction-based services. Although the means are in the predicted direction, we did not find a significant three-way interaction among regret, satisfaction, and service type (F( 1,152) = .14, p > .5). In other words, the type of service in question did not further moderate the interactive effects of regret and satisfaction.
Study 2 Discussion
The results of this study show support for the predicted influence of anticipated regret, a second future-oriented consideration, on consumers' keep/drop decisions. Overall, we find that when consumers are primed merely to consider the regret they might experience from dropping (or discontinuing) a given service in error, they are more likely to continue consuming (or repatronizing) the service. As expected, the level of satisfaction in question moderated the main effect for anticipated regret. However, our simple effects analysis revealed that the effect of regret was limited to consumers who were dissatisfied (supporting H<SUB>7</SUB>). Although we expected a relatively small effect of regret on those who were satisfied, our results show no effect of anticipated regret on these consumers. The marketing implications of these results are discussed in the subsequent section.
The results also suggest that the amount of regret consumers expect to feel over a given keep/drop decision is significantly influenced by the type of service relationship in question; consumers anticipated feeling more regret over dropping ongoing versus transaction-based service relationships. As a result, consumers in ongoing services, for whom dropping the service required a deviation from the default, were even more hesitant to drop the relationship than those who were deciding whether to repurchase or reuse the more transaction-based service, even when the latter consumers were dissatisfied. These results provide support for the conceptual distinction drawn between the two service types.
We did not find that the predicted relationship between regret and satisfaction was further moderated by the nature of the service in question. In other words, we did not find, as we predicted in H<SUB>8</SUB>, that dissatisfied consumers asked to consider anticipated regret would be more likely to remain in the ongoing than the transaction-based relationship. Although we failed to find statistically significant differences, the results indicate that the trend in means is as predicted.
Taken together, the findings support our assertion that consumers are forward-looking with respect to the decision to remain in or leave service relationships. This forward-looking aspect of consumer decision making has several implications for marketing theory and practice.
Theoretical Implications
First, our framework motivates the inclusion of forward-looking aspects into models predicting customer retention. This inclusion is validated not only by our findings regarding the impact of regret on satisfaction but also by the demonstrated effect of expected future use considerations on the keep/drop decision. Specifically, this article contributes to the literature on customer retention in that it investigates the relatively underresearched impact of future use considerations on the customer's decision to remain in or leave the service relationship. We provide empirical and causal evidence to show that customers' future expectations of usage of and benefits from a service relationship have a significant influence on customer retention. We believe that these results extend to many customer-product relationships as well (consider consumers who take vitamin supplements, for example), though we do not specifically test these extensions here.[ 9]
Second, the results provide new insights into the antecedents of customers' perceptions of expected future use as well. We find support for our hypotheses that customers update their expectations of future use following an adaptive expectations approach, incorporating recent usage experiences into their next-period expectations. Taken together with the finding that expected future use is a key element in the customer keep/drop decision, these findings provide additional understanding of this key construct.
Third, our finding that the influence of regret on consumers' decisions is stronger for ongoing services than transaction-based services provides additional insight into the customer retention decision. In particular, by using norm theory to conceptually distinguish ongoing from transaction-based service types, we introduce one theoretical basis for predictions and managerial insights regarding these disparate service types. Our research also highlights yet another marketing domain, consumers' keep/drop decisions, in which the extant literature on anticipated regret seems particularly instructive. Similar to Simonson's (1992) findings, our data show that anticipated future regret can increase status quo-oriented behavior in this domain. In addition, our investigations enable us to test the relative strength of this status quo effect by demonstrating its persistence in situations in which consumers are clearly dissatisfied with the current course of action. Moreover, we advance the understanding of the effects of anticipated regret on consumer behavior by demonstrating how anticipated regret affects the keep/drop decision and how the effects of anticipated regret differ depending on the context of the decision (e.g., ongoing relationship versus transactional exchange).
Finally, this article contributes to the customer retention literature by testing a model in which customer retention is measured as actual customer behavior rather than as "intention to repurchase" the service. Many prior studies (Anderson and Sullivan 1993; Boulding et al. 1993; Zeithaml and Parasuraman 1996) find a strong link among customer satisfaction, perceived service quality, and intent to repurchase, but they have not observed the customer's actions directly (i.e., whether the customer actually remained in or severed the relationship). In light of research by Morwitz, Johnson, and Schmittlein (1993) that suggests that simply measuring intent to purchase or repurchase may affect actual behavior, directly observing customers' actions can reveal greater insights into this relationship.
It is useful to contrast our model with an updating approach to consumer decision making (e.g., Boulding et al. 1993; Rust et al. 1999). In an updating framework, consumers incorporate new experiences of a product or service by updating their existing perceptions of the product or service (e.g., overall satisfaction) on the basis of the new experience. This new, updated perception would then be used as the basis for the decision to continue or discontinue the service relationship. In contrast, our results suggest that it is important to consider the process by which and the extent to which consumers' evaluations of the future benefits (or losses) they expect to receive from the service influence their current decisions. As such, we provide a deeper understanding of the differential effects of these future states (expected future use and anticipated regret) on consumers' keep/drop decisions, over and above the traditional service quality and satisfaction updating mechanism described previously.
Implications for Marketing Practice
The forward-looking model of consumers' keep/drop decisions provides significant insights for marketing managers and their customers. Marketers have moved from a world in which a static understanding of customers (e.g., demographics, psychographics, current satisfaction, current purchase patterns) was sufficient. Marketers now need to understand customers in a dynamic, changing environment and engage in dynamic customer relationship management-understanding that consumers take into account aspects of the past, present, and future, including future expectations (of themselves and of the firm), when determining whether to continue to do business with a firm. If firms fail to take into account this idea that consumers are involved planners and forecasters, as well as "evaluators" of their services, they will miss a key opportunity to manage the customer relationship.
If a firm wants to retain current customers, customer expectations of future benefits should be a primary focus. We have shown that customers' expectations of their own behavior are key in the decision to keep or drop a service. Marketing managers need t consider how such expectations can be managed. In addition to customer satisfaction, firms should measure customers' expected future benefits (e.g., anticipated usage, anticipated future changes) and current usage levels.
Marketing strategies for both new and existing customers should take customer future expectations into account, considering how each element of the marketing mix (e.g., changes in the service, marketing communications, pricing strategy) may affect customers' current usage levels and expectations of future use. For example, will a new service attribute encourage customers to use the service more (or to expect to use the service more)? Marketing actions that either ( 1) increase customer expectations of future use or ( 2) increase actual usage should provide a significant increase in the likelihood that the customer continues the relationship.
In addition, the finding that anticipated future regret significantly influences the customer's decision to keep or drop the service presents an exciting marketing opportunity. Marketing managers, especially in ongoing services, may find it useful to integrate anticipated regret into marketing communications, retention-based marketing, and other interactions with the customer. By making this potential regret salient to customers before the keep/drop decision, firms may be able to reduce churn rates and may get a "second chance"-to turn a dissatisfied customer into a satisfied customer or to regain the trust of a customer who otherwise would not have been retained. Marketing strategies, especially those designed to maintain or enhance relationships with existing customers, should be designed with these ideas in mind. In particular, firms should consider how other aspects of the strategy (e.g., communications placement and content, service enhancement announcements, Internet strategy) might "prime" anticipated regret in current customers and the effect of this potential regret on the customer's relationship with the firm.
This research has implications for consumers as well. As more and more firms seek to build and strengthen relationships with their customers, consumers need to examine whether these relationships provide real value (see Fournier, Dobscha, and Mick 1998). The results of this research suggest a consumer bias toward anticipated regret when a service is thought of as an ongoing relationship rather than as a simple transactional exchange. This suggests that consumers might want to examine whether there is truly additional utility provided by a firm's "relational positioning," so as not to be overly influenced by the marketer's relationship-building efforts.
Taken together, the results presented here suggest that firms that consider satisfaction to be the primary tool to manage customer retention are missing significant opportunities. Our findings suggest that consumers are significantly forward-looking when they make the decision to continue (or discontinue) a service relationship. Failure to consider these components may lead firms to underestimate the likelihood that satisfied customers may defect and to overestimate defection rates for dissatisfied customers, thereby potentially misallocating resources to customer retention efforts.
It is important to note the limitations of this research. First, we have only begun to examine the influence of consumers' forward-looking considerations on the retention decision. Further research should examine aspects such as expectations of future satisfaction and a broader view of expectations of future benefits to be derived from the relationship. Second, although our first study uses real-world customer data, our second study entails a laboratory experiment in which hypothetical scenarios were used. The strength of the laboratory experiment is that it has strong internal validity and provides a critical test of the theoretical framework (for discussions, see Lynch 1999; Plott 1991). However, further research should examine the anticipated regret in an actual (i.e., not imagined) purchase or repurchase setting. Third, our findings are limited to the effects of these future considerations in only two industries, entertainment and grocery delivery. Further studies should test these hypotheses in other industries. Finally, the model does not take into account noneconomic costs of discontinuing service (e.g., opportunity costs, psychological costs). More important, in further research, we hope to examine more fully the interactions of past-focused measures (other than satisfaction), situational variables (such as amount of time the customer has available), and these future-focused aspects of the decision.
In summary, firms must recognize that consumers are active forecasters, taking future considerations into account in their current decision-making efforts. As a result, firms must begin to develop dynamic customer relationship management strategies. These strategies should take into account not only the actions the firm takes to build and manage the relationship but also, insofar as is possible, the future projections of customers.
1 To examine the possibility of nonresponse bias, we compared the percentage of respondents who chose to keep the service (75.4%) with the firm's overall retention rate for the same period. The two were not statistically different, which suggests that the sample did not differ markedly from the overall customer base. Some households dropped the service after the first wave of surveys. Estimating the model on this early sample produced results consistent with those reported here.
- 2 Using the Schwartz Bayesian criterion instead of AIC leads to similar conclusions (see Table 2).
- 3 In addition to the results presented previously, we created calibration and holdout samples to determine how well expected future use and/or overall satisfaction predict customers' keep/drop decisions. These results also support the hypotheses and are available directly from the authors.
- 4 Alternatively, a log-likelihood ratio test provides the same conclusion. The addition of OVSAT to the model solely containing EFU results in only a slight improvement in the log-likelihood (χ² = 1.91, not significant).
- 5 It should be noted that our focus in these hypotheses is the effects on anticipated regret over dropping (or discontinuing) a given service "in error." The effects of anticipated regret over keeping a service in error might also be examined. We would expect that this type of regret would be more influential for transaction-based than ongoing service types (because keeping the service reflects a change from the default for transaction-based services).
- 6 Because we are interested in the effects and not the triggers of anticipated regret, we deliberately provided no information regarding ( 1) the nature of the commercial or ( 2) why the participants found out later that they "shouldn't have" dropped the service. This manipulation is in line with those used in prior experiments in the marketing literature (e.g., Simonson 1992).
- 7 Responses on this variable indicate that, as expected, subjects in the satisfied conditions rated their services significantly higher than did those in the dissatisfied conditions (F( 1,152) = 265.05, p < .0001).
- 8 We find a simple effect in which, within the satisfied/no regret condition, participants in the transaction-based service condition are more likely to stay than those in the ongoing service condition. However, the overall three-way interaction is not significant. This simple effect was not expected but suggests that perhaps the distinctions between transaction-based and ongoing service relationships warrant further research.
- 9 We thank an anonymous reviewer for this suggestion.
Variables Study 1 Measure
Keep/Drop<SUB>T</SUB> Coded as 1 (keep) or 0 (drop) on the basis
of account status on June 30, 1994
(measured at point t + 1).
EFU<SUB>t</SUB> How often do you expect to play XXX in the
next three months? Number of days per week
(measured at point t).
OVSAT<SUB>t</SUB> How satisfied are you with XXX overall?
Extremely dis/satisfied (measured on a
seven-point scale, at point t).
Usage<SUB>t</SUB> Please indicate how often you play XXX?
Number of days per week (measured at
point t). Model 1 Model 3
Expected Model 2 Expected
Future Use Satisfaction Future Use
and only Only
Satisfaction (Standard (Standard
Variable (Standard Error) Error) Only Error)
Intercept -1.332* -1.176* -.534*
(.654) (.580) (.278)
OVSAT .188 .473** NA
(.137) (.118)
EFU .597** NA .641*
(.110) (.101)
-2LL 153.265 195.730 155.181
AIC 159.265 199.730 159.181
Schwartz Bayesian 169.022 206.234 165.686
criterion
χ² 59.883** 17.418** 57.97**
Number of 191 191 191
observations
*Results are significant at p < .05.
**Results are significant at p < .01.1 EFU<SUB>t</SUB> EFU<SUB>t</SUB>
Variables (restricted) (unrestricted)
Intercept -.922* -.795
(.459) (.483)
EFU<SUB>t-1</SUB> .267*** .231***
(.054) (.068)
Usage<SUB>t</SUB> .733** .708***
(.054) (.062)
DirectMkt<SUB>t-1</SUB> .004 .020
(.043) (.046)
MassMkt<SUB>t-1</SUB> .132 .134
(.129) (.129)
Restriction -30.656 NA
(35.999)
Adjusted R² .59 .59
Number of
observations 182[a] 182[a]
* Results are significant at p < .05.
** Results are significant at p < .01.
*** Results are significant at p < .001.
[a]Observations were deleted because of missing observations for the
independent and/or dependent variables. Full Model ANOVA (Dependent Variable:
Keep Likelihood)
Effect F-Value Probability > F
Satisfaction 246.75 .0001
Regret condition 17.58 .0005
Satisfaction × regret 1.71 .1932
Service type 14.52 .0002
Service type × 6.79 .01
satisfaction
Service type × regret 4.77 .03
Service type ×
satisfaction × regret .76 .38
Cell Means Across Experimental Conditions
(Dependent Variable: Keep Likelihood*)
Satisfaction Regret primed Ongoing service
6.1[a] 6.1[a] 6.3[a]
Transaction-based
service
5.3[b]
No regret primed Ongoing service
6.1[a] 5.6[b]
Transaction-based
service
6.5[a]
Dissatisfaction Regret primed Ongoing service
3.1[b] 3.9[a] 4.4[c]
Transaction-based
service
3.4[d]
No regret primed Ongoing service
2.4[b] 2.6[e]
Transaction-based
service
2.1[e]
*Higher numbers are associated with a greater reported likelihood of
keeping (continuing) the service in question.
[a],[b],[c],[d],[e] Column means with different superscripts are
significantly different at p < .05.DIAGRAM: FIGURE 1: The Forward-Looking Model of the Keep/Drop Decision
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~~~~~~~~
By Katherine N. Lemon; Tiffany Barnett White and Russell S. Winer
Katherine N. Lemon is an assistant professor, Carroll School of Business, Boston College. Tiffany Barnett White is Assistant Professor of Business Administration, University of Illinois at Urbana. Russell S. Winer is J. Gary Shansby Professor of Marketing Strategy, Haas School of Business, University of California at Berkeley. The authors thank Jim Bettman, Bill Boulding, Julie Edell, Joel Huber, Rick Staelin, Devanathan Sudharshan, and the three anonymous JM reviewers for their valuable comments.
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Record: 59- Entry Barriers: A Dull-, One-, or Two-Edged Sword for Incumbents? Unraveling the Paradox from a Contingency Perspective. By: Han, Jin K.; Namwoon Kim; Hong-Bumm Kim. Journal of Marketing. Jan2001, Vol. 65 Issue 1, p1-14. 14p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.65.1.1.18133.
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Record: 60- Evolving to a New Dominant Logic for Marketing. By: Vargo, Stephen L.; Lusch, Robert F. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p1-17. 17p. 3 Charts. DOI: 10.1509/jmkg.68.1.1.24036.
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Evolving to a New Dominant Logic for Marketing
Marketing inherited a model of exchange from economics, which had a dominant logic based on the exchange of "goods," which usually are manufactured output. The dominant logic focused on tangible resources, embedded value, and transactions. Over the past several decades, new perspectives have emerged that have a revised logic focused on intangible resources, the cocreation of value, and relationships. The authors believe that the new perspectives are converging to forma new dominant logic for marketing one in which service provision rather than goods is fundamental to economic exchange, The authors explore this evolving logic and the corresponding shift in perspective for marketing scholars, marketing practitioners, and marketing educators.
The forma1 study of marketing focused at first on the distribution and exchange of commodities and manufactured products and featured a foundation in economics (Marshall 1927; Shaw 1912; Smith 1904). The first marketing scholars directed their attention toward commodities exchange (Copeland 1920), the marketing institutions that made goods available and arranged for possession (Nystrom 1915; Weld 1916), and the functions that needed to be performed to facilitate the exchange of goods through marketing institutions (Cherington 1920; Weld 1917).
By the early 1950s, the functional school began to morph into the marketing management school, which was characterized by a decision-making approach to managing the marketing functions and an overarching focus on the customer (Drueker 1954; Levitt 1960; McKitterick 1957). MeCarthy (1960) and Kotler (1967) characterized marketing as a decision-making activity directed at satisfying the customer at a profit by targeting a market and then making optimal decisions on the marketing mix, or the "4 P's." The fundamental foundation and the tie to the standard economic model continued to be strong. The leading marketing management textbook in the 1970s (Kotler 1972, p. 42, emphasis in original) stated that "marketing management seeks to determine the settings of the company's marketing decision variables that will maximize the company's objective(s) in the light of the expected behavior of noncontrollable demand variables.'"
Beginning in the 1980s, many new frames of reference that were not based on the 4 P's and were largely independent of the standard microeconomic paradigm began to emerge. What appeared to be separate lines of thought surfaced in relationship marketing, quality management, market orientation, supply and value chain management, resource management, and networks. Perhaps most notable was the emergence of services marketing as a subdiscipline, following scholars' challenges to "break free" (Shostack 1977) from product marketing and recognize the inadequacies of the dominant logic for dealing with services marketing's subject matter (Dixon 1990). Many scholars believed that marketing thought was becoming more fragmented. On the surface, this appeared to be a reasonable characterization.
In the early 1990s, Webster (1992, p. 1) argued, "The historical marketing management function, based on the microeconomic maximization paradigm, must be critically examined for its relevance to marketing theory and practice." At the end of the twentieth century, Day and Montgomery (1999, p. 3) suggested that "with growing reservation about the validity or usefulness of the Four P's concept and its lack of recognition of marketing as an innovating or adaptive force, the Four P's now are regarded as merely a handy framework." At the same rime, advocating a network perspective, Achrol and Kotler (1999, p. 162) stated, "The very nature of network organization, the kinds of theories useful to its understanding, and the potential impact on the organization of consumption all suggest that a paradigm shift for marketing may not be far over the horizon." Sheth and Parvatiyar (2000, p. 140) suggested that "an alternative paradigm of marketing is needed, a paradigm that can account for the continuos nature of relationships among marketing actors." They went as far as stating (p, 140) that the marketing discipline "give up the sacred cow of exchange theory." Other scholars, such as Rust (1998), called for convergence among seemingly divergent views.
Fragmented thought, questions about the future of marketing, calls for a paradigm shift, and controversy over services marketing being a distinct area of study--are these calls for alarm? Perhaps marketing thought is not so much fragmented as it is evolving toward a new dominant logic. Increasingly, marketing has shifted much of its dominant logic away from the exchange of tangible goods (manufactured things) and toward the exchange of intangibles, specialized skills and knowledge, and processes (doing things for and with), which we believe points marketing toward a more comprehensive and inclusive dominant logic, one that integrates goods with services and provides a richer foundation for the development of marketing thought and practice.
Rust (1998, p. 107) underscores the importance of such an integrative view of goods and services: "[T]he typical service research article documented ways in which services were different from goods. ... It is time for a change. Service research is not a niche field characterized by arcane points of difference with the dominate goods management field." The dominant, goods-centered view of marketing not only may hinder a full appreciation for the role of services but also may partially block a complete understanding of marketing in general (sec, e,g., Gronroos 1994; Kotler 1997; Normann and Ramirez 1993; Schlesinger and Heskett: 1991). For example, Gummesson (1995, pp. 250-51, emphasis added) states the following:
Customers do not buy goods or services: [T]hey buy offerings which render services which create value. ... The traditional division between goods and services is long outdated. It is not a matter of redefining services and seeing them from a customer perspective; activities render services, things render services. The shift in focus to services is a shift from the means and the producer perspective to the utilization and the customer perspective.
The purpose of this article is to illuminate the evolution of marketing thought toward a new dominant logic. A summary of this evolution over the past 100 years is provided in Table 1 and Figure 1. Briefly, marketing has moved from a goods-dominant view, in which tangible output and discrete transactions were central, to a service-dominant view, in which intangibility, exchange processes, and relationships are central. It is worthwhile to note that the service-centered view should not be equated with (1) the restricted, traditional conceptualizations that often treat services as a residual (that which is not a tangible good; e.g., Rathmell 1966); (2) something offered to enhance a good (value-added services); or (3) what have become classified as services industries, such as health care, government, and education. Rather, we define services as the application, of specialized competencies (knowledge and skills) through deeds, processes, and performances for the benefit of another entity or the entity itself. Although our definition is compatible with narrower, more traditional definitions, we argue that it is more inclusive and that it captures the fundamental function of all business enterprises.[1] Thus, the service-centered dominant logic represents a reoriented philosophy that is applicable to ail marketing offerings, including those that involve tangible output (goods) in the process of service provision.
To unravel the changing worldview of marketing or its dominant logic, we must see into, through, and beyond the extant marketing literature. A worldview or dominant logic is never clearly stated but more or less seeps into the individual and collective mind-set of scientists in a discipline. Predictably, this requires viewing the world at a highly abstract level. We begin our discussion with the work of Thomas Malthus.
In his analysis of world resources, Thomas Malthus (1798) concluded that with continued geometric population growth, society would soon run out of resources. In a Malthusian world, "resources" means natural resources that humans draw on for support. Resources are essentially "stuff" that is static and to be captured for advantage. In Malthus's time, much of the political and economic activity involved individual people, organizations, and nations working toward and struggling and fighting over acquiring this stuff. Over the past 50 years, resources have come to be viewed not only as stuff but also as intangible and dynamic functions of human ingenuity and appraisal, and thus they are not static or fixed. Everything is neutral (or perhaps even a resistance) until humankind learns what to do with it (Zimmerman 1951). Essentially, resources are not; they become. As we discuss, this change in perspective on resources helps provide a framework for viewing the new dominant logic of marketing.
Constantin and Lusch (1994) define operand resources as resources on which an operation or act is performed to produce effect, and they compare operand resources with operant resources, which are employed to act on operand resources (and other operant recourses). During most of civilization, human activity has been concerned largely with acting on the land, animal life, plant life, minerals, and other natural resources. Because these resources are finite, nations, clans, tribes, or other groups that possessed natural resources were considered wealthy. A goods-centered dominant logic developed in which the operand resources were considered primary. A firm (or nation) had factors of production (largely operant resources) and a technology (an operant resource), which had value to the extent that the firm could convert its operand resources into outputs at a low cost. Customers, like resources, became something to be captured or acted on, as English vocabulary would eventually suggest; we "segment" the market, "penetrate" the market, and "promote to" the market all in hope of attracting customers. Share of operand resources and share of (an operand) market was the key to success.
Operant resources are resources that produce effects (Constantin and Lusch 1994). The relative role of operant resources began to shift in the late twentieth century as humans began to realize that skills and knowledge were the most important types of resources. Zimmermann (1951) and Penrose (1959) were two of the first economists to recognize the shifting role and view of resources. As Hunt (2000, p. 75) observes, Penrose did not use the popular term "factor of production" but rather used the term "collection of productive resources." Penrose suggested (pp. 24-25; emphasis in original) that "it is never resources themselves that are the 'inputs' to the production process, but only the services that the resources can render."
Operant resources are often invisible and intangible; often they are core competences or organizational processes. They are likely to be dynamic and infinite and not static and finite, as is usually the case with operand resources. Because operand resources produce effects, they enable humans both to multiply the value of natural resources and to create additional operant resources. A well-known illustration of operant resources is the microprocessor: Human ingenuity and skills took one of the most plentiful natural resources on Earth (silica) and embedded it with knowledge. As Copeland (qtd. in Gilder 1984) has observed, in the end the microprocessor is pure idea. As we noted previously, resources are not; they become (Zimmermann 1951). The service-centered dominant logic perceives operant resources as primary, because they are the producers of effects. This shift in the primacy of resources has implications for how exchange processes, markets, and customers are perceived and approached.
Viewed in its traditional sense, marketing focuses largely on operand resources, primarily goods, as the unit of exchange. In its most rudimentary form, the goods-centered view postulates the following:
- The purpose of economic activity is to make and distribute things that can be sold.
- To be sold, these things must be embedded with utility and value during the production and distribution processes and must offer to the consumer superior value in relation to competitors' offerings.
- The firm should set all decision variables at a level that enables it to maximize the profit from the sale of output.
- For both maximum production control and efficiency, the good should be standardized and produced away from the market.
- The good can then be inventoried until it is demanded and then delivered to the consumer at a profit.
Because early marketing thought was concerned with agricultural products and then with other physical goods, it was compatible with this rudimentary view. Before 1960, marketing was viewed as a transfer of ownership of goods and their physical distribution (Savitt 1990); it was viewed as the "application of motion to matter" (Shaw 1912, p. 764). The marketing literature rarely mentioned "immaterial products" or services, and when it did, it mentioned them only as "aids to the production and marketing of goods" (Converse 1921, p. vi; see Fisk, Brown, and Bitner 1993). An early fragmentation in the marketing literature occurred when Shostack (1977, p. 73) noted, "The classical 'marketing mix,' the seminal literature, and the language of marketing ail derive from the manufacture of physical-goods."
Marketing inherited the view that value (utility) was embedded in a product from economics. One of the first debates in the fledgling discipline of marketing centered on the question, If value was something added to goods, did marketing contribute to value? Shaw (1912, p. 12; see also Shaw 1994) argued that "Industry is concerned with the application of motion to matter to change its form and place. The change in form we term production; the change in place, distribution." Weld (1916) more formally defined marketing's role in production as the creation of the time, place, and possession utilities, which is the classification found in current marketing literature.
The general concept of utility has been broadly accepted in marketing, but its meaning has been interpreted differently. For example, discussing Beckman's (1957) and Alderson's (1957) treatments of utility, Dixon (1990, pp. 337-38, emphasis in original) argues that "each writer uses a different concept of value. Beckman is arguing in terms of value-in-exchange, basing his calculation on value-added, upon 'the selling value' of products. ... Alderson is reasoning in terms of value-in-use." Drawing on Cox (1965), Dixon (1990, p. 342) believes the following:
The "conventional view'" of marking as adding properties to matter caused a problem for Alderson and "makes more difficult a disinterested evaluation of what marketing is and does" (Cox 1965). This view also underlies the dissatisfaction with marketing theory that led to the services maketing literature. If marketing is the process that adds properties to matter, then it can not contribute to the production of "immaterial goods."
Alderson (1957, p. 69) advised, "What is needed is not an interpretation of the utility created by marketing, but a marketing interpretation of the whole process of creating utility." Dixon (1990, p. 342) suggests that "the task of responding to Alderson's challenge remains."
The service-centered view of marketing implies that marketing is a continuous series of social and economic processes that is largely focused on operant resources with which the firm is constantly striving to make better value propositions than its competitors. In a free enterprise system, the firm primarily knows whether it is making better value propositions from the feedback it receives from the marketplace in terms of firm financial performance. Because firms can always do better at serving customers and improving financial performance, the service-centered view of marketing perceives marketing as a continuous learning process (directed at improving operant resources). The service-centered view can be stated as follows:
1. Identify or develop core competences, the fundamental knowledge and skills of an economic entity that represent potential competitive advantage.
- 2. Identify other entities (potential customers) that could benefit from these competences.
- 3. Cultivate relationships that involve the customers in developing customized, competitively compelling value propositions to meet specific needs,
- 4. Gauge marketplace feedback by analyzing financial performance from exchange to learn how to improve the firm's offering to customers and improve firm performance.
This view is grounded in and largely consistent with resource advantage theory (Conner and Prahalad 1996; Hunt 2000; Srivastava, Fahey, and Christensen 2001) and core competency theory (Day 1994; Prahalad and Hamel 1990). Core competences are not physical assets but intangible processes; they are "bundles of skills and technologies" (Hamel and Prahalad 1994, p. 202) and are often routines, actions, or operations that are tacit, causally ambiguous, and idiosyncratic (Nelson and Winter 1982; Polanyi 1966). Hunt (2000, p. 24) refers to core competences as higher-order resources because they are bundles of basic resources. Teece and Pisano (1994 p. 537) suggest that "the competitive advantage of firms stems from dynamic capabilities rooted in high performance routines operating inside the firm, embedded in the firm's processes, and conditioned by its history." Hamel and Prahalad (pp. 202, 204) discuss "competition for competency or competitive advantage resulting from competence making a "disproportionate contribution to customer-perceived value."
The focus of marketing on core competences inherently places marketing at the center of the integration of business functions and disciplines. As Prahalad and Hamel (1990, p. 82) suggest, "core competence is communication, involvement, and a deep commitment to working across organizational boundaries." In addition, they state (p. 82) that core competences are "collective learning in the organization, especially [about] how to coordinate diverse production skills." This cross-functional, intraorganizational boundary-spanning also applies to the interorganizational boundaries of vertical marketing systems or networks. Channel intermediaries and network partners represent core competences that are organized to gain competitive advantage by performing specialized marketing functions. The firms can have long-term viability only if they learn in conjunction with and are coordinated with other channel and network partners.
The service-centered view of marketing is customer-centric (Sheth, Sisodia, and Sharma 2000) and market driven (Day 1999). This means more than simply being consumer oriented; it means collaborating with and learning from customers and being adaptive to their individual and dynamic needs. A service-centered dominant logic implies that value is defined by and cocreated with the consumer rather than embedded in output. Haeckel (1999) observes successful firms moving from practicing a "make-and-sell" strategy to a "sense-and-respond" strategy. Day (1999, p. 70) argues for thinking in terms of self-reinforcing "value cycles" rather than linear value chains. In the service-centered view of marketing, firms are in a process of continual hypothesis generation and testing. Outcomes (e.g., financial) are not something to be maximized but something to learn from as firms try to serve customers better and improve their performance. Thus, a market-oriented and learning organization (Stater and Narver 1995) is compatible with, if not implied by, the service-centered model. Because of its central focus on dynamic and learned core competences, the emerging service-centered dominant logic is also compatible with emerging theories of the firm. For example, Teece and Pisano (1994, p. 540) emphasize that competences and capabilities are "ways of organizing and getting things done, which cannot be accomplished by using the price system to coordinate activity."
Having described the goods- and service-centered views of marketing, we turn to ways that the views are different. Six differences between the goods- and service-centered dominant logic, all centered on the distinction between operand and operant resources, are presented in Table 2. The six attributes and our eight foundational premises (FPs) help present the patchwork of the emerging dominant logic.
People have two basic operant resources: physical and mental skills. Both types of skills are distributed unequally in a population. Each person's skills are not necessarily optimal to his or her survival and well-being; therefore, specialization is more efficient for society and for individual members of society. Largely because they specialize in particular skills, people (or other entities) achieve scale effects. This specialization requires exchange (Macneil 1980; Smith 1904). Studying exchange in ancient societies, Mauss (1990) shows how division of labor within and between clans and tribes results in the tendering of "total services" by gift giving among clans and tribes. Not only do people contract for services from one another by giving and receiving gifts, but, as Mauss (p. 6) observes, "there is total service in the sense that it is indeed the whole clan that contracts on behalf of all, for all that it possesses and for all that it does."
This exchange of specializations leads to two views about what is exchanged. The first view involves the output from the performance of the specialized activities; the second involves the performance of the specialized activities. That is, if two parties jointly provide for each other's carbohydrate and protein needs by having one party specialize in fishing knowledge and skills and the other specialize in farming knowledge and skills, the exchange is one of fish for wheat or of the application of fishing knowledge or competence (fishing services) for the application of farming knowledge or competence (farming services).
The relationships between specialized skills and exchange have been recognized as far back as Plato's time, and the concept of the division of labor served as the foundation for Smith's (1904) seminal work in economics. However, Smith focused on only a subclass of human skills: the skills that resulted in surplus tangible output (in general, tangible goods and especially manufactured goods) that could be exported and thus contributed to national wealth. Smith recognized that the foundation of exchange was human skills as well as the necessity and usefulness of skills that did not result in tangible goods (i.e., services); they were simply not "productive" in terms of his national wealth standard. More than anything else, Smith was a moral philosopher who had the normative purpose of explaining how the division of labor and exchange should contribute to social well-being. In the sociopolitical milieu of his time, social well-being was defined as national wealth, and national wealth was defined in terms of exportable things (operand resources). Thus, for Smith, "productive" activity was limited to the creation of tangible goods, or output that has exchange value.
At that time, Smith's focus on exchange value represented a departure from the more accepted focus on value in use, and it had critical implications for how economists, and later marketers, would view exchange. Smith was aware of the schoolmen's and early economic scholars' view that "The Value of all Wares arises from their use" (Barbon 1903, p. 21) and that "nothing has a price among men except pleasure, and only satisfactions are purchased" (Galiani qtd. in Dixon 1990, p. 304). But this use-value interpretation was not consistent with Smith's national wealth standard. For Smith, "wealth consisted of tangible goods, not the use made of them" (Dixon 1990, p. 340). Although most early economists (e.g., Mill 1929; Say 1821) took exception to this singular focus on tangible output, they nonetheless acquiesced to Smith's view that the proper subject matter for economic philosophy was the output of "productive" skills or services, that is, tangible goods that have embedded value.
Frederic Bastiat was an early economic scholar who did not acquiesce to the dominant view. Bastiat criticized the political economists' view that value was tied only to tangible objects. For Bastiat (1860, p. 40), the foundations of economics were people who have "wants" and who seek "satisfactions." Although a want and its satisfaction are specific to each person, the effort required is often provided by others. For Bastiat (1964, pp, 161-62), "the great economic law is this: Services are exchanged for services. ... It is trivial, very commonplace; it is, nonetheless, the beginning, the middle, and the end: of economic science." He argued (1860, p, 43) the following: "[I]t is in fact to this faculty ... to work the one for the other; it is this transmission of efforts, this exchange of services [this emphasis added], with all the infinite and involved combinations to which it gives rise ... which constitutes Economic Science, points out its origin, and determines its limits.
Therefore, value was considered the comparative appreciation of reciprocal skills or services that are exchanged to obtain utility; value meant "value in use." As Mill (1929) did, Bastiat recognized that by using their skills (operant resources), humans could only transform matter (operand resources) into a state from which they could statisfy their desires.
However, the narrower focus on the tangible output with exchange value had several advantages for the early economists' quest of turning economic philosophy into an economic science, not the least of which was economies' similarity to the subject matter of the archetypical science of the day: Newtonian mechanics. The treatment of value as embedded utility, or value added (exchange value), enabled economists (e.g., Marshall 1927; Walras 1954) to ignore both the application of mental and physical skills (services) that transformed matter into a potentially useful state and the actual usefulness as perceived by the consumer (value in use). Thus, economics evolved into the science of matter (tangible goods) that is embedded with utility, as a result of manufacturing, and has value in exchange.
It was from this manufacturing-base view of economics that marketing emerged 100 years later. Throughout the period that marketing was primarily concerned with the distribution of physical goods, the goods-centered model was probably adequate. However, as the focus of marketing moved away from distribution and toward the process of exchange, economists began to perceive the accepted idea of marketing adding time, place, and possession utility (Weld 1916) as inadequate. As we noted previously, Alderson (1957, p. 69) advised, "What is needed is not an interpretation of the utility created by marketing, but a marketing interpretation of the whole process of creating utility" Shostack (1977, p. 74) issued a much more encompassing challenge than to "break [services marketing] free from product marketing"; she argued for a "new conceptual framework" and suggested the following:
One unorthodox possibility can be drawn from direct observation of the nature of market "satisfiers" available to it. ... How should the automobile be defined? Is General Motors marketing a service, a service that happens to include a by-product called a car? Levitt's classic "Marketing Myopia" exhorts businessmen to think exactly this generic way about what they market. Are automobiles "tangible services"?
Shostack concluded (p. 74) that "if 'either-or' terms (product [versus] service) do not adequately describe the true nature of marketed entities, it makes sense to explore the usefulness of a new structural definition." We believe that the emerging service-centered model meets Shostack's challenge, addresses Alderson's argument, and elaborates on Levitt's (1960) exhortation.
Over time, exchange moved from the one-to-one trading of specialized skills to the indirect exchange of skills in vertical marketing systems and increasingly large, bureaucratic, hierarchical organizations. During the same time, the exchange process became increasingly monetized. Consequently, the inherent focus on the customer as a direct trading partner largely disappeared. Because of industrial society's increasing division of labor, its growth of vertical marketing systems, and its large bureaucratic and hierarchical organizations, most marketing personnel (and employees in general) stopped interacting with customers (Webster 1992). In addition, because of the confluence of these forces, the skills-for-skills (services-for-services) nature of exchange became masked.
The Industrial Revolution had a tremendous impact on efficiency, but this came at a price, at least in terms of the visibility of the true nature of exchange. Skills (at least "manufacturing" skills, such as making sharp sticks) that had been tailored to specific needs were taken out of cottage industry and mechanized, standardized, and broken down into skills that had increasingly narrow purposes (e.g., sharpening one side of sticks). Workers' specialization increasingly became microspecialization (i.e., the performance of increasingly narrow-skilled proficiencies). Organizations acquired and organized microspecializations to produce what people wanted, and thus it became easier for people to engage in exchange by providing their microspecializations to organizations. However, the microspecialists seldom completed a product or interacted with a customer. They were compensated indirectly with money paid by the organization and exchangeable in the market for the skills the microspecialists needed rather than with direct, reciprocal skill-provision by the customer. Thus, organizations further masked the skills-for-skills (services-for-services) nature of exchange. Organizations themselves specialized (e.g., by making sticks but relying on other organizations such as wholesalers and retailers to distribute them), thus further masking the nature of exchange.
As organizations continued to increase in size, they began to realize that virtually all their workers had lost sense of both the customer (Hauser and Clausing 1988) and the purpose of their own service provision. The workers, who performed microspecialized functions deep within the organization, had internal customers, or other workers. One worker would perform a microspecialized task and then pass the work product on to another worker, who would perform an activity; this process continued throughout a service chain. Because the workers along the chain did not pay one another (reciprocally exchange with one another) and did not typically deal directly with external customers, they could ignore quality and both internal and external customers. To correct for this problem, various management techniques were developed under the rubric of total quality management (Cole and Mogab 1995). The techniques were intended to reestablish the focus of workers and the organization on both internal and external customers and quality.
The problem of organizations and their workers not paying attention to the customer is not unique to manufacturing organizations. If an organization simply provides intangibles, has some microspecialists who interact with customers, and is in an industry categorized as a "service" industry, it is not necessarily more customer focused. Many non-goods-producing organizations, especially large bureaucracies, are just as subject as goods-producing institutions to the masking effect of indirect exchange; they also provide services through organized microspecializations that are focused on minute and isolated aspects of service provision.
Regardless of the type of organization, the fundamental process does not change; people still exchange their often collective and distributed specialized skills for the individual and collective skills of others in monetization and marketing systems. People still exchange their services for other services. Money, goods, organizations, and vertical marketing systems are only the exchange vehicles.
The view of tangible products as the fundamental components of economic exchange served reasonably well as "Western societies entered the Industrial Revolution, and the primary interest of the developing science of economics was manufacturing. Given its early concerns with the distribution of manufactured and agricultural goods, the view also worked relatively well when it was adopted by marketing. However, marketing has moved well beyond distribution and is now concerned with more than the exchange of goods. Goods are not the common denominator of exchange; the common denominator is the application of specialized knowledge, mental skills, and, to a lesser extent, physical labor (physical skills).
Knowledge and skills can be transferred (1) directly, (2) through education or training, or (3) indirectly by embedding them in objects. Thus, tangible products can be viewed as embodied knowledge or activities (Normann and Ramirez 1993). Wheels, pulleys, internal combustion engines, and integrated chips are all examples of encapsulated knowledge, which informs matter and in turn becomes the distribution channel for skill application (i.e., services).
The matter, embodied with knowledge, is an "appliance" for the performance of services; it replaces direct service. Norris (1941, p. 136) was one of the first scholars to recognize that people want goods because they provide services. Prahalad and Hamel (1990, p. 85) refer to products (goods) as "the physical embodiments of one or more competencies." The wheel and pulley reduce the need for physical strength. A pharmaceutical provides medical services. A well-designed and easy-to-use razor replaces barbering services, and vacuum cleaners and other household appliances make household chores less labor intensive. Computers and applications software can substitute for the direct services of accountants, attorneys, physicians, and teachers. Kotler (1977, p. 8) notes that the "importance of physical products lies not so much in owning them as in obtaining the services they render." Gummesson (1995, p. 251 ) argues that "activities render services, things render services." Hollander (1979, p. 43) suggests that "services may be replaced by products" and compares barber shaves to safety razors and laundry services to washing machines.
In addition to their direct service provision, the appliances serve as platforms for meeting higher-order needs (Rifkin 2000). Prahalad and Ramaswamy (2000, p. 84) refer to the appliances as "artifacts, around which customers have experiences" (see also Pine and Gilmore 1999). Gutman (1982, p. 60) has pointed out that products are "means" for reaching "end-states," or "valued states of being, such as happiness, security, and accomplishment." That is, people often purchase goods because owning them, displaying them, and experiencing them (e.g., enjoying knowing that they have a sports car parked in the garage, showing it off to others, and experiencing its handling ability) provide satisfactions beyond those associated with the basic functions of the product (e.g., transportation). As humans have become more specialized as a species, use of the market and goods to achieve higher-order benefits, such as satisfaction, self-fulfillment, and esteem, has increased. Goods are platforms or appliances that assist in providing benefits; therefore, consistent with Gutman, goods are best viewed as distribution mechanisms for services, or the provision of satisfaction for higher-order needs.
Knowledge is an operant resource. It is the foundation of competitive advantage and economic growth and the key source of wealth. Knowledge is composed of propositional knowledge, which is often referred to as abstract and generalized, and prescriptive knowledge, which is often referred to as techniques (Mokyr 2002). The techniques are the skills and competences that entities use to gain competitive advantage. This view is consistent with current economic thought that the change in a firm's productivity is primarily dependent on knowledge or technology (Capon and Glazer 1987; Nelson, Peck, and Kalachek 1967). Capon and Glazer (1987) broadly define technology as know-how, and they identify three components of technology: (1) product technology (i.e., ideas embodied in the product), (2) process technology (i.e., ideas involved in the manufacturing process), and (3) management technology (i.e., management procedures associated with business administration and sales). Mokyr (2002) reviews historical developments in science and technology to demonstrate that the Industrial Revolution was essentially about the creation and dissemination of propositional and prescriptive knowledge.
In the neoclassical model of economic growth, the development of knowledge in society is exogenous to the competitive system. However, in Hunt's (2000) general theory of competition, knowledge is endogenous. The process of competition and the information provided by profits result in competition being a knowledge-discovery process (Hayek 1945; Hunt 2000). Therefore, not only are mental skills the fundamental source of competitive advantage, but competition also enhances mental skills and learning in society. Dickson (1992) suggests that the firms that do the best are the firms that learn most quickly in a dynamic and evolving competitive market.
Quinn, Doorley, and Paquette (1990, p. 60) state that "'physical facilities--including a seemingly superior product--seldom provide a sustainable competitive edge." Quinn, Doorley, and Paquette's suggestion that "a maintainable advantage usually derives from outstanding depth in selected human skills, logistics capabilities, knowledge bases, or other service strengths that competitors cannot reproduce and that lead to greater demonstrable value for the customer" is consistent with our own views. Normann and Ramirez (1993, p. 69) state, "the only true source of competitive advantage is the ability to conceive the entire value-creating system and make it work." Day (1994) discusses competitive advantage in terms of capabilities or skills, especially those related to market-sensing, customer-linking, and channel-bonding. Barabba (1996, p. 48) argues that marketing-based knowledge and decision making provide the core competence that "gives the enterprise its competitive edge." These views imply that operant resources, specifically the use of knowledge and mental competences, are at the heart of competitive advantage and performance.
The use of knowledge as the basis for competitive advantage can be extended to the entire "supply" chain, or service-provision chain. The goods-centered model necessarily assumes that the primary flow in the chain is a physical flow, but it acknowledges the existence of information flows. We argue that the primary flow is information; service is the provision of the information to (or use of the information for) a consumer who desires it, with or without an accompanying appliance. Evans and Wurster (1997, p. 72) state this idea as follows: "[T]he value chain also includes all the information that flows within a company and between a company and its suppliers, its distributors, and its existing or potential customers. Supplier relationships, brand identity, process coordination, customer loyalty, employee loyalty, and switching costs all depend on various kinds of information." Evans and Wurster suggest that every business is an information business. It is through the differential use of information, or knowledge, applied in concert with the knowledge of other members of the service chain that the firm is able to make value propositions to the consumer and gain competitive advantage. Normann and Ramirez (1993, pp. 65-66) argue that value creation should not be considered in terms of the "outdated" value-added notion, "grounded in the assumptions and models of an industrial economy," but in terms of the value created through "coproduction with suppliers, business partners, allies, and customers."
The move toward a service-dominant logic is grounded in an increased focus on operant resources and specifically on process management. Webster (1992) and Day (1994) emphasize the importance of marketing being central to cross-functional business processes. To better manage the processes, Moorman and Rust (1999) suggest that firms are shifting away from a functional marketing organization and toward a marketing process organization. Taking this even further, Srivastava, Shervani, and Fahey (1999, p. 168) contend that the enterprise consists of three core business processes: (1) product development management, (2) supply chain management, and (3) customer relationship management. They also contend that marketing must be a critical part of all these core business processes "that create and sustain customer and shareholder value." Similarly, Barabba (1996) argues that marketing is an organizational "state of mind."
As we have argued, the fundamental economic exchange process pertains to the application of mental and physical skills (service provision), and manufactured goods are mechanisms for service provision. However, economic science, as well as most classifications of economic exchange that are based on it, is grounded on Smith's narrowed concern with manufactured output. Consequently, services have traditionally been defined as anything that does not result in manufactured (or agricultural) output (e.g., Rathmell 1966).
In addition, as we have suggested, specialization breeds microspecialization; people are constantly moving toward more specific specialties. Over time, activities and processes that were once routinely performed internally by a single economic entity (e.g., a manufacturing firm) become separate specializations, which are then often outsourced (Shugan 1994). Giarini (1985, p. 134) refers to this increasing specialization as "complification." The complification process causes distortions in national economic accounting systems, such as the one used in the United States, that are based on types of output (e.g., agricultural, manufacturing, intangible). The U.S. government is aware of these distortions, as is evidenced in the Economic Classification Policy Committee of the Bureau of Economic Analysis's (1994, pp. 3-4) citation of Hill (1977, p. 320) on the issue:
[O]ne in the same activity, such as painting, may be classified as goods or service production depending purely on the organization of the overall process of production... If the painting is done by employees within the producer unit [that] makes the good, it will be treated as [part of] the goods production, whereas if it is done by an outside painting company, it will be classified as an intermediate input of services. Thus, when a service previously performed in a manufacturing establishment is contracted out, to a specialized services firm, data will show an increase in services production in the economy even though the total activity of "painting," may be unchanged.
It is because of the differentiation of specialized skills (services) in an output-based classification model rather than a fundamental economic shift that scholars definitionally, rather than functionally, have only recently considered that a shift is occurring toward a "services economy" (see Shugan 1994).
Similarly, economists have taught marketing scholars to think about economic development in terms of "eras" or "economies," such as hunter-gatherer, agricultural, and industrial. Formal economic thought developed during one of these eras, the industrial economy, and it has tended to describe economies in terms of the types of output, or operand resources (game, agricultural products, and manufactured products), associated with markets that were expanding rapidly at the time. However, the "economies" might be better viewed as macrospecializations, each characterized by the expansion and refinement of some particular type of competence (operant resource) that could be exchanged. The hunter-gatherer macrospecialization was characterized by the refinement and application of foraging and hunting knowledge and skills; the agricultural macrospecialization by the cultivation of "knowledge and skills; the industrial economy by the refinement of knowledge and skills for large-scale mass production and organizational management; and the services and information economies by the refinement and use of knowledge and skills about information and the exchange of pure, unembedded knowledge.
In both the classification of economic activity and the economic eras, the common denominator is the increased refinement and exchange of knowledge and skills, or operant resources. Virtually all the activities performed today have always been performed in some manner; however, they have become increasingly separated into specialties and exchanged in the market.
All this may seem to be an argument that traditional classificatory systems underestimate the historical role and rise of services. In a sense, it is. Services are not just now becoming important, but just now they are becoming more apparent in the economy as specialization increases and as less of what is exchanged fits the dominant manufactured output classification system of economic activity. Services and the operant resources they represent have always characterized the essence of economic activity.
From the traditional, goods-based, manufacturing perspective, the producer and consumer are usually viewed as ideally separated in order to enable maximum manufacturing efficiency. However, if the normative goal of marketing is customer responsiveness, this manufacturing efficiency comes at the expense of marketing efficiency and effectiveness. From a service-centered view of marketing with a heavy focus on continuous processes, the consumer is always involved in the production of value. Even with tangible goods, production does not end with the manufacturing process; production is an intermediary process. As we have noted, goods are appliances that provide services for and in conjunction with the consumer. However, for these services to be delivered, the customer still must learn to use, maintain, repair, and adapt the appliance to his or her unique needs, usage situation, and behaviors. In summary, in using a product, the customer is continuing the marketing, consumption, and value-creation and delivery processes.
Increasingly, both marketing practitioners and academics are shifting toward a continuous-process perspective, in which separation of production and consumption is not a normative goal, and toward a recognition of the advantages, if not the necessity, of viewing the consumer as a coproducer. Among academics, Normann and Ramirez (1993, p. 69) state that "the key to creating value is to coproduce offerings that mobilize customers." Lusch, Brown, and Brunswick (1992) provide a general model to explain how much of the coproduction or service provision the customer will perform. Oliver, Rust, and Varki (1998) echo and extend the idea of coproduction in their suggestion that marketing is headed toward a paradigm of "real-time" marketing, which integrates mass customization and relationship marketing by interactively designing evolving offerings that meet customers' unique, changing needs. Prahalad and Ramaswamy (2000) note that the market has become a venue for proactive customer involvement, and they argue for co-opting customer involvement in the value-creation process. In summary, the customer becomes primarily an operant resource (coproducer) rather than an operand resource ("target") and can be involved in the entire value and service chain in acting on operand resources.
As we noted previously, marketing inherited a view that value was something embedded in goods during the manufacturing process, and early marketing scholars debated the issue of the types and extent of the utilities, or value-added, that were created by marketing processes. This value-added view functioned reasonably well as long as the focus of marketing remained the tangible good. However, arguably, it was the inadequacy of the value-added concept that necessitated the delineation of the consumer orientation--essentially, the admonition that the consumer ultimately needed to find embedded value (value in exchange) useful (value in use). As Dixon (1990, p. 342) notes, the "conventional view of marketing adding properties to marketing ... underlies the dissatisfaction with marketing theory that led to the services marketing literature" (see also Shaw 1994).
Services marketing scholars have been forced both to reevaluate the idea of value being embedded in tangible goods and to redefine the value-creation process. As with much of the reexamination and redefinition that has originated in the services marketing literature, the implications can be extended to all of marketing. For example, Gummesson (1998, p. 247) has argued that "if the consumer is the focal point of marketing, value creation is only possible when a good or service is consumed. An unsold good has no value, and a service provider without customers cannot produce anything." Likewise, Gronroos (2000, pp. 24-25; emphasis in original) states,
Value for customers is created throughout the relationship by the customer, partly in interactions between the customer and the supplier or service provider. The focus is not on products but on the customers' value-creating processes where value emerges for customers and is perceived by them. ... the focus of marketing is value creation rather than value distribution, and facilitation and support of a value-creating process rather than simply distributing ready-made value to customers.
We agree with both Gummesson and Gronroos, and we extend their logic by noting that the enterprise can only offer value propositions; the consumer must determine value and participate in creating it through the process of coproduction.
If a tangible good is part of the offering, it is embedded with knowledge that has value potential for the intended consumer, but it is not embedded with value (utility). The consumer must understand that the value potential is translatable to specific needs through coproduction. The enterprise can only make value propositions that strive to be better or more compelling than those of competitors.
Interactivity, integration, customization, and coproduction are the hallmarks of a service-centered view and its inherent focus on the customer and the relationship. Davis and Manrodt (1996. p. 6) approach a service-centered view in their discussion of the customer-interaction process:
[It] begins with the interactive definition of the individual customers' problem, the development of a customized solution, and delivery of that customized solution to the customer. The solution may consist of a tangible product, an intangible service, or some combination of both. It is not the mix of the solution (be it product or service) that is important, but that the organization interacts with each customer to define the specific need and then develops a solution to meet the need.
It is in this sense of doing things, not just for the customer but also in concert with the customer, that the service-centered view emerges. It is a model of inseparability of the one who offers (and the offer) and the consumer. Barabba (1995, p. 14) extends the customer-centric idea to the "integration of the voice of the market with the voice of the enterprise," and Gummesson (2002) suggests the term "balanced centricity," concepts that may be particularly compatible with a services-for-services exchange perspective. We also suggest that the interactive and integrative view of exchange is more compatible with the other normative elements of the marketing concept, the idea that all activities of the firm be integrated in their market responsiveness and the idea that profits come from customer satisfaction (rather than units of goods sold) (Kohli and Jaworski 1990; Narver and Slater 1990). Notably, this view harks back to pre-lndustrial Revolution days, when providers were close to their customers and involved in relationships that offered customized services. Hauser and Clausing (1988, p. 64) observe the following:
Marketing, engineering, and manufacturing were integraated--in the same individuai. If a knight wanted armor, he talked directly to the armorer, who translated the knight's desires into a product, the two might discuss the material--plate rather than chain armor--and details like fluted surface for greater bending strength. Then the armorer would design the production process.
Consistent with this view, Gummesson (1998, p. 243) suggests that services marketing research, and its emphasis on relationships and interaction, is one of the two "most crucial contributions" to relationship marketing; the other is the network approach to industrial marketing. Similarly, Glynn and Lehthinen (1995) note that services scholars' recognition of characteristics of intangibility, inseparability, and heterogeneity has forced a focus on interaction and relationships. At least in the U.S. marketing literature (Berry 1983), the term "relationship marketing" originated in the services literature (Gronroos 1994).
Although the output-based, goods-centered paradigm is compatible with deterministic models of moving things through spatial dimensions (e.g., distribution of goods), it is considerably less compatible with models of relationship. In their role as distribution mechanisms the service provision (FP3), goods may be in instrumental in relationships, but they are not parties to the relationship; inanimate items of exchange cannot have relationships. Over the past 50 years, marketing has been transitioning from a product and production focus to a consumer focus and, more recently; from a transaction focus to a relationslhip focus. The common denominator of this customer-centric, relational focus is a view of exchange that is driven by the individual consumer's perceived benefits from potential exchange partners' offerings. In general, consumers do not need goods. They need to perform mental and physical activities for their own benefit, to have others perform mental and physical activities for them (Gummesson 1995; Kotler 1977), or to have goods that assist them with these activities. In summary, they need services that satisfy their needs.
It might be argued that at least some firms and customers seek single transactions rather than relationships. If "relationship" is understood in the limited sense of multiple transactions over an extended period, the argument might be persuasive. However, even in the cases when the firm does not want extended interaction or repeat patronage, it is not freed from the normative goal of viewing the customer relationally. Even relatively discrete transactions come with social if not legal, contracts (often relatively extended) and implied, if not expressed, warranties. They are promises and assurances that the exchange relationship will yield valuable service provision, often for extended periods. The contracts are at least partially represented by the offering firm's brand. Part of the compensation for the service provision is the creation and accumulation of brand equity (an off-balance-sheet resource).
Customers also might not desire multiple discrete transactions; however, a customer is similarly not freed of relational participation. Regardless of whether the service is provided interactively or indirectly by a tangible good, we argue that value is coproduced (FP6), and in the case of all tangible goods, the customer must interact with them over some period that extends beyond the transaction. Service provision and the cocreation of value imply that exchange is relational.
In a service-centered model, humans both are at the center and are active participants in the exchange process. What precedes and what follows the transaction as the firm engages in a relationship (short- or long-term) with customers is more important than the transaction itself. Because a service-centered view is participatory and dynamic, service provision is maximized through an iterative learning process on the part of both the enterprise and the consumer. The view necessarily assumes the existence of emergent relationships and evolving structure (e.g., relational norms of exchange learned through reinforcement over time; see Heide and John 1992). The service-centered view is inherently both consumer-centric and relational.
Perhaps the central implication of a service-centered dominant logic is the general change in perspective. The goods-centered view implies that the qualities of manufactured goods (e.g., tangibility), the separation of production and consumption, standardization, and nonperishability are normative qualities (Zeithaml, Parasuraman, and Berry 1985). Thus, even many services marketers have taken up the implied challenge of trying to make services more like goods. These qualities are primarily only true of goods when they are viewed from the manufacturer's perspective (e.g., Beaven and Scotti 1990). From what we argue the marketing perspective should be, the qualities are often neither valid nor desirable. That is, standardized goods, produced without consumer involvement and requiring physical distribution and inventory, not only add to marketing costs but also are often extremely perishable and nonresponsive to changing consumer needs.
A service-centered view of exchange points in an opposing normative direction. It implies that the goal is to customize offerings, to recognize that the consumer is always a coproducer, and to strive to maximize consumer involvement in the customization to better fit his or her needs. It suggests that for many offerings, tangibility may be a limiting factor, one that increases costs and that may hinder marketability. A service-centered perspective disposes of the limitations of thinking of marketing in terms of goods taken to the market, and it points to opportunities for expanding the market by assisting the consumer in the process of specialization and value creation.
A service-centered view identifies operant resources, especially higher-order, core competences, as the key to obtaining competitive advantage. It also implies that the resources must be developed and coordinated to provide (to serve) desired benefits for customers, either directly or indirectly. It challenges marketing to become more than a functional area and to represent one of the firm's core competences; it challenges marketing to become the predominant organizational philosophy and to take the lead in initiating and coordinating a market-driven perspective for all core competences. As Srivastava, Shervani, and Fahey (1999) suggest, marketing must play a critical role in ensuring that product development management, supply chain management, and customer relationship management processes are all customer-centric and market driven. If firms focus on their core competences, they must establish resource networks and outsource necessary knowledge and skills to the network. This means that firms must learn to be simultaneously competitive and collaborative (Day 1994), and they must learn to manage their network relationships.
Ultimately, the most successful organizations might be those whose core competence is marketing and all its related market-sensing processes (Day 1999; Haeckel 1999). In a service-centered view of marketing, in which the purpose of the firm is not to make and sell (Haeckel 1999) units of output but to provide customized services to customers and other organizations, the role of manufacturing changes. Investment in manufacturing technologies constrains market responsiveness. Together with many goods becoming commodities, as evidenced by the rise in globalized, contract manufacturing services, firms will increasingly become more competitive by outsourcing the manufacturing function. Achrol (1991, pp. 88, 91) identifies "transorganizational firms," which he refers to as "marketing exchange" and "marketing coalition" companies, both of which have "one primary function--all aspects of marketing." Achrol suggests that "the true marketing era may be just over the horizon." Achrol and Kotler (1999) envision marketing as largely performing the role of a network integrator that develops skills in research, forecasting, pricing, distribution, advertising, and promotion, and they envision other network members as bringing other necessary skills to the network.
In a service-centered view, tangible goods serve as appliances for service provision rather than ends in themselves. In this perspective, firms may find opportunities to retain ownership of goods and simply charge a user fee (Hawken, Lovins, and Lovins 1999; Rifkin 2000), thus finding a competitive advantage by focusing on the total process of consumption and use. For example, chauffagistes in France have realized that buyers do not want to buy furnaces and air conditioners and units of energy, but comfort, so they now contract to keep floor space at an agreed temperature range and an agreed cost. They are paid for "warmth service," and they profit by finding innovative and efficient ways to provide these services rather than sell more products. Similar examples are found in the United States, where Carrier is testing "comfort leasing" and Dow Chemical is providing "dissolving services" while maintaining the responsibility for disposing and recycling toxic chemicals. Hawken, Lovins, and Lovins (1999, pp. 125-27) cite these and other examples as indicative of the way firms benefit themselves, their customers, and society by increasing this "service flow," or the "continuous flow of value" as "defined by the customer." The observation of the market in terms of processes and service flows rather than units of output opens many strategic marketing opportunities.
From a service-provision perspective, economic exchange in the marketplace has a competitor: the potential customer (individual or organization) (Lusch, Brown, and Brunswick 1992; Prahalad and Ramaswamy 2000). The potential customer has a choice: engage in self-service (e.g., do-it-yourself activity) or go to the marketplace. However, to be successful at self-service, the entity must have sufficient physical and mental skills and/or the appliances (embedded with knowledge) to make self-service possible. Organizations that recognize this can find opportunities in developing offerings that enable the entity's increasing self-service.
As individual people continue to progress toward finer degrees of specialization, they will find themselves increasingly dependent on the market, both for service provision and for the ability to self-serve. Consequently, consumers will seek to domesticate or tame the market by adopting and developing a relationship with a limited number of organizations. This domestication process increases the consumer's efficiency in dealing with the marketplace and decreases the impact of opportunistic behavior by potential service providers. Consumers will develop relationships with organizations that can provide them with an entire host of related services over an extended period (Rifkin 2000). For example, in the providing for individual transportation, the automobile has associated needs for car insurance, maintenance, repair, and fuel. There will be opportunities for organizations that can offer all these services bundled into periodic user fees. The success of organizations in capitalizing on this need for domesticated market relationships does not come from finding ways to provide efficient, standardized solutions but from making it easier for consumers to acquire customized service solutions efficiently through involvement in the value-creation process.
Achrol and Kotler (1999) extend this service perspective by suggesting that the marketing function may become a customer-consulting function. The marketer would become the buying agent on a long-term, relational basis to source, evaluate, and purchase the skills (either as intangibles or embedded in tangible matter) that the customer needs, wants, or desires. This could be extended to marketers who also serve as selling agents, enabling a customer to exchange his or her skills in the marketplace. This position would enable the marketer not only to evaluate the skills (services) the customer needs but also to advise the customer about which skills (services) he or she can best specialize and exchange in the marketplace and the services (intangible or provided through goods) that might be acquired to leverage his or her own service provision and exchange processes.
Historically, most communication with the market can be characterized as one-way, mass communication that flows from the offering firm to the market or to segments of markets. A service-centered view of exchange suggests that individual customers increasingly specialize and turn to their domesticated market relationships for services outside of their own competences. Therefore, promotion will need to become a communication process characterized by dialogue, asking and answering questions. Prahalad and Ramaswamy (2000) argue that consumers rather than corporations are increasingly initiating and controlling this dialogue. Duncan and Moriarty (1998, p. 3) believe that marketing theory and communications theory are at an intersection; "[They are] in the midst of fundamental changes that are similar in origin, impact, and direction. Parallel paradigm shifts move both fields from a functional, mechanistic, production-oriented model to a more humanistic, relationship-based model." They point out (p. 2) that "many marketing roles, particularly in services, are fundamentally communications positions that take communication deeper into the core of marketing activities. ... which involves the process of listening, aligning, and matching." The normative goal should not be communication to the market but developing ongoing communication processes, or dialogues, with micromarkets and ideally markets of one.
Shostack (1977) and others (e.g., Beaven and Scotti 1990; Schlesinger and Heskett 1991) have indicated that the basic lexicon of marketing is derived from a goods-based, manufacturing exchange perspective. As we believe, if contemporary marketing thought is evolving from an operand-resource-based, good-centered dominant logic to an operant-resource-based, service-centered dominant logic, academic marketing may need to rethink and revise some of the lexicon. The seemingly diverse literature that we cite as converging on this new dominant logic provides the foundation for the revised lexicon. Notably, the need and its existence do not necessarily require discarding the goods-centered counterpart. Its function should be to refocus perspective through reorientation rather than reinvention. For example, the treatment of quality in the services literature has resulted in the distinction between manufactured quality and perceived quality; the former arguably has become a necessary but not sufficient component of the latter. The concept of transaction becoming subordinated to the concept of relationship is another example. Similarly, Rust, Zeithaml, and Lemon (2000) have suggested that the customer-focused term "customer equity" be superordinated to the more traditional, product-focused term "brand equity," which is a component of the former (along with "value equity" and "retention equity").
Marketing educators and scholars should be proactive in leading industry toward a service-centered exchange model. As with the lexicon, this implies reorientation rather than reinvention. This reorientation would not necessitate abandonment of most of the traditional core concepts, such as the marketing mix, target marketing, and market segmentation, but rather it would complement these with a framework based on the eight FPs we have discussed.
A service-centered college curriculum would be grounded by a course in principles of marketing, which would subordinate goods to service provision, emphasizing the former as distribution mechanisms for the latter. The marketing strategy course might be centered on resource advantage theory, building on the role of competences and capabilities in the coproduction of value and competitive advantage. The course could be followed by a new course, one focused on the management of cross-functional businesses processes that support the development of the capabilities and competences needed in a market-driven organization. Integrated marketing communication would continue to replace limited-focus, promotional courses such as advertising. In addition, the course would emphasize both the means and the mechanisms for initiating and maintaining a continuing dialogue with the customer and for enhancing the relationship by using tools such as branding. Likewise, the consumer behavior course might evolve to increased emphasis on relational phenomena such as brand identification, value perception, and the role of social and relational norms in coproduction and repeat patronage. Similarly, courses in pricing would evolve to focus on strategies for building and maintaining value propositions, including the management of long-term customer equity. The marketing channels course would become a course that addressed coordinating marketing networks and systems. Supply chain management concepts would become subordinated to the management of value constellations and service flows.
Complementing this college curriculum could be the emergence of executive education offerings with similar perspectives and frames of references. It is perhaps in the executive education classoom where the rapid dissemination of the service-centered model of exchange is most likely.
If adopted and diffused throughout industry, what does the service-centered model mean for the role of marketing in the firm? It positions service, the application of competences for the benefit of the consumer, as the core of the firm's mission. Marketing's role as the facilitator of exchange becomes one of identifying and developing the core competences and positioning them as value propositions that offer potential competitive advantage. To do this, marketing should lead the effort of designing and building cross-functional business processes. Therefore, marketing should be positioned at the core of the firm's strategic planning. Relationship building with customers becomes intrinsic not only to marketing but also to the enterprise as a whole. All employees are identified as service providers, with the ultimate goal of satisfying the customer. Everyone in the organization is encouraged to reflect on the firm's value proposition. Indeed, from a service-centered dominant logic, a firm's mission statement should communicate the firm's overall value proposition.
Finally, in the service-centered model, marketplace feedback not only is obtained directly from the customer but also is gauged by analyzing financial performance from exchange relationships to learn how to improve both firms' offering to customers and firm performance. Marketing practice accepts responsibility for firm financial performance by taking responsibility for increasing the market value rather than the book value of the organization as it builds off-balance-sheet assets such as customer, brand, and network equity.
The models on which much of the understanding of economics and marketing are based were largely developed during the nineteenth century, a time when the focus was on efficiencies in the production of tangible output, which was fundamental to the Industrial Revolution. Given that focus, perhaps appropriately, the unit of analysis was the unit of output, or the product (good). The central role of the good also fits well with the political goals of exporting manufactured products to the developing and often colonized regions of the world in exchange for raw materials for the purpose of increasing national wealth. In addition, making the good, characterized as "stuff" with embedded properties, the unit of analysis fits well with the academic goals of turning economics into a deterministic science such as Newtonian mechanics. The goods-oriented, output-based model has enabled advances in the common understanding, and it has reached paradigm status.
However, times have changed. The focus is shifting away from tangibles and toward intangibles, such as skills, information, and knowledge, and toward interactivity and connectivity and ongoing relationships. The orientation has shifted from the producer to the consumer. The academic focus is shifting from the thing exchanged to one on the process of exchange. Science has moved from a focus on mechanics to one on dynamics, evolutionary development, and the emergence of complex adaptive systems. The appropriate unit of exchange is no longer the static and discrete tangible good.
As more marketing scholars seem to be implying, the appropriate model for understanding marketing may not be one developed to understand the role of manufacturing in an economy, the microeconomic model, with its focus on the good that is only occasionally involved in exchange. A more appropriate unit of exchange is perhaps the application of competences, or specialized human knowledge and skills, for and to the benefit of the receiver. These operant resources are intangible, continuous, and dynamic. We anticipate that the emerging service-centered dominant logic of marketing will have a substantial role in marketing thought. It has the potential to replace the traditional goods-centered paradigm.
The authors contributed equally to this manuscript.
The authors thank the anonymous JM reviewers and Shelby Hunt, Gene Laxzniak, Alan Alter, Fred Morgan, and Matthew O'Brien for comments on various drafts of this manuscript.
Timeline and Stream of Literature:
1800-1920: Classical and Neoclassical Economics
Marshall (1890); Say (1821); Shaw (1912); Smith (1776)
Fundamental Ideas or Propositions:
Economics became the first social science to reach the quantitative sophistication of the natural sciences. Value is embedded in matter through manufacturing (value-added, utility, value in exchange); goods come to be viewed as standardized output (commodities). Wealth in society is created by the acquisition of tangible "stuff." Marketing as matter in motion.
Timeline and Stream of Literature:
1900-1950: Early/Formative Marketing
- Commodities (Copeland 1923)
- Institutions (Nystrom 1915; Weld 1916)
- Functional (Cherington 1920; Weld 1917)
Fundamental Ideas or Propositions:
Early marketing thought was highly descriptive of commodities, institutions, and marketing functions: commodity school (characteristics of goods), institutional school (role of marketing institutions in value-embedding process), and functional school (functions that marketers perform). A major focus was on the transaction or output and how institutions performing marketing functions added value to commodities. Marketing primarily provided time and place utility, and a major goal was possession utility (creating a transfer of title and/or sale). However, a focus on functions is the beginning of the recognition of operant resources.
Timeline and Stream of Literature:
1950-1980: Marketing Management
- Business should be customer focused (Drucker 1954; McKitterick 1957)
- Value "determined" in marketplace (Levitt 1960)
- Marketing is a decisiommaking and problem-solving function (Kotler 1967; McCarthy 1960)
Fundamental Ideas or Propositions:
Firms can use analytical techniques (largely from microeconomics) to try to define marketing mix for optimal firm performance. Value "determined" in marketplace; "embedded" value must have usefulness. Customers do not buy things but need or want fulfillment. Everyone in the firm must be focused on the customer because the firm's only purpose is to create a satisfied customer. Identification of the functional responses to the changing environment that provide competitive advantage through differentiation begins to shift toward value in use.
Timeline and Stream of Literature:
1980-2000 and Forward: Marketing as a Social and Economic Process
- Market orientation (Kohli and Jaworski 1990; Narver and Slater 1990)
- Services marketing (Gronroos 1984; Zeithaml, Parasuraman, and Berry 1985)
- Relationship marketing (Berry 1983; Duncan and Moriarty 1998; Gummesson 1994, 2002; Sheth and Parvatiyar 2000)
- Quality management (Hauser and Clausing 1988; Parasuraman, Zeithaml, and Berry 1988)
- Value and supply chain management (Normann and Ramirez t993; Srivastava, Shervani, and Fahey 1999)
- Resource management (Constantin and Lusch 1994; Day 1994; Dickson 1992; Hunt 2000; Hunt and Morgan 1995)
- Network analysis (Achrol 1991; Achrol and Kotler 1999; Webster 1992)
Fundamental Ideas or Propositions:
A dominant logic begins to emerge that largely views marketing as a continuous social and economic process in which operant resources are paramount. This logic views financial results not as an end result but as a test of a market hypothesis about a value proposition. The marketplace can falsify market hypotheses, which enables entities to learn about their actions and find ways to better serve their customers and to improve financial performance.
This paradigm begins to unify disparate literature streams in major areas such as customer and market orientation, services marketing, relationship marketing, quality management, value and supply chain management, resource management, and network analysis. The foundational premises of the emerging paradigm are (1) skills and knowledge are the fundamental unit of exchange, (2) indirect exchange masks the fundamental unit of exchange, (3) goods are distribution mechanisms for service provision, (4) knowledge is the fundamental source of competitive advantage, (5) all economies are services economies, (6) the customer is always a coproducer, (7) the enterprise can only make value propositions, and (8) a service-centered view is inherently customer oriented and relational.
Primary unit of exchange
Traditional Goods-Centered Dominant Logic: People exchange for goods. These goods serve primarily as operand resources.
Emerging Service-Centered Dominant Logic: People exchange to acquire the benefits of specialized competences (knowledge and skills), or services. Knowledge and skills are operant resources.
Role of goods
Traditional Goods-Centered Dominant Logic: Goods are operand resources and end products. Marketers take matter and change its form, place, time, and possession.
Emerging Service-Centered Dominant Logic: Goods are transmitters of operant resources (embedded knowledge); they are intermediate "products, that are used by other operant resources (customers) as appliances in value-creation processes.
Role of customer
Traditional Goods-Centered Dominant Logic: The customer is the recipient of goods. Marketers do things to customers; they segment them, penetrate them, distribute to them, and promote to them. The customer is an operand resource.
Emerging Service-Centered Dominant Logic: The customer is a coproducer of service. Marketing is a process of doing things in interaction with the customer. The customer is primarity an operant resource, only functioning occasionally as an operand resource.
Determination and meaning of value
Traditional Goods-Centered Dominant Logic: Value is determined by the producer. It is embedded ïn the operand resource (goods) and is defined in terms of "exchange-value."
Emerging Service-Centered Dominant Logic: Value is perceived and determined by the consumer on the basis of "value in use." Value results from the beneficial application of operant resources sometimes transmitted through operand resources. Firms can only make value propositions.
Firm-customer interaction
Traditional Goods-Centered Dominant Logic: The customer is an operand resource. Customers are acted on to create transactions with resources.
Emerging Service-Centered Dominant Logic: The customer is primarily an operant resource. Customers are active participants in relational exchanges and coproduction.
Source of economic growth
Traditional Goods-Centered Dominant Logic: Wealth is obtained from surplus tangible resources and goods. Wealth consists of owning, controlling, and producing operand resources.
Emerging Service-Centered Dominant Logic: Wealth is obtained through the application and exchange of specialized knowledge and skills, it represents the right to the future use of operant resources.
Thought leaders in marketing
continually move away from
Pre-1900 tangible output with embedded Twenty-first
value in which the focus was Century
Goods-Centered on activities directed at
Model of Exchange discrete or static trans- Service-Centered
(Concepts: action. In turn, they move Model of Exchange
tangibles, toward dynamic exhange (Concepts:
statics discrete relationships that involve intangibles, com-
transactions, and performing processes and petences, dynamics,
and operand exchanging skills and/or exchange processes
resources) services which value is and relationships,
cocreated with the consumer. and operant
The worldview changes from resources)
a focus on resources on
which an operation or act is
performed (operand resources)
to resources that produce
effects (operant resources).
Classical and Neoclassical Economics (1800-1920)
Formative Marketing Thought (Descriptive: 1900-1950)
Commodities
Marketing institutions
Marketing functions
Marketing Management School of Thought (1950-2000)
Customer orientationa and marketing concept
Value determined in marketplace
Manage marketing functions to achieve optimal output
Marketing science emerges and emphasizes use of
optimization techniques
Marketing as a Social and Economic Process (Emerging
Paradigm: 1980-2000 and forward)
Market orientation processes
Services marketing processes
Relationship marketing processes
Quality management processes
Resource management and competitive processes
Network management processesl. Typical traditional definitions include those of Lovelock (1991, p. 13), "services are deeds, processes, and performances"; Solomon and colleagues (1985, p. 106), "services marketing refers to the marketing of activities and processes rather than objects"; and Zeithaml and Bitner (2000), "services are deeds, processes, and performances." For a definition consistent with the one we adopt here, see Gronroos (2000).
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By Stephen L. Vargo, Visting Professor of Marketing, Robert H. Smith School of Business, University of Maryland (e-mail: svargo@rhsmith.umd.edu), and Robert F. Lusch, Dean and Distinguished University Professor, M.J. Neeley School of Business, Texas Christian University, and Professor of Marketing (on leave), Eller College of Business and Public Administration, University of Arizona (e-mail: r.lusch@tcu.edu).
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Record: 61- Exploring New Worlds: The Challenge of Global Marketing. By: Douglas, Susan P.; Clark, Terry. Journal of Marketing. Jan2001, Vol. 65 Issue 1, p103-107. 5p.
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Record: 62- Exploring the Phenomenon of Customers' Desired Value Change in a Business-to-Business Context. By: Flint, Daniel J.; Woodruff, Robert B.; Gardial, Sarah Fisher. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p102-117. 16p. 2 Diagrams, 4 Charts. DOI: 10.1509/jmkg.66.4.102.18517.
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Exploring the Phenomenon of Customers' Desired Value Change in a Business-to-Business Context
Increasingly, organizations are pushed to adopt customer value strategies in order to grow profits and ensure long-term survival. Yet little is known about the dynamic nature of how customers perceive value from suppliers. The authors present findings from a grounded theory study conducted in a business-to-business context that sheds light on the nature of customers' desired value change and related contextual conditions. The authors discover that the phenomenon of customers' desired value change typically occurs in an emotional context, as managers try to cope with feelings of tension. The phenomenon extends well past the change itself into strategies customers use to motivate suppliers to meet their changed needs. Customers' value change provides a reason for customers to seek, maintain, or move away from relationships with suppliers.
These are difficult times for marketing managers. Increasingly, they are pushed to adopt customer value strategies in order to grow profits and ensure long-term survival (Gale 1994; Hamel and Prahalad 1994; Woodruff and Gardial 1996). These strategies require that managers understand what customers want or value from products, services, and supplier relationships. However, customers periodically change what they value, and for some customers in some industries, quite rapidly and extensively. Therefore, suppliers cannot depend on what they currently know about customer value to hold into the future. To retain key customers, suppliers are forced to either anticipate what customers will value next or be ready to react faster than competitors do to these changes. Both approaches demand that managers recognize and understand the implications of customers' desired value change (CDVC) when they see it. Unfortunately, even though much has been written about the dynamic nature of customers and what they value (e.g., Hamel and Prahalad 1994; Morrison and Schmid 1994), there is little evidence that organizations understand much about this phenomenon (Flint, Woodruff, and Gardial 1997; Hamel and Prahalad 1994; Woodruff 1997; Woodruff and Gardial 1996). There is simply little empirical research to guide managers who want to better understand changes in what customers value. Quite likely, organizations' failure to anticipate CDVC may account in part for the loss of key customers, failure of new products, and erosion of brand equity. Emerging customer value research (e.g., Lapierre 2000; Woodruff 1997; Woodruff and Gardial 1996), as well as virtually all customer satisfaction research (e.g., Oliver 1997; Yi 1990), focuses mainly on what customers currently value from suppliers. Other than some recognition of the research need (Flint, Woodruff, and Gardial 1997; Woodruff 1997; Woodruff and Gardial 1996), existing literature stops short of offering theory about how and why customer value changes. The purpose of our research is to explore the CDVC phenomenon and begin to close this gap.
The customer value literature provides an interesting contrast. On the one hand, both the business press and the academic literature build a strong case for the importance of customer value to business practice. The business press argues for managers to adopt a management approach that centers on creating competitive advantage through superior delivery of customer value (e.g., Band 1991; Gale 1994; Naumann 1995; Slywotzky 1996). With regard to CDVC in particular, Gale (1994, p. 388) notes that the 1994 criteria for the Malcolm Baldrige National Quality Award includes "how the company addresses future requirements and expectations of the customer." The academic literature provides a more varied rationale. Several authors link customer value to implementing competitive advantage strategies (e.g., Lai 1995; Slater and Narver 2000), customer-oriented management approaches such as customer value management (e.g., Sinha and DeSarbo 1998), and marketing's concern for buyer-seller exchanges (e.g., Holbrook 1994). Customer value has also become an important concept for understanding buyer behavior, such as shopping and product choice (e.g., Holbrook 1994; Zeithaml 1988).
Despite this importance, the literature suggests that the study of customer value is in its infancy (Day and Crask 2000; Holbrook 1994). Even the term, "customer value," can be confusing because it may bring to mind very different concepts. For example, some authors might think of personal values--the shared, central beliefs about right and wrong, good and bad, that guide behavior. Another concept, value of a customer, is gaining importance because of the growing interest in customer relationship management. This concept refers to the economic (e.g., profit) value to a seller of patronage by a customer over a lifetime.
The concept we are interested in is customer value. The literature suggests that it has two related meanings. Most commonly, customer value means judgments or assessments of what a customer perceives he or she has received from a seller in a specific purchase or use situation (Bagozzi 1999; Walsh 1995; Woodruff 1997). The other meaning, desired value, refers to what customers want to have happen when interacting with a supplier and/or using the supplier's product or service (Flint, Woodruff, and Gardial 1997; Woodruff 1997). This concept is similar to the notion of customer desires in the satisfaction literature (e.g., Spreng, MacKenzie, and Olshavsky 1996). Recent literature suggests that consumers distinguish between desired value and received value judgments (Bagozzi 1999; Holbrook 1994; Huber and Herrmann 2000; Richins 1994).
Although change can occur in both desired and received value, we are interested in changes in what customers want to have happen with regard to suppliers--that is, desired value change. We believe that the most serious gap in knowledge lies here. Most of the literature related to value change, including attitudes, satisfaction, utility, choice, and so forth, involves change in evaluative judgments similar to received value. Therefore, our guiding research question was, "What does desired value change mean to customers?"
Current Focus of Customer Value Research
Some progress has been made in the understanding of how consumers perceive value. Typically, customers may value many aspects of an exchange, which may involve a product, brand, store, or interaction with a salesperson (e.g., Holbrook 1994; Lai 1995; Zeithaml 1988). Furthermore, customer value perceptions may occur throughout all stages of consumption (Huber and Herrmann 2000).
Most authors agree that customer value involves trading off benefit versus sacrifice experiences within use situations (e.g., Hauser and Urban 1986; Lapierre 2000; Slater and Narver 2000; Teas and Agarwal 2000; Zeithaml 1988). More important, the literature generally agrees that the benefit side of value includes more than quality and the sacrifice side includes more than price (e.g., Day and Crask 2000; Holbrook 1994; Slater and Narver 2000), even though many practitioners tend to equate value solely with these two dimensions. Recently, a third dimension, risk, has appeared in the literature. Day and Crask (2000) and Huber and Herrmann (2000) propose that risks associated with a product or service should be included in the customer value phenomenon. Consistent with these views, a growing number of authors support a means-end, hierarchy nature of value(e.g., Gutman1982;Holbrook1994;Lai1995; Woodruff 1997; Zeithaml 1988). In this framework, benefits and sacrifices are types of consequences of product, service, or supplier use that occur in specific situations.
Customer value research has devoted significant effort to developing typologies of value (e.g., Holbrook 1994; Lai 1995; Richins 1994; Sheth, Newman, and Gross 1991; Zeithaml 1988). For example, Sheth, Newman, and Gross (1991) identify five types of value--functional, social, emotional, epistemic, and conditional value. In a business-to-business context, Gassenheimer, Houston, and Davis (1998) distinguish between economic value (i.e., fulfilling economic needs at minimum transaction costs) and social value (i.e., satisfaction with the relationship compared with other alternatives) of business relationships. These typologies are consistent with means-end theory, because they focus on the consequence level in a value hierarchy. Woodruff (1997, p.142) draws on several of these value concepts in defining customer value as a "customer's perceived preference for and evaluation of those product attributes, attribute performances, and consequences arising from use that facilitate (or block) achieving the customer's goals and purposes in use situations."
CDVC in Customer Value Research
Although there is little direct reference to CDVC per se (for exceptions, see Day and Crask 2000; Gassenheimer, Houston, and Davis 1998; Huber and Herrmann 2000; Walsh 1995), we found references to the dynamic nature of value. For example, Richins (1994) notes that value may accrue to an object after purchase through use, suggesting that use experiences could be associated with value change. Day and Crask (2000) observe that value could be assessed before, during, or after purchase, implying that CDVC may occur at any time in this cycle. However, we could find almost nothing in the customer value literature that provides insights into exactly what the aspects of value change are. In one exception, Gassenheimer, Houston, and Davis (1998) recognize that economic and/or social value might change.
Even when CDVC is implied, little is said about its nature. For the most part, external sources of change are typically proposed as factors related to value change. For example, Richins (1994) mentions that television broadcasts, celebrities, and highly visible social subgroups might be related to value changes. Similarly, the attitude literature has been highly concerned with the role of persuasive communications in changing evaluative judgments. In one particularly interesting exception, Gassenheimer, Houston, and Davis (1998) suggest that value change may be related to the deterioration and failure of business-to-business relationships. They discuss external factors associated with changes in the overall value (i.e., evaluation) of relationships, including shifts in moral values in society and business as well as the emergence of extraneous barriers to a relationship (e.g., government intervention in the sale of society-sensitive products such as cigarettes). More important, Gassenheimer, Houston, and Davis (1998) also recognize that shifts that are internal to the organization (i.e., in business purpose goals, organizational culture, and goals for a relationship) can be related to changes in the value of a business relationship.
Because of the early stages of research in this area, we adopted a grounded theory approach (Glaser and Strauss 1967; Strauss 1987; Strauss and Corbin 1990). A growing body of research applies variants of this approach to marketing-related phenomena, which attests to its popularity for generating depth of understanding when little is known about a phenomenon (Celsi, Rose, and Leigh 1993; Schouten 1991).
Data Collection and Analyses
We studied the CDVC phenomenon in the context of business-to-business relationships between multiple firms at different levels in U.S. automobile manufacturing supply chains. This industry accounts for a large portion of the U.S. gross domestic product, and it is closely linked to many other industries. Furthermore, we suspect that business-to-business relationships within U.S. automotive supply chains are similar to business-to-business relationships in many mature, manufacturing-oriented industries in the United States (Lapierre 2000).
Although CDVC in a business-to-business context may be viewed as an organizational phenomenon, we decided to focus on individual managers' perceptions of value change. Significant changes in organizations result from the sharing of knowledge among their managers (Garratt 1987). This information sharing helps transform individual learning to organizational learning. Although researchers have taken different perspectives in terms of equating individual learning to organizational learning (e.g., Bell, Whitwell, and Lukas 2002; Easterby-Smith 1997; Huber 1991), our position is that people learn and share what they know in decision making such that organizations then learn. Therefore, we believe that it is important to begin exploring CDVC through the eyes of individual managers.
We relied heavily on depth interviews to supplement and extend previous research, drawing on the grand tour technique from ethnography (McCracken 1988; Spradley 1979). Interviews took place in participants' offices, all but two of which were located in the upper mid-western United States. The interviews were open-ended and discovery oriented. Interviews lasted approximately one and one-half hours and were audio-recorded. Each interview tape was transcribed verbatim. The primary investigator had an engineering degree and experience in the automobile industry, which facilitated understanding of the heavy manufacturing and engineering focus of the participants and their firms.
Initially, we used an interview guide that broadly outlined the topics of interest. The interviews were designed to draw out autobiographical memories of personal experiences related to changes in what participants valued from suppliers. Although such memories may not depict a "true" representation of what actually happened, it is well established that they accurately represent the meaning of personal experiences (Brewer 1986; Conway1990), are likely "representative of the underlying [memory] structure with respect to both content and organization" (Lynch and Srull 1982, p. 24), and may well influence future behavior because people often make decisions on the basis of how they remember an experience versus how it "actually" occurred (Gardial et al. 1994). The interviews were supplemented, when possible, by observation of meetings with purchasing professionals inside participants' organizations, tours of facilities, and analyses of documents provided by participants.
Analyses of the verbatim interview transcripts followed traditional grounded theory guidelines. We began these analyses early, after the first few interviews, allowing interpretations to inform and direct subsequent interviews. Our analyses tacked back and forth between these interpretations and standard grounded theory coding (i.e., open, selective, and axial). We facilitated coding and interpretations by using QSR NUD*IST4ä (nonnumerical unstructured data--indexing, searching, and theorizing) software (Qualitative Solutions & Research Pty. 1997).
Sampling
Throughout the study, we relied on theoretical, relational, and discriminative sampling to expand theoretical concepts, link them to one another, and provisionally test the emergent theory's limitations. Participants were influential decision makers involved in purchasing and supplier management. Key purchasing-related managers are critical for uncovering and synthesizing what their organizations value from sup-pliers and who those suppliers will be. We expanded to participants from other functional areas as our theory emerged. Sampling ceased when we reached theoretical saturation, indicated by information redundancy. The final sample consisted of 22 participants from nine manufacturing organizations. This sample reflects diversity along several dimensions, such as job position, sex, tenure in the job, organization size, products manufactured, and the organization's position within various supply chains. Table 1 depicts the participants, their pseudonyms, their titles, and selected characteristics.
Analysis of Research Trustworthiness
We assessed the trustworthiness of the research by applying two overlapping sets of criteria. From interpretive research, we focused on credibility, transferability, dependability, confirmability, and integrity (Hirschman1986;LincolnandGuba1985; Wallendorf and Belk 1989). In addition, we applied the criteria of fit, understanding, generality, and control from grounded theory (Straussand Corbin1990). As demonstrated in Table 2, we believe that our data and analyses met these criteria.
In this section, we present our results from grounded theory analyses. In brief, participants' stories can be tied together through an understanding of three critical aspects related to desired value change--namely, CDVC form and intensity, tension management, and action/interaction strategies--all imbedded in complex internal-to-the-organization and external-to-the-organization contextual conditions (Figure 1).
CDVC Form and Intensity
The concept of CDVC form, or more accurately form variety, describes the various forms in which desired value changes appeared in participants' stories. It has four properties, each with accompanying dimensional ranges: value hierarchy level, newness, bar raising, and priority changes. In contrast, CDVC intensity describes the degree or extent of change. It has three properties: rate, magnitude, and volatility. The two characteristics are related in that each CDVC form can vary in its intensity. This interpretation of CDVC is depicted in grounded theory terms in Table 3.
CDVC form variety. CDVC can take on a variety of forms. First, consistent with means-end theory (Gutman 1982; Woodruff 1997), CDVC may occur at any level in a customer value hierarchy. At the supplier attribute level, participants formed new desires regarding what they wanted suppliers to do in the future, such as having a continuous improvement attitude, being involved to a greater extent in participants' product design, offering marketing assistance, delivering integrated systems as opposed to single components, and knowing participants' organizations and customers more deeply.
Participants also described changes at the customer consequence level. These were changes in the experiences and outcomes that participants desired as a result of their inter-actions with suppliers. In the following passage, Ken talked about changes in several desired consequences (shown in italics in the passage) related to a desire for suppliers to be involved in projects earlier. The consequences reflect the nature of supplier situations in automobile supply chains. Time and costs are significant concerns in an industry in which automobile models are the result of two to five years of planning, designing, and testing.
Ken: [We must] evaluate the conditions under which it [product and process decision] was made and give an estimation of viability of the process. Is it safe or is it in danger? Which opens the door for discussing changes and modifications that are required much earlier on. In the past, without any supervision, the soft tool manufacturer could cast up some kirksite tools, whittle out parts by some means, supply those for our early vehicle testing and evaluation. So these parts that are, let's say, I won't use the term "bogus," but suspect perhaps, as far as whether or not they would ever support volume production, get into the vehicle. And then you invest all sorts of time, which is more critical than money, really, you invest all of this time in testing and developing this thing so that then you have your costs in it, sunk costs, that people never want to give up. And somewhere down the line, somebody finally gets the final dies into the stamping press. As soon as they try to make eight or nine or ten strokes a minute, it just doesn't cut it. Now how much negotiation would it have taken 18 months before to sort these things out? (Emphasis added.)
Other consequence-level changes included participants wanting to improve their purchasing productivity, reduce their costs, reduce their cycle times, improve their product quality, and increase their knowledge. We found literally hundreds of examples of both consequence-and attribute-level desired value changes.
Means-end theory posits that customers perceive that attributes and consequences are related (Gutman 1982). However, when applied to CDVC, only sometimes was a change at one level related to a change at another level in the hierarchy. For example, several participants described how they became aware of cost consequences associated with their and their suppliers' operations. These discoveries were related to new cost desires in their own organization and to new attribute-based expectations of suppliers. Thus, changes in customers' desired consequences were linked to changes in desired attributes. However, the reverse was not always true; participants described changes in desired attributes of suppliers without a corresponding change in desired consequences (e.g., suppliers should conduct end-user market research for a customer to help improve that customer's processes--a stable desired consequence). Our findings indicate how complex the relationships can be between levels in a customer value hierarchy, perhaps more than means-end theory indicates.
A second property of CDVC form variety is newness. Participants' stories usually indicated value change as the emergence of a completely new desire, though sometimes participants talked about eliminating old desires. Steve's passage describes new desired value components. Note the specific words used to connote newness, such as "now," "grow in that direction," and "trying to change."
Steve: A lot of focus now that we're putting on our suppliers is, "yeah we're doing great production-wise, but if you want to gain new business you have to take care of the engineers. Do things prototype-wise." Too many times the production houses (suppliers), they just want the print and they want to run a million pieces.... [They may] have all this production business. But sometimes it goes away. Stuff goes obsolete or whatever. So you have to continue to cultivate new business and that may mean making prototypes that your plant is not efficient to do right now but you have to grow in that direction. You have to set yourself up, set the machines up to make prototypes and to work with the engineers on an experimental basis.... And we're trying to change our suppliers to do that. (Emphasis added.)
In some cases, participants described old consequence desires (e.g., a lighter vehicle hood) but described new attribute desires (e.g., a change to supply of an aluminum alloy from steel) that they believed would better provide the old consequence.
Bar raising, a third property of CDVC form variety, typically emerged as participants talked about an increase in the level of supplier performance desired-the proverbial "raising of the bar." Supplier performance improvements were desired because they would help participants manufacture their products more quickly and/or at higher quality levels. For example, Mark described his experiences related to his company's efforts to design new vehicles and bring them to market faster. He perceived that his organization wanted to shorten product development cycle times (benefit consequence), and that goal had a direct impact on what he expected of his suppliers; that is, he wanted them to perform faster too.
Mark: The main thing we want suppliers to do now is perform faster. Cycle times are shortening up. We're trying to bring the vehicle to market much faster. We're down to maybe 30 months now, where before we were quite a bit longer. You know, we think we're the lead right now. Maybe the Japanese are a little bit ahead, maybe about the same. We al ways question where they actually start their clock as to when they actually started working on their vehicle. We try to be a little more honest about that. But, ... you know, things are moving faster and faster and faster. As far as suppliers go, we want them to respond faster. (Emphasis added.)
Other participants made comments such as, "The whole industry has raised the bar on quality. So what we demand out of our suppliers in terms of inspection is no longer acceptable" (Beth), "[We are] elevating that expectancy of them [suppliers]" (Christine), and "We've changed our rating effective May. Before they had up to 72 hours (as a delivery window); now they have 24 hours" (Vicky). Greg described how he bluntly told suppliers, "Our expectations of you have been too low, frankly." Continuous improvement efforts are common and well documented following initiatives such as total quality management, the ISO9000 series, and Six Sigma. Consistent with Bagozzi's (1999) notion of goal setting for a product class, bar raising often appears to apply to all suppliers within a certain class.
Desires may change in terms of relative importance as well. We call this CDVC property priority change. For example, David described the relative shift in priority among price, quality, and service as a result of bringing in a purchasing executive with different ideas to shake up the participant's organization-wide purchasing processes. The ramifications of this person's changes were still felt at this organization, years after the executive had left. David even used this person's name as a demarcation point for the organization (i.e., pre-[executive], post-[executive]).
David: Oh, quality and service are very important..., and price is still pretty important too. When you look at it, the importance of price, to me, hasn't changed. At one point, price was important, but it wasn't that important, pre-[executive]. It was getting there. After [executive came], obviously, price skyrocketed to extreme importance. But what I'm saying is..., I don't think the pressure is off on price. But what has happened is that quality and service have crept up to where they are up to, or in advance of, price now. So relative to the three-legged stool, I would say at one point we had one really long leg, and two really short ones. But as opposed to the long getting short, the two other ones got long. So the stool is much higher off the ground. It's harder to get up there now. (Emphasis added.)
An information-processing view of organizational learning stipulates that people in organizations learn if behaviors change as a result of processing information (i.e., knowledge acquisition, information distribution, information interpretation, and organizational memory) (Huber 1991). Of interest here is that change in key staff may coincide with CDVC. As the previous passage shows, David's organization hired a new vice president of purchasing so that managers in the organization could learn how to manage their supply base more effectively. In this case, changes in desired value from suppliers were partially the result of purchasing managers learning from the new vice president.
CDVC intensity. A second characteristic of CDVC that emerged is intensity. It has three properties: rate, magnitude, and volatility. Rate refers to the speed at which participants perceived desired value changes taking place. They described both gradual and rapid changes in desires. Gradual changes were most often referred to as "evolutionary" changes. Rapid changes were most often referred to as "revolutionary."
Some participants explained that what they wanted from suppliers evolved from the basic to the complex as relationships grew over time. Not surprisingly, given the mature automobile supply chain context, quite often this process was gradual as participants learned about their suppliers' capabilities through daily interactions. As suppliers demonstrated competence in one area, they were asked to perform new tasks in other areas as well as increase their performance level in areas they were currently addressing. Participants used many different words and phrases to describe evolutionary change, including "It's an evolutionary thing," "It originally started about 12 years ago ... slowly, slowly moving (suppliers) in that direction," "It's really a continuum.... You keep finding different rocks and off we go." Consider what Wess had to say:
Wess: I think they are more evolutionary.... You are building on top of your past experiences; I don't see necessarily that we just say eureka, or here are some things we want suppliers to do that we have never asked them to do before. It's that we have evolved from asking [them] to do a certain amount of things and then it just becomes a natural progression that we involve them more and more into our business and what we are doing. So I don't really see in the recent past where we have said, well you know, this is a whole new way or a whole new way of doing a relationship. It is more evolutionary as opposed to revolutionary. (Emphasis added.)
However, CDVC was sometimes revolutionary. When participants found themselves in a near panic situation, they sometimes resorted to introducing changes rapidly in an effort to "stop the bleeding." For example, when the North American operation of Ernest's company was in serious financial trouble, management decided to bring in a purchasing expert from a European firm. This expert completely altered the structure and procedures of the participant's purchasing organization immediately. One result was an increase in the speed at which changes were made in both internal purchasing processes and demands placed on suppliers.
Ernest: You couldn't continue to change at that rate as an organization. We're somewhat settled now in terms of knowing what we want to do. I mean, if you go back to 1992, it was..., I don't know if I should call it "organized chaos" or just "chaos" period. But you had 26 or 27 different purchasing organizations that almost at the flip of a switch became one. And we weren't buying the way we used to. We were going to buy based on market price competitively. (Emphasis added.)
A second property of CDVC intensity, magnitude, refers to the size of the difference from previous desires to altered desires. Some changes, regardless of the level at which they occur, the form they may take, or the rate at which they change, reflect only minor changes from previous desires. Vicky's passage illustrates this:
Vicky: [We] were having to take a box of the one side up and a box of another side up, dump them into the filling machine, it goes down to the tray where the adhesive's put on. So we talked to the supplier and said, how much trouble would it be if you made the pallet one half A's and one half B's? So we can just put the whole pallet up there and that's all we have to do... and they're emptied at the same time? And so we worked it out.... It took about two shipments of getting them right. That's the way we buy those parts now. We don't buy them any other way. (Emphasis added.)
Some changes depicted much greater differences between previous desires and new desires. For example, participants described drastic changes from previous desires, such as their desires for suppliers to do more product design, to self-certify, or to deliver entire systems involving multiple suppliers. The size of the change is likely to lie in the eyes of the beholder. For example, a change in desires may seem relatively small to a customer but may be viewed as quite large to a supplier, particularly a supplier that lacks resources. The converse is also possible. Under conditions in which a supplier is the party with vast resources and expertise, a change that a customer views as significantly different from previous value desires (i.e., of large magnitude) may be something the supplier is already doing for other customers in other markets.
The third property of CDVC intensity, volatility, refers to the sheer number of desired value changes taking place at a given time. Some participants described changes they asked of suppliers as if they emerged one at a time. These participants often thought of CDVC as evolutionary. In contrast, other participants described how they and their colleagues throughout their organization were asking suppliers to make many changes simultaneously. We received this kind of description most often from participants who worked for large organizations that dealt with many suppliers in many parts of their organizations.
Volatility of CDVC is an outgrowth of a fundamental characteristic of customers. Typically, they want many different things from a supplier. This notion is well supported in the customer value literature. For example, customer value typologies (e.g., Gassenheimer, Houston, and Davis 1998; Sheth, Newman, and Gross 1991) suggest that customer desires fall into several different categories that are qualitatively different. Similarly, means-end theory (Gutman 1982) shows how different supplier attributes are related to different kinds of consequences, each of which may be important to a customer. Volatility of CDVC reinforces the notion that customer retention strategies require that suppliers monitor many value dimensions.
Tension Management
Consistent with grounded theory, we looked for factors related to variation in CDVC. We found that participants' stories were saturated with emotion and, in particular, tension. Autobiographical memories involving products and product usage often contain affect (Sujan, Bettman, and Baumgartner 1993). Typically, emotion has been associated with actual experiences with products and services and how the emotion affects or becomes a part of evaluative judgments (e.g., Oliver 1997; Westbrook 1987). We extend these findings to CDVC. We discovered that contextual conditions (described subsequently) within which participants were embedded were associated with varying levels of a strong and often constant emotion that we labeled "tension." Participants described tension in terms of "panic," "pain," "a sense of urgency," and "being pulled" in many different directions. It may be described in terms of the properties and dimensional ranges given in Table 4. Affective strength refers to how powerful the feelings of tension are. Weak tension was represented by comments such as "I'm frustrated" and "It's hard to keep up," whereas strong tension feelings were represented by comments such as "It is sheer panic," "I'm trying to catch up to the train!" "Chaos," and "We were bleeding out of every orifice we have!" Perceived extensiveness refers to whether a participant perceived tension as limited to himself or herself (e.g., "I'm frustrated") or extending to other people within the participant's department or even entire organization (e.g., "Everyone here is upset"). Temporal dynamism refers to variation in tension strength and perceived extensiveness over time (e.g., "The panic was greatest at that time" and "Things are more intense now than they were").
We also discovered that participants' stories throughout each interview conveyed self-identities, that is, unique ways participants perceived themselves and communicated those images, that provided deeper insights to the tension aspects of CDVC. In other words, although all participants described tension feelings, each participant felt tension in unique ways because of his or her perspective that emerged in part from his or her self-identity and life history. For example, Ken's stories conveyed the self-identity of a scientist who valued expertise and thoroughness and who became frustrated when processes, people, and organizational structures detracted from his ability to develop, manage, and use expert knowledge. His interactions with suppliers reflected this need to be an expert, which is depicted in the following details.
Ken valued early supplier involvement programs, because together he and the suppliers could discuss and learn about all the possible product and manufacturing issues that might arise. However, the design engineers, who were assigned to vehicle platform groups whose task was to coordinate all engineering for a particular vehicle, had in the past "off-loaded" much design and planning to these early involvement suppliers, which enabled platform groups to work on other issues. To Ken, this meant that the platform groups lost expertise in the off-loaded areas, which created a significant "brain drain" from his organization. Ken described how he became frustrated when suppliers' systems were designed into vehicles by design engineers before enough was known and documented about the systems. Yet his previous experience as a design engineer gave him an empathy that often conflicted with his purest view of being a thorough expert. At times, he believed that things were moving "so damn fast" that he was constantly "trying to get to the front of the parade" and that expertise sometimes was lost. Yet at other points in the interview, Ken mentioned that people who wanted to document everything sometimes merely slowed the process down. These seemingly contradictory perspectives reflected a tension Ken felt as a result of a deep understanding of the realities of his complex business world. If corners are cut, quality and, later, organizational performance suffer. Yet if the company moves too slowly and cautiously, organizational performance will suffer at the hands of competitors.
Ken's stories reflected varying degrees of tension over time that related to an internal conflict between personal ideals and the recognition of the realities of a fast-paced competitive business environment. The form and intensity with which Ken changed what he valued from suppliers depended in part on his position (i.e., design versus materials engineer) as well as on his past experiences (life history). Even as he changed what he valued from suppliers, he understood both the advantages and disadvantages of doing so. For Ken, change in desired value came at a cost and was a double-edged sword.
In contrast to Ken's perspective, Ted defined himself in terms of being caught in the middle--that is, between large original equipment manufacturer (OEM) customers and his smaller suppliers. His tension rose and subsided depending in large part on how well he could align his suppliers' responses with his customers' changing expectations. As a senior procurement manager for a Tier 1 supplier to large OEM customers, Ted was often pressured to pass on difficult customer demands to his suppliers, demands that often reflected changes in the value he desired from these suppliers. His stories and life histories revealed why this situation was stressful. At the time of the interview, Ted worked for a U.S. firm. However, Ted held positions in a Japanese firm in the past. He described how his former Japanese employer emphasized the necessity of "sending the same message" to everyone within the organization and, more important, to suppliers and customers. This message consistency conveyed a sense of unity, coherent vision, and trustworthiness to suppliers. It was complemented with "acts of good faith" with suppliers to enhance the building of trust and long-term relationships. He perceived U.S. OEM customers as largely interested in the use of leverage and not long-term relationships with mutual trust.
We provide a glimpse of Ken and Ted here as exemplars of our findings across all participants. Analyses on an individual level reveal the role that self-identities and personal life histories play in how people interpret and react to their environments. Here, we have revealed the idiosyncratic way in which tension manifested itself for these participants. It is the abstraction of all participants' stories to a higher level that enables us to describe a general affective component of CDVC called tension.
The customer value literature emphasizes cognitive over emotional aspects of evaluation of an object (e.g., Gutman 1982; Zeithaml 1988). We found only one author who specifically defined customer value as an affective phenomenon. Holbrook (1986, 1994) considers value to be "positive affect." He equates value-as-affect with preference in response to an experience with some object. Holbrook (1986, p. 32) states that "[value] pertains not to an object itself but rather to the consumption experience resulting from its use (extrinsic value) or appreciation (intrinsic value)." Our study extends this affective view of value to the CDVC phenomenon. Emotional responses to unsatisfying experiences, such as participants' feelings of poor performance in responding to a dynamic and competitive environment, intimately intertwined with motivation to change desired value from suppliers. Similarly, others have documented the importance of time pressure (a kind of tension) as an influence in organizations, as in Workman's (1993) research on new product development.
Consistent with the motivational role of emotion (Bagozzi, Gopinath, and Nyer 1999), our results suggest that tension was related to participants' attempts to improve their situation by learning, making improvements, and gaining greater control of their immediate environment. Learning, improving, and extending control are means of managing tension and are similar to coping behaviors found in research on role stress (Keaveney and Nelson 1993). We have extensive transcript support for learning, improving, and controlling efforts. Participants were trying to learn about many changes that were taking place inside and out-side of their organizations. They were trying to improve not only their operations but suppliers' as well. Similarly, they were trying to control as much as they could that was core to their organizations, including suppliers' operations, while giving up control of noncore processes. Beth's passage offers a glimpse of her efforts to learn about changes in her environment that she believed affected her organization's performance:
Beth: There are some acquisitions that I don't really understand [inadequate knowledge]. Who's going to benefit from them? The supplier has gotten larger. But I don't understand where the leverage points are. How are they going to be better because they have merged? They're bigger. But are they better? And so I think the question that we're really striving to ask our teams is, "Is this supplier better?" I just think that we're trying to look much more futuristic.... And that's really a change in focus from when [we were] bleeding to death. [When you are bleeding] you want to get through the day. We've really changed our focus on how are we going to make our next-generation vehicles better. (Emphasis added.)
The learning described by participants sometimes was reflective of organizational learning as "generative learning," in which people in organizations question "long-held assumptions about [the organization's] mission, customers, capabilities, or strategies" (Slater and Narver 1995, p. 64). Similarly, Dickson (1992) suggests that differences in customer demand changes are related to differences in customer learning. Although the notion that business-to-business customers are constantly trying to learn, improve, and control is not new, our study documents how closely tied these activities are to CDVC, the management of tension, and customers' dependence on suppliers.
Action/Interaction Strategies
The CDVC phenomenon does not stop with the change itself. Participants often described ways in which they tried to obtain new desired value from suppliers. Thus, CDVC is related to both supplier selection and supplier relationships. We discovered four strategic categories of action/interaction strategies: ( 1) locating, ( 2) building relationships, ( 3) motivating, and ( 4) coordinating.
Locating. Locating refers to both finding and positioning. Participants needed to find suppliers that were able and willing to respond constantly to the organizations' changing value desires. They also needed to position suppliers' people and facilities in and near their own facilities in the United States and globally. Steve's passage reflects locating as finding the right suppliers:
Steve: So we consider ourselves experts in those areas.... My job in particular is to find suppliers. If they aren't there now that they have the attitude to want to change.... We're trying to find those one or two suppliers in an area who are the best that we've worked with trying to consolidate products into those suppliers.... We're willing to give up some price in order to gain the other things that are as important or even more important to us to reducing the inventories, reducing the lead times ... to getting that proactive attitude to looking to reduce price through value analysis. (Emphasis added.)
Building relationships. To convince suppliers to deliver on different value desires, participants sometimes needed strong, trusting relationships with their suppliers. For example, Ted described an experience that revealed the importance of building strong relationships to induce suppliers to deliver new value:
Ted: I've got to look at the long term. Where do I need this supplier to be in five years, you know, and how can I make sure that he's willing to do what I need him to do in five years..., and how can we help him improve his processes? By going out, seeing the suppliers, and coming over there and telling them someone else is doing it better.... I've had suppliers where they had a barcode printer that was of the ribbon type that was just kind of light, and as I think about it, since I had a good relationship with them I said, you guys need a thermal ink printer for your barcode labels. I think because I had a good relationship with them they went out and spent the $4,000 for the system. (Emphasis added.)
Other participants used such phrases as "The relation-ship between supplier and customer has become so tight for success" (Ken), "We're trying to establish a relationship with a supplier domestically" (Ruth), "You certainly have to have a good relationship with them (suppliers) ... have to really promote a partnership" (Steve), and "[We] try to fix some relationships" (David) to describe how relationships were parts of strategies that helped them obtain the new value components they desired.
Supplier-buyer relationships are growing in strategic importance within supply chains (Dahlstrom, McNeilly, and Speh 1996; Day 1994; Doney and Cannon 1997). Our study suggests that changes in desired value may be related to why customers seek to build strong relationships with suppliers. Wilson (1995) positions relationship building as goal driven but does not list responding to changes in customers' desired value as one of these goals.
Motivating. Participants discussed ways to motivate suppliers to deliver desired new value. These activities varied along an authoritarian level continuum. At one end of the continuum, participants asked suppliers to do things. At the other end, participants demanded suppliers comply. Examples of tactics along this continuum included asking, explaining, putting people at ease, selling, rewarding, using guilt, leveraging the size of a potential contract, setting target prices, using competitive bids, challenging, calling bluffs, bringing in OEMs to force the issue, and bluntly demanding. For example, Larry described his failed attempt to use leverage (the contract) to motivate a supplier to comply:
Larry: There was a meeting with a foreign steel supplier that we had asked ... you know, [participant's company] is now becoming international, as far as our assembly and some of our stamping..., and we have one supplier who was asked to supply business for a vehicle down in Mexico that we're producing in Mexico right now. And we gave them, presented them with the information of what we expect of them as an early supplier involvement supplier, an ESI supplier, which includes doing circle grid analysis and forming simulations, et cetera, on different parts that they were going to be supplying. They were just going to be supplying steel, but they were still expected to do this work.... And they balked at it. They didn't want to do it. They didn't think that was right.... I don't believe that they ever did get on any ESI parts. (Emphasis added.)
If we again examine participants' stories in terms of idiosyncratic self-identities and life histories as we did previously in our discussion of tension, we gain some insight into why certain participants chose to interact with suppliers in the ways that they described. For example, Beth was in a position of power over suppliers as senior procurement manager within a large OEM. Her stories conveyed a view of her organization as "the experts" and suppliers as lucky to be allowed to do business with her organization. Her stories offered a sense of being hardened over time through experiences with suppliers that had taken advantage of her and her organization. She gradually moved from trusting most sup-pliers to being skeptical of many. She viewed herself as a driving force behind change inside her organization and with suppliers. She often used terms such as "driving" sup-pliers to change, "pushing suppliers" to become more global, "demanding out of suppliers," and "gotten the message out to suppliers" on new expectations. Beth decided the processes for which suppliers would and would not be responsible. At the root of her stories was a passionate conviction that she would do her part to help her organization respond to automobile consumers' changing desires better and faster than the competition, that her organization would never again be "bleeding out of every orifice," and that sup-pliers would not detract from this mission. Some of her tension came from her efforts to move quickly and efficiently while being slowed by suppliers that did not do their jobs.
Beth's approach to motivating contrasted with Ted's. Ted used phrases such as "promise," "give them the opportunity," "hold those carrots out there," and "sell," all reflecting his approach to motivating suppliers to respond to new desired value. Ted explained how his organization restructured to better "match" U.S. OEM customers. Every vehicle being supported by his firm had a devoted cross-functional team assigned to it. At one point, his organization was asked by customers to design components rather than merely "build to print." Ted in turn altered what he valued of his suppliers and asked them to help design parts instead of merely building to print themselves. Although he was expected to "push the [design] envelope" for his customers, his suppliers were not ready for the change, despite having stated previously that they could do such design work. Thus, Ted found himself becoming an intermediary, managing more demanding desires from customers while trying to motivate suppliers to keep up. Ted's knowledge that many of his suppliers were small and fearful of global expansion and rapid change, combined with his Japanese-inspired self-identity explained previously, contributed in part to his more coaxing and sales-oriented approach to supplier motivation.
Coordinating. Participants perceived that supplier dependence requires coordinating with many others. Internally, coordination involved many meetings with team members from complementary functional areas as well as peers across the corporation. Coordination with suppliers involved ensuring that the appropriate supplier representatives, such as sales representatives and engineers, were inter-acting with the appropriate people within the participant's company as suppliers considered and delivered on new customer desires.
John: Well, we work with suppliers.... All the steel companies are cooperative. We just had one technical exchange about a month ago, about a month and a half. And they brought the information, particularly what they had been working on, you know, like laser welded blanks, medium-strength steel. They were changing some processes at these plants ... to make better steel, or more uniform steel. Those kind of things, you know, they talked about. We discussed some of our problems with the dent resistance with the higher-strength steel, the forming problems, spring-back issues, manufacturing ... the way we would like it. (Emphasis added.)
This finding supports a recent trend toward customers building closer relationships and greater coordination with fewer suppliers. Our results suggest that CDVC is one reason for this trend. Although there may be many benefits from strong supplier relationships (Johnson 1999), our study suggests another benefit not typically discussed in the literature, that is, a higher likelihood of obtaining new desired value. But Cannon and Perreault (1999) point out that this trend toward closer supplier-customer relationships is not universal. Similarly, we found that the variation in forms and intensity of CDVC account for at least some of the variation among customer firms as to whether they seek closer relationships with fewer suppliers.
Contextual Conditions
Several branches of literature suggest that external forces are linked to CDVC. We found a broader range of contextual conditions than is normally discussed, which we grouped into two sets. The first set is composed of four types of external-to-the-organization contextual conditions, all occurring in the participants' work environments. These are changes in ( 1) customers' customers' desires, ( 2) customers' competitors' strategies/tactics, ( 3) suppliers' offerings and performance, and ( 4) customers' macro environment. Participants often described how they were responding to CDVC. Regardless of whether participants' customers were consumers or business organizations, participants were highly concerned about customer desires and changes in those desires. Not surprisingly, participants also described how they responded to changes in their competitor's strategies-- such as new products; new geographic markets; or changes in quality, price, or service levels. Sometimes participants' suppliers altered their product or service offerings or fell short of performance expectations, both of which seemed to affect CDVC, tension, and action/interaction strategies. Finally, changes in automobile legislation (corporate average fuel economy), technology (e.g., alternative fuel technology), consumer confidence, exchange rates, and even the sitting president were related to all three components of CDVC. These insights are consistent with previous marketing literature with respect to environmental dynamism in general (Dickson 1992; Moorman and Miner 1998). However, we found that each participant focused on different aspects of his or her environment when associating them with stories about changes in desired value. Although different environmental changes were salient to different participant stories about CDVC, the four types of environmental changes that could occur individually and in all possible combinations reflect the large number of potential external contextual conditions related to CDVC.
The second set of contextual conditions is composed of four types of internal-to-the-organization conditions. Our analyses revealed that external environmental changes affected CDVC by filtering through these internal conditions--hence the two concentric circles in our CDVC model. One of these conditions reflected changes taking place within participants' organizations, such as restructuring, changes in processes, and changes in management. Virtually all participants spoke passionately and at length about various internal-to-the-organization conditions and how these changes were closely linked with external-to-the organization changes and CDVC. We refer to the remaining three internal contextual conditions as perceived current capabilities conditions, which include participants' perceptions of their own organizations'( 1) recent performance, ( 2) knowledge levels, and ( 3) levels of control. Here, participants described significant dissatisfaction with their and their organizations' abilities to operate successfully within the dynamic environment reflected in the aforementioned external conditions. We found that participants did not consider their dynamic environments in isolation, but rather they discussed their and their organizations' abilities to deal with that dynamic environment. Participants associated CDVC with environmental changes typically when they felt unprepared for those changes.
Two limitations of this study should be noted. First, our data are weighted heavily toward participant interviews over observation and documents, more so than grounded theory recommends. It took months merely to negotiate access to a single firm (Workman 1993) just to conduct interviews. For this study, we were unable to obtain permission to observe behavior over an extended time period or to study many documents. In many cases, relevant documents apparently did not exist. Deeper insights might have emerged had we been able to observe changes in desired value taking place. Second, as is typical for grounded theory studies, we relied on interviews from relatively few participants (i.e., 22) who represented a relatively few organizations (i.e., 9) in automotive supply chains. This limits the generalizability of our interpretations. Despite these limitations, this study has implications for marketing literature, marketing management, and further research.
Implications for Marketing Literature
The results of our study contribute to the growing body of literature on customer value. Perhaps most important, our findings revealed CDVC as a complex phenomenon, encompassing three interrelated subphenomena: CDVC form/intensity, tension management, and action/interaction strategies (Figure 1). We found little literature that documents the nature of CDVC itself, so our findings about the CDVC phenomenon are largely new. For example, the wide variation in CDVC form and intensity catalogs the many ways in which changes in customer value can appear to both customers and suppliers. In addition, we discovered that CDVC most likely occurs in a context saturated with emotion experienced by managers, and mostly negative emotion (i.e., tension) at that. In many ways, CDVC can be viewed as a problem-solving response to emotion and the need to manage that emotion. In contrast, most of the literature on customer value focuses more on the cognitive aspects of this phenomenon (e.g., Lai 1995; Lapierre 2000; Sheth, Newman, and Gross 1991; Zeithaml 1988). Consistent with this problem-solving view, we discovered that buyers use a wide variety of strategies to induce suppliers to recognize and respond to desired value changes. Our findings of the roles played by participants' self-identities and personal life histories partially explain the variation in all three components of our CDVC model at the individual level and are similar to the kinds of findings that would come from the analysis approach suggested by Thompson (1997).
Our results both support and expand on means-end theory. Supporting this theory, we found that changes in customer value occur at all levels of the hierarchy--attributes, consequences, and, to a much lesser extent, desired end states. Although an attribute-based concept of customer value remains prevalent in the literature (e.g., Bolton and Drew 1991; Levin and Johnson 1984), understanding changes in customer value requires broadening this perspective to include consequences and end states. Although it is not predicted by means-end theory, we found that change at one level is not always associated with change at another level in a customer's hierarchy.
Perhaps customers believe that a desired supplier attribute change will simply maintain a specific consequence. Or consumers may have difficulty linking supplier attributes to specific consequences, at least more than means-end theory suggests. In addition, this finding indicates the importance for future research to learn about how such linkages are broken and reformed as value changes at one level in the hierarchy spread to other levels. Perhaps the most intriguing finding from this study pertains to supplier-customer relationships. Apparently, CDVC provides a major motivation for relationship building and maintenance. Alternatively, CDVC may sow the seeds for relationship dissolution when a supplier cannot or will not conform to the new desired value of customers. Although the literature acknowledges the goal-driven nature of relationships (e.g., Gundlach and Murphy 1993; Sharma and Sheth 1997), we found little literature that makes this important link to CDVC (for an exception, see Gassenheimer, Houston, and Davis 1998). Thus, our findings add new insights into the goal-driven nature of relationship building. Customers' recognition of the role sup-pliers play in attaining new value desires may help explain the current trend in business practice toward relationship building.
In addition, our findings expand customer value theory to incorporate relationships. Specifically, CDVC form may evolve from the basic to the complex as relationships grow. Also, customers and sellers may have different perceptions of the magnitude of CDVC. Failure to achieve specific desired value from a supplier may be related to CDVC magnitude and volatility. Suppliers may unwittingly contribute to customers' changes in desired value by making new demands on customers, such as asking for price increases. Similarly, suppliers that do not respond well to changes in customers' desired value may find that customers generate even more changes.
Finally, our findings highlight the importance of internal-to-the-organization contextual conditions that are influential on CDVC. In the existing literature that focuses on external-to-the-organization contextual conditions (Woodruff and Gardial 1996; Zeithaml, Berry, and Parasuraman 1993), the internal conditions provide an important link between the external conditions and CDVC. For example, our results show that everyday work activities of learning, improving, and controlling intertwine with CDVC.
Cause-and-Effect Speculations
Our interpretations yielded a tripartite CDVC model that reflected close associations among CDVC components. We could not develop a cause-effect structural model based solely on nonlongitudinal depth interviews. However, all 22 interviews consisted of narratives about events that occurred at many points in participants' lives, some years before interviews and some immediately before interviews. This temporal variety of personal stories enabled us to speculate on a causal CDVC model. Such a model does not negate any previous interpretations but instead takes an additional step in proposing direct relationships among components of the model previously discussed. The causal CDVC model (Figure 2) takes a step beyond our data that demands further validation through large samples and longitudinal studies. However, we believe that given our level of immersion in the hundreds of pages of transcript data, we would be remiss if we did not make such a speculation.
Figure 2 posits that CDVC, form variety, and intensity can be modeled as two distinct constructs. Here, CDVC form variety reflects the many ways in which customers' desired value can change, as found in our research--namely, value hierarchy level, newness, bar raising, and priority change. Intensity of CDVC refers to the rate, magnitude, and volatility at which changes occur within a customer's organization. We expect these two constructs to be related; for example, the more intense CDVC is, the more likely there is to be greater variety in the forms value change takes. Both of these CDVC constructs are likely driven by external and internal contextual conditions. However, our findings strongly suggest that the external conditions also operate through the internal conditions, because changes external to the organization most likely partially result in organizational changes and perceptions of poor performance.
Our proposed CDVC model also posits that these external and internal contextual conditions contribute to customers' feelings of tension, which customers then try to reduce through learning, improving, and controlling efforts. Our findings suggest that it is while customers are engaged in these tension reduction activities that they become more acutely aware of their dependence on key suppliers. And it is the recognition of dependence on the supplier that leads to changes in what they value from those suppliers. Therefore, we propose that the tension-management aspects of CDVC could be modeled as a process whereby tension is generated by internal and external conditions, which then leads to tension-reduction efforts followed by recognition of supplier dependence. The more dependent customers believe they are on suppliers, the more likely it is that CDVC will be intense and come in a variety of forms.
Participants' locating, relationship-building, motivating, and coordinating interaction strategies were often means for participants to obtain new value from suppliers. Thus, desired value change leads to interaction strategies. Therefore, we have modeled the four interaction strategies as outcomes of CDVC form variety and CDVC intensity. Similarly, tension-management activities might also lead to these interaction strategies directly. However, the lack of linearity in social reality suggests that the reverse might also exist, that is, that interaction strategies might also affect tension management.
This proposed model has components similar to some offered by extant literature such as external contextual conditions, which may be similar to environmental dynamism (e.g., Achrol 1991), but empirical tests of such a model will require the development of several new scales. However, this alternative model stimulates thoughts on how aspects of CDVC might be related.
Managerial Implications of CDVC
Suppliers may acquire an important source of competitive advantage by paying attention to changes in customers' desired value (Woodruff 1997). However, this requires anticipating when and what change will likely occur in the future to provide lead time that a supplier needs to respond. Our findings about CDVC may help marketers more quickly recognize changes in their customers' desired value and more deeply understand those changes. For example, monitoring tension levels among managers in customer firms may help suppliers predict when and how fast value change will occur. Keeping current on customer value or means-end hierarchies may yield indicators of what kinds of changes are likely. Alternatively, staying attuned to customers' strategies for negotiating may uncover the parts of hierarchies that are most likely to be changing. This kind of deep understanding goes well beyond asking customers what they currently value. It requires creating a customer-oriented culture (Slater and Narver1995) and a customer value-oriented marketing information system (Woodruff 1997) that includes specific CDVC-focused information, such as an assessment of individualized meanings associated with experiences related to CDVC.
Marketers may choose to take either a reactive or proactive position relative to CDVC (Woodruff and Gardial 1996). Reactive marketers wait to respond to changes as they occur, such as when they are asked by customers. In contrast, proactive marketers actively influence changes in customers' desired value by helping customers interpret the changes in their environments, respond to those changes, and possibly avoid undesirable changes (Hamel and Prahalad 1994). Both positions require collection and analysis of data on changes in desired value with each influential member of the customer organizations (Slater and Narver 2000). Such historical data should help in the development of scenarios for possible future desires and, subsequently, contingency plans for those changes.
Finally, marketers could explore segmenting their customers on the basis of CDVC. Customers may be grouped according to the types or degree of changes being observed. For example, some customers may exhibit intense CDVC, but others may not. Because responding to changes in customers' desired value requires intense resource investment in terms of finances, time, and energy, adding this dimension to segmentation decisions should facilitate more appropriate resource allocation.
Future Research Directions and Conclusion
This study's limitations and findings suggest several research directions. One direction consists of empirically testing the generalizability of the proposed models. Validation studies must expand the contexts in which change in desired value is explored to include different industries and supply chains. Valid scales are needed for CDVC and the other concepts in the CDVC models. In addition, more inductive exploration is still needed to assess whether the theory provides a sufficiently parsimonious yet comprehensive explanation for CDVC.
We need to continue the search for other possible insights into CDVC. Some of this research might involve longitudinal observation of many individuals within a variety of organizations.
From the seller's perspective, research should investigate two additional issues. First, what is the impact on suppliers of changes in customers' desired value? What happens when suppliers fail to recognize change or respond poorly? Second, which suppliers are world-class at understanding changes in desired value quickly and accurately? Both market orientation and total quality management philosophies call for marketers to anticipate what customers will value. Which marketers are truly the best at accomplishing this, and do they have a solid understanding of the CDVC phenomenon?
Our intention with this research was to develop a deeper understanding of changes in customers' desired value. We believe that customer value change theory will be essential to improve the state of the art of customer value anticipation as well as advance marketing strategy. We also want to encourage a program of research in this area. Because customer value delivery strategies are so important to business practice, we need much more understanding of the dynamic nature of customer value demands. The findings from this study offer an initial step on this journey of discovery.
Notes:
1 The limited and fragmented research on retail services focuses on specific areas such as quality issues and store image. Most of the quality-related literature involves the measurement of retail service quality, and these studies have generally adapted the SERVQUAL scale (Parasuraman, Zeithaml, and Berry 1988) to a retail store environment (e.g., Dabholkar, Thorpe, and Rentz 1996; Finn and Lamb 1991). Most of the image-related literature focuses on retail services as one component that constitutes retail store image (e.g., Mazursky and Jacoby 1986; Steenkamp and Wedel 1991).
2 We were concerned that some store managers may not have decision-making authority. However, from our data, we find that, when asked this question, 67% of store managers stated that they were completely independent. Another 26% said they needed to respect very general rules but were essentially independent. Only two managers indicated that they receive straight directions from headquarter organizations or have almost no decision-making authority.
Table 1: Study Sample
Legend for chart:
A - Name
B - Participant Details
A
B
Allen
Manager of Strategic Planning, Worldwide Purchasing, OEM1, male,
age 35, 13 years in industry, strategic direction analysis
Beth
Purchasing Director, OEM1, female, age 45, 20 years in industry,
responsible for more than $40 billion in purchasing contracts
Christine
Manager Parts Readiness, OEM1, female, age 40, 8 months evaluating
suppliers for firm, 15 years production and quality experience
David
Manager Training and Communications, OEM1, male, age 37, trains
purchasing department on supplier issues, 2 years in job,
purchasing and supplier management prior to this position
Ernest
Director, Supplier Development, OEM1, male, age 40, 10 years with
firm, helps suppliers improve their products and processes
Frank
Commodity Manager, OEM1, male, age 40, responsible for $30 billion
in metals purchasing at OEM1, in position 18 months,
purchasing/engineering positions prior to this position
Greg
Commodity Manager, OEM1, male, age 45, responsible for $30 billion
in chemicals purchasing, in position 8 months, purchasing experience
Hank
Senior Purchasing Agent, Raw Materials Supplier Management, OEM2,
male, age 45, 2 years in position, 13 years in purchasing
Irving
Materials Development Engineer, OEM 2, male, age 38, responsible for
metals product/service specification design, in position 3 years
John
Senior Engineer, OEM2, male, age 50, 12 years in position as technical
engineer for metals, 28 years experience in the industry
Ken
Senior Engineer, OEM2, male, age 37, 10 years with firm, responsible
for designing supplier product and technical service specifications
Larry
Materials Development Engineer, OEM2, male, age 23, 1 year in the
industry, designs new materials specifications for platform group
Mark
Materials Development Engineer, OEM2, male, age 36, 13 years with
firm, designs materials specifications, (not with Larry)
Nick
Materials Development Engineer, OEM2, male, age 36, 13 years with
firm, designs materials specifications, (not with Larry or Mark)
Paul
Purchasing Manager/Supplier Development, OEM2, male, age 45, 23
years in the industry, global purchasing/supplier development
Ruth
Supervisor, Corporate Purchasing, Tier 1 supplier, female, age
40, responsible for all purchasing for firm ($150 million), in
position 2.5 years, for firm 12 years, firm makes bearings and
related components
Steve
Senior Manager, Component Sourcing, Tier 1 supplier, male, age 35,
responsible for all ($120 million) component and raw materials
purchasing, firm revenues $6 billion, firm manufactures systems
components for OEMs (e.g., seals, bearings, fuel systems,
lighting, pistons)
Ted
Purchasing Supervisor, Tier 1 supplier, male, age 35, responsible
for purchasing of wire harness components, injection molded plastics,
MRO (maintenance, repair, and operations) items, 2 years in position,
5 in industry, firm revenues $5 billion, firm manufactures
electrical and interior trim systems
Unice
Materials Manager, Tier 1 and 2 supplier, female, age 45, responsible
for all purchasing ($6 million), manufactures stamped steel blanks
for auto subcontractors and OEMs, in position 9 years
Vicky
Purchasing Manager, Tier 1 supplier, female, age 45, responsible for
all purchasing for firm, firm manufacturers air, oil, and fuel filters
for OEMs, firm revenues $54 million
Wess
Manager Supplier Development, OEM 3, male, age 50, in supplier
development/purchasing 5 years, all supplier development programs
Zach
Purchasing Supervisor, Tier 1 and 2 supplier, male, age 35, responsible
for purchasing electrical and electronic components for firm, in
position 2.5 years, firm revenues $4.5 billion, firm manufactures
motors such as seat motors, window motors, and lift motors
Notes: All participants are key managers for their firms with decision-making power. Names are pseudonyms. Some ages are estimates. The three OEMs in this study are automobile manufacturers. Annual revenues: OEM1, $150 billion; OEM2, $50 billion; OEM3, $20 billion. All firms are considered world-class in the automobile industry.
Table 2: Trustworthiness of the Study and Findings: Interpretive and Grounded Theory Criteria
Legend for chart:
A - Trustworthiness Criteria
B - Method of Addressing in this Study
Credibility
Extent to which the results appear to be acceptable representations
of the data
Six months conducting interviews.
Five research team members gave input during data collection and
interpretation.
16-page summary of initial interpretations was provided to the
participants for feedback.
Result: Emergent models were altered and expanded; participants
bought into interpretations.
Transferability
Extent to which the findings from one study in one context will apply
to other contexts
Theoretical sampling.
Result: Theoretical concepts were represented by data from all
participants.
Dependability
Extent to which the findings are unique to time and place; the
stability or consistency of explanations
Participants reflected on many experiences covering recent events as
well as long past events.
Result: Found consistency across participants' stories regardless of
when changes occurred.
Confirmability
Extent to which interpretations are the result of the participants and
the phenomenon as opposed to researcher biases
More than 200 pages of interpretations and documents independently
analyzed by a coresearcher.
Summary of preliminary findings to four other team members who acted
as auditors.
Result: Interpretations were expanded and refined.
Integrity
Extent to which interpretations are influenced by misinformation or
evasions by participants
Interviews were professional, of a nonthreatening nature, and
anonymous.
Result: Never believed that participants were trying to evade the
issues being discussed.
Fit
Extent to which findings fit with the substantive area under
investigation.
Addressed through the methods used to address credibility,
dependability, and confirmability.
Result: Concepts were more deeply described, and the theoretical
integration was made more fluid and less linear, capturing the
complexities of social interaction discovered in the data.
Understanding
Extent to which participants buy into results as possible
representations of their worlds.
Executive summary of findings to participants; asked if they reflected
their stories.
Presented a summary to colleagues and practitioners.
Result: Colleagues and practitioners bought into the findings.
Generality
Extent to which findings discover multiple aspects of the phenomenon.
Interviews were of sufficient length and openness to elicit many
complex facets of the phenomenon and related concepts.
Result: Captured multiple aspects of the phenomenon.
Control
Extent to which organizations can influence aspects of the theory.
Some variables within the theory are aspects over which participants
or suppliers would have some degree of control.
Result: Participants and suppliers can influence CDVC.
Table 3: CDVC Properties and Dimensional Ranges
Legend for chart:
A - Properties of CDVC
B - Dimensional Range of Property
A
B
A: CDVC Form Variety
1. Hierarchy level
Attributes only-attributes, consequences, and end-states
2. Newness
Not new-entirely new/unexpected
3. Bar raising
No movement in standards- increase in standards
4. Priority change
No shift-shift in priority among current value dimensions
B: CDVC Intensity
1. Rate
Evolutionary/gradual-revolutionary/ rapid
2. Magnitude
Small-large
3. Volatility
Few-many
Table 4: Tension Properties and Dimensional Ranges
Legend for chart:
A - Tension Properties
B - Dimensional Ranges
A
B
Affective strength
Weak-strong
Perceived extensiveness
Within one individual-across entire company
Temporal dynamism
Growing-subsiding
Figure 1: A General Model of CDVC
Figure 2: A Proposed Casual Model of CDVC
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~~~~~~~~
By Daniel J. Flint; Robert B. Woodruff and Sarah Fisher Gardial
Daniel J. Flint is Assistant Professor of Marketing, and Robert B. Woodruff is Proffitt's Inc. Professor of Marketing and Department Head, Department of Marketing, Logistics and Transportation, University of Tennessee. Sarah Fisher Gardial is Assistant Dean and Associate Professor of Marketing, University of Tennessee. The authors thank the study participants and John T. Mentzer, David W. Schumann, and Eric Haley for their significant contributions during the research and the three anonymous JM reviewers for their valuable comments.
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Record: 63- Firms Reap What They Sow: The Effects of Shared Values and Perceived Organizational Justice on Customers' Evaluations of Complaint Handling. By: Maxham III, James G.; Netemeyer, Richard G. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p46-62. 17p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.67.1.46.18591.
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Firms Reap What They Sow: The Effects of Shared Values and Perceived Organizational Justice on Customers' Evaluations of Complaint Handling
Employing elements of organizational theory and service recovery research, the authors examine how employees' perceptions of shared values and organizational justice can stimulate customer-directed extra-role behaviors when handling complaints. They also investigate how these extra-role behaviors affect customers' perceptions of justice, satisfaction, word of mouth, and purchase intent. The authors capture and match employee and customer perceptions regarding the relevant constructs following a complaint and recovery experience. The results indicate that employees' perceptions of shared values and organizational justice affect customer-directed extra-role behaviors. Furthermore, the authors find that extra-role behaviors have significant effects on customers' perceptions of justice and that these behaviors mediate the effects of shared values and organizational justice on customer justice perceptions. Their study reveals that customer ratings of justice affect the customer outcomes of satisfaction with recovery, overall firm satisfaction, purchase intent, and word of mouth. Finally, the authors show that customers' perceptions of justice mediate the effects that extra-role behaviors have on customer outcomes.
Given that the concepts of customer service and complaint handling are relatively straightforward and intuitive and that the benefits are often quite significant (Goodwin and Ross 1992; Maxham 2001; Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998), why do many firms continue to provide poor service? One reason may be that firms often fail to inspire the employees providing the service. Because front-line employees are often the primary reflection of a firm's image and because they are often critical players in the recovery from failures, it is essential that they believe in the firm's recovery strategy. Apathetic employees may offer lackluster customer service, and disgruntled employees may even sabotage the firm by purposefully treating customers poorly. Some argue that poor recoveries occur partly because frontline customer service agents do not share the firm's values and/or feel they have been treated unfairly by their organization (Bowen, Gilliland, and Folger 1999; Hart-line, Maxham, and McKee 2000). Therefore, it remains critical to identify factors that encourage recovery agents to take extra steps to resolve customer complaints.
The current literature provides little empirical evidence on the best ways to inspire customer service employees to go beyond their job duties to help resolve customer problems. Consequently, many questions remain unanswered. For example, to what extent does the congruence between an employee's values and the values held by the organization (i.e., shared values) affect the employee's propensity to engage in customer-directed extra-role behaviors when handling complaints? Does treating employees fairly affect customers' perceptions of how fairly they have been treated after initiating a complaint? Research addressing such questions could help managers devise recovery strategies that improve organizational and customer outcomes. We seek to bridge the gap between organizational research focused on inspiring employees (e.g., Bettencourt and Brown 1997) and service recovery research focused on customer outcomes (e.g., Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998) by exploring how key organizational factors set the stage for desirable customer outcomes. Although extant research generally views complaint handling as an external customer issue, perhaps it should also be viewed as an internal human resource management issue. Companies may need to internally market the firm to employees just as employees need to externally market the product to customers. As such, managers may reap what they sow in complaint handling. We present a field study that investigates how employees' perceptions of shared organizational values and justice affect customers' perceptions of customer-directed extra-role employee performance, justice, satisfaction, word of mouth, and repurchase intent.
Extra-Role Behaviors in Service Recovery
Much has been written about employee performance that goes beyond "in-role" requirements. Such performance has been defined in at least four ways: ( 1) organizational citizenship behavior, which is viewed as "individual behavior that is discretionary, not directly or explicitly, recognized by the formal reward system and in the aggregate promotes the effective functioning of the organization" (Organ 1988, p. 4; Podsakoff et al. 2000, p. 13); ( 2) prosocial behaviors, defined as "acts that are not directly specified by a job description, but which are of benefit to the organization and which are not of direct benefit to the individual" (O'Reilly and Chatman 1986, p. 493); ( 3) contextual performance behaviors, defined as "behaviors that do not support the technical core [of the organization] itself so much as they support the broader organizational, social, and psychological environment in which the technical core must function" (Borman and Motowidlo 1993, p.73); and ( 4) extra-role behaviors, defined within the context of customer service as "discretionary behaviors of contact employees in servicing customers that extend beyond formal role requirements" (Bettencourt and Brown 1997, p. 41). Within these four classifications, there is a great deal of conceptual overlap, prompting Podsakoff and colleagues (2000) to identify several individual behaviors common to the four classifications. These behaviors include interpersonal helping behavior and altruism, sportsmanship, organizational compliance and loyalty, conscientiousness and civic virtue, and individual initiative and self-development. Furthermore, these performance behaviors have been broken down into those directed toward the firm, coworkers within the firm, and consumers. Although debate exists as to their classification (Organ 1997; Podsakoff et al. 2000), the outcomes of all such behaviors are beneficial to the organization (see meta-analyses by Organ and Ryan [1995] and Podsakoff et al. [2000]). In our research, we focus on the classification that has been the least researched, but perhaps the most beneficial to many firms--customer-directed extra-role behaviors.
Several researchers have conceptualized extra-role behaviors within a customer service setting. For example, Borman and Motowidlo (1993) suggest that favorably representing the organization to customers is part of contextual performance (i.e., not required) that goes beyond the task performance of an employee's formal job requirements. More recently, Bettencourt and Brown (1997) and Betten-court, Gwinner, and Meuter (2001) extend this conceptualization with three customer-directed extra-role behaviors (i.e., loyalty, participation, and service delivery) that go beyond formal role requirements. Loyalty refers to those behaviors in which employees advocate the interests and image of the firm. Participation is defined as those efforts to take initiatives that improve service when communicating with customers, and service delivery reflects conscientious employee efforts to effectively respond to customer concerns.
Consistent with Borman and Motowidlo's (1993) notion of representing the organization to customers, Bettencourt and Brown's (1997, p. 41) view that customer service extends beyond formal role requirements, and the participation and service delivery components that Bettencourt, Gwinner, and Meuter (2001) suggest, we define customer-directed extra-role behaviors as the degree to which customers believe the service agent they dealt with exceeded their expectations in resolving their complaint. Note that our definition and subsequent operationalization are couched in terms of the customer's view of extra-role behaviors in a complaint handling setting. To date, the studies examining customer-directed extra-role behaviors have used either manager ratings of contact service employees (Bettencourt and Brown 1997) or self-reports (Bettencourt, Gwinner, and Meuter 2001). Furthermore, these studies did not investigate extra-role behaviors in the context of complaint handling.
Studies examining customer-rated extra-role behaviors for product or service failures and recoveries are needed for several reasons. First, the constructs encompassing extra-role performance are somewhat convoluted (Organ 1997), leading researchers to suggest a focus on specific types of extra-role behaviors that are most appropriate for a given organization in a given context. Specifically, "service companies have special requirements on dimensions related to dealing with customers and representing the organization to outsiders" (Borman and Motowidlo 1993, p. 90). Thus, research needs to focus on the customer-and service-oriented extra-role behaviors of customer-contact employees (Bettencourt, Gwinner, and Meuter 2001; Podsakoff and MacKenzie 1997). Our research is consistent with this view, specifically assessing customer-rated extra-role behaviors within a complaint-handling context.
Second, some researchers suggest that self-reports may include social desirability bias and defensiveness on the employee's behalf (Podsakoff and Organ 1986). That is, employees may overrate their performance. Manager ratings may be tainted by either excessive strictness or excessive lenience toward employees or limited contact with employees in a given situation (Netemeyer et al. 1997). Given our setting in which customers deal individually with service agents, we believe that customers' perceptions of customer-directed extra-role behaviors are most important and accurate. Because supervisors do not observe all employee behavior, they often are not in the best position to judge whether agents engage in customer-directed extra-role behaviors during all complaint resolution exchanges. Furthermore, what a super-visor or an employee views as extra-role performance may be viewed as in-role performance by the customer, and what the customer views as extra-role behaviors may be considered in-role performance by employees or supervisors. Thus, given the complaint handling setting and the premise that customers' views are the best predictor of firm ratings, we measure customer-rated extra-role behaviors in our research.
Third, because customer service agents are the primary contact between buyers and the firm, their efforts can either augment or weaken customers' perceptions of the firm (Schneider and Bowen 1999). Researchers suggest that ineffective recoveries actually compound the problem and that effective recovery efforts can restore satisfaction and promote customer loyalty (Maxham 2001; Smith, Bolton, and Wagner 1999). Thus, product and service failures offer unique service situations in which customers likely perceive injustices and hold relatively high expectations for redress (Seiders and Berry 1998). Given the confrontational nature of complaints, employees sometimes experience irritated or incensed customers, which makes these employees less likely to engage in customer-directed extra-role behaviors during complaint handling then during more civil customer service exchanges. As such, it is important to identify methods of inspiring employees to perform customer-directed extra-role behaviors while handling complaints. In this study, we uncover some potentially key antecedents and outcomes of customer-directed extra-role behaviors in the context of complaint handling.
Customer-Directed Extra-Role Behaviors, Shared Values, and Organizational Justice
Consistent with the conceptual work by Bowen, Gilliland, and Folger (1999), Figure 1 proposes a framework, or recovery process model, that focuses on achieving customer-directed extra-role behaviors. In the subsequent sections, we offer rationale for why shared values and organizational justice are key factors associated with customer-directed extra-role behaviors and how these extra-role behaviors affect customers' perceptions of justice following a product or service failure and recovery effort. (Note that though shared values and organizational justice are key factors affecting extra-role behaviors, several personality or dispositional factors may also affect extra-role performance; Podsakoff et al. 2000.)
Shared values and extra-role behaviors. Research in organizational psychology suggests that employees and their job environments become intertwined through successive stages of attraction-selection-attrition (ASA). The ASA framework is grounded in the person perspective on organizational behavior, in which people within an organization largely determine a firm's practice, structure, and culture (Schneider, Goldstein, and Smith 1995). Consistent with organizational culture literature, in which culture reflects a set of beliefs, expectations, and shared values that guides the behavior of an organization (Hatch 1993; Schein 1990), a central notion of the ASA framework is that people are attracted to firms whose general core or dominant values they share. The ASA view espouses that companies tend to attract, hire, and retain people who have values consistent with company goals and that the company will function most effectively when populated by employees who share the organization's values. As such, we define shared values as the congruence of general core or dominant values between employees and their organization. This conceptualization is consistent with shared values as a global construct in which values are viewed as fundamental, relatively enduring, and guiding employee behavior (Chatman 1991; Kristof 1996). Although research has yet to investigate the relationship between shared values and customer-directed extra-role behaviors in a failure and recovery context, some studies have examined the association between related constructs and organizational citizenship-type behaviors in other settings. For example, O'Reilly and Chatman (1986) find that person-organization fit is associated with increased levels of employee philanthropy and helping behavior toward the organization. Netemeyer and colleagues (1997) report an indirect relationship between person-organization fit and firm-directed organizational citizenship behaviors in a personal selling context. Goodman and Syvantek (1999) note that person-organization fit affects the employee contextual behaviors of altruism and conscientiousness.
We believe that shared values also have important implications in a failure and recovery context. Given that complainants are often irritated, customer service agents must exude patience and diplomacy. However, it is plausible that service agents lacking shared values may be unable or unwilling to tactfully handle aggravated or demanding customers. In the midst of harsh criticisms from a customer, frontline employees lacking shared values may quickly become frustrated and then retaliate or withdraw from the customer. Alternatively, service agents sharing the core values of the firm are likely to take extra steps to resolve complaints. The ASA framework suggests that values, socially endorsed by the organization and shared by the individual, create an organizational culture in which employees are similar in their behaviors and orientations toward the firm and customers. Thus, when employees share the values of their employer, they will be more likely to take ownership of customer complaints and go out of their way to resolve them (Schneider, Goldstein, and Smith 1995). Thus, we posit the following:
H1: Shared values positively affect customer-directed extra-role behaviors.
Perceived employee organizational justice. Perceived justice represents the extent to which people ascertain the fairness of an exchange between themselves and another party (Deutsch 1985; Greenberg 1990; Lind and Tyler 1988). Perceived justice focuses on the motivational and cognitive processes of weighing justice inputs (e.g., time, effort, opportunity costs) against justice outcomes (e.g., rewards or marginal utility). Injustice arises when people believe their ratio is inequitable when compared with a referent other's ratio; conversely, justice arises when people believe that ratio is equitable. For example, suppose that Customer Service Agent A is rewarded one day of vacation time for every month worked (12 days per year). Agent A may think this reward system is relatively fair until he or she discovers that Customer Service Agent B is rewarded one and a half days of vacation time for every month worked (18 days per year). In this scenario, Agent A may perceive an injustice because, compared with Agent B, he or she is rewarded relatively fewer outcomes (i.e., vacation time) for the same amount of input (i.e., hours worked).
Perceived organizational justice represents an important human resource management practice that facilitates the functioning of the firm and helps create a climate for service both within the firm and to customers (Konovsky 2000; Schneider 1990). Furthermore, perceived justice theories have been useful in explaining the processes and outcomes of organizational conflicts, and studies suggest that fairly treated employees are more likely to engage in extra-role behaviors/organizational citizenship behaviors (Podsakoff et al. 2000). As such, perceived organizational justice provides a worthy backdrop for studying how relationships between employers and employees affect employee extra-role behaviors (Sheppard, Lewicki, and Minton 1992). Consistent with current organizational research, we examine three organizational justice constructs that affect extra-role behaviors-- distributive, procedural, and interactional justice.
Distributive justice and extra-role behaviors. Distributive justice is based in social exchange theory in which people assess the equity or fairness of an exchange by comparing input to outcomes (Deutsch 1985; Greenberg 1990). An exchange is judged as fair when employee input is proportional to outcomes. We define distributive justice as the degree to which employees believe that they have been fairly rewarded for the performance, effort, experience, and stresses associated with their jobs. Consider a scenario in which Customer Service Agent A is paid $10 per hour for performing his or her job. Agent A may think this compensation system is relatively fair until he or she discovers that Customer Service Agent B is paid $15 per hour. In this case, Agent A may perceive a distributive injustice. Compared with Agent B, Agent A is paid less for the same amount of work.
Distributive justice has warranted great attention because of its positive association with desirable job outcomes. Some research concludes that distributive justice is significantly correlated with job satisfaction (Netemeyer et al. 1997) and pay satisfaction (Folger and Konovsky 1989). Others have found relationships between distributive justice and employee-directed or firm-directed extra-role behaviors (Organ and Ryan 1995; Scholl, Cooper, and McKenna 1987). In a customer service context, Bettencourt and Brown (1997) also found a positive relationship between pay level, an element of distributive justice, and job satisfaction. However, no research has examined the association between distributive justice and customer-directed extra-role behaviors in a complaint-handling context. It has been conceptually argued that service recovery agents who are treated fairly with respect to outcomes will likely perform customer-directed extra-role behaviors (Bowen, Gilliland, and Folger 1999). That is, fair internal treatment of employees "spills over" to external customer service in the form of extra-role behaviors. We test the following hypothesis:
H2: Perceived employee distributive justice positively affects customer-directed extra-role behaviors.
Procedural justice and extra-role behaviors. Procedural justice refers to the fairness of the policies and procedures used to achieve organizational outcomes (Lind and Tyler 1988). Even though a final outcome may be unfair, employees can still judge the procedures used to derive the outcome as fair (Folger and Konovsky 1989; Greenberg 1990). In our study, we define procedural justice as the perceived fairness of policies and procedures used in making decisions about employees. Suppose that Customer Service Agent A must consult a manager before offering customers more than $50 in redress. Agent A may think this procedural system is relatively fair until he or she discovers that Customer Service Agent B is empowered to offer redress to complainants on the spot without consulting a manager, regardless of the costs involved with the redress. In this scenario, Agent A may perceive a procedural injustice because, compared with Agent B, Agent A has less autonomy.
In general, researchers have found that employees perceiving procedural justice are likely to perform extra-role behaviors/organizational citizenship behaviors. For example, Skarlicki and Latham (1996) find that people trained to recognize and then engage in procedural fairness exhibit higher levels of organizational citizenship behaviors. Kim and Mauborgne (1996) report that multinational managers who perceived higher levels of procedural justice went "beyond the call of duty" to help the organization (for a review, see also Konovsky 2000). In a service setting, Bettencourt and Brown (1997) note that bank tellers perceiving fair pay rules (i.e., procedural justice) were likely to receive higher supervisor ratings of extra-role customer service behaviors. However, because complaint handling involves a specialized type of customer service often requiring extra efforts beyond those needed for general customer service, studies that investigate these relationships in a complaint setting are needed. Although it has been conceptually suggested that employees' perceptions of procedural justice can affect extra-role behaviors when they handle complaints (Bowen, Gilliland, and Folger 1999; Schneider and Bowen 1999), no empirical studies have tested this premise. We offer the following hypothesis:
H3: Perceived employee procedural justice positively affects customer-directed extra-role behaviors.
Interactional justice and extra-role behaviors. Interpersonal treatment is the cornerstone of interactional justice. It has been suggested that even if the employees perceive the procedures and outcomes as fair, they may still consider themselves unfairly treated if they perceive injustice during interactions with managers. As such, interactional justice can affect job behaviors beyond procedural and distributive justice (Bies and Shapiro 1987; Greenberg 1990). We define interactional justice as the extent to which employees believe they have been treated justly in their interactions with their supervisors. This conceptualization includes elements of courtesy, honesty, interest in fairness, and effort perceived by the employee. Consider a situation in which a supervisor frequently addresses Customer Service Agent A in a terse or rude manner. Agent A may dismiss these personal interactions until he or she notices that the supervisor addresses other customer service agents in a courteous manner, possibly prompting him or her to perceive an interactional injustice.
Some researchers report a positive relationship between interactional justice and organizational citizenship behaviors. In particular, Moorman (1991) finds that employees perceiving interactional justice are more likely to exhibit altruism, courtesy, sportsmanship, and conscientiousness. Skarlicki and Folger (1997) find a strong main effect of interactional justice on organizational-based extra-role behaviors. Betten-court and Brown (1997) similarly note a positive relationship between a supervisor's propensity to value employee feedback before distributing tasks and supervisor ratings of extra-role customer service. Although not empirically tested, scholars have conceptually suggested that interactional justice may affect employee performance of extra-role behaviors in a recovery setting (Bowen, Gilliland, and Folger 1999). On the basis of these writings, we posit that interactional justice influences the extent to which recovery agents engage in customer-directed extra-role behaviors.
H4: Perceived employee interactional justice positively affects customer-directed extra-role behaviors.
Outcomes of Extra-Role Behaviors and the Mediated Effects of Shared Values and Organizational Justice
Our previous discussion hypothesized shared values and organizational justice as antecedents of customer-directed extra-role behaviors when employees handle complaints. What are the likely consequences of customer-directed extra-role behaviors following complaint handling? It seems reasonable that extra-role behaviors should directly affect customers' perceptions of justice and may affect outcomes relating to customer satisfaction, purchase intent, and favorable word of mouth. However, how do extra-role behaviors affect these outcomes? What is the nature of the relationship of customer justice with shared values and organizational justice? Consistent with Figure 1, we contend that mediating hypotheses are tenable and propose the following links: shared values/organizational justice customer-directed extra-role behaviors customer justice customer outcomes. Thus, shared values/organizational justice affect extra-role behaviors, which in turn affect customer justice; that is, extra-role behaviors mediate the effect of shared values/organizational justice on customer justice. Furthermore, customer justice affects customer outcomes, and thus the effects of extra-role behaviors on customer outcomes are mediated by customer justice. The following section offers a rationale for these effects.
Extra-role behaviors and customers' perceptions of justice. The rationale behind the extra-role behaviors customer justice link is twofold. First, the services literature stresses the importance of interpersonal treatment during the service encounter (Bitner, Booms, and Tetreault 1990), and some conceptual writings suggest that service agent extra-role behaviors affect customers' perceptions of justice (Bowen, Gilliland, and Folger 1999; Schneider and Bowen 1999). It therefore seems reasonable that customers' perceptions of the justice they receive from the firm will largely be determined by the customer-agent encounter. Second, if customers perceive extra effort by the service agent, their perceptions of justice should be enhanced. Customers often expect firms to overcompensate or engage in "correction-plus" behaviors after a product or service failure (Kelley, Hoffman, and Davis 1993; Oliver 1997). Customer-directed extra-role behaviors may provide the "plus" needed to raise customers' perceptions of justice after a failure. We posit that a customer's perception of the justice received from the firm is heavily influenced by the service agent's customer-directed extra-role behaviors. Thus, we model extra-role behaviors as theoretical antecedents of customers' perceptions of justice. We examine the distributive, procedural, and interactional dimensions of customer justice in a failure/ recovery context.
Distributive justice is viewed as the extent to which customers believe they have been treated fairly with respect to the final service recovery outcome. Such outcomes may represent refunds, discounts, or other atonement offered to customers after a failure. Thus, customers' perceptions of distributive justice should increase when they believe the agents put forth extra effort by offering attractive compensation. Elements of procedural justice include the timeliness/speed of resolving the failure, the perceived convenience/flexibility of the process to the complainant, and the accessibility of the firm (Tax, Brown, and Chandrashekaran 1998). Our procedural justice construct includes these elements and assesses the overall perceived fairness of policies and procedures in handling the recovery process. We argue that when agents go out of their way to handle a recovery in a timely fashion and explain in detail the procedures involved in the recovery effort (i.e., engage in extra-role behaviors), the customer's view of the firm's procedural justice is enhanced. Interactional justice is viewed as the extent to which customers believe they have been treated with honesty and courtesy by agents who are interested in fairness and who put genuine effort into the recovery (Smith, Bolton, and Wagner 1999). This concern for the customer throughout the recovery is enhanced when service agents go out of their way in their interaction with customers. We propose the following:
H5: Customer-directed extra-role behaviors positively affect (a) customer-perceived distributive justice, (b) customer-perceived procedural justice, and (c) customer-perceived interactional justice.
Extra-role behaviors mediate the shared values/organizational justice → customer justice link. Although research has yet to examine the shared values/organizational justice customer-directed extra-role behaviors customer justice link, theoretical arguments for such associations exist. Consistent with the ASA framework, Schneider, Goldstein, and Smith (1995) suggest that employees must share the values of the firm for an extra-role behavior-type culture to exist. Such extra-role behavior cultures indirectly lead customers to believe they have been fairly treated in service encounters. Bowen, Gilliland, and Folger (1999) and Schneider and Bowen (1999) argue that employees treated fairly by the firm will treat customers fairly (i.e., "justice in-justice out"). That is, internal organizational justice affects external customer service in the form of customer-perceived justice. However, we posit this effect as indirect because organizational citizenship behaviors/extra-role behaviors represent a mediating link between the justice in-justice out chain (Schneider and Bowen 1999). As such, we propose the following:
H6: Customer-directed extra-role behaviors mediate the effects of shared values and perceived organizational justice on customer-perceived justice.
Outcomes of Customer Justice and the Mediated Effects of Extra-Role Behaviors.
Recent research has examined the effects of customer perceived justice on satisfaction with service recovery/ complaint handling, firm commitment and trust, purchase intention, and word-of-mouth decisions (Blodgett, Hill, and Tax 1997; Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998). However, no studies have explored such outcome variables collectively, nor has over-all firm satisfaction been examined as an outcome of perceived justice in a complaint setting. Furthermore, the extra-role behaviors → customer justice → customer outcomes link, in which customer justice mediates the relationships between extra-role behaviors and customer outcomes, has not been addressed. We now advance hypotheses related to these links.
Distributive justice and customer outcomes. Seiders and Berry (1998) contend that as a firm increases the outcomes to its customers through recovery, perceptions of distributive justice increase, affecting several service-related outcomes. This contention has some empirical support. Smith, Bolton, and Wagner (1999) find that distributive justice affects service recovery satisfaction, and Tax, Brown, and Chandrashekaran (1998) find that distributive justice is positively related to satisfaction with complaint handling. Although not yet empirically tested, distributive justice associated with a given failure and recovery may also be related to overall firm satisfaction. Seiders and Berry (1998) suggest that a prime factor affecting overall firm satisfaction is the degree to which customers believe they have been fairly compensated after a failure. Furthermore, beyond its effects on satisfaction, distributive justice during recoveries may be related to purchase intent and word-of-mouth perceptions. Favorable complaint handling incidents include compensation commensurate with customer input, triggering increases in purchase intent (Kelley, Hoffman, and Davis 1993) and increases in positive word of mouth (Schneider and Bowen 1999).
[H sub7]: Customer-perceived distributive justice positively affects satisfaction with the recovery, overall firm satisfaction, purchase intent, and likelihood of positive word of mouth.
Procedural justice and customer outcomes. Theory and research suggest that procedural justice affects service recovery outcomes. A firm can provide a fair outcome (i.e., a full refund) in response to a service failure, but the customer may still deem the process in which the outcome was provided as unfair. For example, if customers experience a lengthy wait to receive refunds because the firm's policy requires frontline employees to clear all restitution offers with a department manager, customers may remain upset despite receiving a fair outcome. Because the service process is often an integral part of the entire product or service offering, firms can enhance satisfaction with the recovery by increasing customers' perceptions of procedural justice (Seiders and Berry 1998). Such theoretical contentions have empirical support. Smith, Bolton, and Wagner (1999) report that procedural justice has a significant effect on service encounter satisfaction, and Tax, Brown, and Chandrashekaran (1998) report that procedural justice has a positive effect on a customer's satisfaction with the firm's recovery.
We also suggest that customers' perceptions of procedural justice during a complaint experience can affect their overall firm satisfaction, purchase intent, and likelihood of word of mouth. Organizational psychologists (Greenberg 1990; Konovsky 2000) and market researchers (Seiders and Berry 1998; Tax, Brown, and Chandrashekaran 1998) argue that procedural justice is important in exchanges involving conflict resolution because it enhances the probability of maintaining long-term satisfaction between parties. Thus, a procedural justice → overall firm satisfaction link is tenable. It has also been argued and empirically demonstrated that procedural justice is related to purchase intent and word-of-mouth decisions. Blodgett, Hill, and Tax (1997) theoretically suggest the procedural justice purchase intent and procedural justice word of mouth links, and others show that some elements of procedural justice are related to word of mouth (Goodwin and Ross 1992).
H8: Customer-perceived procedural justice positively affects satisfaction with the recovery, overall firm satisfaction, purchase intent, and likelihood of positive word of mouth.
Interactional justice and customer outcomes. As previously stated, customers' evaluations of the firm are influenced by their interactions with the firm's agents, and research shows that interactional justice is positively related to satisfaction with recovery and overall firm satisfaction. Smith, Bolton, and Wagner (1999) find effects of interactional justice on satisfaction with service recovery, and Tax, Brown, and Chandrashekaran (1998) report that interactional justice has a strong effect on satisfaction with complaint handling (i.e., a regression coefficient of .457). Spreng, Harrell, and Mackoy (1995) find that the most important determinant of overall firm satisfaction with customer damage claims for a moving service is interpersonal treatment by the firm's agents, and Bitner, Booms, and Tetreault (1990) report that interactional justice is positively related to overall firm satisfaction. Finally, interactional justice is related to purchase intent and word-of-mouth decisions. Blodgett, Hill, and Tax (1997) theoretically argue for and find a positive relationship between interactional justice and purchase intent in a service failure context. They also hypothesize and find that higher levels of interactional justice are associated with lower levels of negative word of mouth.
H9: Customer-perceived interactional justice positively affects satisfaction with the recovery, overall firm satisfaction, purchase intent, and likelihood of positive word of mouth.
Customer justice mediates the extra-role behaviors customer outcomes link. As previously stated, the customer- agent encounter largely determines the customers' perceptions of the justice they receive from the firm. When customers perceive extra efforts by service agents, they likely will believe they have been treated fairly. Combining this with our preceding hypotheses (H7-H9) and the evidence that customer outcomes are affected by perceptions of justice in service recovery (Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998), we contend that customer-perceived justice represents a mediating mechanism between extra-role behaviors and customer outcomes.
H10: Customer-perceived justice mediates the effects of customer-directed extra-role behaviors on customer outcomes (i.e., satisfaction with the recovery, overall firm satisfaction, purchase intent, and likelihood of positive word of mouth).
Procedures, Sample, and Measures
We conducted a field study that focused on online customers who registered telephone complaints about the electronic equipment purchased from and serviced by a well-established electronics retailer. We chose this setting because both the electronics industry and online retailers have historically experienced high rates of customer complaints (Cheng 2000; Consumer Electronics Manufacturing Association 1998).
Customers who purchased online initiated complaints by telephoning the customer service center, which routed them to a customer service agent. The agent logged a "job problem report" into the customer database (all complaints appeared in a complaint register viewed and managed by the customer service director) and then attempted to resolve the problem. Frontline customer service agents handled 96% of the complaints directly (customer service managers handled 4%). When handling the complaints, agents were empowered to determine the appropriate course of action. However, management limited the dollar amount of redress beyond a full refund (e.g., discounts on future purchases). A total of 26% of the complaints involved defects. Of these, 42% were handled by instructing customers how to fix the defect, 37% were handled by replacing the defective product, and 21% were handled by offering full refunds (including shipping costs). A total of 22% of the complaints were shipping mishaps. Of these, 82% were handled by accurately fulfilling the order (shipping the desired items and offering free shipping on returned items). Of the 17% of complaints representing billing inaccuracies, 77% were handled by adjusting the bill in the customer's favor. Another 15% of complaints involved online ordering troubles and were handled by first completing the order by telephone and then offering customers a 20% discount on a future purchase. A total of 12% of the complaints involved poor customer service. All of these complaints were handled by first attempting to resolve the dispute. In 36% of these cases, the customers were not satisfied with the resolution and were subsequently mailed a 20% discount coupon redeemable on the customer's next purchase. All 4% of the complaints handled by management involved customer service process complaints in which the customer specifically asked to speak to a manager. In these cases, a manager generally handled the complaint in a manner similar to a frontline service agent.
In one day, 56% of complaints were handled, and an additional 34% were handled within three days. After exhausting all efforts to resolve the complaint (including the 20% discount on the customer's next purchase), customer service agents "closed out" the job problem report in the customer database and noted the problem history in the buyer's profile. After customer service agents completed the complaint resolution process, surveys were e-mailed to relevant complainants (and to their respective customer service agents). A total of 700 surveys were e-mailed to complainants. Given the recent evidence that delivering a survey by e-mail has little effect on response patterns (Stanton 1998), sending the surveys in this manner was not viewed as problematic. The surveys were introduced with a cover message that explained the firm's desire to improve customer service, and this message asked complainants to complete the survey and submit a hard copy by facsimile or mail. Of the 700 surveys e-mailed, 346 were returned. Of these 346 surveys, 320 had complete responses across all study variables.
Customer survey and measures. We measured customer-rated extra-role behaviors with five items adapted from prior research (e.g., Bettencourt and Brown 1997). These items were specific to the service agent that the customer dealt with and asked the customer to evaluate the extent to which that particular service agent put forth extra effort in the service recovery process (all items are shown in the Appendix). We measured distributive justice with four items from the extant literature that accounted for input (e.g., time, effort, hassle, anxiety, cost) and outcomes in the recovery process (Tax, Brown, and Chandrashekaran 1998). We operationalized procedural justice using four items from Folger and Konovsky's (1989) scale that captured the fairness of policies and procedures in the recovery process as well as input (e.g., time, hassle). We also constructed a four-item scale that measured customer interactional justice for this research. We adapted two of the items from Folger and Konovsky's (1989) research that tapped the interactional dimension of justice, and we adapted the other two items from prior service recovery literature using a perceived justice framework (Tax, Brown, and Chandrashekaran 1998). These items assessed the degree to which firm service agents put forth effort on the complainant's behalf and treated them with respect, courtesy, fairness, and honesty throughout the recovery process.
We measured service recovery satisfaction, overall firm satisfaction, purchase intent, and likelihood of spreading positive word of mouth using three-item scales adapted from prior research (Cronin and Taylor 1994; Goodwin and Ross 1992). We measured all items on seven-point Likert-type scales (see the Appendix). Sixty percent of the sample was male; 69% had been customers for two to four years; 51% reported incomes from $40,000 to $60,000; and 74% held college degrees.
Employee survey and measures. When a complaint was resolved, an automatic e-mail containing a survey was sent to the specific service agent who handled the complaint. As such, surveys were sent to the 700 service agents that specifically handled the 700 customer-initiated complaints. Of the surveys sent to customer service agents, 621 usable responses were collected through facsimile or mail. From these 621 responses, 320 were matched to the fully completed customer survey. That is, 320 service agent surveys were each fully matched to the specific customer survey representing the complainant they served. Thus, all analyses that follow use a sample of 320 customer-agent response pairs.
Service agents handling complaints were asked to complete a survey regarding their perceptions of organizational justice and shared values with the firm over the past six months. To ensure confidentiality, the surveys were sent directly to us, thereby reducing supervisory interference or biases. We measured distributive justice with four items that assessed the degree to which the service agent had been fairly compensated for job responsibilities, experience, efforts, and performance (Netemeyer et al. 1997). We measured procedural justice with four items that assessed the degree to which the company policies and procedures were fair with respect to decision making about employees, fairness of representation by employees, timeliness of decisions about employees, and feedback regarding decisions about employees (Folger and Konovsky 1989). We measured interactional justice with five items that tapped the degree to which employees believed the company supervisors worked hard to be fair; considered the employees' rights; and were honest, respectful, and courteous in interactions with employees (Folger and Konovsky 1989; Moorman 1991). We measured the shared values construct with items from Netemeyer and colleagues' (1997) study and one item adapted from Chatman's (1991) shared values construct. Although one of these items (i.e., "FIRM has the same values as I do with regard to concern for others") reflects a specific value, concern for others has been noted as a core or dominant value (Chatman 1991). Thus, all items assessed the degree to which the employee's core values were congruent with the firm's values (the Appendix also shows these measures). Fifty-three percent of the sample was female; 82% had been employees for five years or less; 81% reported incomes from $40,000 to $70,000; 67% of employees held college degrees; and 58% worked from 35 to 50 hours per week.
Data and Measurement Checks
Sample and nonresponse bias. To check for sample and nonresponse, we queried the firm's database. The firm logs and manages all complaints in its database regardless of a complainant's participation in our study. The firm also asks online purchasers to complete a brief survey before completing their first purchase, which helps create a buying profile for each customer. Thus, we were able to compare our sample's demographic and buying profile with three other customer groups: ( 1) complainants who received our survey, but chose not to respond, that is, nonparticipants (n = 300); ( 2) complainants who did not receive our survey, that is, nonsurveyed complainants (n = 268); and ( 3) customers who have never registered a complaint, that is, noncomplainants (n = 312). There were no significant differences regarding the length of relationship with the firm (means ranged from 3.84 to 3.97 across groups), age (means ranged from 39.0 to 40.5), total number of purchases (means ranged from 5.13 to 5.41), or the dollar value of the order involving the complaint (means ranged from $231.01 to $243.24) between the three database customer groups and our sample respondents (p > .10). In addition, our sample and the database customer groups were similar across sex (males ranged from 40% to 44%), income (the percentage of customers reporting incomes from $40,000 to $60,000 ranged from 51% to 55%), and education (the percentage of customers holding college degrees ranged from 70% to 74%). Likewise, the reasons for the complaint and the firm's complaint handling strategies were similar across groups (i.e., our sample and the database groups that complained).
We also collected demographic data from 300 service employees who were not included in our analyses and compared the demographic profiles of these 300 nonrespondents to our employee respondents. We found no significant differences between the groups regarding the length of tenure with the firm ( µ respondents = 3.77 years, µ nonrespondents = 3.49 years), age (µ respondents = 33 years, µ nonrespondents = 34 years), or the total number of hours worked per week (µ respondents = 45, µ nonrespondents = 46). In addition, the employee respondents and nonrespondents were similar across sex (female respondents = 53%; female nonrespondents = 52%), income (81% of respondents reported incomes from $40,000 to $70,000; 83% of nonrespondents reported incomes from $40,000 to $70,000), and education (67% of respondents held college degrees, 71% of nonrespondents held college degrees). In summary, these results offer evidence that our respondents are a representative sample of the firm's customers and customer service employees.
Measurement properties. We conducted several procedures to examine the psychometric properties of our measures. For the customer measures, we started with 5 extra-role behavior items, 5 distributive justice items, 5 procedural justice items, 5 interactional justice items, 4 purchase intent items, and 3 items each for satisfaction with recovery, over-all firm satisfaction, and word of mouth (a total of 34 items). We subjected these items to principal component and item analyses and our own judgment. On the basis of recommended scaling procedures (Clark and Watson 1995), we deleted one item each from the justice measures and one purchase intent item (5 items total). We then input the remaining 29 items into an eight-factor, 29-item confirmatory factor model for each data set. We conducted several tests of convergence among items in the scales and discriminant validity among constructs that supported the validity of the customer measures across both data sets (Anderson and Gerbing 1988; Fornell and Larcker 1981). We conducted similar psychometric procedures for the employee measures. We started with 27 items: 7 for distributive justice, 6 for procedural justice, 6 for interactional justice, and 8 for shared values. Through principal component/item analyses and author judgment, we retained 4 distributive justice, 4 procedural justice, 5 interactional justice, and 3 shared values items. In the confirmatory factor model, we found strong evidence of convergence among items and discrimination between constructs. (Detailed information about our measurement procedures is available on request.)[ 1]
As a partial validity check of our customer-rated extra-role behaviors measure, we also had agents rate themselves on the degree to which they generally engaged in customer-directed extra-role behaviors. Likewise, we asked supervisors to rate agents on the degree to which the agents engaged in customer-directed extra-role behaviors in the past six months. Both the agent-rated and employee-rated measures were similar to the customer-rated extra-role behaviors measure but were not specific to any particular customer or service event. The correlation between the customer-rated extra-role behaviors and agent-rated extra-role behaviors measures was .50, the correlation between the customer-rated extra-role behaviors and supervisor-rated extra-role behaviors measures was .54, and the correlation between the agent and supervisor rating was .30. Because the agent-rated and supervisor-rated customer-directed extra-role behaviors were not specific to the individual complaints of customers, we did not include them in the analyses.
The Shared Values/Organizational Justice Extra-Role Behaviors Customer Justice Link
Table 1 shows descriptive statistics, correlations among study constructs, and coefficient alpha estimates of internal consistency for study measures. Given our hypotheses, we conducted regressions with mediation analyses. We first analyzed hypotheses that were consistent with the shared values/organizational justice → customer-directed extra-role behaviors → customer justice link presented in Figure 1. In keeping with current writings on mediated regression (Holmbeck 1997; Kenny, Kashy, and Bolger 1998), we examined four conditions to test mediation: ( 1) the predictor variables (shared values/organizational justice) must affect the mediator (customer-directed extra-role behaviors) in the predicted direction, ( 2) the predictor variables (shared values/organizational justice) must affect the dependent variables (customer justice) in the predicted direction, ( 3) the mediator (customer-directed extra-role behaviors) must affect the dependent variables (customer justice) in the predicted direction, and ( 4) the impact of the predictors (shared values/organizational justice) on the dependent variables (customer justice) must be less after controlling for the mediator (customer-directed extra-role behaviors).
We first regressed the mediator on the predictors variables, that is, customer-directed extra-role behaviors as a function of shared values/organizational justice. As Regression Equation 1 in Table 2 shows, shared values and all organizational justice constructs significantly affected customer-directed extra-role behaviors in the predicted direction (standardized regression coefficients ranged from .12 to .29 with an R2 of .31). Thus, mediating "Condition 1" was satisfied, and our hypotheses that shared values and perceived organizational justice affect customer-directed extra-role behaviors was supported (H1-H4).[ 2]
We next regressed shared values/organizational justice on each customer justice construct. Regression Equation 2 in Table 2 shows that for the most part, shared values and the organizational justice dimensions significantly affected the customer justice perceptions in the predicted direction. All but employee distributive justice significantly affected customer distributive justice with an R2 of .38, all but shared values significantly affected customer procedural justice (in the predicted direction) with an R2 of .29, and all employee justice constructs and shared values significantly affected customer interactional justice with an R2 of .40. These results largely satisfy the second mediating condition that the predictor variables affect the dependent variables.
We then regressed customer-rated extra-role behaviors (mediator) on customer justice (the dependent variables) to test the third mediating condition. As Regression Equation 3 in Table 2 shows, extra-role behaviors significantly affected all customer justice dimensions (coefficients of .54, .52, and .69). These results also show support for our hypotheses that when customers perceive that the employee they dealt with put forth extra effort in service recovery, they perceive greater levels of justice (H5).
Next, we regressed both the predictors and the mediator on the dependent variables to test the fourth mediating condition. For full mediation to be statistically supported, the predictor variable effects on the dependent variable must be nonsignificant after controlling for the mediator. However, full mediation is rare in the social sciences. As such, partial mediation is considered supportive of a mediating hypothesis (Kenny, Kashy, and Bolger 1998). Thus, when the effects of the predictor variables on the dependent variables are diminished after controlling for the mediator, mediation is supported. As Regression Equation 4 in Table 2 shows, the mediator (extra-role behaviors) was significant for all three dependent variables (customer distributive, procedural, and interactional justice). Moreover, the regression coefficients for the predictor variables of shared values and distributive, procedural, and interactional justice are generally smaller in Regression Equation 4 (controlling for the mediator) than they are in Regression Equation 2 (not controlling for the mediator) for each dependent variable.
Finally, we conducted a statistical test to determine whether the partial mediation effects were significant. That is, we tested whether the regression coefficients for the independent variables in Equation 4 (the effects of shared values, distributive, procedural, and interactional justice in which the mediating variable of extra-role behaviors was accounted for) were lower than the corresponding regression coefficients of Equation 2 (in which the mediating variable of extra-role behaviors was not accounted for) (Kenny, Kashy, and Bolger 1998). For those cases in which the regression coefficient was significant in Equation 2 (10 of 12 coefficients in the predicted direction), 8 of the 10 coefficients were significantly smaller in Equation 4 (t-values ranged from 1.79 to 9.58, p < .05). Thus, partial mediation was largely supported (i.e., H9 was supported).[ 3]
The Extra-Role Behaviors Customer Justice Customer Outcomes Linkage
To examine the hypotheses and mediating effects associated with the customer-directed extra-role behaviors customer justice customer outcomes link presented in Figure 1, we conducted the same set of regressions. We first regressed the mediator on the predictors variables, that is, customer justice on customer-directed extra-role behaviors. As Regression Equation 1 in Table 3 shows (and as shown in Table 2), extra-role behaviors significantly affected all customer justice dimensions, satisfying mediating "Condition 1." We next regressed customer-directed extra-role behaviors (predictor variable) on the customer outcomes of satisfaction with recovery, overall firm satisfaction, purchase intent, and favorable word of mouth (dependent variables). Regression Equation 2 in Table 3 shows that customer-directed extra-role behaviors significantly affected all outcome variables. These results satisfy the second mediating condition that the predictor variable affects the dependent variable.
We next regressed the customer justice constructs (mediator) on customer outcomes (dependent variables) to test the third mediating condition. As Regression Equation 3 in Table 3 shows, for the most part, the customer justice constructs affected the dependent variables, showing support for the third mediating condition and H7-H9. Finally, we regressed both the predictors and the mediator on the dependent variables to test the fourth mediating condition. As Regression Equation 4 in Table 3 shows, the mediators (justice dimensions) were mostly significant for the outcomes. Furthermore, the regression coefficient for extra-role behaviors was nonsignificant or had a negative value in Regression Equation 4 (controlling for the mediator) compared with Regression Equation 2 (not controlling for the mediator), thus showing a fully mediated effect (i.e., H10 was supported). As such, no partial mediation tests were necessary.[ 4]
The purpose of our study was to examine how shared values and organizational justice influence customers' perceptions of a complaint experience. Although some studies have investigated how employees' perceptions of organizational justice lead to managerial perceptions of performance and other studies have assessed customers' perceptions of service recovery, none has specifically examined how employees' perceptions of the firm influence customers' perceptions following a failure and subsequent recovery. By capturing employee and customer perceptions in a complaint handling experience, our study bridges the gap between organizational theory and service recovery research. We summarize our results as follows:
- Customer-directed extra-role behaviors through shared values and organizational justice. An employee's shared values influenced how customers rated that employee's performance of extra-role behaviors, and all dimensions of organizational justice affected customer ratings of extra-role behaviors.
- Extra-role behaviors and customers' perceptions of justice. Customer-directed extra-role behaviors had strong effects on customers' perceptions of interactional justice, procedural justice, and distributive justice (i.e., regression coefficients of .69, .52, and .54, respectively).
- Shared values/organizational justice → extra-role behaviors → customer justice chain. The effects of shared values/ organizational justice perceptions on customer justice are partially mediated by extra-role behaviors. Thus, by managing employees well, a firm is in effect managing its customers well. That is, justly treated employees are more likely to perform extra-role behaviors, and customer justice is higher when customers perceive that the employee put forth extra effort in handling the complaint.
- Customers' perceptions of justice and customer outcomes. Customer ratings of distributive justice affected all outcome variables (i.e., satisfaction with recovery, overall firm satisfaction, purchase intent, and word of mouth). Procedural justice affected satisfaction with recovery, overall firm satisfaction, and word of mouth, and interactional justice affected overall firm satisfaction and purchase intent. Thus, all aspects of justice affected the customer outcomes, and as Table 3 shows, distributive justice had the strongest effects.
- Extra-role behaviors customer justice customer outcomes chain. The effects that extra-role behaviors had on customer outcomes were fully mediated by customer justice, and customers treated fairly show positive affect and intentions toward the firm. Still, extra-role behaviors affected all dimensions of customer justice, which shows that extra-role behaviors indirectly (and significantly) affect customer outcomes.
Implications
Shared values and extra-role behaviors. Our study indicates that shared values play a significant role in explaining extra-role behaviors, which highlights the importance of selecting (and socializing) employees who share a firm's core/dominant values. Employees who share the organization's values are more likely to feel like an integral part of the system, taking ownership in and responsibility for the firm and its performance. This positive shared values effect, then, hints at the importance of making customer service a core value. Although many firms hang banners and artifacts that pledge customer service as a top priority, they sometimes support and reward employees as if service were an afterthought. Firms can make a commitment to service viable by hiring and training carefully. When hiring employees, managers can measure shared values through structured interviews, customer service simulations, and psychological assessments. As for training and compensation, firms should align their training in a manner that emphasizes shared values and reward employees for performance consistent with those values. Otherwise, firms may be hiring customer employees who will either leave the firm because they do not share its values or become socialized to de-emphasize the importance of handling complaints--both outcomes will erode service levels.
Organizational justice and extra-role behaviors. Our study contributes to the services literature by showing that treating employees fairly inspires them to go out of their way to help resolve customer complaints. Our results show that procedural justice is most influential in spurring extra-role behaviors. In light of this finding, managers should seek employee feedback and incorporate workers' comments into any procedural changes. Simply asking for feedback may itself signal improved fairness. Distributive justice was also significantly related to extra-role behaviors. Managers interested in enhancing extra-role behaviors should first assess the current state of outcome fairness by enlisting employee feedback pertaining to the fairness of compensation, bonuses, and benefits. Service managers, employees, and human resource officers could then work together to modify these programs for enhanced employee distributive justice. By doing so, management can strive to improve complaint handling efforts by concentrating on internal customer service. Interactional justice is important in stimulating extra-role behaviors, quantifying an intuitive "golden rule" of customer service: Treat employees the way you want them to treat customers. Personal interactions from managers to employees send clear signals that can either enhance or detract from an employee's likelihood of engaging in extra-role behaviors. Each interaction may represent a "moment of truth," sending signals to employees regarding acceptable customer service. If managers do not go out of their way to help employees resolve problems, how can they expect employees to do the same for customers?
Extra-role behaviors and customer justice. Our results show that customer-directed extra-role behaviors are positively associated with customers' perceptions of fairness, which provides evidence that extra-role behaviors can indirectly lead to desirable customer outcomes. Specifically, extra-role behaviors have the greatest influence on the extent to which customers believe their personal interactions with frontline employees have been fair. This finding follows logically, given that extra-role behaviors are frequently directed toward customers during personal interactions with frontline employees, and it underscores how beneficial it can be for employees to go above and beyond "in-role" requirements to help customers.
Extra-role behaviors also affect customers' perceptions of fairness involving policies and procedures, which indicates that employees can influence the perceptions of policies and procedures by engaging in extra-role behaviors. On the one hand, perhaps customers see policies and procedures independently of employee behaviors. On the other hand, perhaps customers' perceptions are embedded in and shaped by employee behaviors. Our research supports the latter notion. Our results also show a positive relationship between extra-role behaviors and distributive justice, suggesting that customer redress outcomes are intertwined with employee behaviors. Managers recognizing that employee behaviors play an invaluable role in shaping customers' perceptions of service recovery fairness can train employees in customer service skills that cast policies, procedures, and outcomes in a positive light. These results again emphasize the importance that human resource management plays in enhancing customers' perceptions of justice by encouraging extra-role behaviors.
Customer justice and outcomes. Our results indicate that customer justice significantly affects customer outcomes. For example, distributive justice had the greatest relative influence on overall satisfaction, purchase intent, and word of mouth. Although managers striving to improve long-term customer relations would ideally like to improve all aspects of perceived justice, they may benefit most by investing in resources that enhance compensation (e.g., refunds, future discounts, accurately fixing problems). Indeed, interactional justice during a recovery effort was not significantly associated with satisfaction with recovery or word of mouth, and procedural justice was not related to purchase intent. Given a product failure, customers may be more interested in obtaining fair outcomes than in the manner in which these outcomes are provided.
Service recovery as an e-commerce customer service strategy. Given the context of our study, our results have implications for e-commerce. The promise of e-commerce has enticed firms to rush to market, investing millions of dollars in Web site infrastructure. However, many of these firms have neglected customer service in their quest to capitalize quickly on the e-commerce market. Brandweek reports that e-commerce complaints are dramatically rising and finds that one in four e-shoppers cannot complete their online purchases as a result of inadvertent disconnections, computer freezes, network gridlock, or stock outs (Cheng 2000). These service failures may prove detrimental to firm success. Customers of e-commerce have become accustomed to a certain level of offline customer service and likely expect the same level of online service. Moreover, an astounding 90% of e-shoppers stated that customer service was critical in their decision to choose a particular supplier, and almost half said they would switch providers if they received poor service (Cheng 2000). Given the abundance of online product/service providers, it remains imperative for e-tailers to find competitive advantages. Our study suggests that one such advantage is recovering well from failures.
Limitations and Directions for Further Research
Although this study expands our knowledge of complaint experiences, it must be tempered with certain limitations. First, our study did not account for every important antecedent or consequence of all dependent variables. Thus, the potential for omitted variables bias exists, and our regression coefficients may best be viewed as partial coefficients (Kenny, Kashy, and Bolger 1998). One such omitted variable is disconfirmation (Oliver 1997). Disconfirmation assesses customer expectations that are associated with the service encounter and has been shown to affect customer satisfaction in a failure/recovery context (Smith, Bolton, and Wagner 1999). Although our study assessed the primary predictors of satisfaction in failure/ recovery (i.e., customer justice), future studies need to assess the effects of disconfirmation on satisfaction in relation to the effects of extra-role behaviors. Such studies could also help discriminate between disconfirmation and extra-role behaviors, as both constructs have a performance-based standard.
Second, although recovery research suggests that processes play an influential role in service encounters, it remains unclear whether this holds when multiple failures occur. When consumers are irritated over experiencing multiple failures, they may weigh tangible outcomes (e.g., refunds) more than fair processes. Furthermore, given the emergence of online services, it seems beneficial to recognize potential differences in recovery expectations between these budding services and more traditional services (e.g., online banking versus personal banking). Do the self-service characteristics of online services diminish or enhance the importance of personal interaction processes in complaint handling?
Third, although the online customers in our study complained over the telephone, additional research should focus on if and how complainants view complaint resolution strategies offered online. It has been argued and empirically demonstrated that delivering a compelling customer experience is paramount to creating a competitive advantage for online retailers. In particular, Novak, Hoffman, and Yung (2000) show that certain shopping features create a compelling online experience. These features include easy product returns, customer support, and quick delivery, about which customers commonly have complaints. What is of interest is how an online complaint context might affect responses to the customer justice and outcome variables. For example, given the lack of face-to-face or verbal interactions with a service agent, do customers' perceptions of interactional justice exist in online complaint settings? If so, how do various forms of communication (e.g., chat room dialog) affect these perceptions? Also, given Novak, Hoffman, and Yung's (2000) findings, it would seem imperative that online retailers clearly specify and simplify the policies and procedures for product returns. It has been suggested that online consumers may be more involved with their purchases and may have more complete information about product alternatives (Novak, Hoffman, and Yung 2000). Would these customers expect more in terms of redress (distributive justice) than in-store customers? Thus, future work needs to investigate the relative effects of interactional, procedural, and distributive justice during online complaint handling. Given the increasing popularity of online customer networks (e.g., chat rooms), it is important to determine if positive online recommendations affect customers differently than negative online recommendations. Online retailers would be particularly sensitive to online recommendations, especially given the speed at which such online recommendations can travel.
Fourth, our study also highlights the need for further research on measuring extra-role behaviors and shared values. As previously noted, we collected measures of extra-role customer service behavior from managers, employees, and customers. However, because managers cannot rate each employee on each recovery effort, they rated the extent to which employees engaged in extra-role behaviors over the past six months. The employee measure also captured the extent to which employees rated their own performance of extra-role behaviors over the past six months. However, customer-rated extra-role behaviors specifically measured extra-role behaviors related to a particular complaint handling experience. One viable area for further research is to investigate how different levels of measurement (i.e., general versus specific) affect extra-role behavior ratings. That is, how much, if any, is a general extra-role behavior measure influenced by halo effects and other recall biases? Our study finds significant, positive correlations among manager, employee, and customer ratings of extra-role behaviors despite differences in level of measurement (correlations ranged from .30 to .54). The means of these extra-role behaviors are also notable. The employee extra-role behavior mean (4.45) is significantly higher than the extra-role behavior mean ratings from managers (3.01) and customers (3.37). To what degree are extra-role behaviors in the eyes of employees considered in-role behaviors among managers and customers? If supervisors and customers underrate employee performances of extra-role behaviors and employees overrate themselves, what is the most appropriate way to gauge extra-role behaviors? Such inquiries would help managers choose the most accurate extra-role behaviors measure.
Our shared values measure tapped into a more global/dominant shared values construct. The conceptualization and measurement of shared values have been sometimes confounded with, or considered a dimension of, related constructs, most notably person-organization fit and organizational culture (Kristof 1996; Schein 1990). Thus, the construct of shared values is in need of further conceptual and measurement work. Perhaps the approach suggested by Goodman and Syvantek (1999) is most fruitful, in which they refer to shared values as the match between a person's values and the value system of a specific organizational context (e.g., type of firm). Matching specific individual values to a specific organizational context may enhance the predictive validity of shared values.
Fifth, future work needs to focus on the selection and training of employees best suited for handling complaints. Are there specific employee characteristics that are correlated with solid recovery efforts and good overall customer service? Some noteworthy constructs may include general cognitive ability, conscientiousness, and extraversion, which are considered important predictors of overall job performance. Studies could examine the extent to which these constructs predict an employee's aptitude and attitude for handling service encounters. Moreover, how can firms effectively socialize employees so that they "buy into" a customer orientation and feel compelled to deliver good service recoveries? Studies exploring how factors such as organizational artifacts, formal and informal controls, compensation, and empowerment affect complaint handling could provide useful insights for both scholars and managers.
The authors thank David Mick, Tom Bateman, and four anonymous JM reviewers for their helpful comments on previous drafts.
NOTES [1] We also gathered another data set comprising 132 complainants (and their respective service agents) in a business to-business setting in which all complaints were resolved face-to-face. All measures yielded internal consistency and discriminant validity estimates that mirrored those reported in the text and Table 1.
[2] We also estimated interaction effects among the three organizational justice dimensions as they affect extra-role behaviors. These results were largely inconsistent (mostly nonsignificant or small, negative effects). These results are available on request.
[3] As suggested by Kenny, Kashy, and Bolger (1998), the equation for testing the significance of partial mediation is as follows:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where c = the independent variable regression coefficients of Equation 2, c* = the independent variable regression coefficients of Equation 4, b = the regression coefficient of the mediator on the dependent variable, a = the regression coefficient of the independent variable on the mediator, sa = the standard error of a, and sb = the standard error of b. We also tested our mediating hypotheses so that the mediator was regressed on all predictors simultaneously. That is, consistent with Holmbeck (1997) and Kenny, Kashy, and Bolger (1998), we assessed the significance of any single predictor → mediator effect while accounting for the effects of all other predictors on the mediator.
[ 4] Although our study concentrated on direct effects, we estimated three-and two-way interactions among the customer justice dimensions on customer outcome variables. The results were largely nonsignificant.
DIAGRAM: FIGURE 1 Process Model
Mean S.D. Range 1 2 3 4 5 6 7 8 9 10 11 12
Employee
1. EDJ
4.32 .63 2.00-6.25 .73*
2. EPJ
4.50 1.45 2.25-7.00 .34 .93*
3. EIJ
4.50 .89 2.00-6.80 .45 .53 .82*
4. Values
3.13 .79 1.33-5.00 .13 .34 .23 .89*
Customer
5. ERBs
3.37 1.06 1.00-6.80 .31 .45 .37 .42 .86*
6. CDJ
3.44 1.38 1.00-7.00 .28 .57 .45 .35 .54 .90*
7. CPJ
3.93 1.17 1.50-6.25 .40 .38 .45 .03 .52 .60 .91*
8. CIJ
3.89 1.27 1.00-6.00 .34 .45 .45 .50 .69 .54 .53 .83*
9. Sat-R
5.49 1.01 1.67-7.00 .17 .09 .27 .21 .30 .52 .58 .37 .83*
10. Sat-F
4.27 1.45 1.67-7.00 .43 .66 .55 .12 .40 .62 .53 .50 .35 .82*
11. Intent
3.25 1.03 1.33-6.33 .34 .73 .47 .39 .42 .65 .45 .48 .36 .65 .91*
12. WOM
4.03 .92 2.00-6.33 .14 .22 .28 .32 .33 .50 .41 .35 .50 .49 .56 .93*
Notes: * are coefficient alpha estimates of internal consistency. EDJ = employee distributive justice, EPJ = employee procedural justice, EIJ = employee interactional justice, Values = shared values, ERBs = extra-role behaviors, CDJ = customer distributive justice, CPJ = customer procedural justice, CIJ = customer interactional justice, Sat-R = satisfaction with the recovery, Sat-F = overall firm satisfaction, Intent = purchase intent, WOM = likelihood of favorable word of mouth. Means, standard deviations (S.D.), and ranges are based on summated scale averages.
Regression Equation 1: Shared Values/Organizational Justice → Customer-Directed ERBs
Dependent
Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
EDJ EPJ EIJ Values
ERBs .13(2.45) .24(4.26) .12(1.98) .29(5.29) 36.11 .31Regression Equation 2: Shared Values/Organizational Justice → Customer Justice
Dependent
Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
EDJ EPJ EIJ Values
CDJ .04(.73)n.s. .40(7.38) .19(3.36) .17(3.64) 48.92 .38
CPJ .23(4.23) .20(3.48) .27(4.57) -.13(2.63) 31.97 .29
CIJ .13(2.65) .16(3.97) .22(4.07) .38(8.16) 53.01 .40Regression Equation 3: Customer-Directed ERBs → Customer Justice Dependent
Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
ERBs
CDJ .54(11.60) 134.63 .30
CPJ .52(10.89) 118.74 .26
CIJ .69(17.30) 299.23 .48Regression Equation 4: Shared Values/Organizational Justice → Customer Justice*
Dependent
Variable
Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
ERBS EDJ EPJ EIJ Values
CDJ
.31(6.18) .00(.08)n.s. .32(6.15) .15(2.83) .09(1.70) 51.42 .45
CPJ
.46(9.01) .17(3.45) .09(1.69) .22(4.09) -.27(5.65) 48.31 .43
CIJ
.50(11.63) .07(1.56)n.s. .03(0.77)n.s. .16(3.54) .23(5.58) 85.48 .57
*Analysis controls for ERBs. Notes: ERBs = extra-role behaviors, EDJ = employee distributive justice, EPJ = employee procedural justice, EIJ = employee interactional justice, Values = shared values, CDJ = customer distributive justice, CPJ = customer procedural justice, CIJ = customer interactional justice. Except when noted by "n.s.," all coefficients are significant at the p < .05 level or better.
Regression Equation 1: Customer-Directed ERBs → Customer Justice
Dependent
Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
ERBs
CDJ .54(11.60) 134.63 .30
CPJ .52(10.89) 118.74 .26
CIJ .69(17.30) 299.23 .48Regression Equation 2: Customer-Directed ERBs → Customer Outcomes
Dependent
Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
ERBs
Sat-R .30(5.61) 31.53 .09
Sat-F .40(7.85) 61.56 .16
Intent .42(8.32) 69.15 .18
WOM .33(6.28) 39.47 .11Regression Equation 3: Customer Justice → Customer Outcomes
Dependent
Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[2]
CDJ CPJ CIJ
Sat-R .28(4.68) .41(7.08) .01(.05)n.s. 65.50 .38
Sat-F .41(7.40) .19(3.46) .18(3.31) 84.65 .45
Intent .55(10.13) .03(.61)n.s. .16(3.15) 90.49 .46
WOM .37(5.77) .15(2.40) .07(1.18)n.s. 39.15 .26Regression Equation 4: ERBs → Customer Outcomes* Dependent Variable Predictor Variable Beta Coefficient (t-Value) F-Value R2[ 2] ERBs CDJ CPJ CIJ Sat-R -.14(2.20) .30(5.05) .43(7.37) .08(1.20)n.s. 65.50 .38 Sat-F -.09(1.51) .43(7.57) .20(3.65) .22(2.20) 64.32 .44 n.s. Intent -.02(.38) .56(10.04) .03(.67) .18(2.87) 86.10 .46 n.s. n.s. WOM .01(.09) .37(5.64) .15(2.36) .07(.95)n.s. 39.15 .26 n.s. n.s.
*Analysis controls for customer justice. Notes: ERBs = extra-role behaviors, CDJ = customer distributive justice, CPJ = customer procedural justice, CIJ = customer interactional justice, Sat-R = satisfaction with the recovery, Sat-F = overall firm satisfaction, Intent = purchase intent, WOM = likelihood of favorable word of mouth. Except when noted by "n.s.," all coefficients are significant at the p < .05 level or better.
Customer Measures
Customer-Directed Extra-Role Behavior:
- For this particular encounter, the service representative I dealt with willingly went out of his/her way to make me satisfied.
- For this particular encounter, the service representative I dealt with voluntarily assisted me even if it meant going beyond his/her job requirements.
- For this particular encounter, the service representative I dealt with helped me with problems beyond what I expected or required.
- For this particular encounter, the service representative I dealt with frequently went out of his/her way to help me.
- For this particular encounter, the service representative I dealt with went "above and beyond the call of duty" in servicing me.
Distributive Justice:
- Although the event caused me problems, (firm's) effort to fix it resulted in a very positive outcome for me.
- The final outcome I received from (firm) was fair, given the time and hassle.
- Given the inconvenience caused by the problem, the outcome I received from (firm) was fair.
- The service recovery outcome that I received in response to the problem was more than fair.
Procedural Justice:
- Despite the hassle caused by the problem, (firm) responded fairly and quickly.
- I feel (firm) responded in a timely fashion to the problem.
- I believe (firm) has fair policies and practices to handle problems.
- With respect to its policies and procedures, (firm) handled the problem in a fair manner.
Interactional Justice:
- In dealing with my problem, (firm) personnel treated me in a courteous manner.
- During their effort to fix my problem, (firm) employee(s) showed a real interest in trying to be fair.
- (Firm name) employee(s) worked as hard as possible for me during the recovery effort.
- (Firm name) employee(s) were honest and ethical in dealing with me during their fixing of my problem.
Overall Firm Satisfaction:
- I am satisfied with my overall experience with (firm).
- As a whole, I am not satisfied with (firm).
- How satisfied are you overall with the quality of (firm)?
Satisfaction with Recovery:
- In my opinion, (firm) provided a satisfactory resolution to my problem on this particular occasion.
- I am not satisfied with (firm's) handling of this particular problem.
- Regarding this particular event (most recent problem), I am satisfied with (firm).
Purchase Intent:
- In the future, I intend to use (firm) for electronics purchases.
- If you were in the market for electronics, how likely would you be to use (firm)?
- In the near future, I will not use (firm) as my electronics provider.
Word of Mouth:
- How likely are you to spread positive word of mouth about (firm)?
- I would recommend (firm) for electronics to my friends.
- If my friends were looking to purchase electronics, I would tell them to try (firm).
Employee Measures
Distributive Justice:
- To what extent are you fairly rewarded for the amount of experience you have?
- To what extent are you fairly rewarded for the stresses and strains of your job?
- To what extent are you fairly rewarded for the amount of effort you put forth?
- To what extent are you fairly rewarded for the work you have performed well?
Procedural Justice:
- When decisions about employees are made at (firm), complete information is collected for making those decisions.
- When decisions about employees are made at (firm), all sides affected by the decisions are represented.
- When decisions about employees are made at (firm), the decisions are made in a timely fashion.
- When decisions about employees are made at (firm), useful feedback about the decisions and their implementation is provided.
Interactional Justice:
- When decisions are made about me at (firm), my super-visors/managers deal with me in a truthful and ethical manner.
- When decisions are made about me at (firm), my supervisors/managers treat me with respect and dignity.
- When decisions are made about me at (firm), my supervisors/managers work very hard to be fair.
- When decisions are made about me at (firm), my supervisors/managers show concern for my rights as an employee.
- When decisions are made about me at (firm), my supervisors/managers are courteous.
Shared Values:
- (Firm) has the same values as I do with regard to concern for others.
- In general, my values and the values held by (firm) are very similar.
- I believe in the same values held and promoted by (firm).
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By James G. Maxham III and Richard G. Netemeyer
James G. Maxham III is Assistant Professor of Commerce, and Richard G. Netemeyer is Professor of Commerce, University of Virginia. This research was funded in part by the Bernard A. Morin Fund for Marketing Excellence at the McIntire School of Commerce.
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Record: 64- First in, First out? The Effects of Network Externalities on Pioneer Survival. By: Srinivasan, Raji; Lilien, Gary L.; Rangaswamy, Arvind. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p41-58. 18p. 1 Diagram, 4 Charts, 1 Graph. DOI: 10.1509/jmkg.68.1.41.24026.
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First in, First out? The Effects of Network Externalities
on Pioneer Survival
Network externalities are playing an increasingly important role in the economy, and they have significant implications for firms' marketing strategies. The authors study the effects of network externalities in conjunction with other product and firm characteristics on the survival of pioneers. They apply an accelerated failure time model to data on 45 office products and consumer durables. The authors find evidence that network externalities have a negative main effect on the survival duration of pioneers. However, for more radical products and for technologically intense products, increases in network externalities are associated with increased survival duration. The larger the pioneer, the more network externalities increase its survival duration, whereas incumbent pioneers experience a decrease in survival duration compared with nonincumbents. The findings of this article contribute to theory in marketing strategy and have important implications for firms that are developing market entry strategies for products with network externalities.
As the economy becomes more interconnected, more products in computing, consumer electronics, and telecommunications industries exhibit network externalities (Yoffie 1997).( n1) Positive network externalities exist when a customer's utility for a product increases as the number of customers who use identical or compatible products increases. Some examples of products with network externalities include digital videodisc players, digital cameras, instant messaging systems, interactive televisions, and MP3 players. Given the increasing importance of network externalities in the economy, extensive literature in economics has examined the strategic and welfare implications of network externalities (e.g., Economides 1989; Farrell and Saloner 1986; Katz and Shapiro 1986). A consistent finding in the literature is that network externalities alter customer behavior (e.g., before adopting the product, it is rational for people to wait for others to adopt the product) and have important implications for marketing strategy.
Previous studies have explored the implications of network externalities on several aspects of marketing, including ( 1) customer behavior and market structure (Frels, Shervani, and Srivastava 2003; Goldenberg, Libai, and Muller 2002a; Shankar and Bayus 2003); ( 2) product-related decisions such as preannouncements (Nagard-Assayag and Manceau 2001), timing of product introductions (Padmanabhan, Rajiv, and Srinivasan 1997), and product differentiation (Esser and Leruth 1989); and ( 3) market entry (Gupta, Jain, and Sawhney 1999; Xie and Sirbu 1995). The effects of network externalities are acutely experienced in the high-technology sector, which constituted 14.7% of gross domestic output in 1999, up from 9.6% in 1980 (U.S. Department of Commerce 2000), and has been growing four times as fast as the rest of the economy (Milken Institute 2000). Although there are valuable insights on marketing in high-technology markets (e.g., Heide and Weiss 1995; Weiss and Heide 1993), the area remains underresearched (John, Weiss, and Dutta 1999, p. 78).
There is a rich and diverse stream of research on the rewards of pioneering. Although some of the literature points to an advantage for pioneers (e.g., Carpenter and Nakamoto 1989; Kalyanaram and Urban 1992; Robinson and Fornell 1985; Urban et al. 1986), other research suggests that later entrants may also enjoy advantages (e.g., Bayus, Jain, and Rao 1997; Golder and Tellis 1993; Lilien and Yoon 1990; Narasimhan and Zhang 2000; Shankar, Carpenter, and Krishnamurthi 1998). Thus, the mechanisms that generate pioneering rewards appear to be complex. We next summarize the specific findings in previous research that motivate our article.
First, the detection of pioneering advantage depends on the performance metric used (e.g., market share, profitability, survival duration). VanderWerf and Mahon's (1997) meta-analysis of 22 studies on pioneering indicates that studies that use market share are more likely to find a pioneering advantage than are studies that use other metrics. Indeed, most of the previous research that has detected a pioneering advantage (e.g., Kalyanaram and Urban 1992; Robinson and Fornell 1985; Urban et al. 1986) has used market share as the performance metric. There is now a need for studies that explore pioneering advantages in other performance metrics (Lieberman and Montgomery 1998).
Second, pioneering advantages may be specific to a product class or industry (Kerin, Varadarajan, and Peterson 1992; Szymanski, Troy, and Bharadwaj 1995; VanderWerf and Mahon 1997). In particular, network externalities, a characteristic of many high-technology products, may influence the rewards of pioneering. Network externalities tend to create winner-take-all markets, in which one firm emerges as a dominant player and other firms, which sometimes have superior products, are locked out (Schilling 2002). If the pioneer does not survive, its market share and profitability are moot issues. Thus, survival is a primary performance metric for pioneers in networked markets.
Third, previous research on the survival of pioneers provides mixed results. Across 36 product categories, Golder and Tellis (1993) report a long-term survival rate of 53% for pioneers. Whitten (1979) reports that pioneers in seven cigarette markets survived. Several studies report no difference in survival rates between pioneers and later entrants, including 18 markets for Iowa newspapers (Glazer 1985), 39 markets for chemical products (Lieberman 1989), and 11 markets for consumer nondurables (Sullivan 1992). In contrast, Mitchell (1991) and Christensen, Suarez, and Utterback (1998) find lower survival rates for pioneers in the medical diagnostic-imaging and rigid-disk-drive industries, respectively. Kalyanaram, Robinson, and Urban (1995, pp. G218-19) suggest an emerging empirical generalization "that order of market entry is not related to long-term survival rates," with the caveat that more research is needed to clarify the issue.
In this article, we address the following two questions: ( 1) How do network externalities influence the survival duration of pioneers (main effect)? and ( 2) What factors moderate the effects of network externalities on the survival duration of pioneers (moderating effects)? We develop a model of pioneer survival that incorporates the main effect of network externalities, the moderating effects of two product characteristics (radicalness and technological intensity), and two firm characteristics (size of the pioneering firm and its incumbency with respect to a previous product generation) on the effects of network externalities on the survival duration of pioneers.
We estimate our model by using data on 45 office and consumer durables products and an accelerated failure time (AFT) specification (Cox and Oakes 1984; Kalbfleisch and Prentice 1980). Our results indicate that ( 1) survival duration of pioneers decreases as the network externalities of a product increases; ( 2) radicalness and technological intensity of the product moderate the effect of network externalities to increase the survival duration of pioneers; and ( 3) firm size and the pioneer's incumbency moderate the effect of network externalities to increase and decrease, respectively, the survival duration of pioneers.
In the next section, we define network externalities and present our conceptual arguments. We then describe the data collection and model estimation procedures and the results of our empirical analysis. We conclude by discussing the implications of our results for marketing theory and practice, summarizing the limitations of our work and identifying directions for further research.
Characteristics of both the product and the pioneering firm influence the rewards of pioneering in a complex manner, which involves several possible contingencies (Kalyanaram, Robinson, and Urban 1995; Lieberman and Montgomery 1998). On the basis of previous research on organizational innovation, we consider both product and organizational factors moderators of the effect of network externalities on the duration of pioneer survival. We include two product factors: radicalness (Chandy and Tellis 2000) and technological intensity (Agarwal 1996). We also include two organizational factors: firm size (Audretsch 1995) and incumbency (Henderson 1993; Mitchell 1991). The literature on network externalities provides conflicting indications about the main effects of network externalities and the moderating effects of product and firm characteristics on the effect of network externalities on pioneer survival. Thus, we present arguments for both positive and negative effects of product and firm characteristics on pioneer survival (see P[sub1] and P[sub2]; for such an approach for theory development in the presence of opposing arguments, see Armstrong, Brodie, and Parsons 2001; Bettman, Capon, and Lutz 1975).
Definitions
When the utility of a product to each user in a network depends on the number of users, the product exhibits direct network externalities (Katz and Shapiro 1986). For example, the utility of a fax machine is nil if no one else has one. As the number of people (n) who own fax machines increases, the utility of the fax machine to each user increases in proportion to the number of possible two-way connections, n (n - 1). Indirect or complementary network externalities arise when there is a positive link between the utility to a customer and the number of other users of the product because of complementary products (Katz and Shapiro 1986). Increases in the number of users of the focal hardware product increases the availability of complementary products, which in turn increases the utility that customers derive from the focal product. Videocassette recorders, compact disc [CD] players, MP3 players, and digital videodisc players exhibit indirect network externalities.( n2) To be consistent with previous research (Golder and Tellis 1993; Urban et al. 1986), we define a pioneer as the first firm to commercialize a new product.( n3) We focus on the pioneer's survival in the product market it pioneered, and we measure its survival at time t on the basis of whether the pioneer still maintained a presence in the product market.
Effects of Network Externalities on Survival Duration
Positive effects of network externalities. Given the important role of the installed base for products with network externalities, a pioneer's product may achieve market power through positive feedback (Arthur 1989). A large installed base attracts more developers of complementary and compatible products, thereby enhancing the utility of a pioneer's product and speeding adoption (Choi 1994). Adopters invest in learning to use the product (e.g., videogames, software) and/or in complementary products (e.g., CD music titles for CD audio players), which results in lock-in and prevents defections to offerings of later entrants (Shapiro and Varian 1998). In addition, products with network externalities are sometimes characterized by a standard (e.g., CD audio standard). The emergence of a standard reduces uncertainty about the eventual size of the network, thereby inducing earlier adoption by customers (Chakravarti and Xie 2002) and spurring the development of complementary products. Thus, the pioneer may be able to set the standard and draw customers to its network, resulting in long-term survival. Thus:
P[sub1]: The greater the network externalities of a product, the longer is the survival duration of the product pioneer.
Negative effects of network externalities. Other aspects of network externalities suggest negative effects. First, some innovations (e.g., communication devices) initially diffuse slowly because of uncertainties associated with their potential utility when few adopters exist (Rogers 1995). Prospective customers may adopt a "wait-and-see" attitude, delaying adoption until uncertainties are reduced so that the market exhibits "excess inertia" (Farrell and Saloner 1986). This excess inertia also exists in products with indirect network externalities. Hardware firms want complementors to offer a wide selection of software, but complementors wait until the new hardware has achieved significant market penetration before committing to the hardware platform. Gupta, Jain, and Sawhney (1999) investigate this "chicken-and-egg" coordination problem between producers of hardware and software in the digital television market. Goldenberg, Libai, and Muller's (2002b) study of fax machine adoption shows that network externalities slow the growth of fax machine adoption, thus revealing a "hockey-stick" pattern of slow growth over a long period followed by rapid takeoff. The initial slow sales over a long period may provide a window of opportunity for later entrants.
Second, because of excess inertia, the pioneer's development costs may outpace its revenue, which negatively affects its short-term performance. Thus, the pioneer may curtail its early marketing investments, which hurts its long-term survival. As customer expectations become more certain and complementary goods are developed, later entrants benefit from lower developmental costs as a result of vicarious learning from the pioneer.
Third, when network externalities are sufficiently strong, as additional customers adopt the product, the marginal customer's utility of adoption increases. The increasing utility of the product to customers enables later entrants to have a greater chance of success than the pioneer because of the larger network that exists at their later entry time. Thus:
P[sub2]: The greater the network externalities of a product, the shorter is the survival duration of the product pioneer.
Moderating Effect of Radicalness of Product
We follow Chandy and Tellis (1998, 2000) in defining a radical product as one that incorporates a substantially different core technology and provides substantially higher customer benefits than do previous products in the market. Furthermore, following Chandy and Tellis (2000), we treat radicalness as a continuous construct. There are opposing arguments for how product radicalness can moderate the effect of network externalities on pioneer survival.
Radical products (e.g., instant photography, videodisc) involve new technologies that represent significant technological advances over existing technologies (Levin, Nelson, and Winter 1987). If the pioneering product with network externalities also provides demonstrably superior utility to the consumer, the product's compelling utility may overcome some of the excess inertia that the pioneering product initially faces. The more radical the product, the wider is the window of opportunity, and the pioneer seeks to exploit network externalities in the absence of early competitors. The pioneer can establish a large network, spur the development of complementary goods, and increase the utility of its product to customers. Thus:
P[sub3]: As radicalness of the product increases, the relationship between network externalities and pioneer survival becomes more positive.
In contrast, radical products are based on new technologies that offer a low initial performance-price ratio (Christensen 1997; Utterback 1994). Later entrants often refine the pioneer's product into a market-ready form in several iterations of product development. The changing product often represents different technology platforms (e.g., transitioning from transistor to solid-state electronics to integrated circuits in computers), thereby destroying the firm's investments in the previous technology (Rosenberg 1994). In networked markets, the pioneer is faced with a two-pronged marketing challenge that makes it more difficult to succeed: managing product innovation as the product evolves into a market-ready form and overcoming excess market inertia in developing the product's network. Thus:
P[sub4]: As radicalness of the product increases, the relationship between network externalities and pioneer survival becomes more negative.
Moderating Effect of Technological Intensity of Product
We define technologically intensive products as ones that have significant depth and breadth of technical and scientific knowledge embedded in their creation and functionality (Capon and Glazer 1987; John, Weiss, and Dutta 1999; Rosenberg 1976, 1994). A product's technological intensity is distinct from its radicalness. For example, the microwave oven is high in radicalness but low in technological intensity (it draws primarily from one technology domain: radar technology), whereas the projection television is low in radicalness but high in technological intensity (it draws from multiple technology domains: audio, optical, and computing technologies). As does radicalness, technological intensity may have opposing moderating effects on the effect of network externalities on the survival of pioneers in networked markets, which we describe next.
Higher levels of technological intensity imply greater complexity in product design and commercialization (John, Weiss, and Dutta 1999). Technologically intensive products often involve interdisciplinary, diverse technologies embedded across firms, industries, and users (Iansiti and West 1997). In such a situation, competitors face serious challenges to ensure that the diverse technologies work together well. As a result, the pioneer may have a window of opportunity to establish an installed base before competitive entry, enabling it to secure its long-term survival (Lieberman 1989). Thus:
P[sub5]: As technological intensity of the product increases, the relationship between network externalities and pioneer survival becomes more positive.
Technological intensity of products may also hurt the pioneer's survival in networked markets. Technologically intense products are characterized by rapid changes in the early stages of market development (John, Weiss, and Dutta 1999; Utterback 1994). The pioneer may be forced to keep a rudimentary design that is rendered obsolete by later designs. Technologically intense products are also characterized by heterogeneity in adopter cohorts as the market evolves from introduction to maturity (Moore 1991; Rogers 1995). To appeal to later cohorts, the pioneer must redesign its product often, perhaps relying on disruptive technologies that can make previous investments obsolete (Christensen 1997). The subsequent product designs, based on new technologies introduced by later entrants, may find rapid market acceptance, thereby reducing the chances of the pioneer's survival. Thus:
P[sub6]: As technological intensity of the product increases, the relationship between network externalities and pioneer survival becomes more negative.
Moderating Effect of Size of Pioneer
Two opposing arguments can be made for the moderating effects of firm size on the effect of network externalities on pioneer survival. First, there is a strong positive relationship between size and survival of firms (Audretsch 1995).( n4) The positive relationship may be enhanced in networked markets, in which a large pioneer, by virtue of its resources (e.g., brand capital, financial resources), may reduce uncertainty about the size of the final network among potential customers. Large firms can also access complementary assets (e.g., existing networks of users, complementary products) to develop the network more efficiently than small firms can (Teece 1986). Thus, in networked markets, the large pioneer can reduce the uncertainty for potential adopters, enabling rapid buildup of its product's network and increasing its chances of long-term survival. Thus:
P[sub7]: As the size of the pioneer increases, the relationship between network externalities and pioneer survival becomes more positive.
Second, a large firm has several layers of staff (Blau and Schoenherr 1971), which can delay response to new technologies and market opportunities (Kimberly 1976; Tornatzky and Fleischer 1990). In addition, the structure of large organizations can reduce incentives for individual innovators (Cohen 1995). In networked markets, such bureaucratic inertia provides an opportunity for a later entrant to establish a network and attract customers to its network. Thus:
P[sub8]: As the size of the pioneer increases, the relationship between network externalities and pioneer survival becomes more negative.
Moderating Effect of Incumbency of Pioneer
Consistent with previous research (Chandy and Tellis 2000; Henderson 1993; Mitchell 1991), we define an incumbent as a firm that markets a product belonging to the previous product generation that satisfied the same customer need. Some aspects of incumbency can aid survival, whereas others can hurt it. Having marketed products from the preceding generation, incumbent firms have access to assets such as market knowledge, brand equity, and customer relationships (Thomas 1995). Incumbents are likely to have access to existing customer networks, which may ensure backward compatibility of the pioneering innovation with the product from the previous generation and reduce switching costs for potential adopters. Compatibility advantages may also operate with respect to producers of suppliers' goods and complementary goods. In networked markets, incumbent pioneers can leverage their existing networks and offer greater utility to customers than nonincumbent pioneers, thus securing their long-term survival. Thus:
P[sub9]: The relationship between network externalities and pioneer survival is more positive for incumbent pioneers than for nonincumbent pioneers.
In contrast, incumbents are prone to technological inertia (Foster 1986; Ghemawat 1991), and their efforts in new product marketing are often characterized by underinvestment (Henderson 1993). Thus, incumbent pioneers may be reluctant to make the large investments that are necessary to support the new technology, which may threaten the firm's existing product (Christensen 1997). Such inertia may be intensified in networked markets because of the cannibalizing potential of the new product's network. Thus:
P[sub10]: The relationship between network externalities and pioneer survival is more negative for incumbent pioneers than for nonincumbent pioneers.
In summary, our conceptual arguments suggest that network externalities and the moderating effects of product and firm characteristics exert countervailing forces on the effect of network externalities on the survival of pioneers. Table 1 summarizes these arguments. Figure 1 illustrates how product radicalness can have a positive moderating effect (P3) on the relationship between network externalities and firm survival, resulting in a counterclockwise rotation of the main effect relationship. We next describe the data collection procedure, the measures, and the model we estimate to investigate the effects of network externalities on pioneer survival.
Data
We used three criteria to collect data for this study. First, to provide the necessary variance in network externalities, we identified two classes of products: office products and consumer durables that exhibit various degrees of network externalities. The two product classes have been studied in previous research on innovation diffusion and pioneering (Chandy and Tellis 2000; Golder and Tellis 1993, 1997; Sultan, Farley, and Lehmann 1990), which we build on in this article. Second, we limit our focus to products introduced after World War II, because World War II altered the business environment and the postwar period witnessed the emergence of new technologies (e.g., computing, electronics, telecommunications) that were different in scope and character from those (e.g., mechanical, electromechanical) introduced previously (Teitelman 1994). Third, because of our interest in the survival of pioneers and not the survival of products, we excluded products that did not succeed (e.g., minidisc players). This criterion is consistent with our focus on the survival of pioneers in products demonstrated to be viable, substantive, and managerially relevant. On the basis of these three criteria, we identified 63 office products and consumer durables.
We used the historical method (Golder 2000) to collect data on the pioneer's time of entry, survival, characteristics, and technological intensity of the product. For each product, we obtained information about the pioneer from articles published in scholarly journals, company histories, and online business databases. When it was possible, we used multiple sources to increase the reliability of our data. We were able to collect reliable information about the product, the dates of pioneering, and the survival of pioneers for 45 of the 63 product categories (Table 2). The products we studied span more than 50 years and include all major innovations in office products and consumer durables during this period.
Our 45 products compare favorably in terms of number with those used in recent studies (Chandy and Tellis 2000; Golder and Tellis 1993, 1997; Sultan, Farley, and Lehmann 1990). In addition, the set of products in our study overlaps with 12 of the 16 new (introduced after 1945) consumer durables studied by Golder and Tellis (1997), 9 of the 10 new durables and office products studied by Golder and Tellis (1993), and 20 of the 25 new consumer durables and office products studied by Chandy and Tellis (2000). All the data are from publicly available data sources.
Measures
Pioneer survival. Our dependent variable is the survival duration of a pioneer in the product market it pioneered, which is measured in number of years. We used 2001 as the cutoff year for measuring pioneering survival. Because several pioneers (n = 21) were in existence in 2001, our survival data are right-censored at 2001.( n5)
Network externalities. There are no established measures for our key construct of network externalities. Thus, using two independent sets of raters to assess the reliability of the measures, we developed a new measure. A review of previous research (e.g., Frels, Shervani, and Srivastava 2003; Shankar and Bayus 2003), case studies (e.g., McGahan, Vadasz, and Yoffie 1997), and our discussions with managers indicated that network externalities are a matter of degree and are not dichotomous (i.e., present or absent) and that a product can have direct network externalities, indirect network externalities, or both. We conceptualize the degree of network externalities as a continuous variable that represents both direct and indirect externalities. We used two groups of raters to measure the degree of network externalities: ( 1) academic experts (12 professors at nine business schools who are recognized experts on organizational innovation or high-technology products or network externalities) and ( 2) MBA students (a class of 26 MBA students who had recently completed an elective course on high-technology marketing strategy). We provided the raters with a definition of direct and indirect network externalities and then asked them to rate separately the degree of direct and indirect network externalities associated with each product on a 1 (no network externalities) to 7 (very high network externalities) scale. We computed the degree of network externalities for each product from each rater by adding the scores for direct and indirect network externalities. We computed the reliability of the raters by computing reliability coefficients and eliminating raters (3 academic experts and 5 MBA students) who had item-to-total correlations of less than .40. The intraclass reliability coefficient (Shrout and Fleiss 1979) for the ratings provided by each group of raters (academic experts = .91; MBA students = .95) showed that the measures provided by both the retained academic experts and the students are internally consistent. The average measures of degree of network externalities the two groups of raters provided are highly correlated (.86, p < .01). To assess discriminant validity, we followed the procedure that Campbell and Fiske (1959) suggest. Specifically, we compared the correlations for the network externalities measure across raters (for the same product) with the interrater correlations across products (for the same rater); we found the latter correlations to be small (ranging from .15 to .35).
To bolster our confidence in the ratings measure of network externalities, we also obtained ratings from eight marketing managers, and we found that the ratings provided by the managers were internally consistent (intraclass reliability coefficient = .89) and correlated well with the ratings provided by the academic raters (.83, p < .01). We subsequently used the ratings data from students and managers separately to estimate our model, and, because we found consistent results, we report results based on the data from the academic experts.
Radicalness of product. We used Chandy and Tellis's (2000) radicalness scale, which has two dimensions: ( 1) whether a new product incorporates a substantially different core technology (technology radicalness on a scale from 1 to 9) and ( 2) whether a new product provides substantially higher customer benefits compared with the previous product generation in the category (benefits radicalness on a scale from 1 to 9). We developed a radicalness measure by adding the two scores. We followed a similar procedure for radicalness as we did for network externalities, using both academic and graduate student raters. Our academic raters were 10 professors who are experts on organizational innovation (a different group from that used to rate network externalities); the student raters were 19 midlevel engineer executives enrolled in a master's of technology management program at a leading university. We computed the reliability of the raters by computing reliability coefficients and eliminating raters (1 academic expert and 2 graduate students) who had item-to-total correlations of less than .40. Our retained academic experts had intragroup reliability ratings of .76, and the graduate students had intragroup reliability ratings of .84, which indicates acceptable internal consistency. As occurred previously, the average ratings from the two groups were highly correlated (.88, p < .01). We estimated the model with ratings from both groups of raters, we found consistent results, and we again report results based on data from the academic raters.
Technological intensity of product. To measure the technological intensity of products, we follow Hadlock, Hecker, and Gannon (1991) and use a categorical variable that classifies a product as technical or nontechnical on the basis of the ratio of the number of research and development employees to total personnel of the firms in the product category (for examples of the use of this measure, see Agarwal 1996; Agarwal and Bayus 2002). Of the 45 products, we classified 28 as technologically intensive.
Size of pioneer. Although a firm's size can be measured in several ways, including number of employees, sales volume, and total assets, the most common measure used in the innovation literature is number of employees (Cohen 1995). Because alternative definitions usually yield similar results (Agarwal 1979), we measured size by the number of firm employees at the time of pioneering, and we investigate two such measures: the actual number of employees and a dichotomous variable (small-large) split at the median firm size of 100 employees. For publicly traded firms, we obtained the size information from the COMPUSTAT database. For privately held firms, we obtained the size information from company directories and news archives. Using this method, we classified 25 of the 45 firms as small. We estimated the model with both the dichotomous and the continuous measure of size, and we found similar results. We report results based on the categorical measure because it is more robust and less subject to the influence of extreme values.
Incumbency of the pioneer. We follow the definition of incumbent that has been used in previous research (Chandy and Tellis 2000; Mitchell 1991; Mitchell and Singh 1993) and define a pioneer as an incumbent if it also markets a product belonging to the previous generation of products that satisfied the same customer need. We determined the previous product generation by using historical methods and academic experts. In six cases, the experts determined that there was no previous product generation that satisfied the particular customer need. The third column of Table 2 provides the previous product generation that we used to determine incumbency of the pioneer. We coded an incumbent pioneer as 1 and a nonincumbent pioneer as 0. Of the pioneers we studied, 19 were incumbents.
Pioneer survival times cannot be analyzed by standard regression approaches because such data are typically right-censored (i.e., not all pioneers have failed by the time of the study). In 2001, the cutoff time for our study, 21 of the 45 pioneers continued to be in business. We used the AFT model, which accommodates right-censoring, to investigate the effects of network externalities and the moderation effects of product and firm characteristics on the effect of network externalities on the survival duration of pioneers (Cox and Oakes 1984; Helsen and Schmittlein 1993; Kalbfleisch and Prentice 1980). We provide an outline of AFT models, based on which we specify the following estimation equation, in the Appendix:
( 1) Ln(T[subi]) = μ + β[sub1] X[subi1] + β[sub2]X[subi2] + ... β[subI]X[subik] + σε[subi],
where T[subi] is the number of years firm i has been in existence in the product category as of 2001, and X[subi] is the vector of the covariates associated with the pioneering product introduced by firm i. The covariates used in the model are time invariant and include network externalities and the moderation effects of the two product and the two firm characteristics on the effect of network externalities on pioneer survival, which we described previously. If the results indicate that a particular sign is significant (e.g., a negative sign for incumbency x network externalities), our data support the corresponding form of the proposition (in this case, negative). For completeness, we also include the main effects of product and firm characteristics as covariates in the model. The error distribution ε[sub1] has an extreme value density function (f[ε[subi] ∼ exp[εsubi] - eεi); μ β and σ (a scaling factor for the variance of the error term) are parameters to be estimated. In addition, we included the logarithm of the introduction year as a covariate. We also reestimated the model by including the time of the introduction year as a covariate, and we obtained similar results. We estimated Equation 1 by using maximum likelihood methods implemented in the SAS LIFEREG procedure.
Descriptive Statistics of Products
Table 2 contains the network externalities ratings, and Figure 2 contains the histograms of the network externalities and radicalness ratings for the products in our study. The dispersion of the rating scales for network externalities and radicalness suggests that the raters view the two constructs as continuous rather than dichotomous. Although possible values for the network externalities measure range from 2 (no network externalities) to 14 (high network externalities), the ratings of the products we studied ranged from 3.4 (electric toothbrush) to 12.1 (operating system for personal computers), with a mean of 7.7 and a standard deviation of 2.2. The radicalness ratings, with possible scale values from 2 to 18, ranged from 8.9 (electric toothbrush) to 15.3 (photocopiers), with a mean of 12.8 and a standard deviation of 1.4, which indicates moderate to substantial innovativeness for the products in the study.
Performance of Pioneers and Current Market Leaders
Of the 45 pioneers, 24 (53%) exited the market by 2001, which compares well with the 50% failure rate for the digital/high-technology products reported by Tellis and Golder (2000, p. 43). The average survival duration for the pioneers we studied was 11 years. The average market share for the surviving pioneers is 25%, and only 18% (n = 8) of the pioneers were leaders in the products they pioneered, with an average market share of 47%. The average market share of 25% for surviving pioneers is much higher in our study than that (8%) obtained by Tellis and Golder (2001, p. 44) for digital and high-technology products. We examined the correlations between market shares of all pioneers and the degree of network externalities and found a nonsignificant, negative relationship (ρ = -.14, not significant), but we found a positive relationship between the market shares of current market leaders that are not pioneers and the degree of network externalities (ρ = .30, p < .10). When we performed a median split of the network externalities scale, we found that the 10 surviving pioneers of the products with high network externalities had a market share of 19% compared with the 31% market share of the 11 surviving pioneers that introduced products with low network externalities. The average market share for the 24 later entrants that are current market leaders is 41%, compared with 25% for the surviving pioneers. The average lag in time of entry between the pioneer and the later-entry current market leaders is 8 years. In summary, an aggregate level analysis of the data suggests that there is a negative effect of network externalities on the performance of pioneers and that later entrants are not disadvantaged in relation to pioneers in terms of either survival duration or market share.
Model Estimation Results
We were interested in determining whether the base hazard rate (i.e., the instantaneous probability that the pioneer will fail at time t) was constant, increasing, or decreasing with time, so that we could investigate a pioneer's risk of failure over time. We considered alternative base hazard functions, including the exponential, gamma, log-normal, log-logistic, and Weibull, for which the AFT method allows, and we used a multistep approach to determine the distribution that best represents the survival times of pioneers in networked markets. We were unable to estimate the gamma model with our data because of convergence problems that were partly due to the small sample size. However, the gamma model often displays convergence problems; even if it is estimable, it is difficult to judge the shape of the hazard function from the estimated parameters (Allison 1995, p. 74). We estimated the exponential model, which assumes a constant hazard rate (a special case of the Weibull model, with scale parameter set to 1), and we found that this model can be rejected (p < .001). Therefore, using three distribution functions (log-normal, log-logistic, and Weibull) that accommodate a changing hazard rate, we estimated the model in Equation 1 with the results reported in Columns 1-3 of Table 3.
Although the general pattern of results is similar across the models, based on the Akaike information criterion (AIC), the model estimated with the Weibull hazard function fits the data slightly better than those estimated with the lognormal and the log-logistic functions. The Weibull model can be interpreted as both a (parametric) proportional hazards model and an AFT model (see the Appendix). We also present the results of the Cox proportional hazards model in Column 4 of Table 3. The sign reversals of the estimates for this model compared with the AFT models occur because the proportional hazards model estimates the effects on hazard rate, whereas the AFT model estimates the effects on survival times. By multiplying the Weibull parameter estimates for the AFT model in Table 3 by (-1/σ), we obtained the estimates of the proportional hazards model with a Weibull distribution of survival times. With this transformation, the general pattern of results from the Weibull and the Cox proportional hazards models is similar. However, in terms of the AIC, the overall fit of the proportional hazards model is inferior to that of the Weibull AFT model; therefore, we discuss only the results of the AFT model.
The model χsup2; statistic is significant (χsup2; = 24.74, degrees of freedom [d.f.] =10, p < .01), and the scale parameter of the model is .62, which indicates that the hazard rate for pioneer survival decreases over time (i.e., the longer the pioneer has survived, the greater are its chances of continued survival). Our results indicate that network externalities have a negative effect on the survival duration of pioneers (b = -2.38, p < .01), suggesting that the higher the level of network externalities, the shorter is the survival duration of the pioneer (in support of P[sub2]). Both for more radical products (b = .14, p < .01) and for more technologically intensive products (b = .42, p < .05), network externalities increase the survival duration of pioneers (in support of P[sub3] and P[sub5]). For larger firms, network externalities increase survival duration (b = .89, p < .01, in support of P[sub7]), whereas for incumbent pioneers, network externalities decrease survival duration (b = -1.03, p < .01, in support of Psub10]). In our model, we also observed several unhypothesized main effects for radicalness, technological intensity, size, and incumbency. Table 1 links these empirical findings to our conceptual arguments.
Although the magnitudes of the coefficients are not directly informative, a simple transformation provides an intuitive interpretation. For a continuous variable such as network externalities, the transformation 100(e[supb] - 1) gives the percentage change in the expected survival time for each unit increase in the variable. Thus, 100(e[sup2.38] - 1) = -91% indicates the percentage decrease in the expected survival time for each one-unit increase in the degree of network externalities, when other covariates are held constant. The order of magnitude for the effects is similar to those obtained in other AFT studies of firm survival. For example, Mitchell and Singh's (1993) study of the effects of expansion into new technical subfields on survival in a firm's base business reports similar effect sizes (b = 1.78) for the expansion effect. Note that the coefficient value is a statistical estimate, and given its 95% confidence interval (i.e., -.75 to -4.01), the corresponding change in survival duration ranges from -53% to -98%. Note that in the presence of statistically significant moderation effects in the model, the main effect does not represent the full impact of changes to survival duration associated with a change in the degree of network externalities.
By excluding network externalities and only retaining the main effects of the two products, the two firm characteristics, and time, we also examined the power of network externalities in explaining the survival duration of pioneers (χsup2; = 9.78, d.f. = 5, p < .10). The difference in the model χsup2; statistic between the reduced model and the complete model (Column 3, Table 3), which includes network externalities and all the moderating effects, is significant (χsup2; = 14.96, d.f. = 5, p = .01). We also estimated a model that retained only the main effects of all explanatory variables, excluding all the moderating effects, and we found that the difference in the model χsup2; statistic between the reduced model and the complete model (Column 3, Table 3), which includes network externalities and all the moderating terms, is significant (χsup2; = 14.34, d.f. = 4, p < .01). The results suggest that a model that includes network externalities and the moderating effects of product and firm characteristics provides a significantly improved explanation of pioneer survival over models that exclude network externalities and the moderating effects.
Robustness of Results
Definition of exit. Not all pioneer failures we studied were unambiguous exits from the product market. In two cases (Aldus's desktop publishing software and Kurzweil Technology's flat-bed scanner), other firms (Adobe and Xerox, respectively) acquired the pioneer. In both cases, we treated the exit as a censored exit. In eight other cases (e.g., mainframe computers, database software), the pioneer failed in the market in which it pioneered, but another firm took over the related assets. The appropriate approach is to estimate a model of competing risks to determine the effects of covariates on the multiple types of exits (Allison 1995, p. 185). However, the small number (ten) of ambiguous exits precludes such an approach. Thus, we reestimated the model, excluding the ten pioneers (n = 35) with ambiguous exits (Column 1, Table 4). The model χsup2; statistic is significant (χsup2; = 23.40, d.f. = 10, p < .01), and the scale parameter of the model is .65. The general pattern of results with this smaller sample is consistent with the ones we obtained with the inclusion of the ambiguous exits.
Censoring date. An assumption of duration models is that censoring is conditionally independent of the event and the covariates. For our analysis, we used the full information in the data set and censored the survival of the pioneers in 2001. To explore whether the results were sensitive to the use of 2001 as the censoring date, we reestimated the model with three different censoring dates: 1998, 1995, and 1992 (Table 4). From the results in Columns 2-4 of Table 4, it is evident that the general pattern of results with the different cutoff years is similar to those we reported in Table 3.
Sample. Even though our sample size compares well with those of previous research (e.g., Chandy and Tellis 2000; Golder and Tellis 1993, 1997), it is small, and thus we performed a bootstrap analysis to examine sensitivity of our results to sampling variations (Efron 1979). We generated a list of 50 random numbers (with replacements) from 1 to 45. We then estimated Equation 1 50 times, each time with 44 observations, and we eliminated one observation that corresponded to the random number generated. The results of the bootstrapping analysis (Column 5, Table 4) indicate that our results are robust to sampling variations.
Differential effects of direct and indirect network externalities. We conducted an exploratory investigation by estimating a model that includes direct and indirect network externalities separately and their interactions (not reported herein). Although the overall pattern of results was similar to the results we obtained with the combined measure of network externalities, the effect of direct network externalities in that model was significant at p < .10, whereas the effect of indirect network externalities was not significant. The lack of statistical significance may be because neither direct nor indirect network externalities independently explain pioneering survival.
Academic expert ratings. To cross-validate our estimation results with the ratings of network externalities and radicalness from academic raters, we reestimated Equation 1 with the network externalities and radicalness ratings obtained from student raters and managers; we found results consistent with those we report herein. In summary, our results are robust to different definitions of exit, censoring dates, and other threats to validity, including sampling variations and the use of raters.
First-mover advantages can be powerful and long-lasting in lock-in markets, especially those in information industries where scale economies are substantial. If you can establish an installed base before the competition arrives on the scene, you may make it difficult for later entrants to achieve the scale economies necessary to compete.
-- Shapiro and Varian 1998
In the networked market, whoever gets a small, early advantage in a market may soon have a large, insuperable edge of increasing returns to scale due to the networked effects.
-- Industry Standard,
September 19, 1999
Contrary to conventional wisdom about pioneering advantages in networked markets, our results indicate that network externalities significantly decrease the survival duration of pioneers. Increases in the marginal customer's utility over time and the excess inertia of customers adopting new products, both of which shorten pioneer survival, appear to outweigh the advantages associated with an installed base of customers that may prolong pioneer survival. In addition, the results of our analysis support a contingency-based framework of product and firm characteristics that moderate the effect of network externalities on the survival duration of pioneers.
Theoretical Contributions
The results of our study contribute to research in marketing strategy that explores market entry, network externalities, and high-technology markets.
Market entry. Our study adds to the limited research on the survival of pioneers (e.g., Golder and Tellis 1993; Mitchell 1991). In particular, our finding that network externalities reduce the survival duration of pioneers challenges the proposed empirical generalization that "order of market entry is not related to long-term survival rates" (Kalyanaram, Robinson, and Urban 1995, p. G218). By including network externalities and two product characteristics, we clarify the role of product characteristics in the performance of pioneers and address calls for research on this issue (Kerin, Varadarajan, and Peterson 1992; VanderWerf and Mahon 1997). In addition, by studying office products and consumer durables, we address the calls to extend pioneering research beyond the traditional contexts of packaged goods, pharmaceuticals, and the PIMS databases (Kalyanaram, Robinson, and Urban 1995; Lieberman and Montgomery 1998).
Network externalities. Extant theoretical economics literature on network externalities suggests that there are opposing processes of lock-in that result in market power potentially aiding the survival of the pioneer (Choi 1994) and in excess inertia that can hurt the pioneer's quest for survival (Farrell and Saloner 1986). The results of the negative main effect of network externalities on survival duration of pioneers indicate that the negative excess inertia effects of network externalities outweigh the positive lock-in effects on the pioneer's survival duration.
However, the moderating effects of product and firm characteristics on the effect of network externalities on the survival of pioneers qualify the previous case. Pioneers of more radical and technologically intensive products in networked markets survive longer than pioneers in less radical and less technologically intense products. Thus, it appears that though radical and technologically intensive product categories are risky for pioneers in networked markets (as evinced by the unhypothesized negative main effects for radicalness and technological intensity in Column 3, Table 3), the characteristics also provide entry barriers that mitigate that risk, thereby enabling the pioneer to establish an installed base, wield market power, and secure long-term survival.
Large pioneering firms have access to complementary resources that enable them to establish an installed base and lock in customers to their product's network, positively influencing firms' long-term survival. For incumbent pioneers, increases in network externalities decrease the duration of pioneer survival, a finding that is consistent with previous research on incumbents' inertia to invading innovations (e.g., Ghemawat 1991; Henderson 1993; Mitchell 1991).
In summary, our findings indicate a notable and complex interplay of product and firm characteristics that determine the effect of network externalities on pioneer survival. In our empirical analysis, we were able to elucidate the interplay of factors and explain the relationship between network externalities and market entry strategy (i.e., pioneering), thereby extending the literature on marketing strategy for firms in networked markets (Gupta, Jain, and Sawhney 1999; Padmanabhan, Rajiv, and Srinivasan 1997).
High-technology markets. As the first reported empirical investigation of market entry in high-technology markets, our study addresses a call for more research on marketing strategy issues in such markets (John, Weiss, and Dutta 1999, p. 78). The unhypothesized negative main effects of products' radicalness and technological intensity on pioneer survival indicate that though pioneers of radical and technologically intensive products face considerable risks, the risks are mitigated by the presence of network externalities. These findings point to the role of product characteristics in determining the rewards of pioneering in such markets and suggest that strategy researchers should consider these variables in future theory development in the domain.
Managerial Contributions
Our findings provide several specific managerial insights. First, network externalities have a strong negative effect on the survival duration of pioneers. This finding suggests that firms contemplating entry into such markets should consider taking a wait-and-see approach to launch, allowing other firms to pioneer but readying to enter quickly if the market begins to take off. Thus, it pays to be patient but watchful in those markets. Indeed, for 12 of the 45 products we studied, the later entrant adopted standards set by the pioneer or an earlier entrant, piggybacking on the technological developments. The later entrant firm thus learns vicariously about the product market before market entry, which is consistent with recent evidence for delaying new product entry (Narasimhan and Zhang 2000).
However, not all pioneers in networked markets failed; the surviving pioneers had average market shares of 25%. Why do some pioneers survive and thrive while others fail? Our case histories suggest a possible explanation: a focus on marketing the networked utility of the product. The utility of a networked product to a customer derives from two sources: ( 1) the intrinsic, or the stand-alone, utility of the product, independent of the number of other users of the product (e.g., using a personal computer as a stand-alone computing device), and ( 2) the networked utility that results from other users in the physical (e.g., fax machine) or virtual (e.g., personal computer, CD player) network. Pioneers of products with network externalities must not only market their products but also develop and market the product's network. Firms can build network utility in different ways, including licensing of the product to other manufacturers to build a large installed base quickly to reduce customer uncertainty, promotion of the development of complementary goods (e.g., CD music titles for CD players), and/or ensuring of backward compatibility with existing networks to reduce switching costs to the new network. Pioneers that focused on promoting and delivering network utility to customers survived, but firms (whether pioneers or later entrants) that focused on only the stand-alone utility tended to fail.
Consider Sony, which pioneered the CD player in 1982. Sony worked extensively to develop the CD format accepted by the music industry and entered into extensive licensing agreements for other firms to manufacture the CD player. Sony also recognized that the availability of music titles on CDs was crucial for delivering utility to customers of the CD player, so it leveraged its Columbia Records label and its collaboration with Philips's PolyGram Records, two of the world's largest music producers at the time, to ensure the availability of music titles on CDs. When Sony introduced its first CD player, Columbia Records simultaneously released the world's first 50 music CD titles (for other examples, see Liebowitz and Margolis 2001; Varian and Shapiro 1998).
Second, radicalness and technological intensity of the product moderate the effect of network externalities to increase the survival duration of pioneers. Although radical and technologically intense product markets are risky, these product characteristics actually strengthen the pioneer's survival. Consider again the CD player pioneered by Sony. Because of its laser-based, computerized technology, the CD player offered virtually noiseless sound quality that was impossible to achieve with the prevalent audiocassette player, thereby providing a breakthrough in sound reproduction. Not affected by the scratches, smudges, and heat warping that afflict audiocassettes, CDs maintained their original sound quality for a long time. The CD player was revolutionary and, as an industry analyst (San Diego Tribune 1987, p. B1) notes, was "the most dramatic development in sound reproduction since Edison." Thus, the radicalness of CD players resulted in formidable entry barriers for subsequent entrants, ensuring Sony's long-term market dominance.
Third, firm size moderates the effect of network externalities to increase the survival duration of pioneers, and incumbency moderates the effect of network externalities to decrease the survival duration of pioneers . Large firms can be assured that their size and attendant resources aid their pioneering efforts in networked markets, whereas it might be beneficial for small firms to partner with other firms that have the necessary resources for market development. For example, Philips's large size and plentiful resource base proved useful in pioneering the audiocassette player market. With respect to incumbency, incumbent pioneers in networked markets must guard against technological inertia, which hurts their survival.
Limitations and Further Research
This study has some limitations that present opportunities for further research. We focused on the survival duration of pioneers. In some situations, pioneers may survive for a long period but not realize large market share or high profits. We used firm size as a surrogate for organizational resources, and we did not measure the impact of specific resources (e.g., size of the business unit, marketing mix) on the survival duration of pioneers. Further research should investigate the effects of network externalities on other measures of pioneer performance, such as market share and financial performance, and incorporate the effects of the pioneers' other resource characteristics.
We focused on the survival duration of pioneers, not on the survival of products. Therefore, we restricted our attention to products that had mass-market acceptance. We were unable to collect data for 18 products in smaller product markets (e.g., radar detector) for which historical records were sparse. Other researchers might extend our study to identify the pioneering rewards in a broader set of product categories.
Given the lack of objective measures for network externalities and radicalness, we used retrospective, subjective measures. Because of improved record keeping and contemporaneous accounts of new product innovations, future researchers can use measures that might not be subject to the possible hindsight biases of our subjective measures.
In addition, we focused on two product and two firm characteristics as moderators of the effect of network externalities on pioneer survival. A product characteristic that we did not consider is the appropriability of the innovation (Teece 1986), or the ability of the firm to collect rents for its innovation efforts. In general, appropriability in the product categories we studied is low, because few firms appeared to rely on the protection afforded by patents. Further research should investigate the effects of appropriability and other product and firm factors on the rewards to pioneers for products with network externalities.
In summary, we view this study as a useful base for further investigation of the effects of network externalities on the performance of pioneering firms. We hope our research stimulates additional work in the area.
The authors acknowledge the financial support of the Institute for the Study of Business Markets at Pennsylvania State University and the Office of Vice-President for Research at University of Texas at Austin. The authors thank Reuben Raj, Ursula Alvarado, Sridhar Balasubramanian, Barry Bayus, Rajesh Chandy, Peter Golder, Vijay Mahajan, José Luis Muneura, Robert Peterson, Bill Robinson, Venky Shankar, Rajendra Srivastava, Gerry Tellis, Christophe Van den Bulte, and Rajan Varadarajan for providing helpful input on previous versions of the article. The authors also thank the three anonymous JM reviewers for their helpful comments on previous drafts of the article.
(n1) Early work in the area of network externalities (e.g., Coase 1960) focused on the negative externalities (e.g., pollution, congestion) between the users in a market and their effects on market failures. Thus, to avoid the negative connotation associated with "network externalities," some researchers (Liebowitz and Margolis 2001) use the term "network effects" to describe situations in which there are linkages between users in the network. We use the terms "network externalities," "network effects," and "networked markets" interchangeably.
(n2) Network externalities can be positive or negative (e.g., congestion in telecommunications networks) and tangible or intangible (intangible externalities pertain to the equity of well-established brands whose customers perceive benefits of reduced product uncertainty and peer approval through their large customer bases). To provide focus, we consider only positive and tangible network externalities here.
(n3) We use the terms "pioneer," "pioneering firm," and "market pioneer" interchangeably.
(n4) Although the effects of firm characteristics may apply to all pioneers to some extent, the effects of firm size and incumbency are particularly important in products with network externalities, which are characterized by considerable uncertainty about the potential size of the network, the standard, and the availability of complementary and compatible goods.
(n5) In a few cases, the pioneer (e.g., Polaroid's instant photography) is a single-product company, but in most cases, pioneers are multiproduct firms with several business units. In cases in which the pioneer is a multiproduct firm, we measured the survival duration of the pioneer in the focal product that it pioneered (Mitchell 1991).
Legend for Chart:
A - Variable
B - Positive Effect on Survival Duration of Pioneer
C - Negative Effect on Survival Duration of Pioneer
A
B
C
Network externalities
Lock-in effect of installed base helps
the pioneer. (P[sub1])
Customer inertia because of wait-and-see
attitude hurts the pioneer.
(P[sub2]; supported)
Network externalities x radicalness of
product
Radicalness creates window of
opportunity for pioneer to leverage
network externalities quickly.
(P[sub3]; supported)
Low performance-price ratio slows
acceptance of radical products,
creating a window of opportunity for
later entrants. (P[sub4])
Network externalities x technological
intensity of product
The need to integrate multiple types
of technologies slows down later
entrants, enabling the pioneer to
leverage network externalities.
(P[sub5]; supported)
Fast-changing product designs make
pioneer's product obsolete, enabling
later entrants to enter with an
improved product design. (P[sub6])
Network externalities x size of
pioneer
Access to resources and
complementary assets helps the
pioneer leverage network
externalities. (P[sub7]; supported)
Bureaucratic inertia of a large pioneer
slows down product marketing, which
enables later entrants to leverage
network externalities. (P[sub8])
Network externalities x incumbency
of pioneer
Access to market knowledge and
existing customer networks helps the
pioneer. (P[sub9])
Incumbent inertia and threat of
cannibalization of existing products
slows the incumbent, providing
opportunities for later entrants.
(P[sub10]; supported) Legend for Chart:
A - Product (1)
B - Network Externalities(a) (2)
C - Previous Product Generation (3)
D - Market Pioneer (Year of Entry) (4)
E - Status of Pioneer (Year of Exit) (5)
F - Current Market Leader (Year of Entry) (6)
A
B C D
E F
Antivirus software
6.8 -- Symantec (1982)
Survived Symantec(x)
Audiocassette player
9.3 Spool audio player Philips (1962)
Survived Panasonic
Automatic teller machine
8.9 Bank teller Docutel (1967)
Failed (1986) Diebold (1970)
Computer-aided design
software
6.8 Manual drafting Autodesk (1982)
Survived Autodesk(x)
Camcorder
6.1 16 mm movie camera Kodak (1984)
Failed (1987) Sony (1985)
CD player
9.3 Audiocassette player Sony (1982)
Survived Sony(x)
CD-ROM drive
9.3 3.5 inch floppy disk drive Sony (1984)
Survived LG Electronics (1989)
Cellular telephone
10.0 Telephone AT&T (1979)
Survived Verizon
Color television
8.4 Black-and-white television RCA (1953)
Survived RCA(x)
Cordless telephone
4.3 Corded telephone Keytronics (1975)
Failed (1979) Vtech (1991)
Database software
9.6 Programming Ashton-Tate (1981)
Failed (1991) Microsoft (1992)
Desktop publishing software
8.7 Print typesetting Aldus (1984)
Survived as Adobe QuarkXPress (1987)
Digital camera
6.2 35 mm camera Logitech (1991)
Survived Sony (1997)
Dot matrix printer
6.0 -- Seikosha (1964)
Failed (1979) Okidata (1972)
Digital videodisc player
9.4 VCR Toshiba (1996)
Survived Sony (1997)
Electric toothbrush
3.4 Manual toothbrush Squibb (1960)
Failed (1968) Oral-B (1978)
Fax machine
10.6 Telegraph Magnavox (1960)
Failed (1965) Sharp (early 1980s)
Flat-bed scanner
6.6 -- Kurzweil Technologies
(1978)
Taken over by Visioneer (1994)
Xerox (1979)
Food processor
4.1 Mixer Cuisinart (1972)
Survived Hamilton-Beach (1955)
High-definition television
8.4 Color television Zenith (1998)
Survived Zenith(x)
Home microwave ovens
5.8 Electric/gas range Tappan (1955)
Failed (1985) Sharp (1974)
Home VCR
9.4 16 mm home projection system Sony (1975)
Survived RCA/Matsushita (1977)
Ink-jet printer
6.2 Dot matrix printer Hewlett-Packard (1984)
Survived Hewlett-Packard(x)
Instant photography
5.4 35 mm camera Polaroid (1948)
Survived Polaroid(x)
Internet browser
7.6 -- Netscape/Mosaic (1994)
Failed (1998) Microsoft (1995)
Internet service provider
10.1 -- Compuserve (1980)
Failed (1997) America Online (1985)
Laser printer
6.2 Dot matrix printer IBM (1975)
Survived Hewlett-Packard (1984)
Mainframe computer
9.3 Punched card machine Univac (1946)
Failed (1986) IBM (1953)
Notebook computer
8.7 Desktop computer Teleram Communications
(1980)
Failed (1985) Dell Computers (1984)
Operating system for personal
computer
12.1 -- Digital Research (1976)
Failed (1996) Microsoft (1981)
Personal data assistant
10.7 Electronic organizer Amstrad (1993)
Failed (1995) Palm Computers (1996)
Pager
7.4 Telephone Motorola (1974)
Survived Cobra
Personal computer
9.0 Minicomputer MITS (1975)
Failed (1979) Dell Computers (1984)
Personal finance software
6.8 Manual accounting Dollars and Sense (1982)
Failed (1986) Quicken (1984)
Photocopier
4.7 Cyclostyling 3M Thermofax (1950)
Failed (1962) Canon (1969)
Pocket calculator
3.4 Manual calculation Bowmar (1971)
Failed (1975) Texas Instruments (1972)
Presentation software
7.1 Overhead 35 mm slide Harvard Presentation
Graphics (1986)
Survived PowerPoint (1993)
Projection television
5.6 Overhead 35 mm slide Advent (1973)
Failed (1981) InFocus (1986)
Single-use camera
4.4 35 mm camera Fuji (1986)
Survived Fuji(x)
Spreadsheet software
10.2 Minicomputers Software Arts (1979)
Failed (1983) Microsoft (1985)
Telephone answering machine
4.3 Manually answering Code-A-Phone (1958)
telephone calls
Failed (1993) Panasonic (early 1970s)
3.5 inch floppy disk drive
9.1 5.25 inch floppy drive Sony (1981)
Survived LG Electronics (1989)
Videogame
9.4 Arcade game Magnavox (1971)
Failed (1980) Sony (1991)
Word processing software
10.4 Typewriter MicroPro International
(1979)
Failed (1995) Microsoft (1989)
Workstation
9.6 Minicomputer Three Rivers (1980)
Failed (1983) Sun (1982)
(a) Network externalities ratings are provided by academic
expert raters; scale ranges from 2 to 14.
Notes: If the pioneering firm did not survive and exited
the market, the figure in parentheses in Column 5 denotes
the year the pioneer exited the market. If the pioneer
market leader in its category in 2001, it is depicted
in (x) bold in Column 6. Legend for Chart:
A - Variable
B - Log-Normal Distribution (1) Parameter Estimates
(Standard Error)
C - Log-Logistic Distribution (2) Parameter Estimates
(Standard Error)
D - Weibull Distribution (3) Parameter Estimates
(Standard Error)
E - Proportional Hazards (4) Parameter Estimates
(Standard Error)
A
B C
D E
Intercept
19.71 (6.05)(***) 19.52 (6.63)(***)
23.12 (5.68)(***) --
Product-Level effects
Network externalities
-1.94 (.84)(*) -1.94 (.95)(**)
-2.38 (.83)(***) 3.25(1.42)(**)
Radicalness of product(*)
network externalities
.11 (.06)(*) .11 (.05)(*)
.14 (.05)(***) -.18 (.09)(**)
Technological intensity of
product x network externalities
.42 (.19)(*) .39 (.20)(*)
.42 (.18)(**) -.62 (.30)(**)
Firm-Level Effects
Size of pioneer x network externalities
.70 (.30)(**) .70 (.37)(**)
.89 (.29)(***) -1.20 (.48)(**)
Incumbency of pioneer x network
externalities
-.90 (.33) -.94 (.34)(***)
-1.03 (.33)(***) 1.35 (.55)(**)
Controls
Radicalness of product
-.80 (.36)(**) -.82 (.37)(**)
-1.00 (.31)(***) 1.35 (.55)(**)
Technological intensity of product
-2.89 (1.32)(**) -2.67 (1.44)(*)
-3.10 (1.23)(**) 4.49(2.06)(**)
Size of pioneer
-4.34(1.86)(**) -4.34 (1.96)(**)
-5.67 (1.78)(***) 7.14(3.02)(**)
Incumbency of pioneer
6.31 (2.16)(***) 6.68 (2.29)(***)
7.36 (2.33)(***) -9.91(3.88)(**)
Log (introduction year)
-.82 (.28) -.83 (.29)(***)
-.81 (.26)(***) 1.03 (.49)(**)
Scale parameter
.86 .51
.62 --
Model χ² (d.f. = 10)
18.80 (p = .05) 19.54 (p = .05)
24.74 (p = .01) 18.13 (p = .05)
AIC
104.42 105.48
103.46 157.26
(*) p < .10.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Variable
B - (1) Excludes Ambiguous Exits (n = 35)(a) Parameter Estimates
(Standard Error)
C - (2) Cutoff Year (1998; n = 44) Parameter Estimates
(Standard Error)
D - (3) Cutoff Year (1995; n = 43) Parameter Estimates
(Standard Error)
E - (4) Cutoff Year (1992; n = 41) Parameter Estimates
(Standard Error)
F - (5) Estimates from Bootstrapping Parameter Estimates
(Standard Error)
A
B C
D E
F
Intercept
29.59 (6.60)(***) 21.51 (4.85)(***)
17.86 (4.78)(***) 15.87 (3.84)(***)
22.94 (5.82)(***)
Product-Level Effects
Network externalities
-3.51 (.96)(***) -2.33 (.72)(***)
-1.99 (.71)(***) -1.44 (.58)(**)
-2.36 (.85)(***)
Radicalness of product *network externalities
.19 (.06)(***) .14 (.04)(***)
.13 (.04)(***) .10 (.04)(***)
.14 (.05)(***)
Technological intensity of product *network externalities
.62 (.21)(***) .41 (.16)(***)
.32 (.16)(**) .17 (.14) (N.S.)
.42 (.19)(**)
Firm-Level Effects
Size of pioneer x network externalities
1.00 (.30)(***) .75 (.24)(***)
.99 (.25)(***) .73 (.22)(***)
.88 (.30)(***)
Incumbency of pioneer x network externalities
-1.22 (.45)(***) -.94 (.28)(***)
-1.35 (.29)(**) -.82 (.22)(***)
-1.03 (.34)(***)
Controls
Radicalness of product
-1.23 (.36)(***) -.92 (.27)(***)
-.82 (.27)(***) -.70 (.21)(***)
-.99 (.32)(***)
Technological intensity of product
-4.66 (1.58)(***) -3.03 (1.12)(***)
-2.08 (1.08)(*) -1.33 (.91) (N.S.)
-3.08 (1.27)(**)
Size of pioneer
-6.80 (1.90)(***) -4.84 (1.47)(***)
-5.36 (1.47)(***) -4.35 (1.28)(***)
-5.65 (1.81)(***)
Incumbency of pioneer
9.58 (3.54)(****) 6.52 (1.94)(***)
8.39 (.22)(***) 5.39 (1.48)(***)
7.36 (2.41)(***)
Log (introduction year)
-.75 (.30)(**) -.74 (.22)(***)
-.67 (.10)(***) -.88 (.20)(***)
-.81 (.26)(**)
Scale parameter
.65 .52
.51 .40
.62
Model x χ² (d.f. = 10)
23.40 26.08
(p < .01) (p < .01)
25.98 27.38
(p < .01) (p < .01)
--
(*) p < .10.
(**) p < .05.
(***) p < .01.
(a) Sample excludes ten products for which the pioneering
exit is ambiguous.
Notes: N.S. = not significant.DIAGRAM: FIGURE 1; Moderating Effect of Product Radicalness on the Effect of Network Externalities on Pioneers' Survival Duration
GRAPH: FIGURE 2; Histograms of Network Externalities and Radicalness Ratings
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A pioneer's time of failure, T (from time of product introduction), is a random variable that has a probability density function f(T = t), denoted as f(t), where cumulative density is represented by F(t). The likelihood that a pioneer fails at time T = t, given that it has not failed in the time interval [0, t), is h(t) > 0. The hazard function is h(t) (i.e., the risk of a firm failing at any give time t) and is equal to f(t)/[1 - F(t)]. All pioneers face a base hazard that represents the risk of failure in homogeneous (average) conditions. Various covariates specific to each pioneering product (e.g., firm size, type of product, moderation effects on network externalities) can increase or decrease the hazard (and, consequently, the survival duration) of pioneers.
There are two main ways to estimate the effects of covariates on survival: ( 1) use of nonparametric models to model their effect on the hazard (e.g., Cox proportional hazards model) and ( 2) use of parametric models to model their effect on the duration time (e.g., AFT models). The AFT models can accommodate several distributions (gamma, log-normal, log-logistic, Weibull, or exponential [a special case of Weibull]) for the hazard function. Because we wanted to explore the specific form and shape of the hazard function that underlies the survival of pioneers, we used the AFT model. The AFT method is well accepted in the fields of statistics, engineering, and sociology (see Allison 1995; Kalbfleisch and Prentice 1980; Lawless 1982) but has been used infrequently in marketing (e.g., Bayus 1998; Manchanda et al. 2002).
We illustrate the AFT model with an example. Suppose that there are two firms that are identical in all respects except for group membership (0 and i): A firm in Group 0 with survival time t will have a survival time Φt in Group i (i.e., S[sub0][t] = S[subi][Φt]). For example, if Group 0 consists of large firms and Group i consists of small firms, it might be expected that Φ < 1 (i.e., pioneers in Group i have an accelerated failure time). The hazard functions for the two groups then share the following relationship: h[subi](t) = Φh[sub0](Φt). When incorporating the effects of covariates, we denote the hazard function as h(t,X), where X is a set of covariates. Furthermore, if we specify that Φ is determined by a set of covariates (Φ = eαX[subi]), the hazard function for firms in Group i (compared with that of firms in Group 0) for the AFT model is:
(A1) h[subi](t,X[subi]) = eαX[subi]h[sub0](te(αX[subi]).
If firms in Group 0 have a Weibull distribution of survival times, W(a, b), the distribution of survival times of firms in Group i is also Weibull, W(aebαX[subi], b), so that firms have the same duration distribution conditional on X. Likewise, if firms in Group 0 have a log-logistic distribution of survival times, LL(μ σ), or a log-normal distribution, LN(μ, σ the survival times of firms in Group i are given by LL(μ - αX[subi], σ) and LN(μ - αX[subi], σ] respectively. For estimation of the parameters of the previous distributions, it is easier to work with the distribution of Ln(T) rather than T. Therefore, we specify an estimation model as follows:
(A2) Ln(T[subi]) = μ + β[sub1]X[subi1] + β[sub2]X[subi2] + ... β[subi]X[subik] + σε[subi],
where the terms are described in the text.
To determine the relationship between Equation A2 and the survival distribution, if it is assumed that T[subi] has a Weibull distribution, the correspondence between the parameters of W(aebαX[subi, b) and the parameters of Equation A2 is as follows: b = 1/ σ a = e-(μ/σ); and α = -β. In contrast, if it is assumed that T[subi] has a log-logistic distribution (equivalently, Ln[T[subi]] has a logistic distribution), the correspondence between the parameters of LL(μ - αX[subi], σ) and Equation 2 is as follows: μ = μ = σ and σ = β. The correspondence between the survival time distribution and Equation 2 for log-normal is analogous to that of log-logistic.
We also note that the Weibull model can be interpreted as both a proportional hazards model and an AFT model. For Weibull W(a,b), the base hazard function is given by h[sub0](t) = abt[supb - 1]. The hazard function for the AFT model per Equation A1 is h[subi](t, X[subi]) = eαX[subi]h[sub0](teαX[subi]) = eαX[subi] [ab(te[supαX[subi]])[supb -1]] = (ebαX[subi])h[sub0](t). The last equation in this sequence of identities is the proportional hazards representation; the proportionality constant is equal to ebαX[subi. Note also that ebαX[subi = e- (1/σ)βX[subi when it is represented in terms of the parameters of Equation A2, because b = 1/σ and α = -β
~~~~~~~~
By Raji Srinivasan; Gary L. Lilien and Arvind Rangaswamy
Raji Srinivasan is Assistant Professor of Marketing, University of Texas at Austin (e-mail: Raji.Srinivasan@mccombs.utexas.edu).
Gary L. Lilien is Distinguished Research Professor of Management Science (e-mail: G5L@psu.edu)
Arvind Rangaswamy is Jonas H. Anchel Professor of Marketing (e-mail: arvindr@psu.edu), Pennsylvania State University.
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Record: 65- Free Cash Flow, Agency Costs, and the Affordability Method of Advertising Budgeting. By: Joseph, Kissan; Richardson, Vernon J. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p94-107. 14p. 4 Charts, 5 Graphs. DOI: 10.1509/jmkg.66.1.94.18453.
- Database:
- Business Source Complete
Free Cash Flow, Agency Costs, and the Affordability Method of Advertising Budgeting
The allocation of excess cash has long been recognized in the finance literature as an important aspect of the basic agency conflict between managers and owners. In the advertising budgeting context, marketing scholars report that firms possessing high levels of cash tend to spend more on advertising than what seems necessary or desirable. Indeed, this positive link between excess cash and advertising expenditures constitutes a part of what is commonly referred to as the affordability method of advertising budgeting. Surprisingly, there has been little research that attempts to view this association as a manifestation of agency costs. Therefore, in this article, the authors examine whether agency costs, as measured by managerial ownership, moderate the relationship between excess cash and advertising expenditures. On the basis of received theory, the authors conceptualize that agency costs will first decrease, then increase, and then decrease again with the level of managerial ownership. Accordingly, the authors hypothesize and find that the fraction of incremental earnings reinvested in advertising follows the same pattern in managerial ownership. These findings support the notion that the use of the affordability method is driven, in part, by agency costs. The authors conclude by discussing the theoretical and managerial implications of the findings.
It has long been recognized in the finance literature that the allocation of free cash flow is an important aspect of the basic conflict of interest between managers and owners (Jensen 1986).[ 1] Specifically, free cash flow tempts managers to expand the size of the firm, thereby increasing managers' control and personal remuneration even though such an action may decrease the overall value of the firm. Marketing scholars have also identified a similar phenomenon in the allocation of excess cash to advertising. Tellis (1998, p. 396), for example, writes, "when firms are flush with cash, they tend to spend liberally on advertising, even beyond what seems necessary or desirable." This link between excess cash and advertising budgets constitutes one part of what is generally known in the literature as the "affordability" method of advertising budgeting. However, to date, there has been no attempt to view the use of this heuristic as a manifestation of the agency costs between owners and managers.[ 2] This research is an attempt to explore this connection.
Why does free cash flow tempt managers to expand the size of the firm beyond the optimal point? Jensen (1986) notes that growth typically increases managers' power by increasing the resources under their control. Moreover, growth is also directly related to managerial compensation because changes in compensation are often positively related to growth in sales. Finally, the tendency of firms to reward managers through promotion rather than year-to-year bonuses also creates a strong organizational bias toward growth in order to supply the new positions that such promotion-based reward systems typically require.
In the context of advertising budgeting, free cash flow may give rise to a scenario in which managers reinvest discretionary dollars into advertising with a view to expand sales, even if these investments are not cost effective. Given the economic significance of advertising budgets to both the economy and individual firms,[ 3] our primary objective in this research is to investigate whether the propensity to invest excess cash into advertising is driven, in part, by agency costs in the owner-manager relationship. As such, our research may also be viewed as an investigation into the use of the affordability method in situations in which firms have excess cash.
The mere existence of a positive link between free cash flow and advertising budgets does not imply inefficient use of monies. There may be sound economic reasons for such a link. Nerlove and Arrow (1962), for example, demonstrate that under conditions of constant price and advertising elasticities, the optimal advertising expenditures may well be a fixed percentage of sales. To the extent that sales and free cash flow are correlated, it may indeed be optimal to invest excess cash into advertising. Another reason for the positive link between free cash flow and advertising budgets may be found in the inherent uncertainty about the advertising-sales relationship. In this connection, it is important to recognize that setting the advertising budget in order to achieve the maximum yield is an immensely difficult problem (Bigne 1995). In general, it is challenging to determine the sales response to advertising. Moreover, many studies show little or no impact of advertising on sales in the short run (Eastlack and Rao 1989). Given a lack of understanding of the advertising-sales relationship, a strategy of allocating some fraction of discretionary dollars toward advertising could be akin to purchasing insurance: More is purchased when the firm has a greater amount of discretionary dollars and, consequently, a lower cost of capital. Given these arguments for a positive link between free cash flow and advertising, our incremental contribution is to demonstrate that this association is moderated by agency costs.
We operationalize our emphasis on agency costs by focusing on a key agency variable, namely, the extent of managerial ownership. Indeed, it is well documented in the finance literature that ownership can prove to be a valuable tool in reducing agency costs between managers and owners (Jensen and Meckling 1976). Specifically, ownership motivates managers to use their decision rights efficiently because they bear the rewards and punishments of their actions. Previous research, however, suggests that agency costs need not decrease uniformly with the level of management ownership. Morck, Shleifer, and Vishny (1988), for example, posit that agency costs will first decrease, then increase, and finally decrease again with the level of managerial ownership. Accordingly, they hypothesize that measures of firm valuation will first increase, then decrease, and finally increase again with the proportion of managerial ownership.
Briefly, the rationale for these counterintuitive expectations is as follows: Morck, Shleifer, and Vishny (1988) suggest that ownership first improves firm performance because of the convergence in interests between managers and owners. That is, providing managers with a claim on the firm aligns their goals with those of owners and motivates them to take actions that are value maximizing. However, this beneficial effect of ownership is soon mitigated by an adverse effect. Specifically, as managers begin to hold a substantial fraction of the firm's equity, they become entrenched; this entrenchment, in turn, enables them to pursue non-value-maximizing behaviors without being disciplined by the market. Thus, in this range, firm value decreases with ownership as the adverse effects of entrenchment become increasingly pronounced. This does not imply that convergence effects are absent here-they continue to operate but are dominated by entrenchment effects. Finally, as management ownership increases further, the high level of ownership gives rise to a situation in which convergence effects dominate; consequently, in this region, firm value again increases with ownership.
We follow Morck, Shleifer, and Vishny (1988) and specify that agency costs will vary nonmonotonically with the level of managerial ownership. As do Morck, Shleifer, and Vishny, we measure managerial ownership by the extent of firm ownership among members of the board. We then examine whether the fraction of earnings reinvested in advertising is moderated by the level of ownership in a sample of firms culled from the Compustat database. As hypothesized, we find that ownership by board members has a systematic and economically significant impact on the fraction of earnings that is reinvested in advertising. Moreover, we also find nonmonotonic effects, which suggest the same interplay of convergence and entrenchment effects demonstrated by Morck, Shleifer, and Vishny (1988). We therefore conclude that agency costs play an important role in the reinvestment of excess cash to advertising.
In a broader context, our work explicitly highlights one specific mechanism through which agency problems degrade firm value. Although agency problems have been well documented in the marketing literature (see, e.g., Bergen, Dutta, and Walker 1992), there is little research on the specific mechanisms through which this value dissipation occurs. Our work suggests that the misallocation of excess monies to advertising is one specific route through which agency costs degrade firm value. As such, the current investigation also deepens the understanding of alternative mechanisms through which agency costs affect firm value.
The rest of the article is organized in the following manner: In the next section, we provide a brief review of the literature. We then describe our hypothesis and model. Next, we describe our sample, measures, and estimation equation. Finally, we present our empirical findings and conclude by discussing the contributions and limitations of our research endeavor.
Our literature review is divided into three parts. In the first, we review the impact of agency costs on the allocation of discretionary monies. We then examine how agency costs vary with a key variable of interest, namely, managerial ownership. Specifically, we describe in detail the convergence and entrenchment effects identified in prior work, because it has a direct bearing on our research. Finally, we provide a general overview of advertising budgets. This overview provides the appropriate context and aids in the development of our hypothesis
Impact of Agency Costs on the Allocation of Free Cash Flow
The finance literature has long recognized the impact of agency costs on the allocation of discretionary monies (the so-called free cash flow hypothesis). A large strand of research examines the relationship between agency costs and financial structure. Jensen (1986) posits that leveraged buyout activities are one way of controlling free cash flow because the debt incurred in such transactions forces managers to disgorge excess cash rather than direct it to unprofitable opportunities. Evidence supporting the free cash flow motivation for financial restructuring has been provided by many authors (Gibbs 1993; Griffin 1988; Gupta and Rosenthal 1991; Lehn and Poulsen 1989; Loh 1992; Moore, Christensen, and Roenfeldt 1989).
In the accounting literature, Gul and Tsui (1998) examine the relationship between the amount of free cash flow and audit fees. They hypothesize that because managers will likely engage in non-value-maximizing activities while allocating free cash flow, auditors' assessment of the inherent risk and, in turn, the audit effort will increase with the amount of free cash flow possessed by the firm. Gul and Tsui therefore postulate a positive relationship between high levels of free cash flow and audit fees. As expected, they find this association in their data set.
The free cash flow hypothesis has also been tested in the context of the issue of equity. Mann and Sicherman (1991) hypothesize that shareholders will respond negatively to equity issue announcements because they expect management to misuse such nonbonded funds. Furthermore, they also expect shareholder response to be moderated by the track record of management with respect to previous equity issues. Finally, Wells, Cox, and Gaver (1995) compare the level of cash flow for mutual insurers and stock insurers and find that the latter possess greater levels of cash flow. Wells, Cox, and Gaver posit that management at these firms is able to hoard cash because it is governed by fewer monitoring and control mechanisms. This hoarding of cash, though non-value maximizing, provides management with the important benefit of avoiding the scrutiny of the capital markets when the firm requires additional capital.
In summary, there is a vast body of research in accounting and finance that convincingly demonstrates that agency costs play an important role in the allocation of discretionary monies. As is evident from this brief review, researchers have examined the impact of agency costs on various topics, such as financial structure, audit fees, response to equity announcements, and the level of free cash flow. This stream of research enables us to conceptualize the impact of agency costs on the allocation of discretionary monies in an important marketing context, namely, advertising budgeting.
Agency Costs as a Function of the Level of Firm Ownership by Board Members
As mentioned previously, management ownership may serve as a useful mechanism to reduce agency costs and bring about goal alignment. As their stakes in the company rise, managers bear a larger portion of the cost of pursuing non-value-maximizing objectives; consequently, they will increasingly pursue value-maximizing objectives. It is important to recognize that small levels of firm ownership by managers can prove to be significant when viewed as a proportion of total managerial compensation. Thus, even small levels of firm ownership can prove to be beneficial. Overall, this goal alignment through ownership is referred to as the convergence-of-interests effect.
Many scholars have pointed out limitations in the ability of managerial ownership to bring about goal alignment (Demsetz 1983; Fama and Jensen 1983). These researchers suggest that when a manager owns only a small stake, market discipline (e.g., the managerial labor market [Fama 1980], the product market [Hart 1983], and the market for corporate control [Jensen and Ruback 1983]) may indeed force the manager toward value maximization. However, managers who control a substantial fraction of the firm's equity may have enough voting power and/or influence to indulge in their preference for non-value-maximizing behavior. Examples of such non-value-maximizing behavior include empire building, expensive corporate offices, lavish company trips, purchase of high-priced paintings, installation of a fleet of business jets, and so forth. This so-called entrenchment effect, arising from relatively unfettered power, suggests that ownership may not always lead to goal alignment and value maximization.[ 4]
On the basis of these considerations, Morck, Shleifer, and Vishny (1988) empirically examine how these opposing effects resolve in the case of firm performance. They suggest that whereas the convergence-of-interests effects should increase uniformly over the range of ownership, entrenchment effects may surface only after some threshold level of ownership. In particular, they posit that entrenchment effects may begin to surface above some critical level of ownership and increase to attain their peak well before majority ownership (for a pictorial representation of these effects, see Figure 1). Now, as ownership increases from 0% to 100%, the relative influences of convergence and entrenchment are as follows: Initially, entrenchment effects are absent and increases in managerial ownership give rise to an increase in convergence effects. As ownership increases further, however, entrenchment effects begin to surface. In this range, as entrenchment grows, managers can increasingly indulge in non-value-maximizing behaviors. Finally, as ownership increases even further, convergence effects again begin to dominate because the high level of ownership causes managers to fully bear the costs of any non-value-maximizing action. For these reasons, firm value, as measured by Tobin's Q and profit rate,[ 5] should first rise to reflect the impact of convergence, then decrease as entrenchment effects come into play, and finally increase again as convergence effects dominate. In their empirical work, Morck, Shleifer, and Vishny find these effects, and the results are robust across both measures of firm performance. Figure 2 displays their findings with respect to Tobin's Q.
Advertising Budgets
It is generally recognized in the marketing literature that many firms overspend with respect to advertising. In a well-known article titled, "Are You Overadvertising?" Aaker and Carman (1982) review the findings from both field experiments and econometric studies. They conclude that, in general, firms are overadvertising and suggest that several advertisers should experiment with reduced advertising expenditures. They also suggest that the reward structure at advertising firms is likely to be a key driver behind this observed pattern of overadvertising.
More recently, Prasad and Sen (1999) review the academic literature since 1982 and examine whether there is evidence of systematic overadvertising. Specifically, they review the articles that have appeared in the years following Aaker and Carman's article. Prasad and Sen (1999) report that though the econometric studies yield mixed findings, the evidence from field experiments points to continued overadvertising by firms. They also suggest that incentive structure, among other factors, could likely contribute to the excessive spending with respect to advertising.
Aaker and Carman's (1982) and Prasad and Sen's (1999) work is relevant to our research in two ways: First, it is consistent with the general sentiment documented in the finance literature that managers often misuse the discretionary resources under their control. Second, these researchers also suggest that incentive structure can play an important role in contributing toward the observed overadvertising. Following the suggestion of these researchers, we conceptualize how incentive structure (i.e., managerial ownership) affects agency costs and how these costs, in turn, influence the reinvestment of free cash flow into advertising.
Background
Two issues need to be addressed as we set out to develop our research hypothesis. First, do board members influence lower-level tactical decisions such as advertising budgeting? In this regard, we conjecture that though board members are not directly involved in the day-to-day operational decisions of the firm, their motivations strongly influence the behavior of lower-level managers. Such a view is consistent with Fama and Jensen's (1983) observation that lower-level managers initiate (and implement) decisions and board members ratify them. Thus, depending on the level of ownership, lower-level managers learn to submit plans that have more of a convergence-of-interests flavor or an entrenchment flavor. The approved plans are then implemented and go on to affect firm value positively or negatively, depending on the intent behind the plan. We believe that in this way, board members influence various tactical decisions taken by lower-level managers, including the setting of advertising budgets.
The second issue pertains to the relevance of agency costs to board members. Specifically, because board members often belong to other organizations, it may be reasonably argued that their ability to enjoy the benefits of increased firm size is limited. However, there is strong empirical evidence to show that the compensation of board members is significantly influenced by firm size (Hempel and Fay 1994). Thus, the agency problem of expanding the firm beyond the optimal size applies with equal force to board members.
Finally, as we develop our hypothesis, we note that unlike other investments such as factory equipment, real estate purchases, and so forth, the return on advertising is harder to measure. As such, in many situations, good arguments can be made for increasing the advertising budget, irrespective of whether such an increase is warranted. Consequently, our focus on examining the inherent motivations of key decision makers is particularly salient in the context of advertising budgeting.
Hypothesis
On the basis of the discussion thus far, we posit that managerial ownership will have a systematic impact on the reinvestment of free cash flow into advertising. In effect, we posit that self-interested managers will choose a level of reinvestment in advertising that maximizes their private benefits, namely, control and personal remuneration. Consider the impact of ownership at low levels of ownership. Here, entrenchment effects are absent. As ownership increases within this range, managers begin to bear increasingly the cost of inefficient investments; consequently, they will become increasingly circumspect about investing excess cash into advertising. Therefore, in this range, the fraction of discretionary dollars reinvested in advertising will decrease with ownership.
Next, consider the impact of ownership within an intermediate range. Here, increasing levels of ownership give rise to increasing levels of entrenchment. This increasing level of entrenchment, in turn, gives managers the freedom to reinvest excess cash into advertising. In this case, managers recognize that the private benefits from increased firm size exceed their share of the loss in firm value. Consequently, as managers become increasingly entrenched, the fraction of discretionary dollars reinvested in advertising will increase with ownership.
Finally, as ownership increases even further, another effect emerges. By now, managers are fully entrenched and possess considerable freedom with respect to reinvesting excess cash into advertising. Here, however, the relatively high level of managerial ownership begins to put a check on this behavior. In effect, as ownership increases, managers begin to bear fully the cost of reinvesting excess cash into advertising. Moreover, the magnitude of this cost increases with the level of managerial ownership. As such, in this range, the tendency to reinvest excess cash into advertising will again decrease with ownership.[ 6]
We encapsulate these arguments in the following hypothesis:
H<SUB>1</SUB>: The amount of free cash flow reinvested in advertising will be moderated nonmonotonically by the fraction of management ownership. Specifically, the proportion of free cash flow reinvested in advertising will decrease, then increase, and finally decrease again with the fraction of management ownership.
We note that the postulated nonmonotonic interplay of entrenchment and convergence effects follows directly from Morck, Shleifer, and Vishny's (1988) theoretical and empirical work. This view is also becoming widely accepted by scholars of corporate governance (see, e.g., the review article by Walsh and Seward [1990]). However, its applicability to the advertising budgeting context has not been explored in the literature. Our research efforts aim to redress this gap.
Model
On the basis of our hypothesis, we present the following model of advertising budgeting:
( 1) Advertising = βDiscretionary Monies<SUB>-1</SUB> [1 + Agency Costs] + γX,
where the amount of advertising is influenced by the level of discretionary monies with a lag. Moreover, this relationship is moderated by agency costs. In addition, in Equation 1, X is a large vector of covariates representing firm, market, and industry characteristics. It is included for proper specification.
In our empirical work, it is more convenient to work with the following first-difference model: ( 2) δAdvertising = βδDiscretionary Monies<SUB>-1</SUB> [1 + Agency Costs],
where δ denotes the first-difference operator with respect to time. In this specification, the coefficients associated with time-invariant covariates, γ, need not be estimated because they have been differenced out.
Empirical Context
Because our primary research question pertains to the impact of agency costs in allocating free cash flow, we exclude firms that experience negative earnings (no free cash flow). In addition, we separately analyze firms that experience an increase in earnings across two consecutive periods from those that experience a decrease in earnings across two consecutive periods. We recognize that from an estimation point of view, these firms are identical to firms that experience an increase in earnings. However, it is possible that these latter firms are characterized by somewhat different dynamics. Specifically, to the extent that earnings in the first period serve as a reference point, management may come to view the situation in the second period as one of limited discretionary monies rather than excess discretionary monies. As such, firms that experience an increase in earnings provide us with a better context to examine the impact of agency costs on the allocation of free cash flow. (Subsequently, we also report findings among firms that experience a decrease in earnings.)
Our initial sample is all 10,055 firms described in the Compustat database over the 1990-97 time period (approximately 70,000 firm-year observations). (The Compustat database is a collection of financial statements gathered from the annual reports and Securities Exchange Commission filings of nearly all publicly traded firms.) In this sample, many firms either have no advertising expenditures or invest too small an amount to report advertising expenditures as a separate line item on their income statements. This causes a majority of firms to drop out of the sample. The extent of management ownership is gathered from the Marketbase database. (The Marketbase database provides a summary of stock ownership by directors and officers of a corporation as reported in filings and proxy statements submitted to the Securities Exchange Commission.) Consistent with our previous discussion, we include only firms that report positive earnings across the two periods. In addition, after reducing the sample for the firms that do not report other necessary financial variables or are not covered in the Marketbase database, we are left with 2763 firm-year observations.
Measures
The measures we use in our empirical work follow directly from the model described in Equation 2. For expositional convenience, we describe our variables over the span of an upcoming period as well as a prior period rather than the span of two previous periods as suggested in Equation 2. The earnings at time 0, E<SUB>0</SUB>, report the earnings for the period just ended.[ 7] The earnings at time -1, E<SUB>-1</SUB>, report the earnings for the previous period. We use the difference between these two amounts, E<SUB>0</SUB> - E<SUB>-1</SUB>, as our measure for δDiscretionary Monies. We label this key measure CHG EARN. The dependent variable is the change in the advertising budget between the period just ended, A<SUB>0</SUB>, and the upcoming period, A<SUB>1</SUB>. Our dependent variable is thus A<SUB>1</SUB> - A<SUB>0</SUB>, and we label it CHG ADV.
With respect to management ownership, we follow Morck, Shleifer, and Vishny (1988) and construct the following variable (ownership refers to ownership by the top management team, i.e., board members):
OWNERSHIP LOW = ownership, if ownership < .05
= .05, if ownership ≥ .05
OWNERSHIP MED = 0, if management ownership < .05
= ownership - .05, if .05 < ownership < .25
= .20, if ownership ≥ .25
OWNERSHIP HIGH = 0, if ownership < .25
= ownership - .25, if ownership ≥ .25
We use boundaries of .05 and .25 because they emerge in Morck, Shleifer, and Vishny's (1988) empirical investigation. However, as these authors themselves point out, the exact cutoff points are nebulous because the extent of entrenchment is not likely to be perfectly correlated with ownership. Some management teams, by virtue of their tenure with the firm or status as founding family, may become entrenched with relatively small stakes. Moreover, although higher ownership will typically lead to deeper entrenchment, diminishing returns may set in well before 50% is reached. These arguments notwithstanding, the sensitivity analysis conducted by Morck, Shleifer, and Vishny convinces us that the aforementioned boundaries are appropriate for empirical work.[ 8]
Estimation Equation
In straightforward fashion, we use the following equation to investigate the effects we seek:[ 9]
( 3)CHG ADV = β<SUB>0</SUB> + β<SUB>1</SUB>CHG EARN
+ β<SUB>2</SUB>CHG EARN × OWNERSHIP LOW
+ β<SUB>3</SUB>CHG EARN × OWNERSHIP MED
+ β<SUB>4</SUB>CHG EARN × OWNERSHIP HIGH.
We organize our findings into three main parts. We first describe our sample characteristics. We then present our main findings. This is followed by additional evidence that supports the notion that our observed effects are not driven by omitted variables. Finally, we report findings pertaining to sensitivity analysis and present alternative specifications of our basic model to demonstrate the robustness of our findings.
Sample Characteristics
Here, we report sample characteristics for the 2763 firms that enjoy an increase in earnings across two consecutive periods. In our sample, the mean change in earnings is $15.42 million, and it varies from 0 to $944 million. Despite an increase in earnings, firms chose to decrease their advertising budgets in 780 (28.2%) cases. In the remaining 1983 cases, firms increased their advertising budgets. The mean change in advertising is $2.61 million. It varies from -$97.70 million to $335.75 million.
Figure 3 displays the distribution of management ownership in our sample. The distribution slopes downward as the level of ownership increases. The mean level of ownership on our sample is .30, with standard deviation .22.
Table 1 presents the simple correlation matrix for our analysis variables. We find that CHG EARN and the three interaction terms associated with CHG EARN are all positively and significantly correlated with CHG ADV. As expected, there is a significant, positive correlation between CHG EARN and the three interaction terms associated with this variable. Indeed, two of these three correlations exceed .50. Moreover, there is a statistically significant, positive correlation within the three interaction terms associated with CHG EARN; two of the three correlations exceed .50. Finally, because we include sales in a variant of our basic model, we also report correlations between change in sales (CHG SALES) and the remaining variables. We find that CHG SALES is positively and significantly correlated with CHG ADV, CHG EARN, and the three interaction terms associated with CHG EARN.
We also find that the advertising-to-sales ratio has a mean of 3.82% and a median of 2.06%. More important, the advertising-to-earnings ratio has a mean of 47.12% and a median of 16.75%. This last finding suggests that advertising budgets are a large enough proportion of earnings that they will warrant scrutiny and ratification by the board.
Findings
We use ordinary least squares to estimate the model described in Equation 3, and the results are displayed in Table 2 (Model 1). The results reveal that the overall model is highly significant (p < .0000), with an adjusted R2 of .40. All coefficients have the expected sign and are significant at the .01 level. We find that changes in the level of earnings have a significant impact on the advertising budget for the upcoming period. On average, for a firm with no management ownership, a substantial amount of each discretionary dollar, namely, $.262, is reinvested in advertising. This level of reinvestment in advertising, expressed as a fraction of advertising in the current period, can be fairly substantial. Specifically, the median reinvestment is approximately 28.2% of the current advertising budget. We are quick to point out, however, that this large reinvestment need not indicate the use of the affordability heuristic. It could simply be a manifestation of the percentage of sales method advocated by Nerlove and Arrow (1962) or the insurance argument discussed previously.
In contrast, our findings with respect to the three interaction terms can potentially shed light on the impact of agency costs in allocating discretionary monies. The coefficient associated with OWNERSHIP LOW is negative and statistically significant, which implies that in the 0%-5% ownership range, each percentage point of management ownership reduces the amount of the discretionary dollar reinvested in advertising by $.0612. This finding reveals that as managers begin to bear the cost of advertising, they become more circumspect about investing discretionary dollars back into advertising.
The coefficient associated with OWNERSHIP MED is positive and statistically significant, implying that in the 5%-25% ownership range, each percentage point of management ownership increases the amount of the discretionary dollar reinvested in advertising by $.0136. This finding is consistent with our conjecture that as managers become entrenched, they will begin to pump up advertising expenditures with a view toward empire building.
Finally, as hypothesized, the coefficient associated with OWNERSHIP HIGH is negative and statistically significant. Economically, this implies that in the 25%-100% ownership range, each percentage point in ownership decreases the amount of the discretionary dollar reinvested in advertising by $.0034. As expected, this change in investment policy occurs because the high level of ownership causes managers to become careful about how discretionary dollars are allocated.[ 10]
To better understand the manner in which discretionary dollars are reinvested in advertising, we plot the fraction of earnings that is reinvested as a function of the level of management ownership. We obtain this graph in straightforward fashion by using the coefficients associated with CHG EARN and its interaction with the three ownership terms (see Figure 4).
Figure 4 reveals the aforementioned effects pictorially. Initially, management ownership has a strong negative impact on the fraction of earnings that is reinvested in advertising. However, this pattern reverses for intermediate levels of ownership and reverses again as ownership reaches high levels. The proportion of discretionary earnings reinvested in advertising is approximately similar at zero ownership and at an ownership level of approximately 25%. At both these levels, about one-quarter of each discretionary dollar is reinvested in advertising. Because this latter maxima is observed in a domain in which both convergence and entrenchment effects are at work, it is obvious that the reinvestment due to entrenchment alone is larger than the reinvestment arising at a firm with zero managerial ownership (i.e., no convergence or entrenchment effects). Thus, the singular impact of entrenchment can be fairly substantial.
Overall, our findings are consistent with the postulated interplay between convergence and entrenchment effects: The reinvestment in advertising varies nonmonotonically as the level of managerial ownership increases. Specifically, managers who are entrenched spend a greater proportion of discretionary dollars on advertising than do managers who are effectively disciplined by the market or managers who bear the full cost of advertising expenditures.
Next, we run a second regression in which we include the change in sales as a potential control variable. In effect, we use the difference in sales at time 0, S<SUB>0</SUB>, and time -1, S<SUB>-1</SUB>, to account for the conjecture that some firms may be influenced by the level of previous sales in setting their advertising budgets. Given our first-difference specification, we label this variable, S<SUB>0</SUB> - S<SUB>-1</SUB>, as CHG SALES. The results including this variable are displayed in Table 2 (Model 2).
This model has an adjusted R² of .42, an improvement of only about 2 percentage points over the previous model. As before, all coefficients have the expected signs and are statistically significant. Here, we find that only a relatively small amount, namely, $.0123, is reinvested in advertising per dollar of sales increase. Overall, this set of findings suggests that CHG SALES is not as salient as CHG EARN in the advertising budgeting process. These findings further justify our focus on the role of earnings in explaining changes in advertising budgets.
Finally, recognizing that there may be considerable heterogeneity across industries in the fraction of earnings that is reinvested in advertising, we extend our basic model by including interaction terms for each industry. Following the work of Chauvin and Hirschey (1993) on advertising expenditures, we employ two-digit Standard Industrial Classification (SIC) codes to serve as our proxy for industry dummies. We also include dummies for the different years to account for structural changes that may have occurred over time. These results are also reported in Table 2 (Model 3). Although specifying expected signs by industry is outside the scope of this article, we do find industry-specific effects: The overall R² for the model increases to .63. Moreover, of a total of 28 industries,[ 11] 12 of the SIC × CHG EARN terms are statistically significant. In addition, three of the Year × CHG EARN are significant, implying structural changes over time. Our main findings remain unchanged from both a statistical and a substantive point of view. These findings give us confidence that our observed effects are not being driven by differences in advertising budgeting practices across industries and time.
Omitted Variable Bias
Here, we allow for the possibility that our estimation equation is incompletely specified and our findings are driven by excluded variables. Although such a conjecture is indeed reasonable for any model, it is less of a concern for our model for two reasons: First, the interaction terms are hypothesized to vary in a nonmonotonic manner. As such, any bias arising from omitted variables must also vary in a systematic manner to render our findings spurious. Second, our first-difference specification alleviates this problem to some extent (see, e.g., the discussion of this point by Anderson, Banker, and Ravindran [2000] and the references contained therein). Nevertheless, as a further test, we present results from estimating the model presented in Equation 3 among firms that experience positive earnings in two periods but have a lower level of earnings in the latter period. As mentioned previously, these firms are identical to those that experience an increase in earnings from an estimation point of view. However, they differ in an important manner from a behavioral perspective. Specifically, the drop in earnings may cause management to view the firm's situation as one of limited discretionary monies rather than excess discretionary monies. Consequently, the hypothesized agency effects pertaining to the allocation of excess discretionary monies should be less pronounced within this domain.
Accordingly, we analyze 1141 firms that experience a decrease in earnings across two consecutive periods. Surprisingly, in this subsample, 646 (56.6%) increased their advertising budget despite the decrease in earnings. The remaining 495 decreased their advertising budget as a result of depressed earnings. As before, we use ordinary least squares to estimate a model with CHG ADV as the dependent variable and CHG EARN and its interaction with management ownership as the independent variables. The results are displayed in Table 3.
Table 3 reveals that the model has much lower explanatory power in this domain (R² = .02). If our previous findings were indeed being driven by omitted variables, we would have observed a comparable role for these variables in this domain. This is not the case here. Moreover, three of four coefficients are statistically significant, but with signs opposite from those expected. Overall, these findings suggest that perceptions of excess cash flow are driven by both the presence of positive earnings and the trend in earnings. In any case, these findings weaken the argument that our observed effects are being driven by an omitted variable bias.
Sensitivity Analysis: Cutoff Points and Construction of Ownership Variable
To examine whether our model is sensitive to the cutoff points used for ownership and/or our construction of the ownership variable, we estimate our basic model employing a cubic polynomial in the level of ownership. The results are displayed in Table 4. Compared with our basic model, this model has a somewhat lower fit: The adjusted R² is .36. Using the coefficients, we plot the reinvestment graphically. This is displayed in Figure 5. We find a similar pattern of nonmonotonic effects, though the boundaries seem to be shifted somewhat to the right to 13% and 57%. This rightward shift in the cutoff points may be driven, in part, by the difficulty of the cubic polynomial to change direction. Similarly, the large negative reinvestment at high levels of ownership is likely driven by the fact that the cubic function explodes at high levels of its argument. Overall, however, our main message of nonmonotonic effects prevails, though we concede that some doubt is cast about the exact location of the cutoff points.
Alternative Specifications
We also run a basic model with change in earnings modified by the logarithm and square root transformation. In both cases, all independent variables continue to be statistically significant. The adjusted R², however, drops to 15% and 30%, respectively. Overall, these transformations do not affect the substantive impact of our empirical findings.
Despite large monetary outlays, advertising budgeting is a poorly understood topic. Research conducted over a period of two decades suggests that the vast majority of firms are overadvertising (Aaker and Carman 1982; Prasad and Sen 1999). Moreover, empirical surveys report that practitioners frequently employ heuristics such as the affordability method in setting their advertising budgets (Tellis 1998). Given this state of affairs, it is not surprising that marketing scholars have highlighted the need for additional research on the topic of advertising budgeting. In addition, an area often identified for further research is the impact of the incentive structure on advertising budgeting decisions (Prasad and Sen 1999). This research responds to these calls, from both a theoretical and an empirical perspective.
Following research in the finance literature, we first show that the impact of the incentive structure on the advertising budgeting decision is complex. Specifically, a key incentive tool, namely, managerial ownership, predicts a nonmonotonic effect on the use of the affordability method. In particular, managerial ownership gives rise to a mix of convergence and entrenchment effects. Therefore, as ownership increases from 0% to 100%, we hypothesize that self-interested managers will first decrease, then increase, and finally decrease their propensity to reinvest excess cash into advertising. The identification of such nonmonotonic effects within the advertising context is an important theoretical contribution of our work.
Empirically, we find that the fraction of discretionary dollars reinvested in advertising varies systematically with the level of managerial ownership. These findings are noteworthy because they uncover a hitherto undocumented relationship, namely, that of a nonmonotonic relationship between the use of the affordability heuristic and the level of managerial ownership. This finding, coupled with those reported in the accounting and finance literature, paves the way toward an empirical generalization that would link agency costs and the allocation of discretionary monies in a wide variety of settings. This second contribution of our work is also important because, as Bass and Wind (1995) note, empirical generalizations are the building blocks of science.
Finally, our findings provide an explicit manifestation of the theory documented by Morck, Shleifer, and Vishny (1988). Although these researchers convincingly demonstrate that firm value varies nonmonotonically with ownership levels, our study suggests one route by which this relationship might come about. Specifically, our empirical findings suggest that entrenched managers tend to dissipate firm value by overspending on advertising. This identification of a specific mechanism by which firm value is dissipated is important in its own right. It also underscores the importance of marketing resource allocation decisions in influencing firm value. These insights constitute a third contribution of our research.
Our work also offers specific managerial implications. In particular, our findings suggest that one way to enhance the advertising budgeting process is to redesign the reward structure so as to bring about greater goal alignment between managers and owners. In scenarios in which it is difficult to bring about goal alignment through the right level of managerial ownership, our research informs stakeholders that they need to institute alternative mechanisms to govern the advertising budgeting process. Our research also informs key decision makers that such scenarios are likely to occur for intermediate levels of managerial ownership.
Our research is not without limitations. Given the aggregate level of data we analyze, we are unable to verify with certainty if our observed effects are indeed being driven by the micro-level processes that we suggest. In other words, although our data are consistent with a view in which the motivations of board members are ultimately injected into the advertising budgeting decisions of various divisions of the firm, we are unable to establish that this is indeed the process. There is a need for additional research, conducted perhaps with qualitative techniques, that will provide a deeper understanding of the relationship among free cash flow, agency costs, and the affordability method of advertising budgeting. We hope our research will stimulate such efforts.
1 Formally, free cash flow is defined as cash in excess of that required to fund all positive net present value projects. Loosely, free cash flow may be considered analogous to excess cash on hand.
- 2 Agency costs refer to the sum of the costs of designing, implementing, and maintaining the appropriate control system within organizations and the residual loss resulting from the difficulty of solving control problems completely (Jensen and Meckling 1992).
- 3 Nationally, companies in the United States invested approximately $200 billion on advertising in 1998. This amounts to more than $700 for each of the nearly 270 million men, women, and children in the United States. In addition, several U.S. firms invest more than $1 billion in domestic advertising. Even the U.S. government advertises to the tune of $670 million annually (Czinkota 1999, p. 431).
- 4 In this connection, Weston (1979) reports that no firm in which insiders owned more than 30% has ever been acquired in a hostile takeover. This suggests that managers who control a substantial portion of the firm's equity may be relatively free from the discipline of the market.
- 5 Formally, Tobin's Q is measured as the ratio of the market value of a firm (or the weighted average firm in the financial markets) to the net replacement cost of firm assets (Wernerfelt and Montgomery 1988).
- 6 Our arguments here work best when we assume, as suggested in the literature, that increased advertising is not cost effective in that most firms are overadvertising. Although firms will certainly vary in the degree to which they are overadvertising, it is difficult to describe conditions that will cause this heterogeneity to yield these nonmonotonic effects. In other words, it is unlikely that our hypothesized nonmonotonic effects can be ascribed to this heterogeneity.
- 7 Earnings are frequently used to represent discretionary monies. We use EBITDA, that is, earnings before interest, taxes, depreciation, and amortization. This measure is widely used in accounting and finance studies.
- 8 Subsequently, we also discuss sensitivity analysis pertaining to these boundaries.
- 9 Our measures and specification described in Equation 3 are statistically equivalent to the standard specification recommended to estimate piecewise linear regressions (see, e.g., Neter, Wasserman, and Kutner 1985, p. 348). The standard specification would suggest the following four variables: CHG EARN, CHG EARN × Ownership, CHG EARN × (Ownership - .05) × Dummy 1, and CHG EARN × (Ownership - .25) × Dummy 2. Here, Dummy 1 = 1 if Ownership > .05, 0 otherwise; Dummy 2 = 1 if Ownership > .25, 0 otherwise. This latter specification, however, would require us to collect coefficients to represent the impact of ownership in the [.05, .25] and [.25, 1.00] intervals; consequently, for expositional convenience, we follow the measures and specification described in Equation 3.
- 10 Recall that the partial correlation matrix reveals a positive correlation between the three interaction terms and our dependent variable, CHG ADV. Here, however, we describe two negative coefficients and one positive coefficient. To reconcile this, note that the three interaction terms are all positively correlated with CHG EARN. CHG EARN, in turn, is positively correlated with CHG ADV. Therefore, it is likely that the positive correlation between the three interaction variables and CHG ADV is driven by the underlying association between CHG EARN and CHG ADV. Indeed, after controlling for CHG EARN in the regression, we obtain the hypothesized effects.
- 11 Any SIC codes with few firms are lumped into an "other" category. This is the category that is excluded in the estimation equation.
CHG CHG CHG
EARN × EARN × EARN ×
CHG CHG OWNERSHIP OWNERSHIP OWNERSHIP CHG
ADV EARN LOW MEDIUM HIGH SALES
CHG ADV 1.00
CHG EARN .53 1.00
(.00)
CHG EARN .24 .76 1.00
× (.00) (.00)
OWNER-
SHIP
LOW
CHG EARN .24 .52 .78 1.00
× (.00) (.00) (.00)
OWNER-
SHIP
MEDIUM
CHG EARN .08 .19 .30 .54 1.00
× (.00) (.00) (.00) (.00)
OWNER-
SHIP
HIGH
CHG SALES .47 .70 .63 .57 .15 1.00
(.00) (.00) (.00) (.00) (.00) (.00)
Notes: All correlations are significant at the p < .01 level. Expected Model 1 Model 2 Model 3
Variable Sign Estimate Estimate Estimate
CHG EARN + .2620*** .2250*** .1120***
(38.94) (29.49) (12.44)
CHG EARN × - -.0612*** -.0592*** -.0714***
OWNERSHIP LOW (-22.44) (-22.02) (-29.47)
CHG EARN × + .0136*** .0103*** .0207***
OWNERSHIP MEDIUM (13.74) (10.04) (19.77)
CHG EARN × - -.0034*** -.0019** -.0056***
OWNERSHIP HIGH (-4.32) (-2.42) (-8.28)
CHG SALES + .0123*** .0210***
(9.82) (17.19)
CHG EARN × Two-Digit SIC Dummies
13 Oil -.0480
and gas (-.11)
extraction
15 Build- .0463
ing con- (.31)
struction
16 Heavy -.5420
construc- (-.02)
tion
21 Tobacco .1480
products (1.24)
22 Textile .0267
mill (.23)
products
24 Lumber -.0511
and wood (-.11)
25 Furni- .0155
ture and (.07)
fixtures
26 Paper -.1340***
and allied (-2.83)
products
27 Print- .0061
ing and (.05)
publishing
28 Chemi- .2140***
cals and (21.74)
allied
products
29 Petro- .1460***
leum (-4.99)
refining
30 Rubber .7400***
and plas- (22.86)
tic
products
32 Stone, -.0791
clay, (-.44)
glass,
and
concrete
products
33 Pri- .1020
mary (.48)
metal
industries
34 Fabri- .0423***
cated (2.70)
metal
products
35 Indus- -.0912***
trial (-7.09)
machinery
and
computer
equipment
36 Elec- .1170***
tronic (10.15)
equipment
37 Trans- -.2830***
portation (-11.81)
equipment
38 Measur- .0994***
ing equip- (3.95)
ment,
photography,
and watches
39 Miscella- .4320***
neous manu- (6.99)
facturing
industries
48 Communi- .0284*
cations (1.69)
50 Durable -.2090
goods (-.47)
51 Nondur- .3640
able goods (.57)
57 Home .0546
furniture (.40)
and
furnishings
65 Real -.0330
estate (-.14)
70 Hotels, .0844
rooming (.65)
houses,
and camps
73 Busi- -.1220***
ness (-3.78)
services
87 Engi- -.0111
neering, (-.02)
accounting,
research,
management,
and related
services
CHG EARN × Year Dummies
Y89 -.2280***
(-4.70)
Y90 .0154
(.26)
Y91 -.0598
(-1.04)
Y92 .0612
(1.22)
Y93 .0182
(.04)
Y94 -.0431
(-.65)
Y95 -.0848*
(-1.64)
Y96 .0784***
(8.30)
Adjusted R² .40 .42 .63
*Significant at the p < .10 level.
**Significant at the p < .05 level.
***Significant at the p < .01 level.
Notes: n = 2763; t-statistics are in parentheses. Variable Expected Sign Estimate
CHG EARN + -.0819**
(-5.18)
CHG EARN × OWNERSHIP LOW - .0505**
(2.77)
CHG EARN × OWNERSHIP MEDIUM + -.0148*
(-2.38)
CHG EARN × OWNERSHIP HIGH - .0016
(.98)
Adjusted R² .02
*Significant at the p < .05 level.
**Significant at the p < .01 level.
Notes: n = 1141; t-statistics are in parentheses. Variable Expected Sign Estimate
CHG EARN + .2020*
(35.78)
CHG EARN × OWNERSHIP - -.0029*
(-4.36)
CHG EARN × OWNERSHIP SQUARE + .00002841*
(7.42)
CHG EARN × OWNERSHIP CUBE - -.00000005702*
(-12.16)
Adjusted R² .36
*Significant at the p < .01 level.
Notes: n = 2763; t-statistics are in parentheses.GRAPH: FIGURE 1 Relative Strengths of Convergence and Entrenchment as a Function of Managerial Ownership
GRAPH: FIGURE 2 The Relationship Between Ownership Level and Tobin's Q
GRAPH: FIGURE 3 Distribution of Managerial Ownership in the Sample
GRAPH: FIGURE 4 Fraction of Earnings Reinvested in Advertising as a Function of Ownership Level
GRAPH: FIGURE 5 Fraction of Earnings Reinvested in Advertising as a Function of Ownership Level (Cubic Specification)
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~~~~~~~~
By Kissan Joseph and Vernon J. Richardson
Kissan Joseph is Associate Professor of Marketing and Charles Oswald Research Fellow, and Vernon J. Richardson is Assistant Professor of Business and KPMG Peat Marwick Faculty Scholar, School of Business, University of Kansas. The authors are listed in alphabetical order, both contributed equally. The authors thank the four anonymous JM reviewers for several constructive and insightful comments on previous versions of the article.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 66- Gaining Compliance and Losing Weight: The Role of the Service Provider in Health Care Services. By: Dellande, Stephanie; Gilly, Mary C.; Graham, John L. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p78-91. 14p. 1 Diagram, 1 Chart. DOI: 10.1509/jmkg.68.3.78.34764.
- Database:
- Business Source Complete
Gaining Compliance and Losing Weight: The Role of the
Service Provider in Health Care Services
This research provides and empirically tests a conceptualization of health care services in which customer compliance outside of the service organization is necessary for successful health outcomes. Using data from service providers and customers in a weight-loss clinic, the authors examine the provider's role in gaining customer compliance. They find that provider expertise and attitudinal homophily play a role in bringing about customer role clarity, ability, and motivation. This study demonstrates that compliance leads to goal attainment, which results in satisfaction. More important, compliance also leads to satisfaction directly; consumers who comply with program requirements have greater satisfaction with the program.
The examination of compliance in health care services is important because many of today's major societal problems (e.g., high-fat diets, poor physical fitness, smoking) exist because of the poor health care choices that people make. Petty and Cacioppo (1996) indicate that most of the leading causes of death in the United States could be reduced substantially if people at risk would change just five behaviors: noncompliance with healthful behaviors, poor diet, lack of exercise, smoking, and alcohol and drug abuse. The many societal ills associated with noncompliance and the dearth of knowledge on customer compliance warrant this investigation.
It is important to understand the magnitude of the issue of health care compliance, particularly the social consequences of obesity and overweight. In 2002, people in the United States spent $1.3 trillion (i.e., 12.7% of gross domestic product, or $4,765 per capita) on health care-related products and services (Euromonitor International 2003). Obesity is among the most costly medical conditions. According to the National Institutes of Health, 60% of U.S. adults are overweight or obese. The relationship of obesity to other illnesses such as type 2 diabetes, hypertension, heart disease, stroke, and arthritis is significant; obesity now is the cause of more health problems than smoking, heavy drinking, or poverty (American Obesity Association 2002).
As a result of the problem of overweight and obesity, consumers are spending ever-increasing amounts on weight-loss efforts. In the United States, 40% of women and 25% of men are trying to lose weight at any given time, and approximately 45 million people begin a diet each year. Consumers spend approximately $30 billion per year trying either to lose weight or to prevent weight gain, approximately $1 billion to $2 billion of which is spent on medically supervised and commercial weight-loss programs (American Obesity Association 2002).
The solution to the problem of maladaptive consumer behavior does not lie in scientific breakthroughs in medicine, but in finding how to gain consumer compliance with directives of health care providers. As Jayanti and Burns (1998, p. 6) state, "The marketing challenge is considerable in that unhealthy habits and routines are firmly entrenched in consumers." Our purpose is to provide a conceptualization and empirical investigation of the service delivery process for health care services in which customer/patient compliance outside of the service organization is a necessary condition for a successful health outcome. Given that in many health care situations it is expected that patients continue to engage in certain behaviors for their long-term health after they leave the service organization (Bowen 1986; Bowman, Heilman, and Seetharaman 2002), it is crucial that health care providers understand the factors that influence patients' continuing to perform the prescribed behaviors.
Among social influences on health behavior, the health care provider is important (Jayanti and Burns 1998) and is more easily controlled by the health care organization. Bowman, Heilman, and Seetharaman (2002, p. 8) state that "the most powerful cue for compliance is the instruction" from the health care provider. In the weight-control context, behavioral counselors must provide their patients with support for their efforts as well as skills in lifestyle modification (Foreyt and Poston 1998). Given the importance of health care providers, our focus is on the provider's role in gaining compliance. When a service is complex, customized, and delivered over a series of transactions (e.g., health care services), the relationship between the service provider and consumer is key (Crosby, Evans, and Cowles 1990). We seek to identify and relate providers' characteristics and consumers' attributes to both compliance and its outcomes.
The success of most health care services depends on the consumer's compliance with instructions received from providers. Mills, Chase, and Margulies (1983) posit that the service production process entails a transaction that requires direct involvement of the customer; this involvement can affect a firm's productivity, its positioning relative to that of competitors, its service quality, and its customers' satisfaction (Bowen 1986). In health services, the patient provides the vital information about prior health behavior and the raw material input, such as knowledge and motivation, that is necessary for the transformation. This input is typically received through direct interface with the health care employee. Thus, productivity of health care services entails more than the performance of the service employee; patient performance must be assessed.
A Model of Compliance Antecedents and Outcomes
This research focuses on gaining compliance and particularly on the role of the health care professional in helping the customer attain the necessary attributes to comply. The model of customer compliance in Figure 1 highlights the ways provider characteristics are expected to affect customer attributes that are necessary for customer cooperation in the process of health care services.
Moorman and Matulich (1993) provide an excellent review of health models, drawing on literature from various behavioral fields. The overarching theory for our model is what they classify as a behavioral model: social cognitive theory (SCT). The SCT perspective perceives human behavior as a dynamic interaction between personal factors, behavior, and the environment (Bandura 1977). A person's thoughts, emotions, and so on are developed and modified by social influences in the environment, and these personal factors influence behavior. Thus, our model recognizes the potential of the health service provider to affect consumer attributes that provide direction for behavior (i.e., compliance).
Although several characteristics have been advanced as contributing to interpersonal influence, two that have received a great deal of attention are expertise and homophily (Crosby, Evans, and Cowles 1990; Gilly et al. 1998). Expertise is the mastery of a particular subject (in this case, weight loss), and homophily is the similarity between individual parties. Homophily is a provider characteristic in that it represents the extent to which the provider is similar to the customer; customers will respond to providers as homophilous only if they perceive providers as such (Parrott et al. 1998).
For the customer to take part in the delivery of health services, certain customer attributes are necessary. To the extent that the health care provider possesses expertise and homophily with the customer, the provider will influence the customers' acquisition of the role clarity, ability, and motivation that are necessary for the customer to perform as expected. Role clarity involves understanding the role that must be performed, ability involves the skills needed to perform that role, and motivation is customers' incentive to carry out their role.
On leaving the supervision of the health care provider, customers' compliance with instructions is vital. The model posits key causal paths: ( 1) Compliance leads to goal attainment (i.e., realization of the targeted outcome); ( 2) should customers attain their goal, they are more likely to be satisfied with the service; and ( 3) compliance itself leads to satisfaction. Satisfaction is a favorable response to an outcome (in this case, the weight-loss program). A detailed discussion of the model elements follows.
Provider Characteristic: Expertise
Expertise entails having a special skill or knowledge that represents mastery of a particular subject (Stewart 1989). Simons, Berkowitz, and Moyer (1970) suggest that the greater the expertise of a communicator, the greater is the change toward the position advocated. Research in the sales literature (e.g., Busch and Wilson 1976) supports the idea that experts are more influential than nonexperts. In particular, in long-term buyer-seller relationships, the quality of the relationship is enhanced by seller competency (Crosby, Evans, and Cowles 1990). Thus, the communicator's expertise significantly affects audience reactions to persuasive communication.
Expert providers can take a more active role in structuring the information environment and in clarifying the patient's role. Better-educated nurses tend to have better critical-thinking skills (Tanner 2003). Weight-clinic patients need orientation to address such questions as "Why should I use a pedometer?" The greater the provider's knowledge or expertise about such customer questions, the more likely the provider is to influence positively the customer's role clarity and abilities. Expert behavioral counselors can customize interventions with their patients to maximize their understanding of which changes must be made to lose weight successfully (Foreyt and Poston 1998). Customer education or training can take the form of formal orientation programs, written literature, and so on. Lovelock and Young (1979) and Winslow (1992) suggest that such efforts play a role in helping consumers know which behaviors to adopt and how to perform them. Although expert providers may also need such characteristics as communication skills and effort to affect customer attributes, the foregoing discussion supports the following hypothesis:
H[sub1]: Increased service provider expertise results in customers having (a) greater role clarity and (b) greater ability to carry out their role.
Provider Characteristic: Homophily
Homophily refers to the degree to which people in a dyad are similar on certain attributes, such as demographic variables, attitudes, beliefs, and values (Touhey 1974). The literature suggests that homophily promotes attitude change and/or cooperation in two ways: by clarity of communication and by trust and liking. In the first case, the literature indicates that when a greater degree of homophily exists between communicators (e.g., between a medical expert and a patient), they are more likely to share common meanings for the messages they exchange (Rogers, Ratzan, and Payne 2001). Furthermore, when a target finds a source similar, the target is more likely to listen attentively to the source (Gotlieb and Sarel 1992). Simpson and colleagues (2000) suggest that when the receiver perceives him-or herself as similar to the source of the message, communication between the two is more effective in shaping or changing attitudes. That is, communication is more accurate and efficient.
In the second case, many researchers have noted that similarity leads to feelings of trust and respect (Simons, Berkowitz, and Moyer 1970) and to assumptions about common needs and goals (McGuire 1968). Crosby, Evans, and Cowles (1990, p. 71) add that in goal-interdependent contexts, "similarity (particularly attitude similarity) may be a cue for expecting the other party to facilitate one's goals." In a review of the literature on motivation for health behavior, Carter and Kulbok (2002) identify variables that explain motivation across several studies; a variable they identify is "social support and approval." In a weight-loss clinic setting in which the consumer is assigned to one service provider, the provider is key in offering social support and approval. Foreyt and Poston (1998) stress the importance of the health care provider's developing a collaborative relationship with the patient by using counseling and listening skills. Such a relationship is easier to establish between homophilous parties.
The theoretical support for the importance of demographic homophily has been consistent. For example, Fischer, Gainer, and Bristor (1997, p. 364) suggest that "in service settings where the customer expects to have extensive, repeated interactions" with the provider, customers may anticipate better service from demographically homophilous people because they are more comfortable interacting with them. In many health care services, customer and provider interactions are extensive and repeated. However, the empirical support for the salience of demographic homophily has been spotty (e.g., Brown and Reingen 1987; Fishman 1995). In general, demographic variables are easy to obtain, whereas assessment of customer attitudes requires more effort on the part of the health care organization. This study affords the opportunity to assess the importance of demographic and attitudinal homophily separately to determine whether demographic homophily is sufficient to influence customer attributes.
To the extent that communication between homophilous nurses and patients is clearer and more efficient, we expect that role clarity will be enhanced. The establishment of role clarity is mostly a matter of information and accurate communications, including receiver listening, and homophily can be expected to promote both of these. Relatedly, similarity between nurses and their patients can be expected to enhance the patients' motivation to comply. That is, homophily leads to relationships of liking and trust and therefore motivates the patient to cooperate in the weight-loss program. Again, other provider characteristics, such as communication skills, may moderate the predicted relationships, but this discussion leads to the following hypotheses:
H[sub2]: Service provider-customer dyads that are (a) demographically homophilous and/or (b) attitudinally homophilous result in customers having greater role clarity to carry out their role.
H][sub3]: Service provider-customer dyads that are (a) demographically homophilous and/or (b) attitudinally homophilous result in customers having greater motivation to carry out their role.
Customer Attributes: Ability, Role Clarity, and Motivation
The services marketing literature has focused on customer participation in the service organization, citing the role of customers in service creation. For example, Bowen (1986) and Kelley, Donnelly, and Skinner (1990) suggest that service organizations view customers as quasi employees and manage their behavior in the service organization similarly to other employees. However, the services marketing literature has not invested much effort in customer participation outside of the service organization, which is a requirement of many health care services.
Bowen's (1986) model of the determinants of employee behavior in service performance supports the importance of the role clarity, ability, and motivation attributes in bringing about behavior change. He suggests that these determinants may also prove useful for understanding consumer behavior in service production and delivery. Bowers, Martin, and Luker (1990, p. 62) suggest a three-step process to enhance customer participation that mirrors Bowen's attributes but also provides guidance as to the order of the variables: "Step 1: Define the customer's job. Step 2: Train the customer to perform his or her job. Step 3: Retain the valuable customer by rewarding the customer for a job well done." Thus, we expect that role clarity leads to ability, which in turn leads to motivation. Patients who are not clear on what their role is in the process will be unable to acquire the needed skills to participate appropriately in the process. Furthermore, patients without the ability to perform needed behaviors will become frustrated and will lose motivation. Performance of the role will suffer if the role is not clear, even if patients are motivated and possess the ability to perform the role (Kearney 1978).
H[sub4]: As customers gain role clarity, their ability to perform necessary behaviors increases.
H[sub5]: As customers gain the ability to perform appropriate behaviors, their motivation to do so increases.
Compliance
For many health care services, service quality and customer satisfaction depend on the customer/patient complying with behaviors prescribed by the health care professional. However, compliance to prescribed medical regimens is notoriously low. Typically, adherence rates are only approximately 50% for prescribed medications, and compliance with instructions to lose weight or to stop smoking is much lower. Long-term success rates on these lifestyle prescriptions are lower than 10% (Haynes, McDonald, and Garg 2002).
The literature on compliance is extensive, and research has been conducted in many fields, such as psychology, medicine, and consumer behavior. Often, researchers focus on consumers' compliance with a specific onetime request, considering different ways that the request can be made, including vocal intensity and touch (Remland and Jones 1994), postcompliance touch as an incentive for performing a task (Nannberg and Hansen 1994), impression management strategies (Jackson and Latane 1981; Rind 1992), and the foot-in-the-door technique (Rind and Benjamin 1994). The health literature is less concerned with onetime requests and more concerned with behavior change over a period of time. Fishman (1995) offers additional variables that are important to gaining compliance. He posits that there is mounting evidence that the patient's family, friends, and social support system offer significant contributions to compliance and subsequent improved health. Yet the variable that most consistently has been found to be associated with compliance is the patient-physician relationship.
Without the requisite role clarity, ability, and motivation, compliance is less likely. For example, Bostelman and colleagues (1994) find that between the time the patient is discharged from inpatient hospitalization and before the first appointment for outpatient treatment, many clients experience a personal or mental health crisis and need renewed connections with the health care system. What comes into question is whether the crisis is due to the patient's lack of clarity regarding his or her role, the patient's inability to perform prescribed roles, and/or a lack of motivation to perform when the patient is away from the service organization.
The health belief model (HBM) of compliance supports the predicted relationship between motivation and compliance (Becker 1976; Foxall, Barren, and Houfek 1998). The HBM was originally developed to explain preventive health actions (e.g., vaccinations), but it has subsequently been applied in studies of compliance with medical regimens. The HBM postulates that two key elements determine the likelihood of patients engaging in recommended health behaviors: the patient's ( 1) readiness to take an action and ( 2) evaluation of the feasibility and efficaciousness of the action (Aalto and Uutela 1997). Cues (motivation) to act trigger readiness to take an action. Cues can be internal (e.g., discomfort associated with excess weight) or external (e.g., advice from others, media campaign).
Moorman and Matulich (1993) find that ability and motivation affect consumers' health behaviors, though the influence depends on the health behavior being examined and the health ability characteristic being measured. Jayanti and Burns (1998) include health motivation, knowledge, and what they call "consciousness," a construct similar to role clarity, in their model of preventive health behaviors. In examining the influence of these three exogenous variables on preventive health behaviors, they find that only health motivation and consciousness are significant. Thus, we predict the following:
H[sub6]: The greater the customer's (a) role clarity, (b) ability, and (c) motivation, the greater is the customer's compliance with service provider directives.
Outcomes: Goal Attainment and Satisfaction
When service customers take responsibility for their service outcomes, there is a greater likelihood that they will achieve their goals (Bagozzi and Dholakia 1999). As a result of complying or taking part in the service delivery process, customers become empowered. In so doing, the customer naturally becomes accountable for the performance of the activities involved in the service delivery process. Mills, Chase, and Margulies (1983) indicate that customers not only are involved in their own goal achievement but also must accept some responsibility for their satisfaction with the ensuing results.
As a result of complying with the role outside of the service organization, customers are expected to make progress on their goals, thus influencing their satisfaction with the service delivery process. Although individual customers may have medical conditions that interfere with weight loss, when the customer complies with the service provider's guidelines, he or she is more likely to attain the goal. The discrepancy between what is anticipated and what is received (i.e., disconfirmation) has been shown to predict satisfaction (Oliver 1996). As such, the closer the outcome is to the desired goal, the more likely it is that the customer will be satisfied.
Satisfaction is both a cognitive and an affective evaluation of the service experience (Mano and Oliver 1993; Westbrook 1987). We expect that the cognitive process of assessing goal attainment influences satisfaction directly. In addition, the affective evaluation of the health care experience associated with compliance may enhance satisfaction. Dubé, Bélanger, and Trudeau (1996) find that positive emotions are most important in patient satisfaction with medical care. Furthermore, Dubé (2003, p. 34) states, "Patient satisfaction is not only determined by cognitive expectations and perceptions of quality on a set of dimensions but also by the memories one has of the emotions experienced along the service process."
Kellogg, Youngdahl, and Bowen (1997) find that satisfaction is associated with several customer participation behaviors. Similarly, Kelley, Skinner, and Donnelly (1992) find that satisfaction results from customers contributing to their own service quality. Thus, we also expect that compliance influences satisfaction directly, because achievement of proximate behavioral goals may be satisfying even when the ultimate outcome goals are not attained. Consumers may experience pleasure in the consumption process that leads to satisfaction independent of disconfirmation (Wirtz and Bateson 1999). This is particularly likely in health services in which the consumer is actively involved in the service encounter (Price, Arnould, and Deibler 1995). For example, Johnson and colleagues (2002, p. 182) find that for some types of patients, "compliance with treatment ... [is] significantly associated with patient satisfaction."
H[sub7]: The greater the customer's compliance with service provider directives, the greater is the customer's goal attainment.
H[sub8]: The greater the customer's (a) goal attainment and (b) compliance, the greater is the customer's satisfaction.
Setting
The setting for this study was Lindora Comprehensive Weight Control, established in 1971 by Marshall B. Stamper as a comprehensive, medically based weight-control program with clinics throughout Southern California. As a comprehensive program, both the physiology and the psychology of the customer are addressed to promote permanent lifestyle change. As a medically supervised program, the customers are guided by clinic nurses, who are either registered nurses (RNs) or licensed vocational nurses (LVNs).
Visits to the Lindora clinic are an essential part of the program. The information and support provided exclusively by the customer's assigned nurse during each visit help the customer stay on task and motivated.( n1) The program comprises three phases: weight loss, metabolic adjustment, and lifetime maintenance. This study focuses on the weight-loss process. During weight loss, customers' one-on-one visits provide counseling and medically based solutions to make dietary compliance more comfortable.
Data Collection
We collected data using three methods: Nurses and patients were asked to complete questionnaires, we obtained archival data, and a small group of nurses and patients were interviewed in depth.
Participants and questionnaires. Participants in the study included nurses and their assigned patients. We drew samples from several of the Lindora clinics throughout Southern California. A total of 376 patients (37.6% response rate) and 36 nurses (90% response rate) completed the questionnaires. Surveys were distributed to 40 nurses across 18 clinics. Nurses completed the survey independently and used a postage-paid envelope addressed to the researchers to return the survey. A cover sheet attached to the survey requested nurses' identification to match them to their respective patients. When matching was completed, the cover sheets were destroyed to maintain anonymity.
Each nurse was asked to distribute the patient survey to approximately 25 patients. Patients completed the survey without the supervision of the nurses and returned the survey using a postage-paid envelope addressed to the researchers. A cover sheet attached to the patients' surveys requested the patients' and their nurse's identification for matching purposes. When matching was completed, the cover sheets were destroyed to maintain anonymity.
Archival data. With patients' permission, Lindora also provided data from the records of 213 of the 376 patients in the study. The data included the percentage of required visits made by patients to the clinic and the amount of weight lost by each patient. Such data offer an objective view of customer compliance behavior (albeit narrowly defined) and goal attainment, respectively.
Interviews. On removal of the survey cover sheet, the names of patients and their nurses were noted as potential participants for in-depth telephone interviews. Eight patient participants and nine nurse participants took part in the interviews. In qualitative research, in general, data collection continues until no new insight is provided; McCracken (1988) suggests that usually eight participants are sufficient. The first author conducted structured telephone interviews that lasted approximately 30 minutes each. In general, participants declined to be recorded; thus, the first author took extensive notes.
To better understand the quantitative results, interview questions of nurse and patient participants included, "What nurse characteristics or qualifications are needed to gain customer compliance with the weight-loss program?" "How did you get matched up with your nurse (patient)?" and "In what ways are you similar/dissimilar?" Additional questions were asked about the role the nurse played in clarifying the customer's role and in motivating and enabling the customer to adhere to his or her role. Participants were also asked whether the program works.
Measures
We measured provider expertise in three ways. First, we measured customers' perceptions of providers' expertise using a five-item scale adapted from Bruner and Hensel (1994) (α = .99). Second, nurses were asked to report their credential: "none, RN, or LVN." Third, nurses also reported their years "of experience in the field of nutrition and weight loss." We summed the five-item scale and properly modeled the indicators of the three potentially unrelated aspects of expertise as formative indicators of provider expertise.
We measured demographic homophily using the four characteristics used previously by Brown and Reingen (1987). We gathered data on the sex, education, ethnicity, and age of patients. We also asked patients for their perceptions of their provider on the same items. We then calculated the similarity in the ratings across patient and nurse on each of the four demographic items using formulas such as age homophily = (-1) (absolute value of customer age - provider age), and gender homophily = same sex ( 1), different sex (0). Because we did not necessarily expect the four types of homophily to be correlated, we combined the four scores as formative indicators in the partial least squares (PLS) analysis (Diamantopoulos and Winklhofer 2001; Jarvis et al. 2003).
We measured and modeled attitudinal homophily in an analogous manner, comparing patients' attitudes and patients' perceptions of nurses' attitudes across four separate seven-point items (see the Appendix). Questions about patients' attitudes appeared at the beginning of the questionnaire, and questions about patients' perceptions of nurses' attitudes appeared near the end of the questionnaire.
We measured the customer attributes of role clarity, ability, and motivation using the patients' questionnaires. For each of the three constructs, we developed six-item scales based on the six separate components of Lindora's weight-loss program (see the Appendix). Thus, we modeled each of the three constructs using a formative indicator approach, because we did not expect the six individual items to be correlated.
The face validity of the role clarity, ability, and motivation measures was assessed through the ratings of expert judges. Seven marketing-faculty judges independently assessed how well each of the items reflected the different dimensions of the customer attribute constructs (role clarity, ability, and motivation). Judges used the following rating scale: 1 = "clearly representative," 2 = "somewhat representative," and 3 = "not at all representative" (Bearden, Hardesty, and Rose 2001). Because no item was rated a 3, we retained all 18 items.
We operationalized compliance in two ways. First, we used a nine-item scale on the patients' questionnaires to measure compliance. Examples of the scale items include whether patients visited the clinic as instructed, followed the nurse's weight-loss directives, and kept a daily journal of their weight-loss program activities. We devised scale items on the basis of discussions with Lindora's director of research about tasks expected of program participants. The alpha coefficient for the compliance scale is .80, and we summed the nine items for use in the analysis. Second, we supplemented self-report measures with a behavioral measure of compliance that was comparable to one of the scale items. We obtained the percentage of required visits made to the clinic from Lindora.( n2) Because this archival measure focused narrowly on one aspect of compliance, we modeled the two compliance measures as a formative indicators for the PLS analysis.
We also measured goal attainment, a customer outcome variable, in two ways. First, we summed a four-item scale to measure goal attainment by asking patients whether they ( 1) are attaining, ( 2) think they will achieve, ( 3) are making progress toward, and ( 4) are not attaining their weight-loss goal (α = .86). Second, we measured goal attainment using company data on the actual percentage of each patient's weight loss.( n3) Because the customer self-report and archival data are different measures of the same construct, the two are properly combined as reflective indicators for the analysis.
Finally, we measured the outcome variable, satisfaction, by asking patients about whether they were satisfied with the service (Lindora Comprehensive Weight Control) and the service provider (weight-loss nurse) and about their intention to enroll in the maintenance program on completion of the weight-loss program. The scale used in the study consisted of nine items (α = .79) (Bruner and Hensel 1994), which we summed for the analysis. Elimination of the item that measured intention to enroll in the maintenance program on completion of the weight-loss program improved the scale reliability (α = .85).
Pretest
Before collecting data for the study, we conducted a pretest to ensure the integrity of the data collection instrument and the mode of administration. We conducted pretesting at a Lindora clinic that did not participate in the actual study. The pretest sample consisted of three nurses and eight of each of their patients. The pattern of answers from the pretest was sensible. That is, the meaning of the questions intended by the investigators was the meaning that the respondents attributed to the questions (Hunt, Sparkman, and Wilcox 1982). In addition, the mode of administration was successful; we received all questionnaires within three days of distribution.
Data Analysis
The statistical method we used to determine whether relationships exist between the model variables was PLS structural equation modeling, which entails a mathematically rigorous computation to determine the optimal linear relationships between latent (theoretical) variables. The PLS method is perhaps the best analytical method for this study given the nature of the data and the measures. The data consist of both formative and reflective indicators of the constructs. The PLS method handles both types of indicators, whereas other path analytical methods (e.g., LISREL, EQS) can handle only reflective indicators (Falk and Miller 1992).
We also performed qualitative analysis of the depth interview data, guided by the systematic approach to qualitative research in the work of Glaser and Strauss (1967) and Strauss and Corbin (1998). The theoretical model proposed for this study provided a framework for structuring the depth interviews. We analyzed depth interview data by comparing and explaining the findings relative to the survey or quantitative results. Triangulation of the different sources of data increases our confidence in the validity of our findings.
The findings are summarized in Figure 1. The questionnaire data collected from patient and nurse respondents suggest that the groups are demographically similar. Typically, the nurse was female, a college graduate, Caucasian, and a baby boomer (born between 1946 and 1964). In most cases, the patient was female, had some college education, and was Caucasian and a baby boomer. Nurses were almost evenly split between RNs and LVNs. The range of experience was 1 to 15 years, with a mean of 5.38 years. For a summary of the 34 manifest (i.e., directly measured) variables used in the model, see Table 1.
Expertise
We found the path coefficient or direct effect of provider expertise on customer role clarity (.25, p < .05) to be as predicted in H[sub1a]. Examination of the latent variable weights in Table 1 shows perceived expertise (the five-item scale) to be the most important among the three latent variables that define the construct provider expertise. The depth interviews of both the patients and the nurses revealed that in addition to expertise, experience was an important influence on role clarity as well. A patient reported:
The nurses know the program "inside-out"; they can point to specifics in the book when offering help. They can also provide alternative suggestions when a patient doesn't like a certain food.
The comments of nurses were consistent with this view:
Nurses must have knowledge in the field of nutrition or in the medical field.
Nurses must have experience dealing with different types of patients; different types of patients will require different treatment.
H[sub1b], which predicted that provider expertise influences customer ability (.04), was not supported.
Homophily
The direct effects of demographic homophily on customer role clarity (.0) and customer motivation (-.08) were not statistically significant. The direct effects of attitudinal homophily on customer role clarity (.13) and customer motivation (.10) were weak but statistically significant (p < .05) (H[sub2] and H[sub3]). To learn more about the role of similarity in gaining customer compliance, depth interview questions investigated whether patients and nurses perceived similarity as an important variable in customer compliance. Findings indicate that some patients perceived that they were similar to their nurses and that such similarity was important to compliance:
The nurses have been through the program at my clinic; they know what relapse is like. They are not a bunch of size two people.
If my nurse never had a weight problem, how could she really understand my struggle?
However, nurses did not necessarily believe that being similar to their patients was important in gaining compliance:
I don't have any patients that are like me, and I get their compliance because of years of practice. You need to know personality types and mirror that to make them more comfortable and thus compliant.
It is not necessarily easier to gain compliance with patients who are similar; it really depends on the patients' motivation; this will determine the patients' results. Being similar may actually hurt the outcome because patients may expect their nurse to go easy on them.
Ability, Role Clarity, and Motivation
H[sub4] and H[sub5] were supported. Role clarity influences the acquisition of ability (.59, p <.05), and ability leads to motivation (.72, p < .05). In addition, the direct effects of role clarity, ability, and motivation on compliance (.12, .19, .59, respectively) are statistically significant (p < .05); thus, H[sub6] is supported. The extreme importance of motivation in gaining compliance was also noted in the interviews with nurses:
Self-motivation is the number-one attribute the patient must have in order to comply with the program. People with the desire to change are the most successful people. Those that show up at Lindora and expect us to fix them are less successful.
They must be committed and ready to do the program, willing to set goals, willing to follow instructions, motivated/determined to lose weight, interested, and enthusiastic.
Compliance
The direct effect of compliance on goal attainment is strong (.56) and statistically significant (p < .05), in support of H[sub7]. Patient informants strongly believed that Lindora's weight-loss program worked and that if the patient complied with the guidelines of the program, the patient was likely to attain his or her goal:
The program works. I have to decide to do it. There is only so much Lindora can do; the ball is in my court.
While the knowledge and support they gave me were important, ultimately, I did what I was supposed to do.
Nurse informants agreed:
If patients follow the program, they'll lose weight.
Oh yes, the program works. When they [patients] use the tools, weight loss is greater; when they [patients] don't use the tools, weight loss is slower or ceases.
Goal Attainment
H[sub8a] is supported, and the direct effect of goal attainment on satisfaction (.49) is statistically significant (p < .05). Both patient and nurse participants in the interviews suggested that if the patient achieved his or her goal, the patient was likely to be satisfied with the weight-loss program. As a patient stated, "Yes, I'm absolutely satisfied with the program because I reached my goal." In a similar manner, a nurse stated, "Patient goal attainment ensures satisfaction with the program." Customer compliance also had a direct (unmediated) effect on satisfaction, as H[sub8b] predicted (.23, p < .05).
The root mean square covariance between the residuals of the manifest and latent variables (RMS Cov [E, U]) of .04 indicates that the model fits the data quite well. This index reports the amount of correlation between the variables that is not accounted for by the model specifications. A coefficient greater than .20 is evidence of an inadequate model, and a coefficient of .02 indicates a superior model (Falk and Miller 1992).
Although cross-sectional data limit the strength of inferences about causal connections, our findings suggest that the primary causal chain that runs through the model is the following: provider expertise → customer role clarity → customer ability → customer motivation → customer compliance → goal attainment → customer satisfaction. These findings have important implications for both researchers and practitioners, and we subsequently discuss these in detail.
Provider Characteristics
The findings empirically confirm Swartz's (1982) proposition that expertise is a key characteristic of influencing agents. Expert providers are more likely to influence customer role clarity. The expertise measure that is most important in influencing the customer is the customer's perception of the provider's expertise. Thus, if customers believe that the provider is an expert, they will tend to heed the provider's instructions, which provides empirical evidence for Simons, Berkowitz, and Moyer's (1970) proposition that the greater the expertise, the greater is the change toward the position advocated by the communicator. Organizations should consider ways to communicate staff expertise to their customers, such as including credentials in brochures and posting diplomas and certificates.
We investigated both demographic and attitudinal homophily in this study. Although the relationships of attitudinal homophily to customer attributes are statistically significant, they are weak. The hypotheses about demographic homophily were not supported. Whereas Brown and Reingen (1987) find that demographic homophily predicts consumer-to-consumer influence, the findings in this study are consistent with those of Fishman (1995), who indicates that demography does not appear to be a strong predictor of influence. These results may be partly explained by providers' and customers' demographic similarity (e.g., age, sex, race) in ways that are less meaningful or important to influence. An additional problem may be that there was limited variation on the demographic characteristics of consumers and providers in the sample. More research on this construct is needed in the context of influence processes. Provider expertise and homophily may be required but not sufficient to bring about customer role clarity, ability, and motivation. Further research should examine moderating variables such as communication skills and motivation.
Customer Attributes and Compliance
Compliance is the central construct in this research. Bowen (1986) suggests that customer participation in the service process is facilitated when customers have the ability and are clear about their role and motivated to perform as expected. We extend Bowen's work by empirically determining that these variables are antecedents of compliance with prescribed regimens when customers are expected to continue to perform beyond the face-to-face exchange (i.e., without the direct input of service providers). In addition to determining that these variables are antecedents of compliance, we also determined the nature of the relationships among the variables (i.e., that role clarity leads to ability, which in turn leads to motivation).
The qualitative data were valuable for further exploration of motivation in relation to gaining compliance. For example, a patient stated, "Motivation is number one; skills are important but if you are not motivated, you won't do it." Nurse participants also indicated that customer motivation is the key and that it must come from within the patient. For example, a nurse stated, "If the patient is not self-motivated, I can't give him or her motivation; the learning process, however, may lead to motivation." Our results strongly support this observation; role clarity and ability lead to motivation.
Theories of motivation (e.g., self-efficacy, goal setting, attribution, expectancy value, social cognition) largely focus on beliefs about competency and expectancy for success, values as to why people engage in different activities, and how goals influence self-efficacy and performance. The Latin root of the word "motivation" means "to move"; thus, the study of motivation is the study of action (Eccles and Wigfield 2002). However, these theories do not indicate the attributes that are necessary for a person to move or to act. Kuhl (1987) indicates that many motivational theorists assume that motivation leads directly to outcomes. He posits instead that motivational processes lead only to the decision to act. Our study builds on these theories by identifying specific customer attributes (role clarity, ability, and motivation) that promote or lead to acting or complying.
Goal Attainment and Satisfaction
In this study, we determined that compliance leads to goal attainment. Although this finding is intuitive and not surprising, it is an important result. Mills, Chase, and Margulies (1983) suggest that as a result of taking part in the service delivery process, the customer becomes accountable for the performance of the activities involved in the process, including goal achievement. Because compliance decreases as the duration of the regimen increases, providers can reassure or impress on consumers during periodic meetings that if they stick with the program, they will realize their goal.
The finding that goal attainment leads to satisfaction is somewhat less intuitive in the context of health services. Customers' having to give up an enjoyable habit (e.g., eating junk food) could easily have an adverse impact on satisfaction. The unhealthful habit likely yields immediate gratification, whereas the healthful behavior does not bear fruit until sometime in the future. Consequently, it is easy to realize how consumers might not have a sense of satisfaction from having attained their goal given the sacrifice to realize it. Oliver (1996) indicates that disconfirmation, the discrepancy between what is anticipated and what is received, is a predictor of satisfaction. In support of Oliver's theory, the relationship between goal attainment and satisfaction was strong, which indicates that when goals are attained, customers are satisfied.
Although we found compliance to lead to goal attainment, compliance was also directly related to satisfaction. The act of complying with the service provider's instructions perhaps strengthens the relationship between patient and provider, thus creating a more satisfying relationship. Foreyt and Poston (1998) recommend that behavioral counselors in obesity treatment programs develop a collaborative relationship with the patient by using counseling and listening skills, thus improving the patient-provider alliance. Further research is needed to determine whether the interaction with the service provider contributes to satisfaction or whether the compliance behavior itself is satisfying to customers. Both are likely to contribute, because the relationship between provider and customer is focused on the required behaviors.
Contribution
Extant literature in the fields of consumer behavior, psychology, and medicine is replete with compliance-gaining research that focuses on source actions (e.g., foot-in-the-door, door-in-the-face, vocal intensity, touch) that are useful in gaining compliance with onetime requests and when the provider and customer are in a face-to-face encounter (Dellande and Gilly 1998). Although source actions have been examined in these onetime compliance requests, we contribute to the literature by considering source characteristics that are useful in gaining compliance in services that are long-term in nature and when the customer is not in the presence of the provider. Furthermore, the framework of this study contributes to the literature in that it more completely examines compliance behavior by including the role of the provider, the role of the customer, the compliance process, and postcompliance outcomes.
The findings of the study reveal important drivers of customer satisfaction in the studied health care services setting. Provider expertise leads to customer role clarity, ability, motivation, compliance, goal attainment, and satisfaction. Of particular salience is the testing and sorting out of the relationships among the three customer attributes described by Bowen (1986). Moreover, managers may find it useful to monitor the three customer attributes to determine customer "readiness" (Ostrom 2003) and to make customer selection decisions accordingly (at least in non-life-threatening situations). The hypothesized model provides an excellent "nomological net" in which to demonstrate the theoretical usefulness of the concept of compliance. In addition, as we expected, compliance appears to be a key link in the causal chain investigated. Although all important factors in the causal chain are worthwhile for managers to monitor, the results of this study suggest that compliance deserves special attention. That is, role taking and consumer diaries may be important tools in maximizing consumer goal attainment and satisfaction. Indeed, rewards might be structured for both provider personnel and customers on the basis of such compliance measures.
Conclusion
Although customer noncompliance in health care-related services can have life-and-death ramifications, it is also important to consider services that are not related to health care. Lack of compliance in such services that depend on customer compliance (e.g., long-term financial planning, education, tax preparation, preventive auto maintenance) can also lead to adverse outcomes for consumers, organizations, and society. For example, people who use tax-preparation services but do not keep accurate records or receipts often do not qualify for certain tax benefits. In addition, failure to comply in this area may have other adverse outcomes such as owing unnecessary taxes and/or paying penalties. Thus, our research has implications beyond health care services.
The authors thank Dr. Joseph Risser, Director of Clinical Research, and Cynthia Stamper Graff, President and Chief Executive Officer, Lindora Comprehensive Weight Control, for their assistance on this research project and the three anonymous JM reviewers for their constructive comments.
( n1) Support group meetings in which customers, led by a program counselor, share their dieting experiences are not an aspect of Lindora's program; thus, a single provider is the primary contact for the customer.
( n2) The behavioral measure (percentage of visits that the patient made to clinic) and self-report measure (the scale item: patient visited clinic as instructed) of compliance are weakly correlated, where r = .154 (p < .05).
( n3) The behavioral measure (percentage of weight loss by patient) and self-report measure (the scale item: patient is attaining weight-loss goal) of goal attainment are correlated, where r = .349 (p < .05).
Legend for Chart:
A - Components and Manifest Variables
B - PLS LV Weights
C - Means
D - S.D.
E - Correlation Matrix 3
F - Correlation Matrix 4
G - Correlation Matrix 5
H - Correlation Matrix 6
I - Correlation Matrix 7
J - Correlation Matrix 8
K - Correlation Matrix 9
L - Correlation Matrix 10
M - Correlation Matrix 11
N - Correlation Matrix 12
O - Correlation Matrix 13
P - Correlation Matrix 14
Q - Correlation Matrix 15
R - Correlation Matrix 16
S - Correlation Matrix 17
T - Correlation Matrix 18
U - Correlation Matrix 19
V - Correlation Matrix 20
W - Correlation Matrix 21
X - Correlation Matrix 22
Y - Correlation Matrix 23
Z - Correlation Matrix 24
AA - Correlation Matrix 25
BA - Correlation Matrix 26
CA - Correlation Matrix 27
DA - Correlation Matrix 28
EA - Correlation Matrix 29
FA - Correlation Matrix 30
GA - Correlation Matrix 31
HA - Correlation Matrix 32
IA - Correlation Matrix 33
JA - Correlation Matrix 34
KA - Correlation Matrix 35
LA - Correlation Matrix 36
A B C D E F
G H I J K L
M N O P Q R
S T U V W X
Y Z AA BA CA DA
EA FA GA HA IA JA
KA LA
Expertise
Expert .99 30.35 6.56 1
Credential .24 .60 .49 -.077 1
Years .08 5.38 3.26 .023 -.115
1
Demographic Homophily
Sex .40 .12 .32 .090 -.049
.009 1
Education .85 1.18 1.00 .006 .028
-.018 .047 1
Race -.22 .35 .48 -.035 -.109
.060 -.018 -.033 1
Age -.21 1.29 1.00 -.042 .115
-.161 .016 .046 -.011 1
Attitudinal Homophily
Pills .27 1.20 1.25 .023 -.049
-.056 -.031 -.041 -.007 .055 1
Surgery -.29 1.31 1.21 .061 -.029
-.035 -.116 .025 -.025 -.027 .270
1
Dieting -.68 1.02 1.29 .004 .004
-.076 -.033 .079 .019 .042 .123
.120 1
Diet + exercise .76 .54 .84 -.013 -.021
.001 -.013 .088 -.007 .007 -.045
.089 .091 1
Role Clarity
Activity .55 5.99 .99 .222 -.005
-.017 -.021 -.054 .029 -.010 .061
.060 .127 -.042 1
Carbohydrates .28 6.31 .78 .156 .015
.021 -.059 .013 .043 -.080 -.006
.010 .090 -.045 .368 1
Diary .08 6.05 1.55 .015 .031
.103 -.113 .075 -.051 .030 -.013
-.001 .072 -.052 .102 .180 1
Environment .38 5.71 1.34 .134 .071
-.011 .093 .052 -.029 .021 -.076
-.023 -.012 -.095 .217 .286 .070
1
No supplements .10 5.87 1.54 .118 .026
-.075 -.018 .043 -.070 -.075 -.008
.023 -.015 -.015 .138 .158 .242
.047 1
Yes supplements .12 6.03 1.06 .170 .025
-.077 -.023 -.045 -.021 -.017 .035
.002 .031 -.049 .447 .339 .163
.236 .251 1
Ability
Activity .19 5.88 1.13 .066 .050
.046 -.102 -.012 .019 -.010 .099
.039 -.039 .034 .342 .126 .039
.070 .073 .178 1
Carbohydrates .24 6.11 .92 .111 .045
.049 -.016 .018 .024 -.062 -.003
.020 .059 -.040 .246 .565 .089
.164 .075 .173 .282 1
Diary .36 5.69 1.56 .082 .045
.047 .008 -.024 -.006 -.079 .022
.009 -.022 .019 .220 .218 .097
.084 .183 .122 .177 .279 1
Environment .48 5.93 1.00 .139 -.030
.004 .069 -.004 -.006 -.063 -.065
-.029 .035 .010 .242 .414 -.013
.484 .073 .243 .154 .341 .333
1
No supplements .14 6.21 1.10 .108 .067
.068 -.109 .043 -.020 -.066 -.089
-.003 -.046 -.050 .063 .154 .249
.011 .184 .102 .103 .153 .260
.128 1
Yes supplements .18 6.12 1.08 .192 .021
.072 .036 -.021 .100 -.080 -.014
-.088 -.022 -.088 .147 .204 .046
.059 .171 .381 .286 .344 .172
.202 .249 1
Motivation
Activity .35 5.82 1.13 .067 .093
.096 -.056 -.036 -.016 .065 -.008
.032 -.063 -.109 .280 .159 .018
.163 .036 .112 .329 .214 .220
.248 .096 .132 1
Carbohydrates .26 5.91 1.15 .034 .121
.027 -.088 -.150 .013 .023 .013
.028 .145 -.039 .246 .404 .051
.153 -.023 .121 .178 .503 .337
.294 .064 .204 .352 1
Diary .35 4.91 1.88 .036 -.014
.046 .000 .005 .053 .025 .075
.046 .028 -.011 .192 .141 .142
.167 .020 .063 .167 .260 .502
.205 .196 .085 .180 .358 1
Environment .33 5.88 1.00 .178 .023
.008 .044 -.054 .011 -.036 -.063
-.032 .077 -.047 .238 .446 .036
.414 .090 .240 .146 .407 .265
.739 .180 .257 .332 .416 .280
1
No supplements .12 5.50 1.75 .093 .004
-.023 .021 .019 .009 -.044 -.072
.040 -.021 -.001 .126 .059 .223
.125 .197 .287 .040 .049 .146
.089 .201 .176 .095 .065 .191
.110 1
Yes supplements .22 5.35 1.48 .076 .014
.011 -.041 -.009 -.005 -.038 -.110
-.005 .011 -.067 .120 .116 .128
.162 .085 .340 -.025 .044 -.025
.137 .040 .226 .110 .063 .056
.126 .616 1
Compliance
Subjective 1.00 51.08 7.47 .173 .060
.130 .015 -.081 .055 .043 -.045
.027 .036 -.007 .411 .343 .137
.324 .134 .304 .293 .404 .449
.492 .233 .289 .505 .494 .498
.536 .329 .313 1
Percentage visits .01 .82 .21 -.041 .191
-.033 -.076 .091 .009 .100 -.035
-.009 .070 .089 .003 .040 .000
.119 -.056 .002 -.035 .028 -.060
.088 -.112 -.003 -.006 -.007 -.044
.022 .079 .074 .088 1
Goal Attainment
Subjective .95(a) 23.41 3.44 .066 .040
.096 .048 -.129 .073 -.059 -.037
-.019 .026 -.015 .220 .393 .099
.266 .119 .239 .174 .376 .451
.560 .221 .236 .366 .428 .304
.504 .192 .089 .559 .067 1
Percentage weight loss .63(a) .10 .05 -.008 .004
.032 -.061 .033 .133 -.123 -.030
.000 .146 .091 .096 .247 .017
.131 .162 .012 .101 .255 .151
.191 -.027 -.005 .133 .248 .164
.165 .111 .036 .267 .427 .349
1
Satisfaction
Satisfied 1.00 51.57 4.39 .187 -.021
.085 .115 -.084 -.014 -.011 -.008
.036 .070 -.079 .296 .436 .073
.399 .085 .332 .133 .359 .271
.590 .189 .241 .297 .404 .215
.557 .153 .160 .499 .041 .651
.224 1
(a) Latent variable (LV) loading coefficients for reflective
indicators. Communality coefficient = .36.
Notes: S. D. = standard deviation.DIAGRAM: FIGURE 1 Estimated Latent Variable Model
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Attitudinal Homophily (1 = "Disagree Strongly," 7 = "Agree Strongly")
It is okay to use diet pills to attain ideal weight.
It is okay to have plastic surgery (e.g., liposuction) to attain ideal weight.
Dieting alone is sufficient to manage weight.
It is necessary to include exercising along with dieting to manage weight.
Role Clarity (1 = "Disagree Strongly," 7 = "Agree Strongly")
My weight-loss program has not made it clear how to keep a diary of my daily food/beverage intake. (reverse coded)
My weight-loss program has made it clear how to determine my daily intake of carbohydrates.
My weight-loss program has made it clear the number of prepackaged food supplements to take each day.
My weight-loss program has made it clear how to determine my daily level of physical activity.
My weight-loss program has not made it clear how to take the prepackaged food supplements. (reverse coded)
My weight-loss program has made it clear how to control my environment.
Ability (1 = "Disagree Strongly," 7 = "Agree Strongly")
I am not able to determine how to take the prepackaged food supplements. (reverse coded)
I am able to determine the number of prepackaged food supplements to take.
I am able to determine my daily level of physical activity.
I am able to determine my daily intake of carbohydrates.
I am able to apply the skills my nurse has taught me to help control my environment.
I am not able to keep a diary of my daily food/beverage intake. (reverse coded)
Motivation (1 = "Disagree Strongly," 7 = "Agree Strongly")
I feel motivated to take the prepackaged food supplements prescribed by the program.
I feel motivated to determine my daily level of physical activity.
I feel motivated to calculate my daily intake of carbohydrates.
I do not feel motivated to take the prepackaged food supplements that are suggested. (reverse coded)
I do not feel motivated to keep a diary of my daily food/beverage intake. (reverse coded)
I feel motivated to apply the skills my nurse has taught me to help control my environment.
~~~~~~~~
By Stephanie Dellande; Mary C. Gilly and John L. Graham
Stephanie Dellande is Assistant Professor of Marketing, Chapman University (e-mail: dellande@chapman.edu). Mary C. Gilly is Professor of Marketing (e-mail: mcgilly@uci.edu), and John L. Graham is Professor of Marketing (e-mail: jgraham@uci.edu), Graduate School of Management, University of California, Irvine.
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Record: 67- Geographic Patterns in Customer Service and Satisfaction: An Empirical Investigation. By: Mittal, Vikas; Kamakura, Wagner A.; Govind, Rahul. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p48-62. 15p. 1 Diagram, 5 Charts, 1 Graph, 4 Maps. DOI: 10.1509/jmkg.68.3.48.34766.
- Database:
- Business Source Complete
Geographic Patterns in Customer Service and
Satisfaction: An Empirical Investigation
When firms' customers are located in geographically dispersed areas, it can be difficult to manage service quality because its relative importance is likely to vary spatially. This article shows how addressing such spatial aspects of satisfaction data can improve management's ability to implement programs aimed at enhancing service quality. Specifically, managers can identify areas of high service responsiveness, that is, areas in which overall satisfaction is low but customers are highly responsive to improvements in service quality. The authors estimate the spatial patterns using geographically weighted regression, a technique that accounts for spatial dependence in the variables. They apply this methodology to a large national sample of automobile customers served by a network of dealerships across the United States. The authors also investigate the extent to which factors related to the physical and psychological landscape explain the importance that people in different regions place on dealership service and vehicle quality.
Customer satisfaction is essential to the long-term success of a firm (Rust, Zahorik, and Keiningham 1995; Rust, Zeithaml, and Lemon 2000). However, especially at firms that provide services using a regionally dispersed network of outlets (e.g., automotive dealerships, bank branches), managers face several challenges in implementing a customer satisfaction strategy. For such geographically dispersed outlets, overall satisfaction and the importance placed on service quality will vary from region to region. The firm's ability to provide superior service may also vary geographically. As such, incorporation of the regional differences in customer satisfaction decisions is important.
This article illustrates an approach that enables a firm to identify regional patterns in satisfaction data using geographically weighted regression (GWR), a spatial-econometrics technique that has been used primarily outside of the marketing literature. We apply this approach to a data set of 164,085 customers who represent 21,636 five-digit zip codes across the United States. We identify locations where overall satisfaction is relatively low but where customers are highly responsive to improvements in service quality. Such identification can provide guidance in the implementation of a service strategy on a national basis.
Within marketing, the movement to understand regional differences in consumer consumption began in the 1970s. Responding to the fear that increasing mass production and mass communication would eliminate any regional differences among U.S. consumers, Wells and Reynolds (1979, p. 347) state:
The homogenization hypothesis is not based upon conclusive evidence. "Even the arguments that support homogenization can be seen to work both ways. Consider the common-influences argument: There is ample evidence that the mass media do not have mass effects; rather, they have differing effects according to the predispositions of the audiences." Perhaps we should expect a reinforcement of existing values and beliefs among regions, instead of a convergence.
These regional differences were documented in many descriptive studies (Gillin 1955; Glenn and Simmons 1967; Nicosia and Mayer 1976). In 1981, Hawkins, Roupe, and Coney proposed a framework for understanding regional differences in consumption. As is shown in Figure 1, they posit two sets of causative factors. Factors associated with the physical landscape directly influence the usage situations that consumers face, which in turn influence consumption patterns through homeostatic influences (Parker 1995; Parker and Tavassoli 2000). As the ovals in Figure 1 show, Parker (1995) suggests that climate affects consumer behavior through mediating mechanisms that are both physiological (e.g., homeostatic regulation in the hypothalamus) and psychological (e.g., optimal-stimulation level based on the different senses). This is consistent with research in biology and medicine (Anderson, Deuser, and DeNeve 1995; Hill 1992; London and Teague 1985; Spoont, Depue, and Kraus 1991). For example, differences in the degree of sunlight can affect the chemical balance in the brain, thus affecting customers mood states and, in turn, customer satisfaction (Peterson and Wilson 1992). Factors associated with the psychological landscape can affect consumer values, motivations, and preferences, which in turn determine consumer lifestyles and consumption patterns. In marketing, factors associated with the psychological landscape have received more attention (e.g., Grewal et al. 1999; Ingene 1984).
To implement the framework in Figure 1, the regional patterns must be empirically ascertained and the intervening mediators examined. Both steps require data that represent large geographic regions. This study focuses on the first step: determining the spatial variation in overall customer satisfaction and its response to service provided by the firm.
Wells and Reynolds (1979) compare consumers from the eastern, southern, midwestern, southwestern, and western regions of the United States. Hawkins, Roupe, and Coney (1981) compare coffee preparation and drinking habits in the same regions. Similarly, Gentry and colleagues (1988) compare students from Wisconsin, Washington, Oklahoma, and Massachusetts to draw conclusions about regional variation. All these studies find significant geographic variation in consumer values, attitudes, and consumption. However, they all compare regions that were defined a priori.
In contrast, recent works define regions on the basis of patterns embedded in the data themselves. Ter Hofstede, Steenkamp, and Wedel (1999) analyze data from 11 European countries and find that segmentation in terms of consumer perceptions and attitudes does not overlap with the political boundaries of the countries. In a more recent article, Ter Hofstede, Wedel, and Steenkamp (2002) develop a market segmentation model in which segment-membership probabilities for a focal region depend on the segment memberships of its immediately adjacent neighbors, thus enabling market segments to be spatially contiguous. They apply this flexible segmentation model to a pan-European study of consumers who provided store-image ratings, and they find evidence of spatial dependence in segment membership. Helsen, Jedidi, and DeSarbo (1993) show that countries classified as similar on the basis of macroeconomic variables may or may not exhibit similar patterns with respect to diffusion of consumer durable goods. Hoch and colleagues (1995) and Bronnenberg and Mahajan (2001) incorporate heterogeneous geodemographic data and find that the location of consumers strongly affects their responses to prices and promotions. However, these studies do not address issues related to customer satisfaction. As such, it is not clear how regional differences should be addressed in an examination of customer satisfaction and its antecedents.
In examinations of customer satisfaction data, differences have typically been incorporated at the customer level, though customer characteristics tend to explain less than 10% of the variation in overall satisfaction (Bolton 1998; Bryant and Cha 1996; Danaher 1998; Mittal and Kamakura 2001). The previous section suggests that it might be useful to examine differences across regions as well, especially because some relevant consumer characteristics may show systematic regional patterns. For example, among automobile drivers, the relative emphasis placed on services may vary regionally not only because of the psychological landscape (e.g., different customer characteristics) but also because of the physical landscape (e.g., different climate and geography). In other words, we expect that regional differences exist, but in any large country such as the United States, the pattern of regional variability in overall satisfaction or the importance of service quality is not likely to map onto political boundaries or zones such as states or counties. In such cases, what options are available to the firm for developing a service strategy?
An option is for the firm simply to ignore geographical patterns and to treat each service unit (e.g., bank branch, dealership) as a separate entity. Such a strategy, though conceptually appealing, may be practically infeasible. First, it may not convey a unified brand image to a customer who patronizes different locations. Differences in policies and procedures at the units can also confuse and irritate customers. Second, such a strategy may be costly. The firm may need to collect data for each individual service unit separately. Although this can be done for the larger dealerships, obtaining a large sample from all the smaller service units not only may be costly but also may irritate customers who believe that they are oversurveyed and intruded on (Nunes and Kambil 2001). Third, this strategy may preclude a firm from benchmarking the service units against one another. Another option is for the firm to ignore regional differences and to treat all service units exactly the same. This strategy would fail to capitalize on geographic differences in the customer base. Instead of this supply (firm) focus, we argue for differentiated strategies based on regional patterns in consumers responsiveness to changes in satisfaction drivers.
Such a strategy requires a firm to identify empirically the appropriate "region" in which customers "overall satisfaction" is similarly responsive to improvements in service. A reasonable strategy is to analyze smaller areas, such as the five-digit zip code, in which customers are more likely to be similar. The smaller regions can be aggregated to produce meaningful regional zones in which consumers place a similar level of importance on service and have similar levels of overall satisfaction. Identification of such regional zones in which overall satisfaction is highly responsive to service improvement can be difficult. The firm may not know a priori the regional factors that drive such variability. Even if such factors are known, they may be too numerous to model in a convenient framework, and/or data on many of the factors may simply be unavailable. An empirical approach that can detect geographical patterns in the data without relying on explicit variables may be more useful. Several techniques can be used to accomplish this objective. For customer satisfaction data, GWR is particularly appropriate because of its versatility in addressing geographically sparse data (i.e., when data may not be available for all the regions being analyzed). Typically, managers collect satisfaction surveys from customers who patronize different outlets (e.g., dealerships, bank branches). Although such a strategy provides coverage of the entire service area, it does not provide data from each location (e.g., five-digit zip code). The GWR technique is useful because it statistically borrows data for neighboring regions during estimation. Next, we briefly discuss GWR and show its application in a customer satisfaction context. Because the contribution of this article is not methodological, we do not address the debate about the various available approaches. For this discussion, we refer the reader to the comprehensive review provided by LeSage (1999).
Brunsdon, Fotheringham, and Charlton (1996, 1998) developed GWR in the field of spatial econometrics. Although it has not been applied in the marketing literature, GWR has been used in agriculture and environmental analysis (Nelson and Leclerc 2001), real estate (Fotheringham, Charlton, and Brunsdon 1999), education (Fotheringham, Charlton, and Brundson 2001), and political science (Calvo and Escolar 2002).
Brunsdon, Fotheringham, and Charlton (1996, p. 285) describe how GWR is conceptually similar to kernel regression (Rust 1988). In each, the dependent variable (y) is modeled as a function of the predictors (x) by weighted regression, and weights for an observation are determined by the proximity of the focal observation and the neighboring observation. The key difference is that in kernel regression, the weighting is done on the "attribute space" of the independent variables, whereas in GWR, it is done in the two-dimensional geographic space, thereby avoiding the well-known "curse of dimensionality" that affects kernel-density estimation methods. Another important distinction is that in kernel regression, the dependent variable is related to predictors through a single, highly flexible, nonparametric relationship that applies to all observations or locations. In contrast, GWR estimates a linear relationship between predictors and the dependent variable, and parameters vary across locations.
The objective of GWR is to estimate a linear model that relates the dependent variable to its determinants after taking into account spatial correlation among observations in neighboring locations. This is accomplished by allowing for spatial nonstationarity in the regression coefficients for each location. A "location" is the geographic unit of analysis for which data may be aggregated. For example, in the United States, the five-digit zip code is a location, which enables estimation of a regression coefficient for each location (i.e., five-digit zip code) after accounting for the spatial correlation with neighboring zip codes. The location is defined on the basis of factors such as data availability, similarity of customers within the location, cost effectiveness, and implementation considerations.
Consider the traditional linear regression model pooled across all locations: Y = Xβ + ε. The objective of GWR is to use all the data available (on the dependent variable Y and predictors X, including a column for the intercept) across all locations to obtain location-level estimates of the regression coefficients. Rather than pool all the available data, as in aggregate estimation, or shrink the regional estimates toward a population mean, as in random-coefficient models, GWR assumes that the regression coefficients vary across locations.
The GWR technique takes advantage of spatial dependence in the data. Spatial dependence implies that data available in locations near the focal location are more informative about the relationship between the independent and the dependent variables in the focal location. When calculating estimates for a focal location, GWR gives more weight to data from closer locations than to data from more distant locations. It is assumed that the relative weight of the contributing locations decays at an empirically determined rate as their distance from the focal location increases. Statistically, spatial dependence is operationalized by a weighting scheme in a generalized least squares (GLS) model, such that locations closer to the focal one have a greater weight in determining the regression equation for the focal location. The weighting matrix contains weights (w[subj]) for all locations that are used in computation of the regression equation for the focal location. Using this geographic weighting matrix, we can obtain the weighted least squares estimates for any location j as follows:
( 1) β[subj] = (X'W[subj]X)[sup-1]X'W[subj]y.
This is a traditional regression estimated with GLS, where W[subj] is a (n x n) diagonal matrix that contains {w[subjj']}, (j' = 1, ..., J) in the diagonal, defined by an exponential distance-based decay function,
( 2) w[subjj'] = exp(-d[sup2, subjj;]/θ),
where θ is the distance decay parameter, and d[subjj'] is the Euclidean distance between locations j and j'.
Implementation of the exponential distance-based decay function requires estimation of the optimal bandwidth (θ) before the weighted least squares estimates can be obtained. The bandwidth parameter determines the relevance of each neighboring observation for the estimation of the regionallevel parameters β. When the bandwidth is sufficiently large, the GWR model reverts to a standard regression pooled across all regions. We determine the most appropriate value of the bandwith using the least squares crossvalidation procedure that Cleveland (1979) suggests. Crossvalidation basically relies on the following scoring function to determine the optimal value for θ:
( 3) [Multiple line equation(s) cannot be represented in ASCII text]
where (Multiple lines cannot be converted in ASCII text) represents the fitted value of y with the observations from the focal location j omitted from the calibration process. The value of θ that minimizes this score function is used as the bandwidth for calculating the weighting matrix. Details about the estimation of GWR with cross-validated GLS are found in the work of Brunsdon, Fotheringham, and Charlton (1998).
The GWR model, as Brunsdon, Fotheringham, and Charlton (1998) propose, considers the case with only one observation per location. However, in our analysis, we observe multiple responses in each zip code. Rather than aggregate the data in each zip code, we estimate our model at the respondent level. This individual-level estimation can be problematic because it emphasizes each location according to the number of observations in it. Although this would be acceptable in the case of simple random or proportionate stratified sampling, it is likely to bias the estimates otherwise. Therefore, we retain the concept of equal weighting for each location, as in the traditional GWR model, by weighting each observation with the inverse of the sample size in its location. The altered GWR model can be represented as follows:
( 4) β[subj] = (X'W[subi]fX)[sup-1]X'W[subui]fy.
where f is a (n x n) diagonal matrix that contains the inverse of the sample size in the location j to which the individual observation i belongs, and the diagonal matrix W[subi] now contains the distance-based weights between each individual observation and the focal one (i). Note that the regression coefficients β[subj] are still defined at the location level; the estimates are the same for all observations in the same location. This happens because all observations in the same focal location carry a unit weight, which results in the pooling of all observations from the same location.
Although empirical comparisons between GWR and other approaches are found in the literature (e.g., Brunsdon et al. 1999; LeSage 2001; Wolfinger and Tobias 1998), it is important to compare GWR with three predominant methods: spatial adaptive filtering (Foster and Gorr 1986), random-coefficients regression (Aitkin 1996), and multilevel modeling (Goldstein 1987). Spatial adaptive filtering incorporates spatial relationships in an ad hoc manner through exponential smoothing and produces nontestable parameter estimates, which limits its usefulness. In the other two approaches, the parameter estimates of the regression model are assumed to be randomly distributed over the population of locations with either a finite (Wedel and Kamakura 2000) or a continuous mixture distribution (Aitkin 1996). Random-coefficients regression also requires repeated measures in each sampling unit for reliable estimates at the individual level, which are rarely available in geographic data. For example, in the application we describe, most locations have only a single observation. Multilevel modeling assumes that there is a hierarchical data structure with individual observations nested under another level, such as regions. However, both random-coefficients regression and multilevel modeling are silent about the nature of the spatial dependence in the data, a key factor in the determination of which locations should be treated similarly to constitute a region. Jones and Eldridge (1991) attempt a geographic variation of multilevel modeling, but they predefine a hierarchy of spatial units, which may not be appropriate for satisfaction data.
Rust and Donthu's (1995) two-step approach to capture geographically related misspecification errors in discrete-choice models is also closely related. In our situation, their approach would require us to estimate an aggregate regression model in the first stage and analyze the regression residuals in a second stage, using a cubic spline to relate the residuals to their geographic coordinates. The geographically related misspecification error captured by this approach would then be indistinguishable from the GWR intercept for each sampling unit. The GWR model we use herein allows for different geographic patterns for not only the intercept but also each response coefficient.
Ter Hofstede, Wedel, and Steenkamp (2002) take an entirely different approach. Rather than allow for a continuous spatial variation in the regression coefficients, they identify relatively homogeneous segments of regions under different assumptions about the spatial dependence among the segments. In its most strict form, their model requires spatial contiguity among segment members. In a less restrictive form, they assume only that the probability that a region belongs to a segment depends on the membership of its neighbor to the same segment. Rather than assume that there is spatial dependence among segments of regions, we account for spatial dependence in the original regions themselves.
The GWR technique should be used when there is spatial autocorrelation in the variables. High positive autocorrelation implies that values from neighboring areas are similar to one another, whereas high negative autocorrelation implies that values from neighboring regions are dissimilar to one another. The magnitude and direction of spatial autocorrelation for a variable can be quantified by means of two statistics: Moran's I and Geary's C (Cliff and Ord 1973, 1981). The computational details for each statistic are shown in the Appendix. As is shown in the Appendix, values of Moran's I that are greater than -1/(N - 1)) indicate positive autocorrelation, and vice versa. For Geary's C, values less than 1 indicate positive autocorrelation, and values greater than 1 indicate negative spatial autocorrelation. For both statistics, tests of statistical significance can be conducted to detect spatial autocorrelation. Based on these statistical significance tests, a decision to proceed with GWR can be made.
We conducted the study for a domestic automotive manufacturer that sells and services its vehicles nationally in the United States through a dealership network. Although the manufacturer itself is responsible for the vehicle, it realizes the importance of dealership service as a key driver of overall satisfaction with the vehicle. Dealership service is particularly important during the later stages of vehicle ownership because it plays a significant role in the purchase decision of the next vehicle. Recognizing this, the company conducts a satisfaction survey with customers who have owned their vehicle for 33 months and who have had their vehicle serviced at an authorized dealership in the past 6 months. Thus, all respondents have a relatively high level of experience with the product (vehicle) and at least one service encounter at a dealership. Note that customers who took their vehicle in only for warranty-or recall-related service are excluded from the survey.
We used data from 164,085 customers who filled out the satisfaction survey. From this data set, we created a holdout sample by randomly selecting 32,000 customers from zip codes that contained at least 5 customers. Therefore, the reported analysis is based on 132,085 respondents, who represent a total of 21,636 five-digit zip codes in the United States. The sample is described in Table 1.
Of the 31,956 five-digit zip codes in the United States, we have data for only 21,636, or 67.7%. In other words, 32.3% of the zip codes have no data. Figure 2 displays the number of respondents from each zip code for whom data is available. In summary, a large proportion (32.3%) of zip codes are without data; among the remaining zip codes, more than half have five or fewer data points. A zip code level analysis will systematically exclude approximately one-third of the zip codes. Among the remaining zip codes, zip code level estimates may be unreliable for those that have five or fewer data points. More important, such an analysis will fail to incorporate the advantages of spatial autocorrelation in the estimation process. This is why the benefits of GWR make it the technique of choice for this problem: GWR statistically borrows observations from neighboring locations when estimating the coefficients for a focal location. This feature of GWR is useful because in most cases the focal location has few observations. In such cases, we obtain better estimates because observations from neighboring zip codes provide additional information. Borrowing from neighboring zip codes also helps identify regions that have similar coefficients, leading to a systematic view of the spatial patterns in the data.
We used data from three key variables that we measured using a ten-point scale (1 = "extremely dissatisfied," 10 = "extremely satisfied"). Each customer i answered the following questions on the ten-point scale: On the basis of your experience this far, how would you rate your satisfaction with overall vehicle ownership experience (OVERALLSAT[subi]), vehicle quality (PRODQUAL[subi]), and dealership service (DLRSRV[subi])?
Customers also indicated the five-digit zip code of their current residence. Although we could have aggregated zip codes up to a larger unit of analysis (e.g., county), we considered this inappropriate because it would imply homogeneity within relatively diverse areas. A finer unit of analysis such as the census block was not feasible because we defined the location of each respondent only by zip code. Thus, we performed a zip code level analysis. We obtained the latitude and longitude coordinates for the centroid of each five-digit zip code. From the centroids, we computed the Euclidean distance between each zip code in the data set. We used this distance in the estimation of the GWR model.
Next, we estimated the spatial autocorrelation in the variables. In the absence of spatial autocorrelation, a pooled regression across all the areas should suffice. The measures of spatial autocorrelation (Geary's C and Moran's I) across all 21,636 zip codes for all three variables are shown in Table 2. Moran's I is greater than -1/(N - 1)) (p < .05), and Geary's C is less than 1 (p < .05). This indicates positive spatial autocorrelations for all three variables; values of observations from areas closer to one another tend to be positively correlated.
Using GWR, we estimated the following relationship in which satisfaction with the ownership experience is a function of vehicle quality and dealership service:
( 5) OVERALLSAT[subi] = β[sub0j] + β[subtj]PRODQUAL[subi] + β[sub2j]DLRSRV[subi] + e[subi],
where e[subi] are i.i.d. normal disturbances. Note that we estimated the three parameters for each location j and observed disturbances at the individual i.
Using this model, the automotive firm can identify areas in which it should improve dealership service. For these areas, it can ascertain specific subdrivers of dealership service (Rust, Zeithaml, and Lemon 2000). The GWR model estimates separate coefficients for each five-digit zip code. A listing of coefficients for each location would be neither meaningful nor easy to communicate. Therefore, we depict the results in Figures 3 and 4 by plotting the regression coefficient for each location. In Figures 3 and 4, darker (lighter) color indicates a larger (smaller) regression coefficient (i.e., higher or lower importance) for dealership service and product quality.
Regional Patterns in the Importance of Dealership Service
The regression coefficients for dealership service are depicted in Figure 3. Consider Colorado: In the southeastern part of Colorado, the importance of service quality is high (indicated by the dark color), whereas in the southwestern part of the state, it is lower (indicated by the lighter color). From central to northern Colorado, we find the lowest importance of service, indicated by the white shading. Furthermore, in the northeastern part of Colorado, the importance of service satisfaction is uniformly low, as it is in the adjacent state of Nebraska.
In general, dealership service is more important in the easternmost or westernmost parts of the country, especially in parts of southern Oregon and northern California. Other regions in which service is important include northeastern New Mexico into Colorado and Kansas. In the Midwest, Kentucky, Indiana, and Illinois also share areas in which dealership service has high or medium importance. We were surprised that there is a large area in the western United States--including Nevada, Arizona, Oregon, and Idaho--in which dealership service is less important. In addition, the various major metropolitan areas are located in regions with different levels of importance. Indianapolis; Columbus, Ohio; and Philadelphia have high to medium importance of dealership service, whereas Jacksonville, Fla.; San Francisco; and San Jose, Calif., have medium to low importance.
Regional Patterns in the Importance of Vehicle Quality
Results for the importance of vehicle quality are shown in Figure 4. As we expected, they are essentially the inverse of the results for dealership service. Consider Texas: In northeastern Texas, we find that the importance of vehicle quality is relatively high in determining overall satisfaction. This part of Texas shares the pattern of high importance of vehicle quality with the neighboring states of Louisiana and Arkansas. However, between Dallas and Austin, the relative importance of vehicle quality is medium to low, as it is in northwestern Texas. In contrast, the importance of vehicle quality is high in southern Texas. The pattern of importance of vehicle quality also varies for different metropolitan markets. Whereas Philadelphia, New York, and Washington, D.C., are in areas of low importance, Memphis and San Francisco are in areas of high importance.
We evaluate model performance using two criteria: ( 1) the model's ability to predict overall satisfaction and ( 2) stability of the parameter estimates for the key drivers.
Predicting Overall Satisfaction
To evaluate predictive performance, we used the holdout sample created using 20% of the observations. To create the holdout sample, we selected zip codes that had more than five observations so that we could obtain standard regression estimates for each holdout zip code and so that at least two observations would be available for predictive tests. Note that the minimum of three observations for estimation is a requirement imposed by the benchmark models; GWR can compute the parameter estimates even in a zip code area that has no data, on the basis of data in neighboring areas. From the short-listed zip codes that contained five or more observations, we randomly drew 32,000 observations; a few zip codes had multiple observations in the holdout sample. The final holdout sample of 32,000 observations represents 13,846 zip codes.
Table 3 compares the GWR with simple regression using two measures of predictive performance. The first is the range of predicted satisfaction scores (from the 95% confidence interval) for each location, averaged across all locations in the holdout sample. We performed this using the prediction methodology that Nester (1996) and Brunsdon, Fotheringham, and Charlton (1998) suggest. This measure indicates the model's ability to predict the satisfaction scores on a zip code-level basis, and it indicates the uncertainty associated with the predictions. The second measure is the percentage of observations in the holdout sample for which the actual satisfaction score fell within the 95% confidence interval predicted by the model. This measure provides an empirical verification of the 95% confidence intervals across customers by showing the percentage of times the confidence interval included the actual satisfaction score in a holdout sample.
We examine the results at the zip code level. Model 1 uses GWR estimation at the five-digit zip code level. Model 2 is a zip code-level model without GWR that we estimated by running separate regressions within each zip code. Model 2 uses at least three observations within a zip code and compares predicted satisfaction scores with actual ones for the remaining observations in the zip code. The fourth column of Table 3 shows that the 95% confidence interval around the mean estimate of the predicted values is lower with the GWR model than with the model without GWR (.28 versus 1.07). Thus, the predictions we obtained with GWR carry a lower degree of uncertainty than do the predictions we obtained under the assumption that all regions are independent. The rightmost column of Table 3 shows that when GWR is used (Model 1), 87.05% of the observations in the holdout sample have a satisfaction score that falls within the 95% confidence interval of the predicted value. Without GWR (Model 2), only 40.42% of the values of overall satisfaction in the holdout sample fall within the 95% confidence interval of the predicted value. Therefore, the interval predictions that the GWR model produces are more likely to include the actual value even though the intervals are narrower, which reflects lower uncertainties about the predictions. The confidence intervals based on independent regressions were not only broader, reflecting higher estimation error, but also less accurate in predicting satisfaction in a holdout sample.
For comparison, we also estimated models at the county level (Model 3) and the state level (Model 4). As is shown in Table 3, the actual satisfaction score falls within the predicted 95% confidence interval for 72.56% of the predictions made with the county-level estimation and 83.58% with the state-level model. At first glance, the results appear to be comparable to those we obtained with zip code-level estimation. However, note that the average range of predicted values for each area is much broader for the county-level model (2.39), and even more so for the state-level model (3.87), than for the zip code-level estimates. This means that it is more likely that the actual satisfaction score falls within the broader confidence interval. Because there is more uncertainty in the predictions at the county and state levels, the confidence intervals are much greater, which makes it is easier for the interval to contain the actual satisfaction score. Thus, for managers who want to ensure accurate prediction of overall satisfaction for each zip code under consideration, the zip code-level model with GWR performs the best.
Finally, we also performed a holdout test with another set of observations. In this case, we randomly drew 32,000 observations from the data set without limiting ourselves to the zip codes that had five or more observations. This sample of 32,000 data points represented 17,228 zip codes. We used the remaining 80% of the data to estimate a GWR model. We used the set of parameters we obtained from this model to predict the observations from the holdout sample. The results from this analysis are shown in the fifth row of Table 3. Note that the results from this prediction are not comparable to the estimates from ordinary least squares because for many of the zip codes involved, we had few observations left to be able to estimate an ordinary least squares model. The range of predicted values is now larger than when we included only zip codes with five or more observations (.62 versus .28). This is because we have many zip codes for which we have no observations in the focal region, and thus we needed to form predictions from neighboring locations through the GWR model. In addition, the number of observations that fall within ±1.96(σ/√n) was 71.41%, compared with 87.05% for the previous model. Nevertheless, this predictive performance is still superior to the benchmark models.
Stability of Parameter Estimates
To evaluate the stability of our estimates of the importance of dealership service and vehicle quality, we randomly split the sample within each zip code into two halves. When only one observation was available for a zip code, we randomly assigned it to one of the split samples, and it was designated as missing in the other sample. We then applied GWR on the two halves, computing the coefficients for the zip codes that had no data. To assess parameter stability, we computed the correlation from the two halves and for the complete data for the following three sets of zip codes: ( 1) ones for which the data were available in both samples, ( 2) ones for which the data were missing in one sample, and ( 3) ones for which the data were missing in both samples (i.e., we computed parameter estimates in both halves). We replicated the split-half test ten times, and we report the mean and standard deviation for the correlations across the ten replications. The split-half correlations, computed across 31,956 zip codes (including those with no data), provide an assessment of parameter stability for our application of the GWR model. When data were available in both split halves, the correlation was greater than .65. We consider this strong evidence of parameter stability because we computed the correlations between two estimates and across a large ( 31,956) sample size. When we impute the parameter estimate for one random sample (i.e., no data were available for the particular zip code), the correlation decreases to approximately .40. We expected this attenuation in the split-half correlations because the correlations now involve one parameter estimate and one imputed value. A notable result is that the split-half correlations were not further attenuated when both samples had imputed values (rightmost column of Table 4).
To implement the results, the firm should first identify regions in which overall satisfaction is relatively low. Then, among these regions, it can ascertain the responsiveness of overall satisfaction to dealership-service improvements. Then, the firm can give priority to the regions in which the importance of dealership service is relatively higher.
We selected the subpar satisfaction regions. For this study, we chose regions that were below the median in overall satisfaction. The regional pattern in overall satisfaction is shown in Figure 5. Thus, in these regions, there is relatively more room for customer satisfaction improvements. Note that the criterion and/or cutoff that is used to define subpar satisfaction regions is a subjective issue to be decided with managerial consultation. For the subpar satisfaction regions, we plotted the importance of dealership service (Figure 6). In Figure 6, regions with a darker shade are those in which the firm should implement service improvement first: They are regions in which such improvement has a relatively large impact on overall satisfaction and in which overall satisfaction is relatively low. In deciding where to invest in improving dealership service, the firm must also consider other factors such as market size and competition.
As a next step, performance on various attributes that drive dealership service should be measured. A key driver analysis can identify specific subdrivers of dealership service, and importance-performance charts can help the firm isolate the drivers that need improvement (Rust, Zahorik, and Keiningham 1995). The firm may also gather information on its customer base in such regions (e.g., the Southwest, southern Kansas) for further insights. That is, why is dealership service so important in such regions? Some of this could be related to structural factors and geographic conditions. Although the firm did not have primary data on these factors, we undertook such an exercise using secondary data, which we describe next.
Our goal is to determine empirically the extent to which different factors related to the physical and psychological landscapes affect the importance of the drivers of overall satisfaction. The national coverage of our data set provides a unique opportunity to investigate this issue. To obtain measures of some of the factors, for each zip code we appended variables from the U.S. Census and the Weather Bureau. We included only variables for which data on at least 95% of the zip codes were available. We merged the variables with the survey measures and GWR results at the zip code level. Table 5 shows the variables we used in the final models.
Table 5 shows two models in which dependent variables are the importance of automobile quality and dealership service (as measured by the regression coefficients obtained from GWR). We did not use overall satisfaction because it has the product quality and dealership service embedded in it and is therefore driven more by supply-side factors (for which we do not have data) than by customer characteristics. In contrast, the importance coefficients measure how customers respond to the supply-side factors (product quality and dealership service) and are more intrinsic to the consumer. Thus, they are more likely to be affected by the physical and psychological landscape.
The regression coefficients in Table 5 have been standardized to make them directly comparable. We also tested the predictors for multicollinearity using the variance inflation factor, which was lower than 6, except for a few predictors that we excluded from the model.
Consistent with previous studies, we find that the importance of the automobile and dealership service varies on the basis of customer characteristics (e.g., Bolton 1998; Bryant and Cha 1996; Mittal and Kamakura 2001). In this regard, several notable patterns are evident. For example, as per capita income increases, the importance placed on the automobile (β = .023) and the dealership services (β = .071) increases. However, as the proportion of men in a region increases, the importance of dealership service declines (β = -.031), but the importance of the automobile increases (β = .016). This result is fully consistent with the work of Mittal and Kamakura (2001), who find that men place a lower importance on service than do women. Regarding age, younger buyers (age 25 or younger) place more importance on the automobile (β = .117) than on dealership service (β = -.025). In contrast, buyers older than age 60 place higher importance on both the automobile (β = .062) and the dealership service (β = .090). We found similar results for education. Within an area, as the proportion of people with less than ninth-grade education increases, so does the importance of the automobile (β = .085) and the dealership service (β = .026). However, among people who have a graduate degree, the importance of the automobile seems to be lower (β = -.046), but the importance of service seems to be higher (β = .073). Perhaps increased education and income make consumers more sensitive to service, though older consumers also attach higher importance to both the automobile and the dealership service. Driving habits and driving conditions also seem to influence the importance placed on the automobile and the dealership service. As we expected, as the proportion of carpooling consumers increases, the importance of the automobile increases (β = .077), but the importance of dealership service decreases (β = -.124). The importance of the automobile and dealership service decreases (β = -.041 and -.031, respectively) as the proportion of people who use public transportation in an area increases. Perhaps in such areas alternative means of transportation assume higher importance.
Factors that constitute the physical landscape are also related to the importance placed on vehicle and dealership service, though the pattern of results is complex. Elevation is statistically not significant (p > .05). The mean amount of snow increases the importance placed not only on the vehicle (β = .171) but also on the service (β = .091). The variance in the amount of snowfall has a different effect; it increases (as we expected) the importance of the vehicle (β = .156) but decreases the importance of dealership service (β = .067). The mean amount of rainfall affects only the importance placed on service, whereas the variance in rain has no impact. The mean maximum temperature increases the importance placed on the vehicle (β = .056) but decreases the importance placed on the dealership service (β = .039). Higher variance in weather conditions (e.g., rain, snow, maximum temperature) has no effect or a positive effect on the importance of the vehicle but almost always decreases the importance placed on the service element. In other words, in areas with highly varied climate, consumers seem to be more concerned about the vehicle than the dealer service. However, areas in which the mean amount of snow and rain is generally high deserve special attention because both service and product elements are important.
In summary, factors associated with the psychological and physical landscape are statistically associated with the importance of dealership service and vehicle quality, though the nature of the association is rather complex. Consistent with previous studies (Bryant and Cha 1996; Mittal and Kamakura 2001), we find that though customer demographics are statistically significant, they have low power (R² < 9%) in explaining the importance of satisfaction drivers. Empirically, this provides additional evidence for the robustness of using a GWR methodology that obviates the need for explicit inclusion of the variables in a model a priori. In other words, a strategy for explicit inclusion of the variables in a model that predicts overall satisfaction is unlikely to be useful, and potentially biasing, if the model does not account for heterogeneity across regions beyond these observable characteristics.( n1) The results also indicate the need to improve the understanding of marketing phenomena with respect to the framework shown in Figure 1. Specifically, the mediating constructs (usage situations, homeostatic mechanisms, and consumer values) are not explicitly incorporated in our analysis. We believe that it is this lack of mediating mechanisms that may lead to the low observed explanatory power and that should be addressed in further research.
Based on GWR, our results (for a firm in the automotive industry) show the following:
• There is systematic spatial variability in the pattern of overall satisfaction and the importance placed on its key drivers. Although the specific pattern of regional variation is likely to differ on the basis of the category investigated, the presence of systematic spatial variability should be incorporated in further investigations of satisfaction data.
• Explicit inclusion of physical and psychological factors explains less than 9% of the variability in the importance of key drivers. This may be because we did not explicitly incorporate specific mediating mechanisms into the response model.
• The regional differences in the importance of key drivers and the overall satisfaction patterns enable a firm to identify regions in which it should improve service. The firm can prioritize regions to make investments in improving dealership service.
Two decades ago, Wells and Reynolds (1979, p. 347) asked: If, then, regionalism is persistent, the substantive question is the nature of the regions. How do regions differ in life styles, in consumption-related variables? How are they similar? Our results demonstrate that the nature of the regions and regional differences is unlikely to map onto political boundaries or other impressionistic characterizations based on regional stereotyping (Wells and Reynolds 1979). Firms should take a data-based view of regional differences to improve their decisions.
For identifying the appropriate regions, our results suggest that a strategy of explicitly including demographic and geographic factors in the model may not provide as good a picture as the one produced with GWR. Even with a large set of predictors, less than 9% of the variance in the importance of the automobile and dealership service could be explained. From a practical standpoint, a firm's generating such an exhaustive set of predictors and obtaining information on them could prove cost prohibitive. Furthermore, information at the desired level of granularity simply may not be available, thus leading to higher numbers of missing observations. By obviating the need for collecting such variables, GWR can be used to address such issues more easily. Other empirical approaches may prove equally useful.
Moving forward, it will be important to identify and incorporate mediating factors in the analysis. When we attempted to directly relate regional characteristics (physical and psychological) to the importance placed on service and product, the explained variance was low despite the large number of regional characteristics we included in the analysis. We believe that this happened because we did not incorporate specific mediators, such as usage situations, and consumer motivations and values (see Figure 1) in the analysis. Incorporation of these mediators should not only increase the explanatory power of the models but also improve theorizing by examining how regional characteristics influence distal outcomes such as consumer judgments.
Many scholars have argued that implementation of an appropriate service strategy must account for customer differences (Bolton and Drew 1994). However, prior work has been limited to accounting differences based only on customer demographics (Mittal and Kamakura 2001; Peterson and Wilson 1992) or industry characteristics (Anderson, Fornell, and Rust 1997). Some studies also show that satisfaction ratings and the importance of key drivers can vary over time (Mittal, Kumar, and Tsiros 1999). We show that in addition to the consumer, industry, and time, the geographic location in which ratings are obtained is important. There is a need to develop theory and analytic models that can simultaneously examine all these sources of variation, additively and interactively, to explain customer satisfaction. For example, how do geographic patterns in satisfaction data change over time, and what role do changing demographic patterns play in the observed patterns? Large-scale data (spanning a wide geographic region) with a longitudinal design are needed to answer such questions.
This research can help in the design of satisfaction measurement programs after accounting for regional differences. First, even in the comprehensive and large database we used, data were missing from nearly one-third of the zip codes because the sampling methodology was not designed to obtain the type of data that may address regional issues. Sampling strategies that can provide geographic coverage with limited resources are needed. Second, firms should carefully consider the geographic unit of analysis. Although we used the five-digit zip code as the unit of analysis, this made the data collection task more daunting. A larger unit of analysis (e.g., county) could reduce the data collection burden, but it may result in loss of data resolution. Striking a balance is an important issue for firms. The level at which the firm implements the results is a key deciding factor. Finally, qualitative research is needed to delineate why customer satisfaction and its responsiveness to antecedent variables varies regionally. This should help identify the mediators that link geographic conditions to customer behaviors.
Although the statistically significant impact of weather conditions on the importance of product quality and dealership service is small, it deserves more attention. Peterson and Wilson (1992) mention the relationship, but we are not aware of any large-scale empirical tests of this taken-for-granted relationship. It would be especially useful to test competing mediating mechanisms, such as mood and arousal, by which weather conditions might influence consumer judgments, especially for categories with a seasonal component. Our results imply that the mechanisms are not as straightforward as has been believed. For example, we found variance in weather conditions to affect the importance placed on service and product. In this respect, the homeostatic approach that Parker (1995) and Parker and Tavassoli (2000) advocate seems to account for the pattern observed in the data.
Finally, many methodological challenges need to be addressed. Customer satisfaction data have unique characteristics, such as skewed distributions and high multicollinearity, that make applications of standard spatial models problematic. Different methods should be compared in order to develop guidelines about the conditions in which one class of models may be more appropriate than another. Another important area of research concerns the development of spatial models that can accommodate consumer choice and multiequation systems (Bolton and Drew 1994). Many aspects of the GWR could be improved as well. For example, the assumption of a homogeneous decay parameter for model estimation could be relaxed.
The limitations of our data also deserve attention. Although the results are specific to the automotive industry, the insights developed herein should be universally applicable in several other industries, such as pharmaceuticals, home building, and consumer perishables. As consumers become more mobile, it will become even more important to address the spatial aspects of data. This no doubt will surface new and interesting challenges theoretical, empirical, and managerial ones that should provide the impetus for further research in this area.
This article was supported by funds provided by the Sheth Foundation to the University of Pittsburgh.
( n1) It can be argued that the two-stage approach we use herein, though it produces unbiased estimates, may be less statistically efficient. However, given the large sample sizes involved, efficiency should not be a reason for concern.
Legend for Chart:
A - Sample Characteristics
B - Percentage
A B
Sex
Male 64.2
Female 35.8
Education Level
High school or less 26.3
Some college 27.3
College graduate 46.5
Marital Status
Married 76.0
Single 19.5
Other 4.5
Age
Younger than 30 years 9.4
30-59 years 68.1
60 years or older 22.5
Overall Satisfaction Rating
1 .9
2 .5
3 .9
4 2.1
5 2.8
6 8.7
7 9.4
8 25.4
9 18.5
10 30.7 Legend for Chart:
B - Moran's I
C - Geary's C
A B C
Overall satisfaction -.008 .69
Dealership service -.007 .76
Vehicle quality -.007 .77 Legend for Chart:
A - Model
B - Level of Analysis
C - Estimation Method
D - Range of Predicted Value Averaged Across All Areas(a)
E - Percentage of Observations in a 20% Holdout Sample Falling
Within ±1.96 (σ/√n) of Predicted Value
A B C D E
1 Zip code (13,846) GWR .28 87.05
2 Zip code (13,846) No GWR 1.07 40.42
3 County (n = 3102) No GWR 2.39 72.56
4 State (n = 50) No GWR 3.87 83.58
5 Random sample of GWR .62 71.41
zip codes (n = 17,228)
(a) 95% confidence interval around the mean estimate. Legend for Chart:
A - Parameter
B - Correlation Between the Two Samples Overall
C - Correlation Between the Two Samples Zip Codes with
Observations
D - Correlation Between the Two Samples Data Missing in One
Sample
E - Correlation Between the Two Samples Data Missing in Both
Samples
A B C D E
Intercept .61 (.02) .66 (.04) .41 (.04) .41 (.08)
Dealership service .62 (.04) .68 (.06) .41 (.05) .41 (.07)
Product quality .58 (.06) .70 (.08) .41 (.06) .46 (.11) Legend for Chart:
A - Independent Variables
B - Standardized Regression Coefficients Importance of Automobile
C - Standardized Regression Coefficients Importance of Dealership
Service
A B C
Psychological Landscape Factors
Per capita income .023(**) .071(**)
Sex (percentage of people who are male) .016(*) -.031(**)
Percentage of people who ...
are younger than age 25 .117(*) -.025(**)
are ages 26-35 .064(**) -.010(*)
are ages 36-45 .042(**) .020
are ages 46-60 .053(**) .037(*)
are older than age 60 .062(**) .090(**)
did not go to school .017 -.076(**)
studied until less than ninth grade .085(**) .026(**)
have a high school diploma -.069(**) .072(**)
have an associate's degree -.012 .028(**)
have a bachelor's degree -.031(**) .048(**)
have a graduate degree -.046(**) .073(**)
are Asian -.039(**) -.009
are American Indian -.025(*) -.016
are African American -.047(**) .052(**)
are Caucasian -.031 .016
work in administration .008 .017
work in managerial jobs -.008 .049(**)
work as a laborer -.081(**) .028(*)
work in the farming sector -.051(*) .131(**)
work as a technician .005 -.044(**)
carpool .077(**) -.124(**)
use public transportation -.041(**) -.031(**)
drive less than 20 minutes to work -.056(**) -.002
drive 20-30 minutes to work -.009 .011
drive 31-90 minutes to work .016(*) -.080(**)
drive for more than 90 minutes to work -.020(**) -.031(**)
Physical Landscape Factors
Elevation .016 -.017
Mean minimum temperature .012 .002
Mean rain .015 .085(**)
Mean snow .171(**) .091(**)
Mean maximum temperature .056(**) -.039(**)
Variance in minimum temperature .074(**) .068(**)
Variance in rain -.016 -.012
Variance in snow .156(**) -.067(**)
Variance in maximum temperature -.016 -.053(**)
N 27,584 27,584
R² 6.18% 8.71%
(*) p < .05.
(**) p < .01.DIAGRAM: FIGURE 1 A Framework for Understanding Geographic Influences on Consumer Behavior
GRAPH: FIGURE 2 Frequency of Respondents by Zip Code
MAP: FIGURE 3 Coefficients for Importance of Dealership Services
MAP: FIGURE 4 Coefficients for Importance of Vehicle Quality
MAP: FIGURE 5 Overall Customer Satisfaction
MAP: FIGURE 6 Service Responsiveness for Subpar Satisfaction Areas
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Moran's I
We calculated Moran's I using the following formula:
(A1) [Multiple line equation(s) cannot be represented in ASCII text]
where (Multiple lines cannot be converted in ASCII text), and w(i, j) is the weight given to region i for focal region j. The expected value of Moran s I is -1/(N - 1)). Values of I that exceed -1/(N - 1)) indicate positive spatial autocorrelation. In positive spatial autocorrelation, similar values (either high or low) are spatially clustered. Values of I that are less than -1/(N - 1)) indicate negative spatial autocorrelation (values from neighboring regions are dissimilar), whereas values greater than -1/ (N - 1) indicate positive spatial autocorrelation (values from neighboring regions are similar).
Geary's C
We obtained Geary's C using the following formula:
(A2) [Multiple line equation(s) cannot be represented in ASCII text]
The theoretical expected value for Geary's C is 1. A value less than 1 indicates positive spatial autocorrelation, and a value greater than 1 indicates negative spatial autocorrelation.
For our analysis, we constructed the weighting matrix for the two indexes using the physically adjacent neighbors as regions that influenced the focal region. We identified the immediate physical neighbors using Delauney triangulation (LeSage 1998).
~~~~~~~~
By Vikas Mittal; Wagner A. Kamakura and Rahul Govind
Vikas Mittal is Associate Professor of Marketing, Katz Graduate School of Business, and Associate Professor of Psychiatry, School of Medicine, University of Pittsburgh (e-mail: vmittal@katz.pitt.edu). Wagner A. Kamakura is Ford Motor Company Professor of Global Marketing, Fuqua School of Business, Duke University (e-mail: kamakura@duke.edu). Rahul Govind is Assistant Professor of Marketing, University of Mississippi (e-mail: rahulg@pitt.edu).
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Record: 68- Getting Published: Reflections of an Old Editor. By: Stewart, David W. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p1-6. 6p. 1 Chart. DOI: 10.1509/jmkg.66.4.1.18520.
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Getting Published: Reflections of an Old Editor
My time as editor has been a dynamic period for the marketing community. It has witnessed the maturing of information technology, the Internet boom and bust, and the collapse of some of the most widely recognized and trusted of brands, including Andersen, Sunbeam, Oldsmobile, and Firestone. Even such seemingly powerful brands as Disney, McDonald's, AT&T, and Coca-Cola have struggled. At the same time, the attention of the popular business press and the public at large has become increasingly focused on markets and marketing practices. Issues of intellectual property, consumer privacy, and fair business practices have become part of the daily business headlines. The celebrity attorney has been replaced by the celebrity chief executive officer. The locus of economic growth and new organizational forms has increasingly shifted westward from Europe, the East Coast, and the Midwest to the West Coast and Asia. The only exception to these broad shifts appears to be Japan, once feared as an economic powerhouse and now reduced to a troubled economy.
Change is evitable and is what sustains the need for research and scholarship. Paradigms, assumptions, and even "facts" that dominate thought and practice at one point give way to new facts and modes of thought. In my editorial statement at the beginning of my term as editor (Stewart 1999), I discussed the role of Journal of Marketing (JM) as a tangible artifact of an intellectual community. This intellectual community is very much alive and responsive to change. It is again time for a change in the stewardship of the Journal. This change will, no doubt, bring new energy, new perspectives, and new directions to the Journal. However, editors merely manage in a modest way the real changes that occur in the intellectual discipline. Most of the articles to be published by the new editor during her first year are already written and in the review process. The problems and topics on which scholars within marketing's intellectual community are working and will work reflect the broader social and economic environment of which marketing is a part. An editor has little, if any, influence on this environment. However, an editor plays an especially important role in ensuring that the scholarship that derives from response to change is captured and published on a timely basis.
I discuss three things in my last editorial as editor: First, I describe the changes that have occurred in the Journal over the past three years. These changes suggest a great deal about the nature of the changes in markets and marketing practices that have taken place. Second, I share my perspective as an editor on what is most likely to be valuable to the discipline and find its way into the pages of the Journal. This will be a brief treatise on how to get published. Third, I acknowledge the contributions of the many people who have contributed to the Journal's success.
The marketing discipline has changed in fundamental ways in recent years. The contents of JM reflect that change. The community of which the Journal is a part broadened its focus in recent years. This broadened focus is reflected in the content of the Journal. In preparing for my role as editor, I did a coarse and subjective analysis of the content of JM from 1996 to 1998. I repeated this content analysis for the articles published during my tenure as editor from the beginning of 2000 to the end of 2002. Table 1 provides a summary of these two analyses. Although these analyses are crude and certainly miss important nuances, a comparison of the two analyses is nonetheless revealing.
A particularly striking finding in this comparison is what is not present. The Internet bubble is barely visible in the content of the Journal. I believe this says something positive about the Journal and its review process, as well as the field of marketing. In a journal devoted to the identification of generalizable market and business phenomena, there is no place for fads. This does not mean that JM has ignored the Internet, e-commerce, and the information revolution. Quite the contrary is the case. However, much of what has been published in the Journal about the Internet and e-commerce has not focused on the venue as the rationale for the contribution. Rather, the focus has been on more fundamental market phenomena and business practices. The Internet and e-commerce are merely components of these larger phenomena and practices.
As is now obvious to almost everyone, the Internet and e-commerce are important because they can substantially reduce transaction costs in the value delivery systems that serve some markets, and they can selectively create markets that were not economically viable because geographic dispersion made search for and transactions within them unattractive to both buyers and sellers. However, it is not clear that the Internet created much incremental demand. All of this means that the real benefits of the Internet and e-commerce are related to the well-known and even mundane practice of taking marketing share from competitors through reduced costs or superior quality or service. Some marketing scholars were making these points even at the peak of the Internet frenzy.
It is important for the most influential journals in a field to remain above the hype and faddishness that often characterize the popular business press and many consulting practices. The credibility of the Journal and the discipline rests on the ability to distinguish the important general phenomena from the trivial.
With a modest bit of interpretation, Table 1 demonstrates that the focus of the field of marketing has been changing over time. Relationship marketing has become a more prominent part of JM's content. Similarly, work on the unique characteristics of services marketing has increased. Indeed, there is a strong link between relationship building and much of what is done in the design and delivery of services. Work that once focused exclusively on channels of distribution has largely been replaced with work that focuses on broader strategic issues of design of value delivery systems and interorganizational governance. Business-to-business marketing has similarly been subsumed within broader frameworks for conceptualizing value delivery systems that are ultimately driven by end-user consumers (at least in developed markets).
Focus on customer satisfaction appears to have been replaced by a greater focus on the value of the customer. Time and practice have shown that a satisfied customer is not always a profitable customer, and unsatisfied customers are often cost sinks that the firm would do better ignoring than trying to serve. Pricing is an essential component of the value equation, but in the past several years it has been more closely linked to such topics as the design of value delivery systems, branding, and relationship building.
The marketing mix remains alive and well in the content of JM. Product design, branding, advertising and promotion, personnel selling and sales management, and retailing all continue to be well represented in the pages of the Journal. Close examination of the content of articles on these topics reveals changes in the broader environment. Thus, the markets and industries in which data are obtained tend to reflect growth sectors such as services and high technology.
Some changes in the Journal are not so visible from a cursory examination of its content. The Journal has become more international. However, it has become more international by virtue of its authors rather than because of a specific focus on the unique characteristics of international markets. Almost one-third of the articles published during the past three years have had at least one author who was affiliated with an institution outside the United States. The source of data for empirical studies has also increasingly been from non-U.S. settings. These particular changes do not mean that the Journal has published more "international marketing" articles. Quite the contrary is the case; few articles are published simply because they are international. Rather, the international composition of published authors and the international sourcing of empirical data mean that the community of scholars has expanded and work is now being carried out that is even more likely to be generalizable across geopolitical and cultural boundaries.
One important characteristic of the Journal has not changed over time. The Journal of Marketing continues to welcome the broadest possible array of methodological tools for describing and explaining market and marketing phenomena. Data and empirical results obtained through qualitative methods, interpretive research, model building, experiments, surveys, and analysis of archival records are to be found in the pages of the Journal. This eclectic mixture of data and research methods is indicative of a journal that is driven by theory and substantive problems rather than method. This is a healthy characteristic and suggests an intellectual vitality in the discipline.
The changing character of JM's content and the eclectic mix of methods found in it suggest that there is no easy formula for describing articles that are published in the Journal. Given that JM's mission is to publish the best work in the discipline, it should not be surprising that there is no simple formula for publication. Indeed, if ever such a cookie-cutter approach were to describe the Journal, it would suggest that JM has lost its way and inevitably will lose its influence.
The Journal of Marketing continues to be among the most frequently cited journals in all the social sciences precisely because it publishes articles that make unique contributions.
Although there is no simple formula for publication in JM, articles that are published share some important characteristics. These characteristics may not be so obvious to the reader or author, but they tend to emerge in the course of an editor's work in processing more than a thousand manuscripts. There are only a few basic reasons papers are accepted or rejected, though the precise ways these reasons manifest themselves in specific papers are legion. So what are the keys to success?
Know the Journal
The first step in publishing in JM is developing an under-standing of the role of the Journal, which publishes only about 10% of the manuscripts that are submitted to it. It publishes the best work in the entire field of marketing; it seeks articles that make broad contributions to the field. As a result, an otherwise very good paper in a narrow area may not be acceptable for publication in JM. This does not mean that the paper is poor; rather, it means that the paper does not make enough of a general contribution to the marketing discipline to warrant its selection over other papers. The best papers on advertising compete for space against the best papers on channels of distribution. These papers, in turn, compete for space against the best papers in every other area of marketing. A remarkably large percentage of the papers submitted to JM, including those that are eventually published, do a poor job of describing the incremental contribution of the paper. Therefore, a good place for authors to begin when considering publication in the Journal is to focus on the nature of the contribution of the paper.
Although it is a good practice to review past issues of JM for form and content, examination of past issues is not the best guide to the incremental contribution of a paper. Past issues can indicate what is already known in an area and therefore serve as a benchmark for assessing a paper's contribution. However, just because a topic has been addressed often in prior literature is no guarantee that a paper on the same topic will be publishable. Areas of inquiry mature over time, and research on a given topic tends to become less incremental and less interesting. The thirty-third paper on a topic
is simply not as useful or interesting as the first paper. Similarly, just because something was done in a previously published article is not, in and of itself, justification for a practice. Sloppy methodology or an incomplete model may be over-looked in the first article on a topic(because the topic has not been previously explored), but it is unlikely to be acceptable in subsequent work (because the earlier flaws in need of correction should now be obvious from the prior work).
It is also important to recognize that JM's mission is to publish substantive contributions to the marketing discipline. The Journal seeks papers that make conceptual, theoretical, or empirical contributions; it does not publish papers in which the primary contribution is methodological, measurement related, or modeling oriented. The Journal publishes articles that make a substantive contribution and make secondary contributions to methods, measures, or modeling. Indeed, such secondary contributions can increase the over-all contribution of a paper and sometimes make the difference in whether a paper is published or rejected. Such methodological contributions are not substitutes for substantive contributions, however. Another important characteristic of JM is related to its readership. The Journal of Marketing has the largest circulation of any academic marketing journal. The vast majority of its subscribers are not academics. The content of the Journal must be accessible to these readers. It is also important to recognize that as a general marketing journal, JM is read by academics who are not specialists in a given area of marketing. This means that even well-published academic readers may not be familiar with the unique jargon or specialized research methods employed in a given paper. For this reason, JM places a high premium on the readability of papers. Many papers are rejected simply because they are inaccessible to the reader (usually by reviewers who are more knowledgeable than the typical JM reader about the topic). Authors occasionally complain that reviewers did not seem to understand or were not well versed in an area with which a paper deals. In most cases, this lack of understanding is not the fault of the reviewers. Rather, it was not the reviewer who failed to under-stand but the author who failed to communicate. Most authors are too close to their work to know whether they are actually communicating. A good copy editor, who is always available at a price, can work wonders with otherwise obtuse prose. Presenting a paper or having others read it can also uncover a host of incomprehensible passages of text that seem crystal clear to the author and typographical errors that obscure the intended meaning of a statement.
Knowing the journal to which you are submitting is an essential first step in publishing. The Journal of Marketing has unique characteristics, and prospective authors should be aware of these. A lack of familiarity with the Journal is often all too obvious: I have had papers forwarded to me by past editors (some of whom have not been editors for ten years), I have received papers that identify the Journal by the wrong name, I have received papers in which the cover letter clearly identifies the topic of the paper as inappropriate for JM, and I have received papers so filled with typos or poor language that I could not send them out for review.
Increasing the Odds of Rejection
The most common reason that JM rejects a paper is that it lacks a sufficient incremental contribution. I learned early in my tenure as editor that this explanation for rejecting a paper is especially disconcerting to authors. Such a rationale is based on a subjective judgment (though informed by the opinions of reviewers). It is also difficult to tell an author exactly what must be done to improve the contribution of a paper. Sometimes the methodology in an empirical study is so flawed that the contribution could be improved only by redesigning the study. More often, there is nothing wrong with the methodology; the issue addressed is just not very important.
There are things a prospective author can do to improve the likelihood that a paper will make a sufficient incremental contribution. There are also types of papers that are more likely to be found wanting with respect to incremental contribution. The best approach to making an important incremental contribution is to do something interesting. Whether something is interesting or not is an empirical question. So share your idea with others; ask if something to which you are considering devoting time and resources is interesting to others. You might still wish to work on the idea even if others do not find it interesting, but be aware that such ideas have a low probability of being published. Read the relevant literature and cite it. It is unlikely that you are the first to examine a particular issue or problem. You may be the first to examine a particular dimension of the problem or issue, but you need to articulate clearly what your paper adds or what gaps it fills. This is another area in which authors often complain that reviewers do not understand or have not paid attention to in their papers. What usually happens is that key literature is ignored and a paper's contribution is framed in terms far too general. Interesting may compensate (to a degree) for conceptual or methodological sloppiness, but it is better to offer a sound conceptual and methodological approach that suggests acquaintance with previous work in the same area.
There are types of papers that have a particularly difficult time meeting the necessary hurdle for incremental contribution. This does not mean that such papers are never published, but they carry an especially heavy burden for demonstrating the significance of their contribution, especially in a journal like JM. Venue is seldom a sufficient rationale for publishing a paper. Just because a particular phenomenon has not been examined in a particular venue (such as a specific industry or country) is not a good reason to do a study. This is the problem with much of the research in an international or Internet context. For example, it is unclear why consumer decision making should necessarily be different in Malaysia. If it is not found to be different, investigation of the topic has a "so what?" quality. If it is found to be different, finding a difference alone is insufficient without an explanation for the difference. Unfortunately, all too often the real reason for any obtained difference is trivial (consumers in Malaysia are not as familiar with the stimulus brands as consumers in the United States; the required translation of the questionnaire produced differences in the meaning of the questions compared with the original instrument). Similarly, trust and reputation are important in many markets. Why would it be assumed that there is any difference in an Internet context? Venue-driven research bears the burden of making the case that venue should matter for some important reason and then demonstrating that the expected difference is present for the reason posited. This is a high hurdle.
Replications are not compelling for similar reasons. A replication that works has a "so what?" character. A replication that does not work raises questions about why. Replications may fail for many reasons, and most of these reasons are not interesting. A replication may fail because a different measure was used, a manipulation failed, or the sample was inappropriate. A failure to replicate bears the burden of explanation. Such failures can be important for the establishment of the boundary conditions of phenomena, but this too is a high hurdle.
A variation of replication is the addition of a new variable. This type of research often starts with well-established research and is justified by a finding that adding a previously unexamined variable accounts for some additional variance. Such research can be interesting, especially if the new variable suggests boundaries for a phenomenon, but as with venue-driven research and replications, the burden for demonstrating the importance of a contribution is high. The questions raised are why this particular variable is examined and others are not, why the selected variable is theoretically relevant, and how important the added variance accounted for really is. Some authors employ a strategy of taking selected variables from a larger data set and attempting to make a stand-alone paper from these variables. They then take another subset of variables and attempt to construct another paper. Nothing is inherently wrong with trying to leverage a data collection effort. However, there are some inherent dangers in doing so. Often, a single paper using all the available data makes for a far stronger contribution than several papers each with a far more modest contribution. I have found it common for reviewers to ask for additional information or data and for the authors to respond that, indeed, they already have the necessary data. Another danger in this approach is that each subsequent study is likely to be less important than the last, especially when some of the same variables are used in each study. Therefore, even when the first study is published, the second study may not be. The result is a weaker first paper and no second paper.
Data fitting is not usually interesting no matter how sophisticated the model or method is. This is the problem with much of the recent work employing structural equation modeling. There is an appropriate use for fitting models, but merely showing that a set of data fits a model is not an especially compelling contribution. The same data may fit many different models with different theoretical implications. Data may also fit a model for reasons as uninteresting as the use of variables with highly correlated errors. The burden on the author is to make a compelling case that there is theoretical or practical significance associated with fitting a given model. Similarly, descriptive and purely exploratory work is generally less interesting. Such work certainly can be important for the development of taxonomies or for the identification of important relationships that have heretofore not been identified. Merely describing a phenomenon without some effort to suggest a conceptual basis for the observations leaves much unanswered.
Finally, compelling contributions tend to avoid the obvious. Many relationships have not been tested (especially in specific venues), but there is a reason they have not been tested. If outcomes are obvious, there is no real need to test them. Demonstrations that the obvious does not hold can be interesting, but failures to demonstrate the obvious, when failure means an inability to reject the null hypothesis, are incomplete. There are many reasons for obtaining results that do not differ from the null: insufficient power, insensitive measures, and weak manipulations, among others. Again, the burden of demonstrating an interesting and compelling contribution is high for such research.
Increasing the Odds of Being Published
In addition to selecting interesting problems or questions on which to work, there are other things authors can do to increase the odds of publishing a paper. Get feedback early and often. You can get feedback by presenting the paper at conferences, at other universities, and to your own colleagues. Indeed, the colleagues with whom you interact each day are an especially important source of feedback about how interesting the paper might be, how well you communicate, how you might better design a study and analyze its results, and what the results might mean for theory and practice. This is why choosing colleagues is such an important part of a career decision. Good colleagues who are themselves active scholars are an invaluable resource.
If you do not have a large set of local colleagues, and even if you do, ask others to read your paper. Identify key people working in the same area, regardless of their location; share your work with them; and ask for feedback. Part of the feedback you obtain should be finding out what others are working on in similar areas and reviewing what has been recently published in the area. As an editor, I could usually identify the papers that had been read by no one other than the author before submission. Feedback helps and raises the odds of publication. Early feedback even helps you avoid work on a project that has a low probability of success in the first instance.
Listen to the reviewers; they are generally trying to help. Reviewers often do understand what they read, but authors are often not clear about what they mean. Identifying and correcting such gaps in communication are part of the purpose of the review process. Reviewers disagree far less often than many authors believe. Indeed, reviewers generally agree on the problems that exist in a particular paper. Disagreements sometimes occur when reviewers offer potential solutions for these problems. For example, all the reviewers may agree that one problem with a paper is insufficient power in a statistical test. One reviewer may suggest increasing sample size to solve this problem, another reviewer may suggest using a more sensitive measure, and a third reviewer may suggest using a covariate to reduce the error variance. In such a case, it is not unusual for authors to become concerned that the reviewers disagree about the approach they should take in the revision. They lose sight of the real problem, on which all agree. It is the solution to the problem rather than the particular approach that is important. Authors sometimes use such "disagreements" disingenuously to dismiss reviewers' comments. An editor can help sort through these kinds of disagreements, but it is important for authors to recognize that there is a difference between disagreements on fundamental problems and disagreements with respect to suggestions for resolving the problem. The author who accepts the existence of the problem and works to resolve it will likely be met with a positive response from all the reviewers, regardless of the particular solution that was adopted.
Arguing with reviewers is seldom helpful and may even hurt your case. If you believe a reviewer is wrong, appeal to the editor. Let the editor know the source of your disagreement and offer whatever supporting evidence you can muster. In your response to the reviewer, you need only politely indicate that you disagree and have asked the editor for advice.
When you have a question, contact the editor. It is part of the editor's responsibility to help you through the editorial process. Ultimately, the editor must make a decision about your paper, however. This is a decision about your paper; it is not about you. The goal of the editor is to fill issues of the journal, subject to a quality constraint. This means that your incentives and those of the editor are closely aligned. However, you should listen to what the editor asks you to do. If you are unwilling to make the changes that have been requested, you probably should withdraw the paper and submit it elsewhere. If you are given the opportunity to revise a paper, do so and do so quickly. Any opportunity to revise is a victory and moves you a step closer to publication. The odds of eventually being published go up substantially when a revision is invited. In recent years, 75% of papers have been rejected after the initial review. Of the papers whose authors have been given the chance to revise, half have eventually been published. Of the papers that have gone through a second revision, 90% have been eventually published, though some of these have required several more rounds of revision.
Much of the research in which we are engaged is time sensitive. Other scholars likely are working on similar topics, and some phenomena change over time in important ways. This means that the longer you wait to revise and resubmit a paper, the more likely its contribution will be lower by the time it is reviewed again. Authors occasionally become annoyed at the "inconsistency" of reviewers (and editors), because they were told a paper was interesting and made an important contribution in one round of reviews and subsequently were told that the paper's contribution was not great in the next round of reviews. The odds that this will happen go up the longer it takes the author to complete the revision. In such cases, the reviewers are not being inconsistent; rather, their evaluations are time dependent. Also, the longer the interval between a review and revision, the more likely it is that a reviewer or editor will drop out of the process. This is not always a problem, but sometimes new reviewers (sometimes multiple new reviewers) must be brought into the review process. Editors do not like to do this, and new reviewers often raise new issues. Although this is frustrating to authors, it is often the authors who are to blame for failing to revise on a timely basis.
Appeals are possible but seldom successful. When an editor has made a decision, it is rare that the decision will be reversed. If you believe that an injustice has been done, you can appeal to the editor. In some cases, the editor will then assign the paper to a referee for an assessment. Usually this process involves sharing the paper, all reviews, all responses to the reviewers, and all correspondence by the editor with a senior scholar in the field (often a former editor). The senior scholar is asked for an assessment of the decision and in some cases recommends that the authors be provided a chance to revise the paper. Revisions then go back to the original reviewers. Such appeals are at the discretion of the editor, and authors have the burden of suggesting reasons the request is appropriate. The majority of such appeals are resolved with a negative outcome, and even when revision is invited, it is rare that the author is able to resolve the issues (papers are rejected for good reasons). Rejection is inevitable for active scholars; learn from rejection. The comments of the reviewers and editor often suggest the direction for a new paper. New papers (new data, new conceptual framework) are welcome. Rejected papers often form the basis for new work on the same topic. Indeed, as editor, I have encouraged authors of papers on interesting topics to develop a new paper on the same topic even as I rejected their current submission. I did not do so to be polite. Rather, I was encouraging further work on a subject I believed would be a good addition to the Journal. Also, many of the papers that are rejected by JM are published elsewhere. The feedback from the review process can be helpful to an author in revising a paper for another journal.
Editors want to know the history of a manuscript. If your paper has been reviewed and rejected by another journal, it is in your self-interest to let the editor know this. Often, the editor will contact the editor of the other journals to determine who the reviewers were. This enables the editor to find new reviewers who do not have a previous history with the paper. It is common for the same reviewer to receive a paper from different journals when the history of the paper is unknown to the editor. In the worst case, the reviewer informs the editor that he or she has seen the manuscript before and that the current paper is identical to the one previously reviewed. Needless to say, this does not impress either the reviewer or the editor with respect to the author's willingness to learn from the review process and improve the paper in response to constructive criticism. Editors are also open to suggestions for potential reviewers as well as suggestions for reviewers to avoid because they have seen the paper before or have a different point of view from that expressed in the paper. Editors are not always able to act on these suggestions, because reviewers may have other papers or be otherwise unavailable. However, editors attempt to use suggestions when they are offered.
Summary
There should be no mystery associated with the publication process. It is in the interests of everyone that authors under-stand the mechanics of the publication process. At one level, getting published is quite simple: ( 1) Work on interesting problems; (2) obtain feedback by having others read your work and by presenting it; (3) write well and, if necessary, use a copy editor (especially if English is not your first language); (4) be succinct and use space wisely; (5) provide relevant details (What did you do? What literature did you use? What is your contribution?); (6) if given the opportunity to revise, do so and quickly; (7) listen to the reviewers and the editor; and (8) learn from and build on rejection.
At another level, getting published is difficult. It is about creativity, and creative enterprises are always risky. Managing such risk involves persistence and hard work, but it is worth the effort if the goal is a contribution to the intellectual community.
A Word of Gratitude
In closing, I thank all the members of the marketing community who have contributed to the Journal and made my time as editor easier and enjoyable. I first want to thank our readers, especially readers who do not otherwise directly contribute to the Journal through submission and reviewing of papers. Great journals must have a readership base that is broader than its authors and reviewers. The impact of a journal depends in no small measure on its ability to influence the thinking and behavior of its readers. The Journal of Marketing plays an important role in influencing the marketing discipline through its readers who come to it solely as a source of information and ideas. To the readers of the Journal, I say thank you for your continuing confidence in the ability of the Journal to inform, stimulate thinking and creativity, and motivate behavior. Second, I thank all the authors who have submitted papers to the Journal. The quality of the articles published in the Journal and the Journal's influence are a function of both the papers that are published and those that are submitted but not selected for publication. Because the mission of the Journal is to publish the best conceptual and empirical work in the marketing discipline, the willingness of authors to submit their best work to JM is critical to its success. To all of the scholars who have submitted papers, I say thank you. The quality of the articles published in JM and the impact these articles have on the field of marketing are significantly enhanced by the insights, comments, and constructive criticism of the scholars who review them. I thank the members of the editorial board who have shouldered a heavy burden during my editorship. I also thank the many ad hoc reviewers who have contributed to the review of papers. Authors routinely comment that the reviews they receive from JM are among the timeliest and most constructive they have received from any journal. These comments reflect the quality of the reviewers rather than the editor. The timely and constructive criticism provided in the review process is one reason the Journal is so strong; these characteristics encourage authors to submit their best work.
I also thank the past and future editors of the Journal. Past editors left me a strong legacy on which to build; I look forward to the continuation of that legacy by future editors. I especially want to thank Robert Lusch, my predecessor, for introducing me to the role of editor and for smoothing the transition between editors. I wish Ruth Bolton much success as editor and thank her for her help during the waning portion of my term.
The journal staff of American Marketing Association has made the task of editor far easier than it might have been. I appreciate their cooperation and assistance. My colleagues in the Marshall School at University of Southern California and my co-authors at other institutions deserve particular recognition. They have (usually) cheerfully tolerated my absence from other departmental obligations and delays in the completion of joint research projects.
Finally, I owe a debt of gratitude to Brenda Miller, who has served as my editorial assistant throughout my term. The Journal would not have operated so smoothly or be where it is today without her many contributions.
I appreciate the opportunity to have served as editor of Journal of Marketing. It has been stimulating, educational, and most of all fun. I wish everyone as much fun in the future.
TABLE 1 Comparative Content of Articles in JM
Articles Published 1996-98
Advertising and promotion (16)
Market orientation and organizational design (8)
Personal selling and sales management (8)
Product development and management (7)
Channels of distribution (5)
Marketing strategy (5)
Customer satisfaction (4)
Relationship marketing (4)
Pricing (4)
Services marketing (4)
History and philosophy of marketing (3)
Knowledge management and decision support systems (3)
Social influence (3)
Public policy and regulation (3)
Marketing research and demand forecasting (2)
Internet marketing and interactive shopping (2)
Retailing (2)
Packaging (1)
Buyer behavior (1)
Articles Published 2000-2002
Relationship marketing (10)
Advertising and promotion (10)
Marketing strategy (8)
Services marketing (8)
Product development and management (8)
Personal selling and sales management (5)
Organizational design and performance (5)
Critical analysis/interpretive analysis of marketing phenomena (4)
Retailing (3)
Value of customers (3)
Brands and branding (2)
Buyer behavior (2)
Quality (2)
REFERENCES Stewart, David W. (1999), "Beginning Again: Change and Renewal in Intellectual Communities," Journal of Marketing, 63 (October), 2-5.
~~~~~~~~
By David W. Stewart
David W. Stewart is Robert E. Brooker Professor of Marketing and Deputy Dean, Gordon Marshall School of Business, University of Southern California.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 69- Getting Return on Quality: Revenue Expansion, Cost Reduction, or Both? By: Rust, Roland T.; Moorman, Christine; Dickson, Peter R. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p7-24. 18p. 5 Charts. DOI: 10.1509/jmkg.66.4.7.18515.
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Getting Return on Quality: Revenue Expansion, Cost Reduction, or Both?
Financial benefits from quality may be derived from revenue expansion, cost reduction, or both simultaneously. The literature on both market orientation and customer satisfaction provides considerable support for the effectiveness of the revenue expansion perspective, whereas the literature on both quality and operations provides equally impressive support for the effectiveness of the cost reduction perspective. There is, however, little evidence for the effectiveness of attempting both revenue expansion and cost reduction simultaneously, and some of what little empirical and theoretical literature is available suggests that emphasizing both simultaneously may not work. In a study of managers in firms seeking to obtain a financial return from quality improvements, the authors address the issue of which quality profitability emphasis (revenue expansion, cost reduction, or both) is most effective. The authors examine firm performance using managers' reports of firm performance and longitudinal secondary data on firm profitability and stock returns. Although it is clear that no company can neglect either revenue expansion or cost reduction, the empirical results suggest that firms that adopt primarily a revenue expansion emphasis perform better than firms that try to emphasize cost reduction and better than firms that try to emphasize both revenue expansion and cost reduction simultaneously. The results have implications with respect to how both theory and practice view organizational efforts to achieve financial returns from quality improvements.
Consider the chief executive officer (CEO) of a firm facing an important strategic decision. There are two competing strategic initiatives on the CEO's desk. The chief operating officer notes that Motorola, GE, DuPont, and other high-profile companies have adopted a Six Sigma program (Pande, Neuman, and Cavanagh 2000) that suggests that the route to higher profitability is through improving efficiencies and cutting costs. The vice president of marketing would prefer to increase profits by building revenues through improvements to customer service, customer satisfaction, and customer retention (Johnson and Gustafsson 2000). From these recommendations, it appears that the chief operating officer views quality in terms of internal processes, whereas the vice president of marketing views quality in terms of external customer relations. At this point, the chief financial officer states emphatically that, according to the shareholders, the most important issue is whether the chosen strategy generates acceptable financial return. The purpose of our research is to provide empirical findings that may help determine the primary way of deriving financial returns from quality--what we refer to as a firm's "quality profitability emphasis."
The scenario we depict is a common occurrence in contemporary organizations. Firms increasingly pay attention to the financial return obtained from strategic initiatives (Copeland, Koller, and Murrin 1996). Using such approaches as economic value added (Ehrbar 1998), firms assess the extent to which strategic initiatives increase net operating profits compared with the opportunity cost of capital. This trend has also affected marketing managers, who must focus on the financial implications of their decision making and on conceptualizing marketing expenditures as investments (e.g., Srivastava, Shervani, and Fahey 1998). Consistent with this, efforts to quantify the financial impact of customer-perceived quality have proliferated in recent years (e.g., Anderson, Fornell, and Lehmann 1994; Heskett, Sasser, and Schlesinger 1997; Johnson and Gustafsson 2000; Rust, Zahorik, and Keiningham 1995). An important part of this effort has involved understanding the nature of service quality (Parasuraman, Zeithaml, and Berry 1985) and how its management can produce the greatest impact on financial outcomes.
One of the challenges associated with making strategic decisions about quality is that its conceptualization varies by discipline. In marketing, quality tends to mean quality as perceived by the customer (e.g., Bolton and Drew 1991a; Parasuraman, Zeithaml, and Berry 1985). In operations and quality management, quality tends to mean the efficiency and reliability of internal processes (e.g., Crosby 1979; Deming 1986), even if those processes are invisible to the customer (Ramaswamy 1996). Depending on how quality is defined, different kinds of quality improvement efforts are likely to be appropriate, and most important, they are likely to have different pathways to profitability. Although some quality improvements may increase revenues and decrease costs simultaneously, efforts to improve customer-perceived quality usually increase profits through revenue expansion, and efforts to improve the efficiency of internal processes tend to increase profits through cost reduction. Our conceptualization spans both of these viewpoints and explores their differences by studying three emphases for managing the financial returns associated with quality: revenue, cost, and dual (both revenue and cost combined).[ 1] We now review each quality profitability emphasis in detail and derive competing testable propositions. Table 1 summarizes various features of these emphases.
Although high-quality internal processes can serve the customer (Nilsson, Gustafsson, and Johnson 2001), a revenue emphasis to quality profitability focuses externally--on customer perceptions and attitudes that will lead to more sales.[ 2] Therefore, programs emphasize improving quality by addressing the issues that have the greatest impact on over-all customer satisfaction. These programs may occasionally lower costs, but more often costs rise as the firm delivers a higher level of quality that meets customer needs. Documenting the impact of customer satisfaction and retention on revenues is somewhat more difficult than documenting cost reductions, because the path from customer perceptions to financial results is indirect and must be modeled statistically (e.g., Anderson, Fornell, and Lehmann 1994; Johnson and Gustafsson 2000; Nelson et al. 1992; Rust, Zahorik, and Keiningham 1995). The pathways from customer satisfaction to revenue include customer attraction (Kordupleski, Rust, and Zahorik 1993), customer retention (Bolton 1998), and word of mouth (Anderson 1998; Danaher and Rust 1996). Approaches include measurement of customerperceived service quality (Bolton and Drew 1991b; Kordupleski, Rust, and Zahorik 1993), measuring customer satisfaction (Churchill and Surprenant 1982; Fornell 1992), and measuring the disconfirmation of customer expectations (Oliver 1980; Parasuraman, Zeithaml, and Berry 1988).
Several arguments provide support for the revenue emphasis. One reason may be the capabilities of information technology. In customer relationship management, for example, computational power facilitates the storage and processing of customer data, making it easier to address specific customer needs (Greenberg 2001). Combining computing power with a wide-ranging communication network over the Internet enables firms to listen to customers, store and process their preferences, and respond to them with ever-greater customization (Peppers and Rogers 1999).
The revenue emphasis implies a customer focus and a market orientation, and a voluminous literature has emerged to support each of those ideas. Extensive research linking customer satisfaction and customer-perceived quality to positive business outcomes supports the effectiveness of a customer focus (for a review of this literature, see Zeithaml 2000). Despite the popular press's protests that customer satisfaction is not enough (Gitomer 1998), the academic literature provides overwhelming evidence that customer satisfaction profoundly affects revenue-generating behavior (Zeithaml, Berry, and Parasuraman 1996) and business performance outcomes (Anderson, Fornell, and Lehmann 1994; Danaher and Rust 1996; Fornell 1992; Fornell et al. 1996; Hallowell 1996; Loveman 1998; Rust, Zahorik, and Keiningham 1995). For this reason, the marketing literature has developed considerable knowledge about customer satisfaction (Oliver 1980) and the critical incidents and service environment that produce it (Bitner 1992; Bitner, Booms, and Tetreault 1990). Likewise, the market orientation literature shows strongly that firms that have a market orientation perform better than firms that do not, a finding that is supported by literature in customer orientation and strategy (e.g., Johnson 1997; Porter 1996; Prahalad and Krishnan 1999).
The first source of evidence that a revenue emphasis to quality profitability exerts a strong positive effect on performance outcomes stems from research on customer-or market-oriented approaches to managing organizations. Although several studies on this topic exist (e.g., Jaworski and Kohli 1993; Kohli and Jaworski 1990; Moorman 1995; Narver and Slater 1990), we focus on two that are most diagnostic for our discussion of the three quality profitability emphases.
First, Deshpandé, Farley, and Webster (1993) define four types of organizational cultures that emphasize the customer to various degrees. They find that market cultures that place the customer's interests first are the most profitable. Among the other cultures investigated, the hierarchical culture (which most closely resembles the cost emphasis because of its strategic emphasis on stability, efficiency, and smooth operations) is found to be the least profitable. Second, Day and Nedungadi (1994) show that senior managers tend to adopt one of four types of competitive advantage models (high customer/high competitor, low customer/high competitor, high customer/low competitor, and low customer/ low competitor). A competitor model of strategic emphasis is on low costs through low-cost processing and lowest delivered cost, whereas a customer model builds revenue through superior customer service, market scope, and innovation. The results indicate that customer-oriented models positively affect a firm's financial performance, whereas competitor-centered models negatively affect a firm's financial performance. Note that though a customer focus and a market orientation are necessary conditions for the revenue emphasis, they are not sufficient. That is, a firm possessing both a customer focus and a market orientation may be classified as using the dual emphasis instead of the revenue emphasis if the firm simultaneously emphasizes cost reduction. Either emphasis (revenue or dual) would be consistent with both the customer satisfaction and the market orientation literature. In other words, although the literature states strongly that customer focus and market orientation lead to positive financial outcomes, it does not indicate whether the revenue emphasis will be preferred to the dual emphasis (or vice versa). Because the existing literature does not reveal which quality profitability emphasis is best, our study adds to the conclusions of the market orientation and customer satisfaction literature by disentangling the issue of which emphasis should be preferred.
In summary, advocates of quality profitability programs that emphasize revenues argue that profitability improvements associated with quality efforts will come primarily through serving customer needs that trigger satisfaction and retention. Consistent with a goal of presenting competing hypotheses, given the evidence in the literature and the continuing expansion of information technology and customer relationship management, we predict the following:
H1: A revenue emphasis to quality profitability will have stronger positive effects on firm performance outcomes than either a cost emphasis or a dual emphasis.
The cost emphasis focuses on the efficiency of the firm's processes. General cost reduction efforts (e.g., downsizing) do not necessarily improve efficiency, but quality efforts that reduce costs always do. Successful programs tend to increase the productivity of quality efforts by reducing the input (labor and materials) required to produce a unit of output. These improvements can be incremental (continuous improvement) or discontinuous (process reengineering); in either case, the focus is internal and the goal is to reduce costs. Customer satisfaction improvements are sought only indirectly, through such results as increased reliability or lower prices. Cost reduction programs thus transfer their savings to the bottom line directly. Methods of quantifying cost reductions are referred to as "cost of quality" programs (e.g., Bohan and Horney 1991; Campanella 1990; Carr 1992; Gryna 1988). Philosophically, these programs are akin to the total quality management programs of the 1980s and 1990s (Spitzer 1993), and modern variants have continued to emerge (e.g., Six Sigma; Breyfogle 1999).
Since Crosby (1979) introduced his method of classifying and measuring quality costs, many firms have documented significant profit impacts through improved quality by means of advancements in computation (e.g., mainframe computers, followed by personal computers and micro-processors) and communication (e.g., the Internet, wireless communication networks). Computational advances have enabled widespread use of statistical quality control techniques, thereby increasing companies' abilities to improve operating efficiencies and cut costs (Wheeler and Chambers 1992). This has resulted in a measurable profit impact from the implementation of quality principles and programs (Easton and Jarrell 1998; Hendricks and Singhal 1997). To some extent, information technology, the Internet, and other communication networks have also increased efficiencies by making business faster and easier in general (Lucas 1999) and by coordinating supply chains (Poirier and Bauer 2000).
Advocates of programs that emphasize increasing efficiency and productivity by eliminating defects and unnecessary effort hold that profitability improvements associated with quality efforts will come primarily through cost reduction. Continuing with our goal to present competing propositions, this suggests the following:
H2: A cost emphasis to quality profitability will have stronger positive effects on firm performance outcomes than either a revenue emphasis or a dual emphasis.
Everyone knows that profits are equal to revenues minus costs and that profit improvement must result from increasing revenues, decreasing costs, or both. It would be difficult to find a CEO who did not at least pay lip service to both increasing revenues and decreasing costs. It is also undeniable that ignoring either revenues or costs is a sure path to disaster. All of this seems to imply that a firm should emphasize both revenue expansion and cost reduction simultaneously. The dual emphasis tries to implement tenets of both the revenue building and cost reduction approaches simultaneously.
The possibility that the dual emphasis can be effective seems to be implied by such quality theorists as Juran (1988), who breaks quality into two opposite but presumably complementary categories--"freedom from deficiencies" and quality that "meets customer needs." Likewise, Kano's model of delight (Oliver 2000; Roberts Information Services n.d.) argues for "monovalent dissatisfiers" (quality aspects that can dissatisfy if they are missing, yet their presence does not delight the customer) and "monovalent satisfiers" (quality aspects that the customer will not miss if they are not there but that can delight if present).
Many other quality theorists and practitioners generally support the idea that quality improvement involves both cost cutting and revenue expansion through satisfying and retaining customers (Hiam 1993; U.S. General Accounting Office 1991). This idea is espoused by Deming (1986), who states that improved business processes will inevitably result in both lower costs and more-satisfied customers, thus implying that a company should emphasize both approaches simultaneously (Gitlow and Gitlow 1987). Presumably, improved business processes will result in fewer defects, which creates a higher customer perception of quality and lower costs because of less rework. A reverse but complementary argument holds that improved quality drives market share improvements directly through improved customer perceptions, which result in cost reductions that follow from the operating efficiencies produced by increased scale (Jones and Butler 1988; Phillips, Chang, and Buzzell 1983). Finally, strategic advantages may arise from the dual emphasis. It has been argued that "simultaneous pursuit of several competitive advantages can lead to a stronger position in the market than focusing on a single competitive advantage" (Flynn, Schroeder, and Sakakibara1995, p.666), because a firm that is strong in multiple areas is more difficult for competitors to attack.
Despite the existence of support for the dual emphasis, other literature gives some clues that suggest that the dual emphasis may not be as effective as other emphases. We focus on theories about a firm's learning, system dynamics, and organizational structure and incentive systems.
One perspective theorizes that organizations are bundles of learning routines focused to various degrees on the exploration of new goals, strategies, technologies, and processes or on the exploitation of existing goals, strategies, technologies, and processes (e.g., March 1991). Following from this view, it seems reasonable to suggest that the customer model is more exploration based (given the focus on finding new markets and discovering innovations to satisfy and retain customers) and the cost emphasis is more exploitation based (given the focus on the more effective deployment of existing competencies and the efficiency of internal operations).
Although it is theoretically possible and often practically desirable for exploitation and exploration to exist in organizations simultaneously (as in the dual emphasis), research indicates that one of these approaches will tend to dominate the culture and systems in organizations because of the natural tensions that exist between these two management approaches (Levinthal and March 1993; March 1991). This trade-off between exploration and exploitation is also evident in generic strategy choices of cost leadership (exploitation) and differentiation (exploration) (Porter 1980) and the "productivity dilemma" in operations between efficiency (exploitation) and innovation (exploration) (Abernathy 1978). In support of this view, Capon and colleagues (1992) find that three of four clusters of industrial firms they discover are divided on the issue of exploration (e.g., the investors and the acquirers) versus exploitation (e.g., the improvers of existing processes). Empirical support for this viewpoint is also provided by Ettlie and Johnson (1994).
A similar argument suggests that the dual emphasis might fail simply because of limited budgets. If the quality improvement budget is fixed yet both revenue expansion and cost reduction are attempted, it is possible that neither effort will receive enough resources to reach "critical mass."
Another theoretical perspective that would predict the superiority of the revenue emphasis over the dual emphasis lies in system dynamics. System dynamics examine the recursive relationships among various activities, including negative feedback effects (which create stability) and positive feedback effects (which create change and growth) (Dickson 1992; Dickson, Farris, and Verbeke 2001; Farris et al. 1998). In one dynamic, the implementation of a cost emphasis might have the tendency to initiate firings and loss of benefits and perks, which lowers morale among employees who operate at the market interface. This, in turn, may lower customer service, customer loyalty, and sales, which leads to further cost cutting--creating a vicious circle (Grönroos 1984) or "death spiral" (Rust, Zeithaml, and Lemon 2000). A revenue emphasis, in contrast, is more likely to create a virtuous circle--a dynamic that moves in the opposite direction. These nonreinforcing dynamics mean that the combination is ineffective and that neither approach works as well as it might alone.
A final organizational perspective suggests that a dual emphasis may not be possible because many firms have not developed organizational structures that link areas of the firm involving customers and costs. Moreover, functional differences often reduce the effectiveness of existing structures. Organizational reward structures, for example, are often skewed toward short-term outcomes that favor the cost emphasis. Unless reward systems encourage long-term evaluation horizons as well, it is unlikely that firms will be able to entertain a dual emphasis.
In summary, doubts exist about the efficacy of the dual emphasis because of the tensions among various processes and dynamics as well as the lack of structures within organizations for integrating the two approaches. Proponents of the dual emphasis believe, however, that because the road to satisfying customers is improving efficiency, dependability, and reliability, reducing costs through efficiency improvements should also increase revenues. This means that the dual emphasis should produce the best results with respect to profitability, through simultaneously increasing revenues and decreasing costs. Therefore, we should observe that:
H3: A dual emphasis to quality profitability will have stronger positive effects on firm performance outcomes than either a revenue emphasis or a cost emphasis.
Sample and Procedure
Although firms have long sought to increase profits by improving quality, few have employed formal methods to measure the financial impacts, and there has been no straightforward way to identify those that do. For this reason, our population comprised managers from every company we could identify as employing such a measurement program. Conversations with thought leaders in this area helped us construct a set of roughly 100 U.S. firms, some of which contained multiple business units.[ 3] The firms employed an average of approximately 70,000 people and were from both the service sector and the goods sector; the goods sector was somewhat overrepresented compared with its percentage of the economy. Many of the firms were household name or Fortune-500 companies. Access to the firms was enhanced by one author's personal industry connections; however, this was usually limited to the name of a relevant contact person. Surveying managers about the nature of the quality profitability emphases and various firm performance outcomes of their business units produced our primary data, which were supplemented by secondary data on firm performance outcomes. To generate individual manager respondents for the study, we telephoned a company contact and discussed the study at an abstract level as involving an investigation of "the systems firms have in place for examining the financial return from quality initiatives" and the "factors that influence the operation and effectiveness of these systems." Usually these conversations resulted in the contacts expressing interest in the study and their organizations' participation. There were two models of participation, each of which occurred approximately half the time.
The first model involved the contact providing the names of individual managers in the firm. For those firms, we mailed questionnaires to 185 managers from 75 business units and received responses from 69 managers representing 44 business units, which resulted in a response rate of 37.3%. The second model involved sending questionnaires to the contact person, who was asked to pick randomly among managers who would have exposure to these systems and to send them a questionnaire. Contacts at 35 business units agreed to distribute 664 questionnaires to managers. Of these, 8 business units ultimately did not return any questionnaires, indicating that contacts did not follow through on their commitment despite several reminders. Of the remaining 27 business units and 368 questionnaires mailed to firms, we received responses from 117 managers from all the business units, which resulted in a 31.8% response rate and yielded a total sample size of 186. This reported response rate is likely lower than the actual rate, because if one response was received from a business unit, we assumed that the contact at that business unit distributed all of the questionnaires provided to him or her, as promised (even though we suspect that many questionnaires that were sent to contacts were never distributed).
The two data collections were compared on key independent and dependent variables measures (described subsequently), and no differences were found: revenue emphasis (F( 2, 178) = .598, not significant [n.s.]), cost emphasis (F( 2, 180) = .510, n.s.), and dual emphasis (F( 2, 178) = .598, n.s.). Responding firms were also asked to rate their level of experience in measuring customer satisfaction (mean = 4.89, standard deviation [S.D.] = 1.51) and costs (mean = 5.00, S.D. = 1.36). The difference in firm-level knowledge of the two areas is not significant (t(174) = .825, n.s.), indicating that our sample shows no evidence of bias due to a lack of knowledge of either quality profitability emphasis.
The people who responded to the survey were, on average, knowledgeable about quality initiatives in their organizations, as the average number of hours per week they spent making decisions related to quality was 9.6 (S.D. = 5.00). Moreover, the respondents were self-reported to be knowledgeable in the measurement of areas of importance to the study; all were assessed on a seven-point scale, where 1 = "low" and 7 = "high" (customer satisfaction: mean = 5.15, S.D. = 1.30, and costs: mean = 4.89, S.D. = 1.51). Therefore, these respondents appear to meet the knowledgeability and experience criteria often suggested for key informant status (Campbell 1955).
Informants also reported that their organizations were knowledgeable about how to measure financial performance (mean = 5.78, S.D. = 1.41, on a seven-point scale). They reported an average of 5.8 years of experience (S.D. = 7.8 years) using a system that links quality initiatives to financial performance. Managers also stated that their firms had made important investments in measuring quality (mean = 3.82, S.D. = 1.53) and linking quality efforts to financial performance (mean = 3.33, S.D. = 1.57), both of which were rated on a seven-point scale, where 1 = "low level" and 7 = "high level."
We constructed averages for each item across the informants for each of the 71 business units for which we had reporting respondents. We used these average scores to conduct our firm-level analyses.
Potential Moderating Factors
We tested the influence of several factors we believed might affect our results: industry competitiveness, past emphasis, and quality information processes. First, there are different views about how industry competitiveness might affect our predictions. One view is that in highly competitive industries, prices will be competed down to levels that make sub-sequent cost reductions less attractive. Another view is that competitive pressures make a revenue emphasis more attractive because it differentiates the firm in a field of highly competitive, price-conscious firms, thus leading to economic rents. Second, it is possible that a firm's success with a given quality profitability emphasis may be a function of its past emphasis. After a five-year program of intensive cost cutting, for example, a shift to a revenue emphasis might work better than further cost cutting. Third, the market orientation literature has shown that a firm's development of systems for acquiring, disseminating, and responding to customer information is positively related to the financial performance of the firm (Jaworski and Kohli 1993) and new product development (Moorman 1995). Consistent with this literature, more highly developed quality profitability information processes may influence the effectiveness of the quality profitability emphases.
Measurement
Quality profitability emphases. Given the various meanings associated with the term "quality," we defined it for respondents as "efforts to improve the quality of products and processes within your firm." Each respondent was asked to rate measures designed to tap each emphasis (for a complete list of measures, see the Appendix). To generate an organizational-level view of these approaches, we asked respondents to rate the extent to which "managers in their division agree with statements" that reflect each quality profitability emphasis or "their firm encourages managers to take certain actions to improve the quality of products and processes." The six revenue emphasis items used two questioning approaches. One approach asked managers to rate the firm's agreement that revenue streams from quality improvements are valued (e.g., "Quality improvements that increase future revenue streams are more valuable than investments that reduce future cost streams"). The second approach presumed that customer satisfaction and retention are revenue-building activities and asked informants to rate the extent to which the managers in the organization agree that the focus of quality improvements should be to improve customer satisfaction and retention (e.g., "Quality improvements should be differentiated by their impact on customer satisfaction/ retention").
The three cost emphasis items examined the domain by asking informants to rate the extent to which managers in the organization agree that "The purpose of quality improvements is to reduce cost," "Quality improvements should be differentiated by their degree of cost saving," and "Quality improvements should always result in reduced costs."
The six dual emphasis items examined the extent to which firms try to use both approaches simultaneously. Therefore, all items referred to quality improvements that use revenue (cost) approaches with a consideration of their impact on cost (revenues). Some items, for example, ask informants to rate the extent to which the managers in their organization agree that "It is possible that investments in quality programs can increase customer satisfaction/retention and reduce costs at the same time." Other items asked informants to rate whether the firm encourages managers to "Consider the long-term effect of cost reduction efforts on customer satisfaction/retention," and so on.
Given the centrality of the dual emphasis to this research, we also assessed it by "constructing" dual emphasis from the measured revenue and cost emphasis. Specifically, we created an interaction of the revenue emphasis and the cost emphasis that reflects the organization's ability to manage both of these emphases. Therefore, if a revenue emphasis is high ( 7) and a cost emphasis is low ( 1), the dual emphasis would be low ( 7). If, however, the revenue emphasis is high ( 7) and the cost emphasis is high ( 7), the dual emphasis would be high ( 49).
Firm performance measures. We measured firm performance using both primary and secondary data. Although each data set has limitations, together they reveal a more complete portrait of effects on the firm, and each offsets the weaknesses inherent in the other. The primary measures involved managers' perceptions of business unit performance. Borrowing from Moorman and Rust (1999), we measured financial performance by division performance on sales, market share, and profitability; we assessed customer relationship performance by examining division performance on customer satisfaction, customer retention, and product/service quality.
The secondary data involved two financial measures: return on assets (ROA) and stock returns. The former was measured as the firms' overall 1998 ROA as reported in COMPUSTAT. This time lag enabled us to ascertain the direction of causality in the relationship between the firms' quality emphases(data collected in 1997)and ROA(data collected in 1998). These data were collected at the overall firm level, because business unit-level data were not available.[ 4]
We measured stock returns by calculating a firm's size-adjusted stock return for 1998. Our approach differs from a formal event study of stock returns in which a clear demarcation between new information about a firm (e.g., an announcement of a merger) and a firm's stock price can be assessed (e.g., Fama et al. 1969). Specifically, because we collected our firms' quality emphases in 1997, we assume that they represent "information" that should affect analysts' assessment of the firms' current and future potential earnings in 1998. Given a lack of event study controls, our examination should be considered exploratory. Moreover, we expect that as markets learn about the earnings potential of various quality profitability emphases, our return effects should weaken over time (Fama 1970),[ 5] which is why we investigated stock returns one year after the primary data were collected.
We calculated size-adjusted returns as the difference between a firm's stock return and value-weighted return on the Center for Research in Security Prices (CRSP) size decile portfolio to which the firm belonged at the beginning of the year. We used this procedure to provide an adjustment for a firm's risk because of risk's association with firm size (Ball 1992). We pulled both the firm's return and the portfolio's return for each month in 1998 from CRSP. The firm's return is referred to as its holding period return, which is equal to {[(share price in period t - share price in period t - 1) + (cash and cash dividends)]/share price in period t - 1}.[ 6] We adjusted holding period return data for both stock splits and stock dividends by CRSP. We determined the value-weighted portfolio return from the portfolio assignment number in CRSP for 1998, which provided information about the riskiness of the stock. We pulled the return for this portfolio--referred to as the NYSE/AMEX/ Nasdaq Capitalization Decile--for each firm in each month.
To compute size-adjusted returns, we compounded both holding period return for the firm and the value-weighted returns for the portfolio across the 12 months in 1998: [(1 + return1) (1 + return2) (1 + return3) (1 + return4) (1 + return5) (1 + return6) (1 + return7) (1 + return8) (1 + return9) (1 + return10) (1 + return11) (1 + return12)]. Size-adjusted returns then became the difference between the compounded holding period return for the firm and the compounded value-weighted returns for the portfolio (Barber et al. 2001; Mikhail, Walther, and Willis 1999).
Moderator variables affecting performance of quality profitability emphases. Industry competitiveness was measured on Jaworski and Kohli's (1993) three-item scale ( = .58). The scale was retained despite the low alpha, because its psychometrics have been established in prior research. Past quality profitability emphasis was examined with a single-item scale that asked informants to report on the approach used in their firm five years earlier, in which customer focus was measured on a seven-point scale from 1 = "all efforts directed at cost reductions" to 7 = "all efforts directed at satisfying and retaining customers." Quality profitability information processes were operationalized on a four-item scale adapted from Moorman's (1995) measure of organizational processes for using information ( = .92).
Control variables. We included firm size, because it is a standard variable in all strategy research and it captures, in a crude way, the level of firm resources. We measured this using a one-standard approach--the number of employees in the overall firm in 1999 as reported in COMPUSTAT. We also included a self-reported measure of individual manager performance. This three-item measure asked the reporting manager to rate his or her performance on a seven-point Likert scale(see the Appendix). The resulting scale was reliable ( = .76).
Measure purification. We began measure purification for the primary measures by examining the correlation matrix and Cronbach's alpha (see Table 2). The correlations do not appear to indicate that discriminant validity is a problem; however, we further examined discriminant validity using confirmatory factor analysis in Amos (Arbuckle and Wothke 1999). We employed confirmatory factor analysis on each pair of primary measures for both a constrained model (constraining the measures to be perfectly correlated) and an unconstrained model (permitting any level of intercorrelation). We tested the superiority of the unconstrained model statistically using a chi-square difference test with one degree of freedom (d.f.), reflecting the intercorrelation parameter connecting the measures. If the measures were truly separate, the chi-square difference should be statistically significant. If the two measures reflect a common or distinct domain, the model in which phi is freely estimated should have a significantly better fit than the unconstrained model. Table 3 indicates that the revenue, cost, and dual profitability emphases are distinct measures. In all cases, the model in which phi is free (unconstrained) fits significantly better.
Firm Performance: Primary Data
We begin by discussing the results for the direct measure of the dual emphasis and then the results for the constructed measure of the dual emphasis (i.e., revenue cost).
Measured dual emphasis. Because of the presence of potential moderators that may influence the relationship between the quality emphases and profitability, we used a two-step hierarchical linear moderator regression model to examine our predictions. Step 1 contained the three main-effect quality emphasis predictors (revenue, cost, and dual), the main effects associated with the moderating predictors, and control variables. Step 2 contained the interactions we constructed by mean-centering the main effects and creating products of each potential moderating factor (e.g., industry competitiveness) and each quality profitability emphasis (revenue, cost, and dual).
We then analyzed collinearity levels by computing variance inflation factors for all coefficients in each model. All were well below the acceptability threshold of ten established in the literature. Across both of the dependent variables (customer relationship performance and financial performance), the entry of the interaction effects on Step 2 did not explain a significant level of additional variance in the model(financial performance: change in F( 9, 37) = .863,n.s.,and customer relationship performance: change in F( 9, 38) = .161, n.s.). This means that the moderating factors did not influence the validity of our results. Given these results, we reestimated the models with only the three main-effect predictors and the control variables. Table 4, PartA, reports the results of these models.
Both models were significant (financial performance: F( 5, 53) = 2.653, p = .033, and customer relationship performance: F( 5, 55) = 3.420, p = .003). Across both models, the revenue emphasis had the strongest performance effect. Indeed, it is the only quality profitability emphasis that showed a significant, positive effect on managers' reports of financial performance (b = .477, p = .004)or customer relationship performance (b = .515, p = .001). Both the cost emphasis and the dual emphasis had an insignificant impact on financial performance and customer relationship performance.[ 7]
Constructed dual emphasis. We also examined the impact of the quality profitability emphases using a measure of dual emphasis constructed from the interaction of the revenue and cost emphases. We used a two-step hierarchical linear moderator regression model by entering the mean-centered revenue and cost emphasis main effects and the control variables during Step 1 and the constructed dual emphasis in Step 2. In both cases, the entry of the constructed dual emphasis on the second step does not explain a significant amount of variance (financial performance: change in F( 1, 53) = .694, n.s., and customer relationship performance: change in F( 1, 55) = .795, n.s.). Given these results, the main-effects model results remain the focus. Examining these, we find that only the revenue emphasis had a significant, positive effect (financial performance: b = .341, p = .009, and customer relationship performance: b = .497, p = .000). [ 8] Complete results are given in Table 4, Part B.
Next, as with the measured models, we examined whether interactions reflecting various organizational and environmental factors moderated the impact of the quality profitability approaches. As previously, we introduced these interactions on the second step of the model and found that they did not explain a significant level of additional variance in financial performance (change in F( 9, 36) = .833, n.s.) or customer relationship performance (change in F( 9, 37) = .807, n.s.). This means that the moderating factors do not influence the validity of our results.
Firm Performance: Secondary Data
We analyzed the effect of quality profitability emphasis on future profitability (ROA) and stock returns, partially ameliorating the problems of cross-sectional correlational studies in interpreting causality. The use of secondary data also enabled us to control statistically for unobserved firm-level factors that have a contemporaneous correlation between the independent variables and the error (e.g., Boulding and Staelin 1995; Jacobson 1990; Schmalensee 1987). A typical approach to controlling for the effects of omitted variables when long-term data are available is the instrumental variable approach (Hausman 1978), which uses two-stage least squares (2SLS) to produce coefficient estimates that are not contaminated by omitted variables that may be correlated with the independent variables (Greene 1997, pp. 288-95; Leeflang et al. 2000, p. 334).
For the first stage of 2SLS, we used a set of years (ROA1989, ROA1990, ROA1991, ROA1992) as the independent variable to predict each quality profitability emphasis. We chose those years because they fell before 1998 (our performance measurement year) and therefore by definition can not be correlated with the error term in the 1998 equation.[ 9] We estimated the predicted values of each of these quality emphases, known as instrumental variables, in the model and used them in the second stage of the 2SLS to predict ROA in 1998. We performed the Hausman test of the equality of the estimates produced by the use of the instrumental variables and estimates produced by nonadjusted independent variables. The results indicated the need for the instrumental variables.[ 10]
The stock returns analysis was based on data from CRSP, which reports holding period return, as is frequently analyzed in the finance and accounting literature, in part because it has been "differenced" across the days in the year and therefore is not biased by constant unobserved factors within the year. As a result, instrumental variables were not necessary to deal with omitted variables.
Given the use of instrumental variables in the case of ROA and the construction of stock returns in CRSP, it may not be necessary, strictly speaking, to include any moderating variables, as was the case with the primary data. To be conservative, however, we included the two control variables in the model. We included firm size because it is regularly included in strategy research as a measure of firm resources. We included individual manager performance because we sought to account for the individual manager's biases in evaluating the firm's quality emphases that might be due to his or her own performance in the firm. Recall that we also included these control variables in the primary data analysis.
The individual respondent sample size for our secondary data analysis is somewhat smaller (134 for the ROA analysis, 117 for the stock returns analysis) than the sample size (186) for our primary analysis. This is because some of the firms in our sample are not publicly held; therefore, stock returns and profitability metrics are not available in CRSP and COMPUSTAT. This, in turn, reduces the total business unit sample size from 71 to 53 for the ROA analysis and to 45 for the stock returns analysis.
Measured dual emphasis. We began by estimating models with the measured dual emphasis. As with the primary data, we first examined collinearity levels and found them to be well within the range of acceptability. Following this, we tested whether the interactions should be included. Across both of the dependent variables (ROA and stock returns), the entry of the interaction effects on Step 2 did not explain a significant level of additional variance in the model (ROA: change in F ( 9, 35) = 1.049, n.s., and stock returns: change in F( 9, 23) = .859, n.s.).[ 11]
Given that the entry of the interaction effects was not significant, we report the results from the model that contains only the three quality profitability emphases and the two control variables. The results are given in Table 5, Part A. For ROA, the overall model is significant (F( 5, 47) = 7.746, p = .0001). The revenue emphasis had a positive and significant impact (b = .775, p = .000), whereas the cost emphasis (b = .208, n.s.) and dual emphasis (b = .091, n.s.) were insignificant.
For the size-adjusted stock returns, the overall model is moderately significant (F( 5, 39) = 2.374, p = .057). The revenue emphasis had a significant, positive impact (b = .387, p = .056), whereas the cost emphasis had an insignificant effect (b = -.185, n.s.) and the dual emphasis had a significant, negative impact (b = -.455, p = .021).
Constructed dual emphasis. Following our approach for the primary dependent measures, we also examined the impact of the quality profitability emphases using a measure of dual emphasis constructed from the interaction of the revenue and cost emphases. We again used a two-step hierarchical linear moderator regression model by entering the mean-centered revenue and cost emphasis main effects and the control variables in the first step and the constructed dual emphasis in the second step.
In the case of ROA, the entry of the constructed dual emphasis in the second step did not explain a significant amount of variance (change in F( 1, 47) = 2.223, n.s.). In the case of size-adjusted stock returns, the entry of the constructed dual emphasis was significant (change in F( 1, 39) = 5.862, p = .02). Therefore, the final model results report all three quality profitability emphases.
We next considered whether any of the moderating variables affected our results. As previously, we entered the interactions of the profitability emphases and the moderating variables in the second step of the model. The results indicate that the entry of the interactions for ROA (change in F( 9, 35) = 1.735, n.s.) and stock returns (change in F( 9, 26) = .736, n.s.) was not significant, which indicates that an exclusive focus on our profitability emphases was appropriate (see Table 5, Part B).
Considering ROA, the revenue emphasis had the only significant, positive effect (b = .761, p = .004). The cost emphasis was not significant (b = .211, n.s.). Recall that the constructed dual emphasis was not significant upon entry. For the stock returns, recall that the constructed dual emphasis was significant upon entry; however, its effect on stock returns was significant and negative (b = -.400, p = .02). Conversely, the revenue emphasis was moderately significant and positive (b = .286, p = .103).
Exploring the effect of firm-level data. We measured the dependent measures for the secondary data at the firm level, because business unit data were not available. We tested whether this might have an effect on our results. The sum of squares relating to the dependent variable can be partitioned into between companies sum of squares and within companies sum of squares, and it seems reasonable to assume that the ratio of within company mean square to between company mean square should be roughly the same in the primary and secondary data. We performed one-way analyses of variance with firm as treatment on the financial performance measure and found that the mean square within company was .644 the mean square between companies. We then did one-way analyses of variance on the secondary data and multiplied the between company mean squares by .644. However, this is an overestimate for within-company variance, because independent variable deviations from the company mean should be correlated with the estimated Y.
Therefore, we conducted multiple regressions using firm-level data to obtain the approximate percent variance explained by the explanatory variables, uncontaminated by the within-company variance. Multiplying (1 - R2) by the company variance estimate resulted in an estimated within-company variance, after we controlled for the explanatory variables. Taking the square root produced the estimated standard deviation within company. We then obtained random normal deviates from a normal distribution with mean zero and the preceding square root and added it to the firm measure. This yielded simulated business unit dependent variables, with approximately the correct amount of within-company variance. We then ran the regressions as previously. The ROA results produced the same pattern of significant, positive effects for the revenue emphasis, whereas the stock returns showed insignificant (but directionally similar) effects for the revenue emphasis and replicated the significant, negative effects for the dual emphasis. Therefore, the conclusions from our analyses are mostly unaffected by the use of firm-level dependent measures.
Quality Profitability Emphasis Trends
Our empirical results suggested that the revenue emphasis may produce better financial outcomes, which led us to wonder whether firms were adopting the revenue emphasis over time. Our survey asked managers to evaluate their firm's quality profitability emphases ( 1) five years ago, ( 2) currently, and ( 3) five years from now; relative emphasis was measured on a seven-point scale from 1 = "all efforts directed at cost reductions" to 7 = "all efforts directed at satisfying and retaining customers." Presumably, a pure revenue emphasis would imply the right-hand ( 7) side of the scale, a pure cost emphasis would imply the left-hand ( 1) side of the scale, and a pure dual emphasis would imply the middle ( 4) of the scale. The mean relative emphasis shifted from 3.45 (toward a cost or dual emphasis) five years previously to 4.49 (more of a revenue or dual emphasis) at the time of the study to 5.31 (even more of a revenue emphasis) projected five years into the future.
To test whether there were perceived shifts in quality profitability emphasis over time, we conducted one-sample, two-tailed t-tests of the hypothesis that there was no change. Referring to the three measurements as PREVIOUS, CURRENT, and FUTURE, we calculated changes from one period to the next as DELTA1 = CURRENT - PREVIOUS and DELTA2 = FUTURE - CURRENT. A one-sample t-test for DELTA1 resulted in a t-value of 7.314 (significant at p < .001), and a test of DELTA2 resulted in a t-value of 7.661 (again significant at p < .001). To gain further insight, we then regressed DELTA1 on PREVIOUS and DELTA2 on CURRENT. We observed regression to the mean. The first regression was estimated DELTA1 = 3.784 - .794 PREVIOUS, and the second regression was estimated DELTA2 = 3.804 - .662 CURRENT. This indicates that companies with less revenue emphasis are the ones experiencing greater shifts in their quality profitability orientation.
Summary of Findings
Collectively, these primary and secondary results suggest that firms adopting a revenue emphasis to manage quality profitability may reap the greatest rewards. The revenue emphasis showed a significant, positive impact on financial performance and customer relationship performance, as reported by managers. It also had a one-year-ahead positive impact on ROA and stock returns. The cost emphasis had no effect on primary or secondary measures of performance. Likewise, the dual emphasis had no effect on financial performance and customer relationship performance as reported by managers, nor on one-year-ahead ROA from the secondary data. Both the measured and the constructed dual emphasis, however, exerted a negative effect on one-year-ahead, size-adjusted stock returns.
The Optimal Quality Profitability Emphasis in Organizations
Our research implies that the two faces of quality (revenue expansion through customer satisfaction and cost reduction through efficiency) are not two sides of the same coin. They are distinct and affect firm performance differentially. Furthermore, a company may have different emphases with respect to quality. Our research suggests that companies should clearly determine whether they are emphasizing customer satisfaction (revenue emphasis), efficiency (cost emphasis), or both at once (dual emphasis).
More important, our research indicates that a revenue emphasis may be the most effective quality profitability emphasis for organizations. Across both cross-sectional, manager-reported performance and longitudinal objective performance indicators, firms using revenue approaches to quality profitability outperformed firms that used either cost or dual approaches. This set of results is robust to differences in the turbulence of competitive environments, in firms' past quality profitability emphases, and in the development of firms' quality information systems. Moreover, our results conform to this pattern when either a measured or a constructed dual emphasis variable is used. Finally, our results stand up to four distinct modeling approaches that resolve different empirical challenges associated with our measures and analyses.
As previously noted, prior research in marketing has not resolved whether an emphasis on building revenues through customer-focused activities should be accompanied by an emphasis on reducing costs, even though the literature states strongly that customer focus and market orientation lead to positive financial outcomes. Our results resolve this uncertainty by providing some empirical evidence for the importance of a sole revenue emphasis in firms' financial performance. The results provide some support for the idea that firms should allocate more resources to initiatives such as customer satisfaction programs, customer retention and loyalty programs, customer relationship management programs, and customer equity programs but should allocate fewer resources to quality programs that are designed to improve efficiency and reduce costs. For the most part, both the dual and cost quality profitability emphases had an insignificant impact on firm performance. In the case of size-adjusted stock returns, however, both the constructed and the measured dual emphasis measures negatively affected firm performance. We theorized that organizational systems and structures involved in implementing both a revenue and a cost emphasis might involve nonreinforcing learning systems, system dynamics, and incentive systems that reduce the financial impact of quality profitability efforts. Alternatively, firms in our study may have had a fixed budget, making it difficult for the two concurrent emphases (revenue and cost) to achieve critical mass.
At the same time, we did not expect to find a negative effect. These results may indicate that financial analysts anticipate the types of organizational repercussions we expected under a dual emphasis. These results may also suggest, however, that analysts view a dual emphasis as an attempt by firms to "play the spread," which they perceive as poor management acumen or risk aversion. In either case, such ideas should correct themselves over time as analysts learn more about the true implications of various quality profitability emphases. If so, it is likely that our results provide insights into the possibly deleterious organizational dynamics and conflicts set in motion by the dual emphasis.
Previous research has indicated the possibility of a tradeoff between customer satisfaction and productivity for service firms, but not for goods firms (Anderson, Fornell, and Rust 1997; Huff, Fornell, and Anderson 1996). Therefore, because our results favor the revenue emphasis, one question is whether they might be moderated by the extent to which a company is a service business. Similarly, because productivity improvement is related to internal process quality improvement and cost reduction, it might be inferred from our results that the dual emphasis would perform better for firms with less service intensity. We examined this possibility by testing for the presence of significant interactions between each quality profitability emphasis and the intensity of the firm's service level (i.e., the degree to which a company is a service provider as opposed to a goods provider). We failed to find support for this inference in our model testing. The preference for the revenue emphasis as opposed to the dual emphasis appears to hold across the board.
Further Research
Future work might examine a wider set of contingencies that could influence the financial implications of various quality profitability emphases. The relationship of the business cycle to the effectiveness of quality profitability emphases, for example, would be a fertile area of research, as would the stage of development of the national economy in which the business unit operates.
Further research should also examine the firm, customer, competitor, and environmental factors that tend to create these emphases. In the latter regard, recent exploratory work by Morgan and Piercy (1996) examines a firm's overall strategy on firm performance. The authors focus in particular on a firm's differentiated quality strategy and its low-cost quality strategy and suggest that the two approaches cannot be used within the same firm. In an extension, they describe the role of marketing in each strategy condition as contingent on whether the quality differences are objective or only perceived. Drawing from their work, we would expect a revenue focus to evolve more from a differentiation strategy than from a low-cost strategy and more from perceived than from objective quality. We expect perceived to be stronger than objective quality, because objective quality may increase managers' focus on the product, whereas perceived quality has a clear customer focus.
Another issue for further research that cuts across all these studies is where such an emphasis resides within the organization. Specifically, does the customer focus of a firm reside in the belief systems of the people who make up the organization, or does it instead reside in the collective belief systems of the organizational culture, beyond the people who constitute it? Despite the demonstrated importance of customer focus to firm success, research has not examined the locus of customer-oriented belief systems or investigated whether different locations influence the ability of customer focus to affect a firm's financial performance. Limitations
Several limitations of our current study should also be acknowledged. As is true for a great deal of empirical strategy research, we use self-reported data on such key dependent variables as firm performance. To remedy concerns regarding method bias, we introduced the use of secondary indicators of longitudinal firm performance in the form of ROA and size-adjusted stock returns. These performance measures are also imperfect, because they examine overall firm performance, not business unit performance. It would be optimal to have secondary business unit performance measures to match our business unit-level evaluations of the independent variables, but no such data are available. The strength of our article is that it looks across our objective and subjective measures for trends regarding the impact of quality profitability emphases.
We also acknowledge that our sample does not represent a true probability sample of all organizations, because we created a sample of firms that are actively involved with evaluating returns from quality. It could be that this sample is somewhat more progressive than would be obtained from simple random sampling.
We also recognize that our results may be dependent on the economic climate in which the data were generated. One plausible alternative to our viewpoint, for example, might hold that macroeconomic factors influence which of the quality profitability emphases is best at a particular time. When energy prices rise, for example, the cost emphasis may be more effective; when disposable income is high, the revenue emphasis may do better. It is impossible to know whether this interpretation is correct without replicating our study in a different macroeconomic climate. Replication of this research, in either the past (if possible to do) or the future, would be helpful in confirming the universality of the results.
How a firm should attempt to derive financial benefits from quality might vary depending on the functional perspective it takes. Marketing tends to address the problem from a revenue perspective and operations from a cost reduction or efficiency perspective. Although it might appear possible to double the benefit by using both approaches simultaneously, our empirical findings suggest that firms can achieve greater financial returns from quality improvements by emphasizing revenue generation solely, along with its underlying focus on customer satisfaction and retention. The results from such an emphasis exceed those arising from a focus on costs alone or from attempts to balance a dual emphasis on both revenues and costs. These findings reinforce the literature that describes tensions between revenue building and cost reduction firm dynamics and learning systems. It also contributes to the literature on market orientation by suggesting that a market orientation may not be fully compatible with a concurrent emphasis on cost reduction.
Notes
1 This emphasis approach follows other contemporary strategy approaches. In one example, Treacy and Wiersema (1995) suggest that firms can emphasize one of several value disciplines that focus on operational excellence (costs), product leadership (revenues), or customer relationship building (revenues). In their view, firms cannot ignore any of these value disciplines, but successful firms tend to emphasize just one of them.
- 2 We use the term "revenue emphasis" to describe an emphasis on growing demand through catering to consumers' preferences for quality and increasing consumer preferences for quality--that is, making the market for higher quality (Dickson 1992). We recognize that revenue can also be increased by reducing costs and prices in markets where price elasticity is greater than one.
- 3 Thought leaders consulted included staff and corporate executives affiliated with the Marketing Science Institute, as well as academic authors who are knowledgeable about financial return on quality.
- 4 Later, we investigate the effect that business unit-level data might have on our analysis.
- 5 We use this approach to market adjustment because we lack a sufficient number of months of return to use the market model method that relates the return on a given stock to the return of the overall market (Brown and Warner 1985).
- 6 The virtue of this stock return indicator is that it is constructed by differencing daily stock returns during the year. This differencing removes the potential bias from correlated omitted variables that are not accounted for in the analysis, to the extent that those omitted variables persist across the years.
- 7 We followed this analysis and the analysis involving the secondary measures with a validation approach that randomly removed 25% of the observations several times to check for parameter stability by comparing the estimated parameters on different samples of the whole data set. Although the magnitude of the parameters varied from sample to sample, the overall pattern of our findings was consistent.
- 8 In addition to the constructed dual emphasis, we took precautions and examined our predictions using two other approaches. The first involved entering each one of the quality profitability emphases into a simple regression model. The results indicated that the pattern we observed in the multiple regression models was replicated. Specifically, the revenue emphasis was the only significant, positive indicator. The second approach involved examining the impact of the quality profitability emphases in a structural equation model. The virtues of this approach are that it does not use summated scales and therefore models the error associated with the variables and it permits the latent constructs to be correlated. We tested the two models for which multiple indicators of the dependent variable were available (financial performance and customer relationship performance). The results indicate that the revenue emphasis had a significant, positive impact in both models; the dual emphasis had a significant, negative impact on financial performance and no significant effect on customer relationship performance.
- 9 Before using the ROA to generate the predicted instrumental variables, we took one additional precaution, which was to remove any autocorrelation in the residuals among these years. We accomplished this by regressing, for example, ROAt - 1 on ROAt, ROAt - 2 on ROAt - 1 , and so forth for each of the years. We then used the residuals obtained from each of these models as input for the Hausman test.
- 10 Johnston and DiNardo (1997, p. 259) recommend a modification to the Hausman test involving a test of Y = xregularb1 + x instrumental b2 + where x regular are the original independent variables, x instrumental are the instrumental variables (formed in stage one), and the s are coefficient vectors. If the nested F-test that relates a model with instrumental variables to a model without instrumental variables is significant, then instrumental variables are justified. The results for the measured dual emphasis (F( 3, 44) = 10.688, p = .000) and the constructed dual emphasis (F( 3, 44) = 11.761, p = .000) provided clear evidence that instrumental variables were required.
- 11 Interaction models involving ROA used the noninstrumented version of those predictors. This was necessary because the interactions involving the instrumental variables introduced high levels of collinearity, producing results that could not be interpreted.
Table 1: Characterizing the Quality Profitability Emphases
Legend for chart:
A -
B - Cost Emphasis
C - Revenue Emphasis
D - Dual Emphasis
A
B C D
Profit focus
Cost reduction Revenue expansion Both at once
Quality focus
Internal External Both at once
Quality measures
Defect rate Customer satisfaction/ Both at once
retention
Operational focus
Standardization Customization Both at once
Organizational focus
Operations, accounting Marketing, human resources, Operations,
research and development accounting,
marketing,
human
resources,
research and
development
Typical improvement
Efficiency improvement Service augmentation or Process
product innovation to redesign to
increase customer improve both
satisfaction costs and
revenues
Research programs adopting
this emphasis American Customer Balanced
Six Sigma (Pande, Neuman, Satisfaction Index scorecard
and Cavanagh 2000) Total (Formell et al. 1996) (Kaplan and
quality management Return on quality(Rust Norton 1992)
(Easton and Jarrell 1998) Zahorik, and Keiningham Supply chain
this emphasis 1995) Service profit management
chain(Heskett, Sasser and (Mentzer
Schlesinger 1997) 2000)
Example of a corporate American Airlines spent Right CHOICE's
application &700 million to increase Physician Group
Leigh Valley Hospital cabin legroom in the coach Partners
built a customer information cabin by 3-5 inches per row Program
and tracking system that to improve passenger creates
resulted in shorter hospital satisfaction and loyalty financial
stays and reduced operating (Rust, Zeithaml, and Lemon incentives
costs (Health Management 200). for
Technology 1997) physicians
to improve
both patient
satisfaction
and cost
containment
(RightCHOICE
2000)Table 2: Measure Characteristics and Intercorrelations
Legend for chart:
A - Measure
B - Mean
C - S.D.
D - Items
E - N(a)
F - 1
G - 2
H - 3
I - 4
J - 5
K - 6
L - 7
M - 8
N - 9
O - 10
P - 11
A
B C D E F G H I J
K L M N O P
1. Revenue emphasis
4.842 .880 6 70 .77 - - - -
- - - - - -
2. Cost emphasis
3.948 .934 3 69 -.01 .80 - - -
- - - - - -
3. Dual emphasis
4.515 .849 6 70 .66 -.01 .80 - -
- - - - - -
4. Past quality profitability emphasis
3.387 1.238 1 69 .24 -.32 .09 - -
- - - - - -
5. Industry competitiveness
4.937 .998 3 69 .05 .04 -.02 -.16 .58(b)
- - - - - -
6. Quality probability information processes
4.258 1.140 4 69 .48 .16 .65 -.14 .04
.92 - - - - -
7. Individual manager performance
5.224 .765 3 69 .18 -.09 .13 -.02 .32
.02 .80 - - - -
8. Firm size
70,386 50,734 1 63 .22 -.08 .24 .28 -.06
.13 -.01 - - - -
9. Financial performance
4.361 1.039 3 67 .26 -.03 .06 -.01 -.15
.09 -.03 -.24 .77 - -
10. Customer relationship performance
4.178 .807 3 69 .28 -.04 .20 .17 -.22
.24 -.03 .02 .57 .81 -
11. ROA
5.392 4.050 1 60 .09 -.29 -.03 .31 -.13
-.08 .10 .32 .10 -.01 -
12. Size-adjusted stock returns
.358 .269 1 47 -.05 -.27 .17 .22 -.16
-.11 -.12 .30 .18 -.12 .72
(a)N refers to the number of companies. The total sample size of individual respondents is 186. (b)Although this alpha is below typical standards, we decided to use it because its psychometrics have been established in prior research. Notes: Correlations: r > .15 implies p< .05. Alpha is on the diagonal (in italics) for multi-item measure.
Table 3: Discriminant Validity Analysis Among Primary Data Measures
Legend for chart:
A - Comparison
B - Constrained Model X(sup 2) (d.f)
C - Unconstrained Model X (sup 2)(d.f.)
D - ΔX(sup 2) (1)
A
B C D
Revenue emphasis versus cost emphasis
106.7 (27) 69.0 (26) 37.7**
Revenue emphasis versus dual emphasis
181.0 (54) 161.5 (53) 19.5**
Revenue emphasis versus customer relationship performance
58.3 (27) 44.8 (26) 13.7**
Revenue emphasis versus financial performance
63.7 (27) 49.4 (26) 14.3**
Cost emphasis versus dual emphasis
143.7 (27) 107.4 (26) 36.3**
Cost emphasis versus customer relationship performance
34.6 (9) 6.5 (8) 28.1**
Cost emphasis versus financial performance
37.2 (9) 17.1 (8) 20.1**
Dual emphasis versus customer relationship performance
109.3 (27) 86.4 (26) 22.9**
Dual emphasis versus financial performance
101.3 (27) 75.7 (26) 25.6**
Customer relationship performance versus financial performance
17.5 (9) 11.1 (8) 6.4*
*Significant at p < .05.
**Significant at p < .01.
Table 4: The Impact of Quality Profitability Emphasis on Firm Performance: Primary Data
Legend for chart:
A -
B - Financial Performance
C - Customer Relationship Performance
A B C
A: Measured Dual Emphasis
Final Model Statistics
Adjusted R2 .125 .212
F-statistic 2.653 3.420
d.f. 5, 53 5, 55
p-Value .033 .003
Final Predictors ba (t)b b (t)
Revenue emphasis .477 (2.982)*** .515 (3.420)***
Cost emphasis -.040 (-.323) .007 (.061)
Dual emphasis -.217 (-1.368) -.030 (-.199)
Firm size -.303 (-2.392)* -.293 (-2.452)**
Individual manager
performance -.055 (-.446) -.161 (-1.398)
B: Constructed Dual Emphasis
Model Statistics
Step 1
R2 .172 .277
F-statistic 2.804** 5.361***
d.f. 4, 54 4, 56
p-value .035 .001
Step 2
(containing constructed
dual emphasis)
Change in R2 .010 .010
Change in F-statistic .674 .795
Change in d.f. 1, 53 1, 55
p-Value n.s. n.s.
Final Predictors b (t) b (t)
Revenue emphasis .341 (2.701)** .497 (4.230)***
Cost emphasis -.044 (-.328) .007 (.058)
Firm size -.309 (-2.415)** -.295 (-2.497)**
Individual manager
performance -.043 (-.345) -.160 (-1.403)(a)Standardized coefficients are used throughout. bt refers to the t-statistic for the estimated coefficients. *p < .10. **p < .05. ***p < .01.
Table 5: The Impact of Quality Profitability Emphases on Firm Performance: Secondary Data
Legend for chart:
A -
B - ROA 1998
C - Size-Adjusted Stock Returns 1998
A B C
A: Measured Dual Emphasis
Model Statistics
Adjusted R2 .393 .135
F-statistic 7.746**** 2.374*
d.f. 5, 47 5, 39
p-value .000 .057
Predictors ba (t)b b (t)
Revenue emphasis .775 (3.081)*** .387 (1.967)*
Cost emphasis .208 (.820) -.185 (-1.298)
Dual emphasis .091 (.807) -.455 (-2.408)**
Firm size .220 (1.961)* .202 (1.353)
Individual manager
performance .179 (1.628) -.093 (-.634)
B: Constructed Dual Emphasis
Model statistics
Step 1
R2 .444 .119
F-statistic 9.590*** 1.355
d.f. 4, 48 4, 40
p-value .005 .267
Step 2(containing constructed
dual emphasis)
Change in R2 .025 .115
Change in F-statistic 2.223 5.862**
Change in d.f. 1, 47 1, 39
p-Value n.s. n.s.
Final predictors b (t) b (t)
Revenue emphasis .760 (3.038)** .286 (1.669)*
Cost emphasis .211 (.834) -.145 (-1.018)
Dual emphasis(c) -.400 (-2.421)**
Firm size .234 (2.116)** .233 (1.580)
Individual manager
performance .195 (1.813)* -.034 (-.238)(a)Standardized coefficients are used throughout. bt refers to the t-statistic for the estimated coefficients. (c)The dual emphasis results are reported only for the size-adjusted stock returns model and not the ROA model, because entry of dual emphasis was significant only for the former and not the latter. *p < .10. **p < .05. ***p < .01. ****p < .001.
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Quality Profitability Emphases
Revenue Emphasis Rate the degree to which the managers in your division agree with the following statements about initiatives to improve the quality of products and processes: (1 = "low level," 7 = "high level")
1. The purpose of quality improvement is to improve customer satisfaction/retention.
- 2. Quality improvements should be differentiated by their impact on customer satisfaction/retention.
- 3. It is best to invest in improving those initiatives that greatly increase customer satisfaction/retention.
- 4. Quality improvements should always result in increased revenues.
Rate the extent to which your division encourages managers to take the following actions regarding efforts to improve the quality of products and processes:
- 5. Build revenues by increasing customer satisfaction/ retention.
- 6. Invest in improving those activities that generally
increase customer satisfaction/retention.
Cost Emphasis
Rate the degree to which the managers in your division agree with the following statements about initiatives to improve the quality of products and processes: (1 = "low level," 7 = "high level")
1. The purpose of quality improvements is to reduce costs.
- 2. Quality improvements should be differentiated by their degree of cost saving.
- 3. Quality improvements should always result in reduced costs.
Dual Emphasis
Rate the degree to which the managers in your division agree with the following statements about initiatives to improve the quality of products and processes: (1 = "low level," 7 = "high level")
1. Customer satisfaction/retention efforts should always consider the long-term impact on costs.
- 2. Cost reduction efforts should always consider the long-term impact on customer satisfaction/retention.
- 3. It is possible that investments in quality programs can increase customer satisfaction/retention and reduce costs at the same time.
Rate the extent to which your division encourages managers to take the following actions regarding efforts to improve the quality of products and processes:
- 4. Consider the long-term effect of cost reduction efforts on customer satisfaction/retention.
- 5. Consider the long-term effect of customer satisfaction/retention efforts on costs.
- 6. Manage as if quality programs can increase customer satisfaction/retention and reduce costs at the same time.
Primary Performance Outcomes
Relative to your division's stated objectives, how is your division performing on (1 = "worse," 4 = "on par," and 7 = "better")
Customer Relationship Performance
1. Customer satisfaction?
2. Customer retention?
3. Service quality?
Financial Performance
1. Sales?
2. Profitability?
3. Market share?
Secondary Performance Outcomes
ROA (from COMPUSTAT)
Size-Adjusted Stock Returns (from CRSP)
Variables Affecting Impact of Quality Profitability Emphases
Industry Competitiveness (Jaworski and Kohli 1993) Use the scale at the top of the page to rate your division's operating environment: (1 = "strongly disagree," 4 = "uncertain," and 7 = "strongly agree")
1. Competition in this product/service area is very cutthroat.
- 2. One hears of a new competitive move in this product/service area almost every day.
- 3. Our competitors in this product/service area are relatively weak.
Quality Profitability Information Processes (adapted from Moorman 1995)
Rate your division's processes for using information that ties quality initiatives to financial outcomes. To what extent does your division have processes (either formal or informal) (1 = "low level," 4 = "moderate level," 7 = "high level")
1. That rely on this information to make decisions related to customer satisfaction/retention?
- 2. That use this information to solve specific customer satisfaction/retention problems?
- 3. That use this information to implement various customer satisfaction/retention initiatives?
- 4. That use this information to evaluate customer satisfaction/retention performance?
Past Quality Profitability Emphasis Five years ago, how did your division allocate its quality improvement efforts?
All efforts directed at satisfying and retaining customers
All efforts directed at cost reductions
Service Intensity
Evaluate your division's present operations on the following scale:
Producing goods
Providing services
Control Variables
Firm Size (from COMPUSTAT)
Number of employees
Individual Manager Performance
Use the scale at the top of the page to rate your individual performance: (1 = "strongly disagree," 4 = "uncertain," and 7 = "strongly agree")
1. I have generally performed better than my peers in comparable jobs.
- 2. I am more effective in my job than my peers.
- 3. I have been promoted at a faster rate than my peers.
~~~~~~~~
By Roland T. Rust; Christine Moorman and Peter R. Dickson
Roland T. Rust is David Bruce Smith Chair in Marketing, Robert H. Smith School of Business, University of Maryland. Christine Moorman is Professor of Marketing, Fuqua School of Business, Duke University. Peter R. Dickson is Knight Ridder Eminent Scholar in Global Marketing, Marketing Department, College of Business Administration, Florida International University. This research was sponsored by a grant from the Marketing Science Institute, as well as the University of Maryland's Center for e-Service and Vanderbilt University's Center for Service Marketing. The authors thank the four anonymous JM reviewers for their helpful comments. The authors also are grateful to Bill Boulding, Carl Mela, and Richard Willis for methodological assistance; Holly Barr, Rosie Ferraro, and Joji Malhotra for research assistance; and Randi Huntsman for editorial assistance.
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Record: 70- Grocery Price Setting and Quantity Surchages. By: Sprott, David E.; Manning, Kenneth C.; Miyazaki, Anthony D. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p34-46. 13p. 3 Charts, 4 Graphs. DOI: 10.1509/jmkg.67.3.34.18653.
- Database:
- Business Source Complete
Grocery Price Setting and Quantity Surchages
Quantity surcharges occur when the unit price of a brand's larger package is higher than the unit price of the same brand's smaller package. The authors examine how price-setting practices in the grocery industry help explain the existence of quantity surcharges. Two studies support the authors' contention that common pricing practices aimed at establishing a favorable store--price image can result in quantity surcharges. First, an experiment shows that consumer demand and the importance price setters place on establishing a low store--price image have an interactive effect on price-setting behavior. Second, an examination of retail sales volume, price, and cost data suggests that such price-setting reactions can result in quantity surcharges when certain asymmetries in demand exist across package sizes. The authors also discuss managerial and public policy implications along with areas for further study.
In general, retail executives and consumers expect multiple sizes of a brand (i.e., brand-sizes) to be priced in a quantity-discount fashion, such that a brand's larger package costs less per unit than does a smaller package (Granger and Billson 1972; Manning, Sprott, and Miyazaki 1998; Nason and Della Bitta 1983; Wansink 1996; Widrick 1979b). Contrary to these expectations, quantity surcharges, which occur when the larger brand-size has a higher unit price than an otherwise identical smaller package of the same brand (Widrick 1979a, b), are common in the retail grocery market. Research has found that quantity surcharges occur in 16% to 34% of supermarket brands that are available in two or more package sizes (Agrawal, Grimm, and Srinivasan 1993; Manning, Sprott, and Miyazaki 1998; Nason and Delta Bitta 1983; Walker and Cude 1984; Widrick 1979a, b; Zotos and Lysonski 1993). The most recent investigation finds a 27% incidence of quantity surcharges across two U.S. markets (Manning, Sprott, and Miyazaki 1998).
In light of the evidence that quantity discounts optimize profitability (Dolan 1987; Oren, Smith, and Wilson 1982), the high incidence of quantity surcharges in the marketplace is unexpected. A common but empirically unsupported proposition for why pricing practices result in quantity surcharges is that retail price setters use surcharges to price discriminate against consumers who expect quantity-discount pricing (Agrawal. Grimm, and Srinivasan 1993; Gupta and Rominger 1996; Mason and Della Bitta 1983; Widrick 1985; Zotos and Lysonski 1993). More specifically, this proposition holds that retailers attempt to increase profits by raising the prices of larger packages at the expense of consumers who neither expect nor notice quantity-surcharged items. Although we agree that the actions of retail price setters can lead to quantity surcharges, we contend that surcharges often occur as an unintentional by-product of common price-setting processes and that they can have a positive impact on consumer welfare.
We propose that quantity surcharges occur as retail grocery price setters, who are concerned about having a low store--price image, monitor and respond to competitors' prices associated with popular brand-sizes (i.e., stockkeeping units [SKUs] with the greatest unit sales volume). When a popular brand-size is also a smaller brand-size, we propose that a quantity surcharge is more likely to occur. In the following section, we develop hypotheses about how consumer demand and the importance of a low store--price image influence retail grocery prices. Alter an experimental test of the hypotheses with actual grocery price setters (Study 1), we detail how such pricing practices can lead to quantity surcharges, and we test the premise using data from a regional grocery chain (Study 2).
In the retail grocery industry, price is and likely will remain the predominant basis for cross-chain competition (e.g., Garry 1994; Kahn and McAlister 1997; Mathews 1997; Urbany, Dickson, and Key 1990) Accordingly, establishing a low store--price image is a common priority among grocery firms (e.g., Cox and Cox 1990; Dickson and Urbany 1994; Snyder 1993; Wellman 2000). The industry's enduring focus on price is likely exacerbated by the consistent finding that "low prices" are among the most important attributes consumers consider when selecting which supermarkets to patronize (e.g., Progressive Grocer 1992, 1996, 2000).
Given this strong emphasis on establishing competitive prices and the otigopolistic nature of supermarket competition (e.g., Alderson 1963; Baumol, Quandt, and Shapiro 1964), it is not surprising that grocery retailers actively monitor competitors' prices (Hess and Gerstner 1991; Levy et al. 1997, 1998; Snyder 1993; Urbany, Dickson, and Key 1990). Much of the price monitoring occurs on a monthly or weekly basis (Levy et al. 1998; Snyder 1993) and is conducts by company personnel or external price monitoring services (Levy et al. 1997, 1998).
Instead of monitoring prices on all products, grocery retailers most actively monitor competitors' prices of top-moving items (i.e.. SKUs with relatively high unit sales volume) (Snyder 1993). Retailers focus their price checks on top-moving items because consumers' store--price images depend on their price perceptions of such products. This behavior is consistent with the "price awareness hypothesis" (Cassady 1962; Holton 1957; Nagle and Novak 1988), which holds that consumers form relatively clear internal reference prices for frequently purchased items, and the reference points are influential in evaluating retail prices and forming store--price images. In support of this hypothesis, field studies have found that sales associated with "stock-up" items (i.e., frequently purchased items that can be stockpiled) are particularly responsive to supermarket price changes (Calantone et al. 1989; Litvak, Calantone, and Warshaw 1985; Meloche, Calantone, and Delene 1997).
The nature of the retail grocery industry motivates price setters to establish or maintain prices that are as low as or lower than their key competitors' prices for top-moving items (Dickson and Urban', 1994). As Dickson and Urbany (1994, p. 14) state, "industry executives firmly believe that certain high-volume products are critical bellwethers of a store's price image" and that "executives are highly sensitive to competitive differentials on these items." Researchers exploring supermarket pricing support this assertion, finding that markups are lowest on items with high unit sales volume (Nagle and Novak 1998; Preston 1963).
In addition to attempting to establish a low store--price image, the rationale for maintaining low prices on a subset of items is provided by "market basket pricing" (Preston 1963), or what has more recently been referred to as "implicit price bundling" (Mulhern and Leone 1991). As Mulhern and Leone (1991) demonstrate, it is necessary to anticipate own-price elasticities for products that are offered at a low price and cross-price elasticities between them and other items offered by the retailer to exploit interdependencies in demand and to maximize profitability.
The preceding discussion is summarized by the following:
• Establishing a low store--price image is a common positioning priority among grocery retailers.
• Grocery retailers monitor competitors' prices (through periodic price checks) with a focus on top-moving items (i.e., SKUs with high unit sales volume).
• Price setters concerned about creating a low store--price image are motivated to establish or maintain prices for top. moving items that are as low as or lower than their key competitors' prices.
These processes indicate that grocery price setters believe consumers are particularly sensitive to prices of popular (high volume) items. Thus, price setters may use volume as a surrogate measure of price elasticity; that is, the higher the sales volume for an item, the higher are the perceived own- and cross-price elasticities. As such, when price setters encounter a situation in which key competitors' prices are lower than their own price, they are more likely to decrease the retail price if the particular item is a top-moving (rather than a slow-moving) item. We expect such price decreases to be more prominent among price setters who are highly concerned about establishing a low store--price image. Accordingly, we hypothesize the following:
[H sub 1]: When key competitors' prices are relatively low on an item. (a) sales volume has a negative effect on price, and (b) the greater the importance of a low store--price image, the stronger is the negative influence of sales volume on price.
Study 1 examines the interactive effects of consumer demand and store--price image on the behavior of retail grocery price setters (as hypothesized in H1). We designed the experiment to provide evidence of the retail grocery price-setter behavior that we propose results in surcharges given certain brand-size demand asymmetries, which we discuss (and test) after Study 1.
Pretest
We conducted a pretest to determine the types of information grocery retailers most often use when evaluating and adjusting prices. The pretest participants were price setters who we asked to "consider the times that [they] conduct periodic evaluations of [their] store's regular, nonpromotional prices for various packaged-goods items" and presented with a list of 12 types of information. We instructed the price setters to check the types of information that they "normally use to decide to change any particular SKU's price," and we gave them the opportunity to provide any other information not on the list. (As we describe subsequently, we also collected additional questions to help guide Study 2 analyses.)
The sampling frame comprised all U.S. retail grocery chains as listed in the Chain Store Guide's 2001 Directory of Supermarket, Grocery, and Convenience Store Chains; we used the same sampling frame for the main experiment. We selected grocery chains randomly and telephoned them to identify the primary price setter. We then faxed the pretest to price setters (each represented a different retail grocery chain). Of the 37 price setters who received the pretest, 20 (54.1%) completed it.
None of the open-ended responses (n = 8) were provided by more than one price setter, and thus we did not include them in the subsequent results. For each respondent, we weighted the types of information marked by the inverse of the total number of information types selected; we then summed the values across respondents to develop a usage rating for each informational input.
The top six informational items (starting with the most common usage) were ( 1) the retailer's cost of the SKU, ( 2) the SKU's prices at competitive stores, ( 3) the current regular margin for the SKIS, ( 4) the unit sales volume for the SKU, ( 5) the current average margin for the SKU's product category, and ( 6) the current regular price for other sizes of the brand. We included these six types of information in the experimental scenario presented subsequently. We did not use the seventh most frequently used informational item, promotional support, because of Study 1's focus on regularly priced, nonpromoted items. We did not use the eighth most frequently used type of information, competing brand prices, because Study 1 addresses cross-chain competition rather than within-store cross-brand competition. Respondents reported the other four items (i.e.. sales in dollars and three price-elasticity measures) as rarely used.
H1 Methods and Results
Experimental design and procedure. To test H1, we used a two-level (low versus high unit sales volume) between-participants experimental design in conjunction with a measure of low store--price image importance. We replicated the design across lower and higher competitor prices. We randomly selected retail grocery chains from the same sampling frame used in the pretest. After soliciting participation by telephone, we faxed experiment materials to the main price setter for each firm or region; we sent second and third faxes as needed to increase the response rate.
Pricing scenario and manipulations. We asked price setters to assume they were conducting a periodic evaluation of their store's prices and that one of the brands encountered was a 12-ounce package of "brand X." To provide a basis for any price adjustments to brand X, we gave price setters additional information about the brand. Based on the pretest, this information included the current regular price of the item ($1.89), its cost ($1.65), the current margin (12.7%), the average margin for the product category (14%), an indication of the item's sales volume, and key competitors' current price levels. To assess the potential for quantity-surcharge pricing, we explained that the 24-ounce brand-size had a current regular price of $3.69.
We manipulated sales volume within the scenario. In the low-sales-volume condition, we stated that brand X was "one of your slowest moving SKUs, with unit sales volume in the bottom 5%." In contrast, for the high-sates-volume condition we stated that brand X was "one of your fastest moving SKUs, with unit sales volume in the top 5%."
Although our hypotheses are contingent on lower competitor prices, we included a higher-competitor-prices condition for comparison. In the lower-competitor-prices condition, we stated that "key competitors' current regular prices are $1.81 and $1 .79." In the higher-competitor-prices condition, we presented the prices as $1.99 and $1.97.
Measures. We assessed low store--price image importance by asking, "At the stores for which you are responsible for setting prices, how important is it to have an image of offering low prices?" This measure employed a nine-point scale anchored by "extremely unimportant" ( 1) and "extremely important" ( 9). We mean centered responses to reduce multicollinearity within the subsequently described regression models (Aiken and West 1991).
The dependent measure prompted price setters with the following: "Using the information above that you would normally use when setting prices, and based on your usual price-setting practices, you might change the price of the 12-ounce package of brand X or you might maintain the current price of $1.89. What would be your price for the upcoming period?" We provided two response options: ( 1) "Maintain the current price of $1.89" and ( 2) "Change the price to $( )."
Results. Of the 224 retail grocery price setters we contacted, 197 (87.9%) agreed to participate and 161 (71.9%) actually returned completed materials. The initial analysis involved regressing the new price on sales volume (as represented by a 0.1 dummy variable), low store--price image importance, competitor prices, and the interactions between these variables. Parameter estimates and associated statistics for this full model are shown in Table 1, Panel A.
To assess H1, we restricted analyses to the lower-competitor-prices condition. We conducted moderated regression analysis to test the main effect of sales volume (H1a) and the interaction between sales volume and low store--price image importance (H1b). As such, we regressed price on sales volume, low store--price image importance, and the interaction between these two variables. The results are summarized in Table 1, Panel B. The overall model was significant (F3.78 = 32.43, p < .001 R² = .56). Consistent with H1a, sales volume had a negative effect on price (β = -.67; t = -8.72; p < .001). As illustrated in Figure 1, Panel A, and in accordance with H1b, this main effect was qualified by a significant interaction (β = -.29; t = -2.29; p < .05) between sales volume and importance of low store--price image. A simple slope test indicated that in the high-sales-volume condition, importance of a low store-price image was negatively associated with price (β = -.52; t = -3.39; p < .001). In contrast, in the low-sales-volume condition, importance of low store--price image was not related to price (p > .8). Given this pattern of results and the significant interaction, H1b supported.
Although our hypothesis pertains to conditions in which price setters encounter lower competitor prices, sales volume and store--price image also influenced prices when competitor prices were high. A second moderated regression model within the higher-competitive-prices condition revealed that both sales volume (β = -.32; t = -3.36; p < .001) and store--price image (β = -.50; t = -4.15; p < .001) were negatively related to price, whereas the interaction between these two factors was not significant (p > .1; for model results, see Table 1, Panel C). The simple regression lines for the low- and high-sales-volume conditions are plotted in Figure 1, Panel B.
Study 1 Discussion
Study 1 results provide evidence of the price-setting behavior proposed to lead to quantity surcharges. In the lower-competitive-prices condition, price setters assigned relatively low prices to top-moving items (H1a), a result moderated by the importance retail price setters place on a low store--price image. In support of H1b, when competitor prices are low, responses to sates volume were stronger among those price setters concerned about establishing a low store--price image than among those who were not.
Study 1 demonstrates that in addition to price influencing consumer demand, demand can also affect price through competitive price-setting behavior. This finding suggests that grocery price setters employ sales volume as a surrogate measure of elasticity. Even though price setters are expected to use volume as a pricing input, volume's negative impact on price is counterintuitive: Products with significant market shares (and therefore high sales volume) have been characterized as price inelastic (Nagle and Holden 2002). In the current study, among price setters concerned about creating a low store--price image, high sales volume appears to signal not only high elasticity for the item itself but also the significance of the item's price in achieving the desired low store--price image. Consistent with the price awareness hypothesis, Study 1 findings indicate that by maintaining relatively low prices on high-sales-volume items (for which consumers are expected to have clear internal reference prices), a portion of retailers attempt to reinforce a low-price image. Conversely, when sales volume is low, regardless of the level of low store--price image importance, price setters saw little need to lower prices to a level that would meet or beat key competitors' prices.
In the higher-competitive-prices condition, price setters elected to raise the price of the focal item (i.e., brand X), but they did so In a manner consistent with the process we propose. In particular, price increases were most substantial For low-sales-volume items among price setters who have little concern about establishing a low store--price image (see Figure 1, Panel B). For high-sales-volume items, price increases were more conservative, such that price levels remained below key competitors' prices. This finding is consistent with our hypothesis that price setters attempt to maintain relatively low prices on high-sales-volume items.
Study 1 also is useful in examining the occurrence of quantity surcharges. As we noted previously, the scenario indicated that brand X was also available in a larger 24-ounce package priced at $3.69. As such, a quantity discount existed between the two brand-sizes because the unit price for the smaller brand-size ($.1575 per ounce) was higher than for the larger brand-size ($.15375 per ounce). Because we provided price setters with the price of the larger package, we could explore the extent to which surcharges arose from adjustments to the price of the smaller package. As illustrated in Figure 2, price setters in the higher-competitor-prices condition were unlikely to create surcharges. In the presence of lower competitive prices, price setters still created few quantity surcharges (8.3%) when the focal item was a slow-moving SKU; however, they created significantly more surcharges (79.1%) when the item was a top-moving SKU (χ2, sub d.f. = 1 = 39.38; p < .001). A follow-up logistic regression model (χ2, sub d.f. = 1 = 10.84; p < .001) indicates that within this latter condition, low store-price image importance had a positive impact on surcharge (versus discount) pricing (β = .73; Wald = 8.07; p < .01).
The preceding results suggest that quantity surcharges are more likely to occur when a relatively small package size represents a particularly high unit sales volume and when price setters are particularly concerned about creating a low store--price image. The surcharge results are valid to the degree that grocery price setters tend to adjust prices of high-volume brand-sizes without evaluating and adjusting the prices of other brand-sizes. On the basis of our discussions with price setters, we believe that this situation often is the case. Such anecdotal evidence is consistent with Kumar and Divakar's (1999) assertion that retailers typically fail to conduct brand-size level analysis.( n1)
The Study 1 assessment of grocery-pricing practices suggests that quantity surcharges are more likely to occur under specific patterns of demand for a portion of brands in the marketplace. Given the Study 1 findings, we propose that brands are more likely to include a surcharge when one of the smaller brand-sizes is a top-mover and substantially outsells at least one of its larger counterparts. In such cases, there is an increased likelihood that the price per unit of the more highly demanded smaller brand-size will be set lower than that of the larger brand-size, thereby creating a quantity surcharge.
For example, a retailer may identify the 28-ounce size of a ketchup brand as one of its top-moving brand-sizes (such that the item is on the firm's top-mover list and is monitored weekly). However, the larger 64-ounce brand-size may not have particularly high sales volume, and thus would not be closely monitored. Given the highly price-competitive grocery market and the common goal of creating a low store--price image, it would be expected that efforts to have an attractive price on the top-moving 28-ounce brand-size would create downward price pressure and thus result in a relatively low unit price for the item. Because of the lack of equivalent efforts to establish or maintain low prices on the 64-ounce brand-size, a quantity surcharge would be likely.
For this research, the 28- and 64-ounce brand-sizes in the previous example are considered a brand-size pair, that is, an intrabrand comparison between a particular smaller and larger package size of a given brand. A brand available in two sizes contains one brand-size pair (i.e., small and large), a brand available in three sizes contains three brand-size pairs (i.e., smallest and medium, smallest and largest, and medium and largest), and so on. Brand-size pair serves as the unit of analysis for Study 2, because a surcharge can be reflected in the unit prices of any brand-size pair.
We expect surcharges to be more common among brand-size pairs for which demand for the smaller brand-size is distinct in two respects. First, consumer demand for the smaller brand-size must be relatively strong (i.e., the SKU is a top-mover). As shown in Study 1, such top-moving brand-sizes are subject to considerable downward price pressure among retailers concerned about creating a low store--price image. Second, demand for the smaller brand-size must be substantially greater than that for the larger brand-size. When such demand asymmetry exists between brand-sizes, the smaller brand-size is subject to more downward price pressure than is the larger. From the preceding, we hypothesize the following:
H2: In comparison with all other brand-size pairs, quantity surcharges are more prevalent among brand-size pairs for which the smaller brand-size is a top-moving SKU and substantially outsells the larger brand-size.
Retail Margin Implications
By definition, unit prices associated with quantity surcharges are lower for smaller (rather than larger) brand-sizes. If retail margins are considered, however, quantity surcharges may or may riot result in lower margins for smaller brand-sizes. Although prior research has not explicitly considered retail margins, such an examination should provide insights into the nature of quantity surcharges and the underlying retail-pricing practices that can lead to them. Prior research in the area has suggested that retailers use quantity surcharges to increase profits by raising the price of larger brand-sizes. In line with this perspective, retail margins would be expected to be higher for larger, surcharged brand-sizes than for larger brand-sizes priced as a discount; no differences would be expected between surcharged and discounted smaller brand-sizes. Our account of quantity surcharges, however, suggests a different pattern for retail margins. In particular, the downward price pressure on smaller brand-sizes in surcharged pairs is expected to result in retail margins that are lower than those associated with smaller brand-sizes in discounted pairs. Thus, we offer the following hypothesis:
H3: Smaller brand-sizes associated with quantity surcharges have lower retail margins than smaller brand-sizes associated with quantity discounts.
We used data from a regional grocery chain to test the impact of brand-size demand on the prevalence of quantity surcharges (H,) and to explore the implications of quantity surcharges on retail margins (H3).
Description of Data
A privately held U.S. grocery chain provided the data for Study 2. The data set consists of those products and brands typically found in a grocery store and includes the following product categories: snack foods and crackers; health and beauty care; pet products; cleaners and paper products; canned goods; refrigerated foods, such as milk and cheese, and various frozen foods; condiments and jelly; tea, coffee, and juices; baking goods; and cereal, pasta, and bread.
The data set includes sales volume, retail price, and cost (i.e., wholesale price) for all brands available in two or more package sizes. Unit sales volume is reported at the brand-size (SKU) level and is based on a single year of sales for all stores in the chain. Retail price reflects the price the chain had set at the end of the 12-month sales volume collection period for a particular brand-size. Wholesale price reflects the price charged to the retailer at yearend by its supplier (one of the largest U.S. grocery wholesalers). The structure of the data (i.e., one year of sales volume data and year-end prices) is well suited for our research. Given our focus on the effect of sales volume and competitive price monitoring (and, in turn, the occurrence of surcharges), it is beneficial to have data that is temporally consistent with the proposed causal relationship.
The data did not include a promotion field; thus, it was not possible to identify the retail prices that reflected a temporary price reduction. Previous research, however, indicates that the vast majority of surcharges are not created by temporary price promotions. Field studies investigating surcharges (Nason and Della Bitta 1983; Walker and Cude 1984; Widrick 1979b; Zotos and Lysonski 1993) on average have found (weighted by the number of brands audited) that 14.6% of quantity surcharges were on temporary price promotion at the time of the study. This value actually overestimates the creation of surcharges due to price promotions, because quantity surcharges sometimes exist even before a smaller item is temporarily placed on sale.
In Study 2, brand-size pairs are the unit of analysis. For this research, it was essential that the brand-sizes constituting each brand-size pair were equivalent in all respects except package size. For example, we excluded brands with different packaging materials for various brand-sizes (e.g., a brand of juice with a plastic container for one size and a glass container for another size). The remaining data set comprised exactly 800 brands and 1247 brand-size pairs.
In the current data, the incidence of quantity surcharges at the retail level--15.8% of all brands included one or more surcharges--was similar to that reported in other research (see Manning, Sprott, and Miyazaki 1998). Quantity-surcharge incidence for brands at the wholesale level was lower at 11 .2% (z = 2.69; p < .01). For brand-size pairs, the incidence of quantity surcharges was 11.1% at the retail level and 8.4% at the wholesale level (z = 2.26; p < .05). (Prior research has not reported the incidence of quantity surcharges at the brand-size pair level, and thus we can provide no comparison for our values.) A more detailed overview of the incidence of surcharges in the data is presented in Table 2.
H2 Methods and Results
As we discussed previously, we expect common retail grocery price-setting practices to result in a higher prevalence of quantity surcharges when a top-moving, small brand-size substantially outsells its larger counterpart (H2). We empirically examined this issue through a logistic regression model.
Dependent variable. The focal dependent variable represented the form of pricing (i.e., discount versus surcharge) at the retail level between two brand-sizes. The variable was dichotomous, based on our desire to model the focal phenomenon, namely, the existence of quantity surcharges at the retail level. This dependent variable was based on the quantity surcharge/discount index Qij, which was calculated such that
( 1) Qij = (UPLij - UPSij)/([UPLij + UPSij]/2),
where UPLij is the unit price of the larger package of the brand-size pair i of brand j, and UPSij is the unit price of the smaller package of the brand-size pair i of brand j. (This formula is slightly different from what has been used in prior research, wherein UPSij serves as the denominator; see Manning, Sprott, and Miyazaki 1998; Walden 1988). We calculated unit prices for the two focal brand-sizes in each pair using the same units (e.g., ounces, counts, gallons). A negative value for Qij indicates a quantity discount, and a positive value indicates a quantity surcharge. Accordingly, for each brand-size pair, the form of retail pricing was coded as "0" to represent quantity discounts (i.e., when Qij < 0) and as "1" to represent quantity surcharges (i.e., when Qij > 0).
Independent variables. We included a series of measures as independent variables in the logistic regression and calculated them for each brand-size pair. The dichotomous wholesale pricing variable accounted for quantity surcharges at the wholesale level. For each brand-size pair, this variable indicated whether the wholesale prices reflected a quantity discount (coded as "0") or a quantity surcharge (coded as "1"). The variable tests an alternate explanation for surcharges, namely, that retailers apply constant margins across brand-sizes and that surcharges at the retail level simply reflect the pricing of manufacturers and/or wholesalers. Of the surcharges occurring at the retail level, 39.9% also exist at the wholesale level. In other words, approximately 60% of the retail surcharges in the current data are discounts at the wholesale level. With the addition of this wholesale pricing variable, our model focuses on explaining the existence of surcharges created at the retail level and not those in existence throughout the distribution system.
We accounted for demand asymmetry across smaller and larger brand-sizes by two dummy-coded variables representing sales volume differences within brand-size pairs. We followed a three-stage process to develop these variables.
First, we categorized each brand-size on the basis of annual sales volume as slow moving (sales volume in the bottom 80% of all brand-sizes), moderate moving (sales volume between 80% and 95% of all brand-sizes), or fast moving (sales volume greater than 95% of all brand-sizes). To ascertain the appropriate split into slow-, moderate-, and fast-moving brand-sizes, we included questions about competitor price checks in the Study 1 pretest. Specifically, we asked pretest respondents to indicate how often they check key competitors' prices for the SKUs they consider "fast moving," "moderate moving," and "slow moving." Given the pretest results indicating that both the moderate- and fast-moving items are regularly monitored, we collapsed these categories into a single "top-mover" category that consists of approximately 20% of the brand-sizes.( n2)
Second, we coded each brand-size pair to reflect the account for variance associated with the fundamental nature demand asymmetry existing within the pair. In particular, we coded all brand-size pairs to represent one of three demand asymmetry categories: Category 1 represents pairs in which the smaller brand-size outsold the larger brand-size, and only the smaller brand-size is a top-mover (n = 194); Category 2 represents pairs in which the smaller brand-size outsold the larger brand-size, and both brand-sizes are equivalent in terms of whether they are top-movers (n = 534); Category 3 represents brand-size pairs in which the larger brand-size outsold the smaller (n = 519). The greatest incidence of quantity surcharges should occur in Category I, fewer in Category 2, and the least in Category 3. This expectation is based on our prior theorizing that demand asymmetry, such that the smaller brand-size outsells the larger, results in a higher incidence of quantity surcharges and that such an effect is stronger when the smaller brand-size also is a top-mover.
Third, using reference cell coding, we created two dummy-coded variables to represent demand asymmetry across brand-size pairs (Category 1 served as reference; see Homer and Lemeshow 2000). The first dummy-coded variable compared Category 1 with Category 2; the second variable compared Category 1 with Category 3. Significant and negative coefficients for the dummy-coded variables would support H2, because we expect the greatest incidence of surcharges for brand-sizes in Category 1 . The strongest test of H2, however, is provided by the first dummy-coded variable, because Category 1 and Category 2 are similar (i.e., both contain brand-size pairs in which the smaller brand-size outsells the larger).
We accounted for product category effects in the model with a series of dummy-coded variables. Because of a desire to capture differences in the products with a small number of relatively homogeneous categories, we coded ten primary product categories. Product categories (followed by brand-size pair counts) included snack foods and crackers (n = 109); health and beauty care (n = 213); pet products (n = 93); cleaners and paper products (n = 147); canned goods (n = 109); condiments and jelly (n = 133); tea, coffee, and juice (n = 94); baking goods (n = 161); cereal, pasta, and bread (n = 78); and refrigerated goods (n = 110). Nine dummy-coded variables represented these product categories. The refrigerated-goods category served as reference for each product category dummy variable, because prior research demonstrates the importance of this category to the occurrence of surcharges. Specifically, Walden (1988) finds that refrigerated products are more likely to include a quantity surcharge than shelf-stored products; he attributes this result to the increased unit costs (e.g., per ounce) of cooling refrigerated products packaged in larger rather than smaller packages. Agrawal, Grimm, and Srinivasan (1993) find similar but weaker effects. As such, we expect product category dummy-coded variables to have negative coefficients.
A control variable represented the log of the ratio of package sizes (larger over smaller) being compared (Walden 1988; Walker and Cude 1984; Widrick 1979b). For example, if the larger brand-size is 24 ounces and the smaller is 12 ounces, the ratio is 2. The log of this ratio is included to of quantity-discount pricing. Economic theory suggests that unit prices should decrease for a greater amount of a good (i.e., quantity-discount pricing) because of diminishing marginal returns offered by each additional unit. Thus, the difference in utility per ounce between a 20-ounce bag of potato chips and a 2.5-ounce bag of potato chips should be larger than the difference between a 20-ounce bag and a 12-ounce bag. Accordingly, as the percentage difference between two brand-sizes increases, there should be a greater likelihood of a quantity discount and thus a lower likelihood of a quantity surcharge (see Walden 1988).
Results. We assessed the model with regard to assumptions of logistic regression, and we did not detect any violations. Specifically, there was no evidence of multicollinearity among independent variables based on variance inflation factor and tolerance statistics. In addition, an analysis of the model's residuals (i.e., studentized residuals and dbeta) indicated no cases in the sample that might have an undue influence (Menard 1995). The results of the logistic regression analysis are presented in Table 3.
The overall regression model was statistically significant (p < .001). The wholesale variable was positive and significant, which indicates (as we expected) that wholesale pricing of a brand-size pair (i.e., whether priced as a quantity discount or a quantity surcharge) influenced whether the brand-size pair was priced as a surcharge or a discount at the retail level. The majority of product category dummy variables were not significant, yet based on the significant (and marginally significant) negative coefficients, evidence exists that refrigerated items are more prone to surcharges than are other product categories.( n3)
Of focal interest are the dummy-coded variables representing demand asymmetries. In support of H2, both coefficients associated with these variables were negative and significant (p ≤ .001). The second dummy-coded variable (labeled "volume dummy code 2" in Table 3) demonstrates that more quantity surcharges exist among brand-size pairs in which the smaller brand-size substantially outsells the larger brand-size than exist within pairs in which the larger brand-size outsells the smaller brand-size. The first dummy-coded variable examines only brand-size pairs in which the smaller brand-size outsells the larger. The significant, negative coefficient indicates that more quantity surcharges exist among brand-size pains when the smaller brand-size is a top-mover and the larger brand-size is not than when the smaller outsells the larger brand-size but both are equivalent in terms of whether they are top-movers.( n4)
H3 Methods and Results
We hypothesized that relatively high demand for small brand-sizes leads to downward price pressures and the occurrence of quantity surcharges. Thus, we expect downward price pressure on smaller brand-sizes within surcharged pairs to result in retail margins that are lower than those associated with smaller brand-sizes in discounted pairs (H3).
We calculated margins as a percentage of the retail price for each brand-size (i.e., [retail price -- wholesale price]/ retail price). With retail margins as the dependent variable, we included two independent variables in an analysis of variance model. The first reflects package sizes (small versus large) contained in a particular brand-size pair, and the second indicates whether the focal brand-size pair is priced as a quantity discount or as a quantity surcharge. To avoid double-counting a particular brand-size as both small and large, we used only brands with two brand-sizes for this analysis (n = 635), which represent the bulk of the data and the majority of quantity surcharges. We included wholesale price as a covariate to ensure that the model explained retail-pricing behavior.
The analysis of covariance findings indicate that all effects are significant (all ps < .01). The main effect for brand-size shows that profit margins for small brand-sizes (M = 17.9%) were less than margins for large brand-sizes (M = 23.4%; F1, 265 24.83, p < .01). The other main effect shows that profit margins for brand-size pairs priced as a surcharge (M = 18.8%) were lower than margins for brand-size pairs priced as a discount (M = 22.7%; F1, 1265 = 13.75, p < .01). These main effects, importantly, are qualified by a significant interaction (F1, 1265 = 39.63. p < .01) between brand-size and the form of pricing (see Figure 3).
We used a planned contrast to assess H3. The analysis indicates that the average retail margin of small brand-sizes within surcharged pairs (M = 12.5%) was less than the average margin of small brand-sizes within discounted pairs (M = 23.4%; F1, 632 = 45.72, p < .01). Thus, H3 is supported. Follow-up analysis indicates that the margin for small brand-sizes within surcharged pairs was less than the retail margins for all other brand-sizes (M = 22.8%; including the large, surcharged brand-sizes and both brand-sizes associated with discount pricing; F1, 1267 = 48.51, p < .01).
Furthermore, we assessed the alternative explanation that quantity surcharges are caused by price increases of the larger, surcharged brand-size. This analysis indicates that the average retail margin of large brand-sizes within surcharged pairs (M = 24.8%) was greater than the average margin of large brand-sizes within discounted pairs (M = 220%; F1, 632 = p = 05). Although these results suggest that two processes create surcharges, the decrease in margins for smaller brand-sizes outweighs the increase for larger items. In particular, the effect size associated with lower margins for smaller brand-sizes (η= .26) was significantly larger than the effect size associated with the higher margins for larger brand-sizes (η = .08; z = 3.34, p < .01).
Study 2 Discussion
The Study 2 results support the proposed explanation of quantity surcharges. In particular, the logistic regression analysis demonstrates that the incidence of quantity surcharges is greater among brand-size pairs in which a top-moving smaller brand-size outsells its larger (non-top-moving) counterpart. In addition to being the first field study to assess the association between demand asymmetry and surcharges, this study is also unique in its examination of retail margins. The margin results strongly support the price-setting process we propose. Consistent with the premise that surcharges occur as top-moving small brand-sizes are subjected to downward price pressure, retail margins were lowest for small brand-sizes priced alongside large, surcharged brand-sizes.
An alternative account for the relationship between demand asymmetry and surcharge incidence is that consumers simply shift purchase behavior from larger. surcharged brand-sizes to smaller (less expensive) brand-sizes, which is a finding supported by Manning, Sprott, and Miyazaki (1998). This viewpoint suggests that the significant effect of the volume dummy-coded variables is due, at least in part, to the influence of price on volume as well as to the proposed influence of volume on price. Although such an alternative account cannot be completely ruled out, it is important to note that Study 1 provides the essential causal evidence for the prescribed effects of sales volume on price.
Finally, this study illustrates that no single explanation can account for the existence of quantity surcharges. In addition to supporting the hypothesized role of retail price-setting processes, the results indicate that surcharges at the retail level also result when a retailer passes along surcharges reflected in wholesale prices. The wholesale variable had the strongest effect in the model. We also find support for Walden's (1988) contention that cost-related factors may play a role in determining the existence of surcharges (as indicated by a greater incidence of surcharges for refrigerated products than for some other product categories).
This research explores price-setting practices and the occurrence of quantity surcharges in the retail grocery marketplace and, in so doing, contributes to the pricing literature in several unique ways. First, we demonstrate empirically that sales volume can negatively influence price in a highly competitive industry. Specifically, price setters establish lower prices on top-moving items, and this effect is stronger for retailers that place greater importance on establishing a low store--price image. Second, we provide evidence in a field setting that such price-setting practices result in quantity surcharges when the smaller brand-size is a top-moving SKU and is in greater demand than its larger counterpart. Third, we find support for the predicted differences in retail margins across quantity surcharge and discount brand-sizes; in particular, we find small packages within surcharged brand-size pairs to have lower retail margins than do small packages within discounted pairs.
Managerial and Public Policy Implications
Consistent with extant research (Fader and Hardie 1996; Guadagni and Little 1983; Kumar and Divakar 1999), our results underscore the importance of incorporating brand-size into marketing decision making. As Kumar and Divakar (1999, p. 60) note, "it does not seem that retailers are taking differential brand-size level effects into account while setting pricing and promotional strategies." Retailers should consider brand-size pricing, however, if for no other reason than that the existence of surcharges is not inconsequential.
The conceptual arguments presented in the current research, combined with findings that surcharges can shift purchases to smaller brand-sizes (Manning, Sprott, and Miyazaki 1998; Miyazaki, Sprott, and Manning 2000), suggest a nonrecursive relationship between consumer demand and the Occurrence of surcharges. That is, certain asymmetric brand-size demand conditions lead to surcharges, and surcharges might further shift purchases to smaller brand-sizes. Of particular concern is our result indicating that such small brand-sizes (which are matched with a large, surcharged brand-size) have relatively low retail margins. It follows that retailers should exercise caution when setting prices at levels that create a surcharge, given evidence that surcharges can shift purchases to smaller, low-margin brand-sizes. For retailers that stress a low store--price image, our results support previous contentions that price setters may suffer from a myopic focus on the pricing of top-moving items (see Urbany, Dickson, and Key 1990). This focus may create a favorable store--price image, but it might also have unintended harmful effects on store profits if it is overemphasized (see Dickson and Urbany stand how to use unit price information (Manning, Sprott, 1994). Price setters should consider this trade-off carefully.
In addition to retailer implications, our research also applies to public policy concerns related to the existence of surcharges. The quantity-surcharge phenomenon is often considered a form of retail price discrimination that harms consumer welfare (Agrawal, Grimm, and Srinivasan 1993; Gupta and Rominger 1996; Nason and Delta Bitta 1983; Widrick 1985; Zotos and Lysonski 1993). Gupta and Rominger (1996, p. 1309) clearly reflect this position when they state that "the retailer uses quantity surcharges to increase profit margins by relying on the consumers' mistaken belief in the volume discount heuristic." The results reported in the present article suggest that this is not the case. Study 2 indicates that any increases in margins among larger brand-sizes of quantity-surcharged pairs are largely outweighed by decreases in margins of smatter brand-sizes within quantity-surcharged pairs.
It is worth noting that only approximately 50% of consumers are sure they have encountered quantity surcharges in the marketplace (Manning, Sprott, and Miyazaki 1998; Nason and Della Bitta 1983; Whitfield, Lawson, and Martin 1985). This finding is the basis of additional concerns about consumer welfare and quantity surcharges, because consumers may use a quantity-discount heuristic (i.e., "more is always cheaper"; see Manning, Sprott, and Miyazaki 1998) when shopping and be unaware that they are in the presence of surcharged brand-sizes (e.g., Widrick 1985). The Study 2 finding that more surcharges exist when a smaller brand-size significantly outsells a larger counterpart suggests that, when in the presence of a surcharged brand-size, a large portion of consumers attend to the item's smaller (more popular) counterpart. Because consumers are unaware of information to which they have not first attended, it follows that consumers' unawareness of surcharges may be due, in part, to not attending to larger brand-sizes when the smaller brand-size is more popular. From this perspective, consumer welfare at a general level may be unharmed by the presence of quantity surcharges. Indeed, for those consumers desiring the more popular smaller brand-size (a brand-size with the lowest retail margins), consumer welfare is improved by the presence of surcharges. As quantity surcharges come under increasing media scrutiny (CNBC 2002; Consumer Reports 2000; McCarthy 2002), our article serves to illuminate that surcharges can occur as price setters provide consumers with lower (rather than higher) prices.
Mechanisms are available to aid consumers who prefer larger brand-sizes that are surcharged. In-store information strategies could be developed to reduce consumer information-processing costs (see Russo, Krieser, and Miyashita 1975; Russo et al. 1986) and to ease identification of surcharges. Along these lines, Miyazaki, Sprott, and Manning (2000) find that highly prominent displays of unit prices on shelf labels reduce consumers' selection of surcharged brand-sizes. Furthermore, as shown in previous studies on the processing of price information (e.g., Inman, McAlister, and Hoyer 1990; Srivastava and Lurie 2001), identification and avoidance of surcharges likely depends on consumer characteristics such as price consciousness and search costs and potentially on consumers' ability to understand how to use unit price information (Manning, Sprott, and Miyazaki 2003).
Limitations and Further Research
Although Study 1 manipulated two factors key to retail price setting, further research could manipulate other factors, such as costs and category margins (which we held constant), that may play an important role in setting prices and could therefore affect surcharge pricing. In addition to exploring this possibility, further research could focus on collecting process data (e.g., verbal protocols, thought listings) to substantiate the purported explanations for price setters' responding to higher-sales-volume levels with lower prices. Such research might also explore the extent to which such pricing practices exist in other retail and nonretail contexts.
As we noted previously, a nonrecursive relationship likely exists between consumer demand and the occurrence of quantity surcharges, such that greater demand for smaller brand-sizes may result in quantity-surcharge pricing, and surcharges, in turn, may shift purchases to smaller brand-sizes. A question for further research is, How does this reciprocal relationship begin? When a new product is introduced to the market, average or above-average product category margins may be applied. If the new item attains "top-mover" status, managers who consider a low store--price image particularly important may then start frequently monitoring competitive prices for the item and, when necessary, lower the price to meet or beat competitors' prices. Additional research is needed to determine whether this process accurately reflects the temporal orientation of the expected reciprocal relationship between consumer demand and quantity surcharges.
In terms of quantity surcharges specifically, additional research is needed to establish the generalizability of Study 2's findings. Anecdotal evidence, however, lends support to our findings with an additional retailer serving two different markets. A post hoc analysis of Kumar and Divakar's (1999) peanut butter data (i.e., 131 weeks of Information Resources Inc. data for a major grocery chain) produced results consistent with our findings regarding asymmetric brand-size demand. Specifically, we found that each incidence of a surcharge (six surcharges in 20 brand-size pairs for Market I; eight surcharges in 20 brand-size pairs for Market 2) occurred for brand-size pairs in which the smaller brand-size had a higher sales volume than did the larger brand-size. Ideally, replications of Study 2 would involve the use of longitudinal data such that retail prices, sales volume, and competitive prices are assessed over time. In addition, further research could examine potential moderating effects of product-level factors (e.g., store versus national brands, hedonic versus utilitarian products).
Further research also might explore other causal factors associated with the occurrence of quantity surcharges. One area ripe for inquiry is factors associated with nonretaiter members of the distribution channel. Although our investigation is the first to consider the incidence and nature of quantity surcharges at the wholesale level, our data provide little indication of why surcharges exist at this level of the distribution channel. To determine the causes of surcharges at the wholesaler and manufacturer levels, further research could explore variables focal to wholesalers and manufacturers, such as production, distribution, and storage costs. Finally, as mentioned previously, the results of our research suggest that a single, simple explanation for surcharges does not exist. As such, there is an opportunity for additional research in identifying the relative weights of the various causal factors that determine the occurrence of quantity surcharges.
The authors also are grateful for the suggestions provided by Joe Cannon, Joe Urbany, and the anonymous JM reviewers. All authors contributed equally to this article.
(n1) Given that in some cases price setters may simultaneously evaluate the pricing of multiple brand-sizes, we conducted a follow-up experiment in which we manipulated sales volume of the small brand-size in an identical manner to that in Study 1. We provided price setters (n = 55) with information (i.e., sales volume, cost, margin, and competitor prices) about both the 12- and 24-ounce brand-sizes and asked them to establish a price for both brand-sizes. We held the sales volume of the large brand-size constant at a low level. When the smaller brand-size was presented as having considerably higher sales volume than the larger size, 45.8% of price setters created a quantity surcharge. However, when both brand-sizes were presented as having low sales volume, a significantly lower percentage (12.9%) of the price setters created surcharges (χ2, sub d.f. = 1 = 7.40; p = .01). This pattern of results is similar to that found in Study 1 . A complete description of this follow-up study is available from the authors.
(n2) An alternative analysis in which the top-mover category encompassed items in the top 30% (in terms of unit sales volume) produced substantively equivalent results.
(n3) To explore further the significance of this effect, we substituted a dichotomous variable indicating whether the product is stoned on a shelf (coded as "0") or in some form of refrigeration (coded as "1") for the product category dummy variables, and we reestimated the logistic model. The results paralleled those reported in Table 3. and the refrigeration variable was significant and positive, which indicates that surcharges are less likely for those brands stored on shelves (Wald = 5.241; p = .022).
(n4) Two additional analyses tested the robustness of the findings. First, we conducted an alternate linear regression analysis in which the dependent variable was the continuous quantity surcharge/discount index (i.e., Qij) and independent variables were the same as those in the logistic regression. This model focuses on the magnitude of discounts and surcharges, whereas the logistic regression model focuses on the likelihood of the existence of a surcharge. The second analysis ascertained the influence of relatively small surcharges and discounts on the results by excluding all brand-size pairs containing a quantity surcharge or a discount of less than 10% (as based on Qij n = 333) and then reestimating the logistic regression model. The results of both analyses are in the predicted direction and nearly identical to the logistic regression model detailed in Table 3.
Legend for Chart:
A - Source
B - Standardized Estimate
C - Parameter Estimate
D - Standard Error
E - t-Value
F - p-Value
A B C D
E F
A: Full Model
Intercept 1.89 .008
224.61 .000
Competitor prices .52 .0943 .012
7.99 .000
Sales volume -.46 -.0843 .012
-7.31 .000
Price image -.02 -.0778 .006
-.13 .894
Sales volume x price image -.20 -.0147 .007
-2.01 .046
Competitor prices x price image -.25 -.0161 .007
-2.34 .021
Sales volume x competitor prices .21 .0442 .016
2.73 .007
Competitor prices x price image x
sales volume .23 .0251 .009
2.65 .009
B: Low Competitor Prices
Intercept 1.89 .007
254.94 .000
Sales volume -.67 .0875 .010
-8.72 .000
Price image -.02 .0008 .005
-.15 .879
Sales volume x price image -.29 .0147 .006
-2.29 .025
C: High Competitor Prices
Intercept 1.99 .009
221.80 .000
Sales volume -.32 .0422 .013
-3.36 .001
Price image -.50 .0169 .004
-4.15 .000
Sales volume x price image .19 .0104 .097
1.57 .121
Notes: For Panel A, R² = .71 (adjusted R² = .70);
F7, 153 = 53.27, p < .001. For Panel B, R² = .56
(adjusted R² = .55); F3, 78 = 32.43, p < .001.
For Panel C, R² = .31 (adjusted R² = .29);
F3, 61 = 11.86, p < .001. For all panels, dependent
variable is new price for brand X. Legend for Chart:
A - Sizes of Brand
B - Quantity Surcharges at Brand Level(a,b) Wholesale Price
Brands
C - Quantity Surcharges at Brand Level(a,b) Wholesale Price
Surcharges
D - Quantity Surcharges at Brand Level(a,b) Retail Price Brands
E - Quantity Surcharges at Brand Level(a,b) Retail Price
Surcharges
F - Quantity Surcharges at Brand-Size Pair Level(a,b) Wholesale
Price Brands
G - Quantity Surcharges at Brand-Size Pair Level(a,b) Wholesale
Price Surcharges
H - Quantity Surcharges at Brand-Size Pair Level(a,b) Retail
Price Brands
I - Quantity Surcharges at Brand-Size Pair Level(a,b) Retail
Price Surcharges
A B C D E
F G H I
2 633 52 635 78
633 52 635 78
3 133 25 133 29
397 34 399 34
4 27 8 27 14
162 13 162 20
5 4 4 4 4
40 6 40 6
6 1 0 1 1
15 0 15 1
Total 798 89 800 126
1247 105 1251 139
Surcharge
Percentage 11.15% 15.75%
8.42% 11.11%
(a) Brand level refers to a particular brand that includes
multiple brand sizes. Alternately, brand-size pair level
refers to a particular comparison between two brand-sizes
within a particular brand. The number of brand-size comparisons
increases with the number of brand-sizes available for a
particular brand.
(b) The frequency of brands and brand-size comparisons differs
between retail and wholesale prices because tour brand-size
comparisons had identical unit prices at the wholesale level;
we excluded these from the analysis. Dependent Variable: Form of Retail Pricing(a)
Legend for Chart:
A - Source
B - Parameter Estimate
C - Standard Error
D - Wald
E - p-Value
A B C D E
Intercept 5.800 2.713 4.57 .033
Wholesale(b) 2.751 .254 117.34 .000
Volume dummy code 1(c) -.912 .264 11.96 .001
Volume dummy code 2(c) -2.107 .310 46.29 .000
Snack foods and crackers(d) -.749 .483 2.40 .121
Health and beauty care(d) -.354 .411 .74 .390
Pet products(d) -1.184 .605 3.83 .050
Cleaners and paper products(d) -.532 .444 1.44 .231
Canned goods(d) -.809 .463 3.05 .081
Condiments and jelly(d) -.853 .455 3.51 .061
Tea, coffee, and juices(d) -.314 .487 .42 .518
Baking goods(d) -.683 .424 2.59 .107
Cereal, pasta, and bread(d) -.019 .465 .002 .968
Size ratio(e) 2.820 .679 17.27 .000
(a) Form of retail pricing is a dichotomous variable based on
retail prices, where 0 = quantity-discount pricing and
1 = quantity-surcharge pricing.
(b) Wholesale is a dichotomous variable based on wholesale
prices, where 0 = quantity-discount pricing and
1 = quantity-surcharge pricing.
(c) Demand asymmetry within brand-size pairs was represented by
two dummy-coded variables (with Category 1 serving as reference).
(d) Nine dummy-coded variables represented product categories
(with refrigerated goods as reference.
(e) Size ratio is a continuous control variable indicating the
log of the ratio (larger brand-size over smaller brand-size) of
the package sizes being compared.
Notes: Log-likelihood (intercept only = 742.10; final
model = 534.57); χ²[sub (d.f. = 13 = 207.54;
p < 0001; N = 1247.GRAPHS: FIGURE 1 Simple Regression Lines for Study 1
GRAPH: FIGURE 2 Quantity Surcharges Created for Study 1
Legend for Chart
B - Brand-Size Small
C - Brand Large
B C
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Wellman, David (2000), "The Grocery Empire Strikes Back," Supermarket Business, (April 15), 77, 82, 84, 88.
Whitfield, Justine, Rob Lawson, and Brett Martin (1995), "Consumers' Responses to Quantity Surcharges in New Zealand Supermarkets," paper presented at New Zealand Marketing Educators Conference, Wellington (November).
Widrick, Stanley M. (1979a), "Measurement of Incidents of Quantity Surcharge Among Selected Grocery Products," Journal of Consumer Affairs, 13 (Summer), 99-107.
------ (1979b), "Quantity Surcharge: A Pricing Practice Among Grocery Store Items--Validation and Extension," Journal of Retailing, 55 (Summer), 47-58.
------ (1985), "Quantity Surcharge-Quantity Discount: Pricing As It Relates to Quantity Purchased," Business and Society, 24 (Spring), 1-7.
Zotos, Yiorgos and Steven Lysonski (1993). "An Exploration of the Quantity Surcharge Concept in Greece," European Journal of Marketing, 27 (10), 5-18.
~~~~~~~~
By David E. Sprott; Kenneth C. Manning and Anthony D. Miyazaki
David E. Sprott is Assistant Professor of Marketing, college of Business and Economics, Washington State University.
Kenneth C. Manning is Associate Professor of Marketing, College of Business Administration, Colorado State University.
Anthony D. Miyazaki is Assistant Professor of Marketing, College of Business Administration, Florida International University. The authors thank Finn Andersen, Cristina Calero, Sebastian Fernandez, Troy Ledgerwood, Rayna Uptmor, and Megan Weider for their assistance with data collection and processing.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 71- Handbook of Marketing. By: Rajaratnam, Daniel; Clark, Terry. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p129-131. 3p.
- Database:
- Business Source Complete
Section: Book ReviewsHandbook of Marketing (Book)
Handbook of Marketing
edited by Barton Weitz and Robin Wensley
(Thousand Oaks, CA: Sage Publications, 2002,
582 pp., $99.95)
Most dictionaries or encyclopedias of marketing are hardly worth reading. At best, they are intended for practitioners who lack B-school training and believe themselves deficient in common usage jargon; at worst, they are intended for a general public that knows little about marketing. The Handbook of Marketing is different; that Barton Weitz and Robin Wensley are its editors should suggest something out of the ordinary. A glance at the contributors (e.g., Wilkie, Webster, Day, Shocker, Keller, Hauser, Winer, Stewart, Parasuraman, Zeithaml) puts the matter to rest. The Handbook is an extraordinary effort. The blurb on the dust jacket is an understatement--the "Handbook will be invaluable to advanced undergraduates, graduate students, academics, and thoughtful practitioners in marketing"--the book is far more than that. The editors put it better when they say (p. 1) the "chapters in the Handbook ... summarize research in the substantive domain of marketing." They go on (p. 1) to tell the reader that each chapter "provides an overview of academic research ... I in a] particular substantive area of marketing, offers a bibliography of important research in the topic area. ... [and] identifies productive areas for future research." Although some of the chapters have been previously published in journals, enough of the material is new, and what is old is bundled in such a way that the book itself can essentially be considered a new work. In short, the Handbook is probably invaluable to all academic researchers.
What are the substantive areas the chapters cover? Does the book cover enough of marketing's domain to make it universally appealing? Is there something for everyone? The short answer to all of these questions is yes. The Handbook has 21 chapters organized in live sections: ( 1) Introduction, ( 2) Marketing Strategy, (3) Marketing Activities. (4) Marketing Management, and (5) Special Topics.
The three introductory chapters in the first section--"Marketing's Relationship to Society" (Wilkie and Moore), "A History of Marketing Thought" (Jones and Shaw), and "The Role of Marketing and the Firm" (Webster)--are all masterful essays in their own right and will no doubt be used extensively in doctoral seminars. More important, they set the tone for the entire book; they are sweeping, scholarly, and eminently readable.
Wilkie and Moore open the introductory section with "Marketing's Relationship to Society," an abridged version of their insightful 1999 Journal of Marketing article "Marketing's Contributions to Society," which provides an unusually broad historical panorama and critique of marketing's place (pros and cons) in society. This chapter provides a much needed view for a discipline that rarely looks at itself from an outside perspective.
The evolution of research about marketing as a discipline over the past 100 years is examined in Jones and Shaw's "History of Marketing Thought:' The review is chronologically organized; it begins with studies of marketing ideas in the ancient world and moves on through the writings of late-nineteenth-century economists. The description of the emergence of marketing as a distinct discipline in the first half of the twentieth century and of the subsequent rise of modern schools of thought (marketing management, consumer behavior, macromarketing) is enlightening and instructive.
The introduction concludes with Webster's examination of marketing's place in the firm. In his chapter, Webster considers the changing place of marketing in the firm--marketing as exchange, selling, demand stimulation, market creator, tactics (4Ps), organizational culture, and business philosophy. This morphing/evolving of the discipline has had profound consequences both for practicing managers (not only has "what they do" changed, but the value placed on what they do has changed as marketing has been both more and less central to the received wisdom of how a firm should be constituted) and for scholars, who have wrestled with the whys, hows, and oughts of marketing. Webster's view is that in an era characterized by extensive relationships with other firms, marketing's role in the firm should simultaneously encompass culture (at the corporate level), strategy (at the business unit level), and tactics (at the functional level).
The second section of the Handbook, "Marketing Strategy," Is somewhat broader than its title suggests, and it sweeps deeply into related theoretical areas such as the theory of the firm and market evolution. Indeed, the section title might well stand for the balance of the book.
Day and Wensley's "Market Strategies and Theories of the Firm" relates research on marketing strategies to three theories of the firm: ( 1) the resource-based perspective, ( 2) the positioning perspective, and (3) the configuration perspective. They also present an extensive review of research on marketing strategy issues associated with each perspective.
In "Determining the Structure of Product Markets: Practices, Issues, and Suggestions" (an updated version of Shocker, Stewart, and Zahorik's [1990] article), Shocker reviews research approaches used to analyze market structures.
Gatignon and Soberman round out the section with "Competitive Response and Market Evolution," an impressive review of major issues and research on competitive response and market evolution. They develop a conceptual framework that considers the interactions between these constructs and present the impact that environmental (exogenous) factors can have on both competitive response and market evolution.
The eight chapters in the third section of the book focus on the marketing mix and related issues--branding and brand equity, product development, channel management, sales force management, pricing, promotion, and service quality.
The branding guru Keller gives a tour of branding's theoretical foundations in "Branding and Brand Equity" and touches on brand personality, relationships, experiences, communities, and corporate images. He emphasizes factors that affect the choice and design of brand names and logos, legal issues, brand extension, and leverage of brand equity through brand alliances.
Dahan and Hauser overview new product development in "Product Development--Managing a Dispersed Process." The chapter is particularly valuable for its examination of product development issues in highly competitive, dynamic environments. Presenting new product development as an integrated end-to-end process, Dahan and Hauser identify research challenges unique to the times.
Anderson and Coughlan's "Channel Management--Structure, Governance, and Relationship Management" examines issues and research related to the number of separate firms and levels that constitute the distribution channel, the frameworks used to coordinate and control the activities of channel members, and the management of daily activities by channel members.
Albers's "Sales Force Management--Compensation, Motivation, Selection, and Training" discusses the problems encountered in measuring the performance of salespeople and examines the research directed toward improving sales force performance.
Ofir and Winer's wide-ranging chapter, "Pricing--Economic and Behavioral Models' explores issues and research as diverse as the measurement of customer response to price changes, customer price-information processing, interactions between price and price promotions and brand-choice models, competitive pricing models, and the impact of the Internet on price
Considered together, Stewart and Kamins's "Marketing Communications" and Neslin's "Sales Promotion" constitute a thoughtful, state-of the-art examination of the implications of the firm's communications, broadly construed--including their influence on primary and secondary demand, awareness, and attitudes and their effects over time; the behavioral and economic bases for sales promotion; and customer responses to sales promotion.
The section ends with the masterly "Understanding and Improving Service Quality: A Literature Review and Research Agenda," by the service experts Parasuraman and Zeithaml. Building on their work with Berry, the authors explain the concepts of service quality, touching on its role in customer loyalty and profitability, and perceived value. They discuss paths to improving service quality in the context of the development and application of the SERVQUAL model and the implementation and measurement of customer service in technology-mediated consumer-to-business interactions. Their research agenda points the way to addressing unresolved and emerging issues on service quality.
The three chapters in this section review issues related to how marketing managers actually make decisions, the relative importance of different types of decisions, and how decision support systems are used. Russo and Carlson's "Individual Decision Making" begins the section with a summary of work on the process and phases of decision making. Building on this, Mantrala's "Allocating Marketing Resources" surveys selected normative-theoretical and decision models and related insights for allocating marketing resources directly controlled by the decision maker. Eisenstein and Lodish cap off the section with "Marketing Decision Support and Intelligent Systems: Precisely Worthwhile or Vaguely Worthless?" a review of marketing decision support systems aimed at providing guidance to researchers and practitioners. The taxonomy of decision support systems they lay out is useful in developing additional research.
The last section is a potpourri of various subjects that do not fit well elsewhere, including surveys of global, services, and business-to-business marketing and an assessment of the impact of the Internet on marketing activities. With the exception of the final chapter, the authors provide competent but conventional treatments: Johansson's review of foreign entry, local marketing, and global management is a competent, if cursory, starting point for those doing research in the area; Shugan's "Services Marketing and Management: Capacity As a Strategic Marketing Variable" is of interest for its discussion of the relationship between capacity and service strategy; and Hakansson and Snehota's "Marketing in Business Markets" presents a conventional but useful overview of the business-to-business universe.
The final chapter, Barwise, Elberse, and Hammond's "Marketing and the Internet," is a fascinating exploration of the still emerging, partly understood phenomenon that is reshaping all of our lives: the Internet. No one will come away from this chapter without some new insight. With the inclusion of discussions of Internet adoption, the exploration of usage and consumers' experiences, consumers' online purchasing behavior, Internet advertising practices, the economics of technology and pricing, the impact on channels of distribution, emerging strategies and business models, and future prospects for technology and research opportunities, the chapter is a tour de force.
That a book of some 580 pages devotes approximately 15% of its pages to a bibliography makes it all the more valuable. Handbook of Marketing will prove an efficient place for doctoral students to start rummaging for dissertation topics. The book is a considerable one-stop resource for scholarly marketing; however, it will have enduring value only if it is updated regularly.
REFERENCES Shocker, Allan D., David W. Stewart, and Anthony J. Zahorik (1990), "Determining the Competitive Structure of Product-Markets: Practices, Issues, and Suggestions." Journal of Managerial Issues, 11 (Summer), 127-59.
Wilkie, William L. and Elizabeth S. Moore (1999), "Marketing's Contributions to Society," Journal of Marketing, 63 (Special Issue), 198-218.
~~~~~~~~
By Daniel Rajaratnam, Baylor University, Waco, Texas and Terry Clark, Editor, Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 72- Harry Potter and the Marketing Mystery: A Review and Critical Assessment of the Harry Potter Books. By: Brown, Stephen. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p126-130. 5p. DOI: 10.1509/jmkg.66.1.120.18448a.
- Database:
- Business Source Complete
Section: Book ReviewsHarry Potter and the Marketing Mystery (Book)
Harry Potter and the Sorcerer's Stone, New York:
Scholastic Press, 1998, 309 pp., $19.95; Harry Pot-
ter and the Chamber of Secrets, New York:
Scholastic Press, 1999, 341 pp., $19.95; Harry Pot-
ter and the Prisoner of Azkaban, New York:
Scholastic Press, 1999, 435 pp., $19.95; Harry Pot-
ter and the Goblet of Fire, New York: Scholastic
Press, 2000, 734 pp., $25.95.
"Fads" is the four-letter word of marketing research, more so even than "hype," "puff," "plug," and "spin." For most academicians, fads are an anomaly, a profanity, a pustular pain in the proverbial posterior. Whether it be Beanie Babies, Teletubbies, Pet Rocks, or Rubik's Cubes, fads, crazes, and gimmicks are an affront to the modern marketing paradigm, the absolute antithesis of analysis, planning, implementation, and control. They seem to erupt spontaneously (in an inexplicable, unpredictable fashion), they are the domain of all sorts of disreputable hucksters, drummers, and quick-buck makers (veritable throwbacks in an era of calm professionalism), and they quickly disappear over the marketing horizon (until the next kiddie craze comes hurtling down the preteen pike). At best, fads are an example of word-of-mouth marketing or a component part of the innovation diffusion process (Gladwell 2000). At worst, fads are a mutant form of the product life cycle, commercial instantiations of Mackay's (1995) "extraordinary popular delusions and the madness of crowds." At all times, however, fads are something to be avoided; to be belittled; to be broken, bucking bronco fashion, and transshipped into the more respectable conceptual categories of "trends," "tendencies," and "traits."
The irony, of course, is that marketing itself is incorrigibly faddish, as is management studies generally. True, there is no shortage of scholarly commentators who roundly denounce management by buzzword, readily condemn fad surfing in the boardroom, and repeatedly excoriate the craze-blazing antics of self-appointed marketing gurus, Tom call-me-crazy Peters in particular (Collins 2000; Morris 1998; Shapiro 1998). So voluminous, indeed, is the antifad literature that fadlessness is the latest management fad, according to Harvard Business Review (Wetlaufer 2001). However, if past performance is anything to go by, this too will be unceremoniously abandoned when someone makes the case for crazes and the profad fad kicks in. Conversely, the concept might be "broadened" beyond products and services to embrace fadscapes (Las Vegas), fad ads (Budweiser's "Wazzup"), fad prices ("blue-light" or "13-hour" specials), and, ultimately, fadology (the scientific study of faddishness, faddisms, fadplexes, fadnomena, fadformulae, and so forth).
Management metafads and scholarly denial notwithstanding, an academician would need to be pretty obdurate not to have noticed Harry Potter. The brainchild of the British author J.K. Rowling, Harry Potter is perhaps the most astonishing kiddie craze of recent years (Zipes 2001). To date, approximately 70 million copies of the first four books in a seven-book series have been sold. The texts have been translated into 30 languages and published in multitudinous formats (e.g., illustrated, Braille, audiocassette, adult cover, large print, box sets) and are chart-toppers in 120 countries, Britain and the United States especially. An $85 million live-action movie is scheduled for release in November 2001, and though forecasting the fate of feature films is fraught with difficulty, it is estimated that the release will gross $650 million in tie-in merchandise alone. Harry hysteria, furthermore, has been held responsible for everything from the stratospheric share price of Scholastic Press and capacity pressures in the printing industry to the revival of British boarding schools, as well as increased visitor numbers at "magical" holiday destinations (e.g., Hamilton 2001; Hutton 2000; Wilsdon 2001). Indeed, the Potter parlance of "Muggles" (people without magical powers) and "quidditch" (a popular team sport, akin to hockey, which is played on broomsticks) not only has been inducted into the august pages of the Oxford English Dictionary but also has been appropriated by the advertising industry. A Marketing Muggle, apparently, is an advertising executive who lacks the all-important creative spark or suffers from imagination deficit disorder.
Although many readers might be tempted to dismiss Harry Potter as a passing marketing fad, yet another in a long line of preteen obsessions, it is precipitate so to do. Apart from the inspiration that many cutting-edge management commentators are drawing from kiddie culture (see Frank 2000), Harry Potter is particularly pertinent to the contemporary marketing condition. The books, after all, are as much about marketing as the outcome of marketing. They deal with marketing matters, they are replete with marketing artifacts, they contain analyses of marketplace phenomena, and they hold the solution to an ancient marketing mystery. The books are not merely a marketing masterpiece, they are a marketing master class.
Thus, the Potter portfolio refers to almost every element of the marketing mix as well as aspects of buyer behavior, environmental conditions, marketing research, and many more besides. In Harry Potter and the Goblet of Fire, for example, one character is preparing a market research report on cheap continental cauldrons, most of which fail to conform to U.K. safety standards and, on account of their unacceptably thin bottoms, must therefore be denied access to the great British market. Another aspiring importer wonders whether there is a niche in the U.K. market for flying carpets, the minivans of the wizarding world, only to be brusquely informed that the British will never give up their broomsticks (even though carpets were once the English conveyance of choice). Broomsticks, in fact, provide Rowling with a wonderful vehicle for exploring buyer behavior. Every phase of the purchasing process is described in detail, all the way from the consumer's desperate desire to acquire new and improved models through the information-gathering phase, in which impartial consumer reports are consulted, to the heartbreak of a broomstick owner whose pride and joy is written off in an unforeseen accident:
He didn't argue or complain, but he wouldn't let her throw away the shattered remains of his Nimbus Two Thousand. He knew he was being stupid, knew that the Nimbus was beyond repair, but Harry couldn't help it; he felt as though he'd lost one of his best friends. (Harry Potter and the Prisoner of Azkaban, p. 137)
Advertising, likewise, is incorporated in the shape of huge hoardings, akin to electric scoreboards at football stadiums, with constantly changing sales pitches for broomsticks ("The Bluebottle, a broom for all the family"), detergents ("Mrs Skowers All Purpose Magical Mess Remover-No pain, no stain"), and outfitters ("Gladrags Wizardwear--London, Paris, Hogsmeade"). Pricing figures prominently, furthermore, both in a general sense (the sheer expense of sending a child to Hogwarts school) and more specifically (the exact cost of objects, such as dragon's liver and beetle's eyes, in the wizard currency of Galleons, Sickles, and Knuts). Added-value is not forgotten either, as the Knight Rider Bus bears witness (the flat fare to London is 11 Sickles, but 14 gets a mug of hot chocolate and 15 a hot water bottle, plus a choice of colored toothbrushes). Logistics also get a look-in, albeit in the form of Floo Powder (a magical mixture that transports wizards, Santa Claus-like, to chimneys of their choice), Portkeys (graspable objects, such as old shoes and discarded soda cans, that ferry groups of holders very long distances), the Owl postal service (color coded, naturally, by breed and distance-big Barn Owls cover the country, tiny Scops Owls deal with local deliveries), and the emblematic Hogwarts Express (an old-fashioned steam train that takes pupils to and from Hogwarts School of Witchcraft and Wizardry). Consumption-rich anniversaries and holidays are equally evocatively described (Christmas and birthdays especially, though a St. Valentine's Day extravaganza features in Harry Potter and the Chamber of Secrets), as are personal selling (when Harry gets fitted for his wand and uniform, for example), promotional gimmicks (the Weasleys win a holiday to Egypt, courtesy of a newspaper competition), the cheesy correspondence courses found in the small-ads pages of tabloid newspapers ("Feel out of step in the world of modern magic? Find yourself making excuses not t o perform simple spells? Ever been taunted for your woeful wandwork? There is an answer! "), and, of all things, Harry Potter-ish marketing crazes (Hogwarts pupils collect Pokémonesque wizard cards, which are swapped and traded incessantly).
The books, in short, take the objects and artifacts from traditional fairy stories-cauldrons, wands, broomsticks, flying carpets, magic potions, wizard's apparel, and so forth-and give them a marvelous marketing spin. The Firebolt, for example, is not a bog-standard broomstick but the top of the top of the range. It is the BMW of broomsticks, the Ferrari of flying household effects, a veritable Porsche Carrera of aeronautically engineered cleaning appliances. Harry first spots it in the display window of an exclusive dealership, where he is literally stopped in his tracks by "the most magnificent broom he had ever seen." So enraptured is the apprentice wizard that he returns again and again to stare, agog, at the precious, perfect product. Consumed by commodity fetishism-"he had never wanted anything so much in his entire life"-Harry is completely bowled over by the beautiful object's auratic power, as are his fellow pupils ("Can I just hold it, Harry?"), as is the sports mistress (who waxes lyrical about great racing brooms of the past), as is the official announcer of the climactic quidditch tournament (who spends more time describing the broomstick's attributes than commentating on the match, which prompts one disgruntled spectator to shout, "Jordan! Are you being paid to advertise Firebolts? Get on with the commentary!").
Above and beyond the fabulous Firebolt, almost every product category is given the marketing spit and polish. Codices of curses, spells, and hexes are sold from a Borders-style superstore, Flourish and Blotts, which organizes book signings, arranges special promotions, and retails gimmicky best-sellers such as Where There's a Wand There's a Way, Men Who Love Dragons Too Much, and The Invisible Book of Invisibility. The Quidditch World Cup is accompanied by the promotional razzmatazz that attends major sporting events, everything from an extravagant pregame buildup through the hard-sell antics of team sponsors and outdoor advertising agencies to the plague of cheapjack souvenir sellers, with their rip-off rosettes, overpriced apparel, unlicensed posters, and unofficial programs. Confectionery, furthermore, is brilliantly realized in the form of Cockroach Clusters, Jelly Slugs, Canary Creams, Chocolate Frogs (containing the wizard card collectibles), Sugar Quills (perfect for sucking surreptitiously in class), and Rowling's pièce de resistance, Bertie Bott's Every Flavor Beans. As the brand name implies, these come in every conceivable flavor-chocolate, peppermint, marmalade, toast, coconut, baked bean, strawberry, curry, grass, coffee, sardine, sprouts, spinach, liver, tripe, earwax, booger, and vomit. Aptly, their advertising slogan is "A risk with every mouthful."
Servicescapes, similarly, are arrestingly addressed, thanks to Rowling's remarkable ability to convey a sense of place. Again and again, the author's grasp of genius loci is made manifest, and these exercises in evocation often refer to retailing environments. For example, a wizarding pet emporium, the Magical Menagerie, is cogently described as follows:
A pair of enormous purple toads sat gulping wetly and feasting on dead butterflies. A gigantic tortoise with a jewel-encrusted shell was glittering near the window. Poisonous orange snails were oozing slowly up the side of their glass tank, and a fat white rabbit kept changing into a silk top hat and back again with a loud popping noise. There were cats of every color, a noisy cage of ravens, a basket of funny, custard-colored fur balls that were humming loudly, and, on the counter, a vast cage of sleek black rats which were playing some sort of skipping game using their long bald tails. (Harry Potter and the Prisoner of Azkaban, p. 48)
The olfactory overkill, tactile temptation, and pharmacological intrigue of an old-fashioned potions purveyor are equally persuasively posited:
Then they visited the apothecary's, which was fascinating enough to make up for its horrible smell, a mixture of bad eggs and rotted cabbages. Barrels of slimy stuff stood on the floor, jars of herbs, dried roots and bright powders lined the walls, bundles of feathers, strings of fangs and snarled claws hung from the ceiling. While Hagrid asked the man behind the counter for a supply of some basic potion ingredients for Harry, Harry himself examined silver unicorn horns at twenty-one Galleons each and miniscule, glittery black beetle eyes (five Knuts a scoop). (Harry Potter and the Sorcerer's Stone, p. 62)
As might be expected, however, Rowling reserves her most perspicacious place-imparting powers for Honeydukes candy store in the village of Hogsmeade, adjacent to Hogwarts school:
There were shelves upon shelves of the most succulent-looking sweets imaginable. Creamy chunks of nougat, shimmering pink squares of coconut ice, fat, honey-colored toffees; hundreds of different kinds of chocolate in neat rows; there was a large barrel of Every Flavor Beans, and another of Fizzing Whizzbees, the levitating sherbet balls that Ron had mentioned; along yet another wall were "Special Effects" sweets: Droobles Best Blowing Gum (which filled a room with bluebell-colored bubbles that refused to pop for days), the strange splintery Toothflossing Stringmints, tiny Black Pepper Imps ("breathe fire on your friends!"), Ice Mints ("hear your teeth chatter and squeak!"), peppermint creams shaped like toads ("hop realistically in the stomach!"), fragile sugar-spun quills and exploding bonbons. (Harry Potter and the Prisoner of Azkaban, p. 147)
To be sure, Rowling's reflections on marketing phenomena are not confined to elements of the mix, the external environment, or magical market research reports. She also offers two diverting pen portraits of disreputable marketing types, both deeply unattractive in strangely attractive ways. Vernon Dursley, Harry's oafish stepfather, is a narrow-minded, nit-picking, no-nonsense marketing man, who works for the unspeakable industrial conglomerate, Grunnings. On the first page of the first book, he is anticipating a large order for drills. In the second book, he wines and dines an important client, only to have his sales pitch ruined by the apprentice wizard upstairs. And in the third book, he vaingloriously gloats over his company car, which is admired loudly in front of eavesdropping neighbors. Vernon, in short, is the epitome of marketing pragmatics, obsessed with order, planning, and precision, who has no time whatsoever for magic, mystery, or imagination. He refuses to let Harry use the word "magic" in his house and specifically states that he doesn't approve of imagination. What is more, he doesn't like surprises, as the detailed plans for his order-snaring dinner party attest. The guests arrive on the stroke of 8:00 p.m. At 8:15 precisely, they are escorted into the dining room. At 9:00 p.m., Vernon cracks his joke about the Japanese golfer and brings the subject around to drills. Coffee is served, and "With any luck, I'll have the deal signed and sealed before the News at Ten. We'll be shopping for a holiday home in Majorca this time tomorrow."
If Vernon Dursley personifies the positivistic marketing mindset, Gilderoy Lockhart is a Barnumesque grotesque. The acme of self-marketing and a stranger to self-mockery, Lockhart is a self-centered, publicity-seeking celebrity author; a larger-than-life trickster figure; a twenty-first-century snake oil seller. A complete humbug, in other words. Handsome, hirsute, expensively attired, and orthodontically enhanced, Lockhart is five-times winner of Witch Weekly's Most-Charming-Smile Award, and à la Richard Branson, "it was remarkable how he could show every one of those brilliant teeth, even when he wasn't talking." Like a book-writing Barry Manilow, he is adored by witches of a certain age; he bestrides the best-sellers list with his arresting adventures among outré occultists (Gadding with Ghouls, Holidays with Hags, Travels with Trolls, and so forth); and he is a lion of the book marketing circuit, where he draws huge crowds to his signings, readings, and fan club conventions. He even has a special quill, made from an enormous peacock feather, for such autograph-hungry occasions. Never let it be said, however, that all the attention has gone to Lockhart's head or that he has forgotten his roots. On the contrary, his secret ambition is to "rid the world of evil and market my own range of hair-care potions."
Now, it may be a while before Lockhart's Hair Lotion is available in friendly neighborhood drug stores, but Bertie Bott's Every Flavor Beans are already on sale, as is a host of Harry Potter collectibles (Mahoney 2000). Warner Brothers signed a seven-figure, five-year, two-film deal with Rowling in October 1998, and thus far, the conglomerate has granted 46 licenses to all sorts of corporate supplicants. These include Mattel, for board games and toys; Hasbro, for trading cards and candy; Electronic Arts, for video games and computer-based ancillaries; Lego, for the eponymous building bricks; and the Character Group, for plastic and porcelain figurines. Coca-Cola has also signed a $150 million sponsorship deal, and rumors of everything from Hogwarts theme parks to Harry Potter Happy Meals are circulating (The Economist 2001). It remains to be seen how many of these will come to fruition, but with three books still to be written and possibly six to be filmed, it is fair to assume that the Harry Potter fad will be flourishing for some time yet. Indeed, such is the power of the Potter multiplier that a substandard Warner Brothers comedy, See Spot Run, was carried to the top of the U.S. movie charts on the strength of its "first look" Harry Potter trailer (Entertainment Weekly 2001). It seems that even a crippled dog can be given legs by the tyro wizard's supernatural prowess.
Gratifying as it is to See Spot Run all the way to the bank, thanks to the Harry Potter pyramid-selling scheme, its success raises an intriguing issue about the marketing of Harry. Most commentators on the Harry Potter phenomenon make great play of the fact that it came about without marketing, that Rowling's staggering commercial triumph transpired despite formal marketing, not because of it (Ignatius 2000). In this regard, pontificators on Pottermania invariably refer to the "purity" of his popularity-how it was achieved and is sustained by personal recommendations; schoolyard conversations; Internet chat rooms; and sheer consumer satisfaction, enthusiasm, evangelism (Gladwell 2000; Godin 2000; Lewis and Bridger 2000; Rosen 2000). Although this may have been true for the first two books, it certainly was not the case for subsequent episodes. Harry Potter and the Goblet of Fire, in particular, was given the full marketing treatment (Brady 2000). Press junkets, television appearances, radio interviews, newspaper spreads, book signings, online discussions, launch parties, and every other trick in the arts marketing armory was pressed into Potteresque service. Interestingly, however, Scholastic's Pottermarketing strategy was not predicated on analysis, planning, implementation, and control, the Vernon Dursley school of thought. It was based, rather, on mystery, on intrigue, on unavailability, on postponement, on absence, on deferral, on denial, on tricksterism, on ballyhoo, on P.T. Barnumism, on Gilderoy Lockhartery. Whereas the modern marketing concept aims to make life simple for the consumer by getting the goods to market in a timely and efficient manner, so that they are available where and when they are wanted, at a price people are prepared to pay (Kotler 1999), the Pottermarketing concept deliberately eschews the here-it-is, come-and-get-it, there's-plenty-for-everyone proposition by limiting availability; delaying gratification; heightening expectation ; tantalizing, teasing, and tormenting the consumer; and insinuating that stock-outs are a very real possibility (Brown 2001).
Thus, the mysterious marketing strategy for Harry Potter and the Goblet of Fire comprised a complete blackout on advance information. The original manuscript was reputedly locked in a carefully guarded safe, accessible only to top executives. The title, pagination, and price were kept secret until two weeks before publication. Review copies were withheld, no author interviews were allowed, and foreign translations were deferred for fear of injudicious leaks. Juicy plot details, including the death of a key character and Harry's sexual awakening, were drip-fed to a slavering press corps immediately before the launch. Printers and distributors were required to sign strict, legally enforceable confidentiality agreements. Meanwhile, booksellers were bound by a ruthlessly policed embargo, though some were allowed to display the tantalizing tome (in locked cages) for a brief period prior to "Harry Potter Day," July 8, 2000. Many outlets opened at midnight to long lines of eager-beaver, pajama-wearing, broomstick-clutching, wizard's cloak-clad children and even longer lines of no less excited publicity agents and television crews, who dutifully recorded the late-night revelry and recorded the recordings of the late night revelry and recorded the recordings of the recordings of the late night revelry. The fadplex at its finest. Harry will eat himself.
More than 20 years ago, Jagdish Sheth (1979) pointed out that marketing fads are a mystery, and they are no less mysterious today. Despite recent attempts to map the fadscape (Farrell 2000; Godin 2000; Rosen 2000), fads remain as inexplicably enigmatic as ever. Although I am hesitant to draw lessons from a single case study, let alone a purported passing fad, the Harry Potter megafad suggests that the answer to this marketing mystery is mysteriousness itself. Mystery, admittedly, has no place in the modern marketing paradigm, which is predicated on openness, on trust, on transparency, on integrity, on opalescence, on the avoidance of opacity. But marketing practice has always had a mysterious side. One only needs to peruse the promotional practices of megabrand marketing organizations to appreciate that mystery, enigma, intrigue, and puzzlement are important parts of their appeal. Consider the "secret" recipes that help purvey all sorts of comestibles-Coca-Cola, Heinz Varieties, Kentucky Fried Chicken, Mrs. Field's Cookies, Kellogg's Frosted Flakes, Grey Poupon Mustard, Brach's Chocolate Cherries, Campari, Carlsberg, Chartreuse, Benedictine, Angostura Bitters, and, naturally, HP Sauce. Consider the gift-giving business, which is predicated on secrets, surprises, and agonizingly delayed gratification, as are gift-rich occasions such as Christmas, birthdays, and St. Valentine's Day. Consider the teaser campaigns, advertising soap operas, and who'll-be-the-lucky winner promotions that are launched incessantly by Machiavellian marketers. Consider the self-help marketing gurus, who claim to possess the secrets of success, leadership, efficiency, effectiveness, time management, corporate well-being, or-Heaven help us!-the Harry Potter way to higher profits.
Marketing, then, moves in mysterious ways, in magical ways, in mysteriously magical ways. Yet academicians seem determined to pretend otherwise. Vernon Dursley-like, marketing scholars ceaselessly pursue the chimera of truth, of science, of general theories and axiomatic insights. The unstated assumption is that with more powerful computers, more sophisticated statistics, more elaborate models, and more time please, the secrets of the marketing universe will be revealed. But as Baudrillard (2001, pp. 80-81) astutely observes:
We cannot rely on the pretext of an insufficient development of the scientific, intellectual or mental apparatus. The apparatus has given all that it can give; it has even passed beyond its own definitions of rationality.... It is the event horizon, as they say in physics, beyond which nothing makes sense and nothing at all may be discovered.... That, if there is any, is the secret of the universe. As a metaphor, I would say that at the core of every human being and every thing there is such a fundamentally inaccessible secret. That is the vital illusion of which Nietzsche spoke, the glass wall of truth and illusion. From our rational point of view, this may appear rather desperate and could even justify something like pessimism. But from the point of view of alterity, of secret and seduction, it is, on the contrary, our only chance: our last chance.
Although I hesitate to hold up Harry as "our last chance," marketing fads in general and Potter in particular are reminders that mystery has its place, that intrigue is necessary, that riddle-me-ree is right and proper, that secrecy is the secret of the universe. As the twenty-first century dawns, perhaps Harry Potter should replace Karl Popper as the cynosure of our field. Anyone for quidditch?
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Brady, Diane (2000), "Wizard of Marketing," BusinessWeek, (July 24), 84-87.
Brown, Stephen (2001), Marketing-The Retro Revolution. London: Sage Publications.
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Farrell, Winslow (2000), How Hits Happen-Forecasting Predictability in a Chaotic Marketplace. London: Texere.
Frank, Thomas (2000), One Market Under God: Extreme Capitalism, Market Populism and the End of Economic Democracy. London: Secker and Warburg.
Gladwell, Malcolm (2000), The Tipping Point: How Little Things Can Make a Big Difference. London: Little Brown.
Godin, Seth (2000), Unleashing the Ideavirus. Dobbs Ferry, NY: Do You Zoom Books.
Hamilton, Alex (2001), "Fastsellers 2000: The Hot Paperbacks," Guardian, (January 6), 10.
Hutton, Deborah (2000), "How This Boy Saved the Boarding School," You Magazine, (August 27), 36-37.
Ignatius, David (2000), "The Anti-marketing Wizard," The Washington Post, (June 25), B7.
Kotler, Philip (1999), Kotler on Marketing: How to Create, Win and Dominate Markets. New York: The Free Press.
Lewis, David and Darren Bridger (2000), The Soul of the New Consumer: Authenticity-What We Buy and Why in the New Economy. London: Nicholas Brealey.
Mackay, Charles (1995), Extraordinary Popular Delusions and the Madness of Crowds. Ware, UK: Wadsworth Editions. Originally published 1841.
Mahony, Jeff (2000), Harry Potter Collectibles: Collector Handbook and Price Guide. Middletown, CT: CheckerBee.
Morris, Steve (1998), The Handbook of Management Fads. London: Thorogood.
Rosen, Emanuel (2000), The Anatomy of Buzz: Creating Word-of-Mouth Marketing. London: HarperCollins.
Shapiro, Eileen C. (1998), Fad Surfing in the Boardroom. Oxford: Capstone.
Sheth, Jagdish N. (1979), "The Surpluses and Shortages in Consumer Behavior Theory and Research," Journal of the Academy of Marketing Science, 7 (Fall), 414-27.
Wetlaufer, Suzy (2001), "Against Revolution: An Interview with Nestlé's Peter Brabeck," Harvard Business Review, 79 (January), 113-19.
Wilsdon, James (2001), "Make It Better or Don't Make It," Times Higher, (March 2), 16.
Zipes, Jack (2001), "The Phenomenon of Harry Potter, or Why All the Talk," in Sticks and Stones: The Troublesome Success of Children's Literature from Slovenly Peter to Harry Potter, Jack Zipes, ed. New York: Routledge, 170-89.
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By Stephen Brown, University of Ulster and Terry Clark, Editor, Southern Illinois University
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Record: 73- How Critical Are Critical Reviews? The Box Office Effects of Film Critics, Star Power, and Budgets. By: Basuroy, Suman; Chatterjee, Subimal; Ravid, S. Abraham. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p103-117. 15p. 8 Charts. DOI: 10.1509/jmkg.67.4.103.18692.
- Database:
- Business Source Complete
How Critical Are Critical Reviews? The Box Office Effects
of Film Critics, Star Power, and Budgets
The authors investigate how critics affect the box office performance of films and how the effects may be moderated by stars and budgets. The authors examine the process through which critics affect box office revenue, that is, whether they influence the decision of the film going public (their role as influencers), merely predict the decision (their role as predictors), or do both. They find that both positive and negative reviews are correlated with weekly box office revenue over an eight-week period, suggesting that critics play a dual role: They can influence and predict box office revenue. However, the authors find the impact of negative reviews (but not positive reviews) to diminish over time, a pattern that is more consistent with critics' role as influencers. The authors then compare the positive impact of good reviews with the negative impact of bad reviews to find that film reviews evidence a negativity bias; that is, negative reviews hurt performance more than positive reviews help performance, but only during the first week of a film's run. Finally, the authors examine two key moderators of critical reviews, stars and budgets, and find that popular stars and big budgets enhance box office revenue for films that receive more negative critical reviews than positive critical reviews but do little for films that receive more positive reviews than negative reviews. Taken together, the findings not only replicate and extend prior research on critical reviews and box office performance but also offer insight into how film studios can strategically manage the review process to enhance box office revenue.
Critics play a significant role in consumers' decisions in many industries (Austin 1983; Cameron 1995; Caves 2000; Einhorn and Koelb 1982; Eliashberg and Shugan 1997; Goh and Ederington 1993; Greco 1997; Holbrook 1999; Vogel 2001; Walker 1995). For example, investors closely follow the opinion of financial analysts before deciding which stocks to buy or sell, as the markets evidenced when an adverse Lehman Brothers report sunk Amazon.com's stock price by 19% in one day (Business Week 2000). Readers often defer to literary reviews before deciding on a book to buy (Caves 2000; Greco 1997); for example, rave reviews of Interpreter of Maladies, a short-story collection by the then relatively unknown Jhumpa Lahiri, made the book a New York Times best-seller (New York Times 1999). Diners routinely refer to reviews in newspapers and dining guides such as ZagatSurvey to help select restaurants (Shaw 2000).
However, the role of critics may be most prominent in the film industry (Eliashberg and Shugan 1997; Holbrook 1999; West and Broniarczyk 1998). More than one-third of Americans actively seek the advice of film critics (The Wall Street Journal 2001), and approximately one of every three filmgoers say they choose films because of favorable reviews. Realizing the importance of reviews to films' box office success, studios often strategically manage the review process by excerpting positive reviews in their advertising and delaying or forgoing advance screenings if they anticipate bad reviews (The Wall Street Journal 2001). The desire for good reviews can go even further, thus prompting studios to engage in deceptive practices, as when Sony Pictures Entertainment invented the critic David Manning to pump several films, such as A Knight's Tale and The Animal, in print advertisements (Boston Globe 2001).
In this article, we investigate three issues related to the effects of film critics on box office success. The first issue is critics' role in affecting box office performance. Critics have two potential roles: influencers, if they actively influence the decisions of consumers in the early weeks of a run, and predictors, if they merely predict consumers' decisions. Eliashberg and Shugan (1997), who were the first to define and test these concepts, find that critics correctly predict box office performance but do not influence it. Our results are mixed. On the one hand, we find that both positive and negative reviews are correlated with weekly box office revenue over an eight-week period, thus showing that critics can both influence and predict outcomes. On the other hand, we find that the impact of negative reviews (but not positive reviews) on box office revenue declines over time, a finding that is more consistent with critics' role as influencers.
The second issue we address is whether positive and negative reviews have comparable effects on box office performance. Our interest in such valence effects stems from two reasons; the first is based on studio strategy and the second is rooted in theory. First, although we might expect the impact of critical reviews to be strongest in the early weeks of a run and to fall over time as studio buzz from new releases takes over, studios that understand the importance of positive reviews are likely to adopt tactics to leverage good reviews and counter bad reviews (e.g., selectively quote good reviews in advertisements). Intuitively, therefore, we expect the effects of positive reviews to increase over time and the effects of negative reviews to decrease over time. Second, we expect negative reviews to hurt box office performance more than positive reviews help box office performance. This expectation is based on research on negativity bias in impression formation (Skowronski and Carlston 1989) and on loss aversion in scanner-panel data (Hardie, Johnson, and Fader 1993). We find that the negative impact of bad reviews is significantly greater than the positive impact of good reviews on box office revenue, but only in the first week of a film's run (when studios, presumably, have not had time to leverage good reviews and/or counter bad reviews).
The third part of our investigation involves examining how star power and budgets might moderate the impact of critical reviews on box office performance. We chose these two moderators because we believe that examining their effects on box office revenue in conjunction with critical reviews might provide a partial economic rationale for two puzzling decisions in the film industry that have been pointed out in previous works. The first puzzle is why studios are persistent in pursuing famous stars when stars' effects on box office revenue are difficult to demonstrate (De Vany and Walls 1999; Litman and Ahn 1998; Ravid 1999). The second puzzle is why, at a time when big budgets seem to contribute little to returns (John, Ravid, and Sunder 2002; Ravid 1999), the average budget for a Hollywood movie has steadily increased over the years. Our results show that though star power and big budgets seem to do little for films that receive predominantly positive reviews, they are positively correlated with box office performance for films that receive predominantly negative reviews. In other words, star power and big budgets appear to blunt the impact of negative reviews and thus may be sensible investments for the film studios. In the next section, we explore the current literature and formulate our key hypotheses. We then describe the data and empirical results. Finally, we discuss the managerial implications for marketing theory and practice.
Critics: Their Functions and Impact
In recent years, scholars have expressed much interest in understanding critics' role in markets for creative goods, such as films, theater productions, books, and music (Cameron 1995; Caves 2000). Critics can serve many functions. According to Cameron (1995), critics provide advertising and information (e.g., reviews of new films, books, and music provide valuable information), create reputations (e.g., critics often spot rising stars), construct a consumption experience (e.g., reviews are fun to read by themselves), and influence preference (e.g., reviews may validate consumers' self-image or promote consumption based on snob appeal). In the domain of films, Austin (1983) suggests that critics help the public make a film choice, understand the film content, reinforce previously held opinions of the film, and communicate in social settings (e.g., when consumers have read a review, they can intelligently discuss a film with friends). However, despite a general agreement that critics play a role, it is not clear whether the views of critics necessarily go hand in hand with audience behavior. For example, Austin (1983) argues that film attendance is greater if the public agrees with the critics' evaluations of films than if the two opinions differ. Holbrook (1999) shows that in the case of films, ordinary consumers and professional critics emphasize different criteria when forming their tastes.
Many empirical studies have examined the relationship between critical reviews and box office performance (De Silva 1998; Jedidi, Krider, and Weinberg 1998; Litman 1983; Litman and Ahn 1998; Litman and Kohl 1989; Prag and Casavant 1994; Ravid 1999; Sochay 1994; Wallace, Seigerman, and Holbrook 1993). Litman (1983) finds that each additional star rating (five stars represent a "masterpiece" and one star represents a "poor" film) has a significant, positive impact on the film's theater rentals. Litman and Kohl's (1989) subsequent study and other studies by Litman and Ahn (1998), Wallace, Seigerman, and Holbrook (1993), Sochay (1994), and Prag and Casavant (1994) all find the same impact. However, Ravid (1999) tested the impact of positive reviews on domestic revenue, video revenue, international revenue, and total revenue but did not find any significant effect.
Critics as Influencers or Predictors
Although the previously mentioned studies investigate the impact of critical reviews on a film's performance, they do not describe the process through which critics might affect box office revenue. Eliashberg and Shugan (1997) are the first to propose and test two different roles of critics: influencer and predictor. An influencer, or opinion leader, is a person who is regarded by a group or by other people as having expertise or knowledge on a particular subject (Assael 1984; Weiman 1991). Operationally, if an influencer voices an opinion, people should follow that opinion. Therefore, we expect an influencer to have the most effect in the early stages of a film's run, before word of mouth has a chance to spread. In contrast, a predictor can use either formal techniques (e.g., statistical inference) or informal methods to predict the success or failure of a product correctly. In the case of a film, a predictor is expected to call the entire run (i.e., predict whether the film will do well) or, in the extreme case, correctly predict every week of the film's run.
Ex ante, there are reasons to believe that critics may influence the public's decision of whether to see a film. Critics often are invited to an early screening of the film and then write reviews before the film opens to the public. Therefore, not only do they have more information than the public does in the early stages of a film's run, but they also are the only source of information at that time. For example, Litman (1983) seems to refer to the influencer role in his argument that critical reviews should be important to the popularity of films ( 1) in the early weeks before word of mouth can take over and ( 2) if the reviews are favorable. However, Litman was unable to test this hypothesis directly because his dependent variable is cumulative box office revenue. To better assess causation, Wyatt and Badger (1984) designed experiments using positive, mixed, and negative reviews and found audience interest to be compatible with the direction of the review. However, because their study is based on experiments, they do not use box office returns as the dependent variable.
Inferring critics' roles from weekly correlation data. In our research, we follow Eliashberg and Shugan's (1997) procedure. We study the correlation of both positive and negative reviews with weekly box office revenue. However, even with weekly box office data, we argue that it is not easy to distinguish between critics as influencers and as predictors. We illustrate this point by considering three different examples of correlation between weekly box office revenue and critical reviews.
For the first example, suppose that critical reviews are correlated with the box office revenue of the first few weeks but not with the film's entire run. A case in point is the film Almost Famous, which received excellent reviews (of 47 total reviews reported by Variety, 35 were positive and only 2 were negative) and had a good opening week ($2.4 million on 131 screens, or $18,320 revenue per screen) but ultimately did not do considerably well (grossing only $32 million in about six months). This outcome is consistent with the interpretation that critics influenced the early run but did not correctly predict the entire run. Another interpretation is that critics correctly predicted the early run without necessarily influencing the public's decision but did not predict the film's entire run.
For the second example, suppose that critical reviews are correlated not with a film's box office revenue in the first few weeks but with the box office revenue of the total run. The films Thelma and Louise and Blown Away appear to fit this pattern. Thelma and Louise received excellent reviews and had only moderate first-weekend revenue ($4 million), but it eventually became a hit ($43 million; Eliashberg and Shugan 1997, p. 72). In contrast, Blown Away opened successfully ($10.3 million) despite bad reviews but ultimately did not do well. In the first case, critics correctly forecasted the film's successful run (despite a bad opening); in the second case, critics correctly forecasted the film's unsuccessful run (despite a good opening). In both examples, the performance in the early weeks countered critical reviews. Our interpretation is that critics did not influence the early run but were able to predict the ultimate box office run correctly. Eliashberg and Shugan (1997) find precisely such a pattern (i.e., critical reviews are not correlated with the box office revenue of early weeks but are significantly correlated with the box office revenue of later weeks and with cumulative returns during the run); they conclude that critics are predictors, not influencers.
For the third example, suppose that critical reviews are correlated with weekly box office revenue for the first several weeks (i.e., not just the first week or two) and with the entire run. Consider the films 3000 Miles to Graceland (a box office failure) and The Lord of the Rings: The Fellowship of the Ring (a box office success). Critics trashed 3000 Miles to Graceland (of 34 reviews, 30 were negative), it had a dismal opening weekend ($7.16 million on 2545 screens, or $3,000 per screen), and it bombed at the box office ($15.74 million earned in slightly more than eight weeks). The Lord of the Rings: The Fellowship of the Ring opened to great reviews (of 20 reviews, 16 were positive and 0 were negative), had a successful opening week ($66.1 million on 3359 screens, or approximately $19,000 per screen), and grossed $313 million. In both cases, critics either influenced the film's opening and correctly predicted its eventual fate or correctly predicted the weekly performance over a longer period and its ultimate fate.
These three examples demonstrate that it is not easy to distinguish critics' different roles (i.e., influencer, predictor, or influencer and predictor) on the basis of weekly box office revenue. Broadly speaking, if critics influence only a film's box office run, we expect them to have the greatest impact on early box office revenue (perhaps in the first week or two). In contrast, if critics predict only a film's ultimate fate, we expect their views to be correlated with the later weeks and the entire run, not necessarily with the early weeks. Finally, if critics influence and predict a film's fate or correctly predict every week of a film's run, we expect reviews to be correlated with the success or failure of the film in the early and later weeks and with the entire run. The following hypotheses summarize the possible links among critics' roles and box office revenue:
H1: If critics are influencers, critical reviews are correlated with box office revenue in the first few weeks only, not with box office revenue in the later weeks or with the entire run.
H2: If critics are predictors, critical reviews are correlated with box office revenue in the later weeks and the entire run, not necessarily with box office revenue in previous weeks.
H3: If critics are both influencers and predictors or play an expanded predictor role, critical reviews are correlated with box office revenue in the early and later weeks and with the entire run.
Inferring critics' roles from the time pattern of weekly correlation. Several scholars have argued that if critics are influencers, they should exert the greatest impact in the first week or two of a film's run because little or no word-of-mouth information is yet available. Thereafter, the impact of reviews should diminish with each passing week as information from other sources becomes available (e.g., people who have already seen the film convey their opinions, more people see the film) and as word of mouth begins to dominate (Eliashberg and Shugan 1997; Litman 1983). However, the issue is not clear-cut: If word of mouth agrees with critics often enough, a decline may be undetectable, but if critics are perfect predictors, such a decline cannot be expected. In other words, if there is a decline in the impact of critical reviews over time, it is consistent with the influencer perspective. Thus:
H4: If critics are influencers, the correlation of critical reviews with box office revenue declines with time.
Valence of Reviews: Negativity Bias
Researchers consistently have found differential impacts of positive and negative information (controlled for magnitude) on consumer behavior. For example, in the domain of risky choice, Kahneman and Tversky (1979) find that utility or value functions are asymmetric with respect to gains and losses. A loss of $1 provides more dissatisfaction (negative utility) than the gain of $1 provides satisfaction (positive utility), a phenomenon that the authors call "loss aversion." The authors also extend this finding to multiattribute settings (Tversky and Kahneman 1991). A similar finding in the domain of impression formation is the negativity bias, or the tendency of negative information to have a greater impact than positive information (for a review, see Skowronski and Carlston 1989).
On the basis of these ideas, we surmise that negative reviews hurt (i.e., negatively effect) box office performance more than positive reviews help (i.e., positively affect) box office performance. Two studies lend further support to this idea. First, Yamaguchi (1978) proposes that consumers tend to accept negative opinions (e.g., a critic's negative review) more easily than they accept positive opinions (e.g., a critic's positive review). Second, recent research suggests that the negativity bias operates in affective processing as early as the initial categorization of information into valence classes (e.g., the film is "good" or "bad"; Ito et al. 1998). Thus, we propose the following:
H5: Negative reviews hurt box office revenue more than positive reviews help box office revenue.
Moderators of Critical Reviews: Stars and Budgets
Are there any factors that moderate the impact of critical reviews on box office performance? We argue that two key candidates are star power and budget. We believe that examining the effects of these two moderators on box office revenue in conjunction with critical reviews may provide a partial economic rationale for the two previously mentioned puzzling film industry decisions about pursuing stars and making big-budget films. In the following paragraphs, we elaborate on this issue by examining the literature on star power and film budgets.
Star power has received considerable attention in the literature (De Silva 1998; De Vany and Walls 1999; Holbrook 1999; Levin, Levin, and Heath 1997; Litman 1983; Litman and Ahn 1998; Litman and Kohl 1989; Neelamegham and Chintagunta 1999; Prag and Casavant 1994; Ravid 1999; Smith and Smith 1986; Sochay 1994; Wallace, Seigerman, and Holbrook 1993). Hollywood seems to favor films with stars (e.g., award-winning actors and directors), and it is almost axiomatic that stars are key to a film's success. However, empirical results of star power on box office performance have produced conflicting evidence. Litman and Kohl (1989) and Sochay (1994) find that stars' presence in a film's cast has a significant effect on that film's revenue. Similarly, Wallace, Seigerman, and Holbrook (1993, p. 23) conclude that "certain movie stars do make [a] demonstrable difference to the market success of the films in which they appear." In contrast, Litman (1983) finds no significant relationship between a star's presence in a film and box office rentals. Smith and Smith (1986) find that winning an award had a negative effect on a film's fate in the 1960s but a positive effect in the 1970s. Similarly, Prag and Casavant (1994) find that star power positively affects a film's financial success in some samples but not in others. De Silva (1998) finds that stars are an important factor in the public's attendance decisions but are not significant predictors of financial success, a finding that is documented in subsequent studies as well (De Vany and Walls 1999; Litman and Ahn 1998; Ravid 1999).
Film production budgets also have received significant attention in the literature on motion picture economics (Litman 1983; Litman and Ahn 1998; Litman and Kohl 1989; Prag and Casavant 1994; Ravid 1999).( n1) In 2000, the average cost of making a feature film was $54.8 million (see Motion Picture Association of America [MPAA] 2002). Big budgets translate into lavish sets and costumes, expensive digital manipulations, and special effects such as those seen in the films Jurassic Park ($63 million budget, released in 1993) and Titanic ($200 million budget, released in 1997). Ravid (1999) and John, Ravid, and Sunder (2002) show that though big budgets are correlated with higher revenue, they are not correlated with returns. If anything, low-budget films appear to have higher returns. What, then, do big budgets do for a film? Litman (1983) argues that big budgets reflect higher quality and greater box office popularity. Similarly, Litman and Ahn (1998, p. 182) suggest that "studios feel safer with big budget films." In this sense, big budgets can serve as an insurance policy (Ravid and Basuroy 2003).
Although the effects of star power and budgets on box office returns may be ambiguous at best, the question remains as to whether these two variables act jointly with critical reviews, as we believe they do, to affect box office performance. For example, suppose that a film receives more positive than negative reviews. If the film starts its run in a positive light, other positive dimensions, such as stars and big budgets, may not enhance its box office success. However, consider a film that receives more negative than positive reviews. In this case, stars and big budgets may help the film by blunting some effects of negative reviews. Levin, Levin, and Heath (1997) suggest that popular stars provide the public with a decision heuristic (e.g., attend the film with the stars) that may be strong enough to blunt any negative critic effect. Conversely, as Levin, Levin, and Heath explain (p. 177), when a film receives more positive than negative reviews, it is "less in need of the additional boost provided by a trusted star." Similarly, Litman and Ahn (1998) suggest that budgets should increase a film's entertainment value and thus its probability of box office success, which consequently compensates for other negative traits, such as bad reviews. On the basis of these arguments, we propose the following:
H6: For films that receive more negative than positive reviews, star power and big budgets positively affect box office performance; however, for films that receive more positive than negative reviews, star power and big budgets do not affect box office performance.
Data and Variables
Our data include a random sample of 200 films released between late 1991 and early 1993; most of our data are identified in Ravid's (1999) study. We first pared down the sample because of various missing data for 175 films. We gathered our data from two sources: Baseline in California (http://www.baseline.hollywood.com) and Variety magazine. Although some studies have focused on more successful films, such as the top 50 or the top 100 in Variety lists (De Vany and Walls 1997; Litman and Ahn 1998; Smith and Smith 1986), our study contains a random sample of the films (both successes and failures). Our sample contains 156 MPAA-affiliated films and 19 foreign productions, and it covers approximately one-third of all MPAA-affiliated films released between 1991 and 1993 (475 MPAA-affiliated films were released between 1991 and 1993; see Vogel 2001, Table 3.2). In our sample, 3.2% of the films are rated G; 14.7%, PG; 26.3%, PG-13; and 55.7%, R. This distribution closely matches the distribution of all films released between 1991 and 1993 (1.5%, G; 15.8%, PG; 22.1%, PG-13; and 60.7%, R; see Creative Multimedia 1997).
Weekly domestic revenue. Every week, Variety reports the weekly domestic revenue for each film. These figures served as our dependent variables. Most studies cited thus far do not use weekly data (see, e.g., De Vany and Walls 1999; Litman and Ahn 1998; Ravid 1999). Given our focus and our procedure, the use of weekly data is critical.
Valence of reviews. Variety lists reviews for the first weekend in which a film opens in major cities (i.e., New York; Los Angeles; Washington, D.C.; and Chicago). To be consistent with Eliashberg and Shugan's (1997) study, we collected the number of reviews from all these cities. Variety classifies reviews as "pro" (positive), "con" (negative), and "mixed." For the review classification, each reviewer is called and asked how he or she rated a particular film: positive, negative, or mixed. We used these classifications to establish measures of critical review assessment similar to those Eliashberg and Shugan use. Unlike Ravid's (1999) study and consistent with that of Eliashberg and Shugan, our study includes the total number of reviews (TOTNUM) from all four cities. For each film, POSNUM (NEGNUM) is the number of positive (negative) reviews a film received, and POSRATIO (NEGRATIO) is the number of positive (negative) reviews divided by the number of total reviews.
Star power. For star power, we used the proxies that Ravid (1999) and Litman and Ahn (1998) suggest. For each film, Baseline provided a list of the director and up to eight cast members. For our first definition of star, we identified all cast members who had won a Best Actor or Best Actress Academy Award (Oscar) in prior years (i.e., before the release of the film being studied). We created the dummy variable WONAWARD, which denotes films in which at least one actor or the director won an Academy Award in previous years. Based on this measure, 26 of the 175 films in our sample have star power (i.e., WONAWARD = 1). For our second measure, we created the dummy variable TOP 10, which has a value of 1 if any member of the cast or the director participated in a top-ten grossing film in previous years (Litman and Ahn 1998). Based on this measure, 17 of the 175 films in our sample possess star power (i.e., TOP 10 = 1). For our third and fourth measures, we collected award nominations for Best Actor, Best Actress, and Best Directing for each film in the sample and defined two variables, NOMAWARD and RECOGNITION. The first variable, NOMAWARD, receives a value of 1 if one of the actors or the director was previously nominated for an award. The NOMAWARD measure increases the number of films with star power to 76 of 175. The second variable, RECOGNITION, measures recognition value. For each of the 76 films in the NOMAWARD category, we summed the total number of awards and the total number of nominations, which effectively creates a weight of 1 for each nomination and doubles the weight of an actual award to 2 (e.g., if an actor was nominated twice for an award, RECOGNITION is 2; if the actor also won an award in one of these cases, the value increases to 3). We thus assigned each of the 76 films a numerical value, which ranged from a maximum of 15 (for Cape Fear, directed by Martin Scorsese and starring Robert De Niro, Nick Nolte, Jessica Lange, and Juliette Lewis) to 0 for films with no nominations (e.g., Curly Sue).
Budgets. Baseline provided the budget (BUDGET) of each film; the trade term for budget is "negative cost," or production costs (Litman and Ahn 1998; Prag and Casavant 1994; Ravid 1999). The budget does not include gross participation, which is ex post share of participants in gross revenue, advertising and distribution costs, or guaranteed compensation, which is a guaranteed amount paid out of revenue if revenue exceeds the amount.
Other control variables. We used several control variables. Each week, variety reports the number of screens on which a film was shown that week. Eliashberg and Shugan (1997) and Elberse and Eliashberg (2002) find that the number of screens is a significant predictor of box office revenue. Thus, we used SCREEN as a control variable. Another worthwhile variable reflects whether a film is a sequel (Litman and Kohl 1989; Prag and Casavant 1994; Ravid 1999). The SEQUEL variable receives a value of 1 if the movie is a sequel and a value of 0 otherwise. There are 11 sequels in our sample. The industry considers MPAA ratings an important issue (Litman 1983; Litman and Ahn 1989; Ravid 1999; Sochay 1994). In our analysis, we coded ratings using dummy variables; for example, a dummy variable G has a value of 1 if the film is rated G and a value of 0 otherwise. Some films are not rated for various reasons; those films have a value of 0. Finally, our last control variable is release date (RELEASE). In some studies (Litman 1983; Litman and Ahn 1998; Litman and Kohl 1989; Sochay 1994), release dates are used as dummy variables, following the logic that a high-attendance-period release (e.g., Christmas) attracts greater audiences and a lower-attendance-period (e.g., early December) release is bad for revenue. However, because there are several peaks and troughs in attendance throughout the year, we used information from Vogel's (2001, Figure 2.4) study to produce a more sophisticated measure of seasonality. Vogel constructs a graph that depicts normalized weekly attendance over the year (based on 1969-84 data) and assigns a value between 0 and 1 for each date in the year (Christmas attendance is 1 and early December attendance is .37; these are high and low points of the year, respectively). We matched each release date with the graph and assigned the RELEASE variable to account for seasonal fluctuations.
Table 1 reports the correlation matrix for the key variables of interest. The ratio of positive reviews, POSRATIO, is negatively correlated with the ratio of negative reviews, NEGRATIO; that is, not many films received several negative and positive reviews at the same time. The most expensive film in the sample cost $70 million (Batman Returns) and is the film that has the highest first-week box office revenue ($69.31 million), opening to the maximum number of screens nationwide (3700). In our sample, the average number of first-week screens is 749, the average first-week box office return is $5.43 million, and the average number of reviews received is 34 (43% positive, 31% negative). Using a sample of 56 films, Eliashberg and Shugan (1997, p. 47) reported 47% positive reviews and 25% negative reviews. In our sample, Beauty and the Beast had the highest revenue per screen ($117,812 per screen, for two screens) and the highest total revenue ($426 million).
The Role of Critics
H1-H4 address critics' role as influencers, predictors, or both. To test the hypotheses, we ran three sets of tests. First, we replicated Eliashberg and Shugan's (1997) model by running separate regressions for each of the eight weeks; we included only three predictors (POSRATIO or NEGRATIO, SCREEN, and TOTNUM). In the second test, we expanded Eliashberg and Shugan's framework by including our control variables in the weekly regressions. In the third test, we ran time-series cross-section regression that combined both cross-sectional and longitudinal data in one regression, specifically to control for unobserved heterogeneity.
The replications of Eliashberg and Shugan's (1997) results are reported in Tables 2 and 3. The coefficients of both positive and negative reviews are significant at .01 for each of the eight weeks, and they seem to support H3. Critics both influence and predict box office revenue, or they predict consistently across all weeks.
We added the control variables to the regressions. Tables 4 and 5 report the results of this set of regressions.( n2) The results confirm what is evident in Tables 2 and 3: The critical reviews, both positive and negative, remain significant for every week. For the first four weeks, SCREEN appears to have the most significant impact on revenue, followed by BUDGET and POSRATIO (NEGRATIO). After four weeks, BUDGET becomes insignificant, and critical reviews become the second most important factor after screens. In general, the R² and adjusted R² are greater than those in Tables 2 and 3, suggesting an enhanced explanatory power of the added variables.
For the third test, we ran time-series cross-section regressions (see Table 6; Baltagi 1995; Hsiao 1986, p. 52).( n3) In this equation, the variable SCREEN varies across films and across time; the other predictors and control variables vary across films but not across time. We also created a new variable, WEEK, which has a value between 1 and 8 and thus varies across time but not across films. In this regression, we added an interaction term (POSRATIO x WEEK or NEGRATIO x WEEK) to assess the declining impact of critical reviews over time. The results support H3 and partially support H4. The coefficient of positive and negative reviews remains highly significant (βpositive = 3.32, p < .001; βnegative = -5.11, p < .001), pointing to the dual role of critics (H3). However, the interaction term is not significant for positive reviews, but it is significant for negative reviews, suggesting a declining impact of negative reviews over time, which is partially consistent with critics' role as influencers.
These results are somewhat different from Eliashberg and Shugan's (1997) findings (i.e., critics are only predictors) and Ravid's (1999) results (i.e., there is no effect of positive reviews). There are several reasons our results differ from those of Eliashberg and Shugan. First, although they included only those films that had a minimum eight-week run, our sample includes films that ran for less than eight weeks as well. We did so to accommodate films with short box office runs. Second, the size of our data set is three times as large as that of Eliashberg and Shugan (175 films versus 56). Third, our data set covers a longer period (late 1991 to early 1993) than their data set, which only covers films released between 1991 and early 1992. Fourth, we selected the films in our data set completely at random, whereas Eliashberg and Shugan, as they note, were more restrictive. Similarly, our results may differ from those of Ravid because we included reviews from all cities reported in Variety, not only New York, and we used weekly revenue data rather than the entire revenue stream.
Negative Versus Positive Reviews
H5 predicts that negative reviews should have a disproportionately greater negative impact on box office reviews than the positive impact of positive reviews. Because the percentages of positive and negative reviews are highly correlated (see Table 1; r = -.88), they cannot be put into the same model. Instead, we used the number of positive (POSNUM) and negative (NEGNUM) reviews, because they are not correlated with each other (see Table 1; r = .17), and thus both variables can be put into the same regression model. We expected the coefficient of NEGNUM to be negative, and thus there may be some evidence for negativity bias if |βNEGNUM| is greater than |βPOSNUM|. Table 7 reports the results of our time-series cross-section regression.
Although β[NEGNUM is negative and significant (βNEGNUM = -.056, t = -2.29, p < .02) and βPOSNUM is positive and significant (β[POSNUM] = .032, t = 2.34, p < .01), their difference (|β[NEGNUM| - |βPOSNUM is not significant (F1, 1108 < 1). In some sense, we expected this pattern because we found that negative reviews, but not positive reviews, diminish in impact over time. A stronger test for the negativity bias should then focus on the early weeks (the first week in particular) when the studios have not had the opportunity to engage in damage control. As we expected, the negativity bias is strongly supported in the first week. Although βNEGNUM is negative and significant (βNEGNUM = -.209, t = -3.42, p < .0001), βPOSNUM is not significant (βPOSNUM = .052, t = 1.60, p = not significant), and their difference (|βNEGNUM| - |βPOSNUM is significant (F1, 151 = 3.76, p < .05). Separate weekly regressions on the subsequent weeks (Week 2 onward) did not produce a significant difference between the two coefficients. The combined data for the first two weeks show evidence of negativity bias (Table 7).
It is possible that the negativity bias is confounded by perceived reviewer credibility. When consumers read a positive review, they may believe that the reviewers have a studio bias. In contrast, they may perceive a negative review as more likely to be independent of studio influence. To separate the effects of credibility from negativity bias, we ran an analysis that included only the reviews of two presumably universally credible critics: Gene Siskel and Roger Ebert.( n4) We were only able to locate their joint reviews for 72 films from our data set; of these films, 32 received two thumbs up, 10 received two thumbs down, and 23 received one thumb up. We coded three dummy variables: TWOUP (two thumbs up), TWODOWN (two thumbs down), and UP&DOWN (one thumb up). In the regressions, we used two of the dummy variables: TWOUP and TWODOWN. The results confirmed our previous findings. The coefficient of TWODOWN is significantly greater than that of TWOUP in both the first week (β = -6.51, β = .32; F1, 57 = 4.95, p < .03) and the entire eight-week run (βTWODOWN = -2.28, βTWOUP = .42; F1, 501 = 3.46, p < .06).
Star Power, Budgets, and Critical Reviews
H6 predicts that star power and big budgets can help films that receive more negative than positive reviews but do little for films that receive more positive than negative reviews. Because we made separate predictions for the two groups of films (POSNUM - NEGNUM ≤ 0 and POSNUM - NEGNUM > 0), we split the data into two groups. The first group contains 97 films for which the number of negative reviews is greater than or equal to that of positive reviews, and the second group contains the remaining 62 films for which the number of positive reviews exceeds that of negative reviews. We ran time-series cross-section regressions separately for the two groups. Table 8 presents the results.
Table 8 shows that when negative reviews outnumber positive reviews, the effect of star power on box office returns approaches statistical significance when measured with WONAWARD (β = 1.117, t = 1.56, p = .12) and is statistically significant in the case of RECOGNITION (β = .224, t = 2.09, p < .05). In each case, BUDGET has a positive, significant effect as well. However, when positive reviews outnumber negative reviews, neither the budget nor any definition of star power has any significant impact on a film's box office revenue. The results imply that star power and budget may act as countervailing forces against negative reviews but do little for films that receive more positive than negative reviews.
Critical reviews play a major role in many industries, including theater and performance arts, book publishing, recorded music, and art. In most cases, there is not enough data to identify critics' role in these industries. Are critics good predictors of consumers' tastes, do they influence and determine behavior, or do they do both? Our article sheds light on critics' role in the context of a film's box office performance. We further assess the differential impact of positive versus negative reviews and how they might operate jointly with star power and budget.
Our first set of results shows that for each of the first eight weeks, both positive and negative reviews are significantly correlated with box office revenue. The pattern is consistent with the dual perspective of critics (i.e., they are influencers and predictors). At the simplest level, this suggests that any marketing campaign for a film should carefully integrate critical reviews, particularly in the early weeks. If studios expect positive reviews, the critics should be encouraged to preview the film in advance to maximize their impact on box office revenue. However, if studios expect negative reviews, they should either forgo initial screenings for critics altogether or invite only select, "friendly" critics to screenings. If negative reviews are unavoidable, studios can use stars to blunt some of the effects by encouraging appearances of the lead actors on television shows such as Access Hollywood and Entertainment Tonight (The Wall Street Journal 2001).
Our second set of results shows that negative reviews hurt revenue more than positive reviews help revenue in the early weeks of a film's release. This suggests that whereas studios favor positive reviews and dislike negative reviews, the impact is not symmetric. In the context of a limited budget, studios should spend more to control damage than to promote positive reviews. In other words, there may be more cost effective options than spending money on advertisements that tout the positive reviews. First, studios could forgo critical screenings for fear of negative attention. For example, Get Carter and Autumn in New York did not offer advance screenings for critics, leading Roger Ebert (Guardian 2000) to comment that "the studio has concluded that the film is not good and will receive negative reviews." Second, studios could selectively invite "soft" reviewers. Third, studios could delay sending press kits to reviewers. Press kits generally contain publicity stills and production information for critics. Because newspapers do not run reviews without at least one press still from the film, withholding the kit gives the film an extra week to survive without bad reviews.
Our third set of results suggests that stars and budgets moderate the impact of critical reviews. Although star power may not be needed if a film receives good reviews, it can significantly lessen the impact of negative reviews. Similarly, big budgets contribute little if a film has already received positive reviews, but they can significantly lessen the impact of negative reviews. Therefore, in some sense, big budgets and stars serve as an insurance policy. Because success is difficult to predict in the film business (see, e.g., De Vany and Walls 1999), as is the quality of reviews, executives can hedge their bets by employing stars or by using big budgets (e.g., expensive special effects). These actions may not be needed and, on average, may not help returns; however, if critics pan the film, big budgets and stars can moderate the blow and perhaps save the executive's job (Ravid and Basuroy 2003).
Implications for Other Industries
Although the current analysis applies to the film industry, we believe the results may be applicable to other industries in which consumers are unable to assess the qualities of products accurately before consumption (e.g., theater and performance arts, book publishing, recorded music, financial markets). Critics may influence consumers, or consumers may seek out the critics who they believe accurately reflect their taste (i.e., the predictor role). For example, in urban centers, "theater and dance critics wield nearly life-or-death power over ticket demand" (Caves 2000, p. 189); for Broadway shows, critics appear both to influence and to predict consumers' tastes (Reddy, Swaminathan, and Motley 1998). Similarly, research in the bond market shows that there is little market reaction to bond rating changes when the rating agency simply responds to public information (i.e., the rating agencies simply predict what the public has done already). In contrast, if the rating change is based on projections or inside research, the markets react to the news (see Goh and Ederington 1993).
In addition to the role of critics, all the other issues that we have raised in this article (e.g., negativity bias, moderators of critical reviews) should be of significance in other industries as well. For example, bad reviews can doom a publisher's book (Greco 1997, p. 194), but as with films, readers' reliance on the book critics is reduced when the book features a popular author rather than an unknown author (Levin, Levin, and Heath 1997). When enough data are available, there is ample opportunity to extend our framework to assess the revenue returns of such similar creative businesses.
Ravid thanks the New Jersey Center for Research at Rutgers University and the Stern School at New York University for research support. All authors thank Kalpesh Desai, Paul Dholakia, Wagner Kamakura, Matt Clayton, Rob Engle, William Greene, Kose John, and the three anonymous JM reviewers for many helpful suggestions. The authors owe special thanks to Shailendra Gajanan, Subal Kumbhakar, and Nagesh Revankar for many discussions on econometrics.
(n1) In investigating the role of budgets in a film's performance, we need to disentangle the effects of star power from budgets, because it could be argued that expensive stars make the budget a proxy for star power. However, in our data there is extremely low correlation between the measures of star power and budget, suggesting that the two measures are unrelated.
(n2) Although we report the results using one of the four possible definitions of star power, WONAWARD, rerunning the regressions using the other three measures of star power does not change the results.
(n3) We thank an anonymous reviewer for this suggestion.
(n4) We thank an anonymous reviewer for this suggestion.
Legend for Chart:
B - BUDGET
C - RELEASE
D - POSRATIO
E - NEGRATIO
F - TOTNUM
G - POSNUM
H - NEGNUM
I - WONAWARD
A B C D E F
G H I
BUDGET 1.00
Mean = 15.68
S.D. = 13.90
RELEASE .004 1.00
Mean = .63
S.D. = .16
POSRATIO -.131 .017 1.00
Mean =.43
S.D. = .24
NEGRATIO .042 -.068 -.886 1.00
Mean = .31
S.D. = .22
TOTNUM .605 .150 .252 -.341 1.00
Mean = 34.22
S.D. = 17.46
POSNUM .283 .056 .740 -.704 .760
1.00
Mean = 15.81
S.D. = 12.03
NEGNUM .498 .124 -.579 .556 .448
-.179 1.00
Mean = 9.23
S.D. = 7.06
WONAWARD .358 .077 .126 -.139 .430
.379 .169 1.00
Mean = .15
S.D. = .36
Notes: S.D. = standard deviation. Legend for Chart:
A - Week
B - R² (Adjusted R²
C - POSRATIO Unstandardized Coefficient (Standardized
Coefficient)
D - POSRATIO t-Statistic (p-Value)
E - TOTNUM Unstandardized Coefficient (Standardized Coefficient)
F - TOTNUM t-Statistic (p-Value)
G - SCREEN Unstandardized Coefficient (Standardized Coefficient)
H - SCREEN t-Statistic (p-Value)
I - F-Ratio (p-Value)
A B C D E
F G H I
1 (n = 162) .7268 5.114 2.96 .037
(.7217) (.14017) (.0036) (.07176)
1.49 .00890 17.49 141.03
(.1394) (.85073) (<.0001) (<.0001)
2 (n = 154) .7229 4.02465 3.15 .0498
(.7174) (.15252) (.0020) (.13428)
2.70 .00593 16.22 131.32
(.0076) (.81576) (<.0001) (<.0001)
3 (n = 145) .6542 3.2968 2.79 .03661
(.6469) (.15427) (.0060) (.12538)
.2.17 .00451 13.23 89.56
(.0315) (.77171) (<.0001) (<.0001)
4 (n = 139) .7174 2.15975 2.91 .01495
(.7111) (.14426) (.0042) (.07051)
1.32 .00361 1.32 115.07
(.1891) (.82838) (.1891) (<.0001)
5 (n = 137) .7325 1.709 3.14 .00566
(.7265) (.14897) (.0021) (.03552)
.69 .00302 16.72 122.33
(.4927) (.84327) (<.0001) (<.0001)
6 (n = 132) .7079 1.58248 3.06 -.00147
(.7011) (.15050) (.0027) (-.01003)
-.19 .00299 16.15 104.22
(.8502) (.84839) (<.0001) (<.0001)
7 (n = 130) .5763 2.28437 3.56 -.00396
(.5663) (.20870) (.0005) (-.02546)
-.41 .00299 12.13 57.59
(.6858) (.76491) (<.0001) (<.0001)
8 (n = 122) .7013 1.20016 3.17 -.00551
(.6938) (.16071) (.0019) (-.05212)
-.95 .00262 15.62 93.14
(.3432) (.8577) (<.0001) (<.0001)
Notes: Dependent variable is weekly revenue. Method is separate
regressions for each week. Legend for Chart:
A - Week
B - R² (Adjusted R²)
C - NEGRATIO Unstandardized Coefficient (Standardized
Coefficient)
D - NEGRATIO t-Statistic (p-Value)
E - TOTNUM Unstandardized Coefficient (Standardized Coefficient)
F - TOTNUM t-Statistic (p-Value)
G - SCREEN Unstandardized Coefficient (Standardized Coefficient)
H - SCREEN t-Statistic (p-Value)
I - F-Ratio (p-Value)
A B C D E
F G H I
1 (n = 162) .7290 -6.05792 -3.18 .0285
(.7239) (-.1525) (.0018) (.05479)
1.10 .00888 .17.80 142.58
(.2738) (.84904) (<.0001) (<.0001)
2 (n = 154) .7273 -5.10837 -3.53 .04204
(.7219) (-.17391) (.0005) (.11328)
2.22 .00598 16.51 134.26
(.0276) (.82294) (<.0001) (<.0001)
3 (n = 145) .6518 -3.39389 -2.59 .03451
(.6444) (-.14618) (.0105) (.11819)
1.98 .00447 13.16 88.59
(.0496) (.76423) (<.0001) (<.0001)
4 (n = 139) .7118 -1.97242 -2.38 .01486
(.7054) (-.12094) (.0187) (.07007)
1.26 .00355 15.37 111.95
(.2090) (.81431) (<.0001) (<.0001)
5 (n = 137) .7298 -1.78567 -2.89 .00418
(.7237) (-.14178) (.0044) (.02621)
.49 .003 16.60 120.63
(.6252) (.83882) (<.0001) (<.0001)
6 (n = 132) .7065 -1.73476 -2.95 -.00368
(.6997) (-.14911) (.0038) (-.02515)
-.46 .00299 16.10 103.52
(.6465) (.84649) (<.0001) (<.0001)
7 (n = 130) .5604 -2.10310 -2.76 -.00606
(.5500) (-.1672) (.0066) (-.03903)
-.60 .00296 11.79 53.97
(.5503) (.7576) (<.0001) (<.0001)
8 (n = 122) .6945 -1.20867 -2.68 -.00704
(.6868) (-.13982) (.0083) (-.06662)
-1.18 .00261 15.39 90.20
(.2408) (.85507) (<.0001) (<.0001)
Notes: Dependent variable is weekly revenue. Method is
separate regressions for each week. Legend for Chart:
A - Week
B - Constant
C - WONAWARD
D - G
E - PG
F - PG-13
G - R
H - TOTNUM
I - RELEASE
J - SEQUEL
K - BUDGET
L - POSRATIO
M - SCREEN
N - R²
O - Adjusted R²
P - F-Ratio
A B C D
E F G
H I J
K L M
N O P
1 (n = 162) -6.59(*) .255 -5.101(**)
(.0106) (-.109)
-.857 -.445 -.1210
(-.035) (-.0218) (.0067)
-.032 3.035 5.223(*)
(.0609) (.0545) (.149)
.1763(*) 6.796(*) .007(*)
(.278) (.186) (.938)
.791 .776 51.92(*)
2 (n = 154) -4.50(*) .927 -1.38
(.0547) (.0428)
.05574 -.634 -.186
(.0032) (-.043) (-.015)
.009 .549 1.889(***)
(.026) (.014) (.078)
.097(*) 4.670(*) .005(*)
(.217) (.177) (.697)
.757 .738 40.44(*)
3 (n = 145) -2.416 .966 -.310
(.075) (-.013)
1.485 -1.042 -.059
(.109) (-.093) (-.006)
-.003 -.914 .228
(-.011) (-.030) (.012)
.105(*) 3.590(*) .0035(*)
(.310) (.168) (.601)
.728 .706 32.64(*)
4 (n = 139) -1.92 .521 -.360
(.058) (-.019)
1.204 -.515 .438
(.127) (-.065) (.0634)
-.005 -.159 -.716
(-.024) (-.007) (-.054)
.039(**) 2.424(*) .003(*)
(.164) (.162) (.753)
.758 .738 36.51(*)
5 (n = 137) -1.929(**) .776(**) -.727
(.114) (-.051)
.603 -.101 .3575
(.085) (-.017) (.068)
-.008 1.084 -.578
(-.055) (.067) (.057)
.005 1.867(*) .003(*)
(.027) (.163) (.866)
.768 .748 37.97(*)
6 (n = 132) -1.413 .564(***) .228
(.091) (.018)
.202 .132 .191
(.031) (.024) (.040)
-.006 .744 -.718
(-.044) (.050) (-.075)
-.008 1.416(**) .003(*)
(-.050) (.135) (.892)
.727 .702 29.30(*)
7 (n = 130) -1.608 .477 2.265(***)
(.076) (.176)
.134 .248 .286
(.020) (.045) (.057)
.00011 .285 -.874
(.0007) (.018) (-.084)
-.0109 1.792(*) .003(*)
(-.065) (.163) (.766)
.614 .578 17.19(*)
8 (n = 122) -.937 .511(**) .359
(.118) (.042)
-.135 .072 .081
(-.029) (.018) (.023)
-.004 .678 -.219
(-.037) (.064) (-.032)
-.018(***) .867(**) .003(*)
(-.152) (.116) (.921)
.733 .706 27.65(*)
(*) p < .01.
(**) p < .05.
(***) p < .1.
Notes: Dependent variable is weekly revenue; method is separate
regressions for each week. Standardized betas are reported in
parentheses. Legend for Chart:
A - Week
B - Constant
C - WONAWARD
D - G
E - PG
F - PG-13
G - R
H - TOTNUM
I - RELEASE
J - SEQUEL
K - BUDGET
L - POSRATIO
M - SCREEN
N - R²
O - Adjusted R²
P - F-Ratio
A B C D
E F G
H I J
K L M
N O P
1 (n = 162) -.390 .381 -5.415(**)
(.016) (-.116)
-1.234 -1.055 -.612
(-.051) (-.051) (-.034)
-.036 2.543 4.938(*)
(-.069) (.046) (.141)
.172(*) -7.173(*) .007(*)
(.271) (-.181) (.689)
.789 .774 51.50(*)
2 (n = 154) .019 1.106 -1.742
(.065) (-.054)
-.360 -1.115 -.623
(-.020) (-.075) (-.050)
.004 .215 1.655
(.011) (.005) (.068)
.091(*) -5.476(*) .005(*)
(.205) (-.186) (.710)
.759 .741 41.06(*)
3 (n = 145) .822 1.097 -.441
(.0855) (-.0181)
1.215 -1.380 -.330
(.0899) (-.123) (-.034)
-.004 -1.266 .0998
(-.011) (-.0416) (.005)
.0996(*) -3.573(*) .004(*)
(.292) (-.154) (.602)
.726 .703 32.21(*)
4 (n = 139) .185 .5999 -.229
(.066) (-.012)
1.083 -.698 .281
(.114) (-.089) (.040)
-.005 -.363 -.802
(-.024) (-.017) (-.060)
.038(**) -2.310(*) .003(*)
(.157) (-.142) (.740)
.754 .733 35.73(*)
5 (n = 137) -.280 .838 -.619
(.123) (-.044)
.538 -.213 .273
(.076) (-.035) (.052)
-.010 .942 -.669
(-.063) (.058) (-.066)
.004 -1.952(*) .003(*)
(.021) (-.155) (.862)
.767 .746 37.64(*)
6 (n = 132) -.0526 .604(***) .255
(.097) (.020)
.119 .020 .094
(.017) (.004) (.019)
-.008 .625 -.826
(-.058) (.042) (-.086)
-.008 -1.607(*) .003(*)
(.0513) (-.138) (.891)
.728 .704 29.48(*)
7 (n = 130) .056 .513 2.30
(.082) (.173)
-.084 -.018 .055
(-.013) (-.0033) (.011)
.00029 .133 -.988
(.002) (.0087) (-.096)
-.013 -1.645(**) .0029(*)
(-.082) (-.1308) (.764)
.607 .571 16.72(*)
8 (n = 122) -.133 .524 .348
(.122) (.040)
-.226 -.043 -.015
(-.0488) (-.011) (-.004)
-.004 .616 -.280
(-.039) (.058) (-.040)
-.018(**) -.840(***) .0028(*)
(-.163) (-.097) (.921)
.729 .703 27.27(*)
(*) p < .01.
(**) p < .05.
(***) p < .1.
Notes: Dependent variable is weekly revenue; method is separate
regressions for each week. Standardized betas are reported in
parentheses. Legend for Chart:
A - Variable
B - Using Percentage of Positive Reviews Coefficient
C - Using Percentage of Positive Reviews t-Value
D - Using Percentage of Positive Reviews Significance (p-Value)
E - Using Percentage of Negative Reviews Coefficient
F - Using Percentage of Negative Reviews t-Value
G - Using Percentage of Negative Reviews Significance (p-Value)
A B C D E F G
Constant -1.42 -.98 .33 2.14 1.33 .18
WONAWARD .58 1.46 .14 .69 1.59 .11
G -1.18 -1.07 .28 -1.46 -1.19 .23
PG .102 .10 .91 -.33 -.31 .75
PG-13 -.042 -.04 .96 -.48 -.46 .64
R .22 .24 .81 -.16 -.16 .86
TOTNUM -.006 -.52 .60 -.007 -.59 .55
RELEASE 1.02 1.21 .22 .77 .82 .41
SEQUEL .73 1.30 .20 .55 .89 .37
BUDGET .032 2.24 .02 .023 1.47 .14
POSRATIO 3.321 3.33 .00
NEGRATIO -5.11 -4.41 .00
SCREEN .005 22.06 .00 .005 21.79 .00
WEEK -.436 -2.23 .02 -.55 -2.38 .01
POSRATIO x WEEK -.023 -.14 .89
NEGRATIO x WEEK .42 2.17 .03
R² .47 .43
Hausman test
for random
effects M = 1.00 .60 M = 2.00 .36
Notes: Dependent variable is weekly revenue; method is
time-series cross-section regression. N = 159. Legend for Chart:
B - Fuller-Battese Estimation
C - Week 1 Regression
D - Week 1+ Week 2 Regression
A B
C D
Constant .53 (.38)
-2.94 (-1.34) -2.47 (-1.56)
WONAWARD .55 (1.39)
.08 (.07) .41 (.56)
G -1.65 (-1.50)
-6.21 (-2.46)(*) -4.43 (-2.47)(*)
PG -.58 (-.62)
-2.09 (-1.00) -1.39 (-.93)
PG-13 -.71 (-.78)
-1.50 (-.74) -1.45 (.99)
R -.46 (-.51)
-1.22 (-.63) -1.13 (-.81)
RELEASE 1.10 (1.31)
3.55 (1.70)(***) 2.45 (1.67)(***)
SEQUEL .64 (1.14)
4.85 (3.37)(*) 3.45 (3.51)(*)
BUDGET .03 (2.17)(**)
.18 (5.05)(*) .15 (5.76)(*)
βPOSNUM .032 (2.34)(**)
.052 (1.60) .055 (2.40)(*)
βNEGNUM -.056 (-2.29)(**)
-.209 (-3.42)(*) -.148 (-3.49)(*)
SCREEN .005 (22.70)(*)
.007 (12.82)(*) .006 (15.46)(*)
WEEK -.446 (-2.33)(*)
-- --
F-value for |βNEGNUM|- .54, N.S.
|βPOSNUM|)
3.76(*) 2.71(***)
N 159
162 317
R² .471
.798 .736
(*) p < .01.
(**) p < .05.
(***) p < .1.
Notes: Dependent variable is weekly revenue; methods are
time-series cross-section regression and weekly regressions (Week
1 and Week 1 + Week 2). The t-values are reported in parentheses.
N.S. = not significant. Legend for Chart:
A - Variable
B - When POSNUM - NEGNUM ≤ 0 (i.e., Negative Reviews Outnumber
Positive Reviews) (n = 62) Star Power Is WONAWARD
C - When POSNUM - NEGNUM ≤ 0 (i.e., Negative Reviews Outnumber
Positive Reviews) (n = 62) Star Power Is RECOGNITION
D - When POSNUM - NEGNUM > 0 (i.e., Positive Reviews Outnumber
Negative Reviews) (n = 97) Star Power Is WONAWARD
E - When POSNUM - NEGNUM > 0 (i.e., Positive Reviews Outnumber
Negative Reviews) (n = 97) Star Power Is RECOGNITION
A B C
D E
Constant 1.540 (1.06) 1.234 (.86)
1.238 (.77) 1.250 (.78)
WONAWARD 1.117 (1.56) N.A.
.529 (.99) N.A.
RECOGNITION N.A. .225 (2.09)(**)
N.A. -.069 (-.95)
G -2.372 (-1.86)(***) -2.679(-2.11)(**)
-1.651 (-1.21) -1.451 (-1.05)
PG -.131 (-.19) -.340 (-.49)
-.522 (-.47) -.436 (-.39)
PG-13 -.818 (-1.54) -.978(-1.82)(***)
-.743 (-.69) -.723 (-.67)
R --(a) --(a)
-.503 (-.49) -.387 (-.38)
RELEASE -1.358 (-.90) -.779 (-.53)
1.331 (1.15) 1.212 (1.04)
SEQUEL -.501 (-.63) -.480 (-.61)
1.531 (1.56) 1.057 (1.10)
BUDGET .053 (3.01)(*) .047 (2.65)(*)
-.030 (-1.49) -.017 (-.82)
SCREEN .003 (10.97)(*) .003(11.09)(*)
.006 (19.03)(*) .005 (19.00)(*)
WEEK -.447 (-2.20)(*) -.446(-2.20)(*)
-.482 (2.23)(*) -.480 (2.22)(*)
R² .377 .380
.486 .487
Hausman test for
random effects M = 7.37(*) M = 7.13(*)
M = 8.87(*) M = 8.25(*)
(*) p < .01.
(**) p < .05.
(***)p < .1.
(a) This set did not have any unrated films and thus dropped the
R rating during estimation.
Notes: N.A. = not applicable; dependent variable is weekly
revenue; method is time-series cross-section regression. The
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~~~~~~~~
By Suman Basuroy; Subimal Chatterjee and S. Abraham Ravid
Suman Basuroy is Assistant Professor of Marketing, University at Buffalo, State University of New York.
Subimal Chatterjee is Associate Professor of Marketing, School of Management, Binghamton University.
S. Abraham Ravid is Professor of Finance and Economics, Rutgers University and Yale University School of Management.
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Record: 74- How Firms Relate to Their Markets: An Empirical Examination of Contemporary Marketing Practices. By: Coviello, Nicole E.; Brodie, Roderick J.; Danaher, Peter J.; Johnston, Wesley J. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p33-46. 14p. 4 Charts. DOI: 10.1509/jmkg.66.3.33.18500.
- Database:
- Business Source Complete
How Firms Relate to Their Markets: An Empirical Examination of Contemporary Marketing Practices
The authors examine 308 firms in the United States and four other Western countries to understand how different types of firms relate to their markets. Comparative analysis shows that though there is some support for consumer and goods firms being more transactional and business and service firms being more relational, there are many exceptions. The results also show that firms can be grouped into those whose marketing practices are predominantly transactional, predominantly relational, or a transactional/relational hybrid. Each group constitutes approximately one-third of the sample and includes all types of firms (consumer goods, consumer services, business-to-business goods, and business-to-business services). This suggests that marketing practices are pluralistic and managerial practice has not shifted from transactional to relational approaches per se.
As with most organizational processes, the nature and practice of marketing has evolved over recent decades. The academic field has also developed considerably, and there is a fuller understanding of the complexities of marketing practice in different types of firms and market contexts. Distinct subfields have now emerged within the discipline, reflecting research interests in areas such as business-to-business, services marketing, and, most recently, relationship marketing. Although these developments have enriched the understanding of marketing, there are still certain unresolved issues. Although business-to-business and services marketing are treated as distinct areas for examination (as evidenced by the variety of specialist journals, textbooks, and courses on both topics) and anecdotal reports indicate that they differ from consumer and goods marketing in terms of their practical implementation, little empirical data demonstrating their distinctiveness are available. Similarly, few empirical studies compare market --ing issues across customer and product contexts, fewer still examine marketing practices per se, and none involve comparative research across a combination of market and product types. Finally, although relationships have long been of interest in the business-to-business and services literature, the extant comparative research has been conducted solely in the more traditional, transactional context of the marketing mix.
These issues take on particular importance given Day and Montgomery's (1999) view that a better understanding of how firms relate to their markets is fundamental to the marketing field and their observation that the field has shifted its emphasis from transactional to relational exchanges. However, the practice of relational marketing has not been examined relative to the practice of transactional marketing, nor have the specific approaches implemented by different types of firms been examined in a comparative or cross-national setting. As such, providing empirical data on the contemporary marketing practices of a cross-section of firms, serving different types of customers with different product offers and from several countries, can yield a more encompassing, sometimes overlooked perspective to that typically found in the literature. The objective of this research therefore is to investigate how different types of firms relate to their markets through an examination of their actual marketing practices, including both transactional and relational aspects of marketing. The research leads to an understanding of international contemporary marketing practice in a manner that is integrative and holistic rather than paradigm-or sector-specific. Also, if relation-ship marketing is now relevant to all types of organizations, as Day and Montgomery (1999) imply, it is important to investigate the extent to which this is evident in different types of firms. The study examines the practices of firms that serve different customers (predominantly consumer or business) with different product offers (predominantly goods or services) by addressing two questions: ( 1) How do different types of firms relate to their markets in terms of their contemporary marketing practices? and ( 2) What is their relative emphasis on transactional and relational aspects of marketing? To answer these questions, we operationalize a recent conceptual framework developed by Coviello, Brodie, and M unro (1997). This framework builds on the work of Grönroos (1991), Webster (1992), and Berry (1995) and was developed from a synthesis of the broader literature about how firms relate to their markets through various decisions and activities. It integrates both the transactional and relational views of marketing.
The remainder of the article is structured as follows: We provide an overview of the literature in which we discuss various conceptualizations of marketing practice and highlight key patterns in the empirical studies that compare marketing in different types of firms. We then use the review to develop the research questions that guide this investigation. This section is followed by the research method, a discussion of the results, research conclusions, and implications.
Conceptualizing Contemporary Marketing Practice
According to several academics, firms are now emphasizing the retention of customers and the management of relationships, which extend beyond the buyer-seller dyad to include partners through the value chain(Day and Montgomery 1999; Morgan and Hunt 1994; Webster 1992). This approach to the market is generally referred to as relationship marketing and has been defined by Morgan and Hunt (1994, p. 34) as "all marketing activities directed toward establishing, developing, and maintaining successful relational exchanges."
The role and importance of relationships in business-to-business and services marketing is well recognized in the literature. For example, Webster (1978) argues that business markets are specifically characterized by buyer-seller inter-dependence, and Grönroos (1978, 1991, 2000) suggests that service-based firms are inherently relational because they manage the total buyer-seller interaction process. This is done as part of "attracting, maintaining, and ... enhancing customer relationships" (Berry 1983, p. 25). Although Grönroos (2000, p. 23) notes that the relational perspective is "probably as old as the history of trade and commerce," it was Berry (1983) who introduced the term "relationship marketing" to describe service firm activities.
The relational view of marketing has evolved from efforts by both business-to-business and services scholars to differentiate marketing practices by the nature of the customer served or product offered. Beyond the business-to-business and services arenas, however, there are also theoretical developments pertaining to consumer markets and goods firms, thus extending the relevance of relationships across different contexts (Pels 1999; Sheth and Parvatiyar 1995). Given this evolution, it might be argued that any understanding of "contemporary" marketing should include the concept of relationships, and this is reflected in the argument that relationship marketing offers a new paradigm for the field (Kotler 1992; Sheth, Gardner, and Garrett 1988; Webster 1992). For example, in his review of the changing role of marketing in the corporation, Webster (1992) outlines an extended continuum of marketing relationships and argues for a new paradigm of the marketing function in the firm. Similarly, Berry (1995) identifies different forms or levels of relational marketing, thus suggesting a continuum in terms of the range of relationship-building practices that might be implemented. A third continuum is offered by Grönroos (1991), who argues that the nature of the product offer and the type of customer served affects how a firm relates to its market. He proposes that consumer packaged goods organizations are normally characterized by transaction marketing and dominated by the marketing mix. Slightly less transactional and more relational practices are expected for consumer durable and industrial goods firms. Service organizations are posited to be at the relational end of the continuum.
If Grönroos's (1991), Webster's (1992), and Berry's (1995) schemata are compared, several important implications can be identified for how marketing practice is conceptualized. For example, although Grönroos (1991, p. 10) notes that "for every type of good or service, a variety of strategy approaches can be used," he also prescribes which type of marketing is appropriate to different types of firms. In contrast, Webster (1992) and Berry (1995) do not, and the former argues for a new paradigm. This leads us to question whether marketing practices differ by firm type or whether relational marketing is relevant to and/or practiced by all types of organizations. Similarly, the extent to which transactional marketing is still relevant in contemporary practice might be questioned. Furthermore, whereas Grönroos (1991) compares only transactional and relational marketing, Webster (1992) delineates the concept of relationship marketing into different forms of relational exchange, and Berry (1995) outlines three relationship levels. This suggests that it might be appropriate to broaden the understanding of transactional and relational exchange beyond a simple two-way classification.
A more pluralistic conceptualization of marketing is therefore required, such as that developed by Coviello, Brodie, and Munro (1997) (see Table 1). In contrast with Grönroos's (1991) and Webster's (1992) work, this framework does not view transactional and relational marketing to be separate paradigms, mutually exclusive paradigms, or opposite ends of a continuum. Rather, it suggests that marketing is characterized by multiple complex processes manifested in four different aspects of marketing practice:( 1) transaction marketing: managing the marketing mix to attract and satisfy customers; ( 2) database marketing: using technology-based tools to target and retain customers; ( 3) interaction marketing: developing interpersonal relationships to create cooperative interaction between buyers and sellers for mutual benefit; and ( 4) network marketing: developing interfirm relationships to allow for coordination of activities among multiple parties for mutual benefit, resource exchange, and so forth.
This conceptualization differs from Berry's (1995) in that it integrates transactional with relational marketing and is based on a more complete set of nine dimensions under-lying each aspect of marketing practice. These dimensions were derived from an extensive content analysis of the literature and were refined and operationalized by Coviello, Brodie, and Munro (2000). This results in a conceptualization encompassing the transactional view of marketing, the literature on dyadic interactions, recent developments reflecting the use of technology in marketing, and the role of network relationships. Of particular importance is that the conceptualization focuses on marketing practice and can be used for the purpose of examining contemporary practices in different firm contexts. Thus, it allows for a better under-standing of how firms relate to their markets and their relative emphasis on transactional and/or relational exchange.
Given that the objective of this study is to understand how firms relate to their markets by analyzing contemporary practices across organizations, we distinguish firm type using the two classic approaches to firm categorization found in the general literature and specifically used by Grönroos (1991): that is, the predominant type of product offered (goods or services) and the predominant type of customer served (consumer or business).
One of the earliest attempts to define "services" was offered by Rathmell (1966), who argued that the distinction between goods and services is blurred. This view was strengthened by the later work of Shostack (1977), who suggests that though certain product offerings may be more or less tangible, many products contain elements of both goods and services. At the same time, however, Shostack's work provided impetus to the argument that services marketing is unique (e.g., Grönroos 1978; Gummesson 1978), and Berry (1980) and Lovelock (1981) contend that a different management approach is required for services marketing. In contrast, Enis and Roering (1981), Onkvisit and Shaw (1991), and Wright (1995) challenge this argument; Wright concludes that it is important to identify similarities across the two sectors to help companies tailor their marketing efforts. Indeed, there is a growing view in the literature that all firms compete on the basis of service (Grönroos 2000; Zeithaml and Bitner 2000), and Bitner, Brown, and Meuter (2000, p. 140) argue that all firms can be classified as offering "customer service, service as value-added services, or service offerings as the product." This suggests that the simple goods/services distinction has become less relevant in the context of contemporary marketing practice.
In spite of the debate around the distinction (or lack thereof) between goods and services marketing, we identified only fifteen comparative empirical examinations. Most of these studies focus on comparing (for example) differences in buyer characteristics, and only seven compare how goods and services firms relate to their markets. Three of these focus on advertising content and structure (Abernethy and Butler 1992; Cutler and Javalgi 1993; Zinkhan, Johnson, and Zinkhan 1992), and one (Szymanski 2001) compares sales presentation efforts. The remaining studies compare the implementation of marketing-mix activities (George and Barksdale 1974), market orientation and practices (Hooley and Cowell 1985), and future strategic orientation across goods and services firms (Parasuraman and Varadarajan 1988).
Analysis of these studies shows that only two focus directly on the topic of interest in the current research: how firms relate to their markets (George and Barksdale 1974; Hooley and Cowell 1985), and only George and Barksdale (1974) show clear differences in marketing practice. In contrast, Hooley and Cowell (1985) provide some support for the notion that the goods/services boundary is blurred. Similar results are found in Parasuraman and Varadarajan's (1988) study on future strategic orientation. Given the relative age of these studies, however, it becomes difficult to assess the nature of the goods/services distinction because of the recent and significant changes in the marketing environment. A further limitation is that though George and Barksdale (1974) compare consumer goods and services firms, they do not include firms that serve business markets, and the remaining studies make no distinction on the basis of the type of market served.
Similar to the goods/services literature, early understanding of how firms relate to different markets was established by persuasive theoretical works (Lilien 1987; Webster 1978). These essentially argue that business markets are different from consumer markets in terms of their characteristics and influences, decision processes, and buyer-seller relationships. As such, Webster (1978) argues that the complexity of business markets calls for new approaches, and Lilien (1987) states that any similarities that might be identified between business and consumer markets are superficial.
Even fewer comparative studies (nine) can be identified in the consumer/business marketing context. Of these, five pertain to general marketing activities, as opposed to (for example) profiles of product managers. Parasuraman, Berry, and Zeithaml (1983) and Avlonitis and Gounaris (1997) find differences in the market orientation of consumer and business firms, whereas Turley and Kelley (1997) report no significant differences between consumer and business services advertising. More important, however, only Zeithaml, Parasuraman, and Berry (1985) and Andrus and Norvell (1999) directly consider how firms relate to their markets in terms of their various strategies/approaches, and analysis of these findings suggests that marketing practices are more similar than different.
The general patterns therefore lend some support to Fern and Brown's (1984) concern that the distinction between consumer and business marketing is unjustified. Given that most of the empirical comparisons are also dated (similar to the goods/services literature), the extent to which identified differences between consumer and business marketing practices are a historical artifact rather than current reality might be questioned. Again, this is difficult to assess given the lack of recent empirical data on how firms relate to these two types of markets. Finally, it is also worth noting that the prevalent approach is to compare pairs of firm types such as consumer versus business services (Parasuraman, Berry, and Zeithaml 1983; Zeithaml, Parasuraman, and Berry 1985) or consumer versus business goods (Avlonitis and Gounaris 1997). As in the goods/services literature, no studies can be identified that offer a comparison across consumer goods, consumer services, business goods, and business services firms.
The bodies of literature comparing marketing issues in different customer and product contexts follow similar evolutionary patterns and have developed from strong conceptual rather than empirical arguments. The extant research is dated and shows mixed results. Furthermore, interest in understanding the similarities and differences in marketing across contexts seems to have declined. This is perhaps explained by Brown, Fisk, and Bitner's (1994) conclusion that differences between goods and services appear to be assumed since the mid-1980s. Similar arguments might also be made for the literature that compares consumer and business marketing.
The current study suggests that it is important to compare the contemporary marketing practices of firms that serve different markets with different products for three reasons: First, given the limited amount of research and when it was conducted, there is an identified need for a more current examination of the topic. This is reinforced by Day and Montgomery's (1999) call for further understanding of the issue, one they consider fundamental to the marketing field. Second, there is a lack of research examining contemporary marketing practices in a manner that is both comparative across multiple firm types and based on a common analytical framework. However, this type of research is important given the increasing discussion around the blurring of boundaries between areas that have historically been examined independently (Balasubramanian and Kumar 1990; Bitner, Brown, and Meuter 2000). Extant research also tends to focus on single-country studies, and therefore a broader, more international perspective could add insight to the understanding of contemporary marketing practices.
Third, all the extant studies rely on a simple interpretation of the traditional marketing-mix model as their basis for investigation, and no empirical research comparing transactional and relational marketing practices has been identified. Although this emphasis on the transactional view of marketing is understandable given the time frame of the studies noted previously, we question the relevance of the marketing mix as the sole conceptual and analytical framework, given that major changes are occurring in the marketing environment. Markets are more technologically sophisticated, competition is more intense, and buyers are more demanding. If Day and Montgomery (1999) are accurate in their observation that there has been a shift from an emphasis on discrete transactions to relationships, the examination of marketing practices should go beyond the simple marketing-mix model to incorporate a more relational view of marketing. That is, both transactional and relational approaches need to be measured for an understanding of their relative emphasis in contemporary marketing practice. Thus, the two research questions guiding this study are
1. How do different types of firms relate to their markets in
terms of their contemporary marketing practices?
2. What is the relative emphasis of these firms on transactional
and relational aspects of marketing? Sample and Data Collection Method
A self-administered structured questionnaire was developed to collect quantitative data pertaining to the various aspects of marketing practice and both respondent and organizational demographics. The sample consists of 308 firms comprising five groups of managers in the United States (n = 76), Canada (n = 58), Finland (n = 22), Sweden (n = 20), and New Zealand (n = 132). These countries were chosen on the basis of theoretical reasoning, in that all five can be classified as Type 1 firms according to Hofstede's (1980) three cultural dimensions that are theoretically linked to relation-ship development: individualism, power distance, and uncertainty avoidance (Griffith, Hu, and Ryans 2000). All five countries are characterized as individualistic, with small power distance and weak uncertainty avoidance index levels (Hofstede 1980).
Across the five countries, an average of 66% of firms served other businesses as their primary market and 34% served consumers, whereas 61% characterized themselves as predominantly service organizations and 39% emphasized goods. Four age categories were used: less than 5 years(13%), 6-10 years (17%), 11-30 years (27%), and more than 30 years (43%).An average of 14% of firms reported no change or a decrease in sales growth over three years, 36% reported 1%-10% growth, and 50% had greater than 10% growth. Finally, 27% of the sample had fewer than 20 employees, 23% had 21-100 employees, 27% had 101-500 employees, and 23% had more than 500 employees. The firms were considered representative of the business population in each center.
A comparison across the five countries shows the firms to be similar in terms of customers served, age of firm, and sales growth rate, though the U.S. sample included more service firms and a greater proportion of large firms. These differences are to be expected, given the advanced U.S. economy and larger population base. Slight differences also emerged across all five countries with regard to firm export level and use of technology; however, these were controlled for in all subsequent analyses. Comparative analysis of the main constructs under investigation revealed no significant differences across the five countries in terms of the key variables of interest in this study: ( 1) the construct means used to reflect aspects of marketing practice and ( 2) the results of a cluster analysis. Therefore, data from the five countries were pooled for aggregate examination.
The study involved convenience samples of managers in each country who were participating in part-time MBA programs taught by members of the research team. Though nonrandom in nature, the use of working MBAs in cross-national research is common (see Neelankavil, Mathur, and Zhang 2000), and the approach is both practical and, to a reasonable degree, controllable. The majority of respondents had worked in their present organization (81%) and in their current position (68%) for at least one year. Most of the managers (85%) were between 26 and 45 years of age and had some tertiary training (80%). All respondents were involved with marketing activities in their organization, as defined by their position/role, nature of customer contact, and decisionmaking responsibilities. Positions included those traditionally considered to be marketing related (e.g., account manager, sales and marketing executive) as well as those that are more general in nature but still involved with market planning (e.g., business manager).Assuch, the respondents were informed about the marketing activities of their organization and provided an appropriate match with the topic of interest (Lynch 1999; Neelankavil, Mathur, and Zhang 2000).
The survey was in English, and all respondents were either native or proficient speakers in it. Participants received the survey in the first week of a mandatory course in marketing management and were asked to complete it as a take-home project. The timing of data collection minimized the bias created by classroom exposure to marketing theory and/or the professor's personal views on the topic. Participants were advised to use a variety of sources within the firm to gather the information needed to complete the survey. Thus, although a single manager was asked to report on each organization, respondents not only were reporting on the day-to-day activities that they were familiar with but also were encouraged to include the perspectives of other colleagues. Because completion of the instrument was a required project early in the term, the approach resulted in a high level of response, and considerable care was given by the participant informants. Although there is some potential for demand artifacts associated with this approach, the investigators were careful to brief the participants fully and advise them that the data generated by the research were part of an international study on marketing practices. Further discussion with participants indicated they understood that considered response was necessary.
Firms were classified as serving consumer or business customers on the basis of the managers' responses to a question regarding their primary (most important) customer base. This choice then guided the managers' responses for questions pertaining to marketing practices. Firms were also classified as either predominantly goods or services organizations on the basis of a question that asked respondents to identify the nature of their firm's product offer. Although respondents were able to tick multiple categories, only a few (16 firms, or 5% of the sample) indicated both goods and services. These files were inspected and, when appropriate, were classified as either goods or services. If such a decision could not be made, the file was deleted.
We also defined the variables of firm size, age, and export level to allow for the potential influence of other covariates on marketing practice, as noted in the empirical comparisons of consumer and business firms (Andrus and Norvell 1990; Parasuraman, Berry, and Zeithaml 1983) and goods versus services firms (Parasuraman and Varadarajan 1988). We also included a variable measuring the firm's use of technology, which could influence a firm's practices, on the basis of Coviello, Brodie, and Munro's (1997) argument that database marketing uses technology to get closer to the customer. Finally, it is of interest to identify the impact of marketing practices on firm performance. Therefore, we employed the commonly used measure of sales growth, because this is generally accepted as a reflection of the firm's effectiveness in the market (Walker and Ruekert 1987) and is considered relatively easy information for managers to provide. In contrast, collecting profit or market share data is generally regarded as difficult because of managers' reluctance or inability to provide objective financial data, and as the sample includes a large number of smaller and/or privately held firms, public financial data were likely to be unavailable. On the basis of pretest results and respondent preference, we measured sales growth categorically using average annual growth over the previous three years.
To profile marketing practices, we used the conceptual framework outlined in Table 1 to derive specific measures of marketing practice and thus examine the level/extent of transactional and relational marketing practices in different contexts. This framework allows for the development of a measurement model that identifies four distinct aspects or constructs of marketing practice: transaction marketing (TM), database marketing (DM), interaction marketing (IM), and network marketing (NM).
Development of the model began with the pool of measurement items reflecting the nine dimensions operationalized by Coviello, Brodie, and Munro (2000). Questions using five-point Likert-type scales anchored by "never" ( 1) and "always" ( 5) were used to measure each item (see the Appendix). On the survey instrument, each of the nine dimensions consisted of a set of variables reflecting each of the four marketing practice constructs. Using Item 1 from the Appendix as an example, the first item measures the extent to which the purpose of exchange was to generate an economic return in the form of profit or other financial measures of performance (TM), and the remaining items pertain to each of the three relational aspects of marketing (DM, IM, and NM). Thus, Item 1 measures the extent to which each aspect of marketing is practiced, in the context of one dimension (purpose of exchange).
Each item was evaluated by ten marketing practitioners and five marketing academics, and the questionnaire was pretested with a set of executive students similar to those ultimately targeted to participate in the research. The results of the pretest suggested that the questionnaire was under-standable, was interpreted appropriately, and captured the aspects of marketing practice defined by the conceptual framework. It was therefore concluded that the instrument had adequate content and face validity. Construct validity of the instrument is justified because the measures were developed from a theoretical framework that was derived from an extensive literature review. Convergent and discriminant validity of the four constructs were tested by means of confirmatory factor analysis in a measurement model that combined all four constructs simultaneously. In addition, confirmatory factor analysis was used to further refine the measures so that the final number of items reasonably reflects each construct (Anderson and Gerbing 1988). Starting with the nine items for each construct summarized in the Appendix, LISREL 8.3 (Jöreskog and Sörbom 1993) was used to perform the confirmatory factor analysis. Construct refinement was achieved by an examination of the covariance matrix residuals and modification indices supplied by LISREL and elimination of items until the goodness-of-fit criteria were attained.
Table 2 gives the results of the final model after refinement and item deletion. Although the chi-squared statistic is significant, this is likely due to the large sample size of 308, when the chi-squared statistic is known to be oversensitive (Hair et al. 1998). Indeed, Hair and colleagues (1998) suggest that a better criterion is the ratio of the chi-squared statistic to the degrees of freedom (d.f.), which should be less than 2. In this case the ratio is 1.66, well within recommended tolerance. Other measures of goodness of fit are goodness-of-fit index = .91 and adjusted goodness-of-fit index = .89, which are above or close to the .9 threshold (Hair et al. 1998); a comparative fit index of .91, above the .9 threshold; and a root mean square error of approximation value of .046, well within the recommended .1 upper limit (Browne and Cudek 1993). Therefore, the final measurement model is a good fit to the data.
Table 2 also reports the model's estimated item coefficients and the tests for discriminant validity. Anderson and Gerbing (1988) note that convergent validity is demonstrated by statistically significant path coefficients. In this study, all coefficients are significant at the p < .001 level or lower, apart from the managerial level path for the interaction construct, which is significant at the p < .10 level. Thus, convergent validity is evident. Following Anderson and Gerbing (1988), we also assessed discriminant validity by constraining the correlation between each pair of constructs to be 1. This gives a new chi-squared value for the model. The difference between the chi-squared value for the original model and the constrained model also has a chi-squared distribution, with one degree of freedom. If these differences exceed 3.84 (5% critical value for the chi-squared distribution with 1 d.f.) for each pair of constructs tested, then discriminant validity is established. Table 2 shows that each chi-squared difference is well above 3.84 for the six possible construct pairings, demonstrating discriminant validity for the final model.
For each construct, the final model used either five or six items. The eliminated items are noted in the Appendix and are as follows: First, Item 3 (type of contact) was eliminated from all four constructs. The lack of distinction for this item probably occurs because most firms use multiple forms of contact with their customers, regardless of the approach to marketing practice they emphasize. Second, Item 4 (duration of exchange) was eliminated from all four constructs. Here, respondents may have thought it unlikely that there would be no future contact with customers, even in a transactional environment, and ongoing contact is possible no matter what type of marketing is practiced. Third, Item 5 (formality of exchange) was also deleted from all four constructs. This likely reflects the similarity of the DM, IM, and NM questions, all of which highlighted social contact. Item 7 (managerial focus) was eliminated from the NM construct, as it is likely that firms believe that they focus on both their product offer and customers in their planning processes. Last, Item 9 (managerial level) was eliminated from the TM construct, possibly because the variation in firm size meant that firms did not all have specialized positions for functional managers.
We then used the remaining items to form four constructs: TM, DM, IM, and NM. We accomplished this by averaging the scores across the relevant five or six items and dividing by five (a five-point scale was used). Thus, the construct scores ranged between zero and one. As shown in Table 2, the Cronbach's alpha levels of the four constructs were .62 (TM), .62 (DM), .71 (IM), and .77 (NM). These levels are somewhat low but are generally considered acceptable (Nunnally 1978) and are within the norms of alphas appearing in published studies in a meta-analysis reported by Voss, Stem, and Fotopoulos (2000).
Table 2 also gives the correlations among the four constructs. Notice that the correlation between TM and both IM and NM is low and negative but the one between TM and DM is moderate. This would be expected if higher levels of TM are balanced by lower levels of IM and NM, whereas DM is perhaps an extension of TM. Also, DM has a low correlation with IM and NM, which suggests that it is an element in the relational approach. Finally, a moderate correlation is observed between IM and NM. This reflects the arguments of the Industrial Marketing and Purchasing group (Ford, Håkansson, and Johanson 1986), in which the dyadic relationship established in IM is considered a micro level of NM.
We compared the TM, DM, IM, and NM construct scores across firm type to identify significant differences in how firms relate to their markets. Following this, we used cluster analysis on the construct scores to identify whether we could generate meaningful groups of firms on the basis of their marketing practices, thus reconfiguring the way firms are categorized, instead of using conventional categories such as product offered or market served. This approach also helps answer the question whether contemporary practices emphasize relational marketing.
How Do Different Types of Firms Relate to Their Markets?
The extent to which each of the four conceptualized aspects of marketing are found, by firm type, is presented in Table 3. This table gives the results of the multivariate analysis of variance (MANOVA) used to compare the construct means for TM, DM, IM, and NM across customer type, product type, and the four customer/product combinations. In addition to these factors, the covariates of firm size, age, sales growth rate, export level, use of technology, and country were included in the MANOVA, and only the covariates of firm size and use of technology were significant. That is, larger firms tended to have higher levels of TM, whereas firms reporting a high use of technology had higher levels of IM and NM.
If TM is first considered, Table 3 shows that consumer firms are more transactional than business-to-business firms (p < .001), as are goods firms compared with service firms (p < .001). When the two primary variables are combined to form consumer goods, business-to-business goods, and so forth, the results show a significant difference only for consumer goods firms. This type of organization is more transactional than the other three customer/product combinations (p < .001). Furthermore, the average construct values (for which the maximum value is 1.0) show that consumer goods firms practice the highest level of TM (.87), followed by significantly lower levels for consumer services (.78), business-to-business goods (.77), and business-to-business services (.74). There are no significant differences among the means of the last three combinations.
When practices are examined in terms of the three theoretically defined aspects of relational marketing (DM, IM, and NM), significant differences are found only for DM--and then only for type of market served. That is, firms serving consumer markets are more likely to practice DM than are those serving business markets. When the two primary variables are combined, consumer goods firms practice a higher level of DM than do business-to-business goods or services firms (p < .001). No significant differences are found for either IM or NM across customer or product type or by customer/product combination. A further analysis of Table 3 shows that overall, the mean levels of practice for TM and IM are higher (.79 and .75) than those for DM and NM (.68 and .64). Similarly, more than one-half of all firms practice high levels of TM and IM, and one-third practice a high level of DM and NM.
What Is the Relative Emphasis on Transactional and Relational Aspects of Marketing?
We examined the relative emphasis of transactional versus relational aspects of marketing by assessing the level of practice of each aspect of marketing by firm type. As a basis for comparison, Grönroos's (1991) continuum predicts that consumer goods firms will emphasize transactional marketing and service firms will engage in relational marketing. So as not to artificially categorize a firm as a transaction, database, interaction, or network marketer, however, we conducted a cluster analysis using the TM, DM, IM, and NM construct scores to group the firms in terms of their marketing practice profiles. We then examined each of these clusters to determine how they relate to the four customer/product combinations: consumer goods, consumer services, business goods, and business services. This allows identification of the relative emphasis on transactional and relational marketing across firm type.
We formed the clusters using k-means cluster analysis. The number of clusters varied between one and six, and a three-cluster solution finally resulted based on the average within-cluster difference criterion (Hair et al. 1998). Table 4 shows the average values for the TM, DM, IM, and NM constructs across the three clusters and overall. When the cluster means are compared with those of the entire sample, the first cluster has an above-average score for TM (as shown in boldface italics) but is well below average on the DM, IM, and NM constructs. It is therefore a cluster largely composed of firms that practice TM, and this accounts for 33% of all firms in the sample. The second cluster has above-average construct scores for all four constructs. This might be termed a transactional/relational cluster, because these firms practice high levels of all four marketing approaches. This cluster comprises 35% of the sample. The third and final cluster is above average on IM and NM and well below average on TM and DM. Therefore, this is called a relational cluster, and it represents 32% of the sample. Overall, there are three clearly defined and relatively equally sized clusters.
Table 4 provides further insight on the composition of the clusters, and the three groups are significantly different (p = .03). At one extreme, there is a reasonably equal and substantial number of all four types of firms in the transactional cluster (ranging from 27% to 41%). However, the largest proportions reflect firms that serve consumer rather than business markets. At the other extreme, the relational cluster presents a clear continuum effect whereby only 11% of consumer goods firms emphasize relational aspects of marketing, compared with 41% of business-to-business service firms. Regarding the hybrid transactional/relational cluster, no continuum or similar pattern appears.
If Table 4 is analyzed by firm type, several additional patterns also appear. First, a large number of consumer goods firms are in the transactional/relational cluster (50%). This perhaps reflects the previous evidence indicating their use of DM. Second, although 29% of consumer service firms are in the relational cluster, there is a surprisingly large proportion in the transactional cluster (41%). Third, the business-to-business goods firms are balanced across all three clusters. Fourth, the business-to-business service firms exhibit a classic continuum pattern, from transactional to relational. Although these results imply that business-to-business service firms are more relational, it is also worth noting that more than one-quarter of these firms emphasize a transactional approach, and again, the relative proportions of firms practicing transactional marketing are large across firm type. Of further interest is that business-to-business goods and services firms are comparable to their consumer counterparts with regard to the hybrid cluster results, and all four firm types are represented in each cluster.
Given the results, we then identify how the three clusters can be differentiated on the basis of other firm characteristics. Chi-squared and regression analysis reveal that no statistically significant differences can be attributed to country, firm age, export level, use of technology, or sales growth rate. However, firm size differs by cluster (p = .03), and large firms (more than 1000 employees) are more likely to be transactional. This is expected, given that large firms are likely to serve large markets, which thus makes relational activities more difficult and costly to implement. Of particular interest is the potential impact of various marketing practices on sales growth as a measure of marketing effectiveness. Although the aggregate analysis did not reveal any differences by cluster, this is perhaps to be expected given the broad range of firms in each one and the relatively broad growth categories. However, consumer service firms in the transactional cluster (p = .09) and business-to-business goods firms in either the transactional or the relational cluster (p = .03) achieved higher sales growth (>10% per annum over three years).
This research offers a comparative examination of contemporary marketing practices that is integrative rather than paradigm specific. An important contribution of the research is its organization and empirical examination of four distinct aspects of marketing practice. In particular, the relatively broad concept of relationship marketing is redefined to reflect three separate constructs: database, interaction, and network marketing. By examining these practices, the study assesses the relative emphasis placed on transactional and relational activities and thus provides insight on the extent to which firms employ relationship marketing. Furthermore, it does so in a manner that encompasses different types of firms, whereas the extant literature tends to examine them independently. The international nature of the study also complements the existing U.S.-dominated literature and shows that the marketing practices in certain Western nations are comparable. As in other studies (Homburg, Workman, and Jensen 2000), because the conclusions are based on a range of firm types and multiple countries, the results are not expected to be restricted to specific sectors or nations. Finally, the findings of the study provide a strong empirical foundation for a meaningful investigation of the antecedents and consequences of marketing practice.
The first research question was how different types of firms relate to their markets. The comparison of construct means shows that at a general level, consumer and goods firms relate to their markets in a more transactional manner than business-to-business and service firms do. These findings appear to support the early theoretical arguments that business-to-business and service marketing are different from consumer and goods marketing. Also, the cluster analysis suggests that in the context of relational marketing, Grönroos's (1991) continuum argument is supported. At the same time, although the practice of database marketing appears to vary by type of customer served, other results show that the practice of interaction and network marketing does not differ by firm type. Furthermore, there are no significant differences between goods and services firms for any aspect of relational marketing. This suggests that the continuum effect for the relational cluster may be attributable to market type rather than product offer (confirmed by chi-squared analysis, p = .04). Also, similarities between the practices of goods and services firms can perhaps be explained by Bitner, Brown, and Meuter's (2000) argument that all firms are increasingly positioned as "'solutions organizations." As such, they apply aspects of relational marketing regardless of the nature of the product offer. The current results lend some support for this view, in that high levels of database, interaction, and network marketing are found across firm type and nearly one-third of all firms emphasize a relational approach. As such, the results also offer support for recent assertions that nurturing relationships is a priority for most organizations (e.g., Day 2000). Although this does not necessarily imply that relationships are a top priority for most firms (as stated by Day [2000]), the active implementation of various aspects of relational marketing suggests that relationship development is of interest to many firms.
This is not to suggest, however, that transaction marketing is not practiced by the firms in this study. For example, the cluster results do not provide clear evidence of firms emphasizing relational exchange rather than discrete transactions. Rather, they show that though relational aspects of marketing are implemented by all types of firms, a transactional approach is still very much in evidence, even for nonconsumer goods firms. We also note that more than one-third of the firms in this study have adopted a pluralistic hybrid approach in relating to their markets. Again, this lends support to the solutions organization argument, in that regardless of type, some firms seek a balance between the transactional and relational approaches.
Overall, that there are three identifiable and substantial clusters suggests that within a customer/product type, firms choose to compete in different ways. For example, some business-to-business service firms emphasize relational marketing, others take a hybrid transactional/relational approach, and still others employ a predominantly transactional approach. Although not examined in this study, the third pattern may reflect a conscious decision that relational marketing is inappropriate to a certain competitive or market environment. The second conclusion therefore is that while firms actively practice relational marketing, many firms also rely on transactional marketing. This suggests that though Day and Montgomery (1999) observe that the marketing field has shifted its emphasis from transactional to relational exchange, managerial practice has adopted a more integrative and pluralistic approach, and it appears that certain aspects of marketing are not changing as much as some commentators have indicated. This supports Homburg, Workman, and Jensen's (2000) findings that a traditional approach to marketing remains in most firms.
Two final results are of interest: First, contemporary marketing practices are dominated by the practice of transaction and interaction marketing, and database and network marketing are implemented to a lesser degree. This is an interesting finding given the attention paid to the potential of database technologies (Blattberg and Deighton 1991) and the argument that a network economy is emerging, necessitating a variety of changing roles for marketing (Achrol and Kotler 1999). At the same time, the implementation of database and network marketing may still be at an early stage of market diffusion. Second, it is somewhat surprising that the majority of consumer service firms are classified as transactional in the cluster analysis (41%). This suggests that in spite of their interest in relationships (as is widely described in the literature), the actions of customer service firms (as perceived by their managers) may speak louder than words. This is perhaps explained by the preliminary performance findings that indicate that a transaction marketing focus results in higher sales growth for customer service firms.
Managerial Implications
All four conceptualized aspects of marketing are found in practice, and each of these aspects of marketing can be clearly distinguished from the others. It therefore becomes important for managerial thinking to extend beyond the traditional marketing-mixmodel or a purely relational perspective and encompass multiple aspects of marketing practice. Managers should also recognize that firms compete using transactional marketing, relational marketing, or a hybrid approach. This suggests that managers need an appreciation of the potential role of each aspect of marketing(transaction, database, interaction, and network marketing).
More specifically, it can be argued that transaction marketing is relevant regardless of product or customer type. By recognizing the fundamental role of product, price, promotion, and distribution and learning how to manage these areas competently, all organizations can develop a strong base on which to better develop customer relationships. Given that a relatively high level of interaction marketing is also evidenced in all four types of firms, managers could benefit from understanding, establishing, and facilitating ongoing, individual, and interpersonal relationships with key contacts in their market, regardless of the type of customer served or product offered. Similarly, all firms could investigate the potential relevance of database and network marketing to their organization, particularly in terms of "getting closer" to customers or positioning the organization in a viable network as a defensive measure in increasingly competitive markets. As a cautionary note, the results regarding transaction marketing offer support for Day's (2000, p. 24) observation that "investing in or building close relationships is neither appropriate nor necessary for every market, customer, or company." Overall, however, although not all firms will have the need or capability to apply relational marketing and many will choose to emphasize a transactional approach, it can still be argued that managers will benefit from under-standing the resource requirements and systems underlying database, interaction, and network marketing, given that competitors' activities may involve these aspects of marketing. Understanding the nuances of each aspect of marketing enables managers to make a considered strategic choice regarding their development and implementation.
Limitations and Future Research Directions
As does any research, this investigation has certain limitations that must be considered. First, although the study develops several new measures, and this is in itself a contribution, the reliability levels found for transaction and database marketing suggest that items constituting the scale have some diversity and are not highly correlated. Also, although the correlations between some measures are theoretically explainable (e.g., TM and DM, IM and NM), they are moderate and suggest some overlap of the four constructs. Second, firm classification in terms of the product offer was driven by how respondents defined their organization. As noted previously, the majority of managers (95%) define their firms in a traditional manner, choosing either goods or services. A similar pattern emerged when we identified the nature of the market served, and in the end, using the traditional classification variables follows the general pattern of the literature. More important, it allowed for a useful comparison across four customer/product combinations. Further research might benefit from more subtle firm classifications, such as those suggested by Bitner, Brown, and Meuter (2000).
Third, respondents were asked to focus their answers on activities related to their primary customers. Although a review of the questionnaires indicates that respondents differentiated between end users and intermediaries, managers who defined their firms as serving consumer markets may have responded in the context of practices to channel members. Fourth, although the five country samples are reasonably representative of their markets, they were convenience samples, and further research could involve national random sampling. This would overcome any demand artifacts associated with the use of part-time executive students and allow for a more even distribution of sample size across countries. Finally, performance was captured by a categorical measure of sales growth used to reflect the effectiveness of marketing practices. Although this is appropriate given pretest results, actual sales growth levels as well as profitability and market share measures are also of interest. Ideally, further research would include both objective data and subjective measures of how the firm has performed compared with primary competitors or managerial expectations. In addition, it would be useful to undertake in-depth research in different sectors for a better understanding of the specific factors that influence performance.
Additional areas of investigation are varied. For example, extending this study beyond the current five-country focus to include comparative research across nations with cultural differences could be fruitful. Also, because marketing practice is likely to evolve with technological developments and be tempered by industry conditions, replication of this study would help capture evolutionary patterns of marketing practice (e.g., the practice of database and network marketing). Further research could also examine when and why the various aspects of transactional and relational marketing are more or less used. Ideally, such research would also identify the requisites for the effective practice of all four aspects of marketing and relate their implementation to a broader set of measures of the firm's performance over time. It would also be useful to understand if firms in the hybrid transactional/relational cluster have strategically positioned themselves as such or if they are (for example) inherently transactional but dabbling with relational activities. Linking this information with performance would allow for a better understanding of the risks and payoffs associated with various strategic decisions.
Finally, although this study shows that firms emphasize a transactional, relational, or hybrid approach to marketing practice, it does not identify why such approaches are implemented. One possibility is related to competitive behavior in the market. Therefore, it would be relevant to identify the firm's perceptions of competitor behavior and then examine how the firm's practices reflect and/or deviate from those of the competition. Another possibility is related to customer requirements, and further research could investigate how marketing practices reflect perceived customer need structures and customers' preference for transactional and/or relational exchange. Investigation of these issues could also shed light on why so many consumer service firms emphasize a transactional approach when they might be expected to be more relational.
Four Aspects of Marketing Classified by Exchange and Managerial Dimensions
Legend for Chart:
A -
B - Transactional Perspective, Transaction Marketing
C - Relational Perspective, Database Marketing
D - Relational Perspective, Interaction Marketing
E - Relational Perspective, Network Marketing
A
B
C
D
E
Purpose of
exchange
Economic transaction
Information and economic
transaction
Interactive relationships between a
buyer and seller
Connected relationships between
firms
Nature of
communication
Firm to mass market
Firm to targeted segment or
individuals
Individuals with individuals (across
organizations)
Firms with firms (involving
individuals)
Type of contact
Arm's-length, impersonal
Personalized (yet distant)
Face-to-face, interpersonal (close;
based on commitment, trust, and
cooperation)
Impersonal to interpersonal
(ranging from distant to close)
Duration of
exchange
Discrete (yet perhaps over
time)
Discrete and over time
Continuous (ongoing and mutually
adaptive, may be short or long term)
Continuous (stable yet dynamic,
may be short or long term)
Formality in
exchange
Formal
Formal (yet personalized
through technology)
Formal and informal (i.e., at both a
business and social level)
Formal and informal (i.e., at both a
business and a social level)
Managerial intent
Customer attraction(to
satisfy the customer at a
profit)
Customer retention (to satisfy
the customer, increase profit,
and attain other objectives
such as increased loyalty,
decreased customer risk, and
so forth)
Interaction (to establish, develop, and
facilitate a cooperative relationship for
mutual benefit)
Coordination (interaction among
sellers, buyers, and other parties
across multiple firms for mutual
benefit, resource exchange,
market access, and so forth)
Managerial focus
Product or brand
Product/brand and customers
(in a targeted market)
Relationships between individuals
Connected relationships between
firms (in a network)
Managerial
investment
Internal marketing assets
(focusing on
product/service, price,
distribution, promotion
capabilities)
Internal marketing assets
(emphasizing communication,
information, and technology
capabilities)
External market assets (focusing on
establishing and developing a
relationship with another individual)
External market assets (focusing
on developing the firm's position in
a network of firms)
Managerial level
Functional marketers (e.g.,
sales manager, product
development manager)
Specialist marketers (e.g.,
customer service manager,
loyalty manager)
Managers from across functions and
levels in the firm
General manager
Notes: Adapted from Coviello, Brodie, and Munro (1997, 2000).
Results from Confirmatory Factor Analysis: Coefficients for the Construct Paths, Test of Discriminant Validity, and Correlation Among Constructs
Transaction Database Interaction Network
Marketing Marketing Marketing Marketing
Convergent Validity
Purpose of exchange .27 (4.06) .52 (8.39) .60 (11.38) .78 (12.65)
Nature of communi- .46 (5.34) .38 (6.52) .33 (4.67) .73 (11.30)
cation
Managerial intent .35 (5.31) .40 (7.38) .61 (9.43) .64 (9.48)
Managerial focus .40 (7.18) .38 (6.58) .61 (9.43) .91 (15.38)
Managerial investment .73 (9.53) .47 (7.16) .70 (11.65) .37 (4.53)
Managerial level -- .45 (5.40) .14 (1.64)
Cronbach's alpha .62 .62 .71 .77
Discriminant Validity[a]
TM -- 37.7 52.2 64.4
DM -- -- 9.3 48.5
IM -- -- -- 80.0
Correlation Matrix
TM 1.0 .51* -.15* -.10
DM 1.0 .29* .28*
IM 1.0 .55*
NM 1.0[a]Discriminant validity is tested by constraining the correlation to be 1 between each pair of constructs. The table shows the change in chi-squared values when this constraint is applied. A change exceeding 3.84 (5% critical value) across all pairs of constructs demonstrates discriminant validity.
*Significant at the p < .05 level.
Notes: t-statistics are in parentheses.
Comparing Levels of Different Marketing Practices: Consumers/Business, Good/Services, and Customer/Product Combinations
Legend for Chart:
A - Aspect of Marketing Practice
B - Comparing Construct Means
C - F-Value[a]
D - Consumer Goods Mean
E - Consumer Services Mean
F - Business-to-Business Goods Mean
G - Overall Construct Mean
H - Business-to-Business Services Mean
I - Firms with High Levels (%)[b]
A B
C D E
F G H
I
TM Consumer = .82 Business-to-business = .75
Goods = .82 Services = .76
19.3* .87[c] .78
12.24*
.77 .74 .79
57
DM Consumer = .71 Business-to-business = .66
Goods = .70 Services = .67
11.98* .73 .69
2.54
.66 .66 .68
34
IM Consumer = .74 Business-to-business = .75
Goods = .74 Services = .76
.15 .73 .75
1.16
.74 .76 .75
52
NM Consumer = .64 Business-to-business = .65
Goods = .64 Services = .65
.66 .64 .63
.48
.64 .67 .64
33[a]F-values are from MANOVA. The means reported in this table have taken all the covariates into account.
[b]A high level of practice reflects a mean score of .80 or higher for each construct.
[c]One-way analyses of variance show a difference in means for the TM and DM constructs. Post hoc t-tests reveal significant differences between TM for consumer goods and all three other customer/product combinations (p < .001 ). In addition, significant differences between DM for consumer goods and both business-to-business combinations were apparent. No other means were significantly different.
*p < .001.
Cluster Results: Levels of Marketing Practice and Profile of Firm Types by Cluster
Legend for Chart:
A - Cluster[a]
B - Transaction Marketing Score[b]
C - Database Marketing Score
D - Interaction Marketing Score
E - Network Marketing Score
F - Consumer Goods(%)
G - Consumer Services(%)
H - Business-to-Business Goods(%)
I - Business-to-Business Service(%)
A
B C D E
F G H I
Transactional (n = 103)
.81 .63 .63 .48
38.9 40.6 34.9 26.7
Transactional/relational (n = 107)
.85 .78 .82 .75
50.0 30.4 34.9 32.5
Relational (n = 98)
.65 .60 .79 .71
11.1 29.0 30.1 40.8
Average index score (all 308 firms)
.79 .68 .75 .64
100.0 100.0 100.0 100.0
[a]x( 2) = 14.2, 6 d.f. (p = .03).
[b]Scores range between zero and one. Scores greater than .80 reflect higher levels of marketing practice; scores between .61 and .80 reflect medium levels of marketing practice; scores less than 61 reflect lower levels of marketing practice.
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1. Purpose of Exchange: When dealing with our market, our focus is on
Generating a profit or other "financial" measure(s) of performance. [TM]
Acquiring customer information. [DM]
Building a long-term relationship with a specific customer(s). [IM]
Forming strong relationships with a number of organizations in our market(s) or wider marketing system. [NM]
2. Nature of Communication: Our marketing communication involves
Our organization communicating to the mass market. [TM]
Our organization targeting a specifically identified segment(s) or customer(s). [DM]
Individuals at various levels in our organization personally interacting with their individual customers. [IM]
Senior managers networking with other managers from organizations in our market(s) or wider marketing system. [NM]
3. Type of Contact: Our organization's contact with our primary customers is
Impersonal (e.g., no individualized or personal contact). [TM]*
Somewhat personalized (e.g., by direct mail). [DM]* Interpersonal (e.g., involving one-to-one interaction between people). [IM/NM]*
4. Duration of Exchange: When a customer buys our products, we believe they expect
No future personalized contact with us. [TM]*
Some future personalized contact with us. [DM]*
One-to-one personal contact with us. [IM]*
Ongoing one-to-one personal contact with people in our organization and wider marketing system. [NM]*
5. Formality of Exchange: When people from our organization meet with our primary customers, it is
Mainly at a formal, business level. [TM]*
Mainly at an informal, social level. [DM]*
At both a formal, business and informal, social level. [IM/ NM]*
6. Managerial Intent: Our marketing activities are intended to
Attract new customers. [TM]
Retain existing customers. [DM]
Develop cooperative relationships with our customers. [IM]
Coordinate activities between ourselves, customers, and other parties in our wider marketing system. [NM]
7. Managerial Focus: Our marketing planning is focused on issues related to
Our product/service offering. [TM]
Customers in our market(s). [DM]
Specific customers in our market(s) or individuals in organizations we deal with. [IM]
The network of relationships between individuals and organizations in our wider marketing system. [NM]*
8. Managerial Investment: Our marketing resources (e.g., people, time, money) are invested in
Product, promotion, price, and distribution activities (or some combination of these). [TM]
Technology to improve communication with our customers. [DM]
Establishing and building personal relationships with individual customers. [IM]
Developing our organization's network relationships within our market(s) or wider marketing system. [NM]
9. Managerial Level: In our organization, marketing activities are carried out by
Functional marketers (e.g., marketing manager, sales manager, major account manager). [TM]*
Specialist marketers (e.g., customer service manager, loyalty manager). [DM]
Nonmarketers who have responsibility for marketing and other aspects of the business. [IM]
The managing director or chief executive officer. [NM]
*Indicates an item that was deleted from the final construct. Notes: The related construct (TM, DM, IM, or NM) is given in square brackets.
~~~~~~~~
By Nicole E. Coviello; Roderick J. Brodie; Peter J. Danaher and Wesley J. Johnston
Nicole E.Coviello is an associate professor, Faculty of Management, University of Calgar y. Roderick J. Brodie is a professor, and Peter J. Danaher is a professor, Department of Marketing, University of Auckland.Wesley J. Johnston is CBIM Round Table Professor of Marketing, J. Mack Robinson College of Business, Georgia State University. The authors thank Christian Grönroos, Hugh Munro, and Richard Brookes for their assistance in data collection and Celeste Anderton, Bruce Hardie, and Don Scott for technical support.This article has also benefited from the ideas and comments of Derek Hassay, Al Miciak, RalfWagner, Robin Wensley, David Wilson, and the three anonymous JM reviewers.
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Record: 75- How Is Manifest Branding Strategy Related to the Intangible Value of a Corporation? By: Rao, Vithala R.; Agarwal, Manoj K.; Dahlhoff, Denise. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p126-141. 16p. 1 Diagram, 8 Charts, 1 Graph. DOI: 10.1509/jmkg.68.4.126.42735.
- Database:
- Business Source Complete
How Is Manifest Branding Strategy Related to the
Intangible Value of a Corporation?
Firms exhibit or "manifest" three types of branding strategies: corporate branding, house of brands, or mixed branding. These strategies differ in their essential structure and in their potential costs and benefits to the firm. Prior research has failed to understand how these branding strategies are related to the intangible value of the firm. The authors investigate this relationship using five-year data for a sample of 113 U.S. firms. They find that corporate branding strategy is associated with higher values of Tobin's q, and mixed branding strategy is associated with lower values of Tobin's q, after controlling for the effects of several important and relevant factors. The relationships of the control variables are consistent with prior expectations. In addition, most of the firms would have been able to improve their Tobin's q had they adopted a branding strategy different from the one their brand portfolios revealed. The authors also discuss implications and future research directions.
Over the past decade, there has been significant interest among academics and practitioners in understanding the importance of brand equity. Highly competitive markets make powerful brands essential to accomplishing growth. According to Aaker (1991), firms create brand equity by delivering quality products and by creating strong brand associations through appropriate communication and advertising strategies.( n1) Brands have been widely acknowledged as having a financial value because they are able to generate future cash flows (Aaker and Jacobson 1994). The enhanced cash flows are based on, among other things, customer loyalty, high margins, brand extension and licensing opportunities, and increased marketing efficiency of strong brands (Keller 2002, p. 46). In recognition of the long-term financial contribution of brand equity, financial markets seem to consider brands in their stock valuations (Barth et al. 1998; Simon and Sullivan 1993). Extensive research has been conducted on the estimation of brands' financial value and measurement techniques, as well as on brand extensions (Haigh 1998; Keller and Aaker 1992; Murphy 1989; Reddy, Holak, and Bhat 1994). An intense discussion about the admission of brands in financial accounts is ongoing in the accounting community (Barth et al. 1998; Kallapur and Kwan 2004; Lev and Sougiannis 1996). However, there is no debate that brands are intangible assets of a firm (Lev 2001).
Firms can follow several branding strategies to manage their brands. In general, most firms begin with a single product and become multiproduct firms over time. In such cases, there is a brand name for the first product that most likely is related to the name of the corporation, which marketers refer to as corporate branding. As new products are added, the managers of the firm have the option to use the firm identification in the brand name and to continue the corporate branding strategy. If the initial brand name did not use the firm name and if the firm chooses different names for each new product (without the firm name), this is known as a "house-of-brands" strategy. However, if a firm acquires another firm (or a division of another firm), the products of the acquired firm will have brand names in place; in this situation, the branding strategy of the new entity is a mixed branding strategy. Mixed branding also occurs if a firm uses corporate names for some of its products and individual names for others. In general, the type of branding strategy can be inferred from examination of all the brand names of a firm's products; we refer to this as "manifest" branding strategy and only occasionally use that adjective herein. More important, the manifest strategy is a result not necessarily of deliberate brand decision making but of other decisions that the firm may have made.
Whenever a firm is about to launch a new product or acquire a firm, it needs to make a strategic decision as to which type of branding strategy (corporate, house of brands, or mixed) it should adopt to maximize its intangible value. The intangible value it creates will affect how the financial market will view the firm. Despite extensive research on branding in the marketing literature, there is no available guidance for firms' management because of a lack of systematic research on the financial effects of types of branding strategies. This article is an attempt to fill this gap in the literature. Our work addresses the issue of how a firm's products are branded and how branding strategy is related to the firm's intangible value.
The three branding strategies can be plotted on a continuum: At one end is corporate branding, which entails strategies that use solely the corporate name on products. At the other end is house of brands, which describes strategies that use individual brand names (with no corporate identification). The stock markets presumably value firms differently and impute different brand equity potential for each particular type of branding strategy. For example, corporate branding may be viewed as having higher equity because the firm can build and leverage its overall reputation, whereas a house-of-brands strategy, by definition, requires a firm to build the reputation of each of its individual brands.
In general, advertising expenditures affect the financial markets (Chauvin and Hirschey 1993; Cheng and Chen 1997). The advertising expenditures depend on the branding strategy that the firms follow; corporate branding usually requires fewer expenditures than the house-of-brands strategy. Although a change in advertising expenditures is related to a change in the stock price (Cheng and Chen 1997), is the impact of a change different for different branding strategies? This is an unanswered question.
Despite extensive research on branding in the marketing literature, examination of the relationship between branding strategies and firm value is nonexistent. This article is an attempt to fill this gap. For our research, we classify companies' manifest branding strategies and combine the data with financial data to investigate the relationship between manifest branding strategies and firm financial performance. Our overall objective is to assess the effectiveness of the three main branding strategies on the intangible value of a firm. We believe that our results may also provide guidance for firms in choosing their branding strategies, if they have the opportunity to do so, and in formulating their merger and acquisition decisions.
Against this background, our study seeks to answer two questions: ( 1) How are different manifest branding strategies related to the intangible value of a firm? and ( 2) Do advertising expenditures interact with the relationship between the intangible value of a firm and the type of its manifest branding strategy?
The remainder of this article is organized into five sections. The next section provides a review of the literature that pertains to branding strategies and the relevant financial and accounting research on the financial valuation of a firm. The subsequent section describes our model, which relates intangible assets to other descriptors of the firm, including branding strategy and our method of estimating the model. It also describes ways to measure the intangible value of a firm and other commonly used correlates of the intangible part of firm value. The next section describes the data collection method and provides a description of our data. We then describe and interpret our results, and we conclude by offering some directions for further research.
Branding Strategies
The literature contains several taxonomies for classifying branding strategies; the most important are those of Olins (1989), Murphy (1987, 1989), and Laforet and Saunders (1994). Olins uses a three-category scheme of corporate identities only, corporate name with a subsidiary name, and branded identities. Murphy suggests four categories of corporate-dominant, brand-dominant, balanced systems, and mixed systems. Finally, on the basis of a comprehensive content analysis of brands of major U.S. and European grocery products, Laforet and Saunders propose three categories of brands (all based on the use of the corporation's name in products' brand names). Their categories are as follows: ( 1) The name of the corporation or its subsidiary is prominent in the brand names of the products or services (e.g., FedEx), ( 2) the corporation's name is combined with another name (e.g., Kellogg's Corn Flakes), and ( 3) the corporation's name is not used at all to mark products or services (e.g., Pampers).
We adopt a three-category taxonomy based on Laforet and Saunders's (1994) scheme-corporate branding, house of brands, and mixed branding-which we subsequently describe. We provide examples in Table 1.
Corporate branding : With the corporate branding strategy, the corporate name is dominant in endorsing all or part of the firm's product and service brands. At the least, the corporate name is an element of the product brand names. This holds throughout all its subsidiaries and at all company levels. Examples of companies that employ this strategy are Hewlett-Packard, McDonald's, and FedEx.
Mixed branding: In a mixed branding strategy, firms typically employ a set of house or family brands, such as subsidiary names, in their brand portfolio, in addition to using the corporate name for certain products. Brands with names other than the firm's name are typically strong and significant to the firm. For example, apart from Pepsi's flagship brand, it operates with the Mountain Dew and Aquafina brands, and its subsidiaries Tropicana and Frito-Lay use individual brands at the product level (e.g., Doritos, Ruffles).
House of brands: In the house-of-brands strategy, the firm does not use its corporate name or the name of its subsidiaries for branding its products. Instead, it uses individual brand names to market its products. Companies such as Unilever, ConAgra, and Diageo keep their corporate name in the background and use individual brands for their product lines. Examples are brands such as Dove and Lipton marketed by Unilever and Pampers and Crest marketed by Procter & Gamble.
The three branding strategies are associated with different benefits and shortcomings, which arise from both the supply and the demand sides (for our summary, see Table 2). In general, enhanced cash flows and lower risks are positively associated with the advantages and negatively associated with the disadvantages of the branding strategies.
For the corporate branding strategy, the major advantages are economies of scale in marketing and efficiency in creating brand equity, which can help lower per-item promotion costs. Although this strategy can help brand extensions, there is a risk of dilution or loss of brand identity if a firm overstretches a brand name to product categories that do not match the brand's established associations; therefore, it may limit a firm's ability to expand into some unrelated categories. Under this strategy, the total marketing budget across the portfolio of all products can be lower because of the spillover effects among the products with the same brand name and because consumers are likely to transfer their loyalty between products that carry the same brand name. A corporate brand name is an efficient means to communicate with a firm's stakeholders other than customers (e.g., shareholders, retailers, employees) to build public relations and investor relations.
In contrast, the house-of-brands strategy offers significant possibilities for creating distinctly positioned brands that convey the personality of a firm's products by means of physical or perceptual benefits. Furthermore, each brand creates its own brand equity. By using multiple brands rather than one corporate brand to market different products, a firm can usually command more total shelf space with retailers, which leaves less shelf space for competitors. However, this strategy is quite costly for the firm in building brands and in introducing new products.
The mixed branding strategy can provide both the benefits of the corporate brand strategy and the possibility to create separate product-class associations for various brands of the firm. Both the mixed branding and the house-of-brands strategies can help prevent cannibalization if a firm wants to operate with more than one brand in the same market. Multiple brands enable the firm to serve different market segments better by customizing offers more precisely to the target segment's needs. Therefore, multiple brands are useful if a firm markets products targeted at different segments.
Brands and Financial Value of a Firm
Some studies in finance and accounting examine the connections between brand values and a firm's financial performance. For example, using two cross-sectional regression models, Barth and colleagues (1998) find that brand value estimates of Financial World's annual brand evaluation survey are significantly and positively related to stock prices and returns and that brand value estimates represent valuation-relevant information beyond advertising expenses, operating margin, market share, and earnings forecasts.
Kallapur and Kwan (2004) also show the value relevance of brand assets; they estimate a regression model for the market value of equity on cross-sectional data, using the book value of nonbrand assets, net income, and brand assets (as disclosed in the firms' financial statements) as explanatory variables. The highly significant coefficient of the brand asset variable indicates that brand asset values constitute valuation-relevant information for the stock markets.
There is some related research in the marketing area that relates firms' security prices and returns to brand attributes as predictor variables. For example, Simon and Sullivan (1993) report superior brand equity estimates for industries and firms with well-known brand names. Aaker and Jacobson (1994) use stock returns as a response variable to examine the impact of perceived quality measures. Their models include a quality measure (using the EquiTrend Survey by the Total Research Corporation) and an array of other control variables, such as return on investment, brand awareness, advertising expenditure, and time. Their analysis indicates that stock returns are positively associated with perceived brand quality.
Security price reactions also are examined in two event studies that incorporate news on major decisions on brand strategy as events. Horsky and Swyngedouw (1987) find that company name changes have a positive impact with respect to a firm's return on assets. Likewise, Lane and Jacobson (1995) find that the stock market returns to brand extension announcements depend interactively on brand attitude and brand familiarity.
Complementing the academic research on the recognition of brands' financial value in security prices are the large premiums paid in mergers and acquisitions, representing goodwill, which are largely subscribed to the transferred brands (Buchan and Brown 1989).
In summary, the extant research indicates that brands have a financial value. In addition, brand values are not fully accounted for in the book values of the firm. However, there is no research on the ways different branding strategies are related to the financial value of a corporation.
Conceptual Framework
The value of a firm, which consists of both tangible and intangible assets, represents the collective future cash flows to the firm's equity investors and bondholders, discounted at an appropriate rate. These cash flows are generated by the firm's investment, financing, and dividend decisions (Damodaran 2001). The cash flows and their risk are affected in part by the management of market-based assets, such as customer and partner relationships (Srivastava, Shervani, and Fahey 1998). Brands and brand equity represent the relationship between the firm and its customers and can affect firm value by accelerating and enhancing cash flows or by reducing risk. For example, corporate brands make it easier for a firm to introduce brand extensions and can enhance cash flows as a result of lower costs of promotions and cobranding. Strong brands can also reduce a firm's vulnerability to competition and, in turn, reduce the risk of the future cash flows. Thus, the branding strategies of a firm create long-term brand equity through the customer responses they engender.( n2) In general, this value is not measured in the tangible assets of the firm; it becomes part of the intangible assets of a firm.
The intangible assets of the firm are affected by several firm-specific factors in addition to branding strategy. Some factors reflect the previous operations of the firm, and others reflect future growth opportunities; investors can use both types of factors to assess future cash flows and their risk. Variables such as age of firm, operating margin, leverage, advertising expenditures, and focus all reflect the firm's previous operations. Similarly, factors that affect future growth include research and development (R&D) expenditures, acquisitions, industry characteristics, and competition. Our analysis controls for these other variables while determining the relationship of branding strategy and intangible value. In the next section, we describe their operationalization and linkage to the intangible value of a firm.
We use Tobin's q ratio to measure the intangible assets. Tobin's q is the ratio of the market value of the firm to the replacement cost of the firm's assets. It is a forward-looking measure, providing market-based views of investor expectations of the firm's future profit potential. The long-term equilibrium market value of a firm must be equal to the replacement value of the firm. A q-value of greater than 1.0 reflects an unmeasured source of value attributed to the intangible assets. Beginning with the work of Lindenberg and Ross (1981), the empirical finance literature has used Tobin's q to study many phenomena (e.g., barriers and concentration [Chen, Hite, and Cheng 1989], equity ownership [McConnell and Servaes 1990], managerial performance [Lang, Stulz, and Walking 1989], dividend announcements [Lang and Litzenberger 1989]). In marketing studies, Simon and Sullivan (1993) use Tobin's q to measure brand equity, and Day and Fahey (1988) recommend it to measure the value of marketing strategies. Bharadwaj, Bharadwaj, and Konsynski (1999) use Tobin's q to analyze the effects of information technology on a firm's performance.
Hypotheses on the Effects of Branding Strategies
Our prior discussion on the three branding strategies (see Table 2) leads us to three hypotheses.
Hs[sub1]: The corporate branding strategy is associated with higher values of Tobin's q.
Given that Tobin's q is based on the reaction of the financial market, H[sub1] is justified because of the supply-side advantages of a corporate branding strategy (e.g., lower costs of advertising, new product introduction, economies of production to enhance future cash flows). Furthermore, the demand-side advantages also reinforce this justification. The disadvantages, if any, are not dominant enough to make this effect negative.
H[sub2]: The house-of-brands strategy is associated with lower values of Tobin's q.
H[sub2] is essentially the converse of H[sub1]. According to H[sub2], a firm incurs much higher costs of advertising its portfolio of brands and incurs enormous costs for introducing new products. Furthermore, we conjecture that financial markets pay limited attention to the demand-side advantages of unique positioning and minimal cannibalization. The market finds it difficult to keep track of the idiosyncratic strategies of individual brands and tends to value the firm less because of the lower perceived future cash flows.
H[sub3]: Advertising expenditure interacts with the relationship between branding strategy and Tobin's q.
A specific expenditure on advertising is more effective under the corporate branding strategy than the house-of-brands strategy because of the scale economies obtained under the former. Furthermore, any announcement of such expenditure by a firm that follows the corporate branding strategy becomes much more visible to the financial market. Thus, there is a much greater effect, leading to an interaction effect.
The calculations for Tobin's q used by Lindenberg and Ross (1981) are quite cumbersome. To make the estimation of Tobin's q easier, Chung and Pruitt (1994) suggest a simpler formula.( n3) They then compare their measure with that of Lindenberg and Ross and show that the fit between the two measures over ten years of cross-sectional data is extremely high, with an R² that ranges between .97 and .99. We use the following simpler formula:
( 1) Tobin's q = (MVE + PS + DEBT)/TA,
where
MVE = (share price) x (number of common stock outstanding),
PS = liquidating value of the firm's preferred stock,
DEBT = (short-term liabilities - short-term assets) + book value of long-term debt, and
TA = book value of total assets.
The numerator in Equation 1 represents the total value of the firm and the collective cash flows to the firm's equity investors and bondholders. The denominator is the replacement cost of the assets, which is assumed to equal the book value. The higher the Tobin's q, the higher is the value of the intangible assets of the firm. We use the year-end data taken directly from the annual COMPUSTAT files to compute Tobin's q. Our model for the relationship of branding strategy and Tobin's q is
( 2) Tobin's q = f (branding strategy, control variables).
Control Variables
We include the following control variables in our model to estimate the net effects of branding strategy on Tobin's q: ( 1) focus, ( 2) concentration index, ( 3) operating margin, ( 4) leverage of the firm, ( 5) R&D expenditures, ( 6) advertising expenditures, ( 7) age of firm, ( 8) number of acquisitions, and ( 9) growth rate.( n4) Our selection is based on the discussion in the previous section and on the existing empirical evidence of these variables' relationship to firms' intangible assets (e.g., Chauvin and Hirschey 1993; Hirschey and Weygandt 1985; Lustgarten and Thomadakis 1987; Simon and Sullivan 1993). A subset of these variables (e.g., advertising expenditures, R&D expenditures, concentration) also appears as a determinant of profitability in extensive meta-analyses studies (Capon, Farley, and Hoenig 1990; Szymanski, Bharadwaj, and Varadarajan 1993). The selection of these variables is also partly influenced by the availability of data. A framework for our analysis described in Figure 1 shows the variables we used as controls before teasing out the relationship between branding strategies and Tobin's q. All variables reflect previous operations, and some (e.g., R&D expenditures) are more directly linked with the growth potential of future cash flows and their risk. We further categorize the variables into ones related to the marketing mix, finances, strategy, competition, and other aspects.
Operating margin. In general, a higher operating margin triggers expectations among investors of higher cash flow potential and drives intangible value. Furthermore, there is evidence that higher brand values are significantly associated with higher operating margins and advertising expenses (Barth et al. 1998). Thus, we expect that Tobin's q is positively affected by the firm's operating margins. The relevant data are from COMPUSTAT. We calculate operating margin as the ratio of net income before depreciation to sales.
Leverage. Leverage has been used in several corporate finance studies (Berger and Ofek 1995; Denis and Kruse 2000). We use the ratio of long-term debt to total assets of the firm as a measure of leverage. Firms with higher leverage can enjoy a tax benefit because they can deduct the interest costs, which results in greater cash flow and thus a positive relationship with Tobin's q. McConnell and Servaes (1990) find such a positive relationship. However, Smith and Watts (1992) expect that firms with higher growth opportunities (and thus a higher q-value) have lower leverage. Thus, we do not have any a priori expectation of the sign of the leverage coefficient.
Focus of the firm. We measure firm focus by the number of industry segments in which the firm operates, on the basis of information provided by COMPUSTAT. Comment and Jarrel (1995) find that at more diversified firms (or firms with lower focus), the asset turnover is higher, and thus asset values are closer to market value, which results in a lower q-value and a positive coefficient. In previous studies, this coefficient has been found to have a positive effect in some and a negative effect in others (Lustgarten and Thomadakis 1987); thus, we have no a priori expectation of the sign.
Concentration index. To capture some effects of competition, we use an index to measure the concentration of the primary industry business in which the firm operates, on the basis of its four-digit North American Industry Classification System codes. For this purpose, we compute the Herfindahl index as a measure of concentration. The actual measure is Σ[subi=1,supI[subc(r)]] m[subi,sup2], where m[subi] is the revenue share of the ith company in the primary industry of the rth firm with I[subc(r)] competitors. Because higher concentration can provide more market power, it can lead to a higher q-value (Domowitz, Hubbard, and Peterson 1986). Others contend that a higher q-value reflects better efficiency rather than market power (Smirlock, Gilligan, and Marshall 1984). On the basis of recent empirical support, we expect that the effect of the concentration index on Tobin's q is negative (Bharadwaj, Bharadwaj, and Konsynski 1999; Montgomery and Wernerfelt 1988).
R&D expenditures. Several studies support the premise that R&D expenditures affect a company's market valuation (e.g., Chauvin and Hirschey 1993; Chen, Hite, and Cheng 1989; Kim and Lyn 1990). Lev and Sougiannis (1996) show that investors take R&D information into account when making investment decisions. We expect that R&D expenditures have a positive impact on the firm's intangible value, thereby reflecting better prospects for the firm to generate cash flows. The R&D data we employ are from the COMPUSTAT file. Because companies are not legally obligated to disclose R&D data, much data are missing. This lack of data is a reason we ended up with a small sample. Our operational measure of R&D expenditures is the ratio of R&D expenditures to total assets of the firm.
Advertising expenditures. Advertising expenditures are commonly expected to have a positive impact on a company's performance. Several studies have supported this notion (e.g., Chauvin and Hirschey 1993; Chen, Hite, and Cheng 1989; Klock and Megna 2000). A part of the ample literature on the effectiveness and efficiency of advertising scrutinizes this relationship from a performance viewpoint (for the relationship between brand equity and advertising, see Aaker 1993). In addition, because the advertising expenditures are typically written off in the period they are spent but have a long-term effect on brand equity, they are valued as part of the firm's intangible assets. Some studies uncover that higher advertising expenditures are associated with better corporate performance. We accordingly expect that the advertising variable has a positive impact on Tobin's q.
We collected the advertising data from the publications by Competitive Media Reporting (2001) for the years 1996-2000. Operationally, we use the ratio of advertising expenditures to total assets in our models.
Age of firm. When a firm has been in business for an extended period, investors have extensive information about the firm and thus value firms closer to their true potential. Despite the more accurate evaluation, the intangible value can still be high; with age, the intangible value of brands is actually likely to grow because of advertising, awareness, and loyalty, all of which result in a positive coefficient. However, 1996 to 2000 was characterized by a large speculative element for the newer Internet-based firms, which resulted in a high value of Tobin's q. Thus, we expect that the age of the firm has a negative impact on Tobin's q. We obtained data on how long a firm has been in business from the electronic source Gale Group Business and Company Resource Center (Gale Group 2000).
Acquisitions. The financial market is influenced by a firm's acquisitions, which reflect greater growth opportunities in the future. We simply counted the number of acquisitions during the preceding year. In most cases, this variable is either one or zero. If acquisitions are priced at book value, Tobin's q should not be affected. However, the stock market typically evaluates acquisitions negatively, in part because of the difficulty of efficiently merging operations. Andrade, Mitchell, and Stafford (2001) show that of 3688 mergers between 1973 and 1998, the target firm gained 23.8% in the window beginning 20 days before acquisition announcement and ending on the close, and the acquiring firms lost 3.8% over the same interval. On the basis of this evidence, we expect that acquisitions have a negative impact on Tobin's q.
Growth rate. A higher previous growth rate indicates higher future growth prospects and thus results in a higher value of Tobin's q. Our measure of growth is the compounded annual sales growth rate over the previous three years (e.g., Barth et al. 1998). We expect that this variable has a positive impact on Tobin's q.
The relationship between our hypotheses and the control variables of our model are summarized in Table 3.
Estimating Branding Strategy Effects
We estimate the relationship of branding strategy with the firm value (as measured by Tobin's q) while controlling for advertising expenses and other variables noted previously. We employ two variants of a regression-like model. Our first model, M1, is a standard ordinary least squares (OLS) model, which assumes that the regression coefficients are the same for all firms and industries. Our second model, M2, allows for different firm-specific regression coefficients; we estimated this model using hierarchical Bayesian regression methods.
Aggregate Estimates
Our basic model at the aggregate level (M1) is
( 3) Y[subrt] = Tobin's q for firm r at time t = β[sub0] + β[sub1] Operating margin[subrt] + β[sub2]Leverage[subrt] + β[sub3]Focus[subrt] + β[sub4]Concentration index[subrt] + β[sub5]R&D expenditure[subrt] + β[sub6]Concentration index[subrt] + β[sub7]Age of firm[subrt] + β[sub8]Acquisition[subrt] + β[sub9]Growth rate[subrt] + γ[subcb] Corporate branding dummy[subrt] + γ[subhb]House-of-brands dummy[subrt] + ε[subrt],
where r = 1, ..., R (firms), and t = 1, ..., T (years). Here, the βs and γs are parameters to be estimated, and we assume that the error term ε[subrt] is normally and independently distributed with common variance. The β coefficients measure the effects of the control variables. The coefficients (γ[subcb] and γ[subhb]) measure the average impacts of branding strategy on firm value for the subset of companies that employ the same branding strategy after accounting for the effects of several control variables, which are shown in Figure 1. According to our hypotheses, we expect that γ[subcb] is positive and γ[subhb] is negative. We estimate a second specification of M1 with interaction terms between advertising expenditures and branding strategies. This variant enables us to examine whether there is a differential impact of advertising expenditures with different branding strategies.
Firm-Level Estimates
We employ a hierarchical model (M2) with random coefficients to estimate firm-level effects of branding strategy on Tobin's q. This approach, which allows for parameter variations across firms (Hildreth and Houk 1968; Swamy 1974), has become popular in the marketing literature to represent heterogeneity in parameters (Allenby and Ginter 1995; Bradlow and Rao 2000; Lenk et al. 1996). Accordingly, we posit a hierarchical Bayesian model in which we estimate the firm-level branding coefficients for each firm, assuming that they are randomly distributed around a common mean. Our model at the firm level is as follows:
( 4) Yrt = Tobin's q for firm r at time t = β[sub0] + β[sub1]Operating margin[subrt] + β[sub2]Leverage[subrt] + β[sub3]Focus[subrt] + β[sub4]Concentration index[subrt] + β[sub5]R&D expenditure[subrt] + β[sub6]Advertising expenditure[subrt] + β[sub7]Age of firm[subrt] + β[sub8]Acquisitions[subrt] + β[sub9]Growth rate[subrt] + γ[subcb](r)]]Corporate branding dummy[subrt] + γ[subhb(r)]House-of-brands dummy[subrt] + epsilon[subrt],
where r = 1,..., R (firms), and t = 1,..., T (years). As we did previously, we assume that the errors ε[subrt] are normally and independently distributed with common variance and that γ[subcb(r)] and γ[subh(r)] are firm-specific coefficients
We also compare the results from Bayesian regression with those obtained from OLS regressions using a fixed-effects model. In addition, we test the predictive validity of the two methods. For this purpose, we withhold approximately one-fifth of the observations, reestimate the model, and compare the predictions from the model with the actual values for the withheld observations.
Data Collection
Sample of firms. We sought relevant financial and advertising data for companies in the Standard & Poor's index of the top 500 companies (S&P 500) for five consecutive years as of December 2000 (i.e., 1996-2000).( n5) However, because there was a lack of data on several variables, our final sample consisted of 113 firms (23% of S&P 500 firms), whose total market value was approximately 38% of that of all S&P 500 firms combined. The average market value of our firms is approximately twice that of S&P 500 firms not in the sample. Nevertheless, our sample compares quite favorably with the S&P 500 firms on four variables (Tobin's q; operating margin; leverage; and focus, as measured by the number of industry groups in which the firms operates) on the basis of multivariate T-tests for each year of the sample. The values of Hotelling's T2 values range from .0525 (degrees of freedom [d.f.] = 189.5) for 1996 to 3.10 (d.f. = 185.5) for 2000, and none are significant.
Branding strategy codes. We assigned one of the three branding strategy codes (corporate branding, mixed branding, and house of brands) to each firm on the basis of a review of the firm's Web site, an analysis of the firm's structure, the firm's brands listed in the 2000 Competitive Media Reporting report, and the most recent annual reports. We also consulted revenue data to uncover the significance of a firm's business units and to identify the brands marketed by the business units. Revenue analysis was especially relevant in cases in which it was unclear which code to assign to the branding strategy of the firm. For example, an ambivalent case occurred if a firm predominantly used the corporate brand for its products and services but also owned a minor brand. In such a case, we categorized the firm as having a corporate branding strategy.
Two graduate students assigned the codes. There was a high degree of consistency, and coder reliability, as measured by percentage agreement, was .867. In case of a divergence, one researcher evaluated the information and assigned a branding strategy code. Overall, the classification was fairly straightforward and unambiguous. Operationally, we used two dummy variables for the branding strategies of the firms, with effects coding as ( 1, 0) for corporate branding, (0, 1) for house of brands, and (-1, -1) for mixed branding.
Normalization of the variables. Many studies with diverse research objectives show that the affiliation with a particular industry explains part of the cross-sectional variation of the respective response variable. To account for any systematic differences between industry groups and to make the measures comparable, we first calculated industry medians for the variables for groups based on two-digit North American Industry Classification System codes, and we normalized each firm's data relative to the respective industry medians. We analyzed more than 20,000 cases for each year to obtain the year-specific medians for 30 industry groups. We median-adjusted for Tobin's q, focus, operating margin, leverage, and R&D expenditures variables, which was possible because we had data at the industry group-level from the COMPUSTAT files. We performed no such normalization for the remaining variables because we lacked data.
Descriptive Statistics
Table 4 shows the descriptive statistics and correlations. Considerable variation occurs in this response measure (the median-adjusted Tobin's q), but the mean across all firms is 1.38. The firms in our sample operate in a wide range of industry segments (the median-adjusted value is 3.00; see the "Focus" row of Table 4). Similar variation occurs in other predictors as well. This variation suggests that our sample is probably skewed toward better-performing firms than toward the population of firms included in the COMPUSTAT data set. Most of the correlations are statistically significant from zero, and the multicollinearity among the variables is low.
Before we analyze the results, recall that the dependent variable is Tobin's q, which represents the market's assessment of the future prospects for the firm compared with its book value. Thus, the coefficients of the regression models signal prospects for future cash flows to the financial community. We assess the results only from this market signal perspective, not from any normative view of the optimum strategy for the firm.
Estimates of Effects at the Aggregate Level
Fit. We first describe the analyses from Model M1, which provides estimates of the effects of branding strategy at the aggregate level. We estimated this model with and without interactions of advertising and branding strategy dummies. In each model, we used the core set of nine control variables (i.e., focus, concentration index, operating margin, leverage, R&D expenditure, advertising expenditure, age of firm, number of acquisitions, and growth rate). The results of fit are shown in Table 5. The fits are all significant. The inclusion of interactions between advertising and branding strategy dummy variables shows a small change (a slight decrease for M1A and a small increase for M1B) in the fit.
Predictive testing. The correlation between the predicted values and the actual values for the subset of randomly withheld 20% of observations is .621 for the models with and without interactions. This is quite similar to the fit of the model to the data; therefore, it shows a good degree of predictive validity.
Branding strategy coefficients. The coefficients of the branding strategies in our measurement model of Tobin's q (normalized) after correcting for the control variables are shown in Table 5 for Models M1A and M1B. In both specifications, we consistently find that the corporate branding coefficient is the largest and is positive, whereas the other two strategy coefficients (i.e., house of brands and mixed branding) are negative. Furthermore, the mixed branding strategy coefficient is the most negative.( n6) The relationships of branding strategy and Tobin's q are less pronounced when we include the interactions between the advertising variable and branding strategy dummy variables.
The estimated coefficients of the branding strategy variables (measured in the normalized Tobin's q values) in the model without interactions (M1A) are .32 for corporate branding, -.09 for house of brands, and -.41 for mixed branding.( n7) The coefficient of corporate branding is statistically significant, in support of H[sub1]. However, the coefficient of house of brands, though negative, is not significant, which is not in support of H[sub2]. However, when corrected for the sample selection bias with Heckman's (1979) two-step model, with the first step the selection of 113 companies, the revised estimates for the branding strategy dummy variables in Model M1A are somewhat lower but highly significant; the corrected values are .181 and -.052, with respective t-values of 5.12 and -4.23. Thus, we conclude that the data support H[sub1] and H[sub2].
When we introduced interactions between advertising and branding strategy variables in Model M1B, the estimates of the two branding strategy coefficients and interactions were not significant (even after we corrected for the selection bias). The data do not support H[sub3]. However, the magnitudes of the interactions suggest that if a firm follows a corporate branding strategy, an increase in advertising expenditures increases Tobin's q, but that change is negative if it follows a house-of-brands strategy. A potential reason for this is that investors regard higher advertising expenditures under corporate branding as beneficial for a portfolio of brands with a common brand name; investors may consider such an increase under a house-of-brands strategy harmful (in our sample, a company that adopts the house-of-brands strategy spends an average of $285 million on advertising, compared with an average of $74 million for a company that adopts a corporate branding strategy).
Coefficients of control variables. The coefficients of most of the control variables (also shown in Table 5) are in the expected direction for both M1A and M1B of Model M1. All control variables are significant except for focus, industry concentration, age of firm, and number of acquisitions. It seems that the concentration of a firm on a small number of businesses has no influence on the firm's intangible value.
As might be expected, the growth rate coefficient is positive and significant, which reaffirms the forward-looking nature of the response variable (Tobin's q). In a similar manner, the coefficients of operating margin, advertising, and R&D expenditures are all positive and significant, as we expected.
The leverage variable has a negative coefficient and seems to be consistent with the ambiguity of its effect in the literature. McConnell and Servaes (1990), who analyzed data for 1976 and 1986, show a positive effect. Our analysis period (1996-2000) is characterized by much higher price-to-earnings ratios in the stock market. Thus, an explanation for our significant, negative finding is that the market values firms with a high Tobin's q more as a result of their high perceived future cash flows. The current cash flow for such firms is usually limited, and thus they cannot take on much debt, which results in lower leverage. This might explain the strong, negative correlation of leverage and Tobin's q. In addition, Smith and Watts (1992) find that firms with higher growth options have lower leverage, in support of our argument.
Firm-Specific Estimates
We now turn to Model M2, in which we allowed all specified regression coefficients to vary randomly around a mean value. Furthermore, we specified the two branding strategy parameters to be different for each firm. We estimated this random-coefficient( n8) hierarchical Bayesian model using Markov chain Monte Carlo methods.( n9) We estimated a total of 261 parameters in this analysis; 90% of them passed Heidelberger and Welch's (1983) stationarity test.( n10) The results shown in Table 6 are for the subset of iterations after convergence has been reached.
Fit. Using the average of the residual sum of squares across iterations, we computed a pseudo R² to examine the degree of fit. This pseudo R² is .655, which shows an excellent fit of the Bayesian model to the data. A comparison with the R² of .393 for Model M1A indicates that the aggregate-level Model M1 did not account for a considerable degree of heterogeneity among the sample firms.
Predictive validity. We reestimated the hierarchical Bayesian regression model for a sample of 418 (80%) observations, after we randomly deleted one observation for every firm. The estimates converged, and the results are comparable to those from the full model. We used the firm-level results to predict the value of the (median-adjusted) Tobin's q for the prediction set. The Pearson's correlation between the actual and predicted values is quite high (r = .805, p = .01). This analysis shows high predictive value of our firm-specific results.
Effects of branding strategies. The summary statistics of the effects of the branding strategies across the 113 firms, as estimated by Model M2, are shown in Table 7. Similar to the results in Model M1, the coefficient of corporate branding is the largest and is positive, followed by those of house of brands (second) and mixed branding (third and negative).
Figure 2 shows the means and the 2.5%-97.5% intervals for the 113 firm-specific estimates of the three branding strategy effects. Figure 2 attests to the existence of considerable variation among the sample of firms used in this study.
Effects of other variables. In Table 6, we show the overall parameter estimates and the standard errors of the posterior distributions for the control variables for Model M2. In general, the estimates correspond quite well with the Model M1 results for the control variables.
A way to visualize the impact of branding strategy on Tobin's q value is to calculate the predicted Tobin's q for a typical firm under the assumption that it follows each of the three branding strategies. For Model M2, the predicted average across the strategies is 1.34, which compares quite well with the actual average of 1.38 (shown in Table 4). However, these predictions vary by the type of branding strategy: 1.82 for corporate branding, 1.15 for house of brands, and 1.05 for mixed branding. Compared with the corporate branding strategy, the house-of-brands and mixed branding strategies show reductions in Tobin's q of 37% and 42%, respectively. It seems that investors indeed prefer the corporate branding strategy for a firm.
Inferred best strategies. We performed a similar analysis for all 113 firms and determined the best strategy for the firm to follow if its objective is to maximize the impact of its intangible value (Tobin's q). In Table 8, we compare the best strategies, based on Model M2, with the branding strategies manifest by the firms.
It seems that firms might be better off adopting either a corporate branding or a mixed branding strategy rather than a house-of-brands strategy if their objective is to increase intangible value. This analysis indicates that 39 (20 + 2 + 17) firms (or 35%) manifest the "best" branding strategy that maximizes Tobin's q (or the market value criterion). Furthermore, 50% (56 of 113) of firms might be better off using the corporate branding strategy if their objective is to maximize Tobin's q values.
Estimates for selected firms. Examining detailed estimates for various firms, we find that EMC Corporation, Dell Computers, and Microsoft have the highest corporate branding strategy coefficients; note that all three firms manifest a corporate branding strategy. In addition, the three firms have the most negative estimates for the mixed branding strategy, which seems to suggest that they are following an optimum strategy from the financial market perspective. Computer Associates, which follows a corporate strategy, has an estimated coefficient among the lowest for corporate branding but among the highest for mixed branding, which implies that investors may evaluate the firm more highly if it can implement a change in its branding strategy. We hasten to add that such a conjecture is speculative.
Most of the house-of-brands coefficients are not significant, except for PPG Industries and Darden Restaurants. Both firms currently follow the house-of-brands strategy, but the estimates for this strategy are the most negative for them, which implies that they might benefit from the investor perspective if they have the option of following a different strategy. The highest estimates for a mixed branding strategy are for The Gap and Gillette, both of which have the same manifest strategy.
Summary
This article reports the results of an empirical analysis to determine the relationship between a firm's branding strategy and its intangible value, as measured by Tobin's q. We controlled for nine predictors (i.e., focus, concentration index, operating margin, leverage, R&D expenditures, advertising expenditures, age of firm, acquisitions, and growth rate) and industry grouping variables while estimating the impact of branding strategies on Tobin's q. To account for the interfirm variation in the measures we used in the study, we normalized five variables (i.e., Tobin's q, operating margin, leverage, focus, and R&D expenditures) by subtracting the median values of the firms' corresponding industry groups. We formulated two sets of models in this work and estimated one set by simple regression methods and the other by hierarchical Bayesian methods. The Bayesian methods enabled us to determine the impact of branding strategies at the firm level. In general, the results we obtained are consistent. Furthermore, the predictive validity of our second model is quite high.
In general, our results on the impact of the control variables are in line with what has been reported in the literature. This finding gives us confidence in interpreting the effects of branding strategies on the financial value of a firm.
The coefficient of the corporate branding strategy measured in normalized Tobin's q values is highest, followed by the house-of-brands strategy; the mixed branding coefficient is lowest. We find considerable stability in the order of effects of the three branding strategies. The effects of branding strategies become more pronounced when we include interactions between the type of branding strategy and advertising expenditures in the model. We also find that approximately 65% of the firms in our sample do not manifest the best strategy possible if their objective is to improve Tobin's q.
Discussion
Our primary result, that corporate branding is more positively related to the intangible firm value than are house of brands and mixed branding, may appear to be inconsistent with the concept of market segmentation, which should support implementation of a house-of-brands or mixed branding strategy. However, we recall that our dependent measure is an assessment by the financial community, specifically investors, of a firm's value. Although investors have increasingly come to acknowledge the financial value of brands, it can be presumed that they are not familiar with which brands constitute firms' brand portfolios. It is reasonable to assume that the financial community is more aware of corporate brands than of the individual brands of a firm that follows a house-of-brands strategy. Moreover, financial experts might not value house-of-brands strategies appropriately and might underestimate the potential benefits of a differentiated branding approach for diverse target segments and products. In addition, from a risk management perspective, the investment community might underappreciate that a multitude of brands (i.e., a house-of-brands strategy) distributes risk over more brands, thus improving firms' financial risk profile. This effect does not seem to be reflected in the financial evaluation of a firm that pursues a house-of-brands strategy. The finding that financial valuations are not based solely on purely rational criteria is in line with Frieder and Subrahmanyam's (2002) finding about investors' stock decisions. They find that the perceived quality of brands has an influence on stock holding decisions. In addition, they point out that familiarity with brands influences investment decisions, and they observe a "home bias" (i.e., preference for domestic stocks).
We classified the 113 firms into three broad groups: 40 business-to-consumer (B2C) (mainly consumer goods companies), 33 business-to-business (B2B) (mainly industrial goods companies), and 30 mixed. We estimated Model M1 for the subgroups to determine whether there were any systematic differences among them. In general, a B2B firm has a few organizations as its customers. Furthermore, a B2B firm tends to build customer relationships at an organizational level. Accordingly, we conjectured that a B2B firm's customers depend on the name of the firm more than on a specific brand name as a guide in making purchase decisions. In a similar manner, a B2C firm devotes its resources (e.g., advertising) to create distinct positions for its brands under the house-of-brands strategy and attempts to differentiate its offering in a product category for the end users (consumers). Thus, we were interested in examining any differences across the broad categories. The effect of a corporate branding strategy was significant for B2C and B2B firms, and the coefficient was higher for B2B firms. Furthermore, the house-of-brands strategy effect was not significant for B2B firms, though it was significant and negative for B2C firms.
A firm's manifest branding strategy largely depends on various corporate decisions, such as mergers and acquisitions, global expansion, and the selection of which business fields to compete in (Laforet and Saunders 1999). Therefore, general recommendations to firms about the type of branding strategy cannot be derived from this research. Nevertheless, our study shows how different branding strategies are associated with different effects on the intangible firm value. Moreover, our analysis can assist analysts in computing the level of expenditures on advertising necessary to obtain a desired financial value for a firm with a given branding strategy.
Further Research
The Bayesian regression model we used in this study is quite versatile and is useful in estimating individual-level parameter estimates. Our approach can be applied to various marketing situations, particularly ones that estimate aggregate-level effects with replicated data on a sample of individual units. We show that it is possible to estimate effects (of marketing variables) at the individual unit level.
A related work by Hogan and colleagues (2002) suggests linking customer assets to a firm's financial performance through the basic customer lifetime value model. In principle, branding strategies should increase the value of a firm's customer assets. However, no research has explored how different branding strategies affect customer assets. It can be conjectured that corporate branding strategy adds more value to customer assets because of its higher effectiveness in cross-selling.
We point out that our analysis is not free of limitations. For example, our sample of firms (n = 113) is not truly representative of the population of firms in the economy; however, as we have showed, it is a good subsample of the largest 500 firms. In some industries, there are only a few firms in our sample. Our analysis examines the level of the firm as a whole, whereas a firm may adopt detailed branding strategies for each of its business units and products. Furthermore, our coding of branding strategies is not as refined as we would have liked. A more refined brand strategy coding may involve multiple categories for mixed branding.
Our analysis considers competition effects only indirectly through the use of the concentration index. However, we do not account for the direct effects of competition. As a firm's competitor expands into other products and categories with a particular branding strategy, the firm almost necessarily adapts its own branding strategy to address any harmful effects on its own growth. This issue may have had a formative effect on the manifest branding strategies of firms such as Procter & Gamble (versus Unilever) and Coke (versus Pepsi). Although we cannot address the issue of competitive effects in our analysis because we lack appropriate data, we believe that it is important for further research.( n11)
Our empirical work is necessarily correlational because the branding strategy codes did not change over the period of analysis. A topic for further research is to examine the interdependence of the branding strategy and firm's intangible value; this would require a much longer time series of data and appropriate econometric methods (Granger 1969).
A natural extension of this work is to analyze the effect of a branding strategy with data at the business unit level. Although the current accounting systems do not allow for such an analysis, we expect that future systems will be more disaggregate.
Finally, it is critical to replicate this analysis and test whether our results hold for other samples of U.S. and international firms. Our analysis can be extended to include other descriptors of firms and for longer periods. Furthermore, an examination of the financial impact of branding strategies at the level of individual brands or strategic business units, at least for a few companies, would be beneficial; however, we realize that such an exercise is quite difficult because of the paucity of financial data at those levels.
The authors thank Warren Bailey of Cornell University, Srinivasan Krishnamurthy of Binghamton University, and Christine Moorman of Duke University, as well as participants at the marketing workshops at the University of North Carolina at Chapel Hill and Duke University and the anonymous JM reviewers for their insightful comments on this work.
( n1) Firms frequently use the equity of their current brands to introduce brand extensions. These brand extensions are successful when the parent brand is viewed as having favorable associations and when there is a perceptual fit between the parent brand and the extension product (Keller 1998, p. 473). In efforts to understand the creation and effective use of brand equity, several techniques to measure brand equity have been suggested by consulting, advertising, and investment firms, as well as by the academic community (Agarwal and Rao 1996; Kamakura and Russell 1993; Simon and Sullivan 1993; Swait et al. 1993).
( n2) In a conceptual piece, Ambler and colleagues (2002) posit a brand value chain, which connects a firm's activities by marketing management to shareholder value. Their framework consists of several multipliers to marketing program investment to yield the shareholder value (including intangible assets). Such a detailed analysis requires a significant amount of data for operationalization. Our interest is to analyze the effect of one aspect of the marketing program (branding strategy) at a much-aggregated level.
( n3) Chung and Pruitt's (1994) formula differs from that of Lindenberg and Ross (1981) in that it assumes that the replacement values of a firm's plant, equipment, and inventories are equal to its book value. There is also a slight difference in the way the market value of the firm's long-term debt is calculated. Both methods assume that market and book values for short-term debt are identical.
( n4) A company's reputation has been acknowledged to affect its performance (Fortune 2000). Using an annual survey among 10,000 executives, directors, and analysts, Fortune measures the reputation of the largest U.S. companies on a ten-point scale that uses the criteria of quality of management, quality of products/ services, innovativeness, long-term investment value, financial soundness, employee talent, social responsibility, and use of corporate assets. We could not use these data because they were not available for the study period for all the companies in our sample.
( n5) We use mainly two data sources: the 2000 CD-ROM "COMPUSTAT North America Data for Standard & Poor's Research Insight" for financial data and the Competitive Media Reporting annual books for advertising expenses.
( n6) In an analysis with a different sample of 75 firms, for which we use the COMPUSTAT data for advertising expenses, we find that the corporate branding strategy has the most positive effect on Tobin's q, and the order of the effects for the other two strategies is reversed. Thus, it seems that our result on corporate branding strategy is quite robust.
( n7) Our analysis assumes that the aggregate effects of the branding strategy are fixed because the strategies do not vary in the period of the data. We applied the Hausman-Taylor (1981) instrument variable method, which allows for a consistent estimate of the time-fixed strategy variables using the cross-sectional time-series nature of the data. Corporate brand coefficients were still the highest, and mixed branding coefficients the lowest.
( n8) We compared the OLS model (M1) with fixed effects with a model in which the brand strategy coefficients were random, and we found that the model with random coefficients provides a better fit (the chi-square value for the model comparison is 112, with 2 d.f.). This analysis provides some support for use of a random-coefficients Bayesian model to determine firm-specific effects.
( n9) We use WinBugs (Spiegelhalter, Thomas, and Best 1999) for the estimation. We assumed diffuse and noninformative prior distributions for the parameters so that the data primarily determine the posterior distributions.
( n10) In the Markov chain Monte Carlo iterations, we burned in 11,000 iterations and used the next 5000 iterations, thinned by 10, to test for convergence using Bayesian output analysis (Smith n.d.). We used the last 1000 iterations to report the results.
( n11) We thank an anonymous reviewer for pointing out this issue.
Legend for Chart:
A - Branding Strategy 1 (Corporate Branding) Company and Brand
B - Branding Strategy 2 (Mixed Branding) Company
C - Branding Strategy 2 (Mixed Branding) Selected Brands
D - Branding Strategy 3 (House of Brands) Company
E - Branding Strategy 3 (House of Brands) Selected Brands
A B C
D
E
Nike Gillette Gillette, Oral-B, Duracell,
Braun, Waterman
Procter & Gamble
Pampers, Crest, Ariel,
Tide, Bounty, Always,
Febreze
AT&T The Gap The Gap, Banana
Republic, Old Navy
Darden Restaurants
Red Lobster, Olive
Garden, Bahama Breeze
Dell Computer 3M 3M, Scotch, Thinsulate,
Scotchgard
Bristol-Myers Squibb
Clairol, Aussie, Herbal
Essences, Viactiv, Boost
Notes: The examples are based on information from the companies
as of April 2000. Legend for Chart:
A - Branding Strategy
B - Supply-Side Advantages (+) and Disadvantages (-)
C - Demand-Side Advantages (+) and Disadvantages (-)
A B
C
Corporate Economies of scale in marketing (+)
branding Total costs of advertising/promotion can be lower (+)
Lower costs of creating brand equity (+)
Lower costs of new product introductions (+)
Easier extension of brands (+)
Limits on firm's ability to expand into some
categories (-)
Higher cannibalization among firm's brands likely (-)
Efficient means to communicate to various
stakeholders (+)
House of No identifiable economies of scale in marketing (-)
brands Higher costs of advertising (-)
Can command larger retail shelf space (+)
Significantly higher costs of new product
introductions (-)
Distinctly customized brands can be offered (+)
Lower cannibalization (+)
Mixed Combination of advantages and disadvantages of
branding corporate branding and house of brands
Combination of advantages and disadvantages of
corporate branding and house of brands Legend for Chart:
A - Variable
B - Measure Used
C - Expected Relationship to Tobin's q
D - Support for Expectation
A B
C
D
Operating margin Ratio of net income before
depreciation to sales
Positive (+)
Triggers expectations of
future income potential
Leverage Ratio of long-term debt to
total assets
Not clear-cut
Literature shows both
types of relationships
Firm's focus Number of industry segments
in which the firm operates
Not clear-cut
Literature shows both
types of relationships
Concentration index Herfindahl index using
four-digit North American Industry
Classification system codes
Negative (-)
Recent empirical
evidence
R&D expenditures R&D expenditures/total assets
Positive (+)
Future implied income
due to R&D (previous
research)
Advertising expenditures Advertising expenditures/total
assets
Positive (+)
Literature shows that
advertising affects
market evaluation
Age of firm How long a firm has been
in business
Negative (-)
Investors have more
information on older firms
Acquisitions Number of acquisitions in the
preceding year
Negative (-)
Recent empirical
evidence
Growth rate of sales Compounded annual growth
rate in sales for a
three-year period
Positive (+)
Due to the forward-looking
nature of Tobin's q Legend for Chart:
A - Means (Standard Deviation)
B - Variable
C - 1
D - 2
E - 3
F - 4
G - 5
H - 6
I - 7
J - 8
K - 9
L - 10
M - 11
N - 12
O - 13
P - 14
A B
C D E F G H
I J K L M N
O P
1.38 1. Tobin's q(*)
(2.33)
1
.11 2. Operating margin(*)
(.12)
.387 1
(.00)
.029 3. Leverage(*)
(.14)
-.257 .232 1
(.00) (.00)
3 4. Focus(*)
(3.85)
-.056 .146 .194 1
(.20) (.00) (.00)
.29 5. Concentration index
(.21)
-.215 -.21 .046 .144 1
(.00) (.00) (.00) (.00)
-.018 6. R&D expenses/total assets(*)
(.05)
.121 -.4 -.435 -.08 -.03 1
(.01) (.00) (.00) (.07) (.53)
.014 7. Advertising expenses/total assets
(.02)
.095 -.04 -.016 -.06 .029 .029
(.03) (.32) (.72) (.21) (.50) (.51)
1
67.18 8. Age of firm
(43.79)
-.232 .052 .2 .258 .1 -.31
(.00) (.23) (.00) (.00) (.02) (.00)
.14 1
(.00)
1.69 9. Number of acquisitions
(2.78)
.195 -.04 .308 .001 -.09 .035
(.00) (.03) (.00) (.97) (.03) (.43)
-.17 -.13 1
(.00) (.00)
.15 10. Three-year compounded annual
(.31) growth rate
.37 -.08 .182 -.16 -.16 .031
(.00) (.06) (.00) (.00) (.00) (.47)
-.09 -.38 .098 1
(.05) (.00) (.02)
-.096 11. Corporate branding dummy
(.94)
.254 .095 -.129 -.11 -.07 .191
(.00) (.03) (.00) (.01) (.10) (.00)
-.2 -.37 -.13 .224 1
(.00) (.00) (.00) (.00)
-.38 12. House-of-brands dummy
(.67)
.23 .181 -.142 .011 .001 .094
(.00) (.00) (.00) (.80) (.97) (.03)
-.03 -.19 .123 .114 .73 1
(.54) (.00) (.00) (.01) (.00)
-.0055 13. Corporate branding dummy
(.02) x advertising/total assets
.03 .009 -.168 -.02 .004 .038
(.49) (.83) (.00) (.67) (.97) (.39)
-.72 -.27 .127 .175 .51 42
(.00) (.00) (.00) (.00) (.00) (.00)
1
-.0058 14. House-of-brands dummy
(.02) x advertising/total assets
.001 .074 -.131 .048 .085 -.02
(.97) (.09) (.00) (.27) (.05) (.70)
-.55 -.13 .093 .077 .35 .55
(.00) (.00) (.03) (.08) (.00) (.00)
.84 1
(.00)
(*) Indicates median-adjusted.
Notes: Two-tailed significance levels are shown in parentheses
for correlations. Legend for Chart:
A - Conjecture on the Sign of the Coefficient
B - Model 1A: No Interactions
C - Model With
D - 1B: Interactions
A
B C D
Constant
.52 (2.04) .60 (2.29)
Operating margin(*)
+ 8.26 (10.23) 8.21 (10.07)
Leverage(*)
? (+/-) -4.44 (-6.64) -4.54 (-6.61)
Focus(*)
? (+/-) .013 (.60) .01 (.57)
Industry concentration
- -.70 (-1.76) -.64 (-1.58)
R&D expenditures/assets(*)
+ 6.05 (3.09) 5.84 (2.94)
Advertising/assets
+ 17.63 (4.57) 13.28 (2.20)
Age of firm
- -.003 (-1.37) -.003 (-1.41)
Number of acquisitions
- .01 (.36) .01 (.32)
Three-year compounded annual growth rate
+ 1.69 (5.87) 1.69 (5.87)
Corporate branding dummy
+ .32 (2.36) .26 (1.47)
House-of-brands dummy
- -.09 (-.51) .11 (.44)
Advertising x corporate branding dummy interaction
-- 1.12 (.11)
Advertising x house-of-brands dummy interaction
-- -7.79 (-.92)
Number of firms
113 113
Sample size
531 531
Adjusted R²
.393 .393
F-ratio; d.f.; p-value
32.35; 11, 27.40; 13,
520; .000 518; .000
(*) Indicates that variables are median-adjusted.
Notes: t-values are in parentheses. Legend for Chart:
A - Variable
B - Mean
C - Pseudo t-Values
A B C
Operating margin(*) 7.840 (7.62)
Leverage(*) -4.447 (-5.33)
Focus(*) .037 (1.78)
Industry concentration -.902 (-1.60)
R&D expenditures/assets(*) .265 (.11)
Advertising/assets 19.920 (3.89)
Age of firm -.006 (-1.76)
Number of acquisitions .013 (.38)
Three-year compounded annual growth rate .660 (1.70)
(*) Indicates that variables are median-adjusted. Legend for Chart:
B - Corporate Branding
C - House of Brands
D - Mixed Branding
A B C D
Mean .472 -.195 -.277
Standard deviation .965 .242 1.14
Range (-1.80, 3.96) (-1.09, .63) (-4.20, 2.53) Legend for Chart:
A - Manifest Branding Strategy
B - Best Strategy for Improving Tobin's q (Inferred) Corporate
Branding
C - Best Strategy for Improving Tobin's q (Inferred) House of
Brands
D - Best Strategy for Improving Tobin's q (Inferred) Mixed
Branding
E - Best Strategy for Improving Tobin's q (Inferred) Total
A B C D E
Corporate branding 20 7 18 45
House of brands 6 2 4 12
Mixed branding 30 9 17 56
Total 56 18 39 113DIAGRAM: Figure 1; A Framework for Our Analysis
GRAPH: Figure 2; Firm-Level Estimates of Effects of Branding Strategy
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By Vithala R. Rao; Manoj K. Agarwal and Denise Dahlhoff
Vithala R. Rao is Deane W. Malott Professor of Management and Professor of Marketing and Quantitative Methods, Johnson School of Management, Cornell University (e-mail: vrr2@cornell.edu). Manoj K. Agarwal is Associate Professor of Marketing, School of Management, Binghamton University (e-mail: agarwal@binghamton.edu). Denise Dahlhoff is Senior Client Manager, Centers of Excellence, ACNielsen (e-mail: denise.dahlhoff@acnielsen.com).
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Record: 76- How Organizational Complaint Handling Drives Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach. By: Homburg, Christian; Fürst, Andreas. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p95-114. 20p. 2 Diagrams, 5 Charts. DOI: 10.1509/jmkg.69.3.95.66367.
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How Organizational Complaint Handling Drives Customer
Loyalty: An Analysis of the Mechanistic and the Organic Approach
This article addresses how an organization's complaint management affects customer justice evaluations and, in turn, customer satisfaction and loyalty. In delineating an organization's complaint management, the authors draw a distinction between two fundamental approaches, the mechanistic approach (based on establishing guidelines) and the organic approach (based on creating a favorable internal environment). The empirical analysis is based on a dyadic data set that contains managerial assessments of companies' complaint management and complaining customers' assessments with respect to perceived justice, satisfaction, and loyalty. Findings indicate that though both the mechanistic and the organic approach significantly influence complaining customers' assessments, the mechanistic approach has a stronger total impact. Moreover, the study provides evidence of a primarily complementary relationship between the two approaches. Another key facet of the study is related to the moderating influences of the type of business (business-to-business versus business-to-consumer) and type of industry (service versus manufacturing). The results show that the beneficial effects of the mechanistic approach are stronger in business-to-consumer settings than in business-to-business ones and for service firms than for manufacturing firms.
Despite organizational precautions, problems can occur in the relationship between a company and a customer. Thus, firms are regularly confronted with complaining customers. At this critical stage of a relationship, complaint handling embodies the acid test of a firm's customer orientation. Whereas a poor recovery may result in "magnification of the negative evaluation" (Bitner, Booms, and Tetreault 1990, p. 80), an excellent recovery can increase customer satisfaction and loyalty beyond the degree before the failure (e.g., Smith and Bolton 1998). The relevance of complaint management is also emphasized by studies indicating that its return on investment can be high, sometimes exceeding 100% (Technical Assistance Research Program 1986).
However, there is ample evidence that many companies do not handle complaints effectively. It has been reported that approximately half of the complaining customers are dissatisfied with complaint handling (e.g., Estelami 2000; Grainer 2003). This provides support for Tax, Brown, and Chandrashekaran's (1998, p. 60) statement that many "firms are not well informed … on how to deal successfully with … failures" and for Andreassen's (2001, p. 47) claim that "companies in general must improve their complaint resolution efforts dramatically."
Whereas many complaint studies have analyzed customer behavior (e.g., Singh 1988; Smith and Bolton 1998), there is a lack of research from a company perspective. As Singh and Widing (1991, p. 30) note, "[Q]uestions such as 'What complaint resolution mechanisms are successful?' … have remained largely unexplored." This is also emphasized by Davidow (2003, p. 247), who identifies the following neglected research questions: "Which organizational response affects which type of justice? [and] Which organizational factors most influence the customer's feeling of fairness?" Research that addresses these questions should use data that combine the company and the customer perspective. However, we are not aware of a study in this area based on dyadic data.
Our study attempts to fill these research gaps. We analyze how a company's complaint management affects customer justice evaluations and, eventually, satisfaction and loyalty. In doing so, we introduce a distinction between two fundamental approaches to complaint management, the mechanistic and the organic approach. Our analysis of how these two approaches affect customer evaluations is based on a dyadic sample (i.e., data from companies and their customers). Unlike previous complaint research, our sample covers both the business-to-business (B2B) context and the business-to-consumer (B2C) context and includes service and manufacturing companies. This enhances external validity and enables us to analyze the relative importance of the two approaches in different business and industry settings.
Theoretical Background: The Mechanistic and the Organic
Approach
Our study is primarily rooted in an important theoretical perspective in organizational science that is sometimes referred to as the "behavioral theory of the firm" (e.g., Cyert and March 1992). According to this theory, human beings are characterized by bounded rationality (i.e., limited cognitive capabilities and incomplete information) so that "their actions may be less than completely rational" (March 1994, p. 9). This literature identifies several approaches for influencing employee behavior; two such approaches are particularly relevant for complaint handling.
First, companies can influence individual behavior by developing guidelines (referred to as "standard operating procedures"; e.g., March and Simon 1993, p. 166) for specific activities. In doing so, a firm "does not seek to convince the subordinate, but only to obtain his acquiescence" (Simon 1997, p. 201) to act in the intended manner. The once-and-for-all decision "that a particular task shall be done in a particular way … relieves the individual who actually performs the task of the necessity of determining each time how it shall be done" (Simon 1997, p. 112), and therefore it enables a more rational decision making (March and Simon 1993; Simon 1997). Following terminology in the field of organizational theory (e.g., Burns and Stalker 1994; Mintzberg 1979), we refer to this approach as the mechanistic approach. This approach to guide behavior is closely linked to the "organization as machine" paradigm (e.g., March and Simon 1993; Scott 1998).
Second, organizations can influence behavior by focusing on training and motivating employees and by providing them with shared values and norms. Rather than developing specific guidelines on how to behave in certain situations, this approach aims to establish "in the … employee himself … a state of mind which leads him to reach that decision which is advantageous to the organization" (Simon 1997, p. 9). Human resource management (HRM) and the design of the organizational culture ensuring the "right kind of people and behaviors" form the core of this approach. This organic approach is rooted in the "organization as organism" paradigm (e.g., Burns and Stalker 1994; Scott 1998), which becomes especially visible in its description that the firm "injects into the very nervous systems of the organization members the criteria of decision that the organization wishes to employ" (Simon 1997, p. 112).
Role theory provides further support for the relevance of these two approaches. According to this theory, customer-contact personnel have a strong need for clarity on how managers and customers expect them to perform their jobs (e.g., Bush and Busch 1981; Teas, Wacker, and Hughes 1979). A lack of role clarity has a negative impact on job performance (e.g., Churchill et al. 1985). Research shows that both the mechanistic and the organic approach can significantly contribute to role clarity. For example, Jaworski, Stathakopoulos, and Krishnan (1993) demonstrate that role clarity is highest when there is a strong focus on both standard operating procedures and a supportive cultural environment.
In general, it is accepted that firms can use both approaches simultaneously (e.g., March and Simon 1993; Simon 1997). For example, Simon (1997, p. 9) stresses that "[i]t is not insisted that these categories [for influencing employee behavior] are … mutually exclusive."
Conceptual Framework and Constructs
Our unit of analysis is a company and its complaining customers. Following our previous discussion, our framework (see Figure 1) includes constructs related to guidelines for complaint handling (mechanistic approach) and a construct that captures how favorable the internal environment is with respect to complaint handling (organic approach). We assume that both approaches affect customer justice evaluations with respect to complaint handling. In turn, we expect these to influence customer satisfaction evaluations and, ultimately, loyalty. Davidow (2003) suggests the analysis of such a causal chain as an avenue for further research.
Furthermore, our framework includes moderating effects on the links between complaint-handling guidelines and customer justice evaluations. First, we suggest that these relationships are moderated by the supportiveness of the internal environment. Second, we propose that these links are moderated by the type of business and the type of industry (see Figure 1).
Mechanistic approach. Because complainants base their evaluations on perceptions of the complaint process, interpersonal treatment, and complaint outcome (e.g., Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998), our study considers process, behavioral, and outcome guidelines. Because "institutions … prosper as their standard practices come to match … the demands … of the external world" (March 1994, pp. 77-78), a key aspect of our conceptualization of the quality of complaint-handling guidelines is related to their degree of customer orientation. Further aspects cover their clarity and simplicity (e.g., Bailey 1994; Berry 1995).
We define the "quality of process guidelines for complaint handling" as the degree to which a formal organizational procedure for registering and processing customer complaints exists and is consistent with complainants' needs. This construct captures whether time standards exist that ensure a fast complaint-handling process (e.g., Technical Assistance Research Program 1986) and whether staff is required to inform customers about the status of their complaint within a reasonable period of time (e.g., Andreassen 2000; Berry 1995). Moreover, it includes instructions to record and forward complaints in a quick, complete, and structured way (e.g., Bailey 1994; Van Ossel and Stremersch 1998).
We define the "quality of behavioral guidelines for complaint handling" as the degree to which an explicit organizational policy for employees' behavior toward complainants exists and is consistent with complainants' needs. This construct includes directions for employees to be polite, helpful, and understanding while interacting with complainants as well as to show concern and take responsibility for customer problems (e.g., Bailey 1994; Tax and Brown 1998).
Finally, we define the "quality of outcome guidelines for complaint handling" as the degree to which a formal organizational policy for providing compensation to complainants exists and fits customers' needs. Types of compensation include correction, replacement, discount, and refund (e.g., Kelley, Hoffman, and Davis 1993). For example, the construct addresses the question whether a company gives employees who are responsible for complaint handling the decision authority that is necessary to provide outcomes in such a way that complainants are satisfied (e.g., Hart, Heskett, and Sasser 1990). Furthermore, it encompasses the extent to which guidelines for complaint handling allow for a generous compensation (e.g., Fornell and Wernerfelt 1987) and includes instructions that the form of the outcomes should match complainants' needs (e.g., Mattila 2001).
Organic approach. We define the "supportiveness of the internal environment with respect to complaint handling" as the degree to which HRM practices and the organizational culture favor effective complaint handling. This construct includes the extent to which personnel-related activities (i.e., professional/technical training and leadership behavior, such as setting goals and evaluating and rewarding performance) support employees' customer orientation in general and customer orientation toward complainants in particular (e.g., Berry 1995; De Ruyter and Brack 1993; Maxham and Netemeyer 2003). Another facet relates to the customer orientation of the corporate culture (i.e., shared values, norms, and behaviors) in general (e.g., Deshpandé and Webster 1989). Moreover, this construct includes the existence of a positive attitude toward complaints (e.g., Johnston 2001) and a constructive attitude toward failures (i.e., whether failures are viewed as a chance for organizational learning) (e.g., Tax and Brown 1998).
Justice theories explain people's reactions to conflict situations (e.g., Gilliland 1993; Lind and Tyler 1988). Because a problem with a company (along with a subsequent complaint) is a typical example of a conflict situation, the "concept of perceived justice offers a valuable framework for explaining customers' reactions to complaint episodes" (Blodgett, Hill, and Tax 1997, p. 186). We conceptualize perceived justice of complaint handling as a three-dimensional construct that includes procedural, interactional, and distributive justice (e.g., Clemmer 1993; Smith, Bolton, and Wagner 1999). Whereas these constructs refer to complainants' perceptions of employee behavior (thus taking a customer perspective), the constructs related to process, behavioral, and outcome guidelines as well as the internal environment capture organizational activities that aim to influence employee behavior (thus adopting a company perspective).
"Procedural justice" reflects the perceived fairness of the complaint-handling process. In our study, this construct includes the facets of timeliness (e.g., Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998) and process control (i.e., customers' opportunity to express feelings about the problem and to present information relevant to the firm's decision about the complaint outcome; e.g., Goodwin and Ross 1992; Tax, Brown, and Chandrashekaran 1998). "Interactional justice" refers to the perceived fairness of the behavior that employees exhibit toward complainants. It includes customer perceptions of employee empathy (e.g., Tax, Brown, and Chandrashekaran 1998), employee politeness (e.g., Goodwin and Ross 1989), and employee effort (i.e., the amount of energy spent to solve a problem; e.g., Smith, Bolton, and Wagner 1999). "Distributive justice" describes the fairness of the complaint outcome as the customer perceives it. It includes the facets of equity (i.e., whether the firm and the complainant obtain the same outcome-to-input ratio;( n1) e.g., Tax, Brown, and Chandrashekaran 1998), equality (i.e., whether the complainant receives the same outcome compared with prior complaint experiences with the company; e.g., Tax, Brown, and Chandrashekaran 1998), and need consistency (i.e., whether the outcome meets the requirements of the complainant; e.g., Smith, Bolton, and Wagner 1999).
"Complaint satisfaction" refers to the degree to which the complainant perceives the company's complaint-handling performance as meeting or exceeding his or her expectations (e.g., Gilly and Gelb 1982; McCollough, Berry, and Yadav 2000). "Overall customer satisfaction after the complaint" refers to the degree to which the complainant perceives the company's general performance in a business relationship as meeting or exceeding his or her expectations (e.g., Anderson and Sullivan 1993). This type of satisfaction is cumulative in nature, whereas complaint satisfaction reflects a form of transaction-specific satisfaction (e.g., Bolton and Drew 1991; McCollough, Berry, and Yadav 2000). "Customer loyalty after the complaint" refers to the degree to which a customer has continued the relationship with a company after the complaint and the degree to which he or she intends to do so in the future.
Hypotheses Development
According to the behavioral theory of the firm, guidelines can influence employees to act as the company desires them to act (e.g., March and Simon 1993; Simon 1997). Moreover, guidelines can increase role clarity by informing customer-contact employees how to perform their jobs (e.g., Cummings, Jackson, and Olstrom 1989; Michaels, Day, and Joachimsthaler 1987). The more such guidelines are customer oriented, the lower are employees' perceptions of incompatibility between role expectations from managers and customers, respectively, thus reducing role conflict (for empirical evidence, see Singh, Verbeke, and Rhoads 1996). High levels of role clarity and low levels of role conflict enhance employees' ability to serve customers, thereby improving customer evaluations (Chebat and Kollias 2000; Hartline and Ferrell 1996). Thus, in line with complaint literature (Davidow 2003; Sparks and McColl-Kennedy 2001), we argue (on a general level) that the quality of guidelines for complaint handling positively affects customer justice evaluations of complaint handling by ensuring customer-oriented employee behavior.
For example, the quality of process guidelines is related to time standards and thus is positively linked to the actual speed of complaint handling. Moreover, this construct includes instructions to increase the likelihood that staff will provide timely feedback to customers about the status of their complaints. Such feedback improves customers' perceptions of how quickly their complaint is handled (Gilly 1987). Together, this supports our prediction that the quality of process guidelines increases the perceived timeliness of complaint handling and thus procedural justice. This conclusion is also in line with Smith, Bolton, and Wagner's (1999) experimental findings. Furthermore, instructions to record and forward complaints in a complete and structured manner enhance the probability that staff will give complainants the opportunity to explain their problem, thereby increasing customer perceptions of process control. This conclusion is supported by empirical results that show that "voice" (i.e., customers' chance to communicate their problems to the company) enhances procedural justice (Goodwin and Ross 1992; Hui and Au 2001).
The mere existence of behavioral guidelines for complaint handling shows staff the importance of their interaction style with complaining customers. Combined with a customer-oriented content, this contributes to an adequate interpersonal treatment of complainants (Bailey 1994; Berry 1995) and, in turn, to customer perceptions of empathy, politeness, and effort. In support of this view, empirical studies indicate that employees' customer-oriented interaction style enhances perceived fairness of complaint handling (Goodwin and Ross 1989; Maxham and Netemeyer 2003) and customer satisfaction (e.g., Bitner, Booms, and Tetreault 1990).
By empowering and encouraging employees to provide generous redress, outcome guidelines increase the probability that complainants will receive fair compensation (e.g., Berry, Zeithaml, and Parasuraman 1990). In turn, this enhances customer perceptions of equity (e.g., Smith, Bolton, and Wagner 1999). Furthermore, by adhering to outcome guidelines, staff are likely to provide similar forms and levels of compensation across complainants and over time (Sparks and McColl-Kennedy 2001), thereby increasing customers' perceived equality of the complaint outcome (Tax, Brown, and Chandrashekaran 1998). Moreover, staff's decision authority to award a satisfactory compensation and the instruction to offer redress according to customers' wishes increase the likelihood that employees will fulfill complainants' requirements (Chebat and Kollias 2000; Hart, Heskett, and Sasser 1990). In turn, this leads to customer perceptions of need consistency (Smith, Bolton, and Wagner 1999). In summary, we predict the following:
H1: There is a positive impact of the quality of (a) process guidelines for complaint handling on perceived procedural justice, (b) behavioral guidelines for complaint handling on perceived interactional justice, and (c) outcome guidelines for complaint handling on perceived distributive justice.
The behavioral theory of the firm suggests that by training and motivating employees and by indoctrinating them with shared values and norms, they can be influenced "to make decisions … as the organization would like [them] to decide" (Simon 1997, p. 112). In addition, by clarifying expectations about how to act as an organizational member, the internal environment increases staff perceptions of role clarity (Zeithaml, Berry, and Parasuraman 1988). Moreover, in line with Singh, Verbeke, and Rhoads (1996), we argue that the more the internal environment supports customer-oriented complaint handling, the less likely employees are to perceive incompatibility between role expectations from the internal environment and complainants, respectively, thus reducing role conflict. In turn, high levels of role clarity and low levels of role conflict increase staff ability to serve customers, thereby improving customer evaluations (Chebat and Kollias 2000; Hartline and Ferrell 1996).
More specifically, with respect to a firm's HRM, studies show that adequate staff training and leadership behavior enhance perceived role clarity and reduce perceived role conflict (e.g., Kohli 1985; Shoemaker 1999) as well as contribute to employees' customer-oriented behavior in general (e.g., Grönroos 2000; Jaworski and Kohli 1993) and customer-oriented handling of complaints in particular (e.g., De Ruyter and Brack 1993). In turn, this increases customer satisfaction in general (e.g., Heskett et al. 1994) and complainants' perceptions of procedural, interactional, and distributive justice in particular (e.g., Maxham and Netemeyer 2003).
Perceptions of a firm's culture indicate to employees what is expected of them (Grönroos 2000; Heide and John 1992), thus further contributing to role clarity (e.g., Jones, Busch, and Dacin 2003). The more that customer-contact staff perceive their firm's culture to be customer oriented, the lower is perceived role conflict and the stronger is customer-oriented behavior (e.g., Siguaw, Brown, and Widing 1994), resulting in higher levels of customer satisfaction (e.g., Humphreys and Williams 1996). In addition to the importance of a corporate culture's general customer orientation (e.g., Cook and Macaulay 1997), complaint literature also emphasizes the particular relevance of a positive attitude toward complaints (e.g., Johnston 2001) and of a constructive attitude toward failures (e.g., Tax and Brown 1998). The presence of such attitudes is likely to lead to corresponding employee behavior (e.g., Kraus 1995), which in turn ensures customer perceptions of a fair complaint-handling process, interpersonal treatment, and complaint outcome (e.g., Maxham and Netemeyer 2003). Against this background, we hypothesize the following:
H2: The supportiveness of the internal environment with respect to complaint handling has a positive impact on (a) perceived procedural justice, (b) perceived interactional justice, and (c) perceived distributive justice.
To establish a causal chain between the two fundamental approaches of complaint handling and their ultimate outcome (i.e., customer loyalty), our model includes several additional effects. However, because these effects are well established in the literature, we do not develop explicit hypotheses for them. Specifically, our model includes (presumably positive) effects of customer justice evaluations of complaint handling on complaint satisfaction (e.g., Smith, Bolton, and Wagner 1999); an (presumably positive) effect of complaint satisfaction on overall customer satisfaction (e.g., McCollough, Berry, and Yadav 2000) and customer loyalty (e.g., Gilly and Gelb 1982), respectively; and a (presumably positive) link between overall customer satisfaction and customer loyalty (e.g., Mittal, Ross, and Baldasare 1998).
Supportiveness of internal environment. We argue that the more a firm has succeeded in establishing an internal environment that favors effective complaint handling, the less it needs customer-oriented guidelines to define how complaints should be handled. This is consistent with writings on the behavioral theory of the firm. For example, Simon (1997, p. 311) stresses that the effectiveness of approaches to influence staff behavior, such as implementing guidelines, depends on "the training and competence of the employees," which represents a key facet of the internal environment. More specifically, the more staff are trained and competent, the less a firm needs specific guidelines and other forms of instructions (Simon 1997). Role theory also supports this reasoning. As we mentioned previously, in a company with an internal environment that clearly favors a customer-oriented handling of complaints, there is a high level of perceived role clarity and a low level of perceived role conflict among complaint-handling staff. Therefore, in such a case, there is less of a need to implement specific guidelines to clarify that complaints should be handled in a customer-oriented way. Therefore, we predict the following:
H3: The supportiveness of the internal environment with respect to complaint handling has a negative moderating effect on the relationship between the quality of (a) process guidelines for complaint handling and perceived procedural justice, (b) behavioral guidelines for complaint handling and perceived interactional justice, and (c) outcome guidelines for complaint handling and perceived distributive justice.
Type of business (B2B versus B2C). Theoretical (e.g., Cooke 1986; Lilien 1987) and empirical (e.g., Avlonitis and Gounaris 1997; Coviello and Brodie 2001) work support the notion that business markets differ from consumer markets along several dimensions, leading to various degrees of effectiveness of marketing management approaches depending on the type of business (i.e., B2B or B2C). Characteristics of business markets include, among others, a small number of customers, long-term business relationships, and a high degree of interaction between members of the supplier and the customer company (e.g., Nielson 1998; Webster 1978).
In line with resource dependence theory (e.g., Pfeffer and Salancik 1978), we argue that in business markets, the smaller number of customers and the prospect of a long-term relationship lead to a greater dependence of firms on individual customers. Therefore, to maintain the relationship, staff in B2B companies are likely to provide fair complaint treatment even if there are little guidelines for complaint handling. Thus, there is less of a need for firms that operate in business markets to ensure fair complaint handling by establishing specific guidelines.
Moreover, because of the intensive interaction in long-term B2B relationships, there are often established communication patterns (e.g., Hillebrand and Biemans 2003) and behavioral norms (e.g., Heide and John 1992) in these relationships. This view is supported by Campbell (1998, p. 199), who describes interaction in B2B relationships as "shaped by accepted social guidelines or norms which have become institutionalized." In this case, complaint-handling guidelines, which are relatively standardized across customers, can even have detrimental effects because their content may contradict existing communication patterns and behavioral norms.
In addition, because of the smaller number of customers, the long-term character of relationships, and the higher degree of interaction, a typical "industrial company is often more knowledgeable about its customers and their needs than is the typical ... consumer company" (Webster 1978, p. 22). Thus, in line with role theory, staff in B2B firms are typically less uncertain about how to deal with a complainant, which results in higher levels of role clarity. Thus, in a B2B context, specific guidelines are necessary to a lesser extent. Therefore, we predict the following:
H4: In a B2B context, the impact of the quality of (a) process guidelines for complaint handling on perceived procedural justice, (b) behavioral guidelines for complaint handling on perceived interactional justice, and (c) outcome guidelines for complaint handling on perceived distributive justice is weaker than it is in a B2C context.
In contrast, we believe that the importance of a favorable internal environment with respect to complaint handling does not depend on the business context but rather should be the same in B2B and B2C settings. Thus, we do not find compelling arguments for why the type of business may also moderate the effect of the internal environment on perceived justice. However, we also explore this issue empirically.
Type of industry (service versus manufacturing). In the marketing literature, the inherent differences between services and goods and the resulting implications for marketing management are widely accepted (e.g., Lovelock 1981; Zeithaml and Bitner 2000). Two characteristics of services, the inseparability of production and consumption and the high degree of heterogeneity (e.g., Zeithaml, Parasuraman, and Berry 1985), are particularly relevant for our study.
The inseparability of production and consumption implies that customers must be present during the service production process, which leads to a high degree of personal interaction with service employees. Thus, in a service context, a significant part of complaints is voiced in a face-to-face situation in which frontline staff have considerable freedom in terms of how they react. In addition, the physical presence of the customer typically demands a quick reaction (e.g., Grönroos 1988). Because stress situations such as these increase the likelihood that employees make mistakes (e.g., Sales 1970), adequate guidelines for complaint handling are particularly important in a service setting. Moreover, Goodwin and Ross (1990, p. 59) show that complainants perceive that "they were treated more fairly when they believed the provider followed company procedure, as opposed to circumstances when they believed the provider's decision was ad hoc or arbitrary." Because service customers are more often physically present when their complaint is handled, they are more likely to notice whether employees follow specific company procedures, which in turn leads to the stronger impact of complaint-handling guidelines in a service context.
A further characteristic of services is the high degree of heterogeneity in terms of performance output. This is especially an issue for personnel-intensive services because "[m]any different employees may be in contact with an individual customer, raising a problem of consistency of behavior" (Langeard et al. 1981, p. 16). Moreover, "[p]eople's performance day in and day out fluctuates up and down" (Knisely 1979, p. 58). Combined with customers' need to be involved in the service delivery process, these issues enhance customers' perceptions of the risk associated with a problem (e.g., Guseman 1981; Murray and Schlacter 1990). Thus, in the case of a service failure, a firm's reliability with respect to complaint-handling performance is particularly relevant to customers, which is consistent with empirical research that emphasizes reliability as a key dimension of perceived service quality (e.g., Zeithaml, Parasuraman, and Berry 1990). Several authors (e.g., March 1994; March and Simon 1993) stress that guidelines can increase the reliability of employee behavior. In turn, perceptions of reliability enhance customer justice evaluations (Leventhal 1980). Thus, in line with literature that stresses the importance of task standardization in a service context (e.g., Zeithaml, Berry, and Parasuraman 1988), we expect guidelines for complaint handling to be more relevant for service companies. Thus, we hypothesize the following:
H5: For service firms, the impact of the quality of (a) process guidelines for complaint handling on perceived procedural justice, (b) behavioral guidelines for complaint handling on perceived interactional justice, and (c) outcome guidelines for complaint handling on perceived distributive justice is stronger than it is for manufacturing firms.
However, we regard a favorable internal environment with respect to complaint handling as equally important in all types of industry. Thus, in our view, there are no persuasive arguments for why the distinction between service and manufacturing firms should also moderate the effect of the supportiveness of the internal environment on perceived justice. Nevertheless, we also analyze this issue empirically.
Methodology
In the first phase, we identified a company sample (1786 firms) using data from a commercial provider. The sample covered a broad range of services and manufacturing industries and was restricted to firms with at least 200 employees and an annual revenue of at least $50 million. For 1707 firms, we succeeded in identifying the manager with primary responsibility for complaint management. Subsequently, a questionnaire was sent to these managers. After three weeks, we followed up with telephone calls. As a result, we received 379 useable questionnaires, resulting in a reasonable response rate of 22.2%. We assessed nonresponse bias by comparing early with late respondents (Armstrong and Overton 1977). Moreover, we examined whether the firms we initially addressed and the responding firms differed in terms of size or industry. The findings provide evidence that nonresponse bias is not a problem with the data.
In the second phase, we contacted the responding 379 managers again and requested a list of ten customers who had complained to their firm within the past three months and who had been typical with respect to the reason for complaint, importance to the company, and type of customer. Incentives for managers included a report about customer feedback (on an aggregate basis) and the free participation in a conference on complaint management. In total, 110 managers provided this information, resulting in a response rate of 29.0%. Given the high confidentiality of customer information, this can be considered a satisfactory response. Reasons for declining included legal issues, general firm policies, and lack of support from top management.
In the third phase, we conducted telephone interviews with complainants. For the purpose of motivation, we assured customers that the company in question would receive their feedback in an anonymous form, which in turn might contribute to preventing the problem they had experienced from reoccurring. We achieved our goal of obtaining responses from five complainants per company for all 110 firms. This resulted in a total of 550 interviews with customers.
For subsequent data analysis, we averaged the five customer responses for each firm.( n2) Thus, our data analysis is based on 110 dyads. Each dyad consists of a managerial assessment of the firm's complaint handling and five customer assessments related to perceived justice, satisfaction, and loyalty. Table 1 provides information about the company sample.
We followed standard psychometric scale development procedures (Gerbing and Anderson 1988). We created scales based on a literature review and interviews with 12 practitioners. All items (including selected sources used for scale development) appear in the Appendix.
For measuring the quality of guidelines for complaint handling and the supportiveness of the internal environment with respect to complaint handling, we created new scales because of the lack of existing scales related to a firm's complaint handling. These scales compile aspects that are discussed independently in different studies. Using a seven-point rating scale, we measured each of the three constructs related to the quality of guidelines with six items. With respect to the supportiveness of the internal environment, we also used a seven-point rating scale. Originally, we intended to use 20 items for measuring this construct. However, to establish an internally consistent scale, we eliminated 1 item (which was related to financial rewards for staff with complaint management tasks), which resulted in a total of 19 items. We measured the constructs related to customer justice and satisfaction evaluations as well as customer loyalty on a five-point rating scale. Building on prior research, we operationalized procedural, interactional, and distributive justice with three, five, and four items, respectively, and we assessed complaint satisfaction, overall customer satisfaction, and customer loyalty with three items. With respect to the type of business, we asked firms to indicate the share of their business that comes from business customers. Thus, this moderator variable is continuous rather than binary. To categorize service and manufacturing companies, we used our industry measure (see Table 1). Summary statistics, including means and standard deviations of all constructs (overall and by industry), appear in Table 2.
Using confirmatory factor analysis, we assessed measurement reliability and validity for each factor. Overall, the results indicate acceptable psychometric properties (see the Appendix). Each construct manifests a composite reliability greater than the recommended threshold value of .6 (Bagozzi and Yi 1988). In addition, for all constructs, the coefficient alpha values exceed .8, thus providing evidence for a high degree of internal consistency among the corresponding indicators (Nunnally 1978). For each pair of constructs, we assessed discriminant validity on the basis of Fornell and Larcker's (1981) criterion (see Table 3) and on the chi-square difference test (e.g., Bollen 1989). The results indicate that there are no problems with respect to discriminant validity.
Results
We estimated the main effects using LISREL 8.54 (Jöreskog and Sörbom 1996). The overall fit measures indicate that the hypothesized model is a good representation of the structures underlying the observed data (χ²/degrees of freedom = 1.99, goodness-of-fit index =.93, adjusted goodness-of-fit index =.93, comparative fit index = 1.00, and root mean square error of approximation [RMSEA] = .096).( n3) Figure 2 displays the results of the hypotheses testing. Solid arrows refer to explicitly hypothesized effects, and dashed arrows represent links established in prior research.
H1a, H1b, and H1c predict a positive effect of the quality of a company's guidelines for complaint handling on customer justice evaluations with respect to complaint handling. Each of these hypotheses is confirmed because each of the parameter estimates is positive and significant at the .01 level. From a conceptual point of view, these findings support the relevance of the mechanistic approach to complaint handling. Similarly, we find support for H2a, H2b, and H2c, which suggest a positive impact of the supportiveness of the internal environment on customer justice evaluations. All three parameter estimates are positive and significant at least at the .05 level, thus confirming the relevance of the organic approach in the context of complaint handling.
The findings we have reported so far refer to hypotheses in which we measured dependent and independent constructs on different sides of the dyad. We believe that the confirmation of these hypotheses by data that cross the boundaries of the firm provides strong empirical support for our theoretical reasoning. It is also worthwhile to note that the explanatory power of the model with respect to customer justice evaluations is fairly high (r² values of .39, .56, and .57). This is remarkable in the context of dyadic data because a possible common method bias has been ruled out.
With respect to the additional (not explicitly hypothesized) effects, our results confirm the presumed positive relationships between customer justice evaluations and complaint satisfaction (p <.01) and the expected positive impact of complaint satisfaction on overall customer satisfaction (p <.01) and loyalty (p <.01), respectively. However, we fail to find statistical support for the predicted positive link between overall customer satisfaction and loyalty (p >.10).
Because the moderator variables supportiveness of the internal environment and type of business are continuous, we tested the hypotheses with respect to these effects using moderated regression analysis. We averaged all scales to form a composite. As several authors (e.g., Aiken and West 1993; Cohen et al. 2002) suggest, we standardized the predictor variables by mean centering. Then, we computed interaction terms by taking the product of the mean-centered predictor variables. The results of the moderator analyses appear in Table 4.
H3a, H3b and H3c predict that the supportiveness of the internal environment negatively moderates the impact of complaint-handling guidelines on perceived justice. The results show that as we expected, in all three equations, the estimates of the predictors are positive, and the interaction effects are negative. This pattern indicates an antagonistic interaction and thus a compensatory effect of the predictors on the dependent variable (e.g., Cohen et al. 2002; Neter et al. 1996). In other words, the slopes of the three regression lines that reflect the impact of complaint-handling guidelines on perceived justice are not constant across all values of the supportiveness of the internal environment. Rather, the greater the supportiveness of the internal environment, the smaller is the effect of complaint-handling guidelines on perceived justice. More specifically, the greater the supportiveness of the internal environment, the smaller is the impact of the quality of behavioral guidelines on interactional justice (p <.01), which is consistent with H3b. Moreover, the greater the supportiveness of the internal environment, the smaller is the effect of the quality of outcome guidelines on distributive justice (p <.10), which provides (weak) support for H3c. However, there is no statistical support for H3a, which predicts that the greater the supportiveness of the internal environment, the smaller is the impact of the quality of process guidelines on procedural justice (p >.10).
With respect to the type of business, we predicted that the B2B share would negatively moderate the effect of the quality of complaint-handling guidelines on perceived justice (H4a, H4b, and H4c). Findings show that as we expected, in all three equations, the coefficients of the predictors are positive, and the interaction effects are negative. This indicates that the greater the B2B share, the smaller is the impact of complaint-handling guidelines on perceived justice. Regarding H4a, we find a significant interaction effect (p <.05), which provides statistical support for our prediction that the greater the B2B share, the smaller is the impact of the quality of process guidelines for complaint handling on procedural justice. Furthermore, our results provide (weak) support for H4c (p <.10), thus confirming our notion that the greater the B2B share, the smaller is the effect of the quality of outcome guidelines for complaint handling on distributive justice. However, with respect to H4b, we do not observe a significant interaction effect (p >.10). Therefore, there is no statistical support for our prediction that the greater the B2B share, the smaller is the impact of the quality of behavioral guidelines for complaint handling on interactional justice.( n4) Although we did not formulate hypotheses about the moderating effects of the type of business on the relationship between the supportiveness of the internal environment with respect to complaint handling and perceived justice, we did investigate it. As we expected, the three interaction effects were all nonsignificant (p >.10).
Regarding the type of industry, H5a, H5b, and H5c predict a stronger impact of the quality of complaint-handling guidelines on perceived justice for service firms than for manufacturing firms. Because this moderator variable is categorical, we ran separate regression analyses for both types and tested the significance of differences between corresponding parameter estimates using a Chow test (e.g., Chow 1960). As we expected, in each of the three pairs of equations, the coefficient for service firms is larger. In addition, these differences are all highly significant (p <.01). Thus, our results show that the quality of the three types of guidelines for complaint handling has a stronger effect on corresponding customer justice evaluations in service firms than in manufacturing firms. Although we did not put forward hypotheses regarding moderating effects of the type of industry on the link between the supportiveness of the internal environment and perceived justice, we also analyzed these effects. As we anticipated, we did not find any significant effects (p >.10).
Discussion
Our study advances academic understanding of a company's complaint management by introducing the distinction between the mechanistic and the organic approach. A worthwhile issue for discussion based on our study is whether the two approaches are related in a complementary or compensatory way (i.e., whether they supplement or exclude each other). Prior research in organizational theory (e.g., March and Simon 1993; Simon 1997) does not take a firm stand on the relationship between these two approaches in influencing staff behavior. For example, on the one hand, Simon (1997, p. 310) states that "[t]o a very great extent, these … forms of influence are interchangeable." On the other hand, he stresses (p. 177) that "the several modes of influence by no means exclude one another." Thus, we believe that it is an important contribution to clarify this relationship within a specific context (i.e., complaint management).
More specifically, we provide evidence for a primarily complementary nature of the relationship between the two approaches. First, the complementary nature becomes evident because each approach significantly affects perceived justice, even when we control for the use of the other approach. In other words, each approach explains variance in perceived justice that cannot be explained by the other approach. Second, the finding that procedural and distributive justice are more strongly affected by the mechanistic approach whereas interactional justice is more strongly driven by the organic approach (see Figure 2) also emphasizes the complementary nature of the two approaches.( n5)
In this context, another important issue is the relative importance of the two approaches. To analyze this issue, we computed the total effect on complaint satisfaction for each approach. Based on the estimated parameters (see Figure 2), the mechanistic approach has a total effect of (.55 x.23) + (.29 x.23) + (.54 x.47) =.45, whereas the organic approach has a total effect of (.36 x.23) + (.57 x.23) + (.12 x.47) =.27. Thus, the mechanistic approach is more important insofar as it has a stronger impact on complaint satisfaction.( n6) This finding is particularly interesting considering that, in general, research on complaint management focuses more on HRM and cultural issues (i.e., the organic approach) than on specific guidelines for staff (i.e., the mechanistic approach). Our study indicates that research should pay more attention to the "hard factors" of complaint management (i.e., the implementation of guidelines).
Moreover, we show that the mechanistic approach is more important in the B2C marketing context than in the B2B marketing context. This result is interesting in light of studies that question the relevance of the distinction between B2B and B2C marketing (e.g., Andrus and Norvell 1990; Coviello et al. 2002). Literature points to "a lack of consistent empirical support for the consumer/B2B dichotomy" and of studies that cover both B2B and B2C settings (Coviello and Brodie 2001, p. 389). Our study clearly indicates that for a specific context (i.e., complaint management), the B2B/B2C distinction has some relevance. In addition, we find that the relevance of the mechanistic approach is greater for service firms than for manufacturing firms. This adds to the discussion in the literature about differences between services and goods marketing (e.g., Lovelock 1981; Zeithaml and Bitner 2000) and helps redress the lack of empirical research in this area that several authors identify (e.g., Coviello et al. 2002). For a specific context (i.e., complaint management), we provide evidence that the services/goods distinction is indeed relevant for academic understanding of marketing practice.( n7) Overall, by showing that guidelines have a greater impact in a B2C and service setting, our study contributes to the debate about the circumstances under which a high formalization of organizational policies and procedures for interacting with customers is particularly appropriate.
Finally, a result that is not related to the core of this article is also worth mentioning. We find that complaint satisfaction has a strong effect on customer loyalty, but the impact of overall customer satisfaction on customer loyalty is not significant. Thus, after a complaint, loyalty depends essentially on complaint satisfaction and not as much on satisfaction that has cumulated over time. It seems that immediately following a complaint, customers' perceptions are so dominated by the way their complaint was treated that complaint satisfaction becomes the main driver of loyalty. This further emphasizes the importance of effective complaint handling.
First, although collecting data from 110 companies and their complaining customers required a lot of effort, our sample size is nevertheless relatively small. Thus, the RMSEA value of our model slightly exceeds the recommended threshold value (see n. 3), and the standard errors of the coefficients estimated using moderated regression analysis are rather large, resulting in two interaction effects that are significant only at the .10 level.
Second, we obtained responses from five complainants per company. In a B2B context, this may represent a reasonably high percentage of the total number of customers, but in a B2C context, it might be considered a relatively limited sample. Therefore, future studies should try to verify our results in a B2C context by obtaining responses from a larger number of customers.
Third, our study aimed to analyze the impact of the mechanistic and the organic approach to complaint handling on customer justice evaluations. In doing so, we did not explore a possible causality between the two approaches. Thus, further research should examine this issue in more detail. For example, it can be argued that the organic approach is an antecedent of the mechanistic approach because a company's culture may drive the implementation of guidelines. A longitudinal study would be the most appropriate way to address this issue.
Fourth, we believe that our differentiation between the mechanistic and the organic approach is also applicable to the study of other organizational phenomena in marketing, such as the antecedents of a company's market orientation. Therefore, further research might benefit from using this distinction between two fundamental approaches for influencing employee behavior.
Finally, we agree with Coviello and colleagues (2002, p. 36), who identify a lack of studies "that offer a comparison across consumer goods, consumer services, business goods, and business services firms." Thus, more empirical research should be conducted to gain further insight into similarities and differences in different business and industry settings.
A result that we consider relevant for managerial practice is related to the high importance of effective complaint management. This high importance is illustrated by our finding that customer loyalty after a complaint essentially depends on complaint satisfaction and is largely unaffected by overall customer satisfaction. In practical terms, this means that in the case of ineffective complaint handling, there is a high risk to lose even those customers who were previously highly satisfied. In other words, previous customer satisfaction does not provide a company a buffer against the consequences of ineffective complaint handling.
Our results also provide guidance on how to design a firm's complaint handling. Given the primarily complementary relationship between the mechanistic and the organic approach, our general advice for managers is to use the two approaches in combination. In particular, managers must be aware that some types of complainants' justice evaluations (i.e., procedural and distributive) can be largely influenced by establishing guidelines, whereas interactional justice can be better influenced by designing the internal environment in terms of HRM and corporate culture.
Our finding that the mechanistic approach has a stronger impact on customer evaluations than does the organic approach is also managerially relevant. In line with other authors (e.g., Jackson 2001), we believe that it has almost become a fashion in the managerial literature to emphasize the management of the soft factors, such as leadership and culture, at the expense of the hard factors, such as guidelines. Our study shows that at least for the field of complaint management, the hard factors should receive a lot of managerial attention. Conversely, some executives seem to rely almost exclusively on guidelines. Our advice for these managers is to understand that guidelines cannot cover everything. Especially for situations at the customer interface that are not (or cannot be) covered by guidelines, it is important to develop the soft factors that can serve as a safeguard in such circumstances to ensure effective complaint handling.
Another important finding is related to the relevance of the mechanistic and the organic approach in different business and industry settings. Whereas the organic approach seems to be equally important across different settings, we find that the impact of the mechanistic approach is strongest for firms marketing services to consumers (see n. 7). Thus, for this type of company, a strong emphasis on the implementation of guidelines for complaint handling is especially recommended. However, as we mentioned previously (see n. 7), even for these firms, the organic approach is somewhat important because it has a significant impact on customer evaluations. On a more general level, our study indicates that firms marketing services to consumers have a particularly strong need for relatively formal policies and procedures when interacting with customers.
Our study also provides recommendations for companies that have not yet implemented guidelines for complaint handling. Because outcome guidelines have the strongest total effect on complaint satisfaction (.54 x.47 =.25), followed by process guidelines (.55 x.23 =.13) and behavioral guidelines (.29 x.23 =.07), we advise managers to focus resources at the beginning on the implementation of outcome guidelines and then follow up with the development of process guidelines. This suggestion is particularly valid for firms with a highly supportive internal environment because in such a case, especially behavioral guidelines are less relevant. Beyond following these general recommendations, managers may use our scales related to complaint-handling guidelines and the internal environment as a checklist to assess and improve systematically the quality of their company's complaint management.
The authors thank the three anonymous JM reviewers for their valuable comments on previous versions of this article.
( n1) The complainant's outcome may include, for example, correction, replacement, discount, or refund. His or her input represents the financial and nonfinancial loss caused by the problem and the subsequent complaint statement.
( n2) Such data aggregation may be problematic if there is high variance in judgments related to the same firm. To explore this issue, we computed the intraclass correlation coefficient (ICC) ( 1) for each variable measured on the customer side. This measure can be used to assess the relative consistency of responses among raters (e.g., Bartko 1976; Kozlowski and Hattrup 1992). Therefore, ICC ( 1) is recommended in the literature as a criterion for judging the extent to which data aggregation across respondents is adequate (e.g., James 1982). In our study, ICC ( 1) values range from .22 to .29, which can be considered relatively large (e.g., Bliese 2000; James 1982). Thus, these results indicate a good consistency of responses among customers reporting on the same firm. On the basis of these results and in line with previous studies that use ICC ( 1) as a criterion for aggregating individual responses (e.g., De Jong, De Ruyter, and Lemmink 2004), we believe that our approach of averaging the five customer responses for each company is justified.
( n3) It is worth emphasizing that, all other things being equal, the RMSEA value decreases (i.e., becomes better) as the sample size increases (e.g., Hu and Bentler 1999; Rigdon 1996). Thus, our value, which might be considered a bit high, can be largely attributed to the relatively small sample size. Because the threshold value of .08 typically suggested in the literature (e.g., Browne and Cudeck 1993) does not take into account the sample size (which is considered problematic; e.g., Rigdon 1996) and in line with studies considering values up to .10 as reasonable (e.g., MacCallum, Browne, and Sugawara 1996; Steiger 1989), we believe that our RMSEA value indicates an acceptable fit of the model.
( n4) A possible explanation (suggested by an anonymous reviewer) for not finding complete support for the hypothesized moderator effects of B2B share is the following: In B2B settings, the existence of complaint-handling guidelines is more likely to be known to customers than in B2C settings. It might be argued that the greater visibility of these guidelines in a B2B context would lead to a stronger impact on perceived justice. Such an effect would run counter to our hypothesized negative moderator effects of B2B share.
( n5) It must be mentioned that our finding that the supportiveness of the internal environment negatively moderates the impact of guidelines on perceived justice does not contradict our conclusion that the relationship between the two approaches is essentially complementary. We find that after the inclusion of these moderator effects in the regression models, the effects of guidelines remain significant (see Table 4). Thus, the organic approach weakens the impact of the mechanistic approach but not to the extent that it disappears.
( n6) On the basis of an anonymous reviewer's suggestion, we ran separate analyses for each approach to explore the relative importance of the two approaches further. On an aggregate level (i.e., when a single construct was built for the mechanistic approach and perceived justice, respectively), the results show that the mechanistic approach explains 38% of the variance of perceived justice, whereas the organic approach accounts for only 29%. This finding further supports our statement that the mechanistic approach is a more important driver of complainants' evaluations.
( n7) Our results regarding the moderating effects of the type of business and the type of industry indicate that the strongest impact of the mechanistic approach on perceived justice should occur when the B2C and the service context are combined (i.e., for consumer services firms). On the basis of an anonymous reviewer's suggestion, we conducted further data analyses to explore this issue. More specifically, we computed a correlation coefficient between the aggregate measure of the mechanistic and the organic approach, respectively, and the aggregate measure of perceived justice. We did this for four settings: B2C/services, B2C/goods, B2B/services, and B2B/goods. Indeed, we found that the correlation coefficient for the mechanistic approach was by far the strongest for B2C/services (.67, pb<.01). Yet it is noteworthy that even in this context, the organic approach has a significant correlation with perceived justice (.16, p <.10).
Legend for Chart:
B - %
A: Industry B
Manufacturing Sector
Machine building 14
Chemicals/pharmaceuticals 12
Automotive 12
Electronic 11
Metal processing 11
Service Sector
Banking/insurance 16
Retailing 14
Transport 5
Others 5
B: Position of Respondents B
Head of complaint management 23
Head of quality management 23
Head of customer service 16
Vice president of marketing, vice president sales 15
Managing director, chief executive officer,
head of strategic business unit 13
Others 9
Missing 1
C: Annual Revenues B
<$50 million 4
$50-$99 million 16
$100-$199 million 26
$200-$499 million 18
$500-$999 million 16
$1,000-$2,000 million 6
>$2,000 million 6
Missing 8
D: Number of Employees B
<200 6
200-499 17
500-999 26
1000-2499 22
2500-5000 16
>5000 12
Missing 1
Legend for Chart:
B - Range
C - Mean (Standard Deviation) Overall
D - Mean (Standard Deviation) Machine Building
E - Mean (Standard Deviation) Chemicals/Pharmaceuticals
F - Mean (Standard Deviation) Automotive
G - Mean (Standard Deviation) Electronic
H - Mean (Standard Deviation) Metal Processing
I - Mean (Standard Deviation) Banking/Insurance
J - Mean (Standard Deviation) Retailing
K - Mean (Standard Deviation) Transport
L - Mean (Standard Deviation) Others
A B C D
E F G H
I J K L
1. Quality of process 1-7 5.32 4.84
guidelines for complaint (1.31) (1.47)
handling
5.95 5.13 5.14 5.14
(.85) (1.01) (1.30) (1.25)
5.78 5.17 5.89 4.81
(1.49) (1.34) (.66) (1.85)
2. Quality of behavioral 1-7 5.40 5.27
guidelines for complaint (1.34) (1.28)
handling
5.62 5.31 5.35 4.74
(1.06) (1.38) (1.32) (1.63)
5.77 5.53 5.75 5.08
(1.56) (1.24) (.83) (1.50)
3. Quality of outcome 1-7 4.77 5.39
guidelines for complaint (1.29) (.58)
handling
5.08 4.90 4.60 4.76
(1.00) (.96) (1.59) (1.34)
4.16 5.17 4.44 3.83
(1.56) (1.03) (1.62) (1.70)
4. Supportiveness of internal 1-7 4.71 4.84
environment with respect to (.95) (.77)
complaint handling
4.86 4.65 4.85 5.03
(1.20) (.48) (.85) (.91)
4.53 4.76 4.18 4.23
(1.09) (.91) (.89) (1.51)
5. Procedural justice 1-5 3.72 3.74
(.67) (.47)
4.14 3.76 3.77 3.66
(.45) (.68) (.66) (.52)
3.52 3.73 3.71 3.22
(.80) (.65) (.87) (1.05)
6. Interactional justice 1-5 4.05 4.28
(.60) (.33)
4.18 4.24 4.10 4.14
(.75) (.41) (.58) (.41)
3.79 3.94 4.01 3.51
(.56) (.68) (.66) (.96)
7. Distributive justice 1-5 3.39 3.51
(.83) (.40)
4.09 3.49 3.61 3.65
(.32) (.84) (.69) (.55)
2.74 3.42 2.89 2.84
(1.00) (.77) (1.07) (1.04)
8. Complaint satisfaction 1-5 3.54 3.70
(.74) (.44)
4.07 3.50 3.74 3.53
(.43) (.86) (.61) (.51)
3.07 3.62 3.34 3.11
(.84) (.77) (.97) (.90)
9. Overall customer 1-5 3.90 4.10
satisfaction after the (.62) (.55)
complaint
4.31 3.62 4.01 4.00
(.33) (.67) (.51) (.38)
3.46 4.14 3.62 3.77
(.81) (.56) (.39) (.48)
10. Customer loyalty 1-5 4.29 4.58
after the complaint (.65) (.38)
4.73 4.30 4.31 4.47
(.45) (.52) (.59) (.39)
3.92 3.72 4.83 4.14
(.75) (.80) (.18) (.43) Legend for Chart:
B - Average Variance Extracted
C - Squared Correlations 1 (.58)
D - Squared Correlations 2 (.65)
E - Squared Correlations 3 (.49)
F - Squared Correlations 4 (.42)
G - Squared Correlations 5 (.76)
H - Squared Correlations 6 (.73)
I - Squared Correlations 7 (.69)
J - Squared Correlations 8 (.84)
K - Squared Correlations 9 (.85)
L - Squared Correlations 10 (.80)
A B C D E F
G H I J K
L
1. Quality of process
guidelines for
complaint handling (.58) --
2. Quality of behavioral
guidelines for
complaint handling (.65) .53 --
3. Quality of outcome
guidelines for
complaint handling (.49) .17 .21 --
4. Supportiveness of
internal environment
with respect to
complaint handling (.42) .12 .20 .28 --
5. Procedural justice (.76) .30 .32 .29 .18
--
6. Interactional justice (.73) .11 .24 .25 .41
.48 --
7. Distributive justice (.69) .06 .08 .33 .16
.45 .30 --
8. Complaint satisfaction (.84) .05 .11 .17 .07
.63 .38 .56 --
9. Overall customer
satisfaction after the
complaint (.85) .01 .08 .12 .08
.30 .29 .34 .49 --
10. Customer loyalty after
the complaint (.80) .02 .07 .07 .05
.15 .26 .16 .26 .24
-- Legend for Chart:
C - Dependent Variable Procedural Justice
D - Dependent Variable Interactional Justice
E - Dependent Variable Distributive Justice
A B C
D E
Supportiveness of Internal
Environment as Moderator
Main Effects
Quality of process guidelines .44(***)
Quality of behavioral
guidelines
.17(**)
Quality of outcome guidelines
.47(***)
Internal environment .28(***)
.56(***) .12(*)
Interaction Effects
Quality of process guidelines
x internal environment (H3a) -.07(n.s.)
Quality of behavioral
guidelines x internal (H3b)
environment
-.26(***)
Quality of outcome guidelines
x internal environment (H3c)
-.10(*)
Type of Business (B2B Versus
B2C) as Moderator
Main Effects
Quality of process guidelines .54(***)
Quality of behavioral
guidelines
.52(***)
Quality of outcome guidelines
.51(***)
B2B share .17(**)
.29(***) .25(***)
Interaction Effects
Quality of process guidelines
x B2B share (H4a) -.13(**)
Quality of behavioral
guidelines x B2B share (H4b)
-.03(n.s.)
Quality of outcome guidelines
x B2B share (H4c)
-.11(*)
Type of Industry (Service
Versus Manufacturing)
as Moderator
Quality of process guidelines .66(***)
(service sector)
Quality of process guidelines (H5a) .51(***)
(manufacturing sector) (F = 4.89(***)
Quality of behavioral
guidelines (service sector)
.63(***)
Quality of behavioral
guidelines (manufacturing (H5b)
sector)
.49(***)
(F = 12.43(***)
Quality of outcome guidelines
(service sector)
.68(***)
Quality of outcome guidelines (H5c)
(manufacturing sector)
.37(***)
(F = 10.56(***)
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: Unstandardized coefficients are shown;
n.s. = not significant.DIAGRAM: FIGURE 1 Framework and Constructs
DIAGRAM: FIGURE 2 Results of the Hypotheses Testing (Main Effects)
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Legend for Chart:
A - Construct
B - Items
C - CR/CA
A
B C
Quality of process
guidelines for
complaint handling(a)
To what extent do you agree with the .89/.88
following statements?
In our company/business unit, guidelines for
registering and processing customer
complaints ...
• Are clearly defined.
• Are relatively simple.
• Include time standards that define the
normal or maximum duration of the entire
process.
• Include instructions to inform customers
about the current status of their complaint
within a reasonable period of time.
• Include instructions to record complaint
information in a fast, complete, and
structured manner.
• Include instructions to forward complaint
information to the person in charge in a fast,
complete, and structured manner.
(Selected sources: Andreassen 2000; Bailey
1994; Berry 1995; Technical Assistance
Research Program 1986)
Quality of behavioral
guidelines for
complaint handling(a)
To what extent do you agree with the .92/.91
following statements?
In our company/business unit, guidelines for
employees' behavior toward complaining
customer ...
• Are clearly defined.
• Are relatively simple.
• Include instructions to be polite
and helpful.
• Include instructions to show concern
and understanding.
• Include instructions to take
responsibility for the problem.
• Include instructions to behave in a
customer-oriented way.
(Selected sources: Bailey 1994; Estelami 2000;
Tax and Brown 1998)
Quality of outcome
guidelines for
complaint handling(a)
To what extent do you agree with the .85/.85
following statements?
In our company/business unit, guidelines for
providing compensation to complaining
customers ...
• Are clearly defined.
• Are relatively simple.
• Give employees who are responsible for
complaint handling the decision authority which
is necessary for a satisfactory problem
resolution.
• Empower frontline employees to award
redress up to a certain degree.
• Allow for a generous redress.
• Include instructions that the type
of redress should be in line with
complainants' needs.
(Selected sources: Blodgett, Hill, and Tax 1997;
Bowen and Lawler 1995; Hart, Heskett, and
Sasser 1990; Mattila 2001; Palmer, Beggs, and
Keown-McMullan 2000)
Supportiveness of
internal
environment with
respect to
complaint handling(a)
To what extent do you agree with the .93/.93
following statements?
In our company/business unit,
• The training of employees who are
responsible for complaint management aims at
assuring their sensitivity to the importance
of customer complaints.
• Employees who are responsible for
complaint management are trained how to deal
with complaining customers.
• Managers regularly communicate complaint
management goals, customer satisfaction goals,
and customer retention goals to employees
who are responsible for complaint management.
• Managers include complaint management
goals, customer satisfaction goals, and customer
retention goals into the target definition
for employees who are responsible for
complaint management.
• The performance evaluation of employees
who are responsible for complaint management
includes the achievement of complaint management
goals, customer satisfaction goals, and
customer retention goals.
• Employees are recognized for outstanding
achievements regarding complaint management.
• Managers set a good example in terms of
high customer orientation in general and
effective complaint management in particular.
• Managers regularly communicate the
benefits of an effective complaint management
to employees who are responsible for
complaint management.
• Managers are, with regard to customer
complaints, primarily interested in preventing
failures from reoccurring rather than blaming
employees for problems.
• All employees display a high level
of customer orientation.
• The thinking and actions center around
the customer.
• Customer-oriented values and norms
are deep-seated.
• Employees have a rather negative
attitude toward customer complaints.(R)
• Employees tend to regard customer
complaints as personal criticism rather
than as an opportunity to restore customer
satisfaction.(R)
• Managers tend to regard customer
complaints as a result of own wrong decisions
rather than as an opportunity to prevent failures
from reoccurring.(R)
• Complaining customers are sometimes seen
as trouble makers or petitioners.(R)
• Employees are not fully aware of
the benefits of an effective complaint
management.(R)
• Managers and employees openly talk about
organizational problems and failures.
• Employees try to solve organizational
problems and to prevent failures from
reoccurring.
(Selected sources: Berry and Parasuraman 1991;
Cook and Macaulay 1997; De Ruyter and Brack 1993;
Deshpande and Webster 1989; Johnston 2001;
Tax and Brown 1998)
Procedural justice(b)
To what extent do you agree with the .90/.90
following statements?
• The company quickly reacted
to my complaint.
• The company gave me the opportunity to
explain my point of view of the problem.
• Overall, the company's complaint handling
procedure was fair.
(Selected sources: Smith, Bolton, and Wagner 1999;
Tax, Brown, and Chandrashekaran 1998)
Interactional justice(b)
To what extent do you agree with the .93/.93
following statements?
• The employees seemed to be very interested
in my problem.
• The employees understood exactly
my problem.
• I felt treated rudely by the employees.(R)
• The employees were very keen to solve
my problem.
• Overall, the employees' behavior during
complaint handling was fair.
(Selected sources: McCollough, Berry, and Yadav
2000; Smith, Bolton, and Wagner 1999; Tax, Brown,
and Chandrashekaran 1998)
Distributive justice(b)
To what extent do you agree with the .90/.89
following statements?
• I received an adequate compensation from
the company.
• I received about as much compensation
from the company as in the context of previous
complaints.
• In solving my problem, the company gave me
exactly what I needed.
• Overall, the compensation I received
from the company was fair.
(Selected sources: Smith, Bolton, and Wagner 1999;
Tax, Brown, and Chandrashekaran 1998)
Complaint
satisfaction(b)
To what extent do you agree with the .94/.94
following statements?
• I was not satisfied with the handling of
my complaint.(R)
• I had a positive experience when
complaining to this company.
• I was very satisfied with the complaint
handling of the company.
(Selected sources: Bitner and Hubbert 1994;
Maxham and Netemeyer 2003; Tax, Brown, and
Chandrashekaran 1998)
Overall customer
satisfaction after the
complaint(b)
To what extent do you agree with the .94/.94
following statements?
• Overall, the purchase of the product
from this company was a good decision.
• Overall, after the complaint, I was very
satisfied with the company.
• Overall, so far, I have had positive
experiences with this company.
(Selected sources: Bitner and Hubbert 1994;
Maxham and Netemeyer 2003)
Customer loyalty after
the complaint(b)
To what extent do you agree with the .92/.92
following statements?
• After the complaint, I purchased
the product of this company again.
• It is very likely that I will purchase
the product of this company again.
• I intend to remain loyal to this company
in the future.
(Selected sources: Gilly and Gelb 1982; Maxham
and Netemeyer 2003)
(a) Seven-point rating scale, anchored by "strongly agree" and
"strongly disagree."
(b) Five-point rating scale, anchored by "strongly agree" and
"strongly disagree." Questions for consumers are shown; questions
for business customers are identical except for minor wording
changes (i.e., "My company ..." rather than "I ...").
Notes: (R) = reversed item. CR = composite reliability;
CA = coefficient alpha.~~~~~~~~
By Christian Homburg and Andreas Fürst
Christian Homburg is Professor of Business Administration and Marketing and Chairman of the Department of Marketing, University of Mannheim, Germany (e-mail: prof.homburg@bwl.uni-mannheim.de)
Andreas Fürst was a doctoral student in the Department of Marketing, University of Mannheim, Germany
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Record: 77- How To Be Less Persuaded or More Persuasive: Review of Age of Propaganda: The Everyday Use and Abuse of Persuasion. By: Armstrong, J. Scott; Clark, Terry. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p129-130. 2p.
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Section: Book ReviewsHow To Be Less Persuaded or More Persuasive: Review of Age of Propaganda
The Everyday Use and Abuse of Persuasion How to Be Less Persuaded or More Persuasive: Review of Age of Propaganda: The Everyday Use and Abuse of Persuasion
Age of Propaganda:
The Everyday Use and Abuse of Persuasion
by Anthony Pratkanis and Elliot Aronson
(New York: W.H. Freeman, 2001, 416 pp., $16.95)
This second edition of the Age of Propaganda is excellent. (Should I explicitly tell you my conclusion?) I use a two-sided argument to try to convince readers to read this book. (Is a two-sided argument relevant in this situation, and if so, should I tell you the good news first or last?) I reduce the asides in the rest of this review because they are distracting (and distraction is not useful here, as the arguments to purchase the book are strong); suffice it to say, these are the types of issues that the authors address.
This book is aimed to help general readers protect themselves against propaganda spread by governments and businesses. As Pratkanis and Aronson state (p. 356), "It is our hope that knowledge about the process of persuasion will allow all of us to detect and resist some of the more obvious forms of trickery and demagoguery." If nearly everyone read this book and used its teachings appropriately, it might help society, but the chance of this happening is remote. However, the advice in this book is worth an enormous amount to people who employ propaganda, many of whom strike me as unaware of the persuasion techniques that Pratkanis and Aronson describe. With so much more for propagandists to gain, the book tilts the relationship in their favor; they will work harder at persuading people than people will at protecting themselves.
It seems to me that the book contains an underlying propaganda message against individual freedoms. I found the authors' liberal bias unnecessary and annoying, especially when I tried to follow their advice about how to deal with propaganda. Consider this: Pratkanis and Aronson state (p. 117) that during the Reagan years, the "relaxing of government controls [by the FTC] reopened the door to blatant abuses." On the following page, the authors state, "Ultimately we all have a responsibility to challenge all factoids." So I did just that and found that Pratkanis and Aronson provided no support for their statement on page 117.
This book is not organized for researchers. For example, it took some effort to locate relevant references at the end of the book. Also, on occasion, I would have liked more details on the methodology, conditions, or effect sizes for studies the authors describe.
Sometimes the support the authors offered for statements was anecdotal, as, for example, with respect to escalation bias (pp. 239-41). Contrary to what seems like common sense that people throw good money after bad, the evidence in favor of escalation bias is weak, as summarized by Armstrong, Coviello, and Safranek (1993).
The book has many wonderful qualities. It contains useful knowledge about persuasion for salespeople, advertisers, lawyers, doctors, educators, parents, special interest groups, managers, and others involved in the persuasion business. In addition, the descriptions of propagandists' tricks can help consumers.
Pratkanis and Aronson are widely recognized for their useful research on persuasion. Their credibility is important because some of what they recommend may contradict read-ers current beliefs or behaviors. Their evidence was usually sufficient to convince me that I was wrong. (Or do they use the distraction principle and cause me to lower my defenses?)
Pratkanis and Aronson have made a major contribution by collecting, summarizing, and organizing published research on persuasion. For the past ten years, I have been collecting studies pertaining to persuasion through advertising. Pratkanis and Aronson introduced me to more than 50 important studies that I had missed. I think that this speaks well of them, rather than poorly of me. After all, I examined thousands of studies to come up with my list. Most of the studies the authors describe are from psychology, but some are from other fields, such as marketing and law. These studies are now in the "Bibliography on Persuasion through Advertising," posted at http://advertisingprinciples.com. (I hope that you, dear reader, will point out other omissions. To be included, publications should describe empirical studies that provide evidence on what actions will persuade and under what conditions. You might start by ensuring that your research has been included.)
Pratkanis and Aronson show how interesting and useful academic research can be. For example, predict the outcome of this study (p. 25): If a panhandler asks for 17 cents or 37 cents, will he collect more donations than if he asks for 25 cents? Answer: He will receive about 60% more.
Here is another study (p. 45): Students, acting as fundraisers, went door-to-door asking for donations. With half the houses, they added one sentence to their spiel: "Even a penny would help." Did this have any effect? Answer: It nearly doubled donations.
In another study (p. 78): Is it better to ( 1) lecture students that they should be neat and tidy or ( 2) compliment them for being neat and tidy? Answer: In this study, the lecture method was useless, whereas the compliment method led to a threefold increase in the collection of litter.
With remarkable clarity, Pratkanis and Aronson present studies, translate them to principles, describe conditions under which the principles apply, and show how to use them. I found many of their principles useful for my advertising course.
The writing dances, and the book is laced with delightful examples. The examples cover real-life events such as the O.J. Simpson trial, in which Marcia Clark erred by not providing a two-sided argument. The authors start the book with a powerful example: the case of Demetrick James Walker, who was sentenced to life in prison for killing a teenager because he wanted his pair of $125 Nike Air Jordans, just like the ones advertised on television. (I have used this example to start my advertising course but dropped it when some students informed me that it was not politically correct to do so.)
I liked the authors' historical treatments of issues and the interesting facts scattered throughout the book. For example, who knew that Abraham Lincoln was widely despised in 1863? That Aristotle was the first to develop a comprehensive theory of persuasion? That universities ran courses on Principles of Advertising in the 1890s?
Pratkanis and Aronson's Age of Propaganda compares well with my favorite book on marketing, Robert Cialdini's Influence (2000). It is broader in its coverage than Cialdini's book. I consider both essential reading for those in a persuasion business. Persuasion is a big business. According to McClosky and Klamer (1995), approximately 25% of the nation's economy is involved in persuasion.
(Should I provide an explicit conclusion now? Should it be "you will gain a lot" or "don't lose out to your competitors who will be using these techniques"? As I think about it, given your interest in persuasion, you are just the type of person who can use this book effectively. Do you think it is important to provide people with feedback? Good, I thought you did. Please let me know what you thought of this review. Even a single word helps. Act now. Early replies will qualify for a special list limited to the Top Ten. My e-mail address is armstrong@wharton.upenn.edu. Meanwhile, beware of any subtle attempts at persuasion.)
REFERENCES Armstrong, J. Scott, N. Coviello, and B. Safranek (1993), "Escalation Bias: Does it Extend to Marketing?" Journal of the Academy of Marketing Science, 21, 247-53.
Cialdini, R.B. (2000), Influence: Science and Practice, 4th ed. Boston: Allyn & Bacon.
McClosky, D. and A. Klamer (1995), "One Quarter of GDP is Persuasion," American Economic Review, 85 (2), 191-95.
~~~~~~~~
By J. Scott Armstrong, University of Pennsylvania and Terry Clark, Editor, Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 78- Implementing a Customer Orientation: Extension of Theory and Application. By: Kennedy, Karen Norman; Goolsby, Jerry R.; Arnould, Eric J. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p67-81. 15p. 1 Chart. DOI: 10.1509/jmkg.67.4.67.18682.
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Implementing a Customer Orientation: Extension of
Theory and Application
The marketing literature affirms the value of a customer orientation to organizational performance, but it is relatively silent on the implementation of this orientation. This research reports the results of a paired-comparison ethnographic study of the dynamics of implementing a customer orientation in a major public school district. Changes at a progressing site are compared with those at a struggling site. The study provides answers to the question of how an organization adopts a customer orientation by refining understanding of the roles of leadership, interfunctional coordination, and the collection and dissemination of customer-focused data in the transformation process.
Considerable research has shown that organizations are more successful when they embrace a customer orientation (see Berry 1997; Day 1999b; Deshpandé, Farley, and Webster 1993; Han, Kim, and Srivastava 1998; Jaworski and Kohli 1996; Narver and Slater 1990; Narver, Slater, and MacLachlan 2000; Slater and Narver 1994, 2000). Although scholars continue to refine theory and method, the importance of customer value creation in driving organizational strategy is largely undisputed. Nevertheless, the literature is only modestly descriptive of the processes for implementing this desired orientation. The research described herein juxtaposes the prescriptions of current theory with events experienced during the transformation of an organization to a customer orientation and in so doing refines and expands theory.
Our research context is a major public school district that is establishing a customer orientation. The district manages an annual budget in excess of $800 million; employs approximately 16,000 people; and includes 140 elementary, middle, high, and specialty schools that serve more than 110,000 students. We chose this context because educational institutions have not historically embraced a customer orientation, and school administrators in this district had adopted marketing's customer orientation theory in hope of more accurately specifying external customer requirements, aligning internal efforts with those requirements, and improving performance. School district leaders set detailed outcome requirements based on extensive research with external customers, including businesses, parents, community leaders, and postsecondary educational institutions. The district required each school to align its efforts with these systemwide outcome requirements in a process that cascaded from high school to prekindergarten programs. Thus, the primary customers of middle schools are the high schools to which students are sent, and elementary schools were to satisfy the explicit requirements of their recipient middle schools. Each school implemented a process in which customers were defined as the units that received the student "output" from other units' work; for example, fourth-grade teachers are key customers of third-grade classes. Schools developed a communication process to determine each unit's customers and requirements, and they mutually agreed on methods for measuring and tracking performance.
To study this implementation of a customer orientation, we selected two elementary schools as our laboratory: one that was successfully navigating the transformation to an award-winning, customer-centered model and one that was struggling to implement a customer orientation. The metamorphoses of the schools, as surfaced through a rigorous ethnographic research effort, provide insight for marketing theory and practice. This article is organized as follows: First, we establish the theoretical context of the study. Second, we discuss the research methodology as well as introduce and describe the focal organization we selected for study. Third, we present the findings and insights from our research. Fourth, we merge these insights with current marketing theory. Finally, we provide potential research directions that emerge from the study and limitations of our research.
Market orientation literature has coalesced into an organized knowledge system, replete with theory and a body of empirically derived generalizations. Although scholars have documented the proposed benefits, the intricacies of the adoption of a market orientation have received limited attention (e.g., Deshpandé 1999; Jaworski and Kohli 1996; Webster 1994), and cultural change has emerged as a central focus (Homburg and Pflesser 2000; Narver, Slater, and Tietje 1998). From this perspective, we draw on Deshpandé, Farley, and Webster's (1993, p. 27) definition of customer orientation as "the set of beliefs that puts the customer's interest first, while not excluding those of all other stakeholders ...in order to develop a long-term profitable [viable] enterprise." As such, the cultural transformation process is difficult to investigate, and researchers agree that the understanding of implementing a customer-focused culture is inadequate (see Day 1999a; Narver, Slater, and Tietje 1998).
The gap in research also arises because the study of cultural transformation to a customer orientation spans a disciplinary boundary between academic management and marketing. Management literature discusses generic cultural transformation processes (e.g., Schein 1999; Schneider, Brief, and Guzzo 1996) but does not specifically explore the transformation to a customer orientation, the subtleties of which are important to marketers. Nevertheless, in reviewing the literature from both marketing and management, we have identified three critical organizational variables that structure our research.
First, regardless of their academic background, scholars agree that cultural transformation requires the active role of senior leadership. The management literature stresses senior leadership's role in setting organizational vision (e.g., Argyris 1966; Bass 1990; Bate 1994; House and Podsakoff 1994; Pfeffer 1977; Senge 1990) and the importance of leaders' consistency of message and action (e.g., Avolio 1999; Barabba 1995; Day 1994; Deming 1986, 1994; House and Podsakoff 1994; Kotter and Heskett 1992; Senge 1990). Marketing researchers agree that without senior leadership support, a customer orientation is unlikely to take root (see Day 1994, 1999a, b; Jaworski and Kohli 1993; Kohli and Jaworski 1990; Levitt 1960; McKitterick 1957; Narver and Slater 1990; Slater and Narver 1995). As Webster (1988, p. 37) states: "[C]ustomer-oriented values and beliefs are uniquely the responsibility of top management. Only the CEO [chief executive officer] can take responsibility for defining customer and market orientation as the driving forces." Transformations to a customer orientation are distinctive in that senior leaders must articulate organizational aims in terms that are harmonious with customer satisfaction drivers (Senge 1990), and their behavior must be consistent with customer-oriented mandates (Day 1994, 1999b; House and Podsakoff 1994; Kotter and Heskett 1992). Without managerial vision and a purpose organized to satisfy customers, employees may work conscientiously, but individually they cannot transform an organization (Day 1999b; Kotter 1995; Senge 1990).
Second, both management and marketing scholars posit that interfunctional coordination of work processes is essential to induce cultural transformation. Management researchers have historically focused on such coordinating mechanisms as cross-functional teams (e.g., Hackman 1987; Pinto, Pinto, and Prescott 1993; Rathnam, Mahajan, and Whinston 1995), formal rules and procedures (e.g., Aiken and Hage 1968; Beer, Eisenstat, and Spector 1990), conflict resolution (e.g., Bolman and Deal 1997; Bowen 1995), and reward systems (e.g., Beer and Nohria 2000). Marketing researchers focus on having customer requirements permeate all organizational activities and thus serve to coordinate the alignment required for transformation (e.g., Maltz and Kohli 1996; Narver and Slater 1990; Ruekert and Walker 1987; Webster 1988). Accordingly, for cultural transformation to succeed, a customer focus must permeate the work processes deep into the organization (e.g., Bell and Emory 1971; Kohli and Jaworski 1990; Narver and Slater 1990). As Webster (1994, p. 263) explains, "Everyone's job is defined in terms of how it helps to create and deliver value for the customer, and internal processes are designed and managed to ensure responsiveness to customer needs and maximum efficiency in value delivery."
Third, occupying a relatively unique position in the literature, some marketing researchers contend that the collection and use of market intelligence is essential for a customer orientation to develop. Although the management literature considers fact-based management virtuous (e.g., Senge 1990), marketing researchers emphasize the importance of having accurate market intelligence throughout the organization to ensure that customer requirements are met and competitive forces repelled (Webster 1994). Scholars, particularly Kohli and Jaworski (1990), posit that the generation and dissemination of market intelligence is critical to sustaining a focus on customer satisfaction and ensuring that activities are evaluated in terms of their contribution to customer value. For a customer orientation to advance, market intelligence must be disseminated by formal and informal means, and information must flow both laterally and vertically within the organization (Kohli and Jaworski 1990; Maltz and Kohli 1996; Zeithaml, Berry, and Parasuraman 1988).
Senior leadership, interfunctional coordination, and the use of market intelligence are not an exhaustive list of critical factors that affect cultural transformation. For example, variables such as performance goal-setting and organizational structure may also be significant. Nevertheless, these three factors are widely recognized, and we choose to concentrate on them partly because in our literature review we found only limited insight into implementation processes. For example, Day (1999b) and Webster (1994) provide anecdotal examples of both successful and unsuccessful organizational efforts. Narver, Slater, and Tietje (1998) offer conceptual guidance by proposing "programmatic" and "market-back" approaches for creating a market orientation. However, to our knowledge, nowhere in the literature has cultural transformation to a customer orientation been subjected to rigorous examination. By means of ethnographic research, we juxtapose current beliefs about our three factors with a real-time transformation effort; in doing so, we provide insight to further develop market orientation theory.
Because the success of our research hinged on examining cultural phenomena that occurred at multiple levels in an organization over time, an ethnographic strategy was appropriate (Burawoy 1991; Deshpandé and Webster 1989; Stewart 1998; Strauss 1990). In the following section, we profile our focal organization and then describe our multiple methods of data collection.
Focal Organization
In determining a research context, we desired a partnering organization that ( 1) had a strong commitment to the transformation to a customer orientation, ( 2) was at a relatively rudimentary level of customer orientation, ( 3) incorporated diverse organizational processes, and ( 4) gave the researchers wide access to organizational activities, data, and personnel. The "industry" we selected was public education, and the school district we studied is among the 25 largest in the United States. The school district is a large multiunit organization in an industry that historically has not explicitly considered "customers" in its operations. In response to a range of increasing concerns about performance, the focal organization devised the unprecedented goal of transforming itself by embracing a customer orientation as the centerpiece of a comprehensive improvement initiative, an arduous transformation that required substantial effort, time, resources, creativity, and patience. These unique circumstances enabled the researchers to witness real-time organizational transformation and provided a context in which the extant literature could be examined, insights discovered, and potential modifications to theory recommended.
The school district's leadership realized early on in its efforts to change that the progress of individual schools differed dramatically. To extract the lessons implicit in these differences, district leaders gave the researchers access for ethnographic study in two specific schools: one that was prospering in its transformation and one in which efforts had stalled and had been deemed largely unsuccessful. Exploration of the juxtaposed dynamics in these paired operational units provided the researchers an unusual opportunity to discover success and failure factors that might have been missed in a cross-sectional study or in a study of only successful change. The locations provided an extraordinary opportunity to study a transformation in a natural setting.
The two schools we studied are comparable, with the exception of their progress in adopting a customer orientation; the schools' socioeconomic environments, student populations, and staff configurations are comparable. Both are urban schools that have low-socioeconomic-status populations (approximately 55% of students qualify for free or reduced-price lunches). Before the study, both sites were performing at levels predicted by students' socioeconomic status, that is, approximately 40% of students were meeting grade-level expectations.
The schools participated voluntarily in the study; the researchers obtained cooperation by consulting with the respective principals and their staffs. Other than confidential student and employee records, the researchers were granted access to all documents; given permission to observe all operations, classroom interactions, and school meetings; and encouraged to interview personnel, with the caveat of protecting respondents' identities. Consistent with that commitment, we have changed the names of participants and schools; we label the schools as the "struggling site" and the "progressing site."
Research Activities
We accomplished our work in two stages. First, Goolsby worked independently for two years with the district's top leadership to explore the need for and implications of establishing a customer orientation in the school district. These activities included strategic planning meetings, creation of an implementation plan, and development of training materials. Kennedy collected on-site data at the two schools over two years. During this second stage, the researchers met periodically to discuss data, propose patterns, and question existing theory. Arnould, who has doctoral training in ethnographic research methods, served as an "auditor," critiquing and questioning the other researchers' interpretations. Our various perspectives provide for rigorous and robust examination of ethnographic data (Guba and Lincoln 1985; Thompson, Stern, and Arnould 1998; Wallendorf and Belk 1989).
On-Site Data Collection Methods
Social science research and ethnographic methodologists extol the multiple methods of data collection we employed (Bernard 1988; Campbell and Fiske 1959). Specifically, we collected observational, depth interview, focus group, and documentary evidence. Consistent with prescriptions in the works of Adler and Adler (1994), Arnould and Wallendorf (1994), Glaser and Strauss (1967), Pelto and Pelto (1978) and Sanday (1979), we used multiple methods to offset limitations of individual research techniques, to generate varying perspectives, and to enhance cross-checking and trustworthiness. As is typical of extended ethnographic research, the iterative and flexible research design encouraged exploration, enabling the emergence of additional research questions, unexpected data, and a theoretical understanding. Table 1 details the data collection procedures we used and the informants we queried, and it summarizes the results obtained as keyed to the themes of our research.
Observation. Observation provides a perspective in action (Arnould and Wallendorf 1994); insights gleaned from attending meetings, activities, and interactions served as primary data. The researchers formally recorded and transcribed 99 meetings, inclusive of all stakeholders, which ranged from meetings with 300 participants to serendipitous casual conversations. As Creswell (1994) suggests, the researchers used a written protocol for observational data collection and converted contemporaneous scratch notes into expanded field notes shortly after the observed event in order to incorporate specifics about the setting, context, actions, passages of verbatim conversations, and exchanges. As is prescribed in standard references (Bernard 1988; Emerson, Fretz, and Shaw 1995), notes contained a high level of specificity to facilitate subsequent data analysis.
As Adler and Adler (1994), Arnould and Wallendorf (1994), and Stewart (1998) advocate, the researchers became active participants only when others initiated their involvement. Because of possible reactivity or changes in participants' behavior due to the researchers' presence, involvement included answering questions or responding to advances. The researchers' extended on-site presence helped generate acceptance of data collection. Furthermore, the rapport established between researchers and participants over many months improved researchers' access to candid interactions.
Depth interviews. Intensive probing provides a perspective on action (Arnould and Wallendorf 1994). As Rousseau (1990) suggests, the researchers conducted depth interviews with 65 people to capture the breadth in cultural descriptions and to assess the similarities and differences in perspectives, including supportive and nonsupportive views. Interviews lasted for 20 to 90 minutes and were transcribed for analysis. The researchers also developed interview guides for each of the primary groups interviewed (i.e., faculty, support staff, principals, parents, and volunteers) to probe topical areas identified in the customer orientation literature. As McCracken (1988) suggests, guides help ensure that all topics of interest are discussed, yet enable the researchers to focus attention on informant responses. Use of the guides fostered a conversational style for the interviews and elicited open, unstructured responses. As Creswell (1994), McCracken (1988), and Schwartzman (1993) advise, the researchers explored topics and informant concerns as they arose. A typical, though condensed, interview guide is presented in the Appendix.
Because behavior reported in interviews typically is subject to various well-known biases, the researchers integrated interviews with observation, investigating events by observation and then systematically checking details with participating staff members immediately after the event. Arnould and Wallendorf (1994), Marshall and Rossman (1995), and Schwartzman (1993) recommend such triangulation. When a saturation point was reached (Glaser and Strauss 1967), the researchers collected no additional interviews.
Focus groups. The researchers conducted five student focus groups on a volunteer basis and with written permission from their parents and teachers. Parents that participated in two groups were chosen from the membership roles in the schools' parent--teacher associations. Following Krueger's (1994) guidelines, we used a moderator guide and audiotaped the sessions. When possible, an assistant moderator took field notes. At the close of a session, researchers offered a summary of key research questions and asked participants to verify the summary's accuracy. Immediately afterward, researchers noted the most significant ideas expressed, noteworthy quotes, and surprising findings.
Review of documents. Documents from the district and each school supplemented other data collected. Other than confidential records, district leaders gave researchers unlimited access to materials, including formal policy statements and manuals, training materials, memos, newsletters, budgets, documentary videotapes, and strategic improvement plans.
Data Analysis
To focus, simplify, and organize data from field notes and interview transcripts, we used data reduction techniques of coding, summarizing, memoing, and periodic discussions with other informed researchers. We took care to retain the context of the data while condensing it into manageable form (Emerson, Fretz, and Shaw 1995; Huberman and Miles 1994; Marshall and Rossman 1995; Miles and Huberman 1994). We used Q.S.R. NUD*IST (nonnumerical unstructured data indexing, searching, and theorizing) software to manage, explore, and search transcribed observation and interview texts. Viewing the data from the perspectives previously described, we compared observations with the current status of theory and then revised emerging themes that more accurately reflected the garnered insights.
We primarily judged trustworthiness of results (the counterpart of internal validity in research employing quantitative data) on the criteria of credibility/internal consistency and application/utilization. As Glaser and Strauss (1967) advocate, transparency of data collection methods, data depth, data similarities and variations, and systematic interresearcher questioning of interpretation offer evidence of credibility (see also Thompson, Stern, and Arnould 1998). We also evaluated credibility by asking such questions as, Do the conclusions make sense--to informed researchers, to the informants, to the audience of this research? and Are conclusions plausible to informants and other stakeholders? (Wallendorf and Belk 1989).
Trustworthiness can be assessed by research's contribution to practice (see Spiggle 1994). In this spirit, we assessed findings for their usefulness to the focal organization in assisting decision making. As data analysis progressed, we conducted regular conferences with on-site leadership at the participating sites to share preliminary findings and to assess the managerial contribution of the insights developed. Local leaders of both organizational units altered their actions in response to the analysis and interpretation offered in this research.
The purpose of our analysis is to juxtapose our data with theory as espoused in the marketing literature and then to postulate refinements. We distilled our observations into the most noteworthy insights, and we focus on three pillars of customer orientation theory: senior leadership, interfunctional coordination, and market intelligence. As we noted previously, these three constructs are not exhaustive of the factors that condition organizational change. Nevertheless, both marketing and management researchers consistently discuss them, and they are logically central to the implementation of customer orientation.
Senior Leadership
As we expected, we found support for senior leadership's catalytic role in the transformation to a customer orientation (Jaworski and Kohli 1993). Senior leaders, in this case district officials, consistently communicated their commitment to the transformation and implemented processes for driving the changes throughout the organization. Principals (i.e., local leaders or midlevel managers) were required to participate in a uniform strategic planning process in which each school explicitly identified its customers and the customers' requirements. Senior leaders reviewed the planning documents for alignment to district goals and to ensure that customer requirements, as identified by the school and district, were addressed. If the literature's exhortation for leadership buy-in and involvement were sufficient, both operational units would be adapting equally, yet that was not the case. If senior leadership is necessary but not sufficient, what are the key factors that differentiate successful from unsuccessful implementation? Four potential refinements to theory emerged.
Connectivity to ownership for change. Our observations suggest that there is a strong "leadership proximity effect," or connection to the recognized change leaders, in implementing a customer orientation. Although senior leadership mandate and buy-in may be necessary to initiate transformation, an unbroken "circuit" of buy-in between workers (e.g., teaching staff, specialists, custodial staff, parent volunteers) and senior leaders (e.g., district officials) is necessary. If a break in the circuit occurs, the effect of senior leadership buy-in can even be deleterious. Conditioned through years of failed change initiatives, workers ignore mandates they view as emanating from top management, and they simply go through the motions of implementation by translating old activities into the new language. If workers attributed ownership of the change efforts to senior leaders only, as they did at the struggling site, transformation stalled. Conversely, greater ownership by the principal (i.e., local leader), as happened at the progressing site, produced greater commitment to transformation. Successful implementation exhibited an unbroken circuit of commitment and communication from senior leaders to workers.
At the progressing site, staff seldom mentioned senior leaders; stakeholders considered the impetus for change local. Annette, a veteran specialist, provided a staff perspective:
[The principal] has been the number-one force getting the change started and keeping it going. I would say that she has been the one to promote and keep it going and keep it headed in the direction of ... getting the [school] plan done and putting it into action.
Parents also recognized on-site leadership as a driving force. In a focus group interview, Betsy and June immediately acknowledge the principal's role in the changes witnessed:
Betsy [motioning toward the principal]: The [person] right there.
June: [The principal] is a no-nonsense lady, she has a vision and a direction and she shares it.... She can articulate what she is going to accomplish. I believe she challenges her staff, and those that cannot step up to the plate are someplace else.... So she is cultivating a team of people who are driving and striving for excellence with the kids.
To these parents, the principal is committed to creating a customer-focused organization, and the link to the mandate of top leadership is recognized but transparent.
In contrast, at the struggling site, staff often cited senior leaders' mandate as the driving force for change. When asked, "What do you see facilitating the change here?" Leah stated:
I would imagine that--I'm not sure how to answer that. What's facilitating the changes? I guess ... the county [senior administration] mandates regarding how these things have to be done.
Leah focuses on the district mandates and attributes no ownership to local leadership, namely the principals. Staff members also used such phrases as "program of the month" and "this too will pass." Helen portrays the changes as one more ill-fated program from top management:
We've had [similar] things ... years ago with other superintendents.... [W]ith the ones that have come along most recently, it's my understanding that they've pulled that way of work from the business community in that the business community has been successful using different techniques.... [M]y thought was that they were wanting to do more of that... I haven't really felt a huge impact, myself.
At this site, local leaders did not provide legitimacy for the changes as did leaders at the progressing site. Without an on-site "face" and a message for the change, Helen, Leah, and others used disembodied, third-person pronouns to describe the transformation's leadership. Ironically, workers used this "commitment disconnect" as an excuse for rejecting the initiative, insisting that the effort would fail as previous efforts had.
Even parents attributed the struggling school's challenges to external factors beyond the control of the principal. Wayne, a father of two students, described the mission of the school as "just to get through the year":
It is real[ly] hard to stay focused with all the bureaucracy, all the expectations of the district. The goals are from the legislature. [The principal's] job is tied to [state-mandated guidelines]. He is doing a good job; he listens. But the county needs to explain why things cannot happen.
This pervasive, omnipresent, external locus-of-control perspective enabled parents, teachers, and students to accept excuses for not making the desired changes.
Commitment intensity and emotion. Although the commitment of senior leaders is a sine qua non for initiating transformation, commitment at the local level must also be communicated with passion and be perceived as authentic. Most workers can intellectualize the need for a customer orientation, but the unrelenting passion of local leaders' commitment provides energy necessary for transformation. Staff displayed a keen "authenticity detector" for differentiating sincere efforts from external mandates. The differences in authenticity and passion in the two locations are startling; for example, employees at the progressing site routinely described local leaders' enthusiastic actions and commitment. Paula, a specialist at the progressing site, disclosed the following:
[The principal] is a wonderful role model for us.... She believes in this with all her heart. She will demonstrate the skills. She will go into a classroom and show you how to do it if that will help.
Parents also noted the commitment. A focus group participant relayed that the principal's commitment affected her relocation plans. "I interviewed her, checked into things, called her supervisor to find out more about the school and if what I was seeing was for real; then I made the offer on my house." School documents are peppered with passionate commitment, such as a note about the principal substitute teaching so that a teacher could attend a training session.
In contrast, the staff at the struggling site doubts the principal's commitment. Although the same training documents were often used and some of the same words were spoken in meetings, staff members at the struggling site evaluated the commitment of their local leader warily as they waited to see if the changes were "for real." Janice, a faculty member, describes a particular "mandate" from the principal that masqueraded as a customer-driven, faculty-led decision:
He came in spouting these things about [customer-driven quality], and the few people who did know a few things about it ... were a little bit hopeful. And then he began to do things like make decisions without asking anybody [describes an example involving participation in a science fair].... Is this just rhetoric? That is just what teachers are waiting for ... empty rhetoric so that they can say, " See, we told you."
Staff members at the site observed carefully to determine whether change efforts were more than rhetoric. Janice believed that her colleagues "[were] not going to invest the time and energy in learning a new system that by the time [it's] figured out will be gone."
Driving the commitment with resources. Workers did not believe the sincerity of the transformation effort until leaders allocated resources consistently with the values they espoused. Because of ongoing budget cuts, backing initiatives with budgetary support was foregrounded and distinguished the on-site leaders' approach, especially in terms of creativity for marshaling resources.
Using a consensus process, the principal, staff, and parents at the progressing site agreed to reallocate resources, which sometimes meant accepting additional burdens. For example, they modified class schedules and shifted resources so that faculty members could meet weekly for two hours of uninterrupted training. This required institution of an after-school program on those days and paying for additional transportation charges. Raising the funds and applying the necessary resources certainly signaled a commitment to stakeholders. Personnel recognized the value of the principal's focused networking for resources by bringing experts into the school for seminars and providing additional training. The assistant principal explained:
[The principal] is so focused on high student achievement and making good things happen for the kids here ... that she's out there beating the bushes--"where can I get the money to send my teachers for training."
At the struggling site, there was little effort expended to find additional resources, and workers often used the lack of resources as an excuse for not engaging in customer-focused activities. For example, the parents at both sites requested parenting classes. The reflexive response from the struggling site was, "We can't do that; do you know what that would cost?" At the progressing site, the principal rearranged responsibilities to enable a guidance counselor to offer parenting classes after school. Although each had the same opportunities, local leaders reacted bimodally to financial challenges.
The cascading leadership role. Successful implementation of a customer orientation required pushing leadership and decision making to operational levels. When senior leaders had formally bought into a customer orientation and persuaded local leaders of its importance, decision making could shift to local leaders. When local leadership had become committed and had persuaded staff members of the relevance of a customer orientation to their jobs, decision making could shift even deeper into the organization. This "shared leadership" activates line personnel to become involved, ensuring strategic alignment throughout. Paula, a specialist at the progressing site, acknowledged this importance to the transformation:
I do not believe we would do as well and be as far and have all the involvement and support from the community and district-level people if it wasn't for what [the principal] has done. But, I also realize that she and we ... would not have gotten as far and done as well if all of us ... down here weren't doing our thing either, because she couldn't have done it all by herself.... So, I realize that she needs us and we need her.
The assistant principal at the site also recognized the critical role of shared leadership:
Probably 15 people on this staff that ... you might say are real leaders ... have ... gone over and beyond as far as training and desire to acquire more knowledge.... [They are] key people in working within the individual groups, supporting each other, encouraging others to grow. So, [the principal] is the acknowledged leader and none of this would have happened without her because she's had the good sense to ... share that role with other people.
Through this shared leadership process, ownership for learning was eventually passed to the students, and at the progressing site, teachers were viewed as "learning facilitators" rather than teachers. This is illustrated vividly in the following interaction between a first-grade student and her teacher in which the student has just read an essay to the class from an exam that ends the sentence, "Now I am smart because of [my teacher]":
Teacher: I thought that was a really, really good story, but I just need to ask you one thing about your ending: Are you smart because of me?
Student: Oops...
Teacher: Are you smart because of me?
Student: No.
Teacher: Why are you smart?
Student: Because of me.
Teacher: Because of you. Who's in charge of your learning, me or you?
Student: Me.
This shared leadership model enabled the principal to send the problems to the grade-level teachers and students, who determined the solutions. Ashley, a fifth-grade student, explained:
In the old kind of school, the teacher made all the rules, the teacher made the decisions, the teacher made the discipline systems, but in the new idea, like at [the progressing site], the kids have more of a say in how things are going to be done. The adults say what needs to be learned, but the students say how it is going to be learned.
At the struggling site, a different leadership model was apparent, one that was largely autocratic, rigid, and unshared. As in the earlier statement from Janice, the principal went through the motions prescribed in training to "share" authority and use customer input. However, faculty members remained wary. Leah described the perspective she perceived among her coworkers:
I think that a lot of people on our faculty feel manipulated ... because they're not sure whether they are being included or whether they're not.... There's a progression that you have to see to know whether or not that this is really meaningful ... [or] to know whether it's ... the honeymoon period or ... for real.
Faculty members wanted to know from the principal whether they were once again being manipulated or whether their participation in decision making was valued.
Interfunctional Coordination
The customer orientation literature contends that the focus of effort in the organization must be toward satisfying customers while meeting organizational objectives. Work processes throughout the organization must be designed to add value to the ultimate customer. As do our findings on leadership, our data support the importance of such interfunctional coordination; however, a richer understanding emerges when we compare successful implementation efforts with those at the struggling site. We discuss two refinements to theory in the following sections.
Complex interlocking customer orientations. The customer orientation literature suggests that consumers' needs should arbitrate organizational decision making so that efforts are aligned and coordinated. However, the determination of these needs can be complex. A district document explains the following:
The aim of the system is to meet the customer's requirements. Determining who the customer is and what product or service of value to the customer the organization can deliver is the first step in creating a system of continual improvement.
As we described previously, we defined the entities that received the output of the school's effort as the central, but not the only, customers so that each school could tailor its customer definition to meet particular requirements of the customer schools. Sensitive to its student population, community, and program focus, each school was encouraged to embrace an appropriate complement of customer requirements, as long as meeting those requirements aligned with global district performance measures and requirements. Within each school, programs could have different foci because of varying customer requirements; for example, vocational education had different customer requirements than college preparation programs.
As this process for setting requirements cascaded through the system of schools, leaders admonished each elementary school to set requirements based on its primary receiving middle school and other entities, such as future employers, parents, and students, depending on the nature of the school and its community. When customer requirements had been defined, the challenge was to align the efforts of diverse work units in the school system, including administrators, teachers, and support staff, to achieve shared goals. The principal of the progressing site explained the challenge as follows:
When we are going into the criterion reference testing, the kids are ready to do it and can't wait to get the results back, because it is based on their level, what they've accomplished, and they can see where they are relative to their goals. The learner runs his or her system like that, that is, aligned to the classroom, which is aligned to the school, which is aligned to the district, which is aligned to the state [department of education].
The ways the two schools in our study addressed this "interfunctional coordination" offer insights and extensions to theory. Consistent with the district's initiative, each principal discussed the importance of the middle school in defining success and in setting performance requirements. Each principal conducted a meeting with its respective middle school principal to begin the process of communicating requirements and assessing performance. While observing these meetings, we noted the tension among participants, which primarily arose from long histories of conflict. Both middle school principals seemed to view the exercise as unnecessary work, and they participated only as required.
The principal of the progressing school did not let the middle school's lack of enthusiasm and initial overt rejection of the initiative deter her. District leaders intervened to set up meetings between all fifth-grade teachers at the elementary school and all sixth-grade teachers at its customer middle school. Observational notes document the results of this meeting:
The highly strained meeting began with [the elementary school principal] unequivocally stating that the goal of the school was to satisfy the needs of the middle school teachers, and any deficiency in any student would be addressed with their cooperation. An open and honest dialogue was needed to ensure success in meeting that goal.... Within minutes the atmosphere warmed and a dialogue began that resulted in a list of improvements needed.... Faculty established performance goals for the next year and created a communication system that bypassed administrators, allowing sixth-grade teachers to talk directly to fifth-grade teachers.... Participants listed data, grades, discipline reports, and test scores and created a method for disseminating those data.
Analysis revealed that many deficiencies articulated by the middle school's sixth-grade teachers resulted from performance shortfalls that occurred before the fifth grade. They then created a system inside the progressing site by which they identified fifth-grade teachers as the customers of fourth-grade teachers, fourth-grade teachers of third-grade teachers, and so forth, until the entire school was a tightly connected, interfunctional system of customers meeting other customers' requirements. The principal articulated the traditional problem and the new philosophy uniting functional specialties as follows:
It is teamwork across the board that has to occur. What I [mean] is that it just can't be second-grade teachers that work together efficiently and effectively. It has to be second-grade working with fifth [grade] and second grade working with kindergarten and second grade working with cafeteria and the office staff. It has got to be all those interrelationships. Schools traditionally have been in little silos by grade level. The cafeteria is over here and [physical education] is out here, and the art department is over here. No one is here for the same purpose.
Moreover, individual teacher performance was partly assessed on the basis of student data collected in the following years in subsequent grades, which had a side benefit of reducing any "gaming" of test scores. Because communication and data flowed regularly, performance deficiencies could be identified and corrections made as the faculty united to meet the requirements of the middle school.
At the struggling school, the principals also emphasized the importance of meeting middle school requirements and coordinating activities; however, there was much less enthusiasm, and an inward focus prevailed. An assistant principal explained the following:
The thinking tends to be more in line with aligning everything we do with curriculum, the training so that it all leads or focuses on the child and what's best for the child.... I think in the past, though we've done a lot of good things, ... individual teachers have done what they thought. But, I think that now, with the [state documents],... we're all working toward the same mission. And that's going to affect student achievement in a positive way.
This administrator recognizes the benefits of alignment. Nevertheless, no articulation meetings occurred with middle school teachers, and virtually no data were transferred between grade levels. Although teachers were organized into grade-level teams, minimal communication occurred across the teams; rivalry and blame tossing occurred regularly. Janice, a fifth-grade teacher, articulated the difficulties she faced in attempting to share information:
That could be why we are so compartmentalized, because there is no organized or systematic way to talk to other teachers. And if we do talk to other people ... like if I were to go to the third grade and say "are you teaching the paragraph?" they would be defensive about that; they would think that I was suggesting that they are not teaching the paragraph, when I might just be asking for information. But I would hesitate to do that on my own.... If there were some way of asking those kinds of questions in a nonthreatening way, I think teachers would like to do that.
Internalized, shared mission and vision. Following prescriptions in the literature, both sites created highly visible mission and vision statements designed to create a sense of common purpose and facilitate interfunctional coordination. Although they were distinctive, both schools' statements aligned with the district's well-publicized vision of high student achievement. Because the process follows the prescriptions of management theory, what caused the differences in each site's adoption of a customer orientation? Our data suggest that a philosophical questioning of the true aim of the school and a personalizing of the resultant insights lead to an internalized customer orientation. This synthesis enhanced interfunctional coordination by aligning efforts.
At the progressing site, the participants' questioning of the vision statement produced a dramatic shift in the philosophy of the school and its members. The principal articulated this philosophical shift as follows:
We've changed our teaching model to a learning model.... You see, for years in education we've had a teaching model, in which the only objective of the system has been to teach the curriculum, to teach the textbook.... What we've tried to do at [the progressing site] is to look at what the kids already know. What do we need to take them higher to other levels? We have made the system adapt to meet their needs as the worker[s] in the system.
The result of the revised process was an altered understanding of the school and its purpose that staff members, students, and parents could articulate as a philosophy. For example, Beth, a teacher's aide and parent at the progressing site, explains what she considers the vision of her school:
To have children coming out of each grade level being able to function at that the grade level or higher--to have a love of learning, to be excited about coming to school. For [children] ... to value education and to value what it will do for them, instead of it just being a place that they have to be and have to do their time. A place for them to enjoy to come to see--to feel safe and feel free to express themselves.
Beth has translated the school's mission into a meaningful statement of personal beliefs. This internalization of dedication to student learning indicates personal understanding of and commitment to what the school is creating. This personal commitment extended schoolwide to such mundane changes as custodians modifying the lawn-mowing schedule to avoid disrupting students' exams.
In sharp contrast, at the struggling site, the revised process resulted in a mere restatement of what already existed without substantive questioning or refocusing efforts from participants. Staff response to an interview question about the mission of the school generally yielded vague references to the district's "high student achievement" goal. Articulation of the mission beyond this well-used phrase was scant and without meaning. Anna, a teacher of nearly 20 years, described the vision of the school as follows:
I think the mission statement says it pretty well. I don't remember it all, it was developed a few years ago. It talks about responsibilities. We've got to educate our people to go into the world of work and family, to be better people.
Because there was no questioning or resultant internalization, the process was a mere exercise. Going through the motions of creating statements served little value and was deleterious, because it was mocked and ridiculed. Julia, a new teacher, elaborated on the vision from her perspective:
We wrote up this wonderful vision statement [lightly sarcastic tone]. Did I understand what our true vision was from the vision statement? I don't think so. I don't know whether my personal vision is what [the school's] vision is.
Our data show that when staff members do not share a personalized, internalized vision, their efforts are not coordinated with or guided by a common purpose. Without a common purpose, staff members become advocates for their own situation and become relatively insensitive to others' needs. This pernicious outcome is evident in observational notes from meetings at the struggling site about the use of discretionary funds:
As one person from each group presented their ideas, others questioned and some made comments under their breath, such as "that's stupid," "it's not just for us," and a number exhibited very closed body language. As the discussions became heated, one staff member said, "It's not a war between this grade level and this grade level," to which a teacher responded, "Yes, it is! We need the unit; we want the unit!"
Throughout this lengthy and rancorous decision process, staff members stated that they "wanted what was best for the children." However, without a shared vision, achieving this nebulous goal across functional units proved onerous, and decision making became a battle of functional agendas.
Market Intelligence
Current theory and practice reflect the importance of using market data to facilitate decision making, which ultimately ensures that organizational performance meets marketplace demands. As we discussed previously, marketing theorists emphasize the value of disseminating market intelligence widely in the organization so that improvements can be made that will affect the organization's position in the marketplace. Our observations reinforce previous researchers' emphasis on the importance of external customer and industry data (market intelligence per se) on the implementation of a market orientation. However, we extend this research by showing that the integration and unification of external data with internal customer data prove critical to success, thereby expanding the traditional meaning and use of "market intelligence" in the literature. For example, the market intelligence for third-grade teachers and students became, in part, fourth-grade class requirements, as established by external state mandates and internal fourth-grade teachers' demands. Success arose from articulating both external and internal requirements and unifying them into a comprehensive approach to service delivery.
Our observations reveal the value of robust tracking of multiple customer satisfaction indicators, including those of internal customers, and tying operational performance to measures across multiple stakeholders. Because the district defined the primary receiving middle school as the key external customer, an innovative system reported performance data (e.g., standardized test scores, grade reports, discipline data) as students moved through middle school. The data were presented in comparison with data from other feeder elementary schools to provide a competitive assessment. Both schools used the objective data to drive discussions about improvement, as we discussed previously and as marketing theory suggests. Nevertheless, radically different processes emerged for combining and using robust internal and external data. Articulation of these differences offers some potential insight into theory extension.
Extracting causality from robust stakeholder data. At the progressing site, a desire to make decisions with robust stakeholder data rather than intuition emerged. The end result was the flushing out of causality; that is, rather than merely track the output of the system (i.e., academic performance on test scores at the middle school), stakeholders collected information from many vantage points to understand which operational activities affected performance. The progressing site's principal described some aspects of this intelligence system as follows:
At the end of six weeks after we open the school, I do a survey with parents--how did we do at opening?... And we improve the process.... We do a climate survey in the spring about just general questions to get at how parents are feeling about what we are doing. It was a survey developed by our staff that we do here and collect comparison data. We do a survey with the kids.... All that is put together at the end--what do you like that we are doing and what do you not like?... We do climate surveys for staff to see how they are doing in terms of how they are feeling ... and we use the same survey with them that we use with the parents so that we can have comparative data.... I do needs-assessment surveys with staff on what they need in terms of training. I do that at the end of the year so I can plan for the following year.... We survey after every night event, how we did, whether it met [attendees'] needs. We want to know if we should continue that event the next year or how could we improve it.
For each source of information, the principal recognizes the value of incorporating information into decision making, she understands the need for information, and she is willing to collect what is needed. By systematically generating data from stakeholders and acting on the data, the local leadership demonstrates the importance of these activities to school performance. The principal's desire to make informed decisions based on feedback was transferred to schoolwide stakeholders. To illustrate, a teacher, Lori, discussed her customer groups as follows:
[They include] our parent population, our student population, our community population, the business leaders in this area that are working with us on [committees] and in volunteering situations. I have volunteers that come over every week from [businesses] across the street ... and we are listening to what our parents want, and we are listening to what the kids are saying.
Expressed needs collected from a wide range of stakeholders for use in decision making became integral to the operations of the progressing site. Arlene, a kindergarten teacher, explained, "at the beginning [of the year], of course, all the classes met with the parents to find out what they expect. And it wasn't all the academics." Particularly salient is her casual use of the phrase "of course"--"of course" teachers ask for input from parents at this school. Making decisions based on stakeholder input and customer data became a behavioral norm. Evidence was throughout the school. Data covered hallways, classrooms, and even the cafeteria. School bulletins bristled with charts and graphs. Faculty discussions revolved around data, especially meetings between "supplier" and customer grade-level teachers. Even students used data to drive classroom activities, including surveys developed by students for students. In planning changes to cafeteria seating, students assessed the current level of student satisfaction with the cafeteria using a survey they initiated. More than just external market intelligence, the school became a web of both external and internal markets with mutually aligned needs that were satisfied simultaneously.
At the struggling site, far less importance was placed on using information to make decisions. Intuition and experience dominated decision making. The decision to "team teach," or pair teachers and classrooms so that each teacher specializes in a subject area, illustrates this lack. Anna, a teacher for more than 15 years, explained her motives as follows:
Teaming has advantages and disadvantages for the teacher. You have fewer subjects to teach and therefore fewer lesson plans, but then you have 50 students to deal with and more conferences and more sets of parents.... I don't think it really makes a difference to the students.
In a subsequent interview discussion, a parent in Anna's classroom mentioned the issue of team teaching and the effect it had on her child: "One thing I do have a problem is with the way that they have two teachers sometimes. It's confusing to the kids ... to have two teachers, and teachers don't get to know their children as well." Had teachers solicited feedback from parents and students, a different approach might have been used, one that might have improved student achievement. Because of the absence of feedback and data, discussions at the struggling site were based on opinion, fads, and narrow personal perspectives. Meetings between grade-level teams were contentious and volatile, as illustrated in the observational notes we previously reported. The school's nonreliance on data led to many suboptimal decisions and reinforced ineffective decision processes.
Tying operational performance to customer requirements. At the progressing site, an elaborate system emerged for disaggregating customer data to highlight the effects of specific operations on performance. Thus, the effect of each teacher and classroom on student performance could be ascertained. The principal at the progressing site explained performance in a specific grade as follows:
We have [proved] with hard data of student achievement what can be done. There is no denying it. For those people that have been the naysayers ... their [class's] reading assessments are not as high, their math assessments are not as high, because they are just now starting.... It is pretty obvious that the two high performing teachers that have put these scores at a 93% level are ... the two teachers who have a vision statement. They are the two that have been running their classroom based on that vision statement....
The kids are responsible for their behavior; they are independent workers.
The link between classroom activities and meeting stakeholders' performance requirements was made explicit to teachers and students. Deborah, a teacher, explained to her first-grade students how their performance improved to meet standardized state norms, a customer expectation:
We started below grade level. Did that frighten us? No, it didn't frighten us, because we talked about action plans and our personal goals.... Now, boys and girls, when you started in September, 100% of you were at Stages 1 and 2, below customer expectations. In January, we shared the data and 100% of you in January were on or above.... [W]e didn't take naps in here; we didn't stop working in here. We kept on going. Now, we have had our March assessment, and wait until you see what happened. [She explains that 100% of the students are now fully above grade-level expectations.]
Similarly, Nina, a fourth-grade student, explains a benchmarking system for students:
We keep track of our own grades and averages. It helps us, because before we started keeping track of our grades and averages, we didn't know where we were and what we needed to improve on. So, we always kind of guessed and just kind of went with the flow. But now, anytime you want to see your average or know what you need to work on, we always just go to our desk and open up our data folder and go straight to the back and find it, because we always know where we are, using this chart.... It really helps. We have one of these [data folders] for each subject--reading, writing, math, social studies, and science.
These are but two examples. Throughout the progressing site, data drove decision making from school to grade and from classroom to students' desks. Staff members disaggregated data to create a robust picture of performance, and data were explicitly linked to the performance requirements of stakeholders, primarily but not exclusively customers at the receiving middle school.
In contrast, at the struggling site, there was no analogous system despite measuring and tracking of academic performance as senior leaders required. Staff members gathered informal, ad hoc, disjointed information. A specialist explained her approach as follows:
I like to talk to people and get a reaction from them. "How do you think this is working?" I'm a nut about surveys. I think surveys are important. I think [there are] many people that we can't access to talk to verbally and sometimes they're willing to do some little survey to say ... "This is how I feel about things," where they would hesitate otherwise. And I like to observe and see how things are happening.
In probing for more information about the surveys this faculty member had used, she mentioned only one from the previous year. Tellingly, in it she had discovered at the end of the school year that parent meetings had been scheduled all year on Wednesdays, the day parents considered most inconvenient. Again, going through the motions yielded no actionable result, yet the specialist was proud of her efforts.
Two limitations of the research reported herein provide opportunities for further investigation. First, because of space limitations, we do not offer theoretical contributions or practical recommendations about such issues as conflict resolution, reward systems, and the adoption of formal rules in facilitating the transformation to a customer orientation, all of which are important concerns in the market orientation and organizational change literature. Our data are supportive of their importance, but we leave these issues to a future discussion. Second, although our results contribute to the theory of customer orientation, we cannot assess the generalizability of our contributions without cross-sectional research that assesses the relationships between the practices and outcomes we identify. We expect our results to prove robust in service industries that have decentralized functional units that respond to complex stakeholder requirements, but their predictive value in other contexts is less certain and begs further research.
With these limitations in mind, our purpose was to provide an understanding of how organizations might create and implement a customer-focused culture or orientation. A school district provided a rich empirical context because it had never espoused a formal customer orientation, yet it began a cultural transformation explicitly guided by the market orientation literature. Data collection from a matched sample of organizational units (schools) as this transformation occurred in real time provided empirical insights that are not easily gleaned in case studies of successful transformations alone, cross-sectional surveys, or post-hoc executive interviews.
What do the results contribute to theory and practice? Foremost, although generally supportive of current theory, our research suggests that it is incomplete. This is evident in the dichotomous results we reported for each site's implementation of a cultural transformation guided by market orientation theory. At the end of the research period, virtually all students at the progressing site were at or above grade level, performing at levels comparable to students with the highest socioeconomic status in the district and state. The school was even recognized with the state's quality award for its accomplishments as a business, not necessarily as a school. In contrast, students at the struggling site continued to perform at the level predicted by their socioeconomic status. In defining the mechanisms that differentiated the diametric outcomes, several potential theoretical refinements and practical implications surfaced.
First, as marketing theorists desire to assist executives in fostering a customer orientation, our findings support the importance of focusing research attention on the role of leadership throughout the organization. Unlike current theory, which focuses almost exclusively on senior leaders, our data suggest that for staff members to internalize a customer orientation, they must experience an unbroken circuit of passionate, sincere, unified, and committed leadership from top levels to local managers. Any break in connectivity dilutes and can even negate top leaders' positive influence, especially if the break occurs in close proximity to workers. The history of failed initiatives for change that are evidenced in the school district is not dissimilar to that of private industry, and any reason for believing that senior leaders' admonitions for transformation to a customer orientation are not sincere may be met with cynicism and retrenchment. Although our research points out the importance of resource allocation as evidence of sincerity, the transformation was most effective when workers witnessed unified and concerted leadership "walking the walk" of customer orientation, thereby presenting evidence of its acceptance by the organization as a valid philosophy for improvement. In other words, our work is consistent with Kohli and Jaworski's (1990, p. 9) hypothesis that reducing organizational ambiguity about leadership commitment encourages adoption of a customer orientation. To managers, our research suggests that empowering local-level leadership is key to the practical success of implementing a customer orientation. Further research focused on local leadership or middle-management dynamics could provide additional guidance to organizations that want to transform to a customer orientation.
Second, our findings support the centrality of customer requirements and performance feedback from customers in achieving the interfunctional coordination and alignment required in a customer orientation (Webster 1994). In our research, emphasis on the nonnegotiable necessity of satisfying immediate external customers (middle schools), supported by data, became the glue that held the transformation together and helped unify diverse staff efforts. In other words, both formal (e.g., meetings between grade levels) and decentralized intelligence dissemination led, in Kohli and Jaworski's (1990, p. 9) terms, to enhanced "interdepartmental connectedness." The culture that emerged empowered stakeholders across functional specializations not only to disseminate intelligence as some authors' data suggest (Kohli and Jaworski 1993) but also to deliver value to customers both internal (teachers at the next grade level, students within grade level) and external (parents, supportive business groups, middle school teachers).
When this culture finally reached the students and students internalized performance goals and recognized the school's unified commitment to meet them, their scholastic performance improved. In other terms, as Kohli and Jaworski hypothesize (1990, p. 13; Jaworski and Kohli 1993), organizational consistency led to improved esprit de corps associated with improved performance outcomes. To managers, our data illustrate the advantages in terms of stakeholder commitment that accrues when the organization addresses complex customer requirements head-on. With well-articulated and shared customer requirements, outcomes were easier to evaluate, and improvements based on facts rather than on intuitions and opinions could be made.
For marketing researchers to disentangle the dynamics of interfunctional coordination or interdepartmental coordination as shown in the variable results obtained by Jaworski and Kohli (1993), we emphasize learning more about the role of internal and external customer requirements when establishing an organizational culture that is conducive to a customer orientation. We witnessed a culture emerge that was dedicated to satisfying a wide range of customers, both internal and external, some of whom had competing priorities. When addressed directly, interdepartmental conflicts diminished, in turn reducing tensions and blame in the organization. This transformation galvanized the functional specializations in the organization toward meeting a cohesive set of organization-wide performance requirements. With these interlocking customer needs, staff members connected more tightly with other functional groups and with various key customers, resulting in greater communication of shared expectations and intrastaff support.
In summary, our data support a theoretical argument in favor of interfunctional coordination, or "connectedness," driven by prioritization, personalization, and empowerment in meeting interrelated customer needs. Furthermore, to managers, we argue that this connectedness is a central factor rather than a singular one in implementing a customer orientation. Further research should investigate the cultural transformations of other organizations to determine the extent to which the interfunctional dynamics we witnessed can be generalized, so that more can be understood about interfunctional coordination and how it affects organizational performance.
Third, as Kohli and Jaworski (1990) argue, our research certainly reinforces the desirability of collecting, disseminating, and using market intelligence. Our research suggests that market intelligence, as defined solely by external factors, presents an incomplete portrayal of the intelligence needed for transformation. Although our research does not discount the importance of external customer data, the link of internal customer data with external requirements surfaced as a critical component in implementing the transformation. More needs to be understood about the role of customer-focused data in the transformation to a customer orientation.
Many organizations collect customer-focused data, but our research suggests that when the data are widely circulated and become a shared organization-wide platform from which decisions are made, a customer orientation prospers and becomes self-reinforcing. When fully instituted, the use of both internal and external data becomes a behavioral norm, so that data, rather than intuition or opinion, drive discussion, helping to both reduce discord and improve esprit de corps. The sense of control that emerged empowered stakeholders to be proactive in responding to an increasingly demanding environment. During the transformation, the locus of control shifted from external to internal, especially among students, who uniformly believed they were in charge of their learning. Moreover, for managers, our results suggest that when customer-focused data are habitually used to flush out causation in performance outcomes, it is an indicator of the transformation's maturity.
Our research highlights the desirability of additional research on the effects of a customer orientation transformation on staff empowerment and sense of agency. Although we observed these changes as they occurred incrementally over several years, new arrivals are always amazed at the tremendous sense of control exuded by staff and students at the progressing school. This control partly emerges through the use of customer-focused data, clear expectations, and an interconnected environment that is conducive to meeting the demands that internal and external customers place on the organization. The observed differences made in individual stakeholders at the progressing school, in contrast with the struggling school, are profound. We end this article on a personal note. Our faith in the robustness of the marketing concept, as a pragmatic guiding philosophy for organizations regardless of context, was reinforced and renewed during this research. Witnessing the transformation at the progressing site showed the positive differences that could be made in people's lives when the marketing concept was adapted appropriately to an unusual context. We urge fellow researchers to consider educational and other nonprofit organizations as a venue for applied marketing research and service. The needs are tremendous, and this research illustrates the potential practical and theoretical rewards.
The authors thank the College of Business Administration at the University of South Florida and Honeywell Inc. for their support of this research. The authors appreciate the helpful comments of William B. Locander, Linda Price, Rosann Webb Collins, Carolyn Folkman Curasi, and John Swan on previous versions of this article. In addition, the authors thank the four anonymous JM reviewers for help in greatly improving their work. Finally, the authors acknowledge the efforts of the dedicated professionals of the organization examined in this research.
Legend for Chart:
A - Methods
B - Observations
C - In-Depth Interviews
D - Focus Groups
E - Documentation
A
B C
D E
Systematically
recorded
interactions
99 interactions 65 people
recorded and interviewed, some
transcribed; multiple times;
encounters lasted interviews lasted
from 5 minutes to 2 20-90 minutes; 36
days and ranged people interviewed
from various levels of from the progressing
participation by the site, 29 from the
researchers to struggling site.
nonparticipative
observation; 71
interactions from the
progressing site, 28
from the struggling
site.
7 focus groups 150 documents
conducted; parents reviewed: 85 from
from both sites the progressing site
involved in focus and 65 from the
groups; 3 student struggling site.
groups from the
progressing site, 2
from the struggling
site.
Breadth of
interactions
26 classroom 32 faculty,(a) 11
interactions; 43 support staff, 4
faculty, staff training, principals, 15
and team meetings; parents, 6
4 school advisory volunteers, 4 school
council; 4 leaders, 4 district
parent-teacher leaders, and 4
association; 6 parent community business
and business leaders; leaders
and 17 faculty
lounge/hallway
discussions
2 parent groups and Strategic plans,
5 student groups school manuals,
memos, survey data,
newsletters, district
publications, training
documents, budgets,
and video recordings
Primary insights
gained/value of
method
Commitment of local Value of data to
leadership, use of stakeholders, view of
data throughout the cascading
school, interlocking leadership, and
customer individualized
requirements, perspectives on
involvement of transformation and
stakeholders, and change process
students'
participation
Passion of parents Broad view of
for changes, customers, resource
students' commitment,
involvement confirmation of data
from other sources,
and identification of
areas to pursue
(a) Numbers do not total 65 because interviewees were
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Introduction
Brief explanation of the research purpose: understanding organizations that are in transition from the point of view of the people inside the organization. Tape recording for my purposes. No one else will ever have access or knowledge of these discussions except anonymously. Your name will not be related to anyone.
Would you tell me a bit about yourself? How long have you been teaching?
Broad Questions
How would you describe what is happening here at [the school]?
What are the reasons for the changes?
Who benefits from them?
What effects do the changes have on people here at the school?
Teachers? Staff? Students? Parents?
Are there people who are troubled by these changes? Please describe.
What facilitates these changes? Are there obstacles to the change process?
Reminder: Do they mention customer? Have they indicated whom they view as the customer of [the school]?
Leadership
If they have mentioned leadership in the previous section:
You mentioned ( ) in the facilitators of the changes here at [the school]. I would like to talk a bit more about this.
If they have not mentioned leadership:
I would like to talk a bit about the effect of a few things on [the school].
Questions for both responses:
You mentioned the importance of ( ); what is he/she doing that is making this sort of impact?
Can you give me some examples of what he/she is doing?
Are there any other people that have helped you to understand these changes?
How would you describe the vision/mission of the school?
How did this vision/mission come about?
Do you see changes at [the school] supporting the mission you described?
Interfunctional Coordination
Are any people working together in groups to make changes at [the school]?
Are you working with anyone to make changes here?
Please describe what you are doing. Who is working together?
What is being done?
What are some of the activities that your group is working on?
Market Intelligence
If they have mentioned anything that leads into this topic, such as training, grade-level teams, communication:
I'd like to discuss the ( ) you mentioned.
If they have not:
I would like to talk now about customer/students' needs.
Questions for both responses:
What do you think your students' needs are?
Over the past three years, has your understanding of students' needs changed? How so?
Have you seen the expectations of students and their parents change? In what ways?
Where do you gather this sort of information?
Can you give me some examples of the sources of information you use?
Has this information affected the way you do your job? Please explain.
Has this sort of information affected the way [the school] operates? How so?
Can you give me some examples of programs that have been instituted at [the school] this year?
~~~~~~~~
By Karen Norman Kennedy; Jerry R. Goolsby and Eric J. Arnould
Karen Norman Kennedy is Assistant Professor of Marketing, School of Business Administration, University of Alabama at Birmingham.
Jerry R. Goolsby is Hilton/Baldrige Eminent Scholar of Music Industry Studies and Professor of Marketing, College of Business Administration, Loyola University, New Orleans.
Eric J. Arnould is Professor of Marketing and Interim Director of the College of Business Administration Agribusiness Program, College of Business, University of Nebraska.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 79- Impression Management Using Typeface Design. By: Henderson, Pamela W.; Giese, Joan L.; Cote, Joseph A. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p60-72. 13p. 5 Charts. DOI: 10.1509/jmkg.68.4.60.42736.
- Database:
- Business Source Complete
Impression Management Using Typeface Design
This article develops empirically based guidelines to help managers select typefaces that affect strategically valued impressions. The authors discuss the potential trade-offs among the impressions created by typeface (e.g., pleasing, engaging, reassuring, prominent). The selection of typeface can be simplified with the use of six underlying design dimensions: elaborate, harmony, natural, flourish, weight, and compressed.
The visual aspects of a corporation's marketing materials are receiving increasing attention in marketing research (Childers and Jass 2002; Henderson and Cote 1998; Shapiro 1999; Tavassoli 2001, 2002; Tavassoli and Han 2001, 2002; Veryzer and Hutchinson 1998). By far, the most pervasive design element in marketing materials is typeface. Both academicians and practitioners recognize that typeface design is an important visual tool for accomplishing corporate communication objectives (Childers and Jass 2002; Hutton 1987; McCarthy and Mothersbaugh 2002; Pan and Schmitt 1996; Tantillo, DiLorenzo-Aiss, and Mathisen 1995). Initial research indicates that typeface design affects perceptions of advertised brands, influences the readability and memorability of advertisements (Childers and Jass 2002; McCarthy and Mothersbaugh 2002), creates strategically important impressions (e.g., general positive image to more specific impressions of innovativeness, change, power, or warmth; see Craig 1980; Dolen 1984; Hinrichs and Hirasuna 1990; Hutton 1987, 1997; O'Leary 1987; Solomon 1991; Somerick 2000; Spaeth 1995; Tantillo, DiLorenzo-Aiss, and Mathisen 1995), affects the appropriateness of a typeface for different products (Pan and Schmitt 1996; Walker, Smith, and Livingston 1986), and may affect a company's financial performance (Bloch 1995; Hertenstein and Platt 2001; Hutton 1997; Wallace 2001).
Despite increasing research on the subject, little guidance is available to assist corporations in selecting typeface to create strategically important impressions. Research on the impressions created by typeface has assessed only a few of the many characteristics that differentiate typeface design. Characteristics studied include serif and sans serif (Tannenbaum, Jacobson, and Norris 1964; Tantillo, DiLorenzo-Aiss, and Mathisen 1995), general use and novel display, weight (Rowe 1982), and italics (Tannenbaum, Jacobson, and Norris 1964). In addition, most studies have examined only a small set of typefaces (i.e., ten or fewer), thus limiting the variance that participants see from the extensive pool of fonts from which corporations make their selections. Furthermore, conflicting results continue to surface (e.g., Rowe 1982; Tantillo, DiLorenzo-Aiss, and Mathisen 1995). In general, the only conclusion that researchers agree on is that typeface design affects responses, yet the nature of its effects is not well known. This lack of guidelines led McCarthy and Mothersbaugh (2002) to call for research to create a set of principles that link the features of type with the impressions they create.
In this article, we develop a set of empirically based guidelines to improve managers' ability to choose typefaces that affect strategically valued impressions. In particular, we address four questions:
- What are the strategically relevant impressions created by typeface design?
- What characteristics are most useful for describing typeface design?
- What is the impact of design on each kind of impression?
- What guidelines should corporations follow to achieve their communication goals through the use of typeface design?
We conducted an exploratory study of current typeface designs and the impressions they create. Because our study is exploratory in nature, it is not based on a specific theoretical perspective. However, there are a few theories related to the possible dimensions of impressions and design characteristics that help direct the analysis. Thus, we briefly review theories and empirical research related to the dimensionality of design and the impressions they create for other stimuli.
There are two classes of studies that enable us to anticipate the influence of typeface design characteristics on consumer responses. First, several empirical studies have directly explored the relationship between a typeface's characteristics (e.g., serifs) and a response (e.g., innovativeness). For example, Tantillo, DiLorenzo-Aiss, and Mathisen (1995) investigate the influence of typeface on selected affective responses (e.g., happy/sad, young/old) and find many response differences between the sans-serif and serif typefaces, whereas in a similar study, Rowe (1982) finds few differences. Although both studies are worthy initial efforts, they fail to investigate samples of fonts that have a representative range of design characteristics. The studies also fail to consider the full array of responses to fonts.
A second class of studies that offers insight into the influence of typeface design characteristics on consumer responses is research on aesthetics. Perception-based theories (e.g., Gestalt psychology) suggest that simple and harmonious designs are liked more than complex and disharmonious designs (Bornstein and D'Agostino 1992; Klinger and Greenwald 1994; Van den Bergh and Vrana 1998; Veryzer and Hutchinson 1998; Whitfield and Slater 1979; Whittlesea 1993). Motivation-based theories suggest that more elaborate designs increase arousal and are liked more (i.e., this takes the form of a ∩-shaped relationship; see Berlyne 1971; Hirschman 1980). Other theories, such as prototypicality and conditioning, attempt to explain why some stimuli are more pleasing than others, but they develop no clear link between design characteristics and responses (Martindale 1988; Martindale and Moore 1988; Veryzer 1999; Veryzer and Hutchinson 1998; Whitfield 1983; Whitfield and Slater 1979). Purely empirical work on logo design indicates that there is a linear, positive relationship between a harmonious design and a pleasingness response and a ∩-shaped relationship between an elaborate design and a pleasingness response (Henderson and Cote 1998). Henderson and Cote (1998) also find a positive relationship between natural designs and liking, but they present no theory to explain this link, nor do they address dimensions of response other than pleasingness and liking. In summary, theory and prior research provide insufficient guidance for managing the range of impressions influenced by typeface design.
Responses to Typeface Characteristics
In determining impressions created by typeface design, we first consider what constitutes a response. From a purely empirical standpoint, the design literature has documented various responses to art and design (e.g., honest, distinctive, happy, warm, graceful, beautiful, masculine, powerful, interesting, intense, emotional; see Craig 1980; Ernst 1977; Rowe 1982; Solomon 1991; Tantillo, DiLorenzo-Aiss, and Mathisen 1995). More broadly, we consider what might drive response. First, design adds meaning to the stimulus (beyond the simple depiction of letters). In their seminal research on rating the meaning of concepts, Osgood, Suci, and Tannenbaum (1957) conclude that responses can be accounted for by three underlying dimensions: evaluation (e.g., good, pleasant, beautiful), activation (e.g., hot, active, fast), and potency (e.g., strong, brave, rugged). Second, design also conveys emotion. Mehrabian and Russell (1974) have shown that basic emotions or affective responses are captured by three dimensions: pleasingness (e.g., pleased, contented, relaxed), arousal (e.g., excited, aroused, jittery), and dominance (e.g., important, influential, controlling). Finally, in the context of a corporation, design communicates something about the company. As such, we turn to the spokesperson literature. Much as a spokesperson "dresses up" the advertiser's spoken words, so does typeface design dress up the written word. Spokesperson research involves the conceptualization and measurement of responses to celebrity endorsers as three underlying dimensions: attractiveness (e.g., elegant, beautiful, attractive), trustworthiness (e.g., honest, sincere, dependable), and expertise (e.g., skilled, qualified, experienced; see Ohanian 1990).
There are noticeable similarities to the responses that Osgood, Suci, and Tannenbaum (1957), Mehrabian and Russell (1974), and Ohanian (1990) study. Similarities are evident in the evaluation, pleasingness, and attractiveness dimensions; the activation and arousal dimensions; and the potency and dominance dimensions. In addition, the trustworthiness dimension is discussed in the typology literature and is critical to spokesperson response and to businesses in general. However, expertise seems to be irrelevant to typeface design.
Design Characteristics of Typeface
Typeface design can be distinguished by universal and typeface-specific characteristics. Universal design characteristics are subjective descriptions of the typeface, including characteristics such as symmetry, activity, and complexity. As such, universal design characteristics are holistic descriptions that rely on perception and can be used to describe various stimuli (e.g., symbols, objects, pictures) beyond just type. Typeface-specific design characteristics are graphic descriptions of the fonts, including characteristics such as short/tall, serif/sans serif, and condensed/ extended. Typeface-specific characteristics are not as subjective and provide an opportunity to explain additional variance in responses specific to typeface design. In effect, the examination of universal characteristics allows for greater generalizability of findings, whereas the examination of typeface-specific characteristics provides an opportunity to hypothesize about additional design factors and/or to engineer a typeface to meet specific goals.
Although there is no direct research on the universal characteristics of typeface design, research on logo design has proposed three universal dimensions: elaborateness (complex, active, and depth), naturalness (representative and organic), and harmony (balance and symmetry; see Henderson and Cote 1998; Henderson et al. 2003). These dimensions may or may not hold for typeface. There is no research that indicates the dimensionality of typeface-specific characteristics. It is also difficult to anticipate whether typeface-specific dimensions are independent of universal design dimensions. On the one hand, typeface-specific characteristics, such as serifs and ascenders, appear to be specific to type because they do not clearly relate to a universal dimension. On the other hand, some typeface-specific characteristics, such as a handwritten or typed appearance, seem to relate to universal dimensions for logos, such as naturalness. Unfortunately, neither empirical nor theoretical information regarding typeface characteristics is sufficient to provide much guidance on dimensionality.
Goal of Research
Our review of the design literature suggests that there are no meaningful guidelines for typeface design. The lack of guidelines may lead to designs that do not achieve corporate objectives. For example, reports on corporations changing their logos (logotypes primarily consisting of a specially designed typeface) typically discuss the image that management hopes to communicate through the new typeface (Spaeth 1994, 1995, 1999). Yet the implicit assumption of a single response to a logo is probably incorrect. It is more likely that there are multiple responses to a logo and that the corporation must consider trade-offs among responses when developing its communication goals. Thus, guidelines are needed to assist corporations in managing the range of impressions created through their choices. To develop meaningful guidelines for typeface selection, we conducted an empirical investigation to determine the design dimensions that best capture differences among typefaces, the response dimensions typefaces generate, and how typeface design dimensions are related to response dimensions. On the basis of the empirical findings, we then provide guidelines for corporations to use when selecting typeface.
Data collection required four stages. First, we identified appropriate typeface design characteristics and selected a sample of representative typefaces. Second, professional graphic designers and advertisers rated typefaces on the selected design characteristics. Third, we identified a list of strategically relevant impressions. Fourth, consumers responded to the typefaces on the impression measures.
Phase 1: Selection of Design Characteristics and Typefaces
As we noted previously, universal design dimensions (e.g., natural, harmony, elaborate) should be relevant for all types of stimuli. Universal dimensions can be captured by either universal design variables (e.g., the symmetry variable loads onto the harmony dimension for brand logos) or typeface-specific variables (e.g., handwritten is more organic than machine-made type). In addition, there are also likely to be design dimensions that are unique to the specific stimulus. These typeface-specific dimensions can be captured only by typeface-specific variables.
In Phase 1, we developed a list of universal and typeface-specific design characteristics. We used design characteristics included in publications on typology to construct an initial list. Next, we asked five professional graphic designers from different firms that work with a wide range of corporations to list the primary characteristics that differentiate typeface design. The final list consisted of 16 universal and 8 typeface-specific design characteristics. Table 1 lists the typeface design characteristics and provides illustrative fonts.
The five professional designers each provided an extensive list ( 40-150) of commercially available typefaces representative of variation on the design characteristics they had identified. To broaden the range of typefaces further, we purchased additional typeface software. On the basis of this input, we selected 210 typefaces that represented the full range for each of the 24 design characteristics (e.g., extremely complex typefaces to extremely simple typefaces) and had both upper and lower case sets (some specialty typefaces only have an upper case).
Phase 2: Ratings of Design Characteristics
Eighty-two professional graphic designers, who worked in agencies and corporations, rated the 210 typefaces on seven-point semantic differential scales for each of the 24 characteristics. To minimize fatigue, each designer rated between 10 and 30 typefaces on 12 of the 24 characteristics. We presented typefaces on white paper in 16-point font size in full alphabetic (uppercase and lowercase) and numeric forms. We used a paper-and-pencil method to ensure that typefaces appeared true to form, because computer and software differences across design agencies pose difficulties in maintaining consistent typeface appearances for research purposes. In all, we obtained 17,683 individual ratings from professional designers.
Phase 3: Selection of Impression Responses
Because our goal is to provide guidance to corporations, we researched impressions that corporations and designers aim to create through typeface. Managerially oriented design literature and input from five professional graphic designers revealed the responses believed to result from typeface design and that are relevant to the general communication goals of corporations. These impressions include innovative, calm, liking, interesting, formal, strong, warm, honest, familiar, emotional, masculine/feminine, and attractive. To confirm the relevance of these impressions, 35 additional professional designers rated their perceived ability to select typefaces that elicited the responses. The results indicate that the designers believed that they could select typefaces to create the impressions and that the impressions were meaningful to them and to their corporate clients.
We purposely did not refer to scales found in the meaning (Osgood, Suci, and Tannenbaum 1957), emotion (Mehrabian and Russell 1974), or spokesperson (Ohanian 1990) literature, because we wanted the results of the study to address specific corporate goals. This approach provides a stronger test of the dimensions of response in the context of type than if we had used scales from these studies. Still, there is enough similarity in the evaluations that meaningful comparisons are possible.
Phase 4: Ratings of Impressions Created by Typefaces
Phase 4 involved obtaining responses to the typefaces. Because of the challenge of gathering a large sample of evaluations of 210 typefaces on each of the 12 response variables, we used a laboratory-based computer task. We visually examined and measured the computer display of each typeface to guarantee that appearance, size, and resolution were the same on the monitor as on the printed pages to which graphic designers responded. We dropped one typeface because its appearance on the computer was slightly different from its appearance in print. Software was written to select 20 typefaces randomly to present to the respondents. Each typeface was presented individually as a complete alphabet and number set accompanied by 6 of the 12 seven-point semantic differential scales (e.g., like/ dislike; strong/delicate). The participants controlled the speed of typeface viewing and responding. We obtained more than 60,000 response ratings from 336 upper-division students at a large university, with an average of 24.3 responses per typeface. The use of students in design research has been justified repeatedly by findings that show a surprising consistency in aesthetic response across age groups (Berlyne 1971; Eysenck 1988).
We analyzed the data with the approach used in experimental aesthetics and in research on language processing (e.g., Berlyne 1974; Bradshaw 1984). We conducted analyses at the stimulus level rather than the individual level. This requires averaging across individual ratings of a stimulus (typeface) on a particular characteristic or impression response to obtain a score for the stimulus on that variable. We conducted all remaining analyses using these stimulus scores. Thus, the unit of analysis is the typeface, and the sample size for each analysis is the number of typefaces (209). This approach is particularly appropriate for marketing management because it recognizes that stimuli are designed for, managed for, and responded to by groups of people rather than by individuals.
The variables we used in the analyses were 23 averaged design characteristics and the 12 averaged responses. We dropped 1 of the original 24 design characteristics (frequency of use) because we concluded that it reflected not design, but designers' behavior.
What Are the Strategically Relevant Response Dimensions?
Consistent with the work of Henderson and Cote (1998), we started by conducting an exploratory factor analysis (EFA) of the impressions data. The analysis produced three factors. However, one factor included positive loadings for interesting, emotional, and innovative and negative loadings for calm, formal, honest, and familiar. This factor was cumbersome to interpret because it combined the activation and arousal dimension (Mehrabian and Russell 1974; Osgood, Suci, and Tannenbaum 1957) with a variant of the trustworthy response dimension implied in the spokesperson literature. An attempt to replicate the EFA results using confirmatory factor analysis (CFA) indicated the problems with the EFA factor structure (comparative fit index [CFI] = .658 and low loadings for emotional and interesting). We respecified the CFA to include four factors, which roughly corresponded to the dimensions of evaluation, pleasingness, and attractiveness; activation and arousal; potency and dominance; and trustworthiness found in previous research on emotions, evaluations of objects, and spokesperson perceptions (see Table 2). The model fit the data reasonably well (CFI = .806), and all the loadings were quite high (λ > .7, with the exception of strong/delicate). The correlations among the impression factors were modest but significant (see Table 3). In addition, half the correlations were negative, which indicates an implicit trade-off between impression responses.
On the basis of these results, we used four dimensions to describe the impression variables. Pleasing/displeasing comprised liked, warm, and attractive. Engaging/boring comprised interesting and emotional. Reassuring/unsettling comprised calm, formal, honest, familiar, and a negative loading for innovative (whose opposite endpoint was mainstream). Finally, prominent/subtle included strong (whose opposite endpoint was delicate) and masculine. We used summed scores to capture each dimension.
Which Dimensions Best Capture Typeface Design?
As we described previously, the design characteristics consisted of universal and typeface-specific characteristics. Universal characteristics included distinctive, ornate, special use, conveys meaning, depth, uniform, balanced, smooth, symmetrical, curved, organic, slanted, active, readable (which is known to capture simplicity in type), and handwritten/typed (which indicates natural versus machine-made in type). Typeface-specific characteristics included serifs, ascenders, descenders, heavy, repeat, fat, condense, and x-height. We analyzed the two groups of characteristics separately to ensure that we identified and linked generalizable dimensions to responses so as to better advance design research across stimuli.
We performed EFAs using principal components analysis with Varimax rotation. The factor analysis of the universal characteristics revealed three design dimensions, which explain 69.7% of the variance (see Table 3). The first factor, elaborateness, included ornate, depth, distinctive, meaningful, and negative loadings for readable and common (rather than special-purpose) use. The second factor, harmony, included balance, smoothness, symmetry, and uniformity. The third factor, naturalness, included active, curved, organic, slant, and a negative loading for typed (rather than handwritten). The results are similar to Henderson and Cote's (1998) findings, though we used different design characteristics specified by practitioners. A CFA confirmed the appropriateness of the factor structure (CFI = .858). However, in subsequent analyses, we used the EFA results because orthogonal factor scores could be created to examine the relationship between design and response.
Factor analysis of the typeface-specific variables also uncovered three dimensions, which explain 60.4% of the variance (see Table 3). Flourish comprised serifs, ascenders, and descenders. It might appear that flourish and elaborate would tap the same dimension; however, they were correlated at only .183. Weight comprised heavy, fat, and repeated elements. Finally, compressed comprised condensed and x-height. Again, a CFA confirmed the appropriateness of the factor structure (CFI = .862), and we used the EFA factor scores for the regression analyses.
How Do Design Characteristics Influence Responses to Fonts?
We conducted four separate regression analyses using pleasing, engaging, reassuring, and prominent as the dependent variables. We used the three universal design dimensions (elaborateness, naturalness, and harmony) and the three font-specific design dimensions (flourish, weight, and compressed) as predictors. We first included all the design dimensions in the model. We then added nonlinear relationships using stepwise regression. We also tested for interactions among the design dimensions, but none were significant. Overall, the design dimensions were strongly related to the impressions created by typeface. Explained variance was particularly high (the adjusted R2 ranged from .514 to .734). In addition, both universal and typeface-specific design dimensions influenced response; however, the universal dimensions consistently explained more variance. A summary of the regression results is presented in Table 4.
Pleasing/displeasing. The design dimensions explained 51.4% of the variance in the pleasing/displeasing response factor. Elaborate, harmony, natural, flourish, and compressed all had significant effects. Natural had the largest effect (ΔR² = .320) in creating more pleasing fonts, which leveled off at high values. Elaborateness explained 7.9% of the variance and had a negative effect on pleasingness. Harmony, flourish, and compressed all had modest effects (ΔR² = .045, .039, and .031, respectively). Harmony and flourish both increased pleasingness (harmony was slightly nonlinear in the positive relationship). A curvilinear relationship indicated that moderate values of compressed create the most pleasing fonts.
Engaging/boring. Design explained a large portion of the variance (68.0%) in the engaging/boring response dimension. Natural and elaborate had the greatest effects (ΔR² = .268 and .211, respectively), and higher levels of natural and elaborate created more engaging typefaces. Harmony was also important and created less engaging fonts (ΔR² = .170), though this effect diminished at higher levels of harmony. Compressed and flourish had nominal effects (ΔR² = .018 and .013, respectively), and both increased the engagingness of the font.
Reassuring/unsettling. The design elements explained 73.4% of the variance in the reassuring/unsettling response dimension. Harmony and elaborate had the most influence, explaining 38.5% and 33.0% of the variance, respectively. Harmony made fonts more reassuring, whereas elaborateness made them more unsettling. Flourish explained a nominal amount of variance (ΔR² = .018) and made fonts more reassuring.
Prominent/subtle. Natural, weight, flourish, and harmony explained 52.8% of the variance in the prominent/ subtle response factor. Natural explained the most variance (ΔR² = .291); more natural fonts were perceived as less prominent and more subtle. Weight increased perceptions of prominence and explained 17.1% of the variance. Flourish and harmony created less prominent designs, explaining 4.3% and 2.4% of the variance, respectively.
Analysis for Developing Guidelines
In addition to conducting regression analyses to assist in developing guidelines for corporations' use of typefaces, we conducted a cluster analysis to assess trade-offs. The focus of the cluster analysis was on identifying response profiles that could be achieved through a range of commercially available fonts. We built the response clusters using the 12 raw response variables rather than the factors, so as not to lose any richness in the data. We determined the number of clusters by examining the average distance between clusters and comparing this with the within-cluster distances. In addition, we avoided creating clusters with too few fonts. Six clusters appeared to describe the data best. Although the creation of additional clusters reduced the distance values within clusters, cluster sizes became small. The results and discussion of the cluster analysis, as well as an extended discussion of guidelines for impression management through typeface usage, are presented in the next section.
Before we examine specific guidelines for selecting typeface, we discuss several general conclusions that our analysis supports. The results provide broad empirical support for the contention by chief executive officers, corporate identity analysts, and creative agencies that typefaces convey various strategically important impressions (Dolen 1984; Hutton 1997; Somerick 2000; Spaeth 1995). Just as a spokesperson projects an image of the company, typeface appears to have the potential to influence the impressions created by corporate communications. In addition, the strength of the relationship between typeface design and the resultant impressions (the adjusted R2 ranges from .514 to .734) suggests that corporations can have significant control over the impressions (all other content issues being equal). Because type is inherent to most corporate communications, companies can cost-effectively leverage the benefits of an appropriately designed typeface.
Our findings further reveal that corporations should consider all four responses that their typefaces create. Thus, typeface should be carefully selected to ensure consistency with other elements of the corporate identity strategy. For example, Hilton redesigned its original logo in a script look to make it more friendly (Spaeth 1999):
From Hilton to Hilton
Our results show that natural, script typefaces produce more reassuring and pleasing fonts. However, Hilton's new font is only average on elaborateness and harmony. The combined result is a font that is less prominent (more subtle) and only moderately engaging. The change to a script look in an attempt to make a friendlier impression had a more complex effect in that a combination of responses (pleasing, less prominent, and only moderately engaging) resulted. Focusing on only a single response may lead to unintended consequences in other types of response. Firms must recognize the implications of design for all responses because multiple responses may be elicited.
Firms not only must attend to the breadth of impressions that result from their font selections but also, in many cases, need to make some trade-offs with respect to the desired responses. Ideally, corporations would be able to create any combination of impressions. However, because the design factors have different effects on impressions, a practitioner's ability to create high values on all four response dimensions is limited. Specifically, elaborate designs increase how engaging the design is, but they decrease how pleasing and reassuring it is. Harmony increases pleasing and reassuring responses but decreases engaging and prominence responses. Last, natural designs are pleasing and engaging but are less prominent. As such, some trade-off between responses appears to be necessary. To illustrate, the change in the Citigroup logo,
from CITIGROUP to citigroup
might achieve the company's goal of being "softer, less aggressive, and cozier" (Spaeth 1999, p. 24), but it may also be too uninteresting (i.e., too low on the engaging dimension) and less pleasing. A similar font used in our study (i.e., corporate mono) is reassuring but scored below average on pleasing, low on engaging, and average on prominence.
Common Response Combinations
We draw on the cluster analysis results to illustrate the trade-offs. Among the wide range of commercially available fonts in this study, we found six general profiles (see Table 5). The first cluster comprises pleasing, subtle (not prominent), and engaging fonts that scored average on the reassuring dimension. These fonts are liked, warm, attractive, interesting, emotional, feminine, and delicate (e.g., Scheherezade). The means on design dimensions for this cluster confirm predictions from the regression results. Namely, fonts that evoke these responses elicit high harmony and flourish and low weight (see, e.g., the Angelwizard Films logo in Table 5).
The second cluster comprises unsettling but engaging fonts. These fonts are interesting, emotional, exciting, informal, dishonest, unfamiliar, and innovative (Paintbrush). Although most companies do not want to be perceived as unsettling (not reassuring), these fonts communicate an edginess that is of value in many communications efforts. See, for example, Terrwear.com (see Table 5), which produces clothing for mountain biking. Again, the design dimension means for this cluster confirm the regression analysis predictions that fonts that evoke these responses are natural, somewhat elaborate, and lacking in harmony.
The third cluster of fonts is unlikely to be used heavily by corporations. These fonts are displeasing and unengaging (i.e., boring) but score average on the reassuring and prominence dimensions. They are disliked, cold, unattractive, uninteresting, and unemotional (e.g., Chainlink). The cluster means confirm the regression analysis predictions that such fonts are unnatural, score low on compressed, and have little flourish. Even the trade-offs predicted by the regression (elaborate designs are displeasing but engaging; harmony creates boring but pleasing fonts) are consistent with the cluster results. Although these fonts' use in marketing will be limited, there may be some communications situations in which such a font would be used, such as to display the characteristics or claims of a competing brand or to describe undesirable behaviors in nonprofit advertising (e.g., antismoking advertisements). However, some companies may want to produce this type of displeasing image. See, for example, Cleopatra Records (see Table 5), which produces gothic and industrial music albums.
The fourth cluster of fonts is prominent but scores average on the pleasing, engaging, and reassuring dimensions. These fonts are masculine and strong and are characterized by designs with weighty lines (e.g., NewYorkDeco); they may also have some elaborateness. It appears that any font can be made to fit this category simply by making it thicker. For example, Canon (see Table 5) uses a fairly simple font with thick lines. Again, the regression results predict that fonts are made more prominent by increasing weight (though the regression results suggest that such fonts are less natural and have less flourish than the cluster analysis suggests).
The fifth cluster of fonts scores low on reassuring (i.e., unsettling), displeasing, and engaging but average on the prominent dimension. Such fonts are interesting, emotional, exciting, informal, dishonest, unfamiliar, innovative, cold, disliked, and unattractive (e.g., AluminumShred). Consistent with the regression results, the responses mentioned previously are created by designs low in harmony, below average in naturalness, and above average in elaborateness. As in the third cluster, fonts in the fifth cluster are unlikely to be used heavily by corporations, unless the firms want to convey negative information (e.g., fear-appeal advertising) or to target a niche market. For example, see the logo of Abominable Records (Table 5), which is a punk rock and garage rock record label.
The sixth cluster is highly reassuring but not engaging (i.e., boring). It includes fonts that score average on pleasing and prominent. This cluster contains many common, highly readable fonts (e.g., Georgia). Consistent with the regression results, the fonts in this cluster score low on elaborateness and high on harmony. Such fonts are commonly used in the content of print advertisements and by respected, formal firms, such as law firms and accountants (see, e.g., the logo of Mark Rushing & Associates in Table 5, which conveys the reassuring impression).
The cluster analysis results provide evidence of the trade-offs that are necessary when firms select commercially available fonts. However, further examination of the regression results reveals that new fonts can be created to achieve additional arrays of response profiles. The regression results provide guidance to corporations for enlisting graphic designers to modify existing fonts or to create new, corporation-specific fonts instead of using commercially available fonts that may limit the combination of impressions created. We subsequently discuss guidelines for creating response arrays beyond those we have already described.
Designing Corporation-Specific Fonts
Several strategically attractive response-profile options emerge from our examination of the regression analyses.
Pleasing, engaging, and prominent fonts (average reassuring). This combination of responses is similar to that in Cluster 1, except it is prominent rather than subtle. As we noted previously, simply making the lines thicker can make any font more prominent (e.g., Fluf is much more prominent than the similar Kidstuff). Disney uses this strategy to create a more prominent looking logo than is common for Cluster 1 designs (see Table 5).
Pleasing, reassuring, and prominent fonts (average engaging). The creation of pleasing and reassuring fonts should be fairly easy because no trade-off is required. Pleasing fonts are natural and simple, whereas reassuring fonts are harmonious and simple. In addition, although harmony does not have a large effect on pleasingness, it tends to be positive. The same is true for the effect of naturalness on reassuringness. Thus, pleasing and reassuring fonts can be created with natural, simple, and harmonious designs. These fonts can become more prominent by simply making the lines thicker (which does not affect evaluations of pleasing or reassuring). It is surprising that none of the fonts in our sample had the design characteristics needed to create pleasing and reassuring fonts. All the fonts that scored high in both naturalness and harmony were also elaborate. The closest example to the desired font scored high on naturalness but only average on harmony and slightly below average on elaborateness (i.e., Hamburger). The Hallmark logo is a good example of this type of design (see Table 5).
Pleasing, reassuring, and subtle fonts (average engaging). As we noted previously, it is fairly easy to create pleasing and reassuring fonts. To make them subtle as well, the lines should be thin and natural. There was no example of this type of font in our sample, and it was extremely difficult to find a corporate example; however, the logo for Imagination Web Design is a good example of this combination (see Table 5).
Font combinations. A final option is to combine fonts with different response profiles to create a hybrid response. For example, first initials and delimiters might be used to create a sense of pleasingness and subtlety. This could be followed by the use of generic fonts to create a reassuring impression. For example, the following logo for Elkins & Associates provides a good example of this strategy:
ELKINS & ASSOCIATES
Similar approaches might be used to grab attention (e.g., engaging first initials) without making the typeface overly unpleasant.
The nine design profiles of typefaces, along with the possibility of combining fonts from different profiles, provide corporations with great flexibility in achieving communication goals and in creating differentiated marketing materials. Even greater flexibility and creativity is afforded to corporations because they may achieve the described design dimensions by manipulating any of the design characteristics that underlie the design dimensions. For example, if a corporation chooses to use a natural design to communicate a pleasing, subtle image, it may choose or create a typeface that emphasizes any one of several aspects of naturalness, such as handwriting, curvature, or slant. The typeface need not have all characteristics of naturalness to score high on this dimension.
Thus, rather than constrain a corporation in its use of typeface, the guidelines serve to direct the already-extensive work that goes into selecting, modifying, and using typefaces to accomplish corporations' goals. In addition, by simplifying the myriad characteristics that graphic designers consider into six design dimensions, by simplifying the many impressions that corporations desire into four response dimensions, and by combining these dimensions into nine broad typeface profiles, our guidelines should improve the communication of corporate and brand image goals between executives and their creative partners.
Finally, the guidelines should improve a corporation's ability to distinguish itself in meaningful ways from the communications of its competitors. Without guidance on the design dimensions and profiles that distinguish typefaces, it would be difficult to determine whether a selected font differentiates the firm from competitors or conveys an identical response profile. The guidelines should provide greater insight to both designers and corporations in the audits that they regularly conduct of their competition's communication materials.
The guidelines are useful in creating any type of marketing communication that uses print. In addition, the guidelines should assist corporations in creating and integrating their entire portfolio of print communications. Corporations are increasingly sensitive to integrating communications across various media. Our findings suggest that typeface is a medium with its own message. This makes it critical that the font's message and impression be chosen carefully and held consistent across the variety of communications in which a corporation engages. In some cases, the firm's preferred font may not be appropriate for a particular medium (e.g., a billboard, a Web page). By following the guidelines we have provided, corporations can choose fonts that still have the same response profile. This will increase the company's flexibility while increasing the consistency of the messages it communicates. Although this would not be appropriate for a brand logo, which must be the same in all communications, it is appropriate for other forms of written communication that a corporation generates.
Although this study has aimed to provide corporations with guidelines for managing impressions created by their designed materials, more research is needed in the areas of design in general and of typeface in particular. Fundamentally, research is needed to determine whether people have an innate, "hardwired" inclination to respond to graphic design in ways that are consistent with their innate responses to the natural world (Colarelli and Dettmann 2003). This research should focus on the consistency with which people respond to design elements across a range of stimuli and purposes. Another productive area for further research is the examination of design characteristics and their influence on perception and information processing (e.g., Do elaborate fonts attract greater attention and improve recognition and memory? Do elaborate fonts increase cognitive load and require greater processing time?). In addition, further research should establish baseline or control responses to assess whether typeface design enhances or undermines impressions and to determine the degree to which design can hurt or help in achieving desired responses.
It is important to determine the impact of the impressions created by typeface on other responses of interest to corporations, such as brand attitudes, customer retention, clickthrough behaviors on Web sites, purchase behavior, and corporate identity. Initial research indicates that typeface affects important responses to advertising (e.g., Childers and Jass 2002; McCarthy and Mothersbaugh 2002). More research is needed to determine the extent of impression transfer from typeface to the brand and company itself, as well as its impact on the various responses and behaviors studied in marketing.
Another avenue of research would be to explore the extent to which responses to typeface and other designed stimuli vary across consumers. The approach taken in the current article was a stimulus-level analysis, which is particularly appropriate in marketing, for which efforts are directed at entire segments of consumers. Still, research indicates that individual differences can affect attentiveness to aesthetics (Bloch, Brunel, and Arnold 2003). In addition, there may be differences across countries; some countries' orthographies consist of symbols or characters rather than individual letters, which may affect perceptions of letters in alphabetic name brands (Pan and Schmitt 1996). Notably, research indicates that consumers' perceptions of logos are fairly similar in the United States and Asia (Henderson et al. 2003), but it is unclear whether this holds with typeface.
Research also is needed to determine the public policy implications of typeface selection. For example, typeface selection is of special concern in providing information to the elderly. Although the focus for the elderly is typically on readability, variables such as engagingness may weigh heavily on the extent to which they read warning labels.
More generally, research is needed on the relationship between design and response for other design objects. Empirically based guidelines are needed to help corporations manage, in a more informed and strategic way, their entire design portfolio, including products, advertisements, packaging, Web sites, signage, and physical design (e.g., retail outlets). The present research and previous research on logos (Henderson and Cote 1998) suggests that there may be universal design dimensions that are generalizable across stimuli. In addition, the responses to these designs may be relatively universal and generalizable. As such, researchers may identify the beginning elements of examining design impact for other classes of design stimuli and of assessing the further impact of design on a wider variety of responses. Such research can only help improve the profitability of design for corporations while providing the basis for a more universal theory of design.
The authors greatly acknowledge support from Adobe Systems Inc., EyeWire, Linotype Library GmbH, and Microsoft. The authors gratefully acknowledge Donovan Follette, Theresa Grate, Jeff Boettcher, James Hutton, Andrew Eads, many professional graphic designers, and the anonymous JM reviewers for their contribution to this research.
Legend for Chart:
A - Design Characteristic
B - High
C - Low
D - Characteristics
E - Design Factor
A B C D E
Ornate/plain (x) (x) Universal Elaborate
Special use/common use (x) (x) Specific Elaborate
Depth/flatness (x) (x) Universal Elaborate
Distinctive/not distinctive (x) (x) Universal Elaborate
Conveys meaning/does not
convey meaning (x) (x) Universal Elaborate
Readable/not readable (x) (x) Specific Elaborate
Balanced/unbalanced (x) (x) Universal Harmony
Smooth/rough (x) (x) Universal Harmony
Symmetrical/asymmetrical (x) (x) Universal Harmony
Uniform/not uniform (x) (x) Universal Harmony
Organic/geometric(a) (x) (x) Universal Natural
Looks typed/looks handwritten (x) (x) Specific Natural
Active/passive (x) (x) Universal Natural
Slanted/straight (x) (x) Universal Natural
Curved/angular (x) (x) Universal Natural
Heavy/light (x) (x) Specific Weight
Short and fat/tall and thin (x) (x) Specific Weight
Repeated/no repeated elements (x) (x) Specific Weight
Serif/sans serif (x) (x) Specific Flourish
Ascenders are pronounced/not
pronounced(b) (x) (x) Specific Flourish
Descenders are pronounced/not
pronounced (x) (x) Specific Flourish
Condensed/extended(c) (x) (x) Specific Compressed
x-Height: tall/short(d) (x) (x) Specific Compressed
(a) Organic fonts are more irregular, unplanned, or natural,
whereas geometric fonts resemble objects that are man-made,
planned, or measured.
(b) Ascenders (descenders) are the parts of the letter that go
above (below) the main body, such as the top of a lowercase h
(the tail of a lower- case y). Pronounced ascenders (descenders)
appear to go significantly above (below) the body of the letter
or stand out in their influence on the appearance of the letter.
(c) "Condensed" refers to only the width of the letter. Condensed
letters are narrow, and extended letters have a wider base.
(d) "x-Height" refers to the height of the lowercase x in the
font. Tall letters are letters for which the height of the x
almost equals that of the upper- case letter. Short x-height
occurs when the x is much shorter than the uppercase letter.
(x) *(This character cannot be converted in ASCII text)
Notes: Amore complete set of examples, including examples for
the response variables, is available on request. Legend for Chart:
B - Pleasing
C - Engaging
D - Reassuring
E - Prominent
A B C D E
Like/dislike .834
Warm/cold .838
Attractive/unattractive .855
Interesting/uninteresting .892
Emotional/unemotional .886
Calm/not calm .919
Formal/informal .880
Honest/dishonest .921
Familiar/unfamiliar .916
Innovative/mainstream -.826
Strong/delicate .591
Masculine/feminine 1.000
Correlations Among
Response Dimensions
Pleasing 1
Engaging .341 1
Reassuring .607 -.589 1
Prominent -.543 -.578 .044 1
Notes: χ² = 536.540, with 48 degrees of freedom; the
probability value for the χ² statistic is less than
.001; the normal theory recursive least squares χ²
for this maximum-likelihood solution is 447.318. The
Bentler-Bonett normed fit index = .806; the Bentler-Bonett
nonnormed fit index = .751; and the CFI = .819. Legend for Chart:
B - Elaborate
C - Harmony
D - Natural
E - Flourish
F - Weight
G - Compressed
A B C D
E F G
Distinctive/not distinctive .787 -.247 .132
Ornate/plain .780 -.334 .244
Special use/common use -.720 .449 -.180
Readable/not readable -.711 .475 -.076
Conveys meaning/does not convey meaning .687 -.081 .296
Depth: flat/multidimensional .659 -.101 .208
Uniform/not uniform -.213 .816 -.257
Balanced/unbalanced -.218 .759 -.277
Smooth/rough -.371 .693 .135
Symmetrical/asymmetrical -.406 .600 -.353
Curved/angular .180 .313 .804
Organic/geometric .243 -.373 .742
Slanted/straight .116 -.215 .720
Looks handwritten/looks typed -.307 .431 -.718
Active/passive .380 -.503 .615
Serif/sans serif
.754 .158 .008
Ascenders: pronounced/not pronounced
.720 -.226 .137
Descenders: pronounced/not pronounced
.639 -.197 .156
Heavy/light
-.333 .771 .033
Repeated elements/no repeated elements
.307 .705 -.180
Short and fat/tall and thin
-.382 .677 .397
Condensed/extended
.001 -.024 -.817
x-Height: tall/short
.281 -.017 .583
Notes: We performed the EFA using principal components analysis
with Varimax rotation. Elaborate, harmony, and natural explain
69.7% of the variance in the universal design characteristics,
and flourish, weight, and compressed explain 60.4% of the
variance in the typeface-specific design characteristics. Legend for Chart:
A - Design Elements
B - Direction of Effect
C - Beta Coefficient
D - Size of Effect (ΔAdjusted Rsup2;)
E - Total Adjusted R²
A B
C D E
Pleasing
.514
Natural Positive, plateaus at high values
.428 - .138N² .320
Elaborate Negative, linear
-.275 .079
Harmony (a)
.029 + .208H³ .045
Flourish Positive, linear
.209 .039
Compressed (a)
.118 - .128S² .031
Engaging
.680
Natural Positive, linear
.412 .268
Elaborate Positive, linear
.403 .211
Harmony Negative at a decreasing rate
-.361 + .0645H² .170
Compressed Positive, linear
.153 .018
Flourish Positive, linear
.126 .013
Reassuring
.734
Harmony Positive, linear
.586 .385
Elaborate Negative, linear
-.600 .330
Flourish Positive, linear
.168 .018
Prominent
.528
Natural Increasingly negative
-.436 - .150N² .291
Weight Positive, linear
.329 .171
Flourish Negative, linear
-.220 .043
Harmony (a)
.258 - .229H³ .024
(a) *(This character cannot be converted in ASCII text) Legend for Chart:
A - Design Profile
B - Number of Fonts
C - Responses
D - Level
E - Design
F - Level
G - Fonts
H - Examples
A B C D E
F G H
1 37 Pleasing High Elaborate
Engaging High Natural
Reassuring Average Harmony
Prominent Low Flourish
Compressed
Weight
Average (a) (a)
High (a)
Average (a)
High (a)
Average (a)
Low
2 37 Pleasing Average Elaborate
Engaging High Natural
Reassuring Low Harmony
Prominent Average Flourish
Compressed
Weight
Above average (a) (a)
High (a)
Low (a)
Average (a)
High
Average
3 21 Pleasing Low Elaborate
Engaging Low Natural
Reassuring Average Harmony
Prominent Average Flourish
Compressed
Weight
Above average (a) (a)
Low (a)
Average (a)
Below Average (a)
Low (a)
Above average
4 41 Pleasing Average Elaborate
Engaging Average Natural
Reassuring Average Harmony
Prominent High Flourish
Compressed
Weight
Above average (a) (a)
Average (a)
Average (a)
Average (a)
Average (a)
High
5 19 Pleasing Low Elaborate
Engaging High Natural
Reassuring Low Harmony
Prominent Average Flourish
Compressed
Weight
Above average (a) (a)
Below average (a)
Low (a)
Below average (a)
Average (a)
Average
6 54 Pleasing Average Elaborate
Engaging Low Natural
Reassuring High Harmony
Prominent Average Flourish
Compressed
Weight
Low Georgia MARK RUSHING
Average Verdana & ASSOCIATES
High Janson Text
Average Century Gothic
Average Times New Roman
Average Century Schoolbook
7 -- Pleasing High Elaborate
Engaging High Natural
Reassuring Average Harmony
Prominent High Flourish
Compressed
Weight
Average (a) (a)
High (a)
Average (a)
High
Average
High
8 -- Pleasing High Elaborate
Engaging Average Natural
Reassuring High Harmony
Prominent High Flourish
Compressed
Weight
Low (a) (a)
High
High
Average
Average
High
9 -- Pleasing High Elaborate
Engaging Average Natural
Reassuring High Harmony
Prominent Low Flourish
Compressed
Weight
Low None in data set (a)
High
High
Average
Average
Low
(a) *(This character cannot be converted in ASCII text)
Notes: We present the Author and Viner Hand ITC in bold to make
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~~~~~~~~
By Pamela W. Henderson; Joan L. Giese and Joseph A. Cote
Pamela W. Henderson is Associate Professor of Marketing (e-mail: phenders@tricity.wsu.edu), Joan L. Giese is an assistant professor (e-mail: giesej@wsu.edu), and Joseph A. Cote is a professor (e-mail: cote@vancouver.wsu.edu), Department of Marketing, Washington State University.
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Record: 80- Incorporating Strategic Consumer Behavior into Customer Valuation. By: Lewis, Michael. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p230-238. 9p. 6 Charts, 3 Graphs. DOI: 10.1509/jmkg.2005.69.4.230.
- Database:
- Business Source Complete
Incorporating Strategic Consumer Behavior into
Customer Valuation
The calculation of customer value without regard to marketing policy is problematic because the value of managerial flexibility and the impact of consumer learning are neglected. This article develops a structural dynamic programming model of consumer demand that includes marketing variables and consumer expectations of promotions. The author uses the estimated parameters to conduct policy experiments that yield more accurate forecasts of customer value and to study the impact of alternative marketing policies.
A key idea in customer relationship management (CRM) is that customers should be treated as economic assets. An important corollary to this concept is that firms should identify their most profitable customers and then customize marketing on the basis of customer asset value. As such, the development of methods for forecasting customer value is of increasing importance. Thus far, the majority of customer valuation research has advocated techniques that require strong implicit assumptions about consumer behavior and marketing policies (Berger and Nasr 1998; Blattberg and Deighton 1996; Dwyer 1989). Typically, these techniques assume fixed marketing policies and deterministic retention and revenue rates.
Techniques that compute customer valuations without regard to marketing policy are problematic because they neglect the value of managerial flexibility and the potential for consumer learning. Managerial flexibility is important because as customers make decisions over time, firms can learn about individual customers and adapt marketing policies to the individual customer. However, if firms set policy as a function of transaction history measures, consumers can potentially learn from experience and strategically manage their decisions to receive advantageous terms.
In this article, I illustrate an approach to estimating customer asset value that uses concepts from the marketing science literature to account for several complexities and nuances of consumer behavior. Specifically, I estimate a structural dynamic programming model that replicates consumers dynamic decision-making process (Erdem and Keane 1996; Gonul and Shi 1998). The model is capable of accounting for the effects of marketing variables, prior purchasing activity, consumer expectations of future promotions, and preference heterogeneity. The estimated parameters are structural in nature and are used to conduct policy experiments that simulate customer response to marketing policies over an extended time period. The simulation results provide an estimate of customer value that is directly connected to marketing decisions. The empirical results show that it is important to consider the complexities of consumer behavior when analyzing customer asset value. Relative to a holdout sample, the simulation-based forecasts outperform standard methods in terms of absolute error and are better able to account for variation in long-term values in a heterogeneous customer base. Furthermore, by varying the prices and promotions used in the simulations, I can estimate the long-term consequences of alternative pricing strategies.
The primary contribution of the research is to the customer valuation literature. The results show how customer asset values are a function of marketing tactics, and they highlight the consequences of strategic decision making by customers. From a substantive perspective, the empirical findings are relevant to the issue of how to price over the stages of a customer relationship. This is an important topic because there is little work on individual-level dynamic pricing, despite the increasing ubiquity of CRM systems that facilitate individual-level marketing (Fudenberg and Tirole 2000). The specific problem I address herein is how to price dynamically to newspaper subscribers. Because the individual-level dynamic pricing policy is implemented with targeted promotional discounts, the results are also relevant to the literature that focuses on the long-term effects of promotions (Boulding, Lee, and Staelin 1994; Dekimpe and Hanssens 1999; Mela, Gupta, and Lehmann 1997; Nijs et al. 2001). This research adds to this literature with results from a novel category and by emphasizing the connection between promotional strategy and CRM.
I organize the article as follows: Next, I describe the empirical context of the study and highlight the logical structure of the demand model. Then, I report the estimation results and detail several policy experiments that forecast customer value for various marketing policies. I conclude with a discussion of managerial issues.
Customer Valuation and Dynamic Consumer Behavior
Customer lifetime value (CLV) is an established concept in database marketing and is growing in relevance to the broader marketing community (Berger and Nasr 1998; Gupta, Lehmann, and Stuart 2004; Jain and Singh 2002). It is typically computed with an equation such as
( 1) CLV = ∑Tt=0 rt(Rt - Ct),
where t indexes periods, r is the retention rate, Rt is the single-period revenue, Ct is the single-period cost, and T is the time horizon. Critical limitations of standard CLV formulas are that retention and revenue rates are not explicit functions of marketing actions and the firm's ability to adapt tactics based on observed customer behavior is not represented. The standard formulas also lack the ability to incorporate consumer learning and strategic customer decision making.
In this section, I develop a model of consumer behavior that replicates the dynamic nature of consumer decision making. The model includes elements of learning, an economic assessment of current-period choices, and expectations of the consequences of those choices. This model serves as the basis for a series of policy experiments that are designed to forecast customer value and to compare alternative dynamic marketing policies. By accounting for dynamically oriented consumer behavior and the influence of marketing variables, this approach overcomes many of the limitations in existing customer valuation methods.
The analysis uses transaction histories for 1578 customers of a major metropolitan newspaper. The data include monthly records of pricing, promotions, and subscription activity for each person. The average price in the data is $2.40 per week and ranges from $1.75 to $3.00 per week. An important tactic for the firm is to acquire customers using reduced-rate introductory subscriptions. The typical practice is to use direct solicitation to offer prospects and lapsed customers short introductory subscriptions that are discounted by $.50 to $1.25 from the regular weekly price. Following the introductory subscription, customers are asked to renew at the full price. I divide the sample into a 1078-member estimation sample and a 500-member validation sample to test the predictive capabilities of several valuation methods.
The dependent variable of the model is the consumer's decision each month of whether to subscribe. The reward from buying during period t is labeled rt, and it is assumed to be a function of marketing variables, such as price (Pt) and promotional solicitation (SOLt). The reward associated with the no-buy option is normalized to zero. Although the decision modeled is a binary buy/no-buy choice, the context in which decisions are made depends on the customer's transaction history and the firm's tactics. For example, a promotional solicitation alters the decision environment for lapsed and prospective customers because a decision to buy when directly solicited is fundamentally different from a decision to resubscribe spontaneously. Likewise, the decision context for current buyers is different at the point of subscription expiration compared with the passive decision to continue to buy during a subscription term.
To account for the differences in context, I estimate separate response functions for each type of choice. I define four decision contexts: current customers at renewal, current customers in the middle of a subscription, solicited lapsed customers, and unsolicited lapsed customers. I use a series of binary variables dj(t) to indicate whether a purchase is made at time t when the decision context is of type j. I use the dj(t) terms to define a summary variable D(t), which indicates a purchase at time t, D(t) = ∑jdj(t). The reward functions and decision indicators may be combined to create a convenient, time-specific reward term R(t), defined as R(t) = ∑jrj x dj(t).
In addition to the effects of price and solicitations, consumer decisions may be affected by prior experience with the firm's marketing practices. To account for the effect of past pricing, I use current and past prices to compute a price-change variable PINCt = (Pt - Pt - 1)/(Pt - 1). This price-increase term accounts for the possibility that reference price effects (Kalyanaram and Winer 1995) deters consumer demand beyond the effects of the actual price. In addition, I use two binary variables to track experience with prior promotions: PSOL1 is one if a customer has been previously solicited and is zero otherwise, and PSOL2 is one if a customer has received at least two promotional offers. The solicitation history variables are important in two respects. First, that a lapsed customer has been solicited previously may be important information because the decision to decline a promotion may indicate a lack of interest. Second, the use of promotions may influence a person's expectations of future promotions.
I account for consumer experience and loyalty with variables that track months as a current subscriber (F) and months as a lapsed customer (L). These variables are adjusted each month on the basis of a person's buying decision, as in Equations 2 and 3:
( 2) L(t + 1) = L(t) + 1, if D(t) = 0, and is 0 otherwise.
( 3) F(t + 1) = F(t) + 1, if D(t) = 1, and is 0 otherwise.
Rather than use these measures directly as covariates, I estimate different response functions for classes of experience. This approach eliminates the need for assumptions about the form of the relationship between experience and preferences (Berger, Bowman, and Briggs 2002). The empirical work uses four transaction history based categories: long-term lapsed (and prospects), short-term lapsed, new customers, and long-term customers.( n1) I index these categories by k and define them in Table 1. I index the reward functions, rjk(t), for transaction history category and choice context. These functions appear in Table 2 and vary in terms of the current and lagged marketing variables that are relevant. The β values are parameters to be estimated.
Marketing actions can also affect consumer decision making by changing expectations of the future availability of discounts. Of particular concern in this application is that consumers may learn from prior promotional activity and anticipate future discounts (Gonul and Srinivasan 1996). A customer's expectation of the probability of a promotional discount, Pr(SOL), is modeled as a function of the consumer's previous experience with promotions:
( 4) Pr(SOL) = exp(γ0 + γ1 x PSOL1 + γ2 x PSOL2)/ 1 + exp(γ0 + γ1 x PSOL1 + γ2 x PSOL2)
where the γ terms are to be estimated. This expression gives the expected probability of a solicitation during the next month if a customer chooses to cease purchasing.
The inclusion of expectations necessitates a shift from a single-period choice framework to a dynamic model. I use a dynamic programming structure to replicate the decision-making process when dynamic considerations exist. The model includes factors that affect the merits of current options and a structure that includes dynamic elements. The objective function of a dynamically oriented customer making decisions in response to an evolving environment is
( 5) max E[∑Tt=1 αt - 1R(t)|S(t)],
where S(t) is a vector of information about the environment relevant to the customer's dynamic optimization problem, α is a single-period discount factor, and T is the horizon length. The state space vector s(t) includes elements of the marketing mix and customer transaction histories.
The customer's decision problem involves selecting the option in each period that maximizes the expected utility for the remainder of the relevant time horizon. In dynamic programming terminology, the value function, V, is defined as the maximum value of discounted expected utility over the decision horizon. The alternative specific value functions VD[S(t)] are the expected values of buying ((D = 1) or not buying (D = 0) at time t when the state space is S(t) and then selecting optimal actions thereafter. The form of the alternative specific value functions is given in Equation 6, and it underscores that decisions are based on both the immediate reward provided by an alternative and the expected future utility from the next period onward.
( 6) VD[S(t)] = E[R(t)|S(t)] + αE{V[S(t + 1)]|S(t),D(T)}.
The first term is the current period benefit conditional on the current period state. The second term represents the value function of a process beginning one period in the future. A significant detail is that future benefits can depend on the alternative selected because the evolution of the state from S(t) to S(t + 1) may be conditional on the person s decision, D(t). The relationship between the evolution of a customer's decisions and marketing policy is at the heart of this modeling approach. The customer's state includes transaction history elements, such as prior loyalty, prices, and promotions. Consumer decisions can affect the evolution of the state space by changing the loyalty profile observed by the firm, which may influence how the firm markets to the customer. If customers expect that a cancellation is likely to prompt a future discount, they can manage long-term utility by strategically buying and canceling.
The dynamic structure of the model is best illustrated through an example of how expectations of future promotional discounts can affect multiple period decisions. I begin with a simple single-period reward function of the form r(t) = β0 + βPP(t), where r(t) is the reward from purchasing at time t, P(t) is the price, and the β terms are response parameters. For the example, I also assume that the reward associated with not buying is equal to zero. In a single-period decision, a customer purchases if β0 + βPP(t) is greater than zero.
To illustrate the dynamics, I assume that there is a two-period decision horizon and that the firm has the ability to offer individual consumers either the full price, PF, or a discount, PL. In a myopic decision environment, consumers for whom β0 + βPPF is greater than zero will purchase in both periods and earn a two-period reward of 2(β0 + βPPF). However, when consumers expect promotions, they may engage in strategies that are designed to maximize cumulative rather than single-period rewards. If consumers expect that deciding not to purchase in the first period may result in being offered a promotional discount in the second period, some consumers for whom β0 + βPPF is positive may choose not to buy. If a consumer's expected probability of a promotional offer in the second period is Pr(sol), the optimal two-period reward for the customer from not buying is
( 7) VNo ( 1) = 0 + Pr(sol) x max(β0 + βPPL, 0) + [1 - Pr(sol)] x max(β0 + βPPF, 0),
where VNo( 1) is the two-period reward associated with not buying in the first period and then selecting the optimal action in the second period. Given the assumption that β0 + βPF is positive, this can be reduced to
( 8) VNo( 1) = Pr(sol)(β0 + βPPL) + [1 - Pr(sol)] x (β0 + βPPF).
Customers benefit from strategic cancellations when VNo( 1) exceeds 2(β0 + βpPF). Inspection of the expression for VNo( 1) reveals that the value of a strategic cancellation is based on the discounted price, PL, and the consumer's expectation of being offered a promotion, Pr(sol). Figure 1 illustrates the relationship among promotion expectations, discount depth, and strategic cancellations for a two-period decision horizon, where β0 equals 2.8, βP equals 1.25, and the full price, PF, is $2. In this example, observe that even consumers who gain a positive reward at the full price can benefit by strategically canceling if the discount or likelihood of promotions is sufficiently high. For example, if a consumer expects to receive a solicitation 50% of the time, a discount of $.50 will motivate a cancellation in the first period. In contrast, if the expected probability is 10%, even a discount of $1 does not lead to opportunistic behavior.
From the firm's perspective, this type of strategic behavior can reduce CLV and, thus, firm profitability. If the cost of serving a customer is CT and the cost of solicitation is CS, the reduction in the two-period profit from a customer who strategically cancels is
( 9) 2(PF - CT) - Pr(S) x [(PL - CT) + CS + [1 - Pr(S)] x Pr(buy|PF) x (PF - CS),
where Pr(S) is the actual probability of a promotional offer, and Pr(buy|PF) is the probability that the customer purchases at full price in the second period in the absence of a discount.
The estimation of model parameters is in some ways similar to the approach used in static choice models because the likelihood of observed choices is based on a comparison of the utility of the alternatives. The key distinction in dynamic programming models is that the utility of an alternative involves both current utility and expected future benefits. As such, choices are assumed to be the alternatives that maximize utility over the remaining horizon. If the error terms are distributed extreme value i.i.d., the probabilities of observed choices are given in Equations 10 and 11, where v is the deterministic portion of the alternative specific value functions (Rust 1994):
( 10) Pr[D(t) = 1|S(t)] = exp{vD = 1[S(t)]}/1 + exp{vD = 1[S(t)]}, and
( 11) Pr[D(t) = 0|S(t)] = 1 - Pr[D(t) = 1|S(t)].
The likelihood function is the sum of the logarithms of the choice probabilities defined in Equations 10 and 11. Therefore, estimation requires the repeated solution of a dynamic programming model to calculate the value functions. The estimation procedure involves nesting a dynamic programming algorithm within a maximum likelihood routine (see Rust 1994).
Thus far, I have assumed a population with homogeneous preference and expectations. To account for variability in preferences, I use a latent class approach (Kamakura and Russell 1989; Keane and Wolpin 1997; Lewis 2004). This approach assumes that the population consists of M distinct types with separate response functions. The use of a finite-mixture model to account for consumer heterogeneity increases the computational burden because the optimization problem must be solved for each population type.
Results
Table 3 presents estimated coefficients and standard errors for a specification involving the four transaction history classifications in Table 1 and two latent population types. The model also assumes that consumers use a three-month horizon.( n2) This specification was selected from versions that varied in the number of transaction history classifications, the number of latent classes, and the extent of forward expectations.( n3) The results in Table 3 indicate two distinct customer types. Segment 1 represents only 5.1% of the population, and the parameter estimates suggest a propensity for members to shift back and forth between active and lapsed status. Segment 2 behaves more predictably and exhibits significant duration dependence.
The price sensitivity parameters of Segment 2 customers with fewer than six months as subscribers is -2.63, whereas for customers with more than six months tenure, the estimate is -.29. Similarly, price sensitivity increases as time lapsed increases. For customers who were lapsed fewer than six months, the estimated price parameter is -2.60, whereas for those lapsed more than six months, the estimate is -5.71. This pattern is consistent with findings that price sensitivity decreases with customer tenure (Reichheld and Teal 1996). In contrast, the price parameters of Segment 1 do not evolve in the expected manner. For customers with unexpired subscriptions, price sensitivity increases from -2.42 to -3.06 as tenure increases. For unsolicited lapsed customers, the estimated sensitivity decreases from 3.06 for the short-term lapsed to -2.50 for the long-term lapsed.( n4) These results suggest that this segment s members tend to buy and cancel opportunistically.
Other pertinent results include the effects of multiple solicitations and price increases. For multiple solicitations, the estimated coefficients are negative in all cases, but the majority of these estimates are not statistically significant. The price-increase terms are negative and significant in all cases. Price increases are especially important for the long-term customers in Segment 2. For this group, there is minimal attrition unless the firm implements a price hike.
In terms of future expectations, the segments are fairly different. Segment 1 tends to exhibit a much stronger link between prior promotional activity and expectations of promotions than does Segment 2. This tendency is consistent with the characterization of Segment 1 as a switching segment. The results for Segment 2 show directional evidence that promotions increase the expectations of future discounts, but the effects are not significant. The expected probabilities of solicitations as a function of previous activity appear in Table 4.
Policy Evaluations
The parameter estimates from the dynamic programming model are structural in nature and therefore are policy invariant (Lucas 1976). As such, the estimates may be used to conduct policy experiments through simulation. Policy experiments may be used to calculate customer value and to compare alternative marketing strategies that vary the depth and frequency of discounts.( n5)
Table 5 compares the results for the holdout sample with forecasts from several valuation methods. The first row of the table uses average attrition rates, acquisition rates, and prices to forecast revenue. For a population with the same initial characteristics as the holdout sample, this procedure yields a mean customer value of $75.17. The second row uses the average retention and acquisition rates for each level of the transaction history measures (L = time as a lapsed customer, and F = time as an active subscriber). This approach represents a migration model (Dwyer 1989) and predicts a mean value of approximately $72 and a standard deviation of 33.6. The policy experiment uses the parameter estimates from the dynamic programming model and promotional and regular prices similar to the actual policies to simulate the behavior of a population that is identical to the holdout sample in terms of initial transaction history measures. The simulation predicts a mean value of $78.55 and a standard deviation of 53.9.
The fourth row describes the actual 36-month revenue value of the holdout sample. The average customer value is approximately $85 with a standard deviation of 68.7. The policy experiment is better able to capture variation because it uses more information related to marketing actions and consumer expectations than the other valuation methods. The benefits of including additional information are also reflected in forecast accuracy. In terms of forecast error, the policy experiment approach performs best with a mean absolute error of 8.7, compared with 16.7 for the migration method and 33.5 for the average rate-based calculations.
I report scenarios involving alternative marketing policies in Table 6.( n6) I provide separate valuation estimates for members of each latent population type (Segments 1 and 2). The base policy is a two-price policy involving a large acquisition discount ($2.25 per week) that reverts to the full price after the initial subscription ($2.75). This policy is similar to the pricing observed in the data. The second policy I evaluate is a three-price policy that uses an acquisition price of $2.25 per week and offers a reduced rate of $2.50 when the introductory subscription expires. The full price of $2.75 is then offered at the second renewal. This strategy continues to invest in the relationship at a point (first renewal) when significant attrition tends to occur.
Table 6 reveals significant differences between the segments. The lack or variation in the valuations across the transaction history classifications for Segment 1 indicates that the transaction history measures provide little information. The similar valuations are due to the segment's tendency to move back and forth between active and lapsed states. For Segment 2, the long-term lapsed customers are of very low value, whereas long-term customers contribute approximately $100 in profit. The three-price policy has a strong effect on new customers from Segment 2. For Segment 2, the value of customers who have purchased for fewer than six months increases from less than $30 for the two-price policy to approximately $60. If the population consists of an equal number of consumers in each transaction history classification, a weighted average of the effect of the three-price strategy on both segments predicts an increase in customer value of 27.6% relative to the two-price policy ($39.12 versus 30.66). At the segment level, the three-price policy increases the value of Segment 1 members by 13% and Segment 2 members by 28.7%.
The third policy listed for the segments forgoes promotions and always charges $2.75. For Segment 2, this policy increases the value of long-term lapsed and new customers. The increase in value of the long-term lapsed is due to greater solicitation costs under the base policy, whereas the greater value of new customers is due to the higher price at which customers are acquired. In the two-price policy, new customers are faced with a major price hike at first renewal, whereas in the no-promotion scenario, new customers already pay the full price. Therefore, the no-promotion policy results in greater customer values for new customers but at the cost of acquiring fewer customers. The lower acquisition rate is evidenced by the decrease in the value of lapsed customers from $3 in the base policy to approximately $2 for the no-promotion policy.
The fourth scenario listed for each segment is the best policy identified for that segment. For Segment 1, the suggestion is for lower prices and more frequent promotions. This type of policy is effective because it provides opportunities for Segment 1-type customers to engage in strategic purchasing behavior. A two-price policy that reduces acquisition and regular prices by $.25 and doubles the promotion frequency increases average customer value by approximately 40% to $53.06. For Segment 2, a three-price policy that restricts promotions to one time per customer increases the value of Segment 2 customers by 34% relative to the base two-price policy and by 4% relative to the three-price policy.
Thus far, the discussion has focused on the effects of different pricing and promotional strategies on each population type. I also forecast the relative benefits of applying the same policy across the entire population compared with customizing pricing at the segment level. The application of each segment's best policy results in an average customer value of $41.20. In comparison, the base two-price policy yields a value of $30.66, and the three-price policy results in an estimate of $39.12. The application of the ideal Segment 2 policy across the entire population yields an average customer value of $40.72. These are salient calculations because the benefit of using customized policies based on unobserved types is estimated to provide an improvement of only 1.15% relative to applying the best Segment 2 policy across the population. However, it should be emphasized that the relatively minor benefit of customization based on latent segment type is due to the small size of Segment 1. The recommendation to customize policy only on the basis of observed transaction history measures (and not latent segment membership) is specific to this context and is not a general rule.
Figures 2 and 3 illustrate the relationship between price levels and buying probabilities. Figure 2 shows renewal rates as a function of acquisition price for the total population. Renewal rates are greatly reduced when customers are acquired through steep discounts. Customers acquired with a $.75 weekly discount renew approximately 35% of the time versus a renewal rate of 80% for nonpromotionally acquired customers. Figure 3 shows how discounts influence acquisition rates. For the recently lapsed category, the acquisition rate ranges from 16% for a $.75 discount to 4% for a $.25 discount, and for the long-term lapsed category, the rate ranges from 6% to approximately 1%.
Discussion
In this article, I develop a structural dynamic programming model that can be used as a tool for valuing customer assets and comparing alternative marketing policies. This approach avoids the implicit assumptions about consumer behavior and firm policies that existing valuation methods use. The policy experiment based approach evaluates the impact of alternative marketing policies and yields more accurate forecasts of customer value. From a substantive perspective, the results are relevant to audiences that are interested in pricing aspects of CRM. A strategy of gradually increasing prices is found to be more effective than a single steep acquisition discount. In terms of practice, there is support for this type of pricing strategy in the magazine industry (Freedman 1997).
An issue that merits further comment is the challenge and benefit of customizing marketing tactics on the basis of latent segments. Although the estimation results suggest different strategies for each segment, the identification of a customer's unobserved type is a nontrivial issue. Two approaches to determine latent segment membership are common. Observed choices for a given customer may be used to estimate the posterior probabilities of segment membership (Kamakura and Russell 1989), or alternatively, concomitant variables such as demographics may be used to predict segment membership (Gupta and Chintagunta 1994). In practice, a blend of these methods is likely to be used. The sample used for estimation could be segmented by posterior probabilities and then used to create demographic profiles for the latent segments. Demographics, actual or inferred from census data, might then be used to infer the type of new prospects, and inferences could be updated as the firm observes the customer over time.
Furthermore, although CRM systems frequently provide the means to customize marketing policies to increasingly fine segments, there is reason to be concerned about potential consumer backlash. Customization of prices based on observable behavior or inferences about customer type is potentially controversial (Feinberg, Krishna, and Zhang 2002; Kahneman, Knetsch, and Thaler 1986). Although basing pricing on transaction history measures is generally accepted in categories such as subscription services (e.g., newspapers) or in industries in which loyalty programs provide quantity discounts, other price discrimination mechanisms may negatively affect customer relationships. For example, Coca-Cola earned negative publicity for testing vending machines that varied prices on the basis of the weather (Egan 2001), and Amazon.com came under fire in 2000 when consumers learned they were paying different prices for the same DVDs (Hamilton 2001). Because of adverse publicity, Coca-Cola chose not to implement the temperature-based pricing technology, and Amazon.com used refunds and apologies to placate consumers who were charged higher prices in the dynamic pricing experiment.
In the context of this article, although the firm can benefit by using aggressive promotions when managing the switching segment of the population, the estimated benefit from using latent-segment-specific pricing strategies is only a 1.15% gain in customer value. This increase must be weighed against concerns about negative publicity and consumer ill will. The relative benefit of using customized or one-to-one marketing techniques versus the possible adverse effect on customer relationships or consumer trust (Morgan and Hunt 1994) is a topic that merits additional research.
( n1) The four-category version outperformed specifications with additional categories based on the Bayesian information criteria (BIC).
( n2) The two-segment model detailed in Table 3 required about four hours for estimation using a 2.4 GHz personal computer.
( n3) The model in Table 3 was the preferred specification in terms of the BIC. Removing the expectations structure yielded a log-likelihood of -2181.7 and a BIC score of 7471.66 versus a BIC score of 7433.70 for the full model.
( n4) This pattern is supported only by directional evidence because many of the pricing terms for Segment 1 are insignificant.
( n5) To maintain clarity, I use a 36-month decision horizon, and I do not discount revenues. This horizon is long enough for dynamic factors to be important but short enough to mitigate concerns about the stability of the environment. I exclude costs and nonprice revenues (advertising) from the forecast evaluation calculations but include them in the policy comparisons.
( n6) For the customer valuation analyses, I use a cost-to-serve suggested by the firm and assume that solicitations cost $1.
Legend for Chart:
B - Index
C - Category
D - Definition
A B C
k = 1 Long-term lapsed/prospects L ≥ six months
or new prospect
k = 2 Short-term lapsed L < six months
k = 3 New customers F < six months
k = 4 Long-term customers F ≥ six months Legend for Chart:
A - Decision
B - Reward Function
A
B
Current buyer at renewal (j = 1)
rjk(t) = βjk + βprice,jk x
Pt + βpinc,jk x PINCt
Current subscriber (j = 2)
rjk(t) = βjk + βprice,jk x
Pt
Solicited lapsed customer (j = 3)
rjk(t) = βjk + βprice,jk x
Pt + βsol,jk x PSOL1t x SOLt
Unsolicited lapsed customer (j = 4)
rjk(t) = βjk + βprice,jk x
Pt Legend for Chart:
C - Segment 1 Coefficient
D - Segment 1 Standard Error
E - Segment 2 Coefficient
F - Segment 2 Standard Error
A B C D
E F
Lapsed Six Months or More
Solicitation Intercept 2.21(*) 1.35
-.79(*) .49
Price -3.95(*) 1.33
-5.71(**) 1.74
Multiple solicitation -.61 .99
.10 .32
No solicitation Intercept .72 1.52
-2.92 3.76
Price -2.50(*) 1.62
-6.07(*) 3.86
Lapsed Less than Six Months
Solicitation Intercept .48 .86
-.52(*) .33
Price -.85 1.26
-2.60(*) 1.08
Multiple solicitation -.47 .64
-.63(*) .32
No solicitation Intercept -.73 2.14
-1.34(*) .86
Price -3.06 2.39
-3.59(*) .93
Tenure Less than Six Months
Expiration Intercept 1.40 1.91
2.22(**) .64
Price -2.38 2.23
-2.63(**) .71
Price increase -4.75(*) 2.60
-5.19(**) .73
Subscription Intercept 3.21(*) .77
3.97(**) .39
Price -2.42(*) 1.01
-3.30(**) .42
Tenure Six Months or More
Expiration Intercept .66 1.23
.99 1.34
Price -1.61 1.15
-.29 1.35
Price increase -9.18(*) 3.84
-9.16(**) 1.27
Subscription Intercept 3.62(*) 1.56
3.78(**) 1.03
Price -3.06(*) 1.92
-2.51(*) 1.05
Expectations Intercept -4.15 4.25
-4.13 6.05
Single solicitation 3.57(*) 1.99
2.02 1.89
Multiple solicitation .26(*) .11
.15 .19
Segment size -2.93(**) .30
Log-likelihood -2132.9
(*) p < .10.
(**) p < .01. Legend for Chart:
A - Previous Solicitations
B - Segment 1 (%)
C - Segment 2 (%)
A B C
0 1.5 1.6
1 35.9 10.8
2 or more 42.0 12.3 Legend for Chart:
A - Customer Valuation Method
B - Mean Customer Value ($)
C - Standard Deviation
D - Mean Absolute Error
A B D C
Average retention and acquisition rates 75.17 33.45 8.18
Transaction history specific rates 71.63 16.73 33.55
Simulation based on dynamic programming
model 78.55 8.68 53.86
Holdout sample 85.24 -- 68.72 Legend for Chart:
A - Prices
B - Solicitations
C - Transaction History Classification L ≥ 6 ($)
D - Transaction History Classification L < 6 ($)
E - Transaction History Classification F < 6 ($)
F - Transaction History Classification F ≥ 6 ($)
G - Transaction History Classification Average ($)
A B C D E
F G
Segment 1
$2.25, $2.75 Random 5% 36.11 33.83 39.60
40.70 37.56
$2.25, $2.5, $2.75 Random 5% 42.16 38.33 44.11
44.43 42.26
$2.75 No promotions 32.28 28.86 35.41
35.63 33.04
$2, $2.5 Random 10% 52.63 48.68 54.76
56.16 53.06
Segment 2
$2.25, $2.75 Random 5% .02 3.03 28.81
89.25 30.28
$2.25, $2.5, $2.75 Random 5% 1.37 5.00 60.23
89.25 38.96
$2.75 No promotions .27 2.07 38.77
102.27 32.21
$2.25, $2.5, $2.75 Promote 1X .69 13.33 52.15
96.01 40.55GRAPH: FIGURE 1 Incremental Two-Period Reward of Strategic Cancellation
GRAPH: FIGURE 2 Probability of Renewal Versus Acquisition Price
GRAPH: FIGURE 3 Probability of Acquisition
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By Michael Lewis
Michael Lewis is Assistant Professor of Marketing, Marketing Department, University of Florida mike.lewis@cba.ufl.edu
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Record: 81- Innovations in Product Functionality: When and Why Are Explicit Comparisons Effective? By: Ziamou, Paschalina (Lilia); Ratneshwar, S. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p49-61. 13p. 2 Black and White Photographs, 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.67.2.49.18606.
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Innovations in Product Functionality: When and
Why Are Explicit Comparisons Effective?
The authors investigate the effects of explicit comparisons in differentiating innovations that offer new functionalities to the consumer. Although marketing communications commonly employ explicit comparisons in launching new product functionalities, the authors suggest that such comparisons are not always helpful. The authors show that an explicit comparison of a new functionality with an existing functionality is effective only when the new functionality is offered in a device or physical product that is atypical of the existing functionality. However, when the new functionality is offered in a product that is typical of the existing functionality, explicit comparisons tend to backfire because they merely facilitate the assimilation of the new functionality to the prior functionality. The results of three studies provide significant support for the predictions, and a fourth study demonstrates how the backfire effect might be avoided. The authors discuss implications for communication tactics, new product marketing, and consumer behavior theory.
What communication strategies and tactics are most appropriate for launching product innovations in the marketplace? We examine the case in which the innovation pertains to a new functionality. By functionality, we refer to the opportunity for action that is afforded by a product (Dourish 2001). Product functionalities enable people to engage in purposeful activities, mental and physical, such as eating, traveling, reading, relaxing, communicating, and so forth (Huffman, Ratneshwar, and Mick 2000; Ratneshwar et al. 1999). Innovations in functionality make a novel set of benefits available to the consumer, even though the device or physical product in which the innovation is offered may not itself be new to the market. These benefits represent the more concrete consequences of consuming the functionality. Consider, for example, instant messaging, a new functionality first introduced into the market in 1997, which has contributed significantly to the popularity and market dominance of both America Online and Microsoft Network. Instant messaging is associated with specific benefits such as enabling consumers to view a select listing of people with whom they frequently communicate, determining whether these people are available online at that time, and exchanging text messages with them instantly while also having an online voice conversation.
Prior research suggests that the success of an innovative functionality depends on whether consumers perceive it as a radically differentiated offering or as a minor variation of existing functionalities (Carpenter and Nakamoto 1989; Gatignon and Xuereb 1997; Olshavsky and Spreng 1996; Pechmann and Ratneshwar 1991; Sujan and Bettman 1989). If a marketer wishes to ensure that the innovation is perceived as truly new and different, one key issue is whether introductory communications to the consumer should feature explicit comparisons with existing functionalities (e.g., a comparison of instant messaging with e-mail). Consider the several cases depicted in Table 1 that refer to recent print advertisements in popular magazines such as Good Housekeeping, Vanity Fair, and Wired. Even though the products in question are diverse, in every case a firm has chosen to launch a new functionality using comparisons with an existing functionality. It is plausible that such explicit comparisons drive home the differentiation of the new functionality and thus facilitate the perception that it is new and unique. After all, if the marketer's goal is differentiation, it seems obvious that explicit comparisons should help set apart one functionality from another. Nonetheless, as we demonstrate in this article, there are conditions in which explicit comparisons can backfire.
Note that on account of technological convergence, a new functionality can often be introduced in more than one type of device or physical product (see Bettis and Hitt 1995). Thus, instant messaging can now be used through a variety of devices such as desktop computers, notebooks, pagers, Smart Phones (e.g., wireless telephones with Inter-net capability such as Nokia's Communicator) and Web pads (e.g., Hitachi's Eplate, Cyrix's WebPAD; see Haskin 1999). Consumers are likely to perceive some of these products (e.g., desktop computers) as typical of the existing functionality of e-mail, because those products are used frequently for that particular functionality and/or because their physical features are deemed to be ideally suited for that functionality (Barsalou 1985, 1991; Ratneshwar, Pechmann, and Shocker 1996; Ratneshwar et al. 2001). Conversely, many other products (e.g., wireless telephones) may be perceived as much less typical of the existing functionality of e-mail, either because they are used much less frequently in that context or because their features are not ideal for that purpose.
Might the type of product or device that serves as the physical medium for a new functionality influence the effectiveness of explicit comparisons with existing functionalities? Researchers previously have uncovered several variables that moderate the effects of comparative advertising, but their studies have invariably investigated the impact of comparisons on consumer perceptions only at the brand level and within narrow product categories (see, e.g., Grewal et al. 1997; Pechmann and Ratneshwar 1991; Rose et al. 1993; Sujan and Dekleva 1987).[ 1] A mere extrapolation of the prior findings on brand-level comparisons does not make it clear whether explicit comparisons at the product functionality level are necessarily beneficial or whether other variables need to be brought into the picture. Therefore, the key questions addressed by our research are whether, when, and why explicit comparisons in marketing communications help or hurt the perception and evaluation of a new functionality. We focus on the typicality of the product in relation to the existing functionality as an important moderator of the effects of explicit comparisons. We present four studies.
We first discuss assimilation/contrast and categorization processes in regard to a new functionality when it is explicitly compared with an existing functionality. Next, we draw on prior research on product innovation to link assimilation/ contrast effects to consumer judgments. Our conceptualization is depicted schematically in Figure 1.
Assimilation/Contrast and Categorization Processes
When a new functionality (e.g., instant messaging) is explicitly compared with a preexisting functionality (e.g., e-mail), we posit that the latter should serve as a reference standard or context for judgments of the former. Consequently, the evaluation of the novel stimulus may shift either in the direction of the context or away from it (see Buchanan, Simmons, and Bickart 1999; Schwarz and Bless 1992; Wanke, Bless, and Schwarz 1998). These two outcomes are known as assimilation and contrast, respectively; which of the two will prevail in a given situation depends on the degree of overlap between a person's mental representation of the context and the features of the stimulus to be evaluated (Herr 1989; Herr, Sherman, and Fazio 1983). Assimilation is the expected outcome when there is high overlap between the salient features of the judgment context and the novel stimulus, whereas contrast effects are more likely if there is little overlap.
We further posit that the consumer's mental representation of the context will include products that are typically associated with the existing functionality. Specifically, because functionalities make it feasible for consumers to engage in goal-directed actions, we assume that explicit mention of an existing functionality in a marketing communication will prompt consumers to mentally access a goal-derived category of products that corresponds to that particular functionality (Barsalou 1985, 1991; Ratneshwar, Pechmann, and Shocker 1996; Ratneshwar et al. 2001).[ 2] Consumers should thereby evoke in working memory the product exemplars that are most typical of that goal-derived category (Barsalou 1991; Wisniewski 1995). Barsalou's (1985, p. 636) findings show that the highly typical exemplars of a goal-derived category are products that are frequently instantiated or encountered in relation to the goal or products that have ideal characteristics for serving the goal. Thus, in the previous example, an explicit comparison of instant messaging with e-mail is likely to evoke in working memory typical products such as personal computers that are frequently instantiated or considered ideal in relation to the existing functionality of e-mail.
Given that products typical of the existing functionality are evoked in working memory, is assimilation or contrast more likely when a new functionality is explicitly compared with an existing functionality? The answer should depend on whether the new functionality itself is offered in a physical product that is typical or atypical of the existing functionality. Consider when the new functionality (e.g., instant messaging) is offered in a device that is typical of the existing functionality (e.g., personal computer). In such a case, there should be high feature overlap between the new stimulus and the typical products evoked in working memory by the explicit comparison. Consequently, assimilation of the new functionality to the existing functionality (i.e., standard of comparison) should be promoted. Now suppose that the new functionality (e.g., instant messaging) is offered in a physical product (e.g., wireless telephone) that is atypical of the existing functionality. In this situation, there should be a mismatch between the salient features of the new stimulus and the typical products evoked in working memory. Consequently, the new functionality should be contrasted with the existing functionality.
Consumer Judgments and the Effects of Assimilation/Contrast
Carpenter and Nakamoto (1989) have shown that pioneering products are likely to be judged positively and that consumer preferences for a "follower" product are negatively correlated with its degree of similarity to the pioneer. Thus, a follower product can neutralize the pioneer's advantage only by differentiating itself to the point at which it is classified as completely different from the pioneer, rather than being assimilated into the mental representation of the pioneer. Gatignon and Xuereb (1997, see Table 1, p. 83) find a highly significant negative correlation between the similarity of a new product to competitors' products and the financial performance of the new product. They also find that the more similar an innovation is to its competitors, the lesser is the product's competitive advantage in the marketplace.
Carpenter and Nakamoto (1989) base their work on psychological theory and conduct controlled experiments on consumer preferences to verify their hypotheses. Gatignon and Xuereb (1997), in contrast, approach the issue of product innovation from a strategic management perspective and test their theory regarding the financial outcomes of innovations with survey data from key informants. Notwithstanding these differences in research approach and methodology, the results of both studies suggest that, in general, the assimilation (versus contrast) of a new functionality to an existing functionality should hurt (versus help) consumer evaluations of the innovation. Considered in conjunction with the previous theorizing regarding the effects of explicit comparisons, the implication is that if the typicality of the product influences the relative likelihood of assimilation versus contrast of the new functionality, the typicality should also have a corresponding impact on consumers' judgments.
H1: When a new functionality is offered in a product that is typical of an existing functionality, explicit comparisons
(versus no comparisons) of the new functionality with the existing functionality should result in more negative judgments of the new functionality. However, when a new functionality is offered in a product that is atypical of an existing functionality, explicit comparisons (versus no comparisons) should result in more positive judgments of the new functionality.
Study design and subjects. We operationalized explicit comparisons through print advertisements and tested H1 in a between-subjects experiment with a 2 (typical versus atypical product)x 2 (comparative versus noncomparative advertisement) design. Subjects were undergraduate students majoring in business in the United States and Hong Kong (N = 193); they participated in the experiment for extra credit and were randomly assigned to the various conditions.[ 3]
Stimulus advertisements and manipulation of advertisement format. The advertisements in all four conditions were black-and-white, full-page, and similar in layout. The name of a new functionality, Anytime Entertainment, was used as a headline, and all the advertisements described the new functionality in terms of three specific benefits for the consumer (for a sample advertisement, see Figure 2). The advertisements also showed a picture of the physical product in which the new functionality would be available. In the comparative advertisement conditions, the advertisement's headline contained an explicit claim of superiority over an existing functionality, cable television. Furthermore, the sentence introducing the specific benefits of the new functionality in the comparative advertisements stated, "Unlike Cable Television, with Anytime Entertainment. . . ." In the noncomparative advertisements, the headline did not mention an existing functionality, and the sentence introducing the specific benefits of the new functionality in the noncomparative advertisements simply said, "With Anytime Entertainment, a superior alternative."
Manipulation of product typicality. In the typical product condition, the advertisement's text stated that "Anytime Entertainment is available to subscribers on their TV sets." In the atypical product condition, this was altered to "Any-time Entertainment is available to subscribers through the Internet on their PCs." A manipulation check was done with a pretest group of 32 subjects. On nine-point scales, subjects indicated the extent to which they agreed or disagreed with the following statements: "A TV set (alternatively, personal computer) is a typical means of obtaining cable television." The results showed that respondents perceived a television set as more typical than a personal computer for the functionality of cable television (M = 8.28 versus 2.75, t(31) = 14.5, p < .0001). We then conducted another manipulation test with a separate group of 28 subjects and asked them to rate the perceived idealness of the product in terms of the following items: "A TV set (personal computer in the second statement) is an ideal means of obtaining cable television." The results of this pretest confirmed that respondents indeed perceived a television set as more ideal than a personal computer for the functionality of cable television (M = 7.46 versus 3.50, t(27) = 8.01, p < .0001).
Procedure. Subjects were informed that a major (anonymous) manufacturer was planning to introduce a high-technology innovation into the market and was interested in consumers' reactions to this innovation. No brand names were provided because we did not want subjects' judgments to be based on extrinsic cues. Subjects were handed booklets that included the stimulus advertisement as well as the questionnaire with all dependent measures. The first page of the booklet stated that on the next page they would find an advertisement; they were asked to "look at this ad as you normally look at another ad that you might see in a magazine or newspaper" (see Pechmann and Ratneshwar 1991). Subjects viewed the advertisement in a self-paced manner and then completed all the dependent measures.
Dependent Measures
We measured subjects' judgments of the new functionality with four nine-point scale items: "What is your overall opinion of Anytime Entertainment" (1 = "very negative" to 9 = "very positive"), "How useful is Anytime Entertainment" (1 = "not at all useful" to 9 = "very useful"), "How innovative is Anytime Entertainment?" (1 = "minor variation of existing product" to 9 = "completely new product"), and "How likely are you to subscribe to Anytime Entertainment?" (1 = "very unlikely" to 9 = "very likely"). The four items were averaged to create an overall measure of consumer judgments (alpha = .79).
Results
We analyzed the data using analyses of variance (ANOVAs) and used a priori comparisons of means (one-tailed t-tests) to follow up on the ANOVAs. Means for the dependent variables are shown by experiment condition in Table 2. As predicted in H1, the results revealed a significant typicality of producttype of advertisement interaction (F( 1, 189) = 22.70, p < .0001). When the new functionality was offered in a product that was typical of the existing functionality, subjects expressed less favorable judgments when the advertisement was comparative rather than noncomparative (M = 5.60 versus 6.43, t(189) = 3.32, p < .001). In contrast, when the new functionality was offered in an atypical product, subjects produced more favorable judgments when a comparative (versus noncomparative) advertisement was used (M = 6.46 versus 5.56, t(189) = 3.60, p < .001). None of the main effects were significant (p > .80).
Although our key hypothesis was supported in Study 1, the dependent measures in that study focused on subjects' over-all judgments. Therefore, we designed a new study with a specific focus on obtaining data for investigating the cognitive processes involved in judgment formation. First, we expected that a comparative advertisement format in general should mentally prime the existing functionality and thereby foster the use of the existing functionality as a standard of comparison for evaluating the new functionality. If so, subjects exposed to comparative advertisements should be more likely to generate thoughts that are comparative in nature. Second, we expected that when a new functionality is offered in a product that is typical of the existing functionality, assimilation processes are likely. In this event, subjects' thoughts should relate the new functionality to the existing functionality and discount the extent to which the new functionality offers a novel set of benefits. The comparative thoughts generated by subjects thus should be negatively valenced. In contrast, when the new functionality is offered in a product that is atypical of the existing functionality, contrast processes are likely. As in the previous case, subjects are likely to evaluate the new functionality with the existing functionality as a standard of comparison, but in this case, their comparative thoughts should inflate the newness of the functionality and its benefits. The comparative thoughts generated by subjects therefore should be positively valenced in this case.
H2: Subjects exposed to explicit comparisons (versus no comparisons) of a new functionality to an existing functionality are more likely to generate thoughts that evaluate the new functionality in a comparative manner.
H3: When a new functionality is offered in a product that is typical of an existing functionality, subjects exposed to explicit comparisons (versus no comparisons) of the new functionality to the existing functionality are more likely to generate negatively valenced comparative thoughts. However, when a new functionality is offered in a product that is atypical of an existing functionality, subjects exposed to explicit comparisons (versus no comparisons) are more likely to produce positively valenced comparative thoughts.
Method
Study design and subjects. We tested the preceding hypotheses in a between-subjects experiment with a 2 (typical versus atypical product) x 2 (comparative versus non-comparative advertisement) design. The advertisements used as stimuli in this experiment were exactly the same as in Study 1. Subjects were undergraduate students in the United States majoring in business, and they participated in the experiment for extra credit (N = 108). They were randomly assigned to the different experimental conditions.
Procedure. The procedure was similar to Study 1 except that after subjects reviewed the advertisement, they were asked to list all their thoughts. The thoughts could be about the innovation, the advertisement's claims, how the subjects felt about the product, or anything else that came to their minds when looking at the advertisement. They could write as few or as many thoughts as they wanted, and they were allowed to take as much time as they wished.
Dependent Measures
In subjects' protocols, we first identified the thoughts that involved explicit comparisons with the existing functionality. Next, two independent judges who were blind to experimental conditions coded all such comparative thoughts as either positive or negative in valence. They coded a comparative thought as positive when Anytime Entertainment was perceived as a superior innovation (e.g., "More convenient than cable TV"; "It seems like a good alternative to cable TV"). The judges coded a comparative thought as negative when Anytime Entertainment was perceived as fairly similar or inferior (e.g., "Cable TV is better"; "It's just like cable TV"). Interjudge reliability was high (average r = .91), and all discrepancies were resolved through mutual discussion. Two index measures were then created. The first index was the total number of comparative thoughts listed by a subject, regardless of valence. The second index was a valenced measure of comparative thoughts, which we computed by subtracting the number of negative comparative thoughts from the number of positive comparative thoughts.
Results
As predicted in H2, subjects who were exposed to comparative (versus noncomparative) advertisements listed more total comparative thoughts (M = .77 versus .12, F(1, 104) = 20.60, p < .0001). Also, 50% of the subjects exposed to comparative advertisements listed at least one comparative thought (positive or negative) versus only 11% of their counterparts who were exposed to the noncomparative advertisements (chi2( 1) = 19.61, p < .001). The results for the valenced thought index support the hypothesized interaction (H3) between typicality of the product and type of advertisement (F(1, 104) = 8.28, p < .01). When the new functionality was offered in a product that was typical of the existing functionality, the valenced thought index reflected more negative thoughts when the advertisement was comparative rather than noncomparative (M = -.57 versus -.21, t(104) = 1.89, p < .05). In contrast, when the new functionality was offered in an atypical product, the valenced thought index showed evidence of more positive thoughts when subjects were exposed to the comparative (versus noncomparative) advertisement (M = .48 versus -.04, t(104) = 2.47, p < .01).
Our conceptualization specifies that the variable that moderates the effectiveness of comparative advertisements is the typicality of the product in relation to the existing functionality. Given the stimuli we used in Studies 1 and 2, however, it could be argued that the effects obtained for consumer judgments might have been caused by the mere fact that television sets, compared with personal computers, represent an older generation of technology. Such an explanation would imply that the comparative advertisements for Any-time Entertainment yielded more favorable judgments when the product was a personal computer and not when the product was a television set, just because of the relative newness of the former. Furthermore, this alternative line of explanation suggests that the perceived innovativeness of the new functionality was undermined when the functionality was offered to the consumer in a physical product (television) that represents a mature technology.
Therefore, a key issue left somewhat unresolved by Studies 1 and 2 is the conceptual nature of the moderating variable that is implicated in our findings: Is it indeed the typicality of the product in relation to the existing functionality as we have argued, or is it the perceived newness of the technology (i.e., hardware) involved in the device? One way of corroborating our preferred explanation and ruling out the alternative explanation is to conduct a new study wherein the product roles are switched such that the typical product is a personal computer and the atypical product is a television set. If we find situations in which comparative advertisements are more effective for a new functionality when the device is a television set (versus a personal computer), we would have evidence that the results of Studies 1 and 2 were not driven by the product's technological newness per se.
A second major objective for Study 3 was to strengthen the theoretical explanation by tying our work to prior research on comparison effects by Dhar, Nowlis, and Sherman (1999), who argue that the type and direction of initial comparison processes have a systematic effect on consumers' preference judgments. These authors hypothesize and demonstrate that when subjects are asked to focus on the similarities (versus the differences) between two product alternatives in an initial comparison task, the subjects will differ in a predictable manner in their subsequent judgments of the focal alternative. If the processes we theorized previously are integrated with the comparison processes that have been suggested by Dhar, Nowlis, and Sherman (1999), it would enable us to augment the network of constructs implicit in our theorizing and thereby enhance its nomological validity.
Recall that when an explicit comparison involves a new functionality offered in a typical product, the consumer should detect considerable feature overlap between the new stimulus and the products evoked in working memory (see Figure 1). High feature overlap should lead the consumer to focus on the similarities between the new functionality and the existing functionality. In the case of an explicit comparison that highlights a new functionality offered in an atypical product, the consumer should detect a great deal of feature mismatch; consequently, the consumer should focus on dissimilarities between the new functionality and the existing functionality.
A critical assumption here is that explicit comparisons that feature typical and atypical products should lead consumers to focus spontaneously on the similarities and dissimilarities, respectively, between the new functionality and the targeted existing functionality. If it is true that a comparative advertisement that features a typical product spontaneously leads to a focus on similarities (and therefore assimilation), on the basis of Dhar, Nowlis, and Sherman's (1999) work on comparison effects, this effect should be counteracted if subjects are asked to focus on differences between the new and existing functionalities. Consequently, a task focus on differences (versus no task focus) should engender contrast rather than assimilation, and we should thereby find that the new functionality is judged more positively. In this particular case, however, if subjects are asked to focus on similarities rather than differences, the result should be the same as when no specific task focus is provided, because even without a task focus, subjects are presumed to focus spontaneously on similarities.
Conversely, if an explicit comparison that features an atypical product creates a spontaneous focus on dissimilarities, a task focus on similarities between the existing and new functionalities (versus no task focus) should lead to more negative judgments of the new functionality. However, following a parallel logic, in this case instructing subjects to focus on the differences between the new and existing functionalities should lead to the same outcome as when no explicit task focus is provided.
H4: When a new functionality is offered in a product that is typical of an existing functionality and explicit comparisons are made between the new functionality and the existing functionality, a task focus on differences between the new and existing functionalities (versus no task focus) should result in more positive judgments of the new functionality. However, a task focus on similarities between the new and existing functionalities (versus no task focus) should not affect judgments of the new functionality in this case.
H5: When a new functionality is offered in a product that is atypical of an existing functionality and explicit comparisons are made between the new functionality and the existing functionality, a task focus on differences between the new and existing functionalities (versus no task focus) should not affect judgments of the new functionality. However, a task focus on similarities between the new and existing functionalities (versus no task focus) should result in more negative judgments of the new functionality in this case.
The third and final objective for Study 3 was to extend the generalizability of Study 1's findings to a more representative population. It is possible that the student subjects we used in Studies 1 and 2, on average, have a different level of expertise or motivation than the general population in regard to technological products. As such, it would be desirable to replicate the key findings of Study 1 with nonstudent subjects to establish their generalizability. In summary, we designed Study 3 such that we could ( 1) eliminate a plausible alternative explanation based on the newness rather than the typicality of the product, ( 2) relate our work to prior research on comparison effects by testing the effects of task focus (H4 and H5), and ( 3) extend the generalizability of our main findings in Study 1 to the general population.
Method
Study design and subjects. We employed a between-subjects factorial design with eight cells. Six of the eight cells involved explicit comparisons in comparative advertisements, and they crossed three levels of task focus (similarities-task focus, differences-task focus, no-task focus) with typicality of the product (typical versus atypical product). The other two (control) cells involved noncomparative advertisements with no task focus and with either a typical or an atypical product. Subjects were recruited at a local airport, and they participated in the study in return for a small monetary compensation (N = 185). Of the respondents, 48% were female and 52% were male, the median age of the subjects was 31, their self-reported median income was between $50,000 and $74,999, 51% were college graduates, and 61% were employed full time.
Stimulus advertisements and manipulation of advertisement format. The new functionality in this study was Infoex-change. The advertisements were formatted similarly to the advertisements used in the previous studies, and they described the new functionality in terms of three specific benefits for the consumer (for a sample advertisement, see Figure 3). In the explicit comparison conditions, the new functionality was compared with an existing functionality, e-mail.
Manipulation of product typicality. Following the rationale in the introduction to this study, we designed the stimuli so as to switch the roles of the physical products used in Studies 1 and 2. Therefore, in Study 3 the typical product was a personal computer, and the atypical product was a television set. In the typical product conditions, the text of the advertisement informed subjects that "Infoexchange is available to subscribers through the Internet on their PCs." In the atypical product conditions, the text stated that "Infoexchange is available to subscribers on their TV sets." As in Study 1, we verified our manipulation of typicality of the product with a pretest group of 20 subjects. The results confirmed that respondents perceived a personal computer as more typical than a television set for sending and receiving e-mail (M = 8.55 versus 1.80, t(19) = 23.4, p < .0001). Another manipulation test with a different group of 30 subjects confirmed that respondents perceived a personal computer as more ideal than a television set for sending and receiving e-mail (M = 8.70 versus 2.83, t(29) = 13.70, p < .0001).
Procedure. The procedure was identical to that of Study 1 except for the task focus manipulations. Immediately after looking at the advertisement, subjects in the similarities- task focus conditions were asked to write a brief reply to the following question: "How is Infoexchange similar to e-mail?" Subjects in the differences-task focus conditions were asked to reply to the question, "How is Infoexchange different from e-mail?" In the no-task focus conditions, subjects were simply asked to list the thoughts that came to their minds while they looked at the advertisement. After this task, subjects provided judgments of the new functionality on the same four items as in Study 1 (alpha = .80).
Results
We analyzed the data using ANOVAs with a priori comparisons of means (one-tailed t-tests) to follow up on significant interactions; means are shown by experimental condition in Table 3. We first assessed whether we were able to replicate the results of Study 1 in regard to H1. For this purpose, we took into account only the four no-task focus cells of our design, that is, 2 (comparative versus noncomparative advertisements) x 2 (typical versus atypical product). The results confirmed a significant typicality of producttype of advertisement interaction (F(1, 91) = 12.34, p < .001). When the new functionality (Infoexchange) was offered in a product that was typical of the existing functionality (personal computer in this case), subjects expressed slightly less favorable judgments when the advertisement was comparative rather than noncomparative; however, the contrast did not achieve statistical significance (M = 4.80 versus 5.31, t(91) = 1.41, p = .16). But as anticipated, when the new functionality was offered in an atypical product (television set in this case), subjects recorded more favorable judgments when a comparative (versus noncomparative) advertisement was used (M = 6.15 versus 4.79, t(91) = 4.00, p < .0001). None of the main effects were significant (p > .10).
Next, we tested H4 and H5 by considering the six cells of the design that involved explicit comparisons, that is, 3 (similarities-task focus, differences-task focus, no-task focus) x 2 (typical versus atypical product). The results of this ANOVA for the comparative conditions revealed a significant typicality of the product x type of task interaction (F(2, 132) = 4.49, p = .01). In support of H4, when the new functionality was offered in a typical product, subjects in the differences-task focus (versus no-task focus) condition produced more favorable judgments (M = 6.51 versus 4.80, t(132) = 4.75, p < .0001). Also in support of H4, when the new functionality was offered in a typical product, subjects in the similarities-task focus and the no-task focus conditions did not differ significantly in their judgments (M = 4.96 versus 4.80, t(132) = .42, p > .60). In support of H5, when the new functionality was offered in an atypical product, subjects exposed to comparative advertisements in the differences- task focus and the no-task focus conditions did not vary significantly in their judgments (M = 6.29 versus 6.15, t(132) = .38, p > .70). When the new functionality was offered in an atypical product, subjects in the similarities-task focus (versus no-task focus) condition were more negative in their judgments (M = 4.97 versus 6.15, t(132) = 3.27, p <.001).
The results of the three previous studies provide consistent evidence that explicit comparisons between a new functionality and an existing functionality are counterproductive when the former is offered to the consumer in a physical product that is typical of the latter. In these studies, detailed information regarding the benefits of the new functionality was provided to the consumer in the various advertisements. Nonetheless, the comparative advertisements claimed superiority only at the relatively abstract functionality level (e.g., Infoexchange as superior to e-mail) and did not engage in specific comparisons of the new and existing functionalities at a more concrete benefit level.
When the new functionality is offered in a product that is typical of the existing functionality, might explicit comparisons at a concrete benefit level overcome the backfire effects observed in the previous studies? It may be that in the comparative conditions of the previous studies, the differentiating benefits of the new functionality did not have much impact on judgments because consumers did not mentally access the specific benefits associated with the existing functionality and compare them with those of the new functionality (see also Pham and Muthukrishnan 2002). This line of reasoning is consistent with the evidence in Study 3 that when the new functionality is provided in a typical product, subjects spontaneously focus on the similarities (and not the differences) between the new functionality and the existing functionality. It remains an empirical question whether explicit comparisons at the concrete benefit level would override the assimilation processes engendered by comparisons at the abstract functionality level when the product is typical of the existing functionality.
H6: When a new functionality is offered in a product that is typical of an existing functionality, explicit comparisons (versus no comparisons) of the new functionality to the existing functionality at a benefit-specific level should result in more positive judgments of the new functionality.
Method
Study design, subjects, and procedure. This study had three experimental conditions. Two of them involved exposure to comparative advertisements: one with an explicit comparison at the functionality level only (just as in the previous studies) and the other with explicit comparisons at the benefit-specific level. Subjects in the third (control) condition saw a noncomparative advertisement. All three conditions pertained to a new functionality offered in a product that was typical of an existing functionality. Subjects were undergraduate students majoring in business (N = 94). They received extra credit for participating in the experiment and were randomly assigned to the experimental conditions. The experiment procedure was identical to the one used in Study 1, and subjects provided judgments of the new functionality on the same four items as in that study (alpha = .86).
Stimulus advertisements and manipulation of advertisement format. The advertisements were based on the stimuli employed in Study 3, but with minor editing of the descriptions of the three key benefits. All three advertisements depicted Infoexchange as available "to subscribers through the Internet on their PCs," with pictures of a personal computer; note that the pretests in Study 3 had established that a personal computer was a typical product for the existing functionality of e-mail.
The advertisements used in the noncomparative (control) condition and in the explicit comparison at the abstract functionality level were virtually identical to those in Study 3. However, in the condition in which the comparison was at a benefit-specific level, the advertisement also included an explicit comparison of each one of the benefits of the new functionality to a comparable (but presumably inferior) benefit of the existing functionality. Furthermore, the advertisement in this condition was formatted such that the three benefits of the new functionality were horizontally juxtaposed with the comparable benefits of the existing functionality so as to make the comparisons highly salient. For example, one of the benefits of Infoexchange was described as follows: "You can instantly send and receive pictures and video clips anywhere, anytime. You can personalize your pictures and video clips by adding handwritten notes." On the right of this benefit description, there was a comparative benefit description for e-mail as follows: "You can only send and receive pictures and video clips anywhere, anytime. You cannot personalize your pictures and video clips by adding handwritten notes."
Results
We analyzed the data using a one-way ANOVA. We used a priori comparisons of means (one-tailed t-tests) to follow up on the ANOVA; means for the dependent variable are shown by experiment condition in Table 4. The ANOVA yielded a reliable omnibus main effect for advertisement condition (F(2, 91) = 8.77, p < .001). We then verified that in accord with the findings of the previous studies for the typical product condition, subjects who saw an advertisement with an explicit comparison at the abstract functionality level (versus the noncomparative advertisement) expressed less favorable judgments (M = 4.16 versus 5.00, t(91) = 2.40, p < .01). More important, as predicted in H6, subjects who were exposed to an advertisement with explicit comparisons at the benefit-specific level (versus the noncomparative advertisement) produced significantly more positive judgments (M = 5.70 versus 5.00, t(91) = 2.00, p < .05).
The four studies examine the effectiveness of different communication strategies for launching innovations that involve new functionalities. We argue that when a new functionality is made available in a physical product or device that is typical of an existing functionality, explicit comparisons between the new and the existing functionality cause consumers to discount the novelty of the innovation. But when the new functionality is launched in a product that is atypical of an existing functionality, explicit comparisons foster positive thoughts, and consumers are more likely to think of the new functionality as something truly new and better. We obtained empirical support for our predictions in Study 1 and supported the cognitive process explanation with thought-listing data in Study 2. We ruled out a possible alternative explanation as well as replicated and generalized the findings to a nonstudent population in Study 3. In that study, we also investigated the effects of variations in the task focus provided to subjects so as to obtain converging evidence for our theoretical explanation. Finally, in Study 4 we found that when the physical product is typical of the existing functionality, the backfire effects observed in the previous studies can be counteracted by specific comparisons at a concrete benefit level.
Implications for Comparative Advertising
Prior research has extensively researched and debated the merits of comparative advertising versus noncomparative advertising, but usually in contexts in which comparisons are between brands within the same product category (see, e.g., Gorn and Weinberg 1984; Pechmann and Stewart 1990; Shimp and Dyer 1978). Much of the recent research in this area has focused on a search for moderating variables and on methodological and measurement issues (see, e.g., Grewal et al. 1997; Pechmann and Ratneshwar 1991; Rose et al. 1993). Notwithstanding, as we pointed out previously, the real world abounds with cases in which firms launch innovations with comparisons at the product functionality level rather than at the brand level. The effectiveness of such comparisons cannot be ascertained on the basis of the insights available from previous academic research. Consider, for example, a recent advertisement in Forbes, in which visitalk.com introduced voice and visual communication over the Internet as a new functionality. This functionality was offered in personal computers. The visitalk.com advertisement explicitly compared this new functionality with conventional e-mail, an existing functionality, with sentences such as, "Famous words were never delivered through e-mail." The question is, Does the visitalk.com comparative advertising strategy make sense? Or would the firm be better off not mentioning e-mail at all?
The results of our research clearly suggest that comparative advertising at the product functionality level can be beneficial for the marketer, but only when the new functionality is launched in an atypical product. In other words, such comparative advertisements seem to be effective at product differentiation provided that the new functionality is delivered through a device (or hardware) that is distinctive and unusual in relation to the existing functionality with which the new functionality is being compared. In contrast, comparative advertisements that explicitly compare a new functionality with an older functionality are likely to backfire if the new functionality is offered in a product that is relatively typical of the older functionality. We speculate that the visitalk.com advertisement fits this latter category, because personal computers are typical products for sending and receiving e-mail. Visitalk.com may have done better to launch its innovation with no mention of e-mail at all. Note that the results of Study 4 suggest that the backfire effect can be overcome with detailed comparisons of the superior benefits of the new functionality with respect to counterpart benefits of the existing functionality.[ 4]
Implications for New Product Marketing and Consumer Behavior Theory
Our findings indicate implications for how consumers process information about new products. For example, Olshavsky and Spreng (1996) demonstrate that categorization of an innovation into an existing category may preclude a careful evaluation of the benefits of the innovation and thus undermine its perceived value. They argue that it is important for marketers to know the types of categories that a new product elicits in consumers' minds so that the innovation can be positioned as sufficiently different. Olshavsky and Spreng further suggest that for differentiating a new product, the marketer's goal in general should be to encourage effortful, piecemeal processing of product attribute information.
Consistent with Olshavsky and Spreng's (1996) findings, our results show that assimilation of an innovative new functionality into an existing functionality is likely to under-mine its perceived value. Furthermore, the negative effects of assimilation can be reversed if consumers focus on the benefits of the functionality in a detailed manner (Study 4). Our results also suggest that for effective differentiation, it is not always necessary that consumers should process detailed attribute information piecemeal. Consumers may perceive an innovation as new and different even if the marketer's communication simply prompts a contrast of the innovation to older alternatives. Such a contrast can be brought about, for example, when a new functionality is offered in a product that is atypical of an existing functionality and explicit comparisons are made between the new functionality and the existing functionality.
Our findings also extend theoretical understanding of assimilation/contrast processes. Prior research in this area has usually examined the relative likelihood of assimilation versus contrast effects through direct manipulations of the standard of comparison or context. For example, Herr (1989) varies the judgment context by priming subjects with different categories of cars (e.g., moderately expensive versus extremely expensive cars). Our results, conversely, suggest that assimilation and contrast can be obtained even when the judgment context itself is kept constant. Specifically, these effects can be engendered when the features associated with the target of judgment--in the present research, the type of physical product associated with a new functionality--vary so as to have either considerable or little overlap with corresponding features in a person's mental representation of the judgment context. Moreover, as we theorized previously, when the evoked judgment context relates to functionality or product purpose, the mental representation is likely to include products that are typical of the goal-derived category associated with that functionality. Further research may produce more insights on the relation-ship between assimilation/contrast processes and the category members that are retrieved from memory when a particular judgment context is primed.
Further research could also examine the effects of explicit comparisons that vary in the extent to which they highlight similarities or differences between a new functionality and an existing functionality (cf. Dhar, Nowlis, and Sherman 1999). Still another direction would be to investigate the moderating role of product typicality in more detail. Research in the person perception area by Kunda and Oleson (1997) suggests that people often find extremely atypical exemplars to be implausible and that extreme deviations from the norm may not be accommodated easily in people's knowledge structures (see also Weber and Crocker 1983). Therefore, from a product strategy perspective, it may be optimal to launch new functionalities in products that are moderately atypical rather than completely atypical of existing functionalities.
Further research might also study the manner in which a new functionality alters the hierarchical category structure of physical products and thereby affects product choices. One limitation of our research is that we focused on evaluations of the functionality itself and did not inquire into product perceptions, categorization, or choices. A new functionality can often provide additional benefits to the consumer such that the physical product in which the functionality is offered moves into a higher-level taxonomic category. For example, a personal data organizer that also takes and stores photographs might raise the product itself into the category of "mobile, multimedia documentation systems."[ 5] Prior research by Johnson (1984, 1988) suggests that consumers might compare such innovations with more conventional products (e.g., handheld devices or digital cameras) by evoking abstract criteria based on such higher-order taxonomic categorization. Further research could empirically investigate this possibility as well as the more general issue of the product choice strategies used by consumers in situations in which new functionalities are launched with explicit comparisons.
Finally, our findings also bear on the literature on "meaningless differentiation" (e.g., Carpenter, Glazer, and Nakamoto 1994). Consumers often infer that a unique and salient product attribute such as the "flaked coffee crystals" in Folger's coffee is important and valuable even when that attribute is irrelevant to the functionality of the product (Carpenter, Glazer, and Nakamoto 1994). Consequently, differences in product form may induce the perception that a product provides an improved functionality (e.g., better-tasting coffee) even when that product is objectively no different from others in the category. But what if a product does provide a truly new functionality? Our findings suggest that product form matters even in this case. Marketing communications that attempt to contrast and differentiate a new functionality from an existing functionality are much more effective when the new functionality is offered to the consumer in a product that is atypical or unusual for the existing functionality. In other words, consumers' judgments of meaningfully differentiated offerings also depend on the nature and appearance of the physical product. Conventional wisdom in product design holds that "form follows functionality" (see, e.g., Bloch 1995), but our research indicates that form also influences the perception of functionality.
This research was funded by a Eugene M. Lang fellowship from Baruch College to the first author.
The authors thank Barbara Bickart, Ravi Dhar, and the three anonymous JM reviewers for their helpful comments and suggestions.
1 For research on consumer decision-making processes in situations in which choice options are across different product categories, see Johnson (1984, 1988).
2 A key assumption here is that subjects will spontaneously evoke this mental representation only if explicit comparisons are made between the new functionality and the existing functionality, and not otherwise. The data reported in this article are supportive of this assumption; see also Olshavsky and Spreng (1996).
3 A separate analysis of the main dependent variable in Study 1 with the country factor as an additional blocking variable produced a statistically nonsignificant typicality of producttype of advertisementcountry three-way interaction (p > .11). Furthermore, inspection of the data revealed that the pattern in the means was essentially the same for subjects from both countries. Therefore, for simplifying the presentation, we dropped the country factor from the design and analysis reported in the article.
4 We note that our research was limited to explicit comparisons in print advertisements. Further research might examine the generalizability of our findings to broadcast media, sales presentations, and concept-testing storyboards.
5 We thank an anonymous reviewer for this insight.
Legend for the Chart
A Brand/Firm
B New Functionality
C Existing Functionality
D Physical Product
A
B C D
visitalk.com
Voice and visual E-mail Personal computer
communication
Visor/Handspring
Take and store Organize personal Handheld device
photographs data
TiVo
Watch the television Record and play Digital video
shows you want, television recorder
when you can programs
Olay/Procter & Gamble
Fight signs of aging Clean face Cloth
Huggies/Kimberly-Clark
Bathing costume Protect against Diaper
for children leaks
Abreva/GlaxoSmithKline
Heal cold sores Soothe cold sores Cream New Functionality Is Offered in a Product
That Is Typical of the Existing Functionality
No Comparison of No Comparison of
New Functionality New Functionality
with Existing with Existing
Functionality Functionality
Consumer judgments 6.43 5.60
New Functionality Is Offered in a Product
That Is Atypical of the Existing Functionality
No Comparison of No Comparison of
New Functionality New functionality
with Existing with Existing
Functionality Functionality
Consumer judgments 5.56 6.46Notes: Higher numbers indicate more positive judgments; the theoretical scale range was 1 to 9. Cell sizes ranged from 47 to 50.
New Functionality Is Offered in a Product That
Is Typical of the Existing Functionality
No Comparison
of New
Functionality Explicit Comparison of New
with Existing Functionality with Existing
Functionality Functionality
Similarities- Differences-
No-Task No-Task Task Task
Focus Focus Focus Focus
Consumer 5.31 4.80 4.96 6.51
judgments
New Functionality Is Offered in a Product That
Is Atypical of the Existing Functionality
No Comparison
of New
Functionality Explicit Comparison of New
with Existing Functionality with Existing
Functionality Functionality
Similarities- Differences-
No-Task No-Task Task Task
Focus Focus Focus Focus
Consumer 4.79 6.15 4.97 6.29
judgmentsNotes: Higher numbers indicate more positive judgments; the theoretical scale range was 1 to 9. Cell sizes ranged from 21 to 26.
Legend for the Chart
A No Comparison of New Functionality
with Existing Functionality
B Explicit Comparison of New Functionality
with Existing Functionality at an Abstract
Functionality Level
C Explicit Comparison of New Functionality with
Existing Functionality at Benefit-Specific Level
A B C
Consumer judgments 5.00 4.16 5.70Notes: Higher numbers indicate more positive judgments; the theoretical scale range was 1 to 9. Cell sizes ranged from 31 to 32. All conditions in this study involved a new functionality offered in a product that is typical of the existing functionality.
DIAGRAM: FIGURE 1: Schematic Diagram of the Theoretical Framework
PHOTO (BLACK & WHITE): FIGURE 2: Study 1: Comparative Advertisement for a New Functionality Offered in a Product (Personal Computer) That Is Atypical of the Existing Functionality
PHOTO (BLACK & WHITE): FIGURE 3: Study 3: Comparative Advertisement for a New functionality Offered in a Product (Television) That Is Atypical of the Existing Functionality
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By Paschalina (Lilia) Ziamou and S. Ratneshwar
Paschalina (Lilia) Ziamou is Assistant Professor of Marketing, Baruch College, The City University of New York. S. Ratneshwar is Professor of Marketing and Ackerman Scholar, School of Business, University of Connecticut.
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Record: 82- Internal Benefits of Service-Worker Customer Orientation: Job Satisfaction, Commitment, and Organizational Citizenship Behaviors. By: Donavan, D. Todd; Brown, Tom J.; Mowen, John C. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p128-146. 19p. 3 Diagrams, 6 Charts. DOI: 10.1509/jmkg.68.1.128.24034.
- Database:
- Business Source Complete
Internal Benefits of Service-Worker Customer
Orientation: Job Satisfaction, Commitment, and
Organizational Citizenship Behaviors
Implementation of the marketing concept in service firms is accomplished through individual service employees and their interactions with customers. Although prior research has established a link between service-worker customer orientation and performance outcomes, little research has addressed other potentially important outcomes of customer orientation. Drawing from the literature on person-situation interaction and fit theory, the authors develop and test a model that explains how service-worker customer orientation affects several important job responses, including perceived job fit, job satisfaction, commitment to the firm, and organizational citizenship behaviors. Across three field studies in two distinct services industries, the results indicate that the positive influence of customer orientation on certain job responses is stronger for service workers who spend more time in direct contact with customers than for workers who spend less time with customers. The authors discuss the implications of the results for s ervices marketing managers and researchers.
Marketing theorists have long argued that firms that focus on their customers' needs are better positioned to achieve long-term success than are companies that do not (Deshpandé, Farley, and Webster 1993; Kotler 2000). Indeed, empirical research has demonstrated several positive outcomes of a market orientation, including enhanced profitability (Narver and Slater 1990), employee commitment, and esprit de corps (Jaworski and Kohli 1993). Implementation of the marketing concept in service firms is accomplished through service employees and their interactions with customers. At the individual service-worker level, customer orientation (CO) has been shown to exert positive effects on performance outcomes (e.g., Brown et al. 2002).
One purpose of our research is to investigate additional benefits of service-worker CO beyond its effects on performance. Theorists have noted the importance of worker satisfaction and commitment in retaining service workers, as well as the importance of worker retention to the success of the services organization (Heskett et al. 1994; Schneider and Bowen 1993). Other scholars have noted the significant role of organizational citizenship behaviors (OCBs), or employee behaviors that go beyond specified job requirements, in promoting positive outcomes for an organization (e.g., Bateman and Organ 1983; Podsakoff and MacKenzie 1994). As we describe in the theory section, we predict that CO is associated with higher levels of job satisfaction, commitment, and OCBs. Such findings further highlight the value of hiring and retaining customer-oriented service workers.
Another goal of our research is to begin to establish boundary conditions on the influence of CO. Substantial research suggests that individual characteristics and situational variables often jointly determine outcomes. For example, the interaction between person and situation has been shown to affect job performance (Caldwell and O'Reilly 1990), job burnout (Maslach and Goldberg 1998), job retaliation (Skarlicki, Folger, and Tesluk 1999), and retention (Hayward and Everett 1983). Drawing from fit theory (e.g., Chatman 1989, 1991; Kristof 1996; Nadler and Tushman 1980; O'Reilly, Chatman, and Caldwell 1991; Super 1953), we argue that CO (a personal characteristic) will be more influential on service-worker satisfaction and commitment as workers spend more time in contact with customers (a situational variable).
The article is organized as follows: We initially review prior theory and research pertaining to CO and fit theory. We then develop hypotheses about the internal consequences of CO. Next, we present the methods and results from three field studies with workers in the financial services and hospitality industries. We conclude by discussing the implications for services marketing researchers and managers.
Researchers have investigated the implementation of the marketing concept at both the organizational and the individual levels. Researchers working at the organizational level have identified several organizational outcomes of market orientation (e.g., Jaworski and Kohli 1993; Kohli and Jaworski 1990; Narver and Slater 1990). Narver and Slater (1990) find evidence that as organizations increase their level of market orientation, their organizational performance increases as well. Narver and Slater propose that market orientation involves three behavioral components: ( 1) CO (i.e., focus on customers), ( 2) competitor orientation (i.e., focus on competitors), and ( 3) interfunctional coordination (i.e., coordinated use of company resources).
However, our research addresses how the marketing concept is implemented at the level of the individual worker. Work in this research stream can be traced to a seminal article by Saxe and Weitz (1982), who found evidence that a two-dimensional "selling orientation-customer orientation" measure (i.e., SOCO) was connected to salesperson performance. They propose (p. 344) that customer-oriented selling is a behavioral concept that refers to "the degree to which salespeople practice the marketing concept by trying to help their customers make purchase decisions that will satisfy customer needs." Follow-up research has investigated salesperson CO as consumers and organizational buyers view it (i.e., Brown, Widing, and Coulter 1991; Michaels and Day 1985; Tadepalli 1995) and has examined the relationships among CO and customer satisfaction (e.g., Reynierse and Harker 1992), salespeople's ethical behavior (Howe, Hoffman, and Hardigree 1994), commitment to the organization (Kelley 1992; Pettijohn, Pettijohn, and Taylor 2002), job satisfaction (Hoffman and Ingram 1991, 1992; Pettijohn, Pettijohn, and Taylor 2002), and market orientation of the organization (Siguaw, Brown, and Widing 1994).
Recently, Brown and colleagues (2002, p. 111) defined CO as an "employee's tendency or predisposition to meet customer needs in an on-the-job context." They found that CO was influenced by deeper personality traits and, in turn, influenced worker performance. This perspective is consistent with traditional views of personality. For example, Pervin and John (1997, p. 4) define personality as the "characteristics of the person that account for consistent patterns of feeling, thinking, and behaving."( n1)
As do Brown and colleagues (2002), we treat CO as a surface-level personality trait within a hierarchical personality model. As Mowen (2000) proposes, surface traits are enduring dispositions to act within context-specific situations. From this perspective, CO is an enduring disposition (i.e., consistent over time) to meet customer needs. The context-specific situation is the interaction that takes place between the service provider and the customer. In a hierarchical model, CO is influenced by more basic traits (e.g., agreeability, emotional stability, activity needs); in turn, it influences outcome variables, such as service-worker performance on job-related tasks. Although viewing CO as a trait is inconsistent with Saxe and Weitz's (1982) approach, it is consistent with the research that takes a hierarchical approach to personality (e.g., Allport 1961; Lastovicka 1982; Mowen 2000). Brown and colleagues (2002) demonstrate that CO mediates the relationships between more basic personality traits and service performance. Furthermore, the approach is consistent with the proposal that behavior is a function of both person and environment (Bowers 1973; Magnusson and Endler 1977); that is, any particular customer-oriented behavior will result from the combination of person (e.g., personality, goals, functional motives) and environment (e.g., nature of the job, short-term situational effects). We explore this interactive relationship in our research.( n2)
To develop a four-dimensional conceptualization of CO (i.e., need to pamper the customer, need to read the customer's needs, need for personal relationship, and need to deliver the service required), we use extensive qualitative research and measure development efforts. We argue that CO can produce internal benefits to the service employee (i.e., enhanced satisfaction and commitment) and ultimately to the firm through the performance of OCBs. Furthermore, we believe that the magnitude of the effects of CO on several of the outcomes is contingent on a key aspect of the work environment, that is, the relative amount of time that the service worker spends with customers.
Person-Situation Fit in Organizations
Fit theory offers a rationale for the CO hypotheses that we develop herein. Fit theory derives from interactional psychology, which suggests that the person and the environment or situation combine to affect the person's behavior (Chatman 1991; Nadler and Tushman 1980). Moreover, the interaction between the two variables increases the amount of variance explained.
In an organizational context, organizational behavior and marketing researchers have approached the notion of fit between worker and environment in several ways. As Kristof (1996) notes, there is a distinction between the organization itself and the specific job tasks expected of an employee. Accordingly, in general, prior approaches to worker-environment fit can be grouped into two categories: ( 1) fit between the worker and the specific organization and ( 2) fit between the worker and the tasks associated with a particular job. The latter type of fit, usually labeled person-job (P-J) fit, is the type of person-situation fit that we address herein.
The P-J fit pertains to the degree of match between the personality, skills, and ability of the worker and the requirements of specific jobs or job tasks. People select themselves into jobs that best match their abilities and interests (Wilk, Desmarais, and Sackett 1995). Edwards (1991) defines P-J fit as the congruence between the person's abilities and the demands of a job. However, note that P-J fit is more than just a person's abilities, and it extends to the personality of the worker. For example, Super's (1953) theory of vocational development suggests that people choose vocations on the basis of fit between their own personalities and the career. Holland (1977, 1985) notes both that the worker and the particular job have personalities and that fit is determined by the congruence between the two personalities. Nadler and Tushman (1980) argue that when the demands of the job tasks match the characteristics of the worker, performance is enhanced. In our research, we consider other consequences of the match between the worker and the services job.
Our goal is to examine the effect of CO on service workers' responses to their jobs. In particular, we identify three outcomes of service workers' enhanced CO: higher levels of ( 1) organizational commitment, ( 2) job satisfaction, and ( 3) OCBs. We focus on commitment and satisfaction because of their implications for service-worker retention (e.g., Mobley 1977; Morgan and Hunt 1994; Porter and Steers 1973). Because of OCBs' role in the ongoing functioning of the organization, they are notable (e.g., Organ 1988; MacKenzie, Podsakoff, and Fetter 1993).
Job Satisfaction and Organizational Commitment
On the basis of a P-J fit mechanism, we propose that service workers who have higher degrees of CO will express higher levels of job satisfaction (e.g., Edwards 1991; Super 1953). In contexts in which the primary task is the serving of customer needs, customer-oriented employees fit the service setting better than employees who have lower CO because they are predisposed to enjoy the work of serving customers. Consequently, service employees who have higher degrees of CO will be more satisfied with their jobs than will employees who have less CO.
Researchers have investigated the possible relationship between job satisfaction and CO (Hoffman and Ingram 1991, 1992; Pettijohn, Pettijohn, and Taylor 2002). Using the behaviorally oriented SOCO scale (Saxe and Weitz 1982), each research team concluded that increasing levels of satisfaction produce higher levels of CO. We argue that as a characteristic of the employee, dispositional CO will lead to job satisfaction, not vice versa. That is, a customer-oriented service worker is a more natural fit in a service job and, as a result, will experience greater job satisfaction. The direction of causality is a key issue because of the resulting recruiting implications for services managers. If CO is a consequence of job satisfaction, less emphasis can be placed on identifying customer-oriented job prospects. Conversely, if satisfaction results from CO, managers should devote effort to hiring workers who possess a customer-oriented personality. We address the direction of the causality issue in our empirical work.
H[sub1]: Service-worker CO will exert a positive influence on job satisfaction.
In their research on organizational market orientation, Jaworski and Kohli (1993; Kohli and Jaworski 1990) find that employees experience greater commitment to the organization when they believe the company practices the marketing concept. We suggest that the same effect is also found at the individual level for service workers. Service firms implement the marketing concept through their employees. Thus, as the service workers experience deeper levels of CO, they will become more committed to the organization.
Similar to CO's effects on job satisfaction, we expect that customer-oriented employees will fit the job setting better than employees who have lower levels of CO. Consequently, these workers will experience higher levels of commitment to their organizations. Kelley (1992) and Pettijohn, Pettijohn, and Taylor (2002) argue that organizational commitment is an antecedent of CO rather than an outcome of CO, as we position it. However, we posit that it is the fit of the context and the worker's predisposition toward meeting customer needs that produces the opportunity for organizational commitment to develop. This leads to the following hypothesis:
H[sub2]: Service-worker CO will exert a positive influence on organizational commitment.
Previous research suggests that job satisfaction has a positive influence on commitment (e.g., Brown and Peterson 1993, DeCotiis and Summers 1987; Williams and Hazer 1986). Thus, the influence of CO on commitment will be partially mediated through satisfaction.
H[sub3]: The influence of service-worker CO on organizational commitment will be partially mediated by job satisfaction.
We have argued that workers disposed toward meeting customer needs fit better in a service organization than do workers who are less disposed toward meeting customer needs. However, different jobs, even in the same organization, require different amounts of actual time spent with customers, a variable that we label "contact time."( n3) Consequently, we propose that the positive influence of CO on commitment and satisfaction will be stronger (weaker) for workers who spend more (less) time in contact with customers. For example, a service worker who has higher levels of CO will be especially satisfied with and committed to a job when that job requires higher amounts of time spent with customers. In contrast, the degree of CO may be less relevant to job outcomes for workers who spend little time in contact with customers.
The literature on person-situation interactions offers support for our ideas. Individual responses are often driven by the interplay of personal and environmental factors rather than either factor alone (e.g., Bowers 1973; Magnusson and Endler 1977). A person brings certain characteristics with him or her into a situational context, and the resulting behaviors and responses depend on the interaction of the personal characteristics and situational variables. Thus, we argue that the degree of CO (a personality characteristic) will interact with customer-contact time (a situational variable) in the following manner:
H[sub4]: The positive influence of service-worker CO on service-worker (a) job satisfaction and (b) organizational commitment will be stronger when contact time is high than when contact time is low.
OCBs
We define OCBs as the noncompulsive, helpful, and constructive behaviors that are directed to the organization or to its members (Bateman and Organ 1983; Podsakoff and MacKenzie 1994). Although OCBs are not a part of general job requirements (Organ 1988), they can affect supervisors' evaluations of employees (MacKenzie, Podsakoff, and Fetter 1993). Although employees may not be objectively evaluated on OCBs, research suggests that OCBs positively influence the work environment.
Although several OCB dimensions have been identified, altruism appears to be especially important in the current context. Altruistic OCB (hereafter, OCB-altruism) is defined as one employee helping another employee who has a work-related problem (MacKenzie, Podsakoff, and Fetter 1993). We posit that customer-oriented employees are motivated to help fellow employees as a means of ultimately satisfying customers; that is, customer-oriented employees recognize that for successful exchanges with customers to occur, effective internal exchanges must occur first (George 1990; Grönroos 1990). Contact employees who are inclined to meet customer needs will go beyond the call of duty to assist coworkers. As a result, higher levels of CO will lead to higher levels of OCB-altruism:
H[sub5]: Service-worker CO will exert a positive influence on OCB-altruism.
We further suggest that as service employees become more satisfied with their jobs, helpful behaviors will increase. It has been shown that job satisfaction is correlated with altruism (Bateman and Organ 1983; Organ and Ryan 1995; Smith, Organ, and Near 1983). Consequently:
H[sub6]: The influence of service-worker CO on OCB-altruism will be partially mediated by job satisfaction.
We do not anticipate that the degree of contact time will moderate the relationship between CO and OCB-altruism. On the one hand, it seems reasonable that as a response to CO, OCB-altruism should be subject to the same person-situation influences as other responses (i.e., job satisfaction, commitment). As a result, the match of personality and environment should produce a corresponding enhancement of OCB-altruism. On the other hand, there is a potential countervailing effect: Workers who have high CO but are constrained in lower-contact-time environments may be more inclined than workers in high-contact-time positions to perform OCB-altruism as a means of ultimately satisfying customer needs. That is, if the workers cannot directly meet customer needs as frequently as they would like, they might perform OCB-altruism at an increased rate. As a result, we offer no hypothesis about the possible moderating role of contact time on the relationship between CO and OCB-altruism, though we test the effect in our empirical work.
In the following sections, we present three field studies that test our hypotheses. In Studies 1 and 2, we develop a measure of CO and test our hypotheses in two distinct services contexts, which provides evidence of generalizability. In Study 3, we examine the mediational role of a direct measure of job fit.
In Study 1, we collected data from the employees of a financial institution. The financial services industry was appropriate for testing our hypotheses for various reasons. Financial institutions employ millions of people in jobs ranging from low customer contact (e.g., internal auditing, credit analysis) to high customer contact (e.g., consumer lending, commercial lending, customer service). Furthermore, financial services are "pure" services in the sense that transactions involve few tangibles. Many of the services that financial institutions offer are continuous in nature rather than discrete.
We collected data from the employees of a midsize bank located in a Midwestern city. After bank management's participation was secured, blank questionnaires and self-addressed stamped envelopes were distributed by the managers of each of the bank's departments. We assured all participants that their individual answers would be held in confidence. All 250 of the bank's employees were asked to complete the survey during work time and to mail it directly to one of us. We received 156 completed surveys, for a response rate of 62%. The questionnaire included measures of CO, job satisfaction, organizational commitment, OCB-altruism, and contact time, presented in that order.
Most respondents were female (81%); the median tenure at the bank was 19 months. Contact time ranged from 20% to 100%; 55% of respondents reported spending at least 60% of their time with customers.
Measures and Analysis
CO. In developing a measure of CO, we used appropriate measurement development techniques (e.g., Anderson and Gerbing 1988; Churchill 1979). In particular, we sought to explore the potential dimensionality of the construct. We gathered extensive qualitative data to better define the nature of CO. We conducted personal interviews with six service managers from diverse service settings (e.g., food service, financial services, travel agency) and two focus groups, one with customers and one with nonmanager customer-contact employees. Two judges independently analyzed written transcripts from the interviews and focus groups to identify CO themes.
On the basis of a review of literature and our qualitative research, we developed 98 statements that reflected different aspects of CO. Five academicians who study services marketing and five managers who did not participate in the interviews evaluated the items for face validity. Using their feedback and multiple rounds of data collection and exploratory factor analysis, we reduced the number of items to 23 across four dimensions. We subsequently discuss these dimensions and their relationship to another measure of CO recently introduced into the literature.
Employees' need to pamper the customer represents the degree to which service employees desire to make customers believe they are special, that is, individually important to the service provider. The service provider's need to read the customer reflects the employee's desire to pick up on customers' verbal and nonverbal communication. The service employee's need for personal relationship captures the employee's desire to know or connect with the customer on a personal level. Finally, customer-oriented employees' need to deliver reflects their desire to perform the service successfully. We included the items that assess these four proposed dimensions as our measure of CO.
A review of the items we used to assess CO (see the Appendix) reveals that they are complementary to the needs and enjoyment facets of CO that Brown and colleagues (2002) developed. Indeed, both the desire to meet customer needs and the enjoyment of doing so are reflected across the four dimensions. In Study 3, we compare our results with those we obtained using Brown and colleagues' measure.
Other measures. To assess an employee's organizational commitment, we used three items adapted from Morgan and Hunt's (1994) research (e.g., "The relationship my firm has with me is something to which I am very committed"). We used a global measure of job satisfaction that asked respondents to rate the level of satisfaction with their "overall job" on a 7-point scale (1 = "very dissatisfied" and 7 = "very satisfied"). The use of a global scale enabled us to capture an overall assessment without either focusing on any one of the several reported dimensions of job satisfaction or including many items (e.g., Churchill, Ford, and Walker 1974). To assess OCB-altruism, we used three items that measured the altruism dimension, adapted from MacKenzie, Podsakoff, and Fetter's (1993) work. We assessed the proportion of time spent with customers, or contact time, on an 11-point scale that ranged from 0% to 100% in 10% increments (i.e., 0%, 10%, 20%, and so on). We used this measure in tests of moderation. Finally, we included a six-item measure of socially desirable responding (SDR) based on Strahan and Gerbasi's (1972) short version of Crowne and Marlowe's (1960) scale. (For all the measures we used in our analyses, see the Appendix.)
We analyzed our data using structural equations modeling with Amos 4.0 (Arbuckle 1997). Because we used a single item to assess overall job satisfaction, we assumed a reliability level of .85 to allow for measurement error, and we fixed the path coefficient and error variance accordingly (see Hair et al. 1998; Jöreskog and Sörbom 1993).
To test the moderation hypotheses, we created two groups of employees (i.e., high contact and low contact) based on the measure of contact time. Because the amount of time that a worker spends with a customer might be influenced by the worker's degree of CO, thereby possibly confounding the interpretation of the proposed moderation effect, we removed the effect of CO on contact time before we formed groups. We regressed the contact time measure on CO and then performed a median split on the residuals from the regression analysis to form the high- and low-customer-contact groups. Because of the importance of group formation, we used robust regression (Neter et al. 1996) to control for the effects of outliers on the estimation of the regression equation. As a result of these procedures, any differences in the relationships between CO and its proposed consequences (i.e., job satisfaction, commitment) across groups cannot be an artifact of the relationship between CO and contact time. We then performed a two-group structural equation modeling analysis.
Validation of the CO measure. To purify further the multidimensional measure of CO, we performed a confirmatory factor analysis in which we loaded the indicators on their appropriate dimensions. Of the 23 items, we dropped 9 at this stage because of poor loadings in the confirmatory analyses and/or evidence of cross-loading on one or more additional dimensions. In addition, we deleted one item on the grounds of insufficient face validity: It appeared to be conceptually dissimilar to the other items in its dimension. The remaining items loaded on the four dimensions of CO. According to the criteria recommended by Fornell and Larcker (1981), a confirmatory factor analysis with the four dimensions as latent constructs confirmed discriminant validity between the dimensions. We also tested the validity of our conceptualization by using a second-order factor model. The results (χ² = 119.72, degrees of freedom [d.f.] = 61, p < .01; comparative fit index [CFI] = .96; Tucker-Lewis index [TLI] = .95; and root mean square error of approximation [RMSEA] = .08) indicate that each CO dimension loaded strongly on the second-order factor. Consequently, we computed mean scores for each of the four dimensions of CO and treated them as separate indicators of the CO latent variable in our structural equations analyses.
Results
Table 1 provides descriptive statistics and pairwise correlations for Study 1. Model fit for the measurement model was good (χ² = 81.11, d.f. = 39, p < .01; CFI = .95; TLI = .93; and RMSEA = .08). Composite reliability and average variance extracted were strong for all latent variables (see Table 1). In addition, all model constructs exhibited discriminant validity with respect to the standards Fornell and Larcker (1981) suggest. Given the discriminant validity and evidence of nomological validity (see the subsequent section), we conclude that all measures exhibited construct validity.
Structural model results. We derived the full structural model from our hypotheses; the model is presented in Figure 1. Structural model fit was good (χ² = 81.33, d.f. = 40, p < .01; CFI = .95; TLI = .94; and RMSEA = .08). Table 2 presents the standardized path coefficients (SPCs) and associated t-values for all relationships in the structural model.
A purpose of our research is to consider the effects of service-worker CO on job satisfaction, organizational commitment, and OCB-altruism. H[sub1] suggests that as the employee's level of CO increases, his or her level of job satisfaction also increases. The results reported in Table 2 support this effect (SPC = .34, t = 4.03). We also predicted that CO exerts a positive influence on service-worker commitment to the organization; the results support our hypothesis (i.e., H[sub2]: SPC = .60, t = 5.82). In H[sub3], we predicted that the influence of CO on commitment is partially mediated by job satisfaction. To test this, we examined the linkage between job satisfaction and commitment. Contrary to our expectations, the relationship was not significant (SPC = .01, t = .09). H[sub5] predicted that as the level of CO increases, the level of OCB-altruism increases. The results support this proposition (SPC = .28, t = 3.28). In addition, the expected relationship between job satisfaction and OCB-altruism emerged (SPC = .48, t = 5.11), providing evidence of partial mediation of CO on OCB-altruism by job satisfaction (H[sub6]).
Moderation tests. As we noted previously, we created low-and high-contact-time groups after we adjusted for the effect of CO on contact time.( n4) Both resulting groups contained 78 respondents. In our moderation tests, we compared two models, one in which we constrained all paths in the two groups to be equal and one in which we allowed the path between CO and a particular outcome variable (i.e., commitment, satisfaction, or OCB-altruism) to vary across groups. The resulting single degree of freedom χ² test provides a statistical test of moderation.
The fully constrained model had χ² = 170.68 with 105 degrees of freedom. The effect of CO on both commitment and job satisfaction was statistically stronger for the high-contact group than for the low-contact group (commitment: Δχ² = 14.33, Δd.f. = 1, p < .05; high-contact group SPC = .75, low-contact group SPC = .38; satisfaction: Δχ² = 4.07, Δd.f. = 1, p < .05; high-contact group SPC = .46, low-contact group SPC = .16). These results support H[sub4].
We also tested for the possible moderating effect of contact time on the relationship between CO and OCB-altruism. There was no difference in the influence of CO on OCB-altruism across groups (Δχ² = .03, d.f. = 1, p > .10).
Follow-up tests. To test whether a bias toward SDR influenced our results, we created an index for the SDR scale, fixed the measurement path coefficient and error variance based on coefficient alpha for the measure (α = .74), added paths between SDR and the other latent variables in the model, and reran the structural model. The results demonstrate that SDR had a significant effect on CO (SPC = .33, t = 3.58). However, the addition of the SDR latent variable did little to change the structural paths in the model (see Table 2), so SDR bias cannot account for the results. To assess the effects of common method variance on the results, we used procedures recommended by Williams and Anderson (1994) and MacKenzie, Podsakoff, and Fetter (1993). We added a "method" factor with all indicators for all latent variables loading on this factor and on their respective latent variables. Several indicators loaded significantly on the method factor, but the structural results were completely consistent with the results reported in the structural model (for complete common method results, see Table 2).
Given that previous researchers have argued that CO is an outcome rather than an antecedent of satisfaction and commitment, it was important to determine which causal ordering our data empirically supported. Using a model structure outlined by Rigdon (1995), we fit separate models with reciprocal paths between ( 1) CO and job satisfaction and ( 2) CO and commitment. The basic model is shown in Figure 2. For the models with dual paths to be statistically identified, we included a single antecedent variable, need for activity, which is a variable Brown and colleagues (2002) identify as a determinant of CO. These models enabled us to test which causal path (i.e., a or b), if any, the data support.
The results provide strong evidence that CO leads to higher levels of job satisfaction and commitment, not vice versa. For both job responses, the path from CO to the response was statistically significant and positive (i.e., job satisfaction: SPC = .48, t = 1.71; organizational commitment: SPC = .94, t = 3.31), whereas the path from the response to CO was nonsignificant (i.e., job satisfaction: SPC = -.17, t = -.49; organizational commitment: SPC = -.77, t = -.93).
Aside from the single exception we noted (i.e., H[sub3]), the results of this study support our predictions. To test the generalizability of our findings, we conducted a second study that included service workers from a different environment: the restaurant industry.
The food services industry differs from the financial services industry on several dimensions. First, service is augmented by the presence of a tangible component (i.e., food and drink). Second, the services provided are usually consumed at the service provider's location; consumers of financial services need not be present at the service provider's location to receive services. Finally, most interactions in the food services industry are discrete rather than continuous transactions. For these reasons, we believe that testing our hypotheses with a sample of restaurant employees provides a strong test of the generalizability of the results of Study 1.
We collected data from workers employed in 12 restaurants of a fine-dining restaurant chain in the Midwest. Using a list of all employees at each location, we randomly selected 20 employees from each restaurant, for a total of 240 distributed questionnaires. Employees completed the self-report questionnaire during work hours, sealed it in an envelope, and returned it to a manager. All respondents were assured of the confidentiality of their responses. The questionnaire included our measures of CO, job satisfaction, organizational commitment, OCB-altruism, and contact time, in that order.
We received 211 usable cases; of these, we identified 4 as problematic and removed them on the basis of casewise diagnostics (Cook and Weisberg 1982). Thus, the response rate for Study 2 was 87%, which is due in large part to the cooperation of restaurant chain management in offering extra break time to complete the questionnaire. The majority of respondents were female (67%); median tenure at the restaurant was six months; and customer-contact time ranged from 0% to 100%; 76% of respondents reported spending at least 60% of their time with customers.
Results
Model fit for the measurement model was good (χ² = 62.13, d.f. = 39, p < .01; CFI = .98; TLI = .97; and RMSEA = .05), and all indicators loaded on the appropriate latent variables. The measurement model provided evidence of the reliability, convergent validity, and discriminant validity of our measures. Table 3 provides composite reliability, average variance extracted, and descriptive statistics for this sample.
Structural model relationships. Overall model fit was good (χ² = 62.14, d.f. = 40; CFI = .98; TLI = .98; and RMSEA = .05). The SPCs and associated t-values for all relationships in the structural model appear in Table 4.
The results replicated those of Study 1: CO exerted positive influences on job satisfaction (SPC = .50, t = 6.67), organizational commitment (SPC = .43, t = 5.31), and OCB-altruism (SPC = .42, t = 5.53), which provides support for H[sub1], H[sub2], and H[sub5], respectively. As we predicted in H[sub3], job satisfaction led to greater organizational commitment for the respondents (SPC = .36, t = 4.52). In addition, job satisfaction exerted a positive effect on OCB-altruism (i.e., H[sub6]: SPC = .44, t = 5.80).
Moderation tests. We again split the sample into two groups on the basis of contact time (after we adjusted for the influence of CO). The low-contact group contained 101 members and the high-contact group contained 106 members. The fully constrained model had χ² = 158.17 with d.f. = 105. As we hypothesized, the influence of CO on both job satisfaction and commitment was statistically stronger for the high-contact group than for the low-contact group (commitment: Δχ² = 4.47, Δd.f. = 1, p < .05; high-contact-group SPC = .50, low-contact-group SPC = .29; satisfaction: Δχ² = 5.48, Δd.f. = 1, p < .05; high-contact-group SPC = .59, low-contact-group SPC = .32), in support of H[sub4]. The influence of CO on OCB-altruism did not differ across the two groups (Δχ² = .00, d.f. = 1, p > .10).
Follow-up tests. Follow-up tests on the potential influences of SDR again led us to conclude that the factor cannot account for the obtained results. Although SDR exerted significant influences on CO and commitment, the hypothesized relationships were still statistically significant (see Table 4). We then tested for the effect of common method variance. As shown in Table 4, only three indicators loaded on the method factor (i.e., COM2, COM3, OCB2), and the structural results were essentially unchanged.
As was true for Study 1, the results indicate that CO is a determinant of both job satisfaction and organizational commitment, not vice versa (see Figure 2). For both job responses, the path from CO to the response was statistically significant and positive (i.e., job satisfaction: SPC = .51, t = 2.91; organizational commitment: SPC = .59, t = 3.63), whereas the path from the response to CO was nonsignificant (i.e., job satisfaction: SPC = -.02, t = -.07; organizational commitment: SPC = .02, t = .08).
Although the results of Studies 1 and 2 are important and consistent with our predictions, our arguments for person-situation interactions depend heavily on the notion of a person's degree of fit with his or her job environment, a notion that we did not directly test. A third field study enabled us to test the influence of CO on a direct measure of fit.
We propose that CO exerts a positive effect on job fit, which in turn influences commitment and job satisfaction. We also expect that the influence of CO on job fit will be stronger for employees who have higher levels of customer-contact time. Figure 3 shows the model we tested in Study 3.
Method
We collected data from restaurant employees at a second restaurant chain in the Midwest. All employees (n = 590) from 12 restaurant locations were given an opportunity to participate by completing a questionnaire with relevant measures and then returning it in a sealed envelope to company managers, who forwarded the questionnaires to us. The questionnaire included measures for contact time, OCB-altruism, commitment, CO, and satisfaction, as well as a three-item measure of job fit that we developed for this study (e.g., "My skills and abilities perfectly match what my job demands"; see the Appendix). As we did previously, we assured all respondents of anonymity and the confidentiality of their responses. As an incentive, two random respondents from each restaurant received $100. We obtained 257 usable questionnaires, for a 43% response rate. We subsequently removed four cases on the basis of casewise diagnostics (Cook and Weisberg 1982). Of the respondents, 63% were female; median tenure at the restaurant was ten months; and customer-contact time ranged from 0% to 100%; 74% of respondents reported spending at least 60% of their time with customers.
Results
The measurement model for the augmented model was good (χ² = 171.06, d.f. = 68, p < .01; CFI = .95; TLI = .94; and RMSEA = .08), and all indicators loaded on the appropriate latent variables and exhibited acceptable measurement properties. Table 5 presents descriptive statistics, composite reliabilities, and average variance extracted measures. Table 6 includes the structural paths for the augmented model.
Full mediation model. To test the augmented model, we examined a full mediation model in which the effects of CO on job satisfaction and organizational commitment were fully mediated by job fit. Overall model fit for this model was satisfactory (χ² = 191.35, d.f. = 72, p < .01; CFI = .95; TLI = .93; and RMSEA = .08). As we expected, the path from CO to fit was positive and statistically significant (SPC = .64, t = 9.33), as were the paths from fit to satisfaction and commitment (satisfaction: SPC = .56, t = 7.97; commitment: SPC = .37, t = 4.43), a pattern of results that is consistent with a mediational role for job fit. In addition, the effect of CO on OCB-altruism (SPC = .43, t = 6.45) and the effects of satisfaction on commitment (SPC = .23, t = 2.82) and OCB-altruism (SPC = .20, t = 3.00) were statistically significant.( n5)
Using the same procedures applied in the previous studies, we split the sample into low-contact (n = 123) and high-contact (n = 130) groups. A two-group analysis indicated that the influence of CO on job fit was stronger for high-contact employees (SPC = .80) than for low-contact employees (SPC = .50) (Δχ² = 11.43, d.f. = 1, p < .01), which is consistent with our expectations.
Partial mediation model. Despite the strength of these results, comparison of the fully mediated model with a model that also included direct links from CO to satisfaction and commitment was required to better understand the role of job fit. Thus, we estimated a partial mediation model that allowed for direct effects of CO on these job responses in addition to the effects mediated through job fit. The results are presented in Table 6. The overall fit of the model was better (χ² = 176.03, d.f. = 70, p < .01; CFI = .95; TLI = .94; and RMSEA = .08); addition of the direct links significantly improved the model (Δχ² = 15.32, d.f. = 2, p < .01). The direct link between CO and satisfaction was nonsignificant (SPC = .04, t = .42, p > .10), an indication that the effect of CO on satisfaction was fully mediated by job fit. The moderating effect of contact time on the relationship between CO and job fit remains from the full mediation model (Δχ² = 10.86, d.f. = 1, p < .01). Thus, it appears that CO is related to job satisfaction, but only because greater CO leads to greater job fit, especially for high-customer-contact employees.
The influence of CO on commitment to the organization is more complex. The direct link between CO and commitment in the partial mediation model was statistically significant (SPC = .31, t = 3.85), but the link from job fit to commitment became nonsignificant (SPC = .15, t = 1.54, p > .10). Apparently, the positive influence of service-worker CO on commitment has little to do with job fit, though there remains a smaller effect mediated by the influence of job fit on satisfaction, which in turn influences commitment. The degree of customer-contact time moderates the direct relationship between CO and commitment (high contact: SPC = .46; low contact: SPC = .22; Δχ² = 5.94, d.f. = 1, p < .05), which is consistent with Studies 1 and 2.
The results reveal that in addition to CO's effect on performance, it has strong effects on several employee job responses (e.g., Brown et al. 2002; Hurley 1998; Saxe and Weitz 1982). Managers must understand the factors that will keep their high performers satisfied, committed, and on the job. Our results, obtained across three studies in two different services industries, reveal that CO positively influences job satisfaction, commitment, and the performance of OCB-altruism. The outcomes are largely internal to the organization, but they are important for the motivational well-being of the service worker (i.e., satisfaction and commitment) and successful day-to-day operation of the services organization (i.e., OCB-altruism). The results suggest that service-worker CO plays a much greater role in services organizations than has been understood.
Our results reveal that employees who have higher levels of CO especially thrive in services settings that allow for a high degree of contact time with customers. As we predicted, CO (a personal variable) and contact time (a situational variable) interact to predict job satisfaction and commitment; CO has a stronger influence on the job responses of workers who have higher levels of contact time. Thus, our research establishes boundaries on the influence of CO on job responses. Although even low-contact employees experienced some satisfaction and commitment associated with CO, high-contact employees consistently experienced significantly stronger effects of CO on satisfaction and commitment. Thus, a firm's employing highly customer-oriented people does not necessarily produce the most satisfied and committed employees; the job environment (in this case, degree of customer contact) must also be taken into account.
Even though services managers may understand that it takes a certain kind of employee to flourish in customer-contact positions, our research provides an understanding of why these employees do flourish. In our conceptualization, the employees have an internal drive to ( 1) pamper customers, ( 2) accurately read customers' needs, ( 3) develop a personal relationship with customers, and ( 4) deliver quality service to solve customers' problems. Employment in services industries enables workers to satisfy these needs in the process of performing their jobs. The measure developed herein has the potential for use in hiring (and/or training) customer-contact employees. The measure may also be employed in academic research that addresses service workers. However, additional studies that test the measure's construct and predictive validity are required, particularly before they are employed for employee selection.
Another contribution of our research is the delineation of the process through which CO affects overall job satisfaction. The results of Study 3 support the hypothesis that the effects of CO on job satisfaction are mediated by the perceived degree of job fit. We believe that the failure of job fit to mediate the influence of CO on organizational commitment is intriguing. By taking a narrow perspective on job fit (i.e., we focused on the degree of match between job demands and the worker's skills and abilities), we may not have assessed the kind of fit that is important for the development of commitment. Satisfaction with a particular job or set of tasks is one thing; commitment to a larger entity is something else, and further research should investigate this issue.
An additional contribution of our research is the determination of the directionality of causal relationships between CO and job responses. In contrast to assertions by prior researchers (i.e., Hoffman and Ingram 1991, 1992; Kelley 1992; Pettijohn, Pettijohn, and Taylor 2002), our results suggest an antecedent role for CO. Although customer-oriented performance may be influenced by job satisfaction and commitment, our results support the hypothesis that satisfaction and commitment result from CO rather than cause it.
Managerial Implications
The finding that CO is an antecedent to job satisfaction and commitment holds important implications for services managers who are charged with recruiting new employees. First, even though employees may have similar training and experience, not all prospective employees will react and perform equivalently in the same position. In addition to performing better on the job (Brown et al. 2002; Saxe and Weitz 1982), service workers who possess higher levels of CO can be expected to respond more favorably to the job than can service workers who have lower levels of CO. Second, because CO leads to job satisfaction and commitment, managers must recruit with this personality trait in mind, and they should not expect that CO will simply develop over time in response to job satisfaction and commitment. Our studies are the first to examine the causal ordering of these constructs.
The findings also have implications for the management of people and work tasks in the services organization. Perhaps most obvious is the suggestion that customer-oriented workers will find the greatest level of satisfaction and commitment when placed in high-customer-contact positions. When they are placed in low-contact positions, the internal drive to satisfy customer needs has much less effect on their job satisfaction and commitment to the organization. Of greater concern, perhaps, is the placement of a worker who has a lower degree of CO in a high-contact position: The resulting lower levels of satisfaction and commitment will be magnified as a result of the high-contact environment. The same worker in a low-contact position should experience less reduction in satisfaction and commitment as a result of low levels of CO. Thus, managers who fail to consider adequately the degree of CO of their workers may miss an important non-salary-based driver of satisfaction and commitment. Services managers might also reconsider the organization of job tasks such that highly customer-oriented workers are allowed to spend the maximum amount of time possible in contact with customers. It may be possible, and ultimately profitable, to shift non-customer-contact tasks to other workers in the organization to capture fully the value of the most customer-oriented employees.
We also offer a word of caution about the common practice in services organizations of moving the better line performers into supervisory positions. Given the role that CO may play in driving performance, satisfaction, commitment, and OCB-altruism, in some cases it may be counterproductive for the organization and for the individual worker to move from a high-contact line position to a position that has less direct customer interaction. Services marketing managers must consider that job satisfaction previously obtained by these workers from customer contact may need to be provided in other channels.
On a more macro level, the proliferation of self-service technologies (e.g., telephone banking, automated hotel checkout; Meuter et al. 2000) may limit the overall availability of services jobs that are best suited for high-CO workers. Although research thus far has been limited to consumers of such technologies (e.g., Dabholkar and Bagozzi 2002), the job responses of workers whose jobs have been modified (or eliminated) as a result should be considered.
Limitations and Directions for Further Research
A limitation of our research is that we investigated only overall job satisfaction. Further research should determine whether CO has the same impact across various dimensions of job satisfaction. Similarly, we investigated only one aspect of the situational environment: degree of contact time. Further research should investigate other potentially important aspects of the situation, such as perceived market orientation of the organization, availability of resources necessary to meet customer needs, and employees' perception of managerial fairness in dealing with on-the-job issues. In addition, as we noted previously, further studies should clarify the type of fit that may affect commitment.
A limitation that additional research should investigate is the possibility that a personality trait such as materialism or altruism influences the relationship between CO and OCB-altruism. Furthermore, although previous research demonstrates that basic personality traits predicted CO, only a small portion of the variance was captured. Another variable such as benevolence may drive the CO measure and might account for a larger portion of the variance.
Our studies may also be limited by the samples we obtained. The samples were predominately made up of women (i.e., 81%, 67%, and 63%, respectively, in Studies 1, 2, and 3), and the mean tenures were low, at 19 months, 6 months, and 10 months, respectively. Additional studies might investigate a more diverse workforce and longer tenures to determine whether the same relationships hold.
Another important area for further research involves identifying and testing CO determinants. For example, researchers should determine the degree to which training in the organization has a long-term effect on service-worker CO. If researchers find that training can influence CO, the potential outcomes for a service organization (e.g., enhanced service performance, job satisfaction, organizational commitment, performance of OCBs) are quite positive. In addition, the nature of the hierarchical personality model that underlies CO should be more fully investigated.
Regardless of whether training can influence CO, we believe that service managers must attend to the CO of potential employees during the hiring process. More research is necessary to develop an employee selection instrument that effectively identifies candidates who will flourish over the long run in customer-contact positions. Such an instrument might also help companies identify employees who have a mismatch with their position. Should a mismatch occur, it might be possible to change the employee's job. Even if the situation cannot be immediately remedied, recognizing such mismatches of individual and job might provide the insight necessary for the person to adjust to the stress and dissatisfaction that may result (e.g., Singh, Verbeke, and Rhoads 1996).
In summary, researchers have made significant progress toward understanding the role of CO, particularly as it pertains to employee job performance. The results of this project suggest that the benefits of employing customer-oriented service workers go well beyond improving performance to enhancing other factors that are important to the welfare of employees and the organization: job satisfaction, commitment, and organizational citizenship.
The authors thank Kevin Gwinner for his comments on a previous version of this article, the four anonymous JM reviewers, and the restaurant chains and bank that provided data for our analyses.
(n1) Personality research has a long history in marketing (see Kassarjian 1971). Attention in recent years has focused on using personality to predict such things as salesperson and service-provider performance (Brown et al . 2002; Hakstian et al. 1997; Hurley 1998); ad-evoked feelings (Mooradian 1996); consumers' post-purchase outcomes, such as satisfaction, loyalty, and word-of-mouth behavior (Mooradian and Olver 1997); and brand attitude (Aaker 1999).
(n2) As a reviewer noted, the distinction between dispositional CO and behavioral CO is not great. Allport (1961) describes surface personality traits as summaries of behaviors, and many personality traits are measured by means of items that assess behavioral tendencies (e.g., frugality [Lastovicka et al. 1999], need for uniqueness [Tian, Bearden, and Hunter 2001], consumer susceptibility to interpersonal influence [Bearden, Netemeyer, and Teel 1989]).
(n3) Contact time is largely a function of job requirements, but it may be influenced by individual differences, including CO. Thus, two workers may have identical job descriptions but spend different amounts of time with customers. In our empirical work, we account for the possible influence of CO on contact time.
(n4) The Pearson product-moment correlations between CO and contact time were positive and statistically significant in Studies 1 (r = .24), 2 (r = .24), and 3 (r = .40).
(n5) We also tested the model in Study 3 using Brown and colleagues' (2002) measure of CO. The results were similar to those found in this study. The fit indexes from the model using Brown and colleagues' scale were as follows: χ² = 101.06, d.f. = 49, p < .01; CFI = .97; TLI = .96; and RMSEA = .07. All path relationships were significant as well.
Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
D - Composite Reliability
E - Average Variance Extracted
F - (1)
G - (2)
H - (3)
I - (4)
J - (5)
K - (6)
L - (7)
M - (8)
N - (9)
O - (10)
P - (11)
Q - (12)
R - (13)
S - (14)
T - (15)
A B C D E
F G H I
J K L M
N O P Q
R S T
(1) OCBs 4.80 1.20 .91 .77
1.00
(2) CO 5.52 .91 .88 .65
.43 1.00
(3) Satisfaction 4.78 1.49 .92(b) N.A.
.48 .30 1.00
(4) Commitment 5.81 .86 .96 .90
.27 .52 .18(*) 1.00
Individual Indicators
(5) Pamper 5.49 1.03
.41 .91 .31 .55
1.00
(6) Read 4.70 1.12
.28 .80 .20(*) .39
.66 1.00
(7) Deliver 6.19 .88
.35 .79 .14(a) .37
.67 .49 1.00
(8) Personal
relationship 5.70 1.26
.30 .87 .31 .42
.75 .52 .60 1.00
(9) OCB1 4.67 1.38
.88 .33 .50 .21
.29 .24 .29 .29
1.00
(10) OCB2 5.43 1.29
.79 .48 .26 .26
.47 .36 .44 .35
.60 1.00
(11) OCB3 4.31 1.59
.86 .30 .44 .21
.31 .16(*) .20(*) .33
.65 .48 1.00
(12) COM1 5.50 1.23
.18(*) .38 .12(a) .85
.38 .30 .27 .31
.14(a) .13(a) .20(*) 1.00
(13) COM2 5.96 .85
.28 .51 .22 .86
.57 .33 .38 .44
.23 .33 .17(*) .50
1.00
(14) COM3 5.97 .89
.26 .48 .17(*) .91
.54 .36 .35 .38
.22 .26 .18(*) .60
.85 1.00
(15) SD 4.61 .98
.17(*) .21 .12(a) .24
.30 .09(a) .15(a) .17(*)
.19(*) .14(a) .10(a) .11(a)
.28 .26 1.00
(*) p < .05.
(a) Not significant (for all other correlations
[unless otherwise indicated], p < .01.)
(b) Fixed path.
Notes: N.A. = not applicable. Legend for Chart:
A - Structural Model Statistics
B - Results
C - Results with SDR
D - Methods Test Results
A B C D
χ² 81.33 91.27 57.15
d.f. 40 47 30
CFI .95 .95 .97
TLI .94 .93 .94
RMSEA .08 .08 .08
Legend for Chart:
A - Path
B - Results Path Estimate
C - Results t-Value
D - Results with SDR Path Estimate
E - Results with SDR t-Value
F - Methods Test Results Path Estimate
G - Methods Test Results t-Value
A B C D
E F G
CO → job satisfaction
(H[sub1]) .34 4.03(**) .32
3.57(**) .32 3.70(**)
CO → commitment (H[sub2]) .60 5.82(**) .54
5.33(**) .55 5.36(**)
Job satisfaction →
commitment .01 .09 .00
.01 .01 .11
CO → OCBs (H[sub4]) .28 3.28(**) .25
2.80(**) .28 3.19(**)
Job satisfaction → OCBs
(H[sub5]) .48 5.11(**) .48
5.11(**) .48 5.06(**)
Social Desirability Effects
SDR → CO .33
3.58(**)
SDR → job satisfaction .04
.40
SDR → commitment .16
1.82(*)
SDR → OCBs .07
1.79
Measurement Paths
Y1 (Pamper) 1.00 Fixed 1.00
Fixed 1.00 Fixed
Y2 (Read) .68 10.24 .68
10.23 .70 10.02
Y3 (Deliver) .71 10.85 .70
10.82 .69 10.35
Y4 (Personal relationship) .79 12.87 .78
12.86 .77 12.18
OCB1 1.00 Fixed 1.00
Fixed 1.00 Fixed
OCB2 .87 8.27 .88
8.26 .86 8.06
OCB3 .74 7.78 .74
7.76 .76 7.65
SAT .92 Fixed .92
Fixed .92 Fixed
COM1 1.00 Fixed 1.00
Fixed 1.00 Fixed
COM2 .89 8.56 .89
8.54 .75 4.25
COM3 .95 8.62 .95
8.61 .80 4.33
SD .86
Fixed
Method Effects
Method → personal
relationship
1.00 Fixed
Method → deliver
.17 2.17(**)
Method → read
.05 .56
Method → pamper
.27 4.20(**)
Method → COM1
-.28 -1.40
Method → COM2
.64 4.89(**)
Method → COM3
.39 3.00(**)
Method → SAT1
.12 1.38
Method → OCB1
.25 1.37
Method → OCB2
.11 2.94(**)
Method → OCB3
.01 .08
(*) p < .05.
(**) p < .01.
Notes: n = 156. Standardized path estimates are shown. The error
associated with the common method factor was fixed at .05 because
of a negative error variance. The Study 1 results include (1)
basic model only, (2) basic model with the addition of the social
desirability effects, and (3) basic model with the results of a
common method factor. In a test of a common method factor, two
models are com pared: one in which all the paths from the common
method factor are fixed at zero and one in which the method
factor is freed. The common method factor results shown are from
the freed model only. As shown, only five relationships were
affected by common method factor (i.e., Deliver, Pamper, COM2,
COM3, OCB2); however, the effects did not significantly affect
the results of the hypothesized paths. Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
D - Composite Reliability
E - Average Variance Extracted
F - (1)
G - (2)
H - (3)
I - (4)
J - (5)
K - (6)
L - (7)
M - (8)
N - (9)
O - (10)
P - (11)
Q - (12)
R - (13)
S - (14)
T - (15)
A B C D E
F G H I
J K L M
N O P Q
R S T
(1) OCBs 5.17 1.24 .91 .77
1.00
(2) CO 5.70 .89 .88 .64
.63 1.00
(3) Satisfaction 5.52 1.20 .92(a) N.A.
.65 .50 1.00
(4) Commitment 5.65 1.14 .95 .86
.50 .60 .57 1.00
Individual Indicators
(5) Pamper 5.63 1.04
.55 .90 .45 .53
1.00
(6) Read 5.44 1.06
.37 .85 .24 .36
.64 1.00
(7) Deliver 6.20 .95
.40 .82 .37 .53
.69 .58 1.00
(8) Personal
relationship 5.62 1.27
.41 .76 .25 .37
.63 .52 .53 1.00
(9) OCB1 4.98 1.45
.91 .51 .55 .40
.53 .38 .38 .40
1.00
(10) OCB2 5.58 1.22
.87 .51 .46 .43
.53 .34 .41 .44
.75 1.00
(11) OCB3 4.95 1.56
.86 .36 .45 .34
.40 .25 .27 .25
.64 .58 1.00
(12) COM1 5.56 1.42
.35 .49 .45 .87
.47 .38 .47 .30
.31 .34 .28 1.00
(13) COM2 5.66 1.20
.39 .47 .44 .90
.48 .27 .50 .34
.38 .37 .29 .63
1.00
(14) COM3 5.72 1.20
.44 .48 .47 .92
.48 .30 .46 .37
.39 .44 .34 .67
.81 1.00
(15) SD 4.01 .91
.26 .23 .15(*) .26
.26 .15(*) .20 .17(*)
.21 .27 .21 .16(*)
.31 .24 1.00
(*) p < .05 (for all other correlations, p < .01).
(a) Fixed path.
Notes: N.A. = not applicable. Legend for Chart:
A - Structural Model Statistics
B - Results
C - Results with SDR
D - Methods Test Results
A B C D
χ² 62.14 72.14 42.83
d.f. 40 47 30
CFI .98 .98 .99
TLI .98 .97 .98
RMSEA .05 .05 .05
Legend for Chart:
A - Path
B - Results Path Estimate
C - Results t-Value
D - Results with SDR Path Estimate
E - Results with SDR t-Value
F - Methods Test Results Path Estimate
G - Methods Test Results t-Value
A B C D
E F G
CO → job satisfaction
(H[sub1]) .50 6.67(***) .49
6.16(***) .49 6.46(***)
CO → commitment
(H[sub2]) .43 5.31(***) .39
4.79(***) .46 5.40(***)
Job satisfaction →
commitment .36 4.52(***) .35
4.47(***) .34 4.15(***)
CO → OCBs (H[sub4]) .42 5.53(***) .39
5.06(***) .42 5.42(***)
Job satisfaction → OCBs
(H[sub5]) .44 5.80(***) .43
5.76(***) .43 5.50(***)
Social Desirability Effects
SDR → CO .30
3.73(***)
SDR → job satisfaction .04
.49
SDR → commitment .14
2.09(**)
SDR → OCBs .10
1.58
Measurement Paths
Y1 (Pamper) 1.00 Fixed 1.00
Fixed 1.00 Fixed
Y2 (Read) .71 11.58 .70
11.58 .74 11.30
Y3 (Deliver) .77 13.26 .77
13.27 .77 13.25
Y4 (Personal relationship) .70 11.35 .70
11.35 .69 11.25
OCB1 1.00 Fixed 1.00
Fixed 1.00 Fixed
OCB2 .90 14.33 .90
14.38 .89 13.74
OCB3 .70 10.92 .71
10.97 .69 10.54
SAT .92 Fixed .92
Fixed 1.00 Fixed
COM1 1.00 Fixed 1.00
Fixed 1.00 Fixed
COM2 .88 12.40 .88
12.40 .78 6.22
COM3 .92 12.73 .91
12.68 .82 6.47
SD .91
Fixed
Method Effects
Method → personal
relationship
1.00 Fixed
Method → deliver
.07 .74
Method → read
-.18 -1.38
Method → pamper
.07 .74
Method → COM1
-.09 -.50
Method → COM2
.42 2.90(***)
Method → COM3
.40 2.69(***)
Method → SAT1
.10 .81
Method → OCB1
.15 1.41
Method → OCB2
.19 1.91(*)
Method → OCB3
.11 1.09
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: n = 207. Standardized path estimates are shown. The Study
2 results include (1) basic model only, (2) basic model with the
addition of the social desirability effects, and (3) basic model
with the results of a common method factor. In a test of a common
method factor, two models are compared: one in which all the
paths from the common method factor are fixed at zero and one in
which the method factor is freed. The common method factor
results shown are from the freed model only. As shown, only three
relationships were affected by common method factor (i.e., COM2,
COM3, OCB2); however, the effects did not significantly affect
the results of the hypothesized paths. Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
D - Composite Reliability
E - Average Variance Extracted
F - 1
G - 2
H - 3
I - 4
J - 5
K - 6
L - 7
M - 8
N - 9
O - 10
P - 11
Q - 12
R - 13
S - 14
T - 15
U - 16
V - 17
W - 18
A B C D E F
G H I J K
L M N O P
Q R S T U
V W
(1) OCBs 7.41 1.32 .91 .77 1.00
(2) CO 6.53 1.61 .89 .68 .51
1.00
(3) Satisfaction 5.54 1.15 .92(a) N.A. .36
.37 1.00
(4) Commitment 6.03 1.89 .93 .83 .41
.49 .43 1.00
(5) Fit 6.50 1.72 .94 .83 .37
.63 .56 .47 1.00
Individual Indicators
(6) Pamper 6.43 1.83 .46
.90 .33 .44 .58 1.00
(7) Read 6.38 1.85 .38
.90 .25 .34 .45 .80
1.00
(8) Deliver 7.05 1.65 .42
.83 .28 .38 .47 .72
.68 1.00
(9) Personal
relationship 6.24 2.19 .25
.82 .26 .36 .32 .62
.62 .52 1.00
(10) OCB1 7.20 1.62 .86
.35 .32 .39 .33 .35
.32 .32 .23 1.00
(11) OCB2 7.53 1.37 .91
.40 .31 .31 .34 .43
.34 .43 .21 .63 1.00
(12) OCB3 7.50 1.46 .91
.41 .27 .33 .27 .47
.36 .39 .22 .63 .84
1.00
(13) COM1 5.87 2.08 .30
.35 .26 .87 .29 .34
.27 .32 .28 .32 .22
.26 1.00
(14) COM2 6.25 1.99 .41
.42 .37 .92 .40 .45
.32 .38 .31 .39 .33
.36 .74 1.00
(15) COM3 5.98 2.31 .32
.40 .38 .88 .37 .39
.33 .31 .36 .34 .26
.25 .59 .72 1.00
(16) Fit1 6.69 1.94 .29
.43 .28 .26 .87 .45
.38 .41 .25 .27 .30
.22 .18 .28 .23 1.00
(17) Fit2 6.20 1.96 .27
.48 .47 .35 .89 .54
.43 .36 .34 .25 .25
.22 .23 .34 .36 .64
1.00
(18) Fit3 6.62 1.89 .37
.49 .48 .44 .91 .55
.39 .49 .27 .35 .34
.29 .36 .44 .38 .69
.74 1.00
(a) Fixed path.
Notes: For all correlations, p < .01 (two-tailed).
N.A. = not applicable. Legend for Chart:
A - Structural Model Statistics
B - Full Mediation Model
C - Partial Mediation Model
A B C
χ² 191.35 176.03
d.f. 72 70
CFI .95 .95
TLI .93 .94
RMSEA .08 .08
Legend for Chart:
A - Path
B - Full Mediation Model Path Estimate
C - Full Mediation Model t-Value
D - Partial Mediation Model Path Estimate
E - Partial Mediation Model t-Value
A B C D E
CO → job satisfaction .04 .42
CO → commitment .31 3.85(*)
Job satisfaction →
commitment .23 2.82(*) .24 3.03(*)
CO → OCBs .43 6.45(*) .44 6.50(*)
Job satisfaction → OCBs .20 3.00(*) .20 2.93(*)
CO → fit .64 9.33(*) .63 9.16(*)
Fit → job satisfaction .56 7.97(*) .54 5.87(*)
Fit → commitment .37 4.43(*) .15 1.54
Measurement Paths
Y1 (Pamper) 1.00 Fixed 1.00 Fixed
Y2 (Read) .86 19.66 .86 19.66
Y3 (Deliver) .76 15.50 .76 15.54
Y4 (Personal relationship) .66 12.41 .66 12.46
OCB1 1.00 Fixed 1.00 Fixed
OCB2 .69 12.87 .69 12.89
OCB3 .92 18.92 .92 18.99
SAT .92 Fixed .92 Fixed
COM1 1.00 Fixed 1.00 Fixed
COM2 .94 14.53 .94 14.68
COM3 .77 12.85 .77 12.88
JOBFIT1 1.00 Fixed 1.00 Fixed
JOBFIT2 .83 13.16 .83 13.28
JOBFIT3 .90 13.97 .90 14.00
(*) p < .01.
Notes: n = 253; standardized path estimates are shown.DIAGRAM: FIGURE 1 Empirical Model: Studies 1 and 2
DIAGRAM: FIGURE 2 Model for Testing Causal Order Between CO and Its Proposed Consequences
DIAGRAM: FIGURE 3 Empirical Model: Study 3
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Contact Time with Customers (11-point scale ranging from 0% to 100%)
What proportion of your time do you spend in contact with customers?
Customer Orientation
Need to Pamper Dimension (7-point "strongly disagree-strongly agree" scale) (Y1 in Figure 1)
I enjoy nurturing my service customers.
I take pleasure in making every customer feel like he/she is the only customer.
Every customer's problem is important to me.
I thrive on giving individual attention to each customer.
Need to Read Customer's Needs Dimension (7-point "strongly disagree-strongly agree" scale) (Y2)
I naturally read the customer to identify his/her needs.
I generally know what service customers want before they ask.
I enjoy anticipating the needs of service customers.
I am inclined to read the service customer's body language to determine how much interaction to give.
Need to Deliver Dimension (7-point "strongly disagree-strongly agree" scale) (Y3)
I enjoy delivering the intended services on time.
I find a great deal of satisfaction in completing tasks precisely for customers.
I enjoy having the confidence to provide good service.
Need for Personal Relationship Dimension (7-point "strongly disagree-strongly agree" scale) (Y4)
I enjoy remembering my customers' names.
I enjoy getting to know my customers personally.
Organizational Commitment (7-point "strongly disagree-strongly agree" scale)
The relationship my firm has with me is
• something to which I am very committed. (COM1)
• is very important to me. (COM2)
• is very much like being family. (COM3)
Job Satisfaction (7-point, "very dissatisfied-very satisfied" scale)
How satisfied are you with your overall job? (SAT)
Organizational Citizenship Behaviors (Altruism) (7-point "strongly disagree-strongly agree" scale; 9-point scale used in Study 3)
I help orient new employees even though it is not required. (OCB1)
I always lend a helping hand to others on the job. (OCB2)
I willingly give time to help other employees. (OCB3)
Socially Desirable Responding (Studies 1 and 2 only) (6-point, "strongly disagree-strongly agree" scale)
There have been occasions when I took advantage of someone.
I sometimes try to get even rather than forgive and forget.
At times I have really insisted on having things my own way.
I like to gossip at times.
I have never deliberately said something that hurt someone's feelings.
I'm always willing to admit it when I make a mistake. (Items 1-4 collected in Study 1; all 6 items collected in Study 2.)
Job Fit (Study 3 Only) (9-Point, "strongly disagree-strongly agree" scale)
My skills and abilities perfectly match what my job demands. (FIT1)
My personal likes and dislikes match perfectly what my job demands. (FIT2)
There is a good fit between my job and me. (FIT3)
~~~~~~~~
By D. Todd Donavan; Tom J. Brown and John C. Mowen
D. Todd Donavan is Assistant Professor of Marketing, College of Business Administration, Kansas State University (e-mail: tdonavan@ksu.edu).
Tom J. Brown is Associate Professor of Marketing and Ardmore Professor of Business Administration (e-mail: tomb@okstate.edu)
John C. Mowen is Noble Foundation Chair in Marketing Strategy (e-mail: jcmmkt@okstate.edu), College of Business Administration, Oklahoma State University.
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Record: 83- Investments in Consumer Relationships: A Cross-Country and Cross-Industry Exploration. By: De Wulf, Kristof; Odekerken-Schröder, Gaby; Iacobucci, Dawn. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p33-50. 18p. 2 Diagrams, 5 Charts. DOI: 10.1509/jmkg.65.4.33.18386.
- Database:
- Business Source Complete
Investments in Consumer Relationships: A Cross-Country and Cross-Industry Exploration
This research, investigating retailer-consumer relationships, has three distinct intended contributions: ( 1) It shows that different relationship marketing tactics have a differential impact on consumer perceptions of a retailer's relationship investment; ( 2) it demonstrates that perceived relationship investment affects relationship quality, ultimately leading to behavioral loyalty; and ( 3) it reveals that the effect of perceived relationship investment on relationship quality is contingent on a consumer's product category involvement and proneness to engage in retail relationships. The authors empirically cross-validate the underlying conceptual model by studying six consumer samples in a three-country, transatlantic, comparative survey that investigates two industries.
In the current retail environment, relationship marketing tactics play a predominant role because of the increased importance consumers attach to relational properties of their interactions with retailers (Crosby, Evans, and Cowles 1990; Dorsch, Swanson, and Kelley 1998). In comparison with manufacturers, retailers have an advantage in building enduring relationships with consumers because they are in a better position to detect consumer purchase patterns and apply this knowledge in a cost-efficient way (Sweeney, Soutar, and Johnson 1999). Examples of relationship marketing practices in retailing are widespread. Ritz-Carlton is well known for its personalized welcome and farewell of guests, using the guest"s name when possible. Loyalty programs initiated by airlines consist of not only rewarding the most valuable customers in the form of mileage prizes but also showing recognition and providing special privileges.
Although academics recognize the importance of relationship marketing practices (Berry 1995; Goff et al. 1997), empirical evidence on the nature and extent of the impact of relationship marketing tactics on relationship quality is scarce (Gwinner, Gremler, and Bitner 1998). Specifically, although relationship marketing has a strong theoretical base in industrial and channel marketing (e.g., Doney and Cannon 1997), systematic research on relationship marketing in a consumer environment is lacking (Beatty et al. 1996). Yet several authors agree with Dwyer, Schurr, and Oh (1987), who note that relational bonds create benefits in business as well as in consumer environments (Christy, Oliver, and Penn 1996; Sheth and Parvatiyar 1995). In particular, collecting information from the consumer's side of the retailer"consumer dyad is considered an important future research avenue (Gwinner, Gremler, and Bitner 1998; Sheth and Parvatiyar 1995).
With that in mind, our objectives are threefold. First, we want to determine whether different relationship marketing tactics have a differential impact on consumer perceptions of relationship investment by the retailer.1 We consider this important because retailers are often surrounded by uncertainty and incorrect beliefs about what matters to customers, which results in relationship marketing programs that are ineffectively implemented. Given the observation that retailers largely make use of traditional, defensive strategies, it is especially relevant to collect information on consumer perceptions of alternative, relationship-focused strategies (Beatty et al. 1996; Bolton 1998; Dorsch, Swanson, and Kelley 1998; Sirohi, McLaughlin, and Wittink 1998). Yet few efforts have been made to delineate different relationship marketing tactics (Christy, Oliver, and Penn 1996). Furthermore, hardly any systematic empirical investigation has been published that examines the reactions of consumers to relational strategies (Gwinner, Gremler, and Bitner 1998).
Second, we want to provide empirical evidence for the impact of perceived relationship investment on relationship quality, and ultimately on behavioral loyalty. Based on the reciprocity principle, this effect has been examined extensively in business markets (e.g., Anderson and Weitz 1992; Ganesan 1994; Huppertz, Arenson, and Evans 1978), but to our knowledge, it has not been included yet as a topic of empirical investigation in consumer research.
Third, this research is one of the first empirical studies designed to analyze whether the effect of perceived relationship investment on relationship quality is contingent on consumer characteristics. Several authors stress that relationship marketing practices are not considered effective in every situation or context (Day 2000; Kalwani and Narayandas 1995). Yet few empirical efforts have been made to assess the moderating role of consumer characteristics on relationship marketing effectiveness (Beatty et al. 1996; Bendapudi and Berry 1997).
In addressing these issues, we hope to contribute to the aforementioned existing gaps in the relationship marketing research. Attempts to validate relationship marketing studies across settings are still exceptional (Geyskens et al. 1996), so we conduct a fairly comprehensive and rigorous test of our research hypotheses by empirically cross-validating our conceptual model in a multi-country and multi-industry context. Steenkamp and Baumgartner (1998) stress the need to validate models developed in one country, mostly the United States, in other countries as well.
Although an all-encompassing theory of relationship marketing is still lacking (Bagozzi 1995), the principle of reciprocity is considered a useful framework for investigating exchange relationships (Huppertz, Arenson, and Evans 1978). Reciprocity is identified as a key feature explaining the duration and stability of exchange relationships (Larson 1992). Moreover, it is often considered one of the most robust effects found in psychological literature (Moon 2000). Gouldner (1960, p. 168) states that the generalized norm of reciprocity "evokes obligation toward others on the basis of their past behavior." The principle of reciprocity states that people should return good for good, in proportion to what they receive (Bagozzi 1995). According to the reciprocal action theory, actions taken by one party in an exchange relationship will be reciprocated in kind by the other party, because each party anticipates the feelings of guilt it would have if it violated the norm of reciprocity (Li and Dant 1997).
Reciprocity has regularly been used as a framework of thought or a key variable of interest in research on channel relationships. For example, reciprocity is apparent from the willingness of a firm to give preference to a supplier that is also a customer of the firm's products (Bergen, Dutta, and Walker 1992). Compaq refused to sell directly because doing so would constitute competing with its own dealers. Compaq"s dealers considered this refusal a sign of Compaq"s commitment to them, and the dealers reciprocated by providing the brand greater support and shelf space (Day 1990). In general, reciprocation of behavior will foster a positive atmosphere, remove barriers of risk, and enable channel relationships to move forward (Smith and Barclay 1997).
Bagozzi (1995) indicates that the phenomenon of reciprocity is also present in consumer-firm relationships, and he stresses that further research on relationship marketing should investigate the psychological manifestations of reciprocity and the way it functions in everyday consumer exchanges. Also, Huppertz, Arenson, and Evans (1978) indicate that the principle of reciprocity could be used for understanding consumer behavior in general. Nevertheless, Moon (2000) recently has questioned whether the norm of reciprocity is compatible with the realities of consumer research, since engaging in a reciprocal interaction between a consumer and a company would require a one-to-one interaction with every consumer. Given the recognized importance of the reciprocity principle in consumer relationships and given our focus on relationship marketing tactics that are targeted at individual consumers, we regard the concept of reciprocity as an appropriate framework of thought for building our conceptual model as depicted in Figure 1.
The idea behind our model is consistent with the work of Blau (1964), who recognizes that an investment of time, effort, and other irrecoverable resources in a relationship creates psychological ties that motivate parties to maintain the relationship and sets an expectation of reciprocation. We apply this principle in a consumer context, representing irrecoverable resources by the construct of perceived relationship investment. The resulting constructs of relationship quality and behavioral loyalty, embodying consumers' reciprocation of a retailer's investments, reflect the extent to which consumers want to maintain their relationship. This is similar to Bagozzi"s (1995) argument that consumers demonstrate loyalty to certain sellers in reciprocation of these sellers' investments in the relationship. In addition, Kang and Ridgway (1996) argue that consumers feel obligated to pay back the marketer"s "friendliness." Moreover, to detect the extent to which relationship marketing tactics contribute to perceptions of relationship investment, we assess the relationship between four relationship marketing tactics (direct mail, preferential treatment, interpersonal communication, and tangible rewards) and perceived relationship investment. Finally, we incorporate consumer relationship proneness and product category involvement as moderators between perceived relationship investment and relationship quality. In the sections that follow, we define each of the constructs and describe their expected effects.
Perceived Relationship Investment
When a supplier makes a relationship investment of any kind on behalf of a customer, this customer ought to be favorably impressed (Hart and Johnson 1999). Investing time, effort, and other irrecoverable resources in a relationship creates psychological bonds that encourage customers to stay in that relationship and sets an expectation of reciprocation (Smith and Barclay 1997). Although the predominant approach regarding the construct of specific investment in a business-to-business or channel context has been to examine unrecoverable investments in a specific A-to-B relationship (e.g., Anderson and Weitz 1992; Smith and Barclay 1997), we examine investments that are unrecoverable only in the context of "one A to many B's," that is, a retailer to its set of regular customers rather than a retailer to one specific regular customer. The underlying rationale for this choice is that relationship marketing tactics directed at consumers are most often part of an overall relationship marketing strategy that is applied similarly to all regular customers rather than developed on a case-by-case basis as is common practice in business-to-business settings. Therefore, we define perceived relationship investment as a consumer's perception of the extent to which a retailer devotes resources, efforts, and attention aimed at maintaining or enhancing relationships with regular customers that do not have outside value and cannot be recovered if these relationships are terminated (Smith 1998).
We investigate the mediating role of perceived relationship investment, accounting for the connection between relationship marketing tactics and relationship quality. In line with our theoretical perspective of reciprocation, the measurement items of relationship investment emphasize an aim for reciprocation by consumers that is based on retention efforts made by a retailer (e.g., "This store makes efforts to increase regular customers' loyalty"). We position relationship marketing tactics applied by the retailer as antecedents of relationship investment to provide managerial guidelines as to what affects perceptions of relationship investment. Relationship quality, ultimately influencing behavioral loyalty, is positioned as a consequence of relationship investment. A positive path between relationship investment and relationship quality implies that the consumer reciprocates a retailer's actions.
Relationship Marketing Tactics
Few efforts have been made to define what relationship marketing tactics really are and how valuable consumers perceive them to be (Dorsch, Swanson, and Kelley 1998; Gwinner, Gremler, and Bitner 1998). Nevertheless, the successful establishment of commercial relationships is considered to depend largely on fine-tuning such tactics (Christy, Oliver, and Penn 1996; Dwyer, Schurr, and Oh 1987). In general, the literature distinguishes among three levels of relationship marketing (Berry 1995). A first level relies on pricing incentives to secure customer loyalty and is often referred to as level one relationship marketing. It is considered the weakest level of relationship marketing because competitors can easily imitate price. A second level of relationship marketing focuses on the social aspects of a relationship, which are exemplified by regularly communicating with consumers or referring to their names during encounters. These socially inspired tactics are usually bundled into what is called level two relationship marketing. Level three relationship marketing, offering structural solutions to customer problems, is not investigated in this study. The reason for this choice is that level three relationship marketing does not involve true relationship marketing tactics or skills, as Berry (1995, p. 241) argues: "At level three, the solution to the customer"s problem is designed into the service-delivery system rather than depending upon the relationship-building skills." Consequently, we distinguish among four types of relationship marketing tactics distributed across level one relationship marketing (tangible rewards) and level two relationship marketing (direct mail, preferential treatment, and interpersonal communication).
- Direct mail. We define direct mail as a consumer's perception of the extent to which a retailer keeps its regular customers informed through direct mail (e.g., Anderson and Narus 1990; Dwyer, Schurr, and Oh 1987; Morgan and Hunt 1994). In general, it is recognized that buyer-seller communication increases the probability of discovering behaviors that generate rewards; enhances the prediction of behavior of the other party and clarifies each other's roles (Doney and Cannon 1997; Smith and Barclay 1997); leads to the discovery of similarities; and encourages feelings of trust, special status, and closeness (Anderson and Narus 1990). By conveying interest in the customer, communication is often considered a necessary condition for the existence of a relationship (Duncan and Moriarty 1998). In our study, we limit communication media to direct communication media, because mass media communication does not allow for targeting specific groups such as regular versus nonregular customers. Moreover, the underlying reason for limiting direct communication media to direct mail is that in the research contexts investigated, other types of direct media communication are only occasionally used. As a result, we seek to establish that direct mail, as a way of communicating with customers, should be a strong precursor for consumer perceptions of relationship investment. Therefore,
H1: A higher perceived level of direct mail leads to a higher
perceived level of relationship investment.
Preferential treatment. We define preferential treatment as a consumer's perception of the extent to which a retailer treats and serves its regular customers better than its nonregular customers (e.g., Gwinner, Gremler, and Bitner 1998). For example, account holders at major shops are sometimes offered special shopping evenings or preferential access to certain products for sale. Sheth and Parvatiyar (1995, p. 264) recognize that "implicit in the idea of relationship marketing is consumer focus and consumer selectivity-that is, all consumers do not need to be served in the same way." O'Brien and Jones (1995) criticize companies for inadvertently treating all customers as equal; by not differentiating, companies waste resources in oversatisfying less profitable customers while undersatisfying more valuable, loyal customers. Peterson (1995) argues that distinctive treatment enables a seller to address a person's basic human need to feel important. Thus, we expect to demonstrate that a stronger perception of preferential treatment leads to a higher perceived level of relationship investment made by the retailer. Accordingly,H2: A higher perceived level of preferential treatment leads to
a higher perceived level of relationship investment.
Interpersonal communication. We define interpersonal communication as a consumer's perception of the extent to which a retailer interacts with its regular customers in a warm and personal way (e.g., Metcalf, Frear, and Krishnan 1992). Interpersonal communication differs from preferential treatment in that the former refers to the personal touch in communication between a store and its customers and the latter emphasizes that regular customers receive a higher service level than nonregular customers. The importance of personal exchanges between consumers and retailers in influencing relationship outcomes should not be surprising given that relationships are inherently social processes (Beatty et al. 1996). For example, almost five decades ago, Stone (1954) highlighted the importance of social exchange in recognizing the existence of shoppers who appreciate personal contact in the store. Evans, Christiansen, and Gill (1996, p. 208) state that the social interaction afforded by shopping has been suggested to be "the prime motivator for some consumers to visit retail establishments." Examples of social relationship benefits are feelings of familiarity, friendship, and social support (Berry 1995); personal recognition and use of a customer's name (Howard, Gengler, and Jain 1995); knowing the customer as a person; engaging in friendly conversations; and exhibiting personal warmth (Crosby, Evans, and Cowles 1990). This theorizing is summarized in the following hypothesis:H3: A higher perceived level of interpersonal communication
leads to a higher perceived level of relationship investment.
Tangible rewards. We describe tangible rewards as a consumer's perception of the extent to which a retailer offers tangible benefits such as pricing or gift incentives to its regular customers in return for their loyalty. Babin, Darden, and Griffin (1994) refer to a duality of rewards for many human behaviors, the distinction between performing an act to "get something" versus doing so because "you love it." Many marketers focus on the former, providing rewards that rely primarily on pricing incentives and money savings to secure customers' loyalty (Berry 1995; Peterson 1995). Similarly, our construct of tangible rewards implies that customers receive something tangible in return for their loyalty. Examples of tangible rewards marketers provide as a means of appreciating customers' patronage are frequent flyer miles, customer loyalty bonuses, free gifts, and personalized cents-off coupons (Peterson 1995). Also, trying to earn points-on such things as hotel stays, movie tickets, and car washes-helps customers remain loyal, regardless of service enhancement or price promotions of competitors (Sharp and Sharp 1997). Therefore, we formulate the following:H4: A higher perceived level of tangible rewards leads to a higher
perceived level of relationship investment.
Relationship Quality
The choice of relationship quality as a relationship outcome in our study is consistent with previous studies on relationship marketing (e.g., Kumar, Scheer, and Steenkamp 1995). Relationship quality can be considered an overall assessment of the strength of a relationship (Garbarino and Johnson 1999; Smith 1998). Previous research conceptualizes relationship quality as a higher-order construct consisting of several distinct, though related, dimensions (e.g., Dorsch, Swanson, and Kelley 1998; Kumar, Scheer, and Steenkamp 1995). Although there still exists discussion on which dimensions make up relationship quality, prior conceptualizations mainly emphasize the critical importance of relationship satisfaction, trust, and relationship commitment as indicators of relationship quality. For example, Crosby, Evans, and Cowles (1990) and Dwyer, Schurr, and Oh (1987) consider relationship satisfaction and trust to be indicators of the higher-order construct of relationship quality. Hennig-Thurau and Klee (1997), Leuthesser (1997), and Dorsch, Swanson, and Kelley (1998) further argue to add relationship commitment as a dimension of relationship quality. Therefore, we assume that a better-quality relationship is accompanied by a greater satisfaction, trust, and commitment. We prefer the abstract relationship quality construct over its more specific dimensions because, even though these various forms of attitude may be conceptually distinct, consumers have difficulty making fine distinctions between them and tend to lump them together (Crosby, Evans, and Cowles 1990). Next, we briefly elaborate on the dimensions of relationship quality.
- Relationship satisfaction. Satisfaction with the relationship is regarded as an important outcome of buyer-seller relationships (Smith and Barclay 1997). We define relationship satisfaction as a consumer's affective state resulting from an overall appraisal of his or her relationship with a retailer (Anderson and Narus 1990). Thus, we conceptualize relationship satisfaction as an affective state (Smith and Barclay 1997) in contrast with more rational outcomes (Anderson and Narus 1990). In addition, we view it as a cumulative effect over the course of a relationship compared with satisfaction that is specific to each transaction (Anderson, Fornell, and Rust 1997).
- Trust. The development of trust is thought to be an important result of investing in dyadic buyer-seller relationships (e.g., Gundlach, Achrol, and Mentzer 1995). Drawing on the existing literature (e.g., Morgan and Hunt 1994), we define trust as a consumer's confidence in a retailer's reliability and integrity. Several scholars consider perceived trustworthiness and trusting behaviors as two distinct, though related, aspects of trust. Whereas trustworthiness refers to a belief or confidence, trusting behaviors are related to the willingness to engage in risk-taking behavior, reflecting a reliance on a partner (Smith and Barclay 1997). Although some scholars merge both aspects into one definition of trust (e.g., Moorman, Desphand, and Zaltman 1993), others claim that trustworthiness is a necessary and sufficient condition for trust to exist (e.g., Anderson and Narus 1990). In line with the latter group, our definition encompasses only the notion of trustworthiness.
- Relationship commitment. Commitment is generally regarded to be an important result of good relational interactions (Dwyer, Schurr, and Oh 1987). In our study, we define relationship commitment as a consumer's enduring desire to continue a relationship with a retailer accompanied by this consumer's willingness to make efforts at maintaining it (e.g., Morgan and Hunt 1994). Note that the definition implies the presence and consistency over time of both the desire to continue a relationship and the willingness to make efforts directed at sustaining this relationship (Macintosh and Lockshin 1997). We believe that the desire for continuity is a necessary but insufficient condition for relationship commitment because, for example, it might be driven simply by habitual cues or marketplace constraints. As a result, our measures of commitment incorporate both aspects.
The association between relationship investment and relationship quality has rarely been investigated empirically. A notable exception is the strong support Crosby, Evans, and Cowles (1990) find for a positive path from relational selling behavior to relationship quality. Furthermore, Wray, Palmer, and Bejou (1994) find evidence for a positive relationship between a salesperson's customer orientation and relationship quality. Finally, Lagace, Dahlstrom, and Gassenheimer (1991) find a positive path from ethical salesperson behavior to relationship quality. Although these constructs are not completely similar to our construct of relationship investment, they provide an initial basis for our next hypothesis.
Stronger evidence can be found for the impact of relationship investment on the dimensions of relationship quality. Relationship investment has been shown to predict satisfaction in business marketing relationships (e.g., Anderson and Narus 1990; Ganesan 1994; Smith and Barclay 1997). Customers tend to be more satisfied with sellers who make deliberate efforts toward them (Baker, Simpson, and Siguaw 1999). Also, trust has been shown to be resulting from relationship investment. For example, Ganesan (1994) finds that specific investments made by one partner result in increased trust. With respect to commitment, Dwyer, Schurr, and Oh (1987, p. 19) suggest that commitment is "fueled by the ongoing benefits accruing to each partner." In line with this, Bennett (1996) argues that the strength of customers' commitment depends on their perceptions of efforts made by the seller. Furthermore, several authors have empirically investigated the relationship between relational performance, a construct that shows similarities to relationship investment, and relationship commitment (Anderson and Weitz 1992; Baker, Simpson, and Siguaw 1999; Morgan and Hunt 1994). Therefore, we suggest the following:
H5: A higher perceived level of relationship investment leads
to a higher level of relationship quality.
Behavioral Loyalty
Models that theorize attitudinal as well as behavioral relationship outcomes have strong precedence in relationship studies (e.g., Bolton 1998; Macintosh and Lockshin 1997). Accordingly, we build on existing literature, which states that the effectiveness of relationship marketing tactics should also be evaluated in terms of the behavioral changes they create (Sharp and Sharp 1997). As a result, we included the construct of behavioral loyalty, defined as a composite measure based on a consumer's purchasing frequency and amount spent at a retailer compared with the amount spent at other retailers from which the consumer buys. In other words, behavioral loyalty is measured as a unique combination of behavioral indicators, concordant with suggestions made by Sirohi, McLaughlin, and Wittink (1998) and Pritchard, Havitz, and Howard (1999).
Hennig-Thurau and Klee (1997) argue that relationship quality is an antecedent of repeat purchase behavior. Furthermore, some empirical evidence has been found for relationships between dimensions of relationship quality and behavioral loyalty. With respect to satisfaction as a dimension of relationship quality, Bolton (1998) and Macintosh and Lockshin (1997) find positive paths from relationship satisfaction to both relationship duration and purchase intentions, which can be considered behavioral indicators of loyalty. Regarding trust as a relationship quality dimension, Smith and Barclay (1997), for example, report a positive effect of trust on forbearance from opportunism. Moorman, Desphand", and Zaltman (1993) suggest that customers who are committed to a relationship might have a greater propensity to act because of their need to remain consistent with their commitment. Morgan and Hunt (1994) find empirical support for the relationship between a customer's commitment and acquiescence, propensity to leave, and cooperation, all of which can be regarded as behavioral outcomes of relationships. Derived from these findings, we investigate the following:
H6: A higher level of relationship quality leads to a
higher level of behavioral loyalty.
Factors Moderating the Effect of Perceived Relationship Investment
In addition to testing for the effects we have described thus far, this article also takes an initial step toward assessing the role of moderators that influence the effectiveness of perceived relationship investment. An examination of such moderators enables marketers to understand when investing in relationships is expected to be more effective or less effective. Not all consumers search for more than the timely exchange of a product or service with a minimum of hassles, so making resource-intensive relationship investments is considered neither appropriate nor necessary for every consumer (Bendapudi and Berry 1997; Christy, Oliver, and Penn 1996; Day 2000). Given our focus on and general interest in the consumer, we investigate whether the effects of perceived relationship investment are contingent on either of two consumer characteristics: product category involvement and consumer relationship proneness.
- Product category involvement. In line with Mittal (1995), we define product category involvement as a consumer's enduring perceptions of the importance of the product category based on the consumer's inherent needs, values, and interests. Researchers have suggested that people who are highly involved with a product category reveal a tendency to be more loyal (Dick and Basu 1994; King and Ring 1980). They reason that a relationship can add value only for customers who are already interested in the product. Solomon and colleagues (1985) claim that in low-involvement situations, the treatment of customers as individuals would probably not pay off, whereas in high-involvement situations, customers desire more personal treatment. Gordon, McKeage, and Fox (1998) state that involved buyers are more likely to participate in marketing relationships and to derive value from these relationships. Such relationships may be perceived as invasive or annoying when directed at consumers with lower levels of involvement. Consequently, approaches by the seller, however well-intentioned, could be regarded by the customer as undesirable when the customer's involvement is low (Christy, Oliver, and Penn 1996). We expect the effects of perceived relationship investment to be strengthened in the case of high levels of product category involvement:
H7: A higher level of product category involvement strengthens
the impact of perceived relationship investment on relationship
quality.
Consumer relationship proneness. Gwinner, Gremler, and Bitner (1998) argue that relationship marketing success may depend not only on its strategy or implementation but also on the preferences of the individual customer. Christy, Oliver, and Penn (1996) use the term "psychologically predisposed" to express the idea that some customers are intrinsically inclined to engage in relationships. However, despite the recognized importance of customers' proneness to engage in relationships with sellers, no study has yet investigated its impact on relationship effectiveness (Sheth and Parvatiyar 1995). In this study, we define consumer relationship proneness as a consumer's relatively stable and conscious tendency to engage in relationships with retailers of a particular product category. Several authors stress that a buyer's proneness to engage in relationships may vary across groups of sellers (Bendapudi and Berry 1997; Christy, Oliver, and Penn 1996) (e.g., apparel stores versus supermarkets), so we postulate that consumer relationship proneness must be defined within a particular product category. In addition, we emphasize consumers' conscious tendency to engage in relationships as opposed to a tendency based more on inertia or convenience (e.g., Dick and Basu 1994). From a seller's perspective, investing in relationships with buyers is not always considered a preferable strategy, because not all types of buyers are prone to engage in relationships with sellers (Berry 1995; Crosby, Evans, and Cowles 1990; Sheth and Parvatiyar 1995). We assume that relationship-prone consumers should reciprocate a retailer's efforts more strongly, because by definition, relationship-prone consumers are most likely to develop relationships. Consequently, we test the following:H8: A higher level of consumer relationship proneness strengthens
the impact of perceived relationship investment on relationship
quality.
Setting
An externally valid, fuller understanding of consumer relationships requires that the validity of conceptual models developed in one setting be examined in other settings as well. Our study is conducted in the food and apparel industries, covering a wide variety of retailers, including discount stores, mass merchandisers, traditional department stores, and prestige stores. We consider these industries similar with respect to the competitiveness of their industry environment and the opportunities for consumers to switch. However, the industries differ on many other dimensions. For example, social features of a relationship might be expected to be more important in an apparel context that is characterized by a high degree of personal contact and advice. Conversely, economic features might play a more important role in relationships between food retailers and consumers who have a strong emphasis on discounts and anonymous self-service. In addition to studying various industries, in response to recent calls for cross-cultural research on relationships (Iacobucci and Ostrom 1996), our study is of a transatlantic nature; it includes respondents not only from the United States but also from two highly developed western European countries, the Netherlands and (the Flemish part of) Belgium. The selection of both European countries was a matter of convenience. To the best of our knowledge, this is the first study on consumer relationships that compares survey data from three different countries. According to Hofstede's (1980) classification of countries according to cultural dimensions (power distance, uncertainty avoidance, individualism, and masculinity), large-scale differences exist among these dimensions across the three countries. The power distance scores for the United States, the Netherlands, and Belgium are, respectively, 40, 38, and 65; uncertainty avoidance: 46, 53, and 94; individualism: 91, 80, and 75; and masculinity: 62, 14, and 54. In addition, signif icant variations in competitive conditions and legal environments among the three countries are prevalent. In conclusion, the settings incorporated in our study differ greatly from one another, which should provide a fertile environment for conducting a true cross-validation.
Measure Development
Measures for some of the constructs we are examining were available in the literature, though most were adapted to suit a retail environment. For the four relationship marketing tactics, relationship investment, and consumer relationship proneness, scales applicable to a retail context were not available and were developed for the purpose of this study. First, focus groups were used to examine how consumers described relationship investment, relationship marketing tactics, and relationship proneness. Four focus groups were organized in which participants were asked open-ended questions about their own behavior with respect to shopping for clothing. Then, direct questions were posed to acquire knowledge on relationship investment, relationship marketing tactics, and relationship proneness. Finally, projective techniques were used during the remainder of the discussions (i.e., depth descriptions, photo-sorts). Participants received a monetary incentive in return for their cooperation. The results were helpful in generating items.
Second, in an effort to enhance face validity, a group of Dutch and Belgian expert judges (four academics and three practitioners) qualitatively tested an initial pool of items intended to measure various relationship marketing tactics. Experts were provided with the definitions of the relationship marketing tactics and were asked to classify each item to the most appropriate tactic. Items that were improperly classified were reformulated or deleted. Third, equivalence for all items was sought by conducting back-translation. A U.S.-born American citizen who was fluent in Dutch first translated the original Dutch version of the questionnaire into American English, and a native Dutch speaker who was fluent in American English then retranslated the questionnaire into Dutch. The quality of the English translation was evaluated by a monolinguistic, U.S.-born American citizen on clarity and comprehensiveness of the translated questionnaire. The Dutch questionnaire was used in the Dutch as well as in the Belgian sample (covering the Flemish part of Belgium).
Finally, 12 graduate students in marketing research (4 in each country) were instructed to pretest a questionnaire that included all constructs on a total sample of 60 consumers through personal in-home interviews. Items measuring the various constructs were mixed in the questionnaire to reduce halo effects. To ensure that respondents were distributed across age, sex, and country, students were assigned to particular combinations of quota criteria and were allowed to select respondents who matched these criteria (e.g., friends, family, neighbors). They asked respondents to complete the questionnaire and then describe the meaning of each question, explain their answers, and state any problems they encountered while answering questions. Small revisions to the U.S. and Dutch/Belgian version of the questionnaire were made on basis of the pretest.
Final Measures
Final attempts at measure purification were conducted on a sample (n = 371) drawn to resemble the eventual multi-country, multi-industry sample. We factored the items to investigate whether they correctly measured their intended constructs. Theoretically, it was likely that the latent constructs would be correlated, so we applied an oblique rotation. We only retained items that minimally loaded .65 on the proper latent factor and maximally loaded .30 on the others to enhance the distinctiveness of the intended constructs. The resulting measurement appeared to be clean across scales, countries, and industries. The Appendix contains all (seven-point Likert) scales, organized by construct. Moreover, Table 1 provides an overview of construct means, standard deviations, and correlations.
With respect to relationship satisfaction, trust, and relationship commitment, we first factor-analyzed these multi-item scales for each construct separately; across all samples, a single factor emerged in each case. As Cronbach's alpha values ranged between .70 and .93, reliability was uniformly high in all samples for all three constructs. Then we assessed the second-order factor model with the first-order factors (relationship satisfaction, trust, and relationship commitment) that originated from the higher-order factor relationship quality.2 These measurement results were acceptable in each sample (comparative fit index [CFI] and nonnormed fit index [NNFI] ranged from .93 to .97 for CFI and from .89 to .96 for NNFI). All first-order and second-order factor loadings were significant, demonstrating convergent validity. This provided us with enough confidence to calculate averages for relationship satisfaction, trust, and relationship commitment based on the three items of each construct and use these averages as indicators of the construct relationship quality (see Crosby, Evans, and Cowles 1990; Posdakoff and Mackenzie 1994).
Samples
Information was collected from real consumers as opposed to student samples. Mall-intercept personal interviews were administered in the United States (food: n = 231, apparel: n = 230), the Netherlands (food: n = 337, apparel: n = 338), and Belgium (food: n = 289, apparel: n = 302). Samples were drawn from shopping mall visitors to obtain variance in age (18 to 25 years, 26 to 40 years, 41 to 55 years, and 55 years and over), sex, and allocated share of wallet for the store reported on (0%-20%, 21%-40%, 41%-60%, 61%-80%, and 81%-100%). We also sought even coverage over interviewing time of day and interviewing day of week to reduce possible shopping pattern biases. Across our samples, an average of 37% of the visitors who were approached participated.
Procedure
Participants were first asked whether they had ever made a purchase in the particular product category. If so, they were asked to indicate the names of five stores at which they usually bought food or apparel. Next, respondents indicated their approximate share of wallet for each store listed (measured on a continuous scale from 0% to 100%) and the extent to which they believed they were regular customers of each store (measured on a scale from 1 to 7). Finally, the interviewers selected a specific store to focus on for the remaining questions on the basis of the reported share of wallet figures. Care was taken that respondents reporting low, medium, and high levels of share of wallet were represented in each sample. By definition, a relationship is of extended duration and composed of multiple interactions, so many of the costs and benefits from buyer-seller relationships cannot be assessed a priori (Dwyer, Schurr, and Oh 1987; Parasuraman 1997). Gwinner, Gremler, and Bitner (1998) state that though customers may receive relationship benefits and believe that these benefits are important, they may not always be aware of these benefits- existence in the early stages of a relationship and may not have assessed their value yet. Therefore, only those stores were included for which respondents indicated at least a 4 on the 7-point scale that measured "being a regular customer of the store." To enhance interrater reliability, the cover letter attached to the questionnaire explained the term "regular customer" to respondents as "a customer who regularly buys clothes/food in a store and not simply visits the store to look around."
Examination of Data Pooling
To decide whether we needed to estimate separate models for each sample, we investigated the possibility of pooling data across countries and/or industries by means of several multigroup LISREL analyses. To assess pooling of industry samples, we evaluated two nested models for each country: ( 1) a model in which all structural paths were set equal across the two industry samples (equal model) and ( 2) a model in which all structural paths were set free across the two industry samples (free model). We followed the same procedure to assess pooling of country samples. With respect to pooling across industries, the free model in the Dutch sample obtained a significantly better fit than the equal model, which indicates that not all of the paths were equal across apparel and food. With respect to pooling across countries, the differences between the equal and free models were statistically significant for four of six country comparisons. Therefore, we decided not to pool the data across countries or industries.
Overall Model Evaluation
In Table 2, we report the values of the fit statistics. The chi-squares are all significant (p < .05; Bollen 1989), a finding not unusual with large sample sizes (Doney and Cannon 1997). The ratios of chi-square to degrees of freedom (d.f.) are between 2.01 and 2.59, all within the acceptable range of 2 to 5 (Marsh and Hovecar 1985). The values for CFI, NNFI, root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR) are acceptably close to the standards suggested by Hu and Bentler (1999): .95 for CFI and NNFI, .06 for RMSEA, and .08 for SRMR. Given that these batteries of overall goodness-of-fit indices were accurate and that the model was developed on theoretical bases, and given the high level of consistency across samples, no respecifications of the model were made. This enables us to proceed in evaluating the measurement and structural models.
Measurement Model Evaluation
In Table 3, we report the results of the measurement models. We assessed the quality of our measurement efforts by investigating unidimensionality, convergent validity, reliability, discriminant validity, and metric equivalence. Evidence for the unidimensionality of each construct included appropriate items that loaded at least .65 on their respective hypothesized component and loaded no larger than .30 on other components in a factor analysis. In addition, the overall goodness of fit supports unidimensionality (Steenkamp and van Trijp 1991). Convergent validity was supported by all loadings being significant (p < .01) and nearly all R2 exceeding .50 (Hildebrandt 1987). We assessed reliability jointly for all items of a construct by computing the composite reliability and average variance extracted (Baumgartner and Homburg 1996; Steenkamp and van Trijp 1991). For a construct to possess good reliability, composite reliability should be between .60 and .80, and the average variance extracted should at least be .50 (Bagozzi and Yi 1988). All scales demonstrate good reliabilities.
We tested discriminant validity by means of several subsequent procedures. First, as a basic test of discriminant validity, we checked whether correlations among the latent constructs were significantly less than 1. In all samples, construct correlations indeed met this criterion. Second, we compared a series of nested confirmatory factor models in which correlations between latent constructs were constrained to 1 (each of the 21 off-diagonal elements was constrained and the model reestimated in turn), and indeed chi-square differences were significant for all model comparisons (p < .01) in all samples, again in support of discriminant validity. Third, we performed a stronger test for discriminant validity provided by Fornell and Larcker (1981). This test suggests that a scale possesses discriminant validity if the average variance extracted by the underlying construct is larger than the shared variance (i.e. the squared intercorrelation) with other latent constructs. On the basis of this most restrictive test, we found strong evidence for discriminant validity between each possible pair of latent constructs in all samples (i.e., all pairs of seven factors in all three countries in both industries). Only two exceptions were found. In the U.S. food sample, the squared intercorrelation between preferential treatment and tangible rewards (.79) was larger than the shared variance extracted by both constructs (.76 and .69, respectively). In the Dutch apparel sample, the squared intercorrelation between relationship investment and relationship quality (.67) was larger than the shared variance extracted by relationship quality (.63). However, given that neither problem occurred in the other five samples, we do not consider this a major problem.
Finally, to cross-nationally investigate the interrelationships between constructs in a nomological net, Steenkamp and Baumgartner (1998) indicate that full or partial metric invariance must be satisfied because the scale intervals of the latent constructs must be comparable across countries. We assessed metric invariance by comparing two nested models for each construct separately in terms of the difference in chi-square relative to degrees of freedom, RMSEA, NNFI, and CFI.3 In the first model (base model), all error variances and all factor loadings were allowed to be free across samples. (One marker item was selected, and the same marker item was used in each sample.) Only the factor variance of the latent construct was constrained to be equal across samples. (We measured each latent construct on basis of three indicators, so at least one parameter should be fixed across samples to generate a nonsaturated model.) In the second model (equal loadings model), we additionally constrained the remaining two factor loadings (apart from the marker item) to be equal across the six samples. While metric invariance is "a reasonable ideal", a condition to be striven for, not one expected to be fully realized" (Horn 1991, p. 125), our measurement model supported full metric invariance for three of seven constructs incorporated. For constructs not revealing full metric invariance (direct mail, interpersonal communication, tangible rewards, and relationship commitment), we sequentially relaxed constraints on parameters to test for partial metric invariance. Partial metric invariance was supported for all remaining constructs.
In summary, the measurement models are clean, with evidence for unidimensionality, convergent validity, reliability, discriminant validity, and metric invariance, which enabled us to proceed to the structural model evaluation.
Structural Model Evaluation
Table 4 indicates that in each sample, all significant relationships between latent constructs are in the hypothesized direction, which provides initial evidence for our conceptual model and supports the nomological validity of the constructs. An important finding is that the relationship between perceived relationship investment and relationship quality and the positive path from relationship quality to behavioral loyalty are confirmed across all samples. This result provides strong empirical evidence for the cross-validation of this part of our conceptual model, which is especially noteworthy given that the countries examined differ considerably on demographic, economic, and cultural dimensions. Consequently, there was strong and uniform support for H5 and H6.
In examining H1-H4, which explicate the associations between relationship marketing tactics and perceived relationship investment, only in the United States is there a consistent pattern of effects across the two industries. In addition, only for preferential treatment in the food industry and for interpersonal communication in the apparel industry is there a consistent pattern of effects across the three countries. Apart from these effects, the data provided mixed evidence. Specifically, direct mail had a positive impact on perceived relationship investment (H1) in three of four European samples as opposed to the U.S. samples, in which no significant paths were detected. Preferential treatment revealed a nonsignificant relationship with perceived relationship investment (H2) in all samples except for the Belgian apparel sample. Interpersonal communication had the strongest impact on perceived relationship investment (H3), being cross-validated in all samples except for the Belgian food sample. Finally, the data support a positive path from tangible rewards to perceived relationship investment (H4) in three of four European samples but do not provide evidence for this path in the U.S. samples. We now turn to two model modifications: First, we test a rival structural model to enhance our confidence in the focal model further, and second, we introduce the potential moderators of product category involvement and consumer relationship proneness, in accordance with our prior theorizing.
A Rival Model
It is generally agreed that researchers should compare rival models and not just test the performance of a proposed model (Bagozzi and Yi 1988). In discussing the construct of perceived relationship investment previously, we provided a theoretical basis for positioning perceived relationship investment as a mediating variable. Because our parsimonious hypothesized model allows no direct paths from any of the four relationship marketing tactics to relationship quality or to behavioral loyalty, it implies a central nomological status for relationship investment. A nonparsimonious rival model would hypothesize only direct paths from each of the precursors to the outcomes relationship quality and behavioral loyalty. This model makes relationship investment nomologically similar to the four relationship marketing tactics. The tested rival model (see Figure 2) therefore permits no indirect effects, implying that relationship investment is not allowed to mediate any of the relationships.
On the basis of Morgan and Hunt (1994), we compared our hypothesized model with the rival model on the following criteria:4 overall fit, parsimony, percentage of either model's parameters that were statistically significant, and R2s for the endogenous constructs. With respect to overall fit, the average CFI of the rival model was slightly higher than that of the hypothesized model (.947 versus .938), and the rival model's mean ratio of chi-square to degrees of freedom was slightly lower than that of the hypothesized model (2.24 versus 2.32). Note, however, that to achieve this slight increase in fit, we needed to estimate four additional paths in the rival model, which reduced the rival model's parsimony and partially offset the incremental improvement in fit. In addition, only 47% of the paths in the rival model were significant as opposed to 67% in the hypothesized model, which suggested that the additional paths were not meaningful theoretically or empirically. Finally, the average explained variance of relationship quality was .56 in the rival model as opposed to .47 in the hypothesized model. This is not surprising because in addition to relationship investment, as a precursor of relationship quality, four extra antecedents were modeled to explain relationship quality in the rival model. In contrast, the average explained variance of behavioral loyalty was only .12 in the rival model as opposed to .14 in the hypothesized model. This means that the explanatory power of relationship quality as a single antecedent of behavioral loyalty is stronger than the combined explanatory power of the four relationship marketing tactics plus relationship investment.
On the basis of these findings, we believe that the exercise of fitting a rival model has strengthened the support we found for the meaningfulness and robustness of our hypothesized model. In addition to the conceptual support found for positioning perceived relationship investment as a mediating variable in the hypothesized model, the rival model empirically demonstrates its added value. Neglecting the mediating role of this construct reduces its parsimony and results in a lower percentage of significant path coefficients.
Moderating Influences
We tested moderating effects through multigroup analyses, splitting the samples into subsamples according to whether consumers scored high or low on the moderating variables to ensure within-group homogeneity and between-group heterogeneity. The subgroup method is a commonly preferred technique for detecting moderating effects (Stone and Hollenbeck 1989). For each moderator, Table 5 displays the results for 12 separate structural model estimations in terms of chi-square and degrees of freedom.
- Moderating influence of product category involvement. Considering product category involvement as a moderator, in the equal models, we set all paths of the structural model equal across high' and low'product category involvement subsamples. In the free models, we constrained all paths to be equal across high- and low-product category involvement subsamples, except for the link that was potentially affected by the moderator variable. Differences in chi-square values between models determine whether product category involvement acts as a moderating variable; that is, a significant decrease in chi-square from the equal model to a model in which one relationship is set free implies that the moderator variable has a significant influence on that relationship. Table 5 reveals that the level of product category involvement significantly moderates the impact of perceived relationship investment on relationship quality in three samples (U.S. food, U.S. apparel, and Dutch apparel). For relationships that were moderated, the within-group path coefficients were consistently lower in the low-involvement than the high-involvement subsample. The following differences in path coefficients were found for the link from perceived relationship investment to relationship quality: U.S. food +.19, U.S. apparel +.23, and Dutch apparel +.09. In conclusion, for some industry-country combinations, our data suggest that investing in a relationship generates a higher payoff in terms of increased relationship quality when customers are more involved with the product category. These findings tentatively support H7.
- Moderating influence of consumer relationship proneness. We used the same procedure to assess the moderating impact of consumer relationship proneness. The results show that consumer relationship proneness significantly moderates the impact of perceived relationship investment on relationship quality in three samples (U.S. food, U.S. apparel, and Belgian food). For relationships that were moderated, within-group path coefficients were consistently lower in the low-relationship proneness than the high-relationship proneness subsample. The following differences in path coefficients were found for the link from perceived relationship investment to relationship quality: U.S. food +.30, U.S. apparel +.15, and Belgian food +.30. These findings suggest that the impact of perceived relationship investment may be stronger when customers are more prone to engage in relationships with sellers. These results provide preliminary support for H8.
The development and sustainability of loyalty is increasingly difficult to achieve and is still surrounded with ambiguity regarding its underlying determinants, so we believe that our research makes a significant contribution to relationship marketing theory in three different ways. First, our model contributes to the existing literature by specifying how retailers can guide consumer perceptions of relationship investment by applying four different relationship marketing tactics. Prior studies have rarely investigated the role of such tactics in shaping consumer relationships. Second, our study demonstrates why retailers benefit from investing in consumer relationships by assessing the impact of perceived relationship investment on relationship quality and ultimately on behavioral loyalty. Third, this study is a first attempt to provide insights into the role of contingency factors in determining relationship quality by emphasizing the moderating impact of a newly introduced construct, consumer relationship proneness, and product category involvement. We tested these three research questions comprehensively and rigorously by replicating the study across three countries and two industries.
With respect to our first research question, relationship marketing tactics were found to play a differential yet consistently positive role in affecting perceived relationship investment. Today's retailers increasingly offer comparable merchandise, copy competitors' price promotions, share common distribution systems, and treat customers well in terms of services offered, so there are increased opportunities for directing greater attention to developing and implementing relationship marketing tactics. With respect to the direct mail tactic, we found mixed evidence for its positive effect on perceived relationship investment. Most strikingly, no empirical support was found for positive effects of direct mail in the United States. A likely explanation for this finding is that the longer tradition of sending direct mail to regular customers in affluent U.S. markets has worn out its effect on perceived relationship investment. Whereas in the United States, direct marketing expenditures constituted 57.8% of total advertising expenditures in 1997 (DMA/WEFA 1998), these percentages were significantly lower in the Netherlands and Belgium during the same period: 47.4% and 38.9%, respectively (FEDMA 1998). This is illustrated by the difference across countries in the number of direct mail pieces received per capita. The average number of U.S. direct mail pieces received over the past 50 years has risen from approximately 145 pieces per year to more than 700 per year (James and Li 1993). In 1997, Dutch consumers received an average of only 81.7 pieces of addressed direct mail, and Belgian consumers found an average of 110.1 pieces of addressed mail in their mailbox (FEDMA 1998).
Interpersonal communication proved to be a dominant determinant of perceived relationship investment, being replicated in five out of six samples, an observation that is sensible given that relationships are inherently social. It demonstrates the crucial role of retail employees who are in direct contact with customers. Retailers capable of training and motivating their employees to show warm and personal feelings toward customers can reap the resulting benefits in terms of improved perceptions of relationship investment. Also, when hiring store personnel, store management needs to focus on candidates' social abilities that facilitate social interactions with target consumers (Weitz and Bradford 1999). This is especially important, because the emergence of automated retailing has gradually reduced opportunities for social interaction in the store. Retailers should investigate whether consumers are willing to trade off the loss of social contact for the benefits of automation.
Preferential treatment revealed a nonsignificant relationship with perceived relationship investment in all samples except one, and this contradicts the common opinion that regular buyers should be treated and served differently than nonregular buyers should. A potential explanation for this finding might be that customers do not appreciate being openly favored above other customers. If this is true, it would hold important implications for retailers, because it emphasizes that efforts directed at customers should be made delicately to avoid putting customers in an uncomfortable position. Alternatively, perhaps preferential treatment is simply not as powerful as the other antecedents of perceived relationship investment, and in the presence of the other tactics, preferential treatment is less valued by the consumer.
Finally, mixed evidence was detected for the positive effects of tangible rewards on perceived relationship investment. Again, this was true in the U.S. samples in which no significant paths were found. In U.S. markets, the longer tradition of providing customers with tangible rewards for their loyalty might decrease the impact of such offers. The natural appeal of tangible rewards can be assumed to decrease if more sellers start offering them. As tangible rewards become widespread, their absence may disappoint consumers, whereas their presence would not necessarily boost customer retention. Competitors can easily imitate tangible rewards such as frequent flyer programs, customer loyalty bonuses, and free gifts. Perhaps such "wear-out" effects have simply occurred less in the European markets.
A second key research objective of this study was to assess the effect of perceived relationship investment on relationship quality and ultimately behavioral loyalty. We expected perceived relationship investment to play an important role in determining relationship quality, which was confirmed in all six samples. The path from relationship quality to behavioral loyalty was also demonstrated across samples. These results support the findings of Bagozzi (1995) and Kang and Ridgway (1996), who argue that consumers feel obligated to reciprocate a retailer's investments in the retailer-consumer relationship by increasing their loyalty to this retailer. This finding implies that it pays off for retailers to invest in consumer relationships, because it results in increased loyalty.
Finally, we found initial support for our third research question. We collected empirical evidence for what previously have been only assumptions suggesting that customer characteristics can influence the effectiveness of relationship marketing investments (e.g., Ganesan 1994). The results show that consumer relationship proneness repeatedly acts as a moderator of the effectiveness of perceived relationship investment, perhaps operating as a heightened sensitivity to a seller's efforts directed at buyers (see Dwyer, Schurr, and Oh 1987). In addition, product category involvement moderated the effect of perceived relationship investment in some cases. Paths that are significantly moderated suggest that consumers with a lower degree of product category involvement are less influenced by a retailer's investment in the relationship (e.g., consistent with Solomon et al. 1985). Leuthesser (1997) points out that a buyer's stake in a relationship with a seller tends to be higher with greater involvement in the product category. Our data then might be reasonably interpreted as higher stakes in a relationship, which cause consumers to appreciate a retailer's investments more strongly.
These observations emphasize that retailers should not lose sight of the importance of consumer-related factors in shaping relationship quality. No matter how much trouble the retailer goes to in order to increase relationship quality, the effects of those efforts and resources can be tempered or strengthened by the consumer's level of relationship proneness and product category involvement. Consequently, retailers should not only invest more in consumer relationships but also pay equal attention to finding consumers who are most receptive to such investments. In addition to the more traditional criteria of product-market segmentation such as market size, market growth, and expected market share, segmenting consumers according to levels of consumer relationship proneness or product category involvement could affect expected share of market and share of customer. For example, a practical approach toward accomplishing this objective might be to add a few questions to the registration form of a store's customer loyalty card that measure consumer relationship proneness and product category involvement.
Limitations and Directions for Further Research
Some limitations might be related to collecting our data and interpreting our results. A first limitation might be the omission of important variables. For example, additional tangible elements in the retail mix, such as pricing and promotion, product quality and assortment, and service quality, could be added as antecedents of relationship investment. This is evidenced by the fact that the percentage of explained variance of perceived relationship investment could still be improved. Relationship marketing theory not only should have eyes for typical relationship marketing constructs but also could examine the value of existing instruments such as SERVQUAL in affecting relationships. Although the SERVQUAL measures (Parasuraman, Zeithaml, and Berry 1988) can be applied to a broad spectrum of contexts, no previous research of which we are aware has examined their effects on the relationship outcomes examined in this study. Moreover, it is likely that the relative importance of product, service, and relationship marketing tactics in determining relationship investment varies according to the length of a relationship. We could assume that the longer a relationship exists, the stronger is the relative impact of relationship marketing tactics on perceived relationship investment compared with product and service tactics. Consequently, it could be fruitful to compare research models incorporating all these components across buyer segments that exhibit different levels of relationship length.
Second, this study focused on consumer-specific moderators of perceived relationship investment, but a challenging research avenue would be to assess the role of other contingency factors. For example, it might be interesting to study the differences between large store chains and small, independent neighborhood stores. It could be argued that small stores would demonstrate more relationship-friendly characteristics than large store chains, given that the degree of social exchange and the possibilities for interpersonal communication are generally greater in smaller stores. Whereas larger store chains generally operate on the basis of anonymous self-service, the survival of small, independent stores is often dependent on personal service and knowledge of consumer preferences. A third potential shortcoming in the study is common method bias. We used one questionnaire to measure all constructs included, so perhaps the strength of the relationships among these constructs may be somewhat inflated. A fourth potential limitation is related to the measurement of behavioral loyalty. The true meaning of behavioral loyalty may be only partially captured given that its measure was based on self-reports. Database information could be used as input for measuring actual purchasing behavior. The confidence in our results could be strengthened with access to behavioral data on customer purchase histories that are not subject to potential recall loss. It would then be possible to examine longer strings of purchases and perhaps to incorporate contextual information. These recognized shortcomings could inspire researchers to define their future research agendas.
Footnotes [1]In a first draft of the manuscript, we had originally called the construct "perceived relationship investment" by the label "customer retention orientation." This label, "customer retention orientation," originated from qualitative research in the form of consumer focus groups and was defined as a customer's overall perception of the extent to which a seller actively makes efforts that are intended to contribute to the customer value of its regular customers. In response to one of the reviewers' concerns, we renamed the construct "perceived relationship investment" to convey more clearly the inherent meaning of our original construct and draw more directly from the terms that are strongly established in existing literature.
Legend for Chart:
A - Food Mean
B - Food Standard Deviation
C - Apparel Mean
D - Apparel Standard Deviation
E - Correlation Matrix 1
F - Correlation Matrix 2
G - Correlation Matrix 3
H - Correlation Matrix 4
I - Correlation Matrix 5
J - Correlation Matrix 6
K - Correlation Matrix 7
L - Correlation Matrix 8
M - Correlation Matrix 9
N - Correlation Matrix 10
* p < .05 (two-sided).
** p < .01 (two-sided).
A B C D E F
G H I J K L
M N
UNITED STATES
1. Direct mail
4.22 1.42 4.96 1.68 1.00 .59**
.57** .79** .54** .47** .53** .40**
.34** .15**
2. Preferential treatment
3.71 1.36 4.15 1.54 .55** 1.00
.70** .71** .45** .52** .56** .38**
.45** .27**
3. Interpersonal communication
4.16 1.40 4.30 1.60 .64** .64**
1.00 .67** .55** .63** .66** .48**
.55** .23**
4. Tangible rewards
3.81 1.39 4.42 1.69 .64** .78**
.65** 1.00 .54** .58** .62** .48**
.45** .17**
5. Perceived relationship investment
5.17 1.19 5.29 1.24 .53** .44**
.66** .47** 1.00 .65** .72** .50**
.53** .27**
6. Relationship quality
4.97 1.14 5.15 1.14 .45** .46**
.59** .41** .60** 1.00 .92** .87**
.92** .43**
7. Relationship satisfaction
4.85 1.21 5.05 1.31 .47** .49**
.64** .46** .62** .93** 1.00 .70**
.77** .36**
8. Trust
5.42 1.09 5.37 1.14 .29** .30**
.42** .25** .51** .87** .72** 1.00
.70** .25**
9. Relationship commitment
4.63 1.44 5.04 1.33 .45** .45**
.55** .41** .52** .94** .82** .71**
1.00 .52**
10. Behavioral loyalty
.00 .94 .00 .88 .03 .10
.14* .10 .21** .39** .32** .38**
.37** 1.00
THE NETHERLANDS
1. Direct mail
3.53 1.58 3.71 2.06 1.00 .50**
.22** .34** .48** .40** .43** .26**
.33** .07
2. Preferential treatment
3.00 1.31 3.34 1.45 .32** 1.00
.36** .57** .46** .32** .37** .20**
.25** .07
3. Interpersonal communication
2.74 1.45 3.63 1.83 .40** .42**
1.00 .48** .52** .57** .62** .41**
.43** .27**
4. Tangible rewards
3.90 1.60 3.54 1.81 .30** .49**
.29** 1.00 .53** .47** .54** .29**
.36** .16**
5. Perceived relationship investment
5.02 1.33 4.94 1.44 .25** .30**
.27** .28** 1.00 .68** .71** .54**
.50** .24**
6. Relationship quality
4.80 1.06 5.17 1.07 .28** .12**
.38** .20** .59** 1.00 .91** .82**
.86** .36**
7. Relationship satisfaction
4.64 1.25 4.86 1.39 .34** .18**
.46** .26** .51** .87** 1.00 .68**
.65** .25**
8. Trust
5.46 1.02 5.73 .95 .17** .06
.18** .14** .56** .82** .64** 1.00
.54** .29**
9. Relationship commitment
4.29 1.44 4.92 1.35 .20** .07
.31** .11* .46** .87** .60** .54**
1.00 .39**
10. Behavioral loyalty
.00 .91 .00 .83 -.09 -.08
-.04 .03 .18** .22** .11* .17**
.26** 1.00
BELGIUM
1. Direct mail
4.83 1.65 3.42 1.95 1.00 .29**
.29** .52** .41** .30** .34** .19**
.22** .07
2. Preferential treatment
2.97 1.54 3.04 1.41 .16** 1.00
.44** .44** .34** .28** .33** .15*
.20** .05
3. Interpersonal communication
2.50 1.55 2.49 1.46 .04 .55**
1.00 .47** .32** .31** .34** .11
.30** .15**
4. Tangible rewards
3.86 1.76 3.06 1.73 .46** .48**
.27** 1.00 .39** .19** .25** .08
.12* .03
5. Perceived relationship investment
4.72 1.44 4.45 1.48 .38** .36**
.23** .53** 1.00 .39** .35** .24**
.36** .18**
6. Relationship quality
4.85 1.07 5.10 .94 .32** .28**
.24** .28** .49** 1.00 .86** .81**
.85** .29**
7. Relationship satisfaction
4.81 1.25 4.93 1.18 .38** .34**
.29** .33** .49** .89** 1.00 .60**
.55** .14*
8. Trust
5.44 1.04 5.55 .90 .35** .14*
.05 .23** .44** .83** .67** 1.00
.53** .23**
9. Relationship commitment
4.30 1.43 4.81 1.26 .13* .22**
.23** .17** .36** .87** .62** .55**
1.00 .35**
10. Behavioral loyalty
.00 .92 .00 .84 .17** .02
.03 .11 .27** .34** .29** .29**
.31** 1.00Notes: Correlations above the diagonal are for the apparel samples; those below the diagonal are for the food samples.
Legend for Chart:
A - Fit Statistics
B - Food United States
C - Food Netherlands
D - Food Belgium
E - Apparel United States
F - Apparel Netherlands
G - Apparel Belgium
A B C D
E F G
&chip;2(177) 428.44 458.13 355.49
457.21 390.73 373.51
χ2/d.f. 2.42 2.59 2.01
2.58 2.21 2.11
GFI .85 .88 .89
.83 .90 .89
AGFI .80 .84 .86
.78 .87 .86
RMSEA .079 .072 .061
.087 .058 .060
SRMR .073 .072 .061
.073 .054 .060
NNFI .93 .90 .94
.92 .95 .93
CFI .94 .92 .95
.93 .95 ,94Notes: GFI = goodness-of-fit index, AGFI-adjusted goodness-of-fit index.
Legend for Chart:
A - Construct
B - Composite Reliability Food United States
C - Composite Reliability Food Netherlands
D - Composite Reliability Food Belgium
E - Composite Reliability Apparel United States
F - Composite Reliability Apparel Netherlands
G - Composite Reliability Apparel Belgium
H - Percentage of Variance Explained Food United States
I - Percentage of Variance Explained Food Netherlands
J - Percentage of Variance Explained Food Belgium
K - Percentage of Variance Explained Apparel United States
L - Percentage of Variance Explained Apparel Netherlands
M - Percentage of Variance Explained Apparel Belgium
A B C D E F
G H I J K
L M
Direct mail .89 .76 .83 .93 .94
.93 .73 .51 .63 .82
.84 .82
Preferential treatment .90 .79 .91 .89 .86
.86 .76 .57 .77 .73
.67 .68
Interpersonal communication .89 .87 .89 .90 .87
.83 .73 .69 .73 .74
.72 .61
Tangible rewards .87 .80 .80 .91 .87
.86 .69 .57 .57 .77
.69 .68
Perceived relationship investment .92 .86 .89 .93 .90
.87 .79 .69 .73 .81
.74 .68
Relationship quality .90 .79 .79 .88 .83
.78 .75 .57 .57 .72
.63 .55
Behavioral loyalty .94 .90 .85 .85 .77
.79 .83 .75 .66 .66
.53 .56 Estimate (Standard Error)
Legend for Chart:
A - Hypothesized Path
B - Food United States
C - Food Netherlands
D - Food Belgium
E - Apparel United States
F - Apparel Netherlands
G - Apparel Belgium
* p < .05 (one-sided).
** p < .01 (one-sided).
A
B C D E F G
H1: Direct mail →
perceived relationship
Investment (+)
.09 .08 .16* .22 .27** .28**
(.09) (08) (.07) (.14) (.06) (.07)
H2: Preferential treatment →
perceived relationship
investment (+)
-.10 .04 .08 -.11 .09 .15*
(.16) (.10) (.09) (.12) (.07) (.07)
H3: Interpersonal communication -→
perceived relationship
investment (+)
.74** .19** .08 .42** .40** .16*
(.12) (.08) (.08) (.11) (.06) (.08)
H4: Tangible rewards →
perceived relationship
investment (+)
.02 .24** .47** .18 .18** .12
(.18) (09) (.10) (.20) (.08) (.09)
H5: Perceived relationship investment →
relationship quality (+)
.68** .72** .61** .76** .82** .48**
(-08) (.08) (.08) (.08) (08) (.07)
H6: Relationship quality →
behavioral loyalty (+)
.41** .21** .40** .46** .36** .33**
(.07) (.06) (.07) (.08) (.07) (.07)
Squared Multiple Correlations for Structural Equations
Perceived relationship investment
.44 .18 .41 .44 .55 .31
Relationship quality
.46 .52 .37 .58 .68 .23
Behavioral loyalty
.17 .04 .16 .21 .13 .11 Legend for Chart:
C - Food United States
D - Food Netherlands
E - Food Belgium
F - Apparel United States
G - Apparel Netherlands
H - Apparel Belgium
* p < .05 (one-sided).
** p < .01 (one-sided).
A
B C D E
F G H
Moderator: Product
Category Involvement
Equal model
d.f. 405 405 405
405 405 405
χ2 742.07 782.86 674.91
794.60 758.81 653.99
H7: Perceived relationship investment →
relationship quality: free
d.f. 404 404 404
404 404 404
χ2 735.97 782.84 674.88
781.45 754.51 653.60
Δχ2 6.10* .02 .03
13.15** 4.30* .39
Moderator: Consumer
Relationship Proneness
Equal model
d.f. 405 405 405
405 405 405
χ2 844.85 690.03 664.47
855.85 770.40 741.29
H8: Perceived relationship investment →
relationship quality: free
d.f. 404 404 404
404 404 404
χ2 833.58 689.92 656.50
851.17 769.42 741.18
Δχ2 11.27** .11 7.97**
4.68* .98 .11DIAGRAM: Figure 1: Hypothesized Model
DIAGRAM: Figure 2: Rival Model
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Summary of Measures
Construct Measures
Direct mail This store often sends mailings to regular customers.
This store keeps regular customers informed through
mailings. This store often informs regular customers
through brochures.
Preferential
treatment This store makes greater efforts for regular
customers than for nonregular customers.
This store offers better service to regular
customers than to nonregular customers.
This store does more for regular customers than
for nonregular customers.
Interpersonal This store takes the time to personally get to know
communication regular customers.
This store often holds personal conversations with
regular customers.
This store often inquires about the personal
welfare of regular customers.
Tangible rewards This store rewards regular customers for their
patronage.
This store offers regular customers something extra
because they keep buying there.
This store offers discounts to regular customers
for their patronage.
Perceived This store makes efforts to increase regular
relationship customers' loyalty.
investment This store makes various efforts to improve its
tie with regular customers.
This store really cares about keeping regular
customers.
Relationship
quality
Relationship
Satisfaction As a regular customer, I have a high-quality
relationship with this store.
I am happy with the efforts this store is making
towards regular customers like me.
I am satisfied with the relationship I have with
this store.
Trust This store gives me a feeling of trust.
I have trust in this store.
This store gives me a trustworthy impression.
Relationship I am willing "to go the extra mile" to remain
commitment a customer of this store.
I feel loyal towards this store.
Even if this store would be more difficult to reach,
I would still keep buying there.
Behavioral What percentage of your total expenditures for
loyalty clothing do you spend in this store?
Of the 10 times you select a store to buy clothes at,
how many times do you select this store?
How often do you buy clothes in this store compared
to other stores where you buy clothes?
Product category Generally, I am someone who finds it important what
involvement clothes he or she buys.
Generally, I am someone who is interested in the kind
of clothing he or she buys.
Generally, I am someone for whom it means a lot what
clothes he or she buys.
Consumer Generally, I am someone who likes to be a regular
Relationship customer of an apparel store.
proneness Generally, I am someone who wants to be a steady
customer of the same apparel store.
Generally, I am someone who is willing to "to go the
extra mile" to buy at the same apparel store.Notes: The items formulated in the Appendix were based on the apparel samples. In the food samples, the term "apparel store" was replaced by "supermarket." All are seven-point scales with "strongly disagree" and "strongly agree" as the anchors.
~~~~~~~~
By Kristof De Wulf; Gaby Odekerken-Schröder and Dawn Iacobucci
Kristof De Wulf is Assistant Professor, Vlerick Leuven Gent Management School and Faculty of Economics and Business Administration, Ghent University.
Gaby Odekerken-Schröder is Assistant Professor, Faculty of Economics and Business Administration, Maastricht University.
Dawn Iacobucci is Professor, University of Arizona.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 84- Invited Commentaries on "Evolving to a New Dominant Logic for Marketing". By: Bolton, Ruth N.; Day, George S.; Deighton, John; Narayandas, Das; Gummesson, Evert; Hunt, Shelby D.; Prahalad, C. K.; Rust, Roland T.; Shugan, Steven M. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p18-27. 10p. DOI: 10.1509/jmkg.68.1.18.24035.
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Invited Commentaries on "Evolving to a New Dominant Logic
for Marketing"
In the preceding article, Vargo and Lusch (V&L; 2004) observe that an evolution is underway toward a new dominant logic for marketing. The new dominant logic has important implications for marketing theory, practice, and pedagogy, as well as for general management and public policy. Thus, their observations are likely to resonate with a broad cross-section of the business community. With the goal of stimulating discussion and debate, I invited some distinguished scholars to write brief commentaries on different aspects of V&L's article. I was delighted to receive a thoughtful and diverse set of comments. The ideas expressed in the article and the commentaries will undoubtedly provoke a variety of reactions from readers of Journal of Marketing. I hope you will enjoy reading, and thinking, about these scholars' views on the fundamental premises of marketing as much as I did.
--Ruth N. Bolton
Dominant logics and disruptive technologies apparently evolve in the same way. There is a convergence of streams of contributing technologies, methods, concepts, and theories that crystallize to form something new. This is not an abrupt emergence, because the underlying elements change gradually. Instead, there is usually a "tipping point" that signals and validates a seemingly radical shift. For example, the key elements of wireless communications technologies were largely in place four decades before the "cellular revolution" took place.
Vargo and Lusch (V&L) believe that we have passed the tipping point in the transition from a goods-centered to a service-centered logic for marketing. My purpose is to apply a two-question stress test to their proposition. First, what are the underlying reasons for this transition? If the enablers have endurance, this new dominant logic will likely be sustained and advanced. Second, will it change our view of how marketing resources are converted into competitive advantage? If a service-centered logic prevails, this logic should fundamentally change the mind-sets, schemas, and mental models of the managers and researchers who determine how competitive advantage is conceptualized and how resources are allocated (Bettis and Prahalad 1995; Prahalad and Bettis 1986).
Enabling a New Dominant Logic
Many tributaries feed the "new" dominant logic, including services marketing, market orientation, customer relationship management, networked markets, mass customization, and interactivity. Each tributary has been a high-profile part of the marketing terrain for at least a decade. Why have they converged now? A common denominator is that each draws on information technology advances that enable universal access to knowledge that previously was dispersed and difficult to reach. The drivers are the acceptance of compatibility standards that enable computer systems to converse as well as the escalation of broadband communications and economical computing power.
This connected knowledge system enables the real-time coordination of dispersed organizational activities and groups, the management of cross-functional processes, and the synchronization of the myriad points of customer contact that are integral to the new dominant logic. However, most firms are far from capitalizing on the possibilities, which means that marketing is still in the early stages of the transition to a service-centered dominant logic. Indeed, the tipping-point argument readily leads to the conclusion that the rate of transition is likely to accelerate. By facilitating information flows, and the concomitant knowledge sharing and utilization, the enablers will also speed acceptance of the premise that "knowledge is the fundamental source of competitive advantage" (V&L, p. 9). This raises the question of who will be advantaged or disadvantaged as the competitive landscape changes.
Competing When the Dominant Logic Changes
The crux of V&L's argument (p. 12) is that a service perspective is superior to a goods-centered view because it emphasizes solutions and "points to opportunities for expanding the market by assisting the consumer in the process of specialization and value creation." This is not a new insight. Fully 63% of the Fortune 100 firms already claim that they offer solutions (Sharma, Lucier, and Molloy 2002), but have these firms really encoded the concept of solutions in their dominant logic, or is it merely a fashionable statement of intent?
It is unlikely that most firms are pursuing a true solutions strategy as V&L advocate. This would mean satisfying the following five criteria for a deep relationship that transfers a supplier's skills and knowledge to a customer that lacks them: First, the strategy requires the integration of products with services to offer a complete bundle of benefits. Second, there is a two-way interaction that results in mutual commitments, ranging from information exchanges to cross-firm coordination and even relation-specific investments. This implies the third and fourth criteria: The solution is coproduced by the customer and supplier, and it is tailored to each customer. Fifth, the solution might also mean some absorption of the customer's risk. In light of these stringent criteria, arm's-length referrals, one-stop shopping, and even a tailor-made personal computer that suits a customer's desired configuration do not qualify as service-centered solutions.
An important caution is that many customers may willingly enter into only a few close and committed relationships. They may resist the kind of operational entanglements based on relationship-specific assets that create switching costs (Dyer and Singh 1998), such as ( 1) location of assets in close proximity, ( 2) tailoring of physical assets, or ( 3) human-asset specificity achieved through cospecialization and shared knowledge. For example, GE Plastics installs sensors in customers' injection-molder storage silos to signal an automatic recorder when volume becomes low. It takes a lot of iterative learning to make this work, which underscores the participatory and dynamic nature of the new dominant logic. However, not every customer may want to subordinate its ability to bargain with suppliers or expose itself to the risk of a single source. Thus, both a product- and a service-centered logic will coexist in most markets.
Achieving a Relational Advantage
The emphasis on operant resources in the new dominant logic is well grounded in the resource-based view of the firm. The immediate implication of this theory is that many firms will find it hard to gain and sustain a relational advantage through superior solutions.
A key premise of the resource-based view is that resource and capability development is a selective and path-dependent process. The need for selectivity requires an organization to concentrate attention on a few capabilities that correspond to key success factors in the target market. Thus, firms need to select whether to make superior relational value a central or a supportive element of their strategy. Many firms will not make this choice, perhaps because they have been preempted.
Firms build on what they know (Cohen and Levinthal 1990). As a result, they are path dependent when choosing which resources to develop. Behind the immediate choices are histories of prior choices that sensitize the firms to certain issues, create a knowledge platform on which they can keep building, and constrain or lock in the firm to a particular path.
The choice of a new strategic direction derived from the new dominant logic must overcome the inertia of this path dependency. However, an entrenched logic that is built into the mind-set and mental models of managers is difficult to change. The old logic must be unlearned (Bettis and Prahalad 1995). This can be a slow process because the prevailing dominant logic both supports and is reinforced by the current strategy and structure. Overcoming this inertia takes leadership and resource commitments sustained by a sense of urgency because of the threat of new or existing competitors that are better aligned with changing customer requirements.
Further motivation for change comes from the prospect of firms not being able to overcome the disadvantage of a follower. According to the sustainability premise of the resource-based view, key resources keep their value when they are protected from imitation by causal ambiguity. There is causal ambiguity when it is unclear to competitors how the source of advantage works. Causal ambiguity deepens when the resource is based on a complex pattern of coordination in a process. The complexities of a solutions strategy enabled by the new dominant logic will be difficult to master but even more difficult to copy.
What Role for Marketing?
The emerging dominant logic has many implications, but they are not entirely the ones that V&L have in mind. Vargo and Lusch believe that marketing should be at the center of the integration and coordination of the cross-functional processes of a service-centered business model, but this depends on what is meant by "marketing." It will probably not be the marketing function that is found in most firms; instead, it will be marketing as a general management responsibility of the top team that will have the crucial tasks of ( 1) navigating through effective market-sensing, ( 2) articulating the new value proposition, and ( 3) orchestrating by providing the essential glue that ensures a coherent whole. This broadened role is most effective in a market-driven organization that has superior skills in understanding, attracting, and keeping valuable customers (Day 1999). Thus, the field converges toward and thereby validates V&L's conclusion that the new dominant logic is inherently customer centered and market driven.
George S. Day
Stories can be read as illustrations of theory. This commentary can be read in that spirit, as a story that attempts to vivify what V&L contend is the new dominant logic of marketing. Stories can also be read as challenges to theory, and perhaps some readers will interpret this commentary in that spirit. Either way, telling stories, whose verisimilitude is the primary "fact" that theory is called to account for, is not how marketing literature usually operates. However, telling stories is a tradition in anthropology, history, and some of the other interpretive social sciences, and if V&L are correct that marketing scholarship must increasingly contend with value not frozen in objects but flowing in events, then, as anthropologists and historians do, marketing scholars may find that offering stories to one another to support or repudiate claims about the meaning of a sequence of events is a useful way to perform scholarship.
This story, or history, is of two companies. A comparison of their fates over five years may illuminate what V&L describe as the new dominant logic for marketing. In 1993, SaleSoft was founded in Cleveland to serve the market for sales force automation (SA) software. Siebel Systems was founded in San Mateo, Calif., in the same year and for the same purpose. In 1995, a Gartner Group (1995) report pictured the two as close contenders in the race for market leadership, but by 1997, one was out of business and the other's market capitalization was $2 billion. Can this stark divergence in outcomes be considered evidence weighing on the assertion that what V&L call a "units-of-output" marketing perspective is inferior to a service-flows perspective? Can it be that pathfinding framed within the latter paradigm was bound to be more successful than if framed within the former?
In 1995, SA software represented a $1 billion market. It was used by a quarter of all firms that employed sales forces, including most Fortune 1000 corporations. However, most firms used quite simple contact management software that kept track of a salesperson's itinerary and transmitted progress to headquarters daily or weekly. The opportunity that SaleSoft and Siebel set out to pursue was larger: to automate the sales process and integrate it with marketing and customer service to create a system that, both firms claimed, would unify the fragmentation that characterized sales and marketing, just as manufacturing resource planning had brought order to the production side of the enterprise. In 1995, SaleSoft had built three of eight modules needed to realize this vision and had installations at only five customer sites. Siebel had no complete product and a small set of customers.
SaleSoft (as Narayandas [1998] describes) framed its problem in what might be called "units-of-output" terms. It decided that the difficulty in finding customers pointed to a problem with the product. It believed that its integrated sales, marketing, and service automation offering was too complex; that its sales cycle was too long; that too many people were involved in the purchase decision (several customer business units as well as systems integrators and consultants); and that too much customization was required in each installation. The answer, management concluded, was a simple order-pipeline management product with a crisp value proposition for a single organizational decision maker (the head of sales in the customer firm) that would need no customization by systems integrators. Sales of the simpler product would be a Trojan horse, leading to sales of the multimodule system in due course. SaleSoft projected that it would sell product to a value of $30 million in three years, which would represent 1.3% of the market.
Siebel (as described in the works of O'Reilly and Chang [2001] and Roberts, Lassiter, and Tempest [1998]) framed the problem in what might be called "service-flow" terms. It identified a few large-potential firms that might appreciate what information technology would eventually do for their enterprises. Phase 1 of the service flow was to collect information about the firms' business processes and to propose how the processes might be enabled by information technology. For the firms that respected the expense and complexity of realizing the opportunity and preferred to fund others to do it rather than undertake it themselves, Siebel supplied services to map the processes into code, build systems, and train people to implement the coded systems. Siebel outsourced about 70% of service revenue to systems integrators. It then invited some of the clients and one of the systems integrators to become shareholders. It held out to these investors the vision of a market worth more than $2 billion. When the value-creation process involves coproduction between vendor and client, the marker of a successful collaboration is customer satisfaction, and the measure of satisfaction is continuity of the relationship, as V&L note. That said, transaction revenue is also pleasing evidence of success, and within three years, annual revenue from the shareholders and other clients was $397 million, or 13 times the target glimpsed in SaleSoft's units-of-output framing of the path ahead.
Several features of the successful case that are absent from the failure case are elements of V&L's new dominant logic. In the successful case, the customer was a coproducer, to the point in some cases of being an investor. The company framed the offering as a flow of services, beginning with an interactive definition of the customer's problem and leading to joint development of a solution. Indeed, the head of a competitor once described Siebel's approach as "the client's people, the client's processes, Siebel's tools, so two-thirds of the risk and responsibility was in the client's hands" (personal communication, September 2001). By co-opting the customer throughout the design and implementation of its systems, by holding the customer responsible for making the product work successfully, and by enabling the customer and the integrators to share in the marketplace success, Siebel outran SaleSoft.
Does this story of two responses to the same market conditions support V&L's argument, or does it, on the contrary, hint that the argument fits only particular contingencies, such as the moment when a market is emerging or when the client truly wants a service, not a product? Is there a new dominant logic for marketing or just a familiar set of contingencies? We do not know the answer, and if we did, we doubt that we could be convincing within the word limit imposed on this commentary. However, we do assert that the answer lies in the inductive development of theory from phenomena closely observed and thickly described. Writing of the early history of thermodynamics, Lord Kelvin said that the steam engine had given more to science than science had given to the steam engine. In the same spirit, we suggest that at this point in marketing's evolution, perhaps the marketplace has more to teach scholars than scholars have to teach the marketplace.
John Deighton & Das Narayandas
Vargo and Lusch's intent to develop new marketing theory based on research and market changes of the past few decades and to offer links back to Adam Smith, Wroe Alderson, and others has my full support. There is little integrative marketing theory on a higher level of abstraction and generalization, but there is no shortage of fragmented "textbook theory" that piles ideas, concepts, models, survey data, cases, and hypotheses on top of one another.
My task is to comment on three of eight foundational premises of a new logic of marketing in which service provision is the unifying concept. The three are ( 1) "The application of specialized skills and knowledge is the fundamental unit of exchange," ( 2) "Indirect exchange masks the fundamental unit of exchange," and ( 3) "Goods are distribution mechanisms for service provision." Because I agree, in essence, with the authors, I reflect on some of their issues.
In the spirit of grounded theory (Glaser 2001), collection, analysis, and interpretation of data are targeted to finding a hierarchy of variables, categories, and concepts that results in progressively more general theories. I have ventured to find a core variable that unifies the first three premises, and I have settled for "provider." Rather than claiming that this choice is the best, the stance in grounded theory is investigating where a tentative variable leads and constantly being open to revision.
"Provider" embraces experts, organizations, and goods, each representing one of V&L's three premises. Consumers have moved from self-supporting individuals in local and familiar environments to become dependent on experts, strangers, and external products. Providers stand between consumers and need-satisfaction. Traditional literature offers clear-cut roles and parties: seller/buyer, active producer/passive consumer, and subject/object. For example, physicians provide expert advice and patients receive it ("doctor's orders"), retailers distribute household appliances to consumers, and a washing machine cleans dirty laundry for its owner.
Service research began to realize that these parties, roles, and activities were inseparable and simultaneous, at least in part. Production and consumption were not tied to clearly defined parties, but roles became blurred, and there was a third activity: interaction. The consequence is that service and value are produced through ( 1) independent provider contributions, ( 2) independent customer contributions, and ( 3) joint contributions through interaction.
In this light, the physician provides expertise in certain therapies, but patients are experts of their own experience of a disorder. To arrive at a superior solution, doctors need interaction with patients, and patients must not only consume the therapies but also produce them by taking medication, exercising, and altering their lifestyles. Patients may even know more about a disease than the doctor does, the reason being that doctors know little about health, disease, and cure relative to what there is to know. The patients' possibilities of being updated on a disorder through the Internet have highlighted this even more. This way of reasoning supports the relationship marketing view, in which providers and customers retain win-win relationships. The parties become partners.
How is it in marketing practice and theory? Marketing scholars have not been successful in generalizing this knowledge into actionable theories, even if the Internet has speeded up the interest in interaction. The bulk of relationship marketing and customer relationship management literature reflects the 4P's of marketing management, a traditional perspective in which the role of the firm is to manipulate, manage, and lock in the customer (Grönroos 2000; Gummesson 2002a, b).
The alleged superiority of specialization, large-scale operations, and standardization in boosting productivity worries me, especially when it comes to food. Consumers have lost touch with original food, and little is left of its origin after heavy processing by food technology, genetic manipulation, packaging, transport, storage, and sales to households, which often continue the processing with "kitchen technology." Channel management takes the product for given and is solely concerned with the productivity of the steps in an alleged value chain. Product and operations management knowledge are lacking in marketing, even though it is claimed that both services and goods are in part coproduced and that marketing and production are often simultaneous. What, then, are the net productivity gains when consumer and societal value is considered? Corporations become more productive, but food quality (measured in terms of its core mission to provide nutrition and health) becomes inadequate, thereby causing obesity, diabetes, and a host of other nonquality effects.
Experts, organizations, and goods increasingly depend on sophisticated technology, though it may be most obvious with goods. Technology is axiomatically hailed as good, even as "God." At least initially, technology is science- and producer-centric; it is rarely customer-centric. For example, there is much anecdotal evidence that information technology (IT) has both boosted productivity and increased quality of life. A Harvard Business Review article (Carr 2003) says to the contrary, which caused Fortune (Kirkpatrick 2003) to react vehemently in defense of IT. A study by McKinsey, reported in the media in 2001, concluded that there was no evidence that IT had improved productivity in general (though it might have in specific cases). In a letter, Peter Drucker (2002) expressed his concern: "I have yet to see a company that has really succeeded in integrating information technology into its management structure and into its decision making." For example, it has been observed how relationship marketing principles, transformed into customer relationship management software (eCRM), partially get lost by the neglect of human aspects (hCRM).
Where are the hard facts and metrics, saluted by marketing scholars and managers alike, to prove the net benefits of the providers to the customer, when, for example, food marketing and IT marketing are concerned? Piecemeal surveys of limited data and based on arbitrary assumptions and narrow operationalizations of variables are not sufficient. To begin with, marketers need to do as V&L advocate: reinvent marketing theory to fit the present and the future. The more marketers dare to recognize the complexity and ambiguity of marketing phenomena in this theory, the more useful it will be.
Evert Gummesson
Vargo and Lusch argue that marketing, informed by static-equilibrium economics, has had a goods-centered, "value is embedded in output," dominant logic. However, V&L argue that marketing is now evolving toward a dynamic, evolutionary-process, service-centered view that is informed by resource-advantage theory, competences, knowledge, and relationship marketing. In this view, "value is defined by and cocreated with the consumer" (p. 6). For V&L, marketing should shift toward this customer-centric, market-driven, service-centered view, and it should ( 1) focus on specialized skills and knowledge as operant resources that provide competitive advantage, ( 2) strive to maximize consumers' involvement in developing customized offerings, and ( 3) aim to be "the predominant organizational philosophy ... [that] lead[s] in initiating and coordinating a market-driven perspective for all core competences" (p. 13). Furthermore, marketing scholars should "lead industry toward a service-centered model of exchange," teach principles courses that subordinate "goods to service provision," and teach marketing strategy courses that center on resource-advantage theory (p. 14).
A decade ago, Robert M. Morgan and I struggled to craft an article-length (rather than monograph-length) manuscript that would articulate a new theory of competition (Hunt and Morgan 1995). Similarly, V&L's goal of developing an article-length manuscript on marketing's evolving logic dictated that they could not explore in depth all the worthwhile topics. Therefore, this commentary does not nitpick their argument but, at the editor's suggestion, amplifies and extends it, using the resource-advantage theory on which V&L draw.
Central to V&L's argument, and unique to their work, is the distinction between operand resources (those on which an operation or act is performed) and operant resources (those that act on other resources). However, precisely what is a "resource"? For resource-advantage theory, resources are the "tangible and intangible entities available to the firm that enable it to produce efficiently and/or effectively a market offering that has value for some market segment(s)," and resources are categorized as financial, physical, legal, human, organizational, informational, and relational (Hunt 2000, p. 138). Therefore, resource-advantage theory both conceptualizes "resource" and explicates the kinds of resources that can be operand or operant. That is, operand resources are typically physical (e.g., machinery, raw materials), whereas operant resources are typically human (e.g., the skills and knowledge of individual employees), organizational (e.g., controls, routines, cultures, competences), informational (e.g., knowledge about market segments, competitors, and technology), and relational (e.g., relationships with competitors, suppliers, and customers).
For V&L, the "consumer must understand that the value potential [of an offering] is translatable to specific needs through coproduction. The enterprise can only make value propositions that strive to be better or more compelling than those of competitors" (p. 11). As to what this means, recall resource-advantage theory's nine-celled, competitive-position matrix, the axes of which are relative resource-produced value and relative resource costs (Hunt 2000, p. 137). Because "value refers to the sum total of all benefits that consumers perceive they will receive if they accept a particular firm's market offering" (Hunt 2000, p. 138), the positions of competitive advantage/disadvantage in the matrix further explicate V&L's emphasis on value propositions that are more compelling.
For V&L, operant resources such as competences are valuable to the firm. How, though, is their value determined? For resource-advantage theory, not all resources that are valuable to the firm have an exchange value or price; that is, relatively immobile resources, such as competences, are not commonly or easily bought and sold in the marketplace (save when firms themselves are bought and sold). Therefore, the value of such operant resources is determined not by exchange but by the extent to which each resource contributes to the firm's ability to produce efficiently/effectively market offerings that some market segments perceive as having value. In addition, because relative resource costs in the competitive-position matrix are accounting costs, they may be related only indirectly to key, operant, value-producing resources.
For V&L, competition is a knowledge-discovery process because "in the service-centered model, marketplace feedback not only is obtained directly from the customer but also is gauged by analyzing financial performance from exchange relationships to learn" (p. 14). For resource-advantage theory, competition is a disequilibrating process that involves the constant struggle among firms for comparative advantages in resources that will yield marketplace positions of competitive advantage and, thereby, superior financial performance. In this process, "firms learn through competition as a result of feedback from relative financial performance 'signaling' relative market position, which, in turn, signals relative resources" (Hunt 2000, p. 136). Therefore, to amplify V&L's insight, "[b]ecause command economies lack the process of competition, their firms lack a powerful means (i.e., financial performance stemming from marketplace positions) for determining how efficient and effective they are. Indeed, it ... was the premium prices of Western imports that communicated to socialist planners just how ineffective socialist firms were" (Hunt 2000, p. 174).
For V&L, marketing strategy should be taught from the view of resource-advantage theory. Missing are the arguments for "why" and "how." The arguments are found in Chapter 9 of Foundations of Marketing Theory (Hunt 2002), where resource-advantage theory is argued to be toward a general theory of marketing on three grounds, one of which is that it provides a positive foundation for normative theories, such as those strategies based on market segmentation, resources, skills, knowledge, learning, competences, market orientation, and relationships. Faculty and students report that resource-advantage theory provides a model that enriches the educational experience by integrating the remarkably diverse topics taught in business and marketing strategy. To understand competition (i.e., resource-advantage competition) is to make sense of strategy.
In conclusion, as bespeaks an important and potentially seminal article, V&L's argument is historically informed, finely crafted, properly interdisciplinary, and logically sound. Their position deserves a careful read and thoughtful evaluation, not a quick skim and hasty judgment. I urge marketers to provide such an evaluation. Competition is an evolutionary, disequilibrating, dynamic process that involves firms that use operand and operant resources in their search for competitive advantages and superior financial performance. As it is in competition, so it should be in marketing.
Shelby D. Hunt
I have been asked to comment on V&L's sixth foundational premise: The customer is always a coproducer. I want to congratulate the authors on challenging the dominant logic for marketing by suggesting that services ought to be at the core, and therefore consumers become coproducers. My concern is that V&L do not go far enough. I would like to take a step back and identify the attempts by various scholars to recognize the patterns of customer involvement and engagement in the value-creation process. Then, I would like to illustrate that as scholars, we are behind the reality of how customers engage themselves in the value-creation process.
What is meant by customers as coproducers? There are multiple approaches to customer engagement (Berry and Parasuraman 1991; Heskett, Sasser, and Schlesinger 2002; La Salle and Britton 2002; Peppers and Rogers 1993; Pine and Gilmour 1999; Rust, Zahorik, and Keiningham 1996; Schmitt 1999; Thomke 2003; Zeithaml 1990). First, firms try to persuade customers through advertising and promotions; they try to engage them emotionally, if not physically, in the act of coproduction. The second phase of customer involvement is self-service (e.g., self-service gas stations, self-checkout in grocery stores), which is a transfer of work from the firm to the customer. In that sense, the customer is a coproducer. The third phase is the staging of an experience in which the firm constructs the context and the consumer is part of it (e.g., Disney World). The consumer is involved and engaged, but the context is firm driven. This is labeled the "experience economy." The fourth phase is to allow the customer to navigate his or her way through the firm's system to solve a problem (e.g., call centers). Call centers may provide 24-hour service, but their success depends on both the skill levels and the persistence of customers; this involves transfer of work, use of the customer's time, and use of the customer's skills. The fifth phase is in consumers getting involved in codesigning and coproducing products and services. Consumers have work, service, and risks transferred from the firm, and both the consumer and the firm benefit. Risk can be lowered for both the firm and the customer. There are two common features in all five perspectives on customer engagement and involvement. Although work and risks increasingly are shared, the firm decides how it will engage the customer. It is this premise, a firm-centered perspective on how to engage the customer, that needs to be debated.
Although the aforementioned approaches to customer engagement and involvement are current, there are indications that customers want to engage with the firm in new ways. Three forces are driving this transformation: ( 1) ubiquitous connectivity that enables consumers to be well informed and networked, ( 2) convergence of technologies (and especially the emergence of digital technologies), and ( 3) globalization of information. Four implications result from these drivers:
- Customers are not isolated. The firm-customer relationship is not bilateral. Customers, customer communities, and firms interact. Customer communities can be an integral part of the value-creation process, whether by developing product strategy (e.g., video games) or new distribution channels (e.g., Napster and now Kazaa).
- The outcome of the engagements (be it a single customer with the firm or the customer community and the firm) is the cocreation of value; what is cocreated is the experience. Physical products and services can be the artifacts around which personalized experiences are cocreated.
- New building blocks are needed for the cocreation of value. The new building blocks are dialogue (rather than one-way communication from the firm to the customer), access and transparency to information (to avoid and eliminate the asymmetry of information between the firm and the consumer), and risk assessment (an explicit dialogue among consumers, consumer communities, and the firm of risks).
- No single firm can provide the total cocreation experience. Often, a network of firms must work together to provide a unique cocreation experience (e.g., OnStar)
The central ideas revolve around the individual consumer, the experience, the cocreation of value, the criticality of consumer communities, and the need for a network of firms. My colleague Venkat Ramaswamy and I have been working on these ideas for more than four years (Prahalad and Ramaswamy 2000, 2002, 2003, 2004). We find that when we escape the firm and product-/service-centric view of value creation, which is the dominant logic for marketing and strategy (see Kotler 2002; Porter 1980), and move on to an experience-centric cocreation view, new and exciting opportunities unfold. This new perspective also enables us to challenge the deeply held assumptions about marketing staples, such as the meaning of a brand (experience is the brand), the role of exchange and the market (market as a forum), and innovation (innovating experience environments). This is not the place to detail these implications. I want to congratulate Journal of Marketing for opening up this debate. Marketing scholars need more of the "let us examine our premises" perspective in scholarly work for the field to catch up with and shape next practices.
C.K. Prahalad
I am delighted to respond to V&L's brilliantly insightful article. They do a thorough job of detailing the evolution of marketing thought and demonstrating that marketing is currently in the process of perhaps its most profound paradigm shift. I would first like to expand on their work by providing a historical insight into why this paradigm shift is happening now, rather than, for example, 100 years ago. Second, I wish to explore further some of the implications that this paradigm shift has for marketing academia and marketing practice.
The innate intelligence of Homo sapiens has not changed much in the past 100,000 years, so it is unlikely that the marketers, economists, and philosophers of today are significantly more intelligent than those of 100, 200, or 300 years ago. The implication is that new paradigms are more likely to result from structural changes in the underlying system than from increasing perceptiveness and insight. Perceptiveness and insight are required to sense the structural changes and understand their importance, but it is the structural changes that provide the underlying basis for new paradigms.
Why, then, are scholars only now realizing the full implications of the notion that everything is service? It seems to me that the answer to this question pertains to information technology. As V&L so capably point out, it is ultimately knowledge and information that drive service. It is no coincidence, then, that the information revolution that has accelerated in the past 100 years has brought with it a revolutionary new capability to leverage knowledge and information into service. In particular, it has expanded the intangible aspect of virtually all economic exchanges.
In essence, the service revolution and the information revolution are two sides of the same coin. Information technology gives the company the ability to learn and to store more information about the customer, which in turn gives the company more ability to customize its services and to develop customer relationships. The result is that the utility provided to the customer increasingly is based more on information and less on physical benefits.
Consider the example of General Motors, a traditional bastion of the goods economy, which for many years derived its profits almost exclusively from selling cars and other vehicles. However, as the information economy developed, the provision of service and the manipulation of information became increasingly important, to the extent that eventually the General Motors Acceptance Corporation (the car-loan subsidiary of GM) became even more profitable than the core car-sales part of the company. For similar reasons, IBM shifted from being primarily a computer manufacturer to being primarily a service and consulting company, and American Airlines found that its SABRE reservations company was even more profitable than its airliners. Even large oil companies such as ExxonMobil have realized that their competitive advantages derive more from service elements, such as the Speedpass payment system, than from gasoline.
If it is acknowledged that information technology is the driver of the shift toward service, it is also possible to forecast the future of marketing confidently. Information technology has always moved forward, in a trend that has now lasted for thousands of years. Thus, it can be confidently predicted that information technology will continue to advance. This, in turn, implies that marketing's paradigm shift toward service will only intensify. The past 100 years provide unambiguous confirmation of this conclusion: As information technology has accelerated, the world's leading economies have changed from approximately 30% service to approximately 70% service.
What, then, are some of the implications of this paradigm shift for marketing academia and marketing practice? Many existing concepts and models will need to be modified. For example, traditional marketing relies on many technologies (e.g., conjoint analysis or discrete choice models) that implicitly assume a transactional choice. Such models were used, for example, to predict (wrongly) that New Coke would be much more popular than the original Coke. As marketing proceeds toward more of a service/relationship paradigm, transactional choice models are increasingly incomplete and need to be replaced by models of choice in the context of a relationship. The brand choice models failed to model the effect of customers' relationship with the original Coca-Cola brand. Such relationships occur with even more frequency and intensity in the service economy. Ultimately, it must be realized that it is the lifetime value of the customer relationship that really matters to the marketer and that the transactions in that relationship are driven not only by traditional conjoint choice elements, such as value and brand, but also by relationship elements, involving switching costs, that change and evolve over the course of the relationship.
Some sectors of the economy lead in this paradigm shift. Business-to-business marketing has originated many of the most important new ideas in marketing, due to its relationship intensiveness and its customer databases. Likewise, the service sector has experienced the embrace of its underlying concepts (e.g., customer satisfaction, customer loyalty, customer equity) by an increasing percentage of the marketing world. Predictably, the slowest adopters of the new paradigm are consumer packaged-goods companies, but even there the information/service revolution is inexorably transforming the way that marketing is conducted. Increasingly, customer panel databases are used to understand the dynamics of customer relationships, and information technology provides the capability for statistical techniques that make it possible to personalize marketing efforts.
Marketing is entering a new era, and mainstream marketing in the new era will closely resemble the business-to-business/service/relationship marketing of today. The reason for the shift is the advance of information technology, which has resulted in the service revolution and the use of information to understand and enhance customer relationships. Marketers need to replace their goods-derived transactional concepts and models with service-derived relationship concepts and models. For the foreseeable future, the service/ information parts of every business will continue to increase in importance because of inevitable advances in information technology, and the marketing paradigm as V&L describe will become even more dominant as time passes.
Roland T. Rust
The insightful observations of V&L should dramatically influence academic research in marketing and other disciplines. Vargo and Lusch eloquently and provocatively detail why the mainstream marketing discipline must react to obvious dramatic changes in the world economy. It must be recognized that ( 1) the service sector dominates most developed economies in the world and employs nearly all marketing students; ( 2) the systems that deliver manufactured products (i.e., service) often provide more value-added for the customer than do the delivered manufactured products themselves; ( 3) managing company-consumer service interactions requires adaptation, dynamic strategies, and learning new competences; and, consequently, ( 4) marketing research requires reinvention.
The spirit of V&L is undeniable. As service-dominated economies replace manufacturing-dominated economies, most transactions involve government, high-end business services, health care (e.g., Kahn and Luce 2003), legal services, transportation services, evolving communications, multichannel retailing, financial services, and personal services.
Three questions emerge, the answers to which might dictate the station, and perhaps survival, of the marketing discipline. Although survival seems a hyperbole, consider the following: First, stagnant consumer packaged-goods manufacturers rather than lucrative financial services or rapidly expanding business services predominantly employ marketing's best students. Second, although top corporate officers of consumer packaged-goods firms often have marketing pedigrees, other backgrounds (e.g., finance, law, operations) prevail for most service firms (Doyle 2000; Fredman 2003; Pasa and Shugan 1996). Third, although new marketing faculty members enjoy increased starting salaries this year, their salaries still lag those of hires in information systems, operations, accounting, and finance (AACSB 2002-2003 Salary Survey). Fourth, the gap between academic research and the content of basic marketing textbooks is growing. Fifth, knowledge of the marketing literature is less of a competitive advantage for marketing doctoral students who face competition from nonmarketing doctoral students.
What Are the Risks of Doing Research with a Service Orientation?
Many authors (e.g., V&L) justifiably advocate implementing radical new research directions, but nontrivial impediments and perilous obstacles await researchers. First, much of the marketing discipline concerns itself with developing and refining tools for analyzing numeric data, which historically have been cross-sectional survey data. These data endowed marketing groups with unique advantages: having valuable information not available elsewhere in the organization and having homegrown techniques with which to analyze it. A shift to longitudinal transaction data makes the techniques less valuable. Moreover, longitudinal transaction data are well understood by finance, operations (e.g., the airlines), accounting, information systems, and other business disciplines, which have analyzed the transaction data for years and have developed their own decision-making tools that employ that data. The marketing discipline's distinctive competency in this domain is unclear.
Second, generality is a traditional holy grail of academic research. The developers and zealous stewards of existing methods and theories will enthusiastically proclaim that new methods are unnecessary. Their current treasured methods are equally applicable to data on soap sales or data on surgery.
Third, services (as defined by the U.S. Census Bureau) possess neither entirely unique nor mutually common properties. For example, although psychiatric services are intangible, setting broken bones is no less tangible than the scent of manufactured perfumes. Although consulting services require clients, airplanes fly without passengers. Although inventorying empty airline seats after departure is difficult, many manufactured goods are also highly perishable. Although dry-cleaning services require some customer participation, driving manufactured automobiles requires greater degrees of customer involvement.
Is the Marketing Function Important to Service-Oriented Firms?
Marketing is certainly an essential activity that is worthy of serious academic research. However, for service industries, other disciplines make compelling claims to greater relevancy. In the airline industry, marketing might take a backseat (no pun intended) to maintenance-and safety-related functions. For many public utilities (e.g., electricity, water, emergency services), marketing might take a backseat to legal concerns and regulatory obligations. Do pilots require a customer orientation or flight training? Are restaurant servers more important than the quality of meals? Do deficiencies in marketing or operations explain the high rate of bankruptcies among service providers? Was the titanic battle between Kmart and Wal-Mart resolved on the operations battlefield or on the marketing battlefield (Muller 2002)?
The fundamental marketing concept of a customer orientation can be vague. For example, who is the customer of a hospital? Is it the patient who receives the service, the insurer who pays for the service, the admitting physician who refers the patient, the government regulator who specifies the service, or the employer who chooses the healthcare provider? Are the customers of colleges the students, the parents who pay the tuition, the taxpayers who provide subsidies, the donors who provide funding, the corporations who hire the students, the grant providers, the government, or society at large?
Despite the significant role of marketing, modesty is appropriate. Marketing must coexist with finance and operations. Researchers in marketing must recognize and contemplate the impact of marketing activities on operations as well as their financial impact. For example, complex or confusing promotions might tax servers, thereby causing a dramatic detrimental impact on server time per customer. Moreover, marketing must be accountable to finance and justify marketing activities by measuring both customer satisfaction and the consumption of precious organizational resources. For example, it must be considered how marketing activities affect scarce resources (e.g., server time, space, administrator attention) during times of peak capacity.
What Service-Related Research Problems Crave Attention?
Rather than directly attacking existing views as simply inadequate (despite justification) or arguing for the universal dominance of the marketing function, perhaps a more humble approach is possible. Scholars might focus on overcoming concrete problems and daily challenges commonly faced by particular service industries (Shugan 2003). The primary concern is the implementation of marketing activities, including their financial impact (e.g., Rust, Moorman, and Dickson 2002) and impact on operations (e.g., Evangelist 2002).
As extensions of prior work, further research should explore the following challenges:
• Implementing marketing strategy in an operations-dominated environment (e.g., Eliashberg et al. 2001);
• Measuring the impact of marketing strategies on short-and long-term profits (e.g., Leeflang and Wittink 2000);
• Managing demand and enhancing profits, given capacity constraints;
• Developing new services in which implementation is more critical than design;
• Developing recovery systems for mitigating almost-certain failures in service delivery systems (e.g., Hart, Heskett, and Sasser 1990);
• Making personal selling more effective by adding service;
• Developing marketing strategies for exploiting seasonality and diminishing its deleterious impact on server capacity (e.g., Radas and Shugan 1998);
• Increasing sales and profits when teams deliver the service;
• Marketing when third parties pay for the service or evaluate it (Eliashberg and Shugan 1997);
• Using marketing to train effectively and to retain employees;
• Marketing more effectively information services, entertainment, and services with low marginal costs;
• Developing highly profitable ancillary services to complement low-margin core services (e.g., concessions at movie theaters);
• Balancing self-service and employee-delivered service;
• Determining the optimal amount of customization (e.g., Anderson, Fornell, and Rust 1997) in a rate-based pricing environment;
• Developing internal marketing programs to motivate service employees;
• Determining when and how to advance sell services (Moe and Fader 2002; Xie and Shugan 2001);
• Developing creative pricing ideas for services (e.g., Biyalogorsky and Gerstner 2004);
• Building network externalities for services (e.g., Basu, Mazumdar, and Raj 2003); and
• Measuring the impact of more service on customer welfare (e.g., Liu, Putler, and Weinberg 2004).
Steven M. Shugan
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By George S. Day; John Deighton; Das Narayandas; Evert Gummesson; Shelby D. Hunt; C. K. Prahalad; Roland T. Rust and Steven M. Shugan
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Record: 85- Linking Marketing to Financial Performance and Firm Value. By: Bolton, Ruth N. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p73-75. 3p. 1 Chart. DOI: 10.1509/jmkg.68.4.73.42727.
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Linking Marketing to Financial Performance and Firm Value
In January 2002, Donald R. Lehmann, Executive Director of the Marketing Science Institute, submitted a proposal for a JM Special Section, "Linking Marketing to Financial Performance and Firm Value." The proposal included activities to promote interactions among marketing academics and practitioners, designed to advance research on this topic. I was excited about the opportunity to stimulate and publish new research, and after extensive discussions, the American Marketing Association and the Marketing Science Institute formally agreed to cosponsor the Special Section. Authors submitted their manuscripts through a paper competition as well as directly through JM . Donald R. Lehmann, the Consulting Editor, and a panel of distinguished scholars reviewed every submission. The panel included Tim Ambler, Gregory S. Carpenter, Robert Jacobson, V. Kumar, Roland T. Rust, and Rajendra K. Srivastava. All submissions underwent JM's standard double-blind review process under my editorship, and members of JM's Editorial Review Board served as reviewers. I am pleased to have this opportunity to acknowledge the important contributions of these many individuals: both authors and reviewers. As the subsequent essay suggests, three years' hard work has produced a thought-provoking collection of articles. I hope they will generate further intellectual inquiry and debate about the link between marketing and financial performance in the business community.
Donald R. Lehmann, Consulting, Editor
A major trend of the twentieth century was specialization, initially in production and then for activities in general. The functional areas (silos?) that resulted provided great efficiency in specific tasks and facilitated a deep level of knowledge within each function. Unfortunately, this compartmentalization also led to the development of specialized "languages" (a Tower of Babel?), alienation, and integration problems. In the 1980s and 1990s, the pattern of rough equality among functions moved toward hegemony of a single function: finance. The consequence of this was increasing pressure to "meet the numbers" (i.e., deliver strong financial performance). This pressure only increased as the economy turned down and as global competition and the Internet grew.
Whether finance should have attained its current status is not the issue. Indeed, finance and accounting are not perfect. Even excluding the scandals and abuses that marked the beginning of the twenty-first century, the efficient stock market, which is the fundamental tenet of finance, has been badly tarnished, particularly by the growing field of behavioral finance (which essentially is consumer behavior in the financial investments product category). Similarly, financial accounting rarely captures the intangible assets (e.g., customers, brands) that make up the majority of many firms' value. Still, like it or not, it is appropriate to expect accountability from expenditures. Put differently, in general a free market system measures success in currency (e.g., dollars, euros), so business actions are naturally evaluated in monetary terms.
Given this history, it is not surprising that the Marketing Science Institute (MSI) has designated metrics, the measurement of the impact of marketing, as its top priority for the most recent three two-year priority periods (with the short-lived exception of e-commerce for two years). This led to MSI's proposal competition, which produced 111 entries (its largest-ever competition); to two back-to-back conferences in Dallas in October 2001; and to the articles that appear in this special section of JM , five of which come directly from the proposal competition. The work herein has many ancestors, most recently Srivastava, Shervani, and Fahey's (1998) conceptual development and Ambler's (2003) detailed discussion. What binds the work together is a focus on evaluating marketing actions and assets in financial terms, not marketing ones.
As Figure 1 suggests, much of marketing has focused its attention on the upper levels of the productivity chain, with line marketing people concentrating on customer or product-market (e.g., sales, share) results. The link to financial outcomes and stock price surprisingly is rarely considered. The consequences of this are predictable: A focus on sales or share and growth per se rather than on profits becomes the center of attention. An obsession about customer image can obscure profit relevance. Firms' maximization of measured satisfaction is most attainable by concentrating on a small, and often shrinking, segment of customers. Even a focus on margin or return on investment (which curiously is often measured by sales/marketing budget) can lead to overconcern about short-term results. The point is not that these are bad measures. Rather, the point is that to assess performance, they need to be used (primarily diagnostically) along with financial measures and the value of marketing assets that have long-term value, such as brand equity and customers (primarily evaluative criteria).
There are data and methodological limitations that make development of precise estimates of the links in the value chain extremely difficult. Indeed, it may be "a bridge too far" to move directly from customer associations to stock price. Still, an attempt to lay out such a value chain and to estimate the links seems to be critical. This means that there can be no overreliance on a single measure (it is easy to play games with these) or measure du jour, but there need to be metrics at all the levels. Put simply, if marketing wants "a seat at the table" in important business decisions, it must link to financial performance. Otherwise, by focusing on the measures it is most comfortable with (e.g., awareness, attitude, sales), it will continue to lose ground to other areas in fields such as product development (e.g., research and development, design) and to devolve into a department of "ad copy and cents-off coupons" (Lehmann 1996). The task is not easy, but the reward is great.
In this Special Section, the first article, by Rust, Ambler, Carpenter, Kumar, and Srivastava, is an invited collaboration of five disparate scholars. They were recruited to create an overview of the metrics area. Private conversations (emails) revealed some striking differences in views. What resulted is the "mean," a generally sensible work and a nice overview of what we know and what we need to know.
The first of the articles from the MSI competition is that by Narayanan, Desiraju, and Chintagunta, who focus on the impact of marketing program spending. Specifically, for two different prescription medicine categories, they examine the impact of detailing, direct-to-consumer (DTC) advertising expenditures, and other factors on revenues. In some ways, this is a "traditional" study, but it is one that incorporates advanced methods (e.g., for dealing with heterogeneity). The results are interesting, suggesting that DTC advertising (but not detailing) influences category sales and that both affect share. Several significant interactions exist, and the long-term impact of detailing and DTC advertising are four to five times the short-term impact, which reinforces arguments in support of treating marketing spending as an investment rather than as an expense.
The article by Kumar and Reinartz has a different focus; namely, it shows how to use customer lifetime value (CLV) as the basis for evaluating marketing spending (i.e., treating CLV as an objective). Their empirical work focuses on a particular company and its channel (particularly Web) strategy. The article also demonstrates the usefulness of CLV as a customer selection metric both in competition with and in combination with other bases, such as previous-period purchase.
Three articles focus on the impact of strategic choice on financial performance. First, Rao, Agarwal, and Dahloff address an ambitious topic, evaluating whether a single (umbrella) branding strategy is superior or inferior to a multiple branding strategy, as measured by stock market performance. Using Tobin's q (which is essentially the market value of a firm divided by the book value) as a measure of stock performance, they find that even after controlling for several variables (industry concentration, operating margin, leverage, research and development spending, firm age, number of acquisitions, and growth rate), branding strategy matters. More specifically, a corporate branding strategy appears to outperform a "house-of-brands" strategy significantly. This is important in its own right and because it suggests, as other researchers have (e.g., Mizik and Jacobson 2003), that it is possible to show a link between marketing strategy (rather than spending) and financial and stock performance.
Second, Pauwels, Silva-Risso, Srinivasan, and Hanssens examine the consequences of two different marketing approaches (promotion spending and new product introductions) in the automotive industry. Using state-of-the-art econometric methods, they link marketing actions to stock market performance, as measured by the ratio of market capitalization to book value as well as revenue and income. It is not surprising that both new product introductions and promotions positively affect revenue in the short and long runs. However, in terms of income or stock performance, promotion has a negative long-term impact that more than offsets its short-term benefit. Furthermore, new product introductions have a weak short-term impact but a strong, positive long-term impact on stock price, especially during new market entry.
Third, Lee and Grewal examine the impact of ecommerce strategy on market valuation. Specifically, they examine how retail firms' responses to the Internet affect Tobin's q. Interestingly, alliances and speed of adoption and integration do not have a direct (main) effect on valuation but rather modify the impact of other variables such as resource slack.
Anderson, Fornell, and Mazvancheryl link customer satisfaction, as measured by the American Consumer Satisfaction Index, to shareholder value. In a sense, this completes a gradual movement of the satisfaction literature from a focus on measurement per se to financial impact (e.g., Anderson, Fornell, and Lehmann 1994), which is the "ultimate" dependent variable. Using a version of Tobin's q as a measure, the authors demonstrate a positive link between satisfaction (as well as concentration, share, and weekly advertising) and stock performance, after controlling for industry-and firm-specific factors (advertising-to-sales ratio, share, and industry concentration). More important, the link varies noticeably by industry; it is strongest for department stores, supermarkets, and appliances, and it is weakest for automobiles, apparel, and personal computers. Exploration of the reasons for these links, as well as assessment of the cost (compared with the benefit) of increasing satisfaction, would make it possible to optimize spending on satisfaction improvement programs.
Overall, it seems reasonable to declare victory in this attempt to assess marketing productivity rigorously. Of course, none of the articles are "perfect," which the authors themselves recognize. What they are is quite good, and more important, what they do is help open up new avenues of inquiry. In particular, it is interesting to note that several of the articles focus directly on stock performance as the criterion for assessing marketing performance, a far cry from measures such as awareness or attitude. The hope is that these articles, along with other work in the area, will begin to put marketing on a stronger footing for identifying and defending its successes to those in other areas as well as help eliminate its mistakes.
DIAGRAM: FIGURE 1; Marketing Productivity Chain
REFERENCES Ambler, Tim (2003), Marketing and the Bottom Line , 2d ed. London: Financial Times/Prentice Hall.
Anderson, Eugene, Claes Fornell, and Donald R. Lehmann (1994), "Customer Satisfaction, Market Share, and Profitability," Journal of Marketing , 58 (July), 53-66.
Lehmann, Donald R. (1996), "Some Thoughts on the Futures of Marketing," in Reflections on the Futures of Marketing: Practice and Education , Donald R. Lehmann and Katherine E. Jocz, eds. Cambridge, MA: Marketing Science Institute, 121-35.
Mizik, Natalie and Robert Jacobson (2003), "Trading Off Between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis," Journal of Marketing , 67 (January), 63-76.
Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1998), "Market-Based Assets and Shareholder Value: A Framework for Analysis," Journal of Marketing , 62 (January), 2-18.
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By Ruth N. Bolton
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 86- "Listening In" to Find and Explore New Combinations of Customer Needs. By: Urban, Glen L.; Hauser, John R. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p72-87. 16p. 3 Color Photographs, 6 Charts, 2 Graphs. DOI: 10.1509/jmkg.68.2.72.27793.
- Database:
- Business Source Complete
"Listening In" to Find and Explore New Combinations of
Customer Needs
By "listening in" to ongoing dialogues between customers and Web-based virtual advisers (e.g., Kelley Blue Book's Auto Choice Advisor), the authors identify new product opportunities based on new combinations of customer needs. The data are available at little incremental cost and provide the scale necessary for complex products (e.g., 148 trucks and 129 customer needs in the authors' application). The authors describe and evaluate the methodologies with formal analysis, Monte Carlo simulation (calibrated on real data), and a "proof-of-concept" application in the pickup-truck category (more than 1000 Web-based respondents). The application identified opportunities for new truck platforms worth approximately $2.4 billion-$3.2 billion and $1 billion-$2 billion, respectively.
Identifying new platform opportunities is one of the most important roles of market intelligence. Monitoring [Web-based advisers] provides a rich source of observed in-market customer behavior that complements our current inquiry tools that, by their nature, are forced to ask customers either to state their intentions before they are actually in the market or to remember after the purchase what they did (and why) when shopping for a vehicle. No form of inquiry is perfect, however; whatever its limitations, the currency [of Web-based advisers] presents a valuable source of market understanding that is already streaming by and is of great value when used appropriately.
-- Vince Barabba, General Manager of Corporate Strategy and Knowledge Development, General Motors
The advent of the Internet has given customers more information about products in diverse industries such as travel, health, automobiles, computers, home entertainment, and financial services. For example, the percentage of people using the Internet for information and advice is high in travel (70%), health (56%), and automobiles (62%). The monitoring of Internet searches, undertaken by potential customers in their own vested interests, has the potential to reveal new opportunities for new products and product platforms. In this article, we explore a set of methodologies to use this information to identify new product opportunities. Although our application is drawn from the automotive industry, the basic concepts are applicable to complex products in both consumer and business-to-business markets, such as high-end copiers, home entertainment centers, and financial services (Ulrich and Eppinger 1995).
Automobiles and trucks are indeed complex products. The investment for a new automotive platform can require as much as $1 billion-$2 billion and 1200 person-years of investment. Such investments are justified by the scale of the market. For example, with approximately 150 brands of truck on the market, the average truck needs less than 1% of the marketplace to be profitable; each share point is worth $800 million in annual revenue.
Most automotive platforms are redesigns to provide known combinations of customer benefits (i.e., needs). However, long-term survival requires that new opportunities be identified. For example, in the late 1980s, through a combination of qualitative focus groups and quantitative perceptual mapping studies, a new opportunity was identified for luxury vehicles that could haul moderate loads. Today, the luxury sport-utility-vehicle segment is one of the most profitable automotive segments. Another new product example came from leading-edge users. In the 1960s, teenagers and young adults were customizing inexpensive vintage Fords with V8 engines. Ford recognized the opportunity for inexpensive, sporty cars with large engines. The first production car in this "pony" segment, the 1964 1/2 Mustang, sold 420,000 units in the first year ($10 billion in today's prices; ClassicPonyCars.com 2002). The 1983 Chrysler minivans are another example. Growing families needed a vehicle that could carry a 4′ 8′ sheet of plywood, fit easily in their garages, drive like a passenger car, have a side door for small children, and incorporate a sedanlike liftgate for shopping. Chrysler sold 210,000 units in the first year and dominated the new segment for years to come (Allpar.com 2003). These are but some of the many automotive examples in which profitable new platforms filled previously unrecognized (by the auto industry) combinations of consumer needs. The firms that first identified the new combinations of customer needs were able to exploit the opportunities profitably for many years.
Identification of new combinations of customer needs for complex products is no small challenge. For example, trucks fulfill between 100 and 150 distinct customer needs, and even more if sound and other subsystems are included. Because of the sheer magnitude of combinatorial combinations (e.g., 10<sup>52</sup> in our application), existing products fulfill a tiny fraction of the potential combinations. Complex products require large samples. For example, even if we had hypotheses about a new combination of customer needs, we might still need detailed information on almost 500 or more respondents to be comfortable that a needs-combination segment is worth further investigation. Because multiple needs define a segment, it is not unusual for sample sizes in the automotive industry to approach 10,000 for targeted research and 100,000 for general searches. General Motors (GM) alone spends tens of millions of dollars each year searching for new needs combinations and studying needs combinations when they have been identified. Some studies are in the cost range of $500,000 to $1 million. Automotive firms desire methodologies that are more cost effective and that can be run continuously to identify new needs-combination opportunities as soon as they occur.
In this article, we propose methodologies that provide a practical means to find combinations of customer needs that represent profitable new opportunities. The methodologies exploit new data (i.e., clickstreams from virtual advisers) that are available at little incremental cost but provide the scale (both number of products and number of needs) that is necessary to find opportunities in complex-product categories. For example, there is a virtual adviser sponsored by GM, J.D. Power, Kelley Blue Book, and Car Talk and partly based on the methodologies in this article that has approximately 500,000 annual visitors.
We obtained the new data by "listening in" to ongoing dialogues created when customers use the Internet to search for information and advice about automotive purchases. The data are incentive compatible: Customers are seeking advice and have an incentive to reveal their needs. The virtual advisers generating the data are updated often to include new products and new customer benefits (needs), providing evolving data with which to identify new combinations of needs as soon as customers express them. We focus on the truck market to illustrate the methods. The methodologies extend readily to other complex-product categories, such as travel, medical, and office equipment.
We listen in by combining multiple stages: a Bayesian virtual adviser to obtain the data, an opportunity trigger to identify when existing trucks do not fulfill desired combinations of needs, a virtual engineer to explore and clarify the identified opportunity, a design palette to explore how customers would design their own trucks, and a clustering procedure to estimate the (rough) size of the segment of customers who desire the new combinations of needs. In this article, we illustrate each stage, examine internal validity with Monte Carlo analyses, and provide an example based on a sample of more than 1000 respondents. This "proof-of-concept" research was performed parallel to existing methods, yet it identified a key segment at a much lower cost. It also implied the existence of a segment, still being explored, that existing methods may have missed. We begin by discussing how listening in complements existing methods.
Because so much is at stake, strategic marketing and marketing research groups invest heavily in identifying new opportunities. They speak to leading-edge users, maintain and monitor user groups, sponsor special racing events, monitor chat rooms and user groups, and use various qualitative and ethnographic methods (Barabba 2004; Barabba and Zaltman 1991; Griffin and Hauser 1993; Gutman 1992). For example, automotive firms invest heavily in quantitative methods such as conjoint analyses; activities, interests, and opinions (AIO) studies; and large-scale "clinics" in which customers view and react to prototypes and concepts (Green and Srinivasan 1990; Plummer 1974; Urban, Weinberg, and Hauser 1996). Table 1 summarizes characteristics of existing methods and listening in. The cost and sample-size data are typical for the automotive industry; they are based on our experience and discussions with auto executives and consultants.( n1)
The methods in Table 1 are complementary. For example, qualitative and ethnography interviews are powerful methods to probe in-depth once the research is focused, but they are an expensive means to search for combinations of needs that might be desired by less than 1% of the market. Conjoint analyses provide accurate estimates of the importance of customer needs, but they are most effective when they are targeted to approximately 10 to 20 needs. Even adaptive methods cannot handle all the needs that describe a truck. Furthermore, AIO studies are designed to examine the entire market for new combinations of needs, but they are expensive, performed infrequently, and tend not to collect data on gaps in customer needs. In contrast, AIO studies provide critical input to virtual advisers. Truck clinics provide the most realistic stimuli to customers. They are designed carefully to forecast sales before launch, but their primary use is confirmatory rather than exploratory.
Listening in fills a gap in existing methods by making it feasible to use inexpensive and readily available data to search large numbers of customer needs to find combinations of customer needs that are desired but not currently fulfilled by existing trucks. More important, unlike AIO studies, listening in can immediately and automatically target both quantitative and qualitative questions to explore further the new combinations of customer needs. Because listening in runs continuously and is updated periodically with new vehicles and benefits (needs), it provides an early warning of new needs-combination segments as soon as they appear in the market.
Tailored interviewing (TI) has characteristics that are similar to the Bayesian virtual adviser. Both TI and the virtual adviser classify respondents (e.g., into seven segments, as in the work of Kamakura and Wedel [1995]; into three most preferred trucks [of 148] in our application). There are other technical differences that we discuss in the next section. A key conceptual difference is that to be practical in the truck market, the virtual adviser must be updated almost continuously as new trucks enter the market or as new features are added to the question banks. Although both methods assign respondents with posterior probabilities, the virtual adviser relies on Bayesian methods to update probabilities and uses data from multiple sources, whereas TI relies on a calibration survey and uses maximum-likelihood methods (Kamakura and Wedel 1995, Equations 3-7). Each method works well in its target application.
Listening in is not a panacea, nor can it operate without complementary methods. For example, although the virtual engineer contains qualitative probes, subsequent qualitative and ethnographic research provides greater depth on a segment when it has been identified. Similarly, when new needs combinations have been uncovered, conjoint analyses search the combinations in greater detail and quantify the importance of the alternative needs. Although listening in provides first-order forecasts, truck clinics provide the accuracy necessary before $1 billion-$2 billion is committed to a project. We illustrate in a stylized way how listening in complements existing methods for two practical situations in truck markets. In practice, applications are more iterative and include other methods (Urban and Hauser 1993).
Identify opportunities for a new truck platform:
Listening in ⇒ qualitative interviews ⇒ conjoint analysis ⇒ truck clinics ⇒ launch.
Monitor marketplace changes for vehicle "refresh" opportunities:
Listening in ⇒ conjoint analysis ⇒truck clinics ⇒ launch.
Virtual-adviser data are extensive, available at little incremental cost, and underused as a means to identify unfulfilled combinations of customer needs. Web sites such as Kelley Blue Book (http://www.kbb.com), Microsoft Autos (http://autos.msn.com), Edmund's (http://www.edmunds.com), Autobytel (http://www.autobytel.com), Autoweb (http://www.autoweb.com), NADA (http://www.nadaguides.com), and Vehix (http://www.vehix.com) have changed the way that customers search for information on cars and trucks. Of all new-vehicle buyers, 62% search online before buying a vehicle (J.D. Power and Associates 2001). This search rate has increased from 54% in 2000 and from 40% in 1999. The most important and most accessed Internet content is information about vehicle options and features. Notably, although customers prefer independent sites for pricing and general evaluation, they prefer manufacturers' sites, by more than a two-to-one margin, for feature and option information (J.D. Power and Associates 2001, p. E16).
Virtual advisers come in many varieties, including comparators, which array choice alternatives by features (Epinions.com); feature-specifiers, which ask consumers for preferred levels of features and search the database for products that meet the feature specifications (Kelly Blue Book's online recommendation tool); configurators with detailed specifications and costs for the chosen set of detailed product features (http://configurator.carprices.com/autoadvisors); collaborative filters, which recommend products based on correlations of previous purchases by similar customers (Amazon.com); and utility maximizers, which use methods similar to conjoint analysis to weight features (Activebuyersguide.com). Other advisers use real people who consumers can access by e-mail (Mayohealth.org) or in live chat rooms (Nordstom.com).
The listening-in methodology relies on data from a Bayesian virtual adviser, which is a method that is well-matched to the opportunity trigger mechanism. However, the virtual engineer, the design palette, and the clustering are not limited to working with a Bayesian virtual adviser. These methodologies can work with any virtual adviser that provides recommendations at any point in the questioning sequence and that links customers' responses to benefits that the customers derive from vehicles.
The Bayesian virtual adviser was developed as a prototype for a major automotive manufacturer; a commercial system based, in part, on this adviser is now in place on the Web. This virtual adviser combines two methods to recommend a set of four vehicles to customers: a segmentation gearbox and a Bayesian adviser. The segmentation gearbox divides people into segments on the basis of grouping and assignment rules.( n2) The grouping is based on a cluster analysis of a 114-item AIO questionnaire sent to 100,000 respondents (76 personal viewpoints and 38 preferred vehicle characteristics, including styling and design). The automotive manufacturer's AIO study identified 48 segments, of which 25 were relevant to pickup trucks. Customers were assigned to segments on the basis of answers about their desires for features and options such as comfort, passenger capacity, and prestige as well as about their anticipated use of the truck. In the virtual adviser, one of the four recommended vehicles was the vehicle bought most often by the segment to which the customer was assigned. However, because the segmentation gearbox is designed to allocate people to segments rather than identify new opportunities, it is not the focus of this article. Instead, we focus on the Bayesian adviser that recommends three of the four vehicles.
Bayesian Adviser
The basic concepts behind the Bayesian adviser are ( 1) to select sets of questions, known as question banks, such that the answers provide the most information about which vehicle to recommend and ( 2) to update the probabilities that describe the likelihoods that each vehicle will be most preferred by the customer after each question bank.( n3) Figure 1, Panel A, illustrates the opening screen of the virtual adviser (a neighbor who is a contractor and who has bought many trucks over the years), and Figure 1, Panel B, illustrates one of the question banks asked of customers. We describe the Bayesian updating mechanism and then describe how it can be used to select the maximum-information question bank. We subsequently indicate how we obtained both the conditional and the prior probabilities.
We begin with the notation. We let Q be a set of question banks indexed from q = 1 to N. For each question bank, q, r<sub>q</sub> indexes the potential responses to that question bank, where r<sub>q</sub> is a nominal variable with values from 1 to n<sub>q</sub>. If there is more than one question in a question bank, n<sub>q</sub> represents the number of possible combinations of answers. If one of the questions includes a continuous sliding scale, it is discretized to a finite number of categories.
For each customer, the order of the question banks is chosen adaptively. For a given customer, R<sub>q</sub> - 1 is the set of question banks up to but not including question bank q. The variable v<sub>j</sub> indicates vehicles from 1 to V. At any point in the adviser's questioning sequence, we are interested in the likelihood that the customer will prefer vehicle j after having been asked question bank q. We indicate this likelihood by P(v<sub>j</sub>|R<sub>q - 1</sub>, r<sub>q</sub>).
Suppose that from previous surveys, we have available the conditional probabilities of how customers, who prefer each vehicle, will answer the question banks. We then can use Bayes' theorem to update recommendations.( n4)
( 1) [Multiple line equation(s) cannot be represented in ASCII text]
where P(v<sub>j</sub>|R<sub>q - 1</sub>) is the virtual adviser's recommendation probability to the customer for vehicle v<sub>j</sub> before asking the qth question bank.
However, even with data from full-scale surveys, such as an AIO questionnaire with 100,000 responses, use of Equation 1 is not feasible because the number of potential combinations of responses grows exponentially with the number of question banks. For example, in our study, the dimensionality of RN, the number of unique paths through the adviser's questions, is 1.4 x 10<sup>15</sup>. Fortunately, we can make Equation 1 feasible based on the property of local independence. This property appears reasonable for our data and has proved robust in simulations and applications in the TI literature (e.g., Kamakura and Wedel 1995, Equation 11; Singh, Howell, and Rhoades 1990, Equation 8). Local independence recognizes that there are nonzero correlations across vehicles in the answers to the question banks; customers who prefer a full-sized truck may also prefer a diesel engine. Indeed, it is this combination of preferences on which the adviser bases its recommendations. However, if we limit ourselves to customers who prefer a Ford F350 Supercab, for those customers, responses to the "size" question bank are approximately statistically independent of the responses to the "engine type" question bank. This enables us to write P(r<sub>q</sub>, R<sub>q - 1</sub>|v<sub>j</sub>) ≅ P(r<sub>q</sub>|v<sub>j</sub>)P(r<sub>q - 1</sub>|v<sub>j</sub>)... P(r<sub>1</sub>|v<sub>j</sub>), which implies that P(r<sub>q</sub>|v<sub>j</sub>) ≅ P(r<sub>q</sub>|v<sub>j</sub>, R<sub>q - 1</sub>) by the laws of conditional probability. Using this property, we rewrite Equation 1, in which we recursively obtain P(v<sub>j</sub>|R<sub>q -1</sub>), as follows:
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
Figure 2 gives a simplified example for one customer of the evolution of the recommendation probability. The current recommendation is on the left-hand side, and the probability that the customer will purchase that recommended vehicle is on the right-hand side. Also listed on the left-hand side are the question bank and parts of the answer. For example, after the second question bank on engine size, the customer answers "four cylinders." If the customer were to stop answering question banks and request a recommendation, the adviser would recommend the Mazda B2300 and forecast a.0735 probability that the customer would purchase the Mazda B2300. In Figure 2, the probability of purchase increases for the most preferred truck after each question bank is answered. Note that the recommended vehicle changes after the fifth question bank and again after the eighth question bank.
Question Bank Selection
To select the next question bank, the virtual adviser attempts to gain as much information as possible from the customer. For example, if after reviewing the responses, the adviser decides that a question bank on towing capacity is likely to make one truck more highly probable and all other trucks less probable, that question bank might be a good candidate to ask next. To do this, we turn to formal theory in which information is defined as the logarithm of the relative odds (e.g., Gallagher 1968). That is, the information, I(v<sub>j</sub>|r<sub>q</sub>, R<sub>q - 1</sub>), provided by the response to question bank q equals log [P(v<sub>j</sub>|R<sub>q - 1</sub>, r<sub>q</sub>)/P(v<sub>j</sub>|R<sub>q - 1)</sub>]. This definition has several nice theoretical properties, including that ( 1) under an equal proportional loss rule, information always increases when the probability of the maximum-choice truck increases; ( 2) the expected information is maximized for the true probabilities; and ( 3) the information measure rewards systems that provide more finely grained estimates (Kullback 1954; Savage 1971).( n5)
To compute the expected information, we take the expectation over all possible responses to question bank q and over all possible vehicles. The information that we expect from question bank q is given in Equation 3:
( 3) [Multiple line equation(s) cannot be represented in ASCII text]
We use a two-step look-ahead algorithm. For each potential question bank and response on Step 1, the adviser computes the best second question bank and the expected information for that question bank. It then selects the Step 1 question bank with the highest contingent expected information.
Initial Calibration
Two estimates are necessary and sufficient for the virtual adviser: prior probabilities, P(v<sub>j</sub>), and conditional response probabilities, P(r<sub>q</sub>|v<sub>j</sub>). The virtual adviser obtains the prior probabilities for each individual from a logit model based on five truck characteristics: price, fuel economy, performance, reliability, and safety. Each customer is asked initial constant-sum, self-explicated importance weights (w<sub>c</sub>) for these characteristics. (The prior weights are obtained from questions that are asked before the question banks illustrated in Figure 2.) We estimated the prior probabilities with Equation 4, where w<sub>c</sub> is the importance for the cth characteristic, x<sub>jc</sub> is the value of characteristic c for vehicle v<sub>j</sub>, and Β is a scaling parameter:
( 4) [Multiple line equation(s) cannot be represented in ASCII text]
We obtained the characteristic values for each existing vehicle and the scaling parameters from archival data and managers' and engineers' judgments. For example, prior surveys of owners help establish that the Toyota Tacoma 4 x 4 (regular cab) has a rating of 1.087 on fuel economy and a rating of 1.241 on performance. For the GMC Sonoma two-wheel drive regular cab, the corresponding ratings are 2.116 and .525, respectively (data are disguised slightly). We synthesized the actual data from "an ongoing global effort" by the manufacturer "to understand consumers' needs and wants related to motor vehicles" (quotes from a proprietary study). Part of this ongoing global effort included data from the AIO questionnaire described previously (76 personal viewpoints and 38 vehicle characteristics). When new vehicles become available, managers and engineers provide temporary estimates of the x<sub>jc</sub>'s.
The conditional response probabilities are based on the ongoing AIO surveys, supplemented when necessary by experienced managers and engineers. For example, the survey data suggest that customers who prefer the Toyota Tacoma 4 x 4 (regular cab) are likely to answer that they prefer a four-wheel-drive vehicle 84% of the time. They are likely to answer that they prefer two-wheel drive only 16% of the time. Table 2 illustrates data, disguised slightly, on conditional probabilities for numbers of passengers that are obtained from AIO studies. The data, P(r<sub>q</sub>|v<sub>j</sub>), are sufficient for the updating equations (Equations 2 and 3) if they are available for all question banks in the virtual adviser.
Evolving Question Banks
Virtual advisers and listening-in are not one-shot studies. Markets evolve as customer needs change and as technology improves. Each year brings changing features and new truck brands. To advise customers and identify new opportunities effectively, it must be relatively simple to update the prior and conditional probabilities with data from multiple sources. For example, suppose that four-wheel steering becomes a feature that is important to customers (and a feature that helps the adviser recommend a truck). Suppose further that some truck brands begin offering this feature for the 2003 model year. We add a question bank on steering to the set of available trucks. Because of the local independence property, we need obtain only incremental data for the new question banks. We need to know how owners of each truck brand will rate their vehicles on the new question bank. For new truck brands, we need to know how owners of the new brands will rate their vehicles on the characteristic values (x<sub>jc</sub>) and how they will answer each question bank, P(r<sub>q</sub>|v<sub>j</sub>). We obtained the data from the periodic AIO surveys and from other sources, such as one-time surveys and judgment. In essence, the virtual adviser (and listening in) free rides on surveys undertaken by the manufacturer for other purposes. This adaptability is a key feature that is necessary for practical application and represents a conceptual difference between the Bayesian virtual adviser and TI. The former uses Bayesian methods to incorporate new data from multiple sources, whereas the latter relies on maximum likelihood estimates obtained in a calibration survey. Each method is matched to its application domain. However, further research might combine these relative strengths into an improved methodology.
The next stages of listening in identify when opportunities exist and identify the combinations of customer needs that are not satisfied by existing vehicles.
Trigger Mechanism to Identify When Opportunities Exist
For many customers, an existing vehicle will fulfill their needs, and the updated recommendation probabilities will evolve smoothly as in Figure 2. Existing vehicles satisfy the needs combinations these customers desire. However, for some customers, their answers to question banks reveal inconsistencies. For example, suppose that ( 1) the customer has already answered constant-sum importance question banks, which indicate that reliability and low price are important (price 30 points, performance 10 points, fuel economy 20 points, reliability 30 points, and safety 10 points), and ( 2) the customer's subsequent answers suggest an interest in a small truck with a four-cylinder engine, twowheel drive, and automatic transmission. Through the first four question banks, the Mazda B2300 fits these preferences best (see Figure 3, first four bars from top). Given these answers, the virtual adviser decides that further information on towing and hauling will clarify recommendations. The adviser expects that the customer will want to haul or tow relatively light loads, such as small garden equipment or a Jet Ski. Knowing the exact towing and hauling needs will help the adviser decide among several otherwise comparable light-duty trucks.
However, suppose that the customer says that he or she plans to use the truck to haul heavy materials and to tow a large motorboat (weighing 6500 pounds). No existing lightduty truck can tow such heavy loads effectively and safely. In contrast, no truck that can tow such heavy loads can fill the customer's requirements as expressed in previous question banks. If enough customers desire these combinations of features, this may be an opportunity worth investigating: a light-duty truck that can occasionally haul heavy materials or tow heavy loads. Note that the goal is to define the opportunity by needs (light duty, haul heavy materials) rather than features (V8 engine). In this way, new vehicles can satisfy the newly identified combinations of customer needs with features that may or may not be available in existing vehicles. The intuition in this example is that the question bank on towing and hauling revealed something about the customer's underlying needs. This new information suggests that the customer is not satisfied with the needs combinations provided by existing trucks; the virtual adviser will need to revise its best-truck recommendation probability downward. This drop in the maximum recommendation probability becomes a trigger for further investigation. We illustrate this trigger mechanism with an arrow in the dialogue in Figure 3. The fifth question bank, which included questions about towing and hauling, causes the most preferred vehicle to change from the Mazda to a Ford Ranger (a slightly larger, more powerful compact truck). Utility drops because this more powerful compact truck is an insufficient compromise to meet both the towing and hauling requirements and the requirements expressed in the first four question banks (it has a six-cylinder engine and is more expensive). A fullsized truck, such as the Chevrolet Silverado 1500, can fulfill the towing and hauling requirements, but the adviser does not recommend the Silverado because it has poor ratings on the other desired features. After further question banks, the recommendation probabilities in Figure 3 again increase because the Ford Ranger fulfills the additional requirements.
The intuitive idea in Figure 3 has appeal, but before we incorporate the trigger mechanism, we must investigate it further. For example, the posterior probability might drop because there is error in the customer's response. If the trigger mechanism is too sensitive, it might identify many false need-conflicts, and the true need-conflicts might be lost in the noise. In contrast, if it is not sensitive enough, the trigger mechanism might miss opportunities. We show subsequently, through simulation, how to select a sensitivity level for the trigger mechanism such that segments of customers desiring known combinations of needs are recovered with sufficient precision. In the simulations, we begin with real data for the conditional probabilities and create known segments. We then add error and examine how various sensitivity levels balance false positives and false negatives. The simulations demonstrate that calibration is feasible and that the performance of the listening-in mechanism is reasonably robust in the face of response errors. It is also reasonably robust with respect to the sensitivity levels chosen for the trigger mechanism. Having thus established a reasonable degree of internal validity, we are more confident in applying the methodology to real data. The other issue is theoretical. The intuition assumes that a drop in posterior probability identifies a conflict in the desired customer needs that are fulfilled by existing vehicles. If a question bank affected only the vehicle that was recommended before the qth question bank and if that same vehicle were recommended after the qth question bank, then most random utility models would suggest that a probability drop was an indicator of an underlying utility drop. For example, both the logit and the probit models have this property. However, each question bank can affect the probabilities of all 148 vehicles and change the identity of the recommended vehicle on the basis of the qth question bank. We demonstrate formally in the Appendix that the intuition still holds. If the qth question bank does not change the identity of the recommended vehicle, a drop in posterior probability is a necessary and sufficient condition indicating that the recommended vehicle has characteristics in conflict with the customer's preferences. The more complex issue is when the qth question bank changes the identity of the recommended vehicle. We show formally that if the recommended vehicle changes and the posterior probability drops, it must be the case that a truck with mixed characteristics would have higher utility than the truck recommended either before or after the qth question bank. We also show that the mixed characteristic truck that is better for the customer is not an existing truck.
Analyses to Identify Which Combinations of Customer Needs Are Not Satisfied
When a probability drop identifies a potential conflict, we seek further information to identify which customer needs are in conflict. We consider a null hypothesis that the existing trucks satisfy (almost all) customer-needs combinations. This hypothesis implies that if two truck characteristics are positively correlated among existing trucks, we expect them to be positively correlated among customers' preferences, as revealed by their answers to the questions banks. For example, on the basis of existing trucks, we expect that there is a positive correlation across vehicles of the probabilities that a customer will ( 1) use the truck for towing heavy loads and ( 2) prefer a rugged body style for that vehicle. In addition, we expect that there is a negative correlation of the probabilities that a customer will ( 1) use the truck for towing heavy loads and ( 2) prefer a compact body style. Because no existing truck satisfies these needs simultaneously, recommendation probabilities will drop when the customer requests a compact truck that can tow heavy loads (see the Appendix).
This means that we can identify the needs combinations that caused the drop by examining negative correlations among expected answers to the question banks for the questions answered by customers who experienced a probability drop. The probability drop challenges the null hypothesis and its implications. That is, customers who experience a probability drop want some combinations of customer needs that are negatively correlated in the existing market. To find the desired combinations from the set of all negatively correlated combinations, we limit our search to the need combinations evaluated by customers with probability drops.
Formally, ρ<sub>rqrp</sub> is the correlation across vehicles of the conditional probabilities of a customer answering r<sub>q</sub> to question bank q and answering r<sub>p</sub> to question bank p,( n6) and P is the matrix of these correlations (here P is a capital ρ). Whenever a probability drop implies a potential opportunity, the listening-in algorithm examines all correlations corresponding to that customer's answers to the first q question banks (R<sub>q - 1</sub> ∪ r<sub>q</sub>) and flags the ones that are highly negative (less than -.30 in our application). Such negative correlations indicate why the (triggered) customer's desired benefits (needs) are not fulfilled by existing trucks (subject to statistical confidence). The level of the flagging mechanism is set with simulation.
The opportunity trigger identifies the customers who have combinations of needs that are not satisfied, and it flags specific entries in the P matrix to identify combinations of needs that represent new opportunities. The combinations of needs are a working hypothesis for a new opportunity. However, before the automotive firm can act on the working hypothesis, it needs further information about the potential opportunity, because the number of questions the virtual adviser uses is, by necessity, a compromise between efficient recommendation (fewer questions) and probes for new needs combinations (more questions). To understand and explore the opportunity more completely, listening in complements the virtual adviser and the trigger mechanism.
The virtual engineer (VE) concentrates its questions to obtain relevant, more-detailed information about combinations of customer needs. The VE asks relatively few questions of each targeted customer (six screens in our application), but across many customers, its questions span the needs space. In our application, the VE explores an additional 79 features beyond the 36 features explored in the virtual adviser. As is the virtual adviser, the VE is designed to be flexible; its questions are updated continuously without the need to recommission large-scale AIO surveys.
The concept of a VE is simple; its implementation difficult. To be useful, the VE must ask the customer questions that inform the engineering design decisions that are necessary to design a truck to meet the customers' newly identified (potential) combination of needs. To be credible to the customer, the VE must ask questions in a nontechnical manner that pertains to how the customer uses the truck. Naturally, the VE evolves through application, but we describe here the process by which the initial VE questions are created.
An engineering design team from a major automotive manufacturer considered the basic engineering problem imposed by potential conflicting needs. The team then generated the questions that it would need answered to clarify the opportunity and to decide among basic solutions to conflicts. The engineering team members formulated the questions that they would ask the customer if they were participating in the dialogue between the adviser and customer. For example, if a customer wants a compact truck that can tow a large boat, the engineering team would ask about the type of boat (e.g., modest sailboat, large motorboat, multiple Jet Skis) and the weight of the boat that the customer plans to tow. The engineering team would also ask the customer why he or she wants a compact truck (e.g., low price, tight parking, high maneuverability, fuel economy). All engineering questions are then rephrased into "customer language."
In addition to the questions identified by the engineering team, the VE includes open-ended dialogues that enable the customer to elaborate further the reasons underlying the previously unidentified combinations of needs. Figure 4 illustrates a sample dialogue in which the VE introduces himself, asks about a conflict, gathers quantitative data, and asks for open-ended comments. In this example, the conflict is between a full-sized truck and a six-cylinder engine.
We supplement the VE with a design palette (DP) that covers 14 features. The DP's perspective is the customer's own solutions (von Hippel 1986). The DP is similar to innovation toolkits, configurators, and choice boards that enable customers to mix and match features (Dahan and Hauser 2002; Hauser and Toubia 2003; Liechty, Ramaswamy, and Cohen 2001; von Hippel 2001).
The DP is illustrated in Figure 5. The customer ( 1) receives instructions, ( 2) changes the size of the truck, and ( 3) changes the color. For brevity, we do not show the many intermediate steps, some of which include new state-of-the-art truck features, such as four-wheel steering and extrawide frames. However, changes are not free for the customer. There are sophisticated engineering and cost models underlying the DP. For example, if the customer changes the size of the truck, the price, fuel economy, and towing/payload capacity change accordingly. After completing the redesign, the customer is given the opportunity to indicate whether and by how much he or she prefers the new design. (The customer may not prefer the new design because of accumulated sticker shock or because of a holistic judgment of the final truck.) In the empirical application that we describe subsequently, 73% of the respondents who completed the exercise indicated that they would purchase their custom-designed truck were it available.( n7)
In general, DPs are evolving rapidly. For example, there is a system that enables the customer to adjust the length of the hood of a car or truck while the software automatically ensures the integrity of other design elements, such as the windshield angle and window shape. The customer simply clicks on the hood and drags it forward or clicks on the front bumper and pushes it back. Using this advanced DP, the customer easily creates a "Euro" sports design (short front overhang, high truck deck, low overall height) that is pleasing to the eye and incorporates many design heuristics. In contrast, by lengthening the front overhang and the hood the customer creates a classic look with a long sloping back to the truck. The software is sufficiently advanced that the customer can then rotate the model in all directions for a full three-dimensional view.
Together, the virtual adviser, VE, and DP explore 129 customer needs (10<sub>52</sub> combinations, many of which are multilevel). The detailed data help the firm understand the customer-need conflicts that led some customers to experience a probability drop. The philosophy behind this listening-in search differs from conjoint analysis. Conjoint analysis collects data on the importance of customer needs and searches to find needs combinations that satisfy a minimum share of the market profitably. In contrast, listening in monitors needs requests to identify when customers request combinations of needs that are not fulfilled by existing trucks. After the opportunities are identified, they can be explored further with conjoint analysis.
The next stage of listening in groups customers according to their unmet combinations of needs as revealed through flagging components of the P matrix (supplemented with the VE and DP for interpretation). This estimate of market potential is a rough indicator, but it is sufficient to identify potential opportunities for the fuzzy front end of an iterative product development process. The firm evaluates the opportunities further with targeted qualitative and quantitative research.
Suppose that A<sub>i</sub> represents customer i's answers to the question banks. For each A<sub>i</sub>, we identify a subset, P<sub>i</sub>, of the P matrix that represents strongly negative correlations. By clustering triggered respondents on P<sub>i</sub>, we identify groups of customers with similar combinations of desired needs that are not fulfilled (on average) by existing trucks. Subject to the caveat of self-selected customers, the size of the cluster as a fraction of the initial sample is a rough indicator of the size of the segment that desires the identified combinations of needs.( n8) To simulate a new truck design, we define a concept truck by the needs it fulfills as reflected by customers' answers to the question banks, P(r<sub>q</sub>|v<sub>j</sub>). These data are sufficient to calculate revised posterior probabilities for all trucks, including the new-truck concept (Equation 2). Averaging of the revised posterior probabilities over respondents provides a rough estimate of the potential market share for the new concept truck.
If successful, listening in will affect billion-dollar decisions on new truck platforms. Before we can be confident in its application, we must address the following issues: First, we want to know whether listening in can recover opportunities from noisy data. This issue is best addressed with simulation because we can specify known segments of customers who have unmet needs combinations. Second, applications require that the opportunity trigger be calibrated. Here, too, simulation is best to determine the best trigger sensitivity. Relevance and external validity are better addressed with a proof-of-concept application in which we listen in to real customers in a pilot study to determine whether unmet combinations of needs can be identified. We hope that the pilot study at least can identify combinations of needs that were discovered in parallel by other studies (at much greater expense). Recall that truck manufacturers routinely spend tens of millions of dollars annually on market research.
Simulation Methodology
We use the conditional probabilities, P(r<sub>q</sub>|v<sub>j</sub>), and P-matrix correlations based on the 100,000-respondent AIO study and supplemental managerial judgment. On the basis of the proof-of-concept study we describe subsequently, we select three segments of customers whose needs are satisfied by existing trucks (e.g., full-sized trucks that can tow and haul large loads). The three segments provide a baseline from which to test whether the methodology identifies false opportunities. Next, we generate six segments with combinations of needs that are not satisfied by existing trucks. We define their responses to the question banks to be consistent with their desired benefits (needs). We attempt to test whether listening in can recover these segments from noisy data. Because of the multiple stages of listening in, this is far from ensured. In total, we generate nine customer segments of 500 respondents each, for a total of 4500 simulated respondents.
We next add errors to the customers' responses. For the r<sub>q</sub>'s, which are nominal variables, we randomly select E% of the questions to be answered incorrectly. The incorrect answers are distributed among the remaining categories according to a uniform distribution. For the w<sub>c</sub>'s, which are interval-scaled variables (mean = 20), we simulate response error by adding a zero-mean, normally distributed response error such that the standard deviation of the error equals a specified number of points (e). For simplicity, we truncate negative self-explicated importances that, fortunately, occur with low probability. We then apply the listening-in equations to each of the 4500 simulated respondents. For clustering the P matrix, we use a k-means nontree clustering algorithm based on the Euclidean norm defined on the matrix of negative correlations from triggered respondents (respondents by potential conflict pairs; details are available on request).
Internal Validity: Testing Recovery of Unmet Needs Combinations from Noisy Data
As an initial test of internal validity, we add moderate noise where e = 5 points and E = 10%. We use a relatively sensitive opportunity trigger; we record conflict correlations whenever P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) - P(v<sub>1</sub>|R<sub>q - 1</sub>) ≤.00005. We subsequently examine sensitivity to this parameter.
Table 3 suggests that listening in can recover known needs combinations from moderately noisy data. The entries indicate the number of respondents from a true segment (rows) that were assigned to a cluster (columns). We examine Table 3 at the macro and micro levels.
The managerial focus is at the macro level. First, we notice the diagonal nature of the data in Table 3; even with noise in the data, listening in identified all five segments. Second, we examine the unmet combinations of needs that defined each segment. For example, the first known segment was defined by four need conflicts: compact truck/tow large loads, compact truck/haul large loads, four-cylinder engine/ tow large loads, and a four-cylinder engine/haul large loads. In Cluster 1, the percentages of respondents who had these needs were 95.9%, 82.4%, 77.3%, and 73.3%, respectively. We identified no other need conflict for more than 9.4% of the Cluster 1 respondents. We obtained similar results for the other five known clusters. We identified no false-positive needs combinations at the macro level (Clusters 8 and 9 are redundant with Cluster 6).
At the micro level, we classified 82.7% of the respondents correctly. Most of the misclassifications were respondents who were classified falsely into the null segment because of errors in their responses. The simulation identified 21,096 conflict pairs compared with only 16,500 true conflict pairs: 14% were false negatives, and 36% were false positives. Thus, response errors affect the classification of specific respondents. Fortunately, the macro-level identification of unmet needs combinations appears robust with respect to the micro errors. We now test whether this insight generalizes to other levels of errors (e and E) and other sensitivities of the opportunity trigger.
Setting the Sensitivity of the Opportunity Trigger and Its Relative Robustness
Table 4 repeats the simulations for various trigger sensitivities (t) that vary from extremely sensitive (t =.00000) to extremely insensitive (t =.10000). At both the macro and micro levels, listening in is relatively robust with respect to the trigger level for t ≤.001. For larger sensitivities, performance degrades. For extremely high t, all opportunities are missed. On the basis of Table 4 and simulations with other levels of error, we recommend a sensitive trigger. The exact level is less critical as long as the level is less than.001.
Sensitivity to the Level of Response Errors
We now explore the sensitivity of listening in to response errors in the constant-sum question banks (e) and the nominal question banks (E). We examine performance at both the macro level (percentage of needs combinations identified) and the micro level (percentage of respondents classified correctly). Table 5 suggests that performance is relatively insensitive to errors in the priors (w<sub>c</sub>'s), even for errors that are 50% of the mean response (ten points). For a Bayesian system, we did not find this surprising; the impact of the priors diminishes as more question banks are answered. However, performance is sensitive to errors in the nominal question banks, with clear degradation at a 20% error. Such an error rate would correspond to one of five respondents saying that they want a compact truck when they actually want a large truck. Table 5 indicates that care must be taken in Web design to engage customers with clear questions so that error rates (E) remain at 10% or lower.( n9)
Summary
Together, Tables 3, 4, and 5 suggest that a reasonable level of internal validity exists despite errors in both the prior preferences and the responses to the question banks. As long as the trigger level is relatively sensitive (≤.001) and the nominal error is moderate (le;10%), listening in can identify known segments of customers who desire combinations of needs that existing trucks do not meet. Recovery is not perfect when there are response errors, but this level of recovery should be sufficient for the fuzzy front end of product development, especially when final managerial decisions are refined with subsequent qualitative and quantitative data.
Before bringing online listening in to a situation in which more than 350,000 customers are tracked annually, we believed it was important to test the methodology in a pilot test with real customers. In August 2001, an automotive manufacturer sponsored a study in which 1092 pickup-truck customers were recruited from the Harris Interactive Panel and given a $20 incentive to participate in the test.( n10) On average, each customer spent 45 minutes with the virtual adviser, DP, and VE (when triggered). Most customers found the experience worthwhile. Customers trusted the virtual adviser by an eight-to-one margin over dealers and would be more likely to purchase a vehicle recommended by the virtual adviser by a four-to-one margin over a vehicle recommended by a dealer. For the DP, 78% of participants found using it an enjoyable experience, and 82% believed it was a serious exercise. When the VE was triggered, 88% of participants found the questions easy to answer, and 77% believed that the VE related well to their needs. Notably, 56% of the participants reported that they would pay for the advice provided by the virtual adviser if it were included in the price of the pickup truck that they purchased as a result of using the adviser.
With a sensitive trigger, the most common pairwise conflicts were a maneuverable full-sized truck (38%), a compact truck that could tow and haul heavy materials (14%), and a full-sized truck with a six-cylinder engine (7%). Two segments of customers were identified that expressed unmet combinations of needs. Segment 1 requested large trucks but indicated a desire for maneuverability. Segment 1 consisted of two groups: customers who wanted a top-of-the-line truck and customers who wanted a standard full-sized pickup truck. Segment 2 requested a compact truck that could tow and haul heavy loads. Table 6 provides more detail on Segment 1. From the VE, we learned that respondents use full-sized trucks for city driving. Large trucks fulfill critical needs for large passenger capacity and large payloads. However, the respondents also desired maneuverability: combinations of benefits (needs) that are not available with existing trucks.
The DP explored Segment 1's desires further. The features that they changed most often were truck height (6' to 7'), truck width (6' to 7'), and steering (two-wheel to four-wheel steering). This suggests that these customers desire an even larger truck but that they would be interested in fourwheel steering to gain maneuverability. Using the methods described previously for market sizing, we estimated the potential market share of a full-sized truck with four-wheel steering. On the basis of cost models, we calculated that the extra features would increase the manufacturer's suggested retail price by $3,000. For this concept truck, the listening in equations estimate a market-share increase for the manufacturer of 3%-4% (we coded the exact value for confidentiality).( n11) Such a $2.4 billion-$3.2 billion annual opportunity is worth further investigation. In addition, a compact truck with heavy-duty hauling and towing is estimated to be a $1 billion-$2 billion opportunity (values are coded). Technically, the benefit (needs) combinations are feasible with the use of a small truck platform that has a strong frame, transmission, and engine.
After we completed our study, we learned that an automotive manufacturer was in the process of introducing fourwheel steering to improve the maneuverability of its top-of-the-line pickup truck, which was previously unknown to us. This combination of needs had been identified with traditional methods (Table 1) but at a significantly greater cost. This truck is now selling well. We plan to monitor the sales of this truck to determine whether its sales are in the rough range predicted by the market-sizing equations. We found no indication that traditional methods identified the need for a basic truck with four-wheel steering. We plan to monitor whether traditional methods confirm such a combination of needs.
In this article, we explore a methodology to listen in on customer dialogues with virtual advisers to identify combinations of customer needs that are not fulfilled by existing trucks. Monte Carlo analyses suggest that listening in is internally valid and relatively robust with respect to response errors and trigger sensitivity. A proof-of-concept demonstration suggests that unmet needs combinations for real respondents can be identified.
As with all methodologies, listening in will benefit from continuous improvement. Each stage can be improved; better methods to identify priors, more efficient look-ahead algorithms, improved calibration of the trigger mechanism, and better indicators of conflicting needs all can benefit from further research. The dialogues, the user interfaces, and the presentation of stimuli are all areas of potential improvement. For example, work is now underway to put more stretch into the DP and to give the virtual adviser and the VE personalities based on "talking heads." The various stages of listening in are designed to be modular. Further research might explore other advisers, triggering mechanisms, means to identify and size segments (e.g., latent structure analysis), and applications (e.g., telecommunications, consumer electronics, travel services, financial services).
This research was supported by the Sloan School of Management, the eBusiness Center, the Center for Innovation in Product Development at MIT, and the General Motors Corporation. The authors gratefully acknowledge the contributions of their industrial collaborators, research assistants, and faculty colleagues: Vince Barabba, Iakov Bart, Ahmed Benabadji, Rupa Bhagwat, Brian Bower, Brian Chan, Hann-Ching Chao, Mitul Chatterjee, Shyn-Ren Chen, Thomas Cheng, Stanley Cheung, Frank Days, Ken Dabrowski, Benson Fu, Salman Khan, Christopher Mann, Rami Musa, Joseph Kim, Ken Lynch, Bill Qualls, James Ryan, Bilal Shirazi, Jonathon Shoemaker, Fareena Sultan, Andy Tian, Xingheng Wang, and Irene Wilson. This article has benefited from presentations at the Marketing Science conferences in Wiesbaden, Germany, and Edmonton, Canada; the Marketing Science conference "New Methods for New Products"; the MIT Marketing Workshop; the New England Marketing Conference; and the Stanford Marketing Workshop. Related information is available at http://mitsloan.mit.edu/vc and http://ebusiness.mit.edu. The authors provided external funds for the use of color in the figures.
(n1) Table 1 also includes tailored interviewing, an approach that shows promise for automotive applications, especially for the segmentation gearbox used in the virtual adviser.
(n2) The industry term "gearbox" is an analogy. Just as the gearbox in a car matches engine speed to wheel speed, the segmentation questions match the manufacturer's vehicles to the customer.
(n3) The global set of question banks from which the algorithm selects is drawn from cluster analyses of the ongoing AIO surveys, supplemented with managerial judgment. The set of question banks evolves on the basis of ongoing market intelligence. These methods are state of the art but standard marketing research practice. They are not the focus of this article.
(n4) In most equations, we suppress the individual customer subscript, i, for simplicity.
(n5) For applications in marketing of reward functions based on information theory, see Hauser (1978) and Herniter (1973). For applications in psychology, see Prelec (2001).
(n6) Such correlations across vehicles are consistent with local independence, which assumes response independence conditioned on a given vehicle. Local independence enables customers to be heterogeneous across vehicles in their answers to the question banks.
(n7) Becuase of self-preference learning, memory accessibility, and context effects, the preference for the self-designed truck may be inflated (Simmons, Bickart, and Lynch 1993; Tourangeau, Rips, and Rasinski 2000). This does not diminish the value of the DP as a means to clarify opportunities.
(n8) There is self-selection because customers choose to initiate dialogues with virtual advisers. Nonetheless, a large fraction of self-selected customers might be an important opportunity. We expect less self-selection as more truck customers use the Web to search for information.
(n9) There appears to be a slight anomaly in Table 5. For E = 20%, classification and identification appear to increase slightly with errors in the self-explicated importance. This happens because the combination of errors pushes more respondents to the no-conflict clusters. As a result, a few more no-conflict respondents are classified correctly, making it easier to achieve a majority in the remaining clusters. Neither difference is significant at the.05 level with a two-tailed t-test.
(n10) We based this initial test on a stratified random sample of the panel. For this test, all customers were given the opportunity to use the DP.
(n11) We obtain rough forecasts by adding a full-sized maneuverable pickup truck to the choice sets of the needs-segment customers. We obtain P(r<sub>q</sub>|v<sub>j</sub>) for the new vehicle by assuming a profile similar to an existing vehicle except for the critical responses on the size and maneuverability questions, which we changed to be consistent with the vehicle being both full-sized and maneuverable. The iterative use of Equation 1 provides the estimates.
Legend for Chart:
B - Data Source
C - Incremental Cost per Study
D - Number of Features or Stated Needs per Study
E - Number of Vehicles per Study
F - Number of Feature Combinations
G - Includes In-Depth Probes
A
B C D
E F G
Qualitative and ethnographic
interviews(a)
5-10 groups of 5-10 $40,000-$50,000 50-100
customers per segment
5-10 Open-ended Yes
Tailored Interviews(b)
Segmentation
800 personal interviews $80,000 73 scales
7 segments -- --
Interest or intent
512 (calibration) and 235 $10,000-$15,000 12-15 items
mail questionnaires (Phase 2)
Single scale -- --
AIO studies(a)
100,000 mailed $500,000 114
questionnaires
150 -- --
Conjoint analyses(a)
300 online or in-person $50,000-$100,000 10-20
interviews
5-10 10<sup>6</sup> --
Truck clinics(a)
300 central-facility $500,000 40-50
personal interviews
10-20 -- Yes
Listening in(c)
Bayesian adviser
Track online $10,000-$20,000 36
148 -- --
Opportunity trigger
Track online Included --
-- 10<sup>15</sup> --
Virtual engineer
Invite online Included 79
-- 10<sup>31</sup> Yes
Design palette
Invite online Included 14
-- 10<sup>6</sup> --
Clustering
Track online Included --
-- -- --
(a) Data are as cited in article and/or typical for the
automotive industry. We rounded some estimates for
confidentiality.
(b) Data are from the work of Kamakura and Wedel (1995) and
Singh, Howell, and Rhoads (1990). Automotive cost estimates
are based on sample sizes in Journal of Marketing Research
articles. Cost per respondent is typical for the industry,
as is estimated by auto industry executives and consultants.
(c) Experience is based on the pilot study. The numbers of
vehicles, needs, and combinations may increase in subsequent
applications. Legend for Chart:
B - Conditional Probability P(r<sub>q</sub>/v<sub>j</sub>)(%)
Chevy Avalanche 2WD
C - Conditional Probability P(r<sub>q</sub>/v<sub>j</sub>)(%)
Chewy Silverado 2500 2WD
D - Conditional Probability P(r<sub>q</sub>/v<sub>j</sub>)(%)
GMC Sonoma 4WD Crew Cab
E - Conditional Probability P(r<sub>q</sub>/v<sub>j</sub>)(%)
(1.48 Vehicles)
F - Conditional Probability P(r<sub>q</sub>/v<sub>j</sub>)(%)
Dodge Ram Club 4WD
A B C D E F
1 5% 25% 15% ... 10%
2 15 25 5 ... 15
3 25 25 15 ... 25
4 25 15 25 ... 25
5-6 30 10 25 ... 25
Notes: Data are disguised. 2WD = two-wheel drive;
4WD = four-wheel drive. Legend for Chart:
A - Needs Combinations
B - Number of Respondents Classified to Each Cluster 1
C - Number of Respondents Classified to Each Cluster 2
D - Number of Respondents Classified to Each Cluster 3
E - Number of Respondents Classified to Each Cluster 4
F - Number of Respondents Classified to Each Cluster 5
G - Number of Respondents Classified to Each Cluster 6
H - Number of Respondents Classified to Each Cluster 7
I - Number of Respondents Classified to Each Cluster 8
J - Number of Respondents Classified to Each Cluster 9
K - Number of Respondents Classified to Each Cluster Total
A B C D E
F G H I
J K
Compact truck, large loads 418(x) 0 0 1
0 0 81 0
0 500
Sporty full-sized, short bed 1 422(x) 0 0
0 0 77 0
0 500
Compact truck, diesel 0 0 401(x) 0
0 0 99 0
0 500
Full-sized, extrashort bed 1 0 0 346(x)
0 0 153 0
0 500
Compact truck, ten cylinders 3 27 0 0
336(x) 0 134 0
0 500
Full-sized, maneuverable 0 2 0 0
0 346(x) 92 43
17 500
Null segment 43 0 2 1
0 0 1454(x) 0
0 1500
Notes: Each known segment desired multiple needs combinations.
Here, we list examples for each segment. The largest number in
each row is in (x) boldface. Legend for Chart:
A - Trigger Level
B - Percentage of Respondents Classified Correctly
C - Percentage of Opportunities Identified Correctly
D - Percentage of Needs-Combinations Segments Identified
E - False Opportunities Identified
A B C D E
t = .00000 82.73 100 100 0
t = .00005 82.73 100 100 0
t = .00010 82.69 100 100 0
t = .00100 82.69 100 100 0
t = .01000 56.69 63.6 63.4 0
t = .10000 33.33 0 0 0 Legend for Chart:
A - Response Errors (Updating)
B - Errors in the Self-Explicated Importance(Priors)
e = 0 Points
C - Errors in the Self-Explicated Importance(Priors)
e = 5 Points
D - Errors in the Self-Explicated Importance(Priors)
e = 10 Points
A B C D
Macro Level: Percentage of Unmet Needs Combinations
Identified Correctly
E = 0% 100% 100% 100%
E = 10% 100% 100% 100%
E = 20% 93.9% 75.8% 81.8%
Micro Level: Percentage of Respondents Classified
Correctly
E = 0% 100% 99.9% 99.9%
E = 10% 82.9% 82.7% 81.8%
E = 20% 61.6% 55.0% 56.8% Why I Need A Maneuverable Pickup Truck
Frequent City Driving 66%
Tight Parking 58%
I Make Many U-Turns 26%
Too Many Traffic Jams 28%
Why I Need A Full-Sized Pickup Truck
Large Passenger Capacity 73%
Large Payloads 50%
Full-Sized Style 39%
PHOTO (COLOR): FIGURE 1 Example Question Banks Asked by Bayesian Virtual Adviser
Recommendation/Questions
Banks/Responses
Mazda B2300, prior (points) .0533
Mazda B2300, engine size (four cylinder) .0735
Mazda B2300, transmission (automotive, 2WD) .0861
Mazda B2300, size (compact) .1105
Mazda B2300, towing/hauling (no) .1123
Toyota Tacoma, construction plowing (no) .1200
Toyota Tacoma, brand (all) .1243
Toyota Tacoma, bed length (short) .1328
GMC Sierra 1500, tallest person (6'-6.5') .1376
GMC Sierra 1500, passengers (two) .1440
GMC Sierra 1500, maneuverability (important) .1440
GMC Sierra 1500, big, quiet (not important) .1458
GMC Sierra 1500, styling (sporty) .1467
GMC Sierra 1500, price ($20K-$22K) .1467
Notes: Abbreviated consumer responses to question bank
are in parentheses. 2WD = two-wheel drive.
Recommendation/Question
Banks/Responses
Mazda B2300, prior (points) .0533
Mazda B2300, engine size (four cylinder) .0735
Mazda B2300, transmission (automotive, 2WD) .0861
Mazda B2300, size (compact) .1105
Ford Ranger, towing/hauling (no) .1056
Ford Ranger, construction plowing (no) .1200
Ford Ranger, brand (all) .1243
Ford Ranger, bed length (short) .1328
Ford Ranger, tallest person (<6') .1356
Ford Ranger, passengers (two) .1401
Ford Ranger, maneuverability (important) .1428
Ford Ranger, big, quiet (neutral) .1459
Ford Ranger, styling (conventional) .1478
Ford Ranger price ($20K-$22K) .1498
Notes: Abbreviated consumer responses to question bank
are in parentheses. 2WD = two-wheel drive.
PHOTO (COLOR): FIGURE 4 Virtual Engineer: A. Introductory Screen B: Example Dialogue C: Specific Questions to Elaborate D: Open-Ended Question
PHOTO (COLOR): FIGURE 5 Design Palette: A: Introductory Screen B: Customer Selects Size C: Customer Selects Color D: Customer Evaluates His or Her Design
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The listening-in methodology uses a trigger mechanism to invoke the VE and DP. We argue intuitively in the text that such a drop in the recommendation probability (Equation 1) is an indication that existing trucks do not fulfill desired combinations of customer needs. Here, we demonstrate with a formal analytical model that such a drop identifies opportunities. The issue is not trivial because a question bank, q, potentially affects the updated utilities of each and every product in the market, not just the recommended product. The formal analysis identifies the net effects.
Although our application uses complex question banks for 148 trucks, we can illustrate the basic principles with N = 3 and a dichotomous question bank. (Our propositions generalize to analogs for larger N and for polychotomous question banks, but the notation is cumbersome.) Following the text, j indexes the vehicles. Without loss of generality, v<sub>1</sub> is the recommended product after question bank q - 1. In addition, x<sub>j</sub> represents customer benefits (needs) that are not affected by question bank q, and y<sub>j</sub> represents customer benefits (needs) that are affected by question bank q. In this formulation, we treat price as a characteristic, and it can be in either x<sub>j</sub> or y<sub>j</sub> (for motivation, see Hauser and Urban 1996). Following Blackorby, Primont, and Russell (1975), we model preferences using a utility tree such that u(x<sub>j</sub>, y<sub>j</sub>) = u<sub>x</sub>(x<sub>j</sub>) + u<sub>y</sub>(y<sub>j</sub>) + ε where epsilon; is a Gumbel-distributed error term that represents the uncertainty in utility due to question banks that have not yet been asked (or may never need to be asked). For simplicity, we assume that trucks with y<sub>j</sub> = x<sub>good</sub> experience an increase in utility, and trucks with y<sub>j</sub> = y<sub>bad</sub> experience a decrease in utility. (The dichotomous question bank reveals which customer benefits are desired.) We let v<sub>2</sub> be a surrogate for products with desirable characteristics and v<sub>3</sub> be a surrogate for products with undesirable characteristics (as revealed by question bank q). Following McFadden (1974), we write the recommendation probabilities in more fundamental utility-theory terms (where V is the total number of vehicles):
(A1) [Multiple line equation(s) cannot be represented in ASCII text]
After question bank q, two situations can occur: The recommended truck remains v<sub>1</sub> or it becomes v<sub>2</sub>. It cannot become v<sub>3</sub>, because even if y<sub>1</sub> = y<sub>bad</sub>, v<sub>1</sub> would still be preferred over v<sub>3</sub>. The following propositions address the two situations. Together, they indicate that whenever the recommendation probability drops, an opportunity exists for a new higher-utility truck with mixed characteristics.
P<sub>1</sub>: If the recommended truck after question bank q is the same truck as that recommended after question bank q - 1, then v<sub>1</sub> has undesirable characteristics (y<sub>1</sub> = y<sub>bad</sub>) if and only if P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) decreases. If the probability decreases, a new truck with mixed characteristics has higher utility than does the recommended truck. That new truck is not currently available in the marketplace.
P<sub>2</sub>: If the recommended truck after question bank q is different from the truck recommended after question bank q - 1 and if the recommendation probability decreases, then v<sub>1</sub> has undesirable characteristics (y<sub>1</sub> = y<sub>bad</sub>). A new truck with mixed characteristics has higher utility than both the recommended truck after q - 1 question banks and the recommended truck after q question banks. That new truck is not currently available in the marketplace.
Proofs
Straightforward algebra establishes that P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) - P(v<sub>1</sub>|R<sub>q - 1</sub>) is proportional to (Multiple lines cannot be converted in ASCII text) ≥ 0 if = y<sub>1</sub> = y<sub>good</sub> and that (Multiple lines cannot be converted in ASCII text) ≤ 0 if = y<sub>1</sub> = y<sub>bad</sub>. Algebra also establishes that the proportionality (denominator) is positive. This establishes the first statement in P<sub>1</sub> and implies that y<sub>1</sub> = y<sub>bad</sub>if the probability drops. Because u<sub>1</sub>(x<sub>1</sub>) + u<sub>y</sub>(y<sub>good</sub>) > u<sub>1</sub>(x<sub>1</sub> + u<sub>y</sub>(y<sub>bad</sub>), a new product with x<sub>1</sub> and y<sub>good</sub> has higher utility. If the recommended truck changes after question bank q, then P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) < P(v<sub>2</sub>|r<sub>q</sub>, R<sub>q - 1</sub>), and because the recommendation probability decreases, we have P(v<sub>2</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) < P(v<sub>1</sub>|R<sub>q - 1</sub>). Thus, P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) < P(v<sub>1</sub>|R<sub>q - 1</sub>), and by P<sub>1</sub>, we have y<sub>1</sub> = y<sub>bad</sub>. This establishes the first result in P<sub>2</sub>. Because v<sub>1</sub> was recommended before question bank q, we have u<sub>x</sub>(x<sub>1</sub>) > u<sub>x</sub>(x<sub>2</sub>); by supposition, we have u<sub>y</sub>(y<sub>good</sub>) - u<sub>y</sub>(y<sub>bad</sub>) > 0. Thus, a product with the features x<sub>1</sub> and y<sub>2</sub> will have higher utility than either v<sub>1</sub> or v<sub>2</sub>. This establishes the second result in P<sub>2</sub>. In both propositions, we know that the new truck does not currently exist, because if it were available, it would have higher utility and thus would have been recommended.
Generalizations
If there are n<sub>2</sub> trucks similar to v<sub>2</sub> and n<sub>3</sub> trucks similar to v<sub>3</sub>, the analogs to P<sub>1</sub> and P<sub>2</sub> are readily proved. The numbers n<sub>2</sub> and n<sub>3</sub> enter the equations for P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) - P(v<sub>1</sub>|R<sub>q - 1</sub>), but the basic proofs remain intact. If there are many trucks with y<sub>good</sub> or y<sub>bad</sub> but different x<sub>j</sub>, the expressions for P(v<sub>1</sub>|r<sub>q</sub>, R<sub>q - 1</sub>) - P(v<sub>1</sub>|R<sub>q - 1</sub>) include more terms, but each can be proved to have the correct sign (i.e., increases if y<sub>good</sub> and decreases if y<sub>bad</sub>). With these changes, the remaining portions of the proofs follow as we have showed.
We use Equation 1 to make the proofs transparent. Both propositions can be generalized to other probability models with the appropriate characteristics. We leave the details of these generalizations to readers.
~~~~~~~~
By Glen L. Urban and John R. Hauser
Glen L. Urban is David Austin Professor of Marketing (e-mail: glurban@mit.edu)
John R. Hauser is Kirin Professor of Marketing (e-mail: jhauser@mit.edu), Sloan School of Management, Massachusetts Institute of Technology.
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Record: 87- Logistics Service Quality as a Segment-Customized Process. By: Mentzer, John T.; Flint, Daniel J.; Hult, G. Tomas M. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p82-104. 23p. 6 Diagrams, 7 Charts. DOI: 10.1509/jmkg.65.4.82.18390.
- Database:
- Business Source Complete
Logistics Service Quality as a Segment-Customized Process
Logistics excellence has become a powerful source of competitive differentiation within diverse marketing offerings of world-class firms. Although researchers have suggested that logistics competencies complement marketing efforts, empirical evidence is lacking on what logistics service quality means to customers and whether it has different meanings for separate customer segments. The authors present empirical support for nine related logistics service quality constructs; demonstrate their unidimensionality, validity, and reliability across four customer segments of a large logistics organization; and provide empirical support for a logistics service quality process. Although structural equation modeling offers support for the logistics service quality process across customer segments, the authors find that the relative parameter estimates differ for each segment, which suggests that firms ought to customize their logistics services by customer segments.
Both corporations and researchers are becoming increasingly aware of the strategic role of logistics services in a firm's overall success (Bienstock, Mentzer, and Bird 1997; Bowersox, Mentzer, and Speh 1995; Brensinger and Lambert 1990; Mentzer, Gomes, and Krapfel 1989). Anecdotal evidence from firms such as Dell Computer Corporation, Nabisco, and Federal Express suggest that logistics excellence has a significant impact on revenue and profitability (Mentzer and Williams 2001). Digging deeper, one finds a multibillion-dollar third-party logistics industry dedicated to improving manufacturers' logistics services. Businesses have moved beyond viewing logistics as merely an area for cost improvements to viewing logistics as a key source of competitive advantage within a firm's total market efforts (Novack, Rinehart, and Langley 1994). For example, customer service has been a key focal area of research in the logistics discipline for several years. Stemming from this stream of research, logistics service capabilities can be leveraged to create customer and supplier value through service performance (Novack, Rinehart, and Langley 1994); increase market share (Daugherty, Stank, and Ellinger 1998); enable mass customization (Gooley 1998); create effective customer response-based systems (Closs et al. 1998); positively affect customer satisfaction and, in turn, corporate performance (Dresner and Xu 1995); provide a differentiating competitive advantage (Bowersox, Mentzer, and Speh 1995; Kyj and Kyj 1994; Mentzer and Williams 2001); and segment customers (Gilmour et al. 1994).
The last area, customer segmentation, offers powerful possibilities. If customer segments indeed vary in their logistics desires, it should be possible to customize logistics programs to different customer segments, which would improve both effectiveness and efficiency. If, in contrast, customers view logistics services similarly across segments, and if that view consistently affects outcomes such as customer satisfaction in the same way across segments, suppliers should be able to create logistics services that appear identical across customer segments, enabling them to leverage economies of scale. Therefore, an important research question is, Do different customer segments value different aspects and levels of logistics service quality (LSQ)? Some research suggests that logistics services ought to be customized by market segments (Gilmour et al. 1994; Michigan State University 1995, 1999; Murphy and Daley 1994). However, the research is not yet conclusive, partially because of the conceptualization and operationalization of logistics services. More research is needed to determine if logistics services should be customized by market segment.
Before this research question can be answered, researchers need to know more about what components constitute the overall concept of LSQ from the perspective of the customer. It is essential first to know what LSQ means to customers if researchers expect to examine whether groups of customers place varying degrees of emphasis on specific aspects of this meaning. The purpose of this article is to describe a study in which we examine both of these issues. This study shows that ( 1) LSQ might best be conceptualized as a process of nine interrelated quality constructs, ( 2) these nine distinct constructs are reliable and valid across customer segments, and ( 3) the emphasis placed on each of the constructs differs across some customer segments, which suggests that suppliers should customize their logistics services to the desires of individual customer segments. In subsequent sections, we discuss the importance of resolving the research question, the gap in the general service quality literature in addressing LSQ, theoretical development and hypotheses, the methods used, analyses and results, and the implications of the study.
Logistics excellence has been recognized as an area in which firms can create competitive advantage (Bowersox, Mentzer, and Speh 1995; Kyj and Kyj 1994; Mentzer and Williams 2001; Morash, Droge, and Vickery 1996), in part because of its visible service impact on customers (Bienstock, Mentzer, and Bird 1997; Pisharodi and Langley 1990; Sharma, Grewal, and Levy 1995). To successfully leverage logistics excellence as a competitive advantage to customers, logisticians must coordinate with marketing departments (Kahn 1996; Kahn and Mentzer 1996; Mentzer and Williams 2001; Murphy and Poist 1996; Williams et al. 1997). The quality of logistics service performance is a key marketing component that helps create customer satisfaction (Bienstock, Mentzer, and Bird 1997; Mentzer, Gomes, and Krapfel 1989) and has been recognized as such for some time (Perrault and Russ 1974).
There are many definitions and descriptions of how logistics creates customer satisfaction. The most traditional are based on the creation of time and place utility (Perreault and Russ 1974). The so-called seven Rs describe the attributes of the company's product/service offering that lead to utility creation through logistics service; that is, part of a product's marketing offering is the company's ability to deliver the right amount of the right product at the right place at the right time in the right condition at the right price with the right information (Coyle, Bardi, and Langley 1992; Shapiro and Heskett 1985; Stock and Lambert 1987). This conceptualization implies that part of the value of a product is created by logistics service.
As the business environment has changed, the operations-based definitions of logistics service have evolved. As such, the idea of value has been broadened to include several value-added operational logistics tasks, such as packaging, third-party inventory management, bar coding, and information systems (Ackerman 1989; Mentzer 1993; Mentzer and Firman 1994; Witt 1991). The value-added concept expanded the traditional time and place utilities to include form utility (Ackerman 1991) but was still an operations-based concept. LaLonde and Zinszer (1976) describe customer service as possessing three components: ( 1) an activity to satisfy customers' needs, ( 2) performance measures to ensure customer satisfaction, and ( 3) a philosophy of firmwide commitment. However, these components all focus on the provider firm, not on the customer. Similarly, other research has developed a framework for quantifying the value created by logistics operations that is heavily focused on the service provider (Novack, Langley, and Rinehart 1995). Although this research incorporates internal and external customers, it predominantly involves provider firms--that is, how logistics executives can quantify the value they create for customers. A process is needed to measure customers' perceptions of the value created for them by logistics services, because it is the customers' perspective of service quality that determines their satisfaction level.
Mentzer, Gomes, and Krapfel (1989) argue that two elements exist in service delivery: marketing customer service and physical distribution service (PDS). They recognize the complementary nature of the two elements to satisfy the customer and propose an integrative framework of customer service. This view is shared by others (Rinehart, Cooper, and Wagenheim 1989) and is regarded as an intellectual base for integrating marketing and logistics activities. Here, PDS is composed of three crucial components: availability, timeliness, and quality. We view PDS as a component of LSQ.
An approach to investigate LSQ further is to build on the service quality literature prevalent in marketing. The service quality approach, in general, is an attempt to understand customer satisfaction from the perspective of the differences between customer perceptions and actual customer service on various attributes (Parasuraman, Zeithaml, and Berry 1985). Researchers have begun to examine whether the service quality model can be used to measure logistics service (Brensinger and Lambert 1990). They have modified the original service quality model by developing logistics attributes that fit into the previously customer-defined dimensions and identifying additional gaps that could be applied to the logistics service context (Lambert, Stock, and Sterling 1990). These views of logistics service provide the building blocks to create a customer-based foundation for better definitions and measures of LSQ.
The use of customer-based definitions of LSQ brings physical distribution research, which traditionally has focused on physically observable operational attributes, more in line with marketing, which has devoted attention to understanding such unobservables as customers' perceived value. By recognizing, tapping into, and measuring customer perceptions of LSQ, logistics practitioners and researchers can add to the traditionally measured set of operational service attributes.
Many researchers have tried to replicate empirically the five-dimensional structure (tangibles, responsiveness, empathy, reliability, and assurance) of Parasuraman, Zeithaml, and Berry's (1985) original service quality instrument, SERVQUAL. In developing SERVQUAL, Parasuraman, Zeithaml, and Berry followed a general procedure of qualitative research (interviews and focus groups) to develop the initial scale and then performed quantitative surveys to refine and empirically test the scale. These interviews and surveys included retail consumers of appliance repair or maintenance, retail banking, long-distance telephone service, securities brokers, and credit card services. Additional research has expanded the use of SERVQUAL to include retail consumers of health care, residential utilities, job placement, pest control, dry cleaning, financial services, and fast-food services, and the resultant dimensions have ranged from one to eight (e.g., Babakus and Boller 1992; Babakus and Inhofe 1993; Babakus and Mangold 1992; Babakus, Pedrick, and Inhofe 1993; Brown, Churchill, and Peter 1993; Carmen 1990; Cronin and Taylor 1992; Finn and Lamb 1990; Mishra, Singh, and Wood 1991; Parasuraman, Zeithaml, and Berry 1985, 1988, 1991, 1993, 1994; Spreng and Singh 1993).
Several researchers have argued for the addition of items and/or dimensions to SERVQUAL. For example, from a less sociological and more operational perspective, Crosby (1979) defines quality as conformance to requirements and argues that those requirements should be specifically defined to measure quality. From Crosby's (1979) view and the general total quality management perspective, certain aspects of quality (of services or otherwise) intuitively ought to be incorporated. Along these lines, in applying SERVQUAL to measure perceived quality of retail financial services, Brown, Churchill, and Peter (1993, p. 138) note the "omission of items we a priori thought would be critical to subjects' evaluation of ... quality." Similarly, Brensinger and Lambert (1990, p. 289), applying SERVQUAL to industrial purchasing of motor carrier transportation services, developed a four-factor structure and recommended that further research should supplement SERVQUAL items with "service specific variables" to increase the validity of service quality measurement in an industrial service context.
Bienstock, Mentzer, and Bird (1997) took note of these shortcomings in applying the concept of service quality to an industrial marketing context and suggest a classification scheme based on the work of Lovelock (1983), Grönroos (1984), and Parasuraman, Zeithaml, and Berry (1985). Within this classification scheme, the consumer applications of SERVQUAL are in the context of people receiving intangible actions (services) that are not physically separated from the consumer. Bienstock, Mentzer, and Bird (1997, p. 34) argue that business-to-business logistics services are offered in a context in which people are replaced with "things" and the customer and provider are physically separated. They maintain that the former is appropriate for the SERVQUAL "functional or process dimensions" (p. 33), but the latter logistics service context is composed more of "technical or outcome dimensions" (p. 34). They conclude that an "alternative conceptualization" is necessary for LSQ. As do Parasuraman, Zeithaml, and Berry (1985), Bienstock, Mentzer, and Bird (1997) follow a methodology of a qualitative phase to develop the scale and then perform a quantitative survey to refine and test it. They conceptualize physical distribution service quality (PDSQ) as a second-order construct composed of three first-order dimensions: timeliness, availability, and condition.
We view PDSQ as a component of the broader concept of LSQ. Timeliness, availability, and order condition are critical aspects of the customer's perception of LSQ. However, there are other components as well. In line with traditional service quality research in marketing, logistics services involve people who often take orders and deliver products and procedures for placing orders and handling discrepancies. On the basis of the service quality literature, interactions customers have with these people and procedures should affect their perceptions of overall logistics services.
In conceptualizing PDS, Mentzer, Gomes, and Krapfel (1989) synthesize 26 elements of physical distribution and customer service reported in the logistics literature over more than two decades to arrive at a parsimonious three-dimensional construct composed of availability, timeliness, and quality. This structure was supported by later empirical evidence, with slight reconceptualizations based on additional extensive qualitative research (Bienstock, Mentzer, and Bird 1997). Although the contribution of these studies lies in their parsimonious operationalization of critical aspects of service quality, other aspects that are traditionally mentioned in the literature should be part of a broader concept of LSQ. Specifically, order processing (Byrne and Markham 1991; Langley and Holcomb 1991); quality of contact personnel (Innis and LaLonde 1994); information at order placement (Byrne and Markham 1991; Innis and LaLonde 1994); order accuracy (Byrne and Markham 1991); order completeness, including accuracy, condition, and quality (Byrne and Markham 1991; Sterling and Lambert 1987); and the procedures for handling damaged, inaccurate, or return shipments (i.e., aside from the product condition itself) (Innis and LaLonde 1994; Sterling and Lambert 1987) ought to be incorporated. In short, we found several aspects of customer service that should be combined with PDSQ to conceptualize LSQ. Together with findings of significant situational limitations to the SERVQUAL approach both inside and outside logistics contexts (e.g., Van Dyke, Prybutok, and Kappelman 1999), we thought it best to engage in new qualitative research to complement the aforementioned literature and develop a more comprehensive conceptualization of LSQ.
Following the precedent of the literature, we conducted qualitative research to develop constructs and item pools related to LSQ. For this qualitative exploration, 15 managers within the Defense Logistics Agency (DLA) and 12 DLA customers were interviewed one-on-one to develop preliminary concepts. For this study, DLA was appropriate because its markets are large and diverse and the customers addressed in this study have a choice as to whether they use DLA as a logistics service provider. Following initial depth interviews, 13 focus group sessions were held with key buyers of logistics services for organizations in each DLA customer segment. Each focus group session lasted approximately two hours and was videotaped. Videotapes were combined with extensive notes for content analyses. These focus group sessions addressed the nature of the participant's work with DLA, evaluations of their relationship with DLA, and assessments of critical areas of importance for working with DLA. The qualitative research facilitated the development of a survey designed to measure LSQ. Specifically, the qualitative research revealed that participants representing multiple DLA customer segments were concerned about nine concepts:
- Personnel contact quality,
- Order release quantities,
- Information quality,
- Ordering procedures,
- Order accuracy,
- Order condition,
- Order quality,
- Order discrepancy handling, and
- Timeliness.
Personnel contact quality refers to the customer orientation of the supplier's logistics contact people. Specifically, customers care about whether customer service personnel are knowledgeable, empathize with their situation, and help them resolve their problems (Bitner 1990; Bitner, Booms, and Mohr 1994; Bitner, Booms, and Tetreault 1990; DeCarlo and Leigh 1996; Grönroos 1982; Hartline and Ferrell 1996; Parasuraman, Zeithaml, and Berry 1985). Parasuraman, Zeithaml, and Berry (1985) argue that in most service encounters, quality perceptions are formed during the service delivery. Similarly, Surprenant and Solomon (1987) suggest that service quality perceptions are tied more to the service process, which involves personnel contact, than to the resulting service outcome. As such, personnel contact quality is an important aspect of the employee-customer interface (Hartline and Ferrell 1996; Hartline, Maxham, and McKee 2000).
Order release quantities are related to the concept of product availability. On the basis of several criteria, DLA can release certain order sizes. The organization can challenge customers' requests to ascertain the need behind their volume requests. Customers should be the most satisfied when they are able to obtain the quantities they desire. The importance of product availability has long been realized as a key component of logistics excellence (Mentzer, Gomes, and Krapfel 1989; Novack, Rinehart, and Langley 1994; Perreault and Russ 1974). Although stockouts are believed to have significant impact on customer satisfaction and loyalty, it is difficult to quantify the financial impact of these lost sales (Keebler et al. 1999).
Information quality refers to customers' perceptions of the information provided by the supplier regarding products from which customers may choose (Mentzer, Flint, and Kent 1999; Mentzer, Rutner, and Matsuno 1997; Novack, Rinehart, and Langley 1994; Rinehart, Cooper, and Wagenheim 1989). This information is contained in DLA's catalogs. If the information is available and of adequate quality, customers should be able to use the information to make decisions.
Ordering procedures refer to the efficiency and effectiveness of the procedures followed by the supplier (Bienstock, Mentzer, and Bird 1997; Mentzer, Flint, and Kent 1999; Mentzer, Gomes, and Krapfel 1989; Mentzer, Rutner, and Matsuno 1997; Rinehart, Cooper, and Wagenheim 1989). In particular, focus group participants indicated that it was important for DLA's order placement procedures to be both effective and easy to use.
Order accuracy refers to how closely shipments match customers' orders upon arrival (Bienstock, Mentzer, and Bird 1997; Mentzer, Flint, and Kent 1999; Mentzer, Gomes, and Krapfel 1989; Mentzer, Rutner, and Matsuno 1997; Novack, Rinehart, and Langley 1994; Rinehart, Cooper, and Wagenheim 1989). This includes having the right items in the order, the correct number of items, and no substitutions for items ordered.
Order condition refers to the lack of damage to orders (Bienstock, Mentzer, and Bird 1997; Mentzer, Flint, and Kent 1999; Mentzer, Gomes, and Krapfel 1989; Mentzer, Rutner, and Matsuno 1997; Rinehart, Cooper, and Wagenheim 1989). If products are damaged, customers cannot use them and must engage in correction procedures with DLA and/or other vendors, depending on the source of the damage.
Order quality refers to how well products work (Novack, Rinehart, and Langley 1994). This includes how well they conform to product specifications and customers' needs. Whereas order accuracy addresses the complete set of products in the order (i.e., the accuracy of the kinds and quantities of the products in the order) and order condition addresses damage levels of those items due to handling, order quality addresses manufacturing of products. The focus group participants attributed a portion of their perceptions of the quality of DLA's logistics services to the quality of the products being delivered. Because DLA serves as a general purchasing organization for its customers, this attribution was not surprising.
Order discrepancy handling refers to how well DLA addresses any discrepancies in orders after the orders arrive (Novack, Rinehart, and Langley 1994; Rinehart, Cooper, and Wagenheim 1989). If customers receive orders that are not accurate, in poor condition, or of poor quality, they seek corrections from DLA. How well DLA handles these issues contributes to customers' perceptions of the quality of their services.
Timeliness refers to whether orders arrive at the customer location when promised. More broadly, timeliness also refers to the length of time between order placement and receipt (Hult 1998; Hult et al. 2000). This delivery time can be affected by transportation time, as well as back-order time when products are unavailable (Bienstock, Mentzer, and Bird 1997; Mentzer, Flint, and Kent 1999, Mentzer, Gomes, and Krapfel 1989; Mentzer, Rutner, and Matsuno 1997; Novack, Rinehart, and Langley 1994; Rinehart, Cooper, and Wagenheim 1989).
As is evident, these nine dimensions capture previously supported aspects of PDSQ--namely, availability (in terms of order release quantities), timeliness, and condition--but also capture other aspects of logistics services covered in the literature and discussed previously (e.g., personnel quality, information quality, discrepancy handling). In addition, order completeness is conceptualized as three distinct components--that is, order accuracy, order condition, and order quality--because qualitative research suggests that they differ yet are all considered when customers evaluate whether received orders are complete.
These nine dimensions of LSQ have been proposed as first-order dimensions of a second-order LSQ construct (Mentzer, Flint, and Kent 1999). However, this operationalization has two limitations. First, in a second-order construct, all dimensions are given equal weight and treated as if they occur simultaneously. This is a consistent limitation in the logistics literature. Researchers often provide a laundry list of activities and/or components of logistics services of which customers form perceptions. These operationalizations ignore the processes, that is, the temporal ordering of the components/dimensions being tested. Some components are not just correlated with but dependent on other components. Thus, the process by which perceptions of logistics service components affect one another, and eventually satisfaction, is lost. This omission is surprising considering the general attention given to logistics operations as a set of processes within supply chain management that are aimed at increasing customer satisfaction and reducing costs (e.g., Beinstock, Mentzer, and Bird 1997; Handfield and Nichols 1999; Michigan State University 1995, 1999; Persson 1995). The study of total quality management has long focused on processes, and quality initiatives continue to emphasize operations (e.g., Li and Rajagopalan 1999). Moreover, organizational science researchers have modified their scientific inquiry approach away from variables and toward processes (Mackenzie 2000). Therefore, it is odd that we see little empirical evidence of logistics processes being modeled as the processes perceived by customers.
The second shortcoming of Mentzer, Flint, and Kent's (1999) work is the lack of comparison across market segments. Reported results suggest that market segments place varying degrees of importance on each dimension of LSQ. However, Mentzer, Flint, and Kent did not conduct comparison analysis. The purpose of our article is to improve on the LSQ conceptualization by addressing these two shortcomings. First, we conceptualize the nine components of LSQ in terms of a logical process. After confirming the validity and reliability of these nine dimensions, we empirically test a process model of LSQ and compare the process across market segments.
Although we could not find any articles in the logistics literature that offered a process conceptualization that includes all the dimensions tested here, we did find general presentations of the process that helped us establish a framework within which we could develop our model. Specifically, it is generally understood that customers place orders, orders are processed, orders are shipped, and orders are received (e.g., Byrne and Markham 1991; Mentzer, Gomes, and Krapfel 1989; Persson 1995). Customers have contact with this process when placing and receiving orders. When order receipt is not as expected, customers stay engaged in the logistics process through discrepancy handling. This general framework is presented in Figure 1. This framework helps us begin to place the nine components of LSQ in temporal order (Figure 2).
First, order placement components include perceptions of interactions with DLA personnel when customers place orders (i.e., personnel contact quality), order release quantities, ordering information quality, and ordering procedures. This stage includes what is traditionally referred to as availability (e.g., Bienstock, Mentzer, and Bird 1997; Mentzer, Gomes, and Krapfel 1989). Until the order receipt stage, customers do not have any perceptions of the tangible products that are delivered. At the order receipt stage of LSQ, we place order accuracy, order condition, and order quality. These three components compose what is traditionally referred to as order condition or order fulfillment (e.g., Beinstock, Mentzer, and Bird 1997; Handfield and Nichols 1999). However, timeliness is also part of order receipt. This is the first time customers can really assess the timeliness of the logistics process. Did the product arrive on time as ordered? Thus, perceptions of timeliness fit within the order receipt stage. Perceptions of these four order receipt components (i.e., order accuracy, order condition, order quality, and timeliness) are driven by the order placement components. However, customers sometimes do not receive orders as expected (Bienstock, Mentzer, and Bird 1997; Handfield and Nichols 1999; Langley and Holcomb 1991; Mentzer, Gomes, and Krapfel 1989). In this situation, customers ask the service provider to correct the mistake. Thus, dealing with service providers about orders not received as expected (i.e., discrepancy handling) is still part of order receipt activities but follows an evaluation of the accuracy, condition, and quality of the order. When discrepancies need to be addressed, timeliness is affected. Orders are not considered on time until they are received as ordered. Thus, timeliness is driven by the process of placing orders (i.e., personnel contact quality, order release quantities, information quality, and ordering procedures), the receip t of accurate orders in good condition and of good quality, and the handling of discrepancies.
Finally, satisfaction should be driven by the timeliness of orders received and the manner in which discrepancies are handled. We expect order accuracy, order condition, and order quality to operate through timeliness and through order discrepancy handling to influence satisfaction. This relatively straightforward process is logical, but we drew on an analysis of the qualitative phase of this research and general discussions about logistics services in the logistics literature that heretofore have not specifically modeled all these components of LSQ as a process. However, we also know from the service quality literature that interactions with the service provider are crucial to customer satisfaction (Bitner 1990; Bitner, Booms, and Mohr 1994; Bitner, Booms, and Tetreault 1990; DeCarlo and Leigh 1996; Grönroos 1982; Hartline and Ferrell 1996; Hartline, Maxham, and McKee 2000; Parasuraman, Zeithaml, and Berry 1985; Surprenant and Solomon 1987). This personal interaction reflects both the quality of the personnel and the ease with which customers can interact with the service provider. Incorporating these aspects of service quality into our process model of LSQ adds a direct link between personnel contact quality and customer satisfaction and another between ordering procedures (our construct that addresses ease of interaction) and satisfaction. The reason information quality and order release quantities (the two remaining order placement dimensions) do not operate directly on satisfaction is that they both address issues whose effects should be adequately explained by operating through order receipt dimensions alone.
This logic leads us to the hypothesized model presented in Figure 2. The specific hypotheses that emerge directly from this previous discussion of construct relationships, represented in Figure 2, are discussed next.
We hypothesize that ordering-related constructs affect perceptions of the order when it arrives. Specifically, personnel contact quality, order release quantities, information quality, and ordering procedures all involve interactions customers have with their suppliers when they place orders. Each of these constructs should positively affect perceptions of order accuracy, order condition, order quality, and timeliness. This is reflected in H1 and specifically in 16 distinct subhypotheses:
H1: Perceptions of ordering-related constructs positively affect order
receipt perceptions: (a) personnel contact quality positively affects
order accuracy, (b) personnel contact quality positively affects order
condition, (c) personnel contact quality positively affects order
quality, (d) personnel contact quality positively affects timeliness,
(e) order release quantities positively affects order accuracy, (f)
order release quantities positively affects order condition, (g) order
release quantities positively affects order quality, (h) order release
quantities positively affects timeliness, (i) information quality
positively affects order accuracy, (j) information quality positively
affects order condition, (k) information quality positively affects
order quality, (l) information quality positively affects timeliness,
(m) ordering procedures positively affects order accuracy, (n)
ordering procedures positively affects order condition, (o) ordering
procedures positively affects order quality, and (p) information
quality positively affects timeliness.
As previously discussed, we hypothesized that three of the order receipt constructs have an effect on perceptions of how DLA handles order discrepancies. If orders are inaccurate, of low quality, or in poor condition, customers are forced to interact with DLA to handle the discrepancies. If discrepancies are handled well, such that orders are eventually accurate, of acceptable quality, and in proper condition, customers should have positive perceptions of the supplier's order discrepancy procedures. H2 addresses this issue and is reflected in three subhypotheses:
H2: Perceptions of order receipt positively affects perceptions
of order discrepancy handling procedures: (a) order accuracy
positively affects order discrepancy handling, (b) order condition
positively affects order discrepancy handling, and (c) order quality
positively affects order discrepancy handling.
Timeliness has long been discussed as an important component of logistics services. In addition to the hypothesized positive effects of the four order placement constructs on timeliness, we hypothesize that an order would be considered on time when the order was considered accurate, in good condition, and of acceptable quality. If these three criteria are not met, timeliness is also affected by when the discrepancies are handled adequately. Thus, we hypothesize that perceptions of order accuracy, order condition, order quality, and order discrepancy handling affect perceptions of timeliness.
H3: Perceptions of order accuracy positively affects perceptions of
timeliness.
H4: Perceptions of order condition positively affects perceptions of
timeliness.
H5: Perceptions of order quality positively affects perceptions of
timeliness.
H6: Perceptions of order discrepancy handling positively affects
perceptions of timeliness.
Finally, on the basis of the literature, order timeliness and the handling of order discrepancies should have strong effects on satisfaction. However, as previously explained, two constructs, ordering procedures and personnel contact quality, tie in the broader service quality literature and model direct effects on satisfaction because they involve the ease-of-use aspects of the service and the interpersonal interactions that affect satisfaction. H7 through H10 reflect these concepts:
H7: Perceptions of timeliness positively affects satisfaction.
H8: Perceptions of order discrepancy handling positively affects
satisfaction.
H9: Perceptions of ordering procedures positively affects satisfaction.
H10: Perceptions of personnel contact quality positively affects
satisfaction.
Samples and Data Collection
To examine the constructs and process model of LSQ, we collected samples from customer segments of the DLA. We sent customers in the DLA segments chosen for this study a survey packet including a cover letter, questionnaire, and return envelope. Survey respondents were responsible for logistics ordering from and coordination with DLA but are free to order from other suppliers besides DLA if they are not satisfied with DLA's performance. The total mailing included 5000 to general merchandise customers (n = 2008), 1500 to textiles and clothing customers (n = 505), 1500 to electronics customers (n = 608), and 500 to construction supplies customers (n = 250). The DLA provided the contact names at customer organizations. These numbers of returned, acceptable surveys reflect a 39.66% response rate.
We assessed nonresponse bias by contacting a random sample of 30 nonrespondents from each segment (i.e., general merchandise, textiles and clothing, electronics, and construction supplies customers) by telephone and asking them to answer the three satisfaction questions (SA1, SA2, and SA3). The t-tests of group means revealed no significant differences between respondents and nonrespondents on any of the questions in any of the segments. Thus, nonresponse bias was not considered a problem.
Scale Development
We previously discussed the qualitative research and literature that helped us develop the nine LSQ constructs. We then developed, on the basis of the qualitative analysis, multi-item scales to tap into each of the nine constructs, plus satisfaction. The survey instrument was pretested for readability on a random sample of 200 DLA customers. Analysis of this pretest found that only four items required minor revision of wording for readability. We then mailed the refined instrument to the final sample of 8500 DLA customers in the four segments selected for the study.
Before hypothesis testing, we also engaged in scale purification. We extracted a random sample of 415 surveys from the responses from the four market segments (243 from general merchandise, 59 from textiles and clothing, 78 from electronics, and 35 from construction supplies). Each market segment represented approximately the same percentage of the purification sample as it did in the final analysis sample. Following basic descriptive analyses, including examination for coding errors, normality, skewness, kurtosis, means, and standard deviations, we subjected the purification data set to confirmatory factor analyses (CFA) by means of LISREL (Jöreskog and Sörbom 1996; Jöreskog et al. 1999). In these analyses, items were grouped into a priori conceptualized scales. Modification indices (i.e., initially any greater than 10), standardized residuals (i.e., greater than 4), and fit statistics (i.e., comparative fit index [CFI], DELTA2, relative noncentrality index [RNI], and c2 with corresponding degrees of freedom [d.f.]) flagged potentially problematic items (Anderson and Gerbing 1988; MacCullum 1986).
We then examined these items within the theoretical context of each scale and deleted items on substantive and statistical grounds, if appropriate (Anderson and Gerbing 1988; MacCullum 1986). As a result, we eliminated 27 items from an initial pool of 52 designed to tap the nine LSQ scales, which resulted in 25 items to tap the nine LSQ scales and three items to tap satisfaction. Composite reliability and the average variance extracted compared with the highest variance shared with any other construct were both acceptable for each construct. In addition, the 28 purified items were found to be reliable and valid when evaluated on the basis of each item's error variance, modification index, and residual covariation. The refined scales are provided in Table 1. After the measurement analyses (described in more detail for the samples included in the study in the "Measurement Analysis" section), we proceeded to the hypothesis testing using the refined scales for each of the four final samples (which now had final sample sizes of 1765 for general merchandise, 446 for textiles and clothing, 530 for electronics, and 215 for construction supplies after the pretest responses were removed).
Using the refined scales in each of the four market segment data sets, we subjected the hypothesized constructs of LSQ to a series of CFAs to assess unidimensionality, reliability, and validity and then tested the effects of the nine LSQ constructs on one another and on satisfaction. The results are presented in Tables 2 through 6. Table 2 reports the means and standard deviations of all items for all four segments. Table 3 presents the results of the multisample CFA in which the focus was on testing the invariance of the measurement model across the four DLA segments. Table 3 also reports the testing of all possible pairs of customer segment samples. Table 4 summarizes additional measurement model test results, including parameter estimates, composite reliabilities, average variances extracted, and highest shared variances. Table 5 presents the CFA fit statistics for each DLA customer segment. Table 6 presents the results of all hypothesis tests. Correlation matrices for all four customer samples are provided in the Appendix. We next provide details of the analyses leading to these tables.
Measurement Model
To confirm construct unidmensionality, validity, and reliability, we evaluated the psychometric properties of the nine LSQ and one satisfaction constructs by using the method of CFA by means of LISREL (Jöreskog and Sörbom 1996; Jöreskog et al. 1999). Within this analysis, we incorporated both theoretical and statistical consideration in developing the scales (Anderson and Gerbing 1988). As such, our goal was to achieve a high level of scale reliability and validity and ensure that we had measured each theoretical facet of the intended construct. We evaluated the scales using CFA analyses for each of the four customer segment samples--general merchandise (n = 1765), textiles and clothing (n = 446), electronics (n = 530), and construction equipment and supplies (n = 215). We evaluated the model fits using the DELTA2 index, the RNI, and the CFI. These have been shown to be the most stable fit indices by Gerbing and Anderson (1992). The c2 statistics with corresponding degrees of freedom are included for comparison purposes (Jöreskog and Sörbom 1996).
Using these criteria, a multisample test of the four segments, in which the parameter estimates were constrained to be the same across the four segments (Model 1) (i.e., loadings, factor correlations, and error variances), resulted in acceptable fits to the data (Table 3). Allowing the loadings to be estimated independently from one another in the four segments resulted in similar fit statistics (Model 2). On the basis of the c2 difference test suggested by Anderson and Gerbing (1988), the constrained and unconstrained measurement models were found not to differ significantly. As a further examinination of the potential for differences, multisample tests were conducted on all possible pairs of the customer segment samples. As with the four-sample test, fit indices were acceptable, and no significant differences were found between Models 1 and 2 (Table 3). Similarly, no differences were found between the models when the error variances were allowed to be estimated freely in addition to the loadings (Model 3) or when the loadings were allowed to be invariant but the error variances were allowed to differ (Model 4).
Next we assessed the reliability of the measures. Within the CFA setting, composite reliability is calculated using the procedures outlined by Fornell and Larcker (1981) and based on the work by Werts, Linn, and Jöreskog (1974). The formula specifies that CRh = (Slgi)2/[(Slgi)2+(Sei)], where CRh = composite reliability for scale h, lgi = standardized loading for scale item gi, and ei = measurement error for scale item gi. We also examined the parameter estimates and their associated t-values and assessed the average variance extracted for each construct (Anderson and Gerbing 1988). As is shown in Table 4, the reliabilities for the ten constructs ranged between .76 (order quality for construction segment) and .95 (personnel contact quality for general, textiles, and electronics segments), indicating acceptable levels of reliability for the constructs (Fornell and Larcker 1981). The order quality scale is the only scale below a composite reliability of .79, suggesting that all other scale reliabilities are excellent (Gerbing and Anderson 1992).
We established discriminant validity by calculating the shared variance between all possible pairs of constructs and verifying that they were lower than the average variance extracted for the individual constructs (Fornell and Larcker 1981; Jöreskog et al. 1999). The shared variance was calculated as y2 = 1 - Psi, where y2 = shared variance between constructs and the diagonal element of Psi indicates the amount of unexplained variance. Because n and e are standardized, y2 is equal to the r2 between the two constructs. We calculated average variance extracted using the following formula: Vn = Slgi2/(Slgi2 + Sei), where Vh = average variance extracted for h, lgi = standardized loading for scale item gi, and ei = measurement error for scale item gi. The shared variances between pairs of all possible scale combinations ranged from a low of 8% to a high of 59% between the various scale combinations (Table 4). The average variances extracted ranged between 52% and 85%, all having higher average variances extracted than the shared variances among all applicable pairs of scales (Table 4). To assess discriminant validity further, in line with suggestions by Anderson (1987) and Bagozzi and Phillips (1982), we assessed pairs of scales in a series of two-factor confirmatory models using LISREL. We ran each model twice--once constraining the phi coefficient (f) to unity and once freeing the parameter. We then used a c2 test to test for differences between models. In all cases, the c2 results were higher in the constrained models, thereby indicating discriminant validity between the constructs. These results, in combination with fit indices for each customer segment sample (i.e., in Table 5, DELTA2, RNI, and CFI exceed .90 for all four segments), suggest that the measurement scales are reliable and valid in all four customer segments in this study.
Finally, we examined the validity of each of the 28 individual items in the analysis. First, we maintained our predetermined criteria of modification indices (<10) and residuals (<4). Second, we tested the potential differences among each item (28 items) across the four samples relative to its theoretical construct (10 constructs). This test involved constraining appropriate sets of b estimates, one parameter estimate at a time, to be equal and different across the four samples (general, textiles, electronics, and construction) and then evaluating whether the resulting change in the c2 value was significant with the appropriate difference in degrees of freedom (Bagozzi and Heatherton 1994). The results indicated that all 28 items were robust across the four samples. The c2Ds ranged from .21 to 6.47 with a d.f.D = 3, which was lower than the c2 value of 11.34 to be significant at the p < .01 level. As such, the ten scales and their 28 items were considered reliable and valid in the context of this study.
Hypothesis Testing
The results of the hypothesis tests are provided in Table 6, including the parameter estimates, their corresponding t-values, and the fit statistics. We tested the hypothesized model in Figure 2 using LISREL (Jöreskog and Sörbom 1996; Jöreskog et al. 1999). All scale items were used in the analysis to represent the ten latent constructs. We used the correlation matrix for each segment as input to the SEM analyses (see the Appendix). In testing the hypotheses, we centered our attention on examining the relative emphasis placed on each construct within each segment as opposed to comparing paths across samples.
The main objective of the hypothesis testing was to examine the relative importance of each service quality construct in each of the four distinct DLA customer segments. Initially, however, we examined the implicit proposition that the four DLA segments are different in terms of the service quality process. As such, we conducted a multisample analysis involving all four DLA segments to assess the possible invariance of the model relationships across the segment samples (using procedures similar to the ones employed to assess the individual items in the measurement analysis). The multisample analysis indicated that the models involving constrained (c2 = 22082.65, d.f. = 1761) and unconstrained (c2 = 14811.08, d.f. = 1602) loadings are statistically different (Dc2 = 7271.57, d.f. = 159, p < .01). Thus, these results support our contention that the developed service quality process model (Figure 2) should be examined independently in the four DLA samples.
The fit statistics indicate that in all four segments, the hypothesized model achieves acceptable fit (Table 6). However, a different number of hypotheses was supported in each segment. Within the general customer segment, 23 of the 27 hypotheses were supported at the p < .01 level (Figure 3). In the textiles segment, 15 of the 27 hypotheses were supported at the p < .01 level (Figure 4). In the electronics segment, 12 of the 27 hypotheses were supported at the p < .01 level (Figure 5). In the construction segment, 11 of the 27 hypotheses were supported at the p < .01 level (Figure 6).
The finding that the model generally fits the data for each customer segment (on the basis of fit indices) but that some paths are not significant in certain segments and that the significant paths differ across segments, suggests that customer segments place different levels of emphasis on certain components of LSQ. As such, we find support for the differences across the four DLA segments at the path level (hypothesis) in addition to the explanatory level (as tested in the multisample analysis).
In the general segment, three of four order placement constructs (i.e., personnel contact quality, order release quantities, ordering procedures), order accuracy, and order discrepancy handling drove perceptions of timeliness. Order condition and order quality seemed to work through order discrepancy handling. In the textiles segment, only personnel contact quality and order quality drove perceptions of timeliness. In the electronics segment, timeliness perceptions were driven entirely by order placement constructs (i.e., personnel contact quality, order release quantities, ordering procedures) and not by the order receipt constructs of accuracy, condition, and quality or the handling of discrepancies. Similarly, in the construction segment, only two order placement constructs (i.e., personnel contact quality, order release quantities) drove timeliness perceptions. Thus, customers' perceptions of timeliness are driven by different constructs depending on the market segment in which they exist. Similar comparisons can be made for each of the hypotheses by examining the tables and figures. However, a few intriguing findings are worth mentioning.
The first relates to drivers of satisfaction for each segment. The constructs that drive satisfaction are the ones we might conclude are the most important to the sample. For the construction and textiles segments, only ordering procedures seemed to drive satisfaction, although we also note that for the textiles segment, timeliness and personnel contact quality were significant drivers of satisfaction at p < .05. However, this is interesting given all the emphasis logistics places on receiving the right order at the right time in the right condition. This finding indicates that these customers care most about the ease and effectiveness of the ordering process itself and not necessarily about timeliness. In contrast, both ordering procedures and order discrepancy handling seemed to drive satisfaction for the electronics segment. For the general segment, order discrepancy handling, ordering procedures, and personnel contact quality drove satisfaction. Timeliness drove satisfaction at the p < .05 significance level for the general segment. Thus, for these four segments, there were factors that drove perceptions of timeliness, yet timeliness was not a major factor in satisfaction levels. The question then becomes, Why? Follow-up research with these segments is needed to uncover that answer. We can speculate that there is something similar across these DLA customer segments that reduces the importance of timeliness; however, customer segments of other logistics service providers may place a much higher value on timeliness, as the literature suggests. Again, this finding and others like it suggest that customers' perceptions about various aspects of LSQ and the relative importance they play in determining customer satisfaction differ by market segment.
The purpose of this study was to identify potential components of LSQ that apply across multiple customer segments and examine whether different customer segments place different weights on the components. We know of no other studies that have conceptualized LSQ as a process and then examined it in this way. Examination of these issues should contribute to firms' efforts at using logistics services to differentiate themselves in the marketplace. The results from our study have specific implications for both marketing management and further research.
Managerial Implications
In this study, we presented nine potentially important components of LSQ. The items we generated to tap these components were found to be valid and reliable measures across four customer segments of the DLA. This means that marketing managers, in coordination with their firms' logisticians, can focus on developing services that address these nine components. We found, at least for one organization, that all nine components are important for at least one customer segment. These nine components reveal that LSQ is a complex concept demanding a great deal of attention from supplying firms.
This study also found that LSQ should be conceptualized as a process, rather than merely as a single concept or second-order construct. When viewed as a process, suppliers can identify the drivers of various LSQ perceptions. Our study suggests that customers' perceptions of suppliers' LSQ begin to form as soon as customers try to place orders, and the perceptions develop until customers receive complete and accurate orders, in good condition, with all discrepancies addressed. The process view enables marketers to see the interrelationships among LSQ components.
Finally, we found that customer segments place their emphasis on different components of LSQ, and we believe that this initial evidence will be corroborated by other studies; however, we also found strong similarities across segments. These similarities suggest that in some areas, managers may be able to develop processes that apply to all customer segments. Specifically, personnel contact quality had a positive effect on perceptions of timeliness in all four segments. Perceptions of the effectiveness and ease of use for ordering procedures had the most consistent positive effect on satisfaction. This indicates that the process of placing orders may be more important than order receipt in creating satisfied customers'how the job is done more than what gets done.
Thus, we suggest that managers make their own assessments of the relative weight their customer segments place on each of the constructs developed in this study. If results from their customer segments reveal similar relative emphases, logistics services can be designed to address all these segments similarly, enabling suppliers to take advantage of scale efficiencies. If, conversely, results from suppliers' customer segments reveal marked differences in the LSQ components that customers value, suppliers ought to customize their services to cater to specific customer segment desires.
This kind of thinking enables logistics services to be seen as a differential competitive weapon that can not only improve efficiencies by reducing costs but also improve marketing effectiveness by contributing to customization processes that generate greater revenue for supplier firms.
Research Implications
This study also has implications for further research on LSQ. Although we expand beyond the PDS constructs to include additional constructs in the broader concept of LSQ, the nine constructs identified and tested in this study may not be the only components of LSQ. Although we aimed at being comprehensive in our examination of LSQ issues, further research ought to explore other possibilities. Indeed, such research may lead to the uncovering of omissions and misrepresentations of the relationships tested in the current study and possibly to further conceptual refinement and extension. For example, there may be other logical structures of the interrelationships among the LSQ constructs, especially in contexts other than the ones studied in this research. Finally, we need to improve the operationalization of the constructs. Our reliability and validity assessments showed strong support for the constructs in this study, but two constructs were operationalized with only two items.
As operationalized in this study, LSQ focuses primarily on attributes of the supplier organization. This conceptualization needs to be placed into context with related constructs, such as customers' perceived benefits, sacrifices, and value and their effects on customer satisfaction--concepts all presented in the customer value literature (e.g., Woodruff 1997). Along these lines, LSQ must be linked to other customer outcome measures, such as loyalty, word of mouth, and price sensitivity, as well as supplier outcome measures, such as revenues, market share, and profitability.
Although this study contributes to both business practice and scholastic research, it is limited by several factors. First, the study's reliance on survey methodology as its primary means of data collection may limit the results because of common method bias. Replication studies, as well as studies using maximally dissimilar methods in similar and dissimilar samples over a period of time would lend support to the contention that the concepts measured in this study indeed exist and are stable. A second limitation is that the survey was administered to customer segments of only one organization and this survey was developed on the basis of focus groups and interviews within these same customer segments. Although the samples for each segment were of adequate size, they were from segments of the same supplier organization. Therefore, conclusions from this study may not transfer to customer segments of other firms. Items used to operationalize constructs in this study were worded to be relevant to DLA customers. Other suppliers of logistics services will need to modify the wording of individual items such that they are relevant to their customers yet still maintain the reliability and validity of the constructs they are designed to measure.
Legend for Chart:
A - Scale
B - Item
A B
Personnel Contact Quality
PQ1 The designated DLA contact person
makes an effort to understand my
situation.
PQ2 Problems are resolved by the
designated DLA contact person.
PQ3 The product knowledge/experience
of DLA personnel is adequate.
Order Release Quantities
OR1 Requisition quantities are not
challenged.
OR2 Difficulties never occur due to
maximum release quantities.
OR3 Difficulties never occur due to
minimum release quantities.
Information Quality
IQ1 Catalog information is available.
IQ2 Catalog information is adequate.
Ordering Procedures
OP1 Requisitioning procedures are
effective.
OP2 Requisitioning procedures are
easy to use.
Order Accuracy
OA1 Shipments rarely contain the
wrong items.
OA2 Shipments rarely contain an
incorrect quantity.
OA3 Shipments rarely contain
substituted items.
Order Condition
OC1 Material received from DLA depots
is undamaged.
OC2 Material received direct from
vendors is undamaged.
OC3 Damage rarely occurs as a result
of the transport mode or carrier.
Order Quality
OQ1 Substituted items sent by DLA
work fine.
OQ2 Products ordered from DLA meet
technical requirements.
OQ3 Equipment and/or parts are rarely
nonconforming.
Order Discrepancy Handling
OD1 Correction of delivered quality
discrepancies is satisfactory.
OD2 The report of discrepancy process
is adequate.
OD3 Response to quality discrepancy
reports is satisfactory.
Timeliness
TI1 Time between placing requisition
and receiving delivery is short.
TI2 Deliveries arrive on the date
promised.
TI3 The amount of time a requisition
is on back-order is short.
Satisfaction
SA1 (1 = "terrible,"
5 = "excellent") What is your general impression
of the service DLA provides?
SA2 (1 = "very dissatisfied,"
5 = "very satisfied") Which word best describes your
feelings toward DLA?
SA3 (1 = "very dissatisfied,"
5 = "very satisfied") How satisfied are you with DLA
service?Notes: All nine LSQ construct items were measured on a five-point Likert-like scale (1 = "strongly disagree," 5 = "strongly agree").
Legend for Chart:
A - Item
B - General (n = 1765) Mean
C - General (n = 1765) Standard Deviation
D - Textiles (n = 446) Mean
E - Textiles (n = 446) Standard Deviation
F - Electronics (n = 530) Mean
G - Electronics (n = 530) Standard Deviation
H - Construction (n = 215) Mean
I - Construction (n = 215) Standard Deviation
A B C D E F G H I
IQ1 4.03 1.11 4.13 1.30 4.05 1.22 4.01 1.23
IQ2 3.95 1.18 4.05 1.42 3.87 1.35 3.99 1.29
OP1 3.98 1.02 4.05 1.14 3.97 1.06 3.97 1.17
OP2 3.95 1.06 4.01 1.15 3.95 1.10 4.00 1.18
OR1 3.57 1.32 3.86 1.37 3.57 1.41 3.75 1.42
OR2 3.63 1.50 3.88 1.59 3.62 1.58 3.68 1.65
OR3 3.56 1.48 3.94 1.55 3.54 1.51 3.62 1.64
PQ1 4.44 1.60 4.59 1.60 4.32 1.53 4.39 1.53
PQ2 4.44 1.61 4.62 1.64 4.13 1.61 4.26 1.60
PQ3 4.50 1.53 4.65 1.51 4.33 1.48 4.39 1.52
OA1 3.88 1.02 4.02 1.16 3.86 1.08 3.87 1.17
OA2 3.83 1.05 4.04 1.15 3.84 1.08 3.66 1.24
OA3 3.88 1.01 4.09 1.13 3.81 1.12 3.93 1.16
OC1 3.82 1.05 4.10 1.11 3.91 1.04 3.93 1.17
OC2 3.89 1.04 4.24 1.15 3.96 1.01 3.99 1.17
OC3 3.83 1.08 4.06 1.19 3.83 1.03 3.84 1.16
OQ1 4.02 1.43 4.41 1.74 3.77 1.31 3.91 1.49
OQ2 4.09 .96 4.38 1.19 4.01 .90 4.14 1.07
OQ3 4.07 1.12 4.52 1.47 4.01 .99 4.15 1.08
OD1 3.89 1.47 4.04 1.68 3.69 1.47 3.96 1.57
OD2 3.78 1.50 3.80 1.70 3.56 1.53 3.79 1.61
OD3 3.96 1.60 4.24 1.69 3.80 1.63 3.89 1.71
TI1 3.98 1.84 4.67 2.09 3.78 1.65 3.92 1.98
TI2 3.71 1.97 4.58 2.21 3.54 1.77 3.76 2.05
TI3 3.72 2.00 4.44 2.27 3.38 1.84 3.63 2.06
SA1 3.54 .69 3.64 .78 3.48 .74 3.45 .71
SA2 3.62 .71 3.69 .76 3.52 .79 3.51 .74
SA3 3.61 .74 3.69 .77 3.51 .80 3.51 .74
Notes: PQ = personnel contact quality, OR = order release quantities, IQ = information quality, OP = ordering procedures, OA = order accuracy, OC = order condition, OQ = order quality, OD = order discrepancy handling. TI = timeliness, and SA = satisfaction. All nine LSQ construct items were measured on a five-point Likert-like scale (1 = "strongly disagree," 5 = "strongly agree"). Satisfaction items were measured on five-point scales (see Table 1).
Legend for Chart:
A - Model 1
B - Model 2
C - Model 1 and Model 2 Comparison
(a) Model 1 and Model 2 comparison by means of difference in
χ² and degrees of freedom indicates no significant
difference between the models. Thus, constructs are valid in
four customer segments.
(b) Model 1 and Model 2 comparison by means of difference in
χ⊃ and degrees of freedom indicates no significant
difference between the models. Thus, constructs are valid in
all customer segments.
A B C
All four segments χ2 4233.03 4186.49 46.54(a)
d.f. 1745 1655 90
DELTA2 .96 .96
RNI .96 .96
CFI .96 .96
Segment Pairings
General/textiles χ2 2415.70 2400.32 15.38(b)
d.f. 815 785 30
General/electronics χ2 2189.96 2182.74
d.f. 815 785 30
General/construction χ2 2061.40 2039.29 22.11(b)
d.f. 815 785 30
Textiles/electronics χ2 1930.57 1909.60 20.97(b)
d.f. 815 785 30
Textiles/construction χ2 1573.94 1564.54 9.4(b)
d.f. 815 785 30
Electronics/construction
χ2 1574.59 1549.78 24.81(b)
d.f. 815 785 30 Legend for Chart:
A - Item Loading Reliability Variance Extracted Highest Shared
Variance
B - General (n = 1765)
C - Textiles (n = 446)
D - Electronics (n = 530)
E - Construction (n = 215)
A B C
D E
Personnel Contact Quality
PQ1 .93 .89
.93 .89
PQ2 .96 .93
.97 .92
PQ3 .92 .89
.92 .86
Composite reliability .95 .95
.95 .94
Variance extracted 87% 86%
87% 85%
Highest shared variance 14% 16%
12% 21%
Order Release Quantities
OR1 .66 .59
.64 .62
OR2 .91 .86
.92 .83
OR3 .86 .83
.86 .78
Composite reliability .85 .83
.85 .82
Variance extracted 65% 62%
65% 62%
Highest shared variance 15% 25%
14% 31%
Information Quality
IQ1 .81 .81
.85 .78
IQ2 .91 .90
.92 .86
Composite reliability .85 .85
.86 .84
Variance extracted 75% 74%
76% 73%
Highest shared variance 8% 13%
13% 26%
Ordering Procedures
OP1 .91 .88
.91 .85
OP2 .84 .84
.85 .79
Composite reliability .86 .86
.86 .85
Variance extracted 76% 76%
76% 74%
Highest shared variance 15% 25%
18% 32%
Order Accuracy
OA1 .89 .83
.91 .81
OA2 .90 .85
.91 .79
OA3 .79 .77
.77 .72
Composite reliability .89 .88
.89 .87
Variance extracted 73% 70%
72% 68%
Highest shared variance 49% 59%
52% 58%
Order Condition
OC1 .92 .86
.93 .81
OC2 .86 .80
.90 .78
OC3 .66 .69
.66 .71
Composite reliability .85 .84
.86 .84
Variance extracted 66% 65%
67% 65%
Highest shared variance 49% 59%
52% 58%
Order Quality
OQ1 .65 .63
.70 .62
OQ2 .83 .77
.86 .73
OQ3 .78 .71
.81 .72
Composite reliability .79 .77
.81 .76
Variance extracted 56% 53%
59% 52%
Highest shared variance 26% 36%
29% 50%
Order Discrepancy Handling
OD1 .89 .84
.88 .79
OD2 .91 .88
.93 .83
OD3 .77 .73
.75 .73
Composite reliability .89 .88
.89 .87
Variance extracted 72% 70%
72% 69%
Highest shared variance 26% 27%
20% 41%
Timeliness
TI1 .91 .93
.88 .88
TI2 .94 .93
.93 .90
TI3 .93 .93
.92 .90
Composite reliability .94 .95
.94 .94
Variance extracted 85% 85%
85% 84%
Highest shared variance 12% 23%
13% 24%
Satisfaction
SA1 .83 .84
.85 .83
SA2 .92 .92
.92 .92
SA3 .92 .92
.93 .92
Composite reliability .92 .92
.92 .92
Variance extracted 80% 80%
80% 80%
Highest shared variance 14% 15%
18% 13%
t-Value range 28.79-55.37 13.85-28.53
16.16-31.31 9.78-19.82 Legend for Chart:
A - General (n = 1765)
B - Textiles (n = 446)
C - Electronics (n = 530)
D - Construction (n = 215)
A B C D
DELTA 2 .98 .97 .97 .97
RNI .98 .96 .98 .95
CFI .98 .96 .97 .95
χ2 1231.25 774.38 684.86 603.31
d.f. 350 350 350 350 Legend for Chart:
A - Parameter estimate/t-Value
B - General (n = 1765)
C - Textiles (n = 446)
D - Electronics (n = 530)
E - Construction (n = 215)
A B C
D E
H1a (PQ -> OA) + .12 Supported .13 Supported
t-Value 4.75 2.64
.06 .05
1.17 .78
H1b (PQ -> OC) + .17 Supported .09
t-Value 6.55 1.72
.11 .11
2.20 1.69
H1c (PQ -> OQ) + .24 Supported .21 Supported
t-Value 8.70 3.84
.07 .04
1.44 .58
H1d (PQ -> TI) + .20 Supported .15 Supported
t-Value 7.73 2.89
.13 Supported .23 Supported
2.84 3.15
H1e (OR -> OA) + .25 Supported .26 Supported
t-Value 8.45 4.39
.23 Supported .34 Supported
3.96 4.35
H1f (OR -> OC) + .25 Supported .38 Supported
t-Value 8.29 6.19
.21 Supported .37 Supported
3.75 4.80
H1g (OR -> OQ) + .24 Supported .31 Supported
t-Value 7.73 4.71
.34 Supported .49 Supported
5.71 5.40
H1h (OR -> TI) + .16 Supported .09
t-Value 5.10 1.26
.21 Supported .34 Supported
3.51 2.69
H1i (IQ -> OA) + .08 Supported .18 Supported
t-Value 2.96 3.48
.08 .02
1.54 .27
H1j (IQ -> OC) + .11 Supported .13
t-Value 4.08 2.50
.10 .09
1.90 1.10
H1k (IQ -> OQ) + .11 Supported .17 Supported
t-Value 3.67 2.97
.17 Supported .08
3.36 .98
H1l (IQ -> TI) + .03 .01
t-Value -.97 .14
.07 -.05
1.36 -.52
H1m (OP -> OA) + .23 Supported .25 Supported
t-Value 7.68 4.50
.13 .47 Supported
2.25 4.35
H1n (OP -> OA) + .19 Supported .20 Supported
t-Value 6.26 3.62
.13 .37 Supported
2.30 3.73
H1o (OP -> OQ) + .17 Supported .15
t-Value 5.54 2.46
.07 .35 Supported
1.20 3.35
H1p (OP -> TI) + .17 Supported .01
t-Value 5.35 -.08
.17 Supported .24
3.05 1.63
H2a (OA -> OD) + .11 Supported .16 Supported
t-Value 4.51 3.06
.23 Supported .26 Supported
4.84 3.14
H2b (OC -> OD) + .24 Supported .17 Supported
t-Value 9.53 3.27
.24 Supported .13
5.12 1.63
H2c (OQ -> OD) + .42 Supported .42 Supported
t-Value 14.29 6.75
.25 Supported .47 Supported
5.04 4.74
H3 (OA -> TI) + .08 Supported .13
t-Value 2.93 -2.26
.01 .10
-.21 .92
H4 (OC -> TI) + -.04 .08
t-Value -1.52 1.32
.06 -.14
1.12 -1.36
H5 (OQ -> TI) + .05 .42 Supported
t-Value 1.44 5.28
.09 -.11
1.63 -.77
H6 (OD -> TI) + .10 Supported .05
t-Value 3.32 .80
.01 .13
-.14 1.26
H7 (TI -> SA) + .07 .12
t-Value 2.53 2.50
.05 -.05
1.21 -.67
H8 (OD -> SA) + .07 Supported .07
t-Value 2.57 1.41
.15 Supported -.05
3.41 -.58
H9 (OP -> SA) + .41 Supported .35 Supported
t-Value 14.05 6.48
.46 Supported .57 Supported
8.68 5.22
H10 (PQ -> SA) + .10 Supported .13
t-Value 4.03 2.48
.11 -.03
2.48 -.32
Fit Indices
DELTA2 .89 .88
.87 .89
RNI .89 .87
.87 .88
CFI .89 .87
.87 .88
χ2 4622.68 1798.98
1840.50 1064.89
d.f. 368 368
368 368Notes: PQ = personnel contact quality, OR = order release quantities, IQ = information quality, OP = ordering procedures, OA = order accuracy. OC = order condition, OQ = order quality, OD = order discrepancy handling. TI = timeliness, and SA = satisfaction. Significant hypotheses are supported at p < .01.
DIAGRAM: Figure 1: A General Customer-Perceived LSQ Framework
DIAGRAM: Figure 2: Hypothesized Model of LSQ as a Process
DIAGRAM: Figure 3: General Segment Significant Paths (p < .01)
DIAGRAM: Figure 4: Textiles Segment Significant Paths (p < .01)
DIAGRAM: Figure 5: Electronics Segment Significant Paths (p < .01)
DIAGRAM: Figure 6: Construction Segment Significant Paths (p < .01)
Legend for Chart:
A - IQ1
B - IQ2
C - OP1
D - OP2
E - OR1
F - OR2
G - OR3
H - TI1
I - TI2
J - TI3
K - OA1
L - OA2
M - OA3
N - OQ1
O - OQ2
P - OQ3
Q - OC1
R - OC2
S - OC3
T - OD1
U - OD2
V - OD3
W - PQ1
X - PQ2
Y - PQ3
Z - SA1
AA - SA2
BA - SA3
A B C D E F G
H I J K L M N
O P Q R S T U
V W X Y Z AA BA
A: General Customer Segment
IQ1 1.0
IQ2 .72 1.0
OP1 .26 .27 1.0
OP2 .23 .25 .74 1.0
OR1 .17 .21 .35 .34 1.0
OR2 .19 .23 .31 .30 .58 1.0
OR3 .20 .23 .30 .29 .54 .76 1.0
TI1 .14 .14 .29 .27 .22 .26 .25
1.0
TI2 .13 .13 .26 .23 .25 .30 .28
.84 1.0
TI3 .11 .13 .26 .26 .27 .32 .30
.84 .86 1.0
OA1 .16 .18 .29 .26 .27 .28 .26
.23 .21 .24 1.0
OA2 .18 .19 .27 .28 .30 .28 .26
.26 .24 .26 .78 1.0
OA3 .16 .20 .25 .24 .30 .28 .26
.22 .21 .25 .66 .68 1.0
OQ1 .16 .19 .21 .18 .27 .30 .28
.25 .27 .27 .27 .30 .32 1.0
OQ2 .19 .19 .28 .24 .24 .24 .22
.21 .21 .21 .36 .36 .36 .51
1.0
OQ3 .16 .16 .21 .18 .22 .23 .22
.20 .21 .21 .35 .36 .38 .45
.64 1.0
OC1 .19 .21 .27 .26 .30 .28 .24
.22 .23 .25 .62 .61 .56 .30
.39 .37 1.0
OC2 .17 .21 .24 .19 .27 .29 .25
.19 .19 .20 .56 .55 .53 .31
.37 .34 .77 1.0
OC3 .18 .21 .26 .24 .29 .27 .25
.18 .18 .19 .50 .48 .46 .34
.46 .46 .56 .50 1.0
OD1 .15 .17 .22 .20 .27 .34 .31
.27 .27 .30 .36 .37 .36 .40
.39 .35 .39 .37 .44 1.0
OD2 .13 .16 .21 .20 .28 .31 .30
.24 .24 .26 .33 .34 .33 .36
.35 .33 .37 .36 .39 .81 1.0
OD3 .15 .19 .20 .16 .26 .32 .32
.28 .29 .29 .30 .30 .31 .42
.40 .38 .34 .33 .37 .65 .69
1.0
PQ1 .19 .19 .23 .24 .23 .27 .26
.30 .32 .31 .20 .24 .23 .27
.23 .24 .26 .23 .23 .31 .31
.33 1.0
PQ2 .16 .16 .22 .21 .23 .29 .28
.30 .33 .32 .21 .23 .21 .30
.25 .27 .27 .23 .24 .32 .32
.33 .89 1.0
PQ3 .16 .17 .21 .21 .24 .28 .27
.29 .32 .31 .23 .24 .23 .29
.24 .27 .27 .24 .23 .31 .31
.32 .85 .88 1.0
SA1 .26 .22 .34 .29 .15 .19 .16
.24 .21 .21 .18 .20 .15 .15
.16 .10 .19 .15 .17 .20 .17
.20 .22 .20 .19 1.0
SA2 .26 .26 .33 .29 .14 .22 .19
.25 .22 .23 .16 .20 .15 .14
.18 .13 .19 .14 .16 .21 .20
.21 .25 .23 .23 .77 1.0
SA3 .25 .23 .32 .30 .13 .20 .17
.23 .19 .21 .17 .21 .15 .12
.17 .13 .18 .11 .17 .21 .19
.21 .22 .20 .20 .74 .84 1.0
B: Textiles Customer Segment
IQ1 1.0
IQ2 .74 1.0
OP1 .27 .34 1.0
OP2 .28 .35 .83 1.0
OR1 .19 .22 .39 .33 1.0
OR2 .19 .25 .41 .41 .56 1.0
OR3 .26 .32 .48 .45 .50 .82 1.0
TI1 .16 .21 .26 .24 .18 .30 .36
1.0
TI2 .16 .20 .21 .20 .21 .33 .38
.93 1.0
TI3 .16 .20 .24 .22 .23 .32 .37
.92 .91 1.0
OA1 .25 .28 .41 .37 .30 .36 .35
.24 .24 .24 1.0
OA2 .26 .32 .43 .37 .33 .39 .39
.26 .25 .26 .81 1.0
OA3 .31 .32 .38 .38 .33 .34 .35
.28 .28 .30 .69 .74 1.0
OQ1 .22 .23 .28 .28 .26 .32 .34
.41 .42 .41 .39 .39 .50 1.0
OQ2 .27 .29 .31 .32 .33 .33 .34
.31 .33 .31 .44 .46 .50 .50
1.0
OQ3 .16 .22 .23 .24 .27 .31 .32
.39 .40 .38 .41 .44 .45 .48
.60 1.0
OC1 .24 .27 .40 .38 .35 .45 .42
.31 .31 .30 .69 .68 .66 .41
.45 .42 1.0
OC2 .22 .24 .37 .37 .37 .39 .39
.27 .28 .26 .59 .61 .64 .42
.46 .44 .79 1.0
OC3 .26 .28 .29 .33 .32 .38 .40
.30 .31 .32 .59 .59 .57 .42
.45 .49 .66 .60 1.0
OD1 .20 .18 .36 .32 .36 .40 .42
.30 .33 .33 .38 .42 .44 .38
.41 .33 .42 .36 .41 1.0
OD2 .20 .24 .35 .31 .33 .35 .36
.30 .30 .32 .37 .42 .42 .38
.41 .32 .42 .36 .41 .78 1.0
OD3 .21 .22 .24 .25 .29 .32 .34
.32 .34 .33 .31 .36 .33 .35
.39 .38 .37 .32 .41 .60 .70
1.0
PQ1 .20 .24 .35 .28 .31 .38 .37
.38 .36 .37 .29 .31 .31 .37
.32 .25 .30 .24 .26 .30 .32
.29 1.0
PQ2 .20 .25 .30 .25 .31 .42 .39
.32 .31 .32 .29 .34 .31 .36
.26 .25 .32 .25 .27 .31 .34
.33 .87 1.0
PQ3 .20 .25 .33 .27 .32 .41 .42
.37 .36 .36 .31 .34 .33 .38
.31 .27 .36 .28 .28 .31 .33
.34 .82 .87 1.0
SA1 .18 .12 .35 .32 .01 .14 .21
.34 .30 .29 .16 .18 .17 .33
.24 .18 .18 .17 .17 .26 .23
.22 .26 .26 .27 1.0
SA2 .14 .15 .38 .35 .06 .18 .22
.29 .24 .23 .16 .18 .13 .28
.19 .11 .16 .10 .12 .30 .27
.24 .30 .27 .28 .80 1.0
SA3 .12 .16 .37 .35 .03 .16 .21
.29 .23 .23 .19 .20 .18 .27
.19 .13 .15 .10 .15 .27 .25
.25 .29 .27 .26 .79 .89 1.0
C: Electronics Customer Segment
IQ1 1.0
IQ2 .81 1.0
OP1 .28 .33 1.0
OP2 .28 .38 .74 1.0
OR1 .24 .29 .31 .31 1.0
OR2 .21 .27 .28 .24 .55 1.0
OR3 .21 .30 .28 .26 .48 .73 1.0
TI1 .20 .23 .32 .27 .21 .28 .27
1.0
TI2 .20 .25 .30 .24 .26 .32 .31
.77 1.0
TI3 .16 .23 .23 .16 .23 .30 .30
.75 .82 1.0
OA1 .17 .18 .19 .17 .18 .21 .19
.18 .17 .16 1.0
OA2 .17 .16 .18 .16 .19 .20 .16
.16 .19 .17 .79 1.0
OA3 .17 .19 .16 .14 .20 .25 .25
.16 .17 .16 .64 .64 1.0
OQ1 .22 .27 .15 .16 .23 .33 .30
.22 .27 .25 .28 .24 .41 1.0
OQ2 .27 .28 .21 .20 .21 .29 .29
.26 .23 .20 .34 .36 .38 .57
1.0
OQ3 .20 .19 .12 .13 .12 .28 .24
.20 .19 .16 .40 .36 .39 .51
.67 1.0
OC1 .22 .20 .20 .19 .19 .20 .22
.22 .19 .18 .62 .63 .57 .35
.40 .37 1.0
OC2 .17 .18 .18 .16 .18 .22 .21
.21 .19 .19 .64 .61 .59 .32
.39 .40 .80 1.0
OC3 .24 .23 .22 .17 .19 .22 .18
.19 .23 .22 .44 .48 .41 .38
.48 .51 .54 .54 1.0
OD1 .15 .18 .19 .14 .23 .23 .22
.12 .21 .21 .37 .36 .37 .26
.32 .32 .39 .35 .30 1.0
OD2 .15 .21 .19 .21 .27 .22 .21
.08 .17 .15 .38 .37 .34 .25
.31 .34 .38 .39 .34 .76 1.0
OD3 .19 .21 .16 .17 .27 .24 .23
.15 .27 .26 .30 .27 .24 .26
.29 .31 .33 .34 .32 .58 .65
1.0
PQ1 .16 .12 .25 .25 .27 .30 .25
.27 .26 .25 .13 .12 .13 .18
.20 .16 .21 .18 .21 .23 .23
.30 1.0
PQ2 .13 .13 .24 .22 .27 .32 .28
.25 .26 .25 .16 .14 .17 .17
.18 .15 .20 .19 .21 .24 .26
.33 .86 1.0
PQ3 .13 .10 .20 .22 .24 .30 .25
.19 .26 .25 .15 .13 .18 .21
.15 .14 .17 .15 .19 .22 .24
.31 .81 .85 1.0
SA1 .16 .22 .37 .33 .15 .18 .16
.23 .23 .19 .20 .17 .16 .24
.21 .20 .20 .15 .20 .18 .29
.24 .24 .25 .22 1.0
SA2 .14 .22 .38 .33 .15 .17 .16
.22 .23 .21 .19 .15 .14 .17
.20 .11 .23 .16 .20 .19 .27
.22 .26 .27 .24 .79 1.0
SA3 .17 .25 .38 .35 .17 .18 .18
.25 .26 .23 .18 .17 .16 .20
.23 .16 .20 .15 .19 .22 .30
.25 .26 .27 .24 .79 .87 1.0
D: Construction Customer Segment
IQ1 1.0
IQ2 .73 1.0
OP1 .51 .48 1.0
OP2 .42 .38 .76 1.0
OR1 .20 .23 .36 .36 1.0
OR2 .20 .23 .39 .40 .63 1.0
OR3 .26 .22 .43 .47 .51 .77 1.0
TI1 .21 .14 .37 .39 .31 .44 .38
1.0
TI2 .24 .20 .37 .37 .35 .50 .43
.88 1.0
TI3 .22 .17 .33 .35 .33 .47 .37
.89 .90 1.0
OA1 .34 .36 .45 .52 .44 .44 .39
.42 .40 .38 1.0
OA2 .28 .30 .43 .48 .38 .42 .41
.39 .38 .35 .79 1.0
OA3 .32 .34 .35 .44 .44 .41 .38
.31 .34 .32 .71 .68 1.0
OQ1 .29 .37 .34 .42 .40 .38 .30
.31 .33 .29 .52 .45 .54 1.0
OQ2 .25 .30 .45 .44 .45 .48 .42
.34 .32 .35 .54 .52 .51 .56
1.0
OQ3 .33 .36 .45 .45 .48 .45 .43
.30 .30 .31 .57 .50 .47 .56
.67 1.0
OC1 .38 .32 .46 .53 .40 .39 .38
.34 .38 .32 .69 .60 .62 .50
.56 .63 1.0
OC2 .35 .40 .40 .48 .52 .49 .45
.30 .38 .34 .68 .58 .63 .53
.54 .63 .82 1.0
OC3 .31 .29 .45 .50 .46 .47 .36
.32 .29 .35 .68 .62 .52 .44
.57 .65 .74 .68 1.0
OD1 .30 .33 .38 .41 .47 .48 .42
.31 .34 .38 .51 .47 .50 .48
.50 .55 .49 .53 .47 1.0
OD2 .24 .19 .35 .41 .43 .46 .43
.34 .37 .40 .50 .54 .50 .48
.47 .50 .51 .46 .52 .78 1.0
OD3 .25 .19 .35 .38 .45 .45 .41
.43 .48 .47 .52 .49 .49 .55
.44 .49 .53 .52 .49 .67 .74
1.0
PQ1 .25 .25 .38 .36 .31 .35 .35
.37 .41 .41 .33 .34 .31 .23
.34 .36 .42 .39 .39 .40 .38
.39 1.0
PQ2 .21 .22 .36 .37 .34 .38 .34
.42 .45 .45 .36 .38 .31 .25
.29 .35 .39 .38 .41 .43 .42
.42 .90 1.0
PQ3 .23 .26 .32 .31 .39 .45 .39
.38 .41 .42 .36 .36 .33 .24
.30 .38 .35 .37 .39 .44 .39
.44 .84 .85 1.0
SA1 .25 .22 .33 .23 .04 .03 .02
.19 .13 .13 .22 .23 .18 .19
.14 .08 .12 .09 .20 .19 .18
.20 .16 .16 .13 1.0
SA2 .27 .23 .37 .27 .02 -.02 -.02
.17 .13 .12 .24 .24 .16 .16
.10 .08 .19 .11 .14 .22 .20
.24 .20 .18 .12 .76 1.0
SA3 .24 .20 .33 .27 .01 .03 .00
.21 .19 .16 .21 .21 .12 .19
.07 .06 .13 .09 .16 .18 .19
.23 .14 .15 .11 .75 .86 1.0Notes: PQ = personnel contact quality, OR = order release quantities, IQ = information quality, OP = ordering procedures, OA = order accuracy, OC = order condition, OQ = order quality. OD = order discrepancy handling, TI = timeliness, and SA = satisfaction.
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~~~~~~~~
By John T. Mentzer; Daniel J. Flint and G. Tomas M. Hult
John T. Mentzer is Harry J. and Vivienne R. Bruce Excellence Chair of Business Policy, University of Tennessee
Daniel J. Flint is Assistant Professor of Marketing, Florida State University
G. Tomas M. Hult is Associate Professor of Marketing and Supply Chain Management, Michigan State University
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Record: 88- Making Customer Relationship Management Work: The Measurement and Profitable Management of Customer Relationships. By: Ryals, Lynette. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p252-261. 10p. 4 Charts. DOI: 10.1509/jmkg.2005.69.4.252.
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Record: 89- Making Healthful Food Choices: The Influence of Health Claims and Nutrition Information on Consumers' Evaluations of Packaged Food Products and Restaurant Menu Items. By: Kozup, John C.; Creyer, Elizabeth H.; Burton, Scot. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p19-34. 16p. 4 Charts, 10 Graphs. DOI: 10.1509/jmkg.67.2.19.18608.
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Making Healthful Food Choices: The Influence of Health Claims
and Nutrition Information on Consumers' Evaluations of
Packaged Food Products and Restaurant Menu Items
The authors report the results of three experiments that address the effects of health claims and nutrition information placed on restaurant menus and packaged food labels. The results indicate that when favorable nutrition information or health claims are presented, consumers have more favorable attitudes toward the product, nutrition attitudes, and purchase intentions, and they perceive risks of heart disease and stroke to be lower. The nutritional context in which a restaurant menu item is presented moderates the effects of both nutrition information and a health claim on consumer evaluations, which suggests that alternative (i.e., nontarget) menu items serve as a frame of reference against which the target menu item is evaluated.
Americans have been gaining weight in recent years, and there is significant long-term disease risk associated with this trend. The Nutritional Labeling and Education Act of 1990 (NLEA) was expected to help curtail this trend by providing information to assist consumers in making more healthful food choices. Yet today, more than 50% of U.S. adults are overweight, and 12% of school-aged children are obese, twice the number reported 20 years ago (Liebman and Schardt 2001; Spake 2002). It is estimated that in the United States, more than 300,000 deaths per year (14% of all deaths) are directly related to conditions and diseases associated with being overweight and obese (Centers for Disease Control and Prevention 2002).
The NLEA increased the availability and usefulness of nutrition information on food packages. This was expected to have long-term positive effects on Americans' diets and reduce their risk for heart disease and some types of cancer. The Nutrition Facts panel, mandated on most food packages since 1994, is a widely recognized outcome of the NLEA. Distinctive and easy to read, the Nutrition Facts panel presents information on the amount per serving of saturated fat, cholesterol, dietary fiber, and other major nutrients and provides nutrient reference values expressed as "% Daily Values" (DV). The NLEA also established criteria by which nutrient and health claims can be made on food packaging. Although health claims have been used on package labels since 1984, they have been criticized as vague, trivial, or misleading (Silverglade 1996).
However, not all foods are covered by all aspects of the NLEA. Among the excluded foods are those sold for immediate consumption, such as in restaurants, on airplanes, and from vending machines (Saltos, Welsh, and Davis 1994). Food sold and served in restaurants is an especially important exception. According to the National Restaurant Association, Americans spent 45% of their food dollars outside the home in 1997, up from 25% in 1955. In 1998, 46% of all adults were restaurant patrons on a typical day. Furthermore, 21% of U.S. households used some type of restaurant take-out or delivery on an average day (National Restaurant Association 2001). Because restaurants are not required to present nutrition information about the food items on their menus, the availability and form of presentation of nutrition information varies. Many restaurants do not make information regarding the nutritional content of their food readily available to consumers. In other restaurants, nutrition information is presented on the menu, placed on a sign, or provided in a brochure. There is somewhat greater consistency across restaurants regarding the nature of specific health claims since the Food and Drug Administration (FDA) ruled in 1997 that health claims made for food served in restaurants must be consistent with the claim definitions established under the NLEA. However, unlike processed foods, menu items with a health claim are not held to strict standards of laboratory analyses.
Considerable marketing and public policy research has focused on the effects of changes, such as the use of nutrition information and health claims, brought about by the NLEA (e.g., Balasubramanian and Cole 2002; Ford et al. 1996; Moorman 1996). Prior research has focused almost exclusively on packaged foods. It is therefore unclear whether findings from these prior studies generalize to restaurant foods. This is an especially important issue because consumers spend an increasing percentage of their food budget at restaurants (Jekanowski 1999).
The substantial number of nutrient and health claims appearing on both restaurant menus and packaged food labels highlights the importance of understanding how consumers use health claims, in conjunction with nutrition information, to form product evaluations. Thus, we consider several additional questions. Does the provision of a health claim moderate the effects of nutrition information on product evaluations? How does the nutritional context, that is, the nutritional frame created by other menu items, influence evaluations of a target menu item? Similarly, does the nutritional context in which a specific menu item is presented moderate the influence of a health claim and the influence of nutrition information on target product evaluations? We use three general types of dependent measures to address these questions. First, we consider product evaluations, including attitude toward the product, purchase intention, and nutrition attitude. Second, we examine perceptions of source credibility. Third, we focus on consumers' assessments of the likelihood of developing specific diseases (i.e., heart dis-ease and stroke) if the food is regularly included in their diet. In the remainder of the article, we discuss the FDA's standards regarding the use of health claims. Next, we offer the theoretical foundation and hypotheses. Then, we describe our three studies, present their results, and discuss the theoretical, managerial, and public policy implications of the findings.
The FDA currently allows health and nutrient claims on both food package labels and restaurant menus. "Low fat" or "high fiber" are specific claims that pertain to a food's nutrient content. Health claims address the relationship between a specific nutrient and a disease or health condition. For example, the packaging of foods with low levels of saturated fat and cholesterol may state, "Diets low in saturated fat and cholesterol may reduce the risk of heart disease." On packaged food products, Nutrition Facts information can verify such claims. However, claims made about food items on restaurant menus are more difficult to verify because supporting nutrition information is available only on request. Claims that emphasize "heart healthiness" might have an accompanying symbol, such as a large red heart on the front of the package or next to the menu item.[ 1]
The effects of nutrient claims and health claims have been extensively researched (e.g., Levy, Fein, and Schucker 1996; Russo et al. 1986) in the context of both package design (Ford et al. 1996; Keller et al. 1997) and advertising (Andrews, Netemeyer, and Burton 1998). For example, Garretson and Burton (2000) investigate the effects of nutrition information and nutrient content claims on product evaluations. Their results indicate that differences in the level of fat affected both nutrition attitude and disease risk perceptions. However, large differences in fiber had no effect on nutrition attitude, brand attitude, or disease risk perceptions. Perhaps more interesting, nutrient claims had no influence on attitudes or purchase intentions. This implies that when a Nutrition Facts panel is present, consumers tend to rely on it rather than on the nutrient claim.
Whereas prior research has tended to focus exclusively on foods prepared and consumed in the home, this research examines consumers' evaluations of food prepared by restaurants. A survey conducted by the U.S. Department of Agriculture (USDA) finds that fat contributed 31.5% of the calories in food in the home and almost 38% of the calories in food prepared outside the home (Biing-Hwan, Frazão, and Guthrie 1999). Restaurants typically use fatter meats and add more fats, such as butter and sauces, to improve food flavor but generally do not offer nutrient information about the foods.
Not only are many consumers unlikely to be aware of these facts, but they are also unlikely to be aware of what constitutes a serving size. A study conducted by the Center for Science in the Public Interest (1997) reports that trained dietitians underestimated the calorie content of five restaurant meals by an average of 37% and the fat content by 49%. As a result of consumers' lack of knowledge regarding the fat levels of many restaurant foods and the high disease and death rate directly related to obesity, the USDA has specified that the development of educational programs that enable consumers to better understand the nutritional implications of eating food prepared outside the home is a priority (Centers for Disease Control and Prevention 2002). The effectiveness of such programs is likely to be influenced by how well marketers and public policymakers understand how consumers use health claims and nutrient information to evaluate restaurant menu items. This research is a first step in helping consumers make better choices when dining out.
We suggest that consumers' evaluations of a food item are more favorable when a health claim is presented than when no claim is made. This expectation is consistent with prior research showing that consumers had more favorable ratings of the healthiness of a product and higher purchase intentions after they were exposed to a general statement claiming that the product was "healthy" (Andrews, Netemeyer, and Burton 1998; Roe, Levy, and Derby 1999). Similarly, we expect a main effect of nutrition information on consumers' product evaluations and purchase intentions. Favorable nutrition information should result in more favorable product evaluations and higher purchase intentions for both a packaged food product and a restaurant menu item, which would confirm and extend the findings of previous research (Garretson and Burton 2000; Keller et al. 1997).
Roe, Levy, and Derby (1999) find that consumers considered a product to be healthier when health and nutrient claims were presented. If nutrition information and a health claim influence evaluations of nutritional value, the perceived risk of disease associated with the consumption of that food is also likely to be affected. We presented consumers in this study with a "heart-healthy" claim that identified the relationship between the consumption of saturated fat and cholesterol and the likelihood of coronary heart dis-ease, but not a specific nutrient claim. From this health claim, we expect a main effect on perceptions of disease risk, particularly stroke and heart disease. The health claim should decrease the perceived likelihood of disease if the food is included as a regular part of the consumer's diet. The availability of favorable nutrition information should be associated with the same general pattern of results, though effects on disease risk should be more modest because of the lack of specific disease information.
However, we suggest that such main effects are qualified by significant interactions. Previous research indicates that prior expectations influence subsequent information processing when the subsequent information is ambiguous (e.g., Lord, Ross, and Lepper 1979). A health claim presented on the front panel of the package is typically encountered before nutrition information, which is usually presented on the back or side panel, is processed. Specific expectations created by a health claim may bias the processing of information presented by the Nutrition Facts panel. Thus, we expect disease risk evaluations to be lower when favorable nutrition information is accompanied by a health claim than when no claim is present. However, for nutrition and product evaluations, the claim should not have a positive effect beyond the relevant and specific favorable nutrition information in the panel. When no nutrition information is available, there is no specific information that may be used to test consumers' expectations about the product created by the claim. With only ambiguous information (e.g., a product picture) available to address expectations formed by exposure to the claim, the effect of the claim on product evaluations and disease risk perceptions should be favorable compared with the no-claim condition.
How will consumers respond if the health claim is not consistent with the nutrition information presented on the Nutrition Facts panel? In general, consumers tend to be somewhat more suspicious of health and nutrient claims; the Nutrition Facts panel is often used to verify such claims (Levy 1995). This suggests that when a health claim contradicts information on the Nutrition Facts panel, the perceived credibility of the manufacturer will be diminished and consumer evaluations will be unfavorably affected. On the basis of this discussion, we posit the following hypotheses:
H1: A heart-healthy claim has a favorable influence on consumer evaluations (attitude toward the product, nutrition attitude, and purchase intention) and reduces consumers' perceptions of the risk of heart disease and stroke.
H2: There is a positive (negative) effect of favorable (unfavorable) nutrition information on consumer evaluations, and positive nutrition information reduces consumers' perceptions of the risk of heart disease and stroke.
H3: These dependent measures are influenced by an interaction between a health claim and the provision of nutrition information. When nutrition information is not provided, inclusion of a health claim has a positive effect on evaluations and reduces disease risk perceptions. When favorable nutrition information is presented, inclusion of a health claim has no effect on evaluations but reduces disease risk perceptions. When unfavorable nutrition information is presented, inclusion of a health claim has a negative effect on evaluations and no effect on disease risk perceptions.
H4: Perceived source credibility is influenced by an interaction between a health claim and the provision of nutrition information. Perceived source credibility is lowest when the information presented is contradictory, that is, when a health claim is provided but the nutrition information is unfavorable.
Are consumers' evaluations of a specific menu item influenced by the healthiness of the other items on the menu? For example, will consumers' evaluations of a lean, eight-ounce sirloin steak differ depending on whether it is presented on a "steakhouse" menu (containing many high fat, high cholesterol items) or on a more "vegetarian" menu (containing many low fat items)? Prior research has demonstrated the important role played by reference points during many consumer choice and judgment processes (Dhar, Nowlis, and Sherman 1999; Dhar and Simonson 1992). We suggest that nutrition information presented for the other menu items will serve as a reference point against which the target item is evaluated and thus moderate the effects of both nutrition information and a health claim on the dependent measures.
In the absence of nutrition information for nontarget menu items, favorable (unfavorable) nutrition information is expected to have a positive (negative) effect on consumer evaluations and the perceived likelihood of disease. Compared with the unhealthy nutritional context, in a healthy context, the provision of favorable nutrition information for the target item should have a less positive influence on consumer evaluations and risk perceptions. Similarly, in the unhealthy nutritional context, the provision of negative nutrition information should have a less negative effect on consumer evaluations and disease risk perceptions than in the healthy context. However, in the unhealthy nutritional context, the provision of favorable nutrition information should have a positive effect on consumer evaluations and reduce the perceived risk of disease. In addition, because consumers are somewhat more suspicious of health claims than of nutrient information presented in Nutrition Facts panels (Levy 1995), when a nutritional context is not provided, the inclusion of a health claim will have a stronger (more positive) effect on evaluations and perceived risk of dis-ease than when a nutritional context is provided. We constructed the stimuli so that in the healthy nutritional context, the target item had slightly higher levels of total fat, saturated fat, cholesterol, and sodium than the other two nontarget items. Thus, the provision of a health claim in the healthy nutritional context should decrease perceived source credibility. On the basis of this discussion, we propose the following hypotheses:
H5: When the nutritional content of alternative menu items is unhealthy, consumers' evaluations of the target product are more favorable and their perceptions of disease risk are lower than when the nutritional content of alternative menu items is healthy.
H6: When the nutritional context created by alternative menu items is healthy, favorable nutrition information has a less positive effect on evaluations and risk perceptions than when the nutritional context is unhealthy. Similarly, when the nutritional context is unhealthy, unfavorable nutrition information has a less negative effect on evaluations and risk perceptions than when the nutritional context is healthy.
H7: The nutritional context in which a food is evaluated moderates health claim effects on consumer evaluations and disease risk perceptions. When the nutritional content of alternative menu items is not provided, inclusion of a health claim has a stronger (more positive) effect on evaluations and perceived risk of disease than when the nutritional context is provided.
H8: The nutritional context in which a food is evaluated moderates the effect of a health claim on perceived source credibility. When the nutritional context is healthy, the provision of a health claim reduces source credibility more than when the context is unhealthy or when no context is provided.
Method
Pretests. The first pretest assessed nutrient levels considered for use in the first two experiments. Two Nutrition Facts panels were developed from two frozen lasagna dinner products found in local supermarkets. On the basis of these products, the two panel stimuli had different levels of fat (18 grams and 2.5 grams), saturated fat (10 grams and 1 gram), and cholesterol (50 milligrams and 10 milligrams). Other nutrient levels were identical across the stimuli (e.g., 360 milligrams of sodium). Forty respondents, presented with one of the Nutrition Facts panels, evaluated the overall product nutrition level and nutrient levels. Significant differences (t-values from 2.53 to 5.18, p < .01) occurred between the two Nutrition Facts panels for overall nutrition level and for each individual nutrient. These findings suggest that these nutrient levels are appropriate for the nutrition manipulation in Studies 1 and 2.
The second pretest examined issues related to the provision of nutrition information on the Study 2 menu stimuli. Three nutrition conditions were assessed: the favorable and unfavorable conditions from the first pretest and a control condition with no nutrition information. Results for nutrition perceptions and nutrient evaluations were again significant (Wilks' lambda = .53, F = 4.3, p < .05), and means were in the desired direction. Reliabilities of multi-item measures were all satisfactory. According to discussions with respondents, there were no problems with the realism of the menu stimuli or hypothesis guessing.
Design, procedures, and study participants. Because Studies 1 and 2 differed in only one important respect, we discuss their design, procedures, and participants concurrently. Whereas a packaged food product (a microwaveable, frozen lasagna dinner) was the focus of Study 1, Study 2 focused on a restaurant menu item (a lasagna entrée). The menu and package were constructed to be similar in design.
The picture of the product was identical, the same (fictitious) brand name ("Blue Ribbon") was used on the package and the restaurant menu, the lasagna serving was the same size (255 grams), and the description of the product was invariant. To increase the comparability of the findings of Studies 1 and 2, only the target item (presented as the feature of the day) was presented on the menu. We used a 2 (inclusion or exclusion of a heart-healthy claim)3 (nutrition information level with control [no information], unfavorable, or favorable conditions) between-subjects design in both Studies 1 and 2. In the heart-healthy claim conditions, a heart-shaped symbol identified the target food as a heart-healthy selection and included a footnoted health claim consistent with FDA-permitted claims regarding the relation-ship between saturated fat and cholesterol and the risk of coronary heart disease. The footnoted claim, presented on the front of the package and at the bottom of the menu, stated, "A diet low in saturated fat and cholesterol may reduce the risk of coronary heart disease." In the no-claim condition, the heart-healthy claim information was omitted.
According to the results from the pretest, favorable and unfavorable nutrition conditions differed in nutrient levels on the Nutrition Facts panel for fat, saturated fat, cholesterol, and calories from fat. Nutrient levels of sodium, carbohydrates, fiber, protein, and vitamins and minerals were held constant across conditions. In the favorable nutrition value condition, use of the heart-healthy claim is consistent with FDA requirements for levels of saturated fat and cholesterol. This claim is not consistent with the requirements in the unfavorable nutrition condition.[ 2]
Participants were members of a consumer household research panel for a southern U.S. state and were screened for primary food shopper status. Panel members were mailed packets with stimuli for either Study 1 or Study 2, a survey that included questions of general interest, and a stamped self-return envelope. The combined response rate for the studies was approximately 48%; 147 useable surveys were returned for Study 1, and 145 useable surveys were received for Study 2. Participants ranged in age from 21 to 87 years, and 74% were women. The number of times meals had been purchased at restaurants in the past month ranged from 0 to 63.
Dependent measures. Most dependent measures were assessed with seven-point scales, and all scales were recoded when necessary so that higher values indicated more positive responses. All multi-item measures were divided by the number of scale items, and these mean scores were used in analyses. Items used to measure attitude toward the brand, purchase intentions, nutrition attitude, and source credibility appear in the Appendix. Across the two studies, all coefficient oo exceeded .80. To assess disease risk perceptions, participants considered whether the food product, if eaten regularly as part of their diet, would decrease or increase their likelihood of developing heart disease and having a stroke. These disease risk items were single-item measures employing a nine-point scale.
Results
Multivariate and univariate results for effects of the health claim and nutrition information manipulations on consumer evaluation and disease risk perceptions appear in Table 1; cell means are in Table 2. The top portions of the tables contain information pertaining to Study 1 (the packaged food product), and the bottom portions of the tables contain information pertaining to Study 2 (the restaurant menu item). We discuss the findings from Studies 1 and 2 separately to facilitate comparisons to prior research and between the different contexts (i.e., package versus menu).[ 3]
Findings from Study 1. Multivariate effects of the health claim (Wilks' lambda = .90, F = 2.57, p <.05) and nutrition information (Wilks' lambda = .82, F = 2.40, p < .01) are significant. The two-way interaction between the health claim and nutrition information is not significant (Wilks' lambda = .95, F < 1). Follow-up univariate analyses reveal that the provision of a heart-healthy claim has a favorable influence on nutrition attitude (F( 1, 140) = 10.09, p < .01). The health claim also reduces the perceived risk of heart disease (F( 1, 140) = 8.03, p < .01) and stroke (F( 1, 140) = 7.41, p < .01). These findings offer partial support for Hl.
In H2, we hypothesized that favorable nutrition information would positively affect consumer evaluations and reduce the perceived risk of disease. Consistent with this pattern of predictions, the univariate results show a significant main effect of nutrition information on attitude toward the product (F( 2, 140) = 3.82, p < .05), nutrition attitude (F( 2, 140) = 9.90, p <.01), and purchase intentions (F( 2, 140) = 6.45, p <.01). In addition, favorable nutrition information reduces consumers' perceptions of the risk of stroke (F( 2, 140) = 3.14, p <.05) and heart disease (F( 2, 140) = 3.76, p <.05), as was predicted. However, contrary to the expectations presented in H3 and H4, the health claim does not interact with nutrition information to influence consumer evaluations (F( 2, 140) < 1 for all measures) or perceived credibility (F( 2, 140) = 2.02, p > .10).
Findings from Study 2. Study 2's results focus on the restaurant menu item. As we predicted, there is a multivariate main effect of the health claim (Wilks' lambda = .78, F = 6.28, p <.001). The provision of a health claim reduces the perceived likelihood of both heart disease (F( 1, 142) = 10.62, p <.001) and stroke (F( 1, 142) = 9.41, p <.01). However, the provision of a health claim does not influence attitudes toward the product (F( 1, 142) < 1), nutrition attitude (F( 1, 142) = 2.42, p >.10), or purchase intentions (F( 1, 142) = 1.06, p >.10). H1 is therefore only partially supported.
We also predicted a positive (negative) effect of favorable (unfavorable) nutrition information on the dependent measures. Consistent with the pattern of predictions, there is a significant multivariate main effect of nutrition information (Wilks' lambda = .62, F = 6.27, p <.001). As we predicted in H2, favorable (unfavorable) nutrition information has a positive (negative) effect on attitude toward the product (F( 2, 142) = 14.85, p <.001), nutrition attitude (F( 2, 142) = 33.28, p <.001), and purchase intention (F( 2, 142) = 16.25, p <.001). Favorable (unfavorable) nutrition information also reduces (increases) the perceived risk of heart disease (F( 2, 142) = 19.61, p <.001) and stroke (F( 2, 142) = 15.86, p <.001).
A significant interaction between the provision of nutrition information and a health claim, according to H3, should influence consumer evaluations and perceived risk of disease. The results of a multivariate analysis of variance confirm this expectation (Wilks' lambda = .83, F = 2.27, p <.05). Univariate results are significant for nutrition attitude (F( 2, 142) = 4.42, p <.01), purchase intention (F( 2, 142) = 3.51, p <.05), perceived likelihood of stroke (F( 2, 142) = 7.13, p <.001), and perceived risk of heart disease (F( 1, 142) = 6.37, p <.01) and are marginally significant for attitude toward the product (F( 2, 142) = 2.86, p <.10). Plots for significant interactions appear in Figure 1.
Follow-up analyses provide further insight into the significant interactions. When no nutrition information is available on the menu, there is a significant multivariate main effect of the heart-healthy claim (Wilks' lambda = .67, F = 3.02, p <.05) on the dependent measures. Univariate results show that the claim positively influences both purchase intentions and nutrition attitude (t = 2.46 and 2.67, respectively, p < .01) and reduces the perceived likelihood of dis-ease risk for heart disease and stroke (t = -3.07 and -3.45, respectively, p < .01). In the favorable nutrition condition (when the health claim provides information that is consistent with nutrition information), the multivariate effect of the claim is also significant (Wilks' lambda = .61, F = 4.94, p < .001). As we predicted, the health claim lowers the perceived likelihood of both heart disease (t = -3.33, p < .01) and stroke (t = -3.37, p < .01), but nutrition attitude and purchase intentions are not affected by the claim when there is favorable nutrition information. Thus, when the nutrition information is positive, the heart-healthy claim adds information that affects disease risk perceptions but not general evaluations. In the unfavorable nutrition condition, neither the multivariate nor the univariate effects of a health claim achieve significance, which indicates that the claim does not have any positive effect when the nutrition information is not favorable. In summary, the interaction effect proposed in H3 is partially supported.
H4 suggests that the effect of a heart-healthy claim on perceived credibility is moderated by the provision of nutrition information. The univariate results provide strong support for this prediction (F( 2, 142) = 5.84, p < .01). The relevant plot for the test of H2 is in Figure 2, Panel A. In the favorable nutrition condition and when no nutrition information is presented, the health claim does not influence perceptions of source credibility (t < 1). As shown in Figure 2, Panel A, in these conditions, credibility perceptions are similar regardless of whether the claim is present. However, in the unfavorable nutrition condition (i.e., when the nutrition information does not support the heart-healthy claim), the health claim has a negative influence on perceived source credibility (t = -3.69, p < .01). Credibility is significantly lower when the (inaccurate) health claim is available compared with when no claim is presented (M = 3.32 versus 4.92).
Discussion
The findings show that when a heart-healthy claim is on the package or menu, consumers generally judge the product to reduce the likelihood of heart disease or stroke, but favorable nutrition information leads to more positive attitudes toward the product, nutrition, and purchase intentions, in addition to the belief that the product reduces disease risk. Effects of nutrition information relative to the claim manipulation are particularly strong for the restaurant menu context.
Consistent with prior research examining health claims and nutrition information on packages (e.g., Ford et al. 1996; Garretson and Burton 2000), nutrition information did not interact with the effect of the claim in the package environment. However, the interaction between the health claim and nutrition information was significant in the context of a restaurant menu, which further highlights the usefulness of providing nutrition information to help consumers make healthy food choices when dining out. When favorable nutrition information was presented, the use of a heart-healthy claim further decreased the perceived likelihood of stroke and heart disease.
This pattern of findings contrasts with that of the product attitude and purchase intentions variables, for which the claim has no influence when the nutrition information is favorable. For disease risk, the heart-healthy claim probably serves as incremental, useful information that influences disease risk perceptions, but it does not add information that affects attitudes and intentions beyond the favorable effects of the Nutrition Facts information. When no Nutrition Facts information is presented, however, the claim is the only cue available, and it affects attitudes, purchase intentions, and disease risk perceptions. When unfavorable nutrition information is available, the heart-healthy claim has no influence on either the evaluations or disease risk perceptions. This overall pattern of results suggests that consumers may be somewhat wary of health claims and prefer instead to trust information contained on the Nutrition Facts panel when it is available. This pattern of results also suggests that consumers in general are fairly sophisticated in their ability to use information provided by the Nutrition Facts panel to formulate appropriate conclusions.
This study suggests that though some findings from prior research on labeling may apply to restaurant foods, unfavorable nutrition information does not have equivalent effects when presented in different consumption contexts. Although the positive effects of favorable nutrition information appear similar for food products in both packaged goods and restaurant contexts, the negative effects of unfavorable information are stronger for a menu item than for a packaged good. Furthermore, the provision of nutrition information for a menu item generally has stronger effects than nutrition information presented on a packaged food product. This implies that many consumers do not realize the unhealthiness of many foods prepared outside the home.
In consideration of these findings, the purpose of Study 3 was to provide additional insight into the effects of a health claim and nutrition information on consumers' evaluations of restaurant menu items. There are several key differences between our first two studies and Study 3. Rather than presenting a Nutrition Facts panel on the menu, we presented nutrient information immediately after the item's description. This format is a more practical and commonly used way to present nutrition information on a menu. In addition, three menu items, rather than just a single entrée, were presented. Finally, the nutritional context, that is, the nutrient levels of the other food items on the menu, was varied as a third factor.
Design, Procedures, and Study Participants
Study 3 was a between-subjects experiment that used a 2 (inclusion or exclusion of a heart-healthy claim) x 3 (nutrition information level with control, unfavorable, or favorable conditions) x 3 (nutritional context, or the nutrient levels of the nontarget menu items, with control, healthy, and unhealthy conditions) design. The stimuli identified the target menu item as a heart-healthy selection in the heart-healthy claim condition and included a footnoted health claim consistent with FDA-permitted claims regarding the relationship between saturated fat and cholesterol and the risk of coronary heart disease.
There were three items on the menu: Slow-Roasted Chicken, Chicken Marsala, and Grilled Chicken Fajitas. To avoid introducing bias into the experiment because of consumers' food perceptions and preferences, the target item was counterbalanced across conditions; each item served as the target item for one-third of the subjects, and the order of the menu items was rotated. In the unfavorable nutrition information condition, the target item always contained 35 grams (54% DV) total fat, 11 grams (55% DV) saturated fat, 180 milligrams (60% DV) cholesterol, and 1400 milligrams (58% DV) sodium. In the favorable condition, the target item contained 10 grams (15% DV) total fat, 3 grams (15% DV) saturated fat, 40 milligrams (13% DV) cholesterol, and 600 milligrams (25% DV) sodium. The nutrition levels of the two alternative menu items were also counterbalanced across subjects in both the favorable and the unfavorable nutritional contexts. In the healthy context, nutrient values of the two nontarget items were slightly more positive than the nutrient levels for the favorable target item condition. In the unhealthy context, nutrient values of the alternative items were slightly more negative than the nutrient levels for the unfavorable target item condition. In the healthy context, nutrient values of the two nontarget items were 6 grams and 8 grams (9% and 12% DV) total fat, 2 grams and 2.5 grams (10% and 13% DV) saturated fat, 30 milligrams and 35 milligrams (10% and 12% DV) cholesterol, and 570 milligrams and 560 milligrams (24% and 23% DV) sodium. In the unhealthy context, nutrient values of the two nontarget items were 40 grams and 50 grams (62% and 77% DV) total fat, 13 grams and 18 grams (65% and 90% DV) saturated fat, 190 milligrams and 220 milligrams (63% and 73% DV) cholesterol, and 1750 milligrams and 2000 milligrams (73% and 83% DV) sodium. The actual nutrition levels for the menu items were most similar to the unfavorable nutrition levels, according to information compiled by the Center for Science in the Public Interest (1997), which analyzed the nutritional content of menu items from various fast-food and dinner house restaurants.
Study participants were recruited at a local mall. Inter-viewers set up a table in the mall with a sign stating "Consumer Research Study." Shoppers who agreed to participate received $1. A total of 364 shoppers, equally divided in terms of sex, participated in the study, and they ranged in age from 18 to 82 years. Participants were randomly assigned to 1 of the 18 conditions, handed a booklet containing the stimuli and dependent measures, and then seated at a nearby table. After completing the study, which took approximately ten minutes, participants were thanked, debriefed, and paid.
Dependent Measures
The same dependent measures used in the previous two studies were used in Study 3. Reliabilities for the attitude toward the product (alpha = .97), purchase intention (alpha = .83), nutrition attitude (alpha = .85), and credibility (alpha = .93) scales were all greater than the acceptable minimum level.
Results and Discussion
Multivariate effects of the health claim (Wilks' lambda = .86, F = 9.41, p < .001) and nutrition information (Wilks' lambda = .89, F = 3.37, p < .001) are significant. Multivariate and univariate results for relevant dependent variables appear in Table 3, and means are presented in Table 4.
Univariate results show that, as we expected, the provision of a heart-healthy claim has a favorable influence on nutrition attitude (F( 1, 346) = 23.39, p < .001) and purchase intention (F( 1, 346) = 4.17, p < .05). The provision of a health claim also diminishes the perceived likelihood of heart disease (F( 1, 346) = 38.32, p < .001) and stroke (F( 1, 346) = 20.30, p < .001). Attitude toward the product is not affected by the provision of a health claim (F( 1, 346) = 1.79, p > .10), and therefore H1 is partially supported. In the case of nutrition information, univariate findings are significant for nutrition attitude (F( 2, 346) = 7.63, p < .001), perceived risk of heart disease (F( 2, 346) = 12.26, p < .001), and perceived risk of stroke (F( 2, 346) = 6.65, p < .001). The means in Table 4 indicate that favorable nutrition information leads to more positive nutrition attitudes and lower perceptions of disease risk than does unfavorable nutrition information. Contrary to H3, when nutritional context is included as a factor, the provision of nutrition information and the health claim do not interact (Wilks' lambda = .95, F = 1.47, p > .10). In addition, the interaction of the health claim and nutrition information does not have a significant effect on source credibility. H3 and H4 are therefore not supported.
H5 predicts that when information regarding the nutritional content of alternative menu items is unhealthy, consumer evaluations of the target product will be more favorable and perceptions of disease risk will be lower compared with when the context is healthy. Multivariate results shown in Table 3 are significant (p < .01), and follow-up univariate tests are significant for consumer attitudes, purchase intentions, and disease risk perceptions (p < .01 for all). Thus, H5 is supported.
As hypothesized in H6, the nutritional context in which a food is evaluated moderates the main effects of nutrition information (Wilks' lambda = .89, F = 1.56, p < .05). Univariate results are significant for nutrition attitude (F( 4, 346) = 2.65, p < .05), perceived risk of heart disease (F( 4, 346) = 2.81, p < .05), stroke (F( 4, 346) = 3.34, p < .05), and attitude toward the product (F( 4, 346) = 2.31, p < .057). Plots for nutrition attitude and heart disease are shown in the upper portion of Figure 3. When the nutritional content of alternative menu items is healthy (i.e., healthy context), favorable nutrition information for the target item has a less positive effect on nutrition attitude compared with the no nutrition information control than when the nutritional context is unhealthy.
Similarly, when the nutritional context is unhealthy, unfavorable nutrition information has a less negative effect on nutrition attitude compared with the no nutrition information control than when the nutritional context is healthy. The means in Table 4 show that when nutrition information is not presented for either the target item or the other menu options (i.e., the status quo for most restaurant menu items), consumers' nutrition evaluations and disease risk perceptions are similar to evaluations and perceptions when favorable nutrition information about the target item is presented. Because nutrition levels for the actual menu items were generally consistent with the nutrition level in the unfavorable condition, this indicates that consumers overrate the healthiness and effects on disease risk of these menu items.
As predicted in H7, the multivariate effect of the nutritional context by health claim interaction is significant (Wilks' lambda = .94, F = 1.78, p < .05). Follow-up univariate analyses indicate that attitude toward the product (F( 2, 346) = 5.12, p < .01) and perceived likelihood of heart disease (F( 2, 346) = 3.01, p < .05) are affected by the interaction between a health claim and nutritional context, and nutrition attitude and stroke approach significance (p < .10). Plots for product attitude and heart disease are shown in the bottom portion of Figure 3. When information regarding the nutritional content of alternative menu items is not provided, inclusion of a health claim has a positive effect on evaluations and reduces perceived risk of disease (t-values range from 3.37 to 5.51, p < .01). However, when nutrition information is presented about the menu alternatives, inclusion of a health claim has no significant effect on product attitude (t = 1.42 and .03 for the unhealthy and healthy contexts, respectively) but reduces perceived risk of heart disease (t = -3.03, p < .01 and t = -1.99, p < .05 for the unhealthy and healthy contexts, respectively).
H8 predicts that the nutritional context in which a food is evaluated moderates the main effects of a health claim on perceived source credibility. As we show in Figure 2, Panel B, this expectation is supported (F( 2, 346) = 3.29, p < .05); perceived source credibility was lowest when information was contradictory. That is, when the nutritional context was healthy, provision of a health claim diminished source credibility (t = -2.91, p < .05), but the claim had no effect on credibility when the context was unhealthy or when no context was provided (t < .50, p > .50).
The results of this research, together with the growing epidemic of obesity in the United States (Centers for Disease Control and Prevention 2002; Spake 2002), suggest that a major objective of the NLEA has not been accomplished. Although the NLEA may have helped consumers make more informed choices about foods prepared at home, it has not helped consumers with choices when dining out. Consumers now obtain more than one-third of their calories from food prepared outside the home, and because menus often provide little or no information regarding the nutritional value of the items, most consumers have little knowledge about the types and levels of nutrients they are routinely consuming. Consumers are filing class-action lawsuits against fast-food restaurants, which are charged with use of deceptive marketing practices that have resulted in obesity-related diseases (Tyre 2002).
Across our studies, the multivariate results show that there were positive effects of the inclusion of a heart-healthy claim on a package or menu. As we expected, favorable nutrition information presented on a Nutrition Facts panel (i.e., Studies 1 and 2) also led to more positive attitudes toward the product, nutrition, and purchase intentions and reduced perceived disease risk. As shown in Table 1, the effects of the nutrition information on all dependent variables were more pronounced in the menu context (Study 2) than in the package context (Study 1), even though the nutrient values were identical.
From a theoretical perspective, this research also provides insight into how nutrition information and a health claim interact to influence consumers' attitudes and purchase intentions. Consistent with prior research (Ford et al. 1996; Garretson and Burton 2000; Mitra et al. 1999), the results show that in a package environment, the provision of Nutrition Facts panel information does not moderate the effects of a health claim. However, for the menu stimulus, there are consistent interactions across all dependent variables. As shown in Figure 2, when nutrition information was not available, the heart-healthy claim had a positive influence on nutrition attitude and purchase intentions and reduced disease risk perceptions. However, when favorable or unfavorable nutrition information was presented, the heart-healthy claim had little positive effect on attitudes and intentions. These findings, together with the strong nutrition information main effects in Study 2, suggest that consumers are sensitive to and willing to use any available nutrition information when forming product evaluations and purchase intentions for menu items.
The results of Study 3 suggest that menu nutrition information does not need to be presented on a Nutrition Facts panel to have effects on consumer evaluations. Also, by providing nutrition information for the other menu items, Study 3 offers insight into the effects of nutritional context on consumer evaluations. In this more complex environment, target item nutrition information had a weaker effect, and the significant claimnutrition information interactions were replaced by significant contextnutrition and contextclaim interactions. In general, providing a nutritional context, or frame of reference from which to evaluate a specific menu item, can reduce the influence of the target item's nutrition information on consumer evaluations.
From a conceptual perspective, this research provides new insight into the effects of the context created by competing products on target item evaluations. The effects of context on evaluations have been studied extensively in the psychology and marketing literature (see Eiser 1990; Kahneman and Miller 1986; Viswanathan and Hastak 2002), but to our knowledge, previous research has not examined how the context created by competing items interacts with both a claim about the target item and objective information about the item. The findings pertaining to the modified impact of objective information and more subjectively perceived claims when contextual nutrition information is available suggest that the effects of competing product context are more conceptually intriguing than previously assumed.
Furthermore, our findings extend prior research on labeling issues in two ways. First, the results provide evidence of the potential for interactive effects of health claim and nutrition information, at least when objective nutrition information is not available or accessed. Theoretically, this implies that the effects of the confirmatory bias of a health claim can be reduced by the provision of nutrition information. Second, the results extend findings from prior research on labeling issues to foods marketed by restaurants. Consumer evaluations and disease risk perceptions of restaurant menu items can be significantly influenced by a health claim and, especially, nutrition information on a Nutrition Facts panel. The results from this study also reinforce the notion that misleading health claims can have significant negative consequences for the marketer. A claim that was inconsistent with target item nutrition information or contextual nutrition information diminished the credibility of the restaurant. Claims that cannot be substantiated or are perceived as questionable because of context can cause considerable harm to a marketer's reputation.
The health claim used in this study focuses specifically on the relationship between a diet low in saturated fat and cholesterol and a reduced risk of coronary heart disease. As we expected, consumers perceived the risk of heart disease and stroke to be lower when a health claim was used and no nutrition information was available. The significant interaction between health claim and nutrition information found in Study 2 suggests that a health claim can supplement nutrition information presented on a restaurant menu. Even when favorable nutrition information was available, the provision of a health claim further decreased the perceived risk of stroke and heart disease.
This pattern of findings contrasts with that of the product attitude and purchase intentions variables, for which the claim had no influence when the nutrition information was favorable, as shown in Figure 1. The heart-healthy claim is incrementally useful information that serves to influence disease risk perceptions, but it does not add information that influences attitudes and intentions beyond the favorable effects of the nutrition information. When no nutrition information is presented, however, the claim is the only cue available, and it affects attitudes, intentions, and disease risk perceptions. When unfavorable nutrition information is available, the heart-healthy claim has no influence on either the evaluations or the disease risk perceptions. The overall pattern of results suggests that consumers are fairly sophisticated in their ability to use Nutrition Facts panel information to draw appropriate conclusions and are somewhat wary of health claims, preferring instead to trust specific nutrition information when it is available.
Implications for Marketers
Our findings show that in the absence of nutrition information, a health claim can have favorable effects on product attitudes and purchase intentions. Restaurants using health claims as promotional elements are likely to benefit from such actions, as long as the claims can be supported. It is also potentially beneficial for marketers of packaged food products to place health claims on the front of their packages, especially when targeting consumer segments that are unlikely to access nutrition information presented on the back of the package (Mitra et al. 1999, p. 110). Favorable nutrition information on Nutrition Facts panels has even stronger effects than health claims on product attitudes and purchase intentions; such favorable information seems capable of being used as an effective promotional tool for restaurants and packaged food companies.[ 4] One of the objectives of the NLEA was to promote more healthful foods in the marketplace; findings from this research suggest that such initiatives can be beneficial to marketers.
The results pertaining to the effects of context in Study 3 also have managerial implications. When consumers evaluate a target menu item in the context of alternatives that are unhealthy (i.e., items high in fat and saturated fat), they have more positive attitudes and greater purchase intentions for the target item. This suggests that direct comparisons in advertisements and in-store promotions between a relatively healthy menu item and competitors' less healthy offerings should have positive effects. Essentially, a restaurant promoting healthful choices potentially can benefit from the main effects of nutritional level and nutritional context, as well as from their interaction.
Implications for Public Policy
Although health claims made by restaurants must be consistent with the claim definitions established by the NLEA, guidelines regarding the provision of nutrition information in a restaurant setting are considerably more lenient than those for packaged food products. It may be difficult for consumers to verify claims made by restaurants in many circumstances. In the absence of nutrition information, consumers perceive the food product to be more nutritious when a health claim is presented. Thus, if a consumer considers claim information on the front of a packaged food product (where health claims are typically placed) or on a menu and nutrition information is not examined or available, he or she may have a more favorable impression of the nutritional value of the product than is warranted if the claim requirements are not completely met. When a health claim is made for a specific menu item, restaurants should be encouraged to provide the appropriate nutrient values to substantiate that claim. Although consumers may be somewhat wary of health claims in the absence of other information, the means in Tables 2 and 4 indicate that health claims are persuasive. Presenting nutrition information on restaurant menus should encourage consumers to consider the nutritiousness and healthfulness of the food they consume.
The increasing frequency with which consumers dine outside the home, the high levels of fat and saturated fat in many restaurant foods, and the large serving sizes and caloric content offered by restaurants are of great concern to many consumer welfare advocates and policymakers (Centers for Disease Control and Prevention 2002; Spake 2002; Tyre 2002). In Study 3, when no nutrition information was provided for any of the menu items, consumers rated the nutritiousness of the target product as consistent with the positive nutrition information condition. Because the levels of fat, saturated fat, and cholesterol for the menu items examined were most similar to the unfavorable nutrition condition, this finding suggests that consumers overestimate the healthfulness of restaurant items (e.g., most consumers probably are not aware that some restaurant items provide a full day's worth or more of fat and saturated fat; Hurley and Schmidt 1996). This lack of knowledge and misperception about the healthfulness of restaurant foods suggest that consumers who dine out frequently do not realize or consider the effect of their diet on long-term disease risk. We suspect that if restaurants were required to disclose nutrition levels for at least very unhealthy items, it would affect purchase behavior for many consumers and probably motivate restaurants to improve the nutritiousness of such items. Our research demonstrates that consumers generally can use health claims and nutrition information to make appropriate evaluations; however, many may lack the appropriate information or motivation to influence actual consumption behavior, particularly in restaurants.
Limitations and Opportunities for Further Research
There are several limitations of this study that should be acknowledged. Consumers examined either mock-ups for a packaged food product or restaurant menu items in three studies. Consumers may behave differently in an actual restaurant setting or while grocery shopping.[ 5] In addition, though a mail survey is a widely accepted means of data collection, there is minimal control over the response behavior of consumers. However, observation and greater control over the subjects were possible in Study 3 because the surveys were completed in full view of the interviewers.
We examined the effects for a single, relatively well-known health claim that links a diet low in saturated fat and cholesterol to a lower risk of coronary heart disease. This is clearly an important claim because of its link to diet and the number of people who die each year because of cardiac-related diseases. Lesser-known health claims, such as "calcium reduces the risk of osteoporosis," may have interacted differently with nutrition information. How different types of health claims and different levels of nutrition information influence consumers' attitudes, intentions, and perceptions of disease risk should be explored for a variety of products and contexts. Analysis of actual purchase data across different information-provision environments in restaurants would be especially valuable in better understanding how marketers and public policymakers can help consumers become more informed about the foods they eat. Marketing researchers have much to contribute to the fight against obesity, a national health problem that has reached epidemic proportions with staggering costs. Helping consumers choose more healthful foods will reduce the more than 300,000 annual deaths in the United States directly attributable to diseases and conditions associated with being over-weight and obese, minimize the $110 billion in annual costs associated with diet-related diseases, and improve the over-all quality of life for countless consumers.
The authors thank the anonymous JM reviewers for their many helpful comments.
1 To make a heart-healthy claim, one of two conditions must be satisfied (Kurtzweil 1998). The first condition is that the item is low in saturated fat, cholesterol, and fat. This claim can indicate that a diet low in saturated fat and cholesterol may reduce the risk of heart disease. The second condition is that the item is a significant source of soluble fiber (found in fruits, vegetables, and grain products); is low in saturated fat, cholesterol, and fat; and provides without fortification significant amounts of one or more of six key nutrients. In addition, to make a heart disease claim, the food must contain less than the disqualifying amount of sodium.
2 For a heart disease claim, the product must contain 3 grams of fat or less and 1 gram of saturated fat or less per 100 grams of food content. However, similar claims on menus have not always met this standard. For example, some restaurants have placed a Fit Fare symbol (a heart with the words "fit fare") next to menu items that contain almost 15 grams of fat.
3 We thank an anonymous reviewer for this suggestion.
4 The recent advertising campaign by Subway, which promotes lower calorie and lower fat sandwiches as a means for consumers to obtain substantial weight loss and health benefits, is one example of how nutrition has been used for effective competitive positioning.
5 As suggested by an anonymous reviewer, the responses of some of the consumers may have differed if an actual fast-food restaurant chain, rather than a university business school, had been identified as a the sponsor of this research.
Legend for the Chart
A Independent Variables
B Manova Results: Wilks'lambda
C Manova Results: F Value
D Univariate F Values: Consumer Evaluation Measures:
Attitude Toward the Product
E Univariate F Values: Consumer Evaluation Measures:
Nutrition Attitude
F Univariate F Values: Consumer Evaluation Measures:
Purchase Intentions
G Univariate F Values: Consumer Evaluation Measures:
Perceived Credibility
H Univariate F Values: Disease Risk Measures: Heart Disease
I Univariate F Values: Disease Risk Measures: Stroke
A
B C D E F G H I
Study 1: Package Finding
Heart-Healthy claim (HC)
.90 2.57[b] .45 10.09[c] .45 .10 8.03[c] 7.41[c]
Nutrition information (NI)
.82 2.40[c] 3.82[b] 9.90[c] 6.45[c] 2.83[b] 3.76[b] 3.14[b]
HC x NI
.95 .61 .41 .55 .55 2.02 .01 .43
Study 2: Menu Findings
Heart-healthy claim
.78 6.28[d] .10 2.42 1.06 6.57[c] 10.62[d] 9.41[c]
Nutrition information
.62 6.27[d] 14.85[d] 33.28[d] 16.25[d] 10.71[d] 19.61[d] 15.86[d]
HC x NI
.83 2.27[b] 2.86[a] 4.42[c] 3.53[b] 5.84[c] 6.37[c] 7.13[d]
[a]p < .10
[b]p < .05
[c]p < .01
[d]p < .001
Legend for the Chart
A Consumer Evaluation Measures[a]: Product Attitude
B Consumer Evaluation Measures[a]: Nutrition Attitude
C Consumer Evaluation Measures[a]: Purchase Intentions
D Consumer Evaluation Measures[a]: Perceived Credibility
E Disease Risk Measures[b]: Heart Disease
F Disease Risk Measures[b]: Stroke
A B C D E F
Independent Variables
Study 1: Package Means
Control (none)
No claim 4.12 2.86 3.26 3.98 6.26 6.19
Claim present 4.57 3.56 3.75 4.49 5.38 5.57
Favorable
No claim 4.89 3.77 4.58 4.77 5.59 5.77
Claim present 5.18 4.69 4.94 5.04 4.68 4.61
Unfavorable
No claim 4.20 3.10 3.88 4.90 6.70 6.30
Claim present 4.04 3.48 3.64 4.33 5.72 5.72
Study 2: Menu Means
Control (none)
No claim 3.52 3.32 3.03 4.02 6.64 6.68
Claim present 4.30 4.44 4.32 4.15 4.86 4.96
Favorable
No claim 5.43 4.77 5.23 5.27 5.63 5.63
Claim present 5.24 4.82 5.44 5.05 4.23 4.42
Unfavorable
No claim 4.10 3.03 3.91 4.92 6.74 6.48
Claim present 3.23 2.77 3.31 3.32 7.21 7.04a These measures are based on seven-point scales; higher scores indicate more favorable evaluations.
b A nine-point scale is used for each of these measures; higher scores indicate a greater perceived risk of disease.
Legend for the Chart
A Independent Variables
B Manova Results: Wilks'lambda
C Manova Results: F Value
D Univariate F Values: Consumer Evaluation Measures:
Attitude Toward the Product
E Univariate F Values: Consumer Evaluation Measures:
Nutrition Attitude
F Univariate F Values: Consumer Evaluation Measures:
Purchase Intentions
G Univariate F Values: Consumer Evaluation Measures:
Perceived Credibility
H Univariate F Values: Disease Risk Measures: Heart Disease
I Univariate F Values: Disease Risk Measures: Stroke
A
B C D E F G H I
Study 3:
Heart-healthy claim (HC)
.86 9.41{d} 1.79 23.39[d] 4.17[b] 1.74 38.32[d] 20.30[d]
Nutrition information (NI)
.89 3.37[d] 1.97 7.63[d] 1.98 2.15 12.26[d] 6.65[d]
Nutritional context (NC)
.89 3.17[d] 5.79[c] 10.81[d] 4.93[c] .22 5.36[c] 5.02[c]
HC x NI
.95 1.47 .53 1.00 1.37 .38 4.65[c] 4.37[c]
NI x NC
.89 1.56[b] 2.31[a] 2.65[b] .89 .15 2.81[b] 3.34[b]
HC x NC
.94 1.78[b] 5.12[c] 2.45[a] 1.46 3.29[b] 3.01[b] 2.31[a]
[a]p < .10
[b]p < .05
[c]p < .01
[d]p < .001
Notes: The three-way multivariate and univariate interactions
are all nonsignificant. Legend for the Chart
A Consumer Evaluation Measures[a]: Product Attitude
B Consumer Evaluation Measures[a]: Nutrition Attitude
C Consumer Evaluation Measures[a]: Purchase Intentions
D Consumer Evaluation Measures[a]: Perceived Credibility
E Disease Risk Measures[b]: Heart Disease
F Disease Risk Measures[b]: Stroke
A B C D E F
Independent Variables
Nutritional Context: Control
Control
No claim 5.47 4.47 4.62 5.48 5.69 5.88
Claim present 6.24 5.81 5.37 5.35 3.54 3.79
Favorable
No claim 5.12 4.67 4.97 5.12 5.41 4.65
Claim present 5.52 5.34 5.08 5.39 3.56 3.88
Unfavorable
No claim 4.53 4.00 4.38 5.00 6.41 5.65
Claim present 6.15 5.39 5.75 5.29 4.81 4.65
Nutritional Context: Healthy
Control
No claim 4.64 4.51 4.20 5.60 5.87 5.53
Claim present 5.35 4.83 4.93 5.13 3.87 4.17
Favorable
No claim 5.18 3.92 4.64 5.27 5.14 5.41
Claim present 5.24 4.48 4.75 4.80 5.61 5.50
Unfavorable
No claim 4.91 3.35 4.10 5.62 5.92 5.81
Claim present 4.23 3.82 4.08 4.67 5.45 5.35
Nutritional Context: Unhealthy
Control
No claim 5.16 3.84 4.67 5.62 5.73 5.87
Claim present 5.04 5.00 4.88 5.45 3.65 4.35
Favorable
No claim 5.98 5.21 5.63 5.05 3.50 3.55
Claim present 6.00 5.27 5.25 5.28 3.28 3.56
Unfavorable
No claim 5.86 4.21 4.67 5.19 6.08 5.46
Claim present 5.05 4.48 4.93 5.05 4.71 4.67a These measures are based on seven-point scales; higher scores indicate more favorable evaluations.
b A nine-point scale is used for each of these measures; higher scores indicate a greater perceived risk of disease.
GRAPHS: FIGURE 1: Effects of a Health Claim and Nutrition Information on Consumer Evaluations and Disease Risk Likelihoods for Study 2 (Claim, No claim): Purchase Intention; Nutrition Attitude; Increased Likelihood of Heart Disease; Increased Likelihood of Stroke.
GRAPHS: FIGURE 2: Effects of a Health Claim and Nutrition Information on Perceived Source Credibility. A. Study 2: Health Claim x Nutrition Information; B. Study 3: Health Claim x Nutritional Context
GRAPHS: FIGURE 3: Interaction Effects on Consumer Evaluations and Disease Risk Likelihoods for Study 3. Nutritional Attitude, Increased Likehood of Heart Disease; Attitude Toward the Product, Increased Likehood of Heart Disease
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Nutrition Attitude (coefficient alpha s = .84, .85[b]
1. I think the nutrition level of this product is (poor/good).
- 2. Based on the information provided, how important would this product be as part of a healthy diet? (not important at all/very important)
- 3. This product is (bad for your heart/good for your heart).
- 4. Overall, how would you rate the level of nutritiousness suggested by the information provided? (not nutritious at all/very nutritious)
Attitude Toward the Product (coefficient alpha s = .98, .98)
Based on the information shown for this food product, what is your overall attitude toward the product? (favorable/ unfavorable, good/bad, positive/negative; all reverse coded)
Purchase Intention (coefficient alpha s = .97, .95)
1. How likely would you be to purchase the product, given the information shown?
- 2. Assuming you were interested in buying a lasagna food product, would you be more likely or less likely to purchase the product, given the information shown?
- 3. Given the information shown, how probable is it that you would consider the purchase of the product, if you were interested in buying a lasagna product?
Source Credibility (coefficient alpha s = .89, .84)
Based on the information provided, I believe the company (restaurant) marketing this food product is: (dependable/not dependable [reverse coded], untrustworthy/trustworthy, honest/dishonest [reverse coded]).
a All items were measured using seven-point scales.
b Coefficient alpha estimates are reported for measures in Studies 1 and 2, respectively.
~~~~~~~~
By John C. Kozup; Elizabeth H. Creyer and Scot Burton
John C. Kozup is an assistant professor, Department of Marketing, Villanova University. Elizabeth H. Creyer is an associate professor, and Scot Burton is a professor and Wal-Mart Chair, Department of Marketing and Transportation, Sam M. Walton College of Business, University of Arkansas. Order of authorship was determined by a random draw.
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Record: 90- Managing Business-to-Business Customer Relationships Following Key Contact Employee Turnover in a Vendor Firm. By: Bendapudi, Neeli; Leone, Robert P. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p83-101. 19p. 2 Charts. DOI: 10.1509/jmkg.66.2.83.18476.
- Database:
- Business Source Complete
Managing Business-to-Business Customer Relationships Following Key Contact Employee Turnover in a Vendor Firm
Customers form relationships with the employees who serve them as well as with the vendor firms these employees represent. In many cases, a customer's relationship with an employee who is closest to them, a key contact employee, may be stronger than the customer's relationship with the vendor firm. If the key contact employee is no longer available to serve that customer, the vendor firm's relationship with the customer may become vulnerable. In this article, the authors present the results of two studies that examine what business-to-business customers value in their relationships with key contact employees, what customers' concerns are when a favored key contact employee is no longer available to serve them, and what vendor firms can do to alleviate these concerns and to retain employee knowledge even if they cannot retain the employee in that position. The studies are based on a discovery-oriented approach and integrate input from business-to-business customers, key contact employees, and managers from a broad cross-section of companies to develop testable propositions. The authors discuss managerial and theoretical implications and directions for further research.
Customers frequently form relationships with the employees with whom they interact, as well as with the firms these employees represent. The employees who are closest to the customer, whom we term key contact employees, include the insurance agent who calls on a business, the lead architect on a design project, or the certified public accountant from an accounting firm that does the company's books (Stanley 1985). In many cases, customers' relationships with the vendor firm's key contact employees are stronger than their relationships with the firm itself (Czepiel 1990; Gwinner, Gremler, and Bitner 1998). Vendor firms encourage the relationship-building efforts of their employees with business-to-business customers, as well as with end consumers (Cravens 1995; Dwyer, Schurr, and Oh 1987; Weitz and Bradford 1999), because they view these relationships as means to strengthen the firms' relationships with the customers.
When a vendor firm's key contact employee is no longer available to serve the customer, the loss may fundamentally alter the firm's relationship with the customer. American Express estimates that, on average, 30% of a financial advisor's clients would move with their advisor if he or she were to leave the firm (Tax and Brown 1998). Even if the customer is unwilling or unable to switch to a competitor in the short run because of switching costs and contractual obligations, in the long run, the loss of a favored key contact employee still may be a catalyst for the customer to reevaluate the business relationship with the firm (Anderson and Robertson 1995; Duboff and Heaton 1999), making the customer less open to building additional bonds with the firm and/or more open to moving to a competitor over time.
Historically, firms have dealt with this issue by relying on retention and noncompete agreements. Retention emphasizes building employees' organizational commitment and preventing turnover (Gould 1979; Lee and Maurer 1997). However, in a booming economy and tight labor market, some employee turnover is inevitable. Furthermore, the customer experiences turnover in the key contact employee position not only when the employee leaves the firm but also when the employee is transferred or promoted within the firm. Noncompete clauses and contracts prohibit employees from working for direct competitors to minimize the potential for customer loss following the loss of an employee. However, courts are striking down these clauses as restricting trade (Stafford 1998), and several states have enacted laws to limit or eliminate such clauses (Singleton 1997). Because of these economic and regulatory realities, vendor firms must find other solutions to address potential negative responses from customers in any situation in which their employees are unable to continue to serve customers.
Despite its managerial relevance (Buss 1999; Slater 1998), there has been little academic attention to what happens to the vendor firm-customer relationship when there is key contact employee turnover. We address the gap in academic literature by examining through two studies ( 1) customer responses to key contact employee turnover and ( 2) the strategies firms can use to reduce the vulnerability of the firm-customer relationship in this situation. We adopt a discovery-oriented approach, drawing on the input of business practitioners. We then integrate their input with extant literature to develop a theoretical model and research propositions.
Conceptual Background and Research Questions
In studying business-to-business customer relationships, several authors have made the case for dealing with customer relationships with the firm and its key contact employee as distinct but interrelated constructs (Barnes 1997; Weitz and Bradford 1999). Much of the literature on customer relationships with a vendor firm's employees has focused on understanding the employee characteristics that contribute to strong customer relationships such as familiarity (Brown 1995), expertise (Brown and Swartz 1989), customization (Smith and Smith 1997), similarity (Crosby, Evans, and Cowles 1990), empathy (Pilling and Eroglu 1994), likability (Jones et al. 1998), trust (Doney and Cannon 1997), and power within the organization (Moorman, Deshpandé, and Zaltman 1993). These relationships result in positive emotional ties (Beatty et al. 1996; Price and Arnould 1999) and greater likelihood of the customer continuing to do business with the firm (Seabright, Levinthal, and Fichman 1992).
Studies of the vendor firm-customer relationship have focused on the firm characteristics that result in strong ties with customers (Brown 1998), such as familiarity (Yoon, Guffey, and Kijewski 1993), financial soundness (Hammond and Slocum 1996), leadership competence (Petrick et al. 1999), and corporate social responsibility (Brown and Dacin 1997). Relationships with firms affect customers' product responses (Brown and Dacin 1997), as well as business partners' interorganizational relationships (Dollinger, Golden, and Saxton 1997).
Despite the interrelationships between the two foci of customer relationships-firms and their employees-most studies focus only on one relationship level, either the relationship with the employee (Crosby, Evans, and Cowles 1990) or the relationship with the firm (Morgan and Hunt 1994). Scant attention has been paid to customers' simultaneous relationships with both targets: key contact employees and the firms they represent (Doney and Cannon 1997).
We have identified only four studies that simultaneously examine the relationships of the customer to the employee and the vendor firm. Doney and Cannon (1997) report differential antecedents and consequences of trust in the firm versus trust in its salesperson in a business-to-business context and find that customers' trust in the firm directly affects their intentions to do business with it, whereas trust in the salesperson has an indirect effect through trust in the firm. Macintosh and Lockshin (1997) examine customers' relationships with stores and their employees and find that strong relationships with specific employees have a positive effect on the customers' attitudes toward the store. Brown (1995) reports that there is greater correspondence between evaluations of the suppliers and the suppliers' sales forces when customers evaluate vendors with whom they are familiar than when they evaluate vendors with whom they are less familiar. Reynolds and Beatty (1999), in a study of retail consumers, find that loyalty to the salesperson leads to several firm-level benefits, such as increased spending and positive word of mouth.
These four studies focus on ongoing customer relationships with both the vendor firm and the employee. Only two studies have addressed the impact of key contact employee turnover on customers' relationship with the firm. Beatty and colleagues (1996) report that retail customers, when asked hypothetically, stated that they would follow a store employee to a competing store if comparable products were available. Lovett, Harrison, and Virick (1997) draw on the resource dependence framework (Barney 1991) to present a conceptual framework of whether customer defection follows key contact employee turnover. Because the perceived dependence on a resource is greater when the resource is rare, valuable, inimitable, and nonsubstitutable, Lovett, Harrison, and Virick (1997) posit that customer defection is a function of the relative inimitability of the employee versus the firm. However, no research has empirically examined customers' concerns when a vendor firm's key contact employee leaves. Therefore, Study 1 explores the following two questions: What do customers value in their relationships with key contact employees? and What are customers' concerns about losing key contact employees?
Because our investigation is exploratory, our methods followed the guidelines for grounded theory development (Deshpande 1983; Glaser and Strauss 1967). We collected data from business-to-business managers. Rather than rely on prior theories to test data in the traditional hypotheticodeductive approach (Keaveney 1995), we analyzed practitioner input from representatives of both vendor and customer firms, allowing patterns to emerge from the data. The sequence of research methods is detailed next.
Two researchers initially conducted a series of informal conversations with business-to-business managers: three vendors and seven buyers from seven companies. Five inter-views were conducted in person and five by telephone. Both researchers participated in these conversations, which lasted between 30 and 45 minutes, and took extensive, independent notes during and immediately after the conversations took place. The researchers met and debriefed on the content of the conversations, aspects of business-to-business buyer- seller relationships, and the vocabulary used by the respondents. An illustration of the refinement of our vocabulary is the use of the term "key contact employees" rather than "service providers." It quickly became apparent that though the marketing literature uses the term "service provider" to refer to an employee, business respondents use "service provider" to refer to the firm. An early respondent suggested the term "key contact employee," and when this term was used with subsequent respondents, it was more clearly understood and was adopted for the rest of the study.
On the basis of the insights developed through these conversations, we developed the moderator's guide for sub --sequent focus groups. We recruited focus group respondents using industry directories, personal contacts, and references. Because our primary interest is business-to-business relationships, we included services, industrial goods, and consumer packaged goods companies. We offered a copy of our findings as an incentive for participation. We conducted and moderated six focus group interviews. The size of the focus groups ranged from 10 to 15 subjects for a total of 72 respondents. The sessions lasted from an hour to an hour and a half. We formed the groups and conducted the inter-views in accordance with accepted guidelines in the field (Calder 1977). Because homogeneity of the groups is regarded as an important facet to ensure open participation and because our respondents were business professionals, we emphasized homogeneity of rank and functional area rather than of sex or age. There were two groups of key contact employees (14 and 11), a group of 12 senior-level sales managers, a group of 10 human resources (HR) personnel, a group of 15 purchasing managers, and a group of 10 senior managers. Of the 72 participants, 44 were men and 28 were women. Participants' tenure with their firm varied: 10 had worked at the firm for 5 years or less, 20 for 6 to 10 years, 31 for 11 to 20 years, and 11 for 21 or more years.
Data Analysis
From audiotapes and detailed field notes available, six transcripts were prepared of the focus groups. Each researcher read the six transcripts individually and created lists of the aspects that customers valued in their relationships with employees (or aspects that employees and managers believed customers valued in their relationships) and of customers' concerns about losing favored key contact employees (or what employees and managers believed customers' concerns were). Through careful reading and rereading, researchers detailed passages and quotes that they believed reflected valued aspects of key contact employees or concerns about key contact employee turnover. The two researchers then met and compared their lists to identify are as of agreement and resolve any disagreements. Consensus was reached on all themes.
Results: What Customers Value
The focus group respondents believed that customers valued strong relationships with their key contact employees. For example, one respondent, a star sales representative for the pediatric products division of a major pharmaceutical firm, explained how he builds customer trust and loyalty by treating customers as individuals and providing special services. He remarked:
You know, most pediatricians will remember their first rep from our company. I would meet them first when they were interns and give them free product if they had kids. When they are starting out, I helped them with educational materials for parents and talked to them honestly about our products and our competitors' products. What matters to one doctor may be different from what matters to the next.... You learn about them. It goes beyond just selling the product....
There was this specialist-a pediatric allergist-when she wanted to move, I called a couple of our reps in that area and asked if they knew any practices there that were looking for that specialty. It makes the reps there happy because if the doctor moves there, they are definitely going to have a chance to get their account, and if she doesn't move, she appreciates my help and will recommend me to others. There was remarkable commonality across respondents when they discussed the employee characteristics that resulted in strong customer relationships. Some of the characteristics dealt with more objective performance measures such as expertise or industry experience, whereas others dealt with more subjective issues such as empathy or likability. This is consistent with marketing literature that makes a distinction between technical quality (what is delivered) and functional quality (how it is delivered) (Gronroos 1995). Similarly, in talking about how his employees develop strong rapport with the company's clients on the basis of the company's ability to customize the products, the president of an image management firm remarked,
Our account executives know the customer's business as well as he does. Only then can they really add value. [To customize, account executives] need to be able to anticipate customers' needs, don't wait for the customer to tell you what they need.
The president of a technology applications company suggested that competence was the key to building a strong customer relationship:
In projecting a positive image, there is no substitute for knowledge. An individual's answering "style" can certainly project a positive image during the first few seconds of a customer call, but without an underlying competence it doesn't go far enough....Case studies abound with tales of motivated and empowered interdisciplinary team members that created customer loyalty resulting in the sale of gazillion widgets ...but there are just as many stories that are not reported about foul-ups and customer irritation caused by dealing with personable, well-meaning half-wits, who often know less than the caller about specifics! In my opinion, there is no substitute for good old-fashioned competence.
Key contact employee competence has ramifications beyond the practical application of knowledge. Many respondents alluded to customers' need to feel secure about entrusting their accounts to the key contact employee, and this is influenced by the customers' confidence in the key contact employee's expertise. According to the president of a networks technologies firm,
You should never undermine the authority of the employee in the customer's eyes. Even if the employee is getting significant help from more senior staff, the customer should see the employee as handling the big stuff.
Respondents also emphasized the role of subtler, inter-personal factors that make a key contact employee valuable to the customer. In speaking about her relationship with the manager of a support function, the director of an insurance company indicated that the reasons she valued him were numerous. The key contact employee was extremely competent, but she believed there was also value in the friendly relationship that had developed between them:
I would characterize our relationship as one of professional friendship. It has evolved naturally over time, and now we feel comfortable talking about his kids, our commutes, and soon. The fact that he is always pleasant and easy to like is a big part of why our professional relationship is so cordial.
Results: Customer Concerns About Turnover
After discussing what customers valued in their relationships with favored key contact employees, respondents talked about an actual situation in which a key contact employee was no longer available. As respondents discussed the situation, we explored what their concerns might have been, as well as their reactions. The director of field sales for a pharmaceutical firm spoke about his experience in observing customers in situations when they lost a key contact employee:
If your customer worked in the purchasing department of acute care hospitals, when their sales rep jumps ship and goes to a competitor, they may not immediately follow but they will at least give it a look. The reputation and the relationship the rep has built up get you to at least compare what your old rep's offering now. Before, you may say, why compare? Now, you look.
According to the ensuing focus group discussions, whether customers were concerned about key contact employee turnover in the vendor firm appeared to be a function of whether they believed that the key contact employee was a critical element of their satisfaction with the firm and, relatedly, whether they believed that the firm could assign an acceptable replacement to their account. Concerns were also affected by whether customers believed that the firm followed customer-friendly procedures in managing the turnover.
In terms of criticality, respondents brought up examples of situations in which it did not matter whether a key contact employee was no longer available. Respondents provided examples of strong relationships between the customer and the vendor firm that outweighed the relationships with the key contact employee. In discussing the relationships between a large bank's business customers and personal bankers, the bank's executive vice president remarked that the ties to the bank created a buffer that protected it from the turnover of key contact employees:
Our bank has been in the same location with the same name for 134 years. We have worked with many businesses from the ground up. We have several generations of businesspeople that have turned to us. That is not something our competitors can easily duplicate or that our customers can easily forget.
Focus group respondents indicated that employee turnover may also be offset by the firm's reputation for superior products. The following comment from a sales engineer at a leading consumer products firm who calls on large retailers illustrates this:
We are successful because of our ability to design and market innovative products. Our product development "engine" (designers, product engineers, CAD [computer-aided design] engineers, mold engineers, packaging engineers, prototype managers, product development managers, etc.) is synonymous with quality. Our customers view our company as an expert, whether the customer is Wal-Mart, Kmart, or Target. Customers don't like dealing with different reps from our side, but it's not that big a deal, given the reputation of the company.
Other respondents also mentioned less bottom-line- driven criteria, such as social conscience and image within the community, in assessing vendor firms. This is in keeping with recent conceptualizations of corporate associations along performance and institutional dimensions (Handelman and Arnold 1999). This idea was echoed by the president of a market research firm, who suggested that a firm's social image had much to do with retaining business clients:
People think that the character of the company is less important in business-to-business relationships. But that is wrong. Businesspeople don't want to see themselves as robber barons! If they can, they will do business with a company that they know has a strong image and association with positive social values.
Other respondents discussed cases in which the customer's link with the key contact employee was a critical element in the customer's satisfaction with the firm. Respondents indicated that customers were very concerned about the acceptability of a replacement and the difficulty of replacing the key contact employee because of the special bonds that had been forged. The benefits director of an industrial goods manufacturer, in talking about a vendor's agent, provided an example:
The trustworthiness of the employees that deal with us is critical when vendors want to build strong relationships with us. We work with a vendor in our relocation area, where the customer service rep is very attached to us and vice versa.... We trust her to treat our employees the way we would treat them. We think of her as an extension of our company and that trust is what keeps us loyal to her. If we had to do business with someone else from the vendor's company, I would not be a happy camper. I couldn't be sure that whomever else they send would be able to work with us the same way.
Respondents also mentioned that "the employee must establish genuine familiarity with the customer and know that customer personally."
Another notion that emerged about the acceptability of a replacement employee pertained to time. Respondents indicated that when a customer resents losing a favored key contact employee, it is not necessarily because he or she believes a replacement will always be inferior to the key contact employee. Rather, the customer may be concerned about the time needed to bring the replacement employee up to speed. Consider the comments from the director of work-life strategies at a large insurance company about an internal supplier of data:
I have worked with him over the years, and we have gotten to the point where we work really well together. He always brings me the data when I need it. Great response time. And he sets it up exactly the way I want. He knows my history and customizes everything. If he leaves, someone else would take over, but it would take me a long time to get them to understand my needs and my priorities.
Respondents also mentioned the customer's level of confidence in being assigned an acceptable replacement. From the customer's perspective, there are two related sources of uncertainty: uncertainty about the quality of the replacement employee's performance (will he or she be as good as the previous key contact employee?) and uncertainty about getting the best replacement employee available to service the account. A partner in a major consulting firm talked about its efforts to reduce this uncertainty and build confidence among its clients:
A client of ours is the CEO [chief executive officer] of a major pharmaceutical company. He says that one of the reasons he chooses us over our competitors is that with us, he can count on the consistency of the people we send him. He says to me, "I know you are always sending me new people, but it doesn't bother me because I always know they'll be great."
Respondents discussed the importance of the procedures used to inform customers of employee transitions and to ensure that customer service levels will be maintained. Several respondents indicated that when employees were transferred, promoted, or left the firm, the customers were always the last to know. Procedures to inform customers of impending changes and how they would be handled were viewed as being extremely valuable but also rarely used.
Discussion and Research Propositions
The focus groups confirmed the existence of strong relationships between key contact employees and customers in business-to-business relationships. Respondents indicated that customers valued key contact employees to the extent that they were able to customize the product, were competent, inspired a sense of security, and fostered personal friendships with the customers. It was also evident from the focus groups that customers were concerned about losing favored key contact employees when the employee relation-ship was critical to the customer's satisfaction with the firm. The concerns centered on the acceptability of a replacement employee-that is, the potential performance gap between the key contact employee and a replacement employee-and on the procedures used in the transition.
The insights developed from the focus groups lead to the following propositions regarding customers' reactions when faced with key contact employee turnover:
P<SUB>1</SUB>: In the e vent of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's perception of how critical the key contact employee relationship is to the customer's satisfaction with the vendor firm.
P<SUB>2</SUB>: In the event of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's confidence that an acceptable replacement employee will be available.
P<SUB>3</SUB>: In the e vent of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be directly related to the amount of time the customer believes it will take the replacement employee to match the service level of the former key contact employee.
P<SUB>4</SUB>: In the e vent of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's perception of the replacement employee's knowledge of the product, the industry, and the customer's specific situation.
P<SUB>5</SUB>: In the e vent of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's confidence in the consistency of the quality and performance of the vendor firm's employees.
P<SUB>6</SUB>: In the event of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's confidence in the trustworthiness of the vendor firm's employees.
P<SUB>7</SUB>: In the event of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's confidence in the friendliness of the vendor firm's employees.
P<SUB>8</SUB>: In the event of key contact employee turnover in a vendor firm, the vulnerability of the vendor firm's relationship with the customer will be inversely related to the customer's satisfaction with the procedures used by the vendor firm in the notification and management of the transition.
Conceptual Background and Research Questions
The focus group discussions in Study 1 demonstrated that, in several situations, customers were concerned about losing favored key contact employees and, with them, the knowledge and rapport that had been established. It also became clear that customers, key contact employees, and managers believed that key contact employees were valuable repositories of knowledge about the customers. This suggested that vendor firms must simultaneously address customers' concerns about key contact employee turnover and fashion strategies to retain key contact employee knowledge.
Our careful review of the marketing and management literature revealed that there is no published work that directly addresses what vendor firms can do to alleviate customer concerns. There is a considerable body of literature on information sharing, but it does not address employees sharing information with the firm.
The information that an individual key contact employee possesses may be classified as declarative or procedural. Declarative knowledge is more content-based and deals with facts, events, or propositions (Anderson 1983; Cohen 1991), whereas procedural knowledge deals with "how things are done" (Cohen and Bacdayan 1994). Although these constructs originated in individual-level knowledge, scholars have begun applying them to the study of firms (e.g., Moor-man and Miner 1997, 1998). Relevant research in marketing regarding information sharing has studied how organizational knowledge may affect product innovation (Lukas and Ferrell 2000; Moorman and Miner 1998), the links to market orientation (Baker and Sinkula 1999), and the factors that make it likely that the recipient of information will trust or act on it (e.g., Moorman, Zaltman, and Deshpande 1992). Similarly, much research exists in the management arena that deals with how to create a learning organization (Easterby-Smith 1997; Senge 1990), in which information from employees is collected and analyzed by the firm.
The focus of our study is quite different. By examining when employees are most likely to share information with the firm, we investigate what makes for a "teaching organization," in which knowledge collected and held by the firm is disseminated to its employees. The literature that corresponds to this question relies on examining what firms can do so that employees will perform to set expectations. In an article that deals with performance gaps, Chenet, Tynan, and Money (1999) suggest that employees' compliance with company standards is influenced by several factors, such as trust, cooperation, and shared values. However, Chenet, Tynan, and Money's (1999) article focuses on general compliance with standards, not on capturing employee knowledge. The article is conceptual, and no empirical data are provided in support of the proposed model or implied effects. Therefore, Study 2 is designed to address the gaps in current literature by examining the following two questions regarding key contact employee turnover:
- What can vendor firms do to alleviate customers' concerns about turnover?
- What can vendor firms do to retain the knowledge their employees possess?
Methods
Because of the scarcity of literature addressing these questions, we continued the grounded theory approach of Study 1, relying on practitioner input to generate insights and further theory building. We secured practitioner input through depth interviews and surveys. Figure 1 shows the sequence of research methods, which we discuss next.
Depth interviews. We used a purposive sampling plan to provide broad representation. We contacted 60 managers from 16 companies throughout the world to schedule depth interviews. We described the focus of the study, asked for an hour's time for an interview, and offered a copy of the research findings as an incentive for participation. Of this group, 47 managers agreed to participate, for a response rate of 78%. Each of the 16 companies was represented in the final sample. Interviews lasted between 30 and 60 minutes. We made every attempt to conduct the interviews in person, but because of the large geographic dispersion of the respondents, we conducted 22 of the in-depth interviews over the telephone. We believed that telephone interviews were necessary to obtain the diversity of companies, geographic areas, and levels of management we wanted for this study.
The sample included participants from both vendor companies and customers. Respondents included employees from marketing, purchasing, operations, and information technology and HR managers. These managers came from various levels within their companies, which ranged from Fortune-100 to small and medium-size companies. The respondents were 7 senior managers (presidents, CEOs, and executive vice presidents of functional areas), 9 senior marketing managers, 11 purchasing managers, 4 managers from operations, 1 manager from information technologies, 6 HR managers, and 9 sales representatives. Of the 47 participants, 33 were men and 14 were women. Respondents' tenures with their company varied: 8 people had been with the company for 5 years or fewer, 12 between 6 and10 years, 17 between 11 and 20 years, and 10 for 21 or more years.
The depth interview is a well-established method for collecting data (Peñaloza 2000; Thompson 1997), especially with executives (Kohli and Jaworski 1990; Workman, Homburg, and Gruner 1998). We developed depth interview guidelines in line with recommendations by McCracken (1988) and Thompson, Locander, and Pollio (1989). Respondents were told the purpose of the study, and confidentiality was assured. We began with a "grand tour" question (Ruth, Otnes, and Brunel 1999) to focus on the domain of the study and asked for a description of a case in which a favored key contact employee was no longer available to serve a customer. After the grand tour question, respondents were asked a series of probing questions to encourage them to provide more detailed information. Customers were asked about their responses to key contact employee turnover and the actions that were or could have been taken by the vendor firm. Key contact employees were asked about the actions taken by their firm in managing customer relationships, their insights into customers' perspectives, and their firms' efforts to retain their knowledge. Vendor firm managers from various functional areas were asked similar questions and for their insights about retaining key contact employee knowledge.
Building on this broad response base enabled us to develop a comprehensive perspective of customers' perceptions about losing key contact employees and firm strategies that might affect those perceptions. We avoided questions that asked respondents to explain why they did things in a particular way so that respondents would not feel compelled to justify their actions. This was important in conversing with executives, who may be sensitive about defending their business decisions. Instead, our focus was to obtain answers to descriptive questions that sought information on strategies that were in place to handle key contact employee turnover. We used questions and probes to generate exemplars and obtain clarification rather than to confirm or disconfirm any set hypotheses. When the respondent had insights that we believed were valuable in developing a richer understanding of the problem and/or when a respondent had specific case examples, we probed deeper.
Surveys. Although depth interviews are a valuable research tool, scheduling issues and the required time commitment discourage participation from a broad base of managers. Consequently, to complement the depth interviews, we administered a brief, open-ended questionnaire to a different sample of respondents. We adopted a purposive sampling method and sent both an e-mail version and a hard copy of the questionnaire to 100 executives from different functional areas in 34 diverse companies and geographic regions. The cover letter explained the purpose of the study and assured respondent confidentiality. Respondents were encouraged to write in examples, illustrations, or comments for all questions, and they could send the completed surveys by regular mail, e-mail, or fax. As an incentive for participation, respondents could request a copy of the research findings.
Depending on the respondent's position, he or she was sent one of three versions of a questionnaire. The format and questions were identical to the depth interview, starting with a grand tour question that asked for a description of a situation in which a key contact employee was no longer available. This was followed by questions specific to the respondent's status as a customer, key contact employee, or manager of a functional area. Of the 100 managers contacted, 83 complied, and all 34 companies were represented. Respondents were 15 senior managers (president, CEO, executive vice president of functional area), 11 marketing managers, 15 purchasing managers, 3 managers from operations and logistics, 13 HR managers, 3 managers from management information systems, 3 managers from research and development, and 20 sales representatives. Of the 83 respondents, 51 were men and 32 were women. Respondents' tenures with their company varied as follows: 26 people had been with the company for 5 years or fewer, 25 between 6 and 10 years, 24 between 11 and 20 years, and 8 for 21 or more years.
Data Analysis
To draw insights from the depth interviews and survey responses, we followed a strict protocol for interpretation that proceeded in a series of part-to-whole iterations (Thompson, Pollio, and Locander 1994). Consistent with prior research (Keaveney 1995), we determined that using discrete behaviors in a transcript would be a better unit of analysis than coding the transcript as a whole. The following describes the steps in data analysis, as shown in Figure 1.
Step 1: Identifying behaviors. Two of the researchers involved with the project, Judges A and B, each read all of the 47 interview transcripts and 83 surveys to identify specific discrete behaviors, defined as distinct actions. For example, when a respondent talked about his firm not letting a key contact employee work with an account for longer than two years and about featuring employees in firm newsletters, these were coded as two discrete behaviors. After the specific, discrete behaviors were identified, the two researchers met to compare lists. The interjudge reliability was .92, exceeding the .8 cutoff in prior literature (Keaveney 1995). Points of disagreement were resolved through discussion, and a consensus was reached on all items. A total of 556 discrete behaviors were identified, an average of 4.3 behaviors per respondent.
Step 2: Developing themes for benchmark classification. To generate common themes that incorporated several behaviors, Judges A and B prepared an individual analysis of the ideas presented in the interviews and surveys. The judges developed themes for addressing customer concerns using the input of customers, key contact employees, and managers. They developed themes for retaining key contact employee knowledge using input from key contact employees and managers. The researchers exchanged drafts of the themes, compared their lists, and engaged in a joint analysis whereby they read and reread behaviors to achieve consensus on the themes identified across respondents. On completion of this task, 15 themes were identified, 10 dealing with addressing customer concerns about key contact employee turnover and 5 dealing with retaining key contact employee knowledge.
Judges A and B then reviewed each transcript and survey together to note whether a respondent mentioned any of the themes. If a respondent gave two examples of the same theme, the judges would mark only that the respondent mentioned that theme, not that it was mentioned twice. For example, if a respondent mentioned that his or her firm featured employees in firm newsletters and in annual reports, the judges would mark that the respondent mentioned the theme "showcasing employees." By classifying respondents together, the judges were able to resolve any points of disagreement. Consensus was reached, and these results became the benchmark for comparison.
Step 3: Reliability check of benchmark classification. Two judges, C and D, who were not previously involved with the research project, were recruited as coders. Judges C and D were given a set of all transcripts and surveys, along with the list of themes developed in Step 2. They were instructed to read each transcript and survey carefully and note whether a respondent made any mention of each theme. Any time the judges noted that a theme was mentioned, they were asked to note which specific behaviors they used as indicators of that theme. The coders also were instructed to create new themes if they believed it was necessary and list the specific behaviors they would categorize under that theme. No new themes were identified. The interjudge reliability relative to the benchmark exceeded the .8 cutoff, with values of .88 for C and .92 for D, for an average of .90.
Step 4: Additional verification. As a further check for whether the counts of themes were "intersubjectively unambiguous" (Hunt 1983; Keaveney 1995), as measured by interjudge reliability, a fifth judge, E, previously unrelated to the research project, was given the set of surveys and transcripts along with the list of themes. As in Step 3, Judge E read all the transcripts and surveys in their entirety and noted whether each respondent made any mention of each theme. Judge E also was asked to identify any new themes if necessary, along with behaviors categorized under that theme. No new themes were identified. The interjudge reliability between this judge and the benchmark consensus of Judges A and B, computed as the percentage of agreement, exceeded .8, with a value of .92. The list of themes and the number of mentions of each theme across the respondents, identified by Judges A and B, are given in Table 1. For presentation purposes, the themes were categorized by Judges A and B into three higher-order groupings based on the focus groups, which are shown in Table 1.
Consistent with the focus groups, the depth interviews and survey responses suggested that customers are quite sensitive to the loss of a key contact employee. Consider the statement of a buyer at a major automotive manufacturer:
During my last rotation at _____, as a buyer of braking systems, the sales representative for my primary supplier ... moved to a new position. This person had been in his job for several years and was well respected by both the buyers and the engineers in our company. He was replaced by someone who is also a competent sales rep and had several years of experience but had not developed the same kind of relationships with our personnel. In addition, this company was going to be involved with several critical sourcing decisions in the weeks following this job change. I was very concerned that the sourcing process might be affected by switching the sales rep as these sourcing decisions were being made.
The ten themes that emerged from the interviews and surveys pertaining to customer concerns about key contact employee turnover are presented under three headings: criticality of key contact employees, acceptability of replacement, and procedures used in the transition.
The Criticality of Key Contact Employees
The situations that made the loss of the employee less critical to the customer's satisfaction with the vendor firm all dealt with factors that created mitigating circumstances to lessen the impact of a replacement employee. These included ( 1) rotation, ( 2) teams, ( 3) multiple contacts, and ( 4) vendor firm image.
Rotation. Rotating the key contact employees was a strategy mentioned for lessening the impact of losing any specific key contact employee. A senior manager of a major industrial products manufacturer had this to say about rotation:
We try to make sure that several of our reps interact with our major customers. You don't want the entire relation-ship destroyed because the customer depends on just one person. Many companies ... use rotation and change the contact people by design.
This practice results in the customer being exposed to several employees who can fulfill the key contact function. Respondents in consulting, advertising, and technology systems were quick to point out that they typically presented their use of rotating key contact employees as a strategy to bring fresh, unbiased perspectives to customers. Furthermore, the information about the customer resides with several employees instead of with a single key contact employee (Lahti and Beyerlein 2000). This can minimize the importance of the key contact employee, because no individual dominates the customer relationship over a long period of time.
However, from the customer's perspective, this did not always seem to be a welcome strategy. According to the purchasing manager for a large industrial goods manufacturer, I always prefer having to do business with the same person. It is easier that way. You don't have to rehash the whole story. You don't have to worry about how much they know about your company, the product they're selling. You develop a real relationship over time. You get to the point where you can almost read each other's mind without saying anything. It's hard to deal with different people every so often and have that kind of understanding.
There also may be benefits of rotation to the customer, as the following comment from a purchasing manager illustrates:
Through rotations, I went through four sales representatives at one of my main suppliers. From the standpoint of maintaining a constant stream of information, that made my job a lot tougher.... Actually having to "teach" my suppler representative about his/her product line was an interesting twist.... I was able to teach the supplier representative what I wanted him to learn. This was great because it gave me a lot of negotiation leverage. In many instances, my "adversary" was actually my ally.
Teams. Both vendor firm and customer respondents mentioned teams as reducing the impact of losing key contact employees. Various team structures were described. Some respondents talked about how companies used cross-functional teams to sell and service customers, and others mentioned the pairing of junior and senior salespeople or the pairing of a field salesperson with someone from customer service. A senior bank executive talked about his firm's efforts to build relationships between its business customers and the outside and inside salespeople who work as a team to serve them:
The outside and inside salespeople work as a close-knit team in serving the customer. The outside salesperson may only see the customer once every couple of weeks, but the inside salesperson may talk to them many times, even in a single day. Even if one of them leaves, the team knowledge about the customer is still there.
Companies can use cross-functional teams to sell or to service customers. Such teams ensure that the customer's bonds are spread across team members rather than focused on one individual. Even if the customer believes a replacement employee would not be as good as the current employee (e.g., the customer believes the firm's best sales engineer is on the team), the gap becomes less important given the customer's multiple relationships with team members. The senior purchasing officer of a major automobile manufacturer talked about his experience with the team approach adopted by a vendor:
Our primary battery supplier always has two people working with us-one senior person who manages the account and a more junior person who comes along on all the visits. On one occasion, we were trying to make an important decision and the senior rep was not available because he had an appendectomy. It really made it easier because we already knew the junior person on the team.
Multiple contacts. Another practice mentioned by respondents was the cultivation of multiple contacts with a customer's business. Whereas teams focus on serving one aspect of a customer's business at a point in time, multiple contacts are used to address different aspects of the customer's business. The president of a company that manufactures cameras for scientific applications indicated that he relied on providing multiple contacts to insulate his business from the effects of an individual employee needing to be replaced.
We engage with the customer on many different levels ... for example, a salesperson, engineer, and production/ scheduling person (each a specialist in their field) could engage with the customer to help out at different stages. This is relatively easy to do with the help of e-mail, conference calls, etc. Often, it is enough to give a customer (or a prospective customer) the contact information for all the potential support people that may be available to solve a particular set of issues.
Providing a chain of contact points can complement the use of rotation and teams by making different people or units responsible for different functions for the customer (e.g., one person to contact for after-sales service, a separate person or unit for billing). Providing multiple contacts can also help a firm create a one-stop shop for multiple services (Zinn and Parasuraman 1997), as the following quotation from a customer illustrates:
Since we are building a car, our suppliers who are able to provide several components are a lot easier to work with since they can share information across groups and serve us better. When I go to my guy-even if he might not completely understand the system, I am confident he has access to the information from someone else in his company. This saves me time and helps them deliver a high level of service.
An additional way to create multiple contact points, according to the respondents' input, was to highlight the role of social interactions between the customer and several employees who work on the customer's account at various levels. An executive we interviewed talked about the impact these programs had on him as a customer:
When I go to picnics and barbecues that this vendor holds, I see all the people that are working on my account to keep my business. I even get to see back-office employees that I wouldn't normally deal with. This makes me realize that the company has a lot of employees that really value me as a customer and can service my account.
The more points of contact there are between the customer and the firm, the less critical any single employee relationship seems to be. However, even in the presence of multiple contacts, some respondents expressed a preference for a single key contact employee to serve their account because they wanted to know that someone was always in charge and responsible for the account. A purchasing manager at a Fortune-500 company had this to say:
Because the companies we buy from often make more than just one product, there are other people from within my organization that buy from that supplier too. Many times, suppliers will introduce their buyers to many different people. This can be very confusing, and it is easy for the supplier to pass the buck. That is why I always want to make sure that I understand the sales organization that I interface with. I want to know that I am working with one person that is high enough in the organization to make key decisions about sourcing and pricing.
Vendor firm image. Respondents mentioned that a positive vendor firm image affects the criticality of a key contact employee to customers. This image may be cultivated through tangible cues (uniforms, business cards, stationery, and so on), promotional strategies, patents and other proprietary assets, or corporate citizenship (Maignan, Ferrell, and Hult 1999).
A firm's use of effective tangible cues appears to go beyond the employee to every aspect of the firm that is visible to the customer. A director of marketing for a national chemical products company talked about his firm's difficult decision to outsource the trucking division:
At our company, the clean, polished, well-kept trucks were a point of pride and viewed as a reflection of our company's attention to detail. Outsourcing this function was considered very carefully in terms of the potential loss in customer goodwill and recognition of our company's name and our image. We needed to be sure the company we selected to outsource would maintain the same standards.
In talking about strategies to build firm image, respondents mentioned the use of corporate advertising to emphasize their leadership in the marketplace. The president of a major advertising agency discussed his firm's tactic of advertising that among the agency's clients are the producers of five of the top seven global brands. The senior purchasing officer in an industrial goods manufacturer included such considerations in making purchases:
I worked in the corporate purchase office and was dealing with two furniture companies. I knew this one company has donated money to charity, helps in the community a great deal, so I gave that company the first shot at our business. The other company, I'd never heard about anything they'd done like that. Citizenship, giving back to the community, that mirrors our corporate values, and if I can do business with suppliers like that, great.... I wouldn't have gone with them if they had a lousy rep, bad service, or if I had to pay a premium to work with them.... Still, other things equal, there's no doubt that's where the business would go.
Prior research also shows that the image of a firm is affected by corporate citizenship, which may affect customer responses (Barone, Miyazaki, and Taylor 2000).
Propositions. The input from practitioners leads to the following propositions regarding criticality:
P<SUB>9</SUB>: In the event of key contact employee turnover in a vendor firm, the impact of the loss of the key contact employee on the customer's satisfaction with the vendor firm is inversely related to the vendor firm's use of rotation of key contact employees.
P<SUB>10</SUB>: In the event of key contact employee turnover in a vendor firm, the impact of the loss of the key contact employee on the customer's satisfaction with the vendor firm is inversely related to the vendor firm's use of teams of employees to serve customers.
P<SUB>11</SUB>: In the event of key contact employee turnover in a vendor firm, the impact of the loss of the key contact employee on the customer's satisfaction with the vendor firm is inversely related to the vendor firm's use of multiple contact points with different areas of a customer's business.
P<SUB>12</SUB>: In the event of key contact employee turnover in a vendor firm, the impact of the loss of the key contact employee on the customer's satisfaction with the vendor firm is inversely related to the vendor firm's positive image.
These propositions are consistent with marketing theory that the strength of the relationship with the firm is an important determinant of customer evaluation in the business-to-business setting (Leuthesser 1997; Weitz and Jap 1995). A strong relationship with the firm appears to offset key contact employee turnover in the customer's mind, acting as a buffer against negative responses. Some caveats are in order with regard to the propositions on rotation, teams, and multiple contacts. Although rotation may help familiarize the customer with several key contact employees, customers may resent needing to create new relationships. It also may have the unfortunate consequence of preventing the customer from developing a strong relationship with any employee. With respect to teams and multiple contacts, some respondents mentioned that they felt over-whelmed by having to deal with multiple people from the vendor's side.
The Acceptability of Replacement Employees
The acceptability of a replacement employee emerged as a major concern in both the focus group interviews and the subsequent depth interviews and surveys. Managers in the depth interviews and surveys stated that several avenues were open to vendor firms to improve the perception of potential replacement employees. Four themes captured the specific examples furnished by the managers: ( 1) selection and hiring, ( 2) training, ( 3) showcasing employees, and ( 4) tangible cues. We discuss these strategies next.
Selection and hiring. Respondents indicated that the selection and hiring practices of vendor firms affected customers' perceptions about the quality of replacement employees. The following is an illustrative response from a senior purchasing officer at a large chemical company:
The quality of the people they send you is quite critical. If I am buying material and equipment, and if it's not right, it can shut the plant down, cost us a shift, millions of dollars, then I want to know what kind of people make it through at the vendor's company, and I'll even ask them, what are you guys doing? Who else are you adding to the team this year? If I'm buying paper clips from them, I wouldn't care who they hired. I mean, the worst thing is, hey, we're out of paper clips for a day or two. But if the people who sell me these OEM [original equipment manufacturer] systems don't know what they're talking about, my butt's on the line.
Firms often showcase their reputation for being highly selective employers or desirable places to work. With respect to selection and hiring practices, the director of training for a major industrial manufacturer said,
When our company was selected as a great place to work, it was a critical selling point with customers. Our company made a video of the workplace and placed excerpts on salespersons' computers. Salespeople are encouraged to share snippets with customers. When our salespeople do this, it makes the customers more confident about the company and the type of salespeople we can hire.
Selection and hiring may enhance the image of all employees (Pfeffer, Hatano, and Santalainen 1995). Customers may reason that a vendor firm with stringent standards would hire only the best candidates, and therefore any key contact employee would be viewed more positively than one who works for a firm with less rigorous standards. A firm also may play up its ranking as an employer of choice. Placements in "best places to work" lists or similar rankings may signal that there is a great deal of demand for jobs in the firm and that, therefore, the firm can afford to be selective (Hannon and Milkovich 1996).
Training. The importance of training was another common theme when respondents discussed how vendor firms could improve the perceived quality of replacement employees. The director of customer service at a major pharmaceutical company strongly believed that this was an important aspect of the employees' image with customers:
We put all of our employees through rigorous training. Not just sales techniques, but we teach them about all of our products and our competitors' products. This requires us to teach courses on human physiology, diet, prescriptions, and even toxicology. We let our customers know that when they talk to our reps, they are talking to people with a college degree and many hours of intensive training.
Frequently, vendor firms advertise the rigorousness of the training provided to employees as a way of increasing customers' confidence in the quality of the employees in the firm and the service they provide (Brown 1998). The effects of these training perceptions can be considerable. According to the senior manager of purchasing responsible for carrier contracts at a major industrial goods manufacturer,
I pay attention to how these vendors train their people. We do a lot of business with truckers, and they talk a lot about the selection criteria they use for their drivers, the training they provide, not just one-time but ongoing safety training programs. That kind of thing is very big. If we can't be sure about their training, I just can't take that kind of risk. In some kind of chemicals we buy, the purity o fi t is very important. We use it to process semiconductor chips. Then, again, the training the vendor gives their product and quality assurance folks is critical to us. This supplier I have dealt with over the years does a fantastic job of training all their people and always lets us know of new programs they have, and it really reassures me and keeps me doing business with them.
Showcasing employees. Respondents also spoke about vendor firms showcasing their employees to customers as a way to influence positive perceptions of all employees and increase the acceptability of replacement employees. Several senior managers mentioned that their companies made concerted efforts to showcase their employees by trying to get all personnel promotions, appointments, and awards in the press. Respondents believed that all this visibility enhances the client's perception of the quality of the personnel. Such efforts both pave the way for employees to build new relationships with customers and reassure existing customers of the value of these employee relationships (Howard 1998). The people we interviewed mentioned the prominent role that employees were given in their advertising and annual reports, emphasizing their caring, trustworthiness, competence, and professionalism. As one respondent commented,
There's this one supplier that has a great reputation with our people. When one of their design team folks wins an award, or they are recognized in their industry as best in class, we know about it the very next day. In this business, it is all about the quality of the company and their reliability, and when their people are singled out like that, that recognition really catches your eyes.
Tangible cues. Respondents mentioned also that vendor firms encourage their employees to use tangible cues such as dress, business cards, and class of travel to elevate their image. A senior partner within the business products division of a major consulting firm stressed the role of tangible cues in conveying the desired employee image. He stressed that his firm held orientation sessions for new employees to inform people how important it was for all employees to convey a consistent, high-quality image.
We tell our employees to always dress in a business-appropriate fashion, even when they are traveling on assignment. You never know who might be sitting next to you, and we want any potential customer to get the right idea about our company by looking at any one of our employees.
From the customer's perspective, the tangible cues seem to provide some sense of quality. The comments of a purchasing manager for a supplier of specialty chemicals spoke about this aspect:
I think I'm like the majority of people. If you call on me in Dockers with ratty bottoms or crappy shoes, looking unshaven, unclean, I don't want to do business with you, no matter how smart you are. But sometimes, when a person shows up in a suit or a tie, it's like they're trying to look better than you. We tell suppliers we're a business casual workplace. Sometimes though, it's funny. We have suppliers from overseas or from the East Coast and they still seem to dress more formal. But they kind of apologize. They tell us, "hey, I've got to call on three other accounts after this and they're not real casual."
It was clear from the examples respondents mentioned, such as employees' uniforms and accessories, that these tangible cues may be considered part of the "packaging of the employee" (Solomon 1985). Vendor firms must be conscious of integrating the messages conveyed by every element of the tangibles associated with employees, from their appearance and dress to their identification tags or business cards. Bitner (1990) shows that such physical cues can have a significant impact on the interpretations customers make about various employee actions.
Propositions. We offer the following propositions to summarize the insights on the acceptability of a vendor firm's replacement employee:
P<SUB>13</SUB>: In the event of key contact employee turnover in a vendor firm, from the customer's perspective, the acceptability of a replacement employee is directly related to the vendor firm's reputation for selective hiring of employees.
P<SUB>14</SUB>: In the event of key contact employee turnover in a vendor firm, from the customer's perspective, the acceptability of a replacement employee is directly related to the vendor firm's reputation for training employees.
P<SUB>15</SUB>: In the event of key contact employee turnover in a vendor firm, from the customer's perspective, the acceptability of a replacement employee is directly related to the vendor firm's practice of showcasing its employees.
P<SUB>16</SUB>: In the event of key contact employee turnover in a vendor firm, from the customer's perspective, the acceptability of a replacement employee is directly related to the quality of the tangible cues provided by the vendor firm's employees.
It appears that vendor firms may benefit by creating positive perceptions about the overall quality of their employees. As the quality of the employees increases, there is a greater likelihood that a customer will receive a high-quality replacement if his or her particular key contact employee were to leave. The customer still may believe that his or her key contact employee is superior, but as the absolute level of quality of the replacement employees rises, any difference between the key contact employee and the replacement will be less important.
Procedures Used in the Transition to the Replacement Employee
The depth interviews and surveys reaffirmed how the procedures used by the vendor firm affect how customers respond to the loss of a key contact employee. A review of the transcripts indicated that a vendor firm should keep customers abreast of any personnel changes through ( 1) advance notification and ( 2) a planned transition period.
Advance notification. Many respondents indicated that advance notification of the change by the vendor firm reassures customers. According to a purchasing manager,
It would have been helpful to have the company do something in advance of the transition. They could have provided me with a written notice of the change. They could have given me written or verbal indication as to the readiness of the new person and that person's qualifications.
The marketing manager at a large insurance company presented a way to provide advance notification that he thought would work for key contact employees in his organization.
One thing I would like to do is set up a system so that when our business customers have to deal with a new rep, they get a letter from the old rep that says, "Thank you. I have enjoyed working with you. Now, let me introduce my replacement. They will serve you just as well." Of course, this is easier to do when the old rep has been promoted in our company. But we don't do that. We have turnover, and promotions, and many organizational changes, and nothing goes out to the customers. We send something after but that seems like too little, too late.
A planned transition period. Customers are concerned that a replacement employee may not understand the parameters of their business or that they may not have the same relationship they enjoyed with their old key contact employee. A planned transition period between key contact employees emerged as a critical variable in the successful management of the vendor firm-customer relationship. A buyer for a major auto manufacturer provided the customer's perspective on the management of transitions:
When I lost my sales rep, nothing much was done to address my concerns. There was a brief transition period when the outgoing sales representative, the new one, and I met to discuss open issues and upcoming events. It would have been so helpful to have a longer transition period, where the new sales representative could get up to speed and begin developing relationships with our personnel without the pressures of completing the day-to-day work associated with the position.
Respondents mentioned that such transitions would make it easier for the customer and the new sales representative to share critical information. Suggested transition efforts included having the old employee introduce the new replacement, act as a bridge for the initial transition period, and reassure the customer about the handoff.
However, there was awareness that such transition is not always possible between the old employee and the replacement, especially when the old employee is hired away by a competitor and leaves abruptly, rather than is promoted or moved within the firm. Suggestions were offered to address this scenario as well. According to a purchasing manager,
Companies should provide a transition period-that would be very apparent-where new and old reps attended meetings together, made calls together, etc. This could perhaps occur over a 2-4 week period. In the event the old employee leaves, and there is no time to do this, the sales manager should have taken charge of the account, jointly attended meetings and so on, until the replacement and the buyer are comfortable. This would tell me that they value my business.
Propositions. The following propositions are offered regarding the procedures used in transitions:
P<SUB>17</SUB>: In the event of key contact employee turnover in a vendor firm, from the customer's perspective, satisfaction with the procedures used in the transition to the replacement employee will be directly related to the vendor firm providing advance notification of the transition.
P<SUB>18</SUB>: In the event of key contact employee turnover in a vendor firm, from the customer's perspective, satisfaction with the procedures used in the transition to the replacement employee will be directly related to the vendor firm providing the customer with a planned transition period.
The emphasis on the procedures used in the transition from the key contact employee to the replacement is consistent with current literature on how customers respond to any perceived failure on the part of the firm (Tax, Brown, and Chandrashekaran 1998). Customers pay as much attention to procedural justice issues or the way any recovery efforts are handled as they pay to distributive justice issues or what the recovery efforts deliver (Greenberg 1986). When an employee leaves on good terms, planned transitions between the old and new employees may be easier. When the employee leaves on bad terms, the firm may need to work harder to keep the customer satisfied. This is especially important because customers may perceive a badly handled transition as evidence that the vendor's management is poor or that the vendor does not really value the customer's business.
Because our emphasis was on what vendor firms can do in the event of key contact employee turnover, our focus was on examining how to retain employee knowledge, not how to retain employees.[ 1] Retaining the knowledge a key contact employee possesses, even if the employee cannot be retained, was a key concern for respondents. In the words of a customer service manager at a Fortune-100 company,
If the company uses the exceptional individual employee as a role model and gets that employee to train everyone else, customers won't worry so much about relying on that particular individual to get superior service.
As shown in Table 1, five themes emerged from the depth interviews and surveys regarding strategies to retain key contact employee knowledge. Retention of employee knowledge was thought to be greater when firms fostered a culture of sharing, performance appraisal and reward systems encouraged information sharing, employees trusted the firm, technology was in place for sharing information, and organizational structures facilitated the sharing of information. We organized these five themes into three groups: firms valuing employees' information (culture of sharing), employees' motivation to share information, and employees' ability to share information.
Valuing Employees' Information (Culture of Sharing)
Respondents frequently mentioned the lack of awareness among vendor firms that employees possessed valuable information that could and should be retained. In an inter-view with the president of a vendor company, this lack became apparent:
I am constantly amazed at how little employee knowledge is used in making key decisions. It is as though most management directors have forgotten everything taught in Marketing 101. Companies don't seem to even recognize that it is important to know what your employees know.
Respondents also suggested that the failure to value employee information comes through when firms do little to foster a culture that emphasizes sharing this information. Across the board, a culture of sharing emerged as the most important indicator of whether employees believed firms genuinely recognized the value of employee information and whether employees would share information, as is shown in the following observation from a senior HR executive at one of the big three insurance companies:
I don't think the top management of our company has seriously thought about capturing the information that our employees have. Nobody talks about it. It is not part of any orientation or training programs. We have turnovers and promotions, and each time, it is the same. The new person has to start over, trying to figure out what customers want, or how to get things done. I'm sure if the company asked, many of our people would share the information, gladly! But, nobody has ever asked them! It is not part of the culture.
Proposition. Other researchers also have suggested that a culture of sharing is an important determinant of the effectiveness of efforts to capture employee information (Caylor1999; Phillips 1997).Therefore, we offer the following proposition:
P<SUB>19</SUB>: The employee's willingness to share information with the vendor firm is directly related to the degree to which the vendor firm fosters a culture of sharing.
Employee Motivation to Share Information
As interviewees described how a culture of sharing is created, it became apparent that employee motivation to share information plays a key role. The executive vice president of HR for a Fortune-500 company discussed the reason employees might withhold information:
If I am the top performer in the company, why would I want to share? If I share my information, I used to be a top performer, and now, I am down to average. Granted the over-all company average may be up, but what's in it for me?
Therefore, firms must give their star performers incentives to share their secrets for success with their supervisors, who would then pass them on to all of the employees. In discussing how to motivate employees, respondents mentioned performance appraisals and reward systems in their firm, as well as the trust engendered by the firm.
Performance appraisal and reward systems. The comments of several respondents emphasized the role of performance appraisals and reward systems in encouraging employees to share information. According to the director of sales at an industrial goods manufacturer,
To use a sports metaphor, you can't just reward the guy who makes the baskets. You have to reward and recognize the guy who makes the assists, who sets up the baskets for other people. People who share information with others in the company are setting up other people's baskets and should be rewarded.
A senior partner of a nationally known consulting firm had a different perspective:
Our company wants consultants to share the information they acquire about customers or about effective processes throughout the organization. But what is my incentive? The company's reward system is based on billable, chargeable hours. Any time I take to share information with my colleagues is not billable, chargeable time.... If the firm makes it clear that sharing information with the company is a critical element of performance, it is more likely to encourage this behavior.
Respondents commented that the rewards for information sharing need not be monetary. In our depth interview with the HR director of a pharmaceutical company, we learned that this firm had found that employees were more motivated to share information on what made them effective when they were given proper credit. Therefore, throughout the firm, "tips" on various aspects of dealing with customers were posted by specific employees. According to the HR director, by displaying these ideas in highly visible places, with authorship suitably acknowledged, the firm was able to institutionalize more of the individual employees' insights. The significance of both monetary and nonmonetary rewards also has been acknowledged in the literature on perceived organizational support for sharing (Barker and Camarata 1998).
Trust and commitment. Respondents suggested that an impediment to employees' motivation to share information was fear about how that information would be used. The director of sales training at a large consumer packaged goods company suggested that employees' trust in the firm is critical to motivate employees to share the information about their customers:
We used to have required information sharing. We required people to report on customers and maintain detailed logs of their activities. We quickly learned that rather than reporting what they were doing, our people were reporting what they thought the managers wanted to see. Once we were able to get them to trust that we would not use the information against them and that turning in an honest report would not hurt them, we did not have to require information sharing-people saw the benefits and were more motivated to share accurate and complete information.
Propositions. When key contact employees trust the firm and are committed to it, they are more likely to share information with the firm voluntarily (Butler 1999; Morgan and Hunt 1994; Rousseau and Tijoriwala 1999). It is imperative that the key contact employees be assured that the vendor firm will not use any of the information they provide to put their jobs at risk. It is also necessary for the employee to feel committed to the firm, such that he or she is motivated to improve its well-being. Hunt and Morgan (1994) show that commitment leads to supportive behaviors such as altruism, conscientiousness, and lower intention to quit. On the basis of this discussion, we propose that
P<SUB>20</SUB>: An employee's willingness to share information with the vendor firm is directly related to the degree to which the vendor firm's performance appraisal and reward systems explicitly recognize and reward such behavior.
P<SUB>21</SUB>: An employee's willingness to share information with the vendor firm is directly related to the degree to which the employee trusts the vendor firm and is committed to it.
Employee Ability to Share Information
Even if employees are motivated to share information, they cannot unless the work environment enables them to do so easily and efficiently. Respondents suggested that two strategies were important in cultivating employee ability to share information: the use of technology as an enabling device and the creation of organizational structures to enhance the ability to share.
Technology. Vendor firms increasingly are relying on technology to make it easy for individual key contact employees to share information (Hunsaker and Lixfield 1999). According to the senior vice president of marketing at a major consumer promotions company,
We have an extensive network/system to compile data. Through all these sources, we seek to give our marketing service consultants a leg up on the competition and provide them with a significant marketing advantage. In addition, we put in place a contact management system within Microsoft Outlook to manage customer touch points to record client contact names, hobbies, birthdays, etc. This system is a two-way system that can be accessed by management, internal personnel, and even other consultants.
Requiring the employee to record relevant customer information immediately helps the firm transform itself into a "learning organization" (Senge 1990) and improve the process of learning (Sinkula 1994). Given the ease and affordability of most data management systems, the key to competitive advantage is no longer whether a firm has such a system but how well the firm deploys the system. The critical question is what kind of information should be recorded to avoid information glut (Shenk 1997).
Organizational structures. Respondents presented several ideas on organizational structures that dealt with employees' ability to share information. One refers to the use of transitions. In our previous discussion of procedures to manage a changeover, we presented respondents' insights into the use of transitions. In discussing methods to capture employee information, respondents again referred to transitions as a valuable strategy. For example, a sales representative for a national beverage company talked about his experience with transitions:
One thing the company did was to have the new sales rep ride along with the old rep so that we could counsel them on the things they needed to look for, what each store manager's personality was like, and provide any suggestions for dealing with them. This made it a lot easier and a lot more natural to share information than to put stuff down in memos or something.
A variation on transitions that was mentioned was the use of backups and support staff. The remarks of the president of a high-tech company illustrate this point:
Encourage a backup process where each individual has a backup person(s) who can fill in for them, if needed. In preparing to step in for each other, they act as understudies for the show. It makes it easier for the people to share information, because they know they have to step into each other's shoes to serve the customer. Another thing we do at our company is have a support staff for the customer contact employees. The creation of a support team for each such person makes the sharing of contact information much easier.
Propositions. On the basis of this discussion, we propose that
P<SUB>22</SUB>: An employee's likelihood of sharing information with the vendor firm is directly related to the amount of technology in place to support information sharing.
P<SUB>23</SUB>: An employee's likelihood of sharing information with the vendor firm is directly related to the degree to which organizational structures are in place to support information sharing.
These propositions deal with extracting information from employees. Vendor firms must recognize that this is not the same as ensuring that the information collected is put to use. The use of information is beyond the purview of this article, though it has been addressed in the marketing literature (e.g., Moorman, Zaltman, and Deshpande 1992).
Business-to-business interactions rely on a nexus of relationships: between the customer and the vendor firm's key contact employee, between the customer and the vendor firm, and between the vendor firm and its key contact employee. In Study 1, we examined what customers valued in key contact employees and what their concerns were when key contact employee turnover caused a disruption in the customer-employee relationship. In Study 2, we proposed solutions that vendor firms can use to address customers' concerns through the management of the customer-firm relationship and the firm-employee relationship. Through focus groups; depth interviews; and surveys of business-to-business customers, key contact employees, and managers, we developed a series of propositions. The implications of these two studies are discussed next.
Managerial Implications
From a managerial perspective, this research offers several useful insights into the drivers of customer relationship value. Examining a customer's relationship to the vendor firm versus the customer's relationship to the key contact employee should provide important information to firms about the dynamics of customer relationships and about who really owns the customer relationship.
When the customer's relationship to the vendor firm is weak and the relationship to the employee is strong, the greatest vulnerability lies in the firm's relationship with the customer. The customer perceives the key contact employee as the critical value driver. This frequently occurs when the customer cannot separate the deliverable from the employee (e.g., the creativity of the advertising campaign and the creative lead), the product delivered is a nonbranded commodity (e.g., raw material), or the product can be customized by several firms (e.g., made-to-order specialty chemicals). In these cases, the competitive advantage and differentiation may come from the relationship the customer has with the individual contact employee and the customer's belief that the employee knows the customer's business. Vendor firms in these settings should try to create additional sources of differentiation by offering more services, emphasizing their corporate citizenship, creating multiple links to the firm, and so forth. For example, although AC Nielsen consultants have strong relationships with packaged goods companies, the firm also builds relationships with these customers by offering state-of-the-art systems that the consultants use to analyze data. These systems form structural bonds that bolster the relational bonds (Berry and Parasuraman 1991).
In addition, consider the situation in which the customer's relationship with the vendor firm is strong and the relationship with the employee is perceived by the customer to be less critical. One example would be a customer business development representative for Procter & Gamble who calls on a particular customer. The value associated with Procter & Gamble, the vendor, may be so strong that it lessens the importance of the customer's relationship with the specific key contact employee; there is lower firm vulnerability to employee turnover. Structural bonds that firms develop with customers, such as investment in compatible technologies (Berry and Parasuraman 1991), and financial bonds that tie customers to firms, such as key customer discounting programs, may create situations of this sort as well. Vendor firms in this situation should not ignore the employee-customer relationship but should investigate whether enhancing the value of all of their employees increases the total relationship value for customers.
In some situations, the relationships with both the vendor firm and the key contact employee may be significant contributors to the customer's relationship value. Customers that work with specific consultants from McKinsey & Company may exhibit strong loyalty to both the firm and their specific lead consultant. This is a very positive outcome for the firm as long as the key contact employee continues to serve the customer. However, the firm should recognize its potential vulnerability if the individual employee were to leave or be transferred or promoted and thus be unable to serve the customer. Employees with strong personal ties to customers are employees that the firm should make great efforts to retain. Because turnover is bound to occur, there should be efforts to capture the employees' knowledge about their customers to transfer this information to a replacement. Furthermore, the firm should make strong efforts to convey to customers the high quality of all of its employees because, by inference, a customer will extend this impression to any replacement employee. Vendor firms should also ensure that the customer is kept actively engaged in the transition process so that the procedures are well understood and satisfactory to the customer.
There are two important considerations as firms decide to implement there commendations of this research. The first is the order of implementation. Whether to focus on mitigating negative customer responses to the loss of the key contact employee or on capturing employee information should be determined after a careful audit of the vendor firm's existing systems and culture. For example, the strategy of capturing employee information and sharing it widely among the firm's employees requires greater reliance on the cooperation of key contact employees than does the strategy of building direct firm ties to the customer. A vendor firm must have clear communication with its employees, build employee trust, and be confident of employees' support before launching a program of capturing employee information.
The second issue pertains to the synergy among the various strategies proposed. Because this article focuses on developing a broad perspective of the issues involved in customer relationships, we offered a series of propositions that examine the effects of various vendor firm actions. In measuring its performance on these various strategies, the firm must take a holistic view of managing customer-employee relationships (Bendapudi and Berry 1997; Bendapudi and Leone 2001; Czepiel 1990). A vendor firm should also be careful not to send mixed signals to its employees regarding these activities. A firm may install technology to facilitate easy sharing of information, but if the reward systems still are geared toward individual performance, there will be little impact on employee behavior. Alternatively, the use of rotation of key contact employees may have the positive effect of capturing employee information. It also may have the negative effect of increasing employee (customer) dissatisfaction if employees (customers) dislike being rotated through customers (employees).
Theoretical Implications
This article makes several important theoretical contributions. First, it focuses attention on an increasingly important and rarely studied marketplace phenomenon, the impact of the turnover of a key contact employee. As Doney and Cannon (1997) note, few studies simultaneously examine customers' relationships with employees and the firms these employees represent. Furthermore, the studies that address this problem examine ongoing as opposed to discontinued relationships. This is the first research to provide both an empirical investigation of the phenomenon and a conceptual framework with research propositions to examine the impact of the loss of one of these relationships, the customers' link to the employee, on the customers' evaluation of the second relationship, the link to the vendor firm.
Second, our research goes beyond current work in business-to-business relationships, as well as in consumer relationships, that has focused on identifying characteristics of employees that are conducive to relationship formation. We go beyond such main effect predictions to understand situational factors that could moderate customers' responses to employee loss. The discussion of the perceived criticality of employees and the acceptability of replacement employees addresses the importance and uniqueness of the employee as a resource to the customer and an asset to the firm. The discussion of the procedures used in the changeover complements previous work on procedural justice in organizations. To the extent that the firm is viewed as having failed the customer by not retaining the employee, the findings of this study corroborate the importance of procedural issues in problem resolution (Tax, Brown, and Chandrashekaran1998).
Third, our discussion of the sharing of information by the employee with the firm is a departure from many of the traditional areas of information sharing studied in the literature. For example, studies have focused on the sharing of information between vendors and customers (Cannon and Perreault 1999), between collaborators in alliances (Simonin 1997), and between market researchers and users (Moorman, Zaltman, and Deshpande 1992). There is a crucial distinction between the focus of our study and other efforts to study information sharing. When employees share information with the firm, they are indirectly sharing this information with other employees, or with their potential replacements. Given that the incentive structures inmost firms are tied to employees' relative performance, this is tantamount to sharing information with competitors. In this respect, employees may view information sharing as going beyond typical organizational citizenship behavior, because unlike other civic behavior, information sharing may threaten their position and compensation.
Finally, this article secures the perspectives of customers, key contact employees, and managers on key con --tact employee turnover through focus groups, depth inter-views, and open-ended surveys. Such triangulation of multiple perspectives across multiple data collection methods contributes to a richer understanding of the phenomenon (Peñaloza 2000).
Directions for Further Research
Our study opens several avenues for further research. Consider the literature on celebrity endorsements that has demonstrated a rub-off from the endorser to the brand (Agrawal and Kamakura 1995). Such endorsements are assumed to work because they increase the marginal value of ad expenditures by creating brand equity through the "secondary association" of a celebrity with a brand (Keller 1993). Given the customer's simultaneous relationships to the employee and the vendor firm, it might be hypothesized that a similar process takes place in that evaluations of the employee may affect evaluations of the firm. This rub-off effect from the employee to the firm may be facilitated or inhibited by the attributions customers make about the employee's demeanor and behavior. Attribution literature suggests that customers may make attributions about the locus, stability, and controllability of an event (Bitner 1990; Weiner 1985). If customers attribute the employee's demeanor and behavior to an internal locus, factors intrinsic to the employee, there may be less rub-off from the employee to the firm. Conversely, if the customer believes that the behavior is stable across the firm's employees, the rub-off to the firm may be greater. The rub-off effect may also be greater if the customer believes that the firm controls the employee's demeanor and action. For example, a firm that hires on the basis of empathy or rewards empathy controls the dimension even if the locus is internal to the employee.
Furthermore, exploring customers' perspectives of these issues may lead to a richer understanding of key constructs. For example, we discuss the acceptability of replacement employees as though it were a discrete variable-that is, the employee is either acceptable or not. In reality, there may be a zone of tolerance for the acceptability of the replacement employee, and understanding the organizational, customer, and situational factors that affect acceptability would be a useful extension. Research also is needed to examine situations in which a customer team is involved rather than a single buyer. The use of a customer team may have a significant impact on responses to employee loss and the attributions that are made.
The focus of this article is how a vendor firm can manage its relationships with customers most effectively. Additional research should address how individual employees might respond to these efforts. Research also is needed to understand the employees' perspectives of the problem and comparable strategies that employees may use to ensure that they have strong ties to customers. This is consistent with recent calls to employees to be more proactive in managing their careers (Bridges 1998). Customers' responses to these initiatives also must be addressed.
1 Strategies to build employees' commitment to their own organization and enhance employee retention have been studied extensively in the management literature (see Allen and Meyer 1990; Gould 1979; Lee and Maurer 1997).
Purpose: To understand vendor firm strategies to alleviate customer
concerns about key contact employee turnover and to capture key
contact employee knowledge.
Depth Interviews Surveys
Sample: 60 managers from 16 100 managers from 34
companies contacted. 47 companies contacted. 83
managers participated. All managers participated.
companies represented. All companies
represented.
Data 25 interviews conducted in E-mail and hard copies
collection: person, 22 interviews by mailed to managers.
telephone. Interviews Open-ended questions.
lasted 30-60 minutes.
Data analysis: Transcribed interviews and surveys analyzed as
described below.
Step 1: Independent text analysis of interview transcripts and
surveys by Judges A and B. Each judge identified discrete
behaviors mentioned in the text. Comparison of lists yielded
interjudge reliability of .92, exceeding the preestablished
.8 cutoff.
↓
Step 2: Judges A and B independently developed a list of themes to
capture the discrete behaviors identified in Step 1. The
judges exchanged lists, reviewed the themes identified, and
jointly created a master list of themes. Interview
transcripts and surveys were jointly coded by Judges A and B
for mention of themes by each respondent to develop a
benchmark classification.
↓
Step 3: Independent Judges C and D were given the list of themes
generated from Step 2, along with copies of interview
transcripts and surveys. Judges C and D independently coded
the mention of themes by each respondent. These judges
identified no new themes. Interjudge reliability compared
with the benchmark comparison (Judges A and B) was .88 for
Judge C and .92 for Judge D, exceeding the preestablished .8
cutoff.
↓
Step 4: Independent Judge E was given the list of themes generated
from Step 2 and copies of the interview transcripts and
surveys. Judge E coded the mention of themes by each
respondent. No new themes were identified by Judge E.
Interjudge reliability compared with the benchmark
comparison (Judges A and B) was .92, exceeding the
preestablished .8 cutoff. Strategies to Address Customer Concerns About Losing a Key Contact
Employee (n = 130 respondents)
Themes Frequency
-----------------------------------
Perceived criticality Rotation 12 (9%)
Teams 23 (18%)
Multiple contacts 48 (37%)
Vendor firm image 46 (35%)
Acceptability of Selection and hiring 22 (17%)
replacement employees Training 65 (50%)
Showcasing employees 24 (18%)
Tangible cues 13 (10%)
Procedures in transition Advance notification 12 (9%)
Planned transition period 14 (11%)
Strategies to Retain Key Contact Employee Knowledge
(n = 104 respondents)
Themes Frequency
------------------------- ---------
Valuing employee Culture of sharing 59 (57%)
information
Employee motivation Performance appraisal and 40 (38%)
reward systems
Trust and commitment 25 (24%)
Employee ability Technology 40 (38%)
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~~~~~~~~
By Neeli Bendapudi and Robert P. Leone
Neeli Bendapudi is Assistant Professor of Marketing, and Robert P. Leone is Professor and Berry Chair in Marketing, Fisher College of Business, The Ohio State University
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Record: 91- Managing Marketing Communications with Multichannel Customers. By: Thomas, Jacquelyn S.; Sullivan, Ursula Y. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p239-251. 13p. 6 Charts, 1 Graph. DOI: 10.1509/jmkg.2005.69.4.239.
- Database:
- Business Source Complete
Managing Marketing Communications with Multichannel
Customers
This article presents a marketing communications process that uses customer relationship management ideas for multichannel retailers. The authors describe and then demonstrate the process with enterprise-level data from a major U.S. retailer with multiple channels. On the basis of the results, the authors develop an initial marketing communications strategy for the retailer.
Over the past decade, customer relationship management (CRM) has proved critical in helping firms make more money by enabling them to identify the best customers and then satisfy their needs so that they remain loyal to the firm. More recently, CRM has grown increasingly complex with the proliferation of retailers expanding their channels of distribution (Cleary 2000). This has led to the need for enterprise-level data, which are the aggregation of data gathered from all firm interactions with their customers across all channels. Given this data-rich, dynamic environment, how can a firm identify the customers who will migrate among its multiple channels and predict their migration patterns? More important, how does the firm communicate with these customers to influence their channel choices and, ultimately, their value? This research focuses on answering these questions.
Thus, this article has two general objectives. First, we illuminate a process by which multichannel retailers can leverage enterprise-level data to understand and predict their customers' channel choices over time. Second, we demonstrate how the information gained from this process can be used to develop strategies for targeting and communicating with customers in a multichannel environment. The benefits achieved from the application of this process include increased efficiency in marketing expenditures and enhanced customer value. In the next section, we outline the general process for managing marketing communications (MARCOM) with multichannel retail customers. Subsequently, we demonstrate the application of the process using an enterprise database of a multichannel retailer. We conclude by noting the limitations of the study and ideas for further research in this area.
The Multichannel MARCOM Process
The process of managing MARCOM in a multichannel environment begins with the identification of relevant factors that differentiate among customers who use different channels. It continues with the development of a communication strategy for existing customers, and it ends with the prediction of the right communications strategy for prospects and new customers.
The critical aspect of this step of the process is not choosing the model (e.g., multinomial logit or probit) as much as it is specifying the model. It is important that the variables in the model are factors that ( 1) drive channel choice, ( 2) help classify prospects and customers into segments, and ( 3) measure the efficiency of the MARCOM expenditures and activities. Channel choice research has identified customers' price expectations (e.g., Brynjolfsson and Smith 2000), the product group to be purchased (Young 2001), and convenience (Forster 2004) as factors that may lead to a specific choice among channels or stores (Fox, Montgomery, and Lodish 2004). Balasubramanian, Raghunathan, and Mahajan (2005) assert that the goals (e.g., economic, self-affirmation, socialization) a consumer tries to achieve during his or her shopping experience affect channel choices.
Although descriptors of the actual purchase are relevant, it is also important to include independent measures that can be known before a purchase so that they can be used to classify prospects into segments. The distance that a customer lives from a store is an example. Other examples of factors that may drive channel choice include switching costs and risk aversion (Dholakia, Zhao, and Dholakia 2005). If competitive data are available, both of these factors can be assessed before the customer's first purchase with the focal firm.
Another factor to consider in the channel choice decision is the extent to which prior channel choices influence current channel choices. In general, marketers have shown that prior experiences affect current choices (e.g., Boulding, Kalra, and Staelin 1999; Thomas, Blattberg, and Fox 2004). In a channels context, Shankar, Rangaswamy, and Pusateri (2001) show that a prior positive experience with a brand in a physical store can decrease price sensitivity online. Furthermore, Dholakia, Zhao, and Dholakia (2005) assert that prior channel choices affect subsequent channel choices when customers make repeat purchases. Thus, knowledge of prior purchase channels may help explain future channel choices and develop a MARCOM strategy.
In terms of MARCOM, variables that measure the number, nature, or dollar amount of communication expenditures are also important to include in the channel choice model. For example, a study of migration between the catalog and the Internet channels finds that the number of marketing communications largely predicts the buying behavior of an Internet-oriented segment (Ansari, Mela, and Neslin 2005). Other research asserts that individual- or segment-level media expenditures are essential in the development of a MARCOM strategy (Tellis 2003, p. 45).
Although thoughtful identification of the critical factors that drive channel choice is important, it is also vital to recognize that, similar to attitudes (see Eagly and Chaiken 1993), the consumer's channel choice probabilities may change over time. This change over time is referred to as nonstationarity. In empirical applications, the presence or absence of nonstationarity in choice probabilities is determined by a statistical test (Anderson and Goodman 1957; Montgomery 1969; Styan and Smith 1964). This phenomenon can be captured simply by including a variable in the choice model that indicates time or purchase occasion number.
After the factors associated with channel choice have been examined and the choice model has been estimated, a statistical equation can be used to assign customers to a specific segment. A profile of each segment should then be created to describe the demographics and historical behavior of the segments, including differences and similarities. Doing so not only results in a greater understanding of the kinds of customers who frequent the retailer but also helps firms target and assign new customers to current segments (as can be observed in Step 5).
The purpose of this step is to anticipate the customer's channel choices in the future, thereby becoming more efficient with MARCOM activities in a multichannel environment. Various statistical models can be used for prediction. In this process, we leverage the results from Step 1 to develop a Markov chain. A Markov chain details the probability of a customer sequentially choosing to buy from different channels over time. A firm can then determine which channels a customer is most likely to buy from in the future. Using the knowledge of what drives channel choice (i.e., the results from Step 1), a firm can then attempt to leverage MARCOM to encourage channel choices that enhance customer value.
With this step, a firm can now begin to develop a MARCOM strategy for its existing customers. To develop the strategy, the firm should consider ( 1) the customer types that generate the most value to the firm (e.g., catalog plus Internet customers versus bricks-and-mortar store plus Internet customers), ( 2) a customer's intrinsic channel choice preferences and tendencies given the current MARCOM tactics, ( 3) the degree to which customers respond to MARCOM and the nature of that response, and ( 4) the costs associated with different MARCOM activities.
The objective of Step 5 is to use early information (e.g., demographics, channel of first purchase, revenues from first purchase) from prospects and first-time customers to classify them into the existing segments they most closely resemble. There are various classification and segmentation methods (e.g., discriminant analysis, chi-square automatic interaction detection [CHAID], classification and regression tree [CART], cluster analysis) that can be used in this step. Given the classification, the firm can then tailor communications that will influence purchase behavior similar to other customers in the same segment. The more elaborate the segment profiles (from Step 2) and the more detailed the data on prospects and new customers, the easier this step becomes.
Finally, as current and new customer interaction data become available, the data can then be applied to repeat the prior steps and update the segment assignments and their profiles. In particular, the Markov chain may help update the segment memberships. Note that the six steps we outline for the enhancement of MARCOM efficiency are closely aligned with the four critical actions (i.e., database creation, market segmentation, forecasting customer purchase behavior, and resource allocation) that Berger and colleagues (2002) assert are necessary for the assessment of how marketing actions affect customer value.
Application of the MARCOM Process
To demonstrate the application of the multichannel communications process, we use an enterprise customer database from a major U.S. retailer. This database includes sales from the retailer's three channels: physical retail stores, catalogs, and the Internet. The data were captured using the retailer's proprietary system, which first issues a unique number to a customer and then tracks that customer each time he or she purchases an item from any of the retailer's three channels. Of the more than 4100 customers tracked for this analysis, bricks-and-mortar store-only customers constituted approximately 63% of the total, catalog-only customers constituted 11.9%, and Internet-only customers constituted 12.4%. Dual-channel customers constituted 11.9% of the orders, and three-channel users constituted approximately 1%. Descriptive statistics of customers' purchase behaviors and highlights of the differences between customers appear in Table 1. We determined all of the relative comparisons we note in the bottom portion of Table 1 using a multivariate analysis of variance and planned contrasts for which the critical significance level was at least .05. Note that information from Table 1 is helpful when trying to derive inferences about the relationship between channel usage and customer value. Thus, we also use this information in future steps.
The data cover one year's worth of purchases from only first-time buyers.( n1) Although this one-year time horizon is not ideal for lifetime value assessments, which are common in CRM, this application shows that firms with even a limited enterprise database can begin to assess channel migration and develop communication strategies. As more data become available, a firm can then repeat the process and assess longer-term customer profitability.
Although data integration across channels is a significant challenge for many firms, a failure to do so could distort the firm's view of its customers (Aberdeen Group 2004). In these data, we find that if a channel were to function in a silo, it would fail to capture between 50% and 65% of a multichannel customer's total annual revenues. The magnitude of this distortion will vary by firm, but in these data, we find that it is similar for the store and catalog channels and is the greatest for the Internet channel. Thus, the necessary characteristics of the data are that ( 1) they are a longitudinal record, tracking all of a customer's purchase occasions, and ( 2) they contain elements of the purchase environments (e.g., promotions, pricing) across all three channels. For each purchase occasion, this firm's database tracks the channel from which the customer purchases, the products purchased and their categories, and the prices paid. A notable feature of this retailer is that all products are offered in all three channels, and pricing and promotions are uniform across the channels.
We use a multinomial logit model to estimate channel choice at a given time. A list of the independent variables and their operationalizations appear in Table 2. There are a few things to note with respect to our variable selection. First, we include a quadratic term for MARCOM dollars to explore nonlinearities in the relationship between communication expenditures and channel choice. We based this decision on previous research that has identified a decreasing-returns relationship between MARCOM expenditures and customer acquisition, retention, and long-term profitability of customers (Reinartz, Thomas, and Kumar 2005). Second, we include only the most recent prior purchase as a variable in the model. We based this decision on the statistical tests that Styan and Smith (1964) outline, from which we determined that the channel choice at time (t - 1) affects the channel choice at time t.( n2) Third, we include the purchase occasion variable. Using a test that Anderson and Goodman (1957) outline, we conclude that channel choice probabilities change over time (i.e., they are nonstationary).( n3)
To capture unobserved heterogeneity in the choice probabilities, we estimated the model using a latent class segmentation approach as Kamakura and Russell (1989) describe. The results from estimating the Logit model appear in Table 3. Note that the latent class procedure yielded two distinct segments (Akaike information criterion for Segments 1, 2, and 3 were 1.115, .9326, and .9330, respectively). For comparing the relative impact of the factors related to channel choice, Table 3 includes the elasticities of the statistically significant variables. These elasticities aid us in Step 4 as we develop the MARCOM strategy.
Consistent with the latent class estimation in Step 1, we assign customers to one of the two segments on the basis of their prior choice histories. As Kamakura and Russell (1989, Eqs. 7 and 8) describe, we first assume that a customer belongs to a specific segment. Given that assumption, we compute the likelihood of each customer's channel choice history. The equation to compute this is expressed here as follows:
( 1) L(Hk|i) = ΰtPj(uji,βji, X[sub kt),
where
L(Hk|i) = the likelihood that customer k has channel choice history H given that he or she is in segment i,
Pj(ui,βjXkt) = the probability that customer k chose channel j at time t given that he or she is in segment i,
uji = the preference parameter for channel j given that the customer is in segment i,
βji = the coefficient vector for channel choice j given that a customer is in segment i, and
Xkt = the vector of covariates for customer k at time t.
In the case of these data, we derive the probability in Equation 1 using the parameter estimates from Step 1 of this MARCOM process. In Step 1, we also estimated a parameter that leads to the determination of the size of each latent segment. Specifically, we found that 27% of the customers likely belong to Segment 1 and that 73% likely belong to Segment 2. Using the segment size information, for each customer, we compute the probability that he or she belongs to a particular segment. Borrowing from the work of Kamakura and Russell (1989, Eq. 8), we describe this posterior probability of segment membership in Equation 2:
( 2) P(k ∈ i|Hk) = L(Hk|i)fi/ ∑íL(Hk|i')fi'
where
P(k ∈ i|Hk) = the probability that customer k is in segment given choice history H,
fi = the probability of being in segment i, and
i' = the identifier of a latent segment.
On the basis of the result of Equation 2, we assign a customer to the segment for which he or she has the highest probability of membership.
After all customers have been assigned to a segment, we profile the segments on the basis of the key variables of prior purchase behavior, demographics, and the nature of the communications between the firm and the customers. Table 4 shows the profile for these data. Note that the profile may contain variables that were not included as independent variables in the channel choice model. We use the information in Table 4 to help classify prospects in Step 5.
The goal of this step is to develop a series of Markov switching matrices that reflect the probability of choosing a specific channel in the next period, given that the current channel choice is known. Using the parameter estimates from Step 1 that appear in Table 3, for each segment, we predict the probability of choice at time t at the mean value for the continuous variables (except the purchase occasion variable) and the modal value for the categorical variables (except the prior channel variable). Consistent with our definition, we set the purchase occasion variable equal to two for the first repeat purchase occasion. For subsequent repeat purchase occasions, we increase the variable by one accordingly. The parameter that we use in the prediction for the prior channel choice depends on which row of the matrix we are predicting. For these data, the channel choices for four periods into the future appear in Table 5. We observe that there are two distinct segments in terms of channel use: Segment 1 frequents the catalog and/or Internet, and Segment 2 is channel loyal and frequents the bricks-and-mortar store. Given the degree of loyalty, it is not surprising that the majority of Segment 2 customers made their first purchase from this same channel. This pattern of channel loyalty is consistent with Dholakia, Zhao, and Dholakia's (2005) findings.
Developing a segmented MARCOM strategy begins with some basic questions. Which customer types generate the most value to the firm? From Table 1, we confirm that multichannel buyers generate more revenue for the firm, purchase more items, purchase in more categories, and purchase more frequently than do single-channel buyers. More specifically, we learn that multichannel customers who use catalogs tend to generate more revenue than multichannel customers who do not use catalogs. We even find that a dual-channel customer is equally as valuable as a three-channel customer in terms of revenue, as long as the dual-channel customer buys from the catalog. Although this information does not imply a causal relationship between channel choice and purchase behavior, it provides a guide that we can use to develop a communication strategy for influencing channel choice.
What are the customer's intrinsic channel preferences and tendencies? The answer to this question comes primarily from the Markov matrices we developed in Step 3 (see Table 5). These matrices reveal that Segment 1 is primarily a catalog segment, as long as the prior purchase was not from the Internet. If the customer's prior channel was the bricks-and-mortar store, the probability is high that his or her first and second repeat purchases will be from the catalog. If the customer's prior purchase was over the Internet, there is some migration toward the catalog in the early stages of the life cycle, but this diminishes as the repeat purchase occasions increase. In general, Segment 1 customers could be labeled as those who migrate toward remote channels.
In contrast, Segment 2 customers will most likely stay in the bricks-and-mortar channel or switch to it. Over time, the data predict that Segment 2 customers will not choose to buy from the Internet. Given the infrequent switching that occurs in this segment (see the profile in Table 4), it could also be concluded from the trajectory of the matrices that a small group of customers in Segment 2 who repeat buy from the catalog will likely be those whose first purchase was from this same channel.
Thus, consistent with the segment profiles, these forecasts indicate that there is a significant amount of channel stickiness for buying in both segments. This is an important finding for several reasons. First, it suggests that for these data, the channel of first purchase has a high probability of being the channel choice for subsequent purchases. Second, this finding leads to the next set of questions.
To what extent do customers respond to MARCOM, and what is the nature of their response? On the basis of the elasticities in Table 3, we conclude that the number of communications is a key factor that is associated with the choice of the bricks-and-mortar store over the Internet but not for the choice between the catalog and the Internet in Segment 1 (see Table 3). The prior channel also seems to play a significant role in subsequent channel choices for Segment 1. Specifically, the lack of prior experience on the Internet is related to the decision not to choose the Internet for subsequent purchases. For Segment 2, the elasticities (see Table 3) indicate that the MARCOM dollar expenditures appear to drive channel choice the most. Increasing the number of communications in Segment 2 is associated with the choice of the Internet over the store or the catalog.
Given the significance of both the linear and the quadratic terms in both segments, we show the effect of MARCOM dollar expenditures graphically in Figure 1. This figure shows the change in the log odds of channel choice with respect to MARCOM dollar expenditures. To compute the log-odds ratio, we assume that the customer is making his or her first repeat purchase and that all other continuous variables are fixed at their means and the categorical variables are fixed at their modes.( n4) For Segment 1, we find that the log-odds ratio is negative and decreasing for the bricks-and-mortar store and positive and decreasing for the catalog channel. This means that an increase in MARCOM spending decreases the chance of the customer's choosing the bricks-and-mortar channel over the Internet. In addition, overall, MARCOM expenditures enhance the chances of the customer's choosing the catalog over the Internet, but this probability decreases as MARCOM expenditures increase. Thus, within the range of these data, we conclude that, in general, increasing MARCOM expenditures directed at the average Segment 1 customer motivates him or her to make the first repeat purchase from the catalog.
For Segment 2, we find that the log-odds ratio is positive and increasing for the bricks-and-mortar store and negative and decreasing for the catalog channel.( n5) This means that an increase in MARCOM spending increases the chance of choosing the bricks-and-mortar channel over the Internet and decreases the chance of choosing the catalog over the Internet. On the basis of the magnitude and sign of the log-odds ratios, we conclude that higher levels of communication spending directed toward Segment 2 primarily drives the average Segment 2 customer to make his or her first repeat purchase in the bricks-and-mortar store.
Synthesizing all of this information leads to an initial strategy for communicating with customers. The objective of this strategy is to encourage channel choice behavior that is consistent with the firm's highest-value multichannel customers. Because we are interested in driving net marketing contribution, we ignore the fixed costs that are associated with operating the channels when developing this strategy. Given this objective and the responses to the critical questions, we conclude that though Segment 1 customers are more likely to switch channels than Segment 2 customers, MARCOM plays a more limited role in this decision. The prior purchase channel is a vital if not better predictor of subsequent channel choice.
Many Segment 1 customers (46%) made their first purchase through the catalog. For customers in this portion of the segment, the retailer should attempt to maintain its strong affiliation with the catalog channel and encourage customers to have a multichannel relationship that likely would include the Internet. The rationale for this is that historical data indicate that this type of multichannel customer is desirable because of his or her tendency to be one of the highest dual-channel revenue generators, have a high purchase volume, and purchase at least as often as any other dual-channel buyer (see Table 1). Given that the number of communications does not have a significant effect on the choice between the catalog and the Internet channels, the firm should increase the MARCOM expenditure but direct the increase toward enhancing the quality of the communications, not the number of communications. An opportunity for improving the quality in a way that motivates the use of the Internet would be to feature products that are not in the base product category (i.e., Category 1; see Table 3).
For the 54% of Segment 1 who began a relationship through a channel other than the catalog, the goal is to drive these customers toward the catalog. The odds are against the choice of the bricks-and-mortar store over the catalog, but they improve slightly as the MARCOM dollars increase from $.25 to $5.00. Similarly, the odds are against the choice of the Internet over the catalog, but they improve as MARCOM expenditures increase from $.25 to $5.00. In addition, we know that increasing the number of communications drives this group of customers away from the catalog (see Table 3). On the basis of all this information, we conclude that the best way to drive these customers toward the catalog is to limit the amount of MARCOM spending and the number of contacts.
Although both segments exhibit channel loyalty, Segment 2 customers exhibit even higher levels of channel loyalty. This makes the channel of first purchase an even more important predictor of future choices. This also suggests that encouraging a person in Segment 2 to use all three channels is extremely difficult. Given that Segment 2 is channel loyal and that catalog-only customers generate the most revenue of all the single-channel customers (see Table 1), with limited MARCOM resources, it is better to focus a channel migration initiative on customers in Segment 2 whose first purchase was not through a catalog. Using the data in Table 1, we determine that the most desirable migration profiles for this subgroup are for first-time bricks-and-mortar store customers to become store and catalog customers and for first-time Internet customers to migrate to either of the other two channels. In addition, the estimates and elasticities from Table 3 suggest that for this subgroup, both the amount and the number of MARCOM expenditures are instrumental in achieving these migration objectives.
In Table 6, we summarize the specific MARCOM goals, a tactical plan, and the expected outcome of that plan for both segments. We derived the financial outcomes that appear in Table 6 using a segment-specific comparison of the revenue generation from a single-channel customer becoming a dual-channel customer (e.g., a catalog-only customer becoming a catalog and Internet customer in Segment 1). On the basis of these expected financial outcomes, we conclude that the potential payout may be greater if the firm focuses its incremental MARCOM resources on customers whose first channel was not the catalog.
Although the financial projections of the initial MARCOM strategy appear promising, it is critical that the firm asks one final question before moving forward: What are the costs associated with the different MARCOM activities? This information was not available for these data. However, before adopting any MARCOM plan, the expected payout must be compared with the expected costs that are used to generate that payout.
A significant benefit of this MARCOM process is using it to classify prospects and first-time customers. The earlier a firm can accurately classify a customer, the more efficiently and effectively it can leverage its MARCOM plan. In this step, we classify customers before their first purchase and update this classification after the first and second purchases. We iterate the classification process three times to demonstrate the accuracy and degree of improvement in classification that comes with added customer interaction data.
In this step, we used a tree approach for classifying customers (Breiman et al. 1984). Specifically, we used both CHAID and CART and compared the results in terms of predictive accuracy. The segment profile in Table 4 provides the best guide for variables that can be used to classify prospects and customers. The usefulness of the profile depends on the amount of information the firm has about customer characteristics, early purchase behavior, and the distinctiveness of that information across segments. In these data, the most distinguishing pieces of information are the distance the customer lives from the bricks-and-mortar store and the channel of first purchase. The channel choice estimates from Step 1 of this process confirm that distance is a significant driver of channel choice. Given the channel migration patterns that we uncovered in Step 3, we can conclude that the sequence of channel choices from the first to the second purchase might also aid in classification.
Thus, using only the distance from the closest store, we estimated a classification tree using a training sample of 2081 customers, and we tested it on a different set of 2081 customers. We found that, in general, the CART and CHAID results were similar, but the CART procedure was slightly more accurate with respect to prediction in Segment 1, the segment that generates the most revenues. From the CART results, we found that the overall risk of misclassification using only the distance measure was 21.39%, which we computed by dividing the total number of incorrectly classified customers in the test sample by the total number of customers in the test sample. We accurately classified 52.15% of the Segment 1 customers and 87.69% of the Segment 2 customers; we computed this by dividing the number of customers that CART predicted to be in a segment by the number that are truly in that segment. Using this classification, an initial contact strategy consistent with segment behavior and firm goals would be to select Segment 1 customers and send them a more extensive catalog that emphasizes the breadth of the product line and informs them of the Internet channel. In contrast, the initial contact for potential Segment 2 customers would be a communication piece (e.g., a postcard) that encourages them to make a purchase in a bricks-and-mortar store.
Although the channel of first purchase is not a perfect discriminator of segment membership, it can help classify a fraction of the first-time buyers. This is because a comparison of the segment profiles in Table 4 suggests that a customer whose first purchase is from the catalog is most likely a Segment 1 customer. However, given that a sizable number of customers in both segments make their first purchase in a store or on the Internet (see Table 4), it is difficult to assign these customers to segments. Using both the distance and the channel of first purchase, the CART procedure accurately classified more Segment 1 customers (74.55%) and fewer Segment 2 customers (78.46%). Because Segment 2 is significantly larger than Segment 1, the overall risk of misclassification increases to 23.49%.
On the third iteration of the CART procedure, we used distance, channel of first purchase, and channel of second purchase to classify customers. The motivation for adding this third variable was based on the degree of channel stickiness in Segment 2 and the migration toward the catalog in Segment 1 (see Table 5). The results from this iteration showed that there was only a 12.05% risk of customer misclassification. We accurately classified 74.01% of the Segment 1 customers and 92.74% of the Segment 2 customers. Thus, as the firm gains information, the classification improves. However, using only information that is known before the first purchase (i.e., distance from the store), for these data, the CART procedure predicted segment membership with approximately 80% accuracy.
This step reiterates Steps 1-4 on existing customers. The timing of when to implement Step 6 depends on the dynamics in a firm's market and the degree to which customer behavior changes over time.
Summary, Limitations, and Further Research
Although the insights derived from this analysis are specific to these data, the process that we describe is generalizable. Our application demonstrates that analyses can be conducted to enhance the targeting and management of customers in a multichannel context, even if a firm is just beginning to develop an enterprise database. A key benefit of this process is that it leads to insights that can be used to classify prospects into segments quickly. As MARCOM tactics become more sophisticated and costly, early and accurate customer identification is even more critical, and this process becomes more valuable.
A limitation of the inferences that we derive from using the process is that our data do not account for the nature or content of the MARCOM. For example, the device might be a specialty coupon, such as a buy-one-get-one-free promotion, or it may be a postcard that focuses on a particular category. Given our data, we simply examine the dollar expenditures and the number of MARCOM activities. An avenue for further research is to apply this process to more specific data on the communication devices themselves.
Another area for further research is to gather and incorporate shopping-level data with the purchase data to track customers through all interactions with the retailer (Nunes and Cespedes 2003). The data can be included in the choice model specification as we describe in Step 1. In addition, there is a need for theoretical models of customer buying behavior across multiple channels. Schoenbachler and Gordon (2002) introduce one such model, and researchers such as Kumar and Venkatesan (2005) have tested some of its components and found promising preliminary results for multichannel shopping behavior. The current research is a first step toward helping retailers understand the value and use of enterprise data. Although we demonstrate how MARCOM can benefit from the investments made in developing enterprise data, research in other areas, such as inventory management (e.g., Bendoly et al. 2005), could also benefit from the data that comes from channel integration.
The authors thank the special issue consulting editors, William Boulding and Richard Staelin, for their helpful comments on this article. They are also grateful for the assistance of Len Berry and the Center for Retailing Studies at Texas A&M University.
( n1) We limit our analysis to customers who first began a relationship with the firm during the observation horizon because customers who began before this time may have histories preceding the observation window that affect their current behavior and this analysis.
( n2) For a zero-versus first-order test, we need a customer to have at least two purchase occasions. However, because we do not assume stationarity at this point, for those who had a longer history, we continued to test the zero-order hypothesis at different points in their life cycles. Thus, we computed chi-square statistics at several repeat purchase occasions. The chi-square values for the first, second, third, fourth, and fifth repeat purchases were 137.33, 32.92, 26.17, 28.45, and 17.19, respectively. Assuming that p = .995, the critical chi-square value with four degrees of freedom is 14.86; therefore, we reject the null hypothesis that states that the process is a zero-order model. We conducted a second-order test, but we could not reject the null hypothesis that it was a first-order model.
( n3) Given our data, we let T = 6; we applied a test of stationarity to our data and found a chi-square value of 252.85. If p = .995, the critical chi-square value at 30 degrees of freedom was 50.67. From this, we conclude that we can reject the null hypothesis and that in this application, the channel choice process is nonstationary.
( n4) Given that in the data the average number of unique purchases is between two and three, we determined that it was best to assume that the customer was making his or her first repeat purchase.
( n5) Although we do not show this in Figure 1, we also used the parameter estimates to assess the choice between the physical bricks-and-mortar store and the catalog.
Legend for Chart:
A - Channel Usage
B - Sample Size
C - Percentage of Population
D - Means (Standard Errors) Number of Unique Purchase Occasions
E - Means (Standard Errors) Number of Categories Purchased in
F - Means (Standard Errors) Total Dollars Spent over Relationship
($)
G - Means (Standard Errors) Number of Items Purchased
A
B C D E
F G
H I
Brick only (B)
2620 62.95 3.66 (2.44) 3.31 (2.19)
571.21 (2,022.98) 27.68 (44.66)
Catalog only (C)
491 11.80 2.22 (.51) 1.22 (.55)
1,561.11 (5,176.72) 26.94 (61.35)
Internet only (I)
516 12.40 2.37 (1.31) 1.73 (1.19)
639.47 (622.36) 16.04 (40.74)
Brick and catalog (B + C)
252 6.05 3.98 (2.27) 3.12 (2.29)
2,204.65 (4,492.55) 50.12 (66.41)
Brick and Internet (B + I)
203 4.88 4.14 (2.25) 3.89 (2.39)
720.13 (202.61) 37.24 (82.82)
Catalog and Internet (C + I)
42 1.01 4.06 (1.58) 2.60 (1.58)
2,223.09 (6,702.44) 60.95 (68.87)
Brick, catalog, and Internet (B + C + I)
38 .91 5.71 (3.66) 3.88 (2.55)
2,379.97 (6,585.03) 72.48 (116.40)
Total
4162
Multiple Channel User
Yes
535 12.85 4.15 (1.77) 3.40 (2.13)
1,612.93 (6,256.16) 27.91 (41.47)
No
3627 87.15 3.05 (1.97) 3.01 (2.06)
710.79 (3,552.74) 38.39 (75.05)
Total
4162
Legend for Chart:
B - Summary of Planned Contrasts Number of Unique Purchase
Occasions
C - Summary of Planned Contrasts Number of Categories Purchased
in
D - Summary of Planned Contrasts Total Dollars Spent over
Relationship
E - Summary of Planned Contrasts Number of Items Purchased
A
B C
D E
Single-channel buyer
B > C = I B > I > C
C > B = I B = C > I
Two-channel buyer
B + C = B + I = C + I B + I > B + C > C + I
B + C > B + I C + I > B + C > B + I
B + C = C + I
B + I = C + I
Three-channel buyer versus two-channel buyer
B + C + I > any 2 B + C + I > B + C
B + C + I = B + I
B + C + I > C + I
B + C + I = B + C B + C + I > B + C
B + C + I > B + I B + C + I > B + I
B + C + I = C + I B + C + I = C + I Legend for Chart:
A - Variable Name
B - Operationalization
A B
Price Dollar amount paid for the product
Product Ten dummy variables that represent the
category 11 basic product categories into which all
of the retailer's products are grouped
Distance Number of miles the customer lives from
the closest store
MARCOM Time-varying measure that equals the
dollars dollar amount of the direct marketing
communications that were sent to each
customer after his or her prior purchase
and the current purchase at time t
(MARCOM
dollars)(n2) The square of the MARCOM dollars
Number of Time-varying measure that equals the
MARCOM number of direct communications the
customer receives after his or her last
purchase and before the current
purchase at time t
Prior channel Two dummy variables that indicate the
prior channel from which the customer
made a purchase
Purchase Time-varying measure that equals the
occasion current purchase occasion number of the
customer Legend for Chart:
B - Segment 1 Brick Versus Internet(a) Coefficient(b) (Standard
Error)
C - Segment 1 Brick Versus Internet(a) Elasticity(c)
D - Segment 1 Catalog Versus Internet(a) Coefficient(b) (Standard
Error)
E - Segment 1 Catalog Versus Internet(a) Elasticity(c)
F - Segment 2 Brick Versus Internet(a) Coefficient(b) (Standard
Error)
G - Segment 2 Brick Versus Internet(a) Elasticity(c)
H - Segment 2 Catalog Versus Internet(a) Coefficient(b) (Standard
Error)
I - Segment 2 Catalog Versus Internet(a) Elasticity(c)
A
B C D E
F G H I
Intercept
-.865 (**) .228 (*)
(.086) (.083)
-6.014 (**) -7.271 (**)
(.110) (.130)
Purchase occasion
.100 1.451(**) -.505 -.201(**)
(.008) (.012)
1.918 .000(**) 2.034 .434(**)
(.032) (.033)
Price
-.018 -.027(**) -.017 -.018(**)
(.001) (.001)
.077 .000(**) .052 -.189(**)
(.003) (.003)
Communication dollars
-.0128 -.219(**) -.0470 .081(**)
(.000) (.000)
1.500 23.865(*) -9.976 -6.865(**)
(.004) (.007)
(Communication dollars)(2)
-.0138 (**) -.0450 (**)
(.000) (.000)
-.0445 (**) .177 (**)
(.000) (.000)
Number of communications
.479 2.750(**) -.048 -.039
(.030) (.032)
.723 .000(**) .786 .343(**)
(.035) (.055)
Distance from closest brick
-.107 -12.950(**) -.0001 .007
(.001) (.000)
-.003 .000(**) -.091 -2.510(**)
(.000) (.016)
Prior channel is brick
5.857 2.662(**) 3.736 .541(**)
(.082) (.075)
2.275 .000(**) -2.431 -4.706(**)
(.059) (.110)
Prior channel is catalog
5.857 2.662(**) 3.736 .541(**)
(.083) (.075)
.403 -.002(**) 3.243 2.839(**)
(.089) (.095)
Product Categories
Category 2
-.875 .585(**) -1.709 -.249(**)
(.066) (.060)
1.660 .000(**) .804 -.856(**)
(.057) (.099)
Category 3
-1.404 -.382(**) -1.195 -.173(**)
(.059) (.056)
.599 .000(**) -.408 -1.007(**)
(.036) (.091)
Category 4
-1.049 -.637(**) -.482 -.070(**)
(.069) (.064)
2.295 .000(**) 3.062 .767(**)
(.079) (.116)
Category 5
-1.288 .584(**) -2.191 -.319(**)
(.098) (.102)
28.304 26.204
(74.200) (67.420)
Category 6
-.935 .000(**) -1.093 -.159(**)
(.051) (.048)
2.303 .000(**) 7.742 5.439(**)
(.183) .204
Category 7
-.935 .000(**) -1.093 -.159(**)
(.051) (.048)
20.099 16.447
(125.249) (25.250)
Category 8
-.934 -.001(**) -1.092 -.159(**)
(.051) (.048)
1.064 .000(**) -1.049 -2.113(**)
(.069) (.141)
Category 9
-.933 .000(**) -1.091 -.159(**)
(.052) (.048)
1.162 .000(**) .708 -.454(*)
(.085) (.187)
Category 10
-.935 .000(**) -1.093 -.159(**)
(.053) (.049)
11.271 -81.580
(10.225) (81.655)
Category 11
-.935 -.001(**) -1.093 -.159(**)
(.051) (.048)
.172 .000(*) -.912 -1.084(**)
(.059) (.187)
Segment Size
.27
.73
(*) p < .05.
(**) p < .0001.
(a) To determine the coefficient for choosing the brick store
versus the catalog, we subtracted the coefficient for choosing
the catalog versus the Internet from the coefficient for brick
versus the Internet.
(b) A positive coefficient means that a customer is more likely
to choose channel j than the base channel. The base channel is
the Internet in this case.
(c) We computed elasticities at the mean value of the continuous
variables and the modal value of the categorical variables. Legend for Chart:
B - Segment 1 Mean
C - Segment 1 Standard Deviation
D - Segment 2 Mean
E - Segment 2 Standard Deviation
A
B C D E
Relationship duration (days)
138.39 166.48 137.70 152.48
Number of unique purchase occasions
2.73 1.22 3.73 2.40
Total revenues generated throughout entire relationship
850.29 434.80 727.05 3825.85
Total revenues from first purchase
106.06 236.72 90.36 161.97
Total number of items purchased throughout entire relationship
106.40 116.96 118.15 148.87
Distance from closest bricks-and-mortar store (miles)
121.62 158.78 28.66 21.21
Number of channel switches
.30 .68 .19 .68
Days elapsed between channel switches
118.84 144.49 103.51 115.62
Total amount of direct mail communication dollars
2.60 1.53 2.64 1.45
Total number of direct mail communications
5.29 5.22 5.43 5.04
Percentage of Customers with Channel of First Purchase
Bricks-and-mortar
34.53 75.05
Catalog
46.64 7.94
Internet
18.83 17.01
Percentage of Customer Purchases by Category
Category 1
47.91 49.76
Category 2
8.84 9.45
Category 3
12.49 12.42
Category 4
8.65 6.81
Category 5
2.10 2.55
Category 6
3.83 1.10
Category 7
2.26 2.42
Category 8
4.29 4.46
Category 9
2.01 2.93
Category 10
4.61 5.85
Category 11
3.00 2.26
Percentage of Customers Making up to N Repeat Purchases
1
58.12 21.33
2
26.22 57.44
3
7.79 13.21
4
4.44 2.90
5
1.89 2.77
6
.51 .85
7
.36 1.01
8
.29 .35
9
.36 .13
10+
.00 .00
Percentage of Customers in Segment
27 73 Legend for Chart:
B - Transition Matrices Segment 1: Next Period Channel
Bricks-and-Mortar
C - Transition Matrices Segment 1: Next Period Channel Catalog
D - Transition Matrices Segment 1: Next Period Channel Internet
E - Transition Matrices Segment 2: Next Period Channel
Bricks-and-Mortar
F - Transition Matrices Segment 2: Next Period Channel Catalog
G - Transition Matrices Segment 2: Next Period Channel Internet
A B C D
E F G
First Repeat: Current Channel
Bricks-and-mortar .000 .886 .114
.992 .001 .007
Catalog .000 .886 .114
.737 .231 .032
Internet .000 .198 .802
.888 .049 .063
Second Repeat: Current Channel
Bricks-and-mortar .000 .828 .171
1.000 .000 .000
Catalog .000 .828 .171
.741 .259 .000
Internet .000 .132 .868
.941 .059 .010
Third Repeat: Current Channel
Bricks-and-mortar .001 .751 .248
.999 .001 .000
Catalog .001 .751 .248
.721 .279 .001
Internet .000 .085 .915
.942 .056 .001
Fourth Repeat: Current Channel
Bricks-and-mortar .001 .655 .344
.999 .001 .000
Catalog .001 .655 .344
.700 .300 .000
Internet .000 .054 .946
.940 .060 .000 Legend for Chart:
B - Segment 1
C - Segment 2
A
B
C
First Purchase
Channel Is
Catalog
• Migration objective: Maintain catalog affiliation,
and migrate to the Internet.
• Tactical plan: Focus on increasing the MARCOM
dollar expenditures, and leverage this increase to
enhance the quality of MARCOM.
• Expected plan outcome:
Customer profile: C → C + I
Change in revenue: 118%
• Migration objective: Given limited resources,
accept these customers as single-channel
customers.
• Tactical plan: Maintain status quo MARCOM.
• Expected plan outcome:
Customer profiles: C → C
Change in revenue: 0%
First Purchase
Channel Is
Not Catalog
• Migration objective: Encourage migration to
catalog channel.
• Tactical plan: Limit both the amount and the
number of expenditures, and leverage existing
expenditures to enhance the quality of existing
MARCOM.
• Expected plan outcome:
Customer profiles: B → B + C, or I → I + C
Change in revenue: 138%-199%
• Migration objective: Drive customers to become
dual-channel customers, specifically bricks-and-mortar
store and catalog or Internet plus any
other channel.
• Tactical plan: Focus on increasing the MARCOM
dollar expenditures, and leverage this increase to
enhance both the quality of communications and
the number of MARCOM activities.
• Expected plan outcome:
Customer profiles: B → B + C, I → I + B, or
I → I + C
Change in revenue: 14%-250%GRAPH: FIGURE 1; Effect of MARCOM Expenditures on Channel Choice
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~~~~~~~~
By Jacquelyn S. Thomas and Ursula Y. Sullivan
Jacquelyn S. Thomas is Associate Professor of Integrated Marketing Communications, McCormick Tribune Center, Northwestern University. Ursula Y. Sullivan is Assistant Professor of Marketing, Department of Business Administration, University of Illinois, Urbana.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 92- Market Orientation and Alternative Strategic Orientations: A Longitudinal Assessment of Performance Implications. By: Noble, Charles H.; Sinha, Rajiv K.; Kumar, Ajith. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p25-39. 15p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.66.4.25.18513.
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Market Orientation and Alternative Strategic Orientations: A Longitudinal Assessment of Performance Implications
Although the merits of maintaining a market orientation have been extensively discussed in the literature, studies examining the empirical link between market orientation and performance have shown mixed results. The authors explore the relative performance effects of various dimensions of market orientation using a longitudinal approach based on letters to shareholders in corporate annual reports. Furthermore, the authors examine the relative effects of alternative strategic orientations that reflect different managerial priorities for the firm. The authors also extend previous work by considering the mediating effects of organizational learning and innovativeness on the orientation-performance relationship. The results show that firms possessing higher levels of competitor orientation, national brand focus, and selling orientation exhibit superior performance.
Strategic orientations are the guiding principles that influence a firm's marketing and strategy-making activities. They represent the elements of the organization's culture that guide interactions with the marketplace, both with customers and competitors. Research in marketing has focused almost exclusively on maintaining a market orientation, based on the adoption and implementation of the marketing concept. A market orientation is not the only viable strategic orientation, however. Many successful firms have followed a production orientation, based on the belief that production efficiencies, cost minimization, and mass distribution can be used effectively to deliver quality goods and services to the consumer at attractive prices. Another alternative, a selling orientation, is based on the view that consumers will purchase more goods and services if aggressive sales and advertising methods are employed. This approach emphasizes short-term sales maximization over long-term relationship building. Contrary to the market orientation-or-nothing view that has generally been offered in the literature, we explore these alternative perspectives and their effects on company performance in a longitudinal study of major competitors in a single industry.
For marketers, the emphasis that has been placed on the antecedents and consequences of maintaining a market orientation is not surprising. The main tenets of this view--customer-oriented thinking, market analysis and under-standing, and the imbedding of the marketing concept throughout the organization--are some of the truly fundamental principles in the discipline. The attributes associated with the successful practice of other strategic orientations-- production efficiencies, cost minimization, and "hard sell" tactics--are either outside the realm of marketing or, in the last case, in the domain of what many would consider ethically questionable marketing. Nevertheless, ignoring these or other alternative orientations in our research does not mean that they are not viable and potentially lucrative business approaches.
Within market orientation research, certain empirical challenges have arisen. The fundamental assumption that firms exhibiting greater market orientation will show better financial and market performance has had mixed support in research to date. In part, this has resulted from challenges in effectively measuring the construct. Data in these studies have been largely limited to cross-sectional examinations of current organizational attitudes and practices and their relationship to performance and other outcomes. For the consideration of an organizational characteristic that is as deeply imbedded and slowly evolving as market orientation, a long-term analysis approach is more appropriate.
In this study, we examine the rewards of maintaining a market orientation in a broader context, juxtaposed against alternative marketing management philosophies. The panel of market-leading companies used here enables us to examine the stability of strategic orientation-performance relationships over an extended period in a dynamic industry. Furthermore, although arguments can be made for considering the complex market orientation construct on a holistic basis, we use a disaggregated approach that enables us to consider the relative influence of all elements of market orientation on performance. Recent research has suggested that the effects of market orientation on performance should be considered within a broader context of organizational variables (see Hult and Ketchen 2001). Here, we examine two central factors in the stream, organizational learning and innovativeness, as mediating forces in the relationships between strategic orientations and performance. In summary, this study demonstrates a novel methodology that we use to produce a longitudinal study of the effects of market orientation and other strategic orientations on performance, within a broader context of mediating organizational variables.
The concepts of market orientation, strategic orientation, and culture are closely intertwined. Market orientation has been a central research area for the Marketing Science Institute (MSI) for more than ten years. As the topic of at least two focused conferences and one summary collection of writings, market orientation has been studied from many different perspectives, including both the antecedents and the consequences of being market oriented (Deshpandé 1999). The nature of the concept itself has also been carefully considered. At the most recent conference on the topic, two significant themes emerged from the work in the area (see Deshpandé 1999, p. 2): ( 1) the need to consider market orientation at multiple levels, including as a corporate culture and as a strategic orientation, and ( 2) a need to under-stand both the antecedents and the performance consequences of being market oriented. The research reported here addresses both of these issues.
Any differences among "culture," "strategic orientation," and "market orientation" have not been well established, in part because of different definitions and treatments of the constructs in the literature. In the seminal coverage in marketing literature, organizational culture has been defined as "the pattern of shared values and beliefs that help individuals understand organizational functioning and thus pro --vide them norms for behavior in the organization" (Deshpandé and Webster 1989, p. 4). In this view, culture centers on embedded values and beliefs that guide behavior. Therefore, it is assumed that culture guides the behaviors that ultimately influence performance.
Is market orientation a relatively immutable element of organizational culture? Or is it an organizational choice, related to the adaptive strategies pursued by firms in a given time frame? This question has not been answered definitively in the literature. The former perspective holds that market orientation must be understood within the broader context of organizational culture (Deshpandé, Farley, and Webster 1993). In this view, market orientation is a deep-rooted attribute of the firm, with implications for organizational information processing (Kohli and Jaworski 1990). The alternative view, that a firm's degree of market orientation is largely a matter of choice and resource allocation, is illustrated by Ruekert (1992, p. 228): "[Market orientation is] the degree to which the business unit obtains and uses information from customers, develops a strategy which will meet customer needs, and implements that strategy by being responsive to customers' needs and wants." This perspective suggests that with the proper resources and focus, an organization can become more market oriented in a relatively rapid response to corporate directives. This has a multitude of implications, including the idea that market orientation can be actively managed on the basis of current market conditions and tactical objectives. Adding further complexity to this discussion is an attempt to integrate the concept of strategic orientations.
Strategic orientations have been considered in both marketing and strategic management literature. Miles and Snow's (1978) framework of alternative strategic orientations has been used to study various outcomes, many of which center on performance (e.g., Conant, Mokwa, and Varadarajan 1990). Other studies of so-called strategic orientations have used different typologies, including the propensity of firms to be opportunity seeking or problem avoiding, to maintain an external versus internal orientation, or to pursue differentiation-based or cost-based strategies (e.g., Wright et al. 1995). Yet another approach has examined strategic orientations as reflections of the beliefs and mental models of senior executives (Hitt et al. 1997). This view ties organizational cognition to elements of culture.
There is no definitive view on the nature of strategic orientation. As summarized by Morgan and Strong (1998), the concept has variously been described as strategic fit, strategic predisposition, strategic thrust, and strategic choice. They have summarized this array of perspectives into three general categories of strategic orientations: narrative approaches, which are anchored in qualitative methodologies and generally result in unique case study-like characterizations; classificatory approaches, which attempt to group strategies on the basis of preexisting or derived categories(e.g., Miles and Snow 1978); and comparative approaches, which identify combinations of traits and dimensions of strategy (e.g., Venkatraman1989).
On reviewing these alternative perspectives on strategic orientation and research to date, we offer a fourth possibility and organize these views into a simple framework (shown in Figure 1). The framework we offer considers the determinants used in the assessment of a firm's strategic orientation, either internal priorities and processes or external actions. It also incorporates the descriptive goal of the analyst as either the categorization of the firm relative to established strategic orientations or the development of a unique characterization of the organization. The three elements of Morgan and Strong's (1998) framework fit effectively into this model. We offer a fourth alternative that uses internal priorities and processes to categorize the firm, and we describe this as a competitive culture approach to assessing strategic orientation. We define competitive culture as the dimension of organizational culture that provides the organization's values and priorities in interactions with its marketplace--both customers and competitors--and influences more specific strategies and tactics. This view of strategic orientation is based on the belief that there is a deep, culture-driven characteristic of an organization that influences both the internal processes of that organization as related to marketing and strategic thinking and the strategies that emerge from that organization. Competitive culture should be primarily influenced by long-term management perspectives on the keys to competitive advantage and success in the firm's environment. Furthermore, the orientation can be categorized and compared across organizations.
Our definition first distinguishes between culture and orientation, treating strategic orientation as a sub-dimension of the culture construct. This is consistent with Deshpandé, Farley, and Webster's (1993) work that relates market orientation to culture. The definition also enables us to consider market orientation as one of several strategic orientations that an organization may possess, distinguished primarily by attitudes toward customers and competition. Our definition can be contrasted with that of Gatignon and Xuereb (1997, p. 78), who argue that strategic orientations are "the strategic directions implemented by a firm to create the proper behaviors for the continuous superior performance of the business." Their view suggests a more malleable and less culture-like view of strategic orientations than we take in this research. In summary, we suggest that one possible view of strategic orientations, the competitive culture view, allows the integration of market orientation and alternative strategic orientations as sub-dimensions of the broader organizational culture construct.
Two closely related frameworks have been the foundation for much of market orientation research. Narver and Slater (1990) view market orientation as consisting of three behavioral components (customer orientation, competitor orientation, and inter-functional coordination) and two decision-making criteria (a long-term focus and a profit focus). Kohli and Jaworski (1990) offer a more process-driven model that considers the stages of generating, disseminating, and responding to market intelligence as the essence of market orientation. The two frameworks share many underlying concepts and activities, such as the understanding of customer wants, cross-functional integration within the firm, and the importance of decisive action in response to market opportunities. We chose to operationalize Narver and Slater's (1990) framework for this study, because it was better suited to our data source.
A fundamental benefit of being market oriented is purported to be the creation of superior customer value and "continuous superior performance for the business" (Narver and Slater 1990, p. 20). This relationship between market orientation and performance has been explored by means of a wide range of methodologies, contexts, and measures of market orientation (Deshpandé 1999). Several studies have found support for the fundamental market orientation- performance relationship. For example, Pelham (2000) shows that market orientation has a positive and significant relationship to a range of performance measures, including marketing effectiveness, sales growth, market share, and profitability. In a two-period study, Narver, Jacobson, and Slater (1999) show that market orientation is significantly related to sales growth but not to corporate return on investment. The range of positive outcomes associated with market orientation has been extensive. Market orientation has been shown to have a positive relationship to return on assets (ROA) (Narver and Slater 1990), sales growth, new product success (Slater and Narver 1994), and relative product quality (Pelham and Wilson 1996).
Issues of judgment and perception have been raised as important considerations in market orientation research. Jaworski and Kohli (1993) find a significant market orientation-performance link when using a judgmental assessment as the dependent measure but not when using a more objective measure, market share. Pelham and Wilson (1996) also find significant results when using a subjective relative performance assessment, which suggests that a bias can exist in which firms that view themselves as perceptive regarding customers and competitors may overstate their performance.
Many empirical findings from studies of the relationship between market orientation and performance have produced results that are complex and, in several cases, unsupportive. In several studies of the performance consequences of market orientation in international settings (e.g., Bhuian 1998), no effects have been found, perhaps suggesting a cultural influence on the phenomenon. Across many contexts, various studies have found no direct relationship between market orientation and objective measures of performance (e.g., Han, Kim, and Srivastava 1998). Even in one of the founding pieces in this stream, performance effects vary on the basis of the business context (Narver and Slater 1990). From these findings, it appears that more work is needed to under-stand the range of factors influencing the relationship between market orientation and performance.
Finally, some cautionary issues have arisen in this stream regarding the components of market orientation. Narver and Slater (1990) do not achieve sufficiently high reliability values to evaluate the decision components (long-term focus and profit focus) of their model. Later work in this area has generally avoided an attempt to measure these two dimensions of Narver and Slater's original conceptualization, creating a knowledge gap surrounding these seemingly important factors. In general, studies of disaggregated dimensions of market orientation have been avoided or have encountered similar problems (see Bhuian 1998). Another issue that has been raised pertains to the potential dominance of the customer dimension in this framework, possibly diminishing the importance of other market orientation components (see Han, Kim, and Srivastava 1998). Although it is not possible to conclude a great deal from these isolated cases, they suggest potentially differential effects of the elements of market orientation on various outcome measures.
A few general observations can be derived from the broad body of performance-based market orientation research. It appears that the fundamental link been market orientation and performance has yet to be fully explored and supported. Issues of perception, varying results perhaps due to context, and differences in measurement and methodology have created a set of findings that is rich and interesting but somewhat lacking in clearly established grounding from which to advance knowledge. We propose that the combination of using a longitudinal approach, disaggregating the market orientation construct, and including both related factors and alternative strategic orientations into a single study should result in findings of sufficient breadth to advance knowledge in this area in a meaningful way. The longitudinal approach supports the view that, as a culture-like phenomenon, market orientation should have a great deal of inertia; its development and any associated performance benefits should take time to emerge. The disaggregation of the market orientation construct can be supported both methodologically and theoretically. In terms of research design and interpretation of findings, a disaggregation of the market orientation construct allows for better control of the error or "noise" that may influence more holistic measurement attempts. Narver and Slater's (1990) framework has yet to be completely and effectively studied in a disaggregated manner.
The theory of sustainable competitive advantage (Day and Wensley 1983) offers support for the fundamental hypothesized relationship between the elements of market orientation and performance. From this view, a firmly entrenched market orientation creates an advantage that the competition has difficulty matching. As Morgan and Strong (1998, p. 1053) describe, "The ability of the market oriented firm to outperform its less market oriented competitors is based on the premise that the former can create long-term superior value for the firm's customers in comparison with the latter." If Narver and Slater's (1990) five elements are true dimensions of the construct, it also follows that each should be expected to have the same causal effects. Considering the ultimate goal of market orientation, we suggest that:
H1: The five dimensions of Narver and Slater's market orientation framework will have a positive impact on firm performance.
Brand Focus
Brand development and management have been among the primary focal points of the marketing discipline over the past decade or more. Companies with a history of successful brand development, such as Procter & Gamble and Nike, have created a culture in which all areas of the company are dedicated to the branding process. Developing this brand-focused culture can require significant structural and cultural changes within the firm. For example, an increased focus on branding and brand management can result in a diminished role for outside advertising agencies, significant organizational restructuring, and the enhancement of internal entrepreneurial spirit (Low and Fullerton 1994). Given the increased prominence of branding within firm wide marketing strategy and the organizational commitment required to execute such an approach, it appears that a deep commitment to branding is indeed a reflection of the firm's culture.
Furthermore, the necessary understanding of customers, competitors, and organizational processes associated with successful branding suggests a tie to the market orientation construct. We propose that brand focus adds an additional, important dimension to Narver and Slater's (1990) market orientation framework and treat it as such in this study. We define brand focus as a dimension of market orientation that reflects the firm's emphasis on the development, acquisition, and leveraging of branded products and services in the pursuit of competitive advantage.
Despite the widespread attention to branding across many industries in recent years, there still exist significant within-industry differences in branding approaches. For example, across three grocery chains, Corstjens and Lal (2000) report wide variations for private label (store brand) sales as a percentage of the total for items such as fruit juice (20%, 78%, 12%), pasta (30%, 81%, 15%), and several others. There are clear differences in these firms' philosophies toward branding, perhaps explained by some combination of firm choice variables, organizational inertia, and resource constraints. Evidence such as this makes an exploration into the consequences of various branding approaches more compelling.
In retailing, this issue is complicated by the opportunity to pursue two different branding approaches. Private label, or store-specific, brands represent some of the best-established brands in the marketplace. From its earliest days, Sears, Roebuck and Company has distinguished itself through the development of well-regarded private label brands such as Kenmore appliances, Die Hard batteries, and Craftsman tools. More recently, JCPenney has had great success with its Arizona Jeans Company brand. As an alternative to the private label approach, retailers have the ability to offer national brands that are well known to consumers but are often offered by competitors as well. This can create a situation in which national and private label brands compete directly; for example, Tuff Skins and Levi's jeans both vie for the customer's dollar in a Sears store. Resolving a branding balance is not an easy decision for retailers, as recent research has shown that various consumers are drawn to private label versus national brands because of a complex combination of demographic and psychographic factors (Ailawadi, Neslin, and Gedenk 2001).
Companies dedicated to a brand focus can not only develop compelling propositions for the marketplace but also bind their organizations more tightly and increase their general effectiveness. Strong culture theory (Denison 1984) suggests that a dominant organizational culture provides cohesiveness and focus in planning and tactical activities. This effectiveness should lead to superior organizational performance. Thus, the cultural impact of a brand focus should enhance overall firm effectiveness. As Aaker and Joachimsthaler (2000) note, however, the nurturing of a new brand is a costly and often time-consuming proposition. Estimates on spending for advertising alone for national brand introductions have ranged from $20 million for the Nike Air Max line (Aaker and Joachimsthaler 2000) to $240 million to support the introduction of the Nissan automobile line in the United States (Aaker 1991). Recouping this investment, even for a successful brand, can be an extremely long-term proposition. Using a relatively short time horizon, we expect that a focus on national brands will enhance firm performance because of the previously established equity for these products. Given the considerable investment required for private label brand development, we expect a drag on corporate financial performance to be associated with this approach in the relatively short run. Therefore,
H2: In the short run, private label brand focus will have a negative impact on firm performance.
H3: In the short run, national brand focus will have a positive impact on firm performance.
From the perspective of competitive culture described in Figure 1, we contend that several alternative strategic orientations can be considered at the same level of abstraction as market orientation. Alternative orientations have been explored in a somewhat fragmented body of literature. In a relatively early effort, Keith (1960) examines the historical evolution of a single company through phases of production, sales, and marketing orientation. Miles and Arnold (1991) examine the interrelationship of market orientation and entrepreneurial orientation, finding that the two are indeed discrete management orientations. Other studies have contrasted market orientation or elements thereof with sales orientation, production orientation (Pelham 2000), learning orientation (Baker and Sinkula 1999), and technological orientation (Gatignon and Xuereb 1997).
Despite the compelling nature of the principles of the marketing concept, it is probably myopic to assume that a market orientation is the only legitimate guiding model for business success. Other successful business models exist. For example, eMachines has focused almost exclusively on efficiency and cost minimization to produce personal computers at a substantial cost and price advantage compared with the competition. Maximizing customer satisfaction regarding features, durability, and customization is not the central motivator in this firm's marketing efforts, yet the relatively new company has made substantial inroads in winning market share from established industry leaders. eMachines, as well as companies such as Supercuts, the ultra-quick hair cutting salon; McDonald's; and Southwest Airlines follow to varying degrees what has been described as a production orientation. This orientation is based on the belief that the pursuit of production and other operating efficiencies will produce widely available and relatively inexpensive products and services that will attract consumers (Kotler 2000). As suggested by the preceding examples, this approach has been successfully applied in industries beyond the traditional manufacturing setting. The trade-offs inherent in this approach include a reduced ability to maximize customer satisfaction and, in some cases, reduced quality due to the extreme focus on cost minimization. The demise of the poorly regarded Yugo automobile in the United States may reflect a company that pushed this approach past its limits.
From a theoretical perspective, this approach is based on early work in transaction cost economics by Coase (1937) and others, who describe the incentives available to firms that deliver fixed output through lowered production costs. Although most firms practicing this approach pass a portion of the profit margin advantage on to customers in the form of lower prices, a portion is also typically retained by the firm, which results in economic rents and superior financial performance. Therefore, we expect that:
H4: Production orientation will have a positive impact on firm performance.
Another potential strategic orientation is based on the firm's assumption that consumers will purchase more when subjected to aggressive sales techniques and marketing efforts. A selling orientation focuses on maximizing short-term sales at the possible expense of long-term relationship building(Lamb, Hair, andMcDaniel2000). It is based on the premise that customer hesitancy toward purchase can be overcome through marketing pressures. This approach is often practiced for "unsought goods" that consumers do not normally search for, such as insurance, encyclopedias, and funeral plots (Kotler2000). Time-shared vacation properties, traditional car dealers, and many companies using late-night "infomercials" might also be classified as demonstrating a selling orientation. Although these examples may in some cases represent the antithesis of a market orientation, some of these firms are quite profitable. For example, Direct Focus Inc. (2000), maker of the heavily promoted BowFlex line of fitness equipment, spent a substantial 32.8% of revenues on marketing and selling expenses in a recent fiscal year yet still delivered an impressive 18.5% net profit margin.
From the perspective of value generation, a selling orientation seems to offer little to the consumer. The high advertising expenditures inherent in this approach do not add to the desired product or service attributes, improve the consequences resulting from use of the product or service, or help consumers better achieve the desired end state they associate with the product or service (Day 1994). To the contrary, the costs generated through this approach should inflate selling prices and thereby reduce a consumer's value perception given a fixed bundle of product or service benefits. From a relationship-building perspective, a selling approach may stimulate short-term sales, but little customer loyalty and repeat business can be expected (see Lamb, Hair, and McDaniel 2000). Therefore, we expect that the reduced value offering and low customer loyalty will result in a negative relationship between selling orientation and performance. In particular, we expect this phenomenon to exist in a retail setting where shopping alternatives for the same goods or services can more readily expose inherent shortcomings in value-based offerings from a selling-oriented retailer.
H5: In a retail setting, a selling orientation will have a negative impact on firm performance.
As the conceptual network surrounding market orientation has been explored, several factors potentially mediating the relationship between market orientation and performance have been identified. To deepen our understanding of the relationships among market orientation, alternative strategic orientations, and performance, we consider two mediating factors that may be particularly influential: organizational learning and innovativeness. Organizational learning involves the use of new knowledge or insights to facilitate performance-enhancing organizational changes. Slater and Narver (1995, p. 63) describe the central role of learning as follows:
The critical challenge for any business is to create the combination of culture and climate that maximizes organizational learning on how to create superior customer value in dynamic and turbulent markets, because the ability to learn faster than competitors may be the only source of sustainable competitive advantage.
In this view, market orientation is described as a necessary, but not sufficient, factor in the creation of a learning organization.
Various views suggesting a positive link between organizational learning and performance have been put forth. Learning has been viewed as a complex resource of the firm that can be used to create competitive advantage and, ultimately, superior performance (Hunt and Morgan 1996). Dickson (1996) suggests that learning enables firms to sustain competitive advantages by continuously improving market information-processing activities faster than the competition.
In several ways, the concepts of market orientation and learning are intertwined, perhaps in a synergistic fashion (Baker and Sinkula 1999). For example, Day (1994) considers "outside-in" organizational processes, such as market orientation, contrasted with "inside-out" processes, such as learning. From this perspective, both types of processes influence the boundary-spanning activities that influence firm performance. Hurley and Hult (1998) treat both market orientation and learning orientation as elements of organizational culture that influence innovativeness and other outcomes. We broaden Slater and Narver's (1995) general perspective and consider the effects of cultural elements such as market orientation and other strategic orientations on the accomplished learning of organizations. Consistent with various perspectives on learning, we expect that an organization that is effective in learning will exhibit superior performance. Therefore, we expect that:
H6: Organizational learning will mediate the relationships between strategic orientations (including market orientation) and firm performance.
Organizational innovativeness has been closely tied to market orientation in a range of research. Innovativeness involves the implementation of new ideas, products, or processes (Zaltman, Duncan, and Holbek 1973). Innovativeness has been positively linked to performance in several studies (e.g., Deshpandé, Farley, and Webster 1993) and has been previously shown to mediate the relationship between market orientation and performance (Han, Kim, and Srivastava 1998). In exploring the roots of innovation, Gatignon and Xuereb (1997) consider innovativeness as the outcome of a firm's resources and its strategic orientation (including elements of market orientation). Connor (1999) also posits a causal link between market orientation and innovation, suggesting that the market-oriented dialogue between the firm and its customers provides the identification of issues and source of ideas necessary to foster significant innovation. Hurley and Hult (1998) examine innovation as part of a broader framework that links cultural aspects of the firm to its capacity to innovate and ultimately its performance. The general finding of these studies is that innovation is closely tied to a firm's strategic orientation, perhaps especially to market orientation. As Deshpandé, Farley, and Webster (1993) demonstrate, the most important manifestation of market orientation may be in the success of innovations.
Consistent with the literature in the area, we treat innovation as including both technological and administrative advances by the organization (Daft 1978). In a retail setting, innovations would include new systems, such as those for inventory management and point-of-sale operations, new selling methods and channels, and internal organizational changes designed to enhance value to the customer or operational effectiveness.
Although studies have examined the interaction between market orientation and innovativeness or that between innovativeness and performance, few have integrated these three variables. One exception is Hult and Ketchen's (2001) work, which considers these and other variables as equal contributors to a firm's positional advantages. In extending previous research, we focus on mediating effects and expect that:
H7: Innovativeness will mediate the relationships between strategic orientations (including market orientation) and firm performance.
Research Setting
To examine effectively the relationships between various strategic orientations and performance over an extended time frame, we chose a single-industry setting. This approach enabled us to consider different strategic approaches and their performance consequences in the same competitive environment. The setting chosen for this study was the mass merchandiser or discount sector of the retailing industry. Although the precise grouping of retailers into strategic groups is a challenging task, a simple demarcation among larger store retailers might distinguish among fashion-oriented department stores, mass merchandiser or discount stores, and so-called category killer stores. Fashion-oriented department stores such as Nordstrom and the May Company stores offer high-quality merchandise at relatively high prices and profit margins and have lower inventory turnover than other formats do. Mass merchandiser or discount stores such as Wal-Mart, Kmart, and Sears rely to varying degrees on a high volume, low margin formula and generally offer more moderately priced alternatives than the department stores do. In recent years, so-called category killer stores such as Home Depot, Toys "R" Us, and Circuit City have also emerged, offering a much narrower and deeper product assortment and relying on high sales volume and high inventory turnover.
Several reasons supported our choice of context. First, the mass merchandiser or discount sector is one of the longest-standing in retailing: Sears, JCPenney, and Montgomery Ward all have been in operation for over 100 years. This longevity should have allowed for the development of deep-rooted strategic orientations within competitors. Despite this long history, the sector has gone through changes in recent decades during which fundamental success factors appear to have changed significantly. This is evidenced by the dramatic and relatively recent ascendance of Wal-Mart to become the world's largest retailer. The firm performance differentials in this sector suggest that there are differences in strategies and managerial mind-sets that have led some firms to success and others to relative mediocrity. A second factor in our choice of context involved the level of openness found in the management discussion element of corporate annual reports. In contrast to companies in, for example, high-technology industries, retailers are generally fairly open in their discussion of strategic direction and company priorities in their annual reports, a necessary criterion for the methodology used in this study.
Finally, retailing is an interesting context given the general lack of rent-producing strategic assets in the industry (Barney 1986). In other industries, these assets are generated through proprietary technologies, viable patents, and other unique advantages. In retailing, by contrast, the tools needed to compete effectively are virtually universally available. For example, scanning technology and other technological advances are typically developed by third-party sup-pliers and offered to all major competitors in the industry; the adoption decision is based primarily on managerial judgments. This phenomenon enables most management teams the freedom to pursue a wide range of strategic alternatives based on their interpretation of the competitive situation. This relatively even playing field makes an analysis of the choices made in the industry and their consequences even more interesting.
We used the subject firms in a panel that covered the years 1986-97.[ 1] The companies studied were the market leaders during the period: JCPenney, Kmart, Sears, and Wal-Mart. To develop a better understanding of the industry, we also extensively studied trade publications and popular press articles from the era. Panel data have the primary advantage of allowing for the control of potentially unobservable firm-specific effects (Hausman and Taylor 1981). With the exception of Narver, Jacobson, and Slater's (1999) two-period study, no work has examined the market orientation-performance relationship from a longitudinal perspective.
Mapping Letters to Shareholders
Top management within an organization can be considered the locus of market orientation or other strategic orientations. An examination of the cognitions and mental models of senior management can offer meaningful insights into views on the pursuit of competitive advantage and culture-like elements of the firm such as strategic orientation (Morgan and Strong 1998). However, accessing those managerial insights, particularly over a lengthy time span, poses several challenges.
To assess the strategic orientations present in our subject companies without using unreliable retrospective accounts, we used letters to shareholders (henceforth "letters") in corporate annual reports as the data source. These letters can be used to examine the cognitions or managerial mind-sets of the senior executive group at the time of publication. Cognitive mapping techniques have been used in a variety of applications (see Huff 1990), often relying on annual reports as the data source. Material from annual reports has been used to seek evidence of managerial learning (Barr, Stimpert, and Huff 1992), identify corporate strategies (Bowman 1978), study causal reasoning and attributions within firms (Bettman and Weitz 1983), assess customer orientation (Judd and Tims 1991), and explain differences in joint venture activity (Fiol 1989).
The issues of who prepares the letters and whose views are reflected in them are important ones. Barr, Stimpert, and Huff (1992) raise some noteworthy cautions regarding the use of these documents. They note that public relations functions have taken to writing many of these letters in recent years. On the basis of our discussions with senior executives, we believe that this is a relatively minor concern. Although the nuances and more semantic elements of these statements may be the work of outsiders, it is clear that the underlying beliefs and guidance behind these letters are the work of senior management. In early planning meetings and final approvals of these documents, senior managers generally take great care to ensure that the current corporate vision is expressed to shareholders and employees. It has also been noted that annual reports are becoming an increasingly popular medium for communicating both company image and current strategies (see Judd and Tims 1991), which emphasizes the need for careful executive attention to these documents. These points all suggest that the letters are reasonable reflections of the prevailing managerial mind-set at the time of publication. As described by Barr, Stimpert, and Huff (1992, p. 22),
[W]hile annual reports may not be ideal, few rival data sources exist that can provide insight into the changing mental models of managers over time. This data source also has the critical virtue of being written in the time period of interest.... Further, informal conversations with executives indicate that they do have considerable involvement in preparing communications with investors, particularly in times of poor performance. In the end, we used annual report data because we believe this document is too important not to be given close attention by top management, both in terms of early subject framing and later word level editing.
Data Coding
Text was converted into quantitative data through a form of cognitive mapping. The methodology used in this study involved a sentence-by-sentence coding of the letters for the firms and years studied. Two undergraduate students were hired as coders for the project. Ten years of letters for a retailer that was not included in this study's sample were used as a training forum. The broad parameters of the project (identifying evidence of various types and dimensions of strategic orientations) were explained to the coders.
Coders were then given basic definitions of all strategic orientations (including dimensions of market orientation) and mediating factors, along with general guidelines for identifying evidence of these phenomena. For example, the coding sheets used described sales orientation as "including discussions focusing on maximizing sales, detailed comparisons of current to past sales performance, and any new programs or promotions intended to boost sales in the short term."
Using five years of the training sample, the coders then individually examined the letters for evidence of each strategic orientation and dimension under study. The two coders and one of the principal investigators then met and conducted a line-by-line review of each coder's decisions. Agreement was reached on how certain types of statements were to be coded from then on. It was possible for a single statement to be coded as representative of two variables. For example, it was decided that statements discussing the installation of new inventory management technologies would be treated as evidence of both a production orientation, because of the internal efficiencies with which they were associated, and the innovation variable. The coders then proceeded to code the second five-year block from the training sample, and the interpretation discussion with a principal investigator was repeated. Although the agreement level was not formally tracked, there was approximately a 95% corroboration between the coders on the second set of training data.
In general, the guideline given to coders for inclusion of a statement as representative of a variable was that "it should represent a clear and specific act reflective of the dimension being considered." Several statements along the lines of "we continue to strive to maximize customer satisfaction" and "all areas of the company are dedicated to enhancing performance" were excluded on the basis of this rule. The objective here was to separate vague managerial platitudes from concrete actions and beliefs.
The coders then began the process of analyzing the letters used in this study. They were provided coding sheets that included columns for entering the number of sentences in a particular letter that reflected each variable. The coders evaluated each letter independently for evidence of the phenomena in question. After completing five years for a single firm, the two coders met by themselves to compare their findings, agree on any necessary interpretations of the letters, and determine the final coding scheme for each letter. From the beginning of the coding of the data for the study, none of the principal investigators were involved in any way in the coding process. After the coders finalized the coding scheme for a particular letter and completed the final coding form, they used the total number of sentences in the letter to convert the sentence count to a percentage of the total, thereby creating variables that controlled for the varying lengths of letters. The Appendix shows samples of statements that were coded in each of the variables in the study.
We then examined the financial statements associated with the annual reports to record pretax operating profits, sales, and total asset information for the dependent measures. The data coding was a meticulous process, spanning a full year and several hundred person-hours of effort.
Estimation
The general approach of our data analysis is to use a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. Such models can essentially be viewed as two-way designs with covariates, and the estimation procedure for the regression parameters depends on the statistical characteristics of the error components in the model. If the specification depends only on the cross-section to which the observation belongs, the model is referred to as a one-way effects model. A specification that depends on both the cross-section and the time series to which the observation belongs is called a model with two-way effects. In addition to these effects, it is also common to specify the nature of the cross-sectional and time-series effects. If these effects are specified as occurring in a nonrandom manner, the models are referred to as fixed effects models. For example, the stable and time-invariant effect of managerial expertise or intelligence may be specified in a fixed effects model of market orientation(see Narver, Jacobson, and Slater1999).
We estimated the impact of market orientation and other strategic orientations on firm performance using models devised for panel data wherein sample observations are available for few cross-sections (corresponding to firms in our data) but for a relatively long time frame. We chose not to employ the usual fixed and random effects models, because their estimation requires large sample sizes (Kmenta and Ramsey 1980). We assessed firm performance using two measures, ROA and return on sales (ROS), each of which was used, in turn, as a dependent variable in the models we estimated. Each of the models we estimated took the following form:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where Y it denotes either ROA or ROS and X kit denotes the kth measure of market orientation or other strategic orientations. In estimating this model, we relaxed the usual regression assumptions and estimated a series of models, allowing for heteroscedasticity, autocorrelation, and cross-sectional correlations for the error term (εit). Consequently, we estimated the following four models.
Model 1 (groupwise heteroscedastic). This model enables the error variances (σ2) to vary across the four firms. Specifically,
Variance (εit) = σ 2i
We used this as our base line model, as the Lagrange-multiplier (LM) test strongly rejects the assumption of homoscedasticity for both the ROA and ROS models. Full results for this and the other models are presented in Tables 1 and 2.
Model 2 (groupwise heteroscedastic [Model 1] with cross-sectional correlation). This model additionally enables the disturbances of each firm to be correlated, on the assumption that the competitive environment may affect each firm to varying degrees. This is a testable assumption and is presented because the hypothesis that there is no correlation across firms is rejected for both the ROA and ROS models. Specifically,
Correlation (εit, εjt) = ρ [sub ij for
firm i and firm j.
Model 3. This model incorporates the assumptions in Model 2 plus a common autocorrelation parameter for all firms. Thus, the model allows for heteroscedasticity and cross-sectional correlation based on the previous results; in addition, it allows the error terms to be serially correlated. However, the autocorrelation coefficient is restricted to be the same for each firm. Specifically,
Correlation (ε it, εi(t + 1)) =
ρa for every pair of consecutive time periods t and t + 1.
Model 4. This model incorporates the assumptions of Model 2 plus a separate autocorrelation parameter for each firm:
Correlation (ε it, εi(t + 1)) =
ρ ai for the ith firm, for e very pair of
consecutive time periods t and t + 1.
In summary, we first fit a combination of models for the panel that varies the basic assumptions. These models relax the restrictive assumptions underlying ordinary least squares (OLS) regression as we move from Models 1 to 4. Thus, the four models refer to ( 1) group-wise heteroscedastic, ( 2) group-wise heteroscedastic with cross-sectional correlations, ( 3) Model 2 with an autocorrelation parameter, and ( 4) Model 2 with firm-specific autocorrelation parameters.
The general approach to model estimation is first to pool all the observations and use OLS residuals to estimate the common autocorrelation term. With this estimate in hand, we transform the entire data using the Cochrane-Orcutt transformation. We then use the transformed data for obtaining OLS estimates that have been purged of the autocorrelation and the residual sum of squares, and we use cross-products to obtain the generalized least squares estimates of the model parameters. We use an LM test for the heteroscedasticity specification (chi-square distribution with N - 1 degrees of freedom [d.f.] under the assumption of homoscedasticity). Similarly, we test the group-wise heteroscedasticity as a restriction on Model 2, using the likelihood ratio (LR) statistic. Finally, we test the estimated correlations in Models 3 and 4 for statistical significance by referring the transformation (T - 1) r2/(1 - r2) to the chi-square distribution with 1 d.f. Further details are given by Kmenta and Ramsey (1980). Although these models show highly consistent results, Model 4 is the least restrictive and therefore is the one that should be primarily relied on in the assessment of hypothesis results.
The results for all four models are presented in Table 1(ROS as the dependent variable) and Table 2 (ROA as the dependent variable). The results across all four models for both dependent measures were highly consistent.
Elements of Market Orientation
Of Narver and Slater's (1990) elements of market orientation (H1), only the competitor orientation dimension was significantly related to performance across all models. That is, the more focused the firms in our set were on competitor actions, the more their performance benefited. In the highly competitive industry we studied, this is not surprising. This intensity of competition may lend some explanation to the general lack of significance in the profit focus and long-term t-focus variables. Firms that are focused on marshaling resources to meet a more immediate threat, such as the dramatic rise of a strong competitor, may need to forgo a focus on long-term goals and immediate profits. Inter-functional coordination received some support but not in the most salient model, Model 4. Given the need to coordinate internal resources to both combat competitors and serve customers effectively, this result is interesting. This may represent an underdeveloped aspect of market orientation for the firms in our set.
Finally, the customer orientation variable did not relate to performance. An examination of prior findings of empirical market orientation research reveals few insights to corroborate these findings. Although several studies have gathered a multidimensional measure of market orientation, only the aggregated market orientation-performance relationship has typically been reported (e.g., Jaworski and Kohli 1993). Others report disaggregated market orientation measures but model factors that mediate the relationship to performance, not allowing for an assessment of direct effects (e.g., Han, Kim, and Srivastava 1998). Similarly, Gatignon and Xuereb (1997) identify a moderating relationship in which customer orientation is positively associated with the performance of an innovation only in highly uncertain markets. Although we did not study such potential moderating factors, these limited findings result in a simple conclusion--that the intuitive and theoretically supported relationship between customer orientation and firm performance has yet to be consistently demonstrated in the literature. In this study, customer orientation was not a driver of firm performance.
Next, we consider our extension of the market orientation framework to include brand focus. As developed in our hypotheses, we considered only the relatively short-term effects of the branding approaches. Consistent with our expectation in H2, a private label brand focus was negatively related to performance. That is, the more firms emphasized internally developed brands, the more adversely was performance influenced. It appears that the resource drain imposed by the research, development, and promotion associated with the introduction of a private label brand was not recouped through incremental profits in the short run. Supporting our expectation in H3, a national brand focus was positively related to performance. Firms that placed a greater emphasis on these brands, despite the brands' presence in competitors' outlets, showed superior performance. Among other things, this suggests that the increased volumes generated by these goods offset the lower profit margins they generally carried compared with private label and nonbranded products.
Alternative Strategic Orientations
Among the alternative strategic orientations considered, production orientation (H4) was not significantly related to performance. It appears that having operational efficiencies at the forefront of management thinking did not create the best environment for firms' success. Combined with the significance of competitor orientation, this suggests that at least during this period of this industry's evolution, maintaining an "outward focus" was essential, perhaps because of the rapidly changing nature of the competitive landscape. Contrary to expectations, a selling orientation (H5) was generally positively related to performance. Firms that placed more emphasis on sales promotions and other means of maximizing revenues showed higher levels of financial performance. Combined with the lack of significance for customer orientation, it appears that this industry maintained more of a transactional approach, maximizing short-term sales, rather than a relationship-building orientation toward customers.
The Mediating Effects of Learning and Innovation
We also examined the mediating effects of learning and innovation on the relationships between various strategic orientations and performance. To explore mediation, we followed the analysis strategy recommended by Baron and Kenny (1986), with one additional step (discussed subsequently) to rule out the possibility of spurious relationships that may be induced by multi-collinearity in the data. Baron and Kenny (1986) propose testing for mediation using estimates from three sets of regressions: ( 1) the regression of the mediator variable (i.e., learning or innovation) on the independent variables (i.e., the strategic orientations), ( 2) the regression of the dependent variable (i.e., performance) on the independent variables, and ( 3) the regression of the dependent variable on the independent variables and the mediating variable. The first two regressions seek to demonstrate that the independent variables affect the mediating variable and the dependent variable, respectively. The third regression is done to establish that the mediating variable affects the dependent variable, even when the independent variables are controlled for. The independent variables are included as regressors in the third regression to rule out the possibility that a significant relationship between the mediating variable and the dependent variable is due to the "common causes" of the independent variables on each variable. However, it is also possible that a significant coefficient for the mediating variable in the third regression could be due to multi-collinearity among the independent variables and the mediating variable. This possibility can be ruled out through a fourth regression of the dependent variable on the mediating variable only. If the coefficient for the mediating variable is non-significant in the fourth regression but significant in the third regression, then we conclude that the effect of the mediating variable on the dependent variable in the third regression is a spurious one, due to multi-collinearity among the mediating variable and the independent variables.
The desiderata for establishing mediation are as follows: The independent variables must affect the mediator in the first regression and the dependent variable in the second regression. The mediating variable must affect the dependent variable in the third and fourth regressions. In addition, the statistical significance of the mediated effect (i.e., the effect of an independent variable on the mediator variable in the first regression times the beta for the mediator variable in the third regression) must be assessed by means of the approximate standard error formula given by Baron and Kenny (1986). We examined mediation separately for each mediating variable, as well as for each performance metric (i.e., ROS and ROA). We estimated the impact of learning and innovation on ROA in two simple regressions. The coefficient for innovation was positive (beta = .035) but non-significant (standard error = .063, p-value > .5), and the coefficient for learning was positive (beta = .18) and significant (standard error = .03, p-value < .0001). This suggested that innovation did not mediate relationships between the strategic orientations and ROA but that learning might play a mediating role. Of the independent variables, only three--competitor orientation, private label brand focus, and national brand focus--had significant, direct effects on ROA (Model 4, Table 2). Accordingly, we regressed ROA on learning and these three independent variables and obtained a positive and significant effect for learning (beta = .10, standard error = .02, p-value < 0.0001). In the regression of learning on the three independent variables, only competitor orientation had a significant effect (beta = .38, standard error = .13, p-value < .01). Thus, learning mediated only the relationship between competitor orientation and ROA. The mediated effect (.10 times .38) was also statistically significant (approximate standard error = .015).
The regression of ROS on learning was not significant (beta = .061, standard error = .031, p-value > .05), suggesting that learning had no mediating effect. In contrast, the regression of ROS on innovation yielded a significant but negative coefficient (beta = -.079, standard error = .026, p-value < .003). To explore this anomalous finding further, we performed the other regressions recommended by Baron and Kenny (1986). Only four of the strategic orientations--competitor orientation, profit focus, private label brand focus, and national brand focus--had a significant impact on ROS(Model4, Table 1). When ROS was regressed on innovation and these four strategic orientations, the coefficient for innovation remained negative and significant(beta = -.072,standarderror = .026, p-value < .01). In the regression of innovation on the four orientations, the only significant effect was that of private label brand focus ( beta = .87, standard error = .34, p-value < .012). Although the overall mediated effect(i.e.,.87times-.072) was not statistically significant given its estimated standard error of .034, the pattern in the results may explain the negative impact of innovation on ROS. In summary, we found that organizational learning (H6) positively mediates the relationship between competitor orientation and ROA and that innovativeness (H7) has a weak, negative mediating effect on the relationship between private label brand focus and ROS.
The overall results provide only modest support for the mediating effects of learning and innovation on the relationships between strategic orientations (including market orientation) and performance. The ability to translate marketplace information, such as that gathered by an effective market-oriented firm, into learning is considered an important process in maximizing organizational performance (Hunt and Morgan 1996). We found evidence of that mediating phenomenon only in the relationship between competitor orientation and one performance measure. Of the possible mediating relationships, however, this is perhaps the most intuitive. High-performing firms are those that not only can gather intelligence and understand competitor actions but also can translate that knowledge into learning, leading to actions such as insightful strategic change. Note that because of the nature of the data source, the learning studied here was primarily that of exploitation, or learning from existing routines and actions. An alternative form, exploratory learning, involves the programmatic discovery of new resources and technologies (Sorenson and Sorensen 2001). The more active processes implied by exploratory learning may more strongly mediate the transition of customer and competitor orientations to superior firm performance.
Contrary to our expectations in H7, little evidence was found for the mediating effects of innovativeness on the relationships between strategic orientations and performance. Of the variables considered, we found only a weak, negative mediating effect on the relationship between private label brand focus and ROS. This suggests that in our data, senior management's perceptions of innovation were positively related to the development of private label brands. That is, private labeling was considered a form of innovation in the industry. However, on the basis of our previous discussion of the negative short-term effects of this private label approach, the negative relationship to performance is not surprising. In their study of market orientation dimensions, Han, Kim, and Srivastava (1998) find that the customer orientation-performance relationship is mediated by innovativeness, but mediating effects are not supported when they examine competitor orientation and inter-functional coordination dimensions. Therefore, it appears that the relationship of innovativeness to market orientation and performance has not been fully explained. An alternative view that has received empirical support considers market orientation and innovativeness as equal first-order indicators of the higher-order factor of positional advantage (Hult and Ketchen 2001).
This study has limitations that must be considered. One potential limitation is that the causal chain under examination here assumes that competitive cultures (i.e., strategic orientations) influence the behaviors of organizational members, and those behaviors ultimately influence performance. The methodology used here does not allow for the direct examination of individual behaviors, instead it examines the indirect relationship between cultures and performance. Another consideration is the timing issues that may create variations in an individual firm's strategic orientation- performance relationships. For example, variations in asset growth through new store additions and the resulting lags before performance benefits would influence the ROA dependent measure. Finally, we must consider whether the subjective statements of senior executives reflect the more objective underlying characteristics of their firms. As some research has shown, the difference between subjective and objective evaluations of strategic orientation may yield different results.
We can draw several insights into the nature and consequences of strategic orientations from this study. It appears that there are competitive cultures beyond the traditional view of market orientation that may lead to strong firm performance. A selling orientation was associated with higher levels of performance, as were the competitor orientation and national brand focus elements of our market orientation framework. These findings suggest the importance of a broadened perspective in market orientation research. Different firms, possessing different strategic orientations, may be better suited to succeed in various competitive environments. Our examination of mediating factors provided some support for the view that a broadened perspective is needed for the consideration of the relationship between strategic orientations and performance. Within the market orientation literature, this research illustrates the importance of examining the construct in a disaggregated fashion. Although the sub-concepts are conceptually linked and should also be considered in the aggregate, a disaggregated approach examines the relative value of the various elements. In part, this approach lends itself to more precise insights for managers who are interested in developing a performance-enhancing, market-oriented organization. A focus on holistic measures may explain some of the problems and inconsistencies encountered in prior empirical market orientation research. The lack of significance for the customer orientation dimension in this study is provocative and worthy of further study. We can speculate that the key competitive weapons used in this era--aggressive pricing, dominant store locations, and major store renovations--were intended primarily to create a differentiated positioning from competition and less for pure customer satisfaction benefits.
These findings add weight to the recent emphasis on the importance of branding in marketing strategy and as a component of market orientation. As expected, firms that placed a greater focus on the management of national brands exhibited performance advantages. A noteworthy contribution of this study is the negative performance relationship found between private label brand focus and performance. Although an internal cost assessment for brand development is challenging, because costs go beyond dollar investments in research and advertising to include other resources such as senior management attention, our data suggest that firms need to enter into internal brand development cautiously. It appears that the greater awareness and lower costs associated with the use of national brands created a more favorable profit equation for the firms in our data set.
From a methodological standpoint, this study demonstrates the benefits of a longitudinal approach to the study of market orientation. The use of panel data to examine strategic orientations is meaningful, as these cultural phenomena are likely to evolve over time and show links to performance that may vary from year to year. Finally, the use of the annual report coding methodology represents a contribution to marketing strategy literature, suggesting a viable method to approach the study of a litany of marketing management issues. It represents an effective method for exploring the cognitions and marketing mind-sets of senior managers.
Further research in this area can take several approaches. A configurational approach should be pursued to determine the relative combinations of various strategic orientations that lead to performance success in different competitive situations. This research would provide additional insights into the relative value of alternative strategic orientations. Another approach in future work could examine the nature of the industry and the competitive environment as moderators of the strategic orientation-performance relationship. This contingency approach would improve our understanding of the factors that enable firms pursuing alternative strategic orientations to be successful in different environments. Additional models and conceptualizations of market orientation should also be studied from a longitudinal perspective. The more process-oriented elements of Kohli and Jaworski's (1990) framework would be particularly relevant to study over time.
Although it seems clear that pursuing the principles of a market orientation can help most firms achieve higher levels of marketplace success, the marketing discipline has been remiss in ignoring several other possible strategic orientations that influence the interactions between the firm and its markets. This study has identified several of these alternative orientations and has shown that some are related to higher levels of firm performance. The results show that some elements of one accepted market orientation framework influence performance but several do not. This is consistent with much of the prior research, which has failed to support the performance link fully. These findings highlight a fundamental challenge for marketers--understanding that there is no single strategic orientation that leads to superior performance in all situations and developing an understanding of the environmental, competitive, and other factors that lead one firm and its orientation to greater performance than others.
Note:
1. The years 1992-94 were excluded because of the absence of necessary data from one subject firm.
Table 1: Model Results Based on ROS
Legend for chart:
A - Strategic Orientation Variables
B - Model 1
C - Model 2
D - Model 3
E - Model 4
A
B C D E
Constant
.03 (.006)*** .03 (.004)*** .03 (.003)*** .03 (.003)***
Market Orientation Dimensions
Customer orientation
-.02 (.04) .01 (.03) .01 (.02) -.006(.02)
Competitor orientation
.27 (.09)*** .22 (.06)*** .22 (.04)*** .23 (.04)***
Interfunctional coordination
.13 (.07)* .05 (.05) .002 (.04) .003(.04)
Profit focus
-.10 (.07) -.09 (.06) -.08 (.05) -.13 (.04)**
Long-term focus
-.03 (.07) .002 (.05) .001 (.04) -.016(.04)
Private label brand focus
-.44 (.10)*** -.45 (.09)*** -.35 (.08)*** -.28 (.07)***
National brand focus
.33 (.12)*** .34 (.09)*** .24 (.07)** .15 (.06)**
Production Orientation
-.08 (.05) -.003 (.04) .03 (.03) -.002(.03)
Selling Orientation
.11 (.06)* .12 (.04)*** .11 (.03)** .08 (.03)
Rho
-- -- .27 --
Rho1
-- -- -- -.16
Rho2
-- -- -- .36
Rho3
-- -- -- .48*
Rho4
-- -- -- .40*
LR statistic
-- 26.54b -- --
LM statistic
11.09a -- -- --
*p < .10.
**p < .01.
***p < .001.
aRejects homoscedasticity.
bRejects hypothesis of zero intergroup correlations.
Notes: Standard errors are in parentheses. n = 36 for all models.
Table 2: Model Results Based on ROA
Legend for chart:
A - Strategic Orientation Variables
B - Model 1
C - Model 2
D - Model 3
E - Model 4
A
B C D E
Constant
.009(.01) .009 (.14) .02 (.01) .02 (.01)*
Market Orientation Dimensions
Customer orientation
-.12 (.12) -.08 (.11) -.01 (.08) .01 (.65)
Competitor orientation
.61 (.24)** .50 (.18)*** .36 (.14)** .33 (.10)***
Interfunctional coordination
.90 (.20)*** .73 (.20)*** .27 (.19) .10 (.17)
Profit focus
.08 (.19) -.01 (.17) .04 (.15) .03 (.12)
Long-term focus
.29 (.20) .37 (.18) .22 (.17) .14 (.12)
Private label brand focus
-1.00 (.24)*** -1.11 (.21)*** -.90 (.20)*** -.82 (.17)***
National brand focus
.53 (.32)* .76 (.27)* .68 (.23)** .82 (.19)***
Production Orientation
-.16 (.14) -.04 (.13) .06 (.12) .09 (.10)
Selling Orientation
.53 (.16)*** .49 (.13)*** .27 (.11)* .16 (.84)*
Rho
-- -- .258 --
Rho1
-- -- -- .54*
Rho2
-- -- -- -.19
Rho3
-- -- -- .52*
Rho4
-- -- -- .15
LR statistic
-- 13.31b -- --
LM statistic
7.67a -- -- --
*p < .10.
**p < .01.
***p < .001.
aRejects homoscedasticity.
bRejects hypothesis of zero intergroup correlations.
Notes: Standard errors are in parentheses. n = 36 for all models.
DIAGRAM: Figure 1: Perspectives on Strategic Orientation
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Illustrations of Letter to Shareholder Coding by Strategic Orientation Type
Legend for chart:
A - Strategic Orientation
B - Example of Coded Sentence from Letter to Shareholders
A
B
Customer orientation (a)
"This consumer confidence is well-deserved because of our commitment
to bring high quality, well-made merchandise to all American consumers
at low everyday prices within a friendly, courteous and pleasant
shopping experience" (Kmart 1988).
"Our focus on exceeding our customers' expectations [continues] with
wider aisles and significantly more customer space with our new stores
averaging almost 100,000 square feet" (Wal-Mart 1991).
Competitor orientation (a)
"The intensifying competition in the retail industry is already
altering the landscape" (JCPenney 1995).
"We recently reaffirmed our commitment to low prices with a 'price
promise' advertising campaign that states clearly that Kmart will honor
our competitors' regular or currently advertised prices on identical
items" (Kmart 1992).
Iterfunctional coordination(a)
"As we unite our strengths in merchandising, marketing, customer data,
and technology--and the power of 260,000 associates working together to
identify and exploit these new opportunities--I am confident of our
future success" (JCPenney 1997).
"We have enhanced teamwork within the company, built closer
relationships with suppliers, supported our communities, and
intensified our customer focus" (Wal-Mart 1993).
Profit focus(a)
"Thanks to the hard work of our associates, retail profits rose nearly
20% to surpass $1 billion for the first time" (Sears Roebuck 1995).
"In 1989, we placed even greater emphasis on our pricing leadership,
but the lowering of prices hurt margins, and this contributed
importantly to our earnings decline" (Kmart 1989).
Long-term focus(a)
"While we are committed to executing the current program successfully,
we are also building for the future by identifying major trends in
consumer demand and defining ways we can meet those demands"
(Sears Roebuck 1995).
"[Although disappointing], this year 's performance represents an
extraordinary amount of work completed in order to lay the foundation
for a bottoming out of Kmart's financial decline and a return to
profitability" (Kmart 1995).
Private label brand focus(a)
"We built a formidable sales gain in luggage by introducing appealing
private brand entries that give consumers what they want in
luggage--at prices well below the competition" (JCPenney 1994).
"We also built and strengthened a truly remarkable line of exclusive
private label products such as Martha Stewart Everyday, Sesame Street,
Jaclyn Smith, Kathy Ireland, Penske Automotive..., [which] offer
customers big helpings of quality, style, selection, and value"
(Kmart 1997).
National brand focus(a)
"We will continue to build our merchandising program around
national-brand products sold at everyday low prices" (Wal-Mart 1991).
"In children's, we already offer most of the key national brands
including Healthtex, Spumoni, Weeboks, Toddler University, and
Nickelodeon" (JCPenney 1989).
Production orientation
"[We] maintained better in-stock position on the best selling items,
tightened expense control, and reduced markdowns" (Wal-Mart 1987).
"Benefits from the satellite system have been substantial and include
reduced data transmission costs, lower customer credit handling
expense and improved communication from headquarters personnel to
store management through live video transmissions" (Kmart 1990).
Selling orientation
"We are evaluating ways to drive Kmart's sales growth even faster
through innovations in store design, merchandise offerings, and
execution" (Kmart 1996).
"Wal-Mart stores' record performance in fiscal 1994 included a
same-store sales increase of 8 percent, impressive in a low
inflationary retail environment" (Wal-Mart 1994).
(a)Market orientation dimension.
~~~~~~~~
By Charles H. Noble; Rajiv K. Sinha and Ajith Kumar
Charles H. Noble is Assistant Professor in the Marketing Area, School of Business Administration, University of Mississippi. Rajiv K. Sinha is Associate Professor of Marketing, and Ajith Kumar is Professor of Marketing, Arizona State University. The authors are indebted to the anonymous JM reviewers for their insightful and constructive comments. The efforts of Jason Kaufman, Carrie Luciano, Erin Humphries, and Amanda Roy in the data collection portion of the project are also gratefully acknowledged. This research was supported by a grant to the second author by the Dean's Council of 100, The Economic Club of Phoenix, and the College of Business, Arizona State University.
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Record: 93- Market Orientation, Creativity, and New Product Performance in High-Technology Firms. By: Im, Subin; Workman Jr., John P. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p114-132. 19p. 2 Diagrams, 5 Charts. DOI: 10.1509/jmkg.68.2.114.27788.
- Database:
- Business Source Complete
Market Orientation, Creativity, and New Product Performance
in High-Technology Firms
The ability to generate and market creative ideas in new products (NPs) and related marketing programs (MPs) in response to changing market needs is key to the success of a firm. This research examines the mediating role of NP and MP creativity between market orientation and NP success. The authors investigate ( 1) whether market orientation facilitates or inhibits creativity, ( 2) whether creativity influences NP performance, and ( 3) how to define and measure creativity in the NP development and launch contexts. They use a two-stage sampling frame to collect 312 sets of responses from managers and NP team leaders and thereby address the potential for common method bias in measures of creativity and NP performance. The findings indicate that NP and MP creativity mediates the relationship between market orientation and NP success. The authors also show that the meaningfulness dimension, rather than the novelty dimension, of creativity is of greater importance in explaining the link between market orientation and NP success. The empirical results provide significant theoretical and managerial implications for NP strategy.
An accumulating body of research has established that market orientation leads to better performance in organizations (e.g., Jaworski and Kohli 1993; Narver and Slater 1990). Despite recent efforts to examine the mediating role of innovation as the missing link between market orientation and performance (e.g., Deshpandé, Farley, and Webster 1993; Han, Kim, and Srivastava 1998; Hurley and Hult 1998), there is no clear understanding of this link. A problem is that prior research has focused on the broad construct of innovation (often using the amount of innovations or patents) and has taken the strategic business unit (SBU) as its level of analysis. As Wind and Mahajan (1997) note, research in this area is confounded because of the equivocal definitions and measurements of innovation. This limitation is especially prominent when innovation is linked to performance, because innovation often implies a successful new product (NP) introduced into the market as its outcome. Another limitation is that a common method bias exists when one informant measures both independent and dependent variables.
Following Day and Wensley's (1988) source-position-performance framework, we propose that creativity is a mediator between market orientation and NP success (see also Han, Kim, and Srivastava 1998; Song and Parry 1997a).
We use creativity (rather than innovation) as the positional mediator because it is a more concrete construct and, in general, has been viewed as a construct that precedes innovation. Specifically, Amabile and colleagues (1996, p. 1154) state: "All innovation begins with creative ideas.... [C]reativity by individuals and teams is a starting point for innovation; the first is a necessary but not sufficient condition for the second." In addition, we use creativity in a more specified context (product development teams) and thus avoid overly general responses that can result when innovation is measured at the SBU level.
Compared with prior research on innovation and creativity, our research differs in four significant ways.( n1) First, we examine market orientation as an antecedent of creativity, thereby providing empirical insight into the debate of whether market orientation facilitates or impedes innovation (e.g., Lukas and Ferrell 2000). Whereas the positive impact of market orientation on innovation is well documented (e.g., Han, Kim, and Srivastava 1998; Slater and Narver 1998, 1999), in Christensen's (1997) book. The Innovator's Dilemma, he argues that listening to customers may have a more negative effect on disruptive than sustaining technologies. Second, we examine the impact of creativity on NP performance at the team level. Although, in general, it has been supported that innovation is key to the growth and success of a firm (Andrews and Smith 1996; Sethi, Smith, and Park 2001), some researchers argue that imitation, rather than innovation, is more important (Nelson and Winter 1982; Schnaars 1994). Third, we develop a model that incorporates both NP and marketing program (MP) creativity as positional advantages. Fourth, we develop a measure of creativity that is specific to the new product development (NPD) context. We use a two-stage sampling frame to collect 312 sets of responses from managers and NP team leaders, thus addressing the potential for common method bias in measures of creativity and NP performance.
Conceptualization of NP and MP Creativity
The concept of creativity as a general construct has been researched widely in the fields of psychology and organizational behavior as well as in marketing (for a summary, see Table 1).
Drawing on research in the management and marketing literature (i.e., Amabile 1983, 1988; Andrews and Smith 1996; Sethi, Smith, and Park 2001), as well as on exploratory field interviews in 15 firms, we define NP and MP program creativity as the degree to which NPs and their associated MPs are perceived as representing unique differences from competitors' products and programs in ways that are meaningful to target customers. Consistent with Amabile (1983), we use the "output perspective" of creativity, which identifies two distinct dimensions of creativity: unique differences (i.e., the novelty dimension, defined as the degree to which NPs and MPs are perceived as representing unique differences from competitors) and meaningfulness to target customers (i.e., the meaningfulness dimension, defined as the extent to which NPs and MPs are perceived as appropriate and useful to target customers). Amabile argues that both dimensions must be included in the concept of creativity, because the target audience may perceive ideas as weird or bizarre if they are novel or unique but carry no meaning for the audience.
Augmented elements of the MP (e.g., packaging, warranties, pricing, promotion, distribution) represent efforts to differentiate and facilitate the selling of core products. In the launch of an NP, MPs must be creatively designed and managed to achieve rapid dissemination and maximum penetration of products (Robertson and Gatignon 1986). Our study combines the concept of NP creativity with that of MP creativity to provide a broader perspective of the role of creativity in the development and implementation of NPs. Our study thus conceptualizes four separate dimensions of creativity: NP novelty, NP meaningfulness, MP novelty, and MP meaningfulness.
Why Is Creativity Important in Marketing Strategy?
The creation and development of creative ideas and their manifestations as NPs and MPs are considered the core elements of an innovation strategy (Zaltman, Duncan, and Holbek 1973) for at least three reasons. First, creativity motivates the generation of new ideas, which is one of the key determinants of innovation (Amabile 1988; Amabile et al. 1996). Innovation is conceptualized as the successful development, adoption, and implementation of creative ideas (Rogers 1983; Scott and Bruce 1994; Van de Ven 1986). Thus, creativity, which involves the generation of novel and meaningful ideas, is a necessary though not sufficient antecedent of innovation (Amabile 1988; Amabile et al. 1996; Scott and Bruce 1994).
Second, creativity results in product differentiation, which is an important determinant of a firm's performance (Andrews and Smith 1996; Song and Montoya-Weiss 2001; Song and Parry 1997a, 1999). Product differentiation is viewed as the degree of an NP's superiority relative to competitive products in terms of uniqueness, quality, cost effectiveness, and technical performance (Cooper 1979; Song and Parry 1997a, b). Creativity that focuses on meaningful differentiation provides a competitive advantage because product differentiation improves the performance of a firm by enhancing its customer loyalty and satisfaction (e.g., Andrews and Smith 1996; Sethi, Smith, and Park 2001; Song and Montoya-Weiss 2001; Song and Parry 1997a, b, 1999).
Third, the resource-based theory of the firm suggests that creativity, which is an intangible resource embedded within the firm, can provide a competitive advantage (Barney 1991; Hunt and Morgan 1995). Creativity renders a sustainable competitive advantage to a firm because it is a strategic resource that is valuable, flexible, rare, and imperfectly imitable or substitutable.
In terms of empirical research on creativity in marketing, there have been three general approaches. The first approach has examined individual, group, and organizational characteristics that determine the creativity reflected in NPs (Sethi, Smith, and Park 2001) or MPs (Andrews and Smith 1996). Andrews and Smith (1996) examine determinants of MP creativity, such as individual problem-solving input, individual motivational factors, and situational factors, whereas Sethi, Smith, and Park (2001) focus on team characteristics and organizational contextual factors that influence NP creativity. The second approach defines creativity in terms of the degree of novelty and examines it as an outcome of the organizational learning process (Moorman 1995; Moorman and Miner 1997). The third approach finds that creativity enhances organizational performance at the SBU level when customer ratings of these factors are used but not when manager ratings of them are used (Deshpandé, Farley, and Webster 1993). Despite the presumed importance of creativity, there is no empirical research that examines its consequences at the NPD team level.
We thus have three open issues that we address in this article. First, does market orientation have a positive or negative impact on NP and MP creativity? Second, does creativity affect NP performance? Third, what is the relative importance of NP creativity versus MP creativity?
We develop a model in Figure 1 that adopts Day and Wensley's (1988) source-position-performance framework, in which market orientation is the source, creativity is the positional mediator, and NP success is the performance outcome at the team level (see also Han, Kim, and Srivastava 1998; Song and Parry 1997a).
Our model incorporates both product and program dimensions simultaneously. When NPs are introduced, customers evaluate creativity on the basis of not only the creative features of the products themselves but also the creative ideas reflected in the MPs associated with them. Our model also simultaneously explores market orientation as an antecedent and NP performance as a consequence of creativity. We draw our hypotheses from empirical research that has conceptualized creativity as the combined construct of novelty and meaningfulness (e.g., Amabile 1983; Andrews and Smith 1996). As such, we do not propose different hypotheses for the separate dimensions of creativity (i.e., novelty and meaningfulness) because of the lack of an empirical basis. However, we do empirically explore the dimensionality of NP and MP creativity to determine whether the combined model used in prior empirical research is appropriate for our data.
Our first set of hypotheses addresses the effects of market orientation on NP and MP creativity. Market orientation fosters creativity because it involves the generation and dissemination of and the reaction to market intelligence and knowledge in response to market needs (Kohli and Jaworski 1990; Slater and Narver 1995). Drawing on prior research (e.g., Deshpandé Farley, and Webster 1993; Han, Kim, and Srivastava 1998; Slater and Narver 1995), we hypothesize that there is a positive impact of the three dimensions of market orientation on NP and MP creativity. In the NPD context, customer orientation relates to the collection of intelligence about customers to satisfy their needs and desires as they respond to novel and meaningful stimuli (Day 1994; Deshpandé, Farley, and Webster 1993; Hunt and Morgan 1995; Kohli and Jaworski 1990; Narver and Slater 1990). A customer-oriented firm that closely monitors customers' needs tends to improve creativity by producing novel and meaningful NPs and MPs that, in turn, enhance organizational innovations through the firm's entire business system (Deshpandé, Farley, and Webster 1993; Gatignon and Xuereb 1997; Han, Kim, and Srivastava 1998). Although the debate continues on whether being close to customers fosters or impedes innovation (e.g., Christensen 1997; Christensen and Bower 1996; MacDonald 1995), we propose that the positive effect of customer orientation on creativity exceeds the negative effect:
H<sub>1</sub>: Customer orientation positively influences (a) NP creativity and (b) MP creativity.
We hypothesize that competitor orientation positively influences creativity. Competitor orientation, viewed as a firm's capability to identify, analyze, and respond to competitors' weaknesses and strengths, enhances organizational intelligence (Day and Wensley 1988; Kohli and Jaworski 1990; Narver and Slater 1990). A competitor-oriented firm tends to monitor progress against rival firms continuously, which can lead to opportunities to create products or programs that are differentiated from those of competitors. It thus tends to facilitate innovations to stay ahead of competitors' innovations (Han, Kim, and Srivastava 1998). A competitor-oriented culture infused into an NP team tends to enhance NP and MP creativity because the team is keenly aware of the industry trends through the collection of intelligence from competitors, which can result in the generation of novel and meaningful NPs and MPs in response to competitors' actions. Thus:
H<sub>2</sub>: Competitor orientation positively influences (a) NP creativity and (b) MP creativity.
Finally, we hypothesize that cross-functional integration (XFI) positively influences NP and MP creativity. The significance of XFI in generating superior values for target customers is well documented with respect to NPD (e.g., Ayers, Dahlstrom, and Skinner 1997; Griffin and Hauser 1996; Gupta, Raj, and Wilemon 1986; Ruekert and Walker 1987; Song and Parry 1992; Song, Xie, and Dyer 2000; Xie, Song, and Stringfellow 1998). In NPD, XFI can be represented by the level of interaction and communication, the level of information sharing and coordination, and the degree of joint involvement in conducting specific tasks involved in an NP's development and launch (Song and Parry 1997a). We hypothesize that XFI positively affects NP and MP creativity, because it facilitates the generation, collection, and dissemination of market intelligence about novel and meaningful stimuli across functional areas, thus encouraging creativity (Jaworski and Kohli 1993). In the NPD context, XFI enhances creativity because it involves open generation and sharing of new ideas, resolution of problems and disagreements by means of nonroutine methods and different frames of reference, and responsiveness to change in novel and meaningful ways (Andrews and Smith 1996; Gatignon and Xuereb 1997; Griffin and Hauser 1996; Han, Kim, and Srivastava 1998; Van de Ven 1986). Thus, an NP team that acquires and disseminates divergent ideas and information through close XFI is more likely to generate creative ideas for developing and marketing NPs. Thus:
H<sub>3</sub>: Cross-functional integration positively affects (a) NP creativity and (b) MP creativity.
The Impact of NP and MP Creativity on NP Success
Studies on NP success and failure have suggested that NP creativity provides competitive product advantage by enhancing novel and useful perspectives of the product (e.g., Calantone and Cooper 1981; Cooper 1979; Kleinschmidt and Cooper 1991; Song and Montoya-Weiss 2001; Song and Parry 1997a, 1999).( n2) A creative firm that provides unique and meaningful products and programs will meet the changing needs of consumers by generating highly innovative and superior products and programs in the market (Cooper 1979; Deshpandé, Farley, and Webster 1993). Thus, both NP and MP creativity and proactiveness are strong strategic determinants of NP success (Griffin and Page 1996). Despite the presumed impact of NP and MP creativity on NP performance, there is a lack of empirical study on their linkage.
This study proposes a positive effect of both NP and MP creativity on NP success for three reasons. First, NP and MP creativity play a critical role in solving problems associated with NPD and launch by providing divergent ideas in a unique and meaningful way, which guarantees the successful implementation of NPs (Cooper 1979). Second, NP and MP creativity, which entail differentiation from competitors, provide superior products and programs that improve positional advantage over competitors (Andrews and Smith 1996; Calantone and Cooper 1981; Cooper 1979; Deshpandé, Farley, and Webster 1993; Kleinschmidt and Cooper 1991). Third, NP and MP creativity that are accumulated as organizational intelligence about novel and meaningful ideas can lead to competitive advantage by meeting unique market demands in meaningful ways, which in turn results in superior NP performance (Barney 1991; Hunt and Morgan 1995). Therefore, we propose the following:
H<sub>4</sub>: Both NP and MP creativity, respectively, enhance NP success in terms of relative market share, sales, return on investment, profitability, and whether the NP meets objectives.
Sample
We performed a series of pretests, including exploratory qualitative interviews (N = 15), survey pretests (N = 21), follow-up interviews (N = 21), a research panel review (N = 6), and a pilot study (N = 106), to validate the measurement instruments and to ensure the appropriateness of the survey administration. After the pretests, we collected the final data for the research using a cross-sectional survey of U.S. high-technology manufacturing firms.
In identifying key informants within the participating firms, we adopted a two-stage sampling frame in which a project manager from each firm evaluated the performance of a selected NP, and an NP team leader (designated by the project manager) evaluated NP and MP creativity, market orientation, and control variables. This sampling method, which separates informants for the measures of creativity and NP performance, is essential for this study because the causal attribution by a single informant for perceptually related constructs is considered a major source of common method bias (Ayers, Dahlstrom, and Skinner 1997; Olson, Walker, and Ruekert 1995).( n3) Both project managers and NP team leaders are suitable as sources of NP and MP information because of their level of involvement in NPD activities (4.99 and 5.68 for managers' NP and MP involvement and 5.02 and 5.51 for team leaders' NP and MP involvement on a seven-point scale).
The sampling frame for the final field survey consisted of 1080 project managers drawn from the CorpTech Directory of Technology Companies. We collected a total of 222 sets of responses, for a 20.8% response rate. After we excluded 16 surveys because of incomplete responses, a total of 206 sets of responses remained.( n4) For the final data analysis, we combined 106 sets of responses from a pilot study with 206 responses from the final field study, resulting in a total sample of 312. We pooled the two data sets because testing a complex model by means of a structural equation model requires a substantial amount of data.( n5)
Another sampling issue relates to the choice of the specific NPs and MPs that the project managers selected for evaluation. Managers were asked to select and report on the most recently developed NP, regardless of its level of success, for which their SBU was responsible and that had been in the market for at least 6 months. Limiting the selection of NPs to those that were at least 6 months old helped informants avoid selection and social desirability biases toward more successful products (Montoya-Weiss and Calantone 1994; Olson, Walker, and Ruekert 1995; Sethi, Smith, and Park 2001). To address whether this short-term measure biases the results, a follow-up survey was mailed to the 312 managers in the final sample 12 months after the original survey. We collected a total of 143 sets of usable responses in this follow-up survey, for a 45.8% usable response rate. Measures of NP performance from the final field survey are significantly correlated with those from the follow-up survey at the .01 level, which confirms that short-term measures do not bias the estimation of the creativity-NP performance relationship.( n6) The final field study confirmed that selection bias was not serious, and the overall measure of NP success had enough variance to be estimated (mean of overall NP success on a seven-point scale = 5.16, standard deviation = 1.43).
In addition, the nonsignificant t-test results on major constructs between early and late respondents confirm that there was no significant nonresponse bias (Armstrong and Overton 1977). We also tested whether biases existed from omitted independent variables, as Calantone, Schmidt, and Song (1996) suggest. We found that adding a correlation between measurement errors for any two independent variables does not significantly improve the fit of the model based on chi-square difference tests, thus confirming no bias from omitted variables. Finally, multicollinearity diagnostic tests (Belsley, Kuh, and Welsch 1980; Chatterjee and Price 1991) confirmed that no serious multicollinearity exists for further analysis.( n7)
Measures
We developed the NP and MP creativity measure by following the recommendations of Churchill (1979) and Gerbing and Anderson (1988), but we used existing measures for all other constructs. To finalize the measures that were to be included, 245 surveys were faxed to project managers in a pilot study. We collected 106 sets of responses from a project manager and an NP team leader in each company, yielding a 30.1% usable response rate. Appendix A summarizes the internal consistency of the measurement instruments for the main constructs from the pilot study.
Development of NP and MP creativity measures. Development of valid and reliable NP and MP creativity measures is a necessary first step in the validation of the concepts and structures of NP and MP creativity. Previous marketing literature has adopted either a semantic scale of creativity borrowed from the psychology literature (Andrews and Smith 1996; Sethi, Smith, and Park 2001) or an NP creativity measure that focuses on assessing the degree of the changes by NP ideas (Moorman 1995; Moorman and Miner 1997). We tailored our domain-specific measure of NP and MP creativity to assess creativity in both NPD and launch contexts.
We drew the initial 39 measurement items from an extensive literature review and from exploratory interviews with 15 project managers in high-technology firms. A total of 10 measurement items that exhibited desirable psychometric properties for assessing the creativity of both NPs and MPs remained after we conducted a series of pretests using the traditional measure-development methods that include coefficient alpha, item-to-total correlations, and exploratory factor analysis (for measure items, see Appendix A).
We used the pilot study (N = 106) as a basis for additional scale refinement of the NP and MP creativity measures. Using the sample of team leaders, we purified the remaining measure items in an iterative manner (Churchill 1979). We dropped two items in the measure ("is stimulating" and "reflects a customary perspective in this industry") because of the double-loading problem in the exploratory factor analysis, which deteriorates the internal validity. In addition, we performed a confirmatory factor analysis (CFA) on the remaining eight items using the sample of managers as the validation sample. All the factor loadings were significant and had high R2 values, thus confirming convergent validity (Bagozzi and Yi 1988; Bagozzi, Yi, and Phillips 1991).
In the final field study (N = 206), we validated the NP and MP creativity measures using the team leaders' responses as the trigger sample and the managers' responses as the validation sample. In the final validation process (using correlations of related measures, interrater correlations, and test-retest reliabilities), we found that our measures have good discriminant validity and convergent validity as well as reliabilities (for correlations of related measures, see Table 2, Panel A).( n8) We further collected data (N = 29) from customers, who are the ultimate judges of creativity. The significant correlations between managers' and customers' ratings on creativity (r = .49, p < .01 for NP creativity; r = .53, p < .01 for MP creativity) provide evidence that managers' responses can represent customers' perspectives.
Measures of other constructs. We tested the measure instruments for the other major constructs (i.e., market orientation and NP performance as well as the control variables) for their validity and reliability in two waves. First, in a pilot study, we revised and refined measurement items using traditional measure-development methods as recommended by Churchill (1979). After measure refinement, the results for Cronbach's alphas that are greater than .70 (Nunnally 1978) show that all measures of the major constructs exhibit good internal consistency (for measurement items and coefficient alphas, see Appendix A). Second, in the final field study, the results from the confirmatory measurement model suggest that all indicators are significantly loaded on the subjective latent constructs, thus confirming good convergent validity (Bagozzi and Yi 1988; Bagozzi, Yi, and Phillips 1991). In addition, the significant results from chisquare difference tests in favor of unrestricted models (i.e., correlations are freely estimated) over restricted models (i.e., correlations are fixed at 1) for all pairs of constructs in Table 2 confirmed discriminant validity for all constructs (Anderson and Gerbing 1988). Overall, all measures have good construct validities and desirable psychometric properties.
Market orientation. We adopted Narver and Slater's (1990) measure of market orientation to assess customer orientation, competitor orientation, and XFI. After we excluded 2 items that had low item-to-total correlations, the remaining 13 measurement items represented the three dimensions of market orientation well and had good reliabilities (see Cronbach's alpha, Appendix A).
NP success. As NP strategy researchers (e.g., Montoya-Weiss and Calantone 1994; Song and Parry 1997a, b) recommend, we used multiple measures of NP success to assess different perspectives of NP performance, including market measures (e.g., relative market share, relative sales), financial measures (e.g., relative return on investment, relative profitability), and overall assessment measures (e.g., meeting objectives for customer satisfaction and technological advancement). For the NP success measure, we adopted relative sales, return on investment, market share, and profitability from the work of Song and Parry (1997a), and we added a global measure adapted from the works of Kleinschmidt and Cooper (1991) and Page (1993). Consistent with Song and Parry (1997b), we used relative subjective measures (e.g., performance relative to a firm's other products and original objectives), because objective measures (e.g., financial data) are often inaccurate or unavailable for NPs (e.g., Han, Kim, and Srivastava 1998; Song and Parry 1997a, b).
In the pilot study, the exploratory factor analysis of the measurement items of NP success suggests that the original measurement model with five consequence constructs (Figure 1) can converge into a more parsimonious model with three consequence constructs. Consistent with the work of Lumpkin and Dess (1995), measurement items from relative sales and relative market share converge into one underlying dimension, designated as market performance outcome (MPO). Measurement items from relative return on investment and relative profitability also converge into another dimension, designated as financial performance outcome (FPO). The three items for a global measure constitute the final factor, which we renamed qualitative performance outcome (QPO). The three renamed constructs have good internal consistency, as reflected by their Cronbach's alphas (.91 for MPO, .91 for FPO, and .77 for QPO).
Control variables. We included three control variables that are commonly believed to influence the outcome of NP activities in the high-technology industry: market potential, technological turbulence, and firm size.( n9) After we removed one item that had low item-to-total correlation from a pilot study, the measurement items for market potential and technological turbulence exhibited good reliabilities (see Appendix A). We used market potential, which is defined as the potential demand for the NP in the target market (Han, Kim, and Srivastava 1998; Narver and Slater 1990; Song and Parry 1997a), to control for the environmental impact on NP performance. Technological turbulence, defined as a rapid rate of technological change, is considered an important environmental factor that influences NP performance (Jaworski and Kohli 1993; Narver and Slater 1990; Song and Montoya-Weiss 2001). Finally, we included firm size, defined as the number of employees in a firm (Chandy and Tellis 2000; Narver and Slater 1990), to control for the impact of a firm's resources on NP success.
Before we tested the hypotheses, we examined a correlation matrix for the composite scales of the major constructs (see Table 2). The signs of the bivariate correlations appear to be consistent with the hypothesized relationships. There is also variability in the measures of the major constructs, as reflected by the means and standard deviations shown in Table 2 (Panel B).
Testing the Model of Creativity
We estimated path coefficients using maximum likelihood (ML) estimation in the structural equation modeling method (Bollen 1989).( n10) In testing the main effects from market orientation to NP and MP creativity and to NP success, we followed Anderson and Gerbing's (1988) two-step approach in structural equation modeling, for which the estimation of a confirmatory measurement model must precede the simultaneous estimation of the measurement and structural submodels. The ML estimation results from a confirmatory measurement model show goodness-of-fit indexes that are greater than .95, significant loadings, and high squared multiple correlation (SMC) values (equivalent to R² lowest SMC = .28), thus confirming convergent validity (Bagozzi and Yi 1988). Because interpretational confounding from the measures was no longer an issue, we performed simultaneous estimation of the measurement and structural models to test the hypotheses. Following the general approach to combine novelty and meaningfulness into creativity (e.g., Amabile 1983; Andrews and Smith 1996; Sethi, Smith, and Park 2001), we tested a model that links three dimensions of market orientation to NP and MP creativity with three dimensions of NP success, as illustrated in Figure 2 (Panel A), with standardized coefficients and other fit statistics. To assess the differential effects, we report standardized coefficients as path coefficients.
First, we examine the overall model fit. The chi-square statistic (Χ² = 3442.42, degrees of freedom [d.f.] = 884) is significant because of the sensitivity of the sample size. However, all the baseline comparison indexes (normed fit index [NFI, Δ1] incremental fit index [IFI, Δ2] relative fit index [RFI, pl], and Tucker-Lewis index [TLI, p2] greater than .92 and the root mean square error of approximation (RMSEA) value of .10 indicate an acceptable fit of the data, according to Browne and Cudeck's (1993) cutoff criteria.
Second, H<sub>1</sub>-H<sub>3</sub> examine the impact of three dimensions of market orientation on both NP and MP creativity. The estimation results show that three paths are significant at the .05 level (γ = .22, standard error [s.e.] = .08 between customer orientation and MP creativity; γ = .26, s.e. = .05 between XFI and NP creativity; and γ = .19, s.e. = .08 between XFI and MP creativity). However, the other three paths (customer orientation and NP creativity, competitor orientation and NP creativity, and competitor orientation and MP creativity) failed to reach the desired significance level. Overall, H<sub>1b</sub>, H<sub>3a</sub>, and H<sub>3b</sub> are supported, whereas H<sub>1a</sub>, H<sub>2a</sub>, and H<sub>2b</sub> are rejected.
Third, H4 posits a positive influence of NP and MP creativity on three dimensions of NP success. Our estimation results show that all paths from NP creativity to the three outcome dimensions are significant at the .05 level (Β = .31 [s.e. = .21], .34 [s.e. = .24], and .48 [s.e. = .25] for the links between NP creativity and MPO, FPO, and QPO, respectively). Similarly, all paths from MP creativity to the three dimensions of NP success (Β = .21 [s.e. = .09], .13 [s.e. = .09], and .15 [s.e. = .08] for the links between MP creativity and MPO, FPO, and QPO, respectively) are significant at the .05 level. Therefore, H<sub>4</sub> is supported.
Fourth, we reassessed the model with the three control variables: market potential, technological turbulence, and firm size.( n11) Overall, we find that, in general, the control variables do not influence the three dimensions of NP success at the .05 level (for details, see Appendix B). However, the results indicate that FPO is influenced positively by market potential (Β = .10, s.e. = .06) and negatively by technological turbulence (Β = -.10, s.e. = .06) at the .05 level.
Model Respecification
Although our ML estimation results indicate strong support for our hypotheses (9 of 12 hypotheses are supported at the .05 level), the lack of good fit reflected by the chi-square test and RMSEA led us to respecify the model further. As part of the measure-development process, we explored the dimensionality of the creativity measure using CFA to provide additional evidence of reliability and validity (Anderson and Gerbing 1988; Gerbing and Anderson 1988). To verify whether novelty and meaningfulness converge into the higher-order construct of creativity, we follow the method that Bollen and Grandjean (1981) recommend. They suggest that the chi-square difference between a measurement model with perfect correlation (for the unidimensional model) and another with freely estimated correlation (for the two-dimensional model) be examined to confirm the convergence of the two dimensions. The measurement models with two separate dimensions fit the data significantly better than do those with one dimension for both NP creativity and MP creativity (ΔΧ² = 478.39 [d.f. = 1] for the two dimensions of NP creativity; Chi;² = 366.60 [d.f. = 1] for the two dimensions of MP creativity). In addition, the ML estimation results (Chi;² = 38.85, d.f. = 19, p < .05; NFI = .99; IFI = .99; TLI = .99; CFI = .99; RMSEA = .07) show that all of the coefficient parameters (λs in the two-dimensional model are statistically significant with high SMCs (i.e., SMC equivalent to R² lowest SMC = .54), thus ensuring the convergent validity of the measure (Bagozzi and Yi 1988; Bagozzi, Yi, and Phillips 1991). Thus, in contrast to previous creativity research that employs the average score of the combined dimensions of novelty and meaningfulness (Andrews and Smith 1996; Sethi, Smith, and Park 2001), our empirical results indicate that the two dimensions should be assessed distinctively.
We further examined whether the four dimensions of NP and MP creativity converge into one overarching dimension of creativity in order to explore the possibility of a more parsimonious model with a hierarchical structure. Chi-square difference tests confirm that the first-order condition model fits the data significantly better than does the second-order condition model (ΔChi;² = T<sub>second order</sub> - T<sub>first order</sub> = 83.41 with 2 d.f.), thus ensuring the discriminant validity of the NP and MP creativity measures (Bagozzi and Edwards 1998). Thus, we find that the measure of NP and MP creativity has better construct validity (Peter 1981) when the novelty and meaningfulness dimensions are separately estimated than when they are combined.
As a result of this testing of the dimensionality of creativity, we conclude that our combined model in Figure 2, Panel A, should be respecified with the four dimensions of NP and MP creativity. This is consistent with Han, Kim, and Srivastava's (1998) component-wise approach. Our CFA also confirms the use of separate dimensions of market orientation, creativity, and NP success. Because prior empirical research on creativity has never separated the effects of novelty and meaningfulness, we do not have an empirical basis for reformulating our hypotheses. In addition, theoretical work on creativity has not proposed differential effects for dimensions of creativity. We thus decided to examine the respecified component-wise model in an exploratory fashion, leaving our hypotheses unchanged.
Figure 2, Panel B, displays the respecified hypothesized links with significant paths and standardized coefficients in the component-wise model. First, all the baseline comparison indexes (NFI [Δ1] IFI [Δ2] RFI [p1] and TLI [p2]) that are greater than .95 indicate that the respecified model improved its fit to the data from the original model.( n12) The chi-square difference test (ΔΧ² = 1398.33, ΔΧ² = 22) shows that the respecified model (² = 2044.09, d.f. = 862) in Figure 2, Panel B, fits the data significantly better than does the original model (Χ² = 3444.42, d.f. = 884) in Figure 2, Panel A. The RMSEA value of .07 also indicates that the model fits the data reasonably well, according to Browne and Cudeck's (1993) cutoff criteria. In addition, reasonably high SMC values (.07 for NP novelty, .15 for NP meaningfulness, .16 for MP novelty, .19 for MP meaningfulness, .18 for MPO, .15 for FPO, and .30 for QPO) indicate that a reasonable amount of variance in the endogenous variables is accounted for by relevant constructs in the model.
H<sub>1</sub> proposes that customer orientation enhances the four dimensions of NP and MP creativity. In contrast to our expectation, the estimation results indicate that customer orientation influences NP novelty significantly but negatively at the .05 level (Γ = -.23, s.e. = .13). Customer orientation has a positive relationship with NP meaningfulness at the .10 level (Γ = .19, s.e. = .09). The path from customer orientation to MP novelty is not significant (γ = .08, s.e. = 12), whereas customer orientation has a positive impact on MP meaningfulness (γ = .23, s.e. = .11).
H<sub>2</sub> predicts that competitor orientation positively influences NP and MP creativity. The results show that competitor orientation enhances both NP novelty (γ = .33, s.e. = .14) and MP novelty (γ = .23, s.e. = .13) at the .05 level. In contrast, competitor orientation has no impact on either NP meaningfulness (γ = .03, s.e. = .10) or MP meaningfulness (γ = .09, s.e. = .12).
H<sub>3</sub> posits that XFI has a positive impact on NP and MP creativity. The paths from XFI to NP novelty and MP novelty are not significant (γ = .08, s.e. = .12 for NP novelty; .14, s.e. = .12 for MP novelty). However, XFI has a significant, positive impact on both NP meaningfulness (γ = .21, s.e. = .09) and MP meaningfulness (γ = .18, s.e. = .11) at the .05 level.
H<sub>4</sub> posits that both NP and MP creativity positively influence the three dimensions of NP success. The results regarding NP creativity reveal that NP novelty does not affect MPO (Β = .10, s.e. = .06) or FPO (Β= .03, s.e. = .07), though it does have a positive impact on QPO (Β = .17, s.e. = .06) at the .05 level. The estimation results also confirm that NP meaningfulness has a positive impact on MPO (Β = .28, s.e. = .08), FPO (Β = .32, s.e. = .09), and QPO (Β = .41, s.e. = .08). Furthermore, the results regarding MP creativity indicate that no dimension of NP success is influenced by MP novelty (Β = -.06, s.e. = .07 for MPO; Β = -.05, s.e. = .08 for FPO; Β = .01, s.e. = .07 for QPO). The estimation results further confirm that MP meaningfulness has a positive impact on all three dimensions of NP success at the .05 level (Β = .28, s.e. = .08 for MPO; Β = .16, s.e. = .09 for FPO; Β = .14, s.e. = .07 for QPO).
We reassessed the model after we added market potential, technological turbulence, and firm size as control variables (for details, see Appendix C). Fit statistics for this model are as follows: Χ² = 2654.32 (d.f. = 1226, p < .05), NFI = .95, IFI = .97, RFI = .94, TLI = .97, and RMSEA = .06. When the three control variables are added, all the main effects remain the same, except for the path from MP meaningfulness to QPO. This path, which was significant at the .05 level in the initial model, becomes significant at the .10 level (Β = .13, s.e. = .07, t = 1.86). We find that market potential does not influence either MPO (Β = -.03, s.e. = .05) or FPO (Β = .10, s.e. = .06), though it significantly influences QPO (Β = .13, s.e. = .05). The significant result for QPO indicates that potential demand for the NP in the target market helps a firm achieve its objective with regard to customer satisfaction and technological advancement. In addition, our results indicate that technological turbulence has no impact on MPO (Β = -.09, s.e. = .05) or QPO (Β = -.06, s.e. = .05), but it marginally influences FPO at the .10 level (Β = -.11, s.e. = .06). Finally, we find that firm size does not influence any of the three dimensions of NP success (Β = .01, s.e. = .01 for MPO; Β = -.02, s.e. = .01 for FPO; Β = .05, s.e. = .01 for QPO).
Direct Effects of Market Orientation
To examine the mediating effects of the four dimensions of NP and MP creativity, we examine whether the direct effect from market orientation to NP success is greater than the mediating effect through NP and MP creativity. The direct impact of market orientation on NP performance is well documented (e.g., Ayers, Dahlstrom, and Skinner 1997; Song and Parry 1997a; Song, Xie, and Dyer 2000; Xie, Song, and Stringfellow 1998). To test the direct path from market orientation to NP success, we added a direct path from each dimension of market orientation to each dimension of NP success one at a time and compared the chisquare difference (with one d.f.) from the model proposed in Figure 2, Panel B (Bagozzi and Yi 1988). Chi-square difference tests show that, in general, the addition of a direct path does not improve the fit significantly at the .05 level (except for two cases, between competitor orientation and FPO and between XFI and MPO). In addition, we compared the magnitude of direct and indirect effects calculated from coefficients when each direct path is added to the restricted model. The results show that the indirect effects thorough NP and MP creativity are more dominant than the direct effect in explaining the total effect between market orientation and NP success (except for the two paths mentioned previously).
Our respecified component-wise model in Figure 2, Panel B, shows interesting patterns of significant relationships among the dimensions of market orientation, NP and MP creativity, and NP performance. We find that customer orientation has a positive impact on both NP and MP meaningfulness, though the path from customer orientation to NP meaningfulness is not strong. This means that, in general, the enhancement of customer orientation results in more meaningful marketing programs (e.g., discounts) and new products (e.g., faster processing chips). A notable finding is the negative impact of customer orientation on NP novelty. It appears that enhancing customer orientation is less likely to help a firm create novel products, because current customers may not approve novel product ideas because of their inertia toward existing products in the market. However, the insignificant effect of customer orientation on MP novelty implies that a firm's effort to monitor customers' needs and expectations does not lead to the generation of novel MPs. In relating competitor orientation to creativity, we find that enhancing competitor orientation results in improving novel dimensions of NPs and MPs but not meaningful dimensions of them. This indicates that a firm that carefully monitors competitors' activities tends to produce novel but not meaningful products and programs. We also find that XFI significantly influences meaningful dimensions of NPs and MPs but not novel dimensions of them. A firm that emphasizes the importance of interactions across different departments encourages NP team members to remove the sources of meaningless NPs and MPs and thus accumulate intelligence to enhance their value and usefulness. This result is consistent with previous findings that XFI results in providing creativity through problem solving in meaningful and efficient ways (e.g., Song and Parry 1997a; Van de Ven 1986; Zaltman, Duncan, and Holbek 1973).
In contrast, the insignificant effect of XFI on NP and MP novelty is an indication that NP teams' efforts to produce novel and radical NPs and MPs have been suppressed by opinions from other functional groups. New product teams with high XFI are less motivated to take risks in generating novel and unique ideas, which other functional groups often consider "weird." Our interpretation is that they tend to indulge in groupthink or to exhibit a social loafing effect, which yields a poor search for alternatives and information on novel stimuli (Hogg 1992; Sethi, Smith, and Park 2001; Woodman, Sawyer, and Griffin 1993). In addition, our analysis of the relationship between market orientation and NP success shows that the indirect effect through creativity is more dominant than the direct effect, thus supporting our specification of creativity as a mediator.
We further examine empirical evidence on whether creativity enhances NP success at the NP team level. We also find differential effects between the dimensions of NP and MP creativity and NP performance. In linking novelty and NP performance, five of the six paths from NP and MP novelty to NP success are not significant. In general, these findings imply that an increase in novel features of NPs or MPs does not influence NP success in terms of market, financial, or qualitative performance. Finally, one of the more important results of this study is the finding that both NP and MP meaningfulness positively influence MPO, FPO, and QPO. These empirical findings suggest that the patterns of differential effects for NP success in terms of market, financial, and qualitative performance are driven by the increases in valuable and meaningful attributes of NPs and MPs, not by novel ones.
Theoretical Implications
First, our research demonstrates that novelty and meaningfulness should be examined separately rather than combined into a single creativity construct, as has been done in prior empirical research. Our component-wise model that separates novelty and meaningfulness (in Figure 2, Panel B) provides a clear theoretical explanation of the mediating role of the different dimensions of creativity with a significantly better fit to the data. Consistent with Amabile (1983, 1988), we find that meaningfulness and novelty are distinct, separate dimensions of creativity. We reached this finding through a rigorous measure-development and validation process. As a result, our research contributes to the conceptualization and measurement of creativity in the NPD and launch contexts by developing and validating NP and MP creativity measures.
Second, this study suggests that creativity of MPs should be considered in addition to the creativity of NPs themselves. By supporting the importance of MP creativity, our study implies the need to examine the influence of "augmented elements" of products on NP strategy (Levitt 1980). Creative MPs play a critical role in commercializing products in the implementation stage through meaningfully novel promotion, pricing, distribution, and services (Cooper 1979), whereas NP creativity plays a critical role in generating new ideas in the initiation stage of NPD.
Third, the methodological approaches used to test the model of creativity provide guidelines for further NPD research. We used the two-stage sampling frame with dual informants to avoid the common method bias and the causal attributions caused by measuring the two related constructs, such as creativity and NP success, by one respondent (e.g., Olson, Walker, and Ruekert 1995).
Managerial Implications
First, managers should evaluate the trade-offs between the positive and negative effects of market orientation on creativity instead of assuming that market orientation is a panacea for enhancing creativity. The inconsistent claims about the market orientation and innovation link from previous studies may be because novelty and meaningfulness as key determinants of innovation had not been examined separately in their relationships with dimensions of market orientation. When a firm works to listen and respond to the customer's voice and to interact closely in order to share information across functional groups, it tends to provide meaningful products and programs (though not novel ones). In contrast, a firm that works to monitor competitors' activities tends to provide novel products and programs because it focuses on more salient and novel features.
Second, NP success tends to be driven more by the meaningfulness dimensions of NPs and MPs than by their novelty dimensions. By examining the differential effects of NPs and MPs, we empirically find that meaningfulness is more important than novelty in helping a firm achieve its desired financial and market goals. From our respecified component-wise model, it is important to note that the significant relationship between creativity and NP success is driven by the strong effects from meaningfulness dimensions, not from novelty dimensions of NP and MP.
Third, consistent with Christensen's (1997) claim, we find that customer orientation can be detrimental to the generation of novel perspectives for NPs in high-technology firms. However, by and large, this does not matter, because novelty has little effect on NP outcomes. From a perceptual map perspective, a close focus on competitors may lead a firm to find a novel position in the market (a "hole" in the perceptual map), but such a novel position does not directly affect performance given that the hole may exist because products in this position do not provide meaningful benefits to customers, and thus there is no demand there.
Fourth, the creativity of NPs is more likely to influence NP success than is the creativity of MPs. When we compare standardized coefficients in Figure 2, Panel B, in general, NP novelty and meaningfulness provide a stronger impact on the three factors of NP success than do MP novelty and meaningfulness. To test the differential effects between NP creativity and MP creativity in explaining NP success, we further tested the two models: ( 1) one that includes the paths only from NP novelty and NP meaningfulness and ( 2) one that includes the paths only from MP novelty and MP meaningfulness. The SMC (R2) values for in the first model (MPO = .17, FPO = .16, and QPO = .31) are all greater than those in the second model (MPO = .13, FPO = .09, and QPO = .14), thus supporting the claim that NP creativity explains variances of NP success better than MP creativity does. This implies that consumers tend to recognize novel and meaningful ideas for NPs more saliently than they do those for MPs.
Fifth, MP creativity is important for NP strategy in high-technology firms, as indicated by the significant impact on NP performance, though it is less influential than NP creativity. Despite the tendency to invest more money in NPs, the importance of a firm's committing resources to creative MPs cannot be overstated. When NPs are introduced, customers evaluate creativity on the basis of not only the creative features of the products themselves but also the creative ideas used in the MPs associated with them. For example, the perceived creativity of the Apple iMac computer may stem as much from the way the product was marketed and launched.
In summary, our findings imply that customer orientation and XFI are the driving forces of NP success through the meaningfulness dimensions of NP and MP creativity, whereas competitor orientation fails to influence NP performance, despite its significant influence on novelty. We further find that the indirect effect through meaningfulness is more dominant than the direct effect from market orientation to NP success, thus providing evidence that the meaningfulness dimensions of creativity mediate the market orientation-NP performance relationship.
Limitations and Future Research Directions
As with any study, our results must be evaluated in light of certain key limitations. The first limitation is related to the choice of sample frame. The selection of firms in high-technology industries for the sampling frame excludes other segments that are involved in providing creative ideas in the NPD and launch processes. Thus, the study of creativity should be extended to other industries, such as consumer goods or services, to help generalize the findings.
Second, although this study provides evidence of how creativity as the necessary determinant of innovation influences performance, it does not examine the direct impact of innovation on performance or the impact of imitation on performance. Follow-up research should consider directly examining innovation and imitation as well as other intangible assets, such as entrepreneurship and "intrapreneurship."
Third, additional variables might be added to the model, such as group or organizational antecedents (e.g., group cohesion, formalization, risk taking, motivation), and other mediators might be included (e.g., product differentiation, competitive advantage, product radicalness). Future decisions about the inclusion of more variables must take into consideration the trade-offs between the need for a parsimonious model and the desire for a comprehensive one.
This study was partially supported by the Cato Research Fund, Kenan-Flagler Business School, University of North Carolina at Chapel Hill, for the dissertation of the first author. The authors thank Gary Armstrong, Valarie Zeithaml, Hugh O'Neill, Bill Ware, Bill Perreault, and participants of the research seminar series at the University of North Carolina for providing helpful comments on a previous draft of this article. They also appreciate the helpful comments of Jonlee Andrews, Stanley Slater, Sanjit Sengupta, and Subodh Bhat on previous versions of this article. The authors thank the anonymous JM reviewers for their help in greatly improving the article.
(n1) Because of space constraints, this version of the article does not contain many details that were in a prior draft. For readers who are interested in these details, a longer version of the article is available on request from either author.
(n2) Using the data collected from the final field study (N = 206), we further find that NP novelty and meaningfulness influence product competitive advantage in terms of relative cost, quality, and differentiation (p < .05), whereas MP novelty and meaningfulness fail to affect it. We also find that product competitive advantage has a positive impact on market, financial, and qualitative performance outcomes (p < .05).
(n3) Although all interrater correlations for the four dimensions of NP and MP creativity between managers and NPD team leaders are significant at the .01 level, a substantial amount of variance is not commonly explained between the two respondents, thus confirming the distinctiveness of responses from the two types of respondents (highest interrater correlation = .72 for NP novelty, variance explained = .722 = .52, variance unexplained = .52). See Table 2, Panel A, for interrater correlations.
(n4) The t-tests for major constructs collected by the first respondents (i.e., four dimensions of NP and MP creativity and three dimensions of NP success) indicate that there is no significant difference between complete (N = 206) and incomplete responses (N = 16) at the .05 level (all p values > .15).
(n5) The t-tests on the major constructs show that there is no significant difference at the .05 level between the responses from the pilot study and those from the final field study. In addition, we find no significant difference at the .05 level between the correlation matrix from these two samples, confirming that the two data sets can be pooled.
(n6) The correlations of NP performance measures between the final field survey and the follow-up survey are significant at the .01 level as follows: r = .54 for MPO, r = .57 for FPO, and r = .46 for QPO.
(n7) To address possible problems of multicollinearity from high correlations among independent variables, we regressed each of the four dimensions of NP and MP creativity on three factors of market orientation. All condition indexes (Belsley, Kuh, and Welsch 1980) were less than 30, and all variance inflation factors were much less than 10 (Chatterjee and Price 1991). Thus, multicollinearity is not a concern for further analysis.
(n8) We examined correlations among three different measures: (1) a single-item global measure of creativity, (2) a ten-item semantic scale measure of creativity (adopted from Besemer and O'Quin 1986), and (3) an eight-item new measure of NP and MP creativity (for correlations and means, see Table 2, Panel A). Each of the four dimensions in the new measure of NP and MP creativity significantly correlates with the single-item and the ten-item measures at the .05 level, thus proving convergent validity. In addition, interrater correlations between a team leader in the trigger sample and a manager in the validation sample are all significant (p < .01), indicating good convergent validity (e.g., John and Reve 1982). The correlation analysis using the data collected from the follow-up survey, conducted 12 months after the final field survey, shows good test-retest reliabilities for all four dimensions (p < .01).
(n9) We selected these three variables from the work of Narver and Slater (1990) because these characteristics strongly influence how creativity influences NP performance in NPD processes. We also tested whether these control variables moderate the creativity-NP performance relationships. In general, we found no moderating effects.
(n10) Because we find a significant multivariate kurtosis that violates the multivariate normality assumption in the ML estimation, we use 1000 bootstrap samples, for which the means serve as a proxy for the sampling distribution of the population. The results show that the bootstrapping estimation is statistically equivalent to that using ML estimation, thus confirming that the ML estimation is robust, despite the presence of multivariate kurtosis
(n11) Fit statistics for this model are as follows: Χ² = 3541.88 (d.f. = 1239, p < .05), NFI = .93, IFI = .95, RFI = .92, TLI = .95, and RMSEA = .08.
(n12) We compared the fit of this final model with correlated measurement and latent errors (Χ² = 2044.09, d.f. = 862) with that of a baseline model without correlated errors (Χ² = 2925.78, d.f. = 878). The chi-square difference test shows that this model fits the data significantly better than does the baseline model (Δ² = 88.69, Δd.f. = 16).
Legend for Chart:
A - Authors
B - Primary Focus
C - Sample/Data
D - Measures/ Analysis Method
E - Definition of Creativity and Summary of
Comments and Findings
A
B C
D E
Amabile (1983)
Organizational 120 research-and-development
factors influencing scientists from
individual creativity more than 20
corporations
Content analysis The production of novel, useful
ideas by an individual or small
group of individuals working
together; a model of individual
creativity is integrated into a
model of organizational
innovation.
Amabile and colleagues (1996)
Development of the 306 (main test) and
climate for creativity 160 (validation test)
instrument team members
Measure Five work environment
development dimensions (challenge,
methods, organizational encouragement,
LISREL, and work group supports, supervisory
multivariate encouragement, and
analysis of organizational impediments)
variance influence creative behavior in an
organization.
Andrews and Smith (1996)
Determinants of MP 193 product
creativity managers
Regression Creativity is defined as the
meaningful novelty of some
output relative to conventional
practice in the domain to which it
belongs; MP creativity is
influenced by individual
problem-solving input, motivational
factors, and situational factors.
Besemer and O'Quin (1986)
Development of a 133 student
semantic scale of subjects
creativity
CFA Output perspective of creativity
can be evaluated by three
dimensions: novelty, resolution,
and elaboration and synthesis.
Besemer and Treffinger (1981)
Development of 90 sources of
criteria to explain creativity study
creativity
Theory Based on literature review,
different criteria (e.g., novelty,
resolution, and attractiveness)
can be identified to measure
creative output.
Deshpandé, Farley, and Webster (1993)
The impact of 50 sets of data (i.e.,
customer 50 quadrads)
orientation, culture, collected from
and creativity on Japanese
firm performance managers
Regression Business performance is
positively influenced by the
customer's evaluation of the
supplier's customer orientation
and organizational
innovativeness. Business
performance is not correlated
with the supplier's own
assessment of customer
orientation.
Haberland and Dacin (1992)
Development of a 102 students
measure of subjects
advertising
creativity
Factor analysis, Advertising creativity reflected in
correlation output is measured by Jackson
and Messick's (1965) four
dimensions from the viewers'
judgments.
Jackson and Messick (1965)
Conceptualization N.A.
of creative person,
process, and output
Theory Creativity is composed of four
dimensions that represent(1)
original and unexpected,(2)
appropriate and meaningful,(3)
transformational, and(4)
condensed and simple.
Moorman and Miner (1997)
Organizational 92 sets of data from
memory on NP mangers in
performance and advertising
creativity companies
Regression Organizational memory levels
improve short-term financial
performance of NPs, whereas
memory dispersion enhances
both the financial performance
and the creativity of NPs.
Mumford and Gustafson (1988)
The understanding N.A.
of creative behavior
Theory Creativity is defined as
production of novel, socially
valued products. Creativity is
best conceptualized as a
syndrome involving (1) trait,(2)
process, (3) environment, and
(4)output.
Sethi, Smith, and Park (2001)
Determinants of NP 141 managers of
creativity in NP NP teams
team context
Regression Creativity is defined as the
extent to which the product
differs from competing
alternatives in a way that is
meaningful to customers. New
product creativity is related to
team characteristics (e.g.,
superordinate identity) and
contextual influence (e.g.,
encouragement to take risk and
customers' influence).
Woodman, Sawyer, and Griffin (1993)
Conceptual links N.A.
among creative
persons, processes,
and products
Theory Organizational creativity is
defined as the creation of a
valuable NP, service, idea,
procedure, or process by
persons working together in a
complex social system.
Individual, group, and
organizational characteristics
influence creative behavior,
which determines organizational
creativity in a firm.
Notes: N.A. = not applicable. Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
O - 14
P - Mean (s.d.)
A
B C D E F
G H I J K
L M N O P
A. Correlations Among Creativity Measures (N = 312)
1. NP-novel<sub>TL</sub>
1
18.69
(5.82)
2. NP-meaning<sub>TL</sub>
.27(**) 1
23.56
(3.86)
3. MP-novel<sub>TL</sub>
.48(**) .24(**) 1
15.27
(5.46)
4. MP-meaning<sub>TL</sub>
.18(**) .46(**) .70(**) 1
19.19
(4.41)
5. NP-novel<sub>MGR</sub>
.72(**) .15(**) .32(**) .12(*) 1
18.70
(5.81)
6. NP-meaning<sub>MGR</sub>
.17(**) .59(**) .15(**) .32(**) .26(**)
1
24.01
(3.73)
7. MP-novel<sub>MGR</sub>
.30(**) .14(**) .57(**) .44(**) .39(**)
.14(*) 1
14.95
(5.61)
8. MP-meaning<sub>MGR</sub>
.06 .34(**) .37(**) .62(**) .12(*)
.41(**) .65(**) 1
18.91
(4.39)
9. NP-creat<sub>GLO</sub>
.54(**) .39(**) .38(**) .34(**) .42(**)
.28(**) .30(**) .24(**) 1
5.25
(1.25)
10. MP-creat<sub>GLO</sub>
.07 .24(**) .50(**) .55(**) .08
.20(**) .38(**) .37(**) .45(**) 1
4.38
(1.46)
11. NP-novel<sub>SEM</sub>
.77(**) .27(**) .38(**) .19(**) .64(**)
.18(**) .29(**) .03 .63(**) .13
1 31.73
(6.07)
12. NP-meaning<sub>SEM</sub>
.13 .52(**) .18(*) .32(**) .13
.37(**) .12 .22(**) .31(**) .19(**)
.31(**) 1 24.03
(3.93)
13. MP-novel<sub>SEM</sub>
.12 .25(**) .67(**) .60(**) .07
.09 .43(**) .37(**) .30(**) .73(**)
.19(**) .24(**) 1 25.19
(7.56)
14. MP-meaning<sub>SEM</sub>
-.01 .40(**) .31(**) .57(**) -.01
.29(**) .29(**) .41(**) .26(**) .45(**)
.14(*) .52(**) .55(**) 1 21.43
(4.18)
B. Correlations Among Major Constructs (N = 312)
1. NP-novel
1
18.69
(5.82)
2. NP-meaning
.27(**) 1
23.56
(3.86)
3. MP-novel
.48(**) .24(**) 1
15.27
(5.46)
4. MP-meaning
.18(**) .46(**) .70(**) 1
19.20
(4.41)
5. Customer orientation
.05 .28(**) .32(**) .38(**) 1
23.61
(5.58)
6. Competitor orientation
.14(**) .23(**) .25(**) .31(**) .56(**)
1
19.61
(4.42)
7. XFI
.11 .30(**) .26(**) .32(**) .51(**)
.51(**) 1
18.46
(4.92)
8. MPO
.14(*) .38(**) .18(**) .32(**) .11
.13(*) .05 1
28.40
(8.82)
9. FPO
.11 .37(**) .15(*) .26(**) .15(**)
.22(**) .12(*) .73(**) 1
27.83
(8.76)
10. QPO
.32(**) .46(**) .26(**) .34(**) .18(**)
.16(**) .17(**) .65(**) .60(**) 1
15.54
(3.70)
11. Market potential
.10 .24(**) .20(**) .24(**) .18(**)
.15(*) .14(*) .08 .17(**) .23(**)
1 13.53
(4.42)
12. Technological turbulence
-.01 .17(**) -.01 .08 .09
.11 .10 -.03 -.04 .04
.29(**) 1 19.30
(5.84)
13. Firm size
.06 -.09 -.07 -.11 -.09
-.13(*) -.11(*) -.04 -.05 .02
.01 .01 1 5218.58
(25641)
(*) p < .05 (two-tailed).
(**) p < .01 (two-tailed).
Notes: TL = team leader survey, MGR = manager survey,
GLO = global measure, and SEM = semantic scale.DIAGRAM: FIGURE 1 The Conceptual Model of NP and MP Creativity
DIAGRAM: FIGURE 2 Structural Equation Model of NP and MP Creativity
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A. NP and MP Creativity: New
NP and MP Novelty (seven-point, four-item scale, Cronbach's α = .89 for NP novelty and .90 for MP novelty) Compared to your competitors, the new product you selected [or its associated marketing program](a) Is really "out of the ordinary."
• Can be considered as revolutionary.
• Is stimulating.(b)
• Reflects a customary perspective in this industry. (reverse coded)(b)
• Provides radical differences from industry norms.
• Shows an unconventional way of solving problems.
NP and MP Meaningfulness (seven-point, four-item scale, Cronbach's α = .91 for NP meaningfulness and .90 for MP meaningfulness)
Compared to your competitors, the new product you selected [or its associated marketing program]
• Is relevant to customers' needs and expectations.
• Is considered suitable for customers' desires.
• Is appropriate for customers' needs and expectations.
• Is useful for customers.
B. Market Orientation (Narver and Slater 1990)
Customer Orientation (seven-point, five-item scale, Cronbach's α = .85)
Our business objectives are driven primarily by customer satisfaction.
We constantly monitor our level of commitment and orientation to serving customers' needs.
Our strategy for competitive advantage is based on our understanding of customers' needs.
Our business strategies are driven by our beliefs about how we can create greater value for customers.
We measure customer satisfaction systematically and frequently.
We give close attention to after-sales service.(b)
Competitor Orientation (seven-point, four-item scale, Cronbach's α = .72)
Our salespeople regularly share information within our business concerning competitors' strategies.
We rapidly respond to competitive actions that threaten us.
Top management regularly discusses competitors' strengths and strategies.
We target customers where we have an opportunity for competitive advantage.
Cross-Functional Integration (seven-point, four-item scale, Cronbach's α = .86)
Our top managers from every function regularly visit our current and prospective customers.(b)
We freely communicate information about our successful and unsuccessful customer experiences across all business functions.
All of our business functions are integrated in serving the needs of our target markets.
All of our managers understand how everyone in our business can contribute to creating customer value.
All functional groups work hard to thoroughly and jointly solve problems.
C. NP Success
Relative sales, relative market share, relative return on investment, or relative profits (seven-point, three-item scale each, Song and Parry 1997a; Cronbach's αs = .89, .91, .91, .92, respectively)
Relative to your firm's other new products, this product is very successful in terms of [sales, market share, return on investment, or profits].(a)
Relative to competing products in the market, this product is very successful in terms of [sales, market share, return on investment, or profits].(a)
Relative to your firm's original objectives for this product, this product is very successful in terms of [sales, market share, return on investment, or profits].(a)
Meeting objectives (seven-point, three-item scale, adapted from Kleinschmidt and Cooper 1991 and Page 1993; Cronbach's α = .77)
Relative to your firm's original objectives for this product, this product is very successful in terms of customer satisfaction.
Relative to your firm's original objectives for this product, this product is very successful in terms of technological advancement.
Relative to your firm's original objectives for this product, this product is very successful in terms of overall performance.
D. Control Variables
Market Potential (seven-point, four-item scale, Song and Parry 1997a; Cronbach's α = .77)
There are many potential customers for this product to provide a mass-marketing opportunity.
Potential customers have a great need for this class of product.(b)
The dollar size of the market (either existing or potential) for this product is very large.
The market for this product is growing very quickly.
Technological Turbulence (seven-point, three-item scale, Jaworski and Kohli 1993; Cronbach's α = .88)
The technology in our industry is changing rapidly.
Technological changes provide big opportunities in our industry.
A large number of new product ideas have been made possible through technological breakthroughs in our industry.
Technological development in our industry are rather minor. (reverse coded)
Firm Size (one-item scale) The number of employees in a firm.
(a) We evaluated constructs in brackets separately.
(b) We removed these items from the final analysis because of the low item-to-total correlations.
Legend for Chart:
A - Structural Model
B - Endogenous Variables NP Creativity
C - Endogenous Variables MP Creativity
D - Endogenous Variables MPO
E - Endogenous Variables FPO
F - Endogenous Variables QPO
A
B C D
E F
H<sub>1</sub>: Customer orientation
.15 (.05) .22(**) (.09)
H<sub>2</sub>: Competitor orientation
.07 (.05) .12 (.09)
H<sub>3</sub>: XFI
.26(**) (.05) .19(**) (.08)
H<sub>4</sub>: NP creativity
.31(**) (.21)
.34(**) (.24) .48(**) (.25)
H<sub>4</sub>: MP creativity
.21(**) (.09)
.13(**) (.09) .15(*) (.08)
Control Variables
Market potential
-.03 (.05)
.10(*) (.06) .14(**) (.05)
Technological turbulence
-.08 (.05)
-.10 (.06) -.05 (.05)
Firm size
.01 (.01) -.01 (.01)
.01 (.01)
SMC (R²)
.15 .22 .17
.15 .28
Measurement Model
Customer CustOri1 .75
orientation CustOri2 .79 (.08)
CustOri3 .82 (.07)
CustOri4 .68 (.07)
CustOri5 .61 (.09)
Competitor CompOri1 .67
orientation CompOri2 .80 (.11)
CompOri3 .69 (.11)
CompOri4 .54 (.10)
XFI Xfi1 .66
Xfi2 .80 (.10)
Xfi3 .86 (.11)
Xfi4 .80 (.10)
NP creativity Npn1 .33
Npn2 .34 (.24)
Npn3 .33 (.23)
Npn4 .32 (.22)
Npm1 .88
Npm2 .84 (.27)
Npm3 .88 (.28)
Npm4 .79 (.29)
MPO Mpo1 .78
Mpo2 .87 (.07)
Mpo3 .86 (.07)
Mpo4 .85 (.07)
Mpo5 .84 (.07)
Mpo6 .89 (.07)
FPO Fpo1 .84
Fpo2 .82 (.05)
Fpo3 .91 (.05)
Fpo4 .88 (.05)
Fpo5 .82 (.05)
Fpo6 .92 (.05)
QPO Qpo1 .80
Qpo2 .65 (.07)
Qpo3 .85 (.07)
Market Mp1 .70
potential Mp2 .84 (.09)
Mp3 .73 (.08)
Technological Tt1 .77
turbulence Tt2 .85 (.06)
Tt3 .83 (.07)
Tt4 .72 (.08)
MP creativity Mpn1 .61
Mpn2 .59 (.10)
Mpn3 .61 (.11)
Mpn4 .59 (.10)
Mpm1 .84
Mpm2 .83 (.09)
Mpm3 .83 (.09)
Mpm4 .82 (.09)
(*) p < .10.
(**) p < .05.
Notes: For the measurement model, all standardized
coefficients are significant at p < .05. Fit statistics:
χ² (d.f.) = 3442.42(884), NFI = .92, RFI = .92,
IFI = .94, TLI = .94, and RMSEA = .10. Legend for Chart:
A - Structural Model
B - NP Novelty
C - NO Meaning
D - MP Novelty
E - MP Meaning
F - MPO
G - FPO
H - QPO
A
B C D
E F G
H
Customer orientation
-.23(**) (.13) .19(*) (.09) .08 (.12)
.23(**) (.11)
Competitor orientation
.33(**) (.14) .03 (.10) .23(**) (.13)
.09 (.12)
XFI
.08 (.12) .22(**) (.09) .14 (.12)
.18(**) (.11)
NP novelty
.10 (.06) .03 (.07)
.17(**) (.06)
NP meaningfulness
.28(**) (.08) .32(**) (.09)
.41(**) (.08)
MP novelty
-.06 (.07) -.05 (.08)
.01 (.07)
MP meaningfulness
.28(**) (.08) .16(**) (.09)
.14(*) (.07)
Control Variables
Market potential
-.03 (.05) .10 (.06)
.11(**) (.05)
Technological turbulence
-.08 (.05) -.11(*) (.06)
-.04 (.05)
Firm size
-.01 (.01) -.01 (.01)
.01 (.01)
SMC (R²)
.07 .14 .16
.19 .20 .17
.31
Measurement Model
Customer CustOri1 .75
orientation CustOri2 .79 (.09)
CustOri3 .81 (.08)
CustOri4 .68 (.07)
CustOri5 .61 (.09)
Competitor CompOri1 .67
orientation CompOri2 .80 (.11)
CompOri3 .68 (.11)
CompOri4 .53 (.08)
XFI Xfi1 .65
Xfi2 .80 (.10)
Xfi3 .86 (.11)
Xfi4 .81 (.10)
NP novelty Npn1 .75
Npn2 .88 (.08)
Npn3 .85 (.08)
Npn4 .76 (.08)
MP meaning Mpm1 .86
Mpm2 .88 (.04)
Mpm3 .84 (.05)
Mpm4 .82 (.05)
MPO Mpo1 .78
Mpo2 .86 (.07)
Mpo3 .86 (.07)
Mpo4 .85 (.07)
Mpo5 .84 (.07)
Mpo6 .89 (.07)
FPO Fpo1 .84
Fpo2 .81 (.05)
Fpo3 .91 (.05)
Fpo4 .88 (.05)
Fpo5 .82 (.05)
Fpo6 .92 (.05)
QPO Qpo1 .79
Qpo2 .66 (.73)
Qpo3 .84 (.73)
NP meaning Npm1 .89
Npm2 .85 (.05)
Npm3 .88 (.05)
Npm4 .79 (.05)
MP novelty Mpn1 .82
Mpn2 .83 (.06)
Mpn3 .84 (.06)
Mpn4 .80 (.06)
Market potential Mp1 .70
Mp2 .84 (.09)
Mp3 .73 (.08)
Technological Tt1 .77
turbulence Tt2 .85 (.06)
Tt3 .83 (.07)
Tt4 .72 (.08)
(*) p < .10.
(**) p < .05.
Notes: For the measurement model, all standardized
coefficients are significant at p < .05. Fit statistics:
χ7sup2;(d.f.) = 2659.94 (1226), NFI (Δ1) = .95,
RFI (ρ1) = .94, IFI (Δ2) = .97, TLI (ρ2) = .97,
and RMSEA = .06.~~~~~~~~
By Subin Im and John P. Workman Jr.
Subin Im is Assistant Professor of Marketing, College of Business, San Francisco State University (e-mail: subinim@sfsu.edu).
John P. Workman Jr. is Professor of Marketing, College of Business Administration, Creighton University (e-mail: Workman@creighton.edu).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 94- Market Orientation: A Meta-Analytic Review and Assessment of Its Antecedents and Impact on Performance. By: Kirca, Ahmet H.; Jayachandran, Satish; Bearden, William O. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p24-41. 18p. 2 Diagrams, 7 Charts. DOI: 10.1509/jmkg.69.2.24.60761.
- Database:
- Business Source Complete
Market Orientation: A Meta-Analytic Review and Assessment
of Its Antecedents and Impact on Performance
The authors conduct a meta-analysis that aggregates empirical findings from the market orientation literature. First, the study provides a quantitative summary of the bivariate findings regarding the antecedents and the consequences of market orientation. Second, the authors use multivariate analyses of aggregate study effects to identify significant antecedents of market orientation and the process variables that mediate the relationship between market orientation and performance. In addition, using regression analysis, the authors find that the market orientation-performance relationship is stronger in samples of manufacturing firms, in low power-distance and uncertainty-avoidance cultures, and in studies that use subjective measures of performance. The authors also find that the market orientation-performance correlation is stronger for both cost-based and revenue-based performance measures in manufacturing firms than in service firms. On the basis of the findings, the authors conclude with a discussion of the implications for practice and further research.
The marketing concept, a cornerstone of modern marketing thought, stipulates that to achieve sustained success, firms should identify and satisfy customer needs more effectively than their competitors (Day 1994; Kotler 2002). Much of the prolific market orientation literature examines the extent to which firms behave, or are inclined to behave, in accordance with the marketing concept (Kohli and Jaworski 1990). Market orientation has been conceptualized from both behavioral and cultural perspectives (Homburg and Pflesser 2000). The behavioral perspective concentrates on organizational activities that are related to the generation and dissemination of and responsiveness to market intelligence (e.g., Kohli and Jaworski 1990). The cultural perspective focuses on organizational norms and values that encourage behaviors that are consistent with market orientation (Deshpandé, Farley, and Webster 1993; Narver and Slater 1990). Throughout the past two decades, researchers have investigated several antecedents and consequences of market orientation to better understand its role in organizations, and as we outline next, a thorough quantitative, meta-analytic review of this research stream should benefit both practice and research.
First, the current state of research in market orientation can be evaluated with a meta-analysis by estimating the mean values and range of effects for its relationships with various antecedents and consequences (see Farley, Lehmann, and Sawyer 1995). Second, individual studies typically examine subsets of the antecedents and consequences of market orientation. Meta-analytic evidence obtained by aggregating empirical findings across studies can be used to assess more comprehensive models of factors that drive the implementation of market orientation and mediate its impact on performance (e.g., Brown and Peterson 1993). Third, prior studies in the market orientation literature exhibit variation in their findings regarding the magnitude and direction of the relationship between market orientation and organizational performance. Although the predominant view is that market orientation is positively associated with performance (Jaworski and Kohli 1993; Slater and Narver 1994a), several researchers have reported nonsignificant or negative effects for this association (e.g., Agarwal, Erramilli, and Dev 2003; Bhuian 1997; Sandvik and Sandvik 2003). In addition, research has obtained disparate findings on the effects of moderators of the relationship between market orientation and performance (e.g., Grewal and Tansuhaj 2001; Slater and Narver 1994a). A meta-analysis can provide insights into these inconsistencies by identifying measurement and sample characteristics that affect the market orientation-performance relationship and can assess the generalizability of the relationship (Brown and Peterson 1993).
Previous attempts to consolidate research findings in the market orientation literature have been qualitative (e.g., Jaworski and Kohli 1996; Lafferty and Hult 2000), designed to examine only the market orientation scale (see Deshpandé and Farley 1999), or narrowly focused and based on small samples (see Cano, Carrillat, and Jaramillo 2004). Therefore, to accomplish the objectives mentioned previously, we conduct a meta-analysis of the market orientation literature. First, we present a theoretical framework to guide the meta-analysis. Second, we discuss the development of the database for the meta-analysis. Third, we use the meta-analysis to provide a quantitative summary that documents the mean values and range of effects for the relationships that involve market orientation. Fourth, we use multivariate analyses to reveal the critical antecedents of market orientation. Fifth, we focus on the market orientation-performance relationship and conduct a detailed examination that includes ( 1) a multivariate analysis to illustrate the paths through which market orientation influences performance, ( 2) regression analyses to provide insights into sample and measurement characteristics that moderate the market orientation-performance relationship, and ( 3) a nonparametric assessment of substantive moderators of the market orientation-performance relationship. We conclude with a discussion of managerial and future research implications.
Theoretical Framework
We developed the conceptual framework shown in Figure 1 on the basis of the extant market orientation and meta-analysis research (Jaworski and Kohli 1993, 1996; Narver and Slater 1990; Szymanski, Bharadwaj, and Varadarajan 1993). The framework depicts the relationships among the most frequently examined antecedents and consequences of market orientation, as well as the relationships involving the effects of measurement and sample characteristics and the substantive moderators on the market orientation-performance relationship. Justification for the associations between market orientation and its antecedents and consequences is based on prior marketing literature, and therefore we only briefly discuss them herein. Theoretical rationale for the moderating effects of measurement and sample characteristics and the substantive moderators on the market orientation-performance relationship appears in the subsequent sections in which we examine the effects.
Consistent with Jaworski and Kohli's (1993) work, we classify the antecedents of market orientation into three broad categories: top management factors, interdepartmental factors, and organizational systems. Top managers shape the values and orientation of an organization (Webster 1988). As such, top management emphasis on market orientation has a positive impact on the level of an organization's market orientation (Day 1994; Narver and Slater 1990). Interdepartmental factors include interdepartmental connectedness and conflict. Interdepartmental connectedness, or the extent of formal and informal contacts among employees across various departments, enhances market orientation by leading to greater sharing and use of information (Kennedy, Goolsby, and Arnould 2003). Interdepartmental conflict, or the tension between departments that arises from divergent goals, inhibits concerted responses to market needs and thus diminishes market orientation (Jaworski and Kohli 1993).
The third set of antecedents, organizational systems, consists of two structural variables, formalization and centralization, and two employee-related systems, market-based reward systems and market-oriented training. Formalization, which refers to the definition of roles, procedures, and authority through rules, is inversely related to market orientation because it inhibits a firms' information utilization and thus the development of effective responses to changes in the marketplace (Jaworski and Kohli 1993). Centralization, which refers to a limited delegation of decision-making authority in an organization, negatively affects market orientation, because it inhibits a firm's information dissemination and utilization (Matsuno, Mentzer, and Ozsomer 2002). Market-based reward systems use market-oriented behaviors as metrics to reward employees, thus motivating employee actions that enhance market orientation. Market-oriented training augments employees' sensitivity to customer needs, thus stimulating actions that are consistent with the requirements of market orientation (Ruekert 1992).
The consequences of market orientation are organized into four categories: organizational performance, customer consequences, innovation consequences, and employee consequences (see Jaworski and Kohli 1996). The marketing strategy literature posits that market orientation provides a firm with market-sensing and customer-linking capabilities that lead to superior organizational performance (Day 1994; Hult and Ketchen 2001). Organizational performance consists of cost-based performance measures, which reflect performance after accounting for the costs of implementing a strategy (e.g., profit measures), and revenue-based performance measures, which do not account for the cost of implementing a strategy (e.g., sales and market share).( n1) In addition, researchers have also used global measures that assess managers' perceptions of overall business performance, mostly through comparisons of organizational performance with company objectives and/or competitors' performance (e.g., Jaworski and Kohli 1993).
Customer consequences include the perceived quality of products or services that a firm provides, customer loyalty, and customer satisfaction with the organization's products and services (Jaworski and Kohli 1993, 1996). Market orientation proposes to enhance customer-perceived quality of the organization's products and services by helping create and maintain superior customer value (Brady and Cronin 2001). Market orientation enhances customer satisfaction and loyalty because market-oriented firms are well positioned to anticipate customer needs and to offer goods and services to satisfy those needs (Slater and Narver 1994b).
Innovation consequences include firms' innovativeness; their ability to create and implement new ideas, products, and processes (Hult and Ketchen 2001); and new product performance (i.e., the success of new products in terms of market share, sales, return on investment, and profitability) (Im and Workman 2004). Market orientation should enhance an organization's innovativeness and new product performance because it drives a continuous and proactive disposition toward meeting customer needs and it emphasizes greater information use (Atuahene-Gima 1996; Han, Kim, and Srivastava 1998). For employee consequences, Kohli and Jaworski (1990) argue that by instilling a sense of pride and camaraderie among employees, market orientation enhances organizational commitment (i.e., willingness to sacrifice for the organization), employee team spirit, customer orientation (i.e., the motivation of employees to satisfy customer needs), and job satisfaction. In addition, market orientation can reduce role conflict, which Siguaw, Brown, and Widing (1994) define as the incompatibility of communicated expectations that hamper employees' role performance.
Database Development
To ensure the representativeness and completeness of the database we used in the meta-analysis, we searched the ABI/INFORM, Science Direct, and Wilson Business Abstracts for studies published before June 2004, using the keywords "market orientation," "customer orientation," and "consumer orientation." We also searched the Social Sciences Citation Index for studies that referred to the three most highly cited articles in the market orientation literature (i.e., Jaworski and Kohli 1993; Kohli and Jaworski 1990; Narver and Slater 1990). We examined the references from the market orientation articles identified in these two steps for additional studies. We posted requests on a series of listservs to obtain unpublished research in an effort to address the "file-drawer" problem (Rosenthal 1995).
We selected studies for inclusion in the meta-analysis on the basis of two criteria. First, the meta-analysis included only the studies that reported the r-family of effects (i.e., correlation coefficients or its variants; Rosenthal 1994). Second, we included only the articles that measured market orientation at the organizational level so that results from research that had vastly divergent goals were not aggregated (Franke 2001; Hunter and Schmidt 1990). On completion of the search process in June 2004, we had obtained a total of 418 effects from 130 independent samples reported in 114 studies.( n2)
We followed procedures employed in other meta-analyses in marketing for the development of the final database (e.g., Brown and Peterson 1993; Henard and Szymanski 2001). We first prepared a coding form that specified the information that was to be extracted from each study to reduce coding error (Lipsey and Wilson 2001; Stock 1994).( n3) We then corrected effects obtained from each study for measurement error by dividing the correlation coefficient by the product of the square root of the reliabilities of the two constructs (Hunter and Schmidt 1990). When a study did not report the reliability or used a single-item measure for a relevant construct, we used the mean reliability for that construct across all studies for the reliability correction (see Geyskens, Steenkamp, and Kumar 1998). We transformed the reliability-corrected correlations into Fisher's z-coefficients. Subsequently, we averaged the z-coefficients, weighted them by an estimate of the inverse of their variance (N - 3) to give greater weight to more precise estimates, and then reconverted them to correlation coefficients (Hedges and Olkin 1985).
Antecedents and Consequences: Quantitative Summary of
Bivariate Relationships
Table 1 summarizes the bivariate correlations and other statistics for the relationships between market orientation and its antecedents and consequences (see Figure 1). In total, we collected 63 and 355 effect sizes for the antecedents and consequences of market orientation, respectively. Consistent with traditional hypotheses, we obtained significant, positive reliability-corrected mean correlations for the relationships between market orientation and top management emphasis (r = .44, p < .05), interdepartmental connectedness (r = .56, p < .05), market-based reward systems (r = .41, p < .05), and market-oriented training (r = .54, p < .05). The evidence also shows significant, negative associations between market orientation and interdepartmental conflict (r = -.28, p < .05), centralization (r = -.27, p < .05), and formalization (r = -.12, p < .05).
Among the consequences, the market orientation-performance relationship has been the most frequently examined association. Substantially less attention has been paid to the association between market orientation and customer consequences (only 10% of all effects). Notably, the meta-analysis reveals a positive association between market orientation and performance (r = .32, p < .05) that can be categorized as "above medium" (Cohen 1988) and is consistent with Cano, Carrillat, and Jaramillo's (2004) findings. Furthermore, market orientation positively affects various measures of performance, such as overall business performance (r = .46, p < .05), profits (r = .27, p < .05), sales (r = .26, p < .05), and market share (r = .31, p < .05).
The evidence summarized in Table 1 also reveals that market orientation is positively associated with various customer consequences, such as perceived quality (r = .36, p < .05), customer loyalty (r = .35, p < .05), and customer satisfaction (r = .45, p < .05). For the relationship between market orientation and innovation consequences, the bivariate results indicate that market orientation has positive associations with both an organization's innovativeness (r = .45, p < .05) and new product performance (r = .36, p < .05). Finally, the results we obtained with respect to employee consequences reveal that market orientation is correlated with organizational commitment (r = .71, p < .05), team spirit (r = .51, p < .05), customer orientation (r = .25, p < .05), employee role conflict (r = -.54, p < .05), and job satisfaction (r = .61, p < .05).
Overall, the findings are consistent with the predominant expectations in prior research. The consequences of market orientation, particularly its impact on organizational performance, have received more research attention than its antecedents. The high numbers for availability bias reported in Table 1 indicate that new or unpublished studies not included in the meta-analysis do not represent serious threats to the validity of the findings for the bivariate relationships we discussed previously (Lipsey and Wilson 2001). On the basis of the aggregate data, we now focus on assessing the relative impact of the antecedents of market orientation.
Antecedents of Market Orientation: Multivariate Assessment
Aggregated study effects obtained from a meta-analysis can be used to assess simultaneously the effects of variables that prior research may not have considered jointly (Geyskens, Steenkamp, and Kumar 1999). For a construct to be included in such analyses, there must be multiple study effects that relate it to every other construct in the model (Brown and Peterson 1993). This constraint limited us to examining the following antecedents of market orientation: interdepartmental connectedness, top management emphasis, centralization, formalization, market-based reward systems, and interdepartmental conflict. The correlation matrix we used for the multivariate path analysis of market orientation and its antecedents appears in Table 2.
We provided the theoretical rationale for these relationships previously as part of our description of the conceptual model depicted in Figure 1. As we summarize in Table 3, fit indices suggest adequate model fit (χ² = .76, degree of freedom [d.f.] = 1, p = .38; root mean square error of approximation [RMSEA] < .001; adjusted goodness-of-fit index [AGFI] = .96; normed fit index [NFI] = .99; and root mean square residual [RMSR] = .02). The multivariate findings indicate that though interdepartmental connectedness (β = .36, p < .05) has the strongest impact on market orientation, top management emphasis (β = .25, p < .05) and market-based reward systems (β = .24, p < .05) are also important antecedents of market orientation.
Notably, the path coefficients for centralization and formalization are not significant in the multivariate analysis, though the bivariate results indicate that they significantly correlate with market orientation. The nonsignificant finding regarding formalization is consistent with Kohli and Jaworski's (1993) previous discussion, which posits that the nature of formalized rules may well be more important for market orientation than the extent of formalization because rules can also be designed to enhance market orientation. The nonsignificant result pertaining to centralization in the multivariate analysis could be due to the possibility that interdepartmental connectedness and reward systems counter the tendency of centralization to diminish market orientation by ensuring contact among employees and fostering information flow. We discuss the implications of the latter finding for research and practice subsequently.
As we noted previously, the market orientation-performance relationship is the most frequently examined association in the market orientation literature (i.e., 51% of all effects). Therefore, and also because of the managerial importance of the market orientation-performance relationship, in the next three sections, we focus on examining ( 1) the mediators of the market orientation-performance relationship, ( 2) the variance in the market orientation-performance relationship that is associated with measurement and sample characteristics, and ( 3) the substantive moderators of the market orientation-performance relationship.
Mediators of the Market Orientation-Performance
Relationship: Multivariate Assessment
Explicating the mediators of the market orientation-performance relationship has emerged as a topic of interest in the marketing literature. Although a few studies have directly focused on the routes through which market orientation affects performance (see Han, Kim, and Srivastava 1998; Noble, Sinha, and Kumar 2002), a more thorough examination of mediating effects is possible with the aggregate data. Toward this objective, we employ customer-and innovation-related mechanisms as process variables that mediate the market orientation-performance relationship (Han, Kim, and Srivastava 1998; Hurley and Hult 1998). As we show in Table 4 and Figure 2, Panel A, we could test a correlation matrix that includes four mediating factors (i.e., customer loyalty, customer satisfaction, quality, and innovativeness) with the available data. We could not include new product performance and employee consequences in the path analyses because of the absence of study effects relating them to every other construct in the model.
We previously provided justification for the positive relationship between market orientation and the mediating constructs. Therefore, to elucidate the process effects, we now focus on the influence of the mediating variables on performance. Customer loyalty and satisfaction should have positive associations with organizational performance because they increase repeat purchase behavior and are associated with lower levels of customer complaints and negative word of mouth (Szymanski and Henard 2001). Quality and customer loyalty can influence performance through higher prices, higher market share, and/or lower costs (e.g., Fornell 1992; Slater and Narver 1994b). By enhancing competitive advantage, innovativeness should have a positive effect on performance (Han, Kim, and Srivastava 1998; Hurley and Hult 1998). Therefore, market orientation can improve an organization's performance by enhancing the satisfaction and loyalty of its customers, the quality of its products and services, and its innovativeness.
In estimating the model, the inclusion of customer satisfaction in the analysis was precluded by multicollinearity. Before estimating this model, as Baron and Kenny (1986) recommend, we used regression analysis to confirm the mediating effects of customer loyalty, customer satisfaction, quality, and innovativeness on the market orientation-performance relationship. As Table 5 summarizes, analysis of the initial consequences model did not result in adequate model fit (χsup2; = 48.37, d.f. = 4, p < .001; RMSEA = .27; AGFI = .58; NFI = .71; and RMSR = .12). Therefore, and consistent with the modification indices, we revised the model as shown in Figure 2, Panel B. The goodness-of-fit indices and path coefficients that we report in Table 5 suggest an acceptable fit for the revised model (χsup2; = 2.97, d.f. = 4, p = .44; RMSEA = .00; AGFI = .97; NFI = .98; and RMSR = .03). Notably, subsequent evaluation of prior literature provided support for the revised model. Market orientation affects a firm's innovativeness (e.g., Han, Kim, and Srivastava 1998), and new products enable the organization to meet the evolving needs of customers, thus influencing loyalty and the perceived quality of its products and services (Slater and Narver 1994b). Subsequent analyses of the total (β = .30, p < .05) and indirect effects (β = .13, p < .05) of market orientation on performance also suggest that innovativeness, customer loyalty, and quality account for a substantial portion of the total effect of market orientation on performance, thus demonstrating partial mediation of this relationship through customer-and innovation-related mechanisms. Finally, the direct path between market orientation and performance suggests that market orientation has an impact on performance beyond the mediated effects that we examined (β = .17, p < .10). We discuss implications of the findings for research and practice subsequently.
Market Orientation-Performance Relationship: Sample and
Measurement Characteristics as Moderators
We examined the homogeneity of effects for the market orientation-performance relationship using the procedures that Hedges and Olkin (1985) recommend. The statistically significant chi-square value (χ2173 = 2172.9; p < .01) reveals variability across effect sizes and supports the need to examine theoretically relevant sample and measurement characteristics that explain the variance (Hunter and Schmidt 1990). Therefore, we examined the moderating effects of measurement characteristics (i.e., cost-based versus revenue-based, objective versus subjective, and single-versus multi-item measures of performance) and sample characteristics (i.e., manufacturing versus service firms and cultural context) on the market orientation-performance relationship using regression analysis (see Brown and Peterson 1993; Szymanski, Bharadwaj, and Varadarajan 1993). Such an investigation also provides an opportunity to address inconsistencies in previous research with the market orientation-performance relationship. Consistent with the approach that Tellis (1988) follows, we now present hypotheses to guide the moderator analyses.
Cost-based versus revenue-based performance measures . As we noted previously, organizational performance can be classified into measures that account for the costs involved in implementing a strategy versus measures that emphasize revenues that do not reflect costs. Thus, we examine whether the impact of market orientation on performance varies between measures of cost-based performance (i.e., profits) and revenue-based performance (i.e., sales and market share) (Harris 2001; Jaworski and Kohli 1996). Jaworski and Kohli (1993) argue that though market orientation enhances sales performance, the cost of its implementation might reduce profits. Market orientation may also be more consistent with a revenue emphasis that targets the expansion of the sales and market share of the firm than with a cost emphasis that focuses more on the efficiency of the firm's processes (Rust, Moorman, and Dickson 2002). Thus:
H1: The market orientation-performance relationship is stronger for revenue-based performance measures than for cost-based performance measures.
Objective versus subjective and single-versus multi-item performance measures. The strength of the relationship between market orientation and organizational performance that we assessed using subjective evaluations of performance might differ from relationship tests based on objective measures of performance (Harris 2001). Common methods variance may strengthen the correlation between market orientation and performance when research uses subjective measures to capture both constructs (Doty and Glick 1998). The use of multi-item measures of performance should also be associated with higher market orientation-performance correlations than the use of single item measures because multi-item measures are more capable of capturing various facets of complex constructs (Churchill 1979; Clark and Watson 1995; Henard and Szymanski 2001). Thus:
H2: The market orientation-performance relationship is stronger for subjective measures of performance than for objective measures of performance.
H3: The market orientation-performance relationship is stronger for multi-item measures of performance than for single-item measures of performance.
Manufacturing versus service firms. Services are less tangible, less separable in production and consumption, and more perishable than manufactured goods (Parasuraman, Zeithaml, and Berry 1985). Because market orientation focuses on meeting customer needs, the fulfillment of customer needs involves a higher degree of customization in service firms than in manufacturing firms (Anderson, Fornell, and Rust 1997). Therefore, the implementation of market orientation could entail a higher degree of customization in service firms than in manufacturing firms, which implies that the correlation of market orientation with organizational performance might vary between manufacturing and service firms. Specifically, the relatively higher levels of customization that service firms must use to implement market orientation imply the need to target smaller customer segments, thereby constraining service firms' ability to increase sales and market share (revenue-based performance measures) to the same extent as manufacturing firms. Higher degrees of customization in services could also result in higher costs due to lower production efficiency and the hiring and training of qualified employees (see Anderson, Fornell, and Rust 1997). In turn, such higher costs should generate lower levels of profit (i.e., cost-based performance) for service firms than for manufacturing firms. Thus:
H4: The market orientation-performance relationship is stronger in manufacturing firms than in service firms.
H4a: The market orientation-performance relationship is stronger for revenue-based performance measures in manufacturing firms than in service firms.
H4b: The market orientation-performance relationship is stronger for cost-based performance measures in manufacturing firms than in service firms.
Cultural context. The magnitude of the market orientation-performance relationship may also be country or region specific because of differences in cultural values (Grewal and Tansuhaj 2001; Harris 2001). To examine this variation, we used Hofstede's (2001) dimensions of national culture (i.e., power distance, uncertainty avoidance, individualism, masculinity, and long-term orientation) as moderators of the market orientation-performance relationship. Specifically, using Hofstede's country scores, we arranged countries for which data were collected from low to high for each cultural dimension and used median splits to classify the countries as either low or high on each dimension. We based median splits on the entire set of countries in Hofstede's (2001) work because of the preponderance of U.S.-based studies in the sample. The meta-analysis included studies conducted in the United States, the United Kingdom, Australia, Hong Kong, New Zealand, the Netherlands, China, Finland, Spain, Israel, Japan, Greece, Korea, Saudi Arabia, Taiwan, Zimbabwe, France, Germany, India, Indonesia, Malta, Poland, Slovenia, Thailand, and Turkey. We did not include studies that were conducted across regions spanning multiple countries, such as the European Union, in the analysis.
According to Hofstede (2001), the power-distance dimension of national culture represents the degree to which social inequalities, such as wealth, status, and power, are natural and acceptable among members of a society. The individualism dimension focuses on how people relate to others. Whereas individualist societies tend to prefer loosely knit social frameworks in which individuals are primarily responsible for themselves and exhibit greater self-determination, collectivist cultures tend to prefer greater collaboration and group orientation. Uncertainty avoidance reflects the tendency to seek stability and predictability; masculinity represents the focus on achievement, assertiveness, and material success; and long-term orientation is a cultural disposition that emphasizes values of persistence, thrift, and loyalty.
Employees in low-power-distance cultures, compared with those in high-power-distance cultures, are likely to be more comfortable with and productive in the less-hierarchical structures that are supportive of market orientation (Nakata and Sivakumar 2001). Similarly, employees in countries that rank low on uncertainty avoidance, compared with those in countries that rank high, should be more effective and productive in the less-formalized structures that are associated with market-oriented organizations (see Hofstede 2001). Therefore, firms might implement market orientation more effectively in countries that score low on either power distance or uncertainty avoidance, and market orientation should have a stronger impact on performance in such contexts.
Collectivist cultures should support greater collaboration within the organization, thereby enhancing information dissemination and use and enabling a more effective implementation of market orientation (Nakata and Sivakumar 2001). As such, we posit that market orientation has a stronger association with performance in countries that rank low rather than high on individualism. We also predict that the relationship between market orientation and performance is stronger in countries that rank high rather than low on masculinity because a firm can implement market orientation more effectively in highly masculine societies in which dominant values such as challenge and material success drive a focus on competing successfully through meeting customer needs (Nakata and Sivakumar 2001). Finally, because market orientation promotes durable relationships with customers (Slater and Narver 1994b), it might be more effective and demonstrate higher correlations with performance in more long-term-oriented cultures. Thus:
H5: The market orientation-performance relationship is stronger in low-power-distance cultures than in high-power-distance cultures.
H6: The market orientation-performance relationship is stronger in low-uncertainty-avoidance cultures than in high-uncertainty-avoidance cultures.
H7: The market orientation-performance relationship is stronger in collectivist cultures than in individualist cultures.
H8: The market orientation-performance relationship is stronger in high-masculinity cultures than in low-masculinity cultures.
H9: The market orientation-performance relationship is stronger in long-term-oriented cultures than in short-term-oriented cultures.
Regression analysis. We used dummy-variable regression to test the hypotheses (e.g., Tellis 1988). We dummy coded and used measurement and sample characteristics as independent variables in the following regression model:
ZMO,P = β0 + β1X1 + β1aX1a + β2X2 + β 3 X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9 + εI,
where ZMO,P is the z-transformed value of the corrected correlation between market orientation and performance, βs are parameter estimates, and Xi are the following categorical variables (with the reference level [the level dummy coded '0'] presented first for each Xi):
X1 = cost-based performance versus revenue-based performance,
X1a = cost-based performance versus overall business performance,
X2 = objective versus subjective performance measures,
X3 = single-versus multi-item performance measures,
X4 = manufacturing versus service firms,
X5 = low versus high power distance,
X6 = low versus high uncertainty avoidance,
X7 = low versus high individualism,
X8 = low versus high masculinity, and
X9 = low versus high long-term orientation.
Regression results. The regression analysis results, which we summarize in Table 6, demonstrate that the proposed model is significant (F(9, 73) = 9.53, p < .01) and that the hypothesized moderators account for 54% of the variance in market orientation-performance correlations (cf. Geyskens, Steenkamp, and Kumar 1998: 63%; Sultan, Farley, and Lehmann 1990: 39%, 45%, and 53%; Tellis 1988: 29%). Moreover, the regression model is free of multicollinearity (max variance inflation factor = 2.72) after the exclusion of long-term orientation (variance inflation factor = 17.32).( n4)
The regression results indicate that the strength of the relationship between market orientation and organizational performance does not vary across cost-based versus revenue-based performance measures, contrary to the prediction in H1 (β = -.04, t-value = .46). H2 predicts that subjective measures of performance yield higher market orientation-performance correlations than those obtained when objective measures are used. The results support this hypothesis (β = .33, t-value = 3.08). However, the use of single-versus multi-item performance measures does not affect the strength of the market orientation-performance relationship (β = .06, t-value = .49). Thus, the results do not support H3. Because we made adjustments for measurement reliability by using reliability-corrected correlations in the regression analysis, this finding implies that the use of multi-item performance measures does not necessarily enhance market orientation-performance correlations beyond inherent reliability differences (Churchill 1979). Thus, apart from the impact of subjective performance measures, the market orientation-performance relationship is largely robust across various measurement characteristics.
The results also reveal that the market orientation-performance relationship is stronger for manufacturing firms than for service firms, as we predict in H4 (β = -.43, t-value = 4.81). To test H4a and H4b, we ran two additional regression analyses that incorporated four categorical variables that represent combinations of the industry context (i.e., manufacturing and service) and the type of performance measure (i.e., cost-based and revenue-based performance). The additional regression models also incorporated all the other variables that appear in Table 6 except for manufacturing versus service firm and cost-based versus revenue-based performance dichotomous variables (i.e., X1 and X4), which are redundant when the combination variables are entered into the new equations (see Wooldridge 2003). For all the other independent variables, the results of the additional regressions are similar (see Models 2 and 3 in Table 6).
To test H4a, revenue-based performance in manufacturing firms was the reference category in Model 2 (see Table 6). As we predicted, the results show that the correlation between market orientation and revenue-based performance is lower in service firms than in manufacturing firms (β = -.31, t-value = 3.03). For H4b, manufacturing firms' cost-based performance was the reference category, which enabled comparisons with service firms' cost-based performance. As we expected, the correlation between market orientation and cost-based performance is lower in service firms than in manufacturing firms (β = -.30, t-value = 2.94). Thus, manufacturing firms, as compared with service firms, generate higher profits and appeal to larger markets when they implement market orientation, possibly because of the lower levels of required customization.
Two of the four cultural dimensions affect the market orientation-performance relationship. In support of H5, the regression results suggest that market orientation-performance correlations are higher in low-uncertainty-avoidance cultures than in high-uncertainty-avoidance cultures (β = -.18, t-value = 1.91). In support of H6, market orientation-performance correlations are higher in low-power-distance cultures than in high-power-distance cultures (β = -.29, t-value = 2.39). The estimates for individualism (H7) and masculinity (H8) as moderators of the market orientation-performance association are not significant.
The findings also enable a qualitative assessment of studies that provide negative or nonsignificant associations between market orientation and performance. Although the grand mean of the correlation between market orientation and performance is positive (i.e., r = .32), the extant literature includes several studies that report a negative or nonsignificant association for this relationship (e.g., Agarwal, Erramilli, and Dev 2003; Au and Tse 1995; Bhuian 1997; Greenley 1995; Sandvik and Sandvik 2004; Sargeant and Mohamad 1999). On the basis of the results from the regression analysis, we examined the sample and measurement characteristics of these specific studies. We found that, except for Greenley (1995), these studies typically have at least two of the following three sample and measurement characteristics that significantly affect the market orientation-performance correlation: a service industry sample, data collected in countries that rank high on power distance, and objective measures of performance. Thus, it appears that when a study design combines sample and measurement characteristics that dampen the market orientation-performance correlation, a negative association or no association between these variables may materialize.
In summary, the results of the regression analyses indicate that the variance in the strength of the market orientation-performance relationship can be partially attributed to systematic differences in several theoretically expected sample and measurement characteristics. We discuss the managerial and research implications of these results after the analysis of three substantive moderators of the market orientation-performance relationship.
Market Orientation-Performance Relationship: Substantive
Moderators
Although consistent with prior meta-analyses, the focus on measurement and sample characteristics as moderators of the market orientation-performance relationship leaves one issue unresolved. Specifically, the overall impact of three previously investigated substantive moderators (i.e., market/ environmental turbulence, technological turbulence, and competitive intensity) on the market orientation-performance relationship also warrants consideration (e.g., Grewal and Tansuhaj 2001; Slater and Narver 1994a). Prior research has proposed that market and technological turbulence in the environment, as well as competitive intensity, moderates the strength of the relationship between market orientation and performance (Harris 2001; Slater and Narver 1994a). We expect market turbulence and competitive intensity to enhance the impact of market orientation on performance because market responsiveness is likely to become more important when an organization is faced with an evolving mix of customers and aggressive competitors (Jaworski and Kohli 1993). In contrast, the extant literature predicts that technological turbulence is likely to diminish the impact of market orientation on performance because when technology is changing rapidly, research and development-driven innovation becomes more important to a firm's performance than does the customer-focused innovation that results from market orientation (Grewal and Tansuhaj 2001; Kohli and Jaworski 1990).
A limited number of effects precluded detailed quantitative analyses of the substantive moderators. Therefore, we used a vote-counting procedure in which we categorized prior studies on the basis of the direction and significance of the findings (Bottomley and Holden 2001; Capon, Farley, and Hoenig 1990). As Bushman (1994, p. 195) details, we used a nonparametric "sign test," which tests the hypothesis that the effect sizes from a collection of k independent studies are all zero (null hypothesis, H0: π = .5). This procedure investigates the probability of obtaining results that confirm the proposed hypotheses greater than .5 (alternative hypothesis, HA: π > .5). Accordingly, we categorized studies that explore the moderators of the market orientation-performance relationship into "supportive," "opposite," and "nonsignificant effects" (see Table 7) on the basis of the significance and direction of the results. Next, we counted the number of studies that confirm the hypotheses, and we calculated and evaluated an estimate of π (i.e., the proportion of statistically significant results that support the proposed hypotheses in the population) using critical values from the binomial distribution.
In 5 of 14 studies, market/environmental turbulence was found to moderate the market orientation-performance relationship such that the strength of the relationship between market orientation and performance is stronger in turbulent markets. Using the sign test described previously, the estimate of π is p = .36 (or 5/14), which corresponds to a cumulative probability of .79 from a binomial table. Thus, the nonparametric sign test results indicate that there is insufficient empirical evidence to conclude that market/ environmental turbulence is a significant moderator of the relationship between market orientation and performance. Similarly, sign tests did not support the moderating roles of competitive intensity and technological turbulence on the market orientation-performance relationship.
Discussion and Implications
The meta-analysis reported in this study provides a quantitative summary of bivariate relationships that involve market orientation to document research in this substantial literature stream. The multivariate analyses reveal that internal processes have a greater influence than organizational structure variables in implementing market orientation and that market orientation affects performance through innovativeness, customer loyalty, and quality. An examination of the variance in the market orientation-performance relationship with regression analyses demonstrates that manufacturing firms exhibit higher market orientation-performance associations than do service firms, assesses the generalizability of the relationship across various cultural contexts, and provides research guidance on the effects of performancemeasure-related issues. A nonparametric assessment of prior research into the substantive moderators of the market orientation-performance relationship indicates that the extant research provides limited support for the effects of environmental factors on the market orientation-performance relationship.
In this regard, this study extends prior attempts to summarize the extant market orientation literature by employing a considerably larger number of effect sizes and investigating more comprehensive models. Specifically, previous attempts to consolidate research findings in the market orientation literature include qualitative reviews (e.g., Jaworski and Kohli 1996; Lafferty and Hult 2000) and a meta-analysis focused on the market orientation-performance relationship (see Cano, Carrillat, and Jaramillo 2004). In contrast to our study, Cano, Carrillat, and Jaramillo's (2004) meta-analysis provides a quantitative summary of the market orientation-performance relationship alone, and using subgroup analysis, it offers some preliminary evidence of how this relationship varies across sample and measurement characteristics. Apart from the broader scope of our study, which also has a considerably larger database, we examine the impact of a larger set of sample and measurement characteristics, such as cost-based and revenue-based performance, and multiple dimensions of culture. By doing so, we are able to infer well-supported managerial implications and several interesting research implications. In addition, the use of multiple regression rather than subgroup analysis enables us to limit the problems associated with omitted-variable bias by employing several control variables, such as the type of market orientation scale, journal quality, and business cycles. For example, although a bivariate analysis shows that the type of market orientation scale would have a significant impact on the market orientation-performance correlation, this association vanishes in the presence of other sample and measurement characteristics.
However, this research has several limitations that are common to other meta-analyses in the marketing literature. First, we could not include all studies and constructs in the market orientation literature because of a lack of information necessary for the calculation of effect sizes. Second, the number of variables that we could include in the multivariate models was limited because we used only the constructs that yielded a full correlation matrix in the analyses. Third, the study was limited to the examination of moderators that we could code from existing studies. Regardless, the findings from this meta-analysis could be useful to managers in their efforts to implement market orientation and to understand its impact on performance. The results could also help researchers assess the state of market orientation literature and design further research.
Managers are concerned about four issues pertaining to market orientation. First, how can market orientation be implemented? Second, what is its impact on performance? Third, how does the market orientation-performance relationship vary across cultural and business contexts? Fourth, what are the processes through which market orientation enhances performance? The meta-analysis provides insights into each of these issues.
Implementing market orientation. The results from the multivariate analysis of antecedents of market orientation demonstrate the importance of top management emphasis, interdepartmental connectedness, and market-based reward systems for the implementation of market orientation (Ruekert 1992). Notably, we find that centralization may not hamper market orientation, which implies that an organization with a centralized decision-making structure can prevent that structure from impeding the information flow that is critical for market orientation by focusing on top management emphasis, interdepartmental connectedness, and appropriate reward systems. That is, by ensuring top management emphasis, interdepartmental connectedness, and appropriate reward systems, market orientation can be effectively implemented even in organizations with centralized decision-making structures.
Market orientation-performance relationship. Overall, the results demonstrate that market orientation has a positive impact on organizational performance. Although this conclusion is consistent with several studies in the market orientation literature (see Jaworski and Kohli 1993; Narver and Slater 1990), the present meta-analysis provides managers with a mean value for the expected effect (r = .32). More important, the results help focus managers' attention that market orientation enhances profits (r = .27), which is a cost-based performance measure that accounts for the cost of implementing market orientation, and not merely revenue-based performance measures (r = .26). Although Rust, Moorman, and Dickson (2002) note that market orientation may not be entirely consistent with a focus on cost reduction, our results show that it does enhance profits. In other words, even though the implementation of market orientation may demand resources, it generates profits over and above the costs involved in its implementation while growing revenues.
Impact of industry and cultural contexts. The association of market orientation with performance is lower in service firms than in manufacturing firms (r = .26 versus .37, respectively), possibly because of the higher levels of customization that service firms require. This result varies from Cano, Carrillat, and Jaramillo's (2004) study, in which through the use of subgroup analysis, they report a higher market orientation-performance correlation for service firms. However, the use of a larger database (47 versus 15 effects for service firms and 76 versus 23 effects for manufacturing firms in this study versus Cano, Carrillat, and Jaramillo's) and a more rigorous multiple regression analysis rather than subgroup analysis suggests that, on the aggregate, the relationship between market orientation and performance is stronger in manufacturing firms than in service firms. Furthermore, the extant theory in marketing, as documented by Anderson, Fornell, and Rust (1997), also supports the current findings.
This result is noteworthy because research designed directly to compare market orientation in service firms with that in manufacturing firms is scarce. However, this finding does not necessarily imply that market orientation should receive a greater emphasis in manufacturing than in service firms. We note that this result refers to the strength of the market orientation-performance association in manufacturing and service firms and not to the level of market orientation. Market orientation might be more integral to service firms because of the greater necessity of direct firm-customer interactions. Therefore, market orientation could be viewed as a failure-prevention approach (a "hygiene" factor) in service firms and a success-inducing approach in manufacturing firms (see Varadarajan 1985). In other words, market orientation may be imperative to ensure survival in service firms and may provide a greater competitive advantage that leads to superior performance in manufacturing firms.
Managers should also note that the association between market orientation and performance varied across two of the four national culture dimensions that we tested in the moderator analyses. Specifically, we found market orientation to be more positively associated with performance in countries that are low rather than high on power distance (r = .33 versus .27). In addition, we found market orientation to be more positively associated with performance in countries that are low rather than high on uncertainty avoidance (r = .34 versus .27). Therefore, managers should implement market orientation in accordance with local cultural sensitivities as power distance and uncertainty avoidance describe.
Mediating processes of the market orientation-performance relationship. From a managerial perspective, the explication of the routes through which market orientation influences performance is vital. We examine a more comprehensive model of the mechanisms that mediate the market orientation-performance relationship than those tested in the extant literature (e.g., Han, Kim, and Srivastava 1998; Matsuno, Mentzer, and Ozsomer 2002), and we provide managers with more detailed insights into market orientation's influence on performance. Our findings suggest that measures of the mediating variables--innovativeness, customer loyalty, and quality--may be useful for tracking the impact of market orientation on performance for managers who implement strategic process-measurement frameworks, such as the Balanced Scorecard (see Kaplan and Norton 1993).
Based on the evidence from the meta-analysis, research has made significant progress toward the understanding of the market orientation construct and its nomological network. However, despite the progress, there are several gaps in knowledge about the implementation of market orientation and the market orientation-performance relationship, thus suggesting avenues for further research.
Implementing market orientation. The findings from the meta-analysis about the antecedents of market orientation suggest the following directions for further research: First, researchers must examine how the antecedents of market orientation interact and impact its implementation. Thus, complex relationships, such as the interaction of centralization and market-based reward systems or interdepartmental connectedness and the impact on implementation of market orientation, are fertile topics for further research.
Second, the extant literature needs a better understanding of how the impact of the antecedents of market orientation varies across different business and cultural contexts. Thus, further research should identify profiles of best practices to implement market orientation both in service and manufacturing firms and in different cultural contexts.
Third, the use of customer relationship management technology enables organizations to discover or anticipate continuously what customers need and to fulfill those needs with customized products and services (Bradley and Nolan 1998). Therefore, the use of customer relationship management technology can facilitate more efficient and effective realization of market orientation, and it represents an important topic for further research.
Explicating the market orientation-performance relationship. Our results suggest that research into the following four topics would help enhance knowledge about the market orientation-performance relationship: First, the variance in the market orientation-performance correlations across service and manufacturing contexts is attributed to the higher levels of customization that service firms require and to the subsequent costs involved (Anderson, Fornell, and Rust 1997). To provide further insights into this area, a study examining how customization affects the market orientation-performance relationship would be useful. Researchers could conduct such a study across organizations that offer products or services that require varying degrees of customization.
Second, studies that showed a negative association between market orientation and performance had the following characteristics: service industry sample, highpower-distance culture, and objective performance measure. To further enhance the understanding of the combinations of conditions that limit the effectiveness of market orientation in improving performance, a study with a 2 (service/ manufacturing) x 2 (high/low power distance) design with multiple types of performance measures would be beneficial.
Third, prior research has suggested that reducing role conflict enhances quality and, consequently, performance (e.g., Hartline and Ferrell 1996). Because studies have shown market orientation to reduce role conflict, it is likely that its impact on performance is mediated through role conflict. Data limitations prevented us from examining the mediating role of role conflict, other employee-related consequences, and customer satisfaction on the market orientation-performance relationship. Examining the mediating role of employee-related consequences and customer satisfaction might help further clarify the processes that mediate the market orientation-performance relationship.
Fourth, multicollinearity prevented us from assessing the impact of long-term orientation on the market orientation-performance relationship. Therefore, further research is warranted in which variation is accomplished across long-term orientation and other dimensions of culture while estimating the market orientation-performance relationship.
The authors thank Rajdeep Grewal, Subhash Sharma, Terry Shimp, and Stan Slater for their comments on previous versions of this article.
( n1) We thank an anonymous reviewer for this suggestion.
( n2) A complete bibliography of the studies included in the meta-analysis is available from the authors. Several studies could not be included because ( 1) their results were reported only in multivariate models (e.g., Greenley 1995; 46 studies), ( 2) their results were based on data used in other studies that were already included (16 studies), and ( 3) they reported relationships that were unique and could not be integrated with those in other studies (e.g., Fahy et al. 2000; 10 studies). The inclusion rate of 61% is comparable to other meta-analyses in marketing by Brown and Peterson (1993; 66%), Szymanski, Bharadwaj, and Varadarajan (1993; 63%), and Szymanski and Henard (2001; 59%).
( n3) We revised an initial draft of the coding form on the basis of feedback from three marketing academics who were familiar with the market orientation and meta-analysis literature streams. The final coding form included antecedents and consequences of market orientation, sample and measurement characteristics, and the r-family of effect size indicators, such as correlation coefficients and indicators that could be converted to correlation coefficients (e.g., Student's t, chi-square, F-ratios with one degree of freedom, and p -values for group comparisons; see Rosenthal 1994). We checked coding quality by having an independent investigator code a random sample of 35% of the studies. Following the procedures that Perreault and Leigh (1989) recommend, we calculated an interjudge reliability index for each of the measurement and sample characteristics. The reliability estimate ranged between .91 and 1.0, suggesting that the reliability of the coding process was high (see Perreault and Leigh 1989, p. 147).
( n4) We included dummy variables for the type of market orientation scale (i.e., Narver and Slater's [1990] MKTOR versus Kohli, Jaworski, and Kumar's [1993] MARKOR), journal quality (i.e., top tier versus second tier versus other journals based on Baumgartner and Pieters [2003]), date of publication (1990-1996 versus 1997-1999 versus 2000-2004), and business cycles in preliminary analyses as control variables. These variables were not significant, and thus we excluded them from further analyses.
Legend for Chart:
A - Construct (Traditional Hypothesis)
B - Number of Effects(a)
C - Total Sample Size
D - Corrected Mean(b) r
E - Standard Error
F - Range of r
G - Availability Bias(c)
A B C D
E F G
Antecedents of Market Orientation 63 14,510
Top Management Factors
Top management emphasis (+) 13 4074 .44(*)
.02 -.13-.57 178
Interdepartmental Dynamics
Interdepartmental connectedness (+) 20 3282 .56(*)
.02 .10-.67 353
Interdepartmental conflict (-) 4 530 -.28(*)
.04 -.59-.09 10
Organizational Systems
Centralization (-) 9 2062 -.27(*)
.02 -.43-.07 51
Formalization (-) 9 2185 -.12(*)
.02 -.36-.30 17
Market-based reward systems (+) 5 1297 .41(*)
.03 .20-.54 36
Market-oriented training (+) 3 1080 .54(*)
.03 .43-.57 29
Consequences of Market Orientation 355 61,561
Organizational Performance 214 36,150 .32(*)
.01 -.15-.79 6535
Overall business performance (+) 69 12,732 .46(*)
.01 -.08-.79 3125
Profit (+) 69 11,104 .27(*)
.01 -.13-.46 1812
Sales (+) 58 8735 .26(*)
.01 -.15-.59 681
Market share (+) 18 3579 .31(*)
.02 .00-.50 167
Customer Consequences 43 6530
Quality (+) 16 2361 .36(*)
.02 -.07-.71 127
Customer loyalty (+) 16 2485 .35(*)
.02 .14-.58 170
Customer satisfaction (+) 11 1684 .45(*)
.02 .01-.69 114
Innovation Consequences 60 11,935
Innovativeness (+) 30 6013 .45(*)
.01 -.09-.60 646
New product performance (+) 30 5922 .36(*)
.02 .07-.58 329
Employee Consequences 38 6946
Organizational commitment (+) 12 2203 .71(*)
.03 .24-.82 200
Team spirit (+) 8 1254 .51(*)
.03 .00-.92 74
Customer orientation (+) 7 1214 .25(*)
.03 .11-.56 28
Role conflict (-) 6 1338 -.54(*)
.03 -.05- -.53 58
Job satisfaction (+) 5 937 .61(*)
.03 .26-.64 47
(*) p < .05.
(a) Contains relationships for which at least three effects were
available.
(b) The corrected mean correlation coefficients (r) are the
sample-size-weighted, reliability-corrected estimates of the
population correlation coefficients.
(c) Availability bias refers to the number of unpublished studies
reporting the null results needed to reduce the cumulative effect
across studies to the point of nonsignificance (Lipsey 2001). Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
A B C D E
F G H
1. Market orientation .83
2. Interdepartmental connectedness .46 .82
3. Top management emphasis .36 .32 .76
4. Centralization -.23 -.27 -.17 .88
5. Formalization -.10 .01 -.02 .30
.82
6. Market-based reward systems .30 .22 .13 -.07
-.15 .65
7. Interdepartmental conflict -.20 -.14 -.04 .30
.19 -.06 .78
Notes: Off-diagonal entries represent the average
sample-size-weighted correlation (r) values. Entries on the
diagonal reflect sample-size- weighted mean reliabilities
(Cronbach's α). Legend for Chart:
B - Hypothesized Model Path Coefficient
C - Hypothesized Model t-Value
A B C
Interdepartmental connectedness-market
orientation .36 3.27(*)
Top management emphasis-market
orientation .25 2.55(*)
Centralization-market orientation -.02 -.24
Formalization-market orientation -.02 -.25
Market-based reward systems-market
orientation .24 2.21(*)
Interdepartmental conflict-market
orientation -.14 -1.50
χ²(d.f.) = .76(1)
RMSEA = .00
AGFI = .96
NFI = .99
RMSR = .02
(*) p < .05.
Notes: Error variances for each construct indicator were fixed at
(1 - α), where is the sample-size-weighted average
reliability across studies (see Geyskens, Steenkamp, and Kumar
1998), and the median sample size across studies (n = 147) was
used for estimation purposes (see Henard and Szymanski 2001). Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
A B C D E F G
1. Market orientation .83
2. Organizational performance .27 .81
3. Customer loyalty .27 .33 .85
4. Customer satisfaction .35 .35 .68 .82
5. Innovativeness .35 .29 .40 .26 .78
6. Quality .28 .29 .22 .67 .50 .78
Notes: Off-diagonal entries represent the average
sample-size-weighted correlation (r) values. Entries on the
diagonal reflect sample-size-weighted mean reliabilities
(Cronbach's α). Legend for Chart:
B - Hypothesized Model Path Coefficient
C - Hypothesized Model t-Value
D - Revised Model Path Coefficient
E - Revised Model t-Value
A
B C D E
Market orientation-customer loyalty
.37 4.07(**) -- --
Market orientation-quality
.41 4.34(**) -- --
Market orientation-innovativeness
.50 5.42(**) .46 5.03(**)
Customer loyalty-organizational performance
.30 3.28(**) .28 2.98(**)
Quality-organizational performance
.23 2.47(**) .22 2.27(**)
Innovativeness-organizational performance
.12 1.28 -- --
Innovativeness-customer loyalty
-- -- .49 5.53(**)
Innovativeness-quality
-- -- .64 7.18(**)
Market orientation-organizational performance
-- -- .17 1.78(*)
χ²(d.f.) = 48.37(4) χ²(d.f.) = 2.97(4)
RMSEA = .27 RMSEA = .00
AGFI = .58 AGFI = .97
NFI = .71 NFI = .98
RMSR = .12 RMSR = .03
(*) p < .10.
(**) p < .05.
Notes: Error variances for each construct indicator were fixed at
(1 - α), where is the sample-size-weighted average
reliability across studies (see Geyskens, Steenkamp, and Kumar
1998), and the median sample size across studies (n = 157) was
used for estimation purposes (see Henard and Szymanski 2001). Legend for Chart:
A - Predictor Variables (Reference Level Stated First)
B - Model 1 Hypotheses
C - Model 1 β(a) (t-Value)
D - Model 2 Hypotheses
E - Model 2 β(a) (t-Value)
F - Model 3 Hypotheses
G - Model 3 β(a) (t-Value)
A B C
D E
F G
Performance Measure Type
Cost-based versus revenue-based
performance H1 -.04 (.46)
--
--
Cost-based versus overall business
performance(b) -- .12 (1.17)
--
--
Other Measurement Characteristics
Objective versus subjective
performance measures H2 .33 (3.08)(**)
.34 (3.02)(**)
.34 (3.02)(**)
Single- versus multi-item
performance measures H3 .06 (.49)
.04 (.32)
.04 (.32)
Sample Characteristics
Manufacturing versus service firms H4 -.43 (4.81)(**)
--
--
Manufacturing revenue-based
performance versus service
revenue-based performance -- --
H4a -.31 (3.03)(**)
--
Manufacturing cost-based
performance versus service
cost-based performance -- --
-- --
H4b -.30 (2.94)(**)
Cultural Context
Low versus high uncertainty
avoidance H5 -.18 (1.91)(*)
-.17 (1.75)(*)
-.17 (1.75)(*)
Low versus high power distance H6 -.29 (2.39)(**)
-.25 (1.96)(*)
-.25 (1.96)(*)
Low versus high individualism H7 -.10 (.85)
-.07 (.60)
-.07 (.60)
Low versus high masculinity H8 .14 (1.28)
.08 (.66)
.08 (.66)
Low versus high long-term
orientation H9 --
--
--
F-statistic 9.53(**)
7.32(**)
7.32(**)
Degrees of freedom 9, 73
10, 72
10, 72
R² .54
.50
.50
(*) p < .10.
(**) p < .05.
(a) Standardized coefficients.
(b) Overall business performance refers to performance measures
that cannot be disaggregated into cost-based and revenue-based
measures as shown in Table 1. Legend for Chart:
A - Moderator
B - Supportive
C - Opposite
D - Nonsignificant
A B C
D
Market/ Appiah-Adu (1997) Greenley (1995)
environmental Diamantopoulos and Hart Slater and Narver
turbulence (1993) (1994a)
Harris (2001)
Kumar, Subramanian, and
Yauger (1998)
Pulendran, Speed, and
Widing (2000)
Becherer and Maurer (1997)
Cadogan, Diamantopoulos, and Siguaw
(2002)
Gray et al. (1999)
Jaworski and Kohli (1993)
Rose and Shoham (2002)
Subramanian and Gopalakrishna (2001)
Tay and Morgan (2002)
Competitive Bhuian (1998)
intensity Diamantopoulos and Hart
(1993)
Grewal and Tansujah (2001)
Harris (2001)
Kumar, Subramanian, and
Yauger (1998)
Appiah-Adu (1997)
Appiah-Adu (1998)
Cadogan, Cui, and Li (2003)
Gray et al. (1999)
Jaworski and Kohli (1993)
Kwon and Hu (2000)
Slater and Narver (1994a)
Perry and Shao (2002)
Tay and Morgan (2002)
Pulendran, Speed, and Widing (2000)
Rose and Shoham (2002)
Subramanian and Gopalakrishna (2001)
Technological Rose and Shoham (2002) Grewal and Tansuhaj
turbulence (2001)
Slater and Narver
(1994a)
Appiah-Adu (1997)
Bhuian (1998)
Cadogan, Cui, and Li (2003)
Harris (2001)
Gray et al. (1999)
Greenley (1995)
Jaworski and Kohli (1993)
Pulendran, Speed, and Widing (2000)
Notes: Other less frequently studied moderators of the market
orientation-performance relationship that we found in the
literature include market growth, buyer power, demand
uncertainty, supplier power, and extent of entry barriers.DIAGRAM: FIGURE 1; Conceptual Framework for Meta-Analysis
DIAGRAM: FIGURE 2; Consequences of Market Orientation
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~~~~~~~~
By Ahmet H. Kirca; Satish Jayachandran and William O. Bearden
Ahmet H. Kirca is Assistant Professor of International Business, School of Business, George Washington University.
Satish Jayachandran is Assistant Professor of Marketing, Moore School of Business, University of South Carolina.
William O. Bearden is Bank of America Chaired Professor of Marketing, Moore School of Business, University of South Carolina.
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Record: 95- Market Response to a Major Policy Change in the Marketing Mix: Learning from Procter & Gamble's Value Pricing Strategy. By: Ailawadi, Kusum L.; Lehmann, Donald R.; Neslin, Scott A. Journal of Marketing. Jan2001, Vol. 65 Issue 1, p44-61. 18p. 3 Diagrams, 9 Charts. DOI: 10.1509/jmkg.65.1.44.18130.
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Record: 96- Market Situation Interpretation and Response: The Role of Cognitive Style, Organizational Culture, and Information Use. By: White, J. Chris; Varadarajan, P. Rajan; Dacin, Peter A. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p63-79. 17p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.67.3.63.18654.
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Market Situation Interpretation and Response: The Role
of Cognitive Style, Organizational Culture, and Information Use
Improving marketing decision making requires a better understanding of the factors that influence how managers interpret and respond to a market situation. Building on extant literature, the authors develop a model that delineates antecedents of and responses to the interpretation of a market situation. Using case-scenario methodology, the authors test the model in the context of a marketing decision (annual advertising and promotion budget recommendation) with data collected from a nationwide sample of hospital marketing executives. The results of the partial east squares analysis show that ( 1) cognitive style, organizational culture, and information use affect the extent to which managers perceive a given market situation as one in which they can control the outcomes of their decision; ( 2) the more managers perceive a situation as controllable, the more they appraise that situation as an opportunity; and ( 3) the more managers appraise a situation as an opportunity, the greater is the magnitude of their response.
Perception and cognition are not purely objective, but are also subjectively conditioned. The world exists not merely in itself, but also as it appears in me.
--C.G. Jung, Psychological Types
To survive and prosper in a competitive marketplace, an organization must strive to respond continuously to opportunities and threats posed by a changing environment. Marketing managers typically play a lead role in this task through their responsibility to interpret the environment and make the crucial choices of which customers to serve, competitors to challenge, and products and services to offer (Day 1984). Consequently, how managers interpret a market situation directly affects the solutions considered in their respective organizations, the resources committed to particular projects, and the changes made in products offered or markets served (Thomas, Clark, and Gioia 1993).
There is general consensus among managers and researchers that improving marketing decision making requires a better understanding of factors that influence how managers interpret and respond to information that pertains to a market situation (Barabba and Zaltman 1991; Marketing Science Institute 2002; Moorman 1995; Mullins and Walker 1996; Prabhu and Stewart 2001). Understanding how managers interpret the information they choose to use Is important in light of the growing body of evidence that suggests there are significant differences in the ways individual managers interpret and respond to a situation (Jackson and Dutton 1988; Mullins and Walker 1996; Thomas, Clark, and Gioia 1993).
In this study, we investigate the role of individual differences in managers' interpretations of a market situation to gain insights into why managers arrive at different perceptions of the same situation. Specifically, we focus on the following research questions: ( 1) What factors influence managers' interpretations of a market situation? ( 2) How do managers decide the extent to which a market situation represents an opportunity or a threat for their organization? and ( 3) How does the extent to which managers appraise a market situation as an opportunity or a threat affect the magnitude of their response? Toward this end, we develop and test a model that delineates antecedents of and responses to the interpretation of a market situation. Cognitive appraisal theory provides a unifying theoretical framework for the proposed model (Folkman 1984; Lazarus 1991; Lazarus and Folkman 1984).
Figure 1 presents the proposed model of antecedents and consequences of interpretation of a market situation. The model depicts cognitive style, perceived organizational culture, and information use as key influences of perceived control. Perceived control is modeled as mediating the relationship between these antecedents and appraisal. Appraisal is posited as mediating the relationship between perceived control and magnitude of response.
Antecedents of Interpretation
According to cognitive appraisal theory, individual cognitive traits, the social environment, and information use can affect interpretation of an ambiguous environment (Lazarus 1991; Skinner 1995). In considering potential cognitive traits and social environment variables that can affect interpretation, literature pertaining to cognitive views of organizational information processing suggests that cognitive style and organizational culture are especially relevant. Cognitive style is defined as the relatively stable mental structures or processes that people prefer when they perceive and evaluate information (Jung 1946; Myers and McCaulley 1985). Organizational culture is defined as "the pattern of shared values and beliefs that help individuals understand organizational functioning and that provide norms for behavior in the organization" (Deshpandé, Farley, and Webster 1993, p. 4). Both cognitive style and organizational culture are archetypes that reflect underlying values and assumptions. These archetypes directly influence the ways a manager filters and processes information so that an otherwise ambiguous situation takes on meaning (Deshpandé and Webster 1989; Moorman 1995; Nutt 1990, 1993). Information use, the third antecedent, is defined as the extent to which a decision maker relies on the description of a market situation to make a marketing decision. Information use is an important antecedent of interpretation because of the role of information in reducing the ambiguity and uncertainty associated with a market situation.
Interpretation
Interpretation is defined as the conversion of information into knowledge and understanding. In this study, we attempt to extend cognitive appraisal theory in two ways. First, we apply the theory in a new context, namely, a decision maker's interpretation of a market situation. Second, researchers in this literature stream have theorized that the interpretation of an ambiguous situation may entail two separate but related stages (Folkman 1984; Lazarus and Folkman 1984), perceptions of control and appraisal. We address researcher's calls for an empirical examination of this theoretical framework (Folkman 1984; Lazarus 1991; Skinner 1995) by conceptualizing interpretation as two stages. In the first stage, managers characterize a situation on the basis of their perceptions of control. Perceived control refers to the decision maker's sense of ability to manage a market situation.( n1) The more managers believe they can manage a situation, rather than have a situation manage them, and the more they believe that the outcomes of their decisions are not simply a matter of chance, the more they perceive a situation as controllable. Building on Folkman's (1984) conceptualization of perceived control as antecedent to appraisal, we model perceived control as mediating the relationships between the previously discussed antecedents of perceived control and appraisal, the second stage of interpretation.
Appraisal refers to the extent to which decision makers perceive a situation as an opportunity and the extent to which they perceive it as a threat (Dutton and Jackson 1987; Krueger and Dickson 1994; Lazarus 1991; Skinner 1995). When managers appraise a situation, they attach labels to describe their overall evaluation. The labels chosen stem from cognitive classifications that group objects, events, or ideas with similar perceived attributes and are important because they reflect a categorization process that affects subsequent cognitions and motivations. Managers rely on this process of attaching labels when appraising a situation to reduce the complexity of otherwise ambiguous information. It is important to note that extant research demonstrates opportunity and threat to be empirically distinct dimensions that are related but not identical. In situations that are highly ambiguous, managers are likely to experience both positive and negative emotions at the same time (Folkman and Lazarus 1985). Furthermore, perceptions of opportunity and threat can occur simultaneously and therefore must be considered as separate, albeit related, constructs (Lazarus and Folkman 1984). In accord with cognitive appraisal theory, we model appraisal as mediating the relationship between perceived control and response.
Responses to Interpretation
Finally, cognitive appraisal theory suggests that the assessment of opportunity and threat is followed by an assessment of the potential options and resources for coping (Lazarus 1991). Coping is the psychological equivalent of an action tendency (Lazarus 1991), and in this study we focus specifically on an aspect of coping that we label magnitude of response, which refers to managers' propensity to commit more or fewer resources on the basis of their interpretation of the market situation. Because market situations are seldom perfectly structured, managers must exercise some discretion as the magnitude of their response. Therefore, it is important to understand whether the magnitude of a decision maker's response is related to the perceived magnitude of opportunity or threat to the organization. Researchers argue that the relationship between interpretation and response may hold the key to understanding an organization's ability to adapt successfully to a changing environment (Jackson and Dutton 1988; Thomas, Clark, and Gioia 1993). Next, we develop hypotheses pertaining to each of the links delineated in the model.
Although several classifications of cognitive style exist, the most widely adopted classification scheme is based on the work of Carl Jung and further developed by Isabel Myers and her colleagues (Jung 1946, 1971; Myers and McCaulley 1985). This scheme classifies people along four dimensions of cognitive style: extrovert--introvert, judging--perceiving, sensing--intuiting, and thinking--feeling. The four dimensions refer to a person's preferences for interpreting his or her environment and acting on that interpretation. Research related to cognitive style and perceptions of information has focused primarily on the sensing--intuiting and thinking--feeling dimensions of cognitive style and has largely ignored the extrovert--introvert and judging--perceiving dimensions. However, cognitive style theory suggests that these dimensions reflect predispositions toward interaction with the outside world and therefore may have important implications for how managers interpret information (Nutt 1986). Consequently, we attempt to extend extant literature by investigating the influence of all four dimensions of cognitive style on the interpretation of a market situation.
We conceptualize each of the four dimensions of cognitive style as a bipolar continuum. For example, the greater the score on the extrovert--introvert scale, the more the cognitive style tends toward extrovert; the lower the score, the more the cognitive style tends toward introvert.( n2)
Extrovert-Introvert
The extrovert-introvert dimension of cognitive style refers to people's preferences for interacting with others when making a decision. According to Jung (1946), people with more extroverted cognitive styles are more adept at dealing with the outer world. When extroverts try to make sense of a situation, they place great weight on the opinions of other people (McCaulley 1987). In addition, extroverts are much more prone to share information or ideas freely in their nascent stages in an attempt to build support for their interpretations (Jeffries 1991). Compared with introverted managers, more extroverted managers are better able to shape a discussion because of their ability to deal with people and influence them to accept and support their assessments of new information. Kilmann and Thomas (1975) report that extroverted managers excel at handling conflict because they have the ability to be both assertive and cooperative. Because they can effectively use these somewhat paradoxical skills, extroverted managers should be able to elicit support from others in their organizations; as a result, they are more likely to believe that they can control a new situation. Conversely, more introverted managers are less proactive in seeking input from others, because they prefer to deal with the internal world. Thus, they are less adept at influencing others and may have more difficulty in garnering support for their ideas. As a result, more introverted managers are less likely to believe that they can control a new situation.
H1: The more extroverted a marketing manager's cognitive style, the more the manager perceives a market situation as controllable.
Judging-Perceiving
The judging-perceiving dimension of cognitive style refers to individual preferences with regard to proactiveness when making a decision. Managers with more judging cognitive styles tend to be more proactive, whereas managers with more perceiving cognitive styles tend to be more passive when it comes to their assessment of a situation (Nutt 1986). Managers with more judging cognitive styles are often characterized as organized, purposeful, and decisive (McCaulley 1987). They are "closure" oriented and, all else being equal, are more apt to believe they have adequate information on hand to make an informed judgment (Jeffries 1991). Because they are more likely to believe they have enough information to make an informed decision, judging managers are more likely to perceive less risk in a given situation (Nutt 1986) and to have less uncertainty. When managers have greater certainty, they are more likely to have success in convincing other managers to support their decisions (Dutton and Webster 1988) and are more likely to perceive a greater ability to manage a given situation successfully and resolve problems should they anise (McCall and Kaplan 1985). As a result, they are more likely to perceive a given situation as controllable.
In contrast, managers with more perceiving cognitive styles tend to postpone a decision as long as possible because they are typically concerned with missing one last critical fact (Jeffries 1991; McCaulley 1987). If a manager perceives that critical information is not available, this likely decreases the perceived controllability of a situation. Although cognitive style theory suggests that a manager with a more perceiving cognitive style is often more curious or spontaneous than a more judging manager (Jeffries 1991; McCaulley 1987), Gryskiewicz and Tullar (1995) report that managers with more judging cognitive styles are more adaptive. This suggests that managers with a more perceiving cognitive style are less adaptive because they are concerned with the sufficiency of available information and thus may perceive a situation as less controllable.
H2: The more judging a marketing manager's cognitive style, the more the manager perceives a market situation as controllable.
Sensing-Intuiting and Thinking-Feeling
The sensing-intuiting and thinking-feeling dimensions of cognitive style both pertain to a person's tolerance for ambiguity and risk propensity. Managers with more intuiting and thinking cognitive styles are more adaptive, imaginative, eager to explore new experiences, and ambitious (McIntyre, Wheatley, and Uhr 1996). They tend to tolerate low certainty and high ambiguity and are thus more tolerant of risk (McIntyre and Mokwa 1993). Because of their greater risk seeking propensity, these managers may perceive a greater ability to control an otherwise ambiguous situation (Stumpf and Dunbar 1991). In their study of the effects of cognitive style on choices made in strategic decision situations, Stumpf and Dunbar (1991) find that managers with intuiting and thinking cognitive styles take actions that reflect a "positivity bias." Similarly, Henderson and Nutt (1980) find that such managers are more likely to adopt potentially risky projects. We suggest that managers who have more intuiting and thinking cognitive styles take actions that are more ambitious because of a greater perceived ability to control a situation.
In contrast, managers with more sensing and feeling cognitive styles tend to prefer a more stable environment. The ideal organization for these managers is one characterized by complete control, certainty, and specificity (Kilmann and Mitroff 1976). Cognitive style theory suggests that because of their greater desire for stability and relatively lower tolerance for ambiguity, managers with more sensing and feeling cognitive styles are likely more concerned about their ability to control the outcomes of an ambiguous situation (Mitroff and Kilmann 1975). This concern for control may explain why their strategic recommendations are more likely to be low risk (Nutt 1986).
H3: The more intuiting a marketing manager's cognitive style, the more the manager perceives a market situation as controllable.
H4: The more thinking a marketing manager's cognitive style, the more the manager perceives a market situation as controllable.
According to cognitive appraisal theory, evaluation of information is influenced by the contrived mental reconciliation between individuals and their perceptions about the shared values and beliefs within relevant social settings (Lazarus 1991). For managers, those shared values and beliefs arise from the culture of the organization. Thus, we propose that organizational culture influences perceptions of controllability by providing a cognitive context for decision makers. In this study, we focus on managers' perceptions of organizational culture. To differentiate this level of analysis, we use the term "perceived organizational culture."
The competing values model of organizational culture is (Skinner 1995). When managers use more information, they an appropriate theoretical paradigm for the investigation of issues associated with market situation interpretation because it focuses on the cognitive structure of the manager rather than the operational structure of the organization (Deshpandé, Farley, and Webster 1993; Quinn and Rohrbaugh 1983). The competing values model of organizational culture focuses on competing tensions and conflicts inherent in any organization, such as the conflict between change and stability.
Organizations with a more informal adhocracy or clan culture encourage flexibility, spontaneity, individual initiative, and market responsiveness. Deshpandé (1982) proposes that managers in more flexible organizations may believe they have more freedom in doing their jobs. Furthermore, managers in organizations with informal cultures encourage greater horizontal communication and cooperative action, and the values and beliefs of such organizations are highly conducive to collaborative communication (Brown and Starkey 1994) as well as greater participation in the decision-making process (Ashmos, Duchon, and McDaniel 1998). Moorman (1995) finds that organizations with an adhocracy or clan culture tend to stress participation, teamwork, and cohesiveness, which leads to greater trust, commitment, and cooperation among organizational members. Managers should perceive a market situation as easier to control when they have freedom to act and when they are accustomed to receiving cooperation from others within the organization. Concomitant with an emphasis on flexibility, organizations with an adhocracy or clan culture tend to stress innovation and adaptation (Bluedorn and Lundgren 1993) and thus are more likely to provide adequate resources in the event of unforeseen contingencies. Consequently, managers operating in an informal adhocracy or clan culture are more likely to perceive a given situation as more controllable.
In contrast, an organization with a more formal hierarchy or market culture is primarily concerned with stability and the ability to maintain control. Therefore, managers in such organizations attempt to minimize disruptions in operations (Quinn and Rohrbaugh 1983). Organizations with a formal culture tend to stress tight structure, formal authority, and impersonal coordination (Bluedorn and Lundgren 1993). Managers in these organizations rely more on formal information systems and less on person-to-person information systems (Quinn and Rohrbaugh 1983). As a result, managers in organizations with a formal market or hierarchy culture may not have the same freedom to act, receive the same level of cooperation, or expect the organizational resource support as managers in organizations with informal cultures.
H5: The more a marketing manager perceives the organizational culture as an adhocracy or clan (rather than a market or hierarchy), the more the manager perceives a market situation as controllable.
In general, people try to learn as much as possible before making decisions, especially in new or ambiguous situations (Skinner 1995). When managers uses more information, they reduce uncertainty, reduce perceived risk (McCall and Kaplan 1985), and increase confidence by basing their interpretations on more comprehensive intelligence and a better sense of cause-and-effect relationships (Milliken 1990). Increased information use leads to increased confidence in interpreting market situation information because managers perceive that they are better equipped to cope with ambiguous situations. Eisenhardt (1989) reports that confidence increases as information usage increases, because managers believe that "no stone has been left unturned." Reducing uncertainty and increasing confidence are especially important because they increase perceived competence, which is a key factor is assessing ability to control an ambiguous situation (Bandura 1977, 1989; Skinner 1995). Therefore, increased information use should lead to greater confidence, greater perceived competence, and, in turn, greater perceived ability to control a situation.
H6: The more a marketing manager uses available information to interpret a market situation, the more the manager perceives that situation as controllable.
Experiential knowledge and the need to reduce uncertainty are key factors influencing interpretation (Lazarus 1991; Skinner 1995); that is, a decision maker's information use is guided by past experiences and the need to reduce uncertainty. We propose that because of their experiences and need to reduce uncertainty, decision makers rely more on negative (rather than positive) and external (rather than internal) information when interpreting a market situation. As suggested by prospect theory, managers are more concerned with potential loss than with potential gain (Tversky and Kahneman 1986). Resource dependence theory suggests that decision makers rely more on external information when evaluating a situation because organizations depend on the environment for scarce and valued resources, and therefore external information has a greater capacity for reducing uncertainty (Pfeffer and Salancik 1978). In a notable finding, Jackson and Dutton (1988) report that despite a disproportionate focus on both negative and external information, managers tend to underrecognize and underappreciate the implications of threatening information. Whether it is because of a disproportionate concern for loss, the concern for reducing uncertainty, or simply the need to avoid repeating past mistakes, managers are likely to evidence a bias in favor of negative (rather than positive) and external (rather than internal) information.
H7: Marketing managers use negative information more than positive information when interpreting a market situation.
H8: Marketing managers use external information more than internal information when interpreting a market situation.
The two categories managers use most frequently in appraising a situation are opportunity and threat (Dutton and Jackson 1987). Opportunity is defined as the extent to which the manager perceives a market situation as one in which the firm could experience an increase in sales and/or profits. Threat is defined as the extent to which the manager perceives a market situation as one in which the firm could appraisal mediates the relationship between control and experience a deterioration in sales and/or profits.
Before addressing the relationship between perceived control and appraisal, we first discuss the mediating role of perceived control. In the development of cognitive appraisal theory, researchers have proposed that perceptions of control are shaped by individual cognitive traits, the social environment, and information use (Lazarus 1991; Skinner 1995), and, in turn, perceptions of control are antecedent to appraisal (Folkman 1984; Lazarus and Folkman 1984). In other words, according to Folkman (1984, p. 850), when interpreting an ambiguous situation, "control can be viewed as a cognitive mediator?'
H9: The relationships between antecedents of perceived control (cognitive style, perceived organizational culture, and information use) and appraisal are mediated by perceived control.
When managers perceive that they can control the outcomes of a decision, they are more likely to experience positive emotions (Bandura (977; Folkman and Lazarus 1985), the situation they are faced with seems more attractive (Taylor 1989; Walsh, Henderson, and Deighton 1988), and they may be more inclined to set high goals (Schunk 1990). They are better able to visualize a concrete series of action steps leading to a desired outcome. As a result, they can be expected to assess the situation as one in which their organizations can perform profitably (i.e., an opportunity).
In contrast, when managers perceive a given situation as less controllable and as one in which outcomes are a matter of chance, they are likely to experience negative emotions (Bandura 1977; Folkman and Lazarus 1985), set low goals (Schunk 1990), perceive the situation as one with negative implications (i.e., a threat), and imagine a process full of anxiety, culminating in a poor outcome (Bandura 1989). Consequently, marketing managers' perceptions of the controllability of a situation can be expected to influence appraisal of the situation as follows:
H10: The more a marketing manager perceives a market situation as controllable, the more the manager appraises that situation as an opportunity.
H11: The less a marketing manager perceives a market situation as controllable, the more the manager appraises that situation as a threat.
Before addressing the relationship between appraisal and magnitude of response, we first discuss the mediating role of appraisal. Cognitive appraisal theory, research on perceived control, and extant research on decision making lend support to the assertion that perceived control is an important antecedent for predicting appraisal of an ambiguous situation (Folkman 1984; Jackson and Dutton 1988; Lazarus and Folkman 1984; Skinner 1995). In turn, both theoretical and empirical works on cognitive appraisal theory lend support to the relationship between appraisal and response (see Lazarus 1991). Although empirical evidence is lacking, researchers studying cognitive appraisal have theorized that appraisal mediates the relationship between control and response (Folkman 1984; Lazarus and Folkman 1984). As Lazarus and Folkman (1984, p. 45) state, "Cognitive appraisal is central in mediating subsequent thought, feeling. and action."
H12: The relationship between perceived controllability of and magnitude of response to a market situation is mediated by appraisal of the situation.
Although the exact nature of the relationship between a manager's appraisal of and subsequent response to a situation is subject to debate, as we discuss next, there is strong support in both the cognitive appraisal and the decision-making literature that they are closely linked (e.g., Bateman and Zeithaml 1989; Lazarus 1991 ; Thomas, Clark, and Gioia 1993).
Opportunity Appraisal and Response
Appraising a market situation as an opportunity has a psychologically powerful impact on managers as well as on other members of an organization (Dutton 1992). From an individual perspective, an opportunity appraisal increases the positive affect associated with a situation and serves both to heighten perceptions of control further and to suppress consideration of threatening aspects. As Dutton (1992) suggests, the label "opportunity" seems to give an otherwise ambiguous situation a "positive gloss." Enhanced perceptions of control also result in reduced uncertainty and a greater sense of confidence (Dutton 1992; Dutton and Webster 1988; Jackson and Dutton 1988; Milliken 1990; Taylor 1989; Walsh, Henderson, and Deighton 1988). Because an opportunity appraisal heightens perceptions of control, reduces uncertainty, and positively affects confidence in decision making, it should increase managers' perceptions of the feasibility of accomplishing desired results. This is important because decision theory suggests that managers are likely to recommend greater resource commitments when they believe they can accomplish desired results (e.g., Heath and Tversky 1991; Krueger and Dickson 1994; Mullins and Walker 1996).
From an organizational perspective, when managers assess a situation as an opportunity, that opportunity becomes more attractive to others in the organization because it imbues the market situation with the value of proactiveness and progressiveness, Ashmos, Duchon, and McDaniel (1998) report that the participation of managers is significantly greater when they respond to issues labeled as an opportunity than when they respond to issues labeled as a threat. Indeed, words such as "opportunity" may be considered linguistic symbols of managers attempting to communicate their interpretation and, in doing so, creating interest and excitement in a market situation that others may perceive as vague and ambiguous. Empirical evidence also lends support for a propensity toward a response of greater magnitude when managers label a situation as an opportunity (Ginsberg and Venkatraman 1992; Mullins and Walker 1996; Thomas, Clark, and Gioia 1993). For example, Thomas, Clark, and Gioia (1993) report a positive relationship between managers' labeling of a situation as controllable and actual product and service changes in their businesses. Consequently, when managers perceive a market situation as more of an opportunity, they are likely to be predisposed toward making a greater resource commitment.
H13: The more a marketing manager appraises a market situation as an opportunity, the greater is the magnitude of the manager's response.
Threat Appraisal and Response
As noted previously, prospect theory suggests that because people tend to be more risk averse than risk seeking, potential losses loom larger than potential gains (Kahneman and Tversky 1979). Furthermore, evidence suggests that managers may develop a threat bias over time and thus be more sensitive to negative disconfirmation of their expectations (Jackson and Dutton 1988). Consequently, managers may be predisposed to commit greater resources when they categorize a situation as threat distinctive. In support of this perspective, some studies report that when managers perceive a situation as threatening, they tend to recommend a response of greater magnitude (Fredrickson 1985; Mutt 1984: Tversky and Kahneman 1986).
A contrasting perspective suggests that if managers associate a perceived threat with a lack of control (Jackson and Dutton 1988), threat perceptions result in psychological stress and anxiety, a restriction of the number of alternatives considered, and intensified concerns about efficiency (Staw, Sandelands, and Dutton 1981). Concerns about efficiency have implications for response because they are likely to result in cost cutting, budget tightening, and restriction of a business unit's activities (Starbuck and Hedberg 1977). In light of the equivocal arguments about the nature of the relationship between the appraisal of a market situation as a threat and the magnitude of response, a formal statement of hypothesis is not presented. Instead, we treat the relationship as an empirical issue.
Sample
The sample consists of the directors of marketing and public relations at 2000 randomly selected general medical and surgical hospitals in the United States. Although a multi-industry sample would permit an examination of interindustry effects and potentially broaden the study's generalizability, in this study it is critical that the inherent level of ambiguity associated with a situation remain approximately consistent for all respondents. The best way to accomplish this is to focus on a single industry.
The hospital marketing directors received a letter explaining the purpose of the research, which was followed one week later by a telephone call soliciting their participation. Of the 2000 marketing directors contacted, 87 declined to participate; the remaining 1913 were mailed a questionnaire and cover letter explaining the purpose of the research and promising a summary of results if desired. One week later, a reminder postcard was mailed. Two weeks later, a reminder letter and second questionnaire were mailed to marketing directors who still had not replied.
We assessed nonresponse bias by comparing ( 1) the number of beds in the hospitals of respondents and nonrespondents (t = .523; p > .05), ( 2) the proportion of responses received from respondents affiliated with profit versus nonprofit hospitals (difference not significant at alpha; = .05 for binomial proportion confidence interval), and ( 3) the geographic distribution of respondents and nonrespondents (χ²(3) = 5.56, p > .05). All three of these tests indicated no significant differences between the two groups. Both the number of usable questionnaires returned (757) and the overall response rate (37.9%) compare favorably with the results reported in mail surveys employing the case-scenario approach (e.g., Thomas, Clark, and Gioia 1993).
Case Scenario
We used a case scenario methodology to study interpretation. A key advantage of the case scenario method is that all respondents receive a standardized stimulus in which characteristics of a market situation (information pertaining to strengths, weaknesses, opportunities, and threats) can be balanced. The first step in constructing the case scenario was to compile a list of market situation-related issues that were important to hospital marketing executives. This step entailed interviews with health care marketing executives and reviews of hospital strategic-planning documents, articles in leading health care journals, case studies related to health care issues, and relevant articles in the popular press. We also asked health care executives to provide sources of market information they deemed trustworthy and reliable. During our development of the case scenario and again during pretesting, we asked the participating hospital marketing executives to describe the most important strategic marketing decision they had to make each year. The decision of whether to increase or decrease the annual advertising and promotion budget was by far the most frequent response. Determining the decision most likely to be deemed of strategic importance was important because the processes of evaluating and interpreting strategic information constitute patterns of decision behavior, and therefore the manager's interpretation of a market situation is more likely to be consistent when that information is perceived as strategically important (Fredrickson 1984).
The second step was to draft the case scenario. In creating the scenario, we made every effort to provide a realistic yet balanced market situation. To ensure balance, we constructed the scenario with an equal number of items pertaining to strengths, weaknesses, opportunities, and threats. Before constructing the scenario, a panel of hospital marketing executives evaluated each of the information items under consideration. On the basis of their input, we refined and then resubmitted items to the executives for evaluation. We subjected the information items included in the case scenario to multiple rounds of the refinement process.
We pretested the completed case scenario with a sample of hospital marketing executives to ensure it presented a realistic depiction of information that might be encountered in marketing budget decisions. During development and pretesting, we assessed the overall content validity of the case scenario by asking executives. "On the whole, is the scenario representative of the type of market information typically evaluated by hospital marketing executives when external opportunities and threats. As shown in Appendix B, making decisions about the advertising and promotion budget?" The responses of the 44 executives participating in the final pretest were unanimously affirmative. The resulting case scenario (see Appendix A) describes a hospital faced with several changes (both internal and external) that are likely to affect its advertising and promotion budget.
Measures
The scales we used to measure the various constructs central to the study are summarized in Appendix B. A brief discussion of each follows.
Cognitive style. We used Jung's theory of psychological type, operationalized by the 70-item Keirsey Temperament Sorter, to measure cognitive style (Keirsey and Bates 1984). Although the 128-item Myers-Briggs Type Indicator is the more widely used measure of cognitive style, it is extremely lengthy. The results of correlational comparisons to the Myers-Briggs Type Indicator suggest that the two instruments measure the same constructs (Quinn, Lewis, and Fischer 1992).
Perceived organizational culture. We used the measure of perceived organizational culture developed by Deshpandé. Farley, and Webster (1993). We altered the instructions and descriptions to replace the word "organization" with "hospital." In this study, we treated the items constituting this measure of culture as formative indicators because the intent of this measure is to capture multiple but potentially unrelated facets of organizational culture (e.g., organizational goals, leadership style, operational emphasis). A linear combination of these items defines the culture construct, and therefore a reflective construct would be inappropriate (Hulland 1999). Because this constant-sum measure asks respondents to allocate points to four categories (adhocracy, clan, hierarchy, and market), the four resulting variables are ipsative, resulting in a nonpositive definite input covariance matrix. In the analysis, we omit one culture (hierarchy) and test the impact of the other three relative to the one omitted. The results support H5 if the coefficients for adhocracy and clan are positive and significant (which means they are significantly greater than hierarchy) and the coefficient for market is nonsignificant (which means it is not significantly greater than hierarchy). In other words, this result would indicate that adhocracy and clan cultures are significantly related to perceived control whereas hierarchy and market cultures are not. By omitting hierarchy and limiting the analysis to the three remaining cultures, the resulting input data are not ipsative and can be analyzed in a path model. The descriptive statistics also show that the resulting data are normally distributed and have sufficient range.
Information use. We adapted Thomas, Clark, and Gioia's (1993) measure of information use to measure the extent to which specific pieces of information would be used in a marketing decision. The directions immediately preceding the case scenario provide sufficient information to establish a decision context for the information in the case. In developing the case scenario, managers told us that when they make their budget decisions, they use information pertaining to the organization's strengths and weaknesses along with external opportunities and threats. As shown in Appendix B, the case scenario contains eight distinct pieces of information that, in total, represent a balanced situation analysis; that is, there are two items representing strength, two items representing weakness, two items representing opportunity, and two items representing threat. We asked respondents to rate the extent to which they would use each piece of information in making a recommendation. We treated the eight information items as formative indicators to assess information use. Again, because the intent of this measure is to capture multiple but potentially unrelated facets of a situation analysis, a reflective construct was inappropriate (Hulland 1999).
Perceived control and appraisal. We borrowed the items for measuring managers' perceived control (two items), as well as their perceptions of opportunity (three items) or threat (three items), from the work of Dutton and Jackson (1987), Thomas and McDaniel (1990), and Thomas, Clark, and Gioia (1993). Although Thomas and colleagues use a valence assessment (i.e., the extent to which a situation is interpreted as having positive or negative implications for the organization) as a proxy for opportunity or threat, we combined their items with an additional item that directly assesses perceived opportunity or threat (the first item shown for each measure).
Magnitude of response. The case scenario is one in which we asked marketing directors to make a decision regarding the amount budgeted for advertising and promotion.( n3) To develop measures for this response, we followed the same procedure used to create the case scenario. In the final measure, two items assessed the response. The first item, a single item nine-point scale anchored by "1 = substantial decrease" and "9 = substantial increase," asked marketing managers to indicate how they would recommend changing the current advertising and promotion budget. The second item asked managers to provide a specific estimate of the amount of money they would recommend for the following year's advertising and promotion budget. We treated the two items constituting this measure of magnitude of response as formative indicators because a linear combination of these items defines the construct (Hulland 1999).
Control variables. On the basis of extant literature, we included constructs measuring managerial experience, organization size, organizational performance, and organization type (profit/nonprofit) in the model as control variables (see Hitt and Tyler 1991; Zinkhan, Joachimsthaler, and Kinnear 1987). We operationalized managerial expertise as a formative construct that includes age, education, job title, and job tenure. A database purchased from the American Hospital Association provided information pertaining to the organizational variables size, performance, and hospital type.
Partial Least Squares
To test the hypothesized relationships depicted in Figure 1, we used partial least squares (PLS), specifically, PLS-GRAPH v.3.00. A PLS analysis is most appropriate when the model incorporates both formative and reflective indicators, when assumptions of multivariate normality and interval scaled data cannot be made, and when the primary concern is with the prediction of dependent endogenous variables (Chin 1998; Diamantopoulos and Winklhofer 2001; Fornell and Bookstein 1982). Because PLS considers all path coefficients simultaneously (thus allowing analysis of direct, indirect, and spurious relationships) and estimates multiple individual item loadings in the context of a theoretically specified model rather than in isolation, it enables researchers to avoid biased and inconsistent parameter estimates for equations. Results from the measurement model are discussed next.
Assessments of Validity and Reliability
We attempted to achieve content validity through the depth of literature search used to define the domain, the number of items generated, and the pretest with both managers and academics. We assessed the adequacy of the measurement model by evaluating the reliability of individual items, the internal consistency between items expected to measure the same construct, and the discriminant validity between constructs. We inspected the loadings of measures on their corresponding constructs to assess individual item reliability. In all cases, we maintained a high degree of individual item reliability by retaining only items with factor loadings greater than .50 (as recommended by Hulland 1999). Most final loadings are greater than .70.
We evaluated internal consistency using a measure recommended by Fornell and Larcker (1981) that is similar to Cronbach's alpha, but preferred in this context because it estimates consistency on the basis of actual construct loadings. The internal consistency values for reflective constructs exceed the .70 guideline recommended by Nunnally (1978) for exploratory work. Appendix B presents the measures used in the study.( n4)
We assessed the discriminant validity of each construct in three ways. First, as shown in Table 1, the square root of the average variance extracted is greater than all corresponding correlations (Fornell and Larcker 1981). Second, all constructs exhibit discriminant validity because each correlation is less than 1 by an amount greater than twice its respective standard error (Bagozzi and Warshaw 1990). Third, an examination of the theta matrix confirmed that no item loaded higher on another construct than it did on its associated construct. All constructs exhibited satisfactory discriminant validity.
Test of Hypotheses
In Table 2, we report the beta coefficients and t-values for the model, along with the R² for each endogenous construct, as indicated by the PLS analysis. Because PLS makes no distributional assumptions, traditional parametric methods of significance testing (e.g., confidence intervals, χ²) are not appropriate. Therefore, we used a bootstrapping method (i.e., sampling with replacement method) to ascertain the stability and significance of the parameter estimates. We computed the t-values on the basis of 500 bootstrapping runs. The variance explained for the endogenous constructs ranges from .083 to .224 and is comparable to the values typically reported in similar research (e.g., Henderson and Nutt 1980; Moorman 1995; Stumpf and Dunbar 1991; Thomas, Clark, and Gioia 1993).
Results indicate that extrovert, judging, intuiting, and thinking cognitive styles; adhocracy and clan cultures; and information use are all significantly related to the extent to which managers perceive a market situation as controllable. Thus, H1 through H6 are supported. Perceived control of a market situation is positively related to an opportunity appraisal and negatively related to a threat appraisal, thus in support of H10 and H11. Finally, a positive, significant relationship exists between the extent to which a market situation is appraised as an opportunity and the magnitude of response, in support of H13. Given the equivocal nature of relevant theoretical arguments, we offered no formal hypothesis on the relationship between the appraisal of threat and the magnitude of response; in this study, this relationship is not significant.
Because opportunity and threat are independent continua, there are actually four possible interpretations of a market situation as noted by the following groups: ( 1) high opportunity and high threat, ( 2) high opportunity and low threat, ( 3) low opportunity and high threat, and ( 4) low opportunity and low threat. After performing a mean split (high/low) of managers' appraisals of opportunity and threat, we assessed the relationship between these four groups and magnitude of response using analysis of variance (F = 7.023, p < .001) and then assessed differences among the groups using post hoc contrasts of control for familywise error. The recommended budget was significantly greatest (p < .05) for Group 1 (high-high; $2.55 million), followed by Group 2 (high-low; $2.39 million), and then Groups 3 (low-high; $2.28 million) and 4 (low-low; $2.24 million). Groups 3 and 4 were not significantly different. In other words, the magnitude of the recommended budget was greatest for managers who perceive high levels of both opportunity and threat, it was next highest for managers who perceive high levels of opportunity but low levels of threat, and it was lowest for managers who perceive either low levels of opportunity and high levels of threat or low levels of both opportunity and threat.
We assessed the use of negative versus positive (items with the notation "-" and "+," respectively, in Appendix B) and external versus internal information (items with the notation "E" and "I," respectively, in Appendix B) in two ways. First, we performed a simple t-test to compare the means. We found that the mean for use of negative information is significantly greater than the mean for use of positive information (21.2 versus 18.9; t < .001) and the mean for use of external information is significantly greater than the mean for use of internal information (21.3 versus 18.7; t < .001). Overall, managers indicated they would rely more on negative (rather than positive) and external (rather than internal) information when making a marketing decision, which thus supports H7 and H8. Second, we created two additional PLS models in which we separated information use into two separate constructs: ( 1) negative and positive information use and ( 2) external and internal information use. The beta coefficients were not significantly different in either model. Therefore, this analysis suggests that though managers indicate they would rely more on negative and external information, positive and internal information are equally important in determining perceived controllability of a market situation.
Control Variables
The findings also indicate that individual and organizational characteristics influence interpretation and response. As reported in Table 2, hospital size is significantly related to perceived control, hospital type (profit/nonprofit) is significantly related to opportunity appraisal, and managers' expertise is significantly related to response. Future investigations might further examine the role of these variables, because they appear to affect interpretation.
Test of the Mediating Role of Perceived Control and Appraisal
To test the extent to which perceived control mediates the relationships between antecedents of interpretation and appraisal (H9) and the extent to which appraisal mediates the relationship between perceived control and magnitude of response (H12), we assessed direct (i.e., nonmediated) effects in a two-step process. First, we reviewed the theta matrix to check for potentially significant nonspecified paths. Second, we added those paths to the model and the model rerun to assess significance. The analysis revealed that there are only two significant (p < .05) nonmediated paths: ( 1) between information use and an opportunity appraisal and ( 2) between a clan culture and an opportunity appraisal. The direct path between perceived control and magnitude of response was not significant. Notwithstanding these two exceptions, our results provide reasonably strong support for the mediation effects hypothesized in H9 and H12.
At a managerial level, the insights obtained in this study provide answers to the research questions that served as the foci of the study. Specifically, our key findings suggest that (1 the factors that influence managers' interpretation of a market situation include cognitive styles, perceptions of organizational culture, and extent of information use; ( 2) managers base their appraisal of a market situation as an opportunity or threat on their perceptions of control; and ( 3) the extent to which managers appraise a market situation as an opportunity is positively related to their magnitude of response.
At a theoretical level, the results provide empirical support for a model that delineates the antecedents of and the responses to the interpretation of a marketing situation. Furthermore, our results suggest a path of effect as illustrated in our framework. In developing the model, we address researchers' calls for an extension of cognitive appraisal theory by empirically assessing perceived control as a construct that mediates the relationship between the antecedents of interpretation and appraisal (Folkman 1984; Lazarus 1991; Skinner 1995). In addition, we develop conceptual arguments and provide empirical support for the link between cognitive style and the appraisal process. Consequently, this research contributes to multiple literature streams related to the interpretation of information and its impact on decision making. In addition to general contributions, the results of this study have specific implications for both theory and practice.
Antecedents to Interpretation
Cognitive style. Our findings suggest that researchers investigating individual influences on the interpretation of market information should consider all four dimensions of cognitive style rather than only the sensing-intuiting and thinking-feeling dimensions that are most commonly investigated in the literature. Although organizations may already find measurements of cognitive style useful in selecting managers (Leonard and Straus 1997), our results highlight an important implication for practice. In our study, managers with more extroverted, judging, intuiting, and thinking cognitive styles (compared with those with more introverted, perceiving, sensing, and feeling styles) tend to perceive situations as more controllable. Consequently, they are likely to perceive less risk when interpreting a given market situation, and they are more likely to appraise that situation as an opportunity. In other words, these managers are likely to be proactive and aggressive in their decision making. Conversely, managers with more introverted, perceiving, sensing, and feeling styles are more likely to be cautious and sensitive to threats when evaluating market situations. Because most decisions that are likely to have a major impact on organizations tend to be addressed by a group or team, our results suggest that it may be desirable to sensitize managers as to the ways the cognitive styles of team members influence their information processing and decision making.
Information use. Although we find that managers may use negative information significantly more than positive information and external information significantly more than internal information, the focus on the threatening aspects of a situation does not necessarily result in a negative interpretation.( n5) We find that it is the total amount of information used in a situation rather than the valence of though managers perceptions of control do not add to their information that drives interpretation; managers who use more market information in a given situation perceive that situation as more controllable. These results are consistent with the propositions that increased information use may reduce uncertainty and perceived risk (Dutton and Webster 1988; Milliken 1990). For researchers, these findings suggest that models of the relationship between information use and response need to include perceived control as well as appraisal of a situation as mediators to gain better insights into decision making.
Perceived organizational culture. Researchers have demonstrated a link between organizational culture and business performance (Deshpandé, Farley, and Webster 1993). However, there is a paucity of research pertaining to the role of cultural antecedents in information processing within an organization (see Moorman 1995). We address this gap in our study and find that managers' perceptions of organizational culture influence information processing and the extent to which managers perceive they can control a situation. For example, the results of our study suggest that, compared with marketing managers who perceive their culture as more of a hierarchy or market, marketing managers who perceive their organization's culture as more of an adhocracy or clan are more likely to perceive that they can control the outcomes of an otherwise ambiguous situation. One reason for this may be that managers who perceive themselves in a more formal culture (i.e., hierarchy or market) may also he more prone to perceive that they are unable to control a given situation.
In terms of managerial implications, as with cognitive styles, our findings related to the relationship between organization culture and perceived control of a market situation show that organizations may benefit from sensitizing managers to the ways perceptions of organizational culture may affect information processing and decision making. For researchers, our findings suggest that the influence of organizational culture on the perceptions of individual managers is an important consideration in any quest to better understand the organizational implications of culture.
Interpretation
Our study finds that the relationship between cognitive style and the manager's recommendation is mediated by two stages of interpretation: perceived control and appraisal. The more managers perceive a situation as one in which they can control the outcomes, the more likely they are to appraise that situation as an opportunity. These results are consistent with other findings suggesting similar relationships between perceptions of control and the appraisal of a situation as either an opportunity or a threat (Jackson and Dutton 1988). As with information use, our findings reiterate the importance of incorporating interpretation as a mediating variable when assessing the relationships between cognitive style and decision-making tendencies.
Furthermore, our findings suggest that the extent to which managers perceive they can control a situation mediates the relationship between their information use and their appraisal of a situation. It is important to understand that though managers' perceptions of control do not add to their actual talent or ability, their perceptions may give them greater access to all the resources in their repertoire. That is, greater perceived control may lead to greater realization of extant competence (Skinner 1995).
Magnitude of Response
This study adds to the growing body of evidence that the magnitude of a manager's recommendation is greater for perceived opportunity than for perceived threat. Although most managers in our sample recommended increasing advertising spending in the presence of perceived opportunity (69% recommended more and 8% recommended less), in the face of a perceived threat, managers were less certain whether an increase in advertising expenditures would produce the desired outcome (46% recommended more and 29% recommended less) (test of association χ² = 23.9, p < .01). When faced with making recommendations without the benefit of probabilistic assessments of outcomes, the magnitude of a manager's response may be driven by the cognitive and affective "baggage" associated with the interpretive labels they use. For example, the positive emotional charge associated with the label "opportunity" may foster a greater behavioral response than the stress and anxiety evoked by the label "threat." A recurrent theme in the interpretation literature is that the labels managers attach to situations are powerful determinants of organizational response or lack thereof Magnitude of response also depends on the perceived ability to produce a desired outcome. Thus, another explanation for the results of this study is that managers simply do not perceive increases (or decreases) in advertising expenditures as having equivalent effects in opportunistic and threatening situations.
A notable finding regarding the relationship between appraisal and magnitude of response is that the recommended advertising expenditures were greatest for managers who perceived the market situation as both an opportunity and a threat. Perhaps managers who perceive high levels of both opportunity and threat are more likely to believe that a response of greater magnitude is necessary to ensure a positive outcome.
We conducted this study in a single industry. In a recent study, Prabhu and Stewart (2001) report that context and timing influence the interpretation of market information. Research is needed to investigate whether the findings of this study generalize to other settings. We tested the relationships hypothesized in this study through the use of a hypothetical, though realistic, case scenario. In further research, it might be desirable to use other methods to test these relationships. For example, researchers could observe how managers interpret actual market information. Another methodological improvement would be to replicate this research with a longer case scenario (i.e., a description of a situation containing "richer" information). Such a scenario would also enable researchers to investigate whether relationships involving information use are linear in nature or whether there is a nonlinear relationship associated with information overload.
We posited multiple explanations for our finding that the Mayfield. The past several years, Riverview has spent magnitude of response is greater for perceived opportunity than for perceived threat. We also offered a plausible explanation for why the magnitude of response may be even greater for managers who appraise a market situation as high on both opportunity and threat dimensions. Additional research is needed to examine the validity of these explanations. Our findings also suggest that market situation interpretation mediates the relationship between marketing managers' cognitive style, perceived organizational culture, and information use and their response. However, additional research is needed to gain better insights into the relationship between interpretation and response. For example, a useful extension of our work would be to include estimates of specific, likely outcomes of advertising expenditures and to relate these to the managerial estimates of the importance of these outcomes (in reference to perceived threats and opportunities).
In our study, we measured managers' responses without consideration of the quality of those responses. In addition, the choice of a single scenario limits potential explanatory power. Complementing the dependent variable employed in this research (advertising budget increase/decrease) has the potential to provide further insights. For example, additional dependent variables could measure support for particular advertising themes that are related to the scenario perceptions or relative emphasis on specific advertising themes or directions. It can be argued that increasing advertising of previously ineffective content may have been why those perceiving threats did not support substantially greater budgets. By adding terms for the qualitative (i.e., thematic) elements of advertising decisions, as well as for interactions with dollars budgeted overall, future researchers might be able to increase the explanatory and predictive power of the model we present.
As noted previously, most decisions that are likely to have a major impact on organizations tend to be addressed by a group or team rather than by individual managers. Against this backdrop, another avenue for further research would be to examine whether the effects uncovered in this study change in interactive decision-making settings. For example, it would be worthwhile to compare the interactions and decisions of groups with varying degrees of diversity of cognitive styles.
In summary, in today's knowledge economy, many managers realize that information is as plentiful as sand on the beach. Competitive advantage is more likely to arise from a better understanding of the influences and outcomes of market situation interpretation. Therefore, we hope that the issues addressed in this study contribute to improving marketing managers' ability to analyze and respond to market situations. This research is merely one step in that direction, and we hope it serves as an impetus for further research in the area.
The authors gratefully acknowledge support extended to the first author by the Marketing Science Institute under the auspices of its Alden C. Clayton doctoral dissertation proposal competition. The authors also thank Wynne Chin for providing the partial least squares software, Raj Echambadi for his insightful assistance, and three anonymous JM reviewers for their helpful comments.
(n1) Perceived control of a situation captures both efficacy beliefs (the competence to produce a successful outcome) and strategy beliefs (existence of resources and/or conditions necessary to produce a successful outcome) (Bandura 1989; Skinner 1995). When we asked managers to estimate the extent to which they can control the situation described in the situation scenario, their responses captured both efficacy beliefs and strategy beliefs. Skinner (1995) opines that though conceptually distinct, it may not be possible to disentangle these two beliefs, because efficacy beliefs presuppose the existence of strategy beliefs, and vice versa. In other words, she theorizes that the two beliefs must be combined to understand perceived control. According to Folkman (1984), under conditions of ambiguity, a generalized belief about control (i.e., an efficacy belief) is Functionally equivalent to a perception of controllability with respect to the specific situation (i.e., an outcome-based or strategy belief. Thus, when the environment is ambiguous, generalized control beliefs and situational control beliefs are analogous.
(n2) Although cognitive style has traditionally been analyzed by the creation of categorical combinations of these dimensions, we argue that the use of independent continua rather than a categorical typology permits a more fine-grained theoretical and empirical analysis. Managers may have strong preferences on only one cognitive style dimension (Mutt 1990), and therefore a categorical typology that fails to capture strength of preference could misrepresent how they actually process information (Nutt 1990).
(n3) In this investigation, we asked marketing managers to make an advertising and budget recommendation to establish a familiar decision-making context for their assessment of a market situation. Extant literature provides valuable insights into antecedents of an advertising and promotion budget (e.g., Balasubramanian and Kumar 1990). Our focus here is limited, however, to how a marketing manager's magnitude of response (i.e., make recommendations regarding resources allocated to an advertising and promotion budget) is influenced by his or her interpretation of a market situation.
(n4) Formative indicators can have positive, negative, or no correlation with one another. As a result, observed correlations among these indicators are not meaningful, and traditional assessments of individual item reliability and convergent validity are irrelevant (Hulland 1999).
(n5) We note further that we cannot rule out the possibility that our findings are the result of respondents' perceptions of the relevance of the information to determining opportunities and/or threats, rather than a preference for negative or external information per se. For example, the results could be misleading if the distribution of one positive information item were significantly different from the distribution of the other positive information items. To minimize this possibility, we developed the scenario using an expert panel and then pretested it to ensure that all information items have equivalent expected utility. In addition, an assessment of the means and standard deviation shows that there are no items within the pool of like items (i.e., positive, negative, internal, or external) with a distribution significantly different from any other item within the same pool.
(n6) For all sections, a denotes internal consistency of reflective measures (Fornell and Larcker 1981).
(n7) Numerals in parentheses denote measurement model loadings for retained items of reflective measures. We deleted items with measurement model loadings of less than .50 (shown in italics), as recommended by Hulland (1999). To address possible concerns about the impact of deleting items from an established scale, we also tested the model with all cognitive style scale items included; although the R² of the perceived control construct was reduced (from .172 to .130), all the exogenous constructs remained significant (p < .05).
Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
A B C
D E F G
H I J K
L M
1. Extrovert--introvert cognitive style .692(a)
2. Judging--perceiving cognitive style .068 .662(a)
3. Sensing--intuiting cognitive style -.049 .374
.627(a)
4. Thinking--feeling cognitive style -.062 .317
.261 .624(a)
5. Adhocracy culture .021 .022
.064 .011 --
6. Clan culture .109 .004
.013 .066 .066 --
7. Market culture -.012 .009
.018 .015 -.199 -.198
--
8. Information use .091 .082
-.077 -.010 -.008 .027
-.073 --
9. Perceived control .104 .122
-.148 .120 .129 .113
-.089 .281 .846(a)
10. Opportunity appraisal .127 .020
-.138 .011 .057 .124
-.033 .392 .459 .791(a)
11. Threat appraisal -.065 -.066
.030 -.004 -.026 -.027
.072 -.091 -.391 -.413
.790(a)
12. Magnitude of response .039 -.019
-.122 -.019 -.068 .027
-.048 .146 .132 .232
-.059 --
Notes: (a) Numbers shown in boldface denote the square root of
the average variance extracted (for reflective constructs only). Legend for Chart:
B - Market Situation Interpretation Perceived Control
C - Market Situation Interpretation Appraisal: Opportunity
D - Market Situation Interpretation Magnitude of Response
E - Appraisal: Threat
A B C
D E
Cognitive Style
Extrovert--introvert(a) .062 (2.17)(**)
Judging--perceiving .127 (3.60)(*)
Sensing--intuiting -.202 (-6.33)(*)
Thinking--feeling .130 (3.31)(*)
Organizational Culture
(Relative to Hierarchy)
Adhocracy .095 (2.94)(*)
Clan .082 (2.73)(*)
Market -.035 (-.89)
Information Use
Overall .248 (6.51)(*)
Perceived Control .465 (13.11)(*)
-.398 (-9.77)(*)
Appraisal
Opportunity
.237 (5.24.)(*)
Threat
-.050 (-1.09)
Control Variables
Expertise -.012 (-.27) -.052 (-1.14)
.074 (1.48) -.138 (-2.47)(**)
Size .089 (2.53)(*) -.061 (-1.82)
.082 (1.92) .072 (1.47)
Performance -.018 (-.61) -.014 (-.47)
.042 (.62) -.064 (-1.29)
Hospital type .064 (178) -.089 (-2.18)(**)
-.002 (-.05) -.064 (-1.41)
Construct R² .172 .224
.170 .083
(*) p < .01.
(**) p < 05.
(a) Because these tour dimensions are conceptualized as bipolar
continua, (1) the higher the score on the extrovert--introvert
dimension, the more extroverted is the manager (supporting H[sub
1]), whereas the lower the score, the more introverted is the
manager; (2) the higher the score on the judging--perceiving
dimension, the more judging is the manager (supporting H2),
whereas the lower the score, the more perceiving is the manager;
(3) the higher the score on the sensing--intuiting dimension,
the more sensing is the manager, whereas the lower the score,
the more intuiting is the manager (supporting H3); and (4)
the higher the score on the thinking--feeling dimension, the
more thinking is the manager (supporting H4), whereas the
lower the score, the more feeling is the manager.DIAGRAM: FIGURE 1 Antecedents and Consequences of Market Situation Interpretation Marketing Manager's
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Directions: Imagine that you are the new Vice President of Marketing for Riverview Hospital, and it is your job to make recommendations regarding the annual advertising and promotion budget. With 400 beds and approximately $200 million in annual revenue, Riverview is the third largest hospital in Mayfield. The past several years, Riverview has spent approximately $2 million on advertising and promotion (1% of revenues is about average for hospitals in Mayfield). The organizational culture at Riverview is virtually identical to the culture at your last hospital, and so you have a good sense of the shared values and beliefs in the hospital. However, there have been numerous changes both inside and outside the hospital that suggest a possible need to change the amount budgeted for advertising and promotion. Please read the following situation analysis. Questions that relate to this situation analysis will follow.
The hospital recently completed an extensive renovation, and an article in the local newspaper proclaimed. Riverview now has one of the most attractive facilities in Mayfield. The newly renovated outpatient surgery center is a technological marvel that promises state-of-the-art and yet cost-efficient service." Although it has been suggested that additional advertising might be needed to highlight the new outpatient surgery center, the hospital's chief financial officer has expressed concern that with approximately 90% of outpatient care under managed contracts, it might be unwise to focus more attention on a medical service that stands to gain so little from an increase in noncontract customers.
The hospital's marketing programs were assessed in a recent report prepared by a local marketing firm. The report indicates that although Riverview has spent more money on image rather than medical services advertising in the past, fewer than 30% of area residents have a clear image of Riverview. On the other hand, the hospital's chief executive officer has told you that the hospital has had success in the past offering special seminars (e.g., Wellness Week, Seniors' Day) to increase awareness of targeted health care issues.
The health care market in Mayfield is fiercely competitive and market research indicates that most of the five other local hospitals have been more successful in acquiring exclusive managed care contracts. Further, the managing director of a local preferred provider organization (PPO) has told you that a group of once-loyal physicians may be creating their own physician-sponsored PPO, which could take away contracts and duplicate some of the services in which Riverview Hospital currently has a strong market share. On the other hand, Riverview's Director of Managed Care has privately informed you that the hospital has completed a new contract to provide exclusive service to one of Mayfield's largest health maintenance organizations.
Finally, federal census reports indicate that the population in the ten-county area surrounding Riverview Hospital has been growing steadily for the past live years at 4% per year, and that growth rate is expected to continue.
Cognitive Style
Choose between answer a or b and place a check mark (√) next to your choice.
Extroverted-introverted (α = .82)( n6)
At a party, do you (a) interact with many, including strangers or (b) interact with a few, known to you? (.772)( n7)
At parties, do you (a) stay late, with increasing energy or (b) leave early, with decreasing energy? (.748)
In your social groups do you (a) keep abreast of others' happenings or (b) get behind on the news?
In phoning do you (a) just start talking or (b) rehearse what you'll say?
In company do you (a) start conversations or (10 wait to be approached? (.619)
Does new interaction with others (a) stimulate and energize you or (b) tax your reserves? (.625)
Do you prefer (a) many friends with brief contact or (b) a few friends with longer contact?
Do you (a) speak easily and at length to strangers or (b) find little to say to strangers? (.683)
When the phone rings do you (a) hasten to get to it first or (b) hope someone else will answer it?
Are you more inclined to be (a) easy to approach or (b) somewhat reserved?
Judging-Perceiving (α = .82)
Do you prefer to work (a) to deadlines or (b) just "whenever"?
Do you tend to choose (a) rather carefully or (b) somewhat impulsively?
Are you usually more (a) punctual or (b) leisurely?
Does it bother you more having things (a) incomplete or (b) complete?
Do you usually (a) settle things or (14 keep options open?
Are you usually rather (a) quick to agree to a time or (14 reluctant to agree to a time?
Are you more comfortable (a) setting a schedule or (b) putting things off? (.641)
Are you more comfortable with (a) written agreements or (b) handshake agreements?
Are you more prone to keep things (a) well organized or (b) open ended? (.624)
Do you put more value on the (a) definite or (14 variable?
Are you more comfortable with work (a) contracted or (b) done on a casual basis? (.705)
Do you prefer things to be (a) neat and orderly or (b) optional? (.631)
Are you more comfortable with (a)final statements or (b) tentative statements?
Are you more comfortable (a) after a decision or (b) before a decision?
Is it preferable mostly to (a) make sure things are arranged or (b) just let things happen? (.606)
Is it your way more to (a) get things settled or (b) put off settlement?
Are you more prone to (a) schedule events or (b) take things as they come? (.755)
Are you a person that is more (a) routinized or (b) whimsical?
Is it more like you to (a) make snap judgments or (b) delay making judgements?
Do you tend to be more (a) deliberate than spontaneous or (b) spontaneous than deliberate?
Sensing-Intuiting (alpha; = .86)
Are you more (a) realistic or (b) philosophically inclined?
Are you more intrigued by (a) facts or (b) similes?
Are you a more (a) sensible person or (b) reflective person? (.521)
Are you more drawn to (a) hard data or (b) abstruse ideas?
Are you usually more interested in (a) specifics or (b) concepts? (.678)
Do you prefer writers who (a) say what they mean or (b) use lots of analogies?
Facts (a) speak for themselves or (b) usually require interpretation?
Do you prefer to work with (a) practical information or (b) abstract ideas? (.704)
Traditional common sense is (a) usually trustworthy or (b) often misleading?
Children often do not (a) make themselves useful enough or (b) daydream enough?
Are you more frequently (a) a practical sort of person or (b) an abstract sort of person? (.673)
Which are you drawn to: (a) accurate perception or (b) concept formation? (.706)
Are you more drawn to (a) substantial information or (b) credible assumptions?
Are you more interested in (a) production or (b) research?
Are you usually more interested in the (a) particular instance or (b) general case?
Do you feel (a) more practical than ingenious or (b) more ingenious than practical? (.686)
Do you prize more in yourself a (a) good sense of reality or (b) good imagination? (.637)
Are you more drawn to (a) fundamentals or (b) overtones? (.562)
Do you have more fun with (a) hands-on experience or (b) blue-sky fantasy? (.525)
In writings do you prefer (a) the more literal or (b) the more figurative? (.535)
Thinking-Feeling (α = .91)
Are you usually more (a) fair minded or (b) kind hearted? (.552)
Do you tend to be more (a) dispassionate or (b) sympathetic? (.6 20)
Is it more natural for you to be (a) fair to others or (b) nice to others? (.577)
In first approaching others are you more (a) impersonal and detached or (b) personal and engaging?
Are you more naturally (a) impartial or (b) compassionate? (.635)
In judging are you more likely to be (a) impersonal or (b) sentimental? (.669)
Are you inclined to be more (a) cool headed or (b) warm hearted? (.650)
Would you rather be (a) more just than merciful or (b) more merciful than just?
Are you usually more (a) tough minded or (b) tender hearted? (.620)
Are you more (a) firm than gentle or (b) gentle than firm? (.582)
Which is more satisfying (a) to discuss an issue thoroughly or (b) to arrive at agreement on an issue?
Which rules you more: (a) your head or (b) your heart? (.734) Are you more comfortable when you are (a) objective or (b) personal? (.627)
Do you value in yourself more that you are (a) unwavering or (b) devoted?
Are you typically more a person of (a) clear reason or (b) strong feeling? (.609)
Are you inclined more to be (a) fair minded or (h) sympathetic? (.669)
In judging are you usually more (a) neutral or (b) charitable?
Do you consider yourself more (a) clear headed or (b) good willed? (.690)
Are you usually more (a) unbiased or (b) compassionate? (.707)
Are you typically more (a)just than lenient or (b) lenient than just? (.500)
Perceived Organizational Culture
Distribute 100 points among the four descriptions depending on how similar the description is to your hospital. Adhocracy
My hospital is a very dynamic and entrepreneurial place. People are wiling to stick their necks out and take risks. The head of my hospital is generally considered an entrepreneur, an innovator, Or a risk taker.
The glue that holds my hospital together is a commitment to innovation and development. There is an emphasis on being first.
My hospital emphasizes growth and acquiring new resources. Readiness to meet new challenges is important.
Clan
My hospital is a very personal place. It is like an extended family. People seem to share a lot of themselves.
The head of my hospital is generally considered a mentor, sage, or a father or mother figure.
The glue that holds my hospital together is loyalty and tradition. Commitment to this hospital runs high.
My hospital emphasizes human resources. Nigh cohesion and morale in the hospital are important.
Hierarchy
My hospital is a very formalized and structured place. Established procedures generally govern what people do.
The head of my hospital is generally considered a coordinator, an organizer, or an administrator.
The glue that holds my hospital together is formal rules and policies. Maintaining a smooth-running institution is important here.
My hospital emphasizes permanence and stability. Efficient, smooth operations are important.
Market
My hospital is very production oriented. A major concern is with getting the job done, without much personal involvement.
The head of my hospital is generally considered a producer, a technician, Or a hard driver.
The glue that holds my hospital together is the emphasis on tasks and goal accomplishment. A production-orientation is commonly shared.
My hospital emphasizes competitive actions and achievement. Measurable goals are important.
Information Use
What is the extent (1 = "Small Extent" to 5 = "Great Extent) to which you would use each of the following pieces of information to help you make a budget recommendation? The notations "E" (external), "I" (internal), "+" (positive information), and "(-)" negative information shown in parentheses for items 1 to 8 below, were not on the questionnaire.
1. According to the newspaper, Riverview Hospital has one of the most attractive facilities in Mayfield. (I, +)
- 2. The chief financial officer has expressed concern about focusing more attention on outpatient surgery, because 90% of outpatient care is under managed contracts. (I, -)
- 3. Market research reports that fewer than 30% of residents have a clear image of Riverview Hospital. (I, -)
- 4. The chief executive officer has told you that the hospital has had success offering special seminars. (I, +)
- 5. Market research reports that other area hospitals have been more successful in acquiring exclusive managed care contracts. (E. -)
- 6. The managing director of a local PPO contact tells you that a group of physicians may be creating their own PPO. (E, -)
- 7. The Director of Managed Care informs you that Riverview has completed a new exclusive contract with a large HMO. (E, +)
- 8. According to federal census reports, the area population is growing steadily. (E. +)
Perceived Control (α = .83)
If you were the new Vice President of Marketing for Riverview Hospital, to what extent (1 = "Small Extent" to 5 = "Great Extent) would you:
Feel you can manage the situation rather than it manage you? (.898)
Feel that the outcome of your budget decision will be a matter of chance? (reverse coded) (.789)
Appraisal
If you were the new Vice President of Marketing for Riverview Hospital, to what extent (1 = "Small Extent" to 5 = "Great Extent) would you: Opportunity (α = .83)
Describe the situation overall as an opportunity? (.944) Label the situation as something positive? (.635) Feel the future looks promising for Riverview Hospital? (.750)
Threat (α = .83)
Describe the situation overall as a threat? (.618) See the situation as having negative implications for the future? (.708)
Label the situation as something negative? (.995)
Magnitude of Response
1. Change: Using the scale provided, please circle the number that is the best indicator of how you would recommend changing Riverview's advertising and promotion budget next year. (1 = "Substantial Decrease" and 9 = "Substantial Increase")
2. Budget: Recall that for the past several years, Riverview Hospital has spent approximately $2 million (1% of revenues) on advertising and promotion. Please provide a specific estimate of the amount of money you would recommend for next year's budget for advertising and promotion. Please note that this figure should include all promotion expenditures including print and broadcast advertising, brochures, and promotional seminars.
~~~~~~~~
By J. Chris White; P. Rajan Varadarajan and Peter A. Dacin
J. Chris White is Assistant Professor of Marketing, Department of Marketing and Supply Chain Management, Michigan State University.
P Rajan Varadarajan is Distinguished Professor of Marketing and holder of the Ford Chair in Marketing and E-Commerce, Texas A&M University.
Peter A. Dacin is Professor of Marketing, School of Business, Queen's University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 97- Marketing and the Bottom Line: Creating the Measures of Success, 2d ed. (Book). By: Clark, Terry; Clark, Bruce. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p167-171. 5p. DOI: 10.1509/jmkg.68.1.166.24033a.
- Database:
- Business Source Complete
Section: Book ReviewsMarketing and the Bottom Line: Creating the Mea-sures
of Success, 2d ed. (Book)
Understanding and Justifying Our Existence
Marketing and the Bottom Line: Creating the Measures
of Success, 2d ed.
by Tim Ambler (London: Financial Times/Prentice
Hall, 2003, 336 pp., £21.99)(n2)
"Accountability" is the new (old) buzzword in marketing today. Managers report intense pressure to justify the worth of marketing activities in a flagging economy. For the third consecutive cycle, the Marketing Science Institute's top research priorities include marketing metrics and marketing productivity as key topics (Marketing Science Institute 1998, 2000, 2002). Other institutions such as the Marketing Leadership Council in Washington, D.C., and the Chartered Institute of Marketing in the United Kingdom are urgently researching how to best measure and report the impact of marketing, usually in financial terms.
Sporadic attention has been paid to measuring marketing performance for decades. Economic research on the productivity of marketing in the overall U.S. economy dates back to the 1920s (for a review, see Bonoma and Clark 1988). In the 1960s and 1970s, the first financial perspectives were brought to firm-level activity. Books by Sevin (1965) and Goodman (1970, 1972) advised managers on how to derive the profit effect of marketing from an accounting perspective, and more academic writing tried to bridge the marketing-accounting and marketing-finance interfaces (Shapiro and Kirpalani 1984). When marketing researchers used performance as a dependent variable at all, they tended to focus on sales and market share as the key results.
Sometime during the 1980s, nonfinancial perspectives began taking hold in both academic and management imaginations, beginning with both rigorous research and management success stories in the area of customer satisfaction. From the late 1980s onward, new concepts in performance measurement ran riot. Customer satisfaction, customer loyalty, and customer value management competed for attention as key intermediate performance figures that would ultimately lead to profits. At the same time, brand-measurement research burgeoned and drew attention to other marketing assets as well. Finally, the apparently paradigm-shifting impact of electronic commerce whipped measurement schemes into a frenzy of data, measures, and concepts.
Unprotected by tenure, marketing managers' imaginations, if not their budgets, are being circumscribed. Although the new measurement concepts of the 1990s have not disappeared, a faltering economy has driven managers into survival mode. To paraphrase George Orwell, all measures are equal, but some measures are more equal than others, and financial survival drives an emphasis on financial measures.
The marketing budget has traditionally been considered a possible source of spare change, but there are two reasons to believe that the cries of anguish emanating from marketing managers are worse this time. First, as Sheth and Sisodia (1995) observe in a prescient article, previous recessions and new management practices have led to dramatic rationalization of manufacturing and administrative costs, meaning that the last big item on the income statement to squeeze is the marketing expense. Second, the explosive growth of data and the market-research firms that provide them has changed the problem from a lack of data on marketing performance to the interpretation of vast quantities of data that just might provide a true understanding of the marketing-business performance link.
Into this miasma of fear and loathing steps Tim Ambler, Senior Fellow at the London Business School and former Joint Managing Director at International Distillers and Vintners, which is now a unit of Diageo. The second edition of Ambler's Marketing and the Bottom Line is an ambitious effort to "provide a complete guide to making marketing fully accountable" (p. 3), especially to senior management. The book examines a broad variety of potential measures, how to report marketing performance effectively to key audiences, and how to use measures to manage the marketing efforts of the organization. Although the book is not perfect, it is far and away the best book for a senior manager who is interested in understanding marketing's impact on his or her organization.
Marketing and the Bottom Line 's thesis can be boiled down to five themes: First, top management, not just top marketing management, has a fundamental obligation to measure the performance of marketing activities. Ambler argues that understanding the future financial wealth of an organization requires understanding from where cash will come. The primary provider of future cash flow is customers, so it is necessary to understand exactly how and why customers give organizations their money. Allowing marketing to remain a black box is unacceptable both in terms of accountability to senior management and shareholders and in terms of organizational learning and growth. This book, Ambler notes (p. 3), "is not a paean of praise for marketing." Indeed, the book seems aimed at the board of directors and chief executive officer more than midlevel marketing managers (marketing executives may resist measurement precisely because understanding the effects of their budget can undermine justification of their budget), but marketing executives will still find the book useful in considering what they need to understand and how to report marketing upward in the organization.
Second, financial measurement is not enough. Although understanding the financial impact of marketing is important, it is insufficient on two grounds. First, there is the problem that many income statement measures (e.g., sales, profit) are fundamentally backward looking. The income statement perceived in time t is the product of myriad marketing and other activities that stretch back years and sometimes decades and often reveals surprisingly little about what the income statement in time t + 1 will look like, as recent accounting scandals attest.
Ambler argues that many of the popular financial methods to guide marketing (return on investment, shareholder value analysis, customer lifetime value) are seriously flawed in their application. For example, disagreements among the valuation methods are often pedantic, in that they become debates about how to label cash flow within an organization. He points out that valuation methods for different equities usually rely heavily on discounted cash flows. For example, brand equity can be valued as the incremental cash flows a company would earn beyond those it would earn from an unbranded product. Customer lifetime value similarly examines the total cash flow from a particular customer (or, more frequently, a customer segment). Market capitalization has long been considered a projection of overall future cash flows to the firm. An organization's cash flow can be broken up on the basis of how much each brand brings in or how much each customer group brings in. Either way, it is the same cash.
Third, marketing performance is equal to the outcome of an organization's marketing activities in a given period, adjusted for change in the organization's underlying "marketing assets." Marketing activities have short-and long-term effects. Scholars and practitioners have argued that the assets marketing creates for the corporation (e.g., a strong brand) are too often ignored in the measurement of marketing performance (see Srivastava, Shervani, and Fahey 1998). For example, a promotion might increase sales in the current period but have damaging effects in the long run if it erodes brand equity. Conceptually, Ambler's means of dealing with the long/short distinction is to adjust short-term results for long-term effects on the assets that marketing has created for the organization. This is best done by using a blend of financial and nonfinancial and intermediate and final measures, taken over time and compared to organizational goals. Practically, this does not appear to be so much a quantitative adjustment (e.g., take profit and reduce by 10% because brand equity was damaged) as is the entirely appropriate analogy from financial analysis that changes in both the income statement (short-term activities) and the balance sheet (long-term capabilities) can be used to assess the health of the organization.
Fourth, strategically, the marketing asset represents the reservoir of future customer-based cash flow that has been built up by the firm's previous customer-related activities. Ambler construes the marketing asset broadly: It arises from multiple audiences (e.g., consumer, trade customer, employee, journalist, shareholder, supplier) and thus must be measured in multiple ways. A definition of total (marketing) equity, he avers (p. 42), would be "the sum of the equities in each stakeholder segment." He devotes chapters to brand equity, employee satisfaction, innovation, and investor relations as underlying components of the marketing asset. Given that there are vast research bases in each of these areas, experts in each might disagree with some of Ambler's arguments, but the overall approach is quite practical and managerial.
Fifth, measurement is equal to strategy is equal to management. Ambler effectively turns the aphorism "you can't manage what you can't measure" on its head, making a compelling case that whatever an organization is currently measuring is its strategy and correspondingly its management implementation. Measurement indicates and, in an emergent sense, drives the organization's strategy and implementation. In this sense, tightly controlled measurement is problematic. Ambler argues that tying executive bonuses to measurement is an invitation to counterproductive gaming. Close benchmarking of competitive measurement systems guarantees measurement uniformity in an industry and thus strategic uniformity.
I suspect that practicing managers will ignore this particular theme. The temptation to tie compensation to numbers is too well ingrained to resist in many organizations, and managers seem inordinately fascinated with the measurement schemes that other companies use. In the latter sense, managers will appreciate Marketing and the Bottom Line because it provides so many concrete examples of good measures and good measurement from both individual companies and benchmarking surveys.
The source material for Marketing and the Bottom Line is a broad swath of academic and benchmarking studies of marketing performance measurement. The first edition of the book was based heavily on the Marketing Metrics project in the United Kingdom, a 30-month research effort cosponsored by several marketing-practitioner institutions and the London Business School. The second edition broadens this base significantly, drawing on two major studies by the U.S.-based Marketing Leadership Council, Jean-Claude Larreché's Global Competitiveness project at INSEAD, and a host of more specialized studies from various academics and practitioners. Ambler also has conducted dozens of interviews with U.S. and U.K. executives to pin down specific examples of practice. Indeed, a drawback of the book is that it is sometimes hard to tell the exact scope of the underlying research: An appendix with a paragraph or two on each of the key research projects would have clarified the knowledge base significantly. The book still seems somewhat focused on the United Kingdom, partly because of the preponderance of U.K. firms among Ambler's examples. However, the range of U.S. and European examples (there is relatively little coverage of Asia) should not trouble executives in an increasingly global world.
In terms of measures, Ambler's treatment of the marketing asset is the most innovative part of the book. The breadth of his conception is both a strength and a weakness. While Amber discusses brand equity, he alludes to the tale of blind men each trying to identify an elephant by feeling one part of the animal. Strategically, it is clear that the sum of customer-related activities in an organization is as big as an elephant. Senior management will appreciate this book precisely because it steps aggressively across organizational boundaries: Marketing performance is not a marketing department issue but an issue that cuts across all customer-related parts of the organization.
Unfortunately, because the definition of the marketing asset is so broad, the reader may occasionally be hard-pressed to understand where the elephant ends. If the asset encompasses a half-dozen diverse stakeholders, their concerns, and all the activities that affect them, what part of the organization is not the marketing asset? The case is not helped by some idiosyncratic definitions of terms. When readers have become accustomed to the definitions, there is no problem, but the initial reading can be jarring. For example, brand equity is not specifically tied to a brand name but is defined (p. 34) as "any market-based asset, be it reputation, goodwill or customer satisfaction ... even though the term 'brand' is not generally used by some sectors."
To be fair, Ambler claims that "brand equity" is the term companies most frequently used for the asset in his research, and he is also scrupulous in his examples to identify what each company calls the asset. Other definitions, though, are either more broad or narrow than a marketing reader might expect. Innovation refers (p. 132) to "management inspired changes that alter the firm's position in the market ... [including] introducing new brands and products, finding new customer segments for existing products ... or new ways of selling, [and] servicing or using the brand." The marketing mix is defined (p. 193) as "all expenditures intended to strengthen brand equity" and explicitly excludes spending on promotions and discounts under the assumption that these do not build brand equity.
Leaving aside the specific measures that senior management might use, an important strength of the book is its treatment of the management issues regarding marketing metrics. Ambler describes a pattern of metrics evolution that is intriguing in its implications. On the basis of his research, he posits five stages of evolution (pp. 80-81): ( 1) Top management of the company is unaware of the issue of assessing marketing performance; ( 2) on becoming aware, management considers assessing marketing in terms of financial evaluation; ( 3) in response to the inadequacy of financial measurement alone, a multitude of nonfinancial measures are added to the financial ones; ( 4) the resulting company confusion leads to a streamlined set of financial and nonfinancial metrics that gives a coherent view of the market; and ( 5) mathematical modeling of a measure database provides a short list of predictive marketing metrics.
The evolution appears to suggest a U-shaped relationship between the measurement stage and satisfaction with measures. Because the company is blissfully ignorant in Stage 1, it is relatively satisfied. Early attempts to value marketing financially lead to increasing frustration, and the many-measures stage probably represents a company nadir before satisfaction climbs again across the final two stages. Ambler further suggests that not only is it difficult to shortcut this evolution, but it may not be advisable to try because it will limit the organization's ability to learn. Furthermore, depending on data availability and industry sector, it may not be feasible for all organizations to progress to the fifth stage.
Ambler provides voluminous advice on how to manage marketing metrics regardless of stage. The book provides several sets of useful diagnostic questions to help managers understand their current situation and metrics needs. Ambler dives into the details of managing measurement, is definite in his opinions, and is often quite witty in expressing them. The book is full of provocative metaphors and vivid writing. (I cannot remember the last time I laughed out loud, in a good way, while reading a marketing book.)
Ambler clearly distinguishes the measurement problems of small and medium-sized enterprises as compared with large ones, though the book is definitely slanted toward large firms. He devotes an entire chapter to the tension between developing and applying a single set of global metrics and creating strategy-specific measurement sets for different units, as in strategy maps (Kaplan and Norton 2000). I began this chapter firmly in the strategy-specific camp, and Ambler himself admires the tailored approach, but by chapter's end I found myself seduced by the logic underlying his general set of nine basic marketing metrics. He provides enough detail, including useful diagnostic questions, to help the thoughtful manager pursue either approach.
Ambler also diligently tackles the thorny problem of how to measure in multidivisional, multinational organizations, suggesting separate reporting for the key brand-market units (one brand in one market) that provide the bulk of an organization's shareholder value while relegating the remaining units to aggregate figures. The book is similarly strong in discussing internal and external reporting issues.
Ancient Greeks told the tale of Sisyphus, who was eternally condemned in the afterlife to roll a boulder to the top of a hill, from where it would tumble back to the bottom only to be pushed up again. Marketing performance measurement sometimes seems like just such futile labor. It is difficult to identify the performance impact of any single marketing activity, much less marketing as a whole. Given a sufficient sample size, statistical significance can be generated, but the R2 or practical significance of a given variable is often low. The teasing out of causality among the many factors that might affect overall performance is particularly challenging even with a large sample size. Whether this is because of myriad interactions or because, ahem, no causal path exists between marketing and overall business performance is difficult to discern. By the time researchers figure out what has happened, the market may have changed.
The practical realities of organizational life also mitigate against strong measurement. Given the "fire fighting," "solve-the-latest-crisis" mode that absorbs so much of management time, it will be difficult to devote attention to developing a measurement system when that distracts from tasks such as selling, advertising, and product development. This is probably especially true in the current economic environment, which ironically is simultaneously creating greater pressure to justify marketing activities. In discussing innovation, Ambler notes the paradox that whereas every company needs new initiatives, most managers are buried in what he calls "initiative overload," which leads to lower morale and probably poorer marketing. A marketing metrics system will be one more big, ugly initiative on the organization's plate.
Career incentives to do this measurement spadework are probably low. The costs in time and money are immediate and concrete, whereas the benefits are distant and diffuse. Ambler notes that even if senior marketing managers wanted to develop a strong metrics system, the time it might take probably exceeds their likely tenure in their positions. Furthermore, performing companywide measurement well requires gathering often incompatible data from the four corners of the organization, which the chief marketing officer may not have the power to achieve. Ambler suggests that a better course may be having the chief financial officer in charge of marketing metrics, since he or she is already accustomed to collecting and aggregating data from across the organization. Although marketing managers may pale at this suggestion, it does have the virtue of moving the costs of measurement to the chief financial officer's budget. Ambler also believes that it may give marketing metrics higher credibility, given the profession's reputation for being selective or manipulative with information. At the least, a serious metrics system will need to be developed in consultation with the finance function to ensure comparability of units across financial and nonfinancial measures.
In 1991, the programmer Phil Zimmerman released an encryption software product called "Pretty Good Privacy." Basic in its functionality, the program would not stop dedicated snoopers, but it provided sufficient privacy to stop most attempts to read data in transmission, enough so that the U.S. government tried to prevent Zimmerman from exporting the product. Ultimately, "pretty good measurement" is probably the best that can be done to understand marketing performance. Even if perfect measurement is possible, by the time it is achieved, the strategy will have changed.
Ambler adopts a similar perspective in describing what he calls "the fuzzy future" of marketing metrics. Metrics can be considered as representing a mechanism for control (everything will occur as planned or it will be known why it did not) or direction (where is the firm and where should it be going?). Ambler suggests (p. 237) that "fuzzy future" means that metrics should be used for "broad positioning rather than precision and for illumination rather than control." Rather than worry about precisely identifying the level of an organization's health, a firm should be able to sense whether it is getting sick or better. Perfect alignment between strategy and measurement is not only infeasible but inadvisable as well, because it will cause the company to ossify rather than experiment and change.
Ambler notes that in his research, some respondents found "fuzziness" in measurement uncomfortable, desiring the perfect alignment that an information technology-driven Balanced Scorecard dashboard seemed to promise Kaplan and Norton (1996), but others embraced a freer future. He suggests that a balance between alignment and flexibility is the answer. I agree.
I recommended the first edition of this book to advanced MBAs and executives and will happily do the same with the second. It is most suited for individual reading by senior managers at larger organizations, but it could be used as a text for a performance measurement seminar. Although the book is occasionally idiosyncratic in approach, it has a strategic sweep and insight that will help any senior manager interested in measurement think through the relevant issues. The many practical examples, measures, and implementation tips will maximize the likelihood of any measurement effort being a success. In summary, although it is not perfect, this book is definitely "pretty good," and it is the best general book available on the topic.
(n2) This edition of Marketing and the Bottom Line was released in the United Kingdom in May 2003.
REFERENCES Bonoma, Thomas V. and Bruce H. Clark (1988), Marketing Performance Assessment. Boston: Harvard Business School Press.
Goodman, Sam R. (1970), Techniques of Profitability Analysis. New York: Wiley-Interscience.
------ (1972), The Marketing Controller. New York: AMR International.
Kaplan, Robert S. and David P. Norton (1996), The Balanced Scorecard: Translating Strategy into Action. Boston: Harvard Business School Press.
------ and ------ (2000), "Having Trouble with Your Strategy? Then Map It," Harvard Business Review, 78 (5), 3-11.
Marketing Science Institute (1998), 1998-2000 Research Priorities: A Guide to MSI Research Programs and Procedures. Cambridge, MA: Marketing Science Institute.
------ (2000), 2000-2002 Research Priorities: A Guide to MSI Research Programs and Procedures. Cambridge, MA: Marketing Science Institute.
------ (2002), 2002-2004 Research Priorities: A Guide to MSI Research Programs and Procedures. Cambridge, MA: Marketing Science Institute.
Sevin, Charles H. (1965), Marketing Productivity Analysis. New York: McGraw-Hill.
Shapiro, Stanley J. and V.H. Kirpalani (1984), Marketing Effectiveness: Insights from Accounting and Finance. Boston: Allyn and Bacon.
Sheth, Jagdish N. and Raj S. Sisodia (1995), "Feeling the Heat," Marketing Management, 4 (2), 8-23.
Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1998), "Market-Based Assets and Shareholder Value: A Framework for Analysis," Journal of Marketing, 62 (January), 2-18.
~~~~~~~~
By Bruce Clark, Northeastern University and Terry Clark, Editor, Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 98- Marketing Literature Review. By: LEONARD, MYRON. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p116-128. 13p. DOI: 10.1509/jmkg.67.1.116.18593.
- Database:
- Business Source Complete
Marketing Literature Review
This section is based on a selection of article abstracts from a comprehensive business literature database. Marketing-related abstracts from more than 125 journals (both academic and trade) are reviewed by JM staff. Descriptors for each entry are assigned by M staff. Each issue of this section represents three months of entries into the database.
Each entry has an identifying number Cross-references appear immediately under each subject heading.
1. THE MARKETING ENVIRONMENT
- 1.1 Consumer Behavior
- 1.2 Legal, Political, and Economic Issues
- 1.3 Ethics and Social Responsibility
- 2. MARKETING FUNCTIONS
- 2.1 Management, Planning, and Strategy
- 2.2 Retailing
- 2.3 Channels of Distribution
- 2.4 Electronic Marketing
- 2.5 Physical Distribution
- 2.6 Pricing
- 2.7 Product
- 2.8 Sales Promotion
- 2.9 Advertising
- 2.10 Personal Selling
- 2.11 Sales Management
- 3. SPECIAL MARKETING APPLICATIONS
- 3.1 Industrial
- 3.2 Nonprofit, Political, and Social Causes
- 3.3 International and Comparative
- 3.4 Services
- 4. MARKETING RESEARCH
- 4.1 Theory and Philosophy of Science
- 4.2 Research Methodology
- 4.3 Information Technology
- 5. OTHER TOPICS
- 5.1 Educational and Professional Issues
- 5.2 General Marketing
1.1 Consumer Behavior
See also 65, 78, 79, 89, 93, 101, 103, 104, 108, 110, 111, 113, 120, 123, 125, 127, 129, 130, 145, 153, 160, 164, 175, 188, 191, 195
Beyond First Impressions: The Effects of Repeated Exposure on Consumer Liking of Visually Complex and Simple Product Designs. Dena Cox and Anthony D. Cox, Journal of the Academy of Marketing Science, 30 (Spring 2002), pp. 119-30. [Literature review; Hypotheses; Experiment; Preferences for visually complex product designs tend to increase with repeated exposure, while preferences for visually simple product designs tend to decrease with repeated exposure; Assessment; Implications.] 1
Customer Switching Behavior in Online Services: An Exploratory Study of the Role of Selected Attitudinal, Behavioral, and Demographic Factors. Susan M. Keaveney and Madhavan Parthasarathy, Journal of the Academy of Marketing Science, 29 (Fall 2001), pp. 374-90. [Literature review. Hypotheses, Two field studies. Impacts, Sources of influence. Service usage. Risk talcing behavior. Satisfaction, Interest/involvement, Household income. Education, Statistical analysis. Managerial implications.] 2
Linking Thoughts to Feelings: Investigating Cognitive Appraisals and Consumption Emotions in a Mixed-Emotions Context. Julie A. Ruth, Frederic F. Brunei, and Cele C. Otnes, Journal of the Academy of Marketing Science, 30 (Winter 2002), pp. 44—58. [Literature review. Research questions. Two studies. Systematic relationship between cognitive appraisals and ten consumption emotions. Statistical analysis. Theoretical and practical insights.] 3
The Effect of Customers' Emotional Responses to Service Failures on Their Recovery Effort Evaluations and Satisfaction Judgments. Amy K. Smith and Ruth N. Bolton, Journal of the Academy of Marketing Science, 30 (Winter 2002), pp. 5-23. [Literature review. Conceptual model. Hypotheses, Consumer survey and experiment. Impacts, Disconfirmation, Expectations, Justice (procedural, distributive, interactional). Magnitude, Initiation, Apology, Speed, Compensation, Statistical analysis. Managerial implications.] 4
The Moderating Effects of Country of Assembly, Country of Parts, and Country of Design on Hybrid Product Evaluations. Paul Chao, Journal of Advertising, 30 (Winter 2001), pp. 67-81. [Literature review, Congruity principle. Attitude and behavioral intention. Hypotheses, Experiment, television and stereo component system. Statistical analysis. Implications, United States, Mexico.] 5
Interpreting Consumer Perceptions of Advertising: An Application of the Zaltman Metaphor Elicitation Technique. Robin A. Coulter, Gerald Zaltman, and Keith S. Coulter, Journal of Advertising, 30 (Winter 2001), pp. 1-21. [Literature review; Deep and conceptual metaphors; Thematic categories; Cross-case analysis.
Advertising has positive value, in that it relates information and provides entertainment; Liabilities; Levels of appreciation; Examples.] 6
The "Risky Business" of Binge Drinking Among College Students: Using Risk Models for PSAs and Anti-Drinking Campaigns. Joyce M. Wolburg, Journal of Advertising, 30 (Winter 2001), pp. 23-39. [Literature review. Research questions. In-depth interviews. Essays, Focus groups. Impacts, Perceived risks and their severity. Vulnerability, Response efficacy. Self-efficacy, Ritual influence. Assessment, Recommendations.] 7
Planned or Impulse Purchases? How to Create Effective Infomercials. Tom Agee and Brett A.S. Martin, Journal of Advertising Research, 41 (November/December 2001), pp. 35-42. [Literature review. Consumer survey. Impacts, Comments by experts. Demonstrations, Levels of previous product interest, Prepurchase thinking about product. Prior exposure to advertisement. Children ages 10-14, Statistical analysis. New Zealand.] 8
What Products Can Be Successfully Promoted and Sold via the Internet? Hyokjin Kwak, Richard J. Fox, and George M. Zinkhan, Journal of Advertising Research, 42 (January/February 2002), pp. 23-38. [Discussion, Hypotheses, Survey of Internet users. Impacts, Attitudes, Internet experiences. Demographics, Personality variables. Perceptual map. Regression and correspondence analysis. Managerial implications.] 9
Journal of Business Research, 54 (November 2001), pp. 89-Í84. [Thirteen articles on retail consumer decision processes. Model of schema typicality. Impacts of deviations, Emotional response and shopping satisfaction. Background music pleasure. Salespeople's adaptive selling. Coupon feature manipulations. Control and affect. Store atmospherics. Online retailing. Shopping at less convenient stores. Service quality attributes at sporting events. Store image processing using perceived risk. Children's influence on family decision making.] 10
A Study of Life Events and Changes in Patronage Preferences. Euehun Lee, George P. Moschis, and Anil Mathur, Journal of Business Research, 54 (October 2001), pp. 25-38. [Literature review. Model presentation. Hypotheses, Survey of households. Retail customers. Attitudes, Experience and anticipation of life events or role transitions. Stress, Changes in consumption related lifestyles and patronage preferences. Statistical analysis. Implications.] 11
Automatic Construction and Use of Contextual Information for Product and Price Evaluations. Rashmi Adaval and Kent B. Monroe, Journal of Consumer Research, 28 (March 2002), pp. 572-88. [Literature review. Hypothetical effects of contextual prices on subjective estimation of cost. Hypotheses, Five experiments. Automatic versus deliberative processes. Response language effects. Effects of previously formed product judgments on later ones. Assessment.] 12
Toward a Theory of Consumer Choice as Sociohistorically Shaped Practical Experience: The Fits-Like-a-Glove (FLAG) Framework. Douglas E. Allen, Journal of Consumer Research, 28 (March 2002), pp. 515-32. [Literature review; Field study; Comparison of rational, constructive, and FLAG choice frameworks; Situ context; Social and historical shaping; Assessment; Implications.] 13
Yielding to Temptation: Self-Control Failure, Impulsive Purchasing, and Consumer Behavior. Roy F. Baumeister, Journal of Consumer Research, 28 (March 2002), pp. 670-76. [Discussion, Irresistible and resistible impulses. Standards, Monitoring, Operational capacity to alter one's behavior. Self-control as trait. Implications for theory and research.] 14
The Moderating Effect of Perceived Risk on Consumers' Evaluations of Product Incongruity: Preference for the Norm. Margaret C. Campbell and Ronald C. Goodstein, Journal of Consumer Research, 28 (December 2001), pp. 439-49. [Literature review; Three studies; When consumers perceive high risk associated with a purchase, the moderate incongruity effect is reversed in that the congruent is preferred to the moderately incongruent product; Statistical analysis.] 15
Embarrassment in Consumer Purchase: The Roles of Social Presence and Purchase Familiarity. Darren W. Dahl, Rajesh V. Manchanda, and Jennifer J. Argo, Journal of Consumer Research, 28 (December 2001), pp. 473-81. [Discussion; Hypotheses; Two field studies; Awareness of a social presence during purchase selection and commitment, whether real or imagined, is a motivating factor in creating embarrassment for the consumer; Statistical analysis; Theoretical implications.] 16
Recommendation or Evaluation? Task Sensitivity in Information Source Selection. Andrew D. Gershoff, Susan M. Broniarczyk, and Patricia M. West, Journal of Consumer Research, 28 (December 2001), pp. 418-38. [Literature review. Normative model. Series of experiments. Effects, Knowledge concerning agents' ratings of alternatives in question at time of information source assessment. Conditional as compared with overall assessments. Shortcomings, Statistical analysis.] 17
Counterfactual Thinking and Advertising Responses. Parthasarathy Krishnamurthy and Anuradha Sivaraman, Journal of Consumer Research, 28 (March 2002), pp. 650-58. [Literature review. Hypotheses, Two experiments. Impacts, Solution-focused thoughts. Valence of ad-related thoughts. Ad and brand evaluations. Behavioral intentions. Statistical analysis.] 18
Reexamination and Extension of Kleine, Kleine, and Kernan's Social Identity Model of Mundane Consumption: The Mediating Role of the Appraisal Process. Debra A. Laverie, Robert E. Kleine III, and Susan Schultz Kleine, Journal of Consumer Research, 28 (March 2002), pp. 659-69. [Model proposal and testing, Social communication discourses. Variables, Possessions and performance (reflected appraisal, self-appraisal, pride, shame), identity importance. Statistical analysis.] 19
The Creative Destruction of Decision Research. George Loewenstein, Journal of Consumer Research, 28 (December 2001), pp. 499-505. [Literature review. Problems, Bounded rationality. Impact of context, Intraindividual variability, Decision-making anomalies, Evaluating consequences, Perverse effects of deliberation. Alternative perspective.] 20
We're at as Much Risk as We Are Led to Believe: Effects of Message Cues on Judgments of Health Risk. Geeta Menon, Lauren G. Block, and Suresh Ramanathan, Journal of Consumer Research, 28 (March 2002), pp. 533-49. [Discussion, Hypotheses, Three studies. Impacts, Self-positivity bias. Memory- and message- based factors. Statistical analysis. Implications for media strategy and public health policy.] 21
Consumers' Beliefs about Product Benefits: The Effect of Obviously Irrelevant Product Information. Tom Meyvis and Chris Janiszewski, Journal of Consumer Research, 28 (March 2002), pp. 618-35. [Literature review. Dilution effect. Biased hypothesis testing. Seven experiments. Belief strength. Type of information. Strategy condition. Processing mode. Obviously irrelevant information can have a negative impact on consumers' product perceptions.] 22
Combinatory and Separative Effects of Rhetorical Figures on Consumers' Effort and Focus in Ad Processing. David L. Mothersbaugh, Bruce A. Huhmann, and George R. Franke, Journal of Consumer Research, 28 (March 2002), pp. 589-602. [Literature review. Differential incongruity of schemes and tropes. Hypotheses, Two studies. Combinations of schemes and tropes yield incremental processing gains. Multiple tropes yield no incremental processing. Schemes cause a generalized focus on the entire ad, Tropes generate a more selected focus on message-related aspects.] 23
The Effect of Novel Attributes on Product Evaluation. Ashesh Mukherjee and Wayne D. Hoyer, Journal of Consumer Research, 28 (December 2001), pp. 462-72. [Literature review. Hypotheses, Two studies. Effects, Complexity and search. Attribute information. Benefits, Learning-cost inferences. Statistical analysis, Marketing implications.] 24
Consuming the American West: Animating Cultural Meaning and Memory at a Stock Show and Rodeo. Lisa Penaloza, Journal of Consumer Research, 28 (December 2001), pp. 369-98. [Theoretical discussion. Data collection (field notes, personal interviews, photographs). Cultural production processes. Levels (consumer behavior, situational positioning, subcultural interactions, market interactions). Assessment, Implications.] 25
Walking the Hedonic Product Treadmill: Default Contrast and Mood-Based Assimilation in Judgments of Predicted Happiness with a Target Product. Rajagopal Raghunathan and Julie R. Irwin, Journal of Consumer Research, 28 (December 2001), pp. 355-68. [Literature review. Hypotheses, Three studies. Impacts, Product context characteristics (pleasantness, sequence, domain match, context set size). Statistical analysis.] 26
Factors Affecting Encoding, Retrieval, and Alignment of Sensory Attributes in a Memory-Based Brand Choice Task. Stewart Shapiro and Mark T. Spence, Journal of Consumer Research, 28 (March 2002), pp. 603-17. [Literature review. Hypotheses, Two studies. Competing brands of stereos. Evaluative criteria. Scheme to rate criteria. Product trial. Impacts, Decision performance.] 27
Scripted Thought: Processing Korean Hancha and Hangul in a Multimedia Context. Nader T. Tavassoli and Jin K. Han, Journal of Consumer Research, 28 (December 2001), pp. 482-93. [Discussion; Hypotheses; Three experiments examine contextual interference from auditory and visual stimuli, relational memory between brand names and auditory and visual brand identifiers, and two qualitative processing outcomes, serial-order memory and spatial-relational memory.] 28
Consumer Value Systems in the Age of Postmodern Fragmentation: The Case of the Natural Health Microculture. Craig J. Thompson and Maura Troester, Journal of Consumer Research, 28 (March 2002), pp. 550-71. [Theoretical discussion. Model presentation, Phenomenological interviewing. Consumer articulations (making connections), mindfulness, flexibility, harmonious balance). Assessment, Implications.] 29
Can Mixed Emotions Peacefully Coexist? Patti Williams and Jennifer L. Aaker, Journal of Consumer Research, 28 (March 2002), pp. 636-49. [Literature review. Hypotheses, Three experiments. Duality and the propensity to accept it. Comparisons, Anglo and Asian Americans, Older and younger adults. Happiness and sadness. Felt discomfort. Statistical analysis. Theoretical implications.] 30
Marketplace Metacognition and Social Intelligence. Peter Wright, Journal of Consumer Research, 28 (March 2002), pp. 677-82. [Literature review; People's beliefs about their own mental states and the mental states, strategies, and intentions of others as these pertain directly to the social domain of marketplace interactions; Impacts; Evolutionary psychology; Theory of mind; Multiple life-span intelligences; Everyday persuasion knowledge; Assessment.] 31
Dynamic Customer Relationship Management: Incorporating Future Considerations into the Service Retention Decision. Katherine N. Lemon, Tiffany Barnett White, and Russell S. Winer, Journal of Marketing, 66 (January 2002), pp. 1-14. [Discussion, Model of keep/drop decision. Hypotheses, Two studies. Expected future use and anticipated regret influence service retention decision. Statistical analysis. Theoretical and marketing implications.] 32
Passing the Torch: Intergenerational Influences as a Source of Brand Equity. Elizabeth S. Moore, William L. Wilkie, and Richard J. Lutz, Journal of Marketing, 66 (April 2002), pp. 17-37. [Literature review. Two studies. Mother-daughter dyads. Impacts, Product category. Brand level. Forms they take. Way they have developed. Factors that sustain or disrupt them. Assessment, Managerial implications.] 33
Consumer Trust, Value, and Loyalty in Relational Exchanges. Deepak Sirdeshmukh, Jagdip Singh, and Barry Sabol, Journal of Marketing, 66 (January 2002), pp. 15-37 [Model proposal and testing. Service contexts. Hypotheses, Survey of households. Impacts, Frontline employees and management policies and practices (operational competence and benevolence, problem solving orientation). Statistical analysis. Implications.] 34
The Moderating Role of Commitment on the Spillover Effect of Marketing Communications. Rohini Ahluwalia, H. Rao Unnava, and Robert E. Burnkrant, Journal of Marketing Research, 38 (November 2001), pp. 458-70. [Literature review. Beliefs related to attributes not contained in the message. Hypotheses, Three experiments. Impacts, Positive and negative information. Statistical analysis.] 35
Understanding Reference-Price Shoppers: A Within- and Cross-Category Analysis. Tulin Erdem, Glenn Mayhew, and Baohong Sun, Journal of Marketing Research, 38 (November 2001), pp. 445-57. [Literature review; Hypotheses; Scanner-panel data; Brand choice; Sensitivity; Gains and losses; Sociodemographics; Use of models with continuous, correlated multivariate distributions; Statistical analysis; Managerial implications.] 36
The Differential Impact of Goal Congruency on Attitudes, Intentions, and the Transfer of Brand Equity. Ingrid M. Martin and David W. Stewart, Journal of Marketing Research, 38 (November 2001), pp. 471-84. [Literature review; Brand schema and concept consistency; Feature-, usage-, and goal-based similarity; Structural equation models; Product contexts (goal congruent, moderately and extremely goal-incongruent); Statistical analysis; Theoretical and practical implications.] 37
Search and Alignment in Judgment Revision: Implications for Brand Positioning. Michel Tuan Pham and A.V. Muthukrishnan, Journal of Marketing Research, 39 (February 2002), pp. 18-30. [Literature review. Model presentation. Hypotheses, Four experiments. Abstract versus attribute-specific positioning. Specific versus general challenges. Differential effectiveness. Statistical analysis.] 38
Elderly Latinos in the United States: What Do They Know and Think About HIV/AIDS? Susan Y. McGorry and Judith N. Lasker, Journal of Nonprofit and Public Sector Marketing, 9 (No. 3, 2001), pp. 89-103. [Discussion, National Health Interview Survey, Knowledge, Susceptibility, Comparisons, Young versus older Latinos and non-Latinos, Assessment.] 39
Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior. Terry L. Childers, Christopher L. Carr, Joann Peck, and Stephen Carson, Journal of Retailing, 11 (Winter 2001), pp. 511-35. [Literature review. Model development and testing. Hypotheses, Two studies. Impacts, Navigation, Convenience, Subexperience, Usefulness, Ease of use. Enjoyment, Interrelationships, Statistical analysis.] 40
Experimental Choice Analysis of Shopping Strategies. Peter TL. Popkowski Leszczye and Harry Timmermans, Journal of Retailing, 11 (Winter 2001), pp. 493-509. [Model presentation. Retail formats. Conjoint choice experiment. Cross-effects universal logit model. Statistical analysis. Managerial implications, Canada.] 41
See also 163, 170, 171, 172
Value-Building Growth: A Canadian Challenge. Dean Hillier and Tim MacDonald, Ivy Business Journal (Canada), 66 (November/December 2001), pp. 37-45. [Discussion, Value growth matrix (simple growers, value growers, under-performers, profit seekers). Global financial services. Strategic growth gaps. Assessment.] 42
Look for Disruptions in Imports. Tom Stundza, Purchasing, 130 (October 4, 2001), pp. 24B20-24B26. [Government investigation. Impacts, Steel imports. Appeal, European Union and Japan, Costs, Price declines. Competition from mini-mills. Assessment.] 43
Activists Press Access Issues. Susan Reda, Stores, 83 (September 2001), pp. 28-30,32. [Discussion, Mobility challenges, Americans with Disabilities Act, Court decisions. Retailer concerns. Differing interpretations. Spacing, Fixtures, Key promotional periods. Proactive, Guidelines.] 44
See also 44, 162,206
Ethical Climate's Relationship to Job Satisfaction, Organizational Commitment, and Turnover Intention in the Salesforce. Charles H. Schwepker, Jr., Journal of Business Research, 54 (October 2001), pp. 39-52. [Literature review. Hypotheses, Survey of salespeople. Perceptions of a positive ethical climate are positively associated with job satisfaction and organizational commitment. Statistical analysis. Implications.] 45
Gaining from a Giving Relationship: A Model to Examine Cause-Related Marketing's Effect on Salespeople. Brian V. Larson, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001), pp. 31-43. [Literature review. Hypotheses, Impacts, Perceived fit of relationship partners. Representative tenure. Assessment, Implications.] 46
Big Three Boost Diversity Buy. David Hannon, Purchasing, 130 (August 9, 2001), pp. 31-32, 34-38. [Minority supplier development. Recruiting efforts. Mentoring programs, Standardized requirements. Online initiatives. Examples, Automobile industry.] 47
See also 34, 42, 45, 47, 82, 85, 86, 89, 90, 91, 92, 95, 100, 109, 112, 114, 115, 124, 131, 132, 133, 144, 148, 150, 152, 155, 156, 157, 158, 161, 167, 168, 170, 176, 182, 183, 184, 185, 186, 187, 193, 196, 197, 198, 199, 201, 203, 204, 206, 207, 209
All in a Day's Work. Harvard Business Review, 79 (December 2001), pp. 54-58, 60, 62-64, 66. [Roundtable discussion on leadership. Can leadership be taught?. Are its skills portable?. What makes a leader?. How do the most effective leaders invest their time?. Assessment.] 48
Skate to Where the Money Will Be. Clayton M. Christensen, Michael Raynor, and Matt Verlinden, Harvard Business Review, 79 (November 2001), pp. 72-81. [Discussion, Product life cycles. Integrated companies. Customer satisfaction. Disruptive technologies model, Disintegration, Value chain. Profit shifts. Examples.] 49
Primal Leadership: The Hidden Driver of Great Performance. Daniel Goleman, Richard Boyatzis, and Annie McKee, Harvard Business Review, 79 (December 2001), pp. 42-51. [Management styles. Brain science. Emotional intelligence. Moods, Skills, Self-discovery, Reinvention, Guidelines.] 50
The Real Reason People Won't Change. Robert Kegan and Lisa Laskow Lahey, Harvard Business Review, 79 (November 2001), pp. 84-92. [Discussion, Change-resistant employees. Diagnosing immunity to change. Competing commitments. Perceptions, Big assumptions. Examples.] 51
Insights into Relationship Structures: The Australian Aluminum Industry. Michael P. Donnan and James M. Comer, Industrial Marketing Management, 30 (April 2001), pp. 255-69. [Literature review. Impacts, Parent companies. Host governments. Different organizations and plants often separated by substantial geographic distances. Technical links, Interlevel relationships. Examples.] 52
Managing Culturally Diverse Buyer-Seller Relationships: The Role of Intercultural Disposition and Adaptive Selling in Developing Intercultural Communication Competence. Victoria D. Bush, Gregory M. Rose, Faye Gilbert, and Thomas N. Ingram, Journal of the Academy of Marketing Science, 29 (Fall 2001), pp. 391-404. [Model presentation. Hypotheses, Survey of marketing executives. Measures, Empathy, Worldmindedness, Ethnocentrism, Attributional complexity. Adaptive selling. Stress, Relationships, Communication style. Statistical analysis. Implications.] 53
Customer Mind-Set of Employees Throughout the Organization. Karen Norman Kennedy, Felicia G. Lassk, and Jerry R. Goolsby, Journal of the Academy of Marketing Science, 30 (Spring 2002), pp. 159-71. [Literature review; Hypotheses; Survey of marketing and quality professionals; Scale development; Extent to which an individual employee believes that understanding and satisfying customers, whether internal or external to the organization, is central to the proper execution of his or her job; Statistical analysis.] 54
Alliance Competence, Resources, and Alliance Success: Conceptualization, Measurement, and Initial Test. C. Jay Lambe, Robert E. Spekman, and Shelby D. Hunt, Journal of the Academy of Marketing Science, 30 (Spring 2002), pp. 141-58. [Literature review. Model presentation. Hypotheses, Survey data gathered from alliances. Measures, Joint senior management commitment. Joint alliance competence. Idiosyncratic resources. Complementary resources. Joint alliance success. Interactions, Statistical analysis. Implications.] 55
The Influence of Complementarity, Compatibility, and Relationship Capital on Alliance Performance. MB Sarkar, Raj Echambadi, S. Tamer Cavusgil, and Preet S. Aulakh, Journal of the Academy of Marketing Science, 29 (Fall 2001 ), pp. 358-73. [Literature review. Model presentation. Hypotheses, Survey of international construction contracting firms. Complementarity in partner resources and compatibility in cultural and operational norms have different direct and indirect effects on alliance performance. Implications.] 56
Escalation of Commitment During New Product Development. Jeffrey B. Schmidt and Roger J. Calantone, Journal of the Academy of Marketing Science, 30 (Spring 2002), pp. 103-18. [Literature review; Hypotheses; Experiment; Managers who initiate a project are less likely to perceive it is failing, are more committed to it, and are more likely to continue funding it than managers who assume leadership after a project is started; Statistical analysis.]57
Lessons from the Tech Collapse. Michael A. Podsedly, Journal of Business Forecasting, 21 (Spring 2002), pp. 20-23, 28. [Discussion, Traditional tools are ineffective. Reluctant to accept a change. Proper perspective in conducting analysis, "Scenario Planning Lite", Assessment.] 58
Collaborative Forecasting: An Intra-Company Perspective. John E. Triantis, Journal of Business Forecasting, 22 (Winter 2001-02), pp. 13-15. [Discussion; Combines people with data, knowledge, and experience; Forecast ownership; Participant's role; Establishing the process; Success factors; Determining whether the process is in place; Consequences of non-collaborative forecasting; Guidelines.] 59
Lessons Learned from Implementing Forecasting and Planning Systems. Jeriad Zoghby, Journal of Business Forecasting, 21 (Spring 2002), pp. 17-18. [Discussion; Factors; Buy industry specific forecasting system; No turnkey applications; Consultants are not the experts; Have an in-house expert; Good preparation; Need for robust solutions, automatic tasks, and future planning; Accurate forecasting models; Assessment.] 60
The Indirect Effects of Organizational Controls on Salesperson Performance and Custonier Orientation. Ashwin W. Joshi and Sheila Randall, Journal of Business Research, 54 (October 2001), pp. 1-9. [Discussion, Conceptual model. Hypotheses, Survey of salespeople. Variables, Controls (output, process, professional, organizational), Task clarity. Affective commitment. Performance, Customer orientation. Interactions, Statistical analysis. Managerial implications.] 61
Demystifying Advertising Investments. Lee Mergy and D. Stewart Lade, Journal of Business Strategy, 22 (November/December 2001), pp. 18-22. [Discussion, Value-based approach. Maximizing financial returns. Factors (brand's overall profitability, profitable segments, alternative strategies, value creation of each alternative, calculating return on investment, refine strategic alternatives). Assessment.] 62
Creating Business Value Through Intangibles. Willy A. Sussland. Journal of Business Strategy, 22 (November/December 2001), pp. 23-28. [Discussion, Resources (financial assets; marketing, human, and organizational capital; time- and life-cycles). Management framework. Impacts, Revenues and income. Performance measurement. Assessment.] 63
Managing Business-to-Business Customer Relationships Following Key Contact Employee Turnover in a Vendor Firm. Neeli Bandapudi and Robert P. Leone, Journal of Marketing, 66 (April 2002), pp. 83-101. [Literature review. Propositions, Depth interviews and surveys. What customers value, Criticality of key contact employees. Acceptability of replacements. Transition procedures. Employee information sharing. Assessment, Theoretical and managerial information.] 64
Riding the Saddle: How Cross-Market Communications Can Create a Major Slump in Sales. Jacob Goldenberg, Bara K. Libai, and Eitan Müller, Journal of Marketing, 66 (April 2002), pp. 1-16. [Literature review, Three studies. Sales patterns, "Saddle" effect. Dual market symptom. Early and main market adopters. Cellular automata model, Conditions under which the saddle occurs. Statistical analysis. Consumer electronics industry.] 65
A Configurationally Perspective on Key Account Management. Christian Homburg, John P. Workman, Jr., and Ove Jensen, Journal of Marketing, 66 (April 2002), pp. 38-60. [Literature review, Integrative conceptualization. Constructs (activities, actors, resources, approach formalization). Survey of business-to-business sectors. Cross-industry, Cross-national, Performance outcomes. Statistical analysis. Managerial implications, US, Germany.] 66
Free Cash Flow, Agency Costs, and the Affordability Method of Advertising Budgeting. Kissan Joseph and Vernon J. Richardson, Journal of Marketing, 66 (January 2002), pp. 94-107. [Literature review; Model presentation; Hypothesis; Study of firms (Compustat database); The proportion of free cash flow will decrease, then increase, and finally decrease again with the fraction of management ownership; Statistical analysis; Theoretical and managerial implications.] 67
Role Stress and Effectiveness in Horizontal Alliances. Arne Nygaard and Robert Dahlsrom, Journal of Marketing, 66 (April 2002), pp. 61-82. [Literature review. Model presentation. Organizational outcomes. Hypotheses, Survey of managers. Measures, Transaction-specific assets. Communication modality. Role stress. Competence. Customer satisfaction. Contributions to sales. Coordinated bargaining efforts. Statistical analysis, Theoretical and managerial implications. Norway.] 68
The Value Relevance of Brand Attitude in High-Technology Markets. David A. Aaker and Robert Jacobsen, Journal of Marketing Research, 38 (November 2001), pp. 485-93. [Literature review. Model estimation. Mathematical equations. Data collection. Stock return. Accounting performance. Potential drivers (new products, product problems, change in top management, competitor and legal actions).] 69
Is the First to Market the First to Fail? Empirical Evidence for Industrial Goods Businesses. William T Robinson and Sungwook Min, Journal of Marketing Research, 39 (February 2002), pp. 120-28. [Literature review. Model specification. Hypotheses, Study of market pioneers and early followers. Pioneer's temporary monopoly over early followers plus first-mover advantages typically offset the survival risks associated with market and technological uncertainties. Statistical analysis.] 70
Toward a Customer-Orientation and a Differentiated Position in a Nonprofit Organization: Using the 5th P-People. Vaughan C. Judd, Journal of Nonprofit and Public Sector Marketing, 9 (No. I and 2, 2001), pp. 5-17. [Literature review. Impacts, Employees, Managing people-power (contractors, modifiers, influencers, isolateds, all people). Market strategy. Marketing mixes. Assessment.] 71
An Entrepreneurial Perspective on the Marketing of Charities. Michael H. Morris, Pierre R. Berthon, Leyland F. Pitt, Marie E. Murgolo-Poore, and Wendy F. Ramshaw, Journal of Nonprofit and Public Sector Marketing, 9 (No. 3, 2001), pp. 75-87. [Cause marketing, Entrepreneurship (definition, dimensions, frequency, intensity, process). Obstacles (systems, structure, strategic direction, policies and procedures, people, culture). Assessment.] 72
Making the Most of Uncertainty. Hugh Courtney, McKinsey Quarterly, (Fourth Quarter 2001), pp. 38-47. [Discussion. Shapeor-adapt choices. Levels of residual uncertainty (clear enough future, alternative futures, range of futures, true ambiguity). Examples.] 73
Who Gets Paid the Most? Purchasing, 130 (December 15, 2001 ), pp. 15-16, 18. [Study, Titles shift to higher levels. Electronic buying. Trends (age, experience, supervisory role, bonus, options, education, certification). Assessment.] 74
Supply Chain Stress: Coping with Professional Pressures. William Atkinson. Purchasing, 130 (October 18, 2001). pp. 20-22. [Discussion, Procurement, Workplace stress (time, situational, encounter, anticipatory). Need to focus on physical health, Perceptions of stress. Recommendations.] 75
Smooth Takeoff. Betsy Cummings, Sales and Marketing Management, 153 (October 2001), pp. 34-38, 40. [Turnaround management. Airline industry. Employee motivation. Impacts, Customer service. Projected revenues. Case study.] 76
Steering New Sales. Ron Donoho, Sales and Marketing Management, 153 (November 2001), pp. 30-35. [Management styles. Luxury cars. Market strategy. Target markets. Advertising campaigns. Case study. UK.] 77
See also 9, 10, 11,40,41,44,94,96,99, 102, 106, 119, 162, 172, 198,207,208
The Influence of Multiple Store Environment Cues on Perceived Merchandise Value and Patronage Intentions. Julie Baker, A. Parasuraman, Dhruv Grewal, and Glenn B. Voss, Journal of Marketing, 66 (April 2002), pp. 120-41. [Model presentation. Hypotheses, Two experiments. Factors (social, design, ambient). Store choice perceptions (service and merchandise quality, pricing, time/effort and psychic cost). Statistical analysis. Implications.]78
Multi-Channel Retailers Increasingly Rely on Internet-Based Kiosks to Bridge Gap Between Channels. Timothy P. Henderson. Stores, 83 (October 2001), pp. 28-30. [Discussion, Implementation, Technology advancements. Age groups. Consumer behavior. Value, Assessment.] 79
Customer E-Mail Adds to the Complexity of Managing Customer Relationships. Susan Reda, Stores, 83 (August 2001), pp. 55-56, 58. [Discussion, Full integration. Problems, Prompt and correctly answered responses. Need for standards. Contact-center software. Examples.] 80
Despite Economic Slowdown, Retailers Press Ahead with IT Spending Plans. Susan Reda, Stores, 83 (July 2001), pp. 47-48, 50. [Discussion, Information technology projects. Short-term tactical initiatives. Website integration. Applications that enable retailers to manage their supply base and spending through collaboration. Creating sustainable demand. Competitive pressures. Examples.] 81
Thorough Background Screening Seen as Vital Step in Retail Hiring Process. David P. Schulz, Stores, 83 (October 2001), pp. 80-81. [Discussion, Factors, Employment records. Multiple interviews. Theft database. Credit reports. Assessment, Guidelines.] 82
See also 53, 64, 79, 90, 96, 105, 133, 137, 162
Industrial Marketing Management, 30 (February 2001), pp. 101-253. [Eleven articles on partnering with resellers in business markets. Models, Web-based software. Perceived value. Supplier behaviors. Conceptualizing and operationalizing the value chain. Partnership formation. Building and maintaining relationships. Supply chain management. Integration of logistics in marketing. Electronic commerce and network perspectives. Alienation in distribution channel. Cross cultural relationships. Impacts of antitrust guidelines on competition.] 83
Antecedents of Commitment and Trust in Customer-Supplier Relationships in High Technology Markets. Ko de Ruyter, Luci Moorman, and Jos Lemmink, Industrial Marketing Management, 30 (April 2001), pp. 271-86. [Literature review. Model development. Hypotheses, Two studies. Effects, Characteristics (offer, relationship, market). Relationship building blocks. Loyalty intention. Statistical analysis. Implications, The Netherlands.] 84
The Determinants of Commitment in the Distributor-Manufacturer Relationship. Lester E. Goodman and Paul A. Dion, Industrial Marketing Management, 30 (April 2001), pp. 287-300. [Discussion, Model, Hypotheses, Survey of high-tech distributors. Impacts, Dependence and power. Manufacturer's strengths. Anticipation of trust. Effective communications. Idiosyncratic investments. Ease of sale. Product salability. Statistical analysis. Managerial implications.] 85
Two Sides to Attitudinal Commitment: The Effect of Calculative and Loyalty Commitment on Enforcement Mechanisms in Distribution Channels. David I. Gilliland and Daniel C. Bello, Journal of the Academy of Marketing Science, 30 (Winter 2002), pp. 24—43. [Literature review. Model development. Hypotheses, Survey of manufacturers. Impacts, Relative dependence. Pledges of exclusivity and investments. Distributor's pledges of investments. Manufacturer's trust. Contractual and social enforcement mechanisms. Statistical analysis. Managerial implications.] 86
The Manufacturer-Retailer-Consumer Triad: Differing Perceptions Regarding Price Promotions. Page Moreau, Aradhna Krishna, and Bari Harlam, Journal of Retailing, 11 (Winter 2001), pp. 547-69. [Discussion, Conceptual framework. Three surveys. Relationships (channel, market research, ivory tower, community). Industry and motivational knowledge. Statistical analysis. Managerial implications.] 87
Managing Retail Channel Overstock: Markdown Money and Return Policies. Andy A. Tsay, Journal of Retailing, 11 (Winter 2001), pp. 457-92. [Literature review; Model development; Propositions; Mathematical equations; Retail, wholesaler, and retailer's salvage price per unit; Goodwill loss per unit of retail stockout; Amount ordered; Stochastic demand; Numerical example.] 88
See also 2, 9, 27, 40, 83, 129, 132, 149, 166, 194, 205
Combining On-Line and Off-Line Marketing Strategies. S. Ramesh Kumar, Ivy Business Journal (Canada), 66 (November/December 2001), pp. 14-16. [Discussion, Parameters (type of product and type of consumer). Categories (hopefuls, brand tasters, life consumers, brand ID), Examples.] 89
The Market Valuation of Internet Channel Additions. Inge Geyskens, Katrijn Gielens, and Mamik G. Dekimpe, Journal of Marketing, 66 (April 2002), pp. 102-19. [Literature review; Opportunities; Threats; Hypotheses; Event-study methodology; European newspapers; Impacts; Expected future cash flows; Firm, introduction strategy, and marketplace characteristics; Publicity; Statistical analysis; Managerial implications.] 90
Strategic Internet and E-Commerce Applications for Local Nonprofit Organizations. Roger Gomes and Patricia A. Knowles, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 215^5. [Discussion, Trends, Impacts, Third-party Websites and portals. Market segments. Decision-making, Soliciting funds. Web advertising. Building brand equity. Web site control, Cyber-accountability, Barriers, Assessment.] 91
Purchasing, 130 (September 20, 2001), pp. S1-S22. [Eight articles on e-procurement strategies. Strategic sourcing software. Choosing a software provider. Getting direct suppliers online, Sourcing overhauls. Electronic auctions. System implementation. Third-party buying. Case studies.] 92
Born from Porn. Kathleen Cholewka, Sales and Marketing Management, 153 (October 2001), pp. 50-55. [Business growth. Market strategy. Niche marketing. Web sites. High-tech tactics. Customer orientation. Billing options. Customization, Examples.] 93
Solving the Standards Puzzle. Susan Reda, Stores, 83 (November 2001), pp. 30-32, 34. [Global business-to-business e-commerce standards. Electronic Data Interchange, Costs, extensible Markup Language, Associations, Consortiums, Collaborative efforts. Examples.] 94
See also 75, 83, 181, 183, 205, 221
WMI Can Be Good for Your Forecasting Health. Larry Lapide, Journal of Business Forecasting, 20 (Winter 2001-02), pp. 11-12, 36. [Vendor managed inventory. Impacts, Supply chain operations. Account teams. Time segments. Examples.] 95
The Effect of Collaborative Forecasting on Supply Chain Performance. Yossi Aviv, Management Science, 47 (October 2001), pp. 1326-43. [Discussion, Propositions, Planning, Forecasting, Replenishment, Model presentation. Retailer's inventory replenishment process. Supplier's inventory policy. Cost performance and composite inventory measures. Numerical study.] 96
Does JIT II Still Work in the Internet Age? William Atkinson, Purchasing, 130 (September 6, 2001), pp. 41-42. [Discussion, Speed, Reducing need for face-to-face relationships. Electronic collaborative design. Technological advances are increasing the impact of JIT II.] 97
Base Rates Stahle, But Who Will Pay Extra Security Costs? Tom Stundza, Purchasing, 130 (November I, 2001), pp. 43-45. [Bulk transport rates. The total cost of extra security measures could add $20 billion to the country's annual freight transportation and logistics bill. Slower moving freight. Impacts, JIT, Examples.] 98
Retailers Use Collaborative System to Improve Drop-Ship Performance. Tony Seideman, Stores, 83 (August 2001), pp. 62, 64. [Discussion, Request that a manufacturer ship items directly to a customer, Internet-based service designed to provide up-to-date information on product location and status. Impacts, Customer loyalty. Examples.] 99
See also 12, 36, 43, 87, 88, 98, 112, 177, 223
Integrative Pricing via the Pricing Wheel. David Shipley and David Jobber, industrial Marketing Management, 30 (April 2001), pp. 301-14. [Discussion, Prime pricing determinants (cost-, competition-, demand-based), Integrative (price ceiling, influencing variables, average and direct cost levels), Assessment, Managerial implications.] 100
The Effects of Price on Brand Extension Evaluations: The Moderating Role of Extension Similarity. Valerie A. Taylor and William O. Bearden, Journal of the Academy of Marketing Science, 30 (Spring 2002), pp. 131-40. [Literature review. Hypotheses, Experiment, A high-price introductory strategy used to suggest a high-quality product will likely be more effective for dissimilar extensions than similar extensions, Statistical analysis. Implications.] 101
Image Communicated by the Use of 99 Endings in Advertised Prices. Robert M. Schindler and Thomas M. Kilbarian, Journal of Advertising, 30 (Winter 2001), pp. 95-99. [Literature review. Price- and quality-image effects. Hypotheses, Experiment, Negative impact on quality image in ads sponsored by higher quality retailers.] 102
Reference Price and Price Perceptions: A Comparison of Alternative Models. Ronald W. Niedrich, Subash Sharma, and Douglas H. Wedell, Journal of Consumer Research, 28 (December 2001), pp. 339-54. [Literature review; Conceptual framework; Three experiments; Based on MANOVA and model fitting, range-frequency theory accounted for reference price effects that the other theories could not; Range and frequency effects are moderated by the stimulus presentation condition.] 103
Consumer Price Sensitivity and Price Thresholds. Sangman Han, Sunil Gupta, and Donald R. Lehmann, Journal of Retailing, 77 (Winter 2001), pp. 435-56. [Literature review; Brand choice model; Scanner panel data; Higher own-price volatility makes consumers more sensitive to gains and less sensitive to losses, while intense price promotions by competing brands makes consumers more sensitive to loses but does not influence consumers' sensitivity to gains; Managerial implications.] 104
The Pricing of Franchise Rights. Patrick J. Kaufmann and Rajiv P Dant, Journal of Retailing, 77 (Winter 2001), pp. 537-45. [Discussion, Hypotheses, Survey of fast food restaurants. Initial franchise fee. Ongoing royalty payment. Relationships, Controlling for average outlet sales. Assessment, Implications.] 105 Retailers Play "The Price Is Right" as Interest in Revenue Management Systems Grows. Susan Reda, Stores, 83 (August 2001), pp. 24, 26, 28. [Discussion, Decision making. Software packages. Price and markdown optimization. Impacts, ROI, Data sharing. Examples.] 106
See also 1,5,12,15, 22, 26, 27, 33, 35, 36, 37, 38, 49, 57, 65, 69, 77, 84, 89, 101, 104, 117, 120, 143, 183, 192, 223
Sibling Brands, Multiple Objectives, and Response to Entry: The Case of the Marion Retail Coffee Market. Thomas S. Gruca, D. Sudharshan, and K. Ravi Kumar, Journal of the Academy of Marketing Science, 30 (Winter 2002), pp. 59-69. [Discussion, Multibrand competition. Segment-level brand share attraction model. Mathematical equations, Nash equilibrium. Responses by sibling brands are more accommodating than those of unrelated brands whose responses are consistent with the preservation of preentry levels of sales.] 107
Why a Brand's Most Valuable Consumer Is the Next One It Adds. Ned Anschuetz, Journal of Advertising Research, 42 (January/February 2002), pp. 15-21. [Study of households. Impacts, Buying frequency. Loyalty groups, Period-to-period volume contributions, "Profitable" consumers. Statistical analysis. Implications for advertising strategy.] 108
Why Brands Grow. Allan L. Baldinger, Edward Blair, and Raj Echambadi, Journal of Advertising Research, 42 (January/February 2002), pp. 7-14. [Data collection (Canadian diary panel). Variables, Market share. Market penetration. Customer loyalty. Relative price. Statistical analysis. Managerial implications.] 109
The Effects of Ingredient Branding Strategies on Host Brand Extendibility. Kalpesh Kaushik Desai and Kevin Lane Keller, Journal of Marketing, 66 (January 2002), pp. 73-93. [Literature review. Hypotheses, Laboratory experiment. Impacts, Expansions (slot-filler, new attribute). Ingredients (self-branded, co-branded). Manipulations and stimuli. Consumer reactions and evaluations. Statistical analysis.] 110
Building Brand Community. James H. McAlexander, John W. Schouten, and Harold F. Koenig, Journal of Marketing, 66 (January 2002), pp. 38-54. [Literature review. Ethnographic findings. Model development (customer-centric). Hypotheses, Field study. Impacts, Geotemporal concentrations and the richness of social context. Dynamic phenomena. Brand loyalty. Statistical analysis.] 111
Strategic Bundling of Products and Prices: A New Synthesis for Marketing. Stefan Stremersch and Gerard J. Tellis, Journal of Marketing, 66 (January 2002), pp. 55-72. [Literature, Propositions, Classification and optimality of bundling strategies. Product bundling strategies by symmetry and variation of reservation prices. Objectives of firm. Competition, Consumers' perceptions of bundles. Examples, Managerial implications.] 112
Do We Really Know How Consumers Evaluate Brand Extensions? Empirical Generalizations Based on Secondary Analysis of Eight Studies. Paul A. Bottomley and Stephen J.S. Holden, Journal of Marketing Research, 38 (November 2001), pp. 494-500. [Commentary on the Aaker and Keller model; Evaluations of brand extensions across studies, brand extensions within each study, and across cultures.] 113
The Influence and Value of Analogical Thinking During New Product Ideation. Darren W. Dahl and Page Moreau, Journal of Marketing Research, 39 (February 2002), pp. 47-60. [Discussion; Hypotheses; Three Studies; Originality of the resulting product design is influenced by the extent of analogical transfer, the type of analogies used, and the presence of external primes; Impacts; Consumers' willingness to pay.] 114
Understanding What's in a Brand Rating: A Model for Assessing Brand and Attribute Effects and Their Relationship to Brand Equity. William R. Dillon, Thomas J. Madden, Amna Kirmani, and Soumen Mukherjee, Journal of Marketing Research, 38 (November 2001), pp. 415-29. [Literature review. Brand specific associations. General brand impressions. Sources of bias. Applications, Managerial implications.] 115
See also 87
How to Use a "GOLF" Game to Enhance the Forecasting Process. Kenneth B. Kahn, Journal of Business Forecasting, 20 (Winter 2001-02), pp. 23, 28. [Discussion, Participants play individual holes which represent each forecasting period. Impacts, Salespeople, Incentives, Performance measures. Case study.] 116
Demonstrations and Money-Back Guarantees: Market Mechanisms to Reduce Uncertainty. Amir Heiman, Bruce McWilliams, and David Zilberman, Journal of Business Research, 54 (October 2001), pp. 71-84. [Literature review; Propositions; Risk; Learning; Theoretical findings from economics, marketing, consumer behavior, and psychology are integrated to analyze the performance of the two mechanisms under various conditions and product characteristics.] 117
Promotional Products: Adding Tangibility to Your Nonprofit Promotions. Donald R. Self, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 205-13. [Discussion, Strengths, Weaknesses, Impacts, Fundraising, Donor recognition. Organizational awareness. Member recruitment. Event promotion. Behavior modification programs. Examples.] 118
See also 6, 7, 8, 18, 23, 35, 62, 67, 93, 102, 108, 149, 157, 165, 173, 180
Estimating Differential Lag Effects for Multiple Media Across Multiple Stores. David Berkowitz, Arthur Allaway, and Giles D'Souza, Journal of Advertising, 30 (Winter 2001), pp. 59-65. [Literature review. Model development. Weekly data from three stores of a large national retailer. Radio had longer lagged effects than did billboards. Statistical analysis.] 119
Third-Party Organization Endorsement of Products: An Advertising Cue Affecting Consumer Prepurchase Evaluation of Goods and Services. Dwane Hal Dean and Abhijit Biswas, Journal of Advertising, 30 (Winter 2001), pp. 41-57. [Literature review. Hypotheses, Two factorial experiments. Endorsements (none, celebrity, third party). Impacts (perceived quality, attitude toward the manufacturer, purchase risk, information value of the ad). Statistical analysis. Managerial implications.] 120
"Market Patriotism": Advertising Dilemma. Betsy D. Gelb, Journal of Advertising Research, 42 (January/February 2002), pp. 67-69. [Discussion; Post September 11, 2001; Criticism related to waving the flag for profit, versus criticism for ignoring a patriotic theme; Assessment.] 121
Second-by-Second Looks at the Television Commercial Audience. Robert J. Kent, Journal of Advertising Research, 42 (January/February 2002), pp. 71-78. [Discussion; Data collection (set-top boxes); Audience build and decay; Modeling, analysis, and reporting; Research applications; Advertising practice and accountability; Assessment.] 122
Identifying Viewer Segments for Television Programs. Choong-Ryuhn Kim, Journal of Advertising Research, 42 (January/February 2002), pp. 51-66. [Literature review. Data collection (Media Service Korea), Process, Collect consumers' viewing set data. Calculate viewing co-occurrence. Identify program viewing structure. Classify viewer program segments. Analyze segment profiles and derive. Statistical analysis.] 123
Benchmarking Advertising Efficiency. Xueming Luo and Naveen Donthu, Journal of Advertising Research, 41 (November/December 2001), pp. 7-18. [Data Envelopment Analysis, Descriptive statistics of media spending for top advertisers. Efficiency, Slack/inefficiency, Input Congestion Analysis, Applications.] 124
Using Television Daypart "Double Jeopardy Effects" to Boost Advertising Efficiency. Walter S. McDowell and Steven J. Dick, Journal of Advertising Research, 41 (November/December 2001), pp. 43-51. [Literature review. Data collection (Nielsen diary-based sweep markets). Audience turnover within multi-hour dayparts. Impacts, Media-buying strategies. Statistical analysis.] 125
Who Pays for Magazines—Advertisers or Consumers? David E. Sumner, Journal of Advertising Research, 41 (November/December 2001), pp. 61-67. [Literature review. Data collection (Magazine Trend Report), Advertisers are paying much more while consumers actually paid less for subscriptions (in inflation- adjusted dollars) between 1980 and 1998.] 126
Point of View: Does Advertising Cause a "Hierarchy of Effects"? William M. Weilbacher, Journal of Advertising Research, 41 (November/December 2001), pp. 19-26. [Literature review. Inconsistencies in hierarchy models. Multiple ads may produce singular effects. Competitive hierarchical interactions. Conceptual weaknesses. Problems with understanding specific effects of advertising. Implications for integrated marketing communications.] 127
Assessing Advertising Creativity Using the Creative Product Semantic Scale. Alisa White and Bruce L. Smith, Journal of Advertising Research, 41 (November/December 2001), pp. 27-34. [Literature review; Hypotheses; Fifteen print advertisements were evaluated by advertising professionals, college students, and the general public; Rankings; Demographic variables; Group comparisons; Statistical analysis.] 128
Is the Internet More Effective Than Traditional Media? Factors Affecting the Choice of Media. Sung-Joon Yoon and Joo-Ho Kim, Journal of Advertising Research, 41 (November/December 2001), pp. 53-60. [Survey of internet users. Assessment of mediaproduct relevance. Media-based identification of product characteristics. Use of Katz's functional attitude theory and the FCB Grid, Results suggest that internet advertising is better suited for highly involved as well as rationally oriented consumers. Statistical analysis, Korea.] 129
What to Say When: Advertising Appeals in Evolving Markets. Rajesh K. Chandy, Gerard J. Tellis, Deborah J. MacInnis, and Pattana Thaivanich, Journal of Marketing Research, 38 (November 2001), pp. 399-414. [Literature review; Disaggregate econometric model; Hypotheses; Argument-based appeals, expert sources, and negatively framed messages are effective in new markets; Emotion- based appeals and positively framed messages are more effective in older markets.] 130
Integrated Marketing Communications for Local Nonprofit Organizations: Messages in Nonprofit Communications. Teri Kline Henley, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 179-84. [Discussion, Strategy (frame the issue, connect to audience). Execution (copy and visual issues, shock value, use of celebrities, logos). Examples.] 131
Reversing the Digital Slide. Jacques R. Bughin, Stephen J. Hasker, Elizabeth S.H. Segel, and Michael P. Zeisser, McKinsey Quarterly, (Fourth Quarter 2001), pp. 58-69. [On-line media sector. Problems, Advertising revenue. Pricing, Innovation, Standards, Need for contextual advertising. Guidelines.] 132
Broadband Media: Look Before You Leap. Scott A. Christofferson and Michael A. Gatzke, McKinsey Quarterly, (Fourth Quarter 2001), pp. 48-57. [Discussion, Production costs. Distribution path (origination, backbone, regional and last mile distribution). Consumers' willingness to pay. Value, Assessment, Guidelines.] 133
See also 45,46,61, 140, 141, 142, 149,215,220,222
Salesperson Cooperation: The Influence of Relational, Task, Organizational, and Personal Factors. Cengiz Yilmaz and Shelby D. Hunt, Journal of the Academy of Marketing Science, 29 (Fall 2001), pp. 335-57. [Literature review. Model Presentation, Hypotheses, Survey of salespeople and sales managers. Factors (relational, task, organizational, personal). Interactions, Statistical analysis.] 134
An Initial Evaluation of Industrial Buyers' Impressions of Salespersons' Nonverbal Cues. Thomas W. Leigh and John W. Summers, Journal of Personal Selling and Sales Management, 22 (Winter 2002), pp. 41-53. [Literature review. Hypotheses, Experiment, Cues (eye gaze, formal posture, gesturing, speech hesitations, professional attire). Interactions, Statistical analysis. Managerial implications.] 135
A Measure of Setting Skill: Scale Development and Validation. Joseph O. Rentz, C. David Shepherd, Armen Tashchain, Pratibha A. Dabholkar, and Robert T. Ladd, Journal of Personal Selling and Sales Management, 22 (Winter 2002), pp. 13-21. [Literature review. Model Presentation, Survey of salespeople. Skills (interpersonal, salesmanship, technical). Confirmatory factor analysis. Dimensionality and item reliability, Nomological validity. Statistical analysis.] 136
Strategic Collaborative Communication by Key Account Representatives. Roberta J. Schultz and Kenneth R. Evans, Journal of Personal Selling and Sales Management, 22 (Winter 2002), pp. 23-31. [Model Presentation, Hypotheses, Study of key account representative (KAR)-customer relationships. Constructs, Informality, Bi-directionality, Frequency, Strategic content. Trust in KAR, KAR role performance, Synergistic solutions. Statistical analysis. Implications.] 137
Fast Forward. Erin Strout, Sales and Marketing Management, 153 (December 2001), pp. 36-40,43. [Salespeople, Ability to compete. Factors, Pick your partners wisely. Revisit your mission. Attain diversity and work to keep talent. Meeting customers' needs. Examples.] 138
The Show Must Go On. Erin Strout, Sales and Marketing Management, 153 (November 2001), pp. 52-54, 56, 58-59. [Discussion, Improving sales presentations. Format, Visual aids, Question-and-answer period. Body language and voice pitch. Examples, Guidelines.] 139
See also 46, 66, 116, 134, 136, 137, 138, 139, 179, 220, 222
How to Lose Your Star Performer Without Losing Customers, Too. Neeli Bendapudi and Robert P. Leone, Harvard Business Review, 79 (November 2001), pp. 104-12. [Discussion, Customer concerns. Losing an important contact with the company. Replacement won't be as good. Will have to start all over again. Success, Guidelines.] 140
An Extension and Evaluation of Job Characteristics, Organizational Commitment and Job Satisfaction in an Expatriate, Guest Worker, Sales Setting. Shahid N. Bhuian and Bulent Mengue, Journal of Personal Selling and Sales Management, 11 (Winter 2002), pp. 1 - 11. [Discussion, Alternative models. Hypotheses, Survey of sales expatriates. Variables, Autonomy, Variety, Identity, Feedback, Job satisfaction. Organizational commitment. Statistical analysis. Managerial implications, Saudi Arabia.] 141
Larger Than Life. Melinda Ligos, Sales and Marketing Management, 153 (December 2001 ), pp. 44-47,49-51. [Salesperson's ego. Too important to attend meetings and do paperwork. Rewards and punishment. Examples, Guidelines.] 142
See also 43,47, 52, 56, 58, 60,69,70, 74, 83, 85, 86,96, 135, 167, 186,203,204 Performance of Coupled Product Development Activities with a Deadline. Nitindra R. Joglekar, Ali A. Yassine, Steven D. Eppinger, and Daniel E. Whitney, Management Science, 47 (December 2001), pp. 1605-20. [Literature review. Performance generation model. Optimal policies. Concurrent/sequential/overlapping development. Component and system performance generation. Software engineering. Assessment, Managerial implications.] 143
Big Companies Struggle to Act Their Size. Anne Millen Porter, Purchasing, 130 (November 1,2001), pp. 25-26,28, 32. [Strategic sourcing. Leveraging spending. Process improvement. Centralization of purchasing authority, E-procurement, Internal standardization. Examples.] 144
See also 7, 71, 72, 91, 118, 121, 131, 178, 200, 201, 202
Withholding Consumption: A Social Dilemma Perspective on Consumer Boycotts. Sankar Sen, Zeynep Gurhan-Canli, and Vicki Morwitz, Journal of Consumer Research, 28 (December 2001 ), pp. 399-417. [Discussion, Conceptual framework. Hypotheses, Two experiments. Impacts, Boycott's likelihood of success. Susceptibility to normative social influences. Costs incurred. Pro-boycott message frame. Expectation of overall participation. Perceived efficacy. Preferences for boycotted product. Statistical analysis. Implications.] 145
Identity Marketing: The Case of the Singing Revolution. Alan J. Brokaw and Marianne A. Brokaw, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001), pp. 17-29. [Historical discussion. Songs, Communicating the idea of nationality. Increased Russian population, Baltic liberation movements. Peaceful protests. Examples, Estonia.] 146
The Perceived Fairness of the Human Organ Allocation Process in the United States. Thomas J. Cosse and Terry M. Weisenberger, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001), pp. 45-61. [Literature review. Consumer survey. Attitudes, Race and sex. Statistical analysis. Marketing implications.] 147
The Impact of Health Plan Profit Status on Consumer Satisfaction with Health Care. Scott A. Dellana and David W. Glascoff. Journal of Nonprofit and Public Sector Marketing, 9 (No. 3, 2001), pp. 1-19. [Survey of households. Effects, For- and not-for-profit HMO plans. Dimensions, Access to care. Availability of resources. Technical quality. Financial aspects. Overall satisfaction. Continuity of care. Humaneness, Statistical analysis. Implications.] 148
Integrated Marketing Communications for Local Nonprofit Organizations: Communications Tools and Methods. Teri Kline Henley, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 157-68. [Discussion, Market strategy. Advertising, Direct/interactive marketing. Public relations. Personal selling. Examples.] 149
Integrated Marketing Communications for Local Nonprofit Organizations: Developing an Integrated Marketing Communications Strategy. Teri Kline Henley, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 141-55. [Literature review. Factors (relationship to audience, audience responsiveness, message control, implementation, cost). Writing objectives. Plan outline. Examples.] 150
Utilizing Research to Develop a Plan to Gain Members and Increase Morale in a University Sorority. Teri Kline Henley, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001 ), pp. 103-12. [Literature review. Survey of chapter members. Attitudes, Sorority comparisons, Greek/chapter issues. Important publics. Marketing tools/techniques. Statistical analysis.] 151
Beyond Strategic Control: Applying the Balanced Scorecard to a Religious Organization. John C. Keyt, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4,2001), pp. 91-102. [Discussion, Perspectives (customer, internal business, innovation and learning, financial). Application, Church's mission and vision statement.] 152
"No Problem, Mon": Strategies to Promote Reggae Music as Jamaica's Cultural Heritage. Stephen A. King and P. Renee Foster, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001), pp. 3-16. [Historical discussion. International tourism. Destination and hospitality marketing. Identity/cause marketing, Rastafarian movement. Rhetoric, Assessment.] 153
Measuring Service Quality in a Corporatised Public Sector Environment. Karen McFadyen, Jennifer L. Harrison, Stephen J. Kelly, and Donald Scott, Journal of Nonprofit and Public Sector Marketing, 9 (No. 3, 2001 ), pp. 35-51. [Literature review. Welfare services. Customer survey. Dimensions, Reliability, Personalized proficiency. Empathie professionalism. Tangibles, Access, Statistical analysis, SERVQUAL, Australia.] 154
Cultural Marketing and Archaeology: The Case of Brazil. Joseph C. Miller and Patricia Pitaluga, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001), pp. 63-74. [Preservation and management of archaeological resources. Effects, Concepts and methods of analysis used in social and cultural marketing. Private sector investment. Example.] 155
Nonprofit-Business Alliance Model: Formation and Outcomes. Sridhar Samu and Walter W. Wymer, Jr., Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 45-61. [Discussion, Propositions, Types of alliances. Risks and benefits. Partner selection. Alliance development. Assessment.] 156
Fundraising Direct: A Communications Planning Guide for Charity Marketing. Adrian Sargeant and Michael Ewing, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 185-204. [Discussion, Direct marketing planning framework. Market segmentation. Profiling, Targeting, Direct mail. Communication of offer. Response analysis. Examples, UK.] 157
Conceptual Foundations and Practical Guidelines for Retaining Volunteers to Serve in Local Nonprofit Organizations. Becky J. Starnes and Walter W. Wymer, Jr., Journal of Nonprofit and Public Sector Marketing, (Part II), 9 (No. I and 2, 2001), pp. 97-118. [Discussion, Turnover, Motivations for continuing and discontinuing volunteer service. Retention strategies (selective screening, matching programs, tenure systems). New trends in volunteering.] 158
Learning, Innovation and Competitive Advantage in Not-for-profit Aged Care Marketing: A Conceptual Model and Research Propositions. Jay Weerawardena and Gillian Sullivan-Mort, Journal of Nonprofit and Public Sector Marketing, 9 (No. 3, 2001), pp. 53-73. [Literature review. Impacts, Social entrepreneurship. Learning capability (internally- and market-focused, relational). Organizational and innovation intensity. Assessment.] 159
Conceptual Foundations and Practical Guidelines for Recruiting Volunteers to Serve in Local Nonprofit Organizations. Walter W. Wymer, Jr. and Becky J. Starnes, Journal of Nonprofit and Public Sector Marketing, (Part I) 9 (No. 1 and 2, 2001), pp. 63-96. [Literature review. Model presentation. Determinants of volunteering (personal and interpersonal influences, attitudes, situational factors). Developing and delivering recruitment appeals. Post-recruitment strategies.] 160
Social Entrepreneurship: Managerial, Finance and Marketing Aspects. John T. Zietlow, Journal of Nonprofit and Public Sector Marketing, 9 (No. I and 2, 2001), pp. 19-43. [Literature review. Impacts, Consultants and foundations. Government, Social enterprise initiation. Leadership, Organization missions. Managerial implications and issues.] 161
Charity Groups Aid Retailers in Disposing of Unsaleable Products. Karen M. Kroll, Stores, 83 (September 2001), pp. 34, 36. [Discussion, Product philanthropy. Freeing up warehouse space. Distribution channels. Tax deductions. Examples.] 162
See also 5, 8, 28,43, 52, 56, 66, 68, 77, 84, 90, 94, 123, 129, 135, 141,146,153,154,155,174,223
Cultural Protectionism. Christopher Baughn and Mark A. Buchanan, Business Horizons, 44 (November/December 2001), pp. 5-15. [Discussion, Demands for market access. Concerns for cultural sensitivities. Governmental policies. Argument for free trade in cultural goods. Technology and culture. International accords. Examples.] 163
Cultural Values Reflected in Chinese and American Television Advertising. Carolyn A. Lin, Journal of Advertising, 30 (Winter 2001), pp. 83-94. [Literature review. Hypotheses, Impacts, Group consensus. Soft- and hard-sell. Veneration/elderly, Modernity/youth, Status appeal. Product merit. Individual/independence, Oneness with nature. Time oriented. Statistical analysis.] 164
Advertising Localization Overshadows Standardization. Ali Kanso and Richard Alan Nelson, Journal of Advertising Research, 42 (January/February 2002), pp. 79-89. [Literature review. Hypotheses, Survey of foreign subsidiaries. Two-thirds of the subsidiaries use the localized approach. Many major obstacles impede the standardization of advertising campaigns. Statistical analysis, Sweden, Finland.] 165
Journal of International Business Studies, 32 (Fourth Quarter 2001), pp. 617-791. [Eight articles on electronic commerce and global business. Impact of information and communication technology revolution. Economic geography. Concentration and dispersion in global industries, Territoriality, Institutional environment and international competitiveness. Profiles of Internet buyers. Inferences from retail brokering. Firm specific internationalization factors.] 166
Effects of Post-Privatization Governance and Strategies on Export Intensity in the Former Soviet Union. Igor Filatotchev, Natalya Dyomina, Mike Wright, and Travor Buck, Journal of International Business Studies, 32 (Fourth Quarter 2001), pp. 853-71. [Literature review. Conceptual framework. Hypotheses, Survey of medium and large-sized manufacturing firms. Strategic outcomes. Impacts, External and firm-level factors. Outside and managerial control. Strategic choices. Statistical analysis.] 167
Dynamic Decision-Making: A Cross-Cultural Comparison of U.S. and Peruvian Export Managers. R. Scott Marshall and David M. Boush, Journal of International Business Studies, 32 (Fourth Quarter 2001), pp. 873-93. [Literature review. Survey, Hypotheses, Simulated interactions with business partners. Effects, Trust, Cheating, Personal characteristics. Relationship-specific history. Statistical analysis.] 168
McKinsey Quarterly, Special Edition (Fourth Quarter 2001), pp. 6-116. [Sixteen articles on emerging markets. Surging growth. Global talent drains. Mobile communications. Mobile banking. Revitalizing banks. Family-owned businesses. Corporate reform. Steel industry. Vaccines where needed. Financing non-profits. Mobilizing women. National health insurance. Broadband services. Bond markets. Increasing performance. Many countries.] 169
The Promised Economy. Roger Abravanel, McKinsey Quarterly, (Fourth Quarter 2001), pp. 133-36. [Discussion, Impacts, Foreign investment. Innovation, High-tech sector. Security and military. Software, Optics, Assessment, Israel.] 170
Getting International Banking Rules Right. David Bear, Kevin S. Buehler, and Gunnar Pritsch, McKinsey Quarterly, (Fourth Quarter 2001), pp. 70-81. [Discussion, Regulation (Basel Committee standards). Problems, Risk weight calibrations. Unrated borrowers. Capital requirements. Measuring operational risk. Experience requirement. Assessment, Guidelines, Many countries.] 171
The Euro Countdown Begins. Susan Reda, Stores, 83 (December 2001), pp. 30-32, 34. [Discussion, Conversion rates. Threshold pricing. Euro-timetable, Need for excess cash. Training issues. Consumer education. Assessment.] 172
See also 2, 4, 21, 29, 32, 34, 39, 42, 76, 147, 153, 159, 171, 182, 200
Exploring the Use of Advertising Agency Review Consultants. Fred K. Beard, Journal of Advertising Research, 42 (January/February 2002), pp. 39-50. [Literature review. Research questions. Data collection ("accounts in review" listings). Reasons for consultant use. Client-ad agency relationship success. Extent to which consultants directly influence final review decisions. Statistical analysis.] 173
Journal of Business Research, 54 (November 2001), pp. 189-241. [Six articles on services marketing in Australia, Customer relationships with service personnel. Purchase influence. Justice strategy options in services recovery. Impact of price sensitivity on pricing and capacity allocations. Causes of disruption to franchise operations, Internet uses in the global hotel industry.] 174
Service Recovery's Influence on Consumer Satisfaction, Positive Word-of-Mouth, and Purchase Intentions. James G. Maxham III, Journal of Business Research, 54 (October 2001), pp. 11-24. [Literature review; Hypotheses; Experiment and field study; Results indicate that moderate to high service recovery efforts significantly increase post-failure levels of satisfaction, purchase intent, and positive word of mouth; Implications.] 175
The Customer Orientation of Service Workers: Personality Trait Effects on Self- and Supervisor Performance Ratings. Tom J. Brown, John C. Mowen, D. Todd Donavan, and Jane W. Licata, Journal of Marketing Research, 39 (February 2002), pp. 110-19. [Literature review. Model presentation. Propositions, Field study. Impacts, Emotional stability, Agreeability, Need for activity. Conscientiousness, Statistical analysis.] 176
Temporal Differentiation and the Market for Second Opinions. Miklos Sarvary, Journal of Marketing Research, 39 (February 2002), pp. 129-36. [Literature review. Model presentation. Propositions, Pricing behavior in markets of private information. Client, Consultants, Monopoly, Competition, Assessment.] 177
Consumer Experience Tourism in the Nonprofit and Public Sectors. Mark A. Mitchell and Sheila J. Mitchell, Journal of Nonprofit and Public Sector Marketing, 9 (No. 3, 2001), pp. 21-34. [Consumers bonding with brands. Examples, Consumer motivation. Existing sites. Action plan. Assessment.] 178
Service Slowdown. Kathleen Cholewka, Sales and Marketing Management, 153 (November 2001), pp. 36-38, 40, 42-43. [Customer service responsibilities. Problems, Factors, Repeat calls from same customer about same problem. Management involvement. Sales results. Time needed to fix problems. Post-sales calls. Time spent on fixing routine problems. Examples.] 179
See also 3, 13, 16, 25, 28, 29, 30, 31, 32, 37, 67, 88, 96, 107, 117, 143, 177, 188, 192
On Continuous-Time Optimal Advertising Under S-Shaped Response. Fred M. Feinberg, Management Science, 47 (November 2001), pp. 1476-87. [Literature review. Generalized linear model Contagion model. Effect of discount rate on long-run optima. Assessment, Managerial implications.] 180
The Latest Arrival Huh Location Problem. Bahar Y. Kara and Barbaros C. Tansel, Management Science, 47 (October 2001), pp. 1408-20. [Discussion, Model development, Minimax, Integer programming formulations. Time Zones, Computational results.] 181
Leveraging the Customer Base: Creating Competitive Advantage Through Knowledge Management. Elie Ofek and Miklos Sarvary, Management Science, 47 (November 2001), pp. 1441-56. [Literature review. Model presentation. Knowledge management system design. Monopoly, Competition between symmetric firms. Network externalities. Installed knowledge. Professional services firms. Assessment.] 182
Product Variety, Supply Chain Structure, and Firm Performance: Analysis of the U.S. Bicycle Industry. Taylor Randall and Karl Ulrich, Management Science, 47 (December 2001), pp. 1588-1604. [Literature review. Hypotheses, Data collection, impacts. Firm location. Scale efficient. Return on assets. Return on > sales. Statistical analysis.] 183
See also 1, 2, 3, 4, 6,1,%, 9, 11,12,15, 16,17, 18,21,22,23,24, 26, 27, 30, 32, 33, 34, 35, 36, 37,38,40,41, 53, 65,66,68, 69, 78, 86,87, 101, 102, 103, 104, 105, 107, 108, 109, 110, 111, 115, 119, 120, 122, 123, 124, 125, 126, 128, 129, 130, 136, 145, 147, 151, 154, 164, 165,175, 176,194,210
Predicting Revenue Revisions of Local Networks in Telecommunication Industry. Victor Glass and Maria Petukhova, Journal of Business Forecasting, 20 (Winter 2001-02), pp. 16-22. [Discussion; Techniques; Estimations; Old product, new product, simple regression, and two-step regression methods; Assessment.] 184
How Much Data Should We Use to Prepare Forecasts. Chaman L. Jain, Journal of Business Forecasting, 20 (Winter 2001-02), pp. 2, 10. [Discussion, Product life cycle. Models, Moving averages. Box Jenkins, Forecast horizon. Ex-post forecast errors. Case study.] 185
Strategic Forecasting for the Long Haul. Larry Lapide, Journal of Business Forecasting, 21 (Spring 2002), pp. 12-14. [Discussion, Techniques, Time series. Life cycle. Causal, Judgmental, Delphi Approach, Recommendations.] 186
How to Forecast Demand in Tender Markets. Michael Latta, Journal of Business Forecasting, 21 (Spring 2002), pp. 8-11. [Agreement to buy (sell) a specified quantity of goods or services at a specified price during a specified time period. Uncertainty, Markets, Expected value. Scenarios, Examples.] 187
What If Consumer Experiments Impact Variances as well as Means? Response Variability as a Behavioral Phenomenon. Jordan J. Louviere. Journal of Consumer Research, 28 (December 2001), pp. 506-11. [Random utility theory, Confoundment of means and variances poses problems for published research in many areas of consumer behavior. Examples.] 188
On the Use of College Students in Social Science Research: Insights from a Second-Order Meta-analysis. Robert A. Peterson, Journal of Consumer Research, 28 (December 2001), pp. 450-61. [Discussion, Comparisons, Nonstudents, Impacts, Response homogeneity. Effect sizes. Need for replicating research based on college student subjects with nonstudent subjects before attempting any generalizations.] 189
Hierarchical Bayes Versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery. Rick L. Andrews, Asim Ansari, and Imran S. Currim, Journal of Marketing Research, 39 (February 2002), pp. 87-98. [Literature review. Simulation methodology. Both models are equally effective in recovering individual-level parameters and predicting ratings of holdout profiles. Hierarchical Bayes performs well even when partworths come from a mixture of distributions. Finite Mixture produces good parameter estimates at the individual level.] 190
Extending Discrete Choice Models to Incorporate Attitudinal and Other Latent Variables. Kalidas Ashok, William R. Dillon, and Sophie Yuan, Journal of Marketing Research, 39 (February 2002), pp. 31-46. [Discussion, Two studies. Focus on understanding how softer attributes can influence choice decisions. Integrating structural equation models within the basic framework of binary and multinomial choice models. Empirical applications.] 191
Using Multimarket Data to Predict Brand Performance in Markets for Which No or Poor Data Exist. Bart J. Bronnenberg and Catarina Sismeiro, Journal of Marketing Research, 39 (February 2002), pp. 1-17. [Spatial prediction model. Structure of retailing industry and geographic location of markets. Holdout samples. Price elasticities. Solution of omitted variables problem. Drawing uninformative estimates toward their local averages. Statistical analysis.] 192
A Simulated Annealing Heuristic for a Bicriterion Partitioning Problem in Market Segmentation. Michael J. Brusco, J. Dennis Cradit, and Stephanie Stahl, Journal of Marketing Research, 39 (February 2002), pp. 99-109. [Discussion, Mathematical equations. Comparisons, Krieger and Green two-way algorithm. Experimental study. Industry application. Assessment.] 193
The Field Behind the Screen: Using Netnography for Marketing Research in Online Communities. Robert V. Kozinets, Journal of Marketing Research, 39 (February 2002), pp. 61-72. [Discussion; Comparisons; Netnography is faster, simpler, and less expensive than traditional ethnography and more naturalistic and unobtrusive than focus groups or interviews; Provides information on the symbolism, meanings, and consumption patterns of online consumer groups; Example.] 194
A Multiple Ideal Point Model: Capturing Multiple Preference Effects from Within an Ideal Point Framework. Jack K.H. Lee, K. Sudhir, and Joel H. Steckel, Journal of Marketing Research, 39 (February 2002), pp. 73-86. [Literature review. Exploratory simulations. Estimation procedure. True choice structure. Panel data. Aggregate-level implications.] 195
Designing Conjoint Choice Experiments Using Managers' Prior Beliefs. Zsolt Sandor and Michel Wedel, Journal of Marketing Research, 38 (November 2001 ), pp. 430-44. [Literature review, Bayesian designs. Comparisons, Huber and Zwerina, Monte Carlo studies. Application, Assessment.] 196
See also 19, SO, S\,^2, 92, 99, 106, 133, 211, 214
From IT Solutions to Business Results. Nadim Matta and Sandy Krieger, Business Horizons, 44 (November/December 2001), pp. 45-50. [Data warehousing. Matrix, Scope, Activity and results orientations. Broad systems projects. Factors, Mental shifts. Rapidresult projects. Moving into action. Broad results. Guidelines.] 197
Welcome to the New World of Merchandising. Scott C. Friend and Patricia H. Walker, Harvard Business Review, 79 (November 2001), pp. 133-36, 138, 140-41. [Discussion, Optimization, Software packages. Yield management applications. Demand forecasting, Markdowns, Success, Guidelines.] 198
Challenges in the Post Forecasting Software/System Implementation Phase. Leanne M. Regalia, Journal of Business Forecasting, 20 (Winter 2001-02). pp. 7-10. [Study, Forecasting tools. Length of time since implementation. Planning requirements. Changes made in processes and systems. Resources, Information from other companies. Satisfaction with improvement efforts. Problems, Information sharing. Assessment.] 199
Virtual Community and Connections: Their Relevance to Health Care Marketing. Debbie S. Easterling, Journal of Nonprofit and Public Sector Marketing, 8 (No. 4, 2001), pp. 75-89. [Discussion, Online activities. Health related. Supportive, Altruistic, Breast cancer diagnoses. Personal web sites. Examples.] 200
Information Search: External Secondary Information for Strategic Marketing Planning in Nonprofit Organizations. Vaughan C. Judd and Betty J. Tims, Journal of Nonprofit and Public Sector Marketing, 1 (No. I and 2, 2001 ), pp. 119-40. [Discussion, Information-seeking tactics. Information sources, Internet, Library consortia. Virtual and traditional libraries, CD-ROM databases. Examples.] 201
Expanding Your Communication Budget with Group Collaboration Technology, Donald R. Self and William C. Grayson, Journal of Nonprofit and Public Sector Marketing, 9 (No. 1 and 2, 2001), pp. 169-77. [Discussion, Techniques, Audio teleconferencing. Streaming, Messaging, Nonprofit marketing applications. Video and Web conferencing.] 202
Purchasing Execs Show Keen Interest, But They're Not Buying Yet. Purchasing, 130 (September 20, 2001), pp. 14-15. [E-sourcing software. Survey, Impacts, Productivity, Operating costs. Cycle time reduction, Lower-than-expected returns on investment. Lack of adequate technical and labor resources. Examples.] 203
Measuring Purchasing's Value. Susan Avery, Purchasing, 130 (July 19, 2001), pp. 45, 48^9. [IT Procurement Working Group, Information technology procurement. Cost savings. Cycle time reductions. Customer satisfaction. Examples.] 204
Internet Connections Make Shipments More Visible. David Hannon, Purchasing, 130 (November 15, 2001), pp. 47-49. [High-tech industries are moving to a model of lower inventory and more just-in-time shipping. Lack of Internet access. Online buying exchange movement. Customer relations. Examples.] 205
What PMs Need to Know. Anne Millen Porter and David Hannon, Purchasing, 130 (December 15, 2001), pp. S1-S8. [Purchasing managers. Concerns, Data security. Responsibility, Perceived versus actual value. System design. Protecting data. Guidelines.] 206
Data-Mining Technology Lets Retailers Identify Which Job Candidates are Likely to Remain on the Job. Karen M. Kroll, Stores, 83 (July 2001), pp. 62, 64. [Discussion; Electronic application kiosks; Success patterns; Candidate's interest and aptitude for selling, customer service, and overall reliability; Equal employment issues; Examples.] 207
The Analytics Divide. Patrick A. McGuire, Stores, 83 (October 2001), pp. 32-34, 36. [Discussion, Software packages. Data analysis. Decision-friendly information. Shopping habits. Early adopters. Examples.] 208
See also 151, 189
Schools of Thought in Organizational Learning. Simon J. Bell, Gregory J. Whitwell, and Bryan A. Lukas, Journal of the Academy of Marketing Science, 30 (Winter 2002), pp. 70-86. [Literature review. Comparative analysis (economic, managerial, developmental, process), Applications to market orientation and new product development.] 209
Guidelines for Conducting Research and Publishing in Marketing: From Conceptualization Through the Review Process. John O. Summers, Journal of the Academy of Marketing Science, 29 (Fall 2001), pp. 405-15. [Leading marketing journals. Manuscript acceptance rates. Developing manuscripts. Scholarly research on substantive issues in marketing. State of research in marketing. Assessment.] 210
Journal of Marketing Education, 23 (December 2001), pp. 169-259. [Nine articles on innovation in marketing education. Innovative teaching. Student perceptions of educational technology tools. Utilization of simulation to enhance sport marketing concepts. Learning approach employing student documentation. Web and off-Web marketing research sources. Experiential learning in entrepreneurship and retail management, A department of marketing advisory council. Skill development gaps in marketing and MBA programs. Role of marketing in an integrative business curriculum.] 211
Teaching Portfolios: Uses and Development. Laurie A. Babin, Teri Root Shaffer, and Amy Morgan Tomas, Journal of Marketing Education, 24 (April 2002), pp. 35-42. [Discussion, Definition, Elements (teaching responsibilities and philosophy, evidence of teaching effectiveness, instructional improvement). Assessment.] 212
Exploring Achievement Striving as a Moderator of the Grading Leniency Effect. Donald R. Bacon and Jenny Novotny, Journal of Marketing Education, 24 (April 2002), pp. 4-14. [Literature review. Hypotheses, Evaluation preferences. Achievement striving was found to be inversely related to the tendency to give higher evaluations to lenient-grading teachers at the undergraduate level but not at the graduate level. Statistical analysis. Implications.] 213
Discontinuous Classroom Innovation: Waves of Change for Marketing Education. Richard L. Ceisi and Mary Wolfinbarger, Journal of Marketing Education, 24 (April 2002), pp. 64-72. [Discussion; Comparisons; Technology as a support function; Mirroring; Discontinuous innovation is characterized by unique applications that extend the classroom in ways that result in a more current, active, and interactive learning environment; End goals; Assessment.] 214
Explaining the Appeal of Sales Careers: A Comparison of Black and White College Students. Susan Del Vecchio and Earl D. Honeycutt, Jr., Journal of Marketing Education, 24 (April 2002), pp. 56-63. [Literature review. Research questions. Survey, Attitudes, Job attributes (starting salary, autonomy, education). Statistical analysis. Implications.] 215
Team Learning in a Marketing Principles Course: Cooperative Structures That Facilitate Active Learning and Higher Level Thinking. Sigfredo A. Hernandez, Journal of Marketing Education, 24 (April 2002), pp. 73-85. [Discussion, Comparison with traditional course. Instructional activity sequence. Structure (team selection, rotating roles, team-building activities, peer evaluations, allocation of team resources). Effectiveness, Guidelines.] 216
The Development and Consequences of Trust in Student Project Groups. Lenard C. Huff, Joanne Cooper, and Wayne Jones, Journal of Marketing Education, 24 (April 2002), pp. 24-34. [Literature review. Model presentation. Survey of students. Factors, Determinants of trust. Conditions and facilitating tools. Consequences of distrust. Assessment, Implications.] 217
Enhancing Students' Role Identity as Marketing Majors. Susan Schultz Kleine, Journal of Marketing Education, 24 (April 2002), pp. 15-23. [Literature review. Hypotheses, Survey of students. Measures, Identity importance. Social commitments. Identity-related esteem. Identity time commitment. Subjective adjustment to major. Statistical analysis. Guidelines.] 218
A Professional School Approach to Marketing Education. John A. Schibrowsky, James W. Peltier, and Thomas E. Boyt, Journal of Marketing Education, 24 (April 2002), pp. 43-55. [Discussion; Liberal arts, professional, and vocational approaches to business education; Benefits of professional school approach; Issues (faculty, student, curriculum, other); Assessment.] 219
A Realistic Sales Experience: Providing Feedback by Integrating Buying, Selling, and Managing Experiences. Susan Powell Mantel, Ellen Bolman Pullins, David A. Reid, and Richard E. Buehrer, Journal of Personal Selling and Sales Management, 22 (Winter 2002), pp. 34-40. [Experiential learning. Course descriptions. Schedule of topics and activities. Written communication. Proposal role-play. Sales portfolio. Learning oriented outcomes.] 220
Howard University Launches Supply Chain Curriculum. Damon Francis, Purchasing, 130 (October 4, 2001), pp. 22-23. [Discussion, Goals, Student recruitment. Quality education. Establish network of contacts. Promote continued involvement. Working across departments. Starting salaries.] 221
Sales Ruined My Personal Life. Betsy Cummings, Sales and Marketing Management, 153 (November 2001), pp. 44-46, 48-50. [Survey of sales executives. Impacts, Stress, Workloads, Learning to leave. Regular communication from managers. Work-life balance. Examples.] 222
See also 55, 71,216,218
Marketing-Mix Variables and the Diffusion of Successive Generations of a Technological Innovation. Peter J. Danaher, Bruce G.S. Hardie, and William P. Putsis, Jr., Journal of Marketing Research, 38 (November 2001), pp. 501-14. [Literature review. Model development. Empirical analysis estimates the impact of for two generations of cellular phones in a European country. Assessment.] 223
~~~~~~~~
By MYRON LEONARD, Editor; Western Carolina University
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Record: 99- Marketing Literature Review. By: Leonard, Myron. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p142-155. 14p. DOI: 10.1509/jmkg.66.2.142.18473.
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- Business Source Complete
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Record: 101- Marketing Literature Review. By: Leonard, Myron. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p128-140. 13p. DOI: 10.1509/jmkg.66.3.128.18509.
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Marketing Literature Review
SUBJECT HEADINGS 1. THE MARKETING ENVIRONMENT 1.1 Consumer Behavior
This section is based on a selection of article abstracts from a comprehensive business literature database. Marketing-related abstracts from more than 125 journals (both academic and trade) are reviewed by JM staff. Descriptors for each entry are assigned by JM staff. Each issue of this section represents three months of entries into the database.
Each entry has an identifying number. Cross-references appear immediately under each subject heading.
The following article abstracts are available online from the ABI/INFORM database, which is published and copyrighted by ProQuest Information and Learning. For additional information about access to the database or about obtaining photocopies of the articles abstracted here, please call (800) 521-0600 or write to ProQuest, 300 N. Zeeb Rd., Ann Arbor, MI 48106.
1. THE MARKETING ENVIRONMENT
1.1 Consumer Behavior
1.2 Legal, Political, and Economic Issues
1.3 Ethics and Social Responsibility
2. MARKETING FUNCTIONS
2.1 Management, Planning, and Strategy
2.2 Retailing
2.3 Channels of Distribution
2.4 Electronic Marketing
2.5 Physical Distribution
2.6 Pricing
2.7 Product
2.8 Sales Promotion
2.9 Advertising
2.10 Personal Selling
2.11 Sales Management
3. SPECIAL MARKETING APPLICATIONS
3.1 Industrial
3.2 Nonprofit, Political, and Social Causes
3.3 International and Comparative
3.4 Services
4. MARKETING RESEARCH
4.1 Theory and Philosophy of Science
4.2 Research Methodology
4.3 Information Technology
5. OTHER TOPICS
5.1 Educational and Professional Issues
5.2 General Marketing
See also 89, 98, 100, 101, 103, 106, 107, 108, 109, 117, 118, 120, 123, 138, 139, 142, 144, 150, 158, 159, 166, 169, 181, 182, 186, 192, 193, 196, 198, 202, 208, 209, 214
Days of Our Lives. Rebecca Gardyn, American Demographics, 23 (May 2001), pp. 32-35. [Work-at-home men and women, Shifting gender roles, Homemakers, TV tastes, Education levels, Attitudes, Programming content, Statistical data.] 1
Granddaughters of Feminism. Rebecca Gardyn, American Demographics, 23 (April 2001), pp. 42-47. [Target markets, Generation Y girls, TV, Internet, Personal digital assistants, Mobile phones, Interest in activism, Educational achievement, Market strategy, Examples.] 2
Echo Boomerang. Pamela Paul, American Demographics, 23 (June 2001), pp. 44-49. [College graduates moving back home, Comparisons, Generation X and Y, Parents' expectations, Higher education, Marital trends, Job changes, Financial aspects, Statistical data.] 3
"Stay Tuned--We Will Be Back Right After These Messages": Need to Evaluate Moderates the Transfer of Irritation in Advertising. Bob M. Fennis and Arnold B. Bakker, Journal of Advertising, 30 (Fall 2001), pp. 15-25. [Literature review, Model presentation, Hypotheses, Experiment, People with a high need to evaluate (NE) will show a transfer of irritation more clearly than will low-NE individuals, Statistical analysis, Theoretical and practical implications, The Netherlands.] 4
Advertising's Influence on Subsequent Product Trial Processing. DeAnna S. Kempf and Russell N. Laczniak, Journal of Advertising, 30 (Fall 2001), pp. 27-38. [Literature review, Hypotheses, Experiment (trial only, ad only, ad and trial), Variables, Perceived diagnosticity of trial, Perceived expertise, Expectancy value, Brand attitude, Purchase intentions, Belief confidence, Statistical analysis.] 5
Memory-Based Measures for Assessing Advertising Effects: A Comparison of Explicit and Implicit Memory Effects. Stewart Shapiro and H. Shanker Krishman, Journal of Advertising, 30 (Fall 2001), pp. 1-13. [Literature review; Hypotheses; Lab experiment; Process dissociation procedure; Conscious and automatic components; Implicit memory is preserved even in conditions of delay and divided attention, whereas explicit memory is affected detrimentally by those conditions; Statistical analysis.] 6
The Impact of Parent Brand Attribute Associations and Affect on Brand Extension Evaluation. Sobodh Bhat and Srinivas K. Reddy, Journal of Business Research, 53 (September 2001), pp. 111-22. [Literature review, Model presentation, Hypotheses, Consumer survey, Product category, Parent brand quality, Attribute-and affect-driven, Purchase intentions, Statistical analysis, Managerial implications.] 7
What Makes Open vs. Closed Conclusion Advertisements More Persuasive? The Moderating Role of Prior Knowledge and Involvement. Jean-Charles Chebat, Mathieu Charlebois, and Claire Gelinas-Chebat, Journal of Business Research, 53 (August 2001), pp. 93-102. [Literature review, Model presentation, Hypotheses, Experiment, Information processing, Consumer attitudes, Statistical analysis, Managerial implications.] 8
Consumer Decision-Making in a Multi-generational Choice Set Context. Namwoon Kim, Rajendra K. Srivastava, and Jin K. Han, Journal of Business Research, 53 (September 2001), pp. 123-36. [Literature review, Model proposal and testing, Survey of small business owners (PC purchases), Impacts, Technology and price sensitivities, Information insensitivity, Statistical analysis, Managerial implications.] 9
Conceptual and Operational Aspects of Brand Loyalty: An Empirical Investigation. Yorick Odin, Nathalie Odin, and Pierre Valette-Florence, Journal of Business Research, 53 (August 2001), pp. 75-84. [Literature review, Scale development and testing, Hypothesis, Consumer survey, Repeat purchasing, Impacts, Strong and weak brand sensitivity, Inertia, Statistical analysis.] 10
Age Differences in Memory for Radio Advertisements: The Role of Mnemonics. Malcolm C. Smith and Mark R. Phillips Jr., Journal of Business Research, 53 (August 2001), pp. 103-109. [Literature review; Hypotheses; Experiment; Factors; Age; Version; Timing; Comparisons; Overall aided, unaided, and prompted recall; Overall recognition and memory; Statistical analysis; Managerial implications.] 11
The Short-Term Effect of Store-Level Promotions on Store Choice, and the Moderating Role of Individual Variables. Pierre Volle, Journal of Business Research, 53 (August 2001), pp. 63-73. [Literature review, Multinomial logit models, Hypotheses, Panel data, Impacts, Grocery store loyalty, Involvement toward shopping, Attitude toward products on promotion, Search for promotional information, Statistical analysis, Managerial implications, France.] 12
The Seventh Moment: Qualitative Inquiry and the Practices of a More Radical Consumer Research. Norman K. Denzin, Journal of Consumer Research, 28 (September 2001), pp. 324-30. [Discussion; Cultural studies; Set of interpretive, methodological, and ethical criteria; Examples; Black Arts Movement of the 1970s.] 13
Nonconscious and Contaminative Effects of Hypothetical Questions on Subsequent Decision Making. Gavan J. Fitzsimons and Baba Shiv, Journal of Consumer Research, 28 (September 2001), pp. 224-38. [Literature review, Hypotheses, Experiments and poststudy interviews, Biasing effects, Cognitive elaboration, Relevant information, Choice awareness, Statistical analysis.] 14
Emotional Contagion Effects on Product Attitudes. Daniel J. Howard and Charles Gengler, Journal of Consumer Research, 28 (September 2001), pp. 189-201. [Literature review, Hypotheses, Two experiments, Exposing receivers to happy senders they liked resulted in receivers having a positive attitude toward a product, Observation of facial expressions of senders by receivers was a necessary condition for emotional contagion to occur.] 15
Moderators of Language Effects in Advertising to Bilinguals: A Psycholinguistic Approach. David Luna and Laura A. Peracchio, Journal of Consumer Research, 28 (September 2001), pp. 284-95. [Literature review, Model presentation, Hypotheses, Picture-text congruity, Increasing memory for second-language ads, Reducing impact of language asymmetries on memory, Statistical analysis.] 16
Affect Monitoring and the Primacy of Feelings in Judgment. Michel Tuan Pham, Joel B. Cohen, John W. Pracejus, and G. David Hughes, Journal of Consumer Research, 28 (September 2001), pp. 167-88. [Literature review; Four studies; Impacts; Integral feelings are monitored rapidly, elicit agreement, and are potent predictors of thoughts; Statistical analysis.] 17
The Moderating Effect of Knowledge and Resources on the Persuasive Impact of Analogies. Michelle L. Roehm and Brian Sternthal, Journal of Consumer Research, 28 (September 2001), pp. 257-72. [Literature review, Hypotheses, Four experiments, Impacts, Expertise with base product, Resources, Training on how to process base information, Positive mood, Statistical analysis, Managerial implications.] 18
Two Ways of Learning Brand Associations. Stijn M.J. Van Osselaer and Chris Janiszewski, Journal of Consumer Research, 28 (September 2001), pp. 202-23. [Literature review, Four studies, Impacts, Human associative memory models, Adaptive learning, Feature-benefit associations, Statistical analysis.] 19
The Role of Market Efficiency Intuitions in Consumer Choice: A Case of Compensatory Inferences. Alexander Chernev and Gregory S. Carpenter, Journal of Marketing Research, 38 (August 2001), pp. 349-61. [Literature review, Four experiments; Brand attributes (observable and unobservable), Consumers'intuitive theories about the competitive nature of the market, Assessment, Managerial implications.] 20
What We See Makes Us Who We Are: Priming Ethnic Self-Awareness and Advertising Response. Mark R. Forehand and Rohit Deshpandé, Journal of Marketing Research, 38 (August 2001), pp. 336-48. [Literature review, Hypotheses, Two experiments, Self-categorization, Television and print advertising, Asian and Caucasian participants, Statistical analysis.] 21
Strategic Management of Expectations: The Role of Disconfirmation Sensitivity and Perfectionism. Praveen K. Kopalle and Donald R. Lehmann, Journal of Marketing Research, 38 (August 2001), pp. 386-94. [Literature review; Hypotheses; Laboratory experiment and field study; Consumers who are more disconfirmation sensitive have lower expectations, whereas perfectionists have higher expectations; Postpurchase evaluation.] 22
An Online Prepurchase Intentions Model: The Role of Intention to Search. Soyeon Shim, Mary Ann Eastlick, Sherry L. Lotz, and Patricia Warrington, Journal of Retailing, 77 (Fall 2001), pp. 397-416. [Literature review; Hypotheses; Survey of households; Variables; Attitude; Subjective norm; Perceived behavioral control; Internet purchase experience, search, and shopping intentions; Exploratory factor analysis.] 23
See also 28, 32, 143
Anti-trust: A Threat to Mergers and Acquisitions? David W. Conklin and Peter H. Pocklington Jr., Ivy Business Journal (Canada), 64 (July/August 2000), pp. 38-42. [Discussion, Factors, Availability of substitute products, Reducing international trade and investment barriers, Changes in industry cost structure, International protection of intellectual property, Pace of industry change, Privatization and deregulation, Public opinion, Examples.] 24
Small Retailer and Service Company Accuracy in Evaluating the Legality of Specified Practices. Robin T. Peterson, Journal of Small Business Management, 39 (October 2001), pp. 312-19. [Discussion, Hypotheses, Survey, Comparisons, Assessments, Statistical analysis.] 25
Too Close for Comfort. Mark McMaster, Sales and Marketing Management, (July 2001), pp. 42-48. [Discussion, Customer privacy, Impacts, Sales and marketing teams, New technologies, FTC investigations, Deception, Fraud, Examples.] 26
New Privacy Rules Shake Up Drug Store Operations. Susan Reda, Stores, 83 (April 2001), pp. 52-55. [Federal regulation, Patient consent, Prescription pick up, Transfer from one pharmacy to another, Patient noncompliance, Confidentiality of information, Implementation, Examples.] 27
See also 26, 206, 234
Corporate Social Audits--This Time Around. Homer H. Johnson, Business Horizons, 44 (May/June 2001), pp. 29-36. [Process for identifying, measuring, and reporting the ethical, social, and environmental impact of an organization; Factors; Socially responsible investing; Public interest groups; Internal audits; Examples; Projections.] 28
Reevaluating Green Marketing: A Strategic Approach. Michael Jay Polonsky and Philip J. Rosenberger III, Business Horizons, 44 (September/October 2001), pp. 21-30. [Discussion, External pressures, Strategy implementation (targeting, green design/new product development, positioning, pricing, logistics, marketing waste, promotion, alliances), Impacts, Green levels (tactical, quasi-strategic, strategic), Examples.] 29
Multinationality and Corporate Ethics: Codes of Conduct in the Sporting Goods Industry. Rob van Tulder and Ans Kolk, Journal of International Business Studies, 32 (Second Quarter 2001), pp. 267-83. [Discussion, Contents of codes, Impacts, Sourcing strategies, Degrees of multinationality, National backgrounds, Assessment.] 30
Marketing and the Natural Environment: What Role for Morality? Andrew Crane, Journal of Macromarketing, 20 (December 2000), pp. 144-54. [Literature review, Perspectives, Fair play, Managerialist, Reformist, Reconstructionist, Interpretist, Impacts, Core discipline, Form of morality, Prevalent subject of moral enquiry.] 31
The Role of Codes of Conduct in the Advertising Self-Regulatory Framework. Debra Harker and Michael Harker, Journal of Macromarketing, 20 (December 2000), pp. 155-66. [Discussion, Codes of conduct, Creation (consultation, drafting, dissemination), Application, Australia's Advertising Standards Council.] 32
Marketing Ethics and Behavioral Predisposition of Chinese Managers of SMEs in Hong Kong. Alan K.M. Au and Alan C.B. Tse, Journal of Small Business Management, 39 (July 2001), pp. 272-78. [Literature review, Model presentation, Hypotheses, Survey, Impacts, Money orientation, Egoism, Belief in retribution, Religiosity and gender, Regression analysis.] 33
Watch Out for Counterfeit Parts. David Hannon, Purchasing, 130 (April 19, 2001), pp. 18, 20, 22. [Discussion, Impacts, High demand, Internet use, Online auctions, Know sources, Check packaging, Legal recourse, Examples.] 34
An Affair to Remember. Betsy Cummings, Sales and Marketing Management, (August 2001), pp. 50-57. [Survey of sales reps, Office romances, Impacts, Team morale, Jealousy between coworkers, Loss of key accounts, Sexual harassment lawsuits, Policies regarding workplace relationships, Examples.] 35
See also 24, 27, 28, 30, 33, 70, 73, 74, 75, 76, 77, 79, 82, 85, 86, 88, 93, 97, 98, 99, 104, 105, 110, 112, 132, 135, 136, 137, 145, 146, 147, 149, 151, 152, 153, 155, 156, 157, 158, 160, 161, 162, 163, 164, 165, 170, 171, 172, 173, 174, 175, 176, 177, 180, 189, 190, 195, 200, 203, 204, 206, 211, 213, 216, 225, 236
The Essentials of Scenario Writing. Steven Schnaars and Paschalina Ziamou, Business Horizons, 44 (July/August 2001), pp. 25-31. [Discussion, Characteristics (stylized narratives, sets, tracing progression of present to future), Writing steps, Themes or logics (best guess, good and bad, single issue, independent themes, length and span of scenarios), Strategy, Guidelines.] 36
E-Competitive Transformations. Detmar Straub and Richard Klein, Business Horizons, 44 (May/June 2001), pp. 3-12. [Strategic planning; E-commerce; Order-of-magnitude effects; Alpha-, beta-, and omega-level information asymmetries; Beta-level disintermediation effects; Sustainable advantages over rivals; Assess --ment.] 37
Swarm Intelligence: A Whole New Way to Think About Business. Eric Bonabeau and Christopher Meyer, Harvard Business Review, 79 (May 2001), pp. 106-14. [Discussion, Teamwork, Self-organized, Coordination, Interactions, Rules, Examples.] 38
Want to Perfect Your Company's Service? Use Behavioral Science. Richard B. Chase and Sriram Dasu, Harvard Business Review, 79 (June 2001), pp. 78-84. [Discussion,Operating principles, Finish strong, Get bad experiences out of the way early, Segment the pleasure and combine the pain, Build commitment through choice, Give people rituals and stick to them, Examples.] 39
In Praise of Middle Managers. Quy Nguyen Huy, Harvard Business Review, 79 (September 2001), pp. 72-79. [Study, Contribution areas (entrepreneur, communicator, therapist, tightrope artist), Assessment, Guidelines.] 40
Bringing a Dying Brand Back to Life. Mannie Jackson, Harvard Business Review, 79 (May 2001), pp. 53-56, 58, 60-61. [Turn-around management, Sports, Showmanship, Customer orientation, Media attention, Social commitment, Sponsors, Success, Guidelines.] 41
Get Inside the Lives of Your Customers. Patricia B. Seybold, Harvard Business Review, 79 (May 2001), pp. 80-89. [Customer relations, Using scenarios, Business models, Web strategy, Success, Guidelines.] 42
The Effects of Personal Value Similarity on Business Negotiations. Swee Hoon Ang, Siew Meng Leong, and Georgina P.S. Teo, Industrial Marketing Management, 29 (September 2000), pp. 397-410. [Literature review, Hypotheses, Experiment using executives, Attitudes, Time-processing orientations, Agreement preferences, Adaptability, Statistical analysis, Managerial implications, Singapore.] 43
"Coopetition" in Business Networks--To Cooperate and Compete Simultaneously. Maria Bengtsson and Soren Kock, Industrial Marketing Management, 29 (September 2000), pp. 411-26. [Literature review, Propositions, Impacts, Degree of closeness to customers, Different business units, Dependence due to heterogeneity in resources and to connectedness of positions, Conflicting logic of interaction, Advantages, Case studies, Sweden, Finland.] 44
A Match Made in Heaven? Understanding the Myths and Challenges of Mergers and Acquisitions. Jay Anand, Ivy Business Journal (Canada), 64 (July/August 2000), pp. 68-79. [Discussion, Factors, Performance, Profitability, Large firms, Acquisition of "good" targets, Vertical integration, High-technology or high-growth targets, New growth opportunities, Earnings per share, Pooling accounting, Stock versus cash transactions, Success, Guidelines.] 45
Unlocking the Rusty Supply Chain: The Value Net. David Bovet and Gilles Roucolle, Ivy Business Journal (Canada), 65 (Septem --ber/October 2000), pp. 31-35. [Discussion, Factors, Solve customer problems rather than simply sell products, Quick response to customer demands, Build a strong brand based on valuable services, Build in barriers to competition, Examples.] 46
Today's Board and the Academic Option. Michael Maher and Malcolm Carlyle Munro, Ivy Business Journal (Canada), 64 (July/August 2000), pp. 8-11. [Board composition and strategy, Consideration of academic professionals, Finding a suitable candidate, Academics must "get themselves noticed," Objectivity, Assessment, Canada.] 47
Vanishing Walls--The E-Organization: Function Follows Form. Doug Treen, Ivy Business Journal (Canada), 65 (September/October 2000), pp. 55-59. [Organizational structure, Change, Value proposition, Software packages, Process integration, E-business "cza,r, Teams, Examples.] 48
Effects of Communication Direction on Job Performance and Satisfaction: A Moderated Regression Analysis. Jose R. Goris, Bobby C. Vaught, and John D. Pettit Jr., Journal of Business Communication, 37 (October 2000), pp. 348-68. [Literature review, Model presentation, Hypotheses, Survey of employees, Individual-job congruence, Growth needs strength, Job scope, Assessment, Implications.] 49
Strategic Decision Making in an Intuitive vs. Technocratic Mode: Structural and Environmental Considerations. Jeffrey G. Covin, Dennis P. Slevin, and Michael B. Heeley, Journal of Business Research, 52 (April 2001), pp. 51-67. [Theoretical discussion, Hypotheses, Survey of manufacturing firms, Variables, Sales growth rate, Return on sales, Decision making style, Organizational structure, Environmental technological sophistication, Firm size and age, Statistical analysis.] 50
Internet Startups: So Why Can't They Win? Sydney Finkelstein, Journal of Business Strategy, 22 (July/August 2001), pp. 16-21. [Corporate culture, Market strategy, Competitive advantage, Internal consistency, Paying too much to acquire customers that don't buy enough, Assessment.] 51
Invisible Competition: Some Lessons Learned. Kenneth A. Fox, Journal of Business Strategy, 22 (July/August 2001), pp. 36-38. [Discussion, Segments (stealth competitors, mergers and alliances that change an industry, surprise collaborators, emerging challengers), Examples, Guidelines.] 52
The Impact of Category Management on Retailer Prices and Performance: Theory and Evidence. Suman Basuroy, Murali K. Mantrala, and Rockney G. Walters, Journal of Marketing, 65 (October 2001), pp. 16-32. [Discussion, Strategic framework, Analytical model, Intervention analysis methodology, Scenarios, Cross-price sensitivities, Equilibrium sales and profits, Disaggre-gate analysis, Managerial implications.] 53
Dependence, Trust, and Relational Behavior on the Part of Foreign Subsidiary Marketing Operations: Implications for Managing Global Marketing Operations. Kelly Hewett and William O. Bearden, Journal of Marketing, 65 (October 2001), pp. 51-66. [Literature review, Model presentation, Hypotheses, Survey of marketing managers, Variables, Vertical dependence, Trust, Individualism/collectivism, Acquiescence, Cooperation, Performance, Market program orientation, Statistical analysis.] 54
Managing Customer-Initiated Contacts with Manufacturers: The Impact on Share Category Requirements and Word-of-Mouth Behavior. Douglas Bowan and Das Narayandas, Journal of Marketing Research, 38 (August 2001), pp. 281-97. [Literature review, Model presentation, Survey of customers, Variables, Perceived quality, Disconfirmation, Fairness (procedural, interactional, distributive), Impacts, High loyal, Heavy user, Ask retailer, Complaint, Statistical analysis, Managerial implications.] 55
Contextual Influences and the Adoption and Practice of Relationship Selling in a Business-to-Business Setting: An Exploratory Study. Michael Beverland, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 207-15. [Interviews with experienced salespeople and sales managers, Organizations adopting a relationship approach to selling require flatter organizational structures and need to foster more flexible organizational cultures, New Zealand.] 56
Global R&D Project Management and Organization: A Taxonomy. Vittorio Chiesa, Journal of Product Innovation Management, 17 (September 2000), pp. 341-59. [Literature review, Field research (technology-intensive firms), Specialization-and integration-based structures, Interaction among units, Organizational success factors, Managerial implications, Europe, Japan, North America.] 57
Communication Flows in International Product Innovation Teams. Rudy K. Moenaert, Filip Caeldries, Annouk Lievens, and Elke Wauters, Journal of Product Innovation Management, 17 (September 2000), pp. 360-77. [Literature review, Model presentation, Propositions, Case study research design, Requirements (network transparency, knowledge codification, knowledge credibility, communication cost, secrecy), Firm-and team-level capabilities, Assessment, Managerial implications, Europe.] 58
Small Firms' Motivations for Exporting: To Earn and Learn? William J. Burpitt and Dennis A. Rondinelli, Journal of Small Business Management, 38 (October 2000), pp. 1-14. [Literature review; Survey of firms; Measures; Previous financial success; Increased sales, profit, and growth; Acquisition of new skills, knowledge, and organizational capabilities; Explore new working environments; Impacts on future decisions to export; Managers' attitudes toward risk; Statistical analysis.] 59
Investigating the Existence of the Lead Entrepreneur. Michael D. Ensley, James W. Carland, and JoAnn C. Carland, Journal of Small Business Management, 38 (October 2000), pp. 59-77. [Literature review, Propositions, Survey of firms, Katz/Herron Skill Typology, Carland Entrepreneurship Index, Results confirmed the existence of lead entrepreneurs among macroentrepreneurial firms and suggested that the strength of their strategic vision and self-confidence set them apart.] 60
Quality Practices for a Competitive Advantage in Smaller Firms. Donald F. Kuratko, John C. Goodale, and Jeffrey S. Hornsby, Journal of Small Business Management, 39 (October 2001), pp. 293-311. [Literature review, Survey, Attitudes, Leader-ship, Strategic planning, Customer and market focus, Information and analysis, Human resource focus, Process management, Business results, Statistical analysis.] 61
Sustainability in Action: Identifying and Measuring the Key Performance Drivers. Marc J. Epstein and Marie-Josee Roy, Long Range Planning (UK), 34 (October 2001), pp. 585-604. [Discussion; Strategy, plans, and programs; Structure and systems; Sustainability performance; Stakeholders'reactions; Long-term corporate financial performance; Examples.] 62
Reconfiguration of Value Chains in Converging Media and Communications Markets. Bernd W. Wirtz, Long Range Planning (UK), 34 (August 2001), pp. 489-506. [Literature review, Drivers (deregulation of markets, technological innovations, change of user preferences), Integration of value-added stages, Service and price bundling, Expansion into new markets, Risk diversification, Examples.] 63
Managerial Allocation of Time and Effort: The Effects of Interruptions. Sridhar Seshadri and Zur Shapira, Management Science, 47 (May 2001), pp. 647-62. [Literature review, Virtual experiments, Costly and costless interruptions, Attention, Decision rules, Priority setting, Satisficing, Controlled Markov process, Assessment.] 64
The Trade-Off Between Efficiency and Learning in Interoganizational Relationships for Product Development. Maurizio Sobrero and Edward B. Roberts, Management Science, 47 (April 2001), pp. 493-511. [Literature review, Hypotheses, Data collection (supplier-manufacturer dyadic relationships), Type of problem-solving activities being partitioned and their level of interdependency are important predictors of performance outcomes of the relationship, Statistical analysis.] 65
Managing the IT Buy Brings Out Purchasing's Best. Susan Avery, Purchasing, 130 (June 21, 2001), pp. 23-24, 26-28. [Buying technology goods and services, Early involvement, Cost savings, Teams, Acquisition and support services, Centralizing functions, Case studies.] 66
Top Execs Pinpoint Six Game-Changing Strategies. Jim Morgan, Purchasing, 130 (June 7, 2001), pp. 40-42, 45. [Discussion; Factors; Linkage of sourcing, purchasing, and management of the supply chain to financial plans; Utilization of e-business tools; Planning for globalization; Developing insourcing/outsourcing decisions in terms of corporate strategies; Long-range broadly based strategies aimed at controlling costs; Determining values critical for gaining competitive advantages; Examples.] 67
See also 12, 25, 27, 53, 78, 87, 97, 100, 102, 113, 115, 168, 179, 181, 184, 185, 196, 217, 218, 219
The Retail Power-Performance Conundrum: What Have We Learned? Kusum L. Ailawadi, Journal of Retailing, 77 (Fall 2001), pp. 299-318. [Literature review, Trade and consumer promotions, Store brands, Manufacturer-retailer interactions, Assessment, Research implications.] 68
New Product Introductions, Slotting Allowances, and Retailer Discretion. Ramarao Desiraju, Journal of Retailing, 77 (Fall 2001), pp. 335-58. [Literature review, Mathematical model, Propositions, Preferred method of slotting allowances, Brand-by-brand, Uniform allowance, Optimal contract, Symmetric and asymmetric cases, Assessment.] 69
Drug Chains Adopt New Strategies to Counter Shortage of Pharmacists. Faye Brookman, Stores, 83 (April 2001), pp. 40, 42, 44. [Discussion, Increasing demand, Counseling customers, Impacts, Technology, Quality-of-life initiatives, Joint programs with colleges, Compensation, Examples.] 70
Clicks-and-Mortar Solution Lets On-line Shoppers Get Their Purchases In-Store. Susan Reda, Stores, 83 (April 2001), pp. 82-84. [Discussion, Techniques, Competitive advantage, Providing access to inventory of local stores, Business reengineering, Multichannel customer transaction histories, Examples.] 71
Despite Obstacles, Retailers Expand Use of Internet-Based Transactions, Data Sharing. Susan Reda, Stores, 83 (March 2001), pp. 59-60, 62. [Survey, Building the IT infrastructure, Finance and control, Vendor extranet, Reductions in time and costs, Quality of information, Examples.] 72
Grocery Stores: Leaders or Laggards on Technology? Susan Reda, Stores, 83 (February 2001), pp. 18-20, 22. [Need to shift from buy-side to sell-side decision making, Automation, Monitoring and managing the business, Customer relations, Customized communications, Store-level advances, Examples.] 73
In Survey, Retailers See Management Skills as Top Training Priority. David P. Schulz, Stores, 83 (April 2001), pp. 94, 98. [Discussion, Impacts, Employee satisfaction, Customer service, Measurement and accountability of training, Customization, Electronic teaching systems, Cost effectiveness, Assessment.] 74
See also 34, 46, 55, 65, 68, 86, 95, 96, 137, 141, 147, 153, 162, 194, 216
Bricks and Mortar: 21st Century Survival. Lawrence M. Bellman, Business Horizons, 44 (May/June 2001), pp. 21-28. [Traditional companies, Competition, E-commerce, Innovation, Marketing and service concerns, Issues (legal, privacy, financial), Rethinking marketing approaches, Examples.] 75
Behind Intermediary Performance in Export Trade: Transactions, Agents, and Resources. Mike W. Peng and Anne S. York, Journal of International Business Studies, 32 (Second Quarter 2001), pp. 327-46. [Theoretical discussion, Model presentation, Hypotheses, Survey of firms, Impacts, Knowledge, Negotiation, Title, Specialization, Regression analysis, Implications.] 76
The Severity of Contract Enforcement in Interfirm Channel Relationships. Kersi D. Antia and Gary L. Frazier, Journal of Marketing, 65 (October 2001), pp. 67-81. [Literature review, Conceptual framework, Hypotheses, Survey of managers in franchisor organizations, Factors (dyadic, channel system, network), Variables (performance ambiguity, cost of enforcement, industry, firm size, master franchisee), Statistical analysis, Managerial implications.] 77
Retailer Power and Supplier Welfare: The Case of Wal-Mart. Paul N. Bloom and Vanessa G. Perry, Journal of Retailing, 77 (Fall 2001), pp. 379-96. [Literature review, Regression models, Hypotheses, Data collection (compustat data), Impacts, Small and large market share suppliers, Statistical analysis.] 78
Supplier Selection Practices Among Small Firms in the United States: Testing Three Models. Daewoo Park and Hema A. Krishnan, Journal of Small Business Management, 39 (July 2001), pp. 259-71. [Literature review, Hypotheses, Survey, Executive attitudes, Models (rational/normative, external control, strategic choice), Statistical analysis.] 79
Coordination Mechanisms for a Distribution System with One Supplier and Multiple Retailers. Fangruo Chen, Awi Federgruen, and Yu-Sheng Zheng, Management Science, 47 (May 2001), pp. 693-708. [Literature review, Model presentation, Centralized solution, Discount components (annual sales volume, order quantity, order frequency), Impacts, Channelwide profits, Numerical examples.] 80
Shared-Savings Contracts for Indirect Materials in Supply Chains: Channel Profits and Environmental Impacts. Charles J. Corbett and Gregory A. DeCroix, Management Science, 47 (July 2001), pp. 881-93. [Model presentation, Base and joint investment contracts, Comparative statics of equilibria, Consumption levels, Numerical examples.] 81
Shifting Gears. Mark McMaster, Sales and Marketing Management, (August 2001), pp. 42-48. [Management styles, Relationships, Carmakers, Dealers, Incentives, Customer service, Examples.] 82
See also 23, 26, 34, 37, 42, 51, 66, 71, 72, 75, 102, 117, 148, 179, 215, 228
Hollywood, the Internet, and Kids. Hassan Fattah, American Demographics, 23 (May 2001), pp. 50-55. [Web sites, Impacts, Age groups, Market strategy, Movies, Advertising campaigns, Media mixes, Statistical data.] 83
The Second Coming. Sarah Murray, American Demographics, 23 (April 2001), pp. 28-30. [Internet marketing, Market strategy, Online advertising, Instant Opt-Ins, E-mail, Product branding, Effectiveness, Examples.] 84
Business-to-Business E-Commerce: Models and Managerial Decisions. Pamela Barnes-Vieyra and Cindy Claycomb, Business Horizons, 44 (May/June 2001), pp. 13-20. [Discussion, Impacts, One seller to many buyers, Many sellers to content aggregator to many buyers, One seller to one broker to many buyers, Many sellers to one buyer, Many sellers to many buyers, Examples.] 85
Marketing on the Web: How Executives Feel, What Businesses Do. W. Benoy Joseph, Robert W. Cook, and Rajshekhar G. Javalgi, Business Horizons, 44 (July/August 2001), pp. 32-40. [Study, Marketing with and without intermediaries, Time versus geographic coverage of alternative marketing channels, Strategy importance, Usage patterns, Performance measures, Grading various marketing tasks and activities, Commitment, Balancing prudence and risk taking, Assessment.] 86
Online Grocery Retailing: Success Factors and Potential Pitfalls. Hean Tat Keh and Elain Shieh, Business Horizons, 44 (July/August 2001), pp. 73-83. [Discussion, Industry attractiveness, Growth, Bargaining power, Cost structure, Competitor analysis, Examples.] 87
E-Coms and Their Marketing Strategies. Avraham Shama, Business Horizons, 44 (September/October 2001), pp. 14-20. [Study, Companies, Business growth, Problems, Market shares, Target customers, Marketing mixes, Foreign markets, Examples.] 88
The Effects of Progressive Levels of Interactivity and Vividness in Web Marketing Sites. James R. Coyle and Esther Thorson, Journal of Advertising, 30 (Fall 2001), pp. 65-77. [Literature review, Hypotheses, Experiment, Impacts, Feelings of telepresence, Attitude-behavior consistency, Statistical analysis, Implications.] 89
Journal of Advertising Research, 41 (July/August 2001), pp. 7-81. [Six articles on Web advertising, Advertising placement, Banner ad patterns, "Smart banners, " Forced exposure to banner ads, Advertising effectiveness and content evaluation in print and on the Web, Use of student samples.] 90
Factors Affecting the Adoption of the Internet in the Public Sector. Julie Napoli, Michael T. Ewing, and Leyland F. Pitt, Journal of Nonprofit and Public Sector Marketing, 7 (No. 4, 2000), pp. 77-88. [Literature review, Hypotheses, Survey of managers, Adoption and perceived effectiveness of the Internet is related to the decision-maker's attitude toward this medium as a marketing communications tool, Statistical analysis, Implications, Australia.] 91
Web Ordering, Auctions Will Play Limited Role. William Atkinson, Purchasing, 130 (April 5, 2001), pp. 35-36, 38, 40. [Direct materials procurement, E-sourcing, Integrated information systems, Public versus private exchange networks, Production management system, Examples, Recommendations.] 92
See also 81, 194, 219
Logistics Service Quality as a Segment-Customized Process. John T. Mentzer, Daniel J. Flint, and G. Tomas M. Hult, Journal of Marketing, 65 (October 2001), pp. 82-104. [Literature review, Model presentation, Hypotheses, Survey of customers, Measures (personnel contact quality, order release quantities, information quality, ordering procedures, order accuracy, order condition, order quality, order discrepancy handling, timeliness, satisfaction), Statistical analysis, Managerial implications.] 93
Capacitated Multi-item Inventory Systems with Random and Seasonally Fluctuating Demands: Implications for Postponement Strategies. Yossi Aviv and Awi Federgruen, Management Science, 47 (April 2001), pp. 512-31. [Literature review, Model presentation, Multi-echelon, Markov decision process, Dynamic programming, Benefits of flexible versus dedicated production facilities, Trade-off between capacity and inventory investments, Service levels, Numerical studies.] 94
Sharing and Lateral Transshipment of Inventory in a Supply Chain with Expensive Low-Demand Items. Jovan Grahovac and Amiya Chakravarty, Management Science, 47 (April 2001), pp. 579-94. [Literature review; Mathematical model; Multi-echelon systems; Impacts; Often reduces overall costs of holding, shipping, and waiting for inventory; Sometimes cost reductions are achieved through increasing overall inventory levels.] 95
Who Holds the Inventory? Jim Carbone, Purchasing, 130 (April 19, 2001), pp. 37, 39-40. [Study; Build to order; Inventory levels in the supply chain are shifted to suppliers, distributors, or contract manufacturers; E-commerce; Just-in-time consumption; Outsourcing; Forecasting; Examples.] 96
Logistics Consultants See Varying Needs for Multi-channel Retailers. Michael Hartnett, Stores, 83 (January 2001), pp. 111-12, 114. [Discussion; Store, Internet, and catalog channels; Customer relations; Fulfillment centers; Strategic planning; Examples.] 97
See also 53, 80, 190, 191
Is the Price Right? Peter Meyer, Across the Board, 37 (July/August 2000), pp. 31-34. [Discussion, New markets, Cyberpricing, Pricing too low, Assumption that you get what you pay for, Success, Guidelines.] 98
Pricing as Entrepreneurial Behavior. Minet Schindehutte and Michael H. Morris, Business Horizons, 44 (July/August 2001), pp. 41-48. [Discussion, Price (value, variable, variety, visible, virtual), Strategic perspective, Orientation components (market-versus cost-based, proactive) reactive, risk-assumptive/risk-aversive, flex-ible/standardized), Managerial implications.] 99
Pricey Encounters. Joseph C. Nunes and Peter Boatwright, Harvard Business Review, 79 (July/August 2001), pp. 18-19. [Study, Incidental prices, Impacts on consumer perceptions, Examples.] 100
A Consumer Perspective on Price-Matching Refund Policies: Effect on Price Perceptions and Search Behavior. Joydeep Srivastava and Nicholas Lurie, Journal of Consumer Research, 28 (September 2001), pp. 296-307. [Literature review, Hypotheses, Three studies, Signaling, Store prices, Impacts, High and low search costs, Discontinuing price search, Statistical analysis.] 101
Will the Growth of Multi-channel Retailing Diminish the Pricing Efficiency of the Web? Fang-Fang Tang and Xiaolin Xing, Journal of Retailing, 77 (Fall 2001), pp. 319-33. [Literature review, Hypotheses, Observations, Web sites, Price dispersions, DVD brands, Lower prices among pure Internet retailers than among multichannel retailers online, Statistical analysis, Implications.] 102
See also 7, 9, 10, 18, 19, 20, 41, 46, 58, 65, 69, 87, 95, 102, 115, 119, 135, 146, 161, 168, 169, 170, 188, 192, 193, 210, 213, 229
Behind the Music. Leslie Whitaker, American Demographics, 23 (April 2001), pp. 31-36. [Satellite radio, Market potentials, Pricing, Commuters, Early adopters, Advertising campaigns, Examples.] 103
See Your Brands Through Your Customers' Eyes. Chris Lederer and Sam Hill, Harvard Business Review, 79 (June 2001), pp. 125-33. [Three-dimensional approach to mapping brand portfolios, Molecules, Classification and interpretation, Examples.] 104
Doing It Right: Winning with New Products. Robert G. Cooper, Ivy Business Journal (Canada), 64 (July/August 2000), pp. 54-60. [Stage-gate process (conceptual and operational road map for moving a new product project from idea to launch), Portfolio management, Risk/return bubble diagram, Success, Guidelines.] 105
Consumer Evaluation of Vertical Brand Extensions and Core Brands. Chung K. Kim, Anne M. Lavack, and Margo Smith, Journal of Business Research, 52 (June 2001), pp. 211-22. [Literature review, Hypotheses, Two experiments, Negative impact on core brand, This can be reduced by increasing the perceived distance between core brand and brand extension using graphical and linguistic techniques, Statistical analysis.] 106
Purchase Experiments of Extra-ordinary and Regular Influence Strategies Using Artificial and Real Brands. William H. Motes and Arch G. Woodside, Journal of Business Research, 53 (July 2001), pp. 15-35. [Literature review, Hypotheses, Impacts, Search behavior, Brand penetration, Assisted and unassisted purchase periods, Initial selling price, Models, Forecasting, Assessment, Implications.] 107
The Impact of Brand Extension Introduction on Choice. Vanitha Swaminathan, Richard J. Fox, and Srinivas K. Reddy, Journal of Marketing, 65 (October 2001), pp. 1-15. [Literature review, Three studies (household scanner data), Reciprocal behavior, Market shares, Effects of experience with parent brand, Trial and repeat activities, Statistical analysis, Managerial implications.] 108
Threats to the External Validity of Brand Extension Research. Richard R. Klink and Daniel C. Smith, Journal of Marketing Research, 38 (August 2001), pp. 326-35. [Literature review, Hypotheses, Two studies, Impacts, Limited extension information, Failure to account for consumers' new product adoption tendencies, Single exposure to proposed extensions, Statistical analysis, Implications.] 109
UK Brand Asset Recognition Beyond "Transactions or Events." Tony Tollington, Long Range Planning (UK), 34 (August 2001), pp. 463-87. [Discussion, Intangible assets, Valuation methods (price premium, earnings, royalty payments, original/historic cost), Problem of separability, Assessment.] 110
Warranty Signalling and Reputation. Subramanian Balachander, Management Science, 47 (September 2001), pp. 1282-89. [Literature review, Model presentation, Equilibrium, Profit functions, Market strategy, Competition, Game theory, Assessment, Implications for warranty choices.] 111
The Effect of Incentive Schemes and Organizational Arrangements on the New Product Development Process. Martin Natter, Andreas Mild, Markus Feurstein, Georg Dorffner, and Alfred Taudes, Management Science, 47 (August 2001), pp. 1029-45. [Literature review, Model presentation, Propositions, Neural networks, Agent-based simulation, Search for new products (teambased, House of Quality, trial and error, marketing-production interfaces), Statistical analysis.] 112
Retailers Focus Growing Attention on Brand Building and Sourcing Issues. Susan Reda, Stores, 83 (March 2001), pp. 80, 82, 84. [Discussion, Product development and quality assurance teams, Private brands, Managing consistency, Customer loyalty, Cross-border trading, Sourcing strategy (speed, cost, product), Opportunity and risk, Examples.] 113
See also 192
An Investigation of the Impact of Promotions on Across-Sub-market Competition. P.K. Kannan and Chi Kin Yim, Journal of Business Research, 53 (September 2001), pp. 137-49. [Literature review, Dogit model of brand switching, Scanner panel data, Mathematical equations, Effects, Product differentiation, Intensity of promotional activities, Statistical analysis, Managerial implications.] 114
An Empirical Analysis of the Determinants of Category Expenditure. William P. Putsis, Jr. and Ravi Dhar, Journal of Business Research, 52 (June 2001), pp. 277-91. [Literature review, Data collection (IRI market-level data on food products), National brand and private label promotion can have a significant effect on the level of category expenditure, Significant differences across markets and categories, Statistical analysis.] 115
See also 1, 4, 5, 6, 8, 11, 12, 16, 21, 32, 83, 84, 89, 90, 103, 150, 151, 164, 198, 201, 202, 230
Trying to Clean Up Sweeps. Michael J. Weiss, American Demo-graphics, 23 (May 2001), pp. 42-49. [TV stations, Ratings, ACNielsen, Market strategy, Impacts, Ad revenue, Audience measurement, Alternatives, Assessment.] 116
Ads Unplugged. Leslie Whitaker, American Demographics, 23 (June 2001), pp. 30-33. [Discussion, Connectivity, Wireless, Advertising campaigns, Revenues, Resistance, Age groups, Income levels, Acting locally, Examples.] 117
Highly Attractive Models in Advertising and the Women Who Loathe Them: The Implications of Negative Affect for Spokesperson Effectiveness. Amanda B. Bower, Journal of Advertising, 30 (Fall 2001), pp. 51-63. [Literature review; Model proposal; Hypotheses; Two studies; When sufficient negative affect is generated as a consequence of comparison with beautiful models, evaluations of both the model as a spokesperson and the product argument may be affected adversely because of model derogation.] 118
Exploring Managers' Perceptions of the Impact of Sponsorship on Brand Equity. T. Bettina Cornwell, Donald P. Roy, and Edward A. Steinard II, Journal of Advertising, 30 (Summer 2001), pp. 41-51. [Literature review; Hypotheses; Two-phase survey; Leverage, the use of advertising and promotion to support the sponsor-ship; Management involvement; Effects; Perceived differentiation and financial value of the brand; Statistical analysis; Implications.] 119
Framing Meaning Perceptions with Music: The Case of Teaser Ads. Kineta Hung, Journal of Advertising, 30 (Fall 2001), pp. 39-49. [Literature review; Two studies; Music connects with and accentuates selective visual events, as well as selective aspects of a visual event, to draw out the advertising proposition; Statistical analysis; Implications; Hong Kong.] 120
The Role of Myth in Creative Advertising Design: Theory, Process and Outcome. Gita Venkataramani Johar, Morris B. Holbrook, and Barbara B. Stern, Journal of Advertising, 30 (Summer 2001), pp. 1-25. [Literature review, Propositions, Data collection (creative teams), Only one team engaged in fully diversified idea generation involving a wide range of alternative scenarios, Ad judged most successful by advertising professionals.] 121
Signaling Quality and Credibility in Yellow Pages Advertising: The Influence of Color and Graphics on Choice. Gerald L. Lohse and Dennis L. Rosen, Journal of Advertising, 30 (Summer 2001), pp. 73-85. [Literature review, Hypotheses, Two experiments, A significant effect for process (full) color and photo-graphic-quality graphics on choice of an advertiser from Yellow Pages was found and varied across product categories.] 122
Understanding the Mental Representations Created by Comparative Advertising. Kenneth C. Manning, Paul W. Miniard, Michael J. Barone, and Randall L. Rose, Journal of Advertising, 30 (Summer 2001), pp. 27-39. [Literature review, Hypotheses, Three experiments, Comparisons, Noncomparative ads, Disassociative and associati ve processing, Relative and nonrelative impressions, Assessment, Implications.] 123
See also 131, 132, 133, 210
Salesperson Job Involvement: A Modern Perspective and a New Scale. Felicia G. Lassk, Greg W. Marshall, David W. Cravens, and William C. Moncrief, Journal of Personal Selling and Sales Management, 21 (Fall 2001), pp. 291-302. [Literature review; Survey of salespeople; Variables relating to job satisfaction, job performance, organizational commitment, emotional exhaustion, and turnover intentions; Comparisons; Lodahl and Kejner job involvement scale; Relationship and time involvement; Statistical analysis.] 124
Better with Age. Katharine Kaplan, Sales and Marketing Management, (July 2001), pp. 58-62. [Survey of sales and marketing executives, Older salespeople, Effectiveness, Experience, Enthusiasm, Commitment, Recruitment, Examples.] 125
Flexing Their Muscles. Eilene Zimmerman, Sales and Marketing Management, (September 2001), pp. 34-36, 38-41. [Survey of salespeople, Flexible work options, Impacts, Sales, Loyalty, Productivity, Policy development, Examples.] 126
See also 26, 35, 56, 125, 126, 187, 215, 232, 233, 234
What Buyers Want Most from Salespeople: A View from the Senior Level. Robert M. Peterson and George H. Lucas, Business Horizons, 44 (September/October 2001), pp. 39-45. [Personal interviews, Industrial customers, Requirements (expertise, contribution, representation, trustworthiness, compatibility), Assessment, Implications, Insurance industry.] 127
Torment Your Customers (They'll Love It). Stephen Brown, Harvard Business Review, 79 (October 2001), pp. 82-88. [Retromarketing, Deliberating holding back supplies, Exclusivity, Secrecy, Amplification, Entertainment, Tricks, Examples.] 128
Importance of Alternative Rewards: Impact of Managerial Level. Alan J. Dubinsky, Rolph E. Anderson, and Rajiv Mehta, Industrial Marketing Management, 29 (September 2000), pp. 427-40. [Literature review, Hypotheses, Survey of sales managers, Rewards especially important to sales managers irrespective of hierarchical level, Findings for overall sample, Statistical analysis, Managerial implications.] 129
Marketing Is Not a One-Night Stand. J. Patrick O'Halloran and Todd R. Wagner, Journal of Business Strategy, 22 (September/October 2001), pp. 31-35. [Discussion, Customer relations, The right message, Personalization, Body language, Listening, Focusing on customer needs, Value-added services, Long-term results, Examples.] 130
The Effects of a Stated Organizational Policy on Inconsistent Disciplinary Action Based on Salesperson Gender and Weight. Joseph A. Bellizzi and Ronald W. Hasty, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 189-98. [Literature review, Hypotheses, Data collection, Being overweight produced harsher discipline for saleswomen but had no effect on salesmen, Managerial implications.] 131
Sales Force Activities and Marketing Strategies in Industrial Firms: Relationships and Implications. James Cross, Steven W. Hartley, William Rudelius, and Michael J. Vassey, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 199-206. [Literature review, Survey, Factors, Providing information, Hiring and training salespeople, Assessing and accessing market segments, Market penetration, Product development, Market development, Diversification, Impacts,, Firm size, Type of offering Statistical analysis.] 132
Testing Competing Models of Sales Force Communication. Mark C. Johlke and Dale F. Duhan, Journal of Personal Selling and Sales Management, 21 (Fall 2001), pp. 265-77. [Literature review, Hypotheses, Survey of salespeople, Job outcomes, Impacts, Communication quality (frequency, informal mode, indirect content, bidirectional), Salesperson satisfaction with communication, Job satisfaction and organizational commitment, Statistical analysis.] 133
An Exploratory Assessment of Sales Culture Variables: Strategic Implications Within the Banking Industry. Rick E. Ridnour, Felicia G. Lassk, and C. David Shepherd, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 247-54. [Literature review, Hypotheses, Survey, Variables, Hours of sales training, Frequency of sales activities and performance-based pay, Service quality, Employee and management commitment, Statistical analysis.] 134
See also 34, 50, 55, 57, 65, 67, 92, 96, 127, 132, 148, 149, 154, 178, 188, 189, 195, 205, 211, 215, 222
Moving from Make/Buy to Strategic Sourcing: The Outsource Decision Process. Mike Tayles and Colin Drury, Long Range Planning (UK), 34 (October 2001), pp. 605-22. [Literature review, Model presentation, Product development, Supplier assessment, Decision to outsource releases capital, Integrating cross-functional activities, Cost of retaining competence in-house, Case study.] 135
Cross-Functional Buying: Why Teams Are Hot. James P. Morgan, Purchasing, 130 (April 5, 2001), pp. 27-28, 31-32. [Survey of purchasing professionals, Personal interviews, Problems, Top management support, Information sharing, Turf wars, Training, Time, Trivialization, Projections.] 136
The Shakeout Goes On. James P. Morgan, Purchasing, 130 (May 3, 2001), pp. 43-44, 46-47. [Industrial distributors, Problems, The way alliances are working, The Internet and costs, Centralization, Analyzing the customer base, Distributors and their suppliers, Assessment.] 137
See also 91, 180, 230, 231
Citizen Preferences Regarding New City Services: Demo-graphics Predictors and Patterns of Opinion. Donald Baack, Jerry Rogers, and Kenneth E. Clow, Journal of Nonprofit and Public Sector Marketing, 8 (No. 1, 2000), pp. 41-53. [Discussion, Hypotheses, Survey, Attitudes regarding future city priorities, Variables, Age, Education, Gender, Years of residence, Income, Home owners versus renters, Assessment, Implications.] 138
Using Research to Gain Strategic Insights for the Marketing of a Proposed Museum. Donald W. Caudill and William E. Warren, Journal of Nonprofit and Public Sector Marketing, 8 (No. 1, 2000), pp. 87-94. [Interviews with community leaders and households, Ascertain if there would be community support, Select target market, Determine variables necessary for creation of marketing plan, Recommendations.] 139
Marketing Within the Public Sector. Michael T. Ewing and Albert Caruana, Journal of Nonprofit and Public Sector Marketing, 8 (No. 1, 2000), pp. 3-15. [Literature review, Survey of government departments, Measures, Internal marketing orientation (comprehensive, underdeveloped, intermediate), Statistical analysis, Australia.] 140
Channel Design for Early Intervention Services: Is There a Role for Brokers? Douglas L. Fugate, Journal of Nonprofit and Public Sector Marketing, 7 (No. 4, 2000), pp. 3-15. [Discussion, Legislation, Individuals with Disabilities Education Improvement Act, Parent as channel captain, Impacts, Imperfect competition, Anonymity, Imperfect information, Assessment, Guidelines.] 141
Consumer Based Strategic Planning in the Nonprofit Sector: The Empirical Assessment of a Symphony Audience. Lawrence L. Garber Jr., Jan G. Muscarella, Paul N. Bloom, and Jennifer L. Spiker, Journal of Nonprofit and Public Sector Marketing, 8 (No. 1, 2000), pp. 55-86. [Literature review, Survey, Attitudes, Alternative entertainment events, Perceptions of symphony event, Information source, Manner of ticket purchase, Overall liking and satisfaction, Audience profile, Impacts, Decision to buy, Statistical analysis.] 142
An Exploratory Investigation into Disadvantaged Business Enterprises and Their Role in Airport Commerce. Sean Helmkay and Blaise P. Waguespack Jr., Journal of Nonprofit and Public Sector Marketing, 8 (No. 1, 2000), pp. 17-31. [Discussion, Constitutional origins, Legal aspects of affirmative action programs, Federal compliance, Difficulties, Program success, Local examples.] 143
Marketing Alliances Between Non-profits and Businesses: Changing the Public's Attitudes and Intentions Towards the Cause. Linda I. Nowak and Judith H. Washburn, Journal of Non-profit and Public Sector Marketing, 7 (No. 4, 2000), pp. 33-44. [Literature review, Cause marketing, Hypotheses, Experiment, Cause affinity, Company reputation, Impacts, Trust, Behavioral intentions, Importance of cause, Feelings of responsibility toward cause, Societal consequences, Assessment.] 144
See also 4, 12, 24, 30, 32, 33, 43, 44, 54, 56, 57, 58, 59, 76, 91, 110, 113, 120, 140, 203, 205, 207, 209, 233
WFOEs: The Most Popular Entry Mode into China. Ping Deng, Business Horizons, 44 (July/August 2001), pp. 63-72. [Discussion, Advantages, Wholly foreign-owned enterprises, Problems, Equity joint ventures, Impacts, Deregulated ownership control and resource commitment, Assessment, Managerial implications.] 145
Managing Southeast Asian Brands in the Global Economy. Michael Ewing, Julie Napoli, and Leyland Pitt, Business Horizons, 44 (May/June 2001), pp. 52-58. [Discussion, Problems, Internal (lack of focus, underinvestment in marketing, managerial leader-ship style), Consumer perceptions, Strategic approaches for Asian organizations, Assessment.] 146
Distance Still Matters: The Hard Reality of Global Expansion. Pankaj Ghemawat, Harvard Business Review, 79 (September 2001), pp. 137-38, 140, 142-47. [Discussion, Dimensions (cultural, administrative, geographic, economic), Industry sensitivity, Country portfolio analysis, Example.] 147
The Impact of Internet Use on Business-to-Business Marketing: Examples from American and European Companies. George J. Avlonitis and Despina A. Karayanni, Industrial Marketing Management, 29 (September 2000), pp. 441-59. [Literature review, Model presentation, Hypotheses, Survey of industrial firms, Effects, Use intensity of Internet, Marketing activities, Sales performance, Sales efficiency, Sales and product management, Internet budget, Interactions, Statistical analysis.] 148
Revisiting Firm Characteristics, Strategy, and Export Performance Relationship: A Survey of the Literature and an Investigation of New Zealand Small Manufacturing Firms. David L. Dean, Bulent Menguc, and Christopher Paul Myers, Industrial Marketing Management, 29 (September 2000), pp. 461-77. [Survey, Low-versus high-performance exporters, Measures (annual export sales, export growth, percentage of total sales from export), Statistical analysis, Implications.] 149
How Chinese Children's Commercials Differ from Those of the United States: A Content Analysis. Mindy F. Ji and James U. McNeal, Journal of Advertising, 30 (Fall 2001), pp. 79-92. [Literature review, Hypotheses, Impacts, Social and cultural factors, Economic development, Statistical analysis, Implications.] 150
Organizational Buying and Advertising Agency-Client Relationships in China. Gerard Prendergast, Yizheng Shi, and Douglas West, Journal of Advertising, 30 (Summer 2001), pp. 61-71. [Literature review, Hypotheses, Survey of firms in various industries, Advertising agency power in the campaign development process is not related to the nature of the advertising task at hand, Impacts, Bottom-up processes, Client buying process, Assessment, Recommendations.] 151
The Chinese Approach to International Business Negotiation. Jensen J. Zhao, Journal of Business Communication, 37 (July 2000), pp. 209-37. [Literature review, Content analysis, Categories, Creating negotiation atmosphere, Negotiating position, Making first offer, Counteroffers, Negotiation techniques, Breaking deadlock, Closing the deal, Assessment.] 152
Journal of Business Research, 52 (May 2001), pp. 95-210. [Nine articles on doing business in China; Management and organizational research; Interpersonal and interorganizational commitment; Foreign ownership of equity joint ventures; Self selection, socialization, and budget control; Chinese distribution channels; Justice perceptions of complaint handling; Signal theory for products and services; Conducting business research.] 153
R&D Mode Choices in Central and Eastern Europe. Keith D. Brouthers, Lance Eliot Brouthers, and Steve Werner, Journal of Business Research, 52 (April 2001), pp. 83-91. [Literature review, Hypotheses, Survey of firms, R&D integration strategy can be predicted using Dunning's OLI (ownership-and location-specific, internalization) framework, Firms whose choices are predicted appear to be more satisfied with the performance of their R&D facilities.] 154
The Behaviour of International Firms in Socio-political Environments in the European Union. Amjad Hadjikhani and Pervez N. Ghauri, Journal of Business Research, 52 (June 2001), pp. 263-75. [Literature review; Conceptual framework (network theory); By comparing the management practices of small-and medium-sized enterprises and multinationals, the study shows how smaller exporting firms manage their relationships with political actors; Managerial implications.] 155
Drivers and Outcomes of Parent Company Intervention in IJV Management: A Cross-Cultural Comparison. Jean L. Johnson, John B. Cullen, Tomoaki Sakano, and James W. Bronson, Journal of Business Research, 52 (April 2001), pp. 35-49. [Literature review, International joint ventures (IJVs), Hypotheses, Survey of companies, Impacts, Direct and indirect parental control, Conflicts, IJV experience, Strategic importance, Product similarity, Resource dependence, Statistical analysis, Many countries.] 156
The Market Orientation-Performance Relationship in the Context of a Developing Economy: An Empirical Analysis. Ram Subramanian and Pradeep Gopalakrishna, Journal of Business Research, 53 (July 2001), pp. 1-13. [Literature review, Hypotheses, Survey of manufacturing and service firms, Factors, Customer orientation, Competitor orientation, Interfunctional coordination, Long-term focus, Survival and growth/profit emphasis, Statistical analysis, Managerial implications, India.] 157
Reasons as Carriers of Culture: Dynamic Versus Dispositional Models of Cultural Influence on Decision Making. Donnel A. Briley, Michael W. Morris, and Itamar Simonson, Journal of Consumer Research, 27 (September 2000), pp. 157-78. [Literature review, Hypotheses, Five studies, Reasons and compromise choices, Comparisons, US and Hong Kong, US and Japan, Within a single country, Pro verbs as a source of reasons, Theoretical and marketing implications.] 158
Gift Giving in Hong Kong and the Continuum of Social Ties. Annamma Joy, Journal of Consumer Research, 28 (September 2001), pp. 239-56. [Discussion, Models, Personal interviews, Categories (close, hi/bye, just friends, and romantic other), Assessment.] 159
The Socio-cultural Environment for Entrepreneurship: A Comparison Between East Asian and Anglo-Saxon Countries. Thomas M. Begley and Wee-Liang Tan, Journal of International Business Studies, 32 (Third Quarter 2001), pp. 537-53. [Literature review, Hypotheses, Data collection (aspiring professionals and managerial employees), Demographic and sociocultural variables, Owners versus nonowners, Social status, Shame of failure, Value of work and innovation, Feasibility, Desire, Statistical analysis.] 160
Do Company Strategies and Structures Converge in Global Markets? Evidence from the Computer Industry. Geert Duysters and John Hagedoorn, Journal of International Business Studies, 32 (Second Quarter 2001), pp. 347-56. [Literature review, Hypotheses, Survey, Variables, Size, Specialization, Innovative strength, R&D intensity, Acquisitions and mergers, Strategic technology alliances, Technology specialization, Internationalization, Statistical analysis, Many countries.] 161
A Resource Perspective of Global Dynamic Capabilities. David A. Griffith and Michael G. Harvey, Journal of International Business Studies, 32 (Third Quarter 2001), pp. 597-606. [Discussion, Conceptual framework, Hypotheses, Survey of distributors, Power, Impacts, Asset specificity, Predictability, Market knowledge gap, Type of market, Statistical analysis, Canada, Chile, Great Britain, Philippines.] 162
Cultural Adaptation of Business Expatriates in the Host Marketplace. Sunkyu Jun, James W. Gentry, and Yong J. Hyun, Journal of International Business Studies, 32 (Second Quarter 2001), pp. 369-77. [Literature review, Structural model, Hypotheses, Survey of Korean expatriates, Impacts, Cultural knowledge, Market participation, Market alienation, Statistical analysis.] 163
A Model of Advertising Standardization in Multinational Corporations. Michel Laroche, V.H. Kirpalani, Frank Pons, and Lianxi Zhou, Journal of International Business Studies, 32 (Second Quarter 2001), pp. 249-66. [Literature review, Hypotheses, Data collection (indicies of multinational corporations), Degree of control over subsidiaries, Impacts, Market position, Country environmental conditions, Decision power of subsidiary, MNC's manager's familiarity with foreign context, Statistical analysis, Many countries.] 164
Institutions, Transaction Costs, and Entry Mode Choice in Eastern Europe. Klaus E. Meyer, Journal of International Business Studies, 32 (Second Quarter 2001), pp. 357-67. [Literature review, Multinomial model, Survey of West German and British companies, Variables, Institution building, R&D, Technology transfer, Human capital, Consumer goods, Management transfer, Firm size, Global experience, Regional experience, Statistical analysis.] 165
Cultural Distance Revisited: Towards a More Rigorous Conceptualization and Measurement of Cultural Differences. Oded Shenkar, Journal of International Business Studies, 32 (Third Quarter 2001), pp. 519-35. [Literature review, Conceptual properties (symmetry, stability, linearity, causality, discordance), Methodological properties (corporate and spatial homogeneity, equivalence), Closing cultural distance, Cultural interaction as friction, Recommendations.] 166
The Stampede Toward Hofstede's Framework: Avoiding the Sample Design Pit in Cross-Cultural Research. K. Sivakumar and Cheryl Nakata, Journal of International Business Studies, 32 (Third Quarter 2001), pp. 555-74. [Literature review, Research scenarios, Sets of algorithms that calculate indexes reflecting the power of different samples for hypotheses testing, Rankings, Top multicountry samples are presented in tables for selection when designing studies.] 167
Investments in Consumer Relationships: A Cross-Country and Cross-Industry Exploration. Kristof De Wulf, Gaby Odekerken-Schroder, and Dawn Iacobucci, Journal of Marketing, 65 (October 2001), pp. 33-50. [Literature review, Model presentation and comparison, Hypotheses, Six consumer samples, Impacts, Direct mail, Preferential treatment, Interpersonal communication, Tangible rewards, Perceived relationship investment, Relationship quality, Statistical analysis, Implications, US, The Netherlands, Belgium.] 168
Creating Local Brands in Multilingual International Markets. Shi Zhang and Bernd H. Schmitt, Journal of Marketing Research, 38 (August 2001), pp. 313-25. [Literature review, Three experi --ments, English-and Chinese-brand names, Translation methods (phonetic, semantic, phonosemantic), Cognitive analysis focusing on impacts of primes and expectations on consumer name evaluations, Statistical analysis.] 169
Insights from Senior Executives About Innovation in International Markets. Peter N. Golder, Journal of Product Innovation Management, 17 (September 2000), pp. 326-40. [Literature review, Personal interviews, Attitudes, Product development, Entry timing, Standardization versus differentiation, Strategic goals, Mode of entry, Choice of markets to enter, Organization structure, Assessment, Managerial implications, US, Europe, Japan.] 170 Pioneering Advantage in New Service Development: A Multi-country Study of Managerial Perceptions. X. Michael Song, C. Anthony Di Benedetto, and Lisa Z. Song, Journal of Product Innovation Management, 17 (September 2000), pp. 378-92. [Literature review, Propositions, Survey of managers, Factors, Economic, Preemptive, Technological, Behavioral, Comparisons, West and Asian Pacific region.] 171
Supporting Women Entrepreneurs in Transitioning Economies. Richard T. Bliss and Nicole L. Garratt, Journal of Small Business Management, 39 (October 2001), pp. 336-44. [Literature review, Obstacles, Reasons for starting a business, Perceived success factors, Associations, Effectiveness, Recommendations, Poland.] 172
Networking Trends of Small Tourism Businesses in Post-Social-ist Slovakia. Charles B. Copp and Russell L. Ivy, Journal of Small Business Management, 39 (October 2001), pp. 345-53. [Literature review, Survey, Impacts, Sources of initial funding and business decisions, Training, Professional associations, Locations, Assistance, Assessment.] 173
The Internationalization Process of Small and Medium-Sized Enterprises: An Evaluation of Stage Theory. Harold G.J. Gankema, Henoch R. Snuif, and Peter S. Zwart, Journal of Small Business Management, 38 (October 2000), pp. 15-27. [Literature review; Data collection (INTERSTRATOS group); Domestic marketing; Pre-export; Experimental, active, and committed involvement; DEL analysis; Manufacturing industries; Many countries.] 174
A Crossnational Prediction Model for Business Success. Robert N. Lussier and Sanja Pfeifer, Journal of Small Business Management, 39 (July 2001), pp. 228-39. [Literature review, Model presentation, Survey of firms, Impacts, Human resources (staffing, education level, use of professional advice, planning), Assessment, Implications, Croatia.] 175
Networks, Resources, and Small Business Growth: The Experience in Sri Lanka. S.P. Premaratne, Journal of Small Business Management, 39 (October 2001), pp. 363-71. [Literature review, Conceptual framework, Hypotheses, Survey, Networks (social, support, inter-firm, organizational), Impacts, Money, Information, Other nonmaterial support, Statistical analysis.] 176
Cultural Differences in Planning/Success Relationships: A Comparison of Small Enterprises in Ireland, West Germany, and East Germany. Andreas Rauch, Michael Frese, and Sabine Sonnentag, Journal of Small Business Management, 38 (October 2000), pp. 28-41. [Discussion, Hypotheses, Survey, Variables, Achievement orientation, Start-up business plan, Level of plan's detail, Target planning, Success, Statistical analysis.] 177
Exchange Rates and the Choice of Ownership Structure of Production Facilities. Panos Kouvelis, Kostas Axarloglou, and Vikas Sinha, Management Science, 47 (August 2001), pp. 1063-80. [Literature review, Model presentation, Hypotheses, Data collection, Production strategy, International business, Exporting, Joint ventures, Global pricing, Dynamic programming, Regression analysis, Managerial implications.] 178
Stores, Section 2, 83 (January 2001), pp. 5-141. [Twenty-five articles on global online retailing, Consumer trends, Multichannel imperative, Meeting customer expectations, Tax advantages, Many countries.] 179
See also 25, 39, 41, 66, 70, 82, 83, 93, 97, 127, 130, 134, 138, 140, 141, 142, 171, 173, 190, 191, 196, 216, 227
Examining the Landscape of Managed Behavioral Health Care Through the Market Paradigm. Michael L. Hall and Robert H. Keefe, Journal of Nonprofit and Public Sector Marketing, 7 (No. 4, 2000), pp. 59-76. [Literature review; Survey of social workers, psychologists, and psychiatrists; Market factors; Program impacts; Competition; Services; Income; Assessment.] 180
SERVPERF Utility for Predicting Neighborhood Shopping Behavior. Kimball P. Marshall and J.R. Smith, Journal of Non-profit and Public Sector Marketing, 7 (No. 4, 2000), pp. 45-57. [Literature review, Survey, Validity and predictive utility of subscales, Impacts, Tangibility, Assurance, Empath y, Overall service quality rating and propensity to shop in neighborhood for clothing, Statistical analysis.] 181
The Impact of Satisfaction and Payment Equity on Cross-Buying: A Dynamic Model for a Multi-service Provider. Peter C. Verhoel, Philip Hans Franses, and Janny C. Hoekstra, Journal of Retailing, 77 (Fall 2001), pp. 359-78. [Discussion, Hypotheses, Customer survey, Effects, Lengthy and short relationships, Perceptions of prices, Competition, Statistical analysis, Managerial implications, Insurance industry, The Netherlands.] 182
Outcomes-Adjusted Reimbursement in a Health-Care Delivery System. Prashant C. Fuloria and Stefanos A. Zenios, Management Science, 47 (June 2001), pp. 735-51. [Literature review, Health care financing, Fee for service, Principal-agent models, Dynamic incentives, Medicare's End-Stage Renal Disease program, Application.] 183
Stores, 83 (January 2001), pp. 46-82. [Six articles on saving customer service, Technology tie-ins, Online needs, Web site features, Customer relations, Handling complaints, Core values, Corporate culture, Customer feedback, Case studies.] 184
New Benchmarking Service Gauges Retail Website Performance. Susan Reda, Stores, 83 (March 2001), pp. 26-28, 30. [Discussion, Consultants, Impacts, Time needed to conduct online transactions, Download performance for each site's home page, Distributed monitoring, Examples.] 185
See also 21, 53, 64, 69, 80, 81, 94, 95, 111, 112, 121, 123, 178, 183, 211
The Impossibility and Necessity of Re-inquiry: Finding Middle Ground in Social Science. Richard R. Wilk, Journal of Consumer Research, 28 (September 2001), pp. 308-12. [Literature review, The author takes a position of pluralism and suggests that both positivism and humanism have a great deal to offer consumer research.] 186
An Utility Based Framework for Evaluating the Financial Impact of Sales Force Training Programs. Earl D. Honeycutt Jr., Kiran Karande, Ashraf Attia, and Steven D. Maurer, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 229-38. [Discussion, Economic utility theory, Kirkpatrick model, Benefit estimates, Differences in trained and untrained salespeople, Attrition rates, Time to break even, Sensitivity analysis, Managerial implications.] 187
Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation. Ron Adner and Daniel Levinthal, Management Science, 47 (May 2001), pp. 611-28. [Discussion, Model structure components (characterization of consumers and consumer preferences that comprises the demand environment, mechanism by which products move through this market space), Phases of development activity (attribute equalization, market expansion, demand maturity), Assessment.] 188
The Nonstationary Staff-Planning Problem with Business Cycle and Learning Effects. Edward G. Anderson Jr., Management Science, 47 (June 2001), pp. 817-32. [Literature review, Model presentation, Capacity planning, Knowledge management, Product development, System dynamics, Control theory, Stochastic dynamic programming, Sensitivity analysis, Numerical examples.] 189
Procurement Planning to Maintain Both Short-Term Adaptiveness and Long-Term Perspective. Jewel S. Bonser and S. David Wu, Management Science, 47 (June 2001), pp. 769-86. [Planning under uncertainty, Computational experiments, Fuel procurement problem for electrical utilities, Minimum contract purchases, Stochastic optimization, Managerial implications.] 190
Optimal Pricing That Coordinates Queues with Customer-Chosen Service Requirements. Albert Y. Ha, Management Science, 47 (July 2001), pp. 915-30. [Literature review, Incentive, Delay cost, Service facility, Joint production, Optimal design of queues, Assessment.] 191
Learning and Forgetting: Modeling Optimal Product Sampling Over Time. Amir Heiman, Bruce McWilliams, Zhihua Shen, and David Zilberman, Management Science, 47 (April 2001), pp. 532-46. [Literature review, Propositions, Dynamic analysis, Impacts, Product life cycle, Steady-state goodwill stock, Marginal productivity, Incentives, Managerial applications.] 192
A Generalized Model of Operations Reversal for Fashion Goods. Nikhil Jain and Anand Paul, Management Science, 47 (April 2001), pp. 595-600. [Discussion, Probability and consumer choice models, Process design, Supply chain, Market characteristics, Heterogeneity among customers, Unpredictability of customer preferences, Assessment.] 193
Channel Coordination Under Price Protection, Midlife Returns, and End-of-Life Returns in Dynamic Markets. Terry A. Taylor, Management Science, 47 (September 2001), pp. 1220-34. [Literature review, Supply chain management, Declining price environments, Model presentation, Mathematical equations, Theorems, Incentives, Inventory management, Assessment.] 194
A Nested Decomposition Approach to a Three-Stage, Two-Dimensional Cutting-Stock Problem. Francois Venderbeck, Management Science, 47 (June 2001), pp. 864-79. [Discussion, Model presentation, Trim loss, Knapsack problems, Integer programming, Nested decomposition, Overall algorithm and computational results.] 195
See also 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 36, 53, 54, 55, 69, 77, 78, 89, 93, 101, 102, 106, 107, 108, 109, 114, 115, 116, 118, 119, 120, 122, 123, 124, 134, 138, 139, 142, 144, 150, 151, 153, 154, 158, 159, 167, 168, 169, 181, 182, 214
The Rising Tide. Hassan Fattah, American Demographics, 23 (April 2001), pp. 48-53. [Lower middle class, Stereotypes, Working poor, Demographic characteristics, Purchasing patterns, Brand conscious, Services, Value, Retailers, Statistical data.] 196
Micro Melting Pots. William H. Frey, American Demographics, 23 (June 2001), pp. 20-23. [Ethnic populations, Diversity, Regions, Minorities, Growth, Metropolitan areas, Immigration, Statistical data.] 197
Habla English? Rebecca Gardyn, American Demographics, 23 (April 2001), pp. 54-57. [Hispanic youth, Cultural aspects, Family orientation, Core values, Market strategy, Bilingual, Media planning, Advertising campaigns, Examples.] 198
Size Doesn't Matter. Alison Stein Wellner, American Demo-graphics, 23 (May 2001), pp. 23-24. [States, Growth rates, Boomer migration trends, Generation Y's household-formation years, Statistical data.] 199
Boost Your Marketing ROI with Experimental Design. Eric Almquist and Gordon Wyner, Harvard Business Review, 79 (October 2001), pp. 135-41. [Discussion, Comparisons, Traditional testing, Stimulus-response network, Data collection, Business judgment, Mathematical and statistical sophistication, Examples.] 200
The Unpredictable Audience: An Exploratory Analysis of Fore-casting Error for New Prime-Time Network Television Programs. Philip M. Napoli, Journal of Advertising, 30 (Summer 2001), pp. 53-60. [Literature review, Hypotheses, Data collection (Nielsen reports), Presence of a returning network lead-in or lead-out significantly reduces the amount of forecasting error, Error increased significantly over time, Statistical analysis.] 201
Journal of Advertising Research, 41 (September/October 2001), pp. 7-77. [Six articles on youth research, Ethnic identification on adolescents' evaluations of advertisements, Cyberspace advertising versus other media, Psychographic analysis of Generation Y college students, Student sample on standardization in international advertising, Problems with student samples, Gaining additional customers by stretching the market.] 202
Forecasting with Limited Information: A Study of the Norwegian ISDN Access Market.
Carlo Hjelkrem, Journal of Business Forecasting, 20 (Fall 2001), pp. 18-23. [Discussion, Integrated Services Digital Network, Factors, Gather available information, Select forecasting model, Estimate market potentials, Forecast results, Simulation of uncertainty, Accuracy of forecasts, Examples.] 203
Forecasting Heroes Catch Turning Points. Larry Lapide, Journal of Business Forecasting, 20 (Fall 2001), pp. 11-12. [Discussion, Ways to forecast a turning point (leading indicators, econometrics, adoption models, decomposition methods), Organizational support, Guidelines.] 204
Customer Satisfaction in Industrial Markets: Dimensional and Multiple Role Issues. Christian Homburg and Bettina Rudolph, Journal of Business Research, 52 (April 2001), pp. 15-33. [Literature review, Combinations of satisfaction dimensions in alternative models, Scale development, Propositions, Field interviews and survey, Impacts, Different roles in buying center, Statistical analysis, Managerial implications, Many countries.] 205
The Development of a Scale to Measure Perceived Corporate Credibility. Stephen J. Newell and Ronald E. Goldsmith, Journal of Business Research, 52 (June 2001), pp. 235-47. [Literature review, Five studies, Variables, Trust, Expertise, Attitude ad, Attitude brand, Purchase intent, Endorser credibility, Statistical analysis, Implications.] 206
Developing and Validating a Multidimensional Consumer-Based Brand Equity Scale. Boonghee Yoo and Naveen Donthu, Journal of Business Research, 52 (April 2001), pp. 1-14. [Literature review; Multistep psychometric tests demonstrate that the new brand equity scale is reliable, valid, parsimonious, and generalizable across several cultures and product categories; Theoretical and practical implications; South Korea, US.] 207
Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments. Neeraj Arora and Joel Huber, Journal of Consumer Research, 28 (September 2001), pp. 273-83. [Literature review, Hypotheses, Experiments and poststudy interviews, Biasing effects, Cognitive elaboration, Relevant information, Choice awareness, Statistical analysis.] 208
Optimizing Television Program Schedules Using Choice Modeling. Peter J. Danaher and Donald F. Mawhinney, Journal of Marketing Research, 38 (August 2001), pp. 298-312. [Literature review; Model presentation, Experiment, Program rearrangement, Concurrent and predictive validity, Statistical analysis, New Zealand.] 209 Modeling Hedonic Portfolio Products: A Joint Segmentation Analysis of Music Compact Disc Sales. Wendy W. Moe and Peter S. Fader, Journal of Marketing Research, 38 (August 2001), pp. 376-85. [Literature review, Data collection (store-level sales data), Model can accommodate a large degree of product heterogeneity through product clusters and model covariates.] 210
Critical Evaluation of Porter et al.'s Organizational Commitment Questionnaire: Implications for Researchers. Nathalie Commeiras and Christopher Fournier, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 239-45. [Model presentation, Dimensionality assessment, Two surveys, Industrial salespersons, Convergent validity and reliability, Discriminant and predictive validity, Confirmatory factor analysis.] 211
See also 37, 63, 72, 73, 74, 91, 92, 184, 185, 226, 228
The Persistence of Paper. John Seely Brown and Paul Duguid, Across the Board, 37 (September 2000), pp. 27-30. [Discussion, Paperless office, Electronic newspaper, Digital library, New avenues for paper documents to develop.] 212
Information Technology and New Product Development: Opportunities and Pitfalls. Muammer Ozer, Industrial Marketing Management, 29 (September 2000), pp. 387-96. [Discussion; Impacts; Speed; Productivity; Collaboration, communication, and coordination; Versatility; Knowledge management; Decision quality; Product quality; Examples; Strategy; Guidelines.] 213
Digitizing Consumer Research. Eric J. Johnson, Journal of Consumer Research, 28 (September 2001), pp. 331-36. [Discussion; Changes in the way knowledge is generated and disseminated; Diminishing use of student subjects; Increased use of global samples, panels, secondary data, and information acquisition techniques; Assessment.] 214
Descriptive and Predictive Analyses of Industrial Buyers' Use of Online Information for Purchasing. Karen Norman Kennedy and Dawn R. Deeter-Schmelz, Journal of Personal Selling and Sales Management, 21 (Fall 2001), pp. 279-90. [Literature review, Research questions, Internet use activities and benefits, Variables (personal perceptions of technology, organizational influences, market conditions), Statistical analysis.] 215
Watch These Four Issues When Doing an ASP Deal. Susan Avery, Purchasing, 130 (May 17, 2001), pp. 79-80. [Information technology, Software sourcing strategy, Application service providers, Impacts, Definitions used in contracts, Change control (update to an application, and can occur on both the hosting and using sides), Service levels, Termination, Assessment.] 216
Technology Creates New World at the Point of Sale. Patricia A. Murphy, Stores, 83 (March 2001), pp. 38-40, 42. [Merging selling and service, Software packages, Effects, Broadband and wireless access, Thin-client computing, Web browsers and Web-enabled terminals, Examples.] 217
Data Analysis Seen Adding to Value of Customer Relationship Management. Susan Reda, Stores, 83 (April 2001), pp. 58, 60, 62. [Discussion, Software packages, Databases, Time to market, Customer response to promotions, Order size, Customer lifetime, ROI justification, Examples.] 218
Voice Recognition Matures as Retail Distribution Technology. Tony Seideman, Stores, 83 (February 2001), pp. 44-45. [Discussion, Warehouse systems, Logistics-oriented, Just-in-time, Can help reduce repetitive-motion injuries by allowing workers to use both hands for heavy or stressful movements, Examples.] 219
See also 31, 47, 68, 131, 172, 235, 237
Reference Diversity in JCR, JM, and JMR: A Reexamination and Extension of Tellis, Chandy, and Ackerman (1999). Lance A. Bettencourt and Mark B. Houston, Journal of Consumer Research, 28 (September 2001), pp. 313-23. [Comparative analysis, Small or nonsignificant differences among the journals in discipline and journal variety, Sources, Relationship between reference diversity and article influence, Assessment.] 220
Creating Positive Group Project Experiences: An Examination of the Role of the Instructor on Students'Perceptions of Group Projects. Kenneth J. Chapman and Stuart Van Auken, Journal of Marketing Education, 23 (August 2001), pp. 117-27. [Literature review, Path-analytic model, Survey of marketing educators, Students were more likely to have positive attitudes about group work if the instructor discussed group management issues and used methods to evaluate individual performance within the group.] 221
Integrated Marketing and Operations Team Projects: Learning the Importance of Cross-Functional Cooperation. Jean C. Darian and Lewis Coopersmith, Journal of Marketing Education, 23 (August 2001), pp. 128-35. [Integrating a marketing elective and a required operations management course, Projects (locating a facility, aggregate production planning, monitoring and improving service quality), Course assessment.] 222
Be a Good Teacher and Be Seen as a Good Teacher. Suzanne Desai, Earl Damewood, and Richard Jones, Journal of Marketing Education, 23 (August 2001), pp. 136-44. [Literature review, Model of customer orientation, Survey of students and faculty, Interactions, Initiated activities, Statistical analysis.] 223
The Effects of Class Size and Learning Style on Student Performance in a Multimedia-Based Marketing Course. Fahri Karakaya, Thomas L. Ainscough, and John Chopoorian, Journal of Marketing Education, 23 (August 2001), pp. 84-90. [Literature review, Hypotheses, Large and small class comparisons, Variables, Grade point average, Credit hours completed, Learning styles (accommodators, divergers, convergers, assimilators), Final class average, Statistical analysis.] 224
The Case for Using Live Cases: Shifting the Paradigm in Marketing Education. Ellen J. Kennedy, Leigh Lawton, and Erika Walker, Journal of Marketing Education, 23 (August 2001), pp. 145-51. [Interdisciplinary exercise; Marketing and entrepreneur-ship courses; Marketing plans for small-business owners; Issues for students, clients, and faculty; Problems with institutional inertia.] 225
Marketing Educator Internet Adoption in 1998 Versus 2000: Significant Progress and Remaining Obstacles. Douglas J. Lincoln, Journal of Marketing Education, 23 (August 2001), pp. 103-16. [Literature review, Two surveys, Applications, Distributing course materials, Communicating with students, Use in research and service, Effective use of Internet, Home page effectiveness, Institutional resources and support, Personal intentions, Statistical analysis.] 226
Tailoring a Marketing Course for a Non-marketing Audience: A Professional Services Marketing Course. Kevin M. McNeilly and Terri Feldman Barr, Journal of Marketing Education, 23 (August 2001), pp. 152-60. [Cross-functional course work using market segmentation, Class design, Class discussion topics, Marketing to existing clients and prospects, Assessment.] 227
Practitioner and Academic Recommendations for Internet Marketing and E-Commerce Curricula. Ted Mitchell and Judy Strauss, Journal of Marketing Education, 23 (August 2001), pp. 91-102. [Literature review, Survey, Number of credits indicated for internet and e-commerce programs, Skill-and knowledge-based learning clusters, Importance of academic topics, Company Web site capabilities, In-house versus outsourcing, Contributions from current courses, Suggested courses, Statistical analysis.] 228
Why Some New Products Are More Successful Than Others. David H. Henard and David M. Szymanski, Journal of Marketing Research, 38 (August 2001), pp. 362-75. [Meta-analysis of new product performance literature, Impacts, Product advantage, Market potential, Meeting customer needs, Predevelopment task proficiencies, Dedicated resources, Measurement and contextual factors, Statistical analysis, Implications.] 229
The New Public Relations: Integrating Marketing and Public Relations Strategies for Student Recruitment and Institutional Image Building--A Case Study of the University of Texas at San Antonio. Amiso M. George, Journal of Nonprofit and Public Sector Marketing, 7 (No. 4, 2000), pp. 17-31. [Literature review, Impression management, University image and enrollment, Target markets, Integrated marketing communication campaign, Goals and objectives, Measurement of outcomes, Assessment, Implications.] 230
Student Evaluations and Consumer Orientation of Universities. Robert E. Wright, Journal of Nonprofit and Public Sector Marketing, 8 (No. 1, 2000), pp. 33-53. [Literature review, Survey of students, Variables, Communication, Fairness, Appearance, Age, Study hours, Grade, Assessment.] 231
The Top Ten Sales Articles of the 20th Century. Thomas W. Leigh, Ellen Bolman Pullins, and Lucette B. Comer, Journal of Personal Selling and Sales Management, 21 (Summer 2001), pp. 217-27. [Discussion; Article purpose, content, and contribution; Rankings; Citation and composite analyses; Assessment.] 232
Gender Differences in Attitudes Toward Women as Sales Managers in the People's Republic of China. Sandra S. Liu, Lucette B. Comer, and Alan J. Dubinsky, Journal of Personal Selling and Sales Management, 21 (Fall 2001), pp. 303-11. [Literature review, Research questions, Survey of salespeople, Belief in gender stereo-types, Management traits, Acceptance of female sales managers, Statistical analysis, Managerial implications.] 233
Tough Sell. Erin Strout, Sales and Marketing Management, (July 2001), pp. 50-55. [Discussion, Women in sales, Lower compensation, Bias, Harassment, Industries, Examples.] 234
See also 31, 88, 222, 224
Evidence of a Marketing Periodic Literature Within the American Economic Association: 1895-1936. Dave Bussiere, Journal of Macromarketing, 20 (December 2000), pp. 137-43. [Literature examination and documentation; This study finds that marketing articles began appearing in American Economic Association publications in 1894, 20 years before Weld's noted article on market distribution.] 235
Alfred D. Chandler, Jr. and the Landscape of Marketing History. Pamela Walker Laird, Journal of Macromarketing, 20 (December 2000), pp. 167-73. [Differing responses to Chandler's work, One major division falls between scholars whose academic efforts focus on marketing techniques and strategies and those whose primary focus specializes in historical analysis, Employment of both methodologies.] 236
The Interpretation of Arch Wilkinson Shaw's Thought by Japanese Scholars. Kazuo Usui, Journal of Macromarketing, 20 (December 2000), pp. 128-36. [Literature review, Micro-macro dichotomy, Narrow definition of marketing, Morishita's paradigm, Impacts, Political economy in Shaw's time, Functional approach, Relation to F.W. Taylor's scientific management, Assessment.]237
~~~~~~~~
By Myron Leonard, Editor, Western Carolina University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 102- Marketing Literature Review. By: Leonard, Myron. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p94-106. 13p. DOI: 10.1509/jmkg.65.2.94.18251.
- Database:
- Business Source Complete
MARKETING LITERATURE REVIEW
This section is based on a selection of article abstracts from a comprehensive business literature database. Marketing-related abstracts from more than 125 journals (both academic and trade) are reviewed by JU staff. Descriptors for each entry are assigned by JU staff. Each issue of this section represents three months of entries into the database.
Each entry has an identifying number. Cross-references appear immediately under each subject heading.
The following article abstracts are available online from the ABI/INFORM database, which is published and copyrighted by Bell & Howell Information and Learning. For additional information about access to the database or about obtaining photocopies of the articles abstracted here, please call (800) 521-0600 or write to B&H, 300 N. Zeeb Rd., Ann Arbor, M148106.
1. THE MARKETING ENVIRONMENT
1.1 Consumer Behavior
1.2 Legal, Political, and Economic Issues
1.3 Ethics and Social Responsibility
2. MARKETING FUNCTIONS
2.1 Management, Planning, and Strategy
2.2 Retailing
2.3 Channels of Distribution
2.4 Electronic Marketing
2.5 Physical Distribution
2.6 Pricing
2.7 Product
2.8 Sales Promotion
2.9 Advertising
2.10 Personal Selling
2.11 Sales Management
3. SPECIAL MARKETING APPLICATIONS
3.1 Industrial
3.2 Nonprofit, Political, and Social Causes
3.3 International and Comparative
3.4 Services
4. MARKETING RESEARCH
4.1 Theory and Philosophy of Science
4.2 Research Methodology
4.3 Information Technology
5. OTHER TOPICS
5.1 Educational and Professional Issues
5.2 General Marketing
See also 42, 62, 63, 81, 100, 107, 108, 109, 113, 114, 123, 136, 137, 139, 150, 155, 160, 181, 182, 196, 197, 198, 199, 200, 207, 209, 211, 229
Food for Thought. Roberta Bernstein, American Demographics, 22 (May 2000), pp. 39-40, 42. [Hispanic and Asian consumers, Market potentials, Disposable income, Expenditures, Cultural and language issues, Consumer panels, Shopping behavior, Packaged-goods industry.] 1
Congestion Ahead. John Fetto, American Demographics, 22 (June 2000), pp. 49-50. [Extreme commuting, Regions, Time spent in traffic, In-auto activities, Billboards, Radio promotions, Creative, Examples.] 2
Make Room for Daddy. Rebecca Gardyn, American Demographics, 22 (June 2000), pp. 34-36. [Trends, Fathers, Market potentials, Magazine readership, Household spending decisions, Time spent with children, E-marketing, Statistical data.] 3
The Joy of Empty Nesting. Joan Raymond, American Demographics, 22 (May 2000), pp. 48-52, 54. [Trends, Baby boomers, Discretionary income, Lifestyles, Affluence, Market strategy, Quality of life, Techno-savvy, Health concerns, Examples.] 4
Life's a Beach 101. Nancy Shepherdson, American Demographics, 22 (May 2000), pp. 56-58, 60, 62, 64. [Echo boomers, E-commerce, Customization, Market strategy, Web sites, Surveys, Recent college grads, Brand loyalty, Jobs, Starting salaries, Investing, Examples.] 5
The Facilitating Influence of Consumer Knowledge on the Effectiveness of Daily Value Reference Information. Fuan Li, Paul W. Miniard, and Michael J. Barone, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 425-36. [Literature review, Hypothesis, Experiment, Measures, Trial intention, Attitude, Healthiness (overall, fat, fiber, sodium), Statistical analysis.] 6
Effects of Absurdity in Advertising: The Moderating Role of Product Category Attitude and the Mediating Role of Cognitive Responses. Leopoldo Arias-Bolzmann, Goutam Chakraborty, and John C. Mowen, Journal of Advertising, 29 (Spring 2000), pp. 35-49. [Literature review; Hypotheses; Experiment; Measures; Ad, brand, and product attitudes; Comparisons; Nonabsurd ads; Recall; Statistical analysis; Implications.] 7
An Empirical Test of an Updated Relevance-Accessibility Model of Advertising Effectiveness. William E. Baker and Richard J. Lutz, Journal of Advertising, 29 (Spring 2000), pp. 1-14. [Literature review, Hypotheses, Experiment, Brand names, Choice processes (optimizing, satisficing, indifference), Types of information (evidence of performance superiority, credibility, and liking), Statistical analysis, Implications.] 8
Customer Satisfaction Cues to Support Market Segmentation and Explain Switching Behavior. Antreas D. Athanassopoulos, Journal of Business Research, 47 (March 2000), pp. 191-207. [Literature review, Model presentation, Hypotheses, Survey of banks' business and individual customers, Measures, Corporate, Innovativeness, Physical and staff service, Pricing, Convenience, Statistical analysis, Implications.] 9
Representation of Numerical and Verbal Product Information in Consumer Memory. Terry L. Childers and Madhubalan Viswanathan, Journal of Business Research, 47 (February 2000), pp. 109-20. [Literature review, Conceptual framework based on surface versus meaning level processing of information, Hypotheses, Two experiments, Recognition paradigm, Assessment.] 10
Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent. Margaret C. Campbell and Amna Kirmani, Journal of Consumer Research, 27 (June 2000), pp. 69-83. [Literature review; Model presentation; Four experiments; When an ulterior persuasion motive is highly accessible, both cognitively busy targets and unbusy observers use persuasion knowledge to evaluate a salesperson; Statistical analysis.] 11
Indexicality and the Verification Function of Irreplaceable Possessions: A Semiotic Analysis. Kent Grayson and David Shulman, Journal of Consumer Research, 27 (June 2000), pp. 17-30. [Literature review, Hypotheses, Two studies, Late-adolescent and late-middle-age consumers view irreplaceable possessions as being distinct because of indexicality, Link between verification and irreplaceable possessions, Statistical analysis.] 12
Determinants of Country-of-Origin Evaluations. Zeynep Gurhan-Canli and Durairaj Maheswaran, Journal of Consumer Research, 27 (June 2000), pp. 96-108. [Literature review, Hypotheses, Two experiments, Variables, Evaluations, Beliefs, Information relevance, Total thoughts, Country-of-origin and attribute-related thoughts, Statistical analysis.] 13
Standing on the Shoulders of Ancients: Consumer Research, Persuasion, and Figurative Language. William J. McGuire, Journal of Consumer Research, 27 (June 2000), pp. 109-14. [Literature review, Early communication (tropes, rhetorical figures), Impacts, Creative hypothesis-generating phase of research, Assessment.] 14
Children, Advertising, and Product Experiences: A Multimethod Inquiry. Elizabeth S. Moore and Richard J. Lutz, Journal of Consumer Research, 27 (June 2000), pp. 31-48. [Literature review; Model presentation; Hypotheses; Experiment and depth interviews; Both product trial and advertising have influences, but interplay of these influences differs between older and younger children; Statistical analysis.] 15
Consumer Learning and Brand Equity. Stijn M.J. van Osselaer and Joseph W. Alba, Journal of Consumer Research, 27 (June 2000), pp. 1-16. [Literature review, Series of experiments, Strong blocking effects were found despite a limited number of brand pre-exposures and extensive exposure to predictive attribute information.] 16
The Role of Explanations and Need for Uniqueness in Consumer Decision Making: Unconventional Choices Based on Reasons. Itamar Simonson and Stephen M. Nowlis, Journal of Consumer Research, 27 (June 2000), pp. 49-68. [Literature review, Hypotheses, Series of studies, Explaining decisions shifts the focus from the choice of options to the choice of reasons. Buyers who explain their decisions and have high need for uniqueness tend to select unconventional reasons and are more likely to make unconventional choices.] 17
Qualitative Steps Toward an Expanded Model of Anxiety in Gift-Giving. David B. Wooten, Journal of Consumer Research, 27 (June 2000), pp. 84--95. [Literature review, Model development, Survey of students and nonstudent adults, Givers become anxious when they are highly motivated to elicit desired reactions from their recipients but are pessimistic about their prospects of success.] 18
Understanding the Customer Base of Service Providers: An Examination of the Differences Between Switchers and Stayers. Jaishankar Ganesh, Mark J. Arnold, and Kristy E. Reynolds, Journal of Marketing, 64 (July 2000), pp. 65-87. [Literature review, Hypotheses, Two studies, Consumers' use of banking services, Impacts, Overall satisfaction, Satisfaction with service dimensions, Involvement, Customer loyalty, Statistical analysis, Implications.] 19
Self-Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters. Matthew L. Meuter, Amy L. Ostrom, Robert 1. Roundtree, and Mary Jo Bitner, Journal of Marketing, 64 (July 2000), pp. 50-64. ]Literature review, Critical incident study (satisfying and dissatisfying), Sources, Consumer reactions, Comparisons, Interpersonal encounter satisfaction, Assessment, Managerial implications.] 20
Consumer Response to Negative Publicity: The Moderating Role of Commitment. Rohini Ahluwalia, Robert E. Burnkrant, and H. Rao Unnava, Journal of Marketing Research, 37 (May 2000), pp. 203-14. [Literature review, Hypotheses, Three experiments, Consumers who are committed to a brand counterargue negative information and can resist information that is likely to induce switching behavior.] 21
A Hierarchical Bayes Model for Assortment Choice. Eric T. Bradlow and Vithala R. Rao, Journal of Marketing Research, 37 (May 2000), pp. 259-68. [Literature review, Experiment, Set of eight popular magazines, Effects, Price, Attributes, Features, Selection, Statistical analysis, Managerial implications.] 22
Impact of Product-Harm Crises on Brand Equity: The Moderating Role of Consumer Expectations. Niraj Dawar and Madan M. Pillutla, Journal of Marketing Research, 37 (May 2000), pp. 215-26. [Literature review, Expectations-evidence framework, Hypotheses, Field survey and two laboratory experiments, Impacts, Consumers' interpretation of the evidence from firm response, Managerial implications.] 23
The Evolution of Brand Preferences and Choice Behaviors of Consumers to a Market. Carrie M. Heilman, Douglas Bowman, and Gordon P. Wright, Journal of Marketing Research, 37 (May 2000), pp. 139-55. [Literature review, Logit-mixture model with time-varying parameters, Consumer panel data, Stages (information collection, extended to lesser-known brands, information consolidation), Impacts, Product experience and learning, Statistical analysis, Implications.] 24
Choosing What I Want Versus Rejecting What I Do Not Want: An Application of Decision Framing to Product Option Choice Decisions. C. Whan Park, Sung Youl Jun, and Deborah J. MacInnis, Journal of Marketing Research, 37 (May 2000), pp. 187-202. [Literature review, Model presentation, Hypotheses, Three studies, Option framing, Psychological reactions, Moderators (option prices, product category prices, regret anticipation, product category commitment), Managerial effects.] 25
Assessing a Place to Live: A Quality of Life Perspective. Glen Riecken, Don Shemwell, and Ugur Yavas, Journal of Nonprofit and Public Sector Marketing, 7 (No. 2, 1999), pp. 17-29. [Survey of community leaders, Factors, Weather, Crime, Economy, Education, Health, Housing, Leisure, Transportation, Arts, Importance/performance analysis, Policy implications.] 26
Assessing the Effects of Quality, Value, and Customer Satisfaction on Consumer Behavioral Intentions in Service Environments. J. Joseph Cronin Jr., Michael K. Brady, and G. Tomas M. Hult, Journal of Retailing, 76 (Summer 2000), pp. 193-218. [Literature review, Model testing, Hypotheses, Two studies, Multiple service providers, Direct and indirect effects, Relationships, Statistical analysis.] 27
See also 51,102, 151,176, 188, 198, 199, 203,227
Ethical and Online Privacy Issues in Electronic Commerce. Eileen P. Kelly and Hugh C. Rowland, Business Horizons, 43 (May/June 2000), pp. 3-12. [Discussion, Information gathering, Legal aspects, Freedom of choice, Voluntary and informed consent, Proposed legislation, Industry reaction, Managerial recommendations.] 28
The Measurement of Intellectual Property Rights Protection. Robert L. Ostergard Jr., Journal of International Business Studies, 31 (Second Quarter 2000), pp. 349-60. [Discussion, Empirical research, Comparisons, Countries, Law and enforcement measures, Protection score analysis (copyright, patent, trademark), Assessment.] 29
Covenants Not to Compete. Erica B. Garay, Marketing Management, 9 (Summer 2000), pp. 61-63. [Acquisitions and mergers, Legislation, Court decisions, Enforcing covenants arising in connection with the sale of a business, Limits to enforcement, Assessment.] 30
U.S. Trust Busters Eye Net Markets. Dan Gottlieb, Purchasing, 128 (June 15, 2000), pp. S67, S69, S72. [Discussion, Net trade exchanges, Legal aspects, Market power, Major industry players, Acquisitions and mergers, Anticompetitive effects, Assessment.] 31
See also 28, 151, 210, 228
Representing the Perceived Ethical Work Climate Among Marketing Employees. Barry J. Babin, James S. Boles, and Donald P. Robin, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 345-58. [Literature review, Survey of employees, Models, Responsibility/trust, Peer behavior, Ethical norms, Selling practices, Role ambiguity, Role conflict, Job satisfaction, Organizational commitment, Statistical analysis, Implications.] 32
Crime and Small Business: An Exploratory Study of Cost and Prevention Issues in U.S. Firms. Donald F. Kuratko, Jeffrey S. Hornsby, Douglas W. Naffziger, and Richard M. Hodgetts, Journal of Small Business Management, 38 (July 2000), pp. 1-13. [Literature review, Survey, Level of concern, Crime prevention actions, Training provided, Perceptions of crime against business, Annual cost of crime, Impact of industry type, Statistical analysis.] 33
Making Business Sense of Environmental Compliance. Jasbinder Singh, Sloan Management Review, 41 (Spring 2000), pp. 91-100. [Discussion, Partnerships, Environmental and plant managers, Savings, Strategies, Review plant operations, Find best times to install pollution-control equipment and upgrade production technology, Allocate environmental costs, Integrate business and environmental decisions, Examples.] 34
Corporate Responsibility Audits: Doing Well by Doing Good. Sandra Waddock and Neil Smith, Sloan Management Review, 41 (Winter 2000), pp. 75-83. [Vision versus practice, CEO commitment, Teams, Corporate culture, Mission statement, Stakeholder elements, Existing policies and practices, Functional areas (human resources, environmental practices, quality systems, community relations), Examples.] 35
See also 9, 30, 31, 32, 33, 34, 35, 64, 67, 68, 69, 71, 73, 74, 77, 82, 83,85,86,90,96,97,98, 106, 112, 114, 115, 116, 117, 120, 121, 122, 124, 125, 126, 127, 134, 135, 145, 146, 148, 149, 153, 154, 161,162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 179, 183, 184, 186, 193, 202, 207, 210, 212, 214, 219, 221, 228
Avoiding the Complexity Trap. Alan Brache and Peter M. Tobia, Across the Board, 37 (June 2000), pp. 42--46. [Sustainable niches, Problems, Availability of outsourcing, Broadening capability and plunging price of technology, Workforce mobility, E-commerce, Impacts, Focus, Critical resources, Information on costs, Examples.] 36
Laying Off Risk. Stan Davis and Christopher Meyer. Across the Board, 37 (April 2000), pp. 33-37. [Insuring against risk, Risk-related rewards, Organizing around risk, Hedging, Core competencies, Value creation, Examples.] 37
The Negotiation Industry. Lee Edson, Across the Board, 37 (April 2000), pp. 14--20. [Discussion, Use in hiring process, International, Special training, Educational initiatives, Win-win model, Examples.] 38
The Secrets of Performance Appraisal. Dick Grote, Across the Board, 37 (May 2000), pp. 14-20. [Corporate culture, Organizational expectations, Identification of specific core competencies, Evaluation, Mastery descriptions, Role of objectivity, Examples.] 39
Condition Critical. Phillip L. Polakoff and David G. Anderson, Across the Board, 37 (May 2000), pp. 42-47. [Health and safety programs, Control, Lost time costs, Risk shifting strategies, Helping employees manage their own care, Assessment, Guidelines.] 40
The Effects of Formal Strategic Marketing Planning on the Industrial Firm's Configuration, Structure, Exchange Patterns, and Performance. Andy Claycomb, Richard Germain, and Cornelia Droge, Industrial Marketing Management, 29 (May 2000), pp. 219-34. [Literature review; Survey (Council of Logistics Management); Impacts; Use of integrative committees and mechanisms, specialization, decentralized decision making, and formal performance measurement (both internal and benchmarking).] 41
Implementing a Customer Relationship Strategy: The Asymmetric Impact of Poor Versus Excellent Execution. Mark R. Colgate and Peter J. Danaher, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 375-87. [Literature review; Survey of bank customers; Satisfaction, performance, usage of bank; Personal banker; Good and bad strategies; Switching activity; Behavioral intentions; Statistical analysis; New Zealand.] 42
Superordinate Identity in Cross-Functional Product Development Teams: Its Antecedents and Effect on New Product Performance. Rajesh Sethi, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 330-44. [Literature review, Hypotheses, Survey of key informants in cross-functional teams, Impacts, Special team structure, Traditional team factor, Interaction effects, Statistical analysis, Managerial implications.] 43
Business Planning Practices in Small Size Companies: Survey Results. Surendra S. Singhvi, Journal of Business Forecasting, 19 (Summer 2000), pp. 3-4, 6, 8. [Primary objectives for preparing a plan, Responsibility, Planning horizon, Plan update, Achievement, Annual budget, Board approval, Financial success, Recommendations.] 44
Journal of Business Research, 47 (January 2000), pp. 3-89. [Eight articles on dynamics of strategy, Executive pay and UK privatization, Nonprofit organization responses to anticipated changes in government support for HIV/AIDS services, Evolving complex organizational structures in new and unpredictable environments, Innovation teams, Institutional foundations of success and failure, Impact of technology policy integration on strategy, Business transformation, Impact of environmentally linked strategies on competitive advantage, Many countries.] 45
Relationship of Firm Size, Initial Diversification, and Internationalization with Strategic Change. Parshotam Dass, Journal of Business Research, 48 (May 2000), pp. 135-46. [Theoretical discussion, Hypotheses, Data collection (COMPUSTAT II database), Variables, Initial and changes in product diversity, Industry performance, Risk, Slack, Firm size, International diversification, Interactions, Statistical analysis.] 46
Organizational Values: The Inside View of Service Productivity. Dawn Dobni, J.R. Brent Ritchie, and Wilf Zerbe, Journal of Business Research, 47 (February 2000), pp. 91-107. [Literature review, Survey of service firms, Impacts, Employee mutualism, Market leadership, Customer intimacy, Operational efficiency, Organizational preservation, Change aversion, Social responsibility, Value systems (entrepreneurial, performance-pressured, integrated, temperate), Statistical analysis, Canada.] 47
Firm Characteristics Influencing Export Propensity: An Empirical Investigation by Industry Type. Rajshekhar G. Javalgi, D. Steven White, and Oscar Lee, Journal of Business Research, 47 (March 2000), pp. 217-28. [Literature review, Hypotheses, Survey, Comparisons, Export versus nonexport firms, Variables, Number of employees, Total sales, Years in business, International trade activity, Primary industrial classification, Firm ownership, Statistical analysis.] 48
Benchmarking Cultural Transition. Roger Connors and Tom Smith, Journal of Business Strategy, 21 (May/June 2000), pp. 10-12. [Corporate culture, The best benchmarks are keyed to important before-and-after results the organization must achieve and to the beliefs and actions that produce those results, Assessment.] 49
Investigation of Factors Contributing to the Success of CrossFunctional Teams. Edward F. McDonough I11, Journal of Product Innovation Management, 17 (May 2000), pp. 221-35. [Literature review, Model presentation, Survey of new product development professionals, Outcome and process reasons for adopting cross-functional teams, Interactions, Stage setters, Enablers, Team behaviors, Performance, Assessment.] 50
Environmental and Ownership Characteristics of Small Businesses and Their Impact on Development. William B. Gartner and Subodh Bhat, Journal of Small Business Management, 38 (July 2000), pp. 14-26. [Literature review, Survey, Growth expectations, Effects, Crime, Neighborhood appearance, Ethnicity of owner, Legal structure of firm, Firm type and size, Statistical analysis, Recommendations.] 51
Strategic Planning in the Military: The U.S. Naval Security Group Changes Its Strategy, 1992-1998. William Y. Frentzell II, John M. Bryson, and Barbara C. Crosby, Long Range Planning (UK), 33 (June 2000), pp. 402-29. [Discussion, Creating vision, Middle and top-level management involvement, Stakeholder and SWOT analyses, Scenario planning, Cognitive and oval mapping, Assessment.] 52
The Future.org. Raymond E. Miles, Charles C. Snow, and Grant Miles, Long Range Planning (UK), 33 (June 2000), pp. 300-21. [Collaboration-based organizational model for innovation, Essential conditions (time, trust, territory), Design principles (self-management, behavioral protocols, shared strategic intent, equitable sharing of returns), Barriers (institutional, philosophical, organizational), Examples.] 53
Business Domain Definition Practice: Does It Affect Organisational Performance? Jatinder S. Sidhu, Edwin J. Nijssen, and Harry R. Commandeur, Long Range Planning (UK), 33 (June 2000), pp. 376-401. [Literature review, Hypotheses, Survey of managers, Focus, Stable versus turbulent environments, impacts, Customer need, Technological competence, Assessment, Implications, The Netherlands.] 54
Marketing Decision Support Systems for Strategy Building. Sanjay K. Rao, Marketing Health Services, 20 (Summer 2000), pp. 15-18. [Pharmaceutical products, Customer and market environments, System outputs (interaction between pricing and other marketing mix strategies), Cash flows, Outcomes, Assessment.] 55
What's in a Name? New CPO Title Reflects Buying's Strategic Role. William Atkinson, Purchasing, 128 (June 1, 2000), pp. 45, 49-51. [Chief procurement officer, Organizational structure, Functions, Responsibilities, Top management support, Implications for suppliers, Examples.] 56
Supporting a For-Profit Cause. Guy Kawasaki, Sales and Marketing Management, (May 2000), pp. S 16-S 19. [Corporate culture, Customer focused, Morale, impacts, Creating a good product and service, Sense of ownership, Training, Empowerment, Support, Examples.] 57
A Position of Power. Chad Kaydo, Sales and Marketing Management, (June 2000), pp. 104-106, 108, l l0, l l2, l l4. [Corporate image, Product positioning, Product differentiation, Factors, Identify the difference, Make it relevant, Keep it simple, Watch the competition, Examples.] 58
Technology Is Not Enough: Improving Performance by Building Organizational Memory. Rob Cross and Lloyd Baird, Sloan Management Review, 41 (Spring 2000), pp. 69-78. [Organizational learning, Explicit and tacit knowledge, Databases, Social bonding, Work processes and support systems, Targeting, Structuring, Embedding, Examples.] 59
Outsourcing Innovation: The New Engine of Growth. James Brian Quinn, Sloan Management Review, 41 (Summer 2000), pp. 13-28. [Discussion, Basic and early-stage research, Business processes, New-product introductions, Impacts, Resource limits, Specialist talents, Multiple risks, Attracting talent, Speed, Examples.] 60
Leading Laterally in Company Outsourcing. Michael Useem and Joseph Harder, Sloan Management Review, 41 (Winter 2000), pp. 25-38. [Personal interviews, Senior managers, Leadership capabilities (strategic thinking, deal making, partnership governing, managing change), Assessment.] 61
See also 73, 87, 96, 99, 100, 105, 109, 110, 132, 133, 215, 224
A Longitudinal Analysis of Satisfaction and Profitability. Kenneth L. Bernhardt, Naveen Donthu, and Pamela A. Kennett, Journal of Business Research, 47 (February 2000), pp. 161-71. [Literature review, Hypotheses, Consumer survey, impacts, Customer and employee satisfaction, Statistical analysis, Managerial implications, Fast-food restaurant industry.] 62
Towards Understanding Consumer Response to Stock-Outs. Katia Campo, Els Gijsbrechts, and Patricia Nisol, Journal of Retailing, 76 (Summer 2000), pp. 219-42. [Literature review; Model estimation; Store intercept; Characteristics (product, consumer, situation); Costs; impacts; Item, package size, and store switching; Purchase deferment and cancellation; Statistical analysis; Belgium.] 63
Attention, Retailers! How Convenient Is Your Convenience Strategy? Kathleen Seiders, Leonard L. Berry, and Larry G. Gresham, Sloan Management Review, 41 (Spring 2000), pp. 79-89. [Trends; Access, possession, and transaction convenience; Locating the right product; Integrated approach; Examples.] 64
Customer Relationship Management. Susan Reda, Stores, 82 (April 2000), pp. 33-36. [Target markets, Databases, Software packages, Consultants, Role of marketing department, Examples.] 65
Real Estate, Customer Research Become Key Tools in Service Merchandise Revival. Susan Reda, Stores, 82 (June 2000), pp. 118, 120, 122. [Discussion, Consultants, Customer profiles, Product categories, Point-of-sale information, Software packages, Web site, Internet alliances, Trade area analysis, Case study.] 66
Rethinking the Rules. Susan Reda, Stores, 82 (June 2000), pp. 34-35, 38. [Retailing industry, Impacts, Online revolution, Confronting new realities, More fluid environment, Logistics expertise, Acquisition strategy, Bricks-and-mortar advantages, Examples.] 67
See also 56, 84, 91,92, 94, 95, 104, 147, 163, 165, 185, 190
Industrial Marketing Management, 29 (July 2000), pp. 285-386. [Nine articles on business marketing networks, Implementing programmatic initiatives in manufacturer-retailer networks, Supplier relations, Interconnectedness, Strategic alliances, Partner as customer, Relationship strategy, Quality, Customer retention, Purchasing behavior, Satisfaction in industrial markets.] 68
On Interfirm Power, Channel Climate, and Solidarity in Industrial Distributor-Supplier Dyads. Keysuk Kim, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 388-405. [Literature review, Model estimation, Hypotheses, Survey, Measures, Supplier and distributor power, Coercive and noncoercive influence strategy, Trust, Relationship continuity, Solidarity, Statistical analysis.] 69
A Brand's Advertising and Promotion Allocation Strategy: The Role of the Manufacturer's Relationship with Distributors as Moderated by Relative Market Share. Kenneth Anselmi, Journal of Business Research, 48 (May 2000), pp. 113-22. [Literature review; Hypotheses; Survey of manufacturers; As exchange relationships become more relational, manufacturers increase advertising allocations; More discrete relationships, increase allocation to promotion; Market share moderates the influence of exchange relationship type.] 70
Relationship Marketing Activities, Commitment, and Membership Behaviors in Professional Associations. Thomas W. Gruen, John O. Summers, and Frank Acito, Journal of Marketing, 64 (July 2000), pp. 34-49. [Literature review, Model presentation, Hypotheses, Survey of life insurance agents, Measures, Commitment (affective, continuance, normative), Impacts, Membership retention, Exchange-based participation, Cooperatively based coproduction, Statistical analysis.] 71
Sales Through Sequential Distribution Channels: An Application to Movies and Videos. Donald R. Lehmann and Charles B. Weinberg, Journal of Marketing, 64 (July 2000), pp. 18-33. [Literature review, Model structure and analysis, Data from 35 movies, Exponential sales curves, Optimal release times, Assessment.] 72
Control Mechanisms and the Relationship Life Cycle: Implications for Safeguarding Specific Investments and Developing Commitment. Sandy D. Jap and Shankar Ganesan, Journal of Marketing Research, 37 (May 2000), pp. 227-45. [Literature review, Conceptual framework, Hypotheses, Survey of retailers, Measures, Transaction-specific investments, Relational norms, Explicit contracts, Supplier's commitment, Performance, Conflict level, Relationship satisfaction and phase, Interdependence magnitude and asymmetry, Statistical analysis.] 73
Organizing Distribution Channels for Information Goods on the Internet. Rajiv Dewan, Marshall Freimer, and Abraham Seidmann, Management Science, 46 (April 2000), pp. 483-95. [Electronic commerce, Electronic publishing, Digital and pricing content, Internet service providers, Industrial organization, Spatial competition, Industry structure, Assessment, Managerial implications.] 74
Price Protection in the Personal Computer Industry. Hau L. Lee, V. Padmanabhan, Terry A. Taylor, and Seungjin Whang, Management Science, 46 (April 2000), pp. 467-82. [Literature review, Obsolescence-prone market, Single- and two-buying-opportunity models, Channel coordination, Supply chain management, Incentives, Numerical example.] 75
Pursuing Risk-Sharing, Gain-Sharing Arrangements. James B.L. Thomson and James C. Anderson, Marketing Management, 9 (Summer 2000), pp. 40-47. [Customer-supplier relations, Market strategy, Implementation (assess customer measurement systems, determine products and services, build historical database, measurement responsibility, sharing risks and gains, outline specific actions and initiate the agreement), Case study, Hospital supply industry.] 76
Distributors: How Good Are They? James P. Morgan, Purchasing, 128 (May 4, 2000), pp. 50-52, 54, 58. [Survey of purchasing professionals, Percentage of companies' purchases, Product categories, Needs priorities, Performance ratings, Use of e-business tools, Problems (prices, delivery, damage, cost control, e-business, inventory, personnel, information), Slow implementation, Examples.] 77
See also 3, 5, 28, 31,67, 74, 77, 128, 132, 133, 151, 179, 184, 213
Beating the Banner Ad. Christine Blank, American Demographics, 22 (June 2000), pp. 42-44. [E-mail campaigns, Target markets, Entertainment, Multisensory, Interactive, Click-and-play video messages, Rich media, Costs, Examples.] 78
Mouse-Trapping the Student Market. Rebecca Gardyn, American Demographics, 22 (May 2000), pp. 30, 32-34. tin-school marketing, Ad-supported mousepads, Effectiveness, Comparisons, Internet banner ads, Online sweepstakes, Newspaper ads, Case study.] 79
Cracking the Niche. Christina Le Beau, American Demographics, 22 (June 2000), pp. 38-40. [Online marketing, Market segments, Web-based groups with focused interests, Becoming part of a community, Examples.] 80
Internet: A Vehicle for On-line Shopping. Venkatakrishna V. Bellur, Finnish Journal of Business and Economics, 49 (No. 2, 2000), pp. 191-207. [Literature review, Survey of households, Demographic and socioeconomic profile, Internet access and usage rates, Impacts, Occupation, Income, Discriminant analysis.] 81
Harvard Business Review, 78 (May/June 2000), pp. 84-114. [Three articles on e-business, Syndication, Roles, Structure, Business-to-business marketplaces, E-hubs, Integrating virtual and physical operations, Examples.] 82
How to Acquire Customers on the Web. Donna L. Hoffman and Thomas P. Novak, Harvard Business Review, 78 (May/June 2000), pp. 179-80, 183-86, 188. [Discussion, Banner ads, Affiliate marketing, Integrated strategy (mass media, online advertising, strategic partnerships, word of mouth, free links, PR), Examples.] 83
The All-in-One Market. Paul Nunes, Diane Wilson, and Ajit Kambil, Harvard Business Review, 78 (May/June 2000), pp. 19-20. [Trends, Evolution, Online transactions, Mechanisms, Price competition, Examples.] 84
Going Up! Vertical Marketing on the Web. Sunny Baker and Kim Baker, Journal of Business Strategy, 21 (May/June 2000), pp. 30-33. [Discussion, Mission, Customer needs, Market segments, Building awareness, E-commerce strategy, Assessment.] 85
The Eight Deadly Assumptions of E-Business. Alan Brache and Jim Webb, Journal of Business Strategy, 21 (May/June 2000), pp. 13-17. [Discussion, Technology is the answer, Get on the e-business bandwagon, Expand the customer base, Enables global expansion, Reengineering will help to better serve e-business needs, Web sites will ensure more e-business, Delegate development and implementation to the IT department or to a consultant, Going digital quickly, Assessment.] 86
Frictionless Commerce? A Comparison of Internet and Conventional Retailers. Erik Brynjolfsson and Michael D. Smith, Management Science, 46 (April 2000), pp. 563-85. [Literature review, Data collection, Competition, Price changes, Menu costs, Price dispersion, Asymmetrically informed consumers and search costs, Product and retailer heterogeneity, Assessment.] 87
Debunking the Myths of Web Site Promotion. Joyce Flory, Marketing Health Services, 20 (Summer 2000), pp. 31-35. [Discussion, Site registration, Registration services, Search engines, Specific types of information, Impacts, Promotion efforts, Awards, Press releases, Contests and giveaways, Success, Guidelines.] 88
Branding on the Internet. Larry Chiagouris and Brant Wansley, Marketing Management, 9 (Summer 2000), pp. 34-38. [Discussion, Relationship-building continuum, Impacts, E-branding tactics, Measurement issues, Examples.] 89
Ride or Drive? Ralph A. Oliva, Marketing Management, 9 (Summer 2000), pp. 58-60. [Web-based hubs, Driving (starting your own digital marketplace for multiple buyers and sellers), Riding (signing on to a hub run by another firm), Managing cognitive spaces, Spin ups, Hub wars, Ride versus drive, Assessment.] 90
Buyers Are Hot on Internet, Wary About E-Procurement. Purchasing, 128 (June 15, 2000), pp. S6-S7, SI0, S13. [Survey, Attitudes, Communications (requests for information and quotes from suppliers, shipments tracking and expediting, ordering), Costs, Ease of use, Security, Reliability, Current or projected use, Examples.] 91
E-Auction Model Morphs to Meet Buyers' Needs. Anne Millen Porter, Purchasing, 128 (June 15, 2000), pp. S31-S32, S34, S36, S39, S40, S44, S46. [Discussion, Reverse e-auctions, Bidding involving many suppliers, Impacts, Profit margins, Consultants, Software packages, Requirements, Transactions, Markets, Outsourcing, Examples.] 92
Cashing In. Ginger Conlon, Sales and Marketing Management, (June 2000), pp. 94-96, 102. [Business growth, Dot-corn businesses, Market potentials, Factors, Understand the audience, Act quickly, Risk, Stability, Examples.] 93
Why Dealers Must Buy In to the Web. Brent Keltner, Sales and Marketing Management, (April 2000), pp. 29-30. [Discussion, Benefits, Strategies, Focus on underperforming products, Integrate sales and marketing, Offer incentives, Provide technical support, Examples.] 94
Clicks and Misses. Melinda Ligos, Sales and Marketing Management, (June 2000), pp. 68-70, 72, 74, 76. [E-business, Problems, Alienating channel partners, Not focusing on core competencies, Not integrating customer service systems, Trying to serve mass market instead of existing customers, Not involving salespeople, Not knowing when to outsource, Examples.] 95
Finding Sustainable Profitability in Electronic Commerce. John M. de Figueiredo, Sloan Management Review, 41 (Summer 2000), pp. 41-52. [E-commerce product continuum, Market strategy (commodity products, quasi commodity, look and feel goods, look and feel with variable quality), Incumbents versus new entrants, Sustaining competitive advantage, Examples.] 96
Fast Venturing: The Quick Way to Start Web Businesses. Ajit Kambil, Erik D. Eselius, and Karen A. Monteiro, Sloan Management Review, 41 (Summer 2000), pp. 55-67. [Model presentation; Roles (innovators, equity and operational partners); Stages (illumination, investigation, implementation); Why, when, and how companies should fast venture; Venture networks; Examples.] 97
Building Stronger Brands Through Online Communities. Gil McWilliam, Sloan Management Review, 41 (Spring 2000), pp. 43-54. [Discussion, Traditional user groups, Forum for exchange of common interests, Attracting new members, Links to other sites, Brand owner's control over content, Skills needed to manage online communities, Examples.] 98
Domain Names Emerge as Key Tools for On-line Retail Marketing. Jennifer Karas, Stores, 82 (May 2000), pp. 94, 96, 98. [Discussion, Name by which a company is known on the Internet, Advantages, Professional and credible Web presence, Name competition, Examples.] 99
In-Store Interactive Systems Take on Major Role in Drawing Technology-Savvy Customers. Susan Reda, Stores, 82 (May 2000), pp. 44, 46, 48. [Study, Integration of online and in-store activities (digital signage, electronic messaging, kiosks), Consumer expectations, Shopping behavior, Examples.] 100
See also 67, l I 1, 147, 194, 195
Early Supplier Involvement in Customer New Product Development: A Contingency Model of Component Supplier Intentions. Douglas W. LaBahn and Robert Krapfel, Journal of Business Research, 47 (March 2000), pp. 173-90. [Literature review, Hypotheses, Survey, Customer power advantage, Adherence to agreements, Customer promise, Supplier intentions, Interdependence, Statistical analysis, Implications.] 101
An Industry Still in Need of Integration. Brian Milligan, Purchasing, 128 (May 18, 2000), pp. 147, 149-50. [Business growth, Intermodal transport, Acquisitions and mergers, Government regulation, Examples.] 102
Service Providers Under Pressure to Control Rates. Brian Milligan, Purchasing, 128 (April 20, 2000), pp. 113, 116-17, 119, 121. [Transportation, Third-party logistics, Industry growth, Purchasing managers, Time constraints, Bundled services, Costs, Internet investments, Software packages, Examples.] 103
Supply Chain Software Moves to the Web. Brian Milligan, Purchasing, 128 (April 6, 2000), pp. 67-68. [Transportation, Impacts, Business processes, Forecasting shipments, Demand forecasts, Meeting anticipated transportation requirements, Needed improvements, Examples.] 104
E-Replenishment System Counters Continuing Problem of Supermarket Out-of-Stocks. Susan Reda, Stores, 82 (April 2000), pp. 70, 72. [Supply chain initiatives, POS system investments, Scanning data, Software packages, Collaborative planning, Initialization, Execution, Monitoring, Examples.] 105
See also 55, 75, 84, 87, 103, 113
Industrial Export Pricing Practices in the United Kingdom. Nikolaos Tzokas, Susan Hart, Paraskevas Argouslidis, and Michael Saren, Industrial Marketing Management, 29 (May 2000), pp. 191-204. [Literature review, Survey of export marketing directors from three industrial sectors, High and low competence firms, Pricing orientations, Objectives, Policies, Methods used, Statistical analysis, Managerial implications, UK.] 106
Advertised Reference Price Effects on Consumer Price Estimates, Value Perception, and Search Intention. Bruce L. Alford and Brian T Engelland, Journal of Business Research, 48 (May 2000), pp. 93-100. [Literature review, Hypotheses, Experiment, Plausible and implausible price exposure conditions, Statistical analysis, Practical implications.] 107
An Investigation of Reference Price Segments. Tridib Mazumdar and Purushottam Papatla, Journal of Marketing Research, 37 (May 2000), pp. 246--58. [Literature review, Model development, Data collection (ERIM scanner panel of ACNielsen), Use of internal and external reference prices, Brand preferences and responses to marketing-mix variables, Statistical analysis, Managerial implications.] 108
Insights into Cross- and Within-Store Price Search: Retailer Estimates vs. Consumer Self-Reports. Joel E. Urbany, Peter R. Dickson, and Alan G. Sawyer, Journal of Retailing, 76 (Summer 2000), pp. 243-58. [Literature review, Surveys, Attitudes, Consumer patronage behavior, Price comparison frequency, Search for and responsiveness to price specials, Belief discrepancies, Assessment, Theoretical and managerial implications.] 109
Burden of Frequent Price Changes Spurs Development of Automated Pricing Systems. Michael Hartnett, Stores, 82 (May 2000), pp. 56, 58. [Retail chains; Software packages; Category management; Pricing rules can be applied to maintain family group and parity pricing by item, flavor, size, brand, competitor's pricing, margins, and the retailer's value image.] 110
See also 1, 6, 7, 8, 10, 15, 16, 21, 22, 23, 24, 25, 43, 50, 55, 58, 60, 63, 70, 89, 98, 101, 108, 135, 145, 147, 148, 154, 155, 167, 170, 173, 206
Product Development Partnerships: Balancing the Needs of OEMs and Suppliers. Morgan L. Swink and Vincent A. Mabert, Business Horizons, 43 (May/June 2000), pp. 59-68. [Discussion, OEM needs (providers of scarce resources and capabilities, support of global product strategies, minimized risks), Supplier needs (rewards for up-front involvement, protected business interests, shared wealth), Success, Guidelines.] 111
Building an Innovation Factory. Andrew Hargadon and Robert I. Sutton, Harvard Business Review, 78 (May/June 2000), pp. 157-66. [Knowledge-brokering cycle, Factors, Capturing new ideas, Keeping ideas alive, Imagining new uses for old ideas, Putting promising concepts to the test, Examples.] 112
Price and Brand Name as Indicators of Quality Dimensions for Consumer Durables. Merrie Brucks, Valarie A. Zeithaml, and Gillian Naylor, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 359-74. [Literature review, Model presentation, Hypotheses, Laboratory experiment, Ease of use, Versatility, Durability, Service ability, Performance, Prestige, Consumers' judgment processes and inferences, Statistical analysis.] 113
Introducing Short-Term Brands: A New Branding Tool for a New Consumer Reality. Dan Herman, Journal of Brand Management (UK), 7 (May 2000), pp. 330--40. [Changes in consumer preferences and behavior, Market strategy, Evolving and variety brands, Planned limited life expectancies, Value added, Examples.] 114
A Survey of Brand Risk Management. Rory F. Knight and Deborah J. Pretty, Journal of Brand Management (UK), 7 (May 2000), pp. 353-65. [Discussion; Brand significance and perception; Core qualities of brands across regions, industry sectors, and organizational position; Threats; Protection mechanisms; Brand insurance; Brand valuation; Assessment; Many countries.] 115
Call Branding: Identifying, Leveraging, and Managing New Branding Opportunities. Kevin M. Waters, Journal of Brand Management (UK), 7 (May 2000), pp. 321-29. [Modifying a brand to correspond with its verbal identity (Kraft Mayo, FedEx), Partial declaration and use, Acronyms, Success, Guidelines.] 116
The Incumbent's Curse? Incumbency, Size, and Radical Product Innovation. Rajesh K. Chandy and Gerard J. Tellis, Journal of Marketing, 64 (July 2000), pp. 1-17. [Literature review; Data collection (more than 250 books and 500 articles); Recently, large firms and incumbents are significantly more likely to introduce radical innovations than small firms and nonincumbents; Nationality; Implications.] 117
Impact of a Late Entrant on the Diffusion of a New Product/Service. Trichy V. Krishnan, Frank M. Bass, and V. Kumar, Journal of Marketing Research, 37 (May 2000), pp. 269-78. [Model testing, Mathematical equations, Brand-level sales data in multiple markets, Effects, Market potentials, Diffusion speed of the category and of incumbent brands, Statistical analysis.] 118
Customization of Product Technology and International New Product Success: Mediating Effects of New Product Development and Rollout Timeliness. George M. Chryssochoidis and Veronica Wong, Journal of Product Innovation Management, 17 (July 2000), pp. 268-85. [Literature review, Model proposal, Hypotheses, Interviews with managers in multinational companies, Impacts, Scheduling, Individual country requirements, Statistical analysis.] 119
Company Competencies as a Network: The Role of Product Development. Hanne Harmsen, Klaus G. Grunert, and Karsten Bore, Journal of Product Innovation Management, 17 (May 2000), pp. 194-207. [Literature review, Survey of managers, Rankings, Perceived success factors and problems, Assessment, Implications.] 120
Harnessing Tacit Knowledge to Achieve Breakthrough Innovation. Ronald Mascitelli, Journal of Product Innovation Management, 17 (May 2000), pp. 179-93. [Discussion, Model presentation, Methods, Achieve emotional commitment and personal involvement from design team members, Use of early and frequent prototyping, Face-to-face interaction during product development, Examples, Managerial implications.] 121
Technological Innovativeness as a Moderator of New Product Design Integration and Top Management Support. Morgan Swink, Journal of Product Innovation Management, 17 (May 2000), pp. 208-20. [Literature review, Model presentation, Hypotheses, Study of new product development projects, Effects, Financial performance, Design quality, Time-based performance, Interactions, Statistical analysis, Managerial implications.] 122
Consumer-Level Factors Moderating the Success of Private Label Brands. Rajeev Batra and Indrajit Sinha, Journal of Retailing, 76 (Summer 2000), pp. 175-91. [Literature review, Model estimation, Mall-intercept study, Measures (consequences of purchase mistake, degree of quality variation in category, search versus experience nature of category, price consciousness), Statistical analysis, Managerial implications.] 123
Choosing the Right Branding Expert. Victoria Barkan and Debra Semans, Marketing Management, 9 (Summer 2000), pp. 29-31. [Discussion, Understanding your needs, Approach/methodology, Objectivity and bias, Experience, Future perspective, Leading-edge thinking, Benchmark results, Client satisfaction, Stay involved and visible, Assessment.] 124
Market-Driven Product Development. Stephan A. Butscher and Michael Laker, Marketing Management, 9 (Summer 2000), pp. 48-53. [Target-costing pricing approach, Definition of target segments, Competitive advantages and disadvantages, Product positioning, Fine-tuning product design and pricing, Market simulations, Target costs, Examples.] 125
Brand Waves: Building Momentum Throughout the Ownership Cycle. Peter H. Farquhar, Marketing Management, 9 (Summer 2000), pp. 14-21. [Discussion, Ownership cycle, Trigger, Consideration, Drivers (awareness, relevance, differentiation, quality, affordability, availability), Conversion, Purchase and confirmation, Taking credit, Value, Examples.] 126
How to Build a Billion Dollar Business-to-Business Brand. Don E. Schultz and Heidi F. Schultz, Marketing Management, 9 (Summer 2000), pp. 22-28. [Discussion; Evolution of b-to-b companies; Product-, distribution-, and customer-driven; Brand structures and policies; Building and development; Communication; Measuring results; Example.] 127
See also 70, 75, 88
Redeeming Qualities. Jennifer Lach, American Demographics, 22 (May 2000), pp. 36-38. [Study, Incentives, S&H greenpoints.com, Online participation, Customer retention, Age groups, Incomes, Effectiveness, Examples.] 128
An Evaluation of State Sponsored Promotion Programs. Timothy J. Wilkinson and Lance Eliot Brouthers, Journal of Business Research, 47 (March 2000), pp. 229-36. [Data collection (relationships between program offerings and state exports), Variables (direct exports, trade shows, trade missions, foreign offices, market information activities, population), Statistical analysis, Implications.] 129
Money Isn't Everything. Vincent Alonzo, Sales and Marketing Management, (April 2000), pp. 47--48. [Sweepstakes, Appeals, Effectiveness, Impacts, Long-term sales, Offering prizes appropriate for clients, Examples.] 130
The Shows Will Go On. Danielle Harris, Sales and Marketing Management, (May 2000), pp. 85-88. [Discussion, Trade shows, Factors, Increasing booth traffic and generating quality leads, Motivating salespeople, Budgets, Examples.] 131
Internet Retailers Shift Focus from Attracting to Retaining On-line Customers. Maureen Licata, Stores, 82 (June 2000), pp. 66, 68, 70, 72. [Value-focused customers, Loyalty incentives, Discounts, Giveaways, Contests, Sweepstakes, Free shipping, Customer databases, Impacts, Content, Community, Communication, Examples.] 132
Electronic Coupons Find Growing Uses for Both Stores and E-Commerce Sites. Tony Seideman, Stores, 82 (April 2000), pp. 104, 106. [Target markets, Flexibility, Costs, Customer databases, Profiles, Effectiveness, Examples.] 133
See also 2, 3, 7, 8, 15, 70, 78, 79, 80, 83, 152, 159, 177, 197, 224
Who's Next? Richard Linnett, Advertising Age, 71 (May 29, 2000), pp. 12, 15. [Strategic planning, Advertising agencies, Competitive advantage, Acquisitions and mergers, Impacts, Clients, Business growth, Examples.] 134
Linking Advertising and Brand Value. Irene M. Herremans, John K. Ryans Jr., and Raj Aggarwal, Business Horizons, 43 (May/June 2000), pp. 19-26. [Literature review, Model presentation, Advertising turnover, Marketing investment, Product quality, Market share, Study of firms, High- and low-efficiency brand enhancers, Brand deterioration, Future unknown, Neglect, Examples.] 135
Narrative Music in Congruent and Incongruent TV Advertising. Kineta Hung, Journal of Advertising, 29 (Spring 2000), pp. 25-34. [Literature review, Content analysis, Experiment, Meanings associated with ad components and commercials, Textual elaboration, Assessment.] 136
The Impact of Verbal Anchoring on Consumer Response to Image Ads. Barbara J. Phillips, Journal of Advertising, 29 (Spring 2000), pp. 15-24. [Literature review, Experiment, Attitude toward the ad, Presence and level of verbal anchoring, Comprehension, Statistical analysis.] 137
Journal of Advertising Research, 40 (May/June 2000), pp. 7-52. [Four articles on creativity; Recall, liking, and creativity in TV commercials; Creative differences between copywriters and art directors; Correlates of integrated marketing communications; Customer/brand loyalty in the interactive marketplace.] 138
Advertising Attitudes and Advertising Effectiveness. Abhilasha Mehta, Journal of Advertising Research, 40 (May/June 2000), pp. 67-72. [Literature review, Data collection (Magazine Impact Research Systems), Measures, Attitudinal statements, Intrusiveness/recall, Persuasion/buying interest, Statistical analysis, Implications.] 139
See also 11,142, 217, 218
Sales Call Anxiety: Exploring What It Means When Fear Rules a Sales Encounter. Willem Verbeke and Richard P. Bagozzi, Journal of Marketing, 64 (July 2000), pp. 88-101. [Literature review, Model testing, Hypotheses, Survey of salespeople, Factors, Negative self-evaluations, Negative evaluations from customers, Physiological symptoms, Protective actions, Statistical analysis, The Netherlands.] 140
Independents Day. Dan Hanover, Sales and Marketing Management, (April 2000), pp. 64---66, 68. [Independent sales reps, Motivation, Communication, Support, Rewards, Short-term bonus and incentives programs, Examples.] 141
See also 57, 93, 95, 130, 131, 140, 141, 147, 213
Comparisons of Alternative Perceptions of Sales Performance. Paul A. Dion and Peter M. Banting, Industrial Marketing Management, 29 (May 2000), pp. 263-70. [Study of industrial market triads (salesperson, sales manager, buyer), There were assessment discrepancies in addition to what constituted good performance, Gender evaluation, Statistical analysis, Managerial implications.] 142
Driving Down Costs. Christine Gales, Sales and Marketing Management, (May 2000), pp. 102-104, 106, 108, 110. [Corporate cars, Strategy, Industry, Lease or own, Depreciation, Fuel concerns, Vehicle duration, Reselling, Buy from one manufacturer, Managing risk, Examples.] 143
Masterful Meetings. Erin Strout, Sales and Marketing Management, (May 2000), pp. 68-72, 74, 76. [Discussion, Planning, Choose destination carefully to set the right tone, Set straightforward agenda, Stick to budget, Develop postmeeting action plans.] 144
See also 41, 46, 50, 56, 68, 69, 77, 101, 106, 111, 119, 121, 122, 142, 163, 164, 166, 173, 193, 194, 195,212, 217
Marketing High Technology: Preparation, Targeting, Positioning, Execution. Chris Easingwood and Anthony Koustelos, Business Horizons, 43 (May/June 2000), pp. 27-34. [Discussion; Market strategy; Supply to OEMs; Market education; Distribution; Target innovative adopters, pragmatists, conservatives, current customers, competitors' customers; Emphasize exclusivity, low price, technological superiority; Execution; Examples.] 145
Do Trade-Offs Exist in Operations Strategy? Insights from the Stamping Die Industry. Mark Pagell, Steve Melnyk, and Robert Handfield, Business Horizons, 43 (May/June 2000), pp. 69-77. [Study of firms, Performance, Comparisons, Strategic advantages and disadvantages, Relative fixed costs and lead time, Employee commitment, Assessment.] 146
Strategic Selling in the Age of Modules and Systems. John W. Henke, Industrial Marketing Management, 29 (May 2000), pp. 271-84. [Discussion, OEM impediments to module and system acquisition, Developing a sales strategy, Capabilities and resources, Cooperation among participating suppliers, Design considerations, Markup practices, Supply chain management experience, Case study, Automotive industry.] 147
Differential Effects of the Primary Forms of Cross Functional Integration on Product Development Cycle Time. J. Daniel Sherman, William E. Souder, and Svenn A. Jenssen, Journal of Product Innovation Management, 17 (July 2000), pp. 257-67. [Literature review; Survey of high-technology firms; Variables; Integration of knowledge from past projects; R&D/customer, marketing, manufacturing, supplier integration; Strategic partnership integration; Statistical analysis; US and Scandinavian firms.] 148
Prepping the Supply Base for Leaner Supply Systems. Tom Stundza, Purchasing, 128 (June 1, 2000), pp. 62-64, 66, 68. [Supplier development programs, Quality control processes, Material resource planning, Just-in-time delivery, Make or buy studies, Component kitting, Best supplier evaluations, Outsourcing, Improving performance, Implementation, Case study, Aerospace industry.] 149
See also 26, 52, 129, 158, 180, 208
Demographics, Personality Traits, Roles, Motivations, and Attrition Rates of Hospice Volunteers. Becky J. Starnes and Walter W. Wymer Jr., Journal of Nonprofit and Public Sector Marketing, 7 (No. 2, 1999), pp. 61-76. [Literature review, Volunteer profile, Services to patients and families, Religious beliefs, Personal experiences, Training and expectations, Assessment.] 150
See also 13, 29, 38, 42, 45, 46, 48, 54, 63, 106, 115, 117, 119, 129, 140, 148, 177, 188, 189, 191,200, 201, 211,216, 223
Privacy on the Net: Europe Changes the Rules. William J. Scheibal and Julia Alpert Gladstone, Business Horizons, 43 (May/June 2000), pp. 13-18. [Discussion, Legal aspects, Business impact, EU privacy directive, Impacts, US, Assessment.] 151
Color Usage in International Business-to-Business Print Advertising. Irvine Clarke 1II and Earl D. Honeycutt Jr., Industrial Marketing Management, 29 (May 2000), pp. 255-61. [Literature review, Hypotheses, Content analysis, Comparisons, Black/white ads, Color distribution by magazine, Cultural meanings, Managerial implications, France, US, Venezuela.] 152
Managing International Joint Venture Relationships: A Longitudinal Perspective. Akmal S. Hyder and Pervez N. Ghauri, Industrial Marketing Management, 29 (May 2000), pp. 205-18. [Literature review, Model presentation, In-depth interviews, Motives, Resources, Learning, Network, Performance, Case studies, Telecommunications industry, Sweden, India.] 153
Positioned for Success: Branding in the Czech Brewing Industry. Chris Lewis and Angela Vickerstaff, Journal of Brand Management (UK), 7 (May 2000), pp. 341-52. [Literature review, Brand appeals (function, image and personality), Market strategy, Price, Quality, Traditional, Modem, Effects, Foreign ownership and expertise, Case studies.] 154
Country of Branding: A Review and Research Propositions. Ian Phau and Gerard Prendergast, Journal of Brand Management (UK), 7 (May 2000), pp. 366-75. [Literature review, Hypotheses, Quality perceptions, Brand image, Country of branding versus country of manufacturing, Luxury brands, Development of country, High versus low involvement, Assessment, Asia.] 155
Forecasting Practices in the Pharmaceutical Industry in Singapore. Louis Choo, Journal of Business Forecasting, 19 (Summer 2000), pp. 18-20. [Survey, Extent of involvement, Purpose of forecasts, Techniques used, Sources of information, Forecast drivers, Assessment.] 156
A Systematic Approach to Tourism Policy. Jafar Alavi and Mahmoud M. Yasin, Journal of Business Research, 48 (May 2000), pp. 147-56. [Discussion, Revenues, Model presentation, Mathematical equations, Effects (areawide, region-mix, competitive, allocation), Statistical data, Shift-share analysis, Policy implications, Many countries.] 157
Marketing of a Financial Innovation: Commercial Use of the Euro by European Companies Prior to Mandatory Adoption. Yvonne M. van Everdingen and Gary J. Bamossy, Journal of Business Research, 48 (May 2000), pp. 123-33. [Theoretical discussion, Model presentation, Survey of firms, Measures, Perceived innovation characteristics, Perceptions of political and business environment, Organizational characteristics, Internal communication behavior, Adoption behavior, Statistical analysis, Recommendations.] 158
Effect of a Buy-National Campaign on Member Firm Performance. Graham D. Fenwick and Cameron I. Wright, Journal of Business Research, 47 (February 2000), pp. 135-45. [Literature review, Survey, Comparisons, Nonparticipating firms, Staff member and domestic sales changes, Statistical analysis, New Zealand.] 159
Global Sourcing, Multiple Country-of-Origin Facets, and Consumer Reactions. Zhan G. Li, L. William Murray, and Don Scott, Journal of Business Research, 47 (February 2000), pp. 121-33. [Literature review, Hypotheses, Experiment, Comparisons, Country-of-design, Assembly, Corporation, Dimensions (functional, symbolic, overall quality), Statistical analysis, Implications.] 160
New Rules for Global Markets. Richard W. Oliver, Journal of Business Strategy, 21 (May/June 2000), pp. 7-9. [Discussion; Competitive strategies; Think and act globally; Focus on ethnic group, not country; Focus on neighbors first; Focus on the cities; Culture is an important barrier; Use global market muscle; Focus south, not east-west; Develop new mind-set.] 161
Mode of International Entry: An Isomorphism Perspective. Peter S. Davis, Ashay B. Desai, and John D. Francis, Journal of International Business Studies, 31 (Second Quarter 2000), pp. 239-58. [Literature review, Model development, Hypotheses, Survey of firms, Pressures to conform to behavioral norms within environments, Comparisons, Wholly owned, Exporting, Joint ventures, Licensing agreements, Statistical analysis.] 162
The Determinants of Trust in Supplier-Automaker Relationships in the U.S., Japan, and Korea. Jeffrey H. Dyer and Wujin Chu, Journal of International Business Studies, 31 (Second Quarter 2000), pp. 259-85. [Literature review, Model presentation, Hypotheses, Personal interviews, Measures, Length of relationship, Face-to-face communication, Relationship continuity, Assistance to supplier, Stock ownership, Statistical analysis.] 163
Social Ties and Foreign Market Entry. Paul Ellis, Journal of International Business Studies, 31 (Third Quarter 2000), pp. 443-69. [Literature review, Propositions, Interviews with experienced members of manufacturing firms, Knowledge of foreign market opportunities is commonly acquired through existing interpersonal links rather than through market research, Hong Kong.] 164
Process Standardization Across Intra- and Inter-cultural Relationships. David A. Griffith, Michael Y. Hu, and John K. Ryans Jr., Journal of International Business Studies, 31 (Second Quarter 2000), pp. 303-24. [Literature review, Model presentation, Hypotheses, Survey of distributors, Measures, Trust, Commitment, Conflict, Satisfaction, Statistical analysis, Managerial implications, Canada, Chile, Mexico, US.] 165
Productivity Spillovers from Foreign Direct Investment: Evidence from UK Industry Level Panel Data. Xia ming Liu, Pamela Siler, Chengqi Wang, and Yingqi Wei, Journal of International Business Studies, 31 (Third Quarter 2000), pp. 407-25. [Literature review, Model presentation, Impacts, Situations in which host country is developed, Introduction of advanced technology, Statistical analysis.] 166
The International Biotechnology Industry: A Dynamic Capabilities Perspective. Anoop Madhok and Thomas Osegowitsch, Journal of International Business Studies, 31 (Second Quarter 2000), pp. 325-35. [International diffusion of technology, Propositions, Organizational form and geographic flows, Transactions, Licensing and marketing agreements, Research agreements, Joint ventures, Acquisition, New subsidiaries, Composite groupings, Assessment, Implications.] 167
National Culture and Strategic Change in Belief Formation. Livia Markoczy, Journal of International Business Studies, 31 (Third Quarter 2000), pp. 427-42. [Literature review, Study of Hungarian organizations recently acquired by Anglo-Saxon partners, Individual beliefs, Causal relationships, Impacts, Being a member of the functional area favored by the strategic change, Statistical analysis.] 168
Synergy, Managerialism or Hubris? An Empirical Examination of Motives for Foreign Acquisitions of U.S. Firms. Anju Seth, Kean P. Song, and Richardson Pettit, Journal of International Business Studies, 31 (Third Quarter 2000), pp. 387--405. [Theoretical discussion, Testable hypotheses and empirical predictions, Data collection, Relationship between target gains and acquirer gains, Total gains, Statistical analysis.] 169
Knowledge Flows in the Global Innovation System: Do U.S. Firms Share More Scientific Knowledge Than Their Japanese Rivals? Jennifer W. Spencer, Journal of International Business Studies, 31 (Third Quarter 2000), pp. 521-530. [Discussion, Hypotheses, Data collection (firms' publication and citation patterns), Japanese firms appropriated no more knowledge from the global community than their US counterparts, Statistical analysis.] 170
The Management Implications of Ethnicity in South Africa. Adele Thomas and Mike Bendixen, Journal of International Business Studies, 31 (Third Quarter 2000), pp. 507-19. [Literature review, Hypotheses, Interviews with middle managers, Both management culture and perceived management effectiveness were found to be independent of both race and the dimensions of culture, Implications.] 171
A Case for Comparative Entrepreneurship: Assessing the Relevance of Culture. Anisya S. Thomas and Stephen L. Mueller, Journal of International Business Studies, 31 (Second Quarter 2000), pp. 287-301. [Literature review, Survey of students, Measures, Innovativeness, Locus of control, Risk-taking, Energy level, Impacts, Cultural distance, Many countries.] 172
Customer-Driven Product Development Through Quality Function Deployment in the U.S. and Japan. John J. Cristiano, Jeffrey K. Liker, and Chelsea C. White i1I, Journal of Product Innovation Management, 17 (July 2000), pp. 286-308. [Literature review; Survey of companies; US companies reported a higher degree of quality function deployment usage, management support, cross-functional support, data sources, benefits; Assessment.] 173
Venture Capitalist Involvement in Portfolio Companies: Insights from South Africa. Michael H. Morris, John W. Watling, and Miner Schindehutte, Journal of Small Business Management, 38 (July 2000), pp. 68-77. [Literature review, Survey, Types of companies in which venture capitalists prefer to invest, Factors influencing involvement, Areas of involvement, Interactions, Statistical analysis, implications.] 174
International Competition in Mixed Industries. Roland Calori, Tugrul Atamer, and Pancho Nunes, Long Range Planning (UK), 33 (June 2000), pp. 349-75. [Discussion, Formation of regional competitive territories, Dual effect of marketing intensity, Influence of demand factors, Role of strategic innovators across borders, Examples.] 175
Information Technology and Productivity: Evidence from Country-Level Data. Sanjeev Dewan and Kenneth L. Kraemer, Management Science, 46 (April 2000), pp. 548-62. [Discussion, Production function, Hypotheses, Data collection, Capital investment, GDP per worker, Asset categories, Developed and developing countries, Statistical analysis, Policy implications.] 176
See also 9, 19, 20, 27, 37, 40, 42, 47, 55, 60, 71, 72, 76, 78, 102, 103, 150, 157, 192, 214, 230, 231
Putting the "World" in the World Series. Rebecca Gardyn, American Demographics, 22 (April 2000), pp. 28-30. [Trends, Multicultural players and tans, International, TV viewers, Radio, Impacts, Marketers, Prestige, Brand acceptance, Localism, Examples.] 177
Journal of Business Research, 48 (June 2000), pp. 165-283. [Eleven articles on health care research, Quality-of-life/needs assessment model, Internal marketing, Financial management, Measurement error, Role of nurse practitioners, Market orientation and organizational performance, Antitrust concerns about evolving vertical relationships, Measuring service quality, Modeling health plan choice behavior, Roles of primary and secondary control in older adulthood, Service quality for inpatient nursing services.] 178
One-to-One Marketing Doesn't Have to Be Web-Based. Joel R. Lapointe, Journal of Business Strategy, 21 (May/June 2000), pp. 34-37. [Discussion, Customer relations, Scenarios (hospitality, professional services, customer service/sales), Impacts, Key role identification, High performer profiling, Key information accessibility, Assessment.] 179
Current Resource Constraints and the Role of Marketing in Health Research Organizations. Dennis R. McDermott, Howard P. Tuckman, and David J. Urban, Journal of Nonprofit and Public Sector Marketing, 7 (No. 2, 1999), pp. 3-16. [Survey of CEOs representing national sample of HROs, Attitudes, Fundraising, Revenue sources, Budget allocations, Strategic, Assessment, Recommendations.] 180
A Comprehensive Framework for Service Quality: An Investigation of Critical Conceptual and Measurement Issues Through a Longitudinal Study. Pratibha A. Dabholkar, C. David Shepherd, and Dayle I. Thorpe, Journal of Retailing, 76 (Summer 2000), pp. 139-73. [Literature review, Propositions, Consumer survey, Components and antecedents (reliability, personal attention, comfort, features), Impacts, Behavioral intentions, Measured disconfirmation versus perceptions, Statistical analysis, Implications.] 181
Switching Barriers and Repurchase Intentions in Services. Michael A. Jones, David L. Mothersbaugh, and Sharon E. Beatty, Journal of Retailing, 76 (Summer 2000), pp. 259-74. [Literature review, Model testing, Hypotheses, Consumer survey, Effects, Core-service satisfaction, Interpersonal relationships, Switching costs, Attractiveness of alternatives, Interactions, Statistical analysis, Implications.] 182
Access to Capital and Terms of Credit: A Comparison of Men- and Women-Owned Small Businesses. Susan Coleman, Journal of Small Business Management, 38 (July 2000), pp. 37-52. [Literature review, Model presentation, Data collection (Federal Reserve Board and Small Business Administration), Firm characteristics, Most recent loan, Usage of bank credit products, Interest rates, Collateral, Statistical analysis.] 183
Customer Service: An Essential Component for a Successful Web Site. Cherryl Carlson, Marketing Health Services, 20 (Summer 2000), pp. 28-30. [Discussion, E-mail management, Response (automatic, intelligent agent-aided, intelligent automated), Self help, Live text chat, Outsourcing, Assessment.] 184
Dissecting the HMO-Benefits Managers Relationship: What to Measure and Why. James W. Peltier and John Westfall, Marketing Health Services, 20 (Summer 2000), pp. 5-13. [Discussion, Survey of employee benefits managers, Attitudes, Dimensions (financial/economic, social/responsiveness, structural/partnership), Overall satisfaction and quality, Relationship commitment/loyalty, Statistical analysis, Managerial implications.] 185
Practicing Best-in-Class Service Recovery. Stephen W. Brown, Marketing Management, 9 (Summer 2000), pp. 8-9. [Best practices; Hiring, training, and empowerment; Service recovery guidelines and standards; Easy access and effective response; Customer and product databases; Failure; Companywide recovery; Profits; Technology; Examples.] 186
See also 74, 75, 87, 118, 169
Bayesian Dynamic Factor Models and Portfolio Allocation. Omar Aguilar and Mike West, Journal of Business and Economic Statistics, 18 (July 2000), pp. 338-57. [Dynamic linear models, Exchange rates forecasting, Markov chain Monte Carlo, Multivariate stochastic volatility, Portfolio selection, Sequential forecasting, Variance matrix discounting, Assessment.] 187
Modeling the Sources of Output Growth in a Panel of Countries. Gary Koop, Jacek Osiewalski, and Mark F.J. Steel, Journal of Business and Economic Statistics, 18 (July 2000), pp. 284-99. [Stochastic production-frontier model, Efficiency levels, Bayesian inference, Growth decompositions, Technical change, Numerical implementation.] 188
The Theoretical Foundation for Intercultural Business Communication: A Conceptual Model. Iris I. Varner, Journal of Business Communication, 37 (January 2000), pp. 39-57. [Literature review, Research questions, Impacts, Intercultural communication strategy, Country-specific and comparative studies, Assessment.] 189
Information, Contracting, and Quality Costs. Stanley Baiman, Paul E. Fischer, and Madhav V. Rajan, Management Science, 46 (June 2000), pp. 776-89. [Literature review, Model presentation, Propositions, Internal and external failure, First- and second-best settings, Contractible decisions, Impacts, Information systems, Assessment.] 190
Modeling Intercategory and Generational Dynamics for a Growing Information Technology Industry. Namwoon Kim, Dae Ryun Chang, and Allan D. Shocker, Management Science, 46 (April 2000), pp. 496-512. [Wireless telecommunications service, Market potentials, Asymmetry of effect, Bidirectional interrelationship, Implications, Hong Kong, Korea.] 191
Measuring the Robustness of Empirical Efficiency Valuations. Ludwig Kuntz and Stefan Scholtes, Management Science, 46 (June 2000), pp. 807-23. [Model extension, Propositions, Data envelopment analysis, Hospital capacity planning, Monotone one-parameter perturbations, Assessment.] 192
Behind the Learning Curve: Linking Learning Activities to Waste Reduction. Michael A. Lapre, Amit Shankar Mukherjee, and Luk N. Van Wassenhove, Management Science, 46 (May 2000), pp. 597-611. [Literature review, Organizational learning, Quality, Technological knowledge, Experimentation, Knowledge transfer, Implications.] 193
The Value of Information Sharing in a Two-Level Supply Chain. Hau L. Lee, Kut C. So, and Christopher S. Tang, Management Science, 46 (May 2000), pp. 626-43. [Supply chain management, Mathematical models, Production planning and inventory control, Electronic data interchange, Quick response, Analytical and numerical analyses.] 194
Scheduling Resource-Constrained Projects Competitively at Modest Memory Requirements. Arno Sprecher, Management Science, 46 (May 2000), pp. 710-23. [Model presentation, Branch-and-bound algorithm, Rules, Extended and simplified single enumeration, Local left-shift, Extended global left-shift, Contraction, Set-based dominance, Non-optimality, Heuristic, Computational results.] 195
See also 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 i, 12, 13, 14, 15, 16, 17, 18, 19,20,21,22,23,24,25,26,27,42,62,63,66,72,73,80,81,107, 108, 109, 113, 118, 123, 136, 137, 139, 156, 159, 160, 177, 178, 180, 181, 182, 200, 209, 217, 229, 230
American Demographics, 22 (June 2000), pp. 54-56, 58, 60-62, 64-65. [Three articles on our sense of place, Work-at-home labor force, Desire for complex appliances with simple and easy-to-use designs, Networking, Shortening the distance between places and people, Impacts, Neighborhoods, Cities, Emerging markets, Consumer expenditures, Cities, Statistical data.] 196
What's on Your Mind? Rebecca Gardyn, American Demographics, 22 (April 2000), pp. 31-33. [Electroencephalogram technology, Reading consumers' brain-wave activity, System testing, Problems, Data translation, Acceptance, TV content research applicability, Could be useful in conjunction with focus groups.] 197
Riding High on the Market. Cheryl Russell and Marcia Mogelonsky, American Demographics, 22 (April 2000), pp. 44-46, 48, 50, 52, 54. [Economic expansion, Household incomes, Age groups, Financial assets, Risks, Stock holdings, Home values, Debt, Net worth, Saving for retirement, Statistical data.] 198
The Money in the Middle. Alison Stein Wellner, American Demographics, 22 (April 2000), pp. 56-58, 60, 62, 64. [Economic expansion, Impacts, Middle class, Definition problems, Role of immigration, Age groups, Education, Household income, Internet usage, Statistical data.] 199
The Measurement of Intergenerational Communication and Influence on Consumption: Development, Validation, and Cross-Cultural Comparison of the IGEN Scale. Madhubalan Viswanathan, Terry L. Childers, and Elizabeth S. Moore, Journal of the Academy of Marketing Science, 28 (Summer 2000), pp. 406-24. [Literature review, Consumer socialization, Three studies, Components relevant to marketplace transactions (consumer skills, preferences, attitudes toward marketer supplied information), Comparisons, Parents, Children, US, Thailand.] 200
Innovation and International Business Communication: Can European Research Help to Increase the Validity and Reliability for Our Business and Teaching Practice? Jan Ulijn, Journal of Business Communication, 37 (April 2000), pp. 173-87. [Literature review, Quantitative/qualitative, Real life/simulation, Studying language, Culture (national, corporate, professional), Communication medium, Assessment.] 201
Debunking Executive Conventional Wisdom. Larry Lapide, Journal of Business Forecasting, 19 (Summer 2000), pp. 16-17. [Myths about forecasting; Forecasts are always wrong, so why put any focus on demand planning; All we need is a quantitative expert; Forecasting software will take care of all forecasting needs; Process is too expensive; Assessment.] 202
State Demographic Forecasting for Business and Policy Applications. Jon David Vasche, Journal of Business Forecasting, 19 (Summer 2000), pp. 23, 28-30. [Reliance on a large, multidimensional matrix modeling system with extensive input vectors aids in projection of aggregate population and its characteristics.] 203
Journal of Business Research, 48 (April 2000), pp. 5-92. [Ten articles on replication research, Brand awareness effects on consumer decision making, Credit card effect, Business turnarounds following acquisitions, Organizational growth determinants, Impact of internalization on the diversification-performance relationship, Advertising complex products, Religious symbols as peripheral cues in advertising, Market orientation and business profitability, How salespeople build quality relationships, Conducting marketing science.] 204
Riding the Wave: Response Rates and the Effects of Time Intervals Between Successive Mail Survey Follow-Up Efforts. Cindy Claycomb, Stephen S. Porter, and Charles L. Martin, Journal of Business Research, 48 (May 2000), pp. 157-62. [Literature review; Experiment; Follow-up mailings sent to each of 20 different treatment groups, testing follow-up intervals ranging from 3 to 60 days; Assessment; Implications.] 205
Historical Method in Marketing Research with New Evidence on Long-Term Market Share Stability. Peter N. Golder, Journal of Marketing Research, 37 (May 2000), pp. 156-72. [Literature review, Stages, Select topic and collect evidence, Critically evaluate sources along with evidence, Analyze and interpret, Present conclusions, Application.] 206
Cast Demographics, Unobserved Segments, and Heterogeneous Switching Costs in a Television Viewing Choice Model. Ron Shachar and John W. Emerson, Journal of Marketing Research, 37 (May 2000), pp. 173-86. [Model comparisons, Data collection (ACNielsen), Examination of strategic programming and scheduling decisions, Optimal programming decisions, Goodness-of-fit and ratings predictions, Applications.] 207
The Effectiveness of Survey Response Rate Incentives in a Public Non-profit Environment. Frank H. Wadsworth and Eldon Little, Journal of Nonprofit and Public Sector Marketing, 7 (No. 2, 1999), pp. 53-60. [Literature review, Convenience sample, Treatment groups, Deadlines, Prepaid and promised monetary and non-monetary rewards, Statistical analysis, Recommendations.] 208
See also 53, 59, 78, 82, 85, 86, 91, 92, 94, 97, 98, 104, 105, 110, 176, 184, 190, 191, 195, 225
Teens' Use of Traditional Media and the Internet. Carrie La Ferle, Steven M. Edwards, and Wei-na Lee, Journal of Advertising Research, 40 (May/June 2000), pp. 55-65. [Literature review, Survey, Time spent with media, Media used by activity, Frequency of Internet use by gender, Location of Internet connection, Source of information about Web sites, Internet and interpersonal sources of communication, Statistical analysis, Implications.] 209
Riding Shotgun on the Information Superhighway. Chris Wood, Journal of Business Strategy, 21 (May/June 2000), pp. 38-42. [Internet security, Strategy, Problems, Vulnerability, Costs, Documentation process, Policies, Adding hardware and software, Success, Guidelines.] 210
Understanding the Trade Winds: The Global Evolution of Production, Consumption, and the Internet. Peter R. Dickson, Journal of Consumer Research, 27 (June 2000), pp. 115-22. [Literature review, Economic history, Diffusion technologies, Systems-dynamic perspective, Example.] 211
What Buyers Want in Technology Tools. William Atkinson, Purchasing, 128 (April 20, 2000), pp. 57-58, 61. [Survey, Attitudes, Software packages, Web-based e-procurement systems, Electronic data interchange, Benefits, Satisfaction, Company's internal performance, Supplier selection, After-sales support from suppliers, Management support, Examples.] 212
Web Wise. Patricia B. Seybold, Sales and Marketing Management, (May 2000), pp. S4-S6, 58. [E-business, Customer orientation, Factors, Streamline customer scenarios, Touchpoints and cross-channel solutions, Warehousing and logistics, Staffing and training call/contact center personnel, Managing customer-affecting applications, Examples.] 213
Information Orientation: People, Technology and the Bottom Line. Donald A. Marchand, William J. Kettinger, and John D. Rollins, Sloan Management Review, 41 (Summer 2000), pp. 69-80. [Study of senior managers, Measures of effective information use (information technology and information management practices, information behaviors and values), Achieving high information orientation, Guidelines, Banking industry.] 214
Sophisticated Systems Help Retailers Develop Complete Picture of Each Customer. Susan Reda, Stores, 82 (June 2000), pp. 42, 44. [Customer relationship management, Data strategy, Software packages, Customer analysis, Data warehousing, E-mail response management, Modeling and file integration, Examples.] 215
See also 29, 38, 79, 189, 201
The Ivory Chateau. Stuart Crainer and Des Dearlove, Across the Board, 37 (June 2000), pp. 35-40. [Discussion, INSEAD, Global classroom, Students, Competitive advantage, MBA program, Jobs, Web-based businesses, Assessment.] 216
Cross-National Industrial Mail Surveys: Why Do Response Rates Differ Between Countries? Anne-Wil Harzing, Industrial Marketing Management, 29 (May 2000), pp. 243-54. [Literature review, Survey, Attitudes, Undergraduate courses, Professional and social activities, Work experience, International exposure, Statistical analysis.] 217
Preparing the Next Generation of Industrial Sales Representatives: Advice from Senior Sales Executives. Michael R. Luthy, Industrial Marketing Management, 29 (May 2000), pp. 235-42. [Literature review, Survey, Attitudes, Undergraduate courses, Professional and social activities, Work experience, International exposure, Statistical analysis.] 218
Corporate Universities Crack Open Their Doors. Meryl Davids Landau, Journal of Business Strategy, 21 (May/June 2000), pp. 18-23. [Discussion, Opening training centers to outsiders, Receiving a bigger return on investment, Impacts, Traditional universities, Technology, Assessment.] 219
Publications in Major Marketing Journals: An Analysis of Scholars and Marketing Departments. Aysen Bakir, Scott J. Vitell, and Gregory M. Rose, Journal of Marketing Education, 22 (August 2000), pp. 99-107. [Total number of published articles and a fractional score based on the number of authors of an article, Faculty size, Comparisons, Previous studies.] 220
Using the Theory of Constraints' Thinking Processes to Improve Problem-Solving Skills in Marketing. Marjorie J. Cooper and Terry W. Loe, Journal of Marketing Education, 22 (August 2000), pp. 137-46. [Identify a list of undesirable effects, Generate conflict clouds from the list, Construct a generic conflict cloud, Build a current reality tree that shows the core conflict and how it leads to the undesirable effects, Classroom implementation.] 221
Relating Pedagogical Preference of Marketing Seniors and Alumni to Attitude Toward the Major. Richard Davis, Shekhar Misra, and Stuart Van Auken, Journal of Marketing Education, 22 (August 2000), pp. 147-54. [Literature review, Learning styles, Motivation, Attitudinal enhancement, Survey, Variables, In-class exercises, Lectures, Cases, Association between in-class activities and overall attitude toward the marketing major.] 222
Study Abroad Learning Activities: A Synthesis and Comparison. Charles R. Duke, Journal of Marketing Education, 22 (August 2000), pp. 155-65. [Discussion, Effectiveness, Criteria (location, tour integration with academic credit, time spent on tour), Activities (lecture and test, company visits, journals, treasure hunt, projects, simulation), Assessment.] 223
Improving Students' Understanding of the Retail Advertising Budgeting Process. Myron Gable, Ann Fairhurst, Roger Dickinson, and Lynn Hams, Journal of Marketing Education, 22 (August 2000), pp. 120-28. [Survey of retailing educators; Favorite technique is objective and task; Requires the use of methods of both prioritizing alternative expenditures and setting a cutoff point; These points are often neglected by academics, including textbook writers; Recommendations.] 224
Development of a Web-Based Internet Marketing Course. Shohreh A. Kaynama and Garland Keesling, Journal of Marketing Education, 22 (August 2000), pp. 84-89. [Seven-step systems model; Define purpose of course; Analyze appropriate knowledge, skills, and abilities; Determine what the students should learn and ensure that the learning takes place; Development; Implementation; Assessment; Evaluation.] 225
Determinants of Student Evaluations of Global Measures of Instructor and Course Value. Ronald B. Marks, Journal of Marketing Education, 22 (August 2000), pp. 108-19. [Literature review; Model development; Structural paths; Student evaluations may lack discriminant validity, the extent to which a measure does not correlate with other constructs it is not supposed to measure (e.g., expected/fairness of grading does have a large impact on ratings of teaching ability).] 226
Teaching Marketing Law: A Business Law Perspective on Integrating Marketing and Law. Ross D. Petty, Journal of Marketing Education, 22 (August 2000), pp. 129-36. [Literature review, Marketing law organized by 4 Ps and defined by protected interests, Topics distributed by course, Teaching methods, Assessment.] 227
Introducing Marketing Students to Business Intelligence Using Project-Based Learning on the World Wide Web. Carolyn F. Siegel, Journal of Marketing Education, 22 (August 2000), pp. 90-98. [Discussion, Intelligence process, Business espionage, Overview, Projects, Advantages, Disadvantages, Assessment.] 228
Consumer Primacy on Campus: A Look at the Perceptions of Navajo and Anglo Consumers. Dennis N. Bristow and Douglas Amyx, Journal of Nonprofit and Public Sector Marketing, 7 (No. 2, 1999), pp. 31-51. [Literature review, Marketing lens model, Hypotheses, Survey, Importance ratings among educational attributes, Anticipated preparation after graduation, Satisfaction with educational product, Statistical analysis, Managerial recommendations.] 229
See also 220
An Exploration of the Meaning and Outcomes of a Customer-Defined Market Orientation. Dave Webb, Cynthia Webster, and Areti Krepapa, Journal of Business Research, 48 (May 2000), pp. 101-12. [Literature review, Models, Hypotheses, Survey of bank clients, Relationships, Service quality, Satisfaction, Statistical analysis.] 230
The Four "P"s of Marketing Are Dead. Joel English, Marketing Health Services, 20 (Summer 2000), pp. 21-23. [Discussion, Shifts in channel dynamics within health care, New model (relevance, response, relationships, results), Assessment.] 231
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Section: Book ReviewsMarketing Management Support Systems (Book Review)
by Berend Wierenga and Gerrit van Bruggen
(Boston, MA: Kluwer Academic Publishers, 2000, 341 pp., $120.00)
With developments in information technology, Internet marketing, modeling methodology, and marketing knowledge, it might be argued that we are entering a golden era for marketing management support systems (MMSSs). In that context, Marketing Management Support Systems: Principles, Tools, and Implementation provides a timely discussion as the Internet marketing era matures and the amounts of marketing data increase. Wierenga and van Bruggen (2000, p. 7) define an MMSS as *any device combining information technology, analytical capacities, marketing data, and marketing knowledge, made available to one or more marketing decision maker(s) to improve the quality of marketing management.* In their book, they cover the compass in discussing the state of the art for MMSSs*marketing information systems, marketing models, marketing expert systems, marketing neural networks, and so forth. Written in a clear, logical, and reader-friendly style, avoiding unnecessary mathematical expressions, the book systematically builds the field of MMSSs. In addition, it elaborates on theoretical discussions with several real-world episodes, appropriate data, graphs, diagrams, summary tables, and exemplary systems. All these features make the book easily accessible to a variety of readers.
Wierenga and van Bruggen describe three types of data-driven MMSSs: marketing models, marketing information systems, and marketing decision support systems. Marketing models, which emerged in the first half of the 1960s, are divided into predictive models (the so-called what-if simulations) and prescriptive (or normative) models. The intersection of marketing models with MMSSs comes in the second step of the familiar three-step model-building process: ( 1) model specification (specifying variables and their relationships), (2) model parameterization or estimation (specifying the value of parameters on the basis of data), and (3) model validation (assessing the quality of a model and its parameters).
The second of the data-driven MMSSs, marketing information systems, emerged in the second half of the 1960s to help manage data from various sources so that marketers could make more informed decisions. The most popular marketing information systems are firms* customer data, sales price data, sales data, market share data, and advertising data. Marketing decision support systems are flexible systems that recognize the importance of two-way communication between marketers and the system. They do not replace the marketers but stimulate and support them in solving fairly unstructured problems.
Unlike data-driven MMSSs, which deal with quantitative data, knowledge-driven MMSSs use the knowledge of a marketing decision maker as the primary element in solving problems. The book describes six types of knowledge-driven MMSSs: artificial intelligence, knowledge representation and processing, expert systems, case-based reasoning, neural networks, and creativity support systems.
Rooted in cognitive science, artificial intelligence emphasizes building computer programs that mimic human perception, information processing, thinking, and reasoning. Knowledge representation aims at putting knowledge into a form that makes it accessible to a problem-solving procedure. Knowledge processing carries out operations on the knowledge and derives solutions from it. Expert systems, based on the rules deduced from the knowledge of experts, use heuristic strategies to solve problems. A survey summarized in the book reveals that expert systems are commonly used in several areas of marketing, such as sales promotion, market monitoring, advertising, media planning, and new product development. Case-based reasoning systems solve new problems by retrieving and reusing relevant historical information and knowledge, revising the solutions suggested by the earlier cases, and adding the new solution to the case base. Neural networks attempt to rebuild the physical machinery of the human brain in a computer. In marketing, neural networks are used for time-series data, cross-sectional data, and data mining. Creativity support systems are computer-based tools that enhance the creativity of decision makers and help them produce new and useful ideas that keep an organization competitive in the marketplace.
The book draws an interesting distinction between the demand and supply of MMSSs. On the demand side, the book makes the case that marketing managers* decision-making processes are likely to be less biased if MMSSs are used. The argument is that decision makers, limited by bounded rationality, are more often satisfiers than optimizers. As satisfiers, they will be more likely to employ heuristics. These heuristics lead to biases. Four marketing problem-solving modes are identified: optimizing, reasoning, analogizing, and creating (neatly summarized by the authors by the acronym ORAC). The factors determining which of these marketing problem-solving modes will dominate in a particular decision situation are identified as ( 1) problem characteristics (structuredness, depth of knowledge, and availability of data), (2) decision environment characteristics (time constraints, market dynamics, and organizational culture), and (3) decision maker characteristics (cognitive style, experience, education, and skills).
On the supply side, the authors identify four components of MMSSs: ( 1) information technology (computer hardware, software, and communication networks), (2) analytical capacities (statistical packages, parameter-estimation procedures, marketing models, and simulation and optimizing procedures), (3) marketing data (quantitative information about variables), and (4) marketing knowledge (qualitative knowledge about marketing-related phenomena).
The authors attempt to integrate the demand and supply side of MMSSs in a framework for matching marketing problem-solving modes and MMSSs. In the framework, the three decision situations (i.e., problem characteristics, decision environmental characteristics, and decision maker characteristics) are equated with requirements for decision support, which leads to a recommendation of the type of MMSS for each specific situation. Three additional questions are raised to aid the search for the ideal MMSS/marketing situation match: ( 1) Should the marketing problem-solving mode actually employed be unconditionally taken as the starting point for deciding on the type of support? (2) How does the use of an MMSS affect the marketing problem-solving mode? (3) Should an MMSS reinforce the strengths of the decision makers, or should it compensate for their limitations?
In their efforts to answer the question, *Why does an MMSS succeed?* (or how the supply and demand for an MMSS are effectively matched), the authors recognize two moderating factors: design characteristics and characteristics of the implementation process. Design characteristics reflect the accessibility, system integration, adaptability, presentation of output, user interface, system quality, and information quality, whereas the characteristics of the implementation process reflect user involvement, top management support, communication about the MMSS, marketing orientation, presence of an MMSS champion, attitude of the information systems department, in-company developed versus purchased MMSS, and training of the users. So what is success for an MMSS? Success metrics include technical validity, adoption and use, user impact, and organizational impact.
The book provides an excellent, comprehensive account of MMSSs and will certainly help a wide variety of readers understand, research, develop, and implement MMSSs. Although the book will be most helpful to marketing managers, it will also be helpful to information technology and research and development managers; corporate strategic planners; top executives; and teachers and students at the graduate level of marketing, management, and general business.
Although marketing managers are not the developers of MMSSs, they play a crucial role in developing successful MMSSs by providing their marketing knowledge and decision-making practices as input to those systems. Indeed, they are most certainly and naturally the largest group of buyers and end users of MMSSs. When they become more knowledgeable and demanding customers of MMSSs, better and more MMSSs must be available from the systems suppliers. This book will raise marketing managers* awareness of a broad field of MMSSs and guide them to place right orders and make wise plans for customized systems for their specialized work functions. For years, MMSSs have been proposed, discussed, and dreamed in texts but not fully implemented in practice. This book gives clear pathways to the principles and identifies tools needed to develop successful MMSSs.
Information technology and research and development managers will benefit by knowing what components to consider for MMSSs, what kinds of MMSSs to develop for marketing decision makers, and how to match MMSSs to marketing decision makers* decision characteristics and other relevant circumstances. This book provides these managers with insights on what contents and elements are relevant to MMSSs and helps them perceive the importance of understanding the marketing managers* human side so as to improve collaboration with them to develop successful systems.
Corporate strategic planners and top executives may think of the short- and long-term technical and budgetary requirements essential to developing decision support systems across the corporation in an integrated and synergetic manner to enhance corporate-level decision-making efficiency. Because the use of technology has become an integral part of business, corporate strategists, including the top management, should invest in the development of better systems that enable managers to make objective and scientific decisions in ways that maximize corporate profit and achieve corporate business goals. In addition, top management should be able to calculate the impact of MMSSs on the corporation. Predictably, when well implemented, MMSSs will enhance marketing managers* decision-making efficiency, save time and other expenses, reduce failures, and increase odds of success. However, this improvement may in return require a smaller number of marketing managers. Then, MMSSs may be both good news and bad news to marketing managers*a two-edged sword, as technology often is. Therefore, top management should encourage marketing managers to prepare for their future in a smart way; managers should be trained for such technological advances to improve corporate operations and increase job security.
Marketing teachers and students at the graduate level of marketing, management, information technology, and general business will be immediate beneficiaries of Wierenga and van Bruggen*s book. In particular, a class may run more effectively when it uses the book as a discussion source of historical, theoretical, and philosophical aspects of MMSSs. The learning objectives and key points in each chapter will help students focus on the main ideas of each chapter. Unfortunately, no software comes with the book. The authors should consider offering, for example, BRANDFRAME (Chapter 8) with the book, which would give students a chance to test a successful MMSS.
Because the book summarizes academic research findings, discusses major research topics of MMSSs, and presents a future research agenda, marketing researchers and doctoral students who are interested in MMSSs will find much value in the literature review and research ideas. Particularly, the biographies that cover the classic to the most recent relevant studies are a valuable asset for researchers.
The book consistently emphasizes the fit between the human side (i.e., managers) and the systems (i.e., MMSSs). This emphasis may encourage researchers to direct their research focus toward the interaction of marketing managers and the systems. For example, a manager*s personality, decision-making patterns, cognitive type, marketing knowledge content and amount, marketing experiences, and ethical and cultural tendencies may be useful starting points to examine the impact of managers. As the authors indicate, academic marketing science researchers have contributed to the impressive body of marketing knowledge, methodologies, and theories. But they tend to pay less attention to making their achievements readily usable among managers. Thus, MMSS researchers need to be more actively involved in the MMSS development process, which usually has been led by data suppliers and consulting and software companies.
Readers should also consider this book*s focus on introducing developments, principles, and tools of MMSSs. It is an excellent source of insight to understand and develop MMSSs. Readers who are more interested in learning and exercising established MMSSs or designing/programming new MMSSs should refer to other books that are accompanied with appropriate software programs (e.g., Lilien and Rangaswamy 1998) or with necessary programming languages.
In an age of technological revolutions such as ours, a book of this type necessarily will soon be out of date. For example, amounts of data are exponentially increasing; interactive and accordingly more-complex data are flowing from Internet marketing sites; high-speed hardware, more user-friendly software, and easier programming languages are coming onto the market in new models. All these changes require innovative and adaptive approaches to designing MMSSs. Consequently, the authors will no doubt want to provide revisions for the book to stay relevant.
REFERENCES Gary L. Lilien and Arvind Rangaswamy (1998), Marketing Engineering: Computer-Assisted Marketing Analysis and Planning. Reading, MA: Addison Wesley.
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By Naveen Donthu, Georgia State University and Boonghee Yoo, St. Cloud State University
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Record: 106- Marketing Renaissance: Opportunities and Imperatives for Improving Marketing Thought, Practice, and Infrastructure. By: Brown, Stephen W.; Webster Jr., Frederick E.; Steenkamp, Jan-Benedict E. M.; Wilkie, William L.; Sheth, Jagdish N.; Sisodia, Rajendra S.; Kerin, Roger A.; MacInnis, Deborah J.; McAlister, Leigh; Raju, Jagmohan S.; Bauerly, Ronald J.; Johnson, Don T.; Singh, Mandeep; Staelin, Richard. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p1-25. 25p. 3 Charts, 1 Graph. DOI: 10.1509/jmkg.2005.69.4.1.
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- Business Source Complete
Marketing Renaissance: Opportunities and Imperatives for
Improving Marketing Thought, Practice, and Infrastructure
My three-year term as editor of Journal of Marketing concludes with the October 2005 issue. On the basis of my interactions with various people in the marketing community, I believe that marketing science and practice are in transition, bringing change to the content and boundaries of the discipline. Thus, I invited some distinguished scholars to contribute short essays on the current challenges, opportunities, and imperatives for improving marketing thought and practice.
Each author chose his or her topic and themes. However, in a collegial process, the authors read and commented on one another's essays, after which each author had an opportunity to revise his or her essay. The result is a thoughtful and constructive set of essays that are related to one another in interesting ways and that should be read together. I have grouped the essays as follows:
• What is the domain of marketing? This question is addressed in four essays by Stephen W. Brown, Frederick E. Webster Jr., Jan-Benedict E.M. Steenkamp, and William L. Wilkie.
• How has the marketing landscape (i.e., content) changed? This question is addressed in two essays, one coauthored by Jagdish N. Sheth and Rajendra S. Sisodia and the other by Roger A. Kerin.
• How should marketing academics engage in research, teaching, and professional activities? This question is addressed in five essays by Debbie MacInnis; Leigh McAlister; Jagmohan S. Raju; Ronald J. Bauerly, Don T. Johnson, and Mandeep Singh; and Richard Staelin.
Another interesting way to think about the essays, as Jan-Benedict E.M. Steenkamp suggests, is to group the essays according to whether they address issues of content, publishing, or impact (see Table 1).
These 11 essays strike a common theme: They urge marketers--both scientists and practitioners--to expand their horizontal vision. What do I mean by horizontal vision? In The Great Influenza, Barry (2004) describes the enormous strides that were made in medical science early in the twentieth century. His depiction of William Welch, an extremely influential scientist who did not (as a laboratory researcher) generate important findings, includes a characterization of the "genius" that produces major scientific achievements.
The research he did was first-rate. But it was only first-rate-thorough, rounded, and even irrefutable, but not deep enough or provocative enough or profound enough to set himself or others down new paths, to show the world in a new way, to make sense out of great mysteries. … To do this requires a certain kind of genius, one that probes vertically and sees horizontally. Horizontal vision allows someone to assimilate and weave together seemingly unconnected bits of information. It allows an investigator to see what others do not see and to make leaps of connectivity and creativity. Probing vertically, going deeper and deeper into something, creates new information. (p. 60)
At my request, each author has provided thoughtful and concrete suggestions for how marketing academics and practitioners, both individually and collectively (through our institutions), can work to improve our field. Many of their suggestions urge people and institutions to expand their horizontal vision and make connections, thereby fulfilling their potential to advance the science and practice of marketing. In his essay, Richard Staelin writes (p. 22), "I believe that it is possible to influence directly the generation and adoption of new ideas." I agree. I ask the reader to think about the ideas in these essays and to act on them. Through our actions, we shape our future.
--Ruth N. Bolton
Compared with some of the essays in this issue, this one is less about marketing scholarship per se and more about how marketing scholarship can contribute more broadly to business practice. I believe that marketing scholars can and should position their contributions more to business in general rather than limit them to marketing practice. The underpinnings of this essay stem from a recent executive roundtable discussion that Ruth Bolton and I facilitated specifically for the purpose of developing this essay. Participants in the hour-long teleconference included five executives from IBM, Yellow Roadway, Luxottica Retail (i.e., LensCrafters and Sunglass Hut), McKinsey & Company, and Cisco Systems.( n1) By design, only one of the executives was from marketing, yet all participants had a deep interest in customers and topics of interest to marketing scholars. The executives represented organizations that are active in business-to-business and business-to-consumer spaces, and all were board members of the Center for Services Leadership at Arizona State University.
In the paragraphs that follow, I highlight six business opportunities and imperatives that were discussed in the executive roundtable. Some are directly related to the research of marketing academics, and all are important to marketing scholarship. I conclude by arguing how a broader perspective in marketing scholarship can offer greater value to business practice.
Imperatives for Practice
At the beginning of the discussion, the executives cited opportunities and imperatives in the transformation of organizations through the integration of business processes and the use of technology. First, IBM's Mike Wiley noted an "unprecedented fusion between a focus on business process and the use of technology to transform, reengineer, and then enable new processes." In Wiley's view, this transformation generates competitive advantage, cost saving efficiencies, and enhanced customer experience.
Building on this observation, Gary Bridge of Cisco Systems indicated that most of these improvements require cross-functional integration:
It's between the silos that we see almost all of the really large opportunities now. Within silos, I think there isn't much more to do. There just isn't much more we can do on supply chain management until we get to RFID [radio frequency identification]. There isn't much more we can do on CRM [customer relationship management] until we get everything connected. We do have all these governance issues to overcome that are between silos, not within silos.
According to Greg Reid of Yellow Roadway, the wave of mergers and acquisitions sweeping business adds to the mandate for integration. New organizational forms "[need] to find connectivity and ways to leverage synergies" to increase competitiveness and provide additional value to customers. McKinsey & Company's Nick Semaca agreed with this observation, but he argued that opportunities for improvement still exist within silos for many organizations.
A second major imperative is in the art and science of execution. The roundtable participants observed a growing shift in emphasis from strategy to execution. Bridge noted that major companies know one another's strategies partly because they are announced in the newspaper and are featured in business magazines. Pointing to the Ritz Carlton Hotels, Semaca noted that in some instances, superior execution can be the core of an organization's strategy, as it is with Ritz Carlton's approach to customer service. Although it may not seem that profound, Wiley stressed that executing the strategy is much more difficult than many people think. For many firms, execution occurs at the interface with the customer.
The drive for executional excellence leads to a third imperative: more consistency in organizations. For example, Marriott wants its processes to be the same and its customers' experiences to be consistently positive whether the property is in Shanghai or Chicago. According to Bridge, this imperative also arises from firms' needs for standardization, economies of scale, and better and faster decision making. He also noted that organizations function best when all associates know their roles.
Paralleling this trend toward consistency is a fourth imperative: empowering people who are touch points with customers. Frank Baynham of Luxottica Retail stressed that frontline employees must be given boundaries or parameters but must also have the flexibility to treat each customer as an individual. Bridge cited a U.K. airline that empowers its employees to write up to a £100 check to a customer in real time to address a problem.
A fifth imperative the executives discussed was the notable development of providing customers with solutions rather than simply goods and/or services. According to Bridge, this often manifests in greater service emphasis and better content. He referenced General Electric, which is less interested in selling jet engines than in selling the more profitable multiyear service contracts that take care of everything for the client. Baynham noted that being solution oriented means spending more time with the customer, first listening to understand the customer's needs and then offering solutions that he or she may not know are available. Reid cautioned that a firm must be able to deliver on the basics of its products and services before it can be successful in offering solutions.
The final imperative discussed was who is responsible for the firm's relationships with customers. Semaca and Wiley noted that in their organizations, the people who develop and manage customer or client relationships are the same as those who deliver the actual services and solutions. Wiley added that for key clients, this can be a single person at IBM. Other executives noted that the customer must be a shared responsibility throughout the organization. Notably, none of the executives mentioned marketing as being responsible for the customer. Implicit in the roundtable discussion was the view that marketing and sales often have a major role in making promises to customers and in generating new business. However, the keeping of promises and building customer loyalty is typically considered the responsibility of others in the enterprise.
Bridging Scholarship and Practice
Several of the essays in this issue note the weak linkage between marketing scholarship and marketing practice. Further contributing to this scholarship-practice gap is the diminished role and influence of marketing in companies. Sheth and Sisodia indicate (p. 11) that "many strategically important aspects of marketing … are being taken away by other functions in the organization." The authors also note that at many companies, marketing has become a form of sales support.
The executive roundtable discussion reinforced the decline in marketing's influence in firms. The term "marketing" was mentioned only a couple of times in an hour of intense exchange. Yet customers, clients, and competitiveness were on the executives' minds throughout the discussion.
When they were asked how business scholarship could help business practice, the executives believed that thinking in terms of processes and across disciplines would do wonders, but some also realized that the recognition and reward systems must be revolutionized to jolt academic researchers into thinking and acting outside their disciplinary silos.
The executives also advocated that academic research should be more problem driven and focused on rich contexts. Speaking from the perspective of a former professor, Bridge noted that 20 years ago, he believed that marketing scholarship was driving change in practice. Today, he noted, "I'm not seeing the continuous flow of new relevant ideas. The manipulating of two to three variables in an arcane situation is far a field from the day-to-day trade-offs we must make in business with dozens of variables plus volatile political and human factors."
Wondering whether the answer for building bridges may be in the translation, Semaca pointed to Harrah's Entertainment and its chief executive officer (CEO) Gary Loveman, a former Harvard Business School professor. This CEO catapulted his firm to a leading position in the gaming industry. Drawing on academic research, Harrah's developed sophisticated models of customer behavior and translated this work into guidelines and incentives for frontline and "backstage" employees to follow.
Two roundtable executives believed that business is partly responsible for the scholarship-practice gap. For example, Reid argued that business must play a role in working with academic researchers to determine practical applications. Referencing current research his firm is doing with Arizona State University's (ASUs) Center for Services Leadership (CSL), Baynham stressed the importance of upfront discussions so that both parties understand each other's objectives, the key questions to be studied, and the projected outcomes of the research.
Despite the significance of the scholarship-practitioner gap, the executives voiced notable interest in the customer and other topics of interest to marketing academics. What can realistically be done? Let me offer one recommendation to change our perspective significantly. When thinking about business, as scholars, we must broaden our perspective. In the near future, I do not believe that marketing will assume a more prominent position within organizations. However, many executives and managers outside marketing are interested in what marketing academics study, such as work that links customer metrics to business performance and work on the interface between the customer and the organization.
For scholarly contributions, I believe that we need to broaden our targeted practitioner segments (see Figure 1). Traditionally, academics view the practitioner audience as marketers. I propose an expansion of the practitioner segments as I illustrate in Figure 1.
To illustrate my proposition, I use an example with which I am familiar. The CSL at ASU was launched 20 years ago by the university's Department of Marketing. Although projecting a distinct brand to the business and academic communities, the CSL's leadership and knowledge workers are intertwined with the department. Much of the CSL's substantive research is conducted by marketing faculty and doctoral students working with prominent firms (for more information about how the CSL has engaged firms in scholarly research, see Brown and Bitner 2006). More than 40 leading corporations are CSL members, including IBM, Marriott, Southwest Airlines, Siemens, and Harley-Davidson. The board members that represent the member companies have changed over time. In the early years of the CSL's existence, most of the business leadership came from marketing executives. However, in succeeding years, more board members began to have nonmarketing backgrounds. Today, the business leaders who advise the CSL include general management, operations, supply chain, finance, and marketing executives.
What attracts board members and other business leaders to the CSL is its thought leadership in relation to customer focus and competing through service. With most of the CSL's research being produced by marketing faculty and doctoral students, this business interest suggests that what marketing scholarship offers is too good to be confined to marketing practitioners, especially when many organizations have marginalized the role of marketing. In other words, the bridge from scholarship to practice can be more fruitful if marketing scholars significantly broaden their view of what kind of practitioners may be interested in this work.
A New Practitioner Focus
This essay features a roundtable of business leaders talking about the opportunities and imperatives that confront firms today. The wide-ranging discussion covered issues of contextual and direct interest to marketing scholarship. The leaders' observations demonstrate that the contributions of marketing scholars should be targeted to practitioner audiences beyond marketers. The role and stature of marketing has waned in many firms. Yet the interest in creating and delivering value to customers is at the forefront of business priorities. Thus, as marketing scholars, we can build better bridges to practitioners when we begin to view our target markets as the many parts of a firm that are interested in customers, not just the marketers.
Stephen W. Brown
Marketing thought leaders are taking a critical look at their field. They have identified several issues that have slowed progress in the development of the marketing discipline and are moving toward a new view of marketing as both an intellectual discipline and a professional practice. A successful reconceptualization of the field will recognize that marketing must be understood at three levels and will integrate these three dimensions of the marketing space into a coherent whole.
Marketing as Tactics, Strategy, and Organizational Culture
Marketing has tactical, strategic, and cultural dimensions (Webster 1992). Over the past 60 years or so, the emphasis in research, teaching, and management practice has shifted among these dimensions. At any given point, one has been emphasized to the detriment of the others. For the past two or three decades, the tactical dimension has dominated, with an emphasis on operational (i.e., input-level) marketing decision variables and short-term business performance results, especially sales volume and market share. Methodological rigor has been emphasized over problem importance and relevance and has been facilitated by the availability of large databases on product movement and individual retail transactions. Data and methodology have dominated academic research at the expense of both theory development and practical relevance, retarding the progress of the marketing discipline in a rapidly changing market environment. The focus has been on transactions as the unit of analysis rather than on the long-term enduring economic, interpersonal, and interorganizational relationships that characterize most marketing activity and produce sustainable business performance and growth.
The tactical view of marketing is rooted in the concept of the four Ps (i.e., product, price, promotion, and place) and in the microeconomic optimization paradigm. Mistakenly, the sum of the four Ps was labeled as marketing strategy, even though the most important of marketing variables--market segmentation, targeting, and positioning, which also appeared as marketing concepts in the 1950s--were not part of this tactical formulation.
Short-term tactical outcomes, such as sales volume and changes in awareness, are easily observed and measured, and their effects on performance are easily isolated from those of other decision variables and competitive responses. These measures are the same ones used to evaluate the weekly, monthly, and quarterly performance of most marketing personnel. Pressure from financial markets and shareholders causes many firms to emphasize these short-term outcomes and to devote insufficient time to strategic thinking. Strategy should guide tactics. The sum of tactics without specific strategic analysis and formulation does not equate to a coherent strategy.
Marketing strategy is part of the firm's business-level (as opposed to corporate-level) strategy; it is focused at the level of the strategic business unit (SBU); and it is expressed by the definition of the SBU's served markets--its segmentation, targeting, and positioning choices; its value propositions to customers; its positioning in the value chain; and its strategy for capturing a fair share of the value created for customers as a fair return for the owners. The new definition of marketing recently adopted by the American Marketing Association (AMA) captures this strategic focus:
Marketing is an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders.
The strategic theme of marketing gained dominance in the 1970s as part of a pervasive organizational focus on formal, long-range strategic planning. Marketing responsibility was expanded beyond selling, advertising, promotion, and distribution, with heightened emphasis on product innovation and new business development. However, this emphasis was relatively short lived, and SBU-level marketing strategy was co-opted in many firms by the strategic planning department and in the academic realm by the emergence of the new discipline of strategic management in which market segmentation, value proposition, and customer orientation became key building blocks. Marketing scholars devoted relatively little attention to these macro-organizational and environmental trends that were reshaping the field in practice as they focused increasingly on tactical, not strategic, decisions.
The third dimension of marketing, organizational culture, is the most difficult to define, observe, and measure. A general definition of culture is the things we take for granted, which emphasizes how difficult it is to observe. Organizational culture is a system of values and beliefs that guide the actions of an organization's members. From a marketing management perspective, organizational culture can be defined as organizational cognition, or a knowledge system that expresses itself in assertions about why things happen the way they do in a particular organization, helps members understand the organization's functioning, and provides norms for their behavior (Deshpandé and Webster 1989).
The so-called marketing concept is an expression of organizational culture, a normative statement that the firm should always put customers' interests first. It is a management philosophy asserting that the existence and legitimacy of the firm ultimately depends on satisfying customer needs. The marketing concept was rapidly accepted by managers in many companies, and it quickly became a part of marketing textbooks in the 1960s. It was expressed in statements by some leading executives that the firm would ultimately be organized around the marketing function, that serving customers was not the means to the end of profitability but the end itself, and that marketing was too important to be left to the marketing people.
Despite its inherent moral appeal, the marketing concept has several basic weaknesses. Most important, it lacks strategic content. It says nothing about how the firm should compete. Like the narrow tactical focus, it ignores such basic questions as which needs the firm should focus on, who its customers should be, and how the firm should match its capabilities with the underserved needs of the marketplace. This lack of strategic focus was a major reason many firms brought marketing under the influence of strategic planning departments. When those departments were eliminated--a trend that occurred in the late 1970s and continued through the 1980s--marketing competence often went with it. Although customer orientation has remained a central theme within marketing, it has received little scholarly attention and often is nothing more than lip service in firms that claim to adopt it. Shareholder interests have dominated management attention and corporate values. Both academic and business neglect of the fundamental importance of customer orientation reflect the notion that identifying its impact on business performance requires a long-term perspective and is extremely difficult to measure.
The good news is that significant research in the past decade has found positive associations between long-term business performance and customer orientation (Deshpandé, Farley, and Webster 2002; Narver and Slater 1990). These studies developed reliable scales for measuring market orientation as a combination of customer and competitor orientation and have found these concepts to be correlated with business performance (Deshpandé and Farley 1999). In general, these results suggest strong support for the notion that true marketing competence, including customer orientation and innovativeness as advocated by the marketing concept, is central to the long-term success of the firm. However, the view of marketing as organizational culture remains the least visible of the three marketing dimensions.
Rebuilding the Influence and Integrity of Marketing
Because marketing has been downsized or eliminated as a corporate function in many firms, marketing competence has waned in those organizations. Trying to assign marketing responsibility to the SBU level often fails because SBU managers do not have the necessary marketing skills, have many other demands on their limited time and resources, and are driven by short-term measures of performance. Marketing advocacy depends more on the marketing commitment of a CEO who can articulate the importance of customer orientation as the keystone of corporate culture and the basis for increasing the value of the firm (Webster, Malter, and Ganesan 2003). The position of the chief marketing officer (CMO) was often created to fill the gap left by the elimination of corporate marketing departments, but only rarely has this position been filled by a person with the necessary strategic and analytical skills, the true support of a committed CEO, and a clear mandate to build marketing competence and strategic thinking throughout the organization.
Recent trends in marketing publications suggest that the issues outlined here are being recognized and addressed by both scholars and practitioners. Problems are being more clearly articulated, but comprehensive solutions have yet to be proposed. Although an estimated three-fourths of companies have reorganized their marketing approach in the past five years, these changes are always driven by dissatisfaction with the status quo rather than by a clear vision of the optimal organization. There is still a desperate need for the integration of tactics, strategy, and culture in the reconceptualization of the marketing field as a business practice and an academic discipline. Our understanding of marketing must be embedded in our understanding of organizations, not just markets, and it must focus on issues of implementation, not just strategy formulation.
It is not likely that business leaders or young scholars will be the source of new conceptualizations of our field. Business leaders acting alone seldom have the time or the inclination for such disciplined reflection, though they can be the source of new ideas, insights about how the world of marketing is changing, and challenges to marketing thought leaders. They must be listened to, but we should not expect them to do our work for us. Young scholars, who by definition are the best trained in the latest techniques to do path-breaking research, cannot be expected to have either the perspective on the total marketing field or the ability to commit their time to developing integrated reconceptualizations of their newly chosen discipline. There are few incentives for executives to step outside the owner-focused mandates of transactions-based generally accepted accounting principles and securities regulations to advocate the primacy of customers' interests or for academic newcomers to challenge old paradigms in the field.
Despite the current cry for better measurement of the financial impact of marketing expenditures, we must not limit ourselves to the development of measures of marketing performance at the tactical level. This would be self-defeating. We need to develop measures, which will inevitably be less precise, of marketing's influence at the strategic and cultural levels if we are to understand marketing as an integrated body of knowledge and practice.
The major challenges are conceptual, not methodological. We must show renewed respect for conceptual thinking as opposed to methodological rigor. We must tolerate work that bursts through and redefines the currently accepted boundaries of our intellectual domain. We must respect insight and risk taking as much as we worship empirically verifiable propositions. We must work to advocate a proper balance of rigor and relevance, both theoretical and practical, and bring to bear the results of scholarship that goes back to the roots of the development of all three dimensions of marketing. Not all relevant knowledge is less than 20 years old!
Frederick E. Webster Jr.
Although marketing academics demonstrate diversity by borrowing from many behavioral science disciplines, we are less eclectic when selecting populations in which to test our theories. We derive most of what we know from studies conducted in Western countries, typically the United States, the world's largest economy. Although this research yields a valuable stock of theoretical and empirical findings, three important reasons mandate that we must now move out of the "U.S. silo" and conduct more research on an international basis. In this essay, I discuss these three reasons and delineate the unique research challenges and opportunities for conducting research in emerging consumer markets (ECMs).
Three Reasons to Conduct More Research on an International Basis
Cross-national generalizability and contingencies in marketing theory. To advance marketing as an academic discipline, we must examine the validity of our theories and models, as well as their generalizability and boundary conditions, in international contexts. Cross-national generalization is implicit in our theories; that is, we usually develop theories without explicit reference to their socioeconomic, institutional, and cultural contexts. In many instances, cross-national generalization should not be assumed. To illustrate, consider market orientation, one of the most heavily researched constructs in marketing. Research conducted in Western countries supports the detrimental effect of formalization and centralization on market orientation (e.g., Jaworski and Kohli 1993). However, Burgess and Nyajeka (2005) show that this relationship does not necessarily apply to countries characterized by cultural hierarchy and the lower formal education of employees. In countries such as Zimbabwe, moderate levels of formalization and centralization actually stimulate the information acquisition and dissemination that is necessary for a market focus. Employees with less formal education need to be informed about market orientation and must have their roles clarified in order to deliver. Transaction cost theory, a predominant theoretical framework used to explain organizational boundary decisions, provides another example. Geyskens, Steenkamp, and Kumar (2005) find that the explanatory power of the transaction cost dimensions differs systematically and predictably across national cultural contexts.
These and other examples (e.g., Deshpandé and Farley 2004; Ozsomer and Simonin 2004; Scheer, Kumar, and Steenkamp 2003; Xie, Song, and Stringfellow 1998) illustrate the need to test even our most established theories in an international setting. Including country and cultural variables enlightens us in new ways and clarifies marketing phenomena.
Pushing the theoretical envelope. A reason marketing theories may lack cross-national generalizability is that key country characteristics moderate the structural relations between the constructs in the theories. We can push the theoretical envelope by identifying and including these pivotal country characteristics in our frameworks, leading to true contingency theories of marketing. In marketing, national culture has attracted the most attention.
Cultural norms and beliefs are powerful forces shaping people's perceptions, dispositions, and behaviors. A society's shared cultural priorities frame the social and economic reward contingencies to which people and organizations must adapt for smooth and effective functioning. For example, Tellis, Stremersch, and Yin (2003) find support for the hypothesis that the takeoff of new products is faster in countries that are low in uncertainty avoidance and high in the need for achievement than in countries that are high in uncertainty avoidance and low in the need for achievement. In another example, Steenkamp, ter Hofstede, and Wedel (1999) find that a country's culture systematically and predictably moderates the effects of consumers' personal values on their tendency to purchase new products and brands. These and other results of studies that include cultural variables offer important new insights. Future studies have the potential to expand research on the effect of country contexts by drawing on socioeconomic theory (Etzioni and Lawrence 1991) and institutional theory (North 1990).
Inherently international issues. The globalization of the marketplace is arguably one of the most important challenges that companies face today; firms need to assess how each element of their marketing strategy should be executed along the continuum of internationally standardized to locally adapted. Marketing academics can contribute important new knowledge to these inherently international issues. Although the entire domain of marketing practice could be examined, I choose only two to illustrate this point: international market segmentation and new product launch strategies.
Research evidence shows that international segments of consumers can be identified if data are corrected for methodological biases, both for products (Steenkamp and ter Hofstede 2002; Ter Hofstede, Steenkamp, and Wedel 1999) and services (Bijmolt, Paas, and Vermunt 2004; Bolton and Myers 2003). Much work remains to be done in this area, including accommodating changes in segment sizes and structural properties of international segments over time, developing proper procedures for correcting for response styles, integrating international segmentation in managerial decision making, and extending international segmentation studies to include ECMs more fully.
The choice of product introduction strategy is crucial for companies. In an important game-theoretic article, Kalish, Mahajan, and Muller (1995) derive optimal conditions for the implementation of a waterfall (sequential) versus a sprinkler (simultaneous) product introduction strategy. A waterfall strategy is favored when the product has a long life cycle; when the foreign market is relatively small, exhibits slow growth, and is low on innovativeness and competitiveness; and when fixed costs of entry are high. These analytical findings need to be tested empirically.
The Special Case of ECMs
Most non-U.S. marketing research is conducted in other Western countries, even though more than 80% of the world's consumers live in ECMs. These countries represent the growth and future of our companies. Over the next decade, General Electric expects that as much as 60% of its revenue growth will be from ECMs, and its outlook is echoed by other multinational corporations such as Siemens, Philips, Procter & Gamble, and Volkswagen (Wall Street Journal Europe 2005). As such, ECMs pose specific research challenges, three of which I highlight: business models, unit of analysis, and measurement instruments.
Business models. To be appropriate to ECMs, Western business models must often be recast. The profit maximization goal and the pursuit of self-interest--the very foundation of many Western business models--may not be the driving forces in embedded cultures. Other motivations and constructs must be substituted or incorporated. For example, understanding organizational relations in China requires recognition of guanxi, or the "expectations that, sometime, favors will be returned" (Ambler, Styles, and Xiucun 1999, p. 76). Similarly, in Africa, the cultural concept of ubuntu--a pervasive spirit of caring and community, harmony and hospitality, humility, respect, and responsiveness (Mangaliso 2001)--must be recognized and included in business models. Ubuntu stresses kinship ties, reward systems linked to team performance, and consensus-based decision making. When properly managed, firms derive significant competitive advantages from ubuntu, including intrinsic motivation, loyalty, and long-term effectiveness.
The traditional business focus on relatively expensive, large-unit, overengineered products is unlikely to be successful in ECMs beyond small segments of relatively affluent consumers. To illustrate, 95% of all shampoo units sold in India (representing 60% of total value) are single-serve units, many of which are designed for the poor and do not even require hot water (Hammond and Prahalad 2004). Brown and Hagel (2005) introduce the concept of "innovation blowback"; they expect that ECMs will become important catalysts for product/service and process innovation because of the huge pool of youthful, low-income consumers, who are unusually demanding, open-minded, and adventurous. Companies that are able to develop the new innovation skills to be successful in these markets (e.g., production-driven networks, customer-driven modularity, process-driven services) are likely to gain competitive advantage, both in ECMs and in their home markets (Brown and Hagel 2005). However, little research has been conducted on these innovation skills.
Unit of analysis. The unit of analysis in individualistic Western countries is typically the individual consumer or manager, and research has largely focused on individual decision making. However, group decision making is relatively more important in collectivistic ECMs, which are often characterized by high cultural embeddedness and complex webs of personal and business obligations. We need more theorizing and better tools to conceptualize, measure, and analyze these social networks; to understand their role in group decision making; and to examine the reciprocal and dynamic relations between individual and group norms, attitudes, and behaviors.
Measurement instruments. On average, ECMs are characterized by lower levels of formal education. Unfortunately, many established measurement instruments require a fairly high degree of respondent sophistication. We urgently need simpler and shorter measurement instruments that can be used in ECM market research. Schwartz and colleagues (2001) developed the Portrait Values questionnaire to assess value priorities of less-educated populations, and it has been used successfully in ECMs (Steenkamp and Burgess 2002). A cross-culturally validated short form of the Big Five personality instrument is also available. Other researchers have sometimes constructed their own short form of existing scales on a rather ad hoc basis by including only the highest-loading items in their study (e.g., Batra et al. 2000).
Although some work has begun to address measurement issues in ECMs, much work remains to be done to construct shorter and simpler scales, new scales for concepts that are particularly relevant in ECMs (e.g., embeddedness, guanxi, ubuntu), and different wording and response formats. Some existing scales may need to be adapted at least partially to the local context. Baumgartner and Steenkamp (1998) describe analytical procedures to analyze such combined emic-etic instruments and to compare results across countries.
I hope that future editions of the well-known Handbook of Marketing Scales (Bearden and Netemeyer 1999) will contain more information on the validity of marketing scales in ECMs. In the spirit of the innovation blowback, I except that marketing research in the United States and other Western countries will also profit from the development of scales that are cognitively less demanding.
A Challenge
In her editorial, Bolton (2003) notes that international marketing research is underrepresented in Journal of Marketing (JM). The same applies to the other top marketing journals, which is unfortunate. International marketing research enables us to assess the cross-national generalizability and contingencies of our theories and therefore to push the theoretical envelope in entirely new directions. It also provides answers to inherently international issues. Research in ECMs is especially necessary, given their significant market potential for our companies and their unique research challenges. As a discipline, let us move out of the U.S. silo. The world is beckoning us.
Jan-Benedict E.M. Steenkamp
Has a Larger Sense of Marketing Gone Missing?
Abroad unrest appears to be surfacing about our field's direction and practices, and I appreciate this opportunity to share my observations. In recent years, Elizabeth Moore and I have been pursuing the question, What is marketing, anyway? (Wilkie and Moore 1999, 2003).( n2) Our findings show that the field has changed sharply over time, and some considerable knowledge has been left behind during the general advance. However, this has now gone too far. Some of today's views of marketing scholarship are overly constraining, especially regarding broader conceptualizations of marketing. To illustrate, consider the new official statement of our field.
Limitations of the AMA's New Definition of Marketing
The AMA has recently defined the term "marketing" as follows:
Marketing is an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders.
I appreciate the professional appeal in capturing a marketing manager's role. However, this definition's sole focus is on marketing within an individual organization, which limits scholarship.
Dangers in adopting goals of all organizations engaged in marketing. In my view, the greatest risk of equating all marketing with managerial decisions within organizations is that their goals are being adopted by marketing thinkers without any external appraisal. This leads to something akin to blanket approval of the reality of the marketing world's undertakings. When identifying ourselves with these goals and actions, whose perceived interests are being served, and does this matter? A brief consideration of egregious examples found in political campaigning, lobbying, fraud, bid rigging, energy gouging, channel stuffing, and so forth, alerts us that many organizations are highly imperfect entities with mixed motivations. Furthermore, in most organizations, people other than marketers are setting priorities. Organizational marketing is important, but it should not be taken to represent all of marketing thought.
Limitations in addressing the competitive nature of our marketing system. The sole focus on a firm also leaves us without strong concepts to assess multiple firms engaged in simultaneous marketing activity. For example, when 8 or 12 firms compete in a market, how do we assess the "marketing" that is occurring on all fronts? Inefficiencies would be natural, but they are beyond the managerial purview itself. Is this why our field has not had more of an impact on antitrust enforcement?
Limitations in addressing the marketing system's interactions with consumers. One major task for every consumer is allocating his or her budget for purchases. If we ask, How well do marketers help consumers with their budget and effort allocation decisions?" the answer is, "Very poorly." In the aggregate, all marketers simply propose too much consumption for each consumer. The system acts as if consumer resources and wants are infinite and insatiable: Every product and service category is advocated as worthy of consumption for virtually everyone. Furthermore, within each category, marketers are offering consumers highly conflicting advice as to which alternative to select. To cope, consumers must ignore or resist most marketing programs and respond positively to only a relative few.( n3) These characteristics surely make it difficult to equate each marketer's best interest with each consumer's best interest. (I stress that these are not criticisms but rather characteristics of the marketing system that are not evident from the managerial perspective on marketing.)
Limitations in addressing major societal and public policy issues. There are two good examples of this issue: ( 1) Childhood obesity is a growing problem in the United States. Is a single-firm focus for marketing the most effective way to address this? ( 2) Direct-to-consumer advertising of prescription drugs is actually a public policy experiment in the United States. How helpful have marketing academics been in devising or evaluating this policy?( n4) My point is simple: There are issues in our world that are larger than the problems of a single organization.
Removing research opportunities from many marketing academics who would like to pursue these broader issues. Given the AMA's definition, how are academic marketing thought leaders being prepared to address the role of marketing in society? To examine this, a survey of AMA-Sheth Doctoral Consortium participants was conducted (Wilkie and Moore 1997). The results show a striking gap between personal interest levels and training that is provided: Two-thirds of the doctoral candidates reported having a personal interest in learning about marketing and society, but fewer than one in ten had taken even a single course on the subject, and their self-ratings of expertise were low. Doctoral programs sorely need to reconsider this issue.
It is troubling to realize that knowledge does not necessarily accumulate in a field; knowledge can disappear over time if it is not actively transmitted (e.g., Wilkie 1981). One responsibility of academia is to place a field of study into proper perspective. I believe that the concept of an aggregate marketing system (Wilkie and Moore 1999) should occupy a central position in marketing scholarship. However, this will not happen unless current scholars accept that important knowledge is being lost from the active body of marketing thought. As research specialization has proceeded (with good reason), this risk has increased. Knowledge outside of a person's specialty may first be viewed as noninstrumental, then as nonessential, then as nonimportant, and finally as nonexistent. My particular concern is for the subsequent generations of scholars (both today's and the future's doctoral students) who may not gain enough background to even realize that a choice is available to them.
Understating the scope and importance of marketing. Finally, a key finding in the "Marketing's Contributions" (Wilkie and Moore 1999) article flowed from a system illustration that included 75 marketing-related activities. Of these 75 marketing system activities, we found that marketing managers control only approximately 30, or fewer than half. They influence most other activities, but they are not in control of them; furthermore, these activities are not what is typically considered marketing according to the current view of the field. To me, this understates the importance of marketing and calls for a perspective that is beyond the controllable decisions of marketing managers; such a perspective must reflect inclusive appreciation of organizational operations and of governments' roles in the facilitation of marketing system operations. In brief, we need a larger conception of marketing.
Is Marketing Academia Losing Its Heart?
A spontaneous episode at the 2005 AMA Winter Marketing Educators' Conference sent a signal about the state of our field today. Kent Monroe was named the 2005 Distinguished Marketing Educator (a fitting honor), and I was one of the people asked to speak at his reception. Midway through my remarks, I detoured from my outline and mused, "By the way, there seems to be a meanness creeping into our field, and we really don't need this." Much to my surprise, applause for this sentiment spread across the 100-150 people in the room. A chord had inadvertently been struck that resonates with many marketing academics today.
This little vignette suggests a high level of emotion behind the scenes of our professional lives. Virtually everything in print is about facts, theories, methods, and applications. Behind this, however, is the living reality of our academic lives and pursuits. Collectively, we are the College of Marketing. Individually, we are talented people who have each invested heavily to be in a position to contribute to knowledge.
As the vignette suggests, overt attention needs to be paid to the quality of life in our field today. It is especially painful to talk with people who, as young, aspiring scholars in the field, are now out of research academia (or virtually so), embittered by their experiences and still suffering from the blows to their youthful enthusiasm, idealism, and self-confidence.( n5) Are there steps to improve this situation? I think so.
Briefly, I assert that it is time for a new marketing academic summit, perhaps as a task force on thought development, with the goal of enhancing the participation in and quality of marketing scholarship. In addition to addressing what should be studied and how, I suggest that serious attention should be given to how research quality of life can be improved. For example, informal discussions with senior academics suggest that journal acceptance rates currently hover at approximately 10%, and tenure achievement for first positions at research schools are 25% or less (recall that this is the outlook for presumably the best-trained, most talented people entering our field). It strikes me that these figures reflect a pall on the pursuit of knowledge (at least to the extent that it is internally motivated) and help engender the cynicism and meanness that has entered our college. A key goal for this summit should be to strive to improve these rates and to engender a more positive context for our work.( n6)
Specific topics I believe deserve to be addressed include the following:
• The unrealistic expectations of many universities today in context of a six-year tenure time frame and an overemphasis on "A" journals.( n7)
• The crucial role of a few key journals for the field-a positive or negative in the face of continuing growth and fragmentation?
• The sometimes destructive (and delaying) behaviors of reviewers, coupled with overreaching intrusions into freedoms of thought, theory, and method.( n8)
• Current strengths, weaknesses, and biases in doctoral education, together with exploration of postdoctoral opportunities.
• Opportunities and problems presented by the twin forces of globalization and the Internet, including the explosion of business education around the world and the coming infusion of thousands of new marketing academics.
In closing, let me say that academic marketing has wonderful potentials, and it deserves our care, consideration, and cultivation.
William L. Wilkie
In August 2004, a day-long symposium organized by Bentley College was held in Boston to address the question, "Does Marketing Need Reform?" Speakers were asked to address how the marketing function can simultaneously bolster trust with customers and respect within organizations. The event featured 17 speakers and drew approximately 125 attendees.( n9) Judging by the response, it appears that this topic hit a hot button for many in the discipline. In this brief essay, we highlight some of the perspectives that were suggested at the symposium. The actual presentations can be viewed online at www.bentley.edu/events/markreform/, and we are publishing a book of essays on the theme (Sheth and Sisodia 2006).
The Case for Reform
Speakers were unanimous in the view that marketing indeed needs significant reform. It is ultimately marketing's responsibility to align the interests of customers and the company, and too often, this just does not happen. As Glen Urban remarked, "Marketing effectiveness is down. Marketing is intrusive. Productivity is down. People resent marketing. Marketing has no seat at the table at the board level and top management. Academics aren't relevant. And we have an ethical and moral crisis. Other than that, I think we are in good shape."
A moral dilemma. Phil Kotler noted that marketing's fundamental dilemma stems from two of marketing's central axioms: First, give customers what they want, and do not judge what they want. Customers often want products that are not good for them (e.g., tobacco, high calorie fast foods, sweets, alcoholic drinks). Second, many products may be acceptable to the customer but are harmful to society (e.g., asbestos, lead paint, guns, gas-guzzling automobiles).
The central thesis of Johny Johannson's (2004) recent book is that marketing has become "morally corrupt" and has helped reduce the American way of life to its lowest common denominator while contributing to a rising tide of anti-American sentiment around the world. He suggests that marketing promotes many dangerous and unhealthy products using "preposterous and phony" arguments. Advertising is ubiquitous across old and new media, and there is increasingly nowhere for customers to hide (Johannson 2004).
Marketing's Image Problem
The image of marketing, far from strong to begin with, has taken a beating in recent years. J. Walker Smith presented results from a recent Yankelovich survey that found that 60% of consumers claimed that their opinion of marketing and advertising has become much worse over the past few years and that marketing and advertising is "out of control." On the basis of a study of the image of marketing using approximately 1000 consumers, Raj Sisodia reported that approximately 62% of the respondents had a negative attitude toward marketing, and only 10% had a positive attitude (Sheth, Sisodia, and Barbulescu 2006). On the positive side, marketing is often associated with creativity, fun, humorous advertising, and attractive people, but most people (including most business students) associate negative words, such as "lies," "deception," "deceitful," "annoying," and "manipulating," with marketing.
Marketer Myopia
It could be argued that marketing academics and practitioners alike are suffering from "marketer myopia"; that is, they are so focused on what they do that they fail to notice significant changes in the environment around them. Summarizing his recent book, Jerry Wind suggested that new mental models are necessary to guide the thinking of marketing executives, practitioners, scholars, and journal editors (Wind, Crook, and Gunther 2004). He questioned whether the narrow and deep focus in academic marketing research and modeling is of value to business executives. Wind also criticized the prevailing marketing mind-set that largely ignores the 86% of the world living outside developed countries, most with per capita incomes below $1,000 per year.
Kay Lemon identified three key ways that marketers are myopic and thus fail. First, most marketers fail to take a long-term view. Their typical focus is on short-term gains: improve sales this month, stock price this quarter, market share this week, shelf space compared with competitors. This short-term perspective leads to angry and irate consumers, proliferation of "me too" products (because they are less risky), proliferation of "me too" research (because it is easier to publish), greater resistance to marketing, consumer confusion, and the threat of additional governmental intervention and regulation. Second, marketers often fail to consider all relevant constituencies. Although many do a good job of considering current customers, they fail to consider the effect of marketing on those not directly in the target market. Third, marketers fail to appreciate their own strength and power. Marketing is infusing and transforming cultures around the world. Marketers must be mindful of how powerful their tools are and understand their short-and long-term effects on consumers. Marketing academics must teach students how to use that power responsibly.
Demographic denial. Peter Drucker (1999) has identified the worldwide decline in birth rates as the number one issue that society faces today. David Wolfe highlighted two demographic trends. First, marketing has yet to come to terms with the reality that the majority of adults in the marketplace today are older than 40 years of age. By 2010, annual spending in the United States by households that are headed by people under the age of 45 is projected to be $1.62 trillion, compared with $2.63 trillion by those 45 years of age and older; that is a trillion dollar difference. Yet most marketing remains resolutely youth fixated. Wolfe suggested that marketers need to understand developmental psychology to appreciate how customers evolve continuously over their life spans. Second, around midlife, women begin to outnumber men quite significantly. However, most marketing today remains aggressively masculine in character and fails to speak effectively to women or aging men.
Marketers' Diminished Role and Influence Within the Company
The fundamental value of a marketing mind-set is not in question. As Rajiv Grover put it, "If marketing is defined as satisfying the expressed and latent needs of customers, it is well accepted out there, so marketing is not really being marginalized. But marketers are being marginalized, in the sense that many strategically important aspects of marketing (e.g., pricing, ad budgeting, new product decisions) are being taken away by other functions in the organization." Fred Webster noted that marketing management was once considered destined to assume ultimate influence and control over the U.S. corporation and become the dominant function (Keith 1960). With few exceptions, this has not happened. A key problem is that most marketing managers are not finance literate and have trouble answering questions about the productivity of expenditures. Equally important, other managers are usually not marketing literate. Raj Srivastava suggested that marketing does a poor job of communicating the value it creates because marketers do not speak the "financial language." There is little appreciation for the balance sheet power of brands. Marketing is considered a variable cost, not a committed cost, so its budget is considered "soft money" that can readily be cut. Marketers are hard-pressed to justify their budget requests because they command little trust within most organizations.
An Agenda for Reform
Regain trust with customers. Building on his recently published article (Urban 2004) on the subject, Glen Urban proposed a possible new paradigm for marketing: Instead of just trying to create products their customers might want, marketers should actively advocate for their customers across all departments within the company. Several forces are converging to increase customers' power: the Internet-fueled ability of increasingly skeptical customers to talk to one another, reduced media power, overcapacity, and more stringent government regulations. More and more customers are actively exercising their power, many becoming crusaders for or against companies. In response, companies must choose between "old style marketing" (i.e., the push model that characterized marketing from approximately 1950 to 2000) and "trust-based marketing," whereby companies cooperate and work with customers to make them successful. As Urban said, "If you gave customers full information and the best technical and buying advice you could, would you suggest they buy your product? If you can't say that, then you must work on a better product."
Use technology to enhance mutual value. Sawhney suggested that the implication of widespread connectivity is a greater need for collaboration with customers and business partners because most value creation is now outsourced. He proposed that companies must integrate customers into an entire set of end-to-end processes from ideation to support. Sawhney also noted that technology is enabling customers to move toward "do-it-yourself' marketing. If they choose to, customers can disintermediate marketers from the marketing process; they can self-inform, self-evaluate, self-segment, self-price, self-support, self-organize, self-advertise, self-police, and self-program.
Broaden marketing's perspective. Marketing has come to view itself too narrowly and, in many cases, merely as sales support. It must adopt a much broader perspective centered on improving the quality of life for customers. Marketers should develop new marketing models that focus on long-term issues about which customers really care. To counteract the pressure to produce products that are harmful to people or to society, marketing should take the responsibility to educate customers in ways that positively affect the world. Marketing should also work to promote better corporate citizenship. Kay Lemon noted that a new corporate citizenship metric (www.accountability.org.uk) on global accountability ranks only one U.S. company in the top 100 (Hewlett-Packard). Marketing has great power to align corporate interests with great societal causes, such as alleviating poverty and disease. Jerry Wind highlighted C.K. Prahalad's (2004) recent book, which shows how marketing can help solve real problems and alleviate poverty on a large scale by targeting "the fortune at the bottom of the pyramid."
Make marketing a true profession. Today, anyone can go into business as a marketer. To strengthen marketing as a profession, Sheth suggested requiring certification and recertification of marketing practitioners, similar to the accounting and medical professions. He also suggested the establishment of a National Academy of Marketing, similar in reputation and mandate to the National Academy of Science.
Revitalize marketing within the organization. Srivastava noted that marketers have historically focused on sales-related measures, such as market share, but have largely ignored profitability and shareholder value. Marketing must do a better job managing its resources and demonstrating the value of investing in marketing programs. The role of marketing in achieving price and stock appreciation is beginning to be understood, but other effects also need to be measured. For example, a strong brand confers greater clout in terms of dealing with distributors and can lead to the ability to negotiate lower distribution costs.
Sheth suggested that instead of being managed as a line function, marketing should be designated as a corporate staff function (similar to finance, information technology, legal issues, and human resource management), with both capital and operating budgets. Marketing's domain should include branding, key account management, and business development. The head of corporate marketing should report directly to the CEO, and a standing committee of the board should be formed to oversee the company's marketing activities.
Use new terminology. Reflecting a growing trend, Southwest Airlines has named its department of marketing the "customer department" and its human resources department the "people department." Sheth suggested that the word "marketing" has lost so much credibility that companies would be better off using the designation of chief customer officer rather than CMO.
Many have come to believe that the term "consumer" objectifies customers and creates a one-dimensional image in the minds of marketers. Kotler suggested that using this term creates the image of customers with their mouths open, waiting to be filled by marketers. Instead, he proposed the term "prosumers," which acknowledges that customers participate in the creation of value.
Learn from other disciplines. Several speakers commented on the continuing need for marketing to learn from other disciplines. Wind suggested that marketing is becoming too self-centered and isolated now that it has matured as a discipline. Likewise, Sawhney commented that marketing began as an eclectic discipline but has become increasingly insular and has come to view itself falsely as self-contained. He proposed that marketing transcend its narrow horizons and begin learning again from neighboring disciplines.
Reform marketing academia. Several speakers spoke of the responsibilities of marketing academics in restoring marketing to a position of respect in companies and in society. A recurring theme was the need for greater relevance in academic research. Webster suggested that there has been an increased emphasis on rigor versus practical relevance. Academic research must become more relevant without sacrificing rigor. He added that it is critically important that research become more idea driven, not just data driven. Sheth suggested that academic research should focus on newsworthy domains and discoveries, similar to medicine and engineering. Grover recommended that marketing be taught as an art and a science (analogous to cooking) because the creative dimension will always be crucial to good marketing.
Conclusion
As the world becomes increasingly market driven and globally competitive, marketing is becoming marginalized at a time when it is most needed. Unfortunately, the "side effects" of marketing today often overwhelm its intended main effects. Can marketing's reputation be redeemed? Not unless it resolves the fundamental contradiction at its core: Marketing claims to be about representing the customer to the company, but it remains mostly about representing the company to the customer, using every trick in its bag to make customers behave in the company's best interests.
Speakers at the symposium were unanimous in asserting that "marketing as usual" is not working any more and that fundamentally new thinking is necessary to rejuvenate this vital and potentially most noble of business functions. Done right, marketing is truly an enlightened undertaking. As Phil Kotler and Bill Wilkie reminded the attendees, marketing has made major contributions to raising standards of living around the world. It has played a role in creating markets for products that reduced drudgery, increased convenience, and enriched life in general in the twentieth century.
Jagdish N. Sheth and Rajendra S. Sisodia
Marketing has evidenced a renaissance of sorts in the corporate hierarchy with the creation of the CMO position. According to a recent Booz Allen Hamilton study (Hyde, Landry, and Tipping 2004), 47% of Fortune 1000 companies have a CMO designation on their organizational chart. This study also concluded (p. 37) that "[c]ontrary to prevailing wisdom, the marketing function is more important now than ever before." Marketing has indeed found "a chair at the table" (a phrase to describe elevation to the senior executive suite), but the CMO's chair has proved to be a hot seat. A CMO's tenure averages 22.9 months, only 14% of CMOs for the world's top brands have been with their companies for more than three years, and fewer than half of CMOs have been on the job for 12 months (Welch 2004).
The CMO "churn and burn" statistics are not surprising. My interactions with CMOs are consistent with the impressions of McGovern and Quelch (2004), who cite several reasons for the high casualty rate. Many CMOs mention that the position is often ill-defined; there is little formal authority, corporate expectations are frequently unrealistic, and credibility and legitimacy with other company "chieftains" is absent. I would add that some CMOs simply over-promise and underdeliver on proposed top-line initiatives and bottom-line outcomes. Indeed, it may be that few marketing specialists are up to the task. My essay puts these recent developments into context and considers future prospects for the CMO position, if not an individual, as the embodiment of strategic marketing perspectives and practice in the corporate executive suite. I also address implications for scholarly research in marketing and executive education.
Marginalization of the CMO
The origins of the CMO can be traced to the late 1950s and early 1960s, when the role of chief marketing executive emerged in corporations. At the time, CEOs focused on converting their companies from a production or sales orientation to a market(ing) orientation (Keith 1960). A common practice was to aggregate and centralize marketing staff resources at the corporate level for the purpose of developing marketing policy and planning guidelines that could be adopted within each of the company's business units. Subsequently, CEOs looked to these executives (now labeled corporate vice president or senior vice president of marketing) to ( 1) promote strategic marketing thinking in their organizations, ( 2) represent a genuine marketing presence and mind-set at corporate headquarters, and ( 3) assist in the preparation and implementation of business unit marketing strategy designed to achieve a competitive advantage. By one estimate, about half of the largest U.S. manufacturing firms had an individual who was considered the chief marketing executive in the early 1970s. Most reported to the company chairman or to the president (Hopkins and Bailey 1971).
The following 25 years witnessed the gradual devolution of corporate-level marketing and its strategic role and influence in U.S. companies. Why did this happen? There are at least four interrelated reasons: ( 1) the proliferation of businesses and product lines in the aftermath of aggressive acquisition activity common to companies in the 1970s and 1980s; ( 2) the emergence of strategic-planning staffs largely as an outgrowth of the budgeting and financial-planning process associated with multibusiness firms, which in turn co-opted marketing's strategic role; ( 3) the perceived ineffectiveness of a single, corporate-based marketing head and staff to address an increasingly diverse set of markets and business models; and ( 4) the growing autonomy of business units followed by the dispersion of marketing responsibilities and personnel among individual operating units (Hopkins and Bailey 1984; Kerin, Mahajan, and Varadarajan 1990). By the late 1990s, the corporate marketing presence had become an administrative cost center and assumed a supportive role in many companies under the direction of a vice president for marketing services. This position entailed managing marketing-service supplier relationships (e.g., advertising agencies, research firms) with a primary emphasis on coordination, monitoring, guidance, and dotted-line links extending into business units. Noticeable in this administrative and supportive role was the limited reference to strategic marketing perspectives as a source of insight or direction for corporate or business strategy.
Materialization of the CMO
So what is new? First, the conditions that circumscribed the presence, role, and influence of corporate-level marketing are disappearing. Companies are divesting unrelated businesses and pruning product lines and brands. Corporate strategic planning departments have been dismantled as a consequence. This "deconglomeration" process has heightened senior management's attention to and involvement in business and marketing strategy formulation and execution to generate top-line growth from existing businesses (Varadarajan, Jayachandran, and White 2001). Second, the majority of CEOs today have significant marketing and sales (along with operations) experience in their career history before assuming general management responsibilities at the business unit level (Allen 2005). Most are in their 50s and actually benefited from marketing responsibilities (and accountability) being delegated to business units over the past 25 years. These executives recognize the potential role and contribution of strategic marketing thinking in the executive suite. Otherwise, the CMO position would not exist. Third, previously autonomous business units have been reined in as CEOs mandate "best practices" across units, resource sharing, comarketing efforts, and the like.
These structural changes have cleared the path for corporate-level marketing to find a chair at the table again in the senior executive suite. However, those who occupy the chair will not necessarily be marketing specialists or have the same experience, skills, and knowledge as chief marketing executives 35 years ago, much of which was acquired in brand management systems and advertising agencies (Silver 2003). Rather, the CMO position demands that its occupant combine a broad business outlook, multi-industry experience, and cross-functional management expertise with the analytical skills to interpret extensive market and operational data and an intuitive sense of consumer, customer, and competitor motivations and market-based assets. The position also expects that its occupant view technology not so much as an enabler of marketing processes and activities but as a key differentiator and a means to create, communicate, and deliver value to consumers. Finally, the position requires the action and results orientation of frontline marketing. Chief marketing officers do not have the luxury of hiding behind the pernicious oxymoron "great strategy, poor execution" as evidenced by the position's high turnover.
The resurgence of corporate-level marketing, manifest in the CMO position, has important implications for the academic marketing community. For example, a recurrent theme in the Marketing Science Institute's (MSI's) 2004-2006 research priorities (McAlister and Taylor 2005) is the need for research that has value to senior management. My sense is that this research, regardless of topic, should explore multifunctional, business-level issues; identify cause-and-effect relationships; and focus on metrics that matter to CEOs and corporate boards. Marketing educators in executive MBA and senior executive development programs must address the demands placed on the CMO position and modify course content and pedagogy. For example, emphasis should be placed on improving data and financial analysis skills and encouraging creativity in framing strategic marketing initiatives in light of implementation considerations and financial targets.
Roger A. Kerin
The Domain of Marketing
Marketing is a multifaceted field that leverages perspectives from multiple disciplines to study substantive topics ranging from the study of the macro to the micro, the organization to the consumer, capitalist practice to social welfare, and the local to the global. Marketing also serves an important applied constituency that demands tools (theories, methods, and measures) to inform marketing, consumer, or public policy actions. As is true in most scientific disciplines, the study of the substantive topics in marketing often involves a diverse set of theoretical and methodological perspectives to understand and appreciate fully all aspects of the phenomena under study. For example, the impact of advertising on consumers is enriched by theoretical perspectives from linguistics, literature, psychology, sociology, anthropology, and economics and by such diverse methodological approaches as ethnography, experimentation, survey research, discrete choice models, and quantitative modeling techniques using aggregate data. Through this multimethod, multitheory perspective, we (the "blind men") gain insight into the "elephant" known as advertising.
Marketing and Science
There is general agreement in the philosophy of science that data or ideas are used to advance a theory, which is tested by data, which in turn leads to theory revision and additional testing through relevant methods and acquired data (Zaltman, Le Masters, and Heffring 1982). In an applied discipline such as marketing, there is also hope that findings will provide substantive insights that are relevant to managers, consumers, and policymakers. In this ideal world, progress is made by a strong interface between data and theory, rigor and relevance. Because various methodologies differ in their emphasis on external versus internal validity, their obtrusive versus unobtrusive nature, and the study of universal versus particular systems (McGrath 1981), knowledge accrues through the use of diverse methods that compensate for the weaknesses of others. Such compensation is warranted because the decisions made on the basis of this research have the potential to affect applied constituents (see the essay by Raju).
Self-and Other Categorizations
However, movement toward such multidisciplinary and multimethodological perspectives seems to be inconsistent with a strong trend I have observed in the field. At a fundamental level, academics in marketing identify themselves not by substantive interest (e.g., advertising, materialism) and theoretical orientation but by methodology. Specifically, there is an evolving bifurcation of the field in which researchers categorize themselves and others as "behavioral" (often meaning experimentalists who study consumer behavior but sometimes also meaning "ethnographic types") and "modelers." Some also identify themselves as "managerial types," which typically means behaviorally oriented people who use surveys to study organizations.
Houston, We Have a Problem
Although self- and other categorizations by methodology may be appropriate in some contexts, some intellectual and disciplinary problems accrue when categorization by method becomes a focal lens for viewing the field. Consider the following questions:
What is important? First, categorization by method affects the focus on theory versus data versus relevance. Academics taking a behavioral approach increasingly focus on theory, often at the expense of relevance or generalizability; it is quite possible that we may answer theoretically interesting questions about phenomena that exist only in the artificial world we create for purposes of testing theory. Indeed, in some cases, articles are so far removed from marketing that it is difficult to understand ( 1) what they have to do with marketplace phenomena or ( 2) why they are published in marketing rather than psychology, sociology, or anthropology journals. Modelers seem to focus on data and relevance with increasingly little regard for theory. "Strategy types" focus on theory, data, and relevance; however, little gets through the review process because authors are being subjected to the standards that are used in both behavioral (strong theory, methodological controls) and modeling (real-world data, advanced techniques) contexts (Stewart 2005).
What is good research? Second, categorization by method leads to inconsistent rules about what constitutes "good research." For example, in the behavioral area, articles are not viewed as interesting unless they advance theory or entail interactions (e.g., demonstrate that the theory does not apply under certain conditions). In contrast, in the strategy area, articles are not viewed as interesting unless they demonstrate generalizations across populations, have multi-item scales, and use multiple informants. In modeling, articles are not viewed as interesting unless they advance a new technique.
Is your research rigorous? Third, a focus on methodology can create an overemphasis on empirical articles, particularly those that study short-term marketing tactics (see the essay by Webster), and can encourage "marketer myopia" (see the essay by Sheth and Sisodia), perhaps at the expense of big ideas (see the essay by Staelin), theory development, conceptualization, and integrative frameworks that have broader applicability and more time unbounded potential (Lehmann 2005; MacInnis 2004).
Where's my hammer? Fourth, categorization by method leads to the study of only those things that can be examined with a prevailing methodological approach. For example, because the behavioralists in marketing are over-represented by experimental approaches to marketing and consumer behavior, there are major gaps in the understanding of important and underresearched domains that are difficult to understand with only an experimental approach, such as those applicable to broad economic, social, and societal issues. Several Association for Consumer Research presidents have noted such gaps (e.g., Andreasen 1993; Belk 1987; Lutz 1989; Richins 2000; see also the essay by Wilkie). This enhances our irrelevance to the applied constituency we serve and other disciplines because these underresearched domains address some of the more relevant, interesting, and difficult issues.
Do you think like me? Fifth, categorization by method leads to less intellectual stimulation because articles are reviewed by like-minded researchers who share a common methodological perspective rather than by those whose different perspectives might stimulate provocative thought.
What do we really know? Sixth, categorization by method leads to a narrowing of knowledge among people who study the same thing. Rather than becoming truly an "expert" on a given marketing phenomenon (e.g., consumer choice), regardless of the publication in which an article appears (Marketing Science, Organizational Behavior and Human Decision Processes, Journal of Consumer Research) or of its focus (information processing, behavioral decision theory), we tend to read articles that are relevant only to a particular substantive interest that appears in certain journals. This often leads to a limited understanding of what is collectively known about a substantive topic and a failure to cite work that extends beyond our narrow purview.
Are you with me or against me? Seventh, at a cultural level, this trend toward categorization by method bifurcates the field into "camps," which often view one another as irrelevant or even adversarial. For example, to hire a "modeler" to a faculty is viewed as offering little potential to enrich behaviorally oriented faculty members' academic research programs and/or the intellectual environment. Rather than examining how someone different could offer unique and diverse perspectives that could enrich our understanding of a given marketing phenomenon, we view their presence, though potentially quite pleasant and enjoyable, as less intellectually relevant than someone whose methodological perspective resembles our own. As evidence of these camps, consider the nature of academic conferences and the kinds of journals deemed appropriate for a given academic piece.
Could you ever work with me? Finally, categorization by method has led researchers to view opportunities for joint research among those whose methodological perspectives differ from their own as limited.
Roadblocks
If this trend toward categorization by method is indeed real, where does it come from? A potential cause is a limited understanding about the relative strengths and weaknesses of prevailing methodological paradigms (McGrath 1981). Depth training in a certain methodological perspective can lead to beliefs about the "right" way to do research and to disrespect and disregard research that is limited on the very characteristics we regard as critical to our own methodological paradigm (e.g., internal versus external validity). This attitude may perpetuate some of the "meanness" in our field that Wilkie describes. It may lead us to adopt rigid rules about what constitutes "valid" research (e.g., homogeneous versus heterogeneous samples, single versus multiple informants), "important" research (e.g., those that demonstrate interactions versus those that advance generalizations; see Leone and Schultz 1980), and research that advances theory versus practice and to use our expertise as reviewers to evaluate research more on fit with our own methodological approach than on its capacity to yield intellectual insights that help us understand a substantive domain. Another potential source of the current status is fear of accepting something unknown or unfamiliar. This problem is magnified when that unfamiliar thing is studied by people with whom we are not familiar.
Pathways
Pathways to remedy the pitfalls of self-and other categorizations by methodology require instilling in doctoral students a more intellectually driven and a less methodologically driven approach to the pursuit of marketing phenomena. Doctoral training should help students view a particular method not as a philosophy of how to do research but as exactly what it is--a method by which a phenomenon can be understood. This requires helping students understand that all methodological approaches are flawed, none is better than the other, and intellectual advances can be made only by an approach that attempts to observe the bigger picture that emerges from diverse approaches. This requires that methods are approached from the standpoint of creatively addressing an interesting question rather than as the "right way" to approach a problem. We must let the dog wag the tail, not vice versa.
Toward this end, we might consider organizing seminars by substantive areas (e.g., materialism, advertising effects, branding) rather than by whether they take a behavioral, strategy, or quantitative approach. Such seminars could be led by researchers whose different methodological orientations, yet shared substantive similarities, offer the capacity to spawn intellectual discussions. A similar approach may be taken for academic conferences, which could be organized and attended by people who share a similar substantive interest. In the review process, strong editors could seek broad-minded reviewers who appreciate research based on its substantive contribution rather than on its adherence to methodological paradigms.
Pathways also include actively seeking out others whose perspective (quantitative versus behavioral) is different from our own (behavioral versus quantitative) yet who share a common substantive interest (Staelin 2005). Several articles in the discipline demonstrate the value of this perspective (e.g., blending the theoretical strengths of behavioral researchers with the modeling techniques of more quantitatively oriented researchers). Such alliances focus not on how we differ but on the substantive commonalities that join us. Efforts such as these would make people in the "other camp" seem less like strangers and more like others we can respect and with whom we can personally interact.
Another pathway involves the reformulation of recruiting of both doctoral students and faculty candidates. Too often, we categorize potential recruits in terms of their methodology (e.g., quantitative versus behavioral). Labeling people in this way alters the way we understand both them and their work and may blind us to potential substantive interests we might share. Finally, new pathways mean taking a more open-minded approach, learning more about how problems are approached from various perspectives, and appreciating research for what it is rather than for how similar it is in approach to our own.
Deborah J. MacInnis
The potential for scholars to contribute to marketing thought and practice has never been greater. The MSI research priorities indicate that marketing practitioners have a pressing need to understand return on marketing spending, branding, new products and growth, nontraditional research tools and methods, customer management, and the role that marketing should play in an organization. When clear articulations of practitioners' problems are combined with an increasing number of well-trained marketing scholars, the result should be great strides forward in marketing thought and practice. Unfortunately, this is not necessarily the case. The very expansion of well-trained marketing scholars, which offers such promise, has led to a division of the field. As MacInnis notes in this collection of essays, the field is divided into "camps," each with its own definition of "good research," each viewing other camps as irrelevant or even adversarial. These divisions are exacerbated by the lack of expansion in the number of pages in marketing's "premier" publication outlets. When one camp is in control of a journal, articles from other camps are unlikely to be accepted, and regardless of which camp is in control, articles that address the kinds of important (and inherently messy) problems that make up the MSI research priorities are discouraged. In this essay, I draw on Ellison's (2002) qr theory of the academic review process to help explain the evolution of publication standards for premier marketing journals and to help motivate my call for the premier marketing journals (i.e., JM, Journal of Marketing Research, Marketing Science, and Journal of Consumer Research) to increase the number of pages they publish each year and to move away from "rigid rules about what constitutes 'valid' research" (see MacInnis, p. 15) toward review criteria that ask, Does this article provide new insight? Is this article "not wrong"?
I begin my elaboration of these ideas by first noting that I refer to JM, Journal of Marketing Research, Marketing Science, and Journal of Consumer Research as the "premier journals" in marketing because these are the journals that tend to be considered in marketing promotion and tenure decisions. Although there are several other excellent U.S.-based and non-U.S.-based marketing journals, promotion and tenure committees are familiar with only a few journals from other disciplines (Henderson, Ganesh, and Chandy 1990), and such committees use journal quality as a proxy for the quality of a candidate's publications because research quality is difficult to evaluate outside a person's discipline (Swanson 2004).
As do others before me, I take as a given that our field has a limited set of premier journals and turn my attention to the issues related to "research quality" in those journals. Massachusetts Institute of Technology economist Glen Ellison (2002) developed qr theory to describe the academic review process. According to Ellison's theory, academic articles are evaluated on two kinds of quality: q quality, which is related to the importance and interest of the main ideas in the article, and r quality, which is related to the execution of the article (e.g., exposition, links to the literature, robustness tests, extensions, methodology). In particular, Ellison posits a social norm for publication (α, z), where α is a value judgment parameter (0 ≤ α ≤ 1) and z is an overall quality requirement. Articles are accepted for publication if αq + (1 - α)r ≥ z. Assuming that q quality is determined by the initial work and that r quality is refined in the review process and assuming that reviewers continuously try to learn the current social norm for quality, the qr model predicts that, over time, r quality standards (i.e., execution) will receive increasing focus and q quality standards (i.e., related to the importance of the idea) will receive decreasing focus. This happens because reviewers, being human, overrate the r quality of their own research, and therefore, on the basis of reviews of their own work, they learn that the r quality emphasis is greater than they believed. Other forces that contribute to the increasing emphasis on r quality standards that Ellison mentions are reviewers' attempts to impress editors by requiring complex revisions and reviewers' competitiveness, which can lead reviewers to impose r quality standards above the norm to hold others back. Ellison's qr theory suggests that editors can reverse this process by accepting articles that reviewers have rejected, thereby providing reviewers with new information about the appropriate balance between q quality (ideas) and r quality (execution). However, Ellison notes that editors are reluctant to overrule reviewers because they rely on reviewers to evaluate articles, and they do not want to be viewed as "lowering standards."
The problem that Ellison's (2002) qr model outlines for all academic fields is even worse for the marketing field. In the period between 1980 and 1999, the annual number of articles published in premier marketing journals actually declined by 2.71 articles per year (Swanson 2004).( n12) During this same time period, the supply of well-trained marketing scholars increased. Furthermore, the pool of well-trained marketing scholars targeting premier journals is set to increase again as business schools in Europe and Asia adopt U.S. standards for faculty promotion (Montgomery 2005). As I have written elsewhere, I am concerned about the implications of a rapidly expanding number of well-trained scholars who are faced with a shrinking number of slots in premier journals (McAlister 2005). Even if the field was not divided into "camps" and even if the naturally evolving process of setting journal norms was not driving increased focus on execution and decreased focus on ideas, this combination of an increasing pool of scholars aspiring to a decreasing number of slots in premier journals would cause a narrowing of work that appears in those journals. Think about the problem in terms of Type I and Type II error.( n13) Since the early 1980s, the supply of potential submissions to premier journals has gone up by an estimated factor of five,( n14) but there is no reason to believe that the average quality of that supply has changed. If we assume that premier journals have focused on the job of avoiding Type I error (i.e., accepting articles that might be "wrong"), the greatly increased supply structurally determines an increase in Type II error (i.e., rejecting papers that might be "right").( n15)
However, our field is divided into camps. Staelin (2005, p. 149) tries to reunite those camps:
When reviewing others' work (e.g., for a journal, for promotion, for hiring), screen for breadth and depth and show tolerance for approaches that differ from yours. Do not rule something out just because it is not "sophisticated." Instead, try to determine whether the work has impact and the ability to modify the existing core of knowledge.
In addition, our field is using review standards that favor execution over ideas. Lehmann (2005, p. 142) calls for restoring balance between execution and ideas:
[T]he pendulum may have swung too far in terms of black-belt methods when simple ones would suffice (and be easier to communicate). A consequence of this is that even the topic-oriented journals are increasingly insistent on the latest methods.… [There is a disturbing] tendency to avoid addressing important problems that are inherently messy.
When I became Executive Director of MSI, Don Lehmann passed the responsibility for MSI's working paper series to me and suggested two simple criteria for paper acceptance: Does the paper provide new insight? Is the paper "not wrong"? With the luxury of a working paper series that has essentially no page constraint, these criteria work quite well. If the premier journals adopted similar acceptance criteria, we could rebalance the "execution versus idea" quality standard. If our premier journals, which have begun to expand pages, would accelerate that trend and substantially expand the number of articles published each year, reviewers might find it easier to show the tolerance for different perspectives called for by Staelin in his essay. I believe that doing these things would unleash the potential that resides in the growing body of well-trained marketing scholars. By boosting the incentive for those scholars to produce important and interesting new ideas, we could accelerate the process of addressing those messy problems with which marketing practitioners struggle.
Leigh McAlister
Many essays in this volume have convincingly argued that as a practice profession, marketing faces many challenges and dangers. Several leading organizations have abolished, or have seriously considered abolishing, marketing departments, partly because academics and other thought leaders have convinced companies that "marketing is far too important to be left only to the marketing departments."( n16) In a recent conference at Wharton, it was suggested that the life span of a CMO is relatively short, and many CMOs believe that one of the more serious challenges they face is justifying their own existence. Marketing budgets are being cut in many corporations because it is difficult to justify the return on such expenditures. Thus, marketing professionals no longer have a "seat at the table" (Webster, Malter, and Ganesan 2003). All in all, these are not good signs.
Conversely, it appears that marketing as a discipline in the world of academics is not doing badly. Indeed, a fairly strong case can be made that it has never been better. Casual observation, discussions with colleagues from other universities, and anecdotal evidence lead me to the following conclusions:
• In general, over the past 20 years, the average size of marketing departments has increased at major business schools.
• More marketing courses are being taught, and furthermore, marketing courses are in great demand.
• Marketing academics hold important leadership positions at many business schools. Indeed, the list of marketing academics holding leadership positions (deans, deputy deans, vice deans, associate deans, and similar important administrative titles) has never been greater.
• The number of manuscripts being submitted to major marketing journals is at an all-time high. In the past few years, there have been some of the most dramatic increases. Numbers reported at Marketing Science, Journal of Marketing Research, and Management Science suggest increases of up to 100% over the past five years.
• The number of doctoral degrees granted in marketing worldwide is at healthy levels. Furthermore, the demand for these graduates, though varying from one year to the next, remains quite strong.
It appears that the life on one side of the street is quite different from the life on the other side. It makes me wonder why the two are so different, but it may also explain why some academics are surprised (if not shocked) when they become aware of what is happening on the other side. Should marketing academics care about what is happening on the other side? This essay attempts to argue that we should, and it provides some ideas as to what can be done.
Why are marketing practitioners in trouble? It can be argued that marketing in the world of practice is floundering because practitioners do not fully use the tools that academics and other marketing thought leaders develop. Therefore, they deserve to be in their current state. However, there is the possibility that what we have given them is not good enough, or not potent enough, compared with what other disciplines receive from their thought leaders. To determine which of the two explanations is the cause requires considerable work. A more pragmatic approach is to reconcile that there is some truth to each of these reasons. If there is some truth to the latter argument, academics may need to change. Furthermore, no matter what the reason is for the "plight of the practitioners," to the extent that marketing is an applied discipline and one of the key end customers is the practitioner, we need to do what we can to make sure the customers are healthy. It is with this objective I humbly make the following suggestions.
Choosing Our Audiences
Most marketing academics live and thrive in a university setting. Our colleagues are people from basic disciplines, and this lineage has done wonders for our field. However, rather than focusing on pleasing economists or mathematicians,( n17) we need to keep in mind that one of our key constituencies is the practitioners. We are grateful when our work gets published in Journal of Applied Mathematics, but we need to be equally proud of studying problems that matter to practitioners, and we must provide solutions that they can implement. Another issue worth thinking about is who the right audience is within a company. In general, our audience in companies historically has been people engaged in marketing research. I believe that we need to go further than this.
Doing it Right Versus Doing the Right Thing
Our discipline pays great attention to the precision of arguments and the methodology used, but often, this can lead to incrementalism. We should be more open to studying problems that matter, even if we need to make some limited compromises in terms of the precision with which we study these. As my esteemed colleague Len Lodish (1974) suggested many years ago, approximate answers to important problems or issues are just as useful (if not more useful) than precise answers to wrong, well-defined, narrow problems. This balance will also enable us to appeal to senior levels in the organization; our current audience is at a more junior level.
Directing the Output of Our Doctoral Programs
Our doctoral programs are designed to train future academics, and this is a desirable motive because demographic changes suggest that there may be an acute shortage of marketing academics in the future. However, we should encourage (and if not encourage, at least not discourage) doctoral students to enter the business world. Many leading corporations are led by people who have doctoral degrees in chemistry, life sciences, and engineering. Why not doctoral degrees in marketing? Corporations can gain if they are led by people who have an in-depth knowledge and an appreciation of how to understand customer needs and develop products, services, and programs that enable a company to satisfy such needs profitably. Furthermore, it does not hurt if members of the audience understand our language, because they have been taught to do so.
Valuing Consulting Activity
Virtually all academics in medical schools spend some time taking care of patients. Indeed, in most cases, it is a part of their responsibility. This not the case in business schools. Do our "patients" not need any help, or are we incapable of helping them? Do we not care one way or the other? These are questions that we need to address directly.
Teach What We Study and Discover in Our Research
We all want to teach what we study and discover in our research, and we try hard to do so. However, the structure and design of our courses often limits our intentions. A large majority of students take just one course in marketing; this course goes by different names in different schools, but it is often referred to as "Introduction to Marketing." In most schools, this course teaches the development of a marketing plan using some well-known and useful frameworks that have stood the test of time. However, in comparing our approach with the first courses taught in other disciplines, there appears to be some differences. The first course in finance does not address the development of a financial plan for a company. The first course in operations management also does not attempt to write an operations plan. I believe that we try to cover too much and therefore focus more on breadth than on depth, thus limiting our ability to link teaching with research. What if our first course was titled "Customer Analysis"? Such a course should put equal weight on behavioral and quantitative methods, and it could be cotaught if necessary.
Measure the Value of Marketing Decision Aids and Models
Many other contributors have commented on measuring the value of marketing inputs. Our focus should be to measure not only the value of a particular marketing input (e.g., advertising) or a particular marketing asset (e.g., brand) but also the value of better methods and models we develop. For example, what is the value of better allocation of resources, such as sales force across territories and advertising across products.
Finally, we should be more comfortable with our own identity. Our field has made many important contributions to the business world, to society, and to science. We need to be more comfortable with who we are. We could even be more proud of who we are. If we have a good article that fits equally well in two outlets, we may want to consider publishing it in a marketing journal rather than in an economics journal. If we have two equally good doctoral students who we are considering hiring, one from psychology and the other from marketing, we may want to hire the student from marketing. If we are more comfortable and possibly more proud of our own identity, it will surely help us, and it may also rub on to the practitioners.
Jagmohan S. Raju
If marketers want to communicate across specializations, across functional areas, and outside the marketing discipline and if marketing science is to influence practice, readability is a necessary, if not sufficient, condition to bridge these audiences. We examine the readability levels of JM over its history and find a dramatic decline in the 1966-1971 period. We explore the reasons this occurred and offer suggestions for enhancing readability and thus the impact of marketing.
JM's Readability 1936-2001
Klare (1963, p. 1) defines readability as "the ease of understanding or comprehension due to the style of writing." To measure the readability of JM, we took samples from the introductions of the first five articles in the first issue of JM in each five-year period from 1936 to 2001. We measured the readability of the passages using the Flesch (1948) formula: 206.835 - (.846 x [number of syllables per 100 words]) - (1.015 x [average number of words per sentence]). This commonly used and highly validated measure of readability is standard on word processing software. The longer the words and sentences, the more difficult the passage is presumed to be. A Flesch score of 100 represents the easiest readability, and 0 represents the most difficult readability. As we depict in Figure 2, readability levels dropped abruptly and significantly (p < .01) during the 1966-1971 period and fell into the "very difficult to read" range thereafter. The Flesch formula is just one measure of readability, but Severin and Tankard (1992) find that better Flesch scores lead to better reader comprehension.
From Where Does Difficult Reading Come?
We believe that difficult reading stems from a confluence of factors. Benson (2004) notes the ten-year period following the Carnegie (Pierson 1959) and Ford (Gordon and Howell 1959) Foundation reports as a time of radical change in business education. Benson reports that, on average, a new doctoral school in business was opened every 73 days during that decade and that faculty positions were filled with properly degreed professors. Business degrees were refocused from training workers to training managers; business colleges responded to demands for rigorous research and theory development. Since that time, the evolution of marketing thought, ideas, and issues has increased the complexity of the discipline, and specializations have caused a widening separation among subareas of the discipline. This specialization has also been associated with a proliferation of publication outlets based on various target audiences.
Another factor affecting the level of readability is the authors themselves. Most authors are trained in doctoral programs devoted more to the development of research skills than to the development of writing skills. New professors learn through the interactive research process that writing skills are given limited attention. Professors read published articles, and to increase their chances of being published, they mimic what they read.
Could reviewers be unwittingly contributing to the belief that difficult writing increases the likelihood of publication? In his seminal article about journal readability, Armstrong (1980) finds some support for the proposition that high-prestige publications are expected to have low readability. Armstrong rewrote passages from articles in ten highly ranked management journals to be more readable and then asked groups of faculty participants to read the different versions. Participants rated the less readable versions as being of better quality. In a similar vein, Metoyer-Duran (1993) finds that articles accepted at College & Research Libraries had worse readability scores than rejected articles. Such evidence is a disquieting reminder of the virulence of the difficult-to-read phenomenon.
What contributed to the decline of readability in JM ? In his examination of the literary history of JM, Kerin (1996) details the evolution of JM from a largely descriptive journal to a scholarly and professional one. Journal of Marketing has witnessed dramatic stakeholder realignments over the years (see Table 2). Its editorial staff, editorial review board membership, and authors have all moved dramatically away from practitioners to academicians. As a consequence, merely descriptive articles have been replaced with articles that make a meaningful scholarly contribution. As Kerin (1996, p. 9) notes, this was "determined, in large measure, by logic in argumentation and thoroughness in documentation in both qualitative and quantitative terms."
By the early 1970s, surveys of JM subscribers found that many subscribers had come to view the journal as too "academic" and lacking in "marketing applications" (Grether 1976). More recently, Crosier (2004) found that JM is not alone; readability is quite difficult in other major marketing journals as well. As Staelin (2002) asserts, when articles are written in an academic style, the information must be diffused through other outlets before it is likely to be directly usable by practitioners.
Suggestions for Improving Readability
Nothing less than a true commitment to improved readability is required. We outline several worthwhile options. There is evidence that the editing and review process improves readability (Roberts, Fletcher, and Fletcher 1994). Excellent advice on how to improve readability has been provided by editors (e.g., Kover 2002; Mick 2005; Staelin 2002). Authors can also have their papers professionally edited before submission. Some institutions already provide such services for their faculty either through in-house staff or by paying for external copy editors. Major journals such as JM could assign readability management to an assistant editor whose focus would be to improve the readability of provisionally accepted papers. The motivation for authors would be strong if only an improvement in readability stood between them and publication. Doctoral programs could require a technical writing course with a commitment to helping students develop more readable writing styles. We must train new scholars to recognize that good, readable writing defines good scholarship.
In summary, this collection of invited essays concurs on a critical premise. If marketing is to enjoy its central organizational role, we must collaborate across specialized silos within marketing (see the essay by MacInnis) and across functional and discipline-related areas (see the essay by Raju) and to conduct and disseminate research that is customized to the world markets (see the essay by Steenkamp). Furthermore, as this essay contends, if marketing science is to influence marketing practice, we must work toward making it not only relevant but also readable. Readability provides a critical bridge among specialized silos, functional areas, global markets, and marketing practice. We believe that the English language is a wonderful, flexible, supple tool; in the hands of an experienced and properly motivated writer, it should allow for the effective communication of complex ideas to readers.
Ronald J. Bauerly, Don T. Johnson,
& Mandeep Singh
We all enter the academy believing that our efforts will have an impact. Some of us center our attention on teaching, aiming to diffuse our acquired knowledge to students who act as change agents; others view the creation of new ideas as the best way to modify the practice; and still others attempt to influence the practice through consulting. Yet it is not always clear that marketing academics have had their desired impact. Indeed, this is a recurring theme in all the essays of this issue. Bauerly, Johnston, and Singh worry that our current publications are not readable and therefore are less likely to affect the practice. MacInnis believes that the bifurcation of our profession leads to silos, and as a result, knowledge is lost across these different branches of our profession. She suggests that the people in different areas be open to other perspectives and appreciate research for what it is. Steenkamp also worries about silos. He argues that most of our theories are too U.S. centric, and as a consequence, we are not able to address important global issues. After detailing several of these issues, he calls for marketing scholars to become more international in their research. Kerin calls for marketing academics to broaden their interests so that they can address multifunctional and business-level issues rather than only marketing issues. This theme can also be found in Brown's essay. He believes that we need to expand our target market to managers in all parts of the firm and not only marketing managers. Wilkie believes that most marketing academics focus their attention on helping the firm and thus do not consider societal issues. He points to the possible negative impact that can result from this narrow focus. Raju conjectures that the demise of marketing departments in corporations is partly due to academics not providing practitioners with the correct tools and knowledge. He suggests that we interact more with practitioners and that we modify our core marketing course to provide deeper knowledge. McAlister is less concerned about knowledge generation and more concerned about the knowledge creators who do not have enough outlets for publishing their new findings. She calls for the journals to expand the number of pages published each year. Webster believes that we tend to tackle only part of the problem at any one time and that we need to encourage senior faculty to take on complex issues that simultaneously address tactics, strategy, and organization. Sheth and Sisodia summarize the views of 17 speakers by pointing out that marketing needs to reform. They believe that the field needs to regain the trust of customers, make better use of technology, broaden marketing's perspective, and learn from other disciplines.
Running throughout many of these essays is the need for academics not only to be aware of others' work but also to show tolerance and respect for others' work and to broaden their own perspective. Implicit is the assumption that marketing is a complex discipline that requires people to be open to many difference approaches and ideas, many of which may come from disciplines other than marketing, if our profession is to provide successful solutions. In addition, these essays imply that without this breadth and tolerance, our field will become insular and lose its relevance and impact. With this in mind, I discuss a few ways to broaden our scope of issues and tolerance for new ideas and thus positively influence the practice of marketing.
Big New Ideas
Our field has come a long way since the mid-1960s. Entire new fields have developed. Some of these fields have had a direct impact on the practice of marketing (e.g., the analysis of scanner data, brand equity studies), and others have had a much more indirect effect (e.g., analytic model building, the study of how consumers make decisions). In addition, many of the concepts developed within the field of marketing have migrated into other disciplines. Still, there is a growing perception that our field is closing in on itself and not having any impact on the practice. Wilkie refers to this as a "meanness" that has crept into our field. Raju believes that our field is searching for incremental ideas rather than big ideas. McAlister believes that we are rejecting manuscripts that are not wrong just because there is a page constraint. Using citations, Baumgartner and Pieters (2003) find that other disciplines rarely build on the knowledge developed in marketing journals. The question then becomes, What can be done to make sure our field progresses and broadens its influence?
Let me begin by addressing our journals. I agree with others that if we are to make strides in developing new knowledge, we must learn from others. One way to enhance this learning is for our leading marketing journals to publish articles that cover a wide range of topics and use multiple approaches. Such a view is consistent with the data provided by Baumgartner and Pieters (2003), who analyze the citations from 49 marketing journals. They find that the most influential marketing journals are those that have a broad span of influence; that is, such marketing journals publish articles that are cited by works published in a wide array of marketing journals. This leads me to suggest that at least the editors of our core set of journals should try to attract the best articles in our field, regardless of topic or method. It also leads me to suggest that the authors of such articles should emphasize the substantive or conceptual aspects of their work (versus the methods used) because these aspects tend to be of greatest interest to the largest group of readers.
Now, consider the creation of big ideas. We all teach our students that one of the best ways to learn is to experiment. Experimentation implies some unanticipated variance. This variation is useful because it enables a researcher to begin to understand the underlying forces that influence a particular situation and thus to improve the work. However, this unanticipated variance can also lead to some undesirable outcomes because for every positive draw, there is also a negative draw. Researchers (especially young researchers) tend to be risk averse. Because new ideas are also different ideas, many researchers shy away from looking for the unexpected. This is particularly evident in the selection of potential research projects and in the review process (i.e., in the input and output stages of research).
I certainly do not have the magic bullet to solve this problem, but I believe that it is possible to influence directly the generation and adoption of new ideas. Consider two examples: The first took place in the mid-1980s when MSI provided seed money to a group of researchers who were interested in how consumers actually used the products that firms sold to them (versus the standard paradigm at the time of helping the firm influence consumers to buy the product). Using a diverse set of new (at least to marketing) approaches--now referred to as interpretive research--these researchers traveled from coast to coast one summer in a Winnebago to observe product use (for a description of the trip, see Belk 2005). This resulted in several manuscripts, many of which were sent to Journal of Consumer Research for possible publication. Because this type of research and the questions it asked were new to the field, there was much debate about the relevance of the topics and the appropriateness of this new field to marketing. Many people advocated that the papers should not be published in Journal of Consumer Research. However, the editor at the time, Rich Lutz, was supportive and guided these papers through the review process. As a result, a whole new field of inquiry gained the visibility and legitimacy necessary to sustain its long-term viability.
A second example of the use of seed money and an editor willing to support a budding new field is the case of customer relationship management (CRM). Approximately four years ago, Duke received funding from Teradata, a division of NCR, to foster CRM research and curriculum design. Rather than using the money to fund internal operations, the Duke faculty decided to provide overall guidance and seed money to a large number of researchers throughout the world who were interested in studying different aspects of CRM. Four years later, enough research had been generated that Ruth Bolton, as editor of the JM, decided to dedicate a significant number of journal pages to this one issue. She appointed Bill Boulding and me to usher articles through the review process with the goal of establishing a cohesive body of research that could act as a foundation for further exploration.
These two examples indicate that the field is still open to new ideas. However, in both cases, it took strong leadership to ensure that ideas "saw the light of the day." Because I was part of the review process for the CRM issue, I can safely note that many of the articles would not have been published if the editor (and consulting editors) took only the advice of the reviewers. My conversations with Rich Lutz confirmed that he also played a major role in ensuring that the initial interpretive research papers made it through the review process. In both cases, the editors were willing to experiment, knowing full well that they could be accepting a paper that would not meet the market test for impact.
Editors are not the only people who need to foster new ideas. Senior faculty across our discipline must take it on themselves to facilitate new ideas and approaches. This is certainly the major theme of Webster's essay when he calls for integrative research. However, it goes beyond this. The idea of facilitating new ideas also extends to the establishment of centers that act as umbrellas for a diverse set of scholars and that address major substantive issues. Often, these issues cut across functional lines and require collaborative research. The process of facilitating big new ideas also pertains to those asked to evaluate others' work for possible publication and during the promotion and tenure process. Here, emphasis should be given to the generation of big ideas and approaches versus technical sophistication or the number of publications. Reviewers need to look for the good in a paper rather than view their task as finding the bad. The same issues apply to teaching. We should quickly diffuse new ideas into the classroom and tie these ideas to other areas of business, and we need people to step up and develop new ways to deliver our knowledge to the practicing managers.
Finally, there is the issue of doctoral training. We must prepare these new scholars to have deep knowledge in a particular area of marketing. However, we also need to ensure that they have enough understanding of the diverse approaches found in our field that they respect others' work. Moreover, if this new breed of marketing academics is to make an impact, they will need to understand how our field is integrated into the broader discipline of business. Without this respect and this broader knowledge, there is less chance that they will be able to solve the next set of big problems facing our profession.
Richard Staelin
( n1) Roundtable participants included Frank Baynham, Executive Vice President, Luxottica Retail; Gary Bridge, Vice President, Internet Solutions Group, Cisco Systems; Greg Reid, Senior Vice President and Chief Marketing Officer, Yellow-Roadway; Nick Semaca, Director and Leader, Americas Travel & Logistics Practice, McKinsey & Company; and Michael Wiley, General Manager, Services Transformation, IBM.
( n2) Copies of our articles "Marketing's Contributions to Society" (Wilkie and Moore 1999) and "Scholarly Research in Marketing: Exploring the '4 Eras' of Thought Development" (Wilkie and Moore 2003) can be downloaded at http://web2.business.nd.edu/Faculty/wilkie.html.
( n3) For extended discussion, see Wilkie (1994, Ch. 2).
( n4) For an initiative in this area, see Farris and Wilkie (2005).
( n5) I have developed further thoughts along these lines in an invited Journal of Marketing essay titled "On Books and Scholarship: Reflections of a Marketing Academic" (Wilkie 2002). It can be downloaded at http://web2.business.nd.edu/Faculty/wilkie.html.
( n6) For an earlier report, see AMA Task Force on the Development of Marketing Thought (1988).
( n7) Note that I would personally advocate extending the tenure period to nine or ten years (with options for a person to go up early) and would require that papers actually be read and evaluated for their quality and contribution. It would be difficult for the marketing field alone to gain such change, but our key institutions (i.e., AMA, Association for Consumer Research, and Institute for Operations Research and Management Sciences) could surely approach other areas of business to explore a unified improvement for business schools. I would not leave this up to the deans; they have other agendas beyond scholarship to pursue.
( n8) I wanted to use the subheading "Reviewers and Rigor … Mortis?" but I could not work it in because of space constraints.
( n9) The speakers were Rajiv Grover, Steven Haeckel, Johny Johansson, Philip Kotler, Katherine N. Lemon, Robert F. Lusch, Raj Sisodia, J. Walker Smith, Rajendra Srivastava, Mohanbir Sawhney, Jagdish Sheth, Glen Urban, Rajan Varadarajan, Frederick E. Webster Jr., William Wilkie, Jerry Wind, and David Wolfe.
( n10) Wilkie and Moore (2003) have provided a rich and deep historical perspective on the evolution of the field of marketing. With their systematic study as a background, I comment on observations about more recent history and trends in the discipline that cause concern.
( n11) I thank Ajay Kohli for noting the problem caused by the lack of expansion in our premier journals and an anonymous reviewer for directing me to Swanson (2004). That article, which builds on Ellison's (2002) article, helped reframe this essay.
( n12) Swanson included only JM, Journal of Marketing Research, and Journal of Consumer Research in the set of premier marketing journals for his analysis. I do not believe that adding statistics for Marketing Science to his data set would change his finding that the annual number of articles published in premier marketing journals has fallen steadily since the early 1980s.
( n13) I thank John Hauser and Don Morrison for suggesting that it is important to realize that the review process generates both Type I and Type II errors.
( n14) Swanson (2004) reports Association to Advance Collegiate Schools of Business statistics that show that the number of doctoral-level marketing faculty positions roughly doubled from approximately 1000 to 2000 between 1980 and 1999. However, only a small percentage of those 1000 doctoral-level marketing faculty in 1980 were well trained in the sense that their research skills prepared them to publish in premier journals. Since 1980, a growing percentage of graduating doctoral students have those research skills. Thus, although the total number of doctoral-level marketing faculty only doubled between 1980 and 1999, I estimate that the number of well-trained marketing professors has grown by at least a factor of five.
( n15) As a simple test of the increase in Type II error hypothesis, I pose this question: Is there any person reading this essay who has not had a paper rejected by a premier journal for reasons that did not compromise the contribution of that paper?
( n16) Although many have used similar phrases in different contexts, this particular phrase is assigned to Jack Trout.
( n17) The word "economist" or "mathematician" can be substituted by the word "psychologist" or "statistician" if the reader prefers.
Legend for Chart:
A - Content
B - Research and Publication
C - Impact
A
B
C
Business practice (Brown; Kerin)
Multidisciplinary research (MacInnis)
Readability (Bauerly, Johnson, and
Singh)
Marketing in organizations (Webster)
Serving stakeholders (Raju)
International marketing (Steenkamp)
Scholarship evaluation (McAlister)
Big new ideas (Staelin)
Marketing and society (Wilkie)
Does marketing need reform? (Sheth and Sisodia) Legend for Chart:
B - Year
C - Academic Number
D - Academic Percentage
E - Practitioner Number
F - Practitioner Percentage
A B C D E F
Editorial staff 1966 2 25 6 75
1971 6 75 2 25
2001 4 100 0 0
Review board 1966 24 35 41 65
1971 38 63 22 37
2001 102 98 2 2
Contributing authors 1966 32 58 23 42
1971 63 91 6 9
2001 68 97 2 3 Legend for Chart:
B - Scholarship
C - Practice Areas
A B C
Traditional view Marketing Marketing
Strategic management
Marketing
Enlightened view Marketing Operations
Supply chain
Human resources
FinanceGRAPH: FIGURE 2 JM 's Readability (1936-2001)
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~~~~~~~~
By Stephen W. Brown; Frederick E. Webster Jr.; Jan-Benedict E. M. Steenkamp; William L. Wilkie; Jagdish N. Sheth; Rajendra S. Sisodia; Roger A. Kerin; Deborah J. MacInnis; Leigh McAlister; Jagmohan S. Raju; Ronald J. Bauerly; Don T. Johnson; Mandeep Singh and Richard Staelin
Stephen W. Brown is Edward M. Carson Chair in Services Marketing and Professor of Marketing, W.P. Carey School of Business, and Executive Director, Center for Services Leadership, Arizona State University
Fredrick E. Webster Jr. is Charles Henry Jones 3rd Century Professor of Management, Emeritus, Tuck School of Business, Dartmouth University, and Visiting Scholar, Eller College of Management, Arizona State University
Jan-Benedict E.M. Steenkamp is Center Research Professor of Marketing, GfK Professor of International Marketing Research, and Director of Graduate Studies, Tilburg University
William L. Wilkie is Aloysius and Eleanor Nathe Professor of Marketing, Mendoza College of Business, University of Notre Dame
Jagdish N. Sheth is Charles H. Kellstadt Professor of Marketing, Goizueta Business School, Emory University
Rajendra S. Sisodia is Professor of Marketing, Bentley College</bo>
Roger A. Kerin is Harold C. Simmons Distinguished Professor of Marketing, Edwin L. Cox School of Business, Southern Methodist University
Deborah J. MacInnis is Professor of Marketing, Marshall School of Business, University of Southern California
Leigh McAlister is Executive Director of the Marketing Science Institute
Jagmohan S. Raju is Joseph J. Aresty Professor of Marketing, Wharton School, University of Pennsylvania
Ronald J. Bauerly is Professor of Marketing, Department of Marketing and Finance, Western Illinois University
Don T. Johnson is Professor of Finance, Department of Marketing and Finance, Western Illinois University
Mandeep Singh is Professor of Marketing, Department of Marketing and Finance, and Director of Faculty Development, Western Illinois University
Richard Staelin is Edward and Rose Donnell Professor of Business Administration, Fuqua School of Business, Duke University
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Record: 107- Mass-Communicated Prediction Requests: Practical Application and a Cognitive Dissonance Explanation for Self-Prophecy. By: Spangenberg, Eric R.; Sprott, David E.; Grohmann, Bianca; Smith, Ronn J. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p47-62. 16p. 3 Graphs. DOI: 10.1509/jmkg.67.3.47.18659.
- Database:
- Business Source Complete
Mass-Communicated Prediction Requests: Practical
Application and a Cognitive Dissonance Explanation for
Self-Prophecy
Marketers often promote socially beneficial actions or discourage antisocial behaviors to the benefit of their firms, target markets, and society as a whole. One means by which marketers accomplish such influence is a technique referred to as the "self-prophecy effect," or the behavioral influence of a person making a self-prediction. Researchers have yet to establish the efficacy of self-prophecy in influencing large target markets. In addition, the theoretical mechanism underlying the effect remains in question. The authors report two field studies that demonstrate successful application of self-prophecy through mass-communicated prediction requests. Furthermore, in three laboratory experiments, the authors provide theoretical support for a dissonance-based explanation for self-prophecy, and they discuss practical implications for marketers interested in influencing socially normative behavior.
For-profit marketers often influence socially normative behaviors through components of marketing campaigns (e.g., Drumwright and Murphy 2001; Osterhus 1997). Similarly, affecting normative behavior is a frequent objective of government agencies and consumer groups that use social marketing techniques (e.g., Andreasen 1994). Although various influence strategies are available to marketers, one simple, effective influence technique holds promise for such situations: the self-prophecy effect (e.g., Greenwald et al. 1987; Sherman 1980 Spangenberg and Greenwald 1999). Specifically, self-prophecy entails asking people to make a self-prediction about their intention to perform a future behavior; the prediction becomes a self-fulfilling prophecy such that post-prediction behaviors arc performed differently from what would otherwise have been observed.
The self-prophecy effect, by which behavior change occurs in the direction of associated social norms, has been empirically demonstrated in various situations of interest to for-profit and social marketers. For example, Sprott, Spangenberg, and Fisher (2003) demonstrate that among consumers with strong social norms, self-prophecy influences the choice of low-fat snacks. Research shows that self-prophecy demonstrations are substantial, and the technique likely has practical significance in wide-scale applications (Spangenberg and Greenwald 2001). Although not yet demonstrated, marketers' wide-scale use of self-prophecy should influence normative behaviors of large segments of consumers.
Methodological challenges exist, however, when the implementation of wide-scale application of self-prophecy is attempted in marketing and related environments. Among the most prominent challenge is acquiring prediction requests from large numbers of people through an efficient mechanism, such as advertising. All published demonstrations of the effect have administered prediction requests by personal contact between study administrators and target participants through written or telephone surveys; however, the organizational resources required to establish one-to-one contact are costly. Thus, the effect as demonstrated to date is difficult to bring about efficiently for large target markets. If self-prophecy can be evoked through mass-communicated marketing media, the effect could be used economically to increase participation in socially desirable behaviors across large consumer populations. For example, the environmentally active sportswear retailer Patagonia might increase sales of eco friendly products by asking its target consumers, through a series of advertisements, to make a self-prediction about their support of environmentally friendly firms. Although such an approach appears promising, it is not clear whether self-prophecy is constringed by the absence of personal contact.
The dearth of evidence about the theoretical processes underlying self-prophecy is notable. Sherman (1980) suggests script evocation as explanation for the self-prophecy effect, but alternate accounts have emerged, including impression management (Greenwald et al. 1987), attitude accessibility (e.g., Morwitz, Johnson, and Schmittlein 1993),( n1) commitment and consistency (Cialdini and Trost 1998), and cognitive dissonance (Spangenberg 1997; Spangenberg and Greenwald 1999). Recent empirical evidence helps eliminate some of these theoretical accounts (Sprott, Spangenberg, and Fisher 2003), but no published evidence documents the theoretical process underlying self-prophecy. Untested conceptual arguments provided by researchers to date suggest that cognitive dissonance is the most convincing explanation for the effect. Thus, it is this theoretical perspective that underlies our process tests and field demonstrations of the self-prophecy phenomenon.
As an influence technique, self-prophecy holds considerable promise for substantially increasing the performance of socially normative behaviors among target segments. Following Cialdini's (1980) notion of full-cycle social psychology, the current research investigates self-prophecy in the field and the laboratory to accomplish two elemental objectives: ( 1) to test whether the self-prophecy effect can be efficiently elicited on a wide scale through marketing mass communication and ( 2) to provide evidence of the theoretical process underlying the effectiveness of advertised prediction requests.
Behavioral prediction has been the focus of many scholars and practitioners in marketing and consumer research. A common goal of this research is predicting behavior by using various measured behavioral antecedents (e.g., attitudes, normative beliefs, intentions, expectations). For example, both the theory of reasoned action (Sheppard, Hartwick, and Warshaw 1988) and the theory of trying (Bagozzi and Warshaw 1990) have been used to predict consumption behaviors. A fundamental distinction exists, however, between the aforementioned paradigm and self-prophecy: The former determines whether goals and intentions accurately predict behavior, and the latter pertains to the way people are motivated to respond when they have made a prediction.
Sherman (1980, p. 219) introduces self-prophecy to the literature by suggesting its usefulness to the field of marketing: "The fact that significant behaviors can be changed greatly by simply asking people to predict their behaviors in advance should have intriguing implications for applied work in the areas of consumer behavior, psychotherapy, decision making, and education." Sherman's work shows the efficacy of a prediction in making people more willing to engage in a socially normative behavior, compared with people who made no such prediction, and demonstrates that prediction requests lead to a decrease in the performance of socially undesirable behaviors.
Since self-prophecy's introduction to the literature more than 20 years ago, research has repeatedly demonstrated a self-prophecy effect, whereby behavioral self-prediction influences the future performance of predicted behaviors in socially normative directions (Spangenberg and Greenwald 1999, 2001). Of relevance to for-profit marketers and those who focus on public policy issues, self-prophecy has been shown to increase voter turnout in elections (Greenwald et al. 1987), improve attendance at health clubs (Spangenberg 1997), increase commitment to a health and fitness assessment (Sprott et al., in press), increase recycling of aluminum cans (Sprott, Spangenberg, and Perkins 1999), reduce gender stereotyping (Spangenberg and Greenwald 1999), increase alumni donations to an alma mater (Obermiller and Spangenberg 2000; Obermiller, Spangenberg, and Atwood 1992), and increase the frequency of choosing a low-fat snack (Sprott, Spangenberg, and Fisher 2003). Spangenberg and Greenwald (1999) report in their review and meta-analysis of published and unpublished self-prophecy research that associated effect sizes are homogeneous (p = .99); they range from r = .08 to r = .40, with an average of r = .19 (a small to moderate effect size [Cohen 1988]). Thus, existing evidence suggests promise for consistent, wide-scale application of self-prophecy to socially normative causes for which marketing professionals (both for-profit and nonprofit) may be called on to influence.
Although existing research has convincingly demonstrated self-prophecy as a compelling, straightforward influence technique, the administering of prediction requests can be time consuming and costly. In particular, all prior empirical demonstrations of self-prophecy have relied on personal contact between administrator and research participant to deliver the prediction request. Prior research suggests that if, for example, a marketer wants to increase purchase rates of ecofriendly products through self-prophecy, direct contact (e.g., telephone interview, mail survey) is necessary. This potential operational constraint is especially problematic for firms with large target markets and severely limits the wide-scale application of the technique. In that the broader use of prediction requests (and resultant changes in behavior) may be beneficial to various constituencies (e.g., marketers, government agencies, consumer groups), a primary goal of the current research is to demonstrate the efficacy of self-prophecy administered through mass communication to target populations. Another goal of the research is to test the underlying theoretical process of self-prophecy.
Consistent with the postulations of prior researchers (Spangenberg 1997; Spangenberg and Greenwald 1999), we adopt the working hypothesis that self-prophecy is a manifestation of cognitive dissonance. Festinger (1957) originally conceptualized cognitive dissonance as fundamentally motivational in nature: An inconsistency among important cognitions causes a negative intrapersonal state that thereby motivates a person to alleviate the aversive psychological position. Revisions to Festinger's original conceptualization (e.g., Aronson's [1968] self-concept view, Fazio and Cooper's [1984] "new look" at cognitive dissonance) have emerged in the literature primarily because none of the basic definitions or assumptions of the original theory were precisely stated (see Aronson 1992), including what it is that constitutes important cognitions. Given that the bulk of published evidence tends to support Aronson's self-concept view of the phenomenon (for a review, see Harmon-Jones and Mills 1999), we adopt this framework for the current research.
The self-consistency or self-concept interpretation of dissonance (Aronson 1968, 1992) follows Festinger's (1957) basic premise (i.e., the force behind observed behavioral and cognitive change is motivational in nature and derives from psychological discomfort), yet clearly specifies important cognitions as related to a person's self-concept and behavior; the discrepancy between these concepts results in cognitive dissonance. According to Aronson's (1992) conceptualization, the centrality of the self-concept relies on the expectation that, in general, most people strive to preserve a sense of self that is ( 1) predictable, consistent, and stable; ( 2) competent; and ( 3) morally good. Changes in outcome variables resulting from dissonance, therefore, are self-concept-preserving justifications that attempt to restore consistency between people's self-concepts and their actions. Because most people have a positive sell-concept, they are likely to experience dissonance when they behave in a way they consider incompetent, inconsistent, or immoral.
Specifically, the self-prophecy technique appears to work by motivating people to reduce a values-action discrepancy made salient by self-prediction. In this regard, a dissonance explanation for self-prophecy suggests that prediction request causes psychological discomfort or tension for those who become aware of a discrepancy between the values they hold (e.g., normative beliefs about performing the central behavior, a generalized positive self-concept) and the actions they perform (or have performed in the past). Thus, at the time of prediction request, people become simultaneously aware of what they should do as well as what they have (or have not) done in the past. If these cognitions are discrepant (e.g., "A good person would exercise, but I have not exercised recently"), dissonance likely results. People who make a prediction alleviate associated cognitive dissonance by undertaking the socially normative action they otherwise would have been unlikely to perform.
The literature supports the interpretation that self-prediction makes people consider what they have done and what they should do in terms of a behavior. Across studies, people in a control group consistently perform behaviors in a less socially desirable manner than do those asked to make a self-prediction. Thus, people left to their own accord perform socially normative behaviors at a level below that directed by a view of oneself as moral and ethical. The behavioral self-prediction also likely acts as a reminder for people of their failure to perform the behavior as they "should." Prior research has also demonstrated that people are aware of the norms underlying focal behaviors and tend to make self-predictions in a socially normative direction (e.g., Spangenberg and Greenwald 1999). The sum of these findings indicates that the requisite elements for dissonance to manifest exist in conjunction with making a prediction.
Recent research provides indirect empirical support for a dissonance-based explanation of self-prophecy. Sprott, Spangenberg, and Fisher (2003) demonstrate that the self-prophecy effect is greater when people hold stronger (rather than weaker) normative beliefs about performing a respective behavior. This finding supports a dissonance account of self-prophecy, because greater cognitive dissonance should exist for those people who make a self-prediction and have the strongest social norms (but have not undertaken the normative behavior in the past). It seems most likely that people in this group will change their behaviors after prediction.
In summary, a dissonance-based account of self-prophecy holds that prediction request elicits cognitive dissonance by making salient the incongruity between people's past behavior and their socially normative self-concepts. Ensuing behavior consistent with the social norm reduces the cognitive dissonance associated with this incongruity. Published evidence to document this process directly is nonexistent, which thereby motivates the second goal of this research: to isolate dissonance as the process underlying mass-communicated prediction requests.
We report five studies that provide normative and theoretical contributions to our understanding of self-prophecy. Of particular interest to marketers who want to implement the technique, we explore whether the self-prophecy effect can occur for advertising. Thus, all studies reported herein employ mass-communicated prediction requests, that is, marketing communication tools such as mailings, print advertisements, or outdoor signs prompting people to make an internal prediction. Studies 1 and 2 are direct-effects tests of self-prophecy within such an advertising context. Study 1 Is an interrupted time-series quasi experiment documenting the influence of a mass-communicated prediction request on recycling behavior. Study 2 is a controlled field experiment demonstrating that mass-communicated prediction requests affect the behavior of people attending a fitness club. In that these studies reliably show that mass-communicated prediction requests influence normative behaviors, we adopted the goal of theoretical explanation for self-prophecy effects within such advertising situations. Thus, in Studies 3, 4, and 5, we conducted a series of laboratory-based theory tests (using a combination of behavioral and psychological process variables) to determine whether cognitive dissonance is responsible for self-prophecy effects. In addition to the practical and theoretical contributions detailed previously, a further contribution of this research pertains to the substantively important fields of inquiry: All studies examine self-prophecy in behavioral domains of relevant social interest to the marketing field, including recycling (e.g., Drumright 1994), health and fitness behavior (e.g., Moorman 2002; Moorman and Matulich 1993), and donations to a charitable organization (e.g., Bendapudi, Singh, and Bendapudi 1996).
We conducted Study 1 as an initial assessment of our proposition that socially normative behaviors can be changed by mass-communicated prediction requests. This study derives from research demonstrating that dissonance (e.g., Dickerson et a!. 1992) and, more important, self-prophecy (Sprott, Spangenberg, and Perkins 1999) can influence conservation-related behaviors. In particular, Sprott, Spangenberg, and Perkins's (1999) field study shows an increase in recycling behavior with a self-prediction manipulation. As with other demonstrations of the self-prophecy effect, Sprott, Spangenberg, and Perkins's study required participants (university dormitory residents) to complete individual paper-and-pencil prediction requests about recycling; they found a significant self-prophecy effect, such that the number of aluminum cans recycled (on average) on dormitory floors was significantly higher in the prediction condition than in the control condition. Personal contact was part of the study's design (i.e., each person responded directly to a prediction request on a survey), which is important to the current research. The challenge evolving from Sprott, Spangenberg. and Perkins's work is whether contacting a large group of people with a mass-communicated prediction request can lead to similarly significant increases in the normatively positive behavior of recycling.
The objective of Study 1, therefore, was to change people's recycling behavior through an advertised self-prediction. We adopted a quasi-experimental design for the study that included an advertising campaign containing a prediction request; we measured recycling before, during, and after the campaign. On the basis of prior research, we expected significant improvements in recycling during and after the campaign compared with before the campaign.
Pretest
We conducted a pretest to determine the number and nature of thoughts elicited by participants' exposure to survey- and advertisement-based prediction requests.
Sample, design, and procedures. Seventy-two Washington State University undergraduate students participated in the laboratory pretest in exchange for a modest amount of course credit. The pretest employed a between-participants design with two self-prophecy conditions, including ( 1) a survey-based prediction condition (n = 39) and ( 2) an advertised prediction-request condition (n = 33).
In the survey condition, we provided participants with a prediction request in line with prior research in the field (e.g., Spangenberg and Obermiller 1996; Sprott, Spangenberg, and Perkins 1999): "You are using a lot of products that come in containers/packages/cans that can be recycled. Do you predict that you will (a) not recycle? or (b) recycle?"
We counterbalanced response order. In the advertising condition, we presented participants with a full-page advertisement that showed several recycling containers with the prediction request ("Will You Recycle?") prominently featured. After exposure to their respective prediction requests, we asked participants in both conditions to list the thoughts they had while processing the stimuli. We also asked them to indicate the valence (positive, negative, or neutral) of their thoughts. Finally, participants completed a series of scaled items about norms and beliefs regarding recycling.
Measures. We recorded and standardized the total number of thoughts and the number of positive, negative, and neutral thoughts each participant generated. We deleted data provided by one person with a standardized total number of thoughts greater than 3.0 (Cohen and Cohen 1983). Two trained coders blind to the purpose of the study rated the thoughts listed as related to cognitive dissonance, evoking of social norms, or neither. Thoughts coded as related to cognitive dissonance suggested that people possessed an awareness of a discrepancy between past recycling behavior and a desirable level of recycling (e.g., "Felt bad because I don't recycle at home"). Thoughts coded as evoking of social norms referenced the level of recycling behavior people considered desirable for themselves as well as for others (e.g., "We should all recycle"). Thoughts coded as unrelated to dissonance or social norms included statements such as, "Phone number easy to remember" and "Garbage." Intercoder reliability, as indicated by Perreault and Leigh's (1989) reliability index (Ir), was .71; disagreements were resolved by discussion.
A social norm scale comprised four items ("Students I know recycle," "Students I know think it's important to recycle," "Students I know should recycle those items that can be recycled," and "Students I know are concerned about issues related to recycling"; α = .77). A recycling belief scale included two items ("I feel committed to recycling" and "I recycle"; r = .63). We measured all items on nine-point scales (1 = "strongly disagree" and 9 = "strongly agree").
Results. Multivariate analysis of variance indicated no significant differences in the number of total, neutral, positive, or negative thoughts generated across conditions (all ps > .10). Although a significant difference between survey-and advertisement-based prediction requests did not emerge for any of the thought categories (p > .14), a post hoc univariate analysis revealed a difference in the number of thoughts related to cognitive dissonance between the survey- (M = .42) and the advertisement-based (M = .09) prediction requests (F1, 69 = 3.91; p = .05). There were no differences in the number of thoughts related to social norms (p > .20) and thoughts reflecting neither cognitive dissonance nor social norms (p > .10) across conditions. Multivariate analysis of variance of the belief and social norm scales did not reveal any significant differences in recycling beliefs across conditions (p > .LU). People in the advertising condition, however, reported stronger social norms associated with recycling (M = 6.62) than did those in the survey condition (M = 5.78; F1, 69 = 4.51; p = .04).
Discussion. Pretest results suggest that a prediction request incorporated in a mass-communicated format elicits responses (in terms of belief measures and the number of total, positive, negative, and neutral thoughts generated) comparable to those evoked by a prediction request in a questionnaire format. The findings also generally support the notion that survey-based prediction requests are theoretically grounded in cognitive dissonance, given the greater sales figures of canned beverages in the building for each number of dissonance-related thoughts produced by this format. Because of the similarities between survey- and advertisement-based prediction requests on most cognitive responses collected for the pretest, we were confident that a prediction presented through mass communication was an effective method of eliciting a self-prophecy effect, thereby justifying proceeding with. Study 1.
Method
Context and sample. Study I was a field study conducted during the fall semester in the largest, most frequented classroom building on the Washington State University campus. The university is residential, and most business and classes are conducted Monday through Friday; as such, the building is heavily used on weekdays by thousands of students attending classes as well as by staff and faculty working therein. Before the start of the experiment, we emptied (and replaced if damaged) all existing aluminum-can recycling boxes and made available a few additional boxes as necessary to provide relatively equal coverage throughout the building. A total of 33 aluminum-can recycling boxes were available across the four floors of the building. Importantly, a person could not change floors or exit the building by any means without passing at least one easily accessed recycling box.
Design and procedures. Study 1 employed an interrupted time-series quasi-experimental design (Cook and Campbell 1979). We recorded nine weeks of recycling behavior, with the experimental manipulation occurring during the middle of this time frame. We collected data during three stages: ( 1) a baseline period of four weeks before the implementation of the prediction campaign (to establish a comparison point for the subsequent stages of the study), ( 2) a campaign period of one week during which we presented the mass-communicated prediction request, and ( 3) a post-campaign period of four weeks after the campaign. Stage of the experiment (i.e., baseline, campaign, postcampaign) therefore served as the independent variable in the design.
The experimental manipulation was a marketing campaign aimed to increase recycling behavior. In particular, we displayed the pretest prediction request ("Ask Yourself... Will You Recycle?") on an electronic reader board (approximately 2 feet by 7 feet) at the main entrance of the building, on actual-size wooden stop signs placed on hinged wooden tents (signs were on both sides of the tent) located outside of the three heavily trafficked building entrances, and on stop signs printed on 8 1/2-by-11-inch flyers hung on the bulletin boards in each of the building's classrooms. We completely removed all manipulations associated with the campaign at the end of the five-day campaign period.
Measure. The dependent variable for Study 1 assessed overall recycling behavior as indicated by the percentage of cans recycled in the building (adjusted by the average number of cans sold). Specifically, we collected and counted recycled cans each weekday for the entire building (i.e., 20 days during the baseline period, 5 days during the campaign period, and 19 days during the postcampaign period [20 days less a Friday holiday]). We obtained vending-machine sales figures of canned beverages in the building for each period of the study and converted them into average daily sales figures. It is reasonable to assume that nearly all of the cans recycled in the building were purchased in the building's vending machines because all other commercial sources of beverages near the building were sold in cups or bottles. The focal dependent measure for the study (calculated for the entire building for each weekday of the three experimental periods) was the average number of recycled aluminum cans per day divided by the average daily sales volume in cans (the value of which was expressed as a percentage). For example, on the third day of the study, a total of 148 cans were recycled in the building. During that same period, the building's vending services sold an average of 217 beverages in aluminum cans per day. The value for the dependent variable in this instance is 68.2 ([148/217] x 100), which indicates that on average 68.2% of cans that could have been recycled in the building were recycled.
The use of interrupted time-series designs presents the potential for several threats to internal validity to manifest (see Cook and Campbell 1979). Although selection bias and instrumentation effects were not concerns in our design, history effects related to consumption rates of beverages in aluminum cans could have influenced our results. The dependent variable in Study 1 accounts for this influence because any changes in overall consumption are included in the denominator, that is, the average daily sales of canned beverages in the building.
Results
Interrupted time-series data are analyzed, when appropriate, with a type of time-series analysis, such as an autoregressive integrated moving average model (Cook and Campbell 1979). Examination of the current data, however, showed no significant autocorrelation; therefore, the use of time-series analysis was not warranted, and we deemed a nonparametric analysis most appropriate. Specifically, we conducted a Kruskal-Wallis test to assess overall differences between three stages of the study. We also conducted follow-up pair-wise comparisons using Wilcoxon rank-sum tests.
The Kruskal-Wallis test indicated a significant influence of the experimental stage on the percentage of cans recycled per day (χ2, sub d.f. = 2 = 9.91; p = .007). Overall, recycling was least frequent in the baseline period (15.8%), compared with the campaign (27.6%) and postcampaign (28.2%) periods. Pairwise comparisons revealed significant (one-tail) differences between the baseline period and the campaign (z = 1.87; p = .03; effect size r = .37) and postcampaign (z = 2.98; p = .002; r = .48) periods. The difference between the campaign and postcampaign periods was not significant (z = 0.14; p = .45).
Discussion
Study 1 shows that recycling behavior increased with the implementation of a marketing campaign featuring a self-prophecy prediction request. Compared with prior research in which prediction request is administered personally by an external agent, participants in this study essentially "self-administered" the self-prediction on exposure to the mass-communicated message. Not only was Study 1's self-prophecy effect significant, but the effect size was also much larger (when comparing baseline with the campaign and postcampaign periods) than the average effect size for prior demonstrations of the effect. Study I results therefore suggest that one-to-one contact is not required for a self-prophecy effect to manifest, and they suggest promise in applying the technique to large target markets.
Study 2 was a field experiment assessing the effect of advertised prediction requests on attendance of members at a health and fitness club. Several aspects of Study 2 address the potential shortcomings associated with Study 1's quasi-experimental design. Spangenberg (1997) has demonstrated the success of a self-prophecy technique in this context through individually administered prediction requests; Spangenberg contacted health club members by telephone to ask if they expected to use their health club and then monitored attendance unobtrusively. Health and fitness behaviors are good candidates for self-prophecy because there are strong normative beliefs surrounding the pursuit of exercise and fitness (e.g., Finlay, Trafimow, and Moroi 1999), particularly in the subgroup of people who spend the money to join a club. It is also reasonable to expect that there could be dissonance associated with not making use of a facility for which a person has spent money to join.
Method
Context and sample. Study 2 was a true experiment conducted at a large health and fitness club in Montana's Flathead Valley. The club is owned by and adjacent to a large regional hospital. The facility offers a full range of state-of the-art exercise equipment and sport-specific services (e.g., pools, racquet- and basketball courts, a climbing wall, an indoor track). At the time of the study, there were more than 3000 membership accounts at the facility. Before random assignment to conditions, we dropped people whose memberships were inactive or put on hold (e.g., because of injury or relocation), and we screened club employees and members whose employers paid their dues (this was critical to the treatment to ensure that the mailed advertising manipulation was not delivered to someone other than the person using the membership). Thus, on the basis of these criteria, we had a total available population for Study 2 of 1665 people.
Design, experimental manipulations, and procedures. We used a between-participants design that included three conditions: an advertised prediction-request condition (n = 557) and two advertised control conditions (n = 550 and n = 558). We randomly assigned participants to conditions. We assessed basic demographics and prior attendance figures to confirm equivalency of treatment conditions resultant to random assignment to conditions. There were no differences across conditions regarding prior attendance at the facility (F2, 57 = .11; p = .90), age (F2, 662 = 2.76; p = .06), or sex (χ2, sub d.f. = 2 = 1.14; p = .57).
We modeled treatments after Spangenberg's (1997) study, in which he contacted all study participants by telephone, such that the manipulation was the only difference between conditions (thereby precluding a priming explanation for the observed effects). In the present study, advertisements containing experimental manipulations ("Do you expect to use your club in the next week?" and, for self-prophecy, "Will you work out at [fitness facility name]?") served as stimuli for the three experimental conditions.
Prior research has employed a variety of control conditions to establish the self-prophecy effect, including ( 1) the provision of information related to the behavior but not asking for a prediction (e.g., Greenwald et al. 1987; Spangenberg 1997), ( 2) the measurement of behavior only (i.e., no contact of any sort with the control-condition participants before measuring behavior; e.g., Sherman 1980), and ( 3) the administration of a prediction request unrelated to the focal behavior (e.g., Spangenberg and Obermiller 1996). The present study implemented two control conditions. The first ("Fitness guilt?") asked a question without asking for a prediction. We designed the second ("Work out at [fitness facility name]") to be as close to the prediction request as possible without actually asking for a prediction and to provide the name of the fitness club in a manner as prominent as the prediction request. As such, this condition provides empirical assessment of priming or attitude accessibility; if either of these underlies self-prophecy, we would expect to find similar levels of attendance between this condition and the prediction request (a dissonance-based viewpoint would not predict such a difference).
We provided the advertised messages for the three conditions on an insert included with club members' monthly newsletters and billing statements. The insert was printed on a 3 1/2-by-8 1/2-inch piece of gray, heavy card stock and was provided with the newsletter under the guise of a reader survey. The right half of the insert was a two-question survey regarding newsletter readership and the left half displayed the respective manipulated advertising message (presented in all capital, 36-point Lucida Console font).
Measure. The dependent variable for Study 2 was a measure of attendance behavior recorded by the facility's check-in computer from participants swiping their membership cards at a turnstile entrance (a nearly perfect measure, because members could not otherwise enter the club). We collected attendance data for the four-week period from April 8 through May 1 (allowing two days for members to receive the newsletter and experimental treatment after mailing on April 6). To eliminate the potential confounding effects of special weekend events (e.g., road races and children's activity days) and to maintain a consistent weekly data collection schedule, we used only weekday attendance (i.e., Monday through Friday) to calculate the dependent measure. Specifically, we calculated the dependent variable by dividing the average number of members making a daily visit per experimental condition by the number of members in that condition, and we expressed this value as a percentage. For example, on April 15, 141 of the 557 research participants in the self-prophecy condition visited the facility; the value for the dependent variable is 25.3 ([141/557] x 100), which indicates that 25.3% of members in the condition attended the facility that day.
Results
An analysis of variance model indicated an effect of experimental treatments on daily health club attendance (F2, 57 = 2.79; p = .07). Follow-up contrasts showed that attendance rates were significantly higher (one-tail) in the advertised prediction request than in either control condition. In particular, the average daily attendance rate for the prediction condition was higher (M = 20.6%) than the attendance rate for "Fitness guilt?" (M = 18.9%; t57 = 2.15, p = .02; r = .27) and "Work out at [fitness facility name]" (M = 19.1%; t57 = 1.92, p = .03; r = .25). The two control conditions did not differ (t57 = .25; p = .41).
We conducted additional analyses to determine the nature of the self-prophecy treatment effects on club members' attendance rates. As noted, we conducted the experiment in late April. This time of year typically finds many health club members moving from indoor exercise pursuits (e.g., treadmills, stationary bikes) to outdoor ones (e.g., running, bicycling); this tendency is particularly strong at this health club because of its location near the Rocky Mountains. Indeed, staff and management at the health club indicated in prestudy meetings that the most significant attendance-related problem during that time of year is losing members to outdoor pursuits. In this context, we expected a successful self-prophecy prediction message to be more likely to retard spring attrition rates than would control messages. Thus, we expected to find smaller differences between pre- and post-treatment on behavior for the self-prophecy condition (compared with the two control conditions). To address these issues, we employed a nonparametric method of analysis (similar to Study 1) and conducted pairwise comparisons (i.e., pre- versus post-treatment) using the Wilcoxon rank-sum test across the three conditions. Attendance rates significantly decreased (one-tail) after the promotional campaign for the "Fitness guilt?" (z = 2.60; p = .005) and the "Work out at [fitness facility name]" (z = 2.76; p = .003) control conditions. More important, attendance rates did not significantly decline for the self-prophecy condition (z = 1.56; p = .06). These results are depicted in Figure 1. Notably, pretreatment attendance rates did not differ significantly across the three experimental conditions (F2, 57 = .11; p = .90).
Discussion
The significant self-prophecy effect found in Study 2 builds on the results of Study I using a controlled, true field experiment to support the proposition that actual behaviors can be significantly modified by the implementation of an advertising campaign featuring a self-prophecy prediction request. In particular, with a nonstudent sample in a real-world setting, Study 2 demonstrates that an advertised self-prophecy treatment can lead to a significant increase in daily health club attendance, compared with those people not asked to make a prediction. Study 2 results also provide greater confidence in Study 1 results by testing for pre- and post-treatment differences of the promotional campaign, which indicates that self-prophecy has the capability to retard seasonal health club attrition rates. More important, a pertinent statement about the focal behavior (i.e., "Work out at [fitness facility name]") had less influence on behavior than did a similar prediction request (i.e., "Will you work out at [fitness facility name]?"), which suggests that priming or attitude accessibility cannot account for self-prophecy effects. Given the potential that substantial noise exists in the field setting of Study 2 (e.g., not all health club members likely were exposed to or considered the advertising insert), our significant results and substantive effect size suggest considerable promise for marketers.
Studies 1 and 2 represent the first empirical evidence that self-prediction through mass-communicated messages can influence normative behaviors for large target populations. Practical implications of these held studies for marketers and others interested in influencing human behavior are detailed in the section "General Discussion." A remaining objective of the current article is to test the theoretical process by which the self-prophecy effect occurs. Studies 3, 4, and 5 are a series of laboratory experiments that address this goal by testing the proposed dissonance-based theoretical mechanism for self-prophecy.
All published self-prophecy research to date has focused on the prediction of a person's own behavior. With a slight twist (0 this paradigm, it is possible to use the act of prediction to support the theoretical explanation of dissonance underlying the phenomenon; the twist is to incorporate predictions for other people as an experimental design factor.
According to Aronson's (1968, 1992) self-concept idea of dissonance theory, people's views of themselves as good and morally competent should be threatened when they make a self-prediction inconsistent with previous behavioral patterns. The act of prediction makes people simultaneously aware of what they should do and what they have failed to do in the past. Prior research indicates that when such a threat to self-esteem exists, a person is expected to bolster self-evaluations through downward comparison with others (Hakmiller 1966) to rebuild a conception of the self as morally good and competent. If people are dissonant about having made a prediction, they should feel better if they are not any less conscientious than others regarding a normatively directed but not always adhered to behavior (e.g., "Others don't always recycle, so I'm not that bad if I haven't either"). The reason for expecting such an outcome is grounded in social comparison theory (Festinger 1954), whereby comparison processes are influenced by needs for self-enhancement and self-esteem. In general, we expect that people who view an advertisement containing a self-prediction and subsequently make a prediction about others exhibit less cognitive dissonance than do those who are exposed only to self-prediction in the advertisement.
Prior research has directly measured two generally recognized components of cognitive dissonance that could be used for the purposes of Studies 3A and 3B. The first arousal, which Elliot and Devine (1994, p. 382) refer to as the "drive-like properties of dissonance," has been directly assessed with galvanic skin response (e.g., Elkin and Leippe 1986). The second arousal is the psychological discomfort (or uneasiness) associated with dissonance, for which Elliot and Devine (1994) developed a reliable and valid self-report measure. In the current research, we adopt the latter, more parsimonious approach.
Thus, for people viewing an advertised prediction request, measured psychological discomfort should be significantly lower for those who make a prediction about others' behavior than for those who do not (because such a prediction about others should act as a dissonance-reduction strategy). A dissonance theoretical viewpoint would predict no such difference in a control condition. We report two tests of this hypothesis in the context of exercising at a university health and fitness facility (Study 3A) and recycling (Study 3B).
Method: Study 3A
Context and sample. Study 3A was a Laboratory experiment conducted using paper-and-pencil tasks. The sample (n = 202) included undergraduate marketing students at Washington State University, who participated in the study for course credit.
Design, experimental manipulations, and procedures. The study employed a 3 (advertising treatment) x 2 (order of prediction about others' behavior) between-participants design. The advertising treatment included three conditions: an advertised prediction request condition (n = 68) and two advertised control conditions (for both, n = 67). We randomly assigned participants to conditions. Experimental treatments included copy within color advertisements. In the self-prophecy condition, the copy read, "Ask Yourself: Will you work out at the Student Recreation Center?" The advertisement copy for the control condition read, "Work out at the Student Recreation Center" and "Fitness Guilt? Student Recreation Center."
The other experimental factor manipulated the order in which participants predicted others' behavior. In one condition, the prediction about others came before measurement of psychological discomfort. In the other condition, the prediction of others' behavior followed the measure of psychological discomfort.
Measure. The dependent variable for Study 3A was Elliot and Devine's (1994) measure of cognitive dissonance in terms of psychological discomfort. This measure (validated in a series of studies by Elliot and Devine)( n2) stands alone as a means of directly assessing the psychological discomfort associated with cognitive dissonance. Specifically, we asked participants to "indicate how the information in the advertisement makes you feel right now" using the following descriptors: "uncomfortable," "uneasy," and "bothered." Each descriptor was followed by a seven-point scale anchored by "does not apply at all" ( 1) and "applies very much" ( 7).
In the current study, the scale exhibited favorable psychometric properties: In a confirmatory factor analysis, completely standardized factor loadings ranged from .79 to .93, and individual item reliabilities (Bagozzi and Yi 1988) ranged from .62 to .94. Composite reliability (Bagozzi and Yi 1988) was .93, and average variance extracted (Fornell and Larcker 1981) was .81. Thus, the average of the three items (α = .92) served as the dependent variable.
Results: Study 3A
For research participants completing a self-prophecy prediction, less psychological discomfort was reported (one-tail) when they first estimated other people's behavior (M = 2.11), compared with the condition in which the prediction of others did not come first (M = 2.82; t66 = 1.76, p = .04; r = .21). Neither of the control conditions produced such a difference ("Work out at [fitness facility name]." t65 = .01 and p = .50; "Fitness guilt," t65 = .73 and p = .23). Notably, levels of psychological discomfort observed in the control conditions are comparable to those of control conditions in other research using this methodological paradigm (Elliot and Devine 1994). We discuss Studies 3A and 3B together after the description and results of Study 3B.
Method: Study 3B
Context and sample. Study 3B was a laboratory experiment conducted at the University of Washington. The sample (n = 74) included undergraduate marketing students participating in exchange for course credit.
Design, experimental manipulations, and procedures. The study employed the same between-participants design as Study 3A, and we randomly assigned participants to conditions. With the focal behavior of recycling, treatments were contained within the advertisements' copy. In the self-prophecy condition (n = 23), the copy (derived from Study 1) read, "Ask Yourself ... Will You RECYCLE?" The control conditions included "It's not trash. RECYCLE" (n = 28) and "Ask Yourself... Will You Enjoy Seattle Parks?" (n = 23). All advertisements included similar design elements, namely, stop signs (Study 1) on which the message was prominently displayed. The other experimental factor manipulated the order in which participants predicted others' behavior (i.e., either before or after the measure of psychological discomfort).
Measure. The average of Elliot and Devine's (1994) three-item measure of psychological discomfort (α = .93) again served as the dependent variable with strong psychometric properties (established by means of confirmatory factor analysis): Completely standardized factor loadings ranged from .86 to .99, and individual item reliabilities ranged from .74 to .98. The composite reliability (Bagozzi and Yi 1988) was .94, and average variance extracted (Fornell and Larcker 1981) was .84.
Results: Study 3B
The results of Study 3B mirror those of Study 3A. In particular, participants who completed a self-prophecy prediction had significantly (one-tail) less psychological discomfort after completing a prediction of other people's behavior (M = 1.97), compared with those who did not (M = 3.14; t21 = 2.74, p = .05 r = .35). Neither of the control conditions showed a significant (one-tail) difference ("Recycle," t21 = .83 and p = 21; "Parks," t26 = .55 and p = .29).
Discussion: Studies 3A and 3B
For two behaviors and two populations, Studies 3A and 3B support our proposition regarding the dissonance-reducing aspect of making a prediction about other people's behavior, Those viewing an advertisement containing a prediction request reported significantly lower levels of psychological discomfort after making a prediction about other people's behavior, compared with the group that did not do so. In both experiments, downward comparison appears to have "turned off" dissonance associated with self-prediction, thereby removing the psychological discomfort we expected to lead to a self-prophecy effect. Studies 3A and 3B thus provide support for the proposition that self-prophecy is theoretically explained by cognitive dissonance.
Study 4 begins by asking what would happen if a person were to reaffirm the aspect of the self threatened by the dissonance evoked through making a self-prediction. Self-affirmation theory (Steele 1988), which builds on Aronson's (1968, 1992) conceptualization of dissonance as resulting from a threat to people's view of themselves as competent and moral, can be used to support our interpretation of self-prophecy as a fundamentally dissonance-based phenomenon. Self-affirmation theory proposes that dissonance effects are not the result of mere cognitive inconsistency or a person feeling responsible for producing aversive consequences, but of behaving in a manner that threatens his or her sense of moral and adaptive integrity.
According to self-affirmation theorists, the primary goal of dissonance reduction is to restore the positive integrity of the overall self-system, which suggests that self-focused attention is a necessary condition for self-concept differences to moderate evoked dissonance (e.g., Spencer, Josephs, and Steele 1993). For example, people do not believe there is the need to justify or rectify a regrettable decision at work if reminded of their global sense of self-worth (e.g., being a good parent or an otherwise positively contributing member of society). So long as the affirmed self-concept is strong, positively restoring feelings of self-integrity can obviate the need to resolve the provoking contradiction, because according to Steele (1988, p. 289), "it is the war, not the battle, that orients this [self] system," Relevant affirmations are a mechanism to support people's overall sense of self-worth (Steele 1988) or to reassure them about a personally violated self-concept (e.g., Aronson 1968). Published experiments directly test and support the self-affirmation process within the cognitive dissonance literature (for a review, see Aronson, Cohen, and Nail 1999).
Extant research has used self-affirmation processes to document the existence of cognitive dissonance (e.g., Steele 1988). Within this paradigm, research participants are made dissonant (e.g., by writing a counterattitudinal essay) and then allowed (or not allowed) to self-affirm. Outcomes of dissonance (e.g., change in attitudes) only manifest for those who do not self-affirm; dissonance effects do not manifest for those who do self-affirm. A similar approach documents the existence of cognitive dissonance associated with self-prophecy. Assuming that dissonance underlies the self-prophecy effect, one should be able to eliminate the effects of self-prophecy by making thoughts salient that help restore the person's global self-concept alter making a self-prediction: We test this hypothesis in the current experiment.
Study 4 adopts the preceding logic by exposing participants to advertisements containing prediction requests pertaining to recycling and then allowing some (but not all) of them to affirm values central to their self-concept. Before completing the Elliot and Devine (1994) measure of psychological discomfort, participants selected one of two articles to read. An article about exercising was a baseline condition; we expected an article about the negative consequences of not recycling to increase psychological discomfort (especially for those participants who did not self-affirm important values) because of the dissonance-evoking nature of the prediction request about recycling behavior that we exposed participants to at the beginning of the experiment. If dissonance underlies self-prophecy, self-affirmation should reduce participants' psychological discomfort in response to a prediction request and recycling article. In the absence of such self-affirmation, however, psychological discomfort should be notably higher.
Method
Context and sample. Study 4 was a laboratory experiment conducted concurrently at Washington State University and Seattle University. Undergraduate marketing students (n = 92) participated in exchange for course credit, and we randomly assigned them to conditions.
Design, experimental manipulations, and procedures. The experiment employed a between-participants design in which we manipulated the self-prophecy condition at two levels: ( 1) an advertised prediction request with self-affirmation condition (n = 47) and ( 2) an advertised prediction request without self-affirmation condition (n = 45). The self-prediction for both conditions was contained in an advertisement that read, "Ask Yourself... Will You Recycle?" This advertisement copy was identical to that employed in Studies 1 and 3B.
The self-affirmation manipulation followed the procedures of Sherman, Nelson, and Steele (2000). In particular, we provided participants with the following instructions: "Below is a list of characteristics and values, some of which may be important to you, some of which may be unimportant. Please rank these values and qualities in order of their importance to you, from 1 to 11 ('I' being the most important item, '11' being the least important)."
The list after the instructions included characteristics such as "business/money," "artistic values," "sense of humor," "athletics," and so forth. On completion of this ranking task, we asked all participants to answer two questions. To make salient important aspects of their self-concepts, we asked participants in the self-affirmation condition: "What was your most important value listed on the previous page? (The value you ranked number 1)." We then asked them to respond to the following question: "Why do you think this value might be important to you? Describe a time in your life when it has been important. Write as much or as little as you want during this time." In the non-self-affirmation conditions, we asked participants: "What was your ninth most important value listed above? (The value you ranked number 9)." We then asked them to respond to the following question: "Why do you think this value might be important to a typical [university name] student? Describe a time in the typical student's life when it may be important."
The second component of the experiment was represented by participants' choice of one of two short articles (counterbalanced order of exposure) that we asked them to read and evaluate after the (non-)self-affirmation manipulation was complete. We intended one article ("Not Exercising Is Killing Our Youth") to serve as a baseline and expected it to generate little (if any) dissonance. We expected the other article ("Not Recycling Is Killing Our Planet") to generate considerable dissonance for those participants making a prediction request (without self-affirmation).
Participants began by evaluating the advertisement containing the prediction request and then completed the appropriate (non-)self-affirmation task. Next, we asked participants to indicate the article topic they would be most interested in reading in a student-oriented publication (by placing an X next to one of the titles). After reading the article, participants completed the focal dependent measure--Elliot and Devine's (1994) measure of psychological discomfort. Specifically, we asked participants to indicate "how the information in the article makes you feel right now." With such instructions, we expected discomfort to remain relatively low for those who selected the exercise article (and higher for those who selected the recycling article). Finally, participants provided information on various demographics and completed a demand awareness question.
Measure. The average of Elliot and Devine's (1994) three-item measure of psychological discomfort associated with cognitive dissonance was the dependent variable in this study (α = .90). Completely standardized factor loadings ranged from .76 to .97, and individual item reliabilities (Bagozzi and Yi 1988) ranged from .57 to .94. The composite reliability (Hagozzi and Yi 1988) was .91, and average variance extracted (Fornell and Larcker 1981) was .77.
Results
Of the participants who self-affirmed after making the prediction, 12 (26.7%) chose to read the article about recycling, compared with 14 (29.2%) who did not self-affirm after being exposed to the prediction request. An analysis of variance model showed a main effect for article choice, indicating greater psychological discomfort for those who selected and read the recycling (rather than the exercising) article (F1, 88 = 27.10; p < .001); thus, exposure to a prediction request about recycling behavior may, in general, have resulted in greater psychological discomfort in response to the article about recycling. A main effect for self-affirmation (p = .39) did not emerge. Of particular theoretical import was the significant interaction between self-affirmation and article choice (F1, 88 = 3.78; p = .055). We conducted planned comparisons to interpret this interaction. Participants who did not self-affirm after making a prediction reported greater psychological discomfort for the recycling article (M = 4.69) than for the exercise article (M = 2.14; t43 = 5.69, p < .001; r = .66). A similar (but considerably weaker) pattern emerged for those who self-affirmed after making a prediction, such that greater discomfort was associated with the recycling article (M = 3.69) than with the exercise article (M = 2.53; t45 = 2.13, p.04; r = .30). A comparison of effect sizes associated with these contrasts indicated, as we expected, a significant one-tail difference with a greater effect size in the no-affirmation condition (z = 2.24; p = .01). Results of Study 4 are shown in Figure 2.
Discussion
Study 4 shows that people exposed to an advertisement-based self-prediction request while given the opportunity to affirm values central to their self-concept report relatively lower levels of psychological discomfort than do those making a prediction without the opportunity to self-affirm. The results provide further support for a dissonance-based account of self-prophecy.
Studies 3A, 3B, and 4 focused on measured psychological discomfort to demonstrate that cognitive dissonance is the mechanism underlying the self-prophecy effect. Study 5 shows the effect of mass-communicated prediction requests on behavior within the self-affirmation paradigm.
On the basis of the logic behind Study 4, we should be able to turn off self-prophecy's behavioral effects (assuming a dissonance-based process) by allowing people who make a prediction to self-affirm and restore a positive sell-concept. Study 5 tested this hypothesis with an experiment in which we asked people, through an advertisement, to make a prediction about an important cause (i.e., the American Cancer Society) and then gave them (or did not give them) the opportunity to self-affirm positively; we expected this to reduce participants' dissonance and eliminate any behavioral effects of prediction by providing people with a way to feel good about themselves (Aronson, Blanton, and Cooper 1995). Self-affirmation in this context provides people with a substitute for behaviorally demonstrated compassion toward the American Cancer Society.
Method
Context and sample. Study 5 was a Web-based experiment conducted on the Washington State University campus. Participants were unclassified staff working in departments across the entire campus (to proscribe likelihood of demand artifacts, departments we dropped included psychology, sociology, and several business disciplines). To promote participation, we promised participants in an introductory e-mail solicitation that they would be automatically entered in a drawing for one of two $50 gift certificates at the campus bookstore. After dropping participants as a result of manipulation and process checks described subsequently, the final sample size was n = 83.
Pretest. We conducted a pretest with 40 staff at the university to ascertain a charity that would be important to a significant percentage of the staff that would be contacted for the main experiment. We told pretest participants to assume they had $5 to donate to a charitable cause, and we asked two questions: "What two charitable causes would you be most (least) willing to donate the $5?"
One-third of pretest participants mentioned the American Cancer Society as the cause to which they would be "most willing to donate," and therefore we chose it as the charitable cause for this study (no other cause was mentioned more frequently). In addition, when queried about the causes to which they would be "least willing to donate," no participants mentioned the organization.
Design, experimental manipulations, and procedures.
The study employed a between-participants design with three treatment conditions: ( 1) an advertised prediction request with self-affirmation condition (n = 22), ( 2) an advertised prediction request without self-affirmation condition (n = 25), and ( 3) a control condition (n = 36). The treatments consisted of half-page, full-color advertisements presented through Web browsers. All advertisements contained the American Cancer Society logo and the word "Donate" placed as the first word encountered in the upper-left-hand corner of all advertisements. For the control condition, the copy read, "Support the American Cancer Society." Copy in both the self-prophecy and the self-affirmation conditions read: "Ask yourself: Will you support the American Cancer Society?" We randomly assigned (with the computer program) participants to treatment conditions. The self-affirmation manipulation followed procedures detailed in Study 4 (i.e., ranking and writing about their first or ninth most important value).
We initially contacted participants by personalized email and asked them to participate in an online study about consumers' responses to advertising. To participate, participants merely needed to click on a link embedded in the email that brought them directly to a Web site containing appropriate experimental materials.
On entering the Web site, participants read and agreed to an informed consent statement. They were then sent to the next page, at which they received the following instructions: "On the following page, you will be presented with a mock advertisement. In this study, we are interested in how well you process the copy (i.e., the written content of an advertisement). After viewing the advertisement, you will be asked to indicate what part of the written copy you recall from the advertisement."
After reading the instructions, we presented participants with the advertisement associated with their condition. After viewing the advertisements, participants encountered a page on which we asked them to answer two questions from memory without going back to the advertisement page. The questions were "What was the topic in the advertisement?" and "What was the written copy in the advertisement?"
After answering questions about the advertisements, participants entered a page ("Ranking of Personal Values and Characteristics") containing the value-ranking task (also used in Study 4). When participants had completed writing about condition-appropriate values, we asked them to register for the prize drawing on a separate page. This page contained the focal behavioral response measure of donating time to the American Cancer Society. Specifically, participants had the option to "Log off now" or "Continue" to complete a survey to help the American Cancer Society. Before they were given the choice, we assured all participants that they had been entered in the prize drawing and that either choice would not preclude them from being entered. The survey contained a series of four questions that ostensibly gathered information for the American Cancer Society.
Manipulation and process checks. To ensure that participants included in hypothesis tests were sufficiently engaged in processing the advertisement and tasks associated with our study, we conducted several checks and discarded those participants who did not meet the qualification set for each one. To be included in final analyses, participants needed to remember the topic of the advertisement they saw and correctly recall important aspects of the copy (i.e., "Donate" and "American Cancer Society" in all conditions and the questioning nature of the copy in the self-prophecy and self-affirmation conditions). Participants also needed to complete the values-ranking task correctly to remain in the study. We eliminated participants from analyses if they did not identify the correctly ranked value to write about (1 or 9 depending on the condition). Many participants did not make the cut at several of these stages; thus, the final sample size was n = 83.
Measure. The criterion variable for Study 5 was whether participants donated their time by completing an American Cancer Society survey. We provided research participants with the opportunity (after viewing the advertisement relevant to their respective conditions) to either click on "Log off now" or click on "Continue" to donate their time to complete an American Cancer Society survey. Thus, the percentage of people who decided to donate their time to the American Cancer Society constituted the measure of behavior on which we tested our effect.
Results
We found a significant difference in donation of time to the American Cancer Society research project across the three conditions (χ2, sub d.f. = 2 = 6.27; p = .04). The proportion of participants who donated time (by clicking through to the survey) was higher in the self-prophecy condition (52%) than in the control condition (30.6%) (see Figure 3). Consistent with our hypothesis, participants in the self-affirmation condition were least likely to donate their time (18.6%). The critical contrast for this study was between the self-prophecy and the self-affirmation conditions; as we expected, the 33.4% difference was significant (one-tail) at z = 2.41 (p = .001; r = .35); a lower percentage of participants clicked through in the self-affirmation condition. The 19.4% difference between control and self-prophecy was significant (one-tail) at z = 1.69 (p = .05; r = .22); a higher percentage of participants clicked through in the self-prophecy condition. Control and sell-prophecy with self-affirmation did not differ significantly (p = .15).
Discussion
Study 5 further supports our proposition that people presented with an advertisement-based prediction about a socially normative behavior experience cognitive dissonance. By showing that we can "turn off" the predicted (and observed) behavioral effects of self-prophecy (in this case, donating time to the American Cancer Society) using a self-affirmation manipulation, we provide important evidence of our process explanation. This is especially true given self-affirmation's position within the self-concept paradigm of dissonance.
The current findings combined with prior published research suggest that including a specific behavioral self-prediction in a promotional campaign for social change likely leads to important behavioral effects among targeted consumer segments. The normative value of implementing the self-prophecy technique on a wide scale has heretofore remained a challenge to marketers interested in influencing socially normative behaviors, primarily because of difficulties associated with obtaining behavioral self-predictions from large target segments. Our research addresses this challenge; Studies 1 and 2 show that a mass-communicated prediction request can change behavior in a manner consistent with laboratory-based self-prophecy effects. This article is also the first to provide direct empirical evidence for a theoretical explanation of self-prophecy. Studies 3, 4, and 5 support the proposition that cognitive dissonance is the theoretical process underlying the effect. Taken together, the series of studies reported herein provides compelling theoretical explication for self-prophecy while concomitantly moving marketers closer to wide-scale, real-world applications of the effect.
Managerial Implications
Self-prophecy effects likely have significant effects for marketing organizations that employ the technique, targeted consumers, and society as a whole. Traditional marketing approaches to altering behavior often require marketers to identify most, if not all, salient aspects to a decision context before developing strategies directed toward altering consumer behavior. In contrast, self-prophecy merely requires identification of a socially normative behavior and asks consumers to make a prediction about the target behavior to effect change. The dissonance-based conceptualization of self-prophecy supported by our research suggests that the technique is effective in situations in which clear normative prescription about performance of behaviors exists, yet people do not live up to those standards on a day-to-day basis. Furthermore, we show that efficiencies can be gained by implementing self-prophecy through mass-marketing communications with which target groups are prompted to make predictions to themselves. Thus, marketers of social causes or products and services with socially normative aspects or applications should have keen interest in self-prophecy.
Although it is necessary to be careful when generalizing beyond specific empirical efforts, results of the studies reported herein have clear implications for marketers. For-profit marketers interested in eliciting normative behaviors in societal marketing campaigns could use advertised prediction requests to the benefit of the firm and society. For example, consider Yoplait's recent support of breast cancer research; an advertising campaign prompting consumers to make a self-prediction (e.g., "Will you support breast cancer research with your next purchase?") might increase purchase rates of the product (with concomitant support of breast cancer research). Social marketers employing short, imperative phrases (e.g., "Don't drink and drive," "Buckle up," "Just say no to drugs") might be well advised to adopt a self-prophecy approach (e.g., "Will you buckle up?"). In particular, Obermiller, Spangenberg, and Atwood (1992) suggest that a command is likely to be less effective than a self-prediction as a result of reactance (Brehm 1966). This expectation is borne out by Study 2 results, which show that a command (i.e., "Work out at [fitness facility name]"), compared with a prediction request (i.e., "Will you work out at [fitness facility name]?"), does not alter behavior. Thus, as opposed to an external authority attempting to command people to behave in a normative manner, self-prophecy provides an internal focus that motivates people to do what they know they should do.
More generally, a variety of organizations should find self-prophecy useful for motivating behaviors not yet addressed by the current or prior published research. For example, health care marketers interested in increasing childhood immunization could use prediction requests to improve immunization rates by having expectant mothers complete a prediction about the behavior on standard insurance or hospital admission forms. Brand managers might find self-prophecy potentially useful when faced with the challenge of gathering information from target populations. Indeed, a short questionnaire recently received by one of the current authors included a question to the effect of: "You will soon receive a longer questionnaire from the manufacturer of the new Toyota you just bought. Do you predict that you will fill out this questionnaire? Yes or No." Similarly, a current author received a mass-distributed direct-mail piece from Scientific American that asked, "Will you accept a sample issue?" Recipients of the direct-mail piece in this case were asked to make a prediction to themselves, as were participants in the studies we report herein. It remains to be seen if any of these approaches can alter targeted behaviors (particularly, if cognitive dissonance is not evoked when consumers consider the prediction requests).
As with any decision about promotional strategy, determining how to communicate prediction requests to target populations deserves careful attention to target market characteristics and resource constraints of the organization wanting to use the technique. For some organizations with large target markets and sufficient resources, a mass-communicated advertising campaign (using radio, television, outdoor, or print advertising) may be appropriate. Organizations with smaller target markets and fewer resources should explore other approaches for providing prediction requests to groups of people (e.g., public service announcements, standardized forms completed by the target market). Furthermore, marketers would do well to consider factors that might moderate the effect of mass-marketed self-predictions, such as opportunity costs associated with performing (or avoiding) a predicted behavior. For example, consumers in the studies reported herein experienced opportunity costs to following their predictions (e.g., recycling is inconvenient, going to a health club requires a financial commitment over time).
Theoretical Implications
In addition to the direct managerial implications of this research, marketers interested in implementing self-prophecy on a wide scale would be well advised to consider the underlying theoretical mechanism driving the effect. Such an understanding would enable development of theory-relevant adjustments, which thereby make the technique more effective. A dissonance conceptualization of self-prophecy posits that making a self-prediction reminds people of something to the effect of: "I know what a good person should do and I have largely (or completely) failed to do so in the past. Now that I have an opportunity, I will do what I should do." The associated psychological discomfort with such cognitions is a motivation for the behavior change associated with self-prophecy.( n3)
Tests of our theory (Studies 3 through 5) provide evidence consistent with a cognitive dissonance explanation for the effect. Study 3 parallels previous research on social comparison theory that shows that people's comparisons with others are influenced by their needs for self-enhancement and self-esteem (Festinger 1954). When people's self-esteem is threatened (e.g., when they make a self-prediction), they are expected to bolster their self-evaluation through downward comparison with others (Hakmiller (966). If they are dissonant about having made a prediction, they should feel better if they perceive themselves as not having performed any worse than others with regard to a normatively directed but not always adhered to behavior (e.g., "Other people don't always recycle, so I'm not that bad if I haven't either"). Thus, the simple act of downward comparison can eliminate, or at least ameliorate, the discomfort associated with self-prophecy. Consistent with this reasoning, Study 3 shows that people given the opportunity to denigrate others' performance of a normatively prescribed behavior arc less likely to show evidence of measurable psychological discomfort.
Of theoretical import as well, Studies 4 and 5 coincide with Aronson's self-concept notion of dissonance and self-affirmation theory by indicating that outcomes of dissonance (measured discomfort and behavior, respectively) do not emerge when a prediction request is followed by the opportunity for people to affirm their self-image. As such, these findings are consistent with research in social psychology that demonstrates the "turning off" of the effects of dissonance (e.g., Steele 1988). In addition, Study 2 helps eliminate the alternative explanation of attitude accessibility (or priming) for self-prophecy: A statement with identical information but without prediction did not elicit the change in behavior brought about by the prediction request. Taken together, Studies 2, 3, 4, and 5 provide strong support for dissonance as a process underlying the effects of mass-communicated prediction requests.
Marketers who want to apply self-prophecy in the marketplace should be critically aware that dissonance elicited by self-prediction could be reduced or ignored through several different strategies, including self-affirmation (e.g., Steele 1988), misattribution of arousal to another source (Fried and Aronson 1995), trivialization of the dissonance (Simon, Greenberg, and Brehm 1995), and actual change in behavior (i.e., manifestation of self-prophecy). More important, if alternative methods of alleviating dissonance are available to those who make a prediction, the desired behavior may not be elicited because people feel better about themselves (i.e., cognitively consonant) before having had a chance to perform the target behavior. One way to avoid this situation is by including other elements with prediction requests in advertisements (e.g., reminders about people's past behavior) that increase the likelihood that dissonance is evoked and subsequently eliminated only through specific action.
Future Directions and Conclusion
There are several operational concerns about self-prophecy that further research could address. One issue is related to the nature of self-prophecy over time. Several studies have demonstrated immediate (e.g., Spangenberg and Greenwald 1999), overnight (e.g., Greenwald et al. 1987), or short-term (e.g., Sherman 1980) effects of prediction. Although the current field studies demonstrate effects that last up to four weeks after the initial advertising campaigns, other research indicates that self-prophecy effects can last for much longer periods. For example, recycling effects occurred consistently in each of 11 weeks after the prediction request in Sprott, Spangenberg, and Perkins's (1999) study, and changes in health club patronage emerged over a six-month period in the Spangenberg (1997) study. Because no research to date has systematically explored the temporal limits of self-prophecy, opportunities still exist for contributing to our understanding in this domain.
Another issue requiring further investigation is the use of multiple prediction requests over time. Although most studies have focused on the effect of a single prediction, marketers who want to apply self-prophecy undoubtedly will consider asking customers to make multiple predictions. It is unclear, however, what might happen to behavior over time if a firm such as the American Red Cross were to administer multiple but related prediction requests to the same target population. In any event, there are certainly important unanswered operational questions regarding self-prophecy.
In summary, self-prophecy is an appealing, noncoercive technique that can alter human behavior in a socially normative direction that warrants consideration as a socially relevant marketing strategy. Existing research has shown the efficacy of individually administered prediction requests and has eliminated some explanations as to why the effect occurs. This article contributes to the literature by demonstrating that the self-prophecy effect can be elicited through mass-marketing communications and that cognitive dissonance (and the drive to alleviate cognitive dissonance) likely underlies the effect. Indeed, this research suggests that the wide-scale implementation of self-prophecy has the potential to benefit society as well as those who want to affect society through marketing socially relevant products, services, and behaviors.
(n1) In a tangential stream of research, Morwitz, Johnson, and Schmittlein (1993) propose that attitude accessibility underlies their demonstration that the mere measurement of purchase intentions for consumer durables increases purchase rates.
(n2) To validate the psychometric properties of Elliot and Devine's (1994) psychological discomfort scale, we collected data from a separate sample (n = 84) that used stimuli from Study 3A. Participants completed the measure of psychological discomfort (α = .85), which was followed by a four-item measure of positive affect ("good," "happy," "optimistic," and "friendly"; α = .94) and a 4-item measure of negative affect ("disappointed," "annoyed," "guilty," and "self-critical"; a = .88), anchored by "does not apply at all" (1) and "applies very much" (7). For the measurement model including the three constructs, χ2, sub d.f. = 41 = 76.95; goodness-of-fit index = .86; normed fit index = .89: and comparative fit index = .94. For the Elliot and Devine scale, completely standardized factor loadings ranged from .63 to .91, and individual item reliabilities ranged from .39 to .71. Composite reliability (Bagozzi and Yi 1988) was .88. and average variance extracted was .72 (Fornell and Larcker 1981). Discriminant validity was established: All φs were significantly different from |±1| (Anderson and Gerbing 1988), and average variance extracted exceeded .5 for all constructs (Fornell and Larcker 1981).
(n3) A potentially related stream of research pursued by Higgins and colleagues (e.g., Higgins 1987; Higgins et al. 1994) explains "ideal versus ought predilections." According to this self-regulatory perspective. people pursue ideal psychological states by moving away from things they want to avoid or by approaching things they consider positive with the driving motivation of eliminating "ideal-self" versus "ought-self" mismatches. This research could help explain self-prophecy with regard to the nature of the prediction request; self-prophecy research to date has used prediction requests that focus on the avoidance of inconsistencies (e.g., people know they should recycle, but they have not done so in the past and make changes to avoid the mismatch). Thus, using the wording of the prediction request, researchers could explore whether people approach consistencies or avoid inconsistencies to greater or lesser extents. Higgins's work adds another layer of complexity to the nature of the prediction request in self-prophecy research and bears further investigation.
Legend for Chart:
B - Pretreatment
C - Post-treatment
A B C
Control "fitness guilt" 20.9 18.9
Control "workout" 21.8 19.1
Self-prophecy[a] 21.5 20.6
Experimental Treatment
[a] Sample included 20 weekdays for the pretreatment and
20 week-days for the post-treatment.
Notes: Values represent the average percentage of daily visits
calculated by dividing the average number of members making
a daily visit per experimental condition by the number of
members in that condition and then multiplying by 100 to
express the value as a percentage.
GRAPH: FIGURE 2 Results of Study 4: Self-Affirmation and Measured Discomfort
Experimental Treatment
Control "support" (n = 36)[b] 30.6
Self-prophecy (n = 25)[b] 52.0
Self-prophecy and self-affirmation (n = 22)[b] 18.2
[a] Sample sizes are associated with the number of
research participants per treatment in the experiment.
Notes: Values represent the proportion of research participants
"clicking through" to a Web site containing the American Cancer
Society survey.
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~~~~~~~~
By Eric R. Spangenberg; David E. Sprott; Bianca Grohmann and Ronn J. Smith
Eric A. Spangenberg is Professor of Marketing, David E. Sprott is Associate Professor of Marketing, and Ronn J. Smith is a doctoral candidate in marketing, Washington State University. Bianca Grohmann is Assistant Professor of Marketing, Concordia University. The authors are grateful to Tony Greenwald for his impetus and continuing intellectual contributions to this stream of research. The authors are also indebted to Andy Perkins and a host of undergraduate research assistants for data collection assistance, and they thank three anonymous JM reviewers.
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Record: 108- Maximizing Profitability and Return on Investment: A Short Clarification on Reinartz, Thomas, and Kumar. By: Ambler, Tim. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p153-154. 2p. 1 Chart. DOI: 10.1509/jmkg.2005.69.4.153.
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Maximizing Profitability and Return on Investment: A
Short Clarification on Reinartz, Thomas, and Kumar
Reinartz, Thomas, and Kumar (2005) provide a valuable methodology for balancing resources between customer retention and acquisition. They also conclude that optimal profitability (net return) coincides with optimal return on investment. This article clarifies this perhaps surprising conclusion and shows that it arises from the authors' methodology rather than from the data. More conventional analysis of the same data indicates that maximum return on investment is reached with lower expenditure than maximum profitability.
Reinartz, Thomas, and Kumar (2005) provide a valuable methodology for balancing resources between customer retention and acquisition. A small part of that article concludes that the level and balance of retention and acquisition expenditures that maximize long-term customer profitability also maximize return on investment (ROI). Because ROI (net return divided by expenditure) is usually maximized at lower levels of marketing expenditure, this conclusion is surprising and prompts this note of clarification.
The law of diminishing returns posits that beyond a certain point, the profit return for incremental expenditure begins to decline. As long as the incremental profit exceeds the cost, total profit continues to increase, but ROI progressively decreases. In other words, ROI is usually maximized at a lower expenditure than total profit (or cash flow). There are exceptions. For example, a seller of ice creams on a beach may find 30% of the people present to be on no-ice-cream diets and the remaining 70% happy to buy one item each, but no more. If marketing costs are low, both the seller's profit and ROI are maximized at 70% penetration because a ceiling has been reached.
Reinartz, Thomas, and Kumar's (Table 5, p. 74) data show no ceiling and a flattish response curve around the optimum: 10% more or less expenditure only reduces profit by .08%. The return from the last 25% of expenditure before optimal (7.35%) is greater than the decrease from the 25% above optimal (.49%).
All figures are given per customer, and the profit figures are after marketing costs are deducted. The optimal levels are $754,088, $134.55, and $503.77 for long-term profit, acquisition, and retention spending, respectively (Reinartz, Thomas, and Kumar 2005, Scenario 1, Table 4, p. 72). The remarkably high return is not relevant to this discussion. The ROI at optimal profitability is therefore (754,088/ [134.55 + 503.77]), or 1181, because the profits were net of expenditures. Typically, ROI is expressed as a percentage, but in this case 118,100% is too large a number to make sense; therefore, we can dispense with percentages.
With respect to Tables 5 and 6 (Reinartz, Thomas, and Kumar 2005, p. 74), reducing both acquisition and retention expenditure by 25% gives $.75(134.55 + 503.77), or $478.74. Thus, the ROI at the 75% of expenditure is (698,676/478.74), or 1459 (i.e. 23.5% greater than the ROI in the article at optimal profit level). However, Table 7 (p. 76) shows that when Reinartz, Thomas, and Kumar's methodology is used, it is 116.74 lower. This must be considered more closely.
Reinartz, Thomas, and Kumar's methodology claims to compute ROI in line with Rust, Lemon, and Zeithaml's (2004) approach, which defines ROI consistently with this article, namely, the change in incremental customer equity (long-term discounted profit net of expenditure) divided by the discounted expenditure. Reinartz, Thomas, and Kumar seem to be using that to calculate changes in ROI from the optimal profitability level , which is unusual but defensible. The reduction in profitability at 75% of the spend level is therefore $754,088 - $698,676, or $55,412. Note that Reinartz, Thomas, and Kumar's profitability figures are net of expenditure, whereas customer equity in Rust, Lemon, and Zeithaml's article is before marketing expenditure. The reduced expenditure is $.25(134.55 + 503.77), or $159.58. Therefore, the ROI on the difference between spending at the two levels is 55,412/159.58, or 347. In other words, ROI has increased by this amount because the numerator and the denominator of the ratio have the same sign. This does not equate with Reinartz, Thomas, and Kumar's reduction of 116.74 either, but the gap has been closed.
Reverse engineering provides the answer. For each of the 25 (5 x 5) cells in their Table 7, the methodology used to calculate the change in ROI seems to be as follows:
(Profitability -- Optimal profitability -- Total expenditure for that cell)/Total expenditure for that cell.
This formula is taken verbatim from Rust, Lemon, and Zeithaml's (2004, p. 115) expression, which shows the change in customer equity, but expenditure is not shown as the change in spending. This may be the cause of the misunderstanding because Rust, Lemon, and Zeithaml intended the usual formulation (i.e., either change in profits divided by the change in expenditure or total profits divided by the total expenditure). As I previously noted, Rust, Lemon, and Zeithaml's formula is conventional and does not imply that the incremental net profit should be divide by the total cost. Table 1 shows the calculations using the preceding formula. Apart from one (the third to last) cell, most likely due to a typographical error, all the results match.
This note attempts to clarify Reinartz, Thomas, and Kumar's unusual finding of the optimal profit and ROI points coinciding. This arises from a rather individual, and some might think questionable, formulation of ROI that divides incremental profit by total expenditure, working away from the optimal profit position. Using more conventional means of calculating ROI (e.g., Rust, Lemon, and Zeithaml's [2004] formula), ROI is maximized well before profitability (i.e., at a lower level of marketing expenditure).
Legend for Chart:
A - Level of Acquisition Spend
B - Level of Retention Spend
C - Total Spend
D - Profit (Table 5)
E - Reverse Engineered Change in ROI
F - Reinartz, Thomas, and Kumar ROI Change (Table 7)
A B C D E F
.75 .75 478.7 698,676 -116.75 -116.74
.9 .75 498.9 727,163 -54.97 -54.97
1.0 .75 512.4 726,273 -55.29 -55.29
1.1 .75 525.8 736,623 -34.21 -34.21
1.25 .75 546.0 750,358 -7.83 -7.83
.75 .9 554.3 752,459 -3.94 -3.94
.9 .9 574.5 753,491 -2.04 -2.04
1.0 .9 587.9 753,688 -1.68 -1.68
1.1 .9 601.4 753,491 -1.99 -1.99
1.25 .9 621.6 752,458 -3.62 -3.62
.75 1.0 604.7 752,859 -3.03 -3.03
.9 1.0 624.9 753,891 -1.32 -1.31
1.1 1.0 651.8 753,891 -1.30 -1.3
1.25 1.0 672.0 752,858 -2.83 -2.83
.75 1.1 655.1 752,459 -3.49 -3.49
.9 1.1 675.2 753,491 -1.88 -1.88
1.0 1.1 688.7 753,688 -1.58 -1.58
1.1 1.1 702.2 753,491 -1.85 -1.85
1.25 1.1 722.3 752,458 -3.26 -3.26
.75 1.25 730.6 750,359 -6.10 -6.10
.9 1.25 750.8 751,392 -4.59 -4.59
1.0 1.25 764.3 752,588 -2.96 -4.27
1.1 1.25 777.7 751,391 -4.47 -4.47
1.25 1.25 797.9 750,358 -5.67 -5.67
REFERENCES Reinartz, Werner, Jacquelyn S. Thomas, and V. Kumar (2005), "Balancing Acquisition and Retention Resources to Maximize Customer Profitability," Journal of Marketing, 69 (January), 63-79.
Rust, Roland, Katherine N. Lemon, and Valarie Zeithaml (2004), "Return on Marketing," Journal of Marketing, 68 (January), 109-127.
~~~~~~~~
By Tim Ambler
Tim Ambler is Senior Fellow, London Business School
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Record: 109- Measuring Marketing Productivity: Current Knowledge and Future Directions. By: Rust, Roland T.; Ambler, Tim; Carpenter, Gregory S.; Kumar, V.; Srivastava, Rajendra K. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p76-89. 14p. 1 Diagram. DOI: 10.1509/jmkg.68.4.76.42721.
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- Business Source Complete
Measuring Marketing Productivity: Current Knowledge and
Future Directions
For too long, marketers have not been held accountable for showing how marketing expenditures add to shareholder value. As time has gone by, this lack of accountability has undermined marketers' credibility, threatened the standing of the marketing function within the firm, and even threatened marketing's existence as a distinct capability within the firm. This article proposes a broad framework for assessing marketing productivity, cataloging what is already known, and suggesting areas for further research. The authors conclude that it is possible to show how marketing expenditures add to shareholder value. The effective dissemination of new methods of assessing marketing productivity to the business community will be a major step toward raising marketing's vitality in the firm and, more important, toward raising the performance of the firm itself. The authors also suggest many areas in which further research is essential to making methods of evaluating marketing productivity increasingly valid, reliable, and practical.
Marketing practitioners and scholars are under increased pressure to be more accountable for and to show how marketing expenditure adds to shareholder value (Doyle 2000). The perceived lack of accountability has undermined marketing's credibility, threatened marketing's standing in the firm, and even threatened marketing's existence as a distinct capability within the firm. The Marketing Leadership Council (2001, p. 27) reports that 70% of advertising budgets are in decline, compared with 51%, 47%, and 44% for human resources, information technology, and general counsel functions: "Having exhausted cost-saving opportunities in virtually every other function," marketing is "next in the line of fire."
There are three challenges to the measurement of marketing productivity. The first challenge is relating marketing activities to long-term effects (Dekimpe and Hanssens 1995). The second challenge is the separation of individual marketing activities from other actions (Bonoma and Clark 1988). Third, the use of purely financial methods has proved inadequate for justifying marketing investments: Nonfinancial metrics are also needed (Clark 1999; Marketing Science Institute 2000). Indeed, the Institute of Management Accountants (1996) reports the increasing use of nonfinancial measures.
This article proposes a broad framework for assessing marketing productivity, describes what is already known about marketing productivity, and suggests areas for further research. We conclude that it is possible to show how marketing expenditures are linked to shareholder value. Dissemination of the methods proposed in the past ten years to the business community will be a major step toward maintaining marketing's vitality in the firm and, more important, toward raising the performance of the firm itself.
What We Mean by "Marketing Productivity"
We first need to clarify the ways marketing activities build shareholder value. For example, when we talk of marketing "investment," we must identify the marketing assets in which we invest and understand how the assets contribute to profits in the short run and provide potential for growth and sustained profits in the long run. In this context, the spotlight is not on underlying products, pricing, or customer relationships (see Webster 1992) but on marketing expenditures (e.g., marketing communications, promotions, other activities) and how these expenditures influence marketplace performance. The firm should have a business model that tracks how marketing expenditures influence what customers know, believe, and feel, and ultimately how they behave. These intermediate outcomes are usually measured by nonfinancial measures such as attitudes and behavioral intentions. The central problem we address in this article is how nonfinancial measures of marketing effectiveness drive the financial performance measures such as sales, profits, and shareholder value in both the short and the long run.
It is important to understand that marketing actions, such as advertising, service improvements, or new product launches, can help build long-term assets (e.g., brand equity, customer equity). These assets can be leveraged to deliver short-term profitability (e.g., the advertising and promotional expenditures related to stronger brands are more productive). Thus, marketing actions both create and leverage market-based assets. It is also important to distinguish between the "effectiveness" and the "efficiency" of marketing actions. For example, price promotions can be efficient in that they deliver short-term revenues and cash flows. However, to the extent that they invite competitive actions and destroy long-term profitability and brand equity, they may not be effective. Consequently, we examine both tactical and strategic marketing actions and their implications.
The article is organized around the chain of marketing productivity illustrated in Figure 1. We first discuss the elements of this framework: marketing strategies and tactics, their impact on customers, subsequent marketplace consequences and their financial implications, and their impact on the value of the firm. We next discuss what we already know about elements of this "marketing productivity chain" as the base for establishing what we need to know before summarizing and drawing conclusions.
The Chain of Marketing Productivity
Figure 1 illustrates a broad, conceptual framework that can be used to evaluate marketing productivity. It is a chain-of-effects model that relates the specific actions taken by the firm (marketing actions) to the overall condition and standing of the firm. We begin at the upper right-hand side of Figure 1, with the firm's strategies, which might include promotion strategy, product strategy, or any other marketing or firm strategy.
These strategies lead to tactical marketing actions taken by the firm, such as advertising campaigns, service improvement efforts, branding initiatives, loyalty programs, or other specific initiatives designed to have a marketing impact. Because we are concerned with productivity, this article reduces the full range of marketing actions to tactical actions that require marketing expenditure. The tactical actions then influence customer satisfaction, attitude toward the brand, loyalty, and other customer-centered elements. At the firm level, these customer measures can be aggregated into what we call "marketing assets," which can be measured by such indicators as brand quality, customer satisfaction, and customer equity.
Customer behavior influences the market, changing market share and sales. However, it may also be useful to consider the firm's market position as driven by the firm's marketing assets. At any point in time, tactical actions will have made changes in customers' mental states, but they may not yet have influenced the firm's profit and loss account. Thus, marketing assets represent a reservoir of cash flow that has accumulated from marketing activities but has not yet translated into revenue. They enable the firm to assess the financial impact of marketing (using measures that we describe subsequently). The next section describes the elements of the chain of marketing productivity in more detail.
Elements of the Chain
Strategies and tactics. Marketing strategy plays a central role in winning and retaining customers, ensuring business growth and renewal, developing sustainable competitive advantages, and driving financial performance through business processes (Srivastava, Shervani, and Fahey 1999). A significant proportion of the market value of firms today lies in intangible off-balance-sheet assets, such as brands, market networks, and intellectual property, rather than in tangible book assets (Lusch and Harvey 1994). The leveraging of intangible assets to enhance corporate performance requires managers to move beyond the traditional inputs and outputs of marketing analysis and to incorporate an understanding of the financial consequences of marketing decisions, which include their impact on cash flows.
On a more tactical level, managers implement marketing initiatives to increase short-term profitability. In most settings, this effort requires management of margins and turnover. Because better value to customers (or superior brands) can be tapped in terms of either price or volume, managers need to trade off prices (and therefore margins) against market share. Various programs can be developed to enhance and sustain profitability (e.g., loyalty programs, cross-selling, up-selling); how managers proceed is a matter of strategy. The question is, What type of expenditure has a greater influence on the value of a firm's customer base: a new campaign for advertisements or improvements in the quality of service? How do elements of a coordinated marketing strategy influence the purchase behavior of different marketing segments over time, and how does this affect the firm's revenue streams? What are the disproportionate effects of changes in the structure of pricing on customer acquisition, retention, and cross-buying? How do marketing and operations elements interact to grow or to diminish customer value?
Customer impact. To assess the impact of marketing expenditures on customers, it is important to understand the following five key dimensions (adopted from Ambler et al. 2002), which can be considered particularly important measures of the customer mind-set:
- Customer awareness: the extent to and ease with which customers recall and recognize the firm, and the extent to which they can identify the products and services associated with the firm;
- Customer associations: the strength, favorability, and uniqueness of perceived attributes and benefits for the firm and the brand;
- Customer attitudes: the customer's overall evaluations of the firm and the brand in terms of its quality and the satisfaction it generates;
- Customer attachment: how loyal the customer is toward the firm and the brand; and
- Customer experience: the extent to which customers use the brand, talk to others about the brand, and seek out brand information, promotions, events, and so on.
Because the strength and length of the customer or brand relationship matters (Reinartz and Kumar 2002), the firm must consider multiple aspects of each customer's purchase behavior, not just retention probabilities. Consequently, researchers have begun to model other purchase behaviors, such as cross-selling (e.g., Kamakura, Ramaswami, and Srivastava 1991), word-of-mouth behavior (e.g., Anderson 1998), and profitable lifetime duration of customers (Reinartz and Kumar 2003). These behaviors, at the individual customer level, influence the aggregate level of the marketing assets of the firm.
Marketing assets. Marketing assets are customer-focused measures of the value of the firm (and its offerings) that may enhance the firm's long-term value. We focus on two approaches to assessing marketing assets that have received considerable attention in the marketing literature: brand equity and customer equity.
The concept of brand equity has emerged in the past 20 years as a core concept of marketing. A view of brand equity suggest that its value arises from the incremental discounted cash flow from the sale of a set of products or services, as a result of the brand being associated with those products or services (e.g., Keller 1998). For example, Interbrand estimated the value of the Home Depot brand at $84 billion in 1999 (Tybout and Carpenter 2000). Research on brand equity has sought to understand the conceptual basis for this remarkable value and its implications. The fruits of this research are changing how people think about brands and manage them. Managers have a deeper understanding of the elements of brand equity, of how brand equity affects buyer behavior, of how to measure brand equity, and of the influence of brand equity on corporate value (e.g., Aaker 1991; Keller 1998, 2002). It is also important to note that brand equity leads to strength in the distribution channel. Thus, we assume that brand equity includes channel effects.
Although brand equity takes the brand perspective, customer equity (Blattberg and Deighton 1996; Rust, Zeithaml, and Lemon 2000) takes the firm's customers' perspective. Building on previous definitions, we define customer equity as the sum of the lifetime values of all the firm's current and future customers, where the lifetime value is the discounted profit stream obtained from the customer.( n1) The expansion of the service sector over time, combined with the resultant shift from transaction-to relationship-oriented marketing, has made the consideration of customer lifetime value increasingly important (Hogan, Lemon, and Rust 2002). These events legitimate customer equity (i.e., the aggregation of customer lifetime value across customers) as a key metric of the firm. Customer lifetime value and customer equity are already in widespread use as marketing asset metrics in some industries, most notably in direct marketing and financial services. Customer equity measurement and monitoring is rapidly expanding in other industries as well.
Market impact. Customer impact and the resultant improvements in marketing assets, such as brand equity, influence the firm's market share and sales, thereby influencing its competitive market position. These benefits may be viewed as arising from improvements in the intermediate measures (i.e., the marketing assets of the firm; Ambler 2000). Superior brands (or superior values provided to customers) lead to higher levels of customer satisfaction and perceived value of the firm's offering. The consequences of a superior offering are reflected in many aspects of market performance (Srivastava, Shervani, and Fahey 1998). For example, brands that are better differentiated are characterized by lower price elasticity (Boulding, Lee, and Staelin 1994), have more loyal customers, are less susceptible to competitive actions (Srivastava and Shocker 1991), can command price premiums (Farquhar 1989), can attain greater market shares (Boulding, Lee, and Staelin 1994), can develop more efficient marketing programs because they are more responsive to advertising and promotions (Smith and Park 1992), and can more quickly adopt brand extensions (Dacin and Smith 1994; Keller 1998). The consequences of customer satisfaction also include increased buyer willingness to pay a price premium, to provide referrals, and to use more of the product; lower sales and service costs; greater customer retention, loyalty, and longevity (Hogan, Lemon, and Rust 2002; Reichheld 1996; Reinartz and Kumar 2000); greater market share (Taylor 2002); and greater profitability (Venkatesan and Kumar 2004).
Financial impact. Financial benefits from a specific marketing action can be evaluated in several ways. Return on investment (ROI) is a traditional approach to evaluating return relative to the expenditure required to obtain the return. It is calculated as the discounted return (net of the discounted expenditure), expressed as a percentage of the discounted expenditure. Commonly used retrospectively to measure short-term return, ROI is controversial in the context of marketing effectiveness. Because many marketing expenditures play out over the long run, short-term ROI is often prejudicial against marketing expenditures. The correct usage of ROI measures in marketing requires an analysis of future cash flows (e.g., Larréché and Srinivasan 1981; Rust, Zahorik, and Keiningham 1995). It is also worth noting that the maximization of ROI as a management principle is not recommended (unless management's goal is efficiency rather than effectiveness), because it is inconsistent with profit maximization-a point that has long been noted in the marketing literature (e.g., Kaplan and Shocker 1971).
Other financial impact measures include the internal rate of return, which is the discount rate that would make the discounted return exactly equal to the discounted expenditure (Keynes 1936); the net present value, which is the discounted return minus the net present value of the expenditure; and the economic value-added (EVA), which is the net operating profit minus the cost of capital (Ehrbar 1998). In each case, the measures of financial impact weigh the return generated by the marketing action against the expenditure required to produce that return. The financial impact affects the financial position of the firm, as measured by profits, cash flow, and other measures of financial health.
Impact on the value of the firm. Managers of publicly traded firms aim to explain and enhance market value/ capitalization or shareholder value (Srivastava, Shervani, and Fahey 1998). Linking of marketing actions through customer value to changes in market value (i.e., market value-added [MVA]) is essential to this task, but there are differences between change/flow measures and state measures. Although measures such as EVA and MVA focus on changes in financial performance, others, such as market capitalization, measure the level of performance.
In addition, we can distinguish between forward-looking and retrospective measures. Most accounting measures are retrospective in that they examine historical performance. In contrast, the market value of firms hinges largely on growth prospects and sustainability of profits (i.e., how the firm might be expected to perform in the future). This requires tracking off-balance-sheet metrics (e.g., brand or customer equity) and focusing on both current (e.g., EVA, cash flow) and expected (e.g., MVA, shareholder value) performance.
Several measures of the value of the firm rely on measures of stock market performance. For example, market capitalization is the market value of all outstanding shares of a firm, and book value is the difference between a firm's assets and liabilities, according to its balance sheet. The difference between market value and book value is explained partly by off-balance-sheet assets, such as market-based and intellectual property, and partly by an excess or lack of investor enthusiasm. The ratio of market capitalization to the book value (the market-to-book ratio) is sometimes a useful indicator of the strength of marketing assets.
Similarly, Tobin's q is the ratio of the market value of the firm to the replacement cost of its tangible assets, which include property, equipment, inventory, cash, and investments in stock and bonds (Tobin 1969). A q-value greater than 1 indicates that the firm has intangible assets. Shareholder value is another measure related to economic profit (see Rappaport 1986). Total shareholder return is the cash flow to shareholders through dividends plus the increase in the share price. A large proportion of the value of firms is based on perceived growth potential and associated risks (i.e., the value is based on expectations of future performance). This value may be locked up in marketing assets that can be leveraged to enhance and accelerate current cash flows, and it may enhance the sustainability (reduce the risk) of future cash flows (Srivastava, Shervani, and Fahey 1997, 1998, 1999).
Other Factors
In addition to the previously discussed factors, elements of environment and competition have frequently been shown to be important factors in marketing productivity. The firm's skill in adapting to the environment and competition can do no more than improve performance relative to what would otherwise be the case. Therefore, it is necessary to view Figure 1 within an envelope of context effects.
Environment. No firm is an island: Performance in general and marketing productivity in particular depend on the environmental and competitive context. This is especially true when economic and geopolitical turbulence create unusual amounts of uncertainty. The market orientation literature addresses the firm's willingness to pay attention to such market characteristics (Day 1994; Jaworski and Kohli 1993; Narver and Slater 1990). The firm can choose to be proactive (market driving) or reactive (market driven) (Jaworski, Kohli, and Sahay 2000).
Competition. The competitive environment has a profound influence on the nature of marketing productivity. Marketing expenditure decisions, such as those about advertising, are often made with competitors in mind. Studies on advertising spending have identified two separate effects. On the one hand, competition can drive marketing spending higher, thus producing an escalation effect (e.g., Metwally 1978). Driven by a belief that gaining market share increases profit and enhances firm value (e.g., Buzzell and Gale 1987), firms increase marketing expenditures to gain market share, even as rivals do the same. Little evidence suggests that the expenditures have the anticipated results. For example, examining the brewing industry, Montgomery and Wernerfelt (1991) show that escalating advertising spending destroys value rather than creates it. On the other hand, research has demonstrated that (even taking competitors reactions into account) high-market share brands indeed have an incentive to outspend rivals (e.g., Carpenter et al. 1988). These findings have fueled the escalation in advertising spending. However, the greater wealth associated with the larger share has proved quite elusive.
Chains of Marketing Impact
There already exist several chains of marketing impact. Many of them are practical decision models that have been built for actual implementation, typically for specific marketing decision scenarios. For example, PERCEPTOR (Urban 1975) tracks product design decisions all the way to market share. In the advertising media context, there is a history of models designed to maximize sales or profits, and they usually assume a budget constraint (e.g., Gensch 1973; Little and Lodish 1969; Rust 1986). Similarly, there are several models of the influence of sales promotion on business results (e.g., Little 1975). The business impact of advertising expenditure decisions historically has been addressed through econometric time-series models (e.g., Bass 1969; Eastlack and Rao 1986). The past ten years have witnessed the development of chain-of-effects models of service and customer satisfaction, both across firms (Fornell 1992) and within specific firms (Anderson, Fornell, and Lehmann 1994; Heskett et al. 1994; Kamakura et al. 2002; Rust, Zahorik, and Keiningham 1994, 1995).
More general chain-of-effects models, which are capable of addressing strategic trade-offs across competing marketing expenditures in general, are much rarer. The STRATPORT model (Larréché and Srinivasan 1981, 1982) is an exception: It evaluates the business impact of the allocation of resources across strategic marketing alternatives. More recently, chain-of-effects models that evaluate competing marketing actions on the basis of their influence on customer lifetime value and customer equity have been developed (Rust, Lemon, and Zeithaml 2004; Venkatesan and Kumar 2004).
Strategies and Tactics
The strategic roles of marketing include setting strategic direction for the firm and guiding investments to develop marketing assets that can be leveraged within business processes to provide sustainable competitive advantages. Although marketing investments (e.g., advertising, customer support) and resultant assets are largely intangible, their benefits to the firm are similar to those provided by more tangible resources, such as manufacturing infrastructure. Differentiated brands enable their owners to charge higher prices (Farquhar 1989) and to attain greater market shares (Boulding, Lee, and Staelin 1994). Such brands are more responsive to advertising and promotions and have lower selling costs (Keller 1998). Although the role of marketing actions and assets in influencing sales and market share is well documented and appreciated, it is often forgotten that strategic marketing investments also reduce risk. For example, research shows that advertising can lead to more differentiated, and thus more monopolistic, brands (i.e., brands are less vulnerable to competition). Similarly, investments in brand equity can reduce risks by deflecting competitive initiatives (Srivastava, Shervani, and Fahey 1997; Srivastava and Shocker 1991). Brand equity can also be tapped to reduce marketing expenditures in times of cash flow crunch, thereby reducing risks through "enhanced liquidity" (Srivastava, Shervani, and Fahey 1998).
To deploy suitable strategies and tactics, it is necessary to first try to understand the triggers of customer product purchases. A firm's customer database can be used to develop a purchase sequence model that allows for the identification of which customers will buy which products and when, so that the firm can contact customers at the most appropriate time. A comparison of this type of customer management strategy with the traditional strategy shows that benefits (i.e., profits derived from each individual customer) can be realized by managing on the basis of a 360-degree view of the customer.
The implementation of tactics requires resources. Each year, the firm allocates resources to contact its customers through various channels, including sales personnel, direct mail, telephone sales, and online. However, most of its current contact efforts are ( 1) targeted at the wrong customers, ( 2) targeted at the right customers with the wrong offer, or ( 3) targeted at the right customers with the right offer at the wrong time. The primary challenge is to direct resources toward the right customer, with the right offer, at the right time.
Regarding specific tactics, every firm is eager to understand the effectiveness of various "touch" points. Touch history refers to any contact that the customer has with the firm. With the advent of e-commerce, most firms use various channels. For example, Charles Schwab Corporation has many ways of touching customers. These are activity-based interactions that can be initiated by the customer or the firm (e.g., Bowman and Narayandas 2001). Touches are not normally considered in reach, frequency, and monetary value models that predict whether an individual customer is "due" (i.e., an alive and active customer of the firm) or "dead" (i.e., a customer who has ended his or her relationship with the firm) to purchase. However, contact strategy can take on greater significance in some industry-based contexts, particularly for services that are provided continuously (e.g., finance, telecommunications) and for durables, for which the typical purchase cycle of a business is long. In other words, in addition to the traditionally employed marketing-mix strategies and tactics, customer touch histories are important in the prediction of customer profitability in the future business cycles (Venkatesan and Kumar 2004).
Customer Impact
We consider two major types of customer impact: ( 1) impact on a customer's perceptions and attitudes and ( 2) impact on a customer's summary judgments. The understanding of the psychology of the brand has been deepening over time (e.g., Fournier 1998), and with that comes a clearer understanding of how managerial actions that pertain to the brand affect brand perceptions (e.g., Aaker and Keller 1990; Kamakura and Russell 1991).( n2) Specific marketing actions that have been shown to affect brand perceptions include such wide-ranging corporate activities as advertising (Jedidi, Mela, and Gupta 1999) and corporate ethics (Keller 1993). Customer attitudes toward the brand may be usefully divided (from the standpoint of the marketer) into attitudes and perceptions related to value, brand, and relationship (Rust, Zeithaml, and Lemon 2000). Customer perceptions of value are complex and multifaceted (Holbrook 1994), and many theories and studies explore the mechanisms by which marketing actions affect customers' value perceptions (e.g., Bolton and Drew 1991; Dodds, Monroe, and Grewal 1991; Teas and Agarwal 2000; Zeithaml 1988). In recent years, as the business world has moved toward relationships rather than just transactions, the effect of marketing actions on the perceptions of relationship has been shown to be important. Again, a considerable body of research demonstrates the effect of marketing actions on customer attitudes in relationships (e.g., Anderson and Narus 1990; Gummeson 1999; Häkansson 1982; Kumar 1999; Reinartz and Kumar 2002).
Just as marketing actions can influence customer attitudes and perceptions, ultimately they can also affect the customer's summary appraisals, such as customer satisfaction, loyalty, preference, and purchase intention. The nature of satisfaction and loyalty and their drivers have become much better understood in the past 20 years (for an excellent review, see Oliver 1997), with customer expectations and previous experience assuming a central role. Customer preference (e.g., McAlister and Pessemier 1982) and purchase intention (e.g., Fishbein and Ajzen 1975) also have been heavily explored.
Marketing Assets
In the past 10 to 12 years, marketing scholars have greatly expanded their knowledge of these marketing assets and how they contribute to the economic health of the firm. We focus on two prominent types of marketing asset measures: brand equity and customer equity.
Brand equity. Brands have long been recognized as meaningful, powerful symbols (e.g., Levy 1959), but formal analysis began in earnest with Aaker (1991), who describes brand equity as consisting of four components: brand awareness, perceived quality, brand associations, and brand loyalty. Another widely adopted view offered by Keller (1998) describes brand equity as "the differential effect that brand knowledge has on consumer or customer response to the marketing of that brand" (Keller 2002, p. 7). In both models, a brand can be considered a memory node in a network that links the brand to a set of associations. A more powerful brand is more vivid and has a more favorable and easily recalled set of associations, which increases its overall value.
Various nonfinancial methods have been suggested for the measurement of brand equity. One group of these methods measures buyers' knowledge about brands with free-association tasks, projective techniques (e.g., Levy 1985), techniques designed to elicit the metaphorical meaning of brands (e.g., Zaltman and Higie 1995), and methods to measure the structure of associations more explicitly (e.g., Aaker 1997; Keller 1998). Conjoint analysis also provides insight into brand equity by decomposing overall value into value that arises from product attributes and value that arises from brand names (e.g., Rangaswamy, Burke, and Oliva 1993). In contrast, a second group of holistic methods, called "residual approaches," seeks to estimate the value of brand equity by deduction, that is, by estimating the effect of other factors and then attributing the residual impact to brand equity (e.g., Park and Srinivasan 1994). A third group of methods seeks to measure the value of brands by examining various measures of market performance. Financial World and Interbrand are two of the best-known commercial measures. In calculating brand equity, Interbrand, the first to offer such a measure, includes data on market leadership, stability, internationality, trends of the brand, support, level of protection, and characteristics of the markets in which it operates (Keller 1998).
Research on the influence of brand equity on market value has received less attention, perhaps because of the widely accepted efficient-markets hypothesis that suggests that there is little role, if any, for brand equity. An early effort to measure brand equity used the prevailing finance-based view (Simon and Sullivan 1993). Assuming that a corporation's market value is an unbiased estimate of the future cash flows, Simon and Sullivan (1993) estimate the portion of future cash flow that is attributable to a corporation's brand and derive a financial measure of brand equity. Aaker and Jacobson (1994) examine the influence of brand equity on stock returns more directly by modeling the influence of changes in brand quality perceptions and firm ROI on the market value of 34 corporations. They find that brand equity has a positive impact on stock returns, as does product quality, thus demonstrating the power of brand perceptions. In a subsequent study, Aaker and Jacobson (2001) find that change in brand attitude is positively related to change in stock return in the computer industry. In a related study that explores a wider range of industries, Barth and colleagues (1998) examine the changes in the equity of 1204 brands owned by 183 publicly traded corporations from 1992 to 1997. Their analysis shows that brand equity has a positive statistical association with market value, beyond the effect of two traditional measures: net income and book value of equity. This finding is consistent with other research that suggests that marketing expenditures produce a valuation premium greater than that implied by cash flow (Bowd and Bowd 2002; Kirschenheiter 1997; Srivastava et al. 1997).
Customer equity. Customer equity was first identified as a measure of the marketing asset by Blattberg and Deighton (1996), who define a firm's customer equity as the sum of the lifetime values of the firm's customers. Customer equity models are characterized by models of the lifetime value of individual customers. The early thinking on customer equity arose from the direct marketing paradigm, in which longitudinal data about individual customers and their reactions to marketing efforts (typically promotional mailings) were present (e.g., Blattberg, Getz, and Thomas 2001). Related work on the long-term value of customer relationships arose in the financial services arena (e.g., Storbacka 1994) and in the high-technology industry (Kumar, Venkatesan, and Reinartz 2002). Because customer equity results from customer lifetime value, methods for assessing the lifetime value of a customer became central. Again, such methods typically assumed the existence of longitudinal customer data (e.g., Dwyer 1997; Libai, Narayandas, and Humby 2002; Reinartz and Kumar 2000). This stream of work evolved from measurement of customer lifetime value to evaluation of the influence of marketing effort on customer equity (Berger et al. 2002; Hogan, Lemon, and Rust 2002), thus incorporating an assessment of marketing decisions over time. Because of the data requirements, this approach has largely been restricted to a handful of business scenarios (e.g., direct marketing, subscription sales, financial services, business-to-business) and a handful of marketing variables (e.g., direct mailings, salesperson contacts, telephone sales, price).
More recently, a different approach has emerged that expands the industries and the set of marketing actions to which customer equity may be applied (Rust, Lemon, and Zeithaml 2004; Rust, Zeithaml, and Lemon 2000). This approach combines internal company information, customer survey data, and one-step-ahead purchase information gathered either from panel data (if available) or from a survey. Analogous to "driver analysis" in customer satisfaction measurement (e.g., Johnson and Gustafsson 2000; Rust, Zahorik, and Keiningham 1994), drivers of customer equity are obtained and statistically related to purchase behavior, and inertia from purchase to purchase is incorporated (Guadagni and Little 1983).
Market Impact
Market impact models have mostly arisen in the quantitative research tradition. Such models typically have sought to relate market expenditures over time to effects on such variables as market share and sales. Many comprehensive reviews exist of market impact models (for excellent reviews, see Hanssens, Parsons, and Schultz 1990; Kumar and Pereira 1997; Lilien, Kotler, and Moorthy 1992).( n3) An important lesson from these studies is that long-term impact is very different from short-term impact (e.g., Dekimpe and Hanssens 1995). For example, some marketing actions (e.g., sales promotions) take effect quickly but have little lasting influence, whereas other marketing actions (e.g., service quality improvements, advertising) accumulate their influence over time. This important distinction reinforces the importance of considering the impact on discounted profit flows over time rather than simply investigating short-term effects. Furthermore, the firm's ability to track competitive actions and to react appropriately to them moderates the effects of the environment and competition, so that firm capabilities and context effects become more important in the long run (Kumar 1994; Narver and Slater 1990).
Financial Impact
Although changing customer attitudes, perceptions, and intentions are important, and achieving improved sales and market share is essential to any marketing effort, many managers consider financial impact the most crucial measure of success for any marketing effort. Financial impact involves not only the increase in revenues but also the expenditure required to produce that increase. Marketing expenditures are considered investments, and the financial return is measured as ROI. The long-standing recognition of the importance of ROI in evaluating more general marketing expenditures (Kirpalani and Shapiro 1973) led to early methods for measuring advertising ROI (Dhalla 1976). The connection between marketing efforts and financial performance was subsequently reinforced by analysis of the PIMS company database, which indicated a positive relationship between market share and the firm's aggregate return on net assets (Buzzell and Gale 1987), though that relationship was later challenged on methodological grounds (Jacobson and Aaker 1985). Gale (1994) recanted and later proposed that market share and financial performance were both driven by product quality, though the link between perceived and actual quality is itself complex.
More recently, the "return on quality" model has provided a methodology for projecting a firm's ROI in service quality (Rust, Zahorik, and Keiningham 1994, 1995). Research has shown that there may be trade-offs between service quality improvements that increase revenue and those that reduce costs (Anderson, Fornell, and Rust 1997; Rust, Moorman, and Dickson 2002). Approaches to evaluating financial return have also begun to consider the element of financial risk (Davis 2002; Hogan et al. 2002), as is common in corporate finance.
Impact on the Value of the Firm
Analyses that link market-based assets and marketing actions to shareholder value, though rare, are beginning to emerge. The evidence is encouraging on many fronts. For example, Lane and Jacobsen (1995) show that brand extension announcements lead to abnormal returns on stocks (i.e., returns in excess of those predicted by changes in the market index), thus establishing a link between marketing activity and stock price. Kim, Mahajan, and Srivastava (1995) show a strong relationship between the net present value of cash flows attributable to growth in the number of subscribers (customer base) and stock prices in the cellular telephone industry. Likewise, Ailawadi, Borin, and Farris (1995) demonstrate the impact of marketing actions on EVA and MVA through customer value measures, and they provide a direct link between marketing strategy and changes in a firm's financial fortunes.
Srivastava and colleagues (1997) show that brand equity reduces financial risk and is related to a lower cost of capital and thus to higher market capitalization, whereas Demers and Lev (2000) show that Web site characteristics measured by Nielsen/Netratings, such as stickiness, reach, and loyalty, were correlated with share prices in both 1999 and 2000. Brand reputation (equity) has been shown to be a durable asset that can help reduce the risk of future cash flows for its owners (Deephouse 2000), and customer profitability has been linked to market capitalization for several Internet firms (Gupta, Lehmann, and Stuart 2001). These studies notwithstanding, efforts to link marketing actions to firm performance are few and far between, and more such work is needed.
Other Factors
Environment. Slater and Narver (1994) find limited support for the notion that the competitive environment moderates the market orientation-performance relationship. They argue that the benefits of market orientation are long-term, whereas contextual factors are transient. Harris (2001, p. 33) also finds little relationship between market orientation and both subjective and objective measures of performance, except that "under specific moderating environmental conditions, market orientation is associated with both measures of performance." Pelham (1999) finds that market orientation has a greater influence on small manufacturing firm performance than on industry environment and firm strategy selection, though market turbulence may be a moderating factor. Greenley (1995, p. 7) finds that "for high levels of market turbulence, market orientation is negatively associated with ROI, while for medium and low market turbulence, market orientation is positively associated with ROI." Given all these studies, market orientation remains a strong determinant of performance and, by inference, marketing productivity. However, within that, turbulence is a moderating factor. "In cases where the market is highly dynamic in nature, consistency may be more important than market responsiveness" (Harris 2001, p. 35).
Competition. As we discussed previously, investments in marketing assets, such as brand equity and customer equity, make the firm less vulnerable to competition and directly influence the firm's performance (through market share and sales). First, when the product is associated with a high-equity brand, customers evaluate a product more favorably, believe it to be of higher quality, are more likely to purchase it, and have more confidence in it (e.g., Larouche, Kim, and Zhou 1996). Second, customers are less price sensitive and more responsive to marketing communications spending for high-equity brands (e.g., Simon 1979); thus, marketing expenditures with respect to the competition are effectively leveraged. Third, brand equity can create asymmetries in competition that favor high-equity brands. For example, price cuts or increases in advertising spending draw market share disproportionately from low-equity brands (Carpenter et al. 1988), and competitive imitation by low-equity, me-too brands can increase the share of high-equity brands, such as those of pioneers (Carpenter and Nakamoto 1989). Combined, these forces create important competitive advantages that arise from marketing expenditures on brand equity.
In this section, we explore areas in which current research is insufficient, and we suggest fruitful areas for further progress, especially focusing on the application of marketing productivity measures in the business world.
Chains of Marketing Impact
Few methods currently exist for comprehensively modeling the chain of marketing productivity all the way from tactical actions to financial impact or firm value. Event studies exist that relate tactical actions directly to firm value, but without modeling the intervening steps (e.g., Agrawal and Kamakura 1995), a black-box approach limits insight and understanding. There are many opportunities for firm-level research. For example, how do firm strategies (e.g., promotion strategy, product strategy) influence the firm's brand equity and/or customer equity? How do the firm's market assets relate to firm value and market capitalization (e.g., Gupta, Lehmann, and Stuart 2001)? How does a firm's customer equity affect its long-term market position, financial position, and market capitalization?
A larger question is, Why does linking marketing assets to capitalization matter? A firm contemplating an acquisition would be interested in this linkage, but this article focuses on assessing marketing productivity. Thus, a more challenging issue may be reconciling the short-and longterm approaches. Short-term approaches involve the measurement of the marketing asset, whereas long-term approaches require forecasts of future cash flows. The difficulty of such a reconciliation is that future cash flows are the product both of marketing actions to date and of marketing actions to come.
Strategies and Tactics
Strategies. Several ongoing research agendas continue to be important. For example, how does the relative importance of marketing assets vary as a function of the characteristics of the firm's industry, customer markets, product or service offerings, and competitive strategy? Can these assets be leveraged to provide strategic options? How are marketing and intellectual assets interlinked with other functional resources in creating customer value and competitive advantages (e.g., through core business processes such as product innovation, supply chain management, and customer relationship management)? What is marketing's contribution in managing core business processes? For example, rather than considering marketing research an expense, can the value of market intelligence be assessed in terms of more efficient supply chain processes? Can strong competitive advantages exist in the absence of strong marketing assets? How can these advantages be leveraged to provide marketplace results that fit with company strategy (e.g., when might brand equity be leveraged to opt for a price premium, and when would a share premium be more appropriate?)?
Tactics. New technologies have opened up new channels for customer-vendor interactions (e.g., cable, Internet), which increases the need to manage integrated marketing communications. These developments have led to a critical and immediate need to identify the levels of marketing expenditures for each channel (given expected revenues from customers) that provide firms with maximum opportunities for customer acquisition, retention, and cross-selling, as well as an opportunity for disintermediation. Differences in efficiency across various channels might be captured by the sales response functions in order to identify optimal resource allocations within and across channels. Similarly, firms might rely on long-term customer profitability models to guide direct marketing initiatives. These models should enable firms to improve marketing efficiency. Research that assesses the influence of marketing and communications tactics on multiple measures of customer, market, and financial impact would also be useful.
Customer Impact
Given extensive prior research that relates traditional marketing actions to customer attitudes, preferences, and intentions, further progress in these areas is likely to be incremental rather than groundbreaking. Instead, it is important to model which customers are going to buy, what products they are most likely to buy next, and when they are going to buy the product of highest affinity. In other words, the most fertile area for research on customer impact pertains to how customer behavior (rather than attitudes or intentions) responds to changes in marketing actions (Kumar, Venkatesan, and Reinartz 2002). In addition, there is a need to extend such research to explore these questions for new marketing phenomena and in new environments. For example, there is much yet to be learned about how the Internet environment affects the customer. In general, increased communications and computation capabilities change the nature of the relationship between the marketer and the consumer in ways that are not yet fully understood. Similarly, the changing geopolitical environment (e.g., terrorism, sense of risk) may influence the market in unprecedented ways. In the United States, the persistence of multiple cultures in the society may also change how marketing efforts influence customer attitudes and preferences. In summary, a broader understanding of customer impact is likely to result from studying customer behavior in response to new phenomena and in new environments.
Measuring Marketing Assets
Brand equity. Existing conceptualizations of brand equity have made fundamental contributions to the understanding of brands. However, further conceptual development of key constructs, such as brand knowledge, is crucial for developing better measures of brand equity. Brand dynamics are another important, difficult issue that has received little attention (a notable exception is the work of Keller and Aaker [1993]). Specifically, brands evolve, which changes the fundamental nature of their equity. How should a brand evolve? What are the means of change? When and how should multiple brands be consolidated? These and other questions remain largely open. Most firms manage multiple brands, which raises important issues for their launching new products, seeking to grow profits, or cutting costs, and these issues have received little attention (for more commentary, see Keller 2002).
Customer equity. Bell and colleagues (2002) identify two important areas for further research in customer equity. First, there is a need to build individual-level, industrywide customer databases in industries that do not currently have them (i.e., most industries). Without such data, true longitudinal data analysis of customers' behavioral responses to marketing actions cannot be implemented. Second, there is a need to develop models of customer lifetime value that maximize, not just measure. That is, it is important to determine precisely how much money to spend on each strategic alternative. When longitudinal customer data are not available, businesses must adopt survey-based research methods. It would be useful to validate the effectiveness of such approaches longitudinally. Does customer lifetime value and customer equity, on average, turn out to be as predicted? What adjustments and refinements to the existing models are necessary?
Market Impact
Research on marketing resource allocation (e.g., Mantrala, Sinha, and Zoltners 1992) suggests that ( 1) marketing managers need to optimize investment-level decisions and the allocation of resources across submarkets or customer segments to maximize profitability and that ( 2) interaction between different marketing-mix instruments could lead to differential allocation of resources across marketing channels. As technology progresses, these challenges are magnified. For example, improved communications and computational capabilities greatly extend the marketer's ability to target individual customers. Thus, market impact models increasingly need to be based on individual customer response rather than on aggregate response, which makes them more complex and computationally intensive. Future models of marketing impact may need to employ computational methods (e.g., simulation) more and analytical methods (e.g., closed-form game theoretic equilibriums) less.
Financial Impact
The purest investigations of financial impact involve longitudinal data sources, which means that the construction of customer-level longitudinal data will be a priority, especially in areas in which such data currently do not exist. Ideally, such data sets will include not just one firm's customers, but all the customers in the industry (or a probability sample of the customers in the industry). In addition to the scientific investigation of marketing productivity (or other measures of financial return), practical productivity tools are needed that firms can use when they do not have access to customer-level, industrywide, longitudinal data. These tools need to reflect the state of current knowledge about how marketing productivity works, and their longitudinal validation is required for eventual widespread practical acceptance.
Impact on the Value of the Firm
A strong contender for assessing the value of marketing actions and assets appears to be the shareholder value framework, which includes such variables as the acceleration and enhancement of cash flows, reduction in the volatility and vulnerability of cash flows, and growth of long-term value (Srivastava, Shervani, and Fahey 1998, 1999). However, many questions remain unanswered. Is such value recognized by the stock market and reflected in market-to-book ratios and price-to-earnings multiples? Will larger customer-installed bases or better supply chains and value networks command higher price-to-earnings multiples and market-to-book ratios in mergers and acquisitions activity? Will the stock market reward firms for acquiring other firms with high levels of intangible, market-based assets?
Marketers have made considerable progress in examining the financial implications of their actions on the value of the firm. Indeed, there is the tendency to have the mind-set that customers are assets and to regard the value of the firm in terms of metrics such as (stock) price per subscriber. Strictly speaking, customers are not assets because assets must be owned by the firm. The day firms conclude that they own customers is the day they presume too much. Customer loyalty must be earned. However, it is fair to state that the customer franchise or the customer base is a marketing asset. Metrics such as customer lifetime value and price per subscriber, especially positive trends in such measures, help emphasize marketing's contribution to the firm.
Other Factors
Much work remains to understand how competition and environment influence firm value. Efforts so far have focused on modeling the influence of brand equity on buyer response to marketing spending, such as advertising, to deduce the competitive advantage associated with brand equity (e.g., Brown and Stayman 1992). Models that incorporate competitive effects would provide two types of new insights. First, they would require modeling of the influence of marketing spending on brand equity in a competitive context, which would be valuable indeed. Second, they would require more explicit representations of how brand equity influences firm performance, accounting for the influence of firm expenditures on brand equity itself. The building of such models is challenging, requiring the capture of both competitive effects and brand dynamics. Competitive models have a long tradition in marketing (e.g., Cooper and Nakanishi 1988), and important advances have been made in modeling dynamics (e.g., Dekimpe and Hanssens 1995). Consideration of both in the same framework may offer valuable new insights.
Billions of dollars are spent every year on marketing. As firms struggle to produce ever-higher profits in increasingly competitive environments, calls to justify their expenditures are growing. Existing financial metrics have proved inadequate, leading to the development and increasing use of nonfinancial metrics. Over the past decades, but especially in the past 15 years, considerable progress has been made in developing nonfinancial measures of marketing assets. In this article, we have attempted to bring such methods and measures together in a unified framework and to present them as part of a comprehensive view to describe marketing expenditures on sales, profit, and shareholder value.
The framework we have proposed separates marketing actions, including strategies and tactics, from the overall condition of the firm, as reflected in its assets (including brand equity, customer equity, market position, financial position, and firm value). Only two systems address the important issue of linking short-and long-term outcomes: financial and nonfinancial. The first is based on forecasting long-term outcomes and discounting cash flow (e.g., customer equity). The second represents the future in the state of the marketing asset today. Whether the marketing asset is measured financially or nonfinancially, the long-term picture is provided by the performance of this changing asset and the bottom line.
Our discussion identifies exciting new directions for research in at least seven areas: ( 1) strategies and tactics, ( 2) brand equity, ( 3) customer equity, ( 4) market impact, ( 5) financial impact, ( 6) the environment, and ( 7) competition. A common theme across most of the areas is a greater emphasis on aggregate-level models that link tactics to financial impact. Such models would need to be dynamic and comprehensive but have the potential to yield great insight. Another common theme is the need to account for customer heterogeneity. For example, identification of high-profit customers is a central issue to market segmentation, strategic marketing, and tactics, among other areas. Another common theme is dynamics and competition. The nature of firm performance is fundamentally affected by competition, and it fundamentally changes over time. The capture of both dimensions is essential in virtually every area of marketing productivity measurement. Much work remains.
Despite the opportunities that exist, our review suggests that there currently is a wealth of means to measure marketing productivity. Powerful methods exist to assess marketing tactics; to model the market impact of marketing expenditures; and to assess marketing assets, market position, the value of the firm, and its financial position. These methods reflect the considerable progress that has been made in the past 15 years, and they provide a foundation for exciting further work. More important, these powerful methods provide the tools necessary to affect the practice of management, to bring greater credibility to marketers, and to further advance marketing science and practice by bringing a long-sought understanding of the impact of the billions of dollars that are spent every year on marketing activities.
If there is one conceptual take-away from our review, it is that the evaluation of marketing productivity ultimately involves projecting the differences in cash flows that will occur from implementation of a marketing action. In contrast, from an accounting standpoint, decomposition of marketing productivity into changes in financial assets and marketing assets of the firm as a result of marketing actions might be considered. The devotion of more attention to these marketing assets is likely to transform the way businesses are managed.
The authors thank Don Lehmann for his wise guidance and insightful ideas.
( n1) It would be better to differentiate the customer asset, or customer equity, from the value of that asset. In common usage, though, the term "customer equity" can refer to both. The meaning is usually clear from the context.
( n2) There is a long history of research that relates marketing actions to intermediate outcomes, such as customer attitudes, customer satisfaction, and customer preferences. This extensive body of research (of which we can sample only a small portion) encompasses the behavioral, quantitative, and managerial research traditions. There is an even greater body of literature that relates marketing actions to brand perceptions.
( n3) In many cases, the number of market impact studies is so large that there exist data analyses to summarize the totality of research evidence. For example, Assmus, Farley, and Lehmann (1984) provide a meta-analysis of the findings that relate advertising to sales and find that advertising has variable effectiveness. A 1995 special issue of Marketing Science summarizes many of the generalizations involving the relationship between marketing actions and marketing impact.
DIAGRAM: FIGURE 1; The Chain of Marketing Productivity
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By Roland T. Rust; Tim Ambler; Gregory S. Carpenter; V. Kumar and Rajendra K. Srivastava
Roland T. Rust is David Bruce Smith Chair in Marketing, Chair of the Marketing Department, and Director of the Center for e-Service, Robert H. Smith School of Business, University of Maryland (e-mail: rrust@rhsmith.umd.edu). Tim Ambler is a senior fellow, London Business School (e-mail: tambler@london.edu). Gregory S. Carpenter is James Farley-Booz Allen & Hamilton Professor of Marketing Strategy, Kellogg School of Management, Northwestern University (e-mail: g-carpenter@kellogg.northwestern.edu). V. Kumar is ING Chair Professor and Executive Director of the ING Center for Financial Services, University of Connecticut (e-mail: vk@business.uconn.edu). Rajendra K. Srivastava is Roberto C. Goizueta Chair in e-Commerce and Marketing, Goizueta Business School, Emory University (e-mail: raj_srivastava@bus.emory.edu).
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Record: 110- Measuring the Price Knowledge Shoppers Bring to the Store. By: Vanhuele, Marc; Drèze, Xavier. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p72-85. 14p. 2 Diagrams, 7 Charts, 1 Graph. DOI: 10.1509/jmkg.66.4.72.18516.
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Measuring the Price Knowledge Shoppers Bring to the Store
Reference price research suggests that consumers memorize and recall price information when selecting brands for frequently purchased products. Previous price-knowledge surveys, however, indicate that memory for prices is lower than expected. In this study, the authors show that these price-knowledge surveys provide imperfect estimates of price knowledge, because they focus only on recall and short-term memory. The authors propose, instead, to use a combination of price recall, price recognition, and deal recognition to measure the degree to which consumers use auditory verbal, visual Arabic, or analogue magnitude representations to memorize prices. The authors show how the combination of these three measures provides a much richer understanding of consumers' knowledge of prices. The results suggest that the price knowledge involved in reference prices may often not be accessible to recall but shows up in price recognition and deal recognition. In addition, the authors identify consumer and product characteristics that explain the variations in price knowledge. They find, for example, that frequent promotions increase consumers' ability to remember regular prices and that store switchers do not possess better price knowledge than other shoppers.
Consumers have a strong interest in keeping a knowledge base of prices for products they frequently purchase. This knowledge base enables them to assess the attractiveness of advertised promotions (in flyers, advertisements, and the store itself), alerts them to price increases, and enables them to compare prices across stores. Marketers are interested in finding out how complete and accurate this knowledge base is. The assumption in early economic price theory that consumers are aware of most prices has been invalidated by surveys of price knowledge. Dickson and Sawyer's (1990) in-store price knowledge surveys provide estimates of price knowledge that are surprisingly low. Only 47% to 55% of the respondents could accurately recall the price of an item they had just placed in their shopping cart, and 19% to 23% did not even attempt to give an estimate. These results have been replicated by other researchers (Le Boutillier, Le Boutillier, and Neslin 1994; Wakefield and Inman 1993).
A consumer's price knowledge base is, by definition, stored in long-term memory. Although the results of Dickson and Sawyer's (1990) type of studies are often interpreted as estimates of consumer price knowledge, this interview procedure actually targets in-store attention to prices, by checking the contents of short-term memory. The power of their demonstration is that, because price recall questions are asked immediately after the consumer picked a product from the shelf, "it seems unlikely that shoppers noted the price and forgot it in the short intervening time" (Dickson and Sawyer 1990, p. 50). Although a respondent may call on long-term memory to give a price estimate, short-term memory will dominate the results. This implies that Dickson and Sawyer may have considerably overestimated the extent to which consumers have long-term knowledge of the prices of frequently purchased products. However, it is also possible that the authors underestimated price knowledge, because they relied on price recall. As explained by Monroe and Lee (1999), memory for price information may not be recallable but may still be useful to consumers in making price judgments. Instead of relying on recall, consumers may be alerted to price changes by recognition (or, more precisely, the lack of recognition), and recognition performance is usually better than recall performance. Moreover, Monroe and Lee argue that price knowledge in memory may not be accessible to recall and recognition and still be instrumental in shopping, because it gives a sense of the magnitude of what the normal price is. In the past decade, memory research has focused on this form of memory (referred to as "implicit memory").
To measure the long-term price knowledge consumers bring to the store requires a different interview methodology than that used by Dickson and Sawyer. The main objective of our project is therefore to design and test a methodology that taps into the different forms of long-term memory for prices. We base our design on the recent literature in the area of numerical cognition that shows that numerical information can be represented in three different forms in memory. Only one of these forms is directly accessible to verbal recall. Obtaining a complete picture of price knowledge therefore requires the use of measures that tap into all three forms of representation. In a survey of 400 consumers in a French supermarket, we measure price knowledge with this procedure and examine the extent to which price knowledge is driven by the characteristics of product categories and consumers' shopping strategies. Diagnosing levels of price knowledge and differences across consumers and across product categories is important for managers who need to coordinate prices in a competitive multicategory environment. In addition, academics will be interested in the relationship between the different measures of long-term memory for price and in the determinants of price knowledge in a low-involvement purchase context. They will also be particularly interested in the implications of our research for the apparent conflict between the results of reference price studies and those of price knowledge surveys.
Reference price models are choice models, estimated on scanner data, that include the effect of price knowledge on product choice through a surrogate measure (Kalyanaram and Winer 1995; Winer 1986). In the absence of direct measures of price knowledge, these models measure the reference price of a certain consumer for a certain product as a combination of the past actual prices of this product on previous shopping occasions. The underlying assumption is that the consumer was exposed to the product's price at these shopping occasions and therefore may have memorized it. This assumption is, however, put into question by past price knowledge surveys. Several authors have therefore raised the issue of whether reference price models can "significantly predict brand choice if actual market prices are often not noticed or remembered by consumers" (Urbany and Dickson 1991, p. 51). Price survey results have, however, also been interpreted as an indication that a sizable proportion of consumers are capable of recalling prices (Briesch et al. 1997; see also Kalyanaram and Winer 1995). In this article, we argue that neither interpretation is satisfactory, because Dickson and Sawyer's (1990) type of price knowledge surveys focus on short-term memory for price whereas reference price is, by definition, stored in long-term memory. Moreover, as explained in the previous paragraph, reference price does not need to be recallable to exert its effect on choice. By examining the different forms of long-term memory for price through multiple measures, our study helps advance the debate between reference price and price knowledge survey research. We show that recall-based surveys severely underestimate the level of price knowledge available in the market. Our study also indicates that reference prices may be based on forms of knowledge that are not easily recallable.
We start this article by examining, from a theoretical perspective, how consumers store prices in memory and, consequently, which measures should be used to determine the level of price knowledge. In the second section of the article, these measures are included in a survey procedure that measures the different dimensions of long-term consumer price knowledge that consumers bring to the store. The third section introduces the factors that drive price knowledge, and the fourth section explains in detail the methodology we used. We then present the results of the survey we ran with 400 respondents in a French supermarket and discuss these results in the final section of the article.
To develop measures of price knowledge, we first must understand how prices are stored in long-term memory. Research on numerical cognition focuses on how numbers and arithmetic information are represented in memory and used in cognitive arithmetic tasks (Ashcraft 1992; McCloskey and Macaruso 1995). As a synthesis of the essential findings of this research, Dehaene (1992; see also Dehaene and Akhavein 1995) develops a triple-code model in which numbers can be mentally represented and manipulated in three different forms. The auditory verbal code manipulates a word sequence (e.g., /thirty/ /five/), the visual Arabic code represents numbers on a spatial visual medium (e.g., 35), and the analogue magnitude code represents numbers as approximate quantities on a dimension termed the number line (about 35, or somewhere between 30 and 40).
The triple-code model implies that prices can have three types of representation in memory. The auditory verbal code--which is, for example, the code involved in verbal counting and arithmetical fact retrieval--can be used to store and retrieve price information that has been read out by the consumer, often subvocally. Alternatively, the same information can be stored as a string of visual symbols. Comparing two numbers, for example, usually involves the visual Arabic code. There is evidence that some people are auditory and others visual types, but one person may store some prices in auditory and others in visual form, depending on how the information was coded initially. The same price can also be stored in multiple forms. Which code is accessed at a certain point in time depends on the task at hand. Although conversions can be made from one code to another, there is a cognitive cost involved in this translation, which is revealed through increasing reaction times. The analogue magnitude code, which is the third type of representation of price in memory, is somewhat different from the other two codes because it is formed on the basis of auditory or visual input (i.e., the information passes through the other two codes first), through a fairly automatic process that recodes a number into an approximate quantity.
Important for our study is that Dehaene (1992) posits that each number-related task is tied to a specific code. A verbal recall question is most readily answered through accessing the auditory verbal code. For a recognition question, in which a price is shown visually, accessing the visual Arabic code is the most evident process, and estimates of the normal price range or judgments of the attractiveness of a certain price can be based on the magnitude code. As explained previously, transcoding from one code to another is possible but requires mental switching between notations and increases response time. In the face of a certain numerical task, the most evident code will therefore be privileged.
By using a combination of three price knowledge questions, one for each code, we can take the three possible forms of representation of price knowledge into account and obtain a complete and accurate picture of the level and types of price knowledge a certain consumer holds. Although the literature on numerical cognition was the source of inspiration for the design of our survey procedure, we note that the three ways of measuring price knowledge have also been discussed individually in the marketing literature. Recall has been used most often in prior studies, but Monroe, Powell, and Choudhury (1986) have argued that recognition is a more appropriate measure of price knowledge than recall. They explain that price information can be the result of active search or can be learned incidentally, almost by chance, while consumers are shopping. Active search may be more the exception than the rule, but it is more likely to lead to recallable price information, because it makes explicit links to information already stored in memory. Just picking a product from the shelf and incidentally noticing its price, in contrast, may not lead to recall of this price, even after several repetitions, because no such links to memory are made. Memory researchers refer to these forms of price information processing as interitem processing (several prices are compared) and intraitem processing (one price for one product is examined) (Mandler 1980). If incidental learning is more prevalent, recognition may indeed be more appropriate than recall as a measure of price knowledge.
Regarding the notion that seeking price magnitudes instead of precise price points may in some cases be more appropriate, research suggests the existence of reference price regions instead of price points (e.g., Kalyanaram and Little 1994). Small differences in reference price do not seem to have any effect in some choice models. In addition, Monroe and Lee (1999) explain how the notion of implicit memory expresses the ability to judge the attractiveness of a certain price, without the possibility of explicit recall of an actual price.
On the basis of our literature review, we propose that an investigation of long-term price knowledge needs to tap into all three memory codes for numerical knowledge. In our survey of consumers, we measure three constructs that correspond to these three forms of price knowledge:
- Recallable price knowledge: The consumer knows the actual price of the product in the store "by heart" (Urbany and Dickson 1991). This is the highest level possible and is mainly based on the auditory verbal code.
- Price recognition: Unaided price knowledge is not present, but aided price recognition is (Monroe, Powell, and Choudhury 1986). When people see a price on a product, they can tell if this is the price they are used to and have in mind by accessing the visual Arabic code. If no promotional signal is present, a lack of recognition usually implies that the price has been increased.
- Deal spotting: This form of price knowledge consists of consumers noticing that a price is within or outside the normal range of previous prices, for which accessing the analogue magnitude code is sufficient (Monroe and Lee 1999). People with this level of knowledge do not really know by heart what products cost, and they cannot tell whether a presented price is exactly the one they are used to. They can, however, recognize a good deal or a bad deal when they see one. In other words, they have a sense of what the normal price range is, if they are presented with sufficient cues.
Because our objective is to measure the price knowledge consumers bring to the store and because we want to draw implications for reference price research, our survey procedure must match as closely as possible the context of an actual in-store reference price comparison. In addition to the three-step memory questions, our survey procedure has the following key features:
Our questions are specific to the stock keeping unit (SKU) purchased and are for SKUs that have been purchased in the past by the respondents (unlike, e.g., Urbany and Dickson's [1991] work),
The questions tap into long-term memory and exclude the possibility that price is retrieved from short-term memory (unlike Dickson and Sawyer [1990] and their replications),
We match product and brand cues used in the questioning to those present in the choice context (e.g., through the use of photographs of the products tested), and
The questioning takes place in the store, at the start of a purchase occasion, to maximize the number of contextual cues and to ensure the presence of normally available shopping knowledge (as opposed to in-home surveys such as Urbany and Dickson's [1991], which may underestimate actionable price knowledge).
In addition to our main survey of long-term price knowledge, we ran a survey that measured consumers' memory for prices immediately after they picked a product from the shelf. This survey replicates Dickson and Sawyer's (1990) work and is meant to benchmark our results to theirs in order to elucidate the debate we alluded to previously between researchers who work on scanner data with reference price models and those who use surveys.
Category-Level Drivers of Price Knowledge
A category's characteristics can influence a consumer's ability to accurately store and use the price knowledge of one of its products. If a category had only one item and if the price of that item never changed, it would not be difficult for a consumer who purchases from that category on a regular basis to remember the price of the item. In contrast, if the category had dozens of different items and volatile prices, remembering the price of any given product might be more difficult. We focus here on three dimensions of a category that might affect the consumer's ability to process price information accurately: price volatility, price range, and category clutter. Price volatility measures how often prices change over time. It is a function of the level of promotional activity in the category. Price range is measured as the difference in price from the highest-to the lowest-priced SKU in the category. Category clutter is operationalized as the number of SKUs in the category. Underlying our hypotheses for the effect of these factors is the notion that increased complexity in price information has a negative impact on memory performance.
When a product is often on promotion, its promoted prices will become more salient, and recall and recognition of the normal price should be affected negatively (Johnson 1994). However, if a product is often promoted on price, its normal price range should be salient (Kalyanaram and Little 1994), and deal recognition, which involves reactions to price differences that exceed the typical price promotions (e.g., -20%), should be enhanced. Therefore, our prediction for price volatility depends on the measure considered. Price recall (H1) and price recognition (H2) of the normal price should be lower in often-promoted product categories, whereas deal spotting should be higher (H3) (for the formal hypotheses, see Appendix A). When the price range is large, the product category will be associated in memory with a large number of different prices, and the category therefore becomes an unreliable cue to access any given price in memory (Anderson and Bjork 1994). This should lead to a higher level of confusion about the actual price of any given brand. Thus, prices in categories with a large price range should be more difficult to remember (H4), be it through active or incidental learning. Category clutter is hypothesized to have a similar effect, because confusion again becomes more likely when the number of SKUs in a category increases (H5).
Consumer-Level Drivers of Price Knowledge
Previous studies have examined some of the factors that explain the differences among consumers in their price searches (e.g., Urbany, Dickson, and Kalapurakal 1996) and price knowledge (Dickson and Sawyer 1990 and their replications). To complement this work, we divided consumer characteristics into three groups of variables: the propensity to engage in in-store price search, the propensity to engage in across-store price search, and shopping trip/household size. For each variable, we developed a set of survey items.
Consumers who extensively engage themselves in price search should also be the ones having the best knowledge of prices. They apparently use price as a decision variable (be it to compare brands within a store or prices across stores), and they process more price information than consumers who do not engage in price comparisons. Therefore, we formulate the hypothesis that consumers will have more price knowledge when they engage in in-store price search (H6) or across-store price search (H 7) than when they do not (Dickson and Sawyer 1990).
In terms of average shopping trip size (at the household level), there is more to be gained from accurate price knowledge when the shopping basket is large than when it is small. Therefore, we hypothesize that consumers engaging in larger average shopping trips will have better price knowledge than those engaging in small average trips (H8).
Product Category Selection
The product categories for the survey were chosen so that they represent high or low (but not medium) levels of each of the three factors we studied (price volatility, price range, and category clutter). We selected a representative product category for each cell of our 2 X 2 X 2 factorial design in two steps. We made a first selection of possible candidates on the basis of estimates from a national store panel of approximately 300 product categories. We selected categories that were in either the first quartile (low) or the fourth quartile (high) in terms of price volatility over time, average price range, and number of SKUs. After this first selection, we ran three store checks over a period of two months in the store where our survey took place to determine the eight most representative product categories (see Figure 1).
Each respondent answered price knowledge questions for three of the eight products. The interviewers had a list that determined the selection and order of the products for each interview. This way, all products were presented approximately the same number of times. In case a respondent did not normally buy a given product in a supermarket or hypermarket, the next one on the list was examined.
Respondent Selection and Interview Procedure
The interviews all took place in one hypermarket. It was selected because its prices were almost exactly at the mean of 1524 supermarkets and hypermarkets that were compared in a store price check by a leading French consumer organization (Que Choisir 1998). The objective of our respondent selection was to interview a representative sample of regular shoppers of this store. Respondents were intercepted at one of the store entrances. As soon as an interviewer became available, he or she solicited the third person who entered the store for participation in the interview. In total, 400 shoppers were interviewed. Because we expected to find different types of shoppers at different times of the day and the week, the interviews were scheduled such that we covered each relevant time slot (morning, midday, evening; beginning of the week, normal weekday, and weekend).
The interview was described as being part of a study on consumer products, and the prospective respondents were told they would receive a store coupon of 20 francs at the end of the interview.[ 1] To qualify for the interview, shoppers needed to pass two filter questions: They needed to normally do their shopping themselves, and they needed to do their regular shopping in the store where they were interviewed. We assumed that people who were not regular shoppers would not have reliable reference prices. The problem with those who do not do their regular shopping at the store where the interview took place is that we have no practical way of knowing the actual prices at the store they patronize on a regular basis (i.e., they might have an accurate price knowledge, but for prices that are different from the ones in effect at our store).
As already mentioned, our main survey was designed to assess price knowledge on a shopping occasion before consumers had contact with product prices. This was done to keep respondents from accessing data from short-term memory while still providing them with environmental cues to help retrieval from long-term memory. Pictures were taken of the 174 SKUs sold by the store in the eight selected product categories. These were reproduced in an interview folder sorted in alphabetical order by brand and, at a second level, by SKU. Respondents were interviewed only on product categories they usually buy at the store. After a respondent had indicated in the interview folder which SKU he or she usually purchases, he or she gave an estimate of the SKU's normal nonpromoted price. Respondents could give the price they recalled in the unit they preferred: by item, by pack, by weight, or by volume. Next, a series of hypothetical prices for the product was presented in the interview folder, one at a time. The respondent indicated whether each price represented a good deal, a normal price, or a bad deal. The price presented in this deal-spotting question was a unit price accompanied, if relevant (e.g., for mineral water), with a price per weight or volume. Respondents were assigned to one of two conditions for the deal-spotting questions. In the first condition, the price series started with a price that was 20% below the actual price, followed by prices that were 5% below, 5% above, and 20% above the actual price. In the second condition, the price series started with a price that was 20% higher than the actual price, followed by prices that were 5% above, 5% below, and 20% below the actual price. The two conditions were counterbalanced across the different interview folders such that each product category appeared in each series approximately the same number of times. The selection of the product categories and the counterbalancing of the price presentations were arranged such that respondents received different sequences of prices in the deal-spotting questions and therefore could not predict whether the four consecutively presented prices would be decreasing or increasing.
In the following step, the actual price of the product was shown, along with a 10% higher price and a 10% lower price. The interviewer told the respondent that one of the three prices was the normal price at the store and asked the respondent to indicate which one it was. The presentation order of the three prices in this recognition question was counterbalanced across interview folders. The series of questions was repeated for two other product categories, and the price knowledge questions were followed by 12 questions about the shopping habits and identity of the respondent. To maximize comprehension of the task, all price presentations, questions, and possible answers were presented orally as well as in writing.
In this section, we proceed in two steps. First, we report the overall results for each of the three measures of French consumers' price knowledge, and we examine the degree to which these measures represent incremental levels of price knowledge. Second, we investigate the extent to which these results are category dependent and driven by the demographic characteristics and shopping habits of our respondents.
Measures of Price Knowledge
Price recall. Table 1 shows the percentage of responses that matched the actual price of the products that were tested. It also shows the percentage of the recalled prices that fell within 5%, 10%, and 20% of the actual prices. As Table 1 shows, the levels of price recall are quite different from those obtained in previous U.S. studies. As a comparison, in Dickson and Sawyer's (1990) work, 56% of the respondents were within a 5% range of the actual price, and 47% even recalled the exact price.
There are two possible explanations for this difference in results. One explanation is that we measure long-term memory whereas Dickson and Sawyer measure a mix of long-and short-term memory. Another possibility is that French consumers have much lower price knowledge than their U.S. counterparts. To compare both explanations, we ran a separate survey, in the same store, but now replicating Dickson and Sawyer's study as faithfully as possible on two product categories. Consumers were intercepted immediately after they placed a product in their cart. The categories selected were mineral water and yogurt. We interviewed 100 consumers for each category. As we show in Table 2, the estimates of price recall accuracy are similar for the two categories and, more important, are markedly different from those of Dickson and Sawyer. These data clearly indicate that the French consumers in our study paid less attention to prices and, as a result, had worse short-term memory than their U.S. counterparts.
To check for possible category biases, we considered the unit basis for which a price was given (respondents gave their estimates in the unit that came spontaneously to mind). Different unit responses were observed only for mineral water, milk, and toilet paper, and 43% of the responses for these categories were expressed by item (bottle or roll); the remainder were stated by package. A t-test on absolute percent differences (abs[recall estimate - actual price]/actual price) shows that recall accuracy does not depend on the unit (per item = 22, per pack = 26; t512 = 1.76, p = .08).
Price recognition. In our theory review, we stressed that it may be premature to draw conclusions for retail price policy just from examining the accuracy of price recall. The other two dimensions of price knowledge also must be taken into consideration. Recognition of the actual price can be considered the next step down on the price knowledge ladder. Table 3 gives the percentages of respondents that chose each response option. To correct for guessing and obtain an estimate of the level of genuine recognition, we apply a simple theory of guessing behavior that posits that a certain percentage of the respondents, X, recognizes the actual price with perfect confidence, and the rest, 1 - X, use pure guessing (Morrison 1981). Through random chance, one-third of these guessers will pick the right answer. Thus, the number of observed correct responses (42.2%) is equal to X + (1 - X)/3. Solving for X, we have a corrected percentage of nonguessers of 13.3%.[ 2]
Comparing recognition with recall results provides some interesting insights. The percentage of correct recognition responses is obviously higher than that for recall (13.3% versus 2.1%). This recognition percentage can also be compared with the percentage of recall responses that were within 5%. Indeed, if a consumer were to give a price recall estimate that fell within 5% of the actual price, this person should logically be able to recognize the right price when it is presented alongside prices that are 10% higher and lower. Only 63% of the respondents who recalled a price within 5% of the correct price were able to recognize the actual price. It seems that a sizable number of consumers do not use their recalled price when they need to make a recognition judgment. A possible explanation for this result is that these people access different memory representations to answer the two questions and are not able to make the translation from one memory code for numbers to another. For example, they may have used a pictorial representation of the price to make their recognition judgment, but they used the auditory verbal code to answer the price knowledge question. This is apparently a less effective strategy than if they had used the spontaneously recalled price as a basis for their recognition judgment as well.
Deal spotting. For the price-alert shopper, recall and recognition are useful forms of knowledge, but they require more or less precise representations of price in memory and reliable access to these representations. In our third form of questioning, we measured the extent to which our respondents have only a sense of magnitude of the normal price that guides them in judging which prices are attractive or unattractive. Our analysis combines the response patterns across the four deal-spotting questions into one measure.
We start by dividing our responses into three groups. The first group is composed of consumers who are fairly knowledgeable about price magnitudes and not only respond positively to a large discount (and negatively to a large increase in price) but also respond in the right direction to small changes in prices (i.e., the answer "normal" or "good" for 5% decreases and "normal" or "bad" for 5% increases). This response behavior corresponds to a [good, good or normal, normal or bad, bad] response to a price series of [-20%, -5%, +5%, +20%] or a [bad, normal or bad, good or normal, good] response to a price series of [+20%, +5%, -5%, -20%]. Of all our responses, 32.7% fall into this category.
The second group is formed by consumers who do not even have the most minimal ability for deal spotting. It includes those who label the first presented price as good when it is 20% above the current price and those who label a price reduction of 20% as a bad deal. We find 14.1% of our observations in this case. The remaining responses, which do not belong to the first or second group, in total 53.2%, reflect some intermediate form of deal spotting and constitute our third group. Overall, the price-oblivious segment of respondents is small (14.1%). Most of the respondents in our sample (85.9%) are able to engage in some degree of deal spotting.
As in the case of the price recognition questions, some of the correct answers are the result of guessing. The observed number of responses we considered as an indication of good deal-spotting ability is therefore an overestimate of the true deal-spotting ability. Correcting for guessing is unfortunately not as easy as it is for the price recognition question. We work in three steps (see Appendix B). In the first step, we calculate the percentage of responses that would be categorized as correct deal spotting if in reality all respondents were using pure guessing. In the second step, we infer, from our calculations in the first step, an upper-bound estimate of the proportion of guessers in our sample. This then enables us to derive a lower-bound estimate of the number of true deal spotters. Our calculations give us an estimate of 26.9%. This is a worst-case scenario. In the absence of perfect knowledge on the number of guessers, all we know is that the true number of accurate deal spotters lies between 27 and 33%.
Comparison of Forms of Price Knowledge
In combination, our previous analyses of the three measures of price knowledge suggest the distinction among five steps on the price knowledge ladder (see Figure 2). At the first step, there is no price knowledge. This is reflected in the absence of deal-spotting ability. The second step permits some level of magnitude sensitivity to large price differences but is not perfect. A third step helps a consumer react correctly to prices that depart from the usual price by at least 5%. The fourth step permits, in addition, accurate recognition of the actual price; the highest and rarest form of knowledge is the basis for accurate price recall.
The relative percentages of responses at the last four steps seem to confirm the intuition that deal spotting, price recognition, and price recall are incremental steps on the price knowledge ladder, meaning that some consumers who have accurate deal-spotting ability also have accurate recognition and that some of the latter group have accurate recall.
To check whether the three memory measures are indeed steps of a one-dimensional continuum, we ran a Guttman scalogram analysis (Robinson 1978). We calculated the coefficient of reproducibility of the data and compared it with the reproducibility under the assumption of independence among the measures. At .90, the observed reproducibility is not higher than the chance level of .92. A calculation of the coefficient of scalability (an indication of the extent to which the responses can be scaled on one dimension) confirmed this finding; at .23, it is well below the acceptable range of .60 to .65. In conclusion, although a comparison of the frequencies suggests that deal spotting, price recognition, and price recall are memory measures of increasing difficulty along a single dimension, the Guttman scalogram analysis shows that instead there is more than one dimension at play. This may indicate that these measures tap into different dimensions of our memory system, a notion present in the literature on numerical cognition (Dehaene 1992). This finding is of importance not only to readers interested in memory phenomena but also to those interested in reference price effects; our results imply that consumers who exhibit reference price effects in choice models may have a form of price knowledge that enables them to react accurately to displayed prices but that does not permit recall in a survey.
Drivers of Price Knowledge
We test our hypotheses on price knowledge drivers by running logit regressions on our three measures of price knowledge (price recall, price recognition, and deal spotting) with the knowledge drivers as independent variables. To study price recalled, we use as a binary dependent variable whether responses are within a 5% range of accuracy.[ 3] For recognition, we examine whether the correct response was chosen (we cannot correct for guessing at an individual level) and for deal spotting, we consider the difference between the 32.7% of responses that demonstrate good deal-spotting ability and the other responses. We also run an analysis in which we code, again with a binary variable, whether there was no deal spotting at all (14.1%) or some form of deal spotting (as with recognition, we cannot correct for guessing at an individual level). We expect the answers of price-oblivious shoppers to be completely random, and therefore none of the independent variables should be significant.
To avoid multicollinearity problems in our regressions, we run a factor analysis on the questionnaire items that measure the consumer-level knowledge drivers of H6 to H8 (for details, see Appendix C). This yields the three factors we expected: ( 1) in-store price information search, ( 2) across-store price information search, and ( 3) shopping trip budget. We use the factor scores for these three factors in our analysis rather than the actual survey answers. In our questionnaire, we also tried to assess the effect of memory recency ("When did you last purchase the category?") and salience ("Do you intend to purchase the category today?"). These questions came at the beginning of our questionnaire because we believed that they could function as realistic memory cues for subsequent access to price knowledge.
In addition to our survey results, we have access to national panel data with the average brand loyalty indices per category, to control for switching behavior, and the average purchase frequency for each category. A priori, the effect of switching behavior might go either way. On the one hand, if switching is motivated by price differences, frequent switching would indicate frequent processing of price information and therefore better knowledge. On the other hand, frequent switching may also mean that consumers are exposed to many different prices, and therefore it would be more difficult for them to remember any given price accurately. Regarding the other variables, we expect consumers to remember prices more accurately for products they purchased frequently and recently and for product categories that are more salient because they are on the current shopping list (a priming effect).
Shopper demographics such as sex and age were measured in the questionnaire and were used as control variables. However, they did not contribute to the model and were removed from the final analysis. The parameter estimates for the final model we estimated are reported in Table 4.
The results give several important insights about consumers' knowledge of prices. First, none of the parameters in the deal oblivion regression are significant except for the intercept. This shows that consumers who are oblivious to prices are that way regardless of the characteristics of the product category and regardless of their individual characteristics and shopping behavior. Their knowledge is erratic and, as a result, their responses are random.
A second important finding is that, overall, the characteristics of product categories are significant predictors of price knowledge. As predicted, approximate price recall and recognition are worse for categories with a larger price range (H4a, H4b) and more references (H5a, H5b). The results for price volatility on recall and recognition are also significant but go against our predictions: Price promotions apparently make not only the promoted price but also the normal price better accessible to recall (H1) and recognition (H2). The hypotheses for the effect of price volatility (H3) and range (H4c) are confirmed for deal spotting, but the parameter for category clutter is not significant (H5c), though it has the expected sign (see Appendix D).
Although shopping frequency and brand loyalty are not measured at the level of the respondents but at the national level, these control variables are statistically significant. Frequency has the sign we expected. Recall and recognition are better for categories that have higher-than-average levels of brand loyalty. Recency and salience were not significant predictors of price knowledge. Therefore, being in the store to buy the particular product does not by itself prime price knowledge, and the price of more recently purchased products is not better accessible than that of products purchased in the more distant past.
The price search measures also give some important results. Probably the most surprising result is that people who claim to shop across different stores to "cherry pick" on the basis of price comparisons do not have better price knowledge, whatever measure we consider. Also, shoppers for large households or with big shopping budgets are not more knowledgeable about prices, though they have most to gain financially from better knowledge. We therefore reject H7 and H8. Finally, H6, that consumers who engage in in-store comparisons of prices have better knowledge, is supported by our tests on recall (H6a) and deal spotting (H6c), but not on recognition (H6b).
Summary
Our research clearly shows that a large majority of consumers hold some sort of price information for frequently purchased products in memory. Furthermore, for most people these memorized prices are not an accurate representation of the last price seen but rather a sense of magnitude. Either during the encoding of price information or in between shopping occasions, there is some information loss that makes it difficult for consumers to recall or recognize actual prices (<15% of success) but that is fortunately not so large that consumers are incapable of spotting good deals when they see them (>85% of success). This may indicate that, when confronted with hundreds of weekly purchase decisions, consumers develop a heuristic device for dealing with the vast amount of information to be processed and that such a device provides consumers with the working knowledge necessary to make acceptable decisions (i.e., not pass up good deals or be taken advantage of through large price increases). We also have some evidence that prices are represented in different forms in memory (sound sequences, photographic representations, senses of magnitudes) and that specific memory tasks require the ability to access the corresponding representation. This implies that to obtain an accurate picture of price knowledge, future surveys should tap into the different forms of numerical memory through a combination of the appropriate questions.
Our analysis of the drivers of price knowledge indicates that frequent promotions make normal prices more memorable, which implies that when examining promotions, consumers pay attention to the regular price of the goods promoted and can tell the difference between promoted and regular prices. A wider range between high-priced and low-priced items and a larger number of brands within a category seem to hamper price knowledge, possibly because these factors increase the complexity of the information that customers need to remember. This may imply that in terms of memory organization, prices are not just linked to the respective brands but somehow are also related to the product category. From a learning perspective, it is no surprise that more frequently purchased product categories and more loyalty to a brand lead to better price knowledge.
All these findings are related to the product category, which means that they can be directly applied by retailers for their pricing strategies. This is not the case for shopper characteristics, for which retailers first must identify which type of customer they have in the store in order to adapt their price policies. For these shopper characteristics, our most surprising result may be that the practice of cherry picking has no impact on price knowledge. This can be tentatively explained by the increase in task complexity for cherry pickers. For each product in their shopping basket, they keep either a different reference price for each of the stores they patronize or create a reference price from the aggregate of multiple sources of prices, which would likely reduce the precision and the actionability of their price knowledge. A final finding on price knowledge drivers is that recency does not affect price knowledge, which supports Briesch and colleagues' (1997) claim that reference prices are generated over extended periods of time.
Implications for Retailers
Previous research has warned managers in the retailing industry against their tendency to overestimate the percentage of consumers who search and respond to price information (e.g., Urbany, Dickson, and Kalapural 1996). We find that cherry pickers for frequently purchased goods do not pose a major threat because their price knowledge is not different from that of other shoppers. Overall, shoppers demonstrate low accuracy in price knowledge, but they have the ability to detect attractive and unattractive prices. This lack of precise knowledge helps explain the results obtained by Hoch, Drèze, and Purk (1994) in their study of everyday low pricing (EDLP) versus Hi-Lo pricing. In their article, they show that a change of plus or minus 10% on regular prices has little impact on store sales. This is consistent with our results, because our analysis indicates that only a small minority of consumers has the information necessary to notice such a small change in regular nonpromoted prices. Therefore, consumers would be hard pressed to notice the change in regular price, but we can assume that they would still be able to notice the promotional activity. In short, this suggests that consumers do not know what Hi is, but they can recognize Lo when they see it. In other words, as long as Hi does not increase to the point at which it can be recognized as a bad price, sales will not be affected.
In addition to explaining the lack of performance of the EDLP format in Hoch, Drèze, and Purk's (1994) study, our findings are relevant to the work on interstore competition. The important points here are that we did not find consumers to be proficient at remembering regular prices, but we found them to be proficient at spotting deals. Furthermore, they are better at remembering regular prices when prices are promoted often than when prices are promoted infrequently. This indicates that it will be difficult for two EDLP stores to compete on price, because few consumers have the information necessary to make a valid store comparison. In contrast, two Hi-Lo stores can compete through promotions. Furthermore, in a store format comparison, the credibility of EDLP prices is heightened by the presence of a Hi-Lo store, because the Hi-Lo store makes regular prices more salient.
In terms of in-store pricing, our findings indicate that consumers' knowledge of price is category dependent. The accuracy of knowledge depends on such factors as category clutter (i.e., the number of brands in a category), volatility (the frequency of promotions), and price range (the price difference between the highest-priced item and the lowest priced one). It follows that a retailer's pricing strategy should also be category dependent. To obtain an image of offering low prices, a retailer should first focus on categories that facilitate price knowledge (low clutter, small price range, and frequent promotions), which should have lower prices than categories that make it more difficult to memorize and compare prices (high clutter, large price ranges, and infrequent promotions). The former group of categories will attract price-sensitive consumers to the store, whereas the latter will enable the retailer to maintain a reasonable over-all margin.
Price Knowledge and Reference Price
In the introduction, we referred to reference prices in the context of choice models. This stream of research conceptualizes reference price as the price knowledge residing in long-term memory that results from exposure to past prices. Reference price is, in the absence of direct measures of price memory, operationalized in choice models as some average of the history of actual prices. Considered in this context, the measures of price awareness used in the present study can be viewed as an operationalization of reference price and are a step forward in the direct measurement of reference price. However, what we lack in our survey is the act of choice, and therefore we cannot claim that our measures of price awareness are also measures of reference price. We do not know if consumers would use the price they give in a recall or recognition question to make an actual price comparison. In contrast, the deal-spotting questions require the interviewee to make a comparison between an observed price and some internal benchmark. The deal-spotting question can therefore be considered a direct measure of reference price, though it remains a partial measure because it taps mainly into one of the three forms of memory for numbers, namely, the analogue magnitude code.
Although the link between our measures of price knowledge and the concept of reference price remains to be validated, our research makes several contributions to the debate between reference price and price survey studies. First, our study indicates that price surveys based on short-term recall reflect attention to prices and therefore have only indirect implications for reference price. Such surveys will in most cases overestimate the level of long-term price knowledge in the marketplace. Second, price surveys on long-term recall severely underestimate the amount of actionable consumer price knowledge, because they do not take into account memory representations that are only accessible to recognition and recall. Choice models can therefore detect reference price effects even if price recall is inaccurate or unreliable. Although further validation is required, the two previous points can be considered a reconciliation between previous price surveys that show low price knowledge and reference price models that suggest a much higher price knowledge. Third, reference price models that focus on reference price zones instead of reference price points (e.g., Kalyanaram and Little 1994) represent more valid descriptions of the way the majority of consumers hold price information in memory, because price zones represent deal-spotting ability, a form of price knowledge that is widely available in the market, and correspond to the magnitude code representation.
Limitations
Before concluding this article, we must be cognizant of the shortcomings of the method we used. First, our basic instrument is a survey that was administered at the start of a shopping trip and therefore needed to be as time-efficient as possible. To limit the survey's length, we did not include some factors that probably are important determinants of price knowledge (e.g., individual-level rather than market-level brand loyalty). In addition, environmental variables such as the level of market share, promotion, and advertising of the brands used in our study were not included in our analysis. We only examined a small subset of the relevant knowledge drivers.
A second limitation is that each respondent answered, for a given product, a recall question, four deal-spotting questions, and a recognition question. This setup was essential to examine to what extent the three corresponding forms of numerical memory are dependent or independent. However, it has the drawback that carryover effects from one question to another are possible, though the sequence of questions we adopted was the one that minimized these carryover effects. The most important concern is that our estimate of recognition may be inflated. Respondents saw a sequence of four prices for deal spotting, and the average or midpoint of these prices (which was not stated itself) was the correct answer for the recognition question. Moreover, this correct answer was also the midpoint of the prices presented in the recognition question. It is therefore possible that some respondents figured out our design, even though they did not receive any feedback on the accuracy of their responses.
This is an important concern, and we examined it ex post in a new survey. To obtain an upper-bound estimate of the possible bias of our design, we designed this new survey to maximize our respondents' chance of figuring out the logic in our price structure. We administered our price knowledge questions to a group of people who could not have any idea of the actual prices of the products and therefore needed to rely entirely on logic to determine the best possible answer. A total of 105 U.S. college students answered the deal-spotting and recognition questions for a four-pack of Danone Fruit Yoghurt, a six-pack of 1.5-liter bottles of Evian, and 1 kilogram of sugar. The prices were stated in French francs (the students were not told the current exchange rate between the U.S. dollar and the French franc), and the question pertained to French supermarkets ("Is the price shown a good, normal, or bad price for 1 kilogram of sugar in a typical French supermarket?"). The actual survey instructions and the analysis of the answers are shown in Appendix E. The answers from the store survey are significantly better than the results of the pure guess survey (p = .0001). This indicates that the results obtained in the main survey are not due to logic-based inferences alone. It does not, however, rule out that some guessing occurs, which is why we apply our correction factor to the store survey results for recognition, scaling down the correct response rate from 42.2% to 13.3%.
That we restricted our sample to consumers who made most of their shopping trips at the participating retailer is a third limitation of our study. We did so to increase the validity of our comparison of the remembered prices to the actual store prices, but it would be interesting to record prices at a variety of stores and conduct the same study with a group of known cherry pickers to test whether they keep separate prices in memory for the separate stores. We leave an in-depth analysis to further research, but nevertheless we attempted to verify the impact of this factor on our results. One of our most surprising findings, that respondents who report across-store price comparisons do not have a better price knowledge than those who do not, could be due to our sample selection in that we may have insufficient variance in across-store shopping. This is not the case: Because 19% of the respondent reported that they regularly shop in two stores, 19% in three, and 22% in more than three stores, lack of variance cannot be a concern. An alternative explanation may be that the effect of cherry picking as such was attenuated by the effect of the other items that constituted the across-store search factor. We therefore reran all our logit regressions and replaced Factor 2 first by the reported number of stores visited and then by the item that measured cherry picking. Neither of these variables had a significant effect. In conclusion, the null effect of across-price search cannot be explained by biases introduced by our survey procedure or our analysis.
A final limitation to keep in mind is that we estimated price knowledge for the Universal Product Code a respondent buys most often in a product category. Our results therefore should not be generalized to the respondent's entire shopping basket.
We showed in this study that the accuracy of consumers' price knowledge depends on both the shopping environment (e.g., category clutter, promotion activity) and consumers' idiosyncrasies (e.g., brand loyalty, in-store price search behavior). These findings are important to managers who make pricing decisions in a competitive multiproduct environment. In addition, our distinction among the three forms of number representation in memory and our combination of the three corresponding measures (recall, recognition, and deal spotting) explain the apparent contradiction between the observations made in price knowledge surveys (consumers have low levels of price knowledge) and those made in reference price studies on scanner data (consumers' decisions indicate a high level of price knowledge). Although most consumers do not possess an accurate knowledge of price that permits accurate recall or recognition, they possess a working knowledge of prices that is accurate enough for the consumers to make good purchase decisions. This working knowledge, and not accurate price recall as such, may then be the driving force behind the reference price effects observed in choice models.
Notes:
1 This financial incentive may have introduced a selection bias by attracting price-sensitive shoppers. However, this bias does not affect the nature of our conclusions, as we show in the analyses.
- 2.422 = X + (1 - X)/3 → X = (3 X .422 - 1)/2 = .133.
- 3 Although we report only one analysis of the recall results, we compared different approaches. The results are not substantially different. For example, a first analysis took absolute percentage differences as a dependent variable. When we included all data, we obtained identical results to those reported with one difference: Range was no longer significant. This result was, however, influenced by several extreme outliers. When we excluded the 1% most extreme outliers, there were no differences from the reported results.
Table 1: Price Recall
Legend for chart:
A - Accuracy Level
B - Cumulative Percentage of Respondents
A B
Correct[a] 2.1%
Within 5% 21.3%
Within 10% 37.3%
Within 20% 60.3%
[a]We defined accuracy relative to the current price in the store. This may not always correspond to the reference price--for example, if the reference price is based on several past prices and when the price changed from the previous shopping occasion. It should, however, also be noted that prices in this store are fairly stable, and current price is therefore a good approximation of the "correct" reference price. Notes: N = 1186 (missing values = 2.8%).
Table 2: Replication of Dickson and Sawyer's (1990) Study
Legend for chart:
A -
B - Percent Correct[a]
C - Average Error
D - Percent Estimate[a] Within 5%
E - Percent Estimate[a] Within 10%
F - Percent Estimate[a] Within 20%
A B C D E F
Yogurt 10% 15% 30% 54% 70%
Mineral water 10% 20% 27% 48% 66%
Dickson and Sawyer 47% 15% 56%
[a]Percentage of respondents; n = 100 for each product category.
Table 3: Price Recognition
Legend for chart:
A - Level of Presented Price Relative to Actual Price
B - Percentage of Responses
A B
-10% 32.5%
0 42.2%
10% 25.3%
Notes: N = 1186.
Table 4: Logit Regressions of Drivers of Price Knowledge
Legend for chart:
A -
B - Accurate Within 5% Estimate
C - Accurate Within 5% Odds Ratio
D - Price Recognition Estimate
E - Price Recognition Odds Ratio
F - Deal Recognition Estimate
G - Deal Recognition Odds Ratio
H - Deal Oblivion Estimate
I - Deal Oblivion Odds Ratio
A
B C D E F G H I
Intercept
-4.56** -- -1.12** -- -1.56* -- -2.07* --
Volatility (H1-H3)
1.17** 3.21 .49** 1.63 .58* 1.78 -.36 .69
Range (H4)
-.89** .41 -.39* .68 -.40* .67 .31 1.37
Clutter (H5)
-1.32** .27 -.62** .54 -.23 .80 .31 1.37
Frequency
.09** 1.09 .02* 1.02 .58* 1.79 -.46 .62
Loyalty
.08** 1.09 .03** 1.03 .01 1.01 .01 .99
Recency
.04 1.04 .01 1.01 -.05 .95 -.11 .89
Salience
.15 1.17 -.05 .95 -.01 1.02 .34 1.41
In-store search (H6) (Factor 1)
.15* 1.16 .03 1.03 .16* 1.18 -.12 .89
Across-store search (H7) (Factor 2)
-.09 .91 .09 1.09 .02 1.02 -.01 1.00
Shopping budget (H8) (Factor 3)
.04 1.04 -.02 .98 .05 1.05 -.01 1.00
Chi-square
p = .0001 p = .02 p = .0075 p = .32
*Significant at p = .05.
**Significant at p = .01.
Figure 1: Category Selection
Figure 2: Levels of Knowledge
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Formal Hypotheses
H1: The ability of a consumer to recall the price of an item is negatively affected by the level of price volatility in the category in which the product belongs.
H2: The ability of a consumer to accurately recognize the price of an item is negatively affected by the level of price volatility in the category in which the product belongs.
H3: The ability of a consumer to spot a good or bad deal for a product (i.e., recognize that a price cut of 20% is a good deal or that an increase of 20% is a bad deal) is positively affected by the level of price volatility in the category in which the product belongs.
H4: The ability of a consumer to (a) recall a price, (b) accurately recognize a price, and (c) spot a good or bad deal on a product is negatively affected by the range of prices in the category to which the product belongs.
H5: The ability of a consumer to (a) recall a price, (b) accurately recognize a price, and (c) spot a good or bad deal on a product is negatively affected by the level of clutter in the category in which the product belongs.
H6: Consumers engaging in in-store price research will be better at (a) recalling and (b) accurately recognizing the price of an item and (c) spotting a good or bad deal on a product than those who do not.
H7: Consumers engaging in across-store price search will be better at (a) recalling and (b) accurately recognizing the price of an item and (c) spotting a good or bad deal on a product than those who do not.
H8: Consumers who have large average basket sizes will be better at (a) recalling and (b) accurately recognizing the price of an item and (c) spotting a good or bad deal on a product than those who do not.
Deal-Spotting Guessing Corrections
Probability of Guessing Right
To correct for guessing in the deal-spotting question, we begin by assuming that a guesser will answer at random but will answer in a logical manner. That is, a guesser will not decrease his or her valuation of a deal (e.g., go from "normal" to "bad") when presented with a price that is lower than the previous price. Similarly, the guesser will not increase his or her valuation of a deal (e.g., go from "normal" to "good") when presented with a price that is higher than the previous price.
Therefore, if presented a series of four increasing prices (-20%, -5%, +5%, +20%), a guesser who started with "bad" would be compelled to rate all four prices as "bad." In contrast, a guesser who started with "good" could answer any of the ten following sequences:
1. Good Good Good Good
2. Good Good Good Normal
3. Good Good Good Bad
4. Good Good Normal Normal
5. Good Good Normal Bad
6. Good Good Bad Bad
7. Good Normal Normal Normal
8. Good Normal Normal Bad
9. Good Normal Bad Bad
10. Good Bad Bad Bad
Any other answer (e.g., good, normal, good, bad) would be illogical and therefore is not considered in the analysis. Of these ten possible guessing sequences, only four (sequences 5, 6, 8, and 9) would be considered correct answers for the deal-spotting questions. Guessing sequences 1, 2, 4, and 7 are incorrect because they fail to recognize the +20% price as bad; guess 3 is incorrect because it classifies +5% as a good deal; guess 10 is incorrect because it classifies -5% as a bad deal.
To compute the probability of guessing sequences 5, 6, 8, or 9, we need to recognize that the probability of guessing "good," "normal," or "bad" depends on the choice made on the previous answer. "Good" will be guessed with a 1/3 probability on the first guess or if the previous guess was "good"; it will be guessed with probability 0 if the previous guess was normal or bad (in an increasing sequence). Similarly, "normal" will be guessed with probability 1/3 on the first try or if the previous guess was "good," with probability 1/2 if the last guess was "normal," and with probability 0 if the previous guess was "bad." Finally, "bad" will be guessed with probability 1/3 on the first try or if the previous guess was "good," 1/2 if the previous guess was "normal," and 1 if it was "bad."
We can now compute the probability of sequence 5 (good, good, normal, bad) as 1/3 X 1/3 X 1/3 X 1/2 = 1/54. Similarly, sequence 6 is guessed with probability 1/3 X 1/3 X 1/3 X 1 = 1/27; the result for 8 is 1/36 and that for 9 is 1/18. Therefore, the probability of a correct guess is 1/54 + 1/27 + 1/36 + 1/18 = 13.9%, as illustrated graphically in Figure B1.
Probability of Being Deal Oblivious
To be classified as deal oblivious, a respondent must answer "bad" for the first price in an ascending sequence or "good" for the first price in a descending sequence. As explained in the preceding section, the probability of either of these answers is 1/3. Given that half the series were ascending and the other half were descending, the probability for a guesser to be classified as deal oblivious is 1/3 X 1/2 + 1/3 X 1/2 = 1/3.
Probability of Reflecting Intermediate Deal-Spotting Abilities
We classify as intermediate all shoppers who are neither correct nor deal oblivious. As was shown in the preceding two sections, the probability of being correct is 13.9% and the probability of being oblivious is 33.3%. This leaves 52.8% probability of being intermediate.
Upper and Lower Bounds on Deal-Spotting Abilities
If everybody were guessing, we would find 13.9% correct answers, 33.3% deal oblivion, and 52.8% intermediate answers. We observed 32.7%, 14.1%, and 53.2%. These numbers are different enough to rule out the possibility that everybody was guessing. But how many people truly knew? The observed 32.7% is an upper bound on the percentage of people who can spot a deal accurately, as some of our respondents must have hit on the right answers by chance. Indeed, 13.9% of the guessers would have done so. The question is, What is the lower bound? To figure this out, we need to determine an upper bound on the number of guessers.
The upper bound on guessers is 42%. Indeed, if 42% of the people guessed, they would yield 42% X 13.9% = 5.8% of correct answers, 42% X 33.3% = 14.1% of deal oblivion, and 42% X 52.8% = 22.2% of intermediate answers. If there were more than 42% guessers, we would find more than 14.1% deal oblivion, but we did not. Thus, 42% is the upper bound on guessing. This would yield 5.8% of correctly guessed answers. Therefore, 32.7% - 5.8% = 26.9% is our lower bound on the true number of accurate deal spotters.
Factor Analysis of Consumer and Shopping Characteristics: Rotated Factor Pattern After Varimax Rotation
Legend for chart:
A -
B - Factor 1: In-Store Price Search
C - Factor 2: Across-Store Price Search
D - Factor 3: In-Store Shopping Trip Budget
A
B C D
Do you pay attention to in-store promotions?
.75 .12 .13
Do you compare the flyers you find at the entrance of the store
or in your mailbox?
.71 .29 .07
Do you like shopping at supermarkets?
.57 -.18 -.28
How often do you shop at different stores to buy at the best
possible price?
.30 .75 -.05
Do you compare prices between different stores?
.47 .64 -.01
In how many supermarkets do you do your weekly shopping?
-.21 .79 .03
How many members are there in your household?
.10 .02 .80
How much do you spend on average in this store?
-.07 .09 .80
Notes: The items appeared in a different order in the questionnaire. Loadings in boldface indicate the questions that load the highest on each factor.
Summary of Hypothesis-Testing Results
Legend for chart:
A -
B - Effect on Price Recall
C - Effect on Price Recognition
D - Effect on Deal Spotting
A
B C D
H1-H3: Category volatility
Reverse Reverse Support
H4: Price range
Support Support Support
H5: Category clutter
Support Support No support
H6: In-store price research
Support No support Support
H7: Across-store price research
No support No support No support
H8: Average shopping trip size
No support No support No support
Test of Carryover Effects: Survey Among College Students
When asking the deal-spotting questions, we used series of prices that were either [-20%, -5%, +5%, +20%] or [+20%, +5%, -5%, -20%] off the regular prices. When asking the price recognition question, we used the regular prices along with prices that were 10% below and 10% above regular prices. The combination of these two questions may enable an astute interviewee who does not know prices to guess that the regular price is the middle price in the second question.
To test for this possible bias due to guessing in the results, we ran the same questions using U.S. undergraduate students from a major West Coast school. That is, we asked U.S. students questions about French prices in French francs. Students were not told what the exchange rate from the dollar to the franc was. Furthermore, this questionnaire was run on the last week of the fall semester (15 weeks) to ensure that students would guess the answer(it is unlikely that these students would have had a recent shopping experience in France). The survey began by asking the deal-spotting question for a four-pack of Danone Fruit Yogurt, starting with a price that was 20% below regular price and increasing to 20% above regular price. Students were then asked the price-recognition question for the same product. The questions were repeated for a six-pack of 1.5-liter bottles of Evian, but this time there was a decreasing price sequence for the deal spotting question. Finally, we concluded with the deal-spotting (decreasing sequence) and price recognition questions for 1 kilogram of sugar.
The price recognition questions all used prices that were 10% above, 10% below, and at regular price. However, we rotated the order of the prices using a Latin-square design as shown in Table E1. The price that was picked the most often was not the middle price in terms of franc value but rather the price that was in the middle visually (as opposed to the price on the left or the right). This center price was chosen in 41% of the cases (significantly different from random choice at p = .002). This center bias does not concern us for the store survey, as we took care to rotate the order of the prices in the store survey.
A comparison of the results from both surveys shows that the answers from the main survey are significantly better than the results of the pure guess survey (p = .0001). This indicates that the results obtained in the main survey are not due to guessing alone. However, it does not rule out the possibility that some guessing occurs, which is why we apply our correction factor to the survey results, scaling down the correct response rate from 42.2% to 13.3%.
Figure B1: Guessing Probabilities for an Increasing Sequence
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By Marc Vanhuele and Xavier Drèze
Marc Vanhuele is Associate Professor of Marketing, HEC School of Management (France). Xavier Drèze is Visiting Professor of Marketing, University of California, Los Angeles. The authors thank the HEC Foundation for its financial support and Shantanu Dutta, Gilles Laurent, and the four anonymous JM reviewers for their constructive feedback.
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Record: 111- Negativity in the Evaluation of Political Candidates. By: Klein, Jill G.; Ahluwalia, Rohini. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p131-142. 12p. 1 Diagram, 3 Charts, 1 Graph. DOI: 10.1509/jmkg.69.1.131.55509.
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Negativity in the Evaluation of Political Candidates
Prior research has demonstrated a clear negativity effect (greater weighting of candidate weaknesses compared with strengths) in the evaluation of U.S. presidential candidates in each of the past six elections analyzed. The authors adopt a motivational view and question the robustness of this finding. They reanalyze past National Election Studies data along with new data and conclude that the negativity effect is not universal across voters; it is a robust effect only for voters who dislike the candidate. They argue that previous findings are due to aggregation of data across voters who vary in their motivations.
Millions of dollars are spent marketing political candidates during each election year. An increasing percentage of these dollars is spent on negative campaigning (Ansolabhere and Iyengar 1995; Devlin 1993; Lau and Sigelman 1998) because of the belief that negative information about political candidates is more influential than positive information in swaying voter preferences (Aragones 1997; Bunker 1996; Johnson-Cartee and Copeland 1991; Klein 1991, 1996; Lau 1985; Pinkleton 1997). Consistent with this belief, media gurus often give negative news quadruple weight compared with positive news (as specified by the Merriam formula used to compute media impact; Kroloff 1988). It is because of this firm belief in the weight of negative information that political pundits continue to advocate its use, despite recent data that demonstrate that negativity in political campaigning disenfranchises voters and could lead to low voter turnout and involvement (Ansolabhere and Iyengar 1995).
The belief in the power of negative campaigning has its roots in impression formation studies conducted in the 1980s (e.g., Anderson 1981; Fiske 1980), which demonstrated a robust negativity effect--the greater weighting of negative compared with equally extreme positive information in the formation of overall evaluations--in a laboratory context. This belief gained wider acceptance in political marketing because of multiple case studies that supported it (e.g., the 1988 come-from-behind victory of George H.W. Bush) and empirical studies that demonstrated a negativity effect in the evaluation of presidential candidates (e.g., Klein 1991, 1996; Lau 1985). The latter stream of research consists of studies that analyzed voter interviews available through the American National Election Studies (NES) database for six past Presidential elections (1972, 1976, 1980, 1984, 1988, and 1992) and demonstrates a clear negativity effect for both the winning and the losing candidate in each election.
In the political domain, these studies have led to the conclusion that however popular the presidential candidate, his negatives matter more than his positives to the public, which in turn lends support to the adage that people do not vote for but against candidates (e.g., Ansolabhere and Iyengar 1995; Aragones 1997; Bunker 1996). These results also matter to marketers because they provide the strongest unchallenged source of "real" (as opposed to laboratory) data available in support of the negativity effect and have spurred an increase in the amount of negative product-related advertising (Business Marketing 1992). Recent theorizing in marketing and social psychology (e.g., Ahluwalia, Unnava, and Burnkrant 2001) raises questions about the prevalence of a negativity effect. Specifically, whereas the negativity effect in political candidate evaluations is consistent with the perceptual figure-ground explanation of negativity (e.g., Fiske 1980), it is inconsistent with recent motivational explanations of information processing (e.g., Ahluwalia 2002; Ditto et al. 1998; Till and Shimp 1998), as well as with recent political events (e.g., high Bill Clinton evaluations during the Clinton-Monica Lewinsky affair; the defeat of the more aggressively mudslinging Bush and Bob Dole in 1992 and 1996, respectively). Moreover, we believe that the increasing proportion of marketing dollars claimed by negative campaigning (West 1993) and its deleterious effects on voter turnout signal that the time has come to reassess the likelihood of a negativity effect in the evaluation of political candidates.
The Perceptual Figure-Ground Explanation
The most well-accepted explanation for the negativity effect in the political domain is the perceptual figure-ground theory (or salience explanation; Fiske 1980; Klein 1991), which is based on the argument that people generally exhibit a "person positivity bias" (Sears 1983) whereby they have positive expectations of others. For example, prior research reveals that an overwhelming majority of individual political figures are evaluated positively (e.g., Klein 1996). Against this positive political background, negative information about a specific candidate is likely to stand out (Lau 1985). Thus, negative information may be perceptually more salient and therefore more readily processed and given more weight. It may also be perceived as more credible and more informative (e.g., Fiske 1980). As such, the figure-ground explanation implies a negativity effect for all candidates, irrespective of their individual popularity. Figure 1 presents this conceptual model.
The Motivational Explanation
Most research on negativity grew out of a cognitive approach to judgments. From Anderson's (1981) cognitive algebra theory to the figure-ground explanation, nonmotivational explanations for negativity dominate. However, research in marketing (e.g., Ahluwalia 2002; Ahluwalia, Unnava, and Burnkrant 2001; Till and Shimp 1998) and social psychology (e.g., Ditto et al. 1998; Kunda 2000) has shifted toward an examination of how motivations affect judgments. For example, the cue diagnosticity approach--a dominant cognitive explanation for negativity--suggests that negative information is weighted most heavily because negatives are often more diagnostic or relevant than are positives (Skowronski and Carlston 1989). When the person or object to be evaluated is hypothetical or fictitious (as in most experiments in the impression formation paradigm), negative information is considered more relevant than positive information (Ahluwalia 2002). However, when the perceiver is familiar with the target, even a weak liking or preference is likely to invoke consistency motivation (Chaiken, Giner-Sorolla, and Chen 1996; Russo, Meloy, and Medvec 1998), such that preference-inconsistent negative information about the target may no longer be considered more relevant or diagnostic than preference-consistent positive information (e.g., Ahluwalia, Unnava, and Burnkrant 2001). Therefore, the formation of preferences invokes the biased processing of preference-inconsistent information through various defense mechanisms, such as generation of counterarguments (Ahluwalia, Unnava, and Burnkrant 2001; Chaiken, Giner-Sorolla, and Chen 1996), information distortion (Russo, Meloy, and Medvec 1998), source derogation (Ahluwalia 2000), and even selective avoidance of inconsistent information (Frey 1982). In other words, the relevance or diagnosticity assessment is subjective in nature and driven, in part, by the preferences of the perceiver (Herr, Kardes, and Kim 1991).
The motivational view therefore suggests that the negativity effect is not universal. Instead, a voter's preferences should determine whether a candidate's negatives are weighted most heavily. Consistent with this view, if we were to segment voters by their preference toward a given candidate, only those motivated to dislike the candidate should show a negativity effect; those who support the candidate should not be motivated to dwell on their candidate's negatives any more than his or her positives. Thus, a negativity effect should appear only in evaluations for which the candidate's negatives are preference consistent (see Figure 1).
An implication of this view is that swing voters should not demonstrate a negativity effect, because swing voters by definition do not have strong preferences for one candidate over the other and have not decided against any of the candidates. We presume that swing voters have moderately positive views of all candidates (those who dislike both candidates are likely to be disenchanted and not vote at all; Ansolabhere and Iyengar 1995), which renders negative information preference inconsistent. This prediction is in contrast to the perceptual figure-ground explanation, as well as prior research that explicitly examines undecided or swing voters (Lau 1982). Specifically, in his analysis of 1980 NES data, Lau (1982) reports a stronger negativity effect for swing voters than for voters who had chosen a candidate (though this difference was not tested for statistical significance). Lau's findings have perpetrated the belief that the negativity effect is likely to be stronger and, therefore, that negative campaigning is more effective when many voters are undecided during the initial phases of a campaign (e.g., primaries). This finding, however, could be an outcome of a variety of factors, including the differential importance of the negative and positive traits used in the 1980 NES survey.
The motivational explanation thus predicts a negativity effect for only one segment of voters: those who want the candidate to lose. When voters judge a disliked candidate or the opponent of their favored candidate, negatives are consistent with their desires and thus should receive the greatest weight. However, other voter segments are not likely to weight a candidate's weaknesses more heavily than his or her strengths. This prediction is in direct opposition to expectations based on the figure-ground explanation, which posits a negativity effect for all voter segments, as we discussed previously. Furthermore, the figure-ground explanation predicts that negativity is strongest for swing voters and those who prefer a candidate, namely, those judging candidates against generally positive expectations. The motivational view, however, predicts that negativity is absent for these voters.
We suggest that the finding of a negativity effect in prior research that has used the NES databases could be driven by a subset of voters: those who want the candidate to lose. When the data were aggregated across different segments of voters, an aggregate negativity effect emerged primarily because of this segment of voters. A more disaggregated view of the data may reveal differences across segments of voters. Therefore, we frame the first set of hypotheses in terms of the motivational explanation, as follows:
H1: Voters who prefer a candidate are not likely to exhibit a negativity effect in evaluating the candidate.
H2: Swing voters are not likely to exhibit a negativity effect in the evaluation of either candidate.
H3: Voters who prefer the candidate's opponent are likely to exhibit a negativity effect in evaluating the candidate.
Although we do not make specific predictions about the strength of the voters' opposition or support, we might expect that those who are strong opponents of a candidate will show greater negativity than will weak opponents. Alternatively, all opponents, regardless of the strength of their opposition, might view negative information as preference consistent. To examine these two possibilities, we explore potential differences in negativity based on the strength of the opposition.
Furthermore, if an aggregate-level negativity effect is driven by voters who want the candidate to lose, negativity should not be found in aggregate analyses that are less likely to include this group of voters. The NES data are typically collected just before the election, when most voters have been exposed to substantial information about candidates. By this stage of the campaign, most candidates have acquired a sizable group of opponents. We argue that these opponents are likely to exhibit a negativity effect and drive its occurrence at the aggregate level. In the early phase of the campaign, however, most voters will not have formed strong preferences. Thus, negativity is less likely to emerge in data collected during the primary phase of the presidential race.
There is a caveat to this prediction. Even during the early phases of campaigning, a few candidates may have developed a significant group of opponents as a result of either their extreme platform or behavior or the polarization that occurred previously (e.g., a current vice president running for the presidency). We predict an aggregate-level negativity effect for only these candidates. In contrast, the perceptual figure-ground explanation would predict a negativity effect for all the candidates in the initial phases of a campaign. On the basis of a motivational explanation for negativity, we predict the following:
H4: The likelihood of an aggregate-level negativity effect for a candidate will be related to the proportion of voters who dislike him or are motivated to view him negatively; the higher this proportion, the more likely is an aggregate-level negativity effect.
H5: If the responses from voters except those opposed to a candidate are aggregated, no significant negativity effect will emerge for political candidates.
Summary
In summary, on the basis of recent motivational research in marketing (e.g., Ahluwalia 2002) and psychology (e.g., Kunda 2000), we hypothesize that voters are likely to weight a presidential candidate's weaknesses more heavily than his strengths only when they are motivated to view the candidate negatively (i.e., prefer the opponent and/or dislike the candidate). From a marketing perspective, this group is likely to be the least critical to persuade in a political campaign (e.g., it would be inefficient for a Democratic candidate to pursue diehard Republican voters). However, the segments that likely represent the biggest return on investment from persuasive communications--swing voters and those who have a favorable tendency toward the candidate--are not likely to be more sensitive to negative than to positive information. In addition, we argue that negative information is not likely to be more impactful than positive information in the primaries, when a substantial group of opponents has not emerged for most candidates. Thus, contrary to current belief, we predict somewhat limited benefits of negative compared with equally extreme positive information in the political domain.
We begin our inquiry with a close examination of prior evidence of negativity in political evaluations and speculate that assessments of negativity at an aggregate level mask substantial differences among different segments of the population. In Study 1, we begin with a reanalysis of data from the most recently published negativity effect study in this area, the 1992 presidential election (Klein 1996). We reanalyze this data by disaggregating it into relatively homogeneous groups based on candidate preferences to test H1 through H3. We repeat the same analysis for the 1996 NES data set to examine the generalizability of the effects. In Study 2, we test H4 and H5 by examining candidate evaluations during the 1988 primary season (the most recent NES study of primaries).
The NES database consists of interviews conducted before and after each presidential election by the Center for Political Studies of the Institute for Social Research at the University of Michigan. These studies use a random probability sampling of eligible voters in the United States. The pre-election interviews (which contain the questions used in negativity analyses) are conducted in person from September to Election Day.
Since 1980, the NES interviews also have contained a trait inventory that asks respondents to rate the candidates on a set of traits. Lau (1982, 1985) analyzes the 1980 inventory, in which some of these traits were positively worded (e.g., intelligent) and others were negatively worded (e.g., dishonest). To allow for a more controlled approach (negative and positive items equivalent in extremity, relevance, and so forth), the trait inventory has used only positively worded traits (e.g., honest) since 1984. Respondents could rate the trait as a weakness or a strength of the candidate. Klein (1991, 1996) analyzes the NES data from the 1984, 1988, and 1992 elections to examine whether traits judged to reflect weaknesses were more predictive of final evaluations and voting than were traits that reflected strengths. Through regression analysis, Klein assessed the weight given to each trait in the evaluation of each candidate. The analysis revealed that traits for which the candidates received low ratings (candidate weaknesses) were given more weight than were traits for which they received high ratings (candidate strengths). This result was consistent for both the winning and the losing candidates for each of the three elections and thus supports a robust negativity effect. These findings, however, are based on an aggregate-level data analysis, in which responses from all types of voters, irrespective of their varying candidate preferences (e.g., opponents, supporters, swing voters), were combined in the assessment of negativity.
Data and Methodology
The data. In this study, we reanalyze the 1992 NES data set, along with new analyses of the 1996 NES data.( n1) The NES database contains 2485 (response rate = 72%) interviews for the 1992 election and 1714 (response rate = 71%) interviews for the 1996 election.( n2) The 1992 trait inventory asked respondents how well ("extremely well," "quite well," "not too well," or "not well at all") each of the following nine attributes described each of the candidates: "intelligent," "compassionate," "moral," "inspiring," "provides strong leadership," "cares about people like you," "honest," "knowledgeable," and "gets things done." In the 1996 trait inventory, respondents rated Clinton on the same traits but rated Dole on all but two traits: "intelligent" and "compassionate." The overall evaluation of each candidate was assessed by a thermometer score that asked the respondents to indicate how favorably or unfavorably they were toward the candidate on a 0-100 scale (higher numbers indicate feelings of warmth or favorableness, lower numbers indicate feelings of coldness or unfavorableness, and a score of 50 is neutral).( n3) For ease of interpretation, the four-point trait rating scales and the thermometer ratings were converted to a 0-1 scale, on which higher numbers indicate greater favorableness.( n4)
For the analysis, five groups of voters were identified on the basis of two questions about voting choices. The first asked respondents which candidate they thought they would vote for in the upcoming election; the second asked them whether their preference for the chosen candidate was strong or not strong. Thus, for the 1992 election, the groups were as follows: strong preference Clinton, weak preference Clinton, undecided or swing voters, weak preference Bush, strong preference Bush. The swing voter group included respondents who chose the alternative "don't know" for the candidate choice question but stated that they intended to vote. Unfortunately, the 1996 survey did not include the "don't know" option, and therefore we were unable to identify swing voters through this method. Thus, we used an additional method to identify swing voters in both elections. With the thermometer difference approach, we identified the segment of swing voters by selecting respondents whose thermometer scores for the two candidates were within 10 points (on a 100-point scale) of each other. By definition, this segment comprises voters who have similar evaluations of both candidates and therefore are likely to swing from one candidate to another. We estimated the negativity effect for each candidate at the aggregate level, as well as at the group level (i.e., separately for each of the five groups of voters, with swing voters operationalized in two different ways in 1992).
Computation of trait weights (Stage 1). We estimated the weight given by respondents to each trait in the evaluation of a given candidate. To this end, we specified a regression equation by which we could predict a candidate's thermometer score on the basis of his trait ratings when race, sex, party identification, and political ideology were constant.( n5) Following prior research (Ahluwalia 2000; Klein 1991, 1996), we used the unstandardized slope coefficient obtained for each trait as a measure of its weight in the overall evaluation. This parameter is insensitive to variance differences among the traits (Lewis-Beck 1980; Pedhazur 1997). We used this procedure to generate trait weights for each candidate for each of the five groups of voters, as well as for the entire voter sample.( n6)
Estimation of the negativity effect (Stage 2). Next, consistent with prior research in the area, we examined the relationship between trait weights and trait ratings. A negativity effect would imply a negative relationship between trait weights, as estimated by the slope coefficients generated for each trait (Stage 1), and mean trait ratings. That is, a negativity effect is evident if the traits judged by voters to represent character weaknesses (low ratings) are weighted more heavily in the candidate's overall evaluation than are traits judged to represent character strengths (high ratings). In a univariate regression, we regressed trait weights on the trait ratings; we used the unstandardized slope coefficient as an indicator of the ability of the ratings to predict the weightings. Using this procedure, we estimated negativity for the aggregate and each of the subsamples for each of the candidates.
Results
1992 NES. Consistent with Klein's (1996) findings, a significant aggregate-level negativity effect emerged for both the candidates (Bush and Clinton). However, the group-based analysis reveals that, as hypothesized, the negativity effect occurs only for voters who exhibit a preference for the opponent (Table 1). Specifically, there was a significant negativity effect (p < .05) in the evaluation of Clinton for voters who had a strong preference for Bush. This effect approached significance (p < .10) for those voters who claimed a weak preference for Bush. A statistically significant negativity effect was obtained in the evaluation of Bush by voters who exhibited both strong and weak preferences for Clinton. The difference between the weak and the strong opponent groups was not significant for either candidate. Most other slopes had a negative sign but were smaller than the slopes of the opponent groups, and none approached significance, in support of H1-H3. The negativity effect is not significant for any of the swing voter groups (irrespective of the method used to identify them). Furthermore, if we remove the opponent groups from the aggregate analyses, so that only the supporters and swing voters remain, the aggregate negativity effect is no longer significant for either Clinton (b = -.30, p > .40) or Bush (b = -.47, p > .11).
In Figure 2, Panels A and B, we provide a graphical illustration of the relationship between the trait ratings and trait weights we used to examine negativity in judgments of Bill Clinton. Figure 2, Panel A, demonstrates the absence of the negativity effect for voters with a strong preference for Clinton, whereas Figure 2, Panel B, shows the negativity (negative relationship between trait weights and trait ratings) effect in the evaluation of Clinton by voters who strongly preferred Bush.
1996 NES. As we reveal in Table 2, a negativity effect emerges at the aggregate level for both Clinton and Dole. The group-level analyses, however, reveal a significant negativity effect only for voters who preferred the opponent. This effect tends to be stronger for the group with a strong compared with a weak preference for the opponent (though this difference is not statistically significant). The negativity effect is not significant for swing voters or for the groups who preferred the candidate. When the two opponent groups are excluded from the aggregate analyses for each candidate, a significant negativity effect fails to emerge for either Clinton (-.17) or Dole (-.30; both ps > .30).
Analyses of trait variance differences. A difference in the variance of negative versus positive traits could influence the slope coefficient of the negativity equation. For example, if the negative traits have a higher variance than the positive traits, the coefficients (weight estimates) for these traits may be inflated in the first-stage regression, increasing the likelihood of a negative relationship between trait ratings and trait weighs (the negativity effect) in the second-stage regression. To address this possibility, we used unstandardized regression coefficients to assess trait weights. Prior research has indicated that though beta coefficients are sensitive to differences in the variance of independent variables, unstandardized regression coefficients are not (Johnson and Gustafsson 2000; Lewis-Beck 1980; Pedhazur 1997) and are preferable in situations in which variances may differ. We find that for the groups for which we expected and obtained the strongest negativity effect (strong opponents), the correlation between trait rating and trait variance is either small and insignificant or in the opposite direction (i.e., negative traits had lower, not higher, variance), which increases our confidence in the finding of a negativity effect (i.e., it emerged despite the variance differences working against it). More important, the correlations between the mean trait variance and trait ratings were highly negative, which indicates a higher variance of negative traits (r's ranging from -.59 to -.95, all ps < .10) for all groups (supporters and swing) for which we had neither predicted nor obtained a significant negativity effect. As such, this negative relationship may increase the likelihood of a negative coefficient (and negativity effect), thereby working against our prediction of the absence of this effect. This likelihood could have contributed to the prevalence of negatively signed coefficients for these groups. However, a statistically significant negativity effect does not emerge for these voters, despite negative correlations between the ratings and weightings, which provides even stronger support for our predictions.( n7)
Voting against candidates. Although we do not find a negativity effect for the supporter groups, the data do not rule out the tendency of people to vote against candidates. Simply stated, in a situation in which the voter is evaluating two candidates, the negative evaluation of the opponent (not the positive evaluation of the supported candidate) may drive the formation of preference. One way to determine whether people have a tendency to vote against is to assess whether their preference (and its strength) is determined to a greater extent by their dislike of the opposed candidate than by their liking of the supported candidate.
To this end, we estimated two logit regression models (one for each election). For each voter who had formed a preference (i.e., all voters except the swing voters), we used the overall evaluations of the supported and opposed candidates to predict the strength of the voters' preference for their chosen candidate (1 = "weakly prefer"; 2 = "strongly prefer the candidate"). These analyses help us assess whether the opponent's weaknesses, as opposed to the supported candidate's strengths, are more influential in strengthening a voter's preferences. If people have a tendency to vote against, the coefficient for the opposed candidate should be significantly larger than that for the supported candidate. There were 1638 cases in the 1992 regression and 1282 cases in the 1996 regression. Our analyses reveal that voter evaluations of the supported candidate explain significantly greater variance in the strength of preferences than do evaluations of the opposed candidate for both the 1992 (coefficient(supported) = .08, coefficient(opposed) = -.03, both ps < .001; t = 8.92, p < .001, for the difference between absolute values of the slope coefficients) and the 1996 (coefficient(supported) = .11, coefficient(opposed) = -.03, both ps < .001; t = 10.43, p < .001) elections. This result indicates that people do not have a tendency to vote against candidates. Their strength of preference is driven more by their liking of the supported candidate than by their dislike of the opposed candidate.
Another way to address this issue involves examining the mean thermometer scores. Voting against would translate into evaluations of opposed candidates that are disproportionately lower than the midpoint (neutral point of 50) of the scale compared with the magnitude by which evaluations of the supported candidate are higher than the midpoint. Analyses of the thermometer scores from the 1992 and 1996 data sets do not support such a tendency. In contrast, for all groups of voters who had formed a preference, the mean thermometer score for the supported candidate is farther above the midpoint of the thermometer scale than is the score of opposed candidate below the midpoint (statistically significant for six of eight groups with ps < .001 and directionally more distant for the remaining two groups).( n8) That is, the supported candidate's evaluation is more favorable in magnitude than the opposed candidate's evaluation is unfavorable. Furthermore, the overall evaluation for both candidates is slightly above the midpoint for the swing voters (Bush = 55, Clinton = 52 [1992 candidate choice method]; Bush = 56, Clinton = 57 [1992 thermometer difference]; Dole = 58, Clinton = 57 [1996]), which suggests that they were not deciding on a candidate against whom to vote.
Discussion
The results from both the 1992 and 1996 elections converge in indicating that a negativity effect is not characteristic of all voters. Only those who prefer the opponent weight a candidate's weaknesses more heavily than his strengths. Thus, negative information about a candidate appears to be given more weight only when it is preference consistent. Although there is a slight tendency for strong opponents to show the greatest negativity, this effect is also significant for weak opponents, which suggests that any level of opposition makes negative information preference consistent. Negativity was not found for swing voters or those who prefer the candidate.( n9) Although the full aggregated sample exhibits negativity, this effect is not significant in the aggregate analyses when the opponent groups are excluded. At a more general level, our results suggest that voters are more likely to vote for candidates than against them, and the negativity effect is more often absent than present in the evaluation of political candidates. Thus, our findings suggest a new perspective on negativity in politics.
One point that requires further discussion relates to the possibility that voters base their trait ratings on their overall evaluation (halo effect). This possibility would be especially high if trait ratings immediately followed the overall evaluation question. The overall evaluation and trait ratings, however, were separated by dozens of other questions relating to House and Senate candidates, opinions about a wide range of issues (e.g., affirmative action, religion, foreign affairs), and various other topics. Furthermore, the negativity effect is not dependent on the mean level of trait ratings (lower or higher, as implied by a halo effect) but on the differential weighting of traits based on their favorability (Ahluwalia 2002; Anderson 1981). A negativity effect would not be more likely to emerge for a voter who rates the candidate lower (versus higher) on all traits; instead, it would be much less likely to emerge for a voter who exhibits a strong halo effect for his or her negative overall evaluation (giving all traits similarly low ratings). In such a situation, the voter is not necessarily weighting the candidate's weaknesses more than his or her strengths; he or she is rating every trait as a weakness. This rating reduces the possibility of a negativity effect, which requires both perceived strengths and weaknesses. Therefore, by definition, a halo effect is not consistent with the emergence of a negativity effect.( n10)
The absence of a negativity effect in the 1992 and 1996 analyses for the voters who preferred the candidate is not likely to be a result of either smaller sample size or low power of the tests. The sample size was similar in the supporter and opponent cells for both candidates in both elections. When the data were aggregated across voters with strong and weak preferences for each candidate within each election--thus increasing the sample size--the negativity effect was still not statistically significant for either of the candidates for voters who preferred them. Similarly, even when the ratings of both candidates by swing voters were pooled (for the 1992 NES) or we examined our larger samples of swing voters (from the thermometer difference approach), the relationship between trait ratings and trait weights was still not significant. Thus, the absence of a negativity effect for all groups of voters, except those who preferred the opponent, appears to be a robust finding.( n11)
Data and Methodology
The 1988 NES Super Tuesday study was also conducted by the Center for Political Studies of the Institute for Social Research at the University of Michigan. The study was conducted through telephone interviews between January 17 and March 8 (the early primary season). The sample size was 2076 respondents. The survey included a trait inventory and thermometer ratings for all the candidates.( n12) To test the hypotheses, we assessed negativity for the primary candidates by the same technique used in Study 1.
We predict (H4) that the larger the proportion of voters who dislike a candidate, the higher is the likelihood that an aggregate-level negativity effect will emerge. Furthermore, if this group of opponents is excluded from the data, an aggregate-level negativity effect should fail to emerge (H5). The most conservative test of our hypothesis would be conducted by eliminating the strongest opponents. In the absence of the strength of preference measure used in Study 1, we operationalized this group as those who gave low thermometer scores to a candidate (≤20). We chose this cutoff on the basis of the NES analyses from Study 1, which reveal that the strong opponents group (as defined by the vote choice and strength of preference measures) has a mean thermometer score as low as 22 (for Clinton in 1996).
Results
Aggregate-level negativity analysis. As we show in Table 3, most candidates did not show a significant aggregate-level negativity effect during the primary; significant negativity emerged for only 3 of the 13 candidates, despite the large sample sizes (n > 600 for 10 of 13 candidates). A negativity effect had not yet emerged for Michael Dukakis, who went on to become the democratic nominee. Klein (1991), who analyzed the 1988 main election NES database, reports a significant aggregate-level negativity effect for Dukakis in the data collected just before the final election.
To test H4, we predicted the negativity coefficient (the unstandardized slope coefficient for the relationship between trait ratings and trait weightings) using the proportion of voters classified as opponents; we find that the higher the proportion of opponents for a candidate, the greater is the negativity effect (b = -.61, p < .05).
Next, to confirm that the opponents were driving the emergence of an aggregate-level negativity effect in these data (H5), we ran a new set of aggregate negativity analyses following the same technique but with the strong opponents excluded. As we show in Table 3, with the exception of Pat Robertson,( n13) a significant negativity effect failed to emerge for any of the candidates when opponents (who were motivated to view the candidate negatively) were not included in the analyses. This result suggests that a majority of the voters in the primaries are not motivated to weight a candidate's weaknesses more heavily than his strengths. (Note that the absence of negativity in the primaries is not due to voters' inability to distinguish the candidates' relative strengths and weaknesses; ranges of trait ratings in the primaries were similar to those of the general election candidates examined in Study 1.)
Discussion
The findings of Study 2 support H4 and H5. The lack of a significant negativity effect for most candidates at this stage of the campaign supports our theorizing. More important, we find that the aggregate-level negativity effect is likely a function of the proportion of strong opponents. Negativity is likely to be a hallmark of this group of voters, and their presence appears to drive the aggregate-level finding. Candidates who have not yet developed a group of opponents who are highly motivated to focus on their negatives are not likely to be subject to a significant aggregate-level negativity effect. As such, these opponent groups are most likely to emerge when either the candidate is associated with some controversial policies or personal issues or the voters are faced with a choice between a preferred candidate and an opponent during the final phases of a campaign (as examined in Study 1). In short, the findings of this study further support our central proposition: Only voters motivated to dislike the candidate exhibit a negativity effect in their evaluations.
Consumer behavior researchers have long been interested in both the evaluation (e.g., Ahluwalia 2000; Klein 1991, 1996; Morwitz and Pluzinski 1996; Simmons, Bickart, and Lynch 1993) and the marketing (e.g., Homer and Batra 1994; Newman and Sheth 1985) of political candidates. They have come to market candidates much as they market brands (McManus 1996; Newman 1994; Upshaw 1996).
One empirical finding that has had a substantial impact on the marketing of brands, as well as of political candidates, is the negativity effect. Evidence of a significant negativity effect in six recent presidential elections has provided support for attack strategies in political campaigns and negative product advertising (Ansolabhere and Iyengar 1995; Pinkleton 1997). Prior theorizing and data also suggest a negativity effect for swing voters.
Political pundits apparently advocate negativity, despite its potential ill effects on voter turnout and involvement, because they expect voters to weight negative information more heavily in their evaluations than they do positive information. Prior research is consistent with this expectation, and several salient case studies--such as Bush's victory in the negative 1988 campaign--attest to negativity's effectiveness. In contrast, our research suggests that the influence of negative information is overstated. The time has come to reassess the role of negative information in political evaluations and reexamine the old adage that people only vote against, and not for, candidates.
The NES data from two elections (1992, 1996) and the Super Tuesday data (1988) converge in suggesting that the negativity effect is much less prevalent in the evaluation of political candidates than previously believed; it is significant only in judgments of candidates who the voter is motivated to dislike. This motivation may occur because the voter either has a preference for an opponent or simply dislikes the candidate. Our analysis indicates that this subset of the electorate drives the aggregate-level negativity effect obtained in previous research.
In Study 1, we find a significant negativity effect only for the preferred candidate's opponent. It did not emerge for either swing voters or those who preferred the candidate. In Study 2, we again find that the aggregate-level negativity effect is dependent on the presence of strong opponents. These findings indicate that swing voters and those judging their preferred candidate are less likely to exhibit a negativity effect.
We could argue in favor of negative advertising on the basis of the notion that some voters may become strong opponents because of their exposure to negative candidate information. Thus, a promising strategy would be to create a large group of opponents through negative advertising. Elections research suggests, however, that initial preferences are derived from perceived candidates' positions on high valence issues, party affiliation, and other longstanding ideologies (see Kinder 1998). Furthermore, our results for swing voters (Study 1) and the vast majority of primary voters (Study 2) show that negative candidate information is not given greater weight than is positive information before preferences are formed. In addition, preference strength is better predicted by evaluations of the preferred candidate (presumably based on positive information) than by evaluations of the opposed candidate (potentially based on negative information). Together, these arguments suggest that negative attack advertisements are not more likely than positive advertisements to turn undecided voters into opponents.
We do not find a significant positivity effect in any of our analyses. The tendency toward low (but nonsignificant) levels of negativity implies the possible combined effects of both cognitive and motivational forces and suggests the need to understand relative rather than alternative roles of cognitive and motivational explanations. Negative information is perceptually more salient and therefore likely to garner more attention (e.g., Jones and McGillis 1976). However, the manner in which it is processed (e.g., discounted, supported) and how much weight it receives in overall impressions depends on the motivation of the perceiver. Although a consistency motivation that accompanies a weakly positive attitude may be adequate to attenuate negative information's perceptual advantages, a strong attachment (and high motivation) may be needed to completely reverse them (Ahluwalia, Unnava, and Burnkrant 2001).
Our research paradigm is somewhat different from that of impression formation studies (e.g., Anderson 1981; Fiske 1980), in that we use the voters' self-reported trait ratings and overall candidate evaluations to compute the negativity effect. Although our paradigm is not the most prevalent method for estimating a negativity effect (given the predominance of experimental research that uses unknown targets), it is well established in and consistent with prior field research that has examined the negativity effect in the context of familiar targets (e.g., Ahluwalia 2000; Klein 1991, 1996; Lau 1982). Furthermore, we measured all candidate traits with the primarily positively worded traits used in the NES surveys to avoid confounds associated with the differential wording of items. However, this method may underrepresent the extremity of some negative beliefs. Additional research should examine the extent to which such a bias is likely. Scales with bipolar endpoints (dishonest/honest) may be better able to capture the full range of voter beliefs.
Implications for Political Marketing Management
Our findings challenge the accepted wisdom that negative campaigning is an effective means of persuading critical voters who either have weak preferences or are undecided (Ansolabhere and Iyengar 1995; Lau 1985). Whereas political pundits predict that attack advertising will become more common as more independents and swing voters dominate the U.S. electorate (Ansolabhere and Iyengar 1995; Lau and Sigelman 1998), our findings suggest that the wisdom of this strategy should be reassessed because these target audiences are not motivated to dwell on negatives. The disparagement communicated in negative advertisements will be music to the ears of voters who already dislike the candidate, but preaching to the choir is not the optimal objective of campaign spending.
We note that the absence of a negativity effect does not imply that negative information does not have any impact on these voter segments; it simply means that it is not more effective than equally extreme positive information. If a candidate is the target of a negative advertisement, the advertisement is likely to attenuate his or her evaluation (e.g., Pinkleton 1997). However, this attenuation will be of approximately the same magnitude as the enhancement likely to occur in response to an equally extreme positive advertisement. We speculate that if the negative information presented in an attack is more extreme than the available positive information the candidate offers in support of his or her candidacy, or it corresponds to a particularly salient issue, negative campaigning may be more persuasive than positive advertising, especially for swing and weak preference voters. However, most negative campaigning in the marketplace consists of mudslinging attacks that focus on a small corner of a candidate's career and address trivial issues (Kamber 1997), and therefore its value to the campaign is potentially questionable. Examples abound of situations in which negative campaigning about minor issues failed to influence voter groups. For example, Steve Forbes and Lamar Alexander (1996 primaries) launched negative campaigns against Bob Dole based on narrow issues; Dole emerged as the front runner. In the 2004 Iowa caucus, the two candidates who ran negative campaigns (Howard Dean and Dick Gephardt) finished solidly behind those who promoted more positive campaigns (John Kerry and John Edwards). Even in the general elections, the main attacker has not always emerged as the winner: Consider Bush (1992) and Dole (1996).
In summary, our research suggests that campaign managers might not want to target the opponent's negatives for the sake of negativity. The absence of a negativity effect implies that only when the content of negative information is of an extreme or compelling nature (compared with possible positive information) is it likely to be more effective than its positive counterpart in changing the preferences of the most malleable swing voters.
Implications for Nonpolitical Marketing Management
Our research provides a stringent test of the negativity hypotheses. Voting contexts are different from brand judgments (for which some limitations of the negativity effect have been uncovered; Ahluwalia 2002): Voters are faced with a choice (instead of a judgment), the target is a person (instead of a product), and data are collected in naturalistic settings in which the salience advantages of negative information are magnified (instead of in laboratory contexts in which subjects are directed to process all information, which attenuates the salience advantage). Therefore, a negativity effect should be much more likely to emerge in political candidate research than in experiments that focus on brand judgments. That a significant negativity effect does not emerge for multiple voter segments even within this context presents a greater challenge to the pervasiveness of this effect in the marketplace.
Although extending our findings from the political to the product domain is necessarily speculative, some analogies can be made that could be tested in further research. For example, swing voters are comparable to low-loyalty and switcher segments. Our findings suggest that these consumers will not weight the brand's negatives more heavily than its positives. Thus, attack advertising targeted to these groups to steal market share from the competition will not be more effective than a positive campaign.
The finding that a negativity effect was absent for lesser known candidates in the 1988 primaries has implications for competition with new products and brands. Attacking a new entrant might be less productive than simply promoting the positives of an existing brand, unless the established brand enjoys a base of strong supporters who will represent strong opponents to a new offering. This group will carefully consider negative information about the new entrant. For its part, attack advertisements by the new entrant will be most effective if they are targeted at those consumers who already are dissatisfied with current offerings. Therefore, a proper understanding of the size and passion of segments that represent different loyalty levels is essential in the battle for market share.
Our findings are consistent with those of other studies that find that a positive corporate image and strong consumer loyalty offer some protection to a firm faced with negative publicity (e.g., Ahluwalia, Unnava, and Burnkrant 2001). For example, recent research in corporate social responsibility finds that a prior positive corporate image protects the corporation from the impact of negative information exposed during a crisis (Klein and Dawar 2004). Our research suggests that an understanding of the strength of consumer preferences for a brand or corporation is important for designing effective strategies to respond to negative publicity.
In conclusion, our research joins a growing number of studies that examine the effect of motivations on information processing (e.g., Ahluwalia, Unnava, and Burnkrant 2001; Kunda 2000; Till and Shimp 1998). We find that though negative information may have perceptual advantages, a focus on cognitions alone cannot explain its role in complex naturalistic environments in which people are driven by a variety of motivations (Chaiken, Giner-Sorolla, and Chen 1996). Our results suggest the fruitfulness of reexamining other findings in marketing that are based on a strictly cognitive information processing perspective.
Both authors contributed equally to this research project; the order of authorship was determined by a coin toss. This research was supported by grants from the Oswald Summer Fellowship, as well as from the General Research Fund of the University of Kansas to Rohini Ahluwalia and from the INSEAD Research and Development fund to Jill Klein. The authors thank the participants of the New York University Research camp and Andrew John for their comments on a previous version of this article and ICPSR at the University of Michigan for providing access to American National Election Studies data.
( n1) Unfortunately, the 2000 NES data include only a short version of the trait inventory and thus do not allow for a test of our hypotheses.
( n2) The sample size included in our analyses was actually smaller. To be included in a given regression analysis, the respondent had to have supplied responses to the thermometer score and trait ratings for a given candidate and answered all the control variable questions. Thus, for Clinton in 1992, the sample size was 1658, and for Bush, it was 2090. In 1996, the sample size was 1624 for Clinton and 1491 for Dole.
( n3) The thermometer scores were solicited before the trait inventory. More than 100 questions about other issues (e.g., the Persian Gulf War, Senate and House races and candidates) separated the thermometer questions and the trait inventory.
( n4) Thus, the trait ratings were coded as follows: "not well at all" = 0, "not too well" = .33, "quite well" = .67, and "extremely well" = 1. The thermometer score was divided by 100.
( n5) Klein (1991, 1996) generates separate regression equations, one for each trait (plus control variables), but reports (1996) that the relative weighting of the traits remains the same when the weighting coefficient of each trait is assessed by a regression equation that includes all the candidate's traits and the control variables.
( n6) We conducted analyses to examine the trait rating scale linearity to ensure that the four-point scale had equal intervals. The scale was linear, and minor departures from linearity cannot account for the pattern of results reported.
( n7) We also estimated the negativity equation (relationship between trait ratings and trait importance, using unstandardized b's), controlling for the variance in the trait ratings, with the following equation:
Trait importance (b) = α + b(trait rating) + b(trait standard deviation).
A negativity effect still emerged for the opponent groups, as we predicted. In addition, when variance was controlled, the sign of the unstandardized slope coefficient for the undecided group flipped from (nonsignificantly) negative to (nonsignificantly) positive for both of the 1992 candidates.
( n8) Scores for the 1992 data set were as follows: strong Clinton supporters = +27 versus -16; weak Clinton supporters = +14 versus -6; weak Bush supporters = +17 versus -9; and strong Bush supporters = +32 versus -17 (all ps < .001). For 1996, the scores were strong Clinton supporters = +34 versus -9 (p < .001); weak Clinton supporters = +16 versus -7 (p < .001); weak Dole supporters = +15 versus -12 (not significant); and strong Dole supporters = +28 versus -27 (not significant).
( n9) The findings for swing voters cannot be attributed to a lack of knowledge or low level of political interest. Analyses reveal that these voters were as knowledgeable about politics, not more skeptical about the government, and at least as likely to vote as the other groups.
( n10) As further evidence against a halo explanation for our results, our analyses reveal a high correlation between the trait ratings of the strong supporter and strong opponent groups for each candidate (r(Clinton 1996) = .90, r(Dole 1996) = .94, r(Clinton 1992) = .77, r(Bush 1992) = .85, all ps < .001), which indicates that there was a high level of agreement between the two groups on the candidates' perceived strengths and weaknesses. Consistent with our prior arguments, these data suggest that though the opponents were in general agreement with supporters about the relative strengths and weaknesses of the candidate, a negativity effect emerged for the former group because the candidates' weaknesses were apparently more diagnostic than their strengths in voters' overall evaluations.
( n11) Even the smaller samples in some cells are large enough to produce reliable estimates of trait weighting (Hair et al. 1998). Note also that our negativity analysis examined the relationship between trait ratings and trait means. Thus, we determined n in this analysis by the number of traits and did not vary it across subsamples.
( n12) Respondents in the 1988 study were asked how well each of the following eight attributes described each candidate running in the primaries: "intelligent," "compassionate," "moral," "inspiring," "provides strong leadership," "decent," "cares about people like you," and "knowledgeable." Relative to later NES studies, "decent" appeared in this survey, and "honest" and "gets things done" did not. Respondents indicated that the trait described the candidate "a great deal," "somewhat," "little," or "not at all."
( n13) Even when we removed participants who gave Robertson thermometer ratings of 20 or less from the sample, he was still left with a substantial number of opponents (50% gave a score of less than 50).
Regression Coefficients for the Negativity Analysis of
the 1992 Election
Legend for Chart:
A - Segment of Voters
B - Evaluation of Clinton b
C - Evaluation of Clinton Standard Error
D - Evaluation of Clinton n(a)
E - Evaluation of Bush b
F - Evaluation of Bush Standard Error
G - Evaluation of Bush n(a)
A B C D
E F G
Full sample -.68(**) .27 1658
-.53(***) .13 2090
Strong preference for .23 .47 539
candidate -.24 .36 382
Weak preference for candidate -.35 .25 193
-.27 .16 227
Swing voters (candidate -.91 .62 110
choice question) -.32 .39 157
Swing voters (thermometer -.07 .23 382
difference approach)(b) -.03 .22 511
Weak preference for opponent -.54(*) .27 160
-.42(**) .15 246
Strong preference for -.56(**) .22 294
opponent -.48(***) .14 613
(*) p< .10.
(**) p< .05.
(***) p< .01.
(a) Number of voters in the segment. The sum of n for the
subsamples does not add up to the n of the full sample because
only those who intended to vote could be asked which candidate
they planned to vote for and therefore be included in the
subsamples identified by the voting choice question. Although
the means and trait weights are based on the sample sizes, note
that the slope coefficients are based on the relationship
between the nine trait ratings and the nine trait weightings.
(b) Respondents who gave similar thermometer scores (within ten
points) to both candidates. This group includes almost all swing
voters identified by the voting choice question, plus
respondents who had similar candidate evaluations but did not
intend to vote and respondents who may have been classified
in the preference groups (using the candidate choice question)
even though they had similar evaluations of the two candidates. Regression Coefficients for the Negativity Analysis of
the 1996 Election
Legend for Chart:
A - Segment of Voters
B - Evaluation of Clinton b
C - Evaluation of Clinton Standard Error
D - Evaluation of Clinton n(a)
E - Evaluation of Dole b
F - Evaluation of Dole Standard Error
G - Evaluation of Dole n(a)
A B C D
E F G
Full sample -.35(*) .16 1624
-.73(***) .13 1491
Strong preference for -.16 .18 600
candidate -.33 .36 311
Weak preference for -.11 .19 180
candidate .01 .16 147
Swing voters (thermometer .02 .06 316
difference approach) -.39 .31 291
Weak preference for -.28(*) .14 154
opponent -.69 .15 168
Strong preference for -.44(**) .11 315
opponent -.63(**) .14 544
(*) p < .10.
(**) p < .05.
(***) p < .01.
(a) Number of voters in the segment. Negativity Coefficients by Candidate With and Without Strong
Opponents: Super Tuesday 1988
Legend for Chart:
A - Candidate
B - Negativity Coefficient Including Opponents
(unstandardized b)
C - n(a)
D - Negativity Coefficient Excluding Opponents
(unstandardized b)
E - n(a)
A B C D E
George H.W. Bush -.45(**) 1626 -.31 1478
Gary Hart -.33(***) 1579 -.19 1124
Jesse Jackson -.65 1582 -.16 1281
Bob Dole -.22 1305 -.09 1264
Pat Robertson -.68(***) 1285 -.57(***) 956
Alexander Haig -.36(*) 523 -.24 448
Michael Dukakis -.22 862 -.05 815
Dick Gephardt -.36 822 -.38 773
Paul Simon -.17 720 -.17 671
Jack Kemp -.32 691 -.25 645
Al Gore -.38 635 -.29 604
Bruce Babbitt -.35 248 -.35 223
Pierre Dupont -.24 217 -.37 198
(*) p< .10.
(**) p< .05.
(***) p< .01.
(a) Number of voters who rated the candidate.DIAGRAM: FIGURE 1; Conceptual Models of Negativity in the Evaluation of Political Candidates
GRAPH: FIGURE 2; Relationship Between Trait Ratings and Trait Weights
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~~~~~~~~
By Jill G. Klein and Rohini Ahluwalia
Jill G. Klein is Associate Professor of Marketing, INSEAD
Rohini Ahluwalia is Associate Professor of Marketing, Carlson School of Management, University of Minnesota
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Record: 112- New Products, Sales Promotions, and Firm Value: The Case of the Automobile Industry. By: Pauwels, Koen; Silva-Risso, Jorge; Srinivasan, Shuba; Hanssens, Dominique M. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p142-156. 15p. 10 Charts, 2 Graphs. DOI: 10.1509/jmkg.68.4.142.42724.
- Database:
- Business Source Complete
New Products, Sales Promotions, and Firm Value: The Case
of the Automobile Industry
Year after year, managers strive to improve financial performance and firm value through marketing actions such as new product introductions and promotional incentives. This study investigates the short-and long-term impact of such marketing actions on financial metrics, including top-line, bottom-line, and stock market performance. The authors apply multivariate time-series models to the automobile industry, in which both new product introductions and promotional incentives are considered important performance drivers. Notably, whereas both marketing actions increase top-line firm performance, their long-term effects strongly differ for the bottom line. First, new product introductions increase long-term financial performance and firm value, but promotions do not. Second, investor reaction to new product introduction grows over time, indicating that useful information unfolds in the first two months after product launch. Third, product entry in a new market yields the highest top-line, bottom-line, and stock market benefits. Managers may use these results to justify new product efforts and to weigh short-and long-term consequences of promotional incentives.
For most firms, successful new products are engines of growth (Cohen, Eliashberg, and Ho 1997). Several frameworks, including the product life cycle and the growth share matrix, postulate the need for new products that generate future profitability and prevent the obsolescence of the firm's product line (Chaney, Devinney, and Winer 1991; Cooper 1984). Indeed, Arthur D. Little consultants conclude from a Fortune poll that innovative companies achieve the highest shareholder returns (Jonash and Sommerlatte 1999). At the same time, the new product failure rate is high (ranging from 33% to greater than 60%) and has not improved in the past few decades (Boulding, Morgan, and Staelin 1997; McMath and Forbes 1998; Wind 1982). Moreover, even commercially successful new products may not benefit a firm financially because of high development and launch costs and quick imitation by competitors (e.g., Bayus, Jain, and Rao 1997; Chaney, Devinney, and Winer 1991).
In contrast, sales promotions are effective demand boosters that do not incur the risks associated with new products (Blattberg and Neslin 1990). Sales promotions are relatively easy to implement and tend to have immediate and substantial effects on sales volumes (Hanssens, Parsons, and Schultz 2001, Chap. 8). Consequently, the relative share of promotions in firms marketing budgets continues to increase (Currim and Schneider 1991). However, sales promotions rarely have persistent effects on sales, which tend to return to prepromotion levels after a few weeks or months (Dekimpe, Hanssens, and Silva-Risso 1999; Nijs et al. 2001; Pauwels, Hanssens, and Siddarth 2002; Srinivasan, Leszczyc, and Bass 2000). Consequently, promotions effectiveness in stimulating long-term growth and profitability for the promoted brand is in doubt (Kopalle, Mela, and Marsh 1999).
What are the long-term financial consequences, if any, of these two distinct marketing actions? This is an important question raised by many chief executive officers (CEOs) and chief financial officers (Marketing Science Institute 2002). It is also a difficult question because there are several metrics of financial performance, including revenue (top-line performance), profit (bottom-line performance), and firm value (performance in investor markets). In addition, it is difficult to distinguish between the short- and long-term effects of marketing actions.
Research in this area has focused mainly on the revenue and profit effects of new products, such as demonstrating their benefits in the personal computer industry (Bayus, Erickson, and Jacobson 2003). In terms of investor impact, it is known that new product announcements generate small excess stock market returns for a few days (Chaney, Devinney, and Winer 1991; Eddy and Saunders 1980) and that additional excess returns can be created when the new product is launched in the market (Kelm, Narayanan, and Pinches 1995). As for sales promotions, their effect on revenues is typically positive, albeit short-lived (Srinivasan et al. 2004), whereas their profit impact is often negative (Abraham and Lodish 1990). It is not known whether investors react to firms promotion strategies, nor is it known how such a reaction, if present, compares with the effects of new product introductions.
In this study, we compare the effects of new product introductions and sales promotions on the firm's top-line, bottom-line, and investor performance. We choose the automobile industry for our focus because of its economic importance and its reliance on both new product introductions and sales promotions. Indeed, the automobile business represents more than 3% of the U.S. gross domestic product (J.D. Power and Associates [JDPA] 2002a) and accounts for one of seven jobs in the U.S. domestic economy (Tardiff 1998). Ever since consumers became interested in car styling in the 1920s, manufacturers have invested in innovation in the form of product-line changes (Farr 2000; Menge 1962). However, the costs of such styling changes can be substantial, from up to $100 million in the late 1950s to $4 billion in recent years (Sherman and Hoffer 1971; White 2001). Moreover, the success of styling changes is far from certain, even with extensive marketing research, because product development begins several years before the public launch (Farr 2000).
Research has shown that, overall, styling changes tend to increase sales but often do not pay off financially (Hoffer and Reilly 1984; Sherman and Hoffer 1971). However, these conclusions do not consider entry into new categories (e.g., sport-utility vehicles [SUVs]), nor do they account for potential long-term benefits to top-line performance (e.g., from repeat purchases or replacement sales), bottom-line performance (e.g., from spreading development costs over multiple vehicles), or firm value (e.g., from spillover benefits of successful new products to the manufacturer's image).
Car manufacturers (especially the big three U.S. companies) increasingly use sales promotions as incentives to boost sales and to optimize capacity use in a tough market environment (BusinessWeek 2002). However, concerns about the long-term profitability of such actions persist (The Wall Street Journal 2003). Recently, Chrysler's CEO, Dieter Zetsche, told the Financial Times that his forecasted sales gain of one million cars in the following five to ten years [would] be driven by 12 new product introductions in the next three years rather than by low pricing (JDPA 2002b, p. 1).
The remainder of this article is organized as follows: We first examine how new products and promotions affect financial performance and valuation over time, and we specify a comprehensive model to quantify the relationships. Next, we discuss the marketing and financial data sources and estimate the models. We then formulate conclusions, cross-validate the empirical results, and discuss their strategic implications for marketing.
We begin by considering how new product introductions and promotional incentives influence top-line (firm revenue), bottom-line (firm income), and stock market (firm value) performance. We formulate requirements for a model that aims to capture the long-term effects of such marketing actions on the performance variables. Finally, we show how a vector-autoregressive (VAR) model satisfies the requirements, and we detail the empirical estimation steps.
Short- and Long-Term Performance Effects of Marketing
The top-line performance of new products has been studied extensively in the diffusion-of-innovation literature (e.g., Mahajan and Wind 1991). Among the major findings are that revenue from new products may take considerable time to materialize and that revenue levels depend on several factors, including the degree of product innovation. In addition, new product introductions may have a persistent effect on revenues, compared with the effects of price promotions, which typically produce only temporary benefits (Nijs et al. 2001; Pauwels, Hanssens, and Siddarth 2002; Srinivasan, Leszczyc, and Bass 2000). Therefore, the assessment of new product and promotional effects on revenue should distinguish short-term (immediate or same-week) effects and long-term effects, which can be temporary (adjustment, dust settling) or persistent (permanent).( n1)
Bottom-line financial performance may benefit from new product introductions through increased demand, increased profit margin, and lower customer acquisition and retention costs (Bayus, Erickson, and Jacobson 2003). Geroski, Machin, and Van Reenen (1993) note that a new product can have a temporary effect on a firm's financial position because of the specific product innovation, or it can have a permanent effect because it transforms competitive capabilities. However, several factors can jeopardize such long-term profit benefits, even when top-line performance increases (Sherman and Hoffer 1971). Development and production costs are considerable, notably in the automobile industry, in which new-car platforms cost more than $1 billion (The Wall Street Journal 2002b). New product launches consume considerable marketing resources, especially for a major innovation. Similarly, the profitability of promotional incentives is far from certain (Abraham and Lodish 1990; Srinivasan et al. 2004).
The firm valuation (stock price) implications of marketing activities have not received much research attention to date. In general, it is known from the efficient-market hypothesis that stock prices follow random walks: The current price reveals all the known information about the firm's future earnings prospects, and shocks (surprises) that alter earnings expectations are incorporated immediately (Fama and French 1992). Therefore, the stock market may not react to new product introductions because a firm's current valuation already incorporates the launch, either because it was preannounced or leaked or because the company is known to be an innovator and is expected to produce a steady flow of new products. Instead, the stock market reacts to the extent that the new product introduction updates the forecasts of the firm's future returns (Ittner and Larcker 1997). If investors consider the new product introduction favorably (i.e., expectations are exceeded), the stock price will increase to reflect the expected net sum of future discounted cash flows that result from the new product (Wittink, Ryans, and Burus 1982, p. 3).
However, the efficient-market perspective also acknowledges that investors do not always correctly and immediately forecast the firm's future returns (e.g., Ball and Brown 1968). Although investors have expectations of the firm's general capability in new product introductions, the market success of any specific introduction is usually in doubt (The Wall Street Journal 2002a; Wittink, Ryans, and Burus 1982). Specifically, investors need to assess two major uncertainties correctly: the probability of new product success and the level of profits associated with the product (Chaney, Devinney, and Winer 1991). On the one hand, the stock market may overreact to a product introduction that eventually does not become a financial success (Chaney, Devinney, and Winer 1991). On the other hand, investors may underreact as they focus on current rather than future revenue streams (Michaely, Thaler, and Womack 1995). Therefore, investors should not be expected to be fully able to predict the total financial effects over time of new product introductions at the time of launch. Instead, investors update their evaluation of introductions over time. Helpful information is contained in early success measures such as low days-to-turn and high initial satisfaction ratings,, which indicate high product popularity in the target market and the absence of major technical problems. Therefore, the short-term investor reaction may adjust over time until it stabilizes in the long run as the new product's performance becomes so predictable that it loses its ability to adjust stock prices further.
A similar argument can be developed with respect to promotion effects on valuation. Given the positive revenue effects of promotions for manufacturers, some positive investor reaction can be expected in the short run. However, because promotion effects on sales are typically short lived, it is not evident a priori whether the positive investor reaction will persist, dissipate, or turn around.
Finally, we recognize that dynamic feedback loops may exist among marketing variables, among performance variables, and between marketing and performance variables. Marketing actions such as new product introductions and promotional incentives often become associated with one another over time (Dekimpe and Hanssens 1999; Pauwels 2004). Successful new product introductions can increase a brand's price premium and make promotions redundant. In contrast, a prolonged absence of successful new product introduction may force a company to use promotional incentives to move product (The Wall Street Journal 2002a). Similarly, revenue performance may act as an intermediate variable between marketing actions and firm value. For example, successful new products lead to higher revenues and profits, which in turn can be used to launch other new products (Kashani 2003). Likewise, lackluster revenue performance may prompt some companies to engage in aggressive rebate tactics in an effort to boost sales (USA Today 2002).
Model Requirements
On the basis of these considerations, we maintain four criteria for our model of dynamic interactions among marketing and performance variables. First, the model should provide a flexible treatment of both short-and long-term effects (Dekimpe and Hanssens 1995). Second, the model should be robust to deviations from stationarity, particularly the presence of random walks in stock prices, which can lead to spurious regression problems (Granger and Newbold 1986).( n2) Third, the model should provide a forecast and expected baseline for each performance variable, so that we can capture the impact of unexpected events as deviations from the baseline. Both econometric models and survey methods have been shown to perform well in generating these expectations (Cheng, Hopwood, and McKeown 1992; Fried and Givoly 1982). Consequently, our model uses forecasts based on an econometric model and controls for changes in analyst earnings expectations. Fourth, the model should allow for various dynamic feedback loops between marketing and business performance variables.
In summary, the study of the longitudinal impact of new product introductions and promotional incentives requires a carefully designed system of equations that accounts both for the time-series properties of performance and marketing variables and for their dynamic interactions.
VAR Model Specification
We used VAR models, which are well suited for measuring the dynamic performance response and interactions between performance and marketing variables (Dekimpe and Hanssens 1999). Both performance variables and marketing actions are endogenous (i.e., they are explained by their own past and by the past of the other endogenous variables). Specifically, VAR models not only measure direct (immediate and lagged) response to marketing actions but also capture the performance implications of complex feedback loops. For example, a successful introduction generates higher revenue, which may prompt the manufacturer to reduce sales promotions in subsequent periods. The combination of increased sales and higher margins may improve earnings and stock price and thus further enhance the effectiveness of the initial product introduction over time. Because of such chains of events, the full performance implications of the initial product introduction may extend well beyond the immediate effects.
Depending on the order of integration of the data, VAR models are specified in levels or changes. Our unit-root tests (Enders 1995) reveal evolution in performance variables but stationarity for new product introductions and sales promotions.( n3) Consequently, the VAR model for each brand j in category k from firm i is specified as follows:
( 1) [Multiple line equation(s) cannot be represented in ASCII text]
with B[subn], Γ vectors of coefficients, [u[subVBRi,t], u[subINCi,t], u[subREVi,t], u[subNPlijk,t], u[subSPRijk,t]] ∼ N(0,Σ[subu]) N as the order of the VAR system based on Schwartz's Bayesian information criterion (SBIC), and all variables expressed in logarithms or their changes (Δ). In this system of equations, the first equation explains changes to firm value, which we operationalize as the ratio of the firm's market value to book value (VBR) (Miller and Modigliani 1961).( n4) This variable reflects a firm's potential growth opportunities and is frequently used to assess a firm's ability to achieve abnormal returns relative to its investment base (David et al. 2002). The second and third equations explain the changes in, respectively, bottom-line (INC) and top-line (REV) financial performance of firm i. The fourth and fifth equations model firm i's marketing actions, that is, new product introductions (NPI) and sales promotions (SPR) for brand j in product category k.
With respect to the exogenous variables in this dynamic system, we control for seasonal demand variations in vector C (Labor Day weekend, Memorial Day weekend, and the end of each quarter) and for fluctuations in the overall economic and investment climate (Standard and Poor's [S&P] 500 index, construction cost index, and dollar yen exchange rate).( n5) Finally, we account for the impact of stock market analysts expectations of earnings per share (EPS) (Ittner and Larcker 1997).
Note that the VAR-forecast baseline of market-to-book ratio includes changes to the S&P 500 index, which is the sole predictor of a firm's stock price in the market model used by event studies to calculate excess returns (Chaney, Devinney, and Winer 1991; Eddy and Saunders 1980). In contrast, our model develops a more refined forecast baseline, which also includes changes to the construction cost index and to firm-specific earnings forecasts and financial performance. An argument could be made for an even more extensive VAR model specification (e.g., simultaneous inclusion of competitive product-introduction and promotion variables). However, we want to avoid overparameterization effects on our estimates (Abadir, Hardi, and Tzavalis 1999; Pesaran and Smith 1998), and we aim to balance completeness and parsimony. Permanent effects in the VAR models are possible whenever performance variables are evolving, and SBIC implies lag lengths that balance model fit and complexity.
Vector-autoregressive models have been used extensively in both the marketing and the finance literature. For example, they are used to assess the short-and long-term performance effects of marketing activities such as advertising, distribution, nonprice and price promotions, store-brand entry, and product-line extensions (Bronnenberg, Mahajan, and Vanhonacker 2000; Dekimpe and Hanssens 1999; Nijs et al. 2001; Pauwels 2004; Pauwels, Hanssens, and Siddarth 2002; Pauwels and Srinivasan 2004; Srinivasan et al. 2004). In the finance literature, VAR models have been used to study the relationships within and between stock markets (Eun and Shim 1989), the relationships between capital flows and equity returns (Froot and Donohue 2002), the impact over time of monetary policy on stock market returns (Thorbecke 1997), and the effect of credit interruptions on the economy (Mason, Anari, and Kolari 2000).
Long-Term Impact of Marketing Actions: Impulse-Response Functions
The VAR model estimates the baseline of each endogenous variable and forecasts its future values on the basis of the dynamic interactions of all jointly endogenous variables. Based on the VAR coefficients, impulse-response functions track the impact over time of unexpected changes (shocks) in the marketing variables on forecast deviations from the baseline for the other endogenous variables. This conceptualization closely reflects previous studies of market performance (e.g., Dekimpe and Hanssens 1999), financial performance (e.g., Srinivasan et al. 2004), and stock prices (e.g., Erickson and Jacobson 1992). As Mizik and Jacobson (2003, p. 21) argue, when an unanticipated change in strategy occurs, the markets react and the new stock price reflects the long-term implications such change is expected to have on future cash flows.
To derive the impulse-response functions of a marketing action, we compute two forecasts, one based on an information set without the marketing action and the other based on an extended information set that accounts for the marketing action. The difference between the forecasts measures the incremental effect of the marketing action. This model feature is especially attractive in our investigation of stock market performance, because investors react to shocks, or deviations from their expectations. In the finance area, these expectations derive from econometric forecasting models based on the firm's financial performance records, and the shocks are the model forecast errors (e.g., Cheng and Chen 1997). The VAR model is a sophisticated version of such an econometric forecast. In addition, the dynamic effects are not restricted in time, sign, or magnitude a priori. We adopt the generalized, simultaneous-shock approach (Dekimpe and Hanssens 1999) in which we use the information in the residual variance covariance matrix of the VAR model to derive a vector of expected instantaneous shock values. Because we estimate a model in logarithms, the short-and long-term performance impact estimates are elasticities (Nijs et al. 2001). Finally, we follow established practice in marketing research and assess the statistical significance of each impulse-response value by applying a one-standard error band (e.g., Nijs et al. 2001), as in the work of Pesaran, Pierse, and Lee (1993) and Sims and Zha (1999).
Relative Importance of Marketing Actions: Forecast-Error Variance Decomposition
Although impulse-response functions trace the effects of a marketing change on performance, forecast-error variance decomposition (FEVD) determines the extent to which the performance effects are due to changes in each of the VAR variables (Hamilton 1994). Thus, the variance decomposition of firm value provides information about the relative importance of previous firm value, bottom-and top-line performance, new product introductions, and promotions in determining deviations of firm value from baseline expectations. Of particular importance is the comparison of the short-and long-term FEVD. For example, this comparison may reveal that the initial movements in stock price are mainly due to promotion shocks but that, over time, the contribution of new product introductions gradually becomes stronger. Moreover, FEVD addresses the role of the intermediate performance metrics (revenue and profit). In our context, new product introductions may affect firm value only indirectly through top-and bottom-line performance (in which case, all firm value forecast deviations are attributable to the performance variables) or may have a direct effect beyond the performance impact. For example, in the marketing context, Hanssens (1998) uses FEVD on channel orders and consumer demand data to show that sudden spikes in channel orders have no long-term consequences for the manufacturer, unless movements in consumer demand accompany them. For a detailed overview of all VAR modeling steps, see the work of Enders (1995) and Dekimpe and Hanssens (1999).
Our data come from four major sources: JDPA for weekly sales and marketing, Center for Research in Security Prices (CRSP) and COMPUSTAT for firm performance, and I/B/E/S for earnings forecasts (Ittner and Larcker 1997). We describe these databases in turn and summarize our variable operationalization and data sources in Table 1.
Marketing Databases: JDPA
Sales transaction data for a sample of dealerships in the major U.S. metropolitan areas are available from JDPA. We use data containing every new-car sales transaction of a sample of 1100 California dealerships from October 1996 through December 2001. The detailed data for this region are representative of other U.S. regions, for which available data periods are shorter. Each observation in the JDPA data contains the transaction date; manufacturer; model year; make; model; trim and other car information; transaction price; and sales promotions, which are operationalized as the monetary equivalent of all promotional incentives per vehicle. All observations are retail transactions (i.e., sales or leases to final consumers), excluding fleet sales.( n6) Moreover, the data set is at the detailed vehicle level,, which is defined as every combination of model year, make, and model (e.g., 1999 Honda Accord, 2000 Toyota Camry); body type (e.g., convertible, coupe, hatchback); number of doors (e.g., two door, four door, four-door extended cabin); trim level (e.g., for Honda Accord, DX, EX, LX); drive train type (e.g., two-wheel drive, four-wheel drive); transmission type (e.g., automatic, manual); cylinders (e.g., four cylinder, V6); and displacement (e.g., 3.0 or 3.3 liters) (Morton, Zettelmeyer, and Silva-Risso 2001).
The vehicle information is aggregated to the brand level, which represents a company's presence in a certain category. For example, Chevrolet, GMC, and Cadillac are the three General Motors brands in the SUV category.
Another source of JDPA data is expert opinions on the innovation level of each vehicle redesign or introduction. In line with JDPA (1998) guidelines, experts rate such innovativeness on the five-point scale presented in Table 2.
Our innovation scale ranges from mere trimming and styling changes (Levels 1 and 2), to design and new benefit innovations (Levels 3 and 4), to brand entry in a new category (Level 5). For example, the 2002 Toyota 4Runner with minor exterior styling changes is a Level 1 car, the 1999 Ford Explorer with minor updates to interior and exterior is a Level 2 car, the 1998 Isuzu Rodeo with a major change to vehicle platform is a Level 3 car, the 2001 Ford Explorer with a new platform and additional third-row seating is a Level 4 car, and the 2001 Acura MDX is a Level 5 car. We compared the JDPA classification with the scales used in previous automobile studies in the economics literature (Hoffer and Reilly 1982; Sherman and Hoffer 1971). Although all three approaches converge on most innovation levels, the JDPA scale is more informative in that it acknowledges the introduction of new consumer benefits and includes new brand entry (i.e., the first time a brand enters an automobile category). Furthermore, when there are no visible changes between model years, the scale assigns an innovation value of zero.( n7) The expert ratings operationalize our new product introduction variable, which is timed at the moment of market launch.
Because innovation is vehicle specific and we estimated our models by brand, the innovation variable needs to be converted to the brand level. We define brand-level innovation as the maximum innovation level for all the brand's vehicle changes in the entry week.( n8) We consider 41 brands in six major product categories: SUVs, minivans, midsize sedans, compact cars, compact pickups, and full-size pickups. Table 3 shows that during the period of observation (October 1996 through December 2001), some of the categories experienced many major and minor new product introductions (SUVs and full-size pickups) or a dominance of major introductions (minivans). In other categories there was a more moderate amount of product innovation (midsize and compact cars), and still others were characterized mainly by minor product improvements (compact pickups).
Financial Databases: CRSP, COMPUSTAT, and I/B/E/S
Our measure of firm value is based on the comprehensive data set of firm market capitalization and daily market indexes (S&P 500) of the New York Stock Exchange, which we obtained from CRSP. The CRSP database covers stocks traded on the major U.S. stock exchanges: the New York Stock Exchange, the American Stock Exchange, and NASDAQ. Following financial convention, we used Friday closing prices to compute weekly firm market capitalization (Mizik and Jacobson 2003).
For firm-specific information and quarterly accounting information, such as book value, revenues, and net income, we used S&P's 1999 COMPUSTAT database. The quarterly variables of income and revenue are allocated to quarter weeks in proportion to the retail sales level generated in each week, as obtained from the JDPA database (i.e., we assume that revenue and income generated in a given week are proportional to unit sales in that week). In addition, the COMPUSTAT database provides monthly indexes of the construction cost index and the consumer price index, which we used to deflate all monetary variables. Finally, the I/B/E/S database provides analysts quarterly earnings forecasts for the six major manufacturers in this study Chrysler, Ford, General Motors, Honda, Nissan, and Toyota which represent approximately 86% of the U.S. car market.
Recall that our unit of analysis for the marketing variables is the brand level in each of six product categories. Table 4 provides a listing of the brands in the study as well as the descriptive statistics for the measures that form the basis of our analysis. A casual inspection of Table 4 does not reveal any obvious association between the number of major and minor new product introductions and the ratio of market capitalization to book value. This relationship needs to be assessed longitudinally while controlling for exogenous factors that influence the general stock market and the specific industry, as in our VAR models.
The 41 estimated VAR models (one for each brand), with the number of lags indicated by the SBIC, showed good model fit (the R2 ranges from .25 to .57, and the F-statistic ranges from 3.06 to 14.37).( n9) We first review our results on the performance impact of new product introductions and sales promotions. We then discuss how the effects emerge over time, and we demonstrate the interactions between new product introductions and promotions. Finally, we examine the robustness of our findings across both categories and innovation levels.
Impact of New Product Introductions on Financial Performance and Firm Value
Table 5 shows short-term (same-week) and long-term elasticities of brand-level product introductions and promotions on firm-level performance as sales-weighted averages over all 41 brands for six categories and six companies.( n10)
Because we relate total corporate performance to a new product introduction for one brand in one category, the reported elasticities are small, which is in line with previous research (Kelm, Narayanan, and Pinches 1995) but statistically significant. Overall, new product introductions have a positive short-and long-term impact on the firm's top-line, bottom-line, and stock market performance. Moreover, the impact persists over time.
First, our firm revenue results confirm previous findings of strong sales effects of new product introductions, both in the car industry (Hoffer and Reilly 1984; Sherman and Hoffer 1971) and in other categories (e.g., Booz Allen Hamilton 1982; Kashani, Miller, and Clayton 2000). Notably, we find that the top-line benefits materialize relatively quickly, in six to ten weeks, possibly because the automobile industry is product driven and its end users are highly involved in the category (Farr 2000; JDPA 2002).
Second, the bottom-line impact of new product introductions follows a similar pattern over time as the top-line impact but with lower elasticities. This demonstrates the crucial importance of new product introduction costs in the industry. This observation is consistent with Bayus and Putsis's (1999) research on product proliferation in the personal computer industry and thus may generalize to other industries with substantial innovation costs.
Third, the average short-term firm value impact of new product introductions is low compared with the top-and bottom-line benefits. An explanation for this finding is that investors have already incorporated the firm's product introduction into their valuation (e.g., Ittner and Larcker 1997). In contrast, the long-term firm value effects are typically higher, indicating that relevant new information unfolds as time progresses. Figure 1 illustrates the impact over time of new product introductions for the Honda Odyssey (minivan category) on the valuation of the Honda corporation. After a small initial (short-term) gain, the effect grows and stabilizes at its persistent (long-term) positive value in approximately two months.
Impact of Sales Promotions on Financial Performance and Firm Value
The effects of promotional programs on market and financial performance are significantly different from those of new product introductions. Table 5 shows that incentive programs have uniformly positive effects in the short run; top-line, bottom-line, and stock market performance all increase. In other words, investors reaction mirrors consumers reaction to incentive programs, which is strong, immediate, and positive (Blattberg, Briesch, and Fox 1995). However, the beneficial effects are short-lived for all but a firm's top-line performance, because both long-term bottom-line and firm value elasticities are negative. As we detailed in the validation analysis, this negative long-term elasticity represents most brands in our analysis.
A possible explanation for the sign switch in income and investor reaction between the short and long run is price inertia or habit formation in sales promotions: The short-term success of promotions makes it attractive for managers to continue using them (Krishna, Mela, and Urbany 2001; Srinivasan, Pauwels, and Nijs 2003). In addition, because promotions are known to stimulate consumer demand only temporarily (Srinivasan et al. 2004), they need to be repeated, lest the company is willing to sacrifice top-line performance. Although such repetitive use of incentives is able to maintain, and even grow, the initial revenue effects (which drives the positive long-term revenue elasticity), profit margins erode and bottom-line performance and firm value suffer in the long run (The Wall Street Journal 2002c). This dynamic behavior is the opposite of the positive feedback loop, or virtuous cycle, for new product introductions for which positive consumer and investor reaction stimulate further new product introduction efforts (Kashani, Miller, and Clayton 2000).
Growing Importance of New Product Introductions for Firm Value
Because we find the firm value effects of new product introductions and promotions intriguing, we further investigate their importance in explaining firm value beyond their bottom-line effects. Figure 2 shows the FEVD results of firm value, accounting for all performance and marketing variables.
Although sales promotions initially are more important, an increasing percentage of the forecast deviation variance in firm value is attributed to new product introductions. On average, the ability of product introduction to explain firm value forecast deviations is eight times greater after two quarters than it is in the week of product launch.
Together with the increasing elasticity findings that are illustrated in Figure 1, this result pattern implies that new product introduction per se is a fairly high-entropy signal to investors: Although investors immediate reactions are not strong, they gradually adjust their reactions as emerging consumer acceptance information helps them update their expectations (Kelm, Narayanan, and Pinches 1995).( n11) Moreover, the demonstrated direct effect of new product introductions on firm value implies that investors consider more than only current bottom-line effects. In other words, investors reward firm innovativeness in the form of a premium in firm valuation beyond the new product's impact on top-and bottom-line performance. This finding implies that investors show foresight beyond the extrapolation of firm profits. For example, they may reward the spillover benefits of successful introductions on the manufacturer's image and reputation (Sherman and Hoffer 1971), possibly expecting that the image will enhance consumers acceptance of the firm's future new product introductions.
Interactions Between New Product Introductions and Promotional Incentives
Because new product introductions and promotional incentives have such different long-term effects on firm value, we investigate their interaction in firms decision making. We capture the dynamic interactions by examining the impulse response of promotional incentives to new product introductions (see Table 6).
Notably, new product introductions have a negative and persistent impact on the use of incentives. Because sales promotions are long-term value deterrents (per our previous finding), this finding supports the important strategic conclusion that a policy of aggressive new product introductions is an antidote for excessive reliance on consumer incentives. For example, consider the major redesign of the Honda Odyssey in 1999: The new design had a persistent, beneficial effect on the margins for the vehicle, which continues to enjoy strong sales without virtually any promotional incentives (White 2001).
Robustness of Results Across Product Categories and Innovation Levels
Finally, we assess the robustness of our findings across product categories and innovation levels. First, are the short-and long-term firm value effects of new product introductions and promotional incentives robust across product categories? Table 7 shows the frequency of positive stock market performance effects of new product introductions for the 41 brands in our analysis.
The short-term firm value effects show both negative and positive values across categories, in support of our interpretation that new product introductions are high-entropy signals to investors. Still, the short-term effects of new product introductions are positive for the most part; no product category shows a dominance of negative effects. The long-term effects of new product introductions show a predominantly positive effect on firm value in each of the six categories. Over the total sample, new product introductions have a positive, long-term impact on market capitalization for 81% of all brands.
The short-term effects of promotion incentives vary among categories: SUVs, minivans, and premium midsize cars show a negative impact, and premium compact cars, compact pickups, and full-size pickups show a positive impact. Overall, half of all brands show positive short-term promotion effects. In the long run, this is true for only 43% of the brands.
Second, does the general pattern of our findings hold across innovation levels? To answer this question, we re-estimate our model for each brand and substitute the introduction variable by variables that measure each innovation level.( n12) Table 8 shows the detailed breakdown of the performance impact of styling changes only (Level 1), minor sheet-metal changes (Level 2), major sheet-metal changes (Level 3), all new sheet metal and/or new platform (Level 4), and new market entry (Level 5).
Consistent with the new product literature (Cooper and Kleinschmidt 1993; Holak and Lehmann 1990; Montoya-Weiss and Calantone 1994), we observe an almost linear relationship between the innovation level and its short-term revenue impact. Long-term revenue performance follows a similar pattern, with low impact for mere trim changes and high impact for new market entries; however, there is little difference among the intermediate innovation levels.
The results for bottom-line performance are more complex. The short-term impact on income has a U-shaped relationship with innovation level: Partial sheet-metal changes (Levels 2 and 3) have a lower income impact than mere styling changes, whereas major updates and especially new brand entries yield the greatest income benefits. In the long run, we even observe negative average income effects for Levels 3 and 4. The results reflect and extend previous findings of negative financial returns on new-car models (Sherman and Hoffer 1971) and demonstrate the crucial importance of new product introduction costs in the auto industry.
Finally, the stock market performance impact has a similar U-shaped relationship with innovation level, but there is a preference for new market entries over minor updates. The results again support our interpretation that investors consider more than only current financial returns, such as spillover innovation benefits in the more distant future, which may include a manufacturer's improved image, increased revenues from the opening of new markets, and reduced costs from applying the innovation technology to different vehicles in the fleet (Sherman and Hoffer 1971). Indeed, Booz Allen Hamilton (1982) argues that new-to-the-world products and new product lines (Level 5) offer the highest benefit potential but face manager reluctance because they also pose a major risk compared with incremental innovations. Therefore, investors appear to appreciate new market entries as a signal of confident and bold management.
Our central result is that beyond the impact of the firm's earnings and the general investment climate, product introductions have positive and increasing effects on firm value. In contrast, sales promotions diminish long-term firm value, even though they have positive effects on revenues and (in the short run) on profits. Thus, the investor community rewards new product introductions and punishes discounting beyond the readily observable financial performance of the firm. Table 9 summarizes these findings.
Are the reported elasticities economically relevant? Table 10 reports the size of the monetary effects on market capitalization in dollars.( n13) New product introductions typically generate tens of millions of dollars of long-term firm value, and often several hundred million dollars (up to $302 million). The reverse is true for promotions, which subtract tens or even hundreds of millions of dollars of firm value, or up to $324 million in our calculations. These amounts are especially great given that both product introduction and sales promotions are not isolated events in the auto industry. They occur relatively frequently and, as such, can account for substantial up-or downward movement in auto companies stock prices.
Our results in Table 10 highlight the differences across firms and categories and can be related to firms product strategies and category growth trends. For example, from 1996 to 2001, Ford experienced a shift in emphasis from quality of manufacturing to customer service and cost reductions, under the leadership of its CEO Jack Nasser. The former included service improvements offered by the dealers and improvements in the interface to the consumers through ventures such as Ford Direct. Ford achieved cost reductions through price discounts from its suppliers and manufacturing-related cost savings. In contrast, Chrysler (through its design chief Bob Lutz) was emphasizing innovative, appealing design during this period. For example, Chrysler introduced the highly successful Dodge Durango and Jeep Liberty in the SUV category and the PT Cruiser in the small-cars category. Our results in Table 10 indeed reflect the success of Chrysler's innovation-focused product strategy compared with that of Ford: Chrysler has greater positive effects of new product introductions on firm value than does Ford in all but one category.
For category trends, the SUV category, for example, experienced 12.3% annual growth from 1996 to 2001, whereas the small and midsize sedan categories decreased by 1% and .6% annually, respectively. Our results in Table 10 reflect the market trends because the high-growth SUV category typically has greater effects of new product introductions than do the other lower-growth auto categories.
Our findings have several important implications for new product and promotion strategies. First, to boost the long-term market capitalization of their companies, executives should focus on new product introductions and resist relying on sales promotions. Although consumer incentives may yield increased short-term performance and/or prevent severe sales erosion while new product projects are in the pipeline, they do not provide a viable long-term answer to the manufacturer's challenges in the industry (The Wall Street Journal 2002c).
Second, although in the short run investors often view product introduction favorably, their reactions unfold over time, so market acceptance of the introduction is an important component in determining its long-term impact on firm value. This finding supports the idea that innovative firms need to pay special attention to appropriating new product introduction rewards in the marketplace to enhance stock returns (Kelm, Narayanan, and Pinches 1995; Mizik and Jacobson 2003; Pardue, Higgins, and Biggart 2000). In this regard, investors most value entries into new markets (i.e., Level 5).
Third, managers need not always incur the high development and launch costs that are associated with major product innovations. Indeed, the U-shaped relationship between innovation level and long-term firm valuation implies that firms can benefit from pulsing innovations (i.e., provide minor improvements to their new market entries rather than engage in continuous intermediate-level innovation). This finding corroborates the argument in favor of fast new product development and launch, followed by fine-tuning the product on the basis of market feedback (Smith and Reinertsen 1991). A recent study of many categories indicates that incremental innovations can be drivers of brand growth in their own right if they represent additional consumer benefits and are introduced more frequently than competitor products (Kashani 2003, p. 57). As a case in point, consider Ford's decision to return Lincoln to profitability based on relatively minor changes with lower development costs (aimed to position Lincoln as an American luxury brand), instead of making the major leap toward a global luxury brand at substantially higher costs (The Wall Street Journal 2002b). This move is quite possibly exactly what Lincoln's customers and Ford investors would prefer (The Wall Street Journal 2002b, p. B4).
This study has some limitations that provide worthwhile avenues for further research. First, although our data period (1996 2001) is substantial, it covers only a fraction of the history of the automobile industry and does not feature major innovations that occurred before 1996. Indeed, important breakthroughs and new-to-the-world products, such as four-wheel traction and minivans, may receive considerably greater long-term benefits than even the new market entries in our data period. In the same vein, we focused on new product introductions and did not examine process innovations. Second, we analyzed only one industry, albeit one in which new product introductions and sales promotions play a major role in marketing strategy. A validation of our results in other industries is an important area for further research. Third, this research has assessed the average performance impact of new product introductions, but it leaves the explanations of differences in effects across firms and categories for further studies. Moreover, additional work could address the importance of the relative innovativeness of a company compared with competitive offerings in explaining the observed performance results. Finally, researchers might investigate consumer acceptance ratings that are available before launch and thus may help predict the performance impact of specific introductions. Likewise, knowledge of when management realizes the failure of a new product introduction may shed light on managerial action to remedy the situation, including either more new products or more promotions.
In conclusion, the marketing literature to date has provided several insights into the benefits and risks of new product introductions for consumers and firms. Our research adds an important dimension: The investor community rewards innovative firms by their willingness to pay a premium in valuation, and this premium gradually increases for several weeks after the new product launch. Furthermore, innovation policy is an antidote against firms dependence on sales promotions, which depress firm value. In the words of General Motors chief financial officer, John Devine (JDPA 2002a, p. 1), in terms of driving profits in the [United States], it's about getting products right.
The authors thank Donald Lehmann, David Mayers, two anonymous reviewers of the Marketing Science Institute proposal competition, and the anonymous JM reviewers for their invaluable comments and suggestions. The article also benefited from comments by seminar participants at the 2002 Marketing Science Institute Conference on Marketing Productivity, the 2002 North East Marketing Conference, the American Marketing Association 2003 Winter Educators' Conference, and additional seminars at Erasmus University, University of Groningen, Tulane University, University of California, Los Angeles, and University of California, Riverside. Finally, the authors are grateful to the Marketing Science Institute for financial support.
( n1) Persistent (permanent) effects are defined as the difference between baseline performance before the marketing action and baseline performance after the action's effects have stabilized. For a detailed explanation of the time frame distinctions, see Pauwels, Hanssens, and Siddarth (2002).
( n2) Stationary variables fluctuate as temporary deviations around a fixed mean or trend. Evolving variables, such as random walks, have a unit root (i.e., they fluctuate without reverting to a fixed mean or trend). For technical definitions and applications in marketing, see, for example, Dekimpe and Hanssens (1995).
( n3) We also performed a cointegration test for the existence of a long-term equilibrium among the evolving variables. The test result was negative. Detailed results are available on request from the first author.
( n4) Other measures of firm value include return on assets, return on sales, and return on equity. However, these measures focus on the short run, they are not risk adjusted, and their typical level of temporal aggregation makes it more difficult to link them to specific new product introductions. Furthermore, because accounting measures are based on historical data, they do not adequately reflect future expected revenue streams (Kalyanaram, Robinson, and Urban 1995).
( n5) Although inclusion of the transportation index appears more relevant than the construction index, the big six car manufacturers account for much of the variation in this index, which could cause an endogeneity bias. We performed a sensitivity analysis with the transportation index and found similar results.
( n6) A major source of fleet sales is vehicles sold to rental car companies, which are often affiliated with or owned by a car manufacturer. For this reason, the inclusion of fleet sales could contaminate our measures.
( n7) As we stated previously, we investigate only the launch of new or updated products (which may incorporate process innovations), not process innovations by themselves.
( n8) For example, if Toyota offers two redesigned SUV models in a particular week at Levels 1 and 3, the new product introduction variable has the value of 3 for the Toyota brand in the SUV category in that week.
( n9) Detailed results are available on request from the first author.
( n10) We follow Pauwels, Hanssens, and Siddarth (2002) in adopting static weights (i.e., average share across the sample) rather than dynamic (current-period) weights to compute the weighted prices.
( n11) The emerging consumer acceptance information about the new product could include factors such as vehicle sales, days-to-turn, product reviews, advertising efforts, and consumer awareness. The determination of the exact nature of this information is beyond the scope of this study.
( n12) Because our innovation variables are not continuous, we validate our VAR results by estimating ordered probit models for the five-point innovation scale and probit models for the five innovation-level dummy variables. Comparison of the probit coefficients with the VAR innovation equations yields high correlations (.78 for the major/minor innovation models and .87 for the five-point innovation scale models). We conclude that our main results are robust to the nature of the innovation scale.
( n13) We derive the dollar metric of incremental impact on market capitalization using the estimated elasticities and the end-of-the-observation-period values of the brand's marketing variables (innovation level and rebate) and ratio of market capitalization to book value (Dekimpe and Hanssens 1999, Note 11).
Legend for Chart:
A - Measure
B - VAR Variable
C - Endogeneity
D - Operationalization
E - Temporal Aggregation
F - Data Sources
A
B C
D
E F
Firm value
(VBR)[subi,t] Endogenous
The ratio of firm i's market value
to book value (defined as market
value to book equity)
Weekly CRSP
Top-line performance
(REV)[subi,t] Endogenous
The revenues of firm i
Weekly (quarterly data COMPUSTAT
allocated in proportion to JDPA transactions
the retail sales level in
each week)
Bottom-line performance
(INC)[subi,t] Endogenous
The earnings of firm i
Weekly (quarterly data COMPUSTAT
allocated in proportion to JDPA transactions
the retail sales level in
each week)
Product innovation
(NPI)[subijk,t] Endogenous
The brand innovation variable,
defined at the brand level as the
maximum of the innovation variable
for all vehicle transactions for brand j
in category k in a particular week
Weekly JDPA expert opinion
JDPA transactions
Sales promotions
(SPR)[subijk,t] Endogenous
The monetary equivalent of all
promotional incentives for brand
j in category k in a particular week
Weekly JDPA transactions
S&P 500
S&P 500[subt] Exogenous
The S&P 500 index
Weekly CRSP
Construction cost index
CONSTRUCT[subt] Exogenous
The construction cost index
Weekly CRSP
Earnings forecasts
(EPS)[subi,t] Exogenous
Quarterly earnings forecasts for firm i
Quarterly I/B/E/S
Dollar-yen exchange rate
Exchange[subt] Exogenous
The dollar-yen exchange rate
Weekly Federal Reserve
Foreign Exchange Legend for Chart:
A - Innovation Scale
B - Innovation Level Description
A B
0 No visible change
1 Only styling changes that affect grille, head-light,
and taillight areas
2 Minor changes that affect sheet metal in front
and rear quarter areas and minor changes to
interior but not the instrument panel
3 Major changes that affect exterior sheet
metal and considerable change to interior,
including instrument panel
4 All new sheet metal including the roof panel
(e.g., new platform, change from rear-wheel
to front-wheel drive)
5 New entry into the market Legend for Chart:
A - Category
B - Major Innovations (Levels 3-5)
C - Minor Innovations (Levels 1-2)
D - Total
A B C D
SUV 88 51 139
Minivan 24 4 28
Midsize sedan 21 16 37
Small cars 23 22 55
Compact pickup 19 29 48
Full-size pickup 70 32 112
Total 245 154 399 Legend for Chart:
A - Characteristics
B - Chrysler(a) Dodge, Jeep, Chrysler
C - Ford Ford, Lincoln
D - General Motors Chevrolet, Cadillac, GMC, Buick, Saturn
E - Honda Honda, Acura
F - Nissan Nissan, Infiniti
G - Toyota Toyota, Lexus
A B C D
E F G
Number of models 15 16 30
9 9 19
U.S. market share 15% 21% 28%
8% 4% 10%
Market capitalization ($ in millions) 48,310 52,475 41,770
36,100 15,360 119,140
Market capitalization to book value 1.91 2.36 1.90
2.29 1.51 2.16
Quarterly firm earnings ($ in
millions) 845 1612 988
559 -108 1079
Quarterly firm revenue ($ in
millions) 29,120 39,520 43,355
12,792 13,065 26,780
Number of major introductions
(Levels 3-5) 38 77 64
23 15 28
Number of minor introductions
(Levels 1-2) 29 36 29
19 9 28
Sales promotions per vehicle ($) 633 382 632
24 200 113
(a) We included Chrysler's merger into Daimler-Chrysler (October
1998) in the Chrysler VAR model by including dummy variables (for
a similar treatment of exogenous variables, see Nijs et al.
2001). Legend for Chart:
B - New Product Introductions Short Run
C - New Product Introductions Long Run
D - Sales Promotions Short Run
E - Sales Promotions Long Run
A B C D E
Top-Line Performance
Firm revenue 2.39 4.30 1.48 7.94
Bottom-Line Performance
Firm income .37 .60 1.09 -1.28
Firm Value
Ratio of market capitalization
to book value .02 1.14 .12 -.78
Notes: For readability, we multiplied elasticity estimates by
1000. Legend for Chart:
B - Short Run
C - Long Run
A B C
SUV 58% 58%
Minivan 67% 75%
Premium midsize 50% 75%
Premium compact 36% 78%
Compact pickup 20% 60%
Full-size pickup 62% 75% Legend for Chart:
A - Category
B - New Product Introduction Short Run
C - New Product Introduction Long Run
D - Promotional Incentives Short Run
E - Promotional Incentives Long Run
A B C D E
SUV 58% 92% 42% 50%
Minivan 67% 100% 17% 17%
Premium midsize 67% 83% 17% 17%
Premium compact 71% 57% 86% 57%
Compact pickup 80% 80% 60% 40%
Full-size pickup 50% 75% 75% 75%
Average 66% 81% 50% 43% Legend for Chart:
A - New Product Introductions
B - Level 1
C - Level 2
D - Level 3
E - Level 4
F - Level 5
A B C D E F
Revenue short-term impact 2.47 3.04 3.27 4.67 6.02
Revenue long-term impact 2.32 6.83 5.65 5.68 10.45
Income short-term impact .70 .38 .35 .88 2.09
Income long-term impact .41 2.61 -.40 -4.99 .69
Firm value short-term impact -.01 1.27 -.45 -1.06 1.19
Firm value long-term impact 1.84 1.53 .87 -2.31 3.46
Notes: For readability, we multiplied elasticity estimates by
1000. Legend for Chart:
A - Impact of ...
B - Short Run
C - Long Run
A B C
New product Introductions on top-line performance + ++
New product Introductions on bottom-line performance + ++
New product Introductions on firm value + ++
Promotions on top-line performance + ++
Promotions on bottom-line performance + -
Promotions on firm value + -
New product introductions on the use of promotions - -
Notes: + = significant, positive impact; - = significant,
negative impact; ++ = intensified positive impact. Legend for Chart:
B - Chrysler
C - Ford
D - GM
E - Honda
F - Nissan
G - Toyota
A B C D E F G
New Product Introductions
SUV 302 65 49 102 201 200
Minivan 36 34 36 32 2 184
Midsize sedan 34 10 132 7 4 154
Small cars 115 30 59 60 -29 73
Compact pickup 17 58 138 -- 32 25
Full-size pickup 47 41 13 -- -- 259
Rebates
SUV -148 -26 -72 -36 -39 -92
Minivan -200 -64 -67 -37 -44 -24
Midsize sedan -45 -324 -32 -25 -7 -91
Small cars -64 -58 -24 35 28 37
Compact pickup -65 -43 -93 -- 32 61
Full-size pickup -157 -20 -76 -- -- -35
Notes: Median impact is in millions of dollars.GRAPH: FIGURE 1; Elasticity over Time of Odyssey Introductions on Honda's Market-to-Book Ratio
GRAPH: FIGURE 2; Market Capitalization FEVD
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~~~~~~~~
By Koen Pauwels; Jorge Silva-Risso; Shuba Srinivasan and Dominique M. Hanssens
Koen Pauwels is Assistant Professor of Marketing, Tuck School of Business, Dartmouth College (e-mail: koen.h.pauwels@dartmouth.edu). Jorge Silva-Risso is Assistant Professor of Marketing (e-mail: jorge.silva-risso@anderson.ucla.edu), and Dominique M. Hanssens is Bud Knapp Professor of Marketing (e-mail: dominique.hanssens@anderson.ucla.edu), Anderson Graduate School of Management, University of California, Los Angeles. Shuba Srinivasan is Assistant Professor of Marketing, Anderson Graduate School of Management, University of California, Riverside (e-mail: shuba.srinivasan@ucr.edu).
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Record: 113- On Books and Scholarship: Reflections of a Marketing Academic. By: Clark, Terry; Wilkie, William L. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p141-152. 12p.
- Database:
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Book Reviews
On Books and Scholarship: Reflections of a Marketing Academic
[Note: I should begin with the honest admission that when Terry Clark contacted me about doing this essay, I experienced strong ambivalence. On the one hand, I loved his idea of this series of essays on books and scholarship. It serves a complementary purpose to the journal article, especially in offering the potential to bind our field's scholarship more tightly across time, training, and techniques. In so doing, it can also capture some human dimensions of what it means to pursue learning in the academic sphere of marketing and thus, beyond the information itself, might serve both aspirational and inspirational goals for us readers. So I was very positive about the concept and was flattered to be asked to contribute.
On the other hand, I personally have difficulty in writing on this subject, in overcoming feelings of being presumptuous (I do have some concern that publication practices are not sufficiently sensitive to personality differences among scholars in our field, so I have decided to be open about this right at the outset). My avoidance reaction is strong enough that I would have turned this down had I not recently been consulting Robert Bartels's (1988) classic The History of Marketing Thought. In its last edition, he had added 21 short invited essays to update developments and mentioned that four other invitees had opted not to participate. I was one of those four, and I didn't want this to happen again.
Then came the issues of what to say and how to say it, and the presumptuous feeling was looming strongly. At this time, I happened to begin Kurt Vonnegut's (1997) most recent book, Timequake, for leisure reading. I have always marveled at his capacity for almost instantaneously transporting me, as reader, from one convincing alternative reality into another, then another. Sometimes odd, certainly unconventional, but at almost every reading I come away recognizing some keen insights, and often with humor. So came my hoped-for solution for this essay. To assist with structure, I've arranged the essay in three basic sections: ( 1) reflections on books during my path into marketing academia, ( 2) reflections on the role of institutional initiatives in sustaining the search for scholarship in our field, and ( 3) a few gentle reflections on personal motivations in seeking scholarship in the marketing field and the challenges we face together in this quest. Thus, though I make no effort to emulate Mr. Vonnegut directly (and with no suitable substitute for his frequent narrator, Kilgore Trout), please join me in hopping from one brief topic to another, searching for insights, occasionally with humor.]
"You Can Major in Anything but Business, Son"
With that explicit sentiment, my parents sent me off to college and the path for my future. I happened to enter Notre Dame the year it instituted an experimental "Freshman Year of Studies," in which young learners were to be encouraged to explore the length and breadth of the university setting. I was one who certainly took up this option, managing to change my "intent to major" several times, from science math (a deep but possibly too-confining curriculum) to liberal arts math (a strange, interesting offering in which we spent six weeks exploring "What is a point?" before moving onto another month on "What is a line?") to English (Might I have the soul of a poet? I don't think so, according to the feedback in the special seminar taught by the university's Poet in Residence--who also provided our class, several of whom are now highly regarded writers, a glimpse of the insensitivity possible within college humanities departments). During this sojourn I somehow enrolled in a course in the forbidden field: Business in Society, offered by the Dean of the Business School to nonmajors. The Dean, James Culliton, was a former Harvard marketing professor possessed of creative mind and innovative spirit (though his name is not well known these days, you'll find Neil Borden [1964] crediting him with inspiring the concept for the "marketing mix" at the start of that classic article and E. Jerome McCarthy [1960] also crediting him for inspiring the "4 P's" in McCarthy's ground-breaking text). In my case, among his course readings were several paperback books by Frederick Lewis Allen--Only Yesterday and The Big Change are two titles I recall--that presented business history in a manner that absolutely captivated me. In fact, until writing this essay I had, in the naively egocentric manner of an early college student, simply assumed that these were undiscovered gems. I was shocked when I recently looked them up and found in the Foreword to The Big Change (Allen 1952, pp. v ii-viii), written by famed historian Richard Hofstadter, "For over twenty years [Allen's writings] have been among the most popular books ... in American colleges.... [The Big Change's] seductive facility ... [lures] the minds of many students ... outward from the classroom ... toward a broader interest in the study of man." At any rate, this sent me over to the College of Business, where the first open faculty office happened to be occupied by the aforementioned Jerry McCarthy, who encouraged me to "Try my class in marketing," as well as the new minor in "management science" that he was developing with John Kennedy. This would enable me to pursue more depth in mathematics, as I had wished, albeit in a more applied form. About 20 strong students enrolled in this minor: We were encouraged to read and undertake research by the Notre Dame faculty, and 7 of us went directly on to doctoral study, including two of my present colleagues, Joe Guiltinan and Bob Drevs.
Of course, I did not realize until much later that I was participating in the cusp of a revolution in business academics--that McCarthy's text, along with Kotler's (1967) a few years later, would help alter marketing thought in fundamentally important ways, toward a managerial, decisionmaking focus. Among other influences on me during this period was another text written at Notre Dame, Edgar Crane's (1965) Marketing Communications. Crane was a Stanford Ph.D. in communications who had studied under Nathan Maccoby--which thus brought me into contact with the Hovland/Yale School communications research tradition--and Leon Festinger, who had only recently been working on his concepts for cognitive dissonance. Crane also inserted capsule research study descriptions throughout his book, a device that I, as an undergraduate student, found marvelously interesting. Finally, I should mention Boyd and Westfall's (1964) Marketing Research, as the mere fact that I knew Harper Boyd's name made me feel more comfortable in choosing Stanford for graduate school, where I was honored to get to know this talented man. A little later, Jerry Zaltman's (1965) early book on consumer behavior, as well as Engel, Kollat, and Blackwell's (1968) Consumer Behavior provided important overall frameworks for my choosing this direction for a field of study, and Howard and Sheth's (1969) The Theory of Buyer Behavior provided confidence and impetus for undertaking research studies that would fit comfortably within a larger marketing context. I'm also sure that when I undertook to create my own textbook, Consumer Behavior, some years later (Wilkie 1986), all these sources played a significant role.
In writing this, I deliberately chose to limit the range of books mentioned, because I've come to recognize the parallel significance of the people who were involved (including two of my current colleagues, Jack Kennedy and Yu Furuhashi at Notre Dame, and George Day, Mike Ray, and other superb faculty members at Stanford, who had huge impacts on me, my thinking, and directions I've taken) and the larger educational context within which this was taking place. While a book certainly can have a powerful singular impact, people and contexts also are crucial, a point I want to retain through the remainder of this piece.
Now I'm Wondering, Can Weak Eyesight Lead to Strong Vision?
I've quite enjoyed reading the previous essays in this series, especially when I've found points of personal connection I hadn't realized existed. For example, Len Berry's (1998) discussion of producing books tapped similar sentiments in me, as did much of Morris Holbrook's (1998)discourse-my interactions with Morris over the years have always seemed to tap into some worldviews we hold in common. One element, though, really came as a surprise. Stanley Hollander (1998)mentioned ophthalmic problems in later adulthood, and then Morris described weak eyesight in childhood, having specifically to undertake corrective eye-strengthening exercises each evening using his toothbrush. I had this very same problem, though in mycase the exercises were each morning and evening, using a special card that I extended from between my eyes, striving to bring the three colored dots progressively into focus. I thought this an interesting, likely rare coincidence, until I mentioned it to a co-author (who refuses to allow me to use her name) who volunteered that she too had experienced this as a child. I was startled by this and mentioned it to Joel Cohen, who said that he too had had this childhood condition. Now, one attribute I see in common within this group is exceptional academic "vision." Although I'm not ready to claim any causal links yet, I do wonder whether this is just a common childhood malady and I had never realized it or whether there could be some other factor at work. Maybe we should have a society for weakly focused thinkers (we could callit "SWFT") withinthe field.
"Who Is This Guy Levine?"
Over the years, certain elements have evolved that I try to apply to my teaching, regardless of course level or topic, as well as to my research. One of these is the acronym SFK, which now appears prominently on the first page of each syllabus, just below RFM and above ALE. No explanation is provided, but there is plenty of white space for note-taking on the first class day. Surprisingly, this does seem to provide a useful platform for taking up "search for knowledge" at various points in the course (in case you're curious, RFM stands for "read for mastery," a goal I try to insist my students work on prior to coming to each class meeting, while ALE refers to "affirmative learning environment," which I now insist must be supported by every student as a condition of staying in the course. Although I was slightly embarrassed at resorting to these acronyms a few years ago, I've been surprised at how well they've been received, particularly by the MBAs who drove me to invoke them in the first place!). One point I make with SFK is that at times reading a book isn't quite enough, particularly when a person's grasp of a field's specialized language isn't yet developed, and that asking questions can also be an excellent means of seeking knowledge and clarifying understanding. I tell students in each class that they need not feel embarrassed--that I personally hold the world's record for most embarrassing question and that they won't be able to come close to breaking it. By the time I finish telling the story (complete with writhing, second-by-second memories of my pain), they are loose, laughing, and looking on their hapless professor with actual sympathy. More important, they're much more comfortable about asking questions. I'll share this painful tale with you now, in highly abbreviated fashion.
The trouble started about four minutes into my graduate school career, in a business school seminar on group behavior, with students from various fields. The professor was a world-famous psychologist. I had taken limited psychology courses as an undergraduate and knew my preparation was weak. I was determined to work hard and learn much. The professor began the class by recounting great contributions to the field--James, then Freud--and all the while my pencil was moving speedily, taking notes on these gems of wisdom, most of whom were generally familiar to me. Then the professor moved from Freud to Levine. I kept writing but was bothered by the realization that I had never heard of this great scholar. I looked for signals from the professor: Was this Levine actually prominent? Oh, yes, it was clear the professor thought so, probably more than Freud even. I continued writing, but my thoughts were turning to my personal situation: "I clearly don't belong in this course. We're four minutes in, and I don't know what we're talking about!" I risked a look around. My seminar mates were all writing but were relaxed, looking cool, maybe even a little bored. The professor was excited; he obviously loved this Levine's contributions. "Maybe I shouldn't be in the doctoral program.... Yes, this probably was a huge mistake. I shouldn't be at Stanford, and I shouldn't be after a Ph.D." I began to consider my options: I had a military deferment at the time, but maybe that would be a better choice. "This is terrible...."
I then did something that still amazes me, and I think I did it without a conscious thought. In the midst of angst, I threw up my hand! This stopped the professor in mid-sentence and directed all eyes in the room to me. I detected mild irritation at my interruption.
Professor: Yes?
Student:... Um ...
Professor: What is it you want?
Student: Er ... I don't really know ... [At this point I detected much more irritation.]
Professor: Look, you stopped the class. You must want something.
Student: Well, I just haven't ever heard of Levine before.
Professor: Really? ... Really? ... Hmm, maybe you don't belong in this class.
Student: That's what I was thinking, too.
Professor: What's your name? What's your field?
Student: Wilkie. I'm in marketing. [At this point I hear titters of amusement.]
Professor: Well Mr. Wilkie, what's your question? [I now realize I still don't have one ready.]
Student:... Er ... Who is this guy Levine? [Oh no, I just called him "this guy!"]
Professor: That's what I was just talking about. What do you want to know?
Student:... Um ... Er ... What's his first name? [Oh, great question! That should help a lot!]
Professor: Kurt . . . K-u-r-t ... Levine . . . L-e-w-i-n.
Student: [The light dawned; the sun began to shine again.] Kurt Lewin... Kurt Lewin! Oh, I've heard of him.
Professor: Yes, Americans sometimes mispronounce his name, but in German the "w" is pronouncedas a "v." He's about given up. Can we move on now, Mr. Wilkie?
Student: Yes, sir. Thank you. [Whew!! That was embarrassing, but I'm back in the program!]
At this point, I was deep red with embarrassment and kept my eyes glued to my notebook as I systematically began to erase and replace the "Levines" I had written. After a bit, I risked a glance at my seminar mates to see if I was still the center of scornful attention. Much to my amazement (and, many years later, still to my deepest sense of pleasure), I saw that all but one of them was also busily erasing and replacing! When I glanced to the professor with puzzlement in my eyes, he was looking at me with something that resembled a smile, and I think I saw a quick wink ( I never have been quite sure).
So one challenge I see us all facing is how to properly value and handle the idea of personal ignorance, given our high-powered professional setting. If we think about it, an honest recognition of being ignorant is a real positive for us as academics, in that it can stimulate search and discovery. However, it's not easy to present this openly to a community ready to define it as a limiting weakness.
Within my courses, I try to convey to students that the better they are, the more they'll encounter moments of ignorance, as they come to perceive new possibilities and new frontiers. I call on them to celebrate that wonderful insight: "One mark of an educated person is to know what you don't know," and I invoke the ALE acronym as a course characteristic to help them. As a marketing academic, though, I worry that our world doesn't handle this issue very well, and I wonder how costly this is in the longer run.
Standing in the Corner: My Most Memorable Job Interview
In another recollection related to this essay series and my earlier academic life, I was pleased to discover that Stanley Hollander had worked as a research assistant to Reavis Cox in preparing Vaile, Grether, and Cox's (1952) Marketing in the American Economy. Of all the earlier marketing books I've read, this has always been by far the most impressive to me--sweeping yet analytical and impressively principled. (When recently preparing our article "Marketing's Contributions to Society" (1999) for the special Millennium Issue of Journal of Marketing, Betsy Moore and I began by again consulting this very fine piece of work.)
This book also ties into my respect for the continuing power of marketing thinkers at Wharton over many years. This respect was cemented early in my career, when I presented a seminar there as part of an interview visit for an associate professor position. Perhaps because of my public policy topic, there was a large turnout in the seminar room. Lined up along the right side of the table were the giant names of the 1940s, 1950s, and 1960s, including Reavis Cox (who was likely an emeritus professor then), while on my left side of the table were the giants of the 1970s and later years, including Paul Green, Ron Frank, Jerry Wind, Tom Robertson, and Scott Ward. As I recall, I had barely gotten into my talk when a spark ("Should objective price-quality ratings correlate at 1.00 in our market system, and what if they don't?") set off a remarkable academic conflagration that flew back and forth across the large table. Sharply distinct priorities arose as to the issues, as well as obviously strong wills and love of debate and discourse.
I was quite enjoying being a spectator when I realized that a faculty position was at stake, and I'd better do something to get my talk back on track. I thought it might be humorous to turn off the overhead, leave the table, and go lean in the corner, so this is what I did. However, rather than inviting me to resume my talk, the participants just glanced at me and then went right back to their discussion! I don't recall today how or when I resumed my talk: I do know that overall I said very little and certainly didn't think I'd done well. Much to my surprise, a number of participants, including Reavis Cox, came up and congratulated me on my fine seminar! My dual lessons from that day: ( 1) It's really easy to overestimate your personal importance in an academic setting, and ( 2) never underestimate the value of passion on the part of leading contributors to academic thought. I can't think of another single episode in my career in which this was so admirably evident.
[Note: Although scholarship is essentially a personal activity, academics also work within a larger context. In reflecting on my career, it is striking to realize how much I was affected by the contexts that enveloped my educational enterprise, some of which I was unaware of then and some of which I only dimly perceived. I've now come to believe that in our academic community, it is crucial that we appreciate the potentials of institutional initiatives for advancing scholarship.As my space is limited, I only briefly point to several examples and list books and publications for further pursuit by interested readers.]
Three Exemplary Efforts in Educating Educators
Lifelong learning brings personal growth and satisfaction. Directed faculty experiences, moreover, can bring broader change. My personal nominees for exemplary initiatives in faculty development are ( 1) the Ford Foundation's program to alter business education in the United States, ( 2) the Sloan Foundation's funding for participation in the Stanford- Sloan Program, and ( 3) the Sears-Roebuck Foundation's funding of the Association to Advance Collegiate Schools of Business (AACSB) Federal Faculty Fellowship program for work in the public policy world of Washington. All three directly shaped my opportunities and experiences at formative periods in my professional life. I may simply be unaware, but I've seen nothing even remotely as impressive in recent years.
Relevant to the Ford Foundation initiative, given my parents'instructions, I cannot imagine that I would have opted to major in the business school had that new management science minor not been available. That minor, in turn, was directly the result of Jerry McCarthy's participation in a special Ford Foundation program held at Harvard/Massachusetts Institute of Technology (MIT). This math program, moreover, was only a part of a much larger Foundation effort to bring science into business and ultimately to change the research agendas, doctoral educations, and teaching approaches of faculty members in U.S. business schools. The early portion, beginning in 1953, involved supporting experiments with changing programs at five selected schools: Carnegie, then Harvard, then Columbia, then University of Chicago, then Stanford. In the later 1950s, attention shifted to "trickle down" dissemination efforts, including Gordon and Howell's (1959) famous report and a large series of "new developments" faculty training seminars, held mostly at the chosen five schools during summers, in which more than 1500 faculty from 300 institutions were exposed to suggested changes. Of these, the Harvard/MIT Institute of Basic Mathematics for Application to Business was spectacularly successful. Here a select group of promising young business professors, including the aforementioned Jerry McCarthy of Notre Dame, were tutored deeply for a year by mathematics faculty, for the purpose of returning to their home universities to infuse this discipline into research and teaching. (I don't recall all the names of major researchers who attended, but I do know that two other participants included Frank Bass and Edgar Pessemier, who then went and built the extraordinary marketing doctoral enterprise of the 1960s and 1970s at Purdue, and incidentally provided me with my initial faculty position.) Overall, there's no question in my mind that even a casual tracing of the later contributions of the participants and their students would reveal a huge impact on the course of research in marketing, including especially the evolution of the Journal of Marketing Research and Marketing Science. Readers interested in learning more about this remarkable Ford Foundation undertaking will find a useful overview by Schlossman, Sedlack, and Wechsler (1987) and an interesting retrospective interview with Professor Howell (who taught me in a class in the Sloan Program, though I was again unaware of his activity in this sphere at the time) by Schmotter (1984). My second nomination goes to Alfred Sloan, the man who built General Motors and supported major business education initiatives at MIT and Stanford, and perhaps else-where. By the 1950s, he had become seriously concerned that business faculty members had little experience with the perspectives and needs of top management and policymakers. As one step to help rectify this situation, he endowed six doctoral fellowships for ten years to provide this background through participation in the year-long Stanford-Sloan Executive Development Program, a sort of "finishing school" where firms sent promising executives ready to be moved to the top. Although I was only 22 years of age, the impact on me was major and long-lasting. Among the program's rigorous set of activities were MBA-style business courses; biweekly meetings with chiefs of major corporations; a two-week field trip to Washington and New York for meetings with national leaders; seminars on art, music, and world political developments; and golf outings (which were voluntary but, as in the business world, curiously useful).
Several years later, while an early faculty member at Purdue's Krannert School, I had the opportunity to take a leave to participate in the Sears/AACSB program in Washington. Here, I served as the first in-house consultant on marketing research at the Federal Trade Commission's (FTC's) Bureau of Consumer Protection, setting off a working relationship with this agency that has gone on intermittently ever since. We also participated in a customized public policy seminar series run by the Brookings Institute for our business faculty group. This was similar to the Sloan field trip, in that we met with national leaders, but it was more extensive in time, coverage, and attention to policy-setting processes. The net result of these two experiences is hard for me to describe, but very strong. And though I still don't move easily within the corridors of power, I was able to see enough of them at formative stages to appreciate ( 1) how they generally operate and ( 2) that they deserve serious attention, study, and input from business academics.
The Pioneer Spirit: Helping New Fields Flourish
There's little in academia that can compare with the honest excitement felt by people engaged in pioneering efforts to advance promising areas of study. I've been fortunate-- twice blessed, as it were--to have been present and involved in this respect for ( 1) the consumer behavior area and ( 2) the marketing and public policy area. Some of my points, moreover, will hold for other areas as well.
Advancing consumer research. In consumer behavior, one of the key developments was the founding of the Association for Consumer Research (ACR). (I still picture the informal evening meeting on the screened porch of the Lord Jeffrey Amherst Inn. It was clear that the first ACR conference was a smashing success; now where to hold the second? Montreal and Paris were early favorites. "Wait, what are we trying to do here?" The University of Maryland became the choice.) The field grew quickly: When I became ACR President in 1980, it already had well over 1000 members in some 20 nations, and about two-thirds of all marketing dissertations were being written on consumer behavior topics. The Journal of Consumer Research was by that point also well established: It had begun in 1973 as Research in Consumer Behavior, with Ron Frank as its first editor. (As an aside, I sometimes shake my head at the incredibly heavy workload given those of us from marketing on the original editorial board: While the entire board was balanced across the ten sponsoring fields, almost all submissions came from marketing, which led to very uneven assignments!) And the annual ACR conference had been healthy from the start. Thus, within only a few years, the essential infrastructure for a field of study--an association with newsletter, a journal, and a conference with a proceedings--had been created by the consumer behavior pioneers. Readers may wish to consult the reports on the founding of ACR (Kardes and Sujan 1995, pp. 545-63) and Journal of Consumer Research (Kardes and Sujan 1995, pp. 486-96) in the 25th Anniversary edition of Advances in Consumer Research.
Pursuing academic excellence: "The Florida Experiment."
[Note: I don't know of a written source, but this is a tale of initiative in scholarship that deserves to be documented in the annals of our field.]
In 2001, I was honored to receive the AMA's McGraw-Hill/Irwin Distinguished Marketing Educator Award and was asked in an interview to reflect on the "proudest or most memorable achievement over your career." This question absolutely stumped me at the time--I don't think I'd ever considered it before. Having no decent response, I at least did something intelligent: I asked for a day to think it over. When I did so, I kept coming back to my decision in 1974 to join Joel Cohen in going to the University of Florida to try to build an "academic center of excellence" for scholarship in consumer behavior. Joel and I didn't know each other extremely well but did share an idealistic streak involving scholarly pursuits and interests in both consumer behavior and public policy, and we realized that teaming up made a lot of sense. Joel had also invited Gordon Bechtel, a top-flight psychometrician, to join us, and I fondly recall many broad-ranging discussions with this gentleman scholar.
I should perhaps note that the Florida decision wasn't costless for me. That year there were several attractive job options available, including possibly staying at Harvard, returning to Purdue, possibly returning to Stanford (but now as a faculty member), and joining the powerful group at Wharton. I vividly recall how even my closest friends were shocked at my decision to opt for the "Florida experiment." However, as I hope will be clear by the end of this essay, the quest to strive for scholarship held a powerful allure for me, and at the root of the Florida effort was our belief that it did for many other academics as well. If so, the chance to pursue fine research and help develop a field of study (consumer behavior) ought to be sufficient to attract top young academics to what was already a good faculty at University of Florida. It was an exciting time, particularly because we were striving to help build the overall field of consumer behavior in parallel.
Our building process involved an extensive and continuing search for new faculty members who possessed an elusive combination of intelligence, deep research training, paradigm fit, conscientiousness, ego strength, teaching ability, research passion, and other traits that would someday hopefully translate into powerful and sustained contributions to knowledge. We were in some respects relentless--we built onto the Ph.D. program by converting some MBA electives to joint seminars (the MBA program at the time had few marketing majors) and emerged with an 11-seminar doctoral program that offered breadth, depth, and sophistication in research. The Florida workshops became increasingly hard-hitting explorations into importance of issue as well as excellence in theory and methods, plus capacity to engage in debate (as I relate later in this essay, these sessions clearly emblazoned memories--or scars?--on some candidates).
I can today find several missteps in our process, but I also realize that our original idealism was more than reinforced. I think I need only list some of our hires to make this point--in those first six or seven years Dipankar Chakravarti, John Lynch, Gabe Biehal, Wes Hutchinson, Joe Alba, and John Sherry were among those we brought to Florida (and into the consumer behavior field) for their first marketing faculty positions. Having amassed this array of talent, the next phase of the Florida tale then brought additional senior marketing talent (Rich Lutz, Alan Sawyer, Bart Weitz, and so on) and a broadening of the research focus. For me, though, the crucial lesson lies in the beginnings and in the strength of the attraction of a vision of sheer scholar-ship to fine young academics. In institutional terms, in my view, the "Florida experiment" demonstrated clearly that it is not always best for a department (or college) to slavishly follow current academic conventions. Furthermore, I saw how this type of institutional initiative requires that key faculty members hold to strong academic values and have strong support by administrators who are willing to commit to knowledge development in business schools. Given length constraints, the only other point I'd like to leave with readers here is that it is a shame that Joel Cohen has never received the recognition he deserves for fostering and developing this institutional phenomenon and its now quarter century of contributions to thought in consumer behavior. I've often wondered why.
Participating in public policy: back to exploring "business and society." Although macro marketing issues have a long history in our field, a parallel set of events to those for consumer behavior also occurred around 1970 for the study of marketing and public policy, albeit on a smaller scale. As noted previously, the Sears/AACSB Federal Faculty Fellow-ship program enabled me to pursue participation in public policy, as an in-house consultant to the Bureau of Consumer Protection at the FTC. Given the importance of the policy domain for such research, one initiative I'm pleased to have helped create (along with Commissioners Mary Gardiner Jones, Murray Silverman, and David Gardner) was a set of rotating FTC consultancies for visiting marketing academics, who would then return to their universities to build on these experiences. In our case, I recall how pleased and proud I was when Hal Kassarjian agreed to be my replacement and Neil Beckwith agreed to replace Dave: We had found two terrific people to carry the program forward. During the ensuing years, some 30 academics participated, leading to dissertations and growth of the area. After the program died during the deregulatory era of the 1980s, several of us teamed with FTC Commissioner Andrew Strenio and others to revive it for the 1990s (for an interesting overview of this initiative, see Murphy 1990).
Though still much smaller than consumer behavior, this subfield, now broadened as "marketing and society," has been flourishing in recent years. It now also has a sustaining infrastructure consisting of several journals, several associations, and several related conferences. Rather than delve into detail here, let me highly recommend an informative set of short retrospectives on the development of this area in the Spring 1997 issue of Journal of Public Policy & Marketing (Andreasen 1997; Bloom 1997; Greyser 1997; Kinnear 1997; Mazis 1997; Wilkie 1997), as well as an excellent long-term perspective provided by Hollander, Keep, and Dickinson (1999). Readers might also benefit from Murphy and Wilkie's (1990) book, which presents history and institutional insights from experts on regulation; Handbook of Research on Marketing and Society (Bloom and Gundlach 2001), which offers many research overviews; and Wilkie and Moore's (2002) chapter in The Handbook of Marketing, which provides an overview of this area. For myself, participation in these activities has enabled me to return to and pursue some of the issues that had first caught my interest as a college freshman.
Magical Moments in Marketing
On the basis of my experiences, I would also praise three mainstream marketing initiatives as notable efforts to create and sustain scholarship in our field: ( 1) the creation of the Marketing Science Institute (MSI), ( 2) the AMA Task Force on Developing Marketing Thought, and ( 3) the recent Special Millennium Issue of Journal of Marketing. Again, I'll note each briefly and advocate that original sources be pursued.
Marketing Science Institute is in many respect sour field's "think tank"--what a wonderful concept and organization! I was privileged to serve as MSI's Visiting Research Professor for one year (1973-74), followed by a long, cordial, and productive relationship over the ensuing years. Although the early years, when MSI was first affiliated with Wharton and then moved to Harvard, had their ups and downs (see also George Fisk's [1999] and Paul Green's [2001] essays in this series), the institute was embarking on its long runof success under Bob Buzzell and Steve Greyser when I was there. My purpose here, however, is to focus on the concept on which MSI is founded and the mission it serves, as a bridge between the best of the worlds of academia and business. Its sponsors include some 70 of the most sophisticated national and global firms, and its rotating academic leadership has tapped many of marketing's most impressive academics. It is a place where theory and application are each promoted and where all parties respect the role that knowledge, thought, and research can play in marketing. Paul Bloom's (1986) book provides an interesting foray into earlier contributions MSI has made to the field, and the MSI Web site (www.msi.org) offers handy information on the roster of current offerings and activities.
Broad efforts to ask who we are and where we're going can be highly enriching experiences for a field of study, and my other nominations fit this description. Several knowledge development task forces have been convened by the AMA-- multiyear undertakings with a broad mandate to investigate the development and dissemination of marketing knowledge and with an eye to proposing useful initiatives. I served on the latest of these, convened in the mid-1980s by Steve Brown and Len Berry of the AMA, with Kent Monroeat its head and a motley crew of task force members having disparate backgrounds and views. It seemed to take a long time to attain a group comfort level, but after that point it became a terrifically rich and productive experience. My only regret for that task force is that our report was intended as an interim document showing the progress we were making. Upon submitting the report, however, we were thanked and terminated! I've always thought that our contributions were limited by that action, an unfortunate outcome. The report, however, is itself worthwhile and should be a basic reading for every academic in the field, whether he or she agrees or not. It appears as an article in the October 1988 issue of Journal of Marketing (AMA Task Force 1988), accompanied by invited commentaries (the previous AMA Task Force report by Myers, Massy, and Greyser [1980] is also worth consulting).
Recently, we have seen another undertaking in the same spirit, the Special Millennium Issue of Journal of Marketing, sent out as a fifth issue in late 1999. Underwritten by MSI, this effort complemented the traditional knowledge production process by ( 1) posing only a few key questions to scholars and inviting short proposals on how they might be answered in new ways; ( 2) giving a positive reception to certain proposals, thus encouraging "developmental articles" to begin; ( 3) providing further feedback and encouragement to some of the articles as they were developing; and ( 4) offering the results in a special issue. In my case, this approach (a staged removal of publication risk, along with positive feedback and encouragement) enabled me and my coauthor (who was untenured at the time) to take on a large, multiyear investigation on marketing's contributions to society (Wilkie and Moore 1999) that I would absolutely never have considered doing under typical journal publishing conditions. My purpose in raising this is to point to the entirely affirmative scholarly concept embodied in this journal initiative--one that identified key issues and supported addressing them through new ventures in scholarship. As a long-time academic in marketing, I was pleased and enthused at being able to participate, and I want to salute Bob Lusch (who, as JM editor, pushed for this to happen) and Dave Montgomery and George Day (who, as former heads of MSI, both elicited its support and served as designers and editors for the issue). In addition to the Special Issue itself, readers may wish to consult Roger Kerin's (1996) review of the history of Journal of Marketing and George Day's (1996) commentary for background.
Increasing Institutional Impediments: "Four, Three... Tell Me, 'Who Are We?'"
[Note: Up to this point, the section has been wonderful to write, but I feel compelled to note that not all institutional initiatives in our field have been constructive for scholar-ship, and this situation may be worsening. A moment to pursue the question of self-identity is surely in order, but I feel the need to approach this topic with stealth, so let's take on the second part of the title first.]
I've always been interested in sports and grew up with football as played in Western Pennsylvania, then at Notre Dame. "Gritty, grimy, punishing" are terms that come to mind. Football on the West Coast, however, though every bit the equal in quality, is somehow quite different in spirit. For example, the chant in the title was part of a student offering (possibly only at a single game) by a group at Stanford that had won a contest to conduct a cheer. It was done to a rhythmic drum beat, counting by twos ("One, two, tell me, who are you?" and each time, the crowd answered in time to the beat, "Stanford! Stanford!"), up to ten, then backdown again (our phrase in the title is thus near the end of the cheer, with the same crowd answer). I've never forgotten it. Watching the crowd increasingly join in, hearing its force on repetitions, and noting the assurance of the simple response, I thought it would be worth sharing, because these conditions surely don't pertain to the institution of marketing academia today. We'd be better off, though, if we had a good short answer, and if we agreed on it, and if we were willing to shout it out. Do we, do we, and are we?
Whatever one's proclivities here, marketing's status as an applied field of study brings with it a number of anomalies within a university setting. I hadn't fully appreciated how serious this issue is becoming, though, until a few years ago when I found myself sitting on an invited panel at an AMA conference without a very clear picture of my role. The topic, "Tomorrow's Marketing Professors," had drawn an audience lining the walls of the large room. My three fellow panelists were noted researchers; in addition, each had served as editor of one of our "Big 3" journals, and each had served or was currently serving in an important administrative capacity at his or her school. Except that the others taught at public universities and I taught at a private school, I didn't understand what I could add to the session, so I offered to go last (when your last name starts with "W," this is a familiar enough position in the scheme of things). As the session unfolded, though, I began to see with pristine clarity what I wanted to say.
In brief, each speaker stressed the challenges facing business education as the turn of the century neared, as well as solutions that would require adjustments from faculty members in the future. These included ( 1) corporate part-nerships with business schools, demanding deliverables in the form of value-added research for the business; ( 2) accountability to our constituents, demanding quantitative demonstrations of the value of our production, together with outside audits to document teaching loads, time spent on campus, advising students, and so forth; ( 3) research funding, demanding increased relevance at all stages ("corporate sponsors will desire applications and executive education programming"); ( 4) tenure being under increasing review and in danger of being abolished (as an aside, a Business-Week commentary around that time, titled "Tenure: An Idea Whose Time Has Gone," opened with a vignette of a dean of a leading business school reacting to news of several schools that were considering abolishing tenure by pumping his arm and giving a whooping "holler of joy"; Leonhardt 1996); ( 5) technology driving changes in educational delivery; and ( 6) marketing losing its franchise to other business school areas.
Now, I'll admit that my comments were a bit intemperate. I wish to acknowledge that the other panelists had spoken the truth: These are real and difficult issues. Furthermore, the speakers themselves likely didn't endorse all these changes, such as moves against tenure. However, it irked me that in the entire session, as in outside world discussions of these issues, there had been simply no mention of scholar-ship at all (fortunately, as all three speakers are accomplished scholars, it was clear that the missing mention of scholarship was a property of the issues involved and not of any personal shortcomings of theirs, in my view). Moreover, it was clear that faculty members were not being pictured as having much useful input to either the discussion or future decisions as to directions for business education. Somehow in these discussions, "the faculty" sounds more and more to be a group of recalcitrant employees who don't quite "get it," rather than highly intelligent, conscientious, and accomplished professionals pursuing the highest goals of the academy. I went on to say that though the groups mentioned surely are "constituencies"; this does not and should not reduce us faculty members to some secondary role in which we are first to be instructed in the new purposes of the modern university and then marched out to implement orders.
My basic position is (was) that we faculty are developers and disseminators of knowledge. We are stewards of universities and their roles in society. Throughout modern history, universities have been beacons of light in troubled worlds, serving as fonts from which the future unfolded in terms of science, technology, and significantly liberating worldviews, as well as producing an increasingly educated populace. Within the context of the professional business school, the faculty members should be asserting their visions for the central themes and thrust of each institution and should (in my view) never need to debate being placed in subordinate roles as their institution attempts to seize change opportunities.
My comments that day were much in the spirit of the remarks that the distinguished management educator James March (1996) later made to the Stanford business faculty on the occasion of his retirement--I wish I had known of them at the time I spoke, as I clearly would have been more persuasive. March began his talk by characterizing the guiding rationale for modern business schools, as with the social sciences generally, as in the "consequentionalist" tradition. Here, "action is seen as choice, and choice is seen as driven by anticipations, incentives, and desires" (March 1996, p. 12). While recognizing that this is a powerful and useful perspective, March (1996, p. 12) also notes that John Stuart Mill once described Jeremy Bentham, the father of modern consequentionalism, as having "the completeness of a limited man." Similarly, March (1996, p. 13) points out that extending the marketplace metaphor to business schools leads to a situation in which
The problems of business schools are pictured as problems of creating educational programs (or public relations activities) that satisfy the wishes of customers and patrons rich enough to sustain them.... But [this] fails to capture the fundamental nature of the educational soul.... A university is only incidentally a market. It is more essentially a temple--a temple dedicated to knowledge and a human spirit of inquiry. It is a place where learning and scholarship are revered, not primarily for what they contribute to personal or social well-being but for the vision of humanity that they symbolize, sustain, and pass on.... In order to sustain the temple of education, we probably need to rescue it from those deans, donors, faculty, and students who respond to incentives and calculate consequences and restore it to those who respond to senses of themselves and their callings.
As a final comment on that AMA conference session (interested readers can find a fuller report by Leigh and Mowen [1996]), it was interesting to monitor the audience reactions as I was speaking. I would estimate that only one-fifth to one-quarter of the people were providing affirmative signals, while a very few were demonstrably negative. Most people gave no overt reactions either way. I'm therefore not at all sure where most marketing academics stand on these central issues for our field, but I sense that my positions are not theirs. At a personal level, this is quite all right. I was very pleased that day to be given a chance to speak for myself and on behalf of at least a significant--if minority-- portion of that audience. At an aggregate level, however, I think it is crucial that we not allow ourselves to be diminished, and I do fear that this is what has been happening. On further reading, I particularly liked J. Scott Armstrong's (1995) book review on a report on MBA education, did not favor Ernst & Young's (1995) report on change in business education, and have found Trieschmann and colleagues (2000) and Zimmerman (2001) to be enlightening. I don't want my concerns to go on at length, so I will merely highlight two other themes that are intertwined with the various issues noted here. I think they deserve special mention because there are signs that they have not been handled sufficiently at this point and may well be threats in the future.
Some journal reviewers seem to be out of control, are damaging scholarship, and may even be driving "the gentler people" out of research. This is simply not journal reviewers' role. In making this point, let me stress that this is neither a complaint based on personal outcomes nor one directed at journal editors, all of whom I have found to be quite sensitive to this issue. It is the case, however, that in many of my papers (for years now) and in numerous cases of colleagues who have shown me their review interchanges, at least one reviewer is taking on a role that can be perceived as threatening free thought; free expression; choices of concepts, approaches, and methods; and other foundations of scholarship. Even if unintentional, this issue needs to be pursued because of its destructive impacts on core academic motivations and behaviors. To illustrate (and this is by no means one of the worst cases, in that the review was quite competent and not mean), for one of my papers a (major journal) reviewer recommended "revise and resubmit" but insisted that the paper be entirely altered in style, and he or she opened the comments by saying, "If you want to have a paper accepted in (major journal) you need to...." Now, it happened that I had been on the editorial board of this journal for about ten years, had given hundreds of hours of effort to reviews, and wasn't about to accept that kind of feedback except from the editor, who did apologize for it (but did not retract his cover letter's conclusion that the paper in its present form did not constitute a contribution to knowledge, when my belief was that it clearly did). Later, each time I opened that folder to modify the paper in accord with other feedback, I'd find myself so offended that I would just close it again. Finally, I resigned from that journal's editorial board and withdrew the paper. The entire episode struck me as so wrong that I never did submit the paper to another journal, though I personally vi ew it to be among my best pieces of work and a terrific offering by Peter Dickson, my coauthor on it, on whose dissertation it was based (we had struck a prior agreement on who would do what with the database), who was still untenured at the time, and whose career could have suffered because of my intransigence. Fortunately, it did appear in MSI's Working Paper series, was sought out and reprinted by Hal Kassarjian and Tom Robertson as the lead article in their consumer behavior readings book (they commented that they especially liked its reader-friendly style!), was used as a key basis for a model by Glen Urban and John Hauser in their book, and over the years has been fairly frequently cited, particularly within the marketing science community. I point this out not so much to vindicate my position (had we revised, I believe that the paper would have appeared) but to point to the sharp contrast with the review team's initial summary judgment, which was that in this form the paper did not constitute "a contribution to knowledge." To underscore my point, I don't really think there was a villain in this tale (I believe there are in some other ones I've seen and heard), and Peter and I have survived just fine, but it must be obvious that such efforts to dictate too strongly the directions of research trade-offs impinge on freedom of thought, suppress initiatives, and likely damage scholarship in the long run.
Although damage in this case may have been minimal (and, some may argue, even self-inflicted), there is also the matter of the accumulation of such experiences and what toll this takes on the field. In this longer-term sense, it is especially painful to talk with a few of my former students and many others I met as young, aspiring scholars in the field, who are now out of academia, or virtually so, embittered at their experiences and at dealing with the impacts of these blows to their youthful enthusiasm and self-confidence. Peoples' personalities do differ--some people will fight back and stick with the battle until the work gets in, but others will not, and it's not really evident to me that they aren't equal in scholarship potential. This isn't, moreover, something that "someone else" is doing to us: We're doing it to ourselves. As to readings, three useful works I've found are Holbrook's (1986), the AMA Task Force Report (1988), and John Lynch's ACR Presidential Address (1998), in which he warns against reviewers' "hijacking" of papers.
We need to consider that knowledge does not simply accumulate over time but can be effectively lost from a field of study if it is not transmitted across generations of academics. This insight extends across several issues, so I merely want to raise it to stimulate further thinking. Some people are concerned, for example, that the "marketing mainstream" is disappearing as specialization leads to fragmentation (a striking visual has been available recently by observing the hotel check-out lines for the Society for Consumer Psychology meetings just prior to or in parallel with the hotel check-in lines for the AMA Winter Educators' Conference). Some academics are concerned about incursions from other fields into marketing's previous domains (e.g., Reib-stein 2001), whereas others note major issues for doctoral program design (e.g., Wilkie 1997; Wilkie and Moore 1997). In any event, this is a central issue for marketing scholarship and needs to be seriously addressed by the college of thinkers in the field.
In closing this section, permit me a brief editorial comment. In looking across the positive initiatives I've listed, it strikes me how broad, important, and very impressive they are and what a wonderful opportunity it has been for me to variously observe, participate, and/or benefit from them. As indicated in the final entry, the challenge has now shifted to current thinkers in our field--and in business academia overall--to create bold new initiatives that will best stimulate, direct, support, and sustain our field's scholarship in the future.
In closing, I'd like to just briefly address some special personal aspects of our lives as thinkers about the field of marketing. Because straightforward discussion of some issues might be perceived as trite, I'll continue with musings at times.
Inspiration, Aspiration, Dedication
Terry Clark's invitation to undertake this essay has allowed me to raise certain issues to consciousness, and for this I am most appreciative. Up to this point, I've stayed largely within the province of business and marketing academia, but I think that many of us actually feel a broadened heritage and linkage to the university and its community of scholars. Some of my warmest memories are involved with campus walks and visits. For example, although I never attended there and only briefly taught there, I've always felt a singular level of respect for Harvard, the university. I used to visit Boston often and would typically arise early in the mist, make my way along the river, through the Square, and into Harvard Yard, where I would feel a peculiar sense of comfort or belonging. Sometimes I'd wander aimlessly, observing others, and sometimes I'd imagine footsteps of seekers after knowledge in earlier generations. What an honor, and what a pleasure, to be able to search for knowledge.
I can see that I'm going to need to cut this off quickly, so let me briefly mention a few literature sources in this zone that I've enjoyed over the years. In pursuit of scholarship, it can be easy to lose perspective (I may have already amply demonstrated this!), but I've found that even a brief consideration of the breadth and depth of what is already known in the world brings a bucket of humility to swamp any false pride in this sphere (see, e.g., Wilkie 1981). For example, all we need do is briefly reflecton what we personally know about five great disciplines of the mind and their implications for our work:
- Mathematics enables us to comprehend space and time and is a key to the natural and material sciences.
- Language enables us to add human reason and communicate and transmit knowledge.
- History is our repository of knowledge and requires language for its basic materials.
- Logic introduces the principles of reasoning and uses language to arrive at new facts and knowledge.
- Philosophy transcends narrow principles to consider norms and ideals for human existence.
Beyond these, the aesthetic areas of art, music, and literature offer further opportunities for appreciative humility.
Back when I was first wondering what it might be like to try to pursue a scholarly life, I read two books that offered me inspiration and whose messages have stayed with me. One, The Cultivated Mind by Edward Hodnet (1963), proposed three distinguishing properties:
- The cultivated mind is conceptual. It seeks understanding, desires to know, and is willing to speculate.
- The cultivated mind is discriminating. It is sensitive to value and is willing to distinguish and differentiate.
- The cultivated mind is humane. It regularly moves beyond an obsession with self and the press of daily affairs. It is thus capable of a serious concern with the nature of human existence.
Congenial views to these are also provided in a book by Columbia's University Professor, Jacques Barzun, titled (1959) The House of Intellect. On reflection, one element I find to have been diminished in today's business academic environment is an appreciation for the central role of the academy. In that regard, Barzun is certainly refreshing, if hard-line:
These considerations make only more imperative the safeguard of the master virtues of intellect. They are, once again: concentration, continuity, articulate precision, and self-awareness. Intellect needs the congregation of talents spurring one another to high achievements by the right degree of proximity and discourse; it needs the language and the conversation that maintain its unity like a beneficent air; it needs precision to dispel the blinding fogs of folly or stupidity; it needs self-awareness to enjoy its own sport and keep itself from vainglory.
Finally, I have found James March's retirement comments particularly inspiring, in part because he also attempted to carry out his scholarly calling within the context of a high-powered professional business school. Recalling my previous description of his position, he offers a contrast to the consequentialist tradition--one not found much in business schools, but one to be considered. It is based on a motivation to "fulfill the obligations of personal and social identities and senses of self." Reflecting this driving force are those "who support and pursue knowledge and learning because they represent a proper life, who read books not because they are relevant to their jobs but because they are not, who do research not in order to secure their reputations or improve the world but in order to honor scholarship" (March 1996, p. 13).
Responsibility and Radio Ratings: Is a Zero Possible?
A few years ago, I was scanning the newspaper when a realization struck with stunning impact. A brief report on local radio ratings sweeps contained odd language on the last-place entry, an AM "oldies" outlet. Although the story didn't say it outright, my inference was that the consumer survey might have registered that no one was listening to this station. Now, we all know this is a possible survey outcome, but when you think about it, what does it really mean? I began to think about this:
Could there be times when no single human being is listening to this station? Are there times when no radio on earth is receiving its signal, when not even a single household pet is hearing its messages? And worse, as the survey suggests, could this be a regular event? Is it even possible that no living creature outside the station ever listens to it?
The implications hit me like a bombshell ... visions of a robotic transmitter beaming out signals across a totally inattentive Indiana landscape--the finest efforts of Elvis, the Supremes, the Beatles, Elton, or Whitney--voices and rhythms trapped in invisible, unheard radio waves. I began to picture these waves, a continual stream, over towns and across fields, drifting determinedly into nothingness. The marketing analogue of the philosophical stopper ("Imagine that a tree falls in a forest ...") had in fact arrived, and right here in South Bend, no less.
Several cogent managerial questions did come to mind. Is it feasible for a ratings firm to report a zero rating? If not, what does get reported, and is this fair? What does a zero result mean? For the advertisers, what will their thoughts be when they read these findings? What would mine be? For the station's executives and on-air talent, I felt only sympathy: What a total shock. And for the station's sales team, talk about a credibility gap! [ 1]
Then I began to think about our own field. Do we face a parallel situation? Are there any articles out there that are going entirely unread? Are there valuable insights or findings being sent out but not being perceived by others in the field? How many of us are so busy teaching, producing research, and performing other tasks that we're not learning much else?[ 2] Most of us chose this field because we wanted to learn, to grow, to understand. Given this worthy early motivation, it's reasonable to ask which signals out there we're allowing to go regularly unreceived because our personal "radios" are shutoff or tuned elsewhere. And what is the real cost of this for each of us as an individual scholar? What about the cost to knowledge development in the field? Interesting issues to ponder....
"They've Got the Spirit!"
I'd like to end this essay on a few high notes. One is to comment most favorably on the many opportunities I've had to benefit from, witness, or reflect on the finer attributes of a large number of fellow marketing academics. As just a small nod in this regard, I'd like to refer briefly back to the Levine tale, in which I mentioned that our public handling of gaps in knowledge can be a problem. In this regard, several memorable cases in which young marketing academics have handled this in impressive ways come to mind. For example, as noted previously, in my early years at Florida, the recruiting workshops sometimes became highly intense (and perhaps overly so). On one such occasion, a job candidate was stumped by a faculty query into the true essentials of a construct versus how it was being employed. After two bluffs that didn't fly, the candidate stepped back and simply acknowledged not having ever thought about this issue before. Not impressive in itself, but what the candidate did about it was-- the next year a major conference paper appeared on this exact issue, in which it was addressed very well. Or the time when a talented candidate from a top program finished the job interview with "So that's what I'm doing. However, I've been told that there's a lot I still don't know about this area and that your group (the Florida faculty) would be able to point me in the right direction. What would you recommend I do now?"
Or just recently, when a former job candidate sat down across from me at a Doctoral Consortium dinner and said: "When I interviewed at Florida you asked me a question I couldn't answer. I've thought about it a lot, and now I have the answer." My (shocked) response: "Wasn't that [quickly calculating] ... almost 20 years ago?" "Yes, but it's still a good question, and now I have a good answer." My clever riposte: "Well, now I don't want to hear it!" I got the answer anyway. (By the way, although I can't name names, all three of these people would be instantly recognizable as leading researchers in the field today, and I think their honesty and drive in handling these situations were both impressive and instructive.) Still, given that it is frequently our role to communicate new knowledge, projecting confidence and competence, it's hard to balance this against the natural humility stemming from ignorance.Yet I do believe that this is a wonderful element of the academic life--the acceptance, even celebration, of a lack of knowledge and its stimulating impetus on a search for further understanding. Beyond our freedom to speculate, this may even be its finest attribute!
A Closing Comment: On Marketing as a Field of Study
Several friends who have read this essay suggested that an appropriate ending would be to reproduce a recent quotation in which I tried to summarize my view of marketing into a succinct sound bite (Marketing News 2001, p. 30). On reflection I like this idea, not so much as a measure of the quotation itself but because it does capture a genuine view that I've always held of this field, that I believe many of us share, and that underpins my optimistic view of the pursuit of scholarship in this area:
This may sound odd, but from my earliest days as a student I've seen Marketing as a wonderfully complex and important field. There hasn't been a time for me when it wasn't presenting interesting challenges and paradoxes. For example, it relates and reflects many more basic disciplines in a university, yet it does so in a manner where "the bottom line" counts. It can be quantitative, but it's also always qualitative. It can be very high-tech, but almost always also involves people and their limitations. It can involve duplicitous behaviors, but also can bring wonder to and improvements in peoples' daily lives. I often look back and feel fortunate to have chosen to be in Marketing....I've certainly never regretted my choice for a minute.
[ 1] What happened, you ask? No public comment ensued. The station shifted its format, adding a quite scatalogical syndicated morning show among the changes. This content shift obviously attracted some new listeners. Angry letters to the editor began to appear from offended citizens, demanding to know ( 1) why such tasteless material was allowed on the public airwaves and ( 2) why this station's upstanding ownership (a religious organization) would ever choose to give it air time. Weren't they listening to their own outlet? Station management then issued a public apology and announced it was canceling the new show immediately, in midcon-tract, no less. As a final irony, soon thereafter a leading FM outlet began trumpeting that it would rescue South Bend by offering this popular fare each morning, and it has, ever since.
[ 2] This is an issue we raised in the 1988 AMA Task Force Report on knowledge development.
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By William L. Wilkie and Terry Clark, Editor, Southern Illinois University
William L.Wilkie is the Aloysius and Eleanor Nathe Professor of Marketing Strategy, University of Notre Dame.
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Passing the Torch: Intergenerational Influences as a Source of Brand Equity
In today's competitive battleground, the concept of brand equity has proved to be an important source of strategic insights for marketers. However, one potentially valuable source of brand equity-the operation of intergenerational influences-has generally been overlooked in the marketing literature. This article reports the findings of two studies that show intergenerational impacts on brand equity to be persistent and powerful across an array of consumer packaged goods. However, as a strategic challenge, these effects seem to apply strongly for some brands but not for others-they are selective. In Study 1, the authors use parallel surveys of mother-daughter dyads to isolate and quantify intergenerational impacts, and the surveys reveal a differential range of effects at both the product category and the brand level. In Study 2, the authors use interpretivist methods to delve more deeply into these effects-the forms they take, the way they have developed, and factors that sustain or disrupt them. On the basis of these findings, the authors identify implications for managers and future research needs. Overall, intergenerational influences are a real marketplace phenomenon and a factor that merits much closer attention from marketing strategists who are interested in brand equity issues.
The topic of brand equity has emerged as a central concept in marketing over the past 20 years, raising questions about how to create positive brand images, extend brands into new categories, and build customer loyalty (e.g., Aaker 1996; Aaker and Joachimstahler 2000; Keller 1998). Much has been learned about sources of brand equity as well as its many benefits for a firm and its customers. However, one interesting and potentially powerful contributor-the concept of intergenerational (IG) influence-has generally been overlooked. The purpose of this article is to examine the linkage of IG influences and brand equity. Is this phenomenon a real factor in the marketplace? How substantial is it, and for which consumer goods does it work best? Are certain brands benefiting from this influence whereas others are not? (To the extent that some marketers have been differentially capitalizing on IG effects, they have been adding to their brand's sales, profits, health, and vitality at little or no cost.) More broadly, what exactly are IG influences, how do they come about, and how do they operate? In this article, we present evidence on these questions, drawn from two distinct studies.
Broadly construed, IG influence refers to the within-family transmission of information, beliefs, and resources from one generation to the next. It is a fundamental mechanism by which culture is sustained over time. Its key elements are embedded within socialization theory.
Roots in Socialization Theory
As an area of inquiry, socialization has a long tradition in sociology and cultural anthropology as well as social learning, developmental, and personality theories in psychology (Levine 1969; Peterson and Rollins 1987). Socialization itself is defined as the process through which people develop specific patterns of social behavior (Ziegler and Child 1969) or the process by which people learn the social roles and behaviors they need to participate effectively in society (Brim 1968). Socialization helps society function by reinforcing particular beliefs, traditions, and values. It also helps people develop their personal identities and assume new roles as they move through their life cycles. Although socialization is a life-long process, childhood and adolescence are particularly crucial times. During childhood, the socialization process focuses not only on the present but also on roles and behaviors that will be needed in the future (McNeal 1987). The family is the first and typically the most powerful socialization agent; parents and other family members serve as channels of information, sources of social pressure, and support for one another. As a family develops, it creates a distinct lifestyle, pattern of decision making, and style of interacting (Sillars 1995). Children have continuous opportunities to learn and eventually to internalize the beliefs, attitudes, and values they have observed, accepting these as the norm (Sears 1983).
Basic Findings on IG Influences
Intergenerational research on political and social behavior shows that many forms of influence are transmitted from parents to their children, including political affiliation, religious values, achievement orientation, and gender and racial attitudes. Levels of parent-child consensus vary considerably, being highest within religious and political arenas; lowest for lifestyles; and generally substantial for topics that are concrete, visible, and salient (e.g., Hoge, Petrillo, and Smith 1982; Troll and Bengston 1979). Over time, effects decline; the greatest erosion occurs during the first few years after the young adult leaves home, and then there is a leveling off by the late 20s or early 30s. Thus, IG impacts can endure well into adulthood (e.g., Beck and Jennings 1991; Niemi and Jennings 1991; Whitbeck and Gecas 1988). "Reciprocal socialization," in which children influence their parents, is also studied in this research stream.
Relatively little direct research attention has been given to IG influence in consumer research, though its presence is recognized in early work on family decision making and the family life cycle (e.g., Davis 1976; Wells and Gubar 1966). Relevant research is also present in the literature on consumer socialization, defined as "the processes by which young people acquire skills, knowledge and attitudes relevant to their functioning in the marketplace" (Ward 1974, p. 2). Consumer socialization research has studied the roles of family, peers, and mass media in teaching children about consumption, the impact of parenting style, and the way parents and children interact in making household purchase decisions (e.g., Beatty and Talpade 1994; Carlson and Grossbart 1988; John 1999; Moschis 1987; Palan and Wilkes 1997).
In the early 1970s, IG research was introduced into the study of consumer behavior. From Hill (1970) to Obermiller and Spangenburg (2000), writers have supported the proposition that these effects are significant, interesting, and potentially important in the marketplace. The core empirical research base consists of only some ten articles, and several of these appear in conference proceedings. An interesting and wide set of topics has been studied. The effects of IG can reach impressive magnitudes: For example, Woodson, Childers, and Winn's (1976) study of auto insurance observed that 62% of men in their 20s reported that their insurance company had also supplied coverage to their fathers, and even at age 50 almost 20% met this criterion. Furthermore, Hill's (1970; cf. Miller 1975) longitudinal study finds that financial planning skill levels are transmitted across three generations, particularly among families that are poor financial managers. Arndt (1971, 1972) studied agreement between college students and their parents on dimensions of innovativeness, opinion leadership, and loyalty proneness. Moore and Lutz (1988) uncovered shared marketplace beliefs and choice rules (see also Carlson et al. 1994) and noted greater preference similarity associated with products that are more visible to children in the home. Heckler, Childers, and Arunachalam (1989) assessed perceived purchase similarities and observed stronger impacts for convenience than for shopping goods. Childers and Rao (1992) assessed reference group effects and pointed out that a family's impact will differ from that of peers depending on whether a product is publicly or privately consumed. Olsen (1993, 1995) supplemented the prior survey approach in this area by introducing interpretive methods in her study of brand loyalty transfers between generations. Most recently, Obermiller and Spangenburg (2000) noted linkages between family members on skepticism toward advertising.
In summary, theory suggests that family influences constitute a powerful socializing agent in children's lives, and prior research results provide good reason to believe that IG effects are at work. However, current knowledge about consumer IG influence is grounded in a relatively small research base, a number of research issues remain to be addressed, and further evidence is needed on several key topics. Prominent among these is the matter of IG contributions to a brand's equity in the marketplace.
Ties to Brand Equity
A basic theme in discussions of brand equity is that it represents the added value that accrues to a product as a result of marketing investment and effort (e.g., Aaker 1991; Farquhar 1989; Srivastava and Shocker 1991). Keller's (1998) customer-based brand equity framework identifies a brand's meaning as the key to creating equity. Although IG influences have not explicitly been noted thus far in the brand equity literature, they deserve consideration as a force that develops such meaning in consumers' minds. Consider the number of opportunities family members have, year after year, to jointly consume, shop for, and comment on favored brands, thereby influencing children's brand associations.
Keller's (1998) framework also stresses that meaningful differences among brands derive from brand associations that are unique, favorable, and strong. Theoretically, family interactions are likely to produce high levels on these exact properties. Associations that tie loved ones to a brand bring a unique element. Favorable associations are probable, because IG influences are likely strongest for brands that have provided satisfaction for the household over time. Meanwhile, strong associations result from both personal relevance and consistent information over time. Thus, when a child's attitude about a brand, consumption occasion, or store is formed within the context of daily family life, it can exhibit meaningful characteristics that will sustain it across time.
Finally, Keller's framework points out that firms enjoy a number of brand equity benefits related to growth and profits that ensue from increased customer loyalty levels. The possibility of extending loyalties from one generation to the next is an added benefit that has not yet been well recognized. Each year, new cohorts of potentially brand-loyal consumers go out on their own, continuing to use brands they have been socialized to use at home (Wilkie 1994). Overall, then, the issue of IG influences appears to offer considerable potential for marketers.
This potential likely differs by substantive domain, however, because previous research has shown that IG is partially a function of specific product category or marketplace characteristics. For example, Childers and Rao (1992) report stronger IG impacts for private goods than for those consumed in public (and note that culture may moderate observed levels). Also, Heckler, Childers, and Arunachalam (1989) observe stronger IG preference effects for convenience goods than for shopping goods. Within the packaged goods domain, Moore and Lutz (1988) find stronger IG impacts associated with in-home visibility (when the product is consumed in its original packaging rather than not--e.g., catsup versus canned peas). Each of these studies shows that product category features can affect IG levels. However, none of these studies reports findings for individual product categories (findings are reported only for product types-e.g., convenience versus shopping goods). Furthermore, no study to date has examined IG influences for individual brands.
In this project, we examine IG influences in a domain in which issues of brand equity are extremely important-consumer packaged goods. Here, consumer purchases are made in a highly competitive setting, with frequent new product introductions and substantial promotional activity to encourage brand-switching behavior (Kahn and McAlister 1997). These products are inexpensive and frequently purchased: Trial and brand switching are easy to undertake. Still, many packaged goods meet the basic criteria-having a relatively long life cycle, being used by households with children, providing satisfaction over time-that researchers have identified as important for IG effects to emerge (e.g., Moore and Berchmans 1996; Shah and Mittal 1997; Woodson, Childers, and Winn 1976).
To examine these potentials, we conducted two studies using different methods. In Study 1, we used survey methods to isolate, quantify, and test for IG effects, first for 24 consumer packaged goods categories and then for individual brands in each category. In Study 2, we turned to interpretive methods to delve more deeply into the nature of IG effects. Each study investigated multiple ways in which IG may be operating in this domain. Triangulating across research methods enabled us to probe different aspects of the IG phenomenon, providing a richer perspective than that obtained through a single approach (Denzin 1989; Lutz 1991).
Overview and Method
Our specific purpose for Study 1 was to determine whether and how IG influences are related to brand equity in the packaged goods marketplace. In planning the project, it was clear that a complex analytical approach would be required. We accordingly developed a framework based on four broad expectations:
- Expectation 1: Intergenerational impacts are at work across a spectrum of consumer behavior. Impacts range from brand effects to product category effects to store preferences, marketplace orientations, and broader lifestyles. To appreciate brand equity issues fully, we need to identify and partial out some broader impacts. Here, we examined IG impacts on product nonuse.
- Expectation 2: Intergenerational impacts can be measured at different stages of the consumer decision process. The "hierarchy of effects" (Lavidge and Steiner 1961) provides a useful framework for assessing levels of IG impacts in the marketplace. Here, we focused on IG impacts on consumers' awareness, consideration sets, and brand preferences.
- Expectation 3: Intergenerational influences exist within a tumultuous, differentiated marketplace; various forces affect levels of IG impact. These include differences in product susceptibility to the formation of IG influences and marketplace forces operating to either sustain or disrupt IG impacts in young adulthood. Here, we examined whether observed IG levels were affected by several measurable marketplace factors.
- Expectation 4: Not all brands within a product category are equally likely to benefit from IG influences; certain brands may receive notably high levels of loyalty and support. Our analysis sought to isolate such differences in IG effects for individual brands.
In Study 1, we conducted parallel surveys of 102 mothers and 102 daughters, each mother-daughter pair constituting a dyad for analysis. Our decision to focus on mothers and daughters was based on research indicating that adolescents and mothers influence one another's purchases more than adolescents and fathers do, that maternal influence is particularly strong for household products, and that women tend to exhibit stronger brand involvements than men (Foxman, Tansuhaj, and Ekstrom 1989; Guest 1964; Olsen 1995). We recruited the daughters from an introductory marketing course at a large Southeastern public university, with the proviso that they must currently live off campus and shop for groceries. We developed two similar questionnaires, one for each member of the dyad. Each participant was asked to indicate whether she used each of 24 product categories (listed in Table 1) and then to list the brand she most preferred (by free recall), as well as additional brands she "seriously considers" when purchasing.[ 1] We then asked participants to report independently on their partner's (i.e., mother or daughter) product usage and brand preference in each product category. Students completed their questionnaires and provided their mothers' names and addresses for the mailing of a parallel survey and cover letter that explained the project. Both daughters and mothers were alerted to the requirement that they were not to communicate about survey responses until all forms were returned, and all participants agreed. All 102 parent questionnaires were returned within three weeks of mailing, which indicated a high degree of parental interest and involvement.
There is no single perfect measure of IG influences, so it is worthwhile to briefly consider measurement options. Some key IG studies (e.g., Childers and Rao 1992; Heckler, Childers, and Arunachalam 1989; Woodson, Childers, and Winn 1976) have relied on a one-person, single-item measure from the younger family member only. (For a given product, "Indicate whether you bought/buy the same brand your parents bought/own," with the following response alternatives: [ 1] "same brand as parent[s]," [ 2] "different brand from parent[s]," [ 3] "don't know," or [ 4] "I don't buy/ own this product.") This approach provides an efficient report of perceived parent/child ownership but also requires that the young adult be sufficiently knowledgeable to answer on behalf of his or her parents. This may risk inadvertent memory or estimation errors in the response process, similar to long-standing difficulties that have been studied in family decision-making research (e.g., Corfman 1991; Davis 1971; Dellaert, Prodigalidad, and Louviere 1998; Ferber 1955). Moreover, studies using this measurement approach have generally reported clear findings of IG influence. As an alternative, other key IG studies have measured both sides of the parent-child dyad and then compared responses to assess similarity (e.g., Arndt 1971, 1972; Moore and Lutz 1988; Obermiller and Spangenburg 2000). In these studies, IG results have often been mixed-significant for some variables but not for others (for further consideration of this issue, see Viswanathan, Childers, and Moore 2000).
The present study employed several separate measures from the mother and daughter in each family to provide a basis for conservative tests of IG presence and to allow for the exploration of several levels of IG impacts. This creates the basis for a dyad-specific analytical method, based on an exact match or agreement in the answers provided by a mother and her daughter. When no agreement occurs, we infer that any IG effect that may have existed has not persisted into early adulthood for this daughter in this product class. If a mother-daughter match occurs, this family dyad then becomes a candidate for an IG effect, but matches due to chance levels also need to be considered and statistically removed before such a conclusion is drawn.[ 2] To enhance interpretation, we took measures across several potential levels of IG influence (i.e., product use, daughter's awareness of her mother's favored brands, brands personally considered for purchase, and single most preferred brand) for each of the 24 product categories.
The analysis plan was based on a multistage investigation across the four levels just noted. Within each level, we calculated separate scores for each of the 24 product categories. The score reflects the extent to which mothers and daughters within the same family provide identical responses. We then made a determination, "Is this actual matching score high enough to conclude that IG effects are present for this product class?" We accomplished this through a two-stage process: ( 1) calculation of the expected number of participants who would have a matched brand preference if the mothers and daughters in the sample were paired randomly (i.e., no family influence is present) and ( 2) comparison, through the Z-statistic test, of the actual number of mothers and daughters who match, versus the random expected number if no underlying relationship were operating (Kanji 1993).[ 3] Thus, we infer that an IG effect is operating only when statistical tests indicate that this is likely to be the case. The final feature of the analysis plan was then to examine IG effects for individual brands in each of the 24 product categories. This has not previously been done in the IG literature, and it enabled us to inquire whether some brands benefit from IG effects whereas others do not or whether all brands benefit equally. We discuss the findings in terms of the four expectations noted previously and portray them in Figure 1 and Table 1.
Findings for Expectation 1: IG Impact on Use or Nonuse of a Product Category
An obvious prerequisite to a shared brand preference is that both mother and daughter use the product. Therefore, we began with the question, "Does usage/nonusage of a product category run in families?" Because many of the 24 products in this study are commonly used by virtually all U.S. consumers (e.g., toothpaste, soap), these were not appropriate for this particular analysis. However, as shown in the first section of Figure 1, in nine of the categories, nonusers accounted for at least 15% of the sample, providing at least 30 nonusers. Therefore, to test for an IG nonuse effect in these nine categories, we examined each nonuser of a product to assess whether her mother or daughter likewise did not use the product. We then tested this result against the level of chance alone (were the mothers and daughters unrelated).
As shown in Figure 1, evidence of IG influence appeared for six of the nine product categories. Results ranged widely. The strongest results occurred for frozen juice. Though not shown in this summary figure, 62% of the sample did not use this product form, but as shown, nonuse does run in families. Of the dyads in which a nonuser appeared, 78% had both mother and daughter as nonusers (i.e., stated another way, in only 22% of the cases of frozen juice nonusage did mothers and daughters behave differently from each other). This level of family correspondence is far above that expected by chance, indicating a statistically significant IG impact on nonuse. A similarly strong result emerged for canned vegetables, which showed 49% family agreement on nonuse. Family background was also significant for daughters who do not use baked beans, tea, jams/jellies, and tuna.[ 4]
As indicated at the right-hand side of Figure 1, tests for coffee, candy bars, and peanut butter showed no significant differences from chance: Daughters who do not consume these products have apparently made this choice for reasons other than IG learning (e.g., health, fitness). Overall, Expectation 1 was supported: IG influences help determine the purchase or nonpurchase of some, but not all, products. Managerially, moreover, the finding that this effect is product-specific indicates that customized category research is needed for marketing decision making.
Findings for Expectation 2: IG Influences and the Hierarchy of Effects
One logical model of IG effects suggests that we can expect to find that a daughter's awareness of her mother's preferences is a logical precursor to further consideration of them for personal purchasing, a shared brand preference over time being the key indicator of IG influence in the marketplace. The middle section of Figure 1 presents overall results, averaged across all 24 products. Notice that these cascade from higher to lower levels, as is expected with hierarchical models.
With respect to awareness, mothers were asked to recall up to three brands that they consider when making purchases in each product category. Many provided only a single brand, others listed two, and some listed three brands. Meanwhile, daughters who purchase the product themselves were asked to recall a brand their mothers prefer in each of the 24 categories. The awareness score here shows that the brand named by a daughter appeared somewhere in her mother's consideration set 69% of the time, averaged across all 24 product categories. Although not perfect, this corresponds to approximately 17 correct predictions per daughter, indicating substantial awareness of mothers' preferences. Extreme results were registered for soup, tea, and peanut butter (all above 83%). Inspection of other results suggested that daughters are generally less likely to be aware of household maintenance products, such as dish detergent, paper towels, and household cleaners (all approximately 50%). It is not clear why this is the case: Some daughters may not have used these products while at home, some mothers may have shifted preferences recently, and/or daughters may simply have lower involvement levels with these products. Overall, however, in every product class, a substantial segment of daughters was aware of their mothers' brand preference.
Our next measure shifts focus to the brands the daughter currently considers for purchase. For choice set analysis, we asked whether the brand the mother most prefers is also among those her daughter considers when purchasing. As shown in Figure 1, across all products, this occurred approximately 60% of the time, or for 14.5 of the 24 product categories on average. The extreme categories and pattern of scores were similar to the awareness findings but had a lower level of agreement in almost all ( 21) product categories. Comparing these results in Figure 1 indicates that, on average, 9% of the daughters(i.e., 69% - 60%), though knowing their mother's preference in a given category, have chosen not to consider that brand for their own use. The extent of decline varied considerably, and some personal care products (lotion, soap) and foods (tea, coffee, pasta) were especially susceptible (with drops of approximately 20%).
Given these precursors, the key measure for our assessment of IG impacts on brand equity is, "In how many families do the mother and daughter report an identical brand preference?" We should note that exact brand preference match is a conservative measure, in that it includes all dyads in which one member uses the product and it requires an exact match on the most preferred brand (i.e., here, a nonmatching dyad would have either different brand preferences or one person not using the product, in either case indicating a lack of IG impact). As noted in Figure 1, IG agreement overall families and products averaged 36%-far above chance-and was statistically significant in 23 of the 24 product categories. Only for canned vegetables does the IG brand preference effect not reach statistical significance, but recall that here we had already observed an IG effect on product usage. Thus, we have discovered some form of significant IG impact on all 24 products in this study.
Given that IG effects have been uncovered, just how strong are they? The rightmost columns in Figure 1 present evidence on this question, in terms of gains obtained when actual scores are compared with the baseline chance expected level if no IG influences were operating. Readers will recognize that it is difficult to choose a single statistic that best reflects all situations: We therefore include two indicators in Figure 1. The "Percent Gain" column reports the gain (actual match score minus expected score) as a percentage of the expected baseline level if mothers and daughters had been randomly paired. This statistic indicates a clear impact of IG influences, with a median improvement of 63% over chance levels. Specifically, soap, baked beans, frozen juice, coffee, lotion, dish detergent, household cleaners, and laundry detergent all registered actual IG scores that more than doubled chance matching levels. However, because these percent gains are due in part to low expected levels in some categories, we also include a second measure to provide a fuller picture of IG impacts. The "Of Possible" column reports the statistic recommended as best by Green and Tull (1966,p. 305). Here, the absolute improvement (actual score minus chance score) is divided by the amount of variation left to be explained beyond the baseline expected level: This accounts for the difficulty in improving scores when baseline levels are already high. For example, soup's actual score (which appears in the left-hand column of Table 1 as 76%) was very high because of Campbell's brand dominance in this category. However, this also raised the expected score by chance, so that the soup category did not register an impressive IG gain as a percentage over baseline chance (it was 10%, significant but not large). With the second measure of gain, however, the soup category IG gain score rises to a respectable 23%, which is above the average for all products. According to this measure, the strongest IG effects on brand preference are found for peanut butter, mayonnaise, pasta, and spaghetti sauce, and across all categories, the mean improvement attributed to IG influences is 18% of the variation available.
Overall, Expectation 2 was supported. An adapted hierarchy of effects was useful in examining different levels of IG influences, indicating that an underlying system is operating, in accord with expectations. Furthermore, results on the key measure of IG influence showed significant effects on actual brand preferences for 23 of 24 product categories.
Findings for Expectation 3: Multiple Marketplace Forces Can Affect Levels of IG Scores
As noted previously, the analysis of IG effects can become complex. For example, we needed to compare the mother-daughter match scores in this study with highly varying chance expectation levels to infer whether IG was operating. This reflects the fact that IG influences are but one of many forces operating in the marketplace and are likely to be affected by other factors (e.g., competitive efforts, innovations, peers). Thus, a variety of forces may be at work to either raise or lower levels of IG influences for a particular product. Therefore, as a planned feature of the analysis, we examined four marketplace characteristics anticipated to influence the mother-daughter agreement scores in our data. Each characteristic applied to some of the 24 categories but not to others.
The level of brand preference agreement between mothers and their daughters should be related to the distribution of consumer brand preferences in the market, and when these are heavily concentrated on a single brand, both actual and expected match scores should increase. In three of our categories, a single dominant brand emerged, defined as at least three of five consumers naming it as most preferred. As shown in the third section of Figure 1 (see Test A), categories having a single dominant brand (Campbell's Soup, Heinz Ketchup, and Kleenex Tissue) produced much higher mother-daughter agreement than did categories with more equal competition (65% versus 32%, p ≤ .0001).
Test B shifts attention to another feature of the competitive environment, the number of brands in a product category (operationalized here by the number of unique brands mentioned by respondents). In addition to its substantive marketing implications, this characteristic has statistical impacts in a study such as this: That is, the higher the number of brands, the lower is the level of mother-daughter brand preference matches to be expected by chance alone, and likely the lower are the scores for IG impacts as well. Our two analyses here support this supposition. As shown in Figure 1, mother-daughter matching scores for product categories with a higher number of competing brands-at least 10-were significantly lower than those for categories with fewer brands (29% versus 57%, p ≤ .0001). Visually, moreover, we observe how striking this effect can be by examining category rankings (in the left-hand column of Table 1). The top third of categories (soup to toothpaste) averaged 12 different brands, the middle third (tuna to salad dressing) averaged 18 brands, and the bottom third (paper towels to canned vegetables) revealed an average of 26 brands per category. Therefore, market fragmentation is also a characteristic to be considered in interpreting IG scores.
Though related by family ties, mothers and daughters also belong to unique age cohorts, which are subject to some different lifestyle influences. Therefore, our third test (Test C) examined the question, "What is the impact, if any, of generational usage differences whereby mothers in general may be more likely to drink tea or coffee, or daughters more likely to use prepared spaghetti sauces?" In seven categories, the usage rate for the group of mothers differed statistically from the usage rate for the group of daughters, which led to the hypothesis that dyad matching scores would be suppressed here (because of the greater prevalence of nonusers in one age group, which would thus preclude a brand preference match for those families). However, although our overall results produced mean scores in the predicted direction, these did not reach significance (32% versus 37%, not significant). However, we should note that generational differences could still affect results in a specific category.
Finally, Test D in Figure 1 directly tested the nonusage hypothesis. Here, we examined the nine product categories in which significant levels of consumer nonuse occurred (Expectation 1) and then tested whether IG brand preference agreement might be suppressed, again because nonuse by one family member would rule out a preference match for that family dyad. Statistical tests indicated that this was indeed the case: Product categories with more nonusers had lower agreement scores than categories in which virtually everyone is a consumer (29% versus 40%, p ≤ .05).
Overall, Expectation 3 was supported. The level of mother-daughter agreement-our primary indicator of IG influence-is affected by several marketplace characteristics. It is important to take these forces into account not only in statistical tests for IG impacts, which we did through the calculation and use of chance expectation scores, but also in managerial interpretations of research findings.
Findings for Expectation 4: Some Brands Are Differentially Strong on IG Impacts
Given our observation of IG results at the product class level, our interest now turns to specific brands. As we show, IG impacts are particularly interesting at this level of analysis. Here, we ask, " Do all brands benefit equally from IG brand equity, or are there meaningful differences among competitors? and "Does this picture vary across product categories?" Table 1 presents findings on these issues.
Brands with high-IG brand equity. Relative to our research questions, a visual analysis of Table 1 shows that all brands do not benefit equally from IG impacts, and real differences exist across product categories. Using the category average as a basis for comparison, we find several brands with high-IG brand equity. The highest scores were registered by Newman's Own Spaghetti Sauce (with 86% of its support coming from mothers and daughters from the same families), Campbell's Soup (84%), Heinz Ketchup (80%), Peter Pan peanut butter and Kleenex tissues (both at 67%), Mueller pasta (63%), and Dawn detergent and Crest toothpaste (both at 60%). As shown, several other brands also draw substantial support from IG households. In all these cases, the results were statistically significant when compared with the level of agreement expected if no household influences were operating. (We should note that all brands listed are major market share entries; because our analytic method requires threshold sample sizes, we could not test some regional, ethnic, or specialty brands with small shares in this study, though it is likely that some also benefit from high-IG equity. Marketers conducting research in a given category should be able to examine this as appropriate.)
The IG brand silo effect. In examining the pattern of high-IG results, we note that in only 2 of the 24 categories did no brand emerge as strongly benefiting from an IG influence. However, we also find that most IG matches occur for only a few brands in a category and that brand differences on IG scores are the rule, not the exception. In about half the categories, only a single brand (e.g., Campbell's, Heinz, Tide, Snickers, Fantastik) strongly benefits from IG support compared with the category overall. Beyond this, 11 categories show multiple brands with high-IG scores; we consider these markets as having "IG brand silos," in which large numbers of loyal households continue to prefer a particular brand across generations, but in isolation from one another. Seven of these categories revealed that 2 major brands draw significant levels of IG support. For example, we find Crest households and Colgate households for toothpaste, Kleenex households and Puffs households for tissues, Tylenol households and Advil households for pain relievers, Bumble Bee households and Starkist households for tuna, and, at the bottom of the column, Folger's households and Maxwell House households for coffee. In 4 other categories, 3 brands draw significant portions of their support from IG households. In peanut butter, for example, there are Peter Pan, Jif, and Skippy brand silos; these effects are so strong that no other brand received even a single match from any household. Meanwhile, strong brand silos also appear for Newman's Own, Ragu, and Prego spaghetti sauces; for Dawn, Sunlight, and Ivory dish detergents; and for Miracle Whip, Kraft mayonnaise, and Hellman's mayonnaise (here, we note Kraft's successful segmenting of the market in establishing the first two brands as distinct IG powers). As a final observation, in no category did the number of silo brands exceed three, even though most categories had more than 12 brands competing (up to 48 brands were listed by participants for the lotion category). These brand results are obviously illustrative, arising only from our present sample, and should not be generalized beyond it because of limitations as to size, convenience, and regional setting. However, our interest here is in exploring IG impacts at the brand level. In this regard, these findings are important and merit future study.
Brands with IG potential. The third column of Table 1 lists additional brands that showed reasonable evidence of benefiting from IG brand equity but for various reasons did not qualify for inclusion in the high-IG listing. The most typical issue here was sample size-the scores are reasonably high (and in most cases statistically significant), but the number of consumers noting these brands is just not enough to receive confident mention. In a managerial context, these would be candidates for diagnostic evaluation. Relative to our interest in IG influences and brand equity, however, these brands offer additional evidence of the presence of IG in the marketplace.
Brands with low or no evidence of IG impacts. The emergence of so many well-known and successful brands in the high-IG list might lead us to suspect that IG is a general source of brand equity across the marketplace. However, this is decidedly not what emerged from this study. The right-hand column of Table 1 identifies two types of low-IG performance. First, designated by an asterisk, are well-known brands with sufficient sample size to be tested and whose IG results proved not to differ from chance. That is, members of both generations buy these brands, but there is no family factor involved-purchasers come from different households. Among these brands are Hunt's catsup; Chicken of the Sea tuna; Welch's jelly; and Lysol, Pine Sol, and Formula 409 cleaners. For these brands, we are reasonably confident that IG is not operating, at least within the current study.
Second, for many other cases, sample size is a constraint, and it is difficult to draw confident conclusions about the presence or absence of IG impacts. However, one common result was a failure on the part of many brands to register any household matches at all. We have listed some of these in the right-hand column as well, but given sample size, this listing must properly be viewed as only illustrative. Nonetheless, we note some of the major brand names that drew zero IG support in our sample, including Del Monte catsup, Bufferin and Motrin pain relievers, Cascade dishwashing detergent, Zest soap, and Brawny paper towels. These findings of low or no brand impacts add a useful insight to our understanding of IG effects in the market-place-they are selective.
Overall, the results of Study 1 highlight the many levels at which IG effects are manifested, the measurement challenges that emerge, and the importance of considerations involving the characteristics of particular segments, categories, and brands. At a pragmatic level, IG brand equity is a valuable asset. Further understanding of its sources, nature, and operation is warranted. We delve more deeply into this topic in Study 2.
Study 1's findings show that IG relationships are at work at various levels in the marketplace and offer contributions to brand equity in a selective manner. However, the survey method is limited in providing an explanation of how IG influences work. Therefore, we paired the survey method with an interpretive research method that provided a forum for daughters to express their views on the role of IG influences in their daily lives.
Research Design and Sample
Twenty-five young adult women participated in a phased set of depth interviews. These interviews were conducted over the course of several days, first at home, then in the store, and then at home as part of a pantry audit. Notably, this interview structure enabled us to directly examine brand purchases as well as preferences. Informants were students at a major Midwestern state university, lived in off-campus housing, and shopped for groceries on a regular basis. Some informants had been independent shoppers for as little as two months and others for as long as two and a half years, thus providing some range of experience. Informants were purposively selected (Lincoln and Guba 1985), and each was interviewed for 3-4 hours. Our approach was discovery oriented in nature and based on a topical life history approach (Denzin 1989). The first interview gathered information about shopping histories, personal shopping styles, brand preferences, and family histories. Then, on a different day (typically within one week), a research team member accompanied the informant on a grocery shopping trip, during which informants were encouraged to "think out loud." Here, we observed purchases as they were made, followed up with discussions of the informant's choice considerations (including IG impacts), and gained information on in-store factors in purchase decisions. Immediately following the shopping trip, the third interview involved the researcher and informant returning home, putting groceries away, and continuing the discussion. Also, kitchen cabinets were opened and informants were encouraged to "tell the story" behind the brands there, as well as those just purchased. Life-history information, especially related to mother- daughter relationships, was gathered during this closing session.
Analysis
We audio recorded and then transcribed all interviews. The verbatim transcripts served as the primary data from which conceptual categories and relationships were identified. An extensive and structured process was used to identify and preserve essential insights following the discovery-oriented aims and procedures of grounded theory (Glaser and Strauss 1967; Strauss and Corbin 1998). The first two authors read and independently coded each interview. Emergent categories were compared and discussed in detail for the first six interviews. These initial categories served as a structural basis for coding the remaining interviews, and we incorporated enrichments as necessitated by the data. We independently coded all remaining interviews and then compared them on a line-by-line basis. Discrepancies were few, and we resolved them through discussion. We then sorted, compiled, and assessed data relevant to each emerging category for conceptual fit.
Results
The interviews provided a rich set of findings into the nature of IG influences, how they came to develop in our informants' lives, and how they operate today. Some have been noted in the IG literature, and others have not previously been identified. We have organized these into an extensive framework, as shown in Figure 2, composed of three major sectors. The "Marketplace Manifestations" sector reports the variety of ways IG influences are at work in consumers' lives, the "Factors in Formation" sector provides observations into how IG influences develop, and the "Endurance in Adulthood" sector delves into forces that affect IG influences after the child leaves the family home.
Insights on Marketplace Manifestations of IG Influences
Three aspects of IG stressed in the survey analysis were also evident here. Matches in mother-daughter brand preferences, interest in multiple brands (choice sets), and IG influence on product usage were all commonly reported. Beyond this, however, the interviews provided many additional insights.
Aspects of repetitive purchase. One key aspect of IG influences is that it leads to a daughter's repeated purchases of a favored brand. In probing the motivations underlying these purchases, a mixed picture emerged. At one level, IG purchases had been internalized as simply brand favorites: The purchase was explained as based on knowing that the brand offered good functional performance. In addition, strong elements of familiarity and inertia were often present in descriptions of IG repetitive purchasing:
It's because that's what we've always had at home. It's a "something I grew up on" type of thing. And for some reason the only type of ketchup-I only like Heinz ketchup. I think it's just another thing, ever since I was a little kid-- that's what we've always had. (13a6)[ 5]
Just habit probably-I don't know. It was what I knew, so I'd get it. (12a7)
The things that I like, that we ate at home. I like those, so I'm going to buy them also. If I ate them at home, why am I going to stop eating them? So I just buy them at the store myself now. (2a11)
In contrast to a focus on the brand and its features, these reports reflect IG repetitive brand purchases as a decision rule that offers efficiency. Notice, however, that the brevity of these explanations provides little insight into any other elements of the decision calculus that might be operating. For example, one supporting dimension of IG inertia could be low consumer involvement-with little time, effort, or thought given to the decision:
I got Ragu to go with the pasta.... I'm used to that. You know, I don't really think of this.... I don't really think about what I'm going to buy or why I'm buying it.... I just get what I need. (21a6)
Repetitive brand purchase might at times represent a simplifying consumer decision strategy of daughters relying on their mothers' expertise and experience. Here, favored brands are well justified in the daughter's mind because the mother has tested the alternatives and pronounced her winners:
I buy Dawn just because-my mom always used Dawn ... cleaning products, detergents, Downy softener, and the Downy ball-even when I'm here on my own I use those products.... So when I do my own shopping, I pick those products too. (15a12)
In summary, some evidence supports Heckler, Childers, and Arunachalam's (1989) expectation that one benefit of IG influences is that they offer daughters a simple decision heuristic to use in dealing with a complex consumer marketplace. As noted, however, various forces are reflected in this repetitive behavior.
IG and emotional bonds with brands. In contrast to cases of a simple heuristic, IG influences sometimes create special emotional bonds between the daughter and a particular brand. As indicated in Figure 2, trust was evidenced in many brand selections and descriptions: The brand was appreciated for its long tradition of faithful service to the needs of first the family in years gone by and now the daughter today. This feeling often extended to overt nostalgia, as warm memories of home and family became interwoven with the brand's special image and properties. For example, some IG brands were identified as sources of pleasure through play. Conversely, other brands were appreciated as helpful in overcoming feelings of loneliness and trepidation that were experienced by the daughter in her new life.
I have salad dressing, from Seven Seas-that's what my Grandma buys, and I always spend the summers with her in Michigan, so I like that kind of dressing. (24a5)
I like Jell-O and pudding but ... I didn't do that much this year. My mom even said something about it. When we came here she said, "Oh Beth, you didn't make Jell-O this summer."
I don't buy generic stuff, I buy Jell-O brand, 'cause it is fun and I was brought up on it. (8a3)
It's like what I was saying about the Bisquik. This is going to sound stupid, but it's kind of comforting to have it sitting in my cupboard. It makes the kitchen homey. (23b6)
Finally, IG brand emotional ties also emerged that were symbolic of significant personal relationships (Olsen 1993). Some of these were overtly negative: Marketers should be aware that some IG brand influences can lead to brand avoidance if a brand has come to symbolize long-standing resentments or other negative emotions related to earlier family life. When positive, however, a brand can attain a particularly strong IG preference as a symbol of allegiance to loved members of the family.
I like Brownberry bread ... because it has sentimental value for me. Which sounds really dumb but it is true. Before my dad remarried, we would always eat healthy food, well relatively healthy. And I would eat my vegetables and we would have Brownberry wheat bread. (1a5)
Other IG insights about products and brands. The interviews also illuminated other forms of IG brand influences and choice heuristics. For example, impacts are not limited to a single brand: Daughters may purchase IG favorites as a portfolio of brands. Furthermore, as the survey results show, various dimensions of IG influences act as competitive barriers/opportunities. For a few brands, such as Campbell's Soup, the IG barrier might extend to daughters' failure to have ever sampled any competitive entry, and in other cases, the belief in a brand's superiority appears to be so strong that trial purchases of competitors are extremely unlikely.
We eat a lot of the same cereal, like she always tended to buy-sometimes she'll go off skew and buy something weird like honey crunch something.... But usually it's Cheerios and Special K and Crispix and Rice Krispies, and those are the three or four that we always had, and ... I like those too. (7a11)
I always only buy Campbell's Soup. I've never had anything else. (16a7)
Oh, Tide [detergent]. That's the only kind that really works. (3a8)
Beyond the number of brands considered in a category, the interviews also contained many examples of IG influences on the desired "brand tier." This phenomenon extends across products and includes IG preferences for status brands, as well as strong positive and negative views about private labels and generics. Note that the presence of strong IG influences in all these cases eliminates a large number, even entire tiers, of competitive offerings from receiving any consideration for purchase.
For some reason, I want it to be name brand stuff; that's how my mom always was. And so if I was going to buy crackers or something it would actually be Ritz instead of the store brand. (9a3)
She will only buy Campbell's Soup or Kraft Macaroni & Cheese. Yeah, my mom would never be cheap and buy a generic brand because, well, she's kind of a snob. Yes, I can admit it, I'm a snob. I get it from my mother-because I wouldn't want her to buy a gross generic brand. If she brought that into my house, I think she'd seriously offend a lot of people.... It would be very uncharacteristic of my mom to get something that wasn't a brand name. She just doesn't do that. Image is everything. (21a3)
Intergenerational influences were also evident at a somewhat broader level, but still with implications for brand management. First, a propensity toward new products appeared in some cases: This will tend to work against IG brand loyalty. Second, many instances of IG preferences for a particular product form arose, in which emphasis was on desired attributes rather than the brand itself. Notably, this IG phenomenon would not have been isolated in our survey results. Also emerging more sharply was the operation of IG influence as a limiting force against brands. Included here were daughters who had learned beliefs of no meaningful differences among competing brands in a category or daughters who had learned mistaken perceptions (e.g., the terms for private labels and generics were at times confused).Also, consistent with Study 1, IG influences to avoid the use of certain products were observed:
I like a particular type of noodle. I only buy rotini noodles. But ... I don't care what brand. (1a8)
When you have a peanut butter and jelly sandwich you can't have any other jelly but grape jelly, I don't know why-it has to be grape.... [Interviewer questions about a brand preference.] No, it doesn't matter what kind. Just grape.... It's very important that you have grape jelly. (9b5)
I don't have any one specific [brand of ice cream]. I just remember that I bought one thing of ice cream-and my mom always switches on that too actually, just because.... Well, no, I don't have one particular brand that I go with. I like certain flavors, so I'll just try different brands.(14a12)
Not a big soup fan because ... I never really ate it 'cause I always felt it was a pain. Put it in a pan, heat it up, put it in a bowl, eat it. And it wasn't ever in my house. (10b10)
Finally, as has been discussed in both the sociology and consumer literature, a reverse flow of IG influence was also evident (e.g., Glass, Bengston, and Dunham 1986; Moschis 1988):
I actually introduced my Mom to Downy balls. Because she always used to use Downy, but she hated-she didn't have time to wait for the rinse cycle-and I said mom, there's these great Downy balls that just pop open during the rinse cycle. And so now she uses Downy ... and our clothes smell a lot better. (7a12)
Additional consumer dimensions of IG. The interviews were also helpful in identifying related forms of IG influences beyond products and brands. Because they can direct a consumer's entire approach to the marketplace, these higher-order influences can also offer lessons that are useful for marketing strategy. Virtually every daughter reported elements of IG shopping style and preference, including having learned to enjoy (or not) shopping, how "price conscious" to be, and so forth. In keeping with Hill's (1970) analyses of three generations, some daughters had learned "good consumership" from their mothers. Also, many IG influences seemed to take the form of rules, or norms for behavior, which themselves differ across households. As the final quotation shows, violations of these family rules sometimes provide colorful memories:
She's done that before and it's just really comical. "God, I didn't really need to get all of this." And, you're like "How could you accidentally buy almost $200 worth of groceries, Mom?" She loves shopping. I love it too. I wish I had the money to go grocery shopping every day. (3a16)
Okay, look at the bottom of this apple, the butts are nice and tight and closed-that means it's good. (22b2)
I'm a big nutrition back label reader; like, I always try to look for what has the least amount of calories, the highest amount of vitamins.... I think my Mom is pretty adamant about doing that, because she wants to make the healthiest choice-money doesn't, it's really not an issue in the purchase decision. (24a6)
My Mom was having gall bladder surgery when I was little, and she was in the hospital for about three or four days, so it was just our Dad taking care of us. He had to go grocery shopping, so he piled all three of us kids in the car and off we headed to the Jewel.... Big mistake.... I remember coming home and unloading our groceries, and we had all this junk food. We were in heaven! We had Hungry Man frozen dinners and eight types of cookies and peanut butter-guy food, you know? And dip, you can't forget the dip-it's like my Dad was getting ready for a huge football marathon weekend or something.... I always look back on that and remember it fondly because when my Mom came home she was like, "Steven, what happened here?" ... I think he was just experiencing a moment of temporary insanity. That's what made him buy all those Hungry Man's on an impulse.(22a4)
Daughters also reported IG influences on packages of entire assortments. Reflecting our emphasis on food products, we learned of appropriate ingredients to create a particular dish or entire meals. More broadly, there was clear evidence of IG influences on adoption of a healthy lifestyle for some informants:
I tend to buy a lot of the same types of cooking products as my mom, like this extra virgin olive oil-my mom gets this kind ... or, like in the fridge-I have tofu, and she buys this same package. (24a5)
I'll pack my lunch for school here, and I'll put the same things in it. So I put cut carrots and a sandwich and pretzels and like an apple, maybe.... I remember when we were little we always used to whine.... We got bored of our meals; it was carrots, apple, and a sandwich and pretzels. (7a10) Well she always has a list and very rarely sways from the list.... We were never big junk food people.... We never had sugar cereals, never drank soda.... Most of the stuff that we asked for was pretzels and apple juice, and I don't know- just stuff that we really like. (17a2)
Finally, a key aspect of IG influences is their potentially central role within a daughter's view of herself, reflected in IG ties to personal identity. The following quotations capture three facets: remembered family rituals; ethnic heritage; and, in a final poignant report, a long-term eating disorder with clear IG roots.
My Mom is the type who always takes a day off work each year right before Christmas to make Christmas cookies. When we were really little, we loved this day because our house would smell so good. She'd put all the warm cookies in the dining room to let them cool off, and then we'd all sit there with big glasses of milk and sample everything.... I remember my mom taking me grocery shopping the night before one of these Christmas cookie bake-off days, and she was telling me, "Emily, it's always important to use the best ingredient"-like butter versus margarine and Nestlé Toll House chocolate chips, not generic ones. (22a11)
I just can't eat french fries and hot dogs, just don't like the taste or something.... I like to make, well ... Puerto Ricans make a lot of rice, so I eat a lot of rice, a lot of beans, like we eat a lot of beans, and tomatoes and carrots and potatoes ... [and] check out that lard-that's what a lot of Puerto Ricans use to cook. (19b3,6)
I have an eating disorder. It was much worse during high school, and I've had a chance to talk to counselors about it. I always have been really concerned with my weight, and I do not have a speedy metabolism. But I am very disciplined, so I work out. I don't want to sound like I'm blaming my mom, because I'm not. After all, I'm the one who makes the decision as to what I'm putting in my body. It's just very difficult to grow up in a house where you get really small portions of this weird health food. My mom is very petite, she was Miss [major state name], if that gives you any indication as to what type of frame she has. I take after my Dad, and I know that I'm not fat. I'm just not built like her, and that is difficult. (24a3)
Factors in the Formation of IG Influences
In the second section of the framework, attention turns to how IG influences develop. However, because our focus is on managerial implications of IG for brand equity, we simply highlight major findings on formative aspects of this socialization process (for a more detailed analysis, see Moore, Wilkie, and Alder 2001). As shown in Figure 2, three sets of formative factors merit attention. First, household influencers include structural and lifestyle considerations, people, and roles. It became clear that the specific family form and lifestyle determined dominant interaction patterns and, ultimately, the IG preferences that were learned: This was particularly striking in some blended families and families in transition. Across households, the mother emerged as chief influencing agent, through both her nurturing and supervisory activities. Depending on the household, fathers, grandparents, and/or siblings also exerted significant influences. When multiple influencers were at work, a richer, sometimes confused information environment emerged, in which adaptations and compromises were necessary. Parental roles included both guidance and management of adaptive processes. Although families were generally consistent on these dimensions, we also observed aspects of IG formative processes that reflected each family's unique character.
Our second set of observations involves the substantive content of IG influences. This is a natural subset of what is learned as a child matures and is governed by children's broader learning processes (e.g., John 1999; Moschis 1987; Ward, Wackman, and Wartella 1977). For example, participation in the household's shopping is central to IG formation, as is observation of products and brands in the home (packaging also emerged as having a memorable impact in this regard), as well as direct communication about preferences and practices. Some mothers also used public family lists, rules, and purchase limitations as control devices. The interviews also underscored the importance of efforts to satisfy a child's personal tastes. For example, IG product and brand preferences seemed to emerge more strongly when children believed that their personal desires were being rep --resented in household purchase decisions. Reports of several in-store purchase influence attempts, both successful and not, captured this phenomenon. In this regard, daughters also spoke more broadly of themselves and other family members having to reconcile conflicting preferences: Households with more stability and coherence appeared to yield stronger IG brand preferences. Children also reported several occasions of evaluating-both positively and negatively-one or both parents on a variety of consumership dimensions, including shopping styles, dietary preferences, spending limits, and planning or lack thereof (when direct comparisons were made, it was the father who commonly came out on the short end of these judgments). Overall, the substantive dimensions of IG influence formation involved more evaluation, negotiation, and adaptation than might be apparent at first glance.
Our third set of findings involves IG influences' development across time. Here, we expressly recognize the larger dynamics of this process, which operates in a relatively continuous fashion for approximately 18 years, or until the child leaves the family home. Observations in this area include the recognition that certain experiences are age-defined (e.g., children's cereals, early teen cosmetics), and these temporally bound IG influences will not persist into adulthood (however, some may reappear when these daughters are raising their own children in the future). Furthermore, some IG influences will exist and then be replaced if the marketplace shifts or the household revises its behavior to feature another brand favorite. A special case involves the impact of changes in a family's structure or lifestyle: In these cases, several shifts in IG preferences may ensue as certain family members leave, join, or change their behavior within the unit. As a final dimension, the impact of the family life cycle was apparent in various ways, such as mothers returning to out-side work as children age, shifts in handling older children's influence attempts, increases in influences from peers (a rival to IG influence effects), and teens taking over more of the family's purchasing when they begin to drive. Not surprisingly, the incidence of reverse IG flows also accelerates at this time.
The Endurance of IG Influences in Adulthood: Sources with Disruptive Potential
The long-term impacts of IG influences are felt in adulthood. Prior research has shown that IG impacts initially persist as the child moves away from the family, but that on average they weaken over time. Many factors are at work, some that break down IG patterns and others that sustain them. The rise of new influencers: spouse, roommates, and peers. One of the most striking aspects of change as a young person leaves his or her family involves the new people who will help shape daily living. These "new influencers" literally bring change with them, and the interviews provided many descriptions of this process. In the case of joint purchase decisions, for example, each person arrives with a personal set of beliefs and preferences across products and services. Differences need to be resolved to allow a smooth functioning of the new household's consumption activities (for a useful typology of spousal adjustment mechanisms, see Davis 1976). Discussions with new influencers foster the introduction of additional options and categories for consideration. In a similar vein, joint shopping trips and discussions of personal experiences and preferences bring changes in appropriate criteria for making purchase decisions. Some daughters, for example, use this mechanism to learn about nutritional labels; their new criteria then lead them to shift away from IG brand preferences.
The interviews also presented other interesting dimensions of this general phenomenon. In some cases of apartments with three, four, or five roommates, for example, the role of purchasing agent would be rotated, thus forcing overt consideration of how to handle competing preferences (some of which are IG brands) on each shopping trip. Moreover, in this setting, gifts to one member can unintentionally become a disrupting source for the existing IG preferences of another member (i.e., through a positive product trial experience). Furthermore, not all the new social influences will be positive: Social disdain from peers and media can be a shocking experience for a daughter who holds IG preferences that she learns are not the norm. Finally, at times, overt efforts are made to influence the daughter to conform to practices deemed appropriate by others:
This is a big deal because Meredith always buys the kind that I don't like. I'm so picky. See, at my house we usually get Blue Bonnet or Imperial.... Stacy always buys Parkay, and I can't stand that. (9b7)
I would never buy this on my own because my Mom has never done the boxed spaghetti thing. She always makes it from scratch. I didn't even really know it was available, or if I did, I probably wouldn't think it was very good. But one of my roommates left it and so I ate it one night when there was nothing left to eat, and it was wonderful. So now, I buy it. (6b4)
Brooke went home(and her mother)sent her back to school with so much food, two cases of Diet Coke, tons of frozen food, this thing of Snackwell's, pretzels, a loaf of rye bread- just tons and tons of food. And, then, Katie's parents came to visit and they brought her the two cases of Diet Pepsi.(17a7) [whispers] I have Spam in my cupboard.... [laughs] I grew up with Spam because ... it's pretty cheap. And there's tons of millions of ways to make it..., but usually what I do is cook it in brown sugar ... or scramble an egg and crush some bread crumbs or whatever ... eat it with macaroni and cheese. (10b11)
Everyone always makes fun of me because my Mom always put Miracle Whip on my sandwiches, instead of mayonnaise. And Kevin always yells at me about stuff like that. (11a11)
New lives bring shifting lifestyle demands. Beyond new social interactions, the move away from home can also bring significant alterations in daily living. For many daughters, income constraints and the need to manage money now dictate shifts in choice criteria and brand purchases: Sale prices now serve as a more powerful counterweight to IG brand preferences. Mobility concerns also surface for some daughters, who find themselves constrained to joint shopping or purchasing agent options in which IG preferences are again at risk. Reports also dealt with time at a premium (leading to altered shopping habits) and more personal meal occasions. These led to increased out-of-home dining and fewer super-market purchases overall. Finally, this is a period of experimentation in general, in which new options are explored. For example, nutritional concerns arose, again threatening existing IG brand preferences:
If I could afford to stick with certain brands I would, but due to my budget I more often than not switch. But I don't really want to, I don't think. (1a4)
My sister called me cheap. We were shopping, and she would get the brand that our family would use, and I'd say, "No Annie, get the cheaper one, get that kind." And I would really compare prices, and she has never seen me like that before.... It wasn't like I was being cheap, I wanted to spend money wisely. (13a9)
Chicken, a vegetable, you know, a balanced meal-My Mom made that a lot, we always had that. I don't eat that now 'cause I don't have time, but I want to. I feel better eating like that, 'cause it's healthier, you know? Ideally I want to do that, but I don't. (3a12)
When I go with her I get the low-fat stuff, and when she goes by herself she'll get the cookies that I like but the regular. And that's a difference between the two of us. I like the low-fat and she likes the regular.... I try to go the more healthy route, more so than she does. (15a4)
Altered marketplace experiences. The shift away from home can also involve a physical move to a new marketplace, as had occurred for many in the present sample. This can lead to a lack of IG brand availability (for certain offerings that had been distributed only in the home locality or follow-marketshare brands that are only thinly distributed). Further disruptions were induced by competitive programs in the new marketplace, including sales promotions, sampling, and premiums. The interviews also reflected positive consumer responses to new products, new information, and new metrics related to purchasing. This entry is a reminder that the dynamic nature of the marketplace generally works to undo IG influences, as new and improved alternatives become available and older line items are dropped.
I used to always use Crest toothpaste. But then, I liked Colgate better because it came with a free toothbrush. (10a10)
I was grocery shopping last week looking for my Mom's pizza sauce, and it wasn't there. So I didn't know what to do. I felt like ... I don't know, I wanted that pizza sauce!(16a10)
I started needing deodorant when I was in fifth grade. My mom introduced me to regular Ban, which was absolutely putrid smelling.... So I dealt with that until I started buying it by myself, and then I started getting the Ocean Breeze scent, which I really liked.... It was still Ban. I don't know why I stayed with it. I used to think that it really worked well, and I was fine with it. But then they discontinued Ocean Breeze. (6b9)
Other insights on IG disruption. The final section of Figure 2 on disruptive factors includes some additional observations. First, IG influences based on incomplete knowledge may be more susceptible to disruption in an unsupportive environment. Second, some reports highlighted changes in the family's support for certain IG preferences, because parents changed as their life cycle continued. Behavior is not the only measure of IG, and focus only on changes in behavior can overstate disruptive effects. For example, some of the daughters spoke about having stopped using certain products or services while they were away at school but clearly planned to return to the seat a later time. Time and distance away from home are factors in IG endurance in adulthood, when larger distances, times, or lifestyle changes speed IG disruption. Our sample was chosen to allow for the examination of IG persistence when distance from home is high. In the broader population, however, many consumers never move large distances from family: In these cases, some potential disruptors would be less powerful, leading to higher levels of stability.
I only need ... I'm looking around.... If I saw the right brand, I guess I would know it. But I don't see it, and I can't remember what kind my Mom buys. It has a cow on it, or something. (10b3)
I have noticed that since I have been gone that they don't buy as much ... something my sister and I would like-like Frosted Flakes or-foods that are not good for you. Now I'll come home and there's Total and Special K ... healthier things now ... [not] as many chips or anything like that anymore. (14a16)
The Endurance of IG Influences in Adulthood: Sources with Sustaining Potential
We have just reviewed a lengthy list of forces with the potential to erode the impacts of IG influences in the marketplace. At the same time, we should recall that our survey analyses of IG effects showed that, for a parallel sample of daughters, IG brand and product preferences persist in the marketplace. It therefore became reasonable to identify factors that would be at work to sustain these preferences (as shown in the final section of Figure 2).
Forces that support repetitive purchase. Our first category of sustainers relates to many of the factors discussed previously as manifestations of IG influences on repetitive purchasing-IG brands as offering positive levels of functional performance, personal familiarity with the IG brand, purchasing inertia, and low involvement in a particular product or service category. Beyond these points, we also found evidence that in some cases, the daughter encounters feelings of being a "novice" in her new life, perceives risks of making mistakes, and thus encounters risk aversion. The IG brands can offer protection in this respect, and a continued pattern of purchases can lend reinforcement through regularity in the newly uncertain environment. Finally, the spaced but regular appearance of seasonal rituals offers support for continued enactment of favored IG behaviors:
When I first started, I probably purchased them a lot because I didn't really know what else to buy, and I knew that those would be healthy for me 'cause she always chose things that were healthy for us. And I knew that I liked them, and I didn't want to go wrong 'cause I knew that it was my own money. (7a9)
I bought this squash just because my Mom makes squash a couple times in the fall and I love it. I have no idea what to do with it.... I'm going to call my Mom and figure out what to do with this. (17a20)
Maintenance of self-identity. During life transitions, a sense of self is called on in dealing with (new) sets of outside pressures and opportunities. As noted in the "Marketplace Manifestations" section of the framework, IG influences can be closely bound to the daughter's self-identity and thus are themselves sustained by these ties. Six aspects related to this factor appeared in the interviews. For some daughters, a desire to retain and express an ethnic identity meant that several IG influences would be brought into play on a continual basis. For other daughters, pursuit of a particular set of IG lifestyle activities brings a similar impact. For daughters whose self-identities involve being an athlete or being a vegetarian, for example, continued reliance on IG food preferences provides a useful means of support. These particular lifestyles themselves constrain choice criteria (and, in effect, remove the consumer from some sources of IG disruption, such as marketing promotions or peer influences for forbidden foods for that lifestyle). Meanwhile, IG brands can be "safeharbors," allowing daughters to experiment with new marketplace offerings while preserving an attachment to the reliable IG brand.
External expression also lies within this province. For some daughters, influence attempts on others help communicate more about who they are. This appears to be a fairly common phenomenon, especially when family backgrounds are quite different. Our final two entries, however, are less common. Loosened constraints associated with living on one's own allows for increased expression of self-identity: In a few cases, we found that this also allowed a return to purchasing an IG-preferred brand that had been suspended earlier. Finally, expression of self-identity can also motivate efforts to stimulate a reverse flow of IG influence, bringing parents back to brands recalled fondly from childhood.
My mom always bought Prego spaghetti sauce. So, I used to buy that a lot. I still do. Although recently, I tried something different because it was healthy, Healthy Choice. So I tried it, it was pretty good. (8a7)
And bagels are important. Big and Crusty bagels because they remind me of, you have to have-I'm going to bring back real, real bagels-just because I think that everybody needs to experience a real bagel. Yeah, you guys need to experience the real New York deli bagel. I'll do that over Thanksgiving. (9b9)
We always used to use Crest and now I go home and I find all these other brands that I've never even heard of. Imposters! Like, Pepsodent, like someone in my house is using that, and that tooth lightening stuff and all this stuff. We used to only buy Crest. (3a16)
Parents as IG sustainers. The family also emerged as a major sustaining force. In a few cases, a daughter indicated that her continued patronage of an IG brand symbolized a continuing loyalty and affiliation with parents. Parental proximity allows continued support from personal visits and interactions, and family shopping outings, though now less frequent, also serve as significant reinforcers. Furthermore, parents continuing to act as product suppliers(now as gifts) not only reaffirms the IG brand choice but also temporarily removes the daughter from competitive purchase temptations. In some cases, daughters also reported receiving coupons from parents to ease their continued IG patronage and/or indicated that further parental influence attempts continue to occur in adulthood.
I buy things that my Dad buys. I like my Dad. (1a18)
My Mom and I are very, very, very close. I mean I can tell her everything, you know, and when I go home, she and I always go out and do the lunch and shopping afternoon thing.... I don't get to see them very much. But when I'm home, she and I try to do things together like going grocery shopping. (15a3)
My parents stock me up. Last time I went home, I came back with so much food, I filled up my cabinet and had to start a new one. Because she (mother) said "I know you won't go down there." So, I don't have to go grocery shopping. (18a14)
I go for the Near East brands, stuff like couscous. That's really expensive usually. So, I have 15 coupons for that. My Mom sends me coupons a lot. (9a4)
We had Stove Top tonight from this huge canister. We didn't know how to make small servings so we just followed the directions, and ended up with six servings of it. See, my parents came down a few week ago and asked "Do you want to go to Sam's Club?" Then we were going through the aisles and they asked "Don't you eat Stove Top?" And I said, "Not really." And they're, "Just get it." So I really didn't pick that out. They just threw it in the cart. (16a12)
Managerial Implications
Overall recommendation: IG audits for product categories and brands. As we discovered in Study 1, IG effects are clearly selective by category and by brand. For some products, IG helps determine segments of users and nonusers among young adults. Within most categories, moreover, some brands benefit from strong IG support, and other equally well-known brands do not (see, e.g., Bumble Bee versus Chicken of the Sea tuna in Table 1). Our limited sample provides no direct basis for managerial action. However, our research points to the managerial need for a customized "IG audit" of the marketplace and offers guidance for this undertaking. The approach we developed for Study 1 can provide answers to such questions as, "Are IG effects at work in the category?" and "If so, which brands are benefiting from this influence and which are not?" The approach in Study 2 can also be used to shed more light on the nature of IG within a specific category-how it is perceived by young adult consumers, as well as how it is sustained and/or disrupted as in-store shopping proceeds. Moreover, because this IG audit can concentrate on a single category, diagnostics not attempted in this article are also possible, including classifying IG buyers for competing brands, identifying bases of brand competition, and discovering roots of IG influences in this category.
When an IG effect on brand equity is established, the marketing team can gear its strategy toward the twin goals of combating key IG disruptors and supporting IG sustaining forces (as in Figure 2). As evidenced in Study 2, IG-loyal consumers can be allies in this effort. Also, the IG audit's findings should offer further input for marketing-mix decisions, beyond those suggested subsequently.
IG implications for product decisions. An IG effect can represent a fundamental mechanism for extending a brand's life cycle and thus become a special advantage or asset. In Study2,wefrequentlyencountereddualdriversat work in the IG sphere: desires for both future (e.g., new technology, new benefits, new delivery modes) and past (e.g., familiarity, safety, warmth). Therefore, a key brand challenge is to manage change-to provide an offering that appeals to the young adult's desire for familiarity and stability but also incorporates the needed product improvements, updates, and modifications to stay current in today's marketplace. In the present study, for example, daughters' desire for ease of preparation generally exceeded that of their mothers, as did their interest in nutritional dimensions. This appears to offer opportunities for innovation but also calls for sensitivity to evolutionary rather than revolutionary change in managing strong IG brands.
Beyond updating the core product, brand extensions are also a natural option. For example, some opportunities that arose in our studies would involve extensions appealing to differential IG lifestyle preferences of young adults (e.g., vegetarian diets). In a related vein, Wansink (1997) argues that mature brands can be revitalized through strategies such as suggesting new uses (e.g., Campbell's Soup as a sauce). More generally, umbrella branding strategies should be strongly considered when there is evidence of a favorable IG effect. In contrast, Study 2 pointed to cases in which a brand extension deletion decision left its IG-loyal buyers stranded: Measures to direct them to favored alternatives might also be considered. Finally, managers might seek opportunities to help create IG preferences for a brand's future, such as through package design. Because some of the child's learning of IG brands may be incidental (i.e., repeated observations of the package during home use), marketers can attempt to capitalize by developing interest and uniqueness here, such as with Heinz squeeze bottles of green ketchup. Finally, to help sustain IG among young adults, Study 2 showed that some brands could retain sales if smaller packages were available, reflecting younger IG consumers' smaller households and income constraints.
IG implications for pricing decisions. The monitoring of purchases in Study 2 showed that IG effects are often disrupted by younger consumers' income constraints. It may therefore be worthwhile to seek ways to adjust prices creatively to convert IG preference effects into continued purchase behavior. As noted, smaller packages may help the brand fit into the budget. Furthermore, the implementation of database or other micromarketing techniques could assist in the design of selectively targeted sales promotions to IG-loyal younger consumers, thereby effectively reducing the price they pay. This approach may be especially appropriate for upper-tier brands, that is, prestige brands that younger consumers may consider a luxury at this point in their lives and that are most susceptible to loss of sales.
IG implications for place-related decisions. The Study 2 interviews also made it clear that brand availability is fundamental to sustaining an IG effect. At regional or local levels, zip clustering and/or localized surveys can be used to identify the stores that young IG-prone consumers patronize: Special programs could then be targeted through these outlets. For smaller IG-favored local brands that are unavailable in the young adult's new region, seasonal home promotions (e.g., "stock-up" for back to school), parental suggestive selling (e.g., "Give your daughter her favorite reminder of home"), or a link to "gift-pack" programs run by campus or neighborhood entrepreneurs are all options. Given the IG loyalist's penchant for word of mouth, specialty goods might consider extending local distribution by enlisting some of these young loyalists in an entrepreneurial sales or referral enterprise.
Finally, the Internet is already an accepted presence in young consumers' lives and offers several additional managerial possibilities. For example, our study showed many daughters encountering difficulties in locating favorite brands. If distribution is incomplete in a region, Internet information showing exactly which stores carry the brand (and even where in each store) would be welcomed by young shoppers. Also, the availability of the brand on Web-base detailing sites would help IG loyalists sustain their brand preferences, particularly for specialty goods (e.g., ingredients for a favorite recipe).
IG implications for promotion decisions. Our present studies did not focus directly on promotion decisions, but several possibilities can be deduced for consideration. Perhaps the most obvious implication of IG for advertising strategy is that the brand could be promoted to the entire household, or at least to the specific IG dyads within it. Media vehicles can be used to serve this purpose, and on the creative side, ad executions depicting families consuming the brand together can help both develop and reinforce IG bonds. Casual monitoring indicates that advertisements that invoke IG brand ties and loyalties are not uncommon (e.g., Ivory soap, Crest toothpaste, Dewar's scotch, Frigidaire dishwashers, Werthers' candy). Also, appeals to nostalgia might invoke symbolic "family allegiance" for younger members who have left home, and aspirational appeals for upper-tier brands (coupled with price promotions) may also keep younger loyalists from abandoning a favored brand.
Beyond advertising, sales promotion might also be used to fortify IG brand use. Loyalty programs targeted at young adults may help sustain purchase patterns. Furthermore, promotional programs could also be directed toward younger members of the household to build IG loyalty earlier in life. Finally, social influence-both giving and receiving-is part-and-parcel of an IG effect. Brand promotions providing IG-loyal consumers with information that has news value (e.g., a new technology) can create "buzz" (Dye 2000) and lead to positive word-of-mouth influence attempts.
Theoretically, above all is the significance of unique, favorable, and strong brand associations in equity building (Keller 1998). As noted previously, the brand audit represents an important first step for determining whether IG effects are at work. A key component of the audit should be to analyze the nature of the IG connection, as it is reflected in the set of family-related brand associations. Then, as creative judgments for new campaigns are developed, managers need to be especially vigilant in nurturing and protecting the specific associations that define the IG bond.
Implications for Further Theoretical and Research Consideration
The present project also raises several points that are worthy of further consideration by academics and practitioners who want to delve further into this intriguing area. These arise in two sectors: ( 1) extensions of IG research on brand equity issues and other emerging research streams in marketing and ( 2) development of concepts and methods in the IG area itself.
Extensions of research on brand equity issues. The most prominent findings in the current article involved the selectivity in IG impacts at both product category and brand levels: IG appears to influence usage or nonusage of some products, but not others, and it appears to provide "free" brand equity for some brands, but not others. Note that these are different types of effects. First, our findings of a learned IG impact on use/nonuse decisions means that the size and scope of some markets of the future are being partially determined in households today, as children learn to avoid certain products and services while pursuing others. It would be worthwhile to understand more broadly which products are subject to this IG usage effect, which ones are not, at what ages these product usage effects are being developed, and how long they are likely to persist in adulthood. Second, research on selectivity at the brand level is also needed-for example, what distinguishes brands that benefit strongly from IG effects from those that do not? Third, the research methods we employed for Study 1 revealed considerable complexity deserving additional attention in future research. Issues here include ( 1) comparison of single-informant versus dyadic measurement, ( 2) alternative means of adjusting for marketplace characteristics that affect IG scores, and ( 3) appropriate levels and boundaries of product category definitions to include in IG studies. With respect to this last point, for example, we note that the two strongest IG nonuse findings were for narrow forms of broader product classes, frozen juice and canned vegetables. Although this is a useful IG finding in pointing to the learning of specialized practices, it is not obvious what it might portend for brand equity carryover across different forms of a single category. Finally, although this project has provided strong evidence on some important issues for brand equity, it has not provided answers regarding how long such IG effects are likely to persist or which specific consumer or family factors are especially strong contributors to this phenomenon. These issues could be addressed through approaches similar to Study 1, but with adjustments to incorporate different age groups of adults (for persistence) and/or several household characteristics at the expense of fewer product categories and brands.
The IG research here also provides potential for enhancing studies of market segmentation, as well as rich potential for certain other emerging streams of work in marketing and consumer behavior. With respect to segmentation, our depiction in Study 1 of how high-IG brands exist in the competitive marketplace could offer new strategic insights on marketplace competition. For example, the status of certain brands as cultural icons (i.e., high-IG brand equity from a huge segment of households across the society) is a qualitatively different situation from that in which a single brand receives strong IG support, but from a smaller portion of the market. Moreover, both can be sharply differentiated from our discovery of brand silo markets, in which several brands receive depth of support, but from different households in the marketplace (in this regard, our current approach did not allow exploration of brand-switching behaviors or of portfolios of purchases, both of which should add considerable insights to these phenomena). Also, it would be of interest to determine if and how the silo phenomenon, here defined in terms of exact preference matches, could extend to other components of brand equity, particularly brand associations (Aaker 1991; Keller 1998). This would stretch our under-standing of the IG effect into brand memory networks, or consumers' mental maps of the marketplace.
Meanwhile, the findings in Study 2 could easily link to such promising developments as relationship marketing (e.g., Sheth and Parvatiyar 2000) and consumer relationships with brands (e.g., Fournier 1998), as well as the revitalization of mature brands(e.g., Wansink 1997). Also, IG effects might be pursued as a special case of Muniz and O'Guinn's (2001) brand communities, wherein social groups form around certain brands and socialize and bolster individual consumers' brand preferences. Finally, in estimating the lifetime value of a customer (e.g., Berger and Nasr 1998; Talukdar 1999), these findings on IG effects add the intriguing suggestion of a house-hold level of carry over impact. If feasible, this step should add significantly to value calculations, as well as the validity of resulting estimates. Furthermore, all these areas, along with brand equity, should benefit from advances in the IG area.
Exploring IG influence itself. In this project, we have encountered several elements of IG in addition to those reflecting brand equity. It has taught us many lessons regarding the origins and essence of the IG phenomenon. At the broadest level, IG influences in the consumer context represent specific elements of learning, stemming from consumer socialization, that have become tied to a young person's self-identity and are carried into adulthood. As a research area, its complexity has already been noted, but several useful insights into that complexity have been advanced here. Rather than seek simplicity at this point, we acknowledge the complexity of IG as a welcome challenge for future discoveries. In this regard, Figure 2 is intended as a significant conceptual step forward in explicating some key dimensions of IG influences as we encounteredtheminthisproject.BeyondthosefromStudy1,weshift emphasis to the research challenges that emerged in Study 2. Among these, we highlight the following for future attention:
- How IG is manifested in the marketplace: We raise the following as among the most significant elements in Figure 2's "Market-place Manifestations" section: ( 1) IG might be evidenced at times as a low-involvement, repetitive heuristic, but at other times as a higher-involvement, emotional, or symbolic bond;( 2) IG can offer protection against purchase risks in the marketplace; ( 3) IG can also be sharply negative toward a brand or product; ( 4)IG might easily extend to multiple product versions and perhaps multiple brands in some categories; and ( 5) IG extends to various aspects of personal consumption behavior, including beliefs, shopping habits, price preferences, and so forth.
- The bidirectionality of IG influences: Deserving of separate mention is the matter of the reverse flow of IG effects, in which a younger family member influences a parent's views and behavior. This was not easily distinguished in the present project but was noted sufficiently often in Study 2 to convince us that much closer attention is warranted, especially during the teenage years and early adulthood. Products incorporating new technologies or innovations are likely to be especially susceptible to this phenomenon.
- IG development across heterogeneous households and long timeframes: Although not a major focus here, Figure 2's "Factors in the Formation of IG Influences" section reflects both the heterogeneity of households' lifestyles and the richness of years of daily exposures. This supports views of IG effects as ( 1) based on a large number of consumption episodes; ( 2) occurring across a long span of time, as the son or daughter becomes increasingly independent; and ( 3) of ten actively generated through negotiation or other adaptations and thus reflecting personal preferences as well as media and peer influences. In brief, some IG effects are based on considerable cognitive and emotional investment and are not at all fragile.
- Potentials for reemergence of IG later in life: Somewhat in contrast to the preceding point, some IG preferences are age defined during childhood (e.g., kid's cereals, early teen cosmetics) and will not persist into adult purchase behaviors. They may return in later years, however, as parental role enactments become appropriate.
- Diagnostic challenges in the detection of IG: Implicit in the preceding is the recognition that much of IG influences lies beyond overt purchasing behavior and may not be reflected only in the types of measures we employed in Study 1. In some sense, then, the use of a survey technique alone may risk understatement of actual brand equity levels or other IG effects that exist. Furthermore, the marketplace is dynamic, and mothers adapt as well. Therefore, investigation of both generational cohorts during adulthood is an interesting option, especially if study of their mutual flows of influence over this time period is incorporated as well.
- The persistence of IG overtime: Figure 2's "Endurance in Adulthood" section shows that the forces here are real factors in both new households and in-store environments. This section should also be useful in designing future studies on IG persistence: Some reasonable level of survey quantification seems possible, given that various entries here can be identified as conditions that either are or are not present or can be classified according to what degree they apply. Although IG effects are generally believed to be about stability, the larger context of their application is about change over time and about how adaptation into adulthood is managed. As construed here, both disruptive and sustaining forces are present and active over many consumption episodes. This suggests that IG influences are likely influx over time and that gradual weakening or strengthening of IG effects are alternatives to simple persistence or cessation.
Overall, our appreciation for the complexity of this research area has certainly increased over the course of our investigation. With respect to our initial question, it is clear that IG is an important source of brand equity in today's marketplace, but only for some brands and not for others. It has also become more obvious why IG influences should be present across the marketplace and why they can be significant sources of influence on consumer behavior. In closing, it is clear that the topic of IG influence encompasses a rich set of phenomena and is deserving of further attention by the marketing community.
1 In a preliminary study, 76 parent-young adult pairs provided separate brand preference listings for 120 packaged goods categories (including foods, personal care, and cleaning products) listed in the DDB/Needham Worldwide Lifestyle survey. Examination of parent-child agreement showed considerable variation across product categories, and results generally appeared to be robust, which increased confidence that IG effects would exist within this domain. Because of the multiple levels of detailed analysis desired for the present study, we reduced the list to 24 categories. This product selection process was judgmental but provided representation from a range of dyad-matching scores from the preliminary study-high, moderate, and low-to allow exploration of other factors (e.g., dominant brand, number of brands, product usage) that might mask valid IG effects at the brand level.
- 2 This approach could understate some legitimate IG effects if the mother has shifted her preferences recently and the daughter is truly reporting the IG-generated preferences from her family background. Also, given this cross-sectional survey, it is possible that the direction of the IG influence flow could be reversed, reflecting cases in which the daughter has influenced the mother to shift to a newly favored product or brand offering (because this effect would still be of interest, counting these matches at preliminary stages does not appear to be a serious limitation of the method).
- 3 To illustrate the testing method, we assume a simple market in which there are three brands, 100 daughters, and 100 mothers and that everyone prefers one of the three brands. We further assume that the preferences of daughters are arrayed 60, 20, and 20 for Brands 1, 2, and 3, whereas mothers' preferences are 30, 40, and 30. Thus, pd1 = .6, pd2 = .2, pd3 = .2, pm1 = .3, pm2 = .4, and pm3 = .3. If mothers and daughters were paired randomly, the number of participants expected to be involved in matched preferences is given by the joint probabilities for each brand preference summed across all brands and applied to the entire sample: That is, E = [(pd1 × pm1) + (pd2 × pm2) + (pd3 × pm3)] n = [(.6 × .3) (.2 × .4) + (.2 × .3)] 200 = (.18 + .08 +.06) 200 = (.32)200 = 64 participants in matching dyads (32 daughters and 32 mothers). Now, we count actual matches in the family pairings and observe that 96 participants (48 daughters and 48 mothers) are participating in preference matches, so A = 96 participants. We now test for significance of this difference using the Z test (Kanji 1993, p. 24). Z = (A/n - E/n)/ {[E/n × (1 - E/n)]/n}<SUP>1/2</SUP>, Z = (96/200 - 64/200)/{[(64/ 200) × (1 - 64/200)]/200}<SUP>1/2</SUP>, Z = 4.85, p ≤ .0001.
- 4 Although they are not shown in the figure, we conducted parallel analyses for family agreement on users of these products: In every case, the agreement scores are higher than those shown, except for frozen juice, which still registered a robust 64% agreement.
- 5 Informants are identified by a number ( 1-25), the interview (a = first interview conducted in home, b = second interview conducted in the store and completed in the informant's home), and page number in the interview transcript.
Legend for chart:
A = Product Category and Exact Brand Preference Match[a]
B = High-IG Impact Key Brands: High Score and Significant[b]
C = High-IG Impact Brands with Potential[c]
D = Low or No Impact (Illustrative Brands)[d]
A Soup 76%
B Campbell's 84% +
C Progresso 40%
D (All others)
A Catsup 65
B Heinz 80 +
C
D Hunts*15, Del Monte 0
A Facial tissue 55
B Kleenex 67, Pufs 48 +
C
D (All others)
A Peanut butter 53
B Peter Pan 67, Jif 59, Skippy 48 +
C
D (All others)
A Mayonnaise 51
B Miracle Whip 59, Kraft 51, Hellman's 50 +
C
D (All others)
A Pasta 47
B Mueller 63, Ronzoni 56 +
C Creamette 43
D (All others)
A Spaghetti sauce 46
B Newman's Own 86, Ragu 56, Prego 39 +
C
D (All others)
A Toothpaste 43
B Crest 60, Colgate 42 +
C Arm & Hammer 29
D
A Tuna 41
B Bumble Bee 50, Starkist 48 +
C
D Chicken of the Sea* 19
A Jams/jellies 39
B Smucker's 39 +
C
D Welch's* 14
A Laundry detergent 36
B Tide 55 +
C All 40, Fab 33, Gain 33
D Wisk 14
A Tea 34
B Lipton 44 +
C
D Tetley 13, Bigelow 0
A Dish detergent 34
B Dawn 60, Sunlight 38, Ivory 35 +
C Palmolive 30
D Dove 13, Cascade 0
A Pain relievers 34
B Tylenol 47, Advil 38 +
C Bayer 27
D Bufferin 0, Motrin 0
A Soap 30
B Dial 57, Lever 2000 40 +
C Ivory 33, Dove 32, Irish Spring 33
D Zest 0, Camay 0
A Salad dressing 30
B Kraft 49 Hidden +
C Valley 24
D Seven Seas 14
A Paper towel 25
B Bounty 47 +
C
D Scott* 8, Brawny 0
A Frozen juice 23
B Minute Maid 36 +
C
D Tropicana 14
A Lotion 22
B Lubriderm 38 +
C Jergen's 29
D Vaseline* 23
A Baked beans 22
B
C B&M 29, Bush 24, Campbell's 26
D
A Coffee 20
B Folgers 29, Maxwell House 24 +
C
D
A Candy bars 18
B Snickers 38 +
C
D Hershey* 11
A Household cleaners 13
B Fantastik 34 +
C Soft Scrub 29
D Lysol* 10, Pine Sol* 10, Formula 409* 10
A Canned vegetables 11
B
C Green Giant 38
D Del Monte* 30
[a]The measure used for Table 1 is exact brand preference match (see Figure 1, Expectation 1, where overall average equals 36%). Here, each category average is provided, and some brands' scores are detailed. For example, the top row should be read as follows: "In the soup category overall, 76% of the mother-daughter pairs reported the same brand preference. The Campbell's brand showed an especially high-IG impact, with 84% brand preference matches between mothers and daughters. Progresso also showed promise of IG potential, with 40% preference matches coming from the same families. No other brand showed sufficient evidence of IG influences to be noted here."
[b]Brands in this column show both a high score (relative to their category) on mother-daughter shared brand preference and that this score is statistically above chance levels (z-statistic). Thus, their IG brand equity is real and strong. + symbols are used for emphasis.
[c]Brands in this column show some evidence of IG brand equity but did not attain both levels for the columns at the left. Either the score was high but sample size was too small for confidence, or the result, though significant, reflects a more moderate IG effect.
[d]This column lists only some illustrative brand names that did not perform well on these tests for IG brand equity. Those designated by an asterisk were tested and found to be nonsignificant. The other brands in this column had samples too small to be tested: Their inclusion is based on either no or very low levels of mother-daughter matches.
Expectation 1: IG Influences Help Determine Use or Nonuse of a
Product
Issue: Do mothers and daughters agree on nonusage at a rate more
than expected by chance?
Requirements: At least 30 nonusers in a product category (nine
categories qualified).
Summary Results: Significant IG impact on nonuse of six categories,
IG not significant in three others.
(Percentage of Frozen juice Tea (42%)** Coffee (n.s.)
nonusers in (78%)****
same family) Canned Jams/jellies Candy (n.s.)
vegetables (29%)*
(49%)****
Baked beans Tuna (22%)*** Peanut butter (n.s.)
(46%)*
Expectation 2: IG Impacts Can Be Measured at Different Stages of the
Consumer Decision Process
Issue: Are daughters' reported awareness, choice set, and
preference supportive of IG influence at work?
Requirements: Responses from mothers and daughters on these
measures. All 24 products qualify.
Summary Results:
Exact
In Brand Percent
Daughter Daughter's Preference Signifi- Gain Of
Aware? Choice Set? Match cant? (Median) Possible
Find- 69% 60% 36% Yes**** 63% 18%
ings 17 of 24 14.5 of 24 (23 of 24
(Means) correct match categories)
Expectation 3: A Variety of Forces Affects Levels of IG Impact
Issue: Is the mother/daughter brand preference agreement score
affected by four particular characteristics?
Requirements: Product categories are differentiated on these
characteristics, and then IG preference scores are compared.
Summary Results:
A. Categories with a dominant brand produce higher levels of brand
preference matches.
Mean (dominant) Mean (no single t = 5.27, p ≤ .0001
= 65% dominant) = 32%
B. Categories with a large number of competing brands produce lower
levels of brand preference matches.
Mean (≥10 Mean (<10 t = 1.74, p < .05
brands) = 29% brands) = 57%
C. Categories with generation usage differences produce lower levels
of brand preference matches.
Mean (difference) Mean (same) = 37% t < 1, n.s.
= 32%
D. Categories with a significant number of nonusers produce lower
levels of brand preference matches.
Mean (some Mean (high usage) t = 1.74, p < .05
nonusers) = 29% = 40%*p < .10. **p < .05. ***p < .01. ****p < .001.
I. Marketplace Manifestations
IG Dimensions in Survey IG as Repetitive Purchase
-- Brand preference -- Functional performance
-- Consideration set -- Familiarity
-- Product use/nonuse -- Inertia
-- Reliance on mother's expertise
-- Low involvement
IG as Emotional Bond
-- Trust
-- Nostalgia
-- Overcoming loneliness and trepidation
-- Brand as play
-- Resentments and brand avoidance
-- Brand as a symbol of allegiance
Other IG Insights About Products and Brands
-- IG as a brand portfolio
-- IG as competitive barrier/opportunity
-- IG as desired brand tier
-- IG as new product propensity
-- IG for preference for product forms
-- IG as a limiting brand force
-- IG as a reverse influence flow
Other IG Dimensions: Beyond the Brand
-- Shopping style and preference
-- Good consumership
-- Following the rules: norms for behavior
-- Packaging entire assortment
-- Adoption of lifestyle dimensions
-- Ties to personal identity (remembered rituals, ethnic heritage)
II. Factors in Formation
IG Influencers Within the Household
-- Impacts of a family's structure and lifestyle
-- It's a mother's domain
-- The influencers beyond mom
-- Altogether, a rich information environment
-- Parental roles: guidance and adaptation
-- Heterogeneity across households is the rule
The Substantive Formation of IG Influences
-- Governed by children's learning processes
-- Shopping participation plays a huge role
-- Observational learning also contributes
-- Shopping lists, rules, and limits as control devices
-- Allowing for personal tastes
-- Children's influence attempts as participant learning
-- Coherence helps: resolving inconsistent messages
-- Parents are evaluated too
IG Development Across Time
-- A continuous process across the years
-- Some IG effects are age-specific
-- Preference stability a plus for IG growth
-- Impacts of structural and lifestyle changes on IG
-- As the life cycle moves along, shifts in IG and acceleration of
reverse IG
III. Endurance in Adulthood
A. SOURCES WITH DISRUPTIVE POTENTIAL
New Influencers: Spouse, Roommate, and Peers
-- Joint purchase decisions
-- Introduction of new options and categories
-- Influences on choice criteria
-- New roles: purchasing agent for others
-- Gifts
-- Social disdain: peers and media
-- Conformity pressures
New Lives Bring Shifting Lifestyle Demands
-- Income constraints
-- Mobility
-- Time at a premium
-- Plenty of personal meal occasions
-- Period of experimentation
Altered marketplace Experiences
-- Lack of brand availability results in frustration
-- Sales promotions, sampling, premiums
-- New products, information, and metrics
-- Mismatched package sizes
-- Product line deletion
Other Insights on IG Disruption
-- Incomplete IG knowledge
-- Parents change as life cycle continues
-- Behavior is not the only measure of IG
-- Time and distance
B. SUSTAINING FORCES FOR IG INFLUENCES
Forces that Support Repetitive Purchase
-- Performance, familiarity, inertia, low involvement (see
"Marketplace Manifestations" above)
-- Risk aversion
-- Regularity as reinforcer
-- Seasonal rituals as IG support
Maintenance of Self-Identity
-- Ethnic identify as IG sustainers
-- Select lifestyles shape choice options
-- IG brand as safe harbor
-- IG as basis for influence attempts on others
-- Loosened constraints allow IG enactment
-- Reverse IG: bringing parents back
Parents as IG Sustainers
-- IG purchases as continued loyalty to parent
-- Proximity is a key issue
-- Family shopping outings as reinforcers
-- Parents as product suppliers
-- Inputs from afar: coupons from home
-- IG influence attempts continue to occur in adulthood
Marketer Actions to Help Sustain IG
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By Elizabeth S. Moore; William L. Wilkie and Richard J. Lutz
Elizabeth S. Moore is Assistant Professor of Marketing, and William L. Wilkie is Nathe Professor of Marketing, University of Notre Dame. Richard J. Lutz is Professor of Marketing, University of Florida. The authors thank Carl Mela for his analytic suggestions and Julie A. Alder for her assistance with data collection. They also gratefully acknowledge financial assistance provided by the American Marketing Association to the first author, who was a recipient of a Faculty Advisor Research Grant.
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Record: 116- Plural Governance in Industrial Purchasing. By: Heide, Jan B. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p18-29. 12p. 3 Charts. DOI: 10.1509/jmkg.67.4.18.18689.
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Plural Governance in Industrial Purchasing
This article examines the phenomenon of plural governance, a firm's simultaneous use of market contracting and vertical integration for the same basic transaction. The author studies two particular aspects of plural governance, namely the conditions that motivate firms to deploy plural governance and the manner in which one governance form influences the other when the two coexist in a joint structure. Drawing on agency theory and information economics, the author develops a set of research hypotheses and tests them empirically in the context of industrial purchasing decisions. The results suggest that the plural governance phenomenon can be understood from the perspective of solving information asymmetry problems between buyers and suppliers. The author discusses the implications of a "make and buy" approach to extant theory of interfirm governance, relationship management, and marketing strategy in business-to-business settings.
Much of the recent research on interfirm relationships in marketing has relied on the theoretical notion of governance (e.g., Ghosh and John 1999; Heide 1994). Important insights have been generated both into firms' choices among basic governance forms, such as vertical integration and market contracting (e.g., Anderson 1985; Klein, Frazier, and Roth 1990), and into the specific governance dimensions of individual relationships (e.g., Cannon and Perreault 1999; Lusch and Brown 1996).
Firms' governance decisions involve complex issues. Some authors (Carson et al. 1999, p. 129) have suggested that governance decisions often involve "messy details" that fall outside the power of elegant theory. The present focus is on such an issue, namely the decision to combine governance forms such as market contracting and vertical integration into a single structure, a strategy Bradach and Eccles (1989) describe as a "plural forms" approach.
Bradach and Eccles (1989) describe the plural forms phenomenon in the context of franchising systems, in which firms often rely on a combination of franchised and company-owned outlets. However, the practice of simultaneous deployment of different governance forms is considerably more widespread. Sometimes described as "tapered integration" (e.g., Carlton 1979), joint reliance on internal and external organization has been identified in several marketing settings, including advertising (Belch and Belch 2000), industrial purchasing (Ahmadjian and Lincoln 2001), marketing research (Besanko, Dranove, and Shanley 1996), industrial distribution (Dutta et al. 1995), and foreign market entry (Vanhonacker 1997).
Notably, although plural forms are integral and permanent parts of many firms' strategies (Harrigan 1984; Scherer and Ross 1990), they are not well documented from a theoretical standpoint. In particular, unanswered questions pertain to both the conditions that motivate firms to deploy plural structures in the first place and the influence patterns that exist across individual governance forms within the plural system. Bradach and Eccles (1989) propose that these questions are related to using one governance form to manage the other; however, they did not articulate this general argument in the form of refutable hypotheses. As a consequence, with some exceptions (e.g., Dutta et al. 1995), the plural forms phenomenon has generated little empirical research.( n1)
The objective of this article is to address the preceding issues raised. I draw on agency theory and information economics (Bergen, Dutta, and Walker 1992; Kirmani and Rao 2000) to propose that plural structures can be understood from the perspective of solving information asymmetry problems of various kinds. On the basis of an empirical test in the context of industrial purchasing, in which buyers may purchase components from independent suppliers and make components internally, I examine whether augmenting market relationships with internal organization enables firms to manage information problems that are inherent in (singular) reliance on market governance.
From a theoretical standpoint, I attempt to ( 1) document a common governance practice, ( 2) examine a possible solution to information asymmetry problems, and ( 3) expand the unit of analysis in relationship research from individual dyads to portfolios of internal and external relationships. From a managerial standpoint, the plural forms thesis has important implications for both the firms that pursue such strategies and their exchange partners (e.g., their suppliers). Regarding the former, manufacturers that simultaneously make and buy a particular component are in a unique position to manage the supply function. For suppliers, targeting buyers with plural structures means facing unique buying situations, which in turn may influence the nature of the supplier's marketing program.
The next section presents the conceptual framework and research hypotheses. The subsequent section presents the research design and describes the empirical tests. The final sections discuss the results and their implications.
In the following sections, I discuss conditions that motivate the deployment of plural structures and the influence patterns that exist within such systems. As I discuss subsequently, these issues pertain to solving information problems of different kinds.
The Deployment of Plural Governance
Consider as a starting point an industrial sourcing situation in which a buyer (manufacturer) needs to acquire subassembly components. A strategy for acquiring components is to rely on independent (external) suppliers. In transaction cost terminology (Williamson 1985), such an approach means management of the procurement task through market governance. In the extant literature, market governance is often viewed as an analytical baseline because of its ability to exploit the cost advantage of external specialists and enable firms to concentrate on their core competencies (e.g., Quinn and Hilmer 1994).
At the same time, exclusive reliance on market governance may give rise to problems. A particular problem is information asymmetry, which means that one party (i.e., the external supplier) is better informed about aspects of the exchange than the other (MacMillan 1990). Conceptually, information asymmetry is an example of a market imperfection, which enables a supplier to act opportunistically without being detected. Two forms of opportunism are possible. First, information asymmetry allows for adverse selection, or ex ante misrepresentation of skills and characteristics. For example, there are certain fixed aspects of a supplier firm, such as the technology and processes used, that have the potential to influence product quality but are imperfectly observable to the buyer (Levinthal 1988).
Second, information asymmetry allows opportunism in the form of moral hazard, that is, quality debasement or actual cheating. For example, even suppliers that possess the "right" characteristics or skills may fail to use them if information asymmetry allows such actions and if there are cost savings involved (Mishra, Heide, and Cort 1998). In many situations, buyers can readily evaluate outside suppliers on output measures such as delivery schedule and volume. However, in situations in which output measures do not reflect the true level of performance, a buyer faces the risk of quality debasement.
Thus, buyers that rely exclusively on market contracting may be constrained in their ability both to evaluate supplier performance per se and to make correct attributions about the cause of the performance problems. I propose that a plural governance strategy might help solve these problems. First, with respect to adverse selection, an internal operation provides a buyer with "architectural knowledge" (Venkatesan 1992), which enables direct assessments of supplier characteristics and processes (Ahmadjian and Lincoln 2001). Second, with respect to moral hazard, in-house expertise enables a buyer to design meaningful monitoring systems, including the establishment of relevant measurement standards based on supplier behavior (Anderson and Oliver 1987; Ouchi 1980).
In addition to setting standards ex ante, the presence of an in-house operation also helps in actual monitoring ex post. In the absence of relevant expertise, monitoring provides limited information or simply produces information that cannot be properly interpreted (Jacobides and Croson 2001). Indeed, monitoring under such conditions may produce substantial but unnecessary governance costs.
The preceding discussion suggests that the evaluation or information problem that would exist given exclusive reliance on market governance would be alleviated by augmenting the market transaction with internal organization. Notably, for the buyer to reduce information asymmetry, it is not necessary to establish a large-scale internal operation. From a buyer's viewpoint, the key is the ability to evaluate information about the external party (Arora and Gambardella 1994), which only requires the presence of an internal operation per se. In Dutta and colleagues' (1995) terminology, a plural system can be thought of as a "hierarchy-assisted" market. I offer the following refutable prediction:
H1: The greater the information asymmetry in a market relationship, the greater is the likelihood that the market relationship will be augmented with internal organization.
In the terminology of agency theory, the preceding discussion describes the difficulty a principal (the buyer) faces in ascertaining the characteristics and behaviors of a particular agent (the external supplier). The proposed solution to the problem, namely a plural governance approach, involves using one "agent" to manage another (Varian 1990). However, a plural forms approach involves using a particular type of agent (i.e., an internal one) to reduce information asymmetry in relation to another type of agent (i.e., the external one) due to the nature of the information that becomes available through integration. It would be useful to consider whether these problems could be addressed in a more straightforward manner, namely by using multiple external suppliers. In other words, the issue is whether information asymmetry can be managed by establishing multiple market relationships or by relying on multiple versions of a single governance form. In this study's empirical test, I formally examine this possibility as a means of validating H1.
Influence Across Governance Forms
The preceding discussion suggests that potential information problems in a market relationship are an impetus for augmenting that relationship with a hierarchical one. I now consider the possibility that a plural system serves to influence the structure of the market relationship. As I discuss subsequently, the information economics perspective introduced previously suggests that such influence across governance forms can be understood from a signaling (or self-selection) perspective.
As a starting point, consider Bradach and Eccles's (1989) account of plural forms and their general thesis that a relationship in a plural system is "profoundly affected" by the other. In a later study, Bradach (1997) provides evidence that exposure to hierarchical arrangements within the context of plural systems actually produces similar patterns in market relationships.
Bradach's (1997) study has generated novel insights into plural systems, but its particular scope has left some questions unanswered. Specifically, because the sample in question consisted only of plural systems, it precluded specific comparison between types of systems (e.g., plural and singular). Ultimately, such a comparison is needed to ascertain whether market relationships that coexist with a hierarchical structure are indeed more hierarchical than others. In addition, important questions remain about the processes by which hierarchical characteristics emerge in market relationships.
Consider first the inherent characteristics of the two governance forms that constitute a plural system. An internal manufacturing operation exists within an organizational hierarchy, whose defining characteristics are centralized and formalized decision structures (Weber 1947). Conceptually, centralization refers to the concentration of decision-making authority or the degree of vertical control in the relationship (Hage and Aiken 1967; Heide and John 1992; Poppo 1991). Formalization describes the reliance on fixed rules and standard operating procedures (Dwyer and Oh 1988; John 1984).
In contrast, prototypical market relationships possess limited formal structure and content (Baker 1990; Granovetter 1985). For example, all else being equal, a buyer has limited formal authority over an independent supplier's decisions about subsuppliers and quality-control procedures. Similarly, to a limited extent, market relationships rely on formal rules and policies (Macneil 1980; Simon 1991).
However, it is noteworthy that hierarchical features manifest themselves in relationships between independent firms, but this begs the question, How do buyers acquire the authority to specify decision rights and impose routines? Nishiguchi and Anderson (1995) note that authority acquisition is not automatic; it must be both justified by the buyer and accepted (i.e., viewed as legitimate) by the supplier. Bradach's (1997) study shows that one source of buyer legitimacy is the presence of an in-house operation. According to institutional theory (DiMaggio and Powell 1983; Scott 1987), the presence of an integrated operation gives a firm access to established (and thus legitimate) practices that can be deployed in the market relationship. Walker (1988, p. 228) specifically notes that "in interorganizational contexts, the routines that dominate a relationship may not be the firm's, but those of the organizations with which it cooperates."
The upshot of the institutional argument is that suppliers that are embedded in plural structures signal that they are operating in legitimate ways in relation to the buyer by conforming to the hierarchical arrangements. In the context at hand, these signals are credible because they represent a cost to the sender (i.e., the supplier). For example, there are direct (out-of-pocket) costs associated with conforming to a buyer's decisions and learning the relevant rules and procedures. There are also intangible costs associated with reduced supplier discretion (Cook 1977; John 1984). Regardless of costs' specific nature, signaling theory suggests that they create opportunities for self-selection or for suppliers that possess the "right" characteristics to reveal private information (Kirmani and Rao 2000; Spence 1973). Specifically, because only these suppliers will receive a return on the relevant costs through repeat transactions, only such suppliers will enter the relationship in the first place. Thus, the presence of a hierarchical operation serves as a screening device, which gives suppliers the opportunity to signal their characteristics.
Theoretically, this suggests an additional means by which a plural structure may solve adverse selection problems in relation to the supplier market. Recall from the previous discussion that the key impetus for deployment of plural structures is to manage adverse selection and moral hazard problems directly by improving the ability to evaluate supplier characteristics and behavior, respectively. I now suggest that if a plural structure promotes the emergence of hierarchical characteristics in the market relationship, adverse selection concerns are also addressed in an indirect way, namely through supplier self-selection or signaling. In this scenario, the costs to the supplier are a substitute for direct information acquisition.
However, to the extent that hierarchical procedures constitute signals, they should not be universally desirable. Rather, the need for a supplier to reveal (and for a buyer to extract) private information should be greatest at the early stages of a relationship. Over time, as the supplier's characteristics become known through direct interaction, information asymmetry and the need for signaling diminish. As I noted previously, the upshot of signaling theory (e.g., Kirmani and Rao 2000) is that signals are proxies for direct information. At this later time, although the presence of an internal operation gives a buyer the ability to impose hierarchical arrangements, the actual motivation to centralize and formalize decisions will have decreased. Because the deployment of hierarchical mechanisms means a loss of supplier autonomy, I expect such tactics to be used selectively. Thus, I propose that the positive influence of a hierarchical arrangement on a market relationship in a plural system is time dependent and should decrease with the age of the relationship. I offer the following predictions:
H2: In a plural system, the presence of a hierarchical arrangement (a) increases the market relationship's degree of centralization, but (b) the effect decreases over time.
H3: In a plural system, the presence of a hierarchical arrangement (a) increases the market relationship's degree of formalization, but (b) the effect decreases over time.
Statistically, H2 and H3 involve positive main effects of plural governance on centralization and formalization in the market relationship and negative interactions between plural governance and relationship length. In other words, I expect the positive effect of hierarchy to decrease over the range of relationship length (Schoonhoven 1981).
The time-dependence arguments of H2b and H3b assume that centralization and formalization do not represent solutions to moral hazard. Recall from the previous discussion that moral hazard is an ongoing concern and that even high-quality suppliers may cheat by not using their skills. Although hierarchical features restrict a supplier's decision-making autonomy, they do not necessarily serve a monitoring purpose. In this context, a buyer's safeguard against moral hazard resides in the ownership branch and its detection ability. However, if centralization and formalization serve to control ongoing cheating, the hypothesized time-dependent effects should not materialize.
The empirical context for this study is purchasing relationships between original equipment manufacturers (OEMs) and component suppliers. Specifically, I focused on OEMs in three two-digit Standard Industrial Classification (SIC) major groups: 35 (general machinery), 36 (electrical and electronic machinery), and 37 (transportation equipment).
Preliminary Fieldwork and Pilot Study
The first steps in the research process consisted of attending an industry conference on manufacturer--supplier relationships, conducting personal interviews, and drafting a survey instrument. I personally administered the initial draft of the survey instrument at four different OEM sites. Subsequently, the instrument was revised and administered by mail to 25 OEMs in the designated SIC major groups.
Sample and Data Collection
The sampling frame was a national mailing list of purchasing agents and directors who represented firms in the relevant SIC codes. First, I drew a random sample of 1157 names from the sampling frame. Second, before the administration of the survey, each person was contacted by telephone to verify company and informant characteristics. To further ensure sample homogeneity, I excluded buyers that purchased components for direct resale. In total, 579 people were identified who met the criteria and agreed to participate in the study.
Response rate. After callbacks and a second mailing, I received 175 questionnaires (30% of 579 mailed). To evaluate nonresponse bias, I conducted Armstrong and Overton's (1977) extrapolation method, which compares early and late survey respondents in the sample, and compared the two groups on the study variables and several company demographics. I found no significant differences.
Key informant checks. The survey instrument included post hoc checks on the informants' knowledge and involvement in the firms' sourcing decisions, consistent with Campbell's (1955) criteria. On a seven-point scale, the mean knowledge and involvement scores for the informants were 6.46 (standard deviation [s.d.] = .74) and 6.37 (s.d. = .85), respectively. I eliminated six questionnaires that showed inadequate levels of informant knowledge and involvement.
In addition, I eliminated companies that purchased from distributors rather than from component manufacturers ( 11) or that exhibited excessive amounts of missing data ( 3). In the final sample, 103 key informants were purchasing directors, 40 were purchasing agents, and 12 were materials and operations managers.
Measures
The subsequent sections describe the measures used for the dependent, independent, and control variables, respectively. The actual measures and key descriptive statistics are shown in the Appendix.
Measures of dependent variables. The dependent measure used to test the deployment hypothesis (H1) was based on the following question: What percentage, if any, of your needs for this component do you produce internally? An answer of 0 was coded as 0, and an answer greater than 0 was coded as 1. This particular coding scheme follows from the research hypotheses, which pertain to the use of plural governance per se. Consistent with the work of Bradach and Eccles (1989) and Bradach (1997), my conceptual arguments do not address the relative reliance on different governance forms. Specifically, overcoming performance evaluation difficulties only requires the presence of internal manufacturing.
I based the measure of supplier competition, used as a validity check for H1, on the following question: Including this particular supplier, from how many different suppliers does your company purchase this component? An answer of 1 was coded as 0, and an answer greater than 1 was coded as 1.
An additional comment on the previous measures is in order. Unlike many of the other variables, which are latent constructs and are approached with a set of indicators that tap the relevant domains, the sourcing approach a firm uses (e.g., plural governance) is a readily observable phenomenon. Thus, the procedure of "sampling facets" (e.g., Bollen and Lennox 1991) of a latent construct is not directly applicable here.
In the sample, 31% of the firms relied on a plural governance approach. Of these, 38% were from SIC 35, 35% from SIC 36, and 27% from SIC 37. Of the firms, 41% used more than one outside supplier for the focal component.
The dependent variables in H2 and H3, centralization and formalization, describe the two key dimensions of hierarchical structure, as per Weber (1947). I measured each variable using multi-item scales, which were based on Hage and Aiken's (1967) original items and subsequently adapted by Dwyer and Welsh (1985), Dwyer and Oh (1987), and John (1984). The items in the centralization scale describe the extent to which key decisions in the relationship are concentrated with the buyer, that is, the buyer's degree of vertical control (Heide and John 1992). The formalization items describe the degree to which fixed rules and procedures regulate activities in the relationship.
Measures of independent variables. The key independent variable in H1, information asymmetry, describes the ex ante information gap between the parties, or the buyer's difficulty in verifying the true level of supplier performance. I based the multi-item scale used on items previously developed by Anderson (1985). I measured relationship length, the moderator in H2 and H3, using the historical length (i.e., number of months) of the buyer--supplier relationship.
Control variables. I included a set of several control variables in the analyses to account for additional determinants of governance deployment (H1) and hierarchical structure (H2 and H3). Consider the control variables used in the test of H1. First, it is conceivable that firms deploy a plural structure to manage other governance problems, such as adaptation (Williamson 1985). For example, buyers that face external uncertainty and are constrained in their ability to write complete contracts may enhance their ability to adapt in the supplier relationship to the extent that an in-house operation exists.( n2) I used multi-item measures of external uncertainty and buyer-specific investments, based on those developed by Anderson (1985) and Walker and Weber (1984), to account for this possibility. Second, I controlled both for the extent to which the buyer relied on a formal qualification program to evaluate the supplier ex ante and for whether the supplier also invested in specific assets. For the former, qualification programs may contribute to reducing information asymmetry. Supplier investments may constitute alternative self-selection devices. I measured both qualification and supplier hostages using multi-item scales based on the items developed by Stump and Heide (1996).
I used three additional controls in the deployment model. First, I measured the presence of scale economies on the supplier's part. As Harrigan (1984) notes, the greater an external supplier's cost advantage, the less attractive a plural governance strategy is to a buyer. Because of the difficulty of obtaining a direct measure of costs (Walker and Weber 1984), I used the annual purchase volume from the supplier for the component in question as a proxy. Second, I controlled for firm size. Presumably, the larger the buyer, the greater is its ability to establish an internal operation. The measure of firm size was the buyer's overall annual dollar sales. Third, I included a measure of the characteristics of the focal component. As Anderson and Katz (1998) discuss, particular aspects of the buying situation may influence a firm's sourcing approach. For example, purchases involving technically complex products and/or products that have revenue and risk implications may require greater degrees of buyer control. I attempted to capture this with a measure of the component's degree of customization.
In the models of hierarchical structure (centralization and formalization), I wanted to control for the possibility that power considerations influence a buyer's ability to structure the supplier relationship. To this end, I controlled for both the size of the buyer firm and the annual purchase volume from the supplier. I also controlled for supplier-specific investments (Williamson 1983), given the possibility that such investments enhance a buyer's ability to centralize and formalize decisions. To further account for power considerations, I also included a measure of concentration to account for the possibility that suppliers whose sales are concentrated with the focal buyer relinquish decision-making authority. Finally, I included the qualification measure to control for the buyer's use of a qualification program as a basis for subsequent relationship design (Stump and Heide 1996).
Construct Validity
I subjected the reflective multi-item measures (i.e., information asymmetry, centralization, formalization, external uncertainty, buyer investments, and supplier investments) to a systematic assessment of internal consistency and unidimensionality. I measured qualification using a formative operationalization and did not include the relevant items in this process. I first evaluated each item set on the basis of item-to-total correlations and exploratory factor analysis. Using the elliptically reweighted least squares procedure in EQS (Bentler 1993), I then subjected the entire item set to confirmatory factor analysis.
The overall fit of the model (χ²[309] = 515.33, p < .001, Bentler's [1993] comparative fit index [CFI] = .94, average off-diagonal standardized residual = .06) provides evidence of unidimensionality. All the factor loadings are large and significant (t-values > 2). Each item set, response format, and estimated composite construct reliability is shown in the Appendix. All the reliabilities exceed the threshold that Bagozzi and Yi (1988) recommend.
To assess discriminant validity, I estimated several additional confirmatory factor analysis models in which each pair of factor correlations was constrained to unity. I then compared the fit of each new model with the original unconstrained model. All the measures show evidence of discrimination. The correlation matrix for the variable set is shown in Table 1.
Hypotheses Tests and Results
Recall that the hypotheses pertain to the deployment of plural governance (H1) and influences across governance forms (H2 and H3). I tested the first hypothesis by estimating two logistic regression models. The first of these models estimated the likelihood of augmenting market governance with internal organization (i.e., shifting from a single to a plural governance form, per H1). To further validate H1, a second logistic regression model used a similar specification to predict whether a firm relied on a single supplier (reference category) or multiple external suppliers for the focal component.
The estimation results for the logistic regression models are shown in Table 2. As is evident in Table 2, my key predictions are supported. Information asymmetry increases the problems are magnified in the presence of specific assets. Accordingly, I posit that buyers that make specific investments in the context of a plural structure increase their assets' portability likelihood of a shift from single to plural governance, as H1 predicts (t = 2.37, p < .01). The corresponding term in the supplier competition model is not statistically significant (t = -.88, p >.10). These results support the expectation that information asymmetry creates difficulty for market governance and that this difficulty is not managed by establishing multiple market relationships.
For the other variables in the model, external uncertainty increased the likelihood of augmenting market governance with in-house manufacturing. The interaction between external uncertainty and buyer-specific investments is also significant. Although these variables were not the focus of this study, the results suggest that plural forms are also responses to adaptation concerns. Of the other controls, purchase volume had a negative effect on the likelihood of plural governance, suggesting that the scale advantage of an external supplier creates a disincentive for internal production. As predicted, product customization increases the likelihood of using a plural approach.
In the previous analysis, I treated the plural governance phenomenon as a categorical variable, consistent with Bradach and Eccles's (1989) conceptualization. However, recall that I also obtained information about the actual percentage of the buyer's needs that was produced internally. As a validity check, I also estimated the models with the percentage as a dependent variable. Before estimation, the percentage was transformed to Ln (%/1 - %) to ensure that the predicted values would fall between 0 and 100 on the original scale. None of the focal predictors were significant in this model, which suggests that it is the presence of another governance form that matters.
I tested H2 and H3, which involved influence across governance forms, using ordinary least squares (OLS) regressions, which predicted centralization and formalization, respectively. The independent variables in each model were governance form (a dummy variable indicating exclusive reliance on market contracting [0] or a plural governance approach [ 1]), relationship length, the interaction between governance form and length, and the control variables.
The OLS regression results for the models of hierarchical structure are shown in Table 3. The predicted pattern of effects is evident in both models. Specifically, there is a positive, significant main effect of plural governance in both the centralization and the formalization models (t = 2.34, p < .01; t = 1.86, p < .05, respectively), and there is a negative, significant interaction between plural governance and relationship length (t = -1.78, p < .05; t = -3.08, p < .01, respectively). In combination, these results suggest that augmenting a market relationship with internal organization produces hierarchical elements in the market relationship and that the effect decreases over time. These results are consistent with my expectation that hierarchical elements in a market relationship serve signaling (i.e., adverse selection) purposes at the early stages of a relationship.
As a check on the integrity of the conceptual framework, I reestimated the logistic regression model (for deployment of plural governance) with centralization and formalization as additional predictors to test the possibility that a hierarchically organized supplier relationship is a substitute for internal organization and creates a disincentive for a plural governance approach. In the expanded model, neither of the two predictors was significant, and the other effects remained unchanged. This suggests that some of the benefits of a plural structure cannot be readily duplicated through the organization of a particular market relationship.
I also comment briefly on the control variables in the hierarchy models. As expected, aspects of power influenced the degree of centralization and formalization. In the centralization model, both concentration and supplier investments had significant effects. In the formalization model, significant effects were found for firm size and purchase volume. Qualification was significant in both models, consistent with previous findings (Stump and Heide 1996).
As an additional test of the potential role of power, I estimated the hierarchy models with the actual percentage of the buyer's component needs made internally (rather than the categorical 0-1 measure). Conceivably, the greater the percentage already made internally, the greater the buyer's abilities are to shift the (external) supplier's volume in-house and to impose hierarchical structures. In these models, neither the focal main effects nor the interactions with relationship length were significant. This suggests that in the context at hand, and for these particular problems (i.e., information asymmetry), it is the presence of a plural structure that matters. However, I do not argue that power processes are absent. As institutional theorists (Scott 1987) discuss and as is noted in the development of H2 and H3, the relevant power processes rest primarily on legitimacy rather than on replaceability considerations.
Theoretical Implications
In their much-cited article, Bradach and Eccles (1989) observe that firms do not necessarily make mutually exclusive choices between market contracting and internal organization but that they often combine both into a common structure. I operationalized the plural forms argument by proposing that information asymmetry problems, which would cause difficulties given exclusive reliance on market governance, create incentives for augmenting market contracting with internal organization. Consistent with my a priori expectation, information asymmetry prompted a shift from exclusive reliance on market contracting to a plural system. Notably, I also showed that firms do not manage this problem by establishing multiple market relationships or multiple versions of a single organizational form.
My second research question pertains to the influence patterns in plural systems. Drawing on signaling theory, I proposed that hierarchical arrangements serve as self-selection devices for suppliers. As such, I expected market relationships that coexisted with an integrated operation to deviate from their "ideal type" profile (Weber 1947) and exhibit hierarchical characteristics. In support of this hypothesis, the presence of a hierarchical arrangement significantly increased the degree of centralization and formalization in the focal supplier relationship. However, consistent with the signaling perspective, I also showed that this effect was time dependent.
From a theoretical perspective, the results extend existing perspectives on interfirm governance by showing how firms can manage particular governance problems by purposely combining different governance forms. Although plural structures thus far have received little attention in the literature, they are often permanent and integral parts of firms' strategies (Dant, Kaufmann, and Paswan 1992). Organizational architectures that involve making and buying simultaneously often are more representative of marketplace realities than the conventional make-or-buy perspective.
The conceptual framework was based on the assumption that plural structures are unique in their approach to solving information asymmetry problems. There are two ways such problems are solved, both of which originate from the "ownership" branch of the system. First, the presence of an integrated operation enables a firm to evaluate a supplier's characteristics and its ongoing behavior. In other words, ownership serves the purpose of directly managing both adverse selection and moral hazard problems. Second, in plural governance, ownership addresses adverse selection indirectly by enabling the market relationship to be structured along hierarchical lines. In essence, the ownership branch represents a source of "market domestication" (Arndt 1979).
These effects do not require "making" on a large scale. Thus, an important implication of a plural forms approach is that it enables firms to exploit some of the inherent benefits of market contracting, such as cost advantages, fewer bureaucratic obstacles, and flexibility (e.g., Quinn and Hilmer 1994), without a loss of control. Notably, some of the extant models that rely on a make-or-buy heuristic (e.g., Walker and Weber 1984) often assume that governance decisions involve inherent trade-offs on these criteria.
In a general sense, the plural forms thesis, as developed herein, is based on the premise that there are potential interdependencies across individual governance forms. Instead of viewing governance decisions as choices among discrete alternatives, firms might consider whether the "invisible hand" of the market and the "visible hand" of internal organization can be purposely combined.
From a theoretical perspective, this line of reasoning suggests that the appropriate research focus is sometimes on portfolios of relationships rather than on individual dyads. Uzzi (1997) and Dyer, Cho, and Chu (1998) advocate combining different types of supplier "ties," such as arm's-length and embedded relationships, into larger portfolios. However, in these accounts, the impetus for the portfolio decision has been to acquire a range of relationship properties per se. In other words, the implicit heuristic for portfolio design has been an additive one, and when relationship types have been combined, the individual relationships remain independent of one another. For example, in Uzzi's analysis there is no recognized interaction or synergy among the different relationship types in the larger portfolio.
My perspective on relationship portfolios is slightly different. The upshot of the current framework is that individual governance forms are potentially interdependent and that these interdependencies can be used deliberately. For example, augmenting market contracting with internal organization serves to control the market relationship in different ways. The current research provides some preliminary insight into the role played by particular relationship types in a larger portfolio (Cannon and Perreault 1999). I elaborate on the practical implications of this portfolio perspective in the next section.
Managerial Implications
I consider three categories of managerial implications: ( 1) the supplier-management practices of firms that use plural governance, ( 2) those firms' downstream customer relationships, and ( 3) the marketing programs for suppliers that target firms with plural structures.
First, consider a manufacturer and its strategy in relation to the supply market. Cannon and Perreault (1999) note the lack of "pure" relationship types and how buyers may connect with suppliers in various ways; a given buyer might maintain several types of supplier relationships simultaneously. Dyer, Cho, and Chu (1998) recommend that buyers purposely segment their supplier markets and carefully consider each supplier's contribution to its overall strategy.
In the present study, I consider two general sourcing approaches, namely purchases from independent suppliers and a plural approach that combines outsourcing and internal manufacturing. The results suggest that the latter is a selective strategy and used (in part) for products whose quality is difficult to assess and for products with high degrees of customization (e.g., Anderson and Katz 1998). Beyond these considerations, connecting with the supplier market (Cannon and Perreault 1999) by means of a plural governance approach might have the additional implication of helping in a buyer's segmentation efforts in that the plural setup enables appropriate suppliers to self-select into a buyer relationship. Notably, to the extent that a plural governance approach is capable of controlling the relevant market relationships, it may reduce firms' need to have "close" relationships (in a conventional sense) in a portfolio. Instead, a plural forms approach may be a strategy for "managing" markets. As such, particular institutions, such as internal organization, play important roles in managing relationships. On a somewhat related note, my analyses suggest that tightly controlled market relationships do not substitute for plural structures, which in turn suggests that institutions themselves posses unique properties that cannot be easily duplicated.
Second, consider the implications of a plural governance approach for the focal firm's downstream strategy. Specifically, consider the finding that information asymmetry in relation to a supplier prompts a shift toward plural governance. Notably, information problems in the upstream market may also manifest themselves downstream, to the extent that the characteristics of the components in question are not immediately apparent to the buyers of the end product. In essence, an "experience" good (Nelson 1970) at one level of a supply chain may have implications beyond that particular level.
As I noted previously, a plural forms approach may help manufacturers overcome information asymmetry problems in the supplier market. However, the implications of such a strategy may go beyond the upstream market, to the extent that the plural setup itself constitutes a credible signal to the downstream customers. Specifically, firms that have deployed plural structures have both the ability to detect opportunism (due to the in-house expertise) and the motivation to do so (due to the relevant investments). From an end customer's perspective, to the extent that the manufacturer's plural setup is visible, it may overcome inherent evaluation problems and quality concerns (Kirmani and Rao 2000). As such, a plural governance approach may support a firm's efforts in the downstream market.
Third, consider the implications of a plural forms approach for the suppliers to such systems. The empirical results show that certain purchasing situations motivate buyers both to source from suppliers and to produce internally. Such situations involve unique marketing challenges for the suppliers in question. Buyers that have pursued a plural governance approach most likely have complex buying centers that span multiple functional areas (e.g., Anderson and Katz 1998). This tends to make a supplier's initial sell-in task more complex in terms of both the number of relationships that must be established and the type of information that must be provided to each one. However, the complexity of the buying center may eventually protect the incumbent supplier against other (outside) suppliers, to the extent that the relationships in the buying center involve switching costs in the form of interpersonal attachments, training, or learning by doing (Wathne, Biong, and Heide 2001). Thus, although a plural governance setup represents potential competition for an outside supplier, it may also have positive strategic implications.
On a similar note, to the extent that a plural structure serves to manage adverse selection problems, per the previous discussion, certain suppliers actually stand to benefit from such arrangements. More specifically, if the presence of a plural system enhances a buyer's ability to evaluate product quality, it will enhance high-quality suppliers' ability to differentiate their products from those of competing suppliers (i.e., a separating equilibrium). As such, targeting buyers with plural structures may have distinct benefits.
Limitations and Further Research
I should point out some limitations of this research. Although the homogeneous research context is desirable for theory-testing purposes, it limits the generalizability of the findings. Furthermore, this research only begins to explore why firms rely on plural governance strategies in the first place. Conceivably, such strategies can be used for various reasons, and research could usefully be directed to exploration of the relevant domain.
Finally, the specific performance implications of plural systems remain unanswered. For example, if a plural governance strategy enables high-quality suppliers to signal their characteristics and self-select into a buyer relationship, supplier performance should be greater in such relationships than in others. Given some of the negative evidence about performance effects of outsourcing in general (Walker 1997), establishing a link between particular governance approaches and outcome variables seems an important research priority.
The author thanks Erin Anderson, Neeraj Arora, Charles Halaby, George John, Nirmalya Kumar, Debi Mishra, Jack Nevin, and Kenneth Wathne as well as the three anonymous JM reviewers for their helpful comments. The article has also benefited from comments during presentations at Cambridge University's Judge Institute, University of Florida, INSEAD, IMD, and London Business School.
(n1) Note that the term "plural forms" is used somewhat differently in the extant literature. Cannon, Achrol, and Gundlach (2000) use the term to describe the joint reliance on different control mechanisms in the context of a particular relationship; others, such as Bradach (1997), define plural forms as a combination of distinct institutional forms. Bradach and Eccles (1989) allude to both uses of the term. My current use of the term follows Bradach's conceptualization.
(n2) Transaction cost theory (Williamson 1996) suggests that adaptation problems are magnified in the presence of specific assets. Accordingly, I posit that buyers that make specific investments in the context of a plural structure increase their assets' portability. For example, buyers in a plural system that engage in supplier training (Leenders and Blenkhorn 1988) will simultaneously train their own employees and develop the same set of skills. Similarly, firms that invest in idiosyncratic procedures and supply routines (Heide and John 1990) can transfer them to the internal operation. Thus, although investments are made in relation to a particular external supplier, the joint presence of an internal operation increases the likelihood that some of the relevant assets can be redeployed. Thus, in addition to uncertainty itself, the interaction between uncertainty and specific assets may affect the use of plural governance. I account for this in the empirical test.
Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
O - 14
A B C D E F
G H I J K
L M N O
1. Information asymmetry 1.00
2. External uncertainty -.15 1.00
3. Buyer investments .40 -.30 1.00
4. Firm size .08 .07 .07 1.00
5. Purchase volume .06 -.14 .07 .18 1.00
6. Concentration .16 -.22 .19 .18 -.01
1.00
7. Length -.38 -.02 -.09 -.04 .18
-.19 1.00
8. Qualification .29 -.26 .26 .03 .32
.17 .02 1.00
9. Supplier investments .28 -.31 .49 .31 .15
.52 -.16 .37 1.00
10. Plural governance .20 .14 .01 -.07 -.13
-.13 -.10 .07 .05 1.00
11. Supplier competition .15 -.11 .17 .05 .04
-.13 -.05 .11 -.13 -.13
1.00
12. Centralization .36 -.37 .42 .21 .16
.43 -.19 .43 .53 .13
-.05 1.00
13. Formalization .21 -.28 .18 .22 .33
.16 .06 .25 .30 -.03
.07 .26 1.00
14. Product
characteristics .31 -.26 .34 .11 .04
.26 -.14 .28 .39 .15
.05 .44 .30 1.00
Notes: Correlations greater than .16 are significant at
p < .05 for n = 155. Legend for Chart:
A - Variables
B - Plural Governance Model Coefficient
C - Plural Governance Model t-Value
D - Supplier Competition Model Coefficient
E - Supplier Competition Model t-Value
A B C
D E
Constant -7.08 -3.45(**)
1.66 1.73(*)
Information asymmetry .71 2.37(**)
-.15 -.88
External uncertainty .71 2.56(**)
.10 .63
Buyer investments -.13 -.44
-.10 -.58
External uncertainty x
buyer investments .32 1.64(*)
.17 .15
Qualification .22 .79
.14 .01
Supplier investments .17 .67
-.04 -.26
Firm size -.01 -.22
-.05 -.37
Purchase volume -.02 -1.96(*)
-.01 -.15
Product characteristics .26 1.65(*)
-.01 -.01
Log-likelihood -34.27 -34.27
-74.54 -74.54
χ² (d.f. = 9) 27.65(**) 27.65(**)
4.18 4.18
Classification percentage 72% 72%
60% 60%
(*) p < .05.
(**) p < .01.
Notes: d.f. = degrees of freedom. Legend for Chart:
A - Variables
B - Centralization Coefficient
C - Centralization Standard Coefficient
D - Centralization t-Value
E - Formalization Coefficient
F - Formalization Standard Coefficient
G - Formalization t-Value
A B C D
E F G
Constant .86 .01 2.80(***)
2.58 .01 5.94(***)
Plural .72 .23 2.34(***)
.83 .22 1.86(**)
Length -.01 -.05 -.65
.001 .12 1.29(*)
Plural x length -.03 -.18 -1.78(**)
-.009 -.37 -3.08(***)
Qualification .19 .26 3.05(***)
.17 .19 1.93(**)
Supplier investments .19 .30 3.21(***)
.07 .08 .74
Firm size .01 .09 1.09
.01 .13 1.40(*)
Purchase volume .001 .04 .50
.001 .21 2.16(**)
Concentration .008 .21 2.51(**)
.01 .02 .19
F-ratio 10.04(***)
4.01(***)
Adjusted R² .39
.18
(*) p < .10.
(**) p < .05.
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Information Asymmetry (Reliability = .66)
(Seven-point scale: "strongly disagree"--"strongly agree," mean = 4.07, range = 5.50, s.d. = 1.27)
1. In order to obtain a satisfactory assessment of this supplier's performance, we need to conduct on-site inspection at this supplier's plant.
- 2. Conducting performance evaluations of this supplier requires making sure that they follow the approved production and quality-control procedures.
- 3. Evaluating the performance of this supplier requires extensive incoming inspection.
- 4. It is inadequate to evaluate this supplier based on component prices.
Centralization (Reliability = .70)
(Seven-point scale: "entirely decided by supplier"-"entirely decided by your company," mean = 2.44, range = 5.60, s.d. = 1.05)
1. Supplier's production processes and manufacturing technology
- 2. Ongoing design and engineering changes
- 3. Supplier's level of inventory (raw material, semifinished and finished components)
- 4. Selection of supplier's subsuppliers
- 5. Supplier's quality control procedures
Formalization (Reliability = .68)
(Seven-point scale: "strongly disagree"-"strongly agree," mean = 3.83, range = 6.0, s.d. = 1.36)
1. Our dealings with this supplier are subject to a lot of rules and procedures stating how various aspects of the relationship are to be handled.
- 2. Orders from this supplier are placed periodically according to a formalized routine.
- 3. Deliveries from this supplier are made on fixed days and times.
- 4. The interaction with this supplier involves doing things "by the rule book."
External Uncertainty (Reliability = .72)
(Seven-point scale: "predictable"-"unpredictable," mean =
- 3.86, range = 6.0, s.d. = 1.39)
1. Industry sales volume for end product
- 2. Your company's sales volume for end product
- 3. Your company's volume requirements for the components bought from this supplier
Buyer-Specific Investments (Reliability = .81)
(Seven-point scale: "strongly disagree"-"strongly agree," mean = 2.48, range = 5.50, s.d. = 1.32)
1. We have made significant investments in tooling and equipment dedicated to our relationship with this supplier.
- 2. Our production system has been tailored to meet the requirements of dealing with this supplier.
- 3. Our production system has been tailored to use the particular components bought from this supplier.
- 4. Gearing up to deal with this supplier requires highly specialized tools and equipment.
- 5. This supplier has some unusual technological norms and standards, which have required adaptation on our part.
- 6. Training and qualifying this supplier has involved substantial commitments of time and money.
Supplier-Specific Investments (Reliability = .90)
(Seven-point scale: "strongly disagree"-"strongly agree," mean = 2.85, range = 5.80, s.d. = 1.63)
1. This supplier has made significant investments in tools and equipment dedicated to the relationship with our company.
- 2. This supplier's production system has been tailored to meet the requirements of dealing with our company.
- 3. This supplier has made extensive adaptations in physical plant and equipment in order to deal effectively with our company.
- 4. The procedures and routines developed by this supplier as part of their relationship with our company are tailored to our particular situation.
- 5. Our company has some unusual technological norms and standards which have required extensive adaptation by this supplier.
Qualification (Formative Scale)
(Seven-point scale: "minimal evaluation of supplier"-"extensive evaluation of supplier," mean = 4.63, range = 6.0, s.d. = 1.56)
1. Product quality
- 2. Engineering capability
- 3. Manufacturing capability
- 4. Financial strength
- 5. Personnel/management resources
- 6. Services provided
- 7. Delivery capability
- 8. Price competitiveness
Relationship Length
(Mean = 132 months, range = 654, s.d. = 127) How long has your company been buying these or any other items from this supplier? ( ) months
Firm Size
(Mean = 49 [in $ M], range = 1499, s.d. = 149) What was your firm's total sales volume last year at this location? $ ( )
Purchase Volume
(Mean = 1.13 [in $ M], range = 19.9, s.d. = 2.88) What was the total dollar value of your company's purchases of components from this supplier last year? $ ( )
Concentration
(Mean = 19.46, range = 90, s.d. = 29.49) With regard to this supplier's total annual sales of these components, approximately what percentage is sold to your company (0%-100%). ( )% of supplier's sales accounted for by your company
Product Characteristics
(Seven-point scale: "industry standard component" -- "completely customized component," mean = 4.58, range = 6.0, s.d. = 2.14) Please indicate the degree to which the components that you purchase from this supplier are standardized.
~~~~~~~~
By Jan B. Heide
Jan B. Heide is Irwin Maier Chair in Marketing, School of Business, University of Wisconsin, Madison.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 117- Portfolios of Interfirm Agreements in Technology-Intensive Markets: Consequences for Innovation and Profitability. By: Wuyts, Stefan; Stremersch, Stefan; Dutta, Shantanu. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p88-100. 13p. 4 Charts. DOI: 10.1509/jmkg.68.2.88.27787.
- Database:
- Business Source Complete
Portfolios of Interfirm Agreements in Technology-Intensive
Markets: Consequences for Innovation and Profitability
Despite the high relevance of firms' portfolios of upstream interfirm agreements in technology-intensive markets, little is known about their impact on innovative success. The authors develop a conceptual framework that explains the consequences of different portfolio descriptors for radical innovation, incremental innovation, and profitability. An empirical test in the pharmaceutical industry shows strong support for the developed theory.
There is a rich tradition in marketing of studying diverse aspects of innovation and new product development (NPD). A broad range of prior marketing studies have identified several drivers of NPD, such as the voice of the customer (Griffin and Hauser 1993), internal knowledge development (Madhaven and Grover 1998; Moorman and Miner 1997), and organizational processes and capabilities (Moorman 1995; Moorman and Slotegraaf 1999; Tatikonda and Montoya-Weiss 2001). Marketing scholars only recently have acknowledged an important additional driver: interfirm cooperation (Rindfleisch and Moorman 2001; Sivadas and Dwyer 2000). As Wind and Mahajan (1997, p. 7) point out, firms look beyond their boundaries to access knowledge required for NPD: "Typically, NPD activities are internally focused. Yet, the increased complexity and cost of developing truly innovative products and advances in new technologies often require expertise that the firm does not have; thus, [research-and-development] strategic alliances have emerged."
Especially in technology-intensive (TI) markets, to develop new products, firms need to cooperate with other firms through flexible upstream agreements (Sivadas and Dwyer 2000). However, most recent research has concentrated on interfirm agreements in isolation, with special attention to dyadic information transfer and coordination (Sivadas and Dwyer 2000) and relational embeddedness (Rindfleisch and Moorman 2001). We build on this prior literature and develop a conceptual framework of the nature of knowledge transfer that occurs through portfolios of research-and-development (R&D) agreements rather than through individual isolated agreements. The importance of such agreement portfolios for NPD lies in their facilitating role in the access to and transfer of knowledge (Glazer 1991; Powell, Koput, and Smith-Doerr 1996). We focus on upstream R&D agreements, because these are the agreements that reportedly aid in innovation (Sivadas and Dwyer 2000; Wind and Mahajan 1997). Our focus on the entire portfolio of R&D agreements in which a firm is engaged enables us to capture descriptors that cannot be captured by studying agreements in isolation. We show that the portfolio descriptors have an important impact on a firm's innovative success.
A portfolio approach to interfirm cooperation corresponds with the importance that firms in many TI markets attach to portfolios of R&D agreements (Dutta and Weiss 1997). Industry observers conclude that firm performance in TI markets, such as the pharmaceutical industry, is strongly determined by successful management of entire portfolios of interfirm agreements (e.g., Slowinski 2001). For example, Pfizer has assembled a large portfolio of R&D agreements and claims that these efforts will have a positive impact on innovative output (Humphreys 2002). However, a recent article in McKinsey Quarterly (Bamford and Ernst 2002) reveals the difficulties that managers face when they try to assess their agreement portfolio's payoff to the firm.
We study the effect of portfolio characteristics on both radical and incremental innovation. When innovations incorporate a substantially different core technology and provide substantially greater customer benefits than previous products in the industry, we call them "radical" (Chandy and Tellis 1998); when one or both of the conditions are not met, we call them "incremental." We also study the impact of radical and incremental innovation on profitability, and we study whether the portfolio characteristics have additional direct effects on profitability. In doing so, we account for possible cost and other implications that portfolio characteristics may have on profitability, in addition to their indirect effect through innovation.( n1)
We present an empirical test in the pharmaceutical industry. The test provides strong support for the developed theory but also yields some notable unexpected insights. In what follows, we first present the conceptual framework, hypotheses, and methodology. We then discuss our findings as well as theoretical and managerial implications. We conclude by acknowledging the limitations of our study and proposing several areas for further research.
In many industries, firms form R&D agreements to access knowledge from other firms, which may aid in innovation (Baum, Calabrese, and Silverman 2000; Powell, Koput, and Smith-Doerr 1996; Wind and Mahajan 1997). As such, a firm's portfolio of agreements affects its exposure to external knowledge and its opportunities for the transfer of that knowledge, which in turn affect innovation and profitability.
We focus on two specific descriptors of the R&D agreement portfolio: the portfolio's technological diversity and the level of repeated partnering. Technological diversity refers to the extent to which the agreements in a firm's portfolio cover a diverse set of technologies and thus may facilitate the inflow of more-diverse knowledge. Repeated partnering refers to the extent to which a firm engages in different R&D agreements with the same partners and thus may enable the transfer of more-complex knowledge (i.e., facilitate the inflow of knowledge in depth). These two characteristics are important for several reasons. First, they are theoretically more interesting than a popular but crude portfolio descriptor that is often mentioned in industry reports, namely, portfolio size. Technological diversity and repeated partnering facilitate knowledge transfer along two dimensions that have received considerable attention in prior literature (Dewar and Dutton 1986; Katila and Ahuja 2002): knowledge diversity (see Cohen and Levinthal 1990; Sinkula 1994) and knowledge depth (see Badaracco 1991; Hansen 1999).
Second, experts point to the importance of the agreement portfolio descriptors in TI markets, and in the pharmaceutical industry in particular (Bamford and Ernst 2002; Baum, Calabrese, and Silverman 2000; Gomes-Casseres 1998). Third, the two characteristics are within the managers' reach with respect to both monitoring and managing the portfolio, and thus they may serve as building blocks for a portfolio strategy. Fourth, there is substantial variation in the portfolio descriptors among different firms in TI markets. For example, there is substantial variation in pharmaceutical firms' portfolios of R&D agreements in terms of both technological diversity (e.g., Becton, Dickinson has allied several times on immunoassay technology, but Syntex rarely allies twice on the same technology) and repeated partnering (e.g., Sandoz has allied several times with the biotechnology firm SyStemix, but Johnson & Johnson rarely allies twice with the same biotechnology firm).
We propose hypotheses on the effects of technological diversity and repeated partnering on (radical and incremental) innovation, after which we turn to their effects on profitability. We conclude this section with an overview of other relevant variables (e.g., portfolio size) for which we control.
Agreement Portfolios and Innovation
Technological diversity. A diverse inflow of knowledge affects innovation because it strengthens assimilative powers and enables novel associations (Cohen and Levinthal 1990). The inflow also stimulates broader perspectives and synthesis (Dewar and Dutton 1986; Fichman and Kemerer 1997). We expect that technological diversity affects both radical and incremental innovation.
We first consider radical innovation. Radical innovations are built on new (different from the established) technologies (Dewar and Dutton 1986) and often rely on the integration of different technologies (Iansiti and West 1997); thus, access to diverse knowledge bases is important. Greater technological diversity may lessen a firm's tendency to capitalize on or to be locked into its prior knowledge, and it may stimulate the firm to experiment with new technologies (Chandy and Tellis 1998). Especially in TI markets, which are characterized by rapid technological change, it is imperative for firms to keep abreast of the latest technological developments (Iansiti and West 1997). In this sense, a technologically diverse agreement portfolio facilitates access to new and nonredundant knowledge bases, which will aid in tracking new scientific discoveries and advances. Firms that access highly redundant knowledge bases are less open to and may even be unaware of other new promising technologies (Rowley, Behrens, and Krackhardt 2000). Their restrictive focus on a limited set of technologies makes it increasingly difficult to detect and engage in new promising technologies (Leonard-Barton 1992; Levinthal and March 1993), which may significantly hamper radical innovation in markets that are characterized by rapid technological change (Tushman and Anderson 1986).
In summary, we expect that technological diversity enhances radical innovation. It could be argued that a potential drawback of technological diversity is that it may impede a clear focus and complicate the development of specialist competence, which may constrain innovation. However, we expect that in TI markets, the positive effects of technological diversity dominate.
As for incremental innovation, the mere quantity of incoming information may be more relevant than its novelty. Firms can also arrive at incremental innovations without accessing novel information and without integrating different technologies. A diverse background provides a more robust basis for learning in TI markets (Iansiti and West 1997), because incoming information more likely is associated with what is already known. A more diverse technological background thus provides the firm with the ability to react to more new opportunities for innovation based on external knowledge (Cohen and Levinthal 1990; Henderson and Cockburn 1994). Given that this rationale relies on the number rather than the novelty of opportunities, we also expect that technological diversity enhances incremental innovation. In summary:
H<sub>1</sub>: Greater technological diversity of a firm's portfolio of interfirm R&D agreements enhances the firm's (a) radical innovation and (b) incremental innovation.
Level of repeated partnering. A general benefit of repeated agreements with the same partners is that the focal firm comes to know its partners better, which may enhance its ability to assess its partners' capabilities and consequently identify new opportunities for cooperation. As such, frequent cooperation with the same partner can generate a unique source of information about potential new opportunities (Gulati 1999). We expect that the benefit of repeated partnering leading to better identification of new opportunities enhances both incremental and radical innovation.
Repeated partnering also generates an advantage that is specifically related to radical innovations. Radical innovations encompass major improvements over existing products and therefore benefit from complex (i.e., tacit and interdependent) knowledge transfer (Iansiti and West 1997; Zucker, Darby, and Armstrong 2002). The average scientific discovery is not codified, which illustrates the significance of the tacit component of knowledge in TI markets (Zucker, Darby, and Armstrong 2002). Frequent and repeated interaction facilitates the transfer of tacit knowledge (Hansen 1999) and generates a deeper understanding of new technologies and innovations (Dewar and Dutton 1986; Fichman and Kemerer 1997). Repeated interaction allows for the emergence of relationship-specific heuristics (Uzzi 1997) and induces shared mental models (Madhaven and Grover 1998).( n2) These heuristics and shared mental models in turn facilitate the process of assimilating complex knowledge (Polanyi 1966). The effective assimilation of complex knowledge in turn facilitates radical innovation. In short, we expect the following:
H<sub>2</sub>: Higher levels of repeated partnering of a firm's portfolio of interfirm R&D agreements enhance the firm's (a) radical innovation and (b) incremental innovation.
Note that in line with this reasoning, support for H<sub>2a</sub> and H<sub>2b</sub> would indicate that repeated partnering effectively drives the identification of new opportunities, but support for only H<sub>2a</sub> would indicate that repeated partnering primarily facilitates the transfer of tacit knowledge.
Agreement Portfolios and Profitability
We expect not only that the portfolio characteristics, through their impact on knowledge access and transfer, affect radical and incremental innovation but also that they have additional direct effects on profitability. We distinguish between demand-and supply-side effects of agreement portfolio characteristics on profitability.
First, agreement portfolios affect the demand side of profitability through the stock of radical and incremental innovations they generate. Innovations are often credited for generating sales growth and thereby aiding profitability. In addition, it can be expected that radical innovations are more profitable than incremental innovations, because they represent significant advances in customer benefits, among other reasons. Second, agreement portfolios affect the supply side of profitability. As we argue subsequently, the technological diversity and level of repeated partnering of a firm's R&D agreement portfolio influence the costs of partnering as well as the firm's ability to extract rent from the agreements.
Demand Side: Stocks of Radical and Incremental Innovations and Profitability
Over time, firms build stocks of radical and incremental innovations. Higher levels of innovation enhance a firm's profitability (Geroski, Machin, and Van Reenen 1993). However, it is not clear whether this is true to the same extent for a firm's stock of radical innovations and its stock of incremental innovations. The general belief in marketing is that radical innovations disproportionately contribute to profitability (Wind and Mahajan 1997). The underlying rationale follows directly from the definition of radical innovations. First, radical innovations offer significant improvements over existing alternatives in terms of need satisfaction and thus may trigger higher demand. Second, radical innovations are based on new and complex technologies and are thus more difficult for competitors to imitate (Dutta, Narasimhan, and Rajiv 1999). We hypothesize the following:
H<sub>3</sub>: A firm's stock of radical innovations and its stock of incremental innovations enhance profitability.
H<sub>4</sub>: The effect of a firm's stock of radical innovations on profitability is greater than the effect of a firm's stock of incremental innovations on profitability.
Supply Side: Agreement Portfolio Composition and Profitability
We now turn to the supply-side effects of the portfolio characteristics on profitability in addition to their indirect demand-side effect through (stocks of) radical and incremental innovations. These effects are grounded in cost and rent-extraction rationales.
Technological diversity. Higher levels of technological diversity require higher costs. The cost of building a minimum level of knowledge (unit-one cost) is typically very high (John, Weiss, and Dutta 1999). Therefore, firms that develop a broad technological background typically face higher costs (Gatignon and Xuereb 1997). For example, in the pharmaceutical industry, to a large extent, strategic decision making is determined by the high costs required to acquire new technologies, as is illustrated by Guidant's difficulties in deciding whether to engage in radiation therapy for the treatment and prevention of restenosis (Roberts 2001). Not only was there a great deal of uncertainty about the effectiveness of radiation therapy, but an initial investment cost was estimated at anywhere between $60 million and $100 million. Firms' building a portfolio of R&D agreements that covers a large diversity of technologies may considerably enhance the total investment costs. In contrast, making further advances in technology classes in which the firm is already active requires fewer additional investments than advances in technology classes that are new to the firm. Thus, concentration of the agreement portfolio around fewer technologies may be more cost efficient than diversification of the agreement portfolio over a wide set of technologies.
H<sub>5</sub>: When the level of innovation is controlled for, greater technological diversity of a firm's portfolio of interfirm R&D agreements lowers the firm's profitability.
Level of repeated partnering. The literature offers different rationales for the direct impact of repeated partnering on profitability in addition to its indirect impact through innovation. Repeated partnering may contribute to cost efficiency. Cooperation with the same partner is cheaper than cooperation with a new partner. In the context of industrial purchasing relationships, Stump and Heide (1996) find that partnering with the same partner is cost-efficient because previous qualification efforts reduce the need for new qualification and monitoring practices. In other words, firms are able to examine their prior partners' capabilities (Håkansson 1993). In this sense, a major risk factor to agreements (i.e., the extent to which the partner is capable of doing what it claims to be able to do) is minimized, which may represent a substantial saving of time and money lost in contracting with the wrong partner. However, this positive relationship is unlikely to be linear. Prior research has shown that firms' cooperating too frequently with the same partners may result in more attention for relationship maintenance and loyalty than for the economic outcomes of cooperation. In other words, firms' cooperating too frequently with the same partners can stifle effective economic action if social aspects supersede economic imperatives (Uzzi 1997). As a result, levels of repeated partnering that are too high can cause a decline in profitability.( n3) We posit the following:
H<sub>6</sub>: When the level of innovation is controlled for, the level of repeated partnering of a firm's portfolio of interfirm R&D agreements has an inverted U-shaped effect on the firm's profitability.
Other Variables
In addition to the relationships posited previously, we control for other variables that may affect radical innovation, incremental innovation, and profitability but that are outside our theoretical focus.
Portfolio size. Portfolio size refers to the number of R&D agreements that make up a portfolio and, in general, is considered to facilitate obtaining more exposure to knowledge bases (see, e.g., Dewar and Dutton 1986). Previous studies have documented the positive impact of portfolio size on innovation (Powell, Koput, and Smith-Doerr 1996; Shan, Walker, and Kogut 1994). Large portfolios lead to scale effects in development (Ahuja 2000) and facilitate firms gaining more exposure to knowledge from external sources (Dewar and Dutton 1986). However, portfolio size's effect on radical innovation is not clear. As for profitability, firms' greater experience with interfirm agreements has been associated with positive firm outcomes (Powell, Koput, and Smith-Doerr 1996). The large number of agreements provides the firm with a broad repertoire of experiences that result from previous trials and tribulations (Anand and Khanna 2000). The resulting experience effects not only enhance cost efficiency of cooperation but also make firms better able to extract rent from their agreements (Gulati, Nohria, and Zaheer 2000), which both contribute to profitability.
Resident knowledge. A firm's portfolio of R&D agreements provides insight into its access to external knowledge bases and subsequently into its ability to generate innovations. However, in the process of turning knowledge into actual innovative products, other variables come into play. Firms should be able not only to detect and absorb relevant new technologies and new knowledge but also to apply this knowledge effectively (as formalized in the absorptive capacity argument; Cohen and Levinthal 1990). We expect that a firm's resident knowledge has a positive effect on both innovation and profitability, because it is likely to aid in all processes of detection, absorption, and application.
Experience. In the radical and incremental innovation equations, we also control for a firm's prior experience in developing radical and incremental products, respectively. Prior experience reflects the processes that the firm has in place to innovate successfully. Firms with internal organizational processes that have facilitated radical and incremental innovation in the past are more likely to generate new radical and incremental innovations in the future.
R&D expenditures. Another variable that may influence innovation and profitability is the level of a firm's R&D expenditures. We expect that firms that devote more resources to R&D are more successful with respect to innovation and profitability.
Sales expenditures. In the profitability equation, we further control for sales expenses. In the pharmaceutical industry, the setting of our empirical study, direct selling through medical representatives is by far the most influential marketing instrument (Parsons and Vanden Abeele 1981); there were more than 80,000 sales representatives in the field in 2001 (Shalo 2002). We expect that sales expenditures have a positive effect on profitability.
Trend and industry shocks. Previous studies suggest that the growth of the biotechnology industry has led to more intense competition (Zucker, Darby, and Brewer 1994). We expect that this increasing competitive intensity will be reflected in a negative time trend in the innovation and profitability equations. Apart from a linear industry trend over the entire observation period, there may have been other events that occurred in specific years that affected the outcome variables. We include year dummies to control for such exogenous shocks, and we retain the ones that have a significant impact on the outcome variables in the final model.
Firm size. Much prior research in economics has addressed the relationship between firm size and innovation, building on the seminal work of Schumpeter (1942). Academic research investigating this relationship has found positive, negative, and insignificant size effects (Chandy and Tellis 2000; Cohen and Levin 1989). As for profitability, we expect larger firms to make more profits, in an absolute sense, merely because of a scale effect.
Empirical Setting
The empirical setting of our study is the pharmaceutical industry. In particular, we examine the effect of a pharmaceutical firm's portfolio of agreements with biotechnology firms on innovation and profitability. The discoveries of recombinant DNA (by Cohen and Boyer in 1973) and cell fusion (by Kohler and Milstein in 1975) gave rise to the modern biotechnology industry. Pharmaceutical firms reacted to the biotechnology revolution by building portfolios of upstream R&D agreements with biotechnology firms to access new scientific and technological developments (see, e.g., Pisano 1990).
There are several reasons we chose this context. First, the pharmaceutical industry is a TI industry in which scientific knowledge plays a focal role. Second, interfirm cooperation in the pharmaceutical industry boomed with the rise of biotechnology, especially since the second half of the 1980s. From 1985 on, interfirm agreements with established pharmaceutical firms have overtaken venture capital as the main form of financing the biotechnology industry (Zucker, Darby, and Brewer 1994). At the end of the 1990s, R&D agreements between pharmaceutical firms and innovative biotechnology firms provided eight times more capital to U.S. biotechnology firms than did initial public offerings (Enriquez 1998). As we described previously, pharmaceutical firms developed portfolios of R&D agreements with substantial variation in their composition. Third, secondary data are available on all interfirm agreements between pharmaceutical firms and biotechnology firms in the United States since 1985 (i.e., from the inception of alliance activity in the biotechnology industry).
Data Collection
We collected data to test our theoretical predictions from four different sources. First, we collected data on pharmaceutical firms' upstream R&D agreements with biotechnology firms from the Recombinant Capital database. This database covers all such upstream R&D agreements from 1985 until the present. It provides information on the identity of the parties to the agreement, the nature of the agreement, and the technologies that the agreement covers (categorized into 42 technological classes). Recombinant Capital is a consulting firm that specializes in biotechnology alliances; it is based in the San Francisco Bay Area and was founded by a former manager of business development at Chiron. Recombinant Capital's clients include more than 150 biotechnology and pharmaceutical firms, as well as several universities and investment banking and venture firms active in the biotechnology area. Recombinant Capital uses several sources to ensure the accuracy of its database: trade literature, press releases, and its close links and interactions with experts involved in biotechnology in the pharmaceutical industry.
Second, we collected data on new drugs from the drug approval list of the Food and Drug Administration (FDA). This list provides all drugs approved by the FDA and is updated weekly. Moreover, in this list, the FDA provides additional useful information about each drug, namely, its therapeutic potential and chemical type. We use this additional information to distinguish radical drugs from incremental drugs.
Third, we collected data on profitability, firm size, sales expenses, and R&D expenses from the Compustat database. Fourth, we collected data on biotechnology patents and patent citations from the U.S. Patent and Trademark Office database.
The database we compiled from the four sources contains yearly data on the agreement portfolios of 58 publicly traded pharmaceutical firms from 1985 to 1998. In total, our database covers 991 R&D agreements. For each year (1985-1998) and pharmaceutical firm, the database also contains information on profits, size, sales expenses, R&D expenses, and citation-weighted patents. We used data on new drugs from 1991 to 1999. Before 1991, the FDA did not provide the detailed and complete drug information required for our study (for sample descriptives, see Table 1).
Measurement
Dependent variables. We measured radical innovation of firm i in year t (RADINNOV<sub>it</sub>) as the total number of new radical drugs of firm i that received FDA approval in year t. Given that radical drugs should both provide substantially higher customer benefits than previous drugs in the industry and incorporate a substantially different core technology (or active ingredient) (Chandy and Tellis 1998, 2000), we base our radicalness distinction on two drug properties provided by the FDA: a drug's therapeutic potential and its chemical type. First, the FDA (2002) categorizes all new drugs according to their treatment potential and distinguishes between standard ("therapeutic qualities similar to those of an already marketed drug") and high-potential ("an advance over available therapy") drugs. Second, the FDA assigns a chemical type to each drug. Only drugs of Chemical Type 1 represent a new technology (i.e., different from the established technologies); they involve an "active ingredient that has never been marketed" (see FDA 2002). We refer to all drugs that are labeled both high-therapeutic-potential drugs and Chemical Type 1 drugs as radical drugs. In total, 13.7% of the newly approved drugs in our database are labeled radical drugs, which compares favorably with cross-industry estimates (i.e., approximately 10%; see Wind and Mahajan 1997) and with a recent study in the pharmaceutical industry by the National Institute for Health Care Management (15%; Wechsler 2002).
We measured incremental innovation of firm i in year t (INCINNOV<sub>it</sub>) as the total number of new incremental drugs of firm i that received FDA approval in year t. All drugs that do not satisfy both radicalness conditions (high therapeutic potential and Chemical Type 1) are labeled incremental drugs.
We measured profitability of firm i in year t (PROFIT<sub>it</sub>) as the net income of firm i in year t. Profitability is the net income (loss) variable provided by Compustat.
Independent variables. We measured the technological diversity of a firm's agreement portfolio (TECHDIV<sup>cum</sup> <sub>it</sub>)( n4) as follows (see Powell, Koput, and Smith-Doerr 1996): For firm i up to year t, we denote the number of times that the firm's agreements cover technology j as n<sub>it,j</sub> (j = 1 ... 42).( n5) Then, p<sub>it,j</sub> = n<sub>it,j</sub>/Σ<sub>j</sub>n<sub>it,j</sub> represents the proportion of occurrence of technology j relative to the cumulative occurrence of all technologies in firm i's portfolio. We square each p<sub>it,j</sub> and then take the sum over all technologies j; we subtract the sum from 1, which results in the index of technological diversity:
( 1) [Multiple line equation(s) cannot be represented in ASCII text]
The technological diversity index equals zero when a firm allies on only a single technology, and it is close to one when a firm spreads its alliance activity over many technologies. An example further clarifies how this measure behaves: Suppose that two firms (A and B) both have a portfolio of four agreements. The agreements of Firm A involve three different technologies (Agreements 1 and 2 involve technology x; Agreement 3 involves technology y; Agreement 4 involves technology z), and the agreements of Firm B involve two different technologies (Agreements 1 and 2 involve technology x; Agreements 3 and 4 involve technology y). It is easily computed that Firm A has a technological diversity of .625, and Firm B has a technological diversity of .5. In a sense, this measure is similar to Hirschman-Herfindahl indexes in the economics literature (which are typically used to measure market concentration as the sum of squared market shares).
Repeated partnering (REP<sup>cum</sup>,<sub>it</sub>) is a ratio that measures the extent to which firms cooperate with the same partners in a given period of time.( n6) For firm i up to year t, we denote the number of different partners in its agreement portfolio as Pit<sup>cum</sup><sup>it</sup> and the number of agreements as A<sup>cum</sup><sub>it</sub> We then define repeated partnering of firm i up to year t as
( 2) REP<sup>cum</sup><sub>it</sub> = (P<sup>cum</sup><sub>it</sub>/A<sup>cum</sup><sub>it</sub>
The index of repeated partnering equals zero when a firm never cooperates twice with the same partner, and it is close to one when the firm cooperates frequently with the same partner. In the stylized example of Equation 1, if Firm A cooperates with three different partners and Firm B with two different partners, Firm A's level of repeated partnering is .75, and Firm B's level of repeated partnering is .5.
Finally, we measured a firm's stock of incremental innovations (INCSTOCK<sup>cum</sup><sub>it</sub>) as the cumulative number of incremental innovations (INCINNOV<sup>cum</sup><sub>it</sub>) from 1991 until year t. Similarly, we measured a firm's stock of radical innovations (RADSTOCK<sup>cum</sup><sub>it</sub>) as the cumulative number of radical innovations (RADINNOV<sup>cum</sup><sub>it</sub>) from 1991 until year t.
Control variables. We measured portfolio size of firm i in year t (PORFSIZE<sup>cum</sup><sub>it</sub>) as the total number of R&D agreements, A<sup>cum</sup><sub>it</sub> of firm i from 1985 up to year t. We measured the amount of resident knowledge as the citation-weighted biotechnology patent counts, corrected for truncation bias (Dutta and Weiss 1997; Griliches 1990; Trajtenberg 1990). The U.S. Patent and Trademark Office provides detailed information on all biotechnology-related patents (at year t), including the number of times the patents have been cited in a given year (t + 1, t + 2, ..., T). We included all patents from 1975 (the year in which the citations were registered first) and on, and we constructed a cumulative variable PATENT<sup>cum</sup><sub>it</sub>. We corrected the measure for truncation as follows (see also Hall, Jaffe, and Trajtenberg 2001): For early patents at Time t<sub>0</sub>, we calculated the average citation pattern (the number of times the patents are cited) over the period [t<sub>0</sub> + 1; T]. More specifically, for each year t ∈ [t<sub>0</sub> + 1; T], we derived the proportion of all citations for the patents that occurred in the period [t<sub>0</sub> + 1; t]. For more recent patents (e.g., in year 1996), we had citation data for only a limited number of years. For these recent patents, we calculated the expected number of citations in the future by extrapolation, on the basis of the total number of citations that already occurred in the first years and assuming that the citation pattern is similar to that for early patents.
As proxies for a firm's experience with radical and incremental innovation, we include RADSTOCK<sup>cum</sup><sub>it - 1</sub> and INCSTOCK<sup>cum</sup><sub>it - 1</sub> in the respective innovation equations. These lagged variables reflect the processes that are set by the firm to generate innovations.
Furthermore, the Compustat database contains measures on sales expenses (SALESEXP<sub>it</sub>), R&D expenses (R&D<sub>it</sub>), and firm size (FIRMSIZE<sub>it</sub>). Note that sales expenses are represented in Compustat as sales, general, and administrative expenses. We measure firm size as the number of employees.
Model Estimation
Radical and incremental innovation models. We measured radical innovation of firm i at time t as the total number of radical drugs of firm i approved by the FDA at time t (RADINNOV<sub>it</sub>). We estimated a negative binomial maximum-likelihood regression model, which is an appropriate specification in view of the count character of the dependent variable and the relatively large number of zeros (which are a natural and relevant outcome of the count process). In our case, a (simpler) Poisson specification was not appropriate because of overdispersion.( n7) The underlying assumption of the Poisson model of equality of conditional mean and variance functions is violated, which leads to inefficient Poisson estimates.
As explanatory variables, we included TECHDIV<sup>cum</sup><sub>it - 1</sub> REP<sup>cum</sup><sub>it - 1</sub>. We measured all portfolio descriptors (technological diversity, repeated partnering, and size) at time t over the cumulative portfolio up to year t (Dutta and Weiss 1997). There is likely a lag between firms' partnering activities and the resulting innovative output. Although the cumulative variables partly account for this, we lagged all our portfolio characteristics with one period. We also lagged the control variables with one period. We control for PORFSIZE<sup>cum</sup><sub>it - 1</sub> RADSTOCK<sup>cum</sup><sub>it - 1</sub> PATENT<sup>cum</sup><sub>it - 1</sub>, TREND, year dummy variables, R&D<sup>cum</sup><sub>it - 1</sub>, FIRMSIZE<sup>cum</sup><sub>it - 1</sub>.
We measured incremental innovation of firm i at time t as the total number of incremental drugs of firm i approved by the FDA at time t (INCINNOV<sub>it</sub>). As in the radical innovation model, we estimated a negative binomial model for the incremental innovation equation, with TECHDIV<sup>cum</sup><sub>it - 1</sub> and REP<sup>cum</sup><sub>it - 1</sub> as explanatory variables. We controlled for PORFSIZE<sup>cum</sup><sub>it - 1</sub>, INCSTOCK<sup>cum</sup><sub>it - 1</sub> PATENT<sup>cum</sup><sub>it - 1</sub>, TREND, year dummy variables, R&D<sub>it - 1</sub>, and FIRMSIZE<sub>it - 1</sub>. Table 2 presents a correlation matrix of our portfolio descriptors.
Profitability model. We measured profitability of firm i at time t as the net income of firm i at time t (PROFIT<sub>it</sub>). Because PROFIT<sub>it</sub> is a continuous variable, we used an ordinary least squares regression specification in which we regressed PROFIT<sub>it</sub> on the variables RADSTOCK<sup>cum</sup><sub>it - 1</sub>, INCSTOCK<sup>cum</sup><sub>it - 1</sub>, TECHDIV<sup>cum</sup><sub>it - 1</sub>, REP<sup>cum</sup><sub>it - 1</sub>, PORFSIZE<sup>cum</sup><sub>it - 1</sub>, PATENT<sup>cum</sup><sub>it - 1</sub>, TREND<sup>cum</sup><sub>it - 1</sub>, year dummy variables, R&D<sub>it -1</sub>, SALESEXP<sub>it-1</sub>, and FIRMSIZE<sub>it-1</sub>. In all equations, we mean-centered the independent variables. In line with intuition, we again lagged all independent variables, except for the stock of incremental and radical innovations, because new drugs already affect profitability in the introduction period.
Results
Table 3 presents the estimation results for the radical and incremental innovation equations. Table 4 presents the estimation results for the profitability equation.
Radical and incremental innovation. Technological diversity positively influences both radical innovation (Β = 1.535; p < .001) and incremental innovation (Β = .426; p < .05), in support of H1. A more diverse portfolio strengthens a firm's basis for learning and enhances its absorptive capacity (Cohen and Levinthal 1990), thereby enabling it not to miss the most recent technological developments. As such, a technologically diverse portfolio enhances the firm's number of NPD opportunities and lowers the risk of lock-in with inferior technologies (Levinthal and March 1993).
We find that whereas repeated partnering enhances radical innovation (Β = .680; p = .001), in support of H<sub>2a</sub>, its effect on incremental innovation is not significant (Β = -.013; p = .927), which rejects H<sub>2b</sub>. This finding seems to indicate that repeated partnering serves as more of a facilitator for complex knowledge transfer than an aid for opening up new opportunities.
We also included several control variables in the innovation equations. First, portfolio size has a significant, positive effect on incremental innovation (Β = .252; p < .05) but does not affect radical innovation (Β = .173; p = .350). Apparently, despite the central place of the mere size of the portfolio in industry discourse, it provides access to more opportunities, but it does not provide the depth or diversity of knowledge that stimulates radical innovation. Second, we included the lagged stocks of innovations as indicators of the firm's prior experience with radical and incremental innovation. We find that both have the expected positive sign, but only the prior stock of incremental innovations is significant (Β = .380; p < .01). The stock of prior radical innovations is not significant (Β = .231; p = .181). A large stock of prior incremental innovations seems to aid in the development of new incremental innovations, whereas a track record of radical innovation is no guarantee for future success in radical innovation. Third, we controlled for resident knowledge using a citation-weighted patent variable. Surprisingly, this variable is not significant in both equations (radical: Β = .019; p = .891; incremental: Β = -.093; p = .269). Further exploration reveals a quadratic effect of patents on incremental innovation. More specifically, we find an inverted U-shaped effect (main term: Β = .439; p < .05; quadratic term: Β = -.184; p < .01). The role of patents requires further research. Fourth, we find a negative time trend in both the radical innovation (Β = -1.736; p = .001) and the incremental innovation (Β = -1.024; p < .001) equations. Furthermore, we find one year dummy variable (1996) to be significant; we retained this variable in both the radical innovation (Β = 1.189; p < .001) and the incremental innovation (Β = .646; p < .01) equations. Although the following is only a post hoc interpretation, the 1996 effect may result from the U.S. administration urging the FDA in early 1996 to speed up its approval procedures in major therapeutic classes (as reported on the FDA News Web site; Cruzan 1996). Finally, we find that the effect of R&D expenses is positive and significant in both innovation equations (radical: Β = .491; p < .10; incremental: Β = .313; p < .05). However, the effect of firm size is not significant in any of the two innovation equations (radical: Β = -.268; p = .317; incremental: Β = .080; p = .536).
Profitability. As for the profitability equation, we posited in H3 that both a firm's stock of radical innovations and its stock of incremental innovations enhance profitability. We find only partial support for this, with a positive effect for the stock of radical innovations (Β = 123.379; p = .001) and no significant effect for the stock of incremental innovations (Β = 2.145; p = .962).( n8) In H4, we hypothesized that the stock of radical innovations would have a greater positive effect on profitability than the stock of incremental innovations. A Wald test rejected the null hypothesis that the parameters are equal in size (F = 3.168; p < .10), so we can conclude that the effect of radical innovation indeed is greater than that of incremental innovation. We also conducted additional likelihood ratio tests that consistently point to the same conclusion.( n9)
In accordance with H<sub>5</sub>, we find a negative, direct effect of technological diversity on profitability (Β = -73.400; p < .05). This negative effect indicates that firms have difficulties recouping the high initial investment costs required for a technologically diverse portfolio.
We posited in H<sub>6</sub> that repeated partnering has an additional inverted U-shaped effect on profitability. We find strongly significant main and quadratic effects in support of H<sub>6</sub> (main term: Β = 266.073; p < .001; quadratic term: Β = -141.050; p < .001). Low levels of repeated partnering require substantial partner qualification costs, whereas high levels of repeated partnering restrict economically optimal behavior. When innovative output is controlled for, the optimum lies at medium levels of repeated partnering.
Finally, we included several control variables. First, we find a positive effect of portfolio size on profitability (Β = 300.231; p < .001), in support of the argument that firms with larger portfolios enjoy experience effects that result in cost efficiency and better rent extraction. Second, the amount of resident knowledge has a (weak) positive effect on profitability (Β= 35.084; p = .105). Third, as in the innovation equations, we find a negative time trend (Β = -171.252; p < .001). However, none of the year dummies were significant, which further strengthens our interpretation that the 1996 effect in the innovation equations is related to a temporary extra effort by the FDA. For the other control variables R&D expenditures, sales expenditures, and firm size we respectively find no effect (Β = 44.759; p = .615), a positive effect (Β = 1085.487; p < .001), and a negative effect (Β = -453.765; p < .001).
Robustness of Results
Time lags. In our model estimation, we lagged all explanatory variables, except for innovation stocks in the profit equation, with one year. We examined the sensitivity of our results by applying different lag structures (e.g., two years, three years); the focal results remain unchanged. Note that working with lagged cumulative independent variables further supports our notion of causality, in that a dependent variable at time T is explained by the entire portfolio from t = 0 up to t = T - 1.
Knowledge depreciation and appreciation. Another important issue is whether the value of knowledge changes over time. There are three possibilities: no change, depreciation, or appreciation. Although our analyses assumed that knowledge has a constant value, we also checked the robustness for changes in its value over time. On the one hand, a certain depreciation rate could be specified, which would enable knowledge to become worth less over time, which may be especially relevant in TI markets (Glazer and Weiss 1993). Prior literature typically uses a 20% depreciation rate (e.g., Henderson and Cockburn 1994). On the other hand, it could be argued that complex knowledge is not readily available for use immediately after assimilation and that knowledge becomes worth more as it becomes more embedded in the organization (e.g., Madhaven and Grover 1998). Such reasoning would suggest an appreciation rate rather than a depreciation rate. We estimated our model with depreciation/appreciation rates ranging from .8 to 1.2, and we found our results to be robust for knowledge depreciation and appreciation. Thus, our assumption that knowledge has a constant value does not affect our results.
Alternative model specifications. Finally, we tested alternative model specifications. We specified an ordered probit structure rather than the negative binomial for the innovation models. We also estimated nested models and models containing interaction effects to verify the robustness of our findings. None of the exploratory efforts provided additional insights, and the posited theoretical effects were unaffected and remained similar to the ones we reported.
Our study has several implications for both theory development and practice. We discuss two major theoretical implications and two major managerial implications, respectively.
Theoretical Implications
Our detailed portfolio perspective contributes to both the marketing and the network literature. First, the marketing literature on innovation and NPD may benefit from our study in different ways. By taking a portfolio perspective, our study empirically substantiates a belief shared by many marketing scholars (Achrol 1997; Kotler, Jain, and Maesincee 2002), namely, that such a broadened perspective would significantly enhance the understanding of marketing phenomena in dynamic markets. Despite the shared understanding in conceptual work, empirical studies have been scarce (Stern 1996). Our study points to the importance of considering R&D agreements in TI markets not in isolation but from a portfolio perspective, which provides insight into a firm's ability to access diverse and complex knowledge bases. Thus, this study enriches prior work in marketing on the drivers of innovation. Although several studies have pointed to the importance of R&D capability for success in TI markets (Dutta, Narasimhan, and Rajiv 1999) and for product development broadly (e.g., Moorman and Slotegraaf 1999), the focus has been on internal processes and knowledge domains. Our findings suggest that access to external knowledge domains can also have an important bearing on a firm's ability to develop new products. Prior work has also acknowledged interfirm knowledge sharing as an important driver of innovation (Rindfleisch and Moorman 2001; Sivadas and Dwyer 2000). Our portfolio perspective extends this idea and shows that a holistic view that transcends the individual agreement is required to assess the success of a firm's overall efforts to share knowledge with industry partners.
Second, our study also contributes to the network literature. Recent network studies suggest that repeated and intense cooperation enhances the risk of lock-in with inferior technologies and myopia caused by higher knowledge redundancy (e.g., Rowley, Behrens, and Krackhardt 2000; Uzzi 1997). Our study shows that this rationale may be misleading in TI markets for two reasons. First, contrary to the seminal work in sociology on which this argument is based (Granovetter 1973), the knowledge that is transferred in TI markets does not consist of simple bits of information but has an important tacit component. Frequent cooperation with the same partners facilitates the transfer of tacit knowledge. Second, rather than consider repeated collaboration as a proxy for knowledge redundancy, we show how knowledge diversity can be accounted for more directly. The diversity of technologies that underlie the different agreements is a more direct approximation of the extent to which a firm is able to access nonredundant knowledge. Thus, we were not surprised to find that for given levels of technological diversity, repeated partnering actually enhances radical innovation. On a related note, our findings suggest that in TI markets, both the benefits of nonredundant knowledge and its downside should be considered. Access to diverse or nonredundant knowledge requires high investment costs, and firms often have a difficult time recouping the initial investments. Our findings can help explain why other studies did not find a hypothesized negative relationship between knowledge redundancy and firm performance (see, e.g., Rowley, Behrens, and Krackhardt 2000).
Managerial Implications
Our study reveals how a firm's portfolio of agreements can be managed in accordance with different firm objectives. Our findings also further underscore the importance of radical innovation for profitability.
First, on the basis of our findings, we can provide managers with guidelines as to how to build an effective portfolio according to their specific objectives. We offer a set of useful portfolio descriptors that can be measured and managed when decision makers are prepared to look beyond the individual agreement. Whereas the industry literature over-addresses portfolio size, we provide a richer perspective and point to the importance of portfolio diversity and repeated partnering. Moreover, we acknowledge that firms may have different or multiple objectives (radical innovation, incremental innovation, and profitability), which may bring forth different challenges. As such, we recommend that firms that have the end objective of radical innovation invest in a technologically diverse portfolio to gain access to a diverse knowledge base in which it repeatedly contracts with the same partners to facilitate complex knowledge transfer. Companies that focus on the bottom line (profitability) should balance the demand-side advantages of radical innovations with the supply-side drawbacks of technological diversity and repeated partnering. It is important to note that firms can easily monitor and manage the portfolio descriptors we suggest.
Second, our study empirically underscores the importance of radical innovation and emphasizes the need to develop an appropriate R&D agreement portfolio for radical innovation. Firms should improve the balance between incremental and breakthrough innovation (Wind and Mahajan 1997), but they also may need to turn radical innovation into the core objective of their innovation strategies if their end goal is maximizing profits. Notably, we find that whereas prior experience with incremental innovations entails new incremental innovations, prior experience with radical innovations does not guarantee new radical innovations in the future.
As a first limitation, we note that our sample includes mainly large firms that are publicly traded. Although the sample is a good representation of the industry being studied, it may limit the generalizability of our results. We also focus on only one industry. An interesting area for further research would be to compare industries and test the generalizability of the effects of different portfolio descriptors on performance.
Although we take into account the identity and knowledge domains of a focal firm's partner firms, there may be several other partner characteristics (e.g., the extent to which firms perceive the pharmaceutical firm's other partners as their competitors) that affect the actual transfer of knowledge. Ideally, further research would collect firm-specific data on each of a firm's partner firms. However, we foresee that this may be a challenging undertaking. Many partner firms may not be publicly traded, thereby restricting the available information.
We studied only a firm's portfolio of upstream R&D agreements. Further research might examine a firm's downstream marketing agreements as well and their impact on profitability. We also assumed that all agreements are of similar strength, which may have been a reasonable assumption given our focus on one specific type of cooperation (R&D agreements) but that may be difficult to defend in other empirical settings in which joint ventures and mergers play a more important role.
As for profitability, we do not distinguish between short-and long-term effects on profits. Although we consider this distinction beyond the scope of the current study, future studies might offer the theoretical basis and the appropriate data to disentangle the effects. In addition, our stocks approach to understanding the impact of a firm's innovativeness on profitability can be challenged. This approach somehow conflicts with NPD literature that examines flows of innovations rather than stocks. Future studies that focus on the role of portfolios of interfirm agreements on companies' NPD processes (e.g., the ongoing stream of development projects) would be fruitful.
In addition, we do not provide any information on processes that occur inside the firm. Rather, we control for general proxies such as R&D expenditures, prior innovation experience, and patents. Our theory implies that new drugs result, at least in part, from collaboration efforts. Thus, we do not assess the extent to which new drugs result from purely internal development processes rather than external collaboration. Although internal development processes are affected by resident knowledge, which is in turn gained (at least in part) through collaboration, we do not allow for such an effect explicitly. Further research should focus on the complex relationships between internal development processes and external collaboration.
Finally, by definition, our dependent innovation variables only reflect successful NPD efforts. It may be useful for further research to study the role of agreement portfolios in situations of NPD failure as well. In addition, we believe that our finding that incremental innovations have no significant effect on profitability is somewhat surprising. The role of incremental innovations in conjunction with radical innovations is another interesting issue for further research. It could be argued that firms face a trade-off between radical and incremental innovation that resembles the trade-off between exploration and exploitation discussed in the organizational behavior literature (e.g., March 1991). Garcia, Calantone, and Levine (2003) show that contingent on the level of competition and the profitability of a firm's NPD activities, the exploitation of existing knowledge bases through refinement and recombination might be more advisable than exploration of new knowledge bases in the short run. Translated to our setting, the generation of incremental innovations that represent refinements of prior successful radical innovations may sometimes be an effective shortterm policy. Follow-up studies that address this radical/ incremental balance would also benefit from a better discrimination between research activities (exploration) and development activities (exploitation) (Garcia and Calantone 2003; Garcia, Calantone, and Levine 2003), a distinction that was difficult to draw in our empirical setting.
To conclude, although our study is subject to several limitations, we believe that the phenomenon of agreement portfolios and the managerial question of how to organize the portfolios according to the firm's strategic objectives form an important yet understudied research area. Our findings indicate that a portfolio perspective contributes to the understanding of innovation in TI markets.
This article is based on the first author's dissertation work conducted at Erasmus University Rotterdam.
The authors benefited from the comments of Rebecca Henderson, Prokriti Mukherji, Om Narasimhan, Gerard Tellis, and audience members at the 2002 INFORMS Marketing Science Conference. The authors thank Corine Boon for her help with data coding and gratefully acknowledge the financial support of the Institute for the Study of Business Markets (Pennsylvania State University), the Goldschmeding Center for the Economics of Increasing Returns (Nyenrode University), and the Netherlands Organization for Scientific Research.
(n1) Several of the relationships under study are also on the 2002-2004 Marketing Science Institute research priority list (e.g., valuation of innovation, developing radical innovation, alliances and partnerships), which indicates the high relevance of the topic.
(n2) The argument relies on the frequency of partnering between two actors (see, e.g., Hansen 1999) and does not imply an underlying time dimension.
(n3) Note that this argument provides a novel interpretation of what Grayson and Ambler (1999) refer to as the "dark side" of strong ties. We suggest that the downside of strong ties lies in their restrictive effect on economically optimal behavior.
(n4) The cumulative (up to year t) character of a variable is indicated with the superscript "cum."
(n5) One agreement can cover multiple technologies; one biotechnology firm can have multiple technologies in-house.
(n6) This index measures the extent to which firms cooperate with the same partners (i.e., it does not necessarily refer to relational history).
(n7) In a negative binomial regression, the count variable is believed to be generated by a Poisson-like process, except that the variation is greater than that of a true Poisson (referred to as "overdispersion"). Although in a case of overdispersion the Poisson model still provides consistent estimates, the standard errors are underestimated. The negative binomial model we use does not suffer from this problem.
(n8) We also estimated the model with a ratio variable (per firm and year t) that measures the proportion of a firm's drugs that are radical. This ratio approach did not change any of the other results, and the ratio itself had a positive, though only marginally significant (p = .141), effect on profitability.
(n9) We conducted likelihood ratio tests to study the extra variance explained by the stock of radical innovations and the stock of incremental innovations, respectively. Deletion of radical innovation from a profit model that includes incremental innovation significantly deteriorates the log-likelihood (p < .01), whereas omission of incremental innovation from a profit model that includes radical innovation does not significantly affect the log-likelihood.
Legend for Chart:
B - Minimum
C - Maximum
D - Average
E - Standard Deviation
A B C
D E
Repeated partnering 0 .88
.22 .18
Technological diversity .24 .93
.70 .16
Portfolio size 0 84
6.33 11.57
Number of radical drugs (per year) 0 2
.09 .33
Number of incremental drugs (per year) 0 8
.54 1.16
Profitability (in 10<sup>6</sup>$) -756.15 6821.7
494.90 910.77
Firm size (in 10³ .015 151.9
20.89 26.26
R&D expense (in 10<sup>6</sup>$) 0 4435
399.05 631.37
Selling, general, and administrative
expense (in 10<sup>6</sup>$) 1.63 15877
1700.02 2547.20 Legend for Chart:
B - TECHDIV
C - REP
D - PORFSIZE
A B C D
TECHDIV 1.000
REP -.205 1.000
PORFSIZE .542 -.213 1.000 Legend for Chart:
A - Variable
B - Radical Innovation Equation (RADINNOV) Parameter Estimate
C - Radical Innovation Equation (RADINNOV) Standard Error
D - Radical Innovation Equation (RADINNOV) z-Value
E - Radical Innovation Equation (RADINNOV) p-Value (Two-Tailed)
F - Incremental Innovation Equation (INCINNOV) Parameter Estimate
G - Incremental Innovation Equation (INCINNOV) Standard Error
H - Incremental Innovation Equation (INCINNOV) z-Value
I - Incremental Innovation Equation (INCINNOV) p-Value
(Two-Tailed)
A B C D E
F G H I
TECHDIV 1.535 .430 3.57 .000
.426 .172 2.48 .013
REP .680 .196 3.47 .001
-.013 .144 -.09 .927
PORFSIZE .173 .185 .93 .350
.252 .111 2.27 .023
RADSTOCK(-1) .231 .173 1.34 .181
-- -- -- --
INCSTOCK(-1) -- -- -- --
.380 .130 2.91 .004
PATENT .019 .137 .14 .891
-.093 .084 -1.10 .269
TREND -1.736 .531 -3.27 .001
-1.024 .233 -4.39 .000
DUMMY96 1.189 .341 3.49 .000
.646 .222 2.91 .004
R&D .491 .264 1.86 .063
.313 .151 2.07 .038
FIRMSIZE -.268 .268 -1.00 .317
.080 .129 .62 .536
Intercept -2.688 .301 -8.94 .000
-.664 .132 -5.02 .000
Fit Pseudo R² = .25
Likelihood ratio χ² (9): 75.03;
p < .001
Pseudo R² = .17
Likelihood ratio χ² (9): 147.88;
p < .001
N 426
426 Legend for Chart:
A - Variable
B - Profitability Equation (PROFIT) Parameter Estimate
C - Profitability Equation (PROFIT) Standard Error
D - Profitability Equation (PROFIT) t-Value
E - Profitability Equation (PROFIT) p-Value (Two-Tailed)
A B C D E
RADSTOCK 123.379 35.302 3.49 .001
INCSTOCK 2.145 44.953 .05 .962
TECHDIV -73.400 34.901 -2.10 .036
REP 266.073 81.801 7.06 .000
REP² -141.050 29.892 -4.72 .000
PORFSIZE 300.231 42.516 7.06 .000
PATENT 35.084 21.621 1.62 .105
TREND -171.252 41.675 -4.11 .000
R&D 44.759 88.964 .50 .615
SALESEXP 1085.487 118.178 9.19 .000
FIRMSIZE -453.765 55.363 -8.20 .000
Intercept 617.782 28.837 21.42 .000
Fit Adjusted R² = .80
F(11, 443): 156.05; p < .001
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By Stefan Wuyts; Stefan Stremersch and Shantanu Dutta
Stefan Wuyts is Assistant Professor of Marketing (e-mail: wuyts@few.eur.nl), School of Economics, Erasmus University Rotterdam
Stefan Stremersch is Assistant Professor of Marketing (e-mail: stremersch@few.eur.nl), School of Economics, Erasmus University Rotterdam.
Shantanu Dutta is Professor of Marketing and Tappen Fellow in Business-to-Business Marketing, Marshall School of Business, University of Southern California (e-mail: sdutta@marshall.usc.edu).
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Record: 118- Price-Based Global Market Segmentation for Services. By: Bolton, Ruth N.; Myers, Matthew B. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p108-128. 21p. 1 Diagram, 8 Charts. DOI: 10.1509/jmkg.67.3.108.18655.
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Price-Based Global Market Segmentation for Services
In business-to-business marketing, managers are often tasked with developing effective global pricing strategies for customers characterized by different cultures and different utilities for product attributes. The challenges of formulating international pricing schedules are especially evident in global markets for service offerings, where intensive customer contact, extensive customization requirements, and reliance on extrinsic cues for service quality make pricing particularly problematic. The purpose of this article is to develop and test a model of the antecedents of business customers' price elasticities of demand for services in an international setting. The article begins with a synthesis of the services, pricing, and global marketing literature streams and then identifies factors that account for differences in business customers' price elasticities for service offerings across customers in Asia Pacific, Europe, and North America. The findings indicate that price elasticities depend on service quality, service type, and level of service support and that horizontal segments do exist, which provides support for pricing strategies transcending national borders. The article concludes with a discussion of the managerial implications of these results for effective segmentation of global markets for services.
International services are defined as "deeds, performances, and efforts conducted across national boundaries in critical contact with foreign cultures" (Clark, Rajaratnam, and Smith 1996, p. 15). They constitute a sector of the global economy that is growing exponentially relative to the industrial goods sector (Knight 1999). Organizations are taking an interest in the international marketing of services because of low cost factors and the ability to compete in nearby country markets (Bradley 1995); however, international services pose special challenges for marketing managers (Patterson and Cicic 1995) as a result of the intangibility of services, the extent of customization, and differences in preferences across cultures. Research on international service offerings has focused on entry-mode choices (e.g., Erramilli 1990, 1992), technology growth (e.g., Fisk 2001), geographic roles (e.g., Kassem 1989), service influences on national competitive advantage (e.g., Porter 1990), and strategic differences across services (e.g., Nicolaud 1989). Yet the marketing literature has not investigated several key components of service strategy, particularly in the international domain.
Kinnear (1999) has called for research on the extent of horizontal market segments that transcend national borders. This issue has become particularly pressing as international competition has intensified and regional unification (e.g., the European Union, the North American Free Trade Agreement) has been realized. Price sensitivity is a critical market-segmentation variable, and services involve enhanced contact between members of buying and selling organizations, in which price perceptions often differ significantly across market segments (Erramilli 1992). Consequently, a focus on horizontal segmentation implies that particular attention should be devoted to price-based market segmentation for services. Strategies for segmentation and pricing for services, whether in a domestic or an international context, differ from the strategies for goods for several reasons. First, services are highly perishable, and human resource constraints often restrict short-run capacity, which makes demand-management issues and pricing strategies important in smoothing demand (Kraus 2000). Second, the intangibility of services compared with goods may lead to greater emphasis on extrinsic cues rather than on the intrinsic attributes or quality of the service itself (Kraus 2000, p. 192; Zeithaml 1988). Third, the degree of customization and consumer involvement in service offerings enables services and price to be tailored jointly to suit customer preferences (Lovelock 1996). Therefore, Kinnear's observations identify an important managerial question, How should organizations price services to reach horizontal segments, that is, market segments that transcend national borders?
There is a dearth of research regarding the pricing of services (as opposed to goods) in global markets (see Table I). Tellis (1986) provides a conceptual framework for how pricing strategies vary depending on store, category, brand, consumer, and competitive factors. Several studies have investigated the determinants of price elasticities (e.g., Bolton 1989; Hoch et al. 1995; Shankar and Krishnamurthi 1996). However, with few exceptions (e.g., Wittink 1977), most pricing research has focused on goods sold in a limited number of markets. In their review, Rust and Metters (1996) identify three types of mathematical models of services--customer behavior, service quality impact, and normative service models--but price has typically played a minor role in these models. Recent exceptions are Bolton and Lemon's (1999) model of service usage as a function of price and models of optimal pricing plans involving a flat access fee, usage fee, or two-part tariff (Danaher 2002; Essegaier, Gupta, and Zhang 2002; Shugan and Xie 2000).
Equally important, although studies have investigated preferences for service offerings in consumer settings (e.g., Verma, Thompson, and Louviere 1999), there is almost no research on the antecedents of customer purchase behavior for services. Instead, most empirical work has studied the links between service quality dimensions and behavioral intentions (e.g., Mittal, Kumar, and Tsiros 999; Zeithaml, Berry, and Parasuraman 1996). Little is known about how service quality or relationship properties operate within and across business-to-business relationships (Wathne, Biong, and Heide 2001; Weitz and Jap 1995). Thus, our study extends prior research by identifying price-based, horizontal market segments for services, on the basis of business customers' underlying preferences for service quality, across seven national markets.
This study investigates two main research questions that are critical to the development of pricing strategies for international service offerings. (I) What are the determinants of business customers' price elasticities of demand for service contracts? Specifically, our study seeks to answer the following questions. Under what circumstances will business customers pay a premium for customized services? In other words, will they pay a premium price for higher levels of service and for reliable service delivery? How do business customers' price elasticities differ across market segments within national borders? What are the relative magnitudes of the effects of these different factors? ( 2) What factors account for differences in business customers' price elasticities of demand for service offerings across national borders? How do these differences reflect distinct segments in the global services market? In other words, are cross-border differences in price elasticities of demand for service due to differences in customer or market segment characteristics, the competitive environment, companies' service offerings, or national culture/preferences?
The answers to these questions will help marketing managers understand the extent to which the prices of service offerings must be customized or standardized within and across national borders. This article reviews the literature on customization of services and the targeting of horizontal segments and develops specific hypotheses about how individual business customers' price elasticities of demand for an international service offering vary within and across national borders. We test our hypotheses by estimating an econometric model with an extensive data set that describes business customers' purchases of service contracts from a major multinational firm operating in Asia Pacific, Europe, and North America.
A successful strategy for global marketing depends on a firm's ability to segment its markets so that uniform sets of marketing decisions can be applied to specific groups that exist horizontally, that is, across nations or cultures (Sethi 1971). Researchers historically have segmented international markets by using numerical taxonomy methods to classify segments within countries. Helsen, Jedidi, and DeSarbo (1993) provide evidence of "macro-segments," or segments that exist across borders, in a new product diffusion context. Subsequently, using means--end chain theory applied to consumer survey data (Outman 1982), Hofstede, Steenkamp, and Wedel (1999) develop and apply a methodology to identify cross-national segments by identifying relationships between the consumer and the product at the segment level. We believe that business customers in international markets can be grouped into horizontal market segments on the basis of their underlying preferences for service quality. However, our study explores the existence of horizontal segments for an existing service based on price elasticities rather than (self-reported) survey data describing consumption patterns, attitudinal, personality, and sociodemographic variables. The remainder of this section integrates three diverse streams of literature--service strategy, global strategy, and pricing--to provide a conceptual framework for the development of our model (see Figure 1).
Segmentation of Global Markets for Services
Consumer psychology, behavioral decision theory, and neoclassical economics indicate that different customers place different values on the same product. Consequently, a major challenge for international marketers is to identify global market segments and reach targeted segments with products (i.e., goods or services) that meet the common needs of these customers (Hassan and Katsanis 1994). Product configurations must be developed and marketed with the specific preferences of the target segment in mind (Hofstede, Steenkamp, and Wedel 1999). For two reasons, this goal is particularly crucial to organizations competing in multiple national markets. First, they face diverse customer segments for which standardization of marketing decision variables is often impossible (e.g., Szymanski, Bharadwaj, and Varadarajan 1993). Second, market segmentation can reduce operational costs (by eliminating redundant efforts) and effectively allocate a firm's resources to target markets (Berrigan and Finkbeiner 1992).
Most prior research has focused on the identification of customer characteristics relevant to the segmentation of markets for tangible goods rather than for services. For example, Jain (1989) argues that industrial and high-technology products are more likely candidates for standardization across multiple segments and that a trend toward homogenized use patterns exists for high-technology products. In contrast, services, including postpurchase services attached to tangible goods, are more likely candidates for customization for specific segments because disparate service expectations exist across national and cultural boundaries, enhanced personal interaction frequently occurs in service settings, and service-use patterns frequently differ across countries (Stauss and Mang 1999). In the absence of customization, service quality "gaps" may be created as a result of discrepancies between the performance of the service providers of one nation and expectations of the service recipients of another.
The Role of Price Elasticities in Segment Identification
The goal of market segmentation is to identify individual customers who desire similar benefits and exhibit similar behaviors and thereby form (relatively) homogeneous segments such that there is heterogeneity across segments (Wedel and Kamakura 1999). Identification of market segments is often influenced by customers' response to price, as Hofstede. Wedel, and Steenkamp (2002) illustrate in their international segmentation study. Segmentation strategies are effective when they extract higher prices from those buyers that are willing to pay more to have the service tailored to meet their needs (e.g., Kraus 2000). Customization of a service offering may be warranted when customers are less price sensitive (i.e., willing to pay premium prices for customized services). According to Hofstede, Wedel, and Steenkamp (2002, p. 174), in global markets, "groups of consumers in different countries often have more in common with one another than with other consumers in the same country?' Consequently, the degree of customization for a particular market segment (horizontal or otherwise) requires managers to understand how service attributes explain differences in price elasticities of demand for individual customers. Price elasticities are useful for identifying service segments because repeat purchases, rather than trial purchases, dominate sales of existing services. In their repeat purchases, customers trade off the expected benefits of the service (which they have previously experienced) for the price. Therefore, price elasticities should be particularly useful for market segmentation.
Service Quality Dimensions as Segmentation Variables
There has been extensive modeling of the determinants of consumer price elasticities for frequently purchased goods in domestic markets (e.g., Bolton 1989; Hoch et al. 1995; Narasimhan, Neslin, and Sen 1996; Shankar and Krishnamurthi 1996). Because attributes of goods are fixed over time, these studies typically describe how price elasticities for various brand/store combinations differ because of variables such as retailer promotional activities and consumer characteristics. In contrast, little is known about the determinants of price elasticities for business-to-business services in domestic or international markets. Instead, research in services marketing has focused on cross-sectional studies of the switching behavior of consumers and business customers (e.g., Ganesh, Arnold, and Reynolds 2000; Heide and Weiss 1995 Keaveney 1995). In contrast, this article studies business customers' price elasticities for a single company's services.
Our model describes how price elasticities differ across customer segments as a result of the customization of Service quality dimensions (e.g., reliability) and the organizational characteristics of the customer (e.g., access to information about service prices). We focus on service quality dimensions because, after marketing activities have acquired the customer, variation in the intrinsic attributes of services influences repeat purchase behavior and price elasticities. This article distinguishes between three types of market segmentation variables: horizontal segmentation variables that ( 1) apply to all customers (worldwide) or ( 2) operate across national borders within a region and ( 3) vertical segmentation variables that operate only within national borders. By region, we mean a group of nations in geographic proximity (e.g., Asia Pacific, Europe, North America) that shares certain geographic, economic, political, or cultural characteristics. Consequently, we explicitly distinguish between horizontal segmentation variables that operate across regions (i.e., worldwide) and variables that operate across (some) national borders within a region. This distinction is both necessary and important because service organizations may choose to standardize certain aspects of service operations (e.g., response or distribution centers) at the global or regional level. To make this distinction, in the remainder of the article, we use the terms "global" to refer to horizontal segmentation across regions and "regional" to refer to horizontal segmentation across countries within a region. As an aside, we separately control for the main effects of culture.
This section develops hypotheses regarding price elasticities for international service offerings and incorporates them in a model of the determinants of price elasticities of individual business customers. We predict that price-based, horizontal market segments exist for service, where the segmentation variables are customers' responses to service quality dimensions and organizational characteristics. Then, we consider whether vertical market segments also exist as a result of the moderating effects of national or regional variables.
Horizontal Market Segmentation
The emergence of a global marketplace--fueled by regional unification; standardization of investment and production strategies; and increasing flows of information, labor, and technology across borders--is especially conducive to the emergence of customer groups with common preferences that transcend national borders (Day and Montgomery 1999; Levitt 1983). Consequently, researchers have claimed that some (but not all) service attributes can be standardized across national borders for delivery to horizontal market segments (e.g., Patterson and Cicic 1995). For example, a cross-national segment of business customers might value responsiveness, which could be delivered by providing response centers open 24 hours a day and seven days a week. However, there is no empirical evidence on this issue.
In contrast, recent international market-segmentation studies have provided empirical support for the existence of horizontal market segments for consumer products (e.g., Yavas, Verhage, and Green 1992). Hofstede, Steenkamp, and Wedel (1999) have argued that means--end theory provides a conceptual basis for linking the product and the consumer in international markets. The key idea underlying means--end theory is that product attributes yield benefits on consumption, which in turn yields customer satisfaction or value (Gutman 1982). On the basis of this notion, Hofstede, Steenkamp, and Wedel develop a methodology to identify cross-national segments using hierarchical relations between the consumer and the product; they show that horizontal market segments exist for yogurt sold in 11 countries in the European Union. In subsequent research, they argue that "countries as segments" strategies may no longer be valid, and they demonstrate that store-image attribute importance weights display variation, with spatial concentration and contiguity of segments, across 7 countries in the European Union (Hofstede, Wedel, and Steenkamp 2002).
We believe that price-based, horizontal (i.e., cross-national) segments exist that reflect business customers' underlying preferences for services. Our primary reason is that, consistent with means--end theory, prior research has shown that service attributes yield (higher-level) benefits, such as service quality and value, which in turn yield customer satisfaction and repatronage intentions (Anderson and Sullivan 1993; Bolton 1989; Boulding et al. 1993; Zeithaml, Berry, and Parasuraman 1996). The following paragraphs provide an in-depth discussion of our rationale for the existence of price-based, horizontal segments that reflect business customers' underlying preferences for dimensions of service quality and their organizational characteristics.
Dimensions of Service Quality
Recent empirical research suggests that service organizations that adopt a revenue expansion emphasis in which customization plays a key role perform better than firms that try to emphasize both revenue expansion and cost reduction (Rust, Moorman, and Dickson 2002). An understanding of the value of revenue-expanding strategies to the service organization depends on understanding the demand curve (Szymanski, Bharadwaj, and Varadarajan 1993), where demand depends on a means--end chain that links service attributes with service quality and value (Zeithaml 1988). Although there has been intensive research regarding service quality (Fisk, Brown, and Bitner 1993), marketers have been unable to discover dimensions of service quality that are universally applicable to all customers and markets.( n1) However, higher levels of service quality (on various dimensions) are associated with consumer reports of higher Loyalty levels in many settings (see De Wulf, Odekerken-Schröder, and Iacobucci 2001; Zeithaml, Berry, and Parasuraman 1996). In this article, we explore how dimensions of service quality--identified by qualitative and quantitative research with customers conducted by the company that cooperated in this study--influence price elasticity. Following Parasuraman, Zeithaml, and Berry (1988), we label three dimensions: responsiveness, reliability, and assurance and empathy. We do not study tangibles because they are globally standardized for the company in our study, and such extrinsic cues regarding quality are less important than intrinsic cues when customers have substantial experience with a service and are making a decision about whether to repurchase it.
Responsiveness. Responsiveness can be defined as the willingness to help customers and provide prompt service (see Parasuraman, Zeithaml, and Berry 1988). Customers are more likely to repurchase goods and services from a responsive firm than from a less responsive firm (e.g., Gilly and Gelb 1982). There are high levels of perceived risk in new service encounters (Schlesinger and Hallowell 1993), which are likely heightened in cross-national contexts. Responsiveness reduces the perceived risk of continuing to purchase from an existing service provider, thereby increasing switching costs so that customers will be less sensitive to price increases (i.e., more price inelastic).
Responsiveness is typically represented by the speed with which firms react to service requests from customers; however, responsiveness has multiple aspects. In an international environment, response time can be hours or days, depending on the location of the customer relative to the service provider and the nature of the service. Simple services may be easily executed, whereas complex services may require extensive time and effort to implement. The speed of employee responses to service requests may he limited by geography, whereas the speed of electronic responses to service requests may be accelerated through the use of remote technology. Given that the greatest source of dissatisfaction for customers in technology-based service encounters is technology failure (Meuter et al. 2000), customers place greater emphasis on a firm's ability to respond to technology-driven problems (Bitner, Brown, and Meuter 2000; Lovelock 1999). As a result, we believe that customers are less price sensitive for highly responsive service, where responsive service encompasses initial response time and resolution time as well as the nature of the service request.
H1: A horizontal market segment exists such that customers who receive more responsive service are less price sensitive than customers who receive less responsive service.
Reliability. Reliability, or the ability to perform the promised service dependably and accurately, is typically the most important service quality dimension to customers (Anderson, Fornell, and Rust 1997; Berry, Parasuraman, and Zeithaml 1994). Reliability is particularly critical for services, because (unlike goods) services are typically characterized by heterogeneity (due to differences between employees delivering the service, customers, and context) and simultaneous production and consumption (Berry and Parasuraman 1991). When we say that customers prefer more reliable service, we mean that they prefer lower variability in service attributes over time. For example, response times ranging from two to four days are preferred over (less reliable) response times ranging from one to five days, even when the average response time is the same. In addition, a service representative who is consistently courteous is preferred over a service representative who is intermittently courteous, even if the two representatives are (on average) equally courteous. Research suggests that customers sometimes prefer a lower level of quality that is more certain (i.e., more reliable or consistent over time) to a higher level of quality that is less certain (Rust et al. 1999).
Customers' predictive expectations develop from their service experiences, and inconsistent service increases the possibility of unfavorable disconfirmation and dissatisfaction (Anderson and Sullivan 1993). Unfavorable disconfirmation damages customer retention levels for services at greater rates than favorable disconfirmation benefits them (e.g., Bolton 1989; Bolton and Lemon 1999). Consequently, we predict that customers are more tolerant of price changes (more price insensitive) and less apt to defect to alternative suppliers when they experience highly reliable service.
H2: A horizontal market segment exists such that customers who receive more reliable service over time are less price sensitive than customers who receive less reliable service.
Assurance and empathy Services are intangible; therefore, service quality may be difficult to observe directly, and customers may consider employee behavior a surrogate for service quality (Wolkins 1993). Consistent with this notion, Parasuraman, Zeithaml, and Berry (1988) have identified assurance (i.e., the knowledge and courtesy of employees and their ability to convey trust and confidence) and empathy (i.e., caring, individualized attention the firm provides to its customers) as dimensions of service quality. Employees convey trust and confidence when they make a special effort or commit the company's resources to handling a business customer's service request. They provide caring, individualized attention through frequent visits and direct contact with the customer about service issues.
Empathy is also represented by having operating hours that are convenient to the customer's schedule, exhibiting flexibility in the delivery of services, and considering the customer's other business constraints. In global operations, providing assurance and empathy places an economic burden on the service organization (e.g., when employees make long trips to provide on-site service) and thereby acts as a pledge from the organization to the customer (Anderson and Weitz 1992). Customers perceive the employee's efforts to provide assurance and empathy, recognize that these efforts signal high-quality future service, and (consequently) are more willing to repurchase from the service organization and pay premium prices (Berry, Parasuraman, and Zeithaml 1994).
H3: A horizontal market segment exists such that customers who receive more assurance or empathy from service representatives over time are less price sensitive than customers who receive less assurance.
In our study context, assurance and empathy reflect employee efforts and tend to operate similarly. Consequently, our empirical work explores a single prediction regarding employee efforts to provide assurance. However, we believe that in other industry contexts, the distinction between these two constructs is meaningful.
Organizational Characteristics
Customers are often imperfectly informed about their alternatives in the marketplace because of the large number of product offerings, the many dimensions on which offerings can be evaluated, and the complexity of those dimensions (Tellis and Wernerfelt 1987). The use of information technology reduces complexity and improves marketing effectiveness (Bloom, Mime, and Adler 1994), but information is often expensive to collect and difficult to use. In particular, global firms suffer from a condition of adverse asymmetry in information costs, and disproportionate costs are associated with collecting, synthesizing, and communicating data (Mariotti and Piscitello 1995). We believe that business customers who purchase many services are likely to be more knowledgeable about alternative service offerings and (consequently) more price sensitive than business customers who purchase few services. We also believe that business customers who consider service purchases critical to the success of their core business operations are more price insensitive than business customers who do not view services as critical, because switching service providers or eliminating service purchases may have a negative effect on business performance (ceteris paribus).
H4: A horizontal market segment exists such that customers who purchase few services in a given industry are less price sensitive than customers who purchase many services.
H5: A horizontal market segment exists such that customers who consider service offerings highly critical to their business performance are less price sensitive than customers who view the service offerings to be less critical.
Vertical Segmentation
Cultural factors tend to exert greater influence on customer preferences and evaluations of services than do tangible goods (Mattila 1999) and thereby influence customer repatronage behavior (Kim and Chung 1997; Maignan, Ferrell, and Hult 1999). There are several reasons that cultural differences cause managers from different countries or regions to weigh differentially factors influencing their judgments and decisions about service and thereby to exhibit differences in price sensitivity. First, managerial decision making in a multinational environment is influenced by the cultural distance between the countries representing exchange partners (Kogut and Singh 1988), where cultural distance is the degree to which the cultural norms in one country are different from those in another country. Second, relationships with customers from collectivist societies result in stronger, more intimate, and (thus) more loyal relationships than do relationships with customers from individualistic societies, such as Germany. This loyalty may translate into more price insensitivity on the part of customers from collectivist markets.
Third, cultural differences influence behavioral norms and work-related values (e.g., Markoczy 2000), and these differences reflect disparity in the levels of both commitment to exchange partners and perceived satisfaction with exchange relationships. Fourth, given that customers from different cultures have diverse behavioral norms, they evaluate services differently and have different expectations about optimal and adequate encounters. For example, response centers open 24 hours a day and seven days a week may be more important in some countries or regions than in others, or an organization's characteristics (e.g., perceptions of the business environment, such as the criticality of the service to successful business performance) may be more important in some countries or regions than in others. Taken together, these arguments suggest that national or regional variables moderate the effects of some (or all) service quality dimensions and organizational characteristics on price sensitivity.
H6: Vertical market segments exist such that the effects of dimensions of service quality and organizational characteristics on price sensitivity are moderated by national and regional variables.
Markets may be vertically segmented for some service dimensions (H6) and horizontally segmented for others (H1-H5). We investigate this issue in the empirical portion of this article.
Covariates
Classic economic theory predicts that customers' price elasticities will depend on the point on the demand curve at which they are calculated. This prediction stems from the definition of price elasticity as dynamic in nature and is confirmed in other studies (e.g., Hoch et al. 1995). Thus, we treat price as a covariate. Cross-cultural differences within markets have often been argued to affect buyers' perceived value of services (Dahringer 1991). These cross-cultural disparities are typically captured with Hofstede's (1980) cultural dimensions. As described in the next section, we therefore control for the main effects of cultural distance by employing Kogut and Singh's (1988) measure of cultural distance between markets. Last, we include dummy variables representing geographic markets in the initial models, because the direct effects of these variables must be determined before any test for interaction effects of regional or national differences can take place.
Study Context
The study context is the purchase of system support services by large business customers. System support services are continuously provided services that enable or facilitate the functioning of manufacturing equipment, high-technology equipment, software, and other systems. Some examples include support services for telecommunications, computing, and other information technology; repair and maintenance services for engineering, medical, and/or other equipment; and support services for financial, health, or energy management software systems. In this study, the data set describes a stratified random sample of customers who purchased computing system support services from a company operating in many national markets. We draw the sample from three regions in which this company provides services: Asia Pacific (Japan, Korea, and Singapore), Europe (Germany and the United Kingdom), and North America (Canada and the United States).
Customers purchase service contracts for systems of computer hardware and software. The service contracts are purchased independently from the systems (which have been purchased earlier). The contracts cover both hardware and software support; that is, the two related offerings are bundled (Stremersch and Tellis 2002). The contracts are fixed-price contracts, and therefore customers are not billed on the basis of service-usage levels. Customers purchase a separate contract for each system; they buy multiple contracts if they own multiple systems, and the contracts may act as substitutes and/or complements for other products. There are roughly eight major competitors in any given market. Customers can (and do) purchase system support contracts from many different service organizations, and thus switching costs are low compared with other industries support for different systems. In this study, we focus on two system support contracts (i.e., two different products) that promise different levels of support. Low-support contracts provide primarily reactive responses to customer requests about core hardware and software problems. High-support contracts provide reactive responses, escalation procedures, and proactive consulting in addition to actions that maintain and enhance system effectiveness. Both offerings have specific, contractually defined, targeted or guaranteed performance levels, such as "247 support with a guaranteed response within two hours." The low-support-contract terms and conditions are a subset of the high-support-contract terms and conditions. For example, low-support contracts promise resolution of certain issues within six hours, whereas high-support contracts promise resolution of the same issues within four hours. The duration of system support contracts ranges from three months to one year.
The Data Set
Each customer has (potentially) a separate price elasticity of demand for low- and high-support services. The data set describes purchases of system support contracts during 1995 and 1999 for 184 business customers in Asia Pacific, 216 in Europe, and 341 in North America. Because not all customers purchase contracts at both support levels, the resultant data set contains 508 price elasticities for low-support offerings and 445 price elasticities for high-support offerings. There are only six high-support contracts held by Canadian customers, so these observations are pooled with U.S. customers in our statistical analyses of high-support price elasticities.
The data set contains a description of each customer's interorganizational relationship, including its purchase history, over a three-year period. It combines information from three primary sources: ( 1) a master file that describes the characteristics of each customer account, ( 2) annual billing data for all contracts held by the customer, and ( 3) internal company records of monthly service operations data for all contracts and customers. Customers' organizational characteristics are recorded in the master file, including the number of low-support and high-support offerings each customer held, which is an estimate of customers' total support budget and their report of how critical system support is to their business operations. The billing data are used to calculate each customer's price elasticities for low- and high-support service contracts. The time-series data describing the system support experiences of the customers, obtained from monthly operations reports during 1997 and 1999 (at the contract level), are used to derive objective measures of service customization (reliability, responsiveness, and assurance) for each system support offering level.
The customer's report of how critical system support is to its business operations was originally obtained in a telephone interview with the customer organization's decision maker, who was asked the following question: "Which of the following best describes the impact of four hours unscheduled system downtime on all business at your location? Would you say that the impact is extremely critical ( 5) ... not at all critical Customers may also decide to purchase different levels of With the exception of this measure, we do not use perceptual measures of model constructs, and therefore it is unnecessary to conduct tests for cross-cultural measurement equivalency.
Measurement of Price Elasticities of Demand for Individual Business Customers
Effective measurement of price elasticities can be a complex task in any context (Boatwright, McCulloch, and Rossi 1999). Prior research has typically measured price elasticities for groups of customers by deriving them from aggregate demand functions (e.g., Bolton 1989). A notable exception is Elrod and Winer's (1982) study of market-segmentation issues, which measures price elasticities of individual families. Because our study focuses on market-segmentation issues, we also measure the price elasticities of individual customers. We do not derive our price elasticity estimates from individual customer demand functions, because there is insufficient time-series data to estimate these functions. Nor do we derive our individual customers' price elasticity estimates from aggregate demand functions (e.g., by allowing heterogeneity in the demand function parameters), because this approach requires knowledge of the ways price interacts with other antecedents of customer demand for services. We obtain a separate elasticity for each support-contract type (low or high) by calculating the "arc elasticity" on the basis of customers' repeat purchases at the end of 1999 compared with purchases at the end of 1998. (A one-year period corresponds to most customers' budget cycles and to the maximum contract duration.) By calculating the price elasticity of demand for repeat purchases, we are able to hold fixed (for our consideration) a customer's total number of systems, which thereby eliminates factors that might influence the demand for systems and (consequently) the demand for system support. When a customer decides not to repurchase a contract for a given system, the company may have decided to purchase a system support contract from another supplier, to provide support internally, or to do without support. Such decisions are (naturally) related, but they are made in increments of one contract. Thus, we calculate our measure of price elasticity of demand for low-support contracts for a given customer as the percentage change in the number of low-support contracts purchased by the customer, divided by the percentage change in price paid for low-support contracts by the customer. Similarly, we calculate our measure of price elasticity of demand for high-support contracts for a given customer as the percentage change in the number of high-support contracts purchased by the given customer, divided by the percentage change in price paid for high-support contracts by the customer. These measures are described in Tables 2 and 3. Note that we make a separate calculation for each contract (low or high) for each customer.
Tables 2 and 3 show descriptive statistics for customers holding low-support contracts and high-support contracts, respectively, displayed by country. The two groups of customers overlap in each country but have different characteristics. In Table 2, the average price elasticity for low-support offerings across 104 customers in Germany is -.30, whereas the average price elasticity for low-support offerings across 36 customers in Korea is .67, which implies that German customers are more price sensitive than are Korean customers. Table 2 shows that 104 German customers, who hold (on average) 14.25 low-support contracts, also hold 4.61 high-support contracts. They report an average level of criticality of 4.37, measured on a five-point scale, where 5 = highly critical systems. In Table 3, the average price elasticity for the 53 customers in Germany who hold high-support contracts is -.76. This smaller group of customers holds (on average) 3.38 high-support contracts and 11.71 low-support contracts and reports a high level of criticality of 4.55. Overall, the price elasticity estimates for low- and high-support contracts are similar to elasticity estimates reported in prior research (see Tellis 1988).
The central limit theorem predicts that our measures of price elasticity for an individual customer will be more precise when the customer holds many service contracts.( n2) We recognize the imprecision in price elasticity measures for customers who hold low numbers of contracts by discarding price elasticity estimates that fall outside the range of -11 to 3.5. This cutoff rule discards 6% of the low-support contracts and 10% of the high-support contracts In our study, customers typically hold about ten low-support contracts and five high-support contracts. Consequently, our rule discards a greater percentage of observations for high-support contracts because customers hold fewer high-support contracts. We also recognize imprecision in the price elasticity estimates by weighting the data when we estimate our model. By discarding outliers that are less precise and by using a weighted least squares (WLS) estimation procedure, we increase the statistical efficiency of the estimates of our model parameters. We do not change the substantive results reported in this article.
Measurement of Predictor Variables
Identification of service quality dimensions. Before this study, the cooperating company conducted qualitative and quantitative research with customers in Asia Pacific, Europe, and North America. First, a market research consultancy (that specializes in primary research for high-technology products and services) conducted 88 face-to-face interviews with managers from customer organizations, with the objective of identifying the relevant dimensions of service quality. The interviews focused on the customers' perceptions of exemplary service and service gaps. Although the company is a best-in-class service provider, the majority of respondents identified reliability (i.e., consistently meeting the terms and conditions of the service contracts) as the dimension of service quality that was most important to them. Responsiveness (i.e., willingness to help and provide prompt service) and assurance (i.e., the knowledge and courtesy of employees and their ability to convey trust and confidence) were the two other dimensions of service quality that customers identified.( n3)
Second, the cooperating company conducted a customer satisfaction and loyalty survey that measured both abstract and specific (i.e., actionable) perceptual measures of the dimensions of service quality. The sample sizes and response rates for the survey in each of the three regions are 227 observations from Asia Pacific for a response rate of 34%, 263 observations from Europe for a response rate of 30%, and 340 observations from North America for a response rate of 42%. Quantitative analyses of the survey data identified the same three abstract dimensions of service quality and linked them to perceptions of specific service operations. For example, responsiveness (an abstract measure) can be statistically linked to perceptions of response time for hardware requests. Consistent with means--end chain theory, we mapped the three abstract dimensions of service quality into hard (i.e., objective and concrete) measures that could be derived from the service operations data set using well-established procedures (see Kordupleski, Rust, and Zahorik 1993 Zeithaml and Bitner 2000, pp. 234-38). By linking customer preferences for service quality dimensions to concrete service attributes, we created a basis for market segmentation that is highly actionable for managers (Urban and Hauser 1993).
In summary, we ultimately measured the dimensions of service quality using the service operations data set. Our approach is different from cross-sectional studies that rely on perceptual measures of service quality (which can be standardized across companies and industries). However, it is consistent with within-company studies that relate perceptual service quality dimensions to specific business process metrics to produce actionable results for managers (e.g., Bolton and Drew 1994; Goodwin and Ball 1999, p. 33). Because low and high support have different characteristics, the low- and high-support equations use slightly different measures of service quality.
Measurement of service quality dimensions. Table 4 describes the measures of model constructs. Responsiveness refers to willingness to provide prompt service. Lack of responsiveness is measured by the average time until first response to a hardware request and by average resolution time for a software request. To obtain measures of responsiveness (rather than the lack thereof), we reverse code these measures.
Reliability refers to consistency or dependability in meeting service contract terms and conditions. Because response and resolution time are critical service "promises," lack of reliability in service delivery is measured by counting extreme incidents in which response or resolution time for hardware or software requests were unusually high compared with industry benchmarks. We do not use average response and resolution time to measure reliability; instead, we calculate measures of inconsistency in service by counting extreme values of response and resolution times (i.e., measuring skewness). Few or no extreme values imply that service is very dependable or reliable over time; that is, service operations levels are tightly distributed around average values.( n4) Specifically, we measure lack of reliability by the number of incidents for which resolution time on a software support request exceeded 120 minutes (two hours) and by the number of incidents for which engineer effort to resolve software support requests was unusually high as measured by minutes spent. (In this data set, the service operations records indicate that 13% of hardware support incidents were resolved in more than 240 work minutes, and 11% of software support incidents were resolved in more than 120 work minutes.) In other words, we measure reliability by counting the number of instances of unreliable service (extreme deviations from promised levels). To obtain measures of reliability (rather than the lack thereof), we reverse code these two measures.
We measure the (combined) assurance and empathy dimension by two variables that reflect the knowledge, courtesy, and individualized attention provided by employees. We measure the average amount of engineer time invested in resolving software support requests and occasions of on-site visits to customer locations (when remote handling of the request was insufficient). Qualitative research indicates that customers recognize the additional quality delivered by an engineer relative to a (less knowledgeable) service technician, and they recognize on-site visits (as opposed to remote communications) as evidence of caring and concern within the relationship. Although these two measures seem to correspond to assurance and empathy (respectively), we believed there was some overlap; however, we use the term "assurance" throughout the remainder of this article.
Two organizational characteristics are recognized (by both service providers and customers) as market-segmentation variables for all firms in this industry: (I) the customer organization's breadth of experience with support contracts and ( 2) a measure of the criticality of system support. In the equation for low-support contracts, we include the customers' total number of contracts (both low and high support) and the customer's number of high-support contracts. In the equation for high-support contracts, we include estimates of customers' total dollars spent on systems support (both low and high support) and the customer's number of low-support contracts. In other words, we use two measures in each equation to capture the quantity and quality of the customer organization's support service experience. (The low-support equation includes a measure of the number of high-support contracts, and the high-support equation includes a measure of the number of low-support contracts. We deliberately do not include the number of low-support contracts in the low-support equation or the number of high-support contracts in the high-support equation because these measures are used in the calculation of the equation's respective dependent variables.) The measures of experience might be proxies for the customer organization's size or relative importance of system support expenditures relative to other budget items as well as the organization's experience with system support contract purchases. However, because our study sample is restricted to extremely large businesses, we believe that these measures primarily reflect the customer organization's experience with system support contract purchases. Total support budget was measured by the estimated total dollars spent by the organization on system support (i.e., paid to the cooperating company and its competition). This budget is a numeric value expressed in U.S. dollars. The value has been multiplied by a constant to preserve the confidentiality of the company's records.
The equations for both low- and high-support offerings include covariates to capture national cultural distance. Similar to Kogut and Singh (1988), we estimate national cultural distance as a composite index based on Hofstede's (1980) four national culture scales that incorporate power distance, uncertainty avoidance, masculinity/femininity, and individualism. The index values are Japan ( 46), Korea ( 18), Singapore ( 20), Germany ( 67). United Kingdom ( 89), Canada ( 80), and United States ( 91). (Hofstede's cultural distance measure has a single value for each country, so there is no standard deviation.) In our sample, "regions" included Asia Pacific (Japan, Korea, and Singapore), Europe (Germany and the United Kingdom), and North America (Canada and the United States).
We estimate separate equations for low- and high-support-contract price elasticities. Price elasticity estimates for business customers in different countries of origin may have different amounts of measurement error as a result of different market characteristics. Furthermore, each customer's arc elasticity for a given type of support contract is calculated on the basis of the number of low/high contracts it holds, so that business customers who hold large numbers of contracts should have more precise elasticity estimates. Consequently, the error terms of each of the two price elasticity equations may be characterized by heteroscedasticity; that is, the magnitude of the equation errors may depend on the customer's country of origin and on the number of the low (or high) offerings purchased by the customer. We use Glesjer's (1969) test to test for heteroscedasticity of error terms due to these two features (Johnston 1972). This test confirmed heteroscedasticity stemming from the country of origin and the number of contracts used to calculate the price elasticity estimate as well as the nature of the contract. We use WLS to correct for heteroscedasticity. We calculate the weight for each observation in the data set by ( 1) estimating ordinary least squares regressions for each support level and country, ( 2) calculating the error variance for each combination of support level and country, and ( 3) dividing this error variance by the number of contracts used to calculate the price elasticity estimate. We use these in the WLS estimation of the equations (Greene 1993). Note that we do not use a systems estimation procedure because all customers do not hold both types of contracts.
We use a multiple-step procedure to test for the interaction effects of national and regional differences. First, we test for the existence of national differences in response to service quality dimensions and organizational variables by initially estimating an unconstrained model in which we use dummy variables for the six countries to create main and interaction terms with the measures described in Table 4.( n5) We conduct F-tests to test the null hypothesis that the coefficients of a given predictor variable are equal in magnitude across countries in the same region. The results are reported in the columns labeled Asia Pacific, Europe, and North America in Table 5. A nonsignificant F-statistic in these cells indicates that there are no significant differences across countries within these regions. For example, for low-support contracts in Asia Pacific, we reject the null hypothesis that the coefficients of the predictor variables are equal across countries (Japan, Korea, and Singapore) for only two variables: (I) assurance, as measured by extreme values for time until first response on hardware requests (6.07, p < .01), and ( 2) price (4.20, p c .05). Thus, there are differences in customers' responses to assurance and price across the three countries in Asia Pacific.
Second, we conduct F-tests to test the null hypothesis that the coefficients of a given predictor variable are equal in magnitude across the three regions. (This test is only applicable when we do not reject the null hypothesis at the first step.) These results are reported in the right-hand column of Table 5. A nonsignificant F-statistic in these cells indicates that there are no significant differences across regions. For example, for low-support contracts, we cannot reject the null hypothesis that the coefficients of the predictor variables are equal across regions for reliability (1.26, p > .10) and responsiveness (1.85, p > .10).
On the basis of the results from Table 5, we estimate a pooled model (using data from all nations and regions) that allows for national and regional differences in the effects of service quality and organizational characteristics variables on price elasticity estimates. We examine this penultimate model and delete alt predictor variables that are not statistically different from zero at p < .15. (We use p < .15 rather than a smaller value to be conservative and avoid omitted variable bias). For example, Table 5 indicates that the effect of reliability was the same across countries and regions in the tow-support model. Consequently, reliability is included in the penultimate model as a global effect, but it is not statistically different from zero at p < .15 in that model, so we drop it from the final model. We then estimate the final (reduced) models for both the low-support (Table 6) and high-support (Table 7) models. On the basis of the tests described in Table 5 and the results shown in Tables 6 and 7, we draw conclusions regarding our hypotheses.( n6)
Tables 6 and 7 show that the R² for the low-support model is .28 (p < .001) and .55 (p < .001) for the high-support model. Both models fit well, especially when it is noted that each model accounts for differences in business customers' behavior, as measured by sometimes rather imprecise price elasticity estimates. The quality of fit is also represented in the service quality dimensions by engineering and operations measures, rather than relying on perceptual data, and these measures perform well. It is notable that we are able to explain much more of the variance in the customized or "augmented" product (i.e., high-support contracts) than in the standardized or "core" product. Note that Tables 6 and 7 provide detailed information about customer preferences for contracts. For example, for low-support contracts, Table 6 shows that German customers consider responsiveness, measured by the average response time to software requests, much more important than do U.K. customers.
We test our hypotheses by examining Tables 6 and 7. H1 predicts that customers who receive more responsive service over time are less price sensitive than customers who receive less responsive service (ceteris paribus). In the low-support model (Table 6), U.K. customers are less price sensitive when the average resolution time for software requests is high (p < .01), but non-U.K. customers are not. In the high-support model (Table 7), customers are less price sensitive when average travel times to provide on-site hardware support are high (Europe, p < .01; North America, p < .05) and when average resolution time is high for software requests (North America, p < .01), which indicates horizontal segments exist across countries. These results support H1.
H2 predicts that customers who receive more reliable service over time are less price sensitive than customers who receive less reliable service. In the low-support model, none of the measures of reliability is shown to be statistically significant in Table 6. In the high-support model, European, quality dimensions on price elasticities is moderated by Korean, Japanese, and North American customers are price sensitive when engineer efforts to resolve software service requests are occasionally slow (p < 01), as shown in Table 7. This result supports the hypothesis that customers who experience more reliable service over time are less price sensitive.
H3 predicts that customers who receive more assurance (e.g., because of efforts by employees) are less price sensitive than customers who receive less assurance. In the low-support model, Korean customers are less sensitive to price when technicians' time until first response on an on-site hardware request is occasionally high (p < .01). This initially surprising result is due to an on-site visit seldom being a first response to a hardware request, yet the customer clearly values this effort. In the high-support model, customers in Europe, Japan, Korea, and North America are less price sensitive when average engineer effort to resolve software requests is high and when technicians make infrequent but lengthy trips to resolve on-site hardware requests. These findings support the prediction in H3 that customers who received assurance from employee efforts are less price sensitive.
H4 and H5 pertain to organizational characteristics. H4 predicts that customers who purchase few services in a given industry are less price sensitive than customers who purchase many services. In the low-support model, European customers with a low number of support contracts are more price insensitive than those with large numbers of contracts (p < .01). In the high-support model, European customers with a low number of low-support contracts and low total support budgets are more price insensitive (p < .01). Consequently, H4 is supported throughout Europe. However, in the high-support model, North American customers are more price sensitive when purchases (as measured by the size of their total support budget) are high, which thereby refutes H4. We speculate that this anomalous result is due to the robust North American economy (compared with the rest of the world) during this period. H5 predicts that customers who consider service offerings highly critical to their business are less price sensitive than customers who consider the service offerings less critical. Table 6 shows that this hypothesis is supported in the low-support model for European customers (p < .01); however, Table 7 shows that H5 is not supported in the high-support model.
Through testing H1-H5, we have established that price-based, horizontal market segments exist that reflect dimensions of service quality and organizational characteristics. H6 predicts that the effect of service quality dimensions and organizational characteristics on customers' price elasticities is moderated by national or regional differences. Many differences exist both among countries and among regions for both low-support and high-support service contracts. Here, we examine the (joint) F-test results for the low-support model displayed in Table 5. The price elasticities of European customers showed significant intraregional differences in the effects of responsiveness (p < .01). The price elasticities of Asia Pacific customers showed intraregional differences in the effects of assurance (p < .01). These results show that the influence of service national and regional differences. In the low-support model, we found significant differences across regions for both criticality (p < .05) and number of contracts (p < .01). Thus, the influence of organizational characteristics on price elasticities is moderated by national and regional differences, and H6 is supported by the results of the low-support model for responsiveness, assurance, criticality, and total support.
The results for the high-support model displayed in Table 5 indicate distinct differences across nations and regions. The relationship between responsiveness (measured as both average travel time for hardware on-site requests and average resolution time for software requests) and price elasticity is also moderated by interregional differences (p < .01). The influence of reliability (measured as extreme values of engineer effort to resolve software requests) on price elasticities is moderated by national differences (Asia Pacific, p < .05) and regional disparities (p < .10). The relationship between assurance dimension and price elasticities is moderated by national differences within Asia Pacific (p < 0.05). These results further support H6. We found cross-regional interaction effects in the criticality--elasticity relationship (p < . 10) and the firm size--elasticity relationship (p < .05), and thus H6 is supported by the results of the high-support model for responsiveness, reliability, assurance, criticality, and total support. These results are summarized in Table 8.
Although the principles of market segmentation may appear straightforward, market-segmentation research is still in the early stages of development both theoretically and methodologically (Steenkamp and Hofstede 2002; Wedel and Kamakura 1999). Market segmentation is particularly challenging in global markets where cultural and economic differences influence customer preferences and characteristics. This observation is especially true for managers who are attempting to develop profitable strategies and pricing schedules for services offered in global markets. Prior research on global marketing argues that it may be useful to identify segments on the basis of cultural or demographic differences within and across specific markets. However, our study of price elasticities indicates that market segmentation for international service offerings can be based on the business customers' revealed preferences for different service configurations and characteristics of the customer organization. This approach to market segmentation leads to the identification of horizontal market segments across nations and regions, whereby service offerings are customized to customer preferences for service attributes.
The development of a global market-segmentation scheme based on customers' price elasticities, in addition to service quality preferences and organizational characteristics that influence them, yields two benefits for service organizations. First, revenues can be enhanced by establishing specific price points for service feature bundles that attract and retain customers. Second, service delivery systems can be simultaneously customized to match individual customer preferences for perceived service quality--reliability, responsiveness, and assurance--yet standardized to create global, horizontal segments that are cost effective for the service organization. (Unlike goods, services usually cannot be resold, and thus service organizations are likely to be less concerned with the diverting of sales across national boundaries.) To enjoy these benefits, however, global service providers must understand how price elasticities vary across different service delivery and organizational profiles and how these relationships are moderated by national and regional differences. This study indicates that customer preference, customer retention, and (consequently) price elasticities differ across service quality dimensions in vertical, horizontal, and global segment dimensions.
Simultaneous Vertical and Horizontal Segmentation in the Low-Support Market
A key finding of this study is the identification of both horizontal and vertical segments among service customers. In the low-support model, significant differences in the influence of service responsiveness on customer elasticities exist within the European market, such that only U.K. customers are more price sensitive when average response times for service requests are high. Such vertical segments (i.e., those within one market or culture) follow traditional perspectives toward segment identification (Hofstede, Steenkamp, and Wedel 1999), which requires customization of service dimensions to individual country markets.
The low-support model also demonstrates how horizontal segments can exist at the regional level by identifying organizational characteristics that influence elasticities similarly across countries within a region. Specifically, business customers in Europe are price insensitive when they consider the service critical to the company's business success or when the customer holds few service contracts; otherwise, they are price sensitive. Thus, changes in price to European customers (but not Asia Pacific or North American customers) influence repatronage behavior differently. Some European firms with noncritical systems or many service contracts may be much less willing to tolerate price increases for low-support contracts.
At the same time, there is also empirical evidence identifying a horizontal segment that cuts across national and regional boundaries. The low-support model indicates that service reliability does not influence price elasticities in any country or region, which indicates that current levels of reliability are within the zone of tolerance for all customers worldwide. This observation is consistent with a horizontal segmentation scheme that standardizes certain aspects of service operations within upper and lower bounds that are common across global markets. Furthermore, if competitors offer similar levels of reliability, then the company might also consider deemphasizing reliability in its marketing communications.
How Customer Expectations Influence the Segmentation of Premium Service Markets
The contractual obligations of the service organization are higher for the premium (high-support) service contract than for the core (low-support) service contract. When contractual obligations are high, customers' expectations change. This shift is evident in a comparison of the high- and low-support models. In the premium (high-support) model, service reliability is acutely important, especially the reliability of engineer effort to resolve software service requests. Highly reliable software resolution times lead to price inelasticity in Japan and Korea, as well as in North America and Europe (i.e., horizontal segments across countries within a region). However, the effect of reliability on price elasticity relationship was not statistically significant in the low-support model. Consequently, it is apparent that most customers demand more reliable service as contracts increase in price, though the weights differ somewhat across countries and regions.
Balancing Customization and Standardization of Each Service Quality Dimension
The delicate balance between customization and standardization of service delivery dimensions is particularly evident in the premium service market. The importance of service responsiveness is roughly equivalent across countries within Asia Pacific, European, and North American regions, which provides support for the presence of horizontal segments. However, significant differences exist between these three regions. European and North American customers' price elasticities are affected by the timeliness of the service organization's responses to their on-site requests, and North American customers are also affected by resolution times. Yet price elasticities for Asia Pacific customers are not influenced by either of these dimensions of service quality. This observation illustrates how a service organization may standardize service delivery (e.g., responsiveness) for markets within each region, yet customize service delivery to specific regions This segmentation strategy partially arises from local regional conditions (e.g., physical terrain, national customs procedures, efficiency of transportation and communication hubs) that influence the service organization's response and resolution times.
The high-support model also shows that horizontal segmentation requires subtle customization of service delivery efforts. For example, assurance provided by employees influences price elasticities in virtually all markets. When assurance is represented by employees' willingness to make a long trip to resolve an on-site hardware request, the results show that the effect of assurance on price elasticity is reasonably similar across countries and regions. This result for assurance indicates that a global market exists for this dimension of service quality. In other words, all customers appreciate the effort of the service employee in traveling to fulfill the company's obligations, and they are (consequently) more price insensitive. However, when assurance is represented by engineer efforts to resolve software requests, the size of its effect on price elasticity varies across countries in Asia Pacific and across regions. In Europe, Japan, Korea, and North America, high levels of assurance (due to high engineer efforts to resolve requests) lead to price inelasticity. However, in Singapore, engineer efforts to resolve requests apparently are not noticed or not valued. Marketing managers undoubtedly find such nuances difficult to execute, especially given the complexity of most service delivery processes.
Organizational Characteristics Still Matter
Customer size influences price elasticity in the high-support model, and this relationship is moderated by regional differences. European customers holding many low-support contracts or with large total support budgets are more price sensitive for high-support contracts. Conversely, North American firms with large total support budgets are more price insensitive, probably because of the exuberant U.S. economy in the late 1990s. These findings indicate that from a strategic perspective, organizational characteristics of customer firms influence the price adjustments made by service providers, depending on the regional location of the customer.
As multinational business becomes increasingly service oriented, managers need to develop strategies for segmenting global markets and marketing services to business customers. This challenge is relevant to companies that sell manufactured goods with ancillary pre- and postpurchase service (Hensler and Brunell 1993), as well as to companies that sell conventional services. The effective identification of market segments is critical to the success of multinational companies for several reasons. First, service providers can customize their offerings in ways that maximize customer utility and thereby can charge price premiums. Second, providers may standardize their offerings to a greater degree through the identification of horizontal (regional) or global segments, thereby enjoying cost reductions and more efficient allocation of critical resources. Thus, when customers, as opposed to countries, are used as the basis for identifying global market segments, the effectiveness of marketing strategies will increase (Hofstede, Steenkamp, and Wedel 1999; Jain 1989).
This article has developed and tested a model of how price elasticities depend on dimensions of service quality and organizational characteristics and how these effects are moderated by national and regional differences. Our research shows how these variables can be used to identify nontraditional, vertical, and horizontal market segments. To our knowledge, we are the first researchers to study price elasticities to derive implications for the market segmentation of services. However, several limitations of this study should be recognized. First, our study focuses on two system support services offered by a single global company in a specific industry. Further research should investigate how price elasticities vary across multiple companies and service industries as well as customers. Second, we measure arc price elasticities over a relatively short period during which competitive activity is relatively stable. Additional research could model price elasticities and how they vary within a dynamic model of purchase behavior that incorporates competition. Third, we examine customers from only seven countries. Although these locations are culturally and economically distinctive, further research should consider using data from a larger pool of markets. Fourth, our study uses operational rather than perceptual measures of service attributes Prior research links operational measures to perceptual measures of service quality, or it links perceptual measures to repeat purchase behavior, whereas our study ties operational measures to repeat purchases. Further research is needed to develop a comprehensive model of how customers' perceptions (and cognitive processes) mediate the relationship between service operations and customer purchase behavior. Fifth, we use regional or country dummy variables rather than incorporating characteristics of regions and countries that might act as moderators. Additional research using much more extensive data could use more sophisticated cultural and economic measures to determine any interaction effects.
Our understanding of markets and segments for services is hindered by the blurring of distinct market boundaries. Day and Montgomery (1999, p. 7) remark that "The continuing progression from a world of distinct boundaries to one of linked global markets is being fueled by the persistent forces of the homogenization of customer needs and the recognition of the competitive advantage of a global presence." The homogenization of customer needs will yield horizontal segments that cut across country, and sometimes regional, boundaries. Truly adaptive organizations will be able to develop service strategies that fit an evolving, nonconventional global marketplace characterized by both vertical and horizontal segments. Although our study provides a platform from which further research can begin, additional investigations of global services are needed.
The authors gratefully acknowledge the assistance of the anonymous cooperating company and the programming assistance of Matthew D. Bramlett. This article was originally submitted during the tenure of David W. Stewart as editor of JM; Stewart handled the review process and accepted the article for publication.
(n1) There are (at least) three different streams of research regarding the dimensions of service quality. First, research based on the Nordic school distinguishes between different service processes (see Gronroos 1983). Second, following Juran (1988), researchers have distinguished between design quality, or elements of the service that the customer expects to receive based on benefits promised or stated in the service contract, and experience quality, or the customer's actual experience with each of the elements of the product or service (see Anderson, Fornell, and Rust 1997). Third, Parasuraman, Zeithaml, and Berry (1985, 1988) identify five underlying dimensions of service quality--reliability, responsiveness, assurance, empathy, and tangibles--and develop an instrument, SERVQUAL, to measure them.
(n2) This feature is made evident by considering an extreme case in which a customer begins by holding a single contract and then does not repurchase it because of a price increase of 5%, for example. The resultant elasticity measure is -100/5 = -20. If the same customer held ten contracts and repurchased five after a price increase of 5%, the resultant price elasticity measure is -50/5 = -10. We know that the second price elasticity estimate is more precise because it is based on a larger sample (Len contracts) than the first price elasticity estimate (one contract).
(n3) Respondents included chief information officers, management information system managers, and service technicians who were identified from company records and screened by the consultancy to ensure that they were involved in the decision-making process for service contracts (either recommending or making the final decision). A few brief quotations from customers in different countries are provided here: (1) Responsiveness: "Our biggest problem is to shorten the response time to the trouble." "[We] need a highly flexible emergency team that can respond to ... different needs." (2) Reliability: "It's a full-time job trying to get people to do what we're paying them to do." "They should be concentrating on fulfilling their contracts, which would make me happy." "They have poor performance on their end Let's just say they make faulty promises." (3) Assurance: "I want a partner. Somebody I can trust and I know his background. I completely trust in him. We solve problems together." "Getting the right people .. who know what they're talking about and understand our environment." "I want to talk with someone more knowledgeable." "Get me the right person at the right time.... We need a higher level of expertise."
(n4) Distributions of customer service measures (e.g., employee labor, materials, resources allocated, response time, resolution time) are typically skewed and are characterized by a lower boundary of zero, a majority of observations within a certain range, and a few "extreme" outcomes. For example, a single service technician can usually deliver a service, using certain materials and procedures, within 24 hours of the customer's request. However, a customer's request may occasionally require efforts by multiple technicians, using more extensive materials and procedures, and therefore take much more time. These "extreme outcomes" or infrequent but extremely high (or low) levels of delivered service, are instances of unreliability.
(n5) Cultural distance is not subject to pooling tests because it takes on a single value for a given country. We test whether region coefficients can be constrained to be equal only for those regions where tests indicated that country coefficients could be constrained to be equal. For example, our test constrains price coefficients for North America and Asia to be equal, excluding Europe, because the United Kingdom and Germany have already been shown to have different values.
(n6) As part of this iterative testing procedure, we investigate whether each national or regional variable is a "pure" moderator (i.e., the main effect is not significant in the presence of interaction terms) or a "quasi moderator" (i.e., the main effect and the interaction effects are significant). We investigate this issue using the standard tests of moderated regression analysis (Baron and Kenny 1986; Irwin and McClelland 2001; Sharma, Durand, and Gur-Arie 1981). As shown in Tables 6 and 7, the results of these tests are mixed. National and regional characteristics are pure moderators in the low-support equation and quasi moderators in the high-support equation (because main effects for Germany and the United Kingdom are significantly different From zero). We believe that national and regional variables can be pure or quasi-moderator variables depending on idiosyncratic characteristics of the product markets, and these results will not necessarily generalize to other product markets. Consequently, we do not discuss this issue further.
Legend for Chart:
A - Problem Studied
B - Exemplar Studies
C - Service Domain?
D - Comparative/International?
E - Consumers/Business Customers
F - Study Type or Segmentation Measure
A B C
D E
F
Meta-analysis of Tellis (1988) No
econometric studies
of price elasticity
No Not applicable
Meta-analysis
Determinants of price Bolton (1989); No
sensitivity for Huber, Holbrook,
nondurables and Kahn (1986);
Mulhern, Williams,
and Leone (1998);
Shankar and
Krishnamurthi
(1996)
No Consumers
Across brands
or stores
Segmentation of Elrod and Terry No
relevant markets (1982)
No Family units
Differential
pricing as a
segmentation
variable
Optimal pricing policies Segal (1991) Yes
for services
No Businesses
Fee structures
to service
customers
The effects of specific Andersen (1996); Yes
attributes (including Bolton and Lemon
price) on evaluations (1999)
of services or
purchases of services
No Consumers
The effects of
price on
service
perceptions
across multiple
environments
Control issues in Erramilli and Yes
service firms' Rao (1993)
market-entry strategies
Yes Consumers and
business
customers
Mode of entry,
no segmentation
analysis
Segmentation based Hofstede, No
on consumer-product Steenkamp,
relations and Wedel
(1999)
Yes Consumers
Product
characteristics
Determinants of Present study Yes
business customers'
price sensitivity for
services
Yes Support services
for businesses
Price elasticity
across service
contracts for
international
business
customers Legend for Chart:
A - Variable
B - Asia Pacific Japan
C - Asia Pacific Korea
D - Asia Pacific Singapore
E - Europe Germany
F - Europe United Kingdom
G - North America Canada
H - North America United States
A B C D E
F G H
Price elasticity -.02 .67 -.01 -.30
(.48) (.98) (.49) (1.46)
-.20 -.03 .02
(.85) (.14) (.37)
Average number
of contracts
per customer
used to calculate
elasticity estimate 3.74 6.10 8.26 14.25
(4.22) (5.53) (20.24) (21.04)
10.41 10.40 8.36
(18.63) (10.09) (22.99)
Criticality 4.00 4.41 3.60 4.37
(1.23) (.93) (1.26) (.98)
4.16 3.92 4.02
(1.38) (.94) (.88)
Average number
of high-support
contracts held 1.27 6.04 4.87 4.61
(2.81) (6.81) (10.23) (7.36)
4.11 .31 .96
(8.30) (1.04) (1.21)
Sample size
(number of
customers) 68 36 39 104
62 38 161
Notes: The table shows average price elasticities across
customers, with standard deviations in parentheses. Price
elasticities are measured such that a negative value implies
(relative) price sensitivity and a positive value implies
(relative) price insensitivity. The table also shows descriptive
statistics for selected other variables, such as number of
contracts held The customer's self-report of criticality is
measured on a five-point scale, where 5 = highly critical
systems. Legend for Chart:
A - Variable
B - Asia Pacific Japan
C - Asia Pacific Korea
D - Asia Pacific Singapore
E - Europe Germany
F - Europe United Kingdom
G - North America Canada
H - North America United States
A B C D E
F G H
Elasticity .12 .12 -.03 -.76
(.56) (.80) (.54) (2.52)
-1.04 .00
(2.69) (.43)
Average number
of contracts
per customer
used to calculate
elasticity estimate 2.53 5.51 4.65 3.38
(3.29) (6.18) (9.62) (4.02)
6.97 1.19
(9.45) (.95)
Criticality 4.05 4.39 3.73 4.55
(1.15) (1.09) (1.32) (.85)
4.59 4.05
(1.07) (.83)
Average number
of low-support
contracts held 2.75 3.60 6.52 11.71
(4.83) (5.47) (19.27) (15.50)
14.84 4.16
(20.34) (20.16)
Sample size
(number of
customers) 38 77 44 53
34 N.A. 193
Notes: N.A. = not applicable. The table shows average price
elasticities across customers, with standard deviations in
parentheses. Price elasticities are measured such that a
negative value implies (relative) price sensitivity and a
positive value implies (relative) price insensitivity. The
table also shows descriptive statistics for selected other
variables, such as number of contracts held. The customer's
self-report of criticality is measured on a five-point scale,
where 5 = highly critical systems. Legend for Chart:
A - Construct
B - Measure for Low-Support Offering Equation
C - Measure for High-Support Offering Equation
A B
C
Price elasticity Percentage change in quantity of support revel
contracts of type k, divided by percentage
change in price of support level contracts of
type k for each customer i
Same
Responsiveness Reverse coding of average time until first
response on hardware requests tin hours)
Reverse coding of average time on responses
to software requests (in hours)
Reverse coding of average travel time to
hardware on-site requests (in hours)
Reverse coding of average resolution time for
software requests (in days)
Reliability Reverse coding of extreme values of resolution
time for software requests (number of
occurrences)
Reverse coding of extreme values of engineer
effort to resolve software requests (number of
occurrences)
Assurance Extreme values for time until first response
on hardware request (number of occurrences)
Extreme values for travel time to on-site
hardware requests (number of occurrences)
Average engineer effort to resolve software
requests (in minutes)
Organizational Firm experience: number of high-support
characteristics offerings held
System criticality (self-report on a
five-point scale)
Firm experience: number of low-support
offerings held
System criticality (self-report on a
five-point scale)
Covariates
Cultural Hofstede's cultural index of
characteristics individualistic/collectivistic cultures
Same
Price levels List price expressed in U.S. dollars
Same
Covariates Geographic dummies (where applicable)
Geographic dummies (where applicable), total
budget spent on system support (scaled U.S.
dollars) Legend for Chart:
A - Coefficient
B - Country Coefficients Equal Within Region Asia Pacific
C - Country Coefficients Equal Within Region Europe
D - Country Coefficients Equal Within Region North America
E - Region Coefficients Equal
A
B C D E
Low-Support Model Tests
Responsiveness
Average time until first response on
hardware requests (reverse coded)
.02 2.21 .22 1.85
Average time on responses to
software requests (reverse coded)
.49 9.15(*) .04 .08 (NA = AP)
Reliability
Extreme values of resolution time
for software (reverse coded)
.36 .00 .06 1.26
Assurance
Extreme values for time until first
response on hardware requests
6.07(*) 2.58 .02 1.62 (NA = E)
Organizational Characteristics
Criticality
.27 1.31 .09 5.89(*)
Number of contracts
.34 .27 .07 3.85(**)
Covariates
Price
4.20(**) 3.72(***) .09 No test
Geographic dummies
1.47 1.33 No test 1.52
High-Support Model Tests
Responsiveness
Average travel time for hardware on-site
requests (reverse coded)
.01 .49 .03 6.18(*)
Average resolution time for software
requests (reverse coded)
.40 .10 2.54 11.46(*)
Reliability
Extreme values of engineer effort to
resolve software requests (reverse coded)
7.48(*) 1.35 .08 3.20(***) (NA = E)
Assurance
Extreme values for travel time to on-site
hardware requests
2.31 .01 .00 2.11
Average engineer effort to resolve
software requests
4.09(**) .28 4.00 69 (NA = E)
Organizational Characteristics
Criticality
.33 1.73 No test 2.36(***)
Total support
.61 .83 No test 6.65(*)
Covariates
Price
1.52 2.34 No test .19
Geographic dummies
.73 2.93(***) No test .20 (NA = AP)
(*) p < .01 (**) p < .05. (***) p < .10
Notes: NA = North America; AP = Asia Pacific; E = Europe. In
right-hand column, constraints are across all three regions
unless indicated otherwise. For example, "NA = E" indicates
that the constraint was applied across North America and
Europe. Legend for Chart:
A - Variable
B - Coefficient Estimate
C - Standard Error
D - Standardized Coefficient
A B
C D
Intercept .0075
.1281
Responsiveness
Average time on responses to software
requests (reverse coded), Germany -.0006
.0005 -.04
Average time on responses to software
requests (reverse coded), United Kingdom .0001(**)
.0000 .08
Reliability
Not supported
Assurance
Extreme values for time until first
response on on-site hardware request,
Korea 1.2249(*)
.1362 -.38
Organizational Characteristics
Criticality, Europe -.0092(*)
.0058 .17
Number of high-support contracts, Europe .0119(*)
.0035 .13
Number of contracts, Asia Pacific -.0092
.0058 -.08
Covariates
Hofstede cultural distance .0004
.0018 .01
Price: Europe, Germany -.0202(***)
.0114 -.07
Price: North America -.0054(***)
.0031 -.07
Model Statistics
R² .28
Adjusted R² .27
F-statistic 24.43(*)
(*) p < .01.
(**) p < .05.
(***) p < .10.
Notes: All our hypotheses predict conditions under which
customers will be more price insensitive. Our equations
are specified, and our measures are constructed, so that
a positive coefficient implies more price inelasticity
or insensitivity and a negative coefficient implies more
price elasticity or sensitivity. Legend for Chart:
A - Variable
B - Coefficient Estimate
C - Standard Error
D - Standardized Coefficient
A B
C D
Intercept .7346
.3938
Responsiveness
Average travel time to hardware on-site
requests (reverse coded), Europe 1.6582(*)
.1575 .71
Average travel time to hardware on-site
requests (reverse coded), North America .2741(*)
.1180 .10
Average resolution time for software
requests (reverse coded), North America 2.2135(*)
.2733 .28
Reliability
Extreme values of engineer effort
to resolve software requests
(reverse coded), Japan .0722(*)
.0276 .11
Extreme values of engineer effort to
resolve software requests (reverse
coded), Korea .1401(*)
.0246 .26
Extreme values of engineer effort to
resolve software requests (reverse
coded), Europe and North America .3327(*)
.0497 .35
Assurance
Extreme values for travel time to on-site
hardware requests, all markets .7857(*)
.0949 -.33
Average engineer effort to resolve
software requests, Japan .0070(*)
.0030 -.10
Average engineer effort to resolve
software requests, Korea .0076(*)
.0023 -.16
Average engineer effort to resolve
software requests, Europe and
North America .0107(*)
.0016 -.32
Organizational Characteristics
Criticality, Asia Pacific -.1032
.0652 -.13
Number of low-support contracts, Europe .0286(*)
.0057 .26
Total support budget, Europe .0003(*)
.0001 .09
Total support budget, North America -.0001(***)
.0001 -.06
Covariates
Hofstede cultural distance -.0044
.0051 -.08
Geographic dummy, Germany -1.9537(*)
.3965 -.23
Geographic dummy, United Kingdom -2.3561(*)
.3858 -.45
Model Statistics
R² .55
Adjusted R² .53
F-statistic 33.00(*)
(*) p < .01.
(**) p < .05.
(***) p < .10.
Notes: A positive coefficient implies more price inelasticity or
insensitivity and a negative coefficient implies more price
elasticity or sensitivity. Legend for Chart:
A - Hypothesis
B - Low-Support Offerings Result
C - High-Support Offerings Result
A
B
C
H1: A horizontal market segment
exists such that customers who
receive more responsive service
are less price sensitive than
customers who receive less
responsive service.
Supported in the United Kingdom
only. Customers are less price
sensitive when average resolution
time for software requests is low. See
Table 6.
Supported in Europe and North
America. Customers are less price
sensitive when average travel times
to provide on-site support are high
and when average resolution time for
software requests is low. See Table 7.
H2: A horizontal market segment
exists such that customers who
receive more reliable service over
time are less price sensitive than
customers who receive less
reliable service.
Not supported.
Supported in Korea, Japan., Europe,
and North America. Customers are
less price sensitive when engineer
effort to resolve service requests
does not vary over time (i.e., across
requests). See Table 7.
H3: A horizontal market segment
exists such that customers who
received more assurance or
empathy from service
representatives over time are less
price sensitive than customers
who received less assurance.
Supported in Korea only. Customers
are less price sensitive when
technicians' time until first response
on hardware request (usually for onsite visit)
is high but infrequent. See
Table 6.
Supported in Asia Pacific, Europe,
and North America. Customers are
less price sensitive when average
engineer effort to resolve software
requests is high and when
technicians make infrequent lengthy
trips to respond to on-site hardware
requests. See Table 7
H4: A horizontal market segment
exists such that customers who
purchase few services in a given
industry are less price sensitive
than customers who purchase
many services.
Supported in Europe Customers with
lower numbers of high-support
contracts are less price sensitive.
See Table 6.
Supported in Europe (number of low
support offerings) and North America
(total support budget). See Table 7.
H5: A horizontal market segment
exists such that customers who
consider service offerings highly
critical to their business are less
price sensitive than customers
who view the service offerings as
less critical.
Supported in Europe. Customers with
highly critical systems are less price
sensitive. See Table 6.
Supported in Europe (number of low-support
offerings) and North America
(total support budget). See Table 7.
H6: Vertical market segments exist
such that the effects of
dimensions of service quality and
organizational characteristics on
price sensitivity are moderated by
national and regional variables.
Supported for measures of
responsiveness, assurance, criticality,
and total support. See Table 5.
Supported for measures of reliability,
responsiveness, assurance, criticality,
and total support. See Table 5.DIAGRAM: Figure 1 Factors Influencing Business Customers' Price Elasticities for International Service Offerings
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By Ruth N. Bolton and Matthew B. Myers
Ruth N. Bolton is Professor of Management, Owen Graduate School of Management, Vanderbilt University.
Matthew B. Myers is Assistant Professor of Marketing, Department of Marketing, Logistics, and Transportation, University of Tennessee.
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Record: 119- Product Complements and Substitutes in the Real World: The Relevance of "Other Products". By: Shocker, Allan D.; Bayus, Barry L.; Namwoon Kim. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p28-40. 13p. 1 Diagram, 1 Chart. DOI: 10.1509/jmkg.68.1.28.24032.
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Product Complements and Substitutes in the Real World:
The Relevance of "Other Products"
In the real world, buyer demand for a product can depend directly and indirectly on the marketing efforts of "other products" in different categories. The authors offer a behavioral rationale for the existence of the effects of "other products'" marketing efforts and propose a taxonomy of possible intercategory relationships. The discussion enables the authors to identify several promising new research directions.
Buyers make purchase decisions in a dynamic market environment, which affords them choices from enormous numbers of products and brands as well as influence from a diverse set of marketing efforts. Buyers may also be affected by the context of their previous purchases, ownership, and usage. Given such diversity, demand for a product depends directly and indirectly on many things, including the current or previous marketing efforts of "other products," that is, products in different but related categories. The idea that demand in one product category can be affected by marketing efforts in another is not new (Erdem 1998), but the assumption that a common brand name may be needed for such transfer to occur is too limiting. Categories affect one another in ways that transcend common brand interactions.
Product substitutability and complementarity have long been natural ways to perceive intercategory( n1) relationships.( n2) Products are considered complements (substitutes) if lowering (raising) the price of one product leads to an increase in sales of another (e.g., Bucklin, Russell, and Srinivasan 1998; Russell and Bolton 1988; Russell and Petersen 2000).( n3) Economic theory emphasizes static demand effects associated with "other products," because complements and substitutes usually are defined in terms of extant cross-elasticity measures (e.g., Deaton and Muellbauer 1980). Figure 1 depicts the conventional framework.
Unfortunately, this dichotomy does not fully consider the richness of plausible interproduct effects on buyers and their market behaviors. For example, consider the devices used for personal communication: landline telephones, wireless telephones, and pagers. Are these products complements to or substitutes for one another? How can the interproduct relationship between wireless telephones and personal data assistants (PDAs; which are taking on wireless communication functions) be characterized? These products and the dynamic interrelationships between them do not seem to fit neatly into the conventional complement/substitute framework. A new product introduction is often construed as offering yet another alternative that buyers can choose rather than an alternative that can change the very nature of a market structure (e.g., as Enterprise has done in the rental car market). The introduction of a complement may increase sales of a target product or make it more suitable for more applications than it was previously (e.g., longer-life batteries improved the range of applications for laptop computers). The availability of more (differentiated) alternatives can increase the possibility of buyers finding new uses for existing products (e.g., efforts to improve bottled waters have led to new categories of water) or finding added value in complements that already exist. Recognition of a new complementary entry (e.g., microwave ovens) may alert perceptive marketers of complements to new sales possibilities (e.g., microwave popcorn, addition of a popcorn function button), because a product may be able to provide greater convenience without significant downside (e.g., taste, cost). In addition, a new product introduction can potentially change a market structure by creating new benefits or costs or by extending the range of existing benefits (e.g., Japan's entry in the U.S. automobile market had a significant effect on expectations of quality and reliability; the availability of antilock brakes and air-bag options most likely affected the importance of safety in consumers' car-buying decisions).
The recognition of product complements and substitutes may, in turn, lead to the recognition of features of one product that can usefully be applied to improve another product (e.g., capabilities previously associated with televisions, such as an "instant-on" push button, might improve personal computers [PCs]; MacMillan and McGrath 1997). For example, Kim and Mauborgne (1999) suggest that market-driving new businesses succeed because they incorporate strengths and reduce or eliminate the weaknesses of competing alternatives. For example, they consider personal-finance alternatives that were available before the introduction of Quicken (i.e., accounting software and pencil and paper). Kim and Mauborgne argue that Quicken is successful because it is able to combine the low price and ease-of-use of a pencil with the speed and accuracy of traditional personal-finance software. Knowledge of existing complementary relationships is also essential for the identification of desirable product systems (e.g., wireless telephones with calendars and games may be sensible, whereas wireless telephones with camera capabilities may prove less so, even though both are technologically feasible). The incorporation of existing complements can even legitimize a new product combination because at least one market segment already purchases both separate products.
Despite the pervasiveness of intercategory relationships in the marketplace, most research that addresses competitive effects does not explicitly consider the effects of "other products." Such an omission may limit an understanding of why market structure is the way it is and may create inaccuracies in managers' abilities to predict outcomes of their marketing actions. Thus, a major purpose of this article is to sensitize researchers and managers to the relevance of "other products." As managers find uses for multicategory sales and marketing data, market research firms will be more willing to collect and disseminate them. As occurs with products in general, the ready availability of such data will likely stimulate managers and researchers to find more uses for them (much as has occurred with scanner data). We expect that more attention will be paid to hypothesizing and measuring multicategory effects.
There have already been calls for greater realism in the research of market behaviors. For example, in introducing their research into customer dynamics, Heath and colleagues (2000, p. 291) note the following: "[T]he corpus of decision theory remains focused on single decisions.... This limits our understanding of decision-making.... If we are to understand how earlier choices and ownership influence subsequent choices where competitors attack and defend turf through changes in product, price, and promotion, we will have to expand theories to recognize the many forces at work in complex settings." Day and Nedungadi (1994) strongly question the widespread managerial practice of simplifying market realities. We echo such concerns by noting, for example, that the bulk of market structure analysis (MSA) research has focused only on single-category competition. Greater understanding of the connectedness among products on the part of managers and researchers should help in the design of better strategies and tactics and in the prediction of their market outcomes. We believe this understanding will serve to
• Identify categories and brands that are the key competitor and complementor influences in the market structure (e.g., Brandenburger and Nalebuff 1996),
• Identify who relevant potential customers are and why (e.g., they may already be buying in related categories; Day, Shocker, and Srivastava 1979),
• Determine the attractiveness of potential opportunities (e.g., Lehmann and Winer 2000), and
• Develop appropriate competitive strategies for realizing opportunities (e.g., Porter 1980).
A major reason for the current inattention to "other products" may be a lack of a framework that helps researches think about these effects and gives them a terminology with which to discuss them. Thus, another purpose of this article is to augment the traditional complement/substitute framework by proposing a broader taxonomy that incorporates several important static and dynamic intercategory relationships. Our discussion adds to the marketing literature on competitive dynamics that arise from the interaction of buyer and seller perspectives (e.g., Dickson 1992; Ratneshwar et al. 1999; Rosa et al. 1999) and, more important, enables us to suggest some promising new research questions.
Product Categorization
Before we discuss the rationale for intercategory effects, we consider product categorization and its role in individual-level decision making. The psychology and consumer behavior literature has examined cognitive representations of categories and their ensuing information-processing implications (e.g., Alba and Hutchinson 1987; Barsalou 1991; Murphy and Medin 1985; Rosch 1978; Smith and Medin 1981; Viswanathan and Childers 1999). However, only limited work has addressed key issues of why categories form and how they evolve (e.g., Bettman and Sujan 1987; Rosa et al. 1999) or how to define and distinguish them (e.g., Do different generations of a high-technology product belong to the same or different categories? Ratnesh-war and Shocker 1991).
Both buyers and sellers believe it is useful to categorize products. For buyers, categorization simplifies information processing and decision making, and it facilitates interpersonal communication. Categories provide a context in which similarities and differences among brands can be highlighted. A category name can efficiently communicate much meaning. People are also sensitive to the correlational structure of their environments and, in the interest of cognitive economy, may categorize products (at least temporarily) on the basis of factors such as physical resemblance, perceived similarity of producers, or fit with available category labels (Day, Shocker, and Srivastava 1979). From a seller's perspective, categorization speeds up individual buyer learning about new products and facilitates diffusion and promotion through word of mouth among potential buyers. Categorization also enables easy communication between producers and distributors (e.g., through stockkeeping units and billing information). Thus, product category formation and evolution is the consequence of purposeful behaviors on the part of both buyers and sellers. Rosa and colleagues (1999) present empirical evidence that product markets are socially constructed and evolve from interactions between buyers and sellers. Product markets may not always be coincident with a single product category; that buyers and sellers each need to make sense of the other's behaviors also accounts for the fuzziness of some category boundaries and their seemingly ad hoc nature (e.g., Day, Shocker, and Srivastava 1979; Viswanathan and Childers 1999).( n5)
Strong arguments can also be made for a constructive, flexible, and goal-driven view of product categorization. First, there is considerable evidence that buyer motives and goals are important in determining buyers' mental representations of products, that is, which alternatives they attend to and which aspects they consider more important (e.g., Barsalou 1985; Loken and Ward 1990; Ratneshwar, Pechmann, and Shocker 1996; Ratneshwar and Shocker 1991). Second, category representations may be flexible because they can be contingent on goals that are salient in any given usage situation or context (e.g., Bagozzi and Dholakia 1999; Barsalou 1991; Ratneshwar and Shocker 1991). For example, Ratneshwar and Shocker (1991) find that category typicality judgments people made in the context of specific product-usage situations (e.g., snacks that people might eat while drinking a beer at a Friday evening party) were significantly different from judgments they made in response to simpler category cues (i.e., snack foods). Apparently, the contextual information framed buyers' perceptions by focusing their attention selectively on situation-relevant aspects of products (i.e., whether a snack is salty, crisp, divisible, and convenient to eat at a party).
In mature product markets, many different products that serve the same general need can coexist (e.g., both subcompacts and pickup trucks provide personal transportation). A key reason for the proliferation of categories is that producers face technological barriers to serving multiple, specific buyer goals optimally (e.g., it is difficult to provide both fuel efficiency and roominess in personal transportation). Across buyers or households, there also may be heterogeneity in preferences in terms of the importance they attach to different goals or desired benefits (e.g., fuel efficiency versus roominess). Given both technological constraints and buyer heterogeneity, producers create, label, and position different products to serve disparate buyer goals optimally (Ratneshwar, Pechmann, and Shocker 1996). In such cases, buyers are likely to perceive that products in the same category deliver only on certain goals and that options in different categories have negatively correlated attributes.
Intercategory Effects
Russell and colleagues (1999) identify three ways that choices across different product categories can be linked: ( 1) cross-category consideration, ( 2) cross-category learning, and ( 3) product bundling. In cross-category consideration, several product categories (and possibly many options or brands in each category) are effective substitutes (Srivastava, Alpert, and Shocker 1984). Roberts and Lattin (1991), Shocker and colleagues (1991), and Graonic (1995), among others, provide empirical evidence for the existence of multicategory choice sets.
An intercategory effect can also be activated by the context of previous choices. Such cross-category context or learning effects are present when choice in one category is influenced by the prior possession of, experience with, or use of products in other categories. A buyer who is satisfied with a certain brand (e.g., Maytag) or technology (e.g., digital) in one category (e.g., washing machines, pagers) may be more likely to purchase from another category in which the same brands or technologies appear (e.g., dishwashers, wireless telephones; Erdem 1998; Kim, Chang, and Shocker 2000).
In product bundling, items from multiple categories jointly contribute to fulfill buyers' wants, which leads to buyers selecting several different products (usually on the same or proximate shopping occasions). Most complementary products used together fit into this classification even though they are not always purchased together (e.g., hot dogs and buns, computers and software). Sellers often assemble bundles that consumers can accept or reject (e.g., a package of standard equipment for a new car). In some cases, distributors or consumers assemble the package (e.g., a stereo "system" that comprises complementary components from competing firms, such as a Sony receiver with Yamaha speakers, even though each brand offers both components). It is less recognized that consumers often examine products category by category and create their own (personalized) bundles (e.g., an assortment of liqueurs to serve to guests after dinner, a grocery shopping basket; Farquhar and Rao 1976; McAlister 1979; Russell and Kamakura 1997). Bundles are items that buyers might purchase together, because the items meet a buyer's goal (e.g., convenience) by being available from the same store or supplier. However, a retailer may serve other buyer goals by, for example, prepackaging products to be sold as gifts (saving time) or by assembling different category components (ensuring compatibility and connectivity).
In all three ways, the buyer's purposes or goals are central. Purpose (which is sometimes implicit in a usage or purchase situation) provides coherence for the multicategory decision by helping define the benefits that the buyer wants (Bagozzi and Dholakia 1999; Yang, Allenby, and Fennell 2002). The definition of relevant benefits is often tantamount to the definition of the products that buyers will consider. It is possible that product categories have hierarchical relationships because the purposes that influence their construction are also hierarchical (Ratneshwar, Mick, and Huffman 2000). Products are able to serve multiple purposes because they provide "affordances," which, according to Ratneshwar and colleagues (1999, p. 194), are "the potential benefits and disadvantages of a product... in relation to a particular person" that can be actualized on different occasions. Thus, consideration of product complementarity or substitutability without controlling for the effects of purpose creates ambiguity.
Ratneshwar and colleagues (1999) provide evidence in support of three factors that affect product and service decision making. In addition to purpose, they recognize that the awareness and availability of products and services matter to the decision maker for at least three reasons. First, constraints on the number of available alternatives (e.g., a restaurant with a limited selection of entrees) may force consumers to consider and choose across multiple categories (Johnson 1989). Second, the visual configuration of choice alternatives may juxtapose multiple competing categories and thus prompt cross-category consideration (e.g., restaurant menu, retail store display, mail-order catalog, Web site). Third, access to certain complements already used may enable a buyer to use a core product in particular ways (e.g., a PDA with an add-on that enables it to function as a wireless telephone and offers synergy that may afford it some advantages over more specialized products). The buyer's own preferences (conditioned by past experience and knowledge) are also important. In addition, the context of "other products" and buyer preferences may play a role in defining relevant product substitutes. Thus, the "three P's," or person, products, and purpose, are useful factors for examining why multicategory decisions occur and for predicting their possible outcomes.
We began this section by noting that categorization is an important function of people's decision making. Buyers' and sellers' product categorization is based on commonly understood sets of related products that facilitate communication. Although buyers create categories to simplify decision making, their choice processes often span multiple product boundaries (Ratneshwar, Pechmann, and Shocker 1996; Viswanathan and Childers 1999). A key reason for this is that buyer purposes or goals are situation specific, whereas at least in the short run, categories remain reasonably stable (Srivastava, Leone, and Shocker 1981). Buyers need not respect single category labels if alternatives in a particular category are not adequate to satisfy their purposes or if products in different categories are adequate (Shocker et al. 1991). Sellers have similar freedom, and by offering new product alternatives, they can sometimes even change category meanings (e.g., the phrase "taking an aspirin" became inadequate as a description of all painkillers when acetaminophen, ibuprofen, naproxen, and others entered the market).
In this section, we provide an extended view of intercategory relationships, a view that moves beyond the conventional framework of complementarity and substitutability to consider the richness of plausible effects more fully. Our taxonomy of intercategory relationships in Figure 2 includes both static and dynamic cases. Static relationships are stable and tend to persist largely unchanged for a long time. They are sustainable at an individual level because the categories continue to fulfill similar buyer requirements (stable purposes). Static relationships may offer similar performance/ price ratios (Kim, Chang, and Shocker 2000), and they may include products that can be simultaneous complements and substitutes; for example, a hamburger and a diet soft drink are normally complements, but because consumption of the low-calorie diet soft drink may enable buyers to rationalize consumption of the high-calorie hamburger, the products also have a substitute relationship. Because some purposes arise relatively frequently and others arise only occasionally, static buyer behavior may reflect the different learned responses or environmental circumstances that influence buyer behaviors.
In dynamic relationships, the products and/or their relationships are in transition over time, and the products ultimately may not coexist. Dynamic relationships may reflect ( 1) product order of entry (i.e., a category that already exists and serves as a context for decision making and affects factors such as product appreciation and access to distribution channels); ( 2) transitions between substitutes and complements in which complement bundles become substitutes for the original unbundled products (e.g., a clock radio can substitute for a dedicated clock and dedicated radio), or products that were originally designed as imperfect substitutes come to coexist as complements (e.g., pagers and wireless telephones, e-mail and voice-mail); ( 3) transitions within complements in which originally nonessential complements become more essential; and ( 4) transitions within substitutes in which either the new or the existing product eventually dominates.
Figure 2 is framed in terms of possibilities that operate at the individual-buyer level (i.e., what an informed buyer who is knowledgeable about the relevant categories might comprehend). Because relationships can change with time as categories are modified and because buyers are heterogeneous in terms of their awareness, knowledge, and purposes, aggregate market relationships may not always indicate the individual-level effects that underlie them (i.e., aggregate intercategory relationships may merely represent an averaging of the heterogeneous relationships at the individual level).
Static Intercategory Relationships Across Buyers
Substitutes-in-use. In the case of substitutes-in-use, multiple product categories compete because they are able to serve a similar defining purpose and thus may have similar potential customers (e.g., Srivastava, Alpert, and Shocker 1984; Srivastava, Leone, and Shocker 1981; see Figure 1). In this case, all competing products deliver requisite benefits, even though each may deliver against others as well. Because of trade-offs, one product often does not dominate the other (e.g., digital videodiscs and videotapes offer tradeoffs in picture quality and cost that have enabled them to coexist; however, as cost differences narrow, the coexistence may change). Sometimes a price or distribution channel precludes the categories from competing more directly (e.g., national and private label brands); thus, the categories' substitutability becomes evident only when the more expensive brand is on sale or when they have similar distribution (Blattberg and Wisniewski 1989). The relationship is often asymmetrical in magnitude; for example, a national brand may serve a broader set of purposes than a private label product (particularly in the case of conspicuous consumption), and thus the latter may not substitute effectively even when it is on sale. General purpose products (e.g., laundry detergents) can be substitutes for more specialized or niche products to a meaningful degree (e.g., other cleaning products), but the converse may not hold.
Preferences among substitutes-in-use can largely be a matter of buyer taste rather than performance quality alone. However, these products still negatively influence one another's sales. A desire for variety or redundancy may sometimes motivate the purchase of substitutes, thereby creating a form of complementarity. Stair-climbers, stationary bicycles, rowing machines, and treadmills all offer an aerobic workout and thus may be considered substitutes, even though some are more suitable for certain buyer segments (e.g., recumbent bicycles may put less strain on the back than conventional stationary bicycles; Graonic 1995). Substitutes-in-use need not physically resemble one another, but they can do so if form is essential to function. Services can substitute for products (e.g., leasing rather than owning a car or computer, software-based services that perform the same functions as hardware). What is important is that it is primarily the "person" in the three P's framework that determines the extent of substitutability.
Occasional substitutes. Occasional substitutes satisfy a higher-order, more generic purpose. Purpose is hierarchical; there may be general or superordinate purposes and more specific subordinate purposes. The more general the purpose, the greater is the number of products that provide a degree of competition (e.g., several products all serve the general purpose of providing "pleasure," but a buyer will only purchase one of them because of budget constraints; Lehmann and Winer 2000). Because of this generality of purpose, the specific alternatives and their actual substitutability may be highly idiosyncratic because a person's preferences play an important role.
Even at the same level of specificity, categorization of certain new products may be amenable to cues. An overriding purpose might be suggested by the context of "other products" or the physical form that they assume. For example, granola bars might originally have been credible as candies, cookies, health foods, or a separate snack category, but a section of the supermarket in which they are shelved can suggest a preferred positioning. Store traffic and image can be built by promoting certain brands and categories (Chintagunta 2002). Because inherent ambiguity remains, the focal product may retain some substitutability with products in each of its plausible categories (e.g., granola bars can be substituted for cookies). The packaging of products in containers associated with another category can strengthen (or weaken) associations with the product's category or with another category's major benefits (e.g., gel toothpaste packaged in containers associated with mouthwash may strengthen its breath-freshening associations).
Complements-in-use. Complements-in-use enhance the growth prospects of one another, and their coexistence is affected by user purpose (see Figure 1). For example, PCs and application software have exhibited a positive, reinforcing influence on each other for more than 20 years (e.g., Gates 1998). In many cases, complements-in-use are products that essentially have limited value without the other (e.g., hardware and software; television sets and programming). In other situations, such complementary products can be used independently, but they usually are not because a superior result can be achieved jointly. Recognizing these intercategory relationships, firms have often followed a pure or mixed product-bundling strategy (e.g., Eppen, Hanson, and Martin 1991; Guiltinan 1987). For example, airlines and travel-related Web sites offer mixed bundles that include air travel, lodging, and rental cars; some physicians require that their patients undergo various multicategory diagnostic procedures (pure bundling) with their physical exam.
Occasional complements. Occasional complements offer another array of possibilities. For example, products that are intended to be used together exert design influences on each other (e.g., the size of a briefcase or the trunk of a car should reflect the size and nature of the "other products" they are intended to contain). Prominent features of one product may be used to describe similar features in another. These effects can be unrelated to price. Products that are commonly sold in the same stores or displayed near one another may exert weak effects on one another's sales. A buyer's observation of one product may influence impulse buying of another as a result of a kind of "reminder" promotional effect. A brand name that has strong associations in one product category (e.g., Johnson's Baby Shampoo) may transfer the associations to others (e.g., bandages, talcum powder) that may be weak complements (Loken and John 1993; Russell and Petersen 2000). As part of their rationale, cobranding or branded ingredient strategies have such cross-category associations (Park, Jun, and Shocker 1996).
Dynamic Intercategory Relationships Across Time
Product displacement. Product displacement is a substitute relationship in which "new and improved" categories come to dominate older ones and eventually make them obsolete. It is notable that an older product can contribute to its own demise by sensitizing customers to its deficiencies, which then speeds the adoption of a new product that promises relief. When retailers recognize product superiority for their customers, a newer product may use the same channels of distribution as the older product (e.g., compact discs [CDs] are sold in many of the same outlets that formerly sold cassettes and records). Sellers can force or speed displacement by phasing out and ceasing to supply the older product when both appeal to similar customer bases (e.g., Apple removed floppy disk drives from its new PC models in favor of CD or digital videodisc drives). Successive product generations often fit this case (e.g., among PC peripherals, 3.5 inch disk drives originally displaced 5.25 inch drives because the newer disks were more durable and smaller and offered greater data capacity at little or no extra cost).
Sometimes the displacing product creates a new category instead of serving as a subcategory of the displaced product (e.g., cars replaced buggies as basic transportation, calculators replaced slide rules). Presumably, the greater the differences between the new product and the previous category (e.g., physical appearance, technological platform, manufacturer), the greater is the likelihood that previous category labels no longer suffice. It is also possible that when the first mover appears, initial attempts at categorizing the innovation evoke existing categories (e.g., horseless carriage), but as more competitors enter with similar products, a new category name is created (e.g., car). The speed and magnitude of displacement should depend on whether the benefits and costs (i.e., the value proposition) of the newer product dominate the older product. Writeable CD-ROMs, superdrives, zip drives, and portable hard drives are categories that now compete to replace many applications that the floppy disk previously handled. Displacement seems inevitable whenever a newer product offers higher (equal) levels of all core benefits that are provided by the older product but at little or no added cost (i.e., a higher performance/price ratio). When products are displaced, they may be scrapped or diverted to less prominent uses or less sophisticated users. When this happens, new purposes sometimes become relevant (e.g., calculators are used for more applications than slide rules ever were).
Displacement is an outcome of competitive rivalry, as are coexistence (implied by substitutes-in-use) and product perseverance. What makes these cases dynamic is the method and time frame in which competition occurs. The phrase "predator-prey" characterizes a class of such dynamics (Moore 1993). Targeting similar customer needs, a new product (the predator) that is usually equipped with a higher level of technology than existing products enters the market and encroaches on the incumbent products' (the prey) market potential (e.g., Berryman 1992; Moore 1993). Facing new threats, the incumbent firms either disappear or react by enhancing their competitiveness. These firms' efforts can take the form of product or process improvements, lowered prices, or product repositioning. For example, plastic containers have largely displaced fiber cans for motor oil because of their ability to be opened without a tool and their integration of a pouring spout. Clear plastic has largely displaced glass bottles because of its lighter weight, squeeze-ability, and greater resistance to breakage at only slightly higher cost. However, in the case of all-plastic containers (the predator) threatening paper cartons (the prey) for refrigerated juices and milk, manufacturers of paper containers were able to fight back by adding plastic coating and pouring spouts with screw-on caps to improve the containers' functionality, thereby leading to coexistence.
Product perseverance. Product perseverance is a substitution type wherein a newer category fails to displace the older one. Although many factors have been offered to explain new product failure (including the possibility of inadequate marketing), failure to meet customer needs adequately is a frequently cited reason. A manager may misjudge whether the benefits of the new product exceed the old or fail to understand the full range of added costs that the new product's purchase or use necessitates. For example, in the PC industry, the first handheld PDAs did not fare well against incumbent products (e.g., laptops, paper-based organizers) even though a later variant (the Palm Pilot) has been quite successful. A new product that has poor underlying technology can impede the success of later products based on the same technology (e.g., Microsoft's Bill Gates stated that the Apple Newton fiasco hindered development of the handheld PDA product category; Bayus, Jain, and Rao 1997). Buyers may be sensitized to the aspects of the product (e.g., handwriting recognition) that were troublesome in failed versions, which creates a ready market for a credible improvement. In addition, a brand may be unable to reintroduce an improved version of a failed product (e.g., Apple eventually cut its Newton division because management believed the company was not strong enough to resurrect the brand) despite subsequent evidence of turnaround in the category.
Analogous to the predator-prey relationship is one that we term "prey-predator" (Moore 1993). A prey-predator multicategory relationship is characterized as a kind of competitive role reversal. The new product enters the market because it senses opportunity in the limitations of existing products, but by exposing the limitations, it awakens the existing product, which then becomes the victor. An example is DuPont's Corfam (see Hounshell and Smith 1988). After years of development and heavy research and development expenditures, in 1964 Corfam was heralded as the technological product substitute for leather. Targeting the high-end shoe market, Corfam had proved itself in tests to be equal or even superior to fine leather because it was unaffected by moisture, weighed one-third less than leather, kept its luster, and did not need to be broken in. Although DuPont initially faced retailer and consumer resistance to Corfam shoes, the critical factors that spelled Corfam's death were the entry of European fashion shoes made of many different styles of leather and the leather industry's promotion of glovelike leathers, which Corfam could not duplicate. Thus, Corfam was relegated to competing with cheaper vinyl shoes. Although Corfam was superior to vinyl, DuPont could not earn a profit because of Corfam's high manufacturing costs.
Enhancing complements. Enhancing complements occur when a newer product enhances the sales of an existing one by improving its functionality (e.g., increased availability, easier to use). In general, enhancing complements lead to higher benefit levels for the existing product with which they are used rather than introduce new benefits (Kim and Mauborgne 1999). Well-publicized uses for the newer product increase the likelihood that many buyers will insist on such higher benefit levels in existing and future products. In this case, the newer product positively influences sales of the existing and more basic product. Enhancement also occurs as a result of training or learning. Owning a bicycle may create a feeling of freedom that will subsequently be enhanced by automobile purchases later in a person's life. In such relationships, the interfaces between product components can be especially important, and sales of the enhanced products are furthered by a common standard to ensure compatibility and interchangeability (Shapiro and Varian 1999). Sometimes the interface itself becomes another product that is needed to make a product system function better (e.g., modems enable communication between the Internet and PCs; a car kit enables a portable CD player to play through the existing car radio).
Augmenting complements. Augmenting complements add new benefits that were not formerly present in an existing product (e.g., combining a radio with a clock allowed for an alarm of varying sound and enabled a buyer to program the radio). Augmenting complements often are synergistic and usually are cases in which an existing product has a major sales effect on its newer complement, because its limitations either have created reasons for a complement to exist or have legitimized its existence. For example, e-mail capability (existing product) positively affects a buyer's ownership of a digital camera (newer product) because it enables photographs to be sent with text as well. There may also be priority patterns that determine the order in which related products are purchased; that is, more basic purposes may be satisfied before less basic purposes (e.g., a washing machine before a dryer, a savings account before a mutual fund investment). Again, common purpose may influence people's purchase sequence (Harlam and Lodish 1995). Marketers can influence sales of augmenting complements by means of the ties with older products they emphasize in rationalizing or positioning the newer product. This can help buyers better understand the fuller range of product benefits, that is, the combined benefits of both products (Eppen, Hanson, and Martin 1991).
Sometimes the relationship between products can be both substitute and complement; that is, two products may be complements for one purpose but substitutes for another. These individual-level effects may cancel one another out so that an aggregate intercategory relationship distorts individual-level realities. Different users may purchase products for different reasons or the same users may use products differently at different times or in different contexts. The multiple uses that such products serve may be their major (possibly unrecognized) competitive advantage. For example, a VCR is a complement to a television when it provides an additional tuner to enable picture-in-picture capabilities or the recording of one television show while watching another, but the VCR also is a substitute input to an antenna, cable, or a satellite dish. As another example, television news, news radio, news magazines, the daily newspaper, and the Internet are complements because they are differentiated by timeliness and depth of reporting (e.g., some are immediate and others are delayed, some offer analysis in addition to headlines). However, these products can also be part of a substitute portfolio of products purchased by someone who desires limited detail (McAlister 1979). Product bundling or other product complexity sometimes enables the resulting product to play multiple roles in terms of its relationships with other products.
Dynamics Between Complements and Substitutes
There are dynamics not only within substitute categories or complements but also between the two. Changes in buyer demands may result in a gradual shift from noncompetitive intercategory modes (i.e., complements) to competitive ones (i.e., substitutes), and vice versa. We term these modes reincarnation and rejuvenation, respectively. As an example of reincarnation, consider the relationship between Microsoft's Windows operating system and Netscape's Navigator Web browser, which was initially an augmenting complement. An awakened Windows became the predator after it incorporated its own Web-browsing capability (Gates 1998). Another example is the relationship between wired and wireless telecommunications technologies. Initially, the wireless telephone was an enhancing complement to regular wired telephones (i.e., used for different purposes). Recently, because of "free" long distance and other pricing practices, consistent quality improvements, and the long delays required to obtain wired telephone installation, in many Asian countries and increasingly in the United States it is common for wireless telephones to displace wired telephones for regular use at home.
As an example of rejuvenation, consider film entertainment in the 1950s. When television was first introduced, it was presumed to represent a major threat to motion pictures because a person could watch movies at home instead of traveling to the theater. Both were forms of entertainment, but they had different uses, users, and occasions for use. Television's small screen, inconsistent reception quality, and the initial absence of color were major hurdles. However, because of the perceived threat, movie studios refused to allow their facilities to be used to produce television shows and ran large-scale promotional campaigns that urged consumers not to purchase television sets (Boddy 1990). Television persisted, though, and the two entertainment modes coexist today. Eventually, movie studios became producers of television shows, which became an even bigger business for them than movie production. They belatedly realized that so long as first-run movies were not aired contemporaneously with their showing in theaters, television could serve as a complementary entertainment medium.
In some industries, technological progress and market restructuring occur so quickly that intercategory relationships oscillate in a relatively short period. An example of this is the wireless telecommunications market in Hong Kong (Kim, Chang, and Shocker 2000) and elsewhere. When analog-type wireless telephones were first introduced to Hong Kong in 1986, most users came from the existing pager user group. At that time, people usually owned both a pager and a wireless telephone because of the unstable communication quality of the analog telephone (the products were enhancing complements). Because of continuous technological improvements to the wireless telephone, it began to substitute for the pager. From the late 1990s onward, the relationship has been evolving back to a complementary one in which many wireless telephone users also own pagers to check incoming calls while they have their wireless telephones turned off. In addition, by adding some augmenting complementary accessory functions (e.g., games, calendar, travel information), the pager has developed its own market niche.
In Which Circumstances Are Intercategory Effects Most (Least) Likely to Occur?
Intercategory effects may be a consequence of differences in buyers' and sellers' category definition. If buyers consider benefits and costs and sellers consider product features and prices, such differences can arise. Research is needed both to better understand the nature and level of categorization that different decision makers use and to identify the circumstances in which cross-category consideration and choice are most (least) likely to occur. Whether the stage of product life cycle, individual differences (e.g., experts versus novices, different personality types), purpose, or other factors matter more has not been investigated. Although Ratneshwar, Pechmann, and Shocker (1996) provide empirical evidence that the individual characteristics of goal ambiguity and goal conflict lead to multicategory consideration, the possibility of other explanations (e.g., involving economic factors such as similar prices) needs to be clarified in further research. Managers should also be interested in findings that can provide guidance as to how intercategory effects are best used to the managers' advantage. However, no research has examined how easy or difficult it is to encourage normally single-category decision makers to consider other relevant alternatives.
It seems that economy (i.e., price), brand reputation, design, and versatility are examples of product benefits that can be readily measured across multiple categories. These benefits may serve as general dimensions because they are closely related to superordinate buyer purposes or goals (e.g., buyers will only consider "other product" alternatives in acceptable price ranges or of acceptable brand names because of who they are or the purposes such products serve). Whether there are only special kinds of goals (e.g., gift giving) that favor multicategory over single-category consideration also is worthy of further investigation.
How Does Multicategory Decision Making Differ from Single-Category Decision Making?
Benefits and costs seem desirable for representing product alternatives in the modeling of multicategory decision making. If a new product has similar purposes as others and its benefit levels are known, an informed marketing manager may be better able to predict product success or failure. For example, if whitening ability, safety to clothes and environment, and economy are understood as the major benefits desired of laundry detergent, a product category that offers higher or equal levels of these benefits (assuming little additional cost) can be expected to be successful (product displacement). Similarly, a dominated new product can be perceived as having problems with product perseverance. Research is needed to find the best way to identify all core benefits, because it is their totality that determines a product's market success. This problem is complicated but not unsolvable when a product is not dominant (or dominated) or when different market segments emphasize different benefits and costs.
An attempt to model decisions in terms of benefits and costs rather than physical characteristics raises questions of trust, credibility, and validity. Benefits are inferred by buyers and suggested by sellers (with puffery). Thus, if only the benefits of an alternative are described to a buyer, the buyer must ordinarily assume that the product will deliver the benefits. In addition, if a desired product is described to a seller, the seller must know how to create the bundle. Product analogies may prove useful in successfully describing benefits that may otherwise be ambiguous. Some benefits may be abstract (e.g., as safe as flying) or involve sensory characteristics for which well-developed vocabulary does not exist (e.g., tastes like Belgian chocolate, soft as a luxury hotel's plush towel). Research can usefully determine how well prominent characteristics of highly familiar products are useful analogies for accurately communicating benefit levels (and which types of analogies do it better). This is important to Internet commerce in which certain goods may not be sold successfully against bricks-and-mortar competitors unless their sensory characteristics are validly described.
When do existing products provide a context that affects the evaluation of new substitutes and when will the first mover in a category be able to set its own norms? We have argued that "other products" can affect the reference points used in buyers' decision making in another product category. Are there predictable circumstances when this occurs? Can experiences with "other products" or the marketing changes in those categories be more influential than same-category determinants of reference values? Carpenter and Nakamoto (1989) show that first movers can establish initial reference points or norms, but they do not establish the circumstances in which this would occur. Pricing research has shown that reference points can change; it has identified a list of causes including substitute products (Kalyanaram and Winer 1995; Winer 1988), but it has not attempted either to measure when each has greater impact or to document circumstances in which experiences with substitutes may dominate experiences with the same products. The addition of complements to a core product can sometimes create a new category and affect change in reference values. Managers can benefit from greater understanding of the effects that their plausible actions in the same or related categories might have.
Another area of research importance is what might be termed "transfer of preference" (affect), which presumably is what makes brand extension effective (Bhat and Reddy 2001; Broniarczyk and Alba 1994; Erdem 1998). The literature establishes that the attributes or characteristics of a parent brand are more likely to be transferred to its brand extension than will overall liking or preference. However, the literature does not examine the possibility that modeling of buyer preference in one product category will enable such models to be used to predict choice in a substitute or related category (e.g., one that has benefit and cost similarities). Such a capability would be important in predicting demand for new products (as long as the products were close substitutes). Currently, using conjoint analysis, commercial research emphasizes data for decisions in a single category. It may prove possible (with appropriate scaling of weights) to preserve some information collected in a study (e.g., benefit-cost trade-offs) to approximate decision making in another study in a related category. Several researchers have noted that there are similarities in the importance of similar product characteristics (more likely with benefits than features) across certain categories with respect to decisions by the same person (e.g., Ainslie and Rossi 1998; Andrews and Currim 2001; Russell and Kamakura 1997). Research may find that all benefits need not be identical between categories for such research to be useful. Such findings might enable firms to save on future market research costs and aid a firm's managers in becoming more market oriented.
Research might usefully examine the value that buyers place on the less important benefits that products afford (e.g., augmenting complements) because buyers may pay a higher price to obtain them. Can it be explained how ownership of a product that was purchased for one purpose or use increases the likelihood that a buyer will pursue other purposes for which the product is suitable? For example, when an "all-in-one" device is considered an alternative to dedicated printers, what value (if any) do buyers place on the extra benefits that product provides? Do product benefits that are not important at the initial purchase decision become more important later? (In a personal communication, the Nobel laureate Herbert Simon used the phrase "the importance of the artifact," which is based on the observation that ownership of a computer led users to pursue new uses for it.) Will the all-in-one product be perceived as providing a higher level of an existing benefit (e.g., opportunity to learn new skills, greater versatility) in its competition with items in the dedicated category? Will one component (e.g., a printer) be inferred as higher quality because it was linked to a multitude of others (e.g., fax, scanner)? Do buyers ignore potential benefits (Ratneshwar et al. 1999) at the time of purchase and discover them only later? Research is needed to better understand what makes multicategory decision making different from single-category decision making.
How Do "Other Products" Affect Product-Market Structure?
Market structures have often been determined by means of perceptual mapping, but these maps offer only a snapshot of structure at a particular moment in time. "Other products" might provide key input to the modeling of dynamic changes in structure and enhancement of the prediction of change. Are there predictable patterns to how intercategory relationships will evolve? Intercategory effects seem particularly important when examined as processes over time, and research can usefully examine this. For example, buyer perceptions of product quality change, possibly because of changes in the environment of "other products" (e.g., enhancing complements may suggest quality improvements, thus making current customers less satisfied; augmenting complements may add new criteria by which buyers can judge quality). Quality perceptions often vary with user purpose, because purpose largely affects the benefits attended to by buyers and sellers. However, purpose may change as a consequence of "other product" availability (e.g., components that enable portability may make different product characteristics prominent). It may be possible to generalize about the determinants of quality from "other products" (i.e., substitutes, but perhaps also complements) that are used for similar purposes.
We noted previously that failed or limited technology in one category may limit the competitiveness of "other products" based on the same technology (and success may expand potential). Benefit limits in one category may enhance the appeal of a related product (e.g., pagers may have aided acceptance of wireless telephones). Such dynamic market structures might be used to verify that a particular historical evolution of substitutes and the existence of complements is necessary for a category to evolve similarly, such as in different countries. If a firm introduces only the latest generation of a product that is successful in one country into another country, the product's rate of diffusion and eventual success may differ (e.g., analog pagers and wireless telephones may have been necessary to appreciate fully the subsequent digital versions).
Dynamics of change lead to migration between the intercategory relationships shown in Figure 2. There may be discernable patterns in such migration that historical research methods can reveal (Golder 2000). For example, in developing product strategies, there is often a strong incentive for an existing product to incorporate features that previously were complements (e.g., Microsoft Windows' additions of features such as hard drive management, zip file capability, virus protection, a media player, and Web browsing). As the product evolves, the category itself may be redefined as the new one becomes a substitute for its former complement. Augmenting complements may evolve into a competitive mode as a newer product encroaches upon the existing one (e.g., self-service gas stations have largely made full-service gas stations minor market players, automatic teller machines have replaced bank tellers). Practically every major university is considering the role of multimedia technologies and Internet applications in higher education and distance learning (Matthews 1999). The issue debated by university administrators is less whether the technologies are complements and more whether the technologies offer viable substitutes for face-to-face education in the future.
The intercategory dynamics may lead to either coexistence of related products or survival of some selected products. Bayus, Kim, and Shocker (2000) provide a review of the conditions that eventually lead to a single survivor or coexistence of related products. Empirical studies based on historical methods will increase the understanding of the characteristics of market and technological environments that affect the equilibrium conditions. Further research efforts should usefully examine the role that "other products" play in determining equilibrium from multicategory competition (e.g., dominance of the new entrant, speed of entry of facilitating complements). If it is discovered that substitutes-in-use deliver somewhat different benefit levels or benefit combinations, insights into the future evolution of the category might be obtained.
In dealing with multicategory effects, new methodologies for implementing even static MSA may be needed (for a discussion of challenges and research suggestions, see Elrod et al. 2002). Incorporation of complements, composite products (e.g., clock radios, all-in-one machines), and "other product" substitutes-in-use in the same study may require new modes of representation or different analytic interpretive skills. There will likely be greater problems of aggregation than exist presently because of factors such as heterogeneity in perceptions, multiple purposes or contexts in which different products compete (and the relative incidence of such purpose-defined submarkets; Bucklin and Srinivasan 1991; Yang, Allenby, and Fennel 2002), and different selections of substitutes and complements in each submarket. Different brands in a category may be significant competitors for some purposes (e.g., gift giving) but not for others (e.g., ingredients). Research might usefully examine the value of controlling for purpose (e.g., creating separate MSAs for each major purpose and later finding appropriate ways to integrate them). This research could be valuable for finding out not only how different the market structures are across different purposes but also the extent to which multiple purposes account for some heterogeneity that was formerly believed to be part of a single-category MSA.
What Are Additional Managerial Implications from Influences of "Other Products"?
The study of "other products" may afford insights that are not otherwise available. Some buyers may be proactive, making choices from a broader set because they recognize single-category options as too limiting (e.g., because of inadequate convenience, affordability, and accessibility). For example, a buyer may prefer baking a cake from scratch to buying one ready-made or baking it from a mix because he or she can individualize it. If some buyers actively make choices from a broader set of category alternatives than others do, knowledge of the set can increase the likelihood of discovering important buying criteria that may not have been revealed in a single-category context. Research is needed to confirm that the inclusion of relevant "other products" in a market structure helps reveal otherwise latent dimensions (decision criteria) that affect brand choice and aid in suggesting new product or repositioning opportunity.
Customers purchase from different categories for reasons, which, when better understood, might provide important guidance to marketing action. For example, potential customers of a given brand currently may be buying in substitute categories. However, if firms recognize the category substitutability, they might target those customers. In addition, there may be purposes that normally lead only to occasional substitution. Research might provide insights into ways occasional substitution can be increased (e.g., an exciting new product may encourage more gift giving). Different product design, changes in distribution channels or merchandising strategy, new packaging, or pricing might influence the incidence of intercategory substitution. Emphasis on new product combination with complements might lead to a distinct category whose positioning can be more affected by marketing action simply because it is less familiar to potential buyers. Behavioral research might usefully examine whether cueing "other products" influences which purposes are evoked.
It also seems that multicategory effects influence the ultimate effectiveness of managerial decisions about mergers and acquisitions and strategic alliance formation. An understanding of possible complementors will enable managers to better coordinate their marketing actions. Integrated marketing communications have long recognized the value of a coordinated program for a brand's various promotional options, some of which are not entirely controllable (e.g., word of mouth, press commentary). The same integrative idea might be valuable in designing a marketing strategy in the context of "other products"; for example, a firm in one category may have an incentive to assist firms in another category in developing augmenting complements.
A notable consequence of intercategory effects is that a given product's potential market size is not constant; it depends on what is happening with or could be made to happen to related "other products" (Bayus, Kim, and Shocker 2000; Peterson and Mahajan 1978). Kim, Chang, and Shocker (2000) offer a way to measure the magnitude of the possible positive and negative effects, but their model has been tested only with two data sets. Although the results are encouraging, the benefits of incorporating interproduct effects to improve forecast accuracy must be confirmed by additional research with other multicategory market data sets.
The idea that demand may be interconnected across product categories is a powerful one. Awareness of the interconnections should sensitize academic researchers and managers to the possibilities that "other products" render intercategory effects more controllable or perhaps identify the circumstances in which the effects cannot effectively be controlled. Research results that make use of multicategory data will serve to encourage their subsequent generation and additional analyses, leading to further research results. We hope that this article will stimulate more research to enhance the understanding of these effects. An improved understanding of the roles of "other products" holds great promise for helping managers accomplish what is already a difficult job.
The authors thank David Stewart and three anonymous JM reviewers for their many helpful and constructive comments. They have served to substantially improve this paper. Errors of commission and omission remain the authors' responsibility.
(n1) In this article, we use the term "intercategory" interchangeably with the term "interproduct." As the title of our article suggests, we are concerned with the cases in which decisions in one product category affect buyer and seller decisions in another. We also would treat successive technology generations (e.g., 8-bit, 16-bit, 32-bit microcomputer CPUs) or the subdivisions of a coherent broader product category (e.g., portable computers: desktop computers, notebook computers, handheld computers) as separate categories for discussion purposes. Product category boundaries can be vague (Viswanathan and Childers 1999), but because we concentrate on the relationships between categories rather than their composition, such ambiguity is acceptable.
(n2) For example, the literature addresses product substitution through research on new product success or failure (e.g., Cooper 2001) and technological obsolescence (e.g., Christensen 1997; Utterback 1994). Product complementarity is addressed through research on product bundles (e.g., Eppen, Hanson, and Martin 1991; Gaeth et al. 1991; Guiltinan 1987; Yadav 1994). Our interest is in transcending these static concepts.
(n3) For the sake of clarity, throughout this article, we discuss intercategory effects in terms of just two alternatives. We recognize that in many markets there are more than two products that interact (e.g., a large-screen monitor, color printer, modem, hard drive, CD-ROM, speakers, and memory all interact in the PC market). Such cases need not require a separate discussion because the alternatives can be considered in pairwise fashion. Research is needed to understand more complex cases in which contingencies and interactions among categories are present (e.g., slower technological development of one system component may handicap others; modems capable of faster data transmission speeds than the input-output devices they connect to or the lines over which they transmit may limit the speed of the chain).
(n4) This section borrows extensively from Russell and colleagues (1999).
(n5) Important questions that cannot be answered here are, What exactly is a product category? How are they created (by buyers, sellers, or others)? and How do they evolve? We assume that categories exist, that they have a hierarchical structure (in which superordinate and subordinate categories complement a main category), and that new generations may also be new categories when they are sufficiently differentiated. However, the arbitrariness of category definition should not detract from the points we make herein.
DIAGRAM: FIGURE 1 Conventional Framework of Product Complements and Substitutes
A. Static
Legend for Chart:
A - Level of Purpose
B - Substitutes
C - Complements
Specific 1. Substitutes-in use 3. Complements-in-use
Nonspecific 2. Occasional 4. Occasional
substitutes complements
B. Dynamic
Legend for Chart:
A - Order of Entry
B - Substitutes
C - Complements
A B C
Newer affects 1. Product 3. Enhancing
existing displacement complement
Existing affects 2. Product 4. Augmenting
newer perseverance complement
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~~~~~~~~
By Allan D. Shocker; Barry L. Bayus and Namwoon Kim
Allan D. Shocker is Visiting Professor of Marketing, College of Business, San Francisco State University (e-mail: ashocker@sfsu.edu).
Barry L. Bayus is Roy O. Rodwell Distinguished Professor of Marketing, Kenan-Flagler Business School, University of North Carolina, Chapel Hill (e-mail: Barry_Bayus@unc.edu).
Namwoon Kim is Associate Professor of Marketing, Department of Management and Marketing, Hong Kong Polytechnic University (e-mail: msnam@polyu.edu.hk).
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Record: 120- Product Development Resources and the Scope of the Firm. By: Wernerfelt, Birger. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p15-23. 9p. 3 Charts, 1 Graph. DOI: 10.1509/jmkg.69.2.15.60763.
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Product Development Resources and the Scope of the Firm
This article examines the relationship between a firm's strength in product development and its optimal scope. Firms with product development strength have two options: They can leverage it in horizontally related markets, and they can reach into the supply chain to take full advantage of it. The question is how this should be done. One possibility is for the firm to expand its scope, and another is to manage the linkage through contracts. On the basis of the adjustment cost theory of the firm, the author argues that the former solution is more appropriate when product development is fast-paced. This study tests the argument in a sample of several thousand firms and reports four tests. For both types of expansion, the author examines the incidence and the productivity of increased scope. The author uses several measures and finds results that are consistent with the theory.
Several recent studies on marketing strategy have used the resource-based view (RBV) of the firm (Wernerfelt 1984) as a theoretical lens (e.g., Boulding and Christen 2003; Day 1994; Dutta et al. 2002; Dutta, Narasimhan, and Rajiv 1999; Moorman and Slotegraaf 1999; Ofek and Sarvary 2003; Slotegraaf, Moorman, and Inman 2003). Much of this literature assumes that the resources in question should be leveraged inside the firm, though a few studies (e.g., Rao, Qu, and Ruekert 1999) consider how to leverage a resource through a contract with another independent firm. The current article asks, What does a specific set of resources--namely, those used in product development--imply for the optimal vertical and horizontal scope of the firm?
Expansions of the vertical and horizontal scope are similar insofar as both involve the firm's taking on more activities. Firms have a broader vertical scope when they make, rather than buy, more of their inputs, and they have a broader horizontal scope (i.e., they are more diversified) when they sell more lines of outputs. Although I argue that both types of integration are positively correlated with product development resources, the proposed theoretical mechanisms are somewhat different. I assume that the firm's original stock of resources is exogenous and, in accordance with the RBV, guides its choice of industry. If the industry places a premium on flexibility in dealings with the supply chain, the adjustment cost theory of the firm (Wernerfelt 1997) suggests that the firm expand its vertical scope by bringing in part of the supply chain. If the firm develops more resource capacity, the RBV suggests that the firm transfer any excess to other industries, but this view does not infer whether the firm should govern this transfer through a contract or horizontal expansion. In contrast, the adjustment cost theory is silent with respect to whether this transfer should take place, but it recommends that a horizontal expansion should govern a transfer process if it entails frequent and diverse adaptations.
The argument is independent of the nature of the firm's resources. However, this article focuses on product development resources and presumes that a firm's use of such resources is correlated with a need for frequent and diverse adaptations, which is the central exogenous variable in the adjustment cost theory. I am not claiming that all product development resources should lead to vertical and horizontal expansion or that other resources should not.
I begin the argument by presenting two managerial challenges that many firms engaged in intensive product development face: ( 1) how to manage the demands for flexibility in the supply chain that are brought on by changes in final product designs and ( 2) how to leverage the firm's ever-growing set of product development resources. The ultimate claim is that firms sometimes meet these challenges by expanding their vertical and horizontal scope, respectively. To lay the foundation for making this claim, I review some general theory about the scope of the firm. I summarize the adjustment cost theory of the firm and argue that it provides a lens through which the expansion of vertical scope can be understood. I then combine the adjustment cost theory with the RBV of the firm and use this combination to consider the expansion of horizontal scope. Armed with these theories, I return to product development and formulate several hypotheses. I present a preliminary empirical test and conclude the article with a discussion.
Side-Effects of Product Development
In this section, I attempt to develop the context by describing two managerial challenges that firms may face when competing in industries characterized by fast-paced new product activity. The empirical work reveals the extent to which the challenges are empirically important.
The implementation of a new product design almost inevitably requires that some inputs be changed, and thus it has implications for the supply chain. In many cases, the effects are quite widespread. For example, changes in a single component of a mechanical or electronic product often necessitate changes in several others.
The challenge occurs when the inputs are less standardized and procured under long-term contracts that must be renegotiated. A change is not problematic if it involves standardized items that are traded on the spot market, because in that case, a firm simply stops buying one item and starts buying another item. Similarly, if the changes take place at ex ante known times (e.g., seasonally, annually), they pose less of a problem because a firm is unlikely to have entered into contracts that extend beyond the dates at which it anticipates changes. However, it is unanticipated changes in needs for less-standardized inputs that present problems.
There are many costs with respect to these changes, and firms can incur several of them ex post. In particular, the firm must engage in difficult negotiations with the supplier, which is entitled to compensation for continued delivery of items that the firm no longer wants. This bargaining process takes time and may fail (Myerson and Satterthwaite 1983). In addition, there are some ex ante costs of entering into a contract that both parties know they will eventually need to change. The costs include attempts to withhold certain information from the trading partner (out of fear that it may be exploited at the time of renegotiation) and reductions in efforts due to the anticipated termination. In some cases, the costs are borne, but in others, they are so large that a firm may postpone or even drop otherwise efficient changes.
The managerial challenge is to keep the costs of change down while managing the trade-off between them and the disadvantages of delaying or abandoning improvements in design. As such, the issues are similar to those discussed in the literature on improvisation in new product development (Moorman and Miner 1998). That is, the focus is on finding a process for managing unplanned changes. The literature has suggested many avenues for dealing with the problems, often putting them in the categories of operational (Tang and Tikoo 1999) and strategic (Grewal and Tansuhaj 2001) flexibility. This study is interested in just one of these solutions: the possibility of using vertical integration to ameliorate the costs of unplanned change.
Any firm that undertakes product development will develop resources over time that make repetition easier. Part of this involves the firm's moving down the learning curve, and part of this involves channel partners' and consumers' coming to trust the firm. To some extent, these resources accumulate automatically (Lieberman 1984), but firms can often manage their learning and reputation (e.g., Hurley and Hult 1998; Hult, Ketchen, and Slater 2004).
The challenge that this article presents is associated with the resources that a firm can potentially apply beyond the industry of origin. A firm will accumulate many resources that help it compete more effectively in its current industry. However, some of the resources have potential value in other industries as well, perhaps not in all industries but at least in industries that are similar in some sense to the one in which they originated. I confine attention to two classes of resources.
The first class of such general resources is the "business-process experience," which pertains to the execution of the development process. Some of the skills reside in individual employees, and others are of a more social nature (e.g., the group learns to compromise and cooperate with respect to a set of issues related to product development). Examples include the elicitation of technical and commercial information, the integration of conflicting viewpoints, and the division of labor and authority in the group. The individual components of learning may be quite small, but the aggregate learning-curve effects may be substantial.
The second class of accumulating resources consists of a firm's "reputation" with members of the distribution channel and, ultimately, consumers. If a firm has introduced successful new products in the past, retailers are much more likely to trust its new offerings, and consumers will react favorably to its brand name. To illustrate the frequency with which this happens, Montgomery and Wernerfelt (1992) report that 83% of the products in their sample were umbrella branded.
The managerial challenge is to deploy these two classes of resources in the best possible way. When the resources are sufficiently large, it is rarely optimal to confine their use to the original industry. Thus, the questions are where and how to put them to greater use and, in particular, whether this should lead to an expansion of the horizontal scope of the firm.
Theory
In this section, I summarize the general theories used in the article. I describe the adjustment cost theory of the firm and focus on its implications for the vertical scope of the firm. Then, I outline the RBV of the firm and combine it with the adjustment cost theory to consider the expansion of the horizontal scope. Finally, I examine the context of product development more narrowly and derive specific hypotheses.
To make statements about the optimal scope of the firm, there must be a theory of how firms differ from markets. There is no such generally accepted theory, but most candidates are descendants of Williamson's (1975) transaction cost analysis (TCA), insofar as they stress a subset of the factors invoked in TCA. Variants of TCA have been used extensively in marketing, not only in studies of the choice between direct sales forces and independent representatives (Anderson 1985; Ghosh and John 1999; Klein, Frazier, and Roth 1990; Weiss and Kurland 1997) but also in more general contexts (Heide 2003; Jap 1999). Whereas most applications of TCA, including the property rights theory (Grossman and Hart 1986), have focused on the effects of specific investments, the current study stresses a different class of transaction costs. Specifically, it uses the adjustment cost theory (Bajari and Tadelis 2001; Simester and Knez 2002; Tadelis 2002; Wernerfelt 1997, 2004) to compare the costs of adapting a trading relationship under firm governance rather than under market governance. This theory examines ongoing trading relationships and asks by which process the parties should adjust the relationship by accommodating changes. Possible processes include an ex ante contract that covers all possibilities and an ex post, case-by-case negotiation. If the required changes are diverse and frequent, the theory predicts that an implicit contract in which one party is given decision rights and the other can quit at any time should govern the relationship. The theory defines the latter governance structure as an employment relationship or a firm. It is possible, but beyond the scope of this article, that other theories of the firm, including those based on specific investments, could be used to interpret the results. For example, Rajan and Zingales (2001a, b) suggest that the RBV is consistent with the property rights theory. However, this does not imply that it is inconsistent with the adjustment cost theory. The following is a simple version of the adjustment cost theory and is based solely on the direct adjustment cost that a firm incurs in bargaining and negotiation processes. Other versions are based on indirect adjustment costs (i.e., incentive effects). The exposition is based on Wernerfelt's (1997) work.
To keep things simple, I assume that all adjustments are implemented perfectly such that governance mechanisms can be compared in terms of the direct adjustment costs of implementation. In each period, a buyer may receive a service from a seller. The service is costly to the seller, but it creates value for the buyer. Part of this value can be transferred to the seller through a payment (w). There is a set of feasible services (A), and the ideal service is that which generates the most gross surplus (buyer value less seller costs gross of adjustment costs). Between any two periods, costs and values of all services change, such that the identity of the ideal service changes with probability (.). Thus, this parameter measures the frequency of adjustment. I assume that the buyer always knows the identity of the ideal and that the players always implement it. Given these assumptions, I can compare how alternative game forms govern adjustments. I focus on only three such forms:
- Negotiation-as-needed: The players negotiate a new whenever they switch to another service. Most residential construction projects are governed in this way.
- Price list: The players negotiate a set of prices ex ante, after which the buyer selects from the menu at each opportunity to switch. This arrangement applies, for example, when buyers visit a beauty shop or a tax preparation service.
- Employment relationship: The players first negotiate a constant w, and then they enter into an implicit contract in which the buyer dictates adjustments to the seller, and either player may terminate the relationship at any time. A classical example of this is the relationship between a manager and his or her secretary.
Negotiation-as-needed and the price list represent the market. There are other market game forms as well, but it is clear that a complete list cannot be produced. Conversely, the employment relationship (i.e., the firm) is not an arbitrary choice. There can be no game form that requires players to negotiate fewer prices and, consequently, no game form with lower variable costs per adjustment. Thus, for sufficiently frequent adjustments, no other game form can perform better.
The costs of negotiating a wage contract are Cf, the costs of negotiating a single price are C[sib b], and the costs of negotiating a price list of length (|Ap|) is C(|Ap|). Thus, |A| measures the diversity of adjustments. If a firm implements all adjustments, the price list must cover all elements of A. In an infinite horizon model, the average per-period net surpluses from the three game forms differ only in terms of the direct adjustment costs. If ρ is the rate of interest, the direct per-period adjustment costs are λCb for negotiation-as-needed, ρC(|A|) for the price list, and ρCf for the employment relationship. If the price list is partial, such that |Ap| is smaller than |A|, it does not implement all adjustments and therefore performs worse. Either way, the employment relationship is more efficient than negotiation-as-needed when the frequency of adjustment is high and is more efficient than a price list when the set of possible adjustments is diverse. That is, if many adjustments are required, it is prohibitively expensive to negotiate all of them, and if the set of possible adjustments is large, it is too expensive to negotiate a complete price list (see Figure 1). Phrased as a theory of the vertical scope of the firm, the adjustment cost theory predicts that the firm should internalize supply relationships that require frequent and diverse adjustments.
The RBV (Wernerfelt 1984) is based on the premise that firms differ, even within an industry. The differences occur in the firms' resources, and the main theory is that a firm's strategy should depend on its resources--if a firm is good at something, the firm should try to use it.
The simplest application of the RBV is at the level of business-unit strategy. That is, given that the firm participates in a specific industry, how should it compete? In general, the answers are straightforward. If a firm faces lower variable costs or can produce higher-quality products than those of its competitors, it can often increase profitability by positioning its products accordingly. Similarly, firms with strengths in product development should probably introduce more new products than their differently endowed competitors.
A deeper application pertains to the question of corporate strategy: In which industries should the firm participate? Again, the main prescription is simple: A firm should compete in industries in which its resources are important. For example, this implies that firms that are good at product development are likely to populate industries in which product development is important. If others were to enter, the firms would be at a significant competitive disadvantage and perhaps unable to survive.
Because firms are rarely designed from scratch, industry selection is often accomplished through a process of diversified expansion. In this process, a firm seeks one or more new industries to enter without questioning its continuing participation in its current markets. Under such circumstances, a resource is available only if the firm has capacity in excess of what is needed in its current markets (Penrose 1959).
Resources differ greatly in the likelihood that they appear in excess capacity. To help identify resources that are most likely to be in excess capacity, I classify them according to their short-and long-term capacity (Wernerfelt 1989). Resources with fixed capacity, typically physical assets, rarely play a role in considerations to expand a firm's scope. They support expansion only to the extent that misfortunes, such as declining sales, have caused the firm to have more fixed-capacity resources than the original application can use. At the other end of the spectrum, a firm can potentially use resources with practically unlimited capacity (e.g., brand equity) in a large number of industries. With respect to this type of resource, I place a firm's reputation in this category.
The last category consists of resources with fixed short-term but unlimited long-term capacity. Corporate culture is a standard example of this resource category, but a firm's learning curve may be a more important case. Individual employees can transfer some learning to other firms, but the firm-specific nature of a significant part of the learning curve suggests that much of the effect resides at the team level (Lieberman 1984). Resources developed through a firm's learning provide most of the examples in Wernerfelt's (1984) study, and the current study also examines firms' ever-growing skills in product development as a resource in this category.
Hypotheses
In this section, I apply the theories to the managerial challenges previously discussed and formulate several hypotheses about vertical and horizontal scope. To help situate the concepts, consider Time Warner as a firm with large vertical scope and Procter & Gamble as a firm with broad horizontal scope.
If a firm has resources in new product development, it should leverage them by participating in an industry in which product development, and thus the resource, plays an important role. Given this, the firm must trade with the adjacent stages, even if it brings no special skills to those industries. As I have argued, product development processes typically require frequent adjustments in the supply chain. According to the adjustment cost theory, this implies that it may be too costly to engage in negotiations at every turn and cheaper to bring the supplier in-house. Thus, I hypothesize that, on average, firms that pursue more intensive product development benefit more from a larger vertical scope than a horizontal one.
H1: A larger vertical scope is more efficient for firms with more product development resources.
Assuming that firms optimize, this implies the following:
H2: Firms with more product development resources are more likely to have a large vertical scope.
To apply the adjustment cost theory to the attempt to leverage product development resources, in some detail I consider exactly how the services of these resources enter the development process. I argue that firms typically deploy process-management skills and reputations, two resources that this article examines, in ways that involve frequent reactions to a diverse set of circumstances.
To make the argument for skills in the management of product development processes, I engage in a counterfactual thought experiment. Consider how a firm with expert skills in product development could charge money in exchange for helping another firm, the client, with its product development. The process could begin with advice about the collection of certain kinds of information and then use a particular organizational structure. However, this process is not likely to contain additional news for the client. Because this type of advice is unconditional, it can be codified and sold, so the client probably knows it already. The expert might add more value after the process begins, when the client must react to information as it is revealed. At this point, codification is impossible, and judgment is required. To transfer this, the expert firm would likely move groups of its own experienced people into "shadow positions" so that they could give ongoing advice to key members of the client's organization. Other people in the firm could be consulted occasionally. In the end, the client will have received several small services, mostly in the following form: "In this situation, we will try such-and-such." In other words, such a service may require a lot of contracting. According to the adjustment cost theory, this is likely to be prohibitively expensive, which suggests that the expertise be leveraged through a broader scope instead.
For reputation resources, including brand names, it is less the use and more the value-relevant aspects of this use that change frequently and widely. For example, all the uses of a brand affect its value, and though the brand's owner might approve of a single, specific use of it, even minor variations would need approval. However, I do not mean to make an absolute statement. Some brand names are licensed to other firms, but the majority of names are not. In contrast, it is hardly possible for a firm to rent out its reputation to other retailers.
In summary, attempts to leverage some of the most important resources involved in new product development--namely, process-management skills and reputations--typically involve frequent reactions to a diverse set of circumstances. The adjustment cost theory suggests that a firm should leverage these resources through a broader horizontal scope.
H3: A larger horizontal scope is more efficient for firms with more product development resources.
Again, invoking the optimization assumption, this implies the following:
H4: Firms with more product development resources are more likely to have a broader horizontal scope.
The main focus of this article is the behavior hypotheses (H2 and H4), which predict that firms with more resources in new product development have a larger vertical and horizontal scope. The performance hypotheses (H1 and H3) cannot be tested on real data under orthodox assumptions. If firms are optimizing, there cannot be any observations of less-than-optimal performance, and thus the hypotheses are not testable. A test is only possible if there are some mistakes among the observations. Thus, there is some tension between the two sets of tests. One relies on the assumption that, on average, firms do not make mistakes, and the other requires that at least some firms do. Although it is possible that most, but not all, firms behave correctly, this does not avoid the problem entirely. It is logical to treat scope decisions as endogenous in one test and exogenous in another. However, it is not possible to appeal to a distinction between long-and short-term endogeneity by asserting that firm scope changes more slowly than does performance. A more pragmatic argument is that the equations are likely to suffer from omitted variables. Variance in the equations may allow for the estimation of the effects by driving a wedge between the truly optimal scope and the best guess based on a subset of the relevant variables.
Empirical Test
Theoretical arguments rooted in the RBV have far outstripped empirical tests, at least partially because it has been difficult to obtain objective measures of firms' resource stocks. Some resources are difficult to identify, and even if they can be identified, many measures suffer from endogeneity problems, whereas others require hard-to-get qualitative information. Another problem is that many resources exist at the business-unit level (Rumelt 1991), whereas most data are typically available at the more aggregate firm level. (Montgomery [1994] reports that the average Fortune 500 firm operates in 11 industries.) In an attempt to overcome these problems, I have tested the predictions on the well-known PIMS database, which offers both qualitative and quantitative information at the business-unit level. This database, which Buzzell and Gale (1987) describe, is unique because it contains both financial information and several qualitative items. Researchers have used the database in more than 100 academic papers. In the 1970s and the 1980s, researchers used the database to study all the important questions of the period, such as the value of market share, the profitability of early entry, and the importance of product quality. Currently, the research community uses PIMS more selectively, often in the context of more theory-driven work (e.g., Boulding and Christen 2003). The database is still being updated, and the PIMS Europe consulting company in London maintains it.
Compared with what would be available in a customized data-collection effort, the size of PIMS is a major advantage. A more important advantage is that the unit of observation is a business unit rather than a firm. Because most large firms are diversified, firm-level data are essentially averaged. By avoiding this, PIMS provides a much more precise picture of individual relationships (even if the data are subject to some measurement error from allocation rules). A disadvantage of using this database is that researchers cannot ask new questions with "new" variables but are limited to a fixed set of preexisting items. Furthermore, because the data are disguised, industry or firm effects cannot be controlled.
I test the behavior hypotheses (H2 and H4) by examining measures of scope as functions of a set of exogenous indicators of resources in product development. The logic is standard, and the tests are conceptually straightforward. In particular, I base the tests of the behavior hypothesis on linear regressions of the following form:
( 1) S = δ0 + δ1R,
where S is the vertical or horizontal scope, and R is a set of indicators of resources in new product development. I operationalize H2 and H4 as follows:
H2, H4: δ1 > 0.
It is more difficult to test the performance hypotheses, and though the analysis is informative, the interpretation relies on some slightly uncomfortable assumptions. I propose to test H1 and H3 by estimating strategic-business-unit-level production functions and by examining productivity effects of scope as functions of exogenous indicators of resources in new product development. By using interaction variables, I can isolate circumstances in which changes in scope help and hurt labor productivity. As discussed previously, I estimate the production functions with the implicit assumption that horizontal and vertical scope are exogenous (because of omitted variables and mistakes) or at least fixed in the short run. Although this seems defensible, the same assumptions must be made about labor and capital, and in the case of labor, it is less compelling. To mitigate these problems, I estimate the log of performance on a per-employee basis. I postulate a constant returns-to-scale production function in which value added (V) depends on labor (L), capital (K), scope (S), and indicators of resources in new product development (R). (Because scaling disguises the data, there are not flexible returns to scale. Exploratory analysis implies that the true model has moderate returns to scale, perhaps to a power of 1.1.)
Specifically, I estimate the following:
( 2) V = α (S,R) K βL1 - β,
in the form
( 3) Log(V/L) = γ0 + γ1S + γ2R + γ3SR + βLog(K/L).
If the functional form of Equation 2 is taken seriously, α (S,R) = exp(γ0 + γ1S + γ2R + γ3SR). To test the hypothesis that integration is the more efficient way to leverage resources in new product development, I hypothesize that the cross-derivative is positive. That is, for either type of S and R, I operationalize H1 and H3 as follows:
H1a, H3a: γ3 + (γ1 + γ3ER)(γ2 + γ3ES) > 0,
where E is the expectation operator. If there is uncertainty about the functional form of the production function, a weaker hypothesis is as follows:
H1b, H3b: γ3 > 0.
To test the hypotheses, indicators of the presence of resources in new product development are needed. However, this is not a straightforward task. The most natural resource indicators are firm-level decisions that are subject to endogeneity problems. A correlation between a decision and scope could be interpreted as evidence that both are caused by the presence of product development resources. However, the extent to which the scope of the firm influences the decisions cannot be removed. For example, it would not be possible to interpret a single regression with product quality, firm advertising, or firm research and development. A firm may have a broad scope and advertise frequently because it has product development resources, or it may advertise because it has broad scope. Trying to steer clear of endogeneity problems to the extent that is possible, I found indicators that are reasonably exogenous. Two of them are at the industry level, and one is an outcome measure.
The industry measures capture the time that is typically needed to develop a new product and the extent to which it is difficult to predict when new products will be launched. The use of industry measures relies on the premise that firms elect to compete in industries with certain challenges only if they have the resources to cope with them. For example, in an industry in which the average participant develops new products quickly, all participants are likely to be fairly good at fast-paced product development. In an industry with several participants, this is a fairly exogenous proxy for the resources of any individual firm.
The outcome measure is the percentage of firm sales accounted for by new products. Because price and other marketing variables indirectly control the outcome measure, it is less exogenous than the industry measures. However, it is better than advertising or quality, which are completely endogenous. With more direct indicators, different hypotheses about horizontal and vertical scope might be possible. However, because this does not seem possible, I use the same explanatory variables to predict both aspects of firm scope. I use the most recent observation for each firm of the variables subsequently discussed. This means that some data points are relatively new, whereas others may be as many as 25 years old.
Development time for new products (DT). DT is measured by management responses to the following question: "For this firm and its major competitors, what is the typical time lag between the beginning of a development effort for a new product and its market introduction?" The possible answers are "less than one year," "one to two years," "two to five years, "more than five years," and "little or no product development occurs in this firm." These are coded as 1, 2, 3, 4, and 5, respectively.
Random product changes (PC). PC are measured by management responses to the following question: "Is it typical practice for this firm and its major competitors to change all or part of the line of products or services offered annually? "Seasonally?" "Periodically, but at intervals longer than one year?" Or is there "no regular, periodic pattern of change?" Because this question confounds frequency and randomness, I used only observations with the last two responses and coded them as 0 and 1, respectively. (Approximately 5% of the data are lost by this procedure.) Thus, this is a coarsely measured variable.
Percentage of sales from new products (NP). NP refers to the products that firms have introduced in the past three years and is measured at the firm, rather than the industry, level.
Horizontal scope (HS). HS is measured by management responses to a question about the "extent to which your business unit shares facilities" with other units in the firm. The idea is not that firms have a broader horizontal scope to share assets but that firms with a broader scope have more options to share assets, such that the actual scope is positively correlated with this measure. Because answers are reported on a three-point scale ("less than 10%," "between 10% and 80%," and "more than 80%"), this measure is also coarse. Another concern is the underreporting of the horizontal scope, because many firms may have divisions that do not share the plant and equipment with any other division of the firm. However, it is difficult to tell a natural story in which this creates a bias in favor of the hypotheses. Suppose that the theory is wrong and that firms expand their scope for other reasons. In this case, it would be more difficult to share assets in fast-paced industries, which would result in a negative relationship between the measured incidence of diversification and the extent to which the business operates in a fast-paced industry. The same argument may defend against misinterpretations of the performance model.
Vertical scope (VS). VS is measured by the value added (sales less material costs) as a percentage of sales at the business-unit level. This measure has a long history in the literature that begins with Gort's (1962) work. I have omitted approximately 20% of the business units because they report internal sales or purchases. This is unfortunate because the average firm, thus omitted, would be expected to have larger vertical scope than those included. However, the internal flows cannot be tracked, and thus corporate scope cannot be measured. For the observations I use, VS is equal to both firm-and corporate-level scope for the product line in question. The good news is that there is still plenty of variance on the measure.
Labor productivity (VA/L). VA/L is measured in constant dollars as value added per employee.
Capital per employee (K/L). K/L is measured in constant dollars as total assets per employee.
The descriptive statistics appear in Table 1. Because the sample size is so large, it is not surprising that most of the correlations are significant. The most notable result in Table 1 may be the nonsignificant correlation between VS (= VA over sales) and VA/L. In a model with constant returns to scale, there is no theoretical reason to believe that the two are correlated; all three variables should go up in concert. However, any measurement noise in VA would induce a false positive result. The absence of this boosts confidence in the data.
The correlations in Table 1 show the pattern that the theory predicts at the univariate level. Both HS and VS are negatively correlated with DT and positively correlated with the extent of PC as well as the importance of NP. Five of these six correlations are significant at the 1% level.
The estimates of the behavior regressions appear in Table 2. Whereas the absence of control variables causes the regressions to have low adjusted-R² values, the results of interest are strong. All the signs conform to the predictions, and five of the six coefficients are significant. Although the pattern of significance is different, the results are remarkably consistent with the hypotheses: Firms choose to have a larger scope when DT is short, when PC is important, and when NP is important.
Estimates of the two production functions appear in Table 3. The six test statistics of the form γ3 + (γ1 + γ3ER)(γ2 + γ3ES) all have the predicted sign, and four of the six interaction variables (γ3) are significant. Both vertical and horizontal integration helps productivity when DT is short, when PC is important, and when NP is important. Although there are many reasons for insignificance, the results are also consistent with the claim that, on average, scope neither helps nor hurts productivity. (The net coefficients on VS and HS are .0073 and .0848, respectively, and both have t-values below 1.)
Discussion
I have used the adjustment cost theory and the RBV of the firm to make and test the claim that many product development resources make it more attractive for the firm to have a broader scope. I test this prediction on both vertical and horizontal scope in terms of both actual firm behavior and production functions. Although the measures are coarse, there are several of them. The results are strongly and robustly consistent with the theory.
The limitations of the data, which result in a lack of control variables, open the door for alternative explanations. However, most alternative theories do not speak to the variables that I used, and because the same three variables predict increased scope in four different cases, the bar has been raised for alternative explanations.
Another problem, which I discussed previously, is that not all product development resources justify expansions in the scope of the firm. Because the measures do not allow for distinction between different types of resources, I rely on a maintained hypothesis about the relative importance of different types of resources for the average firm in the sample. This means that there are many counterexamples to the averages that the empirical work has measured. However, it is more difficult to find counterexamples to the theoretical claims.
I do not wish to suggest that vertical and horizontal scope go hand in hand in general. The extent to which a firm has resources in product development may be one of the only variables that correlates positively with both types of scope. In both cases, it increases the need for frequent and diverse adjustments, but the mechanisms that underlie the two correlations are quite different. Resources in product development correlate with vertical scope because they cause demands for adjustments on other levels of the supply chain. They correlate with horizontal scope because their application generates demands for adjustments.
As should be clear from the theoretical part of the article, the empirical research can be refined in several directions. In particular, better measures might allow for the distinction between different classes of product development resources and environments with stronger or weaker demands for adjustments. It would also be interesting to examine other resources.
The article benefited from comments by four anonymous JM reviewers and seminar participants at MIT and Yale and from helpful discussions with Ernst Berndt, Erik Brynjolfsson, and Duncan Simester.
Legend for Chart:
B - Mean (S.D.)
C - VS
D - DT
E - PC
F - NP
G - VA/L
H - K/L
A B C D E
F G H
HS 1.70 (.77) .038(*) -.053(*) .014
.051(*) .252(*) .120(*)
VS 54.8 (16.8) -.049(*) .061(*)
.063(*) -.006(*) -.217(*)
DT 2.98 (1.30) .156(*)
-.262(*) -.041(*) .086(*)
PC 3.74 (.59)
-.184(*) .050(*) .024
NP 8.15 (14.9)
.014 -.043(*)
VA/L 57.7 (50.4)
.718(*)
K/L 50.5 (66.0)
(*) p < .01.
Notes: N ranges from 3469 to 3526. S.D. = standard deviation. Legend for Chart:
A - Independent Variable
B - Vertical Scope
C - Horizontal Scope
A B C
DT -.5530(**) (-2.42) -.0291(**) (-2.68)
PC 2.3487(**) (4.74) .0104 (.28)
NP .0742(**) (3.71) .0022(*) (2.32)
N 3413 3245
Adjusted R² .0106(**) .0042(**)
(*) p < .05.
(**) p < .01.
Notes: t-statistics are in parenthesis. Legend for Chart:
A - Independent Variable
B - Productivity of Vertical Scope
C - Productivity of Horizontal Scope
A B C
Log K/L .6004 (79.23) .5621 (72.13)
VS .0100(**) (2.75)
HS .1438 (1.06)
DT .0262 (1.54) .0279 (2.06)
PC .0241 (.59) -.0750 (-1.57)
NP -.0030 (-1.70) .0006 (.52)
VS x DT (Hyp < 0) -.0013(**) (-4.30)
HS x DT (Hyp < 0) -.0419(**) (-5.76)
VS x PC (Hyp > 0) .0006 (.75)
HS x PC (Hyp > 0) .0835(**) (3.28)
VS x NP (Hyp > 0) .0001(*) (2.32)
HS x NP (Hyp > 0) .0005 (.85)
N 3412 3244
Adjusted R² .6578(**) .6440(**)
(*) p < .05.
(**) p < .01.
Notes: t-statistics are in parentheses.DIAGRAM: FIGURE 1; Efficient Game Forms
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By Birger Wernerfelt
Birger Wernerfelt is J.C. Penney Professor of Management Science, Sloan School of Management, Massachusetts Institute of Technology.
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Record: 121- Promoting Relationship Learning. By: Selnes, Fred; Sallis, James. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p80-95. 16p. 1 Diagram, 6 Charts. DOI: 10.1509/jmkg.67.3.80.18656.
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Promoting Relationship Learning
The authors develop a theory of how management can develop and promote the learning capabilities of targeted customer--supplier relationships. The theory suggests that a supplier and a customer can improve their joint learning activities by facilitating information exchange, developing common learning arenas, and updating their behavior accordingly. The authors suggest that learning within a customer--supplier relationship cannot be mandated by either organization, but rather learning depends on both parties' willingness to cooperate in joint learning activities. Management can promote relationship learning by cultivating a collaborative culture, formulating specific objectives for joint learning activities, and developing relational trust. However, as relational trust develops, the effectiveness of learning is reduced as a result of "hidden costs" of trust. The authors use data from 31 5 dyads to test the theory empirically, and they find that the learning capability of a relationship has a strong, positive effect on performance. Their results also provide insight into how to address the hidden costs of trust.
Learning theories have a prominent role in new theories on competitive advantage (e.g.. Baker and Sinkula 1999; Day 1994a, b; Dickson 1992; Hurley and Huh 1998; Sinkula 1994). Day (1994b) emphasizes the ability of management to question the organizational norms that determine what information is acquired, disseminated, and acted on. The learning orientation of an organization has also been conceptualized along a cultural dimension, which includes a shared vision of learning, a commitment to learn, and open-mindedness (Baker and Sinkula 1999). Learning has been approached not only as an organizational phenomenon but also as an interorganizational phenomenon. The relational view of developing a competitive advantage identifies relationship learning (i.e., interfirm knowledge sharing) as an important avenue for creating differential advantages and "supernormal" profits in relationships (Dyer and Singh 1998; Pine, Peppers, and Rogers 1995). Through relationship learning, both parties in customer-supplier relationships identify ways to reduce or remove redundant costs, to improve quality and reliability, and to increase speed and flexibility. Prom suppliers' standpoint, better knowledge of their customers enables them to provide and develop more valuable products. Likewise, with better knowledge of suppliers, customers are better able to choose products and develop solutions that satisfy their needs and wants. That the learning capabilities of relationships likely foster valuable products is supported by the literature on lead users (von Hippel 1994, 1998). Within rapidly changing markets, there are significant incentives for both customers and suppliers to develop their learning capability related to the domain of relationships.
In principle, relationship learning can be conceptualized in two different ways. The first way is to address the relationship as both a source and a target of organizational learning, which is consistent with the approach Lukas, Hult, and Ferrell (1996) employ in developing a theoretical model for antecedents and consequences of organizational learning in marketing channels. Lukas, Hult, and Ferrell (1996, p. 234) propose that though organizational Learning occurs within individual organizations, it is a function of the interaction among channel members: "It involves aligning the channel members' assumptions about action-outcome heuristics, and simultaneously adapting each channel member's strategy and structure to a selectively attended environment." This conceptualization is consistent with Kohli and Jaworski's (1990) idea of market orientation as an organization-wide processing of market information.
The second way to conceptualize relationship learning, and one we explore in this article, is as a characteristic of the relationship itself. We define relationship learning as a joint activity between a supplier and a customer in which the two parties share information, which is then jointly interpreted and integrated into a shared relationship-domain-specific memory that changes the range or likelihood of potential relationship-domain-specific behavior. Relationship learning is thus a process to improve future behavior in a relationship. We further propose that relationships vary in terms of their learning capabilities, and thus some relationships perform better because they have developed appropriate learning mechanisms.
The conceptualization of relationship learning as a capability of a relationship is consistent with the interaction perspective on relationship building that Hallén, Johanson, and Seyed-Mohamed (1991) and Håkansson and Snehota (1995) address. These authors' interaction perspective suggests that two firms simultaneously affect and are affected by each other in relatively enduring ways. Adaptations stem from the need to coordinate the activities of the individuals and companies involved. Addressing relationship Learning as a capability of the relationship is also consistent with the relational governance perspective (e.g., Heide and John I 990), wherein the two parties collaborate and trust each other in order to secure or improve their economic performance. Several studies in the marketing literature have addressed elements of relationship learning, such as information sharing (e.g., Anderson and Weitz 1992; Cannon and Perreault 1999) and coordination (e.g., Buvik and John 2000; Jap 1999).
Although the learning capability of a relationship has been identified as an important avenue for creating competitive advantage and is consistent with current theories on customer-supplier relationship building, the lack of systematic attempts to examine how learning processes can be designed and promoted is notable. In this article, we address three research questions: ( 1) How can relationship learning be defined and operationalized, and how is it conceptually different from organizational learning? ( 2) How can relationship learning be promoted, and what other factors are likely to affect it? and ( 3) What is the role of trust in effective relationship-learning processes? We first develop the theoretical framework and the set of testable hypotheses and then describe the methodology and results from an empirical test of 315 dyads. Finally, we discuss the results' implications and the study's limitations and provide directions for further research.
We conceptualize relationship learning as a joint activity in which the two parties strive to create more value together than they would create individually or with other partners. We believe that the capability of a relationship to learn is closely connected with how it is managed and the context in which it is embedded. Many theories on interfirm relationships provide different perspectives on the form interfirm governance should take and its antecedent conditions (Cannon and Perreault 1999; Heide 1994). We draw on these theories in developing our theoretical model (Figure 1).
We expect relational trust to facilitate learning, because information sharing and sense making are sensitive issues in customer-supplier relationships in which the parties collaborate not only to increase the "pie" but also to try to maximize their share of the pie (Jap 1999, 2001). Relational trust creates a belief between parties that information sharing increases the size of the pie, more than information withholding increases their share of the pie. Through collaboration and adaptation, the two parties are expected to gradually develop trust and dependence, which again fosters a commitment to collaborate and share information (Dwyer, Schurr, and Oh 1987; Morgan and Hunt 1994). The desire to collaborate creates a climate for relationship-learning activities, and we propose that relationship learning mediates the effect of collaborative commitment on relationship performance.
As relationships evolve, they tend to become more complex in terms of interlinked operational activities across organizational boundaries and operational units. This complexity is expected to drive joint learning activities. Another important motivator for relationship learning is environmental uncertainty, which refers to the forces in the environment over which the parties to the relationship have little or no control, such as changes in end-user buying behavior, competition, and technology. According to resource dependence theory (Pfeffer and Salancik 1978), an organization, as well as a collaborative relationship (Van de Ven 1976), builds relationships in response to environmental uncertainty and organizes its resources accordingly. Therefore, we believe companies are motivated to engage in joint learning activities in order to gain some control or to buffer the consequences of uncertainty. It has been suggested that relationships not only adapt passively to changing environments but also interact more strategically and, through collaboration and joint learning, develop competitive advantage (Dyer and Singh 1998). For example, the locus of innovation is stronger in biotechnology networks than in the individual firms (Powell, Koput, and Smith-Doer 1996).
Finally, relationship learning is closely connected to how the parties have adapted to each other through investments of dedicated resources. Several of the parties' investments have only limited value outside the relationship, which by itself increases interdependence. For example, customizing a production facility to the specifications of a customer such as IKEA has limited value outside that relationship. Increasing interdependence is, according to Rousseau and colleagues (1998), likely to motivate the parties to move toward relational governance mechanisms, subsequently stimulating joint learning activities, which might help explain why higher levels of the transaction-specific assets have a positive effect on relationship performance.
It has been suggested that trust is the strongest governance mechanism in developing collaborative and effective relationships (Dodgson 1993; Doz 1996; Dyer and Singh 1998). Dodgson (1993, p. 78) goes so far as to say that "Effective learning between partners depends on the construction of a `climate' of trust engrained in organizational modes of behavior' Although trust is recognized as carrying potential risks, it is nevertheless generally recognized for its facilitating role in cooperative interorganizational relationships (Ring and Van de Ven 1994), and less attention is paid to its potential "dark side" (e.g., Grayson and Ambler 1999). For example, a customer might not question its trusted supplier, when in actuality the conditions of the transactions are suboptimal despite there being no opportunistic intent. In our theoretical model, we propose that trust actually moderates the effect of relationship learning on performance, which means that the effect of learning levels off as relational trust grows. Thus, attempts to enhance trust might prove dysfunctional unless the parties take action to prevent the negative effects of high trust.
Relationship Learning: The Construct
We developed our definitions of relationship learning and its components through a review of relevant marketing and organizational-learning literature and through 26 in-depth interviews with informants from both sides of 13 customer--supplier dyads. The idea behind the qualitative research approach was to obtain a better understanding of relationship learning and to obtain specific ideas about how to measure it. The participants in the qualitative study came from a variety of industries, including chemical manufacturers with customers from the construction and specialty chemicals industries and farmed-salmon producers with customers from smokers, canneries, sales agencies, and supermarket chains. Informants were typically from sales, procurement, research and development (R&D), quality control, and division management. We developed and employed a standard format for the interview that asked participants to provide examples of joint learning activities, what initiated these activities, and what consequences they had on performance and the nature of their relationship. In particular, we asked participants to elaborate on ways outcomes of learning activities manifested and were stored in memory. The interviews typically lasted approximately 60 minutes, and they were conducted over a period of about three months. All interviews were tape-recorded and written out in protocols that we later analyzed. In conjunction with the literature, we used the more pertinent observations from the interviews in developing a construct of relationship learning, its antecedents, and its consequences.
We consider relationship learning a unique form of the more general construct of organizational learning. Therefore, the literature on organizational-learning theory serves as a natural starting point for developing a relationship-learning construct. A review of the literature reveals several definitions of organizational learning; however, despite the diversity, there seems to be a general consensus that organizational learning involves some kind of information processing. For example, Huber (1991, p. 89) defines organizational learning as follows: "An entity learns if, through its processing of information, the range or likelihood of its potential behaviors is changed'
First, we believe that information sharing between the two parties in a customer-supplier relationship is a starting point and a necessary element of relationship learning. Research related to customer-supplier relationships has identified information sharing as a central element of working relationships. Anderson and Narus (1990) and Cannon and Perreault (1999) discuss how two organizations must exchange information to coordinate and plan the working relationship and thereby achieve operational efficiency. Biong and Selnes (1996) relate exchange of operative information to the tasks of a salesperson in ongoing relationships. In addition to the ongoing management of relationships, information sharing might also affect learning in the relationship. One of the respondents in the field interviews commented: "Mostly, we learn through communication. This is exactly the point we are trying to make with our customers. We want them to refer to us when they are developing new products or if they are making changes. We are trying to find contact points, regional and worldwide, who will work with us. . . . This is something we arc really working with, that is, to gain a mutual understanding with our customers for how we operate."
Second, we believe that dialogue within the relationship constitutes a relationship-specific element of interpretation or sense making (i.e., knowledge development) of the shared information. Fiol and Lyles (1985, p. 811) link interpretation closely with organizational learning when they define learning as "[t]he development of insight, knowledge, and associations between past actions, the effectiveness of those actions, and future actions." Because organizations vary in the ways they make sense of the same information, there likely are differences in the mechanisms involved in making sense of that information. It follows that some of the information acquired might be rejected, not because it is unimportant, but because the organization lacks the ability (i.e., knowledge) to make sense of it. Organizations employ several mechanisms to make sense of information, for example, board meetings, management meetings, and task-force teams. Organizations also introduce specific arenas with the sole purpose to learn, for example, information-sharing forums, as Huber (1996) suggests. Related to customer--supplier relationships, cross-functional teams in customer visit programs have been suggested as a mechanism for creating learning arenas (McQuarrie 1993).
In our field interviews, we wanted to learn how dialogues were organized in different customer-supplier relationships. The interviews revealed that most interactions between the two parties were related to solving some sort of operational problem and thus were addressed in operational kinds of meetings or simply by telephone. There were, however, many examples of face-to-face meetings, such as customer visits and trade shows. The parties used these forums to build a more general understanding of each other.
Third, we believe that organizations develop relationship-specific memories into which acquired relationship-specific knowledge is integrated. Walsh and Ungson (1991) argue that organizational memory is both an individual- and an organizational-level construct. Individuals retain information based on their direct experiences and observations, which are stored in their memories as cognition, beliefs, and values. At the organizational level, memory is decentralized and manifest in several places throughout the organization. This memory includes organizational beliefs, behavioral routines, and physical artifacts (Lukas, Hult, and Ferrell 1996; Moorman and Miner 1997 Walsh and Ungson 1991).
Relationship memory is not unlike organizational memory. It is evident that relationships develop idiosyncratic routines in the form of encoded formal and informal procedures and scripts for how the parties have learned to do things, referred to as operational linkages by Cannon and Perreault (1999). In customer-supplier relationships, each party develops beliefs related to common frames of reference, norms (MacNeil 1980), and symbols, or what others (e.g., Walsh and Ungson 1991) refer to as culture.
Fourth, relationship memories manifest in physical artifacts, such as documents, computer memories, and programming. The unique element of relationship memory is that retention facilities may be external to the organization but internal to the relationship. For example, in our interviews, a manager of a large R&D department said that when confronted with a problem he could not solve alone, his first reaction was to contact someone in his network who he thought might be able to help. His network encompassed relationships outside of his organization and represented a repository for specialized memories. The importance of such social networks across organizational boundaries has been pointed out in the literature, including by Håkansson and Johanson (1988), who find that more than two-thirds of all technical development collaboration is done through informal interpersonal networks.
Relationship learning is conceptually different from organizational learning. First, relationships have idiosyncratic memories that are different from organizational memories. Relationship learning involves, among other things, the common history, frames of reference, and values of the two parties that are different from the respective organizations. The memory is shared, which means that both organizations have access to the memory, independent of where it is located. Second, relationship learning is different from organizational learning with respect to its antecedents and thus how it is managed. Learning within a customer-supplier relationship cannot be mandated by either organization; rather, it depends on the parties' willingness to cooperate in a joint activity. Third, relationship learning is different from organizational learning because the consequences are different. Although the literature on organizational learning is unclear with respect to consequences, the common theme is that organizational learning affects the organization. As we discussed previously, relationship learning can be conceptualized as both a source and a target of organizational learning. However, our perspective is rooted in the belief that a relationship can be conceptualized as an entity of itself (e.g., a marriage). Relationships become quasi organizations, and relationship learning reflects a community of learning wherein what is learned is profoundly connected to the relationship.
Relationship Performance
The primary purpose of a relationship is to connect a customer's buying activities with a supplier's selling activities and services. A relationship can expand in scope and include other activities as well, such as joint R&D, joint marketing, joint quality control, and so forth. A well-performing relationship exists if both the customer and the supplier are satisfied with the relationship's effectiveness (i.e., doing the right things) and efficiency (i.e., doing things the right way). The effectiveness of a relationship can be determined by whether the personnel who interact perceive the relationship as worthwhile, equitable, productive, and satisfying (Ruekert and Walker 1987; Van de Ven 1976). Thus, the purpose of relationship learning is to enhance the effectiveness and efficiency of the relationship. As the parties begin to share information on ways they evaluate the relationship, take this information into a dialogue process, update a common memory, and change their behavior accordingly, relationship learning improves the overall performance of the relationship. Relationship learning can improve efficiency, effectiveness, or both.
Performance related to the efficiency of collaborative relationships is well documented in the literature (e.g., Heide and Stump 1995; Mudambi and Mudambi 1995 Noordewier, John, and Nevin 1990). In a study of long-term manufacturer-supplier relationships, Kalwani and Narayandas (1995) find that suppliers became more efficient with inventory levels and cost control, which results in lower overall costs, part of which suppliers bargained away to the customer as lower prices. In our field interviews, when referring to a joint R&D project with a supplier, a customer stated that service had improved through what was learned: "It's much faster. You know which people to call and you know a lot about the product you have developed together So, it's much easier to improve performance, to improve the product, and to reduce costs."
Research related to organizational learning and market orientation has found strong links to competitive advantage and business performance (e.g., Hurley and Hult 1998; Jaworski and Kohli 1993; Matsuno and Mentzer 2000; Narver and Slater 1990). The association of effectiveness with relationship learning is also supported (e.g., Dyer and Singh 1998). In the long run, high-learning relationships are likely to foster products and services that provide more value and are superior in solving problems for their users (e.g., von Hippel 1994, 1998). As two organizations engage in mutual learning, they are more likely to better understand each other's needs and wants and to respond accordingly (Kalwani and Narayandas 1995). As one supplier stated in of trust is usually accompanied by strong, positive emotions the field interviews: "We have restructured the entire operation. We customize our products for every customer, so in that sense we make changes based on what we learn about what they want."
A potential problem may be that relationship learning results in incorrect insights and thus might actually reduce performance. The relationship partners learn to do the wrong things right or the right things wrong. In such situations, unlearning might be the only way to increase performance (e.g., Hedberg 1981). However, we believe that, in general, relationship learning has a positive effect on the performance of the relationship.
H1: Relationship learning has a positive effect on relationship performance.
Relational Trust
Relational trust is the perceived ability and willingness of the other party to behave in ways that consider the interests of both parties in the relationship, and mainstream thinking states that trust is a facilitator of effective cooperative behavior in customer-supplier relationships (e.g., Dwyer, Schurr, and Oh 1987). The primary rationale for enhanced performance is that high levels of trust reduce reliance on formal control mechanisms, which thereby reduces transaction costs (MacNeil 1980; Nooteboom, Berger, and Noorderhaven 1997). Dwyer, Schurr, and Oh (1987) develop a framework for how interorganizational relationships start, evolve, and dissolve. Central to the development and maintenance of relationships is the establishment of norms of conduct that allow for future exchange and increased risk taking in the relationship. The most fundamental norm is trust, which provides the foundation for understanding expectations and for cooperation in the relationship. Thus, we expect relational trust to enhance relationship performance.
H2: Relational trust has a positive effect on relationship performance.
When the parties in an exchange believe they will not be harmed, exploited, or put at risk by actions of the other party, they are more likely to share information (Jap 1999, 2001; Morgan and Hunt 1994) and to forsake short-term gains at the expense of the other party (Axelrod 1984). This suggests that relational trust facilitates joint learning activities. With relational trust, it is more likely that the parties share information they otherwise would consider sensitive and that they create constructive, creative dialogues around making sense of information they share, to the benefit of both parties. It also follows that as the parties build mutual trust, they are more likely to develop a shared memory with access across company borders.
H3: Relational trust facilitates relationship learning.
As the relationship between two organizations develops, calculative trust, which is a rational choice based on explicit control mechanisms and credible information, gives way to relational trust, which has a strong emotional element (Rousseau et al. 1998). People's development of high levels of trust is usually accompanied by strong, positive emotions and liking (Jones and George 1998). In such atmospheres, a risk exists that negative or critical information is not exchanged because it might endanger the good atmosphere of the relationship. Thus, the benefit of constructive conflict is lost (Eisenhardt, Kahwajy, and Bourgeois 1997). High levels of trust might also produce a lack of critical information search. Alternatively, parties might take advantage of trust and exploit the other party in opportunistic ways (Hamel 1991). Opportunism in the form of self-seeking behavior might also cause high-trust relationships to be less effective (Grayson and Ambler 1999). In addition, as commitment increases, value systems converge and the parties develop a common identity (Gaertner, Dovidio, and Bachman 1996). They might become too homogeneous, which hinders the creative processes found in more heterogeneous groups, as with Janis's (1989) groupthink. Moorman, Zaltman, and Deshpandé (1992) suggest that this reduces the ability to be objective within the relationship, which thus diminishes the capacity to question assumptions on which actions are based.
Thus, high levels of trust might have "hidden costs" that limit the effectiveness of working relationships. We therefore propose that the general positive effect of relationship learning on performance is lower under conditions of high trust. Because these costs are hidden, the parties are not necessarily aware of the negative consequences of their mutual and high levels of trust. This implies that when relationship learning is least costly to initiate, that is, under high levels of trust, it is less effective than under lower levels of trust, and vice versa.
H4: The positive effect of relationship learning on relationship performance is moderated (reduced) under conditions of high trust.
Collaborative Commitment
Central to organizational-learning theory is the notion of the capability to learn, and it is suggested that this capability is related both to a commitment to learn (Day [994a) and to a shared vision of the benefits of learning (Baker and Sinkula 1999). Drawing on research both inside and outside of the marketing field. Morgan and Hunt (1994, p. 23) define commitment as "an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it' Consistent with these observations, it has been suggested that partners might also develop commitment and a shared vision related to collaborative learning activities (Dyer and Singh 1998).
Collaborative commitment can be related to the scope of purposes in the relationship (Borys and Jemison 1989). A narrow scope of purpose might encompass such things as simply providing for reliable deliveries, whereas a broad scope might include more complex objectives such as improving key processes, developing new products, and developing new markets. Some relationships go far in terms of analyzing potential gains from collaboration and developing a common system to monitor how well the collaboration produces results. A common goal, for example, might be to improve productivity by a given percentage every year, where productivity is defined and measured according to a H7: External uncertainty has a positive effect on relationship common unit, such as number of units produced per hour. It follows that the more ambitious the collaborative commitment is in a relationship, the more reason there is to exchange information and learn how relationship performance can be improved. A broad scope of purpose leads to several opportunities for collaboration and thus several opportunities to pursue relationship learning.
H5: Collaborative commitment has a positive effect on relationship learning.
Internal Complexity
Internal complexity reflects the difficulty of matching two operations across organizational boundaries and should increase the need and motivation to learn. It is reasonable to expect that this difficulty is related to the inherent complexly of the products involved in the relationship as well as to the structural complexity of how the relationship is organized.
The greater the internal complexity, the more likely it is that complex information must also be shared (Metcalf, Frear, and Krishnan 1990). Field informants frequently commented that mergers and acquisitions force companies to reorganize their customer-supplier relationships. If two suppliers merge, they often have a set of common customers with which they both have relationships; those customers then must he reorganized under the new structure. A similar picture emerges on the customer side when a supplier that previously had a relationship with two customers must develop a relationship with one customer. Closer collaboration through information sharing and learning should facilitate solving the evaluation problem created by the resulting internal uncertainty.
H6: Internal complexity has a positive effect on relationship learning.
External Uncertainty
External uncertainty creates volatility and hampers organizational decision making (Pfeffer and Salancik 1978). Uncertainty may also give rise to opportunism, because either party can take advantage of the changing situation (Heide and John 1990). Uncertainty external to the relationship refers to the forces in the environment over which the parties to the relationship have little or no control and, at the same time, the forces that have a large impact on the performance of the relationship. Changes in end users' buying behavior, competition, and technology all have a large impact on the potential value created in relationships. Flexibility and the ability to adapt rapidly are important in such uncertain environments. If, for example, end users change their preference for a product, effective collaboration reduces time and improves quality in the way the parties change the way they work together, thus developing new or improved products that reflect the new preferences (e.g., McKee 1992). Therefore, companies might be motivated to engage in relationship learning either to gain some control over externalities or to buffer the consequences (Jap 1999; Oliver 990; Van de Ven 1976).
H7: External uncertainty has a positive effect on relationship learning.
Transaction-Specific Assets
In some relationships, one or both of the parties can enhance their rewards from focal relationships through the investment of dedicated resources. However, these investments may have value only within the relationship. Such transaction-specific investments create a need to safeguard against opportunism (Williamson 1985); Heide and John (1990) suggest developing longer-lasting relationships. Collaboration in the form of joint learning activities thus functions as a safeguard against opportunism and offers a direct check of the other party (Buvik and John 2000; Noordewier, John, and Nevin 1990). Transaction-specific assets should further motivate relationship learning, because this might be a way to enhance the return on investments beyond the initial motivation for the investment.
H8: Transaction-specific assets have a positive effect on relationship learning.
Sample and Data Collection Procedure
The unit of analysis is the relationship, not the individual organization. The relationship is real, but it is simultaneously an abstract phenomenon that is not directly observable, which imposes large potential for measurement error (Heide and John 1994; John and Reve 1982; Phillips 1981). A common measurement method is to use key informants from both sides of the dyad, which John and Reve (1982) find is a valid approach.
We deemed high variance on the relationship-learning variable the key consideration in determining the sampling frame and drawing the sample. We used a list of medium to large companies (more than 50 employees) in Scandinavia as an initial sampling frame to identify seller organizations. A sample of upper management from 780 companies on the list was contacted and asked to participate in the study. When recruited, upper-level managers were asked to supply a few names of people within their companies who were central to their customer relationships and to identify a customer they characterized as important in terms of sales and profit. After seller informants were recruited, they were asked to supply the names of their contacts in a key customer organization. The first informant then contacted the identified informant in the key customer organization and asked him or her to participate in the study. Both customer and supplier informants were then faxed or mailed the questionnaire.
Of the 780 supplier companies contacted by telephone, 665 agreed to participate in the study. We received questionnaires from 3 19 dyads, of which 3 15 were satisfactorily completed for use in the analysis. This represents a 40% response rate based on the 780 suppliers initially contacted. Because customers were recruited directly through suppliers, the response rates were virtually identical. The high response rate indicates the high quality of the sampling procedure and that informants perceived the research as relevant and worthwhile.
On average, seller informants had been involved in the focal relationship for 5.8 years and buyer informants for 5.2 years. The informants had been working with their companies, on average, for 9.3 years (seller) and 8.6 years (buyer). Therefore, the person contacted was expected to be knowledgeable about the phenomenon under study and able and willing to communicate with us (Campbell 1955).
On average, the interfirm relationships were 8.9 years old. The customers accounted for an average of 19.1% of the sellers' total sales, and the suppliers accounted for an average of 18.2% of the customers' purchases. Although the seller companies were recruited from Scandinavia, the buyers were located in several European countries. The objective of heterogeneity is well satisfied because the sampled relationships represent large companies, constitute large volumes of exchange for each party, and typically are international.
Measures
We used Churchill's (1979) approach to questionnaire development. We combined scales from several other relevant empirical studies with new items to make an initial list of questions. We eliminated several redundant items through interviews with businesspeople and colleagues, and we tested a first draft of the questionnaire across 12 dyads. Construct analysis of the results guided final revisions. We used seven-point scales anchored by "strongly disagree" and "strongly agree." Measures are reported in the Appendix.
We received several comments about the questionnaire being in English, which suggests that response rates might have been substantially negatively affected. To alleviate this risk, the questionnaire was translated into German, French, Swedish, and Norwegian and was offered in English as well. All translations were based on the English original, and then back-translations were made from the second language to English. People fluent in both English and the second language checked the questionnaires a third time. We used one-way analysis of variance to test for differences between group means for key variables across languages for both sides of the dyad. F-values for the variables were well below the critical value to assume mean differences across languages.
Relationship learning. Relationship learning is defined as an ongoing joint activity between the customer and the supplier organizations directed at sharing information, making sense of information, and integrating acquired information into a shared relationship-domain-specific memory to improve the range or likelihood of potential relationship-domain--specific behavior. Previous studies by Anderson and Narus (1990), Heide and John (1992), Moorman and Miner (1997), Noordewier, John, and Nevin (1990), and Slater and Narver (1996) provided guidance in developing the items. In the qualitative interviews, we also probed for joint activities related to learning. For example, we asked participants to describe how they learn from their supplier/ customer and to provide examples. We also asked participants to describe how learning is memorized in their organizations. We used 17 items to assess the degree of relationship learning. These items (see the Appendix) tap into the multiple facets of relationship learning incorporated in our definition, including information sharing (7 items), joint sense making (4 items), and integration into a relationship-specific memory (6 items).
The relationship-learning items we developed are formative in nature, and thus we did not expect items to be highly correlated (Bollen and Lennox 1991; Fornell and Larcker 1981). For example, there is no obvious rationale for why a relationship that exchanges product information regularly should also exchange sensitive financial information. Rather, a formative logic suggests that a relationship that exchanges information on many subjects has richer, more extensive information exchange than does a relationship that exchanges information on a few subjects. A confirmatory factor analysis of the I 7 items and three factors for the two data sets is reported in Table I . All loadings are significant. Goodness-of-fit statistics for the supplier data are χ² = 441.90 (p = .00); root mean square error of approximation (RMSEA) = .098; adjusted goodness-of-fit index (AGFI) = .80; normed fit index (NFL) = .87; comparative fit index (CFI) = .90; goodness-of-fit index (GFI) = .85; and root mean residual (RMR) = .054. Fit statistics for the buyer data are χ² = 419.55 (p = .00); RMSEA = .094; AGFI = .81; NFI = .84; CR = .88; GEI = .86; and RMR = .055. The fit statistics are as expected given the formative nature of the scales.
We tested the three-dimensional nature of relationship learning through a second-order factor analysis. All three path coefficients between the higher-order constructs (buyer informant and supplier informant relationship learning higher levels of random error while also accounting for [BRL and SRL, respectively]) and the three dimensions are measurement error and retaining the three-dimensional scale significant at α = .05. Thus, we deemed our second-order scales of relationship learning adequate for the purpose of this study. For hypothesis testing, we aggregated the BRL and SRL scales by summing the measurement items. This follows the arguments provided by Matsuno and Mentzer (2000). Aggregation is validated because ( 1) the second-order relationship-learning scale has been established, ( 2) aggregation enables maximization of the degrees of freedom in estimating path coefficients, and ( 3) aggregation reduces higher levels of random error while also accounting for measurement error and retaining the three-dimensional scale of relationship learning.
Statistical properties of the scales are reported in Tables 2 and 3. The coefficient alpha for the SRL scale is .94 and .92 for the BRL scale. We observe in Table 3 that the correlation between SRL and BRL is .66, which indicates a high degree of convergent validity compared with other studies with multiple informants across dyads. For example, John and Rave (1982) obtained correlations in the range of .34 to .64 for a similar set of variables.
Relationship performance. Relationship performance is defined as the extent to which the partners consider their relationship worthwhile, equitable, productive, and satisfying (Ruekert and Walker 1987; Van de Ven 1976). We developed a scale consisting of seven items on the basis of previous work by Kalwani and Narayandas (1995), Kumar, Stern, and Achrol (1992), and Noordewier, John, and Nevin (1990). Principal component analysis indicated high internal consistency and one-factor solutions for both the supplier and the buyer data, which together with statistical properties (Tables 2 and 3), indicates that the operationalization and measurement of relationship performance is satisfactory.
Relational trust. Relational trust is defined as the perceived ability and willingness of the other party to behave in ways that consider the interest of both parties in the relationship. We measured trust of the other party with five items adapted from the scales developed by Kumar, Scheer, and Steenkamp (1995). Doney and Cannon (1997), Morgan and Hunt (1994), and Zaheer, McEvily, and Perrone (1998). Principal component analysis indicated high internal consistency and one-factor solutions for both the supplier and the buyer data.
Collaborative commitment. Collaborative commitment is defined as the joint belief that the relationship is important enough to warrant joint efforts to maintain and strengthen it. A manifestation of collaborative commitment is that the parties tend to develop common goals and implement joint measures, which thus initiates activities that benefit both parties and subsequently enhances the value of the relationship. In the field interviews with relationship informants, we addressed how collaborative commitment materializes in a relationship. The first issue is whether the two parties discuss a purpose beyond the pure transaction and, if so, to what degree the purpose is prioritized. To varying degrees, the common goals may result from investigation or analysis of potential for improvement. The more ambitious relationships have developed measures that reflected common goals and thus improvements on a more day-to-day basis. We developed a scale with five items on the basis of previous theoretical work by Hamel (1991) and Hakansson and Johanson (1988) as well as empirical work by Heide and John (1992). Principal component analysis indicated high internal consistency and one-factor solutions for both the supplier and the buyer data.
Internal complexity. Internal complexity is defined as the perceived complexity of the relationship itself and reflects the number and complexity of products and operational units involved in the relationship. We addressed internal complexity with four items. Principal component analysis indicated one-factor solutions but only moderate levels of internal consistency. Coefficient alphas for both the supplier internal complexity and the buyer internal complexity relationship complexity scales were initially below the recommended .70 level (Nunnally 1978). A closer examination of the individual items and their impact on the reliability of the scales revealed that the item reflecting the number of products in the relationship was different from the other items, and thus the scale was improved by removing this item on both buyer and seller scales.
Environmental uncertainty. Environmental uncertainty is defined as the perceived pace of change in drivers of market behavior. We assessed environmental uncertainty with five items adapted from Jaworski and Kohli (1993) and Noordewier, John, and Nevin (1990). Principal component analysis indicated high internal consistency and one-factor solutions for both the supplier and the buyer data.
Transaction-specific assets. Transaction-specific assets are defined as investments and adaptations dedicated to the focal relationship. We assessed transaction-specific assets with three items adapted from Heide and Stump (1995) and Heide (1994). Principal component analysis and reliability analysis indicated internal consistency and one-factor solutions.
Measurement Model
The unit of analysis in the theoretical model is the relationship, and the constructs reflect properties of the relationship. Following John and Reve's (1982) procedure, we developed dyadic measures formed by the scales from each informant. Each latent construct, for example, relationship learning, has two indicators, SRL and BRL. We followed the procedure described by Jonsson (1998) for modeling the interaction construct between relationship learning and trust.
We tested convergent and discriminant validity of the scales with a confirmatory factor analysis procedure recommended by Anderson and Gerbing (1988). We first estimated the measurement model with the eight theoretical constructs, assessed with two indicators each, that reflected the key informants. We assessed convergent validity by the GFIs, t-values associated with the individual items, and two reliability indexes for each construct based on the estimated measurement model.
The initial fit statistics were poor: χ² = 457.89 (72 degrees of freedom [d.f.]). Other fit statistics are ACM = .63; RMSEA = .16; NFI= .91; CFI= .93; OH = .80; and RMR = .060. Following the logic of John and Reve (1982), we included two methods factors, one reflecting the buyer responses and one reflecting the supplier responses, which allows for partitioning of the variance into trait (construct of interest), method (systematic informant bias), and random error and should improve the fit of the model (Phillips 1981). With the two methods factors included in the model, χ² for the estimated measurement model improved to 60. 13 (56 d.f., p = .33). Other fit statistics are AGFI = .94; RMSEA = .0 15; NFL = .99; CFI = 1.00; GFI = .98; and RMR = .029. The overall fit of the revised measurement model is thus acceptable. Tables 4 and 5 list the estimates of the model's parameters.
All t-values of the estimated factor loadings of the eight theoretical constructs are significant (p < .01). As shown in Table 4, all theoretical scales are above the recommended level of composite reliabilities (.7) and extracted share variance (.5) (Fornell and Larcker 1981). In summary, the convergent validity of the scales is satisfactory.
We assessed discriminant validity by testing the confidence intervals (± two standard errors) around the standardized correlation estimate between the pair of scales, which did not include 1.0 (Anderson and Gerbing 1988). As shown in Table 5, this test is satisfactory for all comparisons. Together, these results provide good support for the ability of the two key informants to provide valid data on the theoretical variables of the model.
Structural Model
The estimated beta parameters from the structural model appear in Table 6. The goodness-of-fit statistics are satisfactory: χ² = 68.58 (60 d.f., p = .21) AGFI = .94; RMSEA = .021; NFI = .99; CFI = 1.00; GFI = .97; and RMR = .028.
H1 states that relationship learning has a positive effect on relationship performance. In Table 6, it is evident that the effect is positive and significant (t = 3.45), in support of H1. We hypothesized relational trust to have a positive effect on relationship performance (H2) and on relationship learning (H3) and to moderate the effect of learning on performance (H4). As expected, trust has a positive, significant effect on relationship performance (t = 2.72) and relationship learning (t = 3.28) and a negative interaction effect with relationship learning on performance (t = -2.52). The meaning of a negative interaction effect is that the positive effect of relationship learning on performance is lower under conditions of high trust than under conditions of low trust; that is, when relationship trust is low, the effect of learning on performance is high. As relationship trust increases, the positive effect of learning on performance levels off (hidden costs of trust). Thus, as trust grows in a relationship, the effect of relationship learning on performance has diminishing marginal returns on increased performance.
Collaborative commitment (H5), environmental uncertainty (H7), and transaction-specific assets (H8) have the expected positive effects on relationship learning, whereas the hypothesized effect of internal complexity (H6) is not statistically significant at α = .05. The squared multiple correlation is .89 for relationship learning and .88 for relationship performance. Thus, both performance and relationship learning are well explained within the theoretical model.
It has been suggested that researchers should compare rival models and not just test a proposed model (Bollen and Long 1992; Rust, Lee, and Valente 1995). According to the procedure presented by Morgan and Hunt (1994), a rival model is one in which the precursors affect relationship performance directly. In the proposed model, the effects of trust, collaborative commitment, internal complexity, environmental uncertainty, and transaction-specific assets operate through relationship learning. In the rival model, there are no indirect effects; in other words, relationship learning is not allowed to mediate any of the relationships. The most common statistical tests for model comparison between a proposed model and a rival model are ( 1) overall fit of the competing models relative to degrees of freedom, ( 2) number of hypothesized parameters that are significant, and ( 3) ability to explain variance in the outcomes of interest (Morgan and Hunt 1994; Rust, Lee, and Valente 1995). Overall fit of the rival model is somewhat better than the proposed model; χ² improved by 13.56 (5 d.f.), which is significant. However, both model-implied covariance matrices fit the sample covariance matrix well. The second criterion for comparison is the number of significant parameters. In the proposed model, all hypothesized parameters except one are significant. In the rival model, only the effect of transaction-specific assets is significant. Finally, the squared multiple correlation of performance is reduced from .89 to .83 in the rival model. Thus, both the relative number of significant parameters and the relative predictive power provide stronger support for the theoretical model we propose than for the rival model.
The reported findings support our argument that ( 1) relationship performance can be improved through relationship learning, ( 2) relationship learning can he promoted and is accelerated through collaborative commitment, and ( 3) high levels of trust reduce the positive effect of relationship learning.
Within the confines of the selected methodology, we believe our findings have implications for managing customer-supplier relationships. In addition to the importance of addressing relationship learning to create competitive advantage, the study gives management an idea for how such processes should be designed with respect to, for example, information sharing, sense making, and memory mechanisms. In addition to organizing the learning process, management is also encouraged to develop a collaborative strategy that reflects a common, ambitious, though realistic vision for learning within the relationship. A consensus that information sharing increases the size of the pie more than information withholding increases the share of the pie is a necessary condition for creating effective relationship learning. A first step in the process is to analyze carefully the potential for improvement and then operational actions while monitoring one or more key measures reflecting the achieved results. Through developing collaborative commitment and aligning this to relationship-learning activities, the speed of achieving competitive advantage is enhanced.
Our data indicate that trust moderates the positive effect of learning on performance within a relationship, thus diminishing the return of learning. Contrary to common practice in customer relationship management, we warn companies about the hidden costs of high levels of trust. Although trust facilitates information sharing, joint sense making, and development of shared relationship memories, higher levels of trust appear to cause some problems that reduce the effect of relationship learning on performance. We believe it is adequate to refer to these problems as inherent hidden costs of high trust, because the participants in the relationship-learning activities are generally not aware or conscious of them. This moderating effect of trust might happen through one or more of the following processes: The first type of hidden cost in high-trust relationships is a systematic avoidance of negative information. Because the parties' emotions are involved as they start to like each other in high-trust relationships, it is likely that they will start to avoid negative information, because it might signal some sort of dislike and thus risk the "friendship" within the relationship. For this reason, the parties in the relationship might avoid difficult but important aspects of their relationship that should be addressed if high relationship performance is the objective. The second type of hidden cost with high-trust relationships is that the parties relax explicit control mechanisms against opportunities behavior. When trust is low or moderate, each party is likely to perceive some level of risk in which it believes the other party might exploit it, and for that reason both parties seek critical information to monitor each other's behavior. In addition, reduced monitoring means reduced questioning of the status quo and less opportunity for learning. The third type of hidden cost in high-trust relationships is that creativity is lost because of too much congruence among the participants, and thus the parties fall into a state of groupthink.
The combined positive and negative effects of trust suggest that the strongest effect of relationship learning might be found on performance in moderate-trust relationships. This implies that low-trust relationships gain from increasing relational trust, and therefore relational trust should be an objective. How to build relational trust is well described in both academic and management literature; however, to the best of our knowledge, what to do in high-trust relationships has not been properly addressed as a problem in the relationship management literature. The overall positive effects of relational trust on performance and learning are strong, and thus companies gain much from developing high-trust relationships. However, we believe that performance in high-trust relationships can be even stronger if management takes adequate precautions against the inherent hidden costs of high trust. Under conditions of high relational trust, management is advised to impose more heterogeneity into the groups working with the relationship and to search actively for external information to obtain an outside perspective on behaviors and performance. Companies might, for example, impose a policy by which key account managers for any customer are replaced every nth year and develop a process for transferring key account responsibility. Another initiative would be to identify market metrics by which indicators within the relationship are compared with relevant outside indicators. This would force both parties to evaluate their own performance continuously against best practice. We do not suggest that relationship learning is more effective when the parties are "forced to learn," because they do not trust each other. Relational trust is still positive and important for developing effective relationships. The challenge is to manage the inherent hidden costs if management knows they exist. Thus, how companies organize their relationship-learning processes should reflect the level of trust within their relationships.
The major limitation of our study concerns our measurement approach. First, we limit our analysis of relationship learning to aspects that can be retrieved through a questionnaire with a limited number of questions and an assumption that these questions are able to capture the rich dimensionality of these learning processes. Second, we are able to provide only a snapshot of ongoing processes and not measures of the same process over time. Our dilemma was that to test the hypothesized model and provide some general principles, we needed to sample a large number of relationships and measure these in equivalent ways. The previously mentioned concerns and limitations should be considered in understanding the meaning of our findings.
In addition to the antecedents proposed in the theoretical model, other factors are also likely to affect learning capabilities of customer-supplier relationships. One such factor is the strategic fit between the two organizations. This factor relates to the purpose of partnering (e.g., Rangan, Menezes, and Maier 1992), interpersonal relations or social network across the company boundaries (e.g., Håkansson and Johanson 1988; Wathne and Heide 2000), and organizational differences (e.g., Smith and Barclay 1997). It is also likely that the individual organizational-learning capabilities of the two partners affect how successful each is in developing relationship learning. Siguaw, Simpson, and Baker (1998) find that market orientation of the supplier is closely connected to distributor trust, relationship cooperative norms, and distributor commitment. This suggests that collaborative relationships are most successful when both parties are market oriented. Although strategic fit and organizational-learning capabilities are likely important antecedents of relationship learning, we needed to limit the scope of the present study and leave these factors to further research.
We also believe further research should address the role of trust in relationships, in particular, the ways hidden costs evolve and perhaps under which conditions they create costs. Further research could also explore successful mechanisms for limiting or removing the hidden costs of high trust and address the functionality of trust under different relationship conditions. The traditional view has been that the function of trust is related to the development of a relationship in which formal mechanisms (e.g., contracts) are used initially and then replaced, in one or more ways, by trust as the relationship develops (Dwyer, Schurr, and Oh 1987; Dyer and Singh 1998). Because most relationships do not follow a classic linear development from low commitment to high commitment and because most relationships appear more dynamic, commitment to and a shared vision of a relationship likely varies over time depending on changes in needs and opportunities. It follows that the functionality of trust and other governance mechanisms are likely dynamic and context specific. More knowledge is needed about trust under various relationship conditions.
The authors are grateful for the comments of Håkan Håkansson, Jan Heide, Torger Reve, Ulg Henning Olsson, and Kent Grayson and for financial support from the Norwegian Research Council and the Marketing Science Institute. The authors thank the four anonymous JM reviewers for their suggestions.
Legend for Chart:
A - Item
B - Standardized Loading
A B
SRL
SRL1 .73
SRL2 .72
SRL3 .79
SRL4 .76
SRL5 .73
SRL6 .40
SRL7 .66
SRL2
SRL8 .76
SRL9 .77
SRL10 .78
SRL11 .68
SRL3
SRL12 .75
SRL13 .80
SRL14 .76
SRL15 .76
SRL16 .71
SRL17 .68
BRL1
BRL1 .65
BRL2 .64
BRL3 .70
BRL4 .64
BRL5 .64
BRL6 .67
BRL7 .67
BRL2
BRL8 .67
BRL9 .74
BRL10 .81
BRL11 .69
BRL3
BRL12 .71
BRL13 .70
BRL14 .65
BRL15 .64
BRL16 .64
BRL17 .69 Legend for Chart:
A - Construct
B - Scale
C - Items
D - Mean
E - S.D.
F - Skewness
G - Kurtosis
H - Reliability(a)
A B C D
E F G
H
Relationship learning SRL 17 78.27
18.86 -.148 -.696
.94
BRL 77.85
17.15 -.113 -.871
.92
Relationship performance SPF 7 31.86
7.98 -.250 -.560
.88
BPF 32.30
7.28 -.205 -.670
.84
Relational trust STR 5 24.29
6.98 -.390 -.690
.92
BTR 24.53
6.40 -.250 -.773
.89
Collaborative commitment SCC 5 21.67
7.25 .009 -.883
.94
BCC 22.01
6.52 -.055 -.686
.91
Internal complexity SIC 3 11.27
4.10 .243 -.612
.72
BIC 11.19
4.04 .234 -.615
.73
Environmental uncertainty SEU 5 23.47
6.44 -.910 -.952
.85
BEU 23.89
5.86 -.100 -.913
.84
Transaction-specific assets STA 3 13.83
3.71 -.261 -.659
.68
BTA 14.18
3.49 -.192 -.589
.69
(a) Reliability is coefficient alpha.
Notes: S.D. = standard deviation; SPF = supplier relationship
performance; BPF = buyer relationship performance: STR = supplier
relational trust: BTR = buyer relational trust; SCC = supplier
collaborative commitment; BCC = buyer collaborative commitment;
SIC = supplier internal complexity; BIC = buyer internal
complexity; SEU = supplier environmental uncertainty; BEU = buyer
environmental uncertainty; STA = seller transaction-specific
assets; and BTA = buyer transaction specific assets. Legend for Chart:
B - SPF
C - BPF
D - SRL
E - BRL
F - STR
G - BTR
H - SCC
I - BCC
J - SIC
K - BIC
L - SEU
M - BEU
N - STA
O - BTA
A B C D E F
G H I J K
L M N O
SPF 1.00
BPF .58(*) 1.00
SRL .76(*) .54(*) 1.00
BRL .58(*) .75(*) .66(*) 1.00
STR .61(*) .45(*) .74(*) .52(*) 1.00
BTR .43(*) .62(*) .46(*) .62(*) .53(*)
1.00
SCC .62(*) .46(*) .70(*) .55(*) .57(*)
.38(*) 1.00
BCC .51(*) .55(*) .53(*) .62(*) .40(*)
.47(*) .66(*) 1.00
SIC .27(*) .13(**) .29(*) .10(**) .13(**)
.03 .25(*) .22(*) 1.00
BIC .17(*) .24(*) .14(**) .19(*) .02
.07 .12(*) .26(*) .64(*) 1.00
SEU .65(*) .46(*) .65(*) .60(*) .56(*)
.42(*) .57(*) .49(*) .17(*) .06
1.00
BEU .47(*) .57(*) .51(*) .65(*) .40(*)
.48(*) .48(*) .49(*) .09 .08
.58(*) 1.00
STA .66(*) .49(*) .65(*) .54(*) .52(*)
.37(*) .55(*) .48(*) .23(*) .12(**)
.63(*) .47(*) 1.00
BTA .46(*) .61(*) .48(*) .62(*) .38(*)
.48(*) .42(*) .46(*) .10 .12(**)
.52(*) .57(*) .56(*) 1.00
(*) Correlation is significant at the .01 level (two-tailed)
(**) Correlation is significant at the .05 level (two-tailed).
Notes SPF = supplier relationship performance:
BPF = buyer relationship performance; STR = supplier relational
trust; BTR = buyer relational trust; SCC = supplier
collaborative commitment; BCC = buyer collaborative commitment;
SIC = supplier internal complexity; BIC = buyer internal
complexity; SEU = supplier environmental uncertainty;
BEU = buyer environmental uncertainty; STA = seller
transaction-specific assets: and BTA = buyer transaction-specific
assets. Legend for Chart:
A - Parameter
B - Scale
C - Variable
D - Estimate (Standard Error)
E - t-Value
F - Composite Reliability
G - Shared Variance Reliability
A B C D
E F G
λ1,1 PF SPF .87 (.05)
17.33 .77 .62
λ2,1 BPF .70 (.05)
13.17
λ3,2 RL SRL .85 (.05)
16.06 .80 .67
λ4,2 BRL .78 (.05)
15.40
λ5,3 TR STR .82 (.06)
14.96 .70 .54
λ6,3 BTR .64 (.05)
11.93
λ7,4 RL x TR SRL x STR .83 (.05)
15.53 .77 .63
λ8,4 BRL x BTR .75 (.05)
14.75
λ9,5 CC SCC .85 (.05)
16.40 .80 .67
λ10,5 BCC .78 (.05)
14.84
λ11,6 IC SIC .85 (.08)
10.98 .76 .61
λ12,6 BIC .77 (.07)
10.39
λ13,7 EU SEU .88 (.05)
17.24 .76 .62
λ14,7 BEU .68 (.05)
12.79
λ15,8 TA STA .84 (.05)
16.35 .74 .58
λ16,8 BTA .68 (.05)
12.68
λ1,9 S SPF .14 (.07)
1.90
λ3,9 SRL .51 (.08)
6.00
λ5,9 STR .32 (.08)
4.11
λ7,9 SRL x STR .44 (.06)
7.08
λ9,9 SCC .21 (.07)
3.17
λ11,9 SIC .17 (.06)
2.78
λ13,9 SEU .05 (.08)
0.58
λ15,9 STA .08 (.07)
1.22
λ2,10 B BPF .59 (.05)
12.69
λ4,10 BRL .51 (.04)
11.27
λ6,10 BTR .46 (.05)
9.04
λ8,10 BRL x BTR .52 (.04)
11.74
λ10,10 BCC .29 (.05)
5.84
λ12,10 BIC .24 (.05)
4.75
λ14,10 BEU .38 (.05)
7.47
λ16,10 BFA .41 (.05)
8.27
Notes Psi (Ψ) matrix is standardized S and Bar
method scales for supplier and buyer: PF = relationship
performance; RL = relationship learning; TR = relational
trust; CC = collaborative commitment; IC = internal
complexity; EU = environmental uncertainty;
TA = transaction-specific assets. Legend for Chart:
A - Parameter
B - Scale
C - Variable
D - Estimate (Standard Error)
E - t-Value
F - Composite Reliability
G - Shared Variance Reliability
A B C D
E F G
λ1,1 PF SPF .87 (.05)
17.33 .77 .62
λ2,1 BPF .70 (.05)
13.17
λ3,2 RL SRL .85 (.05)
16.06 .80 .67
λ4,2 BRL .78 (.05)
15.40
λ5,3 TR STR .82 (.06)
14.96 .70 .54
λ6,3 BTR .64 (.05)
11.93
λ7,4 RL x TR SRL x STR .83 (.05)
15.53 .77 .63
λ8,4 BRL x BTR .75 (.05)
14.75
λ9,5 CC SCC .85 (.05)
16.40 .80 .67
λ10,5 BCC .78 (.05)
14.84
λ11,6 IC SIC .85 (.08)
10.98 .76 .61
λ12,6 BIC .77 (.07)
10.39
λ13,7 EU SEU .88 (.05)
17.24 .76 .62
λ14,7 BEU .68 (.05)
12.79
λ15,8 TA STA .84 (.05)
16.35 .74 .58
λ16,8 BTA .68 (.05)
12.68
λ1,9 S SPF .14 (.07)
1.90
λ3,9 SRL .51 (.08)
6.00
λ5,9 STR .32 (.08)
4.11
λ7,9 SRL x STR .44 (.06)
7.08
λ9,9 SCC .21 (.07)
3.17
λ11,9 SIC .17 (.06)
2.78
λ13,9 SEU .05 (.08)
0.58
λ15,9 STA .08 (.07)
1.22
λ2,10 B BPF .59 (.05)
12.69
λ4,10 BRL .51 (.04)
11.27
λ6,10 BTR .46 (.05)
9.04
λ8,10 BRL x BTR .52 (.04)
11.74
λ10,10 BCC .29 (.05)
5.84
λ12,10 BIC .24 (.05)
4.75
λ14,10 BEU .38 (.05)
7.47
λ16,10 BFA .41 (.05)
8.27
Notes Psi (Ψ) matrix is standardized S and Bar
method scales for supplier and buyer: PF = relationship
performance; RL = relationship learning; TR = relational
trust; CC = collaborative commitment; IC = internal
complexity; EU = environmental uncertainty;
TA = transaction-specific assets. Legend for Chart:
A - Parameter
B - Scale/Variable
C - Estimate (Standard Error)
D - t-Value
A B C D
Ψ1,2 PF-RL .91 (.02) 40.34
Ψ1,3 PF-TR .80 (.04) 21.30
Ψ1,4 PF-RL x TR .89 (.03) 35.32
Ψ1,5 PF-CC .77 (.04) 20.91
Ψ1,6 PF-IC .30 (.07) 4.55
Ψ1,7 PF-EU .83 (.04) 23.06
Ψ1,8 PF-TA .86 (.03) 25.41
Ψ2,3 RL-TR .82 (.03) 24.76
Ψ2,4 RL-RL x TR .96 (.01) 105.92
Ψ2,5 RL-CC .82 (.03) 26.17
Ψ2,6 RL-IC .24 (.07) 3.50
Ψ2,7 RL-EU .90 (.03) 29.25
Ψ2,8 RL-TA .84 (.03) 24.94
Ψ3,4 TR-RL x TR .94 (.01) 83.13
Ψ3,5 TR-CC .69 (.05) 14.71
Ψ3,6 TR-IC .07 (.07) 1.01
Ψ3,7 TR-EU .77 (.04) 18.04
Ψ3,8 TR-TA .72 (.05) 15.36
Ψ4,5 RL x TR-CC .80 (.03) 24.07
Ψ4,6 RL x TR-IC .15 (.07) 2.10
Ψ4,7 RL x TR-EU .87 (.03) 26.66
Ψ4,8 RL x TR-TA .80 (.04) 21.62
Ψ5,6 CC-IC .29 (.07) 4.45
Ψ5,7 CC-EU .77 (.04) 19.43
Ψ5,8 CC-TA .73 (.04) 16.92
Ψ6,7 IC-EU .18 (.07) 2.68
Ψ6,8 IC-TA .24 (.07) 3.40
Ψ7,8 EU-TA .87 (.04) 23.93
Notes: PF = relationship performance; RL = relationship learning;
TR = relational trust; CC = collaborative commitment
IC = internal complexity; EU = environmental uncertainty;
TA = transaction-specific assets Legend for Chart:
A - Parameter
B - Scales
C - Estimate (Standard Error)
D - t-Value
E - Standard Estimation
A B C
D E
β1,2 RL → PF 1.71 (.50)
3.45 1.84
β1,3 TR → PF 2.33 (.86)
2.72 .82
β1,4 RL x TR → PF -4.71 (1.87)
-2.52 -1.66
β2,3 TR → RL .85 (.26)
3.28 .28
β2,5 CC → RL .73 (.22)
3.26 .24
β2,6 IC → RL .17 (.12)
1.45 .06
β2,7 EU → RL .87 (.41)
2.11 .29
β2,8 TA → RL .70 (.34)
2.04 .23
Notes: PF = relationship performance; RL = relationship
learning; TR = relational trust; CC = collaborative commitment;
IC = internal complexity; EU = environmental uncertainty;
TA = transaction-specific assets.DIAGRAM: FIGURE 1 Theoretical Model of Relationship Learning
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Relationship Learning
1. Our companies exchange information on successful and unsuccessful experiences with products exchanged in the relationship.
- 2. Our companies exchange information related to changes in end-user needs, preferences, and behavior.
- 3. Our companies exchange information related to changes in market structure, such as mergers, acquisitions, or partnering.
- 4. Our companies exchange information related to changes in the technology of the focal products.
- 5. Our companies exchange information as soon as possible of any unexpected problems.
- 6. Our companies exchange information on changes related to our two organization's strategies and policies.
- 7. Our companies exchange information that is sensitive for both parties, such as financial performance and company know-how.
- 8. It is common to establish joint teams to solve operational problems in the relationship.
- 9. It is common to establish joint teams to analyze and discuss strategic issues.
- 10. The atmosphere in the relationship stimulates productive discussion encompassing a variety of opinions.
- 11. We have a lot of lace-to-face communication in this relationship.
- 12. In the relationship, we frequently adjust our common understanding of end-user needs, preferences, and behavior.
- 13. In the relationship, we frequently adjust our common understanding of trends in technology related to our business.
- 14. In the relationship, we frequently evaluate and, if needed, adjust our routines in order-delivery processes.
- 15. We frequently evaluate and, if needed, update the formal contracts in our relationship.
- 16. We frequently meet face-to-face in order to refresh the personal network in this relationship.
- 17. We frequently evaluate and, if needed, update information about the relationship stored in our electronic databases.
Relationship Performance
1. The relationship with the other company has resulted in lower logistics costs.
- 2. Flexibility to handle unforeseen fluctuations in demand has been improved because of the relationship.
- 3. The relationship with the other company has resulted in better product quality.
- 4. Synergies in joint sales and marketing efforts have been achieved because of the relationship.
- 5. The relationship has a positive effect on our ability to develop successful new products.
- 6. Investments of resources in the relationship, such as time and money, have paid off very well.
- 7. The relationship helps us to detect changes in end-user needs and preferences before our competitors do.
Trust
1. I believe the other organization will respond with understanding in the event of problems.
- 2. I trust that the other organization is able to fulfill contractual agreements.
- 3. We trust that the other organization is competent at what they are doing.
- 4. There is general agreement in my organization that the other organization is trustworthy.
- 5. There is general agreement in my organization that the contact people in the other organization are trustworthy.
Collaborative Commitment
1. To what degree do you discuss company goals with the other party in this relationship?
- 2. To what degree are these goals developed through joint analysis of potentials?
- 3. To what degree are these goals formalized in a joint agreement or contract?
- 4. To what degree are these goals implemented in day-to-day work?
- 5. To what degree have you developed measures that capture performance related to these goals?
Environmental Uncertainty
1. End-user needs and preferences change rapidly in our industry.
- 2. The competitors in our industry frequently make aggressive moves to capture market share.
- 3. Crises have caused some of our competitors to shut down or radically change the way they operate.
- 4. It is very difficult to forecast where the technology will be in the next 2-3 years in our industry.
- 5. In recent years, a large number of new product ideas have been made possible through technological breakthroughs in our industry.
Internal Complexity
1. The products we exchange are generally very complex.
- 2. There are many operating units involved from both organizations.
- 3. There are many contact points between different departments or professions between the two organizations.
Transaction-Specific Assets
1. We have made significant investments dedicated to this relationship.
- 2. We have made several adjustments to adapt to the other party's technological norms and standards.
- 3. Our systems and processes can easily be adjusted to a new relationship.
Other Measures
1. Choose the appropriate question: This customer represents approximately ( %) of our total sales.
This supplier represents approximately ( %) of our total supply.
- 2. What is the primary locus of your business? producer, wholesaler, retailer, service provider, other
- 3. How long have you personally been with your company? ( ) years.
- 4. How long have the two companies been involved in the relationship? ( ) years.
- 5. How long have you personally been involved in the relationship with the other company? ( ) years.
~~~~~~~~
By Fred Selnes and James Sallis
Fred Selnes is Professor of Marketing, Norwegian School of Management BI.
James Sallis is an assistant professor, Department of Business Studies, Uppsala University, Sweden.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 122- Promotion of Prescription Drugs and Its Impact on Physicians' Choice Behavior. By: Gonul, Fusun F.; Carter, Franklin; Petrova, Elina; Srinivasan, Kannan. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p79-90. 12p. 3 Charts. DOI: 10.1509/jmkg.65.3.79.18329.
- Database:
- Business Source Complete
Promotion of Prescription Drugs and Its Impact
on Physicians' Choice Behavior
The authors investigate whether and how pricing and promotional activities influence prescription choice behavior using a comprehensive panel of physicians and data on competitive price and promotional activities. The authors find that physicians are characterized by fairly limited price sensitivity, detailing and samples have a mostly informative effect on physicians, and physicians with a relatively large number of Medicare or health maintenance organization patients are less influenced by promotion than other physicians are.
To the best of our knowledge, this article is the first attempt at an exploratory study on the effects of a widely used competitive marketing practice in the prescription drug industry: personal detailing to physicians and dispensing free samples by pharmaceutical companies' sales representatives. Considering that there is not much prior research in this area (partly due, perhaps, to difficulties related to data collection, confidentiality issues, and so forth), we view the primary contributions of our findings to be its initial insights into this matter and its guidance for further research. The health care industry in general and the prescription drug industry in particular employ an unusual combination of marketing effort, namely, personal detailing and free samples. Studying the impact of these marketing activities accomplishes two objectives: First, we can determine their effectiveness from the firm's perspective, and second, we can infer their implications for consumers and thus shed light on related public policy issues.
In the changing health care environment, managed care organizations (MCOs) play an important role in cost containment. Among other things, they encourage physicians to be more cost-conscious and gradually replace more drastic treatment options, such as surgery, with preventive medicine and pharmaceutical treatments whenever possible (Miller and Luft 1994).
Because health care is largely viewed as a social good, marketing expenditures may be viewed as wasteful or excessive unless the marketing activity benefits the consumers. Intense personal detailing to physicians by prescription drug manufacturers is a time-honored practice. Drawing a natural parallel between detailing drugs and advertising consumer goods, it can be argued that detailing, similar to advertising, is both a market power tool and an information source (see Nelson 1974). However, as prior marketing studies have indicated, these two roles of advertising have a fundamentally different impact on consumers' price sensitivity, decreasing it in the former case and increasing it in the latter. Therefore, we consider it useful to glean insights into the mechanisms driving product choice in the prescription drug market and to study these mechanisms' effects on price sensitivity.
The marketing strategies employed in the pharmaceutical industry sharply contrast with those typically adopted in other markets. One of the primary reasons for the difference is that in the prescription drug market there is a distinct breach in the traditional buying decision process: The decision maker is the physician, who chooses among an array of drug alternatives, but it is the patient who takes the drug and ends up paying (either out of pocket or through health insurance coverage) for the choices made by the physician. Therefore, it is conceptually harder to define the customer in such transactions: The intermediary role played by the physician cannot be ignored.
The marketing literature is replete with examples in which the chooser is not the user. Organizational buying, toy purchasing, and textbook buying provide other examples of situations in which the decision maker is necessarily different from the user (Kotler 2000). The complexity of industrial buying situations, in which the buying center makes the decisions on purchases of goods and services that the employees of the company use, are discussed by Bonoma (1982). Krapfel (1985) puts forth a model for the advocate role of organizational buyers. In a similar vein, we expect that in the marketing of prescription drugs there is an important distinction from the traditional marketing practices studied so far, and we suggest that this warrants additional research.
The involvement of physicians as key decision makers is the reason that they are the focus of most promotional efforts of pharmaceutical companies. In addition to detailing, physicians are often supplied with substantial amounts of free products for direct assessment of the effectiveness of a drug, which they can then dispense to patients at no cost. Therefore, from the manufacturer's point of view, physicians are the customers. We believe that physicians have strong incentives to keep their patients satisfied with the provided medical service.
Physicians may be viewed more favorably by their patients if they demonstrate additional responsiveness and empathy by considering the patients' financial situation and the specifics of their health insurance plan when choosing among drugs of similar efficacy for a patient's medical condition (which should be the issue of prime concern). Often the recourse of patients who doubt the judgment of their health care providers is to seek another opinion. Even though switching physicians on the basis of an unsatisfactory experience related to drug costs is unlikely, potential loss of patients' patronage could be a reasonable concern to physicians regardless of its causes.
It is conceivable that physicians can infer a patient's willingness and ability to pay a higher price from either the type of insurance held (e.g., private insurance versus Medicare) or other cues revealed during the discussion with the patient (e.g., if the patient asks how expensive the drug is or indicates price concerns in some other way). Accommodating patients' price sensitivity while accounting for their medical conditions, along with giving free samples to some patients, may be considered a tangible indication of care and involvement that can further enhance the relationship between physician and patient. However, it should be noted that patients' potential difficulty in making price comparisons with other drugs after a prescription is written and filled could dampen the proposed vicarious price sensitivity exhibited by a physician who wants to demonstrate goodwill to the patient. It might often be sufficient to make the patients believe that their price concerns have been addressed in the best possible way given their condition, which thus becomes a credence issue.
Conversely, it can be argued that physicians' utility functions might not always match those of their patients because of constraints imposed by MCO formularies or the increasing involvement of patients that has been enhanced by direct-to-consumer (DTC) advertising of prescription drugs. As noted in the health care literature, physicians are frequently asked to justify not only the drugs they prescribe but also the ones they choose to dismiss. These extra pressures on physicians from MCOs, patients, and manufacturers' sales representatives create a challenging environment in which the prescription decision is made.
Several other pertinent issues should be noted at this point. First, for the physician there is a trade-off between the benefits acquired through time spent with sales representatives (who provide them with information and free samples) and the opportunity cost of that time, which can be spent otherwise (seeing more patients, reading professional materials, conferring with colleagues, or simply enjoying leisure time). Second, information about new drugs and their applications and side effects is largely available from other sources physicians have access to: medical symposia and conferences, research articles, and medical journals, to name a few. Third, there is anecdotal evidence that inertia and loyalty to specific drugs play some role in the choice of a drug prescribed by a physician. All these factors can render the influence of detailing and samples much less important.
We combine several large data sets obtained from Scott-Levin Inc., a pharmaceutical consulting firm. The data sets are collected from nationally representative samples of physician audits, personal detailing audits, and retail pharmacy audits. The prescriptions are written for a specific therapeutic state that is chronic and relatively more common among the elderly population. The end result is a unique data set for this field that combines patient's insurance coverage data, retail price data, detailing and samples data, and the physicians' prescription choice data.
In the next section we set up the background and hypotheses pertaining to pharmaceutical prices, promotion, and insurance coverage. We then describe the data sets and outline the model and the estimation method. In the following section, we present the results of our estimation, and then we discuss managerial and public policy implications and state the limitations of the study. We conclude with a summary of our findings and directions for further research.
Price Effects
A common belief in the theoretical literature is that physicians are not price sensitive when selecting which drugs to prescribe, because they act as the patients' agents and the cost savings accrue to the patient, not to the prescriber (see, e.g., Leffler 1981). The list of empirical studies on physicians' price sensitivity for prescription drugs is relatively short, and the evidence is inconclusive. In a controlled health insurance experiment, Newhouse (1993) finds no conclusive evidence that the average cost of prescriptions written by physicians varies according to the patients' insurance coverage. Thus, the study reports no evidence that physicians prescribe lower-cost drugs to patients who are covered by less generous insurance plans. However, Newhouse suggests that this inconclusive finding could be due to the averaging method used over the duration of the experiment. Hellerstein (1997) examines physicians' preferences for brand-name versus generic drugs. She finds that physicians with a relatively large number of patients who have health maintenance organization (HMO) affiliations are more likely to prescribe generic drugs.<SUP>1</SUP> Hellerstein speculates that this finding can be attributed to the cost-containment emphasis of HMOs and the self-selection of low-cost physicians to HMOs. Alternatively, she suggests that HMO physicians may be more in the habit of writing generic prescriptions because they are sensitive to price. In contrast, she finds that the individual patient's HMO affiliation does not play a role in the prescription of generic drugs. Hellerstein's findings about the patient's affiliation are inconsistent with those from the physicians' pool of patients' affiliations. She explains this inconsistency by the use of dummy variables (generic versus brand name) instead of actual drug prices in the model.
In the changing health care industry, however, prices may be expected to influence the choice of drugs prescribed by physicians. Prescription drug prices have increased at a rate higher than inflation, and progressively patients defray a higher percentage of the drugs' cost. Physicians are increasingly competing for patients. Therefore, we expect that physicians, trying to accommodate their patients' price sensitivity, will act in a price-sensitive way even though they do not directly bear the cost of the drug.
Furthermore, bearing in mind the importance of prescribing the right drug that would lead to efficacious treatment with few side effects or complications given a patient's condition, physicians might choose to forgo the price considerations if they believe that price is an indicator of quality and the patient's condition warrants a higher efficacy treatment. Prior research in marketing has shown that both price and advertising can be perceived as signals of quality (Milgrom and Roberts 1982, 1986; Nelson 1974). If this is the case and physicians are regarded as customers in a situation of incomplete information (in which the uncertainty comes from the unknown efficacy of the detailed drug for a patient's treatment), then it can be expected that physicians might consider the higher price as a credible signal of quality. In addition, prior research on the effects of advertising as a signal of quality for experience and credence goods has shown that the incidence of advertising (of which detailing and free samples are a form) can also be perceived as an indication of higher quality because of the costs and effort associated with it (Nelson 1974).
Prescription products are similar to credence goods whose immediate effects are obvious neither to the user (the patient) nor to the decision maker (the physician). These effects often must be taken on faith, especially for maintenance drugs such as the ones we study. Maintenance drugs are taken for chronic diseases, so there is often no immediately obvious effect from using the drug. In such cases, placebo effects are common, as is cited in medical journals. This creates a situation in which patients and physicians continue to use the drug that the patient perceives as working. There is anecdotal evidence from physician discussions that even when the main ingredients are known to be the same in competitive brands of drugs, physicians keep prescribing the same drug for refills if the drug has been reported as working by the patient, so that possible placebo effects of the original brand remain undisrupted. If the physician believes that a drug works in a particular patient's case, there is no reason to deviate from it in subsequent prescriptions because of the risks associated with switching treatment. Therefore, in these situations price would become less of a concern.
Many generic products do not capture the lead in the pharmaceutical industry because of the strong (and positive) price-quality signaling effects. Generic aspirin is a case in point. It has a low market share despite having a low price and the same ingredients as those found in the leading brands. Its lower price makes it less attractive.
Physicians, being intermediaries in the buying decision process of prescription drugs (a position reflecting their key role between the drug manufacturer and the patient who is the ultimate consumer), are often placed in a situation of uncertainty as to which drug is the best for each particular patient's case. Considering the broad substitutability among many drugs on the market and the similar claims their manufacturers make, the prescription choice decision, often critical, is increasingly harder to make. Physicians might regard a higher price as a signal of quality, a price premium justified by the higher efficacy of the drug, and therefore prescribe the more expensive drug when drug efficacy is of prime consideration.
The type of formulary used by a particular HMO specifies not only which drugs are suggested by the HMO but also what percentage of the drug cost will be covered by the HMO if the prescribed drug is on the HMO formulary.<SUP>2</SUP> The diversity of co-payment schemes and the variable degree of restrictions on drug coverage outside the formulary further complicate the issue of price sensitivity and its importance to both the physician and the patient as a result of the shared-cost effect and the constraints imposed by formularies.
There is a host of diverging arguments that lead to opposite implications about physicians' price sensitivity. Therefore, instead of strongly arguing one way or another about the price effect, we believe that it is best to let the data suggest the impact of price on physicians' choice behavior.
Empirical Question 1: Do direct promotional effects (detailing and free sampling) by pharmaceutical companies affect the price sensitivities of physicians who operate in regulated managed care environments?
Insurance Effects
Patients with private health insurance pay a higher premium and enjoy a wider selection of physicians and hospitals. Patients with HMO insurance tend to have generous prescription drug coverage. In addition, drugs that are on HMO formulary lists enjoy higher prescription rates, as discussed previously.
In contrast, most Medicare patients are retirees with limited income who must pay for prescription drugs themselves, unlike patients with HMO or private insurance who carry prescription coverage. Therefore, Medicare patients are expected to be price sensitive, and their physicians are expected to be more responsive to drug prices than other physicians are. We expect that the physicians' vicarious price sensitivity will be reinforced when patients hold Medicare insurance.
Therefore, we expect the interaction effect between price and Medicare to be negative, decreasing the prescription probability of a drug. That is, we hypothesize that if patients have Medicare coverage, physicians are more price sensitive than if the patients have private or HMO coverage.
H1 : The type of health insurance will have a moderating effect on the prescription probability of a drug, increasing physicians' price sensitivity when patients have Medicare coverage than when they have private or HMO insurance.
Impact of Detailing and Free Samples
Sales representatives in the pharmaceutical industry (detailers) offer information on generic and current modes of therapy, the appropriate drug usage, indications, contraindications, and side effects. In addition to information about drug usage and positioning, detailers give retail price information and dispense free samples. Physicians are expected to benefit from spending time with sales representatives, because the information they receive ultimately leads to higher patient recovery rates that speak well of the physicians' competence and expertise.
Several studies on advertising have suggested that when used as a persuasive tool, advertising affects the consumer by focusing on the differentiating features and attributes of the product and thus reduces price sensitivity. In contrast, advertising that provides information about the existence and availability of competitive products broadens the consideration set and thus increases price sensitivity (see, e.g., Mitra and Lynch 1995; Nelson 1970, 1974; Nerlove and Arrow 1962).
Yet another aspect of direct drug promotion adds to the complexity of the issue. Prior research (Mitra and Lynch 1995) has attempted to reconcile the opposite effects of reminder advertising (which broadens the size of the consideration set and thus increases price sensitivity) and differentiating advertising (which strengthens the preference for a brand and thus decreases price sensitivity). We believe that detailing and samples can induce both reminder and differentiating effects, which makes Mitra and Lynch's (1995) work relevant for our study. They find that for product markets in which consumers must rely on memory to generate alternatives, increased advertising of brands may increase price sensitivity. Conversely, in the case of oint-of-purchase information, the net effect of advertising is to decrease price sensitivity. Although it is clear that physicians retrieve drug alternatives from memory before writing a prescription (rather than check the contents of their medicine cabinet), free samples left by drug representatives after the detailing session might act as long-term reminders of the existence of the drug and dampen the increased price sensitivity effect.
There is a natural similarity between advertising in general and detailing and samples in the prescription drug industry. Because physicians receive visits from the representatives of competing pharmaceutical companies, we expect that the persuasive aspect of the sales presentations will be mitigated by physicians' increased awareness of competitors' prices. In other words, we believe that the persuasiveness of detailing and sampling activity will be canceled out across the visits of different sales representatives, making the increased awareness of drug features and availability the only remaining effect to influence (increase) physicians' price sensitivity. However, we leave this as an open question to address in our analysis:
Empirical Question 2: Do detailing and samples increase physicians' price sensitivity as a result of increased awareness of competitors' prices or decrease it as a result of enhanced perception of product differentiation?
The emergence of managed care has reduced the impact of detailers; however, they are still a strong source of information in the promotion of drugs (Ziegler, Lew, and Singer 1995). There is an unresolved debate whether detailing is a warranted or a redundant promotional activity. The federal government and consumer advocates often criticize pharmaceutical firms for what they consider excessive and wasteful expenditure in detailing and promotion. These expenses, the critics argue, unnecessarily raise the prices of prescription drugs.
Pharmaceutical lobbyists respond that promotional expenditures are necessary to compete effectively in the marketplace, that the generated extra revenues can be allocated to research and development, and that prescription drug expenses are only one-seventh of the total health care costs (PhRMA 1994). In the pharmaceutical advertising literature, Leffler (1981) and Hurwitz and Caves (1988) argue that advertising increases competition and reduces prices. Hence, they posit that limiting advertising expenditures may have negative social welfare effects. Even though our article will hardly help solve the controversy, we hope it will shed light on the effects of detailing on physicians and ultimately on patients' and social welfare.
In the prior health care literature, Berndt and colleagues (1994) find that detailing is critical to increasing industry sales of anti-ulcer drugs. In marketing, there is a large amount of literature on personal selling, albeit in contexts different from our focus on the effects of detailing to physicians. Detailing is a valuable, though not unique or entirely accurate, source of information for physicians, providing them with useful product knowledge about drug toxicity, efficacy, and the cost to the patient. To that extent, detailing may enable physicians to make careful trade-offs between costs and benefits for each patient, thus offering a more customized service and enhancing social welfare.
The effects of samples in nonpharmaceutical contexts have been studied in more detail (Marks and Kamins 1988). However, dispensing samples in the health care industry is different from doing so in nonpharmaceutical markets, because drug samples are often accompanied by detailing and accepting them might imply some commitment to prescribe the product in the future. In addition, samples can be the only visible reminder of the product after the sales representative has left the physician's office. Thus, samples can have a more lasting influence on the physician because they add tangibility to the sales presentation.
H2 : Detailing and samples will have positive main effects on the prescription probability of a drug.
Although exposure to detailing may be useful for the physician, it inevitably takes away from valuable work time. Any communication with the physician-direct mail, direct selling, continuing medical education, show displays, public relations, wellness promotions-competes for share of the physician's time and mind. Consequently, we anticipate that the marginal impact of cumulative detailing and samples will diminish in its effectiveness. There may be a threshold level of detailing and samples beyond which the effect becomes negative.<SUP>3</SUP> Physicians may tire of excessive detailing and samples and may be less willing to prescribe the drug.
H3 : Detailing and samples will have diminishing marginal effects on the prescription probability of a drug.
Impact of Insurance Coverage on the Effects of Detailing and Sampling
Previously we argued that physicians will exhibit greater price sensitivity when prescribing drugs to Medicare patients. Recall that these patients spend a higher percentage of their income on health care. In this context, we set out to examine carefully the role of detailing and samples with Medicare insurance as a moderating factor. The presumption that physicians carefully trade off cost and benefit while prescribing drugs to this extremely price sensitive segment would manifest itself in a more mitigated impact of detailing and samples on the prescription probabilities for the Medicare segment (negative interaction effect). Any absence of such interaction or the presence of a positive interaction may lend some credence to the argument that these costly marketing activities merely convince physicians to prescribe the drug, thus raising justifiable concerns about social welfare.
Physicians who prescribe to HMO patients may also be less susceptible to the promotional efforts of sales representatives because of the restrictive HMO formulary lists. Therefore, we also expect negative interaction between HMO coverage and detailing and samples.
H4 : Compared with private insurance, Medicare and HMO coverage will have a negative moderating effect on detailing and samples, reducing their impact on the prescription probability of a drug.
We summarize our hypotheses in Table 1. In the "Methodology" section, we define the coefficients we use in Table 1. Data
For a comprehensive analysis of the impact of marketing on prescription drug choices, a panel of physicians with information on their exposure to personal detailing and the prescription choices made is needed. The physician-level data sets provided by Scott-Levin Inc. are uniquely comprehensive in this regard.<SUP>4</SUP> For this study we combine three large data sets from Scott-Levin: drug and diagnosis data, personal selling data, and retail price data.<SUP>5</SUP>
The drug and diagnosis data include the physician's identification number, the date the prescription was written, the product code, and the patient's type of insurance. The physicians in the panel kept track of their patients' visits between January 1989 and December 1994. Because physicians' time is valuable, they are asked to fill out survey sheets for a typical week of the month. This gives a sample of patient visits per physician, but it does not contain information about every visit. However, this does not bias our choice model results, because we do not model physicians' longitudinal choice behavior, such as brand switching or brand loyalty.
No physician in our sample prescribes the same drug 100% of the time. Even when we relax the brand loyalty criterion to prescribing the same drug at least 70% of the time, we find that only three physicians could be described as loyal by this criterion. This tentative result indicates that less than 2% of the sample shows evidence of brand loyalty, but again, the data set does not allow for a thorough examination of this issue. However, we do not rule out the possibility that though the majority of physicians appear to be switchers in their overall prescription behavior across patients, they might be persistent in repeatedly prescribing a drug to each patient.
Personal selling data were collected for the same time period as was the first data set. The physicians were asked to keep track of the detailing (minutes) and samples (number of containers) they received from sales representatives for drugs for specific therapeutic states and the dates they received them. We chose a therapeutic state that is a relatively common chronic condition among the elderly population. A physician may prescribe one of seven different products for this therapeutic state.
Retail price data are available for a shorter time span, from January 1991 to December 1994. The data set uses a panel of more than 800 pharmacies throughout the United States. The data contain the full price the pharmacy charges for the prescription drug, regardless of co-payment situations, as well as how the prescription was paid by the consumer: personally or by an insurance plan.
Table 2 displays descriptive statistics for 157 physicians and their patients' visits (related to the specific therapeutic state we have chosen) over a period of four years. The top three products rank high in samples and detailing minutes as well as in market share. Insurance plan frequencies in the data are as follows:
- Private: private insurance (44%),
- HMO: HMO or preferred provider organization (13%), and
- Medicare: Medicare, Medicaid, or Workers' Compensation (42%).
Most of the patients in the second category are covered by an HMO plan (more than 75%); most of the patients in the third category carry Medicare insurance (more than 90%). Although the data set provides a code for no insurance, our sample happens to contain no such patients.
Model
To estimate the effects of price, type of insurance, and direct selling efforts on prescription choice, we use a multinomial logit model (McFadden 1974). However, prescription behavior patterns might be strongly influenced by factors other than the explanatory variables we include in our model. Examples are physicians' unobservable personal characteristics (e.g., inertia, loyalty to certain drugs), unobserved factors related to patients (e.g., the severity of their condition, their health history, other drugs they are currently taking that may cause interactions or exacerbate side effects), or even unobserved specifics unique to the interaction between the physician and the patient (e.g., some patients may like to get more involved in the drug choice because of experience, knowledge, word of mouth, or DTC advertising effects, whereas others leave the choice completely to the physician). Because of data limitations, little can be done to control for these unobserved factors. However, ignoring these factors might bias the coefficients of the included explanatory variables. This is known as aggregation bias (Chamberlain 1980).
We opt for a latent class model that allows for a semiparametric distribution of heterogeneity that is more flexible than any prespecified distribution (Kamakura and Russell 1989). We specify a multinomial logit model with an unknown number of latent classes and interactions and quadratic terms to capture better the specifics of prescription choice and the relationships among model parameters.
We let the intercepts and the price coefficient be segment specific, as noted by their k subscripts. Allowing all coefficients to be segment specific is possible in theory, but our data are not long enough per physician to enable us to estimate reliably the heterogeneity distribution on all coefficients. For identification purposes, one of the three types of insurance coverage should be specified as the default. We chose the default type of insurance plan to be private insurance. Furthermore, we arbitrarily let Brand 7 be the reference brand and set its drug-specific coefficients (a7, gHMO,7, gMedicare,7) equal to zero, again for identification reasons. The drug-specific intercepts must be different for each brand, because they will reveal brand-specific characteristics. The insurance coefficients should also be different, because unlike price, detailing, and samples, the type of insurance itself does not vary by the prescription incidence. The parameters for variables that contain price, detailing, and samples do not need to vary across products, because the variables themselves are product specific. Note that including quadratic terms for Detailing and Samples in our model does not necessarily impose a curvature but will help uncover diminishing marginal effects or an inverted-U shape of the relationship.
Cumulative Discounted Sums of Detailing and Samples
For each prescription physicians write, they are likely to be influenced by past personal selling efforts. We discount the cumulative personal selling effort consistently with the methods used in the advertising literature. The major premise of these methods is that physicians are influenced by the recent visits of sales representatives more than by the distant ones. The discounted formulation for detailing has been used in another context by Berndt and colleagues (1994).
We compute the discounted sum of detailing minutes and the number of received samples for all products by the time of prescription. Henceforth, for brevity we use the terms Detailing and Samples to refer to the cumulative discounted sums.
Model Goodness of Fit
In addition to the multinomial logit model with latent classes specified previously, we estimate several models that are nested within the full model (such as models with no heterogeneity and no interaction or quadratic terms). We find that the full latent class model attains a higher likelihood value than the nested models with fewer parameters. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) also indicate a better fit for the full latent class model than for the nested ones.
We experiment with two different values of the discount factor (d = .99 and d = .80) in the cumulative discounted sums of detailing and samples as specified in Equation 2. We find that the former provides for a better fit as indicated by the AIC and BIC. The results are directionally the same, showing the robustness of our model to the discount factor in that range. Henceforth, we use the results from the better fitting model to guide our discussion. The model has excellent fit between predicted and actual market shares for each data period. The actual market shares for each product are within the prediction confidence limits except for a few cases of extreme highs or lows. (The figures are available on request.) The estimated optimal number of segments is three, as indicated by the AIC and BIC. The estimation results of this model are summarized in Table 3.
Estimates
Intercepts. Of the 18 brand- and segment-specific intercepts, 11 are statistically significant. Brand preferences appear to be polarized when we account for heterogeneity; in other words, each of the three segments is dominated by a different brand. In the first segment of physicians, Brand 2 appears to be dominant, and Brand 6 is least favored judging by the magnitudes of the intercepts (see the first column of Table 3). Brands 1, 3, and 5 are also preferred, but not as strongly as Brand 2. In the second segment, Brand 5 is the most preferred brand, followed by Brand 1. Physicians in this segment appear indifferent to the rest of the products. Dominant in the third segment is Brand 4, but this segment does not favor Brands 1, 3, and 5. Thus, accounting for unobserved heterogeneity in brand preferences reveals that physicians can be split into segments that are characterized by relatively pronounced preferences for certain brands.
The finding that different brands are preferred by different segments of physicians leads us to infer that physicians tend to favor certain brands differently in the absence of external factors such as price and promotion. Accounting for unobserved preferences reduces the estimation biases for the coefficients of the included explanatory variables. It is tantamount to explaining the inherent heterogeneity away. We do not know which physicians belong to these segments, but we have reduced the bias in the coefficients of the remaining explanatory variables by controlling for latent classes. If, in contrast, we had found that the intercepts (i.e., intrinsic brand preferences) were similar across segments, we would have expected more heterogeneity in the impact of price and the promotional variables, which would have rendered our focal results more tentative.
Insurance coefficients. We find that the HMO and Medicare coefficients are significant for most brands, indicating that the type of insurance coverage has a substantial direct impact on the prescription probability of that drug. We point out that all coefficients are relative to that of Brand 7, which was chosen as the reference brand for model identification purposes. The negative coefficients should be interpreted as indicating that the five brands are on fewer formulary lists than the reference brand and Brand 1. Note that we do not have data on formulary lists, which also change over time, so our attempts to interpret the results from the perspective of their inclusion in HMO formularies are speculations.
All six Medicare constants are positive relative to that of Brand 7, and four of them are highly significant. That the signs of the coefficients can be interpreted only relative to Brand 7 indicates that Brand 7 is the least preferred brand for Medicare patients. If we were to expound on the reasons Brand 7 is so unpopular for Medicare patients, given that it has a comparatively reasonable price and is not the least preferred brand overall (see Table 2), a possible explanation would be the presence of side effects that are more common or adverse in the case of elderly patients. However, this is just a speculation, because we lack relevant information.
Price effects. The results of the latent class model unambiguously show that when physicians' distinguishing prescription patterns are accounted for in the basis for segmentation, two of the three segments have positive price coefficients, which thus provides an answer to Empirical Question 1. The two segments together constitute approximately 57% of prescription incidences (segment proportions are shown in the last row of Table 3: Prob[Segment 2] = .31 and Prob[Segment 3] = .26). The relationship between price and prescription probability for the first segment remains weakly negative: The price coefficient is negative but insignificant at p > .10.
The results can be interpreted from various aspects. A possible explanation of the preceding findings is that physicians are often driven by the gravity of a patient's condition and the possible interactions between the drug and other types of medication taken by the patient, which would understandably become a major consideration in the choice of a drug and would override the less critical price concerns. For example, if price is perceived as correlated to drug efficacy and the absence of side effects and contraindications, the incidence of prescribing more powerful drugs will produce positive price coefficients. This is the well-known "price as a signal of quality" argument. Also, prescriptions for refills tend to repeat the initially prescribed drug, so the prescription pattern per patient will persist across the patient's visits, making price a less important factor. As we mention previously, physicians keep prescribing the same drug for refills if the drug has been working for the patient, so that possible placebo effects of the original brand remain undisrupted. The positive price coefficients for two of the three segments could also be a result of some selection process by which patients with more serious conditions would be referred to a specialist who would prescribe a more expensive but more efficacious drug because of the severity of the patient's condition. Unfortunately, we cannot provide support for this explanation because, as already mentioned, our data do not contain the necessary information. However, this issue warrants further examination with the right kind of data.
H1 suggests that the price effect is more negative for elderly patients on Medicare than for patients with private or HMO insurance. In our model, the impact of price for private insurance patients is given by the price coefficient alone, with no interactions, because private insurance is the reference category among the insurance dummy variables. Therefore, we find initial directional support for our hypothesis, because the Price Medicare interaction coefficient is negative but insignificant (Table 3).
The positive interaction effect between price and HMO insurance indicates that the effect of reduced price sensitivity is further enhanced if the patient has HMO insurance. Note that this result is relative to the reference category of private insurance and suggests that physicians appear less price sensitive for HMO patients than for private insurance patients. Although we find this result surprising given that the possession of private insurance might indicate a patient's preference for quality care over cost (which ideally would have produced the lowest prescription drug price sensitivity for patients with private insurance), we believe this finding should be interpreted in the context of three factors: (1) Physicians are restricted by the HMO formularies, and price becomes far less of an issue to physicians if the drug is endorsed by the patient's HMO formulary; (2) HMO patients usually pay nothing or just a small fraction of the drug cost; and (3) there is great variability in private insurance plans' extent of drug coverage. Consequently, price becomes a factor of little concern to physicians and patients in the case of HMO coverage, even when compared with private insurance.
Furthermore, we find that there is a significant, negative interaction effect between price and detailing, indicating that the informative aspect of detailing as a type of advertising overrides the sales pitch persuasiveness. The same kind of negative interaction is also found between price and samples, providing an answer to Empirical Question 2. Thus, we find that personal selling of prescription drugs to physicians as a specific type of brand-level advertising increases price sensitivity, consistent with prior research (Mitra and Lynch 1995).
Personal selling effects. The coefficients of detailing and samples are both positive and significant, providing support for H2 and indicating that the main effects of personal selling are as conjectured, which increases the prescription probability of a drug, ceteris paribus. However, consistent with H3 , we find that excessive detailing or samples are counterproductive: Their quadratic effects are negative and significant, which implies that these promotional activities have an inverted-U shape. This shape implies that too little or too much cumulative personal selling is suboptimal and that any repetitive detailing or free sample activity must be done with caution. The implied adverse effects of excessive detailing and samples can be attributed to frustration caused by waste of time, fatigue with the promotion, or perception that the drug manufacturer is too desperate or too aggressive.
Thus, our model is flexible in offering a variety of alternatives to pharmaceutical companies to help determine how long and how often they should schedule visits to physicians and at what level free samples start lowering the prescription probability of a drug. Pharmaceutical companies could adopt our model and run it through their own databases to arrive at optimal scenarios specific to their products and markets. The sensitivity analysis of the optimal levels of detailing and free samples produces values that exceed those of the currently established practices, as indicated in our data. This empirical result suggests that pharmaceutical companies are operating on the increasing part of the curve and their direct selling efforts are below the level of activity that is most effective. On the basis of our analysis, we conclude that there is room for enhancing the effectiveness of personal promotional efforts by drug manufacturers.
Public policy effects. We turn to public policy issues next and explore the interactions between pharmaceutical promotion variables and health insurance coverage. The estimation results indicate that detailing and samples are less likely to influence physicians who see a higher percentage of HMO or Medicare patients, providing full support for H4 . All four interaction effects mentioned in H4 are negative, and three of them are significant.
Therefore, we conclude that both Medicare and HMO coverage, compared with private insurance, detract from the main positive effect of detailing and samples on the drug prescription probability. These results can be explained by physicians' likelihood of prescribing from fixed formularies suggested by the HMO, which makes them relatively insulated from personal selling efforts. In the case of Medicare, in which physicians have more flexibility in selecting a drug, the revealed diminished effectiveness of personal selling can be attributed to the majority of patients being elderly people who are likely to suffer from other ailments, so considerations related to the patient's condition, other medications taken by the patient, or even cost would prevail. The finding that detailing and free samples to physicians whose patients are largely covered by Medicare or HMOs are not as effective as they are to physicians whose patients are largely covered by private insurance may be an indication of wasted resources and therefore warrants further research.
The results of this study point to conclusions with practical managerial and public policy value. We find evidence that physicians exhibit dissimilar brand preferences in the absence of external factors such as price and promotion. However, we cannot determine whether these distinctive preferences are related to the propensity of physicians to favor a certain set of features in drugs (e.g., ingredients, lack of side effects) or to the particular characteristics of their pool of patients. Drug manufacturers that have the expertise and knowledge to compare the chemical composition and efficacy of alternative drugs would be in a much better position to conduct their own segmentation studies and decide whether it is the drugs or the patients that predetermine the existing preference segmentation of physicians and to adjust their targeting and promotional efforts accordingly.
Our findings indicate that the majority of physicians either demonstrate a lack of price sensitivity or are characterized by fairly limited price sensitivity. Consequently, we suggest that in general, detailing focused on the low price of a drug as its main differentiating feature will not be very effective. However, the increased price sensitivity in the case of Medicare patients suggests that when detailing to physicians with a large number of such patients, sales representatives should point out the lower price of the drug compared with alternatives. In the case of physicians who see mostly patients with HMO and Medicare insurance, we infer that detailing and free samples are not very effective. Therefore, we recommend that the pharmaceutical companies review their personal selling strategies for such physicians, because they could be wasting their promotional resources. In contrast, personal selling to physicians who see mostly patients with private insurance is effective, and drug manufacturers should start targeting these physicians in a more systematic way.
Our study reveals that the scope of personal selling should be carefully scheduled in terms of frequency, length of sessions, and number of free samples given away, so that the company can optimize the effectiveness of its direct promotional efforts and expense. The finding that exposing a physician to personal selling can become counterproductive beyond a certain amount of cumulative detailing minutes and samples is an important insight and should be taken into account. In this regard, we recommend that managers set a system for scheduling visits to physicians and specify the focus of the message (contingent on the prevalent type of insurance held by physicians' patients), the duration of detailing sessions, and the number of free samples to be dispensed per session to ensure optimal effects of personal selling. The exact optimal levels can be computed from the formulas in our model.
Considering other types of marketing activities pharmaceutical companies engage in-for example, DTC advertising-one implication of our research is that setting a schedule for personal selling to physicians and synchronizing it with the timing of DTC campaigns might develop promotional synergy and lead to enhanced effectiveness (for limited empirical evidence on the synergy between pharmaceutical detailing and DTC advertising of prescription drugs, see GonIl, Carter, and Wind 2000). Furthermore, such planning would establish economies of scope for the drug manufacturer by capitalizing on interactions between patients and physicians through a concerted marketing effort targeted at them simultaneously through different promotional channels.
In addition, our analysis sheds light on issues of interest to public policymakers. First and foremost, it helps disperse the concerns that personal selling is ethically objectionable because it might inordinately affect physicians. We find no evidence of such influence, and our findings suggest that detailing and free samples are mostly informative and increase price sensitivity. Another controversial aspect of personal selling to physicians is its cost compared with its social value. We find that the effectiveness of personal selling follows an inverted-U pattern, so that there are optimal values of both detailing minutes and free drug samples. Exceeding those values has dissipative economic impact on the company and potentially on society. Therefore, we find some reasons for concern related to potential waste of resources if this type of marketing activity is not administered systematically or monitored more stringently by the pharmaceutical companies.
Second, because HMO formularies impose restrictions on the drugs to be prescribed by physicians, personal selling to physicians with mostly HMO patients is wasteful. The social value of personal selling to physicians with Medicare patients can be viewed as positive, because it increases physicians' price sensitivity and thus leads to optimized utility on behalf of the patient. In general, we find no reasons to believe that direct selling-as one of the strategies of health care communications-has negative social consequences. However, there is room for pharmaceutical companies to customize their personal selling efforts and optimize the allocation of direct promotional resources, as suggested previously. We acknowledge that because our results are based on a single product category (drugs for a specific therapeutic condition), a cross-category analysis will substantially strengthen or challenge the findings reported in our article.
In our model, we control for unobserved heterogeneity in brand-specific constants and price effects by introducing a segment structure on the sample of physicians. We realize that other coefficients can be made heterogeneous as well. However, to justify a full heterogeneity specification, significantly larger numbers of observations per physician may be necessary to yield stable estimates. The additional data points may also help us resolve the impact of pricing on prescription choice more satisfactorily. For two segments, physicians appear to use price as a signal of quality. Further analysis on larger data sets may be necessary to validate these findings. Also, the reader should bear in mind that price changes over the estimation period are quite limited.
Accurate and comprehensive data sets are scarce in pharmaceutical marketing research. Although our data set is sufficient for the purposes of this study, it still lacks some important data. For example, medical information on patients' condition and treatment history are potentially important covariates. However, we do not know of a data set that includes competitive price and promotion information about manufacturers as well as patient-specific information. This is a subject for further research, given data availability. An interesting area for further research is the pressure that DTC advertising of prescription drugs exerts on the physician's choice of a prescription drug (GonIl, Carter, and Wind 2000). The synergy and potential conflict between DTC advertising and traditional detailing to physicians could also be investigated. Richer data sets that include patient-level advertising exposure and physician-level marketing exposure are needed to untangle these issues in depth.
The significance of public policy issues in the pharmaceutical industry cannot be overstated. The interests of managed care institutions and pharmaceutical companies alike give priority to prescription drug treatments over costly in-patient care. In addition, physicians are limited in their choice of prescription drugs through formulary agreements that lower cost. We find that allowing for segments of physicians is insightful because it reveals intrinsic brand preferences of physicians and reduces the estimation bias. We find evidence that in general, physicians' price sensitivity comes second to considerations about drug efficacy and patients' conditions. We investigate the role and impact of personal selling (detailing and samples) on the choice of prescription drugs. If such a promotional activity primarily provides beneficial information to physicians, it will be regarded as useful. However, if detailing inordinately influences prescription patterns, the expenditure and role of detailing and sampling activities should be reviewed by public policy advocates. We find evidence that detailing positively affects the prescription probability of a drug up to a point, after which excessive detailing becomes countereffective. The effectiveness of dispensing free samples to physicians follows the same pattern.
We find evidence of the informative value of personal selling, which makes physicians aware of new drug alternatives and their specifics and prices. To that extent, we conclude that the impact of detailing and samples is limited and mostly informative. Therefore, the concern that these activities may excessively influence physicians' prescriptions remains unfounded within the context of our analysis. Last but not least, we find that pharmaceutical manufacturers may be wasting resources when sending sales representatives to physicians whose patients carry mostly Medicare or HMO coverage, because detailing and free samples are not as effective for such physicians.
In summary, we conclude that there are no reasons for public concern regarding the social implications of the reviewed personal selling practice employed by drug manufacturers, because its effect is mostly informative. Moreover, we find that there is room for enhancing the effectiveness of direct promotional efforts to physicians by more specific segmentation, targeting, and positioning contingent on the intrinsic brand preferences demonstrated by certain health care professionals and the prevalent type of insurance held by their patients. In addition, the amount and scheduling of detailing and free samples can be optimized for maximizing the return on this type of promotion. Last, we suggest that finding ways to synchronize personal selling to physicians with DTC advertising may achieve further synergies, but the social benefits of such public drug advertising and its possible ramifications should be explored in depth.
.ST.-TABLE 1 Summary of Hypotheses
Legend for Chart:
A - Empirical Questions and Hypotheses
B - Effect
A B
Empirical
Question 1 βP < 0 or βP > 0
H1 βP - Medicare < 0
βP - Medicare < βP - HMO
Empirical
Question 2 POP < βDP < 0 and βSP < 0
or
βDP > 0 and βSP > 0
H2 βD > 0
βS > 0
H3 βD - Sq < 0
βS - Sq < 0
H4 βD - Medicare < 0
βS - Medicare < 0
βD - HMO < 0
βS - HMO < 0 Descriptive Statistics
A= Rx Product
B= Frequency of Choice (%)
C= Price ($)
D= Cumulative Discounted Sum of Sample Containers
E= Cumulative Discounted Sum of Detailing Mintues
A B C D E
Product 1 20.06 34.20 32.28 13.55
(.58) (36.50) (14.53)
Product 2 31.65 42.25 31.00 9.41
(1.83) (31.14) (10.42)
Product 3 23.75 50.49 31.74 12.83
(4.18) (32.25) (13.62)
Product 4 10.42 35.73 24.24 8.82
(1.90) (21.39) (8.10)
Product 5 8.07 34.84 21.62 7.71
(1.05) (33.09) (9.46)
Product 6 2.63 31.75 18.06 8.26
(.90) (23.07) (6.73)
Product 7 3.41 32.83 26.25 8.32
(.54) (41.77) (10.67)
Notes: The number of observations is 1785 patient visits. The
sample spans January 1991 through December 1994. Mean values and
standard deviations (in parentheses) are shown; however, standard
deviations for percentage terms are not computed. Detailing "nd
samples are defined as cumulative discounted sums up to the time
of the writing of the prescription, as described in the
"Methodology" section. Legend for Chart:
A - Covariate
B - Estimate (Standard Error)
* p < .01.
** p < .05.
*** p < .01.
A B
Intercept 2.3817*** .9240*** -.9411***
(α11, (.4032) (.3625) (.3220)
α12,
α13)
Intercept 3.3434*** -.5040 .2105
(α21, (.4533) (.4803) (.4017)
α22,
α23)
Intercept 2.2308*** .3002 -1.4519***
(α31, (.5873) (.6331) (.6080)
α32,
α33)
Intercept -.2435 .3684 .9627***
(α41, (.5336) (.3758) (.2827)
α42,
α43)
Intercept 1.5582*** 1.3693*** -1.1301***
(α51, (.4259) (.3610) (.3644)
α52,
α53)
Intercept -2.8955** -.1747 .0656
(α61, (1.5169) (.4464) (.3152)
α62,
α63)
HMO .6436*
(γHMO,1) (.4332)
HMO -1.1946*
(γHMO,2) (.7450)
HMO -1.5789*
(γHMO,3) (1.2610)
HMO -.6110*
(γHMO,4) (.4969)
HMO -.8400**
(γHMO,5) (.5016)
HMO -.8018
(γHMO,6) (.7323)
Medicare .8116***
(γMedicare,1) (.3584)
Medicare 1.1549***
(γMedicare,2) (.4419)
Medicare 2.1348***
(γMedicare,3) (.6077)
Medicare 1.0108***
(γMedicare,4) (.3515)
Medicare .4108
(γMedicare,5) (.3745)
Medicare .1045
(γMedicare,6) (.3960)
Price -.0085 .0568** .0891***
(βP1, (.0228) (.0254) (.0252)
βP2,
βP3)
Price x HMO .0731*
(.0590)
Price x Medicare -.0224
(.0234)
Detailing .1085***
(.0204)
Detailing² -.0007***
(.0001)
Detailing x HMO -.0091
(.0198)
Detailing x Medicare -.0147**
(.0084)
Detailing x Price -.0012***
(.0004)
Samples .0345***
(.0089)
Samples² -.0001*
(.00003)
Samples x HMO -.0145***
(.0062)
Samples x Medicare -.0141***
(.0040)
Samples x Price -.0002*
(.0002)
Portion of latent
classes (1 2 3) 42% 31% 26%
Log-likelihood -2533.44
AIC 5160.88
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~~~~~~~~
By Fusun F. Gonul; Franklin Carter; Elina Petrova and Kannan Srinivasan
Fusun F. Gonul is Associate Professor of Marketing, Simon School of Business, University of Rochester
Franklin Carter is Assistant Professor of Pharmaceutical Marketing, Haub School of Business, Saint Joseph's University
Elina Petrova is a doctoral student in marketing
Kannan Srinivasan is H.J. Heinz II Professor of Management, Marketing, and Information Systems, Graduate School of Industrial Administration, Carnegie Mellon University.
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Record: 123- Psychological Implications of Customer Participation in Co-Production. By: Bendapudi, Neeli; Leone, Robert P. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p14-28. 15p. 5 Charts. DOI: 10.1509/jmkg.67.1.14.18592.
- Database:
- Business Source Complete
Psychological Implications of Customer Participation in Co-Production
Customer participation in the production of goods and services appears to be growing. The marketing literature has largely focused on the economic implications of this trend and has not addressed customers' potential psychological responses to participation. The authors draw on the social psychological literature on the self-serving bias and conduct two studies to examine the effects of participation on customer satisfaction. Study 1 shows that consistent with the self-serving bias, given an identical outcome, customer satisfaction with a firm differs depending on whether a customer participates in production. Study 2 shows that providing customers a choice in whether to participate mitigates the self-serving bias when the outcome is worse than expected. The authors present theoretical and practical implications and provide directions for further research.
Customers increasingly are being encouraged to take on more active roles in producing goods and services. They go into photography stores and use machines to crop, enlarge, correct, or enhance their photographs; check themselves in and out of hotels; and even routinely scan and bag their own groceries at supermarkets. Customer participation per se is not new. Supermarkets, which are models of customer co-production with customers selecting, carting, and transporting groceries, date to the 1930s. What is new is the recognition that encouraging customers to be "co-producers" in this sense is the next frontier in competitive effectiveness. We are seeing the emergence of the "customizing consumer" (Moyers 1989)--consumers who examine market offerings and create a customized consumption experience for themselves (Firat, Dholakia, and Venkatesh 1995). On the basis of this trend, Lovelock and Young (1979) urge firms to use customers to increase productivity. Schneider and Bowen (1995) suggest that firms should use customer talents to deliver superior service. Lengnick-Hall (1996) urges firms to examine the roles that customers can and do play in the service production process. Recently, Prahalad and Ramaswamy (2000) have advocated co-opting customer competence as a competitive strategy. This shift in the perspective of companies to viewing customers as active co-producers rather than as a passive audience is captured in the move from "What can we do for you?" to "What can you do with us?" (Wind and Rangaswamy 2000).
Until recently, the logic of these exhortations has relied almost exclusively on an economic rationale. Simply stated, when customers participate in production, it frees up labor costs and enables a firm to market the offering at a lower monetary price, resulting in a win-win situation in the buyer-seller relationship (Fitzsimmons 1985). Note that it is the monetary price that is generally lower through customer participation in production. The total cost, comprising monetary price and nonmonetary aspects such as time, effort, and other psychic access costs (Heskett, Sasser, and Schlesinger 1997), may be higher in customer participation for both firms and customers. Although the monetary dimension of participation has received attention, little is known about the effect participation may have on a customer's psychological processes and evaluations. No prior studies have considered explicitly the impact on a customer's psychological response, such as satisfaction, despite calls for such a broader framework (e.g., Czepiel 1990; Dabholkar 1990; Lusch, Brown, and Brunswick 1992; Prahalad and Ramaswamy 2000).
We offer the first empirical investigation of a customer's psychological response to participation in production. Research in social psychology on the self-serving bias forms the theoretical basis for our examination. Literature on the self-serving bias examines how a person assigns responsibility for jointly produced outcomes (Miller and Ross 1975; Sedikides et al. 1998; Zuckerman 1979). In two studies, we examine ( 1) how the self-serving bias affects a customer when he or she participates in production and ( 2) how providing a customer the choice of whether to participate affects this relationship. We conclude with a discussion of the practical and theoretical implications of the studies.
Customer Participation in the Production of Goods and Services
Customer participation has been defined as "the degree to which the customer is involved in producing and delivering the service" (Dabholkar 1990, p. 484). Extending this construct, Meuter and Bitner (1998) distinguish among three types of service production based on customer participation: firm production, joint production, and customer production. Firm production is a situation in which the product is produced entirely by the firm and its employees, with no participation by the customer. Joint production is a situation in which both the customer and the firm's contact employees interact and participate in the production. Customer production is a situation in which the product is produced entirely by the customer, with no participation by the firm or its employees. The purpose of this article is to understand how customers' psychological responses may vary when they have no participation (firm production) versus some production participation (joint production). Therefore, we do not consider the situation of customer production and self-service technologies (Meuter et al. 2000), and when we refer to participation, we mean the joint production of outcomes.
The relevant literature on customer participation in the production of goods and services is summarized in Table 1. An examination of Table 1 reveals two important themes. First, the early work in the area focused largely on the firm, making a business case for why customers should engage in the production process. The benefits to the firm of such customer participation were defined in terms of productivity gains, with customer labor substituting for employee labor (e.g., Fitzsimmons 1985; Lovelock and Young 1979; Mills, Chase, and Margulies 1983; Mills and Morris 1986).
The second theme that emerges is the focus on managing customers as partial employees and the applications and limits of traditional employee management models (e.g., Kelley, Donnelly, and Skinner 1990; Lengnick-Hall 1996; Mills and Moberg 1982). Research in this theme has focused on identifying when customers may be motivated to participate in production as partial employees, such as the effect of propensity for do-it-yourself projects (Bateson 1985), technology readiness (Dabholkar 1996), provision of adequate training (Goodwin 1988), and the resources and constraints in the trade-offs between different levels of customer participation (Lusch, Brown, and Brunswick 1992). Fodness, Pite-goff, and Sautter (1993) examine the potential negative effects of training customers as employees. However, they focus only on the economic effects of creating potential competitors, not the psychological responses.
A broader perspective of co-production is found in the interpretive marketing literature (Firat, Dholakia, and Venkatesh 1995; Firat and Venkatesh 1993). These researchers suggest that a fundamental characteristic of the postmodern era is the reversal of production and consumption: The consumer is usurping the privileged status previously accorded to the producer. Concurrently, Firat, Dholakia, and Venkatesh (1995, p. 50) argue that "the consumer may be finding the potential (sic) to become a participant in the customization of his/her world." Firat and Venkatesh (1995) suggest that customers are demanding a role in production and that to satisfy them, marketers must open up more and more of their processes and systems to consumers' active participation.
Although these studies shed some light on customer participation, the psychological consequences of this phenomenon have been studied only in a limited fashion (Song and Adams 1993). Some authors have suggested that participation may have other important effects on customer satisfaction (Czepiel 1990; Van Raaij and Pruyn 1998; Wind and Rangaswamy 2000), but the paths of this influence have not been specified clearly, and no tests of this effect have been reported. This lack makes it impossible to draw strong conclusions about the effects of participation on customer satisfaction. The previous literature also has focused primarily on the service arena. Although in the past customer participation was much more likely with services than with goods, recent technological advances and competitive realities are creating opportunities for customers to participate in the production of goods (Peppers and Rogers 1997). Thus, we believe it is critical to examine the effects of customer participation in the production of goods as well as services.
Potential Psychological Responses to Customer Participation in Production: The Role of the Self-Serving Bias
Literature on the self-serving bias, a specific area of attribution research that has received limited attention in the marketing literature (Curren, Folkes, and Steckel 1992; Folkes 1988), appears to be especially appropriate in studying psychological responses to customer participation in production. The self-serving bias refers to a person's tendency to claim more responsibility than a partner for success and less responsibility for failure in a situation in which an outcome is produced jointly (Wolosin, Sherman, and Till 1973).
Researchers in the social psychological arena investigating the self-serving bias in jointly produced outcomes have focused on dyadic relationships. Under this paradigm, dyads, strangers to each other, are asked to work together on a specific task. Following the task, the subjects are given bogus success or failure feedback on the task. Subsequently, subjects are asked to provide confidential assessments of their own and their partner's contribution to the task outcome. Campbell and colleagues (2000) identify five lab-based studies that examine the self-serving bias in interdependent out-come contexts (Johnston 1967; Sedikides et al. 1998; Wolosin, Sherman, and Till 1973). Johnston (1967) had subjects believe they were working with a partner, situated in another room, to manipulate a control knob to hold a moving cursor steady at zero. This was a novel task to subjects. After three sessions of 20 trials each, subjects were given bogus success or failure feedback about the dyad's performance. The self-serving bias was not observed in this study.
Arguing that the novelty of the task mitigated the self-serving bias, Wolosin, Sherman, and Till (1973) demonstrate the self-serving bias in two studies. They had pairs of subjects work on decision-making tasks under cooperative or competitive situations; also subjects were asked to assign responsibility to themselves, their partners, or to the situation following bogus success or failure feedback to the dyad. In cooperative situations (subjects and partners working together), subjects tended to take more responsibility for themselves under success feedback, whereas they attributed more responsibility to the partner in the failure feedback condition. In competitive situations (subjects competing with partners), subjects who received success feedback claimed more responsibility than those who received failure feedback; the latter subjects attributed more responsibility to the situation than to themselves. Curren, Folkes, and Steckel (1992) find a similar pattern of results in marketing decision making: In a marketing decision-making simulation task, people claim that their success is due to factors internal to themselves whereas their failure is attributed to external factors.
Sedikides and colleagues (1998) induced closeness in strangers and then asked the dyads to work together to come up with as many uses for an object, a brick, and a candle as they could in five minutes and were given bogus success or failure feedback. The induced closeness reduced the self-serving bias so that subjects accepted responsibility both for success and for failure. Campbell and colleagues (2000) replicated this study by recruiting subjects to work in dyads with their friends or with strangers. Strangers were more likely to take credit for success and blame the partner for failure, whereas friends took responsibility for both success and failure outcomes when assessing the output of their dyad.
These studies of individuals engaging in jointly producing outcomes are similar to situations in which customers work with firms and participate in the production of a deliverable. The customer and the firm jointly produce an outcome, and the customer assigns responsibility to the firm and to him-or herself. Our first study explores the self-serving bias when customers participate in production and investigates its impact on customer satisfaction judgments.
The objective of this study is to examine whether the self-serving bias can be extended to relationships between a customer and a firm. Investigating this extension is interesting for several reasons. First, it is not obvious whether the bias would hold in the commercial arena. For example, in customer-firm relationships, because customers are paying for the good or service, they always might hold the seller more responsible for the outcome, whether they participate or not. Alternatively, customers in relationships with firms may be hypervigilant to what each party gives and gets, and thus they may be less likely to be subject to this bias. Second, previous studies have focused only on bogus feedback regarding success and failure in the task provided by a third party. In a typical consumption situation, the customer actually makes this judgment and then must assign credit or blame.
Traditional explanations have assumed that customer participation may affect customer satisfaction by lowering the price. However, the self-serving bias literature suggests an additional mechanism that may affect customer satisfaction. Relative to the traditional situation in which the customer does not participate in the production of goods and services, in the joint production condition, the customer participates in some part of the production process. Thus, the customer must allocate the credit for a positive outcome or the blame for a negative outcome to him-or herself and to the firm, which in turn may affect a customer's satisfaction.
The Self-Serving Bias and Outcome Quality
Outcomes frequently are characterized in the marketing literature as better than expected, as expected, or as worse than expected (Zeithaml, Berry, and Parasuraman 1993). When the outcome is better than expected, the self-serving bias theory suggests that a person assumes greater responsibility for the outcome (Campbell and Sedikides 1999). Thus, a customer should give him-or herself greater credit for the outcome than he or she gives to the production partner, the firm. In contrast, when a customer does not participate in the production, all the credit for the outcome should go to the firm. Consequently, even for the same level of positive out-come, because a customer who participates in production gives the firm less credit for the outcome, a participating customer may be less satisfied with the firm than a customer who does not participate in production.
A customer may not be as eager to share responsibility when the outcome is negative or worse than expected. According to the self-serving bias, a customer is less likely to take responsibility for the bad outcome, even though he or she participates in the production. Thus, a customer's satisfaction with the firm may be at the same level when the outcome is negative, regardless of whether the customer participated in the production. Note that if the self-serving bias did not exist, a customer would take some of the blame for the negative out-come when he or she participates in production (comparable to taking credit for the positive outcome), and therefore satisfaction with the firm should be greater when the customer participates in production than when the customer does not.
At times, an outcome is neither better nor worse than expected but simply conforms to an expectation. This situation has not been studied in the self-serving bias literature, and we must look elsewhere to make theoretical predictions about satisfaction in this context. Attribution research provides some insight because it has demonstrated that people are more likely to engage in attributional (cause and effect) thinking when confronted with the unexpected and that, when things are as expected, there is less incentive and, consequently, less effort to figure out who is responsible for what share of the task (Weiner 1985). If this is the case, when an outcome is as expected, we would not expect to find differences in a customer's satisfaction with the firm when the customer participates in the production than when he or she does not.
On the basis of the previous discussion, we propose the following:
P1: When an outcome is better than expected, a customer who participates in production with the firm will be less satisfied with the firm than will a customer who does not participate in production.
P2: When an outcome is worse than expected, a customer who participates in production with the firm will be as satisfied with the firm as will a customer who does not participate in production.
P3: When an outcome is as expected, a customer who participates in production with the firm will be as satisfied with the firm as will a customer who does not participate in production.
Method
Subjects were undergraduate students from a major U.S. university (n = 124). All subjects voluntarily participated in return for course credit and a chance to win a prize in a lottery. We informed subjects that the objective of the research was to investigate college students' perceptions of their experiences in purchasing various product categories. We gave subjects a booklet describing various purchase experiences to read at their own pace. After reading the scenarios, subjects provided their evaluations.
Stimulus Materials
We developed scenarios to describe purchases in six product categories. Three involved the purchase of goods: a bookshelf, a poster frame, and custom jeans. Three others were drawn from the services sector: a travel agent, a lawyer, and a weight-loss center. We constructed the scenarios to represent one of the six experimental conditions (two levels of customer participation: participation versus no participation three outcome levels: better than expected, worse than expected, as expected). We carefully pretested all of the scenarios (n = 49) for believability and relevance of the situation to a student population. Believability received average ratings ranging from 5.4 to 6.4 on a seven-point scale anchored by 1 ("not at all believable") and 7 ("very believable"). Relevance received average ratings ranging from 5.3 to 6.2 on a seven-point scale anchored by 1 ("not at all relevant") and 7 ("very relevant"). We also verified the participation manipulation in the pretest. On seven-point scales that measured the extent of work and effort (1 = "low" and 7 = "high") by the customer, the participation option was rated as involving significantly more effort (mean effort rating under nonparticipation = 2.42; mean effort rating under participation = 5.67; p < .01) than the nonparticipation option in all six of the scenarios. The same pattern holds for work ratings across all six scenarios (mean work rating under nonparticipation = 2.23; mean work rating under participation = 5.75; p < .01)
Each subject received six scenarios, one for each product category, which reflected one of the six experimental conditions. The order of presentation of the scenarios was randomized for each subject. Consistent with prior research, the scenarios described the experiences of an undergraduate named "Pat." The name Pat was chosen to be gender-neutral so that both male and female subjects could identify with the character. Students were asked to put themselves in Pat's shoes and indicate how they thought Pat would respond in each setting. The use of projective scenarios is well established in the psychology and marketing literatures and has been shown to minimize social desirability effects and have considerable external validity (Bateson and Hui 1992; Hui and Bateson 1991; Reeder et al. 2001; Robinson and Clore 2001; Voss, Parasuraman, and Grewal 1998). Even if subjects adopted an "observer" stance, analyzing Pat from a distance, instead of an "actor" stance, in which they put themselves in Pat's shoes, research has shown that it still would represent a conservative test of the hypothesis because Jones and Nisbett (1972), among others, have shown that the self-serving bias is reduced when events related to another as opposed to oneself are explained. Consistent with the discussion provided by Meuter and Bitner (1998), for the scenarios used in this study, nonparticipation involves the customer not participating in the production of the product, and participation involves the customer participating in some part of the production. Meuter and Bitner (1998) illustrate nonparticipation with an attendant pumping gas for the customer and taking payment at the pump. For participation, the customer took on some part of the production: Either the customer pumped the gas and the attendant took payment at the pump or the attendant pumped the gas and the customer paid at the pump using automation. Details of the six scenarios used in this study are presented in Table 2.
The scenarios were crafted with two important considerations: control for customization and independence of out-come quality and participation. Satisfaction in the participation condition may be affected by customization in that, when customers produce the good or service, they can make sure it fits their needs exactly (Wind and Rangaswamy 2000) to the extent that they have the requisite expertise (Lusch, Brown, and Brunswick 1992). For example, when customers fix their salads at a salad bar, they can make sure that it is exactly to their liking. When they communicate these preferences to an employee, they may have problems articulating their wants or the employee may have problems understanding them. To control for this customization aspect, in all of the scenarios, the dialogue between the employee and the customer is kept constant and culminates in the customer's choice of a product. For example, in the bookshelf scenario, Pat is described as talking with the salesperson about the type of shelf needed, inspecting the selection, and selecting one. Following this dialogue, depending on a subject's experimental condition, he or she read either about Pat's nonparticipation or about Pat's participation in production. For example, in the bookshelf scenario, participation is manipulated by having a subject read that the employee tells Pat one of the following two things: either that the store would assemble the shelf and deliver it (nonparticipation condition) or that the store would deliver the parts and Pat could assemble it (participation condition).
Equally important, our manipulation of outcome quality is kept independent of participation in production. That is, subjects in both the participation and nonparticipation conditions read that there were no problems with the actual production. Depending on the experimental condition, subjects simply read that the product turned out better than expected, worse than expected, or as expected in a nonproduction-related dimension. For example, whether the store or Pat assembled the shelf, there were no problems with the actual construction of the shelf. Outcome quality was presented as the shelf being much sturdier than expected, much less sturdy than expected, or about as sturdy as expected, independent of the customer's participation in production. This provides a conservative test of the self-serving bias.
Measures and Analysis
After subjects read all scenarios, they provided measures of satisfaction with the firm on a nine-point, single-item scale anchored by 1 ("dissatisfied") and 9 ("satisfied"). In a pretest (n = 41), we used a three-item satisfaction scale (dissatisfied- satisfied, displeased-pleased, terrible-delighted). The reliability coefficient for the scale was .98, and the first item was highly correlated with the latter two (.94 and .96). Consequently, with the high interitem correlations, the length of the task of reading and responding to six scenarios, and precedence in the literature for a single-item measure of satisfaction (e.g., Bitner 1990), we used only the single-item measure in the study to keep the total time to complete the survey reasonable and prevent respondent fatigue. We conducted paired t-tests (participation versus nonparticipation) to test P1-P3 (Kirk 1982) for each product within each out-come level (better than expected, as expected, worse than expected). These results are reported in Table 3.
As predicted in P1, in all six scenarios, when the out-come is better than expected, ratings of customer satisfaction with the firm are greater when the customer does not participate in the production than when he or she does. When the outcome is worse than expected, we proposed in P2 that there would be no difference in customer satisfaction with the firm, whether the customer participates in the production or not. In all six scenarios, customer satisfaction ratings are not significantly different between the participation and nonparticipation conditions.
When the outcome is as expected, we postulated in P3 that there would be no significant differences in satisfaction with the firm based on participation. This turned out to be the case in four of the six scenarios. In the poster frame and hotel stay scenarios, even when the outcome was as expected, customers appeared to take some of the credit for the outcome in the participation condition, as reflected in lower ratings of satisfaction with the firm when the customer participated in production. Therefore, there is partial support for P3.
Discussion
Study 1 is the first empirical investigation of the psychological impact of customer participation in production. It is also the first to demonstrate the self-serving bias in the commercial arena. From a theoretical perspective, the results of Study 1 have several important implications. Most significant, the study shows that the link between outcome quality and satisfaction with the firm is affected by customer participation in production. This study also extends the self-serving bias literature in two directions. First, it incorporates the "outcome as expected" condition. Second, in the place of bogus feedback by the experimenter in a lab, we demonstrate that the bias exists even when the customer provides the judgment of the outcome.
From a managerial standpoint, these results suggest that firms must evaluate customer participation in production carefully. We find that a customer is far more likely to take credit than to take blame in joint production. Therefore, a firm considering a participation strategy must understand how the self-serving bias can be reduced or whether customers can be made to assume credit as well as blame when they participate in production. This is the focus of Study 2.
In Study 2, we examine conditions in which a customer's self-serving bias can be reduced. The psychological literature suggests that increasing a customer's autonomy in a situation may reduce the self-serving bias. Knee and Zuckerman (1996) define autonomy as a situation that fosters choices and a sense of freedom. Using an individual differences perspective, Knee and Zuckerman show that people who have a high autonomy orientation are less subject to the self-serving bias than are those who have a low autonomy orientation. Therefore, a customer's self-serving bias may be reduced if a firm can ( 1) select those customers who have a high autonomy orientation or ( 2) create situations that increase the autonomy for all customers. Identifying and selecting customers on their autonomy orientation may not be feasible for all firms. Consequently, firms may find more value in creating situations that provide choice to foster autonomy. Arkin, Gleason, and Johnston (1976) investigate the effect of providing choice on self-serving bias when a person works alone to produce an outcome. They find that when subjects are provided a choice, they accept responsibility for both positive and negative outcomes. However, when they are not provided a choice, they accept responsibility only for the positive outcome and not for the negative outcome. Study 2 investigates the effect of choice in a joint production context, an area that has not been studied previously.
In Study 2, the effects of participation under choice also are hypothesized to vary on the basis of the outcome quality. Consistent with Study 1, when the outcome is better than expected, satisfaction with the firm should be less for a customer who chooses to participate in production than for a customer who does not. However, in contrast to Study 1, when the outcome is worse than expected, a customer who chooses to participate should take more responsibility for the outcome than one who chooses not to participate, and therefore the former customer should be more satisfied with the firm. Consistent with the attribution literature cited previously, there should not be a significant difference between a customer who chooses to participate and one who chooses not to do so when the outcome is as expected. Consequently, it is proposed that
P4: When a customer is given a choice of whether to participate in production, if the outcome is better than expected, a customer who chooses to participate in production with the firm will be less satisfied with the firm than will a customer who chooses not to participate.
P5: When a customer is given a choice of whether to participate in production, if the outcome is worse than expected, a customer who chooses to participate in production with the firm will be more satisfied with the firm than will a customer who chooses not to participate.
P6: When a customer is given a choice of whether to participate in production, if the outcome is as expected, a customer who chooses to participate in production with the firm will be as satisfied with the firm as will a customer who chooses not to participate.
Participation and Process Versus Outcome Effects on Satisfaction
Customers' satisfaction has been shown to be influenced by both the outcome, or what they receive in the relationship, and the process, or how they receive the outcome (Tax, Brown, and Chandrashekaran 1998). It is possible that the relative weighting of the outcome and the process dimension in determining overall satisfaction may be different for customers who participate in production versus those who do not. There has been no direct, theoretical consideration or empirical test in the literature of this idea of differential impacts of process and outcome based on participation. However, equity theory (Adams 1963; Oliver and Swan 1989), which postulates that individuals' comparison of their inputs and outputs with those of a relational partner affects their overall satisfaction with the relationship, provides a useful framework to examine this notion. In an automobile purchase setting, Swan and Oliver (1991) find that customers monitor their inputs such as their attention, time, and effort and that these affect overall satisfaction. That is, customers' assessment of their own input affects their overall satisfaction. Our study extends Swan and Oliver's work in two ways. First, customer input, participation in production, is by choice in our study, whereas in Swan and Oliver's work, it is not. Second, the input in Swan and Oliver's (1991) study is in the form of negotiation, search, and attention during the car buying process. In contrast, in our study, customer input is even more significant because it constitutes participation in the actual process of production. Thus, building on equity theory and Swan and Oliver (1991), the impact of the production process on overall satisfaction may be greater for those customers who choose to participate in production than for those who choose not to participate. By extension, the impact of the outcome on overall satisfaction may be less important to customers who choose to participate in production than to those who choose not to. Consequently, we propose that
P7: A customer's satisfaction with a firm is affected by satisfaction with both the process and the outcome.
P8: When a customer chooses to participate in production, the effect of the process satisfaction on firm satisfaction will be greater than when he or she chooses not to participate.
P9: When a customer chooses to participate in production, the effect of the outcome satisfaction on firm satisfaction will be lower than when he or she chooses not to participate.
Method
Participants were undergraduate students from a major U.S. university (n = 135) recruited in identical conditions to those described in Study 1.
Stimulus Materials
To achieve comparability across studies, we employed the same scenarios used in Study 1, with one important modification. In Study 2, after the dialogue, the subjects read that the firm gave Pat a choice as to whether to participate in the production. Subsequently, depending on the randomly assigned experimental condition, respondents read that Pat either chose to participate (e.g., chose to build the shelf) or chose not to participate (e.g., chose to have the store build the shelf) in the production process. Respondents in all scenarios read that Pat considered the money, time, effort, costs, and benefits involved in both alternatives before making the decision whether to participate. As with Study 1, we kept the outcome manipulation independent of participation and focused on the same nonproduction-related aspects. The task and order of presentation of stimulus materials were identical to Study 1.
Measures and Analysis
After subjects read each scenario, they provided measures of satisfaction with the firm and with the process involved using single-item, nine-point scales (1 = "dissatisfied" and 9 = "satisfied"). To test P4-P6,weconducted paired t-tests to compare ratings of satisfaction with the firm when the customer chose to participate in production versus when he or she chose not to participate (Kirk 1982) for each product within each outcome level (better than expected, as expected, worse than expected). These results are shown in Table 4.
When Pat is described as having a choice of whether to participate, and the outcome is better than expected, firm satisfaction ratings are significantly greater when Pat chooses not to participate in production than when Pat chooses to participate in five of the six scenarios. This provides support for P4 and replicates the findings of Study 1. The single exception is the poster frame condition for which the difference is in the expected direction but is not significant.
As proposed in P5, when the outcome is worse than expected, firm satisfaction ratings are significantly higher when Pat chooses to participate in production than when Pat chooses not to do so in four of the six scenarios. The differences are not significant in the poster frame and the weight-loss conditions. Overall, the pattern of results provides support for P5 and, more important, reveals a reversal of the results found in Study 1. Thus, choice mitigates the use of self-serving bias when the outcome is worse than expected. When the outcome is as expected, we proposed there would be no significant differences in ratings of satisfaction with the firm based on customer participation. This was the case in four of the six scenarios, which provides some support for P6. In the deposit and the hotel stay scenarios, even when the outcome is as expected, in the participation choice condition Pat is given some of the credit for the outcome, as reflected in lower satisfaction with the firm.
In P7-P9,we proposed customers' satisfaction with the outcome would be affected by their satisfaction with both the process and the actual outcome. Furthermore, we proposed that customers' sensitivity to outcome and process would vary on the basis of their choice of participation in production. Specifically, we expected that a customer would be less sensitive to the outcome and more sensitive to the process when he or she chooses to participate in production than when he or she does not, thus accounting for the muted effect of the actual outcome on satisfaction.
To test these propositions, we conducted regression analyses for each product; satisfaction with the firm was the dependent variable and two dummy-coded outcome variables (dummy variables for "better than expected" and "worse than expected," with the "as expected" condition as the baseline comparison), and satisfaction with the process was the independent variables. These results are shown in Table 5.
As shown in the pooled condition in Table 5 and suggested in P7, satisfaction with the process had a significant effect in all six scenarios. Also, as predicted, the outcome being worse than expected had a significant, negative effect in all six of the scenarios. The outcome being better than expected was not significantly different in any of the six scenarios. Recall that the comparison of the outcomes is with the "as expected" condition.
To determine whether the relative effects are different when a customer chooses to participate versus when he or she does not, we ran two additional regressions for the participation (P) and nonparticipation (NP) choice conditions. Comparing the results from these two regressions with those from the pooled regression allows for a test to be performed for the equality of beta coefficients across the two conditions. The results are shown in Table 5.
The results from the test for the equality of coefficients demonstrate that the relative weights for outcome and process are not the same across participation versus nonparticipation conditions. As predicted, the beta coefficients indicate that the outcome effects are stronger and the process effects are weaker in the nonparticipation condition than in the participation condition. Specifically, satisfaction with the process had greater influence on satisfaction with the firm in the participation condition than in the nonparticipation condition for all six scenarios. In all six cases, though not significant, direction-ally, the coefficients for the better than expected condition are larger in the nonparticipation condition than in the participation condition. In five of the six cases, the coefficients indicate that a worse than expected outcome reduces firm satisfaction to a greater degree in the nonparticipation condition than in the participation condition. The exception is the weight-loss scenario, for which the test for equality of coefficients indicates that relative effects were not significantly different.
Discussion
The results of Study 2 show that providing choice in participation can reduce the self-serving bias and thus make a customer more willing to take the credit as well as the blame for an outcome. When a firm provides a participation choice to a customer, the firm still must decide carefully on a customer participation strategy on the basis of an understanding of the product and its likelihood of falling short versus exceeding a customer's expectations. If a firm believes the outcome will exceed a customer's expectations, encouraging participation may be less attractive because a customer is likely to claim some of the credit when he or she participates. However, if there is some chance that the outcome will not meet a customer's expectations, encouraging customer participation may be a reasonable strategy because the firm may receive less blame for the outcome.
For many companies, a risk-averse strategy may be used to encourage the customer to participate in production to reduce the negative effects of a potential shortfall relative to customer expectations. This may be the safer route to take for two reasons. First, several researchers have shown that the effect of performance on satisfaction is asymmetric (Zeithaml, Berry, and Parasuraman 1996). When a firm falls short of expectations, the penalty in customer satisfaction is far steeper than the benefit when the firm exceeds expectations. Second, in many cases it may be relatively difficult and expensive to try to exceed customer expectations.
In both studies, it appears that a customer takes more responsibility than is warranted when he or she participates. This is because there was an explicit manipulation of out-come quality independent of whether a customer participated in production. In reality, when a customer's participation actually affects the outcome quality, we would expect the effects to be even more pronounced. The insights into how participation choice affects customer satisfaction with the firm are also noteworthy. The standardized beta weights in Table 5 reveal that the impact of a worse than expected outcome can be reduced anywhere from one-third (poster frame scenario) to as much as five times (hotel scenario) when the customer participates in production. Furthermore, focus on the process increases from approximately one-seventh (deposit scenario) to almost two times (poster frame scenario) when a customer participates in production compared with when he or she does not participate. Therefore, when the customer is engaged in production, the firm must ensure that the process of production is involving and of high quality. The process must provide psychic benefits to the customer, whether in the form of enjoyment, accomplishment, self-confidence, or control (Lusch, Brown, and Brunswick 1992).
Conclusion and Directions for Further Research
The research reported here provides evidence for the psychological impact of a customer's participation in production. We show that a customer who participates in production is subject to the self-serving bias and that this tendency is reduced when a customer has a choice of whether he or she will participate in production. Further research is needed to investigate other potential avenues for minimizing the self-serving bias.
Previous research demonstrates that people are less likely to engage in the self-serving bias for joint outcomes when they have a close relationship with the partner. For example, Campbell and colleagues (2000) show that the self-serving bias is significantly lower when the subjects working together are friends rather than strangers. As a person becomes closer to a partner, he or she is still likely to overstate his or her role but does so in a more symmetric fashion and shares the credit as well as the blame (Gilovich, Kruger, and Savitsky 1999). This effect holds even when the closeness is lab induced. For example, Sedikides and colleagues (1998) show that strangers who were made to feel close to each other through an experimental manipulation demonstrated a reduced self-serving bias. These results suggest that when a customer has a close relationship with a firm, he or she may be less subject to the self-serving bias. Therefore, from a strategic point of view, firms may want to encourage participation in production by customers who have a strong relationship with the firm. However, even when dealing with long-established customers, a firm must carefully assess the customer's willingness and ability to participate in production. For example, a customer may have a strong, long-term relationship with the firm precisely because the firm does everything for him or her and he or she does not need to participate in producing the good or service. If the firm now tries to cajole the customer into coproduction, the customer may become dissatisfied and may switch to another company.
Researchers have also contended that distinct cultural differences exist in the likelihood of a self-serving bias, and the bias is more prevalent in individualistic cultures, such as the United States, than in collectivist cultures, such as many Asian countries (Heine and Lehman 1997). Although these cultural differences have been examined in interpersonal relationships, it would be interesting to know whether they translate to firm-customer relationships as well. In addition to cultural differences, there may be individual differences in willingness to accept responsibility. Constructs such as the locus of control (Rotter 1966) may be used to examine whether some people are more likely to be affected by participation in production than others.
Another area for additional research is the timing of a customer's participation. That is, in addition to how much participation, does it matter when a customer participates? The studies reported here do not distinguish between when a customer participates in production. For example, a customer may choose to participate in the initial phase of the production process and then hand off the project to the firm (e.g., a customer who paints a ceramic vase and turns it over for glazing). Or a customer may choose to participate in the end phase of the production (e.g., a customer who has a firm build a deck and then applies the sealer). The management literature on job design suggests that creating task identity--allowing employees to be fully responsible for one aspect of the work--increases employees' awareness of how their piece of work fits into the whole, and this increases their job satisfaction (Hackman and Oldham 1976). If this relation-ship also holds for a customer, firms may do well to select the choice points for customer participation carefully so that they stand out as separate and distinct to the customer.
In current literature, there is an assumption that coproduction always facilitates better customization of the product (Wind and Rangaswamy 2000). However, the assumption of greater customization under co-production may hold only when the customer has the expertise to craft a good or service to his or her liking (Lusch, Brown, and Brunswick 1992). Furthermore, perceived expertise may affect the customer's psychological responses to co-production. A customer who believes he or she has the expertise and chooses to co-produce may be more likely to make self-attributions for success and failure than a customer who lacks the expertise. A customer who lacks the expertise but feels forced to co-produce (e.g., a customer who enters a department store seeking help from store personnel but is forced to make decisions on his or her own because of the scarcity of store personnel) may make more negative attributions about co-production.
We adopt a narrow view of customer co-production in this article, focusing on customer participation in the construction of goods and services. However, consumer coproduction extends to the construction of meanings as well. Consumers are not just passive receptacles of brand identities projected by marketers; they are active co-producers of brand meanings (Cova 1996; Firat and Venkatesh 1993; Ritson and Elliott 1999). Greater attention to the implications of such consumer co-produced marketing images is warranted given the empowerment of consumers through the Internet and customers' militancy in protecting their brand icons (Levine et al. 2001). Another limitation is the reliance on single-item measures of satisfaction. Even though the decision to use the single items was driven by high intercorrelations among multi-item measures in pretests and a concern for reducing respondent fatigue, further research should consider using multi-item measures for the variables to investigate whether these provide stronger tests and greater insight.
Legend for Chart
A = Author(s)
B = Focus
C = Nature of Study
D = Findings and Conclusions
A
B
C
D
Lovelock and Young 1979
Consequences of customer participation in production of services.
Conceptual
Customers can be a source of productivity gains.
MILLS AND MOBERG 1982
The organizational technology needed to manage the services sector
as opposed to the goods sector.
Conceptual
Suggests that one key difference between the two sectors is the
customer/client's role in the production process. Customer
contributions to services are described as information and effort.
MILLS, CHASE, AND MARGUILES 1983
Managing the customer/client as a partial employee to increase
system productivity.
Conceptual
Suggests that greater customer involvement in the production process
can be a source of productivity gains. Customers' input needs to be
monitored and assessed the same way as regular employees' input.
BATESON 1985
Understanding the motivations of the self-service consumer.
Empirical
Examines the differences between customers who would choose to
do-it-yourself and those who would choose to be served. Shows that
a segment of customers would prefer the do-it-yourself option even
when no incentives are offered to encourage participation.
FITZSIMMONS 1985
The consequences of customer participation on service sector
productivity.
Conceptual
Suggests that customer participation through substitution of
customer labor for provider labor, smoothing of demand, and use
of technology in place of personal interaction may yield greater
service sector productivity.
MILLS AND MORRIS 1986
Conceptual
Customers as partial employees.
Customers may serve as partial employees in a service setting
by sharing some of the production responsibilities.
GOODWIN 1988
Training the customer to contribute to service quality.
Conceptual
Suggests that customers' sources of training and willingness
to be trained are a function of their commitment to the provider
and the presence of other customers. When customers are committed
to the provider, they are more willing to invest in learning how
to contribute. Customers may be trained by both the provider and
other customers.
CZEPIEL 1990
The nature of the service encounter and directions for research.
Conceptual
Suggests that customer participation in the production process and
the satisfaction with this role may affect customer satisfaction.
BOWEN 1990
Taxonomy of services based on customer participation.
Empirical
Participation is a meaningful construct for customers describing
various services. It may be possible to segment customers on the
basis of their willingness to participate in the creation of
services.
BOWERS, MARTIN, AND LUKER 1990
Treating employees as customers and customers as employees.
Conceptual
Suggests that treating employees as customers through internal
marketing and treating customers as employees through training
and reward systems enhance overall system productivity.
KELLEY, DONNELLY, AND SKINNER 1990
Managing customer roles when customers participate in
service production and delivery.
Conceptual
Suggests that customers may be managed as partial employees
when participating in service production and delivery by
focusing on customers' technical and functional quality input
to the process. Suggests that customer participation may affect
overall quality and productivity, employee performance, and
employees' emotional responses.
DABHOLKAR 1990
Using customer participation to enhance service quality perceptions.
Conceptual
Suggests that customer participation may influence perceptions of
the waiting time and thus affect perceived quality.
FODNESS, PITEGOFF, AND SAUTTER 1993
The downside of customer participation.
Conceptual
Suggests that customers who are trained to do more of the service
for themselves may develop into a potential competitor by performing
for themselves services that were previously purchased.
FIRAT AND VENKATESH 1993
Argues for the reversal of roles of consumption and production.
Conceptual
Among the postmodern conditions discussed is the reversal of
consumption and production as customers take on more active roles
in production.
SONG AND ADAMS 1993
Using customer participation in production and delivery as
opportunities for differentiation.
Conceptual
Customer participation should not always be examined merely as
a cost-minimization problem. Instead, firms can examine
opportunities for differentiating their market offering by
heightening or lessening customers' participation in the
production and delivery of products.
CERMAK, FILE, AND PRINCE 1994
Distinguishing participation versus involvement effects.
Empirical
Attempt to distinguish involvement from participation, but
authors conclude that participation construct was confounded by
operationalization as level of involvement.
FIRAT AND VENKATESH 1995
Distinguishes between the consumer perspectives of modernism
and postmodernism.
Conceptual
Argues that the modernist perspective confines the consumer by
arguing for the "privileging" of production over consumption.
Postmodernism provides a basis for understanding a greater
consumer role in production as well as consumption.
FIRAT, DHOLAKIA, AND VENKATESH, 1995
Presents a postmodern perspective of consumer as customizer
and producer.
Conceptual
As consumers have become customizers, marketing organizations'
offerings will increasingly become processes rather than finished
products. Consumers who are integrated into the production systems
will need to be conceptualized as producers.
HULT AND LUKAS 1995
Customer participation in health care.
Conceptual
Suggests that classifying health care tasks in terms of customer
participation and complexity of the task has important implications
for marketing the services.
LENGNICK-HALL 1996
Customer contributions to quality.
Conceptual
Customers influence quality by their roles: as resources,
as co-producers, as buyers, as users, and as product. Garnering
customer talents in these roles can yield competitive advantages.
VAN RAAIJ AND PRUYN 1998
Customer control and its impact on judgments of service validity
and reliability.
Conceptual
Suggests that customers may perceive more or less sense of control
in three stages in the service relationship: input, throughput,
and output. The greater the sense of control, the more customers
will feel responsibility for and satisfaction with the service.
PRAHALAD AND RAMASWAMY 2000
Coopting customer competence.
Conceptual
The changing roles of customer from passive audience to active
cocreators of experience. Companies can achieve a competitive
advantage by leveraging customer competence.
WIND AND RANGASWAMY 2000
Customerization: The next revolution in mass customization.
Conceptual
In the digital marketplace, customers are becoming active
participants in product development, purchase, and consumption.
Firms must become customercentric and adopt "customerization" to
add value.
I. Dialogue between Pat and the employee and selection of product (common to all subjects).
Talks to salesperson and selects the bookshelf to buy (S1).
Talks to salesperson and selects the mat and frame for the
poster (S2).
Talks to salesperson and selects the fabric and color for
custom-fit jeans (S3).
Talks to lawyer and decides what to say in letter to landlord
for a return on security deposit (S4).
Talks to travel agent and selects hotel room to stay at for
spring break (S5).
Talks to weight counselor at weight-loss center and selects
weight-loss plan (S6). II. Manipulation of participation in production (between-subjects).
Pat is told the store will assemble and deliver shelf.
Pat is told the store will deliver the shelf parts and
Pat must assemble it.
Pat is told the store will build the frame.
Pat is told Pat must build the frame.
The employee takes Pat's measurements for custom-fit jeans
and enters them.
The employee takes Pat's measurements for custom-fit jeans
and Pat enters them.
The lawyer shows Pat a form letter and drafts and mails it
to Pat's landlord.
The lawyer shows Pat a form letter, and Pat drafts and mails
it to the landlord.
The travel agent calls to reserve the room for Pat.
Pat calls to reserve the room.
The center uses the food list to shop for Pat's food at
the grocery store.
The center gives Pat the food list to shop for food at
the grocery store. III. Manipulation of outcome independent of participation (between-subjects).
The shelf is assembled fine, but Pat thinks it is sturdier than expected, much less sturdy than expected, or about as sturdy as expected.
The frame is built fine, but Pat thinks it matches the room much better than expected, much worse than expected, or about as expected.
The jeans are tailored fine, but Pat thinks the color is much better than expected, much worse than expected, or about as expected.
The letter is written fine, but Pat thinks the refund from the landlord is much more than expected, much less than expected, or about as expected.
The room looks fine, but Pat thinks that the view is much better than expected, much worse than expected, or about as expected.
The list is used to shop, but Pat thinks the weight loss is much more than expected, much less than expected, or about as expected.
Legend for Chart
I = Results of t-tests of P[sub1]:
SatisfactionNP > Satisfaction [subP] when outcome is
better than expected
II = Results of t-tests of P[sub2]:
SatisfactionNP > Satisfaction [subP] when outcome is
worse than expected
III = Results of t-tests of P[sub3]:
SatisfactionNP > Satisfaction [subP] when outcome is
as expected
(NP) = No Participation
(P) = Participation
A
I (NP) (P) t-Value
Bookshelf Mean = 8.40 Mean = 7.74 1.85*
n = 20 n = 19
s.d. = .940 s.d. = 1.28
Poster frame Mean = 8.52 Mean = 7.12 3.07**
n = 23 n = 17
s.d. = .665 s.d. = 1.79
Jeans Mean = 8.36 Mean = 7.59 2.39**
n = 22 n = 22
s.d. = .902 s.d. = 1.22
Deposit Mean = 8.47 Mean = 6.95 3.57**
n = 19 n = 21
s.d. = .772 s.d. = 1.77
Hotel Stay Mean = 8.06 Mean = 6.87 2.78**
n = 18 n = 23
s.d. = 1.16 s.d. = 1.48
Weight Loss Mean = 8.67 Mean = 8.18 1.95*
n = 21 n = 22
s.d. = .577 s.d. = 1.01
Legend for Chart
II = Results of t-tests of P[sub2]:
SatisfactionNP > Satisfaction [subP] when outcome is
worse than expected
(NP) = No Participation
(P) = Participation
A
II (NP) (P) t-Value
Bookshelf Mean = 5.95 Mean = 5.77 .290
n = 19 n = 22
s.d. = 1.98 s.d. = 1.90
Poster frame Mean = 7.00 Mean = 6.52 .820
n = 19 n = 22
s.d. = 1.98 s.d. = 1.90
Jeans Mean = 7.05 Mean = 6.37 1.42
n = 21 n = 19
s.d. = 1.43 s.d. = 1.61
Deposit Mean = 5.09 Mean = 5.28 -.28
n = 22 n = 18
s.d. = 2.26 s.d. = 1.81
Hotel Stay Mean = 5.00 Mean = 5.30 -.54
n = 20 n = 23
s.d. = 2.13 s.d. = 1.43
Weight Loss Mean = 4.55 Mean = 5.28 -1.06
n = 20 n = 21
s.d. = 2.43 s.d. = 2.00
Legend for Chart
III = Results of t-tests of P[sub3]:
SatisfactionNP > Satisfaction [subP] when outcome is
as expected
(NP) = No Participation
(P) = Participation
A
III (NP) (P) t-Value
Bookshelf Mean = 8.05 Mean = 7.68 .970
n = 21 n = 22
s.d. = 1.02 s.d. = 1.39
Poster frame Mean = 8.42 Mean = 7.62 2.63**
n = 19 n = 21
s.d. = .610 s.d. = 1.24
Jeans Mean = 8.41 Mean = 7.89 1.57
n = 22 n = 18
s.d. = .854 s.d. = 1.23
Deposit Mean = 7.77 Mean = 7.91 -.32
n = 22 n = 22
s.d. = 1.51 s.d. = 1.27
Hotel Stay Mean = 7.90 Mean = 6.80 2.44**
n = 20 n = 20
s.d. = 1.25 s.d. = 1.57
Weight Loss Mean = 7.79 Mean = 7.48 .880
n = 19 n = 21
s.d. = 1.13 s.d. = 1.12*Significant at p < .05.
** Significant at p < .01.
Notes: s.d. = standard deviation.
Legend for Chart
I = Results of t-tests of P[sub4]:
SatisfactionNP > Satisfaction [subP] Given choice,
when outcome is better than expected
(NP) = No Participation
(P) = Participation
A
I (NP) (P) t-Value
Bookshelf Mean = 8.37 Mean = 7.44 3.06**
n = 22 n = 25
s.d. = .79 s.d. = 1.23
Poster frame Mean = 8.09 Mean = 7.90 .590
n = 22 n = 20
s.d. = .87 s.d. = 1.21
Jeans Mean = 8.22 Mean = 7.74 1.65*
n = 23 n = 23
s.d. = .67 s.d. = 1.21
Deposit Mean = 7.74 Mean = 6.71 1.80*
n = 19 n = 21
s.d. = 1.6 s.d. = 1.90
Hotel Stay Mean = 8.08 Mean = 5.74 6.48
n = 23 n = 23
s.d. = .90 s.d. = 1.48
Weight Loss Mean = 8.31 Mean = 7.74 1.63*
n = 26 n = 23
s.d. = .93 s.d. = 1.48
Legend for Chart
II = Results of t-tests of P[sub5]:
SatisfactionNP > Satisfaction [subP] Given choice,
when outcome is worse than expected
(NP) = No Participation
(P) = Participation
A
II (NP) (P) t-Value
Bookshelf Mean = 4.67 Mean = 6.08 -2.71**
n = 24 n = 23
s.d. = 2.1 s.d. = 1.50
Poster frame Mean = 5.60 Mean = 6.41 -1.36
n = 22 n = 20
s.d. = .87 s.d. = 1.21
Jeans Mean = 5.60 Mean = 6.85 -2.33**
n = 20 n = 20
s.d. = 2.1 s.d. = 1.10
Deposit Mean = 4.40 Mean = 5.90 -2.52**
n = 19 n = 21
s.d. = 1.6 s.d. = 1.90
Hotel Stay Mean = 3.50 Mean = 5.09 -2.87**
n = 20 n = 22
s.d. = 1.8 s.d. = 1.74
Weight Loss Mean = 3.56 Mean = 3.54 .040*
n = 26 n = 23
s.d. = .93 s.d. = 1.48
III = Results of t-tests of P[sub6]:
SatisfactionNP > Satisfaction [subP] Given choice,
when outcome is as than expected
(NP) = No Participation
(P) = Participation
A
III (NP) (P) t-Value
Bookshelf Mean = 7.65 Mean = 7.52 .370
n = 20 n = 21
s.d. = 1.2 s.d. = .980
Poster frame Mean = 8.00 Mean = 7.67 .890
n = 24 n = 24
s.d. = 1.4 s.d. = 1.20
Jeans Mean = 7.77 Mean = 7.95 -.57
n = 26 n = 22
s.d. = .99 s.d. = 1.25
Deposit Mean = 7.75 Mean = 6.76 2.19*
n = 24 n = 21
s.d. = 1.4 s.d. = 1.61
Hotel Stay Mean = 7.32 Mean = 5.81 2.78**
n = 22 n = 27
s.d. = 1.5 s.d. = 2.13
Weight Loss Mean = 7.38 Mean = 7.50 -.29
n = 21 n = 20
s.d. = 1.5 s.d. = 1.00*Significant at p < .05.
** Significant at p < .01.
Notes: s.d. = standard deviation.
Legend for Chart
A = Product
B = Condition
C = Process β
D = Better than expected outcome β
E = Worse than expected outcome β
F = N
G = Pooling Test F-Value
A B C D E F G
Bookshelf Pooled .427*** .027 -.472*** 131 4.92***
P .540*** -.05 -.344*** 67
NP .340*** .094 -.574*** 64
Poster frame Pooled .384*** .065 -.378*** 133 2.52**
P .524*** .055 -.309*** 67
NP .282*** .056 -.401*** 66
Jeans Pooled .368*** -.01 -.430*** 130 4.68***
P .532*** -.04 -.220* 63
NP .330*** .005 -.588*** 67
Deposit Pooled .189*** -.04 -.435*** 126 4.61***
P .249*** -.08 -.157* 60
NP .219*** .030 -.583*** 66
Hotel stay Pooled .440*** .100 -.355*** 133 8.20***
P .478*** .058 -.111 70
NP .277*** .111 -.627*** 63
Weight loss Pooled .238*** .096 -.617*** 133 .56
P .252*** .011 -.628*** 65
NP .233*** .175** -.605*** 68*Significant at p < .10.
**Significant at p < .05.
***Significant at p < .01.
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By Neeli Bendapudi and Robert P. Leone
Neeli Bendapudi is Associate Professor of Marketing, and Robert P. Leone is Professor and Berry Chair in Marketing, Fisher College of Business, Ohio State University.
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Record: 124- Pursuing the Value-Conscious Consumer: Store Brands Versus National Brand Promotions. By: Ailawadi, Kusum L.; Neslin, Scott A.; Gedenk, Karen. Journal of Marketing. Jan2001, Vol. 65 Issue 1, p71-89. 19p. 2 Diagrams, 2 Charts. DOI: 10.1509/jmkg.65.1.71.18132.
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Record: 125- Reciprocal Spillover Effects: A Strategic Benefit of Brand Extensions. By: Balachander, Subramanian; Ghose, Sanjoy. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p4-13. 10p. 8 Charts. DOI: 10.1509/jmkg.67.1.4.18594.
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Reciprocal Spillover Effects: A Strategic Benefit of Brand Extensions
A commonly advanced rationale for the proliferation of brand extensions is companies' motivation to leverage the equity in established brands, thereby developing profitable products relatively easily. A more interesting strategic argument for brand extensions that has been advanced is that extensions would favorably affect the image of the parent brand and thereby influence its choice. In this research, the authors investigate the existence of such reciprocal spillover effects emanating from the advertising of a brand extension. The authors use scanner panel data and study spillover effects of advertising on brand choice. They develop implications for brand and product line management.
In recent years, there has been a plethora of line and brand extensions. Between 1977 and 1984, 40% of the 120 to 175 new brands introduced each year in supermarkets were extensions (Aaker 1990). One commonly advanced rationale for this proliferation of extensions is companies' motivation to leverage the equity in established brands and develop profitable products relatively easily (Morein 1975). A second motivation for extensions is to affect the image of the umbrella brand favorably, which thereby influences sales in other categories. Aaker (1996) offers several examples in which existing products obtain this reciprocal benefit from brand extensions. He suggests that Gallo opted to attach its name with a "jug-wine" reputation to its line of upscale, corked wines, Ernest and Julio Gallo Varietals, to improve the quality perception of its low-end wine product. In another example, Contadina, which was perceived as a strong canned-foods brand with an authentic Italian heritage, was revitalized by its entry into fresh refrigerated pastas and sauces.
If introduction of a brand extension can produce such (reciprocal) spillover benefits to existing products, it can be expected that advertising of the brand extension will also have a positive spillover effect on sales of existing products. For example, Aaker (1996) suggests that advertising of the brand extension Hidden Valley Honey Dijon Ranch salad dressing made the advertising for the Hidden Valley brand group more effective. Such advertising spillover effects would also have implications for allocation of advertising budget among the new brand extension and existing products with the same brand name. However, it is surprising that there is little documentation of the existence of advertising spillover effects in academic literature in spite of the availability of high-quality, single-source scanner panel data. In the only field research known to us, Sullivan (1990) studies the effect on the demand of used Jaguar models arising from the introduction of a new Jaguar model. Her results can be interpreted as somewhat mixed. Although she found that the event of introduction of the new model increased demand for used Jaguar models (positive spillover), advertising of the new Jaguar model depressed demand for used Jaguars (negative spillover). If the event of product introduction is considered an "information shock" and advertising represents a more steady flow of information, similar effects for both forms of information should be expected. However, Sullivan does not find such a result. A possible reason is that Sullivan does not separate the negative substitution effect that arises from a brand extension, depressing sales of existing products from a likely positive spillover effect. Thus, unlike the spillover effect of information shock caused by a product introduction, spillover effects from ongoing advertising may not be strong enough to over-whelm a substitution effect.
In this article, we separate the substitution and spillover effects by estimating demand at the disaggregate level using ACNielsen's single-source scanner panel data. We find that the advertising of brand extensions produces significant reciprocal spillover that favorably affects the choice of the parent brand. Thus, we find empirical support for the anecdotal evidence presented previously. Our separation of spillover and substitution effects may enable us to find a positive spillover effect of advertising, in contrast to Sullivan (1990) who finds a negative spillover effect for Jaguar advertising.[ 1]
In contrast to our use of disaggregate data, Sullivan uses aggregate data from published sources. Tellis and Weiss (1995) suggest that aggregation may lead to bias in estimating the effects of advertising. Our use of disaggregate data also provides a strong test for the existence of advertising spillover effects because advertising effects have been difficult to establish with such data (Tellis and Weiss 1995). In a laboratory study, Morrin (1999) finds that advertising of a brand extension facilitates recall of the parent brand. However, our research focuses on brand choice and examines the advertising effect of both a brand and its extension in a field setting.
In the remainder of this article, we present the conceptual background that pertains to spillover effects. We then develop the empirical models and present the analysis and results. Finally, we discuss the managerial implications of our findings and limitations of our study and offer a brief conclusion.
Spillover Advertising Effect
Advertising spillover becomes relevant when a brand name is used on two or more products that are separately advertised. Consider two products, A and B, that carry an umbrella brand name in common (e.g., Yoplait yogurt and Yoplait non-fat yogurt). We conceptualize a spillover effect as the impact of Product A's (B's) advertising on the utility to the consumer of Product B (A). In general, the spillover effects between products may not be symmetric. In particular, we distinguish between the spillover effects from advertising of the parent (the product that originally used the brand name) and those from advertising of the child or extension (the line or brand extension). We refer to the former effect as the forward spillover effect and the latter effect as the reciprocal spillover effect. The definition of parent as the product that originally used the brand name is similar to the definition of "core" brand in Keller and Aaker's (1992) study. In a more theoretical sense, we consider the parent as the product most closely associated with the umbrella brand name in the consumer's mind (Farquhar, Herr, and Fazio 1990; Morrin 1999). This perspective is similar to the concept of a flagship product used by John, Loken, and Joiner (1998) or "instance dominance" used by Herr, Farquhar, and Fazio (1996).
We offer several commercial examples in which a positive reciprocal spillover effect is anticipated. There are two main theoretical reasons to expect such a positive reciprocal spillover effect. First, a positive spillover effect would be consistent with the existence of economies of information in advertising when an umbrella or "range" brand is applied to different products (Aaker 1996; Morein 1975). Indeed, in an empirical study, Smith (1992) finds that advertising expenditures for umbrella-branded products are lower, which is consistent with economies of information. As Aaker (1996, p. 295) notes, such economies are realized because "the fixed cost of maintaining a brand name can be spread across different businesses." The implication of this rationale is that umbrella-branded products benefit one another with their advertising because of positive spillover effects, resulting in less advertising expenditure for each product. In a similar vein, Morein (1975) suggests that economies of information are realized because an advertised product produces a "halo effect" that increases sales of other umbrella-branded products. Thus, we expect both reciprocal and forward spillover effects to be positive because of economies of information.
Although the economies of information argument is intuitively reasonable, the specific mechanism through which advertising spillover or halo occurs is not clear. Wernerfelt's (1988) analysis of umbrella branding suggests one such mechanism. Using a signaling model, Wernerfelt theorizes that umbrella-branded products are perceived to offer higher quality because profits from other umbrella-branded products act as a "performance bond" for the quality of any of the umbrella-branded products. In other words, if a low-quality product is offered with an umbrella brand name, it leads consumers to conclude that all other products with the same brand name are also of low quality, which thus threatens the profits from these other products. Therefore, a firm would optimally extend an established brand name only to high-quality products, thus rendering consumers' perceptions (that umbrella-branded products are of high quality) accurate. Wernerfelt's analysis offers a mechanism by which advertising can spill over and enhance sales for other umbrella-branded products.[ 2] Essentially, the advertising of other products with the same brand name makes consumers aware of the performance bond at stake for the firm, thereby increasing quality perceptions of unadvertised products and enhancing their sales. In summary, the theory of economies of information suggests positive reciprocal and forward spillover effects.
Second, Anderson (1983) offers a consumer memory- based explanation for the existence of reciprocal spillover effects with the associative network theory. Brand associations in consumer memory are a key component of brand equity and brand-related effects (Aaker 1996). The associated network theory has been particularly useful in analyzing the effect of brand associations (see, e.g., Herr, Farquhar, and Fazio 1996; John, Loken, and Joiner 1998; Keller 1993; Morrin 1999). This theory conceptualizes knowledge about a brand as being a network of nodes (or concepts) connected by links, which represent associations between the concepts. Moreover, the strength of a link is a measure of the association strength between the concepts. Thus, the brand (e.g., Yoplait), the parent (e.g., Yoplait [regular] yogurt), and the brand or line extension (e.g., Yoplait non-fat yogurt), as well as beliefs about the brand, are conceptualized as nodes in a knowledge network, and the links between the nodes vary in strength. A consumer retrieves a particular piece of knowledge from memory when the corresponding node is activated above a threshold level, through priming by external cues such as advertising or by "spreading" activation from other linked nodes. The extent of spreading activation to a new node increases with the strength of the link between the new node and the previously activated node. A stronger link facilitates the spreading activation to the new node above the threshold to be retrieved from memory.
Because we expect the parent to be strongly associated with the brand in consumers' memories, the link between the parent node and the brand node is likely to be strong (Farquhar, Herr, and Fazio 1990). When exposure to advertising of the brand extension activates the brand node, the activation will likely spread to the parent because of the strength of the link between the two nodes. The resulting retrieval of the parent will produce a positive spillover effect. In this manner, exposure to advertising of Yoplait non-fat yogurt may activate the parent category, regular yogurt, through activation of the Yoplait name because the Yoplait node is strongly linked to the regular yogurt node. Thus, we form the following hypothesis for the reciprocal spillover effect:
H[sub1]: Both the economies of information theory and the associated network theory suggest that the reciprocal spillover effect, that is, the spillover effect of exposure to advertising of a child on the revealed preference for a parent, is positive.
Consider the forward spillover effect of parent's advertising on the choice of the child. As previously discussed, arguments based on economies of information favor a positive forward spillover effect. Moreover, such arguments do not suggest any asymmetry in the magnitudes of forward and reciprocal spillover effects. In particular, Wernerfelt (1988) argues that both the parent and child are perceived to be of high quality as a result of umbrella branding, which suggests that the forward and reciprocal effects are equal in magnitude. In contrast, although the associated network theory favors a positive forward spillover effect, it suggests that the forward spillover effect is weaker than the reciprocal spillover effect.[ 3] In particular, exposure to the parent's advertising will activate the brand node, whence activation will spread to the child, with potential for a positive spillover effect. However, because the child is newer, the link between brand name and child is likely to be weaker than the link between brand name and parent, especially in the early stages of introduction of the child. Activation of the child node may not always exceed the threshold level needed for retrieval from memory. Therefore, exposure to parent's advertising is less likely to activate the child in consumers' memories as compared with the effect of the child's advertising on activation of the parent. Thus, the associated network theory suggests that the forward spillover effect is likely to be positive but weaker than the reciprocal spillover effect.
H[sub2]: Both the information economies theory and the associated network theory suggest that the forward spillover effect, that is, the spillover effect of exposure to advertising of a parent on the revealed preference for a child, is positive.
H[sub3]: If the theory of information economies holds, both forward and reciprocal spillover effects will be equal in magnitude.
H[sub4]: If the associated network theory of consumer memory holds, the forward spillover effect will be weaker than the reciprocal spillover effect.
Model Specifications
We use the multinomial logit model to study the impact of advertising and other marketing variables on consumer choice.[ 4] The logit model has been extensively applied in marketing literature (e.g., Guadagni and Little 1983; Kanetkar, Weinberg, and Weiss 1992; Krishnamurthi and Raj 1991; Tellis 1988). With this model, the probability that household i chooses brand j on choice occasion t is given by
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where J is the number of products, and Uijt is the (revealed) indirect utility of household i for product j on choice occasion t. We use two formulations of Uijt here. In the first formulation, we use brand loyalty to capture unobserved heterogeneity in preferences across households as do Guadagni and Little (1983) and Kanetkar, Weinberg, and Weiss (1992). Thus, Uijt is given by
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
In this equation, α j is the intercept for brand j, and Xijkt is the value of the explanatory variable k for household i and product j on choice occasion t. The parameter β k is the unknown coefficient of explanatory variable k that is to be estimated, and εijt is the random error that follows an extreme value distribution. The household's brand loyalty is one of the m explanatory variables in Equation 2 and is computed from household purchase history as described by Guadagni and Little (1983). Thus, brand loyalty Lij,t for household i toward brand j at purchase occasion t is given by
Lij,t = αLij,t - 1 + (1 - α)Y[ij,t] - 1'
where Yij,t - 1 = 1 if brand j is purchased at purchase occasion t - 1 and 0 otherwise, and α is the smoothing constant. Following Gupta (1988), we use = .8.
Recent research suggests that the previous brand loyalty measure, which has traditionally captured household heterogeneity in logit models, biases parameter estimates because of correlation of the measure with the error term (e.g., Chintagunta, Jain, and Vilcassim 1991; Gonul and Srinivasan 1993). Thus, we use a second formulation of utility that captures heterogeneity in intrinsic preference across households using the latent class approach proposed by Kamakura and Russell (1989). Given that recent research by Ailawadi, Gedenk, and Neslin (1999) finds little difference in the estimated response elasticities across different methods of incorporating heterogeneity, the Kamakura-Russell (K-R) approach has the advantage of being computationally less burdensome. Thus, we use a second formulation,
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
In this equation, Uisjt is the indirect utility to household i for product j on choice occasion t, and household i belongs to latent household segment s, s = 1, 2, ..., M. The parameter αsj is the intrinsic preference of household segment s for brand j and replaces the brand loyalty measure in this model. Other terms in Equation 4 have similar interpretations to those in Equation 3. We determine the parameters and the proportion of the latent segments fs using a maximum likelihood procedure. The number of segments, M, is selected to minimize Schwarz's (1978) Bayesian information criterion (BIC).
We estimated the previous models using the ACNielsen scanner panel data for two product-markets: yogurt in the Springfield, Mo., market and powdered detergents in the Sioux Falls, S.Dak., market. Although household purchase data in this data set are available between January 1986 and August 1988, the data used for estimation pertain to the period between September 1987 and August 1988, during which household exposure to advertising was recorded. However, we used purchase data before the estimation period to calibrate loyalty of sample households.
Choosing brand or line extensions for analysis involves some judgment. At the extreme, each item with a distinct Universal Product Code can be considered a brand or line extension. Thus, we used prior knowledge to identify significant extensions (from a consumer perspective) on which to focus. For example, in yogurts, prior knowledge suggests that extension of a brand into the non-fat yogurt subcategory is important. As another criterion, we considered an extension significant if it was advertised separately. After we identified significant extensions, we considered a purchase of any Universal Product Code for each brand a purchase of that brand. We ignored brand sizes for reasons similar to those offered by Kanetkar, Weinberg, and Weiss (1992). First, advertising focuses on brands and not package sizes. Second, package size decisions are not likely to be purchase-to-purchase decisions according to the literature (Blattberg, Eppen, and Lieberman 1981; Krishnamurthi and Raj 1988; Tellis 1988).
For purposes of manageability, we restricted the study to brands with share of purchases exceeding 5% and their extensions. The resulting set yielded nine and six brands, respectively, in the yogurt and powdered detergent categories. For yogurt, spillover advertising came from line extensions within the category. Thus, there was advertising from line extensions in the Dannon and the Yoplait families of brands. However, for powdered detergents, spillover advertising came from liquid detergents. In this case, there was advertising from brand extensions in the liquid detergent category for the Cheer, Surf, and Tide brands. Table 1 identifies the selected brands and provides descriptive statistics, advertising exposures, and introduction dates for yogurts. Table 2 provides the same information for detergent brands. From households that purchased only the selected brands, we removed light-user households, which we define as those that made less than eight purchases in the entire period for which purchase data were available or less than three purchases in the estimation period. After removing purchases used for loyalty calibration, in the yogurt category we were left with 157 households that accounted for 1674 purchases in the estimation period. The corresponding numbers in the powdered detergents category were 163 households and 1336 purchases.
Own advertising: This variable captures the effect of a brand's own advertising on its utility to the household. Similar to Tellis and Weiss (1995), we formulated advertising as a stock variable that is an exponentially weighted average of past exposures and current exposures. The current exposure, Aij,t of household i to product j's own advertising at purchase occasion t, was measured by the number of television advertisements that a household was exposed to in the time period between the previous purchase and the current purchase occasion. This definition of current household advertising exposure is similar to that used by Kanetkar, Weinberg, and Weiss (1992). Also, similar to Kanetkar, Weinberg, and Weiss, we considered a household to have been exposed to an advertisement if the television set was tuned to the advertisement for more than half the advertisement's duration. Let ASij,t be the stock of own-advertising exposures for product j for household i at purchase occasion t. As in Tellis and Weiss's (1995) study, ASij,t is given by
Asij,t = λAsij,t - 1 + (1 - λ)Aij,t'
The element ASij,t - 1 is the advertising stock for product j, household i, and purchase occasion t - 1. The parameter is a smoothing constant to be determined.
Spillover advertising: We examined the order of entry of products that share the same brand name and designated the product that originally used the brand name as the parent. We inferred introduction dates of products on the basis of the earliest date the product occurred in the data set. In cases in which the products were available from the beginning of the data collection period, we consulted trade magazines to ascertain the order of introduction of products. Tables 1 and 2 give estimated introduction dates of umbrella-branded products.
In all cases, the parents identified in this manner appear to be the product that is most closely associated with the brand name, consistent with our theoretical perspective. The relatively high market shares of the parent in all cases also supports this view. For example, in the Dannon brand family, Dannon low-fat yogurt was deemed the parent, and Dannon mini-pack, Dannon non-fat, and Dannon fresh flavors as its children. In the other case of multiple products with a common brand name in the yogurt data, Yoplait yogurt was considered the parent of Yoplait non-fat yogurt. The remaining three products in yogurt data are stand-alone products with no parent or children. In the powdered detergent data, none of the six brands had significant extensions in the same category; however, some benefited from spillover advertising of branded relatives in the liquid detergent category. In this case, all powder brands were considered parents. For example, Tide Liquid is the child of Tide Powder (the parent).
In product j's utility function, a parent spillover advertising variable measured stock of exposures of household i to advertising of the parent of product j at purchase occasion t. Similarly, a child spillover exposure variable in product j's utility function measured stock of advertising exposures of product j's children for household i at purchase occasion t. In both cases, the stock variable was an exponentially weighted average of past stock and current exposures as in Equation 5 with the same smoothing constant as was used for own advertising.
Although the spillover advertising variables capture spillover effects in our model, the own-advertising variable captures the substitution effect from a brand's advertising. Thus, we separately estimate the substitution and spillover effects by including both advertising variables in our model and using household scanner-panel data. In contrast, Sullivan (1990) could not distinguish between these two effects in her measurement of spillover effects: Her data show the aggregate impact of both effects.
Consumer sales promotion: We employed two separate 0/1 (absence/presence) variables. They were features and in-store displays.
List price: As in Tellis and Weiss's (1995) study, this variable represents the price before coupons for the product at purchase occasion t and is expressed in dollars per ounce.
Coupon value: The data provide information on the value of coupons redeemed with purchase, but they have no information on coupon availability to households. As do Gonul and Srinivasan (1993), we assume that if a manufacturer's coupon was redeemed for a brand in a particular week, it is available to all households during that week. For store coupons, we assume that if a store coupon was redeemed for a brand in a week, that coupon was available to all households shopping that store during that week.
Brand loyalty: When we estimated the model given by Equation 2, we used brand loyalty given by Equation 3 as an explanatory variable. We used the first five purchases of a household or all of a household's purchase before the estimation period (whichever was larger) for calibrating the loyalty measure for each household.
As do Tellis and Weiss (1995), we estimated different logit models using values of the advertising carryover parameter that varied from 0 to 1 in increments of .1. We found that a value of .5 for λ provided the best fit on the basis of the BIC for both yogurt and detergent. Thus, we set equal to .5 throughout our analysis.[ 5]
Yogurt Data
In Table 3, we present the estimation results for a sequence of models leading up to the full model, M2, containing brand loyalty, display, feature, list price, coupon value, own advertising, spillover advertising from children (child-advertising), spillover advertising from parent (parent-advertising), and the brand dummies (Guadagni and Little 1983).[ 6] List price, coupon value, brand loyalty, display, and feature are strongly significant in all models and have the expected signs. Consistent with the scanner literature on advertising effects (e.g., Tellis and Weiss 1995), own advertising is not significant in the different models. The coefficient of child-advertising is relevant to us in the context of H1. In both M1 and M2, the coefficient of child-advertising is positive and strongly significant (p < .006). Thus, the results suggest the existence of a significant and positive reciprocal spillover effect.
As can be seen from M2, the coefficient of parent-advertising is not statistically significant, thus H2 is not supported. This result, combined with the finding of a significant, positive reciprocal spillover effect, offers some support for the associated network theory hypothesis, H4. Although a one-tailed significance test of the difference in the coefficients of parent-advertising and child-advertising in M2 (using the estimated variance-covariance matrix for the vector of coefficients) rejects the null hypothesis of no difference in the coefficients (p < .06), a two-tailed test marginally fails to reject the null hypothesis (p = .11). Given the directional prediction of H4, we believe that a one-sided test is more appropriate in this case. Overall, given the lack of significance of parent-advertising in comparison with the strong significance of child-advertising, and given the previous results of the one-sided test, we conclude that our results weakly favor the associated network theory hypothesis, H4, over the information economies hypothesis, H3.[ 7] The Þ2 values in Table 3 suggest a good fit of the models. We performed predictive validation on a holdout sample as a further test of a model with spillover effects. In Table 3, the likelihood ratio test rejects model M0 in favor of model M1 ( χ2 < .01), though we fail to reject model M1 in favor of model M2. Thus, model M1 seems to offer the best fit to the data. To test further the appropriateness of this model, we used models M0 and M1 calibrated on a randomly selected estimation sample of 110 households to make market share predictions in the remaining separate holdout sample of 47 households. (The results from the estimation sample of households were similar to those of the overall sample: Model M1 was favored over model M0 and child- advertising was significant.)[ 8] Marketing literature has customarily characterized the accuracy of market share predictions using measures such as root mean square error (RMSE) and mean absolute deviation (MAD) (e.g., Montgomery 1997). The RMSE penalizes larger deviations in predictions more than MAD does. The RMSE between predicted and actual market shares in the holdout sample deteriorates by 15.2% to a value of .811 with model M0, from a value of .704 obtained with M1. The MAD between predicted and actual market shares in the holdout sample deteriorates by 15.03% to a value of .680 with model M0, from a value of .591 obtained with M1. A chi-square test of the difference in market share predictions between the two models M0 and M1 rejects the hypothesis that there is no difference between the two predictions ( χ2 with 8 degrees of freedom [d.f.] = 25.157, p < .005), showing that the improvement in prediction is statistically significant. We also compared disaggregate predictions of models M0 and M1 in the validation sample at the individual choice level using hit rates as suggested by Gensch (1987). To compute the hit rates, we defined the predicted choice on each purchase occasion as the alternative with the highest predicted probability based on the model (M0 or M1). We then compared the predicted choice with the actual choice for each purchase occasion to obtain a hit rate (percentage of choices correctly predicted). Computed in this manner, the hit rate improved from 73.3 for model M0 to 74.0 for model M1. We found the improvement in disaggregate prediction of model M1 over M0 to be statistically significant using the Krishnan test of strength of prediction at the individual choice level (p < .007) (for additional details on the Krishnan test, see Gensch 1987). Thus, the predictive results further support the model with reciprocal spillover effect, M1. Using standard tests from the literature, we eliminated alternative explanations for our results such as collinearity, influential observations, or outliers.
The K-R model estimated on yogurt data yielded five preference segments on the basis of minimization of the BIC. We present the results of the full model here. The estimated parameters in Table 4 reveal that child-advertising is positive and significant (p = .03), whereas parent-advertising is not significant. In contrast to the brand loyalty models, own advertising is significant (p = .04) in the K-R model. Because the K-R model avoids potential bias in parameter estimates that are possible with the brand loyalty model, it appears that own-advertising effects may be significant. Overall, it is reassuring that the results on spillover advertising from the K-R model are consistent with those of the brand loyalty model.
Detergent Data
Results of the brand loyalty model are presented in Table 5. The parent-advertising effect was not estimated with this data because we modeled the choice of powdered detergents alone (there were too few observations of liquid detergent choices for us to model their purchase). Child-advertising is again positive and significant in this category (p < .002), whereas own advertising is not significant. The likelihood ratio test rejects model M0 in favor of model M1 ( χ2 < .005). Similar to the procedure for yogurt, we used models M0 and M1 calibrated on a randomly chosen estimation sample of 100 households to make market share predictions in the remaining separate holdout sample of 63 households. (The results from the estimation sample of households were similar to those of the overall sample: Model M1 was favored over model M0 and child-advertising was significant.) The RMSE between predicted and actual purchase shares in the holdout sample deteriorates by 12.2% to a value of .852 with model M0, from a value of .759 with model M1. The MAD between predicted and actual market shares in the holdout sample deteriorates by 10.7% to a value of .613 with model M0, from a value of .554 obtained with M1. A chi-square test of the difference in market share predictions between the two models M0 and M1 rejects the hypothesis that there is no difference between the two predictions ( χ 2 with 5 d.f. = 13.376, p < .025), showing that the prediction improvement is statistically significant. Disaggregate predictions for detergents, as measured by the hit rate, improved from 83.2 for model M0 to 84.1 for model M1. We found the improvement in disaggregate prediction of model M1 over M0 to be statistically significant using the Krishnan test of strength of prediction at the individual choice level (p < .001). Thus, the prediction results affirm the model with (reciprocal) spillover effects as being the most consistent with the data. Again, using standard tests we eliminated alternative explanations for our results such as collinearity, influential observations, or outliers.
The K-R model estimated on these data yielded seven preference segments by means of the BIC. Parameter estimates of the K-R model are presented in Table 6. Consistent with results of the brand loyalty model, child-advertising is once again positive and significant (p = .03), whereas own advertising is not significant.
Managerial Implications
We estimated spillover effects in two different product categories and geographic markets, using two different ways of representing unobserved heterogeneity. Our results provide strong and consistent support to the hypothesis of a positive spillover effect from advertising of a child on choice of a parent brand (reciprocal spillover effect). Thus, exposure to advertising of Yoplait non-fat yogurt, for example, had a positive effect on the choice probability of its parent, Yoplait yogurt, by households. However, we did not find evidence to support the existence of forward spillover effects (i.e., advertising of a parent increasing the choice probability of a child). This result is consistent with the prediction of associated network theory that forward spillover effect would be weaker (H4). Statistical testing weakly supports this hypothesis while contradicting the expectation of symmetric spillover effects based on information economies (H3). The principal theoretical rationale for a weaker forward spillover effect was that parent's advertising might not evoke the child in the consumer's mind as much as the child's advertising would evoke the parent.
Consistent with previous studies (Kanetkar, Weinberg, and Weiss 1992; Tellis 1988; Tellis and Weiss 1995), the effect of own advertising is weak or nonexistent. In particular, own advertising has a positive, significant effect for yogurt in some models, but has no significant effect in detergents. The significance of reciprocal spillover effects when own-advertising effects are weak or nonexistent can be ascribed to both the newness of child-advertising and the flagship nature of the parents that benefit from the spillover. Because of their flagship position for the umbrella brand, parents can gain considerably, if not the most, from the news value and interest generated by the advertising of the child (Aaker 1996). Indeed, the newness and freshness of child-advertising may make such advertising more effective in garnering attention for the parent than the parent's own advertising. In summary, spillover effect from a child may be of equal or greater importance than a parent's own advertising. A key managerial implication of this finding concerns the allocation of advertising spending between the parent and its children. To the extent that the parent benefits from the advertising of its children and to the extent that such spillover advertising may be more effective in increasing the choice share of the parent, less advertising money may be allocated to the parent.
The productivity or effect size of an advertising exposure can be best captured by the brand-choice elasticities, which measure the increase in choice probability (purchase share) that results from increase in exposure. We follow the method used for computing choice elasticity for feature advertising in the literature (Chintagunta 1993; Chintagunta, Jain, and Vilcassim 1991) by calculating advertising (own or spillover) elasticity as the fractional relative change in the probability of purchase due to an advertising exposure. As an illustration of the effect size comparison, in Table 7 we present the relative productivity of a parent brand's own advertising and spillover advertising of a child, for the two parent yogurt brands, Dannon low-fat and Yoplait. We computed the elasticities using parameters from the brand loyalty model.[ 9] Although the own-advertising effect was not significant in the brand loyalty model, we have chosen to present the point elasticity for own advertising for comparison. For interested readers, we also provide the 95% confidence intervals for the choice elasticities.
As an example, the choice elasticity for Dannon lowfat's own advertising in Table 7 should be interpreted as follows: One exposure to Dannon low-fat advertising results in an average increase of 5.7% in its choice probability. The table shows that spillover advertising from its children has an impact on purchase share of Dannon low-fat that is more than twice the impact of Dannon low-fat's own advertising. In the case of Yoplait, spillover advertising from Yoplait non-fat has nearly twice the impact on purchase share of Yoplait, as does Yoplait's own advertising. Therefore, optimal allocation of advertising spending may favor the line extensions in the case of both Dannon and Yoplait.[ 10]
Analogous productivity comparisons for detergent advertising (Table 8) yield conclusions similar to that for Yoplait advertising. The main difference between the detergent and the yogurt results is the somewhat higher magnitude of the reciprocal spillover elasticities for detergents. We believe that this outcome may be due to the presence of fewer brands in the detergent category, which makes advertising exposures more productive. However, consistent with the yogurt results, the reciprocal spillover has a greater impact on the purchase share of parents than the respective parent's own advertising, for all three extended brands of powdered detergents. The apparent managerial implication is that these brands are better off devoting relatively more advertising spending to the newer brand extensions. Morrin (1999) reaches the opposite conclusion that shifting of advertising funds to extensions may hurt the parent. However, she studies the effect of brand extension advertising on recall and recognition of the parent rather than its choice. It is possible that advertising of the child may change beliefs about the parent and thus influence its overall evaluation and choice, without affecting recall or recognition. For example, advertising of the extension may increase perceptions of parent quality (Dacin and Smith 1994; Wernerfelt 1988) or parent innovativeness. In such a case, a strong reciprocal spillover effect on a parent's evaluation may more than compensate for a weaker spillover effect on recall of the parent (as Morrin finds), resulting in a overall spillover elasticity that is greater than the parent's own-advertising elasticity (as we find). The different conclusions between our studies may also be due to the attention to advertising that was forced on subjects in Morrin's laboratory study, while it is possible that advertising of newer brand extensions command greater attention from the audience in real-world settings (Pieters, Rosbergen, and Wedel 1999).
The existence of beneficial spillover effects from brand extensions suggests an additional strategic benefit of introducing line or brand extensions (Aaker 1990). This strategic benefit may be distinguished from and may supplement the commonly advanced rationale for line or brand extensions, which is to satisfy new market segments profitably by leveraging existing brand equity. Proliferation of brand extensions may have another strategic benefit, which is to crowd the product space and deter entry (Schmalensee 1978).
The information in Tables 7 and 8, when combined with the introduction dates from Tables 1 and 2, yields the following interesting observation: The superiority of reciprocal spillover advertising in comparison with a brand's own advertising is greatest for recently introduced brand extensions and, at least within a given product category, tends to diminish more or less monotonically with age of the extension. This conclusion is consistent with the intuitive expectation that with passage of time, the child's advertising may provide less by way of new information that may influence the choice of the parent. Also, as time passes, the child may gain a stronger presence in the consumer's mind and be less likely to evoke the parent with its advertising. The associated network theory predicts that such inhibition of the parent in consumers' memories will result from a strengthening of the link between the brand and child nodes. A caveat to our previous conclusion regarding the relation between the spillover effect and age of the child is that it comes from a cross-brand comparison and not a within-brand comparison. Further research might examine this issue with more extensive longitudinal data. Overall, it is important to note that a positive and statistically significant reciprocal spillover effect was observed over a wide variety of relative introduction times of the parent and child in the two categories.
Methodological Advantages and Limitations of the Study
The advantage offered by the high quality of scanner panel data is well known. However, the household-level data also help mitigate the problem of reverse causality faced with advertising studies using aggregate data. This problem arises because managers might set advertising budgets as a percentage of sales or market share. As Tellis (1988, p. 142) points out, "it is unlikely that managers set advertising exposures in expectation of purchases at such disaggregate levels" (individual household levels). Tellis also indicates that between-brand analysis with such data provides greater power and efficiency than a separate analysis for each brand.
As with typical econometric studies, the conclusions from our study should be considered tentative subject to replication by other studies using different models or methodologies or by replication in other product-markets. Furthermore, our results may be sensitive to the level of advertising by competing brands observed in the categories and markets we examined.[ 11] Although we used a variety of tests to verify that a few influential observations did not affect the pattern of our findings, it would nevertheless be worthwhile to replicate our analysis with categories having a greater number of advertising exposures. Future studies could also explore how advertising content affects spillover effects.
In this article, we study the reciprocal spillover effect of advertising of a line or brand extension on the choice of the parent brand. Using scanner panel data on two product categories and in two geographical markets, we find evidence for a significant reciprocal spillover effect. Indeed, we find that such spillover advertising can increase the choice probability of the parent more than is possible with the parent's own advertising. Our results suggest that firms should favor the line or brand extension with a greater allocation of the advertising budget than otherwise. These results also indicate a new strategic benefit from line or brand extensions whereby a firm introducing the extensions can expect positive reciprocal spillover effects for the parent brand.
The authors acknowledge the helpful comments of Dennis Gensch, Jugal Ghorai, P.K. Kannan, Manohar Kalwani, Vithala Rao, and Brian Ratchford and a seminar at Carnegie Mellon University for sparking this inquiry. Part of this research was conducted when the second author was a Senior Visiting Fellow at the National University of Singapore.
NOTES [1] Category differences (yogurt is a category we analyze) probably account for some of the differences between Sullivan's (1990) results and ours. In a category such as yogurt, a consumer can purchase both parent (low-fat yogurt) and extension (non-fat yogurt), as a result of complementarity or variety-seeking behavior. In such a case, positive spillover effects may be enhanced. We thank an anonymous reviewer for suggesting the role of category differences.
[2] In experimental studies, Dacin and Smith (1994) find that consumers' evaluation of the quality of an umbrella-branded product increases with the number of products using the same brand name. Because several products using the same brand name imply a bigger performance bond, the result appears to support Wernerfelt's (1988) analysis. See also Erdem (1998).
[3] We thank an anonymous reviewer for pointing out the different predictions of the two theories regarding the relative magnitudes of the forward and reciprocal spillover effects.
[4] We focus on brand choice because, using coffee data, Gupta (1988) finds that brand-switching accounts for 84% of the overall sales increase due to promotions.
[5] A long stream of literature suggests that the effect of advertising repetition produces a greater response (e.g., attitude, purchase intention, sales) among consumers loyal or familiar with the advertised brand than among consumers who are not (Calder and Stern-thal 1980; Raj 1982; Sawyer 1973; Tellis 1988). In our analysis, we tested for such effects by including loyalty advertising variables for both own advertising and spillover advertising. None of the interactions was significant, so we present the results without these interactions. Another stream of literature suggests that advertising has an indirect effect on utility through its effects on price-sensitivity (e.g., Kanetkar, Weinberg, and Weiss 1992; Krishnamurthi and Raj 1985). Similar to Kanetkar, Weinberg, and Weiss (1992), we modeled this effect using a list price own-advertising interaction term. We found that the interaction was not significant and have chosen to present the results without these interaction terms.
[6] We omit the brand intercept parameters in the presentation of all results to conserve space.
[7]This conclusion is not critical to our central finding that reciprocal spillover effects exist. If the null hypothesis, H3, is instead deemed to be supported by virtue of the two-sided test, we would infer that the forward spillover effect is of comparable magnitude to the reciprocal spillover effect, thereby supporting the information of economies hypothesis. Thus, the outcome of the comparison between parent-advertising and child-advertising is mainly of theoretical interest.
[8] In Tables 3 and 5, we present the estimation results using data from all households to maintain comparability with the K-R models, in which the additional data were helpful, considering the large number of parameters estimated with those models.
[9] Ailawadi, Gedenk, and Neslin (1999) find that different heterogeneity formulations cause little change in elasticity estimates.
[10] A more formal analysis of the implication of spillover effects for advertising allocation, using an analytical model, can be obtained directly from the authors on request.
[11] We thank an anonymous reviewer for pointing out this limitation.
Price per Number of
Yogurt Share Ounce Advertising
Product (Percentage) (Dollars) Display* Feature* Exposures
Dannon low-fat
(prior to 1981)**
11.77 .084 0 .023 154
Dannon non-fat
(1987) 1.97 .074 0 0 0
Dannon fresh flavors
(around 1985)
3.58 .081 0 .021 36
Dannon mini-pack
(1985) .12 .133 0 0 68
Nordica low-fat 18.51 .065 .0305 .220 2
Wells-Bunny low-fat
8.18 .053 .0203 .056 66
Weight Watcher's
11.41 .076 .0024 .030 344
Yoplait non-fat
(1986) 9.80 .106 .0018 .019 296
Yoplait
(1976-77) 34.65 .100 .0024 .035 121*Proportion of purchase occasions in estimation-period data.
**Numbers in parentheses indicate estimated introduction dates of the brands.
Price per Number of
Detergent Share Ounce Advertising
Brand (Percentage) (Dollars) Display* Feature* Exposures
Bold powder 10.46 .052 .0082 .0067 --
Cheer powder
(1950) 2.04 .049 .0239 .0247 82
Oxydol powder 8.94 .053 .0052 .0060 111
Purex powder 8.76 .030 .0449 .0254 --
Surf powder
(before 1985) 10.15 .047 .0284 .0419 95
Tide powder
(1946) 59.64 .040 .0861 .1871 172
*Proportion of purchase occasions in estimation-period data.
Notes: Number of advertising exposures for Cheer liquid, Surf liquid, and Tide liquid is 43, 39, and 28, respectively. Numbers in parentheses in the first column indicate estimated introduction dates of the brands. Estimated introduction dates for Cheer liquid, Surf liquid, and Tide liquid were 1986, 1988, and 1984, respectively.
Parameter Estimates (t-Ratio)
Variables Model M0 Model M1 Model M2
Brand loyalty 4.7027 (38.750) 4.7201 (38.678) 4.7160 (38.645)
Display 1.0813 (3.478) 1.1118 (3.571) 1.1090 (3.565)
Feature .5863 (3.698) .5773 (3.641) .5782 (3.648)
List price -31.591 (-6.342) -31.978 (-6.408) -31.912 (-6.397)
Coupon value 54.978 (8.876) 55.451 (8.938) 55.388 (8.929)
Own advertising .1515 (1.178) .1625 (1.147) .1852 (1.305)
Child-advertising .3971 (2.996) .3775 (2.752)
Parent-advertising -.3698 (-.782)
LL (n = 1674) -1366.9 -1363.1 -1362.7
[p2*] .530 .531 .531
[X2](-2LL)** 2733.8 2726.2 2725.4
BIC 1418.9 1418.7 1422.1
[p2*] = Þ2 values are with respect to model with brand intercepts only.
[X2**] = χ2 Likelihood ratio test rejects model M0 in favor of model M1 (p < .01), whereas model M1 is not rejected in favor of model M2.
Notes: LL = log-likelihood.
Variables Parameters t-Ratio
Display .9215 3.229
Feature .4656 3.432
List price -40.8099 -8.069
Coupon value 60.2923 9.883
Own advertising .2478 2.014
Child-advertising .3120 2.152
Parent-advertising -.2840 -.671
Parameter Estimates (t-Ratio)
Variables Model M0 Model M1
Brand loyalty 4.887 (26.583) 4.927 (26.486)
Display 1.6629 (6.104) 1.6463 (6.034)
Feature .9115 (3.561) .9452 (3.686)
List price -92.652 (-8.802) -92.540 (-8.759)
Coupon value 153.148 (10.217) 155.385 (15.103)
Own advertising .1133 (.568) -.0761 (-.343)
Child-advertising 1.7804 (3.137)
LL (n = 1336) -551.5 -547.4
[p2]* .692 .694 2
[X2 (-2LL)]** 1103 1094.8
BIC 591.1 590.6
[p2]* = p2 values are with respect to model with brand intercepts only.
[X2 (-2LL)]** = χ2 Likelihood ratio test rejects model M0 in favor of model M1 (p < .005).
Notes: LL = log-likelihood.
Variables Parameters t-Ratio
Display 1.4875 5.179
Feature .9402 3.553
List price -136.0419 -12.161
Coupon value 166.0217 17.020
Own advertising .0959 .496
Child-advertising 1.4658 2.089
Choice Elasticity of
Parent Brand
Parent's Spillover
Own Child-
Parent Brand Advertising Advertising
Dannon low-fat .057 .144
[-.046, .171] [.043, .255]
Yoplait .041 .079
[-.027, .093] [.017, .144]Notes: 95% confidence intervals are in brackets.
Choice Elasticity of
Parent Brand
Parent's Spillover
Own Child-
Parent Brand Advertising Advertising
Cheer n.s. .600
[.160, 1.273]
Surf n.s. .893
[.236, 1.956]
Tide n.s. .145
[.056, .229]Notes: 95% confidence intervals are in brackets. n.s. = not significantly different from zero.
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~~~~~~~~
By Subramanian Balachander and Sanjoy Ghose
Subramanian Balachander is Assistant Professor of Marketing, Krannert Graduate School of Management, Purdue University. Sanjoy Ghose is Professor of Marketing, School of Business Administration, University of Wisconsin-Milwaukee.
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Record: 126- Reducing Adverse Selection Through Customer Relationship Management. By: Cao, Yong; Gruca, Thomas S. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p219-229. 11p. 1 Diagram, 5 Charts. DOI: 10.1509/jmkg.2005.69.4.219.
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Reducing Adverse Selection Through Customer
Relationship Management
Adverse selection is an important problem for marketers. To reduce the chances of acquiring an unprofitable customer, companies may screen prospects who respond to a marketing offer. Prospects who respond are often not approved. At the same time, prospects who are likely to be approved are unlikely to respond to a given marketing offer. Using data from a firm's customer relationship management system, the authors show how to target prospects who are likely to respond and be approved. This approach increases the number of customers who are approved and reduces the number of applicants who may defect after being turned down. This method can be extended to new customer acquisition and more effective targeting of costly promotions to migrate customers to higher levels of lifetime value.
In many businesses, the customers most likely to sign on are precisely the worst customers you could possibly find.
--Reichheld (1996, p. 76)
The customers you want to attract don't respond, and the ones you don't want to attract do.
--Richard E. Mirman, Chief Marketing Officer, Harrah's
Entertainment, quoted in Levey (2002, p. 1)
Most of the people applying for a card with, say, a 12 percent APR, were the last people issuers would approve.
--Lazarony (2000)
Successful customer relationship management (CRM) begins with acquisition of the right customers. Many writers on CRM issues focus on the identification, valuation, and retention of good customers (e.g., Dowling 2002; Rigby, Reichheld, and Schefter 2002; Verhoef 2003; Winer 2001). A firm should put a great deal of emphasis on discovering who its best customers are and how to find new customers who will be similarly loyal and profitable. At the same time, many companies would benefit immensely from avoiding customers at the other end of the value spectrum (i.e., bad customers).
Bad customers are those who buy only when deep discounts are offered, buy in much smaller quantities than normal, or are otherwise much more costly to serve. In other cases, they defect to another company before their acquisition and other up-front costs are recovered. Unfortunately, in firms attempts to attract new customers, they often elicit responses from the very type of customer they wish to avoid.
This predicament, known as adverse selection, is most closely linked with the marketing of products such as loans or insurance. However, the experiences of many failed Internet retailers are case studies in how to attract and reward bad customers. A leading online grocer found that 75% of its customers were price butterflies, bargain hunters chasing deep discounts from one Web site to the next (Reichheld and Schefter 2000). Regrettably, this problem is not limited to online neophytes. Every company has the potential to attract customers whose loyalty and value to the firm are suspect. Long-distance carriers spent billions of dollars courting disloyalty by sending large checks to tempt competitors customers into switching (Naik 1995). A multi-industry study by McKinsey found that bad customers may account for 30% 40% of a typical company's revenue (Leszinski et al. 1995). It is clear that many firms do a poor job screening out bad customers (Reichheld 1996).
In markets for risk products, the purchase of the product imposes risks on the seller rather than on the buyer (Mitchel 2002). In such situations, a company's survival depends on its decisions about which customers to seek and acquire. These firms use a screening process to determine whether a customer responding to an offer should be approved. This step reduces the likelihood of acquiring an unprofitable customer. Ultimately, many people who respond to a favorable marketing offer will not be approved. At the same time, because the most attractive prospects for any offer are likely to have many alternatives from which to choose, they are less likely to respond.
The problems of adverse customer selection are exacerbated for firms attempting to cross-sell (or up-sell) a product to their existing customers. Several recent articles have tried to understand why customers would be interested in buying more from the same company. Some determinants of cross-buying that have been studied thus far include ownership of other products from the same firm (Kamakura, Ramaswami, and Srivastava 1991), satisfaction with those products (Wangenheim 2004) and their prices (Verhoef, Franses, and Hoekstra 2001), and environmental factors, such as technological volatility (Stremersch et al. 2003).
The core of any relationship is loyalty and respect (Fournier, Dobscha, and Mick 1997). By soliciting a response to an offer from an existing customer and then rejecting that customer's response, the firm shows contempt for its relationship with that customer. It is likely that such consumers may reduce their commitment to the firm or defect completely. Such a reaction by consumers has a real economic cost in lost future revenues and other benefits, such as positive word-of-mouth advertising and referrals of new customers (Reichheld 1996).
Fortunately, for firms implementing CRM, there is a solution to the twin problems of adverse selection and costly screening. In this article, we develop a modeling framework to identify the customers who are more likely to respond to an offer and to become more profitable customers (i.e., by being approved for the product). Using data from a major financial institution, we show that our approach to prospect selection results in more approvals and fewer rejected customers. Overall, these effects result in increased profitability.
We provide a practical method for effectively implementing cross-selling activities, a cornerstone to increasing profitability through CRM (Dowling 2002). Specifically, we show how CRM enables a firm to target its marketing efforts better to current customers (Rigby, Reichheld, and Schefter 2002). Furthermore, this modeling framework can be easily extended to parallel cross-selling situations, such as those involving costly sampling of nonfinancial products.
Modeling Framework
In this article, we focus on the situation in which a firm tries to cross-sell a product to its existing base of customers through direct marketing. This is a key situation in which CRM technology can fulfill its promise to improve firm performance (Rigby, Reichheld, and Schefter 2002, p. 6). Because we are focusing on marketing a risk product, we assume that the firm incurs substantial costs to screen its customers and that not all of the customers who respond to the offer will be approved. (We discuss the extension of this framework to the situation of marketing nonrisk products subsequently.)
Typically, a direct marketing campaign has two phases. In the first phase, a relatively small sample of the population is selected to evaluate and respond to a particular offer from the firm. The results of this test (i.e., who responded and who did not) are combined with the firm's data about these customers to develop a prospect selection model. The prospect selection model links observed behavior with household characteristics, such as prior purchase behavior and geographic, demographic, and lifestyle variables, to predict response probability. By ranking the remaining customers from the most likely to the least likely to respond to an offer, the firm can select the most promising set of customers for a larger solicitation. During the second phase, the company contacts only those prospects who are most likely to respond.
Under adverse selection, there are limitations to the traditional approach of selecting prospects on the basis of their propensity to respond. Although the firm would elicit many responses, most of these prospects are unlikely to be approved. Therefore, the number of rejected customers will be high, and it is possible that this rejection could damage the firm's relationships with these customers.
An alternative is to use screening criteria to identify prospects that would be approved if they responded to the offer. Financial institutions use this approach when they use third-party credit data to solicit customers with offers of preapproved credit cards. However, the best customers are unlikely to respond to any particular offer because they have more options than do less desirable customers. To illustrate this point, consider the response to a major bank's mailing of approximately two million preapproved credit card applications. At the end of the campaign, the bank issued only 15,000 new credit cards, a response rate of less than 1% (Ausubel 1999). The response rate was low despite the bank's offering various levels of favorable introductory rates and time periods.
Adverse selection necessitates the costly screening of customers who respond to a firm's offer. The customers who are deemed acceptable will be approved, and the others will be rejected. For a firm to identify the best prospects from its remaining customers, it must understand the factors that drive response and approval. This entails the development of a model that enables the firm to identify prospects who are likely to respond and to be approved. We illustrate this process in Figure 1.
Using this simultaneous approach, we can identify prospects who are likely to respond and to be approved. As a result, the number of approvals should be higher than either alternative we previously discussed. This is due to the lower number of rejected applications compared with the approach in which prospects are selected solely for their propensity to respond. We also expect a greater number of responses from using our simultaneous approach than from selecting prospects on the basis of their likelihood to be approved. Thus:
H1: Under adverse selection, identifying prospects using a simultaneous model of response and approval likelihoods results in (a) a greater number of approvals than selecting prospects on the basis of their likelihood to be approved and (b) fewer rejections than selecting prospects on the basis of their likelihood to respond.
In the next section, we present the results of an empirical study that demonstrates the use of a simultaneous model for prospect selection under adverse selection, and we provide a comparative evaluation of the financial impact of using this modeling framework.
Empirical Study
Our data were provided by a major financial institution that was considering a large mailing for a new secured loan product. We were provided with a random sample of 11,710 records from a larger test mailing that used the bank's database. To test our hypothesis, we divided the entire sample into an estimation (two-thirds) and holdout (one-third) sample. The estimation sample consisted of 7854 households, 3844 of which responded and 1414 of which were approved. The holdout sample included 3856 households, 1870 of which responded and 690 of which approved.
The company that provided the data for this study was concerned about revealing the true response rates for this test mailing; this was information the company considered confidential. Therefore, rather than using the entire sample of nonrespondents, they provided a random sample of nonrespondents that was approximately the same size as the number of respondents (i.e., a synthetic retrospective sample; see Mantel 1973). This sampling method affects only the estimation of the intercept term, not the other parameters in a binary response model. This bias in the intercept term does not affect the use of a model estimated on data of this type for ranking purposes, because the intercept is the same for every observation by definition.
For this sample of customers, the company's CRM system provided measures of each household's general credit history and experience. We describe these items in Table 1. The credit status and history variables supply a great deal of detailed but redundant information about the household. To reduce the overlap across these indicators, we used principal components analysis to reduce this set of variables to a more manageable number of factors (i.e., related linear combinations of the original measures).
From the original set of 24 measures, we identified six factors that accounted for 72% of the total variance in our sample. We present a Varimax rotation of the factor loadings in Table 2. The first factor is associated with the borrower's use of credit compared with current limits. We designate this factor credit limits. A higher score on this factor indicates that a borrower is using a high percentage of the credit available to him or her. This reduces the attractiveness of the borrower for many lenders (Delaney 1997). The second factor is related to recent approvals for bank cards, including premium bank cards. We call this factor new bank cards. Borrowers with a high score on the third factor, which we designate payment problems, have a less desirable payment history for unsecured debt. The fourth factor is related to the number of finance company accounts specifically and the total number of accounts in general. We designate this factor finance company accounts. Both the second and fourth factors are related to the breadth of credit the borrower uses. The fifth factor measures the length of credit history and is so named. Only one measure, the number of inquiries, has a high loading on the sixth factor (i.e., inquiries).
The factors identified in our sample are consistent with the categories of inputs that commercial suppliers of credit scores use (Delaney 1997). In our empirical study, we use the factor scores as indicators of creditworthiness. In addition to these measures of general creditworthiness, we were also provided with a measure of recent payment history with respect to mortgage payments. The credit-scoring firm Fair Isaac considers such data important in evaluating secured home loans (Delaney 1997).
The theory of adverse selection suggests that people who respond to an offer for a secured loan product are likely to be greater credit risks than nonrespondents, but creditworthiness is not the only determinant of demand. Canner, Durkin, and Luckett (1998) find that households that have home equity loans or lines of credit tend to have higher incomes and higher levels of education. Most of these borrowers are between the ages of 35 and 64 years. These households are also more likely to have substantially more equity in their homes. In addition, data from the 2002 American Home Survey show that married households are also more likely to have a home equity loan.
The financial institution augmented its database of credit data with measures indicating income, age, marital status, presence of children in the home, and occupation. The income data (INCOME) was estimated by means of microlevel geodemographic data (e.g., census tract averages). We did not have data beforehand about a prospect's actual level of home equity. Therefore, we used a proxy measure, an estimate of the average mortgage amount (MORTGAGE) from a small area sample. All else being equal, households with higher mortgages might be less interested in a home equity loan because they may have recently refinanced their first mortgage, leading to a higher current payment (Canner, Durkin, and Luckett 1998). We included an indicator for households headed by a single adult (SINGLE) because we expect such households to be less likely to use this type of product. Because education expenses are a major use for home equity loans (Canner, Durkin, and Luckett 1998), we also included an indicator for the presence of children in the home (CHILDREN). Although we did not have access to data on the borrower's level of education, we used occupation as a proxy. We constructed an indicator (MANAGER) for the households that owned their own business, were professionals or executives, or held middle-management positions. For age, we created separate indicators for the 35 44 age group (35T44), the 45 54 age group (45T54), the 55 64 age group (55T64), and the 65 and over age group (65PLUS). We expected higher levels of response from the 35 55 age groups. We were also provided with a variable that indicated whether this household had responded to a mail solicitation from a financial services firm in the recent past (MAILBUY). We list these variables in Table 3.
To test our hypotheses, we compared the prospects identified using the proposed simultaneous response and the approval model with prospects selected solely on the basis of their response probability and the prospects with the highest likelihood of being approved. We began by dividing the entire sample into an estimation sample and a holdout sample as we previously discussed.
We used the model parameters from the estimation sample to identify prospects in the holdout sample using the same criteria. For example, using the estimated parameters of a model of response likelihood, we computed the probability that a prospect in the holdout sample would respond to the offer. This information provided a rank ordering of all elements of the holdout sample with respect to their attractiveness as a prospect.( n1)
To proxy the selection of the best prospects from the remaining customers (e.g., for the simultaneous model of response and approval, see Phase 2 in Figure 1), we compared the expected results for the top 30% of the prospects for each alternative method. The differences between the methods are more pronounced for smaller subsets of the holdout sample (10% or 20%). However, we chose this larger subset to avoid appearing as if we capitalized on chance variations in smaller samples (e.g., there are fewer than 60 approvals in the first decile of prospects chosen for their response likelihood). We discuss these empirical results subsequently.
The first model focused on the probability that a household would respond, given the household's creditworthiness and other lifestyle characteristics that we previously discussed. Using a binary probit formulation, we modeled the responses on the 7854 households in the estimation sample, 3844 of which responded to the offer. The resultant coefficient estimations appear in Table 4 (Column 2). The overall chi-square fit statistic for the model is significant at the p < .01 level.
Households with lower incomes and lower mortgages and applicants between 35 and 64 years of age were more likely to respond. Previous response to direct mail was also positively associated with a response to this offer. Neither marital status (SINGLE) nor the presence of children in the home (CHILDREN) was a significant determinant of the propensity to respond to the offer. Business owners and middle managers (MANAGER) were also less likely to respond. A possible explanation for this is that occupation is a poor choice for a proxy variable in this sample. Alternatively, because these customers probably have higher incomes, they might be better able to afford refinancing to extract equity or to combine various types of debt (e.g., first mortgages, credit card debt) into a new mortgage.
The impact of the creditworthiness factor scores provides clear evidence of adverse selection. Poor mortgage payment performance, a higher ratio of credit used to available credit, a greater number of inquiries, a shorter credit history, and more indicators of problems with prior payment history were all positively associated with an increased probability of responding to this offer. The respondents were also less likely to have been recently approved for a new bank card and had less broad credit experience.
Using these results, we predicted the probability of response for the holdout sample of 3856 households. We then ranked the results and assigned each household to one of ten (nearly) equally sized segments (i.e., deciles). We expected that the households in the highest decile would be the most likely to respond to the offer.
Because we have the actual response data (and approval data) for these households, we can examine how well the response-oriented selection model would have performed if it were used to score the profiles of the holdout sample. The average response rate for the top three deciles is 72%, compared with 38% for the remaining deciles. However, the approval rate for these same households was only 26%, compared with the rate of 46% for the remaining prospects. Therefore, under adverse selection, selecting prospects on the basis of response likelihood results in a predictably high response rate but a low approval rate.
Selecting prospects using approval as the dependent variable involves a change in the sample to accompany the change in the dependent variable. We can only model the probability of approval using data from that subset of customers who responded to the offer. This means restricting ourselves to the 3844 observations for which we have data on the outcome of the approval decision. To predict approval, we relied on the key income-, credit-, and mortgage-related variables.
The modeling of approval as a probabilistic process might seem unnecessary in today's environment of automated credit scoring and online credit card approvals. However, in this particular application, important variables such as current income and size of mortgage are unknown at the time of prospect selection, and verification of such information is an important component of the costs of screening applicants.
Using a binary probit model, we estimated the approval likelihood for the households in the estimation sample that had responded to the offer. The resultant coefficient estimations appear in Table 4 (Column 3). The overall chi-square fit statistic for the model is significant at the p < .01 level.
Applicants with higher incomes, good records of keeping current with mortgage payments, and fewer inquiries into their credit history are likely to be approved. In addition, applicants with a recent approval for a bank credit card are likely to be approved, whereas applicants with poor records of payment on unsecured debts are less likely to be approved. We used the results from this second model to estimate the approval probabilities for all 1870 households in the holdout sample. We then ranked the households and assigned them to deciles; again, those in the highest decile were the most likely to be approved.
Using the actual approval data for the validation sample, we observe how well this approach would have performed had it been used to identify prospects. If we consider only the customers who responded, the approval rate for a mailing to the top three deciles of this sample (62%) is greater than the approval rate (32%) for the remaining applicants. However, the response rate for the top three deciles is much lower (28% versus 57% for others in sample).
Consistent with the executives experiences reflected in the opening quotations, households that are likely to be approved for the offer are the same ones that are less likely to respond. This is because good customers (e.g., customers with more favorable credit ratings) receive a large number of offers, including those from companies trying to acquire them as a new customer. Thus, their propensity to respond to any one particular offer is predictably quite low.
To identify prospects who are likely to both respond to our offer and be approved, we need to consider two dependent variables: response and approval. If we follow the probit model formulations, we have the following system of equations to estimate:
( 1) {R = β1X1 + μ, {A = β2X2 + ν
where X1 and X2 are descriptors of the individual household. These include the measures of creditworthiness, income, mortgage, and lifestyle characteristics that we previously discussed. The dependent variable R is the observed response to the offer (0/1). The dependent variable A is the observed binary approval outcome (0/1). Note that the dependent variable A is observed only when the other dependent variable R (response) has a value of 1. We further assume that the error terms μ and ν follow a standard bivariate normal distribution and that the correlation between the error terms (μ, ν) equals ρ.
That the dependent variable A (approval) is observed only when the other dependent variable R (response) has a value of 1 introduces the possibility of bias in the estimates of the approval prediction equation. One source of this bias may be an omitted variable (or a set of correlated variables) that explains both response and approval. To estimate such a system, we used a bivariate probit model with sample selection (Meng and Schmidt 1985). This formulation is often used in the credit-scoring literature (Banasik, Crook, and Thomas 2003; Boyes, Hoffman, and Low 1989; Jacobson and Roszbach 2003).
The simultaneous estimation of the response and approval models is only the first step. We must then combine the resulting information to identify households that are likely to respond and to be approved. To identify such prospects, we estimate the probability that both R = 1 and A = 1. This probability is given by the following:
( 2) P(R = 1 and A = 1) = Φ2(B1X1, B2X2, ρ,
where Φ2 is the cumulative bivariate standard normal distribution, B1 and B2 are the parameters estimated in the system given in Equation 1, and ρ is the correlation between the error terms. The terms X1 and X2 are the respective variables used to predict response and approval, as we previously noted.
For our empirical study, we estimated the system of equations (Equation 1) using the bivariate probit with the selection procedure in LIMDEP. The results appear in Columns 4 and 5 of Table 4. Because we observe the responses of the entire estimation sample, there should be little difference between the coefficients of the response model in the bivariate probit system (Table 4, Column 4) and those in the preceding response model (Table 4, Column 2). The differences are negligible.
Contrary to our expectations, we find that the correlation between the error terms in the two equation system is not statistically significantly different from zero (ρ = .062, p < .83).( n2) This implies that the estimates of the coefficients of the approval model are not significantly biased because of their being estimated with only the subset of respondents. This result is confirmed by two other findings. First, in comparing the coefficients for the stand-alone approval model (Table 4, Column 3) and the coefficients estimated for the approval model in this simultaneous system (Table 4, Column 5), we find few, if any, differences. Second, we find that the improvement in the fit of the bivariate probit model over the separate response and approval probit models is not significantly different from zero (χ² = .54, p < .46, degree of freedom = 1).
The estimated coefficients of the response and approval models in the simultaneous model (Equation 1) do not differ significantly from those estimated by the separate models. However, the prospects we identified using the formula in Equation 2 yield different results. By avoiding elicitation of responses from prospects who will ultimately be rejected and by reducing the number of prospects unlikely to respond, the simultaneous approach to identifying prospects should result in better outcomes for the company.
To identify prospects who are likely to respond and to be approved, we used the estimated coefficients from the system in Equation 1 as inputs into Equation 2.( n3) Using the independent variables for each household in the holdout sample, we computed the probability that a given household would respond and be approved. As before, we ranked the entire holdout sample and separated the households into deciles. The households in the highest decile are the most likely to respond and to be approved.
Using the actual response and approval results for the holdout sample, we determined the expected number of responses and approvals for each decile. Concentrating on the first three deciles again, we observe the superiority of this simultaneous approach to prospect selection. The response rate is lower (60%) than that for the approach that focuses on selecting prospects on the basis of their response likelihood (72%). The approval rate (41%) is lower than that for the approval-focused selection approach (62%). However, this combined approach results in more approved customers overall (284 versus 198 for approval-based selection). This finding supports H1a. In addition, the simultaneous approach results in fewer rejected customers (405 versus 624 for approval-based selection), in support of H1b.
It is worthwhile to compare the number of approvals for each method of identifying prospects with random selection. A random selection of 30% should yield 30% of the approvals. The top three deciles of prospects based on response likelihood yielded 214 (31%) approvals, which is slightly better than random selection. The top three deciles of prospects based on approval likelihood yielded only 198 (29%) approvals, which is actually worse than random selection. This is because so few of the households in the most highly rated deciles actually respond to the offer. In contrast, the prospects identified by our simultaneous model yielded 284 (41%) approvals, a 37% increase over random selection.
In this section, we attempt to assess the financial impact of selecting prospects using a model focused simultaneously on response and approval probabilities. This entails computing the relative profits, including approval costs for the firm and the potential costs of losing customers who respond to an offer only to be turned down.
We make several simplifying but realistic assumptions in this analysis. First, we restrict ourselves to the top three deciles (30% of the holdout sample) for each method of identifying prospects. Second, we assume that the unit costs of contacting ($2) and approving ($15) are the same for all customers. For the value of the offer to the firm, we continue with our example of a secured loan product. Currently, the average home equity loan is approximately $30,000. Some loans are resold to the secondary market, and others are retained in-house. Without further information, we estimate the value of an approval at 1% of the origination value (i.e., $300).
To estimate the potential value lost if a current customer terminates his or her relationship with the financial institution as a result of being turned down for a secured loan, we assume that the financial institution is using its database of its credit card customers. Reichheld (1996) estimates the average profits from a credit card customer to be approximately $100 per year. A 10% discount rate yields a lifetime value of $1,000.
We do not know the probability that a credit card customer who is turned down for a loan by a bank will drop the credit card associated with that bank. Therefore, we consider two possibilities, a .5% defection rate and a 2% defection rate. In Table 5, we compare the outcomes of mailing the offer to the top 30% of prospects, using each of the alternative prospect selection approaches.
By using the usual direct marketing metrics of response rates and cost per response, the optimal approach would be to emphasize response likelihood in the selection of prospects. In contrast, selecting prospects on the basis of approval likelihood is more efficient because this approach yields the lowest screening cost per approval and the highest approval rate. The simultaneous emphasis on response and approval in selecting prospects is dominated by the other strategies with respect to these functionally oriented metrics.
With respect to the overall profitability of the campaign, the results are different. Selecting prospects on the basis of both their likelihood of response and approval is the most profitable approach. When we incorporate the possible losses of future revenue from the defection of customers who were rejected, our proposed method for identifying prospects remains superior.( n4)
Discussion
This article makes an important contribution to CRM research by showing how to reduce the negative effects of adverse selection and costly screening using CRM data. In an empirical study of cross-selling a secured loan product, we show that the negative effects of adverse selection are considerable. They can have a detrimental effect on the profitability of a marketing campaign in the short term. Furthermore, because of rejected customers potential defections, adverse selection can have an important negative influence on the future profitability of the firm. Our model for selecting prospects on the basis of their likelihood of responding to an offer and being approved relies on the CRM data that most financial firms already possess. This approach is superior to alternative methods for identifying prospects within a database of current customers.
In addition to being more profitable, this modeling approach is more consistent with the goals of CRM insofar as it is sensitive to customers needs. There is a monetary value in treating all customers, not just the best customers, with respect. For the customers who are unlikely to respond, firms should not bother them with an offer. For those who are unlikely to be approved, firms should not set them up for disappointment. Our proposed modeling framework results in a win-win situation for the firm and all its customers.
This study emphasizes the importance of the method that companies use to identify prospects for cross-selling to their own customers. The selection of a method to identify such prospects depends on two factors. The first of these is the relationship between a household's propensity to respond and the likelihood that it will become a more valuable customer (e.g., be approved). Under adverse selection, these factors have an inverse relationship. The most attractive customers are unlikely to respond, whereas potentially unprofitable customers respond in droves. If the relationship is positive or zero, the second factor, screening costs, becomes important. The firm must consider the costs of determining whether a prospect will become a more valuable customer. In our empirical study, these were the screening costs. If these screening costs are relatively large, the firm should identify prospects on the basis of their likelihood of becoming a more valuable customer; this increases the chances that costs will be recouped. If these costs are comparatively small, the firm should target prospects who are likely to respond to the offer, thereby increasing the expected number of customers whose value to the company is increased over time. Although these guidelines should hold for companies selling products or services outside the typical boundaries of risk products, more research is necessary to evaluate their effectiveness in practice. In the next sections, we discuss how this modeling framework can be adapted to assist managers with similar marketing problems.
Although our motivating example is a secured loan product, the application of this modeling framework to cross-selling to current customers in associated markets, such as credit cards, lines of credit, home improvement loans, and so forth, is readily apparent. However, it is also worthwhile to consider the extension to nonfinancial markets.
Marketers often face a problem that is parallel to that of costly screening due to adverse selection. Many companies use costly sampling programs to increase a consumer's lifetime value. Consider a cable company that is interested in upgrading customers to a digital service that would necessitate the in-home installation of a decoder box or digital video recorder. Often, the cable company will offer free installation of the equipment and a limited period of reduced fees. This cost is incurred in the expectation that responding households will be sufficiently satisfied to keep the equipment and continue to pay full price for the upgraded service after the expiration of the trial offer.
Using their CRM database, the cable company could determine the characteristics of the type of households that respond to such offers and identify which households tend to continue with the higher-priced services (at least until the installation costs are recovered). By incorporating such information into a bivariate probit model such as the one we previously described, the cable company can more selectively target their promotions to households that are more likely to be profitable over time.
Many firms use costly promotions to move customers from one level of consumption to a higher, more profitable level. By combining offer testing and the data in their CRM systems, such companies can use this modeling framework more profitably to identify good prospects for costly upselling promotions. Such a modeling approach represents an important departure from previous research on crossbuying behavior (e.g., Kamakura, Ramaswami, and Srivastava 1991; Verhoef, Franses, and Hoekstra 2001). In these studies, there is a tacit assumption that firms should try to increase cross-buying and that this is a universally good thing for the firm. However, under adverse selection, firms must also consider whether attempts to broaden their relationship with some customers might be ultimately detrimental to profitability.
With some adaptation, this modeling framework can also be used to identify high-quality prospects among noncustomers. For example, when a firm uses an established list to find new customers, it can identify which households responded to a promotion and which ones turned into profitable customers. The next step would be to append identifying information onto this purchasing and profitability data from third-party data vendors.
This augmentation of lifetime value data would enable the firm to build a model to predict which households are likely to respond and which of those become profitable. The model can then be used to guide the firm's selection of future prospects to approach with similar offers. A variation of this method is currently being used by some insurance companies to identify good automobile and home insurance prospects using credit bureau data (Simon 2002).
The marketing executives quoted at the beginning of this article suggest that adverse selection is a serious problem for marketers. The customers who are likely to be profitable acquisitions are unlikely to respond, whereas prospects with less favorable future values to the firm are more likely to respond. This article provides the first empirical evidence we know of that supports this widely held view of who responds and who does not respond to marketing offers.( n5)
We have shown the impact of adverse selection for an important but limited case of cross-selling risk products. Further research is necessary on the magnitude and impact of adverse selection in the response to promotions, especially costly ones. Whether adverse selection is as significant a problem for new customer acquisition or for companies selling nonrisk products is an important area for further research.
In this study, the firm used a fairly stable model to approve the applications of responding prospects. With nonrisk products, the situation may change. In these applications of our framework, approval is replaced by a customer moving to a new, more profitable level of consumption. How firms should model these transitions and how soon they can identify those who will become highly valued customers is an important extension of our study and a key problem in CRM research in general.
Conclusions
A positive by-product of our approach to customer selection is the inherent coordination between the marketing and the credit functions of the firm that is selling risk products. Selecting prospects on the basis of their likelihood to respond and to be approved removes a major source of conflict. Such interfunctional coordination is a major goal of CRM as a whole (Rigby, Reichheld, and Schefter 2002). Furthermore, our proposed approach to prospect selection addresses an important problem in the customer relationship life cycle, namely, the identification of the right prospects. Its narrow focus and modest goals are consistent with the successes that are now being experienced in CRM practice (Rigby and Ledingham 2004).
The authors thank an anonymous company for providing the data used in this study. They also thank Wagner Kamakura, Bruce Klemz, Lopo Rego, and Gary Russell for their comments on this article. Author names are listed in alphabetical order. Professor Gruca acknowledges the support of the Lloyd J. and Thelma W. Palmer Research Fellowship.
( n1) The response and approval rates for the validation sample will be overstated compared with actual results, given the artificially high response rate in our synthetic retrospective sample. However, the results are comparable across models, which is the key to our analyses.
( n2) This result is largely due to the wealth of information we have about each household from the firm's CRM system. For example, if we were unable to obtain estimates for income or mortgage size, the correlation between the error terms for the two equations would be large (ρ = .46) and significant (p < .02).
( n3) In this particular case, the estimates from the separately estimated models would also perform well because the coefficients in the approval model do not suffer from a great deal of bias. However, this situation is likely to be unique to this data set. Managers interested in this method should use the bivariate probit formulation to avoid the usual problems with censored data.
( n4) As with any simulated results, the numbers in Table 5 depend on the assumptions about contact costs, screening costs, and so forth.
( n5) All of the prospects who responded in Aubusel's (1999) study were preapproved for the product being promoted. His results demonstrate adverse selection within a set of prospects who were already judged to be attractive by the firm (i.e., bank approval preceded customer response).
Legend for Chart:
A - Measure
B - Sample Mean
A B
Average number of months that accounts are on
file 78.68
Months on file 142.70
Months since most recent account opened 20.95
Months since most recent bank card account
opened 29.42
Number of accounts 15.03
Number of accounts ever 30 or more days
past due 2.46
Number of accounts 30 or more days past due
in 24 months 1.22
Number of bank card accounts open in 6
months .15
Number of bank card accounts open in 24
months .80
Number of bank cards, finance, personal
revolving accounts 4.51
Number of currently active personal finance
accounts .29
Number of different subscribers 12.37
Number of finance accounts verified in 12
months 1.44
Number of inquiries 2.97
Number of premium bank card accounts open in
24 months .18
Number of public record and account line
derogatory remarks 1.00
Number of satisfactory finance company
revolving accounts .84
Percentage of accounts never delinquent 78.45
Percentage of active accounts with positive
balance 67.27
Percentage of bank card accounts greater than
50% of limit 29.00
Ratio: balance to high credit 44.26
Ratio: bank card balance to high credit 2.94
Ratio: retail balance to high credit 16.24
Ratio: revolving balance to high credit 33.39
Legend for Chart:
A - Measures
B - Credit Limits
C - New Bank Cards
D - Payment Problems
E - Finance Company Accounts
F - Length of Credit History
G - Inquiries
A
B C D E F G
Ratio: bank card balance to high credit
.903 .163 .045 .107 .075 -.062
Percentage of bank card accounts greater than 50% of limit
.893 .144 .038 .088 .065 -.059
Ratio: revolving balance to high credit
.866 -.055 .165 .074 -.098 .028
Ratio: balance to high credit
.653 -.192 .202 .062 -.317 .134
Percentage of active accounts with positive balance
.631 -.230 .088 .112 -.288 .195
Ratio: retail balance to high credit
.575 -.184 .226 .051 -.132 .081
Number of bank card accounts open in 24 months
.050 .833 -.136 .109 -.212 .038
Number of bank cards, finance, personal revolving accounts
-.060 .752 -.098 .409 .278 -.090
Number of premium bank card accounts open in 24 months
-.195 .740 -.196 .107 .005 -.004
Number of bank card accounts open in 6 months
-.006 .669 -.155 -.055 -.239 .106
Number of accounts ever 30 or more days past due
.251 -.104 .908 .100 -.021 .053
Number of accounts 30 or more days past due in 24 months
.240 -.085 .839 .102 -.062 -.006
Number of public record and account line derogatory remarks
.013 -.129 .817 -.151 -.114 .128
Percentage of accounts never delinquent
-.136 .295 -.778 .274 .210 -.044
Number of finance accounts verified in 12 months
.224 -.045 -.021 .836 -.231 .076
Number of satisfactory finance company revolving accounts
.074 .129 -.135 .806 -.032 -.074
Number of accounts
.060 .536 .051 .751 .168 .075
Number of different subscribers
.026 .625 -.002 .678 .239 .011
Number of currently active personal finance accounts
.272 -.311 .168 .398 -.296 .225
Average number of months accounts on file
-.210 .036 -.131 -.078 .862 -.005
Months since most recent bank card account opened
.011 -.163 -.176 .062 .708 -.112
Months on file
-.126 .142 -.072 .116 .616 .591
Months since most recent account opened
-.152 -.226 .059 -.401 .540 -.103
Number of inquiries
.166 .044 .187 .007 -.171 .819
Eigenvalue (unrotated solution)
5.9 4.8 2.1 2.0 1.5 1.1
Notes: We used principal components analysis with Varimax
rotation. Legend for Chart:
A - Measure
B - Description
C - Sample Mean
A B C
INCOME Estimated income/$10,000 46.30
MORTGAGE Estimated mortgage 64,601.09
SINGLE Head of household is single .07
CHILDREN Presence of children under the age of .16
18
MANAGER Borrower is a business owner, .04
professional, executive, or middle manager
35T44 Borrower is between the ages of 35 and .38
44 years
45T54 Borrower is between the ages of 45 and .27
54 years
55T64 Borrower is between the ages of 55 and .13
64 years
65PLUS Borrower is 65 years of age or older .04
MAILBUY Household responded to mail solicitation .08
from a financial company recently Legend for Chart:
A - Variable
B - Response-Oriented Selection Model Coefficient
C - Approval-Oriented Selection Model Coefficient
D - Simultaneous Response and Approval Selection Model
(Bivariate Probit Model) Response Coefficient
E - Simultaneous Response and Approval Selection Model
(Bivariate Probit Model) Approval Coefficient
A B C
D E
Constant .1459(**) -.8363(**)
.1453(*) -.8727(**)
Credit limits .1985(**) .0092
.1983(**) .0163
New bank cards -.1891(**) .0377
-.1890(**) .0309
Payment problems .2983(**) -.2961(**)
.2984(**) -.2860(**)
Finance company accounts -.0897(**) .0823(**)
-.0899(**) .0791(**)
Length of credit history -.1915(**) .0025
-.1911(**) -.0034
Inquiries .1262(**) -.0572(**)
.1260(**) -.0533(*)
INCOME -.0032(**) .0128(**)
-.0031(**) .0126(**)
MORTGAGE -3.80E-06(**) 8.22E-07
-3.80E-06(**) 6.67E-07
Number of 30 day or worse
ratings on mortgage
accounts .0411(**) -.0557(**)
.0410(**) -.0540(**)
SINGLE .0810 --
.0838 --
CHILDREN .0403 --
.0419 --
MANAGER -.1585(*) --
-.1578(*) --
MAILBUY .2169(**) --
.2184(**) --
35T44 .1994(**) --
.1992(**) --
45T54 .2085(**) --
.2078(**) --
55T64 .1734(**) --
.1702(**) --
65PLUS .1229 --
.1172 --
Chi-square (model) 1618.9(**) 352.7(**)
.54 ρ (error
correlation)
.062
(*) p < .05.
(**) p < .01. Legend for Chart:
B - Prospect Selection Based on Response Likelihood
C - Prospect Selection Based on Approval Likelihood
D - Prospect Selection Based on Likelihood of Response and
Approval
A
B C D
Mailing size (top three deciles)
1157 1157 1157
Total contact costs ($2 per household)
($2,314) ($2,314) ($2,314)
Number of responses
835 319 689
Response rate
72% 28% 60%
Contact cost per response
($2.77) ($7.25) ($3.36)
Total screening costs ($15 per response)
($12,525) ($4,785) ($10,335)
Number of approvals
214 198 284
Screening cost per approval
($59) ($24) ($36)
Approval rate
26% 62% 41%
Contact and screening cost per approval
($69) ($36) ($45)
Total revenue ($300 per approval)
$64,200 $59,400 $86,200
Gross profits (without defection costs)
$49,361 $52,301 $72,551
Expected losses due to rejections
(½%/2% defection rate and $1,000 lifetime value)
($3,105)/($12,420) ($605)/($2,420) ($2,025)/($8,100)
Net proceeds (½%/2% defection rate)
$46,256/$36,941 $51,696/$49,881 $70,526/$64,451DIAGRAM: FIGURE 1 Prospect Selection Under Adverse Selection and Costly Screening
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~~~~~~~~
By Yong Cao and Thomas S. Gruca
Yong Cao is Assistant Professor of Marketing, Department of Business Administration, College of Business and Public Policy, University of Alaska
Thomas S. Gruca is Associate Professor of Marketing, Department of Marketing, Henry B. Tippie College of Business, University of Iowa
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 127- Reducing Assortment: An Attribute-Based Approach. By: Boatwright, Peter; Nunes, Joseph C. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p50-63. 14p. 2 Charts, 4 Graphs. DOI: 10.1509/jmkg.65.3.50.18330.
- Database:
- Business Source Complete
Reducing Assortment: An Attribute-Based Approach
Most supermarket categories are cluttered with items, or stockkeeping units (SKUs), that differ very little at the attribute level. Previous research has found that reductions (up to 54%) in the number of low-selling SKUs need not affect perceptions of variety and therefore sales, significantly. In this research, the authors analyze data from a natural experiment conducted by an online grocer, in which 94% of the categories experienced dramatic cuts in the number of SKUs offered, particularly low-selling SKUs. Sales were indeed affected dramatically, increasing an average of 11% across the 42 categories examined. Sales rose in more than two-thirds of these categories, nearly half of which experienced an increase of 10% or more; 75% of households increased their overall expenditures after the cut in SKUs. In turn, the authors examine how different types of SKU reductions-defined by how the cuts affect the available attributes or features of a category (e.g., the number of brands)-affected purchase behavior differently. The results indicate that consumers experienced divergent reactions to the reduction in sizes, but they uniformly welcomed the elimination of clutter brought on by the reduction in redundant items. In addition, of households that were loyal to a single brand, size, or brand-size combination that was eliminated, nearly half continued purchasing within the category. Also, contrary to previous research on SKU reductions, the authors find that category sales depend on the total number of SKUs offered. The authors extend the previous research by showing that (1) category sales depend on the availability of key product and category attributes and (2) two particularly important attributes to consumers in an assortment are brand and flavor.
Regarding product assortment, the conventional wisdom among supermarket managers has been that "more is better." Consequently, the average number of stockkeeping units (SKUs) at a supermarket has grown from 6000 a generation ago to more than 30,000 items today (Dreze, Hoch, and Purk 1994; Food Marketing Institute 1993). Recognizing that a reduction in SKUs can clear away clutter and lower costs, grocery retailers have been under immense pressure in recent years to begin offering a more efficient assortment by simply eliminating the low- or nonselling items within a category. Yet retailers are generally reluctant to cut items for fear of losing consumers who will be unhappy with their offerings.
Most grocers realize that consumers often prefer stores that carry large assortments of products for several reasons (Arnold, Oum, and Tigert 1983). For one, the larger the selection, the more likely consumers are to find a product that matches their exact specifications (Baumol and Ide 1956). In addition, more products mean more flexibility, which is important if the consumer has uncertain preferences (Kahn and Lehmann 1991; Koopmans 1964; Kreps 1979; Reibstein, Youngblood, and Fromkin 1975) or is predisposed to variety seeking (Berlyne 1960; Helson 1964; Kahn 1995; McAlister and Pessemier 1982).
Recent research, however, suggests that consumer choice is affected by the perception of variety among a selection, which depends on more than just the number of distinct products on the shelves. The consumer's perception of variety can be influenced by the space devoted to the category, the presence or absence of the consumer's favorite item (Broniarczyk, Hoyer, and McAlister 1998), the arrangement of an assortment and the repetition of items (Hoch, Bradlow, and Wansink 1999), and the number of acceptable alternatives (Kahn and Lehmann 1991). Therefore, many observers in industry and academia believe that, if they do it properly, grocers can make sizable reductions in the number of SKUs offered without negatively affecting sales. In a study by Dreze, Hoch, and Purk (1994), aggregate sales went up nearly 4% in eight test categories after experimenters deleted 10% of the less popular SKUs and dedicated more shelf space to high-selling items. This experiment lasted 16 weeks and tracked sales at 30 test stores and 30 control stores.
Broniarczyk, Hoyer, and McAlister (1998) examine the link between the number of items offered, assortment from the consumer's perspective, and sales. They find that reductions (up to 54%) in the number of low-selling SKUs need not affect perceptions of variety and therefore sales. In a field study, the researchers eliminated approximately half of the low-selling items in five categories (candy, beer, soft drinks, salty snacks, and cigarettes) in two test convenience stores while holding shelf space constant. Neither sales nor consumers' perceptions of variety differed significantly between two test stores and two control stores. As Broniarczyk, Hoyer, and McAlister (1998, p. 175) point out, however, their findings are limited by the extent to which their results would generalize to other categories and how the specific features of the category might make consumers more or less sensitive to SKU reductions (e.g., a category with a small number of brands). This research addresses both of these issues directly.
First and foremost, we examine how different types of SKU reductions-defined by how they affect the attributes available in a category (i.e., the number of brands, sizes, and flavors)-affect sales differently. Fader and Hardie (1996, p. 451) pose the question, "Is it sufficient to drop the lowest-selling items or is it wiser to eliminate all items sharing an ineffective SKU attribute level?" Few retailers would eliminate any item that is selling briskly, but category managers must wonder whether all low-selling SKUs can be cut with equal (or lack of equal) impact on sales and, if not, which SKUs should be the first to go. We believe that the answer can be found by tracking changes in attribute offerings. To date, no work we are aware of has examined the effect on sales resulting from different types of SKU reductions or, more specifically, cuts based on product characteristics other than being low- or nonselling.
Second, although the increases in sales were not significant at Broniarczyk, Hoyer, and McAlister's (1998) test stores, our results are similar to Dreze, Hoch, and Purk's (1994) in that sales can change significantly following a reduction in the number of SKUs offered (see Figure 1 and Table 1). Unlike Dreze, Hoch, and Purk (1994), who found that aggregate sales went up nearly 4% in 8 categories, we observed an average sales increase of 11% across the 42 categories we examined. In 35 categories, sales changed by more than 4%, and nearly half of all categories experienced an increase of 10% or more. Our research is the first to examine how these changes depend on the particular nature of the cuts (i.e., which attributes were affected). Our model finds that the resulting change in sales depends on how the reduction in items affects the features of the category (i.e., the availability of alternative attributes such as brands and flavors). Our model also allows for divergent consumer reactions to the assortment reduction, measuring not just the results for the average consumer but also the percentage of consumers who embrace (or reject) different aspects of the change in assortment.
Third, our results indicate that even while sales increased, purchase probabilities decreased slightly. Not all consumers welcomed a reduction in their selection, and these consumers stopped purchasing or purchased less frequently. But the majority of consumers bought larger quantities, and their increased purchases easily outweighed the loss in sales due to the minority that preferred the greater selection. Even so, our results raise questions about customer retention, and we therefore investigate why some households ceased purchasing in some categories. Although we document that customers who lose their favorite brand are likely to stop or reduce category purchases, we find that a large percentage (nearly 40%) of consumers who were extremely loyal to a single brand continued to purchase in the category after that brand was eliminated.
Our investigation differs from previous work for several other important reasons. First, the data come from a retailer that (1) cut the number of SKUs offered in almost all categories (383 of 407 categories) and (2) implemented these cuts indefinitely. Second, the data come from an online grocer, which enables us to track the purchase behavior of a large, stable panel of regular shoppers who made purchases both before and after the reduction. These shoppers all made real purchase decisions with their own real dollars. Third, the purchase history of a much larger control panel of online customers, for whom the assortment did not change, provided a base for comparison. This control group's purchase data came from consumers in the same market during the same time period.
We should be clear about the objectives of this research up front. We do not attempt to measure consumers' perceptions of assortment, nor do we address issues related to shelf space allocation and product display. Instead, our primary goal is to explore how different types of SKU reductions affect category sales differently. Fader and Hardie (1996, p. 450) have argued that individual SKUs can be described in terms of a small set of discrete attributes and that "consumer choice is often made on the basis of these attributes." If the consumer views the category in terms of its attributes, we believe that the marketer should pay attention to changes in the availability of options among the attributes within a category. By monitoring how additions and deletions affect the presence or absence of attribute options, we believe that managers can better anticipate how category sales will respond to changes in SKU offerings and thus fine-tune their selection of SKUs in a more profitable manner. Our model tests how various changes in the availability of options among meaningful attributes, which are brought on by changes in product offerings, affect category sales.
We also believe that an understanding of which attributes are meaningful to consumers can help explain how different types of SKU reductions can affect category sales differently. Our secondary goal is to determine which attributes, if any, are meaningful across a wide variety of categories and are susceptible to changes that have a direct impact on assortment. Just as we believe that attributes should matter, we believe that not all attributes should matter equally. We recognize that a reduction in the availability of certain attributes could have a positive or negative effect and that the direction probably depends on the number of options originally offered within a category. It stands to reason, however, that changes in the availability of meaningless attributes will have no significant effect on category sales, whereas the magnitude of the effect of meaningful attributes will differ. For this work, we chose only attributes we believed would be meaningful in the vast majority of categories, yet we still expect that some will be more meaningful than others.
The rest of this article is organized as follows: In the next section, we briefly discuss how we selected the particular attributes we test. We then describe the data in more detail and explain why they are remarkably appropriate for this research. In the section that follows, we specify our measures and outline the development of a formal model that is used to examine how the particular nature of different SKU cuts affects sales differently. The results suggest that changes in category sales can be explained by changes in the availability of attributes. Finally, we discuss some secondary findings and the noteworthy managerial implications that emerge from these results. We conclude by reviewing some limitations of this work and offering suggestions for further research.
The first step in our analysis was to determine what would be treated as an SKU attribute. Marketing research firms typically use three important criteria (Fader and Hardie 1996). First, an SKU attribute must be consumer recognizable, that is, immediately observable to the consumer. Second, it must be objective, or precise. Third, it must be collectively exhaustive; in other words, it must apply to every SKU in the category.
At the onset, we attempted to be as inclusive as possible, yet the nature of the research, which examines issues that affect all categories, required that the attributes we chose transcend product categories. For this reason, we began by including brand, size, flavor, package, and form, all five of the key product characteristics articulated by the Food Marketing Institute in its 1993 report on variety and duplication. None of the categories examined included a significant number of different packages (e.g., Marlboro soft versus hard pack) or forms (e.g., Grape Nuts versus Grape Nut flakes), and therefore the categories could not experience significant changes along these attributes. Consequently, the three SKU attributes included in our final analysis were brand, size, and flavor. These attributes conveniently mimic the product descriptions onscreen at most online grocers (see Figure 2) as well as those Broniarczyk, Hoyer, and McAlister (1998) mention in their discussion of the cognitive aspects of assortment perceptions.
Not every product cut will lead to the elimination of a brand, size, or flavor. Merchandise managers might purposefully or inadvertently eliminate entire brands and sizes from a selection, or they might just trim several specific brand-size combinations. For example, if 12-packs of Sprite are eliminated but the consumer can still get 12-packs of alternative sodas (e.g., 7UP) and other sizes of Sprite (e.g., 2-liter bottles), then only a specific brand-size combination was cut; the brand and size themselves are still available. The total number of brand-size combinations cut could be viewed as indicative of a reduction in redundant items as long as no brands or sizes are eliminated. Consequently, we expect these types of cuts to have a positive effect on sales, because eliminating clutter makes it easier for consumers to find what they are looking for. If too much choice is truly demotivating (Iyengar and Lepper 1998), eliminating redundant items should help boost category sales (a simple positive relationship).
In addition, we included a variable that indicated the percentage of total items cut within each category. Unlike previous research that included either one level (10% in Dreze, Hoch, and Purk's [1994] work) or low, moderate, and high levels of cuts (25%, 50%, and 75% in Broniarczyk, Hoyer, and McAlister's [1998] work), the categories in our data experienced 27 different levels of reductions. Although this variable is identical in its operationalization to previous research, we recognize that it incorporates many of the differences among products that are specific to a particular category. For example, the Bounty brand of paper towels comes in white, medley, or fun prints. Strictly speaking, pattern is neither a package or form (e.g., extra strength, shorter sheets) difference, but if cut, a unique attribute disappears from the assortment. Consequently, we would expect a change in the number of items to be correlated with a change in sales (i.e., unexplained variance) because of these types of product distinctions disappearing. These attributes differ by category, occur in small numbers, and may or may not be valued by consumers. Therefore, a priori, we cannot predict the effect of item cuts on sales. We test the effect of varying degrees of cuts among brands, sizes, flavors, items, and brand-size combinations with data from a natural experiment that was conducted independently by an online grocer.
Electronic commerce has begun to change the nature and economics of food marketing radically as millions of computer-savvy consumers have begun shopping for groceries online. At the time of the experiment, 435,000 of the 53.5 million U.S. consumers online had purchased food products over the Internet, and a study by Anderson Consulting predicted that the online market for groceries and related products would reach $85 billion by 2007 (Thompson 1998). Most online grocers were reliant on the professional Ihoppers they hired to pick up items that were ordered for delivery at a local supermarket. This system contributed to above-average operating expenses, which hovered between 12 and 23% in 1998. Online grocers needed to cut costs desperately, and most planned to do so by moving to a system with central warehouses and bin shelving, where a severe reduction in the number of SKUs was seen as essential and inevitable. Industry analysts and executives agreed that the product offerings at online grocers should be cut roughly in half (Food Marketing Institute 1997).
In November 1996, a national online grocer began servicing a major market on the east coast. For eight months, all of its customers were offered the exact same selection online as was available at a local, affiliated grocery chain. In July 1997, this grocer undertook an enormous experiment, severely reducing the number of products available to a test panel or experimental group of consumers.<SUP>2</SUP> The company simultaneously monitored the purchases of everyone else in the market (i.e., the control group), for whom the assortment remained unaltered. The reduction did not appear to affect attrition; only 9 of the 292 (3%) active consumers in the experimental group quit the service. In the control group, 28 of the 455 (6%) customers quit.
In January 1998, the company provided the authors with all the recorded information for 1997 (e.g., SKU, quantity, date of purchase, unit price, shopping basket) for both panels. Accordingly, we compared purchases made by households in the two groups for six months before and five months after the SKU reduction.<SUP>3</SUP> To measure the effect of the SKU reduction on sales within a category, we modeled dollar sales to individuals in the experimental group compared with sales to a control group. The use of a control group increases the reliability of the statistical results and simplifies the analysis by establishing credible purchasing baselines and eliminating the need for a large number of covariates. Because we compare data from identical time periods for the two consumer panels, we do not need to account separately for any effect on sales caused by holidays (e.g., Thanksgiving), seasons (e.g., summer), or any other variables (e.g., price changes) that we can assume affected both panels equally. In addition, the simplified shopping environment online (no shelves, no lines, and so forth) offers a unique opportunity to explore assortment as a separate issue from these complexities.
To make the task more manageable, the analysis focused on 42 of the 47 top-selling categories, each of which had at least $5,000 in sales for the period in question.<SUP>4</SUP> On average, the households in the test panel shopped online 1.56 times per month and spent $72.32 dollars per shopping "trip" among just those 42 categories examined. Within these categories, the test panel placed 5924 separate orders for 135,979 items (an average of 23 items per order). The panel chose among 4181 SKUs for the period before the cut versus 1852 SKUs afterwards (a 56% reduction). As might be expected, the 2329 SKUs eliminated were mainly small-share items (see Figure 3). Individual categories lost between 20 and 80% of the products previously offered (see Table 1); none of the 42 categories was eliminated entirely. This variance helps provide a rich source of real-world data on consumers' reactions to a broad spectrum of different types of SKU reductions.
In this experiment, the assortment was simultaneously altered in a large number of categories. At the most basic level, our model compares sales before and after the manipulation, which is the format for the standard t-test for the difference in group means. Although the t-test offers a nice conceptualization of our model, the t-test itself is simplistic. It requires the assumption that any difference in the sales in the two time periods could be attributed to the experiment, whereas we desire to measure effects of covariates on the change in sales. Some of the covariates are manipulations of the experiment, and others are outside the experiment. Because this experiment was not conducted in a lab but in a real purchasing environment, many factors are not accounted for in a simple t-test. Probably most important to our experiment are seasonal consumption patterns. For example, coffee sales are much higher in winter months than in summer months, and bottled water sales peak in late spring and early summer months. Because we have only one year of data, the periods of the experiment are potentially confounded with these seasonal consumption patterns. In addition, the prices of the individual products were not held constant but varied with the typical plethora of product promotions. Furthermore, these seasonal and promotional effects vary by category as well. In addition, the pre-cut period is six months, whereas the post-cut period is five months. Given the high variance in sales at the category level, the single month of additional observations can dramatically affect the variance of the estimate of mean sales, meaning that the mean sales estimates of the two periods do not have the same variance. Whatever statistical test is used to compare sales across the two time periods must account for differing variances.
Where Sjt is sales of the experimental group in category j for month t; IREDUCT is an indicator variable that takes the value of 1 after the SKU reduction; Cjt is the sales of the control group in category j in month t; {xijt}, i = 1, ..., q are covariates; and ejt is an error term. Linear regression can be used to estimate the parameters of the logged form of the model. This model can be considered a modified version of the basic t-test for differences in means, where the additional terms make the necessary adjustments for the complications mentioned in the preceding paragraph. Use of sales information from a control group (Cjt ) adjusts Sjt for price changes, seasonality, and promotions. Both the control group and the experimental group received the same prices, promotions, and advertising. Furthermore, both populations reside in the same city, so their seasonal consumption patterns would be roughly identical. Therefore, as Cjt increases, the probability of purchase for an individual household would increase. The use of monthly data makes the homoskedasticity assumption on log(ejt ) (for hypothesis tests) more plausible than the data aggregated to two periods.
The basic regression model ignores the diverse tastes and needs of the consumers, however. Consumer heterogeneity is an additional source of variation, which is not accounted for by Equation 1. One possible method of incorporating consumer heterogeneity would be a mixture model, in which we would assume the consumers would belong to one of k segments of homogeneous consumers. Another possibility is to allow consumers all to differ from one another through a random effects model specification. A priori, there are no particular reasons to expect the consumer preferences to fall readily into one of a small number of segments. Furthermore, Allenby, Arora, and Ginter (1998) provide evidence that consumer heterogeneity is often much greater than that measured by a mixture model (latent class model).
Where Shjt is sales to household h of category j in month t; Qh = [kh , qh , f1h , ..., fqh ], h = 1, ..., H; H is the total number of households in the data; and m = [kw, qqw, fw1 , ..., fwq ] is a vector of the population means. Note that the household-level model includes an exponent g for Cjt ; because of the asymmetry of the log-normal distribution, individual household sales will not be exactly proportional to sales aggregated over a group of consumers. For simplicity, we specify S to be diagonal; that is, S = diag(s2k , s<SUP>2</SUP>q , s<SUP>2</SUP>f1 , ..., s<SUP>2</SUP>fq ), the assumption that characterizes this model as variance components. Such models have been used extensively in the sciences to allow for variance at different levels in the data (Searle, Casella, and McCulloch 1992).
An additional complication arises with the model at the household level. Many households do not purchase from the online service in every category in every month, meaning that our data contain a large number of observations where Shjt = 0.
Although Equation 3 appears similar to a Tobit, it is not a censored model but an uncensored model mixed with a point mass. Similar to the Tobit and unlike latent class mixture models, however, mixture membership is observed in the data. The parameter p captures the percentage of observations that contain a purchase (the "when" of Gupta 1988), and the remaining parameters characterize the quantity of purchase (the "how much" of Gupta 1988). We estimate the coefficients of the model using maximum likelihood, where the likelihood for the model is given in the Appendix. The sample size of our data was 74,305 total observations, and for 43,219 observations Shjt > 0.
Covariates
For the q covariates {xi } in Equation 3, we construct variables that measure category attributes: market share, number of available items (SKUs), number of brands, number of sizes, number of brand-sizes, number of flavors, and category price. Our measure of market share (MKTSHRjt ) is defined as the category dollar market share of the eliminated items in category j at time t. Product market shares, wnj , are calculated relative to the category, or where snjt is the dollar sales of item n (n = 1, ..., Nj ) in category j for week t (t = 1, ..., T1 ) for the experimental group before the assortment cut,<SUP>5</SUP> and S<SUP>Nj</SUP>m = 1 S<SUP>T1</SUP>t = 1 smjt is the dollar sales of category j for the experimental group of consumers before the assortment cut. The share of the eliminated items in category j is then where l = 1, ..., Ljt indexes the products eliminated from category j at time t. Because all items were available at first, MKTSHRjt equals 0 before the SKU reduction.
Similar to MKTSHRjt , ITEMSjt equals 0 before the SKU reduction, because all items were available at that time. Similarly, BRANDjt , SIZEjt , FLAVORjt , and BRNDSZjt are the percentages of brands eliminated, sizes eliminated, flavors eliminated, and brand-size combinations eliminated, respectively. Finally, PRCCHGjt is the ratio of the category price at time t to that before the SKU reduction. Because we need the category price of offered products rather than purchased products, we calculated category price using the data for the universe of consumers rather than for our experimental or control group.
Where n = 1, ..., Nj indexes the items within category j; w<SUP>u</SUP>nj is the market share of item n in category j calculated over the universe of consumers; t = 1, ..., T1 indexes weeks before the assortment reduction; and pnjt is the price of item n in category j during week t. Our measure of category price change is then where products m = 1, ..., Mjt represent the products retained in category j. Before the SKU reduction, PRCCHGjt takes the value of 1.
To allow for increasing/decreasing marginal effects of the independent variables, we also include the squares of the independent variables in our model. The loss of one brand versus zero brands of ten total may not affect sales, but the loss of seven brands versus six of ten total may affect sales greatly.
To facilitate interpretation of qh , we recode the variables so that they are mean-centered after the SKU reduction and equal to 0 before the SKU reduction. The parameter qh then can be interpreted as the average change in sales coinciding with the SKU reduction. Also, in 13 categories, flavors were either nonexistent (e.g., diapers) or ill-defined (e.g., frozen dinners). We therefore multiply the flavor covariates by an indicator variable IF , which takes the value of 1 when flavors exist, 0 otherwise. In the hypothesis tests on flavor, we adjust the degrees of freedom to correct for the reduced number of observations used to estimate the flavor parameters.
Therefore, [x1jt , ..., xqjt ] = [BRANDjt , BRANDjt <SUP>2</SUP>, SIZEjt , SIZEjt <SUP>2</SUP>, IF FLAVORjt , IF FLAVORjt <SUP>2</SUP>, ITEMjt , ITEMjt <SUP>2</SUP>, BRNDSZjt , BRNDSZjt <SUP>2</SUP>, MKTSHRjt , MKTSHRjt <SUP>2</SUP>, PRCCHGjt ]. This matrix can also be summarized as [ATTRIBUTES, ITEMS, COVARIATES], where ATRRIBUTES is a matrix composed of the brand, size, and flavor variables; the ITEMS matrix is composed of the two items variables; and the remaining variables are included in the COVARIATES matrix.
The results can be divided into two general classes: population mean parameters and heterogeneity (variance) parameters. The estimates of the population mean parameters m = [kw, qqw, fw1 , ..., fwq ] and g of the model are presented in the top portion of Table 2. Almost all of these parameters are statistically significant. At least one component (linear or quadratic) of market share, brand, and flavor is significant at the .05 level; however, size is not significant.<SUP>7</SUP>
These results indicate that (1) sales are affected by changes in the number of available SKUs; (2) the market share of items eliminated, the number of brands eliminated, and number of flavors eliminated all affected sales; (3) on average, category sales increased on the order of 11% in the experimental group; (4) a decline in sales due to brand reductions can be partially offset by reductions in brand-size combinations; and (5) reductions of assortment characteristics do not have a simple proportional relationship to sales.
The coefficient estimate for qh is .104, indicating that sales increased, on average, by e<SUP>.104</SUP>, or approximately 11% after the assortment reduction, in which the average effect is across both consumers and categories. Overall, therefore, the experiment of reducing the assortment increased rather than decreased sales. For the effects of category attributes, most involve multiple parameter estimates. To clarify these effects, we have included graphs of some of these nonlinear relationships in Figure 4.
As shown in the graphs, initial reductions in share, the number of brands, and the number of flavors tend to increase sales, whereas further cuts engender sales declines. On average, the retailer in our data enjoyed maximal sales increases for reductions of approximately 25% of the brands, 18% of the flavors, and items accounting for 15% of the market share. Because the majority of the eliminated SKUs were small-share items (see Figure 3), a 15% reduction is already a large reduction of market share. Reductions in the clutter of brand-size combinations (when the number of brands and the number of sizes are held constant) increase sales of the category, though the marginal benefit of the reductions is decreasing. Finally, item reductions (not shown in Figure 4) decrease category sales, for which the negative effect is marginally decreasing. In all panels of Figure 4, the data histograms are included to show that the curves are estimated using data on both sides of the inflection points.
Brand reductions and brand-size reductions have opposite effects. Because of the close relationship of these two factors, along with their opposing impacts, it is theoretically plausible that brand reductions end up having a net effect close to zero. Altsough it is possible to eliminate brand-sizes without affecting the number of brands and/or sizes available, it is impossible to eliminate whole brands or sizes without affecting the number of brand-size combinations available. Typically, a grocery retailer does not carry all the brand-size combinations that are available from the manufacturer. In our data, a 10% reduction in brands resulted, on average, in approximately a 4% reduction in brand-size combinations (holding the number of available sizes constant). This relationship can be used to test the multivariate hypothesis Lb = 0, where L = [1, 1, .4, .4<SUP>2</SUP>] and bAis a vector of the parameter estimates of brand, quadratic-brand, brand-size combinations, and quadratic-brand-size effects, respectively. The F-statistic for this hypothesis was significant (p < .0001), meaning that, on average, a reduction in brands affects sales even after the positive effect of the reduction of brand-size combinations is taken into account.
The estimates of consumer heterogeneity are included in the lower portion of Table 2. Of particular importance is the heterogeneity on the change in sales due to the assortment reduction, qh , which is estimated as qh ~ N(.104, .0241). Of the mass of this normal density, 75% is greater than zero. Therefore, in addition to a significantly positive estimate for the population mean (i.e., average sales increased), the heterogeneity distribution estimates that 75% of consumers increased their category expenditures after the assortment cut.
For heterogeneity with respect to the elimination of product attributes, the results show that consumers are heterogeneous in their reactions to cuts of each attribute.<SUP>8</SUP> The largest heterogeneity parameter among the linear components of brand, size, and flavor is that for size, indicating that a reduction in the assortment of sizes results in a mixed reaction from consumers. The large degree of heterogeneity indicates that some consumers were upset with the reduction, but others welcomed the shorter list. Thus the heterogeneity parameter highlights an important result that is masked by the statistical insignificance of the population mean for size: The loss in sizes affects some members of the population quite strongly. Sales from some consumers decreased with the size deletions, but sales from other consumers balanced those losses such that the overall effect (the population mean) was close to null.
Although the heterogeneity parameter on the size reduction indicates a divergence of reactions, the small degree of heterogeneity indicated by the brand-size parameter suggests uniformity with respect to the elimination of clutter. When the estimated distribution of the brand-size linear term is N(2.419, .2136), a reduction in brand-size combinations elevates sales for the whole population, not just for a portion of it. In essence, no consumers are upset with the reduction of brand-sizes, provided that there is little change in the number of available brands and sizes.
se can also investigate the purchase probability of the population (i.e., the estimate of p of Equation 3, which is shown in Table 2). In some respects, this is a measure of category-level customer retention, in that it reflects the likelihood that a customer will make a category purchase, irrespective of the size of the shopping basket. (Recall that the distribution of qh indicates that 75% of consumers increased their category expenditures after the assortment cut.) The parameter p is purchase incidence, the probability that an observation (sales for a random household in a random month and random category) is not zero. In our data, 58.2% of the observations were nonzero. It is also possible to estimate p before and after the assortment reduction and test if the purchase probability changes because of the manipulation in assortment, for p is a proportion, and the difference in proportions is asymptotically normally distributed. We estimate p1 to be .595 and p2 to be .566, where p1 refers to the purchase probability prior to the assortment cut. As for the hypothesis that p1 - p2 = 0, the p-value for the t-test of difference in proportions is less than .0001, which indicates that purchase probability has changed over the same time period as the change in assortment. For the control group, p1 = .551 and p2 = .635, which are significantly different from each other as well as from the experimental group estimates for p1 and p2 . Therefore, during a season in which the purchase probability would be expected to increase, purchase probabilities for the experimental group significantly decreased. Again, the parameter p is a timing measure, a probability of purchase. This parameter decreases if some households buy less frequently in some categories. Our results indicate that some households did buy less frequently in some categories after the assortment reduction, though the loss in sales due to a smaller p was outweighed by an increase in purchase quantities, because overall sales increased.
In summary, we found that category sales increased, though the likelihood of making a purchase decreased, and that the attributes of a collection of products affected sales of the individual items within the collection. In addition, consumers' responses to changes in the availability of attributes differed across attributes (e.g., more mixed for size, uniform for brand-size combinations). In short, changes in the availability of attributes (i.e., attribute-based assortment) affect consumers differently and can help explain how category sales respond to changes in the SKUs offered.
The primary objective of this research was to test the relationship between assortment and purchase behavior by examining how different types of changes in an assortment (i.e., SKU reductions) might affect sales differently. Unlike previous research on SKU reductions, our results indicate that a modest reduction, even one focusing on small-share items, can have a sizable impact on sales. Overall, we found that category sales tended to increase rather than decrease, on average, as a result of the SKU reduction. Broniarczyk, Hoyer, and McAlister (1998) and Dreze, Hoch, and Purk (1994) have suggested that SKU reductions can result in sales increases, and our model indicates that pragmatic cuts can boost sales significantly. Industry analysts have discussed how inventory reductions can lead to substantial cost savings, but our results indicate that pragmatic reductions in assortment do not just reduce costs but also can significantly increase sales.
Our secondary goal was to determine some meaningful attributes that indirectly affect sales, notably those that transcend a variety of categories and directly affect attribute-based assortment. Eliminating brands and flavors to a small degree helped sales, but deep cuts led to a decrease in sales. Therefore, the number of brands and flavors available within a category was meaningful and should be taken into account when category managers consider changes in their product offerings. Managers should be aware that consumers value the availability of their preferred brands and flavors, but pragmatic cuts in product offerings can increase overall category sales. The results may be summarized by a simple guideline: Eliminate redundant attributes while, on the margin, minimizing the number of brands, sizes, and flavors eliminated. More generally, the results suggest that simplifying choice can increase the sales of Web-based firms.
As mentioned previously, the formal attrition rate for the test panel was smaller than that for the control group, on the basis of membership cancellations. Yet there was a significant decrease in the category purchase probability despite the increase in overall sales, indicating that some consumers stopped purchasing in some categories even though the average consumer welcomed the assortment reduction by buying more. The category purchase probability is conditional on the customer returning to the store, but retailers may also be interested in the likelihood that a customer will not return. It is extremely difficult to obtain a reliable estimate of this measure of customer retention, as it must be estimated using interpurchase times, which in our data exhibit strong periodicity (people tend to purchase in weekly intervals). We are not aware of an applicable model that allows for this type of periodicity in the statistics or marketing literature. Rather, given our findings on the importance of category attributes, we investigated retention at the attribute level. We did this by examining the willingness of consumers to make purchases within a category in which their favorite brand, size, or brand-size combination was eliminated. Although the SKUs eliminated were typically small-share items, they frequently constituted the sole class of products along a particular attribute purchased within a particular category for a household. By focusing on only those purchase records in which a household bought a singular brand, size, or brand-size combination within a category before the SKU reduction, we are most likely to capture the cases in which a household lost its favorite attribute.
When a household lost the only brand it had purchased within a category before the cut, that household returned to make a purchase in the category 38.7% of the time (see Table 3). As a point of comparison, the proportion of households that bought a singular brand that was not cut and returned to buy in the category was 69.4%. This difference was highly significant (z-score = -7.57, p < .0001). Consumers were much less likely to buy in a category when their favorite brand was eliminated. Similarly, fewer consumers returned to shop in a category if their preferred size was eliminated (50.7%) than if it remained (67.8%), and this difference, though much smaller than the brand effect, was also highly significant (z-score = -4.11, p < .0001). In the case in which consumers purchased only one brand-size combination before the cuts and that combination was eliminated, 53.4% came back to buy in the category. Those whose sole brand-size combination was not cut were far more likely (67.1%) to buy in the category again during the post-cut period (z-score = -3.83, p < .001).
The loss in category purchasers seems large, yet retailers may be surprised to learn that nearly 40% of consumers who were extremely loyal to a singular brand and 50% who bought a single size or brand-size combination exclusively returned to purchase in the category after that attribute was eliminated. We should also note that eliminating brands had a more profound effect than either size or brand-size combination on leading these consumers to stop shopping in the category, which is not surprising given the results of our model regarding the significant impact of brand cuts on sales.
Overall, because of the heterogeneity of consumer preferences, elimination of any items will almost certainly cause some consumers to stop purchasing in the category. However, many of even the most product-loyal buyers will switch to an alternative product if their favorite item is eliminated. Furthermore, pragmatic product cuts can lead to sales increases, which greatly outweigh the loss in sales from the relatively few consumers who leave the category.
Although we develop a functional approximation of how sales are affected by changes in the availability of particular category attributes, our goal is exploratory and descriptive rather than explanatory. We use a random effects model that contains only a few category attributes and several covariates across a large number of categories to determine (1) if the availability of category attributes affects sales and (2) what some of those attributes are. Therefore, our research provides guidance to managers who aspire to alter their assortment. One common rule of thumb for an assortment reduction has been to eliminate small-share items. Although individually, small-share items contribute less to category sales, we find this rule of thumb to be overly simplistic. We find that judicious cuts of small-share items can increase sales rather than maintain current category sales levels. Cuts that eliminate redundancy boost sales, whereas cuts that eliminate key attributes reduce sales. For example, consider a simplistic category containing, among other items, 30-ounce regular Cascade, 30-ounce lemon-scented Cascade, and 75-ounce regular Cascade dishwasher detergent. Assuming that all these items have the same market share, the best item to eliminate would be the 30-ounce regular Cascade, because the consumer still would have access to lemon-scented and regular Cascade, and the consumer also would have the choice of 30 ounces or 75 ounces. Although our results do not offer optimal selections for individual categories, we find that the online grocery retailer would benefit by offering a smaller selection than the typical full-service bricks-and-mortar grocer offers.
Our results also raise several additional managerial issues worthy of further research. As would be expected, overly sparse assortments yield low sales. Although new products are typically viewed as either beneficial or neutral, our results imply that adding redundant new products may be harmful to category sales. We emphasize that our findings are based solely on item reductions. However, if these results hold for category additions, additional items (not necessarily new items) may depress overall category sales rather than increase them. We do not know of research that has investigated how the addition of category attributes (e.g., new brands) has affected category sales. Given the frequent introduction of new products into mature categories, we view this topic as one that is relevant to both managers and academics.
Future work could also investigate meaningful category-specific attributes, which might be tied to specific product types (e.g., food, nonfood). Although our work finds brand and flavor to be meaningful attributes, individual categories or clusters of similar categories should be considered if the goal is to develop a more accurate functional relationship between attributes and sales rather than the general reduced form function proposed here. For example, the relevant attributes in the toilet paper category (e.g., softness, olor/pattern, ply, brand, size) might be very different from those in the ice cream category (e.g., flavor, fat content, brand, size) but very similar to those in the paper towels category.
Although we find that brand, for whatever reason, is one of the most important category attributes, an analysis at the category level would be better suited to exploring specific brand associations. Consider the following example from Pan and Lehmann's (1993, p. 77) work: "Sony electronics are expensive (compared to other brands)." Or, a more appropriate example for this research: Hefty bags are "strong" plastic bags. Therefore, eliminating Hefty brand bags from an assortment might be synonymous with eliminating strong plastic bags. This research has shown that consumers respond strongly to the loss of brands, yet we recognize that brand may act as a surrogate for or may be confounded with other category attributes. In addition, the various associations with brand might differ across product categories. Exploring these associations is well beyond the scope of this article, yet in spite of this potentially high degree of variation in brand associations, brand appears as an important attribute across categories.
Future work in this area could also explore whether an optimal selection of items exists, which could depend on the availability of attributes and attribute options within a category. In other words, is there an absolute ideal for an assortment, or are consumers affected only by changes in items available (absolute or relative)?<SUP>9</SUP> Furthermore, do consumers tend to adapt to the selection offered by a retailer, as adaptation theory suggests (Bawa, Landwehr, and Krishna 1989; Helson 1964)? Another potential issue for further research pertains to the role of small grocery stores. Small grocery stores may end up serving a dual role-not only are they usually closer to consumers and decrease driving time, but they also may reap the demand-side benefits of reduced assortment (as well as the benefit of smaller inventory costs). Grocery chains may find it desirable to have multiple types of stores serving the same consumers-the megastore where everything can be found, as well as the streamlined outlet where the consumer can quickly and easily find general items.
More work could also be done using a model such as that developed by Rossi, Allenby, and Kim (2000) to determine how readily consumers substitute across attributes. Our research focuses on existing regular customers. Future work may more fully explore how assortment affects customer acquisition and retention. Finally, our results and those of other studies (Broniarczyk, Hoyer, and McAlister 1998; Dreze, Hoch, and Purk 1994; Food Marketing Institute 1993) show that sales are increased or at least unaffected by reductions in item counts. To understand fully how consumers respond to different assortments and changes in assortment, researchers must begin modeling the mental process consumers employ when confronted with an assortment of items in a category in which they are considering, or are intent on, making a purchase. We have already begun conducting a series of laboratory experiments and are making significant progress in determining the link among attribute availability, assortment perceptions, and purchase intention.
The parameters of Equation 3 can be estimated using conventional techniques, as the likelihood can be written as a binomial likelihood for p and a linear random effects model for the remaining parameters.
Category Repurchase Rates for Households Loyal to a Single Attribute
Brand Size Brand-Size
Combination
Within-category
purchase records 3583 3422 2615
Purchased Item Eliminated
Records in which sole
item was cut 150 (4.2%) 150 (4.4%) 208 (8.0%)
Continued purchasing
in category 58 (38.7%) 76 (50.7%) 111 (53.4%)
Purchased Item Retained
Records in which sole
item was not cut 3433 (95.8%) 3272 (95.6%) 2407
(92.0%)
Legend for Chart:
A - Category
B - Percent Reductions of Items
C - Percent Reductions of Market Share
D - Percent Reductions of Brand
E - Percent Reductions of Size
F - Percent Reductions of Flavor
G - Percent Reductions of Brand-Size
A B C D E F G
Cat food 22% 13% 15% 33% 27% 35%
Baby food 29 13 29 30 17 25
Facial tissue 37 20 17 25 31
Water 38 18 29 20 36
Tuna 38 29 0 36 0 38
Butter 38 3 25 0 20
Low-fat milk 42 3 50 20 0 44
Long-life milk 45 10 14 0 29
Laundry detergent 45 17 28 39 38
Soup 45 18 27 14 38 23
Milk 45 10 44 0 0 35
Flavored water 46 12 50 30 24 50
Paper towels 47 43 31 0 39
Bread 47 23 37 12 37 39
Cereal 50 24 55 16 31 41
Dish soap 50 37 0 40 47
Orange juice 50 10 50 33 0 56
Eggs 50 4 71 33 0 67
Waffles 51 40 40 43 23 47
Plastic bags 53 34 33 31 46
Frozen dinners 53 23 37 29 38
Refrigerated pasta 53 20 25 0 50 20
Soda/pop 54 25 29 54 40 46
Diet/sugar-free pop 54 21 27 50 37 47
Breakfast bars 56 14 33 33 35 41
Yogurt 57 29 27 33 54 29
Garbage bags 58 25 57 60 63
Coffee 59 38 38 26 67 39
Pasta 60 36 61 50 0 53
Cigarettes 61 42 53 0 0 53
Toilet paper 61 54 14 29 50
Cat litter 62 47 31 20 50
Juices 66 32 67 43 64 50
Crackers 66 40 56 41 58 53
Shredded cheese 68 52 40 33 25 50
Candy 71 27 45 58 45 67
Spaghetti sauce 72 47 67 68 50 74
Ice cream bars 72 42 45 53 58 66
Chunk cheese 74 55 40 42 53 50
Ice cream 76 42 40 0 66 61
Cookies 78 44 61 54 60 69
Diapers 82 62 50 61 74
Average 54 28 38 31 33 46
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GRAPH: FIGURE 1: Effect of SKU Reduction on Sales
GRAPH: FIGURE 2: Typical Display at an Online Grocer: Sample from the Spaghetti Sauce Aisle
GRAPH: FIGURE 3: Market Shares of Retained and Eliminated Items
GRAPH: FIGURE 4: Effects of Attribute Cuts on Sales
~~~~~~~~
By Peter Boatwright and Joseph C. Nunes
Peter Boatwright is Assistant Professor of Marketing, Graduate School of Industrial Administration, Carnegie Mellon University.
Joseph C. Nunes is Assistant Professor of Marketing, Marshall School of Business, University of Southern California.
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Record: 128- Reference Price Research: Review and Propositions. By: Mazumdar, Tridib; Raj, S. P.; Sinha, Indrajit. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p84-102. 19p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.2005.69.4.84.
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Reference Price Research: Review and Propositions
A substantial body of research evidence has now accumulated in the reference price literature. One stream of research has identified the antecedents of reference price and has assessed their effects through experimentation. Others have calibrated a variety of reference price models on panel data and reported the effects on brand choice and other purchase decisions. In this article, the authors review the published literature on reference price in both the behavioral and modeling streams. They offer an integrative framework to review prior research on ( 1) the formation of reference price, ( 2) retrieval and use of reference price, and ( 3) influences of reference price on various purchase decisions and evaluations. In doing so, the authors examine the influences of consumers' prior purchase history and contextual moderators, such as specific purchase occasions, promotional environment of the store, and product category characteristics. They summarize the key findings, identify the unresolved issues, and offer an agenda for further research, which includes a set of testable propositions. They also identify the methodological challenges that face reference price research. In the concluding section, the authors discuss the managerial implications of reference price research.
Reference prices are standards against which the purchase price of a product is judged (Monroe 1973). Numerous articles on the topic of reference price have been published in marketing journals and presented at marketing conferences. These articles provide insights into such issues as the conceptualization of reference price, how it can be measured or modeled, and its effects on consumer purchase behavior. The effects of reference price on consumer choice have been accepted as an empirical generalization in marketing (Kalyanaram and Winer 1995), and the idea of reference point has been extended to other stimuli such as price promotions (Lattin and Bucklin 1989) and product quality (Hardie, Johnson, and Fader 1993). A few researchers have also incorporated reference price into economic theory and have developed models of consumer choice (e.g., Putler 1992). Others have considered reference price effects in modeling competitive behavior of firms and have developed managerial guidelines for retailers and manufacturers (Greenleaf 1995; Kopalle, Rao, and Assunção 1996).
Despite the wealth of available findings and the acknowledged theoretical and managerial importance of the reference price concept, there is no cohesive framework that has systematically examined its antecedents, the mechanisms by which it is formed, and its use by consumers. Winer (1988) provides a survey of the theoretical foundations and modeling of the reference price concept, Briesch and colleagues (1997) present an empirical comparison of different reference price models, and Kalyanaram and Winer (1995) draw empirical generalizations, but these reviews focus primarily on modeling-based investigations that use panel data for frequently purchased packaged goods (FPPG). Thus, there has not been a comprehensive assessment of what is known about reference price and what remains unresolved.
The goal of this article is to present an integrative review of published articles on reference price and related topics. In our attempt to synthesize the empirical evidence, we identify two fairly independent streams of research. The first stream takes a behavioral perspective and uses experimental approaches to assess the effects of external stimuli on consumers' internal reference price (IRP), price judgments, and other evaluations (e.g., Alba et al. 1999; Urbany, Bearden, and Weilbaker 1988). The second stream of research models alternative reference price formulations and tests their effects from the statistical fit of models calibrated on consumer panel data. (e.g., Briesch et al. 1997; Winer 1986).
We offer a framework that synthesizes the findings from both behavioral and modeling-based research streams, and we assess our current understanding of ( 1) the formation of reference price, ( 2) the retrieval of IRP from memory and the relative use of memory versus information available externally (hereafter, external reference price [ERP]), and ( 3) the effects of reference price on purchase decisions and evaluations. For each of the three areas of reference price research, we first review available prior research and present the findings as summaries. We then identify "research gaps" and provide directions for further research, which include a set of propositions. Next, we highlight the methodological challenge that arises from the confounding effects of consumer heterogeneity when reference price effects are estimated. We conclude with a brief review of the different domains of reference price construct and a discussion of the managerial implications.
A Conceptual Framework
Reference price has multiple conceptualizations. A common conceptualization views reference price as a predictive price expectation that is shaped by consumers' prior experience and current purchase environment (Briesch et al. 1997; Kalyanaram and Winer 1995). The theoretical rationale for this conceptualization comes from adaptation-level theory (Helson 1964), which holds that people judge a stimulus relative to the level to which they have become adapted. Thus, in a pricing context, the expectation-based reference price is the adaptation level against which other price stimuli are judged (Monroe 1973). Other conceptualizations of reference price include normative and aspirational standards (Klein and Oglethorpe 1987). A normative reference price is one that is deemed "fair" or "just" for the seller to charge (Bolton and Lemon 1999; Bolton, Warlop, and Alba 2003; Campbell 1999), and an aspiration-based reference price is based on what others in a social group pay for the same or similar product (Mezias, Chen, and Murphy 2002). Although we offer a brief review of the latter two conceptualizations in the concluding section of this article, our main focus is on the expectation-based reference price (for a comprehensive account of the fair price conceptualization, see Xia, Monroe, and Cox 2004).
Figure 1 serves as a framework for organizing the presentation of this review. At the core of the framework (see the left box in Figure 1), we include three main areas of reference price research. The first area of research examines the formation of reference price. The relevant areas of research interest here are identification of the inputs to IRP, integration and assimilation of the information, and the different representations of IRP in memory. The second area of research focuses on the retrieval and use of IRP. The key research issues here are the moderating effects of the accessibility of price information in memory (i.e., IRP) versus those available externally (ERP), retrieval of IRP under different task contingencies, and the biases that may occur during consumers' retrieval process. The third aspect of reference price research focuses on the effects of using reference price on a variety of buying decisions (e.g., brand choice, purchase quantity) and on making other evaluations and attributions.
Consistent with adaptation-level theory, the framework also proposes that consumers' prior purchase experiences, the current purchase context, and individual characteristics of consumers influence certain aspects of reference price formation, retrieval, and effects either directly or indirectly. Prior experiences during which consumers are exposed to price and promotional information create a price memory, the retrieval of which has subsequent effects. However, several contextual factors may moderate this influence. In our framework, we consider three contextual moderators: ( 1) the purchase occasion or task, ( 2) the store environment, and ( 3) the type of product being purchased.( n1) The purchase occasion and task moderators differentiate one purchase occasion from another (e.g., planned versus opportunistic purchase) and one purchase task from another (e.g., brand choice versus store choice task). The store environment moderators include retail pricing and promotional strategies, which are implemented by altering the depth and frequency of promotions through everyday low price (EDLP) or hi lo pricing; such promotions are often accompanied by the retailer's explicit provision of the advertised reference price at the point of purchase. The inclusion of product category moderators expands the scope of reference price research beyond FPPG to include durable products and services as well.
Finally, the framework considers the possibility that prior experience can vary across consumers as a result of individual differences in price sensitivity, brand loyalty, demographics, and so forth. These differences influence consumers' purchase history and incidence. Accounting for individual differences in assessing the reference price effects presents methodological challenges in reference price research. We consider each of these issues in greater depth in the following sections.
Reference Price Formation
We divide this section into three parts. First, we identify the information that consumers acquire over time and contextually, which serves as input to the formation of IRP. Second, we review the processes that consumers may use to integrate memory-based and contextual information. Third, we consider alternative mental representations of reference price. We offer a summary at the end of the section.
Prior purchase experience. Because panel data provide extensive information on consumers' prior purchases, modeling-based reference price research has used consumer purchase history as the main determinant of IRP for FPPG (for a summary of previously used IRP models, see Briesch et al. 1997). The following is a commonly used IRP model for brand i and consumer H on purchase occasion t:
( 1) IRPiHt = α X PriceiH(t - 1) + (1 - α) X IRPiH(t - 1) + βPromPromiH(t - 1).
This model of IRP is entirely memory based and is influenced by prior prices and promotions. The first two terms capture the effect of prior prices on IRP and have been shown to be the strongest predictors of price expectation. Parameter α ≤ α ≤ 1, signifies the recency effect of prior exposures to price on IRP. Studies have found that this parameter ranges from approximately .60 to .85 in different product categories, which indicates that prices encountered beyond two to three prior purchase occasions have negligible direct influences on IRP (for the results of a field study, see Dickson and Sawyer 1990). In addition to prior prices, consumers use previously encountered promotions to create a promotion expectation for a brand (Lattin and Bucklin 1989) that reflects their interest in obtaining transaction utility (Thaler 1985). Because the promotion expectation indicates the extent to which a consumer has been conditioned to promotions, it is usually operationalized as the proportion of times he or she purchased (or observed) a brand on promotion in the past. The greater the deal expectation, the lower is the IRP for the brand (Kalwani et al. 1990).
Summary 1: The following factors involving a consumer's prior purchase experiences have been shown to influence IRP:
• The strongest determinant of a consumer's IRP is the prior prices he or she observes.
• Prices encountered on recent occasions have a greater effect on IRP than distant ones.
• The greater the share of prior promotional purchases, the lower is the consumer's IRP.
Purchase context moderators. Although the reference price model presented in Equation 1 has been used frequently, it does not allow for differences in purchase contexts. Thaler (1985) demonstrates that reference points for an identical product differ simply because of differences in purchase contexts. One such purchase context is the type of shopping trip consumers make for FPPG (e.g., planned versus unplanned trip, regular versus fill-in trips). Bucklin and Lattin (1991) show that consumer processing of in-store promotional activities varies depending on whether a shopping trip is planned or opportunistic. Kahn and Schmittlein (1989, 1992) find that out-of-store promotions have a stronger effect on brand purchase decisions during a regular shopping trip (i.e., larger basket size), whereas in-store promotions have a stronger effect when the trips are fill-in (i.e., smaller basket size). Bell and Lattin (1998) demonstrate that large-basket shoppers are less price elastic in their individual category purchase incidence decisions but are more price elastic in their store choice decisions.
Although the preceding research was not conducted in the context of reference prices, it suggests that the shopping occasions should moderate the influence of prior price and promotional history on IRP.( n2) Further research might investigate whether the salience of prior prices in the formation of IRP varies by shopping trip types. Prices encountered during prior planned and regular shopping trips may be more salient (than those encountered during opportunistic and fill-in trips) for the IRP used for subsequent planned and regular shopping trips. Likewise, the effect of prior promotional purchases on the formation of IRP may also vary by shopping trip type. Out-of-store promotions may be more salient in the formation of IRP when a shopping trip is planned and the basket size is large than when the trip is opportunistic and the basket size is smaller. In-store promotions may be salient for both opportunistic and planned purchases.
Store environment moderators. A brand's IRP may vary by store because of the level of service provided, assortment offered, or store types (e.g., factory outlet, specialty store, mass merchandiser) (Berkowitz and Walton 1980; Biswas and Blair 1991). For example, the same price of a bottle of wine could be judged more favorably if it is sold in a specialty wine store than if it is sold in a discount wine store. Likewise, consumers may be more (less) price sensitive and thus have lower (higher) IRP when buying products from online retailers that provide comparative price (quality) information aimed at lowering search costs (Lynch and Ariely 2000). In addition, the promotional strategies that stores use may influence consumers' IRP. Stores implement these strategies by altering the frequency and depth of promotion by their adoption of either an EDLP or a hi lo pricing policy (see Neslin 2002).
Frequent deals and deep price cuts have been shown to lower consumers' IRP (Alba et al. 1999; Kalwani and Yim 1992). Kalwani and Yim (1992) report that the price consumers expected to pay for an item was significantly lower after they observed either more frequent or deeper promotions for the item on previous purchase occasions. However, there are several factors that have been shown to bias consumers perception of deal frequencies. Consumers tend to distort perceptions of deal frequency when they are random; in addition, their perceptions of deal frequency of a certain brand are affected by the dealing pattern of a rival brand (Krishna 1991). Krishna, Currim, and Shoemaker (1991) find that consumers tend to overestimate the deal frequency of infrequently promoted brands and underestimate the deal frequency of brands that are promoted more heavily. Distortions are also found to occur for depth of promotions based on how the promotion is framed. DelVecchio, Krishnan, and Smith (2003) find that promotions framed as a percentage off (versus cents off) influence consumers' price expectations more when the depth of promotion is high for low-priced products and when the depth is low for high-priced products. The effect of depth is found to decrease beyond a high level of discount (Gupta and Cooper 1992).
Summary 2: The negative effect of deal frequency on consumers IRP is moderated by (a) the dealing pattern (i.e., regular versus random) of the purchased brands, (b) the dealing pattern of competing brands, and (c) the framing of the deal (percentage off versus cents off). In addition, the marginal (negative) effect of deal frequency and depth on IRP decreases as the frequency and depth of promotions increases.
The effects of depth and frequency of promotions found in prior research have not been adequately integrated into the research on the formation of IRP at an EDLP versus a hi-lo store (cf. Kopalle, Rao, and Assunção 1996). Because all brands in an EDLP store are, in effect, being promoted as brands "always" on sale, this promotional strategy can be considered one of moderate discount depth but infinite frequency. Because promotion frequency has been shown to have a stronger influence than promotion depth on price perceptions (Alba et al. 1999), IRPs for brands sold in an EDLP store are likely to be lower than those of brands sold in a hi-lo store, ceteris paribus (Alba et al. 1994; Shankar and Bolton 2004). However, hi lo stores can influence IRPs for selected brands within these stores by deep and simple dichotomous discounts--that is, one regular (high) and one sale (low) price (Alba et al. 1999).
Another potential area for research is to investigate the effects of "rollback" prices on IRP. In advertising rollback prices, EDLP stores (e.g., Wal-Mart) often convey the message that additional cost savings they are able to obtain from suppliers are being passed on to customers. This explanation of additional price cuts within an EDLP store is presumably to minimize the negative effects of promotions on IRP. However, frequent use of rollback prices in predictable categories is likely to be noticed by consumers and incorporated into their price expectations, much like promotions in a hi-lo store.
Product category moderators. The variables included in Equation 1 are not appropriate for durables and services. Winer (1985) proposes a model for durable products in which IRP is a function of ( 1) price trend, ( 2) current and anticipated economic conditions (e.g., inflation), ( 3) predictive signals of future prices, and ( 4) household demographics. Focusing on the personal computer category, Bridges, Yim, and Briesch (1995) find that consumers' price expectations are also influenced by the relative level of technology used (e.g., processor speed) by a specific model in the same product category.
Because durables have longer interpurchase time than FPPG, the attribute configuration, technology used, and price of a durable may change significantly. The information acquired during prior purchase occasions is therefore less salient in the formation of a reference price for a durable product than it is for an FPPG. Thus, current prices of competitive products and economic and technological trends are likely to be better predictors of IRP for durable products. Moreover, compared with FPPG, the variations in attributes and features across choice alternatives are typically more discernible for durables. Thus, IRPs of durable products may be a hedonic function of the features and attributes they contain.
Summary 3: IRPs for durable products are influenced by such aggregate factors as anticipated economic conditions (e.g., inflation) and household demographics. In addition, in the formation of IRPs for durable products, competitive prices and differences in attribute configurations and features across alternatives are more salient than historical prices; historical prices of durable products are used only to discern a price trend, if it exists. Finally, consumers' price expectations are influenced by the technology used in a specific brand compared with other brands in the same durable product category.
In addition to the relative level of technology of a brand (or model), IRP for high-technology durables should also be influenced by consumers' estimates of cost of key inputs (e.g., Intel versus AMD microprocessor) and other externalities such as expected installed base and availability of complementary products. In addition, in many durable products, consumers use the price of a "default option" provided by the seller (e.g., Dell Web site) as an initial reference point. Further research is necessary to understand the influences of the default provider's characteristics and the attribute configurations of the default option.
There is limited research on the formation of reference prices for services. Services range from those that are purchased at regular intervals (e.g., oil change, hair cut, car wash) to those that are infrequently purchased and sometimes have long temporal separation between their purchase and consumption (e.g., cruise) (Shugan and Xie 2000). Because the former class of services is conceptually similar to FPPG, the usual factors, such as prior and competitive prices and promotions, and store characteristics (e.g., dealership versus an independent repair shop) should be significant predictors of IRP. For the latter type of services, extrinsic signals (e.g., reputation of the service provider, word of mouth, endorsements) (Bolton and Lemon 1999) and tangible signals (e.g., time spent on performing a service, cruise itinerary, refund policies) are likely to influence consumers expectations about service quality and, therefore, price expectations.
There is another class of services in which consumers make a long-term commitment to buy the service from a service provider, but the consumer's usage rate may vary (e.g., cable television, telephone calling plans, health club memberships). To investigate how consumers evaluate continuously provided services, Bolton and Lemon (1999) propose that consumers use a priori norms (i.e., reference points) of expected payments, performance, and usage rates. Consumers maintain mental accounts of whether the actual outcomes exceed (or fall below) the norms, which results in the assessment of fairness or "payment equity. The assessment of gains and losses in payment equity influences customer satisfaction and service usage rates aimed at reestablishing payment parity.
Because consumers have been shown to use reference points to evaluate a service, a relevant area for further research is to explore how IRP is formed for continuously provided services. One of the determinants of IRP is the type of pricing scheme that the service provider offers and what consumers adopt.( n3) When consumers adopt a usage-independent fixed fee or access charge (e.g., Internet service), the prices that competing providers charge may serve as a basis for comparison. Consumers may also convert the fixed (e.g., monthly) fee into a dollar per unit of expected consumption (e.g., dollar per minute) and use it as an IRP to monitor their usage pattern (Bolton and Lemon 1999). For a purely usage-based pricing scheme (e.g., calling card, metered parking), IRP is likely a weighted average of prior usage based payments; recent payments tend to receive greater weights (e.g., first two terms in Equation 1).
When consumers adopt a two-part pricing scheme for a service (e.g., mobile communication), a question that arises is whether they retain two separate IRPs, one for the fixed part and another for the variable component, or integrate the two components into a single IRP. Factors that may influence the formation of either a single IRP or multiple IRPs include ( 1) the relative magnitude of the fixed and the variable part of the price, ( 2) the consumer's need for controlling spending for the expense category (e.g., monthly cellular phone bills), and ( 3) the extent to which consumers link the amount spent with actual usage. When the variable part of the price is small compared with the fixed component and when the need to control the budget is high, consumers may retain an integrated IRP for the service category. However, when consumers' propensity to link price with usage is strong, consumers may retain separate IRPs for the fixed and variable components. On the basis of our preceding discussion, we offer the following proposition:
P1: For continuously provided services, IRP depends on the pricing scheme adopted.
(a) For a fixed-fee option, IRP is a function of competitors prices for similar services; in addition, consumers retain IRP as a dollar per unit of expected usage for monitoring actual usage.
(b) For a strictly variable pricing, IRP is a recency-weighted average of amount spent in the past.
(c) For a two-part pricing scheme, consumers retain either dual IRPs or a single IRP, depending on the relative magnitude of each part, budget importance, and perceived price usage equity.
In the preceding sections, we identified several antecedents of reference price. We now review the literature that has investigated the mechanisms by which consumers integrate the input information to form and/or update a reference point.
Theoretical perspectives. Researchers have adopted one of two theoretical perspectives to study how consumers construct and update IRP. One perspective uses theories from social psychology (e.g., Parducci 1965; Sherif and Hovland 1964), and the other relies more on economic theories on the formation of price expectations (e.g., Muth 1961; Nerlove 1958). The psychological perspective uses assimilation contrast theory (Sherif and Hovland 1964) to investigate how consumers integrate external information into their IRP (e.g., Lichtenstein and Bearden 1989). The theory suggests that for a given quality level, a consumer has a distribution of prices that are considered acceptable. The new price information is assimilated only if the observed price is judged as belonging to that distribution; the distribution of IRP is then updated in a manner akin to Bayesian updating. Several researchers have attempted to investigate the assimilation process empirically as a function of the distributional properties of price. Kalyanaram and Little (1994) find that in the unsweetened drinks category, consumers assimilate a price if it falls within approximately .75 times the price variability of the product.
Kalwani and Yim (1992) find that consumers assimilate prices that are within ±4% of the regular price of the brand. Han, Gupta, and Lehmann (2001) propose that the thresholds for assimilating prices are "fuzzy" or probabilistic.
The assimilation contrast theory was later augmented by range theory (Volkmann 1951) and range-frequency theory (Parducci 1965) that make predictions about the effects of the properties of the acceptable price range (e.g., end points and distributions) on price judgments. Recent marketing applications of these theories have shown that the assimilation (i.e., judgment) of a purchase price depends on the end points of the price distribution (Janiszewski and Lichtenstein 1999) and on the frequency distribution of prices (Niedrich, Sharma, and Wedell 2001).
Economists' views of integration of previously acquired price information in forming price expectations are based on economic interactions between the buyer and the seller. For example, the "rational expectation" model ((Muth 1961) suggests that consumers form expectations using the same decision rules that firms use. Therefore, the current price (set by firms) is an unbiased predictor of the price consumers expect to pay. Although several researchers in marketing have used this model to estimate reference price equations for FPPG, the empirical support of the rational expectation model is somewhat mixed (see Briesch et al. 1997; Jacobson and Obermiller 1990; Kalwani et al. 1990; Winer 1985, 1986).
The "adaptive expectation" model ((Nerlove 1958) introduces a mechanism by which consumers can adjust their prior expectations on the basis of the discrepancy between the observed and the expected prices. A consumer's expected price at time t can be expressed as follows:
( 2) IRPt + IRP (t - 1) + βAE [P[sub(t - 1) - IRP [sub(t - 1)]].
Note the similarity between the adaptive expectation model and a model derived from assimilation contrast theory. For a given difference between an observed price and IRP, the parameter βAE can be viewed as an assimilation parameter. If the parameter is close to zero, consumers are unaffected by the difference between IRP and P, and a contrast is deemed to have occurred. Conversely, a high value of βAE indicates that the observed price is assimilated. Note that by rearranging terms in Equation 3, we obtain the following:
( 3) IRPt = βAE X P(t - 1) + (1 - βAE) IRP(t -1).
Therefore, consumers' reference price is a weighted average of the last period's reference price and observed price. This form of updating has been used extensively in both modeling-based and behavioral research on reference price.
Summary 4: Research on how previously encountered prices are integrated to form a reference price has produced the following results:
• Assimilation contrast theory and the adaptive expectation model seem to depict the process of integration of prior prices and contextual information accurately.
• Consumers update their reference prices (a) by weighting their existing reference price and the observed prices and (b) by factoring in a price trend observed from prior prices.
Store environment moderators. In addition to integrating previously encountered information (e.g., prices) temporally, consumers contemporaneously integrate contextual information available in the store environment to form IRP. One stream of research involving the contextual influences on IRP has been in the area of retailer-provided advertised reference point (ARP). In many product categories, retailers explicitly provide ARP at the point of purchase to encourage either competitive comparisons (e.g., "compare at) or temporal comparisons (was now) of the actual purchase price (Biswas and Blair 1991; Lichtenstein and Bearden 1989; Mayhew and Winer 1992). Researchers theorize that ARP is first assimilated into consumers' IRP, which in turn influences purchase behavior or evaluations (e.g., Lichtenstein and Bearden 1989; Urbany, Bearden, and Weilbaker 1988). The assimilation process is captured in Equation 4:
( 4) IPRt = ω X ARPt + (1 - ω) X IRPt - 1.
The assumption is that a consumer enters a purchase environment with a prior IRP and adjusts it on the basis of the retailer's ARP. The weight ϖ 0 ≤ ϖ ≤ 1, signifies the extent to which the seller-provided ARP has an effect on consumers IRP. The ability of ARP to influence IRP is found to be affected by the plausibility of the ARP (Urbany, Bearden, and Weilbaker 1988), the difference between the ARP and the actual selling price (Kopalle and Lindsey-Mullikin 2003), and the semantic cues (e.g., was--now versus compare at) that retailers use to frame the sale (Lichtenstein, Burton, and Karson 1991). The literature on ARP is vast and has been reviewed and meta-analyzed in recent articles (see, e.g., Grewal, Monroe, and Krishnan 1998).
Bearden, Carlson, and Hardesty (2003) examine the effects of multiple ARPs for automobiles (e.g., dealer invoice price and manufacturer suggested retail price) on the judgment of an offer's fairness; they find that invoice price is more likely to be assimilated than manufacturer suggested retail price. In an Internet auction context, the provision of a reserve price, compared with a minimum bid, has been shown to raise the average bid. When both reserve and minimum bid are provided, the reserve is found to have a greater effect on the final bid (Kamins, Dreze, and Folkes 2004).
In addition to ARP, the retail environment provides a variety of other external price stimuli (i.e., ERPs), which consumers integrate when forming a reference point. Rajendran and Tellis (1994) explicitly model a context-based reference price and, on the basis of model fit, conclude that consumers use the lowest price in the category as an ERP. Mayhew and Winer (1992) suggest that the retailer-provided "regular" price of a brand serves as its ERP. Hardie, Johnson, and Fader (1993) propose that the current price of the brand chosen on the previous purchase occasion is a relevant ERP.
Because a purchase environment typically provides a large amount of information, consumers must be selective in their choice of which pieces of externally available information they attend to and assimilate in their IRP. An important determinant of selectivity is the size of a consumer's consideration set. Because consumers are expected to pay greater attention to the prices of brands they purchase more frequently, Mazumdar and Papatla (1995, 2000) propose a model in which current prices are weighted by the respective shares of purchases devoted to a brand. Deal-sensitive consumers may also be selective by integrating prices of only those brands that are on sale (i.e., featured or displayed) during a purchase occasion (Bolton 1989). Recent studies have also shown that when a purchase environment does not contain diagnostic price information, consumers unknowingly integrate "incidental" price information ((e.g., prices of completely unrelated products) (Nunes and Boatwright 2004).
Summary 5: The findings on the integration of information at the store environment are summarized as follows:
• Retailer-provided ARP that exceeds the selling price raises the consumer's IRP, even when the ARP is deemed to be exaggerated. The effect of ARP on IRP is nonlinear; it has an inverted-U shape. A moderately discrepant ARP has a stronger impact on IRP than either very similar or very dissimilar (i.e., implausibly high) ARP.
• The use of semantics aimed at competitive comparison (i.e., compare at) is more effective in raising IRP than is the use of temporal comparisons (i.e., was now). Cues that are distinctive in relation to the competition and have low consistency have stronger effects on IRP.
• In an automobile purchase context, the seller's invoice cost information is more readily integrated into an IRP than is a manufacturer's list price. In an Internet auction context, reserve prices are more readily integrated into an IRP than is a minimum bid.
• When faced with a large amount of externally available information, consumers are selective in deciding which pieces of contextually provided information are salient. Customers who are loyal to a few brands integrate prices of only the favorite brands, whereas switchers tend to integrate prices of promoted brands. In addition, lacking diagnostic information in the purchase environment, consumers unwittingly integrate readily available incidental and irrelevant price information.
Product category moderators. Other than the investigations of the assimilation of ARP, which sellers of durable products often provide, there is practically no research on how different pieces of information for durable products are integrated. We suggested previously that attribute differences are a significant predictor of IRP for durables. An important research question is how consumers integrate the attribute information in constructing an IRP as they sequentially evaluate attributes of different models of a durable product. For example, consumers who are interested in buying a personal computer may begin with a default option and then adjust their IRPs upward or downward as they consider either adding or subtracting attributes. Park, Jun, and MacInnis (2000) consider two default alternatives: a loaded model from which consumers subtract and a base model to which consumers add. They find that a loaded-model default yields higher prices paid and more optional attributes included as a result of insufficient adjustment from the initial anchor. This finding can be extended in the context of reference price.
For services, a fertile area for further research is to investigate how the fixed and variable parts of a two-part service price are integrated (see P1). Literature on partitioned pricing (e.g., Morwitz, Greenleaf, and Johnson 1998) suggests that consumers can use as an anchor either the fixed or the variable component and then insufficiently adjust for the other component of the price. Which of the two components of price serves as an initial anchor may depend on their relative magnitudes. In addition, when the fixed part serves as an anchor, the degree of adjustment of the variable part may depend on how frequently consumers pay the variable part and the magnitude of the variable portion when they do. The frequency effect may also be stronger than the magnitude effect because frequent payment of a moderate variable fee (in addition to the fixed fee) is more likely assimilated into IRP than rare occurrences of large magnitude.
P2: (a) IRP for a durable product depends on the default option that serves as an initial anchor from which consumers insufficiently adjust their IRPs upward or downward on the basis of addition or deletion of product features, respectively. (b) In integrating the fixed and the variable part of two-part prices of services, consumers use either the fixed or the variable part as an anchor depending on their relative magnitude and then insufficiently adjust upward to account for the other part. Frequent payment of a moderate variable fee is more likely assimilated into IRP than are rare occurrences of large magnitude.
Researchers typically assume that a reference price is stored in memory in a numeric form and at the brand or item level (e.g., a 64-ounce liquid Tide normally sells for approximately $5). We now consider other forms and levels of IRP and discuss when these alternative representations may occur.
Numeric versus nonnumeric forms of IRP. Price standards may not always be stored in shoppers' minds as "precise quantitative prices" ((Dickson and Sawyer 1990, p. 51). Price information is also encoded in memory as price ranks (e.g., Tide is usually more expensive than Wisk) or as price beliefs (e.g., Wisk is frequently on sale). Mazumdar and Monroe (1990) show that when people acquire price information incidentally (rather than under directed learning), they are more accurate in recalling price ranks than in estimating numerical prices.
A better understanding of the alternative forms of IRP may require further investigation on at least two fronts. First, research should investigate how prices are encoded at different stages of a purchase process. For example, price encoding during the initial stages of information search may result in prices being encoded at a more sensory level without strong associations with other information. In later stages, consumers may integrate price with nonprice information, which leads to a more evaluative representation of price (e.g., a Kenmore dishwasher is a good value for the money). The second potential area of inquiry is how consumers extract meaning from numeric price information (e.g., Schindler and Kirby 1997; Thomas and Morwitz 2005) or adaptively convert price evaluations (e.g., prices are "reasonable" in this restaurant)) to numeric price estimates.
Levels of IRP. Although IRP is typically modeled at the brand level, reference price may also be represented in memory at more aggregate levels. We present a hierarchy of IRP levels and discuss the contexts in which IRP may be conceptualized at each of these levels. Economists have proposed a two-stage purchase process in which consumers first decide how much to budget for an expenditure category and then decide which item within that category to purchase (e.g., Deaton and Muellbauer 1980). Thus, a consumer may set spending limits that represent how much he or she wants to allocate to different expenditure categories (e.g., weekly grocery shopping, Christmas shopping, eating out). The spending limit or the mental budget may serve as a reference point for monitoring the actual spending (Heath and Soll 1996; Thaler 1980) and may also help time-constrained consumers simplify the task of price comparison.
The reference price may also be encoded at a product category level, and it may be an average of prices of different brands (Monroe 1973) or the price frequently charged in a category (Urbany and Dickson 1991). Consumers may retain a category-specific reference price in product classes with low variability in brand quality and price, because small differences across brands may not justify the cognitive burden of attending to and retaining price information for several brands in memory.
As we noted previously, IRP is typically conceptualized at the brand level. Indeed, a brand-specific model of IRP has been shown to provide the best fit for data in several grocery product categories (Briesch et al. 1997). Unlike a category-level IRP, the brand-specific IRP assumes that each brand has its own reference point. Substantively, this conceptualization of IRP implies that consumers are interested in capitalizing on price and quality or unit price differences across brands. Within the brand-and item-specific IRP, consumers may also retain separate reference points for promotional and regular prices.
P3: (a) Consumers retain IRP in both numeric and evaluative forms. The form changes from a numeric form to a more evaluative form with repetitive purchase experiences. Consumers use the numeric structure (e.g., spatial location of digits) of price to form price evaluations or to derive numeric price estimates from evaluations. (b) The representations of IRP in memory are ordered at different levels of aggregation (i.e., spending level, product category level, and brand and item level). The level of aggregation in which IRP is represented depends on consumers' assessments of the cost and benefits of detailed price comparisons at the brand and item level.
Section summary. A summary of what is known about the formation of reference price and what remains unresolved appears in Table 1. Additional research is necessary on how purchase occasions may moderate the IRP formation, how reference prices for services are formed, and how retail pricing strategies may shape consumers' reference prices. Prior research has focused on the integration of information acquired over time and on integration of ARP and other contextual information. More work is necessary to understand whether consumers retain reference points at more aggregate (e.g., spending, category) levels and whether reference price could be represented in memory in nonnumeric forms as well.
Retrieval and Use of IR
We begin this section with a review of existing research on the moderating role of accessibility of IRP in memory in consumers' relative use of IRP versus ERP. We then identify the research gaps, examine how different purchase tasks may influence the IRP retrieval process, and consider the biases in consumers' price retrieval process.
The extent to which consumers use an IRP to make a purchase decision depends on the accessibility of price in memory (e.g., Biehal and Chakravarti 1983) and the perceived appropriateness of the remembered price versus the information available externally when making a price judgment (Feldman and Lynch 1988). To investigate the extent to which consumers use memory versus external information, researchers have used a hybrid (of IRP and ERP) model of reference price and have identified factors that determine the differential weights that consumers assign to memory versus external information. Supporting the accessibility diagnosticity principle, consumers who devote their purchase share to only a few brands are found to use IRP (or temporal) more than ERP (or contextual) reference price (Mazumdar and Papatla 2000; Rajendran and Tellis 1994). Driven by their idiosyncratic preference for certain brands, these consumers do not consider contextual prices of other brands salient for price judgment, and therefore they tend to use their favorite brands' prior prices as reference points. Moreover, being focused on only a few brands, these consumers can more readily remember prior prices of their favorite brands than consumers who tend to switch across a large number of different brands.
Mazumdar and Papatla (2000) also find that consumers who primarily buy during promotions tend to make greater use of external information. In addition, categories that are characterized by higher absolute price levels, shorter interpurchase time, and more stable prices (i.e., less frequent promotions) are associated with greater use of memory than external information, and vice versa. Kumar, Karande, and Reinartz (1998) show that the relative use of IRP and ERP is also moderated by a household's inventory position.
Summary 6: Research on the differential use of memory for prior prices versus externally available information has produced the following findings:
• Consumers use both memory and external information, but they assign weights to each that depend on consumer and product characteristics.
• The weight placed on memory (relative to external information) is related (a) negatively to the size of the consumer's consideration set, (b) negatively to the frequency of purchases during promotions such as features and displays, (c) positively to the price level of the product category, (d) negatively to the increase of interpurchase time of the category, and (e) negatively to the frequency of promotions in the category.
Because prior research has focused on the retrieval and use of price in the context of brand choice, there is no known research on how IRP is retrieved in other types of purchase tasks. In addition, research on less effortful price retrieval and the factors that bias the retrieval process is somewhat sparse.
Although the retrieval and use of IRP is relevant in many purchase contexts, we consider only two purchase tasks here: store selection decision and consideration set formation.( n4) The former task is performed outside of the store, and the latter may take place either in the store or out of the store.
Store choice decision. Deciding which store to visit depends on factors such as store location, assortment and quality of products, overall price level of the store, and prices of specific brands. As we noted previously, consumers retrieve store-specific reference prices to decide which store to visit. Because the retrieval of store prices depends on consumers' prior experience with the store, consumers are prone to draw a sample of prices of product categories (or brands) from memory that they are more familiar with and place greater weights on these prices in judging the overall store price levels. Moreover, the availability hypothesis (Tversky and Kahneman 1973) suggests that estimates of the probability that certain products will be on promotion depend on how easily the consumer can remember a previously encountered promotional episode. Thus, promotional frequencies may be over-or underestimated on the basis of what a consumer readily remembers about a prior purchase experience. When consumers make store choices (or switching) based on externally available price and promotional information (e.g., feature advertisements), they may evaluate the attractiveness of the sale price by comparing it with prices previously paid in the store or prices charged by competing stores. In either case, retrieval of prior and competitive prices is subject to the same set of previously discussed biases.
Consideration set formation. The decision to include certain items in the consideration set is influenced in large part by consumers' idiosyncratic preferences for specific brands and their prices. When the consumer forms a consideration set before actually visiting the store, the decision to include or not to include a brand in the set is mostly memory based. If the consumer forms the consideration set at the store, the decision to include a brand involves a mixed task in which recalled prices serve as reference points to judge the observed price of the same and other brands. A research question that requires investigation is how consideration set size or the frequency of promotions of the brands within the set affects retrieval. When the consideration set size is small (e.g., due to strong brand loyalties), the consumer can recall the encoded prices more easily than when the set size is large. This is also the case when the brands in the consideration set are infrequently promoted and their prices are relatively stable over time (Mazumdar and Papatla 2000; Rajendran and Tellis 1994).
P4: (a) In making a store choice decision, consumers retrieve store-specific reference prices as a basis for price comparison. However, the retrieval of store-specific reference prices may be biased as a result of erroneous sampling caused by relative familiarity with prices of different product categories and retail promotional strategies. (b) In consideration set formation, retrieval accuracy is moderated by the consideration set size and the frequency of promotions of the brands in the set.
Retrieval heuristics. In recent years, researchers have proposed retrieval processes that rely on simplifying heuristics (e.g., Schwarz and Vaughn 2000). Monroe and Lee (1999) argue that in many low-involvement purchases, price memory is implicit in that it serves as an input to performing a task successfully without the consumer being aware of the input information. Menon and Raghubir (2003) demonstrate that consumers use "ease of retrieval" as a heuristic to judge the appropriateness of the retrieved information, and the heuristic use occurs outside of awareness. Mere accessibility serves as an input to judgment even when the source of the information is discounted. Thus, factors that increase the ease of retrieval of previously encoded price information (e.g., small consideration set, dichotomous promoted/ regular prices) will increase the likelihood of consumers using prior prices as IRP. In addition, as Menon and Raghubir (2003) note, if distant information is unexpectedly remembered, consumers may use the remembered information to make judgments.
Biases in price retrieval. We previously discussed how consumer perceptions of promotions may be distorted and how retrieval of store prices may be biased as a result of consumers' prior experiences and store promotions. We now identify a few additional factors that may introduce biases in the retrieval of price. One such factor is the way the price is structured (e.g., product price, cost of delivery). Morwitz, Greenleaf, and Johnson (1998) argue that when the total price of a product is partitioned, consumers tend to allocate greater attention and processing resources to the product price, making it more memorable than the delivery cost component, thus resulting in retrieval biases. Specifically, this study shows that consumers tend to underestimate (i.e., recall a lower) total price more when the price is partitioned than when prices are aggregated. This finding can be extended to two-part prices of services and product bundles that are composed of a core component and add-ons (Janiszewski and Cunha 2004).
Retrieval biases may also occur because of spatial locations of digits in price. Recent research has shown that consumers convert the digits of price into an analog magnitude scale, and the spatial locations of the digits in price determine the extent to which the digits are attended to and processed and, therefore, recollected and used in price judgments (Dehaene 1997; Thomas and Morwitz 2005). The retrieval of previously encoded prices may also be influenced by interference caused by multiple tasks. For example, a vacationer evaluating the online price of an airline ticket may be distracted by an advertisement that depicts a low-price offer for a hotel in the destination city. An important research question is how consumers handle dual (retrieval) tasks and whether certain characteristics of the interfering tasks actually help retrieval.
Biases may also occur when consumers use readily available nonprice information to infer IRP because prices are not accessible in memory. Prices can be inferred from product quality levels (Bettman, John, and Scott 1985) or distinctive product features (e.g., class, make, or trim level of an automobile) (Murray and Brown 2001). However, literature on intuitive covariation assessment in social psychology has shown that people often detect illusory relationships because of deeply entrenched beliefs, which results in biased inferences (Crocker 1981). Thus:
P5: (a) In low-involvement purchase tasks, price memory (i.e., IRP) is implicit and is retrieved outside of awareness by invoking heuristics such as ease of retrieval. (b) Retrieval and use of IRP is biased because of partitioning of price (i.e., consumer's cost), spatial positions of the digits in price, and task interferences. (c) Consumers may infer IRPs of a brand based on available nonprice information. However, the inference may be biased as a result of consumers prior beliefs about the relationship between price and these nonprice attributes.
Section summary. Research on the accessibility diagnosticity moderators of the relative use of IRP and ERP is fairly extensive (Summary 6). However, more research on how IRP is retrieved from memory under different task contingencies is necessary. We consider store choice and consideration set formation and relate these tasks to certain types of price encoding that are activated in memory (P4). We also identify several variables that may influence and bias the retrieval process (P5).
Effects of Reference Price
Research using panel data has focused mainly on reference price effects on consumer brand choice decisions and, to a lesser extent, on purchase quantity and purchase-timing decisions. The behavioral stream has studied the effects of reference price on constructs such as perceived value of the offer, intentions to search for lower prices, and purchase intention (for a review, see Grewal, Monroe, and Krishnan 1998).
Increased availability of individual-level panel data spurred a flurry of research activity on reference price effects on consumer brand choice decisions. A summary of this research stream appears in Table 2. Because reference price is unobserved, its effect is typically inferred by comparing the fit of a brand choice model that contains no reference price with that of a model that incorporates a reference price term. The utility specification of the baseline model (Equation 5 in Table 2) contains the usual price and promotional variables, consumer preference, and brand loyalty (Guadagni and Little 1983).
Symmetric "sticker shock" effect. Winer (1986) was the first to propose a "sticker shock" model of reference price, which includes an additional term that captures the difference between the brand's reference price and its purchase price (Equation 6 in Table 2). The assumption is that a positive difference between the reference price and the purchase price increases the utility of the item, and a negative difference lowers it. However, responsiveness to a positive difference is assumed to be the same as that to an equal negative difference.
As we show in Table 2, several researchers have used this particular specification and found that the model outperforms the baseline model, thus making the sticker shock effect of reference price empirically generalizable (Kalyanaram and Winer 1995). However, Lattin and Bucklin (1989) find that the inclusion of a similar reference effect of promotion (i.e., actual versus expected promotion) makes the symmetric reference price effect not significant. Bell and Lattin (2000) find that after they account for heterogeneity in price sensitivities, the sticker shock effect is somewhat reduced, but it remains significant. Chang, Siddarth, and Weinberg (1999) find that the sticker shock effect in brand choice disappears when the heterogeneity of consumers purchase timings is taken into account.
Asymmetric reference price effect. According to prospect theory (Kahneman and Tversky 1979; Thaler 1985), when an observed price is higher (lower) than the reference price, consumers encode it as a loss (gain). Loss aversion dictates that consumers are more sensitive to losses than to gains. The asymmetric utility function appears in Equation 7 in Table 2. This table shows that the evidence of loss aversion is mixed. When consumers are segmented on the basis of their brand preferences (or loyalties) or their price sensitivities, either the loss aversion effect is reduced or it disappears (Bell and Lattin 2000; Krishnamurthi, Mazumdar, and Raj 1992; Mazumdar and Papatla 1995).
Krishnamurthi, Mazumdar, and Raj (1992) empirically investigate the reference price effects on purchase quantity decisions in frequently purchased product categories. The study finds a significant effect of reference price, but the effect is mediated by consumer brand loyalty and household inventory levels. When household inventory reaches a stock-out level, brand-loyal consumers are found to be more sensitive to perceived gains than to losses when shopping for their favorite brands. However, brand-loyal consumers are more sensitive to losses when the purchase quantity decision is made before the stock-out. No such difference is found for the switcher segments.
Bell and Bucklin (1999) consider how reference price affects purchase timing. Using Loewenstein's (1988) framework of intertemporal choice, Bell and Bucklin posit that at every purchase occasion, consumers compare the relative attractiveness of buying into a category with the prospect of postponing the purchase. Thus, expected category attractiveness acts as the benchmark and is assumed to be a function of individual background factors (e.g., inventory) and marketing-mix variables (e.g., promotions). The study finds that during a given shopping visit, the purchase postponement that results from a perceived loss (i.e., negative difference between actual category value and the reference category value) significantly exceeds the purchase acceleration that takes place when a gain is perceived.
Summary 7: (a) The symmetric sticker shock effect of reference price on brand choice is empirically generalizable. However, the evidence for loss aversion is mixed. (b) The effect of reference price on purchase quantity is mediated by household inventory position and brand loyalty. (c) Reference price has a significant effect on consumers purchase-timing decisions, in which they evaluate the "attractiveness" for buying into a category now or later.
Although there is considerable research demonstrating reference price effect on brand choice decisions, little is known about how reference price affects store choice decisions and consideration set formation. The relevant questions here are, What are the likely decision sequences? Are the antecedents that are identified in brand choice models still relevant here? If not, what additional factors might be relevant? and How might these factors interact with reference price? Further research should also investigate ( 1) the role of reference prices in services and durable goods purchase decisions and ( 2) the effects of reference price in other evaluations and attributions.
Research on the reference price effects on purchase quantity can be extended to include service quantity (i.e., usage) decisions as well. Prior research has found that the actual usage depends on consumers' price expectations and satisfaction with the overall payment equity (Bolton and Lemon 1999). Further research might also investigate the reference price effects on service quantity decisions under different pricing policies that are postulated to shape consumers price expectations (P1). The methodological challenge here is that reference price formation may not be exogenous to usage expectation and prior usage level.
Purchase timing is a critical decision for most durable product purchases. A fertile area for further research is to investigate reference price effects on durable goods purchase timing. In the past, researchers have used a conditional hazard function approach to investigate timing of frequently purchased products (Jain and Vilcassim 1991).
Research could use similar methodologies in which the reference price term and other variables of interest are included as covariates.
Brand extensions and brand equity. Aside from the effects on purchase decisions, reference price may also influence evaluations of brand extensions. Jun, MacInnis, and Park (2003) demonstrate that consumers' price expectations of a brand extension are affected by the price of the parent brand, and the effect is moderated by the parent brand's relative price in the parent category and the dispersion in prices in the extension category. The parent brand's prices may also evoke quality associations and influence expectation of quality of the brand extension. Reference price may also moderate the effects of frequent promotions on the long-term negative impact on brand equity (Jedidi, Mela, and Gupta 1999).
Attributions. Nonpurchase influences of reference price include consumers' attributions of fairness of the price that a seller charges and imputation of the seller's motives behind raising or lowering prices. Urbany, Madden, and Dickson (1989) find that consumers appear more ready to accept price increases when sellers provide explicit cost justifications. Campbell (1999) demonstrates that people are more likely to judge the increased price as unfair when they infer that the seller is attempting to earn greater-than-normal profits. However, the seller's reputation moderates this effect. Consumers accord the benefit of the doubt to more reputable sellers. Xia, Monroe, and Cox (2004) propose that perceived unfairness of a price results in lower value perception and evokes negative emotions, which may result in behavioral responses such as withdrawing from a purchase, spreading negative word of mouth, and even engaging in legal actions. This stream of literature can be extended to further study whether reference prices influence other inferences, including consumers' perceptions of sellers deceptive practices.
Section summary. The research on reference price effect on consumer brand choice decisions is extensive. There is also evidence of effects on purchase quantity and category purchase. The purchase quantity effects should be extended to service usage, and purchase-timing effects need to be tested in the context of durable purchases. The role of reference price on other purchase decisions, such as store choice and consideration set formation, should also be examined. In addition, research is needed to understand how reference prices may influence evaluations of brand extensions and brand equity.
Methodological Challenges
Because IRP is a latent construct, its inferred existence and effects are inevitably open to questions about the appropriateness of methodologies used to model the construct and measure these effects. One such question is directed at the stream of research that uses panel data to infer reference price effects, and it is related to the role of customers (cross-sectional) heterogeneity confounding the reference price effects. There are also questions about the effects of reference price overlapping with the effects of other price-related constructs.
When assessing reference price effects, researchers model reference price using temporal data (e.g., prior prices paid, promotions, stores visited), which differ across consumers because of differences in price sensitivities, brand preferences, loyalties, and purchase timing. The reference price effects are estimated by pooling cross-sectional purchase history data for all households. This approach introduces potential confounding effects that arise from customers heterogeneity in their price sensitivities (Bell and Lattin 2000) and, thus, in their purchase timings (Chang, Siddarth, and Weinberg 1999).
Heterogeneity and loss aversion. Bell and Lattin (2000) contend that price sensitive (insensitive) consumers have lower (higher) reference points because, on average, they pay a lower (higher) price. Therefore, these consumers experience more losses (gains) in their purchase histories. Thus, the inferred loss aversion in the results may simply be due to the cross-sectional heterogeneity in price sensitivities rather than to the same consumer exhibiting a greater sensitivity to losses than to gains. In addressing the heterogeneity issue, Bell and Lattin (2000) consider both common (occasion-specific) and brand-specific reference price formulations (note that the latter permits each brand to have its own reference price and therefore is less correlated with price sensitivity) and use a mixture model (Kamakura and Russell 1989) to account for heterogeneity in not only price sensitivity but also preference. They find that ignoring heterogeneity in price sensitivities significantly overstates the loss aversion parameter, but it remains significant in the refrigerated orange juice category. The study includes additional product categories, and in all 11 categories, the loss aversion parameters in multisegment models are smaller than those in single-segment models (i.e., no heterogeneity) and are significant in only 6 of the 11 categories. Other studies that have accounted for heterogeneity also find that the loss aversion phenomenon is attenuated, and in some cases, the gain parameters are greater than the absolute values of loss parameters (e.g., Krishnamurthi, Mazumdar, and Raj 1992; Mazumdar and Papatla 2000).
Heterogeneity and symmetric sticker shock effect. The sticker shock model assumes that the responsiveness to gains is the same as the responsiveness to losses. Bell and Lattin (2000) report significant sticker shock effect in 10 of 12 categories after accounting for consumer heterogeneity in price responsiveness. Briesch and colleagues (1997) use a latent class mixture model to account for heterogeneity and find that the symmetric reference price model fits better in all four categories. Krishnamurthi, Mazumdar, and Raj (1992) carry out a priori segmentation of consumers into loyals and switchers and allow for each brand to have its own price and sticker shock parameters. This study shows that for all six brands in two product categories, the symmetric reference price model performs significantly better than the model that does not include a sticker shock term.
Chang, Siddarth, and Weinberg (1999) consider purchase-timing heterogeneity in which price sensitive consumers time their purchases to capitalize on lower prices and, thus, have a lower reference price than consumers who do not. Using simulated data, the authors find that when purchase timing and price responsiveness heterogeneities are not accounted for, there is a significant upward bias in the reference price estimate. Although the existence of purchase-timing heterogeneity is found to be sufficient for the bias to occur, the price responsiveness heterogeneity alone is not sufficient (i.e., the range of price sensitivities must be beyond what is observed in prior research) for the bias to be significant.
Note that reference price effects may manifest in brand choice (e.g., switching) and in purchase timing (e.g., purchase postponement or acceleration). Studies that use choice data account for heterogeneity in price sensitivities and brand preference, conditional on the models being estimated only on choice data (Bell and Lattin 2000; Briesch et al. 1997). As we noted previously, these studies find that the loss aversion effect of reference price is significantly attenuated, but the symmetric reference price effect remains significant. Bell and Bucklin (1999) focus exclusively on purchase timing and find a significant reference price effect in consumers' decision to buy now or later. Thus, if purchase timing is explicitly included in the model and if consumers exhibit a propensity to delay a purchase to avoid a loss and to accelerate a purchase to capitalize on a gain, the reference price effect in brand choice should be reduced or even disappear (e.g., Chang, Siddarth, and Weinberg 1999).
Summary 8: The confounding roles of consumer heterogeneity in the estimation of loss aversion and sticker shock effects are as follows:
• The loss aversion effect in brand choice models is significantly attenuated (and may even disappear) when consumer price response heterogeneity is considered.
• Accounting for heterogeneity in consumer price sensitivities reduces the sticker shock effect in brand choice models, but the effect remains significant.
• When reference price effect is present in purchase timing, ignoring purchase-timing heterogeneity overstates the reference price effects (both loss aversion and sticker shock) in brand choice.
Given the confounding effects of consumer heterogeneity found in these studies, two research issues must be resolved. The first issue is whether the effect proposed by prospect theory is indeed present in frequently purchased grocery product categories. Because the attenuating effect of heterogeneity has been demonstrated in many product categories, adding more product categories to verify the existence of the loss aversion phenomenon may not be fruitful. Instead, the answer may lie in the design of controlled experiments similar to that of Kalwani and Yim (1992) that can experimentally induce the reference points, verify their existence through manipulation checks, and assess whether people exhibit loss aversion in their choice decisions.
Second, reference price research that uses panel data requires a comprehensive assessment of the role of heterogeneity in the estimation of not only the loss aversion effect but also the symmetric reference price effect. It is important to identify the different sources of individual-level heterogeneity, such as price responsiveness, purchase timing, and brand preference. In addition, a comprehensive study should use multiple methods to account for heterogeneity. These methods could range from a priori classification of segments (e.g., loyals versus switchers) to more rigorous statistical procedures, such as random coefficient models or latent class mixture models. The random coefficient model should permit different assumptions about variables that are susceptible to heterogeneity and their distributional characteristics.
In addition to cross-sectional heterogeneity confounding the reference price effect, there is also a question of overlap between the reference price construct and other price-related constructs, such as price and promotion sensitivities. Erdem, Mayhew, and Sun (2001) find that the reference price effects for both gains and losses are significantly correlated with consumer sensitivities to price, promotions (i.e., features and displays), and brand loyalty both within and across categories. Although the authors conclude (p. 451) that "reference price sensitivity is distinct from other sensitivities," it would be useful to investigate further the causal links among price and promotional sensitivities, brand loyalties, and reference price effects.
Discussion and Conclusion
We provide an assessment of our current understanding of ( 1) how reference prices are formed, ( 2) how reference prices are retrieved and used, and ( 3) the effects of reference price. We offer summaries of prior findings and an agenda for further research, which includes a set of propositions. We also provide a critical assessment of the role of customer heterogeneity, which has raised questions about the validity of reference price effects found in modeling-based research. In this section, we discuss the alternative domains of the reference price construct and briefly review the normative models that have incorporated reference price into the demand function to draw managerial implications.
In this review, we conceptualized reference price as price expectation, which is based on consumers' memory or contextual information. However, justifications of the reference price construct are also drawn from other theoretical domains that conceptualize reference points as normative and aspirational. A normative reference price may be the price that consumers consider fair or just (Bolton and Lemon 1999; Campbell 1999; Kahneman, Knetsch, and Thaler 1986). The judgment of fairness is determined not only by prior and competitive prices but also by consumers assessment of the seller's cost and what is deemed to be a normal profit (Bolton, Warlop, and Alba 2003; Thaler 1985). The dual entitlement principle (Kahneman, Knetsch, and Thaler 1986) suggests that manufacturers are expected to abide by community standards of cost and profit, and consumers "punish" errant sellers that stray from these norms. Xia, Monroe, and Cox (2004) have developed a conceptual framework for the price fairness construct that identifies the key factors influencing consumer price fairness and outcomes of perceived unfairness.
Aspiration-based adaptation levels have been conceptualized in organizational research (Cyert and March 1963). The level at which an organization aspires to perform depends on its prior aspirations, discrepancies between the aspired and actual performance, and how performance compares with that of others in the group (Mezias, Chen, and Murphy 2002). This view is also consistent with the social comparison theory, which postulates that entitlements received by an individual are compared with those received by others in the group (Major and Testa 1989). In a pricing context, aspirational reference price is therefore a function of not only the usual prior and contextual prices but also what others in a social group pay for the same or similar products. If someone pays a low price, the aspiration level of others in the social group is also adjusted downward, and vice versa.
The existence of multiple conceptualizations of reference price raises the question whether there are certain conditions under which one type of reference price is more likely to be evoked than others. We propose that the relative propensity to use one of the three types of reference price (i.e., expectation based, normative, and aspirational) is a function of ( 1) temporal stability or predictability of prices, ( 2) level of competition within a category, ( 3) price transparency, and ( 4) the extent to which a consumer is locked in to the consumption category. An expectation-based reference price is likely to be used in product categories that are characterized by a high level of competition (i.e., many alternatives), relatively stable prices over time, and transparent pricing. However, a fair or just price benchmark is likely to be evoked when a category is monopolistic or contains few competitors, when prices charged by competing firms lack transparency, and when consumers are locked in to the category because of either the essential nature of the product (e.g., medicine, gasoline) or long-term contracts. Finally, when firms use discriminatory pricing that lacks transparency (e.g., airline pricing, negotiated pricing), which causes significant variation in prices paid across consumers, aspirational benchmarks are likely to be evoked. All three conceptualizations of reference price may come into play in any given decision, though the specific factors or contexts we previously noted are likely to determine which reference price concept becomes more dominant.
A limitation of our framework is that it is not suitable to assess the profit-maximizing implications at the firm level explicitly. Therefore, it is useful to review selected studies that have developed analytical models to assess the profit implications for firms when reference price is included in the consumer demand function. Greenleaf (1995) shows that reference price effects can increase profits on promotions, and he demonstrates how a retailer can develop an optimal strategy for repeated promotions over time that maximizes profits from such effects. The study shows that in the presence of reference price effects, the optimal strategy of a monopolist is to institute a cyclical (high low) pricing policy. Kopalle, Rao, and Assunção (1996) generalize this result to an oligopoly while considering customer heterogeneity in both reference price formation and differential weighting of gains and losses. They show that when heterogeneity has been accounted for, cyclical pricing policies are optimal. However, if the market consists only of loss-averse buyers, the optimal strategy is a constant price.
These findings suggest that reference price should be an important component of managerial decisions about pricing and promotional strategies. There is a growing interest in assessing the impacts of price promotions on category demand (Nijs et al. 2001), the long-term profitability of firms, and brand equity (Dekimpe and Hanssens 1995; Jedidi, Mela, and Gupta 1999). This stream of research can be augmented by incorporating reference price effects in the assessment of the impacts of promotions on long-term category expansion (or contraction) and profitability and the usual short-term effects.
In addition to the normative prescriptions, this review presents summaries and propositions that may offer useful managerial insights into the roles of reference price in consumer purchase decisions in different product categories. For example, this review proposes that attribute configurations of the default option influence the formation of IRP. In many online (e.g., computer) and in-store (e.g., automobile) purchases, firms can present a default option to create an initial anchor and control the sequence of subsequent additions or deletions of attributes. Likewise, firms can invoke consumer interest in a product bundle by framing a low price for the core component (e.g., central processing unit) that serves as an initial anchor for evaluating bundles.
For services, we propose that the pricing scheme (i.e., fixed, variable, or two-part) should significantly influence consumers' IRP, which in turn might influence consumers usage of the service. A high fixed fee may encourage consumers to increase usage of a service so that it justifies the fee, whereas a variable fee may discourage heavy usage of the service. Firms (e.g., electronic retailers) may capitalize on the increased usage by advertising and cross-selling products to generate additional revenue. Conversely, firms (e.g., utilities) that want to discourage usage may inform users when their usage has exceeded their norms. Some of these propositions may also be useful in the development of normative models for services, in which IRP is a function of different pricing schemes and is included in the demand function. Profitability of a pricing scheme can be assessed under different cost structures and capacity constraints.
With respect to pricing and promotional strategies, many sellers routinely provide ARP to influence consumers reference points. In addition, as we note in the review, a retailer can frame a selling price relative to the cost either by directly providing the cost information (e.g., invoice price) or by influencing consumers' perceptions of the retailer's cost (e.g., rollback prices). Our review also offers managerial guidance on how EDLP and hi lo stores may compete. An EDLP strategy creates a favorable store-level reference point. Nevertheless, a hi lo store can achieve a competitive advantage if it selects certain product categories in which it regularly offers deep and dichotomous discounts. The goal here is to create a stable and accessible memory for low prices.
The type of reference price a consumer uses and the effect of the reference price have been shown to vary across consumers, creating an opportunity for segmenting and targeting consumers on the basis of reference price. Consumers can initially be divided into a reference price segment and a non reference price segment and then can be characterized by behavioral (e.g., price and promotion sensitivity, brand loyalty) and sociodemographic factors (Arora, Kopalle, and Kannan 2001; Erdem, Mayhew, and Sun 2001). Reference price consumers can be further segmented into an IRP and an ERP segment (Kumar, Karande, and Reinartz 1998; Mazumdar and Papatla 2000; Moon and Russell 2004). Because different reference price segments use different referents, firms should use appropriate strategies to target each segment. A was now framing is likely to be effective for an IRP segment, whereas a compare-at framing is more suited for ERP users who construct reference points at the point of purchase.
Finally, we propose an expanded view of reference price that includes more aggregate levels of conceptualization, such as spending levels and product category-level IRP. If consumers set a spending limit as a reference point for a purchase task (e.g., family vacation), a strategy that asserts that the total spending will be below the limit is likely to be more effective than a strategy that focuses on prices of a specific component of the purchase. In addition, consumers may also retain IRP in nonnumeric forms. An understanding of the different representations of IRP is significant because price communication messages must be consistent with these representations. Simply advertising a low price may not convey the notion that it represents a good value for the money.
( n1) The three moderators and the components in each cover the main areas of prior and potential research on reference price. However, note that the list of moderators is by no means exhaustive.
( n2) We review the research on planned/regular versus opportunistic/fill-in to illustrate the potential effect of purchase context moderators. There can be a variety of other purchase context moderators (e.g., purchase for gift giving, purchases made during a vacation).
( n3) For expositional ease, we assume that pricing scheme is exogenous. When a firm offers multiple pricing schemes, the scheme that consumers adopt likely depends on their expected use of the service (see, e.g., Danaher 2002).
( n4) These tasks are not independent. Items in the consideration set may influence the store choice decision, and vice versa. A brand choice task can be considered a special case of consideration set formation performed at the point of purchase. Other tasks for which price retrieval is necessary include purchase timing decisions.
The authors thank the three anonymous JM reviewers for their insightful and helpful comments at each stage of the review process. The authors also thank Yong Liu and Jason Pattit for their comments on the manuscript. They are grateful for the financial assistance granted to the first author by the Earl V. Snyder Innovation Management Center of the Whitman School.
Legend for Chart:
A - Research Areas
B - Extent of Research
C - Prior Findings and Further Research
A
B
C
Antecedents
Purchase History
Extensive
Effects of prior prices, promotions, and recency of
purchase are well established (Summary 1).
Contextual Moderators
• Purchase occasion
Low
Further research: moderating effects of different shopping
occasions (e.g., planned versus unplanned) on the roles of
prior prices and promotions on IRP.
• Store environment
Moderate
Effects of depth, frequency, and framing of promotions on
IRP have been demonstrated (Summary 2). Further research:
effects of store pricing policy (hi-lo versus EDLP) on IRP.
• Product category
Durables:
moderate
Effects of economic conditions, technology, and attribute
configuration have been demonstrated (Summary 3). Further
research: effects of input costs, externalities, and
default options on IRP.
Services: low
Further research: effects of pricing schemes (e.g., fixed,
variable, two-part pricing) on IRP (P1.)
Integration
Purchase History
Extensive
Temporal integration is captured well by the adaptive
expectation model and assimilation-contrast theory
(Summary 4).
Contextual Moderators
• Store environment
Extensive
Effects of ARP on IRP, the weighting of contextual
information, and the influence of irrelevant information
are well established (Summary 5).
• Product category
Durables: low
Further research: investigating the anchoring effects of
a default option and sequential addition/deletion of
attributes on IRP for durables (P2a.)
Services: low
Further research: investigating the integration of
two-part prices of services (P2b.)
Representations
Low
Further research: identifying alternative forms (e.g.,
numeric versus nonnumeric) and levels (budget, category,
brand/item) of IRP (P3.) Legend for Chart:
A - Models
B - Utility Specifications
C - Sample Studies
D - Effects Studied
E - Product Categories
F - Special Features
G - Results
A B
C D
E F
G
Baseline (5) UiHt = β0,i
+ βp x PriceiHt
+ βProm x PromiHt
+ βLoy x LoyaltyiHt
+ εiHt
Guadagni and Little No reference price
(1983) effect
Symmetric (6) UiHt = β0,i
sticker + βp x PriceiHt
shock + βProm x PromiHt
model + βLoy x LoyaltyiHt
+ βref(RPiHt - PriceiHt)
+ εiHt
Winer (1986) Sticker shock
Coffee (three brands) Multiple price
expectation models.
Significant sticker
shock effect for two
brands.
Lattin and Bucklin Sticker shock and
(1989) reference promotions
Ground coffee(ten Includes promotion
stockkeeping units) expectation term.
Reference price is
not significant in the
presence of
promotion effect.
Mayhew and Winer Sticker shock
(1992)
Yogurt Multiple reference
price (IRP and ERP).
Both IRP and ERP
effects are
significant.
Rajendran and Tellis Sticker shock
(1994)
Salted and unsalted Multiple reference
saltine crackers prices.
Significant sticker
shock effects when
ERP is included.
Krishnamurthi, Sticker shock
Mazumdar, and Raj
(1992)
Ground coffee and an Consumers
undisclosed category segmented on the
basis of brand loyalty;
brand-specific effects.
Significant sticker
shock effect for both
loyals and switchers.
Chang, Siddarth, and Sticker shock
Weinberg (1999)
Yogurt, ketchup Purchase-timing
heterogeneity
accounted for.
Sticker shock effect is
not significant.
Bell and Lattin (2000) Sticker shock
Orange juice, bacon, Price-sensitivity
butter, margarine, heterogeneity
crackers, sugar, paper accounted for.
towel, ice cream,
detergent, hot dogs,
tissue, soft drinks
Sticker shock effect is
significant in 10 of 12
categories.
Asymmetric (7) UiHt = β0,i
reference + βp x PriceiHt
price + βProm x PromiHt
model + βLoy x LoyaltyiHt
+ Li, βL(PriceiHt
- RPiHt)
+ GiβG(RPiHt
- PriceiHt) + εiHt
where
Li = 1 if PriceiHt > RPiHt,
0 otherwise;
Gi = 1 if PriceiHt < RPiHt,
0 otherwise.
Kalwani et al. (1990) Loss aversion
Ground coffee Reference price is
separately modeled.
Loss aversion is
supported.
Krishnamurthi, Loss aversion
Mazumdar, and Raj
(1992)
Ground coffee and Consumers
another undisclosed segmented on the
category basis of brand loyalty;
brand-specific effects.
Loss aversion is
supported for one of
six brands.
Hardie, Johnson, and Loss aversion in price
Fader (1993) and quality
Refrigerated orange Reference price is a
juice current price of a
previously chosen
brand.
Loss aversion for
both price and quality
is supported.
However, quality loss
is greater than price
loss.
Kalyanaram and Little Latitude of price
(1994) acceptance, loss
aversion
Sweetened and Zone of indifference
unsweetened drinks around the reference
price.
Latitude is supported;
loss aversion is
supported.
Mazumdar and Loyalty-based
Papatla (1995) segmentation of IRP
and ERP use
Margarine, liquid Estimate brand
detergent loyalty threshold to
assign to IRP or ERP
segment.
Brand loyalty
influences IRP or
ERP use. Loss
aversion is not
supported in either
segment.
Briesch et al. (1997) Identify the
best-fitting reference
price model
Peanut butter, liquid Heterogeneity
detergent, tissue, accounted for.
coffee
Brand-specific IRP
provides the best fit
for the data. Loss
aversion is not
supported in the any
of the four categories.
Mazumdar and IRP- and ERP-based
Papatla (2000) segmentation
Liquid detergent, Use of mixture model
ketchup, tissue, to segment
yogurt consumers.
Consumers are found
to be segmented on
the basis of IRP and
ERP use. Loss
aversion is present in
only one segment in
each category.
Bell and Lattin (2000) Loss aversion
See previous Bell Preference and price
and Lattin (2000) sensitivity accounted
entry for.
No evidence of loss
aversion.
Erdem, Mayhew, and Consumer
Sun (2001) heterogeneity in gain
and loss
responsiveness
Ketchup, peanut Considers
butter, tuna cross-category
correlations
and accounts for
consumer
heterogeneity.
Loss sensitivity is
heterogeneous;
reference price
effects are correlated
with other
sensitivities.DIAGRAM: FIGURE 1; A Conceptual Framework for the Review of Reference Price Research
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~~~~~~~~
By Tridib Mazumdar; S. P. Raj and Indrajit Sinha
Tridib Mazumdar is Professor of Marketing, Martin J. Whitman School of Management, Syracuse University (e-mail: mazumdar@syr.edu). S.P. Raj is Professor of Marketing, Cornell University (e-mail: spr24@cornell.edu). Indrajit Sinha is Associate Professor of Marketing, Fox School of Business and Management, Temple University (e-mail: isinha@temple.edu).
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Record: 129- Reflections of a Marketing Educator with Scholarly Ambitions (Book). By: Kerin, Roger A.; Clark, Terry. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p186-190. 5p.
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Section: Book ReviewsReflections of a Marketing Educator with Scholarly
Ambitions (Book)
Some years ago, I had the pleasure of teaching an undergraduate marketing principles class in a 500-seat auditorium at the University of Texas at Austin. Toward the end of the semester, a student in the class came to my office and politely inquired, "Are you Professor Kerin?" "Yes, come in," I replied. An astonished look covered the student's face as he exclaimed, "You look so different up close!" I tactfully ignored the remark but wondered what he expected to see in a close encounter. The same thought surfaced when Terry Clark asked me to write an essay that described the "personal and idiosyncratic aspects of my research, my research process, as well as the story of my formation and development as a scholar." However, I came to view Terry's invitation as an occasion to recall events and experiences, to recognize influential people and perspectives, and to acknowledge the role that choice and chance have played in shaping my academic career and my diverse scholarly pursuits. I also viewed his invitation as an opportunity to reflect on the origin and context of some of my published work and the context in which it was written. In doing this, I touch on my research orientation and highlight how my teaching interests and research endeavors have reinforced each other over the past two decades.
I am now beginning my fourth decade as a marketing educator. My biography shows the receipt of a doctorate in business administration from the University of Minnesota in 1973 and a faculty appointment at Southern Methodist University (SMU) the same year. So much for recorded history.
The experiences preceding these events would affect my approach to marketing education and scholarship for the next 30-plus years. Let me explain.
I have long considered myself an accidental academic. As a twentysomething pragmatist of the 1960s, graduate business education meant earning an MBA and embarking on a corporate career. Acceptance to the master's program at Minnesota was an auspicious start. I soon found myself immersed in classics such as Alfred Sloan's My Years with General Motors and Chester Barnard's The Functions of the Executive, enrolling in courses with names such as "Executive Leadership and Business Policy," and satisfying MBA core curriculum requirements. Application-based classes that featured case analyses, role-playing exercises, and field projects were of special interest to me.
My preference for hands-on classes was first noticed by Bill Rudelius, my graduate advisor. As I approached graduation, Bill asked me to join his business and university-sponsored consulting team to help assess retailing opportunities in a lower-income area of Minneapolis. The area of the city had suffered from civil unrest and vandalism of the kind common in the late 1960s, and its commercial structure was distressed. Over the following several months, I interviewed local residents and shop owners, deciphered trade-area census data, and prepared detailed feasibility studies for 14 separate retail outlets being considered for a neighborhood strip shopping center. This engagement was a fitting capstone for the practical graduate education I had sought. The experience of dealing with a consequential problem and of delivering actionable recommendations to an appreciative client on time and on budget was enormously gratifying. I have sought out that same gratification again and again over the years. The Minneapolis study also marked the beginning of a valued friendship with Bill Rudelius, who would become a trusted and tireless mentor. We subsequently published an account of the Minneapolis study (Rudelius, Hoel, and Kerin 1972). However, my attention at the time focused mainly on finding employment, which I secured one month before graduating with my MBA in August 1970.
I was offered a job with General Foods, in White Plains, N.Y., in its Maxwell House division, for the princely salary of $14,000. However, the next day, I received a letter from the University of Minnesota notifying me that I had been accepted into the doctoral program in marketing. I was dumbfounded, having never applied for admission! After confirming that the letter was not a hoax, and for reasons still mysterious to me, I declined General Foods' offer and entered the doctoral program. Bill Rudelius agreed to serve again as my advisor.
In the early 1970s, the Minnesota doctoral program had a strong behavioral thrust. The curriculum included courses in psychology, experimental design, inferential statistics, and psychometric theory and methods, as well as requisite courses in marketing theory, marketing research, marketing management, and consumer behavior. Given my interest in general management, I added several classes in organizational theory. All in all, I suspect that my course of study was similar to that in contemporary Big Ten doctoral programs in marketing. My texts included Alderson's Dynamic Marketing Behavior; Howard and Sheth's Theory of Buyer Behavior; Green and Tull's Research for Marketing Decisions; and the Brown et al. casebook, Problems in Marketing. Kotler's Marketing Decision Making: A Model-Building Approach rounded out my formal graduate education in marketing. At the time, I had no idea that I was reading what would become classics of the field.( n1) What I did know was that the experiential learning I had gained during my MBA was less frequent. This was about to change.
During the summer of 1971, Dick Cardozo asked me to join him in a case development effort at General Mills. I had completed Dick's marketing management case study course the previous term, and he was about to introduce me to case writing. The time spent at General Mills preparing the "General Mills: Jet 24" case study was a crash course in new product development and commercialization for consumer packaged goods. Jet 24 was a fruit-flavored concentrate that could be sprayed into a glass of water from an aerosol can. The product was the firm's first venture into the beverage market, and it was a commercial failure. Our task was to document General Mills' new product planning for Jet 24 from concept development through an early attempt at premarket testing, thus leading to the launch decision. Interactions with General Mills' executives ignited a passion in me for case writing and teaching, which endures to the present day. However, Bill Rudelius wisely pointed out that a collection of case studies "does not a thesis make," and he urged me to begin thinking about a dissertation topic while preparing for my comprehensive exams.
My thesis examined the quality of self-report data in mail survey research. I would later publish two thesis-related articles followed by a review and synthesis of empirical and methodological research on self-report data quality. However, the real impact of my thesis (for me) was a collaborative relationship with Bob Peterson. But I'm getting ahead of myself. A week after defending my thesis, I packed my few belongings and drove to Dallas, where I joined the marketing faculty at SMU. I was deeply appreciative of the position, given the weak job market at that time.
When You Come to a Fork in the Road, Take It
-Yogi Berra
The School of Business at SMU was in transition when I arrived in August 1973. The school's emphasis on undergraduate education and teaching had been gradually broadening to emphasize its MBA program and faculty scholarship. New faculty recruitment was viewed as the best way to achieve this. The opportunity to be involved in this transformation appealed to me, and the faculty (notably Dick Hansen, the department chair and fellow Minnesota graduate) welcomed my enthusiasm for teaching and saw publication potential in my research.
The environment at SMU was ideal for me. Course development was encouraged, and consulting was common. In six years, I prepped eight different courses (ranging from sales and business forecasting to consumer behavior); I taught undergraduate, MBA, and executive MBA classes; and I authored a dozen case studies (many based on my consulting). Early success in teaching indicated promise as an educator at SMU; however, the same could not be said for my scholarly achievements. As things turned out, my scholarly ambitions were kindled while visiting the University of Texas at Austin during the 1976-77 academic year.
I visited Austin primarily to work with Bob Peterson, another fellow Minnesota graduate. Bob's scholarly talent was already obvious when, in 1970, Bill Rudelius introduced us; Bob was finalizing his dissertation, and I was completing my MBA. We became reacquainted shortly after I settled in at SMU. To my surprise, Bob was familiar with my thesis research and suggested the possibility of collaborative work. Bob invited me to visit Austin, and I accepted without hesitation. The results of my visit far exceeded my expectations. Bob and I crafted several papers that year that lay the foundation for many future studies. For good measure, we delivered the first of (now) ten editions of our casebook, Strategic Marketing Problems: Cases and Comments. At that time, Bob also introduced me to Vijay Mahajan, a future SMU colleague and an indefatigable coauthor. Collaboration with Bob Peterson fueled my appetite for research. However, our research efforts were not much related to my teaching and case writing interests. I considered myself at the proverbial fork in the road, believing that my pedagogical interests were incompatible with my nascent scholarly ambitions. Coincidence and opportunity would intervene to prove me wrong.
Adventures in Serendipity
Upon returning to SMU, I reverted to case writing and course development. The summer of 1980 found me at LaQuinta Motor Inns, preparing a case for an MBA marketing management course. The case focused on the company's decision to broaden its target market beyond business travelers to include pleasure travelers. While I was writing the case, the question arose of how the company could compare the complete preference order of motels in the two segments to determine LaQuinta's relative position in the motel industry. As fate would have it, I was also a committee member on an SMU statistics department doctoral thesis that examined alternative approaches for determining rank-order agreement on a common set of objects between two separate groups (albeit from a theoretical perspective). A light bulb went off, and I suddenly understood the LaQuinta problem as a practical application of the method (later published by Palachek and Kerin [1982]).
This serendipitous episode changed my perspective on how case writing and research might be linked. I began to appreciate how case writing could provide topics and ideas suitable for academic study. A then recently published report by the Commission on the Effectiveness of Research and Development for Marketing Management (Myers, Greyser, and Massey 1979) offered encouragement for the research orientation that I envisioned for myself. The report recognized problem-oriented research as a legitimate scholarly activity to the extent that the issues, methods, or problems studied evidenced generalizability. It concluded with a statement that I have often come back to: "Understanding practice, and contributing to it, can lead to major contributions to knowledge-development" (Myers, Greyser, and Massey 1979, p. 29).( n2) This report also conceded that problem-oriented research still lacked the respectability of basic research for knowledge building by the academic marketing community and therefore was not without risk as a career choice. Undeterred, I decided to revisit previous studies and to pursue future case study (and consulting) settings with promising problem-oriented research opportunities and publication potential.
The decision to stake my scholarly future on case writing and consulting activities fit my inductive approach to research. Fortunately for me, the SMU marketing faculty had become populated by kindred spirits, including Bill Cron, Dan Howard, Michael Levy, and Vijay Mahajan, who were also inclined to turn problem-oriented research projects into scholarly manuscripts. Our joint efforts included applying conjoint analysis to the design of channel-directed support programs; identifying marketing and trade show strategy-related variables that affect trade show performance; developing a methodology for assessing market penetration opportunities and saturation potential for multistore, multimarket retailers; and modeling word-of-mouth effects in the diffusion process for new products. Dan Howard and I would explore causal relationships among variables that influence "value-for-the-money" perceptions of supermarket shoppers and would show empirically that brand name sound and meaning affect consumer brand-source inferences, while also highlighting the implications of this result for trademark infringement and brand name research. Raj Sethuraman, Bill Cron, and I would use our collaboration with a large consumer electronics firm to show that choice-based conjoint analysis tasks conducted in an online environment may yield different (and more valid) results than those observed in conventional paper-and-pencil methods in new product research.
To the casual observer, this bevy of projects may appear to be unrelated, but they shared a common purpose, consistent with the research orientation I had adopted. All these projects originated from, benefited from, or were inspired by field-based case-writing efforts and consulting engagements, and all aimed to improve the marketing practice of the phenomenon under investigation. Equally important, as a marketing educator, I found the coalescence of case writing and problem-oriented research indispensable to course development (and vice versa.)
Course Development and Programmatic Research
New and ongoing course development has played a central role in motivating my research and textbook writing (Kerin et al. 2003). For me, course development and case writing are synonymous. Since 1973, I have authored some 60 case studies and have been involved in the creation of a dozen courses. What I have found is that course development efforts discipline my examination of marketing phenomena. For example, I developed an MBA course ("Interactive and Multichannel Marketing") to determine how marketing concepts and practices (as I understood them) were affected by the Internet. Although case writing is time consuming and labor intensive, I have discovered that for me, as an element of course development, it has a triple purpose: ( 1) It serves as a pedagogical device, ( 2) it aids deeper thinking, and ( 3) it stimulates innovative research. Two of my course development experiences stand out in this regard.
During the summer of 1981, I began revising a popular executive MBA and MBA course that featured Abell and Hammond's Strategic Market Planning: Problems and Analytical Approaches. The revision was initiated by student requests to broaden the case assignments beyond the technology-based companies that appeared in the text. I chose to write a case on Zale Corporation, which then was one of the largest publicly held diversified specialty retailing companies in the United States and the world's leading retailer of jewelry merchandise. My original intent was to illuminate market strategy and resource allocation options for a multidivisional retailing firm. However, a more interesting question soon became apparent: Are corporate sales and asset growth objectives on the one hand and financial policies on the other hand synergistic (or otherwise) in creating shareholder wealth? This question sparked my curiosity about the interface between marketing and finance in strategy formulation, implementation, and evaluation. The Zale Corporation case prompted three articles coauthored with finance faculty who also thought the topic worthy of study. The first article (Higgins and Kerin 1983) documents sustainable growth challenges for a cross-section of retailers, outlines marketing and financial "solutions" for balancing sales and asset growth and earnings, and indirectly examines the influence of each on shareholder wealth creation. The second article (Kerin and Varaiya 1985) explores the shareholder wealth consequences of retail mergers and acquisitions and demonstrates why this popular growth strategy rarely yields the intended value to stockholders of the acquiring firm. The third article (Varaiya, Kerin, and Weeks 1987) explores shareholder wealth creation and erosion from the perspective of value-based planning models, which were then receiving extensive but uncritical coverage in the business press. This research examines the empirical validity of these models for assessing corporate strategy and cautions prospective users to the conditionality of their prescriptions for managerial action. I wrote a supplemental course note to summarize this research to accompany both the Zale Corporation case and the course lecture titled "Strategic Financial Analysis."
By 1986, the strategic market planning course had been substantially revised. Vijay Mahajan was teaching a companion class at the time, and he suggested that we document the changes reflected in our course material. He would introduce me to Rajan Varadarajan, whose encyclopedic knowledge of the literature was instrumental in our preparation of a "state-of-practice" article on strategic market planning (Mahajan, Varadarajan, and Kerin 1987). We would later elaborate on this work with the publication of Contemporary Perspectives on Strategic Market Planning (Kerin, Varadarajan, and Mahajan 1990). A few years later, the Vice President of Strategic Planning at a large telecommunications firm showed me a dog-eared copy of the book and asked me to deliver an executive seminar on the material he had highlighted. The seminar led to an invitation to integrate our material into the firm's planning process. A valuable lesson on implementation issues in market planning was, in turn, brought back to the classroom.
My research on consumer product and brand management and an MBA course with the same title benefited from multiple cases written at Frito-Lay and a 20-year association with Dwight Riskey, presently the Senior Vice President for Consumer and Customer Insights at PepsiCo. Dwight's eye for strategic issues and fondness for challenging case development were instrumental in the formation of my views and writing on first-mover advantage, product cannibalism, brand equity, and brand valuation. For example, insight I gained in chronicling Frito-Lay's successful pioneering effort with SunChips Multigrain Snacks resulted in two articles. The first article (Kerin, Varadarajan, and Peterson 1992) proposes a conceptual framework for identifying the sources of first-mover advantages and product-market contingencies that moderate the relationship between order of entry and competitive advantage. The second article (Kerin, Kalyanaram, and Howard 1996) shows that the magnitude of order-of-entry effects depends on whether a firm pioneers a product class or product form and whether a new brand or brand extension strategy is used.
My interest in brand valuation was stimulated while I was drafting a case on Frito-Lay's marketing and financial analysis, before the acquisition of the Cracker Jack brand from Borden in 1997. Subsequent research (Kerin and Sethuraman 1998) inspired by this case helped identify a positive relationship between a firm's accumulated brand values and its ratio of market value to book value, but it also revealed that the functional form of the relationship appears to be concave, with decreasing returns to scale. An additional dividend from my association with Dwight Riskey has been his willingness to work with me on writing intelligible and practicable supplemental class reading materials to accompany course case studies and lectures. We would subsequently publish two of these course notes: one on product cannibalism (Kerin and Riskey 1994) and the other on PepsiCo's global brand equity model (Kish, Riskey, and Kerin 2001).
As I look back on my educational and professional experiences, I humbly acknowledge that I have been fortunate in many ways. My mentors, notably Bill Rudelius and Dick Cardozo, provided opportunities and guidance at critical moments in my graduate education. Both have been role models for me whenever I have been called on to guide graduate students. Providential introductions to Bob Peterson, Vijay Mahajan, and Rajan Varadarajan stimulated my intellectual curiosity and furthered my research and writing interests. I am deeply indebted to this charmed circle of marketing scholars as well as to my many talented coauthors. They, more than me, can describe the idiosyncratic aspects of my research style.
Above all, I have been fortunate in being able to combine my passion for case writing and teaching with scholarly pursuits. In doing this, I take some satisfaction in developing course content that is uniquely my own, for better or for worse. Continuous course development through case writing and related research fuels my enthusiasm for the subject matter, which I hope is conveyed to and appreciated by students.
In writing this essay, I have no illusions that my research orientation and process is suitable for everyone, let alone anyone. However, it has served me well. I fully expect that tomorrow, if not the next day, will bring a case-writing opportunity that will change my view on marketing practice and plant the seed for a new course or research topic. This is a comforting thought as I embark on my fourth decade as a marketing educator.
( n1) It would take me several years to appreciate fully my doctoral training. For example, while I was preparing a commemorative essay on the sixtieth anniversary of Journal of Marketing (Kerin 1996), I uncovered the readings packet and lecture notes from the marketing theory class taught by Edwin Lewis and discovered an orderly, historical account of marketing thought from the early 1900s through 1970. As a student of Wroe Alderson, Ed's perspective on the development of marketing theory and practice was an invaluable reference for me.
( n2) During my term as editor of Journal of Marketing (1988-90), I would frequently cite this study and quotation. I like to think that some of the best articles published during my editorial term were based on problem-oriented research that contributed to practice improvement and knowledge development in marketing.
BIBLIOGRAPHY Higgins, Robert C. and Roger A. Kerin (1983), "Managing the Growth-Financial Policy Nexus in Retailing," Journal of Retailing, 59 (Fall), 19-48.
Howard, Daniel J., Roger A. Kerin, and Charles Gengler (2000), "The Effect of Brand Name Similarity on Brand Source Confusion: Implications for Trademark Infringement," Journal of Public Policy & Marketing, 14 (Fall), 250-64.
Kerin, Roger A. (1996), "In Pursuit of an Ideal: The Editorial and Literary History of the Journal of Marketing," Journal of Marketing, 60 (January), 1-13.
-----, Eric N. Berkowitz, Steven W. Hartley, and William Rudelius (2003), Marketing, 7th ed. New York: McGraw-Hill/ Irwin.
----- and William L. Cron (1987), "Assessing Trade Show Functions and Performance," Journal of Marketing, 51 (July), 87-94.
-----, Ambuj Jain, and Daniel J. Howard (1992), "Store Shopping Experience and Consumer Price-Quality-Value Perceptions," Journal of Retailing, 68 (Winter), 376-97.
-----, G. Kalyanaram, and Daniel J. Howard (1996), "Product Hierarchy and Brand Strategy Influences on the Order of Entry Effect for Consumer Packaged Goods," Journal of Product Innovation Management, 13 (January), 21-34.
----- and Robert A. Peterson (1977), "Personalization, Respondent Anonymity, and Response Distortion in Mail Surveys," Journal of Applied Psychology, 62 (February), 86-89.
----- and ----- (1983), "Scheduling Telephone Interviews," Journal of Advertising Research, 23 (April-May), 41-47.
----- and ----- (2004), Strategic Marketing Problems: Cases and Comments, 10th ed. Upper Saddle River, NJ: Prentice Hall.
----- and Dwight R. Riskey (1994), "Product Cannibalism" in Marketing Managers Handbook, 3d ed., Sidney J. Levy, George R. Frericks, and Harold L. Gordon, eds. Chicago: Dartnell Company, 880-95.
----- and Raj Sethuraman (1998), "Exploring the Brand Value-Shareholder Value Nexus for Consumer Goods Companies," Journal of the Academy of Marketing Science, 26 (Fall), 260-73.
-----, P. Rajan Varadarajan, and Vijay Mahajan (1990), Contemporary Perspectives on Strategic Market Planning. Boston: Allyn & Bacon.
-----, -----, and Robert A. Peterson (1992), "First-Mover Advantage: A Synthesis, Conceptual Framework, and Research Propositions," Journal of Marketing, 56 (October), 33-52.
----- and Nikhil Varaiya (1985), "Mergers and Acquisitions in Retailing: A Review and Critical Analysis," Journal of Retailing, 61 (Spring), 9-34.
Kish, Paulette, Dwight R. Riskey, and Roger A. Kerin (2001), "Measurement and Tracking of Brand Equity in the Global Marketplace: The PepsiCo Experience," International Marketing Review, 18 (1), 91-96.
Levy, Michael, John Webster, and Roger A. Kerin (1983), "Formulating Push Marketing Strategies: A Method and Application," Journal of Marketing, 47 (Winter), 25-34.
Mahajan, Vijay, Eitan Muller, and Roger A. Kerin (1984), "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, 30 (December), 1389-1404.
-----, Subhash Sharma, and Roger A. Kerin (1988), "Assessing Market Penetration Opportunities and Saturation Potential for Multi-Store, Multi-Market Retailers," Journal of Retailing, 63 (Fall), 315-33.
-----, P. Rajan Varadarajan, and Roger A. Kerin (1987), "Metamorphosis in Strategic Market Planning," in Contemporary Views on Marketing Practice, Jagdish N. Sheth and Gary L. Frazier, eds. Lexington, MA: Lexington Books, 67-110.
Myers, John, Stephen A. Greyser, and William F. Massey (1979), "The Effectiveness of Marketing's 'R&D' For Marketing Management: An Assessment," Journal of Marketing, 43 (January), 17-29.
Palachek, Albert D. and Roger A. Kerin (1982), "Alternative Approaches to the Two-Group Concordance Problem in Brand Preference Rankings," Journal of Marketing Research, 19 (August), 386-89.
Peterson, Robert A. and Roger A. Kerin (1977), "The Female Role in Advertisements: Some Empirical Evidence," Journal of Marketing, 41 (October), 59-63.
----- and ----- (1981), "The Quality of Self-Report Data: Review and Synthesis," in Review of Marketing 1981, Ben M. Enis and Kenneth J. Roering, eds. Chicago: American Marketing Association, 5-20.
----- and ----- (1983), "Store Image Measurement in Patronage Research: Fact and Artifact," in Patronage Theory and Retail Management, Robert F. Lusch and William Darden, eds. New York: Elsevier North Holland, 293-306.
Rudelius, William, Robert F. Hoel, and Roger A. Kerin (1972), "Assessing Retail Opportunities in Low-Income Areas," Journal of Retailing, 48 (Fall), 96-114.
Sethuraman, Raj, Roger A. Kerin, and William L. Cron (2004), "A Field Study Comparing Online and Offline Data Collection Methods for Identifying Product Attribute Preferences Using Conjoint Analysis," Journal of Business Research, forthcoming.
Varaiya, Nikhil, Roger A. Kerin, and David Weeks (1987), "The Relationship Between Growth, Profitability, and Firm Value," Strategic Management Journal, 8 (September-October), 487-97.
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By Roger A. Kerin and Terry Clark, Editor, Southern Illinois University
Roger A. Kerin is Harold C. Simmons Distinguished Professor of Marketing, Edwin L. Cox School of Business, Southern Methodist University (e-mail: rkerin@mail.cox.smu.edu).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 130- Relationship Governance in a Supply Chain Network. By: Wathne, Kenneth H.; Heide, Jan B. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p73-89. 17p. 1 Diagram, 4 Charts, 2 Graphs. DOI: 10.1509/jmkg.68.1.73.24037.
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- Business Source Complete
Relationship Governance in a Supply Chain Network
The authors examine how a firm's strategy in a (downstream) customer relationship is contingent on how a related relationship outside of the focal dyad is organized. Drawing on emerging perspectives on interfirm governance and networks, the authors propose that the ability to show flexibility toward a (downstream) customer under uncertain market conditions depends on the governance mechanisms that have been deployed in an (upstream) supplier relationship. The governance mechanisms take the form of ( 1) supplier qualification programs and ( 2) incentive structures based on hostages. The authors develop a set of contingency predictions and test them empirically in the context of vertical supply chain networks in the apparel industry. The tests show good support for the hypotheses. The authors discuss the implications of the findings for marketing theory and practice.
Interfirm relationships and relationship governance issues are receiving considerable attention in the marketing literature. A growing body of research addresses different aspects of firms' relationships with exchange partners from a variety of theoretical perspectives (e.g., Bergen, Dutta, and Walker 1992; Cannon and Perreault 1999; Wilson 1995). For example, several studies have relied on the new institutional economics literature, including transaction cost analysis (TCA), to examine how particular governance processes are carried out between firms (e.g., Ghosh and John 1999; Heide 1994; Noordewier, John, and Nevin 1990).
The predominant focus in much of the existing research has been on individual dyadic relationships between firms, such as those between a manufacturer and a customer. However, some scholars have suggested that to understand fully the nature of dyadic interfirm relationships, greater attention must be directed to the larger networks in which the relationships exist (e.g., Anderson, Håkansson, and Johanson 1994; Iacobucci 1996; Levy and Grewal 2000; Möller and Wilson 1995). For example, the industrial networks perspective, as presented by the Industrial Marketing and Purchasing Group (e.g., Håkansson and Snehota 1995; Wilkinson 2001), posits that the implicit assumption of ceteris paribus in other relationships, which underlies much of the extant dyadic research, is an unrealistic one.
In this article, we begin with a particular governance process: adaptation to uncertainty (Williamson 1985). Consistent with TCA, we argue that uncertain conditions in a focal dyadic relationship require the use of governance structures that allow for flexible adaptation to changing circumstances (Williamson 1991). However, we broaden the established TCA model by drawing on extant network perspectives (e.g., Cook and Emerson 1978; Hakansson and Snehota 1995), and we posit that adaptation to uncertainty in a focal dyad depends on how a connected relationship is organized.
We test our conceptual arguments in the context of vertical supply chain networks in the apparel industry. Specifically, our study examines the relationships ( 1) between a manufacturer and an independent (downstream) customer and ( 2) between the manufacturer and an independent (upstream) supplier. On the basis of the existing governance literature, we identify two governance mechanisms that a manufacturer can use to structure its relationship with the upstream supplier: supplier qualification and incentive design. Next, we describe the effect of these governance mechanisms on the manufacturer's ability to adapt in a flexible manner to uncertainty in the downstream relationship. The research design used to test our hypotheses about governance effects across levels involves data from matched pairs of manufacturers and retailers in an overall supply chain.
We seek to make the following contributions to the literature: First, from a theoretical standpoint, we want to broaden existing models of interfirm governance. More specifically, we examine whether the normative predictions from TCA about a firm's governance response in a dyadic relationship depend on another one in its immediate network context. As such, we attempt to expand the unit of analysis relative to extant governance research.
Second, from a practical standpoint, firms are increasingly recognizing that relationship management involves more than a single relationship. For example, many manufacturers are recognizing that their downstream customer relationships are constrained by other relationships elsewhere in the larger supply chain (The Economist 2001). We respond to the call for research on supply chain issues in marketing (e.g., Stewart 1999) by identifying specific strategies that can be used to manage supply chain relationships and by describing their effects across levels in the overall chain.
The remainder of this article is organized as follows: We begin by presenting our conceptual framework and research hypotheses. We then describe the research method used to test the hypotheses and the empirical results. We conclude with a discussion of the implications of our findings, the study's limitations, and possible topics for further research.
Figure 1 shows a vertical supply chain network that involves relationships at two different levels: ( 1) between a manufacturer and a (downstream) customer and ( 2) between the manufacturer and an (upstream) supplier. As an example, the manufacturer might be an apparel company (e.g., Jockey), the customer an independent retailer (e.g., Marshall Field's), and the supplier an independent contractor. We begin by considering the dyadic relationship between a manufacturer and a downstream customer.
Level 1: Manufacturer-Customer Relationship
In the fashion apparel industry, rapid changes in consumer demand create considerable uncertainty in the downstream market. For example, as a result of continuously changing consumer tastes, retailers face uncertainties in terms of both product design and volume needs (Djelic and Ainamo 1999; Iyer and Bergen 1997). The inherent characteristics of such markets have important implications for the relationships between the firms that serve the end consumers (i.e., retailers) and the firms that supply them (i.e., apparel companies). Specifically, the uncertainties faced by downstream retailers affect the relationship with apparel companies in the form of ongoing needs for flexibility or relationship modification. In the terminology of TCA, because the relevant downstream uncertainties cannot be easily contracted for in advance (i.e., complete contracts that define all the relevant contingencies in the manufacturer-customer relationship cannot be written a priori), they create significant adaptation problems. Transaction cost theorists have suggested that adapting to uncertainty is the "central problem of economic organization" (Williamson 1991, p. 163).
Adaptation problems require a specific governance response. In relationships with high levels of uncertainty, TCA suggests that firms will try to craft agreements with good adaptation properties that can economize on ongoing transaction costs (Williamson 1985, 1991). In relationships that require coordinated responses between two independent exchange partners (e.g., an apparel company and a retailer), TCA predicts reliance on so-called relational interfirm contracts (Dwyer, Schurr, and Oh 1987; Gibbons 1999; Noordewier, John, and Nevin 1990; Williamson 1991). Such contracts are based on particular contracting norms (Kaufmann and Stern 1988; Macneil 1980) that enable the parties to overcome planning gaps and adapt in a flexible manner as circumstances change.
However, as Noordewier, John, and Nevin (1990) and other scholars (e.g., Granovetter 1994; Masten 1993) note, TCA is based on a normative decision heuristic that emphasizes a firm's motivation to craft adaptive governance structures. It does not directly address a firm's ability to achieve relational solutions. Drawing on the network literature, we argue that a firm's ability depends in part on how other connected relationships are organized.
Level II: Supplier-Manufacturer Relationship
As we noted previously, scholars have argued for the need to expand the unit of analysis from dyads to business networks (for a historical analysis of network thinking in marketing, see Wilkinson 2001). A noteworthy dimension of the network perspective is that individual relationships are connected (Anderson, Håkansson, and Johanson 1994; Cook and Emerson 1978) in the sense that exchange in one relationship is contingent on or has consequences for exchange in the other relationship (Yamagishi, Gillmore, and Cook 1988).
Consider again the supply chain network in Figure 1. The product that the manufacturer sells to the downstream customer (i.e., the retailer) is obtained from an upstream supplier. For example, many of Jockey's branded garments are not manufactured internally but are sourced from outside contractors subject to company specifications. Thus, when conditions in the downstream market create a need for manufacturer flexibility in relation to the customer, demands are also placed on the upstream relationship. In other words, accommodating the downstream retailer requires modifications in the relationship between Jockey and its upstream contractor.( n1)
Consider next the likelihood that a manufacturer's request for (upstream) modifications will be accommodated.
Because the manufacturer and the supplier are separate companies that have individual goals (Iyer and Bergen 1997), it is not definite that the supplier will support the manufacturer's request. In addition, opportunism may undermine the manufacturer's downstream strategy in various ways. For example, the supplier may have misrepresented its production capacity when the relationship was being established (i.e., an ex ante adverse selection problem, as per Akerlof's [1970] research). In addition, the supplier may fail to make necessary capacity adjustments ex post and opportunistically exploit the manufacturer's request for changes by demanding concessions. As Williamson (1991, p. 278) notes, "although it is in the collective interest of exchange parties to fill gaps, correct errors, and effect efficient realignments, it is also the case that the distribution of the resulting gains is indeterminate." Research in the transaction cost tradition shows that various forms of opportunistic behavior are common in interfirm relationships, especially during renegotiations of original agreements (John 1984; Masten 1988; Wathne and Heide 2000; Williamson 1985).
As we describe in the next section, the firm's ability to adapt to uncertainty downstream ultimately depends on its having deployed particular governance mechanisms in the (connected) upstream relationship that mitigate the potential problems of incompatible goals and/or opportunism. In the absence of appropriate governance efforts upstream, management of the ongoing supplier relationship may be associated with substantial renegotiation costs, which may actually undermine the manufacturer's ability to respond to uncertainty in the downstream market. Consider next the specific governance mechanisms that can be used in a relationship with an (upstream) contractor to ensure flexible adaptation downstream.( n2)
Governance of Supplier Relationship
Extant theory has proposed several strategies or governance mechanisms that can be used to manage relationships with exchange partners. In general, the mechanisms fall into two categories (Eisenhardt 1985; Heide 1994). First, a firm can a priori identify or select exchange partners that possess the ability and motivation to support its strategy (Ouchi 1980). For example, an apparel company may require a potential contractor to participate in a formal qualification program. Second, a firm can design incentive structures (Williamson 1983) that reward the necessary behaviors and/or penalize noncompliance in the ongoing relationship. Our general expectation is that the greater the investment by a firm (e.g., an apparel company) in either form of governance in the upstream (contractor) relationship, the greater is its ability to adapt to uncertainty in the downstream (retail) market. We discuss each of these governance strategies subsequently.
Supplier qualification. Firms frequently require potential exchange partners to participate in formal qualification programs (Stump and Heide 1996). In a broad sense, qualification programs are designed to ascertain certain aspects of an exchange partner in a prerelationship phase (e.g., Dwyer, Schurr, and Oh 1987). From a theoretical standpoint, the general purpose served by such programs is proactively solving potential governance problems by means of systematic selection.
Selection is discussed in different streams of literature, some of which make different assumptions about its specific effects. In the organizational theory literature, the conventional rationale for selection is to assess a party's likely "fit" on particular criteria (Chatman 1991; Etzioni 1975). Per our previous example, an apparel company may use a qualification program to evaluate potential contractors in areas such as product quality, manufacturing capability, and financial strength (e.g., Gadde and Håkansson 2001; Sarkis and Talluri 2002). Contractors that fail to meet established thresholds on the relevant criteria will be eliminated from further consideration.
However, such assessments are limited because they only provide evidence about a contractor's particular skills or abilities. Such assessments do not guarantee that the contractor will actually apply the skills to the relationship in question (Kirmani and Rao 2000). To the extent that holding back efforts produces cost savings for the contractor, this scenario is not unlikely.
However, organization theory suggests that selection efforts serve an additional purpose, namely, that of a socialization process (Ouchi 1979). More specifically, the qualification program may be specifically designed to expose an exchange partner to a firm's goals and values, and the interaction during the program may promote internalization of the relevant goals (Dwyer, Schurr, and Oh 1987). To the extent that the parties' goals become aligned ex ante in this way, the likelihood of subsequent motivation-related problems is greatly reduced (Coleman 1990).
Agency theory (e.g., Bergen, Dutta, and Walker 1992) recognizes another possible effect of selection efforts that is due to the opportunities they create for imposing costs on the other party. Per our previous example, if the qualification process is costly or time consuming for potential contractors, "appropriate" contractors (i.e., ones with the right skills and motivation) can self-select, because only such contractors will get a return on their efforts through repeat sales. Similarly, a firm may use selection criteria that directly or indirectly have a cost dimension. For example, an apparel company may select on the basis of contractor reputation or observed behavior in other relationships (e.g., Ganesan 1994; Kumar, Scheer, and Steenkamp 1995; Rubin 1990). To the extent that a given contractor's reputation is valuable, subsequent behaviors that contradict the established reputation may lead to a monetary loss and therefore are less likely to take place.
Consider the specific effects of partner qualification in the context of a particular supply chain network (see Figure 1). Recall from our previous discussion that uncertainty in the downstream market creates the need for relational contracts between the manufacturer and the customer, which ensure flexible adaptation to changing circumstances. In transaction cost terms (e.g., Williamson 1985), uncertainty requires agreements with good adaptation properties. However, when the manufacturer relies on an independent supplier to produce the products, the ability to be flexible (or the actual effect of downstream uncertainty) is contingent on the nature of the manufacturer's upstream relationship.
If the manufacturer has made insufficient efforts to organize the upstream relationship (i.e., limited qualification efforts), renegotiation difficulties may prevent the firm from accommodating customers' needs. In effect, the relationship with the supplier will serve as a constraint on the firm's actions (Håkansson and Snehota 1995). From the downstream customer's perspective, this implies insufficient flexibility when circumstances require it or a lack of responsiveness on the part of the manufacturer. In contrast, if the necessary qualification efforts have been made upstream, the greater is the likelihood of having identified a supplier that is able and motivated to support the manufacturer, the lower are the focal renegotiation costs, and the greater is the likelihood that the need for adaptation in the downstream market can be met.
The previous discussion implies that the effect of uncertainty in the downstream market on manufacturer flexibility will be nonmonotonic (Schoonhoven 1981) and that it will shift over the range of the manufacturer's (upstream) supplier qualification efforts. More generally, it suggests that the normative TCA prediction for adaptation to uncertainty in the (dyadic) manufacturer-customer relationship is contingent on the governance efforts made in the manufacturer-supplier relationship. We summarize our theoretical discussion in the following contingency hypothesis:
H[sub1]: Downstream uncertainty will have (a) a negative effect on the manufacturer's flexibility toward the downstream customer for lower levels of upstream qualification efforts and (b) a positive effect on the manufacturer's flexibility toward the downstream customer for higher levels of upstream qualification efforts.
Incentive design. Another general governance strategy that is available to a firm is the design of an incentive structure in which the long-term gains from maintaining the relationship exceed the short-term payoffs from potential opportunism (Eisenhardt 1985).
A specific strategy available to a manufacturer is reliance on a "hostage" from the supplier (Williamson 1983). For example, a supplier may provide a performance guarantee in the form of manufacturer-dedicated assets (e.g., Mariotti and Cainarca 1986). By dedicating assets, the supplier essentially reduces its ability to replace the particular manufacturer, because the assets can be used only in that particular relationship. As Mariotti and Cainarca (1986, p. 361) describe, "should this particular supply agreement terminate prematurely, the dedicated assets... lose [their] value, as with other types of asset specificity." The effect of the hostage is to lock in the supplier (Heide 1994) and thereby create incentives for behaviors that support the relationship and its continuity. Thus, when circumstances in the downstream market require the manufacturer to make requests of the upstream supplier, the presence of a supplier hostage increases the likelihood that the renegotiation requests will be accommodated. As such, at least in principle, an upstream supplier hostage has the ability to promote flexible adaptation to uncertainty in the downstream market in a way that is parallel to upstream qualification efforts (see H[sub1]).
As we described previously, the conventional hostage model assumes that the hostage from the supplier actually increases the manufacturer's control. However, this is not necessarily the case. From the supplier's perspective, hostages can create expropriation hazards, because they allow the manufacturer to extract the supplier's profits. For example, when a supplier is locked in, the manufacturer can demand costly reductions in order-cycle time. As a consequence, unilateral supplier lock-in may actually reduce, rather than increase, the supplier's willingness to support the manufacturer (Buchanan 1992; Heide 1994).
On the basis of the preceding discussion, we propose that the effect of a supplier hostage, as per the conventional hostage model, is contingent on the extent to which the manufacturer has made a corresponding investment in the relationship. If both parties commit, a condition of mutual lock-in is created. To the extent that both parties have constrained the alternatives that are open to them or have made each other irreplaceable (Barney and Ouchi 1986; Jackson 1985), the parties' incentives are aligned because opportunism on the part of one firm can be (credibly) retaliated against by the other firm (Provan and Skinner 1989). Ultimately, from the supplier's standpoint, a bilateral exchange of hostages significantly reduces the ongoing expropriation risks in the relationship.
Thus, we posit that the ability of an upstream supplier hostage to promote flexible adaptation to changing circumstances downstream depends on both the level of the supplier's hostage and the match with a manufacturer hostage. Under mutual and high lock-in, the resulting incentive structure enables the supplier to accommodate the manufacturer's request without the risk of exploitation. However, under unilateral supplier lock-in, expropriation concerns on the part of the supplier may undermine flexible adaptation to downstream uncertainty. Under such conditions, renegotiation difficulties with the upstream supplier will be a constraint on the downstream relationship, and the manufacturer will come across as unresponsive to the downstream customer.
In summary, the discussion in the preceding paragraphs implies that (upstream) supplier hostages have the potential to promote adaptation to (downstream) uncertainty. However, the actual nature of the effect depends on the other party's (i.e., the manufacturer's) hostages. Overall, we predict that the effect of supplier hostages is nonmonotonic over the range of manufacturer hostages.
H[sub2]: Downstream uncertainty will have (a) a negative effect on the manufacturer's flexibility toward the downstream customer when supplier hostages are not accompanied by hostages from the manufacturer (i.e., unilateral lock-in, as per the conventional hostage model) and (b) a positive effect on the manufacturer's flexibility toward the downstream customer when supplier hostages are accompanied by hostages from the manufacturer (high mutual lock-in).
Other Effects
As we described previously, the main premise of our theoretical arguments is that flexibility downstream is partly a function of a manufacturer's governance efforts in the upstream supply market. Similarly, the downstream customer's own governance efforts with respect to the manufacturer may increase the chances of identifying and keeping an exchange partner that is able and motivated to accommodate the ongoing needs for adaptation. As we describe in the "Research Method" section, we control for the customer's own use of both qualification and incentives with respect to the manufacturer.
We also recognize that manufacturers themselves may rely on operational strategies that affect their flexibility downstream. For example, some apparel companies may use a speculation strategy (Bucklin 1967) to buffer against unpredictable changes in the retail market; specifically, apparel companies can stockpile inventory (e.g., Abernathy et al. 1999). Some manufacturers may rely on a postponement strategy (Bucklin 1967). For example, some apparel companies may try to delay product differentiation until the last possible moment to compress lead times (and to limit finished goods inventory) and thereby secure flexibility in a more cost-effective way (e.g., Abernathy et al. 1999; Johnson and Anderson 2000). To control for the possibility that manufacturer flexibility toward the downstream customer is due to such operational strategies, we included them as additional controls in our empirical test.
A manufacturer's degree of flexibility may also be affected by the firm's view of the focal customer. Recall from our discussion of the upstream manufacturer-supplier relationship that one party's unilateral efforts may be exploited by the other. Conceivably, manufacturer flexibility may lead to increasing customer demands, which ultimately serve to extract the manufacturer's profits. If there is a risk that the customer will exploit the manufacturer, the manufacturer's motivation to show flexibility in the first place is reduced. To control for this, we account for ways the manufacturer can deploy safeguards. For example, we control for retailer lock-in with respect to the apparel company. In addition, we include a measure of the nature of the product being sold, which may serve as a safeguard against customer exploitation that is quite separate from the incentive structure created in the downstream market. Specifically, higher-fashion garments, which represent a critical source of retailer differentiation and for which there are typically fewer alternative sources of supply available (Buchanan 1992), put the apparel company in a position to restrict retail supply. In a broad sense, the nature of the product contributes in unique ways to create retailer dependence on the apparel company. From the apparel company's standpoint, this ensures that the retailer's request can be accommodated without the risk of subsequent exploitation.
Another characteristic of the downstream relationship that may affect a manufacturer's flexibility is the percentage of sales to the focal customer, or the degree of downstream concentration. Presumably, in supply chains that have a higher degree of exchange concentration in the downstream market, the stronger is the bargaining position of the focal customer, and the higher is the likelihood that the manufacturer will accommodate the customer's request. Finally, a manufacturer's degree of flexibility may be affected by its relative size as compared with that of the upstream supplier and the downstream customer. Specifically, larger apparel companies may be able to extract concessions from the upstream contractor (e.g., in the form of reduced order-cycle time) and may be less pressured to accommodate the need for flexibility downstream. To account for these additional effects, we control for relative size in both relationships.
Research Context
The empirical context for our study is the U.S. apparel industry. Specifically, our research setting focuses on apparel companies in the Standard Industrial Classification Group 23 (apparel manufacturing) and their relationships with (upstream) contractors and (downstream) retailers. Group 23 comprises companies that are primarily engaged in manufacturing cut-and-sew apparel (men's, women's, children's, and infant-wear) from woven fabric and purchased knit fabric. The contractors and retailers represent suppliers and customers, respectively, in our conceptual framework (Figure 1). The specific unit of analysis for the study is the sourcing arrangement used for a particular garment.
We used three main criteria in selecting this empirical context. First, all our main independent variables needed to manifest themselves in the setting to various degrees. Most important, the context needed to exhibit substantial variation in downstream market uncertainty. Second, we required a context in which customer flexibility involved a significant and ongoing effort on the focal manufacturer's part, rather than just maintenance of excess inventory to meet demand fluctuations. Third, the manufacturer, the downstream customer, and the upstream supplier needed to be independent (i.e., not integrated, no equity cross-holdings).
With respect to our first two criteria, the apparel industry faces several categories of consumer demand, from supplying consumers with utilitarian attire that changes little in style from year to year to providing fashion apparel that is characterized by short product life and fickle consumer preferences (Djelic and Ainamo 1999; Richardson 1996). Fashion apparel poses considerable manufacturing and marketing difficulties. Highly unpredictable consumer demand makes it difficult for retailers to select appropriate merchandise and to specify clearly terms of exchange with apparel companies (Mariotti and Cainarca 1986). Furthermore, because timing is a major determinant of consumer value for these products, and because fashion apparel is characterized by short life cycles, apparel companies must continuously adapt their product lines (Buchanan 1992). In this context, relationships between retailers and apparel companies are not easily governed by complete or explicit contracts. Although long-term relationships exist between apparel companies and retailers, the relationships must continuously adapt in response to changing circumstances (Djelic and Ainamo 1999).
With respect to our third criterion, apparel companies are increasingly relying on ( 1) external contractors to manufacture their products and ( 2) independent retail outlets to sell the products (including discount stores, off-price retailers, specialty stores, department stores, and major chains) (Djelic and Ainamo 1999).( n3)
Questionnaire Development
We used mail surveys of apparel companies and their retail customers to measure the relevant theoretical variables. To develop the questionnaires, we used the procedures recommended by Churchill (1979) and Gerbing and Anderson (1988). Initially, we conducted in-depth interviews with ( 1) production managers at four different apparel companies, ( 2) purchasing managers at two retail companies, and ( 3) two directors of the American Apparel Manufacturers Association. In total, we spent more than 15 hours on personal interviews. On the basis of the interviews and a review of previous research on buyer-supplier relationships, we developed preliminary versions of the questionnaires. When it was possible, we used existing scale items (e.g., Ko and Kincade 1998; Stump and Heide 1996), after we adapted them to our research context. Subsequently, the questionnaires were sent to a sample of ten production managers to verify the appropriateness of the terminology used, the clarity of the instructions, and the response formats. Six questionnaires were returned, and no particular problems appeared to exist with the scales. We also conducted telephone interviews with all of the managers ex post to verify the relevance and clarity of the survey questions.
Measures
We operationalized the key constructs in our conceptual framework by using multi-item reflective scales (Bollen and Lennox 1991). The Appendix contains a description of response formats and specific items for the multi-item scales.
Downstream market uncertainty. In our context, the main domains in which downstream uncertainty exists are demand and design characteristics (Djelic and Ainamo 1999; Iyer and Bergen 1997). Specifically, uncertainty exists to the extent that apparel companies are unable to forecast accurately the sales volume and style preferences in the downstream market. The actual items are based on the ones developed by Heide and John (1988, 1990) and Ko and Kincade (1998).
Apparel company flexibility. We conceptualized flexibility as a global contracting norm. In our particular context, flexibility describes the retailer's perception of the apparel company's flexibility in the focal relationship. For example, one of the items measures the retailer's perception of the apparel company's flexibility in response to its requests for changes. Note that though the primary governance or adaptation problem in this context pertains specifically to volume and design changes for particular garments, the accommodation of such changes requires modifications (i.e., flexibility) across a broad range of relationship dimensions (e.g., delivery, pricing, terms). Thus, we needed to measure flexibility in global terms. Our conceptualization is consistent with that in the existing literature (Heide and John 1992; Noordewier, John, and Nevin 1990).
Unlike many of the other measures that pertain to the apparel company's upstream governance efforts, we obtained the flexibility measure from the retailer. Given that our ultimate focus is on flexibility in the downstream customer relationship, the most appropriate source of data on flexibility is the customer in question. The customer's perspective need not coincide with an apparel company's self-report. For example, our asking the apparel companies to report on their flexibility with respect to a retailer may have introduced a social desirability bias into the study (Mick 1996). Social desirability can manifest itself as a tendency for apparel companies to present themselves as "good" companies. In addition, having the same source (i.e., the apparel company) report on the independent and the dependent variable may introduce common method biases. For example, informants may theorize about underlying relationships (Podsakoff and Organ 1986), which can affect the relationships among the variables in the study (e.g., by inflating the correlation between variables and thereby creating a relationship in which no true relationship exists).
We define contractor qualification as the scope and extent of selection efforts that are undertaken by the apparel company ex ante to verify the contractor's ability (e.g., technical expertise, manufacturing capacity) and motivation (e.g., general business philosophy, reputation) to perform as needed. We asked the apparel company to consider the time when it first established the relationship with the contractor and to indicate the extent of qualification efforts undertaken by the firm. We adapted the specific items from the ones used by Heide and John (1990) and Stump and Heide (1996), and we modified them on the basis of the in-depth interviews.
Incentive design. As we discussed previously, the incentive structure in the upstream relationship is captured by the existence of supplier and manufacturer hostages, which create lock-in or replaceability problems. Specifically, we relied on two different scales that measure the degree to which the contractor can replace the apparel company ("contractor hostages") and whether the apparel company faces a corresponding lock-in situation ("apparel company hostages"). Both parties committing bilaterally creates a condition of high mutual lock-in, which in turn serves to align the respective parties' interests. We adapted our measures from the ones used by Heide and John (1988) and Heide (1994).
Control variables. In addition to the focal theoretical variables, we included nine control variables in the model. As we discussed previously, the first set of control variables pertains to the retailer's governance efforts with respect to the apparel company. We included measures of qualification efforts and incentive design. We then included a second set of variables that captures operational strategies that apparel companies can use to meet fluctuations in customer demand. These strategies are inventory maintenance and delayed product differentiation. The third set of control variables pertains to the apparel company's safeguards with respect to the retailer. In addition to controlling for the retailer's investments with respect to the apparel company, we included a categorical measure of garment characteristics (budget/ mass, moderate, better, bridge, and designer). In our model, the garment characteristic variable is represented by four dummy variables (with budget/mass as the reference category). Finally, we also included measures of relative firm size (upstream and downstream) and percentage of sales to the focal customer (i.e., retailer concentration).
Data Collection
Our research design involves a multilevel effort, because data are collected from two parties in the supply chain network (Figure 1). We obtained measures of the key independent variables (i.e., downstream market uncertainty, contractor qualification, and incentive design) from the apparel companies. We obtained the key dependent variable, apparel company flexibility in the downstream market, from purchasing managers and buyers in retail companies.
Apparel companies. Our sample was a national mailing list, purchased from List Source USA, that contained names of managers at 9574 independent U.S. apparel companies. All managers were contacted personally by telephone to screen their firm for eligibility and to locate a key informant in the production, planning, and control department. Campbell's (1955) criteria of informants being knowledgeable about the phenomena under study and able and willing to communicate with the researcher constituted the criteria for selection. In many cases, our presurvey screening process required multiple telephone calls or successive "snowballing" to locate an appropriate key informant.
On the basis of the telephone contacts, we identified 1764 managers who ( 1) were knowledgeable about the phenomena under study, ( 2) worked in companies we judged appropriate for the study, and ( 3) agreed to complete the questionnaire. The remaining firms could not be reached (36%), were not eligible for the study (38%) on the basis of the established criteria (i.e., use of independent contractors and sales to independent retailers), or refused to participate (8%).
Each of the 1764 managers received a mail questionnaire and was asked to complete it with respect to one particular contractor and retailer about which he or she was knowledgeable. The managers were asked to select and describe a particular sourcing arrangement in which the contractor was the largest source for a particular apparel item (in terms of annual dollar value). If the item was sold to more than one retailer, the managers were asked to select the largest retailer of the item (in terms of annual purchase volume).
As an additional step toward increasing the quality of the informant reports, each questionnaire included post hoc checks on the informant's knowledge about his or her firm's dealings with the contractor and retailer, respectively (seven-point scale). The questionnaire packet contained a cover letter, a prepaid envelope, and the questionnaire. To motivate informants to respond, they were offered an incentive in the form of a report that summarized the results of the study.
Three follow-up telephone calls were made to nonrespondents, and a second mailing was sent to informants who had lost or not received the first survey. Of the 1764 mailed questionnaires, 497 were returned, for an overall response rate of 28%. Although the response rate is somewhat modest, it is consistent with other distribution channel (e.g., Mishra, Heide, and Cort 1998) and alliance (e.g., Rindfleisch and Moorman 2001) studies that have relied on the same data collection strategy. Of the 497 questionnaires received, we discarded 13 because of an excessive amount of missing information. In addition, on the basis of the post hoc test of informant quality, we eliminated 63 companies, which had scores lower than four on either of the two knowledge scales. The average knowledge scores for the informants were 6.3 (standard deviation [s.d.] =.89) and 6.2 (s.d. =.99), respectively, which indicates that the selected informants were highly qualified to report on their firm's relationships with contractors and retailers. The final sample of apparel companies consisted of 421 firms, for a usable response rate of 24%.
To assess whether nonresponse bias was an issue, we compared data from early and late survey respondents, following the procedure that Armstrong and Overton (1977) suggest. Specifically, we tested the null hypothesis of no mean difference across the two groups (using t-tests) with respect to the key independent variables in the conceptual framework (i.e., downstream uncertainty, contractor qualification, and incentive design). In our final sample, approximately 32% of the questionnaires were received before two weeks and 68% were received after two weeks. We found no significant differences between the two groups on any of the variables, which suggests that nonresponse bias is not a problem.
In addition to comparing early and late respondents, we were also able to compare our final sample of firms with the larger sample of U.S. apparel companies with respect to the number of employees. Again, our hypothesis of no mean difference was supported, providing additional evidence that nonresponse bias may not be a problem.
Retailers. We used a similar procedure to that described previously to identify an informant within the retailer's firm. The informant from the apparel company was asked to identify a person in the customer firm who was knowledgeable about his or her firm's relationship with the apparel company. In total, 218 names were obtained and subsequently contacted by telephone. Of the 218 retailers that were contacted, 178 (82%) agreed to participate and were mailed a questionnaire. In total, 81 questionnaires were returned, for a response rate of 46%. We did not eliminate any cases on the basis of the post hoc test of informant quality. The final score on the knowledge scale was 6.5 (s.d. =.74).
In our final sample, 36% of the retailer questionnaires were received before two weeks and 64% were received after two weeks. To evaluate nonresponse bias, we compared the two groups on the basis of the key control variables collected from the retailers (i.e., qualification efforts by the retailer and incentive design in the downstream relationship, respectively). We found no significant differences between the two groups on any of the variables, which suggests that nonresponse bias is not a problem.
Our last assessment of nonresponse bias involved comparing the sample of 421 apparel companies from the first phase of data collection with the subsample of 81 apparel companies that we used to test our hypotheses. We again tested the null hypothesis of no mean difference across the two groups with respect to the key independent variables in the conceptual framework, and we found no significant differences. Together, the tests suggest that nonresponse bias is not a problem.
Measure Validation Procedure
To identify items that did not belong to the specific construct domain, we initially subjected each set of items used for our multi-item scales to an examination of item-to-total correlations. We examined the items that were deleted from the initial set and compared them with the original conceptual definitions of the constructs. In each case, we concluded that deleting the item did not significantly change the domain of the construct, as it was initially conceptualized. To verify unidimensionality, we subsequently subjected the resulting pool of items to confirmatory factor analysis with LISREL 8.3 (Jöreskog and Sörbom 1995).
In total, we used a pool of 47 items to measure the eight constructs. Because we could not include all the items in a single-factor model without violating the ratio of sample size to number of parameters (Jöreskog and Sörbom 1995), we divided the set of scales into two subgroups consisting of ( 1) the focal theoretical variables (i.e., apparel company flexibility, downstream market uncertainty, contractor qualification, contractor hostages, and apparel company [upstream] hostages) and ( 2) the control variables (i.e., qualification by retailer, retailer hostages, and apparel company [downstream] hostages), respectively (Bagozzi and Edwards 1998). We then employed a partial disaggregation model for each subgroup of scales to increase further the ratio of sample size to number of parameters (Bagozzi and Heatherton 1994; Marsh and Hocevar 1985).
To evaluate each factor model, we used a combination of absolute fit indexes (χ² and root mean square error of approximation [RMSEA]) and incremental fit indexes (incremental fit index [IFI] and comparative fit index [CFI]). All indexes met or exceeded the critical values (Model 1: χ² = 107.53, p =.16, RMSEA =.04, IFI =.97, and CFI =.97; Model 2: χ² = 28.45, p =.24, RMSEA =.05, IFI =.99, and CFI =.99) for good model fit (Bentler 1990; Bollen 1989; Browne and Cudeck 1992).
We then assessed the reliability of the scales. We calculated coefficient alpha (Cronbach 1951) for the completely disaggregated scales and composite reliability for the partially disaggregated scales (Fornell and Larcker 1981). We also examined the parameter estimates and their associated t-values and assessed the average variance extracted for each construct (Fornell and Larcker 1981; Gerbing and Anderson 1988).
As is shown in Tables 1 and 2, the coefficient alpha levels all exceed the .7 level that Nunnally (1978) recommends. Moreover, all the factor loadings for the partially disaggregated multi-item scales are significant, and the composite reliabilities range from .80 to .91, indicating acceptable levels of reliability for the scales (Fornell and Larcker 1981). The average variances extracted range between 56% and 78%, and all are greater than the recommended .50 level (Fornell and Larcker 1981).
Finally, we investigated discriminant validity by calculating the shared variance between all possible pairs of constructs and demonstrated that they were lower than the average variance extracted for the individual constructs. As is shown in Tables 1 and 2, all possible pairs of constructs passed Fornell and Larcker's (1981) test, which is evidence of discriminant validity among the measures. To assess discriminant validity further, we assessed pairs of scales in a series of two-factor confirmatory models, in line with the suggestions of Bagozzi and Phillips (1982). Following the procedure Jöreskog (1971) describes, we respecified the two-factor models by restricting the factor intercorrelations to unity, and then we performed χ² difference tests (with one degree of freedom) on the values we obtained for the constrained and unconstrained models. In all cases, the χ² was significantly higher in the constrained models, thereby indicating discriminant validity between the constructs. The results, in combination with the fit indexes for each factor model, suggest that the measurement scales are reliable and valid. Table 3 shows the correlations between our study variables. As we expected, some of the correlations between the interaction variables and their components are high. However, as Buvik and John (2000) note, significant results for higher-order interaction terms in a regression model in the presence of lower-order terms mean that the imprecision (reduced power) due to multicollinearity is not a valid threat.
Hypotheses Tests
Our research hypotheses specify that the effect of downstream uncertainty on apparel company flexibility will shift across the range of upstream qualification and incentive structure. To test the hypotheses, we estimated an ordinary least squares regression model and treated the upstream governance mechanisms as moderators (Sharma, Durand, and Gur-Arie [1981] refer to these as "specification variables") of the relationship between uncertainty (the predictor variable) and flexibility (the criterion variable).( n4) We specified the model as follows:
(1) Apparel company flexibility = α[sub0] +
β[sub1]X[sub1]
+ β[sub2]X[sub2]
β[sub3]X[sub3]
β[sub4]X[sub4]
β[sub5]X[sub3]X[sub1]
β[sub6]X[sub4]X[sub1]
β[sub7]X[sub3]X[sub4]
β[sub8]X[sub1]X[sub2]
β[sub9]X[sub3]X[sub1]X[sub4]
+ control variables + ε[sub1]
where
X[sub1] = downstream market uncertainty,
X[sub2] = contractor qualification,
X[sub3] = contractor hostages, and
X[sub4] = apparel company (upstream) hostages.
Table 4 shows the estimated coefficients and associated t-statistics.( n5) First, the model explains a sufficient amount of variance to justify examination of the individual coefficients (adjusted Rsup2; =.33). Second, the prediction for H[sub1] is captured by the combination of the effect of downstream market uncertainty (β[sub1] and the interaction between contractor qualification and uncertainty (β[sub8] on downstream flexibility. As we predicted, the effect of uncertainty on flexibility is significant and negative (t = -3.21, p <.025), and the effect of the interaction between contractor qualification and uncertainty is significant and positive (t = 2.07, p <.025). Together, the effects provide support for H[sub1].
Our contingency prediction (i.e., the effect of downstream uncertainty on apparel company flexibility will shift in a nonmonotonic way over the range of contractor qualification) can be examined more formally by graphing the partial derivative of the regression equation (Equation 1) following the procedure that Schoonhoven (1981) suggests. As is evident in Figure 2, downstream market uncertainty has a negative effect on apparel company flexibility for lower levels of contractor qualification and a positive effect for higher levels of contractor qualification. This indicates that upstream qualification efforts increase the apparel company's ability to accommodate the ongoing need for flexibility under uncertain market conditions.
The prediction for H[sub2] is captured by the combination of ( 1) the two-way interaction between downstream market uncertainty and contractor hostages (β[sub5] and ( 2) the three-way interaction among uncertainty, contractor hostages, and apparel company hostages (β[sub9] As is evident in Table 4, the two-way interaction between uncertainty and contractor hostages is significant and negative (t = -1.78, p < .05), which is consistent with our theoretical argument about unilateral hostages. The three-way interaction captures our expectation that the ability to secure flexibility downstream by means of upstream hostages requires that a hostage exchange takes place. As we predicted, the interaction is significant and positive (t = 2.06, p <.025), which, together with the negative two-way interaction, provides support for H[sub2].
Figure 3 shows the contingency prediction that underlies H[sub2] more formally through partial derivatives, which is similar to our previous analysis. However, because H[sub2] involves a higher-order interaction, the graph in Figure 3 is based on the partial derivative of the former equation (Figure 2) (i.e., the second derivative of the original regression equation [Equation 1], per Fisher's [1988] procedure). Specifically, Figure 3 shows how apparel company hostages enable contractor hostages to promote flexible adaptation to uncertainty. As is evident in Figure 3, when contractor hostages are not accompanied by hostages from the apparel company (i.e., a condition of unilateral supplier lock-in), downstream market uncertainty has a negative effect on apparel company flexibility. However, the effect turns positive for higher levels of apparel company hostages (i.e., a shift toward mutual lock-in). This suggests that the effect of hostages as governance devices depends on both the level at which a focal hostage is deployed and the match with the other party's hostage.
For the control variables, as we expected, qualification by the retailer has a positive and significant effect on apparel company flexibility (t = 2.49, p <.025), and apparel company (downstream) hostages have a significant and negative effect on apparel company flexibility (t = -2.06, p <.025). Retailer hostages do not have a significant effect on apparel company flexibility. With respect to the impact of inventory (speculation) and delayed product differentiation (postponement), we found a positive and significant effect of postponement (t = 1.63, p <.1), whereas apparel company inventory had no significant effect on the dependent variable. As we expected, higher-fashion garments (relative to the baseline category), measured through the dummy variables, give rise to apparel company flexibility in the downstream market (all t-values > 1.98, p <.025). Finally, both size (measured in the downstream market) and retailer concentration have significant effects on apparel company flexibility (size: t = -2.62, p <.05; retailer concentration: t = 1.60, p <.1) in the expected directions.( n6)
The predominant focus in previous research on interfirm relationships has been on individual dyadic relationships. Recently, scholars have suggested that to understand fully the nature of dyadic relationships, greater attention must be directed to the network context in which they exist. In this study, we drew on emerging perspectives on interfirm governance and networks to develop a theoretical framework of connections between relationships at different levels in a vertical supply chain network. Our starting point was a particular governance process, namely, adaptation to uncertainty (Williamson 1985). Using established TCA logic, we argued that uncertain market conditions require that relational elements (i.e., flexibility) be built into a focal relationship to facilitate adaptation to changing circumstances (Macneil 1980; Williamson 1991). However, drawing on the network literature, we augmented the basic TCA model by positing that the actual ability to build flexibility into a dyadic relationship may depend on how other connected relationships in the firm's larger supply chain are governed.
We tested our conceptual arguments in supply chain networks that consisted of relationships ( 1) between a manufacturer and an independent (downstream) customer and ( 2) between the manufacturer and an independent (upstream) supplier. We identified two specific governance strategies for the upstream supply market: supplier qualification and incentive design. We then described the effect of the governance strategies on a manufacturer's ability to adapt to uncertainty in a flexible manner in the downstream market.
Overall, the empirical results show good support for our theoretical arguments that individual relationships in a larger network are connected and the ability to adapt to uncertainty in one relationship depends in part on a firm having deployed particular governance mechanisms (qualification and hostages) in another. However, we note that the process by which hostages promote adaptation is considerably more complex than the one for qualification. In general, incentives and selection seem to work in different ways. Governance deployment is a complex matter, and additional research into the properties and effects of alternative mechanisms is a priority. We return to this issue in the "Limitations and Further Research" section.
In general, this study emphasizes the importance of broadening the unit of analysis, both in transaction cost theory and in relationship research. We note, however, that proponents of TCA have actually acknowledged this themselves. For example, Williamson (1985, p. 393) suggests that because TCA normally examines each trading nexus separately, "interdependencies among a series of related contracts may be missed or underevaluated as a consequence." However, to date, this idea has received little attention in the governance literature. An exception is Antia and Frazier (2001), who document how contract enforcement in a particular dyad is influenced by factors outside of the dyad itself. Another exception is Heide and John (1988), who describe how agents' bonding efforts in one (customer) relationship serve to discourage opportunism in another (i.e., by a principal). Finally, Mishra, Heide, and Cort (1998) show how the strategies used to manage relationships with end customers influence how a firm manages its employee relationships.
Our current research builds on these studies. By drawing on extant network perspectives, our study highlights the important implication of accounting for "related contracts," namely, their effect on the ability to respond in a given relationship in accordance with TCA's prescriptions.
Managerial Implications
Supply chain management has emerged in recent years as a key source of competitive advantage. As companies continue to outsource business activities, they are realizing that practicing relationship management involves more than managing an individual relationship (Byrnes 2001; The Economist 2002). For example, many manufacturers are recognizing that their downstream customer relationships are often constrained by other relationships in the larger supply chain (The Economist 2001). Our study has two key implications for managers. First, we identify specific strategies that can be used to manage supply chain relationships. Second, we describe the effects of these strategies across levels in an overall chain.
Consider a manufacturer's ability to meet a downstream customer's (e.g., a retailer's) continuously changing needs. Although a manufacturer cannot accurately predict future conditions in the downstream market (and the specific needs for adaptation), it nevertheless can make proactive efforts to structure the supply chain. Specifically, the manufacturer needs to consider possible constraints throughout the supply chain that may affect its ability to meet customers' needs. The constraints include possible governance problems due to incompatible goals and/or opportunism in the upstream supply market. On the basis of an assessment of the potential problems, the manufacturer can deploy particular governance mechanisms in the relationship. In this study, we show how supplier qualification programs and incentive structures increase a manufacturer's ability to adapt in a flexible manner to uncertainty in the downstream customer relationship.
Although flexibility is one of the most commonly noted dimensions of strong customer relationships (e.g., Achrol and Kotler 1999), it is not a goal in itself. Because promoting flexibility requires investments on a firm's part (e.g., qualification programs involve substantial costs), a firm should make such efforts selectively. However, whenever a manufacturer seeks downstream flexibility, promoting support throughout a supply chain becomes an important part of a firm's overall strategy.
Limitations and Further Research
The results of our study must be interpreted in view of certain limitations. For theory-testing purposes, we decided to test our hypotheses in a particular (and homogeneous) context: the U.S. apparel industry. Restricting our sample in this way served the dual purposes of controlling for extraneous sources of variation and developing grounded measures. At the same time, caution should be used in extrapolating our results to other contexts.
Furthermore, as is evident from our previous discussion, in our hypotheses testing, we faced certain challenges that pertained to our ( 1) using matched apparel company-retailer pairs (because of the need to obtain the dependent and independent variables from different parties), ( 2) controlling for many variables, and ( 3) testing a three-way interaction. A joint effect of the three challenges was that we needed to estimate a relatively complex model with a somewhat modest sample size, which may raise concerns about the robustness of our results. To increase confidence in our findings, we undertook a validation task based on a regression procedure that Anderson and Weitz (1989) previously used. The procedure involved estimating a model that included all the focal variables used to test the two research hypotheses and then regressing the residuals from the first model against all the control variables (including the remaining second-order terms). Finally, we included the significant variables in the second model in a reestimation of the first model. The results from this model, which has a more modest set of parameters (and raises fewer concerns about potential overfitting), were almost identical to the model in Table 4 and suggest that our results are not sensitive to model specification.
We also limited our study to two specific governance aspects of the manufacturer-supplier relationship: qualification and incentive design. A natural extension of our study would be to consider whether other governance mechanisms that may facilitate downstream adaptation exist. For example, a key construct in the general literature on governance is monitoring. Unfortunately, as Wathne and Heide (2000) note, the role of monitoring in interfirm relationships has not always been stated clearly. More specifically, it is not entirely clear whether monitoring (as a governance strategy) matches up with specific governance problems (e.g., adaptation to uncertainty). Thus, a significant topic for further research is to specify in greater detail the range of governance mechanisms that can be used to manage supply chain relationships and the properties of each mechanism with respect to specific governance problems.
Further research could also be usefully directed toward exploring particular supply chain initiatives, such as quick response and efficient consumer response. Unfortunately, because these are umbrella terms, there is little consensus about their meaning. For example, in recent literature (e.g., Coughlan et al. 2001; Dunne, Lusch, and Griffith 2002; Levy and Weitz 2001), some authors use the terms interchangeably and some try to develop distinctions. However, at a conceptual level, our understanding of the terms is that there are potentially three different components involved in each one: ( 1) production and inventory management, ( 2) relationship management, and ( 3) information technology (for a similar categorization, see Handfield and Nichols 1998).
In our model, we capture the first two components through the operational strategies variables (i.e., postponement and speculation) and the governance mechanisms (i.e., supplier qualification and incentive design), respectively. To address the third component, we estimated an additional model that included a measure of electronic data interchange implementation in the relationships ( 1) between the apparel company and the (downstream) retailer and ( 2) between the apparel company and the (upstream) contractor, respectively. None of the variables has a significant effect in the model. Although our a priori expectation was that both variables would have an effect on downstream flexibility, we recognize that we may not have fully captured the phenomenon in our measure. Thus, an important topic for further research is to specify in greater detail other components of supply chain initiatives, such as quick response and efficient customer response, to describe how each will manifest itself in a network and, most important, to outline the function of each component with respect to specific governance problems.
Finally, a promising avenue for further research is expansion of the current model to include network effects at the customer level. Conceivably, the way a manufacturer manages a focal customer relationship may affect, and be affected by, the firm's other customers. We hope that future projects will be directed toward extending the unit of analysis in relationship research in this and other directions.
The authors gratefully acknowledge the financial support of the Marketing Science Institute and the constructive comments on the article provided by University of Iowa Marketing Camp participants (May 2001), Erasmus Research Institute of Management conference participants (November 2001), and University of Wisconsin, Madison (April 2003) and University of Melbourne (June 2003) seminar participants. The authors also thank the JM reviewers for their constructive comments and Natascha Wathne for considerable assistance with data collection.
(n1) We assume that the manufacturer is motivated to respond to the downstream customer as circumstances change. In addition to the TCA arguments we noted previously, we base our assumption on a manufacturer decision calculus that involves a positive trade-off between the opportunity costs of failing to satisfy the customer (Rindfleisch and Heide 1997) and the direct costs of responding in a particular way. At the end of this section, we discuss how we have tried to explicitly account for (in our empirical test) conditions that affect the manufacturer's decision calculus.
(n2) Our theoretical argument here follows the transaction cost notion that though decision makers are boundedly rational, they are far-sighted in the sense that they have the ability to "look ahead, perceive potential hazards," and factor these into the organization of the supplier relationship (Williamson 1996, p. 9). Thus, although manufacturers cannot accurately predict specific conditions in the downstream market (or potential adaptation problems), manufacturers that operate in such markets will make the necessary efforts to structure their upstream relationships appropriately.
(n3) As we describe in the "Research Method" section, we purposely eliminated from the study firms that relied on vertical integration as a governance approach because of the different way adaptation problems are managed under ownership. Rindfleisch and Heide (1997) point to the mixed pattern of results for uncertainty in previous research, which may partly be due to differences in terms of the governance approaches used.
(n4) We considered two additional approaches to estimating interaction effects: indicant product and subgroup analysis. Given some of the possible limitations of these approaches, such as dichotomizing a continuous variable (see, e.g., Jaccard and Wan 1996), we chose to test the hypotheses by using product-term regression analysis.
(n5) To mitigate the potential threat of multicollinearity among the interaction terms and the other variables in the regression model, we mean-centered all independent variables (Aiken and West 1991). When the focal independent and moderator variables are mean-centered, the regression coefficient for the independent variable reflects its influence on the dependent variable at the average value of the moderator variable (Jaccard and Wan 1996). The interpretations of the interaction effects remain the same.
(n6) As is consistent with our theoretical arguments, we found support for a model in which the effect of downstream uncertainty on apparel company flexibility was contingent on the firm's upstream governance efforts. However, it may be useful also to consider a competing model in which uncertainty is a direct antecedent of upstream governance mechanisms (i.e., the governance mechanisms are mediators, rather than moderators, of the relationship between uncertainty and flexibility). We examined this possibility by estimating two path models; the governance mechanisms served as complete and partial mediators, respectively. Neither model showed acceptable levels of fit. Although we hesitate to emphasize individual parameters in light of poor overall model fit, we note that none of the mediating paths were significant. Overall, this suggests that governance decisions at one level in a supply chain network are not directly driven by market conditions at another level and do not in themselves affect flexibility across relationships. Moreover, the results indicate that both governance mechanisms conform to the psychometric definition of a pure moderator variable (Sharma, Durand, and Gur-Arie 1981).
Legend for Chart:
A - Items
B - Apparel Company Flexibility
C - Contractor Qualification
D - Contractor Hostages
E - Apparel Company (Upstream) Hostages
F - Downstream Market Uncertainty
A B C
D E
F
X1 .87 (9.56) --
-- --
--
X2 .83 (8.83) --
-- --
--
X3 .94 (10.84) --
-- --
--
X4 -- .85 (9.26)
-- --
--
X5 -- .81 (8.61)
-- --
--
X6 -- .70 (6.92)
-- --
--
X7 -- .92 (10.41)
-- --
--
X8 -- --
.93 (10.02) --
--
X9 -- --
.82 (8.47) --
--
X10 -- --
.72 (7.10) --
--
X11 -- --
-- .83 (7.40)
--
X12 -- --
-- .76 (6.76)
--
X13 -- --
-- .64 (5.67)
--
X14 -- --
-- --
.85 (8.93)
X15 -- --
-- --
.81 (8.36)
X16 -- --
-- --
.87 (9.19)
Coefficient alpha(b) .93 .88
.84 .74
.86
Composite reliability .91 .89
.87 .80
.88
Variance extracted 78% 68%
69% 56%
71%
Highest shared variance 12% 14%
14% 3%
12%
(a) Item loadings, t-values, coefficient alpha, composite
reliability, variance extracted, and highest shared variance.
(b) Completely disaggregated scales. Legend for Chart:
A - Items
B - Qualification by Retailer
C - Retailer Hostages
D - Apparel Company (Downstream) Hostages
A B C
D
X1 .83 (8.84) --
--
X2 .93 (10.46) --
--
X3 .88 (9.66) --
--
X4 -- .82 (8.29)
--
X5 -- .81 (8.14)
--
X6 -- .85 (8.67)
--
X7 -- --
.87 (9.35)
X8 -- --
.86 (9.29)
X9 -- --
.85 (9.15)
Coefficient alpha(b) .90 .83
.89
Composite reliability .91 .86
.90
Variance extracted 77% 68%
74%
Highest shared variance 3% 5%
5%
(a) Item loadings, t-values, coefficient alpha, composite
reliability, variance extracted, and highest shared variance.
(b) Completely disaggregated scales. Legend for Chart:
A - Construct
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
O - 14
P - 15
Q - 16
R - 17
S - 18
T - 19
U - 20
A B C D E F
G H I J K
L M N O P
Q R S T U
1. ACF 1.0
2. CQ .06 1.0
3. CH -.05 -.30 1.0
4. ACUH .02 -.07 .16 1.0
5. MU -.24 -.04 -.14 .06 1.0
6. CQ x MU .06 -.01 -.11 .03 .14
1.0
7. CH x MU -.17 -.11 -.05 -.26 -.26
-.30 1.0
8. ACUH x MU -.09 .03 -.24 .00 -.18
.26 .07 1.0
9. CH x ACUH -.06 -.13 .10 -.48 -.24
-.06 .16 .06 1.0
10. CH x MU x
ACUH .20 -.05 .14 .05 .06
-.26 -.16 -.73 -.03 1.0
11. RQ .27 -.02 -.16 -.00 -.02
-.00 .11 -.12 -.22 .11
1.0
12. RH .07 -.11 .13 .03 -.02
-.12 .14 .01 -.08 -.06
-.03 1.0
13. ACDH -.10 -.18 .16 -.02 .07
.00 .09 -.02 .01 .08
.22 -.20 1.0
14. RH x ACDH -.16 .05 -.18 .02 -.02
-.10 .13 .05 -.06 -.16
-.08 .13 -.13 1.0
15. Size: apparel
company >
contractor -.01 -.10 .31 -.22 .08
.05 .09 -.11 .19 -.02
-.02 -.15 .22 -.11 1.0
16. Size: apparel
company >
retailer -.23 -.08 -.14 -.16 .02
.23 -.10 .15 .07 -.21
-.22 -.12 -.33 -.01 -.12
1.0
17. Postponement .06 .04 .02 -.17 -.20
-.13 -.07 .03 -.03 -.10
.07 -.00 .07 -.05 -.11
-.10 1.0
18. Inventory .15 .12 -.16 .02 -.02
.05 -.04 .20 -.15 -.16
.05 .15 -.11 .08 -.03
-.05 .21 1.0
19. Garment .32 -.02 -.13 -.02 -.15
.06 -.01 .03 -.00 -.02
-.15 -.27 -.25 .07 -.13
-.16 -.24 .08 1.0
20. Retailer
concentration .17 -.06 .03 -.01 -.05
.12 -.06 .05 -.09 -.17
-.24 -.01 -.23 .01 -.20
-.13 -.12 .24 -.13 1.0
Mean 5.35 4.79 3.59 3.61 3.65
-.05 .24 .10 .37 -.70
4.80 3.36 3.62 .60 .38
-.36 3.35 4.25 3.4 27.4
S.D. 1.1 1.1 1.6 1.5 1.1
1.3 1.9 1.9 2.7 4.0
1.4 1.7 1.8 3.1 .8
.8 1.9 1.8 1.1 24.9
Notes: R > .22 are significant at p < .05 (two tailed) for
n = 81. Acronyms are defined as in the Appendix. Legend for Chart:
A - Independent Variables
B - Unstandardized Coefficients
C - Standardized Coefficients
D - t-Value
A
B C D
MU
-.35 -.34 -3.21(***)
CQ
.03 .03 .29
CQ x MU
.20 .23 2.07(***)
CH
.03 .05 .38
ACUH
-.02 -.03 -.21
CH x MU
-.12 -.20 -1.78(**)
ACUH x MU
.06 .10 .70
CH x ACUH
.01 .03 .26
CH x MU x ACUH
.09 .32 2.06(***)
Retailer Governance Efforts
RQ
.22 .26 2.49(***)
RH
.04 .06 .59
ACDH
-.15 -.23 -2.06(***)
RH x ACDH
-.02 -.07 -.65
Other Controls
Apparel company inventory (speculation)
.01 .01 .12
Delayed product differentiation (postponement)
.11 .19 1.63(*)
Garment characteristic, dummy 1 (designer)
1.52 .41 2.84(***)
Garment characteristic, dummy 2 (bridge/difference)
2.22 .43 3.54(***)
Garment characteristic, dummy 3 (better)
.91 .37 2.16(***)
Garment characteristic, dummy 4 (moderate)
.77 .34 2.01(***)
Size: apparel company > contractor
.09 .06 .56
Size: apparel company > retailer
-.39 -.29 -2.62(***)
Retailer concentration
.01 .18 1.60(*)
Adjusted R²
.33
(*) p < .1 (one-tailed test).
(**) p < .05 (one-tailed test).
(***) p < .025 (one-tailed test).
Notes: Dependent variable is apparel company's flexibility
toward retailer (n = 81). Acronyms are defined as in the
Appendix.DIAGRAM: FIGURE 1 Supply Chain Network
DIAGRAM: FIGURE 2 Impact of Contractor Qualification on the Relationship Between Downstream Market Uncertainty and Apparel Company Flexibility
DIAGRAM: FIGURE 3 Impact of Apparel Company Hostages on the Ability of Contractor Hostages to Promote Flexibility Under Downstream Market Uncertainty
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Downstream Market Uncertainty (MU; Seven-Point Semantic-Differential Scale: Predictable/ Unpredictable)
• Consumer demand
• Sales forecasts
• Retail sales
• Consumer style preferences
Apparel Company Flexibility (ACF; Seven-Point Likert-Type Scale: Completely Inaccurate/Accurate Description of Apparel Company)
• Flexibility in response to requests for changes is a characteristic of this apparel company.
• In this relationship, the apparel company is open to the idea of making changes, even after we have made an agreement.
• In this relationship, the apparel company makes it possible for us to make adjustments to cope with changing circumstances.
• This apparel company is open to modifying our agreement if unexpected events occur.
• If a situation arises in which we have different assumptions about our agreement, this apparel company is open to working out a new deal that is acceptable to both of us.
• When unexpected situations arise and we disagree on how to proceed, this apparel company is open to working out a new deal that is acceptable to both of us.
• If our views differ regarding events in our relationship, this apparel company is open to developing a common understanding.
Contractor Qualification (CQ; Seven-Point Likert-Type Scale: Minimal/Extensive Qualification Effort)
• Garment quality (e.g., ability to meet specifications)
• Technical capability (e.g., technical expertise)
• Manufacturing capability (e.g., capacity)
• Financial strength
• Labor conditions (e.g., workers are treated fairly)
• Price competitiveness
• Contractor's performance in other relationships
• Contractor's general business philosophy
• Contractor's reputation among other apparel companies
• Contractor's reputation among other contractors
• Contractor's reputation for on-time delivery
Contractor Hostages (CH; Seven-Point Likert-Type Scale: Strongly Disagree/Agree)
• If we canceled our sourcing agreement with this contractor, the contractor would be required to write off substantial investments.
• If we canceled our sourcing agreement with this contractor, it would hurt this contractor's operations in the season in question.
• If we canceled our sourcing agreement with this contractor, the contractor would have difficulty finding another apparel company to source for in the season in question.
• If we canceled our sourcing agreement with this contractor, finding another apparel company to source for in the same season would have a negative impact on the price this contractor could charge.
Apparel Company (Upstream) Hostages (ACUH; Seven-Point Likert-Type Scale: Strongly Disagree/ Agree)
• Replacing this contractor for this particular garment would require us to write off substantial investments.
• If we canceled our sourcing agreement with this contractor, we would have difficulty shipping the required quantity of this garment to the retailer on time for the season in question.
• If we canceled our sourcing agreement with this contractor, we would be forced to compromise on the quality of this garment for the season in question.
• If we canceled our sourcing agreement with this contractor, it would be difficult to find another contractor for this particular garment in the same season.
Qualification by Retailer (RQ; Seven-Point Likert-Type Scale: Minimal/Extensive Qualification Effort)
• Garment quality
• Manufacturing capability (e.g., capacity)
• Financial strength
• Price competitiveness
• Apparel company's general business philosophy
• Apparel company's reputation among other apparel companies
• Apparel company's reputation among other retailers
• Apparel company's quality reputation
• Apparel company's reputation for on-time delivery
Retailer Hostages (RH; Seven-Point Liker-Type Scale: Strongly Disagree/Strongly Agree)
• Replacing this apparel company for this particular garment would require us to write off substantial investments.
• If we canceled our purchase agreement with this apparel company, we would have difficulty obtaining the required quantity of this garment on time for the season in question.
• If we canceled our purchase agreement with this apparel company, we would be forced to compromise the quality of this garment for the season in question.
• If we canceled our purchase agreement with this apparel company, it would be difficult to find another apparel company for this particular garment in the same season.
Apparel Company (Downstream) Hostages (ACDH; Seven-Point Likert-Type Scale: Strongly Disagree/Strongly Agree)
• If we canceled our purchase agreement with this apparel company, the apparel company would be required to write off substantial investments.
• If we canceled our purchase agreement with this apparel company, it would hurt this apparel company's operations in the season in question.
• If we canceled our purchase agreement with this apparel company, the apparel company would have difficulty finding another retailer to sell to in the season in question.
• If we canceled our purchase agreement with this apparel company, finding another retailer to sell to in the same season would negatively impact the price this apparel company could charge.
~~~~~~~~
By Kenneth H. Wathne and Jan B. Heide
Kenneth H. Wathne is Assistant Professor of Marketing, School of Business, University of Wisconsin, Madison (e-mail: kwathne@bus.wisc.edu).
Jan B. Heide is Irwin Maier Chair in Marketing, School of Business, University of Wisconsin, Madison (e-mail: jheide@bus.wisc.edu).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 131- Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and Interpretations. By: Lambert-Pandraud, Raphaëlle; Laurent, Gilles; Lapersonne, Eric. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p97-113. 17p. 4 Charts, 4 Graphs. DOI: 10.1509/jmkg.69.2.97.60757.
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- Business Source Complete
Repeat Purchasing of New Automobiles by Older
Consumers: Empirical Evidence and Interpretations
In a large empirical study, the authors find that older consumers, who constitute an important market segment, repurchase a brand more frequently when they buy a new car. Older consumers consider fewer brands, fewer dealers, and fewer models, and they choose long-established brands more often. To interpret the results, the authors rely on four age-related theoretical perspectives: biological aging, cognitive decline, socioemotional selectivity, and change aversion.
In developed countries, the growing segment of older consumers has become increasingly important economically. In France, people age 60 and older constitute 20% of the current population, a percentage that is likely to increase to 27% by 2010 (Daguet 1996). In the United States, the number of people age 65 and older is 35 million and is projected to increase to 50 million by 2010 (Polyak 2000). The purchasing power of the older segment is also increasing. Compared with the average household in the United States (American Demographics 2002), households whose members are age 65 and older spend more not only on drugs (+180%) and medical services (+67%) but also on fresh fruit and vegetables (+50%) and general household expenses (+39%). For the new-car market, buyers age 60 and above represent 21% of the U.S. market (Krebs 2000) and 29% of the French market.
The sheer size of the older segment, though managerially important, does not justify a specific research project if older consumers behave similarly to younger ones, but older consumers behave differently, at least in their repeat purchase behavior. Repeat purchases and brand loyalty are of great managerial importance (Uncles and Laurent 1997), as is evidenced by the many articles and books devoted to the greater profitability of keeping existing customers rather than acquiring new ones (e.g., Reichheld and Teal 1996). Most studies concentrate on marketing actions that may lead to more frequent repeat purchases, but there has been little analysis of the impact of permanent customer characteristics, such as their demographics. Whereas various marketing practitioners report higher levels of repeat purchase behavior by older consumers (e.g., see Secodip's [1998, p. 1] analysis of "60 markets and 600 brands"), to preserve confidentiality, they rarely present detailed results. In contrast, Burnett (2002) finds that older consumers are neither more loyal nor more committed to brands of bath soap, shampoo, deodorants, and cellular telephones. Given the contradictions and the practical importance of the question, it is surprising that the possible impact of age on repeat purchase behavior has seldomly been mentioned in academic literature (Phillips and Sternthal 1977; Tongren 1988), though we review a few exceptions subsequently.
In this study, we attempt to achieve three main objectives: ( 1) to measure precisely and reliably whether older consumers have greater tendencies to repurchase; ( 2) to identify related characteristics of older consumers' purchase processes, which may be unique to their demographic; and ( 3) to investigate possible reasons for the underlying differences in purchase behaviors between older and younger consumers.
We analyze new-car purchases, a category with strong consumer involvement, a high average repeat purchase rate, and great economic importance. Each year, 2.2 million new cars (versus 5.4 million used cars) are sold in France, and most are purchased by individual consumers, not by companies for their fleet. We review the few previous studies that examine older consumers' repeat purchase behaviors, and then we justify the choice of the new-automobile market. Next, we present results related to repeat purchasing and to characteristics of the purchase process. We then interpret the results in light of several theoretical approaches, highlighting the extent to which each theory can explain a result. We summarize this discussion with four theory-based propositions that are compatible with the results and can be tested by further comparative studies.
Previous Studies
Few, if any, studies have explicitly analyzed the impact of age on repeat purchase behavior. More often, researchers examine its impact on consideration sets and find that a smaller consideration set should increase the probability of a repeat purchase if the previous brand is included in the set. Deshpandé and Zaltman (1978) and Deshpandé and Krishnan (1982) find that older consumers tend to make fewer price comparisons and collect less information before they purchase. Uncles and Ehrenberg (1990) observe that, on average, older households, in which members are age 55 and older, buy fewer brands of frequently purchased consumer goods, partly because of their smaller purchase rate. Cole and Balasubramanian (1993) find that older people consider fewer brands and varieties of cereals before a purchase. Similarly, Aurier and Jean (1996) report that the number of drinks considered during specific purchase occasions decreases with age. In contrast, neither Gruca (1989) nor Campbell (1969) observes a significant relationship between consumer age and the size of the consideration set for coffee and grocery products, respectively.
Prior results converge more for new-car purchases. Johnson (1990) and Srinivasan and Ratchford (1991) observe that older consumers search for less information before they make a decision. Maddox and colleagues (1978) find that the increased age of consumers decreases the number of car brands they consider. From another perspective, Punj and Cattin (1983) find that car buyers who consider a single dealer are significantly older, which they attribute to the higher psychological cost of information search. Lapersonne, Laurent, and Le Goff (1995, p. 55) mention the link between a smaller consideration set and more frequent repeat purchases, and they indicate that being age 60 and older significantly increases the probability that the consumer will have a "consideration set of size one" before the purchase of a new car, which in four of five cases leads to a repeat purchase. The studies describe the "shrinkage" of the decision process with age, including fewer brands considered and bought, fewer price comparisons made, fewer drinks considered, less information sought, and fewer dealers considered, all of which mean that older consumers consider fewer options on the basis of the more limited information they attain.
Using a large data set, we consider this shrinkage more broadly in terms of brands, dealers, and models, and we analyze repeat purchases for both brands and dealers. We also analyze separately older (ages 60-74) and very old (age 75 and older) buyers. We rely on the most relevant age-related theoretical approaches to interpret our results.
Methodology and Data
We use a survey rather than experimental methodology. Given our goals, external validity is essential, and therefore we study real-life decisions. The use of hypothetical decisions and artificial brands would present major drawbacks. New-car purchases provide a pertinent study domain for several reasons. Car purchases are important and visible, and we can check brand and model choices through official registration documents. We can also study the steps in the purchase process precisely, because they take place in an environment (in France at that time) in which each dealer sells only one brand of new car; we prefer this option because collecting information about multiple brands or models entails significant costs in terms of time, transportation, energy, and mental resources.
Therefore, we analyze a secondary survey of recent individual new-car buyers that a leading French market research company conducted over a one-year period (from July 1997 to June 1998). For 40 years, this survey has been conducted annually in the five largest Western European countries and every other year in approximately ten other countries with a similar questionnaire and methodology.( n1) Questionnaires are mailed to a random sample that is drawn from the mandatory, government-run registration system for new cars. The questionnaire takes approximately 60 minutes to complete, and the response rate is slightly less than 40%. Responses are weighted to match the sample exactly to the population of car purchasers in terms of purchased brands and models. The sample (see Appendix A) is representative of the population of individual new-car purchasers and comprises 31,497 buyers who are the main users of the car. We analyze only the 28,913 respondents who bought a new car to replace a previous car.
Because of the long intervals between consumers' car purchases, the cross-sectional sample represents current purchasers rather than a permanent panel of households. The mail survey is sent eight times a year, so on average it reaches each buyer three months after his or her car purchase. A possible bias in the survey could result from the greater likelihood that older people underreport the number of brands, dealers, and models they considered because they suffer memory problems. However, we found no link in our data set between these variables and the delay between the car purchase and the survey. This finding may be a result of the special characteristics of our study; that is, cars are extremely involving products, and therefore buyers are much more likely to remember the purchase process for cars than for lower-involvement, frequently purchased products. In addition, respondents have a greater chance of remembering the details of the purchase process because the delay between the purchase and the survey is short (with a long delay between the two, there could be a risk of respondents' forgetting details of the purchase).
Among the 193 variables that the survey measures, we use items that describe the recently purchased car (brand and model), the previous car, other brands and models considered, dealers visited, and the level of satisfaction with the previous car and previous dealer. In addition, general descriptors of the respondents include not only age but also complete demographics with respect to education, income, gender, location, occupation, retirement status, and marital status. By taking individual characteristics into account, we can reduce the bias that is induced by cohort effects in the cross-sectional analysis (Schaie 1965; Whitbourne 1996). The questions we use are allocated across the eight-page survey as follows: the car recently bought (p. 1), the previous car (p. 5), brands and models considered (p. 3), dealers visited (p. 3), satisfaction (p. 6), and demographics (p. 7).
Age is the main explanatory variable of interest in this study. Investigating older consumers' behavior poses several methodological problems. Tongren (1988) notes that three-quarters of studies of older consumers make no comparisons with younger consumers. In our study, we surveyed buyers of all ages and demographic characteristics, which enabled us to compare the choice process across age groups. In addition, age limits have varied across previous studies, which makes comparisons of the results even more difficult. In contrast to previous studies, we know the exact age of each respondent, and therefore we could use age as a ratio-scaled explanatory variable with a linear impact on the variables of interest. However, literature on gerontology and psychology (on which we rely because almost no data on this problem appear in marketing literature) suggests that this approach would be erroneous because of the nonlinear impact of age on daily life and decisions.
That is, aging occurs through a series of qualitative changes rather than as a continuous, underlying process. The changes occur at varying times for different people because of individual and environmental differences. Nevertheless, gerontology and psychology literature offers a reliable basis for defining "older" consumers. Typically, elderly people are defined as older than age 60 or 65 (Heslop and Marshall 1990), far beyond the age of 50, which marketing practitioners often use (Treguer 1994). As Lesser and Kunkel (1991) explain, people between ages 40 and 59 are at their peak in terms of problem-solving abilities and social maturity. Gerontology research based on social psychology relies on the retirement age, which is usually near age 60. Research based on cognitive psychology studies people older than age 60 or 65, and it reports a stronger cognitive decline in daily life among people older than approximately age 75 (Chasseigne, Mullet, and Stewart 1997). Therefore, we coded age as a categorical variable and based the limits between successive categories on psychological literature. We adopted Schaie's (1996) categorization of the elderly, distinguishing "young-old" (ages 60-74) from "old-old" (age 75 and older) consumers. We divided the remaining respondents into two subgroups: a "middle-aged" group (ages 40-59), the reference group against which the young-old and old-old consumers can be compared, and a "young" group (age 39 and younger), which is not of interest but whose indicator variable we include in the statistical analysis.
We also controlled for other factors that may have an impact on purchase behavior. As we noted previously, the survey provides a complete set of categorical demographic variables. For each of the following variables, we used one category as the reference and created dummies for the other categories: education, income, occupation, city size, marital status, gender, and retirement status (defined only for people ages 55-65).
In addition, several variables describe the context of the car purchase. We measured satisfaction with the previous car and previous dealer (if the dealer took care of that car) by a single item (in both cases, respondents provide a rating on a scale from 1 to 10), and we noted the absence of such a dealer (a binary variable), whether the previous car was bought secondhand, and how long the consumer owned the previous car. We considered these variables because previous research has indicated that they influence car repurchase (Lapersonne, Laurent, and Le Goff 1995).
Most of the dependent variables are binary (e.g., whether the previous brand is repurchased), and we analyzed them through logistic regression. We used an analysis of variance on the quantitative dependent variables (e.g., number of brands considered). Given the large number of aspects of purchase behavior that we investigate and the many explanatory variables other than age, we present the results in three complementary forms. In Table 1, we describe the impact (.2 in the logistic regression, F in the analysis of variance) of the explanatory variables (i.e., age, demographics, and context of the car purchase) on each of the dependent variables. Because of space limitations, we do not comment on the results for the explanatory variables other than age (e.g., as Table 1 shows, satisfaction with the previous car and with the previous dealer is important). Age is always a significant variable and is often the most significant. In Table 2, we provide the estimated parameters for age that correspond to the young, young-old, and old-old groups; the middle-aged group is the reference. Finally, we illustrate the impact of age and show how the percentage of answers (for binary variables) or the average value (for quantitative variables) varies with age.
Results
Age had a strong impact on the probability that a consumer would repurchase the previous brand (Χ²[ 3] = 77.69, Table 1). Of the young buyers, 42% repurchased the previous brand versus 54% of the middle-aged, 66% of the youngold, and 72% of the old-old (Figure 1, Part A). Accordingly, older consumers were also more likely than were middle-aged or young buyers to consider their previous brand (Χ²[ 3] = 42.65). However, the effect was significant only for old-old buyers. Of the young buyers, 61% considered their previous brand, compared with 73% of the middle-aged, 80% of the young-old, and 83% of the old-old (Figure 1, Panel B). Finally, an extreme focus on the previous brand occurred when buyers considered nothing but that brand. Empirical data marginally support (p = .06, Table 2) the hypothesis that old-old buyers are more likely to consider only one brand: 6% for the young, 11% for the middle-aged, 21% for the young-old, and 27% for the old-old (Figure 1, Panel C).
Thus, older consumers considered the previous brand more often, considered it alone more often, and, most important, purchased it more often. The results we report in Tables 1 and 2 indicate that this finding was not due to the spurious effect of important context variables, such as satisfaction with the previous car, satisfaction with the previous dealer, the absence of a regular dealer to handle the previous car, or whether the previous car was bought secondhand or long ago. Although these variables, as well as several demographic factors, significantly affected the focus on the previous brand, the impact of age remained strong, even when we took all of the other variables into account.
We illustrate the findings in Figure 2. In each case, we plot separate curves for consumers younger than age 60 and for those age 60 and older. The curves show how the predicted probability varies as a function of satisfaction with the previous car. For a given satisfaction level, older buyers were more likely to consider the previous brand (Figure 2, Panel A), consider nothing but it (Panel B), and repurchase it (Panel C). The three results converge: Whereas satisfaction with the previous car was a powerful driver of the consideration and repurchase of the previous brand, age remained a strong, specific effect.
In the peculiar structure of the car market, another form of repeat purchasing is the purchase of a new car from the previous dealer. Older buyers were more likely to purchase from a previous dealer (Χ²[ 3] = 101.95, Table 1), and the effect was stronger for old-old consumers (21% for the young, 34% for the middle-aged, 44% for the young-old, and 49% for the old-old; Figure 1, Panel D). Again, this finding was not due to greater satisfaction among older consumers. For a given satisfaction level with the previous brand, older consumers (age 60 and older) were more likely to purchase from the previous dealer, and the effect was stronger for old-old consumers.
According to Lapersonne, Laurent, and Le Goff (1995), the repeat purchase of cars should be associated with a reduced consideration set, and in our study, age had a significant impact on the number of brands considered (F[ 3, ∞] = 18.91, Table 1). The average number of brands considered was as follows: 2.24 by young buyers, 2.16 by middle-aged buyers, 1.92 by young-old buyers, and only 1.77 by old-old buyers (Figure 3, Panel A). Older buyers of new cars were much less likely to consider three or more brands (24% for the young, 22% for the middle-aged, 14% for the youngold, and 7% for the old-old). The extreme form of this phenomenon was buyers who considered only a single brand (Χ²[ 3] = 41.79, Table 1), whose percentage strongly increased with age (11% for the young, 15% for the middle-aged, 26% for the young-old, and 33% for the old-old). The results confirm that repeat purchasing is only one component of the more general shrinkage of the decision process that is associated with age. Older buyers make their purchase decisions from a reduced framework in which the number of alternative cars they consider is smaller. The peculiar phenomenon of a consideration set of size one (Lapersonne, Laurent, and Le Goff 1995) applies to 25% of young-old car buyers and to 33% of old-old buyers.
Another way for a buyer to simplify the purchase process is to consider a single dealer, though any given brand is offered by multiple dealers, and different prices might be negotiated by visiting more than one dealer. Age had a strong effect on the probability of considering a single dealer (Χ²[ 3] = 128.60, Table 1). The percentage of new-car buyers who considered a single dealer strongly increased with age (47% for the young, 53% for the middle-aged, 66% for the young-old, and 79% for the old-old; Figure 3, Panel B). Again, the results suggest a simplified, considerably shrunken purchase process, throughout which older consumers rarely collect information from additional dealers.
However, a given brand offers many models, and a consumer could go through a complex decision process if he or she considered all the possible models that a single brand and dealer offer. Here again, the impact of age was significant (F[ 3, ∞ ] = 31.83, Table 1). The average number of models considered was as follows: 2.37 by young buyers, 2.26 by middle-aged buyers, 2.00 by young-old buyers, and 1.83 by old-old buyers (Figure 3, Panel C). The consideration of a single model increased markedly with age (6% of young buyers, 11% of middle-aged buyers, 20% of young-old buyers, and 28% of old-old buyers), whereas the percentage of buyers who considered three or more models dropped sharply with age (30%, 26%, 16%, and 8%, respectively).
The preceding group of analyses leads us to the following conclusions: Among older buyers, we observe a shrinkage of the choice set before the purchase of a new car. Older buyers considered fewer brands and often just a single brand (25% of young-old purchasers and 33% of old-old purchasers). In addition, they considered a single dealer and a single model more often.
The last set of results relates to long-established brands (i.e., brands that have been offered for a long period and have become familiar to older consumers). In the French automobile market, as in many other countries, an easy and reliable distinction can be made between two brand groups: national brands (i.e., Renault, Peugeot, Citroén), which have been around for approximately a century, and foreign brands, which typically have entered the market much more recently (e.g., Fiat was introduced in France in 1966, Toyota in 1983, and Daewoo in 1997). We argue that older consumers may have developed knowledge about the market as it was at the time of their previous purchases, that is, when French brands held the leading position (e.g., 95% of car sales in 1960 versus less than 50% today). Because their previous purchase was probably a national brand, older consumers have accumulated more long-term knowledge of these brands than they have of recently entered foreign brands. In addition, French car manufacturers retain more dealers that have been established for much longer periods. French consumers might encounter a third-generation dealer of a French brand, whereas a foreign brand has no such long-term presence. Thus, older French consumers also are likely to have better long-term knowledge about the dealers of French brands. Therefore, older consumers should be more likely than younger consumers to consider long-established national brands, even if they are not the consumers' previous brand.
To measure this phenomenon, we studied only respondents who did not repurchase their previous brand. We found that among these respondents, older buyers were more likely than younger buyers to consider national brands (Χ²[ 3] = 34.00, Table 1). The direction of the age effect was as we expected (Table 2; Figure 4, Panel A). The acid test of this bias in favor of long-established brands was their actual purchase, and when older consumers changed brands, they were more likely to switch to a national brand than to a foreign brand (Χ²[ 3] = 11.30, Tables 1 and 2; Figure 4, Panel B). Among buyers who previously owned a national brand car, the percentage of switches to another national brand regularly increased with age (40% for the young, 45% for the middle-aged, 59% for the young-old, and 66% for the old-old). Among buyers who previously owned a foreign car, the percentage of switches to a national brand also increased with age, though less markedly (43% for the young and middle-aged, 48% for the young-old, and 49% for the old-old). However, for young and middle-aged buyers, this figure does not appear to depend on whether the previous car was French or foreign, whereas for young-old and old-old buyers, the likelihood of a French car purchase is clearly higher when their previous car was French. Thus, in addition to older buyers' focus on their previous brand and their reduced consideration set, they also had a bias toward well-known, long-established brands. When older consumers switched brands, this bias, combined with their tendency to repurchase the previous brand, led them to purchase long-established, national brands more often: 49% for the young, 56% for the middle-aged, 69% for the young-old, and 74% for the old-old (Figure 4, Panel C).
Our initial motivation for this article was to investigate the repeat purchase behavior of older consumers. From a large representative sample of new-car buyers, we observe not only a higher rate of repeat buying among older consumers but also a general shrinkage of their decision process, which includes a greater focus on the previous brand and dealer and the consideration of fewer brands, dealers, and models. In addition, young-old buyers and especially old-old buyers were more likely to switch to national, long-established, familiar brands when they switched away from the previous brand.
Discussion
What are the mechanisms that underlie the results? Literature in marketing, gerontology, and psychology suggests various explanations. In this section, we consider four tentative marketing explanations and evaluate them on empirical grounds using another data set (i.e., a nationally representative survey of 1015 recent purchasers of a new car). We then present four potential explanations derived from gerontology and psychology and assess more precisely which rationales can explain each of our results.
Consumer behavior literature suggests four potential explanations for buyers' actions: involvement, expertise, national preference, and gender. In each case, the variable known or hypothesized to have an impact on our dependent variables is likely to vary with age.
Involvement. A tentative explanation for the shrinkage of the purchasing-decision process could be older buyers' reduced involvement in the product and, consequently, in the purchase process. Among the different facets of involvement (Laurent and Kapferer 1985), interest appears to be the most relevant. Older people are less interested in cars, which causes them to "shrink" their purchase process, consider fewer brands, and stay with the previous brand. If they switch, this lack of interest leads them to return to long-established brands rather than to learn about newer brands. Unfortunately, the main data set does not measure involvement. Therefore, we use the second, smaller data set to measure interest in the product category, using Laurent and Kapferer's (1985) scale, and find that it has good reliability (4 items, α = .80) but is not correlated with age (r = .014, not significant [n.s.]). Furthermore, in contrast to age, interest has no significant impact on the three available measures of prepurchase information seeking (i.e., number of brands considered, number of information sources, or probability of test driving the new car). Thus, we do not find support for the results in the tentative explanation that older consumers have decreased interest in cars.
Expertise. Several classical contributions have demonstrated the role of expertise in the consumers' decision processes (e.g., Alba and Hutchinson 1987). The logic behind the theories is that accumulated experience in a product category, or a personal history of purchases and usage, leads to a consumer's higher level of expertise with respect to the products offered in this category, as well as to a routinized decision process (Howard and Sheth 1969). In contrast, neophytes have everything to learn and must spend much more time collecting and processing information.
The expertise construct applies to the car market, because, in general, older consumers have accumulated more experience by purchasing more cars over their lifetime than have younger consumers. We could also argue that more experience leads to increased expertise, which is defined as a good knowledge of cars (Punj and Staelin 1983) and high self-confidence about that knowledge (Furse, Punj, and Stewart 1984). Therefore, older (more expert) buyers should be able to identify their preferred car better, which in turn leads them to consider fewer brands and dealers (they know the "good" ones) and be more loyal to those brands and dealers (they repeatedly choose the same "good" one). Because the main survey did not measure expertise, we resort again to the second data set in which expertise is measured by a three-item scale (a = .74). (Although it would be interesting to analyze specific facets of car expertise separately, it would require distinct measures of each facet.) We measure experience by the number of cars that consumers previously bought. The results indicate that expertise increases with age through the mediation of experience (see Appendix B).
However, the impact of expertise on consumers' search for information (i.e., the number of information sources they use, the number of brands they consider, and the number of test drives they take) is the opposite. Expertise increases information search, whereas age significantly decreases it. This finding represents an interesting complement to the questions that Cole and Balasubramanian (1993) pose about the relationship between category knowledge and age. Our results may demonstrate the specific characteristics of car purchases, which differ from those of many other categories, including the category that Cole and Balasubramanian (1993) study (i.e., cereals). Car purchases are made at intervals of a few years. Although brands tend to be stable, the models change markedly from one purchase occasion to the next and almost entirely over a ten-year period. Therefore, expertise that a consumer may acquire during the purchase process of a model is of limited usefulness by the time the next car purchase occurs. Overall, our results cannot be explained by older consumers' increased expertise.
National preference. We could argue that older people tend to have a stronger national preference, which may lead them to consider long-established national brands and thus have smaller consideration sets. Again, we use the secondary survey to test this argument. As we indicate in Appendix B, we use two items to create a brief scale of national preference for cars with a low but acceptable reliability (a = .58). The measure of national preference has a significant, positive impact on the likelihood of purchasing a French car (Χ² = 99.51, degree of freedom [d.f.] = 1 in the logistic regression), but it is not correlated with age (r = .025, n.s.). Note that both national preference and age have significant but separate effects on the purchase of a national brand. Thus, we find no support for the tentative explanation that increased national preference mediates the impact of age.
Gender. Another tentative explanation of greater loyalty toward brands and dealers could be the allegedly higher percentage of female consumers among older buyers and the alleged tendency of female consumers to be more cautious. We can dismiss this explanation easily, because in our sample, as in the overall population, the percentage of female car buyers is smaller among older groups (37% among the young, 31% among the middle-aged, 17% among the young-old, and 13% among the old-old; Χ² = 948, d.f. = 3). We control for gender in all our statistical analyses.
Therefore, we set aside these four classical consumer behavior variables as explanations for our results; instead, we propose alternative explanations based on previous research in gerontology and psychology (Salthouse 1991).
We consider four possible mechanisms that may explain our results (see Table 3): biological aging, cognitive decline, socioemotional selectivity, and decision aversion. Whereas biological aging and cognitive decline make the decision process more difficult for unknown, unfamiliar solutions, socioemotional selectivity and decision aversion make known, familiar solutions more attractive.
To provide illustrative vignettes of the theoretical interpretations, we interviewed six older consumers who had recently bought a new car. We held the qualitative interviews in Paris or its western suburbs. Potential respondents were approached and asked whether they were still driving a car, whether they had bought the car themselves (or with their spouse), and whether the newly purchased car had replaced a car of the same make. The respondents (three men and three women) were ages 69-85 and were retired. All six interviewees indicated that they had bought their car at a dealership that they had known for a long time (at least 10 years and, in one case, 40 years) without visiting another dealership. In addition, each had considered a single brand, a single model, and a single dealer.
We then let interviewees explain the rationale for their choice, using a simple follow-up question: "For this car's purchase, you only considered brand XX?" We also asked them about their previous car, including their likes, dislikes, and satisfaction. The final question on the topic asked, "Why didn't you consider another brand for your new car?" We then asked the participants to talk about the dealer that sold the new car. We also asked questions related to their knowledge of the manager and their likes, dislikes, satisfaction, length of relationship, trust, and previous purchases at the dealership. We asked each interviewee to list the dealerships located near his or her home. We ended the interview with demographic questions, including the year of birth, educational level, former profession, and marital status (the participant's gender was easy to observe). We conducted the interviews during the first week of July 2004, and each lasted an average of 30 minutes. Finally, we transcribed the interviewees' answers word for word; we use the excerpts in the following discussion.
Biological aging. A possible explanation for the results could be the decline of physical capacities that occurs because of biological aging, or "the array of modifications happening in the organism with age, and lowering its resistance and adaptability to the pressures of the environment" (Barrère 1992, p. 16). First, older people with serious physical problems are unlikely to drive a car and therefore to belong to our population of interest. Second, according to Jean-Claude Henrard, the chief surgeon at St. Perrine Hospital's Geriatrics Department, no clear biologically based slowdown occurs in daily social life until an advanced age of 80 and older. A majority of people between ages 60-74 report having no problems walking and say that they shop "often" or "every day." In contrast, of people age 80 and older, more than half report that they have problems walking and shop only "occasionally" or "never." Among people ages 60-74, more than 80% "go everywhere," whereas only 34% of those age 80 and older do so (David and Starzec 1996).
Thus, physical impairment may reduce the number of dealers that old-old consumers visit, but the same may not be true for young-old consumers. In the follow-up interviews, three people indicated that they bought cars from dealers that were located farther away from their homes than were the closest dealerships of their brand. There was a respondent who even continued to buy from a Citroën dealer near the plant in the southern suburbs of Paris where he was a production manager 40 years ago, even though another Citroën dealer is located closer to his current address in the western suburbs. When we asked why he took the trouble of traveling to a distant dealership instead of buying from the one nearby, he explained that he was "very happy about this dealership." When we asked what other dealerships existed in their neighborhood, the two other interviewees who purchased cars from dealers that were far from where they lived answered, "I don't care" or "I don't know." Thus, it appears that physical impairment does not explain the behavior of the three respondents, who range from age 73 to 85. Furthermore, physical problems should have no impact on the number of models considered or the nature of the brands considered (long-established or recent). Overall, biological aging can explain only a few of the results (Table 3).
Cognitive decline. Older consumers may behave differently because their memory limits their consideration set to the previously owned or already known brands or because they are no longer able to evaluate several complex options in minute detail. Research in cognitive psychology offers some reliable lessons on this aspect of aging. Various psychometric tests in different countries at different dates, using both cross-sectional and longitudinal designs, have shown that cognitive capacities decline with age. The conclusions have been reinforced and better explained by more recent studies that use neuroimaging techniques (Hedden and Gabrieli 2004) to show the age-related decline of functions that are active in the dorsolateral prefrontal cortex, such as working memory (MacPherson, Phillips, and Della Sala 2002). Working memory mediates the encoding of information in long-term memory and the conscious retrieval of recent events (Park and Gutchess 2004). Beginning at approximately age 60, people may also experience a reduction in their explicit memory, the form of memory that makes it possible to retrieve pieces of information and their sources consciously (e.g., remembering an advertisement for a car manufacturer, when it was seen, and where it appeared). For example, the free recall of a series of words or a text declines significantly with age (Zelinsky and Burnight 1997). Working memory also enables people to manipulate several pieces of information simultaneously to compare them (Mather 2003), and with their depleted working memory, older people may avoid cognitive efforts, such as a comparison of alternative choices, by relying on facilitation heuristics. For example, Johnson (1990) measures the use of a noncompensatory intra-attribute heuristic to facilitate the evaluation of different car options, and Cole and Balasubramanian (1993) measure the more frequent choice of the first satisfying option when a problem is made more complex. In line with these theories, an interviewee explained that she considered only one brand to keep the decision process simple; she then made a spontaneous mention of her age, stating that "the older one gets, the less one wants to make one's life difficult with this kind of thing."
Sorce (1995) also suggests that older people often rely on a store loyalty or an advice-seeking heuristic, and we encountered two examples of these heuristics. An interviewee mentioned a classical heuristic when she indicated that she had chosen a Mercedes-Benz because "of the brand reputation." Another interviewee combined two heuristics: She had chosen a Renault "because her husband would have chosen a Renault." Her husband had relied on a politically based heuristic to make a choice: "[Renault] was owned by the French state at the time, and my husband thought it was nice to buy a car from them." She continued to rely on that same heuristic, though Renault is no longer owned by the state. Finally, Yoon (1997) shows that heuristic inference facilitates the recognition of television programs. Her theoretical analysis develops the heuristic of schematic information processing, which consists of relying on known schemas rather than on a new detailed analysis.
These aspects of cognitive decline should have an impact on the variables that we analyze. For a complex, durable good, such as a car, consumers must decide whether to purchase when alternative choices are not physically present. In addition, age is negatively correlated with fluid intelligence (i.e., intelligence required for new problems and situations), but crystallized intelligence (i.e., intelligence based on prior experience and learning) remains intact (Salthouse 1991). The decline of fluid intelligence is continuous, but it passes through performance thresholds, and Chasseigne, Mullet, and Stewart (1997) observe that after age 65, people have difficulty identifying an inverse relationship between indicators and a consequence.( n2) For example, in the car market, if a price goes down when a rebate goes up, consumers age 75 and older are unable to use this inverse relationship, even with a visual aid. Therefore, the decline in working memory and fluid intelligence should decrease the number of brands, dealers, and models that older consumers consider, and it should increase their tendency to consider familiar options, which helps induce repeat purchases. Older people also should rely more on decision heuristics that orient brand choice at the consideration stage without any intensive search or evaluation (Cole and Balasubramanian 1993). These effects of age appear for people age 60 and older and more strongly for people age 75 and older. We summarize this discussion with a proposition, which should be tested by further experimental or survey research:
P1: When the comparative analysis of possible options becomes cognitively more difficult, older consumers are more likely to (a) consider fewer options and (b) repeat their previous choice.
Socioemotional selectivity. Socioemotional selectivity theory claims that older people who perceive their time horizon as limited place greater emphasis on feelings and emotions, and their interest in new information declines. They give priority to close, well-known, emotional contacts over new, informative ones (Carstensen, Isaacowitz, and Charles 1999; Isaacowitz, Charles, and Carstensen 2000). When asked to choose between potential social partners who represented three degrees of familiarity (a member of their immediate family, a recent acquaintance, and the author of a book they read), 65% of older subjects chose the most familiar social partner, whereas only 35% of younger subjects chose this option. Similar results were obtained for younger people whose time horizon was artificially shortened because of, for example, an unexpected move within a week or a disease that was suddenly diagnosed as fatal (Fredrickson and Carstensen 1990). The increased importance of emotional goals influences the daily relationships of older people, such that "elderly couples often accept their relationship as it is, to appreciate what is good, and ignore what is troubling, rather than seek new solutions to problems" (Carstensen, Isaacowitz, and Charles 1999, p. 167). As a result, "older people not only interact with fewer people, they interact primarily with people who are well-known to them" (Field and Minkler 1988, qtd. in Carstensen, Isaacowitz, and Charles 1999, p. 169).
Similarly, we can analyze the relationship between older consumers and familiar dealers with which older consumers have developed a rapport over the years, especially with respect to highly involving purchases such as automobiles. Several interviewees spontaneously mentioned that they had a long-lasting, special relationship with a person associated with the dealership, who was often the dealer: "The dealer, he was the boss, we had a contact with the boss." This relationship can also involve other members of the dealership's staff, as the following quotes show: "I knew well the one who repairs cars, their shop foreman," and "The sales girl, I've known her since she was 20. She has always been very helpful. She even had my car's painting fixed for free." This connection can even extend to the dealer's family members: "The dealer, he's very kind, his family, a long-established one in [this city]"; "I taught catechism to his daughter"; and "It's very familiar, it's very nice." These elements lead us to the following:
P2: The longer a consumer has had a relationship with a supplier, the more likely he or she is to (a) analyze fewer options and (b) repurchase from the supplier.
Socioemotional selectivity also may bias people's memory of their prior choices. In an experiment, Mather and Johnson (2000) find that older people are more likely than younger people to attribute positive features to an option they had chosen and negative features to an option they had rejected.( n3) Accordingly, in our large-scale survey we find that older buyers are more likely to be satisfied with their previous car. On a scale of 1 to 10, high scores (8 or higher) were rated by 56% of the young, 67% of the middle-aged, 78% of the young-old, and 86% of the old-old. Furthermore, as we indicated previously, for a given satisfaction level, older buyers are more likely to consider the previous brand, consider nothing but the brand, and repurchase it (Figure 2). Their tendency to be more supportive of their prior choices may explain their higher brand and dealer loyalty, an extreme version of which is to consider only the previous brand and previous dealer (Figure 1, Panel C). Thus, we posit the following:
P3: Older consumers are more likely than younger consumers to remember positive features of the previously chosen option and therefore (a) analyze fewer options and (b) repeat their previous choice.
Change aversion. An aversion to the risks linked to changes, even if the present solution is far from ideal, is a well-documented phenomenon in gerontology. Wallach and Kogan (1961) and Botwinick (1966) asked participants to choose between two options: to stay in a secure but mediocre occupation with limited prospects for a pay increase or to change to an occupation that would lead, with probability p, to a high salary increase and, with probability 1 - p, to financial disaster. Older subjects were markedly more likely to choose not to change, whatever the value of p. Botwinick (1978) suggests two hypotheses to explain this resistance to change. First, because of their intellectual decline, older people may avoid making decisions. The preceding results corroborate this hypothesis. Second, older people may avoid the risk that is associated with a bad decision, especially that which may lead to a financial risk. However, Botwinick (1978) also notes that when the option not to change is not available or the more difficult option (e.g., a word that is difficult to explain versus an easy one) has a higher probability of success or is more rewarding (Okun and Elias 1977), older people have a utility function similar to that of younger ones. Recent laboratory studies, in which older participants selected cards in the high-reward/high-risk deck as often as younger ones did, confirm this concept (MacPherson, Phillips, and Della Sala 2002). Thus, the purchase behavior of older people could be the consequence of change aversion, which would lead them to repeat their previous choice; staying with the same brand and the same dealer is a way to avoid the complexity of a new decision, as is considering a single model. For example, one interviewee stated, "I don't like to change my habits; maybe I'm loyal." All six interviewees indicated that they had been repurchasing the same brand for a long time (15-50 years) and that their relationship with the same dealer had lasted 15-40 years. The discussion leads us to the following:
P4: When the stakes associated with a potential decision become higher, older consumers are more likely to avoid the burden of making a decision by (a) repeating their previous choice or (b) analyzing only a few options.
In summary, we leave aside biological aging because it explains only part of our results. The other three age-related explanations converge in predicting that preference for familiar choices increases with age. However, there are small differences in the three predictions: Cognitive decline is a tentative explanation for all our results; cognitive decline and change aversion, but not socioemotional selectivity, predict the consideration of fewer car models; and change aversion does not predict a preference for long-established brands.
Limitations and Further Research
Further research could use experiments and include other product categories, consumers who play roles other than that of the main decision maker, other stages in the decision process, or other types of data. Our study is based on a large-scale, representative survey of actual car purchasers. Because survey data make it difficult to delineate the effects of collinear age-related variables (Hedden and Gabrieli 2004), additional research should experimentally test our propositions. Cognitive difficulty (P1) and length of a relationship (P2) are theoretical variables that increase naturally with age but that can be manipulated experimentally. The amount at stake (P4) can also be manipulated. Finally, the biased memory of previous choices (P3) could be studied by replicating Mather and Johnson's (2000) research and by adding measures of the number of options and whether the final choice is a repeat purchase.
In addition, our study is restricted to a specific, albeit important, purchase: new automobiles. Can the results be extrapolated to other purchases? We offer two important tentative explanations: the reduced cognitive abilities of older respondents (cars are complex products, and the number of brands and models is enormous) and the increased aversion to change (automobile purchases are financially, physically, and socially risky). Therefore, for secondhand automobiles, which can be more risky purchases than new cars, we expect to find more brand repurchases and dealer loyalty among older buyers than among younger buyers. (Note that older consumers who purchase secondhand cars are likely to be less wealthy than are those who buy new cars, and therefore they face a higher financial risk.) Furthermore, we argue that our findings are likely to be replicated in other high-complexity, high-stakes categories, such as financial products, pharmaceutical drugs, trips, and so forth. Javalgi, Thomas, and Rao (1992) find that older people buy more travel packages rather than organize trips themselves, though they may have more time to do so. Conversely, the findings may be different, or even opposite, in categories that involve limited risk and require less information to process, such as a new yogurt flavor.
A crucial characteristic of the car market is its slow changes in brands and brand shares. The three leading national brands in France (i.e., Renault, Peugeot, and Citroën) have existed for a century and have been sold by dealers that have remained in the same locations for decades. This characteristic makes a long-term acquisition of brand knowledge and preferences possible, though biased toward long-established brands and selective expertise. In many other categories, long-established brands and relative newcomers coexist. We hypothesize that the preferences and actual choices of older consumers are biased toward older brands, whereas younger consumers lean toward more recent brands. Similar phenomena may occur for hedonic preferences. People may acquire preferences when they discover a domain for the first time, and they may keep them for life. An example is provided by Holbrook and Schindler's (1994, p. 414) finding that "consumers tend to form enduring preferences for cultural products [e.g., movie stars] during a sensitive period," namely, late adolescence. This finding has been replicated for various other categories. For example, Schindler and Holbrook (2003) analyze how male consumers' preferences for car styles (evaluated from anonymous photographs) depend on the correspondence between the consumers' age and the date the car appeared on the market. In agreement, Schuman and Scott (1989, p. 377) state that "memories of important political events and social changes are structured by age, and adolescence and early adulthood [are] the primary period for generational imprinting ... of political memories." Similar effects may also occur for strictly utilitarian products, which additional research should investigate.
In addition, we study individual buyers of new cars as a function of their age, but it would be interesting to determine whether the age of the person, if any, who accompanies the primary buyer (e.g., friend, family member) affects purchase behavior. Unfortunately, information about that person is not available from the survey.
We also study actual buyers of new cars. Further research might consider potential buyers, sampled before they decide whether to buy, to analyze what leads some of them to buy and others not to buy. Note that our sample is representative of new-car buyers, not of the general population. Among other characteristics, older buyers of new cars, compared with older nonbuyers, are more often men, have a higher average income, and, we posit, are more likely to be in good health. Our results may generalize to similar buyer populations, but other populations may behave differently (Yoon 1997).
Although our study relies on a large representative sample of actual buyers, it is cross-sectional, and it is always difficult to compare the impact of age on people who belong to different cohorts, unless the cohort effect is moderated by all demographic variables (Schaie 1965; Whitbourne 1996), as we did in this study. However, it would be worthwhile to replicate the results using a series of similar samples collected at regular intervals to analyze the aging versus cohort effects. In addition, other research methods--such as ethnographic research of older buyers and nonbuyers before the decision is made whether to purchase; studies based on consumer panels, which are inappropriate for car purchases but useful for frequently purchased goods; and experiments that measure psychological variables-could enrich our findings.
Our research has immediate implications not only for the current marketing environment but also for emerging environments. For example, we observed a tendency of older car consumers to repeat purchase and to limit their purchase process to a few brands, giving a privileged status to the previous brand and long-established brands. This predisposition makes it more efficient for managers of certain brands and less efficient for managers of others to target older consumers. Nevertheless, some managers are suspicious of the greater loyalty of older consumers because they fear the perception that their brands have aged. In addition, older consumers may be open to innovations that make life easier for them, such as new ergonomic features. Along this line, Ford uses drivers wearing "third age suits" to pretest its new cars (Krebs 2000, p. 52). Brands may benefit both from older buyers' loyalty and by introducing innovations in their models, which would enable them to maintain a modern image.
Potential interpretations of our cognitive decline, socioemotional selectivity, and change aversion propositions also lead to specific suggestions for marketing actions. Managers should provide older consumers with reassuring information in an easy-to-acquire format, such as testimonials by well-known spokespeople; advertisements that feature simple sentences, slow speech, and large fonts; and easily accessible Web sites. Well before the product search begins, the brand should establish trustful, long-term relationships with older buyers through potential advisors. Preemptive actions, such as being the first brand to suggest itself as a possible choice, may be particularly effective for older consumers. The product or service should also include well-known features or characteristics that help locate the brand or model within already memorized frameworks and avoid radical frame changes. In this vein, we question recent trends toward renaming well-established companies or products, which removes the support provided by well-ensconced references and forces older consumers to confront what may appear to be new categories. Similarly, easily recognizable patterns should be preserved for packages, logos, models, and variety names. For example, Peugeot has used the same pattern to identify its car models for a century: a three-digit number with a zero in the middle (e.g., 305, 607). The first digit identifies the size of the car (1 for very small cars, 6 for the largest cars, and 9 for a prototype entered at Le Mans), and the last digit describes successive generations and increases slowly (3 in the 1950s, 4 in the 1960s, and 7 in the late 1990s and early 2000s). This pattern enables older consumers to understand the positioning of, for example, a newly appearing 207 model instantly and in a perfectly mastered, crystallized framework.
In the car market, the Internet represents an important change. Ratchford, Lee, and Talukdar (2003) find that Internet users are younger and search more information than do nonusers, but they would have searched even more had they not used the Internet. Thus, it appears that the Internet, though it makes a search easier, does not stimulate greater search. Therefore, we expect that older buyers will not search for as much information as will younger buyers.
Finally, in response to new European regulations, in October 2003, France officially switched from its decade-sold system of exclusive brand dealerships to multibrand dealerships (though the implementation has begun slowly). According to socioemotional selectivity theory, personal relationships with a dealer may become more important than the relationship with a brand because of this change. However, consistent with arguments based on cognitive decline and decision aversion, the brand will still play a major role by offering a cognitive heuristic that facilitates choice.
This article is based on Raphëlle Lambert-Pandraud's dissertation, which was written under the supervision of Gilles Laurent at HEC School of Management. The authors thank Peugeot SA, especially Daniel Bachelet and Claude Le Minor, for supplying the data. They gratefully acknowledge Amitava Chattopadhyay, Carolyn Yoon, Marc Vanhuele, and the four anonymous JM reviewers for their constructive comments.
( n1) Another year of the same survey is the basis for the comparative analysis of brand-switching methods that Colombo, Ehrenberg, and Sabavala (2000) report.
( n2) Salthouse (1991, qtd. in Chasseigne, Mullet, and Stewart 1997, p. 2) quotes the following example of an inverse relationship: "R and S do the opposite, Q and R do the same. If Q increases, what happens to S"?
( n3) Participants were asked to choose between two options that were characterized by positive and negative features. After the choice, participants were shown positive and negative features to attribute to each option. The degree of their choice supportiveness was measured by an asymmetry score, which was calculated as follows: (proportion of positive features attributed to the chosen option + proportion of negative feature attributed to the rejected option) - (proportion of negative features attributed to the chosen option + proportion of positive feature attributed to the rejected option).
( n4) We use the weights to replicate the brand and model market shares observed in the French market (N = 28,913; we analyze replacement purchases only).
Legend for Chart:
A - Dependent Variable
B - Age
C - Education
D - Income
E - Occupation
F - City Size
G - Marital Status
H - Gender
I - Retired (if 55-65)
J - Satisfied with Previous Car
K - Satisfied with Previous Car's Dealer
L - No Dealer for Previous Car
M - Previous Car Was Secondhand
N - Length of Use of Previous Car
A B C
D E F
G H I
J K L
M N
Degrees of freedom 3 2
2 3 2
1 1 1
1 1 1
1 1
Number of brands
considered (F) 18.9(†) 51(†)
48(†) 5(**) 21(†)
3.2 (p = .07) 57(†) 2.4
2.2 216(†) 34(†)
.8 111(†)
Considering only one
brand 42(†) 38(†)
125(†) 5.9 5.8(*)
.5 10.4(**) 1.5
7.1(**) 67(†) 42(†)
1.7 34(†)
Considering only one
dealer 129(†) 76(†)
11.6(**) 13.7(**) 44(†)
33(†) 49(†) 6.9(*)
1.2 158(†) 18.8(†)
10.4(**) 74(†)
Number of models
considered (F) 32(†) 63(†)
47(†) 4.7(**) 30(†)
5(*) 82(†) 1.4
.2 196(†) 22(†)
10.9(**) 118(†)
Considering the
previous brand 43(†) .1
15.2(***) 35(†) 28(†)
14.05(***) 18.5(†) .4
235(†) 231(†) 207(†)
48(†) 21(†)
Considering only the
previous brand 50(†) 27(†)
78(†) .6 4.8
3.3 (p = .07) 3.9(*) 1.6
53(†) 127(†) 81(†)
.2 46(†)
Repurchasing the
previous brand 78(†) 8.7(*)
.8 6.8 (p = .07) 26(†)
10.4(***) 3.7(*) 1.1
59(†) 522(†) 352(†)
45(†) 177(†)
Repurchasing from
previous dealer 102(†) 15.7(***)
7.9(*) 12.5(**) 66(†)
6.6(*) 3.4 6(*)
18.3(†) 559(†) 296(†)
561(†) 596(†)
Considering
long-established
national brands 34(†) 31(†)
3.4 18.95(***) 35(†)
0 7.3(**) 8.1(*)
.3 0 2.9
4.4(*) 80(†)
Switching to
long-established
national brands when
changing brand 11.3(*) 4.15
1.5 7 (p = .07) 30(†)
4.6(*) 0 .2
3.6 5.5(*) 32(†)
17.5(†) .3
(*) p < .05.
(**) p < .01.
(***) p < .001.
(†) p < .0001. Legend for Chart:
A - Dependent Age Categories
B - 18-39
C - 60-74
D - 75 and Above
A
B C D
Number of brands considered
.07(†) -.08(**) -.19(†)
Considering only one brand
-.34(†) .16 (p = .06) .31(**)
Considering only one dealer
-.22(†) .34(†) 1.04(†)
Number of models considered
.08(†) -.11(†) -.27(†)
Considering the previous brand
-.26(†) .13 .25(*)
Considering only the previous brand
-.47(†) .15 .24 (p = .06)
Repurchasing the previous brand
-.28(†) .29(†) .58(†)
Repurchasing from previous dealer
-.42(†) .20(**) .32(**)
Considering long-established national brands when changing brand
-.19(†) .18(*) .45(***)
Switching to long-established national brands when changing brand
-.07 .32(**) .43(*)
(*) p < .05.
(**) p < .01.
(***) p < .001.
(†) p < .0001.
Notes: Controlling for the other variables (other demographics,
variables describing the context of the car purchase). Legend for Chart:
B - Repeat Brand
C - Repeat Dealer
D - Consider Fewer Brands
E - Consider Fewer Models
F - Consider Fewer Dealers
G - Back to Long- Established Brands
A B C D E F G
Biological aging + +
Cognitive decline + + + + + +
Socioemotional selectivity + + + + +
Change aversion + + + + +GRAPH: FIGURE 1; Older Consumers Focus More on the Previous Brand
GRAPH: FIGURE 2; Satisfaction Level and Focus on the Previous Brand
GRAPH: FIGURE 3; Older Consumers and Consideration Set
GRAPH: FIGURE 4; Older Consumers Give a Privileged Status to Long-Established Brands
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Legend for Chart:
A - Variables
B - Weighted Distribution(4) (%)
A B
Age Groups
18-39 32
40-59 39
60-74 25
75 and above 4
Education
Primary 37
High school 24
Higher 39
Income
Lower third 42
Medium third 22
Upper third 36
Gender
Women 28
Men 72
Occupation (at 18-55)
Workers, employees 30
Intermediary (middle management and trade staff) 25
Managers, executives, professionals 8
Age 55 and above 37
City Size
Small: Fewer than 5000 inhabitants 33
Medium-sized: 5000-100,000 inhabitants 32
Large: 100,000 or more inhabitants 35
Marital Status
Living alone 24
In couple 76
Retired (defined only for respondents
aged 55-65)
Retired 81
Active 19
(4) We use the weights to replicate the brand and model market
shares observed in the French market (N = 28,913; we analyze
replacement purchases only). Impact of Expertise
We measured expertise on a three-item scale: "I keep informed about news of the automobile market," "I could give good advice on automobiles if I was asked to," and "I know a lot about cars" (α = .74). We measured experience by the number of cars previously bought. We used three measures of prepurchase information seeking: number of information sources used, number of brands considered, and number of times the buyer test drove the car. As we predicted, preliminary regressions indicate that expertise increases with age and is mediated by experience. Age is a significant predictor of expertise when it is the only explanatory variable, but it becomes nonsignificant when experience (which is also significant) is included in the equation. Therefore, we use age and expertise as predictors of prepurchase information search, with the following results:
(B1) Number of information sources = 3.143
(t = 17.80) R² = .066 + .00537 Expertise
(t = 5.31) F(2, 1005) = 35.45 - .0225 Age (t = -6.93)
(B2) Number of brands considered =
3.961 (t = 17.89) R² = .088 + .00134 Expertise (t = 1.55)
F(2, 1005) = 48.35 - .0272 Age (t = -9.80).
In a logistic regression in which the dependent variable is
whether the buyer test drives a car,
(B3) -.694 (Χ² = 7.71) + .0041 Expertise (Χ²
= 8.11) - .0149 Age (Χ² = 10.12)
Overall Χ² = 17.18 (d.f. = 2).
The results provide a consistent pattern. Age significantly decreases information search, whereas (self-assessed) expertise increases information search. We find two significant results for information sources and test drive and a directional but nonsignificant result for the number of brands considered.
Impact of National Preference
Two items ("I always buy French cars," and "I buy nothing but foreign brands") provide a brief scale of national preference for cars with low but acceptable reliability (a = .58). This measure of national preference is not correlated with age (r = .025, n.s.). In a logistic regression, both national preference and age have significant but separate effects on the purchase of a national brand:
(B4) 1.258 (Χ² = 37.19) + 479 National preference (Χ² = 49.00) + .0027 Age (Χ² = 38.71)
Overall Χ² = 92.93 (d.f. = 2).
~~~~~~~~
By Raphaëlle Lambert-Pandraud; Gilles Laurent and Eric Lapersonne
Raphaëlle Lambert-Pandraud is Associate Professor of Marketing, Negocia.
Gilles Laurent is Carrefour Professor of Marketing, Marketing Department, HEC School of Management, Paris.
Eric Lapersonne is Maître de Conférences, Département des Techniques de Commercialisation, Institut Universitaire de Technologie, Université de Cergy-Pontoise
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 132- Resolving the Capability—Rigidity Paradox in New Product Innovation. By: Atuahene-Gima, Kwaku. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p61-83. 23p. 1 Diagram, 5 Charts, 4 Graphs. DOI: 10.1509/jmkg.2005.69.4.61.
- Database:
- Business Source Complete
Resolving the Capability—Rigidity Paradox
in New Product Innovation
Managers face an important strategic dilemma in product innovation: how to exploit existing product innovation competencies (competence exploitation) while avoiding their dysfunctional rigidity effects by renewing and replacing them with entirely new competencies (competence exploration). Although the resolution of what is termed the "capability-rigidity paradox" is considered a fundamental managerial task in enhancing product innovation outcomes and the firm's competitive advantage, it has received little research attention. The author argues and finds support that market orientation provides a key to this paradox. Specifically, customer and competitor orientations ensure simultaneous investments in exploiting existing product innovation competencies and exploring new ones. The author also finds that the effects of these orientations on competence exploitation and exploration are differentially moderated by interfunctional coordination and perceived market opportunity. Regarding outcomes, competence exploitation and exploration have opposing relationships with incremental and radical innovation performance. However, the relationship between competence exploration and radical innovation performance is positively moderated by interfunctional coordination. Overall, the results of this study suggest that market orientation can prevent a firm from becoming operationally efficient but strategically inefficient by simultaneously engendering competence exploitation and exploration, which are differentially related to incremental and radical product innovation outcomes.
In the development of new products, firms face an important strategic dilemma: Exploiting existing competencies may provide short-term success, but competence exploitation can become a hindrance to the firm's long-term viability by stifling the exploration of new competencies and the development of radical innovations (Levinthal and March 1993; March 1991). Although many firms are adept at exploiting existing capabilities, they appear to falter in simultaneously developing new ones (Dougherty 1992; O'Reilly and Tushman 2004). Leonard-Barton (1992) aptly terms this phenomenon the "capability-rigidity paradox."( n1) Many business observers (e.g., Abell 1999; Williamson 1999) view the resolution of this paradox as perhaps the toughest managerial challenge in sustaining a firm's competitive advantage. Although the literature is replete with warnings about the dire consequences for firms unable to resolve this paradox, to date, attempts to find a solution have been limited to anecdotal reports (e.g., Abell 1999) and a few case studies (e.g., Danneels 2002; Dougherty 1992; Leonard-Barton 1992).
This study attempts to resolve this paradox and, in doing so, addresses four research gaps in the extant marketing literature. First, the essence of the capability-rigidity paradox is that competence exploitation tends to crowd out competence exploration (Leonard-Barton 1992). Thus, the key to the paradox may be organizational factors that can ensure simultaneous investments in both the exploitation of existing product innovation capabilities and the exploration of new ones. The firm's market orientation appears to be such a factor because scholars (e.g., Day 1994, p. 41; Hurley and Hult 1998, p. 47) posit that market orientation is a precursor to capability building. Reports of how DuPont overcame its problems in developing radical innovations (BusinessWeek 2003b, p. 103), Woolworth's renewed ability to respond to upstart competitors such as Wal-Mart (Williamson 1999, pp. 119-20), and Hewlett-Packard's leadership position in the printer business (BusinessWeek 2003a) appear to concur with this proposition. In each case, the firm's resource allocations to exploit existing capabilities and to develop new ones were affected substantially by its knowledge of current and future customers and competitors.
Second, despite market orientation's potential to unlock one of the most intriguing managerial dilemmas in product innovation, previous studies have examined its effect on the firm's innovation performance with little attention to the possible mediation by product innovation competence exploitation and exploration (e.g., Atuahene-Gima 1995, 1996; Baker and Sinkula 1999; Lukas and Ferrell 2000). In related research, Noble, Sinha, and Kumar (2002) find that exploitation mediates the link between competitor orientation and firm performance, and Han, Kim, and Srivastava (1998) show that innovativeness mediates the link between customer orientation and performance but not between competitor orientation or interfunctional coordination and performance. However, these studies did not examine exploitation and exploration simultaneously. Noble, Sinha, and Kumar (2002, pp. 35-36) argue that high-performing firms not only gather market intelligence but also translate knowledge into learning and insightful strategic actions. Thus, they speculate that because exploration is a more active process involving programmatic discovery of new resources and technologies, it may play a more important role than exploitation in the transition of customer and competitor orientations to firm performance. With a focus on product innovation, in this study, I highlight the previously overlooked differential mediating roles of competence exploitation and exploration because findings could provide support for the central role that marketing theory ascribes to innovation capabilities in the link between market orientation and innovation performance (Day 1994; Hurley and Hult 1998). For managers, support for a full or partial mediation will provide evidence of the differential power of the facets of market orientation on innovation competencies, thus ensuring better resource allocation decisions.
Third, resolving the capability-rigidity paradox also requires insights into why firms that have analogous customer and competitor knowledge exhibit differential capacities for competence exploitation and exploration. Grant (1996, p. 380) notes that the source of competitive advantage is how knowledge is coordinated and integrated among functional units rather than knowledge itself. This observation implies the need to examine the moderating role of the firm's coordination mechanisms in its use of customer and competitor knowledge. Although interfunctional coordination is a key knowledge integration mechanism (Gatignon and Xuereb 1997), few studies examine how it facilitates the effects of customer and competitor orientations on the firm's product innovation competencies.
Relatedly, Day (1994) observes that managers' mental models provide a shared ideology that enables collective interpretation of market reality, and thus these models play a key role in managerial decisions about capability enhancement and renewal. This insight resonates with research showing that managers are more willing to invest resources in new strategic initiatives when the market situation is interpreted as an opportunity rather than as a threat (Dutton and Jackson 1987; White, Varadarajan, and Dacin 2003). Despite its salience, no studies report on how managers' interpretations of the market situation influence their decisions on product innovation competencies. I contend that the differential ability of firms to transform analogous customer and competitor knowledge into product innovation competencies lies in their differential interfunctional coordination abilities and in the interpretation of the market situation as an opportunity rather than as a threat (i.e., perceived market opportunity).
Fourth, competence exploitation and exploration are believed to have direct but opposing and interactive relationships with incremental and radical innovations (see Levinthal and March 1993; March 1991). Thus, a search for a solution to the capability-rigidity paradox necessarily requires the determination of whether by enhancing incremental innovations competence exploitation crowds out competence exploration and inhibits radical innovations (Leonard-Barton 1992). More important, the interactive effect of competence exploitation and exploration determines the nature of their balance, which ensures the firm's simultaneous pursuit of incremental and radical innovations. In addressing this research gap, I also note that researchers in both marketing (e.g., Day and Wensley 1988, p. 7; Gatignon and Xuereb 1997) and strategy (e.g., Grant 1996; Henderson and Cockburn 1994) observe that superior resources are converted into positional advantages through the firm's knowledge integration processes. Thus, I suggest that interfunctional coordination not only transforms customer and competitor orientations into product innovation capabilities but also plays the dual role of integrating innovation capabilities to facilitate incremental and radical innovations. Figure 1 presents the conceptual model I tested to address the previously identified research gaps.
Theory Development
Successful product innovation demands that a firm must exploit its existing competencies while trying to avoid their dysfunctional rigidity effects by renewing and replacing them with entirely new ones (Leonard-Barton 1992). A competence or capability refers to the knowledge, skills, and related routines that constitute a firm's ability to create and deliver superior customer value (Day 1994, p. 38). Thus, it reflects behavior processes that engender procedural knowledge or skill (i.e., knowing how to do something) (Kogut and Zander 1992). This definition reflects the notion that competencies are developed through path dependent learning processes (Henderson and Cockburn 1994). In this study, competence exploitation refers to the tendency of a firm to invest resources to refine and extend its existing product innovation knowledge, skills, and processes. Its aims are greater efficiency and reliability of existing innovation activities. In contrast, competence exploration refers to the tendency of a firm to invest resources to acquire entirely new knowledge, skills, and processes. Its objective is to attain flexibility and novelty in product innovation through increased variation and experimentation. This distinction draws on March's (1991, p. 85) view of exploitation as "the refinement and extension of existing competencies, technologies, and paradigms" and exploration as "experimentation with new alternatives that have returns that are uncertain, distant, and often negative." Exploitation and exploration reflect an organizational attitude that manifests in investment decisions (Chandy and Tellis 1998, p. 477). Because the benefits of exploration are distant and uncertain, managers tend to put more resources into exploitation than into exploration (March 1991). Thus, the key to the capability-rigidity paradox may be factors that can prevent exploitation from crowding out exploration so that the firm can develop incremental and radical innovations simultaneously. The resource-based view (RBV) of the firm and marketing theory suggest that the components of market orientation play this role.
The first component of market orientation, customer orientation, involves generating information about current and future customers and disseminating and using it within the firm. The second, competitor orientation, refers to generating information about current and future competitors and disseminating and using it within the firm (Jaworski and Kohli 1993; Narver and Slater 1990). The RBV argues that the performance differences among firms result from knowledge resources that can be used to create idiosyncratic, inimitable internal capabilities (Amit and Schoemaker 1993; Barney 1991). Managers exercise discretion over the use of firm resources by making decisions to convert them into superior products and services (Penrose 1959). Thus, Kogut and Zander (1992, p. 384) note that "the theoretical challenge is to understand the knowledge base of the firm as leading to a set of capabilities that enhance the chances of growth and survival." Therefore, according to the RBV, competitive advantage results not from the mere possession and control of rare and valuable resources but rather from the idiosyncratic internal competencies by which a firm translates its resources into superior customer value (Amit and Schoemaker 1993; Barney 1991). Such internal competencies sustain competitive advantage because they are difficult for competitors to imitate (Reed and DeFillippi 1990).
Market knowledge is a resource with which managers can uncover current capability deficiencies in the firm and emerging market opportunities that may require the development of new capabilities. Thus, the RBV scholars affirm that a firm's internal capabilities are a function of its interactions with the market, the opportunities available to it, and the limitations of its current capabilities (Schroeder, Bates, and Junttila 2002, p. 106). For this reason, Cockburn, Henderson, and Stern (2000) observe that managers are sensitive to environmental cues such that the origins of the firm's competitive advantage may lie in their ability to invest in appropriate internal competencies in response to those cues. Barney and Zajac (1994, p. 6) echo this view, noting that "as firms learn how to overcome specific competitive challenges, they develop potentially valuable resources and capabilities." Levinthal and Myatt (1994, p. 46) also explain that how a firm's capabilities evolve is intimately linked with its knowledge about how the competitive markets it serves evolve.
Marketing theory coincides with these tenets of the RBV. For example, Day's (1994, p. 41) thesis on capabilities of market-driven firms posits that the superior market-sensing capabilities of firms "inform and guide both spanning and inside-out capabilities," such as product development capabilities. Several other studies note that market orientation plays a role in building firm capabilities for innovation (Atuahene-Gima and Ko 2001; Hurley and Hult 1998; Slater and Narver 1995; Sorescu, Chandy, and Prabhu 2003) and for dealing with economic crisis (Grewal and Tansuhaj 2001). Some scholars suggest that a focus on current market conditions could lead a firm into a "competency trap" by diverting attention away from emerging customers and competitors (Christensen and Bower 1996). However, Barnett, Greve, and Park (1994, p. 12) argue that an awareness of changing market conditions "can cause current practices in the organization to be considered inadequate. Hence, a firm that faces competition is more likely to refine current routines or to make innovations " (emphasis added). In particular, market-oriented firms not only respond to current market conditions but also anticipate future market conditions (Chandy and Tellis 1998; Day 1994; Kohli and Jaworski 1990; Slater and Narver 1995). With deeper knowledge of the current and future customers and competitors, managers become dissatisfied with the inadequacies of current capabilities, which results in investments in new capabilities (Huff, Huff, and Thomas 1992) and insightful strategic change (Noble, Sinha, and Kumar 2002, p. 35). In brief, both the RBV and marketing theory indicate that a firm cannot exploit its existing innovation capabilities or develop new ones without knowledge of market conditions. Thus:
H1: Customer orientation is positively related to (a) competence exploitation and (b) competence exploration.
H2: Competitor orientation is positively related to (a) competence exploitation and (b) competence exploration.
The moderating role of interfunctional coordination. This construct refers to the degree to which the functional units in the firm interact, communicate, and coordinate with one another to collect and use market information (Jaworski and Kohli 1993; Narver and Slater 1990). Knowledge has attributes (e.g., complexity, tacitness) that make it difficult to create and transfer it within the firm (Galunic and Rodan 1998; Kogut and Zander 1992; Szulanski 1996). Thus, Grant (1996) and other researchers (e.g., Zahra, Ireland, and Hitt 2000; Zahra and Nielson 2002) note that the conversion of knowledge into value-creating processes depends on the firm's knowledge integration mechanisms. Interfunctional coordination enables firms to synthesize, integrate, and apply current and newly acquired external knowledge (Henderson and Cockburn 1994; Kogut and Zander 1992). Marketing scholars acknowledge the internal stickiness of market knowledge and highlight the integrative role of interfunctional coordination (Day 1994, p. 44; Olson, Walker, and Ruekert 1995). Building on this literature, I suggest that interfunctional coordination moderates the effects of customer and competitor orientations on product innovation competencies for two reasons. First, it results in lateral communication that deepens knowledge flows across functional boundaries. By enhancing efficient knowledge exchange, it ensures that the firm generates new and broadened insights from market knowledge through constant reinterpretation of each functional perspective (Grant 1996; Kohli and Jaworski 1990). Second, interfunctional coordination builds trust among different functional units, creating conditions for harnessing the diverse functional perspectives in the use of market information (Jaworski and Kohli 1993; Narver and Slater 1990). This permits a critical, unbiased assessment of the firm's product innovation competencies and enables the cross-fertilization of ideas that ensure better decisions about refining existing competencies and developing new ones (Zahra, Ireland, and Hitt 2000).
H3: The positive effect of customer orientation on (a) competence exploitation and (b) competence exploration is stronger when interfunctional coordination is high than when it is low.
H4: The positive effect of competitor orientation on (a) competence exploitation and (b) competence exploration is stronger when interfunctional coordination is high than when it is low.
The moderating role of perceived market opportunity. This construct refers to the tendency of managers to interpret a market situation as having positive rather than negative implications for the firm, as representing a potential gain rather than loss, and as being controllable rather than uncontrollable. Opportunities and threats are the mental schemata that commonly underlie managers' interpretations of the market environment. Dutton and Jackson (1987, p. 80, emphases in original) describe a threat as a " negative situation in which loss is likely and over which one has relatively little control," and an opportunity as a " positive situation in which gain is likely and over which one has a fair amount of control." Because a market situation typically involves both threats and opportunities, it is often defined by the three continua: positive-negative, gain-loss, and controllable-uncontrollable (Thomas, Clark, and Gioia 1993); these are reflected in the preceding definition of perceived market opportunity.
Mental models shape managerial interpretations of a market situation, thereby significantly influencing how a firm's market orientation affects the development and use of its capabilities (Day 1994, p. 43). Managers who perceive threats rather than opportunities in the market tend to become risk averse and respond to market events by focusing on current domains in which they perceive greater control in order to improve efficiency and reliability of operations (Thomas, Clark, and Gioia 1993). In contrast, managers who perceive market opportunities engage in actions that involve greater risk and resource commitments (Dutton and Jackson 1987). It follows that perceived market opportunity is more likely to enhance competence exploration than competence exploitation. In addition, perceived market opportunity is more likely to amplify the positive effect of customer and competitor orientations on competence exploration than on competence exploitation. The logic is that such a mental model imbues managers with greater boldness and proactiveness, thus encouraging a more critical assessment of the efficacy of the firm's current competencies. Interpretation of a market situation as an opportunity also helps engender greater support in the firm for initiatives to overcome its competence deficiencies to take advantage of the opportunity discovered (White, Varadarajan, and Dacin 2003).
H5: The positive effect of customer orientation (a) on competence exploitation is weaker when the perceived market opportunity is high than when it is low and (b) on competence exploration is stronger when the perceived market opportunity is high than when it is low.
H6: The positive effect of competitor orientation (a) on competence exploitation is weaker when the perceived market opportunity is high than when it is low and (b) on competence exploration is stronger when the perceived market opportunity is high than when it is low.
Resolving the capability-rigidity paradox also requires an assessment of the effects of the firm's product innovation competencies on its performance in incremental and radical innovations (Dougherty 1992; Leonard-Barton 1992). Performance refers to the number of new product innovations introduced by the firm, percentage of sales of new product innovations, and the relative frequency of introducing innovations compared with competitors. Incremental innovations are product improvements and line extensions that are usually aimed at satisfying the needs of existing customers. They involve small changes in technology and little deviation from the current product-market experiences of the firm. In contrast, radical innovations involve fundamental changes in technology for the firm, typically address the needs of emerging customers, are new to the firm and/or industry, and offer substantial new benefits to customers (Chandy and Tellis 1998). Exploiting existing competencies increases efficiency and productivity through the search for and use of solutions to customer problems in the neighborhood of the firm's current experience (March 1991). Thus, competence exploitation increases incremental innovations and may hinder radical innovations because it focuses attention on variety reduction and productivity improvements in existing products (Christensen and Bower 1996; Danneels 2002). Competence exploration involves experimentation that focuses on emerging markets and technologies for ideas to produce radical rather than incremental innovations that offer entirely new value for customers. Leonard-Barton (1992) finds that current competencies lead to new product projects that align with those competencies and hinder those lacking such alignment.
H7: Competence exploitation is (a) positively related to incremental innovation performance and (b) negatively related to radical innovation performance.
H8: Competence exploration is (a) negatively related to incremental innovation performance and (b) positively related to radical innovation performance.
March (1991) argues for a balance between exploitation and exploration tendencies, cautioning that a firm that is too oriented toward exploitation is likely to suffer because of a lack of novel ideas. Similarly, a firm that is too oriented toward exploration suffers the costs of experimentation without gaining many of its benefits because it exhibits too many new and risky ideas and little refinement of its existing competencies. This notion of balance reflects the RBV tenet that competitive advantage is a function of the unique bundling of heterogeneous resources and capabilities that increases the complexity and ambiguity of organizational actions (Amit and Schoemaker 1993; Barney 1991). For example, according to Reed and DeFillippi (1990, p. 93, emphasis added), "ambiguity may be derived from the complexity of skills and/or resource interactions within competencies and from interaction between competencies." From this perspective, the interaction between competence exploitation and exploration reflects a complex capability whose value exists only in their relationship (Colbert 2004). Although each of competence exploitation and exploration may affect a firm's innovation performance, their interrelationship provides an additional source of competitive advantage beyond those provided by each one individually (see Colbert 2004, p. 349). This notion of balance is often interpreted as implying that firms need to combine high exploitation with high exploration to achieve superior performance. However, this overlooks March's (1991) caution that both exploitation and exploration have inherent limitations. Indeed, exploitation and exploration thrive under different organizational conditions, which makes their combination difficult (O'Reilly and Tushman 2004).
Thus, Nerkar (2003) argues that the notion of balance could also imply that a high (low) exploitation needs to be coupled with a low (high) exploration to enhance firm performance. Too much exploration could be costly because the firm may move from one new idea to the next without exploiting prior learning and experience (Levinthal and March 1993; March 1991). In addition, novel products may be underdeveloped, and their fit with customer needs may be unknown. A dose of exploitation tempers these potential excesses of exploration by helping the firm evaluate and assimilate new ideas more effectively (Danneels 2002). Similarly, too much competence exploitation involves costs because the firm lacks the novel skills and knowledge to generate new insights in product innovation (March 1991). Overcoming these costs requires a dose of exploration (March 1991, p. 71). Given the different notions of balance, I posit the following nondirectional hypotheses:
H9: The interaction between competence exploration and exploitation is related to (a) incremental innovation performance and (b) radical innovation performance.
The moderating role of interfunctional coordination. The RBV suggests that it is the heterogeneity not only of competence endowments but also of competence deployment abilities that accounts for differences in competitive advantage among firms (Barney 1991). The competitive advantage that a firm's capabilities confer depends largely on the efficiency with which they are integrated (Day and Wensley 1988; Grant 1996). For example, Henderson and Cockburn (1994, p. 65) suggest that a firm's competitive advantage is enhanced when its "component competencies" (i.e., knowledge and skills) are combined with "architectural competencies" (i.e., the ability to coordinate an extensive flow of information within the firm to use component competencies). Without such coordination, conflicts and mistrust among functions stand in the way of a firm's effective use of its capabilities (Zahra and Nielson 2002). Interfunctional coordination reduces cross-functional conflict and promotes commitment and the efficient combination of different functional insights that are necessary for turning a firm's competencies into superior customer value (Kohli and Jaworski 1990; Olson, Walker, and Ruekert 1995). Indeed, Gatignon and Xuereb (1997) find that interfunctional coordination enables the firm to use its resources to achieve desired innovation characteristics and outcomes. Thus, I posit that interfunctional coordination strengthens the positive and weakens the negative aspects of competence exploitation and exploration on incremental and radical innovation performance. Formally,
H10: The positive effect of competence exploitation on incremental innovation performance is stronger when interfunctional coordination is high than when it is low.
H11: The negative effect of competence exploitation on radical innovation performance is weaker when interfunctional coordination is high than when it is low.
H12: The negative effect of competence exploration on incremental innovation performance is weaker when interfunctional coordination is high than when it is low.
H13: The positive effect of competence exploration on radical innovation performance is stronger when interfunctional coordination is high than when it is low.
In addition to the previously described factors, product innovation competencies and outcomes may be affected by several other firm-specific and environmental factors. For example, firms with domain defensive strategies tend to defend their existing markets by preserving traditional product lines, suggesting a tendency for competence exploitation and incremental innovation. In contrast, firms that pursue domain offensive strategies have high first-mover predispositions and thus a penchant for new competencies to develop radical innovations (Abell 1993, 1999). The firm's willingness to cannibalize (i.e., its propensity to reduce the actual or potential value of its investments in current products), its incumbency, and the degree of dominance of the firm in the industry all may influence the firm's tendencies for competence exploitation and exploration and for incremental and radical innovations (Chandy, Prabhu, and Antia 2003; Chandy and Tellis 1998, 2000).
The firm's new product control systems are particularly salient for innovation competencies and their outcomes (e.g., Cardinal 2001; Olson, Walker, and Ruekert 1995) because they affect managers' assessment of performance risk (Atuahene-Gima and Li 2002; Hitt et al. 1996). Specifically, output control (the measurement and reward of project teams based on results achieved) encourages low-risk activities, such as competence exploitation and incremental innovations. This is because project members bear a disproportionate share of the project's performance risk and thus develop risk-averse behaviors. In contrast, behavior control (the measurement and reward for the achievement of process and strategic objectives rather than their outcomes) engenders exploration and radical innovations by encouraging risk-seeking behaviors (Hitt et al. 1996). Factors such as firm size and slack reflect greater resources and market power to exploit existing competencies, build new ones, and develop innovations (Chandy and Tellis 1998; Gatignon and Xuereb 1997). In addition, product development alliances also affect a firm's innovation capabilities and success by exposing it to external knowledge and opportunities (Li and Atuahene-Gima 2002; Rindfleisch and Moorman 2001). Environmental turbulence reflects rapid market and technological changes that managers perceive as hostile and stressful conditions for their firm. Turbulence often renders current firm competencies obsolete (Tushman and Nelson 1990), leading managers to upgrade existing capabilities and develop entirely new ones (Day 1994). Finally, market launch capability, which refers to the firm's ability to design and implement new product launch activities effectively, should affect innovation performance (Day and Wensley 1998). Although this review shows several potential control factors in testing the proposed model, Figure 1 shows the variables for which data were available.
Research Methods
China is an ideal context for this study because the complexity and dynamism of this transitional environment means that firms must confront the challenges of new (often dysfunctional) competition and also collapsing capabilities (Li and Atuahene-Gima 2001, 2002). Thus, scholars suggest that success in China's market requires both prospector (exploration) and defender (exploitation) orientations (Luo and Park 2001, p. 145). As a testimony to the importance of market orientation in competence exploitation and exploration, Luo (2002, p. 60) reports that Kodak's success in China is due to the adaptation of its existing competencies and the development of new ones to respond to market changes.
The instrument was prepared in English and then translated into Chinese. It was checked for accuracy following the conventional back-translation process. It was tested with 25 managers who had at least three years' business experience in China to ensure the face validity and appropriateness of the measures in the Chinese context. The sample was 500 firms located in Guangdong province; they were randomly selected from a mailing list of 1650 electronics firms provided by a local consulting firm. An interviewer scheduled appointments with two key informants in each firm, presented the questionnaire to them, and collected the questionnaire after completion. In China, this procedure is critical for ensuring quality control and reliability of the data (Li and Atuahene-Gima 2001, 2002).( n2) I received 227 usable questionnaires for a participation rate of 45.4%. Given the on-site data collection, a test of response bias by comparing early and late respondents was not appropriate. I compared a sample of participating and nonparticipating firms. The analysis of variance test was not significant for firm age (F = 1.05), number of employees (F = .98), and sales (F = 1.32), suggesting no response bias.
To assess informants' quality, they indicated on a seven-point scale their degree of knowledge (1 = "very limited knowledge," 7 = "very substantial knowledge") about the issues under study. The means for the first and second informants were 6.22 and 6.31, respectively. The first informant (marketing managers 75%, chief executive officers [CEOs] 20%, and research and development [R&D] managers 5%; mean industry experience = 12.50 years; mean firm experience = 9.45 years) gave data on customer and competitor orientations, incremental and radical innovations, environmental turbulence, market launch capability, and product development alliances. The second informant (marketing managers 25%, CEOs 60%, and R&D managers 15%; mean industry experience = 13.97 years; mean firm experience = 10.69 years) provided data on competence exploitation and exploration, interfunctional coordination, perceived market opportunity, slack, firm size, output, and behavior controls. This procedure separates the informants for the measures for the main predictor and criterion variables, thus eliminating common method bias (Slater and Atuahene-Gima 2004). I pooled the data because the analysis of variance test showed that the constructs did not differ significantly (p > .10) among different respondents.
Table 1 reports the measures and their sources. I ran two confirmatory factor analyses, grouping measures of theoretically related constructs to ensure acceptable parameter estimate-to-observation ratios. The fit indexes reported in Table 1 indicate that each model fits the data reasonably well. All the t-values for the estimated factor loadings for the theoretical constructs are significant, suggesting convergent and discriminant validity. I conducted a series of confirmatory factor analyses to test whether a two-factor model of their measures would fit better than a one-factor model for every pair of constructs (Bagozzi, Yi, and Philips 1991). As further evidence of the discriminant validity of the measures, in each case, the chi-square for the constrained model was significantly greater than the chi-square for the unconstrained model.( n3) The composite reliability for each construct is greater than the recommended .70. All but competence exploitation met the required .50 threshold for average variance extracted.
To assess interrater reliability for the measures, participating firms were interviewed a second time 14 months after the primary survey. Data were received from 127 firms. The responses of the first and second respondent in the second survey were correlated with those of the second and first respondent in the primary survey. Interrater reliability for the variables ranged from r = .95 (p < .01) for environmental turbulence to r = .63 (p < .01) for output control. These results provide evidence of the reliability of the data obtained in the primary survey.( n4)
Analysis and Results
I used the data obtained from the first (primary) survey for the analyses. Table 2 presents the correlation matrix and descriptive statistics of the measures. Examination of the skewness and kurtosis values for all the variables (see Table 2) indicated that firm size was skewed. I transformed firm size by taking its logarithm to ensure normal distribution. I estimated moderated regression equations to test the hypotheses. I mean centered customer and competitor orientations, interfunctional coordination, and perceived market opportunity before creating the interaction terms (Aiken and West 1991). A Levine test for the threat of unequal variances was not significant (p > .10) for any of the variables indicating the presence of homoskedasticity. On the basis of studentized residuals and Cook's D tests, I deleted five outlier cases. Visual inspection of the plots of the histogram and normal probability plots reaffirmed the multivariate normality of the data (Hair et al. 1998). The variance inflation factors in each regression model were all below two, indicating that multicollinearity was not a serious problem. An interview survey raises the potential that errors of prediction may not be independent of one another over the sequences of cases (Tabachnick and Fidell 1989, p. 133). However, the Durbin-Waston statistic check for nonindependence of errors was not significant in the regression models.
Table 3 (Model 1) shows that the control variables explain 21% of the variance in competence exploitation. Adding the independent variables in Model 2 increased R² by 6% (ΔF = 3.72, p < .05). I added the interaction terms in Model 3, which resulted in a further increase in R² of 1% (ΔF = 1.10, not significant [n.s.]). Model 3 shows that customer orientation is positively related to competence exploitation (b = .13, p < .01), in support of H1a. Competitor orientation is positively related to competence exploitation (b = .16, p < .05), in support of H2a. These relationships are not moderated by interfunctional coordination or by perceived market opportunity. Thus, H3a, H4a, H5a, and H6a are not supported. Perceived market opportunity is a predictor of competence exploitation rather than a moderator (b = .11, p < .05). Three control variables are related to competence exploitation: organizational slack, firm size, and environmental turbulence.
The data in Table 3 (Model 4) show that the control variables explain 23% of the variance in competence exploration. The independent variables increase R² by 11% (ΔF = 7.44, p < .001) (Model 5). The interaction variables contributed an additional 9% (ΔF = 6.56, p < .001) to explained variance (Model 6). The data in Model 6 show that customer orientation is positively related to competence exploration (b = .26, p < .001), in support of H1b. Competitor orientation has a significant, positive effect on competence exploration (b = .16, p < .001), in support of H2b. I discuss the significance of the differential strength (based on standardized coefficients) of the effects of customer and competitor orientations on competence exploitation and exploration subsequently.
The product of customer orientation and interfunctional coordination is positively related to competence exploration (b = .10, p < .01), in support of H3b. To gain further insight into these relationships, using the unstandardized coefficients and following procedures that Aiken and West (1991) outline, I plotted the interactions and conducted simple slope tests. The simple slope test involved splitting the moderator (interfunctional coordination) into a high group (two standard deviations greater than the mean) and a low group (two standard deviations less than the mean) and reestimating the relationship between customer orientation and competence exploration. The plot in Figure 2, Panel A, shows that when interfunctional coordination is high, the positive relationship between customer orientation and competence exploration is stronger (simple slope: b = .28, t = 3.98, p < .001) than when it is low (simple slope: b = .21, t = 3.67, p < .001).
The interaction term for competitor orientation and interfunctional coordination is positively related to competence exploration (b = .12, p < .05), in support of H4b. Figure 2, Panel B, shows that when interfunctional coordination is greater, there is a positive link between competitor orientation and competence exploration (simple slope: b = .34, t = 3.36, p < .001). There appears to be no relationship between the two constructs when interfunctional coordination is low (simple slope: b = .09, t = 1.10, n.s.). Interfunctional coordination is a pure moderator because it is unrelated to competence exploration.
The interaction between customer orientation and perceived market opportunity is positively related to competence exploration (b = .12, p < .001), in support of H5b. The plots in Figure 3, Panel A, show that the positive link between customer orientation and competence exploration is stronger when perceived market opportunity is high (simple slope: b = .42, t = 5.73, p < .001) than when it is low (simple slope: b = .14, t = 2.29, p < .05). Similarly, the product of competitor orientation and perceived market opportunity is positively related to competence exploration (b = .11, p < .01), in support of H6b. Figure 3, Panel B, shows a positive relationship between competitor orientation and competence exploration when perceived market opportunity is high (simple slope: b = .38, t = 4.06, p < .001) but no relationship when it is low (simple slope: b = .01, t = .13, n.s.). Perceived market opportunity is a quasi moderator because it also has a positive relationship with competence exploration (b = .10, p < .01). Three control variables are related to competence exploration: organizational slack, behavior, and output controls.
Table 4 presents the results for incremental and radical innovation performance. The addition of the independent variables to the control variables in Model 2 increased R² by 7% (.F = 7.20, p < .001) over the explained variance in incremental innovation performance in Model 1. The interaction variables increased R² by 1% (ΔF = 1.36, n.s.) in Model 3. Competence exploitation is positively related to incremental innovation performance (b = .16, p < .01), in support of H7a. In contrast, competence exploration is negatively related to incremental innovation performance (b = -.14, p < .01), in support of H8a. Interfunctional coordination does not moderate these relationships but rather is a predictor of incremental innovation performance (b = .15, p < .001). Thus, H[sub 9a, H10 and H12 are not supported, because none of the interaction terms is significant. Output control, a control variable, was positively related to incremental innovation performance. I replicated the analyses using the measure of incremental innovation performance obtained from the second survey. The results remain unchanged.
Model 5 in Table 4 shows that the independent and moderator variables increase explained variance in radical innovation performance by 12% (ΔF = 11.82, p < .001) over the explained variance in Model 4. The interaction terms contribute an additional 8% to explained variance (ΔF = 7.05, p < .001) (Model 6). Competence exploitation is negatively related to radical innovation performance (b = -.14, p < .05), in support of H7b and H8b, whereas the effect of competence exploration is positive (b = .14, p < .05). The interaction between competence exploitation and exploration is negatively related to radical innovation performance (b = -.17, p < .001), in support of H9b. The plot in Figure 4, Panel A, shows no relationship between competence exploration and radical innovation performance when competence exploitation is high (simple slope: b = -.04, t = -.43, n.s.), but it shows a positive effect when competence exploitation is low (simple slope: b = .26, t = 3.63, p < .001). The plot in Figure 4, Panel B, shows the reverse relationship; that is, there is a nonsignificant relationship between competence exploitation and radical innovation performance when competence exploration is low (simple slope: b = .13, t = 1.45, n.s.) but a significant, negative effect when it is high (simple slope: b = -.28, t = -3.62, p < .001).
The interaction term for interfunctional coordination and competence exploitation is positively related to radical innovation performance (b = .12, p < .05), in support of H11. However, Figure 5, Panel A, suggests that at higher levels of interfunctional coordination, competence exploitation has no relationship with radical innovation performance (simple slope: b = .09, t = 1.24, n.s.), but at lower levels, it has a negative effect (simple slope: b = -.19, t = -2.90, p < .01). Finally, the positive link between competence exploration and radical innovation performance is stronger when interfunctional coordination is high (b = .20, p < .01), in support of H13. This result is confirmed by the plots in Figure 5, Panel B, which show that competence exploration is related to radical innovation performance when interfunctional coordination is high (simple slope: b = .42, t = 5.32, p < .001), but it has no effect when it is low (simple slope: b = -.02, t = -.18, n.s.). Interfunctional coordination serves as a quasi moderator for this outcome because it is also positively related to radical innovation performance (b = .25, p < .001). Market launch capability, organizational slack, and output control predicted radical innovation performance. A replication analysis that substituted the measures for radical innovation performance obtained from the second survey with those from the primary survey confirmed the original findings.
My model (see Figure 1) posits that competence exploitation and exploration fully mediate the effects of customer and competitor orientations on innovation performance. I used Baron and Kenny's (1986) tests of mediation to verify this claim. With the entry of competence exploitation and exploration (see Table 4, Model 3), the effects of customer orientation (b = .19, p < .001) and competitor orientation (b = .19, p < .01) on incremental innovation performance are reduced (but remain significant): customer orientation (b = .14, p < .01) and competitor orientation (b = .12, p < .05). This suggests partial mediation. In contrast, Table 4 (Model 5) shows that with the entry of competence exploitation and exploration, the significant effect of competitor orientation on radical innovation performance (b = .13, p < .10) in Model 4 becomes nonsignificant (b = .01, n.s.). This suggests full mediation. Customer orientation has no direct effect on radical innovation performance, suggesting that its effect occurs entirely through its positive influence on competence exploration previously reported in Table 3. Thus, the mediating proposition is partially supported. Table 5 summarizes the hypotheses and empirical conclusions of the study.
Discussion and Implications
Marketing scholars have paid little attention to resolving the capability-rigidity paradox despite its immense hindrance to the effective management of product innovation, an activity that lies at the heart of marketing's role in enhancing the firm's competitive advantage (Day 1994). In this study, I examined the role of market orientation in resolving this paradox. The results advance the marketing literature in several ways. First, the results show that both customer and competitor orientations guide managerial decisions to allocate resources to exploit existing product innovation competencies and to develop new ones. These findings support propositions in the RBV and marketing theory that because market-oriented firms are sensitive to environmental cues, they are in a better position than their non-market-oriented counterparts to uncover and overcome potential internal competence deficiencies (Barney and Zajac 1994; Day 1994; Hurley and Hult 1998; Schroeder, Bates, and Junttila 2002). Instead of being plagued by the capability-rigidity paradox, it appears that market-oriented firms are able to make judicious judgments in resources allocations for product innovation competencies based on market information. Yet expenditures for acquiring and using market knowledge are typically considered operating costs. This study provides justification for the view that such expenditures must be considered investments rather than operational costs of the firm (Srivastava, Shervani, and Fahey 1998).
Second, I find that the effects of customer and competitor orientations on competence exploration (unlike competence exploitation) are positively moderated by interfunctional coordination. This implies that because competence exploration is a highly risky and uncertain endeavor, the use of market information to inform such a process requires a high degree of interfunctional coordination. In contrast, because of its greater experience with existing competencies, a firm's use of market information to guide resource allocation for competence exploitation does not necessarily require high interfunctional coordination efforts. A related finding is that because of the uncertainties and risks involved in developing radical innovations, firms require greater interfunctional coordination in deploying both existing and new competencies for this purpose. As people from different functions interact, there is likely to be reinterpretation of one another's perspectives in deploying the firm's competencies (Henderson and Cockburn 1994), developing new solutions, and recombining existing competencies for use in new product areas (Zahra and Nielson 2002). Interfunctional coordination helps explain the differential capacities of firms to exploit existing competencies and to explore new ones simultaneously to develop radical innovations and thus escape the capability-rigidity paradox.
Third, I find that a managerial mental model that perceives the market environment as an opportunity rather than as a threat has significant implications for the firm's decision to develop entirely new product innovation competencies. A possible explanation for this result is that with such a mental model, managers are likely to discover competence gaps that need to be overcome to benefit from the available market opportunities. Because customer and competitor orientations lead to greater understanding of the firm's strengths and weaknesses in relation to those of its competitors (Day and Wensley 1988), when perceived market opportunity is high, managers are more likely to develop new competencies than to focus entirely on exploiting existing ones. Perceived market opportunity reduces the perceived costs of investments into new competencies (White, Varadarajan, and Dacin 2003). This is an important insight because scholars tend to examine the role of market orientation on product innovation activities without explicitly considering how the managers' interpretations of the market conditions affect the linkages. The role of mental models may represent an underdeveloped aspect of the study of market orientation and should be given attention in future studies.
Fourth, the new evidence of the differential direct and interaction effects of competence exploitation and exploration on product innovation performance is particularly poignant. Although the differential effects affirm conventional wisdom, the negative effect of their interaction on radical innovation performance is counterintuitive. It implies that competence exploration will be more valuable to the firm when it is matched with a lower level of competence exploitation, and vice versa. Because too much of both competence exploitation and exploration may have undesirable costs for the firm (March 1991; Nerkar 2003), this result implies that a firm at the forefront of new knowledge creation through exploration is more likely to succeed in developing radical innovations by recombining this knowledge with some level of exploitation. Existing competencies provide the necessary absorptive capacity to use new competencies (Danneels 2002). Conversely, a firm that is extremely competent in exploiting its current competencies will be successful with radical innovation only with a little dose of exploration. This finding reflects the argument that many radical innovations are the locus of a meeting between a problem and its solution, even when neither the problem nor the solution is itself new (Galunic and Rodan 1998; Kogut and Zander 1992). This insight is apt in the context of this study in which firms may exploit existing capabilities in new ways to solve emerging customer problems (Luo 2002). Radical innovations to the Chinese market often result from the recombination of known technology and market elements. The product novelty stems from the act of combination, not necessarily from the novelty of the technology and market solutions combined.
Finally, the differential strengths of the direct effects of customer and competitor orientations on competence exploitation and exploration are noteworthy. Competitor orientation has a greater effect on competence exploitation, a key driver of incremental innovation performance, than does customer orientation. This suggests that compared with customer focus, competitor-centered practices enable firms to marshal resources to meet more immediate threats of competitors through competence exploitation and incremental innovations (Noble, Sinha, and Kuma 2002). Thus, the partial mediation of the effects of customer and competitor orientations on incremental innovation performance by competence exploitation and exploration suggests that examining only their direct effects leads to an underestimation of their differential explanatory power. Furthermore, compared with competitor orientation, customer orientation has a stronger effect on competence exploration, which is the only means by which customer orientation is positively linked to radical innovation performance. Yet competence exploration fully mediates the positive effect of competitor orientation on radical innovation performance. These findings provide some support for Noble, Sinha, and Kumar's (2002, p. 36) conjecture that exploration plays a stronger role in the transition of customer and competitor orientations to firm performance than does exploitation. Given that competence exploration involves the acquisition of entirely new knowledge and skills, these results suggest that it is through customer rather than competitor orientation that firms build stronger capacities for radical innovation. I contend that these differential effects may be the result of firms having greater knowledge of their future customers than of their future competitors. Indeed, few, if any, empirical frameworks exist for marketing managers to identify and analyze future competitors. Deeper insight is necessary in future research on how firms develop competitor orientations. In brief, in failing to examine the differential routes by which customer and competitor orientations affect innovation performance, these findings imply that previous research may have arrived at a premature and perhaps an overly simplistic view of the relationships.
Theoretical Contributions
This study contributes to marketing theory in five main respects: First, the study incorporates market orientation into the RBV research stream that views firms as responding to environmental conditions through existing competencies and the development of new ones (Barney and Zajac 1994; Cockburn, Henderson, and Stern 2000). According to the RBV, knowledge is the most important resource that a firm can control, but the challenge is how firms turn knowledge into internal competencies for innovation (Barney 1991; Kogut and Zander 1992). By addressing the link between customer and competitor orientations and product innovation competencies, this study meets this challenge and presents a new perspective of the role of market orientation in product innovation; that is, market orientation can contribute to competitive advantage insofar as it elicits and reinforces investments in existing product innovation competencies and simultaneously leads to the development of new competencies. Although marketing scholars have theorized about this linkage (e.g., Day 1994; Hurley and Hult 1998), there has been no empirical evidence until now. This evidence contributes to the marketing literature by providing a new theoretical mechanism by which market-oriented practices are linked to incremental and radical innovations simultaneously.
Second, the finding of a significant moderating role of interfunctional coordination resonates with research that suggests a dual role for organizational coordination mechanisms: one of transformation of knowledge into functional competencies (Grant 1996) and one of integration of functional competencies into performance outcomes (Grant 1996; Henderson and Cockburn 1994). The knowledge-sharing benefits of interfunctional coordination ensure the collective assimilation of efforts among functional units to use market knowledge to engender competence exploration. In addition, by enhancing connectedness among functional units, interfunctional coordination ensures the effective use of the firm's new product innovation competencies to engender radical innovation outcomes. These findings underscore the wisdom of a disaggregated view of market orientation and suggest a more nuanced view of the different roles of interfunctional coordination than has previously been provided in the extant literature.
Third, resource allocations for capability building involve a trade-off between exploitation and exploration (March 1991). In finding a significant moderating impact of the firm's perceived market opportunity on the effect of customer and competitor orientations on competence exploration, this study reveals an important managerial factor that may enable firms to strike an appropriate trade-off. The finding validates Day's (1994) thesis about the salience of managers' mental models in using market information to build firm capabilities. It also emphasizes the notion that building new capabilities does not involve the mere acquisition and use of market knowledge. More critically, the process involves the use of interpretation schema to determine how the managers respond to the market situation (White, Varadarajan, and Dacin 2003). This finding throws new light on why different firms faced with the same objective market conditions develop different product innovation capabilities.
Fourth, this study contributes to marketing theory by being perhaps among the first to test empirically the proposition that competence exploitation and exploration have direct and opposing relationships with incremental and radical innovations performance. In particular, the negative impact of their interaction on radical innovation performance suggests new theoretical implications that are unavailable in the extant literature: Both exploitation and exploration require a little dose of each other to enhance radical innovations. This study suggests that a firm must exploit some level of its current competencies to leverage its new competencies to develop radical innovations. This echoes Danneels's (2002, p. 1097) finding that "rather than trapping the firm, current competencies may be used as leverage points to add new competencies." The additional new insight that is missing in the literature but is offered here is that existing competencies, when coupled with some level of new competencies, may also enhance radical innovations. This new insight lends some support to the idea that effective balancing of exploitation and exploration requires a high-low matching rather than a high-high matching (Nerkar 2003). Further research should address the organizational designs and processes that could ensure appropriate levels of interaction between competence exploitation and exploration.
Fifth, given the context of this study, the results have implications for the role of market orientation in the firm's adaptation to turbulent environments. As I argued previously, the significant risks and uncertainties in a transitional environment indicate that firms in China must confront not only the challenge of new competition, changing technologies, and new customer preferences but also collapsing capabilities (Li and Atuahene-Gima 2002). Exploitation of existing capabilities may not be adequate and may quickly become hazardous to competitive advantage. Systematic efforts are necessary to track the market changes and to assess the firm's competence deficiencies to refine existing competencies and to develop the necessary new ones for the new environment. By empirically linking market orientation to the simultaneous exploitation and exploration of product innovation capabilities and outcomes in China, I demonstrate that market orientation has promise for understanding how firms adapt to complex and turbulent environments.
Studies indicate that market-based assets, such as market orientation, play an important role in creating and sustaining shareholder value and should play an equally significant role in investment decisions (Srivastava, Shervani, and Fahey 1998). By showing that market orientation affects product innovation competencies, this study reinforces this perspective and provides further empirical evidence with which marketing managers can buttress the argument that marketing expenditures may be better viewed as capital investments rather than as operational costs. Therefore, the results suggest an important role for marketing managers in planning and executing the firm's resource allocation decisions about product innovation. To exercise this role, marketing managers can use the results reported here to make the case for increased interface with finance to ensure ( 1) that resource allocation decisions take into account the firm's needs for new product innovation competencies and ( 2) that such decisions are appropriately guided by the firm's knowledge of current and emerging market conditions.
Traditionally, resource allocation in product innovation has been largely guided by the level of sales (e.g., R&D as percentage of sales) and competitors' expenditures and/or based on innovation typologies, such as process and product innovations. This study suggests an additional criterion: competence exploitation and exploration to prioritize resource allocation in product innovation. The ten-item measure for competence exploitation and exploration used in this study could be a useful starting point for marketing managers in developing such a decision-making yardstick. In addition, the results suggest that marketing managers should be sensitive to the need for knowledge sharing and integration among functional units within the firm. This is because the mere allocation of a scarce resource to competence exploitation and exploration is unlikely to yield radical innovations without effective coordination among cross-functional units to translate this competence into effective outcomes.
This study suggests that to ensure the effective allocation of resources for new competencies, marketing managers should work to prevent threat-rigidity tendencies when interpreting the market situation in the firm. When marketing managers successfully persuade other functional units about the potential opportunities that market conditions offer, the firm is likely to consider its current innovation capabilities critically and devote resources to the exploration of new ones. In doing so, marketing managers would be helping prevent competence exploitation from crowding out competence exploration in their firms. Therefore, reducing threat-rigidity tendencies in the interpretation of market information should be an important task for marketing managers. This could be achieved by marketing's advocacy for functional diversity, reward systems that encourage risk taking, and interdependencies among functions in the acquisition and use of market information.
Finally, findings suggest that because marketing managers have the ability to balance the potential tension between competence exploitation and exploration, they are likely to experience greater success in their efforts to enhance the firm's product innovation. As the results indicate, managers may need to combine high competence exploration with low competence exploitation (and vice versa) to develop radical innovations. This requires careful attention to the use of ambidextrous structures that separate exploration and exploitative activities (O'Reilly and Tushman 2004). As an example, FirstDirect and SKF set up autonomous new business units to develop new competencies for new markets, but they left the exploitation of existing competencies to existing business units (Abell 1999).
This study has several limitations. First, the measure for market orientation neither captures all its different components nor covers all the various stakeholders (e.g., suppliers) that are likely to be the focus of a firm's information collection efforts (Matsuno and Mentzer 2000). I measured innovation competence and performance across three years. However, the question remains whether market orientation may be causally an antecedent to competence and performance across multiple years. Thus, a second limitation of the study is that causal relationships cannot be inferred in the results reported. Further research might adopt a longitudinal design to tease out these linkages more clearly. Third, I used data from a sample of firms from a single industry. Although this offered several advantages for this study, it limits the generalizability of the results. Finally, I controlled only for a limited set of the potential antecedents of product innovation competencies and outcomes because of data limitations.
In addition to alleviating these limitations, there are other fertile avenues for further research. First, although I focused on product innovation competencies, the theory I developed herein can conceivably apply to several other organizational competencies that Day (1994) describes as spanning and inside-out capabilities. Further research should examine the potential effects of market orientation on these other capabilities. Second, I show that market orientation may have relatively stronger effects on competence exploration and on radical innovations. This echoes Atuahene-Gima's (1995, p. 279) argument that "in contrast with incremental innovations, firms need greater degree of market orientation not only to cope with the high level of uncertainties associated with developing radical innovations but also in establishing and educating the market." Yet some scholars argue that market orientation may lead to competency traps that stifle competence exploration and radical innovations. To date, however, the literature lacks clarity on the nature and types of these traps and the effect that market orientation has on them. The linkage between market orientation and competency traps requires empirical scrutiny in further research to advance the understanding of product innovation.
Third, this study suggests a synergistic perspective of interfunctional coordination by considering both the direct and the moderating effects of this integrative mechanism. Further research should adopt this perspective in studying other formal (e.g., cross-functional teams) and informal
(e.g., social networking) integrative mechanisms to ensure a better understanding of the outcomes of customer and competitor orientations. Although it is useful for sharing ideas and gathering interpretations, interfunctional coordination may also carry costs. However, there is little research that considers costs and drawbacks of interfunctional coordination. Research is necessary on the conditions that moderate the effect of this integrative mechanism on product innovation competencies and their outcomes. Fourth, the results of this study point to the importance of the mental models in capability building and underscore the need for further research to examine the factors that affect the interpretation of a market situation as an opportunity or as a threat.
Finally, although the capability-rigidity paradox is typically conceptualized with respect to product innovation, it occurs in any marketing activity in which the choice between exploitation and exploration is an issue (see Kyriakopoulos and Moorman 2004). This raises several research questions that may be the focus of further research: What factors influence the allocation of resources between exploitation and exploratory marketing strategies? To what extent do firms differentiate between exploration and exploitation in their relationships with customers and other firms, and what are the performance effects of such relationships? Under what conditions are exploratory and exploitative innovations and marketing strategies related? and How do firms maintain a balance between exploitative and exploratory marketing strategies? Research on these and other related questions could provide a better understanding of marketing's role not only in sustaining the firm's current competitive advantage but also, and perhaps more important, in building its sources of future competitive advantage.
Conclusion
Taken together, the results of this study suggest that market orientation can prevent a firm from being operationally efficient but strategically inefficient by enhancing both product innovation competence exploitation and exploration. Market orientation appears to be a key mechanism by which firms can reap the benefits of their innovation capabilities without incurring the costs associated with potential rigidities. In my view, this more nuanced assessment of market orientation is only one of the benefits, both for theory building and practice, of an inquiry into how firms generate and exploit product innovation competencies. Given the Chinese context of the study, I believe that this study provides a significant clarification of the role of market orientation in firms' adaptation in turbulent environments through product innovation. However, this line of inquiry is still in its infancy. The complexity of the research questions that this article raises is only comparable to the magnitude of the expected returns from the advancement of the knowledge of marketing's role in the firm.
The author thanks the three anonymous JM reviewers for their constructive feedback. Previous versions of this article also benefited from comments and suggestions by Namwoon Kim, Stan Slater, Ian Wilkinson, and Rajan Varadarajan. The article also benefited from presentations at the Center for Innovation Management and Organizational Change, Department of Management, City University of Hong Kong, and at the 2004 annual meeting of the Academy of Management Conference in New Orleans. The author thanks Liang Xiangfen, Guoqing Guo, and Victor Lee for their assistance in developing the instrument and collecting the data and Ziguang Chen for assisting in analysis of the data. The work described in this article was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CityU 1121/02H).
( n1) In line with other researchers (e.g., Danneels 2002; Day 1994; Grant 1996; Henderson and Cockburn 1994), I use the word "competence" interchangeably with "capability."
( n2) The interviewer returned each completed questionnaire along with the business card of the respondent. This procedure facilitates ( 1) quality control by allowing for independent verification through telephone calls to ascertain that the informants were interviewed and that they completed the questionnaires and ( 2) the delivery of the summary of the research results to informants.
( n3) The results of the most closely related theoretical constructs in the study are as follows: customer orientation versus competitor orientation (constrained model: χ² = 457.16, degrees of freedom [d.f.] = 44; unconstrained model: χ² = 136.52, d.f. = 43; χ² difference = 320.64, d.f. = 1), customer orientation versus incremental innovation performance (constrained model: χ² = 246.91, d.f. = 35; unconstrained model: χ² = 91.78, d.f. = 34; χ² difference = 155.13, d.f. = 1), customer orientation versus radical innovation performance (constrained model: χ² = 337.55, d.f. = 44; unconstrained model: χ² = 121.03, d.f. = 43; χ² difference = 216.52, d.f. = 1), competitor orientation versus incremental innovation performance (constrained model: χ² = 197.75, d.f. = 14; unconstrained model: χ² = 35.76, d.f. = 13; χ² difference = 161.99, d.f. = 1), competitor orientation versus radical innovation performance (constrained model: χ² = 361.82, d.f. = 20; unconstrained model: χ² = 30.20, d.f. = 19; χ² difference = 331.62, d.f. = 1), perceived market opportunity versus interfunctional coordination (constrained model: χ² = 633.83, d.f. = 35; unconstrained model: χ² = 73.86, d.f. = 34; χ² difference = 559.97, d.f. = 1), competence exploitation versus competence exploration (constrained model: χ² = 567.81, d.f. = 28; unconstrained model: χ² = 77.67, d.f. = 27; χ² difference = 490.14, d.f. = 1), incremental versus radical innovation performance (constrained model: χ² = 163.49, d.f. = 14; unconstrained model: χ² = 17.76, d.f. = 13; χ² difference = 145.73, d.f. = 1), and behavior control versus output control (constrained model: χ² = 212.46, d.f. = 9; unconstrained model: χ² = 13.76, d.f. = 8; χ² difference = 198.70, d.f. = 1). All chi-square differences are significant at the .001 level.
( n4) The interrater reliability for each construct measured with multiple items are as follows: customer orientation (r = .89, p < .01), competitor orientation (r = .82, p < .01), interfunctional coordination (r = .73, p < .01), perceived market opportunity (r = .87, p < .01), competence exploitation (r = .79, p = .01), competence exploration (r = .77, p < .01), incremental innovation performance (r = .67, p < .01), radical innovation performance (r = .88, p < .01), organizational slack (r = .86, p < .01), environmental turbulence (r = .95, p < .01), behavior control (r = .69, p < .01), output control (r = .63, p < .01), product development alliance (r = .81, p < .01), and market launch capability (r = .87, p < .01).
Legend for Chart:
A - Construct and Source
B - Operational Measures of Construct
E - SFL(a)
F - t-Value
A
B
E F
Model 1
Model Fit Indexes: χ² = 662.89, d.f. = 423;
χ²/d.f.= 1.57; RMSEA = .04, GFI = .90,
CFI = .93, and NNFI = .92
Customer orientation(b)
(Narver and Slater
1990)
1. We regularly meet customers to learn about their
current and potential needs for new products.
.68 12.10
2. We constantly monitor and reinforce our understanding
of the current and future needs of customers.
.80 13.93
3. We have a thorough knowledge about emerging customers
and their needs.
.85 10.52
4. Information about current and future customers is
integrated in our plans and strategies.
.65 10.52
5. We regularly use research techniques such as focus
groups, surveys, and observations to gather customer
information.
.82 14.50
6. We have developed effective relationships with
customers and suppliers to fully understand new
technological development that affect customers' needs.
.51 7.72
7. We systematically process and analyze customer
information to fully understand their implications
for our business.
.76 12.94
Competitor orientation(b)
(Narver and Slater
1990)
1. We regularly collect and integrate information about
the products and strategies of our competitors.
.55 8.88
2. We systematically collect and analyze information
about potential competitor activities.
.61 9.13
3. Managers in this firm regularly share information about
current and future competitors within the company.
.69 11.51
4. Our knowledge of current and potential competitors'
strengths and weaknesses is very thorough.
.75 12.94
Interfunctional
coordination(b)
(Narver and Slater
1990; Zahra and
Nielson 2002)
1. The activities of functional units are tightly
coordinated to ensure better use of our market knowledge.
.60 8.99
2. Functions such as R&D, marketing, and manufacturing
are tightly integrated in cross-functional teams in
the product development processes.
.74 10.89
3. R&D and marketing and other functions regularly
share market information about customers, technologies,
and competitors.
.72 10.78
4. There is a high level of cooperation and coordination
among functional units in setting the goals and priorities
for the organization to ensure effective response to
market conditions.
.64 9.97
5. Top management promotes communication and cooperation
among R&D, marketing, and manufacturing in market
information acquisition and use.
.63 9.37
6. People from marketing, R&D, and other functions play
important roles in major strategic market decisions.(f)
Incremental innovation
performance(c)
(Chandy and Tellis
1998)
1. % of total sales from incremental product introduced by
your firm in the last three years (less than 5%, 5%-10%,
11%-15%, 16%-20%, >20%).
.89 13.25
2. This firm frequently introduced incremental new
products into new markets in the last three years
(1 = "strongly disagree," 5 = "strongly agree").
.59 7.70
3. Compared to your major competitor, this firm introduced
more incremental new products in the last three years
(1 = "strongly disagree," 5 = "strongly agree").
.67 9.76
4. Number of incremental products introduced by the firm
in the last three years (converted into five-point
scale: 1-10, 10-15, 16-30, 31-75, more than 75).(f)
Radical innovation
performance(c)
(Chandy and Tellis
1998)
1. % of total sales from radical product introduced by
your firm in the last three years (less than 5%, 5%-10%,
11%-15%, 16%-20%, >20%)
.81 13.83
2. Number of radical products introduced by the firm in
the last three years (converted into a five-point scale:
0-3, 4-6, 7-9, 10-12, >12)
.77 10.01
3. Compared to your major competitor, this firm introduced
more radical new products in the last three years
(1 = "strongly disagree," 5 = "strongly agree").
.78 11.56
4. This firm frequently introduced radical new products
into markets totally new to the firm in the last three
years (1 = "strongly disagree," 5 = "strongly agree").
.88 14.02
Competence
exploitation(d)
(Zahra, Ireland, and
Hitt 2000)
Over the last three years, to what extent has your firm
1. Upgraded current knowledge and skills for familiar
products and technologies?
.87 16.49
2. Invested in enhancing skills in exploiting mature
technologies that improve productivity of current
innovation operations?
.89 16.85
3. Enhanced competencies in searching for solutions
to customer problems that are near to existing solutions
rather than completely new solutions?
.78 12.90
4. Upgraded skills in product development processes in
which the firm already possesses significant experience?
.63 9.05
5. Strengthened our knowledge and skills for projects
that improve efficiency of existing innovation activities?
.68 9.89
Competence
exploration(d)
(Zahra, Ireland, and
Hitt 2000)
Over the last three years, to what extent has your firm
1. Acquired manufacturing technologies and skills entirely
new to the firm?
.60 8.23
2. Learned product development skills and processes (such
as product design, prototyping new products, timing of new
product introductions, and customizing products for local
markets) entirely new to the industry?
.81 14.46
3. Acquired entirely new managerial and organizational
skills that are important for innovation (such as
forecasting technological and customer trends; identifying
emerging markets and technologies; coordinating and
integrating R&D; marketing, manufacturing, and other
functions; managing the product development process)?
.75 13.37
4. Learned new skills in areas such as funding new
technology, staffing R&D function, training and
development of R&D, and engineering personnel for
the first time?
.73 12.57
5. Strengthened innovation skills in areas where it had
no prior experience?
.51 6.75
Model 2
Model Fit Indexes: χ² = 481.07, d.f.= 297;
χ²/d.f.= 1.62; RMSEA = .05, GFI = .87,
CFI = .93, and NNFI = 91
Environmental
turbulence(b) (Jaworski
and Kohli 1993)
Indicate your degree of agreement about how well these
statements describe the market and competitive environment
during the last three years.
1. The actions of local and foreign competitors in our
major markets were changing quite rapidly.
.60 7.11
2. Technological changes in our industry were rapid and
unpredictable.
.55 6.77
3. The market competitive conditions were highly
unpredictable.
.65 10.56
4. Customers' product preferences changed quite rapidly.
.51 6.06
5. Changes in customers' needs were quite unpredictable.
.75 12.77
Perceived market
opportunity (Dutton
and Jackson 1987)
In your company, different functions such as R&D,
marketing, and manufacturing communicate, share, and use
information about current and prospective customers,
competitors, and other market information in product
development. Overall, how does each of the following
describe your firm's tendency in interpreting new market
information? We have a general tendency to view market
information and conditions as
1. Representing a loss/gain.
78 12.78
2. Uncontrollable/controllable.
.85 15.51
3. Having a negative/positive implication for the firm.
.81 14.83
4. Representing a threat/opportunity for the firm.
.77 12.45
D E
Organizational slack(b)
(new scale)
1. This firm has uncommitted resources that can quickly
be used to fund new strategic initiatives.
.79 11.63
2. This firm has few resources available in the short run
to fund its initiatives. (reverse scored)
.69 8.56
3. We are able to obtain resources at short notice to
support new strategic initiatives.
.73 9.97
4. We have substantial resources at the discretion of
management for funding new strategic initiatives.
.55 6.09
Output control(b)
(Atuahene-Gima and
Li 2002)
In evaluating and rewarding new product projects teams
in this firm, emphasis is placed on
1. Achievement of financial performance objectives.
.60 8.96
2. The quantity and quality of the final outputs achieved.
.71 10.81
3. The market performance of products.
.80 12.75
4. Objective criteria such as cost savings, quantity of new
ideas, and patents filed.(f)
Behavior control(b)
(Atuahene-Gima and
Li 2002)
In evaluating and rewarding new product projects teams in
this firm, emphasis is placed on
1. Subjective criteria such as the quality attributes of
the products.
.86 12.75
2. Quality of decisions made rather than the results
achieved.
.68 10.03
3. Completing major stages of the development process
cost-effectively.
.59 7.71
4. Meeting of specific product development process
deadlines rather than the actual results.(f)
Product development
alliances(e) (Li and
Atuahene-Gima
2002)
To what extent does each of the following statements
describe your firm relative to your major competitors
over the last three years?
1. Marketed complementary new products with other firms.
.60 8.44
2. Established cooperative R&D agreements with other
firms.
.63 9.30
3. Jointly introduced new products to market with other
firms.
.60 8.68
4. Jointly designed and manufactured new products with
other firms.
.89 13.01
Market launch
capability(e) (new
scale)
To what extent does each of the following statement
describe your firm relative to your major competitors
over the last three years?
1. Ability to speedily introduce new products to market.
.75 10.98
2. Access to a wide distribution network for new products.
.56 7.83
3. Ability to develop creative marketing strategies for
new products.
.66 9.56
4. Ability to invest significant resources in marketing
new products.
.82 12.04
(a) SFL = standardized factor loading.
(b) The scale format for each of these measures was 1 = "strongly
disagree" and 5 = "strongly agree."
(c) I standardized items in these scales before creating the
scores to measure each construct for analysis.
(d) The scale format for each of these measures was 1 = "no
extent" and 5 = "to a great extent."
(e) The scale format for each of these measures was 1 = "worse
than major competitor" and 5 = "far better than major
competitor."
(f) I deleted this item during the scale purification process.
Notes: RMSEA = root mean square error of approximation,
GFI = goodness-of-fit index, CFI = comparative fit index, and
NNFI = nonnormed fit index. Legend for Chart:
A - Variables
B - Mean
C - Standard Deviation
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
K - 8
L - 9
M - 10
N - 11
O - 12
P - 13
Q - 14
R - 15
A
B C D E F
G H I J K
L M N O P
Q R
1. Customer orientation
3.33 .94 1
2. Competitor orientation
3.05 .79 .19(**) 1
3. Perceived market opportunity
2.56 1.09 .13(*) .20(**) 1
4. Interfunctional coordination
3.06 .92 .12 .36(**) .33(**)
1
5. Competence exploitation
3.14 .80 .29(**) .24(*) .25(**)
.14(*) 1
6. Competence exploration
3.46 .80 .42(**) .22(**) .29(**)
.19(**) .41(**) 1
7. Incremental innovation performance
3.41 .72 .38(**) .34(**) .29(**)
.35(**) .35(**) -.40(**) 1
8. Radical innovation performance
2.86 .90 .17(*) .23(**) .22(**)
.38(**) -.40(**) .38(**) .36(**) 1
9. Organizational slack
2.69 .95 .28(**) .10 .31(**)
.11 .32(**) .35(**) .22(**) .27(**)
1
10. Environmental turbulence
3.56 .96 .21(**) .32(**) .16(*)
.40(**) .28(**) .16(**) .26(**) .19(**)
.14(*) 1
11. Firm size (log)
1.75 .27 .04 .11 -.01
-.09 -.02 .05 -.05 .01
-.10 -.06 1
12. Behavior control
3.33 .89 .20(**) .10 .00
.01 .06 .20(**) -.08 .12
.10 .04 .09 1
13. Output control
2.68 .80 .25(**) .13 -.06
-.01 .17(**) .21(**) .20(**) -.15(**)
.02 .09 .06 .33(**) 1
14. Product development alliance
2.92 .96 -.01 -.11 -.09
-.13(*) -.16(*) -.06 -.10 -.08
-.03 -.11 .11 .01 -.10
1
15. Market launch capability
3.48 1.16 -.00 .01 .14(*)
.05 .04 -.05 .05 .16(*)
.00 -.02 .06 .06 .05
.30(**) 1
Skewness
-.33 -.17 .26
-.15 -.33 -.29 -.11 -.00
.44 -.09 -.03 .46 .05
-.01 -.63
Kurtosis
-.38 .49 -.77
-.21 .86 .20 -.51 -.66
.02 -.24 .79 -.21 -.50
-.25 .19
Composite reliability
.86 .74 .89
.85 .86 .83 .70 .88
.79 .78 .76 .79
.75 .79
Average variance extracted
.60 .59 .70
.59 .49 .52 .50 .72
.50 .52 N.A. .52 .53
.53 .55
(*) p < .01.
(**) p < .001.
Notes: N.A.= not applicable. Legend for Chart:
A - Variables
B - Hypotheses
C - Competence Exploitation Model 1
D - Competence Exploitation Model 2
E - Competence Exploitation Model 3
F - Competence Exploration Model 4
G - Competence Exploration Model 5
H - Competence Exploration Model 6
A
B C D
E F
G H
Controls Variables
Constant
2.58 2.06
(4.36)(***) (3.38)(***)
2.05 2.95
(3.29)(***) (5.51)(***)
2.19 2.02
(4.08)(***) (3.85)(***)
Organizational slack
.24 .17
(4.29)(***) (2.83)(***)
.18 .30
(2.95)(***) (5.74)(***)
.20 .23
(3.78)(***) (4.59)(***)
Firm size (log of number
of employees)
-.38 -.43
(-1.95)(*) (-2.30)(**)
-.42 -.02
(-2.25)(**) (-.23)
-.12 -.07
(-.71) (-.45)
Environmental turbulence
.29 .22
(3.57)(***) (2.46)(**)
.23 .14
(2.58)(**) (1.81)(†)
-.00 .03
(-.01) (.48)
Behavior control
.02 .03
(.42) (.52)
.03 .00
(.64) (.02)
.04 .12
(.70) (1.85)(†)
Output control
-.08 -.04
(-1.34) (-.36)
-.05 -.19
(-.71) (-3.11)(***)
-.13 -.13
(-2.15)(*) (-2.10)(*)
Product development
alliance
.03 -.02
(.52) (-.70)
-.01 .03
(-.20) (.68)
.05 .03
(.96) (.69)
Independent Variables
Customer orientation
H1a, H1b .13
(2.15)(**)
.13
(2.11)(**)
.23 .26
(4.43)(***) (5.02)(***)
Competitor orientation
H2a, H2b .17
(2.18)(**)
.16
(1.99)(*)
.12 .16
(1.66)(*) (2.36)(***)
Interfunctional
coordination
-.05
(-.79)
-.08
(-1.17)
.02 -.02
(.18) (.60)
Perceived market
opportunity
.09
(1.88)(*)
.11
(2.00)(*)
.08 .10
(1.87)(*) (2.31)(**)
Relevant Interaction Effects
Customer orientation x
interfunctional coordination
H3a, H3b
-.06
(-1.03)
.10
(2.00)(**)
Competitor orientation x
interfunctional coordination
H4a, H4b
.08
(1.21)
.12
(1.87)(*)
Customer orientation x
perceived market opportunity
H5a, H5b
.02
(.37)
.12
(2.61)(***)
Competitor orientation x
perceived market opportunity
H6a, H6b
-.11
(-1.44)
.11
(2.05)(**)
R²
.21 .27
.28 .23
.34 .43
Adjusted R²
.18 .23
.23 .20
.30 .38
F value
7.73(***) 6.41(***)
4.91(***) 8.66(***)
8.92(***) 9.06(***)
R²
.06
.01
.11 .09
Partial F value
3.72(*)
1.10
7.44(***) 6.56(***)
Degrees of freedom
6/179 10/175
14/171 6/178
10/174 14/170
(†) p < .10.
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: I report unstandardized regression coefficients (t-values
are in parentheses; I used a two-tailed test for control
variables and a one-tailed test for all hypotheses). Legend for Chart:
A - Variables
B - Hypotheses
C - Incremental Innovation Performance Model 1
D - Incremental Innovation Performance Model 2
E - Incremental Innovation Performance Model 3
F - Radical Innovation Performance Model 4
G - Radical Innovation Performance Model 5
H - Radical Innovation Performance Model 6
A
B C D
E F
G H
Control Variables
Constant
1.82 1.09
(4.61)(***) (2.66)(***)
.87 1.50
(2.01)(*) (2.87)(***)
.31 .25
(.59) (.47)
Organizational slack
.08 .01
(1.77)(†) (.23)
.08 .23
(.16) (3.64)(***)
.11 .17
(1.73)(†) (2.77)(**)
Firm size (log of number
of employees)
-.03 -.04
(-.52) (-.67)
-.03 .03
(-.55) (.43)
.02 .05
(.29) (.76)
Environmental turbulence
.13 .06
(1.64)(†) (.83)
.07 .15
(.95) (1.62)(†)
.06 .08
(.72) (1.00)
Behavior control
-.09 -.11
(-1.03) (-1.17)
-.12 .02
(-1.24) (1.66)(†)
.02 .02
(1.68)(†) (1.42)
Output control
.13 .11
(1.77)(†) (1.62)(†)
.17 -.13
(1.77)(†) (-1.32)
-.15 -.15
(-1.67)(†) (-1.78)(†)
Product development
alliance
-.01 -.02
(-.17) (-.23)
-.00 -.17
(-.06) (-2.21)(**)
-.08 -.06
(-1.15) (-.95)
Market launch capability
.02 .01
(.66) (.21)
.00 .17
(.02) (3.12)(***)
.14 .17
(2.70)(**) (3.48)(**)
Customer orientation
.19 .14
(3.78)(***) (2.68)(**)
.14 .04
(2.58)(**) (.67)
.04 .03
(-.71) (.48)
Competitor orientation
.19 .12
(3.14)(**) (1.95)(*)
.12 .13
(2.08)(*) (1.65)(†)
.01 .02
(.15) (.29)
Perceived market
opportunity
-.08 -.03
(-1.47) (-.57)
-.02 -.05
(-.05) (-.85)
-.06 -.09
(-1.03) (-1.59)
Independent Variables
Competence exploitation
H7a .11
(1.62)(*)
.16
(2.10)(**)
-.19 -.14
(-2.19)(**) (1.60)(*)
Competence exploration
H8a -.15
(-2.31)(**)
-.14
(-2.08)(**)
.25 .14
(2.99)(***) (1.67)(*)
Interfunctional
coordination
.16
(2.86)(***)
.15
(2.84)(***)
.24 .25
(3.52)(***) (3.87)(***)
Relevant Interaction Effects
Competence exploitation x
competence exploration
H9a
.06
(1.20)
-.17
(-2.94)(***)
Competence exploitation x
interfunctional coordination
H10
.06
(1.11)
.12
(1.83)(*)
Competence exploration x
interfunctional coordination
H12
.06
(.91)
.20
(2.24)(***)
R²
.27 .34
.35 .21
.33 .41
Adjusted R²
.23 .30
.30 .17
.28 .36
F value
7.25(***) 7.76(***)
6.60(***) 5.16(***)
7.34(***) 6.60(***)
R²
.07
.01
.12 .08
Partial F value
7.20(***)
1.36
11.82(***) 7.05(***)
Degrees of freedom
10/199 13/196
16/193 10/200
13/197 16/194
(†) p < .10.
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: I report unstandardized regression coefficients (t-values
are in parentheses; I used a two-tailed test for control
variables and a one-tailed test for all hypotheses except
H9a and H9b.) Legend for Chart:
A - Hypotheses
B - Expected Sign
C - Empirical Conclusions
A
B C
H1: Customer orientation is positively related to
a. Competence exploitation.
+ Supported
b. Competence exploration.
+ Supported
H2: Competitor orientation is positively related to
a. Competence exploitation.
+ Supported
b. Competence exploration.
+ Supported
H3: The positive effect of customer orientation on
competence:
a. Exploitation is stronger when interfunctional coordination
is high than when it is low.
+ Not supported
b. Exploration is stronger when interfunctional coordination
is high than when it is low.
+ Supported
H4: The effect of competitor orientation on competence:
a. Exploitation is stronger when interfunctional coordination
is high than when it is low.
+ Not supported
b. Exploration is stronger when interfunctional coordination
is high than when it is low.
+ Supported
H5: The positive effect of customer orientation on
competence:
a. Exploitation is weaker when the perceived market
opportunity is high than when it is low.
- Not supported
b. Exploration is stronger when the perceived market
opportunity is high than when it is low.
+ Supported
H6: The positive effect of competitor orientation
on competence:
a. Exploitation is weaker when the perceived market
opportunity is high than when it is low.
- Not Supported
b. Exploration is stronger when the market opportunity
orientation is high than when it is low.
+ Supported
H7: Competence exploitation is
a. Positively related to incremental innovation performance.
+ Supported
b. Negatively related to radical innovation performance.
- Supported
H8: Competence exploration is
a. Negatively related to incremental innovation performance.
- Supported
b. Positively related to radical innovation performance.
+ Supported
H9a: The interaction of competence exploration and
exploitation is related to incremental innovation
performance.
N.A. Not supported
H9b: The interaction of competence exploration and
exploitation is related to radical innovation performance.
N.A. Supported
H10: The positive effect of competence exploitation
on incremental innovation
performance is stronger when interfunctional coordination
is high than when it is low.
+ Not supported
H11: The negative effect of competence exploitation
on radical innovation performance is weaker when
interfunctional coordination is high than when it is low.
+ Supported
H12: The negative effect of competence exploration
on incremental innovation performance is weaker when
interfunctional coordination is high than when it is low.
+ Not supported
H13: The positive effect of competence exploration
on radical innovation performance is stronger when
interfunctional coordination is high than when it is low.
+ Supported
Notes: N.A. = not applicable.DIAGRAM: FIGURE 1; Conceptual Model of the Relationships Among Market Orientation, Product Innovation Competencies, and Innovation Performance
GRAPH: FIGURE 2; Interaction of Interfunctional Coordination and Customer and Competitor Orientations on Competence Exploration
GRAPH: FIGURE 3; Interaction of Perceived Market Opportunity and Customer and Competitor Orientations on Competence Exploration
GRAPH: FIGURE 4; Interaction of Competence Exploitation and Competence Exploration on Radical Innovation Performance
GRAPH: FIGURE 5; Interaction of Interfunctional Coordination and Competence Exploitation and Competence Exploration on Radical Innovation Performance
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~~~~~~~~
By Kwaku Atuahene-Gima
Kwaku Atuahene-Gima is Professor of Marketing and Innovation Management, China Europe International Business School (CEIBS) in Shanghai, and Professor of Innovation Management and Director of the Center for Innovation Management and Organizational Change, City University of Hong Kong (e-mail: kwaku@ceibs.edu).
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Record: 133- Return on Investment Implications for Pharmaceutical Promotional Expenditures: The Role of Marketing-Mix Interactions. By: Narayanan, Sridhar; Desiraju, Ramarao; Chintagunta, Pradeep K. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p90-105. 16p. 13 Charts, 4 Graphs. DOI: 10.1509/jmkg.68.4.90.42734.
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- Business Source Complete
Return on Investment Implications for Pharmaceutical
Promotional Expenditures: The Role of Marketing-Mix Interactions
The authors empirically explore the revenue impact of marketing-mix variables and their interactions. The findings include the following: pharmaceutical direct-to-consumer advertising and detailing (sales force) affect demand synergistically, detailing raises price elasticity, and detailing has a higher return on investment than does direct-to-consumer advertising. The authors also discuss other implications and provide future research directions.
In recent years, there has been explosive growth in direct-to-consumer advertising (DTC) by pharmaceutical manufacturers. Pharmaceutical DTC expenditures varied from slightly less than $1 billion in 1996 to $2.5 billion in 2000. Compare that with expenditures on detailing (i.e., sales representatives detail physicians in their offices)), which have increased from $8 billion in 1995 to only approximately $9 billion in 2000. Industry sources predict that by 2005, DTC spending will reach $7 billion. It is widely recognized that such growth was partially fueled by a change in the Food and Drug Administration's (FDA) policy toward DTC.( n1)
However, no matter how many patients DTC may induce to walk into a physician's office, if a physician says no to a request for a specific medicine, significant advertising dollars have been potentially wasted. Given this, drug manufacturers are struggling to measure the cost effectiveness of their multimillion-dollar DTC campaigns. For example, according to Lee Weinblatt, chief executive officer of PreTesting, an advertising consultancy company in Tenafly, N.J., Over 80% of our clients are questioning the efficiency of their consumer advertising ((PreTesting.com 2004).
Identifying marketing investments that generate a proven return on investment (ROI) is now a main concern for companies because such investments take funding priority over ones made on faith (see, e.g., Lehmann 2002). Moreover, firms are being urged to understand any synergy among the various elements of the marketing mix, such as detailing and DTC, in order to leverage the impact of such interactions. As Gatignon and Hanssens (1987, p. 257) point out, the optimal resource allocations that arise from models that account for marketing-mix interactions can be significantly different from those inferred from, for example, constant elasticity models.
Intuitively, if there were a positive, synergistic effect between DTC and detailing, it would be valuable to employ both simultaneously. However, if there were negative interactions, it might benefit firms to limit the overlap among the mix elements and perhaps devote more resources to the one that has a better return on the dollar. The arguments for examining interaction effects between detailing and DTC also hold for interaction effects between other promotional activities and between price and any other element of the marketing mix.( n2) For example, with respect to the latter, if greater advertising reduces (raises) price sensitivity, it enables firms to raise (lower) prices.
However, although academics have had a long-standing interest in understanding the effectiveness of marketing activities, limited attention has been devoted to the study of interactions among the elements of the marketing mix. For example, researchers have investigated the role of price x advertising interactions (for an overview, see Kaul and Wittink 1996), but little is known about the interactions among the various marketing-mix elements that are at the disposal of companies (e.g., in the pharmaceutical industry, these include price; detailing; DTC; and other marketing efforts [OMEs] such as journal advertising,, meetings, and events). Our search of the literature, which spans a few decades, identified only eight studies that examine marketing-mix interactions at the aggregate level (see Table 1).
Similarly, after reviewing 320 empirical studies on the determinants of (firms) financial performance, Capon, Farley, and Hoenig (1990, pp. 1144, 1159) make the following observations: "Only a handful of studies made an explicit attempt to model interactions among the causal factors, and There may be synergies (positive and negative) leading to various optimal combinations.... [W]ork on interaction of causal factors is badly needed if the analysis is to move towards optimal allocation of resources among controllable variables."
Analogous sentiments are shared by other researchers in related contexts; for example, Blattberg, Briesch, and Fox (1995, p. G125) note: "Few empirical results have been generated regarding the synergies between feature advertising, displays, and price discounts" ((for a recent review of that literature, see Lemon and Nowlis 2002). In summary, an understanding of whether marketing-mix elements such as DTC and detailing have a positive or negative synergistic effect appears to be important and warrants research attention.
To that effect, our empirical analysis focuses on two questions: First, are there any significant interaction effects between pairs of marketing-mix elements? For example, is the impact of detailing enhanced by DTC? Second, if there are interaction effects, what is their likely impact on revenues?
The latter question is relevant because previous academic research has largely been concerned with measurement (e.g., estimated demand elasticity, lagged effects of advertising), and the link to revenues (or other measures of financial performance) has rarely been made (see, e.g., Leeflang et al. 2000; Little 1979). There are two exceptions, both sponsored by the Association of Medical Publications: the work of Neslin (2001) and Wittink (2002). The main goal of these studies is to explore the "average" or "median" impact (across categories) of medical journal advertising along with DTC, detailing, and other promotional expenditures. Given the relatively large amount of data (e.g., Neslin analyzes monthly data from 391 brands), it is quite challenging to characterize the interaction effects among promotional expenditures in these studies (see, e.g., Wittink 2002). A common observation across the two studies is that the ROI for DTCs is quite low.
In a complementary way, our empirical analysis examines the second-generation antihistamine category of drugs. In addition, to assess the extent of our results category specificity, we analyze antiviral drugs that treat genital herpes. These two categories span the range from extensive use of DTC (antihistamines) to more limited use (antivirals), and they help ensure that the qualitative nature of our results is not driven entirely by the fact that antihistamines constitute a category with significant DTC.
Overall, the distinguishing features of our article are the following: ( 1) We explore the full range of interactions between pairs of marketing-mix elements and determine their differential impact on category sales and brand shares in two distinct prescription drug categories, ( 2) we assess the financial implications of detailing and DTC expenditures by providing ROI measures, and ( 3) we measure the impact of the interactions between marketing-mix elements both on the ROIs for detailing and DTC and on the price elasticities.
The rest of this article is organized as follows: In the next section, we review the relevant literature. The subsequent four sections discuss our model, the data, estimation-related issues, and the results, respectively. The penultimate section summarizes the managerial implications of our analysis, and the final section concludes with directions for further research.
Two main streams of research are relevant to our analysis: ( 1) research on the interaction between marketing-mix elements and ( 2) other research that examines pharmaceutical promotional spending. We discuss each of these subsequently.
The impact of detailing and DTC can be simply additive. Two recent studies (Neslin 2001; Wittink 2002) report such a possibility. Neslin (2001) uses monthly data from 391 branded products from various pharmaceutical categories and, for a median brand,, estimates the impact of detailing and DTC on the number of prescriptions written by physicians.( n3) He finds that the ROI for $1 of detailing is $1.72, and the corresponding ROI for DTC is $.19.( n4) Wittink (2002) expands on this analysis by considering more data points and by distinguishing between the size of the brands and their launch date in characterizing the average response to promotional investments. Wittink also finds considerable difference between the ROIs of detailing and DTC; for example, for brands that have at least $500 million in annual revenues and that launched between 1998 and 2000, ROI for detailing is $11.60 and $1.30 for DTC.( n5)
Benchmark estimates, such as the preceding ones, obtained from pooling the data across a variety of pharmaceuticals are valuable to the understanding of the impact of promotional expenditures on market response. It is important to extend such research by exploring whether the results, articulated in terms of a median (or average) drug, apply to specific product-markets. Our study provides such an exploration.
In contrast to the additive effects we have outlined, other extant research finds evidence for synergies between marketing-mix elements (for a list of such studies, see Table 1). For example, Swinyard and Ray (1977) find that advertising's effectiveness is enhanced when it follows a personal selling encounter. However, there is also evidence in the literature of a negative interaction between detailing and DTC (e.g., Azoulay 2001); this effect is referred to as jamming. The premise of the argument is that detailing typically is a scientific source of information for physicians and that DTC swamps the positive effect, perhaps when physicians generate counterarguments to the claims in the advertisements.
Overall, the conclusions in extant research appear to be that the net interaction effect between detailing and DTC can be positive, nonexistent, or negative. Kaul and Wittink (1996) report evidence for both positive and negative interactions between price and advertising. As we have noted, the interactions also have important managerial implications. In this article, we explore the full range of possible interactions between pairs of the various marketing-mix elements.
Several researchers have estimated demand functions of oligopolistic branded products in various pharmaceutical subcategories (e.g., Berndt et al. 1997; Chintagunta and Desiraju 2004; Rizzo 1999). Although the demand model herein is similar to that in the work of Chintagunta and Desiraju (2004), our study differs in three important ways: First, we allow for a comprehensive set of interaction effects among the various marketing-mix elements. In contrast, Chintagunta and Desiraju focus on the strategic interactions among multimarket competitors in several countries. Second, the antidepressant category that Chintagunta and Desiraju examine does not have significant DTC expenditures for the data duration of their study. In contrast, we investigate two different product categories (antihistamines and antivirals) that have various degrees of DTC investment. Consequently, we account for the effects of DTC. Third, we explicitly include carryover effects of marketing activities in the analysis.
Two other related studies are also worth noting: those of Rosenthal and colleagues (2002) and Wosinska (2002). Both studies consider DTC's impact on demand. Rosenthal and colleagues find that 9% to 22% of category growth can be attributed to DTC. However, the focus of their study is not on profitability implications; furthermore, because of data limitations, they treat promotional spending as a simple flow and do not consider carryover effects. Wosinska uses a large panel of insurance prescription claims (Blue Shield of California) for cholesterol-lowering drugs and empirically makes two observations: First, DTC affects only the market shares of drugs on the formulary (i.e., the approved drug list); even for such drugs, the marginal impact of DTC on demand is lower than the marginal impact of detailing. Second, as we do, Wosinska finds that the impact of DTC is lower than that of detailing. In addition, we estimate the interaction between DTC and detailing and the profitability implications. We now discuss the various elements of our model.
In discussing the model, we refer to the antihistamines product category for expositional convenience. We decompose the sales of each of the focal brands in a given period as the product of the category sales and the share of that brand. Category sales denote the number of prescriptions of all antihistamines (for both the focal brands and other antihistamines) in that period. Category sales are a function of category-level marketing activities and variables that account for product diffusion over time. The conditional share equation reflects the share of each of the three focal brands and a fourth brand that represents all the other antihistamines available in the market. We use a linear sales model for category sales and a discrete choice model for brand share. This hybrid model is appropriate for various reasons, which we discuss subsequently.
First, the category-level sales specification enables us to model category-level diffusion and the marketing activities role in the process. It has been suggested in prior research (Wosinska 2002) that marketing activities such as detailing and DTC play a different role in category sales and brand switching. A goal of our study is to investigate the differential effects; in particular, we aim to explore the hypotheses that DTC is the primary marketing activity that drives category expansion and that detailing is the primary marketing activity that drives brand switching.
Second, a discrete choice model is particularly appropriate for modeling brand shares in our problem because physicians make a discrete choice from the available drugs. Our discussions with industry experts revealed that there are virtually no cases of multiple brands of antihistamines being prescribed simultaneously because they are considered substitutes. Furthermore, the models are parsimonious. This is important because we are interested in modeling interaction effects. If we were to introduce all possible interactions between different marketing variables and for different brands, the dimensionality of the problem would make alternative models, such as linear sales models, extremely difficult to estimate with the available data.
In addition, we need to accommodate time-varying choice-sets. Initially, there was only one drug in the category: Claritin. Subsequently, Zyrtec and Allegra were introduced. The mixed-logit discrete choice specification that we use can easily account for such changes in the choice set. Most alternative models cannot account for this without having regime-specific effects (e.g., when there is one brand or two brands) in addition to all the other estimated parameters.
The preceding are our various reasons for employing a hybrid model to explore the issues at hand. We subsequently describe the category sales and conditional share models.
Category Sales Model
Let Q[subjt] denote brand j's sales in month t, where j = 1, 2, ..., K + 1, the first K brands are the focal brands of interest, and the K + 1 brand is the all-other brand.. We determine category sales by aggregating the sales of all brands. The relationships between brand sales, category sales (CQ[subt]), and share (S[subjt]) are given by the following:
( 1) [Multiple line equation(s) cannot be represented in ASCII text]
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
The category sales level depends on the prices, detailing, DTC, and OMEs of all brands in the category. In addition, it depends on factors such as seasonality (SD[sub1] and SD[sub2]).( n6) To capture possible diffusion effects due to the introduction of new brands and category growth, we include linear and quadratic time trends (t and t²). The category sales regression model is given as follows:
( 3) ln (CQ[subt]) = ρ[sub0] + ρ[sub1]CD][subt] + ρ[sub2]CA[subt] + ρ[sub3]CP[subt] + ρ[sub4]COME[subt] + ρ[sub5]CDP[subt] + ρ[sub6]CDA[subt] + ρ[sub7]CDOM[subt] + ρ[sub8]CAP[subt] + ρ[sub9]CAOM[subt] + ρ[sub10]COMP[subt] + κ[sub1]SD[sub1] + κ[sub2]SD[sub2] + τ[sub1]t + τ[sub2]t² + e[subt].
The category stocks of goodwill associated with detailing, DTC, and OMEs (e.g., meetings) are denoted by CD[subt], CA[subt], and COME[subt], respectively. The Verispan data we use in the empirical analysis reports a category price for each period, which is the share-weighted price of the individual brands. We denote this price as CP[subt]. The interaction terms between detailing and price, DTC, and OMEs are denoted by CDP[subt], CDA[subt], and CDOM[subt], respectively. Similarly, CAPt and CAOM[subt] represent interaction terms between DTC and price and OMEs, respectively, and COMP[subt] represents the interaction term between OMEs and price. Thus, we account for all possible paired interaction effects.( n7) Finally, in Equation 3, e[subt] is the random error term and ρ[sub0] - ρ[sub10], κ τ[sub1], and τ[sub2] are parameters to be estimated.( n8)
We employ the standard Nerlove Arrow (1962) exponential decay goodwill model (see also Lilien, Kotler, and Moorthy 1992, p. 280) for each brand. Let d[subjt], a[subjt], and ome[subjt] represent brand j's level of detailing, DTC, and OMEs, respectively, in period t. Thus, the jth focal brand's goodwill stocks in period t (D[subjt], A[subjt], and OME[subjt]) are
( 4) D[subjt] = θ[subD]D[subj, t = 1] + √d[subjt],
( 5) A[subjt] = θ[subA]A[subj,t = 1] + √a[subjt],
and
( 6) OMEjt = θ[subome] OME[subj, t = 1] + √ome[subjt],
where Θ[subD], Θ[subA] and Θ[subome] are the carryover coefficients for detailing, DTC, and OMEs, respectively, and the square root captures diminishing effects (Erickson 1992). We constructed the category stock variables for detailing, DTC, and OMEs exactly as we did for category detailing, DTC, and OME variables, respectively. Note that we could also have captured diminishing effects with quadratic terms or logarithms. A quadratic specification would involve estimation of a large number of additional parameters, particularly because of the interaction terms. A logarithmic specification is unable to deal with zeros in the variables. However, the square root specification avoids such problems and is appropriate for our analysis.
Conditional Share Model
We employ a mixed-logit formulation (see, e.g., Chintagunta and Desiraju 2004; McFadden and Train 2000) to specify the focal brand j's share in period t, denoted by S[subjt]:
( 7) [Multiple line equation(s) cannot be represented in ASCII text]
and for the all-other brand, denoted by S[subK + 1,t]:
( 8) [Multiple line equation(s) cannot be represented in ASCII text]
where
( 9) v[subjt] = α[subj] +γD[subjt] + ΔA[subjt] + ΩOME[subjt] + βP[subjt] + μ[sub1]D[subjt]P[subjt] + μ[sub2]D[subjt]A[subjt] + μ[sub3]D[subjt]OME[subjt] + μ[sub4]A[subjt]P[subjt] + μ[sub5]A[subjt]OME[subjt] + μ[sub6]OME[subjt]P[subjt].
where α = {α[sub1] .... αK} is the vector of brand-specific intrinsic preference, and α[sub(K + 1)] is normalized to zero; Β is the price sensitivity parameter; Pjt is the price of brand j in period t; D[subjt], A[subjt], and OME[subjt] are the stocks of goodwill defined in Equations 4 6; and γ, δ and ω represent the corresponding sensitivities, respectively. The parameters μ[sub1] - μ[sub6] capture the sensitivities to the interaction terms. We include both linear and nonlinear trend terms (t and t²)( n9) as a second-order approximation to a more general specification for diffusion of the new drugs in the market;( n10) ψ[sub1] and ψ[sub2] are the associated coefficients. The seasonal dummy variables are SD[sub1] and SD[sub2], and λ[sub1] and lambda;[sub2] are their associated coefficients.
Our rationale for introducing the term ε[subjt] in Equation 7 is as follows: There are several unobserved brand- and time-specific factors--potentially correlated with prices, detailing, and DTC--that could influence a brand's sales. These include the influence of organizations such as health maintenance organizations and other factors such as the publication of medical studies and newspaper articles about newly discovered side effects. All such factors are reflected in ε[subjt] and we assume that it is a mean zero term.
In Equation 7, we explicitly account for heterogeneity by assuming that the α[subj] parameters are draws from some unknown underlying distribution.( n11) We use α = {α[subj], j = 1, 2, ..., K} to denote the parameters of the share specification, f(a) to denote the joint density of the distribution of the parameters, and Z to denote the region of support of this mixing distribution that results in the choice of brand j.
This mixed-logit formulation does not suffer from the restrictive elasticities that are typically associated with the standard logit model, which is the main reason we use this specification. In addition to the mixed-logit model's allowing for more flexible substitution patterns, because it preserves the basic logit structure, it can be interpreted as the aggregation of choice probabilities of heterogeneous utility-maximizing agents.( n12) Note that we could have specified a mixing distribution on the other parameters as well (e.g., price sensitivity, detailing sensitivity). However, because of data limitations, we restrict ourselves to specifying this distribution only for the brand intercepts.
In this section, we discuss the data for the first category we study: second-generation antihistamines. A reason for our focus on antihistamines is the increasing use of DTC by leading firms in the category. Furthermore, industry experts indicate that in categories in which generics have been introduced, firms tend to reduce (if not discontinue) detailing activities. Therefore, to better understand the impact of detailing, it is critical to select a category in which generics have not yet accrued significant market shares. For these reasons, we focus on the marketing-mix decisions of three branded antihistamines: Claritin, Zyrtec, and Allegra.
Our data, obtained from Verispan, comprise observations on three brands of second-generation antihistamines and the fourth all-other brand, which is the aggregation across all other antihistamines (first-generation antihistamines and prescribed over-the-counter medications). The second-generation antihistamines perform much like the first-generation drugs (e.g., Chlor-Trimeton, Benadryl) but improve quality of life considerably as a result of improved side-effects profiles (e.g., less drowsiness).
The observations in the data are for the entire U.S. market, on a monthly basis, from April 1993 through March 2002. For each brand, the data set tracks the total prescriptions written, the average retail price of the prescription, detailing expenditures, DTC expenditures, and OME expenditures. Descriptive statistics for the data appear in Tables 2 and 3, and Figures 1 4 depict the time series of sales, detailing expenditures, DTC expenditures, and OME expenditures, respectively. Note that the timing of introduction of the three brands is as follows: Claritin in April 1993, Zyrtec in January 1996, and Allegra in August 1996.
Methodology
The category demand function in Equation 3 is a simple linear regression. We used the methods laid out in the work of Berry (1994) and Nevo (2000) to obtain estimates for the parameters of the brand-share function in Equation 7. We deflated prices using the consumer price index for all urban consumers. We deflated detailing expenditures using the wage series for all workers. We obtained the consumer price index and wage series from the Bureau of Labor Statistics (BLS).
Our price variable is the average retail price per prescription. We believe that this is a reasonable measure because, according to industry experts, physicians or patients often use price per prescription to compare medications.
Instruments
Our analysis assumes that firms prices, detailing, and DTC activities are endogenous in the category sales and brandshare models. Thus, we needed to instrument for these variables in the estimation because they could be correlated with the error terms. We used three sets of instruments in the estimation. The first set has variables that could potentially drive the costs of producing the drugs and includes the producer price index (PPI) for pharmaceuticals, obtained from BLS. This series captures the prices for bulk drugs and therefore a component of the costs faced by the marketers of brand name antihistamines. In the estimation, we allowed the instruments to influence prices of the various brands differentially by interacting them with the brand intercepts in the estimation. Furthermore, we used lagged values (up to 12 periods) of the PPIs interacted with the brand intercepts as additional instruments. Thus, we constructed 36 (12 - 3 = 36) instruments from the PPIs.
For detailing instruments, we compiled the data for the number of employees from the annual reports of the firms in our data set. Although firms report financial numbers to the Securities and Exchange Commission every quarter, they report the number of employees only annually. We assumed that the number of employees in any month was the same as for the year. Thus, we developed one instrument from the employee data.( n13) Finally, we used the PPIs for television, radio, and print advertising (which we obtained from BLS) as instruments for DTC. We also included interactions of the instruments for price, detailing, and DTC to construct the overall instrument matrix.( n14)
The Heterogeneity Distribution
We assumed that the set of parameters, α = {α[subj], j = 1, 2, ..., K}, is heterogeneous, and we specified a normal distribution to represent this heterogeneity. Because we assumed that brand preferences are not correlated, we had three mean parameters and three variance parameters to estimate. We could have allowed for a richer structure of heterogeneity, including the sensitivities to marketing activities and the interaction terms and correlations between different parameters; however, data limitations did not enable us to estimate the large number of parameters that such a specification would entail.
Carryover Parameters
The carryover parameters (Θ[subD], Θ[subA], and Θ[subome] could not be estimated and needed to be fixed. Our preliminary analysis with simple models that allow for the estimation of these parameters revealed that carryover parameters were 86%, 75%, and 92% for detailing, DTC, and OMEs, respectively. For our estimation, we fixed the carryover rates at these values.( n15) The rates seem to be consistent with other findings in the literature. For example, Roberts and Samuelson (1988, Table 2) report advertising retention rates of greater than 80% in the cigarette industry, and Berndt and colleagues (1997) report a carryover rate of 85% for advertising in the antiulcer pharmaceutical market.( n16)
Standard Errors
We computed the heteroskedasticity and autocorrelation consistent standard errors using Newey and West's (1987) estimator.
This section is divided into several subsections that are organized as follows: We begin by discussing the parameters of the demand function. We then discuss the single-and multiperiod ROI measures for DTC and detailing. We next discuss the impact of marketing-mix interactions on ROI and consider the impact of the interactions on price elasticity. Finally, we present the results from the antiviral category.
Parameters of the Demand Function
In Table 4, we provide the parameter estimates and the standard errors from the category sales regression model in Equation 3. Note that in this model, we explicitly account for the endogeneity in price, detailing, and DTC stock variables by using PPIs for pharmaceuticals as instruments for prices; employees in all the three firms in the category as instruments for detailing; and PPIs for radio, television, and print advertising as instruments for DTC. We find that after controlling for the two seasonal effects and the diffusion effects through the time trend, only DTC has a statistically significant effect. This finding is consistent with previous research (Wosinska 2002). Table 4 also indicates that none of the marketing-mix interactions are statistically significant. We find that the two seasonal dummy variables have significant effects, which reflects the strong seasonality in the category as a whole.
We report the estimates for the parameters of the conditional share model, from Equation 7, in Table 5.( n17) We find that Claritin is the most preferred brand, followed by Zyrtec and Allegra, in that order. The parameter estimates for the main effects of price, detailing, and DTC are statistically significant and have the expected signs. Detailing and price are significant at the 95% level, and DTC is significant at the 90% level. However, the parameter for the direct effect of OMEs is insignificant. These results, together with the ones for the category sales model (Table 4), reveal a notable contrast. Although detailing has a significant effect on brand switching but not category sales, DTC has significant effects both on category sales and on brand switching.
It is worthwhile to compare these findings with ones from previous empirical research. Rosenthal and colleagues (2002) find that detailing and DTC affect only category sales (not brand share); furthermore, DTC has a greater impact than detailing on category sales. Wosinska (2002) finds that both DTC and detailing affect brand share, but the impact of detailing is greater than that of DTC. As does Wosinska (2002), we find that detailing has a greater effect on brand switching than DTC.
We find that there are four interaction effects that are significant at the 95% level: the interactions between ( 1) detailing and price, ( 2) detailing and DTC, ( 3) detailing and OMEs, and ( 4) DTC and OMEs. In addition, the interaction between OMEs and price is significant at the 90% level. The interactions between detailing and price, between detailing and OMEs, and between DTC and OMEs are all negative. The interactions between OMEs and price and between detailing and DTC are positive.
The positive interaction between detailing and DTC implies that there is synergy between the two marketing activities. In other words, a physician sales call (which usually emphasizes the therapeutic benefits of a brand) has a greater impact when combined with the brand's television or print advertisement (which induces consumers to ask their physician about it). In the context of pharmaceutical promotional expenditures, to our knowledge, this is the first time that such a positive synergy has been documented.
In contrast, the finding of significant, negative interactions between OMEs on the one hand and DTC or detailing on the other hand points to the lack of synergy between these pairs of activities. Coupled with the finding of no significant main effect for OMEs, it appears that OMEs influence on the demand for the three antihistamines is limited.
The set of interactions between price and detailing and between price and OMEs is also notable. As we noted previously, the detailing x price interaction is negative and significant. Because the main effect of price is negative, a negative interaction implies that greater detailing increases price sensitivity. In turn, this implies that more detailing reduces the prices that firms can charge for their drugs. The OMEs x price interaction is significant at the 90% level but, in contrast to the detailing x price interaction, is positive. This implies that greater amounts of OMEs reduce price sensitivity and therefore enable firms to charge a higher price for their drugs. The interaction between price and DTC is not statistically significant.
Next, to understand the joint impact of main and interaction effects of the promotional expenditures, we compute the short-term promotional elasticities. For example, by "short-term detailing elasticity," we refer to the percentage change in current-period sales (prescriptions written, in our case) for every percentage change in detailing investments. These elasticities enable us to compare the estimated magnitudes of the coefficients of detailing and DTC investments for the three brands. We compute the elasticities by simulating the new sales of each brand by varying the respective promotional variable by 1%. In principle, when a variable changes for a particular brand, it also changes for the category as a whole. Therefore, the elasticities incorporate the effects through the brand-share model and the category model. Detailing share and DTC elasticities are reported in Table 6.
Inspection of the elasticities reveals that, overall, detailing has a much greater impact on shares than does DTC.( n18) Furthermore, Allegra's detailing has the greatest negative effect on the shares of the other two brands, and Zyrtec's detailing investments have the least effect. Although the DTC own-elasticity for Claritin is the lowest of the three brands, its DTC cross-elasticities are higher than those of the other brands. Thus, although the own-effect of Claritin's DTC is low, it is still able to affect the other brands shares more than they affect its share.
This pattern of elasticities implies that the nature of competition across brands is highly asymmetrical. For example, the first entrant (Claritin) is able to affect sales of the other brands negatively through DTC investments, but it is not as susceptible to the other brands DTC investments. In the case of detailing investments, Allegra has the greatest effect on the sales of the other brands, and the effect of Claritin is smaller but comparable. Coupled with the higher intrinsic preference for Claritin (Table 5), its high cross-elasticities imply that the brand is in a strong position in the antihistamine marketplace. Allegra's detailing has a large impact on other brands, but its DTC has a relatively small impact. In contrast, the effect of Zyrtec's detailing and DTC investments on the sales of the other brands is relatively small. Nevertheless, that Zyrtec can influence its sales through promotional investments is good news for the brand; the high own-elasticities suggest that it is able to influence its own sales by stealing share from the all-other group that we included in the analysis.
ROI Measures
We now turn to short-term or single-period ROI, which is the revenue impact of the marginal dollar spent on a particular promotional activity:
( 10) [Multiple line equation(s) cannot be represented in ASCII text]
where g is the promotional activity and G is the goodwill stock associated with the promotional activity (i.e., d and D denote detailing, and a and A denote DTC), p is the price, Q is the sales, CQ is the category sales, and S is the share of the respective brand. We also computed the ROIs by simulation; we varied the respective promotional activity by one dollar and simulated the new sales. Table 7 presents the current-and multiperiod ROIs.( n19) Note that these ROIs are in terms of revenues that correspond to retail prices. Thus, actual revenues for the manufacturing firm would be lower because the revenues would be net of retail and wholesale margins. This must be kept in mind during discussions of the ROI numbers.
Table 7 reveals that average current-period detailing ROIs are $1.28 for Allegra, $1.49 for Claritin, and $1.10 for Zyrtec. Thus, the marginal $1 spent on detailing returns more than $1 in revenue. This finding supports an increase in the level of detailing, except if it induces competitive reaction that raises rival detailing expenditures and makes the increased spending not pay out. We find that the ROIs for DTC are lower than the ROIs for detailing and less than $1. Specifically, the average current-period ROIs are $.85 for Allegra, $.66 for Claritin, and $.76 for Zyrtec. That ROIs are less than $1 suggests that firms would be better off reducing their expenditure on this marketing activity. However, as we discuss in the next paragraph, to obtain a more complete picture of investment returns, both single-and multiperiod ROIs should be assessed.
Current-period ROIs ignore that effects of marketing investments last more than one period. Indeed, in our specification, there is carryover of 86%, 75%, and 92% for detailing, DTC, and OMEs, respectively. When a firm raises its promotional expenditure by one dollar, that one dollar affects goodwill not only for the current period but also for future periods. When this intertemporal linkage is taken into account, a multiperiod ROI needs to be reported.
We used the following procedure to compute a multiperiod ROI: For a given set of values of all the independent variables, we computed the expected value of the dependent variables (shares, category sales, and consequent revenues). Then, we raised a given promotional expenditure for a single focal period by one dollar and computed the new revenues in the current and subsequent periods (under the assumption that competitors do not react to such an increase in expenditure). We repeat this experiment for all periods (except the last 11 periods) and report the multiperiod ROI as the average change in the sum of revenues for the current and 11 subsequent periods for every additional one dollar spent on that promotional activity in the current period. That is, multiperiod ROI represents the change in revenues for a year from the period in which the promotional activity changes. These ROIs are also reported in Table 7 for both detailing and DTC. We note that long-term ROIs for detailing and DTC are much higher than for single-period ROIs (Table 7). Thus, detailing continues to have a greater effect than DTC, even over a longer horizon.
It is worth comparing our findings with those in previous studies that report ROIs for detailing and DTC. Neslin (2001) reports (multiperiod) ROIs of $1.72 for detailing and $.19 for DTC. These are considerably lower than the ROIs in our study. However, when we compare our ROIs with those reported for large categories (i.e., categories with median brands that have revenues of more than $200 million per year; under this definition, antihistamines are a large category), our results are more comparable. Neslin reports the detailing ROI for large categories launched in the 1994 1996 period to be $6.76; for large categories launched after 1996, the detailing ROI was $10.29. These are comparable to the ROIs in our study (between $7.65 and $9.19). However, we find that ROIs for DTC are much higher than even those that Neslin reports for large categories: $1.37 for large categories launched after 1996. This could reflect the antihistamine category's status as a poster child for DTC.
Similar conclusions can be drawn from a comparison of our results with those of Wittink (2002). Our detailing ROIs are consistent with those in Wittink's study (for the large brands introduced in 1998 2000), but the ROIs for DTC are much larger in our case.( n20) This contrast underscores the importance of examining different submarkets of the industry and parsimoniously accounting for category-expansion, share-stealing, and carryover effects to grasp the revenue impact of promotional investments fully.
It is useful to note three other studies that examine ROIs for advertising in nonpharmaceutical categories. Lambin (1970) studies a mature category of consumer durables in Europe and finds that ROI for advertising is $1.25 (i.e., the incremental $1 causes revenues to increase by $1.25). Horsky (1977) finds that the total incremental gross margins over the course of a year due to an incremental $1 spent on advertising are $1.77 without discounting, $1.60 with discounting, and $1.04 for the current period. Peles (1971) reports a current-period incremental gross margin of $.77 and a long-term incremental gross margin of $1.70 for every incremental $1 spent on advertising. Our results are similar to the ones in these studies in that an additional $1 spent on advertising raises revenues by more than $1.
Impact of Marketing-Mix Interactions on ROI
Because our focus is interaction effects, we assess the impact of all the significant interaction effects on ROIs for detailing and DTC. All interaction effects except that between price and DTC are significant at least at the 90% level. We assessed the impact of these interactions on ROIs by determining the effects of changes in price, OMEs, and DTC on detailing ROIs and those of detailing and OMEs on ROIs for DTC. For example, to study the impact of the interaction effect between detailing and price, we varied price by 5% to determine the effect that change has, for example, on detailing ROIs. We implemented the price variation by simulating the category sales and brand shares using the category and share models at 95% and 105%, respectively, of the actual price levels. Similarly, we analyzed the effect of DTC on detailing ROIs and price and detailing on ROIs for DTC. We report these results in Tables 8 and 9.
An examination of these results reveals that compared with the other variables, price has a much greater impact on detailing ROIs. A 5% increase in price decreases detailing ROIs by 16% to 21% for the three brands. Conversely, a 5% decrease in price increases detailing ROIs by 15% to 23%.
Detailing and DTC both have a positive effect on each other's ROI; that is, increased DTC causes detailing ROI to increase, and vice versa. Detailing ROI increases by 3% to 4% with a 5% increase in DTC, and it decreases by a similar amount when DTC decreases by 5%. The decrease in ROI for DTC is between 6% and 16% for a 5% decrease in detailing, and the ROI for DTC increases by 9% to 12% with a 5% increase in detailing.
A notable result is that detailing has a greater effect on ROIs for DTC than vice versa. This is because the interaction effect between detailing and DTC constitutes a greater component of the total DTC effect than of the total detailing effect (because the main effect of DTC is much less than the main effect of detailing). Thus, the interaction effect plays a bigger role for ROIs for DTC.
In addition, OMEs have a relatively small effect on detailing ROIs but a much greater effect on ROIs for DTC. A 5% increase in OMEs causes detailing ROIs to decrease by 3% to 5%. There is a similar percentage increase in detailing ROIs if OMEs decrease by 5%. However, ROIs for DTC decrease by 14% to 28% when OMEs increase by 5%. When OMEs decrease by 5%, ROIs for DTC increase by 12% to 23%. The different magnitudes of effects on ROIs for detailing and DTC reflect that the interaction effect is a much greater proportion of ROIs for DTC than for detailing.
In summary, note that price has a large, negative effect on detailing ROI, larger than effects due to OMEs. Next, detailing has a greater effect on ROIs for DTC than vice versa. Here, we have documented not only the synergy between detailing and DTC but also the asymmetrical effects on firms revenues. In addition, OMEs have a greater (negative) impact on ROIs for DTC than for detailing.
It is worthwhile to note that an interaction effect exists between DTC and detailing on brand sales simply because DTC has a significant effect on category volume and because detailing has a significant effect on brand shares. Thus, even in the absence of any explicit interaction effect in the brand-share model, we would still observe an implicit interaction effect. Higher DTC would increase category volumes; thus, detailing ROI would be affected even if the explicit interaction were to be shut off. To investigate this, we conducted a simulation in which we set the explicit interaction terms in the category and brand-share models to zero and computed the change in detailing ROIs for a 5% change in DTC. The results are reported in Table 10. Note that because there is no explicit interaction in the share model for this simulation, the absolute levels of these ROIs are different from those reported in Table 7. In particular, note the percentage changes in ROIs with 5% change in DTC: We find that a 5% increase in DTC causes detailing ROIs to increase by approximately .7% to 1.2%. A 5% decrease in DTC causes detailing ROI to increase by 1% to 3.5%. This is a notable result because it demonstrates a unique synergistic effect in pharmaceutical categories: DTC drives category volume by leading patients to talk to their physicians, and detailing then steals share by inducing physicians to prescribe the focal drug.
Impact of Marketing-Mix Interactions on Price Elasticities
We have discussed the implications of the interaction effect between price and the marketing variables on ROIs. However, the two significant interaction effects between price on the one hand and detailing or OME on the other hand also have implications for price elasticities. We begin this discussion by recalling the debate in the literature on advertising's effect on price elasticities (see Kaul and Wittink [1996], who report empirical evidence for both positive and negative effects of advertising on price elasticities). We explore these interactions using an approach similar to the one used for analyzing the interaction effects on ROIs. We vary detailing and OMEs for the focal brand by 5% and record the change in the price elasticities. These results are reported in Table 11.
We find that greater detailing increases price elasticities. A 5% increase in detailing increases price elasticities by 1.5% to 2%. A 5% decrease in detailing similarly decreases price elasticities by approximately 1.5% to 2%. The interaction effect between OMEs and price has the opposite sign of that between detailing and price. Thus, an increase in OMEs causes price elasticity to decrease. For example, if OMEs increase by 5%, price elasticities decrease by approximately 1.5% to 2.5%. There is a similar increase of 1.5% to 2.5% in price elasticities when OMEs decrease by 5%. These results can have important implications for firms pricing policies (see, e.g., Managerial Implications herein).
Results from the Antivirals Category
The results we have reported are for the antihistamine category. Although there are likely to be differences between categories in returns on promotional activities, it would be useful to determine whether our key results on ROI are specific to the category under study or whether the results can be applied more generally. Of specific concern is that the antihistamine category has an extremely high level of DTC among all therapeutic categories. Therefore, we conducted an identical analysis for the antivirals category in which DTC expenditures are nonzero but still are much smaller than those in the antihistamine category.
Although genital herpes is an incurable disease, it can be controlled and its symptoms mitigated through the use of specific antiviral drugs. The first drug to be introduced in this category was Glaxo Wellcome's Zovirax (generic: acyclovir), which is available both in tablet and topical (cream) forms (Glaxo Wellcome subsequently merged with SmithKline Beecham to form Glaxo-SmithKline). SmithKline Beecham launched Famvir (generic: famciclovir) in July 1994 but sold it to Novartis in 2000 when Smithkline merged with Glaxo (to meet Federal Trade Commission requirements). The merged entity GlaxoSmithKline launched another drug in the category, Valtrex (generic: valacyclovir), in September 1995. In April 1997, Zovirax went off patent, and generic substitutes became available.
The highest-share brand in the category is currently Valtrex. Before its introduction, Zovirax was the market leader. Valtrex is the only brand that had significant DTC levels, whereas Famvir and Zovirax had almost none. The expenditure on detailing was relatively steady, and all brands have invested in detailing.
Our data set for the category consists of a monthly time series that extends over 11 years of sales, prices, and promotional expenditures for all brands in the category. We set Zovirax topical as the base brand for the brand-share model because, unlike the antihistamine category, there is no all other brand in the antiviral case.
We report the detailing elasticities for the antiviral category in Table 12. (Valtex is the only brand in the category that invested in DTC activity, and its current-period DTC elasticity is .1777.) All the elasticities have the expected signs, and their magnitudes are comparable to those for the antihistamine category. Furthermore, we report the ROIs for detailing and DTC in Table 13. The current-period detailing ROI is greater than one for all three brands in the category, which suggests that the marginal dollar spent on detailing returns more than one dollar in marginal revenues for all three brands in the category. The ROI for DTC is only reported for Valtrex, because it is the only brand in the category that invested in DTC. For Valtrex, the ROI for DTC is less than one, which suggests that the marginal dollar spent on DTC for the brand returns less than one dollar in marginal revenue in the current period. Although the magnitudes of the ROIs vary from those for antihistamines, the nature of results we obtained is nevertheless similar to the nature of results in the antihistamine category.
The multiperiod ROIs for detailing and DTC are also reported in Table 12. The nature of these multiperiod ROIs for detailing and for DTC is similar to that for antihistamines. Specifically, we find that the detailing and DTC ROIs are both greater than one in the long run. Although we were unable to conduct an analysis of the full set of interactions between pairs of marketing-mix elements, as we did in the antihistamine category, the interaction effects are consistent with those for that category. For example, detailing has a similar effect on price elasticity as it does in the case of antihistamines. A 5% reduction in detailing causes price elasticities to decrease by 1.34% for Famvir, .91% for Valtrex, and 2.07% for Zovirax. A 5% increase in detailing similarly causes price elasticities to increase by 1.31%, .9%, and 2.01%, respectively. Thus, greater detailing leads to higher elasticities and thus lower prices.
This article's purpose has been to explore the interactions between pairs of marketing-mix elements and how the interactions affect ROI. Accordingly, our econometric analysis identified several statistically significant interactions and estimated the impact of the interactions on ROI. Although we have reported and discussed our results, here we reiterate the four results that we deem the central takeaways of the analysis.
First, our analysis finds that the interaction between price and detailing is negative. Because a higher price adversely affects demand, the negative interaction implies that at higher levels of detailing, the demand is even more sensitive to higher prices. A manager who employs higher detailing levels faces a downward pressure on prices. We believe that this finding counters the often-repeated tenet, marketing tends to raise prices by (artificially) differentiating the products. Contrary to such expectations,, detailing efforts seem to reduce differentiation. However, it must be noted that there are many other factors that drive managers pricing decisions, such as firms objectives for recovering research and development costs, health maintenance organizations formulary decisions, and other intermediaries policies. Thus, pricing policy may not always reflect the interaction effect that we have identified.
Second, a brand's expenditures on detailing and DTC can affect sales in two ways: by affecting product category sales and by affecting the brand's share of category sales. Our analysis reveals that whereas DTC has a significant effect on category sales, detailing does not. In contrast, both detailing and DTC affect brand shares, and we find that detailing has a much greater effect than DTC. The implication is that when managers want to drive up category volume, perhaps in the early stages of a product's life cycle, they should consider allocating a substantial portion of the marketing budget to DTC. In contrast, in the more mature phases of the drug life cycle (but before the entry of generics), it seems beneficial for them to use a mix of detailing and DTC, with a greater emphasis on detailing.
Third, when making resource allocation decisions, it is important for managers to consider synergies among the various marketing investments. In this context, two of our findings are particularly relevant: ( 1) the positive interaction between DTC and detailing and ( 2) the negative interaction between OMEs and either DTC or detailing. Industry observers often question the value of pharmaceutical manufacturers increased DTC spending, particularly when studies such as those of Neslin (2001) and Wittink (2002) show relatively poor returns on DTC. However, the positive interaction that we identified implies that higher levels of DTC may indeed make detailing more effective. These two expenditures work hand-in-hand, and managers can generate an effect that is greater than the sum of its parts by using these marketing-mix elements simultaneously. Next, the negative interaction between OMEs and detailing (or DTC) implies that managers approach OME resource allocation more carefully. It appears that OME spending jams the value of the other marketing-mix elements. In this case, managers are well advised to limit the overlap between OMEs and either DTC or detailing, perhaps by targeting different segments (or groups) of physicians with OMEs or by separating OMEs and DTC (or detailing) temporally.
Fourth, our analysis developed both short-and longterm ROI benchmarks for specific subcategories of pharmaceuticals. Recall that unlike our study, which focuses on one category of pharmaceuticals at a time, both Neslin (2001) and Wittink (2002) approach ROI estimates by aggregating various (categories of) drugs. However, the commonality among the studies (including ours) is that the ROI for DTC is less than that for detailing; although the exact numerical values vary from study to study, the pattern seems to be fairly robust. We also provide explicit long-term ROI estimates and find that long-term ROIs are approximately four to seven times single-period ROIs. We note that an increase in detailing has a greater impact on ROIs for DTC than vice versa. Finally, although there are individual differences between the antihistamine and antiviral categories, our results for both categories suggest that detailing ROIs are higher than DTC ROIs.
The main implication is that whereas the ROI estimates developed for a given product-market may or may not apply to other categories, it is relevant for managers to identify and understand the underlying drivers of ROI. In this article, we endeavored to highlight the role of interactions in some detail. Research that explores other drivers of financial performance of marketing variables is certainly warranted.
We opened this article with the objective of addressing two issues: ( 1) the empirical characterization of the interactions between pairs of marketing-mix elements and ( 2) the development of benchmarks for the impact of promotional expenditures (with a particular focus on detailing and DTC expenditures) on both short-and long-term ROIs.
Using monthly observations from April 1993 to March 2002, we examined market shares, prices, and promotional expenditures of three brands (Claritin, Zyrtec, and Allegra) in a subcategory of pharmaceuticals. We focused on this particular category because of the high expenditures on DTC and because generic products have not yet appeared in the market for second-generation antihistamines. To provide a robustness check, we repeated our analysis on a second category: antivirals to treat genital herpes.
Our findings reveal that detailing primarily affects the brand share positively; in contrast, DTC has a significant, positive effect on both brand share and category sales. Detailing ROIs are greater than DTC ROIs. Furthermore, there is a synergy between the effects of the two promotional variables on brand share. We find that changes in detailing have a greater impact on DTC ROI than vice versa (i.e., changes in DTC have a smaller impact on detailing ROI). In addition, we find significant interaction effects between price and promotional expenditures, and we quantify the impact of these interactions on detailing and DTC ROIs. Next, long-term ROIs are several times greater than single-period ROIs; for example, for DTC, they are approximately five times greater.
A feature of DTC is that it is less targeted than detailing; that is, whereas the detailing activity ensures that the target physician is appropriately informed, DTC does not have the power to ensure a 100% reach to target (potential) patients. Consequently, it may not be surprising that DTC ROI is less than that of detailing. However, what is more surprising is the industry's increasing expenditures on DTC despite the relatively poor short-term returns. Our analysis suggests that a positive interaction between DTC and detailing may be a reason for increased expenditures; however, other potential reasons for the expenditures are worth exploring.
Therefore, further research should explore alternative paths by which DTC affects sales and should consider measuring the impact of different media used in DTC. Further research will also help characterize the extent of strategic interactions (i.e., levels of competition and cooperation) among brands. These issues underscore the importance of investigating firms optimal budget allocation problems. Overall, our analysis provides a valuable benchmark for further research, and as our brief discussion suggests, much work remains to be done in this important area of marketing.
The authors are listed in reverse alphabetical order and contributed equally to the project. Partial funding for this research was provided by the Kilts Center at the University of Chicago, the College of Business Administration at the University of Central Florida, and the Marketing Science Institute. The authors thank the participants at the Marketing Science Institute conference on marketing metrics in Dallas and at the Marketing Science Conference in College Park, Maryland; Don Lehmann; Marta Wosinska; and the anonymous JM reviewers for their valuable comments and suggestions.
( n1) From the 1970s to the early 1990s, the pharmaceutical industry communicated with consumers mainly through nonbranded public service announcements. During that period, the FDA required that DTC advertising mention a specific indication for a brand name drug and include a brief summary of side effects and adverse reactions from the FDA-approved label. In 1997, the FDA relaxed restrictions on the content of DTC, essentially making the advertisements considerably less expensive to broadcast.
( n2) These include interactions between meetings or events and detailing and meetings or events and DTC.
( n3) The study examines two other promotional variables: ( 1) professional and medical journal advertising and ( 2) physician meetings and events.
( n4) The study also finds that the ROI for journal advertising is $5.00, the highest among all promotional variables.
( n5) Wittink (2002) also considers three therapeutic categories (hypertension, asthma, and arthritis) and examines the impact of promotions without distinguishing between brand-size and launch date. The results for these categories are similar to those that he finds for the difference between the ROIs of detailing and DTC.
( n6) Note that we use SD1 = 1 for March June (the spring allergy season) and SD2 = 1 for September October (the fall allergy season).
( n7) We do not explore higher-order interactions because the data are insufficient to obtain precise estimates for such effects.
( n8) Note that category prices, detailing, and DTC are likely to be endogenous (i.e., correlated with the error term). We address this issue by using instruments for these variables.
( n9) Note that t denotes the time, in months, since the introduction of the first of the three brands under consideration and captures the time trend in the share growth (decay) of the focal brands in the market compared with that of all-other brands. If we change this to a variable specific to each drug based on the time of its introduction, only the estimates of the brand-specific intercepts are affected.
( n10) We also included higher-order terms, but they did not affect the results significantly.
( n11) The variation in the parameters could be due to various reasons, including cross-sectional heterogeneity, by which the preferences and sensitivities to marketing activities could vary.
( n12) For a more detailed discussion of how this mixed-logit approach allows for flexible substitution patterns, see, for example, Nevo (2000) and Berry, Levinsohn, and Pakes (1995).
( n13) Note that the firms in the data set are Schering-Plough (maker of Claritin); Aventis (maker of Allegra), which was formed by the merger of Rhone-Poulenc and Hoechst (thus, for earlier periods, we used the sum of employees for the two firms); and Pfizer (maker of Zyrtec), for which we added Warner-Lambert's figures before it merged with Pfizer.
( n14) We also conducted two additional sets of analysis. First, we conducted a robustness check using another set of instruments that contained promotional expenditures in the nasal steroid category. There are common underlying factors that drive promotional spending in both categories. In addition, regulatory factors, seasonality, and other institutional factors are similar between the two categories. Thus, the nasal steroid promotional activities are likely to be correlated with those in the antihistamine category but uncorrelated with the error term. Second, we also compared predictions for a holdout sample with the sets of instruments. The overall results are consistent; all these analyses are available on request from the authors.
( n15) We found the parameters using a grid search for a simple aggregate logit model with an outside good to represent category growth. The aggregate logit model reduces to a linear regression, and we chose the parameters with the highest R2. Such a gridsearch method is a fairly standard way of estimating the carryover parameters. For example, Wittink (2002) uses a grid search to find the carryover parameter; similarly, Guadagni and Little (1983) use a grid search to find the carryover for the loyalty smoothing parameter. Berndt and colleagues (1997) also use a grid-search methodology similar to ours, in which we estimate the model for various values of carryover and choose the set of parameters for which the sum of squared residuals is the lowest. Note that if we conducted this grid search for our full model, each iteration of the mixed-logit model would take several hours.
( n16) For further evidence on retention rates (including on monthly data), see Clarke's (1976) survey of results from more than 70 studies that use the distributed lag structure.
( n17) Note that we instrumented for all endogenous variables in this estimation, and the "base brand" is the all--other brand that has been included in the analysis.
( n18) These results appear to be largely consistent with those reported by Wosinska (2002), who used individual-level data. They are also consistent with Wittink's (2002) analysis of large brands (i.e., with revenues of more than $500 million) introduced in 1998 2000 (see "Literature Review" herein)). However, Wittink does not present explicit elasticity estimates.
( n19) We discuss how we computed the multiperiod ROIs later in this section.
( n20) In addition, our long-term ROIs are higher than those reported in the work of Wittink (2002) for the smaller brands (i.e., less than $500 million in annual revenues) and for the larger brands that were launched before 1998.
Legend for Chart:
A - Article
B - Interactions Examined
C - Effect
A B
C
Lemon and Nowlis (2002) Feature advertising x price
cut Display x price cut
Depends on price tier
Azoulay (2001) Advertising x sales force
Negative
Rizzo (1999) Advertising x price
Negative
Mela, Gupta, and Lehmann (1997) Advertising x nonprice
promotions
Positive
Gatignon and Hanssens (1987) Advertising x sales force
Positive
Parsons and Vanden Abeele (1981) Sales force x samples
Positive
Swinyard and Ray (1977) Advertising x sales force
Positive
Kuehn (1962) Advertising x product quality
Positive
Notes: The research listed here does not focus on interactions
that involve price. For research that focuses on price
x advertising interactions, see Kaul and Wittink (1996). Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
A B C
Prescriptions (000)
Allegra 922.88 510.42
Claritin 1364.97 652.32
Zyrtec 807.16 408.39
Total 5927.65 1913.32
Shares (%)
Allegra 16.23 7.23
Claritin 36.43 14.16
Zyrtec 13.30 4.98
All other 44.12 28.94 Legend for Chart:
A - Variable
B - Mean
C - Standard Deviation
A B C
Prices
Allegra 39.19 4.32
Claritin 48.32 4.98
Zyrtec 40.84 .95
Detailing ($ in millions)
Allegra 7.3342 2.9058
Claritin 6.8024 2.4725
Zyrtec 6.2402 2.0198
DTC ($ in millions)
Allegra 5.2632 4.1873
Claritin 5.5307 6.4225
Zyrtec 4.5424 4.5543
Meetings ($ in millions)
Allegra 1.2417 .8213
Claritin 1.0337 1.0706
Zyrtec 1.1230 .8789
Notes: Prices and expenditure on detailing, DTC, and meetings
are all expressed in constant January 1993 dollars. Legend for Chart:
A - Parameters
B - Estimate
C - Standard Error
A B C
Intercept 8.9681(**) .7245
Price -.0063 .0131
Detailing stock -.0093 .0560
DTC stock .0152(*) .0084
OMEs stock .0145 .0173
Detailing x price .0210 .0307
Detailing x DTC .0156 .0194
Detailing x OMEs -.0035 .0055
DTC x price -.0033 .0024
DTC x OMEs .0006 .0005
OMEs x price -.0004 .0004
Time -.0097 .0063
Time² 4.6976e-05 .0001
Spring season (SD[sub1]) .1452(**) .0309
Autumn season (SD[sub2]) .0859(**) .0267
(*) Significant at the 90% level.
(**) Significant at the 95% level. Legend for Chart:
A - Parameter
B - Mean Parameters Estimate
C - Mean Parameters Standard Error
D - Heterogeneity Parameters Estimate
E - Heterogeneity Parameters Standard Error
A B C
D E
Intercept: Allegra -4.0348(**) .3840
.1996(**) .0065
Intercept: Claritin -1.9550(**) .3885
.8174(**) .0034
Intercept: Zyrtec -3.9195(**) .3922
.2271(**) .0056
Price -.0977(**) .0215
Detailing stock .2853(**) .0295
DTC stock .1946(*) .1117
OMEs stock .0521 .0652
Detailing x price -.0061(**) .0010
Detailing x DTC .0185(**) .0051
Detailing x OMEs -.0114(**) .0056
DTC x price 4.4855e-05 .0034
DTC x OMEs -.0373(**) .0097
OMEs x price .0054(*) .0032
Time .0960(**) .0079
Time² -.0004(**) 4.4730e-05
Spring season (SD[sub1]) .1007(**) .0492
Autumn season (SD[sub2]) .1086(*) .0605
(*) Significant at the 90% level.
(**) Significant at the 95% level. Legend for Chart:
A - Change in Number of Prescriptions
B - Allegra
C - Claritin
D - Zyrtec
A B C D
Change in Detailing
Allegra .1772 -.0423 -.0311
Claritin -.0455 .0950 -.0369
Zyrtec -.0481 -.0436 .1440
Change in DTC
Allegra .0909 -.0352 -.0138
Claritin -.0151 .0543 -.0107
Zyrtec -.0205 -.0377 .0717 Legend for Chart:
A - Every Marginal $1 Spent
B - Additional Revenue ($) Allegra
C - Additional Revenue ($) Claritin
D - Additional Revenue ($) Zyrtec
A B C D
Detailing
Current period 1.2814 1.4923 1.1049
Multiperiod 8.8778 9.1920 7.6583
DTC
Current period .8498 .6597 .7567
Multiperiod 3.8073 3.6964 2.7861
Notes: Multiperiod indicates current period + 11 subsequent
periods. Legend for Chart:
A - Levels
B - Multiperiod Detailing ROI Allegra
C - Multiperiod Detailing ROI Claritin
D - Multiperiod Detailing ROI Zyrtec
A B C D
Price
Actual-5% 10.7303 10.5548 9.4172
(% +/- w.r.t. actual level) (+20.86%) (+14.83%) (+22.97%)
Actual level 8.8778 9.1920 7.6583
Actual +5% 7.0399 7.6658 6.0492
(% +/- w.r.t. actual level) (-20.70%) (-16.60%) (-21.01%)
DTC
Actual-5% 8.5476 8.8763 7.4055
(% +/- w.r.t. actual level) (-3.72%) (-3.43%) (-3.30%)
Actual level 8.8778 9.1920 7.6583
Actual +5% 9.2027 9.5736 7.9082
(% +/- w.r.t. actual level) (3.66%) (4.15%) (3.26%)
OMEs
Actual-5% 9.3413 9.5761 7.9043
(% +/- w.r.t. actual level) (5.22%) (4.18%) (3.21%)
Actual level 8.8778 9.1920 7.6583
Actual +5% 8.4306 8.9148 7.3882
(% +/- w.r.t. actual level) (-5.04%) (-3.02%) (-3.53%)
Notes: w.r.t. = with respect to; multiperiod indicates current
period + 11 subsequent periods. Legend for Chart:
A - Levels
B - Multiperiod DTC ROI Allegra
C - Multiperiod DTC ROI Claritin
D - Multiperiod DTC ROI Zyrtec
A B C D
Detailing
Actual-5% 3.5622 3.1028 2.4063
(% +/- w.r.t. actual level) (-6.44%) (-16.06%) (-13.63%)
Actual level 3.8073 3.6964 2.7861
Actual +5% 4.3066 4.1635 3.0309
(% +/- w.r.t. actual level) (+13.11%) (+12.64%) (+8.79%)
OMEs
Actual-5% 4.6725 4.3177 3.1277
(% +/- w.r.t. actual level) (22.72%) (16.81%) (12.26%)
Actual level 3.8073 3.6964 2.7861
Actual +5% 3.2809 2.6784 2.1916
(% +/- w.r.t. actual level) (-13.83%) (-27.54%) (-21.34%)
Notes: w.r.t. = with respect to; multiperiod indicates current
period + 11 subsequent periods. Legend for Chart:
A - DTC Level
B - Own-Price Elasticity Allegra
C - Own-Price Elasticity Claritin
D - Own-Price Elasticity Zyrtec
A B C D
Actual-5% 3.6943 1.2142 4.3062
(% +/- w.r.t. actual level) (-1.00%) (-3.53%) (-1.23%)
Actual level 3.7318 1.2586 4.3600
Actual +5% 3.7679 1.2672 4.4126
(% +/- w.r.t. actual level) (.97%) (.68%) (1.21%)
Notes: Effects pertain to category sales only; that is, the
interaction effect in the share model is turned off. w.r.t.
= with respect to. Legend for Chart:
A - Level
B - Own-Price Elasticity Allegra
C - Own-Price Elasticity Claritin
D - Own-Price Elasticity Zyrtec
A B C D
Detailing
Actual-5% -3.0081 -2.3308 -2.9330
(% +/- w.r.t. actual level) (-1.58%) (-1.76%) (-2.11%)
Actual level -3.0563 -2.3726 -2.9964
Actual +5% -3.1006 -2.4117 -3.0565
(% +/- w.r.t. actual level) (1.45%) (1.65%) (2.01%)
OMEs
Actual-5% -3.1045 -2.4117 -3.0695
(% +/- w.r.t. actual level) (1.58%) (1.65%) (2.44%)
Actual level -3.0563 -2.3726 -2.9964
Actual +5% -3.0078 -2.3342 -2.9249
(% +/- w.r.t. actual level) (-1.59%) (-1.62%) (-2.38%)
Notes: w.r.t. = with respect to. Legend for Chart:
A - Change in Number of Prescriptions
B - Change in Detailing Famvir
C - Change in Detailing Valtrex
D - Change in Detailing Zovirax
A B C D
Famvir .5216 -.1086 -.0371
Valtrex -.0885 .2980 -.0123
Zovirax -.1666 -.1092 .0735
Legend for Chart:
A - For Every Marginal $1 Spent
B - Additional Revenue($) Famvir
C - Additional Revenue($) Valtrex
D - Additional Revenue($) Zovirax
A B C D
Detailing
Current period 2.6786 1.6421 3.7380
Multiperiod 11.7172 8.1088 17.6368
DTC
Current period .6852
Multiperiod 2.8578
Notes: Multiperiod indicates current period + 10 subsequent
periods.GRAPH: FIGURE 1; New Prescriptions
GRAPH: FIGURE 2; Detailing Expenditures
GRAPH: FIGURE 3; DTC Expenditures
GRAPH: FIGURE 4; OMEs Expenditures
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~~~~~~~~
By Sridhar Narayanan; Ramarao Desiraju and Pradeep K. Chintagunta
Sridhar Narayanan is a doctoral student (e-mail: sridhar@uchicago.edu) Ramarao Desiraju is an associate professor and Ph.D. Program Coordinator, Department of Marketing, College of Business Administration, University of Central Florida (e-mail: rdesiraju@bus.ucf.edu). Pradeep K. Chintagunta is Robert Law Professor of Marketing (email: pradeep.chintagunta@gsb.uchicago.edu), Graduate School of Business, University of Chicago.
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Record: 134- Return on Marketing: Using Customer Equity to Focus Marketing Strategy. By: Rust, Roland T.; Lemon, Katherine N.; Zeithaml, Valarie A. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p109-127. 19p. 1 Diagram, 5 Charts, 3 Graphs. DOI: 10.1509/jmkg.68.1.109.24030.
- Database:
- Business Source Complete
Return on Marketing: Using Customer Equity to Focus
Marketing Strategy
The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm's customer equity relative to the incremental expenditure necessary to produce the change. The change in the firm's customer equity is the change in its curr ent and future customers' lifetime values, summed across all customers in the industry. Each customer's lifetime value results from the frequency of category purchases, average quantity of purchase, and brand-switching patterns combined with the firm's c ontribution margin. The brand-switching matrix can be estimated from either longitudinal panel data or cross-sectional survey data, using a logit choice model. Firms can analyze drivers that have the greatest impact, compare the drivers' performance with that of competitors' drivers, and project return on investment from improvements in the drivers. To demonstrate how the approach can be implemented in a specific corporate setting and to show the methods used to test and validate the model, the authors illustrate a detailed application of the approach by using data from the airline industry. Their framework enables what-if evaluation of marketing return on investment, which can include such criteria as return on quality, return on advertising, return o n loyalty programs, and even return on corporate citizenship, given a particular shift in customer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the greatest return.
Top managers are constantly faced with the problem of how to trade off competing strategic marketing initiatives. For example, should the firm increase advertising, invest in a loyalty program, improve service quality, or none of the above? Such high-level decisions are typically left to the judgment of the chief marketing or chief executive officers, but these executives frequently have little to base their decisions on other than their own experience and intuition. A unified, data-driven basis for making broad, strategic marketing trade-offs has not been available. In this article, we propose that trade-offs be made on the basis of projected financial impact, and we provide a framework that top managers can use to do this.
Although techniques exist for evaluating the financial return from particular marketing expenditures (e.g., advertising, direct mailings, sales promotion) given a longitudinal history of expenditures (for a review, see Berger et al. 2002), the approaches have not produced a practical, high-level model that can be used to trade off marketing strategies in general. Furthermore, the requirement of a lengthy history of longitudinal data has made the application of return on investment (ROI) models fairly rare in marketing. As a result, top management has too often viewed marketing expenditures as short-term costs rather than long-term investments and as financially unaccountable (Schultz and Gronstedt 1997). Leading marketing companies consider this problem so important that the Marketing Science Institute has established its highest priority for 2002-2004 as "Assessing Marketing Productivity (Return on Marketing) and Marketing Metrics." We propose that firms achieve this financial accountability by considering the effect of strategic marketing expenditures on their customer equity and by relating the improvement in customer equity to the expenditure required to achieve it.
Although the marketing concept has reflected a customer-centered viewpoint since the 1960s (e.g., Kotler 1967), marketing theory and practice have become increasingly customer-centered during the past 40 years (Vavra 1997, pp. 6-8). For example, marketing has decreased its emphasis on short-term transactions and has increased its focus on long-term customer relationships (e.g., Håkansson 1982; Storbacka 1994). The customer-centered viewpoint is reflected in the concepts and metrics that drive marketing management, including such metrics as customer satisfaction (Oliver 1980), market orientation (Narver and Slater 1990), and customer value (Bolton and Drew 1991). In recent years, customer lifetime value (CLV) and its implications have received increasing attention (Berger and Nasr 1998; Mulhern 1999; Reinartz and Kumar 2000). For example, brand equity, a fundamentally product-centered concept, has been challenged by the customer-centered concept of customer equity (Blattberg and Deighton 1996; Blattberg, Getz and Thomas 2001; Rust, Zeithaml, and Lemon 2000). For the purposes of this article, and largely consistent with Blattberg and Deighton (1996) but also given the possibility of new customers (Hogan, Lemon, and Libai 2002), we define customer equity as the total of the discounted lifetime values summed over all of the firm's current and potential customers.( n1)
Our definition suggests that customers and customer equity are more central to many firms than brands and brand equity are, though current management practices and metrics do not yet fully reflect this shift. The shift from product-centered thinking to customer-centered thinking implies the need for an accompanying shift from product-based strategy to customer-based strategy (Gale 1994; Kordupleski, Rust, and Zahorik 1993). In other words, a firm's strategic opportunities might be best viewed in terms of the firm's opportunity to improve the drivers of its customer equity.
Because our article incorporates elements from several literature streams within the marketing literature, it is useful to point out the relative contribution of the article. Table 1 shows the contribution of this article with respect to several streams of literature that influenced the return on marketing conceptual framework. Table 1 shows related influential literature streams and exemplars of the stream, and it highlights key features that differentiate the current effort from previous work. For example, strategic portfolio models, as Larreché and Srinivasan (1982) exemplify, consider strategic trade-offs of any potential marketing expenditures. However, the models do not project ROI from specific expenditures, do not model competition, and do not model the behavior of individual customers, their customer-level brand switching, or their lifetime value. Our model adds to the strategic portfolio literature by incorporating those elements.
Three related streams of literature involve CLV models (Berger and Nasr 1998), direct marketing-motivated models of customer equity (e.g., Blattberg and Deighton 1996; Blattberg, Getz, and Thomas 2001), and longitudinal database marketing models (e.g., Bolton, Lemon, and Verhoef 2004; Reinartz and Kumar 2000). Our CLV model builds on these approaches. However, the preceding models are restricted to companies in which a longitudinal customer database exists that contains marketing efforts that target each customer and the associated customer responses. Unless the longitudinal database involves panel data across several competitors, no competitive effects can be modeled. Our model is more general in that it does not require the existence of a longitudinal database, and it can consider any marketing expenditure, not only expenditures that are targeted one-to-one. We also model competition and incorporate purchases from competitors (or brand switching), in contrast to most existing models from the direct marketing tradition.
The financial-impact element of our model is foreshadowed by two related literature streams. The service profit chain (e.g., Heskett et al. 1994; Kamakura et al. 2002) and return on quality (Rust, Zahorik, and Keiningham 1994, 1995) models both involve impact chains that relate service quality to customer retention and profitability. The return on quality models go a step farther and explicitly project financial return from prospective service improvements. Following both literature streams, we also incorporate a chain of effects that leads to financial impact. As does the return on quality model, our model projects ROI. Unlike other models, our model facilitates strategic trade-offs of any prospective marketing expenditures (not only service improvements). We explicitly model the effect of competition-an element that does not appear in the service profit chain or return on quality models. Also different from prior research, our approach models customer utility, brand switching, and lifetime value.
Finally, we compare the current article with a recent book on customer equity (Rust, Zeithaml, and Lemon 2000) that focuses on broad managerial issues related to customer equity, such as building a managerial framework related to value equity, brand equity, and relationship equity. The book includes only one equation (which is inconsistent with the models in this article). Our article is a necessary complement to the book, providing the statistical and implementation details necessary to implement the book's customer equity framework in practice. The current work extends the book's CLV conceptualization in two important ways: It allows for heterogeneous interpurchase times, and it incorporates customer-specific brand-switching matrices. In summary, the current article has incorporated many influences, but it makes a unique contribution to the literature.
In the next section, on the basis of a new model of CLV, we describe how marketing actions link to customer equity and financial return. The following section describes issues in the implementation of our framework, including data options, model input, and model estimation. We then present an example application to the airline industry, showing some of the details that arise in application, in testing and validating our choice model, and in providing some substantive observations. We end with discussion and conclusions.
Conceptual Model
Figure 1 shows a broad overview of the conceptual model that we used to evaluate return on marketing. Marketing is viewed as an investment (Srivastava, Shervani, and Fahey 1998) that produces an improvement in a driver of customer equity (for simplicity of exposition, we refer to an improvement in only one driver, but our model also accommodates simultaneous improvement in multiple drivers). This leads to improved customer perceptions (Simester et al. 2000), which result in increased customer attraction and retention (Danaher and Rust 1996). Better attraction and retention lead to increased CLV (Berger and Nasr 1998) and customer equity (Blattberg and Deighton 1996). The increase in customer equity, when considered in relation to the cost of marketing investment, results in a return on marketing investment. Central to our model is a new CLV model that incorporates brand switching.
Brand Switching and CLV
It has long been known that the consideration of competing brands is a central element of brand choice (Guadagni and Little 1983). Therefore, we begin with the assumption that competition has an impact on each customer's purchase decisions, and we explicitly consider the relationship between the focal brand and competitors' brands. In contrast, most, if not all, CLV models address the effects of marketing actions without considering competing brands. This is because data that are typically available to direct marketers rarely include information about the sales or preference for competing brands. Our approach incorporates information about not only the focal brand but competing brands as well, which enables us to create a model that contains both customer attraction and retention in the context of brand switching. The approach considers customer flows from one competitor to another, which is analogous to brand-switching models in consumer packaged goods (e.g., Massy, Montgomery, and Morrison 1970) and migration models (Dwyer 1997). The advantage of the approach is that competitive effects can be modeled, thereby yielding a fuller and truer accounting of CLV and customer equity.
When are customers gone? Customer retention historically has been treated according to two assumptions (Jackson 1985). First, the "lost for good" assumption uses the customer's retention probability (often the retention rate in the customer's segment) as the probability that a firm's customer in one period is still the firm's customer in the following period. Because the retention probability is typically less than one, the probability that the customer is retained declines over time. The implicit assumption is that customers are "alive" until they "die," after which they are lost for good. Models for estimating the number of active customers have been proposed for relationship marketing (Schmittlein, Morrison, and Columbo 1987), customer retention (Bolton 1998), and CLV (Reinartz 1999).
The second assumption is the "always a share" assumption, in which customers may not give any firm all of their business. Attempts have been made to model this by a "migration model" (Berger and Nasr 1998; Dwyer 1997). The migration model assigns a retention probability as previously, but if the customer has missed a period, a lower probability is assigned to indicate the possibility that the customer may return. Likewise, if the customer has been gone for two periods, an even lower probability is assigned. This is an incomplete model of switching because it includes purchases from only one firm.
In one scenario (consistent with the lost-for-good assumption) when the customer is gone, he or she is gone. This approach systematically understates CLV to the extent that it is possible for customers to return. In another scenario (consistent with the migration model), the customer may leave and return. In this scenario, customers may be either serially monogamous or polygamous (Dowling and Uncles 1997), and their degrees of loyalty may vary or even change. We can model the second (more realistic) scenario using a Markov switching-matrix approach.( n2)
Acquisition and retention. Note that the brand-switching matrix models both the acquisition and the retention of customers. Acquisition is modeled by the flows from other firms to the focal firm, and retention is modeled by the diagonal element associated with the focal firm. The retention probability for a particular customer is the focal firm's diagonal element, as a proportion of the sum of the probabilities in the focal firm's row of the switching matrix. Note that this implies a different retention rate for each customer x firm combination (we show the details of this in a subsequent section). This describes the acquisition of customers who are already in the market. In growing markets, it is also important to model the acquisition of customers who are new to the market.
The switching matrix and lifetime value. We propose a general approach that uses a Markov switching matrix to model customer retention, defection, and possible return. Markov matrices have been widely used for many years to model brand-switching behavior (e.g., Kalwani and Morrison 1977) and have recently been proposed for modeling customer relationships (Pfeifer and Carraway 2000; Rust, Zeithaml, and Lemon 2000). In such a model, the customer has a probability of being retained by the brand in the subsequent period or purchase occasion. This probability is the retention probability, as is already widely used in CLV models. The Markov matrix includes retention probabilities for all brands and models the customer's probability of switching from any brand to any other brand.( n3) This is the feature that permits customers to leave and then return, perhaps repeatedly. In general, this "returning" is confused with initial "acquisition" in other customer equity and CLV approaches. The Markov matrix is a generalization of the migration model and is expanded to include the perspective of multiple brands.
To understand how the switching matrix relates to CLV, consider a simplified example. Suppose that a particular customer (whom we call "George") buys once per month, on average, and purchases an average of $20 per purchase in the product category (with a contribution of $10). Suppose that George most recently bought from Brand A. Suppose that George's switching matrix is such that 70% of the time he will rebuy Brand A, given that he bought Brand A last time, and 30% of the time he will buy Brand B. Suppose that whenever George last bought Brand B he has a 50% chance of buying Brand A the next time and a 50% chance of buying Brand B. This is enough information for us to calculate George's lifetime value to both Brand A and Brand B.( n4)
Consider George's next purchase. We know that he most recently bought Brand A; thus, the probability of him purchasing Brand A in the next purchase is .7 and the probability of him purchasing Brand B is .3. To obtain the probabilities for George's next purchase, we simply multiply the vector that comprises the probabilities by the switching matrix. The probability of purchasing Brand A becomes (.7 x .7) + (.3 x .5) = .64, and the probability of purchasing Brand B becomes (.7 x .3) + (.3 x .5) = .36. We can calculate the probabilities of purchase for Brand A and Brand B as many purchases out as we choose by successive multiplication by the switching matrix. Multiplying this by the contribution per purchase yields George's expected contribution to each brand for each future purchase. Because future purchases are worth less than current ones, we apply a discount factor to the expected contributions. The summation of these across all purchase occasions (to infinity or, more likely, to a finite time horizon) yields George's CLV for each firm. Note that if there are regular relationship maintenance expenditures, they need to be discounted separately and subtracted from the CLV.
The bridge of actionability. We assume that the firm can identify expenditure categories, or drivers (e.g., advertising awareness, service quality, price, loyalty program) that influence consumer decision making and that compete for marketing resources in the firm. We also assume that management wants to trade off the drivers to make decisions about which strategic investments yield the greatest return (Johnson and Gustafsson 2000). The drivers that are projected to yield the highest return receive higher levels of investment. Connecting the drivers to customer perceptions is essential to quantify the effects of marketing actions at the individual customer level. Therefore, it is necessary to have customer ratings (analogous to customer satisfaction ratings) on the brand's perceived performance on each driver. For example, Likert-scale items can be used to measure each competing brand's perceived performance on each driver; perceptions may vary across customers.
The firm may also want to assemble its drivers into broader expenditure categories that reflect higher-level resource allocation. We refer to these as "strategic investment categories." For example, a firm may combine all its brand-equity expenditures into a brand-equity strategic investment category, with the idea that the brand manager is responsible for drivers such as brand image and brand awareness.
Modeling the Switching Matrix
Thus, the modeling of CLV requires modeling of the switching matrix for each individual customer. Using individual-level data from a cross-sectional sample of customers, combined with purchase (or purchase intention) data, we model each customer's switching matrix and estimate model parameters that enable the modeling of CLV at the individual customer level.
The utility model. In addition to the individual-specific customer-equity driver ratings, we also include the effect of brand inertia, which has been shown to be a useful predictive factor in multinomial logit choice models (Guadagni and Little 1983). The utility formulation can be conceptualized as
( 1) Utility = inertia + impact of drivers.
To make this more explicit, U[subijk] is the utility of brand k to individual i, who most recently purchased brand j. The dummy variable LAST[subijk] is equal to one if j = k and is equal to zero otherwise; X is a row vector of drivers. We then model
( 2) U[subijk] = β[sub0k]LAST[subijk] + X[subik]β[sub1k] + ε[subijk],
where β[sub0k] is a logit regression coefficient corresponding to inertia, β[sub1k] is a column vector of logit regression coefficients corresponding to the drivers, and ε[subijk] is a random error term that is assumed to have an extreme value (double exponential) distribution, as is standard in logit models. The β coefficients can be modeled as either homogeneous or heterogeneous.( n5) For the current exposition, we present the homogeneous coefficient version of the model. In a subsequent section, we build and test alternative versions of the model that allow for heterogeneous coefficients. The model can also be estimated separately for different market segments.
The individual-level utilities result in individual-level switching matrices. Essentially, each row of the switching matrix makes a different assumption about the most recent brand purchased, which results in different utilities for each row. That is, the first row assumes that the first brand was bought most recently, the second row assumes that the second brand was bought most recently, and so on. The utilities in the different rows are different because the effect of inertia (and the effect of any variable that only manifests with repeat purchase) is present only in repeat purchases.
Consistent with the multinomial logit model, the probability of choice for individual i is modeled as
( 3) P[subijk*] = Pr[individual i chooses brand k[sup*], given that brand j was most recently chosen] = [Multiple line equation(s) cannot be represented in ASCII text]
Thus, the individual-level utilities result in individual-level switching matrices, which result in an individual-level CLV.
Brand switching and customer equity. To make the CLV calculation more specific, each customer i has an associated J x J switching matrix, where J is the number of brands, with switching probabilities p[subijk], indicating the probability that customer i will choose brand k in the next purchase, conditional on having purchased brand j in the most recent purchase. The Markov switching matrix is denoted as M[subi], and the 1 x J row vector A[subi] has as its elements the probabilities of purchase for customer i's current transaction. (If longitudinal data are used, the A[subi] vector will include a one for the brand next purchased and a zero for the other brands.)
For brand j, d[subj] represents firm j's discount rate, f[subi] is customer i's average purchase rate per unit time (e.g., three purchases per year), v[subijt] is customer i's expected purchase volume in a purchase of brand j in purchase t,( n6) π[subijt] is the expected contribution margin per unit of firm j from customer i in purchase t, and B[subit] is a 1 x J row vector with elements B[subijt] as the probability that customer i buys brand j in purchase t. The probability that customer i buys brand j in purchase t is calculated by multiplying by the Markov matrix t times:
( 4) B[subit] = A[subi]M[subi, sup t].
The lifetime value, CLV[subij], of customer i to brand j is
( 5) CLV[subij] = Σ[supT[subij], [subt=0]] (1 + d[subj][sup-t/f[subiV[subijt]π[subijt]B[subijt]]],
where T[subij] is the number of purchases customer i is expected to make before firm j's time horizon, H[subj] (e.g., a typical time horizon ranges from three to five years), and B[subijt] is a firm-specific element of B[subit]. Therefore, T[subi] = int[H[subj]f[subi]], where int[·] refers to the integer part, and firm j's customer equity, CE[subj], can be estimated as
( 6) CE[subj] = mean[subi](CLV[subij]) x POP,
where mean[subi](CLV[subij]) is the average lifetime value for firm j's customers i across the sample, and POP is the total number of customers in the market across all brands. Note that the CLV of each individual customer in the sample is calculated separately, before the average is taken.
It is worth pointing out the subtle difference between Equation 5 and most lifetime value expressions, as in direct marketing. Previous lifetime value equations have summed over time period, and the exponent on the discounting factor becomes -t. However, in our case, we are dealing with distinct individuals with distinct interpurchase times (or equivalently, purchase frequencies f[subi]). For this reason, we sum over purchase instead of time period.( n7) The exponent -t/f[subi] reflects that more discounting is appropriate for purchase t if purchasing is infrequent, because purchase t will occur further into the future. If f[subi] = 1 (one purchase per period), it is clear that Equation 5 is equivalent to the standard CLV expression. If f[subi] > 1, the discounting per purchase becomes less than the discounting per period, to an extent that exactly equals the correct discounting per period. For example, for f[subi] = 2, the square root of the period's discounting occurs at each purchase.( n8)
We can also use the customer equity framework to derive an overall measure of the company's competitive standing. Market share, historically used as a measure of a company's overall competitive standing, can be misleading because it considers only current sales. A company that has built the foundation for strong future profits is in better competitive position than a company that is sacrificing future profits for current sales, even if the two companies' current market shares are identical. With this in mind, we define customer equity share (CES, in Equation 7) as an alternative to market share that takes CLV into account. We calculate customer equity share for each brand j as
( 7) [Multiple line equation(s) cannot be represented in ASCII text]
ROI
Effect of changes. Ultimately, a firm wants to know the financial impact that will result from various marketing actions. This knowledge is essential if competing marketing initiatives are to be evaluated on an even footing. A firm may attempt to improve its customer equity by making improvements in the drivers, or it may drill down further to improve subdrivers that influence the drivers (e.g., improving dimensions of ad awareness). This requires the measurement of customer perceptions of the subdrivers about which the firm wanted to know more.
A shift in a driver (e.g., increased ad awareness) produces an estimated shift in utility, which in turn produces an estimated shift in the conditional probabilities of choice (conditional on last brand purchased) and results in a revised Markov switching matrix. In turn, this results in an improved CLV (Equations 4 and 5). Summed across all customers, this results in improved customer equity (Equation 6). We assume an equal shift (e.g., .1 rating points) for all customers, but this assumption can be relaxed if appropriate, because our underlying modeling framework does not require a constant shift across customers.
Projecting financial impact. It is often possible to devote a strategic expenditure to improve a driver, but is that investment likely to be profitable? Modern thinking in finance suggests that improved expenditures should be treated as capital investments and viewed as profitable only if the ROI exceeds the cost of capital. Financial approaches based on this idea are known by such names as "economic value-added" (Ehrbar 1998) or "value-based management" (Copeland, Koller, and Murrin 1996). The increased interest in economic value-added approaches has attracted more attention to ROI approaches in marketing (Fellman 1999).
The discounted expenditure stream is denoted as E, discounted by the cost of capital, and δCE is the improvement in customer equity that the expenditures produce. Then, ROI is calculated as
( 8) ROI = (δCE - E)/E.
Operationally, the calculation can be accomplished by using a spreadsheet program or a dedicated software package. Note, though, that even if δCE is negative, the ROI expression still holds.
Cross-Sectional Versus Longitudinal Data
Our approach requires the collection of cross-sectional survey data; the approach is similar in style and length to that of a customer satisfaction survey. The survey collects customer ratings of each competing brand on each driver. Other necessary customer information can be obtained either from the same survey or from longitudinal panel data, if it is available. The additional information collected about each customer includes the brand purchased most recently, average purchase frequency, and average volume per purchase. The logit model can be calibrated in two ways: ( 1) by observing the next purchase (from either the panel data or a follow-up survey) or ( 2) by using purchase intent as a proxy for the probability of each brand being chosen in the next purchase.
Obtaining the Model Input
The implementation of our approach begins with manager interviews and exploratory research to obtain information about the market in which the firm competes and information about the corporate environment in which strategic decisions are made. From interviews with managers, we identified competing firms and customer segments; chose drivers that correspond to current or potential management initiatives; and obtained the size of the market (total number of customers across all brands) and internal financial information, such as the discount rate and relevant time horizon. In addition, we estimated contribution margins for all competitors. If there was a predictable trend in gross margins for any firm in the industry, we also elicited that trend. From exploratory research, using both secondary sources and focus group interviews, we identified additional drivers, which we reviewed with management to ensure that they were managerially actionable items. On the basis of the combined judgment of management and the researchers, we reduced the set of drivers to a number that allows for a survey of reasonable length. The drivers employed typically vary by industry.
Estimating Shifts in Customer Ratings
The calculation of ROI requires an estimate of the rating shift that will be produced by a particular marketing expenditure. For example, a firm may estimate that an advertising campaign will increase the ad awareness rating by .3 on a five-point scale. These estimates can be obtained in several ways. If historical experience with similar expenditures is available, that experience can be used to approximate the ratings shift. For example, many marketing consulting firms have developed a knowledge base of the effects of marketing programs on measurable indexes. Another way, analogous to the decision calculus approach (Blattberg and Deighton 1996; Little 1970), is to have the manager supply a judgment-based estimate. The manager may reflect uncertainty by supplying an optimistic and a pessimistic estimate. If the outcome was favorable for the optimistic estimate, but unfavorable for the pessimistic estimate, the outcome would be considered sensitive to the rating shift estimate, indicating the need for more information gathering. Another limited cost approach is to use simulated test markets (Clancy, Shulman, and Wolf 1994; Urban et al. 1997) to obtain a preliminary idea of market response. Finally, the marketing expenditure can be implemented on a limited basis, using actual test markets, and the observed rating shift can be monitored (e.g., Rust et al. 1999; Simester et al. 2000).
Calibrating the Data
It is typical in many sampling plans to have respondents with different sampling weights, w[subi], correcting for variations in the probability of selection. We can use the sampling weights directly, in the usual way, to generate a sample-based estimate of market share, which we denote as MS[subsample]. If the sample is truly representative, MS[subsample] should be equal to the actual market share, MS[subtrue]. To make the sample more representative of actual purchase patterns, we can assign a new weight, w[subi,new] = (MS[subtrue]/MS[subsample]) x w[subi] to each respondent, with market shares corresponding to that respondent's most recently chosen brand, which will correct for any sampling bias with respect to any brand. The implied market share from the sample will then equal the actual market share.
If purchase intent rather than actual purchase data is used, the application must be done with some care. Previous researchers have long noted that purchase-intention subjective probabilities occasionally may be systematically biased (Lee, Hu, and Toh 2000; Pessemier et al. 1971; Silk and Urban 1978). We assume that the elicited purchase intentions, p[subij], of respondent i purchasing brand j in the next purchase need to be calibrated. In general, we assume that there is a calibrated probability, p[sup*, subij], that captures the true probability of the next purchase. These probabilities can be calibrated in two possible ways. First, if it is possible to follow up with each respondent to check on the next purchase, we can find a multiplier K[subj] for each brand that best predicts choice. (We set K[subj] for the first brand arbitrarily to one, without loss of generality, to allow for uniqueness.) The K's can be quickly found using a numerical search. If p[sup*, subij] is the stated probability of respondent i choosing brand j in the next purchase, the calibrated choice probability is p[sup*, subij] = K[subj]p[subij]/Σ[subk]K[subk]p[subik]. Second, if checking the next purchase is not possible, it is still possible to calibrate the purchase intentions by making an approximating assumption. Assuming that the market share (as the percentage of customers who prefer a brand) for each brand in the near future (including each respondent's next purchase) is roughly constant, we employ a numerical search to find the K[subj]'s (again setting K[sub1] = 1) for which MS[subtrue] = mean[subi](p[sup*, subij]).
Model Estimation
Principal components regression. In this application, as in customer satisfaction measurement, multicollinearity is an issue that needs to be addressed (Peterson and Wilson 1992). For this reason, we adopt an estimation approach that addresses the multicollinearity issue. Principal components regression (Massy 1965) is an approach that combats multicollinearity reasonably well (Frank and Friedman 1993), yet it can be implemented with standard statistical software. Principal components regression is a two-stage procedure that is widely known and applied in statistics, econometrics, and marketing (e.g., Freund and Wilson 1998; Hocking 1996; Naik, Hagerty, and Tsai 2000; Press 1982). Principal components multinomial logit regression has been used successfully in the marketing literature, leading to greater analysis interpretability and coefficient stability (e.g., Gessner et al. 1988).
The idea is to reduce the dimensionality of the independent variables by extracting fewer principal components that explain a large percentage of the variation in the predictors. The principal components are then used as independent variables in the regression analysis. Because the principal components are orthogonal, there is no multicollinearity issue with respect to their effects. In addition, eliminating the smallest principal components, which may be essentially random, may reduce the noise in the estimation. Because the principal components can be expressed as a linear combination of the independent variables (and vice versa), the coefficients of the independent variables can be estimated as a function of the coefficients of the principal components, and the coefficients (after the least important principal components are discarded) may result in better estimates of the drivers' effects. Estimation details are provided in Appendix A.
Importance of customer equity drivers. The results from the model estimation in Equation 2 provide insight into which customer equity drivers are most critical in the industry in which the firm competes. When examining a specific industry, it is useful to know what the key success factors are in that industry. Ordinarily, this might be explored by calculating market share elasticities for each driver. However, that approach is not correct here, because the drivers are intervally scaled rather than ratio scaled. This means that it is incorrect to calculate percentages of the drivers, as is necessary in the calculation of elasticities. Moreover, our focus is customer equity rather than market share. To arrive at the impact of a driver on customer equity, we need to determine the partial derivative of choice probability, with respect to the driver, for each customer in the sample. That is, if a driver were improved by a particular amount, what would be the impact on customer choice and, ultimately, on CLV and customer equity? Appendix A provides details of these computations and significance tests for the drivers.
Data and Sampling
Survey items. We illustrate our approach with data collected from customers of five industries. We assume three strategic investment categories: ( 1) perceived value (Parasuraman 1997; Zeithaml 1988), ( 2) brand equity (Aaker and Keller 1990), and ( 3) relationship management (Anderson and Narus 1990; Gummeson 1999). The three categories span all major marketing expenditures (Rust, Zeithaml, and Lemon 2000). We drew heavily on the relevant academic and managerial literature in these areas to build our list of drivers and ensured that the drivers could be translated into actionable expenditures. The resulting survey contained questions pertaining to shopping behavior and customer ratings of each driver for the four or five leading brands in the markets we studied. In addition, several demographics questions were asked at the end of the survey. We selected industries (airlines, electronics stores, facial tissues, grocery, and rental cars) that represented a broad set of consumer goods and services. To save space, we present the details for the airline industry analysis only; however, our approach was similar across the other four industries. The complete list of the survey items used in our analysis of the airline industry appears in Appendix B.
Population. We obtained illustrative data from two communities in the northeastern United States: an affluent small town/suburb and a medium-sized city that adjoins a larger city. Respondents were real consumers who had purchased the product or service in the industry in question during the previous year. Demographic statistics suggest that the sample is representative of similar standard metropolitan statistical areas in the United States, with the exception of generally high levels of education and income. For example, in the small town (with a population of approximately 20,000), the average age of the respondent was 47, the average household had two adults and one child, the average household income was $91,000, and the average years of education was 17. In the larger city, the average age was somewhat lower (39 years), the average household had two adults and one child, the average household income was $70,000, and the average years of education was similar to that of the small town.
Sampling. We obtained respondents from three random samples. The first sample, drawn from the city population, answered questions about electronics stores and rental car companies. The second sample, also drawn from the city population, addressed groceries and facial tissues. The third sample, drawn from the small town, focused on airlines. Potential respondents were contacted at random by recruiters from a professional market research organization (by telephone solicitation or building intercept). The screening process consisted of two criteria: ( 1) the respondent had purchased from the industry in the past 12 months, and ( 2) the respondent had a household income of at least $20,000 per year. Respondents agreed to participate and received $20 compensation for completing the questionnaire. In the electronics stores and rental cars survey, 246 consumers were approached: 153 were eligible, 144 cooperated, and 7 were disqualified, resulting in a total of 137 total surveys completed. In the groceries and facial tissues survey, 177 consumers were approached: 124 were eligible, 122 cooperated, and 4 were disqualified, resulting in a total of 118 surveys completed. In the airline survey, 229 consumers were approached: 119 were eligible, 105 cooperated, and 5 were disqualified, resulting in a total of 100 surveys completed.
Data collection and preliminary analysis. Data were collected in December 1998 and January 1999 at the firm's offices in each location. The respondents came to the facility to complete the pencil-and-paper questionnaire, which took about 30 minutes. They were then thanked for their participation and compensated. In addition, we obtained aggregate statistics on the small town and city (e.g., percentage of population that uses rental cars, average spent at grocery store) from secondary sources and used them in subsequent analysis. For purposes of financial analysis, we used local population and aggregate usage statistics for predominantly local industries (electronics stores and groceries) and national statistics for predominantly national industries (airlines, facial tissues, and rental cars). Although our random samples may not be fully representative of U.S. users, we extrapolated to the national market for national industries to show the type of dollar magnitudes that can arise given a large population. Because our examples are illustrative, truly precise dollar estimates are unnecessary.
Data were cleaned to eliminate obvious bad cases and extreme outliers. Because listwise deletion of cases would have resulted in too many cases being removed (even though only a relatively small percentage of responses were missing for particular items), we employed mean substitution as our missing data option for all subsequent analyses.( n9) Because we suspected that the relationship drivers would affect primarily repeat purchasers, we collected relationship items only for the brand most recently purchased.( n10) We mean-centered the relationship-related drivers for the cases in which the brand considered was the previously purchased brand, and we set them equal to zero for the cases in which the brand considered was different from the previously purchased brand. This enabled the "pure" inertia effect to be separated from the relationship effect of the drivers.
Choice Model Results
Principal components analysis results. We reduced the dimensionality of the predictor variables in each industry by conducting a principal components analysis. We used an eigenvalue cutoff of .5, which we judged to provide the best trade-off between parsimony and managerial usefulness.( n11) The airline analysis began with 17 independent variables, and we retained 11 orthogonal factors. Table 2 shows the loadings on the rotated factors. The resulting factor structure is rich. All the factors are easily interpretable. The few negative loadings are small and insignificant; they are zero for all practical purposes. All drivers load on only one factor, and many (e.g., inertia, quality, price, convenience, trust, corporate citizenship) load on their own unique factor.
There is some degree of discrimination among the value, brand, and relationship strategic investment categories in that drivers in the three strategic action categories of different drivers do not correlate highly on the same factors. As we expected, the strategic investment categories, value, brand, and relationship are not unidimensional. The drivers that constitute the categories can be grouped for managerial purposes as managers consider them, but drivers in a particular strategic investment category may be quite distinct in the customer's mind.
Logit regression results. Using the resulting factors as independent variables, we conducted multinomial logit analyses, using the analysis we described previously. Table 3 shows the coefficients that arise from the multinomial logit regression analysis, highlighting the significant factors. Using Equations A1-A9, we converted the factor-level results to the individual drivers. The resulting coefficients, standard errors, and test statistics are shown in Table 4. All the drivers are significant and have the correct sign, but some drivers have a larger effect than others. The most important drivers span all three strategic investment categories. In addition to the drivers, inertia has a large, significant impact (.849, p < .01). Among the value-related drivers, convenience has the largest coefficient (.609), followed by quality (.441); for brand-related drivers, direct mail information has the largest impact (.638), followed by ad awareness (.421) and ethical standards (.421). The loyalty program (.295) and preferential treatment (.280) are the most important relationship-related drivers.
Model Testing and Validation
We tested and validated the core choice model in several different ways. We tested for brand-specific effects, heterogeneity of response, a more general covariance matrix, and the reliability of the coefficient estimates.
Brand-specific effects. The model in Equation 2 assumes that there are no brand-specific effects. We tested the validity of this assumption by including brand-specific constants in the model of Equation 2. Testing the significance of the more complicated model can be accomplished through the use of a nested-likelihood-ratio chi-square test (in the airline application, this involves three degrees of freedom, reflecting a number of brand-specific constants that is equal to the number of brands minus one). The resulting nested model comparison was not significant (χ²[sub3] = .977), from which we conclude that brand-specific constants are not required.( n12)
Heterogeneity of response. It is reasonable to suspect that there may be unobserved heterogeneity of response across the respondents. That is, expressed in terms of Equation 2, the βs may be different across respondents. To test this, we employed a random coefficients logit model (Chintagunta, Jain, and Vilcassim 1991) in which we permitted the driver coefficients to be distributed as an independent multivariate normal distribution. The log-likelihood improved from -97.58 to -93.24, which is an insignificant improvement (χ²[sub11] = .8.68). Therefore, we conclude that the random coefficients logit formulation does not produce a better model and that it is not worthwhile in this case to model unobserved heterogeneity in the parameters.
Correlated errors. Another way the independence from irrelevant alternatives property can be violated is if the error terms in Equation 2 are correlated. For example, it is possible that people who prefer American Airlines more than the model predicted will systematically dislike Southwest Airlines more than the model predicted. To address this issue, we turned to a multinomial probit model (Chintagunta 1992). In this model, the error terms in Equation 2 are assumed to be normally distributed rather than extreme value, and they are permitted to have a general covariance matrix.
Our original logit model is no longer a constrained version of the more complicated model, so we cannot do the nested likelihood-ratio chi-square test. However, by comparing the general multinomial probit model with a multinomial probit model in which the error terms are assumed to be independent, we can address the issue of whether modeling the more general covariance matrix is useful. This results in a nested test. We found that the uncorrelated errors version of the model resulted in a log-likelihood of -98.46 (slightly worse than the multinomial logit log-likelihood) and that the more general model produced a log-likelihood of -97.57. The improvement is insignificant (χ²[sub3] = .82). We also can compare the general multinomial probit model with the original multinomial logit model by using the Akaike information criterion. The improvement from -97.58 to -97.57 does not compensate for the additional three estimated parameters (we would need a log-likelihood improvement of at least 3.0), suggesting that the general multinomial probit model is not better than our multinomial logit model. Thus, we conclude that the more general covariance matrix is not warranted.
Coefficient reliability. Given our relatively small sample size (96 usable data points after the data are cleaned), we were unable to pursue split-half tests or complete holdout samples. However, to further understand the reliability of our model estimates, we randomly split our sample into three parts (A, B, and C) and estimated our model on AB, AC, and BC. The mean range (and median range) of the coefficient estimates across the 11 factors was .14; that is, on average, the swing between the largest and smallest coefficient estimate across the three samples was .14, which comes out to about .6 standard errors, on average. Thus, the model appears to produce reasonably stable coefficient estimates.
CLV
Using Equation 5, we calculated CLV for American Airlines for each respondent in our airline sample. To operationalize the equation, we assumed a time horizon of three years, a discount rate of 10%, and a contribution margin of 15%. The 15% figure was approximately equal to the average operating margin for the industry for the five years preceding the survey, according to annual reports of the four firms we studied (since our study, airline industry operating margins have declined). We also based our contribution margin figures in the other four industries on financial data from annual reports. To extend the CLV figures to the firm's U.S. customer equity, we used U.S. Census data to determine the number of adults in the United States (187,747,000), and we then combined this with the percentage of U.S. adults who were active users of airline travel (23.3%), yielding a total number of U.S. adult airline customers of 43,745,051. To approximate the total customer equity, we multiplied this number by the average CLV across our respondents. Note that though we used average CLV to project customer equity, we calculated CLV at the individual customer level for each customer in the sample.
Customer loyalty and CLV. Some insights can be obtained from examining American Airlines' CLV distribution. For example, Figure 2 shows the distribution of CLV across American Airlines' customers. The $0-$99 category includes more than 60% of American's customers, and the $500-plus category includes only 11.6% of customers, indicating that the bulk of American's customers have low CLV. Figure 3 also indicates that American's customers are fickle. Almost half of American's customers have a 20% or less share-of-wallet (by CLV) allocated to American. Only 10.5% give more than 80% of their CLV to American. This percentage shows dramatically that the vast majority of American's customers cannot be considered monogamously loyal. Figure 4 shows a startling picture of the percentage of American's customer equity that is contributed by each CLV category. The $0-$99 category, though by far the largest (more than 60% of American's customers), produces less than 10% of American's customer equity. In contrast, the $500-plus CLV category, though only 11.6% of American's customers, produces approximately 50% of American's customer equity.
Comparison with the lost-for-good CLV model. Previously, we proposed that some models of CLV that do not account for customers' returning systematically underestimate CLV and customer equity (for an exception, see Dwyer 1997). Using the airline sample, we explored the degree to which this was true. The lost-for-good model is simply a constrained version of our switching model, such that all probabilities of switching from another brand to the focal brand are zero. In other words, to calculate the results, we considered only the customers who were retained from the first purchase. When the customer chose another brand, we gave a probability of zero to any further purchase from the focal brand. For American Airlines, our brand-switching model gives a customer equity of $7.303 billion. Without accounting for switching back, the estimated customer equity declines to $3.849 billion. Thus, the lost-for-good model provides a systematic underestimation of customer equity that, in this case, is an underestimation of 47.3%.
Customer equity and the value of the firm. It has been suggested (Gupta, Lehmann, and Stuart 2001) that customer equity is a reasonable proxy for the value of the firm. Our analysis of American Airlines provides some support for this idea. Multiplication of American's average CLV ($166.94) by the number of U.S. airline passengers ( 43,745,051) yields a total customer equity for American of $7.3 billion. Given American's opening share price for 1999 ($60) and its number of shares outstanding at that time (161,300,000) (AMR Corporation 1999), we calculate a market capitalization of $9.7 billion. Because our projection ignores profits from international customers and nonflight sources of income, our customer equity calculation is largely compatible with American's market capitalization at the time of the survey.
Projected Financial Return
Our framework enables the financial impact of improvement efforts to be analyzed for any of the usual marketing expenditures. For example, American Airlines recently spent a reported $70 million to upgrade the quality of its passenger compartments in coach class by adding more leg room. Is such an investment justified? To perform an analysis such as this, we estimated the amount of ratings shift and the costs incurred in effecting the ratings shift. We then used the ratings shift to alter (for each respondent) the focal brand's utility, switching matrix, and CLV (see Equations 2-5), which, when averaged across respondents and projected to the size of the population (see Equation 6), resulted in a revised estimate for the firm's customer equity. In this way, and using the discount rate and contribution margin we discussed previously, we analyzed the recent American Airlines seating improvement. We used the $70 million cost figure reported by the company.
If we assume that the average for the item that measures quality of the passenger compartment (a subdriver of quality) increases by .2 rating points on the five-point scale, our analysis (see Table 5) indicates that customer equity will improve by 1.39%, resulting in an improvement in customer equity of $101.3 million nationally, or an ROI of 44.7%, which indicates that the program has the potential to be a large success. Table 5 shows the results of similar analyses from the other four industries. For example, a $45 million expenditure by Puffs facial tissues to improve ad awareness by .3 rating points would result in a $58.1 million improvement in customer equity and an ROI of 29.1%.( n13)
It is even possible to measure the financial impact of corporate ethical standards or corporate citizenship. For example, if Delta spent $50 million to improve customers' perceptions of Delta's ethical standards by .1 rating points, this would project to a customer equity improvement of $85.5 million (a 1.68% increase). Such findings may cause some airlines to reconsider practices such as canceling flights that are not full in order to be profitable.
Not all investments will project to be profitable. For example, suppose that the grocery store Bread & Circus decides to spend $100,000 in the local retail area to improve its loyalty program ratings across two measures by .5 points. The projected benefit is not enough to justify the expenditure, and the ROI is -12.5%.
The preceding examples illustrate only some of the marketing expenditures that can be evaluated by means of the customer equity framework. Any marketing expenditure can be related to the drivers of customer equity, measured, and evaluated financially. This capability enables a firm to screen improvement ideas either before application or after a test market has nailed down the expected degree of improvement.
Model Sensitivity
The preceding analyses are based on point estimates, but how sensitive is the ROI model to errors of estimation or measurement? Sensitivity to errors of estimation can be analyzed by considering the sampling distribution of β[subx] Appendix A shows how to construct confidence intervals for β[subx]. Then, by applying the end points of the confidence interval to the ROI model, it is possible to analyze the sensitivity of ROI to estimation error. In general, this error is of more concern on the low side, because overoptimism may result in inappropriate expenditures. With this in mind, we suggest calculation of a coefficient, β-[subx] which will be greater than the true value only 5% of the time. Assuming that there is a large n, this is calculated as
( 9) β-[subx] = β[subx] - 1.645(standard error of β[subx]
Then, β-[subx] can be used to produce "conservative" projections of the customer equity change and the ROI. This can be done by inserting β-[subx] directly into the customer equity calculations. For example, if we calculate a conservative estimate of customer equity impact for the American Airlines example in Table 5, we obtain a $93.9 million increase in customer equity, or a 1.29% increase. This would result in a 34.2% ROI, indicating that even a conservative estimate shows a quite favorable return.
Sensitivity to errors of measurement can be addressed by considering the sampling distribution of the sample mean. In Equation A5 in Appendix A, unlike the case in regression analysis, the level of a variable affects the extent to which a change in the variable affects choice and thus utility, CLV, and customer equity. By evaluating the end points of the confidence interval for the sample mean of a variable to be improved, we can thus obtain a confidence interval for the ROI that will result from a shift in that variable. We performed this analysis for the Delta Airlines corporate ethics example in Table 5. A 95% confidence interval for the mean on the corporate ethics variable was 3.346 ± .188, which results in a 95% confidence interval for corporate ethics improvement of $83.1 million/$87.8 million and a 95% confidence interval for ROI of 66%/76%.
If the projected rating shift results from a test market, the sampling distribution of the rating shift can also be employed to generate a confidence interval. Under the assumption that the sources of error are independent, which is not unreasonable, it would then be straightforward to simulate an all-inclusive confidence interval for ROI, incorporating errors in the model coefficient estimate, estimated sample mean, and estimated shift that are based on an assumption of a multivariate normal distribution with orthogonal components.
Contributions to Theory and Practice
We make several contributions to marketing theory and practice. First, we identify the important problem of making all of marketing financially accountable, and we build the first broad framework that attempts to address the problem. We provide a unified framework for analyzing the impact of competing marketing expenditures and for projecting the ROI that will result from the expenditures. This big-picture contribution extends the scope of ROI models in marketing, which to date have focused on the financial impact of particular classes of expenditure and have not addressed the general problem of comparing the impact of any set of competing marketing expenditures. Our work is the first serious attempt to address this issue in its broadest form: the trading off of any strategic marketing alternatives on the basis of customer equity. Marketing Science Institute member companies have identified this research area as the most important problem they face today.
Second, we provide a new model of CLV, incorporating the impact of competitors' offerings and brand switching; previous CLV models have ignored competition. We also discount according to purchase rather than time period. Previous CLV models have been limited to the consideration of purchases made in prespecified time units, which is realistic for some businesses (e.g., subscription services, sports season tickets) but not for others (e.g., consumer packaged goods). By discounting according to purchase, at the individual level, our model is more widely applicable. The approach set out previously considers customer equity for the entire relevant competitive set, which has two advantages over existing approaches. First, this approach considers the expected lifetime value of both existing customers and prospective customers, thereby incorporating acquisition and retention (for the focal firm and competitors) in the same model. Second, by explicitly considering competitive effects in the choice decision, it is possible to use the model to consider the impact of competitive responses on the firm's customer equity.
Third, we provide a method for estimating the effects of individual customer equity drivers, testing their statistical significance, and projecting the ROI that will occur from expenditures on those drivers. We present a principal components multinomial logit regression model for estimating the Markov brand-switching matrix, and we separate the driver effects from the inertia effect. The identification and measurement of key drivers has been a process widely and successfully employed in the fields of customer satisfaction measurement and customer value management (e.g., Gale 1994; Kordupleski, Rust, and Zahorik 1993). We extend this idea to customer equity. By doing so, companies can answer questions such as, "Should we spend more on advertising, or should we improve service quality"? and "Which will have a bigger effect"?
Fourth, customer equity provides a theoretical framework for making the firm truly customer centered, and it is applicable to a wide variety of market contexts and industries. Basing strategic investment on the drivers of customer equity is an outside-in approach that directly operationalizes these fundamental marketing concepts. In other words, the customer equity approach provides a means of making strategic marketing decisions inherently information driven, which is consistent with the long-term trends of decreasing costs for information gathering and information processing.
Fifth, application of the customer equity framework is consistent with practical management needs. The results provide insight into competitive strengths and weaknesses and an understanding of what is important to the customer. By contrasting the firm's customer equity, customer equity share, and driver performance with those of its competitors, the firm can quickly determine where it is gaining or losing competitive ground with respect to the value of its customer base. In addition, the model results include the distribution of CLV across the firm's customers, the distribution of CLV share (discounted share-of-wallet) across the firm's customers, and the percentage of the firm's customer equity provided by the firm's top X% of customers. Collectively, the information gives useful information about how to segment the firm's customers on the basis of importance. Finally, the mathematical infrastructure of our framework can be implemented by means of widely available statistical packages and spreadsheet programs, and we have conducted all the analyses by using only standard, commercially available software packages.
Limitations and Directions for Further Research
In this article, we have developed and illustrated a practical framework for basing marketing strategy on CLV and customer equity. As with any new endeavor, there is much work yet to be done. Specifically, we have determined seven key areas for further research. First, the effects of market dynamics on customer equity should be examined. For example, if the market is rapidly expanding or rapidly shrinking, an assumption of stable market size is inappropriate. In such markets, it would be necessary to model the changing size of the market and relate that to customer equity. This also implies the explicit modeling of a birth and death process for customers in the market. New-to-the-world products and services and markets in which firms are expanding globally are examples of contexts in which we believe this will be particularly important.
Second, our model assumes that there is one brand or product in the firm and does not consider cross-selling between a firm's brands or products. We believe that the model we have described provides a solid foundation for firms to understand what drives customer equity in a given brand or product category. However, because many firms have multiple offerings and hope to encourage customer cross-buying of these products, it will be important to understand the influence of the drivers of customer equity on customer cross-buying behavior and to incorporate the impact of cross-selling on customer equity. This is particularly important for firms that rely on customer cross-buying behavior for long-term customer profitability (e.g., financial service firms).
Third, we adopt the assumption that a customer's volume per purchase is exogenous. An extension of this research would permit volume per purchase to vary as a function of marketing effort. For example, it will be important to understand whether marketing efforts that may result in forward buying (e.g., short-term price discounts) have a long-term effect on customer equity.
Fourth, there is a need to develop dynamic models of CLV and customer equity. Traditional models of CLV have been adopted from the net-present-value approach in the finance literature. Understanding how the value of the firm's customers (and overall customer equity) changes over time will enable managers to make even better marketing investments. There is also an opportunity to develop richer models of CLV that incorporate a deeper understanding of consumer behavior.
Fifth, there is an opportunity to relate customer equity to corporate valuation (Gupta, Lehmann, and Stuart 2001). This should involve the evaluation of corporate assets, liabilities, and risk, as well as the estimated customer equity. Sixth, applications of this framework and further empirical validation of its elements would be useful, especially across different cultures. For example, in what kinds of cultures are various drivers more important or less important, and why? Seventh, although our model incorporates competition, it makes no provision for competitive reactions. An extension of this work might involve a game theoretic competitive structure in order to understand the effects of potential competitive reactions to the firm's intended improvements in key drivers of customer equity.
Summary
We have provided the first broad framework for evaluating return on marketing. This enables us to make marketing financially accountable and to trade off competing strategic marketing investments on the basis of financial return. We build our customer equity projections from a new model of CLV, one that permits the modeling of competitive effects and brand-switching patterns. Customer equity provides an information-based, customer-driven, competitor-cognizant, and financially accountable strategic approach to maximizing the firm's long-term profitability.
This research was supported by the Marketing Science Institute, University of Maryland's Center for e-Service, and the Center for Service Marketing at Vanderbilt University. The authors thank Northscott Grounsell, Ricardo Erasso, and Harini Gokul for their help with data analysis, and they thank Nevena Koukova, Samir Pathak, and Srikrishnan Venkatachari for their help with background research. The authors are grateful for comments and suggestions provided by executives from IBM, Sears, DuPont, General Motors, Unilever, Siemens, Eli Lilly, R-Cubed, and Copernicus. They also thank Kevin Clancy, Don Lehmann, Sajeev Varki, Jonathan Lee, Dennis Gensch, Wagner Kamakura, Eric Paquette, Annie Takeuchi, and seminar participants at Harvard Business School, INSEAD, London Business School, University of Maryland, Cornell University, Tulane University, University of Pittsburgh, Emory University, University of Stockholm, Norwegian School of Management, University of California at Davis, and Monterrey Tech; and they thank participants in the following: American Marketing Association (AMA) Frontiers in Services Conference, MSI Customer Relationship Management Workshop, MSI Marketing Metrics Workshop, INFORMS Marketing Science Conference, AMA A/R/T Forum, AMA Advanced School of Marketing Research, AMA Customer Relationship Management Leadership Program, CATSCE, and QUIS 7.
(n1) For expositional simplicity, we assume throughout much of the article that the firm has one brand and one market, and therefore we use the terms "firm" and "brand" interchangeably. In many firms, the firm's customer equity may result from sales of several brands and/or several distinct goods or services.
(n2) It is also possible to model the share-of-wallet scenario that is common to business-to-business applications by using the concept of fuzzy logic (e.g., Varki, Cooil, and Rust 2000; Wedel and Steenkamp 1989, 1991).
(n3) The Pfeifer and Carraway (2000) Markov model considers only one brand and does not capture brand switching. Its states pertain to recency rather than brand.
(n4) Actually, George's CLV also depends on word-of-mouth effects (Anderson 1998; Hogan, Lemon, and Libai 2000), because George may make recommendations to others that increase George's value to the firm. To the extent that positive word of mouth occurs, our CLV estimates will be too low. Similarly, negative word of mouth will make our estimates too high. Although these two effects, being of the opposite sign, tend to cancel out to some extent, there will be some unknown degree of bias due to word of mouth. However, word-of-mouth effects are notoriously difficult to measure on a practical basis.
(n5) To the extent that heterogeneity in the regression coefficients exists, the state dependence effect will likely be overestimated (Degeratu 1999; Frank 1962). This would result in underestimation of the effects of the customer equity drivers, which means that the effect of violation of this assumption would be to make the projections of the model more conservative. However, it has been shown that our approach to estimating the inertia effect performs better than other methods that have been proposed (Degeratu 2001).
(n6) To simplify the mathematics, we adopt the assumption that a customer's volume per purchase is exogenous. We leave the modeling of volume per purchase as a function of marketing effort as a topic for further research.
(n7) If standard marketing costs (e.g., retention promotional costs) are spent on a time basis (e.g., every three months), they may either be discounted separately and subtracted from the net present value or be assigned to particular purchases (e.g., if interpurchase time is three months, and a standard mailing goes out every six months, the mailing cost could be subtracted on every other purchase).
(n8) We should also note that the expression implies that the first purchase occurs immediately. Other assumptions are also possible.
(n9) Mean substitution can result in biased estimates, but in our judgment, the additional effort of employing a more sophisticated missing values procedure (e.g., data imputation) was not justified in this case, given the relatively low percentage of missing values.
(n10) This decision was based solely on the researchers' best judgment. Our model does not require this.
(n11) The 1.0 eigenvalue cutoff (Kaiser 1960) is typically employed in marketing, but it is just one of many possible cutoff criteria (for two alternatives, see Cattell 1966; Jolliffe 1972). As Kaiser (1960, p. 143), who proposed the 1.0 cutoff, points out, "by far [the] most important viewpoint for choosing the number of factors [is] ... psychological meaningfulness." In other words, the cutoff should be chosen such that the results are substantively meaningful, which is our justification for using the particular cutoff level that we chose.
(n12) This nested chi-square was also insignificant in the other four industries that we studied (electronics = 6.79, facial tissues = 1.00, grocery = 5.82, and rental cars = .28).
(n13) To conserve space, we show the details only for the airline example, but the details of the other industry examples are similar.
(n14) Actually, convergence is faster, because the probabilities of next purchase are given directly, so the law of large numbers does not need to be evoked with respect to the dependent variable.
(n15) For convenience, we suppress the j subscript for D, P, and U, because for any customer i in the sample, j is fixed.
(n16) We could also use the equilibrium probabilities. In practice, there is little difference between the two.
Legend for Chart:
A - Type of Model
B - Exemplars
C - Strategic Trade-offs of Any Marketing Expenditures
D - ROI Modeled and Calculated?
E - Explicitly Models Competition?
F - Calculation of CLV?
G - Can Be Applied to Most Industries?
H - Net Present Value of Revenues and Costs?
I - Brand Switching Modeled at Customer Level?
J - Statistical Details?
A B C D E
F G H I
J
Strategic Larreché and Yes No No
portfolio Srinivasan
(1982)
No Yes Yes No
Yes
CLV Berger and No No No
Nasr (1998)
Yes No Yes No
Yes
Direct Blattberg and No Yes No
marketing: Deighton
customer (1996);
equity Blattberg,
Getz, and
Thomas (2001)
Yes Yes Yes No
Yes
Longitudinal Bolton, Lemon, and Yes Yes No, unless
database Verhoef (2004); panel data
marketing Reinartz and
Kumar (2000)
Yes No Yes No, unless
panel data
Yes
Service profit Heskett et al. No No No
chain (1994);
Kamakura et
al. (2002)
No No No No
Yes
Return on Rust, Zahorik, No Yes No
quality and
Keiningham
(1994,1995)
No Yes Yes No
Yes
Customer Rust, Zeithaml, Yes Yes Yes
equity book and Lemon
(2000)
Yes Yes Yes No
No
Return on Current paper Yes Yes Yes
marketing
Yes Yes Yes Yes
Yes Legend for Chart:
A - Driver
B - F1
C - F2
D - F3
E - F4
F - F5
G - F6
H - F7
I - F8
J - F9
K - F10
L - F11
A B C D
E F G H
I J K L
Inertia -.013 -.004 .033
.038 .015 .116 -.024
.984(b) .029 .043 .002
Quality .097 .058 .174
.076 .147 .212 .014
.049 .068 .904(b) .083
Price .044 -.007 .128
.054 .078 .023 .039
.030 .975(b) .059 .034
Convenience .078 .068 .219
.161 .043 .830(b) .066
.163 .018 .260 -.015
Ad awareness -.031 .130 .038
.938(b) -.010 .022 .048
-.011 .074 .101 .058
Information .216 -.077 .248
.656(b) .322 .299 -.207
.125 -.038 -.058 .016
Corporate citizenship .011 .122 .150
.093 .880(b) .001 .256
.021 .077 .137 .006
Community events .021 .100 .188
-.042 .226 .051 .921(b)
-.026 .041 .011 .029
Ethical standards -.016 .044 .605(b)
.105 .458 .266 .028
-.034 .104 .109 .218
Image fits my personality .098 .112 .878(b)
.107 .069 .092 .203
.058 .110 .142 .081
Investment in loyalty program .921(b) .044 .090
.032 -.007 -.060 .018
.014 -.003 .137 -.103
Preferential treatment .898(b) .087 .082
-.002 .022 -.071 -.029
.032 -.007 .077 .104
Know airline's procedures .708(b) .232 -.022
.116 .029 .166 .058
-.033 .010 -.069 .240
Airline knows me .681(b) .309 -.073
-.059 -.001 .356 -.012
-.061 .136 -.075 .219
Recognizes me as special .214 .851(b) .069
.077 -.036 .042 .138
-.004 -.044 .118 .092
Community .175 .876(b) .065
.031 .166 .035 -.015
.001 .036 -.042 .129
Trust .246 .227 .179
.069 .041 -.003 .031
.006 .038 .091 .889(b)
Notes: (b) Loadings greater than .5 are shown in bold. Legend for Chart:
A - Independent Variable
B - Coefficient
C - Standard Error
D - b/s.e.
E - p
A B C D E
F1 .325(**) .122 2.65 .008
F2 -.031 .118 -.26 .795
F3 .421(*) .210 2.00 .045
F4 .459 .285 1.61 .107
F5 .212 .228 .93 .352
F6 .331(*) .159 2.08 .037
F7 -.081 .279 -.29 .771
F8 .633(**) .100 6.36 .000
F9 .034 .164 .21 .835
F10 .184 .147 1.26 .209
F11 -.033 .115 -.29 .772
Log-likelihood = -98.46
Chi-square (11 degrees of freedom) = 69.246(**)
(*) p < .05.
(**) p < .01.
Legend for Chart:
A - Driver
B - Coefficient
C - Standard Error
D - b/s.e.
A B C D
Inertia .849 .075 11.341(*)
Quality .441 .041 10.871(*)
Price .199 .020 9.858(*)
Convenience .609 .093 6.553(*)
Ad awareness .421 .099 4.242(*)
Information .638 .082 7.819(*)
Corporate citizenship .340 .045 7.617(*)
Community events .170 .024 6.974(*)
Ethical standards .421 .053 7.901(*)
Image fits my personality .390 .050 7.874(*)
Investment in loyalty program .295 .027 10.956(*)
Preferential treatment .280 .026 10.857(*)
Know airline's procedures .238 .027 8.779(*)
Airline knows me .249 .041 6.108(*)
Recognizes me as special .167 .017 9.771(*)
Community .151 .016 9.412(*)
Trust .203 .023 8.725(*)
(*) p < .01.
Legend for Chart:
A - Company (Industry)
B - Area of Expenditure
C - Geographic Region
D - Investment
E - Amount Improved
F - Percentage Improvement in Customer Equity
G - Dollar Improvement in Customer Equity
H - Projected ROI
A B C
D E
F G H
American Passenger United States
(airlines) compartment
$70 million .2 rating point
1.39% $101.3 million 44.7%
Puffs Advertising United States
(facial tissues)
$45 million .3 rating point
7.04% $58.1 million 29.1%
Delta (airlines) Corporate United States
ethics
$50 million .1 rating point
1.68% $85.5 million 71.0%
Bread & Circus Loyalty Local market
(groceries) programs
$100,000 .5 rating point
in two measures
7.04% $87,540 -12.5%DIAGRAM: FIGURE 1 Return on Marketing
GRAPH: FIGURE 2 Distribution of CLV: American Airlines
GRAPH: FIGURE 3 Distribution of CLV Share (Share of Wallet): American Airlines
GRAPH: FIGURE 4 Percentage Customer Equity by CLV Category: American Airlines
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Principal Components Regression
The independent variables for the principal components analysis are all the drivers and the LAST variable. The vector X[subijk] denotes the original independent variables for each customer i by previously purchased firm j by next-purchase firm k combination. Treating the customer by firm combinations as replications, we extract the largest principal components of X[subijk] and rotate them using varimax rotation to maximize the extent to which the factors load uniquely on the original independent variables, thereby aiding managerial interpretability. The vector F[subijk] denotes the rotated factor. These form the independent variables for our logit regression, which we describe subsequently.
Expressing Equation 2 in terms of the underlying factors leads to the following:
(A1) U[subijk] = F[subijk] γ + ε[subi],
where γ is a vector of coefficients.
From factor analysis theory, it is known that the factors are linear combinations of the underlying variables X[subijk]. In other words, there exists a matrix A for which F[subijk] = X[subijk] A. However, the idea of the principal components analysis was to discard the potentially muddling effects of the least important components. Denoting A[sup*] as the subvector of A that corresponds to the reduced factor space (discarding the principal components that do not meet the eigenvalue cutoff) and γ[sup*] as the estimated γ that corresponds to the reduced space, Equation A1 can be expressed as
(A2) U[subijk] = (X[subijk]A[sup*]) γ[sup*] = X[subijk](A[sup*] γ[sup*]),
where U[subijk] is the estimated utility, which means that β[sup*] = A[sup*]γ[sup*] can be the estimated coefficient vector. In other words, the coefficients of X[subijk] are obtained by multiplying the regression coefficients obtained from the logit regression on the factors by the factor coefficients that relate the drivers to the factors.
Logit Estimation
Usually in multinomial logit regression, the observed dependent variable values are ones and zeroes, corresponding to the purchased brand (1 = "brand was purchased," 0 = "brand was not purchased"). This will be the case if the next purchase is observed from a longitudinal panel or follow-up survey. However, if purchase intent is used as a proxy for next purchase, the dependent variable values will be proportions that correspond to the stated (or calibrated) purchase intention probabilities. This does not create any difficulties. Equation 9, we have U[subijk] = F[subijk] γ[sup*] + ε[subi], after discarding the principal components that did not meet the cutoff. Using the laws of conditional probability, we can express the likelihood of a particular parameter vector γ[sup*] given respondent i's observed next purchase (or purchase intention) vector p[sup*, sub i] as
(A3) [Multiple line equation(s) cannot be represented in ASCII text]
where Y[subij] equals one if customer i chooses brand j and equals zero otherwise, the likelihoods on the right side are the usual 0-1 logit likelihood expressions obtained as in Equation 3, and p[sup*, sub ij] is the element of p[sup*, subi] that corresponds to firm j. The resulting likelihood for the sample is then the product of the individual likelihoods across the respondents. It is easily shown that with this adjustment in the likelihood, the standard logit regression maximum likelihood algorithms can be employed (Greene 1997, p. 916, 1998, pp. 520, 524). The same adjustment of the likelihood does not affect the derivation of the asymptotic distribution of the regression coefficients( n14) (as is evident in McFadden's [1974, pp. 135-38] work), which means that the usual chi-square statistics, as given in standard logit software such as LIMDEP, can still be employed, even if the p[sup*, sub ij] vector is not all zeroes and ones.
From Equation 3, it is easily shown that the partial derivative of probability of choice with respect to utility, for respondent i and firm k, is( n15)
(A4) [Multiple line equation(s) cannot be represented in ASCII text]
Then, from Equation A2 we have
(A5) ∂P[subik]/∂X[subijk] = ∂P[subik]/∂U[subik] x ∂U[subik]/∂X[subijk] = (A[sup*]γ[sup*]D[subik] = = (A[sup*]γ[sup*]) p[sup*, sub ik](1 - p[sup*, sub ik]).
This equation shows how each customer's brand-switching matrix will change given a change in any driver (or changes in more than one driver). This result is nonlinear and implies diminishing returns for any driver improvement. By applying the altered switching matrix in Equation 4, reestimating CLV by using Equation 5, and aggregating across customers by using Equation 6, we find the impact on customer equity.
The relative importance of each driver, measured as the impact of a marginal improvement in the driver on utility, can also be addressed as a proportion of the total marginal impact summed across all drivers. In other words, the importance of driver x is the per-unit amount that it contributes to utility, and the relative importance is that amount expressed as a percentage.
(A6) [Multiple line equation(s) cannot be represented in ASCII text]
(A7) Relative importance
[Multiple line equation(s) cannot be represented in ASCII text]
where C is the set of retained principal components, A[subcx] is the factor coefficient relating driver x to factor c, and γ[subc] is the logit coefficient corresponding to factor c.
In addition, we address the statistical significance of the drivers. The coefficients, γ[subc] as estimated by the logit model, are distributed asymptotically normally, and mean and variance are estimated and reported by standard logit regression software. If the estimated logit coefficient and variance of the estimate for factor c are γ[subc] and σsup2, sub c], respectively, and β[subc] = Σ[subc]A[subcx]γ[subc] is the coefficient estimator for driver x, then, if we assume that the γ[subc]'s are distributed independently, β[subx] is a linear combination of independent normal distributions and thus is also normally distributed. Specifically:
(A8) Standard error of β[subx] [Multiple line equation(s) cannot be represented in ASCII text]
which results asymptotically in the following z-test for β[subx]:
(A9) [Multiple line equation(s) cannot be represented in ASCII text]
which is easily calculated from the results of the principal components analysis (for A[sup2, sub cx]) and logit analysis (for β[subx] and σsup2,s ub c]).
Computational Issues in Estimating CLV
If the time horizon is long or the customer's frequency of purchase is high, there may be many purchases expected before the time horizon, which increases computation considerably. Therefore, it is useful to make a simplifying approximation that can speed up the computation. In practice, the expected purchase probabilities, B[subijt], approach equilibrium and change little after about 15 purchases. This enables us to employ the approximation that the purchase probabilities do not change after 15 purchases. If T[subi] ≤ 15, we can estimate CLV[subij] as in Equation 5. However, if T[subi] > 15, we can simplify the calculations. Let CLV[subij]( 15) denote the lifetime value of customer i to firm j in 15 purchases, as calculated by Equation 5, and let CLV[subij, sup*](T[subi]) and CLV[subij, sup*]( 15) denote the lifetime values that would occur (through T[subi] purchases and 15 purchases, respectively) if the purchase probabilities were constant and equal to B[subij,15].( n16) The expected lifetime value of the purchases beyond purchase 15 can be approximated as CLV[subij, sup*](T[subi]) - CLV[subij, sup*]( 15). This is helpful because CLV[sup*] can be viewed as a net present value of an annuity, and it can be calculated in closed form because the probabilities B[subij,15] are constant. Expressing the individual-specific discount rate per purchase as d[subi, sup*] = d[subi, sup -1/f[subi]], we have the standard expression for the net present value of an annuity:
(A10) CLV[subij, sup*](t) = v[subijt]π[subijt]B[subij,15](1/d[subi, sup*])[1 - (1 + d[subi, sup*]) [sup-t]], from which we obtain the estimated lifetime value of
(A11) Estimated CLV[subij] = CLV[subij]( 15) + CLV[subij, sup*](T[subi]) - CLV[subij, sup*]( 15).
Here are some examples of survey items that might be used to measure customer equity and its drivers. These items are from the survey that we used to analyze the airline market. (The headings in this Appendix are for explanatory purposes and would not be read to the respondent.)
Market Share and Transition Probabilities
1. Which of the following airlines did you most recently fly? (please check one)
2. The next time you fly a commercial airline, what is the probability that you will fly each of these airlines? Probability (please provide a percentage for each airline, and have the percentages add up to 100%)
Size and Frequency of Purchase
3. When you fly, how much on average does the airline ticket cost?
( ) less than $300
( ) between $300 and $599
( ) between $600 and $899
( ) between $900 and $1199
( ) between $1200 and $1499
( ) between $1500 and $1799
( ) between $1800 and $2099
( ) 2100 or more
4. On average, how often do you fly on a
commercial airline?
( ) once a week or more
( ) once every two weeks
( ) once a month
( ) 3-4 times per year
( ) once a year
( ) once every two years, or less
Value-Related Drivers
- 5. How would you rate the overall quality of the following airlines? (5 = "very high quality," 1 = "very low quality")
- 6. How would you rate the competitiveness of the prices of each of these airlines? (5 = "very competitive," 1 = "not at all competitive")
- 7. The airline flies when and where I need to go. (5 = "strongly agree," 1 = "strongly disagree")
Brand-Related Drivers (5 = "Strongly Agree," 1 = "Strongly Disagree")
- 8. I often notice and pay attention to the airline's media advertising.
- 9. I often notice and pay attention to information the airline sends to me.
- 10. The airline is well known as a good corporate citizen.
- 11. The airline is an active sponsor of community events.
- 12. The airline has high ethical standards with respect to its customers and employees.
- 13. The image of this airline fits my personality well.
Relationship-Related Drivers (5 = "Strongly Agree," 1 = "Strongly Disagree")
- 14. I have a big investment in the airline's loyalty (frequent flyer) program.
- 15. The preferential treatment I get from this airline's loyalty program is important to me.
- 16. I know this airline's procedures well.
- 17. The airline knows a lot of information about me.
- 18. This airline recognizes me as being special.
- 19. I feel a sense of community with other passengers of this airline.
- 20. I have a high level of trust in this airline.
~~~~~~~~
By Roland T. Rust; Katherine N. Lemon and Valarie A. Zeithaml
Roland T. Rust is David Bruce Smith Chair in Marketing, Director of the Center for e-Service, and Chair of the Department of Marketing, Robert H. Smith School of Business, University of Maryland (rrust@rhsmith.umd.edu).
Katherine N. Lemon is Associate Professor, Wallace E. Carroll School of Management, Boston College (e-mail: lemonka@bc.edu).
Valarie A. Zeithaml is Roy and Alice H. Richards Bicentennial Professor and Senior Associate Dean, Kenan-Flagler School of Business, University of North Carolina, Chapel Hill (e-mail: zeithamv@bschool.unc.edu).
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Record: 135- Revenue Premium as an Outcome Measure of Brand Equity. By: Ailawadi, Kusum L.; Neslin, Scott A.; Lehmann, Donald R. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p1-17. 17p. 2 Diagrams, 9 Charts. DOI: 10.1509/jmkg.67.4.1.18688.
- Database:
- Business Source Complete
Revenue Premium as an Outcome Measure of Brand Equity
The authors propose that the revenue premium a brand generates compared with that of a private label product is a simple, objective, and managerially useful product-market measure of brand equity. The authors provide the conceptual basis for the measure, compute it for brands in several packaged goods categories, and test its validity. The empirical analysis shows that the measure is reliable and reflects real changes in brand health over time. It correlates well with other equity measures, and the measure's association with a brand's advertising and promotion activity, price sensitivity, and perceived category risk is consistent with theory.
The concept of brand equity has been widely discussed in the marketing literature; much of the research stems from a Marketing Science Institute (MSI) conference on the topic (Leuthesser 1988). Researchers such as Aaker (1991), Aaker and Keller (1990), Broniarczyk and Alba (1994), Farquhar (1989, 1990), Feldwick (1996), Keller (1993), Loken and Roedder-John (1993), and Park, Milberg, and Lawson (1991) have written extensively about the concept of brand equity and about how to build, manage, and extend it. At the same time, advertising and market research executives have emphasized the importance of brand equity (Baldinger 1990, 1992; Blackston 1992, 1995); companies have paid increasing attention to brands, often creating the position of brand equity manager; and consulting practices have been established to evaluate and track brand equity (e.g., Interbrand, Total Research Corporation, Millward Brown).
The steadily growing literature contains several often-divergent viewpoints on the dimensions of brand equity, the factors that influence it, the perspectives from which it should be studied, and the ways to measure it. However, there is agreement among researchers on the general definition of the concept. Brand equity is defined as the marketing effects or outcomes that accrue to a product with its brand name compared with those that would accrue if the same product did not have the brand name (Aaker 1991; Dubin 1998; Farquhar 1989; Keller 2003; Leuthesser 1988). The specific effects may be either consumer-level constructs, such as attitudes, awareness, image, and knowledge, or firm-level outcomes, such as price, market share, revenue, and cash flow. As Leuthesser (1988) summarizes, Al Shocker and Bart Weitz define brand equity from the consumer perspective as a utility, loyalty, or differentiated clear image not explained by product attributes and from the firm perspective as the incremental cash flow resulting from the product with the brand name compared with that which would result without the brand name.
Despite all the attention paid to brand equity, the existence of a generally accepted definition, and both brand equity's and marketing metrics' positions as priority MSI topics for the past ten years, remarkably few academic researchers have addressed brand equity measurement per se. This may partly be due to disagreement about whether equity should be measured from the consumer or the firm perspective; although, the two perspectives are linked because firm-level outcomes, such as incremental volume, revenue, price commanded, cash flow, and profit, are the aggregated consequence of consumer-level effects, such as positive image, attitude, knowledge, and loyalty.
The purpose of this article is to propose and validate revenue premium as a measure of brand equity. We describe the measure and compute it for various brands across several packaged goods categories. We validate the measure by examining its correlation with other commonly available measures, its behavior over time and across product categories, and its association with price elasticity and marketing activities, such as advertising and promotion. The following section provides the conceptual background for our work by reviewing the purposes for which managers use brand equity measures, desirable characteristics of the ideal measure, and existing measures of brand equity. Subsequently, we present our measure and its theoretical basis, advantages, and limitations; we present an empirical validation of the measure; and we conclude with implications for researchers and managers.
Why Measure Brand Equity?
The academics and practitioners who gathered at an MSI (1999) workshop on brand equity metrics summarized the following broad purposes for measuring brand equity: ( 1) to guide marketing strategy and tactical decisions, ( 2) to assess the extendibility of a brand, ( 3) to evaluate the effectiveness of marketing decisions, ( 4) to track the brand's health compared with that of competitors and over time, and ( 5) to assign a financial value to the brand in balance sheets and financial transactions. They also developed the following list of desiderata for the ideal measure. It should be
1. grounded in theory;
- 2. complete, that is, encompassing all facets of brand equity, yet distinct from other concepts;
- 3. diagnostic, that is, able to flag downturns or improvements in the brand's value and provide insights into the reasons for the change;
- 4. able to capture future potential in terms of future revenue stream and brand extendibility;
- 5. objective, so that different people computing the measure would obtain the same value;
- 6. based on readily available data, so that the measure can be monitored on a regular basis for multiple brands in multiple product categories;
- 7. a single number, to enable easy tracking and communication;
- 8. intuitive and credible to senior management;
- 9. robust, reliable, and stable over time, yet able to reflect real changes in brand health; and
- 10. validated against other equity measures and constructs that are theoretically associated with brand equity.
Recognizing that no single measure is likely to satisfy all these criteria, the workshop attendees recommended that the usefulness of a measure should be evaluated against the primary purposes it is to be used for and that efforts should be made to build a database for use in validating existing and new measures of brand equity.
Existing Measures of Brand Equity
Keller and Lehmann (2001) divide existing measures of brand equity into three categories. The first category, which they call "customer mind-set," focuses on assessing the consumer-based sources of brand equity. The second and third categories, which they call "product market" and "financial market," focus on the outcomes or net benefit that a firm derives from the equity of its brands.
Customer mind-set. Customer mind-set measures assess the awareness, attitudes, associations, attachments, and loyalties that customers have toward a brand and have been the focus of much academic research (e.g., Aaker 1991, 1996; Ambler and Barwise 1998; Keller 1993, 2003) and industry offerings (e.g., Millward Brown's Brand Z, Research International's Equity Engine, Young & Rubicam's Brand Asset Valuator). These measures are rich in that they assess several sources of brand equity, have good diagnostic ability, and can be used as input to predict a brand's potential. Thus, they are well suited for the first three purposes of brand equity measurement listed previously. However, because the measures are typically based on consumer surveys, they are not easy to compute and do not provide a single, simple, objective measure of brand performance. Furthermore, because they do not culminate in a dollar value for the brand, they are not appealing for financial valuation purposes. Even marketers argue that it is not enough to assess brand image, attitudes, and so on; the dollar-value connection to the bottom line is imperative (Kiley 1998; Schultz 1997).
Product-market outcomes. The logic underlying product-market measures is that the benefit of brand equity should ultimately be reflected in the brand's performance in the marketplace. The most commonly mentioned such measure is price premium, that is, the ability of a brand to charge a higher price than an unbranded equivalent charges (Aaker 1991, 1996; Agarwal and Rao 1996; Sethuraman 2000; Sethuraman and Cole 1997). Price premium is measured either by asking consumers how much more they would be willing to pay for a brand than for a private label or an unbranded product or by conducting conjoint studies in which brand name is an attribute. Other product-market outcome measures include market share, relative price (Chaudhuri and Holbrook 2001), share of category requirements (Aaker 1996), market share adjusted by a "durability" factor (Moran 1994), the constant term in demand models (Srinivasan 1979), the residual in a hedonic regression (Hjorth-Andersen 1984), or an economic theory-based measure of the difference between the brand's profit and the profit it would earn without the brand name (Dubin 1998).
The advantages of such measures are that they are more "complete" than any single customer mind-set measure because they reflect a culmination of the various mechanisms by which the brand name adds value and that they can be given a dollar value, which is appealing to senior management and is critical for financial valuation. Many such measures are also rooted in the conceptual definition of brand equity because they quantify the incremental benefit due to the brand name.
The disadvantages are that some of the measures rely on customer judgments of what they would buy in hypothetical situations rather than actual purchase data and are subject to several biases, such as context effects (Simonson and Tversky 1992). Other measures, such as conjoint-based measures, require fairly complicated statistical modeling, which makes them time consuming and impractical to monitor on a regular basis, or are sensitive to model specification (Steenkamp and Wittink 1994). In addition, some product-market measures can result in an incomplete and therefore misleading estimate of brand equity. For example, a brand might have high market share, but if that share simply has been "bought" by severe price cuts, market share will overestimate brand equity. Other brands might not command a price premium, but that does not mean they do not have equity. Indeed, in today's value-conscious consumer market, there are many examples of strong, value-priced brands (e.g., Southwest Airlines, Wal-Mart, Suave). Finally, because of their focus on outcomes rather than sources of brand equity, all product-market measures have limited diagnostic ability: They are diagnostic to the extent that they can flag when a brand is in trouble or when it is strong, but they cannot explain the reasons for either situation. Thus, they are more suited for the last three purposes of brand equity measures that we listed previously.
Financial market outcomes. Financial market measures assess the value of a brand as a financial asset; such measures include purchase price at the time a brand is sold or acquired (Mahajan, Rao, and Srivastava 1994) and discounted cash flow valuation of licensing fees and royalties. The Interbrand consultancy combines both product-market and financial market measures to adjust a brand's current profits for growth potential. Simon and Sullivan (1993) determine the residual market value after other sources of firm value are accounted for.
Although financial market measures have many of the same advantages and disadvantages as do customer mind-set measures and product-market outcomes, they differ in one key respect. In general, product-market outcomes quantify the current strength of a brand, whereas financial market outcomes also attempt to quantify future potential. However, this difference introduces a substantial element of subjectivity and/or instability into the measures. Future potential is assessed by means of subjective judgment (e.g., the multiples Interbrand applies) or stock market value, which is highly volatile (e.g., Snapple's sale price went from $1.7 billion in 1994 to $300 million in 1996, and then back to $1 billion in 2000), and has less immediate relevance to marketing, because many things other than marketing activities influence it.
Summary. Researchers have used three major approaches to measure brand equity, and each approach has its own advantages and disadvantages. No single measure can possess all the characteristics that marketers desire in the ideal brand equity measure. Product-market measures offer an attractive middle ground between customer mindset and financial market measures in terms of objectivity and relevance to marketing. However, our review reveals a need for a measure of this type that combines high external validity, strong conceptual grounding, completeness, and ease of calculation. In the next section, we propose the revenue premium measure as a way to satisfy this need.
We define revenue premium as the difference in revenue (i.e., net price x volume) between a branded good and a corresponding private label:
( 1) Revenue premiumb = (volumeb)(priceb) - (volumepl)(pricepl).
Theoretical Basis
Figure 1 shows the role of equity in determining a brand's sales volume. Sales are influenced by the marketing mix of both the brand and its competitors. Equity influences sales directly by means of consumer choice and indirectly by enhancing the effectiveness of the brand's marketing efforts and insulating the brand from competitive activity (Keller 2003). In turn, equity is created by the marketing mix of both the brand and its competitors and by the firm's previously existing strength from its corporate image, product line, research and development (R&D), and other capabilities. For example, Sony's equity arises from its superior products and marketing programs, company reputation, and expertise; this equity makes consumers pay more attention to Sony advertising, enables better trade support, and reduces Sony's vulnerability to competitors' product improvements and price cuts. Exogenous category characteristics, such as market size and perceived risk, also influence the level of equity that brands can achieve. The incremental value that consumers are likely to give to a well-respected branded product compared with an equivalent unbranded one is greater if the perceived risk in buying or consuming the category is high (Batra and Sinha 2000; Erdem and Swait 1998; Sethuraman and Cole 1997). In equation form, Figure 1 can be represented as follows:
( 2) Sj = fj(Mj, Pj, Mk, Pk, MjEj, PjEj, MkEj, PkEj, Ej), and
( 3) Ej = gj(Mj, Pj, Fj, Cj, Mk, Pk),
where
S = unit sales,
M = marketing mix,
P = price,
E = equity,
F = preexisting firm strength,
C = category characteristics, and
j and k = indexes of brands j and k.
In the competitive marketplace defined by the sales and equity functions in Equations 2 and 3, brands j and k decide on their marketing mix and price to maximize profits.( n1) This yields an equilibrium set of marketing-mix, price, and brand equities (Mj, sup *, Pj, sup *, Mk, sup *, Pk, sup *, Ej, sup *, Ek, sup *), resulting in the following equilibrium revenue for brand j:
( 4) Rj, sup * = Sj, sup *Pj, sup * = fj(Mj, sup *, Pj, sup *, Mk, sup *, Pk, sup *, Mj, sup *Ej, sup *, Pj, sup *Ej, sup *, Mk, sup *Ej, sup *, Pk, sup *Ej, sup *, Ej, sup *)Pj, sup *.
If brand j did not have a brand name, the resulting equilibrium would be Mj, sup **, Pj, sup **, Mk, sup **, Pk, sup **, Ej, sup ** = 0, Ek, sup **.( n2) This would yield the following revenue for brand j:
( 5) Rj, sup ** = Sj, sup **Pj, sup ** = fj(Mj, sup **, Pj, sup **, Mk, sup **, Pk, sup **)Pj, sup **.
Therefore, the outcome of the brand's equity is its revenue premium, Rj, sup * - Rj, sup **, that is, the revenue it achieves in the market less the revenue it would achieve if it had no brand name.
This theoretical development provides two major insights. First, the revenue outcome is achieved in competitive equilibrium, where brands adjust their marketing mix and prices to maximize profits. Therefore, revenue premium does not need to control for the marketing activities of either the brand or its competitors: The marketing mix, and equity itself, is part of equilibrium and is manifest in the revenue the brand achieves. This is the reason outcome measures generally do not control for marketing activities in quantifying the value of a brand. Keller (2003, p. 492) critiques as static measures that hold everything else constant and attempt to isolate only preferences for the product itself and highlights the importance of including differential response to marketing activities.( n3)
Second, an exact calculation of equity requires structural estimates of the demand and equity functions for each brand, which in general are not available. Equations 2 and 3 could be combined to yield one "reduced form" equation, but even in the simplest case in which both equations are linear, the reduced form would still have interactions and quadratic terms. If the demand and equity functions were available, equilibrium marketing mix, prices, and revenue could be calculated by first using the equity function for brand j and then setting it equal to zero. The difference in equilibrium revenue is the revenue premium measure of equity. However, this process is difficult to implement in practice because it requires knowledge of the demand and equity functions, and it still may not yield closed-form equilibriums.
Therefore, we take a pragmatic approach to approximate Rj, sup * - Rj, sup **. We take the brand's current revenue as Rj, sup * and the revenue of the private label in its category as Rj, sup **. Subtracting the latter from the former yields the revenue premium for brand j. Two key assumptions underlie this calculation. The first is that brands pursue rational equilibrium strategies so brand revenue approximates Rj, sup *. This assumption is most likely to hold over long periods, such as an annual time frame (Ailawadi, Kopalle, and Neslin 2002). Weekly demand may be subject to random shocks and out-of-equilibrium knee-jerk reactions to competitors' actions, but over the long run, this "dust settles" (Dekimpe and Hanssens 1999), and the market is in equilibrium.( n4)
The second assumption is that the private label mimics how the brand would perform if it had no brand name, so private label revenue approximates Rj, sup **. The generally low expenditures of private labels on brand-building activities, such as advertising and R&D, and their low prices provide face validity to this assumption, and other researchers who have used private labels as a benchmark to compute a brand's price or market share premium (Park and Srinivasan 1994; Sethuraman 2000) provide precedence. Still, there are some potential complications. First, we assume that the demand function facing the private label is identical to that which brand j would face if it had no equity. If this is not the case, private label revenue may not be a good surrogate for Rj, sup **. However, note that many, if not all, of the differences in demand parameters between national brands and private labels are likely due to brand equity, and our model accounts for these differences by means of the main and interaction effects of brand equity. Second, there will be an obvious zero-equity brand in some markets, most often it will be a private label, that provides a good surrogate for what the brand would achieve if it had no brand name and thus no equity. In other markets, a new entrant or a weak brand may need to be used as the benchmark. Third, private labels vary across retailers and markets. However, unlike measures such as price or market share premium, total revenue premium has the advantage that it can be computed as the sum of revenue premiums for individual retailers and/or markets (indexed by s):
( 6) [Multiple line equation(s) cannot be represented in ASCII text]
Advantages of the Revenue Premium Measure
The external validity and objectivity of the measure are obvious because revenue premium is computed with actual market data, not responses to hypothetical scenarios or subjective judgments. Revenue premium is logical, intuitive, and linked to a key performance measure that marketers and the investment community care about: revenue. Revenue premium is easy to calculate because it does not require consumer surveys, estimates of demand elasticities, or assumptions about consumer choice. The data required for calculating revenue premium are readily available in existing internal and secondary data (e.g., annual reports, Information Resources Inc. and ACNielsen data). Therefore, it can easily be monitored for a large number of brands and categories.
Revenue premium is also more complete than some other outcome measures because it considers both volume premium and price premium. Consider four possible cases, depicted in Figure 2, that depend on the price and unit sales of the brand relative to a private label. In each case, price is on the x-axis, and unit sales are on the y-axis. The B represents the branded product, and PL represents the private label equivalent. The area depicted by the plus sign represents a positive contribution to the brand's revenue premium, and the area depicted by the negative sign shows a negative contribution.
Case A represents the ideal situation: The brand is priced higher and sells more than the private label does. Its revenue premium is the shaded area depicted with a plus sign. In Case B, the brand sells at a higher price but has fewer unit sales than the private label does. Major brands in the cigarette and diaper markets have faced this situation in recent years as consumers switch to private label and discount brands and are no longer willing to pay high price premiums (Keller 2003, p. 106; Miller 1993). In such cases, revenue premium may be positive or negative, depending on the relative size of the positive premium due to higher price (depicted by "+" in Figure 2) and the negative premium due to lower sales (depicted by "-").( n5) In Case C, the branded good enjoys greater sales than the private label does (depicted by "+") but at a lower price (depicted by "-"). Again, total revenue premium may be positive or negative, depending on the size of the components. Although it is not common for strong brands to be priced below private labels, several low-priced brands do have strong equity in today's value-conscious market, as we noted previously. Finally, Case D is the opposite of Case A: The brand sells fewer units at a lower price than the private label does. Revenue premium is negative in this case, and prospects for a brand in this position are not encouraging.
The four cases illustrate the completeness of our measure compared with some other product-market measures. For example, in Case A, value-priced brands may be labeled as low equity by means of a price premium measure when their true strength is better reflected in revenue premium. In Case C, the market share measure would label brands as high equity, ignoring that the brand might have "bought" share by cutting prices. Behavioral brand loyalty, which is sometimes quantified as share of category requirements (i.e., the percentage of customers' total category purchases of the given brand), does not account for either the number of customers or the price they pay. Therefore, tracking revenue premium and determining in which of the four cases in Figure 2 their brand lies enables brand managers to flag a problem or an upturn in brand strength more readily than would one of these measures alone.
Limitations of the Revenue Premium Measure
As we noted previously, no single measure of brand equity is ideal on all fronts. First, as do all outcome measures, revenue premium has limited diagnostic ability: It does not provide insight into the customer-level sources of equity and thus the "quality" of this equity. Second, revenue premium does not explicitly consider a brand's extendibility and future potential, though it represents a reasonable floor on the overall long-term value of a brand (see Dubin 1998, p. 78). A multiple could be applied to a brand's revenue premium to reflect its future potential, but any forecasting attempts are necessarily subjective or complex. The subjectivity of such multiples is evident in the rule of thumb that accountants allegedly use to price a brand: four to six times the annual profit realized by products bearing the brand name (Keller 2003, p. 495). Third, our measure does not include costs. To adjust the revenue premium measure for variable costs, we define the following:
( 7) Adjusted revenue premiumb = (volumeb)(priceb - variable costb) - (volumepl)(pricepl - variable costpl).
Inclusion of variable costs has a negative impact on our measure in Cases A and C and a positive impact in Cases B and D. In this article, we use the gross revenue premium rather than the adjusted revenue premium measure partly because we do not have reliable data for variable costs. However, it could be argued that in some sense, gross revenue premium is a more appropriate measure because it reflects market demand rather than the firm's internal production costs.
The validity of a brand equity measure can be assessed by examining whether it ( 1) is stable (reliable) over the short and medium runs and correlates ( 2) with other measures of brand equity; ( 3) in expected ways with the brand's marketing effort; ( 4) in expected ways with other variables, such as the characteristics of the product category; and ( 5) in expected ways with price sensitivity.
Stability over Time
Brand equity is an enduring phenomenon because it is built with long-term effort and investment (Aaker 1991; Farquhar 1990). In general, therefore, brand equity should be fairly stable in the short and medium runs. However, conventional wisdom maintains that the equity of brands eroded in the 1990s as consumers became more price conscious and as private labels gained market share (Dunne and Narasimhan 1999). Thus, although a measure of brand equity should not change drastically from one year to the next, it should reflect overall market trends.
Correlation with Other Measures
In theory, various measures of brand equity reflect the same underlying construct. However, equity is a multidimensional construct (Aaker 1996), and each measure may tap somewhat different dimensions. A new measure should correlate well with other conceptually similar measures, but it should not correlate so highly as to be redundant.
Correlation with Marketing Activities
As Figure 1 shows, marketing activities influence brand equity. It is widely accepted that advertising increases equity (Aaker and Biel 1993; Kirmani and Zeithaml 1993; Mela, Gupta, and Lehmann 1997). In contrast, some researchers argue that promotions erode brand loyalty and equity (Jedidi, Mela, and Gupta 1999; Keller 2003, p. 310; Yoo, Donthu, and Lee 2000); others suggest that promotions do not have a negative effect on brand loyalty (Ehrenberg, Hammond, and Goodhardt 1994; Gedenk and Neslin 2000) and even expand the brand franchise by increasing penetration (Ailawadi, Lehmann, and Neslin 2001). We validate the revenue premium measure by examining whether its correlation with these variables is in line with what is expected of a brand equity measure. Note that this is strictly a test of association, not causality. The causal relationship between marketing actions and brand equity, as Figure 1 indicates, occurs through a complex chain of simultaneous relationships that we do not model.
Correlation with Category Characteristics
As Figure 1 shows, a driver of variation in equity across categories is the level of risk that consumers perceive. Risk may be related to performance, financials, or social aspects (e.g., Dunn, Murphy, and Skelly 1986). Brands should have higher equity in categories with greater perceived risk. The perceived risk of using unbranded products is greater ( 1) if the average time between purchases is high or if consumers stockpile, because consumers then must endure their choice for a longer time; ( 2) if the category is consumed more for pleasure than for utility, because it is easier for consumers to compare functional attributes than hedonic ones; and ( 3) if there is a greater difference in quality between branded and unbranded products (Batra and Sinha 2000; Richardson, Jain, and Dick 1996; Sethuraman and Cole 1997). Thus, brand equity should be positively associated with length of purchase cycle, stockpileability, hedonic products, and quality differences between branded and unbranded products.
Correlation with Consumer Price Sensitivity
Brand equity makes consumers less sensitive to price increases and thus enables the brand to charge a premium price. In contrast, a high-equity brand should make significant sales gains when it cuts its price. Thus, a high-equity brand may have a weaker (less negative) "up" self-elasticity and a stronger (more negative) "down" self-elasticity (Keller 2003; Keller and Lehmann 2001; Sivakumar and Raj 1997).( n6)
We base our empirical investigation on two separate data sets for the consumer packaged goods industry, both of which cover the period from 1991 to 1996. The first data set includes weekly price, promotion, sales, and retail margin data for several product categories sold in 85 stores owned by Dominick's Finer Foods, a major grocery retailer in the Chicago market. We study the 17 categories in which Dominick's had a private label offering during the entire period of our study. We calculate revenue premium and all other product-market measures possible for each of the 111 brands in each year. We provide definitions of all the variables in Table 1.
The second data set includes the entire U.S. grocery channel and contains share, price, promotion, and advertising data for 102 brands in 23 product categories from 1991 to 1996. We compile annual data on share, sales, price, promotion, and category characteristics from Information Resources's Marketing Fact Book, which tracks purchases of a panel of thousands of randomly selected households in markets across the United States. The Fact Book provides nationwide grocery sales of each category on a per-thousand-households basis and unit market shares of each brand, from which we compute unit sales of each brand per thousand households. We supplement Fact Book data with advertising expenditures from LNA/Media Watch, Narasimhan, Neslin, and Sen's (1996) measure of category stockpileability, Hoch and Banerji's (1993) data on private label quality, and a classification into hedonic versus utilitarian categories per Sethuraman and Cole's (1997) survey and the judgments of several experts. In each category, we include two to four major brands, apart from private labels, and at least one small-share brand that existed during the entire study period, were sold nationally, and were not niche players. Definitions of the variables in this data set are also listed in Table 1.
The benefit of the local data set is that it covers a single market and uses the private label from a single retailer. Thus, it is free from issues of heterogeneity in private label quality across retailers and of differences in equity across markets, though the levels of brand equity may not be representative of the national market. In contrast, the national data set enables us to examine how much revenue premium packaged goods brands possess and how it has changed over time in the entire country. In obtaining this nationwide view, differences across retailers and markets are averaged out. Thus, the two data sets complement each other and together contribute much more to our empirical analysis than either one would by itself.
Change over Time
The correlation of revenue premium with its lagged value in the local sample is .96. This high correlation speaks to its stability from one year to the next. However, as we noted previously, the 1990s were a period of eroding brand equity. Table 2 provides a summary of trends in private label share and revenue premium for each category.
In general, the trends in Table 2 support conventional wisdom. From 1991 to 1996, the median percentage change in Dominick's private label share is 13.5%, and the median percentage change in revenue premium is -11%. For individual categories, we find that the private label share increased in all but 5 of the 17 categories and the median revenue premium decreased in 11 categories. Although the percentage change in revenue premium seems large in some cases, recall that the change is over six years. For example, the 77% increase in canned broth translates to a 12% annual increase.( n7)
Correlation with Other Measures
Table 3 summarizes the correlation of revenue premium with other measures; there are several notable results. First, our measure correlates strongly with revenue, but the correlation is not perfect, showing that revenue premium captures something different from revenue. Second, our measure is much more simple to compute than Dubin's (1998) measure (see the Appendix), yet it correlates well with it (.82). Third, the correlation is also strong with revenue premiums for the smallest-share (.90) and lowest-price (.83) brands, which are useful in categories with no private label. Fourth, our measure correlates strongly with volume premium obtained (.79) but not with price premium charged. As we discuss subsequently, this reinforces the need for a measure that combines both volume and price premiums.( n8)
Volume Premium Versus Price Premium
We determine the breakdown of our sample in terms of the four cases depicted in Figure 2. Of the brands, in 1991 33% were Case A; 55%, Case B; 5%, Case C; and 7%, Case D. Thus, only one-third of the sample enjoy both a price and a volume premium over the private label, and more than one-half charge a price premium but are not strong enough to sell more than the private label does. The existence of this sizable latter group explains the lack of correlation between revenue premium and price premium charged.
Table 4 displays the price and volume premium components for the brands with the highest and lowest revenue premium in each category. It supports the distribution of the four cases, showing that the vast majority of brands charge a positive price premium but that many are unable to get a positive volume premium. For example, consider the lowest-revenue-premium brands in categories such as juice, broth, soup, and cheese, which belong to Case B. Consideration of only price premium charged paints a relatively rosy picture of the brands, but their revenue premium, as shown in Table 2, is mostly negative. They are subject to significant upside price elasticity and therefore do not have much equity. Consideration of changes over time also reveals that the three measures can differ significantly. For example, consider the American processed cheese and liquid fabric softener brands with the highest revenue premium. From 1991 to 1996, the former lost 35% of its price premium but gained 37% in volume premium; the latter's price premium rose by 71%, but its volume premium declined by 64%. Whether these brands gained equity overall during this period cannot be ascertained from these numbers. Table 2 shows that the overall impact was a 21% increase in the revenue premium of the cheese brand, reflecting an increase in equity, and a 23% decrease in the revenue premium of the fabric softener brand, reflecting a decrease in equity.
These patterns demonstrate that revenue premium provides a more complete single measure of equity than either volume premium obtained or price premium charged. Volume premium may be bought by means of lower price premiums, and revenue premium is needed to determine whether this is the case. Price premium may result in significant losses in volume premium; again, revenue premium is needed to determine this. That less than one-half the cases are unambiguous (revenue, price, and volume premiums are all positive or all negative) underscores the need to examine revenue premium as the overall descriptor of the brand's equity.
Revenue Premium in Partitioned Markets
A significant issue in calculating revenue premium is the definition of the market. A market definition that is too broad may make a niche or regional player appear to be much weaker than it really is. In contrast, a market definition that is too narrow can make even a weak brand appear to be strong. For example, we define bottled, refrigerated, and frozen juice drinks as three separate categories in our data. If we aggregated these products into one category, juice, then Gatorade, which sells only bottled juice drinks and not the other products, would appear to be much weaker than it really is. As shown in Table 5, Gatorade's revenue premium was -$161,537 in the bottled juice drinks category, but it would appear to be considerably worse at -$7,601,469 if we inappropriately evaluated the brand in an aggregated juice category. If we define the market more narrowly as sports drinks, Gatorade's revenue premium is high. In contrast with Gatorade, Tropicana sells products in all three categories, though it is strongest in refrigerated juice and weakest in bottled juice drinks. In an aggregate juice category, Tropicana's strong showing in refrigerated juice would not be revealed; rather, it would be offset by the weaker showing in bottled and frozen juices.
We cannot prescribe the "right" way to define the market, but we recommend that a rigorous method be used when the market structure is not obvious (e.g., Kalwani and Morrison 1977; Urban, Johnson, and Hauser 1984). Moran (1994) recommends that the served market be defined quite narrowly on the basis of the segment in which the brand enjoys the greatest loyalty. However, this may be a slippery slope, because any brand can appear strong if its served market is defined narrowly enough. Ultimately, the breadth of the market definition should depend on the pattern of interbrand competition and switching as well as the firm's aspirations for the brand.
In summary, the key findings from the local data set are that ( 1) revenue premium is highly correlated from year to year, suggesting stability; ( 2) its trend over the six-year period is consistent with conventional wisdom about the eroding equity of brands; and ( 3) it correlates in expected ways with other measures of brand equity.
Change in Measure over Time
The correlation of revenue premium with its lagged value is .98, showing that the measure is highly reliable even at the aggregated national level. Table 6 summarizes the median revenue premium in each category and median percentage changes over time.
The trends in our measure are again consistent with conventional wisdom about brand equity; there is an improved position of private labels and a decrease in revenue premium. The median percentage loss in revenue premium across all brands in our sample is 29% over the six-year period (translating to an approximate 6.6% decrease per year), and the median percentage gain in private label share is 69%. The change in private label share is positive for all but three categories, and median change in revenue premium is negative for all but four categories.
Two of the worst hit categories are cold/allergy/sinus tablets and liquids. In these categories, the median decreases in revenue premium are 235% and 275%, respectively, over the six-year period. To understand why such drastic changes occurred in these categories, consider that private labels increased their share by approximately 80% during the period. At the same time, direct-to-consumer advertising of prescription drugs increased significantly, and consumers became more aware of these alternatives. Furthermore, given the co-pay system of most health maintenance organizations, prescription drugs became more like over-the-counter products in terms of consumers' out-of-pocket costs. The result was that marginal over-the-counter brands, such as Alka-Seltzer, Chlor-Trimeton, and Drixoral, lost out to both private label and prescription drugs. Because these brands had little revenue premium to begin with, the percentage decrease was even greater.
Diapers are another category in which brands experienced substantial losses in revenue premium. In this category, private labels more than doubled their share from 1991 to 1996. At the same time, the category leaders Kimberly-Clark (Huggies brand) and Procter & Gamble (Luvs and Pampers brands) were locked in a price war and a struggle for share. As a result, they lost almost 70% and 90%, respectively, of their revenue premium during the six-year period. That the equity of these brands suffered is borne out by Total Research Corporation's EquiTrend study (Miller 1993, p. 8).
Correlation with Other Measures
Table 7 summarizes the correlations of revenue premium with other measures. With annual national data, there are not enough observations to estimate a demand function separately for each brand. As a result, we were unable to compute Dubin's (1998) measure of equity. All the other measures we computed for the local data set are included in Table 7. We also included a measure of behavioral brand loyalty, the brand's share of requirements (SOR), and the SOR premium over private labels.
The pattern of correlations in Table 7 is similar to that obtained for the local data set. We particularly note three results. First, the high correlations of revenue premium using private label as the benchmark with revenue premium using the smallest-share national brand (.92) or lowest-price national brand (.91) as benchmarks are reassuring. In addition to confirming the robustness of the measure, the correlations also alleviate concerns about the aggregation of multiple private labels in the national data set. Second, the correlations with SOR and SOR premium are .20 and .54, respectively. Neither SOR nor SOR premium reflects the number of consumers who buy the brand or the price they pay, so the measures are less complete than revenue premium. However, the correlation with SOR premium is stronger because it is more similar to the conceptual definition of brand equity in that it compares with a benchmark. Third, the correlation with price premium is almost zero, as it is in the local data set. Again, this reinforces the importance of including both volume and price premiums in equity measurement. Referring back to the four cases depicted in Figure 2, we find that in 1991, 54% of the brands were in Case A; 30% in Case B; 11% in Case C; and 5% in Case D. Therefore, even nationwide, a substantial number of national brands charge a price premium but are not strong enough to achieve a volume premium, which explains the lack of correlation between price premium and revenue premium.
Association with Marketing-Mix and Category Variables
Having established the stability, face validity, and convergent validity of revenue premium, we examine whether it is associated in expected ways with other variables by estimating the following regression:
( 8) Revenue Premiumijt = α + β1 Revenue Premiumijt-1 + β2SOVijt + β3SOPijt +β4PurCyclej + β5Stockpilej + β6Hedonicj + β7PLQualityj + β8Catrevjt + εijt.
In Equation 8, the revenue premium of brand i in category j in year t is a function of its revenue premium in the previous year, its share of total advertising (SOV) in category j in year t, its share of total promotion (SOP) in category j in year t, the average purchase cycle (PurCycle) and stockpileability (Stockpile) of category j, the hedonic nature of category j (Hedonic), and the average quality of private labels compared with national brands (PLQual) in category j.( n9) Because we estimated the regression by pooling data across categories, we controlled for differences in market size across categories by including the revenue of category j in year t (Catrev) in the model.
In general, the results summarized in Table 8 confirm our expectations. First, the coefficient of lagged revenue premium is .93, again confirming the stability of revenue premium from year to year. Second, a brand's share of category advertising has a significantly positive association with revenue premium. Third, category characteristics such as purchase cycle, hedonic nature of the category, and relative quality of private label are significantly associated with our measure, and their coefficients are of the expected sign. The only two variables that are not significant are the brand's share of category promotion and the stockpileability of the category. As we discussed previously, the lack of a significantly negative coefficient for share of category promotion is actually consistent with recent work that shows that promotion increases penetration and has little negative impact on SOR (Ailawadi, Lehmann, and Neslin 2001). As a result, the positive impact on unit sales offsets the decrease in price that comes with increased promotion. Thus, revenue premium's association with most of the brand and category characteristics examined is consistent with theory and prior research.
Impact on Price Elasticity
Finally, using a modified version of Ailawadi, Lehmann, and Neslin's (2001) market share response model, we tested whether high-revenue-premium brands exhibit asymmetric up and down price elasticities. Specifically, we estimated a first-differenced log-linear model on data pooled across brands and categories:
( 9) Shareict = eα(Priceβ[sub 1ic, sub ict])(Advtβ[sub 2ic, sub ict])(Dealβ[sub 3ic, sub ict])(Coupβ[sub 4ic, sub ict]) (Priceβ[sub 5ic, sub ict]) (Advtβ[sub 6ic, sub ict])(Dealβ[sub 7ic, sub ict])(Coupβ[sub 8ic, sub ict])eε[sub ict],
where all the variables are transformed into first differences of logarithms, that is, logarithm of the value in year t less the logarithm of the value in year t - 1. The independent variables are the brand's own price, advertising, dealing, and coupons and the share-weighted average price, advertising, dealing, and coupons of the competing brands. Following Ailawadi, Lehmann, and Neslin (2001), we accounted for brand and category differences in elasticities by including interactions of all the marketing-mix variables with two dummy variables for brand size (Smallic and Midic) and four category characteristics (average category dealing, advertising, purchase cycle, and stockpileability). Thus, for k = 2 ... 8:
( 10) βkic = βk0 + βk1Smallic + βk2Midic + βk3CatDealc + βk4CatAdvtgc + βk5PurCyclec + βk6Stockpilec,
where Bkic is the coefficient of the kth marketing-mix variable in Equation 9. For the self-price coefficient (k = 1), we also included an interaction with a dummy variable (β17 that is equal to one if there was a price increase from the previous year and equal to zero if there was not. We estimated this model separately for high- and low-revenue-premium brands.( n10)
Rather than report the large number of coefficients in the regression model, we focus on the coefficient of the up price interaction (β17 The first row of Table 9 reports the estimated coefficients of the interaction for low- and high-revenue-premium brands. Our expectation about the up versus down asymmetric effect is confirmed. The interaction term with the brand's own price is not statistically significant for low-revenue-premium brands, but it is positive and significant for high-revenue-premium brands. To illustrate what these interaction coefficients mean for the up and down self-price elasticities of high- and low-revenue-premium brands, we calculate the base (i.e., the down elasticity) for each brand by plugging in its values for each of the independent variables in the model (we report the average down elasticity across all brands in the second row of Table 9). We then added the estimated coefficient of the up interaction term to determine the average up elasticity across all brands. Table 9 shows that low-revenue-premium brands have an average down price elasticity of -1.195 and an average up price elasticity that is not much different at -.921. In contrast, high-revenue-premium brands have an average down price elasticity of -.747 but an up price elasticity that is much less negative at -.183. As we expected, high-revenue-premium brands gain share when they cut prices but lose relatively little when they increase price.
We have proposed revenue premium as a measure of brand equity, discussed its theoretical underpinnings, and validated the measure. Revenue premium is conceptually grounded in the fundamental definition of brand equity and theoretically grounded as the equilibrium outcome of a competitive marketplace. It is stable over time, yet reflects conventionally accepted industry trends, correlates reasonably with other product-market measures, and is more complete. Revenue premium's association with marketing actions and category characteristics is consistent with theory, as is its association with up and down price elasticities.
Implications for Managers
It is highly unlikely, if not impossible, for a single measure of brand equity to satisfy all the characteristics of the ideal measure. Still, the revenue premium measure has several strengths that make it attractive to managers. It is a single, objective number that is credible to senior management and the financial community, and it provides a useful guide to the value of a brand during mergers and acquisitions. Revenue premium is easy to calculate with readily available data and thus can be monitored on an ongoing basis for several brands in several product categories. At the same time, it is more complete than some other product-market outcome measures and thus provides a more accurate summary of brand health. Managers can also use the revenue premium measure to monitor the impact of marketing decisions on the long-term value of their brands.
The most challenging aspect of calculating revenue premium is the identification of the benchmark brand, that is, the product that mimics what the subject brand would achieve if it had no equity. We used private label as the surrogate, but some private labels arguably have brand equity, and in some categories private labels do not exist. That private label-based revenue premium correlates highly with lowest-price or lowest-share brand-based revenue premiums suggests that as long as the choice of the benchmark is sensible, the measure is robust. We recommend that managers identify a reasonable benchmark brand and use it consistently.
The most significant limitations of revenue premium for managers are that it does not provide insight into the consumer-based sources of brand equity or quantify a brand's future extendibility and potential. Customer mindset measures are crucial for diagnosing the underlying reasons for changes in equity that may be signaled by revenue premium, and financial market measures are crucial for examining long-term potential, even if the assessment is subjective. All these measures are needed to provide a rich picture of current and future brand health. We recommend that managers regularly use revenue premium for tracking brand health over time compared with that of their competitors and periodically examine customer mind-set measures to guide marketing decisions and fully diagnose problems flagged by revenue premium and its price and volume premium components. We also caution managers not to become complacent simply because their brands enjoy a large revenue premium. It is imperative to have a sense of both the consumer-based sources of the revenue premium and the future challenges and opportunities the brand faces.
Implications for Researchers
We believe the contribution of this article lies not only in proposing the revenue premium measure of brand equity but also in providing a framework within which the reliability and validity of various brand equity measures can be evaluated and in starting that validation process with the revenue premium measure. We hope that our work will encourage others to conduct such validation of the measures they develop. Although we validated our revenue premium measure against as many other measures of equity as we could calculate, we were limited by the availability of data. For example, we could not correlate our measure with customer mind-set or financial market measures. Although we recognize that measures used in industry are often based on proprietary data, we hope that researchers will share data with one another whenever possible to promote better measurement of this construct.
Our work also suggests some specific avenues for further research. First, revenue premium reflects the equilibrium realization of all the complex interrelationships among the brand name, its marketing decisions, and its competitors' marketing decisions. A worthwhile research project would be to estimate these structural relationships and understand the process by which firms develop high-equity brands. A second research need is an outcome measure of brand equity that is explicitly linked to the different consumer-based sources of brand equity. Park and Srinivasan (1994) take a step in this direction by decomposing equity into attribute-based and non-attribute-based components; however, more work is needed to combine some of the diagnosticity of customer mind-set measures with the financial valuation ability of market outcome measures. Third, a significant portion of the benefit of a brand name is its future potential. Current methods for valuing future potential depend on subjective multipliers or on the swings of the supposedly "efficient" stock market. It would be valuable to test validity of historical values against present values. For example, researchers could test the predictive validity of brand valuations done in the early 1990s using Interbrand or Simon and Sullivan's (1993) methodology by comparing them with the actual performance of those brands in recent years.
Further research should also quantify the long-term financial value of a brand. A relatively simple, objective approach for obtaining this from the current revenue premium is based on the premise that without further brand-building investment (e.g., advertising) in the brand, the brand's revenue premium will gradually decay to the level of a private label. Thus, researchers could estimate the carryover or persistence and treat it as an annuity. For example, if the estimated carryover coefficient is .9 (i.e., 10% of the value decays each year) and the discount rate is 10%, the long-term value of a brand is (1 + .1)/(1 + .1 - .9) = 5.5 x the current revenue premium. Alternatively, researchers could assume that further brand-building expenditures will keep revenue premium constant, and they could treat the current revenue premium less annual brand-building costs as an annuity. This does not account for the extendibility of the brand name to other products, but it is a reasonable starting point.
As technology and new distribution channels continue to intensify the competitive environment, the viability and health of the brand will continue to be prominent, even dominant, in the minds of managers. This argues for the importance and potential impact of more research of the type we suggest.
The authors thank Paul Farris, Kevin Keller, and Al Silk for their many valuable suggestions; the University of Chicago, Steve Hoch, and Raj Sethuraman for providing some of the data; Paul Wolfson for computing assistance; and Bethanie Anderson for editorial assistance. Thanks are also due to Marketing Science Institute conference participants and seminar participants at Case Western Reserve University, Dartmouth College, Erasmus University, Harvard Business School, Massachusetts Institute of Technology, Syracuse University, Tilburg University, Tulane University, University of Connecticut, and University of Michigan for their comments. Finally, the authors thank the four anonymous JM reviewers for their many helpful suggestions.
(n1) For exposition purposes, we portray two competing brands, but there could be several.
(n2) We set the equity the brand would achieve if it did not have its brand name equal to zero, without loss of generality.
(n3) Market size, which is not determined in equilibrium, could be controlled for by taking revenue premium as a percentage of category revenue. We present the absolute size of the revenue premium because it provides a dollar value of the brand. We believe it is important to control for market size when comparisons are made across categories, and we do so later in this article.
(n4) Note that equilibrium does not mean stable or zero growth. It simply means that during the period of interest, firms maximize their profits while taking into account one another's actions, new entrants and exits in the market, and environmental influences such as growth in the category.
(n5) It is important to define the market appropriately because a strong niche or regional player may incorrectly appear to belong to Case B if its revenue premium is calculated in a broad market that it does not serve. We examine this issue in our empirical analyses.
(n6) This is related to but distinct from the concept of tier-based asymmetric price competition (Blattberg and Wisnewski 1989). The latter considers the amount a high-tier brand takes from a low-tier brand compared with the amount a low-tier brand takes from a high-tier brand. In contrast, our analysis compares the up self-elasticity of a high-equity brand with its own down self-elasticity.
(n7) The percentage change is also amplified when the base is small. For example, all but one of the natural cheese brands had negative revenue premium in 1991. By 1996, this brand's premium also had become negative. The change appears great (-2108%) because it is calculated from the small base in 1991 ($68,300).
(n8) Note that the price premium charged in the market is not the same as the price premium measure used in the literature. The latter is the premium that consumers report they are willing to pay for a brand over a private label; this is obtained from consumer survey data, which were not available to us. Sethuraman (2000) is the one researcher who provides data on a survey-based price premium measure. Although only six of Sethuraman's categories overlap with ours and he measures national brand equity at the category level rather than the brand level, the correlation with median revenue premium as a percentage of category revenue is .61.
(n9) We use share of advertising rather than dollar advertising because the latter may have a positive coefficient simply as a scaling artifact. Companies often use a target advertising-to-sales ratio as a budgeting rule, and therefore categories and brands that have high sales will also have greater dollar spending. For promotion, results are unchanged whether we use promotion or share of promotion.
(n10) We define these using a median split of revenue premium as a percentage of the category's revenue in 1993. We use the percentage figure to control for the size of different categories so that we do not classify a brand as low simply because the category is small, and vice versa.
Legend for Chart:
A - Variable
B - Definition
C - Source
A
B
C
Variables in Local Data Set
Price
Net selling price per unit volume
DD
Brand volume
Number of equivalent units of the brand sold
DD
Price premium charged
Brand's price-private label's price
DD
Percentage market share
(Brand's unit volume sold)/(category's
unit volume sold)
DD
Market share premium
Brand's market share-private label's market share
DD
Volume premium
Brand's unit volume-private label's unit volume
DD
Revenue
Unit volume x price
DD
Revenue premium
(Brand's unit volume x brand's net price per unit
volume) - (private label's unit volume x private
label's net price per unit volume)
DD
Revenue premium over
smallest-share brand
(Brand's unit volume x brand's net price per unit
volume) - (smallest-share brand's unit volume x
smallest-share brand's net price per unit volume)
DD
Revenue premium over
lowest-price brand
(Brand's unit volume x brand's net price per unit
volume) - (lowest-price brand's unit volume
x lowest-price brand's net price per unit volume)
DD
Dubin's equity
(Brand's unitvolume)(Brand's net price)
{1 - [Sb(1-Sb)(εb - 1)/
(1- Shareb)(εpl - Sb]}
DD(for
details,
see the
Appendix)
Additional Variables in National Data Set
Category volume per
1000 households
Number of equivalent units of the category sold
Fact Book
Brand volume per 1000
households
Brand market share x category unit volume
Fact Book
SOR
Among households that bought the brand,
the percentage of their total
category purchases represented by the brand
Fact Book
SOR premium
(Brand's SOR)-(private label's SOR)
Advertising
Total advertising expenditure (millions
of dollars) across 10 media computed by
monitoring advertisements in each
medium/program and applying a relevant
rate to each advertisement
LNA/Media
Watch Ad
$ Summary
Promotion
Percentage of brand sales made on a promotion
Fact Book
Small-brand dummy
Equal to 1 if brand accounts for less than
5% of the sales of the top three
brands in the category, equal to 0 otherwise
Fact Book
Medium-brand dummy
Equal to 1 if brand accounts for 5%-40% of the
sales of the top three brands
in the category, equal to 0 otherwise
Fact Book
Purchase cycle
Hedonic category dummy
Average number of days between consecutive
purchases of the category Equal to 1 if mean
summed score from consumer mail-survey
response (three-point scale) to
two items (The product is fun to have; The product
gives me pleasure) is greater than 2, equal
to 0 otherwise
Fact Book
Sethuraman
and Cole
(1997);
expert
judgment
Stockpileability
Mean factor score from consumer survey response
(five-point scale) to two items (It is easy to
stock extra quantities of this product in my home;
I like to stock up on this product when I can)
Narasimhan,
Neslin, and
Sen (1996)
Private label quality
Mean mail-survey response (five-point scale)
by retail experts to: How does the quality
of the best private label supplier
compare to leading national brands in this category?
Hoch and
Banerji
(1993)
Notes: DD = Dominick's database, University of Chicago. Following
Ailawadi, Lehmann, and Neslin (2001), we combined all items sold
by a manufacturer in a given category in the brand. Therefore, we
computed Procter & Gamble's revenue premium in the diaper
market, Colgate's revenue premium in the toothpaste market, and
so on. Legend for Chart:
A - Product Category
B - Private Label Share 1991(%)
C - Private Label Share Percentage Change(a)
D - Median Revenue Premium 1991($)
E - Median Percentage Change(a)
F - Highest-Revenue-Premium Brand 1991($)
G - Highest-Revenue-Premium Brand Percentage Change(a)
H - Lowest-Revenue-Premium Brand 1991($)
I - Lowest-Revenue-Premium Brand Percentage Change(a)
A B C D E
F G
H I
Food Products
Bottled juice 18.3 14 -1,344,392 -57
1,824,308 -48
-1,698,933 -22
Canned broth .5 852 515,501 77
688,630 26
342,372 128
Canned soup 3.4 50 1,263,653 -41
7,631,917 -10
-154,807 -41
Canned tuna 8.5 3 -239,926 68
1,820,494 -93
-434,458 72
Cheese, American 27.4 -46 -1,799,205 43
4,647,035 21
-2,211,581 43
Cheese, natural 43.3 15 -1,379,057 -62
68,330 -2108
-2,152,881 -62
Frozen juice 33.8 -1 -2,767,301 39
-787,483 83
-3,535,425 39
Ready-to-eat cereal 6.2 -18 1,329,812 8
13,074,037 8
-1,261,071 -24
Refrigerated juice 23.7 -11 -3,229,130 -3
3,481,530 88
-3,762,568 -4
Personal Care Products
Toothbrushes 8.5 76 -4,275 -38
287,376 -38
-47,272 97
Toothpaste 1.7 32 409,002 12
1,556,056 -16
99,554 172
Paper Products
Toilet tissue 4.5 49 -26,390 -9
4,908,696 -66
-405,530 11
Cleaning Products
Dishwasher detergent 9.6 -19 185,233 32
1,161,689 -17
-142,423 -7
Dishwashing liquid 6.8 36 683,427 -55
2,121,942 -55
377,736 -54
Laundry detergent 1.3 61 638,577 -15
14,301,874 -15
186,084 63
Liquid fabric softener 5.7 191 10,103 -274
1,999,097 -23
-58,904 -348
Sheet fabric softener 14.8 76 -187,161 -60
1,022,546 -42
-251,012 -51
Overall sample 8.5 13.5 -51,786 -11
13,074,037 8
-3,762,568 -4
(a) Change from 1991 to 1996 as a percentage of the 1991
absolute value.
Notes: Median values of private label share and percentage change
in private label share are reported for the overall sample. Legend for Chart:
A - Product-Market Measure
B - Correlation with Revenue Premium
A B
Volume .62
Volume premium .79
Market share .65
Market share premium .73
Price premium charged -.00
Revenue .89
Private label revenue -.36
Dubin's (1998) equity .83
Revenue premium over
smallest-share brand .90
Revenue premium over
lowest-price brand .82
Revenue premium lagged
one year .96 Legend for Chart:
A - Product Category
B - Highest-Revenue-Premium Brand 1991 Volume Premium (Units)
C - Highest-Revenue-Premium Brand Percentage Change(a)
D - Highest-Revenue-Premium Brand 1991 Price Premium ($/Unit)
E - Highest-Revenue-Premium Brand Percentage Change(a)
F - Lowest-Revenue-Premium Brand 1991 Volume Premium (Units)
G - Lowest-Revenue-Premium Brand Percentage Change(a)
H - Lowest-Revenue-Premium Brand 1991 Price Premium ($/Unit)
I - Lowest-Revenue-Premium Brand Percentage Change(a)
A B C D E
F G H I
Food Products
Bottled juice 15,664,952 -182 .019 20
-53,153,400 -21 .012 -6
Canned broth 13,873,356 -1 .007 58
7,012,374 83 .006 50
Canned soup 129,112,598 -29 .012 -10
-4,468,052 7 .06 -41
Canned tuna 10,988,986 -95 .034 18
-3,826,213 77 1.725 2
Cheese, American 19,200,240 37 .051 -35
-14,396,264 49 .085 5
Cheese, natural -3,126,070 -205 .074 28
-10,873,124 -48 .041 66
Frozen juice -15,249,504 60 .026 -5
-38,392,210 39 .020 30
Ready-to-eat cereal 69,346,274 -4 .061 -19
-10,812,402 6 -.002 -2365
Refrigerated juice 30,048,960 304 .015 -19
-148,956,488 2 .085 5
Personal Care Products
Toothbrushes 136,478 -76 .536 153
-32,998 -5 1.350 1
Toothpaste 4,383,046 -26 .090 24
108,649 244 .241 15
Paper Products
Toilet tissue 13,767,254 -68 -.003 1273
-889,896 -5 -.079 26
Cleaning Products
Dishwasher detergent 19,782,890 -8 .017 -36
-4,803,364 11 .164 6
Dishwashing liquid 28,863,102 -53 .020 -15
4,734,086 -31 .015 -93
Laundry detergent 190,379,659 -12 .033 -38
4,079,715 54 .002 -161
Liquid fabric softener 33,607,198 -64 .032 71
-4,335,198 -67 .419 -2
Sheet fabric softener 14,931,282 -48 .015 4
-5,211,640 -68 -.011 35
(a) The change from 1991 to 1996 as a percentage of the 1991
absolute value. Legend for Chart:
A - Market
B - Revenue Private Label
C - Revenue Gatorade
D - Revenue Tropicana
E - Revenue Premium Gatorade
F - Revenue Premium Tropicana
A B C D
E F
Bottled juice drinks $1,707,803 $1,546,266 $ 67,638
-$ 161,537 -$1,640,165
Frozen juice 3,671,137 0 2,883,654
-3,671,137 -787,483
Refrigerated juice 3,768,795 0 4,491,976
-3,768,795 723,182
All juice 9,147,735 1,546,266 7,443,268
-7,601,469 -1,704,466 Legend for Chart:
A - Product Category
B - Private Label Share 1991(%)
C - Private Label Share Percentage Change(a)
D - Median Revenue Premium 1991 ($/1000 HH)
E - Median Percentage Change(a)
F - Highest-Revenue-Premium Brand 1991 ($/1000 HH)
G - Highest-Revenue-Premium Brand Percentage Change(a)
H - Lowest-Revenue-Premium Brand 1991 ($/1000 HH)
I - Lowest-Revenue-Premium Brand Percentage Change(a)
A B C D E
F G H I
Food Products
Brownie mix 5.5 4 390 -18
711 -7 -71 -5
Frosting 3.6 76 483 6
788 6 152 32
Potato chips 5.8 -30 -422 23
3085 138 -1567 22
Shortening 14.5 31 637 -9
1456 -30 -181 12
Health Care Products
Cold/allergy/sinus liquids 23.3 79 -31 -275
204 -135 -122 -80
Cold/allergy/sinus tablets 19.4 83 -134 -235
34 -234 -313 -128
Cough syrup 15.4 89 233 -95
367 -33 98 -156
Personal Care Products
Bar soap .2 508 1259 -18
2829 -29 326 -18
Hair conditioner 1.9 -39 59 24
403 -8 11 160
Liquid soap 1.8 234 181 30
420 -17 80 11
Mouthwash 16.2 89 140 -121
593 -10 -152 -142
Shampoo 2.7 35 56 -30
1089 9 10 -493
Toothbrushes 10 31 203 -28
239 -54 -42 55
Toothpaste 1.6 70 491 -20
1845 -27 -29 -85
Paper Products
Diapers 11.1 157 1500 -104
5763 -88 -1030 -127
Paper towels 12.2 70 810 -69
2372 -10 -1093 -69
Facial tissue 16.2 34 -382 -31
2972 5 -975 -26
Toilet tissue 6.5 80 1877 -45
4467 -35 -1205 -61
Cleaning Products
Dishwashing liquid 4.1 52 1098 -44
3063 -44 924 -57
Dishwasher detergent 6.2 37 627 -18
1938 -29 573 -18
Dry bleach 2.7 -55 244 -38
559 -38 -4 2845
Liquid laundry detergent 1.7 39 218 -10
4516 -10 24 -44
Powdered laundry detergent 1.5 69 726 -41
10,431 -41 -209 -55
Overall sample 5.5 69 225 -29
10,431 -41 -1567 22
(a) The change from 1991 to 1996 as a percentage of the 1991
absolute value.
Notes: HH = households; median values of private label share and
percentage change in private label share are reported for the
overall sample. Legend for Chart:
A - Product-Market Measure
B - Correlation with Revenue Premium
A B
Volume .57
Volume premium .75
Market share .48
Market share premium .53
Price premium -.07
Revenue .91
Private label revenue -.02
Revenue premium over smallest-share brand .92
Revenue premium over lowest-price brand .91
SOR .20
SOR premium .54
Revenue premium lagged one year .98
Legend for Chart:
A - Independent Variable
B - Regression Coefficient Unstandardized
C - Regression Coefficient Standardized
A B C
Lagged revenue .93(*) .96(*)
premium (90.37)
Share of category 740.19(*) .02(*)
advertising (2.20)
Share of category 407.28 .01
deals (.44)
Average purchase 7.75(*) .11(*)
cycle (4.65)
Stockpileability 62.34 .01
(.76)
Hedonic category 166.59(*) .04(*)
dummy (3.95)
Private label quality -137.57(**) -.03(**)
(-1.79)
Category revenue .02(*) .11(*)
(5.13)
Adjusted R² .97 .97
F-statistic (d.f. 1,
d.f. 2) 1762 (8490) 1762 (8490)
(*) p < .05.
(**) p < .10.
Notes: The t-statistics are in parentheses;
d.f. = degrees of freedom. Legend for Chart:
A - Parameter/Elasticity
B - Revenue Premium Low
C - Revenue Premium High
A B C
Self-price increase dummy x .274 .564
self-price coefficient (β17) (.69) (2.41)
Average down self-price
elasticity -1.195 -.747
Average up self-price
elasticity -.921 -.183
(*) p < .05.
Notes: The t-statistics are in parentheses.DIAGRAM: FIGURE 1 Role of Customer-Based Equity in Determining Unit Sales
GRAPHS: FIGURE 2 Revenue Premium Measure: Four Possibilities
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Dubin's measure is the incremental profits the branded version receives compared with those of an unbranded version. Using oligopoly economic theory and a series of simplifying assumptions, Dubin derives the following formula for brand equity:
(A1) Dubin's equityb = volumeb(priceb - variable costb)
{1 - [Sb(1 - Sb)(εb - 1)/(1 - shareb)(εpl - Sb)]},
where Sb is the volume of brand b divided by the sum of the volumes of brand b and the unbranded (i.e., private label) products in the market, and εb and εpl are the price elasticities of brand b and the private label product, respectively. The entire term in braces represents the proportion of the brand's margin that is due to the brand name.
To calculate Dubin's measure, we obtained the price elasticity of each brand (and the private label) in each category by using weekly data pooled across stores to estimate a demand function for each brand. The demand function specifies the logarithm of unit sales of brand i in store s in week t (Lnvolist) as a function of the logarithms of prices of all n brands in the store in week t (Lnpricejst); the percentage of items belonging to each of the n brands that are on promotion in store s in week t (Promojst); the percentage of items belonging to brand i that were on promotion in store s in week t - 1 (Promoist - 1); 85 store dummy variables (Strdumks), where the kth dummy variable is 1 if s = k and 0 otherwise; 9 dummy variables for special events during the year (e.g., Easter, Labor Day, Thanksgiving, Christmas) (Splevdumlt), where the lth dummy variable is 1 if that event occurs in week t and 0 otherwise; and a trend variable (Trendt) that takes values 1, 2, 3 ..., N for each week in the data:
(A2) [Multiple line equation(s) cannot be represented in ASCII text]
The demand function controls for store-specific effects, special events and holidays during the year that may affect sales, any general trend in sales of the brand, and any lagged effects of the brand's promotion in the previous week, and it provides estimates of price and promotion (self and cross) elasticities for every brand in every category. We then used the self-price elasticities obtained for each brand to compute Dubin's measure of equity. Because we did not have information on variable costs, we computed the amount of the brand's revenue (not profit) that is due to the brand name:
(A3) Dubin's equityb) = (volumeb)(priceb) {1 - [Sb(1 - Sb)(εb - 1)/(1 - shareb)(εpl - Sb)]}.
~~~~~~~~
By Kusum L. Ailawadi; Scott A. Neslin and Donald R. Lehmann
Kusum L. Ailawadi is Associate Professor of Business Administration, and Scott A. Neslin is Albert Wesley Frey Professor of Marketing, Tuck School of Business, Dartmouth College.
Donald R. Lehmann is George E. Warren Professor of Business, Graduate School of Business, Columbia University.
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Record: 136- Riding the Saddle: How Cross-Market Communications Can Create a Major Slump in Sales. By: Goldenberg, Jacob; Libai, Barak; Muller, Eitan. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p1-16. 16p. 1 Diagram, 3 Charts, 12 Graphs. DOI: 10.1509/jmkg.66.2.1.18472.
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Riding the Saddle: How Cross-Market Communications Can Create a Major Slump in Sales
Using data on a large number of innovative products in the consumer electronics industry, the authors find that between one-third and one-half of the sales cases involved the following pattern: an initial peak, then a trough of sufficient depth and duration to exclude random fluctuations, and eventually sales levels that exceeded the initial peak. This newly identified pattern, which the authors call a "saddle," is explained by the dual-market phenomenon that differentiates between early market adopters and main market adopters as two separate markets. If these two segments-the early market and the main market-adopt at different rates, and if this difference is pronounced, then the overall sales to the two markets will exhibit a temporary decline at the intermediate stage. The authors employ both empirical analysis and cellular automata, an individual-level, complex system modeling technique for generating and analyzing data, to investigate the conditions under which a saddle occurs. The model highlights the importance of cross-market communication in determining the existence of a saddle. At low levels of this parameter, more than 50% of the cases of new product growth involved a saddle. This percentage gradually decreased as the parameter increased, and at values close to the within-market parameters, the proportion of saddle occurrences dropped below 5%.
The year 1985 was not a good one for personal computer (PC) manufacturers and vendors. Although the state of the U.S. economy could not be described as recessionary, sales were reported to be "sluggish" and the market was described as "despondent." In December 1985, BusinessWeek reported that "Importers of two new Asian imitations of the IBM PC are betting that their low prices will boost a despondent personal computer market" (Lewis 1985a, p. 142D). This slowdown in sales was all the more unexpected because it followed two years of rapid growth that exceeded an average annual growth rate of 50%. In light of the satisfactory health of the U.S. economy, other explanations for the slump were offered, ranging from lack of training to lack of compatibility to industry shakeout (Bonnett 1985; Roman 1985; Steffens 1994). With today's hindsight, it is now known that the market was to lose 30% of the 1984 peak sales, creating a slump that lasted for seven years before sales recovered and returned to their previous high levels.
This phenomenon, which we term a "saddle" in this article, is not isolated. As an illustration, Figure 1 presents three markets in which a saddle is apparent: PCs, videocassette recorder (VCR) stereo decks, and cordless telephones. A saddle is a pattern in which an initial peak predates a trough of sufficient depth and duration to exclude random fluctuations, which is followed by sales eventually exceeding the initial peak (a detailed definition is given subsequently).
In each case, a local maximum of sales is indicated (occurring in 1984 for PCs, 1987 for VCRs with stereos, and 1984 for cordless telephones) after the initial takeoff. After this local maximum, a considerable drop in sales occurs over a period of a few years (a drop of 30% for PCs over seven years, 30% for VCRs over three years, and 35.5% for cordless telephones over three years).
These cases are not isolated incidents; in fact, we found that the saddle pattern is quite prevalent. The three cases presented in Figure 1 constitute part of a data set compiled by the Consumer Electronics Association of 32 innovations. In approximately one-third of these cases, a saddle is evident. Using a different data set, Golder and Tellis (1998) also report a phenomenon of an early peak and slowdown in sales during the growth stage of the product life cycle.
Cases of significant and unexpected decline in sales in the relatively early stages of the product life cycle are critical to marketers, as such a decline inevitably casts doubt on product viability. In the case of the PC market in 1985, the following quotation represents one view of the contemporary market, admittedly reflecting a more pessimistic position: "Stephen Wozniak, founder of Apple Computers and now head of his own California firm, seems to have lost his optimism about the personal computer (PC) industry that he helped to pioneer. Wozniak feels that there is no market for PCs as an aid to carrying out household chores, and that most small businesses can get along with only a couple of small computers" (Lewis 1985b, p. 32).
Although reactions to a significant decline in sales may be diverse, ranging from trying to save the product by pouring more money into aggressive marketing campaigns to pulling the plug and terminating the product altogether, both types of decisions involve an investment of considerable financial and personal stakes by the firm and its executives. Given both the large percentage of cases in which we found a saddle and the criticality of this phenomenon to marketing decisions, an investigation of the phenomenon, its sources, and its managerial implications is of considerable importance.
Before we measure the prevalence of saddles, a careful definition of the phenomenon is necessary to exclude both fluctuations in the sales of a new product and noise in measurements. Let d be the depth of the saddle, measured as the sales difference from the initial peak to the minimum of the saddle. Let w be the duration of the saddle, measured as the time elapsing from the time of the initial peak (Ts) to the time at which sales recovered their previous peak levels. To distinguish between a saddle and random perturbations in the market growth pattern, both parameters w and d must be of some minimal size for the pattern to be considered a saddle. Because any specification of these minimal sizes is of an arbitrary nature, we selected the following conservative measures to define a saddle as a trough following an initial peak in sales, reaching a depth of at least 20% of the peak (10% for the relaxed case), lasting at least two years, followed by sales that ultimately exceed the initial peak.
Denoting h as the initial peak sales level, and d* as the relative depth, that is, d/h, we then define the following conditions for the occurrence of a saddle (see Figure 2):
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Note that the strict definition leads to a conservative estimation of saddle occurrences in any sample. In Figure 1, all three sales patterns satisfy our strict definition. However, in the full data set, there are saddles that are definitely noticeable yet do not satisfy our conditions, and they are not included in our analyses. We therefore added a more relaxed definition using a relative depth of at least 10%, so as to include cases in which the saddle is less dramatic but still noticeable. A minimum depth is required in any event to exclude random deviations.
We begin with a preliminary inquiry that demonstrates the prevalence of the saddle phenomenon. Our empirical analysis is based on a comprehensive data set of new product sales made available by the Consumer Electronics Association, a major source of data on new product growth. The original data set includes 62 innovations, primarily in the consumer electronics industry. By eliminating all cases that contained less than eight points of data or that did not contain data on unit sales, we remain with a sample of 32 valid cases.
Table 1 lists descriptive data of the sample investigated. Of the 32 cases, 10 are found to have a duration and relative depth that satisfied our strict condition (a relative depth of at least 20% and a minimum duration of two years), and one case satisfies our relaxed condition. Note that the resulting frequency of 31% increases to 50% if the constraints are further relaxed to include saddles of a minimum duration of one year or a minimum relative depth of 10%. We therefore conclude that an occurrence of a saddle in new product growth is indeed common.
In the remainder of this article, we show that a saddle can be the direct consequence of a dual-market phenomenon, and we continue to explore the conditions under which a saddle is likely to occur. If two segments of the market-an early market and a main market-adopt at different rates and if the difference is pronounced, then overall sales to the two markets will exhibit a temporary decline at the intermediate stage. Note that, other than the existence of a dual market, the only assumption we make regards the relative magnitudes of the communication parameters.
A recent perspective in the marketing literature views the market for many new products as composed of early and main markets, which require differential treatment by marketers (Mahajan and Muller 1998). The basis of this approach is the premise that adopters in the early market are sufficiently and meaningfully different from main market adopters as to call for a significant differentiation in product and/or marketing strategy. This view is supported by the observation of the existence of segments of adopters that differ in their inclination or reluctance to adopt new concepts and innovative products(see, e.g., Rogers 1995; Tanny and Derzko 1988).Another premise is the existence of a discontinuity in the diffusion process or, stated otherwise, deficient communications between early market adopters and main market consumers (Moore 1991).
Although Rogers (1995) uses the term "innovators" to describe the first 2.5% of the population that ultimately adopts a given product, "innovators" has also been used in a more general sense to describe the early market, which consists of the first 16% of adopters, a group that includes Rogers's innovators and early adopters (Mahajan and Muller 1998; Midgley 1977).
Until recently, it was accepted as fact that these innovators tend to be opinion leaders (Kotler 1997; Perreault and McCarthy 1996).Opinion leaders can be viewed as those who "exert disproportionate influence on others through personal influence" (Summers 1971). Early adopters seek guidance from this group of opinion leaders, whose influence lies in their tendency to spread information by word of mouth (Perreault and McCarthy 1996).This view is supported by Rogers (1995, p. 274), who suggests that early adopters, more than any other group, show a high degree of opinion leadership.
Moore (1991) builds on Rogers's normal diffusion curve and his division into adopter categories along the curve to explain how new products spread in the market. He identifies a discontinuity in the process after approximately 16% of the population adopts the innovative product. The social process of contagion is broken at this point, because the later adopters (whom Moore labels the main market) refuse to rely on the earlier adopters (the early market) for information.
Main-market adopters are different from early-market adopters. Recent literature suggests that, at least regarding high-tech products, not only are main-market adopters not opinion leaders, but they also have no personal influence over others who have yet to adopt the product (Moore 1991, 1995). In addition, industry studies claim that the early-and main-market consumers adopt an innovative product for different reasons: Whereas main-market consumers are fundamentally utilitarian and are primarily interested in a product's functionality, early-market adopters are attracted to attributes that are not exclusively functional. In high-tech markets, early-market adopters are characterized as technophiles, fascinated by cutting-edge technology and applications.
A dual-market perspective has also been suggested by academic researchers in various disciplines. In the field of communications, Rogers(1986) suggests that for interactive innovative products-innovative products consumers use for communications-diffusion can be analyzed as a two-phase process, that is, before and after a critical mass of sales is reached. In the organizational science literature, Cool, Dierickx, and Szulanski (1997) expand this critical market mass concept to include diffusion of innovations within organizations. In marketing, Mahajan and Muller (1998) posit that market failures can be avoided by modifying marketing strategy and concentrating efforts on tailoring product attributes to main-market needs or by reallocating the respective resources invested in early versus main markets.
The technology management literature (e.g., Anderson and Tushman 1990; Utterback 1994) has focused on the impact of what is conceptualized as a "dominant design," or standard, on the evolution of technologies, exploring various penetration processes preceding and following the establishment of a product's dominant design. In geography, Brown (1981) links the early market to the duration required to set up distribution outlets for a new innovation. Behind these diverse approaches lies the common idea that the initial product offered to consumers is meaningfully different from that offered in the later phase and that the consumers in the two stages of the product life cycle differ in a meaningful way.
From all this literature, we adopt the dual-market concept of an early and a main market, each of which is relatively homogeneous, with a strong word-of-mouth effect within each market and weaker communication ties between markets.
The relationship of the dual market to the saddle phenomenon can be best illustrated by Figure 3. The two markets begin at the same point in time, and though not isolated from each other (more precise parameters of the relationship between the two markets are specified subsequently), each has its own market attributes and potential, which are indicated when the diffusion and growth of the product are plotted and calculated separately for each market.
In the schematic illustration of Figure 3, a saddle phenomenon is observable if the growth of sales in the main market begins late; that is, the main market takes off shortly after sales in the early market reach their peak (see Figure 3, Panel A). If the two markets take off simultaneously or, as is the case in Figure 3, Panel B, the main-market takeoff begins slightly before the early-market sales reach their peak, then a saddle is not observable.
The Dual Market for Citizens Band Radios[1]
As an illustration of the dual-market phenomenon, consider the case of the citizens band (CB) radio market, a two-way communications radio that any civilian (as distinguished from police) can use to communicate with any other person who operates a CB radio. The beginning of the CB radio industry is considered 1958, when the Federal Communications Commission formed the basis for the citizens band, as we know it today. The early diffusion of CB radios peaked around 1963 and afterward generally declined. Growth began again in the early 1970s and increased rapidly in the mid-1970s, creating what was described in the popular press as a "market explosion." By 1976, the CB radio was considered the "consumer electronics star performance of the year" (Electronic Market Data Book 1976), and more than five million CB license applications were submitted in 1977 alone. After 1977, demand decreased but remained considerably higher in the 1980s than in the early years.
According to industry literature, there was a noticeable difference between the average CB user in the 1960s and the user in the 1970s. The former were considered more serious users who needed CB radios for work (such as farmers or boat owners who wanted to have a connection with shore) and deeply involved hobbyists interested in radio transmission. In the 1970s, CB radio became a mainstream market composed of many consumers with no record of or high need for radio communication. The product became a widespread hobby available through mainstream consumer outlets. By the end of the 1970s, many CB enthusiasts had left the market, turning to new hobbies.
Because of the different scales involved in the early and late CB unit sales growth, we present the penetration of CBs as occurring in two stages. The early penetration of CBs to the U.S. market, based on Federal Communications Commission records, is presented in Figure 4, Panel A, and the later years in Figure 4, Panel B.
The increase in demand for CB radios in the early 1970s after the decrease of the late 1960s is intriguing, because no major change in the product itself or its price can account for it. The Electronics Industry Association, the main industry data source that followed the growth of CB radio, attributed the change to communication factors, suggesting that it was an "increased awareness of the availability and utility of citizens radio," which happened around 1972, that brought about the change (Electronic Market Data Book 1976, p. 60). It does not report which was more influential on this awareness among mainstream market adopters: the word-of-mouth communication of the early market of the 1960s or the advertising and mass media; but what is clear is that a large mainstream market emerged in the early 1970s, which created a clear and strong saddle effect.
In the next section, we move beyond the specific examples given in Figures 3 and 4 to a more general analysis and understanding of the driving forces behind saddles in the dual-market case. To do that, we employ an important simulation tool called cellular automata.
Social interactions among consumers in distinct segments and the aggregation of these interactions can be considered a complex system problem. The social sciences have recently exhibited an increasing interest in complex systems (Anderson 1999), which are systems consisting of a large number of members who maintain linear interactions with one another to form nonlinear macrobehavior (Casti 1996). A well-established technique, which is both appropriate and convenient to model such complex systems, is cellular automata (Casti 1996; Rosser 1999; Wolfram 1984).
Cellular automata models are simulations of aggregate consequences that are based on local interactions among members of a population. The models track members' changing states and parameters over time (for detailed descriptions of this technique, see Adami 1998; Goldenberg, Libai, and Muller 2001; Toffoli and Margolus 1987).[ 2]
Applying this approach to our context here, consider a cellular automata model in which an environment is characterized as an array of cells. Each cell, representing a potential consumer, can accept one of two states: 0, representing a potential consumer who did not adopt the innovative product, and 1, representing a consumer who has adopted the new product. In addition, irreversibility of transition is assumed, so that consumers cannot "unadopt" after adoption.
The rules that define transitions of potential adopters from State 0 to State 1 can be classified into two types:
- External factors: Some probability p exists, such that in a certain time period, a consumer will be influenced by external influence mechanisms, such as advertising or mass media, to adopt the innovative product.[ 3]
- Internal factors: Some probability q exists, such that during a single time period, a consumer will be affected by an inter-action with a single other person who has already adopted the product.
For illustration purposes, consider a simple homogeneous case in which p and q are constant for all potential adopters. In such a case, a time-dependent individual probability of adoption, PA(t), given that the consumer has not yet adopted, is based on the following binomial formula:
( 1) PA(t) = 1 - (1 - p)(1 - q)<SUP>k(t)</SUP>,
where k(t) is the number of previous adopters with whom the consumer maintains interactions.
We solve this general cellular automata model computationally by running a stochastic process in which, at each period, each individual probability of adoption is given by Equation 1. The results for a particular realization of the stochastic process are depicted in Figure 5.
Unlike the simple case described previously, the dual-market perspective assumes that adopters are more often affected by information flows within their own proximal group than by communications disseminated throughout the entire population. We consider, therefore, a market that is divided into two main groups: ( 1) the early market (indexed by i) and ( 2) the main market (indexed by m), for which we define the following parameters:
p<SUB>i</SUB> = the probability that an early-market consumer will adopt the innovative product as a result of external forces such as marketing efforts,
p<SUB>m</SUB> = the probability that a main-market consumer will adopt the innovative product as a result of external forces such as marketing efforts,
q<SUB>ii</SUB> = the probability that an early-market consumer will adopt an innovative product because of an interaction with another consumer from the same group (within-early-market communication parameter),
q<SUB>mm</SUB> = the probability that a main-market consumer will adopt an innovative product because of an inter-action with another consumer from the same group (within-main-market communication parameter), and
q<SUB>im</SUB> = the probability that a main-market consumer will adopt an innovative product because of an inter-action with a consumer from the early market (cross-market communication parameter).
Whereas q<SUB>ii</SUB> and q<SUB>mm</SUB> represent within-market communications, associated with strong ties within each community of adopters, q<SUB>im</SUB> represents cross-market communications between the early market and the main market, whose ties are typically weaker.
Models of this type are frequently solved computationally (Casti 1989). The following step-by-step outline describes the cellular automata algorithm for a dual-market case:
Period 0: This is the initial condition, in which none of the consumers has yet adopted the product (receiving the value of 0).
Period 1: The probabilities for each consumer p(t) are realized. Only advertising is at work during this period, because word of mouth requires consumers who have already adopted the product to start the process. A random number U is drawn from a uniform distribution in the range [0,1]. If U < p(t), then the consumer moves from nonadopter to adopter (receiving the value of 1). Otherwise, the consumer remains a nonadopter.
Period 2: The consumers who have adopted the product begin the word-of-mouth process by deploying communications within their own market (early or main) and cross-market communications from the early to the mainmarket.ProbabilitiesarerealizedasinPeriod1, and the random number is drawn so that when U < p(t), the consumer moves from nonadopter to adopter.
Period n: This process is repeated until 95% of the total market (e.g., 1000 consumers) has adopted the product.
To illustrate the outcomes of a computational solution, consider a hypothetical case of p<SUB>i</SUB> = .01, p<SUB>m</SUB> = .0005, q<SUB>ii</SUB> = .005, q<SUB>mm</SUB> = .0005, and q<SUB>im</SUB> = 5 × 10<SUP>-7</SUP>. The results of this case are presented in Figure 6. In this case, a saddle with a relative depth of 59% and duration of seven periods is indicated.
The cellular automata solution approach consists of running the program for a range of parameters, allowing sensitivity analysis to identify the crucial factors that govern the phenomenon. To generate realistic data sets, it is essential to define a proper range of parameters. In the area of new product growth, the Bass model is one for which a large body of empirical data exists. It is therefore reasonable to use the parameter range of this body of knowledge to calibrate our cellular automata parameter range. We followed previous research on the values of the Bass model parameters (Parker 1994; Sultan, Farley, and Lehmann 1990) and assumed that the individual-level parameters relate to the aggregate-level ones in the following manner:
- The relationships between cellular automata probabilities (lowercase letters) and the aggregated Bass parameters (uppercase letters) are fairly simple: P (Bass) and p (cellular automata probability) are of the same order of magnitude, because they both represent probabilities of adoption as influenced by external sources of information.
- The case of the communications parameters is more complicated: Q, the aggregate-level internal effect parameter, refers to the overall internal effect on an individual potential adopter. However, q<SUB>ii</SUB>, q<SUB>mm</SUB>, and q<SUB>im</SUB>, our cellular automata individual-level parameters, are the probabilities that a given potential adopter will be affected by a single previous adopter. To the extent that many potential adopters exist, these parameters are smaller than Q, by a magnitude equal to the potential market population size (in our case, M + I = 1000). For example, where q<SUB>ii</SUB> = .0005, the probability that one potential early adopter will be affected by his or her interaction with all the other previous adopters, given as 500, will be 1 - (1 - .0005<SUP>500</SUP>) = .22.
- Following the dual-market view, we set the range of p<SUB>i</SUB> to be of an order of magnitude larger than the range of p<SUB>m</SUB>. Note that this does not exclude cases in which marketing effects have an equal impact on the early and main markets, because the entire range of parameters is scanned.
- The same rationale leads us to set the range of q<SUB>ii</SUB> (normalized by the size of the group) higher by an order of magnitude than the normalized range of q<SUB>mm</SUB>.
- Consistent with the assumption that low levels of communications are maintained between adopters across the two markets, we set q<SUB>im</SUB> to be smaller than q<SUB>mm</SUB>.
In the next section, we first use cellular automata to conduct two studies. Study 1 explores the effect of communications across the early and main markets (q<SUB>im</SUB>) on saddle occurrence. Study 2 explores the effect of all available parameters on the depth of a saddle and its duration. Following the cellular automata studies, Study 3 empirically tests the model to replicate the occurrence of saddles.
We begin our analysis by closely examining the effect of q<SUB>im</SUB>-or the parameter of communications across the early and main markets-on saddle prevalence. We emphasize this particular parameter for two reasons: First, the main contention of this article is that saddles are driven by the two-market phenomenon. What makes two markets different is that the communications between them are different from those within each one. Thus, the level of communications across markets is central to their definition as a dual market. Indeed, dual-market proponents such as Moore (1991) claim that lack of communications between the two markets is the main reason for a "chasm" in new product growth. As we presently show, our results support the relationship between low-level cross-market communications and the existence of saddles. However, a complete break in communications between the early and main markets is not a necessary condition for the creation of a saddle.
A second reason for a specific interest in the cross-market communications effect is the lack of empirical knowledge on its range of values. The new product diffusion literature provides a wealth of information on what may constitute reasonable levels of communications within a group. However, there is almost no indication regarding the value of q<SUB>im</SUB>, especially given that much of the material on the dual-market phenomenon is qualitative in nature.
Method
The purpose of this study is to demonstrate the importance of the cross-market communications parameter (q<SUB>im</SUB>) on the prevalence of the saddle. Unlike the next study, in which we change all parameter values, here we keep the following parameters fixed at these values (for parameter range justification, see the previous section): p<SUB>i</SUB> = .01, p<SUB>m</SUB> = .001, and the ratio between the sizes of the early market and the main market is fixed at 1:9. We focus on the internal communications parameters and therefore change them as follows: q<SUB>ii</SUB> from .001 to .04, q<SUB>mm</SUB> from .0001 to .0009, and q<SUB>im</SUB> from .00006 to .0006. Manipulating three parameters so as to receive nine different values each, we obtain 729 different adoption processes generated by cellular automata. For each process, we identify the existence of a saddle using a SAS application. The results are reported for the more relaxed saddle definition of 10%; however, nearly identical results appear for the 20% case as well.
Results
To show the centrality of the cross-market communications parameter in the existence of saddles, we computed the percentage of times a saddle appeared for each value of q<SUB>im</SUB>. For example, for a value of .0005, there were 81 different adoption processes, corresponding to nine different values each for the other communications parameters (q<SUB>mm</SUB> and q ii). Of these 81 cases, 11 cases (i.e., 13.6%) showed a saddle. These results are summarized in Figure 7.
The striking feature of Figure 7, Panel A, is that in the dual-market model, for a wide range of relatively large values of the cross-market communications parameter, this parameter has a considerable influence in determining the existence of a saddle. Thus, for a q<SUB>im</SUB> of .00006, more than 50% of the cases involved a saddle. This percentage gradually decreases as this parameter increases, until at values that are close to the within-market parameters (q<SUB>ii</SUB> and q<SUB>mm</SUB>), the percentage of saddles drops to below 5%. In a narrower range of relatively low values of the cross-market communications parameter, there is no clear-cut relationship between the values of this parameter and the existence of saddles, as shown in Figure 7, Panel B.
No consistent relationship was found between the within-market communications parameters (q<SUB>ii</SUB> and q<SUB>mm</SUB>) and the prevalence of a saddle. Moreover, a logistic regression on the existence of a saddle indicates that the (standardized) coefficient of the cross-market parameter is by far the largest of the three.
Thus, our contention that the dual market drives the saddle phenomenon receives strong support from Study 1. As the difference between the two markets is reduced (when we allow the cross-market communications parameter to come closer to the within-market parameters), the occurrence of a saddle becomes more rare.
Although the previous study clearly indicates that the dual market drives the saddle prevalence for a wide range of the cross-market communications parameter values, this does not hold in its smaller values. Some other factors are at work, determining the fate of saddles in this smaller range. Therefore, we conduct another study that explores the effect of all available parameters when the cross-market communications parameter is relatively small.
Method
The purpose of this study is to explore the effect of all available parameters on the depth of a saddle, its slope, and its duration when the cross-market communications levels are relatively low.
The cellular automata model is set to represent a social system of 1000 potential adopters, varying p<SUB>i</SUB> from .01 to .09, p<SUB>m</SUB> from .001 to .009, q<SUB>ii</SUB> from .01 to .09, q<SUB>mm</SUB> from .0001 to .0009, q<SUB>im</SUB> from 10<SUP>-7</SUP> to 25 × 10<SUP>-7</SUP>, and the ratio between the sizes of the early market and the main market from .11 to .20 (relative market size is reflected by changing I, while M is kept constant at 900).
To cover all the various values in the defined ranges (six parameters receiving five different values each), we generated 15,625 different adoption processes by cellular automata modeling. For each process, we automatically identified and analyzed a saddle using a SAS application. For each identified saddle, we calculated the starting time, depth, duration, and slope parameters according to the definitions specified previously (see Figure 2). We assigned a dummy variable to reflect the existence versus nonexistence of a saddle in the process.
We calculated initial descriptive statistics with the aim of isolating the parameter ranges implicated in saddle formation, allowing further exploration of the general structure of saddles. We performed regressions between the model parameters and the saddles' parameters to analyze and uncover saddle formation mechanisms and prediction probabilities of occurrence. We report the results for the more relaxed saddle definition of 10%; however, nearly identical results appear for the 20% case as well. In addition, we per --formed discriminant analysis (for two groups, with and without saddle) to provide support for our ability to predict the formation of a saddle.
Results
The results indicate that when we compare the relative depths of the saddles in our sample data and the cellular automata data, the relative depths of the saddles in the real-life cases are smaller than those in the cellular automata results (32% compared with 58%). However, we suggest that this difference is due to a selection bias in our sample. For example, some cellular automata saddles reflect a relative depth of 70%. In real life, firms experiencing such a severe drop in sales may either consider the product a failure or encounter consequent financial problems. In both cases, the cessation of marketing efforts and the termination of the product are realistic options. Consequently, we filtered data reflecting the more pronounced end-of-saddle parameters from our sample. Our results indicate that at least some of these products might ultimately have become successful if the decline after the initial peak had not been interpreted as the end of the product's life cycle. Table 2 presents the results of a regression analysis performed for the saddle cases identified by cellular automata; each column represents a different dependent variable.
The effect of early market size. Given the existence of a saddle, the larger the early market, ceteris paribus, the larger are the relative depth and duration of a saddle. When the main market is slow enough to adopt so as to create a saddle, an increase in the initial peak leads to a corresponding increase in saddle parameters. The main effect is indicated in the saddle's depth rather than in its duration.
The main market effect. Both the marketing efforts parameter p<SUB>m</SUB> and the within-market communications parameter q<SUB>mm</SUB> have a strong, negative impact on the relative depth and duration of the saddle. The rationale for this indication is that, to the extent that P<SUB>m</SUB> and q<SUB>mm</SUB> increase, main-market adopters become a more powerful factor in the total market at an earlier stage in the process. Accelerated main-market growth decreases saddle parameters in terms of both relative depth and duration.
The effect of the cross-market communications parameter. Recall that the Study 1 finding that cross-market communication drives saddle prevalence for a wide range of the q<SUB>im</SUB> parameter values did not hold for smaller values. Because Study 2 involves these smaller values of q<SUB>im</SUB>, it is not surprising that q<SUB>im</SUB> has a very small (and insignificant) main effect on saddle parameters at these levels. In contrast to Study 1, in this study, within-market communication parameters (q<SUB>mm</SUB> and q<SUB>ii</SUB>) have a significant impact on saddle formation.
The effect of within-early-market communications on saddle depth. Of all the parameters, early-market word-of-mouth (q<SUB>ii</SUB>) has the largest effect on relative saddle depth. This result is consistent with our observation that word-of-mouth effects are stronger than marketing effects (represented here by p<SUB>i</SUB>).
The effect of within-early-market communications on saddle duration. Counterintuitively, high values of q<SUB>ii</SUB> significantly reduce saddle duration. When q<SUB>ii</SUB> is large, we expect the early market to adopt at a faster rate, creating a larger window for the more reluctant main-market consumers to adopt. Accordingly, saddle duration would be expected to increase when within-early-market communications accelerate early-market growth (especially in the case of a reluctant main market). However, our findings indicate that in such a situation, saddle duration decreases.
To explore this seemingly counterintuitive finding further, we added a nonlinear term (q<SUB>ii</SUB><SUP>2</SUP>) and an interaction term (q<SUB>ii</SUB>q<SUB>im</SUB>). Indeed, the resulting sign of the nonlinear parameter is negative, indicating that saddle duration increases, but only for larger values of q<SUB>ii</SUB>. What is the reason for this nonlinearity in effect? Recall that with small values of q<SUB>ii</SUB>, saddle formation is possible only with small values of q<SUB>mm</SUB> and pm. In this subrange of parameters, a small increase in q<SUB>ii</SUB> increases the pool of early-market buyers, allowing cross-market communications (q<SUB>im</SUB>) to activate the main market earlier. This indirect effect induces a reduction in w (but only for small values of q<SUB>ii</SUB>). Supporting this explanation is the finding that the interaction term of q<SUB>im</SUB> with q<SUB>ii</SUB> is significant, even though the main effect of q<SUB>im</SUB> is not significant.
The effect of early-market marketing efforts. Another seemingly counterintuitive result is the negative impact of marketing efforts directed at the early market (p<SUB>i</SUB>) on the relative depth and duration of the saddle. To understand this result, note that all of these correlations are contingent on a saddle existing. Thus, given the existence of a saddle, for small values of p<SUB>i</SUB>, q<SUB>ii</SUB> must be large. However, this also implies that early market growth is steep (Mahajan, Muller, and Srivastava 1990; Rogers 1995), leading, in turn, to a high initial peak. As p<SUB>i</SUB> increases, the range of q<SUB>ii</SUB> expands to include increasingly lower values, which results in a slower rise to a somewhat lower initial peak and a shallower relative saddle depth. Such a formation will not occur when p<SUB>i</SUB> is large to begin with, as is evidenced by the positive effect of p<SUB>i</SUB><SUP>2</SUP> (ninth row of Table 2). This also explains the effect on saddle duration: To the extent that the initial peak is higher, a longer duration is required to regain the initial peak level of sales.
The interplay between early-market size and the delay in adoption of the main market. Note that of all the main and significant effects, early-market size (I) has the least impact on saddle duration (column w of Table 2). The reason for this is that, ceteris paribus, large values of I delay the initial peak, requiring more time for most early-market members to become buyers. This delay enables the main market to enter the process at a stage preceding the completion of the early-market adoption process. Therefore, an increase in I, in its lower range of values, will induce higher values of w, and an increase in early-market size at the higher range of values decreases the saddle's duration.
If we are correct in our hypothesis that the saddle phenomenon is governed, inter alia, by an intrinsic dynamic mechanism, predicting its appearance is an imperative. We performed discriminant analysis using the identified parameters. Table 3 presents the confusion table of the discriminant analysis. The model correctly predicts 77% of the saddles and 79% of the nonsaddle cases. These results strongly imply that the selected parameters govern a noticeable portion of the saddle formation dynamics.
Studies 1 and 2 demonstrate the frequency and influencing factors of the saddle phenomenon in simulated markets. However, the question remains whether we can tie our dual-market model to the saddle phenomena identified in real markets, as presented in Table 1. In this study, we use the data in Table 1 to examine the extent to which a dual-market model using the mean of the stochastic cellular automata framework can help us understand the aggregate-level saddle phenomenon.
Method
To examine the dual-market model with the aggregate-level product growth data in Table 1, we constructed a dual-market aggregate model that is based on the individual-level model presented in the cellular automata Studies 1 and 2. The model we employed uses the mean of the stochastic cellular automata model presented previously to describe the growth of a market in a dual-market case. Basically, such a model should have seven parameters: two external marketing variables (one for each market), p<SUB>i</SUB> and p<SUB>m</SUB>; two internal word-of-mouth parameters (one for each market), q<SUB>ii</SUB> and q<SUB>mm</SUB>; one cross-market communication parameter, q<SUB>im</SUB>; and two market potentials, N<SUB>i</SUB> and N<SUB>m</SUB>. Calculating the mean probabilities of adoption given these parameter values, we can estimate at each period the expected number of adopters (from both early and main markets), compare the number of adopters with the actual data that contain saddles, and investigate ( 1) whether the model fits the data using classical fit measures such as R-square and ( 2) specifically, whether it can capture the saddle phenomenon identified in the data.
However, shifting to an aggregate level of analysis raises problems similar to those of classic aggregate-level modeling, namely, the need to examine complex multiparameter models with few data points. In our case, of the ten cases with saddles in our sample data, seven cases had 19 data points or fewer. Because fitting fewer than 19 points of data with seven parameters is highly unstable, we made the following changes in the model to reduce the number of parameters.
First, as noted previously, we can focus on the ratio between the market potentials of the early and mainstream markets rather than on the potentials themselves. Therefore, we fixed the size of the main market and estimated the size of the early market N<SUB>i</SUB>. Second, we set the main-market external marketing coefficient (p<SUB>m</SUB>) to zero. Thus, the early-market word of mouth is left as the only external influence working on the mainstream market.[ 4] We therefore run a standard nonlinear search procedure on a five-parameter model instead of seven, acknowledging that even in this case our ability to generalize from the results is limited.
Results
Figure 8 presents the estimation results for the PC case we highlighted in this article. The saddle is well captured by our estimated saddle, beginning in 1984 and lasting for five years, just like the real one.
Capturing the saddle phenomenon. In general, we find that of the ten cases with saddles (see Table 1), the estimated growth pattern produces a saddle in eight cases. Of these eight cases, six are saddles according to our strict definition. These are PC monitors, compact audio systems, digital corded telephones, cordless telephones, personal computers, and VCRs with stereo. Two more cases involve a saddle in the predicted growth pattern that does not meet the strict criteria: The growth pattern of PC printers has a saddle of a 7.7% relative depth, and in the growth pattern of monochrome television, the model captures the saddle but not the peak, such that the estimated peak is lower than the estimated saddle. Still, even in the last two cases, the real saddle is explained by the early and late markets. Only two of the eight did not produce a saddle: color television and home radios. Of these two, although in color television there is a breakdown between early and late markets, the estimated break between the two is enough to cause a delay but not a saddle. Only in home radios do we find that the model does not work; apparently, the saddle should be explained by factors other than the existence of early and late markets.
Considering, for example, the case of the PC presented in Figure 8, 1987 can be viewed as a milestone in the development of this market. In this year, which is right in the midst of the saddle, the number of mainstream new adopters equaled that of the early market. Examining industry publications from that time, we indeed find that increasing attention began to be drawn to the mainstream market that was expected to fuel the expected growth (e.g., Murphy 1987).
Model fit. Considering the model estimation, the model shows good fit to the data. The adjusted R-square varies from 79% to 99.1%, with an average of 92.9%.
Note, however, that we do not consider the relative success of Study 3 to be demonstrated merely with high measures of fit. High R-squares indicate that the model fits the data, but that itself is insufficient, because a standard aggregate-level Bass equation also yields reasonably high measures of fit. What the Bass model cannot capture, however, is a saddle, because the Bass model necessarily has a single maximum. In contrast, as presented previously, the dualmarket model we examine captures the saddle in most cases and can give us an impression of the dynamics between early and main markets in these cases.
Cross-market communication. On the basis of the dual-market theory and the cellular automata results, we expect that the cross-market communication level (q<SUB>im</SUB> parameter) should be ( 1) lower than the early and main within-market communication levels (q<SUB>ii</SUB> and q<SUB>mm</SUB>) and ( 2) smaller for cases of larger saddles.
The empirical results of Study 3 support both these points. First, on average, we find that the value of the cross-market parameter is lower than the within-market parameters (one-half and one-tenth of q<SUB>ii</SUB> and q<SUB>mm</SUB>, respectively; we do not report statistical differences because of the low number of cases).
To examine the second point, we also estimate the model for the six smaller saddles of the relaxed definition of the saddle (see Table 1 and our definition in the second section of the article) and compare it with the strict definition analyzed previously. We expect that the cross-market communication parameter for the larger saddles of the strict definition would be considerably lower than that of the smaller saddles of the relaxed definition. Indeed, we find that, on average, q<SUB>im</SUB> in the strict case is much lower: 9.4% of the value of q<SUB>im</SUB> in the relaxed case.
In a concluding remark to this study, the aggregate-level results seem to give empirical support to the dual-market theory of the saddle presented previously. However, although they support our theory, they also highlight the advantage of the cellular automata approach. As an example, consider Figure 7, which depicts a monotonic decline in the number of saddles as the value of the cross-market communication parameter increases. It is difficult to envision the extensive set of empirical data needed to replicate this result. For example, for a specific value of q<SUB>im</SUB>, we would need a few different adoption processes, corresponding to different values each, for the other communications parameters (q<SUB>mm</SUB> and q<SUB>ii</SUB>). The cellular automata gave us the possibility of generating 81 such simulated markets with pure individual-level assumptions.
In this article, we demonstrate that the saddle is a relatively common phenomenon. Using a data bank of a large number of innovative products in the consumer electronics industry, we found that a saddle pattern was evident in one-third to one-half of the cases investigated. From a managerial point of view, this pattern warrants attention, because a significant and unexpected decline in sales in the relatively early stages of a product's life cycle may erroneously cast doubt on the product's viability. This is especially true for high-tech and innovative products that are in the vulnerable point after introduction to the market, when firms typically engage in further research and development (R&D) and product improvements. The occurrence of a saddle may lead to a sudden, unexpected drop in cash flow just as the firm is in the highly precarious situation of simultaneously launching and improving the product.
We offer an explanation of the saddle phenomenon based on the dual-market approach. If two segments of the market-an early market and a main market-adopt at different rates and if the difference is pronounced, then overall sales to the two markets will exhibit a temporary decline at the intermediate stage. Other explanations for the saddle phenomenon exist. The temporary decrease in sales may be attributed to stockpiling, changes in technology, industry performance, or macroeconomic events. As we explain next, although these idiosyncratic explanations are certainly valid in many cases, we do not believe that any of them can give a comprehensive explanation for the saddle.
- Stockpiling: The data we report are based on shipments rather than sales, and these data do not always track sales. Therefore, it is possible that if resellers over forecast sales, they accumulate excess inventory and reduce orders accordingly, thus creating an artificial peak.
- Changes in technology: Rapid changes in technology might induce a period in which consumers are reluctant to adopt a new product, causing them to skip one generation and leapfrog directly to the next one. This postponement in adoption might create a temporary dip in sales.
- Industry performance: Poor industry performance phenomena such as slow emergence of dominant design or a perceived low quality/price ratio might induce a decline in sales when consumers become fully aware of these shortcomings.
- Macroeconomic events: Macroeconomic conditions such as a recession will have a dampening effect on sales, thus creating a saddle.
- Other product-specific variables: Golder and Tellis (1998) suggest that the phenomenon of an early peak and slowdown could be explained on the basis of product-specific variables such as market penetration, price, and year of takeoff.
It is not clear, however, to what degree these variables yield a comprehensive and unified explanation of the saddle phenomenon. For example, in Table 1 we report a duration of 5.1 years for the average saddle. This duration is incompatible with the stockpiling explanation. In the same fashion, we found no correlation between recessionary periods and saddles.
Our model highlights the importance of cross-market communications in determining the existence of a saddle. For low levels of this parameter, more than 50% of the cases in the cellular automata data involve a saddle. This percentage gradually decreases as the cross-market communications parameter increases, until at values that are close to the within-market parameters, the percentage of saddles drops to below 5%. The parameters governing the depth and duration of a saddle are the intensity of the various channels of communications within and across the two consumer groups (early market and main market), as well as the relative sizes of these markets.
It should be noted that though we find that cross-market communications are an important factor driving a saddle, our results do not necessarily support Moore's (1991) view that there is no communication between the early and the main markets. We suggest that a lower level of cross-market communication is correlated with saddles, but not necessarily that cross-market communication is completely absent. Furthermore, the fact that in most cases of the data we present (both sample data and data generated by cellular automata), a saddle does not appear suggests that often this cross-market communications level is high enough, even among product categories close to the ones that Moore himself describes.
What options are open to firms that wish to reduce the parameters of a saddle, after it is predicted? The parameters under the direct control of the firm are the marketing effort parameters, p<SUB>i</SUB> and p<SUB>m</SUB>. Assuming a fixed budget, increasing p<SUB>m</SUB> at the expense of p<SUB>i</SUB> will reduce the delay in the adoption pattern of the main market, thus diminishing the size of the saddle.
Such action will be effective if firms are aware of the differences between the two types of market groups and are able to address them separately. In contrast, firms can have only limited control over word-of-mouth parameters. Examples of such indirect influence are programs in which the firm rewards customers who enlist friends and acquaintances as further customers.
Intuitively, to reduce the size of a saddle, firms need to increase both the within-main-market communications parameter and the cross-market communications parameter. However, our findings indicate that low levels of the latter parameter have no significant effect on the saddle, implying that firms can more efficiently achieve reduction of saddle size by stimulating main-market word of mouth.
We also find that the within-early-market communications parameter (q<SUB>ii</SUB>) has the strongest effect on relative saddle depth. However, a high value of q<SUB>ii</SUB>, though necessary for a rapid takeoff and penetration process, simultaneously precipitates increased relative depth and duration of the saddle. The sharp decrease in sales may be erroneously interpreted as a signal of the rejection of the product by the market, rather than a consequence of intensive word-of-mouth communications in the early market.
Assuming that firms have some degree of control over saddle size, it is questionable whether firms would prefer to eliminate the saddle entirely, for example, by reducing p<SUB>i</SUB> to zero. In such a case, no saddle would form, and a consequent later takeoff should be expected. However, minimizing the adverse effects of a saddle by eliminating the early market is only one-and the least desirable-of several possible strategies.
First, ascribing the responsibility for product adoption and takeoff to the main market is an expensive decision. Manipulating the reluctant main-market adopters may be far more costly than investing in the early market. Second, by reducing or eliminating the early market, firms lose a critical source of information. By "listening to their voices," firms leverage feed-back from the early market as important input in further R&D and product improvements. The costs of longer and more isolated R&D, as well as marketing to a slow response group, may be too high for the launching of brand new products.
This argument implies that a saddle might be a necessary stage for the successful introduction of an innovative product, enabling simultaneous introduction and improvement. In this case, firms may prefer to optimize the saddle size and timing by allocating their resources and deciding on appropriate marketing strategies based on the saddle's predicted appearance.
One question is whether it is possible to know a priori which real-life cases are more prone to saddle occurrences. Our results suggest that a principal communication channel that governs the saddle appearance is the cross-market communications between the early and the main markets. The smaller the cross-market communications, the more likely a saddle is to appear. We therefore expect that certain variables that influence this value would be dominant in a priori evaluations of the likelihood of a saddle appearance.
As an example of a product-based variable, consider the issue of dominant design (Utterback 1994). If the new product versions are not compatible with one another or with previous products, communications between the main market and the early market are less likely to occur. The main market might view the early market as much too tolerant for the personal discomfiture caused by incompatibility of designs. The early market's level of risk taking, as manifested by its immediate approval and adoption of a new, incompatible technology, is also suspect by the main market. In addition, the existence of many incompatible products and technologies is likely to inhibit effective personal communications, because communications are likely to be more effective among users of the same technology. Thus, the larger the role of a dominant design in the evolution of a market and the larger the difference between predominant design and the standard platforms, the larger is the expected place of a saddle in the new product growth. Similarly, an increased novelty level of a new product is expected to reduce cross-market communications as well as lack user friendliness and the frequency of product attribute changes.
Limitations
Our study has several limitations. The data presented are limited to electronics-based durable goods. Although this factor is in line with much of the anecdotal evidence related to the kinds of products that drive a dual-market phenomenon, further research is needed to examine the saddle on a broader product base. From a modeling point of view, we present a simple dual-market model, making only a few assumptions. In a total modeling of real life, the communications pattern should be more complex and involve a large number of parameters.
One of the advantages of the use of cellular automata is the knowledge that a complication of the model is feasible and there is no uncertainty regarding an analytical solution. However, in our view, that our simple dual-market assumptions modeling approach drives a clear saddle phenomenon gives stronger support to the relation-ship between the two than more complicated models would.
In this study, we investigate the determinants of the occurrence of a saddle and its attributes in terms of depth and duration. We have not discussed the important issue of the timing of a saddle, which would require a comprehensive study that dealt with both the empirical issues and the theoretical underpinning of the timing of a saddle.
In addition, we rely on prior theory to support the dual-market phenomenon that drives the result of this study. In some cases, however, more than two segments of various sizes could be present. The results could change according to whether this segmentation is significantly different from the dual segmentation used in this article.
One question associated with the robustness of our cellular automata results involves their sensitivity to the definition of a saddle. We checked in both studies whether the saddle definitions conform to either the strict or the relaxed definitions. The differences in the results are minimal. The main reason is that the requirement of a two-year duration is the one that distinguishes between a saddle and a random perturbation. When we impose the two-year duration condition, the difference between 10% or 20% depth is not critical. For example, in the first study, if we relax the condition of the two-year duration, the number of saddles increases by 70%, but if we relax the depth requirement from 20% to 10%, total saddle occurrence increase is less than 10%.
In addition, the difference in the results in terms of both the graphs and the regressions is minimal in the case of the depth conditions. However, we believe that the two-year requirement is important, because idiosyncratic forces such as the ones discussed in the previous section could be powerful explanatory variables in the case of a one-year perturbation.
Another approach would specify the depth criterion as a function of the standard error of the time series. This type of volatility measure is common in other fields, such as finance. When no pattern of growth is evident, the standard error itself is used; that is, the errors are measured from the mean of the series. However, if consistent growth is noticeable, the errors are measured from a regression line; that is, a model is used to predict the growth curve, and the errors are measured from the line. For a growth curve to emerge that will serve as a basis for comparison, we develop a two-stage aggregate-level diffusion model in the spirit of Mahajan and Muller's (1998) model.
We checked the deviations of the time series from the model's prediction for three products: cellular telephones, camcorders, and telephone answering devices. We chose these products because none of them has a saddle and they belong to relatively noncorrelated industries. We computed the standard deviation from the real data in each case as a percentage of the average sales level to be the following: 6.4%, 7.7%, and 7.4%, respectively. The average of the three is 7.2%. Thus, the 20% rule that is approximately three times the average error seems a conservative measure, so we used 10% as an alternative. As we mentioned, we found few differences in the analyses of these two cases.
In a more general sense, the two-stage aggregate diffusion model could be viewed as a substitute for the cellular automata model. The performances of this approach in predicting a saddle are limited by the stochastic nature of the market. For example, a specific innovator may appear to be the last one to adopt, and a laggard can be among the first adopters because of pressure. An aggregate model cannot capture this aspect and therefore can be used only for sensitivity analysis.
In contrast, cellular automata models take into account the stochastic aspect (indeed a cell, or consumer, can adopt with a certain probability at a time other than expected). By running the cellular automata repeatedly on the same values, we can evaluate the odds that a saddle will appear, as well as the range of its timing, depth, and duration. Thus, the final result of our modeling is a curve with stochastic disturbances, as we would expect in reality. Distinguishing a saddle in such an environment is more of a challenge and is closer to reality than if we were to take the aggregate approach, which results in smooth curves.
Returning to our PC market example, we are left to verify that the dual-market concept can explain the saddle that occurred during 1984-91 (the PC industry recovered its lost ground and exited the saddle in 1991, as shown in Figure 1). If our proposed explication of the saddle is tenable, we should be able to find evidence that implicates the main market in the industry's steep (second) takeoff. In 1994, in his Fortune magazine column, Kirkpatrick (1994a) attributed the rapidly growing home segment in the PC market to the following five factors: More consumers were working from home, prices were falling, PCs were becoming easier to set up, shopping for PCs was becoming more convenient, and customers were attracted by exciting new software.
In our view, these are signals that the dominant player had become the main market, with its orientation to product functionality, rather than the early-market technophiles. Note that the Internet was not even mentioned as a contributing factor; the real explosion in PC sales was yet to come. Seven months after his report, Kirkpatrick (1994b, p. 110, emphasis added) cited major industry sources that identified the main market as the source of the growth: "With less than four months to go, this is already shaping up as the landmark year for that rapidly evolving electronic marvel, the personal computer . . . . Consumers will pay $8 billion to buy 6.6 million PCs . . ., add in the $3.4 billion that ordinary Joes and Janes will shell out for PC software, and the total is more than Americans will spend on televisions [this year]."
The authors thank David Mazursky, Moshe Givon, John Hogan, and the five anonymous JM reviewers for many helpful comments and suggestions. This research was supported by grants from the Israeli Science Foundation, the Kmart International Center of Marketing and Retailing, The Hebrew University, The Davidson Center, Technion, and the Israeli Institute for Business Research at Tel Aviv University.
1 The following section is based on sources including Smith (1977), Bibb (1976), and Perkowski and Stral (1976) and various issues of CB Yearbook, Electronic Market Data Book, and FCC Annual Report.
- 2 Cellular automata stochastic dynamics have been analyzed using specific methods to discover their statistical properties (e.g., partition function, cross entropy, renormalization groups). For a review of these methods and the unique statistical properties, see Parisi's (1998) book. The fundamental idea of the analysis of cellular automata is presented by Von-Neuman (1966). Technical aspects of the analysis on a practical level are presented by Burks (1970) and Toffoli and Margolus (1987).
- 3 Although it is customary in the diffusion literature to think of the external parameter as representing marketing variables, in reality they can represent any influence other than interaction with other market participants.
- 4 It should be noted that by deleting the main-market marketing parameter from the empirical analysis, we do not suggest that it is insignificant in real life but rather that, for the data set we have, it is the one parameter that has the least effect. Indeed, when we checked the inclusion of the parameter on the three products in our sample data that have more than 40 data points, the difference in the results was minimal.
Product Relative Duration
Description Saddle Depth (%) (Years)
PC monitors Yes 25.8 5
Blank audio cassettes Yes* 6.6 2
Blank floppy disks
Video cassettes
Camcorders Yes* 5 2
Cellular telephones
Color television Yes 35.6 4
Color television with stereo
Compact audio systems Yes 53.5 11
Digital corded telephones Yes 36.7 7
Cordless telephones Yes 36.5 2
Direct broadcast satellite (DBS) Yes* 21.4 1
Fax machines
Home radios Yes 33.6 6
Laser disk players Yes* 11.8 1
LCD monochrome television
LCD color television
Fax modems
Monochrome television Yes 27.8 3
PC printers Yes 25.8 5
PCs Yes 25.8 5
Word processors
Portable compact disk equipment
Projection television Yes* 12.3 1
Rack audio systems
Portable tape and radio
Answering machines
Compact disk players
Television/VCR combination
VCR decks Yes* 18.7 5
VCR decks with stereo Yes 30 3
Videocassette players
Percent saddle (strict definition) 34.4
Percent saddle (* definition) 50
Averages (strict definition) 31.8 5.1
Averages (* definition) 25.4 3.9
Notes: Yes* denotes cases in which a trough exists but its relative depth is less than 20% (but above 10%) or the trough lasts less than two years (this is an even more relaxed condition than that described in the text). For example, the trough in the videocassettes case had a relative depth of 7.4% with only a one-year duration. We therefore did not include it as a saddle. If we include the yes* cases as well, exactly half of the cases would have included a saddle.
Legend for chart:
A = d* Relative Depth (Standardized)
B = d* Relative Depth (Nonstandardized)
C = w Saddle's Duration (Standardized)
D = w Saddle's Duration (Nonstandardized)
A B C D
1 I Size of group I .20 .001 .04 .007
2 p<SUB>m</SUB> Marketing to majority -.57 -50 .33 -499
3 q<SUB>mm</SUB> Within-majority-market
communications -.35 -433 -.59 -12,609
4 q<SUB>im</SUB> Cross-market -.01 -3499 -.02 -83,171
communications
5 q<SUB>ii</SUB> Within-early-market .59 6 -.55 -101
communications
6 p<SUB>i</SUB> Marketing to early -.39 -3 -.52 -74
market
7 q<SUB>ii</SUB> q<SUB>im</SUB> -.01 -111,745 -.001 -106,844
8 q<SUB>ii</SUB>² -.52 -62 .34 696
9 p<SUB>I</SUB>² .31 27 .34 499
10 Adjusted R² .36 .36 .41 .41Notes: All variables are significant at the p < .01 level except for Rows 4 and 7 (q<SUB>im</SUB> and q<SUB>ii</SUB>q<SUB>im</SUB>).
Observed Observed
Saddle (%) Nonsaddle (%)
Predicted saddle 77.2 22.8
Predicted nonsaddle 21 79Notes: The average correct prediction is 78.1%.
GRAPH: FIGURE 1 Prevalence of Saddles A: Saddle in PCs
GRAPH: FIGURE 1 Prevalence of Saddles B: Saddle in VCR Decks with Stereo
GRAPH: FIGURE 1 Prevalence of Saddles C: Saddle in Cordless Telephones
GRAPH: FIGURE 2 Saddle Parameters
GRAPH: FIGURE 3 The Dual-Market Case A: The Saddle Case
GRAPH: FIGURE 3 The Dual-Market Case B: The Nonsaddle Case
GRAPH: FIGURE 4 Penetration of CB Radios A: 1958-75
GRAPH: FIGURE 4 Penetration of CB Radios B: 1973-82
DIAGRAM: FIGURE 5 Illustration of the Cellular Automata Process
GRAPH: FIGURE 6 A Saddle Formation in Cellular Automata Data
GRAPH: FIGURE 7 Prevalence of Saddles as a Function of Communications Between Markets (q<SUB>im</SUB>) A: The Upper Range (.00006 < q<SUB>im</SUB> < .0006)
GRAPH: FIGURE 7 Prevalence of Saddles as a Function of B: The Lower Range (.00004 < q<SUB>im</SUB> < .0006) Notes: The scales are different in the two figures. Whereas the upper range covers a wide interval of values of q<SUB>im</SUB>, the lower range covers a much more limited interval.
GRAPH: FIGURE 8 Growth of PC: A Dual-Market Empirical Model
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Practical Aspects in Cellular Automata Modeling
In this Appendix, we detail our experience in applying the cellular automata model to new product growth. Although the market for new products is indeed a complex system on the aggregate level, often the behavior on the individual level can be broken into relatively simple and known relationships among individuals. When this is the case, the cellular automata approach is capable of untying the complexity of the phenomenon so as to better understand the underlying governing mechanisms. This approach, however, requires careful attention to some aspects.
Cellular automata modeling requires a reliable micro representation of the relations among the individuals. Therefore, this modeling technique needs a strong individual-level theoretical background.
Although cellular automata models are not limited by the number of variables, it is recommended to develop a parsimonious model to reduce the level of the statistical analysis in the second stage and to increase the understanding of the underlying mechanisms.
We should note that the individual-level probabilities are different in both their values and their interpretation when compared with the aggregate-level parameters that are often reported in the marketing literature (e.g., Q from the Bass model differs from q<SUB>i</SUB> or q<SUB>m</SUB>).
Assigning ranges of small values to q (low level of inter-actions among individuals) leads to regimes with excessive levels of volatility. This regime is not likely to represent real-life situations and therefore can be used as an indicator to the lower bound of values.
When a phenomenon is monotonic or linear in most of its duration, aggregate-level modeling (e.g., simple diffusion models) may be more convenient to employ, and the results are plausible. In contrast, when the phenomenon becomes complex, with nonlinear or nonmonotonic regimes with a noticeable duration, it is much more expedient and accurate to employ cellular automata modeling.
When extreme levels of values are used, cellular automata can uncover hidden regimes that compose the boundaries of the phenomenon under discussion. Cellular automata is more representative at its boundaries.
Our analysis in this article is based on a C program written specifically for this study. Although the advantages of a computer language-based program are clear, including the ability to conduct complex experiments, we found that a simple cellular automata can be built using an Excel spread-sheet. Although limited in its size and manipulation ability, the spreadsheet can promote intuition and a first impression of whether the model itself has the potential to capture the observed phenomenon.
~~~~~~~~
By Jacob Goldenberg; Barak Libai and Eitan Muller
Jacob Goldenberg is Senior Lecturer of Marketing, School of Business Administration, Hebrew University of Jerusalem. Barak Libai is Lecturer of Marketing, Davidson Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, and Leon Recanati Graduate School of Business Administration, Tel Aviv University. Eitan Muller is Professor of Marketing, Leon Recanati Graduate School of Business Administration, Tel Aviv University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 137- Role Stress and Effectiveness in Horizontal Alliances. By: Nygaard, Arne; Dahlstrom, Robert. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p61-82. 22p. 1 Diagram, 4 Charts, 3 Graphs. DOI: 10.1509/jmkg.66.2.61.18474.
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- Business Source Complete
Role Stress and Effectiveness in Horizontal Alliances
Horizontal arrangements are increasingly deployed in organizational networks, yet research has rarely examined the effectiveness of these alliances. The coalition of disparate corporate cultures yields appreciable levels of role stress for people in boundary-spanning positions. Dedicated assets and communication modality are factors that influence the level of role ambiguity and conflict. The authors implicate these facets of role stress as antecedents to four forms of effectiveness drawn from the competing values framework. The authors present alternative perspectives that examine the relationship between stress and performance. The received view frames role stressors as linear, negative antecedents to organizational outcomes. The authors contrast this perspective with theories that espouse triphasic, parabolic, and interactive influences of stressors on organizational outcomes. Data gathered with 218 managers of dual-branded retail oil outlets indicate that the relevance of these alternative perspectives is mitigated by the form of effectiveness pursued by the organization. The results support a linear relationship between role conflict and bargaining efforts, yet they also offer evidence of nonlinear influences of role ambiguity on contributions to sales, customer satisfaction, and competence. The study concludes with a discussion of relevance of the findings to the management of horizontal alliances and to interorganizational theory.
Marketing practice and thought have benefited substantially from the adoption of a long-term perspective in the analysis of exchange. Theory has been developed that addresses the domestication of markets (Arndt 1979) and the breadth of governance structures employed to manage long-term vertical exchange relationships (Heide 1994).
Increases in the magnitude of horizontal alliances have been concomitant with the rise in emphasis on long-term vertical exchange. Merger-related activities surpassed US$2.30 trillion in 1998, and the top ten mergers alone exceeded US$550 billion (Jackson 1999), yet empirical work has not kept pace with the rise in horizontal alliances. Bucklin and Sengupta (1993) examine success-related factors in comarketing pacts, and Johnson and Houston (2000) offer evidence of synergistic returns in horizontal joint ventures. Unfortunately, complementary work has not addressed interaction and performance in other horizontal arrangements.
Mergers and acquisitions are horizontal exchange relationships that dynamically change the organizational structure of multiple entities. Parties to these relationships seek scale economies in production and marketing, yet research suggests that many mergers have a negative influence on profits (Cartwright and Cooper 1995). The new organization must not only consolidate supply chain factors but also bring together disparate corporate cultures. Nevertheless, marketing research has not examined the organizational challenges that emerge in the wake of a horizontal alliance. Role ambiguity emerges because of the paucity of relevant information as well as the level of complexity and change (Kahn et al. 1964). Role conflict similarly materializes as employees evaluate responsibilities in light of historical obligations and associations with new partners. Ambiguity and conflict jeopardize efforts to achieve objectives, but research has not examined stress in mergers and acquisitions.
The goal of this research is to examine role stress in horizontal alliances. Three objectives underlie our pursuit of this goal. The first objective is to examine influences of ambiguity and conflict on multiple facets of effectiveness. The competing values framework (Cameron and Quinn 1999) emphasizes the simultaneous pursuit of several dimensions of performance. Examinations of the determinants of a single facet of performance are ill equipped to evaluate the consequences of these antecedents to other channel outcomes (Kumar, Stern, and Achrol 1992). Focusing on a single element of performance constrains advancements in theory and limits researchers' ability to make policy recommendations (see Singh 2000). Our research, however, augments channel thought with an examination of role-based antecedents to multiple facets of effectiveness. Analysis of diverse facets of effectiveness informs managers about the multiple consequences of stress.
Our second objective is to examine alternative theoretical perspectives on the influences of role stress on organizational outcomes. Role stress is often implicated as a determinant of performance, yet prior research has not consistently supported this hypothesis (Tubre and Collins 2000). We present the linear negative influence of stress on performance (House and Rizzo 1972) as the received view. We contrast this perspective with theories that espouse triphasic (Selye 1950), parabolic (Yerkes and Dodson 1908), and interactive influences (Fried et al. 1998) of stressors on performance. We contribute to contemporary role research by offering evidence that the influences of role stress vary on the basis of the form of effectiveness. Moreover, managers of horizontal alliances can gain insight into the positive and negative influences of stress.
The third objective of our research is to identify organizational and communicative processes that influence the level of stress in alliances. Williamson (1985) underscores the importance of transaction-specific assets, yet the influence of these assets on stress has not been examined. Prior research offers insight into the content and flow of communication, but the medium employed for interaction has received less attention (Frazier 1999). Identification of these factors enables management to prepare for conflict and ambiguity. Furthermore, identification of these factors enables managers to discount some opportunities because of inevitably high levels of stress.
Our empirical context is the distribution system of two oil companies that operate through a horizontal alliance at the retail level. Mergers or acquisitions have recently been completed in a variety of retail settings (Zecher 1998), and the retail sector accentuates some inherent challenges associated with interfirm alliances. Retail executives who super-vise mergers must monitor multiple aspects of integration that are deemed critical to the success of alliances. These integration issues include consolidation of financial systems; product delivery systems; pricing, promotion, and customer service policies; information systems; and human resources (Harvey, Price, and Lusch 1998). There is often a significant gap between the expected value of an acquisition and its actual performance, and the acquisition may lead to layoffs or divert attention away from core brands and markets (Weston and Chiu 1996). Brand managers should value research that addresses efforts to integrate divergent firms into a unified, effective enterprise. Nevertheless, research has rarely addressed postmerger integration (Kerin and Varaiya 1985; Kumar, Kerin, and Pereira 1991).
The article proceeds as follows: In the next section, we present our theoretical model and hypotheses. We then present the method and data collection procedures, along with the results. The final section outlines implications of the research for channel management and theory.
Role theory uses the metaphor of playacting to characterize the interaction between a focal person and a role sender (Kahn et al. 1964). The focal person encounters pressure because of expectations placed on him or her by role senders. For example, franchisees are endowed with role expectations from McDonald's upon investing in the franchised system.
Inherent to the nature of boundary-spanning positions is the likelihood that people occupying these roles will experience ambiguity and conflict. Role ambiguity refers to the "lack of clarity and predictability of the outcomes of one's behavior" (House and Rizzo 1972, p. 475). Interfirm theory and research on boundary-spanning occupations indicate that organizational factors and coping mechanisms have important influences on role clarity (Singh 1998). Standard industry practice is to develop comprehensive operating systems that are at the retail managers' disposal (Keating 1991), yet retailers still encounter ambiguity. For example, retail managers experience ambiguity when retail traffic history is unavailable to assist in the scheduling of personnel.
Role conflict refers to the "degree of incongruity or incompatibility of expectations associated with the role" (House and Rizzo 1972, p. 475). House and Rizzo's analysis of conflict addresses incompatibilities among standards or values, time, resources, or capabilities; multiple role responsibilities; and various organizational inputs. Prior studies of boundary personnel recognize that each of these facets may influence conflict (Ford, Walker, and Churchill 1975; Singh, Verbeke, and Rhoads 1996). Retail managers encounter incompatibilities emanating from a variety of sources. For example, franchisees experience conflict when order quantities suggested by the franchisor are not consistent with the recommendations of local suppliers.
The Received View of Role Stress and Organizational Outcomes
Our theoretical model incorporates multiple perspectives on the relationship between stress and performance in horizontal alliances. The influences of ambiguity and conflict on performance have been the focus of considerable research, yet the findings do not support a strong, direct relationship (see Table 1). Consequently, researchers have proposed non-linear (e.g., Schuler 1980) and interactive relationships (Fried et al. 1998) between stress and performance. Our model examines the independent influences of role stress on organizational outcomes and treats nonlinear and interactive effects as competing hypotheses. Treatment of conflict and ambiguity as independent factors is consistent with Kahn and colleagues' (1964) framework, yet it stands in contrast to several analysts'(e.g., Dubinsky et al. 1992) treatment of conflict as antecedent to ambiguity. Because the conditions leading to the sources of stress are similar, Kahn and colleagues (1964) address the independent effects of ambiguity and conflict and posit modest correlation among facets of stress. We incorporate this premise into our analysis.
We present the competing values framework (Cameron and Quinn1999) as a means to contrast alternative stress theories and differentiate among forms of performance. The competing values framework reveals two major dimensions of effectiveness. The first dimension differentiates effectiveness on the basis of the extent to which adaptation and change are valued over predictability and stability. The second dimension contrastsaninternalorientationthatemphasizesunityandintegrationwith an external orientation that focuses on differentiation and rivalry. Simultaneous consideration of the two dimensions reveals four perspectives on effectiveness. The clan approach reflects an internal emphasis with individual discretion. Its polar opposite, the market approach, refers to an externally oriented firm that values control. The adhocracy approach refers to an external orientation with value placedon flexibility. The hierarchical approach contrasts with adhocracy through an internal emphasis coupled with a desire for stability.
Organizations vary in the extent to which they emphasize one form of effectiveness over other facets. Our analysis focuses on multiple facets of effectiveness pursued at the retail level. Table 2 summarizes research on the relationship between effectiveness and stress and categorizes operationalizations within the competing values framework. Most recent research acknowledges multiple facets of effectiveness, yet the empirical analyses develop composite measures that are drawn from multiple, competing forms of effectiveness (see Singh 2000). For example, Michaels, Day, and Joachimsthaler (1987) describe six elements of performance, yet their analysis solely addresses the composite measure. Composite measures provide insight into overall effectiveness, yet the influence on independent facets of performance is not established. In contrast, our model examines influences of stress on each mode of effectiveness. Consider first the influence of stress on the clan perspective.
Clan-based effectiveness and role stress. The clan-based approach emphasizes the shared goals and values of team members in conjunction with participative decision making. The primary tasks of managers in this approach are to empower employees and foster their commitment and loyalty to the organization (Cameron and Quinn 1999). Our treatment of clan-based effectiveness examines the competence of the retailer. Competence reflects the experience, product knowledge, and capabilities acquired at the retail site (see Kumar, Stern, and Achrol 1992). Domino's Pizza, for example, attributes its success to the experience and skills acquired by franchisees (Boroian and Boroian 1997).
Research with organizational teams (Orsburn et al. 1990) suggests that clearly defined role responsibilities influence efforts to achieve confidence. The human resources within a team are unlikely to be channeled toward common objectives unless the responsibilities of the managers are well defined. Clear expectations enable retailers to gain confidence in the abilities of their trading partners. In contrast, poorly defined roles preclude personnel from recognizing expectations and foster lower perceptions of competence (Dubinsky and Mattson 1979; Singh 1998).
Role conflict should also influence competence. Research by Cohen (1980) suggests that role stress affects the availability of cognitive resources. As more resources are dedicated to coping with the stress, fewer resources are devoted to performing job responsibilities. Therefore, Dubinsky and Mattson (1979) report a negative relationship between conflict and retail salespeople's perceptions of competence. As stress-related demands increase, local managers are less likely to recognize factors critical to success and become uncertain about their job-related abilities. Inconsistencies in the messages sent by role partners should cloud managers' evaluations of their capabilities.
H<SUB>1a</SUB>: The manager's role ambiguity is negatively associated with managerial competency.
H<SUB>2a</SUB>: The manager's role conflict is negatively associated with managerial competency.
Adhocracy-based effectiveness and role stress. An adhocracy parallels a clan in its emphasis on flexibility and discretion, yet an adhocracy places greater emphasis on external positioning and differentiation. Our adhocracy-based analysis addresses local efforts to enhance customer satisfaction. Copy Mat photocopy systems offer evidence of the value derived from attending to customer satisfaction (Boroian and Boroian 1997). Although operating in a commodity business that has a poor reputation for quality, the company succeeds through managerial efforts to raise customer satisfaction.
Substantial research has addressed the relationship between role stress and boundary spanners' satisfaction with customers, yet few studies have considered the influence of stress on efforts to enhance customer satisfaction. When sufficient knowledge is not provided, boundary spanners lack the information necessary to attend to the needs of buyers. Singh (1993) reports that performance wanes in the presence of customer-based role ambiguity encountered by sales and customer service agents. Role ambiguity among retailers should have a strong influence on the level of customer satisfaction. When retailers perceive that obligations associated with job performance are unclear, they should experience greater difficulty in their efforts to meet customer expectations.
Role conflict should have a similar influence on efforts to enhance customer satisfaction. Pfeffer and Salancik (1978) maintain that competing demands inhibit people from achieving objectives. Retailers faced with the demands of multiple parties may perform tasks that have countervailing influences on customer outcomes. Conflicting demands may also lead retailers to prioritize obligations to role partners and downplay customer needs. In either case, the likelihood of addressing the demands of external constituents is likely to suffer.
H<SUB>3a</SUB>: The manager's role ambiguity is negatively associated with managerial efforts to enhance customer satisfaction.
H<SUB>4a</SUB>: The manager's role conflict is negatively associated with managerial efforts to enhance customer satisfaction.
Market-based effectiveness and role stress. Market-based organizations focus on relationships external to the firm, yet they simultaneously value stability. Market-based effectiveness emphasizes performance compared with competition and is embodied in contribution to sales (see Kumar, Stern, and Achrol 1992). This construct reflects the revenues generated by a location as compared with other dealers. For example, to varying degrees, managers engage in local marketing activities that are designed to fuel sales.
A fundamental premise of the rational goal approach is the notion that organizations are collectivities oriented to specific goals. Ambiguity and conflict constrain efforts to achieve these goals. Consistent with this perspective, Bagozzi (1980) reports a negative influence of role ambiguity on sales performance. As role responsibilities become less clear, retail managers must dedicate more cognitive resources to identify role expectations. Because cognitive resources are limited, resources allocated to clarifying responsibilities cannot be dedicated to pragmatic, goal-based action (Cohen 1980). Consequently, managers encountering unclear expectations should contribute less to the firm's revenue stream. An increase in the level of conflict similarly requires managers to dedicate more energy to sorting out the demands of multiple stakeholders. As more resources are allocated to sorting out role obligations, contributions to performance should falter.
H<SUB>5a</SUB>: The manager's role ambiguity is negatively associated with managerial contributions to sales.
H<SUB>6a</SUB>: The manager's role conflict is negatively associated with managerial contributions to sales.
Hierarchy-based effectiveness and role stress. Interfirm relationships with a hierarchical orientation parallel market-based organizations in their emphasis on control, yet their internal focus is consistent with clan-based organizations. The simultaneous attention to stability and internal operations is reflected in values that center on efficiency and smooth-flowing production. Berkowitz's (1980) analysis of industrial sales territories is one of the few studies that links role clarity to sales-to-expense ratios. Our investigation of efficiency-based factors examines the degree to which negotiation between firms reflects coordinated bargaining efforts, which address the extent to which negotiation between the transacting parties is systematic and effective (Milgrom and Roberts 1991). For example, retailers negotiate with suppliers over order quantities, deliveries, training, and staffing.
Ambiguity and conflict are likely to inhibit efforts to coordinate bargaining sessions. When role obligations are poorly understood or underspecified, trading partners are inclined to use bargaining sessions as vehicles for clarifying responsibilities. In addition, managers experiencing high levels of ambiguity are unlikely to focus on relevant factors during negotiation periods. Conflict should similarly lead to lengthy negotiation sessions, but the motivations for negotiating are different. Retail managers who are cognizant of high conflict may view bargaining sessions as opportunities to reconcile disputes, and recognition of points of contention may lead them to stake out negotiation stances that are unacceptable to their trading partners. Conflict may also lead trading partners to avoid interaction and interact less openly when bargaining commences (see Michaels, Day, and Joachimsthaler 1987). Consequently, ambiguity and conflict require the dedication of additional resources to the bargaining process.
H<SUB>7a</SUB>: The manager's role ambiguity is negatively associated with coordinated bargaining efforts.
H<SUB>8a</SUB>: The manager's role conflict is negatively associated with coordinated bargaining efforts.
Alternative Perspectives on Role Stress and Organizational Outcomes
General adaptation syndrome (G-A-S). Many studies have examined relationships between role stress and effectiveness, yet the results do not unequivocally support the hypotheses. For example, analyses in retail (e.g., Kelly, Gable, and Hise 1981) and other contexts (Singh, Verbeke, and Rhoades 1996) fail to support the negative influences of stress on performance. Research in biology and psychology suggests that responses to stress may not be linear. Yerkes and Dodson's (1908) laboratory experiments illustrate a nonlinear relationship among habit formation, performance, and stressors. Their research indicates that intermediate levels of stimuli outperform weak or strong stimuli when tasks are challenging. Thus, stress is viewed as having a parabolic relationship with performance. Activation theory (Scott 1966) similarly maintains that low levels of stress constrain performance because of minimal stimulation. High levels of stress yield similar performance because of overstimulation and disorganization of responses. Intermediate levels of stressors, however, provide appropriate stimulation to yield desirable outcomes.
Marketing researchers (e.g., Singh 1998) have incorporated logic from habit formation and activation theory, but their results do not corroborate these perspectives. Selye (1950) offers a contrasting perspective in his presentation of the G-A-S as a three-phase model of reactions to stressors (see Figure 1). The alarm phase (A to B) is characterized by increasingly lower levels of performance. For example, biological research characterizes body weight loss as a symptom of this phase. During the reactance stage (B to D), performance factors increase and resistance to stress increases. Selye (1974) uses the term "eustress" to describe positive consequences of stress. Eustress is accompanied by coping behaviors that enable people to overcome stress and accomplish tasks that are considered worthwhile. Beyond some threshold, however, the exhaustion phase (D to E) is observed, in which reactions are similar to those in the initial phase.
The G-A-S perspective was developed from laboratory observations of within-subject responses to increasing levels of stimuli. The experimental results do not evince constant thresholds of responses to stress but reflect consistent patterns of results across subjects (Selye 1974). The thresholds (B and E) vary across subjects, but the triphasic response pattern should be consistent.
The periodicity outlined in G-A-S augments the hypotheses examined in prior research. The linear effects offered in many studies are embedded into the initial and final phases of the function(arcs A to B and D to E, respectively, in Figure 1). The function also incorporates logic from Yerkes and Dodson's (1908) research on habit formation to account for nonlinear influences of stress. This relationship is incorporated into the G-A-S function by the arcs from points C to E in Figure 1.
Our investigation of G-A-S effects examines the discrete influence of stress on four performance factors that are drawn from the competing values framework (Cameron and Quinn 1999). Interorganizational systems that value flexibility maintain that people should be granted the opportunity to implement their own styles to achieve objectives. This perspective emphasizes an internal orientation and personal flexibility and is embodied in an organization's efforts to nurture competence. Low levels of ambiguity and conflict provide limited opportunity to hone capabilities and yield lower levels of competency. At high levels, stress factors are overwhelming and similarly constrain competence. In contrast, intermediate levels of ambiguity and conflict pose challenges to boundary spanners, and accomplishments achieved in this context heighten competence.
H<SUB>1b</SUB>: The manager's role ambiguity has a nonlinear, triphasic relationship with managerial competency.
H<SUB>2b</SUB>: The manager's role conflict has a nonlinear, triphasic relationship with managerial competency.
Values related to the open systems framework under-score a firm's simultaneous emphasis on external positioning, flexibility, and individuality. When ambiguity and conflict are present in trivial amounts, managers have little incentive or opportunity to implement strategies that enhance customer satisfaction. As ambiguity and conflict increase, coping skills are developed that enable managers to initiate action that raises satisfaction. Nevertheless, the positive influence of stress operates within a limited spectrum. Excessive levels of ambiguity preclude managers from identifying action that is relevant to customer satisfaction. Extreme conflict requires the dedication of resources to evaluate and reconcile interrole disagreements. As the resources dedicated to reconciliation of responsibilities mount, less effort is channeled toward customer satisfaction.
H<SUB>3b</SUB>: The manager's role ambiguity has a nonlinear, triphasic relationship with managerial efforts to enhance customer satisfaction.
H<SUB>4b</SUB>: The manager's role conflict has a nonlinear, triphasic relationship with managerial efforts to enhance customer satisfaction.
The triphasic influence of stress outlined in the G-A-S should also be germane to contributions to sales. The rational goal model emphasizes the pursuit of productivity through a strong emphasis on external positioning and control. At low levels of ambiguity and conflict, retailers are not motivated to engage in activity to raise revenues. As clarity begins to wane and conflicts intensify, the retailer is likely to feel challenged and dedicates more energy to enhancing sales. At extreme levels, however, unclear expectations and inconsistencies in requirements preclude the manager from recognizing and engaging in efforts that enhance contributions to sales.
H<SUB>5b</SUB>: The manager's role ambiguity has a nonlinear, triphasic relationship with managerial contributions to sales.
H<SUB>6b</SUB>: The manager's role conflict has a nonlinear, triphasic relationship with managerial contributions to sales.
The G-A-S effects should also be germane to bargaining efforts. Initial observations of unclear guidelines and conflicting expectations warrant consideration in bargaining sessions. As ambiguity and conflict increase, managers develop skills that enable them to function efficiently in interactions with brand management. At excessive levels, uncertainty and competing expectations void any gains associated with coping skills. Trading sessions become opportunities to clarify obligations and prioritize conflicting demands, and bargaining sessions are prolonged.
H<SUB>7b</SUB>: The manager's role ambiguity has a nonlinear, triphasic relationship with coordinated bargaining efforts.
H<SUB>8b</SUB>: The manager's role conflict has a nonlinear, triphasic relationship with coordinated bargaining efforts.
Interaction effects. Fried and colleagues (1998) offer a perspective on the influences of stress that stands in contrast to those outlined in the G-A-S. According to their approach, each facet of stress reduces a person's capacity to control the work setting. As multiple dimensions of stress materialize, the person's ability to maintain control over the work setting diminishes. Although people can control single components of stress, the simultaneous presence of ambiguity and conflict inhibits the ability to exercise control, which results in lower performance.
We propose that these interactive influences of ambiguity and conflict are relevant to organizational values that emphasize stability and control. Therefore, the pursuit of market-based objectives such as contributions to sales should be impaired by the simultaneous presence of ambiguity and conflict. When responsibilities are poorly defined, consistent role obligations emanating from multiple partners enable local retailers to attain acceptable sales levels. In contrast, when retailers are confronted with inconsistent demands from multiple trading partners, clearly delineated responsibilities enable retail managers to garner high contributions to sales. The recognition that roles are poorly specified and multiple partners are placing inconsistent demands, however, leads retail managers to generate lower contributions to sales.
The interactive influences of role stressors should also affect bargaining efforts. Ambiguities in obligations may not lead to higher bargaining expenditures when role expectations are compatible, yet well-defined roles may identify relevant bargaining issues despite substantial conflict. High conflict exacerbates role demands, and low clarity complicates efforts to prioritize expectations. Thus, effort is focused on addressing multiple demands throughout negotiations.
H<SUB>5c</SUB>: The manager's role ambiguity and conflict have an inter-active influence on contributions to sales. Managerial contributions to sales decrease as role ambiguity and conflict increase simultaneously.
H<SUB>7c</SUB>: The manager's role ambiguity and conflict have an inter-active influence on coordinated bargaining efforts. The degree to which bargaining efforts are coordinated declines as role ambiguity and conflict increase simultaneously.
Antecedents to Role Stress
Our model addresses economic factors and communication processes that influence the level of stress operating in alliances. Consider first how economic factors influence role stress.
Economic antecedents to role stress. Transaction cost analysis is a theory that underscores the importance of the nature of investments made in a relationship (Williamson 1985). To varying degrees, assets dedicated to a relationship have limited value in other settings. For example, operating procedures for an online cash register system have limited value in other retail networks. Transaction cost theory refers to these investments as transaction-specific assets. Specific assets take on importance because managers must ensure that these investments are employed productively.
A manager's commitment to a retail distribution system involves several forms of capital that have limited value out-side of the network. For example, hoteliers become well acquainted with the chain's reservation and operating systems (Shook and Shook 1993), and the nuances of the system cannot be redeployed in other hotel systems. Investments in learning the operating system of a retail network are investments in the role outlined by the role sender. Horizontal alliances complicate specific assets because managers enter the new contract with a history of dedicated investments. Managers carry over expectations based on their knowledge of the operating system developed for the old branded concept. These investments augment the expectations on the local managers and should raise conflict. Similarly, obligations endemic to the old system should escalate ambiguity for retailers. Investments in understanding the prior operating system complicate the managerial task to the extent that current policies are inconsistent with prior obligations. Managers more familiar with the old system should experience less clarity about expectations in the dual-branded system.
H<SUB>9</SUB>: Transaction-specific investments in the former operating systems are positively associated with managerial role ambiguity.
H<SUB>10</SUB>: Transaction-specific investments in the former operating systems are positively associated with managerial role conflict.
Investments in the current operating system also have implications for role stress. Agency contracts such as franchises are designed to bring the best possible outcome for the principal by aligning the incentives of both parties to the contract (Bergen, Dutta, and Walker 1992). As the agents dedicate more effort to learning the system, they are likely to recognize facets of the system that favor the principal. As a result of this process, they experience higher levels of conflict in their relationships with network management. Their investment should have a contrasting effect on ambiguity. Efforts to learn the operating system should clarify the principal's expectations.
H<SUB>11</SUB>: Transaction-specific investments in the current operating systems are negatively associated with managerial role ambiguity.
H<SUB>12</SUB>: Transaction-specific investments in the current operating systems are positively associated with managerial role conflict.
Communication-based antecedents to role stress. The mode of communication should have a strong influence on retail managers' stress. The medium used to transmit information has been operationalized in a variety of ways, including personal/impersonal and formal/informal means. Huber and Daft (1987) present a hierarchy of communication modes that ranges from face-to-face contact to less personal communiques such as form letters. As the vehicle becomes more personal and multidimensional, greater opportunity is presented to interpret and transmit information. For example, face-to-face communication provides the opportunity to send verbal and nonverbal messages, yet form letters are more sterile means of communication. Employees prefer personal communication because it provides opportunities for dialogue that is more difficult to achieve in other modes (Larkin and Larkin 1996). Personal communication underscores the social link between the values of the employee and the firm, and conflict emerges when this link breaks down (Strebel 1996). Dialogue enables employees to express contingencies associated with multiple role obligations, and resolutions can be designed that meet each party's needs without raising conflict. Hallowell (1999) uses similar logic in his description of the human moment in interpersonal communication. The human moment involves occupying the same physical space and dedicating physical and emotional attention to interaction. Electronic media offer efficiencies in transmissions of sterile information, but personal cues cannot be transmitted as effectively in modes such as fax or e-mail. Body language, intonation, and emotion are constrained in less personal communication. Group gatherings and written correspondences are less capable of transmitting the meaning embodied in nonverbal communication. The meaning underlying words is muddled as the communication becomes less personal and ambiguity becomes more pronounced.
H<SUB>13</SUB>: Personal modes of communications are negatively associated with managerial role ambiguity, yet group and impersonal modes are neither positively nor negatively associated with role ambiguity.
H<SUB>14</SUB>: Personal modes of communications are negatively associated with managerial role conflict, yet group and impersonal modes are neither positively nor negatively associated with role conflict.
Empirical Context
The Norwegian distribution system of two oil refiners served as the setting for our study. The refiners recently elected to merge their operations at the retail level. The stations adopted dual-branded trademarks and signs, and their shops were redesigned to reflect uniformity in the product setting and offerings. The retail setting provided a consistent image for consumers, yet the retail operating system brought together two corporate systems. The Norwegian state-owned company became a retailer in 1986 through the purchase of the retail network of a global refiner. Before the merger, this company had limited access to oil retailing. The global partner began Norwegian operations in 1930 and established a highly bureaucratic system with extensive reporting lines.
An internal proprietary study suggested that stress increased after the merger. The proprietary study, collected at the same time as the focal survey, revealed that managers believed that role responsibilities within the system were unclear and managerial decision making was performed on a day-to-day basis. Similarly, area supervisors indicated that they lacked the responsibility to perform their jobs properly. Managers reported that information related to new programs was insufficient, and no area supervisor could describe the strategy for the market or for the dual-branded concept. Unclear internal responsibilities, ad hoc decision making, and limited empowerment of supervisors fueled conflict in the system, and insufficient information in the system exacerbated role ambiguity.[ 1]
The method for analysis was a between-subjects design, and data were collected with retailers and the refiners' area managers. Much of the theory pertaining to responses to stress has been developed in laboratory settings that employ within-subjects designs. The laboratory setting provides an opportunity to decrease the variance in response to stress (Carroll and Nelson 1993), but a controlled within-subjects design is rarely feasible in applied settings. The effects are weaker in a between-subjects design, yet their observation suggests that the effects are appreciable (Grice and Hunter 1964).
Sampling Procedure and Data Collection
The data were collected through surveys sent to each retail manager in the network. Respondents were informed by telephone that they were about to receive the survey. The survey was accompanied by supporting letters from the retail union, the refiners, and the principal researcher. The response rate was 55.9% (218 of 390 responses). Among the responding firms, 89 had been associated with the state-owned network, and 129 had been associated with the global refiner. The mean level of managerial experience was 16.4 years, and all managers had at least one year of experience before the merger. Analysis of several variables (e.g., annual sales) and the focal constructs indicated no significant differences between early and late responses (Armstrong and Overton 1977). This analysis suggests that nonresponse bias is not an issue, but a stronger test would have been to contact nonrespondents.
Measure Development
Measure development followed the guidelines outlined by Gerbing and Anderson (1988). The survey instrument was developed with retail management. The director of the retail network, the director of the retail managers' union, and an industry consultant provided insight into the proper wording of the measures. In addition, pretests with four managers offered us an opportunity to make semantic changes in variables and make the study more meaningful to the respondents.
Although additional respondents enable researchers to control for informant bias (Kumar, Stern, and Anderson 1993), the pilot study indicated that additional respondents were not available at the retail level. Nevertheless, we employed a matching dyad approach using data gathered with the refiners' area supervisors. Given that supervisors oversee four to ten retail outlets, it was infeasible to obtain reports for each outlet. We obtained 71 reports, which reflected a 95% response rate from supervisors. The manager and supervisory surveys used parallel wording. We employed coefficient alpha, item-to-total correlation, and exploratory factor analysis to purify the measures. We eliminated items that did not load properly in either factor analytic procedure. Eigenvalues and screen tests illustrated that one factor was sufficient to represent the items within each conceptual domain.
Measures
Transaction-specific assets. The dealer's investments in the current and prior operating systems were analyzed by separate sets of measures drawn from Anderson's (1985) company-specific asset scale. Four seven-point Likert-type items assessed the investment dedicated to learning the retail operating system.
Communication modality. We assessed this construct using formative measures of the frequency of usage of communication channels (Mohr, Fisher, and Nevin 1996). Managers interact with the refiner through face-to-face contact, telephone, letters, Internet, union meetings, seminars, and local retailer meetings. The refiner uses these modes but also communicates through advertisements, sales literature, and newsletters. We grouped the measures into three categories: personal (face-to-face messages and telephone calls), group (union meetings, local managers' meetings, and seminars), and impersonal interactions (letters and Internet contacts, refiner promotions, sales, and newsletters).
Role stress. The role stress scales were based on the seven-point measures developed by Rizzo, House, and Lirtzmann (1970). The conflict and ambiguity measures consisted of six and seven items, respectively.
Competence. This construct was analyzed by two seven-point items that addressed the knowledge and skills acquired by the local retail personnel. The scale was adapted from Kumar, Stern, and Achrol (1992).
Customer satisfaction. The satisfaction scale was based on Kumar, Stern, and Achrol's (1992) customer satisfaction scale. Three seven-point Likert items assessed efforts to achieve customer satisfaction.
Contributions to sales. This construct was assessed through three seven-point Likert items derived from Kumar, Stern, and Achrol (1992). The items addressed sales' contributions compared with competitors' and other retailers' sales.
Coordinated bargaining efforts. This construct examined the extent to which negotiation sessions were systematic and effective. Two seven-point items drawn from Nygaard (1992) were employed.
Construct Validity
We employed Anderson and Gerbing's (1988) two-phase approach to assess the model. We assessed the first phase, confirmatory factor analysis of the reflective measures, using EQS. Initial analysis (χ²(406) = 635.546, p < .05; comparative fit index [CFI] = .921) indicated that elimination of some items would enhance the fit indices. Nevertheless, pursuit of optimal fit can limit the conceptual domain. Standardized residuals and Lagrange multipliers indicated significant cross-loadings for one conflict item. Removal of the item (see the Appendix) offered a stronger representation of the data (χ²(377) = 589.979, p < .05; CFI = .926). We assessed discriminant validity by comparing this model with others in which the correlation between two constructs was set to one. In each case, the chi-square difference test evinced discrimination. For example, the discriminant test for conflict and current assets was significant (χ²( 1) = 14.504, p < .05). Table 3 provides correlations and descriptive statistics for the constructs.
The second phase of Anderson and Gerbing's (1988) framework employs structural equation modeling to assess the proposed relationships.[ 2] We developed two series of models. The first series assessed the received view, interactive, and parabolic influences of stress. As a means for assessing interaction effects, we incorporated Ping's (1995) estimation technique. Because multiplicative terms raise the possibility of multicollinearity and Type II errors, we mean-centered and standardized the individual measures before developing the nonlinear terms. The initial model examined the linear influences of stress on effectiveness. We employed Lagrange multipliers to assess whether interactive and parabolic influences enhanced the received view model (Bentler 1993). Structural analysis of the linear model indicated some correspondence between the model and data (χ²(529) = 610.049, p < .05; CFI = .976), yet chi-square difference analyses warranted the deletion of paths from the received view model. In addition, sequential chi-square difference tests suggested a group-based communication determinant of cooperative bargaining efforts (χ²( 1) = 5.222, p < .05), a parabolic influence of ambiguity on competence (χ²( 1) = 5.735, p < .05), and interaction effects for contributions to sales (χ²( 1) = 5.763, p < .05). The resulting analysis offers a more parsimonious model (χ²(537) = 605.534, p < .05; CFI = .980).
The second series investigated the G-A-S effects as a competing model to the received view. The G-A-S effects were modeled as sine functions of the ambiguity and conflict variables. This model offers some correspondence to the data (χ²(496) = 607.337, p < .05; CFI = .967), yet chi-square difference tests warrant the deletion of several proposed paths. The Lagrange procedure indicates a relation-ship between group-based communication and bargaining efforts (χ²( 1) = 6.686, p < .05). In addition, personal inter-action has a direct influence on contributions to sales (χ²( 1) = 7.911, p < .05). The resulting model offers a more efficient representation of the relationships among the constructs (χ²(502) = 607.785, p < .05; CFI = .970). The Friedman comparison test (Rigdon 1999) suggests that this model provides superior fit to the trimmed multiplicative model (χ²( 1) = 7.52, p < .05), yet the pattern of results warrants examination of both models (see Table 4).
The influence of role stress on retailer competence is the focus of H<SUB>1a</SUB>, H<SUB>1b</SUB>, H<SUB>2a</SUB>, and H<SUB>2b</SUB>. Ambiguity has the anticipated negative, linear effect (β = -.373, t = -3.21), and the parabolic effect (β = .228, t = 2.76) is significant. The graph outlined in Figure 2, Panel A, suggests that these results do not reflect the inverted-U effects outlined in habit formation and activation theory, but they are consistent with the alarm and reactance phases of the G-A-S. The sine function, however, is not significant (β = .155, t = 1.82). The linear (β = .077, t = .47) effects of conflict are nonsignificant. Although the trigonometric (β = -.154, t = -1.80) effects of conflict are marginally significant, they are contrary to the hypotheses. At low levels, increases in conflict yield greater competence, yet this trend subsides rapidly. Together, these results support H<SUB>1a</SUB>, yet they do not support H<SUB>1b</SUB>, H<SUB>2a</SUB>, and H<SUB>2b</SUB>.
H<SUB>3a</SUB>, H<SUB>3b</SUB>, H<SUB>4a</SUB>, and H<SUB>4b</SUB> examine the effects of stress on customer satisfaction. The linear ambiguity parameter (β = -.201, t = -1.16) is nonsignificant, yet the trigonometric (β = .157, t = 1.96) function supports H<SUB>3b</SUB>. Graphic presentation of the relationship (Figure 2, Panel A) is suggestive of the initial and reactance stages of G-A-S, yet the degradation stage is not observed. H<SUB>4a</SUB> and H<SUB>4b</SUB> are not supported, given the nonsignificant, linear (β = .082, t = .86) and sinusoidal (β = -.050, t = -.66) effects.
The linear, trigonometric, and interactive influences of stress on contributions to sales are addressed in H<SUB>5a</SUB>, H<SUB>5b</SUB>, H<SUB>5c</SUB>, H<SUB>6a</SUB>, and H<SUB>6b</SUB>. Ambiguity (β = -.292, t = -2.94) is significantly related to the dependent variable, yet the trigonometric function is also significant (β = .165, t = 2.49). Simultaneous evaluation of the linear and sine functions by means of constraint analysis (Bentler 1993) indicates (χ²( 1) = .88, p < .35) that the influence of the linear function does not exceed that of the sine variable.[ 3] In addition, the linear (β = -.010, t = -.06) and trigonometric (β = .057, t = .91) effects of conflict are nonsignificant. The interactive influence of ambiguity and conflict on contributions to sales is significant (β = .186, t = 2.49), yet the effect is contrary to the hypothesis. To examine these effects, we generate dichotomous levels of ambiguity and conflict on the basis of a median split. We estimate mean levels of contributions to sales for each pair of dichotomous values. The results (see Figure 2, Panel B) indicate that ambiguity has a negative influence on contributions to sales, yet role conflict moderates the effect. When conflict is low, increases in ambiguity lower contributions to sales (t<SUB>104</SUB> = 3.88, p < .05). When conflict is high, increases in ambiguity do not significantly lower contributions to sales (t<SUB>110</SUB> = 1.58, p > .05). Together, these results support H<SUB>5a</SUB> and H<SUB>5b</SUB> but fail to support H<SUB>5c</SUB>, H<SUB>6a</SUB>, and H<SUB>6b</SUB>.
The influences of stress on bargaining efforts are the focus of H<SUB>7a</SUB>, H<SUB>7b</SUB>, H<SUB>7c</SUB>, H<SUB>8a</SUB>, and H<SUB>8b</SUB>. In contrast to H<SUB>7a</SUB>, H<SUB>7b</SUB>, and H<SUB>7c</SUB>, ambiguity is not a significant, linear (β = -.164, t = -.90) or trigonometric (β = .073, t = .94) antecedent to bargaining efforts, and the interaction with conflict (χ²( 1) = .85, p < .36) does not influence bargaining efforts. H<SUB>8b</SUB> is rejected because of the nonsignificant, trigonometric effect (β = .043, t = .57), but the linear effect of conflict outlined in H<SUB>8a</SUB> (β = -.311, t = -1.99) is supported.[ 4]
H<SUB>9</SUB>-H<SUB>12</SUB> examine whether transaction-specific assets influence role stress. Although previous investments in the operating system do not influence conflict (γ = .000; t = .00, p > .05), they have a significant influence on ambiguity (γ = .336; t = 3.68, p < .05). Currently dedicated assets are associated with conflict (γ = .575; t = 3.56, p < .05), yet they do not influence ambiguity (γ = .079; t = .89, p > .05). These results support H<SUB>9</SUB> and H<SUB>12</SUB>, but they offer no support for H<SUB>10</SUB> and H<SUB>11</SUB>.
Facets of communication are the focus of H<SUB>13</SUB> and H<SUB>14</SUB>. Personal interaction lowers ambiguity (γ = -.247; t = -3.07, p < .05), yet it does not influence conflict (γ = -.086; t = -1.23 p > .05). The results lend partial support to H<SUB>13</SUB>, but not H<SUB>14</SUB>.[ 5]
Our goal has been to examine role stress in horizontal alliances. The results support the received view prediction of a linear, negative influence of conflict on efforts to streamline bargaining and of ambiguity on competence and contributions to sales. In addition, nonlinear effects that reflect the alarm and reactance phase of G-A-S are observed in the treatment of ambiguity as a determinant of competence, customer satisfaction, and contributions to sales. The data also suggest that conflict moderates the influence of ambiguity on contributions to sales. Role ambiguity is positively associated with dedication to the old operating system, but it is quelled through personal communication. In contrast, conflict is positively associated with current specific investments.
Theoretical Implications
Our analysis of stress in horizontal alliances has incorporated three underlying goals. The first goal has been to examine influences of ambiguity and conflict on facets of effectiveness drawn from the competing values framework (Cameron and Quinn 1999). Human resource development values, such as competence, focus on flexibility and an internal focus, yet market-based values such as contributions to sales favor control along with an external orientation. Retailer efforts to enhance customer satisfaction and other open-system values emphasize autonomy and an external orientation, whereas coordinated bargaining efforts and related factors emphasize an internal, control-based approach to performance. Our treatment of these facets of effectiveness underscores the need to identify the dimensions of effectiveness that are focal to the research. Discrete organizational outcomes not only may be uncorrelated but also may have different antecedents. Theory and practice benefit through explicit identification of the forms of effectiveness addressed in the research. The competing values approach provides a rich theoretical foundation for the analysis of multiple channel outcomes.
Our second objective has been to examine alternative theoretical perspectives on the influences of role stress on organizational outcomes. We offered rationales that are associated with the received view (House and Rizzo 1972), G-A-S (Selye 1950), and the interaction approach (Fried et al. 1998). Although no support is provided to the interaction approach, our research suggests that the form of effectiveness mitigates the merits of the other approaches. The negative effects associated with the received view are observed in the relationship between conflict and bargaining efforts. The received view effects of ambiguity also garner some support, but these effects are augmented by other factors. When the system places value on personal autonomy or an external orientation, there is evidence that managers develop skills that yield competence, customer satisfaction, and contributions to sales despite rising levels of ambiguity. These results suggest that the effects outlined by G-A-S (Selye 1950) are germane when organizational values emphasize personal flexibility or an external focus. These results are tentative, yet they point to the need to consider multiple theories when examining the influences of ambiguity on effectiveness.
The third objective of our research has been to identify organizational and communicative processes that influence alliances. Dedicated assets have been implicated as determinants of organizational structure, but the deleterious consequences of specific assets have not been considered. Our research indicates that investments in the previous retail operating system raise ambiguity that influences competence, customer satisfaction, and contributions to sales. Current investments in the operating system increase conflict and subsequently constrain bargaining sessions. These results reinforce the need to consider multiple consequences of specific assets. Investments not only have implications for control structures but also influence working relationships.
Our study also has implications for theory addressing the mode of communication employed to distribute information. Mohr, Fisher, and Nevin (1996) emphasize the influence that communication style has on commitment, satisfaction, and coordination, yet scant research has examined the influence of communication media on other facets of interfirm process and performance. Our research suggests that personal modes of communication reduce ambiguity and thus indirectly influence effectiveness. In addition, group-based communication raises the efficiencies of inter --firm bargaining. These results are consistent with research that underscores the merits of personal communication (Hallowell 1999). Personal media facilitate dialogue that enables parties to resolve ambiguities, and they facilitate gestalt evaluations of verbal and nonverbal aspects of inter-action. The overriding implication of these results is the need to consider the mode of communication. As electronic media evolve and become more personal, research should examine their influence on multiple facets of effectiveness.
Implications for Organizational Design
Although the context, pattern of results, and method limit the extent to which generalizations can be drawn from our research, some tentative recommendations should be acknowledged. First, our work underscores the need to consider the underlying values associated with effectiveness. Prior studies indicate that the influences of stress vary with the form of effectiveness, yet prior research does not offer a framework for categorization and analysis of alternative forms of effectiveness. The competing values framework classifies forms of performance, and our research augments this framework by linking alternative stress theories to the underlying values pursued by the organization. When organizational values downplay personal autonomy, role conflict has a deleterious influence on interfirm outcomes. Therefore, organizations grounded in this perspective should seek decision-making structures that limit the level of stress encountered by trading partners.
Other performance-related values stand in contrast to the control-based internal orientation of a hierarchy, and our research suggests that these values systems have more complex relationships with stressors. Organizational values that emphasize flexibility recognize the merits of enabling local managers to develop their own approaches to problem solving. Similarly, organizational values that emphasize an external orientation recognize that boundary spanners interact with the environment to exact outcomes. To the extent that these outcomes are valued within the organization, local managers should anticipate some uncertainty. Ambiguities in a role facilitate learning by the role player, and the learning process garners positive outcomes despite increasing levels of stress (Selye 1974).
A second implication of our work is the need to under-stand that action taken to achieve one objective may have countervailing influences on other factors. For example, training programs designed to make local managers more knowledgeable about the operating system may result in more prolonged bargaining sessions. The competing values framework emphasizes contradictory outcomes realized because of managerial action, yet few research studies have addressed this contingency. Our work reinforces the need for local and area managers to recognize multiple influences of stress.
The results provide insight into mechanisms that can be used to control the level of ambiguity and conflict, notably through transaction-specific investments and personal modes of communication. Our preliminary results suggest that facets of the investment in the retail system yield higher levels of role stress. Consequently, upper-level managers of these networks should recognize that role issues are most likely to emerge among local managers with the most invested in the retail system. Retailers contemplating dual branding should recognize that ambiguity would be greater among local managers with large personal investments in the system. Similarly, the investment made upon entering the new system will raise the level of role conflict. Upper-level management can confront these issues by placing greater emphasis on personal interactions with the local managers. As the perceived level of personal interaction increases, ambiguity diminishes. Impersonal communiques offer efficiency in the transmission of policies, but they do not quell conflict or ambiguity. Consequently, organizations that seek to contain role stress should place greater emphasis on personal modes of communication.
Limitations and Further Research
The implications drawn from this study must be viewed in light of the limitations inherent to the research. The outcome variables were based on measures developed in prior research. Replication facilitates comparison, yet replicated measures may provide relatively poor statistical properties. Therefore, the results associated with competence and bargaining efforts are limited by the constraints of two-item scales. The role stress measures have been criticized because of the counterbalancing of positive and negative role stress measures. McGee, Ferguson, and Seers (1989) have developed a procedure for the comparison of the ambiguity and conflict factors with a singular-factor model that incorporates negatively keyed ambiguity items. Their results indicate that the factor structure for Rizzo, House, and Lirtzmann's (1970) measures of ambiguity and conflict is due to the wording of the items. Although our replication is not fully consistent with their findings,[ 6] their study underscores the need to address the breadth of the construct by means of measures that are not confounded by wording biases.
Our Norwegian oil retailing milieu can also be augmented through replication of the model in contrasting empirical contexts. Oil retailing may differ from other contexts on the basis of the resource advantages and near-monopoly power wielded by the refiners. In addition, state participation in the oil industry may limit the ability to generalize our findings to contexts in which government adopts a more laissez-faire approach to commerce. Moreover, the findings associated with retail managers may not replicate with other groups. Retail managers interact with customers, yet they also dedicate time to administrative tasks. The influence of stress on efforts to achieve customer satisfaction and contributions to sales may be more pronounced among customer service employees than among managers.
This survey-based research uses logic drawn from laboratory research to examine the effects of stress on effectiveness. An important distinction between the methods is the manner in which the influences of stimuli are recorded. Laboratory methods chart the within-subjects responses of organisms to progressively higher levels of stress, yet surveys record between-subjects self-reports of multiple levels of stimuli. Future efforts can augment our research by longitudinally tracking responses to stress. Time series provides the opportunity to chart the initial distribution of respondents along the G-A-S thresholds. The researcher has the opportunity to observe whether the G-A-S or other stress-based effects are realized. This design would be particularly insightful in the tracking of stress throughout the planning and completion of an alliance. The research can move beyond the limits of our study and isolate changes in stress incurred through the alliance. Furthermore, the research could track bidirectional changes in the mode of communication and pre-/postmerger specific assets and their influences on stress.
Our research suggests that stress influences organizational outcomes, but it does not consider factors that mitigate the influence of stress. Empowerment should be germane to organizational values that emphasize personal flexibility (Quick et al. 1997). Organizations that empower their managers grant them a level of autonomy that raises perceptions of competence. Because empowered managers operate under a broader set of parameters, they should be equipped to achieve levels of customer satisfaction that exceed those achieved by highly scrutinized managers. If empowerment is to yield desired outcomes, it is essential for managers to be psychologically involved and interested in the decision-making process (Quick et al. 1997). They must also have the ability to express their thoughts about decisions and believe that their participation is personally relevant to their future. When these conditions hold, empowerment should lead to heightened levels of competence and customer satisfaction.
Analysis of the dominant organizational culture operating within the network can also augment our research on competing values. Multiple facets of effectiveness are associated with specific organizational cultures, yet few firms place equal emphasis on all facets of effectiveness. Singh, Verbeke, and Rhoads (1996) illustrate that organizational archetype mitigates the influences of stress. A procedural archetype characterized by an emphasis on efficiency catalyzes the effects observed in the relationships among ambiguity, conflict, and bargaining efforts, yet an achievement configuration exacerbates the influences of stress on contributions to sales. Research that examines organizational culture and archetype would offer insight into processes by which stress influences effectiveness.
In addition to the institutional constraints on stress, the research can also be augmented by analysis of coping skills. Coping refers to efforts to manage demands that tax a person's resources (Latack and Havlovic 1992). Research should consider the extent to which the focus of coping is problem-centered and the degree to which the method for coping is cognitive, proactive, and social. When stressors exceed people's ability to cope, burnout is manifested as emotional exhaustion, reduced accomplishment, and depersonalization (Singh, Goolsby, and Rhoads 1994). Stress directly lowers psychological outcomes, yet the eustress effects of stress on behavioral outcomes are cancelled out by the mediating effects of burnout. Research seeking to isolate positive and negative influences of stress would benefit from analysis of coping mechanisms.
In conclusion, the purpose of our research has been to investigate antecedents and consequences of stress in horizontal alliances. Our results suggest that economic conditions and communicative mechanisms influence role stress. Furthermore, ambiguity and conflict influence multiple facets of effectiveness, and the form of influence is mitigated by the underlying values pursued by the organization. We hope that these findings are informative to retail management and interorganizational theory.
1 After the data-collection period, the refiners implemented two systems to alleviate stress associated with the transition to the dual brand. The refiners established a program designed to guarantee consistency in role expectations and reporting responsibilities throughout the system, and they developed a comprehensive online operating system intended to clarify managerial role expectations.
- 2 We also assessed the proposed model using ordinary least squares regression. We extracted the trigonometric and multiplicative terms from equations that regressed them against their relevant linear constructs (see Singh 1998). This procedure eliminates problematic multicollinearity among the antecedent variables. Although the resulting models are biased because of measurement errors, the pattern of results is similar to that resulting from the structural equation models.
- 3 The constrained model treating the absolute value of the coefficients as equivalent provides beta coefficients of -.132 (t = -3.08) and .205 (t = 3.08) for the linear and sine functions, respectively. In the unconstrained model, the beta coefficient for the linear variable is -.202 (t = -2.20), and the sine coefficient is .172 (t = 2.30).
- 4 No other parabolic or interactive terms are significant. The Lagrange multipliers do not implicate the squared ambiguity term as an antecedent to customer satisfaction (χ²( 1) = 2.600, p < .11), contributions to sales (χ²( 1) = .595, p < .44), or bargaining efforts (χ²( 1) = .237, p < .63). Similarly, the quadratic conflict term is not related to competence (χ²( 1) = 2.182, p < .14), customer satisfaction (χ²( 1) = 1.767, p < .18), contributions to sales (χ²( 1) = .617, p < .43), or bargaining efforts (χ²( 1) = .854, p < .36). The interaction term is unrelated to competence (χ²( 1) = 1.897, p < .17), customer satisfaction (χ²( 1) = 1.184, p < .28), and bargaining efforts (χ²( 1) = .000, p < .99).
- 5 The Lagrange multipliers indicate that neither written (χ²( 1) = 1.258, p < .262) nor group-based (χ²( 1) = .174, p < .676) communication influence role ambiguity. Similarly, the Lagrange multipliers do not indicate an influence of written (χ²( 1) = .000, p < .99) or group-based (χ²( 1) = 1.091, p < .30) communication on role conflict.
- 6 Replication of the procedure developed by McGee, Ferguson, and Seers (1989) failed to illustrate that differences in the scales are solely due to wording or construct discrepancies. The procedure involves the comparison of the oblique ambiguity and conflict factors model with a singular factor model that incorporates negatively keyed ambiguity items. The chi-square for the oblique model is greater than that of the second model (χ²( 76) = 210.468 versus χ²( 71) = 205.780), and the rho statistic for the second model exceeds that of the oblique model (P = .805 versus P = .801). In contrast, the delta statistic and CFI for the oblique model ([Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.] = .833, CFI = .805) are greater than the values generated for the second model ([Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.] = .821, CFI = .801). The root mean square residual (RMSR) statistics are identical for the two models (RMSR = .05).
Legend for chart:
A = Authors
B = Role Constructs[a] Role Ambiguity Items
C = Role Constructs[a] Role Conflict Items
D = Setting
E = Results
A Donnelly and Ivancevich (1975)
B 3
C 3
D 86 sales representatives, 46 supervisors
E Ambiguity is negatively associated with three facets of
satisfaction.
A Churchill, Ford, and Walker (1976)
B 41: 21 company, 7 manager, 7 customer, 6 family
C 30: company, manager, customer, family
D 265 sales representatives
E Ambiguity lowers job, coworker, company, and pay-based
satisfaction. Conflict lowers supervisor and company-based
satisfaction.
A Oliver and Brief (1977-78)
B 6 (Rizzo, House, and Lirtzmann 1970)
C 8 (Rizzo, House, and Lirtzmann 1970)
D 114 retail managers
E Ambiguity and conflict lower satisfaction.
A Bagozzi (1978)
B 12 (Ford, Walker, and Churchill 1975)
C 15 (Kahn et al. 1964)
D 161 industrial sales representatives
E Conflict and ambiguity are negatively associated with sales and
job satisfaction. Stepwise regression suggests that only
conflict lowers sales and satisfaction.
A Busch and Bush (1978)
B 5 (Donnelly and Ivancevich 1975)
C
D 78 industrial sales representatives, 39 women, 39 men
E Ambiguity is negatively associated with performance. Ambiguity
is negatively associated with customer (female representatives),
work, and supervisor satisfaction.
A Dubinsky and Mattson (1979)
B 24: company, supervisors, customers
C 24: company, supervisors, customers
D 203 retail sales representatives
E Conflict and ambiguity are negatively associated with performance
and satisfaction.
A Teas, Wacker, and Hughes (1979)
B 6 (Rizzo, House, and Lirtzmann 1970)
C
D 107 sales representatives
E Ambiguity lowers higher-order need fulfillment.
A Bagozzi (1980)
B 12 (Ford, Walker, and Churchill 1975)
C 15 (Kahn et al. 1964)
D 122 industrial sales representatives
E Role ambiguity is negatively associated with sales and
satisfaction in the structural equation model, but conflict is
not related to either endogenous variable.
A Berkowitz (1980)
B 5 (Hamner and Tosi 1974)
C 15 (Secord and Backman 1970)
D 49 sales managers, 148 sales representatives
E Ambiguity raises the sales-to-expense ratio for sales managers.
Ambiguity and conflict lower job satisfaction.
A Kelly and Hise (1980)
B 5 (Lyons 1971)
C 8 (Rizzo, House, and Lirtzmann 1970)
D 198 brand managers
E Ambiguity and conflict lower satisfaction.
A Kelly, Gable, and Hise (1981)
B 5 (Lyons 1971)
C 8 (Rizzo, House, and Lirtzmann 1970)
D 179 chain store managers
E Ambiguity and conflict are unrelated to performance. Ambiguity
is negatively related to job satisfaction.
A Behrman, Bigoness, and Perreault (1981)
B 19: 4 customer, 5 company, 5 family, 5 manager (Ford, Walker, and
Churchill 1975)
C
D 193 industrial sales representatives
E Family-based ambiguity raises performance. Manager- and
customer-based ambiguity lower performance. Manager-based
ambiguity lowers satisfaction.
A Teas (1983)
B (Rizzo, House, and Lirtzmann 1970)[b]
C (Rizzo, House, and Lirtzmann 1970)[b]
D 116 sales managers
E Conflict lowers satisfaction.
A Behrman and Perreault (1984)
B 8 (Rizzo, House, and Lirtzmann 1970)
C 18 (Rizzo, House, and Lirtzmann 1970)
D 196 sales representatives
E Ambiguity lowers and conflict raises performance. Ambiguity and
conflict lower job satisfaction.
A Kohli (1985)
B 6 (Rizzo, House, and Lirtzmann 1970)
C
D 114 sales managers
E Ambiguity lowers satisfaction.
A Chonko, Howell, and Bellenger (1986)
B 36: 3 family, 12 job, 7 company, 7 supervisor, 7 customer
C 30: 4 family, 4 job, 11 supervisor, 6 customer, 5 self
D 121 industrial sales representatives
E Congruence of evaluation and compensation is lowered through
job-related ambiguity, but it is raised through company and
supervisor ambiguity.
A Fry et al. (1986)
B 8 (Rizzo, House, and Lirtzmann 1970)
C 7 (Rizzo, House, and Lirtzmann 1970)
D 216 sales representatives
E Ambiguity lowers job, company, and customer satisfaction.
Conflict lowers all INDSALES facets except customer satisfaction.
A Hampton, Dubinsky, and Skinner (1986)
B 6 (Rizzo, House, and Lirtzmann 1970)
C 8 (Rizzo, House, and Lirtzmann 1970)
D 116 retail sales representatives
E Ambiguity and conflict are unrelated to performance. Conflict
lowers overall satisfaction.
A Michaels, Day, and Joachimsthaler (1987)
B 10 (Rizzo, House, and Lirtzmann 1970)
C 13 (Rizzo, House, and Lirtzmann 1970)
D 952 industrial buyers
E Ambiguity lowers conflict and raises performance. Both role
factors lower satisfaction.
A Yammarino and Dubinsky (1990)
B 6 (Rizzo, House, and Lirtzmann 1970)
C 5 (Dansereau, Graen, and Hage 1975)
D 109 retailers, 116 insurance agents
E Conflict and ambiguity are negatively associated with
performance.
A Lusch and Jaworski (1991)
B 4 (Kahn et al. 1964)
C 4 (Kahn et al. 1964)
D 182 retail store managers
E Role stress lowers performance
A Singh and Rhoads (1991)
B 45: 9 company, 9 boss, 8 customer, 6 ethical, 4 other managers, 5
coworkers, 4 family
C
D 472 SMEs: sales and marketing executives
E All seven dimensions are negatively associated with performance
and satisfaction.
A Dubinsky et al. (1992); Singh (1995)
B 9 (Rizzo, House, and Lirtzmann 1970)
C 11 (Rizzo, House, and Lirtzmann 1970)
D Sales representatives: U.S., 218; Japan, 220; Korea, 156
E Ambiguity lowers performance; conflict has a positive, direct
effect on performance, but the total effect is nonsignificant.
Conflict and ambiguity (Japan only) lower satisfaction.
A Singh (1993)
B 45: 9 company, 9 boss, 8 customer, 6 ethical, 4 other manager, 5
coworkers, 4 family (Singh and Rhoads 1991)
C
D 472 SMEs: marketing executives, 254 IS marketing and support
representatives
E SME: Customer-based ambiguity lowers performance, company and
supervisor ambiguity lower satisfaction, and family-based
ambiguity raises satisfaction. IS: company, customer, and other
ambiguity lower performance; supervisor-based ambiguity lowers
satisfaction
A Singh, Goolsby, and Rhoads (1994)
B 3 (Rizzo, House, and Lirtzmann 1970)
C 3 (Rizzo, House, and Lirtzmann 1970)
D 377 customer service representatives
E Direct effects of conflict and ambiguity on performance are not
significant. Burnout mediates the effects of stress on
performance and satisfaction.
A Singh, Verbeke, and Rhoads (1996)
B 3 (Rizzo, House, and Lirtzmann 1970)
C 3 (Rizzo, House, and Lirtzmann 1970)
D 188 boundary spanners
E Ambiguity lowers performance, and the effect is moderated by
organizational archetype. Ambiguity lowers satisfaction.
Conflict lowers satisfaction for the affective archetype.
A Hartline and Ferrell (1996)
B 17 (Chonko, Howell, and Bellenger 1986)
C 12 (Chonko, Howell, and Bellenger 1986)
D 797 hotel employees
E Conflict lowers self-efficacy. Ambiguity lowers self-efficacy
and satisfaction.
A Singh (1998)
B 7 (Rizzo, House, and Lirtzmann 1970)
C 8 (Rizzo, House, and Lirtzmann 1970)
D 703 marketing executives and representatives
E Ambiguity and conflict lower performance. Conflict lowers
satisfaction.
A Singh (2000)
B 9 company, 8 customer (Singh and Rhoads 1991)
C 2 intersender, 4 resource demands (House 1980)
D 182 customer support representatives, 177 bill collectors
E In both samples, customer-based ambiguity lowers quality and
productivity, and resource-based conflict lowers productivity.
[a]Names in parentheses correspond with the sources of the measures. [b]The number of items was not reported. Notes: Churchill, Ford, and Walker's (1974) INDSALES assesses satisfaction with the job, fellow workers, supervision, company, pay, promotion potential, and customers. SMEs = Small and medium-sized enterprises. IS = information systems.
Legend for chart:
A = Authors
B = Satisfaction Items[a]
C = Performance Items[a]
D = Competing Values Framework: Conceptual Domain of the Performance
Items Human Relations
E = Competing Values Framework: Conceptual Domain of the Performance
Items Open Systems
F = Competing Values Framework: Conceptual Domain of the Performance
Items Rational Goal
G = Competing Values Framework: Conceptual Domain of the Performance
Items Internal Process
A Donnelly and Ivancevich (1975)
B 6
C
D
E
F
G
A Churchill, Ford, and Walker (1976)
B 95 (Churchill, Ford, and Walker 1974)[b]
C
D
E
F
G
A Oliver and Brief (1977-78)
B 18 (Brayfield and Rothe 1951)
C
D
E
F
G
A Bagozzi (1978)
B 8 (Pruden and Reese 1972)
C 1
D
E
F 1 Sales
G
A Busch and Bush (1978)
B
C 1 (Pruden and Reese 1972)[c]
D
E
F
G
A Dubinsky and Mattson (1979)
B
C 1 (Pruden and Reese 1972)[c]
D
E
F
G
A Teas, Wacker, and Hughes (1979)
B 13 (Porter and Lawler 1968)
C
D
E
F
G
A Bagozzi (1980)
B 8 (Pruden and Reese 1972)
C 1
D
E
F 1 Sales
G
A Berkowitz (1980)
B 9 (Ivancevich and Donnelly 1974)
C 5[c]
D
E
F 1 Sales 1 Turnover
G 1 Net profit
1 Sales/expense ratio
A Kelly and Hise (1980)
B 9 (Ivancevich and Donnelly 1974)
C
D
E
F
G
A Kelly, Gable, and Hise (1981)
B 9 (Ivancevich and Donnelly 1974)
C 5
D
E
F 1 Return on assets, 1 Volume
1 Volume/square foot, 1 Contribution, 1 Contribution/square foot
G
A Behrman, Bigoness, and Perreault (1981)
B 95 (Churchill, Ford, and Walker 1974)[b]
C 5
D 1 Technological Knowledge
1 Providing information
1 Sales presentations
E
F 1 Meeting sales objectives
G 1 Controlling expense
A Teas (1983)
B 36 (Smith, Kendall, and Hulin 1969)
C
D
E
F
G
A Behrman and Perreault (1984)
B 95 (Churchill et al. 1974)[b]
C 31 (Behrman and Perreault 1982)
D 6 Technical knowledge
5 Providing information
6 Sales presentations
E
F 7 Quantity and quality sales objectives
G 7 Controlling expense
A Kohli (1985)
B 13 (Porter and Lawler 1968)
C
D
E
F
G
A Chonko, Howell, and Bellenger (1986)
B
C 11[d]
D 1 Ability to reach quota
1 Company knowledge
1 Customer knowledge
1 Product knowledge
1 Competitor knowledge
E 1 Customer relations
F 1 Sales volume 1 Percent increase in salary
G 1 Expense accounts
1 Time management
1 Planning
A Fry et al. (1986)
B 95 (Churchill, Ford, and Walker 1974)[b]
C
D
E
F
G
A Hampton, Dubinksy, and Skinner (1986)
B 8 (Hackman and Oldham 1975)
C 9 (Pruden and Reese 1972)[c]
D
E
F
G
A Michaels, Day, and Joachimsthaler (1987)
B 18 (Smith, Kendall, and Hulin 1969)
C 6 (Reck 1978)
D 1 Departmental relations
1 Workload
E 1 Vendor relations
F 1 Price variances
G 1 Cost savings
1 Negotiations
A Yammarino and Dubinsky (1990)
B 15[e] (Dansereau, Graen, and Hage 1975)
C 5[e] (Dansereau et al. 1982); 10 (retail), 12 (insurance)
D 1 Product knowledge
1 Resourcefulness
5 Satisfaction with performance
E
F
G 1 Profitability
A Lusch and Jaworski (1991)
B
C 31[c,e]
D 22 Competence
8 Satisfaction with performance
E
F
G
A Singh and Rhoads (1991)
B 26 (Churchill, Ford, and Walker 1974)[b]
C 6[e]
D 1 Ability
1 Product knowledge
1 Performance potential
E 1 Customer relations
F 1 Quantity of sales
G 1 Time management
A Dubinksy et al. (1992); Singh (1995)
B 5 (Hackman and Oldham 1975)
C 10[e] (Yammarino and Dubinsky 1990)
D
E
F
G
A Singh (1993)
B 26 (Churchill, Ford, and Walker 1974)[b]
C 6 (Dubinsky and Mattson 1979)
D 1 Ability
1 Product knowledge
1 Performance potential
E 1 Customer relations
F 1 Quantity of sales
G 1 Time management
A Singh, Goolsby, and Rhoads (1994)
B 26 (Churchill, Ford, and Walker 1974)[b]
C 6 (Dubinsky and Mattson 1979)
D 1 Ability
1 Product knowledge
1 Performance potential
E 1 Customer relations
F 1 Quantity of sales
G 1 Time management
A Singh, Verbeke, and Rhoads (1996)
B 6 (Ironson et al. 1989)
C 6 (Dubinsky and Mattson 1979)
D 1 Ability
1 Product knowledge
1 Performance potential
E 1 Customer relations
F 1 Quantity of sales
G 1 Time management
A Hartline and Ferrell (1996)
B 8 (Brown and Peterson 1993)
C 8 (Jones 1986)
D 8 Self-efficacy
E
F
G
A Singh (1998)
B 26 (Churchill, Ford, and Walker 1974)[b]
C 6 (Dubinsky and Mattson 1979)
D 1 Ability
1 Product knowledge
1 Performance potential
E 1 Customer relations
F 1 Quantity of sales
G 1 Time management
A Singh (2000)
B
C 17 (Hartline and Ferrell 1993);[f] 7 developed
D
E 3 Trust, 5 promptness, 5 reliability, 4 attention
F 4 Contact outputs
G 3 Backroom work[a]Names in parentheses correspond to the sources of the measures. [b]Churchill, Ford, and Walker's (1974) INDSALES assesses satisfaction with the job, fellow workers, supervision, company, pay, promotion potential, and customers. [c]A single-item, global measure of performance was incorporated into this study. [d]In this study, the effectiveness measure addressed the congruence between the sales representatives' increase in salary and the other performance factors. [e]All items are not reported. [f]Hartline and Ferrell's (1996) measures address quality; the new measures address productivity.
Legend for chart:
A = Mean
B = S
C = Correlations[a] 1
D = Correlations[a] 2
E = Correlations[a] 3
F = Correlations[a] 4
G = Correlations[a] 5
H = Correlations[a] 6
I = Correlations[a] 7
J = Correlations[a] 8
K = Correlations[a] 9
L = Correlations[a] 10
M = Correlations[a] 11
A B C D E F
G H I J K
L M
1. Current specific assets 5.19 1.02 .65b
2. Prior specific assets 3.88 2.46 .042 .95
3. Personal mode 3.14 1.46 .121 .038 -c
4. Group mode 2.99 1.35 .085 .056 .348
- c
5. Written mode 3.45 1.22 .045 -.043 .188
.408 - c
6. Role ambiguity 2.96 1.06 -.030 .263 -.224
-.125 -.132 .81
7. Role conflict 4.07 1.43 .424 .025 .000
-.047 .012 .280 .84
8. Competence 4.92 1.23 .042 -.082 .127
-.015 .126 -.299 .075 .69
9. Customer satisfaction 6.43 .86 .113 .002 .115
.054 .144 -.260 -.027 .234
.88
10. Contributions to sales 4.67 1.24 .071 -.012 .229
-.025 .047 -.230 .000 .260
.092 .81
11. Bargaining efforts 3.82 1.49 -.110 -.007 .106
.198 .152 -.401 -.316 .079
.149 .062 .75[a]Correlations with absolute values greater than .13 are significant at p < .05. [b]Internal consistency measures are on the diagonal. [c]Formative scale.
Legend for chart:
A = Initial Received View Model Role Ambiguity
B = Initial Received View Model Role Conflict
C = Initial Received View Model Competence
D = Initial Received View Model Customer Satisfaction
E = Initial Received View Model Contributions to Sales
F = Initial Received View Model Bargaining Efforts
G = Trimmed Received View Model Role Ambiguity
H = Trimmed Received View Model Role Conflict
I = Trimmed Received View Model Competence
J = Trimmed Received View Model Customer Satisfaction
K = Trimmed Received View Model Contributions to Sales
L = Trimmed Received View Model Bargaining Efforts
A B C
D E F
G H I
J K L
Prior specific .349 .000 --
-- -- --
.336 -- --
-- -- --
assets (3.613) (.002) --
-- -- --
(3.675) -- --
-- -- --
Current specific .079 .606 --
-- -- --
-- .575 --
-- -- --
assets (.886) (3.568) --
-- -- --
-- (3.563) --
-- -- --
Personal -.277 -.086 --
-- -- --
-.247 -- --
-- -- --
mode (-3.175) (-1.230) --
-- -- --
(-3.065) -- --
-- -- --
Group -- -- --
-- -- --
-- -- --
-- -- .192
mode -- -- --
-- -- --
-- -- (2.465)
Role -- -- -.406
-.201 -.297 -.164
-- -- -.373
-- -.292 --
ambiguity -- -- (-3.055)
(-1.164) (-2.704) (-.898)
-- -- (-3.206)
-- (-2.939) --
Role -- -- .077
.082 -.010 -.327
-- -- --
-- -- -.311
conflict -- -- (.473)
(.862) (.065) (-2.108)
-- -- --
-- -- (1.989)
(Role -- -- --
-- -- --
-- -- .228
-- -- --
ambiguity)² -- -- --
-- -- --
-- -- (2.759)
-- -- --
Ambiguity × -- -- --
-- -- --
-- -- --
-- .186 --
conflict -- -- --
-- -- --
-- -- --
-- (2.486) --
Summary
Statistics X2(529) = 610.049 CFI = .976
p < .01 RMSR = .051 X2(537) = 605.534
CFI= .980 p < .01 RMSR = .053
Legend for chart:
A = Initial Trigonometric Model Role Ambiguity
B = Initial Trigonometric Model Role Conflict
C = Initial Trigonometric Model Competence
D = Initial Trigonometric Model Customer Satisfaction
E = Initial Trigonometric Model Contributions to Sales
F = Initial Trigonometric Model Bargaining Efforts
G = Trimmed Trigonometric Model Role Ambiguity
H = Trimmed Trigonometric Model Role Conflict
I = Trimmed Trigonometric Model Competence
J = Trimmed Trigonometric Model Customer Satisfaction
K = Trimmed Trigonometric Model Contributions to Sales
L = Trimmed Trigonometric Model Bargaining Efforts
A B C
D E F
G H I
J K L
Prior specific .341 .001 --
-- -- --
.341 -- --
-- -- --
assets (3.734) (.011) --
-- -- --
(3.798) -- --
-- -- --
Current specific .020 .564 --
-- -- --
-- .547 --
-- -- --
assets (.255) (3.333) --
-- -- --
-- (3.329) --
-- -- --
Personal -.212 -.060 --
-- -- --
-.217 -- --
-- .202 --
mode (-2.768) (-0.872) --
-- -- --
(-2.852) -- --
-- (2.793) --
Group -- -- --
-- -- --
-- -- --
-- -- .205
mode -- -- --
-- -- --
-- -- --
-- -- (2.607)
Sine (role -- -- .155
.200 .126 .073
-- -- --
.157 .165 --
ambiguity -- -- (1.815)
(2.550) (1.945) (.944)
-- -- --
(1.958) (2.194) --
Sine (role -- -- -.174
-.050 .057 .043
-- -- -.154
-- -- --
conflict) -- -- (-2.023)
(-.656) (.914) (.566)
-- -- (-1.795)
-- -- --
Summary
Statistics X2(496) = 607.337 CFI = .967
p < .01 RMSR = .058 X2(502) = 607.785
CFI= .970 p < .01 RMSR = .056DIAGRAM: FIGURE 1 General Adaptation Syndrome Effects of Stress on Performance
GRAPH: FIGURE 2 Effects of Stress on Multiple Facets of Effectiveness A: Nonlinear Effects of Ambiguity on Effectiveness
GRAPH: FIGURE 2 Effects of Stress on Multiple Facets of Effectiveness B: Interactive Effects of Stress on Contributions to Sales
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Legend for chart:
Initial Trimmed
Scale Items Model[a] Model[a]
Investment in Current System
It is vital that a manager builds strong
relationships within our company. .322[b] .324[b]
The refiner's procedures and policies are
complicated. .807 .810
It is difficult for a manager to learn all
tasks necessary to function effectively in
the system. .655 .653
A newcomer to the system must learn our
language and culture before being capable to
function effectively. .356 .352
Investment in Previous System
Before the merger, it was difficult for a new
station manager to work effectively. .869[b] .869[b]
The refiner's procedures and policies were
complicated prior to the merger. .917 .917
Before the merger, it was difficult for a
manger to learn all tasks necessary to
function effectively in the system. .951 .951
Prior to the merger, a newcomer to the system
had to learn the company's language and
culture before being capable to function
effectively. .901 .901
Role Ambiguity
I feel certain about how much authority I
have. (r) .422[b] .422[b]
I know what my responsibilities are. (r) .616 .615
I have just the right amount of work to
do. (r) .422 .420
I know that I have divided my time
properly. (r) .566 .567
I know exactly what is expected of me. (r) .751 .752
Explanation of what has to be done is
clear. (r) .738 .738
I perform work that suits my values. (r) .594 .594
Role Conflict
I receive assignments without the personnel
to complete the task. .818[b] .822[b]
I have to circumvent rules or policies to
complete assignments. .635 .644
I receive incompatible requests from two or
more refiner personnel. .446 .446
I often get assignments without adequate
resources and materials to execute them. .843 .841
I work on unnecessary things for the refiner. .709 .703
I have to work under vague directives or
orders. .389 --
Competence
We believe that we have acquired the
necessary business skills to operate
successfully in my market. .721[b] .724[b]
We believe that we have a great deal of
knowledge about the features and attributes
of the refiner's products. .731 .728
Customer Satisfaction
We do everything we can to make our customers
happy. .715[b] .715[b]
We provide quality assistance in the solution
of any problem involving the refiner's
products. .902 .901
We provide information that lowers
uncertainty about use of the refiner's
products. .705 .705
Contributions to Sales
Relative to other dealers, we have been
successful in selling the refiner's products. .820[b] .820[b]
Compared to other dealers, we have achieved a
higher level of market penetration for the
refiner. .946 .946
We have contributed more to the refiner's
business in this market than other dealers. .746 .746
Bargaining Efforts
Our meetings with the refiner's
representatives are very effective and
systematic. .753[b] .751[b]
Both parties are always well prepared in the
meetings with the refiner so that decisions
can be made. .804 .805
Summary Statistics
χ² 635.546 589.979
(d.f.) 406 377
p-Value .001 .001
RMSR .050 .047
CFI .921 .926[a]All factor loadings have t-values that exceed 2.0. [b]These items are fixed for the purpose of scaling. Notes: Items marked (r) were reverse scored.
~~~~~~~~
By Arne Nygaard and Robert Dahlstrom
Arne Nygaard is a professor, Graduate School, Norwegian School of Management. Robert Dahlstrom is Bloomfield Endowed Associate Professor of Marketing, Carol Martin Gatton College of Business and Economics, University of Kentucky. The authors thank the four anonymous JM reviewers for their insightful critiques of this article. In addition, the authors thank Michelle Duffy, Scott Kelley, and Steve Skinner for their valuable evaluations of a previous version of this article. The authors are listed in random order. Both contributed equally to the study. The U.S.-Norway Fulbright Foundation provided partial funding for this research.
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Record: 138- Service Orientation of a Retailer's Business Strategy: Dimensions, Antecedents, and Performance Outcomes. By: Homburg, Christian; Hoyer, Wayne D.; Fassnacht, Martin. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p86-101. 16p. 2 Diagrams, 1 Chart. DOI: 10.1509/jmkg.66.4.86.18511.
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Service Orientation of a Retailer's Business Strategy: Dimensions,
Antecedents, and Performance Outcomes
Augmenting products with services is a major way retailers have of gaining differentiation in today's competitive market. Despite its importance, this topic has received relatively little research attention. Unlike previous research, this study adopts a more comprehensive perspective on retail services by examining three important research gaps related to a service-oriented business strategy: First, the authors elaborate on the dimensions of a service-oriented business strategy and introduce a new measure of this strategy. Second, the authors examine the antecedents of a service-oriented business strategy. In practice, there appears to be considerable variability in terms of the extent to which retailers demonstrate a service orientation, but there is a major gap in the understanding of what factors influence this orientation. Third, the authors investigate the neglected link between a service-oriented business strategy and performance outcomes. To examine these three important areas, the authors conducted an empirical study of clothing and furniture retailers in both the United States and Germany. This study provides evidence for the proposed conceptualization of a service-oriented business strategy. The authors also find that the investigated antecedents account for some variance of a service-oriented business strategy, which in turn positively affects company performance in the market and thereby profitability. Furthermore, the authors discuss several important research issues as well as managerial implications and future research avenues.
It is widely recognized that today's retail environment is highly competitive. This requires that the retailer gain some form of differential advantage. Unfortunately, retailers often offer the same assortments at similar prices and have identical operating hours, and location is not as important as it used to be (Hummel and Savitt 1988). Against this background, the predominant way for retailers to differentiate is increasingly to pursue a service-oriented business strategy (Ellis and Kelley 1993; Wortzel 1987). In support of this view, Berry (1986, p. 3) stresses that "retail businesses are services businesses." Despite its importance, this topic has been given little attention in the academic literature. In his comprehensive review article, Mulhern (1997, p. 111) comes to the conclusion that "very little research has addressed the service aspects of retailing." Although there has been a large body of research in the context of services in general, this research typically focuses on settings in which the service is the core offering (Fisk, Brown, and Bitner 1995). In the case of retailing, services are designed more to augment the core offering or add value rather than represent the core offering itself.[ 1]
Given this general void in the literature, we approach retail services as a strategic perspective by focusing on the service orientation of a retailer's business strategy. If a retailer decides to strategically augment its products with services, it is essential that the retailer make this change systematically, with a long-term orientation. We therefore examine three important research gaps related to a service-oriented business strategy. The first research gap is related to the content of a service-oriented business strategy (Varadarajan and Jayachandran 1999). Although the notion of a service-oriented business strategy has been discussed in the literature, previous research has not defined the scope of such a strategy. A key goal of the current study therefore is to identify the strategy's important dimensions. As depicted in Figure 1, there are two levels at which a service orientation can be conceptualized. On the one hand, a service orientation can be examined at the individual level. In this research stream, a service orientation is treated as a personality measure whereby certain people are more service oriented than others. Examples of studies in this area are presented in Figure 1.
On the other hand, it is also important to examine a service orientation at the organizational level. According to Lytle, Hom, and Mokwa (1998, p. 456), "Scholars and business executives have become increasingly interested in the concept of an organizational service orientation." Two different perspectives can be distinguished at this level. First, service orientation can be examined in terms of internal organizational arrangement parameters, which involve internal design characteristics such as the organizational structure, climate, and culture (e.g., Bowen, Siehl, and Schneider 1989; Lytle, Hom, and Mokwa 1998; Schneider, Wheeler, and Cox 1992).
Second, a service orientation can be applied to a business strategy. In contrast to the internal perspective, the service orientation of a business strategy is more externally focused because it is related to the extent to which services are an important element of the firm's marketing strategy. As we have argued, adopting a service-oriented business strategy can be a key way for retailers to perform effectively in today's competitive environment. This requires that retailers more intensively focus their attention on services on a strategic level. A key gap in our knowledge, however, is related to the dimensions that constitute a service-oriented business strategy. When a retailer adopts a service-oriented business strategy, several important strategic decisions must be made. Therefore, we attempt to clarify conceptually what these decisions are. To the best of our knowledge, our study is the first to conceptually and empirically address the issue of a service-oriented business strategy and therefore fills an important research gap.
The second research gap is related to the apparently considerable variability in the extent to which retailers adopt a service-oriented business strategy. Therefore, research is needed to systematically explain some of this variance. One perspective in the strategy literature that has been useful in understanding the nature of organizational strategy is contingency theory (Hambrick 1983; Zeithaml, Varadarajan, and Zeithaml 1988). One key aspect of this theory is that to gain an understanding of a business strategy, it is important to specify its antecedents. A basic proposition of contingency theory is that strategic orientations depend systematically on certain environmental and organizational variables. By looking at antecedents of a service-oriented business strategy, we adopt a strategy-formulation perspective (Gins-berg and Venkatraman 1985) that deals with the impact of external and internal antecedents on the content of strategies.
The third research gap deals with the question of whether a service-oriented business strategy pays off in terms of company performance (both non financial and financial). Contingency theory also supports the relevance of this question by proposing that adaptation to external and internal antecedents can lead to higher company performance outcomes (Zeithaml, Varadarajan, and Zeithaml 1988). A significant strategy research stream has investigated whether certain types of business strategies increase company performance (e.g., Capon, Farley, and Hoenig 1990; Pearce, Robbins, and Robinson 1987). However, studies linking a service-oriented business strategy to performance have been notably lacking. Although it is generally believed that retail services can have a positive impact on company performance (e.g., Judd and Vaught 1988; Morey 1980), studies are needed to empirically verify this important assumption.
Empirically investigating whether a service-oriented business strategy affects company performance is extremely important, because strongly pursuing this type of strategy implies significant costs for retailers (e.g., costs for service personnel). In some cases, there may be a concern that these costs could outweigh the financial benefits associated with a service-oriented business strategy. In other words, although retailers often acknowledge that a service-oriented business strategy can lead to higher profits, they may be afraid to apply this strategy because of a concern that significant costs may reduce profits. Therefore, it is far from obvious that a service-oriented business strategy pays off in terms of financial company performance.
As shown in Figure 2, our overall framework addresses the three research gaps mentioned previously: the content of a service-oriented business strategy, its antecedents, and its consequences.
Focal Construct
The key construct of the present study is that of a service-oriented business strategy. On the basis of strategy theory and research, we argue that the service orientation of a business strategy should be defined in terms of three dimensions: ( 1) the number of services offered, ( 2) how many customers these services are offered to (broadness), and ( 3) how strongly these services are emphasized.
The number of services itself is one important facet. Logically, retailers that are not service-oriented offer few or no services. However, as a service orientation increases, retailers should offer more services. Thus, all other things being equal, a retailer that offers more services would be considered more service oriented. In the strategy literature, the number of offerings has been identified as a key strategic decision (Aaker 1998; Miller 1987; Murray 1988). Because retailers offer both products and services, they must decide not only which products they will offer but also the number of services that will accompany them. In support of this view, Anderson and Narus (1995, p. 76) state that "managers should analyze their services and decide which must be offered."
Furthermore, retailers must decide to whom they should offer these services. This facet of a service-oriented business strategy is termed broadness and relates to the number of customers to whom the services are offered. Offering services to only a limited number of customers reflects special treatment for a certain group and is not an indication of an overall service-oriented business strategy. Therefore, we argue that, all other things being equal, providing services to a greater number of customers reflects a higher service-oriented business strategy. In strategy research, the decision as to how many customers are served is considered a key strategic decision (Day 1990; Hambrick 1983; Miller 1987). This notion is also consistent with that of Porter (1985), who considers the scope of activity to be a key strategic decision--offering a given number of services to many customers (a broad target) or to few customers (a narrow target).
Finally, although retailers can offer many services to a large number of customers, these services must also be emphasized for the firm to be strongly service oriented. The emphasis placed on service is the degree to which a retailer actively offers services to customers. In other words, some retailers may stress services, whereas others may offer services only if customers explicitly ask for them. The importance of this variable is also highlighted in both the retailing and industrial marketing areas (Morris and Davis 1992). In particular, in the retailing area, Dotson and Patton (1992) suggest that emphasizing services actively to customers is a crucial factor for a firm to be more service oriented. Furthermore, Wright, Pearce, and Busbin (1997) use this variable to differentiate between two strategic groups. An emphasis on services also identifies companies that have clearly recognized that they offer services as well as the core product offering (Bowen, Siehl, and Schneider 1989).
Thus, although there has not been formal theory or research on the dimensions of a service-oriented business strategy, the literature suggests that each of these dimensions represents a key strategic decision. Furthermore, we propose that no one dimension alone captures the concept of a service-oriented business strategy. All three are needed to represent this construct.
Conceptually, a highly service-oriented business strategy would require a retailer to perform highly on all three dimensions. If a retailer is high in number and broadness but low in emphasis, this retailer is demonstrating a reactive orientation regarding services that is not consistent with a highly service-oriented business strategy. Being high in number and emphasis but low in broadness would be considered exclusive treatment for a limited set of customers rather than a highly service-oriented business strategy. Similarly, being high in emphasis and broadness but low in number of services could be termed a focused service-oriented behavior. If one or more of the three dimensions is low, the retailer is not pursuing a highly service-oriented business strategy. Therefore, we argue that a company exhibits a highly service-oriented business strategy only if all three dimensions are high.
Categories of Antecedents
As we have argued, one perspective that has been useful in the understanding of the nature of organizational strategy is contingency theory. A basic proposition of this theory is that strategic orientations depend systematically on certain environmental and organizational variables. Hofer (1975) groups these variables into several classes, including the environment, competitors, market and consumer behavior, and organizational characteristics and resources.
Therefore, on the basis of contingency theory, we identify three categories of antecedents that may influence the adoption of a service-oriented business strategy: ( 1) aspects of the external environment that may either enhance or detract from the store being service oriented (including competitors), ( 2) internal aspects of the store (including organizational characteristics and resources), and ( 3) characteristics of the store's customers. Figure 2 presents a framework that identifies the variables within each of these categories that are likely to play a key role in determining the level of a service-oriented business strategy. Although a wide variety of variables could be meaningful for the understanding of a service-oriented business strategy, we restrict our study to several variables that have the strongest theoretical support. These are discussed in more detail in the "Hypothesis Development" section.
Performance Outcomes
In the effort to understand whether a service-oriented business strategy pays off, it is also important to examine its link to performance outcomes. In terms of performance outcomes, we make an important distinction between nonfinancial and financial company performance measures (Bharadwaj, Varadarajan, and Fahy 1993; Srivastava, Shervani, and Fahey 1999). Nonfinancial company performance is related to the effectiveness of an organization's marketing activities and includes variables such as customer satisfaction, customer loyalty, customer benefit, and market share (Menon, Bharadwaj, and Howell 1996; Morgan and Piercy 1996). Financial company performance essentially is related to profitability measures including return on sales, return on investment, and return on assets (Chakravarthy 1986).
Previous research supports the notion that nonfinancial performance leads to improved financial performance (Rust, Zahorik, and Keiningham 1995). However, the understanding of whether marketing strategy activities affect the company's financial performance is still at a less-than-desirable level. According to Srivastava, Shervani, and Fahey (1998, p. 4), "In the absence of a strong understanding of the marketing-finance interface, marketing professionals cannot but have great difficulty in assessing the value of marketing activities. This, in turn limits the investment in marketing activities." This point is also echoed by Webster (1981).
Environmental Characteristics
Competitive intensity in the local market. Competitive intensity refers to the level of competition the retailer must face in its immediate trading area and is one key driver of strategic decisions that is identified by contingency theory (Hall 1980; Miller 1987). Specifically, competitive intensity is related to the number of local competitors, the frequency of using certain marketing techniques (e.g., advertising, pricing activities) to gain market share, the number of competitors using these techniques, and the intensity of usage of these techniques (Jaworski and Kohli 1993; Slater and Narver 1994).
In highly competitive environments, organizations need to be more attentive to the changing needs of customers in the marketplace (Lusch and Laczniak 1987). Adopting a service-oriented business strategy is one of the key ways firms can accomplish this (Lytle, Hom, and Mokwa 1998). That is, services represent a way of fulfilling customer needs more thoroughly than the product alone, thereby providing additional customer value (Grönroos 1997). In addition, a service-oriented business strategy is a key for building strong customer relationships and reducing customers' responses to competitive marketing efforts (Homburg and Garbe 1999). This is particularly critical when competitive intensity is high, because there is a greater degree of competitive marketing activity that aims at breaking up existing competitors' customer relationships. Organizations that provide superior customer value will build loyalty and commitment and reduce the customers' motivation to shop around. Furthermore, when competitive intensity is high in the local market, the retailer is typically under greater pressure to differentiate from the competition than when competition is lower. Having a service-oriented business strategy rather than a classical focus on merchandise and prices is also a key way of providing this differentiation (Hummel and Savitt 1988). As Ohmae (1982) has emphasized, competitive differentiation must be based on some form of competitive advantage. One of the requirements of a competitive advantage is sustainability. This means that there must be some level of protection against fast imitation by competitors. This requirement is especially met by services, because they are delivered through people and are therefore difficult to imitate. Therefore, the following hypothesis is offered:
H1: The higher the competitive intensity in the local market, the higher is the service orientation of the business strategy.
Local retail innovativeness. Local retail innovativeness refers to the extent to which competitors in the local market adopt new merchandising or service ideas. This is similar to the construct of organizational innovativeness, which refers to organizations that exhibit innovative behavior over time (Subramanian and Nilakanta 1996; Wolfe 1994). Note that this is a multi-item construct that refers to the number of innovations adopted, the time of adoption, and the consistency of adoption over time.
Empirical strategy research based on contingency theory has consistently suggested that the level of dynamism in the environment created through innovativeness is a key driver of a company's strategic decisions (Miller 1988; Miller and Dröge 1986). In highly innovative environments, there is greater pressure for individual retailers to be innovative. In contingency theory terms, this means that there is a greater need for firms to adapt to the environment. Given the classical merchandising orientation of many retailers (Mulhern 1997), one promising way for a firm to innovate is by adopting a service-oriented business strategy. Therefore,
H2: The higher the local retail innovativeness, the higher is the service orientation of the business strategy.
Store Characteristics
Of the many possible store characteristics, the present study focuses on a customer versus product orientation, the relative quality level of merchandise, the relative choice of merchandise offers, and the number of full-and part-time employees. With reference to store characteristics, the issue arises whether these variables should be conceptualized as antecedents of a service-oriented business strategy. An alternative view would be to consider them either together with a service-oriented business strategy as simultaneous facets of an overall retail strategy or as the consequences of a service-oriented business strategy. In this study, we chose to conceptualize store characteristics as antecedents of a service-oriented business strategy for several reasons.
Treating merchandise-related decisions such as the quality and choice of merchandise as antecedents is relatively straightforward. Historically, merchandise-related decisions have been at the core of strategy making in retailing companies (Berry, Gresham, and Millikin 1990; Mulhern 1997). However, in the case of other store characteristics, such as customer orientation and the number of full-and part-time employees, the direction is less clear. Specifically, although these variables could be viewed as either antecedents or consequences of a service-oriented business strategy, already having a customer orientation or a greater number of employees could increase a firm's probability of adopting a service-oriented business strategy. In other words, these variables certainly can be antecedents of a service-oriented business strategy. Furthermore, research in the strategy area shows that strategic decisions in companies are made sequentially and past strategic decisions set the frame for and influence subsequent strategic decisions (Bowman and Hurry 1993; Rajagopalan and Spreitzer 1996). Because adopting a service-oriented business strategy in cases in which services augment products is a fairly recent strategic development, the possibility exists that earlier decisions regarding customer orientation and the number of employees will influence the decision to adopt a service-oriented business strategy. Against this background, we treat store characteristics as antecedents of a service-oriented business strategy.
Customer orientation. Customer orientation refers to the extent to which salespeople practice the marketing concept by helping their customers make purchase decisions that will satisfy the customers' needs (Saxe and Weitz 1982; Siguaw, Brown, and Widing 1994). Thus, the firm focuses more on a commitment to customers and their needs rather than the product itself. This involves activities such as creating customer value, understanding customer needs, and focusing on customer satisfaction (Narver and Slater 1990).
Retail organizations that monitor and are sensitive to customer needs will take active steps to fulfill those needs. As mentioned previously, customer service is a key way of accomplishing this goal (Liu and Davies 1997; Narver and Slater 1990). Furthermore, customer service, in addition to products, can be a source of customer value (Grönroos 1997). Customer-oriented companies can create "benefit bundles" that comprise products and value-added services. Thus, services represent a key way of delivering customer value for these companies. Therefore,
H3: The stronger the customer orientation, the higher is the service orientation of the business strategy.
Relative quality level of merchandise. Retail outlets often vary in terms of the quality of the merchandise they carry, and this has been found to be an important factor in determining store image and attitudes among customers (e.g., Mazursky and Jacoby 1986). When consumers purchase high-quality items (which are also typically higher in price), they also expect a higher level of service to accompany the product. These services add value to these high-quality items (Garvin 1984). In contrast, low-quality items are typically sold with a low price/low cost orientation in mind. One of the key ways firms lower costs and thus prices is to restrict the number or broadness of services offered or emphasize them less. Therefore,
H4: The higher the relative quality level of merchandise, the higher is the service orientation of the business strategy.
Relative choice of merchandise offers. The relative choice of merchandise offered refers to the difference between a store and its most important competitors in terms of the assortment or variety of merchandise offered. This includes the number of stock keeping units, the number of different merchandise categories, and the amount of merchandise in stock. This factor has also been a key factor in determining store attitudes (e.g., Samli, Kelly, and Hunt 1998).
When a greater amount of merchandise is offered, it is more difficult for firms to service these items (Lee and Tang 1997). Even if a firm wants to stress both products and services, it is often difficult to do so, and trade-offs must be made. Organizations that try to perform too many tasks often become too complex and inefficient (Child et al. 1991). Furthermore, having a large assortment of merchandise requires heavy marketing activity directed toward the products rather than the services. In other words, a large product assortment biases a retailer toward a product orientation. As a result, a service-oriented business strategy is less likely. Organizations are limited in terms of the amount of complexity they can handle or deal with at any point in time, and they are most likely to focus on the aspects that are most critical at the time. This notion is similar to the concept of attention allocation in the psychological literature: Paying attention to one particular aspect means taking away attention from others because of limitations on processing ability (Kahneman 1973). Therefore,
H5: The higher the relative choice of merchandise offers, the lower is the service orientation of the business strategy.
Number of full-and part-time employees. According to Berry, Gresham, and Millikin (1990), a retailer's ability to adopt a service-oriented business strategy is contingent on the human resources that are available in the store. Companies that augment their products with services must have personnel who are capable of dealing with the many customer interactions that a service-oriented business strategy involves. Specifically, having a greater number of such employees would enable a retailer to offer more services to a greater number of customers with a stronger emphasis. In particular, full-time employees are more likely than part-time employees to fulfill this role. In other words, full-time employees should provide more consistent service delivery and have more commitment than employees who are there for only a short period of time. Full-time employees will have more contact time with customers because of their higher availability. Thus, full-time employees--as the primary link between the retailer and its customers--play an important role in the formation of long-term retailer- customer relationships (Weitz and Bradford 1999; Wotruba 1991). Taking all this together, having a greater number of full-time employees will enable a retailer to have a higher service-oriented business strategy.
In contrast, part-time employees are less likely to fulfill the requirements of service interactions. Because these employees spend less time in the store (because of shorter working hours) and may have a lower commitment to the store's customers, they should be less effective in delivering services than full-time employees are. Less time in the store also makes it more difficult for these employees to build strong customer relationships, because they have fewer contacts with customers. Part-time employees are typically paid less than full-time employees, and this should reduce their motivation to serve customers compared with that of full-time employees. Furthermore, emphasizing services to a broader number of customers requires permanent training activities, which are less likely to be given to part-time employees. Taking all this together, it should be more difficult to adopt a service-oriented business strategy when there is a large number of part-time employees. Therefore, we offer the following:
H6: The greater the number of full-time employees, the higher is the service orientation of the business strategy.
H7: The greater the number of part-time employees, the lower is the service orientation of the business strategy.
Customer Characteristics
Price consciousness. Price consciousness is defined as the degree to which the store's average customers focus predominantly on paying low prices (Lichtenstein, Ridgway, and Netemeyer 1993). Adopting a service-oriented business strategy raises a retailer's costs in terms of personnel, training, design of services, monitoring of service quality, and so on. Firms typically cover these costs by raising the price to the customer. Therefore, adopting a service-oriented business strategy is more likely to occur when customers are less price conscious.
In contrast, when the majority of a store's customers are perceived as price conscious by the management, the retailer is more likely to adopt a low-price orientation. In other words, the retailer must find ways to lower costs in order to meet the low price demands of customers. One of the key ways of doing so is to minimize the level of service-oriented business strategy. Therefore,
H8: The greater the customers' price consciousness, the lower is the service orientation of the business strategy.
Time pressure when shopping. Time pressure when shopping reflects the customer's time availability and thus time costs (Srinivasan and Ratchford 1991). In today's world, consumers engage in many different activities that place severe demands on their time. Time pressure when shopping is high when customers have less time to shop because of the pressure to perform competing activities (Dickerson and Gentry 1983; Eroglu and Machleit 1990). Research has shown that time pressure can have a negative impact on information search (Srinivasan and Ratchford 1991) as well as the ability to make intended purchases and impulse purchases and switch brands (Park, Iyer, and Smith 1989).
One way for retailers to help time-pressured customers is to provide and actively emphasize services that help customers save time and that make the shopping process easier (e.g., pickup of merchandise at the customers' home for repair and maintenance). Therefore, when retail managers perceive that a large share of their customers is under time pressure, they should be motivated to adopt a service-oriented business strategy. Therefore,
H9: The higher the customers' time pressure when shopping, the higher is the service orientation of the business strategy.
Performance Outcomes
Company performance in the market. As previously argued, services can be a source of customer value in addition to products (Grönroos 1997; Wilkie and Moore 1999). Therefore, retailers that pursue a service-oriented business strategy can create "benefit bundles" that comprise products and services. Furthermore, because services are delivered by employees to customers, personal relationships can be developed. Thus, retailers that pursue a service-oriented business strategy can build long-lasting relationships with customers (Dwyer, Schurr, and Oh 1987; Heide and John 1990). Building relationships is generally viewed as leading to nonfinancial performance outcomes such as customer satisfaction and loyalty and building a positive store image (e.g., Anderson and Narus 1990; Garbarino and Johnson 1999). In addition, through the personal encounters between employees and customers, a retailer can obtain important information from the customers that it can use to increase the benefits provided to the customer (Berry and Parasuraman 1997). Against this background, we hypothesize the following:
H10: The higher the service orientation of the business strategy, the higher is the company performance in the market.
Financial performance. In general, previous studies have found a positive link between customer satisfaction (a market-related performance outcome) and the company's profitability (Anderson, Fornell, and Lehmann 1994; Anderson, Fornell, and Rust 1997). It has also been empirically shown that customer loyalty can increase organizational profitability through the absence of acquisition costs, lower operating costs, higher price tolerance, and referrals (Kalwani and Narayandas 1995; Loveman 1998; Reichheld and Sasser 1990). Furthermore, several studies (Buzzell and Gale 1987; Szymanski, Bharadwaj, and Varadarajan 1993) have found that, on average, market share has a positive effect on business profitability. Therefore, the following hypothesis is offered: H11: The higher the company performance in the market, the higher is the company profitability.
Respondents
Data were collected by means of a survey questionnaire, which was sent to 1410 retail store managers in both the United States and Germany. In total, 411 were returned, for a response rate of 29.1%. Of these, 245 were from Germany, and 166 were from the United States.
We sampled two retail industries: 217 questionnaires were from clothing stores (139 from Germany and 78 from the United States), and 194 were from furniture stores (106 from Germany and 88 from the United States). We selected the clothing and furniture industries for the study because of the variety of services offered in these industries. Also, to make the project manageable and to enable the employment of the same questionnaire (i.e., containing the same constructs and measures), we needed to narrow the focus of the study to certain industries. For clothing retailers, we randomly sampled from men's clothing, boys' clothing and furnishings shops, women's clothing shops, and family clothing shops (Standard Industrial Classification codes 5611, 5621, and 5651). Furniture shops were also randomly sampled (Standard Industrial Classification code 5712).
Note that the key focus of this research is to understand the extent to which there is a strategic orientation toward services at the store level. Because strategic decisions are driven by managers' perceptions of the environment's, customers', and store's characteristics, the constructs are examined on the basis of managers' perceptions rather than customers'. Although customers' perceptions of retail services are examined in several other studies (e.g., Dabholkar, Thorpe, and Rentz 1996; Finn and Lamb 1991), the specific focus of our study is to provide a different perspective focused on the managerial view of a business strategy.
Note also that the unit of analysis is the individual store rather than a company. This level of analysis is chosen because it enables both individually owned stores and large chain stores to be included in the sample.[ 2] Furthermore, even when chain stores are sampled, there is generally some autonomy or individual decision-making authority at the store level, even if there must be coordination with the organization's headquarters.
Questionnaire Development and Pretesting
Standard psychometric scale development procedures were followed (Gerbing and Anderson 1988). When possible, multi-item scales were generated on the basis of previous research on the constructs of interest. In cases in which no previous scales or items existed, the developed items were based on conceptual definitions.
The questionnaire was designed in English. To ensure translation equivalence, one bilingual person translated the questionnaire into German, and a second back-translated it into English (Douglas and Craig 1983). The original and back-translated versions were compared for conceptual equivalence, and translation was refined when necessary. The resulting English and German versions were then pretested and further refined on the basis of feedback from ten store managers in each country. The managers were also asked to comment if they offered other services that were not listed in our survey.
Data Collection
A random sample of store and manager names was acquired from commercial database providers in both the United States and Germany. Each store manager was then contacted by telephone and asked to participate in the survey. Questionnaires were immediately sent to those who agreed to participate. As an incentive to participate, managers were told that they would receive a specially tailored report on the state of services in their industry. Furthermore, to increase the response rate, follow-up telephone calls were made to those who had agreed to participate but had not yet returned the questionnaire.
We tested for nonresponse bias by comparing early and late responders on all constructs. Most of the late responders had responded only after the follow-up telephone calls. We found no significant differences between the two groups. Therefore, there is evidence that nonresponse bias was not a problem with these data.
Configurational and Metric Equivalence
To determine whether the U.S. and German samples could be combined for subsequent assessment procedures, we tested whether configurational and metric invariances are supported for the multi-item constructs measured by reflective indicators (Steenkamp and Baumgartner 1998). Configurational invariance implies that the factorial structure underlying a set of observed measures is the same across groups. A stronger test of invariance is metric invariance, which implies that the units of measurement or scale intervals are equivalent across groups. As recommended by Steenkamp and Baumgartner (1998), we used multiple-group confirmatory factor analysis to test for configurational and metric equivalence. The results indicated that full configurational and partial metric invariances are supported (for each construct, at least two items are metric invariant), which enabled us to combine the U.S. and German samples for subsequent assessment. In addition, we ran analyses for configurational and metric invariance across the two retail industries applying multiple-group confirmatory factor analysis. The results of this analysis indicated that full configural and partial metric invariance was supported, which enabled us to combine data from the furniture and clothing industries.
Assessment of Antecedent Measures
The complete list of antecedent measures is reported in the Appendix. Table 1 lists summary statistics for the measurement scales using the combined sample. All antecedents together, with the exception of the single-item measures, were then subjected to a confirmatory factor analysis. Although the chi-square test is statistically significant (X[sup2]209 = 401.12, p < .01), the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA) indicate a good fit with the hypothesized measurement model (GFI = .95, AGFI = .93, CFI = .95, RMSEA = .05). The reliabilities of the individual scales reported in Table 1 provide further evidence of the measure's sound psychometric properties--ranging from .61 to .84 for coefficient alpha and .69 to .87 for composite reliability (Bagozzi and Yi 1988; Baumgartner and Homburg 1996).
We also used confirmatory factor analysis to assess discriminant validity for all antecedents together, with the exception of the single-item measures. We conducted the stringent test recommended by Fornell and Larcker (1981). Discriminant validity is established by demonstrating that the average variance extracted exceeds the squared correlation between all pairs of constructs. This series of tests provided evidence of discriminant validity in line with this recommendation. Together, the results provide evidence that the measures have the sound psychometric properties necessary for hypothesis testing.
Measure of the Business Strategy's Service Orientation
As mentioned previously, a service-oriented business strategy is conceptualized in terms of three aspects: ( 1) number, ( 2) broadness, and ( 3) emphasis. On the basis of a review of the limited retailing literature with respect to services (e.g., Dotson and Patton 1992; Samuolis and Morganosky 1996) and the store manager pretest mentioned previously, we selected 24 retail services, which are defined as intangibles provided by retailers to their customers to enhance the marketing of goods (see the Appendix). For each service, we asked whether it is offered (0 = "not offered," 1 = "offered"). We then represented the number of services offered by the total of the services for which the retailer checked 1. If the corresponding service is offered, we asked the number of customers to whom the service is offered (broadness) and the extent to which the service is actively emphasized to the customers (emphasis) using Likert-type scales. We calculated each variable as a mean of the corresponding items.
Latent variables with multiple items can be operationalized in a reflective or a formative manner. When indicators of a construct present unique aspects of the construct, the construct can be viewed as a sum or a composite of the individual indicators (Bagozzi 1994; Bollen and Lennox 1991). All three constructs--number, broadness, and emphasis-- reflect such a total across different, unique sources and are therefore operationalized effectively in a formative way (Howell 1987). Because each of these items measures a particular dimension of the underlying construct, they need not correlate significantly, but they still contribute to the total value of the corresponding construct. The validity and reliability of formative scales cannot be assessed by conventional statistical techniques such as coefficient alpha and confirmatory factor analysis (Bagozzi 1994; Cohen et al. 1990), which are based on the assumption of high interitem correlation. Although marketing researchers have largely relied on reflective conceptualizations of measures, formative operationalizations have become more widely used (e.g., Diamantopoulos and Winklhofer 2001; Kumar, Scheer, and Steenkamp 1998).
As conceptualized, in designing a measure of the service orientation of a business strategy, we required that retailers be high in all three dimensions to be classified as pursuing a highly service-oriented business strategy. We therefore multiplied the scores for the three formative components (number, broadness, and emphasis) to create the variable of a service-oriented business strategy. Thus, the impact that a change in one dimension has on the change of the overall index depends on the level of the other two dimensions. In
other words, offering an additional service increases the service-oriented business strategy to a greater extent when the other two dimensions are high than when they are low. This is consistent with our conceptualization that a service-oriented business strategy depends on the level of all three dimensions. This holds true for building the total score as well as in case of changing one dimension. The latter would not be the case for an additive index.
Note, however, that the number of services is an additive measure (0 to 24) and would be given more weight in a multiplicative model if used as is. We therefore converted this measure to a five-point scale to correspond it to the scales for the other two variables in the index. Thus, the theoretical maximum score is 5 X 5 X 5 = 125. The descriptive values of a business strategy's service orientation and its three dimensions are reported in Table 1.
Assessment of Performance Measures
Measures of performance(company performance in the market, profitability) are also reported in the Appendix. Table 1 lists summary statistics for the measurement scales using the combined sample. The reliability of the scale for company performance in the market provides evidence of the measures' sound psychometric properties--.87 for coefficient alpha and .90 for composite reliability (Bagozziand Yi 1988).
Ideally, the analysis would involve an examination of all hypotheses simultaneously using causal modeling. However, with our sample size and the number of parameters to be estimated, we do not meet the recommended ratio of sample size to number of free parameters (5 to 1) needed to obtain trustworthy parameter estimates (Baumgartner and Homburg 1996). Therefore, we split the whole model into two separate analyses. The first model examines the antecedents of a service-oriented business strategy; the second examines the performance outcomes of this strategy. For both models, all reported coefficients are standardized.
Antecedents of a Business Strategy's Service Orientation
With the same previous reasoning (recommended ratio of 5 to 1 of sample size to number of free parameters), the hypotheses are tested by means of a regression model (rather than a causal model) in which each of the predictor variables from the three categories (environmental, store, and store's customers' characteristics) is regressed on the criterion variable (business strategy's service orientation). In addition, three control variables--size of store, country, and industry --are added to the model.
Overall, the set of predictor variables displayed a strong relationship to the service-oriented business strategy, with an R of .47 and an R2 of .23. Therefore, these factors help us explain why some retailers are more service-oriented than others.
The two key environmental characteristics studied are the competitive intensity in the local market and local retail innovativeness. H1 is not supported, as competitive intensity does not contribute to a service-oriented business strategy ( β = .001, not significant [n.s.]). However, retailers in a more innovative retail environment tend to be more service oriented (β = .09, p ≤ .05). Therefore, H2 is supported.
Five store characteristics are studied. H3 states that having a customer orientation also leads to a higher service-oriented business strategy. Support for this hypothesis is in evidence ( β = .16, p ≤ .01). Also, consistent with H4, the higher the quality level of merchandise, the higher is the service-oriented business strategy ( β = .14, p ≤ .01). There is also a higher service-oriented business strategy when there are more full-time employees (β = .26, p ≤ .01) and fewer part-time employees ( β = -.15, p ≤ .01). Thus, H6 and H7 are supported. Finally, H 5 is only marginally supported. A greater choice of merchandise tends to lead to a slightly lower service-oriented business strategy ( β = -.07, p ≤ .10).
Price consciousness and the perceived customer time pressure when shopping are the customer characteristics studied. In support of H 8, the higher the level of customers' price consciousness, the lower is the service-oriented business strategy ( β = -.11, p ≤ .05). In contrast, customer time pressure when shopping does not appear to be related to a service-oriented business strategy ( β = .03, n.s.; H9). In addition, we classified services according to only those that are time saving and related the use of these services to time pressure while shopping. Unfortunately, this relationship was also non significant.
The type of industry, size of store, and country are included as control variables. The results indicated that there is a stronger service-oriented business strategy in the furniture than in the clothing industry (β = -.19, p ≤ .01) and that larger stores are more service-oriented than smaller ones (β = .12, p ≤.05). Finally, although the United States is generally considered a more service-oriented country, this does not appear to be supported by our data (β = -.03, n.s.).
Performance Outcomes of the Business Strategy's Service Orientation
We estimated the hypothesized model for performance outcomes by causal modeling techniques using the LISREL 8 program (Jöreskog and Sörbom 1996). The overall fit measures suggest that the model provides a good fit for the data. The chi-square/degrees of freedom ratio (3.27) showed satisfactory results. The other overall measures also meet the recommended values (GFI = .97, AGFI = .95, CFI = .97, RMSEA = .08) (Bagozzi and Yi 1988).
Consistent with H10, there is a strong positive link between a service-oriented business strategy and company performance ( γ11 = .75, p ≤ .01). That our measure of a service-oriented business strategy explains 57% of the variance in company performance provides support for its usefulness in capturing key dimensions of a service-oriented business strategy. Furthermore, increased company performance leads to higher profitability ( β21 = .23, p ≤ .01), in support of H11. In terms of control variables, company profitability was slightly higher in the clothing industry ( γ22 = .09, p ≤ .10) and significantly higher in the United States (γ32 = .28, p ≤ .01).
Research Issues
The goal of this article was to empirically address three major research gaps in the literature. The first research gap is related to the content of a service-oriented business strategy. As is shown in Figure 1, almost all the previous research in the area of service orientation has been conducted in terms of either the individual or the internal organizational arrangement parameters. The current study is the first to apply a service orientation to a business strategy. This is important because this strategy represents a key way retailers can perform effectively in today's competitive market.
If a retailer decides to adopt a service-oriented business strategy, this change must be made systematically, with a long-term orientation. Because there has been almost no research in this area, there is no clear understanding of what it means to apply a service-oriented business strategy. Therefore, an important contribution of our study is to clarify the content of a service-oriented business strategy by providing an empirical way of describing this construct in terms of three key dimensions: the number of services offered, the broadness of the service offering, and the emphasis placed on services. A critical point is that no single dimension is enough to capture the service-oriented business strategy construct. Rather, we provide a more comprehensive view by examining all three of these dimensions simultaneously. Strong evidence for this approach is that our composite measure explained 57% of the variance in company performance in the market. Thus, we have developed a measure that can be useful in guiding further research on a service-oriented business strategy.
As mentioned previously, there is another organizational level at which a service orientation can be examined (e.g., service orientation of the organizational culture--see Figure 1). Further research is needed not only to develop measures for the service orientation of these organizational arrangement parameters but also, more importantly, to investigate whether a service-oriented strategy influences these parameters. By doing so, researchers could examine whether these parameters play an important role in establishing the link between a service-oriented business strategy and company performance. Furthermore, the relationships between service orientation at the organizational and the individual level are of interest. One worthwhile issue to be investigated is whether a service-oriented business strategy increases the service orientation of individual employees. In addition, our conceptualization of a service-oriented business strategy can be applied to other strategic orientations, such as the innovation orientation of a business strategy. In this case, the dimensions would relate to the number of innovations a company offers, how many customers these innovations are offered to, and how strongly these innovations are emphasized.
In terms of the second research gap, our study adopts a strategy-formulation perspective (Ginsberg and Venkatraman 1985) to explain the considerable variability in retailers' adoption of service-oriented business strategies. Drawing on contingency theory, our research demonstrates that the variability of a service-oriented business strategy can be partially explained by a variety of environment, store, and customer characteristics.
More specifically, we identify which categories of antecedents demonstrated the strongest relationship to a service-oriented business strategy in terms of the magnitude of standardized effects and significance levels. We find that store characteristics demonstrate a stronger effect than the two other categories of antecedents. Thus, the internal store environment appears to drive the service orientation of a business strategy to a greater extent than the external environment does. Because prior research applying the strategy-formulation perspective has focused more on the external environment, this suggests that a more intensive investigation of internal antecedents that drive the business strategy would be important.
Regarding specific antecedents, local retail innovativeness appears to be an environmental characteristic that is related to a service-oriented business strategy. Based on contingency theory, our results suggest that if competitors are offering new service ideas, there is pressure to adapt to this environmental situation by adopting a service-oriented business strategy. In contrast, we find that competitive intensity is not related to a service-oriented business strategy. This may suggest that a service-oriented business strategy is driven more by the extent to which there is innovation in the environment than by general competitive intensity. Another possibility is that competitive reactions depend not so much on the overall competitive intensity per se but rather on the nature of competition (e.g., price-based, quality-based). Further research could adopt a more comprehensive, multi-dimensional conceptualization of competitive intensity.
Finally, as mentioned in the "Hypothesis Development" section, some antecedents (e.g., customer orientation, number of full-and part-time employees) could be viewed as consequences rather than antecedents of a service-oriented business strategy. Further research should explore this possibility. Such an exploration would require a longitudinal study of these factors.
In addressing the third research gap, we wanted to gain a better understanding of whether a service-oriented strategy affects performance outcomes. By doing so, we build on research that examines the impact of strategy on performance (e.g., Capon, Farley, and Hoenig 1990). Although it has been posited that retail services can have a positive impact on company performance (e.g., Judd and Vaught 1988; Morey 1980), studies demonstrating this effect in a rigorous manner are lacking. Our study provides needed empirical evidence that a service-oriented business strategy (one of the key ways managers can compete in today's highly competitive environment) can pay off. If this is the case, this strategy merits more research attention than it has received in the past.
One way to gain a deeper understanding of the link to performance outcomes is to identify moderators that may strengthen or weaken the positive effect of a service-oriented business strategy on company performance. It might be argued that some of the antecedents identified in the present study also moderate the relationship between a service-oriented business strategy and performance. For example, a service-oriented business strategy might produce higher dividends in more highly competitive environments. However, further research is needed to establish whether this is the case.
Note that though this research is conducted in a retailing context, this issue is important for any type of company that augments or plans to augment their products with services. This holds especially true for the industrial marketing area (Anderson and Narus 1995). Therefore, research in this area could also apply and develop further the measure of a service-oriented business strategy and its link to company performance.
Managerial Implications
In terms of managerial implications, our results support the view that a service-oriented business strategy has a positive impact on performance in the market and profitability. Thus, although there are significant costs (both time and financial) involved in applying a service-oriented business strategy, our study suggests that this strategy will pay off.
Strategies that improve performance attract the attention of managers. We conceptualized a service-oriented business strategy and developed it in terms of three dimensions (number of, broadness of, and emphasis on services). These three dimensions give managers clear guidance on how to pursue a highly service-oriented business strategy. This index of a service-oriented business strategy can also provide companies with a benchmark of how they compare with their competitors and the industry in general. This type of benchmark has been found useful in other areas, such as customer satisfaction (Fornell et al. 1996).
We also linked a service-oriented business strategy to the nonfinancial performance measures. Our empirical results found evidence for a strong relationship between these two variables. This suggests that managers who adopt a service-oriented business strategy should experience improvements in customer satisfaction, loyalty, retention, and market share.
Also, we mentioned previously that one of the key concerns about a service-oriented business strategy is related to the financial costs of applying it. However, our findings suggest that a service-oriented business strategy increases financial performance in terms of return on sales through the company's performance in the market. In other words, the benefits of a service-oriented business strategy appear to outweigh the costs. These findings suggest that companies should not be overly concerned about the costs associated with offering services. Not only are costs often one of the key reasons that services are not offered, but also services are often one of the first areas that receive cuts when companies are trying to tighten their belts financially. However, companies should not be reluctant to pursue a highly service-oriented business strategy.
There was also a positive relationship between a customer orientation and service-oriented business strategy. Being highly customer oriented means having a strong commitment to customers, trying to create customer value, and understanding customer needs (Narver and Slater 1990). Our results suggest that having a customer orientation can result in the active offering of a high number of services to many customers. Thus, managerially speaking, a service-oriented business strategy represents one concrete way that a customer orientation can be put into action.
Finally, there was a strong relationship between a service-oriented business strategy and the number of full-time (positive relationship) and part-time (negative relation-ship) employees. As mentioned previously, full-time employees are more likely to understand and develop relationships with the store's customers and to be more committed to consistent service delivery over time (Wotruba 1991). Therefore, managers should be aware that adopting a service-oriented business strategy requires a focus on full-time rather than part-time employees.
Table 1: Summary Statistics for Measurement Scales
Legend for chart:
A - Scale Name
B - Scale Mean/ Standard Deviation
C - Range Covered in the Sample(a)
D - Coefficient Alpha (b)
E - Composite Reliability (b)
Strategy Variable
Service orientation
40.50/19.55 5-120.83 - -
Number of services (original scale)
16.42/3.62 1-24 - -
Number of services (converted scale)
3.68/.63 1-5 - -
Number of service offerings
2.84/.75 1-5 - -
Broadness of service offerings
3.67/.73 1-5 - -
Environmental Characteristics
Competitive intensity in the local market
3.67/.74 1-5 .74 .77
Local retail innovativeness
2.69/.79 1-5 .81 .86
Store Characteristics
Customer orientation
4.35/.57 1.67-5 .76 .84
Relative quality level of merchandise
3.56/.55 2.33-5 .61 .69
Relative choice of merchandise offers
3.18/.65 1.20-5 .70 .73
Number of full-time employees
11.00/22.08 0-240 - -
Number of part-time employees
6.45/19.66 0-250 - -
Store's Customers' Characteristics
Price consciousness
3.24/1.05 1-5 .84 .87
Time pressure when shopping
2.58/.88 1-5 .81 .85
Control Variable
Size of store
12,962.84/32,489.66 sq. ft. 50-376,740 sq.ft - -
Performance Measures
Company performance in the market
3.62/.60 2-5 .87 .90
Company profitability
4.67/2.50 1-8 - -
(a)Number of full-time employees, number of part-time employees, and size of store were open-ended questions. (b)Coefficient alpha and composite reliability are not applicable for formative and single-item measures.
Figure 1: Perspectives of Service Orientation
Figure 2: Framework
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Measures: Scale Name, Response Cue, and Individual Items
Service Orientation
To each of the listed retail services below there are three questions. We want to stress that there are no right or wrong answers, and it is perfectly all right not to offer some of these services. First, do you offer this service or not (scored on a dichotomous scale with 0 = "not offered" and 1 = "offered" for each retail service)? If you offer this service, how many customers do you offer this service to (scored on a five-point Likert scale with anchors 1 = "not at all" and 5 = "to all customers") and how actively do you emphasize this service to your customers (scored on a five-point Likert scale with anchors 1 = "not at all" and 5 = "very actively")? Listed 24 services:
Information/advice concerning merchandise, information/advice to merchandise usage and maintenance, information desk, visits to the customer's home to provide information, invitation to special events (e.g., fashion shows, special sales), extended guarantee/ warranty on merchandise, liberal return of merchandise, merchandise repair, merchandise alteration/ adjustment, delivery of merchandise to the customer 's home, pickup of merchandise at the customer's home for repair and maintenance, merchandise installation/assembly, can order by mail, can order by phone/fax, can order by Internet, payment by checks, payment by credit/debit cards, availability of credit, child care, free parking, extended store operating hours, offer of free beverages during the sales process, layaway of merchandise, availability of gift certificates.
Competitive Intensity in the Local Market
(Scored on a five-point Likert scale with anchors 1 = "strongly disagree" and 5 = "strongly agree"; adapted from Jaworski and Kohli 1993; Slater and Narver 1994)
To what extent do the following statements describe the competition related to your local market?
We have a lot of competitors in our local market. Temporary price discounts of merchandise are very often used in our local market.
Everyday low pricing for merchandise is very often applied by many competitors in our local market. Most of our competitors regularly advertise.
The magnitude of temporary price discounts of merchandise in our local market is typically very high.
Local Retail Innovativeness
(Scored on a five-point Likert scale with anchors 1 = "strongly disagree" and 5 = "strongly agree")
To what extent do the following statements describe the competition related to your local market?
In general, the retailers in my trading area adopt a lot of new merchandising or services ideas relative to other areas of the country.
In general, the retailers in my trading area adopt new merchandising or services ideas more quickly than retailers in other areas of the country.
In general, the retailers in my trading area consistently adopt new merchandising or services ideas over time relative to other areas of the country.
Customer Orientation
(Scored on a five-point Likert scale with anchors 1 = "strongly disagree" and 5 = "strongly agree"; adapted from Narver and Slater 1990)
To what extent do you agree or disagree with the following statements regarding your store?
Relative to our competitors, our store is committed to customers.
Relative to our competitors, our store tries to create customer value.
Relative to our competitors, our store understands customer needs. (Our store sets customer satisfaction objectives.)[a]
Relative Quality Level of Merchandise
(Scored on a five-point Likert scale with anchors 1 = "much worse" and 5 = "much better")
How does your store compare to your most important local competitors on the following dimensions?
Quality of merchandise. Fashionability of merchandise. General price level of merchandise. (1 = "much lower" and 5 = "much higher")
Relative Choice of Merchandise Offered
(Scored on a five-point Likert scale with anchors 1 = "much less" and 5 = "much more")
How does your store compare to your most important local competitors on the following dimensions?
Number of stock keeping units within merchandise categories (depth of products).
Number of different merchandise categories (breadth of products).
Amount of merchandise in stock. Number of different national brands of merchandise. Variety of price levels.
Number of Full-Time Employees
On average, how many full-time employees were employed in your store over the last three business years?
Number of Part-Time Employees
On average, how many part-time employees were employed in your store over the last three business years?
Price Consciousness
(Scored on a five-point Likert scale with anchors 1 = "strongly disagree" and 5 = "strongly agree"; adapted from Lichtenstein, Ridgway, and Netemeyer 1993)
To what extent do you agree or disagree with each of the following statements regarding the customers of your store?
On average, my customers are willing to go to extra effort to find lower prices.
On average, my customers will shop at more than one store to take advantage of low prices.
Time Pressure When Shopping
(Scored on a five-point Likert scale with anchors 1 = "strongly disagree" and 5 = "strongly agree"; adapted from Srinivasan and Ratchford 1991)
To what extent do you agree or disagree with each of the following statements regarding the customers of your store?
On average, my customers seem to be pressed for time during the shopping process.
On average, shopping appears to be a real burden on the time of my customers.
Size of Store
How many square feet (meters) did your store have on average over the last three business years?
Company Performance in the Market
(Scored on a five-point Likert scale with anchors 1 = "much worse" and 5 = "much better"; adapted from Menon, Bharadwaj, and Howell 1996; Morgan and Piercy 1996)
Relative to your competitors, how has your store performed over the last three business years with respect to ...
Achieving customer satisfaction? Providing customer benefit? Attaining desired market share? Attaining desired growth? Keeping existing customers? Attracting new customers? Building a positive store image?
Profit (Before Tax) as a Percentage of Sales (Before Tax) (1 = negative, 2 = 0%-.4%, 3 = .5%-.9%, 4 = 1%-1.4%, 5 = 1.5%-1.9%, 6 = 2%-3.9%, 7 = 4%-7.9%, 8 = 8% and more) What was the profit (before tax) as a percentage of sales (before tax) of your store on average over the last three business years?
[a]Item not kept after the item purification process.
~~~~~~~~
By Christian Homburg; Wayne D. Hoyer and Martin Fassnacht
Christian Homburg is Professor of Business Administration and Marketing and Chair of the Marketing Department, University of Mannheim. Wayne D. Hoyer is Chair, Department of Marketing, and The James L. Bayless/ William S. Farish Fund Chair for Free Enterprise, The University of Texas at Austin. Martin Fassnacht is Associate Professor of Marketing and Chair of Marketing, University of Paderborn.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 139- Six Degrees: The Science of a Connected Age (Book). By: Clark, Terry; Iacobucci, Dawn. Journal of Marketing. Jan2004, Vol. 68 Issue 1, p166-167. 2p. DOI: 10.1509/jmkg.68.1.166.24033.
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Section: Book ReviewsSix Degrees: The Science of a Connected Age (Book)
Six Degrees: The Science of a Connected Age
by Duncan J. Watts (New York: W.W. Norton, 2003,
368 pp., $27.95)
As a marketer, I do not know what to make of this book. As a network researcher, I do not know what to make of this book. As a consumer of pop-science readings, I do not know what to make of this book.
There is already quite a bit of hype around Six Degrees: The Science of a Connected Age, and the result is a network phenomenon itself. The buzz that the book has generated is functioning much like the game of telephone: The earliest sources may have actually read the book, but later players almost assuredly have not, given the errors in the publicity that the book has received. For example, reviewers in both the popular press and at Web sites where the book can be purchased hail Watts as a pioneer of the "six degrees of separation" or "small-world phenomenon." Given that Milgram's small-world experiment ("Can a random sample of Nebraskan citizens reach a particular stimulus Boston stockbroker?") was published in 1967, and Six Degrees' cover puts Watts at 31 (i.e., born in 1971), he must have been precocious indeed to have been one of the phenomenon's pioneers. Fortunately, Watts himself makes no such claims and appropriately gives Milgram his due (pp. 37-42), though he also pulls out the rug from under Milgram when he presents Judith Kleinfeld's attempt to replicate the research (pp. 133-35). Kleinfeld discovered that a third of the sources asked to contact the Bostonian broker actually lived in Boston, another third lived in Nebraska but were in the stockbrokerage business, and of the remaining third (truly random people in Nebraska who were not in the financial field), fewer than 20% of their attempts to contact the stimulus person were successful at all, never mind in six links. Hmm....
It is easy enough to see why Six Degrees has a good deal of broad appeal. Consider its opening paragraph (p. 19):
The summer of 1996 was a sizzler. Across the nation, the mercury was climbing to record highs and staying there, a mute testimony to climatic unpredictability. Meanwhile, closeted in their domestic fortresses, Americans were stacking their refrigerators, cranking the air-conditioning, and no doubt watching a record amount of mind-deadening television. In fact, no matter what the season or the weather, Americans have become increasingly reliant on a truly staggering and ever growing array of devices, facilities, and services that have turned a once hostile environment into the lifestyle equivalent of a cool breeze. No amount of inventiveness or energy is excessive if it results in the creation of leisure, the increase of personal freedom, or the provision of physical comfort. From climate-controlled vehicles the size of living rooms to climate-controlled shopping malls the size of small cities, no effort or expense has been spared in modern America's endless crusade to impose strict discipline on a once unruly and still occasionally uppity planet.
An example of a power outage and the interconnections in the electric grids across the nation follows the introduction. As the excerpt illustrates, the writing is clear, lively, and engaging. It is also rather glib for an academic.( n1) This tone carries throughout the entire book, which is probably fun for the nontechnical reader, but the serious academic tires of the constant "Aren't I clever!" overtures. My first question about this book is a marketing one: What is the book's intended target audience? I can only presume it is intended to be a popularization of networks and systems science for a general-science readership, analogous to Stephen Hawking's books that popularize astrophysics (e.g., The Universe in a Nutshell). Hawking achieves this so much more effectively, but he is a genius. Still, Hawking gives his readers a sense of "Wow!" "Oh my gosh!" and "The world is magnificent!" whereas Watts at best elicits an "Oh, hey, yeah, that's cool." Still, I suppose that is more affect than the typical journal article yields.
What is the point of popularizing network and systems science? Perhaps it is a personal agenda, or perhaps it is an attempt to convince the National Science Foundation of the area's importance and worthiness of additional funding. However, I do not believe that there is enough depth in Six Degrees for even the casual reader to change his or her thinking about the world. The book is too long to be an elementary introduction, the type a popular science magazine might offer readers.
Assume that popularizing this particular science was Watts's goal. There are many examples spanning many disciplines that Watts abstracts and incorporates into network (connected systems) thinking. He treats most topics superficially (though to be fair, he develops a few examples more completely). Here are examples of the puff and many superficial treatments that Watts discusses:
• Exercises equivalent to children's party games (e.g., origami tricks to build a torus [the doughnut-shaped function learned in calculus], p. 85).
• Current manifestations of networks, such as chain e-mails (p. 158).
• The "Kevin Bacon game" (p. 93; i.e., an actor who stars in a movie with Bacon is assigned a score of 1, an actor who stars in a movie with an actor who starred with Bacon receives a 2, and so on), which is a contemporary version of the Erdös numbers (i.e., if someone has published with this prolific mathematician, that person's Erdös number is a 1; if someone has published with one of Erdös's coauthors, that person is a 2; and so on, p. 137). Apparently, the biggest challenge with Erdös numbers is tracking the network to determine one's own number (I'm a four, if that helps anyone who has worked with anyone I've worked with). This reduces quickly to parlor games.
• AIDS and the central character in a network that helped the medical community turn the corner in learning about the disease's epidemiological spread (p. 164), as is also depicted in a fabulous though brief scene in And the Band Played On in which a blackboard is covered with a network diagram.
• The network phenomenon of contagion in general: The word can mean communication or friendship patterns, but originated in medical applications, and is applied to the spread of viruses among humans (e.g., Ebola plagues), animals (e.g., foot-and-mouth disease), and computers (he boldly offers prescriptions to Microsoft, making at least this reader wonder, If Watts wanted to talk to Bill Gates in person, what room would be big enough for both of their egos?).
• Word of mouth (e.g., Harry Potter, The Blair Witch Project), opinion leaders, and cognitive networks and information processing.
• The puffery of the dot-coms, which now seems a bit stale and even a little mean-spirited given their massive passing.
• The Bible, which he boldly misinterprets (p. 108), which surely carries the penalty of going to network hell (presumably watching the movie Six Degrees of Separation and all Kevin Bacon movies over and over or inverting huge sociomatrices by hand).
What is the point of all the illustrations? "Here's an example of networks!" "Look, here, too!" To what end?
Watts discusses several phenomena and disciplines, attributing their underlying mechanisms to networks. It is acceptable that networks are the lens through which he tries to make sense of his world, but he is not successful in convincing readers that several examples have much to do with networks at all (e.g., the synchrony of runners in a race clumping together around a track [p. 32], the rich getting richer [p. 108]).
Yet I type these criticisms with a grin on my face. The guy has guts. Who do you know who likens "a pack of hungry physicists" to both "lords of the academic jungle" and to intellectual "scavengers" who "descend with fury" (p. 62)? The breadth of network applications offered in this book speaks for itself, and it means to me that Watts is intellectually playful and curious, which is an admirable (and surprisingly rare) quality in a scientist. However, I cannot determine his actual depth of understanding. He gleefully admits trying to learn about network issues without reading the literature so as to allow for creative solutions and to be unencumbered by existing paradigms. It is not clear whether he has subsequently read a few literatures deeply, rather than many literatures broadly and lightly, because the level of discussion is so superficial. However, for a popular press outlet, it probably must be so. Still, his discussions, knowledge, references, and disciplines cited seem spotty. The transportation literature seems an obvious oversight; nowhere does Watts address difficult network issues (e.g., sampling, modeling), and our own personal network idols and revered cites overlap only a little, with few actors spanning both cliques. Thus, an exhortation to budding academics: Begin by steeping yourself in the literature, and know a few things very well.
Watts's book is not a work of scholarship. The bibliography is rather good (more extensive than it would be if the target audience is indeed a popular readership). However, with the exception of the studies that he actually discusses, Watts spends no time giving credit where credit is due (which may well have led to the popular, though mistaken, belief that he is one of the original small-world thinkers). This error violates a chief principle to which academics adhere.
What does the author claim that the book's contribution is? The conclusion (p. 299) states that "distance is deceiving" (presumably, Watts means physical distance, rather than social or perceptual distance). Another conclusion offered is that cause and effect are difficult to pinpoint in interconnected systems. Finally, he closes with a praise to scientists and their heroic endeavors. Again, I say, hmm....
(n1) The criticisms of U.S. energy usage (even if readers agree) rankle unless Watts considers himself to have adopted his host nation (he is an expat, of the Australian variety--a comment only on the book's author, certainly not of Australians in general).
REFERENCES Milgram, Stanley (1967), "The Small World Problem," Psychology Today, 2, 60-67.
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By Dawn Iacobucci, Northwestern University and Terry Clark, Editor, Southern Illinois University
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Record: 140- Some New Thoughts on Conceptualizing Perceived Service Quality: A Hierarchical Approach. By: Brady, Michael K. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p34-49. 16p. 3 Charts, 4 Graphs. DOI: 10.1509/jmkg.65.3.34.18334.
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SOME NEW THOUGHTS ON CONCEPTUALIZING PERCEIVED
SERVICE QUALITY: A HIERARCHICAL APPROACH
Through qualitative and empirical research, the authors find that the service quality construct conforms to the structure of a third-order factor model that ties service quality perceptions to distinct and actionable dimensions: outcome, interaction, and environmental quality. In turn, each has three subdimensions that define the basis of service quality perceptions. The authors further suggest that for each of these subdimensions to contribute to improved service quality perceptions, the quality received by consumers must be perceived to be reliable, responsive, and empathetic. The authors test and support this conceptualization across four service industries. They consider the research and managerial implications of the study and its limitations.
The conceptualization and measurement of service quality perceptions have been the most debated and controversial topics in the services marketing literature to date. This debate continues today, as is evident from the ongoing and largely failed attempts either to integrate the SERVQUAL/SERVPERF conceptualization into new industries (e.g., Dean 1999; Durvasula, Lysonski, and Mehta 1999; Kettinger, Lee, and Lee 1995) or to replicate its conceptual structure (e.g., Asubonteng, McCleary, and Swan 1996; Kettinger and Lee 1995; Mels, Boshoff, and Nel 1997; Robinson 1999; Van Dyke, Kappelman, and Prybutok 1997). Indeed, perceived service quality has proved to be a difficult concept to grasp. It has been referred to as "elusive" (Parasuraman, Zeithaml, and Berry 1985; Smith 1999), and research relative to the construct is still considered "unresolved" (Caruana, Ewing, and Ramaseshan 2000, p. 57) and "far from conclusive" (Athanassopoulos 2000, p. 191). A call for research that specifically examines the "dimensionality" of the service quality construct (Parasuraman, Zeithaml, and Berry 1994, p. 221) has yet to be successfully addressed.
In the literature, there has been considerable progress as to how service quality perceptions should be measured (e.g., Babakus and Boller 1992; Brown, Churchill, and Peter 1993; Cronin and Taylor 1992; Parasuraman, Zeithaml, and Berry 1985, 1988, 1991, 1994; Teas 1993) but little advance as to what should be measured. Researchers generally have adopted one of two conceptualizations. The first is the "Nordic" perspective (Gronroos 1982, 1984), which defines the dimensions of service quality in global terms as consisting of functional and technical quality. The second, the "American" perspective (Parasuraman, Zeithaml, and Berry 1988), uses terms that describe service encounter characteristics (i.e., reliability, responsiveness, empathy, assurances, and tangibles). Although the latter conceptualization dominates the literature, a consensus has not evolved as to which, if either, is the more appropriate approach. Moreover, no attempt has been made to consider how the differing conceptualizations may be related.
Although it is apparent that perceptions of service quality are based on multiple dimensions, there is no general agreement as to the nature or content of the dimensions. Two (e.g., Gronroos 1982; Lehtinen and Lehtinen 1982; Mels, Boshoff, and Nel 1997), three (e.g., Rust and Oliver 1994), five (e.g., Parasuraman, Zeithaml, and Berry 1988), and even ten (e.g., Parasuraman, Zeithaml, and Berry 1985) dimensions have been proposed. However, it is apparent that service quality evaluations are highly complex processes that may operate at several levels of abstraction (Carman 1990). The missing link appears to be a unifying theory, or conceptualization, that reflects this complexity and the hierarchical nature of the construct.
The objective of this study is to identify a new and integrated conceptualization of service quality in order to move forward with this research agenda. Given the ambiguity in the literature and the importance of developing favorable service quality perceptions among consumers (Zeithaml, Berry, and Parasuraman 1996), this new perspective is well justified.
The Origins of Service Quality Theory: The Disconfirmation Paradigm
The foundation of service quality theory lies in the product quality and customer satisfaction literature. Early conceptualizations (e.g., Gronroos 1982, 1984; Parasuraman, Zeithaml, and Berry 1985) are based on the disconfirmation paradigm employed in the physical goods literature (e.g., Cardozo 1965; Churchill and Surprenant 1982; Howard and Sheth 1969; Oliver 1977, 1980; Olshavsky and Miller 1972; Olson and Dover 1976). This suggests that quality results from a comparison of perceived with expected performance, as is reflected in Gronroos's (1982, 1984) seminal conceptualization of service quality that "puts the perceived service against the expected service" (Gronroos 1984, p. 37, emphasis in original). In addition to adapting the disconfirmation paradigm to the measurement of service quality, Gronroos (1982) identifies two service quality dimensions, as shown in Figure 1, Panel A. Functional quality represents how the service is delivered; that is, it defines customers' perceptions of the interactions that take place during service delivery. Technical quality reflects the outcome of the service act, or what the customer receives in the service encounter.
The disconfirmation paradigm also is the basis for Parasuraman, Zeithaml, and Berry's (1985) SERVQUAL model (see Figure 1, Panel B), which views service quality as the gap between the expected level of service and customer perceptions of the level received. Whereas Gronroos (1982) suggests two dimensions, Parasuraman, Zeithaml, and Berry (1988) propose five: the reliability, responsiveness, assurances, empathy, and tangibility characteristics of the service experience.
Alternative Conceptualizations of Service Quality
Three themes are evident in more recent work on service quality. First, several studies advance modified versions of the SERVQUAL model (e.g., Boulding et al. 1993; Cronin and Taylor 1992; DeSarbo et al. 1994; Parasuraman, Zeithaml, and Berry, 1991, 1994; Zeithaml, Berry, and Parasuraman 1996). These modifications drop expectations altogether (e.g., Cronin and Taylor 1992), add dimensions to the expectations portion of the model (such as "will" and "should" expectations; see Boulding et al. 1993), or employ alternative methods (such as conjoint analysis) to assess service quality perceptions (Carman 2000; DeSarbo et al. 1994).
A second theme involves the heightened interest in the technical and functional quality dimensions Gronroos (1982, 1984) identifies. For example, Rust and Oliver (1994) offer a three-component model: the service product (i.e., technical quality), the service delivery (i.e., functional quality), and the service environment (see Figure 1, Panel C). Rust and Oliver do not test their conceptualization, but support has been found for similar models in retail banking (McDougall and Levesque 1994) and health care samples (McAlexander, Kaldenberg, and Koenig 1994).
The third theme relates to the structure of the service quality construct. Because of the reports of SERVQUAL's inconsistent factor structure, Dabholkar, Thorpe, and Rentz (1996) identify and test a hierarchical conceptualization of retail service quality that proposes three levels: (1) customers' overall perceptions of service quality, (2) primary dimensions, and (3) subdimensions (see Figure 1, Panel D). This multilevel model recognizes the many facets and dimensions of service quality perceptions. In other words, retail service quality is viewed as a higher-order factor that is defined by two additional levels of attributes.
In summary, scholars have advanced modified versions of either Parasuraman, Zeithaml, and Berry's (1988) five-factor American model or Gronroos's (1982) two-factor Nordic conceptualization (Rust and Oliver 1994). That is, service quality is defined by either or all of a customer's perception regarding (1) an organization's technical and functional quality; (2) the service product, service delivery, and service environment; or (3) the reliability, responsiveness, empathy, assurances, and tangibles associated with a service experience.
When assessed collectively, the SERVQUAL model appears to be distinct from the others because it uses terms that describe one or more determinants of a quality service encounter. In other words, the five dimensions of SERVQUAL are terms that might be used to refine some aspect of service quality. However, of major concern should be the question as to what should be reliable, responsive, empathic, assured, and tangible if service excellence is to be ensured. From a theoretical perspective, if service quality perceptions represent a latent variable, something specific must be reliable, responsive, empathetic, assured, and tangible.
We suggest that identifying this "something" is critical in the literature. Specifically, a conceptualization that recognizes the significance of the SERVQUAL factors and defines what needs to be reliable and so forth will respond to the call (e.g., McDougall and Levesque 1994; Oliver 1997) for identifying the attributes that influence service quality perceptions. Such a framework is needed if the true effects of service quality perceptions are to be better understood by both marketing researchers and practitioners.
We adopt Rust and Oliver's (1994) view that the overall perception of service quality is based on the customer's evaluation of three dimensions of the service encounter: (1) the customer-employee interaction (i.e., functional quality; see Gronroos 1982, 1984), (2) the service environment (see Bitner 1992), and (3) the outcome (i.e., technical quality; see Gronroos 1982, 1984). Given the growing support for revisiting Gronroos's seminal conceptualization (e.g., Bitner 1990; Lassar, Manolis, and Winsor 2000; Mohr and Bitner 1995; Oliver 1997; Rust and Oliver 1994) and the recent evidence that the service environment affects service quality perceptions (e.g., Baker 1986; Bitner 1990, 1992; Spangenberg, Crowley, and Henderson 1996; Wakefield, Blodgett, and Sloan 1996), a framework that incorporates these three dimensions is justified.
We also adopt the view that service quality perceptions are multilevel and multidimensional (Dabholkar, Thorpe, and Rentz 1996). Carman (1990) was perhaps the first to note that customers tend to break service quality dimensions into various subdimensions. Such a structure more fully accounts for the complexity of human perceptions (Dabholkar, Thorpe, and Rentz 1996). There is theoretical support for a multidimensional, multilevel model (e.g., Carman 1990; Czepiel, Solomon, and Surprenant 1985; Dabholkar, Thorpe, and Rentz 1996; McDougall and Levesque 1994; Mohr and Bitner 1995), but there has been little effort to identify the attributes or factors that define the subdimensions. Given the complexity of evaluating the quality of service interactions, the service environment, and the service outcome, this is a significant gap. To address this gap, we undertook the qualitative study described in the next section.
The Qualitative Study
A review of the services marketing literature reveals many examples of qualitative research. Parasuraman, Zeithaml, and Berry (1985) use it to identify dimensions for their SERVQUAL model. On the basis of a qualitative study, Bitner, Booms, and Mohr (1994) and Bitner, Booms, and Tetreault (1990) categorize various determinants of critical service encounters. Likewise, Grove and Fisk (1997) and Tax, Brown, and Chandrashekaran (1998) employ this approach to study customer interactions and service complaint handling. We use qualitative research to identify the subdimensions customers consider when evaluating the quality of the interaction, physical environment, and outcome dimensions of a service experience.
The qualitative data were obtained from responses to open-ended surveys that were administered by trained student assistants. Data collection took place over three weeks and yielded a final usable sample of 391 completed surveys. The age, sex, and income level of respondents were controlled by a quota sampling method. Respondents were asked to complete an open-ended questionnaire about the specific attributes they perceived as influences on the interactions, environments, and outcomes encountered during recent service experiences. Responses were collected across eight industries (amusement parks, full-service restaurants, health care facilities, hair salons, automobile care facilities, dry cleaning, jewelry repair, and photograph developing) and multiple service providers in each industry. The industries and providers were chosen to maximize the diversity of outlets and ensure recent use of the service by the respondents.
The respondents were encouraged to list all factors that influenced their perception of each of the three primary dimensions, with one exception. Dabholkar, Thorpe, and Rentz (1996) report that in their study, respondents regularly listed price as a factor, but the literature clearly suggests that price is a determinant of service value (e.g., Bitner and Hubbert 1994; Chang and Wildt 1994; Drew and Bolton 1987; Heskett, Sasser, and Hart 1990; Zeithaml 1988). More specifically, price is a component of sacrifice that, when combined with service quality, defines a customer's value assessment. Therefore, we followed Dabholkar, Thorpe, and Rentz's (1996) methodology in eliminating price from the decision set.
To code the data, we used an inductive categorization method that involves labeling recurring factors found in a passage of text (Spiggle 1994; Strauss and Corbin 1990). Similar processes are referred to as content analysis in the services literature (de Chernatony and Riley 1999; Tax, Brown, and Chandrashekaran 1998), and the method is used extensively in consumer behavior research to identify and document thematic relationships among various text passages (McCracken 1988; Richins 1997; Schouten and McAlexander 1995; Thompson 1997, 1999; Thompson and Hirschman 1995). In our case, three independent coders processed the survey results to identify and categorize the factors that influenced customer perceptions of the service interaction, physical environment, and service outcome. The coders were familiar with qualitative research procedures but not the marketing literature and were unaware of any a priori conceptualization. The process was therefore data driven, as the objective was to identify emergent factors from the open-ended responses (Spiggle 1994).
Coders discussed disagreements; if a resolution could not be reached, the incident counted against the reliability assessment (Kassarjian 1977). All potential subdimensions emanating from the responses were counted, but only those judged distinct from the other variables in the model were included in the conceptualization. Also, subdimensions needed to be consistently attributed to their respective primary dimensions; if a factor was listed under more than one primary dimension, it counted against the reliability estimate. Coder reliability was 89%.
The categorization yielded nine distinct subdimensions that were divided evenly among the three primary dimensions, as shown in Figure 2. Although the terms varied slightly, the factors were consistent. Moreover, a subsequent review of the literature revealed much support for the relationships identified between the factors and the primary dimensions. For example, subdimensions of interction quality (i.e., attitude, behavior, and expertise) and physical environment quality (i.e., ambient conditions, design, and social factors) are similar to those specified in the literature (e.g., Baker 1986; Bitner 1992; Bitner, Booms, and Tetreault 1990). These qualitative results are further discussed in the section on hypotheses, along with the literature that supports the findings.
Our model suggests that each of the primary dimensions of service quality (interaction, environment, and outcome) has three subdimensions. Furthermore, customers aggregate their evaluations of the subdimensions to form their perceptions of an organization's performance on each of the three primary dimensions. Those perceptions then lead to an overall service quality perception. In other words, customers form their service quality perceptions on the basis of an evaluation of performance at multiple levels and ultimately combine these evaluations to arrive at an overall service quality perception.
In addition to the qualitative study, the work of Parasuraman, Zeithaml, and Berry (1985, 1988) was used to refine the definition of the subdimensions. There is debate about SERVQUAL's five-factor structure, but there is wide agreement that these dimensions are important aspects of quality service (Fisk, Brown, and Bitner 1993). For example, it is clear that reliable service is an important part of attaining a favorable perception of quality. Yet there are many aspects of a quality service encounter that should be reliable, such that reliability is not a clear dimension unless it is known what needs to be reliable. We maintain that the dimensionality problems with SERVQUAL/SERVPERF (Cronin and Taylor 1992; Parasuraman, Zeithaml, and Berry 1994) hinge on this issue.
se therefore reposition the SERVQUAL factors as modifiers of the nine subdimensions (see Figure 2). These subdimensions provide the necessary foundation for answering the question of what needs to be reliable, responsive, and so on. In turn, the SERVQUAL dimensions capture how consumers differentiate performance on these dimensions. In other words, they define how the subdimensions are evaluated. Undeniably, the relative importance of the SERVQUAL factors may vary across each dimension depending on individual or situational differences. That is, the SERVQUAL factors theoretically may be an important determinant of any of the nine subdimensions. This may account for the problematic factor structure of the SERVQUAL scale (e.g., Babakus and Boller 1992; Carman 1990; Cronin and Taylor 1992; McDougall and Levesque 1994; Parasuraman, Zeithaml, and Berry 1994; Simon 1995).
In our revised framework, the reliability, responsiveness, and empathy variables are retained, but they are not identified as direct determinants of service quality. Rather, they serve as descriptors of the nine subdimensions identified in the qualitative study. The tangibles dimension is not identified as a descriptor because of evidence that customers use tangibles as a proxy for evaluating service outcomes (Booms and Bitner 1981; McDougall and Levesque 1994). This evidence is supported by our qualitative data, because respondents regularly listed tangible elements as influencing their perceptions of service outcome quality. Therefore, tangibles are included as a subdimension of outcome quality. The assurance dimension was subsequently dropped because it did not remain distinct in factor analyses. Specifically, assurance measures have been found to load on several different factors depending on the industry context (see Babakus and Boller 1992; Carman 1990; Dabholkar, Shepherd, and Thorpe 2000; Frost and Kumar 2000; Llosa, Chandon, and Orsingher 1998; McDougall and Levesque 1994; Mels, Boshoff, and Nel 1997), and they failed to remain distinct in a pretest that was conducted as part of our research.
The conceptualization of service quality advanced here recognizes the complexity of the construct and the two seemingly conflicting perspectives advanced in the literature. We suggest that neither perspective is wrong; each is incomplete without the other. Our qualitative research identifies nine subdimensions that define the three direct determinants of service quality. Thus, these subdimensions reflect the composite set of factors customers consider when they evaluate the quality of the service interaction, environment, and outcome. On the basis of that underlying logic, we tested several hypotheses.
Interaction Quality
Services are often inextricably entwined with their human representatives. In many fields, a person is perceived to be the service.
G. Lynn Shostack (emphasis in original)
Because services are inherently intangible and characterized by inseparability (Bateson 1989; Lovelock 1981; Shostack 1977), the interpersonal interactions that take place during service delivery often have the greatest effect on service quality perceptions (Bitner, Booms, and Mohr 1994; Bowen and Schneider 1985; Gronroos 1982; Hartline and Ferrell 1996; Surprenant and Solomon 1987). These interactions have been identified as the employee-customer interface (Hartline and Ferrell 1996) and the key element in a service exchange (Czepiel 1990). Their significance is captured in Surprenant and Solomon's (1987) suggestion that service quality is more the result of processes than outcomes. Therefore, there is strong support in the literature for including an interaction dimension in the conceptualization of perceived service quality.
H1: Perceptions of the quality of service interactions directly contribute to service quality perceptions.
Our qualitative study indicates that three distinct factors constitute customer perceptions of interaction quality. With few exceptions, the responses referred to some aspect of the attitude, behaviors, and/or expertise of the service personnel. Consider the following comment: "The staff was friendly [attitude], knowledgeable [expertise], and I was greeted as soon as I walked in the door [behavior]." Additional examples are presented in Table 1.
A review of the literature supports these factors as subdimensions of interaction quality. For example, Czepiel, Solomon, and Surprenant (1985, p. 9) suggest that the attitude, behavior (in their term, "manifest" function), and skill of service employees define the quality of the delivered service and ultimately "affect what clients evaluate as a satisfactory encounter." Similarly, Bitner, Booms, and Tetreault (1990) divide the employee-customer interaction into three distinct aspects: demeanor, actions, and skill of employees in resolving failed service incidents. Gronroos (1990) also suggests that the attitudes, behavior, and skills of employees are factored into service quality assessments. Finally, Bitner (1990) proposes that the attitudes and behavior of service personnel largely influence consumer perceptions of functional quality. She adds that these perceptions are subsequently combined with customer evaluations of technical quality and the service environment to define service quality.
H2: Perceptions of employee attitudes directly influence the quality of service interactions.
H3: Perceptions of employee behaviors directly influence the quality of service interactions.
H4: Perceptions about employee expertise directly influence the quality of service interactions.
Service Environment Quality
A body of work (e.g., Baker 1986; Baker, Grewal, and Parasuraman 1994; Bitner 1990, 1992; Spangenberg, Crowley, and Henderson 1996; Wakefield, Blodgett, and Sloan 1996; Wener 1985) considers the influence of the physical or "built" environment on customer service evaluations. Because services are intangible and often require the customer to be present during the process, the surrounding environment can have a significant influence on perceptions of the overall quality of the service encounter (Bitner 1992). Research from such disciplines as marketing, environmental psychology, and sociology has been integrated into the study of the service environment, or what Bitner (1992) terms the "servicescape."
Early works in the services marketing literature exemplify the discipline's interest in how the servicescape affects consumer service evaluations. These studies indicate that it is a fundamental factor. Parasuraman, Zeithaml, and Berry (1985) were perhaps the first to identify several environmental considerations. Rys, Fredericks, and Luery (1987), in their study of restaurant patrons, find that customers infer quality on the basis of their perceptions of the physical facilities. In a cross-sectional qualitative study, Crane and Clarke (1988) report that the servicescape influences perceptions, because customers in four different service industries listed the service environment as a consideration factor in their service quality evaluations. Similar results have also been reported for patient evaluations of the service provided by their physicians (Baumgarten and Hensel 1987; McAlexander, Kaldenberg, and Koenig 1994). In view of the prominence of the environment during service delivery, it appears that the servicescape plays an integral role in the formation of service quality perceptions.
H5: Perceptions of the quality of the physical environment directly contribute to service quality perceptions.
Our qualitative study reveals that three factors influence the perceived quality of the physical environment: ambient conditions, facility design, and social factors (for examples, see Table 1). Ambient conditions and design factors (facility layout in Bitner 1992) are well supported by prior research (Baker 1986; Baker, Grewal, and Parasuraman 1994; Bitner 1992). Ambient conditions pertain to nonvisual aspects, such as temperature, scent, and music (Bitner 1992). Facility design refers to the layout or architecture of the environment and can be either functional (i.e., practical) or aesthetic (i.e., visually pleasing).
The last factor, social conditions, refers to the number and type of people evident in the service setting as well as their behaviors (Aubert-Gamet and Cova 1999; Grove and Fisk 1997). The negative influence of unruly crowds or the disturbance caused by a crying baby fall in this category. This factor was identified by Baker (1986), but there is disagreement over it in the literature. Bitner (1992, p. 66) suggests the term "social cues," which refer to the "signs, symbols, and artifacts" evident in the service setting that influence customer perceptions.
Our qualitative data support Baker's (1986) conceptualization, because respondents repeatedly cited social factors as influencing their perception of the service environment. In contrast, only a handful listed factors pertaining to what might be considered signs, symbols, or artifacts, and nearly all these cases were categorized under the service outcome as indicative of a tangible aspect of the service. There seems to be ample justification for including ambient, design, and social factors as underlying dimensions of the service environment.
H6: Perceptions of ambient conditions in the service facility directly influence the quality of the physical environment.
H7: Perceptions of the facility design directly influence the quality of the physical environment.
H8: Perceptions of social conditions in the service facility directly influence the quality of the physical environment.
Outcome Quality
There is consensus in the literature that the technical quality of a service encounter significantly affects customer perceptions of service quality (e.g., Carman 2000; Gronroos 1982, 1984, 1990; Rust and Oliver 1994). Gronroos (1984, p. 38) defines this factor as "what the customer is left with when the production process is finished." Czepiel, Solomon, and Surprenant (1985) refer to the technical outcome as the "actual" service and posit that it is a determinant in assessing the quality of a service encounter. Rust and Oliver (1994) refer to the service outcome as the "service product" and suggest that it is the relevant feature customers evaluate after service delivery. McAlexander, Kaldenberg, and Koenig (1994) refer to the service outcome in health care industries as "technical care" and find that it is a primary determinant of patients' service quality perceptions. Similarly, de Ruyter and Wetzels (1998) include the service outcome in their health care investigation and find a direct link to service quality. On the basis of the literature and our qualitative study, as well as its pragmatic appeal, it is reasonable to expect outcome quality to affect perceived service quality.
H9: The service outcome directly contributes to service quality perceptions.
Marketing scholars have yet to identify attributes that define outcome quality. Gronroos (1984) and Rust and Oliver (1994) simply state that this is what the customer is left with when service is rendered. Given the lack of empirical work, we used the results of our qualitative study to identify the subdimensions of outcome quality.
Our survey indicated that waiting time influences outcome quality perceptions. In most cases, responses were negative, though a few were positive when the service delivery was especially timely. In either case, the effect of waiting on outcome quality appears strong. This has considerable support in the literature. Parasuraman, Zeithaml, and Berry (1985) find that customers identify service punctuality as an integral part of their overall evaluation. Maister (1985) reaches a similar conclusion on the basis of qualitative interviews with service personnel. Katz, Larson, and Larson (1991) and Taylor and Claxton (1994) provide empirical verification of this relationship in their studies of the effect of waiting time on bank and airline customers, respectisely. Moreover, Houston, Bettencourt, and Wenger (1998) incorporate waiting time into their analysis of service encounter quality and find it to be an important predictor. Therefore, perceived waiting time is identified as a subdimension of outcome quality (see Figure 2).
It is intuitive to infer a negative relationship between waiting time and perceptions of outcome quality, but this only holds if waiting is measured in absolute time. That is, longer waiting periods have a negative effect on quality perceptions (Hui and Tse 1996; Katz, Larson, and Larson 1991; Taylor 1994). In our research, however, waiting time is not measured in absolute terms, because this would require controls associated with experimental conditions (Hui and Tse 1996). Rather, it is evaluated in a manner similar to that used by Taylor and Claxton (1994). They suggest a positive relationship: More favorable perceptions of waiting time are associated with enhanced outcome quality perceptions. We conceptualize perceived waiting time as a subdimension of outcome quality and predict a positive relationship.
H10: Perceptions of waiting time directly influence service outcome quality.
Another influence on service outcome perceptions is tangible elements, which represent almost half the factors cited by respondents in the qualitative study. Theory suggests that customers use any tangible evidence of the service outcome as a proxy for judging performance (see Booms and Bitner 1981; Hurley and Estelami 1998; Shostack 1977; Zeithaml, Parasuraman, and Berry 1985). The research relating physical evidence to customer evaluations is largely based on economic signaling theory (Murray 1991), but Parasuraman, Zeithaml, and Berry (1985) show that tangible evidence is a factor that service customers consider when forming quality perceptions. Furthermore, tangibles are one of the SERVQUAL dimensions that are generally retained in factor analyses (Mels, Boshoff, and Nel 1997). On the basis of our qualitative research and the literature, we propose the following:
H11: Perceptions of the tangible evidence directly influence service outcome quality.
Our qualitative research also supports the addition of valence as a determinant of outcome quality. Respondents listed various factors that reflect this dimension, which covers the essence of the service outcome above and beyond waiting time and tangibles. That is, valence captures attributes that control whether customers believe the service outcome is good or bad, regardless of their evaluation of any other aspect of the experience. For example, consider a customer who approaches a bank to inquire about a mortgage loan. Service performance may be irrelevant if the loan is not approved. Our research indicates that many of the factors that shape the valence of the outcome are outside the direct control of service management, yet they still influence perceptions of the service outcome. Other examples can occur in such services as sporting events ("We lost the game"), entertainment outlets ("The movie was disappointing"), law offices ("I lost the case"), repair services ("The damage was irreparable"), and airlines ("There was a blizzard"). In these scenarios, the customer may have a positive perception of each service quality dimension, but the negative valence of the outcome can ultimately lead to an unfavorable service experience.
The theoretical basis for incorporating valence is the general belief that service quality is similar to an attitude (Cronin and Taylor 1992; Parasuraman, Zeithaml, and Berry 1985, 1988). As such, the service outcome evaluation process is similar to that described in the attitudinal literature (Fishbein 1961, 1963; Rosenberg 1956). People's attitudes toward an object are based on a summation of their beliefs and evaluations of whether those beliefs are good or bad (Lutz 1975). This good/bad dimension is termed valence and reflects the degree to which the object of interest is considered favorable or unfavorable (Mazis, Ahtola, and Klippel 1975). On the basis of our qualitative research and theory, we conceive valence as a subdimension of outcome quality.
H12: The valence of the service encounter directly influences service outcome quality.
Measures for some of the variables, such as waiting time (Taylor 1994) and tangibles (Parasuraman, Zeithaml, and Berry 1988), are identified in the literature, but these either are specific to an industry or contain potentially confounding subdimensions. It was therefore necessary to develop measures, and we followed Churchill's (1979) recommended procedure for scale development (see Parasuraman, Zeithaml, and Berry 1988).
On the basis of a review of the literature, we generated an initial pool of 59 items. We iteratively assessed the items for internal consistency (by means of coefficient alpha estimates) and factor analyzed them using convenience samples of students in undergraduate marketing classes at a large state university. We continued the process until the scales exhibited acceptable measurement properties. The result was a final group of 35 items to measure the 13 constructs in the model. Items are listed in the Appendix.
The sample was drawn from four service industries: fast-food, photograph developing, amusement parks, and dry cleaning. These were selected because (1) each allows the customer the opportunity (albeit disproportionately) to evaluate the quality of interactions with employees, the environment, and the outcome; (2) they represent an array of service providers (Lovelock 1981); and (3) they are similar to the service industries used in other service quality research (e.g., Parasuraman, Zeithaml, and Berry 1988, 1991; Cronin and Taylor 1992). Two providers per quadrant were examined to enhance the generalizability of the results. The selection of specific firms was based both on their familiarity to the population as evidenced by responses to our qualitative survey and on their comparable service offerings.
The data were gathered in a medium-sized metropolitan area that was dominated by three state-supported universities. The survey method was self-completed questionnaires distributed at various locations. Respondents were allowed to complete only one survey and were asked to base answers on their cumulative experiences with the service provider (see the Appendix). Participants were initially screened to ensure that they had used the service within the previous 12 months. The resulting sample consisted of 1149 respondents.
To ensure authenticity of the data, 15% of the respondents (n s 175) were contacted by telephone and asked to verify selected responses to demographic questions. The surveys were also checked for obvious instances of yea-saying and incompleteness. This process eliminated less than 2% of the sample. The final sample consisted of 1133 participants. A comparison revealed that the sample closely mirrors the general population except for age (the 25-44 age group is slightly overrepresented) and education level (the sample is slightly more educated). The latter can perhaps be attributed to the presence of three universities in the area in which the survey was administered.
The psychometric properties of the items were evaluated through a comprehensive confirmatory factor analysis using LISREL 8. All items were tested in the same model and were restricted to load on their respective factors. Scale statistics, including intercorrelations, shared variances, and construct reliabilities, are given in Table 2. The results of the confirmatory factor analysis are presented in Table 3, along with several descriptive and diagnostic statistics (means, standard deviations, average variances extracted, parameter estimates, and t-values).
The means reported in Table 3 appear similar across constructs. However, this is somewhat misleading in that the variables were measured across the respondents' aggregate experiences and the means were reported across all four industries. When disaggregated, the means and standard deviations were notably different both across and within service industries. For example, the amusement parks and photograph developing industries had higher means and less variation than the fast-food and dry cleaning samples. This may be a reflection of the hedonic nature of these industries. In contrast, dry cleaning had the lowest means for nearly every variable. There was also variation within the service industries. For example, the food and waiting time at one fast-food restaurant were rated much higher than those of its competitor, but the latter had a clear advantage in atmospherics. The waiting time perceptions are likely a reflection of the different queuing systems used by the two restaurants.
Because of the large sample size, the model fit was evaluated using the comparative fit index (CFI), DELTA2 (Bollen 1989), and relative noncentrality index (RNI) (McDonald and Marsh 1990), as these have been shown to be the most stable fit indices (Gerbing and Anderson 1992). Other fit indicators (chi-square, root mean square residual [RMSR] and normed fit index [NFI]) are included in Table 3 for evaluative purposes (Hu and Bentler 1999). As shown in Table 3, the results (CFI, DELTA2, RNI = .95) indicate that the comprehensive model fits the data well.
The internal consistency of the scales was assessed through the construct reliability estimates (see Fornell and Larcker 1981) reported in Table 2. The reliability estimates ranged from .72 (social factors) to .93 (attitude and ambient conditions). Convergent validity was evaluated by an examination of both the significance of the t-values and the average variances extracted (Fornell and Larcker 1981). All the t-values were significant (p .001), and all but one of the average variance extracted estimates were greater than .50 (Fornell and Larcker 1981); the exception was social factors, which was 47%. Discriminant validity was tested by means of Fornell and Larcker's (1981) criteria, whereby the explained variance for a construct indicator (see Table 3) is compared with the shared variance (see Table 2) between the construct and the other variables in the model. The results indicate discriminant validity, because the average variance extracted by each of the scales was greater than the shared variance between the construct and all other variables.
The conceptualization depicted in Figure 2 suggests that service quality is a multidimensional, hierarchical construct. It therefore can be described as a third-order factor model suitable for testing through traditional structural equation modeling techniques. Given that our goal was to assess the proposed framework, testing the model in its entirety was a priority. Despite a lack of precedent for simultaneously analyzing a third-order factor model (Dabholkar, Thorpe, and Rentz 1996), we tested the conceptualization in a single structural model using LISREL 8. Because such a test has not been reported in the literature and because the items play a central role in this study, we performed two supplementary tests to assess further the model's structure, as shown in Figures 3 and 4. Similar to the procedure of Dabholkar, Thorpe and Rentz (1996), we first tested the primary dimensions and then tested the subdimensions. We added an analysis of the overall model to complete the three-stage design. Thus, the fit of the models determines the degree to which the items measure the same hierarchical factor as well as whether the variables depicted in Figure 2 are well supported as subdimensions of service quality (Dabholkar, Thorpe, and Rentz 1996).
The first stage of the process was to test the second-order factor model (see Figure 3). The purpose was to determine whether the three primary dimensions can be viewed as appropriate indicators of overall service quality. Because the three primary dimensions have not been empirically tested, such an analysis has added merit. The results of the first stage test, presented in Table 4, indicate that the model fits the data well (CFI, DELTA2, RNI = .99).
The second stage assessed the nine subdimensions (see Figure 4). We argue that explicit dimensions are needed to anchor the SERVQUAL factors. Accordingly, we developed 27 descriptive measures to assess the nine subdimensions. This stage tests these descriptors as well as their relevance in conceptualizing service quality. The results reported in Table 4 support these descriptive measures (CFI, DELTA2, and RNI = .93).
The results of the overall model test are also presented in Table 4 and indicate an acceptable fit to the data (CFI = .91; DELTA2, RNI = .92). Although the structural model appears to support the conceptualization, the significance of the individual paths identified in Figure 2 provides a more comprehensive test. All the paths depicted in the research model were supported, as the t-values associated with the paths were positive and significant (p .001). For exploratory and generalization purposes, the model was also assessed on the disaggregated industry samples. The results were similar to those reported for the overall sample (CFI estimates ranged from .89 to .92 and R<SUP>2</SUP> ranged from .76 to .89), with one exception. The path from interaction quality to service quality was insignificant in the photograph developing sample. All other paths were significant and exhibited similar loadings in each of the four subsamples.
Consideration of Alternative Paths
Although the research model is well grounded and appears to be robust, the potential for model respecification needs to be considered (Anderson and Gerbing 1988). The objective is to increase the degree to which the conceptualization captures the data and, in turn, improve the validity of the conceptualization (Bentler and Chou 1987). In so doing, it is recommended that theoretical paths be avoided (Robles 1996). These are based on relationships established in the literature and should be eliminated from the respecification consideration set. Ignoring this decree is referred to as "data snooping" (Bentler and Chou 1987; MacCallum 1986) and is not a recommended practice.
In contrast, empirical paths have conceptual structures that are not yet well defined and therefore can be considered for respecification (Robles 1996). We identified several such paths. They were selected either because there is little research on these relationships (such as the valence construct) or because there is some evidence that a subdimension may influence more than one primary dimension (i.e., load on more than one factor). For example, it appears that waiting time may affect not only outcome quality but also perceptions of employee performance. Taylor (1994) and Taylor and Claxton (1994) infer that this can occur in the short run through the development of negative affect (e.g., anger, a bad mood). Therefore, a path between waiting time and interaction quality should be examined.
A second prospective path was identified between tangibles and physical environment quality. The servicescape literature identifies artifacts (Bitner 1992) as a possible determinant of the quality of the physical environment. As noted previously, our qualitative data support Baker's (1986) social dimension, but the path should be investigated nonetheless.
A third and fourth possible respecification was identified from Grove and Fisk's (1997) study on other customers. Their research in the amusement parks industry supports a relationship between a service firm's clientele and customer perceptions of the service outcome. Moreover, their literature review indicates that this relationship may carry over to perceptions of the firm's employees, though this effect was not tested. Therefore, paths between social factors and interaction/outcome quality should also be considered.
These paths, along with several associated with valence, were added to the research model both collectively and individually (i.e., one at a time). The results were consistent across methods and industries. With one exception, the added paths were not supported because they either were insignificant or did not improve the model fit or both. The lone exception was the relationship between social factors and outcome quality (see Figure 2). This path was significant in all samples, and its addition consistently and significantly (p .001) improved the model fit (see Table 4). Moreover, a comparison of the common parameters across the original and respecified models resulted in a correlation between the two solutions that approaches 1.00. This both supports the stability of the model and suggests that the original conceptualization was incomplete before the addition of the path (Bentler and Chou 1987)
A review of the qualitative data also provided some support for this relationship; a few comments were classified as social factors and categorized as outcome quality elements. From a practical perspective, this addition indicates that the appearance and behavior of other customers not only influence perceptions of the physical environment but also can enhance or detract from the service outcome. These results are consistent with those reported by Grove and Fisk (1997).
Despite two decades of study and much lively debate, conceptual work on service quality can best be described as divergent. At the core of the debate are two competing perspectives, termed the Nordic and American schools (Asubonteng, McCleary, and Swan 1996; Lam and Woo 1997; Mels, Boshoff, and Nel 1997). The point of contention is that the former defines service quality using overall categorical terms, whereas the latter uses descriptive terms. Both perspectives highlight important aspects of service quality, but neither fully captures the construct. The resulting impasse has led to a call for research that reconsiders the various dimensions of service quality.
The results presented here are an effort to integrate the two schools and move forward. We provide qualitative and empirical evidence that service quality is a multidimensional, hierarchical construct. The paths in the research model are all confirmed, which indicates that each subdimension is appropriately conceived as an aspect of service quality. Collectively, it appears that these results contribute to the discipline in several areas.
First, we provide evidence that customers form service quality perceptions on the basis of their evaluations of three primary dimensions: outcome, interaction, and environmental quality. The first two are adapted from the Nordic school, in particular Gronroos's (1982, 1984) seminal idea that service quality is assessed according to customer evaluations of outcomes as well as interactions with service employees. The third primary dimension reflects the influence of the service environment on quality perceptions. Therefore, we offer the first empirical evidence for Rust and Oliver's (1994) three-component conceptualization of service quality.
Second, our qualitative and empirical results also indicate that the three primary dimensions are composed of multiple subdimensions. Customers base their evaluation of the primary dimensions on their assessment of three corresponding subfactors. The combination of all these constitutes a customer's overall perception of the quality of service. On the basis of these findings, it appears that a hierarchical conceptualization of service quality is appropriate.
Third, the results indicate that the reliability, responsiveness, and empathy of service providers are important to the provision of superior service quality, as is suggested by the American school (e.g., Parasuraman, Zeithaml, and Berry 1985, 1988). Yet we argue that these are modifiers of the subdimensions, as opposed to direct determinants. The implication is that they represent how each subdimension is evaluated (reliable or not, responsive or not, and so on), whereas the subdimensions answer the question as to what about the service should be reliable, responsive, and empathetic.
Our study achieves two important objectives. First, it consolidates multiple service quality conceptualizations into a single, comprehensive, multidimensional framework with a strong theoretical base. Second, it answers the call for a new direction in service quality research and may help alleviate the current stalemate. These advances are particularly significant because a high level of service quality is associated with several key organizational outcomes, including high market share (Buzzell and Gale 1987), improved profitability relative to competitors (Kearns and Nadler 1992), enhanced customer loyalty (Zeithaml, Berry, and Parasuraman 1996), the realization of a competitive price premium (Zeithaml, Berry, and Parasuraman 1996), and an increased probability of purchase (Zeithaml, Berry, and Parasuraman 1996).
Managerial Implications
Our model can greatly assist managers in understanding how their customers assess the quality of service experiences. Essentially, we address three basic issues: (1) what defines service quality perceptions, (2) how service quality perceptions are formed, and (3) how important it is where the service experience takes place. These three factors require managerial attention in efforts to improve consumer perceptions of service quality. Therefore, our framework can guide managers as they endeavor to enhance customers' service experiences.
The potential applications of this study are numerous. From a strategic standpoint, the conceptualization can be used to categorize customers across the nine subdimensions. Segment profiles then can be created to identify areas of core competency as well as service deficiencies. Isolating and resolving problems noted by loyal customers (Bolton 1998) and by those prone to word-of-mouth behavior (Zeithaml, Berry, and Parasuraman 1996) is especially important in view of the profit deterioration associated with even a small percentage of customer defections (Reicheld 1996). The relative performance of organizational units across the subdimensions also can be tracked. From a competitive standpoint, the identified variables can be used to compare service levels with competitors' offerings. For example, the fast-food data indicate that one competitor is a clear leader in perceptions of food quality, whereas the other derives a competitive edge from the ambience of its servicescape.
Our multilevel conceptualization is unique in that it allows for analysis at several levels of abstraction. For example, a practitioner interested in perceptions of service on a cumulative basis can use the global measures to determine an overall service quality evaluation. For researchers who focus on the quality of the primary dimensions, the six items pertaining to Rust and Oliver's (1994) conceptualization can be used as an effective service quality proxy. Or if a practitioner desires a comprehensive service quality analysis, the complete scale can be used both to determine an overall service quality assessment and to identify specific areas that are in need of attention. Analyses performed in this fashion enable managers to devote resources to improving either service quality collectively or specific aspects of the service act. This flexibility can have important implications for managers who operate in more than one service industry. As was evident in the photograph developing sample, the importance of the subdimensions can vary across service contexts (Parasuraman, Zeithaml, and Berry 1988). In that industry, the results suggest that the interaction dimension is not a key driver of service quality perceptions. Therefore, in other industries with a low level of customer-employee interaction, managers may need to concentrate only on a subset of the dimensions.
For users of SERVQUAL/SERVPERF, the findings suggest that delivering reliable, responsive, and empathetic service is indeed related to improved service quality perceptions. Of this there is little doubt, both intuitively and empirically, but the literature and this research further suggest that guidance is needed as to what is supposed to be reliable and so on. We have identified nine constructs that answer this question. With this kind of focused information, managers not only can diagnose service failures but also can isolate their origins. For example, the ERVQUAL/SERPERF items would convey that one or more aspects of the service delivery are unreliable or unresponsive or lack empathy. Alternatively, our conceptualization not only can identify waiting time as the problem but also can indicate (1) whether waiting time is consistent, (2) whether efforts are being made to minimize the wait, and/or (3) whether the company recognizes customers' time constraints. An explicit and actionable remedy then can be devised.
Research Implications
There are several implications for further research. The most obvious extension is to investigate the interrelationships between service quality and other service constructs. The discipline has made great strides in understanding the relationships between service quality and expectations, satisfaction, and service value, but additional work is needed. In particular, there is a notable lack of discriminant validity between measures of perceived service quality and customer satisfaction. Determining whether our conceptualization can help overcome this problem should be of great interest to researchers. Moreover, any improvement in the ability to capture service quality perceptions will enhance the understanding of service value. Specifically, value attributions are defined in part by what a customer "gets" from a service experience. A more complete representation of service quality improves the ability of researchers to capture this portion of consumers' value attributions.
Given the interest in investigating internal service quality (Bitner, Booms, and Tetreault 1990; Brown and Swartz 1989; Frost and Kumar 2000; Parasuraman, Zeithaml, and Berry 1988), our conceptualization could be extended to analyze service quality from an employee perspective. Indeed, Parasuraman, Zeithaml, and Berry (1988) argue that overall service quality measures can easily be adapted to serve in such a fashion.
Further work investigating valence as a subdimension of service quality is needed. Our findings indicate that the valence of the service outcome can have an effect on overall perceptions of service quality. Because the factors driving valence tend to be beyond the control of service managers (e.g., bad weather, bad credit, the wrong verdict), more research is needed to identify possible strategies for counteracting these effects. It may be that one or more of the other subdimensions help neutralize negative valence. For example, exceptionally friendly service or a particularly attractive setting might outweigh these effects. The loyal support of athletic teams such as the Chicago Cubs and Boston Red Sox is a testament to this potential. Revenue generation for these franchises is insulated from the performance of the team largely by the tradition and atmosphere of their stadiums (i.e., the physical environment).
Finally, the scale developed in this study can be used to examine each primary dimension of service quality in greater depth. The literature review suggests that relatively few scholars have empirically analyzed the quality of the customer-employee interaction, the service environment, and the service outcome. Moreover, our findings indicate that the importance of the dimensions may vary depending on industry characteristics. Future studies could employ the items to investigate each construct more fully or even the interactions among the set. Questions remain as to whether customer perceptions of the quality of interactions, the service environment, or outcomes dominate overall service quality perceptions, as well as such issues as customer willingness to revisit or offer positive word-of-mouth endorsements. Our model can facilitate these and other research efforts.
As does any research project, our study has some limitations. The four services tested account for only a small portion of service industries, which makes generalizing the results risky. Also, the 12-month interval in data collection may have influenced the variance in responses and therefore should be considered a limitation. In addition, although the measures used in the study perform well in multiple tests and across several industries, further analysis of the items is needed in other contexts to establish more definitive proof of reliability and validity.
Finally, our conceptualization is intended as a global view of service quality. Our goal was to develop a model that identifies the structure and factors considered when customers evaluate a typical service encounter and, in so doing, offer a unifying theory that draws from the literature to date. Yet we acknowledge that it may be impossible to develop a model that is equally applicable across all service industries. For example, highly automated services (e.g., remote services) would require the evaluation of only a subset of the factors discussed here (i.e., remote services have little or no customer-employee interaction). We therefore stress that modifications of this conceptualization to account for industry-specific factors are critical.
All items were scored on a seven-point Likert scale (1 = "strongly disagree," 7 = "strongly agree"). In the following items, r indicates a reliability item, sp indicates a responsiveness item, and em indicates an empathy item.
Interaction Quality
Overall, I'd say the quality of my interaction with this firm's employees is excellent.
I would say that the quality of my interaction with XYZ's employees is high.
Attitude
You can count on the employees at XYZ being friendly (r).
The attitude of XYZ's employees demonstrates their willingness to help me (sp).
The attitude of XYZ's employees shows me that they understand my needs (em).
Behavior
I can count on XYZ's employees taking actions to address my needs (r).
XYZ's employees respond quickly to my needs (sp).
The behavior of XYZ's employees indicates to me that they understand my needs (em).
Expertise
You can count on XYZ's employees knowing their jobs (r).
XYZ employees are able to answer my questions quickly (sp).
The employees understand that I rely on their knowledge to meet my needs (em).
Service Environment Quality
I would say that XYZ's physical environment is one of the best in its industry.
I would rate XYZ's physical environment highly.
Ambient Conditions
At XYZ, you can rely on there being a good atmosphere (r).
XYZ's ambiance is what I'm looking for in a (sp).
XYZ understands that its atmosphere is important to me (em).
Design
This service provider's layout never fails to impress me (r).
XYZ's layout serves my purposes (sp).
XYZ understands that the design of its facility is important to me (em).
Social Factors
I find that XYZ's other customers consistently leave me with a good impression of its service (r).
XYZ's other customers do not affect its ability to provide me with good service (sp).
XYZ understands that other patrons affect my perception of its service (em).
Outcome Quality
I always have an excellent experience when I visit XYZ.
I feel good about what XYZ provides to its customers.
Waiting Time
Waiting time at XYZ is predictable (r).
XYZ tries to keep my waiting time to a minimum (sp).
This service provider understands that waiting time is important to me (em).
Tangibles
I am consistently pleased with the at XYZ (r).
I like XYZ because it has the that I want (sp).
XYZ knows the kind of its customers are looking for (em).
Valence
Directions: These questions refer to whether you think the outcome of your experience was good or bad. Please choose the number which best reflects your perception of whether your experience was good or bad.
When I leave XYZ, I usually feel that I had a good experience (r).
I believe XYZ tries to give me a good experience (sp).
I believe XYZ knows the type of experience its customers want (em).
Service Quality
I would say that XYZ provides superior service.
I believe XYZ offers excellent service.
Examples from the Qualitative Research of the Nine Subdimensions as Determinants of Service Quality
Attitude
- "The receptionist seemed very snotty. The nurse and doctor were nice, but the attitude of the receptionist ruined my visit."
- "The attitude and personality of the service personnel definitely influenced my opinion. They were very friendly and warm."
Behavior
- "When I told the waitress that my order was wrong, she apologized and promptly brought over the correct order. Excellent service!"
- "The one incident that stands out in my mind was when some money had fallen out of my pocket.... The workers ran me down to give me my money back."
Expertise
- "The first person I spoke with did not seem to know much about what I needed. Even after I had finally selected the tires I wanted, he did not seem to be experienced in entering the data in the computer. My perception of the personnel was directly affected by their lack of knowledge of what they sold."
- "The service personnel really knew their jobs. They were very knowledgeable about the park and knew the answers to all of my questions."
Ambient Conditions
- "The office was peaceful and tranquil. It had nice plants all over, and they had beautiful soft music in the background that calmed my nerves a bit."
- "The photo shop was decorated with bright lights and lots of color. This caught my eye and evoked warm, comfortable feelings."
Design
- "The doctor's office was clean, but people were sitting too close to each other. Germs were spread whenever someone sneezed or coughed. But the examination rooms were spread out and private, so I felt comfortable talking to the doctor."
- "The photo processing area was located in an inconvenient area. It was at the front of the store where all of the traffic was coming in. This made it hard to organize a line."
Social Factors
- "Another factor that influenced my perception of the service surroundings was the volume of business. I equate volume with quality; the more people inside, the higher the quality of the restaurant."
- "It wasn't a pleasant experience, since everyone there was pretty unhygienic."
Waiting Time
- "The amount of time that it took to receive my car was the determining factor of my evaluation of the service outcome, which was a poor one. Even though the car was in great condition, the length of time it took to do it was unreasonable."
- "Overall I didn't like the outcome because it took the doctor only three minutes to check me, and I had to wait three hours just to see him."
Tangibles
- "The operation was a success. There were no scars."
- "The quality of the whole park left an impression on me. The food was great, all the rides were clean, and I left with some nice souvenirs."
Valence
- "I didn't like the experience because I didn't feel well. I think if I had felt better, I would have had a better time."
- "I would have had more fun if my kids had behaved better. They made it difficult for me to have a good time."
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Scale Statistics
A= Variable
B= INTQ
C= OUTQ
D= PHYQ
E= WT
F= TANG
G= VAL
H= ATT
I= BEH
J= EXP
K= AMB
L= DES
M= SOC
N= SQ
A B C D E F G H
I J K L M N
INTQ .87 .49 .25 .14 .15 .45 .64
.59 .50 .31 .23 .30 .52
OUTQ .70 .90 .36 .19 .21 .58 .52
.52 .56 .42 .29 .36 .58
PHYQ .50 .60 .85 .06 .12 .37 .27
.26 .29 .64 .49 .30 .36
WT .38 .44 .25 .82 .26 .26 .15
.19 .18 .08 .11 .20 .16
TANG .39 .46 .34 .51 .91 .37 .14
.18 .15 .09 .10 .23 .28
VAL .67 .76 .61 .51 .61 .89 .45
.50 .50 .42 .34 .42 .62
ATT .80 .72 .52 .39 .38 .67 .93
.76 .61 .36 .27 .34 .50
BEH .77 .72 .51 .44 .42 .71 .87
.92 .66 .35 .28 .36 .53
EXP .71 .75 .54 .42 .39 .71 .78
.81 .91 .40 .30 .35 .50
AMB .56 .65 .80 .26 .30 .65 .60
.59 .63 .93 .56 .36 .44
DES .48 .54 .70 .33 .32 .58 .52
.53 .55 .75 .85 .41 .34
SOC .55 .60 .55 .45 .48 .65 .58
.60 .59 .60 .64 .72 .49
SQ .72 .76 .60 .49 .53 .79 .71
.73 .71 .66 .58 .70 .90
Notes: Intercorrelations are presented in the lower triangle of
the matrix. The content reliability of each scale is depicted in
boldface on the diagonal. Shared variances in percentage form are
given in the upper triangle of the matrix. INTQ = interaction
quality, OUTQ = outcome quality, PHYQ = physical environment
quality, WT = waiting time, TANG = tangibles, VAL = valence, ATT
= attitude, BEH = behavior, EXP = expertise, AMB = ambient
conditions, DES = design, SOC = social factors, and SQ = service
quality.
Summary Measurement Results
A= Variable
B= Mean
C= Standard Deviation
D= Average Variances Extracted
E= Parameter Estimates
F= t-Values
Interaction quality 4.76 1.43 77% .86-.90 35.23-37.47
Attitude 4.65 1.48 82% .86-.93 36.33-41.16
Behavior 4.63 1.40 80% .87-.91 36.88-39.42
Expertise 4.75 1.38 77% .84-.90 34.74-38.39
Physical environment
quality 4.83 1.45 84% .90-.93 38.37-40.40
Ambient conditions 4.76 1.46 82% .90-.92 38.71-40.12
Design 4.84 1.27 66% .73-.87 27.56-35.39
Social factors 4.57 1.11 47% .62-.72 22.06-26.14
Outcome quality 4.87 1.56 81% .87-.93 36.42-40.09
Waiting time 4.84 1.37 61% .60-.86 20.79-33.53
Tangibles 4.66 1.43 76% .85-.90 34.91-38.16
Valence 5.12 1.32 73% .73-.91 27.83-39.49
Service quality 4.85 1.43 81% .87-.93 36.60-40.21
Notes: n = 1133; c2 = 2442, 482 degrees of freedom; RMSR = .05;
NFI = .94; CFI = .95; DELTA2 = .95; RNI = .95.FIGURE 3
Structural Equation Results
A= Model (n=1133)
B= X2
C= Degrees of Freedom
D= RMSR
E= NFI
F= CFI
G= 2
H= RNI
Model 1: test of the
primary dimensions 67.27 14 .01 .99 .99 .99 .99
Model 2: test of
the subdimensions 2260.24 312 .07 .92 .93 .93 .93
Model 3: test of
the overall model 3801.38 545 .07 .90 .91 .92 .92
Model 3a: the
respecified model 3604.02 544 .06 .91 .92 .92 .92
GRAPH: FIGURE 1: Conceptualizations Advanced in the Literature
GRAPH: FIGURE 2: The Research Model
GRAPH: FIGURE 3: Test of the Second-Order Factor Model
GRAPH: FIGURE 4: Test of the Subdimensions
~~~~~~~~
By Michael K. Brady
Michael K. Brady is Assitant Professor of Marketing, Carroll School of Management, Boston Colleg
J. Joseph Cronin Jr. is Professor of marketing, Florida State University.
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Record: 141- Sources and Financial Consequences of Radical Innovation: Insights from Pharmaceuticals. By: Sorescu, Alina B.; Chandy, Rajesh K.; Prabhu, Jaideep C. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p82-102. 21p. 10 Charts, 6 Graphs. DOI: 10.1509/jmkg.67.4.82.18687.
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Sources and Financial Consequences of Radical
Innovation: Insights from Pharmaceuticals
Radical innovations are engines of economic growth and the focus of much academic and practitioner interest, yet some fundamental questions remain unanswered. The authors use theoretical arguments on the risk associated with radical innovations, and the resources needed for them, to answer the following questions on the sources and financial consequences of radical innovation: ( 1) Who introduces a greater number of radical innovations: dominant or nondominant firms? ( 2) How great are the financial rewards to radical innovations, and how do these rewards vary across dominant and nondominant firms? ( 3) Is it only a firm's resources in the aggregate or also its focus and leverage of resources that make its innovations more financially valuable? and ( 4) Which are more valuable: innovations that incorporate a breakthrough technology or innovations that provide a substantial increase in customer benefits? The authors pool information from a disparate set of sources in the pharmaceutical industry to study these questions. Results indicate that a large majority of radical innovations come from a minority of firms. The financial rewards of innovation vary dramatically across firms and are tied closely to firms' resource base. Firms that provide higher per-product levels of marketing and technology support obtain much greater financial rewards from their radical innovations than do other firms. Firms that have greater depth and breadth in their product portfolio also gain more from their radical innovations.
Truly innovative products are important engines of economic growth. Firms ramp up their research budgets in the hope of discovering the next blockbuster product before their competitors do. Financial analysts keep a close eye on firms' product pipelines in the hope of finding the next soaring company stock. Who succeeds at the radical innovations game? Which firms introduce these radical innovations and which firms gain most from them?
Questions such as these have inspired generations of writers attempting to document the sources and consequences of radical innovation (Smith and Alexander 1988; Teitelman 1994). Since Schumpeter (1934, 1942) pondered whether small or large firms are the main sources of radical innovations, the debate on the relationship between firm size and innovativeness has become the second largest body of literature in industrial organization economics (Cohen 1995). Radical innovation has also been the focus of study in marketing and management research (e.g., Chandy and Tellis 2000; Gatignon and Xuereb 1997; Henderson 1993; Olson, Walker, and Ruekert 1995; Stringer 2000).
Although knowledge of radical innovation has improved considerably during the past several years, some persistent limitations in the research remain. These limitations are both conceptual and methodological in nature. Conceptual limitations involve the range of questions addressed in the research thus far. Methodological limitations involve potential problems in the data and methods used in the research. Our study is motivated by calls to address the methodological issues (Fisher and Temin 1973; Scott 1984) and to explore a hitherto unexplored set of questions regarding the valuation of radical innovations (Wind and Mahajan 1997).
Conceptually, although research has explored the antecedents of radical innovations, virtually nothing is known about their performance and financial value. Firms spend billions of research and development (R&D) dollars in trying to create radical innovations. For example, the cost of developing a blockbuster drug that involves a completely new technology has been estimated between $250 million and $350 million (Van Arnum 1998). However, there remain nagging suspicions that the returns to innovation may be scarce (Fortune 2000; Golder and Tellis 1993).
All that is known thus far is that radical innovations are more valuable than incremental ones (Chaney, Devinney, and Winer 1991), but do firms gain more from products that involve a substantially new technology or from products that respond to an unfulfilled consumer need? Furthermore, do some firms gain more from their products than other firms do? Just as there are reasons some firms are better at generating radical innovations, there may be reasons some firms gain more from them. Such reasons, which might include the resources firms own and the ability to protect and leverage new products, have been unexplored thus far.
Methodologically, one of the thorniest problems in the study of radical innovation is also one of the most fundamental: how to determine whether an innovation is truly radical. Research has used one of two methods, surveys and retrospective coding, to assess radical innovation. Researchers who use the survey method typically provide respondents with a definition of radical innovation and ask them for an evaluation of the extent to which their firm is radically innovative (e.g., Chandy and Tellis 1998; Ettlie and Rubinstein 1987; Gatignon and Xuereb 1997). Thus, survey-based studies essentially end with managers' word on whether the firm has introduced or will introduce radical innovations. Therefore, this type of data potentially suffers from self-report bias in measuring innovation (Price and Mueller 1986). Innovation is a desirable outcome, and managers may, consciously or unconsciously, believe there is a need to appear more innovative than they really are. This need not be a problem if all managers are equally prone to this bias and the research questions simply involve comparisons across firms. However, there may be reason to believe that responses from some firms (e.g., those for which innovation is an explicit corporate goal) are more prone to this bias than others are.
A different kind of bias is possible when retrospective coding is used to assess the radicalness of the innovation: memory and retrospection bias (Golden 1992; Golder and Tellis 1993). Researchers who use retrospective coding typically provide a panel of experts a definition of radical innovation and a sample of products introduced at varying points in time and then ask the panel for an evaluation of the extent to which each product is radically innovative (e.g., Blundell, Griffith, and Van Reenen 1999; Pavitt, Robson, and Townsend 1987). However, products that failed may have faded from memory, or their failure may bias the way the coders evaluate their innovativeness (e.g., Louie, Curren, and Harich 2000). Alternatively, radical innovations that have been widely adopted and are an integral part of the current commercial landscape may be taken for granted and may not be perceived as radical as they truly were on introduction. For example, the sewing machine now seems a prosaic piece of household machinery; however, in 1841, when Barthélémy Thimonnier first introduced the machine, it was a revolutionary product (Cook 1922; Cooper 1976). Upon hearing of the machine, Parisian tailors were so threatened by it that they burned the army tailoring shop where 80 of the machines had been first installed. Thimmonier himself barely escaped with his life (Cooper 1976).
This research attempts to address the preceding outlined conceptual and methodological limitations. We study a broad array of research questions using a unique data set from the pharmaceutical industry that spans ten years (1991-2000). Pharmaceuticals is a knowledge-intensive industry; moreover, innovation is its lifeblood (Gambardella 1995; Scherer 2000). In these respects, pharmaceuticals is similar to other industries (e.g., consumer electronics, fiber optics, semiconductor manufacturing) that are commonly studied in the context of innovation (e.g., Chandy and Tellis 2000; Dekimpe and Hanssens 1999; Dutta, Narasimhan, and Rajiv 1999). Findings from pharmaceuticals research may have implications for other knowledge-intensive and innovation-based industries (see Blundell, Griffith, and Van Reenen 1999). Moreover, pharmaceuticals provide rich sources of data that do not suffer from self-report and retrospective coding concerns and that enable the study of hitherto understudied research questions.
This article addresses the following questions about the sources and financial consequences of radical innovation: ( 1) Who introduces a greater number of radical innovations: dominant or nondominant firms? ( 2) How great are the financial rewards to radical innovations, and how do these rewards vary across dominant and nondominant firms? ( 3) Is it only a firm's resources in the aggregate or also its focus and leverage of resources that make its innovations more financially valuable? and ( 4) Which are more valuable: innovations that incorporate a breakthrough technology or innovations that provide a substantial increase in customer benefits? To address these questions and to link the two issues of the sources and financial consequences of radical innovation, we develop a single theoretical framework centered on the concepts of risk and resources. We distinguish three types of innovations: market breakthroughs, technological breakthroughs, and radical innovations. We measure financial consequences by examining how stock market returns vary across firms and across innovations.
By using stock market measures, we attempt to contribute to the recent stream of research on the marketing-finance interface. Srivastava, Shervani, and Fahey (1998, p. 2) note that "marketers [can no longer] afford to rely on the traditional assumption that positive product-market results will translate automatically into the best financial results." By adopting a forward-looking, stock market measure of the financial impact of radical innovations, we respond to recent calls to adopt performance metrics that can be related directly to shareholder value (e.g., Day and Fahey 1988; Srivastava, Shervani, and Fahey 1999). Our stock market measure, as we discuss in the "Method" section, also has considerable managerial significance, thus increasing the relevance of our findings.
Definitions
Radical innovations. Chandy and Tellis (1998) review the literature on radical innovation and note that two common dimensions underlie most definitions of the construct--that is, ( 1) the extent to which the product incorporates a new technology and ( 2) the extent to which it fulfills key customer needs better than existing products do. They propose a taxonomy that differentiates innovations along these two dimensions (Table 1). According to this taxonomy, a radical innovation is a product that is high on both the technology and the market dimension; it involves a substantially different technology while offering a substantial increase in customer benefits. A market breakthrough provides substantially greater benefits than existing products, but its core technology is not significantly new. A technological breakthrough uses a substantially different technology than existing products without considerably increasing the benefits to consumers. We adopt Chandy and Tellis's taxonomy of innovations along the technology and market dimensions; this taxonomy is consistent with many other definitions of the "newness" of an innovation (see Garcia and Calantone 2002). We conduct our study in a context that enables us to differentiate empirically among all three types of breakthroughs.
Dominance. We define dominance as the level of market power a firm wields (e.g., Scherer 1980). Authors have historically equated dominance with market share (see Szymanski, Bharadwaj, and Varadarajan 1993). However, more recently, some authors have noted that there is more to dominance than a firm's share of sales in a particular market (e.g., Borenstein 1990, 1991; Pleatsikas and Teece 2001). This broadened view of dominance incorporates three dimensions: ( 1) market share, which reflects revenue from the firm's current position in the market; ( 2) assets, which reflect the tangible and intangible factors that the firm can bring to bear on the market (Borenstein 1990); and ( 3) profits, which reflect the financial resources the firm can bring to bear on the market (Borenstein 1991). Our definition and measures incorporate this more recent, multidimensional view of dominance, because each of the three dimensions could independently influence the resources that a firm brings to its innovation activity. Thus, market share could provide firms with brand equity that they can leverage to stimulate adoption of their innovations, and profits could ensure that firms have adequate financial resources to develop and support innovations. Because firms may vary in the extent to which they dominate in each of these dimensions and because these dimensions may bring different benefits, it is necessary to account for firms' dominance on all three dimensions together (e.g., Pleatsikas and Teece 2001).
Financial value. We assess the financial value of radical innovations using the net present value (NPV) of the future cash flows expected from the innovation (Ross, Westerfield, and Jaffe 1999). The NPV is a fundamental criterion for appraising investment projects and has been widely used by academics and practitioners (Fisher 1965; Ross, Westerfield, and Jaffe 1999). By definition, in our context NPV captures the expected value of all future discounted cash flows generated by an innovation. Therefore, it is a forward-looking measure of the overall value of an innovation as reflected in the stock market's expectation of the success of the product and the level of profits it will generate.
Product support and product scope. In addition to dominance, we examine whether firms' focus and leverage of resources make their innovations more financially valuable. We use two concepts to capture firms' focus and leverage of resources: product support and product scope. We define product support as a firm's per-product marketing and technology expenses. Marketing and technology resources have been frequently linked, often in conjunction, to the success of new products (e.g., Cooper and Kleinschmidt 1987; Moorman and Slotegraaf 1999; Song and Parry 1997). Product support reflects a firm's ability to protect and support an innovation on the market. We define product scope as the extent of a firm's product portfolio within an industry. Product scope encapsulates both breadth and depth of the product portfolio; as such, it reflects the leveraging opportunities of the radical innovation within the firm.
Theoretical Framework
We organize our theoretical arguments around the two fundamental concepts of risk and resources. Risk refers to the uncertainty associated with a course of action (e.g., Singh 1986): A product is deemed risky if there is high uncertainty associated with its outcomes. There may be a higher risk associated with a radical innovation than with an incremental product (see Golder and Tellis 1993; Robinson and Min 2002; for a different view, see Kleinschmidt and Cooper 1991), and this risk is apparent at two stages.
First, at the development stage, there is uncertainty associated with when and whether a process directed at creating breakthroughs will materialize into actual, ready-for-market innovations. Firms can encourage cutting-edge research by dedicating sizable resources to R&D, but they cannot command or even predict the moment when a scientist's mind will conceive of a product beyond the frontier of existing knowledge. Second, at the introduction stage, there is uncertainty associated with the extent and time frame of consumers' adoption of the product (Griffin 1997). In particular, firms involved in radical innovation face both an unknown probability of their products' success (i.e., the likelihood of extracting cash flows from the products) and an unknown extent of their products' success (i.e., the expected magnitude of the cash flows to be extracted from the products).
Which firms can better handle these risks? Firms that can spread risks over a larger asset or product base face lower costs in raising money to develop or introduce a radical innovation. In addition, firms with more resources are in a better position to bear the costs and support radical innovation (Cohen and Klepper 1996). Resource-rich firms may have a greater ability to absorb, interpret, and commercialize critical information on a timely basis, which in turn can lower the risks that the firm faces (Lane and Lubatkin 1998). Moreover, at the introduction stage, marketing and organizational resources can help the firm stabilize and increase the cash flows resulting from radical innovations.
The previous arguments point to a relative advantage of dominant firms, both in terms of who introduces and who gains more from radical innovations. But dominance and aggregate resources may tell only part of the story. Indeed, the literature in strategy and organizational theory emphasizes that the deployment of resources is as valuable as their magnitude (see Barney 1991; Makadok 2001). In addition to dominance, we highlight two aspects of resource deployment: ( 1) product support, or the extent to which individual products are supported with marketing and technology resources on introduction (i.e., firms' per-product levels of marketing and technology investments), and ( 2) product scope, or the extent of the product portfolio over which the radical innovation can subsequently be leveraged.
Who Introduces More Radical Innovations?
The literature presents conflicting conclusions about whether dominant or nondominant firms are better at radical innovation (see Cohen 1995; Stringer 2000). Some researchers argue that dominant firms tend to be more bureaucratic (Tornatzky and Fleischer 1990) and find it difficult to adapt and reinvent themselves when the technological environment changes. Alternatively, they may fail to evaluate the long-term market potential of new technology because the very basis of competition changes with it (Christensen 1997; Stringer 2000). Some organizational theorists also suggest that the research efforts of dominant firms are less productive than those of new entrants because dominant firms fail to update their set of "information-processing assets" or to develop new ones (Arrow 1962; Nelson and Winter 1982). Furthermore, dominant firms may be less likely to introduce innovations because such innovations have the potential to decrease the rents such firms extract from their current products (Chandy and Tellis 1998).
If bureaucracy, myopia, and reluctance to change the status quo prevent dominant firms from introducing innovations in general, these should be even stronger deterrents of radical innovations. However, recent empirical research suggests the opposite. Using a retrospective coding of 64 radical innovations in two industries, Chandy and Tellis (2000) conclude that though small firms and new entrants introduced more radical innovations before World War II, this trend has reversed more recently. What explains this change? In the following paragraphs, we propose some reasons that dominant firms may introduce more radical innovations than do other firms.
Radical innovations and the technology necessary to generate them have become increasingly complex, and their undertaking requires sizable resources (e.g., Mowery and Rosenberg 1998; Teitelman 1994). Dominant firms have greater technological, financial, and market-related resources, which put such firms in a better position than nondominant firms to handle the risks associated with radical innovation. Specifically, dominant firms enjoy economies of scale and scope both in R&D (Scherer 1980; Teece 1980) and in marketing (Comanor 1965). Economies of scale in R&D entail a more efficient use of research resources, which in turn enables firms to dedicate a larger fraction of resources to uncertain projects. Economies of scope and the synergies they imply may lead to a greater base of ideas that can be combined and materialized into new products. A greater knowledge base is also likely to be associated with higher absorptive capacity, that is, the ability to recognize the value of new information, assimilate it, and apply it to commercial ends (Cohen and Levinthal 1990). This suggests that radical innovations are more likely to arise from well-funded, sophisticated research labs where many top scientists spend their days putting together the technologies of the future. Such labs are more likely to be found in dominant firms, which have the critical mass for research and often have entire divisions dedicated to pioneering research.
Dominant firms also have better financial resources than do nondominant firms. They have greater access to funds to finance the risky pursuit of radical innovation, and they can spread these risks over a large volume of sales (Arrow 1962; Comanor 1965). In contrast, nondominant firms may not get second chances; their first failure may be their last, as has often been shown to be the case with small firms (Dunne, Roberts, and Samuelson 1989).
Finally, economies of scale and scope in R&D suggest that dominant firms are able to diversify their research portfolios and introduce more of all types of breakthroughs: technological, market, and radical innovations. Although their technical capabilities help dominant firms create technological breakthroughs, the better understanding of the market and customers they obtain while building their market power offers them a competitive advantage in creating market breakthroughs.
For all these reasons, the necessity of handling the riskiness of radical innovations and their increased complexity, we expect the advantages of resources available to dominant firms to outweigh the pitfalls of their bureaucracy and inertia. Thus:
H1: Dominant firms introduce significantly more (a) radical innovations, (b) technological breakthroughs, and (c) market breakthroughs than do nondominant firms.
Who Gains More from Radical Innovations?
Innovate or Die? Sorry, that misses the point. There's actually an innovation glut. The real shortage is profits.
-- Fortune 2000
Recent research indicates that new product introductions can have a positive impact on the market value and profitability of firms (e.g., Blundell, Griffith, and Van Reenen 1999; Geroski, Machin, and Van Reenen 1993) and that the more innovative these products are, the greater their financial value is. For example, Chaney, Devinney, and Winer (1991) find that original new products have a greater financial value than updates of existing products, and Kleinschmidt and Cooper (1991) find that highly innovative products surpass moderately innovative products in terms of their success rate and return on investment.
However, firms may not gain equally from innovation. Our thesis is that it is not only what is introduced that matters, but also who introduces it. Investors value a new product on the basis of how successful they expect the firm to be in commercializing it; specifically, investors evaluate the likelihood of success and the level of success they expect the radical innovation to attain.
The product's level of success is based on the magnitude of the net cash flows that it can generate relative to the investment made in the product. These cash flows depend, in turn, on the tangible and intangible resources the firm can deploy to sustain and protect the innovation. In particular, dominant firms have greater marketing resources, such as advertising and promotional budgets, which can sustain the innovation and increase the adoption rate of the new product (Chandy and Tellis 2000). Because of dominant firms' involvement with previous generations of products, they are likely to have built a better knowledge base and a stronger set of market-based assets (Srivastava, Shervani, and Fahey 1998). Market-based assets such as brand equity can reduce the perceived risk that consumers associate with radical innovations (see Dowling and Staelin 1994). Dominant firms can also stimulate the adoption rate through superior access to distribution channels (Mitchell 1989).
Financial markets evaluate the product's likelihood of success on the basis of how well the firm that introduces it can handle the uncertainty of the cash flows the product is expected to generate. Resources can both increase the magnitude and reduce the uncertainty of the cash flows that an innovation is expected to generate. Alternatively, this uncertainty is related to the perceived riskiness of the firm. The literature in finance and industrial organization suggests that dominant firms face less risk and that the market uses a smaller discount rate when evaluating such firms' future prospects (e.g., Aldrich and Auster 1986). Although lower perceived risk is mainly an indication of the stability of the firm, it is also an indication of its access to future resources. Even if current resources are not sufficient to sustain a radical innovation, dominant firms are better positioned than nondominant firms to augment these resources through credit markets. Specifically, nondominant firms face a greater disadvantage than do dominant firms in the cost of external sources of funds. Evidence from federal credit surveys suggests that, on average, small firms are more likely to face credit rationing (i.e., higher interest rates and smaller loans), which can impair growth or even lead to failure after the introduction of a new product (Scanlon 1984).
In summary, better current financial and organizational resources and easier access to future resources put dominant firms in a better position than nondominant firms to undertake the risks of radical innovations, market breakthroughs, and technological breakthroughs. Thus:
H2: Radical innovations, technological breakthroughs, and market breakthroughs introduced by dominant firms are valued more highly than are those introduced by nondominant firms.
In the following paragraphs, we explore other factors that, in addition to dominance (and aggregate resources), have an impact on the value of radical innovations.
Aggregate Resources Alone Do Not Tell the Full Story
Previously, we argued that the financial value of a radical innovation depends not only on the intrinsic advantages of the product over competing alternatives but also on how well positioned the firm is to exploit these advantages (Kelm, Narayanan, and Pinches 1995). Therefore, the greatest economic returns go to firms that can extract the most rents from their products. We now highlight the concepts of product support and product scope to argue that it is not only resources in the aggregate that provide a competitive advantage to the firm but also the firm's ability to focus and leverage its resources.
Product Support
We previously argued that greater marketing and technology resources are a reason the radical innovations introduced by dominant firms are valued more highly than are those introduced by nondominant firms. However, some dominant firms spread these resources over a greater number of products. This suggests that in addition to aggregate resources, it is also necessary to examine the per-product level of resources deployed by the firm, or product support. Product support addresses the firm's commitment to individual products rather than its commitment to its entire product portfolio.
The role of marketing and technology investments in the success of new products is well documented (e.g., Cooper and Kleinschmidt 1987; Yeoh and Roth 1999). These investments can build brand equity and create barriers to entry for competitors. Specifically, marketing builds awareness, which is essential for the success of a product that is completely new to consumers. Similarly, investors can view technology investments that are associated with the product (as reflected in patents and R&D spending) as evidence of higher quality, which in turn is associated with higher market value (Aaker and Jacobson 1994). Furthermore, a strong set of patents indicates that the firm's products are well protected from the early entry of competitors, which means the firm will generate cash flows for a longer period of time (Bunch and Smiley 1992). However, investors evaluate innovations one at a time; therefore, in addition to evaluating a firm's overall set of patents, they also value how well each innovation is protected by patents. This again highlights the importance of viewing resources on a per-product basis in addition to doing so at an aggregate level.
In addition to their individual effects on financial value, marketing and technology investments may also play a joint role. Moorman and Slotegraaf (1999) predict that both marketing and technology capabilities must be present for effective product development. Similarly, Dutta, Narasimhan, and Rajiv (1999, p. 547) find that "the most important determinant of a firm's performance is the interaction of marketing and R&D capabilities." Indeed, the value of marketing in supporting a new product will be diminished if the product has a shorter lifetime because it is not protected by a strong set of patents. Similarly, a strong set of patents alone cannot increase the sales of a radically new product if the marketing resources necessary to create awareness and increase the speed of adoption are lacking.
Overall, the preceding reasoning suggests that investors recognize product support as a source of competitive advantage for firms that introduce radical innovations. Thus:
H3: Radical innovations, technological breakthroughs, and market breakthroughs introduced by firms with high product support are valued more highly than are those introduced by firms with low product support.
Product Scope
Theory about product sequencing (Helfat and Raubitschek 2000) suggests that the creation of new products depends on both existing products and the underlying path-dependent knowledge and capabilities of a firm. A radical innovation is a real option (e.g., Brown and Eisenhardt 1997) and an avenue of "preferential access to future opportunities" (Bowman and Hurry 1993, p. 762). A firm with a broad product portfolio offers more opportunities for the radical innovation to be extended or leveraged, perhaps by developing other products based on the technology or simply by cross-selling the innovation with other products, and as such can increase the future cash flows that are expected from the innovation.
A greater product scope involves both depth and breadth of expertise. A broad product scope is an indication of greater expertise in dealing with new products in various settings and of better ability to adapt the strategy for commercialization of each radical innovation. Firms that extend their product portfolio in related areas by building on their current knowledge base have been shown to obtain economies of scale as well as synergies based on exchanges and transfers of skills and resources from one category to another (Aaker 1984). A firm with high product scope not only has more opportunities to leverage the new product in one of its areas of expertise (because of breadth) but also is more likely to have the ability to leverage the product (because of depth). This ability to exploit synergies can extend the commercial life of the radical innovation and lead to more successful extensions, thus making the innovation more valuable.
A narrow product scope may also signal to investors that the firm has a deeply embedded knowledge set and that its core competence, though well defined, is limited and associated with a certain rigidity in dealing with projects outside the scope of the core competence (Leonard-Barton 1992). Such a firm may lack either the ability to identify all areas in which the radical innovation can be leveraged or the expertise to leverage it. Furthermore, if the firm's product scope is narrow and the new product is introduced within the firm's current scope, the risk of cannibalization increases. If the new product is introduced outside of the firm's narrow scope, investors may fear that the firm has limited experience in the new domain. This assessment would be reflected in the stock market's evaluation of the product. Thus:
H4: Radical innovations, technological breakthroughs, and market breakthroughs introduced by firms with high product scope are valued more highly than are those introduced by firms with low product scope.
Are All Breakthroughs the Same?
At first, it may appear that market breakthroughs are more highly valued by investors than are technological breakthroughs, because their benefits are likely to be more apparent to consumers. But market breakthroughs are often not technologically advanced, or they involve technology that is no longer new, and as such they are easier to imitate than are technological breakthroughs. Thus, the economic rents that the firm can extract from market breakthroughs may be short lived.
Although there is more uncertainty associated with technological breakthroughs, they are much more likely to be further leveraged than are market breakthroughs. Firms that initiate technological changes have been shown to grow more rapidly than other firms (Geroski, Machin, and Van Reenen 1993). Technological breakthroughs carry the promise of this growth, and investors will view them both as platforms for future product introductions and as signals that the firm is committed to and successful in the innovation process. They are "options" (Bowman and Hurry 1993; Sharp 1991) in the sense that they can offer new strategic choices for the firms should the opportunity to leverage the technology in these products arise.
Introducing market breakthroughs that are not technological breakthroughs may, in turn, signal a commitment for incremental innovation and may position the firm as an entity that exploits existing knowledge rather than one that strives to extend the frontier of knowledge (see Cohen and Levinthal 1990). Thus:
H5: Technological breakthroughs are valued more highly than are market breakthroughs. Radical innovations are valued the highest.
Overall, our hypotheses indicate our search for insights into the sources of radical innovations and the financial gains they generate. Drawing on marketing, strategy, and industrial organization, the hypotheses highlight the role of risk and resources in determining the sources and consequences of radical innovation. Any tests of the preceding hypotheses should take into account some of the limitations of existing research on radical innovation: small, convenience samples; potential self-report bias; and bias introduced by retrospective coding. The following section describes the data and methods we used, including several novel features that can help alleviate some of the methodological problems of prior research.
This section presents an overview of the data and empirical context of the article and describes how we translated each of our conceptual variables into empirical measures and how we specified the models in the empirical analysis.
Data and Empirical Context
To test our hypotheses, we need ( 1) a comprehensive sample of radical innovations, ( 2) objective measures of the radicalness of innovations, ( 3) a measure of the financial value of innovations at the time the innovation is introduced, and ( 4) a context with adequate variation in resources across firms but that nevertheless allows for comparability in radical innovations across firms.
The pharmaceutical industry is a context that meets these requirements well. Because the Food and Drug Administration (FDA) has closely documented the pharmaceutical industry since 1939, researchers have access to a uniquely rich trove of carefully compiled historical data. The industry is driven by innovations, yet there is enough variation in firms' resources to enable us to study their effects on the sources and consequences of radical innovations. Moreover, pharmaceuticals form a pillar of the national economy, and innovations in this industry can literally make the difference between life and death for individual consumers (Scherer 2000).
Restriction of the empirical context to a specific industry allows for a degree of comparability between radical innovations that would be impossible to obtain in a cross-industry study. A comparison of Viagra to microwave ovens, for example, is not an easy (or advisable) task. In the interest of internal validity and given the lack of objective classifications in other industries, we concentrate on pharmaceuticals as our empirical context. In doing so, we follow in a long tradition of marketing researchers who have chosen this industry as their empirical context (e.g., Dekimpe and Hanssens 1999; Gatignon, Weitz, and Bansal 1990; Rangaswamy and Krishnamurthi 1991).
For the purposes of this research, perhaps the most attractive feature of the pharmaceutical industry is that it enables us to distinguish between incremental innovations, market breakthroughs, technological breakthroughs, and radical innovations using an external, objective classification system. The FDA classifies new drugs along two dimensions at the time of approval: therapeutical potential and chemical composition. On the basis of their therapeutical potential, drugs are classified into two classes: priority review drugs, which represent a therapeutical advance over available therapy, and standard review drugs, which have therapeutical qualities similar to those of an already marketed drug. On the basis of their chemical composition, drugs are classified as either new molecular entities (NMEs) or drugs that are either new formulations or have new indications of use. The NMEs are the most technologically advanced products, because they are based on an active ingredient that has never been marketed before. Table 2 presents the FDA definitions of these categories and the operationalizations of the two types of breakthroughs and radical innovations.
The two dimensions of the FDA classification coincide precisely with the two dimensions in our classification of product innovation. Specifically, the FDA's therapeutical potential dimension corresponds to our customer benefits dimension, and the chemical composition dimension corresponds to our product technology dimension. Recall that a radical innovation is a product that involves a substantially new technology and provides substantially greater customer benefits than do existing products. A market breakthrough provides substantially greater customer benefits, but its core technology is not substantially new. A technology breakthrough uses a substantially different technology than existing products but does not provide substantially greater customer benefits. On the basis of these definitions, we classify product innovations as follows:
• innovations: priority review and NME,
• breakthroughs: priority review and non-NME, and
• breakthroughs: standard review and NME.
Our sample is based on a census of innovations from 1991 to 2000 that we obtained from the NDA Pipeline.( n1) The total number of products introduced in that period that were market breakthroughs, technological breakthroughs, or radical innovations was 380. We were able to retrieve accounting and financial information for 255 innovations (226 of these had complete data on all measures of dominance, and 212 had complete measures on both dominance and stock market data). We eliminated 17 observations from the analysis because of confounding effects of firm announcements unrelated to the approval of the drug. Specifically, we checked for equity offerings, earnings, dividends, and mergers and acquisitions announcements made in the time window used in the NPV measure that could have distorted the abnormal returns.
The 255 breakthroughs in our sample were introduced by 66 publicly traded firms. The total number of new products introduced by these firms from 1991 to 2000 was 3891. This number underlines the fact that breakthroughs are rare; they represent less than 7% of the total number of new introductions. The breakthroughs and radical innovations that we excluded from our sample are from divisions of large conglomerates, private firms, firms that were no longer in business in 2000 (and for which financial data are unavailable), or joint ventures (Table 3). The figures presented in Table 3 indicate that our focus on public companies does not cause us to disproportionately include innovations by dominant firms in our sample. The innovations that we dropped from our sample that nondominant firms introduced are roughly equal in number to the dropped innovations that dominant firms introduced.
The 66 firms in our sample have headquarters in seven countries: the United States, the United Kingdom, France, Belgium, Switzerland, Germany, and Japan. Four of the firms in the data set were acquired before 2000. For those firms, we included accounting data until the year of their acquisition; we treated data in the remaining years as missing. Testing the hypotheses required compiling data from 14 different databases. Table 4 lists the variables and the sources of data used in the study.
Measures and Models
The sample is a cross-sectional time-series data set that is composed of 255 breakthroughs introduced by 66 firms over a ten-year period. We therefore must analyze an unbalanced panel of data. We also must choose appropriate econometric models to accommodate the two dependent variables of interest (the number and the financial value of radical innovations) and to account for any unobserved heterogeneity due to firm-specific effects.
As we noted previously, the literature suggests that dominance is a multidimensional construct that involves three variables: market share, assets, and profits (e.g., Borenstein 1990, 1991; Pleatsikas and Teece 2001). Principal factor analysis generates a common factor that captures information from all three components of dominance. We therefore operationalize dominance as the factor score associated with this factor. This operationalization incorporates not only the multifaceted nature of dominance but also its relationship to firm resources, one of our key theoretical constructs. Therefore, a dominant firm is more than a large firm; it is a profitable firm with resources.
Because all our data are from one industry, we use firm sales as a proxy for market share. Profits are estimated as the product of assets and return on assets. To check for robustness, we also conduct additional analyses using employees as a measure of dominance (e.g., Yeoh and Roth 1999).
Measures and Model for the Test of Who Introduces More Radical Innovations (H1)
To assess who introduces more radical innovations, the dependent variable is the count of radical innovations introduced in each year by various firms (e.g., Baltagi 2001; Blundell, Griffith, and Van Reenen 1999; Hausman, Hall, and Griliches 1984). This variable has two unique properties: It is nonnegative (i.e., a firm cannot have -5 innovations), and it involves integers (i.e., a firm cannot have 2.35 innovations). Ordinary least squares is inappropriate for count data. Moreover, the data extend over multiple years for the same firms; that is, they form a time-series cross-sectional panel. In the tradition of Blundell, Griffith, and Van Reenen (1999) and Hausman, Hall, and Griliches (1984), among others, we account for these properties using a Poisson model to test H1. The basic Poisson probability specification is
( 1) P(nit = exp(-λit)λit, sub it/nit!,
where nit is the innovation count for firm i in year t.
We model the parameter λit as a function of dominance and a set of control variables. In addition to controlling for time and the country in which the firm is based, we include two measures of overall firm innovativeness as control variables: number of incremental innovations introduced and number of patents applied for in each year. Although we do not formally hypothesize a relationship between radical innovativeness and the firm's incremental innovation output, this model enables us to explore whether radical innovations are "accidents" or part of a more substantial innovation output at the firm level. We include a dummy for country (U.S./ non-U.S.) in the model to account for any effects in valuation that may exist between U.S. and non-U.S. firms.
The panel nature of the data also enables us to control for firm-specific unobserved heterogeneity. We use a random effects model and specify the Poisson parameter as follows:
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
where
αi = a random firm-specific effect;
No.Prodit = control variable, the number of incremental products introduced in the same year with the radical innovation;
No.Patentsit = control variable, the number of patents applied for in the year the radical innovation was introduced;
Countryi = control variable, a dummy that has a value of 1 if the drug is introduced by a firm with U.S. headquarters and a value of 0 otherwise;
Year = a matrix of dummies for year of introduction;
μi = the unobserved firm-specific effect; and
μ0 = the overall intercept.
The Poisson probability specification becomes
( 3) P(nit/Xit, μi) = exp(-λitexp μi)(λitexp μi)n[sub it]/nit!,
and the joint density is
( 4) [Multiple line equation(s) cannot be represented in ASCII text]
We test for the equality of the mean and variance in the Poisson distribution and the appropriateness of a negative binomial specification as part of a robustness check for the counts model. We also check the results from a fixed-effects (rather than a random-effects) specification of unobserved heterogeneity. We report the results of these checks in a subsequent section.
Measures and Model for the Test of Who Gains More from Radical Innovations (H2-H5)
Measuring innovation valuation. Srivastava, Shervani, and Fahey (1998) argue that the key to bridging research in marketing and finance lies in examining the impact of various marketing actions and market-based assets on a firm's cash flows, which ultimately define shareholder value. In line with this argument, we assess the financial value of radical innovations using NPV, which captures the expected value of all future discounted cash flows generated by the innovation (Fisher 1965; Ross, Westerfield, and Jaffe 1999). We discuss how NPV is measured and its theoretical and managerial implications in Appendix A.
Measuring product support. Product support has two components: marketing support and technology support. We assessed marketing support on the basis of investments in the firm's sales force. (In a subsequent section, we report results from a measure of product support that also includes direct-to-consumer advertising.) Because sales force expenditures are considered the most important promotional expenditures in the pharmaceutical industry (e.g., Yeoh and Roth 1999), we obtained three measures of firm investments in the sales force: ( 1) the size of the sales force, ( 2) the number of sales calls placed by the salespeople, and ( 3) the amount of dollars that the firm has spent on its sales force. We purchased the data from Verispan, a marketing research firm that tracks the performance and investments made by pharmaceutical firms. We computed the relative size of these marketing investments to the number of new products introduced per year. Specifically, we operationalized marketing support by using the factor scores from a principal component analysis on the relative measures of sales force. Similarly, we assessed technology support on the basis of the firm's R&D expenditures and the patent support that the firm's products enjoy. We used citation-weighted patents in light of recent research that shows that citation-weighted patents are a better measure than unweighted patents of a firm's ability to appropriate returns from its innovations (Hall, Jaffee, and Trajtenberg 2000). We computed the relative size of the R&D investments and citation-weighted patent stocks to the number of new products introduced per year. We then operationalized technology support using the factor scores from a principal component analysis on the relative R&D and patents measures. We measured product support as the sum of the (standardized) marketing and technology support variables.
Measuring product scope. By definition, product scope is more than a mere measure of the diversification of a firm's product portfolio; it is a measure of the breadth of its expertise and the depth of the multifaceted knowledge that arises from introducing many innovations across multiple product categories. We therefore needed to modify existing measures of diversification to account for not only the breadth of the product portfolio but also its overall depth. One of the most commonly used measures of diversification is entropy (Varadarajan 1986):
( 5) [Multiple line equation(s) cannot be represented in ASCII text]
where
pj = Pj/P, the fraction of the firm's products in the jth product category relative to its overall product portfolio;
Pj = the number of products in a specific therapeutic category (as defined by the FDA classification of these categories) that the firm has at time t; and
P = the firm's overall number of products at time t.
The entropy measure does not differentiate among firms that have the same breadth of product portfolio but different depths. A firm with 5 products, 1 in each of five different product categories, has the same entropy as a firm with 500 products, 100 in each of five different product categories. Our conceptual definition of scope, as we noted previously, also takes into account the depth of the product portfolio, because it rests on theoretical arguments related to the firm's knowledge base. We therefore multiply the entropy measure by the overall number of products in the product portfolio to obtain a measure of product scope:
( 6) [Multiple line equation(s) cannot be represented in ASCII text]
Quantification of product scope requires the collection of data on all the innovations that are currently in the portfolio of the 66 firms in our sample. The data are from the National Drug Code (NDC) directory database. The NDC directory is an FDA-maintained database that the FDA describes as "a universal product identifier for human drugs." The NDC database not only contains all the FDA-approved drugs available in the United States but also classifies the drugs into 21 major therapeutic categories, which in turn are divided into subcategories. We use these major therapeutic categories to define each firm's product categories and to construct product scope as in Equation 6. Our data cover drugs introduced since 1970. We use a rolling window of 17 years (to correspond to the duration of the patent life) to count a product in a firm's product portfolio. We also conduct additional analyses on rolling windows of 14 and 21 years to test the robustness of the results.
Model for H2-H5
To test H2-H5, we estimated the following model (e.g., Baltagi 2001; Dutta, Narasimhan, and Rajiv 1999; Geroski, Machin, and Van Reenen 1993):
( 7) NPVikt = β0 + β1Dominanceit + β2ProductSupportit + β3ProductScopeit + β4 RIkt + β5MBkt + β6Licensedkt + β7WACCit +β8Countryi + β9NRIit + γYear + λCategory + ζi + ηit,
where
RIkt and MBkt = dummy variables with a value of 1 if the product is a radical innovation (respectively a market breakthrough) and a value of 0 otherwise; the effects of RIkt and MBkt are therefore interpreted relative to the third type of innovation, TBkt;
Licensedkt = a dummy variable with a value of 1 if the product was invented by the firm that introduced it and a value of 0 otherwise;
WACCit = cost of capital for firm i in year t (for a description of this variable, see Appendix B);
NRIit = the number of breakthrough innovations introduced by firm i in year t;
Year = matrix of dummies for the year in which the innovation was introduced;
Category = matrix of dummies for the therapeutic class to which the drug belongs; and
ζ = the unobserved firm-specific effect.
Who Introduces More Radical Innovations?
A major concern in assessing the sources of radical innovation is distinguishing between who invented the innovation and who introduced it. It is conceivable that entrepreneurs develop a radically new product but do not have the means to commercialize it and thus sell it to a larger organization. Because dominance is a central variable in our study, it is especially important to account for this possibility. The FDA data indicate only to whom the approval to market the drug was granted, and thus we also needed to determine the original source of the innovation, or its inventor. A comprehensive search that included "The Pink Sheet" (a detailed newsletter about pharmaceutical and biotechnology products, published by F-D-C Reports), the Pharmaprojects database of pharmaceutical projects, and trade press articles published while the drugs were in development enabled us to determine that the original inventors introduced 193, or approximately 75%, of the 255 breakthroughs studied; 62 breakthroughs were licensed or bought from other firms. Only 25% of the radical innovations introduced by dominant firms were licensed or acquired before FDA approval, and the rest were invented in-house by the firms. In addition, because the Bayh-Dole Act (35 U.S.C. §sect; 200-212) sets up considerable incentives in the pharmaceutical industry for commercializing university research, we also used the Pharmaprojects database to determine how many of the drugs in our database were invented at universities. We found that only 4 of the 255 drugs in our data set were invented at universities. We further address the issue of invention versus product acquisition in analyses later in this section.
H1 suggests that dominant firms introduce more radical innovations and more breakthroughs than nondominant firms do. Dominance is significant as a continuous variable in the Poisson model that predicts the count of innovations (eβ = 1.58; p < .001; for results, see Table 5). For ease of exposition, the coefficients for the time dummies are not included in Table 5. A significant covariate of the count of radical innovations is the number of incremental new products introduced in the same year as the radical innovation (eβ =1.02; p < .001). The number of patent applications submitted by the firm, a common measure of innovativeness previously used in the literature, was not significant (p = .44). Even after we accounted for whether the innovation was invented in-house or acquired, dominant firms still introduce more radical innovations. There is no significant difference in the proportion of licensed innovations introduced by dominant versus nondominant firms (likelihood ratio χ² = .51, p = .48). We obtained similar results with the same level of significance if we used ( 1) firm size (operationalized in terms of number of employees) rather than the measure of dominance reported herein; ( 2) marketing and technology support measures computed using the standardized sum of their components, rather than factor scores (for details, see Sorescu, Chandy, and Prabhu 2003); and ( 3) a fixed-effects specification of firm-specific unobserved heterogeneity rather than the random-effects specification reported herein. We also used a negative binomial model of counts rather than a Poisson model. The overdispersion parameter is not significantly different from zero; thus, the negative binomial distribution is equivalent to the Poisson distribution.
To better understand the difference between dominant and nondominant firms, we also present a bivariate categorical analysis of the innovation counts. For exposition purposes, we use a median split on dominance among our sample of 66 radical innovators.( n2) Figure 1 suggests that dominant firms introduce more than twice as many radical innovations and breakthroughs than do nondominant firms. Although we draw our data from a single industry, this result is in line with the findings of Chandy and Tellis (2000), who use data on radical innovations in different industries. The convergence in findings offers some confirmation of the external validity of our results.
Our data also indicate that dominant firms have the advantage for all three types of products studied: They introduce more radical innovations, market breakthroughs, and technological breakthroughs (Figure 2). The greatest difference arises for technological breakthroughs, which suggests the possibility of economies of scale in R&D for dominant firms.
Table 6 presents a ranking of the top 15 firms with the greatest number of breakthrough innovations. It is notable that the top 15 most innovative firms introduced 161 breakthrough innovations, more than half of all breakthrough innovations introduced in the entire 1991-2000 period. Results in Table 6 further indicate that firms that introduce more radical innovations also tend to introduce more incremental innovations. Thus, contrary to popular belief (e.g., Utterback 1996), radical innovation is not necessarily a substitute for incremental innovation: The two appear to go hand in hand among the most innovative firms.
What Is the Impact of Product Support and Product Scope on Radical Innovations?
H2 argues that dominant firms gain more from radical innovation. H2 is supported; results suggest that though a new product introduced by a dominant firm is valued at about $456 million, it is only valued at about $37 million if it is from a nondominant firm (Figures 3 and 4). This difference is significantly different from zero (p < .01). Again, if firm size (number of employees) is used rather than the composite measure of dominance, the differences between dominant and nondominant firms are even more pronounced. Dominance is also significant as a continuous variable in all three random-effects models that we tested. A ranking of the highest NPV drugs is presented in Table 7.
To estimate the effect of product support, we first ran separate principal component analyses on the three sales force measures to extract a measure of marketing support and on R&D expenditures and citation-weighted patents to extract a measure of technology support. We then computed product support as the sum of marketing and technology support. The standardized coefficients for product support (β = .38; p < .001) and product scope (β = .26; p < .05) are significant; addition of these variables to the model more than doubles the R². Therefore, H3 and H4 are supported: Greater support and scope significantly increases the financial value of a radical innovation. (Table 8 presents the results for the model with and without product support and product scope.)
Furthermore, product support and product scope can explain differences in the NPV of radical innovations even among dominant firms (firms with higher than the median value on the dominance factor score). Figures 5 and 6 show the average NPV for dominant firms with high versus low product support and product scope, respectively. These differences are statistically significant for both support (p < .05) and scope (p < .01). We report the results for product scope using a 17-year rolling window, but significance is also maintained if we use a 14- or 21-year window. We controlled for the therapeutic class to which the drug belongs; only the coefficient for the therapeutic class "diuretics" is significant. None of the year dummies is significant. To conserve degrees of freedom, we did not include the nonsignificant therapeutic class and year dummies in the final model. We also checked whether the market reaction to the drugs introduced by biotechnology firms was different from the reaction to drugs introduced by all other firms. We found no significant differences between the two types of firms. To further explore the relationship among dominance, support, and scope, we present results from four additional analyses. First, we examined the effects of product support with its components, marketing and technology support, included separately in the model. The results in Table 8 (Model 3) indicate that both components of product support, marketing support (β = .34; p < .001) and technology support (β = .49; p < .05), have a significant, positive effect on NPV. Furthermore, including the two components maintains the significance of the other relevant independent variables.( n3)
Second, we expanded the operationalization of product support by including advertising as an additional component of marketing support. In recent years, pharmaceutical firms have viewed direct-to-consumer advertising as an increasingly important marketing expenditure. However, we were able to collect advertising data for only 16 firms in our sample, corresponding to 85 innovations. We found that advertising expenditures are highly correlated with the other marketing variables: The correlation between advertising and dollars spent on sales calls is .80 (p < .001) and that between advertising and number of calls is .83 (p < .001). The sign of the coefficients and their significance levels are maintained when we added advertising to the marketing support variable in our random-effects model (see Table 9). We report the results of this analysis separately, because advertising data are available only for a limited subsample of firms and because its inclusion does not substantively modify the results.
Third, we also checked for any significant interactions between dominance and product support, using dummies based on median splits. The results show that the highest value is created for firms with high dominance and high product support. The NPV for high dominance, high support is significantly greater (p < .05) than the NPV for high dominance, low product support. In addition, the NPV for low dominance, high support is not significantly different from high dominance, low support (p = .84). Thus, investment in product support may provide nondominant firms with a means to equalize their NPV position relative to low-support dominant firms.
Fourth, we checked whether nondominant firms provide more monetary incentives to their salespeople. In theory, such firms could compensate for small sales forces by spending more per sales person and per sales call. Empirically, we found that the average detailing expenses per salesperson from 1991 to 2000 were $116,000 and $79,000 for dominant and nondominant firms, respectively. The dominant firms in our sample spent an average of $132 per sales call, whereas nondominant firms spent an average of $129. Overall, these richer measures provide additional evidence of dominant firms' resource advantages.
Are Different Breakthroughs Valued Differently?
H5 maintains that technological breakthroughs are valued more highly than market breakthroughs, and radical innovations will be valued most. H5 is partially supported: Radical innovations are valued significantly more than either technological or market breakthroughs (see Table 8). However, we did not find any significant differences between the financial valuation of technological and market breakthroughs. Although technological breakthroughs have a higher mean value, their variance is also higher, highlighting their riskiness. Figure 4 presents the NPV of the three types of innovations.
Short-Term Versus Long-Term Horizon
Using recent methodology from the finance literature (Barber and Lyon 1997; Mitchell and Stafford 2000), we also computed one-and two-year buy-and-hold long-term abnormal returns for the firms in our sample. Given the newness of radical innovations, we were concerned about the possibility that their effect on the market value of a firm may not be entirely captured by the short-term abnormal returns around the announcement. The long-term results reveal no overall abnormal returns beyond the short-term ones (p-values for the tests of zero one-year and two-year buy-and-hold abnormal returns were greater than .10). Moreover, the results show no significant differences between dominant and nondominant firms in the long run. The stock market appears to incorporate most information about the expected financial value that a radical innovation can add to the firm within two days from the announcement date (additional details of this analysis are available on request from the authors).
The pharmaceutical industry provides a clean, data-rich, and economically and socially important context for this study, but it is always perilous to speculate about the applicability of results from one industry to another. The remarkable advantage that we find dominant firms enjoy in the radical innovation process may raise questions about the generalizability of our results. For example, it could be that the pharmaceutical industry is highly concentrated, and small players cannot break the barriers to entry that their larger counterparts impose. If this is true, then making generalizations is especially imprudent. Some commonly studied industries in the innovation context are household appliances, electronic computer manufacturing, fiber-optic cable manufacturing, and semiconductor manufacturing. As data from the U.S. Economic Census (U.S. Census Bureau 1997a) show, the pharmaceutical industry is less concentrated than most of these industries (see Table 10).
Another cause for concern is that nondominant firms are disadvantaged because of the long process involved in obtaining FDA approval for innovations; nondominant firms may therefore lack the incentive to innovate. However, innovations in this industry are also well protected by patents, thus sheltering innovations by small firms and encouraging such firms to dedicate resources to innovation. The Waxman-Hatch Act of 1984 (21 U.S.C. § 301 et seq.) includes provisions that can extend the patent life for a drug that was delayed in the approval process by up to five years, increasing the chances that firms will collect economic rents from their drugs beyond their initial R&D investments (Scherer 2000). Indeed, several authors have noted that firms in the pharmaceutical industry enjoy a relatively high level of appropriability of the returns from innovations (Gambardella 1995). Firms also have greater access to venture capital than do those in many other industries (Fugazy 2002). All these factors could help protect the investments of nondominant firms and help explain why firms with fewer than 100 employees account for 73.83% of all pharmaceutical firms (U.S. Census Bureau 1997b). The long approval process does not appear to hinder the participation of nondominant firms, at least not much more than in other technology-intensive industries.
Theoretical and empirical arguments have long indicated that dominant firms are proficient in making incremental changes to existing products but inept in commercializing breakthrough ideas. Stringer (2000, p. 71) notes that "[they] seem to be 'genetically' incapable of commercializing radical innovation and they cannot bring themselves to learn by doing." Henderson (1993, p. 268) suggests that such firms are "significantly less productive than entrants in their attempt to introduce innovations that were radical." Our findings point to the contrary: Dominant firms introduce significantly more radical innovations than do nondominant firms. Moreover, nondominant firms suffer from double jeopardy in the radical innovations game; not only do they introduce fewer radical innovations than the dominant firms, but their innovations are also valued less by the stock market.
Perhaps the main theoretical implication of this study is that the value of radical innovations, namely products that provide substantially greater benefits for consumers and include substantially new technology, cannot transcend the characteristics and capabilities of the firms that introduce them. A radical innovation is only as good as the firm that commercializes it.
Contradicting the often-held belief that new, discontinuous technologies signal the swan song of dominant firms because they do not recognize the markets for such technologies, our results suggest that radically new technology can actually reinforce the market position of dominant firms by generating larger cash flows than the technology can for their nondominant counterparts. Our results also offer a rationale for the consolidation trend in the pharmaceutical industry, which has increased at a brisk pace in the last decade: Firms may be seeking economies of scope to increase their productivity, innovativeness, and profitability.
Do our results mean that small, nondominant firms are doomed in their quest for radical innovations? Not necessarily. In addition to firm-level resources, the stock market also recognizes the extent to which the firms deploy these resources at the product level. Our results indicate that high product support increases the value of breakthrough innovations, thus offering a means for nondominant firms to gain from innovations by focusing their resources on key products. A medium-sized firm that deploys high levels of technology and marketing support toward its key products could gain as much (or more) from its radical innovations as a dominant firm that fails to support its products adequately.
Our findings also offer managers of nondominant firms an indication of how much their breakthrough innovations are worth, both to them and to a dominant firm (or a firm with greater marketing expertise) that would be interested in marketing their products. The substantial differences in valuation uncovered in this analysis leave considerable room for licensing activities that would benefit both the small inventors and the large firms that are better positioned to commercialize the inventions.
In a marketplace with intense competitive forces, radical innovations arguably are the last type of product for which the old belief "make a good product and customers will beat a path to it" is still applicable. This article suggests that dominant firms are able to build "highways" to their radical innovations and gain more from their products. An unequal path leads even to the best products, a path that depends on the resources of the firm that introduces the innovation.
This article is based on the first author's dissertation work conducted at the University of Houston. This research was supported by grants from the Marketing Science Institute, the Mays Business School at Texas A&M University, and the University of Minnesota.
The authors thank Inigo Arroniz, Ed Blair, Betsy Gelb, George John, Akshay Rao, Sorin Sorescu, and four anonymous JM reviewers for their valuable input to this research and Luis Wasserman and Raghunath Rao for research assistance. They appreciate the comments of participants at seminars at Duke University, Emory University, Ohio State University, Penn State University, University of Houston, University of Georgia, University of Minnesota, University of Pittsburgh, Texas A&M University, and University of Washington.
(n1) The NDA Pipeline is a database of drugs tracked from discovery through preclinical and clinical trial phases, to ultimate approval or rejection by the FDA. It is administered by F-D-C Reports.
(n2) This median split is conservative. For example, the median number of employees per firm in our sample of radical innovators is 1013 for U.S. firms. The 1997 U.S. Economic Census (U.S. Census Bureau 1997b) reports that less than 3% of the pharmaceutical companies in the United States have more than 1000 employees.
(n3) We also tested a model with main and interaction effects of the marketing and technology support variables. The interaction between marketing and technology support is not significant and is not included here.
Legend for Chart:
C - Customer-Need Fulfillment Low
D - Customer-Need Fulfillment High
A B C
D
Newness of Technology Low Incremental innovation
Market breakthrough
High Technological breakthrough
Radical innovation
Source: Chandy and Tellis (1998). A B
C
FDA Definitions
Chemical Composition NME
An active ingredient that has never
been marketed in the United States.
Update
Adrug that is a new formulation,
a new dosage of existing components,
or a commercialized drug
that has a new usage.
Therapeutical Potential Priority review drug
Adrug that appears to represent
an advance over available therapy.
Standard review drug
Adrug that appears to have
therapeutical qualities
similar to those of
an already marketed drug.
Operationalization
of Innovations
Legend for Chart:
C - Therapeutical Potential Standard Review
D - Therapeutical Potential Priority Review
A B C
D
Chemical Composition Update Incremental innovation
Market breakthrough
NME Technological breakthrough
Radical innovation Legend for Chart:
A - Nature of Breakthroughs
B - Details
C - Detailed Count
D - Count
A
B C D
Used in the sample (introduced by public firms)
226 226
Introduced by public firms in the sample, but
data on one of the three components of dominance
or the stock market were missing in the year the
product was introduced
29 29
Introduced by divisions of dominant firms
Division of 3M 1 22
Division of BASF 3
Division of Ciba Geigy (Ciba Vision) 3
Division of DuPont 3
Division of Kodak (Sterling) 1
Division of Merck KGaA 1
Division of Nestle 7
Division of Procter & Gamble 1
Division of Sigma-Tau Pharma (Italy) 1
Division of Snow Brand Milk Products (Japan) 1
Introduced by firms that were acquired before 2000
and for which financial data were unavailable
Upjohn, acquired in 1995 18 31
American Cyanamid, acquired in 1994 1
Ciba, acquired in 1996 3
Syntex, acquired in 1994 1
Wellcome, acquired in 1995 8
Introduced by private firms
32 32
Introduced by public firms for which
financial data were unavailable
37 37
Introduced by joint ventures of public firms
Joint venture of Astra and Merck 1 3
Joint venture of Abbott and Takeda 1
Joint venture of L'Oreal and Nestle 1
Total
380 380 Legend for Chart:
A - Conceptual Variable
B - Measured Variable
C - Data Source
A B
C
Dominance f (sales, assets, profits)
• Compustat, Thomson
Datastream, Standard
& Poor's
Type of breakthroughs Market breakthrough: FDA
priority review
Technological breakthrough:
FDA NME classification
Radical innovations: NMEs that
are also priority review drugs
• NDA Pipeline
• "The Pink Sheet"
Value of radical NPV
innovations
• Center for Research
in Security Prices
• Datastream
Marketing support Sales force/number
(product support) of new products;
number of sales calls/number of
new products; detailing dollars/
number of new products;
advertising expenditures
• Verispan (Scott Levin Inc.)
• NDA Pipeline
• Schonfeld & Associates
Technology support Citation-weighted patents/number
(product support) of new products; R&D
expenditures/number of
new products
• U.S. Patent and
Trademark Office database
• Compustat
Product scope Entropy x number of new products
• NDC directory
• Freedom of Information
database of drugs
Original source Dummy for inventor
of innovation
• Pharmaprojects
• "The Pink Sheet"
• LexisNexis
Riskiness of projects Cost of capital
undertaken by the firm
• Lehman Brothers Fixed
Research Program
• Datastream, etc. Legend for Chart:
B - Incidence Rate Ratio (eb)
A B
Dominance 1.58(*)
Number of new products introduced in the same year 1.02(*)
Number of patents applied for in the same year 1.00
Country .95
Log-likelihood -383.12
Wald χ² 116.72(*)
(*) p < .01.
Notes: Dependent variable is number of breakthrough innovations.
Legend for Chart:
A - Company
B - All Breakthroughs
C - Radical Innovations
D - Total Innovations
A B C D
GlaxoSmithKline 19 8 382
Roche 15 7 147
Bristol-Myers Squibb 15 4 320
SmithKline (before Glaxo merger) 12 4 177
Abbott Laboratories 11 2 284
Merck 11 7 489
Johnson & Johnson 10 2 136
Aventis 9 4 83
Hoechst 9 3 79
Novartis 9 0 163
Wyeth-Ayerst 9 2 144
Pfizer 9 1 118
Parke-Davis 9 4 93
AstraZeneca 8 0 117
Eli Lilly 6 2 231 Legend for Chart:
A - Drug Name
B - Drug Class
C - Company
D - Approval Date
E - Innovation Type
F - Licensed
G - NPV (in $ M)
A B C
D E
F G
Singulair Respiratory; pulmonary Merck
asthma/antiasthmatic
February 20, 1998 Technological
breakthrough
No 6981.7
Tikosyn Cardiovascular; Pfizer
arrhythmia/
antiarrhythmic
January 10, 1999 Technological
breakthrough
No 6313.5
Viagra Gynecological; Pfizer
genitourinary
impotence
March 27, 1998 Radical innovation
No 6189.9
Rapamune Immunology/autoimmune Wyeth-Ayerst
disease
September 15, 1999 Radical innovation
No 5745.0
Mylotarg Cancer; blood cancer; Wyeth-Ayerst
leukemia
May 17, 2000 Radical innovation
No 5552.9
Glucovance Metabolic disorders; Bristol-Myers Squibb
diabetes; diabetic
complications
July 31, 2000 Technological
breakthrough
Yes 5428.8
Rebetron Infectious diseases Schering-Plough
and viral diseases;
antiviral hepatitis
June 3, 1998 Market breakthrough
Yes 4910.0
Aggrastat Cardiovascular Merck
May 14, 1998 Radical innovation
No 4807.4
Relenza Infectious diseases GlaxoSmithKline
and viral diseases;
antiviral influenza
July 27, 1999 Radical innovation
Yes 4112.7
Temodar Cancer, brain cancer Schering-Plough
August 11, 1999 Radical innovation
Yes 3281.4 Legend for Chart:
B - Model 1 (N = 195)(a)
C - Model 2 (N = 117)(a)
D - Model 3 (N = 117)(a)
A B C
D
Dominance .16(**) .51(**)
.50(**)
Radical innovation .17(**) .31(***)
.30(***)
Market breakthrough .06 .11
.11
Product support -- .38(***)
--
Marketing support -- --
.34(***)
Technology support -- --
.49(**)
Product scope -- .26(**)
.23(*)
Number of breakthroughs .04 .15
.15
Cost of capital .10 .35(***)
.33(**)
Diuretics .17(**) .54
.50
Country -.09 .06
.04
Licensed -.06 -.13
-.14
Wald χsup2; 23.55 48.37
48.55
(p-value) (.0027) (<.0001)
(<.0001)
R² within .09 .29
.28
R² between .14 .24
.24
R² overall .11 .31
.31
(*) p < .10.
(**) p < .05.
(***) p < .01.
(a) Standardized coefficients. Legend for Chart:
B - Model 1 (N = 85)(a)
C - Model 2 (N = 85)(a)
A B C
Dominance .63(**) .60(*)
Radical innovation .38(***) .38(**)
Market breakthrough .00 -.02
Product support .48(***) --
Marketing support -- .49(***)
Technology support -- .47(*)
Product scope .37(**) .39(**)
Number of breakthroughs .12 .14
Cost of capital .51(***) .56(***)
Diuretics .25 .15
Country .45(**) .44(*)
Licensed -.10 -.09
Wald χ² 51.17 49.20
(p-value) (<.0001) (<.0001)
R² within .38 .38
R² between .44 .43
R² overall .40 .40
(*) p < .10.
(**) p < .05.
(***) p < .01.
(a) Standardized coefficients. Legend for Chart:
A - Industry
B - Prior Research
C - Value of Shipments Accounted by the Largest 20
Companies(a) (%)
D - Herfindahl-Herschmann Index for the 50 Largest Companies(a)
A B C
D
Pharmaceuticals Dekimpe and Hanssens (1999), 69.7
Gatignon, Weitz, and Bansal (1990),
Rangaswamy and Krishnamurthi (1991)
441.5
Household Sultan, Farley, and Lehmann (1990), 82.7
appliances Chandy and Tellis (2000)
839.8
Electronic Chandy and Tellis (1998), 90.0
computer Eisenhardt and Tabrizi (1995)
manufacturing
658.2
Semiconductors Dutta, Narasimhan, and Rajiv (1999) 62.1
688.7
(a) Available from the U.S. Census Bureau (1997a).
Legend for Chart:
B - Acquired
C - Invented
A B C
Nondominant 18 57
Dominant 35 116 Legend for Chart:
B - Nondominant
C - Dominant
A B C
Market breakthroughs 13 19
Technological breakthroughs 30 73
Radical innovations 25 50 Nondominant 37.3
Dominant 456
Market breakthroughs 153.7
Technological breakthroughs 187.5
Radical innovations 636.4
Low Product Support 143.9
High Product Support 929.1
Low Product Scop 122.2
High Product Scope 760.2
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Theoretically, the NPV for a particular product is given by
(A1) [Multiple line equation(s) cannot be represented in ASCII text]
where
CFt = the cash flow that the product is expected to generate at time t,
k = the required rate of return for that specific project, and
I0 = initial investment in the product.
The theoretical appeal of this measure resides in its ability to explicitly reflect variations in financial value predicted by the theoretical constructs on which we have built our arguments: risk and resources. Any firm characteristics or actions that reduce risk are reflected in a lower discount rate, k, which results in higher NPV. In turn, higher levels of resources, such as marketing or technology resources, can increase the size of the cash flows and decrease the uncertainty of these cash flows, resulting in a lower discount rate and consequently a higher NPV.
An estimate of the cash flows expected from the innovation can be obtained from the stock market's assessment of the value that the innovation will add to the value of the firm that introduces it. The theory of efficient markets (Fama 1970) postulates that investors are forward-looking and incorporate all publicly available information in a firm's stock price as this information becomes available. Specifically, when a new product approval is announced, investors will adjust the stock price to account for the expected cash flows that the innovation will generate. For pharmaceuticals, the announcement occurs when the FDA gives its final approval for immediate commercialization of the drug.
Measuring NPV
Empirically, we measure NPV by the increase in the market value of the firm over a three-day window after the announcement associated with the introduction of the new product (we find no indication of information leakage over the two-day period before approval). We use a market-adjusted model to calculate returns. We also use a market model (not reported here) for robustness checks (Brown and Warner 1985). The NPV equation is
(A2) [Multiple line equation(s) cannot be represented in ASCII text]
where
Rt = rate of return on the firm's stock, calculated as Rt = [(Pt + dt)/Pt - 1] - 1;
Pt = stock price at time t;
dt = the dividend per share paid by the firm on day t (dt = 0 if no dividend was paid on day t);
Rmt = the equally weighted rate of return of all publicly traded equities on the market;
Nsharest - 1 = the number of outstanding shares that the firm had the day before the announcement; and
Pt - 1Nsharest - 1 = market value of the firm on the day before the announcement.
We collected stock market data for firms traded on U.S., European, and Japanese exchanges. For non-U.S. firms, we based currency conversions on daily exchange rates collected from Datastream. We used the stock market on which each firm traded as the benchmark to calculate abnormal returns and conducted robustness checks using the appropriate pharmaceutical indexes.
The abnormal change in market value is frequently used to assess the value of firm investments or actions and has the advantages of comparability and managerial appeal (see Dowdell, Govindaraj, and Jain 1992; Hendricks and Singhal 1996; Klassen and McLaughlin 1996). This measure assigns a unique dollar value to each radical innovation instead of examining the innovations' effect on a percentage increase in the value of the firm. It therefore enables us to compare the value of new products across firms. The dollar value, as an absolute measure, has the additional benefit of ensuring symmetry and consistency between the measures we use for the "who introduces more" and "who gains more" questions.
The NPV measure also has considerable managerial appeal. First, the NPV measure is forward-looking and provides a metric for managers to assess the value of products before a time series of revenue data becomes available. This sets the NPV measure apart from measures such as sales or return on investment, which are not forward-looking and capture the performance of a product over a specific, limited period of time, and then only after the fact. Second, the NPV measure is based on excess returns that result from the innovation, net of the expected loss in cash flows from existing products. Because the measure takes into account the extent to which the radical innovation can draw sales from the firm's existing products, it provides a comprehensive metric of the impact of an innovation.
Impact of Information Leakage
A concern common to all event studies that deal with new product announcements is whether any information about the product was incorporated in the stock price before the announcement. In our case, the large amount of uncertainty attached to the FDA-approval process prevents investors from incorporating a substantial amount of information while the drugs await approval (DiMasi et al. 1991). First, there is uncertainty about the outcome of the approval process per se, because more drugs are rejected than approved. Second, there is uncertainty attached to the announcement date, also compounded by the fact that it takes an average of eight years for a drug to proceed from clinical trials to FDA approval (DiMasi et al. 1991). Third, because of FDA regulations, firms cannot release specific claims for the product before it is approved. Finally, dominant firms are under closer scrutiny by investors and the trade press. Therefore, if any information is leaked before the drug's approval, it is more likely to be about dominant firms. Thus, even if information is incorporated in the stock price before the announcement, the effect will decrease the difference in returns at introduction between dominant and nondominant firms. Our metric is therefore conservative because it makes support for our hypotheses more difficult to demonstrate.
Cost of capital is a control variable that accounts for the riskiness of the investments undertaken by the firm (Ross, Westerfield, and Jaffe 1999). High cost of capital is an indication that the firm works on projects perceived by the market as risky. It is not necessarily a proxy for the firm's propensity to produce radical innovations, because riskiness may also be associated with projects such as orphan drugs or drugs that are researched simultaneously by other firms. However, it is a measure of investors' expectations for that firm's products. We therefore include it as a control variable in our model. The cost of capital is given by
(A3) WACC = Kd D/A(1 - T) + KeE/A,
where
Kd = cost of debt = risk-free rate + credit risk premium;
Ke = cost of equity = Rf + β (Rm - Rf), where Rf is the risk-free rate and Rm is the rate of return on the market;
D = market value of the firm's debt (approximated by book value);
E = market value of the firm's equity (calculated as number of shares outstanding x market price per share);
A = market value of the firm's assets, approximated as D + E; and
T = corporate tax rate.
~~~~~~~~
By Alina B. Sorescu; Rajesh K. Chandy and Jaideep C. Prabhu
Alina B. Sorescu is Assistant Professor of Marketing, Mays Business School, Texas A&M University.
Rajesh K. Chandy is Assistant Professor of Marketing, Carlson School of Management, University of Minnesota.
Jaideep C. Prabhu is Assistant Professor of Marketing, Judge Institute of Management, University of Cambridge.
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Record: 142- Strategic Bundling of Products and Prices: A New Synthesis for Marketing. By: Stremersch, Stefan; Tellis, Gerard J. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p55-72. 18p. 2 Diagrams, 3 Charts. DOI: 10.1509/jmkg.66.1.55.18455.
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Strategic Bundling of Products and Prices: A New Synthesis for Marketing
Bundling is pervasive in today's markets. However, the bundling literature contains inconsistencies in the use of terms and ambiguity about basic principles underlying the phenomenon. The literature also lacks an encompassing classification of the various strategies, clear rules to evaluate the legality of each strategy, and a unifying framework to indicate when each is optimal. Based on a review of the marketing, economics, and law literature, this article develops a new synthesis of the field of bundling, which provides three important benefits. First, the article clearly and consistently defines bundling terms and identifies two key dimensions that enable a comprehensive classification of bundling strategies. Second, it formulates clear rules for evaluating the legality of each of these strategies. Third, it proposes a framework of 12 propositions that suggest which bundling strategy is optimal in various contexts. The synthesis provides managers with a framework with which to understand and choose bundling strategies. It also provides researchers with promising avenues for further research.
Bundling is the sale of two or more separate products in a package. This strategy is pervasive in markets today in one form or another. In the past decade, bundling has received growing attention in the marketing literature. However, the published studies are fuzzy about some basic terms and principles, do not discuss the legality of bundling, and do not provide a comprehensive framework on the economic optimality of bundling. As a result, marketing researchers may not appreciate the full meaning of bundling and the variety of strategies encompassed by the term. Marketing managers may not appreciate the hazards involved in this strategy and fully exploit the advantages of bundling in various markets.
Examples of bundles that come to mind readily are opera season tickets (tickets to various events sold as a bundle), luggage sets (various luggage items sold as a bundle), and Internet service (bundle of Web access, Web hosting, e-mail, personalized content, and an Internet search program). Less straightforward examples include multimedia personal computers (PCs), fixed-price menus, executive MBA programs, and premium brokerage accounts. The multimedia PC is a bundle of the traditional PC plus speakers, a CD-ROM, and other multimedia gadgets. A fixed-price menu is a bundle of a choice of appetizer, entrée, and dessert. The executive MBA is a bundle of selected business education modules that managers could otherwise obtain separately at various conferences and educational organizations. A premium brokerage account provides stock trades, stock research, margin trading, retirement planning, and free check writing in one account.
These examples show the pervasiveness and strategic importance of bundling. Firms need to resort to bundling cautiously because of the legal pitfalls involved. For example, the landmark antitrust case against Microsoft is, at the core, a case against its bundling of Windows and Explorer. Indeed, the U.S. Congress has a long history of legislating on bundling issues, and the U.S. Department of Justice extensively monitors its use by firms. Recently, the Justice Department has prosecuted substantially more cases. For example, the number of antitrust cases it handled between 1996 and 1999 is approximately double the number of cases it handled between 1890 and 1996.
Prior marketing literature on bundling has examined the optimality of bundling (Bakos and Brynjolfsson 1999, 2000; Eppen, Hanson, and Martin 1991; Guiltinan 1987; Wilson, Weiss, and John 1990), consumer evaluation of bundles (Johnson, Herrmann, and Bauero1999; Soman and Gourville 2001; Yadav 1994, 1995; Yadav and Monroe 1993), and firms' pricing and promoting of bundles (Ansari, Siddarth, and Weinberg 1996; Ben-Akiva and Gershenfeld 1998; Hanson and Martin 1990; Mulhern and Leone 1991; Venkatesh and Mahajan 1993). Economists have focused mainly on the optimality of bundling for monopolists (Adams and Yellen 1976; Burstein 1960; Carbajo, De Meza, and Seidman 1990; Long 1984; McAfee, McMillan, and Whinston 1989; Pierce and Winter 1996; Schmalensee 1982,B1984; Stigler 1963; Whinston 1990), equilibrium theories of bundling (Chen 1997; Kanemoto 1991; Matutes and Regibeau 1992), and the welfare implications of bundling (Dansby and Conrad 1984; Martin 1999; Salinger 1995; Whinston 1990).
We identify the following three shortcomings in the literature: First, the domain of bundling is ill defined, and terms that refer to distinct phenomena are used interchangeably. Second, there is no clear, comprehensive, and coherent discussion of the legality of bundling. Third, there is no integrative framework that explains the optimality of bundling conditional on various factors. On the contrary, the literature contains ambiguity about some key conditions for optimality, and theory on others is incomplete or absent.
This article provides a new synthesis of the field of bundling based on a critical review and extension of the marketing, economics, and law literature. In particular, this synthesis makes three important contributions to the literature. First, it clearly and consistently defines bundling terms and principles. It identifies two key underlying dimensions of bundling that enable a comprehensive classification of bundling strategies. Second, it formulates clear rules to evaluate the legality of each of these strategies. Such rules must complement any discussion of economic optimality to ensure that economically optimal strategies are optimal in practice, after taking into account legal proscriptions and risks. Third, it proposes a framework of 12 propositions that prescribe the optimal bundling strategy in various contexts. The framework is a logical one that uses uniform terms and assumptions. The propositions incorporate all the important factors that influence bundling optimality. These propositions synthesize a body of knowledge that is at least partly supported by verbal logic, mathematical proof, or empirical evidence.
The rest of the article is organized as follows: The next section presents a primer on bundling strategies. The following section develops a set of key propositions about bundling. The final section presents our conclusions, implications, and limitations.
This section first defines terms used in bundling. It then classifies the entire domain of bundling strategies and clearly demarcates their legality.
Definitions
This subsection first explains the current confusion in the bundling literature. It then proposes clear definitions of key terms that are parsimonious and rooted in the law literature.
Confusion in literature The confusion in the literature arises from inconsistent use of terms, ambiguous distinctions between important constructs, and an unclear domain of application. We explain each of these problems with relevant examples.
First, bundling does not have consistent, universally accepted definitions. Adams and Yellen (1976, p. 475) define bundling as "selling goods in packages." Guiltinan (1987, p. 74) defines bundling as "the practice of marketing two or more products and/or services in a single package for a special price." Yadav and Monroe (1993, p. 350) define it as "the selling of two or more products and/or services at a single price." Without consistent definitions, the legality of bundling becomes fuzzy and its practical implications become imprecise.
Second, the distinction between a product and a bundle is not clear. For example, Salinger (1995) treats a pair of shoes as a bundle of a left and a right shoe. Telser (1979) considers a car a bundle of different parts, such as the engine, wheels, and so forth. As such, every product would be a bundle of parts, and the term would lose its strategic and legal importance.
Third, the domain of bundling strategies is not clear. Mulhern and Leone (1991, p. 66) introduce the concept of implicit price bundling as "the pricing strategy whereby the price of a product is based on the multitude of price effects that are present across products without providing consumers with an explicit joint price." By this term, the authors imply that retailers that decrease price in one category must consider potential sales increases or decreases in other categories. However, this extension if the meaning of bundling runs the risk of increasing ambiguity about the concept and its domain without enhancing understanding of the core concepts and principles of bundling.
In the interest of generality, we use definitions that are parsimonious, rooted in the law literature, and as close as possible to the intent of authors in economics and marketing.
Bundling. Bundling is the sale of two or more separate products in one package.[ 1] The term "separate" has enormous implications for understanding the legality and optimality of the phenomenon, so it merits precise definition. We define separate products as products for which separate markets exist, because at least some buyers buy or want to buy the products separately. For example, combined offerings of banking and insurance products are bundles because at least some consumers buy insurance and banking separately. A travel package including air and ground travel is a bundle consisting of procedurally separate services. Note that products can be separate at one level in the channel, while being mere parts of a product at another level. Although a processor and a hard disk drive are parts in a PC for an end user, they are separate products for a PC manufacturer. This article focuses on bundling from an end user's perspective and does not deal with bundling in a channel context.
Bundling focus: product versus price bundling. At present, researchers use the terms product bundling and price bundling interchangeably without clearly distinguishing between the two strategies. Our article is the first in marketing to clarify this distinction, articulate the ramifications of each-strategy, and relate the two to each other.
We define price bundling as the sale of two or more separate products in a package at a discount, without any integration of the products. Because the products are not integrated, the reservation price for the price bundle is, by definition, equal to the sum of the conditional reservation prices of the separate products.[ 2] In other words, bundling itself does not create added value to consumers, and thus a discount must be offered to motivate at least some consumers to buy the bundle. Think of a set of luggage items, a six-pack of beer, a combo meal, a software suite, or a season ticket for the opera.
We define product bundling as the integration and sale of two or more separate products or services at any price. This integration generally provides at least some consumers with added value, such as compactness (integrated stereo systems), seamless interaction (PC systems), nonduplicating coverage (one-stop insurance), reduced risk (mutual fund), interconnectivity (telecom systems), enhanced performance (personalized dieting and exercise program), or convenience from an integrated bill (telecom calling plans). The greater value raises consumers' reservation prices for the product bundle compared with the sum of the conditional reservation prices of the separate products.
A product bundle can therefore be thought of as having an integral architecture (Ulrich and Eppinger 1995). It implements the different functions of the bundled products in a single product bundle. The multimedia PC has an integral architecture, in that it integrates functions such as connection (e.g., modem), data storage, and retrieval (e.g., CD-ROM), which were separate physical chunks before the advent of the multimedia PC.
The distinction between price and product bundling is important because it entails different strategic choices with different consequences for companies. Whereas price bundling is a pricing and promotional tool, product bundling is more strategic in that it creates added value. Managers can therefore use price bundling easily, at short notice, and for a short duration, whereas product bundling is more of a long-term differentiation strategy. In the case of physical goods, product bundling requires a new design, research to optimize the design, and retooling to manufacture the product bundle. In the case of services, product bundling requires redefinition of services, optimization of the interfaces among the services, and redesign of service delivery processes. Managers frequently approach product bundling from a (new) product development perspective, involving the research and development and manufacturing departments. Price bundling decisions are often the sole prerogative of the marketing department.
For example, consider the strategic options of Dell, which markets to consumers who want to buy a portable computer system consisting of a basic laptop, a modem, and a CD burner. First, it can sell these products as separate items, such that the price of each item is independent of consumers' purchase of the other item. In this case, consumers could easily forgo purchasing a modem or CD burner, or they could purchase it from a competitor. Second, Dell can sell the products as a price bundle. For example, it could, without physically changing any of the products, give a discount to consumers if they buy all three products together. This offer would probably motivate at least some consumers to buy all three products from Dell. Third, Dell can sell the three items as a product bundle. To meet the latter classification, Dell must design some integration of the three separate products. For example, it could create an enhanced laptop. Not only could this trigger some consumers to buy all products from Dell, but through the value added they might even do so at a premium price.
Bundling form: pure versus mixed. Bundling may take one of three forms: pure, mixed, or unbundling (Adams and Yellen 1976). Unbundling is a strategy in which a firm sells only the products separately, but not the bundle. Typically, because this strategy is a base strategy for most firms, the strategy is called unbundling only when contrasting it with a bundling strategy. Pure bundling is a strategy in which a firm sells only the bundle and not (all) the products separately. Pure bundling is sometimes called "tying" in the economics and legal literature.[ 3] A tying product is a separate product that is bundled with other separate products. Tie-ins are secondary products that are bundled with the primary product. Mixed bundling is a strategy in which a firm sells both the bundle and all the separate products in the bundle separately. Table 1 presents a tabular view of these terms.
Classification and Legality of Bundling Strategies
Classification of bundling strategies. To classify and relate various bundling strategies, we identify two key dimensions of bundling: ( 1) the focus of bundling, whether on price or product, and ( 2) the form of bundling, whether pure or mixed. These dimensions encompass a rich set of bundling strategies that have substantially different characteristics and implications. By using these two dimensions, focus and form, Figure 1 classifies the domain of bundling strategies. The focus of bundling is along the horizontal axis, that is, on either price or product. The form of bundling is along the vertical axis, that is, none, pure, or mixed. Figure 1 considers a general case with two products, X and Y. Combinations of X and Y represent the terms of the sale. Thus, (X, Y) represents the sale of a price bundle, (X (Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.) Y) represents the sale of a product bundle, and X and Y without parentheses represent the sale of separate products.
When the products are sold separately, the strategy is unbundling and remains the same for the price and product columns (Cell 1). Sears sells Kenmore home appliances unbundled. Cell 2 represents a case of pure price bundling. In this case, a firm bundles the two products for one fixed price, without integrating the products or offering them separately. A classic example would include restaurants that offer only a fixed price menu, with appetizer, entrée, and dessert. Cell 3 represents a case of mixed price bundling, in which case the firm sells the separate products (unchanged) in a price bundle and also sells the products separately at their regular prices. An example would be Samsonite's strategy of selling different sizes of suitcases separately as well as complete sets at a discounted price. Cell 4 represents a case of pure product bundling. In this case, the firm physically integrates the products and sells only this integrated product bundle. An example is Apple Computer's strategy of selling its computers and software as one package. Cell 5 represents the case of mixed product bundling. In this case, the firm sells an integrated product bundle at one price and also sells the separate products at their regular prices. An example would be Circuit City's sale of integrated stereo systems alongside those for the separate products in the system.
Legality of bundling. The preceding classification helps us sort out the legality of various strategies (see Figure 1). This is valuable in view of the limited attention devoted to a clear delineation of the legal rules on bundling in the marketing, economics, and law literature. In particular, the marketing literature avoids all discussion of the legality of bundling, though it covers the legality of truth in advertising or price discrimination substantially (also see Werner 1991, 1993). Yet bundling is pervasive in marketing and is as important as price discrimination or advertising. Moreover, understanding the legality of bundling is crucial to developing successful price and product bundling strategies.
Legal and economic analysts have not made an effort to abstract clear rules from past cases, despite a body of case law. On the basis of a review of the law literature and case law, we synthesize the proscriptions contained in the relevant federal laws in two clear rules, the per se rule and the rule of reason. This simple distinction helps explain much of the apparent conflict in court rulings on various cases over the past century. The spirit underlying both rules is that the bundling strategy of a firm should not hurt buyers by limiting competition. The per se rule is the more stringent of the two rules.
We describe the per se rule in terms of four conditions, as follows: Bundling is illegal per se when it involves ( 1) pure bundling ( 2) of separate products ( 3) by a firm with market power and ( 4) when a substantial amount of commerce is at stake. We have already clarified the meaning of pure bundling and separate products. Here, we explain market power and substantiality.
Market power means that the bundling firm can "force a consumer to do something that he would not do in a competitive market" (Soobert 1995, n. 87) with regard to the tying product. Although a monopoly is a clear indication of dominant market power, a company's power does not have to be complete over all buyers in the market (Fortner Enterprises v. United States Steel Corp 1969).
Substantiality means that the amount of commerce that is at stake should be high. If this amount is not high, the practice is legal. How high is high? The U.S. Supreme Court has noted that as little as $60,800 would be considered substantial (United States v. Loew's 1962). This small number means that this condition is easily met in most markets.
Note that a firm may try to circumvent the law by adopting a mixed bundling strategy in which it prices individual products so high that consumers buy only the bundle. In this case, mixed bundling is de facto pure bundling and will receive the same legal treatment (Northern Pac Ry v. United States 1958).
We describe the rule of reason in terms of six conditions, as follows: Bundling is illegal under the rule of reason when it involves ( 1) pure bundling ( 2) of separate products ( 3) by a firm with market power, ( 4) involving a substantial amount of commerce, ( 5) which poses a threat that the bundling firm will acquire additional market power over at least one of the products that is bundled with the tying product, and ( 6) no plausible consumer benefits offset the potential damage to competition. Therefore, under the rule of reason, each of the four conditions mentioned under the per se rule is still necessary, but not jointly sufficient, for bundling to be illegal. Although assessing the legality of bundling under the per se rule can be relatively easy and objective, doing so under the rule of reason is generally more difficult because of these two additional conditions, which we explain next.
Under the rule of reason, the mere existence of market power over the tying product is not sufficient for bundling to be illegal. In addition, there should be a substantial threat of the bundling firm acquiring additional market power over at least one of the products that are bundled with the tying product. For example, Sandoz Pharmaceuticals bundled Clorazil, a drug for schizophrenia, with CPMS, Clorazil Patient Management System, a system that monitored the side effects of the drug on the patient (Hurwitz 1991). Although Sandoz possessed market power through the patented Clorazil drug, it could not acquire additional market power in the market for monitoring systems because the specific use to monitor schizophrenics' reaction to Clorazil was a small part of the total market for monitoring systems. Therefore, this strategy was not illegal.
The sixth condition for bundling to be illegal, under the rule of reason, is that it produces no benefits to buyers that may offset the potential damage to competition (Meese 1999). If such benefits are present, bundling can still be legal, even though all five previous conditions are met. Typical offsetting benefits are substantial reductions in costs or major increases in value when the products are bundled. By this logic, pure product bundles may be legal if they provide added value and are not merely a bolting together of products. For example, in United States v. Jerrold Electronics Corp. (1961), the court used this factor to find an otherwise illegal bundling strategy lawful. Jerrold Electronics, an early producer of cable television equipment, sold community television antennas only bundled with a service contract. The equipment was very sensitive, and customers had no expertise in using it, which thus warranted a bundling strategy to ensure quality. Recently, the D.C. Circuit (appeals court), in the context of the Microsoft case, ruled that any plausible claim of consumer benefits is enough to satisfy this condition (United States v. Microsoft Corp. 1998). Note that this statement gives a liberal interpretation of offsetting buyer benefits. Indeed, the D.C. Circuit strongly discouraged courts from second-guessing manufacturers' design decisions (United States v. Microsoft Corp. 1998).
Both of these rules, the per se rule and the rule of reason, have been used over the course of different legal cases, and therefore different applications seem to display a "conflicting set of rules" (Dansby and Conrad 1984, p. 377). The IBM case (1936) showed rigid legal scrutiny of bundling practices, in that the Supreme Court applied the per se rule avant-la-lettre. The first explicit application of the per se rule occurred in 1947 (International Salt Co. v. United States 1947). In this case, International Salt Co. leased patented salt-dispensing machines on the condition that the lessee purchased salt for the machines from the company. Without analyzing evidence of substantial anticompetitive effects or taking into account evidence presented by the company that bundling was necessary to maintain quality control, the court found the company per se guilty of illegal bundling (Soobert 1995).
In 1969, the Supreme Court relaxed the harsh per se rule and moved toward the rule of reason (Fortner Enterprises v. United States Steel Corp 1969). Although it is uncertain which of these two rules a court will use in a specific bundling case, use of the rule of reason is growing more common. Therefore, the rule of reason is the most suitable benchmark for judging the legality of various bundling strategies.
Legality of various bundling strategies. We apply the previous discussion on legality to our classification of bundling strategies (see Figure 1). We discuss only the case in which a firm with market power bundles separate products, because these are necessary conditions for illegality. All unbundling (Cell 1) and mixed bundling strategies (Cells 3 and 5) are legal. Pure price bundling (Cell 2) is always illegal, under both the per se rule and the rule of reason. Pure product bundling (Cell 4) is legal under the rule of reason if the benefits to consumers offset potential damage to competition. Note, however, that it would be illegal under the per se rule. Bolting products together is also illegal. Merely bolting products together does not constitute genuine integration and thus cannot be beneficial to consumers.
Although we have specified clear rules for legality and applied them to various bundling strategies, ambiguity may still occur in the factual evidence of specific cases. A good example is the landmark antitrust case United States v. Microsoft Corp. If we take the position that Microsoft does not possess a monopoly position (as Massachusetts Institute of Technology economist Richard Schmalensee did) or that the bundling of Windows and Explorer provides consumer value (as Microsoft chief executive officer Steve Ballmer did), then the bundling of Explorer and Windows is legal. However, if we take the position that Microsoft is a monopolist and the bundling of Explorer with Windows by Microsoft does not add value for consumers (as the U.S. government did), then Microsoft's bundling of Explorer and Windows is illegal. Thus, the critical issue in the Microsoft case is the factual assessment of market power and consumer benefits.
First, does Microsoft possess market power? The presence of market power is a difficult fact to establish objectively. In particular, the definition of the relevant market is the subject of intense economic and legal debate. In the Microsoft case, Judge Jackson (1999) defined the relevant market narrowly as "the worldwide licensing of Intel-compatible PC operating systems." In this market, Microsoft Windows indeed has market power because of its dominant market share (95%). However, we question this definition. Would it be more relevant also to include other desktop operating systems such as Apple or Linux? How about network operating systems, such as UNIX or Novell?
Second, does the bundling of Explorer and Windows provide consumers with added benefits, or were the two software packages bolted together just to temper competition? As the D.C. Circuit Court indicated, it is difficult for an outsider to second-guess a company's design choices, especially in high-tech markets. Besides, in this case, what would be the optimal degree of integration between the two software packages? Who would determine that?
Thus, although our identification and formulation of clear rules reduce the ambiguity in case law, ambiguity still remains in the empirical evidence to which the rules apply. This problem can be clarified by separating two stages in the legal process: findings of fact and conclusions of law. Establishing findings of fact is an empirical issue, which could be clear in some cases and highly controversial in others, such as the Microsoft case. However, based on those findings, the conclusions of law should3become much clearer with the rules we formulated.
This section discusses the optimality of the various bundling strategies. It explains under what factors which strategy becomes dominant. Relative to work done in the literature, this section makes the following contributions: First, the economics literature has focused primarily on profit maximization by a monopolist (Adams and Yellen 1976; Pierce and Winter 1996; Schmalensee 1982, 1984) without considering other objectives of firms, other forms of competition, a firm's cost structure, or consumers' perception of bundles. We formulate propositions that cover all the key factors that affect the optimality of bundling: consumers' conditional reservation prices, objectives of the firm, competition, costs, and consumers' perception of bundles. Second, the literature is ambiguous about the heterogeneity of reservation prices. In particular, most authors focus entirely on asymmetry of reservation prices. However, the distribution of reservation prices involves asymmetry and variation, each of which can affect the optimum strategy. Our discussion clearly explains the role of each. Third, the literature has largely ignored the important distinction between product and price bundling. Product bundling is an important alternative focus for bundling strategies, especially in high-tech markets. We formulate propositions that cover the area of price and product bundling.
Of the propositions we advance, most have never been discussed. Of those previously discussed, at least one (P<SUB>2</SUB>) has previously been imprecisely stated, and a few are at least partially supported in the literature (P<SUB>1</SUB>, P<SUB>8</SUB>, and P<SUB>12</SUB>). We discuss and classify all of these propositions in the interest of completeness. Whenever the literature contains a partial or full proof for a proposition, we cite it. Although our propositions are firmly grounded in marketing or economic literature, we also develop a simulation that illustrates the mechanisms underlying most of our propositions. The use of simulation to do this kind of sensitivity analyses is a bit uncommon in marketing, though it has been used successfully before (Rajendran and Tellis 1994; Tellis and Zufryden 1995). Before we proceed to the propositions, we explain the simulation in detail.
Simulation
We illustrate the logic of some of our propositions with numerical examples (see Tables 2 and 3). These examples give the optimal prices for a supplier based on various distributions of consumers' reservation prices or costs of the supplier. To generate these examples, we developed a program that runs on Microsoft Excel. To determine the optimal prices, we use a subroutine called Evolver from Palisade (see www.palisade.com). This is a powerful optimization routine based on an innovative genetic algorithm. It replaces the subroutine, Solver, in Microsoft Excel, which does not work well when a spreadsheet has "if-then-else" statements, as our program does.
Figure 2 provides a flowchart of our program. It consists of the following five steps or components:
A. The user must first specify the segments, their sizes, and the distribution of reservation prices by segment and product.
B. The user then assigns an array for the optimal prices that the program tries to determine.
C. Based on those prices, the program contains formulas to determine consumer surplus for the various offerings (i.e., product and price combinations).
D. Next, an array computes unit sales for each offering. This array contains formulas that incorporate the following rules: ( 1) Sales occur for a particular offering if and only if consumer surplus is positive and a maximum among alternatives is available. ( 2) If consumers are exactly indifferent between buying and not buying, they buy a product. ( 3) If consumers are exactly indifferent between buying a bundle and the separate products in the bundle, they buy the bundle.
E. Finally, the program calculates the revenues based on the product of sales, the segments' size, and the prices offered.
The researcher then uses Evolver to maximize revenues in Cell E by varying values in Array B, subject to certain constraints. The basic constraints specify the minimum (the lowest reservation price) and maximum (the highest reservation price) values that Array B can take. These constraints are not essential, but they ensure a faster convergence to the optimum. (A technical note and the spreadsheet program are available from the authors.)
The program easily determines the optimal prices for a variety of price distributions, segments, segment sizes, and products. As such, it is easier and more flexible than any program available in the literature. It has three benefits. First, it can generate suitable examples with minimal computation effort, for papers, classroom use, or demonstrations. Second, it can help managers determine what strategy is optimal in specific situations. Third, it can be used to evaluate the robustness of various propositions when the user generates examples that stretch the bounds of a particular proposition.
We next proceed to discuss the propositions under each of the five factors: consumers' conditional reservation prices, objectives of the firm, competition, costs, and consumers' perceptions of bundles. Where appropriate, we integrate legality in the disc9ssion based on the principles we elucidated previously.
Consumers' Conditional Reservation Prices
An important factor in determining which strategy is optimal is the distribution of conditional reservation prices. We first explain the basic principles about heterogeneity of conditional reservation prices. We then develop and explain our propositions. We do so in two general cases: a base case, in which only price bundles are possible, and an extended case, in which product bundles are possible.
Heterogeneity of conditional reservation prices. Many researchers have stated that heterogeneity of conditional reservation prices is an important factor in determining the optimality of bundling strategies. However, this heterogeneity has two dimensions, asymmetry and variation. Mainstream economics and marketing research focus exclusively on asymmetry in reservation prices (Adams and Yellen 1976; Guiltinan 1987; Tellis 1986). Only Pierce and Winter (1996) discuss the effect of variation in reservation prices. This section clearly defines each type of heterogeneity, relates the two types to each other, and presents a comprehensive treatment of the effect of different forms of heterogeneity on the optimality of various bundling strategies.
An asymmetric distribution of conditional reservation prices for two products, X and Y, occurs when one consumer segment has a lower conditional reservation price for Product X than another consumer segment and the former segment has a higher conditional reservation price for Product Y than the latter segment. In other words, an asymmetric distribution of conditional reservation prices results in a negative correlation of conditional reservation prices for two products across consumer segments (Adams and Yellen 1976). A segment consists of an identifiable group of consumers within a market with relatively homogeneous conditional reservation prices. For example, consider the demand for magazines such as Sports Illustrated and Entertainment Weekly. Some consumers (sports fans) will be more interested in a subscription to Sports Illustrated than in a subscription to Entertainment Weekly, whereas others (movie buffs) will prefer the latter to the former (as is displayed in Cases 3 and 4 in Table 2, Part A). This is a case of asymmetric distribution of conditional reservation prices.
Variation refers to the difference among consumers' reservation prices for the bundle of products. Suppose Time Inc. considers bundling Sports Illustrated and Entertainment Weekly. Although some consumers, who read many magazines or are both sport fans and movie buffs, may value such a bundle, others may not. As a result, valuation of the bundled subscription of Sports Illustrated and Entertainment Weekly may vary considerably among consumers. Variation refers to the contrast in Cases 2 and 4 from Cases 1 and 3 in Table 2, Part A. We next cover the optimality of bundling strategies in more detail for two cases: ( 1) a base case, in which only price bundling is possible, and ( 2) an extended case, in which both price and product bundling are possible.
Price bundling. The base case assumes potential for only a price bundle and not a product bundle. Again consider the magazine example stated previously. The reservation price of a bundled subscription for Sports Illustrated and Entertainment Weekly is equal to the sum of the conditional reservation prices of both magazines. How does heterogeneity affect the best strategy for a firm in terms of revenues? Under the assumptions outlined previously, we formulate the following proposition:
P<SUB>1</SUB>: A price bundling strategy (either pure or mixed) yields higher revenues than unbundling if conditional reservation prices are asymmetric.
Although this proposition has found substantial support in economics (Adams and Yellen 1976; Schmalensee 1982), it has not been stated unambiguously, in that asymmetry has rarely been isolated from variation in reservation prices. The underlying reason for the strategy is the following: When there is asymmetry, different consumer segments highly value different products in the bundle. In such a scenario, a bundle can be designed to appeal (and more profitably sell) to consumers who would otherwise buy only one product or buy both products at prices below their reservation prices. In particular, if a firm wanted to maximize sales, it could price the separate products at the minimum of consumers' reservation prices for them. However, such a pricing strategy would leave untapped a considerable amount of consumer surplus. In contrast, a well-designed price bundle can capture most of the surplus arising from the asymmetry in conditional reservation prices. We call this process the extraction of consumer surplus.
We can also consider price bundling a price discrimination instrument. Price discrimination is a strategy in which a supplier sells the same product at different prices to different segments that value a product differently. The strict case in which the supplier is charging a different price to each consumer is called first-degree price discrimination. The supplier extracts the full value of each consumer's surplus. By properly choosing a price for a bundle, a supplier can capture different segments with substantially different valuations for the individual products in the bundle. As such, price bundling is called second-degree}price discrimination, because in this case the supplier will not be able to take all the consumer surplus of each consumer.
We illustrate the logic with the example in Table 2, Part A. Suppose (as in Case 3 in Table 2, Part A) some consumers-we call them sports fans (Segment A in this case)-value a subscription to Sports Illustrated so highly that they are willing to pay $50 for it, but they are willing to pay only $30 for a subscription to Entertainment Weekly. Others-we call them movie buffs (Segment B in this case)-are willing to pay $50 for a subscription to Entertainment Weekly but only $30 for Sports Illustrated. What pricing strategy will maximize revenues? Table 2, Part B, shows that price bundling generates more revenues than unbundling. Table 2 presents average revenues per consumer, which are derived as total revenues divided by the number of consumers.
Although the sizes of Segments A and B are initially assumed equal, this result applies to every proportion of sports and movie buffs. If 90% of the consumers are sports fans, the optimal unbundled prices for Sports Illustrated and Entertainment Weekly are $50 and $30, respectively. Revenues are equal to ($50 × .9 + $30 × 1) × (number of consumers). Therefore, all consumers would buy a subscription to Entertainment Weekly, but only sports fans would subscribe to Sports Illustrated. A price bundle at $80 would generate ($80 1) × (number of consumers) in revenues, again larger than the unbundled revenues. Also, if 90% of the consumers are movie buffs, unbundled revenues are maximized at a price of $50 for Entertainment Weekly and $30 for Sports Illustrated. This generates ($50 × .9 + $30 × 1) × (number of consumers) in revenues. A price bundling strategy, in which the bundle is offered at $80, generates ($80 × 1) × (number of consumers) in revenues, which is again higher than the revenues when the supplier does not offer a price bundle and offers the magazines separately. Thus, the proposition is independent of segment size.
P<SUB>2</SUB>: Mixed price bundling yields higher revenues than pure price bundling only when reservation prices for the bundle vary across consumers. In all other cases, pure price bundling yields at least the same revenues.
This proposition contradicts often-cited statements of Adams and Yellen (1976), Guiltinan (1987), and others that mixed price bundling is always at least weakly better than pure price bundling. In contrast, we state that mixed price bundling is superior only when the reservation price for the bundle varies. The reason for our different conclusion is that our analysis disentangles two key dimensions of heterogeneity, asymmetry, and variation in conditional reservation prices. Previous analyses did not make this distinction clear, and their conclusions for optimality were not complete or accurate. In contrast to mainstream bundling literature, this proposition has received partial support in the literature (Pierce and Winter 1996).
The rationale of the proposition is the following: When the bundle reservation prices vary sufficiently, the firm can price the separate products to extract surplus from the segment that values one of the bundled products highly, while pricing the bundle to attract the other segment. So a mixed price bundling strategy dominates. When bundle prices do not vary, the bundle will be equally attractive to both segments. Thus, a pure price bundling strategy dominates or equals a mixed price bundling strategy. For example, contrast Cases 1 and 3 with Cases 2 and 4 in Table 2, Part B.
Note that in these cases, the revenues from a mixed price bundling strategy merely equal the revenues from a pure price bundling strategy. The reason is that in these cases, mixed price bundling reduces to a de facto pure price bundling strategy. (Recall from the legality section that a de facto pure price bundling strategy is one in which all consumers buy the bundle because the prices of the separate products are relatively high.) In a valid mixed price bundling scheme, the prices of the separate products would need to be such that at least one consumer segment buys one separate product. Then mixed price bundling would become inferior to pure price bundling. For example, one such pricing strategy would optimally charge $49 for Sports Illustrated, $50 for Entertainment Weekly, and $80 for the bundle. In that case, Segment A would buy only Sports Illustrated, and Segment B would buy the bundle. This would generate a revenue of $64.50, on average.
In practice, legality limits the choice of strategies. Although it may be economically optimal for a firm that faces low price variation to use pure price bundling, this choice may be illegal if the firm has dominant market power (see the prior discussion of the per se rule). A mixed price bundling strategy is also illegal if the strategy reduces to a de facto pure price bundling strategy, as discussed previously. Thus, consideration of economic optimality must proceed with evaluation of what is legally prudent.
Product bundling. The possibility of a product bundle enriches the set of potential bundling strategies (seeFigure 1).
P<SUB>3</SUB>: A product bundling strategy (either pure or mixed) yields higher revenues than unbundling for both symmetric and asymmetric conditional reservation prices, though the difference in revenues will be larger when reservation prices are asymmetric.
Prior research has not addressed the optimality of product bundling. Product bundling strategies yield higher revenues than unbundling strategies because they exploit consumers' willingness to pay for added value. Because of this added value, the asymmetry in conditional reservation prices is not necessary for the optimality of product bundling. However, the difference in revenues between product bundling and unbundling is larger when conditional reservation prices are distributed asymmetrically rather than symmetrically. The reasoning is the same as in the case of price bundling in P<SUB>1</SUB>--transferring consumer surplus from a segment with high conditional reservation prices to that with low conditional reservation prices. Again, this logic can be easily illustrated by the following example.
Consider the pricing for an integrated stereo system, composed of a receiver and CD player, in Table 3, Part A. Note that a product bundling strategy yields much higher revenues than an unbundling strategy in all four cases (Table 3, Part B). However, note that the difference in revenues is higher when conditional reservation prices are asymmetric (Cases 3 and 4 in Table 3, Part B) than when they are symmetric (Cases 1 and 2 in Table 3, Part B).
P<SUB>4</SUB>: Mixed product bundling can yield higher revenues than pure product bundling only when reservation prices for the bundle vary. Pure product bundling yields equal or higher revenues than mixed product bundling when reservation prices do not vary.
P<SUB>3</SUB> suggests that product bundling strategies yield higher revenues because they exploit consumers' willingness to pay for added value. In addition, by adopting a mixed product bundling strategy, a supplier can exploit the variation in the bundle reservation prices. If consumers vary in their valuations of the product bundle, offering only the product bundle leads to either a loss of consumers with a low reservation price for the bundle (at the high price) or a loss of potential revenues from the segment with a high reservation price for the bundle (at the low price). Both alternatives result in lower revenue compared with mixed product bundling, in which a supplier can accommodate all possible segments at optimal prices.
For example, in Table 3, Part B, note how mixed product bundling yields higher revenues only in one of the cases (Case 4) when reservation prices vary. In all other cases, mixed product bundling yields the same revenues as pure product bundling. The exact point at which mixed product bundling becomes superior to pure product bundling is dependent on the configuration of the conditional reservation prices. As yet, we have no precise formula to determine this point, though our simulation can determine whi1h strategy is optimal as configurations change.
Although mixed product bundling is superior to other bundling strategies in specific contexts, when a firm has market power or the benefits to consumers are not clear, mixed product bundling is far superior to the other strategies because of the current legal environment. Pure product bundling strategies by high-profile firms with market power are likely to be challenged by the U.S. Department of Justice. Even if the company is not found guilty in court, the legal battle with the government may involve enormous legal costs and management time. In such cases, mixed product bundling is the best defense against prosecution because it is legal. Thus, implementation of the economic optimum must be tempered by legal considerations when strategies are implemented.
P<SUB>5</SUB>: Combining a product with a price bundling strategy is superior to mere product bundling if consumers' conditional reservation prices (a) for the separate products and (b) for the price bundle and the product bundle are asymmetric.
Asymmetry between price and product bundles occurs when one segment values the price bundle but not the integrated product bundle, and another segment values the integrated product bundle but not the price bundle.
No previous research addresses combining a price and a product bundling strategy, let alone its optimality. P5 states two conditions for the optimality of such a strategy. First, conditional reservation prices for the separate products must be asymmetric. If not, price bundles will gain no more revenues than will selling the products separately (by P<SUB>1</SUB>). Second, consumers' reservation prices for the price bundle and the product bundle must be asymmetric. In this situation, the firm can price the integrated product bundle to demand a premium price from the latter segment and can price the price bundle to exploit the asymmetry in reservation prices of the former segment.
An illustration for this proposition is the frequency with which companies combine a product and price bundling strategy in a variety of industries, such as information systems and sound systems, in which consumers are presented the full array of separate products, price bundles, and (integrated) product bundles. The reason is the asymmetry in consumer reservation prices for all these combinations. Some consumers value a CD player, others value a receiver, and still others value a good set of speakers. Also, some consumers value an integrated system, whereas others like to mix and match their own system and buy the separate products.
Objectives of the Firm
The literature on bundling has not dealt with any goals of firms other than profit or revenue maximization. Consequently, the propositions we formulate in this section have not been addressed in any way in previous literature. Further research could refine and test these propositions.
An important alternative goal to profit or revenue maximization may be maximizing market penetration. This goal is relevant for a new product, particularly in high-tech and Internet environments. In the latter case, rapid market penetration becomes paramount because a rapidly growing product has the potential to monopolize the market ("winner takes all"; see Liebowitz and Margolis 1999), so profit maximization may be secondary, at least initially. In such contexts, bundling a new product with an existing product can be a critical strategy for success.
Price bundling. We offer the following proposition:
P<SUB>6</SUB>: When a firm's goal is to maximize market penetration first and profits second, pure price bundling either is the best strategy or is no worse than any other strategy.
Comparing pure price bundling with unbundling is straightforward. If a firm strives for maximum penetration, it would price the separate products in an unbundling strategy at the minimum of consumers' reservation prices for the separate products. In a pure price bundling strategy, it would price the bundle at the minimum of consumers' reservation prices for the bundle. However, from P<SUB>1</SUB>, the revenues from the latter strategy will always be higher than (in case of asymmetric reservation prices) or equal to (in case of symmetric reservation prices) the revenues of the former strategy.
As we discussed previously, the profitability of mixed price bundling stems from selling separate products to consumers with a high valuation for them, while selling the bundle to the other consumers. In other words, its optimality is based on excluding some consumers from buying all products in the bundle. However, if the company's prime objective is to increase market penetration, it will not want to exclude any consumers from buying one of its products. Therefore, if the goal is market penetration, pure price bundling will be superior.
In addition, pure price bundling may have other strategic advantages. Pure price bundling may serve as a means of subsidizing trial. Some segments may not even have heard of the new product. In that case, pure price bundling provides both visibility and trial for the new product. To the extent that visibility and trial are important in new product diffusion (Rogers 1995), they provide additional reasons for pure price bundling. Although this proposition may appear to be a bold statement at first, many real-life examples provide some validation. New software is often included for free with purchased software or hardware (e.g., Microsoft's Money with the Windows operating system). Free samples of new products are often included with already existing products in fast-moving consumer goods. New financial services often are free at first to existing customers; when they are well adopted, firms start to charge for them.
The following example can explain the intuition of this proposition. Consider the marketing manager at McDonald's responsible for the introduction of a new item, the McFlurry. To maximize market penetration, the marketing manager can bundle the McFlurry with each sandwich on the menu at the additional price that equals the lowest that any consumer is willing to pay for it. This is a strategy of pure price bundling and will ensure that every consumer of any of McDonald's sandwiches tries the McFlurry. Thus, the strategy provides the new product with high visibility, trial, and penetration. In addition, if consumers value the McFlurry more than the additional price added to the bundle, the strategy creates consumer surplus for the bundle. In every other price strategy for the McFlurry, its market penetration would be equal or less.
However, pure price bundling of separate products is illegal if the supplier has market power. In such situations, firms may need to adopt mixed price bundling even when pure price bundling would be superior. Here again, when firms implement bundling strategy, they must temper what is economically optimum with what is legally prudent.
Product bundling? For product bundles, we formulate the following proposition:
P<SUB>7</SUB>: When a firm's goal is to maximize market penetration first and profits second and a product bundle is possible, pure product bundling is as good as, if not better than, any other strategy; the only exception is the case in which consumers' conditional reservation prices (a) for the separate products and (b) for the price bundle and product bundle are asymmetrically distributed. In the latter case, combining a pure price bundling strategy with a pure product bundling strategy may be optimal.
The first part of this proposition is in line with P6. That product bundling also creates added value for consumers only makes the case for the optimality of pure product bundling stronger. Again, pure product bundling will be superior to mixed product bundling when the optimality depends crucially on the exclusion of certain buyers from buying the bundle. The optimality of pure product bundling over unbundling is even clearer, because a supplier can capture added value by selling the product bundle, compared with selling the separate products. In addition, pure product bundling may provide a new product with higher visibility and trial. Moreover, because it involves a product bundle, the link with perceived functionality is even clearer than in the case of price bundles. Thus, pure product bundling is superior to any other strategy. The second part of the proposition runs parallel to P<SUB>5</SUB> for the same rationale as for that proposition.
We now can fully appreciate Microsoft's bundling strategy of Internet Explorer from an economic perspective. Considering Microsoft's objective of rapid market penetration for Explorer, a pure product bundling strategy as implemented by Microsoft makes perfect economic sense. By integrating the browser into the operating system at no extra charge, Microsoft effectively maximized its market penetration by maximizing the browser's visibility and trial and also maximized consumer surplus. However, Microsoft clearly misjudged the optimality of a pure product bundling strategy from a legal perspective. The legality of the practice is not always clear, because it depends on a judgment of a firm's monopoly power and the offsetting benefits of its product bundling. Even if Microsoft were legally right and won on appeal, the whole investigation and court case cost the company dearly. The case consumed a great deal of top management's time and attention, it lowered employee morale, talent left to join competitors, and the firm lost approximately 35% of its market value.
Competition
The literature on how competition influences the optimality of different bundling strategies is rather thin. Further research in this area would be fruitful.
Price bundling To discuss the impact of competition on the optimality of different bundling strategies, we again start with our base case, in which the supplier cannot develop a product bundle.
P<SUB>8</SUB>: In competitive markets, a mixed price bundling strategy dominates a pure price bundling strategy.
Research by Matutes and Regibeau (1992) supports this proposition. In competitive markets, from oligopoly to perfect competition, companies cannot differentiate themselves from competitors by bundling. Even if a pure price bundling strategy were more profitable, the strategy would encourage competitors to offer both bundled and separate products. This mixed bundling strategy would be more attractive to consumers and consequently would cut into the market share of the firm using pure price bundling. Consequently, this firm would also adopt a mixed price bundling strategy. It can be shown mathematically that in the long run, both companies sustain their mixed price bundling strategies (Matutes and Regibeau 1992). However, all the firms could be better off if they could collude on an unbundling strategy, because then they could commit to not offering a discount on the bundle, which would increase their combined profits. Of course, price collusion is illegal.
It is still unclear how a mixed price bundling strategy compares with an unbundling strategy from a competition perspective. Anderson and Leruth (1993) provide some reasons firms may prefer unbundling to mixed price bundling in highly competitive environments. First, mixed price bundling is a readily observable strategy and thus an easy signal to trigger a response from competitors. Conversely, if a firm's marginal costs were unobservable and declining, retaining its prices at the same level and thereby increasing its profits would not be detected by competitors. Second, unilaterally adopting a mixed price bundling strategy could likely trigger aggressive pricing by competitors. Third, firms may try to avoid competing on multiple domains, in essence on a separate-products market and a bundle market. However, Anderson and Leruth's (1993) assumption that firms recognize the implications of embarking on a mixed price bundling strategy-that is, lower profits for both firms-is questionable.
An illustration of P8 is the pervasiveness of mixed price bundling strategies in highly competitive industries, such as telecommunications and banking services. In telecommunications, the degree of asymmetry in consumers' conditional reservation prices is high. Most telecom products are more or less substitutes (cellular versus fixed telephony). Thus, the revenue potential from price bundling strategies is substantial. But competition is so intense that firms cannot force pure price bundles on consumers. Therefore, the number of calling plans (various forms of bundling) has exploded to accommodate the superiority of mixed over pure price bundling.
Product bundling The analysis is similar in the case when a supplier can introduce a product bundle, though with a slightly different outcome. We propose the following:
P<SUB>9</SUB>: In competitive markets, if the supplier can introduce a product bundle, mixed product bundling strategies dominate unbundling and pure product bundling strategies.
This proposition is consistent with P8 and can be supported, at least partially, by the same logic. Competitive markets preclude clearly differentiated positions. Even if a firm develops a unique, superior product bundle, a differentiated position is not sustainable. Competitors imitate the product bundle immediately, with substantial downward pressure on prices. In addition, consumers who wish to mix and match products and compose their own system are left unserved. This creates an opportunity for firms to escape price pressure by adopting a mixed product bundling strategy. A mixed product bundling strategy increases variety, which in turn increases consumer demand. This result will motivate competitors to adopt and sustain mixed product bundling strategies (Matutes and Regibeau 1988).
An unbundling strategy is also not optimal. When adopting an unbundling strategy, a company would forgo a profitable opportunity, namely, offering a product bundle to consumer segments that value integration at a premium price. Note that if markets are not competitive, in the sense that product bundles can create sustainable differentiation, pure product bundling can be optimal. In this case, the product bundle will provide the company with a local monopoly and thus reduce price competition (Chen 1997).
To clarify P<SUB>9</SUB>, consider Apple's strategy. Initially, the Apple Macintosh was sold as a pure product bundle of hardware and software. This strategy was optimal because the Apple Macintosh was unique in performance and ease of use as a result of its graphical user interface. However, as time went on and other suppliers rapidly gained on Apple through product improvements, the Macintosh lost its uniqueness. Consumers found more flexibility and choice possibilities with competing suppliers, which led to a decline in Apple's market shares. At that time, a mixed product bundling strategy would have been superior for Apple.
Costs
Little work has been done in marketing or economics that relates costs to the optimal bundling strategies. Still, costs may be as important to the optimality of bundling as are the other factors we identified. Therefore, costs warrant further academic attention.
Three cost aspects appear relevant to bundling: the relative contribution margin, economies of scale and scope, and additivity of costs in the bundling process. Note that the immediate impact of price bundling is to increase revenues. The relative contribution margin is relevant to price bundling because it increases profits from revenue increases. Economies of scale and scope are relevant to price bundling because they decrease the costs of additional sales. Additivity of costs is relevant to product bundling because it determines the extra margin that the product bundling strategy generates.
However, costs will not be pertinent to the optimality of mixed versus pure bundling strategies because the costs do not vary much between these two strategies. For example, the costs for an opera house of selling only season tickets (pure bundling) or season tickets as well as individual tickets (mixed bundling) will not be very different.
The relative contribution margin is equal to (price -- variable costs) divided by price. Products such as home appliances, with high variable costs relative to price, have a low contribution margin. Products such as software, with low variable costs relative to price, have a high contribution margin.
Economies of scale are decreases in costs per unit as the scale of operation increases. Economies of scope are decreases in costs per unit of two or more products due to producing or marketing them together instead of separately. Economies of scale and scope are often present in technology and telecommunication markets.
Almost all articles on bundling assume costs to be additive. The term additive means that the ratio of the costs of the bundle to the sum of the costs of the separate products is equal to 1 (for an exception, see Hanson and Martin 1990). A variety pack of cereals is a good example of a price bundle with approximately additive costs. Subadditive costs, in which the ratio is smaller than 1, or superadditive costs, in which this ratio is greater than 1, have largely gone unresearched. The multimedia PC is an example of a product bundle with subadditive costs. Because the modem, CD-ROM, and speakers can be miniaturized (no casing, separate ports, cable, and so forth) and integrated in the PC, production, packaging, and distribution costs are lower than the sum of the costs of the separate products. A turnkey computer network is a product bundle with superadditive costs. Beyond the costs of the separate components (such as server, terminals, network software, and application software), the supplier has extra costs in seamlessly integrating the network. Again, we first develop the base case in which only price bundles are possible.
Price bundling. We can formulate the followingproposition:
P<SUB>10</SUB>: The profitability of price bundling is likely to be higher than that of unbundling (a) the higher the relative contribution margin and (b) the stronger the economies of scale or scope.
The rationale for P<SUB>10a</SUB> is that discounts on high-margin products are better able to raise profits than discounts on low-margin products, assuming constant price elasticity. For example, consider a marketing manager at Sears who is considering a bundle of a blender and a food processor. Assume that the blender has variable costs of $80 and sells for $100 and the food processor has variable costs of $700 and sells for $800. If Sears offers a bundle of the two products at $810 (amounting to a 10% discount), it gains only $30 contribution per bundle sold. Because this bundled offer will cut into regular sales margins, the sales increase must be 300% to make bundling profitable. Compare this with a marketing manager at Microsoft who considers offering a temporary price bundle, in cooperation with original equipment manufacturers, of Microsoft Windows and Microsoft Office. Assume that these products sell at $100 and $60, respectively, and have variable costs of $4 and $5, respectively. If Microsoft prices the bundle at $144 (10% discount), it will gain $135 in contribution per bundle sold. A sales increase of only 12% will make bundling profitable. Therefore, assuming constant price elasticity and equal incremental and cannibalized sales, Microsoft will benefit more from a price bundling strategy than Sears will.
When economies of scale exist, an increase in sales volume will lower costs and increase profits. Because price bundling can increase sales, it will be more profitable than unbundling strategies when economies of scale are present. When economies of scope are present, a firm can jointly produce and market a portfolio of products more economically than doing so separately. Price and product bundling can increase sales of a portfolio of products. Thus, such bundling strategies are more profitable than unb8ndling strategies when economies of scope are present.
Product bundling. Product bundling strategies almost always call for attention to costs of the bundling process. This situation contrasts sharply with price bundling, in which the cost structure of the individual products is important. The reason is that in price bundling, the bundled products do not change.
P<SUB>11</SUB>: If costs of product bundling are subadditive, a product bundling strategy is always superior to an unbundling strategy, irrespective of consumers' reservation prices, the firm's strategic objectives, or the nature of competition.
In many cases, product bundling will generate diverse cost savings. The multimedia PC is a good example of such cost savings. By internalizing the CD-ROM in the PC, costs can be saved in the casing and the connection of the CD-ROM. The same goes for thy speakers and the modem. Also, on-site assembly costs may be lower. Costs that would accrue from connecting and installing separate components on-site, such as the CD-ROM and modem, to the reseller (e.g., CompUSA) or the manufacturer directly (e.g., 9ell) are avoided by the "out of the box" multimedia PC.
In case product bundling generates extra costs, the optimality of product bundling depends on the trade-off between the extra costs generated and extra revenues from the added sales to consumers who appreciate the product bundle. The existence of system integrators in many industrial markets (Wilson, Weiss, and John 1990) points to the potential profitability of such a strategy. System integrators typically integrate a set of different products into a system. For example, companies can buy an entire computer network through a system integrator, such as Andersen Consulting, versus mixing and matching the separate components, such as server, terminals, and software, from different vendors. In many cases, companies will contract from a system integrator, though its total price may be higher.
Consumers' Perceptions of Bundles
The previous discussion is largely driven by economic principles. However, in the past two decades, considerable behavioral research has focused on consumers' perceptions of bundles. Insights developed from these studies may help companies in fine-tuning their formulation and presentation of bundling strategies. We synthesize the main conclusions of this literature in these final propositions on optimality. Previous research shows support for these propositions. Research in this area is so recent and the area is so rich with phenomena that further research promises to be quite fruitful.
Most of the behavioral research on bundling is grounded in prospect theory (Kahneman and Tversky 1979) and mental accounting (Thaler 1985). Central to prospect theory is the value function. In prospect theory, outcomes are framed as positive (gains) or negative (losses) deviations from a reference point. The value function is concave in gains and convex in losses. Mental accounting suggests that people perceive multiple gains as more rewarding and multiple losses as more punishing than a single gain and a single loss of the same amount. What are the implications of these principles for bundling strategies?
P<SUB>12</SUB>: For price information, it is optimal for companies to (a) integrate all price information in a single bundle price rather than present it in a list of separate product prices and (b) separate the bundle discount in multiple savings rather than presentit as a single saving.
This optimality is driven by considerations on purchase likelihood as well as subsequent consumption behavior. Several researchers show that presenting consumers with a single bundle price lowers price sensitivity and increases purchase likelihood (Drumwright 1992; Gaeth et al. 1990; Yadav and Monroe 1993). The theoretical rationale is the following: Consumers perceive a single loss as less punishing than multiple losses. Therefore, they value a single bundle price more than one that explicitly sums the prices of the separate products.
For example, suppose consumers are confronted with two possible offerings for portable PCs:
a. Take advantage of this great deal: Buy now a portable PC for only $2,500 and get a Deluxe Case at $99 and printer HP DeskJet 932C at $199, or
b. Take advantage of this great deal: Buy a portable PC, with a Deluxe Case and printer HP DeskJet 923C for only $2,798.
P<SUB>12a</SUB> suggests that consumers prefer Offer b to Offer a. Therefore, it is optimal for companies to present consumers with a single bundle price. Also, this mechanism sometimes enables consumers to buy more than they would if products were offered individually.
In contrast, consumers prefer their gains segregated. They perceive multiple savings in the bundle as more favorable than a single saving (Johnson, Herrmann, and Bauer 1999; Mazumdar and Jun 1993). Consider the following two offerings as illustrations:
a. Take advantage of this great deal: If you buy all your telecom services from AT&T, get $200 cash back on your long distance calls and a credit of $100 on international calling.
b. Take advantage of this great deal: If you buy all your telecom services from AT&T, get $300 cash back.
Research shows that consumers value Offer a more than Offer b.
Considerations of consumption behavior also may drive the optimality of presenting consumers with a bundled price rather than a list of separate product prices. Recent research has found that consumers who buy a bundle of products at a bundled price consume less of the bundle than do consumers who are presented with separate product prices (Prelec and Loewenstein 1998; Soman and Gourville 2001). Consumers who buy a bundle of products at a bundled price perceive far greater ambiguity on the sunk cost of their purchase than do consumers presented with separate product prices. This greater ambiguity "decouples" the sunk cost of the purchase from the extra benefit of consuming the entire bundle. In other words, consumers who are presented with a bundled price will account less for the sunk costs of their purchase than will consumers who are presented with separate prices.
Consider two consumers, John and Robert, who have purchased tickets for a series of four NBA games at $20 each. John is presented with a bundled price of $80, whereas Robert has paid $20 separately for each of the four games. Because of their mode of payment, Robert has less ambiguity of the cost of each ticket ($20) than does John. As such, the sunk cost of each ticket looms larger for Robert than it does for John. For this reason, prior research shows that Robert has a higher likelihood of seeing all four games.
Although most research indicates support for P<SUB>12</SUB>, there may be exceptions to this general guideline. For example, Morwitz, Greenleaf, and Johnson (1998) show that partitioned pricing, in which a firm divides a product's price into two mandatory parts, the product and shipping charges, can increase consumer demand because of lower recalled costs. This example suggests the need for more research on potential causes for the discrepancy. Important topics in this research could be the price differential between products in the bundle, the salience of the product and bundle prices, and the cognitive effort involved in evaluating the bundle.
Although the economics literature has some in-depth analyses of bundling in specific situations, the topic has enjoyed only scattered research in marketing. Moreover, the published studies are fuzzy about some basic terms and principles. In addition, the literature lacks a unifying classification of the strategies, clear norms for the legality of the strategies, and a comprehensive framework for the optimality of bundling strategies. We try to address these limitations. This article provides a new synthesis of the field of bundling based on a critical review and extension of the marketing, economics, and law literature. This article makes the following three contributions:
First, the article defines bundling terms and principles to reveal a new, rich set of bundling strategies. It presents a classification of these strategies that provides a clear understanding of the relationship among them. In particular, the classification shows that price bundling and product bundling are independent strategies, which firms can mix and match to best meet consumer demand.
Second, this article reviews the legal literature to articulate certain fairly simple conditions that guide the legality of bundling strategies. In particular, it clarifies the current ambiguity in case law by identifying the per se rule and the rule of reason. The exposition distinguishes between issues of law, in which clear norms are discernable, and issues of fact, in which empirical cases may be quite ambiguous and controversial.
Third, the article develops a framework of 12 propositions that prescribe the optimal bundling strategy depending on five important factors. The literature contains partial empirical or mathematical support for only three of the propositions (P<SUB>1</SUB>, P<SUB>8</SUB>, and P<SUB>12</SUB>), and it imprecisely describes one of the propositions (P<SUB>2</SUB>). All the other eight propositions have been proposed here for the first time. The propositions synthesize a body of knowledge about the trade-offs managers must make when choosing among bundling strategies in specific contexts. The article emphasizes that such trade-offs should account for the legality as well as the economic optimality of a bundling strategy.
Implications for Marketing Management
Bur synthesis offers answers to the following managerial concerns.
When is bundling illegal? The controversy about and probable strategic errors in the recent Microsoft case show that bundling is not well understood, even by well-financed major corporations. History shows that engaging in illegal or even potentially illegal bundling strategies can be costly. The legal battle takes many years, costing valuable management time and large financial resources. An eventual conviction is even more costly, because in most cases, judges mandate monetary penalties or radical organizational changes. We have defined clear rules by which managers can easily assess whether a certain strategy is illegal. Most important, firms with dominant market power that consider implementing pure bundling strategies should scrutinize the legality of their bundling strategy. Although pure price bundling for such firms is illegal at all times, pure product bundling may be legal if the bundle offers substantial added value to consumers that cannot be achieved when firms sellthe bundled products separately. Faced with these legal constraints, companies with dominant market power may find it optimal to resort to value-added product bundling for long-term benefits rather than to short-term price bundling to gain market share. In this respect, it would have been better for Microsoft to have invested in unambiguous value-enhancing integration of Internet Explorer and Windows at the start, instead of merely packaging the browser and the operating system. This latter initial strategy triggered the original lawsuit.
What are the drivers of the optimality of bundling? This article shows that bundling is profitable for a variety of reasons and thus deserves more attention from managers. In particular, we find that price bundling of existing products may be optimal because it is a form of price discrimination between different consumer groups and because it decreases price sensitivity and increases individual consumers' purchase likelihood. We also find that price bundling yields larger profit increases the higher the relative contribution margin and the stronger economies of scope and scale are. Thus, services or goods with high development costs-such as high-tech products-generally have more to gain from price bundling than do goods with high marginal costs, such as consumer durables or industrial goods. We find that product bundling of existing products may be optimal because it creates added value for consumers, saves costs, and creates differentiation in highly competitive markets. We also argue that bundling a new product with an existing product is an ideal introduction strategy because it allows extraction of more consumer surplus at equal sales levels. In addition, price bundling will increase the visibility and trial of the new product, which are important in new product adoption by consumers. Product bundling may also improve consumers' perceptions of the functionality of the new product when it is bundled with existing complements.
This discussion suggests that firms that exploit opportunities offered by bundling will enjoy increases in market shares and profits. Thus, developing expertise in designing bundling strategies may be of prime importance in achieving long-term success. The guidelines we posit in this article may be the first step in enhancing managerial insights on the optimality of bundling.
Choose mixed or pure bundling? Prior research generally views mixed bundling as at least weakly superior to pure bundling. Our discussion shows that this literature may be misguided because it assumes that pure bundling can never be optimal. In contrast, we propose that mixed bundling is superior to pure bundling only in highly competitive environments or when consumer reservation prices vary a fair amount. Moreover, we argue that for new products, pure bundling strategies tend to outperform mixed bundling strategies. Pure bundling strategies necessarily bring all consumers of an existing product in contact with the new product, so they grow aware of it and can easily try it out. Thus, developing expertise on the proper choice between pure bundling and mixed bundling is important for using bundling strategies profitably.
Limitations and Further Research
This article has several limitations that further research could address. First, product bundling is relatively new, and its use in high-tech markets can benefit from further research. Two questions seem especially pressing: What factors drive customer preferences for product bundles in high-tech environments? and How can suppliers optimally organize themselves to offer product bundles when they do not have competence on all products in the bundle?
Second, limitations of time and space prevented a formal mathematical proof or empirical validation for each of the propositions. The field would benefit especially from research that defines the domain and validity of the newly proposed propositions. The most promising areas of further research appear to be the impact of competition and alternative strategic objectives on the optimality of bundling. Although prior analytical research has developed some insight on the impact of competition, it is limited mostly to monopoly and relatively simple forms of bundling.
Third, the article does not indicate the relative importance of each of the conditions for optimality. Intuitively, for price bundling, we suspect that the distribution of conditional reservation prices is probably the predominant condition for optimality. The reason is that price bundling, by nature, tries to exploit the heterogeneity in consumers' conditional reservation prices. For product bundling, costs seem to be the important condition for optimality. The reason is that costs determine the amount of value firms can build into the product bundle. Empirical research that tests our intuition on this issue would be fruitful.
Finally, this article focuses on the optimality of bundling toward the end user (either consumers or businesses). It does not discuss the optimality of bundling in channels (i.e., the optimality of bundling by a manufacturer to a retailer). An example of-the latter is full-line forcing, in which a manufacturer forces a retailer to carry an entire line of products. Research into this area should be fruitful.
In summary, the current article underscores the centrality of bundling in marketing. It integrates research from a variety of perspectives to provide a deeper and more complete understanding of bundling than is as yet available to marketers. However, it suffers from several shortcomings, which we hope will stimulate further research in this area.
1 The term "product" in this definition and the rest of the text refers to both goods and services.
- 2 The reservation price of a product is the maximum price a consumer is willing to pay for the product. The conditional reservation price is the reservation price of a product, conditional on the consumer buying another product.
- 3 Many economics scholars will approach tying more narrowly, as the pure bundling of products in fixed proportions; for example, a bundle of a car and car insurance is always the combination of one car with one insurance policy.
Term Definition Examples
Bundling Bundling is the sale of two or more Opera season tickets,
separate products in one package. multimedia PC
Price Price bundling is the sale of two or Luggage sets, variety
bundling more separate products as a package pack of cereals
at a discount, without any
integration of the products.
Product Product bundling is the integration Multimedia PC, sound
bundling and sale of two or more separate system
products at any price.
Pure Pure bundling is a strategy in which IBM's bundling of
bundling a firm sells only the bundle and not tabulating machines
(all) the products separately. and cards
Mixed Mixed bundling is a strategy in which Telecom bundles
bundling a firm sells both the bundle and
(all) the products separately. A: Reservation Prices for X, Y in Four Cases of Symmetry and Variation
When Only a Price Bundle Is Possible
Reservation Prices ($)
Case 1: Symmetric Invariable Case 2: Symmetric Variable
Products Sports Entertain- Price Sports Entertain- Price
Segments Illus- ment Bundle Illus- ment Bundle
trated Weekly trated Weekly
A 50 30 80 50 30 80
B 50 30 80 40 10 50
Case 3: Asymmetric Invariable Case 4: Asymmetric Variable
A 50 30 80 50 30 80
B 30 50 80 10 40 50
B: Prices and Sales Generating Maximum Revenues for Alternative Price
Bundling Strategies (Equal-Sized Segments)
Case 1: Symmetric Invariable Case 2: Symmetric Variable
Average Average
Revenue Revenue
per per
Sales Consumer Sales Consumer
Strategy Prices ($) A B ($) Prices ($) A B ($)
Unbundling Sports 1 1 80 Sports 1 1 55
Illustrated Illustrated
= 50 = 40
Entertain- 1 1 Entertain- 1 0
ment Weekly ment Weekly
= 30 = 30
Pure price
bundling Price 1 1 80 Price 1 1 50
bundle = 80 bundle = 50
Mixed Sports 0 0 80[a] Sports 0 1 55
price Illustrated Illustrated
bundling = 50 = 40
Entertain- 0 0 Entertain- 0 0
ment Weekly ment Weekly
= 30 = 30
Price 1 1 Price 1 0
bundle = 80 bundle = 70
Case 3: Asymmetric Invariable Case 4: Asymmetric Variable
Unbundling Sports 1 1 60 Sports 1 0 55
Illustrated Illustrated
= 30 = 50
Entertain- 1 1 Entertain- 1 1
ment Weekly ment Weekly
= 30 = 30
Pure price
bundling Price 1 1 80 Price 1 1 50
bundle = 80 bundle = 50
Mixed Sports 0 0 80[a] Sports 0 0 60
price Illustrated Illustrated
bundling = 50 = 50
Entertain- 0 0 Entertain- 0 1
ment Weekly ment Weekly
= 50 = 40
Price 1 1 Price 1 0
bundle = 80 bundle = 80
[a]Note that revenue-maximizing prices here reduce mixed price
bundling to de facto pure price bundling (only bundled sales occur).
Every pricing strategy in which consumers buy separate products will
reduce profits. This further underlines the validity of P<SUB>2</SUB>. A: Reservation Prices for X, Y in Four Cases of Symmetry and Variation
When a Product Bundle Is Possible
Reservation Prices ($)
Case 1: Symmetric Invariable Case 2: Symmetric Variable
Products Product Product
Segments Receiver CD player Bundle Receiver CD player Bundle
A 500 250 800 500 250 800
B 500 250 800 450 100 600
Case 3: Asymmetric Invariable Case 4: Asymmetric Variable
A 500 250 800 500 250 800
B 250 500 800 100 450 600
B: Prices and Sales Generating Maximum Revenues for Alternative
Product Bundling Strategies (Equal-Sized Segments)
Case 1: Symmetric Invariable Case 2: Symmetric Variable
Average
Revenue Revenue
per per
Sales Consumer Sales Consumer
Strategy Prices ($) A B ($) Prices ($) A B ($)
Unbundling Receiver 1 1 750 Receiver 1 1 575
= 500 = 450
CD player 1 1 CD player 1 0
= 250 = 250
Pure Product 1 1 800 Product 1 1 600
product bundle bundle
bundling = 800 = 600
Mixed Receiver 0 0 800[a] Receiver 0 1 600
product = 500 = 450
bundling
CD player 0 0 CD player 0 0
= 250 = 250
Product 1 1 Product 1 0
bundle bundle
= 800 = 750
Case 3: Asymmetric Invariable Case 4: Asymmetric Variable
Unbundling Receiver 1 1 500 Receiver 1 0 500
= 250 = 500
CD player 1 1 CD player 1 1
= 250 = 250
Pure Product 1 1 800 Product 1 1 600
product bundle = 800 bundle = 600
bundling
Mixed Receiver 0 0 800[a] Receiver 0 0 625
product = 500 = 500
bundling
CD player 0 0 CD player 0 1
= 500 = 450
Product 1 1 Product 1 0
bundle = 800 bundle = 800
[a]Note that revenue-maximizing prices here reduce mixed product
bundling to de facto pure product bundling (only bundled sales occur).
Every pricing strategy in which consumers buy separate products will
reduce revenues. This further underlines the validity of
P<SUB>4</SUB> and P<SUB>5</SUB>.DIAGRAM: FIGURE 1 A classification of Bundling Strategies
DIAGRAM: FIGURE 2 Flow Chart of Excel Optimization Program (Displayed Here to Generate a Two-Segment, Two-Product Example)
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By Gerard J. Tellis
Stefan Stremersch is Doctoral Student in Marketing, Tilburg University. Gerard J. Tellis holds the Jerry and Nancy Neely Chair in American Enterprise, Marshall School of Business, University of Southern California. This article was completed when the first author was visiting doctoral student, Marshall School of Business, University of Southern California. The authors thank Rajesh Chandy, Ruud T. Frambach, Peter Golder, R. Venkatesh, Eden Yin, and the four anonymous JM reviewers for their many valuable comments on previous versions of this article. Financial support from the Intercollegiate Center for Management Science is gratefully acknowledged.
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Record: 143- Strategic Firm Commitments and Rewards for Customer Relationship Management in Online Retailing. By: Srinivasan, Raji; Moorman, Christine. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p193-200. 8p. 1 Chart, 2 Graphs. DOI: 10.1509/jmkg.2005.69.4.193.
- Database:
- Business Source Complete
Strategic Firm Commitments and Rewards for Customer
Relationship Management in Online Retailing
Academic studies offer a generally positive portrait of the effect of customer relationship management (CRM) on firm performance, but practitioners question its value. The authors argue that a firm's strategic commitments may be an overlooked organizational factor that influences the rewards for a firm's investments in CRM. Using the context of online retailing, the authors consider the effects of two key strategic commitments of online retailers on the performance effect of CRM: their bricks-and-mortar experience and their online entry timing. They test the proposed model with a multimethod approach that uses manager ratings of firm CRM and strategic commitments and third-party customers' ratings of satisfaction from 106 online retailers. The findings indicate that firms with moderate bricks-and-mortar experience are better able to leverage CRM for superior customer satisfaction outcomes than firms with either low or high bricks-and-mortar experience. Likewise, firms with moderate online experience are better able to leverage CRM into superior customer satisfaction outcomes than firms with either low or high online experience. These findings help resolve disparate results about the value of CRM, and they establish the importance of examining CRM within the strategic context of the firm.
The study and practice of customer relationship management (CRM) has experienced explosive growth over the past decade. Extant research provides two sets of insights into the relationship between a firm's CRM investments and its performance. The first set focuses on CRM as expenses. Gupta, Lehmann, and Stuart (2004) find that customer acquisition and retention expenses have a significant, positive effect on firm value. Other studies report a positive relationship between a firm's CRM technology investments and CRM performance (Jayachandran et al. 2005; Mithas, Krishnan, and Fornell 2005). The second set of studies envisions CRM as a firm capability and, again, reports its positive effects on both CRM and business performance (Day and Van den Bulte 2002; Reinartz, Krafft, and Hoyer 2004).
However, these findings are in contrast to increasing practitioner skepticism of CRM expenditures. As Day and Van den Bulte (2002) note, practitioners report that the majority of CRM initiatives fail to meet expectations (Dignan 2002). Indeed, CRM has been decried as one of the biggest blunders of the early twenty-first century (Infoworld 2001); there is evidence that most CRM initiatives do not deliver the anticipated return on investment (Gartner Group 2003).
This divergence in the effectiveness of CRM across theory and practice is both troubling and intriguing. It is troubling because though CRM as a way to manage customers is here to stay, increasing skepticism among practitioners signals that CRM will face intense scrutiny and accountability. From a theoretical perspective, the divergence is intriguing because it implies that the observed variability in CRM performance may be explained by moderating factors.
Considering possible explanations for the observed variability, researchers have examined ( 1) CRM data-related techniques (e.g., Ansari and Mela 2003), ( 2) marketing strategies for customer profitability (e.g., Reinartz and Kumar 2000, 2003; Rust, Lemon, and Zeithaml 2004; Rust and Verhoef, 2005; Venkatesan and Kumar 2004), ( 3) the balance between customer acquisition and retention efforts (e.g., Reinartz, Thomas, and Kumar 2005), and ( 4) effective CRM implementation (Day and Van den Bulte 2002; Reinartz, Krafft, and Hoyer 2004). Although impressive in scope, extant research offers few insights on the strategic choices that are associated with the effective deployment of CRM. This is remiss because strategic conduct influences the effect of customer satisfaction on firm value (Anderson, Fornell, and Mazvancheryl 2004).
We address this gap in the literature. Specifically, we ask whether the effect of a firm's CRM on CRM performance, as measured by customer satisfaction ratings, is influenced by its prior strategic commitments. Strategic commitments can involve any long-term firm decision, such as the choice to enter specific markets or invest in products, brands, channels, or partnerships. Using the empirical context of the emerging online retailing market, we investigate whether CRM performance is weakened or strengthened by two relevant prior strategic commitments of the retailer: ( 1) bricks-and-mortar or offline experience and ( 2) online entry timing. We test our predictions using manager ratings of CRM and strategic commitments and customer ratings of satisfaction in 106 online retailers.
Predictions
Bricks-and-mortar experience refers to the firm's level of offline experience before its online entry. Bricks-and-mortar experience is a form of strategic commitment that reflects the retailer's incumbency in offline retailing. Traditionally, studies of incumbency have focused on an incumbent s ability to innovate an emerging technology (e.g., Chandy and Tellis 2000). In this study, we examine the effects of incumbency on the effectiveness of a firm's CRM investments in an emerging market. Our review of the literature suggests that there are both customer and firm explanations for this question.
Customer factors. Several customer-based factors imply that a firm's bricks-and-mortar experience may strengthen the effect of its CRM on performance. First, bricks-and-mortar retailers have existing supply chain infrastructures, which should improve fulfillment efficiency, a key success factor in online environments. Second, bricks-and-mortar online retailers have access to extensive customer information from their offline operations, which may improve their ability to deploy CRM effectively to serve online customers. Third, bricks-and-mortar firms offline brand and relationship equities can be leveraged in their online operations (Geyskens, Gielens, and Dekimpe 2002). These market-based assets (Srivastava, Shervani, and Fahey 1998) may help the firm establish strong online customer relationships. For example, bricks-and-mortar operations may serve as a "source of advertising to pre-sell merchandise" (Alba et al. 1997, p. 48).
Finally, bricks-and-mortar retailers enable online customers the option to experience products before they purchase them, which reduces customers' uncertainty and helps them identify products that closely match their preferences, thus increasing their satisfaction (Alba et al. 1997). Customers may also prefer returning products purchased from online retailers to the offline store, saving shipping costs. Thus, the bricks-and-mortar experience of retailers can complement their online operations and increase the returns on their online CRM investments.
Firm factors. Although there are positive effects involving customer-based factors, several features of bricks-and-mortar online retailers suggest the opposite. First, incumbents with a long history of offline retailing may be concerned about cannibalizing their bricks-and-mortar operations (Alba et al. 1997; Lynch and Ariely 2000). As Ghosh (1998, p. 127) notes, Established businesses that … have carefully built brands and physical distribution relationships risk damaging all they have created when they pursue commerce in cyberspace." As a result, bricks-and-mortar firms may be less aggressive in their online CRM efforts. As Kanter (2001, p. 92) notes, "Ask big companies about their goals for the Web, for example, and they are likely to reply, 'Cautious testing.' Ask dot-coms and they declare, Total world domination!'"
Second, bricks-and-mortar experience of online retailers may be viewed as an organizational routine involving tacit knowledge (Nelson and Winter 1982). Transfers of such tacit knowledge are often characterized by stickiness and misapplication (Szulanski 1996). Thus, bricks-and-mortar retailers may inappropriately transfer knowledge from their offline operations to their online operations, negatively affecting online performance. Indeed, because offline and online business models are distinct, bricks-and-mortar retailers may need to unlearn what led to their offline success in the design of their online CRM systems (Kanter 2001).
Prediction. Integrating the evidence, we expect that moderate levels of bricks-and-mortar experience should produce the highest performance returns on a firm's CRM investments. Moderate bricks-and-mortar experience provides access to key market-based assets without concerns about cannibalization or the well-entrenched routines that may create incumbency inertia. Conversely, low bricks-and-mortar experience offers only freedom from the inhibiting aspects of incumbency, and high bricks-and-mortar experience offers only access to customer relationships and knowledge. Thus:
H1: The positive effect of CRM on performance is stronger for firms with moderate bricks-and-mortar experience than for firms with low or high levels of bricks-and-mortar experience.
A retailer's decision to enter online markets represents an important strategic commitment. We use the term "firm online experience" to capture the firm's entry-timing strategy, and we define it as the firm's online experience relative to the first entrant in the industry. Several aspects of online retailing, especially in its early years, suggest that the returns on a firm's CRM investments are influenced by its entry timing. In line with H1, we offer both customer and firm explanations to derive our prediction.
Customer factors. Some aspects of online customers imply that a firm's online experience may influence its performance rewards for CRM investments. Customers gain efficiencies when switching to online (from offline), which may increase switching costs, satisfaction, and loyalty (Johnson, Bellman, and Lohse 2003; Zauberman 2003). Thus, early entrants may be able to extract the greatest performance rewards from their CRM investments. However, this argument overlooks a key feature of emerging markets; namely, early markets are different from the mass market in terms of customers' willingness to take risks (e.g., Rogers 1995). In addition, customers in early markets have weaker expectations given the nascent status of these markets (Boulding et al. 1993). Thus, early entrants that establish customer relationships with early adopters may be disadvantaged when targeting later adopters (Degeratu, Rangaswamy, and Wu 2000). This is problematic because most customers enter the market during the middle and later stages of market evolution. Thus, firms may expect the strongest response to their CRM investments when they enter in the middle stages of market evolution, not in the earlier or later stages.
Firm factors. Several aspects related to a firm's online experience suggest that there are advantages for later entrants to achieve higher performance from their CRM investments. First, online retailing is a new technology that is characterized by firm (e.g., changes in Web design) and customer (e.g., learning how to use the online interface) experimentation. Indeed, online retailers continually reengineer their strategies to meet the evolving needs of online customers (Wind and Mahajan 2002). Thus, later entrants may have an advantage over early entrants in configuring cost-effective CRM systems.
Second, emerging markets, such as online retailing, are characterized by technological turbulence. For example, the performance-price ratios of online CRM technology increased dramatically over time, such that later entrants implemented more cost-effective CRM investments than early entrants. As such, early online entrants with large investments in vintage CRM technology incur considerable upgrade costs to remain competitive (The Gartner Group 2003). In turn, early entrants' unwillingness to incur these costs creates gateways for later entrants (Golder and Tellis 1993).
Prediction. Integrating these arguments, we expect that firms with moderate online experience receive the greatest rewards for CRM investments. We argue that when online entry moves from moderate to early, there may be reductions in the effectiveness of CRM investments due to differences in online customer cohorts or to evolving information technologies that are costly to upgrade. Conversely, late entrants may fail to achieve strong performance because of customer loyalty to early entrants. Thus:
H2: The positive effect of CRM on performance is stronger for firms with moderate online experience than for firms with low (late entrants) or high (early entrants) online experience.
Method
We test the predictions using a multimethod approach in a sample of online retailers. The population consisted of online retailers that were enrolled in BizRate.com's rating service in the summer of 2001. BizRate.com inserts a popup HTML that invites an online retailer's customers to participate in a survey that rates their satisfaction with a retailer after completion of a purchase from the retailer. After order fulfillment by the retailer, BizRate.com sends a second e-mail survey to these customers to obtain customer satisfaction ratings.
BizRate.com sent a Web link by e-mail to the senior managers of firms enrolled in its service on May 1, 2001, inviting them to participate in our study. In return, firms were promised information about how their firm compared with other firms on key variables. A total of 187 of the 978 online firms responded to the survey, for a response rate of 19%. Key informants, who averaged 46 months' tenure, reported high levels of confidence (5.80/7.00) in the information they provided. The average firm'size in the sample was 202 employees (standard deviation [s.d.] = 747), and the average age of online operations was 41 months (s.d. = 24); in addition, most retailers had offline experience (63%).
To investigate selection bias, we randomly selected 100 nonrespondent firms and compared them with the respondent firms on variables obtainable from public sources: ( 1) publicly held versus privately held and ( 2) bricks-and-mortar operations or not. We found no significant differences between respondent and nonrespondent firms.( n1) Of the 187 retailers that responded to our survey, BizRate.com had customer satisfaction data for 106, which formed the sample for this study.( n2) We found no significant differences between the 106 retailers with customer-ratings data and the 81 retailers without customer-ratings data.( n3)
We focus on customer satisfaction as the performance metric associated with CRM success. In addition to its inherent value as a key CRM performance metric, satisfaction positively affects other performance metrics, including retention, share-of-wallet, and even shareholder value (Anderson, Fornell, and Mazvancheryl 2004).
Customer satisfaction has been defined either as transaction specific or as cumulative (Boulding et al. 1993). Transaction-specific customer satisfaction is the customer's postchoice evaluative judgment of a specific purchase occasion (Boulding, Kalra, and Staelin 1999). In contrast, cumulative customer satisfaction is the customer's overall evaluation of the accumulated customer experiences with the firm (Fornell 1992). In this study, we focus on transaction-specific satisfaction. Given our emphasis on the performance of a retailer's online CRM investments, cumulative satisfaction is not appropriate, because it also includes customers experiences with a retailer's bricks-and-mortar operations, when such operations are available.
Furthermore, because order fulfillment is a crucial element of CRM in online retailing (Reibstein 2002), we use satisfaction ratings that customers provided after order fulfillment. Specifically, BizRate.com asks, "How satisfied are you overall with this purchase experience at (merchant name) site?" on a scale that ranges from 1 ("not at all") to 10 ("highly") (see the Appendix). We averaged three months of a firm's postfulfillment customers satisfaction ratings following our manager survey (i.e., June, July, and August 2001) to the firm level to obtain a firm-level measure of customer satisfaction performance (mean = 8.68, s.d. = .58; α = .80).
We use two measures of firm CRM. First, we use an eight-item measure that reflects the firm's CRM system investments, which we obtained from a senior manager. Six items assess the firm's investments in CRM activities (1 = "low investments," 4 = moderate investments," and 7 = "high investments"; see the Appendix). Two items assess the online retailer's CRM acquisition and retention expenses relative to the industry (1 = "worse than industry average," 4 = "on par," and 7 = "better than industry average"; see the Appendix).( n4) Together, these eight items form our measure of CRM system investments (mean = 5.04, s.d. = .93; α = .77).
Second, we complement the measure of the firm's CRM system investments with an assessment of its CRM capability. Reinartz, Krafft, and Hoyer (2004) and Day and Van den Bulte (2002) developed measures of a firm's CRM capability. Unfortunately, these measures were not available when our survey was launched. Fortunately, these new measures are theoretically founded in the firm's market orientation, an organizationwide system for acquiring, disseminating, and responding to customer information (Kohli and Jaworski 1990). This foundation reinforces the importance of market orientation to a firm's CRM capability. However, a firm's CRM capability extends beyond its market orientation, and our use of market orientation represents a weak test of the role of a firm's CRM capability.
Given length constraints imposed by BizRate.com, we used 14 items from Kohli, Jaworski, and Kumar's (1993) 20-item market orientation scale (see the Appendix; mean = 4.92, s.d. = .78; α = .76).( n5) Notably, CRM system investments and CRM capability, as measured by market orientation, are only moderately correlated (ρ = .32, p < 01).
Bricks-and-mortar experience. We constructed this measure from managers' reports of dates. The difference (in days) between "days since firm founding" and "days since firm Web entry," both measured from our survey date (May 1, 2001), is our measure of bricks-and-mortar experience (mean = 2161 days or 5.92 years, s.d. = 4432 days or 12.14 years).
Online experience. We also constructed this measure from manager reports of the dates of their firm's online entry. Two coders assigned firms to one of eight industries (shoes and apparel, books and music, electronics and computers, health and medicine, flowers and gifts, home and kitchen furnishings, sporting equipment, and specialty occasion [e.g., bridal, birthday, baby]). Interjudge reliability was 88%, and disagreements were resolved through discussion.
Using this industry classification, we calculated the number of days since entry for each firm from the date of the first entrant in the firm's industry. We computed the firm's online experience as the difference between the number of days since entry for the industry's first entrant and the number of days since the firm's online entry. To facilitate interpretation such that the first entrant into a category had maximum online experience and smaller numbers indicate less online experience, we subtracted the firm's online experience from May 1, 2001, our survey date (mean = 1180 days or 3.23 years, s.d. = 670 days or 1.84 years).
Finally, we controlled for the well-known effect of consumer experience on customer satisfaction (Johnson, Bellman, and Lohse 2003). We measured the firm's customer online experience level by the average number of online purchases the firm's customers made in the product category in the previous six months (mean = 3.19, s.d. = 1.12).
Results
To examine the moderating effect of a firm's strategic commitments on the effectiveness of its CRM, we used a three-step hierarchical linear regression model. Step 1 included the main effects of firm CRM, strategic commitments, and the control variable. Step 2 included the two-way interactions between CRM and its strategic commitments. Finally, Step 3 included the interactions between CRM and quadratic forms of the strategic commitment variables. Thus, our model is as follows:
( 1) SATi = Step 1: β0 + β1CRM_Investi + β2CRM_Capi + β3BMEi + β4OEi + β5Cust_Expi + ε1i;
Step 2: β6 (BMEi x CRM_Investi) + β7 (BMEi x CRM_Capi + β8 (OEi x CRM_Invest[sub i + β9 (OE i x CRM_Capi + ε2i;
Step 3: β10BME²i + β]sub 11]OE²1 + β12 (BME²i x CRM_Investi) + β13 (BME² i x CRM_Capi) + β14 (OE² i x CRM_Investi) + β15 (OE² i x CRM_Capi) + ε3i,
where SATi is customer satisfaction, CRM_Invest is CRM system investments, CRM_Capi is CRM capability, BMEi is bricks-and-mortar experience, OEi is online experience, and Cust_Expi is customer online experience for firm i. We mean centered all explanatory variables before creating the interaction terms to avoid multicollinearity. To assess the potential threat from multicollinearity, we examined variance inflation factors and found them to be below harmful levels (Mason and Perreault 1991).
Step 1 (main effects) was significant (F( 5, 100) = 6.78, p < .01). Step 2, with the two-way interactions between CRM and the strategic commitments, was also significant (F( 9, 96) = 4.16, p < .01) as was the change in F associated with entry of this step (change in F( 4, 96) = 3.43, p < .01). Finally, Step 3, with the interactions between CRM and quadratic forms of the strategic commitments, was also significant (F( 15, 90) = 3.85, p < .01) as was the change in F associated with entry of this step (change in F( 10, 90) = 3.76, p < .01). Given these results, we interpret the full model results in Table 1.
The results indicate that the firm's CRM system investments (b = .30, p < .01) and CRM capability, in the form of market orientation (b = .29, p < 0.05), positively affect customer satisfaction.( n6) In addition, the control variable, customer online experience, negatively affects customer satisfaction (b = .28, p < .01). We conjecture that increasing consumer experience may increase customers' "should" expectations, producing a negative effect (Boulding et al. 1993). We next examine tests of H1 and H2 pertaining to the moderating effects of bricks-and-mortar experience and online experience on the rewards for CRM.
In H1, we predict an inverted U-shaped effect of a firm's bricks-and-mortar experience on CRM effectiveness. We first discuss the results with respect to a firm's CRM system investments, followed by its CRM capability.
CRM system investments. The first-order interaction term (BMEi x CRM_Investi) is positive and significant (b = .20, p < .10), and the second-order interaction term (BME²i x CRM_Investi) is negative and significant (b = -.80, p < .01), in support of H1. To determine the nature of the moderating effect, we examine the returns on CRM system investments at different levels of bricks-and-mortar experience. To do so, we use the unstandardized parameter estimates from Equation 1 (not the standardized estimates we report in Table 1) that are pertinent to bricks-and-mortar experience and CRM system investments to calculate and plot the estimated coefficients of CRM system investments for different levels of bricks-and-mortar experience in Figure 1.( n7)
As expected, the inverted U shape in Figure 1 indicates that moderate bricks-and-mortar experience strengthens the effects of a firm's CRM system investments more than low and high levels of bricks-and-mortar experience.( n8) Approximately 12 years of bricks-and-mortar experience maximizes customer satisfaction returns on CRM system investments (.22). Additional analysis indicates that CRM returns drop below the average return of the firms in our sample (.19) when bricks-and-mortar experience is less than 4 years (.17) and greater than 20 years (.18).( n9) In summary, the significant parameter estimates of the first-order (b = .20, p < .10) and the second-order (b = -.80, p < .01) interaction terms, combined with the inverted U-shaped relationship of the returns on CRM system investments at different levels of bricks-and-mortar experience, support H1.
CRM capability. We next examine the effect of firm bricks-and-mortar experience on the effects of its CRM capability. Notably, both the first-order (BMEi x CRM_Capi) (b = -.26, not significant [n.s.]) interactions are order (BMEi[CRM_Capi) (b = -.10, n.s.) interactions are not significant. Combined with the positive main effect of CRM capability (b = .29, p < .05), these results suggest that the effect of a firm's CRM capability on customer satisfaction is impervious to its bricks-and-mortar experience.
CRM system investments. The first-order interaction (OEi x CRM_Investi) is positive and significant (b = .28, p < .01), and the second-order interaction (OE²i x CRM_Investi) is negative and significant (b = -.25, p < .05), in support of H2. We plot the estimated coefficients of CRM system investments at different levels of online experience in Figure 2.
The expected inverted U shape in Figure 2 indicates that moderate online experience strengthens the effects of a firm's CRM system investments more than low and high online experience. Online experience of 4.5 years maximizes customer satisfaction returns on CRM system investments. Additional analysis indicates that CRM returns drop below the average (.19) when online experience is less than 3.0 years (.16) and greater than 6.5 years (.15). Notably, customer satisfaction returns on CRM system investments of very young firms (<2 years) are negative. We conjecture that this may be due to the challenges that confront these nascent firms in the turbulent online retailing market.
In summary, the significant parameter estimates of the first-order (b = .28, p < .01) and second-order (b = -.25, p < .05) interaction terms, combined with the inverted U-shaped relationship of the returns on a firm's CRM system investments at different levels of online experience, support H2.
CRM capability. Finally, we examine the effect of a firm's online experience on the effects of its CRM capability. Neither the first-order (OE²i x CRM_Capi) (b = .17, n.s.) nor the second-order (OE² i x CRM_Capi) (b = .04, n.s.) interactions are significant. These results, combined with the positive main effect of CRM capability (b = .29, p < .05), suggest that the effect of a firm's CRM capability on customer satisfaction is impervious to its online experience.
Discussion
The study s findings indicate that firms' prior strategic commitments have impressive effects on the performance of their CRM investments. Specifically, the customer satisfaction effects of CRM system investments are greater for online retailers with moderate levels of bricks-and-mortar experience than for firms with low and high levels of bricks-and-mortar experience. In light of this finding, managers of bricks-and-mortar retailers with moderate bricks-and-mortar experience (approximately 12 years) can consider their offline experience an asset. Higher levels of bricks-and-mortar experience (>20 years) produce diminishing customer satisfaction returns on CRM system investments. Perhaps these older incumbent retailers' core rigidities dampen the returns on their CRM system investments in the emerging online market. From an operational perspective, CRM executives can assess the rewards for their firm's and competitors' CRM system investments, given their bricks-and-mortar experience.
With respect to online experience, customer satisfaction effects of online CRM system investments are greater for firms with moderate online experience (approximately 4.5 years) than for firms with low and high online experience. This suggests that there is a window of opportunity in the online retailing market for fast followers to generate greater customer satisfaction returns on their CRM activities than early and later entrants. Finally, managers can use this approach to predict the optimal return on their investment and on their competitors CRM investments, given online experience levels.
Unlike CRM system investments, our findings indicate that a firm's strategic commitments do not moderate the customer satisfaction effects of its CRM capability, which is embodied in its market intelligence acquisition, dissemination, and responsiveness processes. Thus, a firm's market orientation appears to be a robust and effective organizational capability that operates independently of the two strategic commitments of bricks-and-mortar and online experience. This null result, though not compelling in isolation, is powerful when it is considered in conjunction with the significant moderating effects of CRM system investments.
Given the complex relationship between customer satisfaction and other performance metrics (Anderson, Fornell, and Lehmann 1994), the generalizability of this study s findings to other performance metrics is an important issue. Specifically, studies using profit-based performance metrics would provide insight into cost-based effects and revenue-based effects of a firm's CRM investments. In this study, we focused on the role of strategic commitments on CRM returns in online retailing, an important emerging market. Further research that examines this issue in other emerging and mature markets would extend the study s findings in important ways. In addition, researchers could investigate the generalizability of our findings using other strategic commitments, CRM investments, and CRM capabilities.
Conclusion
In summary, our study makes four contributions. First, we offer a contingent effect of a firm's CRM investments on its performance, shedding some light on the divergent findings between the CRM literature and CRM practice. Second, we offer a strategic vantage point, highlighting the role of two key strategic commitments on the rewards for CRM investments. Third, we provide important insights into CRM activities in online retailing, for which the complex intersection of firm and customer forces shapes firm performance. Fourth, our study offers guidance to practitioners on the contingent nature of rewards for their CRM investments, which should be useful in managing their firms investments and monitoring their competitors CRM investments.
The authors thank the Marketing Science Institute for its financial assistance; Dave Reibstein for his assistance in securing the data; BizRate.com for providing data; the consulting editors Bill Boulding and Rick Staelin; and commentators Rajesh Chandy, Marnik Dekimpe, Abhijit Guha, Jennifer Francis, Wayne Hoyer, Julie Irwin, Mitch Lovett, John Lynch, Vijay Mahajan, Arvind Rangaswamy, Roland Rust, Raj Srivastava, and Rajan Varadarajan for helpful comments on previous versions of the article.
( n1) Tests find no differences in public versus private (χ²( 1) = .13, not significant) and presence versus absence of bricks-and-mortar operations (χ²( 1) = 2.08, not significant) for respondent and nonrespondent firms.
( n2) At the time, BizRate.com offered two plans. In the first plan (the 106 online retailers that responded to our survey), BizRate.com surveyed customers and provided firms with firm-specific customer data. In the second plan (81 firms), BizRate.com did not survey customers and offered these online retailers overall aggregate data instead.
( n3) The t-tests of difference between the 87 firms (with no customer data) and the 106 firms (with customer data) were not significant on size (t = 1.612, not significant), CRM system investments (t = .787, not significant), or CRM capability (t = .973, not significant).
( n4) A reviewer raised the concern that the two CRM acquisition and retention expense questions may have been answered on a per customer basis so that the "better than" anchor (rating 7) may have been viewed as "lower" (more efficient); thus, a higher rating ( 7) may actually reflect lower CRM system investments per customer. However, because we provide explicit instructions to the respondent to evaluate these items at the overall firm level, these two items form a reliable scale together with the remaining six items; separate analysis involving the six-item scale and the two-item scale produce similar results, so this concern does not seem problematic.
( n5) We also estimated the model with Homburg and Pflesser's (2000) measure of market-oriented organizational culture, and we obtain similar results.
( n6) Note that the regression coefficients for the first-order terms in mean-centered models are conditional effects at mean values of the other predictor variables and must be interpreted with caution (Irwin and McClelland 2001). As we subsequently show in Figure 1 and Figure 2, CRM system investments have a positive effect on performance, except at low online experience.
( n7) Rearranging Equation 1, the parameter estimate for CRM_Investi is (β̂1 + β̂6BMEi + β̂12BME²i). However, Equation 1 is mean centered; thus, BMEi is BMEi(observed) - µ(bme). Thus, the effect of CRM system investments at BMEi(observed) is (β̂1 + β̂6[BMEi(observed) - µ (bme)] + β̂12 [BMEi(observed) - µ(bme)]²).
( n8) For presentation convenience, we plot bricks-and-mortar experience up to 28 years in Figure 1. We observe similar diminishing returns to CRM system investments for higher levels of bricks-and-mortar experience.
( n9) The average return level on CRM system investments (unstandardized b = .19, p < .01) corresponds to the main effect of CRM system investments (standardized b = .30, p < .01) in Table 1.
Legend for Chart:
A - Variables
B - Standardized Coefficients
A B
Step 1(a)
CRM system investments .30(***)
CRM capability .29(**)
Bricks-and-mortar experience -.67(***)
Online experience .13
Customer online experience -.28(***)
Step 2(b)
Bricks-and-mortar experience x CRM
system investments .20(*)
Bricks-and-mortar experience x CRM
capability -.26
Online experience x CRM system
investments .28(***)
Online experience x CRM capability .17
Step 3(c)
Bricks-and-mortar experience² .14(***)
Online experience² -.24(***)
Bricks-and-mortar experience² x CRM
system investments -.80(**)
Bricks-and-mortar experience² x CRM
capability -.10
Online experience² x CRM system
investments -.25(**)
Online experience² x CRM capability .04
Overall Intercept 17.43(***)
Overall F associated with complete model
(degrees of freedom = 15,90) 3.85(***)
Overall R² associated with complete model .39(***)
(*) p < .10.
(**) p < .05.
(***) p < .01.
(a) The results from the three-stage model are shown.
(b) The change-in-F associated with the introduction of the
interaction of the strategic commitment variables (e.g.,
bricks-and-mortar experience) and with the CRM investment
variables is significant (change-in-F(4, 96) = 3.43,
p < .01).
(c) The change-in-F associated with the introduction of the
squared strategic commitment variables (e.g., bricks-and-mortar
experience²) and their interaction with the CRM investment
variables is significant (change-in-F(10, 90) = 3.76,
p < .01).GRAPH: FIGURE 1 The Moderating Effect of Firm Bricks-and-Mortar Experience on Rewards for CRM
GRAPH: FIGURE 2 The Moderating Effect of Firm Online Experience on Rewards for CRM
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Firm Customer Satisfaction (mean = 8.68, s.d. = .58, range = 1 10; α = .80)
How satisfied are you overall with this purchase experience at (merchant name) site? (1 = "not at all" to 10 = highly")
(Note that the reported mean and standard deviation are across firms, and the alpha is across individuals within firms.)
Firm CRM System Investments (mean = 5.04, s.d. = .93, range = 1 7; α = .77)
Rate the level of investments your firm makes in the following areas: (1 = "low investments," 4 = "moderate investments," and 7 = "high investments")
1. Developing a large installed base of customers
- 2. Enhancing the performance of our website
- 3. Providing optimal product pricing
- 4. Improving the ease of ordering
- 5. Building a strong attachment to our brands
- 6. Enhancing the quality of customer support
Rate your firm relative to industry average. (1 = "worse than industry average," 4 = "on par," 7 = better than industry average")
1. CRM acquisition expenses
2. CRM retention expenses
Firm CRM Capability (measured by firm market orientation) (mean = 4.92, s.d. = .78, range = 1 7; α = .76)
Rate the extent to which the following statements describe your firm: (1 = "strongly agree," 7 = "strongly disagree")
Information generation
1. In this business, we do and/or buy a lot of market research.
- 2. We are slow to detect changes in our customers' product preferences.
- 3. We are slow to detect fundamental shifts in our industry (e.g., competition).
Information dissemination
1. We have frequent interdepartmental meetings to discuss market trends.
- 2. Marketing personnel spend time discussing customers future needs with other departments.
- 3. Data on customer satisfaction are disseminated at all levels on a regular basis.
- 4. When one department finds out something important about competitors, it is slow to alert other departments.
Responsiveness
1. It takes forever to decide how to respond to our competitors price changes.
- 2. We tend to ignore changes in our customers' products or service needs.
- 3. If a major competitor launched an intensive campaign targeting our customers, we would respond immediately.
- 4. The activities of the different departments are well coordinated.
- 5. Customer complaints fall on deaf ears in our firm.
- 6. Even if we came up with a great marketing plan, we probably would not be able to implement it in a timely fashion.
- 7. When our customers want us to modify a product or service, the departments involved make an effort to do so.
Firm Bricks-and-Mortar Experience (mean = 2161 days, s.d. = 4432 days, range = 0 27,393 days)
The difference (in days) between the "days since firm founding" and "days since firm Web entry," both measured from the survey date of May 1, 2001, is the measure of bricks-and-mortar experience.
Firm Online Experience (mean = 1180 days, s.d. = 670 days, range = 1 2497 days)
The difference (in days) between the days since the industry's first entrant and the days since firm's Web entry. To facilitate interpretation such that larger numbers denote higher online experience, we subtracted the firm's online experience from our survey date of May 1, 2001.
Customer Online Experience (mean = 3.19, s.d. = 1.12, range = 1 10)
The number of online purchases made by the firm's customers in the product category in the previous six months.
~~~~~~~~
By Raji Srinivasan and Christine Moorman
Raji Srinivasan is Assistant Professor of Marketing, Red McCombs School of Business, University of Texas at Austin.
Christine Moorman is T. Austin Finch Jr. Professor of Business Administration, Fuqua School of Business, Duke University.
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Record: 144- Strategic Interdependence in Organizations: Deconglomeration and Marketing Strategy. By: Varadarajan, P. Rajan; Jayachandran, Satish; White, J. Chris. Journal of Marketing. Jan2001, Vol. 65 Issue 1, p15-28. 14p. 2 Charts. DOI: 10.1509/jmkg.65.1.15.18129.
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Record: 145- Strategic Responses to New Technologies and Their Impact on Firm Performance. By: Lee, Ruby P.; Grewal, Rajdeep. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p157-171. 15p. 2 Diagrams, 6 Charts. DOI: 10.1509/jmkg.68.4.157.42730.
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Strategic Responses to New Technologies and Their Impact
on Firm Performance
Modern corporations must adopt and assimilate new technologies to build and sustain competitive advantage. The authors develop a theoretical framework to understand the relationships among ( 1) strategic responses to new technologies, ( 2) organizational resources, and ( 3) firm performance. Specifically, they theorize that a strategic response can be categorized according to the dimensions of magnitude, domain, and speed, and they conceptualize organizational resources as tangible and intangible. The authors operationalize this framework for the adoption of the Internet by traditional store-based retailers, for which they posit strategic responses as the speed of ( 1) adopting the Internet as a communications channel, ( 2) adopting the Internet as a sales channel, and ( 3) forming e-alliances. In addition, they use resource slack to represent organizational resources. Results from nine years (1992-2000) of data on 106 firms establish the influence of strategic responses on firm performance (i.e., market valuation of the firm, operationalized as Tobin's q). Specifically, the results show that both the adoption of the Internet as a communications channel and e-alliance formation positively influence firm performance. The positive effect of communications channel adoption on firm performance is enhanced further by the use of slack resources. Post hoc analysis reveals that the adoption of the Internet as a sales channel seems to matter only to firms that have preexisting catalog operations.
As new technologies become ubiquitous, firms must adopt and assimilate them to gain and sustain competitive advantage (e.g., Abernathy and Utterback 1978; Tushman and Anderson 1986). Recent examples--such as the adoptions of biotechnology by pharmaceutical firms, semiconductors by machine tools firms, video banking by commercial banks (Pennings and Harianto 1992), and information technology (IT) by a diverse set of firms such as small office professionals (Kim, Han, and Srivastava 2002) and market makers (Grewal, Comer, and Mehta 2001)--illustrate the importance of this organizational phenomenon. Although the literature has focused on strategic responses to competitive threats (Heil and Robertson 1991), theoretical developments and empirical investigations of strategic responses to new technologies have been lacking.
As a result of inherent differences in organizational routines (Nelson and Winter 1982), business processes (Srivastava, Shervani, and Fahey 1999), and resources (Wernerfelt 1984), firms are expected to differ in their strategic responses to new technologies. Organizational routines and business processes are likely to determine the efficacy of resources while the firm adopts and assimilates new technologies. Therefore, firm differences in strategic responses to new technologies and firm resources should result in performance heterogeneity (Rumelt 1974). Thus, it becomes important to understand ( 1) the impact of strategic responses on firm performance and ( 2) the role of organizational resources in the relationship between the strategic responses and firm performance. To address these research questions and to understand the relationships among strategic responses to new technologies, organizational resources, and firm performance, we theorize about strategic responses along the dimensions of magnitude, domain, and speed (Heil and Robertson 1991), and we posit that organizational resources consist of tangible and intangible resources (Barney 1991).
The intensity of the organizational response to a new technology represents the response magnitude. At the extremes, firms may decide either not to adopt the new technology or to reengineer their business processes to assimilate the new technology (e.g., Gatignon, Anderson, and Helsen 1989). Midrange responses are also possible, in which firms adopt a technology on an experimental basis. In addition to the intensity of response, firms may differ in the business process (response domain) that they decide to alter to adopt the new technology (Srivastava, Shervani, and Fahey 1999). For example, one firm may use advances in communications technology to revamp its logistics activities; another may use the same technology to streamline customer relationship management (CRM). Finally, the speed with which firms adopt the new technology may vary across firms, and timely responses should enable them to gain early-mover advantages (e.g., Lieberman and Montgomery 1988). By combining these three dimensions, we capture a firm's strategic response.
Firms are likely to use their resources to implement their strategic responses to new technologies. Resources include both tangible and intangible organizational assets, such as managerial skills and know-how, organizational processes and routines, market-based assets, human assets, and financial assets such as cash (e.g., Hunt and Morgan 1995; Srivastava, Shervani, and Fahey 1998), that are valuable, rare, inimitable, and nonsubstitutable (Barney 1991). Resources are the products of a firm's history and the outcomes of its prior strategic actions (Barney 2001). The endowments of such resources affect a firm's competitive position and its ability to generate economic rents (Rumelt 1974). We suggest that resources help determine the efficacy of strategic responses to new technologies.
To test this framework empirically, we need a context (industrial sector) in which ( 1) a new technology has had widespread influence, ( 2) there are differences in strategic responses to this new technology, and ( 3) there are differences in organizational resources. Internet technology for retailers represents such a case. Internet technology has had a widespread impact on retailing, to the extent that some researchers even predicted that the Internet would lead to the demise of traditional retailing (e.g., Chen and Leteney 2000). Strategic responses to the Internet vary significantly across retailers; for example, some retailers (e.g., Best Buy) formed alliances with IT firms, whereas others did not (e.g., Gottschalks); some adopted early (e.g., Wal-Mart), whereas others were slow (e.g., Jacobson Stores); and some had catalog operations (e.g., J.C. Penney), whereas others did not (e.g., May Department Stores). Finally, retailers differ significantly in the amount of resources they own, and some firms (e.g., Wal-Mart) have more than others (e.g., Good Guys).
Our research makes several important theoretical and managerial contributions. As we elaborate subsequently, it highlights the importance of strategic responses and organizational resources during the assimilation of new technology. We develop a typology of strategic responses to new technologies and demonstrate its efficacy. Furthermore, we provide secondary data-based measures to assess strategic responses and organizational resources, which then can be used to design longitudinal studies for various strategic marketing issues. With Tobin's q as the measure of firm performance, we also contribute to the understanding of the drivers that create shareholder wealth (e.g., Day and Fahey 1988; Srivastava, Shervani, and Fahey 1998). For managers, this research helps uncover the impact of their responses to new technologies.
The three components of strategic responses--magnitude, domain, and speed--suggest that a response is likely to include one or more of the three components (thus, the overlapping circles in Figure 1). Response magnitude, or the intensity of a firm's response to new technology, can be assigned to a spectrum whose endpoints represent ignoring the new technology or assimilating it completely. Closer to the lower end of the spectrum, firms can adopt the technology on an experimental basis to "test the waters" and learn about the technology (Dickson 1992). At relatively higher levels, firms may decide to adopt the technology as a backup, viable alternative, or complement to their existing technology. Response magnitude also might be measured according to money spent or number of employees involved with a task, after controlling for firm size and other confounding variables.
Response domain pertains to the business process, such as supply chain management, new product development, or CRM, that is affected by the new technology (e.g., Srivastava, Shervani, and Fahey 1999). Response domain can be conceptualized in terms of organizational routines, in the sense that routines represent "all regular and predictable behavioral patterns of firms" (Nelson and Winter 1982, p. 14). For example, a loyalty management program (e.g., frequent shopper program), which is a component of a larger CRM process, can be viewed as a routine and thus as a possible domain of responses to new technologies.
Response speed represents the timeliness of the response, in that faster responses can preempt competitive actions and provide the firm with greater opportunities to learn the benefits of the new technology (Lieberman and Montgomery 1988, 1998). First-mover advantage arises when learning curves generate barriers to the entry of other firms (Robinson 1988; Spence 1981), and it enables the first mover to preempt scarce assets, such as physical resources (e.g., Robinson and Fornell 1985; Schmalensee 1978), and shape consumer preferences, which lead to high switching costs (Carpenter and Nakamoto 1989; Grewal, Cline, and Davies 2003). Speed also has been shown to be vital for diverse strategic issues, including introducing new products, entering new markets, and responding to competitive threats (e.g., Bayus, Jain, and Rao 1997; Bowman and Gatignon 1995; Kerin, Varadarajan, and Peterson 1992). Thus, we expect that firms that respond to a new technology more quickly than their rivals will be able to garner first-mover advantage.( n1)
Most responses to new technologies are likely to encompass two or more of the three response components. For example, although the prototype of an industrial robot was developed in the United States in the 1950s and its first commercial use was by a U.S. firm in 1961, it was Japanese automakers who adopted the technology en masse. Thus, the advantage went to the Japanese, whose slower speed was overcome by their higher magnitude and different domain application of the new technology (Tanzer and Simon 1990). Similarly, firms that adopted electronic markets early and expended considerable efforts to learn about the new medium were able to use it successfully as an alternative sales channel (Grewal, Comer, and Mehta 2001). Thus, according to existing research, various strategic options can be derived from any combination of response magnitude, domain, and speed.
To test the conceptual framework (Figure 1), we apply it to the strategic responses of store-based retailers to the advent of the Internet. To be successful, store-based retailers must build knowledge and capabilities with regard to store and merchandise management, which are useful in designing store layouts, controlling traffic flow inside a store, and managing and maintaining the quality of in-store customer services, among other benefits. However, with the emergence of the Internet, store-based retailers needed to develop new capabilities to execute online retail operations (Amit and Zott 2001). Specifically, online retailing requires IT capabilities to maintain a Web interface that is linked to back-end order-processing systems, logistics management systems, and strategic decision support systems. Online retailers also must develop or acquire logistic management capabilities to complete an order by ensuring timely physical delivery to the homes of individual consumers, compared with traditional retailers, which need logistic capabilities to ensure in-store product availability.( n2) This disparity in capabilities makes Internet adoption a consequential decision. Furthermore, as we reason subsequently, Internet adoption involves diversity in response magnitude, domain, and speed across firms. The diversity in the response components and the strategic importance of Internet adoption make the advent of the Internet for the retail industry an appropriate setting to study the relationships among strategic responses, organizational resources, and firm performance (see Figure 2).( n3)
Strategic Responses
A traditional retailer may adopt the Internet as a communications channel, whereas another may adopt it as a sales channel. As a communications channel, the Internet conveys information about products and store locations to consumers and information about finances to investors and shareholders. To use the Internet as a sales channel, retailers must develop online order-taking capabilities and build logistic capabilities to ensure the timely delivery of products. Thus, the adoption of the Internet as a sales channel represents a response of greater magnitude (i.e., it involves greater investments) than its adoption as a communications channel. Furthermore, communications and sales channels involve different business processes and therefore represent responses from different domains. In addition to communications and sales channel adoption, firms may respond to new technologies by forming e-alliances. E-alliances are strategic alliances that traditional retailers (e.g., Best Buy, Wal-Mart) form with IT firms (e.g., America Online [AOL], Microsoft) to acquire surrogate capabilities that facilitate information management, online transactions, and invoicing, among other things. To incorporate response speed, we theorize the responses as the speed of ( 1) communications channel adoption, ( 2) sales channel adoption, and ( 3) e-alliance formation.( n4)
Communications/sales channel. Retailers that adopt the Internet as a communications channel can use it to convey company information, which represents an initial step in which a retailer learns about the new technology before adopting it as a sales channel. Sales channel adoption enables the retailer to provide its customers with an alternative to physical stores and thereby goes beyond communications channel adoption with its emphasis on generating sales (Burn, Marshall, and Wild 1999).
Although adoption of the Internet seems important, we believe that it is critical to adopt the new technology in a timely manner. Consistent with extant literature that documents the importance of speed in strategic responses (Kerin, Varadarajan, and Peterson 1992; Lieberman and Montgomery 1988), we expect that the speed with which retailers adopt the Internet as a communications and/or sales channel will have a positive effect on their performance.
E-alliances. By forming alliances with IT-capable firms, retailers can draw on the IT expertise of their partners. For example, Barnes & Noble partnered with AOL to market its Web site and to keep abreast of changes in the virtual world, and Best Buy allied with Microsoft for similar purposes.
Diverse theoretical perspectives imply the advantages of such alliances. Transaction cost economics suggests that the alliances should help retailers reduce transaction costs (Gulati 1995), and organizational learning theory suggests that the alliances provide retailers with knowledge bases that they may lack (Parkhe 1993). Alliances should enable retailers to expand their resource bases and to help build competitive advantage by reducing risks, generating scale economies, and facilitating cooperation in the development of production and technological know-how (e.g., Sivadas and Dwyer 2000; Varadarajan and Cunningham 1995).
In contrast, resource dependence theorists argue that alliances may create asymmetrical resource situations between partners, which can lead to adverse performance outcomes (Pfeffer and Salancik 1978). For example, Das, Sen, and Sengupta (1998) find that firms receive a smaller share of an alliance "pie" when they rely on their partners for technological know-how. The literature on strategic alliances also cautions about the significance of bandwagon effects, which occur when firms form alliances simply because other firms are doing so. Such motivation for forming alliances usually affects firm performance negatively, because the alliances are formed primarily to meet legitimacy needs, not to achieve efficiency. Thus, whether e-alliances benefit retailers remains an open empirical question.
Organizational Resources
Consistent with extant literature (Bourgeois 1981; Gatignon, Weitz, and Bansal 1990), we use the amount of slack resources that a firm possesses to assess organizational resources. Slack resources are the buffer of idle resources that enables firms to be flexible and improvise (Grewal and Tansuhaj 2001; Moorman and Miner 1998). We suggest that these idle bundles of resources enhance a firm's ability to assess, adopt, and assimilate new technologies.
Nevertheless, slack resources have opportunity costs associated with them, in that ex post unused resources would have been productively used (Nohria and Gulati 1996). Thus, if slack resources are unused, they could have negative impacts on the firm. As opportunity cost reasoning suggests, as the level of resource slack increases, its marginal utility decreases. Thus, we include both linear and squared resource slack terms in our model to capture the diminishing marginal returns.
Strategic Response and Organizational Resources
Slack resources instill flexibility in firms, which enables them to respond to environmental opportunities and threats (Bourgeois 1981; Nohria and Gulati 1996). The adoption of the Internet as a communications channel is such a situation, in which firms use the Internet to supplement and/or substitute for the communication they previously carried out through traditional channels. Given that the economic benefits of Internet communications are well documented (e.g., Rayport and Jaworski 2001), the deployment of slack resources for adopting the Internet as a communications channel should prove beneficial for retailers.
The adoption of the Internet as a sales channel requires retailers to develop knowledge in previously unexplored domains. Slack resources thus offer store-based retailers the ability to study the new capabilities needed and to expend efforts to acquire or develop the capabilities (e.g., on-time delivery, effectively linked Web interface). Because online trade is fairly recent, it is difficult for many firms to understand its underlying cause-and-effect relationships. Firms with slack resources may display greater abilities to absorb and use new information, which thereby makes it easier for them to assimilate new technologies (Zahra and George 2002) and to uncover causal ambiguities (Reed and DeFillippi 1990).
Control Variables
It is important to account for firm-, industry-, and economy-specific variables, because the market valuation of a firm (Tobin's q) can depend on these factors. We use catalog operations, cost of capital, and return on assets (ROA) to control for firm-specific effects; the Herfindahl index to control for industry-specific (competitive) effects; and changes in the gross domestic product (GDP) to control for the volatility of the economy. Firms with catalog operations, such as J.C. Penney, already had logistic systems in place to deliver products to the homes of individual consumers. Because developing such a logistic management capability is necessary for successful online sales, it becomes important to account for catalog operations. The cost of capital indicates the average interest rate that a firm pays for its long-term liabilities (Dutta, Narasimhan, and Rajiv 1999). Capital usually comes from two sources: equity and debt. The nature of these two sources of equity is quite distinct, and their effects need to be considered. Regardless of profits or losses, firms must pay interest on liabilities. However, when losses are reported, the firms need not pay dividends on equity. We also account for ROA, which indicates the profitability of a business. Because a firm's current profitability is likely to affect its market valuation positively, it is important to account for this link. Profitability also depends on competitive intensity. Thus, we include the Herfindahl index as a control variable that accounts for industry-specific competitive effects. Finally, because we are conducting a longitudinal study, we must control for changes in the economy. We use the change in the GDP to achieve this control.
Thus, we present our research model in Figure 2 and Equations 1 and 2:
( 1) Q = β[sub0] + β[sub1] x SCC + β[sub2] x SSC + β[sub3] x SECA + β[sub4i] x RS
+ β[sub5] x CAT + β[sub6] x CC + β[sub7] x ROA + β[sub8] x GDP
+ β[sub9] x HERF + ε[subit], and
( 2) β[sub4i] x γ[sub0] + γ[sub1] x RS + γ[sub2] x SCC + γ[sub3] x SSC,
where β and γ represent the effects of hypothesized and control variables; Q stands for Tobin's q; and SCC, SSC, SECA, RS, CAT, CC, ROA, GDP, and HERF stand for speed of communications channel adoption, speed of sales channel adoption, speed of e-alliance formation, resource slack, catalog operations, cost of capital, return on assets, changes in gross domestic product, and the Herfindahl index, respectively. We expect β[sub1], β[sub2], γ[sub0], γ[sub2], and γ[sub3] to be positive and γ[sub1] to be negative. We are unsure of the direction of β[sub3].
Data Sources
Because commercial use of the Internet began in 1994, we decided to study the nine-year period from 1992 to 2000 to capture the evolutionary effects of resource slack and strategic responses before and after the advent of the new technology. We drew the sample of traditional retailers for this study primarily from the COMPUSTAT database and supplemented it with other sources. The COMPUSTAT database for Standard Industrial Classification (SIC) codes 53 (general merchandise), 54 (food stores), 56 (apparel and accessories), 57 (home furniture and furnishings), and 59 (miscellaneous) consists of 259 retailers. However, the database does not provide complete data for each of the retailers because ( 1) some retailers went out of business during that period (e.g., County Seat Stores), ( 2) a few retailers were acquired by other organizations (e.g., in 1998, Fred Meyer Inc. acquired Food 4 Less Holdings), and ( 3) new retail organizations were formed during the period (e.g., Amazon.com). For other missing data, we searched sources such as Compact Disclosure, EDGAR, Standard and Poor's industry reports, and company annual reports. We made every possible effort to obtain complete data for the nine-year period on all retailers.
Furthermore, to derive Tobin's q (our measure of firm performance), we needed to know the closing share prices and preferred share values, which we obtained from the Center for Research in Security Prices (CRSP) tapes. Inadequacies in the CRSP database, similar to those of the COMPUSTAT database, further reduced the sample size. Because our research questions center on the efficacy of traditional retailers' strategic responses, we omitted retailers that employed only online operations (e.g., Amazon.com). Ultimately, we obtained complete data on 83 retailers that adopted the Internet between 1994 and 2000. We also obtained complete data on an additional 23 retailers that had not adopted the Internet during the period of our study. Thus, the sample consists of nine years of data for 106 retailers, for a total of 954 data points (Table 1).
To record the strategic responses of the 83 retailers, we searched their Web sites and annual reports, as well as articles in business publications such as BusinessWeek, Forbes, Fortune, The Economist, and The Wall Street Journal. The sample constitutes approximately 41% of public retail firms. To determine whether there was bias in sample selection, we compared the sample with the population (all public retail firms) in terms of number of employees, sales, and retained earnings for the nine-year period. None of the t-statistics were statistically significant (p > .10). We therefore concluded that the sample was representative of the population.
Measures
Performance. We use Tobin's q as a measure of retailer performance. In marketing literature, Tobin's q has been used in studies related to brand equity (Simon and Sullivan 1993) and customer satisfaction (Anderson, Fornell, and Mazvancheryl 2004). As a performance metric, Tobin's q has several advantages. First, it is derived from stock market price, which reflects future performance and therefore is a forward-looking measure. Second, Tobin's q reflects a firm's long-term profitability because it captures the relationship between the replacement cost of a firm's tangible assets and the market value of the firm (Bharadwaj, Bharadwaj, and Konsynski 1999). Third, Tobin's q can be used to compare across industries because it is not affected by accounting conventions (Chakravarthy 1986).
Specifically, we use Chung and Pruitt's (1994) method to calculate Tobin's q as follows:
( 3) Q = MVE + PS + DEBT/TA,
where
Q = Tobin's q,
MVE = (closing price of shares at the end of the financial year) x (number of common shares outstanding),
PS = liquidation value of the firm's outstanding preferred stock,
DEBT = (current liabilities - current assets) + (book value of inventories) + (long-term debt), and
TA = book value of total assets.
The year-end stock price is an important component of the Tobin's q formula. Therefore, the results and findings depend on a single measure of stock price, which can be volatile. To overcome this volatility problem, we took the average stock price at the end of the four quarters (i.e., March 31, June 30, September 30, and December 31) to calculate Tobin's q. (We thank an anonymous reviewer for this suggestion.) The number of common shares outstanding also was taken as the average at the end of each of the four quarters. (The correlation between the quarterly and the year-end measures of Tobin's q is .89.)
Speed. We code speed as an ordinal variable that increases by an increment of one with every year that passes after a retailer makes a strategic response (i.e., adopts the as a communications/sales channel or forms e-alliances). For example, if a retailer adopted the Internet as a communications channel in 1996, we code the speed of communications channel adoption in years 1992 through 1995 as 0, 1996 as 1, 1997 as 2, and so on. With this measure of speed, retailers that adopted earlier score higher, and the annual incremental increases capture experiential gains (learning).
Resource slack. We use factor scores calculated from two financial ratios to assess resource slack: ( 1) retained earnings to total assets and ( 2) working capital to total assets. Retained earnings capture the resources that a firm decides to maintain as strategic options for unforeseen eventualities and implementation strategies (Bourgeois 1981). The ratio of retained earnings to total assets accounts for firm size (total assets) and suggests that larger firms should have higher retained earnings. Thus, similar to other proxies for resource slack (e.g., earnings before interest and tax; Gatignon, Weitz, and Bansal 1990), the retained ratio of earnings to total assets suggests that firms with higher performance usually have greater amounts of slack resources (Nohria and Gulati 1996; Singh 1986). The working capital measure is the firm's current assets less its current liabilities, which normally include inventory, account receivables, and cash. Unlike fixed assets, they are not subject to depreciation, though the devaluation of inventory may be a concern. However, different accounting practices--such as cost methods; first in, first out; and last in, first out--adopted by retailers should address the issue of inventory devaluation. In addition, the ratio of working capital to sales has been used in extant research (e.g., Chakravarthy 1986) to represent how effectively a firm uses its liquid assets to generate sales. The ratio of working capital to total assets controls for the effects of firm size (as reflected in total assets) and indicates that larger firms need greater amounts of working capital.
Control variables. We control for catalog retailers by coding a dummy variable as 1 if the retailer had catalog operations and as 0 otherwise. Consistent with the literature (e.g., Dutta, Narasimhan, and Rajiv 1999), we use the interest rate for long-term liabilities to account for the effect of cost of capital. We measure ROA according to reports in the COMPUSTAT database and the change in GDP, as reported by the Bureau of Economic Analysis of the U.S. Department of Commerce. For the Herfindahl index, we square the market share of the top four firms classified in the same SIC code as the firm of interest. In Table 2, we provide the bivariate correlations among the measures.
Identification of Strategic Responses
We use a historical approach to data collection (Golder 2000; Savitt 1980; Smith and Lux 1993) that involves careful examination of relevant published records. We follow Golder (2000) and evaluate the criticality of archival data obtained from at least two different sources to ensure that at least one data source is neutral, that all data sources are reliable, and that the data sources are independent. We carry out a structured content analysis of company annual reports, press releases, and articles available on LexisNexis, BusinessWeek, The Economist, Fortune, Forbes, and The Wall Street Journal, as well as respective company Web sites, to identify when a retailer adopted the Internet as a communications and sales channel and when a retailer formed an e-alliance. For each retailer, we searched for the following key words: "online," "e-commerce," "Internet," and "Web."
For example, we searched for information on Walgreen's using the specified key words. In its 1995 1998 annual reports, Walgreen's stated that shareholders could access company information, such as press releases and financial data, on its Web site. Walgreen's announced in its 1999 annual report that the company became an Internet player in January 1998. Following contemporary historical research (e.g., Golder 2000), we reviewed all available information with respect to Walgreen's adoption of the Internet. After critically analyzing the available information, we coded 1995 as the year Walgreen's adopted the Internet as a communications channel and 1998 as the year it adopted the Internet as a sales channel. In Table 3, we provide a sample of qualitative information we found and the way we coded the information.
Measure Validation
To assess the validity of the nominal data, which are based on qualitative judgments, two judges (one not involved with the research) independently analyzed the content of annual reports and other sources of information (e.g., Frankwick et al. 1994; Houston et al. 2001). The judges first reviewed all company annual reports available on LexisNexis. Missing information was collected from alternative sources such as business journals. For 10 of the 83 retailers in the sample (randomly drawn), the judges worked together to develop a protocol and common understanding of the coding procedure. For the remaining 73 retailers, the judges completed the tasks independently.
We then assessed the Pearson product correlations between the judges' coding and the classifications of the 73 retailers. We found a high level of correlations for the three major constructs: for e-alliances, r = .91 (p < .01); for adoption of the Internet as a communications channel, r = .93 (p < .01); and for adoption of the Internet as a sales channel, r = .93 (p < .01). Any disagreements were resolved by discussion and revisiting the information sources. Finally, factor analysis showed that the two items for resource slack loaded on the latent construct with factor loadings equal to .85.
Estimation Procedure and Model Selection
The estimation procedure was driven by the need to control for unmeasured and unobservable variables (Jacobson 1990). Because there is no single model recommended for controlling for unobserved effects (Boulding 1990), we estimated two models ( 1) a two-factor fixed-and random-effects linear panel model (TPM) and ( 2) a single-factor fixed-and random-effects autoregressive panel model (ARPM). Specifically, the fixed-effects TPM model can be represented as follows:
( 4) y[subit] = μ + α[subi] + γ[subt] + β'X[subit] + ε[subit],
where
μ = the overall constant;
α[sub1] = firm-specific constants;
γ[subt] = time-specific constants;
X = the explanatory variables;
β = a vector of the influence of the explanatory variables; and
ε[subit] = the error term, such that E(ε[subit]) = 0, and E(ε[subit, ²] = σ[subε].
A parallel representation of the random-effects model is given as follows:
( 5) y[subit] = μ + β'X[subit] + ε[subit] + V[subi] + λ[subt],
where
V[sub1] = the random disturbance characterizing the ith firm that is constant through time, and the assumptions are E([subi]) = 0, and E(V2[subit, ²) = σ[subv]; and
λ[subt] = the random disturbance characterizing the tth year that is constant across firms, and the assumptions are E(λ[subt]) = 0, and E(λ[subit, ²]) = σ[subλ]
The following assumptions apply: E(ε[subit], V[subj] = 0 E(ε[subit, λ[subs) = 0, and E(V[subi], λ[subt] = 0 ∀ i, t, j, and s. Furthermore, E(ε[subit], ε[subjs]) = 0, and E (V[subi], V[subj] = 0 and E(λ[subt], λ[subs]) = 0, it i ≠ j and t ≠ s, ∀ i, t, j, and s.
The fixed-effects ARPM model is given as follows:
( 6) y[subit] = α[subi] + β'X[subit] + ε[subit], and ε[subit] = ρε[subi,t-1] + η[subit],
where ρ represents the autocorrelation coefficient, and η[subit] is the error term, such that E(η[subit]) = 0 and E(η[subit, ²) = σ[subη]. A parallel representation of the random-effects model is given as follows:
( 7) y[subit] = μ + β'X[subit] + ε[subit] + V[subi], and ε[subit] + ρε[subi,t-1] + η[subit].
For both the TPM and the ARPM models, we use the Hausman (1978) test to compare the fit of the fixed-effects model with that of the random-effects model. This statistic is distributed χ², and a statistically significant value favors the fixed-effects model. We estimated the following six models:
M1: the constant term (μ in Equation 4) as the only explanatory variable;
M2: firm effects only (i.e., μ and α[subi] in Equation 4);
M3: X variables only;
M4: X and firm effects;
M5: X, firm, and time effects (this configuration does not apply for the ARPM); and
M6: a random-effects version of M5 for TPM and of M4 for ARPM.
Note that for ARPM, M1-M4 are autoregressive models. We use ordinary least squares to estimate models M1-M5, with dummy variables for firm and time effects when appropriate, and we use generalized least squares for M6 (for details, see Baltagi 1995). Finally, to compare TPM with ARPM, we used the Akaike, Bayesian, and consistent Akaike information criteria (AIC, BIC, and CAIC, respectively).( n5)
Model Selection Results
We used a series of likelihood ratio tests to compare the first five models, whereas we compared M6 with M5 for TPM and with M4 for ARPM using the Hausman (1978) test. We report the results of these tests in Table 4. The tests show that the two-way fixed-effects model (M5) outperforms the other submodels for TPM and that the one-way fixed-effects autoregressive model outperforms its submodels for ARPM. The Hausman test shows that the fixed-effects model outperforms the random-effects model for TPM (χ⊃2; = 60.24, degrees of freedom [d.f.] = 12, p < .01). Similarly, the Hausman test shows that for ARPM, the fixed-effects model outperforms the random-effects model (χ² = 66.06, d.f. = 12, p < .01). The autocorrelation coefficient for ARPM is positive and statistically significant (ρ = .32, p < .01). Finally, the information criteria show that the ARPM (R² = .77) outperforms the TPM (R² = .72).( n6)
Research Model Results
The results provide some support for the main effects of strategic responses (see Table 5).( n7) The speed of communications channel adoption appears to enhance firm performance (TPM: b = .085, p < .05; ARPM: b = .076, p < .05), and slack resources strengthen this positive effect (TPM: b= .055, p < .05; ARPM: b = .060, p < .05). The results show that the speed of e-alliance formation also enhances firm performance (TPM: b = .227, p < .10; ARPM: b = .263, p < .05). However, we do not find support for the main and interaction effect of sales channel adoption. This unexpected finding may be due to the empirical correlation between communications channel adoption and sales channel adoption (ρ = .687, p < .01; see Table 2) or the cannibalization potential of the Internet channel. Alternatively, because we study market-based performance, the finding may reflect that the value of a firm increases when it adopts the Internet as a communications channel. Furthermore, it is possible that investor attention focused on the presence of nonincumbent (new entrant) firms, the so-called dot-coms, and as a result, the market valuation of incumbents was not influenced.
To explore these conjectures further, we estimate two models. In the model without communications channel adoption variables, the statistical results for sales channel adoption remain the same. When we combine the speed of sales channel adoption with a dummy variable for catalog operations, the interaction effect is positive and statistically significant, indicating that the value of retailers with catalog operations increases with the adoption of the Internet as a sales channel (TPM: b = .199, p < .05; ARPM: b = .197, p < .05). Finally, we find that slack resources tend to decrease Tobin's q (TPM: b = -.279, p < .01; ARPM: b = -.278, p < .01). The firms in our sample may have excess slack resources, and the opportunity costs associated with these resources may outweigh the gains that can be obtained from them. Managers often accumulate slack resources to pursue their own interests, which can be at odds with the organizational needs to create the strategic options that slack resources provide (Child 1972; Cyert and March 1963).
Model Robustness Results
We took three steps to ensure that the results were robust. First, we estimated the research model (Equations 1 and 2) with data until 1999 as opposed to 2000. Because the Internet bubble burst in 2000 (i.e., the NASDAQ market value dropped from $5.2 trillion in 1999 to $3.6 trillion in 2000), we thought it would be interesting to study how the results might change. As is shown in the two right-hand columns of Table 5, the results are substantively the same. Second, we estimated three submodels nested in the research model (Equations 1 and 2). Submodel 1 included the control variables, Submodel 2 included the control and resource variables, and Submodel 3 added the main effects of the strategic response to the control and resource variables. The signs and statistical significance of the parameter estimates did not change across the models. Third, we estimated two models nested in the research model (Equations 1 and 2). Model 1 included the interactions between the speed of e-alliance formation and the speed of communications channel adoption and sales channel adoption. Model 2 included the square terms of speed of communications and sales channel adoption. None of the effects across the two models were statistically significant. Thus, we have a fair degree of confidence that the results are robust.
As new technologies continue to proliferate, firms in diverse industries increasingly must respond. Future economic rents and competitive advantage rests on the organizational ability to assimilate new technologies in a timely manner. To address this important research issue, we develop a general framework that describes the influence of organizational strategic responses on firm performance (Figure 1). We theorize strategic responses according to the three dimensions of magnitude, domain, and speed, and we suggest that a response can encompass one or more of these dimensions. In this framework, we rely on the resource-based view of the firm (Hunt and Morgan 1995) and the organizational learning literature (Sinkula 1994) to suggest that organizational resources determine the efficacy of the strategic response. We also operationalize the framework for retailers response to the advent of the Internet. The results highlight the importance of organizational strategic responses and resources and demonstrate the potency of our framework.
Speedier communications channel adoption, useful in preempting competition for scarce resources (Lieberman and Montgomery 1988) and learning from experience (e.g., Sinkula 1994), seems to yield significant returns. Furthermore, firms with slack resources seem to garner greater returns from communications channel adoption. For example, the comparison of Staples and Office Depot illustrates this advantage. Staples adopted the Internet as a communications channel in 1995 and as a sales channel in 1998, whereas Office Depot, its major competitor, did not adopt it until 1998. The difference in the year 2000 Tobin's q for Office Depot (.97) and Staples (2.08) can be attributed in part to their responses to the advent of the Internet. Similarly, The Gap adopted the Internet as a communications and sales channel in 1996, whereas Urban Outfitters did not adopt it until 2000. The year 2000 Tobin's q for The Gap was 4.81, compared with 1.11 for Urban Outfitters. In Table 6, we present details about the strategic response variables and firm performance at various points in time for 40 retailers. Careful scrutiny of Table 6 provides more examples of the benefits of appropriate strategic responses.
However, sales channel adoption does not seem to influence firm performance (Christensen 1997). Perhaps the market does not reward firms for adopting the Internet as a sales channel because the adoption may cannibalize existing store operations. Alternatively, the market may reward firms for their initial communications channel adoption, and then the effect of the sales channel adoption filters in through enhanced sales and profitability. Furthermore, as a result of the attention focused on dot-com new entrants, investors may have overlooked and undervalued the online sales of incumbents. Thus, the only thing that mattered for incumbents was whether they had a Web presence. The data lean in favor of this conjecture; communications and sales channel adoptions are correlated (ρ = .687, p < .01; see Table 2), and profitability (ROA) positively influences Tobin's q. Furthermore, the interaction between slack resources and sales channel adoption is not statistically significant. Thus, it seems that slack resources do not enable firms to develop the new capabilities needed for sales channel adoption. Nonetheless, our post hoc analysis reveals a positive interaction between speed of sales channel adoption and the dummy variable for catalog operations (p < .05). This result suggests that firms with catalog operations that adopted more quickly tended to have higher performance.
The literature is unclear about whether e-alliances yield positive or negative consequences for retailers. On the one hand, e-alliances yield the benefits of lower transaction costs (Gulati 1998) and surrogate knowledge bases (Parkhe 1991). On the other hand, such alliances can manifest asymmetrical resource dependence, which can be harmful for the nontechnical, or store-based, partner (Das, Sen, and Sengupta 1998). In addition, if e-alliances are formed because others are doing so (i.e., the bandwagon effect), the consequences of the alliances can be negative (e.g., Pangarkar and Klein 1998). Our results show that the gains of lower transaction costs and surrogate knowledge bases seem to overshadow any losses due to asymmetrical resource dependence.( n8)
Limitations and Future Research Implications
We rely on secondary data to test the model, and thus our investigation is limited to the variables for which we could obtain data. For example, a measure such as dollar spending on online operations would be appropriate to capture response magnitude. Furthermore, our sample is limited to public firms, which means that many private retailers are not captured in this research. Although we use the Herfindahl index to control for competitive effects, we do not include nonincumbent retailers that used Internet-based business models. It would be interesting to study the competition between incumbents that adopt a new technology to supplement existing operations and nonincumbents that rely primarily on the new technology. In addition, we suggest that strategic variables determine the market valuation of the firm. However, it is possible that market valuation drives some strategic response variables, specifically e-alliance formation. In other words, firms that are highly valued may seem to be attractive alliance partners. Further research is needed to rule out such endogeneity. Finally, as in most secondary data research, we do not account for measurement error, which can lead to biased estimates. Such measurement error manifests itself in measures obtained from COMPUSTAT, such as resource slack and Tobin's q, and from strategic response measures we coded using other secondary data sources, such as searches on LexisNexis. These limitations provide potentially fruitful avenues for further research.
Theoretical Implications
The two primary theoretical contributions of this research are the development of a conceptual framework for strategic responses to new technologies and the linking of marketing actions to market valuation of the firm. First, we suggest that strategic response and organizational resources together affect firm performance. Our conceptualization of organizational strategic responses is a potent tool that researchers can use to study the adoption and assimilation of new technologies. The illustration of the adoption of the Internet demonstrates the potency of the proposed framework and exemplifies how the framework can be operationalized.
Second, when the value of marketing as a discipline is questioned, the linking of marketing actions to shareholder wealth is likely to go a long way toward further establishing the worth of the discipline (Day and Fahey 1988). We theoretically link marketing actions in response to new technologies with the market valuation of the firm and empirically establish this link. For example, the results show that shareholders value the improvements in marketing communications that can be attributed to the adoption of the Internet. In other words, shareholders value marketing decisions related to new technology and foresee future economic gains in the organizational adoption and assimilation of new technologies.
From a methodological standpoint, we use secondary data from multiple sources to study a strategic marketing research issue. Whereas most research on marketing strategies relies on survey data and suffers from common methods bias, we show that the relationship between strategic marketing actions and firm performance is not an artifact of common methods. The longitudinal nature of the study also illustrates the dynamic nature of the effects of strategic marketing actions.
Finally, we contribute to the growing literature that attempts to make sense of the implications of the emergence of the Internet. Although the advent of the Internet is considered critical for the business arena, to date we are unaware of any attempt to link strategic responses to its advent with the market valuation of the firm (cf. Geyskens, Gielens, and Dekimpe 2002). The model sheds some light on this important question and emphasizes the importance of modeling the interactions between organizational strategic responses and resources (Figure 2).
Managerial Implications
This research also sheds light on two important strategic issues for managers: the value of strategic responses to new technologies and the market valuation of the firm. The results suggest that an appropriate response--as defined by response magnitude, domain, and speed--is needed to assimilate new technologies successfully. In our results, retailers that speedily adopted the Internet as a communications channel were more valued by their shareholders. For example, our model predicts a Tobin's q of .907 for a retailer that adopted the Internet as communications channel in 1994, 2.4 times greater than the Tobin's q of .375 for a retailer that did not adopt the Internet until 2000 (if all other variables are fixed at their mean values) and almost twice that of a retailer that waited until 1999 to adopt (.527). The results also suggest that firms not oriented toward technology can benefit from partnering with technology firms because such alliances lower transaction costs and provide surrogate knowledge bases. For example, a firm that adopted the Internet as a communications channel in 1994 and engaged in an e-alliance would have a Tobin's q of approximately 2.729, more than three times greater than that of a firm that adopted in 1994 but did not form any e-alliances. Our results also highlight the importance of using slack resources to adopt the Internet as a communications channel. However, although slack resources may be useful in ventures that do not require the development of new capabilities, they are not effective for reengineering business processes when new capabilities must be developed (e.g., sales channel adoption).
Both authors contributed equally to the research. The article benefited from the feedback of Bill Ross, Amit Saini, Raji Srinivasan, Patriya Tansuhaj, and the three anonymous JM reviewers. The authors thank seminar participants of the JM /Marketing Science Institute competition at the Linking Marketing to Financial Performance and Firm Value Conference and Don Lehmann for their support of the research effort.
( n1) Some researchers have found that first-mover advantage does not always pay off and that motivated late movers become winners (e.g., Golder and Tellis 1993; Shankar, Carpenter, and Krishnamurthi 1998; for more details, see Tellis and Golder 2001). Lieberman and Montgomery (1988) discuss first-mover disadvantage and suggest that late movers ( 1) can free ride on early movers, ( 2) can gain from the resolution of technological and market uncertainty, ( 3) can jump to reliable technologies, and ( 4) are less likely to succumb to incumbent inertia. Nonetheless, Lieberman and Montgomery's synthesis of literature on first-mover advantage shows more support for first movers than for late movers. Lieberman and Montgomery question Golder and Tellis's (1993) operationalization of first movers and recognize that innovative late movers can be more successful than first movers.
( n2) The mismatch of capabilities between store-based and online retailing is amply illustrated by the case of Toys "R" Us. Somewhat surprised by the sudden emergence and gain in prominence of eToys.com, Toys "R" Us hurriedly launched its e--commerce operations. Poor navigability and technical glitches highlighted the company's dearth of IT capabilities, and its inability to physically deliver products on time (i.e., promised Christmas Day delivery) raised questions about its logistics capabilities (The Economist 2000). In contrast, the success of some traditional retailers, such as J.C. Penney, in online ventures underscores the promise of this new medium for store-based retailers.
( n3) We use Tobin's q, a forward-looking measure (e.g., Anderson, Fornell, and Mazvancheryl 2004), to operationalize firm performance. Because the formula for Tobin's q contains share price, we refer to this measure as reflecting the market valuation of the firm. Thus, we use firm performance, market valuation of the firm, and Tobin's q interchangeably.
( n4) Another possible theoretical response is the integration of store-based and online operations. However, this response is empirically correlated with the speed of adoption of the Internet as a sales channel, and therefore we do not use it.
( n5) We calculate the AIC as AIC = -2 x LL + 2 x NP, where LL and NP stand for the log-likelihood value and the number of parameters, respectively. We calculate the BIC and the CAIC as BIC = -2 x LL + ln(SS) x NP, and CAIC = -2 x LL + [1 + ln(SS)] x NP, respectively, where SS stands for the sample size and ln is the natural logarithm transformation.
( n6) Multicollinearity did not seem to be a concern; the highest variance inflation factor was 2.4, and the highest condition index was 3.9.
( n7) Descriptive statistics for the 83 firms that adopted the Internet are as follows: for communications channel adoption (83 firms), there were 3 in 1995, 17 in 1996, 12 in 1997, 14 in 1998, 25 in 1999, and 12 in 2000; for sales channel adoption (50 firms), there was 1 in 1995, 3 in 1996, 3 in 1997, 9 in 1998, 23 in 1999, and 11 in 2000; and for e-alliances (34 alliances), there was 1 in 1996, 1 in 1997, 5 in 1998, 11 in 1999, and 16 in 2000. The mean year-end Tobin's q is 1.92 with a standard deviation of 1.61; the quarterly Tobin's q is 1.38 with a standard deviation of 1.54. The Tobin's q ranges from -.80 to 26.58.
( n8) It is important to recognize that because of the way we operationalize e-alliances, we will find positive, negative, or statistically nonsignificant parameter estimates. Thus, we will find support for at most one of the two perspectives. It would be valuable to come up with other operationalizations of e-alliances (e.g., that use two operationalizations) to find support for both positive and negative effects. (We thank an anonymous reviewer for this suggestion.)
Legend for Chart:
A - SIC Codes (Two-Digit)(a)
B - All Retailers in COMPUSTAT Database
C - Complete Information (Nine-Year Data Available on COMPUSTAT
and CRSP tapes) Retailers with Online Operations
D - Complete Information (Nine-Year Data Available on COMPUSTAT
and CRSP tapes) Retailers Without Online Operations
A B C D
53 43 17 2
54 48 13 4
56 52 21 12
57 39 12 2
59 (exclude 596) 77 20 3
Total 259 83 23Legend for Chart:
A - Variable
B - All Retailers
C - Descriptive Statistics(b) Retailers with Online Operations
D - Descriptive Statistics(b) Retailers Without Online Operations
A B C D
Number of 25.88 30.03 10.81
employees (46.68) (50.96) (19.48)
Sales 3076.79 3636.42 1047.45
(5768.97) (6346.26) (1646.94)
Retained 416.56 500.17 113.36
earnings (991.64) (1096.09) (277.07)
Earning 163.81 196.76 44.32
before (341.78) (376.65) (89.03)
interest
and tax
(a) Retailers were classified into eight major groups: SIC codes
52-59. In this study, we excluded SIC codes 52 (building
materials), 55 (gasoline and car dealers), 58 (eating and
drinking), and 59 (nonstore retailers).
(b) We provide mean values with standard deviations in
parentheses. Number of employees is measured in thousands; sales,
retained earnings, and earnings before interest and tax are in
millions of dollars. Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
A B C D
E F G
H I J
K
Retailers That Adopted
the Internet
1. Tobin's q
2. SCC .080(*)
3. SSC .022 .675(**)
4. ALLY -.008 .287(**) .480(**)
5. RS -.010 -.044 -.012
-.037
6. CAT .071 .069 .097(**)
.029 .034
7. CC .101(**) .031 -.047
-.027 -.081(*) -.020
8. ROA .449(**) -.041 -.022
-.011 .377(**) .072(*)
.024
9. GDP1 .048 .566(**) .382(**)
.212(**) -.068 .011
-.030 -.027
10. HINDEX -.055 -.008 -.114
-.024 -.057 .016
.090(*) -.044 -.134(**)
Mean 1.359 .820 .340
.080 .068 .110
.118 4.083 41.133
1037.131
Standard deviation 1.343 1.372 .903
.435 .982 .308
.323 9.171 11.440
374.585
Retailers That Did Not
Adopt the Internet(a)
1. Tobin's q
2. RS -.168(*)
3. CC .103 .115
4. ROA .369(**) .516(**) .147(*)
5. GDP1 -.084 .022 -.099
.010
6. HINDEX .171(*) .124 .011
.086 -.290(**)
Mean 1.311 -.248 23.967
2.832 41.197 954.878
Standard Deviation 1.186 1.028 163.698
9.535 11.451 296.224
(*) p < .10.
(**) p < .05.
(a) None of the nonadopting retailers in our sample had catalog
operations.
Notes: Tobin's q, ROA, GDP1, and HINDEX are ratio indexes; RS and
CC are factor scores; and SCC, SSC, ALLY, and CAT represent
categorical data. Details can be found in the "Measures" section.
SCC, SSC, SECA, RS, CAT, CC, ROA, GDP, and HERF stand for speed
of communications channel adoption, speed of sales channel
adoption, speed of e-alliance formation, resource slack, catalog
operations, cost of capital, return on assets, changes in gross
domestic product, and Herfindahl index, respectively. Legend for Chart:
A - Strategic Response
B - Examples
A B
E-alliances • We started forming an alliance with
Microsoft. Microsoft is going to
provide our company marketing and
technological support.
• We have e-alliances with Micron
Electronics, Redline Entertainment,
Simplexity.com, and etown.com.
• Our strategic partners include AOL
and Sony Electronics.
• We develop alliances with other
leading retailers to strengthen our
e-commerce strategy.
• We partner with AOL and became an
exclusive bookseller on AOL.
• We partner with major Web portals
and content sites such as AOL,
Lycos, and MSN.
Adoption of the • Our Web site provides product
Internet as a information, company background,
communications and current news.
channel
• Our home page provides news and
other financial information to
shareholders.
• Our Web site provides information
such as 10-K reports, press releases,
and other stockholder information.
• Our Web site does not focus on
Internet selling yet, but investors can
visit our Web site for financial
information and other company news.
• In 1995, we first entered the e-
commerce space with the launch of
xxx.com.
Adoption of the • We began to develop sales on the
Internet as a Internet.
sales channel
• In 1997, we launched our public
Internet site, which enables
customers to shop online.
• During the second quarter of this
year, we began selling about 1500
items from our Web site. Legend for Chart:
A - Model
B - TPM Log-Likelihood
C - ARPM Log-Likelihood
A B C
1: Constant term only -1609.5 -1403.6
2: Group effects only -1078.3 -836.2
3: X variables only -1465.3 -1264.2
4: X and group effects -1012.2 -776.8
5: X individual and time effects -995.1 --
Legend for Chart:
A - Likelihood Ratio Test
B - TPM χ² (d.f., p-Value)
C - ARPM χ² (d.f., p-Value)
A B C
2 versus 1 1062.4 (105, .00) 1134.9 (105, .00)
3 versus 1 288.5 (12, .00) 278.9 (12, .00)
4 versus 1 1194.6 (117, .00) 1253.6 (117, .00)
4 versus 2 132.2 (12, .00) 118.7 (12, .00)
4 versus 3 906.1 (105, .00) 974.7 (105, .00)
5 versus 4 34.1 (8, .00) --
5 versus 3 940.2 (114, .00) --
Legend for Chart:
A - Information Criteria
B - TPM
C - ARPM
A B C
AIC 2276.4 1787.6
BIC 2888.7 2342.4
CAIC 3014.7 2459.4 Legend for Chart:
A - Variables
B - Tobin's q TPM for Data to 2000
C - Tobin's q ARPM for Data to 2000
D - Tobin's q TPM for Data to 1999
E - Tobin's q ARPM for Data to 1999
A B C
D E
Control Variables
Catalog operations -.190 -.492
(.601) (.702)
-.136 -.548
(.599) (.759)
Cost of capital x 100 .015 .004
(.043) (.039)
-.007 -.037
(.045) (.038)
ROA .033(***) .034(***)
(.004) (.004)
.036(***) .024(***)
(.004) (.004)
Change in GDP (ΔGDP) x 10 .347(*) .018
(.230) (.026)
.364(*) .037(*)
(.226) (.024)
Herfindahl index x 100 .038(**) .020
(.020) (.019)
.048(**) .001
(.024) (.027)
Strategic Response
Speed: communications .085(**) .076(**)
channel (SCC) (.042) (.033)
.118(**) .119(***)
(.051) (.043)
Speed: sales channel (SSC) -.026 -.045
(.056) (.051)
.012 -.010
(.077) (.074)
Speed: e-alliance (SECA) .227(*) .263(**)
(.176) (.159)
.181 .303(*)
(.226) (.190)
Organizational Resources
Resource slack (RS) -.279(***) -.278(***)
(.077) (.077)
-.274(***) -.180(**)
(.083) (.081)
RS x RS -.008 -.010
(.018) (.017)
-.011 -.005
(.019) (.018)
Interaction Between Strategic
Response and Organizational
Resources
RS x SCC .055(**) .060(**)
(.030) (.027)
.086(**) .082(**)
(.039) (.037)
RS x SSC -.029 -.056
(.066) (.060)
.066 -.032
(.106) (.094)
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: Standard errors are in parentheses. We report one-tailed
tests. Legend for Chart:
A - Firm Name
B - SIC
C - Year of Channel Adoption Communications
D - Year of Channel Adoption Sales
E - Tobin's q Year 1994
F - Tobin's q A Year Before Communications Channel Adoption
G - Tobin's q Year 2000
H - E-Alliance Year Formed
I - E-Alliance Selected Partner Name
A B C D
E F
G H I
Albertson's Inc. 54 1999 1999
3.59 4.48
1.83 1999 Adam.com
Ames Department Stores Inc. 53 1996 1998
.69 .70
.82 2000 RetailExchange.com
AnnTaylor Stores Corp. 56 2000 2000
1.74 1.74
1.21 2000 Digitas Inc.
Bed Bath & Beyond Inc. 57 1999 1999
6.79 8.80
6.28 1999 WeddingNetwork.com
Best Buy Co Inc. 57 1997 1998
1.30 .80
1.84 1999 Microsoft Corp.
Dillard's Inc. 53 1997 2000
1.10 1.18
.63 -- --
Federated Department Stores 53 1996 1999
.67 .82
.78 1998 IBM
Foodarama Supermarkets 54 1999 1999
1.06 1.51
1.38 -- --
The Gap Inc. 56 1996 1996
2.75 3.33
4.81 -- --
Good Guys Inc. 57 2000 2000
1.20 1.04
1.23 -- --
Gottschalks Inc. 53 1997 1997
.78 .70
.77 -- --
Ingles Markets Inc. 54 1999 1999
1.35 1.36
1.45 -- --
Jacobson Stores 53 1999 1999
.87 1.07
.95 -- --
Jo-Ann Stores Inc. 59 1999 1999
1.01 1.06
.90 2000 IdeaForest.com
Kmart Corporation 53 1995 1995
1.15 1.15
.98 1999 Yahoo! Inc.
Longs Drug Stores Corp. 59 1999 1999
1.32 2.51
1.46 1999 planet U
Marsh Supermarkets 54 1998 1998
1.04 1.18
1.08 -- --
May Department Stores 53 1996 2000
1.43 1.55
1.74 2000 WeddingNetwork.com
Michaels Stores Inc. 59 1996 1999
1.64 .94
1.30 -- --
Mothers Work Inc. 56 1999 1999
1.26 1.36
1.42 -- --
Neiman Marcus Group 53 1999 1999
1.16 1.43
1.54 2000 RichFX Inc.
Nordstrom Inc. 56 1999 1999
2.23 2.96
1.76 -- --
Office Depot Inc. 59 1998 1998
2.45 1.75
.97 1998 Microsoft Corp.
Pier 1 Imports Inc. 57 2000 2000
1.15 1.42
2.04 -- --
RadioShack Corporation 57 1999 1999
1.36 3.33
4.66 1999 Microsoft Corp.
Rex Stores Corporation 57 1999 1999
1.13 .83
.82 1999 Zengine Inc.
Rite Aid Corporation 59 1999 1999
1.67 2.01
1.12 1999 Drugstore.com
Safeway Inc. 54 1997 2000
2.18 3.98
3.26 2000 GroceryWorks.com
Saks Inc. 53 1998 2000
1.07 1.59
.89 -- --
Smart & Final Inc. 54 1996 1996
1.56 2.01
.99 -- --
Sport Chalet Inc. 59 1999 1999
1.12 1.27
.74 -- --
Staples Inc. 59 1998 1998
2.13 2.52
2.08 1999 Point.com Inc.
The Talbots Inc. 56 1999 1999
2.60 2.27
4.77 -- --
Target Corporation 53 1998 1999
1.23 2.01
2.50 1999 E*Trade
Tiffany & Co. 59 1998 1998
1.49 1.85
3.52 -- --
Todays Man Inc. 56 1999 1999
1.46 1.12
.89
Toys "R" Us Inc. 59 1998 1998
1.90 1.77
1.11 1999 Amazon.com
Ultimate Electronics Inc. 57 1998 1998
1.67 .79
1.91 -- --
Urban Outfitters Inc. 56 2000 2000
4.28 4.03
1.11 2000 WebLinc
Winn-Dixie Stores Inc. 54 1998 2000
5.86 6.56
5.64 -- --DIAGRAM: FIGURE 1; Conceptual Framework
DIAGRAM: FIGURE 2; Research Model for the Adoption of the Internet
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~~~~~~~~
By Ruby P. Lee and Rajdeep Grewal
Ruby P. Lee is Assistant Professor of Marketing, College of Business, University of Nevada at Las Vegas (e-mail: ruby.lee@ccmail.nevada.edu). Rajdeep Grewal is Assistant Professor of Marketing, Smeal College of Business, Pennsylvania State University (e-mail: rug2@psu.edu).
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Record: 146- Subject and Author Index. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p266-272. 7p. DOI: 10.1509/jmkg.2005.69.4.266.
- Database:
- Business Source Complete
Subject and Author Index
TO VOLUME 69 2005 SUBJECT INDEX
Item Number
Authors
1. The Concept of Hope and Its Relevance to Product Evaluation and Choice Vol. 69, No. 1, January 2005, 1-14
Deborah J. MacInnis & Gustavo E. de Mello
2. Negativity in the Evaluation of Political Candidates Vol. 69, No. 1, January 2005, 131-42
Jill G. Klein & Rohini Ahluwalia
3. The Role of Spokescharacters as Advertisement and Package Cues in Integrated Marketing Communications Vol. 69, No. 4, October 2005, 118-32
Judith A. Garretson & Scot Burton
- 4. Harold H. Maynard Award Vol. 69, No. 2, April 2005, iv
- 5. Marketing Science Institute/H. Paul Root Award Vol. 69, No. 2, April 2005, iv
- 6. Sheth Foundation/Journal of Marketing Award Vol. 69, No. 2, April 2005, iii
7. Corporate Associations and Consumer Product Responses: The Moderating Role of Corporate Brand Dominance Vol. 69, No. 3, July 2005, 35-48
Guido Berens, Cees B.M. van Riel, &
Gerrit H. van Bruggen
8. The Impact of Cobranding on Customer Evaluation of Brand Counterextensions Vol. 69, No. 3, July 2005, 1-18
Piyush Kumar
9. Maximizing Profitability and Return on Investment: A Short Clarification on Reinartz, Thomas, and Kumar Vol. 69, No. 4, October 2005, 153-54
Tim Ambler
10. Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and Interpretations Vol. 69, No. 2, April 2005, 97-113
Raphaëlle Lambert-Pandraud, Gilles Laurent, & Eric Lapersonne
11. The Role of Spokescharacters as Advertisement and Package Cues in Integrated Marketing Communications Vol. 69, No. 4, October 2005, 118-32
Judith A. Garretson & Scot Burton
12. The Social Influence of Brand Community: Evidence from European Car Clubs Vol. 69, No. 3, July 2005, 19-34
René Algesheimer, Utpal M. Dholakia, & Andreas Herrmann
13. Decomposing Influence Strategies: Argument Structure and Dependence as Determinants of the Effectiveness of Influence Strategies in Gaining Channel Member Compliance Vol. 69, No. 3, July 2005, 66-79
Janice M. Payan & Richard G. McFarland
14. Does Distance Still Matter? Geographic Proximity and New Product Development Vol. 69, No. 4, October 2005, 44-60
Shankar Ganesan, Alan J. Malter, & Aric Rindfleisch
15. How Organizational Complaint Handling Drives Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach Vol. 69, No. 3, July 2005, 95-114
Christian Homburg & Andreas Fürst
16. Cherry-Picking Vol. 69, No. 1, January 2005, 46-62
Edward J. Fox & Stephen J. Hoch
17. Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies Vol. 69, No. 2, April 2005, 61-83
Matthew L. Meuter, Mary Jo Bitner, Amy L. Ostrom, & Stephen W. Brown
18. The Concept of Hope and Its Relevance to Product Evaluation and Choice Vol. 69, No. 1, January 2005, 1-14
Deborah J. MacInnis & Gustavo E. de Mello
19. Corporate Associations and Consumer Product Responses: The Moderating Role of Corporate Brand Dominance Vol. 69, No. 3, July 2005, 35-48
Guido Berens, Cees B.M. van Riel, & Gerrit H. van Bruggen
20. Decision Making and Coping of Functionally Illiterate Consumers and Some Implications for Marketing Management Vol. 69, No. 1, January 2005, 15-31
Madhubalan Viswanathan, José Antonio Rosa, & James Edwin Harris
21. Incorporating Strategic Consumer Behavior into Customer Valuation Vol. 69, No. 4, October 2005, 230-38
Michael Lewis
22. Reference Price Research: Review and Propositions Vol. 69, No. 4, October 2005, 84-102
Tridib Mazumdar, S.P. Raj, & Indrajit Sinha
23. Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and Interpretations Vol. 69, No. 2, April 2005, 97-113
Raphaëlle Lambert-Pandraud, Gilles Laurent, & Eric Lapersonne
24. The Role of Spokescharacters as Advertisement and Package Cues in Integrated Marketing Communications Vol. 69, No. 4, October 2005, 118-32
Judith A. Garretson & Scot Burton
- 25. The Social Influence of Brand Community: Evidence from European Car Clubs Vol. 69, No. 3, July 2005, 19-34 René Algesheimer, Utpal M. Dholakia, & Andreas Herrmann
- 26. Why Do Customer Relationship Management Applications Affect Customer Satisfaction? Vol. 69, No. 4, October 2005, 201-209 Sunil Mithas, M.S. Krishnan, & Claes Fornell
27. Conflicts in the Work-Family Interface: Links to Job Stress, Customer Service Employee Performance, and Customer Purchase Intent Vol. 69, No. 2, April 2005, 130-43
Richard G. Netemeyer, James G. Maxham III, & Chris Pullig
- 28. Customer Satisfaction, Cash Flow, and Shareholder Value Vol. 69, No. 3, July 2005, 115-30 Thomas S. Gruca & Lopo L. Rego
- 29. Do Satisfied Customers Buy More? Examining Moderating Influences in a Retailing Context Vol. 69, No. 4, October 2005, 26-43
Kathleen Seiders, Glenn B. Voss, Dhruv Grewal, & Andrea L. Godfrey
- 30. Do Satisfied Customers Really Pay More? A Study of the Relationship Between Customer Satisfaction and Willingness to Pay Vol. 69, No. 2, April 2005, 84-96 Christian Homburg, Nicole Koschate, & Wayne D. Hoyer
- 31. The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention Vol. 69, No. 4, October 2005, 210-218
Anders Gustafsson, Michael D. Johnson, & Inger Roos
32. How Organizational Complaint Handling Drives Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach Vol. 69, No. 3, July 2005, 95-114
Christian Homburg & Andreas Fürst
33. Strategic Firm Commitments and Rewards for Customer Relationship Management in Online Retailing Vol. 69, No. 4, October 2005, 193-200
Raji Srinivasan & Christine Moorman
- 34. Understanding Firms' Customer Satisfaction Information Usage Vol. 69, No. 3, July 2005, 131-51 Neil A. Morgan, Eugene W. Anderson, & Vikas Mittal
- 35. Why Do Customer Relationship Management Applications Affect Customer Satisfaction? Vol. 69, No. 4, October 2005, 201-209
Sunil Mithas, M.S. Krishnan, & Claes Fornell
36. The Concept of Hope and Its Relevance to Product Evaluation and Choice Vol. 69, No. 1, January 2005, 1-14
Deborah J. MacInnis & Gustavo E. de Mello
37. Customer Strategy: Observations from the Trenches Vol. 69, No. 4, October 2005, 262-63
Martha Rogers
38. Decision Making and Coping of Functionally Illiterate Consumers and Some Implications for Marketing Management Vol. 69, No. 1, January 2005, 15-31
Madhubalan Viswanathan, José Antonio Rosa, & James Edwin Harris
39. Decomposing Influence Strategies: Argument Structure and Dependence as Determinants of the Effectiveness of Influence Strategies in Gaining Channel Member Compliance Vol. 69, No. 3, July 2005, 66-79
Janice M. Payan & Richard G. McFarland
40. Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions Vol. 69, No. 1, January 2005, 32-45
Itamar Simonson
41. Making Customer Relationship Management Work: The Measurement and Profitable Management of Customer Relationships Vol. 69, No. 4, October 2005, 252-61
Lynette Ryals
42. Maximizing Profitability and Return on Investment: A Short Clarification on Reinartz, Thomas, and Kumar Vol. 69, No. 4, October 2005, 153-54
Tim Ambler
43. Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and Interpretations Vol. 69, No. 2, April 2005, 97-113
Raphaëlle Lambert-Pandraud, Gilles Laurent, & Eric Lapersonne
44. Balancing Acquisition and Retention Resources to Maximize Customer Profitability Vol. 69, No. 1, January 2005, 63-79
Werner Reinartz, Jacquelyn S. Thomas, & V. Kumar
45. Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research Vol. 69, No. 2, April 2005, 1-14
Pierre Chandon, Vicki G. Morwitz, & Werner J. Reinartz
46. Managing Marketing Communications with Multichannel Customers Vol. 69, No. 4, October 2005, 239-51
Jacquelyn S. Thomas & Ursula Y. Sullivan
47. Reducing Adverse Selection Through Customer Relationship Management Vol. 69, No. 4, October 2005, 219-29
Yong Cao & Thomas S. Gruca
48. Marketing Renaissance: Opportunities and Imperatives for Improving Marketing Thought, Practice, and Infrastructure Vol. 69, No. 4, October 2005, 1-25
Ruth N. Bolton, Stephen W. Brown, Frederick E. Webster Jr., Jan-Benedict E.M. Steenkamp, William L. Wilkie, Jagdish N. Sheth, Rajendra R. Sisodia, Roger A. Kerin, Deborah J. MacInnis, Leigh McAlister, Jagmohan S. Raju, Ronald J. Bauerly, Don T. Johnson, Mandeep Singh, & Richard Staelin
49. Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study Vol. 69, No. 4, October 2005, 133-52
Yakov Bart, Venkatesh Shankar, Fareena Sultan, & Glen L. Urban
50. Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies Vol. 69, No. 2, April 2005, 61-83
Matthew L. Meuter, Mary Jo Bitner, Amy L. Ostrom, & Stephen W. Brown
51. Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions Vol. 69, No. 1, January 2005, 32-45
Itamar Simonson
52. Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research Vol. 69, No. 2, April 2005, 1-14
Pierre Chandon, Vicki G. Morwitz, & Werner J. Reinartz
53. Strategic Firm Commitments and Rewards for Customer Relationship Management in Online Retailing Vol. 69, No. 4, October 2005, 193-200
Raji Srinivasan & Christine Moorman
54. Why Do Customer Relationship Management Applications Affect Customer Satisfaction? Vol. 69, No. 4, October 2005, 201-209
Sunil Mithas, M.S. Krishnan, & Claes Fornell
55. Corporate Associations and Consumer Product Responses: The Moderating Role of Corporate Brand Dominance Vol. 69, No. 3, July 2005, 35-48
Guido Berens, Cees B.M. van Riel, & Gerrit H. van Bruggen
56. Marketing Renaissance: Opportunities and Imperatives for Improving Marketing Thought, Practice, and Infrastructure Vol. 69, No. 4, October 2005, 1-25
Ruth N. Bolton, Stephen W. Brown, Frederick E. Webster Jr., Jan-Benedict E.M. Steenkamp, William L. Wilkie, Jagdish N. Sheth, Rajendra R. Sisodia, Roger A. Kerin, Deborah J. MacInnis, Leigh McAlister, Jagmohan S. Raju, Ronald J. Bauerly, Don T. Johnson, Mandeep Singh, & Richard Staelin
57. Customer Strategy: Observations from the Trenches Vol. 69, No. 4, October 2005, 262-63
Martha Rogers
58. The Incomplete Autobiography of an Immigrant Marketing Professor Vol. 69, No. 3, July 2005, 169-73
Vijay Mahajan
59. Marketing Renaissance: Opportunities and Imperatives for Improving Marketing Thought, Practice, and Infrastructure Vol. 69, No. 4, October 2005, 1-25
Ruth N. Bolton, Stephen W. Brown, Frederick E. Webster Jr., Jan-Benedict E.M. Steenkamp, William L. Wilkie, Jagdish N. Sheth, Rajendra R. Sisodia, Roger A. Kerin, Deborah J. MacInnis, Leigh McAlister, Jagmohan S. Raju, Ronald J. Bauerly, Don T. Johnson, Mandeep Singh, & Richard Staelin
60. Negativity in the Evaluation of Political Candidates Vol. 69, No. 1, January 2005, 131-42
Jill G. Klein & Rohini Ahluwalia
61. Do Suppliers Benefit from Collaborative Relationships with Large Retailers? An Empirical Investigation of Efficient Consumer Response Adoption Vol. 69, No. 3, July 2005, 80-94
Daniel Corsten & Nirmalya Kumar
62. Customer Strategy: Observations from the Trenches Vol. 69, No. 4, October 2005, 262-63
Martha Rogers
63. The Impact of Acquisitions on Innovation: Poison Pill, Placebo, or Tonic? Vol. 69, No. 1, January 2005, 114-30
Jaideep C. Prabhu, Rajesh K. Chandy, & Mark E. Ellis
64. Technological Evolution and Radical Innovation Vol. 69, No. 3, July 2005, 152-68
Ashish Sood & Gerard J. Tellis
65. Benchmarking Marketing Capabilities for Sustainable Competitive Advantage Vol. 69, No. 1, January 2005, 80-94
Douglas W. Vorhies & Neil A. Morgan
66. A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to Go Vol. 69, No. 4, October 2005, 155-66
William Boulding, Richard Staelin, Michael Ehret, & Wesley J. Johnston
67. The Effects of Strategic Orientations on Technology-and Market-Based Breakthrough Innovations Vol. 69, No. 2, April 2005, 42-60
Kevin Zheng Zhou, Chi Kin (Bennett) Yim, & David K. Tse
68. Market Orientation: A Meta-Analytic Review and Assessment of Its Antecedents and Impact on Performance Vol. 69, No. 2, April 2005, 24-41
Ahmet H. Kirca, Satish Jayachandran, & William O. Bearden
69. A Marketing Perspective on Mergers and Acquisitions: How Marketing Integration Affects Postmerger Performance Vol. 69, No. 1, January 2005, 95-113
Christian Homburg & Matthias Bucerius
70. The Performance Implications of Fit Among Business Strategy, Marketing Organization Structure, and Strategic Behavior Vol. 69, No. 3, July 2005, 49-65
Eric M. Olson, Stanley F. Slater, & G. Tomas M. Hult
71. Resolving the Capability-Rigidity Paradox in New Product Innovation Vol. 69, No. 4, October 2005, 61-83
Kwaku Atuahene-Gima
72. Understanding Firms' Customer Satisfaction Information Usage Vol. 69, No. 3, July 2005, 131-51
Neil A. Morgan, Eugene W. Anderson, & Vikas Mittal
73. A Marketing Perspective on Mergers and Acquisitions: How Marketing Integration Affects Postmerger Performance Vol. 69, No. 1, January 2005, 95-113
Christian Homburg & Matthias Bucerius
74. Decomposing Influence Strategies: Argument Structure and Dependence as Determinants of the Effectiveness of Influence Strategies in Gaining Channel Member Compliance Vol. 69, No. 3, July 2005, 66-79
Janice M. Payan & Richard G. McFarland
75. Do Suppliers Benefit from Collaborative Relationships with Large Retailers? An Empirical Investigation of Efficient Consumer Response Adoption Vol. 69, No. 3, July 2005, 80-94
Daniel Corsten & Nirmalya Kumar
76. The Formation of Buyer-Supplier Relationships: Detailed Contract Drafting and Close Partner Selection Vol. 69, No. 4, October 2005, 103-117
Stefan Wuyts & Inge Geyskens
77. Managing Marketing Communications with Multichannel Customers Vol. 69, No. 4, October 2005, 239-51
Jacquelyn S. Thomas & Ursula Y. Sullivan
78. Balancing Acquisition and Retention Resources to Maximize Customer Profitability Vol. 69, No. 1, January 2005, 63-79
Werner Reinartz, Jacquelyn S. Thomas, & V. Kumar
79. The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories Vol. 69, No. 2, April 2005, 114-29
Michael Tsiros & Carrie M. Heilman
80. The Role of Relational Information Processes and Technology Use in Customer Relationship Management Vol. 69, No. 4, October 2005, 177-92
Satish Jayachandran, Subhash Sharma, Peter Kaufman, & Pushkala Raman
81. Negativity in the Evaluation of Political Candidates Vol. 69, No. 1, January 2005, 131-42
Jill G. Klein & Rohini Ahluwalia
82. The Formation of Buyer-Supplier Relationships: Detailed Contract Drafting and Close Partner Selection Vol. 69, No. 4, October 2005, 103-117 Stefan Wuyts & Inge Geyskens
83. Benchmarking Marketing Capabilities for Sustainable Competitive Advantage Vol. 69, No. 1, January 2005, 80-94
Douglas W. Vorhies & Neil A. Morgan
84. Does Distance Still Matter? Geographic Proximity and New Product Development Vol. 69, No. 4, October 2005, 44-60
Shankar Ganesan, Alan J. Malter, & Aric Rindfleisch
85. Resolving the Capability-Rigidity Paradox in New Product Innovation Vol. 69, No. 4, October 2005, 61-83
Kwaku Atuahene-Gima
86. Understanding Firms' Customer Satisfaction Information Usage Vol. 69, No. 3, July 2005, 131-51
Neil A. Morgan, Eugene W. Anderson, & Vikas Mittal
87. The Performance Implications of Fit Among Business Strategy, Marketing Organization Structure, and Strategic Behavior Vol. 69, No. 3, July 2005, 49-65
Eric M. Olson, Stanley F. Slater, & G. Tomas M. Hult
88. Do Satisfied Customers Really Pay More? A Study of the Relationship Between Customer Satisfaction and Willingness to Pay Vol. 69, No. 2, April 2005, 84-96
Christian Homburg, Nicole Koschate, & Wayne D. Hoyer
89. Incorporating Strategic Consumer Behavior into Customer Valuation Vol. 69, No. 4, October 2005, 230-38
Michael Lewis
90. Reference Price Research: Review and Propositions Vol. 69, No. 4, October 2005, 84-102
Tridib Mazumdar, S.P. Raj, & Indrajit Sinha
91. Does Distance Still Matter? Geographic Proximity and New Product Development Vol. 69, No. 4, October 2005, 44-60
Shankar Ganesan, Alan J. Malter, & Aric Rindfleisch
92. The Effects of Strategic Orientations on Technology- and Market-Based Breakthrough Innovations Vol. 69, No. 2, April 2005, 42-60
Kevin Zheng Zhou, Chi Kin (Bennett) Yim, & David K. Tse
93. The Impact of Acquisitions on Innovation: Poison Pill, Placebo, or Tonic? Vol. 69, No. 1, January 2005, 114-30
Jaideep C. Prabhu, Rajesh K. Chandy, & Mark E. Ellis
94. Product Development Resources and the Scope of the Firm Vol. 69, No. 2, April 2005, 15-23
Birger Wernerfelt
95. Resolving the Capability-Rigidity Paradox in New Product Innovation Vol. 69, No. 4, October 2005, 61-83
Kwaku Atuahene-Gima
96. The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories Vol. 69, No. 2, April 2005, 114-29
Michael Tsiros & Carrie M. Heilman
97. The Impact of Cobranding on Customer Evaluation of Brand Counterextensions Vol. 69, No. 3, July 2005, 1-18
Piyush Kumar
98. Technological Evolution and Radical Innovation Vol. 69, No. 3, July 2005, 152-68
Ashish Sood & Gerard J. Tellis
99. A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to Go Vol. 69, No. 4, October 2005, 155-66
William Boulding, Richard Staelin, Michael Ehret, & Wesley J. Johnston
- 100. Customer Satisfaction, Cash Flow, and Shareholder Value Vol. 69, No. 3, July 2005, 115-30 Thomas S. Gruca & Lopo L. Rego
- 101. Making Customer Relationship Management Work: The Measurement and Profitable Management of Customer Relationships Vol. 69, No. 4, October 2005, 252-61
Lynette Ryals
102. Maximizing Profitability and Return on Investment: A Short Clarification on Reinartz, Thomas, and Kumar Vol. 69, No. 4, October 2005, 153-54
Tim Ambler
103. Reducing Adverse Selection Through Customer Relationship Management Vol. 69, No. 4, October 2005, 219-29
Yong Cao & Thomas S. Gruca
104. Cherry-Picking Vol. 69, No. 1, January 2005, 46-62
Edward J. Fox & Stephen J. Hoch
105. Decision Making and Coping of Functionally Illiterate Consumers and Some Implications for Marketing Management Vol. 69, No. 1, January 2005, 15-31
Madhubalan Viswanathan, José Antonio Rosa, & James Edwin Harris
106. Do Satisfied Customers Buy More? Examining Moderating Influences in a Retailing Context Vol. 69, No. 4, October 2005, 26-43
Kathleen Seiders, Glenn B. Voss, Dhruv Grewal, & Andrea L. Godfrey
107. Do Suppliers Benefit from Collaborative Relationships with Large Retailers? An Empirical Investigation of Efficient Consumer Response Adoption Vol. 69, No. 3, July 2005, 80-94
Daniel Corsten & Nirmalya Kumar
108. The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories Vol. 69, No. 2, April 2005, 114-29
Michael Tsiros & Carrie M. Heilman
109. Managing Marketing Communications with Multichannel Customers Vol. 69, No. 4, October 2005, 239-51
Jacquelyn S. Thomas & Ursula Y. Sullivan
110. Reference Price Research: Review and Propositions Vol. 69, No. 4, October 2005, 84-102
Tridib Mazumdar, S.P. Raj, & Indrajit Sinha
111. Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research Vol. 69, No. 2, April 2005, 1-14
Pierre Chandon, Vicki G. Morwitz, & Werner J. Reinartz
- 112. Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study Vol. 69, No. 4, October 2005, 133-52 Yakov Bart, Venkatesh Shankar, Fareena Sultan, & Glen L. Urban
- 113. Cherry-Picking Vol. 69, No. 1, January 2005, 46-62
Edward J. Fox & Stephen J. Hoch
114. Determinants of Customers' Responses to Customized Offers: Conceptual Framework and Research Propositions Vol. 69, No. 1, January 2005, 32-45
Itamar Simonson
115. Incorporating Strategic Consumer Behavior into Customer Valuation Vol. 69, No. 4, October 2005, 230-38
Michael Lewis
116. Conflicts in the Work-Family Interface: Links to Job Stress, Customer Service Employee Performance, and Customer Purchase Intent Vol. 69, No. 2, April 2005, 130-43
Richard G. Netemeyer, James G. Maxham III, & Chris Pullig
117. Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies Vol. 69, No. 2, April 2005, 61-83
Matthew L. Meuter, Mary Jo Bitner, Amy L. Ostrom, & Stephen W. Brown
- 118. Conflicts in the Work-Family Interface: Links to Job Stress, Customer Service Employee Performance, and Customer Purchase Intent Vol. 69, No. 2, April 2005, 130-43 Richard G. Netemeyer, James G. Maxham III, & Chris Pullig
- 119. Do Satisfied Customers Buy More? Examining Moderating Influences in a Retailing Context Vol. 69, No. 4, October 2005, 26-43
Kathleen Seiders, Glenn B. Voss, Dhruv Grewal, & Andrea L. Godfrey
120. The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention Vol. 69, No. 4, October 2005, 210-218
Anders Gustafsson, Michael D. Johnson, & Inger Roos
121. How Organizational Complaint Handling Drives Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach Vol. 69, No. 3, July 2005, 95-114
Christian Homburg & Andreas Fürst
122. Making Customer Relationship Management Work: The Measurement and Profitable Management of Customer Relationships Vol. 69, No. 4, October 2005, 252-61
Lynette Ryals
123. Reducing Adverse Selection Through Customer Relationship Management Vol. 69, No. 4, October 2005, 219-29
Yong Cao & Thomas S. Gruca
124. Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study Vol. 69, No. 4, October 2005, 133-52
Yakov Bart, Venkatesh Shankar, Fareena Sultan, & Glen L. Urban
125. Balancing Acquisition and Retention Resources to Maximize Customer Profitability Vol. 69, No. 1, January 2005, 63-79
Werner Reinartz, Jacquelyn S. Thomas, & V. Kumar
126. Benchmarking Marketing Capabilities for Sustainable Competitive Advantage Vol. 69, No. 1, January 2005, 80-94
Douglas W. Vorhies & Neil A. Morgan
127. A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to Go Vol. 69, No. 4, October 2005, 155-66
William Boulding, Richard Staelin, Michael Ehret, & Wesley J. Johnston
128. Customer Satisfaction, Cash Flow, and Shareholder Value Vol. 69, No. 3, July 2005, 115-30
Thomas S. Gruca & Lopo L. Rego
129. The Effects of Strategic Orientations on Technology-and Market-Based Breakthrough Innovations Vol. 69, No. 2, April 2005, 42-60
Kevin Zheng Zhou, Chi Kin (Bennett) Yim, & David K. Tse
130. The Impact of Acquisitions on Innovation: Poison Pill, Placebo, or Tonic? Vol. 69, No. 1, January 2005, 114-30
Jaideep C. Prabhu, Rajesh K. Chandy, & Mark E. Ellis
131. The Impact of Cobranding on Customer Evaluation of Brand Counterextensions Vol. 69, No. 3, July 2005, 1-18
Piyush Kumar
132. Market Orientation: A Meta-Analytic Review and Assessment of Its Antecedents and Impact on Performance Vol. 69, No. 2, April 2005, 24-41
Ahmet H. Kirca, Satish Jayachandran, & William O. Bearden
133. A Marketing Perspective on Mergers and Acquisitions: How Marketing Integration Affects Postmerger Performance Vol. 69, No. 1, January 2005, 95-113
Christian Homburg & Matthias Bucerius
134. The Performance Implications of Fit Among Business Strategy, Marketing Organization Structure, and Strategic Behavior Vol. 69, No. 3, July 2005, 49-65
Eric M. Olson, Stanley F. Slater, & G. Tomas M. Hult
135. Product Development Resources and the Scope of the Firm Vol. 69, No. 2, April 2005, 15-23
Birger Wernerfelt
136. The Role of Relational Information Processes and Technology Use in Customer Relationship Management Vol. 69, No. 4, October 2005, 177-92
Satish Jayachandran, Subhash Sharma, Peter Kaufman, & Pushkala Raman
137. Strategic Firm Commitments and Rewards for Customer Relationship Management in Online Retailing Vol. 69, No. 4, October 2005, 193-200
Raji Srinivasan & Christine Moorman
138. A Strategic Framework for Customer Relationship Management Vol. 69, No. 4, October 2005, 167-76
Adrian Payne & Pennie Frow
139. Technological Evolution and Radical Innovation Vol. 69, No. 3, July 2005, 152-68
Ashish Sood & Gerard J. Tellis
140. A Strategic Framework for Customer Relationship Management Vol. 69, No. 4, October 2005, 167-76
Adrian Payne & Pennie Frow
Legend for Chart:
B - Item Number
A B
Ahluwalia, Rohini 2, 60, 81
Algesheimer, René 12, 25
Ambler, Tim 9, 42, 102
Anderson, Eugene W. 34, 72, 86
Atuahene-Gima, Kwaku 71, 85, 95
Bart, Yakov 49, 112, 124
Bauerly, Ronald J. 48, 56, 59
Bearden, William O. 68, 132
Berens, Guido 7, 19, 55
Bitner, Mary Jo 17, 50, 117
Bolton, Ruth N. 48, 56, 59
Boulding, William 66, 99, 127
Brown, Stephen W. 17, 48, 50, 56, 59, 117
Bucerius, Matthias 69, 73, 133
Burton, Scot 3, 11, 24
Cao, Yong 47, 103, 123
Chandon, Pierre 45, 52, 111
Chandy, Rajesh K. 63, 93, 130
Corsten, Daniel 61, 75, 107
De Mello, Gustavo E. 1, 18, 36
Dholakia, Utpal M. 12, 25
Ehret, Michael 66, 99, 127
Ellis, Mark E. 63, 93, 130
Fornell, Claes 26, 35, 54
Fox, Edward J. 16, 104, 113
Frow, Pennie 138, 140
Fürst, Andreas 15, 32, 121
Ganesan, Shankar 14, 84, 91
Garretson, Judith A. 3, 11, 24
Geyskens, Inge 76, 82
Godfrey, Andrea L. 29, 106, 119
Grewal, Dhruv 29, 106, 119
Gruca, Thomas S. 28, 47, 100, 103, 123, 128
Gustafsson, Anders 31, 120
Harris, James Edwin 20, 38, 105
Heilman, Carrie M. 79, 96, 108
Herrmann, Andreas 12, 25
Hoch, Stephen J. 16, 104, 113
Homburg, Christian 15, 30, 32, 69, 73, 88, 121,
133
Hoyer, Wayne D. 30, 88
Hult, G. Tomas M. 70, 87, 134
Jayachandran, Satish 68, 80, 132, 136
Johnson, Don T. 48, 56, 59
Johnson, Michael D. 31, 120
Johnston, Wesley J. 66, 99, 127
Kaufman, Peter 80, 136
Kerin, Roger A. 48, 56, 59
Kirca, Ahmet H. 68, 132
Klein, Jill G. 2, 60, 81
Koschate, Nicole 30, 88
Krishnan, M.S. 26, 35, 54
Kumar, Nirmalya 61, 75, 107
Kumar, Piyush 8, 97, 131
Kumar, V. 44, 78, 125
Lambert-Pandraud, Raphaëlle 10, 23, 43
Lapersonne, Eric 10, 23, 43
Laurent, Gilles 10, 23, 43
Lewis, Michael 21, 89, 115
MacInnis, Deborah J. 1, 18, 36, 48, 56, 59
Mahajan, Vijay 58
Malter, Alan J. 14, 84, 91
Maxham, James G., III 27, 116, 118
Mazumdar, Tridib 22, 90, 110
McAlister, Leigh 48, 56, 59
McFarland, Richard G. 13, 39, 74
Meuter, Matthew L. 17, 50, 117
Mithas, Sunil 26, 35, 54
Mittal, Vikas 34, 72, 86
Moorman, Christine 33, 53, 137
Morgan, Neil A. 34, 65, 72, 83, 86, 126
Morwitz, Vicki G. 45, 52, 111
Netemeyer, Richard G. 27, 116, 118
Olson, Eric M. 70, 87, 134
Ostrom, Amy L. 17, 50, 117
Payan, Janice M. 13, 39, 74
Payne, Adrian 138, 140
Prabhu, Jaideep C. 63, 93, 130
Pullig, Chris 27, 116, 118
Raj, S.P. 22, 90, 110
Raju, Jagmohan S. 48, 56, 59
Raman, Pushkala 80, 136
Rego, Lopo L. 28, 100, 128
Reinartz, Werner J. 44, 45, 52, 78, 111, 125
Rindfleisch, Aric 14, 84, 91
Rogers, Martha 37, 57, 62
Roos, Inger 31, 120
Rosa, José Antonio 20, 38, 105
Ryals, Lynette 41, 101, 122
Seiders, Kathleen 29, 106, 119
Shankar, Venkatesh 49, 112, 124
Sharma, Subhash 80, 136
Sheth, Jagdish N. 48, 56, 59
Simonson, Itamar 40, 51, 114
Singh, Mandeep 48, 56, 59
Sinha, Indrajit 22, 90, 110
Sisodia, Rajendra R. 48, 56, 59
Slater, Stanley F. 70, 87, 134
Sood, Ashish 64, 98, 139
Srinivasan, Raji 33, 53, 137
Staelin, Richard 48, 56, 59, 66, 99, 127
Steenkamp, Jan-Benedict E.M. 48, 56, 59
Sullivan, Ursula Y. 46, 77, 109
Sultan, Fareena 49, 112, 124
Tellis, Gerard J. 64, 98, 139
Thomas, Jacquelyn S. 44, 46, 77, 78, 109, 125
Tse, David K. 67, 92, 129
Tsiros, Michael 79, 96, 108
Urban, Glen L. 49, 112, 124
Van Bruggen, Gerrit H. 7, 19, 55
Van Riel, Cees B.M. 7, 19, 55
Viswanathan, Madhubalan 20, 38, 105
Vorhies, Douglas W. 65, 83, 126
Voss, Glenn B. 29, 106, 119
Webster, Frederick E., Jr. 48, 56, 59
Wernerfelt, Birger 94, 135
Wilkie, William L. 48, 56, 59
Wuyts, Stefan 76, 82
Yim, Chi Kin (Bennett) 67, 92, 129
Zhou, Kevin Zheng 67, 92, 129 Breen, T.H., The Marketplace of Revolution: How Consumer Politics Shaped American Independence, Vol. 69, No. 3, July 2005, 173-74 (Dennis J. Cahill)
Miller, Vincent J., Consuming Religion: Christian Faith and Practice in a Consumer Culture, Vol. 69, No. 4, October 2005, 264-265 (Daryl McKee)
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Record: 147- Successful and Unsuccessful Sales Calls: Measuring Salesperson Attributions and Behavioral Intentions. By: Dixon, Andrea L.; Spiro, Rosann L.; Jamil, Maqbul. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p64-78. 15p. 9 Charts. DOI: 10.1509/jmkg.65.3.64.18333.
- Database:
- Business Source Complete
Successful and Unsuccessful Sales Calls: Measuring
Salesperson Attributions and
Behavioral Intentions
Applying attribution theory to consumer behavior issues is quite common. In the managerial arena, previous research suggests that salespeople's attribution processes affect their expectancies for success and future behavior. However, no published research has developed adequate measures that might be used to examine the full range of attributional responses for sales success or failure and the behaviors that are likely to follow such attributions. The goal of this research is to develop a complete set of attributional and behavioral scales for sales success and failure and validate such scales in a real-world context-among field sales representatives. Following Churchill's (1979) recommended process, the authors develop a complete set of attributional and behavioral intention scales that is applicable to a field sales force setting. The authors then measure 228 financial services representatives' performance attributions for a previous sales interaction; their intended behaviors for a future, similar selling situation; and their personal characteristics. The authors test the validities of the scales and examine the usefulness of applying the scales within a theoretically justified nomological network of relationships.
A potential client ... refused to buy what I recommended, offering really poor reasons for his decision.... On the way back to the office, I thought about why he really refused to take my recommendation.
This excerpt from a salesperson interview represents a process that attribution theorists call "naive psychology." Previous research suggests that people use this cognitive process frequently to seek explanations for events around them, particularly when an unexpected, important, or negative outcome occurs (Dalal 1988; Kelley 1967; Weiner and Kukla 1970; Wong and Weiner 1981).
Unfortunately, unexpected and sometimes negative outcomes are just a part of the job for people engaged in sales. Salespeople aspire to minimize important negative occurrences, such as failing to close a sale. By reflecting on why they have failed, salesseople may recognize behaviors that might be changed. Thus, attributional search-asking why something occurred-serves an adaptive purpose, propelling sales representatives toward better understanding and new behavior and helping them reach their sales goals (Weiner 1986).
Reducing the incidence of failing to close a sale is a concern of people in industry. If salespeople are making erroneous attributions about what is causing them to fail, their subsequent behaviors are not likely to lead to success. Sales managers need to understand the type of attributions their salespeople are making and what behaviors are driven by these attributions. If specific attributions for failure lead to negative, counterproductive behaviors or positive, proactive behaviors, managers will benefit greatly from understanding the attributional/behavioral patterns of their salespeople as well as the forces driving such attributions. In addition, if dispositional factors relate to specific attributional tendencies, this has direct implications for the salesperson selection process.
Initial work in this area by Teas and McElroy (1986) provides a theoretical framework for understanding the kind of attribution salespeople might make following a successful or unsuccessful sales encounter. However, subsequent empirical work does not provide a complete set of usable scales to measure salespeople's attributions and their subsequent behaviors. Testing Teas and McElroy's (1986) propositions through an experimental design, Johnston and Kim (1994) fall short of developing a full set of attributional and behavioral measures in their work, which involves hypothetical sales scenarios and college students. DeCarlo, Teas, and McElroy (1997) test an attribution model that focuses on the links among performance, attributions, and expectancies; however, they note that their measures do not adequately tap the domain of the attributional concepts. Both of these studies provide insight into the salesperson's attributional processes yet do not provide a complete set of attributional and behavioral scales that might enable us to investigate important attribution-behavior linkages.
Sujan (1986) provides usable attributional and behavioral scales in his research; however, the scope of his scales is limited. Sujan focuses solely on effort and strategy behaviors of working harder and working smarter, yet sales representatives have other behavioral options available (e.g., obtaining assistance from a peer or supervisor, avoiding a future similar situation, making no behavioral changes). In addition, several researchers have applied attribution theory to consumer behavior issues (for an extensive review, see Folkes 1988; more recent work includes Folkes and Kamins 1999; Folkes, Koletsky, and Graham 1987; Mittal, Kumar, and Tsiros 1999; Taylor 1994), but these studies provide scant insight into how attribution theory can be operationalized within the sales domain.
We attempt to bridge the gaps by developing a complete set of attributional and behavioral intention scales for sales success and failure. We validate the new scales in a real-world context-among field sales representatives-and ask subjects to consider their attributions and behavioral intentions in a real-world (not hypothetical) context. Following Churchill's (1979) recommended process, we develop a complete set of attributional and behavioral intention scales that is applicable to a field sales force setting. Then we measure sales representatives' performance attributions for a previous sales interaction; their intended behaviors for a future, similar selling situation; and their personal characteristics. We tested the validities of the scales and examined the usefulness of applying the scales within a theoretically justified nomological network of relationships.
In its broadest sense, attribution theory involves the attempts of ordinary people to understand the causes and implications of events (Ajzen and Fishbein 1983; Fincham 1983; Kelley 1967; Monson 1983; Ross and Anderson 1982). Weiner and colleagues (Weiner 1972, 1979, 1985, 1986, 1990; Weiner et al. 1971; Weiner, Nierenberg, and Goldstein 1976; Weiner, Russell, and Lerman 1978, 1979), as well as others (Kelley 1967, 1973), have provided the framework within which most researchers examine attributions. Their work has been primarily theoretical (versus empirical) in its contribution, and they do not, as Teas and McElroy (1986, p. 76) note, explore "the theory's relevance to sales force management issues." Snyder, Shultz, and Jones (1974) and Kelley (1973) suggest that attributions for successes and failures influence expectancy of future success and failure and subsequent behavior.
Integrating attribution theory and expectancy theory (Teas 1981; Vroom 1964; Walker, Churchill, and Ford 1977), Teas and McElroy (1986) develop a conceptual framework that describes the linkages among salespeople's performance, their attributions for thaI performance, and the impact of those attributions on their expectancy estimates. Teas and McElroy's framework relates the types of attribution made (controllable/uncontrollable, internal/external, stable/unstable; from Weiner 1979) by the salespeople for their performance (high or low performance) to how they view the performance in relation to prior events (i.e., whether the event information is perceived as consistent and distinctive and as having consensus; from Kelley 1967, 1973) and to the impact (positive, negative, or neutral) on the salespeople's expectancies for future performance (Walker, Churchill, and Ford 1977). Teas and McElroy (1986) also suggest that some dispositional characteristics are associated with particular attributional tendencies. (For additional details, see Teas and McElroy 1986.)
Properties Underlying Attributions
Weiner (1979) proposes three properties of the causes underlying successful or unsuccessful situations: locus of causality (internal versus external), causal stability (stable versus unstable), and controllability (controllable versus uncontrollable). By definition, if the locus of causality is internal, the cause resides within the salesperson whose success/failure is being examined; if it is external, the cause resides in other persons/entities within the environment. A stable cause persists over time and across situations; an unstable cause is subject to changing temporal or situational conditions. If the attribution is controllable, the salesperson has the power to change the cause; if it is uncontrollable, the environment or other persons/entities possess control of causation (Bettman and Weitz 1983; Teas and McElroy 1986; Weiner 1979, 1986). It should be noted that according to Weiner, the controllability dimension might not be distinct from that of the locus and stability dimensions. For example, salespeople probably view most external causes as uncontrollable and most internal causes as controllable (Weiner 1986, p. 346).
To extend the current knowledge (see DeCarlo, Teas, and McElroy 1997; Johnston and Kim 1994; Sujan 1986; Teas and McElroy 1986), we develop a complete set of attributional and behavioral scales for sales success and failure, which reflects the dimensions discussed previously. Using a sample of sales representatives, we validate these scales and examine their usefulness within a theoretically justified nomological network of relationships.
Scale Development
The goal of the research was to develop and validate a complete set of attributional and behavioral intention scales for use in the sales arena. To ensure that the domain of each attributional and behavioral intention dimension was tapped, we developed multi-item scales in concert with Churchill's (1979) recommendations.
In-depth interviews and item development. The first step in the scale development was to conduct in-depth interviews with a convenience sample of seven salespersons. We audiotaped these interviews to ensure that we appropriately captured the salespersons' descriptions of their attributions and behaviors. The objective of these interviews was to see whether salespeople, using a free-elicitation approach, could easily and clearly recall their most recent successful and unsuccessful sales calls and whether their attributions for these successes and failures fell into the groups suggested by Teas and McElroy's (1986) framework. (Teas and McElroy provide conceptual dimensions of attributions but do not provide measurement scales with their conceptual framework.) Another purpose of these interviews was to explore the behavioral intentions that followed the salespersons' attributions for success and failures.
The salespersons were able to recall their most recent success and failure, and their descriptions of their attributions for these successes and failures closely matched those in Teas and McElroy's (1986) framework. From the taped interviews, we found that Sujan's (1986) items (he provides two items for the effort attribution and two for the strategy attribution) did not match the language reported in the interviews. Therefore, we used the audiotaped free-elicitation descriptions of the attributions and behaviors as a basis to develop all the original measurement items. Therefore, we wrote multiple items for each of the attributions that are suggested in Teas and McElroy's (1986) framework: effort, ability, strategy, task difficulty, and luck. We also wrote multiple items for the behavioral intentions that were uncovered in the literature search and in our pretest interviews: increase effort, change strategy, seek assistance, avoid the situation, and no change in behavior.
Item refinement. The four sets of items (attributions and behavioral intentions for the successful sale and the unsuccessful sale) were then subjected to item-sort exercises by four different groups: undergraduate students, MBA students, academicians, and salespeople. Several items were eliminated on the basis of the results of these item-sort exercises. This resulted in a list of five items for each attribution and five items for each behavioral intention, all of which were measured using six-point (forced-choice) Likert scales (where 1 = "strongly disagree" and 6 = "strongly agree").
Validity and reliability testing. To refine and further validate the scales, we used a survey methodology. We incorporated the items into a questionnaire that we administered to two samples: a pilot sample of 42 and a pretest sample of 100 salespeople. To achieve external validity, the questionnaire asked these respondents to recall their most recent sales success (in which they made a sale) and their most recent sales call in which they were not able to close the sale or schedule a follow-up appointment (in other words, an unsuccessful sales experience). Then the salespeople responded to a series of questions about these calls, including their attributions for success on the successful call and their attributions for failure on the unsuccessful call as well as their behavioral intentions in each case for the next similar call. The samples and survey procedures used for this refinement as well as the results are described next.
Samples and survey procedures. We obtained data from sales representatives working for a large Fortune-500 financial services company. Representatives in this sales division call on individual consumers to sell mutual funds; annuities; and life, automobile, and property insurance. Selling primarily in middle-income consumer markets, these sales professionals make sales calls to both existing customers and prospects. Most prospective customers are identified through referrals, seminars, special events, and/or cold calling. Because these sales are commission driven, the representatives have a strong degree of control over their sales call patterns and activities.
For the initial pilot, we used a convenience sample of sales representatives (a single district). The general manager for this district agreed to encourage participation in the study. Several communications were sent from the researchers to the sales managers (who report to the general manager). The researchers further encouraged response rates by sponsoring breakfasts at district field sales team meetings. A total of 42 sales representatives participated in this initial pilot. The primary purpose of this pilot was to ensure that the questions were understood and the length of time to complete the questionnaire was reasonable. On the basis of the feedback from this pilot, we reworded several items.
For the pretest, a random sample of 100 field sales representatives was selected and sent survey packets. The purposes of this pretest were to determine the expected rate of response for the full mailing (when breakfast incentives and personal contact with management would not be possible) and examine further the properties of the scales. This pretest provided us with adequate data for a power analysis and enabled us to determine the final survey sample size. We also conducted exploratory factor analysis and reliability analyses. The results supported the hypothesized structure of the scales; therefore, no additional changes were made to the scale items (see Appendices A through D).
Finally, using an nth-order sampling procedure, we sent surveys to 1200 salespeople, who were randomly selected from among those who had worked for the company as salespeople for a minimum of one year. (We believed that recently hired representatives would have different success and failure perspectives than would representatives having a base of experience; therefore, we excluded the new hires from the final sample.) Sales representatives received a questionnaire and a cover letter from the researchers asking for their cooperation and assuring them of confidentiality. A preaddressed, stamped envelope was provided for subjects to return the questionnaires directly to the researchers. The district sales managers distributed the questionnaires to the sales representatives' mailboxes; a letter from the company indicating support of the project and encouraging participation was distributed through intercompany mail. Three weeks after the initial mailing, a follow-up letter was sent to the sales managers reporting how many of their representatives had responded and asking them to encourage their representatives to participate. Usable questionnaires were obtained from 228 salespeople (response rate of 19%)-a reasonable response rate given that no response incentive was employed.
The sample was composed primarily of men (87%) with an average age of 43 years. As is typical of the financial industry, the sample was generally highly educated: 89% of the sales associates held an undergraduate or advanced college degree. On the average, the sales associates had worked for their sales units for 9 years and had 15 years of sales experience. It is important to note, therefore, that most of the salespeople in this sample are relatively successful. An analysis of the early and late respondents revealed that there were no significant differences in their demographics or their responses on the study variables. This analysis suggests that the likelihood of a nonresponse bias in this study is minimal. Additional reliability and validity tests described subsequently were conducted on the data collected from the final sample.
Measurement models. To refine and validate the scales further, we submitted each of the multi-item constructs for the successful and the unsuccessful sales experience to confirmatory factor analysis (using LISREL 8.20). Items having low squared multiple correlation coefficients and/or high cross-loadings were dropped. All attributional and behavioral scales were trimmed to three items on the basis of this analysis. At this point, we assessed the fit of the resulting model. For the sales success situation, the results are good (c<SUP>2</SUP> = 218.51, degrees of freedom [d.f.] = 120, p .01; goodness-of-fit index [GFI] = .91; comparative fit index [CFI] = .97; root mean square error of approximation [RMSEA] = .06). For the unsuccessful sales experience, the model fit is also good (c<SUP>2</SUP> = 629.15, d.f. = 360, p .01; GFI = .85; CFI = .96; RMSEA = .06). Although the chi-square goodness-of-fit measures for the overall models were statistically significant, other fit indices are close enough to the .90 benchmark (Bentler 1990) to suggest that the model fits the observed data acceptably. In addition, we obtained reasonable RMSEA estimates. Browne and Cudeck (1993) suggest that a value of .08 or less for an RMSEA fit index indicates a reasonable error of approximation and close model fit to the observed data. The scale items and their means, standard deviations, and item reliabilities are reported in Tables 1 and 2 for the successful and unsuccessful sales situation. The scale intercorrelations are reported in Table 3.
According to Anderson and Gerbing (1988), convergent validity can be assessed by determining whether each indicator's estimated pattern coefficient on its posited underlying construct factor is significant (greater than twice its standard error). An examination of Tables 1 and 2 indicates that all the factor loadings for the individual items are significant. Thus, convergent validity was confirmed for the scales.
We examined the multi-item scales for attributions and behavioral intentions for internal consistency using several criteria. In Tables 1 and 2, we report Cronbach's alpha (an indicator of construct reliability) and Fornell and Larcker's (1981) composite reliability estimate. These statistics for the attribution measures suggest strong internal consistency across the items in the constructs (.83 to .93 for alpha and .81 to .92 for composite reliability) for a sales success (Table 1), in that all scales exceed Nunnally's (1978) suggested Cronbach's alpha level of .70. Cronbach's alphas and composite reliability estimates for attribution measures for unsuccessful sales experiences (Table 2) also suggest strong internal consistency across the items in the constructs (.89 to .94 for alpha and .80 to .90 for composite reliability). Similarly, Cronbach's alphas and composite reliability estimates for behavioral intention measures for both successful (Table 1) and unsuccessful (Table 2) sales experiences indicate strong internal consistency across the items in the constructs.
The entries in Tables 1 and 2 also suggest that a reasonable level of discriminant validity was achieved, because all construct intercorrelations were significantly less than 1.0 (p < .05). To examine the discriminant validity of the constructs, we estimated Fornell and Larcker's (1981) average variance measure (Tables 1 and 2). If average variance is less than .5, the variance due to measurement error is larger than the variance captured by the construct, and the validity of the individual indicators, as well as the construct, is questionable. To satisfy fully the requirements for discriminant validity, the average variance measure must be greater than the squared correlation between the two constructs. In our study, the shared variance between any two constructs (i.e., the square of their intercorrelation) was less than the average variance explained in the items by the construct. Last, the t-values for all loadings across the scales were significant (p < .01), and item-to-total correlations were above .50. These results support the internal consistency and discriminant validity of each of the scales.
Concurrent Validation: Proposed Relationships and Results<SUP>
As a test of concurrent validation of these scales, we examined their ability to substantiate relationships suggested by other researchers' theoretical frameworks (e.g., Teas and McElroy's [1986] work pertaining to effort, strategy, task difficulty, ability, and luck attributions) or empirical findings (e.g., Sujan's [1986] research that explores the effort and strategy attribution process). Figure 1 summarizes the nomological network of relationships between attributions and behavioral intentions. It should be noted that for the sake of brevity, the nomological framework was proposed for only unsuccessful selling situations.
We estimated the proposed model (in Figure1) using LISREL 8.20. The results indicate a reasonable fit between the model and the observed data (c<SUP>2</SUP> = 903.25, d.f. = 385, p = .00; GFI = .80; CFI = .92; RMSEA = .07), thereby suggesting that the nomological network of relationships fits these data (another indicator of support for the validity of these scales) (Churchill 1979). Excluding the GFI, the traditionally reported fit indices are within the accepted range (Hu and Bentler 1999). Exploring additional fit indices (as recommended by Hoyle and Panter [1995]), we find that both the nonnormed fit index (.91) and the incremental fit index (.92) both exceed the recommended .90 threshold levels (Hu and Bentler [1995] recommend these Type II and III incremental indices over the GFI, an absolute fit index). Additional restrictions to the proposed model (e.g., fixing insignificant paths) did not yield better fit of the model.
To examine the validity of these scales further, we tested the individual paths in the structural model to investigate whether the standardized estimate for each path is significantly different from zero (Table 4). Next, for an even stronger assessment of concurrent validity, we tested whether the proposed path is significantly stronger than all alternative (nonhypothesized) paths from the same attribution to other behavioral intentions. (We tested this by comparing the chi-square difference of a model in which all the paths from the attribution in question are freed with models in which the hypothesized path is constrained to be equal to an alternative path. The results of these comparisons are shown in Table 5.) We provide the theoretical justification and the results of the analysis for each of these relationships.
Effort attribution. Sujan (1986) suggests that if salespeople believe that they failed to make a sale because of insufficient effort, they are likely to exhibit greater levels of effort in similar future sales situations. Because Sujan (1986) examines this relationship in the context of only one other attribution (strategy), we attempt to confirm Sujan's findings in the context of the full range of attributions as well as in the context of a broader group of possible behaviors. Therefore, the first path states,
P1 : Attributing an unsuccessful sales call to a lack of effort is more likely to lead to plans to increase effort in a similar sales situation in the future than to plans to seek assistance, change strategies, avoid similar situations, or make no change.
We find a significant standardized LISREL estimate (Table 4) for the effort attribution to the intention to increase effort (P1 ), and the relationship between the effort attribution and the intention to increase effort is significantly stronger than any of the alternative relationships. These results support the validity of this scale within this nomological network (see Table 5).
Ability attribution. As shown in Figure 1, a causal ascription associated with ability may lead to different behaviors depending on whether the salespeople believe they can do anything about the situation. For example, if salespeople believe that they can improve their selling skills or knowledge, they are likely to obtain assistance from a manager or other, more knowledgeable representatives. To increase their skill or knowledge in the event that no one is available to help, salespeople may simply seek to work harder (i.e., increase their effort) to compensate for their deficiency in this area. However, as other researchers suggest (Graham 1990; Teas and McElroy 1986), failure that is attributed to salespeople's innate ability or aptitude may also lead to the belief that there is no response in their repertoire to alter the course of failure; consequently, ability attributions may also lead to an avoidance of similar situations. In many sales situations, the sales representatives have the latitude to alter their call plans, thereby determining which customers/prospects are called on and which are not. As summarized in Figure 1, this suggests the following:
P2 : Attributing an unsuccessful call to the lack of ability is more likely to lead to (a) plans to seek assistance in a similar sales situation in the future than to plans to change strategy or make no change, (b) plans to increase effort in a similar sales situation in the future than to plans to change strategy or make no change, and (c) plans to avoid similar situations in the future than to plans to change strategy or make no change.
Significant standardized LISREL estimates (Table 4) connect the lack of ability attribution with (1) the intention to seek assistance (P2a ) and (2) the intention to increase effort (P2b ). The path between the lack of ability attribution and the intention to avoid the situation (P2c ) was not significant in the LISREL model. In the more stringent chi-square test of P2a and P2b , we find that the relationship between the lack of ability attribution and the intention to seek assistance is significantly stronger than the alternative relationships (i.e., those with avoiding the situation, changing strategy, or not making any changes in behavior), but the relationship between the lack of ability attribution and the intention to increase effort is not stronger than the alternative relationships. Consequently, we find only partial support for its convergent validity within this nomological network.
Task difficulty attribution. Although few researchers have focused on task difficulty attributions (see Teas and McElroy 1986), it seems reasonable to presume that environmental/task difficulty attributions may result in varying behavioral outcomes. Salespeople may believe that the situation is so difficult that there is nothing anyone can do to improve it, and as Weiner (1986) suggests, they may become angry and frustrated. Salespeople may decide to avoid such situations in the future. Alternatively, they may believe that a change of strategy will help or that someone with more experience and/or ability will be able to assist them. These possibilities suggest the following:
P3 : Attributing an unsuccessful sale to the difficulty of the task or situation is more likely to lead to (a) plans to seek assistance in a similar sales situation in the future than to plans to increase effort or make no change, (b) plans to change strategy in a similar situation in the future than to plans to increase effort or make no change, and (c) plans to avoid similar situations in the future than to plans to increase effort or make no change.
The standardized LISREL estimates (Table 4) for these three proposed relationships are significant (indicating that the individual paths associated with this attribution are supported). Through the more stringent test of alternative relationships (chi-square tests; see Table 5), we find that the relationship between making the task attribution and the intention to avoid the situation is significantly stronger than the alternative relationships. The relationships between making the task attribution and the intention to change strategies or seek assistance were not significantly stronger than alternative relationships. Thus, this task difficulty scale fits the overall measurement model and demonstrates good measurement properties (discriminant validity and reliability); however, we find only partial support of its convergent validity within this nomological network.
Strategy attribution. Sujan's (1986) results suggest that salespeople who believe that they failed to make a sale because of an incorrect approach or strategy are likely to change their strategy in similar future sales situations (see Anderson 1983; Sujan 1986). We seek to confirm his findings by examining this relationship within the context of a broader range of attributions and subsequent behaviors:
P4 : Attributing an unsuccessful sale to the use of an incorrect strategy is more likely to lead to plans to change strategy in similar sales situations in the future than to plans to increase effort, seek assistance, avoid similar situations, or make no change.
In strong support of the scale's validity, we find a significant standardized LISREL estimate (Table 4) for an incorrect strategy attribution to the intention to change strategy (P4 ) relationship, and this relationship is significantly stronger than any of the alternative relationships.
Luck attribution. Attributing a sales call failure to bad luck leads to an attribution that is external and unstable according to the properties underlying attributions (Weiner 1986). Because causality is perceived to be outside of the salesperson's power, subject to change, and not subject to anyone's control, little can be done to change the situation. Therefore, salespeople who face such situations may not alter their behaviors. However, to avoid failure, salespeople may choose to avoid similar selling situations in the future. For example, they may choose not to call on a particular type of customer if they believe that the outcomes are somewhat random because of luck. This avoidance behavior is possible in many sales settings that are typified by sales representatives determining their own sales call patterns. Therefore,
P5 : Attributing an unsuccessful sale to bad luck is more likely to lead to (a) plans not to make any changes in a similar sales situation in the future than to plans to increase effort, seek assistance, or change strategy and (b) plans to avoid similar situations in the future than to plans to increase effort, seek assistance, or change strategy.
The relationship between the bad luck attribution and the intention to avoid the situation (P5b ) is significant, whereas the path between the luck attribution and the intention not to make any changes (P5a ) is not significant. In the more stringent chi-square tests, we find that the relationship between the luck attribution and the intention to avoid the situation is not significantly stronger than the alternative relationships. Consequently, this luck scale fits the overall measurement model and demonstrates good measurement properties (discriminant validity and reliability); however, we find only partial support of its convergent validity within this nomological network.
Dispositional antecedents. In an effort to provide additional concurrent validity for the scales, we also tested the extent to which there is a relationship between the types of attributions salespeople make and their dispositional characteristics. On the basis of Teas and McElroy's (1986) discussion of these relationships, we chose to examine two dispositional characteristics in this validation effort: interpersonal control and personal efficacy.
Interpersonal control involves the degree to which people believe that they have an effect on other people in dyads or groups. People having a high level of interpersonal control believe that they have a great impact on (or control over) other people when involved in dyadic (e.g., one-on-one selling) or group settings (Paulhus 1983). Therefore, we expected that salespeople who have a high degree of interpersonal control would be more likely than others to attribute both their successes and failures to themselves rather than to other people or events.
According to Teas and McElroy (1986), people who have a high global self-esteem can be expected to perceive a life history of success. Therefore, when these people experience success, they consider it a consistent event and make stable attributions, both internal and external (i.e., an ability or easy-task attribution). Alternatively, when they experience failure, they consider it an inconsistent event and make attributions that are unstable. To test these relationships, we use personal efficacy (which is the confidence a person feels with regard to personal achievement of a specific task), because it captures the task-and goal-oriented nature of personal selling better than the more global self-esteem measure (Paulhus 1983).
Therefore, we expect to find the following:
P6 : Salespeople who are high in interpersonal control will make more internal attributions following performance, regardless of the level of performance, than salespeople who are low in interpersonal control.
P7 : Salespeople having higher personal efficacy will have tendencies to (a) make more stable than unstable attributions following success and (b) make more internal unstable than stable attributions following failure.
For the analysis, we split the sample into thirds on the basis of the respondents' scores on the personality trait scales and then compared the top third and bottom third of the sample by testing for the differences in the mean level of attributions (paired comparisons test). The results show that in both high and low performance situations, salespeople with a greater interpersonal control made significantly more internal attributions than salespeople with external interpersonal orientation (successful means of 5.31 versus 4.79, p < .00, and unsuccessful means of 3.22 versus 2.54, p < .02) (in support of P6 ). In examining the relationship between salespeople's personal efficacy and attributions, we found that salespeople with high personal efficacy made significantly more stable attributions (mean of 4.12) than unstable attributions (mean of 3.98, p < .08) after a sales success (P7a ). However, following an unsuccessful sales experience, salespeople with higher personal efficacy still made more stable attributions (mean of 3.31, p < .00) than internal, unstable attributions (mean of 2.67, p < .00).
Following the recommendations of Churchill (1979), we used a rigorous methodology to develop and test multi-item scales for measuring five types of attributions: effort attributions, strategy attributions, task difficulty attributions, ability attributions, and luck attributions. Using this same process, we also developed scales for measuring the intended behaviors that might stem from the attributions: increase efforts, change strategies, seek assistance, avoid the situation, or make no changes. The scales' reliabilities, as measured by coefficient alpha, are all well above recommended levels (.77 to .96). The shared variance among the constructs (scales) is less than the average variance of the items within the construct, which thus demonstrates strong discriminant validity for the scales. Finally, concurrent validity of the scales was demonstrated in a nomological network of relationships.
Research Contributions
Over the past two decades, researchers (Decarlo, Teas, and McElroy 1997; Johnston and Kim 1994; Sujan 1986; Teas and McElroy 1986) have demonstrated an interest in gaining a better understanding of the attributions salespeople make following their sales successes and failures. The primary contribution of this research was to develop a more complete set of scales that researchers or managers can use to measure salespeople's attributions for sales successes and failures. A second contribution was the development of the scales to measure salespeople's subsequent behavioral intentions. We believe that researchers must explore both salespeople's attributions for performance and their subsequent behavior to better understand the keys to improving sales performance. Finally, in the process of examining the concurrent validity of these scales, we have found several substantive findings worthy of note, which we discuss next.
Substantive findings. First, it appears that representatives do not fall prey to simply blaming their environment for their failures (the fundamental attribution error). On the contrary, they are likely to identify both internal (effort, ability, and strategy) and external (task and luck) factors to explain their failure. Perhaps experienced salespeople often believe that there is something they can do to change an unsuccessful situation into a successful one. Otherwise, they would avoid unsuccessful situations more often than is reported in this study. (In an unsuccessful situation, the lowest mean of 2.09 was reported for the avoidance behavior.) Consequently, this research prompts us to reconsider the applicability of the fundamental attribution error among salespeople.
Second, the general pattern of relationships between attributions and behavioral intentions shown in the model (Figure 1) was supported (the overall model fit was acceptable). As expected, salespeople who blamed their failure on their own lack of effort plan to put forth more effort in the next situation that is similar to the one in which they failed. Similarly, salespeople who believed that they used the wrong strategy planned to change their strategy. Both findings provide additional support for Sujan's (1986) work. Most salespeople who believed that their lack of ability contributed to the failure indicated that they would be more likely to seek assistance when facing a similar situation than to avoid the situation or redouble their efforts. Apparently, representatives are willing to admit when they believe that they are in over their heads, and they do not try to compensate for ability deficits by increasing their efforts. This result provides support for Sujan's (1986) general conclusion that successful salespeople work smarter rather than harder.
In addition, when salespeople believe they failed because the situation was fraught with difficulties, they indicate that they will not seek assistance or try a new strategy; rather, they plan to avoid such situations in the future. This is consistent with Teas and McElroy's (1986) suggestion that when salespeople believe they have failed because of factors beyond their control, this has a negative impact on their expectancies. It is interesting to note that if salespeople make internal, stable attributions (lack of ability) for failure, they are blaming themselves for the failure; therefore, they intend to seek assistance to improve their own abilities. In contrast, if they make external, stable attributions (task difficulty) for failure, they may believe that they cannot do anything about the situation, and therefore they plan to avoid such situations. This represents a difficult coaching scenario for managers, because they must convince their representatives not to avoid these situations.
As expected, salespeople having high personal efficacy made more stable than unstable attributions in a successful situation, in support of Teas and McElroy's (1986) proposition. However, contradicting Teas and McElroy (1986), salespeople with high personal efficacy also view the causes of their failings as stable. It is possible that efficacious salespeople perceive their failure as so inconsistent with normal events that they attribute it to events that are beyond their immediate control, such as the nature of the task or their ability in that particular situation.
Managerial Implications
This research provides a useful set of scales for measuring attributions and behavioral intentions among field sales representatives. The scale development process yields items appropriate for a successful sales situation as well as an unsuccessful sales situation. The tests of concurrent validity offer several points that should be of interest to managers.
Our research demonstrates that the attributions salespeople make for their successes and failures are significantly related to their behavioral intentions in future sales interactions. Because the primary role of sales managers is to direct and improve the performance of salespeople, they should recognize the importance of the salespeople's attributions for success and failure. If salespeople make incorrect attributions for their failures, it will be more difficult for a manager to help those salespeople improve performance than if the representatives make correct attributions.
However, this study highlights the complexity of the attributional processes and the links between that process and subsequent behaviors. It is not easy for sales managers to know what attributions their salespeople are making for successes and failures or whether those attributions are correct. Sales managers should not assume that they know what attributions their salespeople are making. This becomes most critical when a salesperson has failed.
The scales developed in this study will enable managers to assess more accurately which types of attributions salespeople are making and what their intentions are with regard to the situations in which they are not successful. When an assessment has been made, sales managers should discuss the results with their salespeople. Salespeople should be asked to detail what they believe are the causes for their unsuccessful outcomes and what future actions they intend to take with regard to that or similar situations. If sales managers agree with the salespeople's attributions and intentions, they can reinforce them by agreeing or by offering encouragement and assistance. If sales managers believe that salespeople are making incorrect attributions and/or behavioral choices, then the sales managers must help the salespeople understand the correct causes of the unsuccessful sales encounters and/or the appropriate actions to take. Otherwise, the salespeople are not likely to improve their performance.
The validation work also suggests that companies should hire people who have high interpersonal control, because these people are more likely to make internal attributions than are people with low interpersonal control. Also, with regard to their subsequent behavior, they are less likely to avoid difficult situations. In contrast, employees with high personal efficacy may need guidance in accepting responsibility for choosing the wrong strategy or for not working hard enough.
Limitations and Extensions
In this study, we validated the scales by examining the attributions and behavioral intentions that salespeople make after a particular interaction with a customer. However, we did not examine whether or not salespeople have attributional tendencies and subsequent patterns of behavioral tendencies across multiple sales interactions with different customers or even with the same customer. Additional studies are needed to further understand what circumstances may lead to particular attributional patterns and whether these patterns are stable across situations.
Also, in this study we did not ascertain whether the salespeople's attributions were correct or incorrect. Another valuable use for these scales would be to examine whether more successful salespeople make more correct attributions than their less successful counterparts. It would be helpful for sales managers to know whether more successful representatives' attributions have greater veracity than those of less successful sales representatives.
In conducting future work in this area, researchers might examine actual behaviors (rather than behavioral intentions) and determine how those behaviors are related to attributions. Ultimately, we are interested in behaviors, and the behavioral intention measures that we developed are not a perfect predictor of behaviors. Therefore, a study that measures actual behavior following an attribution would overcome this limitation.
Another extension to consider for this research area is to integrate attribution theory with rational decision making. In a sales success scenario, researchers might examine whether representatives choose to reduce effort in an attempt to shift their resources (i.e., time) to other activities to further enhance their performance.
Finally, because the study was conducted in the financial services industry, researchers can use these scales to investigate other types of selling, for example, business-to-business or consumer packaged goods sales. Also, our sample excluded salespeople who had worked for the company for less than one year. Understanding the attribution patterns of younger, less experienced salespeople who are more likely to experience failure is as important as understanding the patterns for more experienced salespeople.
Measurement Development Results for Successful Sales Attributions and Behavioral Intentions:[a] Means, Standard Deviations, and Confirmatory Factor Analysis Estimates
Legend for Chart:
B - Mean
C - Standard Deviation
D - Standardized Loading
E - Item Reliability
[a] Scale items were based on six-point (forced-choice) Likert
scales (1 = "strongly disagree," 6 = "strongly agree").
[b] Entries in parentheses for multi-item constructs are
Cronbach's alpha, composite reliability estimates, and average
variance captured, respectively.
* p < .001.
A B C D E
Attributions
Effort (.83, .86, .68)[b] 5.01
I put forth the effort needed to
make this sale. 5.06 .83 .95 .90
I worked hard and it paid off. 4.93 1.05 .63* .40
I put in the necessary time to make
this sale. 5.06 .94 .85* .72
Ability (.89, .92, .78)[b] 5.09
I have the necessary skills. 5.51 .90 .87 .75
I have the knowledge and skills
required to be successful. 5.22 .85 .91* .83
My sales abilities led to my success. 4.91 .90 .80* .64
Task (.89, .81, .58)[b] 3.19
Most sales reps find this type of
sales call to be pretty easy. 3.50 1.42 .78 .61
This type of sales call is relatively
easy for just about everyone. 2.98 1.31 .84* .71
Most reps find this type of sale
easy to close. 3.09 1.31 .96* .93
Strategy (.88, .83, .67)[b] 5.05
I picked the right strategy for
this type of client. 5.08 .84 .83 .68
My sales strategy was right for
this client. 5.03 .83 .91* .82
I used the right strategy for this
type of situation. 5.02 .85 .85* .71
Luck (.93, .91, .78)[b] 1.86
As luck would have it, the sale
just happened. 1.95 1.13 .87 .76
It just worked out by chance. 1.81 1.03 .92* .84
It was just good luck. 1.85 1.09 .90* .81
Behavioral Intentions
No Change (.77, .82, .61)[b] 4.84
I would do the same thing. 5.09 .87 .72 .52
I will not change anything I did. 4.42 1.35 .67* .45
I would do things pretty much the
same. 5.00 .85 .89* .80
Increase Effort (.93, .89, .73[b] 3.04
I will work much harder. 3.08 1.25 .90 .81
I would out greater effort. 3.04 1.32 .89* .79
I will put in more time. 2.99 1.26 .93* .86
Change Strategy (.86, .86, .66)[b] 2.20
I will change the strategy that I
use. 2.20 1.03 .81 .66
I would approach the client
differently. 2.22 1.00 .81* .66
I will try a different tactic with
the client. 2.17 1.11 .86* .74
Seek Assistance (.94, .91, .77)[b] 2.46
I will seek advice in how to deal
with the situation. 2.52 1.23 .91 .83
I would seek advice in dealing with
the situation. 2.40 1.19 .91* .83
I would get help from someone in
the organization. 2.46 1.21 .92* .85
Avoid Situation (.87, .90, .76)[b] 1.53
I will avoid such situations in the
future. 1.48 .81 .90 .81
I would avoid similar situations. 1.57 .87 .79* .62
I would not call on that type of
prospect again. 1.53 .90 .82* .67 Measurement Development Results for Unsuccessful Sales Attributions and Behavioral Intentions:[a] Means, Standard Deviations, and Confirmatory Factor Analysis Estimates
Legend for Chart:
B - Mean
C - Standard Deviation
D - Standardized Loading
E - Item Reliability
[a] Scale items were based on six-point (forced-choice) Likert
scales (1 = "strongly disagree," 6 = "strongly agree").
[b] Entries in parentheses for multi-item constructs are
Cronbach's alpha, composite reliability estimates, and average
variance captured, respectively.
* p<.001.
A B C D E
Attributions
Effort (.94, .90, .75)[b] 2.49
I didn't work hard enough. 2.63 1.32 .90 .82
I didn't put in the necessary time
to make this sale. 2.51 1.32 .93* .86
I didn't put forth the effort needed
to make this sale. 2.47 1.28 .91* .82
Ability (.94, .87, .69)[b] 3.22
I need more skill and knowledge to
be successful. 2.97 1.49 .88 .78
I need to improve my skills to be
successful. 3.40 1.48 .94* .88
I need to increase my knowledge in
order to be successful. 3.07 1.37 .92* .84
Task (.89, .80, .57)[b] 3.67
This type of sales call is difficult
for everyone. 3.69 1.45 .88 .78
Everyone finds this to be a tough
selling situation. 3.57 1.41 .89* .79
This was a difficult selling
situation. 3.78 1.44 .79* .63
Strategy (.92, .86, .68)[b] 3.06
I used the wrong selling strategy
for this type of situation. 3.04 1.39 .88 .77
I picked the wrong strategy for this
type of client. 3.07 1.36 .92* .85
My sales strategy was incorrect for
this type of client. 3.07 1.37 .88* .77
Luck (.90, .84, .63)[b] 2.44
This situation was just an unlucky
one. 2.49 1.35 .84 .70
It was just an unlucky break. 2.42 1.36 .96* .92
It was just bad luck. 2.26 1.32 .81* .66
Behavioral Intentions
No Change (.88, .79, .56)[b] 3.34
I would do the same thing. 3.57 1.44 .85 .72
I will not change anything I did. 2.95 1.36 .81* .66
I would do things pretty much the
same. 3.51 1.35 .86* .73
Increase Effort (.94, .90, .76)[b] 3.46
I would work harder at making the
sale. 3.51 1.38 .92 .84
I would put out greater effort. 3.47 1.34 .91* .82
I will put in more time. 3.41 1.33 .94* .89
Change Strategy (.93, .88, .70)[b] 3.89
I will change the strategy that I
use. 3.94 1.30 .89 .79
I would use a different strategy
with the client. 3.87 1.40 .93* .86
I will try a different tactic with
the client. 3.82 1.36 .88* .77
Seek Assistance (.96, .91, .77)[b] 3.55
I will get input from someone who
may have had a similar experience. 3.69 1.47 .92 .84
I will seek advice in how to deal
with the situation. 3.52 1.46 .95* .90
I would seek assistance in dealing
with this situation. 3.41 1.46 .95* .90
Avoid Situation (.93, .91, .78)[b] 2.09
I will stay away from situations
like this one. 2.14 1.24 .87 .76
I will avoid such situations in the
future. 2.08 1.17 .97* .93
I would avoid similar situations. 2.07 1.12 .89* .80 Construct Intercorrelations
1 2 3 4 5
6 7 8 9 10
1. Effort 1.00 .69** -.04 .71** -.34**
.41**
2. Ability .38** 1.00 -.06 .78** -.39**
.37**
3. Task .00 .33** 1.00 .02 .35**
.05
4. Strategy .57** .36** .00 1.00 -.32**
.51**
5. Luck .09 .19** .34** .01 1.00
-.25**
6. No change -.53** -.38** .00 -.65** .08
1.00
7. Increase
effort .57** .48** .11 .34** -.05
-.47** 1.00
8. Change
strategy .45** .42** .11 .66** .04
-.77** .48** 1.00
9. Seek
assistance .20** .60** .33** .25** .13
-.32** .44** .49** 1.00
10. Avoid
situation .15* .04 .32** .04 .25**
-.03 -.06 .00 .05 1.00
a The numbers in the lower half of the matrix are the
correlations for the unsuccessful attribution and behavioral
intention scales. The numbers in the upper half of the matrix are
the correlations for the successful attribution and behavioral
intention scales.
*p < .05
**p < .001.
Attributions and Behavioral Intentions for an Unsuccessful Sales Experience: LISREL Estimates
Standardized
Estimate t-Value
Effort increase effort .45** 7.14
Ability seek assistance .56** 8.68
Ability increase effort .31** 5.00
Ability avoid situation -.09 -1.27
Task seek assistance .15* 2.38
Task change strategy .12* 2.16
Task avoid situation .29 3.72
Strategy change strategy .67** 10.51
Luck no change .08 1.07
Luck avoid situation .17* 2.36
Model Fit Statistics
X2 d.f. p-Value GFI CFI RMSEA
903.25 385 .000 .80 .92 .07
a Scale items were based on six-point (forced-choice) Likert
scales (1 = "strongly disagree," 6 = "strongly agree").
*p < .05
**p < .001
Legend for Chart:
A - Po: Type of Attribution Made
B - Behavioral Intentions Increase Effort
C - Behavioral Intentions Seek Assistance
D - Behavioral Intentions Avoid Situation
E - Behavioral Intentions Change Strategy
F - Behavioral Intentions No Change
G - Behavioral Intentions Conclusion
[a] These numbers are the standardized estimates for the
significant LISREL path coefficients A nonsignificant path is
reported as N.S.
[b] "N.S. and weaker" entries should be read as follows: The
effect of inadequate effort (the predictor) on seek assistance
(the criterion) was a nonsignificant path and was weaker than
the hypothesized effect of inadequate effort on increase effort
(H1).
[c] No chi-square comparison was made, because this attribution
is hypothesized to be related to this behavioral intention in
another hypothesis (e.g., H2b).
[d] Chi-square comparison tests were performed for only
hypothesized paths that were significantly different from
zero (significant path coefficient).
A
B C
D E
F G
P1: Inadequate effort
.45[a] N.S. and weaker[b]
(25.20)
N.S. and weaker N.S. and weaker
(10.97) (13.52)
N.S. and weaker Accept
(123.09) P1
P2a: Lack of ability
[c] .56[d]
N.S. and weaker N.S. and weaker
(56.90) (20.20)
N.S. and weaker Accept
(111.51) P2a
P2b: Lack of ability
0.31 [c]
N.S. and weaker N.S. but as strong
(20.76) (1.66)
N.S. and weaker Reject
(64.27) P2b
P2c: Lack of ability
[d] [d]
N.S. [d]
[d] Reject
P2c
P3a: Difficult task
N.S. but as strong .15
(3.09)
[c] [c]
N.S. and weaker Reject
(4.20) P3a
P3b: Difficult task
N.S. but as strong [c]
(1.94)
[c] .12
N.S. but as strong Reject
(3.03) P3b
P3c: Difficult task
N.S. and weaker [c]
(7.06)
.29[c] [c]
N.S. and weaker Accept
(8.51) P3c
P4: incorrect strategy
N.S. and weaker N.S. and weaker
(13.04) (5.90)
N.S. and weaker .67
(9.39)
N.S. and weaker Accept
(190.67) P4
P5a: Bad luck
[d] [d]
[d] [d]
N.S. Reject
P5a
P5b: Bad luck
N. S and weaker N.S. and weaker
(13.40) (4.49)
.17 N.S. but as strong
(2.99)
N.S. but as strong Reject
(.89) P5b
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Legend for Chart:
B - Component 1[b]
C - Component 2
D - Component 3
E - Component 4
F - Component 5
[a] Principal component analysis using varimax rotation (Kaiser
normalization).
[b] Only loadings above .40 are shown.
A B C D E F
Effort Attribution Items
I worked hard and it .604
paid off.
I tried very hard to .832
make this sale.
I put out a lot of effort .806
for this sales call.
I put in the time needed .667
to make the sale.
I gave the effort needed .463 .663
to make the sale.
Ability Attribution Items
My skills/knowledge made .675
me succeed on this type
of call.
I have the skills to be .824
successful.
I have the necessary skills. .773
I have the knowledge and .838
skills for success on
this type of call.
My sales abilities led .687
to my success.
Task Attribution Items
Most reps find this type .826
of call to be pretty easy.
This type of sales call .877
is relatively easy for
just about everyone.
It wasn't a very tough .742
selling situation.
This was an easy selling .790
situation.
Most reps find this sale .908
easy to close.
Strategy Attribution Items
My sales strategy was .764
effective for this customer.
An appropriate selling .778
strategy was used for
this situation.
I picked the right .811
strategy for this client.
My strategy was right .807
for the client.
I used the right strategy .801
for the situation.
Luck Attribution Items
It was just a lucky break. .810
I was lucky. .912
Luckily, the sale just .819
happened.
It just worked out by chance. .856
It was just good luck. .910 Legend for Chart:
B - Component 1[b]
C - Component 2
D - Component 3
E - Component 4
F - Component 5
[a] Principal component analysis using varimax rotation (Kaiser
normalization).
[b] Only loadings above .40 are shown.
A B C D E F
Effort Attribution Items
I didn't work hard enough. .401 .730
I really did not fry very .846
hard.
I didn't put out enough .863
effort for this sales call.
I didn't put in the necessary .890
time to make this sale.
I didn't put forth the .658
effort needed to make this
sale.
Ability Attribution Items
I need more skill and .876
knowledge to be successful
in this type of sales call.
I lack some of the necessary .845
skills for this type of call.
I need to improve my skills .902
to be successful in this type
of call.
I need to increase my .923
knowledge to be successful
In this type of call.
I need to learn more in .915
order to be successful in
this type of call.
Task Attribution Items
Sales reps find this type .826
of sales call to be difficult.
This type of sales call is .869
difficult for everyone.
Sales reps struggle with .891
this situation.
This was a difficult selling .854
situation.
Everyone finds this to be .830
a tough selling situation.
Strategy Attribution Items
My sales strategy was .807
incorrect for this client.
A different selling strategy .870
was more appropriate for this
situation.
I used the wrong selling .840
strategy for this type of
sales call.
The sales strategy I used .878
was not effective for this
customer.
I picked the wrong strategy .854
for this type of client.
Luck Attribution Items
It was just an unlucky .835
break.
This situation was just an .909
unlucky one.
As luck would have it, the .869
sale just didn't happen.
The circumstances surrounding .936
this call were unlucky.
It was just bad luck. .908 Legend for Chart:
B - Component 1[b]
C - Component 2
D - Component 3
E - Component 4
F - Component 5
[a] Principal component analysis using varimax rotation (Kaiser
normalization).
[b] Only loadings above .40 are shown.
A B C D E F
Increase Effort Behavior Items
I would put forth more .721
time and effort.
I would work harder at .796
making the sale.
I would work much harder. .880
I would put out greater .846
effort.
I would put in more time. .843
Seek Assistance Behavior Items
I would seek assistance .745
from someone else in our
company.
I would get input from .773
someone who may have had
a similar experience.
I would seek advice in .838
how to deal with the
situation.
I would seek assistance .852
in dealing with this
situation.
I would get help from .848
someone in the organization.
Avoid the Situation Behavior Items
I would stay away from .817
situations like this one.
I would avoid such .885
situations in the future.
I would avoid similar .795
situations.
I would not call on that .847
type of prospect again.
I would not put myself .748
in that situation again.
Change Strategy Behavior Items
I would change the strategy .682
that I use.
I would use a different .642
strategy with this client.
I would approach the .748
client differently.
I would adapt my strategy. .648
I would try a different .531
tactic with the client.
Make No Change in Behavior Items
I would do the same thing. .575
I would not change anything .782
I did.
I would do things pretty .663
much the same.
I would not change anything .746
very much.
I would not do anything .723
differently. Legend for Chart:
B - Component 1[b]
C - Component 2
D - Component 3
E - Component 4
F - Component 5
[a] Principal component analysis using varimax rotation (Kaiser
normalization).
[b] Only loadings above .40 are shown.
A B C D E F
Increase Effort Behavior Items
I would put forth more .777
time and effort.
I would work harder at .811
making the sale.
I would work much harder. .865
I would put out greater .871
effort.
I would put in more time. .861
Seek Assistance Behavior Items
I would seek assistance .877
from someone else in our
company.
I would get input from .899
someone who may have had
a similar experience.
I would seek advice in .909
how to deal with the
situation.
I would seek assistance .919
in dealing with this
situation.
I would get help from .909
someone in the organization.
Avoid the Situation Behavior Items
I would stay away from .895
situations like this one.
I would avoid such .934
situations in the future.
I would avoid similar .944
situations.
I would not call on that .904
type of prospect again.
I would not put myself .921
in that situation again.
Change Strategy Behavior Items
I would change the strategy .786
that I use.
I would use a different .786
strategy with this client.
I would approach the .778
client differently.
I would adapt my strategy. .744
I would try a different .728
tactic with the client.
Make No Change in Behavior Items
I would do the same thing. .799
I would not change anything .696 -.402
I did.
I would do things pretty .755 -.401
much the same.
I would not change anything .792
very much.
I would not do anything .775
differently.~~~~~~~~
By Andrea L. Dixon; Rosann L. Spiro and Maqbul Jamil
Andrea L. Dixon is Assistant Professor of Marketing, college of Business Administration, University of Cincinnati.
Rosann L. Spiro is Professor of Marketing, Kelley School of Business, Indiana university.
Maqbul Jamil is a marketing consultant, Eli Lily Corporation.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 148- Teaching Old Brands New Tricks: Retro Branding and the Revival of Brand Meaning. By: Brown, Stephen; Sherry Jr., John F.; Kozinets, Robert V. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p19-33. 15p. DOI: 10.1509/jmkg.67.3.19.18657.
- Database:
- Business Source Complete
Teaching Old Brands New Tricks: Retro Branding and the
Revival of Brand Meaning
Retro brands are relaunched historical brands with updated features. The authors conduct a "netnographic" analysis of two prominent retro brands, the Volkswagen New Beetle and Star Wars: Episode I--The Phantom Menace. that reveals the importance of Allegory (brand story), Aura (brand essence), Arcadia (idealized community), and Antinomy (brand paradox). Retro brand meanings are predicated on a utopian communal element and an enlivening paradoxical essence. Retro brand management involves an uneasy, cocreative, and occasionally clamorous alliance between producers and consumers.
America has no now.... Our culture is composed of sequels, reruns, remakes, revivals, reissues, re-releases. recreations, re-enactments, adaptations, anniversaries, memorabilia, oldies radio, and nostalgia record collections.
--George Carlin, Brain Droppings. 1998
Brand extension, the use of an existing brand name to introduce a new product or service (Keller 1993, 1998), is an important marketing tactic that has attracted considerable academic interest (e.g., Desai and Keller 2002; John, Loken, and Joiner 1998). However, another form of brand extension strategy is gaining prominence and requires urgent research attention. Many long-abandoned brands have recently been revived and successfully relaunched (Franklin 2002; Mitchell 1999; Wansink 1997), so much so that marketers appear in the midst of a "retro revolution" in which revivals of old brands and their images are a powerful management option (Brown 2001).
The rise of retro brands places marketing in an interesting conceptual quandary. On the one hand, marketers are continually reminded of the need for product differentiation, that today's marketing environment demands strong brand identities and decries imitation (Aaker 1996). On the other hand, contemporary markets are suffused with updated imitations, such as retro brands, many of which are proving enormously popular (Franklin 2002; Naughton and Vlasic 1998; Wansink 1997).
How can marketing academics and practitioners make sense of this conceptual conundrum? What are the causes of retro brand proliferation? How do retro brands help improve understanding of the management of brand meanings? What do retro brands reveal about such important issues as brand personality, person--brand relationships, and brand communities? We investigate retro brands to develop practical theory that contributes to marketing principles and practice, particularly brand management. Akin to Keller (1993), we aim to inform managers and researchers interested in the strategic aspects of brand equity. We study retro brands from the perspective of consumers and conceptualize the implications of this information for marketing practitioners.
We animate and illustrate our investigation of retro brand marketing through an empirical analysis of two prominent exemplars. After briefly examining the background literature on three interdependent concepts--brand revival, brand heritage, and nostalgia--we develop our conceptualization of retromarketing and retro brands. We then describe our methodology, which is followed by a detailed analysis of two retromarketing exemplars, and we conclude with a consideration of our findings' implications for practicing brand managers.
Nostalgia and Heritage
As the epigraph exemplifies, the late twentieth century was characterized by an astonishing "nostalgia boom" (Naughton and Vlasic 1998, p. 58), and many marketing scholars have examined that phenomenon (see also Harris 2000; Leadbeater 2002; Redhead 2000). Stern (1992), for example, attributes the tatter-day advent of nostalgic advertising to the fin de siècle effect, or humankind's propensity to retrospect as centuries draw to a close. Belk (1991) contends that personal possessions, such as souvenirs, photographs, heirlooms, antiques, and gifts, serve as materializations of memory and evoke a powerful sense of the past. Holbrook and Schindler (1989, 1994, 1996) have developed a "nostalgia proneness scale" and have tested it in various memory-rich domains (e.g., music, movies, fashion models, classic cars) and among different demographic cohorts. Peñaloza (2000, p. 105) notes the importance of expanding the conception of history as "a source of market value" and a cultural marker of legitimacy and authenticity. Thompson, Pollio, and Locander (1994) report that classic brands not a particular brand, including all its personal and cultural only embody the moral values of craftsmanship and lasting value but also hark back to a time when the world seemed safer, more comprehensible, and much less commercial. There are also numerous conference papers and analogous academic analyses of the recent retrospective propensity (Baker and Kennedy 1994; Baumgartner 1992; Goulding 1999, 2000; Havlena and Holak 1991, 1996; Hirsch 1992; Holak and Havlena 1992; Rindfleisch and Sprott 2000; Romanyshyn 1989; Stevens, Brown, and Maclaran 1998).
Although there is a rich marketing literature on the mainsprings of today's nostalgia boom, the scholarly touchstone remains Davis's (1979) much-cited distinction between personal and communal nostalgia. The former is associated with individual life cycles; as people age, they are wont to reflect on the palmy days of their youth. The latter, conversely, occurs at a societal level in the wake of epochal changes precipitated by wars, revolutions, invasions, economic dislocations, or environmental catastrophes. Thus, the Great Depression of the 1930s was accompanied by a profoundly retrospective perspective (Lears 1994); the social turmoil of the late 1960s triggered the nostalgia outbreak of the 1970s (Schulman 2001); and the post-Communist, new world order of the early 1990s created conditions conducive to the subsequent rise of retro (Leadbeater 2002).
Personal and communal nostalgia are closely intertwined, nowhere more so than in marketing. Long-established brands evoke not only former epochs but also former selves. Old brands serve to bind consumers to their pasts and to the communities that shared those brands. According to McAlexander, Schouten, and Koening (2002), brands link people into communities with common interests (see also Muniz and O'Guinn 2001). A temporal component can readily be added to this (Bergadaa 1990), whereby old brands evoke past events. Because brands can he linked with events (Keller 1993), the associations of the event become associated with the brand.
Indeed, old brands may link people together even more powerfully, because they strongly evoke a sense of a utopian past and because of the close-knit "caring and sharing" communities that are associated with it (Kozinets 2002a, p. 21). Therefore, it might be expected that in times of threat or of sociocultural and economic turbulence, nostalgia would provide a sense of comfort and close-knit community, a safe haven in an unsafe world. Conceptualizing brands is this manner combines the individual nostalgia that Belk (1991) explores and Holbrook and Schindler (1989, 1994, 1996) psychometrize with the communal nostalgia that Stern (1992) cogently theorizes. In this conception, old brands are rich with both personal and communal associations. They can be invested with the same legitimizing, authenticating, market value of history that Peñaloza (2000) finds in entire industries and that Thompson, Pollio, and Locander (1994) uncover among contemporary consumers.
The scholarly implications of this personal-communal melding are clear. Conventional marketing wisdom suggests that repositioning is one method of revitalizing a brand (Aaker 1991) and that the royal road to rejuvenation lies in the skillful exploitation of the associations linked to a brand's heritage (Aaker 1996). Brand heritage is perceived as using marketing-mix variables that invoke the history of a particular brand, including all its personal and cultural associations. An example is the rich historical associations of the Coca-Cola brand with Americana, patriotism, globalization, Santa Claus, and Christmas. However, because cultures are complex and individuals heterogeneous, heritage is often an ambivalent legacy. In launching new, improved, or cutting-edge products, aspects of heritage might prove a liability. Heritage, moreover, might need to be created and managed, as the literature on "invented traditions" attests (Hobsbawm and Ranger 1983). Although conceptions of brand heritage provide one route to an understanding of the process of brand renewal, they do not holistically capture the intriguing dynamics of retromarketing in general and brand revival in particular.
Brand Revival and Retromarketing
There is considerable overlap among nostalgia, brand heritage, and brand revival. Revived or retro goods and services (we use retro synonymously with revived brands) trade on consumers' nostalgic leanings. Familiar slogans and packages, for example, invoke brand heritage and evoke consumers' memories of better days, both personal and communal. The success of the Museum Store, Past Times, Restoration Hardware, and similar retailers of "exact" reproductions and the continuing popularity of heritage-based campaigns for brands such as Budweiser, John Hancock, and Ivory indicate that demand exists for allegedly authentic reproductions of past brands.
The problem with exact reproductions, however, is that they do not meet today's exacting performance standards. Retro products, by contrast, combine old-fashioned forms with cutting-edge functions and thereby harmonize the past with the present (Brown 1999, 2001). In this regard, consider the Chrysler PT Cruiser, which amalgamates the shape of a 1940s sedan with the Latest automotive technology to produce a futuristic car with anachronistic styling. Another striking example is Nike's Michael Jordan XI Retro Sneakers. These shoes may look like a monument to 1950s hoop dreams, yet their cushioned soles, aerated uppers, and recommended retail prices are state of the marketing art. Denny's retro diner is an homage to eateries of the 1950s, but its registers are computerized, the kitchen equipment is cutting edge, smoking is prohibited in the dining area, and vegetarian dishes are available for those unwilling to revert to carnivorous habits of yore.
We define retro branding, therefore, as the revival or relaunch of a product or service brand from a prior historical period, which is usually but not always updated to contemporary standards of performance, functioning, or taste. Retro brands are distinguishable from nostalgic brands by the element of updating. They are brand new, old-fashioned offerings.
Reconceptualizing Nostalgia
To begin to understand the elements of retro brands and their implications for brand management, we employ a more nuanced idea of nostalgia than the notion that "things were better back then" (e.g., Holbrook 1995). For example, in his monumental history of Western culture from 1500 to the present, Jacques Barzun (2000) describes the alternation and coexistence of two powerful antithetical themes, progress and primitivism. The former is characterized by a secular, scientific, technological, and future-looking worldview; the latter is dominated by a profound sense of loss, a feeling that there is a price to be paid for progress and that price is the destruction of community, solidarity, empathy, and closeness to nature, which are markers of the past (see also Gombrich 2002).
Boym's (2001) recent reflections on the future of nostalgia further develop this notion of an uneasy balance between past and future. Moved by the post-Communist nostalgia sweeping Eastern Europe, Boym asserts that people live in a world where progress and primitivism combine, where the latest technological and scientific advances increasingly are used to re-create visions of the past, whether of the sinking of the Titanic, the gladiatorial contests of ancient Rome, or the scientifically cloned dinosaurs of Jurassic Park. Progress and primitivism, Boym argues, are like Jekyll and Hyde--two contrasting personalities simultaneously occupying the same body.
Boym's (2001) bittersweet yearning for what is gone but not forgotten is counterpointed by the theories of the leading literary critic Fredric Jameson (1991), who contends that today's "neo-nostalgia" has nothing to do with the deep emotional disturbance that afflicted nostalgia sufferers of times past. To the contrary, Jameson argues that contemporary nostalgia is essentially emotionless, an aesthetic response to evocations of the past. Another prominent literary critic, Walter Benjamin (1973, 1985, 1999) has proposed theories that are rich with references to marketing, consumer behavior, and advertising. Although he was affiliated with the critical theorists of the Frankfurt School, who abhorred commercial life, Benjamin was fascinated by marketing, obsolete objects, abandoned possessions, superseded technologies, long-forgotten fads, and the remarkable fact that new ideas often come wrapped in old packaging.
We discern four themes relevant to our investigation of retromarketing and to contemporary brand management from Benjamin's interwar writings. These themes relate to and usefully synthesize extant conceptual elements of brand management and marketing. These elements are Allegory (brand story), Arcadia (idealized brand community), Aura (brand essence), and Antinomy (brand paradox). Together, these themes constitute the 4As of retro branding.
Allegory. Brand allegories are essentially symbolic stories, narratives, or extended metaphors. As Stem (1988) notes, allegory is frequently used in advertising. Allegories successfully convey didactic messages that invoke and then offer resolutions for consumer states of moral conflict. In addition, allegories are dynamic; they alter and change in response to popular tastes and trends (Stern 1988). We examine allegorical brand stories from the perspective of consumers (as Keller [1993] suggests; see also Stern 1995, 1998; Thompson 1997). We use the reception and discourse surrounding retromarketed products, or those products that combine qualities of old and new, to study the links among brand meanings, brand heritage, and the morality tales that consumers tell one another.
Arcadia. For Benjamin, arcadia relates both to his own (unfinished) study of Parisian shopping arcades and to the golden age of the ancient Greeks. In arcadia, an almost utopian sense of past worlds and communities is evoked. This sense of the past as a special, magical place is an integral part of retromarketing's appeal, insofar as an idealized past is festooned with the latest technological magic. The presence of this utopian communal ideal has only barely been hinted at by scholars who have examined various manifestations of brand community (McAlexander, Schouten, and Koening 2002; Muniz and O'Guinn 2001; Schouten and McAlexander 1995). In this article, we attempt to explore further the conceptual links among brand meaning, idealized place, consumer community, and times past.
Aura. In Benjamin's most celebrated conception, aura pertains to the presence of a powerful sense of "authenticity" that original works of art exude. As many scholars note (e.g., Belk and Costa 1998; Holt 1997; Kozinets 2001, 2002a, b; Peñaloza 2000; Thompson and Tambyah 1999), consumers' search for authenticity is one of the cornerstones of contemporary marketing, notwithstanding the "inauthenticity" of today's consumer culture (Hartman 2002). Authenticity is also vitally important to brands; uniqueness is an important aspect of brand identity (see, e.g., Aaker 1986; Keller 1993). Kelly (1998) considers "brand essence" the core values for which a brand stands, which he compares to its "marketing DNA." Brand essence is thus highly related to authenticity; it is composed of the brand elements that consumers perceive as unique. In this study, we explore aura-based relationships among the authentic, the past, and brand essence in the context of consumer dialogue about retro brands.
Antinomy Irresolvable paradox lies at the heart of Benjamin's philosophy. For example, he considers scientific and technological progress both as unstoppable, and almost overpowering, and as the root cause of people's desire to return to simpler, slower, less stressful times. In the marketing field, paradoxes are a central theme that explains how technology products are consumed (Fournier and Mick 1999; Mick and Fournier 1998). The paradox premise also offers a rich departure point for investigations of the complexity and open-endedness of brand meaning management.
In this regard, the novelist Alex Shakar (2001, p. 61) postulates that every product has "two opposing desires that it can promise to satisfy simultaneously. The job of a marketer is to cultivate this schismatic core, this broken soul, at the center of every product." Shakar suggests that this paradoxical essence, or "paradessence," is the crux of consumer motivation. Literary theorists likewise maintain that ambiguity and paradox offer places for readers to insert their hopes and dreams into texts (e.g., Derrida 1985). Analogously, it is arguable that this paradoxical "soul" of brands offers an opening for consumers to invest themselves emotionally into mass-produced goods and services and thereby form the elusive connections that result in lasting loyalty. Through the inherent paradoxes of retro brands, we consider consumers' responses to the simultaneous presence of old and new, tradition and technology, primitivism and progress, same and different.
To develop our conceptualization of retromarketing, we explore it in its empirical context, as realized in the interactions of relevant consumers. We focus on two prominent, much-lauded retromarketing exemplars, both of which are cult brands with high levels of customer commitment and strong ties to popular culture. The first is Volkswagen's New Beetle, a modern brand that builds on a famous and indeed infamous brand heritage. The second is Star Wars: Episode I--The Phantom Menace, a long-awaited brand revival of George Lucas's celebrated cinematic trilogy of the 1970s and 1980s.
In keeping with the study's retrospective spirit, the empirical investigation uses a brand new, old-fashioned research technique called "netnography." As Kozinets (2002b) explains, netnography involves the transplantation of ethnography, one of the most venerable marketing research procedures, to cyberspace, the latest marketing milieu. As in the case of its offline counterpart, netnography necessitates in-depth immersion in and prolonged engagement with the many consumer cultures that populate the World Wide Web (Kozinets 1999, 2002b). According to Muniz and O'Guinn (2001), the Web is a place where abundant information on online consumer groups' belief systems, buying behaviors, and object relations is readily available.
We began our investigation with an overview of the relevant topical news groups and the Web pages related to them that were available from our local server (see the Appendix). We chose sites both for the quantity and for the directed focus of their Web postings (e.g., alt.fan.starwars holds Google's highest activity rating, at approximately 130 messages posted per day). Recent statistics are difficult to find, but Arbitron ranked rec.arts.sf.starwars 294th of all news groups and estimated that it was read by 118,545 people worldwide (in 1995). Similarly, Arbitron ranked rec.auto.vw 599th and estimated that it had 82,315 worldwide readers (Reid 1995).
As part of an ongoing research project on popular brands, media fans, and virtual communities of consumption, we followed the aforementioned news groups and downloaded noteworthy messages during three months beginning in spring 2001 (we performed follow-up data collection and member checking in spring 2002). In this regard, it should be noted that using carefully chosen message threads in netnography is akin to purposive sampling in market-oriented ethnography (Arnould and Wallendorf 1994). Because findings are interpreted in terms of a particular sample, it is not necessary that the sample be representative of other populations (Kozinets 2002b). Although we attempted to find diverse message types and message postings in our data set, we intended our sampling strategy not to offer representativeness or transferability, but to focus on analytic depth and the provision of realistic examples of retro brands and their receptions.
The volume of downloaded text amounted to 560 double-spaced, ten-point type size pages, which represents 432 postings containing 131 distinct e-mail addresses and user names (likely related to the number of distinct message posters). There were 76 unique message-poster identifiers within the downloaded New Beetle messages and 55 unique poster identifiers included with the downloaded Star Wars messages. The messages we downloaded were posted between 1999 and 2002. We classified the 432 postings (before downloading) into topics either relevant or not relevant to the brand management topics of interest. We each manually coded the data and analyzed it in an initial step to identify themes relevant to our hypotheses (on data analysis, see Spiggle 1994). Through group comparison and in-person discussion, we then allowed a more impressionistic, hermeneutic, and grounded interpretation to emerge from the data. We identified recurrent social and cultural tendencies within the data and constantly compared these emergent themes with our preconceptions derived from Benjamin's work. In this step, unexpected findings led us to stretch the boundaries of our original definitions (e.g., to place more emphasis on community and its influence) and to devise new notions (i.e., antinomy, or brand paradox). Our overall approach is in keeping with the precepts of mainstream qualitative inquiry (e.g., Arnould and Wallendorf 1994; Sherry and Kozinets 2001; Thompson 1997).
All told, we examined several thousand news-group messages and dozens of Web sites and Web rings representing the perspectives of several hundred consumers of New Beetles and Star Wars prequels.
The New Beetle
The original Volkswagen Beetle was the stuff of motor-enthusiast legend. Created by the pioneering automotive designer Ferdinand Porsche, with a past grounded in the common classes of Third Reich Germany, the car proved wildly popular across postwar Europe and North America. The Volkswagen Beetle was globally cherished for its durability, economy, user-friendliness, and idiosyncratic design. At the time, it was considered an exemplary vehicle of the people. Everyone from commune-bound hippies and middle-class couples with children to eccentric multimillionaires drove Beetles. The Beetle even begot a series of live-action Disney movies starring the "Love Bug" as "Herbie," the sentient vehicle with a heart of gold.
The same enthusiasm coalesced around the New Beetle, which Volkswagen launched in 1998 at the Detroit Motor Show. A happy combination of the traditional bubble shape and state-of-the-art automotive technology, the New Beetle rapidly attained cult status. With their totemizing and fetishizing of the retro auto's shape and flower-power history, New Beetle enthusiasts are analogous to the ardent fans of Beatlemania. The result is a retromarketing exemplar that potentially extends our understanding of brand meaning management.
Allegory: The Beetle's brand stories. To understand the brand management implications of the New Beetle, it is necessary to be attuned to the narratives surrounding it, which includes attending to the heritage stories circulated by producers and cultural intermediaries such as the media and advertisers. In this consumer-oriented study of brand meaning, this attention also includes consumers. We therefore pay particular attention, as Stern (1988) does in her study of advertising allegories, to the moral overtones present in the narratives that consumers tell one another about the brand. These "morallegorical" qualities are captured in the narrative of "Adam," a news-group poster who sought to encapsulate the perceived significance of the Beetle's history as follows:
The VW [Beetle] was designed at a time when only big businessmen and other wealthy individuals were able to have an automobile. The VW [Beetle] was designed specifically to and did bestow on the working man, who could otherwise never afford it, the feasibility of obtaining the freedom that comes with automobile ownership. The noble thoughts that went into the production of this machine included considerations of economy of operation and ease of repair. (posted by "Adam," rec.autos.makers.vw.aircooled news group, June 21, 1996)
Adam's posting recapitulates Adolph Hitler's propagandist use of Porsche's design for the Volkswagen Beetle; it is also notable for overlooking this historical fact. The original Beetle was grounded in Nazi Germany's narrative of egalitarian totalitarianism, which in turn is related to the Volkswagen brand's genealogy as car of the people. As historical commentators have often noted, Hitler's regime was nothing if not utopian for the German people, and it seems that populist brand meanings are inextricably intertwined with the reliable design and do-it-yourself qualities of the original Volkswagen Beetle. However, Adam's narrative also reworks the populist Nazi-utopian narrative in terms of an American discourse of self-reliance, autonomy, and a can-do attitude. Slotkin (1992), for example, makes a compelling case that these mythic values are central to the construction of American identity.
It is easy to find moral standing, even moral brand meaning, in Adam's recounting of the Beetle brand's Americanesque egalitarianism. An honest, brand-of-the-people ethos was also articulated in the groundbreaking advertising campaign of DDB Needham for the Volkswagen Beetle in the 1960s. Using copy such as "Lemon" and "Ugly is only skin deep," the campaign used ironic, reflexive, self-deprecating humor, which helped make Volkswagen the best-selling foreign automaker of its time (see Kiley 2002). Given that honesty has intrinsic moral overtones, the campaign creatively counterpointed the infamous puffery of advertising with a healthy dose of truthfulness, albeit marketing-mediated truthfulness.
In an important extension of this history, online consumers create tales of their own that build on the brand, emphasize its uniqueness, personalize it, and demonstrate to others how they can individualize the brand. For example, the following story of singularity, personality, and idiosyncrasy is typical of the types of postings of many Beetle enthusiasts as they carefully recount their brand experiences for other news-group members:
I inspected it in the driveway of our mom's home, where my brother and I were visiting, and then drove it around for half an hour on her neighborhood's streets, with light to no traffic, in a suburban area with no traffic signals, and in light rain and mist.... The overall impression of the exterior styling is "extremely cute." Almost huggable. The front of the car presents an almost literal face that appears to smile. The old running boards are just hinted at in the new design, which is nothing but round, sensual curves from stem to stern. The seats are VERY comfortable and firm, although they're equipped with perhaps the most bizarre set of adjustment controls I've ever used.... The strange looking, wide stereo system is in the middle of the dash. Now comes One of the weirder parts of the styling--the huge, steeply sloped windshield must have been almost three feet from the steering wheel. It's the most unusual windshield placement I've ever experienced.... The windshield is SO far away from your face as you sit in the seats, it's almost like you've turned around and are looking out the back window! This makes the top surface of the dash absolutely huge. The dashtop is so big and deep, in fact, you could probably set up an entire model train track on it. :) You could put a good sized dog on it. On your entire collection of Star Wars action figures. You could plant a lawn on it. It's simply the strangest looking and feeling dash-top/windshield design I've ever seen in any car. Period, (posted by "Larabee," alt.volkswagen.beetle news group, April 12, 1998)
Larabee's posting is seeded with autobiographical detail and structured as a stony. It has all the hallmarks of personal narrative, which folklorists recognize as a genre. At points, Lanabee's evaluation reads less like a dispassionate, deliberative analysis than an emotional family saga chronicling an adoption. In the multifaceted narrative, Larabee reveals not only his anthropomorphization of the product and the brand, but also that he is not seeking a perfect copy of a classic Beetle. He emphasizes the brand's individuality and filters this through his own idiosyncrasies. It becomes clean from the way Larabee negotiates the changes between old and new--judging some of them positively, some of them negatively--that consumers like him consider netro brands not reproductions of namesake brands, but radical redefinitions of them. Larabee considers the New Beetle brand a restyling that updates and transcends. The allegory of the New Beetle reflects this because it tells a tale of thrift. It is thus a brand story with moral overtones. Old Beetle and New Beetle share personalities, origins, names, and values, forming a brand allegory that is moral, functional, and yet prone to individualization through consumer storytelling.
Arcadia: The Beetle brand's idealized communities. We previously mentioned the original 1960s advertising campaign for the Volkswagen Beetle in relation to the moral qualities of allegony. These moral qualities are extended and romanticized into a type of utopian brandscape by Arnold Communication's more recent, award-winning marketing campaign for the New Beetle. The campaign references both Sixties nostalgia and the cheap-and-cheerful brand associations of the original Beetle. Arnold's campaign uses lines such as "Less flower, more power" and "Comes with wonderful new features, like heat." The reference to flower power intends to evoke more than the old vehicle itself, but also a romanticized, upbeat, optimistic, times-are-a-changin' attitude associated with the Sixties. However, just as Adam's allegory overlooked the Volkswagen Beetle's Nazi propaganda past, so too today's happy-hippie marketing campaign suppresses much of the turmoil of the Sixties. By stressing superficial symbols such as flower power, Arnold Communications co-opts and convents the tempestuous Sixties into a marketable golden age.
This association of the brand with a time and place is no less evident in the multiplicity of books dedicated to people's affectionate memories of their original Beetles (see, e.g., Jacobs and Klebahn 1999; Rosen 1999). In these recollections, the car evokes the rose-tinted interlude of peace, music, and love that was the 1960s. The New Beetle represents an eternal return to such utopian dreams and associated attempts to better the material and spiritual condition of humankind. Among online community members, the hippie associations of the Beetle brand are a common topic of conversation. In one large message thread (41 postings) titled "Hippies and Volkswagens," "Frank" and "Richard" describe the relationship as they see it:
Hippies wanted to be different and defy society. They did it by driving a car that at that time went against every other car on the road--If you drive an older VW you are no different than the `605 hippies--and as history shows the hippies were right because the Japanese took off where VW left off and filled the gap of what people really wanted. (posted by 'Frank," rec.autos.makers.vw.aircooled news group, June 21, 1997)
I think that the interest that the hippies have in VW ownership has a lot to do with the unity of owner and vehicle. With a Volkswagen, you attain a certain self-sufficiency when you can do most of the work on your car yourself This fits in a lot better with the ideals of less participation in the horrifically wasteful commercial/industrial process, where half of all employed people waste time and resources quarreling. in one form or another, over what belongs to whom, instead of doing anything productive and beneficial to society as a whole. Hippies like to feel a direct connection with the things in the world that support them, and a Volkswagen is conducive to that, both in the way the owner should communicate with it frequently through maintenance and in the way it feels to drive one. (posted by "Richard," rec.autos.makers.vw.aircooled news group, June 23, 1997)
Frank casts the brand in quasi-moral terms of Sixties rebellion and revolution, mixing that with a striking economic vindication. For Richard, the brand takes on many meritorious characteristics. It is associated with independence, environmentalism, anticommercialism, social participation, and a sense of the mechanic's mindfulness that recalls Pirsig's (1974) New Age classic Zen and the Art of Motorcycle Maintenance.
Frank's and Richard's brand meanings both hint at the way old Volkswagen cars allowed the expression of nonconformity, of individuality, similar to the meanings ascribed to other cult brands such as Macintosh computers (Muniz and O'Guinn 2001) and the Star Trek franchise (Kozinets 2001). All three are inimitable brands that have attracted enormous consumer loyalty among their customer bases. Three aspects--low market share and struggling brand (Volkswagen in the United States, Star Trek, Macintosh), an idealized time (the Sixties in each of these examples), and a somewhat marginalized yet cohesive social group (hippies, television nerds, computer geeks)--seem to have become associated with a strong sense of affiliation and belonging in these brand communities. For example, "Trevor" celebrates another new member coming into the fold of Volkswagen ownership. The new owner inquired about the presence of a Beetle owners' club (common for Beetles). Trevor welcomed him with these words: "Trust me, buying a Beetle causes you to be part of a club whether you realize it or not. If nothing else, you have to wave `hi' to the other Beetle drivers, because they're waving `hi' to you" (posted by "Trevor," alt.volkswagen.beetle news group, April 3, 2000). Muniz and O'Guinn (2001) term this type of identification through brand community "consciousness of kind" and the type of welcoming behavior "shared ritual." Yet unlike the brand communities McAlexander, Schouten, and Koening (2002) study, the arcadian ethos of the retro brand seems to be strongly associated with the upstanding individuals and caring-sharing society of a dear departed golden age.
Aura: Authenticating the Beetle brand. Aside from historical and trademarked continuity, the challenge to Volkswagen's managers was animating the New Beetle with the same brand essence as the original. As the Sixties references and advertising examples indicate, brand managers attempted to rebuild the Beetle's brand essence and the physical vehicle by piecing it together from pop culture and retro references. They tried to make the car an "original" again, refashioning it to read as both old-fashioned and new-fangled, simultaneously retro and techno. Readapting the vehicle to a new social and historical context refreshes the brand meaning that has been denuded through time and by repeated reproduction, just as Benjamin asserted that aesthetic aura was wont to do. In conceptualizing design in the broadest, most culturally significant sense possible, the product's creators sought to reanimate the brand's mojo, so to speak.
Our study of consumer reception to the New Beetle, however, reveals bitter skeptics and true believers. Consumers demonstrate that they are sophisticated interpreters of marketing cues about a brand's authenticity. "Jane" posted a simple, one sentence statement that precipitated a heated debate: "In my view, the `new' Beetle is a Beetle in name only" (alt.volkswagen.beetle news group, February 17, 1998). Jane's comment and its polarizing effect on the news group were profound. In an immediate response, "Matthew" expressed his agreement: "I agree. It's nothing but a smart trick. Add a bit [of] old shapes and there you are. Let's hope the commerce will fail. The end of the so-called beetle." As with Jane, Matthew expresses his outrage at marketers and refuses to equate the shared brand name with a shared authenticity. He is searching for other, less superficial cues.
Another emotional debate about supporters of the old versus the new unfolded on the alt.volkswagen.beetle news group in mid-September 1998.
"Jaco": Does the term "New Beetle" in this news group refer to a "new" Air Cooled, Rear Engined, Beetle or the Front Engined, Water Cooled, Marketing Con Trick vehicle? ... I see no direct lineage between the "old" Beetle and the "new" Beetle in terms of engineering. And lets face it, the old Beetle was pure engineering.... The bottom line is that ANY automotive company could have produced a modern car with a couple of styling cues suggesting it "looked" liked a Beetle.
"Orrin": It's by VW, it has the styling cues you mentioned, and people love the car for the car itself. What would it take for you to call it a New Beetle? To have a rear engine that's air-cooled? To be prone to fire? To be uncomfortable? To be unsafe? Obviously, they wouldn't release that. It's 1998, things have changed. As beautiful as the old v-dubs are, people want something that has more power stock, has all the creature comforts of a `90s automobile, doesn't require someone to be mechanically inclined to maintain, and has that intangible charm the old one did for so many. (exchange posted between "Jaco" and "Orrin," alt.volkswagen.beetle news group, September 15, 1998)
This debate between Beetle fans revolves around definitions of authenticity. Jaco equates certain physical characteristics, particularly engine-related ones, of the old Beetle with the brand's values and thus its brand essence. Orrin, conversely, is looking beyond these physical characteristics for definitions of Beetle brand essence. He is more concerned with design and ineffable atmospherics; he references the brand associations that are more closely related to advertising and marketing than to automotive assembly lines. Jaco lashes out at Beetle brand managers in terms similar to Matthew, suggesting that the brand managers are devious tricksters. The debates among consumers raise important points about the management of brand essence. Part of the challenge for the New Beetle's marketers is to shift the terms of comparison so that the core values of the brand are maintained while the physical properties of the car are radically altered.
Antinomy: Brand paradox in the Beetle brand. Recall that we have conceptualized brand paradox as an irresolvable contradiction that manifests itself at the level of brand consumption. The New Beetle brand, as with retro brands per se, exhibits many such irresolvable tensions. For example, we have illustrated how the car juxtaposes a Third Reich history with a worker's utopia, the rebellious American Sixties, flower-power hippies, and the middle-class ethos of contemporary American consumers. We have also demonstrated the anthropomorphization of the patently mechanical automobile, expressed perhaps best in Disney's cartoonlike "Love Bug," Herbie (for an argument that this human sense of relationship building obtains in all brand relationships, see Fournier 1998).
The New Beetle also expresses the central retro brand paradox between old and new, then and now, past and future. In the case of the Beetle brand, a tension between past and present is made manifest in a debate between supporters of old Beetles and new Beetles. In a heated exchange in the rec.autos.makers.vw.watercooled news group in May 2002, "James" contends that the old Beetle is inferior:
In this era of the Cadillac Escalade, Lincoln Navigator. and even the modern crashworthy Passat, it astounds me that anyone would dream of venturing out into regular traffic in one of these 1930s-era death traps. Even in moderate collisions (that wouldn't even bigger an airbag in today's cars) old Beetles collapse like beer cans at a frat party.... Old Beetles are nostalgia pieces, no doubt. Romantic even. But safe in today's driving conditions? No more than a Model A. I say preserve a few old Beetles as trailer queens, Sunday drivers, and museum pieces, and let the rest of `em rot. (posted by "James," rec.autos.makers.vw.watercooled news group. May 23, 2002)
James's advice demonstrates that he believes the Beetle brand must be updated. The reason for this is straightforward, as James explains in a later, follow-up e-mail: "The old Beetle's 70-year-old technology incorporates NO modern safety design" (rec.autos.makers.vw.watercooled news group, May 24, 2002). There is a strong progressive undercurrent here: James considers the Beetle brand part of the entire progressive project of modern society. As technology is brought to bear on product categories, brands become more sophisticated, more reliable, and more advanced. Although the Beetle brand of the past may have rich symbolic meanings (as evidenced by James's references to nostalgia, romanticism, and museum worthiness), the brand can and must be updated if it is to be used rather than merely admired.
Yet there are others who believe that the old Beetle brand has lasting relevance. They collect the cars; refurbish them; correct their defects with specially engineered new pans; and buy the newer, updated models still produced in Mexico. Another old Beetle detractor, "Alex," did not understand this and asked the news group, "Why would anyone want an old Beetle, especially when they cost thousands of dollars now?" (rec.autos.makers.vw.watercooled news group, May 24, 2002). The answer he received from "David" illustrates the perspective of an old Beetle supporter:
Because they're the last remnant of when quality meant something--a car not meant to be disposable. They're fun to drive, reliable, and cheap to run. Supers in particular have handling that front-wheel-drive cars can only dream about. Compared to [old] Beetles, Rabbits/Golfs (or any modern car [such as the new Beetle], for that matter) are tinny, plasticy, flimsy. overweight pieces of junk where most of the engineering work has gone into making sure you'll need to replace them when the factory wants some more money from you. (posted by "David," rec.autos. makers.vw.watercooled news group, May 24, 2002)
David's perspective helps provide an understanding of the irresolvable temporal paradox underlying retromarketing. David regards the old brand as a repository of better times. To David, it is physical evidence, a fast-fading signal, that affordable and durable cars once existed. David's narrative is a testament to tradition, conservatism, the enduring quality of times past, and times past of enduring quality. In stark contrast to James, David dismisses the progressive and the modern as cheap marketing gimmicks. Similar to Richard, Jaco, and Matthew, David's anticorporate and antimarketing comments reveal a popular discomfort with corporate power in contemporary culture, which consumer and marketing scholars have recently begun to explore (see, e.g., Holt 2002; Kozinets 2001, 2002a, b; Thompson and Haytko 1997).
Can this brand paradox be resolved? A posting from "Tony" illustrates a careful negotiation of the consumer ambivalence surrounding the conflicting meanings of retro brands:
There's no way in hell a car could be sold in today's market based on the original. The design is simply obsolete.... The old beetle was designed in the mid- I 930s for a nation in economic trouble with few cars, at the request of Hitler. The new beetle was designed in the `90s in the U.S. for mostly the U.S. and Europe, for people with more money, in an utterly different marketplace.... En other words, OF COURSE the new Beetle has very little in common with the old other than the outline. It couldn't realistically be any other way. It's essentially a Golf with a different shell. This is a very good thing.... Is there another car on the market with a design more audacious than the new Beetle? The sixties are long over. So are the thirties, for that matter. I'd thank VW for still making small cars with excellent design and personality. You can always blend in with a Civic, Corolla, Cavalier (yech), etc. if you'd rather, (posted by "Tony." alt.volkswagen.beetle news group, February 17, 1998)
Tony argues from the perspective of a moderate rationalist laying out a logical argument to ease the tensions between two warring factions. Discounting the intrinsic value of the past, he insists that the old brand must be adjusted for a new time, place, and set of target consumers. Akin to Orrin, he shifts the argument about brand essence to superficial design elements and advertising-laden symbolic associations. The result, for Tony, is an up-to-date vehicle that still shares the enchanting personality of the old Beetle brand. Enchantment indeed is the operative word, as many Star Wars fans testify.
Star Wars
The original 1977 Star Wars movie attempted to disorient its consumers temporally by offering a faraway future world of spacecraft and intelligent robots subsumed within a fairy tale set in the distant past. Star Wars: Episode I--The Phantom Menace attempted to top this temporal dislocation. Setting the fourth movie three episodes before the first was a stroke of marketing genius that created the neologism prequel. This prequel demonstrates retro branding in the realm of connected products and services that characterize today's "entertainment economy" (Wolf 1999). As a retro brand, The Phantom Menace stamps an established brand name, Star Wars, on a new movie that couples cutting-edge special effects with a cast of contemporary actors. As with the new Beetle, it imaginatively melds a familiar brand name with an all-new, up-to-date product.
The Phantom Menace not only contained history but also created history. People waited in lines to sit through the trailers for the movie and then left the theaters before the main feature (Gaslin 1999). Die-hard fans in 30 U.S. cities camped out on sidewalks for more than a month to be among the first to see the film, which spawned a documentary movie about their rite of endurance (Gaslin 1999). Fans exhumed old Star Wars collectibles, new Star Wars toys graced toy store shelves, and box office records were broken once more. The buzz surrounding the rerelease was closer to a din. Our analysis of consumer responses to this retro brand reveals additional insights into the management of brand meaning.
Allegory: Star Wars's brand story. The movies Star Wars, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, and Attack of the Clones are parts of a saga set in a mythical world that blends past and future. The saga is branded by the name Star Wars, and its story is a coming-of-age morality tale that segues into a tragic fall from grace. As an entertainment brand, Star Wars contains a rich set of associations deriving from the narrative that is its core product. The brand is a universal myth populated with familiar mythic signs and boasting archetypal heroes, sorcerers, sages, demons, fairy princesses, clowns, and elvish entities (for more on consumer myths, see Stern 1995). Each character in the Star Wars movies forms a subbrand in a complex, multidimensional brand constellation.
Many of the consumers who discuss Star Wars and its sequels, prequels, and even sequels of prequels often refer to the moral aspects of its narrative arc. The heroic quest and its obstacles and overall purpose transcend consumers' ordinary experiences and give them a taste of the philosophical and the sublime previously associated mainly with religion, mysticism, and other spiritual practices. For example, consider the importance of the central brand story and its mythic references in the following news-group postings:
As Yoda said, the Dark Side is always hard to see. Except with someone like Darth Maul, who just reeks of evil. Still, even him, when he's on Coruscant, probably doesn't go anywhere near the Jedi Temple. Especially for (some might say cliché) views on good (heroic and brave) and evil (sneaky and in-Sidious, pun intended), it would be natural for Sidious to be disguised and deceptive. (posted by "Randy," parenthetical comments in original, rec.games.frp.gurps news group, May 22, 1999)
Darth Vader may have ended up becoming a twisted version of Anakin Skywalker but he started out acting with the best intentions. Unfortunately, we all know the road to hell is paved with the best intentions. Sure, Palpatine admits to using the Dark Side of the Force in E6 [Star Wars: Episode 6--Return of the Jedi], but that's just [the] name the Jedi have applied to the side they don't use. Palpatine is willing to use it in order to achieve the ordered and peaceful galaxy he doesn't see existing under the Republic and the Senate. Jedi would never use an ends to justify a means and that what separates the dark and light sides. This is why the young Anakin will be willing to follow Palpatine, because of the good he can do, not the evil. (posted by "Cam," rec.arts.sf.starwars.misc news group, July 10, 2002)
"Erik": In Return of the Jedi the emperor is not able to detect Luke or seemingly Yoda. In the Phantom Menace the Jedi are not able to detect Darth Sidious or Darth Maul even though Darth Sidious was amongst them the whole Lime. Does this mean if you are totally good and totally evil you can not see one who is your opposite?
"Roseanne": Well, while the Emperor/Sidious is presented as totally evil, who is totally good? I don't think that Luke is meant that way at all. In fact, a good deal of the psychological drama is made out of Luke's struggles against the temptations of evil. (exchange posted between "Erik" and "Roseanne," rec.arts.sf.starwars.misc, January 30, 2001)
These postings powerfully demonstrate two important points about the consumption of the Star Wars brand story. First, the archetypal characters and plot elements of The Phantom Menace are interpreted allegorically as timeless didactic symbols alluding to a reality that transcends consumers' mundane daily existence. Star Wars is a myth of good versus evil. It refers to human universals such as the battles between temptation and resistance, selfishness and generosity, personal and political, means and ends. As consumers decode George Lucas's cosmology--inspired by the archetypal reflections of the comparative religion scholar Joseph Campbell--they inevitably are also defining and processing moral characteristics and the meaning of morals for themselves. For example, it may not be acceptable to favor ends over means, as Darth Vader does, but Cam finds it comprehensible and even honorable. The brand story is deeply implicated in this personal activity of mapping out acceptable definitions and behavior.
Second, the way consumers weave together notions from all five Star Wars movies is noteworthy. Interpreting the story as a continuing saga, they treat the brand as one single text rather than five separate texts. By using common characters and motifs in marketing images (such as the giant head of the villain as the looming background for both movie posters), the producers cue consumers to make comparisons. These comparisons between texts suggest that the prequels successfully blend into the same brand story of the first motion picture. Consumers consider the separate products a single narrative, a single brand, a single "morallegory."
Arcadia: Star Wars's idealized community. The first Star Wars movie begins with a trumpet flourish and the words, "A long time ago, in a galaxy far, far away." With this resonant phrase, the creator George Lucas marks the imaginary realm of Star Wars as a "liminal," or borderland, state that exists beyond ordinary life (Turner 1967). It is in the openness that accompanies this displacement that consumers find their sense of connection and meaning. Consumers have roamed for almost three decades in the wild, if imaginary, frontier that Star Wars founded. Anthropologists report that liminal places and states contribute to altered states of consciousness, assist people in escaping from everyday routines, and hold out the possibility of self-transformation. Like sidewalk squatters outside movie theaters, consumers occupy the space created by Star Wars's brand story and use elements of it to construct their own utopian domains.
This process is encouraged, and even accelerated, by Star Wars's setting in a bygone age of adventure, imagination, and magic. As a brand, Star Wars represents a timeless tale of liberating empowerment. In addition, for many Star Wars consumers, the sense of nostalgia they derive from the brand also derives from their personal experiences with the brand during childhood (see also Holbrook and Schindler 1989, 1994, 1996; Moore, Wilkie, and Lutz 2002). By turning the inner sanctum of a darkened theater into a summoning space for viewers' inner child, the saga combines the nostalgic with the paradisiacal. "Allan" said that seeing The Phantom Menace's opening title, with its direct evocations of Star Wars symbolism, "took me back to when I was II, and me and my brother got to see Star Wars in my grandfather's theater. Front-row center when the blockade runner and Star Destroyer wooshed right over my head" (rec.games.frp.gurps news group, May 25, 1999). Allan's recollections precipitate a para-Proustian return to the past by commingling family ties, privileged viewing positions, and preteen fantasies. His wonderment mixes fantasy with reality; the demarcation between being part of an audience in a theater and floating suspended in outer space has become one. Many other message posters echoed Allan in exhibitionistic displays of childhood dreams and nostalgia:
Having seen the original Star Wars films more times than I can count (more times than any adult cares to admit), I so wanted to love this movie. I was mentally prepared to be swept back into a world I haven't seen anew since I was 17. With the imagination behind the first trilogy reinvigorated by a long rest, and equipped with technology not even imagined in 1977, I expected an unequalled triumph of the imagination.... I have to admit that I did not mind the character of Jar Jar Binks, Qui-Gon Jinn's reluctant comic sidekick. So much negative hype about how annoying this character was going to be had spread like wildfire though the Star Wars fan community that it was impossible for Jar Jar to live down to it. (posted by "Peter," rec.arts.sf.starwars.misc news group, May 23, 1999)
Peter's posting also reinforces the close connection between Star Wars's retro brand and childhood or adolescence. It is as if the brand has magical powers to transport consumers back in time, to thrill them in a way they have not been thrilled since they were children. In his detailed review (most of which is cut here for brevity's sake), Peter's narrative also shows that he sees himself as a spokesperson for an enormous Star Wars fan community of which he is a member. The brand community had been anticipating the film for a long time, and its word-of-mouth connections, especially over the Internet, were formidable (see Kozinets 1999, 2001; McAlexander, Schouten, and Koening 2002; Muniz and O'Guinn 2001). Peter must defend his own opinion of the character Jar Jar Binks against the extant strong negative consensus of the community, which demonstrates how the brand community influences consumer opinion.
In this vein, some community members contend that the revived Star Wars film was not sufficiently distant from the present--a necessary condition for it to enable suspension of disbelief and evoke a wondrous place set apart. A common news-group complaint was that the film contained too much contemporary vernacular, such as "that's gotta hurt." "Calvin," for example, unequivocally stated: "Look. I'm a fan here. I'm willing to really work to suspend disbelief. I'm willing to go out of my way to try to get into the mood. But, c'mon, George Lucas--meet me halfway!" (rec.games.frp.gurps news group, May 22, 1999). Calvin's comments suggest that though he is highly motivated to believe in the movie, the text must also conform to accepted conventions. Even before the release of Star Wars: Episode II--Attack of the Clones, there was a major fan outcry reported in the news and shared on the news groups. The outcry pertained to the cameo roles given to the pop-music boy band N'Sync:
The online response from the huge Star Wars fan community was immediate and condemnatory; many felt that the independent, fly-in-the-face-of-convention sci-fi franchise was become [sic] just another pop culture marketing gimmick, and that N'Sync's involvement was another nail in its coffin. This week, however, LucasFilm proved that it does listen to its fans. Because of the outcry and dismay expressed on various fan Web sites (notably TheForce. net), they made the decision to cut N'Sync's parts from the film. (news release posted by Anthony," rec.music.artists.kings-x news group, January 14, 2002)
This posting reinforces the existence, power, and peril of an active, involved brand community. The inference from these complaints is that if the film is too contemporary it shatters the illusion of iconic events unfolding in a place set apart from today. Ironically, the original Star Wars films (not least, the notorious Star Wars Holiday Special) were as much a part of their own era, the Seventies and Eighties, as the retro prequels are a part of theirs. In addition, there is concern, as there is for the New Beetle, that the retro brand is merely a marketing gimmick. For the Star Wars brand, its idealized community is composed of not only the famous fan base but also the communities associated with at least two other idealized past times partially evoked by the story, which are held in high esteem by community members. These are, first, the brand story's close-knit community of rebels and sorcerers and, second, the rich associations of childhood delight evoked by the original Star Wars brand.
Aura: Star Wars wanes. As Peter's aforementioned posting attests, when the new Star Wars movie was released, many Internet news groups were awash in rankings, ratings, interpretations, and autopsies of the movie. The majority of these messages assessed the extent of the movie's authenticity, that is, the presence of the original Star Wars brand essence in the prequel. There was considerable commentary on the precious, incomparable, singular, and sacred qualities of the original, which is analogous to Benjamin's conception of the almost holy aura of original artworks. Consumers complained that the movie's representation of "medichiorians," an essentially biological explanation for the hitherto ineffable Force, was "lame," "anti-mystical," and generally ran against the grain of the original movies (see various postings at rec.games.frp.gurps news group, May 22, 1999). Consumers' concerns are captured in a more general sense in the following news-group message:
Seriously. People who think Phantom Menace and Jedi are in any way close to the quality of the first two movies just did not get what was special about those two movies in the first place. Phantom Menace and Jedi are JUST MOVIES. Star Wars and Empire seemed something more. (posted by "Todd," alt.fan.starwars news group, August 18, 1999)
Todd's comments, which must resort to emotional generalities, suggest that consumers can only struggle to describe precisely their judgments of brand essence--whether the reproduction has the core values and authentic identity of the original brand. In Todd's posting, these qualities seem beyond rational terms; they are only recognized when viscerally grasped. Consumers appreciate the ineffable, mystical nature of their attachment to the brand in frequent self-deprecating jokes about their own obsessions. After one message poster stated that The Phantom Menace is just a movie and people need to get on with their lives (apparently a reference to the extreme sidewalk-sitting behavior preceding the prequel's release), "Chandler" replies:
Foolish Heretic. The Archangel Gabriel descended from heaven and instructed the prophet Lucas with the Fourth Testament. He is in the process of filming the more dramatic parts. This is authentic Holy Scripture you pass off with "it's just a movie?' Do you accuse the Bible of just being a book? The Tablets of Gold and the Ruby Spectacles of just being jewelry? The Ark of the Covenant of just being a bug tamp?" (rec.games.frp.gurps news group. June 10, 1999)
Although Chandler is clearly being playful with his use of religious language, many Judeo-Christian believers might consider his comparisons blasphemous. His comparisons are founded on the assertion that The Phantom Menace possesses vivid brand authenticity and powerful authority. They also suggest that the Star Wars brand is a deeply meaningful creation affecting some people's lives profoundly. As with the New Beetle, however, consumers are divided on the authenticity issue and cannot agree on the criteria that should be used to judge it. Although the concepts of brand identity and brand essence seem relatively straightforward, this analysis of the Star Wars brand suggests that they are much more complex from the consumer standpoint. Consumers cocreate the brand meaning by carefully reading and interpreting brand-related communications, adding their own personal histories, and continually delving into definitions of the brand's authenticity.
Antinomy: Star Wars's brand paradox. The stewardship of Star Wars's commercial cosmos offers important insights into brand meaning management. The series creator George Lucas and his creative team have spent more than a quarter of a century carefully managing their brand extensions. To do this, they have had to control the antinomy, or the paradoxical essence, the "paradessence" (Shakar 2001), of their brand. One of the key challenges Lucas and his marketing team have faced is consistency. In the dynamic deal-making and almost embarrassingly merchandized world of successful entertainment opportunities, could they bolster brand essence and avoid brand dilution? If one story maintained that the Force was all-powerful and another maintained that it had limits, what would the effects be on the extended brand story?
To answer this question, consumers must and do follow sets of rules about what constitutes a "real" or "authentic" Star Wars brand story. In fan debates on the alt.startrek.vs. starwars news group, "Pat" (May 23, 2002) told other followers to consult the official guidelines on the Star Wars Web site to help determine what constitutes its actual brand story. According to the Web site, the "real story of Star Wars" is contained in the films "and only the films." It discounts the authority and authenticity of book and comic book novelizations, trading cards, and other subnarratives. On the official Web site and in Star Wars fan idiom, the guidelines are called "canon" and generally considered sacrosanct.
However, some Star Wars fans resist the edicts of the series' marketers and those telling them that any product bearing the official brand is the genuine article. As is clear from the preceding comments of Calvin and Todd, despite the increased technological sophistication of the prequel, some perceive the newer Star Wars film as failing to fit in with the spirit of the past. The retro brand is thus perceived as inauthentic, inadequate, and ultimately unacceptable.
In the fascinating, activist, emotionally charged comments of "Bill," precipitated by the latest Star Wars prequel, Attack of the Clones, it seems that virtually everything from the retro-branded movie is deemed inferior to the original (see alt.fan.starwars news group, May 10, 2002). In perhaps the ultimate act of consumer resistance, Bill vehemently argues that fans should wrest control of Star Wars from its official owners and produce and distribute their own, more authentic version of The Phantom Menace:
We the fans need to take Star Wars back from its evil creator. I propose that we form a secret, rebel group that will create a REAL EPISODE I, in secret, and release it over the Internet using encrypted c-mails and distributing DVD's. Once it gets out, there will be no stopping it, because people will copy it to each other. It will he illegal, but what will happen. will the F***ING THOUGHT POLICE OF GEORGE LUCAS come and try to take it from us? Nobody owns the characters of Star Wars. That's so much bullshit. You can't own my religion! You can't own the very metaphor I interface reality with! Well, OK, maybe you could if you were going to MAKE A GOD-DAMN GOOD STAR WARS MOVIE, but this crap just won't do. (alt.fan.starwars news group, May 10, 2002)
In a different discussion about the religious responses to the Star Wars brand (a message thread titled "Star Wars--A New Religion"), fans reached equally resentful conclusions. "Adam" began by suggesting that Australian Star Wars fans write "Jedi" as their religion on the country's census form, because if 10,000 people subscribe to the same sect, it becomes a fully recognized and legal "religion" (see aus.dvd news group, April 3, 2001). The "Jedis" then might even name their own holidays and gain government benefits. To this, "Zack" responded:
There is a downside to this business. Since George Lucas is THE CREATOR, that would make him GOD. It's bad enough in the ancient religions having a God that is either cruel or apathetic but to have one that constantly milks you for money while in return giving you extra Computer Generaled muppets as a Special Edition is just ludicrous, (aus.dvd news group, April 4, 2001)
Bill's diatribe offers opposing poles of the profane market and the sacred myth (see Belk, Wallendorf, and Sherry 1989). Zack's posting is resentful of the myth- and money-making power manifested in George Lucas as a person. Both message posters demonstrate that though the brand is clearly recognized as a commercial creation, it is also a deeply meaningful part of some consumers' lives--it is enthusiastically represented as a powerful metaphor for living and even for religion. These comments suggest not only that commerce and the sacred are cultural opposites but also that their intermixing in brands such as Star Wars has considerable cultural power (see also Kozinets 2001, pp. 76-78). In support of these Findings having wider marketing relevance, Peñaloza (2000, p. 105) finds similar tensions between "commerce" and "soul" among ranchers of the American West and concludes that the West is a site for "the production of profound cultural meanings." Bill's and Zack's postings express these important tensions in terms of a brand and the different responses to them. They respond to the brand paradox behind Star Wars's combination of creed and commerce, piety and profanity, mana and money.
Implications for Understanding Retro Brands
Retro brands will have continuing appeal as a marketing strategy for two important reasons. First, technology and imitation quickly eradicate first-mover advantage, yet a competitive edge is gained by Upping into the wellsprings of trust and loyalty that consumers hold toward old brands. Second, consider Davis's (1979) contention that communal nostalgia increases during chaotic times. The tumultuous post--September 11 world is likely to see more rather than less retro branding. Early indicators seem to confirm this trend. Firms such as River West Brands LLC, headquartered in Chicago, are actively acquiring neglected brands such as DUZ detergent and Aero Shave foam to attempt to relaunch them nationwide (Van Bakel 2002). Our analysis suggests that these two reasons, the rapidity of new product launches and the increasingly unstable cultural environment, are important causes of the rise of retro branding.
Yet despite the evident popularity of old-style products among contemporary consumers, our findings suggest that managing retro brands is a complex affair. It is not simply a matter of rebroadcasting an old advertisement, relaunching a long-delisted brand, or boasting about an organization's illustrious heritage (cf. Aaker 1996). It is more intractable than this because the brand is reanimated jointly by stakeholders. The brand is a milieu where marketing management and consumer commitment coexist. As Fournier (1998) demonstrates, consumers have deep relationships with brands; our data bring to bight some of the complex historical, allegorical, and paradoxical qualities of those relationships. Consumers' arguments and agreements about the New Beetle and Star Wars, for example, demonstrate that the management of brand meanings can be a jointly told tale or a vicious verbal duel.
In relation to past research, our data especially bring to light the communal elements of brand meaning management in a retro context. Given its mythological status and historical context, retromarketing represents the acme of community-based relationship management. A retro brand is a powerful totem that regathers its loyal users into a contemporary clan. Members of the clan share an affinity that situates them in a common experience of belonging, both to a brand community (Muniz and O'Guinn 2001) and to a particular era and its ethos. The retro brand carries and concretizes these important symbolic elements in perpetuity.
Furthermore, our findings demonstrate that retro brands enable consumers to negotiate the moral geography of their lives. Despite, or perhaps because of, the nomadic character of contemporary social relations, consumers use retro brands to return briefly to an imagined era of moral certainty. This moral element is an important source of a brand's continuing reinvigoration and a singular contribution of this study. It is inscribed, for example, in the Star Wars paradox, in which good and faithful consumers are exploited by the evil, money grubbing George Lucas. Morality is also at the core of brand essence, which is the central tension driving the Volkswagen Beetle's brand story of idealism, environmentalism, independence, and nonconformity.
In summary, the communities that coalesce around retro brands differ from other brand communities (e.g., McAlexander, Schouten, and Keening 2002; Muniz and O'Guinn 2001) in the moral and utopian character of the "consciousness of kind" (Muni, and O'Guinn 2001, p. 418) to which their members subscribe. The retro brand is a portal to a temporal sanctuary, which admits the community periodically for purposes of renewal and rejuvenation. The retro brand is a creative anachronism, imaginatively employed by the community as a metasocial commentary on the contemporary cultural scene. The preservation and active reconfiguration of collective memory is the lifework of the community. It should come as no surprise, then, that brand managers are increasingly turning to the mythological and poetic (Randazzo 1993) as well as the archetypal (Mark and Pearson 2001) in their attempts to reinvigorate ailing brands.
Which brands most qualify for revival? Our analysis suggests several salient qualities. Although it may lie dormant in collective memory, the brand must still exist as a brand story, yet it should remain relatively undisturbed by recent marketing attention. The brand must have a vital essence; that is, it must have existed as an important icon during a specific developmental stage for a particular generation or cohort. It must be capable of evoking vivid yet relevant associations for particular consumers. Perhaps most important, the retro brand must be capable of mobilizing a utopian vision, of engendering a longing for an idealized past or community. In this respect, the brand must inspire a solidarity and sense of belonging to a community. Ideally, the brand should be amenable to both technological and symbolic updates so as to ensure its perpetual relevance to consumers, who constantly revise their own identities. In these considerations, we find the central elements of Allegory (brand story). Aura (brand essence), Arcadia (idealized community), and Antinomy (brand paradox) that inform our 4As analysis.
Implications for Brand Meaning Management
Marketing scholars from Alderson (1957) to Zaltman (1997) have recognized the importance of the experiential nature of the brand, but perhaps not since the heyday of motivation research (e.g., Dichter 1960) has there been such a resurgence of interest in brand phenomenology. The most succinct if overstated justification for this interest is the contention that some conceptualize a product as "no more than an artifact around which customers have experiences" (Prahalad and Ramaswamy 2000, p. 83). From the pioneering work of Levy (1999) and Hirschman and Holbrook (1982) through the current wave of postpositivist inquiry (e.g., Brown 1995; Fournier 1998; Fournier and Mick 1999; Holt 2002; Kozinets 2001; Peñaloza 2000; Sherry 1998; Thompson 1997), the tendency to regard brands as symbolic creations has led to the conclusion that the management of meaning must underlie marketing strategy. That marketers are quintessentially meaning managers, shaping the experience of consumers, is intuitively plausible. That meaning management involves attending to the creative activity of consumers, or that consumers might justly be regarded as the cocreators of brand essence, is equally plausible, if less fully appreciated.
Strategic brand management models, both comprehensive (Keller 1998) and circumscribed (Aaker 1991, 1996), tend to downplay the complex, heterogeneous, and experiential nature of consumer--brand relations. These models generally adopt a more cognitive or structural view of the brand and overlook much of the emotional complexity that endows the brand with texture, nuance, and dimensionality. Furthermore, although customer-based approaches to brand equity allow for the existence of idiosyncratic consumer response, they encourage managers to view this experience as a passive or reactive result of marketing intervention. For example, recent studies on brand extensions conceptualize brand meanings as well-developed networks of associated beliefs and feelings (e.g., Desai and Keller 2002; John, Loken, and Joiner 1998). A study of the process element of brand meanings is valuable, but it does not and should not substitute for a sophisticated knowledge of the cultural contents of those meanings. By seeking singular, aggregate brand meanings among populations, these association-based network approaches tend to underplay the complexity and difference among consumers--let alone consumers' own active making of meaning--that our approach brings to the fore.
In this article, we show consumers to be partners in the cocreation of brand essence and importers of meaning from beyond the marketplace. Consumers draw holistically from their lived experiences with products, history, mass media, and one another, as well as marketing sources, for the meanings they ascribe to brands. Whereas Stem (1988) recommends that advertising be considered intrinsically allegorical, our research suggests a broadening and extension of this perspective. Summarized by the 4As abbreviation, we believe that brands themselves can be usefully considered complex stories and that the most successful brands have "morallegorical" qualities.
Brand stories are partly composed of the meanings and associations emanating from advertisers and marketers; however, they are also constructed by the mass media, press releases, news stories, and related celebrities. Most important for retro brands, they are redolent of historical periods; temporal connections; and their attendant national, regional, and political associations (e.g., the Beetle's vestigial links to Nazi Germany). Consumers construct their own brand stories using the raw material of producer and cultural intermediary stories and adding their own idiosyncratic viewpoints, needs, goals, and experiences. As our data set demonstrates, networked information technology has made the widespread sharing of these brand stories among interested consumers much easier and much more global than it has been previously. These consumer communities play an important role in cocreating brand stories. They can also serve as settings in which those stories take place. As Meredith and Schewe (2002) note, cohorts of consumers are nostalgic together and can idealize similar versions of utopia. Consumers use idealized times and places--those of Seventies childhood innocence and Sixties flower power, for example--as settings that lend depth and vitality to brand meanings. The result of this animation is a brand with core values, a brand animated by its story and containing a moral character and identity. Our study thus suggests that Aura (brand essence), Allegory (brand stories), and Arcadia (idealized community) are the character, plot, and setting, respectively, of brand meaning.
Antinomy, the final element of our 4As abbreviation, is perhaps most important of all, for brand paradox brings the cultural complexity necessary to animate each of the other dimensions. The brand is both alive and not alive, a thing and a personality, a subject and an object: This is the paradoxical kernel of brand meaning. The story is both truth and fiction, composed of clever persuasions and facts, devised by distant copywriters and real users. This is the central conundrum of brand story and consumer--marketer codependence. The idealized community is both a real community and a pseudocommunity, moral and amoral, in thrall to a commercial creation and a rebellious uprising, dependent and independent, a gathering of both angry activists and covetous consumers. For a retro brand, the tension between past and present--and even, as in our two examples, the future--also vivifies brand meanings. Retro products seem custom-made to address a core paradox at the heart of brand management. Retro combines the benefits of uniqueness, newness, and exclusivity (with its hints of higher functionality, class, styling, and premium prices) with oldness, familiarity, recognition, trust, and loyalty. These intrinsic paradoxes underpin a product's élan vital, the creative life force at the heart of the retro brand's extraordinary appeal.
According to the acerbic comedian George Carlin (1998, p. 110), contemporary consumer culture is beset by "yestermania," an inordinate fondness for revivals, reenactments, remakes, reruns, and re-creations. Certainly, the merest glance across today's marketing landscape reveals that retro goods and services are all around. Long-abandoned brands, such as Airstream (trailers), Brylcreem (pomade), and Charlie (cologne), have been adroitly reanimated and successfully relaunched. Ostensibly extinct trade characters, such as Mr. Whipple, Morris the Cat, and Ms. Chiquita Banana, are standing sentinel on the supermarket shelves once more. Ancient commercials are being rebroadcast (e.g., Ovaltine, Alka-Seltzer); timeworn slogans are being resuscitated (e.g., "Good to the last drop," "Look, Mom, no cavities"); and long-established products are being repackaged in their original eye-catching liveries (e.g., Necco wafers, Sun-Maid raisins).
We have examined the rise of retro brands in an attempt to develop tractable theory that contributes to marketing principles and practice. We have reviewed the pertinent literature on nostalgia and, using an appropriately retro research method, have empirically investigated two exemplars of retro branding, the Volkswagen New Beetle and Star Wars: Episode I--The Phantom Menace. We found that the meaning of retro brands inheres in four key characteristics: Allegory, Arcadia, Aura, and Antinomy. These characteristics indicate that the social and cultural forces that animate brand meaning are considerably more complex than prior conceptualizations suggest. Brands mean more than relatively fixed arrangements of associative nodes and attributes. Complexity, heterogeneity, dynamism, and paradox are integral aspects of the consumer--brand relationship. Not only are brands fixed cognitive associations of meanings; they are also dynamic, expanding social universes composed of stories. They are social entities experienced, shaped, and changed in communities. Therefore, although brand meanings might be ascribed and communicated to consumers by marketers, consumers in turn uncover and activate their own brand meanings, which are communicated back to marketers and the associated brand community. This is not to say that brand management is impossible in a world of consumer-mediated meanings, but only that it is more complex than before and cocreated rather than imposed by managerial dictate.
Our analysis of brand stories and their managerial significance complements the increasingly accepted view that brands are no longer expected to be reassuring to consumers; they must inspire consumers to take risks (Kapferer 2001). Fournier and Mick (1999) note that consumer satisfaction with technological products should be built on surprise, the unexpected, a challenge. To create this challenge, brand managers must take risks and boldly go where managers have been reluctant to go before. Nike's championing of global labor standards in response to a boycott of its brands is a perfect example of this propensity. Brand meanings, moreover, must be managed as community brands, as brands that belong to and are created in concert with groups of communities (Kozinets 1999, 2001, 2002b; McAlexander, Schouten, and Koening 2002; Muniz and O'Guinn 2001). These communities exist in local and technologically mediated arenas. They are ambivalent, dynamic, and contested cultural spaces. In brand-building efforts, marketers must locate relevant gatherings of these groups and address them collectively as well as continue to address solitary consumers. The effect of undertaking these new strategies will be an enlivening of our evolving understanding of brands and their cultural meanings.
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The news groups we examined include the following: rec.autos.makers.vw.watercooled rec.autos.makers.vw.aircooled; rec.autos.marketplace; alt.volkswagen.beetle; rec.games.frp.gurps; alt.fan.starwars; rec.arts.sf.starwars. collecting.misc; rec.arts.sf.starwars.collecting.vintage; rec.arts.sf.starwars.games; rec.arts.sf.starwars.misc; and rec.arts.movies.current-films.
The Web sites we investigated include Peter's New Beetle Experience at http://broca.aecom.yu.er.iu/beetle/new_beetle.shtml; http://www.newbeetle.org; Retro New Beetle Headquarters at http://homepage.mac.com/retro%5fnb; Countdown to Star Wars at http://www.countingdown.com/movies/ episode-ii; TheForce.Net at http://www.theforce.net/ prequels/; and a range of online fan Star Wars movie reviews (e.g., http://www.mindspring.com/∼-sejohnson/mag/reviews/ Epl_review.html).
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By Stephen Brown; John F. Sherry Jr. and Robert V. Kozinets
Stephen Brown is Professor of Marketing Research, University of Ulster, Newtownabbey, Northern Ireland. Robert V. Kozinets is Assistant Professor of Marketing, and John F. Sherry Jr. is Professor of Marketing, Kellogg School of Management, Northwestern University. The authors thank Brian Sternthal and the Kellogg marketing faculty for constructive suggestions. The authors also thank the three anonymous JM reviewers for their constructive advice, enthusiasm, and encouragement.
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Record: 149- Technological Evolution and Radical Innovation. By: Sood, Ashish; Tellis, Gerard J. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p152-168. 17p. 6 Charts, 6 Graphs. DOI: 10.1509/jmkg.69.3.152.66361.
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Technological Evolution and Radical Innovation
Technological change is perhaps the most powerful engine of growth in markets today. To harness this source of growth, firms need answers to key questions about the dynamics of technological change: ( 1) How do new technologies evolve? ( 2) How do rival technologies compete? and ( 3) How do firms deal with technological evolution? Currently, the literature suggests that a new technology seems to evolve along an S-shaped path, which starts below that of an old technology, intersects it once, and ends above the old technology. This belief is based on scattered empirical evidence and some circular definitions. Using new definitions and data on 14 technologies from four markets, the authors examine the shape and competitive dynamics of technological evolution. The results contradict the prediction of a single S-curve. Instead, technological evolution seems to follow a step function, with sharp improvements in performance following long periods of no improvement. Moreover, paths of rival technologies may cross more than once or not at all.
Understanding technological innovation is vital for marketers for several reasons. Technological change is perhaps the most powerful engine of growth. It fuels the growth of new brands (e.g., Gillette's Mach 3), creates new growth markets (e.g., digital video recorders), and transforms small outsiders (e.g., Intel) into market leaders (Chandy and Tellis 1998; Christensen 1997; Foster 1986). To date, the topic of technological evolution has been studied primarily in the technology management literature. A central premise is that performance of a new technology starts below that of an existing technology, crosses the performance of the older technology once, and ends at a higher plateau, thus tracing a single S-shaped curve (see Figure 1). There is scattered empirical support for the premise and limited theoretical support for various aspects of the S-shape curve (e.g., Foster 1986; Utterback 1994a).
Belief in this premise is so strong that it has become almost a law in the strategy literature, from which authors have derived strong managerial implications. For example, they have warned that even though managers might be able to squeeze out improvement in performance from a mature technology at the top of its S curve, improvement is typically costly, short lived, and small. Thus, a primary recommendation in the strategy literature and the trade press is that managers should abandon a maturing technology and embrace a new one to stay competitive (e.g., Christensen 1997; Foster 1986). A central, practical problem that managers face is when to shift investments from the current to the future technology. If the S curve is indeed valid, the appropriate time would be the inflection point of the S curve. After this point, performance improves at a decreasing rate until maturity.
New product development and major investments in research depend on a correct understanding of technological evolution in general and of the S-shaped curve in particular. To foster this understanding, this study addresses the following questions:
• How do new technologies evolve? Do they follow the S-shaped curve or some other pattern? Are technological changes predictable? Is the rate of technological change increasing?
• How do rival technologies compete? What are the performance dimensions of competition? What are the transitions between technological changes?
• Which firms carry out and survive technological evolution? Who introduces radical innovations? Do incumbents survive the change?
The primary focus of the current study is empirical. We test hypotheses derived from prevailing literature and examine the evolution of 14 technologies in four markets or industries. In the next three sections, we present the hypotheses, method, and results. In the final section, we discuss the findings, limitations, and implications of the research.
Hypotheses Development
The field does not enjoy a single, strong, and unified theory of technological evolution. To guide our empirical work, we reviewed available theory from the literature and derived testable hypotheses about the path, shape, source, and speed of technological evolution and the competition among rival technologies. Findings in this area have been partly confounded by the use of circular definitions. Thus, we begin by defining types of technological innovations independent of their effects.
Beginning with Schumpeter's (1939) early study, researchers have used a wide variety of terms to describe innovations. Many terms, such as "revolutionary," " disruptive," "discontinuous," or "breakthrough" (Freeman 1974; Garcia and Calantone 2002; Tushman and Anderson 1986), are intrinsically problematic because they define an innovation in terms of its effects rather than its attributes. If the definitions are then used to predict market outcomes (e.g., new entrants that displace incumbents with disruptive technologies), researchers risk asserting premises that are true by definition. To avoid such circularity, we define technological change in terms of intrinsic characteristics of the technology. As such, we identify and define three types of technological change: platform, component, and design.
We define a "platform innovation" as the emergence of a new technology based on scientific principles that are distinctly different from those of existing technologies. For example, the compact disk (CD) used a new platform, laser optics, to write and read data when the prior technology used magnetism. We define a "component innovation" as one that uses new parts or materials within the same technological platform. For example, magnetic tape, floppy disk, and zip disk differ by use of components or materials, though all are based on the platform of magnetic recording. We define a "design innovation" as a reconfiguration of the linkages and layout of components within the same technological platform. For example, floppy disks decreased from 14 to 8 inches in 1978, to 5.25 inches in 1980, to 3.5 inches in 1985, and to 2.5 inches in 1989, though each was based on magnetic recording (Christensen 1993).
These definitions are refinements of the technological dimension of radical innovations that Chandy and Tellis (2000) define. In our study, we use the term technology synonymously with platform. Furthermore, we note that improved performance in platform innovation results from innovations in component or design. In the interests of parsimony, this study does not explicitly identify the component and design innovations that improve performance in new platforms.
In the technology literature, a consensus has developed about the shape of technological evolution, and a consensus is emerging about the major explanation for this phenomenon. Regarding the phenomenon, prior research suggests that technologies evolve through an initial period of slow growth, followed by one of fast growth, and culminate in a plateau. When plotted against time, the performance resembles an S curve (see Figure 1, Panel A). Support for this phenomenon comes primarily from the work of Foster (1986), Sahal (1981), and Utterback (1994a). These authors address the progress of a technology on some primary dimension that is critical to consumers when the innovation emerges. Some examples of this are resolution in monitors and printers and recording density in desktop memory products. Subsequent authors have either accepted this view or found additional support for it. We did not find any article that has questioned it.
Authors have not developed any single, strong, and unified theory for the S curve. However, an emerging, and probably the most compelling, explanation revolves around the dynamics of firms and researchers as the technology evolves. We call this explanation the technology life cycle because it explains the occurrences of three major stages of the S curve: introduction, growth, and maturity (see Abernathy and Utterback 1978; Utterback 1994a). We describe these stages as emerging from the interplay of firms and researchers over the life of the technology.
Introduction stage. A new technological platform makes slow progress in performance during the early phase of its product life cycle. Two reasons may account for this: First, the technology is not well known and may not attract the attention of researchers. Second, certain basic but important bottlenecks must be overcome before any new technological platform can be translated into practical and meaningful improvements in product performance. For example, the laser beam was a new platform that required much time and effort to achieve the safety and miniaturization required to use it as a surgical tool.
Growth stage. With continued research, the technological platform crosses a threshold after which it makes rapid progress. This stage usually begins with the emergence of a dominant standard around which product characteristics and consumer preferences coalesce (Utterback 1974). That consensus stimulates research on the new platform, which in turn leads to improvement in its performance. Furthermore, publicity of the standardization draws a large number of researchers to study the new platform. Their cumulative and interactive efforts also lead to rapid increases in performance. The rapid progress leads to increases in sales of products based on the new technology, which increases revenues and profits and offers further support for research. In turn, these added resources fuel further improvement in performance (Klepper 1996).
Maturity stage. After a period of rapid improvement in performance, the new technology reaches a period of maturity after which progress occurs slowly or reaches a ceiling (see Brown 1992; Chandy and Tellis 2000; Foster 1986; Utterback 1994b). Authors propose several reasons for this change. Foster (1986) suggests that maturation is an innate feature of each platform; a technology is good for only so much improvement in performance. Utterback (1994b) and Adner and Levinthal (2002) suggest that as a market ages, the focus of innovation shifts from product to process innovation. As such, performance increases are few and modest, leading to technological maturity. Reinganum (1985) and Ghemawat (1991) suggest that maturity occurs when there is less incentive for incumbent firms to innovate because of fears of obsolescence or cannibalization from a rival platform. Thus, the rate of innovation reduces relative to the growth stage. Perhaps the best explanation is that of Sahal (1981). He proposes that the rate of improvement in performance of a given technology declines because of limits of scale (i.e., things become either impossibly large or small) or system complexity (i.e., things become too complex to work flawlessly). When these limits are reached, the only possible way to maintain the pace of progress is through radical system redefinition-that is, a move to a new technological platform.
Hypotheses
On the basis of the preceding explanation, we derive hypotheses about six aspects of technological evolution: shape, path, and dynamics of technological change on a primary dimension; progress on secondary dimensions; and the source of innovations and pace of technological transition.
Although the extant literature suggests that technological evolution follows an S curve, it does not indicate the slope of this S curve, the duration of the stages, or the timing or steepness of the turning points. We try to determine whether it is possible to identify any patterns or generalizations about these parameters. However, in terms of a testable hypothesis regarding shape, the most precise hypothesis we can formulate is as follows:
H1: Technological progress on a primary dimension follows a single S-shaped growth curve.
Do the paths of two technologies ever cross? If so, how many times? A crossing signals whether a new technology is robust and productive enough to supplant the old one. The existing literature suggests that such paths cross once. This conclusion is based on three implicit premises. First, performance of successive technologies each follows an S curve. Second, the performance of the new technology starts below that of the old technology. Third, performance of the new technology ends above that of the old technology.
Support for the first premise follows from that for H1 Support for the second premise is widespread. Utterback (1994a, p. 158) asserts that "[a]t the time an invading technology first appears, the established technology generally offers better performance or cost than does the challenger, which is still unperfected." Foster (1986) claims that the performance of competing new technologies is much less than that of established technology during the fast growth phase of the established technology. Adner and Levinthal (2002, p. 56) claim, "It is unlikely that a new technology will initially dominate an established technology in its primary domain of application." Christensen (1992a, b) and Anderson and Tushman (1990, 1991) support the general phenomenon that when it first appears, any new technology provides much lower benefits than the old technology. Several authors provide arguments and examples in support of the third premise. Utterback (1994a, p. 160) states that "the new technology often has so much more potential for better performance that it" ultimately "surpasses the old." Two common examples cited in support of these arguments are steamships replacing wind-powered ships (Foster 1986) and airplanes' turbo jet engines replacing internal combustion engines (Constant 1980).
On the basis of the preceding premises, Foster (1986) and Christensen (1997, p. 398) postulate the following chain of events in the evolution of competing technologies: Sometime in the life of an old technology, a new technology emerges. Initially, it makes slow progress on the primary dimension. However, at some point, it enters its growth phase and improves rapidly. In contrast, the old technology improves at a much slower rate even though major commitments are made to develop products using old technology. As a result, a point is reached when the new technology crosses the old technology in performance (see Figure 1, Panel B). This crossing of the old technology is a signal of the end of its efficient progress. Thus, the threat to the old technology on the primary dimension is always from below. This pattern of intertechnology competition results in overlapping S curves, with each new S curve starting below but ending above the old. For example, some empirical studies indeed show a single crossing between any two technologies (Christensen 1997; Foster 1986). This line of argument is represented in the following hypotheses:
H2: When a new technology is introduced, its performance is lower than that of the old technology.
H3: When a new technology reaches maturity, its performance is higher than that of the old technology.
H4: The performance path of a pair of successive technologies intersects once when the new technology surpasses the old technology in performance.
Pace of technological change refers to the rate at which innovations are introduced in the market. The pace may be essentially stochastic because of the uncertainties in both the frequency of improvements and the magnitude of gain realized through each innovation. However, some authors believe that innovations are occurring more rapidly for three reasons. First, every year, greater resources are devoted to research and development (R&D). Second, every year, an increasing number of countries and people become involved in this R&D. Third, progress in one area (e.g., computers) enables greater efficiencies in another area (e.g., materials design).
At least two studies have found empirical support for this thesis. For example, Qualls, Olshavsky, and Michaels (1981) find that the percentage of products in the introductory and growth phases of the product life cycle increased over the past 50 years. In addition, research in diffusion of new technologies (Danaher, Hardie, and Putsis 2001; Pae and Lehmann 2003) also suggests a reduction in intergenerational time over time in many markets. Thus:
H5: The time interval between successive introductions of a new technology (i.e., a new platform) decreases over time.
Similarly, Golder and Tellis (1997, 2004) and Tellis, Stremersch, and Yin (2003) find that the time for takeoff of new products is shorter now than in previous decades. This finding implies that the rate of innovation within technologies is higher now than in previous decades, and new technologies improve at a quicker rate. Kayal (1999) finds that in the past 25 years, there had been increasing recency in the median age of the patents cited on the front page of a patent document. This finding suggests that the cited patents are relatively recent and that the technology is experiencing a frequent replacement of one generation of inventions with another, which is a sign of a rapidly progressing technology. Fernald and Ramnath (2004) find the greatest increase in total factor productivity in industries that use emerging information technologies. Total factor productivity is defined as the growth of real output beyond that which is attributable to increases in the quantities of labor and capital that are used. Economists often use this measure to represent the improvements in productivity that result from technological change (Hulten 2000). The increase in the pace of technological change could result from the larger size of successive jumps in performance, more frequent jumps, or both. Thus, we propose the following two hypotheses:
H6: The time interval between successive improvements in performance of a given technology decreases over time.
H7: The percentage increase in performance, calculated relative to the previous year, increases over time.
Conversely, Bayus (1994, 1998) believes that even though more products and product variations are available in the market at any point, the current rate of change is no higher than in previous decades.
Which types of firms are more likely to introduce platform innovations: incumbents or new entrants, large firms or small firms? This topic has been the subject of research for decades. The conventional wisdom is that platform innovations come primarily from small firms or new entrants. These firms are ridiculed and ignored by incumbents in the beginning, but subsequently, they become successful with the progress of the new technology. Scherer (1984) shows how new entrants contribute to a "disproportionately high share" of revolutionary industrial products. Previous studies proposed many reasons for large incumbents' failing to introduce innovations, including incumbents' technological inertia (Ghemawat 1991), complacency (Robertson, Eliashberg, and Rymon 1995), arrogance (Lieberman and Montgomery 1988), and unwillingness to cannibalize their current products (Chandy and Tellis 1998). Thus, the dominant view in the literature is as follows:
H8: Platform innovations are introduced primarily by small entrants.
Method
A ready-made database does not exist for the study of technological evolution. We collected our own data using the historical method, following a growing trend in marketing (see Chandy and Tellis 2000; Golder 2000; Golder and Tellis 1993). The benefits of using the historical method include lower survival and self-report bias, ability to assess causality through longitudinal analysis, and new insights from a fresh reading of history (Golder 2000; Tellis and Golder 1996). Next, we detail our sample selection, sources, and procedure for data collection.
We used two criteria to select categories: some overlap with prior research and an adequate number of platform innovations. We selected a portfolio of categories such that it included some that had been investigated in prior studies (e.g., memory) and others that had not been researched (e.g., data transfer). This coverage enables us to compare our results with prior studies and to validate these findings in new categories. However, the current study goes further than previous studies in one important aspect: Within each category, we selected all technologies. We also required that the category have had at least two platform innovations. On the basis of these criteria, we chose data transfer, computer memory, desktop printers, and display monitors. Note that the first is utilities, and the next three are consumer electronics. Thus, the sample crosses a broad spectrum of products.
The information required for this study is technical data on product performance for various technologies at different stages of their evolution. The primary sources of our data were reports in technical journals, industry publications, white papers published by R&D organizations, and annual reports of industry associations. We sourced industry reports of market research firms (e.g., Disk/Trend, Stanford Resources), press releases, timelines of major firms, and records in museums that profiled innovations and the development of industries. We recorded the current performance of many technologies from product information bulletins released by firms.
We followed the general rules for data collection for the historical method (Golder 2000). We explain specific problems we encountered and the rules we used to resolve them. First, within a technology, as the performance of that platform, we used the performance of the best component or design or the combination of the two. Second, if two sources provided conflicting data for a period within the series, we chose the one whose starting and ending values were more consistent with the rest of the series. Third, we used the date of first commercialization of a product based on each technology as the standard starting point to compare the relative performance of any two technologies.
Results
First, we present the identification of platform innovations and the performance attributes in each category. Second, we present findings on the hypotheses regarding the shape, path, and dynamics of technological changes. Third, we present findings on the competition, rate of improvement, and source of new technologies. We used nonlinear regression to test the first and primary hypothesis, the existence of the S curve. We used cross tabulations, chi-square and binomial tests, and regression analysis for the other hypotheses.
We identified various technologies in each of the markets, each of which was initiated by a platform innovation: four in desktop printing and display monitors and three in desktop memory and data transfer. We describe these 14 technologies briefly in the Appendix. (Detailed definitions for these innovations are available on request).
We found that some of the platform technologies may not be readily distinguishable to the customers for one major reason: Even when a new technology differs radically from an old one, firms try to facilitate consumer adoption by maintaining a uniform interface for the new product based on the new technology. For example, many secondary storage devices based on magnetic and magneto-optical principles used almost identical housings and interface accessories. We considered the underlying technologies distinct if they were based on fundamentally different scientific principles. We adopted this rule to avoid the confusion of differences in technologies based on their characteristics with superficial differences based on derived products.
The literature is quite consistent in recommending the use of performance as the key dependent variable when testing the S curves (e.g., Christensen 1999, p. 19; Foster 1986, p. 274; Utterback 1994a, p. 158). In each category, at a particular stage of technological evolution and consumer needs, certain dimensions of performance assume primacy. We did not have difficulty identifying these dimensions based on the historical description of the technologies. Fortunately, each of the dimensions has fairly clear performance metrics. In choosing metrics, we were careful to take into account output per unit of input (see Table 1).
In H1, we predict that technologies evolve through S curves. We plotted the performance of technologies on the y-axis against time on the x-axis (see Figure 2, Panels A-E). We excluded organic light emitting device (OLED) technology from the formal tests of shape because it has only two data points. As we hypothesized, these figures reveal that technologies have a slow start and a sudden growth spurt. However, we found a single S-shaped path with a single inflection point followed by a permanent plateau or maturity in only four technologies. In nine technologies, we did not find a single S curve. Rather, we found long periods of static performance interspersed with abrupt jumps in performance. The plots suggest a series of step functions, each of which could approximate an S curve. To test H1 that evolution follows an S-shaped function, we performed the following two tests.
First, we fit the generalized logistic function to the four technologies that reveal a single S-shaped curve:
( 1) Yt = d + a/1 + e-b(t - c),
where Yt = performance of the technology in year t, and a, b, c, and d are parameters to be estimated: b is the growth rate, c is the time of maximum growth or the inflection point, and a + d is the upper asymptote of the S curve. We used the nonlinear regression techniques in SAS to estimate the model over the entire data. Second, for the nine technologies that seemed to exhibit multiple S curves, we fit the generalized logistic function both to the entire series of data and to a subsample of data that exhibited an S curve. We used two criteria to select a subset of data for this purpose: ( 1) Performance of the technology during the subsample had an upper plateau that was longer in duration than was the duration of the just-preceding growth phase, and ( 2) the subsequent jump in performance in the one year immediately after the plateau was almost double the performance during the entire plateau. Our practical goal is to test how well an S curve fits on the whole sample and whether it fits better on a subsample than on the whole data.
We found that for the four technologies with an apparent single S-shaped curve, the generalized logistic function provides a good fit with the data (see Table 2, Panel A, and Figure 3, Panels A and B). For the remaining nine technologies, an S-shaped curve fits better over a subsample of data than over the whole data, even after we take into account degrees of freedom (see Table 2, Panel B, and Figure 3, Panels C and D). Table 2, Panel B, shows that the fit over the subsample gives an average reduction of 97% in the mean square error compared with the fit over the whole sample. Furthermore, Table 2, Panel C, shows that after adjusting for degrees of freedom, the parameter estimates of the fitted generalized logistic function for the subsample are significantly different from the parameter estimates over the whole sample. For this test, we used the t-test for differences in parameters with unequal variances over the two models. More important, the upper asymptote in the subsample is significantly and substantially different from that in the whole series, leading us to reject H1.
To summarize, the hypothesis of a single S-shaped growth in performance is supported for only 4 of the 13 technologies.( n1) For 1 technology, we did not have enough data, and for the remaining 9 technologies, change in performance follows a series of irregular step functions that are better approximated with multiple S curves than with a single S curve. Across these step functions within a technology, estimates of growth rate and especially performance at maturity (the upper asymptote) differ substantially. Using functional analysis, James and Sood (2005) obtain similar results and confirm significant departures from an S curve for these technologies. The importance of this finding is that an analyst expecting a single S curve may prematurely abandon a promising technology at the first plateau in performance.
In H2, H3, and H4, we predict three characteristics of technological competition: performance of a new technology at its introduction and at its maturity (Points 1 and 2 in Figure 1, Panel B) and a single crossing when the new technology crosses the old technology in performance. Our results are contrary to the hypotheses (see Table 3, Panel A). A majority of new technologies performed better than the old technology from the time they were introduced. In addition, many new technologies never improved over the old technology, whereas others enjoyed brief spells of dominance over the old technology before the old technology regained dominance.
This unexpected pattern of evolution results in three distinct types of crossings between any pair of successive technologies (see Table 3, Panel B). First, three of the ten technology pairs showed no crossing at all. In these cases, new technologies either started higher than the old technology and remained higher or started lower than the old technology and never crossed the old technology long after their introduction. Second, many technologies (three of the ten) showed multiple crossings. In such cases, the new technology passed an older technology but was not able to sustain its advantage. Third, the expectation of a single crossing, of new passing the old from below, was satisfied in only four of the ten technologies, thus rejecting H4.
In summary, we find no support for any of the three hypotheses on performance of competing technologies. Thus, the final status of each technology cannot be determined solely from the direction of the attack or timing of introduction. As such, it would be unwise for an incumbent to scan for competition only among technologies performing worse than its current technology and to make decisions on that basis.
Three hypotheses (i.e., H5, H6, and H7) suggest that the pace of technological transition increases over time (the null hypothesis is that the pace of change is constant over time). Tests of all three measures support an increasing pace of technological change (see Figure 4, Panels A - C). The negative slopes of trends for both the measures of duration suggest declining duration between introductions of successive new technologies and declining duration between successive improvements in each technology. The positive slope of trend for the rate of improvement over the past year suggests an increase in the pace of technological change.
To test these three hypotheses, we pooled the categories and estimated the following regression equation for each of the three measures.
( 2) Ymt = α | βm log (t) + εt m = 1 - 3…,
where Ymt represents each one of the preceding three measures of pace of technological change in year t, a and b are coefficients to be estimated, m is the measure of technological change, and e t are the errors assumed to be i.i.d. normal. The coefficients are significantly different from zero for all three measures (see Table 4). Thus, we reject the null hypothesis for H5, H6, and H7 that pace of technological evolution is constant over time. Note that our test is in the same spirit as meta-analyses, which pool estimates across multiple categories (Assmus, Farley, and Lehmann 1984; Tellis 1988). Such pooling increases the power of the test and reduces the probability of a Type II error.
The dominant view in the literature is that new technologies come primarily from small entrants. To shed more light on this issue, we operationalized incumbency and size. An incumbent is a firm that was in the category before the introduction of the new platform technology. All other firms are entrants. For technologies introduced before 1950, we determined the size of a firm on the basis of the total assets (million of dollars) from the COMPUSTAT database. Because of limitations of data, for technologies introduced before 1950, we determined a firm 's size on the basis of market share or the range of products across industries.
In contrast to the dominant view in the literature (H8), we find only 1 platform innovation introduced by small entrants. All the remaining 13 platform innovations came from large firms (7 incumbents and 6 new entrants). Although our results run counter to the dominant view in the literature, they are consistent with two recent findings in the literature (Chandy and Tellis 2000; Sorescu, Chandy, and Prabhu 2003). A possible reason is that in recent decades, innovation has become far more complex. The deeper pockets of large firms enable incumbents to maintain state-of-the-art facilities to conduct research, and incumbency provides them with opportunity and resources for developing and introducing platform innovations. This reason is further supported by the results: Of the 13 innovations introduced by incumbents, none was introduced by small incumbents.
Prior research suggests that certain secondary dimensions become important as technology evolves. Progress occurs systematically along the first dimension and then moves to the second, then to the third, and so on. These dimensions form the bases of intertechnological competition. They also form the bases by which consumers choose among rival technologies or products.
The literature also suggests that the basis for such competition is standard and occurs in the same form across markets. For example, Christensen (1999) notes four generic dimensions of intertechnological competition: functionality, reliability, convenience, and cost. Product functionality is the primary attribute on which consumers choose products in that category. Similarly, Moore (1991) suggests that products begin to compete on consistent performance, or higher reliability, after subsequent innovations increase functionality beyond a certain point. Christensen suggests that after product functionality and reliability requirements are satisfied, firms become more willing to customize product designs to meet customers 'specific requirements, such as convenience. Abernathy and Clark (1985) propose that the product becomes a commodity, and progress occurs through price reductions after the technology has progressed up the S curve sufficiently on functionality, reliability, and convenience. The occurrence of such generic dimensions can be important in guiding firms on the path of evolution and in the direction of the next competitive attack.
However, in contrast to expectations about the dimensions of technological competition, our results suggest a sequence of random, unpredictable secondary dimensions in each of the four categories (see Table 5). Each platform technology offered a completely new secondary dimension of competition while competing on the primary dimension. For example, consider four successive technologies in monitors: CRT, LCD, plasma, and OLED. The CRT monitor was initially introduced on the basis of resolution. Each subsequent technology was inferior in resolution at the time of introduction but introduced a new important secondary dimension: resolution, compactness, screen size, and efficacy.
Discussion
The current research leads to six major findings:
- Technologies do not show evidence of a single S-shaped curve of performance improvement. Rather, they evolve through an irregular step function with long periods of no growth in performance interspersed with performance jumps. A jump in performance appears to be largest after a long plateau of no improvement.
- New technologies may enter above or below the performance of existing technologies. The performance curves of a pair of competing technologies rarely have a single crossing.
- The path of technological evolution seems partially predictable. Previous improvement in performance of the same technology, improvement or crossing by a rival technology, and especially crossing by a rival firm tend to signal immediate improvement in performance.
- The rate of technological change and the number of new technologies increase over time.
- New technologies come as much from new entrants as from large incumbents.
- Each new technology introduces a sequence of random, seemingly unpredictable secondary dimensions as a new basis of competition.
To test the robustness of the results, we performed several analyses that covered reference technology, gestation time, censoring bias, survival bias, criterion variables, and multiattribute performance.
Reference technology Our results are based on comparing one technology with another technology introduced just before it. To address the question whether these results are in any way sensitive to the reference point of the comparison technology, we redid our analyses using the first technology and the dominant technology in the category. The results were not materially different from those that we report herein.
Gestation time We also examined the gestation time of each technology, which is defined as the time it takes for a firm to convert a patent to a commercial product. The average gestation time for technologies is 14.5 years for display monitors, 14.3 years for desktop printers, 9.7 years for desktop memory, and 22.7 years for data transfer technologies. The overall average for all categories is 15.1 years. Given this long gestation period, our results show that investors must be patient and managers must persevere to bring a new technology to fruition.
We examined whether the gestation period shrinks over time given the increasing pace of technological change. To examine this hypothesis, we did a median split of the gestation period by the year 1970. Each group had technologies from all four categories. Pre-1970 technologies have a significantly larger (t = 2.5) gestation time (25 years) than do post-1970 technologies (7.8 years). This result further supports H5, H6, and H7 regarding the pace of technological change.
Censoring bias To check whether our results were at the cost of a censoring bias from not allowing enough time for the new technology to improve, we compared the time taken for the technologies that failed to cross old technologies with those that did cross. The average number of years for new technologies to reach the point of first crossing the old technology is 6.3 years. In contrast, categories in which the new technology never crossed the old have been in existence for 14.6 years. Thus, the lack of a crossing cannot be due to a censored time frame.
Survival bias It is impossible to rule out survival bias, though we took great pains to minimize its role. First, we tried to include every technology that was commercialized in the markets that we covered for the time period that we studied. Second, to examine the possible effect of inadvertently excluding any technologies from the analyses, we defined a class of technological failures: non-starters. Nonstarters are those that were used in related fields and could have been used in the target markets with some modification but were either never used or failed to show sufficient improvements in performance, cost, and features soon after initial introduction (e.g., chain printers in desktop printers, wire recorders in desktop memory). In other words, these are potential technologies that were never mass commercialized. The key issue is whether the exclusion of failures biased our results, just as survival bias upwardly biases the alleged advantages of market pioneers (Golder and Tellis 1993).
We believe that nonstarters do not affect any of our results for three reasons: First, our definition of nonstarters is stringent; they are technologies that were never commercialized. Second, most of our analysis tracks the progress of individual technologies without averaging performance across technologies. As such, the exclusion of nonstarters does not bias computed performance levels. Third, our entire analysis tracks the performance of a technology given that it was commercialized. We do not make any predictions or test any hypotheses about the productivity of R&D, in which case nonstarters would loom large.
We find common factors in each of these cases. First, each of the nonstarter technologies failed to develop an acceptable standard or to be included in a prevailing standard. For example, wire recorders were excluded from the standard-setting process in favor of magnetic tape technology by the recording industry. Such exclusion in the standards-setting process, also termed "technological lockout" (Schilling 1998), leaves the technology in a weak market position (Shapiro and Varian 1999). Second, either a new and better technology was introduced early in the life of the nonstarter technology (e.g., magnetic tape and wire recorders) or the performance of the new technology was exceptionally superior to (or growing at a fast rate than) the nonstarter technology (e.g., dot matrix printers and chain printers). Perhaps as a result of these two factors, the nonstarter technology did not show any improvement in performance on all dimensions that we tracked.
The exclusion of these technologies does not lend support to any of the alternative hypotheses that we tested and rejected, such as a single S-shaped curve, single crossing, or generic dimensions of competition. However, because we excluded nonstarter technologies, it would be wrong to conclude from our results that performance always improves over time.
Alternative criterion variable Some authors propose that when testing the S curves, benefits per dollar should be used as the key dependent variable instead of performance (Chandy and Tellis 1998). Although all our current performance measures also have a denominator for proper scaling (e.g., megabits per second), we investigate the sensitivity of our results to using this alternative metric. We collected data on benefits per dollar for three categories: desktop printers, desktop memory, and display monitors. For each technology, we identified the product that offers the highest benefit per year and the price at which it was offered at its introduction. The results in Figure 5, Panels A - C, do not provide support for any of the hypotheses that we rejected. For example, we observed multiple crossings in all categories and new technologies being introduced with higher benefits per dollar. Moreover, we found that the evolution of technologies is not even a monotonic function of benefits per dollar. One possible reason is that firms charge higher prices for technologically advanced products until competition drives the price down.
Multiattribute performance To address the question whether our results hold when we take into account multiple dimensions of performance simultaneously, we repeated the analysis using multiple dimensions in two categories: desktop printers and display monitors. For printers, we collected data on the speed of printers, which we measured as pages per minute, and print resolution. For monitors, we collected data on screen size, which we measured in diagonal inches, and resolution. Note that our findings on shape, path, and crossing patterns are robust to the use of this second dimension (see Figure 6, Panels A and B). We also calculated standardized values of performance on each dimension over the category for each platform, computed the sum of these standardized values over all dimensions of performance, and then plotted the latter index by time (see Figure 6, Panels C and D). The use of multiple dimensions simultaneously using this crude index fails to yield any patterns that might support consistency with S curves.
Although the results of our study are not strictly normative, this study has several implications for managers. First, using the S curve to predict the performance of a technology is quite risky and may be misleading for two reasons: ( 1) Most of the technologies do not demonstrate an S-shaped performance curve, and ( 2) several technologies show multiple S curves, suggesting that a technology can show fresh growth after a period of slow or no improvement. The critical importance of this issue is the following: An analyst expecting an S-shaped curve would conclude that the first curve (on the subsample) meets the hypothesis. He or she would then wrongly conclude that the technology has matured at the upper asymptote when indeed it has not. As a result of this incorrect conclusion, the analyst would suggest abandoning the old technology. The average period for the subsample S curves is 16 years, compared with an average of 23 years for the full period. Thus, this error may result in premature abandonment of a promising technology as early as at least 7 years before its life to date.
Second, the continuous emergence of new technologies and the steady growth of most technologies suggest that relying on the status quo is deadly for any firm. Moreover, technological progress is occurring at an ever-increasing pace. As such, vigilance for the emergence of new technologies coupled with efforts to improve the old technology can help an incumbent sustain and advance its position or even preempt competitors.
Third, our findings indicate that the attack from below remains a viable threat. Many new technologies start by offering low performance but subsequently threaten old technologies by improving at a much quicker rate. Incumbents are prone to disregard these new technologies initially because they often cater only to a niche and not to the mass market. However, these niches can grow into mass markets and eventually replace the old technology. Furthermore, some new technologies can outperform old technologies even at the time of introduction.
Fourth, another threat to incumbents is the emergence of secondary dimensions of competition. Old technologies may be completely vulnerable to these dimensions. Faced with such threats, incumbents need research to identify technological solutions to improve the value of the old technology and to identify market segments that value the contributions of the old technology. Alternatively, incumbents need to explore R&D options on multiple dimensions to react appropriately to threats posed by entrants. Fifth, first-mover advantages may not be lasting because entrants introduce even more innovations than do incumbent firms.
This study has several limitations. First, we needed to limit our analysis to only four categories because of the time-consuming nature and difficulty of data collection. Second, our analysis of performance did not include cost to buyers. Third, we did not incorporate sales of products based on each technology within a category. Finally, despite our efforts to be as comprehensive as possible in data collection based on historical records, we must acknowledge the potential limitation of incomplete data availability in the first half of the twentieth century. All of these limitations are potential opportunities for further research. In addition, further research might also explore whether S curves are evident at the subplatform level, why there are long periods of no improvement in performance, and how firms should compete given the pattern of technological evolution.
The study benefited from comments of participants at the 2003 American Marketing Association Winter Educators' Conference, the 2004 Marketing Science Conference, the 2004 Hot Thoughts on Innovation Conference, the 2004 Utah Conference on Product Process Innovation, the 2004 Workshop on Marketing Strategy at Free University Amsterdam, the 2002 MSI Trustees Meeting, and seminars at Cambridge University's Judge School of Management and the Wharton School of Business. The study was supported by a grant from the Marketing Science Institute.
( n1) We also analyzed these and two other categories: analgesics and lighting (Sood and Tellis 2005). Because of reviewers ' concerns about the category definitions and the substitutability of technologies, we do not include these analyses in the article.
Legend for Chart:
A - Category
B - Primary Dimension
C - Metric
A B C
Desktop Storage Bytes per
memory capacity square inch
Display Screen Dots per
monitors resolution square inch
Desktop Print Pixels per
printers resolution square inch
Data Speed of data Megabits
transfer transmission per second
A: Fit of Logistic Model to Technologies with Single S Curves
Legend for Chart:
A - Technology
B - Parameter Estimates Upper Asymptote (t-Value)
C - Parameter Estimates Growth Rate (t-Value)
A B C
1. Magnetic memory 4.28 (8.5) .50 (24.8)
2. Optical memory 1.20 (7.4E +06) 30.95 (24.2)
3. Magneto-optical memory 3.51 (5.2) .51 (7.8)
4. Wireless data transfer 1.57(234) 6.29 (5.3)
B: Differences of Logistic Model Fit Between Subsample and Full
Data (for Technologies with Multiple S Curves)
Legend for Chart:
A - Technology
B - Improvement in Fit of Subsample over Full Data (Measured as a
Reduction in the Mean Square Error)(a) Full Data Number of
Years
C - Improvement in Fit of Subsample over Full Data (Measured as a
Reduction in the Mean Square Error)(a) Full Data Mean Square
Error
D - Improvement in Fit of Subsample over Full Data (Measured as a
Reduction in the Mean Square Error)(a) Subsample Number of
Years
E - Improvement in Fit of Subsample over Full Data (Measured as a
Reduction in the Mean Square Error)(a) Subsample Percentage
Reduction
A B C D E
1. Dot matrix printers 24 .04 14 95%
2. Inkjet printers 18 .05 13 97
3. Laser printers 17 .02 10 100
4. Thermal printers 11 .09 9 95
5. CRT monitors 31 .09 27 97
6. LCD monitors 19 .20 18 93
7. Plasma monitors 19 .14 7 100
8. Copper/Al cables 42 .57 21 95
9. Fiber optics 27 .06 25 99
Mean 23.1 -- 16 97
C: Differences of Logistic Parameters Between Subsample and Full
Data (for Technologies with Multiple S Curves)
Legend for Chart:
A - Technology
B - Difference in Parameter Estimates for Upper Asymptote
(a + d): Difference (t-Value)
C - Difference in Parameter Estimates for Growth Rate (b):
Difference (t-Value)
A B C
1. Dot matrix printers 2.2 (68) -4.9 (-42)
2. Inkjet printers 2.6 (79) 1.8 (-9)
3. Laser printers 1.6 (63) -20.8 (-80)
4. Thermal printers 2.5 (14) -7.2 (-14)
5. CRT monitors 97.3 (44) -.2 (-11)
6. LCD monitors 89.6 (21) -.4 (-11)
7. Plasma monitors 1.1 (10) -28.6 (-222)
8. Copper/Al cables 17.7 (425) .6 (14)
9. Fiber optics -1.6E + 12 (-3.0E + 08) -12.6 (-19)
(a) We excluded OLED displays from the analysis. A: Performance of New Technology Compared with Old Technology
Legend for Chart:
A - Technology Category
B - H2: Proportion of New Technologies with Low
Performance Compared with Old Technologies at Introduction
C - H3: Proportion of New Technologies with High
Performance Compared with Old Technologies at Maturity
A B C
1. Desktop memory 0/2 ½
2. Display monitors 3/3 1/3
3. Desktop printers 2/3 1/3
4. Data transfer ½ ½
Total 6/10 4/10
B: Number of Crossings Between New and Old Technologies
Legend for Chart:
A - Technology
B - Single Crossing
C - Multiple Crossing
D - No Crossing
A B C D
1. Desktop memory 1 1 (3)(a) 0
2. Display monitors 1 1 (3)(a) 1
3. Desktop printers 2 1 (3)(a) 0
4. Data transfer 0 0 2
Total 4/10 3/10 3/10
(a) Figures in parentheses indicate the total number of
crossings in the technology pair with multiple crossings.
Notes: Panel A presents the results of a binomial test to
determine the probability that technology performs in accordance
with H2 and H3, and Panel B presents the results of
a binomial test to determine the probability that all crossings
are single, and for both, p < .001. Legend for Chart:
A - Measure of Dependent Variable: Technological Change
B - Effect of Independent Variable: Time β
C - Effect of Independent Variable: Time t-Value
D - Effect of Independent Variable: Time R²
A B C D
1. Time interval between introductions -5.54 -2.1 .27
of successive technologies
2. Time interval between successive -2.64 -10.9 .81
improvements in performance
within a technology
3. Percentage increase in performance .03 2.9 .23
relative to the previous year Legend for Chart:
A - Market
B - Secondary Dimensions
A B
1. Desktop memory Areal density, reliability, and cost
2. Display monitors Resolution, compactness, screen size,
and efficacy
3. Desktop printers Resolution, graphics, speed, and
continuous color rendition
4. Data transfer Transfer speed, bandwidth, and
connectivity/mobilityGRAPH: FIGURE 1 Technological Evolution
GRAPH: FIGURE 2 Technological Evolution in Four Categories
GRAPH: FIGURE 3 Fit of Logistic Model over Multiple and Single S Curves
GRAPH: FIGURE 4 Pace of Technological Transition
GRAPH: FIGURE 5 Performance per Unit Price
GRAPH: FIGURE 6 Multiattribute Performance
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Legend for Chart:
A - Technology
B - Principle
A B
Desktop Memory
Magnetic Records data by passing a
frequency-modulated current through
the disk drive's magnetic head, thus
generating a magnetic field that
magnetizes the particles of the disk's
recording surface.
Optical Stores data using the laser
modulation system, and changes in
reflectivity are used to store and
retrieve data.
Magneto-optical Records data using the magnetic-field
modulation system, but it reads
the data with a laser beam.
Display Monitors
CRT Forms an image when electrons,
fired from the electron gun, converge
to strike a screen coated with
phosphors of different colors.
LCD Creates an image by passing light
through molecular structures of liquid
crystals.
Plasma Generates images by passing a high
voltage through a low-pressure,
electrically neutral, highly ionized
atmosphere using the polarizing
properties of light.
OLED Generates light by combining
positive and negative excitons (holes
emitted by anodes and electrons
emitted by cathodes) in a polymer
dye through the principle of
electroluminescence.
Desktop Printers
Dot matrix Creates an image by striking pins
against an ink ribbon to print closely
spaced dots that form the desired
image.
Inkjet Forms images by spraying ionized
ink at a sheet of paper through
micronozzles.
Laser Forms an image on a photosensitive
surface using electrostatic charges.
Then, it transfers the image onto a
paper using toners and heats the
paper to make the image permanent.
Thermal Forms images on paper by heating
ink through sublimation or phase
change processes.
Data Transfer
Copper/aluminum Transmits data in the form of
electrical energy as analog or digital
signals.
Fiber optics Transmit data in the form of light
pulses through a thin strand of glass
using the principles of total internal
reflection.
Wireless Encodes data in the form of a sine
wave and transmits it with radio
waves using a transmitter-receiver
combination.~~~~~~~~
By Ashish Sood and Gerard J. Tellis
Ashish Sood is an assistant professor, Goizueta School of Business, Emory University
Gerard J. Tellis is Neely Chair of American Enterprise, Director of the Center for Global Innovation, and Professor of Marketing, Marshall School of Business, University of Southern California
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Record: 150- Technological Opportunism and Radical Technology Adoption: An Application to E-Business. By: Srinivasan, Raji; Lilien, Gary L.; Rangaswamy, Arvind. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p47-60. 14p. 6 Charts. DOI: 10.1509/jmkg.66.3.47.18508.
- Database:
- Business Source Complete
Technological Opportunism and Radical Technology Adoption: An Application to E-Business
Using the resource-based view of the firm, the authors hypothesize that differences in adoption of radical technologies among firms can be attributed to a sense-and-respond capability of firms with respect to new technologies, which is termed technological opportunism. Using survey data from senior managers in business-to-business firms, the authors study the adoption of e-business, a radical technology with the potential to alter business models. The authors first establish the distinctiveness of technological opportunism from related constructs, such as organizational innovativeness, and show that it offers a significantly better explanation of technology adoption than existing constructs do. In a follow-up survey of senior managers, the authors investigate the antecedents of technological opportunism and find that organizations can develop technological opportunism by taking specific actions such as focusing on the future, by having top management advocate new technologies, and by becoming more of an adhocracy culture and less of a hierarchy culture. The proposed technological opportunism construct can inform theory development on the relative emphasis on internal (research and development) versus external (buying, licensing) development of technologies and the complementarities in technology orientation and market orientation in the firm. The results can be used by managers who seek to develop the technological opportunism capability of their firms and by those in technology vendor firms who seek to develop segmentation strategies based on the technological opportunism capabilities of their customer firms.
Business history offers many examples of industries (e.g., lighting, photography, steel, and telecommunications) in which radical technologies emerged and eventually overwhelmed established technologies (Utter-back 1994).[ 1] In each industry, some firms did not adopt a radical technology and failed to survive in the marketplace,[ 2] whereas other firms leaped from one generation of technology to the next and adapted their business models on the basis of such technologies. In this article, we focus on why some firms readily adopt radical technologies whereas other firms are either unwilling or unable to do so. This issue is important for both adopter firms and firms that sell radical technologies. For adopter firms, the decision to adopt radical technologies is difficult because of the associated uncertainties, the possibility that prior investments may be rendered obsolete, and high switching costs in adopting new technologies. Yet if a new technology is promising, it will create attractive market opportunities. For seller firms, understanding how their potential customers adopt technologies can help in formulating marketing strategies. Studying radical technology adoption is also important to researchers in marketing. The marketing literature on organizational adoption of radical technologies is sparse (Chandrashekaran and Sinha 1995; Gatignon and Robertson 1989; Robertson and Gatignon 1986). Furthermore, the adoption of radical technologies by firms is linked to their marketing strategies in the area of product design, distribution, and pricing (Capon and Glazer 1987). Although research on technology-intensive markets (Heide and Weiss 1995; Weiss and Heide 1993) provides useful insights on firms' strategic behaviors when they buy radical technologies, the area remains underresearched. For example, consider e-business, a radical technology that has been transforming business models and processes, resulting in the disruption of old industries and the creation of new ones. A casual review of the business press suggests that some organizations proactively adopt e-business to transform their business models and others adopt e-business merely for supporting functions, such as communications. Research in organization theory, information systems, economics, and technology management has contributed to the understanding of organizational adoption of innovations. However, most of the research on organizational adoption has involved innovations that have limited impact on the firm's business strategy. Radical innovations that have been studied in the past include production technologies in shoe manufacturing (Dewar and Dutton 1986), software engineering innovation (Fichman and Kemerer 1997), packaging innovations in the food processing industry (Ettlie, Bridges, and O'Keefe 1984), and adoption of laptops by sales executives (Gatignon and Robertson 1989). Although these innovations incorporate new technologies, their impact at the organizational level is limited. Indeed, some of these adoption decisions (e.g., production processes, sales force automation) likely took place at the functional level (e.g., manufacturing, sales and marketing, respectively).
We extend theoretical developments in the resourcebased view of the firm (Wernerfelt 1984) to investigate why some firms proactively adopt radical technologies whereas others do not. We identify technological opportunism, a sense-and-respond capability of firms with respect to new technologies, as an important determinant of radical technology adoption. To assess the incremental contribution of technological opportunism in explaining technology adoption, we also include in our model variables identified in prior research --############ pressures on the firm from the environment (DiMaggio and Powell 1991), complementary assets that help generate value from new technologies (Tripsas 1998), and the perceived usefulness of the technology (Venkatesh and Davis 2000). We develop and test our technology adoption model in the context of e-business adoption in business-to-business firms, a context in which e-business has the potential to radically alter the relative profitability of firms' business models.
We report the results of two studies. In Study 1, we use field interviews and national surveys of senior executives in 183 firms to develop the new construct of technological opportunism. We develop reliable measures of technological opportunism and establish its distinctiveness from the related constructs of technological orientation (Gatignon and Xuereb 1997), organizational innovativeness (Deshpandé, Farley, and Webster 1993), and market orientation (Kohli, Jaworski, and Kumar 1993). We find that technological opportunism explains significantly more variance in radical technology adoption than do constructs currently proposed in the literature. The results of Study 1 raise the question of why some firms are technologically more opportunistic than others. In Study 2, we build a model of the antecedents of technological opportunism to address the following research issues: ( 1) What are the organizational factors that influence technological opportunism? and ( 2) To what extent does a firm's environment influence its technological opportunism? We test our Study 2 model using data from a national survey of senior managers in 200 firms. Our results suggest that though the firm's technology environment influences technological opportunism, firms can become more technologically opportunistic by ( 1) having a future focus, ( 2) having a top management that advocates the use of new technologies, and ( 3) developing an adhoc-racy culture within the firm.
The article is organized as follows: In the next section, we introduce our concept of technological opportunism and describe its distinctiveness from existing constructs. We then present our conceptual framework, hypotheses, and model for technology adoption. In subsequent sections, we describe the method we use to test our model and present the results of our analyses. Following that, we present our conceptual framework, hypotheses, and model for the antecedents of technological opportunism and the results of a test of that model. We conclude by discussing the theoretical and empirical contributions of our research, summarizing the limitations of the study and identifying future research extensions.
Importance of Sensing and Responding to New Technologies
A firm's ability to sense and respond to new technology developments is critical for several reasons. First, technological change is a principal driver of competition--destroying monopolies, creating new industries, and rendering products and markets obsolete. Second, in-house technology development, a traditional source of technical know-how for firms, is increasingly being complemented by additional sources both within and outside an industry (Pisano 1990). Third, it is difficult for firms to predict which of several technology options under development will eventually succeed commercially, and therefore it may be expedient for firms to hedge their positions with alternative new technologies (Schilling 1998).
Defining Technological Opportunism
Strategy theorists emphasize how firms build competitive advantage by developing resources and capabilities (Wernerfelt 1984). Resources include difficult-to-imitate, firm-specific know-how (e.g., patents, licenses) or assets (e.g., plant and equipment, human capital). Capabilities include skills exercised through organizational processes (e.g., market sensing) that enable firms to use their assets (Day 1994; Teece, Pisano, and Shuen 1997).
Much previous research has focused on a negative form of opportunism (e.g., opportunistic behavior by trading partners) in interorganizational relationships (John 1984; Williamson 1975). However, some researchers have explored a benign form of opportunism (Hutt, Reingen, and Ronchetto 1988; Isenberg 1987), wherein managers are proactive in responding to new opportunities in a way that does not violate principles of fairness. Our notion of technological opportunism is of the latter kind. We identify two components of technological opportunism: technology-sensing capability and technology-response capability.
Technology-sensing capability is an organization's ability to acquire knowledge about and understand new technology developments, which may be developed either internally or externally. An organization that has strong technology-sensing capability will regularly scan for information about new technological opportunities and threats (Daft and Weick 1984). It will identify, sense, and evaluate internally produced innovations and scan for external innovations through meetings with vendors, debriefings from salespeople, and discussions with competitors.
Technology-response capability is an organization's wilingness and ability to respond to the new technologies it senses in its environment that may affect the organization. An organization that senses new technologies may not be willing or able to respond to these new technologies, because such technologies can cannibalize existing products, markets, and organizational relationships and result in switching costs (Chandy and Tellis 1998). Therefore, technology-response capability also includes the firm's ability to reengineer its business strategies to exploit the opportunities or stave off the threats posed by new technologies. A firm may respond to a radical technology in several ways, including ignoring the technology, monitoring it, forming alliances to exploit the technology, doing limited experimentation, and adopting the technology within the firm.
Our conceptualization of technological opportunism as a generalized firm-level capability with respect to new technologies is analogous to the treatment of consumer innovativeness as a generalized underlying disposition, distinct from the adoption of a specific innovation in consumer behavior research (Midgley and Dowling 1978; Steenkamp, Hofstede, and Wedel 1999). Just as consumer innovativeness can affect behavior in a variety of contexts (e.g., innovation adoption, creativity, variety-seeking behavior), the technologically opportunistic firm canrespond in several ways to new technologies.
Technological Opportunism and Related Constructs
Technological opportunism as a firm-level capability is consistent with multiple research perspectives on organizational traits (Daft and Weick 1984; Miles and Snow 1978; Teece, Pisano, and Shuen 1997). Similar to the prospector firm in Miles and Snow's (1978) typology, a technologically opportunistic firm senses and responds proactively to capitalize on (or counter) these technology opportunities (or threats). Similarly, technologically opportunistic firms are in an enactment mode (Daft and Weick 1984) with respect to new technologies, exploring several new technologies that could be potential threats or opportunities for them. Technological opportunism is also consistent with the growing stream of research in marketing on firm-level traits including marketing capabilities (Day 1994), culture (Deshpandé, Farley, and Webster 1993; Moorman 1995), market orientation (Jaworski and Kohli 1993), and willingness to cannibalize (Chandy and Tellis 1998).
Technological opportunism differs from other, related concepts important to innovation management, including organizational innovativeness (Deshpandé, Farley, and Webster 1993) and technological orientation (Gatignon and Xuereb 1997). Organizational innovativeness is the degree to which a firm deviates from existing practices in creating new products and/or processes (Deshpandé, Farley, and Webster 1993). We also note an alternative conceptualization of innovativeness (e.g., Rogers 1995) as a dependent variable of innovation adoption. Here, we consider organizational innovativeness more broadly as a capability and not in its more restricted definition as innovation adoption. Gatignon and Xuereb (1997, p. 78) define technological orientation as "the ability and the will to acquire a substantial technological background and use it in the development of new products."
Technological opportunism is distinct from both organizational innovativeness and technological orientation in an important way. As conceptualized and measured, both organizational innovativeness and technological orientation refer to the capability of the organization to develop new technologies, products, and processes. In contrast, technological opportunism is the capability of the organization to sense and respond to new technologies, regardless of whether those technologies are developed externally or internally or are used in developing new products. If an organization has the foresight and the will to invest in an in-house research facility to develop a radically new production process, the organization is both innovative and technologically oriented but not necessarily technologically opportunistic. For example, Xerox Corporation's Palo Alto Research Center produced various revolutionary technologies in the 1970s, including the laser printer, the mouse, and graphical user interface. Xerox was both innovative and technologically oriented, but it was not technologically opportunistic because it did not sense and respond to its own technologies. Indeed, other (technologically opportunistic) companies including Hewlett-Packard (laser printer) and Apple (graphical user interface) commercialized these new technologies. In contrast, when IBM approached Microsoft in 1980 for an operating system for IBM's forthcoming personal computer, Microsoft was aware of another software QDOS (Quick and Dirty Operating System) developed by Seattle Computer Products that might work for this purpose, bought the rights to it, and developed MS-DOS based on QDOS. Microsoft was technologically opportunistic, innovative, and technologically oriented. Thus, technological opportunism pertains to a sense-and-respond capability of the organization with respect to new technologies (whether developed internally or externally), whereas organizational innovativeness and technological or ientation pertain to the creation of new technologies, products, and processes within the organization.
As a sense-and-respond capability, technological opportunism is conceptually similar to market orientation. Jaworski and Kohli (1993) define market orientation as organization-wide gathering of market intelligence pertaining to customer needs, dissemination of intelligence among departments, and organization-wide responsiveness to it. Market orientation researchers have focused on the sense-and-response capabilities with respect to a firm's market environment of customers and competitors. Technological opportunism differs from market orientation in two ways: First, new technologies can arise from many other sources outside the market environment (e.g., suppliers, universities, other industries). Thus, the substantive sensing domain of technological opportunism is distinct from that of market orientation. Indeed, research suggests that some market-oriented firms are unable to adopt new technologies because their current customers do not find them useful (Christensen 1997). Second, market responsiveness is a strategic imperative with tangible rewards, whereas responsiveness to new technologies is risky because it may not be clear a priori whether the new technology will benefit the organization. Thus, market responsiveness need not imply technology responsiveness. We provide empirical support for the distinctiveness of these constructs subsequently. We next develop a model that relates technological opportunism to radical technology adoption.
Several strategy researchers (Bourgeois 1984; Child 1972) have proposed that organizations proactively manipulate their environments to achieve their objectives. Consistent with this line of reasoning, we suggest that firms that are technologically opportunistic proactively seek and adopt new technologies. We incorporate this basic idea in a model to explain radical technology adoptionthat includes technological opportunism, institutional pressures on the firm to adopt the technology, and complementary assets that help the firm generate value from new technologies. In addition, consistent with prior research, we include perceived usefulness of the technology as an explanatory variable (Venkatesh and Davis 2000). Previous researchers have used multiple definitions of technology adoption, including time to adopt, the dichotomous measure of adopt/not adopt, and the extent of technology adoption. When the technology is amorphous and variations in the form of adoption are high, as is the case in e-business, it is appropriate to assess the extent of technology adoption. Therefore, we measure the extent to which firms adopt the radical technology (in this case, e-business).
Technological Opportunism
Firms that are aware of changes in their environment are likely to create pressures for change. We expect technologically opportunistic firms to be aware of technology developments and be more likely to invest resources in adopting new technologies. Strategy research suggests that when an organization's decision makers percei ve a strategic issue as an opportunity (compared with when they perceive it as a threat), they consider that situation to be positive and perceive greater control over the outcomes (Dutton and Duncan 1987). In such cases, managers are likely to take proactive measures. Technologically opportunistic firms will perceive technology developments as potential sources of growth for the firm and will respond proactively to adopt radical technologies. Therefore,
H1: The greater a firm's technological opportunism, the greater is its extent of technology adoption.
Institutional Pressures
Organizational sociologists have long argued that firms adopt technologies because of institutional pressures from constituencies in their environments. We consider two components of institutional pressures: stakeholder pressures and competitive pressures. Stakeholder pressures are forces on the firm from its customers, trading partners, investors, bankers, suppliers, general public, media, and employees to adopt a technology. Some resource-dependency theorists (Pfeffer and Salancik 1978) have argued that managers lack the power to do anything beyond allocating resources to developments and actions that customers require. Neoinstitutional theorists have proposed that an organization will conform to the social expectations of its stakeholders, because such conformity gives it access to the scarce resources it needs to survive and succeed (DiMaggio and Powell 1991). Competitive pressures force a firm to adopt a technology or risk losing competitive advantage (Abrahamson and Rosenkopf 1993). When these arguments are applied to e-business technologies, an organization's early and extensive adoption signals its technological astuteness and gives it social legitimacy with its stakeholders. In addition, fear of being left behind competitors also results in technology adoption. Therefore,
H2: The greater the institutional pressures on a firm to adopt the technology, the greater is its extent of technology adoption.
Complementary Assets
Complementary assets help the firm derive value from new technologies. Prior research indicates that complementary assets positively affect the technology adoption process (Rogers 1995; Tripsas 1998). Personal computers initially diffused more rapidly among consumers and firms that had prior experience with mainframes or minicomputers than among those that did not. Tripsas (1998) finds that specialized complementary assets buffer incumbents from the effects of destruction by invading radical technologies. The costs of learning new technologies will be affected by the extent to which the new technology is related to the preexisting knowledge base or its absorptive capacity (Cohen and Levinthal 1990). Therefore,
H3: The greater a firm's ownership of assets complementary to a radical technology, the greater is its extent of technology adoption.
Field Interviews, Sample, and Procedure
We conducted field interviews with 18 senior managers in 15 organizations to obtain initial insights into the technology adoption process. We then acquired a mailing list of business-to-business firms from Corptech for the formal surveys. To improve the generalizability of our findings, we surveyed a cross-industry sample of executives in firms covering six industry groups: computer hardware, computer software, chemicals, heavy manufacturing, light manufacturing, and telecommunications. Of the 630 surveys we mailed out, 22 surveys were returned because of incorrect addresses, and 10 managers returned the surveys because they were not qualified to respond. We received 183 completed surveys (of 598 surveys remaining), which yielded an effective response rate of 30.6%. Table 1 contains the descriptive statistics of the sample and indicates that our sample represents a broad range of firms in terms of size and industries. We used a senior marketing executive as the key informant because our field interviews indicated that marketing executives were frequently responsible for e-business adoption decisions in business-to-business firms. Most (70%) of the managers in our sample were at the level of director and above, which suggests that they were knowledgeable about their firms' capabilities and actions.
Measure Development
We measured all constructs at the level of the strategic business unit (SBU). Because scales for the key constructs in our research were not available, we developed them when necessary, using an appropriate refinement procedure (Churchill 1979). Table 2 contains the measures used in the study.
E-business adoption ranges from simply using e-mail to communicate within the organization to developing entirely new business models. To account for this range of adoption behavior, we defined technology adoption as the breadth and depth of e-business usage in a firm's business processes and measured it using a four-item interval scale (TECADOPT). Because of the centrality of technology adoption to our research, we also measured the scope of a firm's e-business adoption using specific applications (the eADOPT scale in Table 2). The two dependent variable measures, TECADOPT and eADOPT, correlate well (r = .68, p < .01). We use TECADOPT as the dependent variable for our analysis.
We measured technological opportunism (TECHOPP) using an eight-item scale. Consistent with the idea that capability includes organizational processes (Day 1994), we measured technological opportunism on the basis of behaviors related to organization-wide sense-and-respond capabilities with respect to new technologies. Four of the eight items pertain to technology-sensing capability and four to technology-response capability. For institutional pressures (INPRES), we developed an eight-item scale specific to e-business. We provided a detailed definition of the term "stakeholders" for our respondents. Six items pertain to stakeholder pressures and two items to competitive pressures. Our field interviews indicated that for e-business adoption, the firm's information technology (IT) capability was the most important complementary asset. Therefore, for complementary assets (CASSETS), we used a three-item scale to measure the existing IT of the firm. For perceived usefulness, we used a five-item scale. For organizational innovativeness, we modified Deshpandé, Farley, and Webster's (1993) scale to include timeliness in the development of new products, processes, and markets and modified the response mode to a Likert scale to be consistent with the measurement of the other constructs.
Validity of Measures
We constructed equally weighted additive measures for all constructs and took several precautions to ensure their validity. We asked informants to report their confidence levels about the information provided (Kumar, Stern, and Anderson 1993). The final sample showed mean scores (on a scale of 1 to 7) of 5.99 (standard deviation [S.D.] = .95) and 5.27 (S.D. = 1.14) for confidence levels about the accuracy of information on firms' characteristics and e-business adoption, respectively. We obtained information on an appropriate second informant (the information technology manager) from our mailing list for 130 of the 183 firms in our sample. We received 28 responses, too small a sample for a formal multitrait, multimethod assessment. However, t-tests of the difference in means of the key variables between the two informants' reports indicated that these means were not statistically different (K = key informant and S = second informant): technological opportunism (K = 35.62, S = 35.29, not significant [n.s.]), institutional pressures (K = 36.13, S = 35.68, n.s.), perceived usefulness (K = 22.40, S = 21.86, n.s.), complementary assets (K = 12.62, S = 13.86, n.s.), and technology adoption (K = 13.76, S = 12.86, n.s.). To assess the threat from nonresponse bias, we performed a test using the extrapolation procedure suggested by Armstrong and Overton (1977) and found no significant difference between early and late respondents on the key variables. We estimated the reliability of each scale by computing its Cronbach's alpha. The reliabilities range from .77 to .91, which exceed the .70 recommended for exploratory research (Nunnally 1978). Table 3 provides descriptive statistics, the pairwise correlations, and the reliabilities of the multi-item scales.
Distinctiveness of Technological Opportunism
In our first round of data collection from 183 firms, we collected measures only of the most closely related construct of organizational innovativeness. To establish the discriminant validity of technological opportunism from other related constructs, we subsequently mailed a survey containing organizational innovativeness, technological orientation, and market orientation measures to 400 firms. That sample included 190 firms from the sample described previously and 210 firms from a second sample of firms used in the second study. We received completed surveys from 130 of these 400 firms, which we use to establish the discriminant validity of technological opportunism from the related constructs.
Cronbach's alpha (a) for the technological opportunism scale is good (a = .89). The descriptive statistics indicate that the firms in our sample rated themselves as rather technologically opportunistic, with a mean score of 34.93 and a standard deviation of 9.49 (n = 130). The scale exhibited good variance and ranged from 12 to 54 (of a possible range of 8 to 56). We performed a confirmatory factor analysis to check for the distinctiveness of technological opportunism from organizational innovativeness, technological orientation, and market orientation. All factor loadings are large and significant (p < .01), indicating that the items display good measurement properties. Our model yields nonnormed fit index (NNFI) = .84, comparative fit index (CFI) = .85, root mean square error of approximation (RMSEA) = .07, and standardized root mean square residual (SRMR) = .08. Although the NNFI and the CFI indices are below the desirable level of .90, RMSEA and SRMR are less than .10, indicating a reasonable fit of data to the model. The complexity of the model (20 items in the market orientation scale load onto one factor) may be lowering the fit indices. Our indices compare well with those obtained by Kohli, Jaworski, and Kumar (1993).
Next, we examined the convergent and discriminant validity of technological opportunism (Fornell and Larcker 1981). The composite reliability (CR) and average variance extracted (AVE) are as follows: technological opportunism: CR = .91, AVE = .56; organizational innovativeness: CR = .93, AVE = .76; technological orientation: CR = .93, AVE = .59; market orientation: CR = .92, AVE = .56. Overall, the results indicate that the four constructs demonstrate satisfactory levels of internal consistency and convergent validity. Regarding discriminant validity, the 95% confidence intervals of the correlation between the constructs are well below 1.00 (p < .05). The AVEs of technological opportunism (.56), organizational innovativeness (.76), technological orientation (.59), and market orientation (.56) exceed the squared correlations between them. Therefore, technological opportunism is empirically distinct from innovativeness, technological orientation, and market orientation.
Model Fit and Hypothesis Tests
We tested our hypotheses using regression analysis. In addition to variables corresponding to hypotheses H1-H3, we included the perceived usefulness of the technology to the firm (Venkatesh and Davis 2000), firm size (measured by the number of employees), and industry variables as controls in the adoption model. Before testing our hypotheses, we established the discriminant validity of the constructs in our technology adoption model by examining the distinctiveness of technological opportunism, perceived usefulness, complementary assets, and technology adoption. All factor loadings are positive and significant. The model fit is as follows: NNFI = .89, CFI = .91, RMSEA = .08, and SRMR = .06. The CR and AVE are as follows: technological opportunism: CR = .89, AVE = .51; perceived usefulness: CR = .85, AVE = .53; IT: CR = .86, AVE = .67; technology adoption: CR = .77, AVE = .47. Therefore, except for technology adoption, for which AVE (.47) is less than the recommended .50, the other conditions for convergent and discriminant validity are satisfied.
Table 4 summarizes the standardized estimates of the technology adoption model. The model for technology adoption has a good fit, with R2 = .37 (F ( 13, 169) = 7.55, p < .01). Size and industry dummy variables have no main or moderating effects on the relationship between technological opportunism and technology adoption, which indicates that our results may be generalized to firms of different sizes and in different industries. In support of H1, H2, and H3, respectively, technological opportunism (b = .24, p < . 01), institutional pressures (b = .29, p < .01), and complementary assets (b = .10, p < .10) are positively related to technology adoption. Consistent with prior research, we also find that perceived usefulness positively influences technology adoption (b = .19, p < .05). Model Comparisons and Implications
We next assessed how well technological opportunism, compared with existing constructs, explains radical technology adoption. From the data we used to examine the discriminant validity of technological opportunism (n = 130), we find that technological opportunism is correlated with organizational innovativeness (.52, p < .01) but not with technological orientation (-.10, n.s.) and market orientation (.05, n.s.). Therefore, we examine the explanatory power of technological opportunism, after accounting for organizational innovativeness's role in radical technology adoption. We compared the model of technology adoption, which included technological opportunism, organizational innovativeness, and institutional pressures, with a model that excluded technological opportunism. When both technological opportunism and organizational innovativeness are included, we find that technological opportunism has a significant, positive effect on adoption (b = .31, p < .01), but organizational innovativeness has no effect on adoption (b = -.01, n.s.). In addition, we find that the R2 for a model that includes both technological opportunism and organizational innovativeness is .37 (F( 14,168) = 6.97, p < .001), whereas the R2 for a model that includes organizational innovativeness (b = .12, p < .10) but excludes technological opportunism is .34 (F( 13, 169) = 6.73, p < .001). The F-test of the difference in R2 between the two models is significant (F( 1, 169) = 8.00, p < .01), indicating that technological opportunism provides a significant incremental explanation of technology adoption over a model that includes institutional pressures and organizational innovativeness.
Our results suggest that the extent of radical technology adoption is influenced by a firm's technological opportunism. The next question is why some firms are technologically opportunistic and others are not. In Study 2, we explore the following two questions: ( 1) What are the organizational drivers of technological opportunism? and ( 2) To what extent does a firm's environment influence its technological opportunism?
Because technological opportunism is a new construct, we used a discovery-oriented approach (Deshpandé 1983) to identify the factors influencing technological opportunism. Our field interviews with 15 managers in six industries indicated that ( 1) firms in technologically turbulent environments (e.g., telecommunications) were more technologically opportunistic than firms in less turbulent environments (e.g., chemicals) and ( 2) firms within the same industry differed substantially in their technological opportunism. These findings suggest that the technological opportunism capability is influenced by both organizational and environ-mental factors. On the basis of these field interviews, we develop a conceptual model with three organizational factors that influence technological opportunism: ( 1) the firm's future focus, ( 2) top management's advocacy of new technologies, and ( 3) organizational culture; we also include one environmental factor: technological turbulence.
Future Focus
Firms differ in the extent to which they focus on developing capabilities for their future relative to their past and current capabilities. Hamel and Prahalad (1994) stress the importance of "unlearning the past" and "learning to forget" in developing strategies for competing in today's business environments. Dominant firms in the disk drive, copier, tire, minicomputer, and mainframe computer markets stayed too close to existing customers (thereby lacking future focus) and consequently lost their market positions to new, emerging technologies (Christensen 1997). Similarly, Chandy and Tellis (1998) find that radically innovative firms pay closer attention to future markets than to current markets. Our field interviews suggested that technologically opportunistic firms focus more on developing capabilities for managing their future than the present. We term this orientation future focus and define it as the extent to which a firm emphasizes its future opportunities and capabilities relative to its current capabilities. Future-focused firms review their current technology options and actively monitor new technologies to assess how these technologies may advance or hinder the achievement of their objectives. In addition, because of their focus on the firm's future rather than on the past or the present, these firms are also willing to cannibalize existing investments in responding to new technologies. Therefore,
H4: The greater the firm's future focus, the greater is its technological opportunism.
Top Management's Advocacy of New Technologies
The critical role of top management in championing the development of firm-level capabilities is reflected in diverse branches of literature. Top management advocacy is important in mobilizing the resources for internal corporate venturing (Burgelman 1983) and new product development (Howell and Higgins 1990). Top management emphasis on market orientation plays an important role in fostering market orientation throughout the organization (Jaworski and Kohli 1993). Consistent with the literature, our field inter-views indicated that senior management support was an important factor in fostering technological opportunism. We define top management's advocacy of new technologies as the efforts of the top management team to emphasize the importance of organizational responsiveness to new technologies. Top management's role is important because new technologies may entail destruction of existing assets for which management's approval will be required. If top managers advocate new technologies, middle and junior managers will devote the resources necessary for sensing and responding to new technologies. Therefore,
H5: The greater a firm's top management's advocacy of new technologies, the greater is its technological opportunism.
Organizational Culture
Organizational culture is the pattern of shared values and beliefs that help individuals understand organizational functioning and provide norms for behavior in the organization (Deshpandé, Farley, and Webster 1993; Moorman 1995). Consistent with this perspective, our field interviews indicated that technologically opportunistic firms differed systematically in organizational culture from firms that were less technologically opportunistic. We use the typology of organizational culture based on the competing values model (Deshpandé, Farley, and Webster 1993)--which proposes four types of organizational cultures: adhocracy, market, hierarchy, and clan to develop hypotheses of the effects of organizational culture on technological opportunism (Moorman 1995).
Adhocracy culture values flexibility and emphasizes entrepreneurship, creativity, and adaptability. Moorman (1995) notes that entrepreneurial cultures, such as adhocracy, thrive on information acquisition and that such firms are likely to be informed about new technology developments. Furthermore, because adhocracy cultures foster risk taking, managers in these firms are willing to experiment with new technologies. Therefore, we hypothesize that adhocracy culture will be positively related to technological opportunism.
Market culture emphasizes customer focus, goal achievement, productivity, and efficiency. Firms with market culture are focused on acquiring market information to improve their performance (Moorman 1995). Given the focus of market culture on efficiency, we expect a reduced focus on exploring new technology opportunities. Furthermore, the emphasis on efficiency in market culture may result in an aversion to experimenting with new technologies. Therefore, we hypothesize that market culture will be negatively related to technological opportunism.
Hierarchy culture emphasizes order, efficiency, stability, and control, reflecting internally oriented and formalized values. Hierarchy cultures do not support transmission of market information (Jaworski and Kohli 1993). Therefore, firms with hierarchy culture may not generate and share information about new technologies. Furthermore, the rigidity of the hierarchy culture may hinder responsiveness to emerging technologies. We hypothesize that hierarchy culture will be negatively related to technological opportunism.
Clan culture stresses participation, teamwork, and cohesiveness. The emphasis is on the development of shared organizational understanding through participative processes. Clan culture is positively related to market information transmission (Moorman 1995). Thus, firms with clan culture are likely to share information about emerging technologies. However, the consensual nature of clan culture may inhibit rapid adaptation. In summary, we hypothesize a positive relationship between clan culture and technological opportunism. Therefore,
H6: The greater the adhocracy culture of a firm, the higher is its technological opportunism.
H7: The greater the market culture of a firm, the lower is its technological opportunism.
H8: The greater the hierarchy culture of a firm, the lower is its technological opportunism.
H9: The greater the clan culture of a firm, the higher is its technological opportunism.
Technological Turbulence
Organizational learning depends on the setting in which the organization operates. In technologically turbulent environments, the value and impact of prior stored learning deteriorates with environmental change. As Weiss and Heide (1993, p. 221) note, "a rapid pace of technological change creates uncertainty that can be competency destroying." Rapidly changing technological environments will require constant surveillance of markets and technologies and create a need to experiment with new technologies. Firms in such environments will, over time, gain experience in sensing and responding to emerging technologies. Therefore,
H10: The greater the technological turbulence in the firm's environment, the higher is its technological opportunism.
Procedure
Using the mailing list described previously, we collected data from a survey of senior managers using a cross-industry sample of firms covering the same six industry groups as in the first study. We mailed 798 surveys and received 200 completed surveys, which yielded an effective response rate of 25.1%.
Instrument Development and Refinement
We used previously published scales to measure our constructs when possible, and when scales were not available, we developed new ones (Table 2). For technological opportunism, we used the same eight-item scale that we developed for the first study. For future focus, we used a three-item scale adapted from Chandy and Tellis (1998). For top management's advocacy of new technologies, we used a new four-item scale. We measured the four types of organizational culture using the scales developed by Moorman (1995). For technological turbulence, we used the five-item scale for pace of technological change developed by Jaworski and Kohli (1993). We measured all constructs at the level of the SBU.
Validity of Measures
We use key informants as our data source. The person responsible for technology management may have been the ideal key informant, but only 5% of the firms in our sample frame had senior-level titles that indicated responsibility for technology management. Therefore, we used senior marketing executives as key informants. Our field interviews indicated that senior marketing executives in the selected industries are closely involved in developing the technology strategies of their firms. We ensured the validity of our key informant reports by including self-reports on the informants' knowledge of the technology area(Kumar, Stern, and Anderson 1993). The respondents in our sample (n = 200) show mean scores (on a scale of 1 to 7) of 5.62, (S.D. = 1.05), 5.80 (S.D. = 1.03), and 5.87 (S.D. = .98) for confidence levels about the accuracy of information provided about the firm's technology strategies, characteristics, and the environment, respectively. To assess the threat from nonresponse bias, we performed a test using the extrapolation procedure suggested by Armstrong and Overton (1977) and found no significant difference between early and late respondents on the key variables. Table 5 provides descriptive statistics, pairwise correlations, and reliabilities of the scales.
We first established the discriminant validity of the constructs using confirmatory factor analysis. All factor loadings are positive and significant. The model fit was as follows: NNFI = .86, CFI = .88, RMSEA = .07, and SRMR = .08. The CR and AVE are as follows: technological opportunism: CR = .90, AVE = .54; future focus: CR = .79, AVE = .56; top management's advocacy of new technologies: CR =
.87, AVE = .67; adhocracy culture: CR= .62, AVE = .54; market culture: CR = .70, AVE = .46; hierarchy culture: CR = .73, AVE = .52; and clan culture: CR = .76, AVE = .47. Therefore, except for clan and market culture, for which the AVE is less than the recommended .50, the other conditions for convergent and discriminant validity are satisfied. We first examined a model with only technological turbulence. Technological turbulence has a significant effect (b = .18, p < .01) on technological opportunism. However, the low R2 of .03 for this model suggests that technological turbulence alone does not adequately explain technological opportunism.
Table 6 presents the standardized estimates for the factors that influence technological opportunism. The model has a good fit, with R2 = .54 (F ( 16, 183) = 13.50, p < .01). In support of H4 and H5, respectively, future focus (b = .36, p < .01) and top management's advocacy of new technologies (b = .25, p < .01) have a positive impact on technological opportunism. We find partial support for the effects of organizational culture on technological opportunism: ( 1) An adhocracy culture (H6) is positively related to technological opportunism (b = .15, p < .05), and ( 2) a hierarchy culture (H8) is negatively related to technological opportunism (b = -.08, p < .10), but we find no significant effects for the other culture forms (H7 and H9). Even though we find no relationship between technological turbulence (H10) and technological opportunism (b = .05, n.s.), three of the five industry variables have a positive, significant effect. When we removed the industry control variables from the model, technological turbulence has a significant, positive effect (b = .10, p < .05). Thus, technological turbulence influences technological opportunism, and its effect can be detected either directly or through industry-specific indicators.
Theoretical Contributions
We cite four theoretical contributions of this article: The first contribution is the development of the technological opportunism construct. Although the existence of a sense-and-respond technological capability has been alluded to in prior strategy research (Teece, Pisano, and Shuen 1997), we develop the domain of the construct, measure it reliably, and demonstrate its distinctiveness from innovativeness. In doing so, we extend prior research (Deshpandé, Farley, and Webster 1993; Gatignon and Xuereb 1997) by showing that the organizational capability to sense and respond to new technologies is distinct from a firm's capability for creating new products. The new construct of technological opportunism can inform theory development on important strategic issues in technology strategy. For example, what are the trade-offs in emphasizing internal technology development (research and development) versus purchasing or licensing externally (Pisano 1990)? Researchers can use technological opportunism to examine such issues as resource allocation for internally based capabilities (organizational innovativeness, technological orientation) and externally based capabilities (technological opportunism) and their effects on new product development and performance outcomes for the firm.
A second contribution is that by testing a model of the drivers of radical technology adoption by organizations, we extend the marketing literature on buyer behavior in high technology markets, which has focused primarily on pre-adoption strategic behaviors including information search (Weiss and Heide 1993) and vendor consideration and switching (Heide and Weiss 1995).
A third contribution is our extension of the existing literature on organizational adoption of innovations. A factor not emphasized in that literature is the notion that firms proactively seek and respond to technologies of their own volition, even in the absence of external pressures. Our results of the role of technological opportunism on e-business adoption suggest that such a perspective is misleading. An integrated model of technology adoption that includes a proactive driver (technological opportunism) and a reactive driver (institutional pressures) provides a more complete depiction of the adoption process.
Finally, our substantive domain, e-business, is a new radical technology, which has substantially influenced and, in some industries, even changed marketing practice. Therefore, e-business merits investigation in its own right. Despite its wide-reaching effects on marketing practice, there is limited academic research in marketing on e-business. By using e-business as a context for testing our proposed theory, this article also contributes to the limited academic literature in marketing on this important radical technology.
Managerial Contributions
Technology is no longer just an enabler of business processes but is increasingly becoming the core of the firm's business strategy. Our results on the domain and distinctiveness of technological opportunism indicate that the capabilities to produce new technologies and sense and respond to new technologies are distinct. Managers may want to emphasize different strategies for managing innovativeness compared with sensing and responding to new technologies. In addition, our results are useful to firms that are seeking to develop the technological opportunism capability. Specifically, we identified two actionable drivers of technological opportunism capabilities: top management's advocacy of new technologies and its enabling of certain types of organizational cultures.
Our insights on the role of technological opportunism in technology adoption are useful to managers in technology vendor firms for developing segmentation strategies based on the technological opportunism capabilities of their customer firms. Specifically, we have shown that the extent of e-business adoption depends on both technological opportunism and institutional pressures (consistent with our framework). In addition, e-business vendors can improve their success rates by focusing on companies that are technologically opportunistic and likely to feel institutional pressures(e.g., other firms in their industry adopting e-business).
Limitations and Possible Extensions
Our research has several limitations that qualify our findings and present opportunities for further research. Because we used a cross-sectional method focused on technology adoption, we do not explore the effect of technological opportunism on other strategic behaviors (e.g., alliances, technology commercialization). In addition, there may be specific conditions that influence the effects of technological oppor --tunism on the technology choices made by the firm.
We used a cross-sectional study design to generate exploratory insights, which raises possible concerns about retrospective justification bias. Furthermore, our cross-sectional design precluded an investigation of the evolutionary effects of factors (e.g., competitive intensity, institutional pressures) on technological opportunism. Further research could use a multiple-informant, longitudinal methodology that may capture the time-dependent dynamics of the adoption process. Even though we establish the discriminant validity of technological opportunism in this study, an important area for further research is the refinement of the technological opportunism measure with consideration of the psychometric properties of the scale.
Because we did not collect data on the antecedents of technological opportunism and the technology adoption process in the same round of data collection, we were unable to determine the extent to which technological opportunism mediates the effects of its antecedent variables on technology adoption. We focused on only a few organizational antecedents of technological opportunism in this study, and further research could explore the effects of other organizational antecedents. We also did not examine how technological opportunism affects firm performance. Studying the effect of technological opportunism on performance in conjunction with other factors, such as organizational culture and market orientation, promises to be an important area for further research. Specifically, it may be useful in investigating the complementarity of market orientation and technological opportunism on different firm outcomes, including new product development and financial performance.
In summary, we believe that both researchers and practitioners will find the technological opportunism construct useful and that much more research remains to be done to refine and extend the construct, explore its drivers, and quantify its impact on or ganizational outcomes.
[ 1] A radical technology contains a high degree of new knowledge compared with a current technology and represents a clear departure from existing practices (Dewar and Dutton 1986). We use the terms "innovation" and "technology" interchangeably in the article.
[ 2] We use the terms "firm," "organization," and "strategic business unit" interchangeably in the article.
Profile of Firms in Sample
Number of Number of
Respondents Respondents
Size (% of Sample) (% of Sample)
Study Study 1 (n = 183) Study 2 (n = 200)
Industry Groups
Computer hardware 40 (21.9) 32 (16.0)
Computer software 22 (12.0) 14 (7.0)
Chemicals 40 (21.9) 22 (11.0)
Heavy manufacturing 33 (18.0) 88 (44.0)
Light manufacturing 38 (20.8) 16 (8.0)
Telecommunications 10 (5.5) 28 (14.0)
Total 183 (100) 200 (100)
Sales Turnover
<$100 million 70 (38.3) 87 (43.5)
$101 million-$499 million 67 (36.6) 72 (36.0)
$500 million-$999 million 18 (9.7) 14 (7.0)
$1 billion-$4.99 billion 14 (7.7) 17 (8.5)
>$5 billion 14 (7.7) 10 (5.0)
Total 183 (100) 200 (100)
Number of Employees
<500 55 (30.1) 8 (4.0)
500-999 42 (23.0) 70 (35.0)
1000-4999 54 (29.5) 59 (29.5)
5000-10,000 17 (9.3) 43 (21.5)
>10,000 15 (8.1) 20 (10.0)
Total 183 (100) 200 (100) Items of Multi-item Scales
Measure: Technology adoption[a] (TECADOPT) (α = .77)
Items:
1. We have implemented e-business in all our business processes.
- 2. E-business has had a very limited impact on our business operations. (R)
- 3. Relative to the potential of e-business for our business, our e-business implementation is extensive. 4. E-business has substantially changed our business processes
Measure: Technology adoption[b] (eADOPT, an alternative measure)
Items:
1. External communications: We use e-business as a tool to communicate with our trading partners and stakeholders. (Trading partners include customers, suppliers, and such third parties as banks and distributors; stakeholders include shareholders, financial analysts, employees, media, and general public.) Typical applications: corporate communications, statutory reports, marketing communications, recruiting.
- 2. Transaction-based support: We use e-business with transaction capabilities to support our firm's traditional commercial activities but not to conduct commercial transactions electronically. Typical applications: presales support, product catalogs, pricing information, order status tracking and returns, and so forth. Similar applications would exist for other trading partners, such as suppliers and bankers.
- 3. Fully e-commerce enabled: We use e-business to conduct commercial transactions. Typical applications: presales support, product catalogs, pricing information, order status tracking, returns, and electronic ordering and payment systems.
Measure: Technological opportunism (TECHOPP) (α = .89)
Items:
Technology-sensing capability (TS) (α = .77):
- We are often one of the first in our industry to detect technological developments that may potentially affect our business.
- We actively seek intelligence on technological changes in the environment that are likely to affect our business.
- We are often slow to detect changes in technologies that might affect our business. (R)
- We periodically review the likely effect of changes in technology on our business.
Technology-response capability (TR) (α = .83):
- 5. We generally respond very quickly to technological changes in the environment.
- 6. This business unit lags behind the industry in responding to new technologies. (R)
- 7. For one reason or another, we are slow to respond to new technologies. (R)
- 8. We tend to resist new technologies that cause our current investments to lose value. (R)
Measure: Institutional pressures (INPRES) (α = .88)
Items:
1. Satisfying the needs of our major customers was an important factor in implementing our e-business initiative.
- 2. Some of our major customers demanded that we implemented e-business in our relationships with them.
- 3. Our relationships with our major customers would have suffered if we had not implemented e-business initiatives.
- 4. Our customers' needs did not influence the design of our e-business initiative. (R)
- 5. Having a state-of-the art e-business confers status for our business unit with our stakeholders.
- 6. Our stakeholders would have perceived our business unit as being technologically backward if we had not implemented e-business.
- 7. If we had not undertaken e-business, we might have lost our edge over competitors.
- 8. Being ahead of our competitors' e-business capabilities is a key factor in our e-business initiative.
Measure: Complementary assets (CASSETS) (α = .86)
Items:
1. Our business unit lags behind industry in the implementation of information technology (IT) systems. (R)
- 2. Our business unit uses state-of-the-art IT systems.
- 3. Relative to major competitors, our IT implementation is very advanced.
Measure: Perceived usefulness (USE) (α = .85)
Items:
We implemented e-business to
- Streamline business processes.
- Reduce costs.
- Improve service quality to our customers.
- Open new distribution.
- Develop new markets.
Measure Organizational innovativeness (adapted from Deshpandé, Farley, and Webster 1993) (α = .91)
Items:
Compared to others in our industry, our firm tends to be
1. First to market with innovative new products and services.
- 2. First to develop a new process technology.
- 3. First to recognize and develop new markets.
- 4. At the leading edge of technological innovation.
Measure: Future focus (FUTURE) (α = .79)
Items:
1. This firm's planning activities are more oriented toward the future than the present.
- 2. This firm's future plans are based more on past performance rather than on future potential. (R)
- 3. Our firm plans actively for the future instead of resting on past successes.
Measure: Top management's advocacy of new technologies (TOPADV) (α = .87)
Items:
1. Top managers keep telling managers that this firm must gear up now to meet changing technology trends.
- 2. Top managers make an effort to convince managers of the benefits of a new technology.
- 3. Top managers encourage employees to develop and implement new technologies.
- 4. Top managers in this firm are frequently the most ardent champions of new technologies.
[a]We provided the following definition of e-business at the beginning of the survey: "For the purpose of this research, we define e-business as the use of Internet-based systems to share business information, maintain business relationships, and/or conduct business transactions."
[b]This is a formative measure, and therefore we do not report its reliability.
Notes: The unit of analysis was the SBU. All items were scored using a seven-point scale, where 1 corresponds to "strongly disagree" and 7 to "strongly agree." (R) indicates an item that is reverse-coded. All scales are new except the organizational innovativeness scale, which was adapted from Deshpandé, Farley, and Webster (1993).
Correlation Matrix of Constructs in the Technology Adoption Model (n = 183)
Legend for Chart:
A -
B - Range
C - Means
D - (S.D.)
E - 1
F - 2
G - 3
H - 4
I - 5
J - 6
A
B C D E F
G H I J
1. Technological opportunism (TECHOPP)
8-56 35.62 (9.62) .89
2. Institutional pressures (INPRES)
8-56 36.13 (10.34) .19* .88
3. Perceived usefulness of technology (USE)
5-35 22.40 (7.03) .27 .63
.85
4. Complementary assets (CASSETS)
3-21 12.62 (4.20) .52 .19*
.16* .86
5. Technology adoption (TECADOPT)
4-28 13.76 (4.94) .39 .44
.44 .31 .77
6. Organizational innovativeness(OI)
4-28 17.80 (5.39) .55 .17*
.26 .41 .28 .91
Notes: Reliabilities of measures are on the diagonal in italics. All correlations are significant at p < .001, except that * indicates correlation significant at p < .05.
Model for Technology Adoption, Showing the Effects of Technological Opportunism on Radical Technology Adoption (Study 1)
Standardized
Parameter
Estimates
Variables (Standard Error)
Technological opportunism
(TECHOPP) (H1) .24 (.08)***
Institutional pressures (INPRES) (H2) .29 (.08)***
Complementary assets (CASSETS) (H3) .10 (.08)*
Perceived usefulness (USE) .19 (.09)**
Size1 (500-999 employees)[a] -.08 (.08)
Size2 (1000-4999 employees) .00 (.00)
Size3 (5000-10,000 employees) -.06 (.07)
Size4 (>10,000 employees) -.08 (.08)
Industry dummy 1 (computer software)[b] -.02 (.02)
Industry dummy 2 (chemicals) -.12 (.08)*
Industry dummy 3 (heavy manufacturing) .10 (.08)
Industry dummy 4 (light manufacturing) -.01 (.08)
Industry dummy 5 (telecommunications) .05 (.06)*p < .10.
**p < .05.
***p < .01.
[a]Size dummies have been coded so that <500 employees serves as the base relative to which the effects of the other dummies are measured.
[b]Industry dummies have been coded so that the computer hardware industry serves as the base relative to which the effects of the other dummies are measured.
Notes: R2 = .37 (F( 13, 169) = 7.55, p < .01).
Correlation Matrix of Constructs in the Model of Factors Influencing Technological Opportunism (n = 200)
Legend for Chart:
A -
B - Range
C - Means (S.D.)
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
K - 8
A
B C D E F
G H I J K
1. Technological opportunism opportunism (TECHOPP)
8-56 34.93 (9.49) .89
2. Future focus (FUTURE)
3-21 13.24 (3.78) .63 .79
3. Top management advocacy of new technologies (TOPADV)
4-28 18.16 (4.93) .61 .66 .87
4. Adhocracy culture (AD)
4-28 16.38 (5.29) .57 .57 .62
.82
5. Market culture (MKT)
4-28 18.65 (3.37) .08 (n.s.) .17* .22
.14* .44
6. Hierarchy culture (HY)
3-21 12.93 (3.31) -.04 (n.s.) .17* .08 (n.s.)
-.04 (n.s.) .24 .61
7. Clan culture (CLAN)
4-28 16.78 (4.95) .43 .43 .42
.61 -.02 (n.s.) .21 .76
8. Technological turbulence(TT)
5-35 24.24 (6.58) .18* .06 (n.s.) .18*
.15* 0 -.02 (n.s.) .10 (n.s.) .86
Notes: Reliabilities of measures are on the diagonal in italics. All correlations are significant at p < .01, except that * indicates correlations significant at p < .05 and n.s. = not significant.
Model of Factors Influencing Technological Opportunism (Study 2)
Standardized
Parameter
Estimates
Variables (Standard Error)
Future focus (FUTURE) (H4) .36 (.07)***
Top management's advocacy of new
technologies (TOPADV) (H5) .25 (.07)***
Adhocracy culture (AD) (H6) .15 (.07)**
Market culture (MKT) (H7) -.03 (.05)
Hierarchy culture (HY) (H8) -.08 (.05)*
Clan culture (CLAN) (H9) .08 (.07)
Technological turbulence (TT) (H10) .05 (.07)
Size1 (500-999 employees)[a] -.01 (.10)
Size2 (1000-4999 employees) -.05 (.06)
Size3 (5000-10,000 employees) .01 (.10)*
Size4 (>10,000 employees) .06 (.06)*
Industry dummy 1 (computer software)[b] .02 (.06)
Industry dummy 2 (chemicals) .03 (.07)
Industry dummy 3 (heavy manufacturing) .14 (.08)*
Industry dummy 4 (light manufacturing) .15 (.08)*
Industry dummy 5 (telecommunications) .20 (.07)****p < .10.
**p < .05.
***p < .01.
[a]Size dummies have been coded so that <500 employees serves as the base relative to which the effects of the other dummies are measured.
[b]Industry dummies have been coded so that the computer hardware industry serves as the base relative to which the effects of the other dummies are measured.
Notes: R2 = .54 (F( 16, 183) = 13.50, p < .01).
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By Raji Srinivasan; Gary L. Lilien and Arvind Rangaswamy
Raji Srinivasan is an assistant professor, The University of Texas, Austin. Gary L. Lilien is Distinguished Research Professor of Management Science, and Arvind Rangaswamy is Jonas H.Anchel Professor and Professor of Marketing, Pennsylvania State University. This article is based in part on the first author's doctoral dissertation, which was funded by a grant from the Institute for the Study of Business Markets at Pennsylvania State University. The authors thank Reuben Raj, Christophe Van den Bulte, Sridhar Balasubramanian, Hans Baumgartner, Herman Bierens, Hubert Gatignon, Eelko Huizingh, Christine Moorman, Tom Robertson, Kevin Steensma, Srikant Vadali, and Rajan Varadarajan for their generous help and advice during the research.The authors also thank the four anonymous JM reviewers for their helpful comments on previous drafts of this article.
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Record: 151- The Acquisition and Utilization of Information in New Product Alliances: A Strength-of-Ties Perspective. By: Rindfleisch, Aric; Moorman, Christine. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p1-18. 18p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.65.2.1.18253.
- Database:
- Business Source Complete
THE ACQUISITION AND UTILIZATION OF INFORMATION IN NEW
PRODUCT ALLIANCES: A STRENGTH-OF-TIES PERSPECTIVE
In this article, the authors examine the acquisition and utilization of information in new product alliances. Drawing from research in social network theory with a focus on the strength-of-ties literature, the authors suggest that horizontal alliances have lower levels of relational embeddedness and higher levels of knowledge redundancy than vertical alliances. The authors then suggest that though embeddedness enhances both the acquisition and utilization of information in alliances, redundancy diminishes information acquisition but enhances information utilization. The authors test these ideas using a sample of 106 U.S. firms that recently have participated in new product alliances. Although the results are broadly supportive of the predictions, they are also surprising because they question key underlying assumptions of the strength-of-ties literature. For example, closely tied individual actors are typically assumed to share both high levels of embeddedness and high levels of redundancy, but the present research finds that this assumption does not hold for organizational actors. The authors discuss the implications of these findings specifically for new product alliances and for research on tie strength among organizations in general.
Perhaps the most fundamental area of research activity in the field of marketing involves the nature, antecedents, and consequences of various forms of exchange relationships (Bagozzi 1975; Dwyer, Schurr, and Oh 1987; Webster 1992). In the domain of interorganizational relations, this exchange-based paradigm has informed inquiries into the relations between buyers and suppliers (e.g., Frazier, Spekman, and O'Neal 1988; Lusch and Brown 1996), service providers and clients (e.g., Heide and John 1988; Moorman, Zaltman, and Deshpande 1992), and manufacturers and distributors or sales agents (e.g., Anderson and Weitz 1989; Anderson and Narus 1990).
Despite this wealth of interorganizational research, several aspects of interfirm relations have eluded inquiry. First, although marketing scholars acknowledge that the nature of interorganizational relations may be substantially different among firms that are competitors rather than channel members (e.g., Achrol 1997), the bulk of marketing's interorganizational relationship literature focuses on vertically related firms. As recently noted by Sheth and Sisodia (1999, p. 84), "we have good theories on vertical integration but not on horizontal integration or alliances." Likewise, Robertson and Gatignon (1998, p. 529) suggest that "It might be particularly useful to separate horizontal alliances (between competitors) vs. vertical alliances of firms operating at adjacent stages of the value chain." Second, although increasing numbers of firms are developing new products within a web of interorganizational exchange relationships (Millson, Raj, and Wilemon 1996), the marketing literature has little research on interorganizational new product development activity (for exceptions, see Kotabe and Swan 1995; Robertson and Gatignon 1998; Sivadas and Dwyer 2000). Therefore, Wind and Mahajan (1997, p. 7) identify new product alliances as an important research issue that represents the forefront of "the changing dynamics of competition and cooperation."
In this article, we seek to enhance marketing's understanding of these issues by examining interorganizational relations in new product development. These relationships, which we term new product alliances, are defined as formalized collaborative arrangements among two or more organizations to jointly acquire and utilize information and know-how related to the research and development (R&D) of new product (or process) innovations (adapted from Link and Bauer 1989, p. 5). Because of the rising costs of R&D, increased global competition, and a need for standardization, growing numbers of firms are conducting new product activities through such alliances. However, as noted by several scholars (e.g., Rosenfeld 1996; Wang 1994), the outcomes of these alliances are largely unknown. Managers, for example, have expressed great concern about their firms' ability to acquire information from fellow alliance participants as well as their ability to use this information to enhance new product-related outcomes (Mowery 1998; Sivadas and Dwyer 2000).
At least part of the uncertainty surrounding these alliances may arise because the structural and motivational aspects of cooperation among competitors appear to be quite different from those found in traditional vertical channel-based relationships (Galaskiewicz 1985). To understand these differences, we draw on concepts and findings from social network theory with a focus on the strength-of-ties literature. This perspective, which has been applied to several recent interorganizational studies (e.g., Achrol and Kotler 1999; Gulati 1998; Hansen 1999; McEvily and Zaheer 1999), conceptualizes information flows among social actors as heavily dependent on both their social structure (e.g., Burt 1992; Granovetter 1973, 1982) and their motivation to engage in information exchange (e.g., Brown and Reingen 1987; Frenzen and Nakamoto 1993; Uzzi 1999). Given that know-how transfer is widely cited as the major objective behind alliance participation (Macdonald 1995; Rosenfeld 1996; Watkins 1991) in specific and new product development in general (Moorman 1995; Moorman and Miner 1997), a tie-strength perspective is appropriate for the study of new product alliances.
We suggest that compared with vertical interorganizational relationships (i.e., channel members), firms in horizontal relationships (i.e., competitors) may be faced with the double jeopardy of trying to acquire information from participants that have little complementary knowledge and lack the motivation to share this knowledge (because of common structural linkages and access to similar types of information). This combination of structural and motivational features is dramatically different from the characteristics of the interindividual networks typical of most strength-of-ties research.
In contrast to these information acquisition deficiencies, we posit that horizontal alliances may realize information utilization benefits, as redundancy in the form of similar product development knowledge and skills enhances the ability of weakly tied social actors (i.e., competitors) to develop creative new products and launch them quickly. Although conceptualizations of the strength of weak ties among individuals (e.g., Granovetter 1973) assume that weak ties are advantageous because a low degree of redundancy enhances information acquisition, we suggest that in an interorganizational context, weak ties may be advantageous because a high degree of redundancy enhances information utilization. Thus, our research uncovers hidden aspects of the strength of weak ties that have not been articulated by network scholars.
The strength-of-ties literature is primarily concerned with the nature of the relational bond between two or more social actors, as well as the effect of this bond on their information sharing activities (e.g., Frenzen and Nakamoto 1993; Granovetter 1973, 1982; Hansen 1999; Uzzi 1999). Tie-strength researchers typically classify the relation between social actors as being linked by either a strong tie or a weak one. Strong ties are viewed as having higher levels of closeness, reciprocity, and indebtedness than weak ties (Granovetter 1973; Marsden and Campbell 1984). Although there is considerable debate about the relative advantages of these two different types of ties, it is widely accepted that strong ties increase the likelihood that social actors will share sensitive information with each other, whereas weak ties provide access to a greater amount and diversity of information (Frenzen and Nakamoto 1993; Hansen 1999).
The notion that tie strength could be used gainfully to understand information flows among social actors was first advanced by Granovetter (1973), who demonstrates the power of weak ties in information diffusion. In this classic study, Granovetter shows that information about employment opportunities was more likely to be obtained from acquaintances (i.e., weak ties) than family members (i.e., strong ties). Since then, tie strength has been applied to a variety of information-sharing contexts, ranging from word-of-mouth behavior among consumers (Brown and Reingen 1987) to information transmission among medical professionals (Butt 1987). Although tie strength typically has been applied to relationships among individuals, a growing body of researchers has applied tie-strength concepts to understand information flows in both organizational (e.g., Hansen 1999; Krackhardt 1992) and interorganizational settings (e.g., McEvily and Zaheer 1999; Uzzi 1999).
Concurrent with this shift in focus, researchers have also begun to reexamine the basic concept of tie strength itself. According to Granovetter's (1973) original conceptualization, strong ties are distinct from weak ties in terms of both structure and motivation. Specifically, Granovetter views strong ties as social networks that are structured by a high degree of redundant information and motivated by a high degree of emotional closeness and reciprocity. This conceptualization has been broadly shared by other tie-strength researchers as well. For example, using a bridge metaphor, Frenzen and Nakamoto (1993, p. 373) suggest that there is a "structural tendency for strong ties to cluster in dense, island-like cliques and weak ties to scatter widely as nonredundant bridges that link cliques together."
Growing numbers of researchers have begun to question the validity of this traditional view of tie strength (e.g., Hansen 1999; McEvily and Zaheer 1999; Reingen 1994). For example, recent research suggests that in an alliance context, strong ties may provide access to nonredundant information (Achrol and Kotler 1999; McEvily and Zaheer 1999). These findings are consistent with those reviewed by Reingen (1994, p. 154), who notes that "the little empirical research that directly relates to this issue suggests a surprising lack of redundancy among people in strong-tie relations.'' One reason for this growing debate over the role of tie-strength may be that researchers do not share a common conceptualization of strong ties (see Krackhardt 1992).
This growing debate regarding the conceptualization and components of tie-strength is an outcome of applications of tie-strength concepts to situations characterized by social actors in complex role relations. Much of the early research that emerged from Granovetter's (1973) original conceptualization of the strength of ties deals with information exchange activity among individual actors. This early literature assumed that social actors occupy a singular role (e.g., close friend or casual acquaintance); therefore, tie strength could be viewed as a broader concept, consisting of both high levels of embedded relations and high levels of redundant knowledge (i.e., close friends display both of these qualities). Although this broader view may be suitable for interindividual relations, it may be an oversimplification of interorganizational relations, in which firms may simultaneously hold multiple roles (e.g., competitor and collaborator) and therefore may display more complex structural and motivational features. Thus, studies of information-sharing activity within interorganizational relationships have a greater need to distinguish among the dimensions of tie strength. Following previous research, we focus on two key tie-strength dimensions: (1) relational embeddedness (Gulati 1998; Uzzi 1999) as an indicant of the motivational aspect of tie strength and (2) knowledge redundancy (Butt 1987, 1992) as an indicant of the structural aspect of tie strength. We define and explain both of these constructs in the following section.
Considering the nature of the roles played by competitors in an alliance setting, we suggest that horizontal alliances are characterized by a low degree of relational embeddedness and a high degree of knowledge redundancy. Conversely, we suggest that vertical alliances are characterized by a high degree of relational embeddedness and a low degree of knowledge redundancy. This idea is based on previous research, which reveals that compared with channel members, competitors are characterized by higher levels of conflicting goals (Park and Russo 1996) and greater access to similar types of information (Powell, Koput, and SmithDoerr 1996). Thus, as is shown in Figure I (and developed in our hypotheses), the relationship between embeddedness and redundancy among interorganizational actors appears to be the inverse of the relationship found for individual actors. In the next section, we examine these alliance characteristics (i.e., embeddedness and redundancy) in greater detail and offer a set of hypotheses about their relationship to new product alliance activity.
Tie Strength and Alliance Composition
Relational embeddedness. We define relational embeddedness as the degree of reciprocity and closeness among new product alliance participants. This conceptualization is derived from recent work that observes that interorganizational networks are linked by both structural and relational embeddedness (e.g., Granovetter 1992; Gulati 1998; Uzzi 1999). To date, most studies in the tie-strength literature focus on the structural embeddedness between actors by examining the position of a given organization in a broader network structure (see Gulati 1998). However, in a growing number of recent studies, researchers have begun to explore the impact of relational embeddedness on interorganizational outcomes through examinations of interorganizational reciprocal helping relations (Hansen 1999), cohesive ties (Gulati 1998), and reciprocal obligations (Uzzi 1999). These studies suggest that alliances characterized by a high degree of relational embeddedness display high levels of cooperation (Gulati 1998).
McEvily and Zaheer (1999) suggest that relational embeddedness should be higher among channel members than competitors, as channel members are more likely to have a vested interest in the success of their partners. The prospect of direct competition, in contrast, lowers a firm's incentive to engage in cooperative information-sharing activity and increases the incentive for hoarding valuable information (Achrol 1997; Vonortas 1997). This idea is indirectly supported by recent research by Park and Russo (1996), who find that joint ventures between competitors are more likely to fail than joint ventures between partners that do not compete. They suggest that this higher rate of failure is due to the competitors being more likely to face conflicting goals and objectives. On the basis of this research, we predict the following:
H1: Vertical new product alliances will have higher levels of relational embeddedness than horizontal new product alliances.
Knowledge redundancy. Whereas relational embeddedness focuses on the quality of the relationship between social actors, redundancy is broadly viewed as the degree of overlap in the knowledge base between two or more social actors (Burt 1992; Krackhardt 1992). Overlapping knowledge is the product of social actors sharing equivalent structural positions in which they are exposed to similar types of information. Given our focus on the domain of new product development, we define knowledge redundancy as the degree of similarity in the new product-related information, capabilities, and skills among new product alliance participants.
The strength-of-ties literature indicates that knowledge redundancy is typically higher among actors that occupy similar social positions (Burt 1987; Granovetter 1973). Extending this literature, we suggest that competitors are likely to occupy similar positions within a larger social structure and share similar patterns of relations to other social actors (i.e., customers and suppliers). As Galaskiewicz (1985, p. 287) notes, "Firms that are horizontally interdependent compete with each other in obtaining similar resources and disposing of similar goods and services. One could argue that these organizations are structurally equivalent.'' This assertion is supported by a broad base of literature that suggests that in many industries, horizontally related firms have access to similar types of information because of common structural linkages through trade associations (Vives 1990), industry-based norms and procedures (Thomas and Soldow 1988), networks of informal knowhow trading (von Hippel 1987), and membership in a common technological community (Powell, Koput, and SmithDoerr 1996). Not all horizontally related firms share high levels of redundant knowledge. However, because of their high degree of structural equivalence, alliances composed predominantly of competitors should possess more redundant knowledge than alliances composed mainly of channel members. Thus, we predict the following:
H2: Horizontal new product alliances will have higher levels of knowledge redundancy than vertical new product alliances.
Relational Embeddedness, Knowledge Redundancy, and Information Acquisition
We view information acquisition as the quantity of information related to new product development acquired from other new product alliance participants. Specifically, our focus is the acquisition of technical information directly relevant to new product development, because other types of information (e.g., consumer or market information) fall outside the boundaries of most new product alliances (Hemphill 1997; Wright 1986).
Relational embeddedness. According to strength-of-ties researchers, information sharing among social actors is facilitated by a high degree of relational embeddedness in their social network (Granovetter 1973). As an example of the effects of a high degree of embeddedness, Frenzen and Nakamoto (1993) find that consumers are more likely to transmit information about a sale to a close friend than to a casual acquaintance (see also Brown and Reingen 1987). More recently, Hansen (1999) finds that frequent contact and emotional closeness among internal product development team members enhance the amount of complex knowledge transferred among team members. Finally, Krackhardt (1992) argues that in an organizational domain, information exchange is highly dependent on the degree of emotional closeness among social actors. On the basis of this research, we expect the following:
H3: Relational embeddedness will be positively related to information acquisition in new product alliances.
Knowledge redundancy. In addition to the benefits of embedded ties, strength-of-tie researchers also find that information sharing is enhanced by a low degree of redundancy among social actors' knowledge structures. As an example of the effects of knowledge redundancy, Uzzi (1999) finds that within the banking industry, a firm's access to information about loan opportunities and market prices is facilitated by having a network of loosely connected, nonredundant, arm's-length ties to small business lenders. This finding supports both Granovetter's (1973) conceptualization of the "strength-of-weak ties" and Burt's (1992) conceptualization of "structural holes," which posits that information is more likely to flow among social actors that have different sets of contacts. These differential contacts lead to lower levels of knowledge redundancy and are more likely to provide access to novel information (Hansen 1999). Thus, we propose the following:
H4: Knowledge redundancy will be negatively related to information acquisition in new product alliances.
Relational Embeddedness, Knowledge Redundancy, and Information Utilization
Although strength-of-ties research has largely focused on the influence of tie strength on information acquisition (e.g., Butt 1987; Granovetter 1973; Uzzi 1996), a few studies have explored the effects of tie strength on higher-level information activities (e.g., Debackere, Clarysse, and Rappa 1996; Duysters, Kok, and Vaandrager 1999; McEvily and Zaheer 1999). We follow and extend these prior studies by employing our disaggregated view of tie strength to examine the independent effects of relational embeddedness and knowledge redundancy on information utilization in new product alliances. As McEvily and Zaheer (1999) note, information acquisition is not a discrete event but rather a part of a multistage process that includes the eventual utilization of this information to achieve organizational objectives.
The marketing literature has identified various types of information use activities (e.g., Kohli and Jaworski 1990; Moorman 1995; Slater and Narver 1995). For example, in their seminal research on market orientation, Kohli and Jaworski (1990) identify two components of organizational information utilization: response design (i.e., the use of information in developing plans) and response implementation (i.e., the speed with which plans are executed). Following this literature, we focus on the creativity and speed of development activities as two indicators of information utilization in new product alliances. We define new product (or process) creativity as the degree to which a firm utilizes new product alliance information to develop output that is novel to the industry and challenges existing standards (adapted from Moorman 1995). We define new product development speed as a firm's efficient utilization of new product alliance information in moving from conceptualization to the market introduction of a new product (adapted from Griffin 1993b).
Relational embeddedness. In addition to its positive effect on information acquisition, recent strength-of-ties research suggests that relational embeddedness should also enhance information utilization. For example, Uzzi (1999) finds that firms with embedded ties to their lending institutions are able to achieve lower financing costs than firms that share more arm's-length ties with their lending institutions. He suggests that this occurs because the lender can use information about the firm to create innovative and low-cost loans. In a new product development context, Hansen (1999) finds that weak ties (i.e., a lack of relational embeddedness) among new product development team members may lengthen project completion time because they impede the transfer of complex knowledge among team members. In effect, embedded relations appear to enable product development activities to proceed more efficiently by lowering concerns about the loss of proprietary skills and knowledge and diminishing the likelihood of conflict over goals and implementation.
The notion that higher levels of relational embeddedness facilitate the utilization of information has also been noted by interorganizational relationship researchers. For example, Sabel (1993) documents how the development of embedded relations (what he terms "studied trust") among industrial firms in Pennsylvania provides benefits in the form of improved training and technological processes. Also, Moorman, Zaltman, and Deshpande (1992) find that embedded relations (in the form of organizational trust) between market research providers and their clients indirectly enhances the utilization of market research information by improving the quality of the relationship between exchange partners. In a recent extension and replication of Moorman, Zaltman, and Deshpande (1992), Grayson and Ambler (1999) find that trust is positively related to a marketing manager's utilization of information provided by an advertising agency representative. In summary, relational embeddedness appears to enhance information utilization in terms of both new product/process creativity (Uzzi 1999) and new product development speed (Hansen 1999). Thus, we suggest the following:
H5: Relational embeddedness will be positively related to information utilization in new product alliances in the form of (a) new product creativity, (b) new process creativity, and (c) new product development speed.
Knowledge redundancy. In contrast to its negative effect on information acquisition, recent evidence from the tie-strength literature indicates that knowledge redundancy may improve information utilization. For example, Debackere, Clarysse, and Rappa (1996) find that biotechnology firms improve their innovative output by forming strong network ties to other firms in their industry (i.e., higher levels of redundant ties). Conversely, Kotabe and Swan (1995) find that firms that form strategic alliances with organizations outside their industry (i.e., low redundancy) introduce products that are significantly more innovative than firms that form ventures with organizations within their industry (i.e., high redundancy).
Thus, the effects of redundancy on creativity appear somewhat mixed. However, previous studies that focus on the negative influence of horizontal collaboration on innovative activity have not fully accounted for the fact that innovation in horizontal alliances is likely to be diminished by competitors' low degrees of relational embeddedness (see Figure 1). We believe that when the effects of relational embeddedness are removed, knowledge redundancy should have a positive effect on information utilization.
Building on research by both marketing and organizational scholars, we suggest that redundant knowledge enhances innovation by providing a shared base of tacit understanding, similar organizational routines, and common beliefs that serve as building blocks for innovation (Dougherty 1992; Hutt, Reingen, and Rochetto 1988). As Powell and Brantley (1992, p. 368) note, "Typically, innovation builds on existing know-how." The conception of innovation as a process of building from existing stores of knowledge and expertise underlies Cohen and Levinthal's (1990) well-known concept of absorptive capacity, which suggests that existing knowledge structures enhance a firm's ability to use new information. More recently, Madhavan and Grover (1998) note that information redundancy enhances innovation by capitalizing on the absorptive capacity of organizational actors. Likewise, Hutt, Reingen, and Rochetto (1988) find that shared knowledge structures increase an organization's level of creative new product initiatives.
Collectively, this literature suggests that knowledge redundancy in the form of similar new product development capabilities will have a positive effect on information utilization in the form of new product/process creativity. At times, however, alliance members may engage in innovation that fails to build on existing competencies. For example, "competency-destroying innovations" (see Tushman and Anderson 1986) may require firms to develop new skills and capabilities. In these cases, firms may intentionally seek out alliance partners that possess nonredundant knowledge structures. However, these conditions do not necessarily negate the importance of shared knowledge structures and common skills. Indeed, shared knowledge structures are likely to provide a basis for effective communications and actions even in these more uncertain environments. For example, the innovative benefits of shared knowledge structures is demonstrated in research that shows that radical innovation is effectively diffused through knowhow exchange among industry competitors (Allen 1983).
In addition to its positive effect on new product/process creativity, we suggest that redundancy should also enhance information utilization in the form of new product development speed. Specifically, we believe that the presence of shared knowledge structures and similar capabilities has a positive effect on a firm's ability to absorb, incorporate, and transform acquired knowledge into new products or processes in a timely manner. Although the strength-of-ties literature is largely silent on the effects of redundancy on speed of action, new product development researchers broadly suggest that the presence of shared knowledge and similar capabilities should enhance the speed of new product development because this redundancy lowers the need for planning and coordination among new product alliance members.
Although planning is an essential component of the new product development process, it tends to have a negative relationship with the speed of product introduction (Dickson 1992; Stalk 1988). According to McDonough and Barczak (1991), technological familiarity facilitates the speed of product development by easing decision making and reducing the dangers of the "not invented here" syndrome. Likewise, Griffin (1993a, p. 9) notes that diversity of technical expertise is negatively related to speed of new product development and observes that "As the number of different technical inputs to projects increases, stronger coordination across groups is required." Thus, a high degree of redundancy among alliance participants should enable firms to spend less time planning and coordinating their activities and thus be better able to introduce new products quickly. In summary, we predict the following:
H6: Knowledge redundancy will be positively related to information utilization in new product alliances in the form of (a) new product creativity, (b) new process creativity, and (c) new product development speed.
Sample and Procedure
The sampling frame for this study is U.S. firms that have recently participated in new product alliances. Historically, new product alliances among U.S. companies have been limited by a federal antitrust policy that has been described as largely "hostile toward R&D collaboration among industrial firms" (Mowery 1998, p. 38). However, starting in the mid-1980s, new product alliances blossomed following the passage of the National Cooperative Research Act (NCRA) of 1984. One of the principal intents of the NCRA was to increase information sharing and cooperative R&D activity among industry rivals by decreasing the threat of antitrust prosecution (U.S. House 1984). Although the NCRA was primarily focused on enhancing cooperation among competitors, this act has also fostered several alliances among channel members.
In accordance with the NCRA, new product alliance participants may file written notification of their alliance with the U.S. Attorney General to minimize the threat of antitrust prosecution. These filings are published in the Federal Register and provide information about the formation date, identity and location of the parties, and basic purpose of each alliance. Although new to marketing, Federal Register filings have served as the sampling frame for several studies of new product alliances (e.g., Aldrich and Sasaki 1995; Bolton 1993; Link 1996; Scott 1988; Vonortas 1997). We examined all the alliances filed in the Federal Register from January 1, 1989, to March 15, 1995.
During this time period, 242 new product alliances were filed in the Federal Register. After omitting alliances that either were deemed too large for respondents to evaluate (i.e., more than 12 participants) or consisted solely of firms that were already included in a prior alliance, we had a sample of 153 alliances, which represent the relevant population for our sampling frame. Within each alliance, we identified between one and six firms for inclusion in our sample. If an alliance had six or fewer participants, we included all members in our sampling frame. For alliances containing more than six participants, we used a random selection procedure. Because prior research suggests that international alliances may be systematically different from domestic alliances (e.g., Harrigan 1985; Kogut and Singh 1988), we included only firms that were either U.S. companies or domestic divisions of multinational corporations. To maximize the diversity of organizations included in this study, firms that belonged to multiple alliances were sampled only once. These procedures resulted in 380 firms for inclusion in our study.
The next stage of the sampling procedure involved finding the name of a key informant. As detailed by Campbell (1955), the key informant approach enables researchers to obtain information about a group (i.e., a firm) by collecting data from selected people within that group who are highly knowledgeable about the phenomena under study. The key informant approach has been successfully employed in several studies of interorganizational relationships (e.g., Lusch and Brown 1996; Morgan and Hunt 1994; Stump and Heide 1996). As in other studies in this domain (e.g., Bolton 1993; Link and Bauer 1989; Robertson and Gatignon 1998), our targeted key informants were vice presidents of R&D within each firm. Vice presidents of R&D are ideal respondents because of their high levels of knowledge about the firm, its strategic environment, and its new product alliances (Link and Bauer 1989).
Before mailing questionnaires, we attempted to precontact each key informant by telephone to (1) assess the informant's ability to serve as a key informant by asking if he or she was knowledgeable about the alliance in question, (2) obtain cooperation, and (3) verify the informant's mailing address. In the majority of cases, we talked directly with a key informant. This process eliminated 39 firms (across six alliances) in which we could not reach or identify a knowledgeable executive. Therefore, the population for our final sampling frame consisted of 341 (380 - 39) firms. Each informant was mailed a cover letter, a one-page summary description of their new product alliance, a survey, and a postage-paid reply envelope. As an incentive to participate, informants were told they would be provided with a customized summary report of the study results. Three weeks after this initial mailing, we telephoned nonrespondents, and we sent a handwritten postcard one week later. Informants who did not reply within six weeks were mailed a second set of survey materials.
The surveys for eight firms were returned as undeliverable, and another 33 firms replied that they were willing to participate in this study but did not have enough knowledge about their alliance to provide useful information. This left an effective sampling frame of 300 firms (across 147 alliances), of which 106 usable surveys were returned, for a 35% response rate. These 106 surveys represent 70 different alliances (for a 48% response rate at the alliance level). This sample size and response rate compare favorably with similar studies of this population (e.g., Bolton 1993; Chen 1997; Littler, Leverick, and Bruce 1995; Sivadas and Dwyer 2000). As Armstrong and Overton (1977) recommend, potential nonresponse bias was assessed through an extrapolation method of comparing early with late respondents. No significant differences in either mean scores or variances were found for any key constructs between early (i.e., before second mailing) and late (i.e., after second mailing) respondents. Only 12 (11%) of our responses were from firms involved in two-party alliances, and the average respondent was involved in an alliance with 5.4 other participants. Thus, these responses capture the multifirm aspect of new product alliance activity.
As a validity check, respondents provided information regarding their position, the number of years they had worked for their firm, and their level of familiarity with the alliance in question. Results indicate that the sampling approach was quite successful in identifying key informants. Respondents were highly knowledgeable about their firm's involvement in the new product alliance (5.8 on a seven-point scale) and had worked for their firm for an average of 14.8 years. Two-thirds (66%) were presidents or vice presidents of their firm.
Measurement
Measure development began with field interviews and an early pretest version of the survey among product development personnel at IBM. These early interviews helped develop the measurement scales and were instrumental in crafting a pretest survey that was mailed to key informants in 50 firms (of which 23 responded) who had participated in new product alliances from March 16, 1995, to October 31, 1996. Respondents were asked for their suggestions for improving the survey instrument. All the scales used in the pretest were examined for internal consistency, unidimensionality, and content validity. This analysis revealed that the survey instrument was generally sound; however, a few items appeared in need of modification and were revised. The final survey contained measures of the key constructs and a set of control variables. The items in these key measures are detailed in the Appendix and the intercorrelations, reliability, and descriptive statistics are provided in Table 1.
Alliance composition. We consider alliance composition to be the nature of the relationship among alliance participants. To capture this relationship, we listed the names of all organizations participating in the alliance and asked respondents to classify each collaborator as a customer, a supplier, a competitor, or other (adapted from Littler, Leverick, and Bruce 1995). On the basis of these classifications, we calculated the percentage of competitors in each alliance as an indicant of the degree of horizontal collaboration. The mean percentage of alliance participants classified as competitors by each respondent was 37%. In terms of the distribution of our sample, 35.8% of responses were from alliances composed solely of channel members, 24.5% of the responses were from alliances in which 1%-49% of the participants were competitors, and 39.6% of the responses were from alliances in which 50%-100% of the participants were competitors. This distribution is consistent with the composition of alliances found in prior studies in this domain (e.g., Robertson and Gatignon 1998; Vonortas 1997) and provides a wide array of alliance types in which to examine our conceptual framework.
Relational embeddedness. In the extant strength-of-ties literature, embeddedness typically is derived by an estimation of the frequency of contact (typically using a single item) between social actors (see Granovetter 1982; Krackhardt 1992). However, as noted by Frenzen and Nakamoto (1993, p. 369, italics in original), "frequency of contact reflects the opportunity rather than the motivation to transmit.'' Recognizing the limitations of this type of revealed measures of embeddedness, both Krackhardt (1992) and McEvily and Zaheer (1999) call for measures that capture social actors' motivation to engage in information exchange. We sought to answer these calls by obtaining a direct assessment of relational embeddedness through a multi-item measure of its underlying components.
Using related work in the relational exchange literature as a guide (e.g., Dwyer, Schurr, and Oh 1987; Heide and John 1988; Lusch and Brown 1996), we view embedded ties as evolving in a temporal fashion, in which both prior dealings and anticipated future interactions exert an influence on the pattern of relations among organizational actors. In addition, our measure taps the degree of reciprocal services and mutual closeness among social actors, because prior research suggests that these two constructs are the best indicants of embedded ties (e.g., Marsden and Campbell 1984; Mathews et al. 1998). Therefore, we developed a four-item, Likert-type scale that asked respondents to assess their firms' degree of reciprocity and closeness with fellow alliance participants. This measure demonstrates acceptable reliability (alpha = .76).
Knowledge redundancy. Network researchers typically assess redundancy by examining the degree of overlap in the network contacts of social actors (e.g., Butt 1987, 1992). Although this measure is rich in analytical properties, it provides only an indirect assessment of shared knowledge and skills. Therefore, we decided to assess knowledge redundancy more directly by developing a four-item semantic differential scale that asked respondents to evaluate the degree of similarity in new product development skills, knowledge, and resources of one of their fellow alliance participants (selected at random by the researchers). We adopted this single-firm approach because we believed that respondents would have difficulty responding to a more global (i.e., alliance-level) measure of knowledge redundancy.
In the vast majority of cases (91%), firms for which competitors accounted for half or more of their fellow participants evaluated competitors, and firms for which channel members accounted for the majority of their fellow participants evaluated channel members. In addition, there is no significant difference in the mean level of redundancy reported for two-party alliances (3.52) versus multiparty alliances (3.65). Therefore, although our measure only assesses a firm's degree of redundancy with a single alliance participant, we believe that this measure is indicative of the degree of redundancy among alliance participants in general. Our knowledge redundancy scale was developed on the basis of the descriptions of capability similarity discussed in the writings of Best (1990), Richardson (1972), and Teece (1992) and is similar to the measure of technological linkage employed by Olk (1997). This measure displays good reliability (alpha = .85).
Amount of new product-related information acquired. Drawing on research in both cognitive and organization science (Anderson 1983; Kogut and Zander 1992), we investigate two different forms of new product-related information. First, we examine product-related information, such as a product's underlying components, features, and specifications. Second, because new product alliance activity often involves process innovation, we also examine process-related information, which is the techniques and procedures used to develop new products. We measured the amount of new product-related information acquired using a new tenitem, seven-point Likert-type scale that asked informants to rate the amount of new product-related information their firm acquired from fellow alliance participants. Our measure contained five items that assess product information (i.e., facts and findings) and five items that assess process information (i.e., techniques and tasks). Each of these two forms of information acquisition demonstrates a high degree of internal consistency (product information acquisition alpha = .89, process information acquisition alpha = .92).
New product/process creativity. Our measure of new product/process creativity is adapted from Moorman's (1995) new product creativity scale. Because firms can develop innovation in both products and processes, our measure consisted of two separate scales: (1) an assessment of the degree of creativity of the product itself and (2) the degree of creativity in the processes designed to manufacture the product. Each of these two measures employed seven items with a seven-point Likert-type scale. Both new product and new process creativity demonstrate a high degree of reliability, as each had an alpha of .96.
New product development speed. To measure product development speed, we used a five-item, seven-point semantic differential scale. Scale items asked informants to rate the speed of development associated with the new products generated from their alliance participation. These items focus on how fast the firm has been able to develop new products or processes compared with the firm's norms and expectations. This measure, which borrows from the work of Griffin (1993a, b) and McDonough and Barczak (1991), displays good reliability (alpha = .81).
Control variables. These variables were designed to control for individual firm differences and features of new product alliances that might serve as potential confounds or alternative explanations for our hypotheses about the relationship between tie-strength characteristics and new product outcomes. These control variables fall outside our theoretical focus on tie-strength characteristics but have been shown to influence interfirm cooperation in previous studies that use alternative conceptual foundations.
At the firm level, we control for relationship history between the focal firm and its fellow alliance participants, because a history of prior dealings has been shown to enhance interorganizational cooperation (Morgan and Hunt 1994; Smith and Barclay 1997). To assess the extent to which the focal firm participated in prior alliances with each of the other alliance participants, we used a single item with a seven-point scale ranging from "few relationships" to "many relationships." We then calculated the average score among all alliance partners to form an aggregate measure of relationship history. We also control for the type of objective a firm is trying to accomplish, because prior research suggests that firms enter alliances as a means of either reducing costs or enhancing skills (Hladik 1988; Sakakibara 1997; Vonortas 1997). As a measure of these objectives, we asked informants to rate the importance of four objectives that focus on cost-based goals (such as reducing the costs associated with product development, alpha = .71) and four objectives that focus on skill-based goals (such as keeping abreast of changing technologies, alpha = .72).
In addition to these firm-level variables, we also control for three alliance-level variables. First, because a broad base of research demonstrates that it is easier to achieve coordination in small groups than large ones (Day 1990; Heil and Robertson 1991; Pfeffer and Salancik 1978), we control for the number of firms by counting the number of participants in each alliance as listed in the Federal Register. Second, as the level and nature of interfirm interaction is likely to vary depending on the stage of development of alliance projects (Garud 1994; Link and Bauer 1989), we control for the stage of development of product innovation. As a measure of this construct, we asked informants to assess retrospectively the stage of product development at the time of alliance formation using a five-item scale based on the work of Garud (1994), Link and Bauer (1989), and Tushman and Anderson (1986). This measure displayed adequate reliability (alpha = .77). Third, because multiple-project alliances may differ systematically from single-project alliances, we control for alliance scope (i.e., whether the alliance was a short-term, single-project venture or a long-term, multiple-project venture) by having two doctoral students classify each alliance as involving either single or multiple projects on the basis of the statement of alliance objective contained in the Federal Register. Interrater agreement level among the coders was 85%, and all discrepancies were resolved through a discussion between the coders and one of the authors.
Measure purification. Our key measures were purified through a process that examined their internal consistency by means of coefficient alpha and their unidimensionality and discriminant validity by means of a series of confirmatory factor analysis models using LISREL 8 (Joreskog and Sorbom 1993). The sets of measures were selected from theoretically similar subsets, which permitted joint examination of maximally similar latent constructs. These sets consisted of the three types of information utilization (i.e., new product creativity, new process creativity, and product development speed), two types of information acquisition (i.e., product information and process information), and two dimensions of tie strength (i.e., relational embeddedness and knowledge redundancy). As Campbell and Fiske (1959) note, this type of grouping of maximally similar constructs provides a stringent test of discriminant validity.
We also chose this submodel approach because all the observed variables could not be included in a single model without violating the five-to-one ratio of sample size to parameter estimates as recommended by Bentler and Cho (1988). This type of submodel analysis has been employed in several previous studies (e.g., Fisher, Maltz, and Jaworski 1997; Moorman 1995; Moorman and Miner 1997). In general, these submodels have fit indices close to or above recommended levels (information utilization model: X2(149) = 377, comparative fit index [CFI] = .89, root mean square residual [RMR] - .07; information acquisition model: x2(34) = 111, CFI = .90, RMR = .09; strength-of-ties model: x2(19) = 14, CFI = .97, RMR = .03), and each observed variable had significant (p is less than or equal to .01) factor loadings associated with its theorized latent construct.
To assess the discriminant validity between the latent constructs, we ran each submodel twice; in the first run, we freely estimated the correlation between the latent constructs, and in the second run, we constrained the correlation to unity (Anderson and Gerbing 1988). For the information utilization model (which contained three constructs), we constrained the correlation between product and process creativity. For each of the models investigated, the chi-square values for the unconstrained models were significantly lower than the chi-square values for the constrained models (information utilization model: delta[x sup 2](1) = 494, p is less than or equal to .0001; information acquisition model: delta x2(1) = 141, p is less than or equal to .0001; strength-of-ties model: delta x2(1) = 130, p is less than or equal to .0001), providing evidence of discriminant validity.
Tie Strength and Alliance Composition
We examined the relationship between tie strength and alliance composition by conducting a one-way analysis of variance (ANOVA) (with a Scheff6 test of multiple contrasts) in which we specified three conditions of alliance composition (i.e., 0% horizontal, 1%-49% horizontal, 50%-100% horizontal) as the criterion variable. Although we conceptualize and measure alliance composition as a continuous variable (i.e., percentage of participants classified as competitors), we treat alliance composition in a categorical manner for inclusion into an ANOVA format (similar results are obtained when alliance composition is used in a continuous manner in a correlation analysis; see Table 1). As predictor variables, we included relational embeddedness (H1) and knowledge redundancy (H2). As hypothesized, we find that relational embeddedness is negatively related to the degree of horizontal alliance composition (F(2, 101) = 5.0, p is less than or equal to .01), as firms in purely vertical alliances have a higher level of embeddedness ([mu] = 4.64) than firms in alliances dominated by competitors ([mu] = 3.81). Thus, H1 is supported. We also find that knowledge redundancy is positively related to the degree of horizontal alliance composition (F(2, 101) = 13.6, p is less than or equal to .001), as firms in alliances dominated by competitors have a higher level of redundancy ([mu] = 4.54) than both firms in alliances dominated by channel members ([mu] = 3.38) and firms in purely vertical alliances ([mu] = 2.82). Thus, H2 is supported.
The Effects of Embeddedness and Redundancy on Information Acquisition and Information Utilization
We tested the effects of relational embeddedness and knowledge redundancy on information acquisition (H3 and H4) and information utilization (H5 and H6), through a multivariate general linear regression model (GLM) for purposes of statistical efficiency. Product information acquisition, process information acquisition, product creativity, process creativity, and speed of product development were the dependent variables; relational embeddedness and knowledge redundancy were the predictor variables; and relationship history, number of firms in the alliance, stage of development, skill-based objectives, cost-based objectives, alliance scope, and alliance composition (i.e., percentage of alliance participants classified as competitors) were control variables. We included alliance composition as a control variable to help determine the effects of redundancy and embeddedness independent of their relationship to the composition of a given alliance. In contrast to our previous hypotheses (H1 and H2), in this GLM regression analysis, we are interested in the direct effects of tie-strength characteristics (rather than the direct effect of alliance composition) on new product outcomes.
Multivariate tests reveal that both knowledge redundancy (Wilks' Lambda = .62, F = 8.2, p is less than or equal to .0001) and relational embeddedness (Wilks' Lambda = .76, F = 4.5, p is less than or equal to .001) are significantly related to our five dependent variables. In addition, both knowledge redundancy (E2 = .24) and relational embeddedness (E2 = .36) have large effect sizes. Among our control variables, only skill-based objectives have a significant multivariate relationship (at p is less than or equal to .05) to our five dependent variables (Wilks' Lambda = .70, F = 6.1, p is less than or equal to .0001). Because of these significant multivariate effects for our two key predictor variables, we explored the individual regression models for each of our five dependent variables.
As reported in Table 2, relational embeddedness and knowledge redundancy have differential effects on information acquisition. Specifically, although relational embeddedness is strongly and positively related to the acquisition of product information (B = .42, t = 3.51, p is less than or equal to .001), it is only marginally related to the acquisition of process information (B = .24, t = 1.86, p is less than or equal to .07). Conversely, although knowledge redundancy is negatively related to the acquisition of process information (B = -.22, t = -2.11, p is less than or equal to .04), it is unrelated to the acquisition of product information (B = -.11, t = -1.17, p is less than or equal to .25). Thus, both H3 and H4 receive partial support, because relational embeddedness appears to enhance the acquisition of product information whereas knowledge redundancy appears to diminish the acquisition of process information.
As reported in Table 2, both relational embeddedness and knowledge redundancy have significant, positive effects on two of our three measures of information utilization. Specifically, relational embeddedness is positively related to both new product creativity (B = .33, t = 3.15, p is less than or equal to .002) and speed of new product development (B = .34, t = 3.90, p is less than or equal to .0001) but is unrelated to new process creativity (B = .13, t = .97, p is less than or equal to .34). Likewise, knowledge redundancy is positively related to both new product creativity (B = .19, t = 2.23, p is less than or equal to .03) and speed of new product development (B = .15, t = 2.10, p is less than or equal to .04) but is unrelated to new process creativity (B = .12, t = 1.13, p is less than or equal to .26). Thus, the hypothesized effect of relational embeddedness on information utilization is generally supported, because closer relational ties appear to enhance both new product creativity (H5a) and speed of new product development (H5c). Similarly, the hypothesized effects of knowledge redundancy on information utilization are generally supported, because overlapping skills and knowledge appear to enhance both new product creativity (H6a) and speed of new product development (H6c).
In recent years, both marketing scholars (e.g., Sheth and Sisodia 1999; Wind and Mahajan 1997) and marketing professionals (see Gupta and Wilemon 1996) have expressed a considerable degree of interest in horizontal forms of cooperation in general and new product alliances in particular. A topic of concern to both groups is the issue of how to achieve cooperation among competing firms. As recently noted by McEvily and Zaheer (1999, p. 1154), "The balance between interfirm cooperation and competition, while a popular idea, warrants greater research attention." Our study directly addresses these concerns about the tension between cooperation and competition by showing that horizontal alliances differ from vertical ones in both structure and motivation. In addition, we show how these different alliance characteristics affect interfirm cooperation through both the acquisition of information from alliance participants and the use of this information to develop new products and processes. Thus, our study provides a first step in the direction toward understanding how to achieve and sustain cooperation among competitors compared with channel members. In this final section, we highlight the key implications of our findings, discuss potential limitations, and identify future research directions.
Theoretical and Substantive Implications
Tie strength and alliance composition. As suggested by a growing number of interorganizational researchers (e.g., Achrol 1997; Sheth and Sisodia 1999; Sivadas and Dwyer 2000), relations among competitors are qualitatively different from relations among channel members. Our results support this view by showing that participants in horizontal alliances possess both higher levels of knowledge redundancy and lower levels of relational embeddedness compared with vertical alliance participants. In effect, competitor-centered alliances can be thought of as networks dense in overlapping knowledge but sparse in relational norms. This combination of dense knowledge and sparse relations runs counter to the traditional conceptualization offered in the strength-of-ties literature. Thus, although individual actors may display both high levels of redundancy and high levels of embeddedness (i.e., close friends), this combination seems unlikely among interorganizational actors. This finding is important because it questions the existing notion that weak ties serve as important bridges through which information is transmitted (e.g., Frenzen and Nakamoto 1993; Granovetter 1973). In an interorganizational context, strong ties (i.e., channel members) are more likely to serve this bridging function than weak ties (i.e., competitors) because of their higher level of relational embeddedness and lower level of knowledge redundancy.
As a result, the challenges facing managers in horizontal alliances appear to be quite different (and much stiffer) than the challenges facing their counterparts in vertical alliances. Specifically, mangers seeking to develop new products through alliances with competing firms face the dual challenge of cooperating with firms that can provide relatively little complementary knowledge and are reluctant to share their knowledge. This may be an important reason that horizontal alliances are found to be less stable than vertical ones (Park and Russo 1996). In summary, it appears that despite their collaboration as alliance participants, horizontally related firms have difficulty balancing the tension between cooperation and competition (see McEvily and Zaheer 1999).
One possible solution for minimizing tension between horizontal alliance participants may be to establish stronger informal linkages among competitors through such activities as trade show meetings, informal know-how transfer among engineers, and active membership in trade associations. As noted by Lee and Lee (1992), the vast majority of cooperative R&D activity is informal in nature and occurs through these types of mechanisms. For example, engineers often engage in informal know-how trading activity through impromptu meetings at conferences or trade association meetings. Likewise, manufacturers often solicit informal advice from channel members when designing a new product. Thus, formal new product alliances may be more successful when they develop as an extension of these informal processes.
Relational embeddedness, knowledge redundancy, and information acquisition. As expected, our results show that both relational embeddedness and knowledge redundancy play an important role in determining the amount of new product-related information a firm acquires from its fellow alliance participants. However, our results also suggest that the impact of these two features of tie strength varies depending on the type of information considered. Specifically, embeddedness appears to influence the amount of product information a firm acquires, whereas redundancy appears to influence the amount of process information a firm acquires.
The differential effects for relational embeddedness on product versus process information suggest that information transfer among alliance participants is more than a passive diffusion process. Thus, the nature of the relationship between alliance participants plays an important role in regulating the flow of information. As Frenzen and Nakamoto (1993, p. 363) note, "When transmitters are allowed to behave as gatekeepers, the flow of word-of-mouth information can be greatly disrupted." In a new product alliance context, participants appear to guard their gates carefully to ensure that valuable product-related information is not transferred to partners with whom they share low levels of embeddedness because of fears of having this information opportunistically exploited (see Williamson 1985). This finding is congruent with a recent study by Macdonald (1995), which finds that interfirm cooperation is hampered by many senior managers' reluctance to release product-related information, because they commonly view it as a commodity to be hoarded. Such concerns seem less salient for process information, as this type of technology is more tacit and less accessible through reverse engineering (Teece 1998).
Our finding that redundancy has a negative influence on the acquisition of process information but is unrelated to the acquisition of product information may have important implications for researchers concerned about the role of structure versus motivation among social actors. In his initial conceptualization, Granovetter (1973, p. 137 l) suggests that weak ties provide key information benefits because of their structural network characteristics (i.e., low degree of knowledge redundancy). Therefore, he argues for "the primacy of structure over motivation" in terms of the relationship between tie strength and information flow. Since then, the issue of the information-related value of structure versus motivation (Frenzen and Nakamoto 1993) has been hotly debated by strength-of-ties researchers. However, these researchers have paid little attention to the type of information flowing between social actors. Our findings offer a conceptual refinement by distinguishing between information about techniques and skills and information about facts and findings, as well as an empirical contribution by finding that the structural aspects of interorganizational ties are more important for acquiring information about processes than products. This finding may be due to the likelihood that competing firms are working on similar technologies independently (Allen 1983) and thus have less need to acquire process-related information from each other.
Relational embeddedness, knowledge redundancy, and information utilization. As described thus far, the portrait of horizontal new product alliances appears rather bleak. We have found that compared with vertical alliance participants, horizontal alliance participants have lower levels of relational embeddedness and higher levels of knowledge redundancy and that this combination is associated with lower levels of information acquisition. Therefore, it may seem surprising that despite these challenges, many firms choose to partner with competitors rather than channel members (Hladik 1988). For example, 40% (42 of 106) of the firms in our sample were engaged in an alliance in which half or more of the participants were competitors.
To date, existing explanations for horizontal collaboration have centered on economic efficiency, such as the desire for competitors to develop common industry standards or lower the collective costs associated with new product development (e.g., Sakikabara 1997; Vonortas 1997). Our findings suggest an alternative explanation. Specifically, although efficiency concerns may be valid, horizontal new product alliances also appear to enjoy benefits of new product development effectiveness in the form of higher levels of new product creativity and faster speed of development due to the synergy created by the redundancy of their product development-related knowledge, skills, and capabilities (see Olk 1997; Sivadas and Dwyer 2000). Thus, our findings offer a novel, information utilization-based explanation for the popularity of horizontal alliance activity. These findings also offer an alternative view of the strength of weak ties (Granovetter 1973). Traditionally, weak ties are viewed as advantageous because weakly connected actors share a low degree of knowledge redundancy, which enhances information acquisition. In contrast, our findings suggest that in an interorganizational context, weak ties may be advantageous because a high degree of knowledge redundancy among competing firms enhances information utilization.
In contrast to the information utilization benefits of redundancy (as well as embeddedness) for both new product development speed and new product creativity, this aspect of tie strength has little effect on new process creativity. Traditional economic thought suggests that compared with product innovations, process innovations are more likely to be internally generated as by-products of production (Klepper 1996). Therefore, if process innovation is merely a secondary outcome of product innovation, the structural and motivational aspects of a new product alliance may be relatively unimportant for the development of new processes. Although the exact nature of the relationship between alliance characteristics and new process development is hard to determine from a single study, our results support traditional economic thought and suggest that the tie strength has a larger impact on the development of new products than new processes. As seen in Table 1, this assertion is supported by the respondents' indications that their new products were significantly more creative than their new processes (product creativity - 5.29, process creativity = 4.81; t = 4.14, p .001).
Potential Limitations
Some researchers have expressed concern about organizational studies that employ the view of only a single informant (e.g., Phillips 1981). Although our study uses a single-informant approach, we believe that this approach is warranted for several reasons. First, because our research objective focuses on obtaining global measures of new product-related activities rather than an aggregation of individual perceptions of these activities, the use of a single key informant seems appropriate. Second, as recommended by Campbell (1955), these informants were carefully selected for their unique expertise (which was verified through validity checks). Finally, as noted by Griffin (1993a, p. 120), for most new product development studies, the estimates provided by individual informants "are surprisingly robust-they usually fall within 5%-10% of each other."
A related limitation pertains to our use of subjective managerial perceptions rather than more objective new product-related outcomes. Although objective outcome measures may be desirable, they are difficult to acquire and even harder to interpret (Griffin 1993a). As Smith, Carroll, and Ashford (1995, p. 17) note, subjective measures of managerial perceptions are appropriate for studies of interorganizational collaboration, because "many of the benefits of cooperation ... can be defined in noneconomic terms." Furthermore, Dess and Robinson (1984) show that managerial perceptions are generally consistent with objective measures of performance. Finally, objective measures may also be problematic, because they usually require higher level of measurement aggregation, which often leads to higher levels of respondent uncertainty and greater rates of survey error (Hu, Toh, and Lee 1996).
It should also be noted that our sample is composed of firms that have voluntarily filed their alliance with the U.S. Department of Justice in order to seek the protection of the NCRA. Thus, data obtained from these respondents could reflect a self-selection bias. Specifically, participants concerned about antitrust prosecution may be more likely to file under the NCRA. Given these antitrust concerns, these firms may be wary about sharing sensitive information with their fellow participants. This wariness may have influenced our findings about the effects of tie strength on information acquisition. However, we believe that the possibility of such contamination is remote, because our findings suggest that highly competitor-centered alliances account for only a small portion of all NCRA filings. In addition, because the NCRA provides many benefits and filing is cheap and easy, any firm engaged in new product alliance activity has a strong incentive to register under the NCRA. Nevertheless, given the diversity of collaborative activity in general, there are likely to be several new product alliances not filed under the NCRA for various reasons. Unfortunately, other than the NCRA filings, there is no systematic data source of U.S. firms engaged in new product alliance activity (Hemphill 1997).
As a final limitation, we focus our analysis on the relationship between alliance composition and tie-strength dimensions (by ANOVA) and then assess the impact of these dimensions on new product outcomes (by GLM regression). Using a strength-of-ties perspective, we view relational embeddedness and knowledge redundancy as simply correlates (rather than consequences) of alliances with varying composition (i.e., horizontal versus vertical). Therefore, the precise causal linkage among alliance composition, tie-strength dimensions, and new product outcomes remains to be determined. As a means of clarifying the causal sequence among these variables, alternative conceptual models should be tested. For example, one such model could examine the moderating influence of embeddedness and redundancy on the relationship between alliance composition and new product outcomes. In addition to alternative conceptualizations, future research efforts should consider alternative (and finer grained) measures of alliance composition, because our classification focuses on the percentage of competitors in a given alliance and ignores possible distinctions between buyers and suppliers. Thus, although our findings generally support our hypotheses, these limitations suggest possible boundary conditions for our results.
Future Research Issues
We encourage other scholars to use our research as a starting point to investigate the interorganizational dimension of new product development. Many marketing and management scholars note that a firm's survivability and growth is highly dependent on its ability to develop innovative new products (e.g., Dickson 1992; Zander and Kogut 1995). Because of the critical importance of new product development for both individual firms and the U.S. economy, marketing scholars need to adopt a broader perspective on product development issues and examine the role of new product alliances and other types of interfirm cooperation as a source of new product innovations. For example, researchers interested in intraorganizational new product teams (e.g., Walker and Ruekert 1987) may wish to examine the applicability of their concepts and findings to the interorganizational product development teams that populate new product alliances.
We also encourage marketing orientation scholars to investigate the relationship between alliance participation and market orientation. The market orientation literature indicates that intraorganizational information exchange is a key component of a market orientation (Kohli and Jaworski 1990; Slater and Narver 1995). In addition, market-oriented firms may also engage in a high degree of interorganizational information exchange. Therefore, researchers could explore the relationship among alliance participation, information exchange, and market orientation through a longitudinal study that tracks alliance participants from initial alliance formation to dissolution. As part of this longitudinal study, researchers could explore the impact of informational exchange activities on managerial perceptions of competitors and customers.
Finally, we encourage research on the broader societal and consumer implications of new product alliance activity. As sanctioned by the NCRA, the U.S. Department of Justice views new product alliances as a means of encouraging innovation and enhancing the competitiveness of U.S. industry (Harris and Mowery 1990; Hemphill 1997). However, many scholars and public policy officials remain deeply concerned about the potential anticompetitive effect of collaborative R&D (Link 1996; Wright 1986). This concern seems somewhat warranted, because prior studies have found that new product alliances are disproportionately composed of large firms making R&D investments in existing lines of product development (Scott 1988; Vonortas 1997). As this debate unfolds, some researchers (e.g., Petit and Tolwinski 1999; Sakakibara 1997; Wright 1986) claim that alliances among competitors are especially problematic from a public policy perspective because of the risks of collusion and underinvestment in R&D. Our findings suggest that alliances among competitors may actually enhance innovation, because their high degree of knowledge redundancy appears to lead to products that are both more innovative and more quickly introduced to the marketplace. However, these results are preliminary, and further research is needed to understand more fully the complex relationship among social welfare, economic efficiency, and interfirm cooperation.
Legend for chart:
1 = Measure
2 = Mean
3 = Standard Deviation
4 = a
5 = b
6 = c
7 = d
8 = e
9 = f
10 = g
11 = h
12 = i
13 = j
14 = k
15 = l
16 = m
17 = n
1
2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17
a. Alliance composition
.37 .37 (n.a.)
b. Relational embeddedness
4.16 1.22 -.21 (.76)
c. Knowledge redundancy
3.63 1.66 .48 -.06 (.85)
d. Product information acquisition
3.45 1.44 -.43 .47 -.28 (.89)
e. Process information acquisition
2.98 1.41 -.27 .33 -.22 .66 (.92)
f. New product creativity
5.29 1.30 -.36 .29 -.08 .38 .26 (.96)
g. New process creativity
4.81 1.38 -.12 .10 .02 .31 .33 .65
(.96)
h. Product development speed
4.01 1.03 .19 .38 .24 .23 .29 .12
.27 (.81)
i. Number of firms
6.42 2.68 .34 -.02 .18 -.26 -.10 -.37
-.07 .13 (n.a.)
j. Relationship history
2.67 1.50 -.20 .36 -.08 .30 .16 .14
.03 .20 -.21 (n.a.)
k. Stage of development
4.83 1.30 -.09 .01 -.01 .13 .00 .34
.35 .11 -.26 .17 (.77)
l. Skill-based objectives
5.28 1.31 -.42 .14 -.29 .36 .21 .49
.42 .17 -.16 .06 .35 (.72)
m. Cost-based objectives
5.16 1.41 .10 .18 .11 .03 .07 .15
.15 .26 .01 .05 .14 .32 (.71)
n. Alliance scope
1.74 .44 -.02 -.09 -.10 -.06 .04 -.09
-.10 -.14 .04 -.08 -.11 -.01 -.08 (n.a.)
Notes: The coefficient alpha for each measure is on the diagonal, and the intercorrelations among the measures are on the off-diagonal. Correlations [greater than or equal to][plus or minus] .19 are significantly different from zero at p [is less than or equal to] .05; correlations [greater than or equal to] [plus or minus] .25 are significantly different from zero at p [is less than or equal to] .01. n.a. = not applicable.
Legend for chart:
A = Product Information Acquisition: B
B = Product Information Acquisition: t-Score
C = Process Information Acquisition: B
D = Process Information Acquisition: t-Score
E = New Product Creativity: B
F = New Product Creativity: t-Score
G = New Process Creativity: B
H = New Process Creativity: t-Score
I = New Product Development Speed: B
J = New Product Development Speed: t-Score
A B C D E
F G H I J
Key Predictor Variables
Relational embeddedness .42 3.51*** .24 1.86* .33
3.15** .13 .97 .34 3.90***
Knowledge redundancy -.11 -1.17 -.22 -2.11** .19
2.23** .12 1.13 .15 2.10**
Control Variables
Number of firms -.03 -.62 .02 .28 -.13
-2.73*** .04 .64 .07 1.87*
Relationship history .12 1.27 .12 1.18 -.04
-.43 -.04 -.34 .12 1.70*
Stage of development -.01 -.07 -.05 -.41 .17
1.64* .27 2.17** .04 .51
Skill-based objectives .30 2.24** .09 .63 .43
3.64*** .47 3.22*** .28 2.82***
Cost-based objectives -.15 -1.46 -.06 -.53 -.05
-.52 -.06 -.50 -.01 -.14
Alliance scope .02 .06 .41 1.23 -.02
-.09 -.12 -.37 -.11 -.49
Alliance composition -.30 -.66 -.17 -.36 -.33
-.92 .38 .77 1.03 3.16**
R2 (adjusted) .38(.31) .22(.13) .46(.40)
.27(.18) .40(.34)
*Significant at p [is less than or equal to] .10.
**Significant at p [is less than or equal to] .05.
***Significant at p [is less than or equal to] .01.
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Amount of New Product-Related Information Acquired (new measure; seven-point Likert scale)
Please rate the amount of the following types of information that your firm has acquired from the other participants in this venture:
Product Information Acquisition
1. Information about venture participants' R&D projects.
- 2. Research findings related to the development of new products.
- 3. Information about key product specifications.
- 4. Information about end-user requirements.
- 5. Information about competitors' technology.
Process Information Acquisition 1. Information about new manufacturing processes.
- 2. Insights into new ways to approach product development.
- 3. Information about new ways of combining manufacturing activities.
- 4. Insights about key tasks involved in the production process.
- 5. insights into new ways to streamline existing manufacturing processes.
New Product/Process Creativity (adapted from Moorman 1995; seven-point semantic differential scale)
Product Creativity
In regard to new product creativity, please rate the degree to which the new products generated by your firm's participation in this venture are or are expected to be
1. Very ordinary for our industry-very novel for our industry.
- 2. Not challenging to existing ideas in our industry--challenging to existing ideas in our industry.
- 3. Not offering new ideas to our industry-offering new ideas to our industry.
- 4. Not creative--creative.
- 5. Uninteresting-interesting.
- 6. Not capable of generating ideas for other products-capable of generating ideas for other products.
- 7. Not promoting fresh thinking-promoting fresh thinking.
Process Creativity
In regard to new process creativity, please rate the degree to which the processes used to manufacture the new products generated by your firm's participation in this venture are or are expected to be
1. Very ordinary for our industry-very novel for our industry.
- 2. Not challenging to existing ideas in our industry--challenging to existing ideas in our industry.
- 3. Not offering new ideas to our industry--offering new ideas to our industry.
- 4. Not creative-creative.
- 5. Uninteresting-interesting.
- 6. Not capable of generating ideas for other products-capable of generating ideas for other products.
- 7. Not promoting fresh thinking-promoting fresh thinking.
New Product Development Speed (adapted from Griffin 1993a, b and McDonough and Barczak 1991; seven-point semantic differential scale)
In regard to the speed of development, please rate the degree to which the new products or processes generated by your firm's participation in this venture are or are expected to be
1. Far behind our time goals-far ahead of our time goals.
- 2. Slower than the industry norm-faster than the industry norm.
- 3. Much slower than we expected-much faster than we expected.
- 4. Far behind where we would be had we gone it alone-far ahead of where we would be had we gone it alone.
- 5. Slower than our typical product development time-faster than our typical product development time.
Relational Embeddedness (new measure; seven-point Likert scale)
Please rate the degree to which the following items accurately describe the nature of your firm's overall relationship with the other organizations participating in the cooperative research venture:
- We feel indebted to our collaborators for what they have done for us.
- Our engineers share close social relations with the engineers from collaborating organizations in this venture.
- Our relationship with our collaborators can be defined as "mutually gratifying."
- We expect that we will be working with our collaborators far into the future.
Knowledge Redundancy (new measure; seven-point semantic differential scale)
Please rate the degree to which the participant listed below compares to your firm in general:
- Produces very different products-produces very similar products.
- Has complementary new product development skills-has overlapping new product development skills.
- Their engineers have different knowledge from ours-their engineers have the same type of knowledge as ours.
- Has very different resources-has very similar resources.
DIAGRAM: FIGURE 1 Relationship Between Relational Embeddedness and Knowledge Redundancy Among Individual Actors Versus Organizational Actors
~~~~~~~~
By Aric Rindfleisch and Christine Moorman
Aric Rindfleisch is Assistant Professor of Marketing, Eller College of Business and Public Administration, University of Arizona. Christine Moorman is Professor of Marketing, Fuqua School of Business, Duke University. This article is based on a dissertation by Aric Rindfleisch under the supervision of Christine Moorman while both were at the University of Wisconsin-Madison. This research was funded through the 1996 Business Marketing Doctoral Support Award Competition sponsored by the Institute for the Study of Business Markets at Pennsylvania State University. The authors thank Yeon Koo Che, Peter Dickson, Don Hausch, and Jan Heide for their insights throughout this research project and Maura Belliveau, Rajesh Chandy, Dan Freeman, Shankar Ganesan, Peter Reingen, Christophe Van den Bulte, Melanie Wallendorf, seminar participants at the University of Virginia, and the three anonymous JM reviewers for their helpful comments on previous drafts of this article.
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Record: 152- The Chain of Effects from Brand Trust and Brand Affect to Brand Performance: The Role of Brand Loyalty. By: Chaudhuri, Arjun; Holbrook, Morris B. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p81-93. 13p. 2 Diagrams, 3 Charts. DOI: 10.1509/jmkg.65.2.81.18255.
- Database:
- Business Source Complete
THE CHAIN OF EFFECTS FROM BRAND TRUST AND BRAND AFFECT TO
BRAND PERFORMANCE: THE ROLE OF BRAND LOYALTY
The authors examine two aspects of brand loyalty, purchase loyalty and attitudinal loyalty, as linking variables in the chain of effects from brand trust and brand affect to brand performance (market share and relative price). The model includes product-level, category-related controls (hedonic value and utilitarian value) and brand-level controls (brand differentiation and share of voice). The authors compile an aggregate data set for 107 brands from three separate surveys of consumers and brand managers. The results indicate that when the product- and brand-level variables are controlled for, brand trust and brand affect combine to determine purchase loyalty and attitudinal loyalty. Purchase loyalty, in turn, leads to greater market share, and attitudinal loyalty leads to a higher relative price for the brand. The authors discuss the managerial implications of these results.
Price premiums and market share have been closely associated with the increasingly salient concept of brand equity (Aaker 1996; Bello and Holbrook 1995; Holbrook 1992; Park and Srinivasan 1994; Winters 1991). These outcomes, which in turn drive brand profitability, depend on various aspects of brand loyalty. Specifically, brand-loyal consumers may be willing to pay more for a brand because they perceive some unique value in the brand that no alternative can provide (Jacoby and Chestnut 1978; Pessemier 1959; Reichheld 1996). This uniqueness may derive from greater trust in the reliability of a brand or from more favorable affect when customers use the brand. Similarly, brand loyalty leads to greater market share when the same brand is repeatedly purchased by loyal consumers, irrespective of situational constraints (Assael 1998). Furthermore, because of various affective factors, loyal consumers may use more of the brand--that is, may like using the brand or identify with its image (Upshaw 1995). In summary, superior brand performance outcomes such as greater market share and a premium price (relative to the leading competitor) may result from greater customer loyalty. This loyalty, in turn, may be determined by trust in the brand and by feelings or affect elicited by the brand.
The importance of brand loyalty has been recognized in the marketing literature for at least three decades (Howard and Sheth 1969, p. 232). In this connection, Aaker (1991) has discussed the role of loyalty in the brand equity process and has specifically noted that brand loyalty leads to certain marketing advantages such as reduced marketing costs, more new customers, and greater trade leverage. In addition, Dick and Basu (1994) suggest other loyalty-related marketing advantages, such as favorable word of mouth and greater resistance among loyal consumers to competitive strategies. Yet despite the clear managerial relevance of brand loyalty, conceptual and empirical gaps remain. Specifically, with some exceptions (Oliver 1999; Zeithaml, Berry, and Parasuraman 1996), our conceptualizations of brand loyalty emphasize only the behavioral dimension of that concept, thereby neglecting its attitudinal components and its relationship with other variables at both the consumer and market levels. Therefore,
Even though many marketers have emphasized the need to define brand loyalty beyond operational measures (mostly sequence of purchases), the nomology of brand loyalty in behavioral theory (i.e., its relationships with other concepts in the expanding vocabulary of marketing research) requires stronger integration. (Dick and Basu 1994, p. 99)
The present study explores the relationship among brand trust, brand affect, and brand performance outcomes (market share and relative price) with an emphasis on understanding the linking role played by brand loyalty. Toward this end, we further examine the effects of two general product-level, category-related control variables (hedonic and utilitarian value) on brand trust and brand affect and the effect of two brand-level control variables (brand differentiation and share of voice) on market share and relative price. If these relationships exist, measures of brand trust and brand affect can be included (along with existing measures of brand loyalty and brand equity) in our assortment of brand-valuation techniques (Keller 1993). Moreover, marketing managers can justify expenditures on promotions to create such long-term consumer effects as brand trust and brand affect. Furthermore, our understanding of the process of brand loyalty and brand performance will benefit from an empirically supported explanation for these crucial marketing concepts.
We use brands--that is, specific branded versions of particular product classes--as the units of analysis in this study. This enables us to bring consumer-level notions of trust and affect toward brands into the same plane as market-level measures of brand performance such as market share and relative price, which are at the level of the brand. We do this by averaging across consumer responses and thus arriving at single brand-specific scores for the notions of brand trust, brand affect, and brand loyalty. We then merge these scores with data on market share and relative price to create a single data set at the level of brands as the units of analysis. We do not mean to suggest in any way that brands themselves are capable of affect or trust, but rather that brands have the response potential to elicit affect and trust from consumers. The brand scores thus represent the average response potential of the brand in terms of the trust, affect, or loyalty that it is capable of eliciting from consumers. These brand scores also include data on the product-category characteristics of the brand. As explained in the "Methods" section, these product-level, category-related scores control for the effect of the product category on the theoretical relationships of interest. This helps us extricate the relationships that are at the level of the brand alone.
In what follows, we begin by defining the constructs of interest and developing a model of the relationships among these constructs. To develop our hypotheses, we draw from the new and emerging concepts of relationship marketing, brand equity, and double jeopardy. Here, we propose that instead of representing separate, competing, or antithetical orientations, these conceptualizations can be reconciled and integrated as crucial aspects in an overall process of brand development and brand performance. In this direction, we present the methods, measures, and results of three surveys designed to test the hypotheses of interest. We discuss the results in terms of their managerial relevance and implications for further research.
Background
Oliver (1999, p. 34) defines brand loyalty as
a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior.
This definition emphasizes the two different aspects of brand loyalty that have been described in previous work on the concept--behavioral and attitudinal (Aaker 1991; Assael 1998; Day 1969; Jacoby and Chestnut 1978; Jacoby and Kyner 1973; Oliver 1999; Tucker 1964). Behavioral, or purchase, loyalty consists of repeated purchases of the brand, whereas attitudinal brand loyalty includes a degree of dispositional commitment in terms of some unique value associated with the brand. We propose in Figure I that brands high in consumer trust and affect are linked through both attitudinal and purchase loyalty (also among consumers) to greater market share and premium prices in the marketplace.[1]
Consider, for example, a diner who patronizes only one restaurant. One explanation for this behavior could involve a lack of knowledge of other restaurants and thus habituation to a single place of patronage. Another possible explanation is that the consumer has visited other restaurants; has found that restaurants differ in quality, convenience, service, and so forth; has discovered a particular restaurant that can be trusted and relied on in terms of these criteria; and now chooses to frequent this restaurant rather than other, less trustworthy places. Another scenario is that the customer might have developed strong emotional ties with the restaurant or with its staff'. This brand affect leads to greater commitment in the form of attitudinal loyalty and a willingness not only to revisit the restaurant but also to pay a premium price for the pleasure involved. Moreover, the loyal consumer may even increase his or her usual frequency of eating out every week (instead of cooking at home), thus providing the favorite restaurant with increases in sales. The consumer may now also find other uses for the restaurant, such as ordering take-out food when in a hurry, encouraging group visits with friends, asking the staff to cater a party, and so on. All this will generate additional sales and consequent profitable brand outcomes for the restaurant.
In the present study, brand affect is defined as a brand's potential to elicit a positive emotional response in the average consumer as a result of its use. In consonance with the definition of trust provided by Moorman, Zaltman, and Deshpande (1992, p. 315) and Morgan and Hunt (1994, p. 23), we define brand trust as the willingness of the average consumer to rely on the ability of the brand to perform its stated function. Moorman, Zaltman, and Deshpande (1992) and Doney and Cannon (1997) both also stress that the notion of trust is only relevant in situations of uncertainty (e.g., when greater versus lesser differences among brands occur). Specifically, trust reduces the uncertainty in an environment in which consumers feel especially vulnerable because they know they can rely on the trusted brand.
Doney and Cannon (1997, p. 37) suggest that the construct of trust involves a "calculative process" based on the ability of an object or party (e.g., a brand) to continue to meet its obligations and on an estimation of the costs versus rewards of staying in the relationship. At the same time, Doney and Cannon point out that trust involves an inference regarding the benevolence of the firm to act in the best interests of the customer based on shared goals and values. Thus, beliefs about reliability, safety, and honesty are all important facets of trust that people incorporate in their operationalization of trust, as we discuss subsequently. Overall, we view brand trust as involving a process that is well thought out and carefully considered, whereas the development of brand affect is more spontaneous, more immediate, and less deliberately reasoned in nature.
The model in Figure 1 also includes certain product-level, category-related control variables (hedonic and utilitarian value) and certain brand-level control variables that are discussed fully in a later section (see "Control Variables''). Researchers have suggested that the product-category characteristics will influence brand-level effects (such as brand trust, brand affect, brand loyalty, or brand performance). Categorization and schema theory (Lurigio and Carroll 1985; Sujan 1985) appears to bear this out. These theories both suggest that product-category cognitions are likely to precede thoughts and feelings about brands within the product category. According to categorization theory (Sujan 1985), people form categories of the stimuli around them, and new stimuli (e.g., brands) are understood according to how they fit into these existing categories. Thus, prior knowledge of the product category determines the type of evaluation that a brand stimulus will evoke. Similarly, schema theory (Lurigio and Carroll 1985) suggests that people form abstract schemata from prior knowledge and experience and then use these schemata (say, product categories) to evaluate new information (say, on brands). Hedonic and utilitarian values can thus be conceived of as abstractly representing two types of knowledge gathered from prior experience with the product category for use in evaluating individual brands within that product category.
Hypotheses
As mentioned previously, it has been suggested that brand loyalty includes some degree of predispositional commitment toward a brand (Aaker 1991; Assael 1998; Beatty and Kahle 1988; Jacoby and Chestnut 1978). Therefore, our notion of brand loyalty in this study includes both purchase loyalty and attitudinal loyalty (Figure 1). Purchase loyalty is defined as the willingness of the average consumer to repurchase the brand. Attitudinal loyalty is the level of commitment of the average consumer toward the brand.
We propose that brand trust and brand affect are each related to both purchase and attitudinal loyalty. This proposition stems from the emerging theory of brand commitment (similar to brand loyalty) in relationship marketing (Fournier 1998; Gundlach, Achrol, and Mentzer 1995; Moorman, Zaltman, and Deshpande 1992; Morgan and Hunt 1994; Webster 1992). Brand trust and brand affect appear to serve as key determinants of brand loyalty or brand commitment, consistent with the concept of one-to-one marketing relationships.
Brand trust leads to brand loyalty or commitment because trust creates exchange relationships that are highly valued (Morgan and Hunt 1994). Indeed, commitment has been defined as "an enduring desire to maintain a valued relationship" (Moorman, Zaltman, and Deshpande 1992, p. 316). Thus, loyalty or commitment underlies the ongoing process of continuing and maintaining a valued and important relationship that has been created by trust. In other words, trust and commitment should be associated, because trust is important in relational exchanges and commitment is also reserved for such valued relationships. In this connection, Moorman, Zaltman, and Deshpande (1992) and Morgan and Hunt (1994) find that trust leads to commitment in business-to-business relational exchanges. Thus, we suggest that brand trust will contribute to both purchase loyalty and attitudinal loyalty. Trusted brands should be purchased more often and should evoke a higher degree of attitudinal commitment.
H1: Brand trust is positively related to both (a) purchase loyalty and (b) attitudinal loyalty.
In the context of maintaining brand relationships, the emotional determinants of brand loyalty or commitment need to be considered separately. Gundlach, Achrol, and Mentzer (1995) suggest that commitment is associated with positive affect and that though this may prevent the exploration of other alternatives in the short run, steady customer benefits are likely to accrue from such affective bonding in the long run. In particular, these authors view such a relationship or "affective attachment" (p. 79) to be most beneficial in uncertain environments. Our expectation of a positive relationship between brand affect and brand commitment or loyalty is further predicated on the ties between positive emotional feelings and close interpersonal relationships (Berscheid 1983). In this connection, Berscheid (1983) isolates two critical aspects of a close emotional relationship-namely, the magnitude of the affect (intensity) and its hedonic sign (positive/negative). We suggest that the close relationship of a brand with its consumers (i.e., commitment) also tends to reflect the level of positive affect generated by that brand. Strong and positive affective responses will be associated with high levels of brand commitment. Similarly, Dick and Basu (1994) have proposed that brand loyalty should be greater under conditions of more positive emotional mood or affect. Thus, brands that make consumers "happy" or "joyful" or "affectionate" should prompt greater purchase and attitudinal loyalty. People may not always purchase the brands they "love" for reasons of high price and so forth. In general, however, brands that are higher in brand affect should be purchased more often and should encourage greater attitudinal commitment. Therefore,
H2: Brand affect is positively related to both (a) purchase loyalty and (b) attitudinal loyalty.
Figure 1 further suggests that the variables of purchase loyalty and attitudinal loyalty contribute to brand outcomes such as market share and relative price. Here, as elsewhere, market share is defined as a brand's sales taken as a percentage of sales for all brands in the product category. We expect that brands higher in purchase loyalty will also be higher in market share because of higher levels of repeat purchases-by the brand's users. This expectation is predicated on the theory of double jeopardy (McPhee 1963), which has been advanced as one of the few "lawlike" generalizations in marketing (Ehrenberg, Goodhardt, and Barwise 1990, p. 90) and is supported by a considerable body of evidence (see also Donthu 1994; Fader and Schmittlein 1993).
The double-jeopardy theory specifies that brands with smaller market share are at a disadvantage compared with brands with larger market share in two ways: First, they have fewer buyers; second, they are purchased less frequently by these few buyers. In contrast, more popular brands with larger market shares have more buyers and are purchased more often by these buyers. In short, relevant to our present concerns, brands with greater purchasing loyalty should and do exhibit greater market shares, with a correlation of approximately r = .60 for frequently purchased products (Ehrenberg, Goodhardt, and Barwise 1990, p. 83). Accordingly, we can expect a positive relationship between a brand's market share and the purchase loyalty of its buyers. The caveat must be made that increasing purchase loyalty results in increased market share only if the size of the targeted segment is large enough and if other segments (e.g., present heavy users of the brand) are not alienated by any changes in marketing strategy. Also, this discussion may be more appropriate for national or international brands than for regional or local brands. These caveats notwithstanding,
H3: Market share increases as purchase loyalty increases.
Relative price is defined as the price of a brand relative to that of its leading competitor. We use relative price as an aspect of brand performance with the caveat that in evaluating this performance, price should be considered in conjunction with the costs of maintaining the brand (which, in the present case, we assume to be roughly equal among competitors and/or held constant by partialing out share of voice as a control variable, as described subsequently).
Consumers' price perceptions of brands have been found to be unrelated to brand loyalty (Yoo, Donthu, and Lee 2000). However, when actual rather than perceived relative price measures are used, we propose that brands higher in attitudinal loyalty will command higher relative prices. This proposition draws on the theory of brand equity, which has been described by the Marketing Science Institute as "the set of associations and behavior on the part of a brand's customers, channel members, and parent corporation that permits the brand to earn greater volume or greater margins than it could without the brand name" (Leuthesser 1988, p. 31). Winters (1991) and Aaker (1996) have reviewed different ways of assessing brand equity, and both authors reach the conclusion that the price of a brand in the marketplace is a critical aspect of its brand equity. Furthermore, Holbrook (1992; Bello and Holbrook 1995) defines brand equity operationally as the price premium associated with a given brand name across a range of product categories. Moreover, to cite Keller (1993, p. 9), "Consumers with a strong, favorable brand attitude should be more willing to pay premium prices for the brand." In other words, greater attitudinal loyalty should lead to greater willingness to sacrifice by paying a premium price for a valued brand. Therefore, on the basis of the literature, we expect a significant and positive relationship between a brand's attitudinal loyalty and its relative price in the marketplace.
H4: Relative price increases as attitudinal loyalty increases.
Control Variables
Although they are not of primary theoretical interest to our study, we include in our model control variables that have been found in prior research to affect brand outcomes. Beyond whatever substantive interest these control variables possess in their own right, their major purpose here is to help remove statistical noise due to omitted-variables bias in a case in which we can capture effects that have been shown elsewhere to make a difference.
Brand-level control variables. Smith and Park (1992) find that the degree of brand differentiation is significantly related to market share. With some exceptions, the brand's share of voice has also tended to account for market share (Jones 1990). Furthermore, brand differentiation may justify a higher relative price. Also, share of voice may reflect differences in advertising expenditures and therefore may also tend to affect relative price. Thus, controlling for these variables statistically by including them with the other independent variables of interest provides for a stronger test of our hypotheses regarding the impact of brand loyalty on the relevant brand performance outcomes (while brand differentiation and share of voice are held constant).
Product-level, category-related control variables. In presenting an alternative to the usual decision-oriented perspective on consumer behavior, Holbrook and Hirschman (1982) advocate research on the experiential aspects of human consumption in which emotions and feelings of enjoyment or pleasure are key outcomes. They also propose two different types of consumption: utilitarian products with tangible or objective features and hedonic products with nontangible or subjective features that produce a pleasurable response from consumers. More recently, other researchers have attempted to measure the hedonic versus utilitarian aspects of consumption (Babin, Darden, and Griffin 1994; Batra and Ahtola 1991; Mano and Oliver 1993; Spangenberg, Voss, and Crowley 1997). Viewed broadly, these two aspects of hedonic and utilitarian value correspond to the archetypal constructs of emotion and reason. In this connection, it has been found that affect and reason meaningfully describe a variety of product categories (Buck et al. 1995). In a similar spirit, we adopt the hedonic and utilitarian value of products as basic and fundamental descriptors of product-category characteristics. We define hedonic value as the pleasure potential of a product class and utilitarian value as the ability to perform functions in the everyday life of a consumer. Note that hedonic value and utilitarian value are not considered in this study to represent two ends of a single continuum. Instead, we view them as two potentially orthogonal types of value, and we suggest that products are best conceived as offering some degree of both.[2]
Hedonic and utilitarian value reflect two contrasting paradigms in consumer behavior theory. Specifically, the information-processing paradigm (e.g., Bettman 1979) regards consumer behavior as largely objective and rational and as oriented toward problem solving. Thus, brand trust (which involves a calculative process, as described previously) toward a particular favored brand may be greater when the utilitarian value in the product category is high in terms of tangible product attributes, such as quality or convenience. In contrast, in the experiential paradigm, consumer behavior pursues the more subjective, emotional, and symbolic aspects of consumption (e.g., Hirschman and Holbrook 1982; Holbrook and Hirschman 1982). More hedonic products have nontangible, symbolic benefits and are likely to encourage a greater potential for positive brand affect. When the emotional elements of pleasure are high and positive for a product category, consumers should experience more favorable affect toward the brand consumed.
Allowing for these kinds of relationships helps control for that part of the trusting or affective response to a brand that depends on the product category itself rather than the brand alone. Some of the benefits of a brand may indeed accrue from the product category it belongs to, and accordingly we control for both hedonic and utilitarian aspects of products, which may account for certain tangible and nontangible aspects of brands. This helps ensure that whatever brand-related effects appear in this study are due to the brand and not to its product-category characteristics.
The Unit of Analysis
This study used brands, rather than individuals, as the units of observation. This approach, which aggregates across consumers to produce scores for (in this case) brands or (elsewhere) advertisements (Holbrook and Batra 1987; Olney, Holbrook, and Batra 1991; Smith and Park 1992; Stewart and Furse 1986), avoids the pitfalls of experimental manipulations that examine only two or a few cases across people (thereby giving rise to alternative hypotheses) while carrying greater significance for practitioners (who must consider the effects of their decisions on individual brands).
Independent Measures
The aggregate-level, brand-specific data for the study were compiled from three separate surveys conducted in three phases. Collecting these responses independently for almost every stage in the model ensures that linkages between any two variables are not artifacts of consistency bias due to asking the same respondents to provide both sets of answers in a single questionnaire. The use of three separate samples guards against this kind of consistency bias and thereby provides a more valid test of the key relationships (Holbrook and Batra 1987; Olney, Holbrook, and Batra 1991; Smith and Park 1992).
In Phase 1, the data on utilitarian and hedonic value were collected in the form of product-level data (i.e., ratings of product categories that pertain to the particular brands surveyed later in Phases 2 and 3). Note that no brand-specific data were collected in Phase 1. In Phase 2, measures of brand performance (market share, relative price) were obtained from a survey of product managers. In Phase 3, the data on brand trust, brand affect, and brand loyalty were gathered by a survey of consumers who were users of the brands in the study.
Phases 2 and 3 were completed during a three-month period in the year immediately following Phase 1. The aggregate-level data generated during each phase were then merged to form a single brand-specific data set for the study. Details regarding the procedures and measures used in the three phases are described in the remainder of this section.
Phase 1
Data collection. A sample of 146 products was randomly selected from the Standard Industrial Classification (SIC) manual (1987). Four-digit SIC codes were selected at random from the manual's index of manufacturing and nonmanufacturing industries. Next, a specific subdivision was randomly selected from within each industry, and its good or service was taken as a unit of observation. Industrial products were not included in the selection, so that commonly known brands for consumer products could be surveyed in the later phases of data collection. As discussed subsequently, the final data set consisted of 107 brands in 41 of these product categories.
A field survey of 30 actual users was conducted for each of the 146 products, requiring an overall sample of 30 x 146 = 4380 respondents (mean age = 32.2 years). Respondents were first asked if they were users of the good or service and, if thus qualified, were then invited to participate. If they agreed, they were shown the survey and asked to complete it. Reasons for nonparticipation were mostly either nonusage of the product or lack of time to complete the survey. Overall, 11,139 total approaches were made in the Northeastern United States, mostly in Massachusetts, Connecticut, New York, and New Jersey. Insofar as possible, surveys were conducted at places where the product was consumed or purchased. Thus, for example, the surveys for hair tonics were conducted at a hairstyling salon, potato chips at a grocery store, electric fans at the appliance section of a department store, and so forth.
The surveys consisted of a sell-administered paper-and-pencil questionnaire that contained the scales for the measures relevant to the present study and for some other measures not relevant to this study. The surveys began with an introductory statement that asked respondents to administer their own responses, assured them of confidentiality, and so forth. This was followed by the measures and a request for demographic information. The surveys were distributed and immediately collected by 49 college students enrolled in two sections of an upper-level marketing course at a private university in the Northeastern United States. The students volunteered for the task (in place of completing alternative class assignments) and received course credit on successful collection of 30 consumer interviews for each of three product categories (i.e., 90 completed responses per student). Their work was carefully supervised, and they were well rehearsed in the procedures to be followed in the distribution and collection of the questionnaires.
The individual-level responses of consumers were combined to produce aggregate-level scores by averaging across the 30 respondents in each of the 146 product categories. An aggregate data set for a representative sample of 146 randomly selected products was thus compiled.
Measures of product-level control variables: hedonic and utilitarian value. Hedonic and utilitarian value were each measured on indices composed of two items accompanied by seven-point scales of agreement ( I = disagree, 7 = agree). For hedonic value, the two items were "I love this product" and "I feel good when I use this product." For utilitarian value, the two items were "I rely on this product" and "This product is a necessity for me." Coefficient alphas for the two-item indices were .74 (hedonic) and .95 (utilitarian), respectively.
Phase 2
Data collection. Of the original 146 products in Phase 1, 50 were included in Phase 2 by virtue of (1) having easily identifiable branded alternatives and (2) representing commonly used offerings for which it would be feasible to locate 30 users of a brand in Phase 3. Questionnaires were mailed to product managers of 372 brands in these 50 product categories.[3] Only one manager was used for each brand. Three weeks later, a second mailing was sent out. A personalized cover letter stating the academic purpose of the study and promising absolute confidentiality was enclosed. Follow-up personal telephone calls were made to encourage participants to complete the survey. Through this approach, 160 completed surveys were obtained, for a response rate of 43%, which was judged quite satisfactory, given the sensitivity of the data requested.
Despite this healthy response rate, it was important to rule out nonresponse bias. In this connection, 42 of the original 50 product categories were represented in the returned surveys. The eight products that were not represented included canned soft drinks, shampoos, synthetic sweeteners, ballpoint pens, women's underwear, cigarettes, flashlights, and razor blades. Our best efforts to contact these managers and to persuade them to complete the surveys were not successful. In general, we were told that the information was confidential and not publicly available. The eight product categories appear to group together as frequently purchased and widely distributed consumer goods. Therefore, their absence was likely to be compensated by the large number of similar products that remained in the data set (e.g., bottled iced tea, hair tonic, candy, coffee, hosiery, laundry soap, chewing gum, suntan lotion, cereal, bacon, beer, margarine, ice cream). A full list of product categories in the final data set appears in Table 1. In general, this table reveals a wide representation of brands drawn from a variety of product categories.
Care was also taken that the sample was not biased toward any one viewpoint or opinion. For example, bias could result from managers with poor outcome measures for their brands not responding to the survey. However, examination of sample statistics on brand outcomes shows that the sample contains a substantial representation of brands with both low and high scores.
Finally, the sample was split into early and late respondents (Armstrong and Overton 1977). The two were compared in terms of the key brand performance outcomes, market share and relative price. This comparison showed no difference in means or variances between the early and late respondents, which further suggests that nonresponse bias in Phase 2 is unlikely to distort the findings of the present study.
Measures: market share, relative price, and brand-level control variables. All measures in Phase 2 were obtained from the questionnaire responses provided by the product managers. Specifically, these product managers were asked to define the served market of their brand and answer a series of questions on this brand while keeping its served market in mind. For example, market share was measured by asking respondents directly for the brand's market share within its served market. Relative price was constructed as the ratio of retail price per unit of the brand (numerator) to the retail price of the brand's leading competitor (denominator). The leading competitor was defined as the market share leader in the product category. If the brand itself was the market leader, the next strongest brand was taken as the leading competitor. It was deemed preferable to obtain market share and relative price information directly from the brand managers rather than to try to obtain these data through published secondary sources (e.g., Market Share Reporter). Such secondary sources do not report all the brands of interest to the study and report market shares from different years and different markets. Thus, obtaining reliable secondary data on these variables (especially relative price) proved to be impossible.
Furthermore, data on brand-level control variables were also collected from the brand managers. Share of voice was estimated as the ratio of a brand's annual advertising expenditures to those for the entire industry (all brands). Brand differentiation was operationalized as the sum of two questions, which asked the managers to give five-point ratings of (1) how different their brand was from all other brands in its category in terms of actual product attributes, defined as "those features of the brand which can be physically identified by touch, smell, sight, taste, etc.," and (2) how different their brand was in terms of overall perceived quality, defined to include nontangible, psychological perceptions that consumers have about the brand in addition to its physical attributes. Coefficient alpha for these items was .75.
Phase 3
Data collection. Interviews to collect data on brand trust, brand affect, and brand loyalty were conducted by 50 students enrolled in a senior-level market research course at a private university in the Northeastern United States. Interviewers volunteered for the task (again, in place of alternative class assignments) and received course credit on successful completion of 30 consumer interviews for each of three brands in a single product category. One interviewer was assigned to each of the 50 product categories. Interviewers were trained on data collection using a mall-intercept technique. Their work was carefully supervised and checked for accuracy by random callbacks (to telephone numbers obtained in the interviews).
Overall, 47 interviewers collected data from 30 different respondents for each of three brands, and two interviewers obtained data for four brands (one interviewer was omitted because of errors), which resulted in a total of 149 brands in 49 product categories represented by 149 x 30 = 4470 respondents (mean age = 35.8 years). To obtain this sample, interviewers made a total of 13,386 approaches in Connecticut, Massachusetts, New Jersey, and New York. They conducted surveys mostly in shopping centers and malls. For some products, such as barbecue grills, this approach was not viable for producing actual users of the product. In these instances, interviewers found users in places where the product was purchased or consumed. For example, the barbecue grill interviewer went to a hardware store to obtain the requisite number of users per brand. Interviews were conducted around the middle of the semester and mostly during the mid-semester break.
After qualification for product usage and willingness to participate, respondents were asked which brands of the product they used. They were then interviewed with reference to the first target brand mentioned. If respondents did not use any of the targeted brands from Phase 2, their responses were taken for the brand they did use, but these responses were not included in the final data set, as is discussed subsequently. In this manner, a field survey of 30 actual users was conducted for each of 149 brands in 49 product categories. The means across the 30 responses were calculated for each item on the survey, which resulted in a data set with 149 brands as the units of observation.
Measures: brand trust, brand affect, and brand loyalty. Brand trust was measured as a four-item index based on seven-point ratings of agreement (i = very strongly disagree, 7 = very strongly agree) with the following four statements: "I trust this brand," "I rely on this brand," "This is an honest brand," and "This brand is safe." Coefficient alpha for this four-item index of brand trust was .81. Brand affect was measured by the sum of three similarly rated items: "I feel good when I use this brand," "This brand makes me happy," and "This brand gives me pleasure." Coefficient alpha for brand affect was .96. In general, brand loyalty was measured by agreement with four statements constructed to reflect either the purchase-related or attitudinal aspects of brand commitment (Jacoby and Chestnut 1978). Specifically, purchase loyalty was measured by agreement with the following two statements: "I will buy this brand the next time I buy [product name]" and "I intend to keep purchasing this brand." Coefficient alpha for purchase loyalty was .90. Attitudinal loyalty was measured by two statements: "I am committed to this brand" and "I would be willing to pay a higher price for this brand over other brands." Coefficient alpha for attitudinal loyalty was .83.
Note that at least one of the measures for brand trust and brand affect corresponds closely to the measures cited previously for utilitarian and hedonic value. This correspondence was introduced intentionally to control for the variance due to the product category when effects due to the brand alone are examined. Thus, for example, we capture the variance due to affect toward the product category with the hedonic value item cited previously ("I feel good when I use this product"), and we separately estimate the variance due to affect toward the brand with the brand affect item ("I feel good when I use this brand"). As stated previously, the product-level, category-related variables of hedonic and utilitarian value act as control variables in the sense that they capture product-category effects that might otherwise be subsumed in the brand-level data. By relating the product-category variables to the brand-level variables of trust and affect, we can isolate the variance that is due to the brand alone from the variance that is due to the product category.
As a test of discriminant validity, Fornell and Larcker (1981) have suggested that the average variance extracted for each construct should be higher than the squared correlation between that construct and any other construct. To demonstrate this for the four constructs just described, we conducted a confirmatory factor analysis with LISREL 8.14 (Joreskog and Sorbom 1996) using the aggregated data for the 149 brands in Phase 3. Fornell and Larcker's (1981) test of discriminant validity held for all four constructs considered separately; specifically, the largest squared correlation between any two of the constructs was .46, whereas the average variance extracted ranged from .67 to .88. Accordingly, we then summed the relevant items to form multi-item indices of brand trust, brand affect, purchase loyalty, and attitudinal loyalty.
Final Data Set
To construct the final data set, we merged the aggregated consumer-survey data set (Phase 3) based on the means of 30 responses for each of 149 brands with the data set from the managerial survey (Phase 2) for the corresponding brands in the 41 product categories covered by both sets of responses. Next, we entered the appropriate product-category data (Phase 1) on hedonic and utilitarian value for each brand in the data set. This resulted in a combined data set for 107 brands with complete observations on all variables except, in a few cases, one or more of the final brand performance outcomes. These brand performance variables were not always provided by the product managers. In Table 1, we provide a list of the 41 product categories in the final data set of 107 brands (with the number of brands in each category shown parenthetically). Confidentiality agreements with the product managers prevent us from divulging the specific brand names in the final data set.
In Table 2, we provide the full set of correlations among the constructs of interest in the study. Note that the two brand performance outcomes, market share and relative price, were essentially independent (r = .03, n.s.), with a vanishingly small shared variance (r2 = .0009).
Path analysis (LISREL 8.14) was used for testing the model and hypotheses shown in Figure 1. In this path analysis, the multiple indicators were summed together for each construct, and the resulting summated score was used to represent that construct in the simultaneous equation model.[4] Path analysis (LISREL 8.14) testing the proposed model (Figure 1) resulted in the following fit statistics: x2(18) = 20.32, p = .32, root mean residual (RMR) = .036, goodness-of-fit index (GFI) = .96, adjusted goodness-of-fit index (AGFI) = .89, normed fit index (NFI) = .94, nonnormed fit index (NNFI) = .96, comparative fit index (CFI) = .99, incremental fit index (IFI) = .99. Fourteen structural paths and 13 correlations were estimated for the model containing the ten constructs in Figure 1.
Three of the paths in the proposed model (utilitarian ----> trust, hedonic --> trust, and differentiation ---> market share) were not statistically significant (p < .05). These departures from the model refer to relationships involving control variables not represented by H1 to H4 (i.e., not of specific theoretical interest in the present study). The statistically nonsignificant x2 indicates a good fit of the model with the data, and the other indices of fit further confirm this. Note that the final model explained 16% of the variance in market share and 24% of the variance in relative price, respectively.
Standardized path coefficients for the model appear in Table 3, which shows that the results support all four hypotheses at p < .05 or better. As diagrammed in Figure 2, these results also indicate that brand trust and brand affect are both indirectly related to market share and relative price, and the indirect linkage occurs through the constructs of purchase loyalty and attitudinal loyalty. Note also that as expected the two components of loyalty have different outcomes in terms of brand performance. Purchase loyalty explains market share but not relative price, whereas attitudinal loyalty explains relative price but not market share.
To check for reverse causality, we also tested a nonrecursive model that freed the paths from market share back to purchase loyalty and from relative price back to attitudinal loyalty. Both feedback effects were nonsignificant (t-value < 1.96, p > .05).
To determine the robustness of the model to variations among specific groups of products, we ran the same model on durable and nondurable product categories within the final data set. Path analysis (LISREL 8.14) to test the model for durable product categories resulted in the following fit statistics: x2(18) = 34.34, RMR = .05, GFI = .94, AGFI = .82, NFI = .92, NNFI = .89, CFI = .96, IFI = .96. With the exception of H2a and H2b, all hypotheses in the study were supported again. Only the paths from brand affect to attitudinal and purchase loyalty were not significant at p < .05. However, both paths were positive in direction, as hypothesized. It appears likely that with a larger sample of products, these relationships would become significant.
Path analysis (LISREL 8.14) was also used to test the model for nondurable product categories and resulted in the following fit statistics: x2(18) = 50.45, RMR = .06, GFI = .92, AGFI = .75, NFI = .87, NNFI = .76, CFI = .90, IFI = .91. Here, two of the six hypothesized paths (H1b and H3; brand trust ---> attitudinal loyalty and purchase loyalty ---> market share) had standardized coefficients of. 15 but were not significant at p < .05. However, both paths again were positive in direction, as hypothesized, and it seems likely that with a larger sample size, these relationships would prove to be significant.
We further tested the robustness of the model by running it separately on utilitarian and hedonic product categories within the final data set. Path analysis (LISREL 8.14) to test the model for utilitarian product categories resulted in the following fit statistics: x2(18) = 68.69, RMR = .06, GFI = .90, AGFI = .69, NFI = .84, NNFI = .67, CFI = .87, IFI = .88. All the hypotheses in the study were supported (p < .05) in this version of the model.
The fit statistics for hedonic product categories were x2(18) = 51.94, RMR = .08, GFI = .92, AGFI = .74, NFI = .86, NNFI = .75, CFI = .90, and IFI = .91. Three of the six hypothesized paths (H1a, H1b, and H3; brand trust --> purchase loyalty, brand trust ---> attitudinal loyalty, and purchase loyalty --> market share) were not significant at p < .05. However, all paths were positive in direction, as hypothesized, and would be expected to become significant with larger sample sizes.
In summary, we are confident that the model also applies at the level of more specific product categories, perhaps with a need for some variations in the paths included (to be determined in further research). Such deviations from the norm when testing for segments within the overall "population" of product categories are not uncommon (for a vivid description of the issue, see Wells 1993). However, pending further research, they do not appear to pose a serious threat to the validity of the present findings.
Empirical Findings
Almost all conceptualizations of brand equity agree that the phenomenon involves the value added to an offering by consumers' perceptions of and associations with a particular brand name (Aaker 1996; Baldinger 1990; Baldinger and Rubinson 1996; Bello and Holbrook 1995; Dyson, Farr, and Hollis 1996; Holbrook 1992; Keller 1993; Park and Srinivasan 1994; Winters 1991; see also the special issue of the Journal of Advertising Research [1997] on brand equity). Therefore, there are two aspects to brand equity--from the viewpoints of the firm and the consumer. The firm-related side of brand equity emphasizes such brand-related outcomes as relative price and market share, whereas customer-based brand equity appears to hinge at its core on psychological associations with the brand (Keller 1993, p. 1). Furthermore, several authors have suggested that these psychological associations with a brand name account for brand equity outcomes such as greater market share or differential consumer responses to marketing-mix variables such as relative price (Aaker 1996; Baldinger and Rubinson 1996; Bello and Holbrook 1995; Keller 1993; Smith and Park 1992). It also has been noted that brands with high market share tend to have high levels of repeat purchase among their users (Ehrenberg, Barnard, and Scriven 1997; Ehrenberg, Goodhardt, and Barwise 1990). However, in this large and growing literature, the role that brand trust and brand affect play in the creation of brand loyalty as a determinant of brand equity outcomes has not been explicitly considered. In the latter connection, our findings suggest that brand trust and brand affect are separate constructs that combine to determine two different types of brand loyalty--purchase loyalty and attitudinal loyalty--which in turn influence such outcome-related aspects of brand equity as market share and relative price, respectively.
This conceptualization has been corroborated by our empirical results, in which very different outcomes were evidenced for brand trust and brand affect as opposed to brand loyalty. Although brand trust and brand affect were each directly related to both purchase and attitudinal loyalty (Table 3), they were indirectly related to market share and relative price. Specifically, brand trust and brand affect contributed to both purchase loyalty and attitudinal loyalty, which in turn contributed significantly to market share and relative price, respectively. From this, it follows that brand loyalty may be viewed as a link in the chain of effects that indirectly connects brand trust and brand affect with the market performance aspects of brand equity.
Brand trust, brand affect, and brand loyalty are also relevant constructs in the relationship marketing literature, which considers trust and commitment or loyalty to be "key mediating variables" in relational exchanges (Morgan and Hunt 1994). As contributors to brand loyalty, brand trust and brand affect have distinct antecedents. In this connection, our results show that different product-category characteristics influence brand trust and brand affect differently. For example, hedonic value in the product category was significantly and positively related to brand affect. Conversely, the utilitarian value of the product category was significantly but negatively related to brand affect. In summary, we find that every level in our model (Figure 1) is necessary to understand fully the chain of effects from the product-level, category-related control variables at one end to the brand performance outcomes at the other.
Although they are not of theoretical interest to the present study, some of the nonhypothesized findings relevant to the purely endogenous variables, market share and relative price, bear repeating. For example, the lack of any correlation between market share and relative price is an interesting finding. Perhaps this relationship is moderated by other variables. Also, it appears from the findings that brand differentiation does not lead to greater market share for the brand but does influence the brand's relative price.
Managerial Implications
One goal of our study was to explore the relationship between the concepts of brand loyalty (purchase loyalty and attitudinal loyalty) and firm-level brand outcomes (market share and relative price) in ways that would tie the roles of brand trust and brand affect to the overall structure of brand equity. If the relevant relationships can be replicated in other studies, measures of these constructs can be included in our assortment of brand valuation techniques (Keller 1993). Accordingly, the results tentatively encourage managers to include measures of brand trust, brand affect, purchase loyalty, and attitudinal loyalty in performing brand valuation analysis. Our study has shown the potential importance of brand loyalty in general and as a link in the determination of brand performance outcomes in particular, while also providing some useful measures of the construct. These measures appear to be reliable and valid predictors of brand performance outcomes. With more work, it should be possible to arrive at even better brand loyalty indices, which can then be combined for use as one among other crucial methods of brand valuation.
Also, marketing managers can interpret these results as helping to justify expenditures on design, communication, and merchandising strategies that create such long-term effects on consumers as brand trust, brand affect, and brand loyalty insofar as these consumer-level constructs contribute to profitable brand performance outcomes. Moreover, as we better relate the consumer and market levels on which brands perform, our overall understanding of the antecedents to brand performance should improve, which will lead to more effective marketing-mix strategies. Brand communication strategies might also be designed with special regard to the product-level, category-related determinants of brand outcomes. For example, understanding that favorable brand affect may be more prevalent in certain product categories--those associated with low utilitarian value and high hedonic value--suggests different advertising themes and strategies for these product categories.
Our study has distinguished among brand trust, brand affect, and brand loyalty while also suggesting that brand loyalty includes components related to both repeat purchase and attitudinal commitment (Jacoby and Kyner 1973). Thus, the results provide managers with evidence for theories of both double jeopardy (through purchase loyalty) and brand equity (through attitudinal loyalty). On the one hand, the evidence suggests that higher brand trust and brand affect, working through higher purchase loyalty to the brand, lead to sales-related brand outcomes such as market share. On the other hand, the evidence also suggests that brand trust and brand affect, working through attitudinal loyalty, lead to premium-related outcomes such as higher relative prices in the marketplace. Most important, there is evidence from this study that brand trust and affect are only indirectly related to market share and relative price through their combined impacts on purchase loyalty and attitudinal loyalty, respectively (Table 3 and Figure 2). Thus, in both cases, the roles of brand loyalty in general and of its attitudinal or purchase-related aspects in particular are critical in understanding the contrasting brand performance outcomes.
Limitations and Further Research
As previously discussed at length, the results of this study are largely in accord with our theoretical expectations. However, as in any study, further research is needed to replicate and extend our findings. In general, these findings should be replicated with different product categories and brands. To assess the generalizability of the model, we have provided fairly consistent results for different product categories. Studies on other product classes, such as luxury goods, services, and impulse purchases, might reveal findings that corroborate or extend our approach. Also, the present study did not examine such personal factors as product involvement, variety seeking, impulsiveness, and so forth. Such individual differences or consumer-based segmentation variables should be incorporated in future studies. Overall, we still need to develop a more detailed understanding of the relationship between brand loyalty and other marketing-related variables.
Furthermore, additional measures of brand trust, brand affect, purchase loyalty, and attitudinal loyalty should be developed, which would lead to a better explanation of brand performance outcomes. Despite the importance of the concept, brand loyalty measurement has not flourished in the marketing literature. For example, there is only one brand loyalty scale included in the 1305 pages of the Marketing Scales Handbook (Bruner and Hensel 1992) published by the American Marketing Association, and that lone scale is specific to soft drinks. Scales for both types of brand loyalty (purchase and attitudinal) exist (for some examples, see Jacoby and Chestnut 1978), but they generally are not used in conjunction with one another. Most often, we measure brand loyalty--neglecting its attitudinal component--according to the past purchasing patterns of consumers. The present study has moved toward considering both purchase and attitudinal loyalty, but there is room for further development in that direction and beyond. Similarly, in addition to our measures of market share and relative price, other brand performance outcomes, such as the brand's direct contribution to profits, should be assessed.
Our aggregate-level model using brands as the units of analysis has depicted paths from purchase loyalty to market share and from attitudinal loyalty to relative price. We also checked for possible feedback paths from the brand performance outcomes to the two components of brand loyalty. As mentioned in the "Results" section, we found these feedback effects to be nonsignificant in our data. However, such nonrecursive effects might emerge when people rather than brands are used as the units of analysis. In other words, reverse causality is always a possibility and should continue to be considered in future studies that use different methodological designs. For example, we have suggested that brand trust and brand affect are key determinants of brand loyalty, but this does not preclude the possibility that continuous brand loyalty in turn may also create additional brand trust and brand affect. Indeed, it is likely that studies over time will find that these relationships are ongoing and reciprocal.
Finally and perhaps foremost, we recognize that other determinants of brand loyalty and performance outcomes might supplement the variables included here. In the present study, 16% of the variance in market share and 24% of the variance in relative price were accounted for. This leaves room for potential improvements in explanatory power achieved by more comprehensive models. As researchers increasingly probe the area of relational exchanges between brands and their consumers (Fournier 1998), other constructs that are prevalent in the literature on interpersonal relationships, such as similarity, attraction, love, familiarity, or power, should be examined for their potential relevance to brand loyalty and brand outcomes (e.g., Ahuvia 1999). Also, topics such as sex differences in the development of these constructs should be explored in studies that use group-level brand scores as the units of analysis. We have shown that brand trust and brand affect may differ according to the type of product, but do men and women also differ in their responses to brands or in their subsequent brand loyalty? Furthermore, additional aspects of brand affect abound with research potential. For example, now that the role of emotions has been energetically researched in advertising studies related to marketing and consumer behavior, there remains a need to examine emotional experiences that arise from other product- and brand-related aspects of consumption (Holbrook 1995, p. 14; Mano and Oliver 1993).
1 This framework draws on assumptions made at the level of individual consumers, whereas the data in the study are compiled at the level of aggregated responses. This is not uncommon. As Fox, Reddy, and Rao (1997, pp. 253-54) point out, "The conceptual basis for most observed aggregate (macro) phenomena is at the disaggregate, individual (micro) level." See also the other references cited by these authors in defense of this treatment.
- 2 As one of the reviewers of this article points out, the distinction between hedonic and utilitarian value may depend on whether the relevant satisfaction is immediate (utilitarian value) or in the future (hedonic value). Pharmaceuticals, for example, may be considered utilitarian in their initial use but result in relief from pain, which may be viewed as a gratifying and pleasurable end result. Here and elsewhere, a given product category potentially contributes to both types of customer value.
- 3 These brands were derived from an extensive search through both secondary information sources and personal observation at points of purchase for each of the 50 relevant product categories. Examination of the data provided by the product managers in the final data set reveals that 79% of the brands were nationally distributed in 50 states. The remainder of the brands were regionally or locally distributed brands. No dealer brands were used in the study.
- 4 The path-analytic procedure used here is becoming common in studies in which a small sample size restricts the use of the full structural equation model. For a similar use of the technique, see Li and Calantone (1998) and the references cited by these authors in defense of this approach.
Personal computers (3) Macaroni (3)
Women's handbags (3) Hotels (3)
Chewing gum (3) Men's underwear (1)
Mattresses (3) Potato chips (1)
Analgesics (3) Hair tonic (1)
Cameras (3) Margarine (2)
Ice cream (3) Electric fans (3)
Cottage cheese (1) Salad dressing (1)
Suntan lotion (3) Microbrews (3)
Children's wear (3) Laundry soap (3)
Cereal (3) Room air conditioners (2)
Microwave ovens (3) Vegetable cooking oil (2)
Perfume (3) Golf clubs (3)
Bacon (3) Kitchen utensils (3)
Barbecue grills (3) Boys/men's slacks (1)
Gasoline (3) Bottled iced tea (3)
Canned fruit (3) Cooking ranges (3)
Beer (3) Candy (3)
Trucks (3) Coffee (3)
Hosiery (3) Automotive tires (2)
Light bulbs (3)
Notes: Numbers in parentheses indicate the number of brands for each product category in the final data set of 107 brands.
Legend for chart:
A = 1
B = 2
C = 3
D = 4
E = 5
F = 6
G = 7
H = 8
I = 9
J = 10
A B C D E
F G H I J
1. Utilitarian value 1.00
2. Hedonic value .07 1.00
3. Brand trust .15 .06 1.00
4. Brand affect -.24 .30 .66 1.00
5. Share of voice -.17 -.07 .04 -.05 1.00
6. Differentiation -.13 .11 .04 .07 .06
1.00
7. Purchase loyalty .02 -.09 .63 .55 .03
-.03 1.00
8. Attitude loyalty -.02 .08 .52 .51 -.03
-.03 .64 1.00
9. Market share -.03 -.01 .19 .08 .35
.02 .22 .12 1.00
10. Relative price -.03 .14 .17 .05 .33
.31 .12 .22 .03 1.00
Legend for chart:
A = Hypothesis
B = Coefficient
A B
Hypothesized Links
Brand trust --> purchase loyalty H1a .46
Brand trust --> attitudinal loyalty H1b .33
Brand affect --> purchase loyalty H2a .25
Brand affect --> attitudinal loyalty H2b .30
Purchase loyalty --> market share H3 .21
Attitudinal loyalty --> relative price H4 .21
Control Variables
Utilitarian value --> brand affect -.26
Hedonic value --> brand affect .32
Share of voice --> market share .35
Share of voice --> relative price .32
Differentiation --> relative price .27
Notes: All coefficients are significant (t-value > 1.96,
p < .05).
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DIAGRAM: FIGURE 1 A Model of Brand Loyalty and Brand Performance
DIAGRAM: FIGURE 2 Significant Paths and Correlations
~~~~~~~~
By Arjun Chaudhuri and Morris B. Holbrook . Heide d Peter H. Reingen
Arjun Chaudhuri is Associate Professor of Marketing, School of Business, Fairfield University. Morris B. Holbrook is W.T. Dillard Professor of Marketing, Graduate School of Business, Columbia University. The authors thank David A. Kenny, Sharmila Chatterjee, and the three anonymous JM reviewers for their help. The authors also gratefully acknowledge support by grants from Fairfield University's Research Committee, Fairfield's School of Business, and the Columbia Business School's Faculty Research Fund.
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Record: 153- The Concept of Hope and Its Relevance to Product Evaluation and Choice. By: MacInnis, Deborah J.; de Mello, Gustavo E. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p1-14. 14p. 3 Charts. DOI: 10.1509/jmkg.69.1.1.55513.
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The Concept of Hope and Its Relevance to Product
Evaluation and Choice
This conceptual article uses an appraisal theory perspective to define the construct of hope and describe its relevance to consumer behavior, marketing, and public policy in the domains of product evaluation and choice. Using the appraisal dimensions of hope, the authors identify a set of marketing tactics that are designed to stimulate hope. They posit that hope plays a moderating role in the relationship between well-known antecedent variables (e.g., involvement, expectations) on the one hand and evaluative judgments (e.g., attitudes, satisfaction) and consumer choices on the other hand. The authors advance specific propositions that describe the effects. Furthermore, they argue that the effects have implications for marketing and public policy. The article concludes with a call for additional research relevant to the study of hope.
Each day, millions of consumers engage in such behaviors as investing in the stock market, buying lottery tickets, attempting to lose weight, reading self-help books, undergoing cosmetic surgery, visiting therapists, and seeking a renowned physician for a given illness. What do all these consumption situations have in common? Among other things--hope.
Indeed, hope is a marketable entity that affects the economic viability of many industries. The beauty industry alone is a multibillion dollar business that influences the viability of cosmetics companies, pharmaceuticals, plastic surgeons, department stores, salons, spas, beauty parlors, magazines, and books. The latter two industries are sources of hope, promoting "secrets," "tools," "tips," and "tricks" to better looks, a more alluring body, improved romantic relationships, and enhanced self-esteem. Charles Revson, the founder of Revlon, aptly captures the impact of the marketplace on hope: "In the factory we make cosmetics; in the store we sell hope." It comes as no surprise that "hope" is a common word in everyday language; Shimanoff (1984) finds that, in everyday conversations, hope is among the most frequently named emotions.
In their motivational theory of emotions in advertising, Rossiter and Percy (1987) propose that hope is one of four basic emotions, along with fear, relief, and disappointment. Still, despite its relevance to human behavior in general and consumer behavior in particular, little has been written about the concept of hope. Lazarus (1999, p. 653) writes, "With a modest number of exceptions ... there has been a great reluctance on the part of psychologists to address the concept of hope." Averill (1991; Averill, Catlin, and Chon 1990) believes that the dearth of a systematic study of hope is attributable to traditional emotion theories' failure to recognize it as an emotion. Such theories tend to focus on emotions that have a strong biological and physiological emphasis (e.g., fear, anger, love) that may not encompass more intellectual emotions such as hope. Only with the development of appraisal theory as a theoretical approach to emotions has hope emerged as a bona fide emotion (Frijda 2000; Lazarus 1991; Roseman 1991; Shaver et al. 1987; Smith and Ellsworth 1985).
The purpose of this conceptual article is to shed light on the relevance of hope to consumer behavior, marketing, and public policy. First, we use an appraisal theory perspective to define hope and to differentiate it from potentially related constructs. Second, we use the appraisal dimensions of hope to identify tactics that marketers use to stimulate hope. Third, we describe the relevance of hope to the discipline. Although its relevance is potentially wide reaching, we focus on product evaluation (e.g., attitudes, satisfaction) and consumer choices. We conclude with additional research questions relevant to the study of hope.
According to appraisal theory, emotions are caused by a person's interpretation of a given situation (Frijda 1986, 1991; Roseman 1991; Smith and Ellsworth 1985). Although researchers have articulated several diverse appraisal theories of emotion, all contend that emotions are responses to a person's interpretation or appraisals of the environment and its relevance to goals.
Appraisal theorists suggest that people appraise their environment along several appraisal dimensions, namely, ( 1) goal congruency, ( 2) personal agency, ( 3) certainty, ( 4) normative/moral compatibility, and ( 5) importance.( n1) According to appraisal theory, various appraisal dimensions combine to evoke a specific emotional response. Hope is a positively valenced emotion evoked in response to an uncertain but possible goal-congruent outcome. Of particular relevance to hope are the dimensions of goal congruency, certainty, and importance.
The goal-congruency dimension reflects the extent to which the environment is or is not conducive to goal fulfillment. In a benign environment, "goal congruent" means that a favorable outcome could occur. In an aversive or threatening environment, "goal congruent" means that a negative outcome could be avoided or solved. Environments appraised as goal congruent (incongruent) are evaluated as good or desirable (bad) and evoke positive (negative) emotions.
Research has empirically confirmed that hope is a positive emotion (Shaver et al. 1987) that arises from environments or outcomes characterized as goal congruent (e.g., Ellsworth and Smith 1988; Lazarus 1991) and uncertain but possible (e.g., Roseman 1991; Roseman, Spindel, and Jose 1990). Outcomes regarded as certain will not evoke hope. Averill, Catlin, and Chon (1990) find that college students reported terminating hope when the time during which the goal-congruent outcome should have occurred had passed (i.e., students were certain that the outcome would not happen) and/or when they were certain that the goal could not be achieved. A person feels hopeless when he or she is certain that a goal-congruent outcome will not occur (Seligman 1975).
In this article, we sometimes use the term "possible" as a shortcut to the expression "uncertain but possible" or simply "uncertain" for three reasons: ( 1) The term "possibility" is endemic in the definition of hope offered by several hope researchers (Gelwick 1979; Haase et al. 1992); ( 2) it is logically correct (i.e., a person does not hope for something that is uncertain per se but for something that is possible even though it is uncertain); and ( 3) from a clarity of exposition perspective, it is simpler (i.e., stating that a person regards an outcome as "impossible" is simpler than stating that a person is "certain that an outcome will not occur"). The term "possibility" is embedded in "uncertainty" and thus is strongly consistent with appraisal theory.
Although the existence of hope is predicated on appraisals of outcomes viewed as goal congruent and possible, appraisal theorists link its intensity to variation in the importance and goal-congruity dimensions. The more important a person perceives a goal-congruent outcome, the greater is the value attached to its occurrence and the more severe are the potential consequences of its failure. Averill, Catlin, and Chon (1990) find that objects of hope are described as highly important. Hope is not viewed as an appropriate emotion when the outcome is trivial. Lazarus (1991) indicates that in some cases, the value or importance is driven by perceived deficiencies between the current and the desired end state. Lazarus (1999, p. 653) writes, "A fundamental condition of ... hope is that our current life circumstance is unsatisfactory--that is, it involves deprivation." Consistent with this idea, Hamilton (1978) indicates that perceived deficiencies in economic assets stimulated hope for gold in the California Gold Rush. Similarly, participation in the lottery is greatest among consumers with the most limited economic means (Clotfelter and Cook 1989).
The intensity of hope also varies as a function of variation in goal congruity or of the degree to which the outcome is positive or desired. Averill, Catlin, and Chon (1990) find that people no longer feel hope when they no longer desire the outcome.
Following Lazarus (1991), we use the term "yearning" and conceptualize it as the joint combination of the degree of importance and goal congruity. Yearning, and thus the intensity of hope, can be conceptualized in terms of the degree to which the outcome is positive or goal congruent, weighted by the outcome's importance. Hope is not equated with yearning; yearning for a goal-congruent outcome that is regarded as certain evokes emotions other than hope. Yearning for a goal-congruent outcome that is appraised as certain evokes emotions such as joy (when oriented toward the present) and nostalgia (when oriented toward the past). Likewise, yearning for a goal-congruent outcome that is appraised as certain not to occur evokes despair, not hope.
Distinguishing Hope from Other Constructs
Expectations. The distinguishing of hope from expectations is critical because several consumer behavior phenomena are predicated on the concept of expectation (e.g., expectancy value as in subjective expected utility and multiattribute attitude models, expectancy disconfirmation in the satisfaction literature). First, hope is an emotion, whereas expectations are beliefs. Second, hope reflects situations described as goal congruent, whereas expectations and perceived probabilities encompass situations that are goal congruent, goal incongruent, or goal irrelevant. Third, feelings of hope are based on appraisals of possibility, not of probability. A person can experience hope even when the probability of an outcome is low. For example, many examples from medicine indicate that consumers experience hope about overcoming disease even in the face of overwhelming odds (Lazarus 1999) and that people given the exact same probabilities of survival vary greatly in the degree of hope that they feel. The difference is that some people interpret even extremely low probability estimates of survival as evidence of the possibility of recovery (Taylor and Brown 1988; Taylor et al. 2000).
Involvement. Park and Mittal (1985) define involvement as goal-directed arousal. Hope and involvement may be empirically related; for example, a consumer who has strong hope for a goal-congruent outcome (e.g., profiting from the stock market) may be highly involved in activities that support its occurrence (e.g., by consulting prospectus sheets, friends, and brokers).
However, the two concepts are conceptually distinct. First, whereas involvement reflects arousal or energy, hope is more: It is a positive emotion, and an emotion attached not to an advertisement, a message, a brand, a medium, or a decision but to a goal. Second, hope is linked to outcomes from a decision, not the decision itself. Third, importance is an antecedent to involvement, though it is a critical component of the yearning dimension of hope. Finally, hope can vary even when involvement is high. For example, two consumers may both be highly involved in finding a one-bedroom apartment in a large city, because the task is high both in personal relevance and in potential risk (e.g., economic, psychological, social, time). However, one of the consumers may have considerably stronger hope of finding an apartment because the goal-congruent outcome is both yearned for and uncertain. Specifically, one of the consumers may have greater yearning to find a one-bedroom apartment because he or she finds it important to self-esteem to create an independent life (perhaps because of feelings of deficiency in the self-concept as an independent person) and/or finds the prospect of getting away from family problems at home highly goal congruent and desirable.
Rossiter and Percy (1991, p. 103) suggest that "all ads make a 'promise' and thereby invoke hope--whether this hope be for termination of a negative state (negative reinforcement) or for onset of a positive state (positive reinforcement)." In other words, because hope is evoked when a goal-congruent outcome is uncertain but possible, varying the hope that consumers feel should be influenced by marketing activities that affect appraisals of possibility and/or the extent to which they yearn for the goal-congruent outcome. Table 1 summarizes an illustrative though nonexhaustive set of tactics that affect the appraisals and thus elicit hope. The first set of principles identifies factors that induce hope by making possible a goal-congruent outcome previously appraised as impossible. The second set identifies factors that enhance hope by affecting yearning.
Inducing Hope by Altering Certainty
Tversky and Fox (1995) suggest that events have the greatest impact on cognitive assessments of the environment when they turn an outcome perceived as impossible (certain not to occur) into one that is possible (and thus uncertain). As Table 1 shows, the possibilities may reside in the product, the person, or the process of goal pursuit.
Suggest possibilities in the product. Innovations reflect new products or attributes that imply possibilities for achieving goal-congruent outcomes that formerly were viewed as impossible. Therefore, marketing communications that imply possibilities of goal-congruent outcomes with respect to novel aspects of products should induce hope. Possibilities are inferred when communications claim that products involve "breakthrough technology" or offer "revolutionary" designs. Because the outcomes are breakthrough and the designs revolutionary, perceptions that the new product can make goal-congruent outcomes possible seem to be natural.
Consumers may experience the possibility of a goal-congruent outcome thwarted by existing products' inability to achieve the goal-congruent outcome, even given the personal or environmental contingencies that hinder goal achievement. However, customization implies the possibility of a goal-congruent outcome by creating a match between a specific product configuration and a consumer-specific yearned outcome. It also implies the possibility of goal achievement by eliminating or greatly reducing personal or environmental contingencies that may thwart goal achievement. For example, personalized weight-loss programs "custom designed" to fit a person's metabolism may enhance a person's hope to achieve previously unattainable goals.
Suggest possibilities in the person. In contrast, it may be proposed that possibilities for achieving the goal-congruent outcome lie in the person (see Table 1). Curry and colleagues (1997) indicate that the difference between people who feel strong hope and people who feel weak hope is that the former perceive that they have personal control over the occurrence of goal-congruent outcomes, because control suggests that achievement of such outcome is inherent in the person. Analogously, Seligman (1975) indicates that people feel hopeless when the situation affords no control over aversive outcomes (see Bandura 1997). Therefore, products, brand names, and marketing communications that suggest that they help consumers control a consumption outcome should enhance hope. Titles of books in the self-help industry usually imply that a person has power and can take charge of life forces to achieve hoped-for outcomes. Nike's "Just do it!" slogan also implies that control resides in the consumer.
Consumers may also perceive that the possibility for achieving a goal-congruent outcome lies with them when communications model similar others who have successfully achieved the yearned-for outcome. Such models evoke hope because achievements by similar others are perceived as diagnostic of possibilities that a person may achieve him-or herself (Bandura 1997). For example, the weekly announcement of new lottery winners and before-and-after pictures of similar others lead to the inevitable conclusion: "If it can happen to them, why not me?"
Anticipatory self-imagery may also promote the perception that goal-congruent outcomes are possible and reside in the person. Considerable research suggests that imagining future outcomes increases people's assessments that a goal-congruent outcome will occur (for a review, see Johnson and Sherman 1990). As evidence of the impact of anticipatory self-imagery on hope, Wilkinson (1990) finds that anticipatory self-imagery of positive outcomes helped cancer patients replace depression and despair with hope.
Suggest possibilities in the process. Achievement of goals is impossible if a person does not know how to do so. Accordingly, perceptions that a goal-congruent outcome is possible should be enhanced by knowledge revelation, specifically, by revealing secrets, or previously unknown steps to achieving them. Several advertisements, books, and magazines suggest that goal-congruent outcomes are now possible given the revelation of "secrets," "tips," or "tricks."
Considerable research suggests that "pathways" thinking (i.e., the perceived ability to generate multiple, plausible routes to achieve a goal-congruent outcome) enhances hope by affecting perceptions of possibility (Curry et al. 1997; Snyder 2000). When multiple pathways are identified, the attainment of the goal-congruent outcome is still regarded as possible, should any one pathway be blocked. For example, in a personal selling context, salespeople stimulate consumers' pathways thinking by showing multiple ways to achievement associated with product use. For example, such is the case when a salesperson at a gym demonstrates the many machines and classes that can facilitate fitness and points out the location of affiliated clubs near home, work, and travel destinations.
Enhancing Yearning
Hope can also be induced by enhancement of the degree of yearning for the goal-congruent outcome. The antecedents to yearning (i.e., importance and goal congruence) can be used to effect this inducement.
Enhance perceived importance. Products should be regarded as more important when they achieve lower-order goals, but they also are instrumental to achieving higher-order goals (Austin and Vancouver 1996). For example, greater yearning should be created by claims that a product helped a consumer not only lose weight but also have a more romantic and physical relationship with his or her spouse, thereby enabling higher-order goals such as social approval and love.
Because the importance of an outcome can be stimulated by perceived deficiencies, yearning and hope should also be enhanced by affecting perceived deficiencies through comparisons of current states with ideal states. Such comparisons may enhance hope not only by making salient what could be (i.e., by affecting possibility) but also by enhancing yearning with a focus on the discrepancy between the current and the ideal states.
Consumers can also make social comparisons to an ideal other, an ideal past self, or an ideal future self. Appeals to any of these ideal entities should enhance yearning. For example, lottery advertisements that emphasize the glamorous life achieved by the few lucky winners induce a comparison and perceived discrepancy between this ideal life state and a person's current state. Similarly, weight loss and beauty advertisements that feature ultraslim and superfit models who epitomize beauty and athletic prowess induce perceived deficiencies and create yearning for the achievement of that ideal state. Advertisements that affect self-deficiencies by inducing consumers to make comparisons between themselves now and an ideal past are likely to create similar effects.
Enhance the degree of goal congruity. Enhancement of the degree to which an outcome is regarded as goal congruent and desired should also affect hope. Research in psychology links positive fantasy imagery with desire (Oettingen 1996; Przybyla, Byrne, and Kelley 1983). Evidence of tactics that stimulate fantasy is found in lottery advertisements that encourage consumers to fantasize about what they would do with a winning jackpot. The dating industry induces fantasies of finding a "soul mate" and emphasizes how much better life is when shared with the right partner.
Another tactic designed to enhance the degree of goal congruity is to suggest that the product resolves an approach or avoidance conflict (Rossiter and Percy 1987). For example, several diet advertisements and packages claim that products enable consumers to lose weight even when they eat as they normally would. Some skin care systems claim to provide outcomes that are as good as what could be provided by a dermatologist, but without the expensive dermatology bills; most abdominal machines guarantee great abdominal muscles with little effort.
The elicitation of hope takes on additional significance when its effects on consumer behavior are considered. We argue that the relevance of hope lies in its moderating effect on well-established antecedents of product evaluation and choice and that the moderating effects not only augment consumer behavior theory but also have important implications for marketing management and public policy (Table 2). We also argue that hope has effects that differ from what might be anticipated for expectations and involvement and that it explains consumer phenomena (e.g., susceptibility to fraud) in ways that have not yet been identified.
A long-standing body of research suggests that the impact of advertising content on brand attitudes depends on involvement. Theoretical accounts, including the elaboration likelihood model (Petty and Cacioppo 1983) and the systematic-heuristic processing model (Eagly and Chaiken 1993), propose effects such as the ones summarized in the first two columns of Table 3. Specifically, when involvement is low (because of lack of personal relevance), product evaluation is based on easily processed (peripheral or heuristic) persuasion cues, such as the valence of the pictures, the likeability of the source, or the pleasantness of background music. Attention to, search for, and elaboration of message-relevant information is limited. In contrast, when involvement is high, consumers engage in central route or systematic processing. Consumers attend to arguments, elaborate on their merit, and form enduring attitudes based on their assessment of argument strength. Several empirical studies support the moderating role of involvement on the advertising content-attitude favorability relationship (e.g., Chaiken and Trope 1999; Eagly and Chaiken 1993; Petty and Cacioppo 1986).
When hope is strong, by definition, outcomes are regarded as personally relevant. Personal relevance should stimulate involvement and result in systematic processing. Furthermore, because it is linked to outcomes appraised as uncertain, hope should trigger systematic processing so as to reduce uncertainty (Tiedens and Linton 2001). However, we argue that the yearning component of hope alters the nature of information processing. When hope is strong (because of intense yearning), consumers are motivated to preserve their feelings of hope. In reducing their uncertainty about the possibility of attaining their goal-congruent outcome, they prefer to conclude that such an outcome is possible, not impossible. In other words, when hope is strong, processing is tainted with directionality, and consumers seek to reduce uncertainty in the direction of the possibility (rather than the impossibility) of the outcome.( n2) Thus, we suggest that when hope is strong, attitude favorability is based less on the strength of message arguments than on the extent to which the arguments suggest that the goal-congruent outcome is possible. Rather than engaging in objective and systematic processing, consumers engage in motivated reasoning, another form of high-involvement processing.
We define "motivated reasoning" as a desire to think about and evaluate information in a way that supports a particular directional conclusion. Although both motivated and objective reasoning involve "motivation," the former involves a motivation to arrive at a yearned-for conclusion, whereas the latter involves a motivation to arrive at an accurate conclusion. With hope, the conclusion that consumers yearn for is that the goal-congruent outcome is possible. The greater the yearning for this goal-congruent outcome, the more motivated consumers are to process information in a way that suggests its possibility.
The potential impact of hope on motivated reasoning is supported by research that describes the seductive power of hope in judgment processes. Chaiken, Lieberman, and Eagly (1989) suggest that motivated reasoning is likely when people are ego-involved with an issue, as might be the case when an outcome is not only goal congruent but also associated with strong yearning (i.e., desired, important, and entailing deficiency). In a related vein, Alcock (1995) notes that people are most vulnerable to believing what they want to believe when they strongly yearn for a goal-congruent outcome. The third column of Table 3 summarizes how hope-induced motivated reasoning produces outcomes that differ from the ones expected under conditions of high-involvement objective processing.
Attention to Information
Research on attention reveals an encoding bias called perceptual defense, which is evidenced when the valence of information affects the likelihood that information is encoded and the speed with which it is processed (Bruner and Postman 1947; Matlin and Stang 1978). Because the outcomes people hope for are goal congruent, we expect that consumers are more likely to encode information that suggests that the outcome is possible (rather than impossible). The intensity of hope, driven by yearning, should magnify the extent of perceptual defense. As such, we anticipate outcomes such as the ones we describe in P1.
P1: Under conditions in which involvement is high and hope is weak, consumers attend to message arguments that relate to the assessment of the true merits of the product. In contrast, when involvement is high and hope is strong, consumers attend to the message arguments that suggest that a goal-congruent outcome is possible.
As P1 suggests, such a bias should not be evident when hope is weak (and processing is thus objective).
Length of Information Search
With objective processing, consumers attend to information carefully and thus search for and process message-relevant arguments. Thus, the length of information search should be affected by the number of message-relevant arguments or by the length of the message. However, when hope is strong, the length of information search should be affected less by the number of message-relevant arguments or message length than by the extent to which claims suggest that the goal-congruent outcome is possible. Edwards and Smith (1996) argue that people should terminate information search earlier when the information supports a desired conclusion than when it does not. We surmise that this is the case when hope is strong. Because additional search runs the risk of a person identifying information that does not support the outcome's possibility, we expect that search terminates when consumers have evidence that the goal-congruent outcome is possible. The greater the yearning is for the outcome, the faster consumers may terminate search when information supports the outcome's possibility. Thus:
P2: When involvement is high and hope is weak, information search is affected by the number of message arguments and/or the length of the message. In contrast, when involvement is high and hope is strong, information search is affected by the extent to which the information supports the possibility of the goal-congruent outcome; the more quickly consumers identify information that supports the possibility of the goal-congruent outcome, the more quickly they terminate search.
Elaboration of Information
When involvement in a message is high and consumers engage in objective processing, they generate support and counterarguments based on the strength of message arguments. Strong (weak) arguments generate support arguments (counterarguments). However, when involvement in a message is high and hope is strong, the nature of the elaboration process may differ, because support and counterarguments are based less on the strength of the message arguments than on the extent to which they suggest that the goal-congruent outcome is possible. Because hope is dependent on the favorability of such evaluation, the uncertainty reduction that Tiedens and Linton (2001) predict should be directed not so much by an accuracy goal but by the favored hypothesis that the product is effective, that is, that the outcome is possible. Reviews by Frey, Schulz-Hardt, and Stahlberg (1996) and Kunda (1990) indicate that when reasoning is motivated, people tend to focus on cases that confirm a favored hypothesis, a bias labeled as the "confirmation bias."
The yearning dimension associated with hope should affect the magnitude of the confirmation bias. If consumers are confronted with information that supports the possibility of the congruent outcome, they generate support arguments. In contrast, consumers may tend to ignore or counter-argue information that suggests that the goal-congruent outcome is not possible, which is known as the disconfirmation bias (Lord, Ross, and Lepper 1979). Edwards and Smith (1996) find that "emotional conviction" affects the magnitude of the confirmation and disconfirmation bias; greater bias is exhibited when people are most committed to the desired conclusion. Likewise, Ahluwalia, Burnkrant, and Unnava (2000) find that when consumers have strong commitment to a company, they are more likely to counterargue negative publicity written about the firm. Therefore, we expect that the more intensely a person hopes (yearns) for a goal-congruent outcome, the more confirmation and disconfirmation biases we will observe. Thus, we propose the following:
P3: When involvement is high and hope is weak, consumers elaborate on message claims, generating support arguments to claims that are strong and counterarguments to claims that are weak. When hope is strong, consumers elaborate on message claims, generating support arguments to claims that suggest that the goal-congruent outcome is possible and counterarguments to claims that suggest it is impossible.
The existence of confirmation and disconfirmation biases may be driven by variation in acceptance criteria for message arguments; previous research has linked motivated reasoning to differential acceptance criteria. For example, Edwards and Smith (1996) find that when people are confronted with information that goes against a favored conclusion, they tend to judge it as weaker than information compatible with a favored conclusion. Thus, we expect that consumers who hope that an outcome is possible may have more lenient acceptance criteria for information that supports the outcome's possibility and also stricter acceptance criteria for information that suggests its impossibility. When processing is objective, the perceived strength of an argument should not depend on the extent to which information supports the possibility of a goal-congruent outcome. For example, consider the argument "stays fresh longer" for a chewing gum designed to help consumers stop smoking. When involvement in the message is high and hope is weak, consumers may evaluate this argument as relatively weak because it does not pertain to the true merits of the product as a smoking-cessation device. However, when hope is strong, the consumer may interpret this argument as evidence of the product's efficacy at staving off tobacco cravings for a longer period. We anticipate:
P4: When involvement is high and hope is strong, consumers' evaluations of the strength of message arguments depend on whether the product information suggests that the goal-congruent outcome is possible or impossible. The possibility of the outcome does not affect assessments of argument strength when involvement is high and hope is weak.
Implications for Marketers
The preceding section suggests that under conditions in which consumers have strong hope for an outcome, marketers can enhance brand attitude favorability by including cues and message factors that support the possibility that consumers can achieve the goal-congruent outcome with product use, as is suggested in the tactics we described previously (see Table 1). The efficacy of the tactics is further enhanced when an advertisement contains cues that increase yearning (and thus intensity of hope) for the outcome. The preceding section also suggests that when hope is strong, brand attitudes are more favorable when advertisements focus on outcomes from product use than on the product and its attributes, because the former speaks more clearly to the goal-congruent outcome and its possible achievement through product use. Finally, the preceding section suggests that when hope is strong, disclaimers and product warnings are relatively ineffective as input to attitude formation or change because they are unlikely to be processed in light of perceptual defense and search termination. Even if encoded, disclaimers may have less impact because they may be counterargued or subjected to stronger acceptance criteria. Although prior research has identified a set of advertising and cognitive factors that impact disclaimer/product warning effectiveness (e.g., Johar and Simmons 2000), no previous research has considered the impact of emotional factors such as hope on their efficacy.
Implications for Public Policy
Scams and fraud deprive U.S. consumers of more than $100 billion annually (Langenderfer and Shimp 2001). An important public policy implication of P1-P4 is that when hope is strong, consumers are more susceptible to fraudulent activities. Prior research indicates that vulnerability to fraud is affected by demographic, social, and cognitive factors (e.g., Lee and Soberon-Ferrer 1997); however, research has not considered how emotional factors such as hope affect vulnerability. Indeed, a reason that poor, less educated, and older consumers may be more vulnerable to fraud is that they are the most deficient in money and/or products, experience greater yearning, and thus are more willing to act on scam-oriented transactions that imply the possibility of deficiency reduction. Thus, scams may work by suggesting that what is yearned for but seemingly impossible is actually possible; that is, they induce hope.
Another implication is that hope may make consumers vulnerable to products that have no scientific backing or make outlandish claims about outcomes (e.g., as is true of many diet and sexual-enhancement products), because information processing focuses not on the true merits of the product or service but on its promise of making the goal possible. A laissez-faire doctrine that places the onus of deciding whether a product is appropriate for consumption on the consumer may not be effective, because motivated reasoning may preclude objective product evaluation. More stringent policies that regulate marketing communications coupled with consumer education and awareness programs may be necessary. Notably, and in support of the previous public policy implications, stricter standards for diet and nutritional supplements and products that claim to treat or cure disease (Anthony 2003), contexts in which hope is likely to be strong, are under consideration by the Federal Trade Commission.
Beyond attitude formation processes, we anticipate that hope is also relevant to consumer satisfaction. Consumers' satisfaction with products has important implications for marketers, given satisfaction's effects on repeat purchasing, negative or positive word of mouth, and complaining behavior. As such, several theoretical perspectives have been leveraged to identify antecedents to satisfaction judgments (e.g., Spreng, MacKenzie, and Olshavsky 1996; Szymanski and Henard 2001). For example, the expectancy disconfirmation perspective suggests that consumers compare actual performance levels with expected performance levels. If the product performs worse (better) than expected, a negative (positive) disconfirmation occurs, and consumers are dissatisfied (highly satisfied) (e.g., Oliver and DeSarbo 1988). Equity theory suggests that satisfaction is based on the ratio of input to output (cost to benefit) received from the product in comparison with a referent other (Fisk and Young 1985). The emotions perspective proposes that emotions such as contentment, anger, or joy experienced during consumption can leave affective tags on the product's memory trace that are accessed in satisfaction evaluations (Westbrook and Oliver 1991). Finally, attribution theory (Folkes 1988) proposes that consumers' dissatisfaction with a failed product depends on whether they blame the manufacturer, uncontrollable factors in the environment, or themselves for failed outcomes. For the most part, these perspectives are noncompeting, and there is evidence for all of them.
Although all prior theories contend that satisfaction evaluation is contingent on the valence of the consumption outcome, we anticipate that hope moderates the impact of outcome valence on satisfaction because hope affects many of the previously identified antecedents to satisfaction, specifically ( 1) whether consumers perceive a disconfirmation, ( 2) the extent to which they perceive the exchange as equitable, ( 3) the nature of the emotions they experience on achievement of the goal-congruent outcome (or lack thereof), and ( 4) to whom they attribute product success or failure.
When the Goal-Congruent Outcome Is Realized
Consider the impact of hope on satisfaction when the goal-congruent outcome is realized and thus the outcome valence is positive. We expect that consumers are more likely to be satisfied with the same product experience when hope is strong than when it is weak. First, consumers are more likely to experience a positive disconfirmation when hope is strong rather than weak, because the yearning associated with strong hope should engender motivated reasoning, thus encouraging consumers to engage in tactics such as biased information search, selective attention, and biased hypothesis testing so as to conclude that the outcome is possible.
Second, greater yearning associated with more intense levels of hope suggests that consumers are willing to bear whatever costs are necessary to achieve the outcome. Thus, from an equity perspective, the cost-benefit ratio decreases as the benefits are strongly yearned and loom large in relation to the costs incurred.
Third, the uncertainty surrounding the occurrence of the goal-congruent outcome should magnify the intensity of positive emotions such as joy, happiness, and potential relief (if the goal-congruent outcome is driven by others or by environmental factors) or pride (if it is due to personal efforts). Van Dolen and colleagues (2001) show that the intensity of positive emotions such as these positively affects product satisfaction.
Finally, because achievement of the outcome is appraised as uncertain, consumers may attribute its occurrence to the product, because they themselves may not have been able to make the outcome happen in absence of the product. Thus, we anticipate effects such as the ones we describe in the subsequent P5a.
When the Goal-Congruent Outcome Is Not Realized
Consider how hope affects consumer satisfaction when product purchase or use does not result in achievement of the goal-congruent outcome. We predict that consumers experience less dissatisfaction when hope is strong than when it is weak. Again, the yearning and uncertainty appraisals of hope explain this counterintuitive result. The yearning component of hope affects the magnitude of the disconfirmation. The more consumers yearn for the goal-congruent outcome, the more motivated they are to perceive that the outcome has actually occurred or that some positive benefit, even if not the one originally intended, has been realized. Thus, consumers may notice that though a given product does not eliminate their wrinkles, it does seem to make their skin look more "dewey." Consumers may also use selective attention and notice that the product does seem to have made the lines around their lips finer (and not notice that it has had no impact on lines around the eyes). It is entirely possible that hope-induced motivated reasoning also causes consumers to fail to encode a product failure as such. Taylor and colleagues (2000) have found evidence of such illusions of success and well-being. Likewise, Gilovich (1983) finds that gamblers "rewrite" their histories of success and failure by scrutinizing and explaining away their losses; they count some losses as "near-wins." Similarly, a consumer who strongly hopes to achieve a goal through product usage may rewrite the product's failure into a quasi success. This logic may explain consumers' continued use of products (e.g., nutritional supplements, antiaging creams) despite their negligible effects.
Even if consumers perceive a product failure, they may not see the costs borne in product acquisition and usage as significant (e.g., "What did I have to lose by trying?"). Thus, perceptions of equity may not be negative. Furthermore, because the outcome is uncertain, consumers may have already anticipated potential failure, which in turn should minimize negative emotions such as disappointment. Finally, consumers may attribute lack of outcome achievement (if perceived) to their own unrealistic expectations or to other circumstantial factors (e.g., "It is very difficult to lose weight over the holidays"). On the basis of the preceding, we predict the following:
P5a: Under conditions in which the outcome is positive, consumers for whom hope is strong are more satisfied with the product than are consumers for whom hope is weak.
P5b: Under conditions in which the outcome is negative, consumers for whom hope is strong are less dissatisfied with the product than are consumers for whom hope is weak.
Although it may appear odd that actual product performance is potentially unrelated to satisfaction, Szymanski and Henard's (2001) meta-analysis reveals that actual product or service performance is not a significant predictor of satisfaction. Thus, situations are possible in which a product fails to perform yet consumers do not become dissatisfied.
Implications for Marketers
The foregoing suggests that marketers are often in a favorable position by enhancing consumers' hope, because high levels of hope create delight and consumer satisfaction if the product delivers the goal-congruent outcome and limited dissatisfaction if it does not. Notably, the effects are not explained by the concept of consumer expectations. Although marketers may always be in a positive position by enhancing hope, their enhancing expectations may cause negative effects for them because it sets consumers up for a potentially negative expectancy disconfirmation. Moreover, if hope has an effect on an expectation disconfirmation process, it operates through the disconfirmation component, not the expectancy component. Indeed, that consumers regard the goal-congruent outcome as possible but uncertain suggests that they may have relatively weak expectations of the occurrence of the outcome. Furthermore, we argue that the impact of hope on satisfaction and dissatisfaction operates through not only disconfirmation but also additional antecedents to satisfaction, such as equity and attributions.
Implications for Public Policy
The potentially limiting effect of hope on dissatisfaction has consequences for both the individual consumer and the marketplace as a whole. Insensitivity to product failure or the cataloging of a previous failure as a near-success may result in continued expenditures on useless products as well as disincentives for marketers to invest in significant product improvements.
In addition to the effects of hope on product evaluation, hope may also influence consumer choice. We examine two choice-relevant contexts: ( 1) the impact of advertised risks on choice and ( 2) consumption choices in the context of self-regulation.
Perceived risk reflects the extent to which use of a product or service is perceived as having ( 1) uncertain, ( 2) personal, and ( 3) severe consequences (Bauer 1960; Cunningham 1979). Because several products entail risk (e.g., investments, gambling, cosmetic surgery, diets), marketers are encouraged to warn consumers of the risks associated with product use in advertising. Indeed, in certain industries such as pharmaceuticals, government mandates require explicit risk information in advertising (Dickinson 1999). However, marketers are ambivalent about advertising risks because of the potentially negative impact of risk advertising on choice, as is reflected by their use of creative tactics to avoid explicit advertising of risks (Adams 2002).
We argue that hope moderates the negative relationship between advertised risk and choice; specifically, when hope is strong, the negative impact of advertising risks on choice is diminished compared with conditions in which hope is weak. The reason is that one or more appraisal dimensions of hope (e.g., goal congruity, importance) affect one or more dimensions of perceived risk (e.g., uncertainty, personal nature, severity of consequences).
First, research from several areas suggests that the yearning associated with hope should affect consumers' uncertainty about the consequences of risky choice, which shifts their assessment to the possibility that negative consequences will not occur. Research in the medical domain shows that consumers overestimate medical risk when their emotions are negative, and they underestimate medical risk when their emotions are positive (Bowen et al. 2003). In a marketing domain, Chaudhuri (2002) finds that negative (positive) emotional experiences induce greater (lower) perceptions of product risk. Because hope is a positive emotion whose intensity is affected by yearning, we expect that stronger levels of hope are associated with stronger perceptions that product usage does not result in negative consequences.
Uncertainty about the consequences of product use may also be affected by the yearning component of hope through its effects on motivated reasoning. Perceptual defense processes may focus consumers' attention on non-risk-related message information. If risk information is processed, consumers may counterargue it or use stricter acceptance criteria. Combined, such processes should reduce uncertainty about the consequences by making negative consequences seem less likely.
Second, the yearning and uncertainty components of hope may reduce the perceived severity of the consequences. Specifically, the yearning component of hope may encourage elaborated imagery (MacInnis and Price 1987), because consumers envision the positivity of life when the goal-congruent outcome occurs. If consumers imagine negative outcomes at all, they may imagine outcomes that are more benign or less severe. This is because the positivity associated with hope brings to mind favorable images associated with achieving a goal-congruent outcome rather than unfavorable outcomes, which makes the latter less salient and less available.
Finally, independent of risk perceptions, hope may moderate the relationship between advertised risks and choice, because the yearning component of hope alters consumers' perceptions of the risk-reward trade-off (Bell 1995). That is, when hope is strong, consumers may yearn for the goal-congruent outcome so intensely that they are willing to bear whatever risk is involved to achieve it. Considerable anecdotal evidence supports this effect. Peiss (1998) and Brumberg (1998) discuss several risky practices that women historically have engaged in to look more beautiful, such as taking the poison belladonna to achieve "bedroom eyes," ingesting arsenic to improve their complexion, or undergoing carcinogenic X-ray treatments to reduce acne. Today, consumers who hope to look beautiful use botulism to smooth wrinkles, silicone implants to achieve a more attractive bosom, and cosmetic surgery to attain a youthful appearance. Consumers who hope to overcome illness are willing to take untested medications and controversial, expensive methods of alternative healing. In the interpersonal domain, teens who hope to be "cool" adopt such risky behaviors as underage smoking, drinking, drug use, shoplifting, and unprotected sex. Childless couples who desperately hope for children are willing to undergo expensive and potentially dangerous physical procedures to become pregnant. Hamilton (1978) indicates that people risked lives, families, fortunes, and the ability to return to their homeland for the hope of striking it rich during the Gold Rush. Thus:
P6: The impact of advertised risks on decisions not to purchase a product is considerably greater when hope is weak than when it is strong because more intense levels of hope (a) increase perceptions that positive outcomes will occur, (b) reduce perceptions that negative consequences may occur to the self and/or (c) be severe, and (d) increase the risk-reward trade-off.
The effects in P6 cannot be explained by involvement or expectations. High involvement coupled with weak hope would induce systematic processing, thus increasing the attention to and processing of advertised risks because they constitute strong arguments as to why the product should not be chosen. Such processing should increase expectations that risks could occur to the self.
Implications for Marketers
P6 leads to the conclusion that consumers are more prone to undertake risks when hope for the outcome is strong. Consistent with this idea, Averill, Catlin, and Chon (1990) report that respondents indicated that they took additional risks and "stuck their neck out" for things they hoped for. The previous anecdotal information also supports this idea. Thus, by enhancing consumers' hope or by targeting markets of consumers whose hope is strong, marketers of innovative products may be able to turn normally conservative buyers (i.e., late adopters) into innovators who are willing to adopt the product despite the risks of buying early. In other words, hope may cause a steeper slope of the product adoption curve of goods and services that position themselves as means to attaining goal-congruent outcomes.
In addition, hope may reduce consumers' price sensitivity. Although a high price entails economic risk, consumers may be willing to bear higher levels of economic risk when their hope for a goal is strong. Thus, an advertised high price may prove less of a deterrent to choice for consumers for whom hope is strong rather than weak.
Implications for Public Policy
P6 also suggests that hope may affect consumers' vulnerability to product harm (Smith and Cooper-Martin 1997). Although the Spring 1995 Special Issue of Journal of Public Policy & Marketing indicates that medically underserved, poor, older, and rural consumers are more vulnerable to product harm, research has not considered that vulnerabilities may be tied to hope. That is, it is the consumers who yearn most for a goal-congruent outcome (e.g., the ones who are in-need) who are most likely to ignore, discount, or assume as unlikely risks from consumption.
To reduce vulnerability to product harm, communications may demonstrate safer means to achieving the yearned, goal-congruent outcome or may reduce hope by demonstrating the possibility of severe and negative consequences, thus shifting yearning to other outcomes that entail lesser risks. Notably, though, prior research has found that tactics that highlight the possibility of harm also result in defensive and biased processing, because such tactics induce fear, an emotional concomitant of hope (Pechmann et al. 2003). That research supports the impact of intense anticipatory emotions on biased processing, and it also suggests that public policy campaigns are least likely to induce biased processing and thus be more effective if they show consumers how to prevent aversive consequences through their own actions.
Many consumers are beset by problems of self-regulation as evidenced by overeating, compulsive shopping, gambling, drug use, smoking, and alcoholism. In many cases, problems exist despite consumers' rational knowledge that their consumption means forgoing a larger and more important long-term goal. Hoch and Lowenstein (1991) call these "time-inconsistent preferences," because the immediate behavior that consumers want to engage in is inconsistent with the longer-term goal they would like to achieve. Time-inconsistent preferences occur when the desire for a given behavior (e.g., eating, drinking, smoking) is greater than the consumer's willpower to forgo the behavior in light of a larger goal (e.g., weight loss, sobriety, nicotine-free living). Recent research has verified that desire and willpower are indeed related to time-inconsistent preferences, at least in the domain of economic spending (Karlsson 2003).
Although the marketplace is often a source of temptation, consumers also turn to marketplace products, services, and self-help groups to help them reduce desire and enhance willpower. Unfortunately, little is known about factors that may moderate the relationship between desire (or willpower) on the one hand and self-regulatory success on the other hand. We argue that an important moderator is hope.
Specifically, we posit that whether desire enhances the likelihood of a time-inconsistent choice depends on whether hope is strong or weak. The yearning and possibility dimensions of hope enhance willpower, thus rendering the impact of desire on time-inconsistent preferences less significant. As such, desire may affect time-inconsistent choices only when hope is weak. Several factors underlie our reasoning.
First, the uncertainty dimension of hope reminds consumers that the goal-congruent outcome is possible and thus makes the benefits of the long-term goal salient in light of the more immediate outcome. Second, yearning for the longer-term goal may render the immediate choice less desirable when weighed against the longer-term, yearned-for outcome. Finally, yearning associated with the goal-congruent outcome may cause consumers to imagine the anticipated regret they might feel from forgoing a longer-term, yearned-for goal for a short-term, time-inconsistent choice. Combined, these factors suggest the following:
P7: The impact of desire on time-inconsistent choice is minimized when hope is strong rather than weak. Thus, consumers will more successfully resist time-inconsistent choice when hope is strong.
Implications for Marketers
The preceding propositions have several implications for marketers of self-regulatory products and services. Specifically, marketers should be better able to enhance the success of their self-regulatory products and services to the extent that they create hope in consumers. For example, consider the success of programs such as Weight Watchers: The organization influences hope in meetings as attendees relate their success stories and achievements publicly. Such stories remind other attendees that goal achievement is possible. They also reinforce yearning as attendees relate the many and varied ways that goal achievement results in higher-order, yearned-for goals related to weight loss (e.g., greater energy, respect and admiration from others, better romantic interactions).
Implications for Public Policy
Several nonprofit organizations engage in public health campaigns designed to encourage consumers to give up smoking, drinking, drugs, and the like. Considerable scholarly research in the public health arena is devoted to understanding the efficacy of advertising practices that encourage cessation of self-destructive and problematic consumption behaviors (e.g., Pechmann et al. 2003). If hope is related to consumers' motivation to achieve self-regulatory outcomes, advertisers that promote cessation of maladaptive consumption practices may be more successful if they enhance consumers' hope about cessation and thus their motivation to heed social marketing messages. Specifically, enhancement of hope may also increase a critical predictor of intentions to stop consumption: self-efficacy. Consumers may be more likely to believe that they can stop a behavior if they are made to understand that stopping is possible. Hope may also enhance another critical predictor of intentions to stop: beliefs about the consequences of stopping. The yearning component of hope may enhance beliefs that the consequences of quitting are positive. Thus, marketing messages that encourage hope about cessation of the maladaptive behavior may be more effective than ones that do not.
The objective of this conceptual article is to introduce the concept of hope and suggest its potential relevance to product evaluation and choice. Using appraisal theories of emotion, we argue that hope is a positively valenced emotion whose intensity is a function of the extent to which a goal-congruent outcome is yearned for and appraised as uncertain but possible. We also identify tactics that can stimulate hope, and we suggest that hope moderates well-established relationships that are relevant to marketing and public policy, including the relationships between ( 1) involvement and attitude-formation processes, ( 2) outcome valence from product use and satisfaction, ( 3) advertised risks and choice, and ( 4) marketing tactics and self-regulatory behaviors. Several additional research questions relevant to evaluation and choice and other contexts warrant attention. Given space constraints, we briefly identify a few.
In the domain of attitudes, managerial and theoretical research based on the multiattribute attitude model has been extremely useful in understanding consumer behavior and in guiding managerial practice. It is not known whether multiattribute attitude formation based on attribute yearning and possibility leads to attitudes that differ in valence, strength, or behavioral prediction from attitudes based on traditional attribute evaluation and belief strength, as well as the extent to which hope moderates the relationship between attitudes and behaviors.
Notable questions pertaining to the impact of hope on choice timing can be identified. Does the intensity of hope encourage impulsive buying? Are consumers more likely to delay gratification and delay choice, so as to live off the hope that the product, when purchased, results in the goal-congruent outcome? For example, consumers sometimes delay scratching off lottery tickets to savor the hope of a win. Is waiting more difficult or easier as hope becomes stronger?
The normative and ethical implications of creating hope should also be examined. Although the inducement and enhancement of hope may create positive social welfare outcomes (e.g., self-regulation), it may also create outcomes that are negative from the standpoint of social welfare (e.g., susceptibility to risk, susceptibility to fraud) as well as others we have not considered. For example, does hope for products lead to excessive consumption and subsequent debt or credit abuse? Does hope for outcomes achieved through the marketplace encourage materialism? To what extent do the poor feel hopeless and in despair when they perceive consumption of yearned-for products impossible?
At present, answers to these and other provocative questions remain elusive. The concept of hope is rich in its potential to provide insight into consumer behavior, marketing practice, and public and social policy. Although the discussion here only alludes to hope's potential, we hope that it can guide theoretical and empirical work in the future.
The authors thank Valerie Folkes, Allen Weiss, C.W. Park, Allison Johnson, and Shashi Matta for their comments on previous drafts of this article. The authors also thank the three anonymous JM reviewers for their insightful suggestions throughout the review process.
( n1) Although many different terms have been used to describe the appraisal dimensions, most fit the set of dimensions articulated by Johnson and Stewart (2004) and described herein.
( n2) In Tiedens and Linton's (2001) experiments, the uncertainty emotion under study was incidental and dissociated from the cognitive task that followed. Participants were motivated to reduce uncertainty, but they did not have a directional motivation as to the outcome of the evaluation. However, the emotion of hope is intimately linked to the evaluative task at hand and depends on the yearning associated with the outcome related to the evaluation.
Tactics That Induce and Enhance Hope
Legend for Chart:
A - Principle
B - Tactic
A
B
Turn Impossibility into Possibility
Suggest possibilities in the
product
Promote innovations: New product features to
overcome obstacles to attainment of the hoped-for
outcome (e.g., "Our new, state-of-the-art, patented
formula brings you results you've never been able to
achieve before!")
Promote product customization: The product or
service can be adapted specifically to fit the
needs of the consumer (e.g., "A diet plan just
for you.")
Suggest possibilities in the
Appeal to personal control (e.g., "Just do it!")
person
Model others' actions and outcomes (e.g.,
"Look at me now!")
Promote anticipatory imagery (e.g., "Just see
yourself free of joint pain.")
Suggest possibilities in the
process
Imply knowledge revelation (e.g., "The best-kept
secret for keeping sex alive.")
Reflect pathways thinking (e.g., "Anew workout
you can do at home, at work, or in the car.")
Enhance Yearning
Enhance perceived importance
Link to the achievement of higher-order goals
(e.g., "Losing weight makes you healthier,
increases your self esteem, and gains you
respect and admiration from others.")
Comparisons with ideal others (e.g., upward
comparisons to successful others, celebrities:
"The same diet that changed Oprah's life.")
Comparisons with ideal past self (e.g., "Remember
when you were able to run five miles without
breaking a sweat?")
Comparisons with ideal future self (e.g., "Be
all that you can be.")
Enhance the goal congruity of
the outcome
Encourage positive fantasy (e.g., "What would you
do with a million bucks?")
Suggest resolution of an approach-avoidance
conflict (e.g., "Eat all you want and
still lose weight!") Why Study Hope
Legend for Chart:
A - Implications
B - Moderating Role of Hope on Product Evaluation
Attitude-Formation Processes
C - Moderating Role of Hope on Choice and Consumption
Behavior Effects of Advertised Risks on Choice
A B
C
For consumer behavior Systematic versus motivated
theorists processing
Factors that affect risk encoding
For managers Enhancing attitude favorability
Reducing the negative impact of
advertising risks
For public policy Consumer susceptibility to fraud
Susceptibility to product harm
Legend for Chart:
A - Implications
B - Moderating Role of Hope on Product Evaluation
Postconsumption Satisfaction
C - Moderating Role of Hope on Choice and Consumption
Behavior Effects of Marketing Practices on Self-Regulatory
Outcomes
A B
C
For consumer behavior Understanding satisfaction
theorists processes
Understanding self-regulatory
efficacy
For managers Tactics to enhance satisfaction
Tactics to enhance success of
self-regulation programs
For public policy Competitive pressures for market
improvements
Efficacy of public health campaigns Moderating Role of Hope on Brand Attitudes and Attitude-Formation
Processes
Legend for Chart:
B - Low Involvement
C - High Involvement, Weak Hope
D - High Involvement, Strong Hope
A B
C
D
Type of processing Heuristic/Peripheral Route
Processing: Evaluation of
information is based on
easily processed
persuasion cues
Systematic/Central Route
Processing: Evaluation of
credibility of information
based on its true merits
Motivated Processing: Evaluation of
information based on its suggestion
that achieving a yearned-for,
goal-congruent outcome is possible
Attention to information Limited: Attention to easily
processed persuasion
cues
Extensive: Pay attention
to all information relevant
to assessing true merits
of the argument
Selective (perceptual defense):
Attend only to information that
suggests that a goal-congruent
outcome is possible
Length of information Limited
search
Extensive: Search
terminates when
sufficient evidence to
pass judgment has been
gathered
Depends on the nature of the
information encountered: If
information confirms that
goal-congruent outcome is possible,
search terminates; if it suggests
it is not, search continues.
Elaboration of Limited
information
Extensive: Elaborate on
information to evaluate
the true merits of the
argument
Because of yearning and uncertainty
components of hope, elaboration is
extensive if information suggests
that achievement of yearned-for,
goal-congruent outcome is possible;
limited if information suggests that
achievement of yearned-for,
goal-congruent outcome is not
possible (biased hypothesis testing).
Confirmation bias for information
suggesting that achievement of
yearned-for outcome is possible;
disconfirmation bias for information
suggesting that it is not.
Criteria for judging Limited
information
High
Selective: Because of yearning and
uncertainty components of hope;
weaker acceptance criteria for
information, suggesting that
yearned-for, goal-congruent outcome
is possible; stronger acceptance
criteria for information, suggesting
that yearned-for, goal-congruent
outcome is not possible.
Attitude toward Dependent on the
advertised brand heuristic/emotional nature
of peripheral cues
Dependent on strength of
message arguments
Dependent on extent to which
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~~~~~~~~
By Deborah J. MacInnis and Gustavo E. de Mello
Deborah J. MacInnis is Professor of Marketing, Marshall School of Business, University of Southern California.
Gustavo E. de Mello is a doctoral student in marketing, Marshall School of Business, University of Southern California.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 154- The Effect of Corporate Social Responsibility on Customer Donations to Corporate-Supported Nonprofits. By: Lichtenstein, Donald R.; Drumwright, Minette E.; Braig, Bridgette M. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p16-32. 17p. 1 Black and White Photograph, 2 Diagrams, 4 Charts. DOI: 10.1509/jmkg.68.4.16.42726.
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The Effect of Corporate Social Responsibility on Customer
Donations to Corporate-Supported Nonprofits
Both theory and recent research evidence suggest that a corporation's socially responsible behavior can positively affect consumers' attitudes toward the corporation. The effect occurs both directly and indirectly through the behavior's effect on customer-corporation identification. The authors report the results of four studies designed to replicate and extend these findings. Using a field survey design, Study 1 provides evidence that perceived corporate social responsibility affects not only customer purchase behavior through customer-corporate identification but also customer donations to corporate-supported nonprofit organizations. Using experimental designs, Studies 2 and 3 replicate and extend the Study 1 findings by providing additional evidence for the mediating role of customer-corporate identification on the relationship between corporate social responsibility and customer donations. However, the combined results of Studies 2 and 3 also show that because of a "perceived opportunity to do good" by supporting a company that is changing its ways, consumers are more likely to donate to a corporate-supported nonprofit when the corporation has a weaker historical record of socially responsible behavior. Finally, Study 4 tests the relationship between the nonprofit domain and the domain of the corporation's socially responsible behavior as a boundary condition for this effect.
Each year, companies donate millions of dollars to various nonprofit organizations through various initiatives, including philanthropy, cause-related marketing, employee volunteerism, and other innovative programs. For example, Avon has raised more than $200 million for breast cancer education and early detection services through the Avon Breast Cancer Awareness Crusade. Coca-Cola has signed an agreement to provide $60 million and significant staff time to the Boys & Girls Clubs of America over ten years. Starbucks is the largest North American contributor to CARE, an international relief organization with programs in coffee-growing countries.( n1) Patagonia donates 1% of its sales to groups that are focused on environmental protection and restoration. Calphalon, a gourmet cookware manufacturer, has raised millions of dollars to feed hungry people through its sponsorship of Share Our Strength's Taste of the Nation events. Home Depot donates materials and its employees to build houses for Habitat for Humanity. As these examples show, many companies engage in corporate social responsibility (CSR) initiatives (Sen and Bhattacharya 2001; Strahilevitz and Meyers 1998). Companies are called on to address deep and persistent social ills of great magnitude, ranging from malnutrition and HIV to illiteracy and homelessness (Margolis and Walsh 2003). The Fortune cover story "America's Corporate Social Conscience" (Ioannou 2004) and the more than $9 billion that U.S. companies alone spent on social causes in 2001 (Cone, Feldman, and DaSilva 2003) provide further testament to corporate involvement with serious social problems.
The increase in CSR initiatives has been prompted both by companies that increasingly recognize it as a key to success and by nonprofits that have ever-increasing needs for resources. Corporate social responsibility refers to the obligations of the firm to society (Smith 2003), and we use the term "CSR initiatives" to refer to the various forms of company involvement with charitable causes and the nonprofits that represent them.( n2) Research findings provide support for the benefits that CSR initiatives provide companies, particularly in terms of enhanced consumer perceptions of the company (e.g., Brown and Dacin 1997; Drumwright 1996; Sen and Bhattacharya 2001), but more research is needed. In particular, little is known about the effects of CSR initiatives on nonprofits. Despite some obvious benefits, CSR initiatives can be particularly risky for nonprofits, which usually are the less powerful partner, and they may not ultimately serve the nonprofit's long-term interests (Andreasen 2003; Berger, Cunningham, and Drumwright, in press). It is important not only that nonprofits benefit from CSR initiatives but also that the extent to which firms make effective use of such relationships is ultimately influenced by the benefits to the nonprofits. Thus, the purpose of our research is to investigate effects of CSR initiatives on nonprofits and companies.
A way that CSR initiatives create benefits for companies appears to be by increasing consumers' identification with the corporation, or customer-corporate (C-C) identification, which is the degree of overlap in a consumer's self-concept and his or her perception of the corporation (Dutton, Dukerich, and Harquail 1994). For example, when a company undertakes a CSR initiative, to the extent that the initiative signals to consumers that the company has traits that overlap with their self-concept (e.g., civic minded, compassionate), consumers have higher degrees of identification with the company and, in turn, are more likely to support the company. Sen and Bhattacharya (2001) manipulate CSR to find that it positively affects C-C identification. In addition, CSR has a positive effect on consumer evaluations of the company, and this effect is partially mediated by C-C identification. The rationale for the mediated effect is that when consumers perceive companies as behaving in a socially responsible manner, they are more attractive targets for C-C identification, and they are more likely to support corporations with which they identify.
We extend this rationale to hypothesize that CSR-induced C-C identification leads not only to customer support for the corporation but also to increased support for nonprofits that the corporation supports. This rationale is based on the premise that when a company visibly aligns with a nonprofit, consumers may reasonably infer that support of the nonprofit is also support of a goal of the corporation with which they identify.
Although researchers have recognized benefits that accrue to the entities directly involved in the identification process (e.g., Ashforth 1998; Dutton, Dukerich, and Harquail 1994; Elsbach 1998; Glynn 1998; Sen and Bhattacharya 2001), to our knowledge, no research has addressed benefits that may accrue to third parties to the identification process, such as nonprofits. As collaborative marketing relationships flourish in various forms (e.g., CSR initiatives, cobranding, cross-promotions, strategic alliances), it is particularly important to understand any potential effects of identification on third parties. Our research addresses this void with respect to third parties in the context of CSR initiatives that involve both companies and nonprofits.
Over the past 15 years or so, organizational behavior researchers have studied the process by which people (usually employees) come to identify with some organization (usually the employer company). Researchers have coined the term "organizational identification" to refer to the overlap of a person's self-perceptions with his or her perceptions about the organization. For example, Dutton, Dukerich, and Harquail (1994, p. 242) define organizational identification as "a cognitive link between the definitions of the organization and the self." They also note that "when [people] identify strongly with the organization, the attributes they use to define the organization also define them" (p. 239). The concept has moved beyond the employee to others, such as the consumer (Bhattacharya and Sen 2003; Sen and Bhattacharya 2001).
A factor that is hypothesized to underlie consumer motivation to identify with an organization is the perceived attractiveness of the organization's identity. People identify with organizations with which they believe, or with which they want to believe, that they share common traits and that provide for a sense of self-enhancement (Ashforth 1998; Bhattacharya, Rao, and Glynn 1995; Elsbach 1998; Sen and Bhattacharya 2001). Thus, corporations attempt to position themselves in an attractive manner to increase consumer identification with the corporation. With the application of this logic to the present study, when a corporation behaves in a manner that is perceived as socially responsible, consumers are likely to infer that it has certain desirable traits that resonate with their sense of self. As a result, consumers are more prone to identify with the corporation; in so doing, they behave in a manner that supports the corporation's goals. For example, consumers who are aware of Timberland's CSR initiative with City Year, a youth service corps that works with disadvantaged urban youth, might infer that Timberland is compassionate and civic minded.( n3) If the same customers believe that they too have these traits and thus identify with Timberland, they are more likely to support Timberland by buying its products. There is evidence to support this contention (Sen and Bhattacharya 2001), and Bhattacharya and Sen (2003) theorize that the range of identification-driven behaviors extends beyond patronage to include behaviors that support other corporate goals, such as company promotion, customer recruitment, and resilience to negative corporate information.
Our research hypothesizes that consumers' identification with a corporation may lead to their support of additional corporate goals that have more direct implications for the welfare of third parties to the identification process, namely nonprofits with which the corporation is affiliated. We posit that when a socially responsible corporation is visibly aligned with a nonprofit cause, corporate customers can reasonably infer that support of the cause is a goal of the corporation. As such, to the extent that customers identify with the corporation, they are more likely to support the particular nonprofit cause and the corporation. Thus, we hypothesize that the perception of CSR positively affects C-C identification; through that identification, customers are more likely to support directly the nonprofits that the corporation supports and the corporation itself (see Figure 1).
We assessed constructs depicted in Figure 1 in a field survey of 1000 customers at four locations of a national food chain (n = 250, per store), two in each of two different nearby cities over a two-day period. As shoppers paid for their groceries, trained interviewers positioned at the end of the checkout aisles observed whether they handed the cashier a frequent shopper card. We restricted the sample to only consumers who used a frequent shopper card because we could track one of the dependent variables (i.e., year-to-date dollars spent at the store) for only customers who used the frequent shopper card. Interviewers approached the shoppers as they exited the cashier line and asked for their participation in a customer perception study that was conducted by professors from a local university. If a shopper agreed to participate, the interviewer asked the shopper to surrender his or her cash register receipt and to answer a few questions. Respondents then were given a take-home survey with a postage-paid return envelope addressed to the university and two $3.00 vouchers to encourage participation. The customer's store receipt, survey, and vouchers were all cross-coded with identification numbers. The two $3.00 vouchers were in the form of two postage-paid postcards addressed to corporate headquarters, either or both of which could be used as cash in the store or mailed to the corporation, which in turn would make a donation to a nonprofit that it supports. The reverse side was a postage-paid postcard. The vouchers were valid for purchases or donation for three weeks from the time of the study. The voucher side of the card is shown in Figure 2. Before conducting the study, we pretested the entire procedure.
The take-home survey contained measures of several of the study constructs. All scale items, descriptive statistics, reliability estimates, and sources are provided (for all studies) in the Appendix. We measured perception of CSR with a five-item scale that assessed perceptions of the corporation's commitment to giving back to the community by supporting nonprofits through both traditional philanthropy (i.e., check writing in response to nonprofit fund-raising appeals) and newer forms of CSR initiatives that involve integrating charitable activities into business activities (Drumwright and Murphy 2001; Hess, Rogovsky, and Dunfee 2002). We measured C-C identification by having subjects complete a trait-matching battery of 12 character traits. Subjects were provided with 12 traits and asked to circle the 5 that best described themselves and the 5 that best described the corporation. We then used the number of traits that customers circled as being common to themselves and the corporation (0-5) as a measure of C-C identification. The traits were based on interviews with corporate personnel and customers.
We assessed benefits that accrue to the corporation by two different constructs: perceptual benefits and behavioral benefits. We operationalized perceptual benefits by standardizing and then summing three different measures: ( 1) a three-item measure that assessed customer loyalty to the store, ( 2) a four-item measure that assessed emotional attachment to the store, and ( 3) a single-item measure that assessed customer interest in learning more about the store. We assessed behavioral benefits by standardizing and then summing two measures: ( 1) a single-item measure through which customers estimated the percentage of their total grocery shopping done at the store and ( 2) the year-to-date total dollar purchases at the store, printed on the grocery receipt of each customer (printed for all customers who used a frequent shopper card). Because we conducted the study in mid-November, we coded the ten-and-a-half month cumulative dollar amount spent at the store for each customer (X = $1,103.09, range = $0-$8,855.00, standard deviation [s.d.] = $1,294.64).
We operationalized nonprofit donation as the customer's decision of whether to use the $3.00 vouchers as cash or to donate them to a nonprofit that the corporation supports. Thus, each customer could have a score of 0 (did not donate either voucher), 1 (donated one voucher), or 2 (donated both vouchers) (X = .60, range = 0-2, s.d. = .87).
Results
For the 1000 customers across the four stores who were approached and who agreed to participate in the survey, we deemed 31 of the surveys unusable for various reasons (e.g., customer was an off-duty store employee). Of the 969 respondents who completed the in-store interviews, 508 take-home surveys were completed and mailed back (response rate = 52.4%). Of the 2000 $3.00 vouchers distributed (2 for each of 1000 people), 314 people redeemed a total of 582 for either cash (419) or donation (163).( n4) Consistent with the store's target market, the 508 customers who returned their take-home survey were socioeconomically "upscale." The median age group was 35-44, 79% of the sample was female, the median income was $50,000-$64,999, and the median education was college graduate.
Tests of Predictions
The results of regression analyses that test hypothesized relationships are reported in Table 1, where each row represents a separate regression model. The dependent variables are in the first column, and the independent variables entered into the equation for each of the respective regression models are in subsequent columns. As Table 1 shows, perception of CSR was related to perceptual corporate benefits (p < .01), and perceptual corporate benefits were positively related to behavioral corporate benefits (p < .01). Furthermore, perception of CSR was related to C-C identification (p < .01), and C-C identification was related to perceptual corporate benefits (p < .01). Finally, C-C identification was related to nonprofit donations (p < .01).
Discussion
Findings of Study 1 provide support for the proposition that the beneficiaries of organizational identification are broader than has previously been conceptualized. In addition to benefits that accrue to organizations and individuals, Study 1 provides evidence that customers who identify more strongly with the corporation are more likely to donate to corporate-supported nonprofits. The findings from Study 1 also are consistent with the proposition that CSR has both a direct and an indirect effect, through C-C identification, on corporate benefits. Although Sen and Bhattacharya (2001) find evidence in support of this by manipulating CSR and assessing student subject response in a lab setting, our research provides support in a field setting with actual shoppers as subjects and actual purchase behavior as a dependent variable. Thus, the results of the two studies jointly provide support for the internal and external validity of our finding.
However, our findings must be considered in light of the study limitations, most of which pertain to the use of a correlational field design. For example, three limitations include the inferring of construct sequence from cross-sectional data, respondent self-selection, and the possibility of omitted variable bias. Another limitation is the missing values in the data in general and (partially due to administration error) the voucher redemption variable in particular. In addition, there are theoretical questions about the operationalization of CSR. We operationalized CSR using measures of the corporation's commitment to supporting nonprofits. However, CSR has been conceptualized more broadly as the company's status and activities with respect to its perceived societal obligations (Brown and Dacin 1997; Hess, Rogovsky, and Dunfee 2002; Sen and Bhattacharya 2001; Smith 2003). Thus, our measures may underrepresent the CSR construct. The generalization of results to the CSR construct would be enhanced by a broader operationalization with regard to how the corporation performs its core business activities. Consequently, given the number of remaining questions about the influence of CSR on donation behavior, we conducted a second study.
In Study 2, 61 subjects enrolled in marketing courses were randomly assigned to one of two experimental conditions. Subjects were given an experimental packet that consisted of four pages (the second page contained the manipulation) followed by several pages of dependent measures. Subjects were told that the experimenters were working with a company that produced computers and calculators, which, for reasons of confidentiality, would simply be referred to as Company X in the materials that they would read. They were told that Company X was researching to gain information for input into a decision about whether it should offer a unique type of promotion. However, subjects were told that before they "responded" to the promotion, they needed to have an understanding of Company X and its corporate practices and attitudes so that they would be able to evaluate the promotion in context. Company X's corporate practices then were presented in a scenario description that served as the manipulation. The positive condition explained that Company X had been a "pioneer" in issues of combating sweatshop conditions in overseas manufacturing, and the negative condition explained that Company X had been a "laggard" on this issue (this is the exact CSR manipulation that Sen and Bhattacharya [2001] use). Because Sen and Bhattacharya show these manipulations to have an effect on C-C identification, our use of them increased our confidence that we would obtain the necessary variance in C-C identification to test for its effect on the outcome variable of primary interest (i.e., nonprofit donations). This manipulation of CSR is also broader and more encompassing of the construct as Brown and Dacin (1997), Hess, Rogovsky, and Dunfee (2002), Sen and Bhattacharya (2001), and Smith (2003) conceptualize than that in Study 1.
After reading the scenario, subjects were told that to understand the promotion that Company X was considering, it would help to review a Barnes & Noble promotion, because Company X's promotion would be patterned after it. The experimenter then reviewed an actual Barnes & Noble promotion with the subjects to make certain that they understood it (see Figure 3). As Figure 3 shows, the promotion allowed existing customers of Barnes & Noble to receive a 5% commission for referring new customers to Barnes & Noble, or customers could elect to have the commission donated directly to one of five nonprofits that Barnes & Noble supports. The experimenter then told subjects that given Company X's record on sweatshop operations, they were considering a similar promotion. Subjects then reviewed a description of the product that Company X was considering placing on promotion (i.e., the Model #XP15 calculator) and the promotion itself. The promotion was described as follows:
Given its record on issues of international fair labor practices, Company X is considering introducing a new "International Fair Labor Partnership Program" on its bestselling calculator (Model #XP15). The International Fair Labor Partnership Program is designed to represent a partnership between Company X and nonprofit organizations that have increasing monitoring of fair labor practices in "less-developed countries," especially manufacturing "sweatshops," as their focus. The program would run through Company X's online ordering on its Web site. Here is how it would work:
- A customer visits Company X's Web site to purchase the Model #XP15 calculator.
- The customer will be able to click on one of the following two pricing options.
This description was followed by two boxes that were designed to look like Web-clickable boxes. In each of the two boxes, the pricing option was named (see the Appendix). Directly under each box was a description that detailed the implications of choosing the particular pricing option. The first box contained the text "You Pay the 5% Discounted Price of $123.45, You Save $6.50" and was accompanied by the text "If the customer chooses this option, the customer keeps the 5% ($6.50) discount." The second box contained the text "You Pay the Regular Price of $129.95, You Donate $6.50 to Nonprofits" and was accompanied by the following text:
If the customer chooses this option, the customer gets to donate 5% ($6.50) of the purchase price to a nonprofit organization that has promoting international fair labor practices as its goal. Clicking on this option brings up a Web page where five different fair labor practice-related nonprofits appear on the screen, and the customer can click on the one that he or she wants to receive the $6.50 donation.
The experimenter then verbally reviewed the promotion with subjects to ensure that all subjects understood it, after which subjects responded to the dependent measures on the pages that followed. Respondents then were debriefed and thanked for their participation.
Dependent Measures
Dependent measures included a five-item manipulation check scale to assess the success of the experimental manipulation. Because subjects might respond differentially to a given nonprofit cause on the basis of the importance of the cause to them, we added a six-item scale to assess the perceived importance of issues in the manufacturing sweatshop domain. As we noted previously, we developed the operationalization of C-C identification used in Study 1 and customized it to the specific natural foods supermarket that represented the target of identification. Given this and our use of Sen and Bhattacharya's (2001) scenario manipulations in Study 2, we also assessed the C-C identification construct with the same two operationalizations that they used and showed to be sensitive to the scenario manipulations across two studies. The first of the two measures is a Euclidean distance measure based on subjects' responses to 40 scale items that assessed perceptions of both themselves and Company X across 20 common traits. The second measure we used to assess C-C identification was Bergami and Bagozzi's (2000) seven-point, single-item identity circle measure of the overlap between self and organization. We coded the two measures of C-C identification so that higher values reflected higher identification, and we standardized and summed them to form a two-item scale.
Finally, subjects responded to two donation measures. The first measure assessed probability of donation on a seven-point scale. The second measure was a dichotomous donation choice measure. We initially standardized and summed the two measures to form an overall measure. However, because analyses showed that the dichotomous measure was more sensitive to the manipulation and other study constructs, we used it alone for testing the hypothesized relationships.
Tests of Predictions
Before testing the predictions, we conducted an analysis of variance to assess the success of the experimental manipulation. The effect of the manipulation on the manipulation check was significant (F[sub1, 59] = 128.74, p < .01, and X = 14.90 and 29.22 for negative and positive conditions, respectively). The results of regression analyses that test the relationships between study variables are reported in Table 2. As Table 2 shows, when the CSR manipulation was the sole predictor, it had a direct effect on C-C identification (p < .01) but not on nonprofit donations. In addition, the bivariate relationship between C-C identification and nonprofit donations was not significant, but when we used both CSR and C-C identification as predictors of nonprofit donations, both were significant (p < .05). However, the direct effect of the CSR record was unexpectedly negative: A positive record on fighting sweatshops led to a decrease in customer donations. Thus, CSR had a direct, negative effect on donation choice and an indirect, positive effect on donation choice through C-C identification. Finally, nonprofit domain importance did not significantly affect donation behavior (nor was it affected by the manipulation).
Discussion of Results
Consistent with Study 1, Study 2 provides evidence for the indirect, positive effect of CSR on donation behavior, through C-C identification. However, Study 2 also provides an unexpected finding: When the CSR record is accompanied by C-C identification as a second predictor, it has a direct, negative effect on nonprofit donations. It seems to be counterintuitive that a positive record on social issues leads to fewer nonprofit donations in the particular domain while also leading to increased identification (Studies 1 and 2), increased donations as a result of identification (Studies 1 and 2), and more favorable corporate perceptions (Study 1). However, it is possible that consumers identify with a corporation that "does the right thing" (thereby leading to increased corporate benefits) but do not believe that corporations should ask consumers to donate directly to nonprofits. Perhaps by supporting socially responsible corporations with their purchases, consumers believe that they have done their share (i.e., they have "given at the office"; Andreasen and Drumwright 2001) and thus that corporations cross a boundary when they ask consumers to make additional donations. Given the potential implications of this finding, we conducted a third study to explore this effect further.
We designed Study 3 on the basis of Study 2, but we included additional measures to assess possible explanations for the negative effect of CSR on donations. We added a three-item scale to assess the "corporate boundary" explanation we provided previously. Furthermore, after the donation choice measure, we added an open-ended question that asked subjects about the reasons for their choice. Finally, we assessed the seven-point donation probability scale along with the dichotomous choice measure. We standardized and summed the measures as the operationalization of donation choice.
Coding of Open-Ended Responses
Before we tested study relationships, responses to the open-ended question were coded into categories. The experimenter first read through all responses and, on the basis of the reading, constructed ten categories. Then, two coders who were blind to the experimental conditions independently coded each of the responses across all ten categories. Three categories emerged that contained enough responses to be relevant and that did not overlap with factors already assessed by scale items. They were "to save money" (i.e., frugality), "negative attributions about Company X," and "perceived opportunity to do good." Coder agreement across categories was 100%, 99%, and 91%, respectively. Discrepancies between coders were reconciled after discussion with the experimenter.
Tests of Predictions
Before testing the predictions, we conducted an analysis of variance to assess the success of the manipulation. The effect of the manipulation on the manipulation check was highly significant (F1, 90 = 500.45, p < .01, X = 8.24 and 28.43 for negative and positive conditions, respectively). The results of the regression analyses that test study relationships are reported in Table 3.
Consistent with results of Studies 1 and 2, the CSR manipulation positively affected C-C identification. Also consistent with Study 2, when we used the CSR manipulation and C-C identification simultaneously as predictors of nonprofit donations, both were significant (p < .05). Furthermore, the direction of effects was consistent with the results in Study 2; C-C identification had a positive effect and CSR had a negative effect on donations.
To investigate the relationship between CSR and donation choice further, we added the additional variables assessed in Study 3 to the analysis. First, we individually regressed corporate boundary, domain importance, frugality, negative Company X attributions, and perceived opportunity to do good on the manipulation using ordinary least squares (OLS) or logistic regression as appropriate. Corporate boundary, domain importance, and frugality were not affected by the manipulation; however, both negative attributions toward Company X and perceived opportunity to do good were affected by it (both at p < .01, see the second and third regression models in Table 3).
Second, we regressed donation behavior on frugality, negative Company X attributions, corporate boundary, domain importance, organizational identification, perceived opportunity to do good, and the CSR manipulation. As is shown in the last regression model in Table 3, with the exception of the CSR manipulation, all variables were significant (p < .05 or better) and in the hypothesized direction. Follow-up analyses showed that when perceived opportunity to do good was absent/present in the model, the CSR manipulation had/did not have (p < .01 and p > .10, respectively) a negative effect on the donation measure, and this was the only variable that mediated the negative relationship. In line with Kenny, Kashy, and Bolger's (1998) procedures, with all other variables in the model, perceived opportunity to do good completely mediated the effect of the CSR manipulation (z = 4.37, p < .01).
Discussion of Results
The findings in Study 3 replicate and extend findings from Studies 1 and 2. Of particular interest in Study 3 is the negative relationship between CSR and consumer donation behavior. This negative effect may be generated by a differential negative influence on respondents in the positive scenario to give or a differential positive influence on those in the negative condition to give. From our review of the nature of the open-ended responses to perceived opportunity to do good, it seemed that many respondents stated motivations that could not be present in the positive condition. For example, a more frequent reason that was coded in this category was the "desire to help Company X change its ways" or, in other words, to help rehabilitate Company X. This represents a motivation to give in the negative condition that is not present in the positive condition, and it results in the negative relationship mediated by perceived opportunity to do good. Thus, from a substantive perspective, the mediated negative effect of CSR on customer donations is a positive outcome for nonprofits because it reflects a desire for consumers who were in the negative condition to donate rather than a motivation for consumers in the positive condition not to donate.
That said, an issue that remains unaddressed by the Study 2 and 3 results pertains to boundary conditions for the negative relationship between CSR and donation behavior. Specifically, an aspect of the promotion that may have implications for this relationship is the connectedness of the nonprofit domain to that of Company X's CSR record. In both Studies 2 and 3, consumers could choose to donate to one of five charities that targeted unfair labor practices in foreign manufacturing operations, which was the exact domain in which Company X had a poor record of CSR behavior (in the negative condition). Seemingly, for consumers in the negative condition to be motivated to give, they would need to perceive Company X as sincere about changing its ways. Thus, when the nonprofits were in the same domain as when Company X had previously behaved in a socially irresponsible manner, there was a "match" in that Company X was undertaking behavior to address its previous negative behavior. However, consider a situation in which the nonprofits are in a domain other than that in which Company X has a poor record of CSR behavior (e.g., increasing the reading skills of youths). Consumers may be less likely to perceive this as a sincere effort on the part of Company X to change its ways because the company is silent about whether it is trying to "right its wrongs." Rather, consumers may make some other attribution (e.g., a public relations ploy). Consequently, as a boundary condition on the direct, negative relationship found in Studies 2 and 3, there is reason to expect that the nonprofit domain must be connected to the domain in which Company X had previously behaved in a socially irresponsible manner. We designed Study 4 to test for this boundary condition.
We designed Study 4 on the basis of Studies 2 and 3, and the major differences are the addition of a second independent variable and the inclusion of additional dependent measures.( n5) Specifically, 115 subjects participated in one of four experimental sessions in a 2 x 2 between-subjects design in which each of the four sessions was randomly assigned to one of the experimental conditions. We used the same operationalization of CSR behavior as in Studies 2 and 3, so Company X had either a positive or a negative record in overseas manufacturing practices (coded as 1 and 0, respectively). The other variable was connectedness of the nonprofit domain to the domain of Company X's previous CSR behavior. In the connected condition, the nonprofits were the same as in Studies 2 and 3: They had "promoting international fair labor practices" as their goal (coded as 1). In the unconnected condition, they had "increasing the reading skills of America's youth" as their goal (coded as 0).
Dependent measures included C-C identification, corporate boundaries, and nonprofit donations, all of which we assessed as in Study 3. We also included two single-item measures to capture frugality and negative corporate attributions, both of which were open-ended variables in Study 3. We also measured nonprofit domain importance in Study 4. However, because we manipulated the nonprofit domain in Study 4, we developed new measures that, with minimal alteration of wording, assessed nonprofit domain importance for overseas manufacturing practices and reading skills of U.S. youths, respectively. In addition, we included two variables to assess their potential impact on donation choice. First, we included a single-item measure to assess respondents' self-perceptions of social consciousness. Second, on the basis of Hess, Rogovsky, and Dunfee's (2002) contention that CSR is evidenced by corporate programs that reflect the core values of the firm, we included a measure that assessed this perception (which we called "promotional consistency with core values").
Results
The results of regression analyses for Study 4 are provided in Table 4. Consistent with previous studies, CSR had a positive effect on C-C identification (p < .01), as did connectedness (p < .05, see the first model). As in Study 3, CSR negatively affected the propensity for respondents to make negative attributions toward Company X (p < .01, see the second model). In addition, CSR positively affected respondent perception that the promotion was consistent with Company X's core values (p < .01, see the third model). Finally, respondents perceived the connected nonprofit domain (fair labor practices) as significantly less important than the unconnected domain (reading skills) (p < .05, see the fourth model). None of the other variables were affected by the manipulations.
The results relevant to the central question motivating Study 4 are provided in the final model in Table 4. There is a significant interaction between the two manipulated variables (p < .05). Replicating the results of Studies 2 and 3, the tests of the simple effects show that when the nonprofit domain was connected, the effect of CSR on donation behavior was negative (beta = -1.23, p < .05). However, when the nonprofit domain was unconnected, CSR had no effect on donation behavior (p > .10).( n6) Beyond this effect, nonprofit domain importance and promotional consistency with core values both positively affected nonprofit donations (p < .05 and p < .01, respectively), and frugality negatively affected nonprofit donations (p < .01). However, inconsistent with previous studies, C-C identification and corporate boundaries failed to affect donation choice.
Given the centrality of the relationship between C-C identification and nonprofit donations to the current investigation, in conjunction with the findings in Studies 1-3 of a significant relationship between the two variables, we performed additional analyses to gain insights into the nonsignificant relationship between C-C identification and nonprofit donations in Study 4. Specifically, we estimated three models in which we first regressed the donation variable on C-C identification; then on C-C identification, CSR record, and connectedness; and finally on C-C identification, CSR record, connectedness, and CSR record - connectedness. The unstandardized beta and associated significance level for C-C identification in the three models was .19 (p < .06), .21 (p < .12), and .19 (p < .15), respectively. The near-significant bivariate effect in the first model, in conjunction with the decreasing level of statistical significance as we added the additional independent variables, suggests that the lack of an effect is at least partially the result of collinearity between C-C identification with the other independent (and manipulated) variables. The significant effect of both manipulations on C-C identification is consistent with this explanation. Furthermore, the reduced beta estimate (in relation to the .19 bivariate beta estimate) of C-C identification in Table 4 suggests that some explanatory power of C-C identification is attenuated by the additional independent variables estimated in the final model in Table 4. In particular, the respondent's perception of promotional consistency with core values in Study 4 was correlated with C-C identification and nonprofit donations at .66 and .31, respectively. Thus, when we added this variable as a predictor to the third model, the unstandardized beta for C-C identification dropped from .19 to .01. This was the only added variable in Study 4 that produced such a drastic effect. Because this variable was only present in Study 4, it accounts for much of the obtained difference in the relationship between C-C identification and nonprofit donations compared with that in the previous studies.
The results of our four studies (one field-based survey and three follow-up laboratory experiments) provide support for the notion that CSR behavior can result in ( 1) an array of corporate benefits (e.g., more favorable corporate evaluations, increased purchase behavior) and ( 2) increased nonprofit benefits in the form of consumer donations to corporate-supported nonprofits. The relationships we found among all constructs investigated across the four studies provide general support for the model provided in Figure 4.
CSR and Corporate Benefits
The Study 1 results provide evidence that CSR has both a direct and an indirect effect, through C-C identification, on perceptual corporate benefits, which in turn translate into behavioral corporate benefits. These findings, which we obtained in a natural field setting using consumer purchases as a measure of behavioral corporate benefits, support the external validity of benefits that companies can realize through CSR. Given the convergence of results from Study 1 with the results of the lab experiment of Study 2 and with Sen and Bhattacharya's (2001) experimental results, there is strong support for the direct and indirect effects of CSR on corporate benefits.
CSR and Nonprofit Benefits
Our results also show that CSR may provide benefits from the identification process to third parties. Specifically, across three of the four studies (one in which we measured CSR and two in which we manipulated it), CSR had an indirect, positive effect on nonprofit donations through C-C identification. In addition, for all three studies in which the nonprofit domain was the same (i.e., "connected") as that for which the company had a record of CSR (i.e., for Studies 2 and 3 and the connected condition in Study 4), there was a negative, direct effect of CSR on donation choice.
However, that the effect of CSR on donation choice was negative in a statistical sense should not be equated to a negative effect in a substantive sense. The results of Study 3 provide evidence that the negative effect occurred because socially conscious people in the negative CSR condition had a motivation to donate that people in the positive condition did not have. The mediated negative relationship suggests that when corporations with a poor record of CSR attempt to change their ways by affiliating themselves with nonprofits, consumers in the negative condition who perceive an opportunity to do good by helping rehabilitate the corporation have a different motivation to give than do consumers in the positive condition. This finding seems akin to variables related to promoting change in a recent study of consumer boycotting behavior. Klein, Smith, and John (2004) find that variables that reflect a boycotter's desire to bring about some kind of change and to communicate a message to the target firm are important moderators of a consumer's decision to boycott. Likewise, upon encountering a company with a poor record on social responsibility, some consumers viewed the donation to the corporate-supported nonprofit as a way to support a company that had made the decision to "do the right thing" and possibly as a way to communicate support to the company.
That said, we emphasize that our findings should not be taken to suggest that it is to a nonprofit's benefit more to partner with a company that has recently changed its ways than to partner with a company with a consistently strong CSR record. Rather, our findings suggest that there is a positive influence that may be realized by their doing so. The results of follow-up regression analyses in Study 3 reveal that the positive effect of a historical record of CSR on customer donations (through reduced negative attributions and increased identification) is three times that of the negative effect (through perceived opportunity to do good). Thus, our results suggest that it is in a nonprofit's interest to partner with companies with a strong historical record of CSR.
Another issue that warrants consideration is the generalizability of our findings. At a theoretical level, we believe that the type of promotion we used corresponds to many marketplace decisions that consumers commonly make. For example, Hess, Rogovsky, and Dunfee (2002) note that consumers often face trade-offs between their desire for lower-priced goods and their moral desires (e.g., donation of a dollar to a retailer-sponsored charity at time of payment, donation of a can of food to a "feed the hungry" charity when exiting a grocery store). As with the Barnes & Noble promotion, these types of decisions involve trade-offs between consumer economic self-enhancement and altruistic socially responsible behavior. Couched at this level of abstraction, our theory would predict that to the extent that a corporation (e.g., a grocery chain) is perceived as socially responsible, it will engender greater identification among its customer base, thus leading customers to make the trade-off of economic self-enhancement (e.g., donate rather than keep canned food) in favor of their moral desires, in the form of supporting third-party nonprofit causes (e.g., a particular homeless shelter) that are affiliated with the corporation.
Following this logic, we view these findings as having important implications for theory. To our knowledge, this is the first demonstration of transference of the affect and goodwill that a customer has for a corporation to a third-party organization. In addition, the mechanism by which this transference occurs is specified and empirically supported across three of four studies.
Managerial Implications for Nonprofits
Nonprofits can benefit from CSR not only through the donations and other resources that companies provide but also through donations from the company's customer base through corporate-nonprofit promotion programs. This provides an attractive "double-kick" for nonprofits and mitigates concerns that a nonprofit's involvement with the company will lessen its support among the company's customers. Beyond that, our studies provide guidance to nonprofits in choosing company partners. First, nonprofits should choose company partners that are attractive targets for C-C identification and already are adept at prompting it. Ideally, a company partner should have a strong CSR record, and its organizational values should be widely perceived as consistent with the CSR initiative. The risk to a nonprofit is always increased when a company partner has a tarnished CSR reputation. However, our findings suggest that if the dimension on which a company's CSR record has been criticized is related to the nonprofit's cause, the risk may be offset, at least in part, by support from consumers who perceive the CSR initiative as a genuine attempt by the company to reform its ways. Second, at a minimum, nonprofits that are considering working with a company with a tarnished record should be certain that the CSR initiative and their involvement in it can be clearly and credibly positioned as a genuine and enduring attempt to reform on the company's part.
Managerial Implications for Companies
Corporate social responsibility can be a viable promotional strategy that leads to broader company benefits than immediate purchase behavior. The finding that company benefits are based, at least in part, on C-C identification has several implications. An important implication pertains to the building of corporate brand equity. Some scholars have identified our measures of perceptual corporate benefits (company loyalty and emotional attachment to the company) as measures or strong correlates of brand equity (see Keller 1998). Moreover, the creation of meaningful associations that pertain to an organization's identity is particularly key to building a strong brand identity. Aaker (1996) argues that organizational identity, which focuses on the attributes of the organization, is one of four key perspectives around which brand identity is organized. The more attractive the organizational attributes and the more meaningful the associations they create, the more brand equity is enhanced. Given that consumers identify with an organization because its attributes are meaningful, attractive, and similar to their own characteristics or to characteristics that they aspire to have, it stands to reason that an increase in C-C identification through CSR initiatives is likely to enhance brand equity. However, we emphasize that increases in brand equity through CSR initiatives are typically predicated on the use of a cause that resonates with, or at least does not offend, the company's customer base. If customers find that a company is supporting a cause that is inconsistent with their values, the CSR initiative is unlikely to increase brand equity and may even harm it.
Aaker (1996) also asserts that organizational attributes are more enduring and resistant to competitive claims than are product attributes. He argues that it is easier for competitors to copy product attributes than organizational attributes, which in large part are based on people, values, and programs. Inasmuch as CSR initiatives enhance and communicate organizational attributes and increase C-C identification, they may contribute to the creation of a competitive advantage. These effects are likely to be particularly strong for retailers whose company name is, in essence, the brand name and for other companies that use a corporate branding strategy.
Finally, our results suggest that if a company has a poor CSR record, it should choose a nonprofit partner with a cause directly related to the area of criticism, and it should engage in the CSR initiative as part of a genuine effort to change its ways. This speaks to the importance of fit between the firm and the nonprofits with which it may align. This also highlights the importance of a company's ability to position its alliance in a credible way.
Further Research
Although the findings and implications of our studies provide an important extension of previous examinations of C-C identification, they also raise other issues, particularly with respect to the effect on nonprofits. First, we found that nonprofits can benefit from donations from the company's customer base, but do CSR initiatives also increase the probability that customers will support nonprofits in non-monetary ways, such as by volunteering their time or by supporting political initiatives that benefit the nonprofit? Second, we discovered an important boundary condition related to the willingness of customers to donate to the nonprofit: the degree to which a cause is connected to the dimension on which a company had a poor CSR record. What other boundary conditions may come into play? For example, does the degree to which consumers believe that their donations are likely to have an impact matter? Does the influence that a nonprofit is perceived as having over the issue matter? Do the nonprofit's reputation and/or competencies come into play?
Although we examined C-C identification driven by CSR initiatives, consumers identify with corporations on the basis of other factors as well (e.g., athletes and Nike, bikers and Harley-Davidson). To date, the extent to which identification created in one domain (e.g., athletics) can be leveraged in another (e.g., CSR) is unknown. Thus, it is unclear whether the influence of identification is limited to the basis of that bond or whether it transfers. Research of this nature is likely to provide guidance not only for CSR initiatives but also for other forms of collaborative marketing relationships. In summary, opportunities provided by CSR seem to be vast but complex. The same can be said for further research in the area.
The authors gratefully acknowledge the comments and help of Ida Berger, Dipankar Chakravarti, Amar Cheema, Peggy Cunningham, Chris Janiszewski, Charles Judd, Peggy Sue Loroz, John Lynch, Gary McClelland, Rick Netemeyer, H.W. Perry Jr., and Kelly Tian.
( n1) We note that there often are normative debates about what is and what is not CSR behavior. For example, Starbucks, which has been lauded for its donations to CARE, has also been criticized for its expansion policy that allegedly drives out small independent companies and for its allegedly insufficient use of fair-trade coffee.
( n2) Joint nonprofit-company initiatives now encompass a plethora of approaches that extend beyond traditional corporate philanthropy, which historically largely involved companies writing checks in response to fund-raising appeals (Drumwright and Murphy 2001). Nonprofit-company initiatives have been referred to by various terms, including cause-related marketing, corporate social initiatives, joint issue promotion, sales-related fund-raising, social alliances, and corporate community involvement (Andreasen 2003; Berger, Cunningham, and Drumwright 2004; Hess, Rogovsky, and Dunfee 2002). Although such initiatives do not adequately represent the totality of a firm's CSR, they are typically designed to be expressions of it.
( n3) For a description of the Timberland-City Year initiatives, see Austin (2000).
( n4) Sample sizes reported in the regression analyses for the voucher dependent variable are smaller than for other dependent variables for two reasons. First, because of administrative error, there were several missing values for voucher redemptions. Many of the customers who redeemed the voucher for cash cut along the dashed line (see Figure 2) and redeemed only the inner portion of the voucher. However, we had placed the unique respondent identification number in the upper-right-hand corner of the card; thus, we were unable to match the survey responses or cash register receipts to vouchers of customers who did this. For coding purposes, we coded the donation variable as 2 for people who mailed in both vouchers, as 1 for people who mailed in one voucher, and as 0 for people who did not mail in either voucher but for whom we had a valid (i.e., respondent identification present) redemption for cash. The effect was that we treated some of the cash redemptions as missing values, and we based the interpretation of the donation variable on the population for which we knew either donated the voucher or used it as cash. Although we do not believe that this resulted in any bias, in Studies 2 and 3, there were no missing data on the donation choice variable, and the results were consistent with those of Study 1. The second factor underlying reduced sample sizes for analyses involving the voucher dependent variable pertained to missing values on measures we used as independent variables to predict voucher redemptions in the regression analyses. Specifically, some of the 314 voucher redeemers did not return a survey. Thus, for analyses for which we assessed predictor variables in the survey, sample sizes were further reduced. Follow-up analyses revealed no significant differences on several variables assessed during the store encounter between voucher redeemers who did and did not return their surveys.
( n5) On the basis of the suggestion of a reviewer, we also dropped the introductory phrase "Given its record on issues of international fair labor practices" from the description of the promotion.
( n6) Because of a loss of statistical power, our splitting the sample on the basis of the level of the connectedness manipulation and our investigating the effect of CSR on donation choice revealed nonsignificant effects in both levels of connectedness. However, to aid substantive interpretation of the overall interaction, we estimated both models. The results showed that for the unconnected condition, the unstandardized beta for CSR record was -.16, t = -.28, and p < .78. These values for the connected condition were -1.00, t = -1.29, and p < .22, respectively. Thus, considering these effects in the context of the significant overall interaction (and the significant simple effect) for the total sample, the effect of CSR on donation choice was negative in the connected condition but not in the unconnected condition.
Legend for Chart:
A - Dependent Variables
B - Independent Variables Perception of CSR
C - Independent Variables C-C Identification
D - Independent Variables Perceptual Corporate Benefits
E - Degrees of Freedom
F - Model F
G - Model R²/Adjusted R²
A B C D
E F G
Perceptual corporate benefits .20(**)
1, 428 95.58(**) .18/.18
.67(**)
1, 475 48.56(**) .09/.09
.18(**) .54(**)
2, 427 68.07(**) .24/.24
Behavioral corporate benefits .31(**)
1, 471 86.18(**) .15/.15
.06(*)
1, 446 9.70(*) .02/.02
-.01 .32(**)
2, 425 38.09(**) .15/.15
C-C identification .03(**)
1, 449 9.87(**) .02/.02
Nonprofit donations .15(**)
1, 211 9.35(**) .04/.04
.02
1, 189 2.28 .01/.00
.01 .14(*)
2, 188 4.40(*) .05/.04
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Dependent Variables
B - Independent Variables Manipulation of CSR
C - Independent Variables C-C Identification
D - Independent Variables Nonprofit Domain Importance
E - Degrees of Freedom
F - Model F/-2 Log-Likelihood(a)
G - Model R²/Adjusted R²(b)
A
B C D E F G
C-C identification
2.85(**) 1, 55 80.33(**) .59/.59
Nonprofit donations
.10 1 70.67 .01
-.77 1 75.28 .03
-3.30(*) .84(*) 2 63.01(*) .13
-2.74 .75(*) .08 3 61.25(*) .16
(*) p < .05.
(**) p < .01.
(a) For logistic regressions, this is the log-likelihood ratio;
in addition, the significance level refers to the likelihood
ratio chi-square.
(b) For logistic regression, the R² values are Cox and
Snell's R² values.
Notes: We estimated models using OLS and logistic regression for
continuous/dichotomous dependent variables. For ease of
readability, we indicate logistic models for which there is a
single numeric value for degrees of freedom. Legend for Chart:
A - Dependent Variables
B - Independent Variables Manipulation of CSR
C - Independent Variables C-C Identification
D - Independent Variables Negative Corporate Attributions
E - Independent Variables Nonprofit Domain Importance
F - Independent Variables Corporate Boundaries
G - Independent Variables Frugality
H - Independent Variables Perceived Opportunity to Do Good
I - Degrees of Freedom
J - Model F/-2 Log- Likelihood(a)
K - Model R²/Adjusted R²(b)
A
B C D E
F G H I
J K
C-C identification
2.83(**)
1, 86
130.21(**) .60/.60
Negative corporate attributions
-2.27(**)
1
72.16(**) .13
Perceived opportunity to do good
-1.58(**)
1
83.16(**) .08
Nonprofit donations
.22
1, 86
3.88 .04/.03
.09
1, 90
.06 .00/.00
-1.27(*) .49(*)
2, 85
4.12(*) .09/.07
-.63 .33(**) -1.18(**) .05(*)
-.08(*) -1.33(**) 1.95(**) 7, 79
18.01(**) .62/.58
(*) p < .05.
(**) p < .01.
(a) For logistic regressions, this is the log-likelihood ratio;
in addition, the significance level refers to the likelihood
ratio chi-square.
(b) For logistic regression, the R (2) values are Cox and
Snell's R² values.
Notes: We estimated models using OLS and logistic regression for
continuous/dichotomous dependent variables. For ease of
readability, we indicate logistic models for which there is a
single numeric value for degrees of freedom. Legend for Chart:
A - Dependent Variables
B - Independent Variables Manipulation of CSR
C - Independent Variables Manipulation of Connectedness
D - Independent Variables CSR x Connectedness Interaction
E - Independent Variables C-C Identification
F - Independent Variables Negative Corporate Attributions
G - Independent Variables Nonprofit Domain Importance
H - Independent Variables Corporate Boundaries
I - Independent Variables Frugality
J - Independent Variables Promotional Consistency with Core
Values
K - Independent Variables Social Consciousness
L - Degrees of Freedom
M - Model F
N - Model R²/Adjusted R²
A
B C D E F
G H I J K
L M N
C-C identification
2.55(**) .77(*) -.71
3, 105 24.08(**) .41/.39
Negative corporate attributions
-1.67(**) -.34 -.71
3, 111 7.84(**) .17/.15
Promotional consistency with core values
2.63(**) .29 .61
3, 111 30.23(**) .45/.44
Nonprofit domain importance
.97 -3.75(*) 2.23
3, 111 4.01(**) .10/.07
Nonprofit donations
.02 .67 -1.25(*) .01 -.01
.07(*) -.06 -.33(**) .31(**) -.09
10, 97 8.90(**) .48/.43
(*) p < .05.
(**) p < .10.DIAGRAM: FIGURE 1; The Effect of Perceptions of CSR and C-C Identification on Corporate and Nonprofit Benefits
PHOTO (BLACK & WHITE): FIGURE 2; Donations and Cash Vouchers for Study 1
PHOTO (BLACK & WHITE): FIGURE 3; Barnes & Noble Promotion
DIAGRAM: FIGURE 4; The Effect of Perceptions of CSR on Corporate and Nonprofit Benefits
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For the following scale descriptions, unless we note otherwise, we developed all scale items, and we measured all items on seven-point scales (1 = "Strongly disagree," and 7 = "Strongly agree") and (re)coded the items such that higher scores reflect higher levels of the constructs.
Perceived CSR (Study 1: X = 27.10, range = 5-35, s.d. = 5.26, α = .90)
1. (Company name) is committed to using a portion of its profits to help nonprofits.
- 2. (Company name) gives back to the communities in which it does business.
- 3. Local nonprofits benefit from (company name)'s contributions.
- 4. (Company name) integrates charitable contributions into its business activities.
- 5. (Company name) is involved in corporate giving.
C-C Identification (Study 1: X = 2.03, range = 0-5, s.d. = 1.13)
Consider the following lists of character traits. On the lefthand [side of the] list, circle the five that best describe you; on the right-hand [side of the] list, please circle the five that you think best describe (company name).
Legend for Chart:
A - You (Circle 5)
B - Company Name (Circle 5)
A B
Committed Committed
Progressive Progressive
Community involved Community involved
Entrepreneurial Entrepreneurial
Gutsy Gutsy
Active Active
Responsible Responsible
Compassionate Compassionate
Competitive Competitive
Free-spirited Free-spirited
Assertive Assertive
Risk taking Risk taking Perceptual Corporate Benefits
In Study 1, we measured perceptual corporate benefits as the sum of store loyalty, emotional attachment to the store, and store interest. We adopted all of the following measures taken from the work of Hess (1998).
Store Loyalty (X = 12.93, range 3-21, s.d. = 4.89, α = .87)
1. I could easily switch from (company name) to another store. (reverse coded)
- 2. I am a committed shopper at (company name).
- 3. I feel a sense of loyalty to (company name).
Emotional Attachment to Store (X = 17.10, range 4-28, s.d. = 5.85, α = .85)
1. The emotional reward I get from shopping at (company name) makes it worth it for me.
- 2. Shopping at (company name) gives me a sense of warmth and comfort.
- 3. Shopping at (company name) makes me happy.
- 4. I would experience an emotional loss if I could no longer shop at (company name).
Store Interest (X = 3.99, range 1-7, s.d. = 1.67)
1. I am interested in learning about (company name).
Behavioral Corporate Benefits
Percentage of Shopping at Store (Study 1 [first two and last two categories collapsed]: X = 2.94, range = 1-5, s.d. = 1.55)
1. What percentage of your grocery shopping do you normally do at (company name)?
( ) less than 10% ( ) 10-20% ( ) 21-40% ( ) 41-60% ( ) 61-80% ( ) 81-90% ( ) 91-100%
CSR Record Manipulation Check (Study 2/3: X = 18.85/18.55, range = 5-35/5-35, s.d. = 10.59/11.02, α = .98/.97)
1. Company X has a strong record on fair labor practices in its overseas manufacturing plants.
- 2. Company X has a strong record of compensating foreign employees fairly.
- 3. Company X has a strong record of providing fair benefit packages for all of its employees.
- 4. Company has a record of not hiring underage children in its overseas manufacturing plants.
- 5. Company X's working conditions in overseas factories are equal to those in U.S. factories.
Nonprofit Domain Perceived Importance (Study 2/3: X = 36.36/36.24, range = 15-42/13-42, s.d. = 6.60/6.21, α = .93 /.91)
1. I strongly believe that companies should treat workers in their foreign manufacturing plants as well as they treat workers in their U.S. manufacturing plants.
- 2. I am committed to the corporate practice of treating workers in foreign and U.S. manufacturing plants equally well.
- 3. I believe that corporations should monitor their overseas manufacturing operations to make sure their business practices are fair to their workers.
- 4. I believe that corporations have a responsibility to make sure that the working conditions in their overseas manufacturing plants are as good as the working conditions in their U.S. plants.
- 5. Standing up for fair manufacturing practices in overseas plants is important.
- 6. Corporations will have a better foreign workforce if workers are treated the same as workers in their U.S. plants.
Organization Identification Euclidean Distance Measure (Study 2/3/4: X = 9.28/10.17/9.04, range 3.32-24.25/3.00-21.54/2.83-21.21, s.d. = 4.44/4.64/ 4.21)
We adopted the organizational identification Euclidean distance measure from the work of Sen and Bhattacharya (2001).
Match 20 self-corporate attribute pairs along dimensions of activist, best, capable, dishonest, innovative, enlightened, a leader, expert, progressive, compassionate, fair, risk averse, conservative, high quality, sincere, cooperative, inconsiderate, sensitive, democratic, and inefficient. Format as follows:
Me Company X
Strongly Strongly Strongly Strongly
Disagree Agree Disagree Agree
Organization Identification Identity Overlap Scale (Study 2/3/4: X = 2.75/2.87/2.89, range 1-7/1-7/1-6, s.d. = 1.45/1.40/1.38)
We adopted the organization identification identity overlap scale from the work of Bergami and Bagozzi (2000).
Donation Choice (0 = donation not chosen/1 = donation chosen; Study 2 = 20/41; Study 3 = 55/37, Study 4 = 74/41)
Assume that you decided to buy the Model #XP15 calculator from Company X. Which button would you click? Please circle one.
You Pay the 5% Discounted Price of $123.45
You Save $6.50
You Pay the Regular Price of $129.95
You Donate $6.50 to Nonprofits
Corporate Boundary (Study 3/4: X = 9.89/9.83, range = 3-21/3-21, s.d. = 4.11/4.31, α = .65/.70)
1. Corporations are asking too much of consumers if they ask them to donate to charities.
- 2. It is not a corporation's role to give to charities: They should pass the savings on to consumers instead and let consumers donate to whatever charities they want.
- 3. If I purchase products from corporations that support charities, I'm doing my part: I shouldn't be asked to donate directly to the charities in addition.
Donation Probability (Study 3/4: X = 3.98/3.85, range = 1-7/1-7, s.d. = 2.32/2.45)
1. If you made the decision to purchase the Model #XP15 calculator from Company X, how likely is it that you would choose to pay the full price whereby you would donate 5% of the purchase price to nonprofits" (1 = "Very unlikely," and 7 = "Very likely").
Frugality (Study 4: X = 3.96, range = 1-7, s.d. = 2.65)
For the frugality variable, respondents were asked to state the "extent to which you thought of each of the following factors when you considered whether or not to donate the $6.50 to the charity," on a seven-point scale of 1 = "Did not think about at all," and 7 = "Thought of a lot."
1. I simply do not have the extra money at this stage in my life.
Negative Corporate Attributions (Study 4: X = 3.45, range = 1-7, s.d. = 2.26)
For the negative corporate attributions variable, respondents were asked to state the "extent to which you thought of each of the following factors when you considered whether or not to donate the $6.50 to the charity," on a seven-point scale of 1 = "Did not think about at all," and 7 = "Thought of a lot."
1. I do not like the way that Company X does business.
Nonprofit Domain Importance (Study 4 [labor practices and reading skills]: X = 17.71/20.99, range = 5-28/4-28, s.d. = 6.21/5.49, = .92/.86)
1. Supporting nonprofits that fight manufacturing sweatshops (help increase the reading skills of U.S. youths) is important to me.
- 2. I could see myself donating some of my time to supporting nonprofits that help fight manufacturing sweatshops (help increase the reading skills of our youths).
- 3. Nonprofits that have the goal of fighting manufacturing sweatshops (increasing the reading skills of our youth) make this world a better place to live.
- 4. I can identify with nonprofits that have the goal of fighting manufacturing sweatshops (increasing the reading skills of our youths).
Social Consciousness (Study 4: X = 5.29, range = 1-7, s.d. = 1.38)
1. I consider myself to be a socially conscious person.
Behavior Consistent with Core Values (Study 4: X = 4.22, range = 1-7, s.d. = 2.08)
1. The promotion that Company X is considering running is consistent with its core values.
~~~~~~~~
By Donald R. Lichtenstein; Minette E. Drumwright and Bridgette M. Braig
Donald R. Lichtenstein is Professor of Marketing, Leeds School of Business, University of Colorado, Boulder (e-mail: Donald.Lichtenstein@colorado.edu). Minette E. Drumwright is Associate Professor of Advertising, College of Communication, University of Texas at Austin (e-mail: mdrum@mail.utexas.edu). Bridgette M. Braig is an independent consultant (e-mail: bridgettebraig@earthlink.net). This research was funded by a Leeds School of Business Research Grant to the first author and a research grant from the University of Texas at Austin to the second author.
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Record: 155- The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories. By: Tsiros, Michael; Heilman, Carrie M. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p114-129. 16p. 9 Charts, 1 Graph. DOI: 10.1509/jmkg.69.2.114.60762.
- Database:
- Business Source Complete
The Effect of Expiration Dates and Perceived Risk on
Purchasing Behavior in Grocery Store Perishable Categories
In this article, the authors examine consumers' behavior with respect to expiration dates for grocery store perishable products. A better understanding of such behavior can both guide efforts to educate consumers about the risks associated with perishables that are approaching their expiration dates and help managers implement effective promotional strategies for these products throughout the course of their shelf lives. Both of these approaches can help reduce waste due to spoilage.
In the current era of "super-grocery stores," it is no longer branded items that bring in business but rather full-service delis, fresh baked goods, premium meats, and elaborate arrays of produce. Simply stated, perishable products drive grocery store traffic. According to Glen Terbeek, managing partner of Anderson Consulting, "In today's world, branded grocery items are the same everywhere; Coke is Coke and Tide is Tide, ... but perishables and their presentation are unique," and such presentation draws consumers into the store (Hennessy 1998, p. 63).
The importance of perishables for store profitability and store image has been supported by several market facts. First, in 1999, sales of perishable goods rose 4.5% and accounted for 69.4% (approximately $305 billion) of all retail food sales and just more than 50% of all supermarket retail sales (Supermarket Business 2000). Second, produce represents 12.7% of total store sales and is the second most profitable category behind frozen foods (Berner 1999). Third, meat and produce tend to be the departments on which consumers base their value judgments about stores (Kerin, Jain, and Howard 1992). These trends may explain why grocery stores advertise 19 varieties of peppers and stock more than 400 produce items (Turcsik 2003).
Despite their importance, perishable products are difficult to manage because of their random weights, lack of specific Universal Product Codes for different product variations, and different forms of sale (e.g., raw, semiprepared, fully prepared), to name just a few issues. Perishables are so complex that many retailers are unable to implement any type of category management strategies for them (Litwak 1997). However, according to the Blattberg, Chaney & Associates consulting firm, "the key to successful category management (of perishables) is to understand how the consumer makes category decisions" (Litwak 1997, p. 166). Such understanding could lead to actions that benefit grocery store managers, consumers, and society as a whole.
A better understanding of consumers of perishable goods would enable managers to implement discounting policies that reduce shrinkage (waste due to spoilage), a major problem in perishable categories. Departments such as produce, bakery, and meat lose approximately 4.5% of their goods to spoilage (compared with 2% overall), which can cost a grocery store $70,000-$340,000 a year, depending on the size of the store (Supermarket News 1997). Furthermore, a grocery store may be able to increase its profits by as much as 15% by minimally reducing the shrinkage of perishable goods (Hennessy 1998). Discounting aging perishables may be an effective way to accomplish this goal, especially in light of evidence that the short-and long-run effectiveness of price promotions is greater for perishable goods than for other categories (Nijs, Dekimpe, and Steenkamp 2001). In addition, discounting perishables would benefit consumers by enabling them to make tradeoffs between buying a more expensive but fresher item and buying one that is discounted but may be perceived as riskier because of its approaching expiration date (though it is still safe to consume). This policy could also build trust between consumers and managers who avoid selling products that are close to their expiration dates at full price. In the end, discounting perishables would benefit society as a whole by reducing the unnecessary waste that spoilage causes each year. Despite these benefits, however, it seems difficult and perhaps imprudent to implement such discounting strategies without a better understanding of consumers' awareness and interpretation of expiration dates, as well as the way that consumers' willingness to pay (WTP) for a perishable varies throughout the course of the product's shelf life.
A better understanding of consumer behavior with regard to perishable categories could also aid efforts to educate consumers about food dating (the dates found on product packaging). Currently, there is ambiguity about the usage and regulations of what consumers often refer to as "expiration dates." Except for infant formula and baby food, the Food and Drug Administration does not require manufacturers to date their products, nor does it have federal regulations governing dating (Tufts University Health & Nutrition Letter 1997). Therefore, food dating is a voluntary process typically provided in one of three forms: ( 1) "Best before," which indicates the date after which a product is no longer of its "best" quality, generally is used for products such as baked goods, cereals, snacks, and some canned foods; ( 2) "use by," which indicates the date after which a product is no longer of sufficient quality and should not be consumed, is used for products such as eggs, yeast, and refrigerated dough; and ( 3) "sell by," which indicates the last day a product should be sold (though most remain safe to eat up to seven days past this date or longer if stored in a freezer), is most commonly used with meat, seafood, poultry, milk, and bread. Because of the different forms of product dating, misconceptions among consumers about their meaning is common.( n1)
However, the problem is not limited to expiration dates;( n2) it applies to food labels in general. A recent Food Standard Agency survey (Whitworth 2001) reports that more than one-quarter of all consumers find labels difficult to understand, and one-third do not know the significance of "use by" or "best before" dates. Although approximately 60% of consumers claim that they check food labels and expiration dates on food packages, only one-third of them know what to do with food when it is one day past its "use by" date (Whitworth 2001). As the findings show, research is necessary to understand consumers' awareness and interpretation of expiration dates to implement programs that could change misconceptions about and behavior with respect to product dating.
Despite this evidence, perishable products have received little or no attention in the marketing literature (Krider and Weinberg 2000). In response, our main objectives are to examine ( 1) consumers' awareness of expiration dates, ( 2) their WTP for a perishable throughout the course of its shelf life, and ( 3) the role that risk plays in the choices consumers make when buying perishable goods.( n3) Whereas studies have investigated the influence of perceived risks on brand preference (Dunn, Murphy, and Skelly 1986), product classification (Murphy and Enis 1986), product liking (Cardello 2003), and attitudes toward pesticide use on produce (Huang 1993), our research is the first to investigate the impact of perceived risks and expiration dates on consumer behavior in grocery store perishable categories.
We develop a set of hypotheses that we test empirically through a survey of consumers' perceptions and behaviors with respect to the following important perishable categories: chicken, beef, milk, yogurt, lettuce, and carrots. We use products from the meat and poultry, fresh produce, and dairy categories because of the large percentage of all grocery store perishable sales in these categories: 30.4%, 22.5%, and 16.9%, respectively (Supermarket Business 2000). We chose two products from each perishable category to obtain insights into behavior both across products and within product types. Our findings support many of the hypotheses, and we use the findings to make recommendations to managers of grocery store perishable goods and to develop a research agenda for further studies in these under-researched but important categories.
Literature Review
To date, much of the research on perishable goods has come from the operations research literature and has focused on optimizing pricing, ordering, and restocking strategies for aging goods, most of which do not appear in grocery stores (e.g., fashion goods, broadcast spots). In Table 1, we present a subset of these studies and summarize the problems investigated, the characteristics of the categories studied, and the main findings.
Although these studies address some issues relevant to managers of perishable goods, such as pricing, restocking, and ordering policies, the characteristics of the products studied differ from consumable grocery store perishables, which makes the findings from these studies impractical for managing such products. For example, a grocery store perishable has a relatively short shelf life, typically one to three weeks. Therefore, a manager's ability to update information throughout the life of the product, as is done in many of the operations research studies, is limited. In addition, the quality of grocery store perishables decreases until they eventually spoil and can no longer be sold, unlike fashion goods, for which the utility of the product, though it may decay over time, never reaches zero, or broadcast spots, for which the value of the good may actually increase over time if the expected number of viewers increases before the sale of the broadcast spot (e.g., when two teams from big, metropolitan cities advance to the Super Bowl). Because grocery store perishables spoil, managers must understand consumers' WTP across the shelf life of these products to be able to sell them before they go bad. However, the most important distinction between our research and that of the operations research literature is that most of the studies in Table 1 make assumptions about consumer effects in their models. In contrast, we explicitly study consumers' perceptions and behavior in grocery perishable categories through a survey study.
The few studies that have considered grocery store perishable goods and goods with similar characteristics have modeled optimal inventory policies (Giri and Chaudhuri 1998; Hariga 1997; Khouja 1996), pricing policies (Gold 1981; Rajan and Steinberg 1992), ordering and issuing (restocking) policies (Fujiwara, Soewandi, and Sedarage 1997), and the impact on store choice (Krider and Weinberg 2000). Although the management of goods such as fresh meat, poultry, and dairy depends on many of the factors captured in these studies (e.g., seasonality, inventory, buyback deals), an understanding of consumers' awareness of, perceptions of, and behavior with respect to expiration dates is missing. A study that is similar in spirit to ours is Huang's (1993), which addresses the effect of risk perceptions of pesticide use on consumers' WTP for residue-free produce. However, that study does not include expiration dates, and it focuses solely on perishables that use pesticides; in contrast, we focus on a broader range of grocery perishables that includes meat, poultry, dairy products, and produce.
Our study is also different from those mentioned previously in that we explicitly focus on consumers' perceptions and behaviors in specific, important grocery store perishable goods categories. Therefore, our study represents the only work to explicitly investigate consumers' awareness of expiration dates, their WTP for a perishable as it ages, and the influence of perceived risk on such behaviors.
Hypotheses Development
During the past 15 years, perishable food scares--such as salmonella in eggs, milk, and poultry; listeria in pate and certain soft cheeses; and bovine spongiform encephalopathy (more commonly known as mad cow disease) in meat--have caused health concerns among consumers and have brought about significant changes in their purchasing habits (Mitchell 1998). For example, the retail volume sales of beef and veal dropped 63% in 1996 after a public announcement linked bovine spongiform encephalopathy to Creutzfeldt-Jakob disease, a fatal brain disease in humans, and in 1989, retail sales of eggs dropped 21% after an outbreak of salmonella (Yeung and Morris 2001). Furthermore, these scares have caused investors to devalue stocks of companies that sell related products (e.g., McDonald's, Jack in the Box, Outback Steakhouse) by up to 5% (Shell 2003). The main factor driving the behavior of consumers and investors in these situations is the perceived risk associated with purchasing and consuming an unhealthy perishable good.
Perceived risk, defined as the expected negative utility associated with the purchase of a particular brand or product (Dunn, Murphy, and Skelly 1986), influences consumers not only for categories affected by highly publicized food scares but also for everyday, common purchase decisions. As a result, consumers take actions to lower the perceived risk associated with a purchase by ( 1) shifting or postponing their purchase, ( 2) purchasing well-known brands, ( 3) seeking advice or endorsement from a trusted source (Yeung and Morris 2001), or ( 4) in the case of perishable goods, searching for visual and other cues of freshness, such as expiration dates in the case of perishable goods.
The types of perceived risks that influence consumer decision making include functional, performance, physical, psychological, social, and financial (Greenleaf and Lehmann 1995; Havlena and DeSarbo 1990; Jacoby and Kaplan 1972; Roselius 1971) (for definitions of these types, see Table 2). The influence of the different types of risk on shopping behavior varies depending on the brands considered or the categories of interest. For example, Dunn, Murphy, and Skelly (1986) study the influence of perceived risk on preferences for generic, store, and national packaged-goods brands and find that social risk plays a minor role compared with financial and performance risks. Alternatively, Murphy and Enis (1986) classify product categories on the basis of consumers' shopping effort and price risk dimensions. They report that convenience goods, which include produce and other grocery staples, tend to rank lower than preference, shopping, and specialty goods in terms of the effect of effort and risk on behavior. However, recent evidence demonstrating the importance of perishables for store choice and shopping experience and the flood of recently publicized food scares involving perishable goods may lead to different results than those found approximately 20 years ago.
Prior studies have shown that as the risks associated with a product increase, so does consumers' desire for information before they make a purchase (Blackwell, Miniard, and Engel 2001; Dowling and Staelin 1994; Greenleaf and Lehmann 1995). In an attempt to reduce the risks, consumers may search for product attributes and information before making a purchase. Because a relevant piece of information for a perishable good is its expiration date, we propose the following:
H1: Consumers who perceive high levels of risk associated with a perishable product check its expiration date more frequently than do consumers who perceive low levels of risk (positive main effect).
In Table 2, we define the risks included in our study. As we mentioned previously, no theory exists in the literature about the relative importance of these risks in any context, let alone in the context of perishable grocery store goods. However, we expect that functional, performance, and physical risks dominate the other risks (psychological, social, and financial) because of their relationship with product quality and the highly publicized and important health risks associated with many of the products in our study.
Consumer research recognizes familiarity and expertise as characteristics that influence consumers during the various stages of their decision-making process (Bettman and Park 1980). Consumers with greater category experience are better able to search out, encode, and recall information than are those with less experience (Alba and Hutchinson 1987; Johnson and Russo 1984; Maheswaran and Sternthal 1990). Because expiration dates provide valuable information about a product's remaining shelf life, we expect that consumers with greater category experience are better able and more likely to search out expiration dates. Furthermore, as their category experience increases, the impact of uncertainty and risks associated with a perishable should decrease because consumers rely more on the expertise that they have accumulated in the category. Therefore, we expect that consumers' increased category experience dampens the impact of perceived risks on the frequency of checking expiration dates. Alba and Hutchinson (1987, p. 411) define familiarity and expertise as "the number of product related experiences accumulated by the customer." Therefore, we use household consumption rate as a proxy for category experience and propose the following:
H2: Consumers from households with high consumption rates of a perishable product check its expiration date more frequently than do consumers from households with low consumption rates (positive main effect).
H3: The effect of perceived risk on how frequently consumers check expiration dates is smaller for consumers from households with high consumption rates than for those from households with low consumption rates (negative interaction effect).
Consumers can take measures to stop or extend the aging process of most perishables, such as freezing, cooking, or using the product immediately. In such situations, the information that the expiration date provides is of less value to consumers because the product is less likely to spoil. Therefore, such consumers may be less likely to seek out expiration information. Similarly, stopping the aging process should reduce the impact of the risks associated with spoiling, and the effect of perceived risks on the frequency of checking expiration dates should be dampened. Thus:
H4: Consumers who plan to stop the aging process of a perishable product check its expiration date less frequently (negative main effect).
H5: The effect of perceived risk on how frequently consumers check expiration dates is smaller for consumers who plan to stop the aging process of a perishable product (negative interaction effect).
When consumers buy a perishable, they must consider the likelihood that the product will spoil. As the likelihood of spoilage increases, the value of the product, and thus consumers' WTP for it, should decrease. Because the number of days before the product reaches its expiration date is a measure of the likelihood of spoilage, we expect that as the product approaches its expiration date and the number of days decreases, WTP for the product decreases.
H6: Consumers have a higher WTP for perishable products that have more days before their expiration date (positive main effect).
The inverse relationship between perceived risk and WTP has been documented for products that use pesticides (Eom 1994; Huang 1993). In addition, when the risks of failure are high, consumers will trade some risk by reducing the resources that they allocate to acquire the product (Huang 1993; Yeung and Morris 2001). Therefore, we propose that as the perceived risks associated with a perishable increase, consumers' WTP decreases in exchange for accepting a greater risk.
H7: Consumers with higher perceptions of risk for a perishable product have a lower WTP (negative main effect).
However, risks may be consumer specific. For example, perishables consumed by households with higher consumption rates are less likely to spoil, and therefore their value to such consumers is greater than it is to households with lower consumption rates, especially as the product approaches its expiration date. As a result, household consumption rate should have a direct and positive impact on WTP, both overall and with respect to how WTP decreases as the expiration date approaches. Furthermore, as the household consumption rate increases, the negative impact of risk perceptions on WTP should decrease because the risk of spoilage is reduced. Thus, we propose the following:
H8: Consumers from households with higher consumption rates have a higher WTP (positive main effect).
H9: The effect of the number of days before the expiration date on consumers' WTP is smaller for consumers from households with high consumption rates than for those from households with low consumption rates (positive interaction effect whereby WTP decreases at a slower rate as the number of days decreases).
H10: The effect of perceived risk on consumers' WTP is smaller (less negative) for households with high consumption rates than for those with low consumption rates (positive interaction effect whereby the negative effect of risk is dampened).
Finally, in cases in which consumers plan to stop the aging process of a perishable, because the product is less likely to spoil, its value to consumers should be greater. Thus, stopping the aging process should have a direct and positive impact on WTP, both overall and with respect to how WTP decreases as the expiration date approaches. Furthermore, stopping the aging process should lower the impact of the perceived risks associated with the perishable on WTP.
H11: Consumers who plan to stop the aging process of a perishable product have a higher WTP (positive main effect).
H12: The effect of the number of days before the expiration date on consumers' WTP is smaller for consumers who plan to stop the aging process of a perishable product (positive interaction effect whereby their WTP decreases at a slower rate as the number of days decreases).
H13: The effect of perceived risk on consumers' WTP is smaller (less negative) for consumers who plan to stop the aging process of a perishable product (positive interaction effect whereby their WTP decreases at a slower rate).
In Table 3, we summarize these hypotheses and the variables we use to test them.
Empirical Research
We conducted a survey to test our hypotheses with regard to consumer behavior in six perishable categories with printed expiration dates on their packaging: prewashed/precut lettuce, prewashed/precut carrots, milk, yogurt, chicken, and beef. A convenience sample of 300 consumers participated in our study, for which the only screening criterion was that the respondent did a substantial amount of grocery shopping for his or her household. Participants came from two large metropolitan areas in the Midwest and Southeast United States. To assess consumers' WTP throughout the course of the products' shelf life, we distributed three versions of the survey in which we inquired about participants' WTP for a perishable with seven (85 surveys), four (93 surveys), or one (92 surveys) day(s) remaining before it reached its expiration date. After we cleaned the data of missing observations, there were 270 usable surveys. We report summary statistics about consumers' perceptions of the products in Table 4.
A majority of consumers (69%-84%, depending on the product category) believed that the quality of perishables deteriorates throughout the course of their shelf life, though retailers maintain that the quality remains relatively constant until the product passes its expiration date. This finding suggests an opportunity for retailers to smooth demand throughout the course of the products' shelf life by educating consumers about the actual risks of purchasing a product close to its expiration date and about the way perishables age over time. However, because such efforts might be received with skepticism, it would be in retailers' best interests to obtain the support of the Center for Food Safety and Applied Nutrition, which distributes printed materials that describe food labels; to encourage cookbook authors and food editors to emphasize food safety; and to disseminate public service announcements by all media (Hunter 1994). However, until such coordination occurs, discounting products as they approach their expiration dates may be the most viable strategy.
We conducted an exploratory factor analysis of the six risk dimensions to determine if they could be reduced to fewer constructs. We used a principal components analysis with Varimax rotation to obtain the factor loadings (oblique rotation gave similar results). In all six categories, a two-factor solution, in which both factors had eigenvalues greater than 1 and the remaining factors were less than 1, provided the best fit. We report the results of this analysis in Table 5.
For all six products, functional, performance, and physical risks load more heavily onto the factor that we label "product quality risk" (PQR), and psychological, social, and financial risks load more heavily onto the factor we label "personal risk" (PR). The PQR factor captures the perceived risks associated with product quality as a perishable approaches its expiration date and the associated health risks, and the PR factor captures risks associated with the negative emotions a consumer experiences when a product fails, such as the impressions others form about the consumer, the way the consumer feels about him-or herself, and the frustration of a financial loss. Using the results from Table 5, we recast the data to create PQR and PR. Although both models (two-and six-factor risk) provide similar results, for parsimony we use the two-factor model to capture the perceived risks associated with perishables. We also include a set of consumer characteristics in an exploratory effort to assess their influence on the frequency of checking expiration dates and consumers' WTP. We used the following estimated equations to test our hypotheses:
( 1) Freq_Checkij = β0 + β1(PORij) + β2(PRij) + β3 (HH Consumeij)
+ β4(HHConsumeij) x [PQRij + PRij])
+ β5(Stopij + β6(Stopij x [PQRij + PRij])
+ β7(Sexi) + β8(Agei) + β9(Incomei)
+ β10(FullTimei) + β11 (HHSizei) + Εl,
and
( 2) WTPij = β0 + β1(Daysj) + β2(PQRij) + β3(PRij)
+ β4(HHConsumeij)
+ β5(HHConsumeij x Daysj)
+ β6(HHConsumeij x [PQRij + PRij
+ β7(Stopij) x [PQRij + PRij]) + β10(Sexj)
+ β11(Agei) + β12(Incomei) + β13(FullTimei)
+ β14(HHSizei) + Epsilon2,
where j indicates the category of interest (i.e., 1-6), Sexi = 1 if consumer i is male and 0 if otherwise, Agei = 1 if consumer i is older than age 45 and 0 if otherwise, Incomei = 1 if the household income for consumer i is greater than $50,000 and 0 if otherwise, FullTimei = 1 if consumer i works full time and 0 if otherwise, HHSizei is the number of people living full time with consumer i, and Ε1 and Ε2 are random error terms. We defined the remaining variables previously in Table 3. We determined the cutoffs for each variable from a median split analysis. To normalize WTP across categories, we define it as the consumer's WTP for the perishable divided by the original shelf price.( n4) We present summary statistics for the variables in Equations 1 and 2 in Table 6. We also tested alternative functional forms of the variables in the model (e.g., linear, log-linear, curvilinear). In the next section, we present the results of the best-fitting models.
To test H1-H5, we estimated Equation 1 using the PROC MIXED procedure in SAS. This procedure allows for repeated measures within consumers, which was a characteristic of our data, given that participants answered similar questions across categories. We omitted the data from the milk category because of a lack of variance in the dependent variable (93% of consumers "always" or "usually" check expiration dates in this category; see Table 4). We provide the results in Table 7.
We find support for H1; the greater the risks associated with a product, the more frequently consumers check expiration dates. However, this finding holds true only for PQR in all five categories but not for PR. Therefore, PQR, or the perceived risks associated with the quality of a perishable that is approaching its expiration date and the resultant health risks, are the most salient types of risks and are positively correlated with the frequency with which consumers check expiration dates. However, a higher level of PR is not associated with a greater frequency of checking expiration dates. This finding suggests that marketers should encourage consumers to check expiration dates in categories in which PQR is low and in which consumers therefore do not frequently check expiration dates, thus enabling consumers to make more informed decisions.
We also find strong support for H2.( n5) In all five categories, we find that consumers with greater category experience, as indicated by their household consumption rate, are more likely to check expiration dates. We also find that when PQR perceptions are high, the impact of the household consumption rate on the frequency of checking expiration dates is minimized, in support of H3. However, the same cannot be said for PR in terms of its support for H3.
We do not find convincing evidence to support H]4 or H5; thus, stopping the aging process does not appear to affect the frequency with which consumers check expiration dates. This might be because the act of stopping the aging process is usually situational, whereas the habit of checking expiration dates is an act that spans multiple shopping trips. In addition, the decision to stop the aging process may depend on the consumer's knowledge of the number of days before the product expires, something that is obviously unknown prior to the consumer's checking the expiration date.
Finally, when we consider the demographic variables, we find that older consumers are more likely to check the expiration dates of chicken, carrots, and beef, whereas consumers who do not work full time are more likely to check lettuce and carrots. We could generalize from these findings that consumers with more free time are more likely to check expiration dates, and therefore marketers should focus on consumers with less free time (e.g., younger consumers, those working full time) to ensure that they are aware of expiration dates and are making informed decisions. However, because of the lack of strong consistency in the findings across the five categories, more research is necessary before we can make conclusions of this nature.
To test H6-H13, we estimated Equation 2 (WTP), again using the PROC MIXED procedure in SAS, and we present the results in Table 8. The results support H6 in all six categories. Furthermore, we note that WTP decreases linearly for produce and dairy and exponentially for beef and chicken as the number of days left before the product's expiration decreases.( n6) In Figure 1, we graph the decline in WTP for all six categories as the products go from seven days to one day before their expiration dates, holding all other variables constant. The way that WTP decreases throughout the course of the product's shelf life provides information about consumers' perceptions of how product quality deteriorates over time and the likelihood of the product spoiling at various points. In turn, this information provides insights into how discounts can help retailers sell products as they approach their expiration dates.
The different functional forms for chicken and beef suggest that a deeper discount is necessary earlier in their shelf lives to encourage a purchase. In the stores of the managers we interviewed, chicken and beef are the only perishable products discounted when they approach their expiration dates. This trend may explain the different functional forms for these categories; namely, consumers become conditioned to expect a discount when discounting is already practiced. In addition, chicken and beef are statistically the most risky of the six categories in terms of PQR.( n7) Therefore, another possible explanation for the exponential relationship between WTP and days before expiration for beef and chicken is that products with high PQR require a multiplicative rather than additive discount to entice a purchase. However, more research on this topic is necessary to make such a conclusion decisively.
On the basis of the trends in Figure 1, we also note that WTP (as a percentage of the list price) for products with one day left before expiration is the lowest for milk, perhaps because milk is the only product for which the aging process is difficult to stop (e.g., milk cannot be cooked, except perhaps in a recipe, or frozen). Therefore, the only means of stopping the aging process is by consuming the gallon of milk immediately, a task that takes more than a day for an average household. Therefore, with only one day before expiration, milk's value should be low, as is reflected by the low WTP.
As with H1, we find support for H7 with respect to PQR (in all categories but yogurt) but not PR. As consumers' PQR perceptions increase, their WTP for the product decreases. However, this relationship does not hold for risks related to social and financial forces. It is not surprising that PR is not significant, because the products we study are not "status" goods, nor do they require a substantial financial investment. Therefore, for products for which PQR is high, such as beef and chicken, managers should consider greater discounts to compensate for the greater risks associated with these products if they want to sell aging inventory. Other products with high PQR that we do not study herein might include fish or lunch meats. Alternatively, discounting may not be as crucial for perishables for which PQR is lower.
Next, there is no consistent evidence that consumers with higher household consumption rates are willing to pay more for a fresh product, regardless of the number of days remaining before the product reaches its expiration date (H8). However, we find support for H9 because as the product approaches its expiration date, WTP decreases at a faster rate for households with lower consumption rates in every category but chicken. In all categories except lettuce, the negative effect of PQR on WTP is dampened for households with higher consumption rates, in support of H10 for PQR. However, the same does not hold true for PR. Thus, consumers with higher consumption rates are willing to pay more for a product as it approaches its expiration date than are those with lower household consumption rates. This trend presents an opportunity for managers to target discounts at households with lower consumption rates, and therefore WTP, to sell perishables nearing their expiration dates.
We also find that though stopping the aging process does not affect overall WTP for a fresh perishable, regardless of the number of days remaining before expiration (H11), consumers who plan to stop the aging process are willing to pay more for a perishable as it approaches its expiration date than are those who do not plan to do so (H12). The results strongly support H12 in all categories except milk, whose aging process, as we noted previously, largely cannot be stopped. In all categories except lettuce, the negative impact of PQR on WTP is moderated for consumers who plan to stop the aging process, in support of H13 for PQR. However, again, the same does not hold true for PR. According to these findings, managers should encourage consumers to stop the aging process of a perishable immediately, especially in categories with high PQR. Some encouragements might include communicating the importance of freezing a product (at the point of purchase or otherwise), providing recipe ideas for the appropriate quantity of the perishable as it is packaged and sold, and selling smaller packages of perishables so that the entire product may be consumed or used in a recipe on the day it is purchased.
Finally, from the demographic variables, we find supporting evidence that older consumers and larger households are willing to pay more for perishables, if all else is equal. For example, it is not surprising that larger households have a higher WTP if we assume that they are able to consume the same amount of perishable goods more quickly than are smaller households. In turn, their risk of not being able to consume the product before it expires is lower, and their value of and WTP for the perishable is higher.
Conclusions and Further Research
We investigate consumers' behavior in six important grocery store perishable categories with printed expiration dates: yogurt, milk, precut lettuce, precut carrots, beef, and chicken. We focus on three main aspects of decision making in these categories: ( 1) the frequency with which consumers check expiration dates, ( 2) their WTP for a perishable product throughout the course of its shelf life, and ( 3) the perceived risks associated with perishables as they approach their expiration dates. We develop a set of hypotheses that predict the relationship among these three aspects of behavior in perishable categories and the variables that moderate them, and we test the hypotheses using a survey study. We present a summary of our results in Table 9.
When investigating the frequency with which consumers check expiration dates, we find that increases in PQR--a factor that captures functional, performance, and physical risks--have a positive impact on the frequency with which consumers check expiration dates. However, this effect does not hold for the PR factor, which captures psychological, social, and financial risks. We also find that consumers with greater category experience, as measured by household consumption rate, check expiration dates more frequently.
Discounts may be necessary to sell aging inventory to consumers who are aware of expiration dates, and our findings suggest that such promotions should be targeted at experienced consumers, those with higher perceptions of PQR, and those who shop in categories in which there is a greater tendency to check expiration dates, such as beef and chicken. For example, experienced shoppers could be identified by means of scanner data, which reveal those who shop more frequently in a category, and the technology necessary to target these consumers with price promotions is on the horizon. As Keenan (2003, p. 63) notes, "with new gadgets such as electronic shelves and digital price labels coming down the pike, retailers may eventually be able to change prices in the blink of an eye-and send electronic messages to shoppers' carts for custom-made deals."
However, marketers also must pay special attention to educating consumers who infrequently check expiration dates (e.g., inexperienced consumers, consumers with low PQR, younger or full-time employed customers) about the existence and importance of such dates, especially in categories for which PQR is low for most consumers, such as carrots and lettuce. This effort would help avoid the potential ill will that is created in cases in which uninformed consumers unintentionally buy a perishable good close to its expiration date only to get it home and place blame on the store when it spoils sooner than they expected. In addition, marketers can minimize confusion by educating consumers about the different meanings of expiration dates or by adopting a more uniform classification method to date foods.
With respect to WTP, we find support for the hypothesis that WTP decreases throughout the course of the product's shelf life, which suggests the need to educate consumers that product quality, and thus the value of the perishable, does not decline as the product approaches its expiration date. To be credible, this education would need support from third-party sources, such as the Center for Food Safety and Applied Nutrition (Hunter 1994). However, in the meantime, discounting may be an effective way to persuade consumers to purchase a perishable close to its expiration date.
We also find that WTP decreases linearly throughout the shelf life for lettuce, carrots, milk, and yogurt (products with relatively lower PQR) and exponentially for beef and chicken (products with relatively higher PQR). Therefore, a deeper discount earlier in the shelf life may be necessary to sell beef, chicken, and similar perishables. In addition, we find that WTP is lower for households with lower consumption rates, smaller households, and younger consumers. Therefore, discounting perishables that are approaching their expiration date will be most effective when it is targeted at these segments.
Finally, we find that WTP is greater when consumers plan to stop the aging process and that this effect is stronger the closer the product is to its expiration date. On the basis of this finding, we suggest that managers ( 1) remind consumers at the point of purchase of the importance of freezing a product, ( 2) provide recipe ideas for the appropriate quantity of the perishable as it is sold and locate common combinations of food items together (e.g., strawberries next to angel food cake) to encourage ideas for immediate consumption, or ( 3) sell smaller package sizes so the perishable can be consumed or used in a recipe on the day it is purchased.
In conclusion, we recognize that the decision of how to market grocery store perishables, including whether and how much to discount perishables as they approach their expiration date, requires a more in-depth analysis of the profit margins of each product and of the effects of discounting on store traffic, category volume, and crosscategory effects. However, our results provide grocery store managers with some insights into the impact of discounting perishables on consumers' behavior and perceptions. Furthermore, we show how perceived risk, a widely studied topic that has not been applied to perishable goods, can be used to understand and generalize behavior more effectively in these important categories. We hope that this research encourages others to examine consumer behavior in these profitable and important categories in an effort to aid managers in determining their product strategies.
Further research attempts might investigate the PQR dimension to determine whether our results for this important risk dimension are generalizable to other perishable categories. It also might be beneficial to conduct a field experiment to examine the impact of discounting perishables on purchase behavior. Such an effort would facilitate a more comprehensive analysis of consumers' WTP for a perishable throughout the course of its shelf life and may answer questions such as when and by how much the product should be discounted. It would also be worthwhile to examine how the WTP function changes when a fresher, more expensive batch of goods is shelved alongside an older, discounted set of products.
Finally, a more in-depth study of the management of perishables and its effect on store image would be of interest. Our interviews with managers revealed that many stores discount beef and chicken but are reluctant to discount dairy or produce for fear that such actions will tarnish the store's image. However, our findings reveal that consumers do not perceive any differences among the products examined in our study in terms of the effect that discounting them has on store image (see Table 4). Furthermore, some products that managers traditionally do not discount (e.g., carrots, lettuce, yogurt) are less likely to affect store image negatively when discounted than are those that managers already discount (e.g., chicken, beef). Therefore, for each product category, managers should weigh the trade-offs between the potential benefits of discounting to sell inventory and its potential negative effects on store image. As recent research suggests, organizations can also publicize socially responsible programs to create a competitive advantage in the marketplace (Lichtenstein, Drumwright, and Braig 2004). By presenting their discounting as an alternative to throwing away the product, stores can promote a socially responsible image that may provide both short-and long-term benefits for the store.
The authors thank Tom Jansen, a grocery store manager in the St. Louis, Mo., area for his invaluable insights into the issues studied herein. They also thank four anonymous JM reviewers whose comments improved the contributions of this article.
( n1) We identified consumers' misconceptions of how grocery store perishables age during their shelf lives in a pilot study in which the majority of consumers (61%) perceived that "sell by" dates represent the last day on which the product should be consumed rather than sold. Furthermore, a large proportion of consumers (42%-82%, depending on the category) reported the misconception that the quality of a perishable deteriorates continuously from the time it is put on the shelf. The results are available from the authors.
( n2) We use the term "expiration date" to denote "sell by," "best before," and "use by" dates.
( n3) We thank two anonymous reviewers for suggesting perceived risk as an important driver of behavior in perishable goods categories.
( n4) We thank an anonymous reviewer for this suggestion.
( n5) We find additional support for this hypothesis through an observation study and personal interviews with consumers, which revealed that those who shop infrequently in a category rarely, if ever, check expiration dates, and many do not even know that expiration dates exist in those categories.
( n6) These functional forms were supported in other similar studies that we conducted for this research, which are available on request.
( n7) We base this finding on an analysis of variance that tests for statistical differences in PQR and PR across the six product categories. The results are available from the authors.
Legend for Chart:
A - Reference
B - Problem Investigated
C - Category Characteristics/Assumptions
D - Findings/Contribution
A B
C
D
Hahn, Hwang, and Shinn Use a period-review
(2004) inventory model under
"last-in-first-out" and
"first-in-first-out,"
issuing policies to
examine retailers' operating
policies for perishables.
Products for which the
supplier agrees not to buy
back unsold products but
provides the retailer some
discount on the wholesale
price.
Cases in which the retailer
and supplier are better or
worse off because of a
no-return policy.
Abad (2003) Presents an analytical model
for pricing and lot size
decisions.
Perishable goods under
finite production, exponential
decay, and partial
backordering.
When demand is price
sensitive, pricing and
production are related, and
the retailer may need to
backlog demand to avoid
high costs due to
deterioration.
Chun (2003) Studies (1) optimal product
pricing based on demand
rate, buyer preference, and
length of sales period and
(2)the optimal ordering
quantity that maximizes the
seller's total expected profit.
For perishable commodities
for which demand is
represented by a negative
binomial, the seller must
determine the price for
several units of a perishable
with a limited shelf life, and
any product not sold at the
end of the selling period is
disposed of at a lower price.
Presents superior solutions
to those previously found in
the literature.
Li (2001) Uses a series of linear
programming models to
study optimal pricing
decisions.
Nonstorable perishable
goods or services (e.g.,
airline seats, hotel rooms).
Shows that there is an
optimal pricing policy of, at
most, three prices.
Hariga (1997) Presents analytical solution
methods to determine
optimal replenishment
schedules.
Exponentially decaying items
and perishable products with
fixed lifetimes.
Provides two efficient
solution methods that
determine the optimal
replenishment schedules for
the types of products
studied.
Subrahmanyan and Present an optimal pricing
Shoemaker (1996) and restocking model that
allows demand to be
updated throughout the
course of the life of the
product.
Products with uncertain
demand, a limited selling
season, and inventory left at
the end of the selling season
are greatly reduced in value
(e.g., fashion goods, toys).
Present a model that offers
advantages for new items
with the characteristics
studied.
Lodish (1980) Presents a dynamic model
for pricing and ordering that
accounts for inventory,
anticipated demand during
current and future periods,
and future pricing decisions.
Products whose value to the
customer changes over time
(e.g., broadcast spots).
Provides modelers an easily
implemented framework for
structuring and solving
dynamic pricing problems. Legend for Chart:
A - Dimension of Risk
B - Definition
C - Cited Study
A B
C
Functional risk The product does not perform as expected.
Jacoby and Kaplan (1972)
Performance risk The product does not meet standards of
quality.
Dunn, Murphy, and Skelly (1986); Roselius
(1971)
Physical risk Consumers' safety in using the product.
Jacoby and Kaplan (1972)
Psychological risk Poor product choice harms consumers' ego.
Jacoby and Kaplan (1972); Roselius (1971)
Social risk Product choice may result in embarrassment
before family or friends; others will think
less of a person as a result of
a poor product choice.
Dunn, Murphy, and Skelly (1986); Jacoby
and Kaplan (1972); Roselius (1971)
Financial risk The product is not worth the financial
price.
Dunn, Murphy, and Skelly (1986); Jacoby
and Kaplan (1972); Roselius (1971) Legend for Chart:
B - Dependent Variable
C - Independent Variable
D - Predicted Sign
E - Question Used to Capture Construct
F - Scale
A B C D
E
F
H1 Frequency of Functional risk +
checking(a)
"How likely is it that the following product
will not meet your expectations as it
approaches its expiration date?"
1 = "very unlikely,"
5 = "very likely"
H1 Frequency of Performance risk +
checking(a)
"How likely is it that the quality of the following
product gets worse as the product approaches its
expiration date?"
1 = "very unlikely,"
5 = "very likely"
H1 Frequency of Physical risk +
checking(a)
"How likely is it that consuming a spoiled product
of the following grocery item may lead to a health
risk?"
1 = "very unlikely,"
5 = "very likely"
H1 Frequency of Psychological risk +
checking(a)
"How likely are you to think less of yourself as
an experienced shopper if you were to buy the
following grocery item and then find that it did
not meet your standards of quality?"
1 = "very unlikely,"
5 = "very likely"
H1 Frequency of Social risk +
checking(a)
"How likely would guests in your home be to
think less of you for serving them a poor
quality product?"
1 = "very unlikely,"
5 = "very likely"
H1 Frequency of Financial risk +
checking(a)
"How likely would you be to feel financial angst
from paying for the following product and then
having it not perform up to its expectation?"
1 = "very unlikely,"
5 = "very likely"
H2 Frequency of HHConsume +
H3 checking(a) HHConsume x risk(c) -
"How often do you purchase in the product category
in an average month?"
0 = "never," 4 = "four
or more times/month"
H4 Frequency of Stop -
H5 checking(a) Stop x risk(c) -
An indication of whether the consumer plans to
stop the aging process (e.g., use, freeze) of
the perishable on arriving home.
1 = "use or freeze,"
0 = "otherwise"
H6 WTP(b) Days +
The number of days remaining before the product
reaches its expiration date.
Days = 7, 4, or 1
H7 WTP(b) Functional risk -
See H1
See H1
H7 WTP(b) Performance risk -
See H1
See H1
H7 WTP(b) Physical risk -
See H1
See H1
H7 WTP(b) Psychological risk -
See H1
See H1
H7 WTP(b) Social risk -
See H1
See H1
H7 WTP(b) Financial risk -
See H1
See H1
H8 WTP(b) HHConsume +
H9 HHConsume x days +
H10 HHConsume x risk(c) +
"How often do you purchase in the product category
in an average month?"
0 = "never," 4 = "four
or more times/month"
H11 WTP(b) Stop +
H12 Stop x days +
H13 Stop x risk(c) +
An indication of whether the consumer plans to stop
the aging process (e.g., cook, use, freeze) of the
perishable on arriving home.
1 = "cook, use, or
freeze," 0 =
"otherwise"
(a) "How often do you check for an expiration date when buying
each of the following products?" 1 = "never," 5 = "always."
(b) "What is the most you would be willing to pay for product j
if it were due to expire in (7, 4, or 1) days?" (divided by
shelf price).
(c) In the interest of space, we use "risk" here to represent an
interaction with all risk variables. When estimating the model,
we included a separate term to capture the interaction of
HHConsume and Stop with all risk variables in the model. Legend for Chart:
B - Average Shelf Life (Days)
C - Average Retail Price ($)
D - Aware of the Existence of Expiration Dates (%)
E - "Always"/"Usually" Check Expiration Dates (%)
F - Perception of Store Quality Would Decrease if the Store
Were to Discount Perishable Goods (%)
G - Believe Quality Deteriorates as the Product Approaches
Its Expiration Date (%)
A B C D E F G
Precut/prewashed lettuce 10 2.49 53 42 44 84
Milk 14 2.70 99 93 47 84
Chicken breast 7 2.99 80 74 46 82
Precut/prewashed carrots 21 1.69 35 29 40 69
Yogurt 21 .62 87 70 44 74
Beef 7 2.68 78 59 47 84 Legend for Chart:
A - Risk Variable
B - Lettuce Factor 1 (PQR)
C - Lettuce Factor 2 (PR)
D - Milk Factor 1 (PQR)
E - Milk Factor 2 (PR)
F - Chicken Factor 1 (PQR)
G - Chicken Factor 2 (PR)
H - Carrots Factor 1 (PQR)
I - Carrots Factor 2 (PR)
J - Yogurt Factor 1 (PQR)
K - Yogurt Factor 2 (PR)
L - Beef Factor 1 (PQR)
M - Beef Factor 2 (PR)
A B C D E
F G H I
J K L M
Functional .884 .082 .864 -.014
.833 .084 .822 .067
.865 .042 .863 .083
Performance .877 .160 .837 .015
.747 .159 .773 .007
.836 .089 .745 .254
Physical .472 .261 .468 .205
.648 -.029 .513 .329
.649 .159 .681 -.042
Psychological -.070 .791 -.064 .806
-.056 .817 .009 .737
-.062 .785 -.047 .820
Social .089 .744 .065 .751
.088 .799 .089 .778
.128 .794 .113 .794
Financial .299 .486 .290 .504
.304 .432 .136 .616
.301 .551 .303 .416
Cronbach's alpha .70 .72 .70 .71
.73 .71 .74 .75
.81 .74 .77 .72
Percentage variation explained .317 .308 .323 .282
.330 .285 .291 .310
.360 .294 .345 .288 A: Descriptive Statistics
Legend for Chart:
B - Lettuce
C - Chicken
D - Carrots
E - Yogurt
A B C D
E F G
FreqCheck(a) 2.81 (1.80) 4.73 (.75) 4.01 (1.48)
2.33 (1.69) 3.84 (1.57) 3.81 (1.63)
WTP (7 days)(a) 2.15 (.63) 2.14 (.82) 2.58 (.73)
1.39 (.53) .51 (.19) 2.33 (.83)
WTP (4 days)(a) 1.63 (.70) 1.46 (.99) 1.65 (.92)
1.00 (.60) .42 (.22) 1.54 (.82)
WTP (1 day)(a) 1.08 (.79) .71 (.78) 1.38 (.93)
.70 (.56) .26 (.22) 1.20 (.90)
PQR(a) 9.51 (2.02) 10.39 (2.02) 11.10 (2.17)
8.28 (2.11) 10.71 (2.47) 11.52 (2.17)
PR(a) 9.60 (2.20) 8.50 (2.01) 8.56 (2.05)
8.90 (2.16) 8.76 (2.12) 8.84 (2.09)
HHConsume(a) 1.80 (1.42) 3.05 (1.12) 2.47 (1.14)
1.14 (1.15) 1.57 (1.42) 1.66 (1.32)
Stop(b) 27% 11% 34%
33% 13% 31%
Sex(b) 25%(c) -- --
-- -- --
Age(b) 50%(c) -- --
-- -- --
Income(b) 63%(c) -- --
-- -- --
FullTime(b) 57%(c) -- --
-- -- --
HHSize(a) 2.67 (1.33)(c) -- --
-- -- --
B: Correlation Matrix (Pooled Across Categories)
Legend for Chart:
B - PQR
C - PR
D - HH Consume
E - HHCon x PQR
F - HHCon x PR
G - Stop
H - Stop x PQR
I - Stop x PR
J - Sex
K - Age
L - Income
M - FullTime
N - HHSize
A B C D E F G H
I J K L M N
PQR 1
PR .19 1
HHCon .01 -.01 1
HHCon x PQR .29 .24 .16 1
HHCon x PR .25 .28 .16 .19 1
Stop .01 -.01 -.01 .01 -.01 1
Stop x PQR .37 .31 .01 .24 .20 .24 1
Stop x PR .33 .33 -.01 .21 .20 .23 .18
1
Sex .22 .17 -.01 .16 .11 -.01 .10
.07 1
Age .25 .26 .05 .17 .18 -.01 .12
.13 .09 1
Income .23 .21 .04 .17 .16 -.02 .10
.08 .19 .33 1
FullTime .24 .29 .02 .18 .20 -.01 .13
.15 .31 .30 .31 1
HHSize .22 .21 .04 .17 .14 -.01 .10
.08 .16 .31 .26 .17 1
(a) Mean with standard deviation is in parentheses.
(b) Percentage of respondents who planned to stop the aging
process of the product on arriving home; they were male, were
older than age 45, had a yearly income greater than $50,000, and
worked full time.
(c) Because respondents answered questions across all six
categories, these figures do not differ across products. Legend for Chart:
A - Variable (Hypothesis)
B - Expected Sign
C - Lettuce
D - Chicken
E - Carrots
F - Yogurt
G - Beef
A B C D E
F G
Intercept .72(**) 3.92(**) 1.21
(.25)(a) (.75) (.79)
2.23(**) 3.35(**)
(.72) (.54)
PQR (H1) + .24(**) .21(**) .19(**)
(.07) (.09) (.04)
.17(**) .12(*)
(.04) (.07)
PR (H1) + -.06 .02 -.05
(.06) (.13) (.07)
-.10 -.04
(.07) (.10)
HHConsume (H2) + .29(**) .15(**) .35(**)
(.09) (.04) (.14)
.18(*) .39(**)
(.09) (.09)
HHConsume x PQR (H3) - -.03(**) -.04(*) -.02
(.01) (.02) (.03)
-.04(**) -.04(*)
(.01) (.02)
HHConsume x PR (H3) - -.01 -.04 .03
(.01) (.04) (.02)
.04 -.01
(.03) (.03)
Stop (H4) - -.34 -.39(**) .13
(.45) (.11) (.10)
-.33 -.19(*)
(.23) (.11)
Stop x PQR (H5) - .01 -.05 -.01
(.01) (.07) (.11)
-.08 -.01
(.24) (.07)
Stop x PR (H5) - -.03 .09 -.02
(.02) (.09) (.10)
.11 .07(*)
(.29) (.03)
Sex -.12 -.13 -.39(*)
(.21) (.21) (.21)
-.19 -.13
(.21) (.21)
Age .30 .55(**) .43(**)
(.20) (.20) (.19)
.13 .65(**)
(.19) (.19)
Income .25 -.07 .21
(.22) (.19) (.19)
.12 -.13
(.19) (.19)
Full time -.32(*) -.07 -.36(*)
(.18) (.18) (.18)
.12 .11
(.18) (.18)
Household size .18 -.04 -.12
(.21) (.07) (.08)
.05 -.10
(.07) (.07)
N = 1350
R²: Main effects model only = .28
R²: Full model = .30
F-statistic: Full model = 8.05
(*) p < .05.
(**) p < .01.
(a) Numbers in parentheses are standard errors. Legend for Chart:
B - Expected Sign
C - Lettuce
D - Milk
E - Chicken(b)
F - Carrots
G - Yogurt
H - Beef(b)
A B C D E
F G H
Intercept 1.19(*) 1.83(*) 2.03(*)
(.69)(a) (.84) (.98)
.74(*) .25(**) 2.14(**)
(.42) (.09) (.72)
Days(b)(H6) + .11(**) .04(**) .0007(**)
(.05) (.01) (.0001)
.03(**) .02(*) .0004(**)
(.01) (.01) (.0001)
PQR (H7) - -.03(**) -.02(**) -.16(**)
(.01) (.007) (.05)
-.06(**) -.02 -.10(*)
(.02) (.04) (.05)
PR (H7) - -.04 -.01(**) -.02
(.03) (.005) (.05)
-.03 -.02 -.03
(.02) (.03) (.04)
HHConsume (H8) + .02 .05 .02
(.03) (.04) (.04)
.03 .01(**) .02(*)
(.04) (.003) (.01)
HHConsume x days + .01(**) .01(**) .0001
(H9) (.003) (.004) (.0001)
.03(**) .03(*) .0001(*)
(.01) (.01) (.0001)
HHConsume x PQR + .001 .002(*) .004(**)
(H10) (.001) (.001) (.001)
.002(*) .002(*) .004(*)
(.001) (.001) (.002)
HHConsume x PR + .002 .001 .0001
(H10) (.003) (.001) (.0003)
.003 .002 .002(*)
(.004) (.003) (.001)
Stop (H11) + .11 .08 .18
(.13) (.15) (.11)
.13 .06 .37(**)
(.10) (.20) (.11)
Stop x days (H12) + .03(**) .06 .0002(*)
(.01) (.08) (.0001)
.03(**) .02(*) .0002(*)
(.01) (.01) (.0001)
Stop x PQR (H13) + .02 .01(**) .03(**)
(.02) (.003) (.01)
.03(**) .02(*) .04(**)
(.01) (.01) (.01)
Stop x PR (H13) + .03 .02 .01
(.03) (.03) (.02)
.01 .03 .02(*)
(.01) (.02) (.01)
Sex -.13 -.15 -.04
(.21) (.22) (.18)
-.18 -.11 .07
(.16) (.21) (.09)
Age .07(**) .02(*) .25(**)
(.03) (.01) (.06)
.37 .06 .28(*)
(.25) (.19) (.15)
Income .03 -.09 .10
(.02) (.19) (.13)
.26 .11 -.20
(.19) (.09) (.14)
Full time .02 .04 -.08
(.08) (.18) (.06)
-.38(*) .13 -.21
(.20) (.11) (.15)
Household size .03(**) .04 .07(**)
(.01) (.07) (.02)
.11(*) -.04 .15(*)
(.05) (.07) (.07)
N = 1620
R²: Main effects model only = .27
R²: Full model = .36
F-statistic: Full model = 8.43
(*) p < .05.
(**) p < .01.
FT.-
(a) Numbers in parentheses are standard errors.
(b) The best-fitting model for chicken and beef operationalizes
Days as e (days). Legend for Chart:
A - Behavior
B - Main Findings
C - Implications
A
B
C
Frequency of checking
expiration dates
As PQR decreases,
consumers are less likely to
check expiration dates.
Because lack of awareness of expiration dates is
bad for consumers (they make uninformed decisions)
and marketers (who may be blamed by consumers who
purchase products close to expiration at full
price), marketers must educate consumers whose
perceptions of PQR are low, especially for
categories in which the majority of consumers tend
to have low perceptions of PQR (e.g., carrots,
lettuce).
Consumers with greater
category experience check
expiration dates more
frequently.
Discounting perishables that are approaching their
expiration dates may be most effective when
targeted at consumers with greater category
experience. In addition, marketers must educate
inexperienced consumers about expiration dates so
that they can make more-informed decisions.
WTP for a product
throughout the course of
its shelf life
WTP decreases throughout
the course of the shelf life
of a product.
Discounting may be a necessary and effective way
to entice consumers to purchase a perishable close
to its expiration date.
WTP decreases linearly for
products with low PQR and
exponentially for products
with high PQR.
A deeper discount may be necessary to sell a
perishable with a high PQR (e.g., beef, chicken)
earlier in the shelf life of the product.
WTP is lower for younger
consumers and consumers
with lower household
consumption rates.
Marketers should target younger consumers and
consumers who are shopping for households with
lower consumption rates with price incentives to
persuade them to purchase perishables close to
their expiration dates.
The greater the perceptions
of PQR, the lower is the
WTP. However, household
consumption and stopping
the aging process moderate
this effect.
Marketers must educate consumers to reduce
perceptions of product quality risks and
health risks associated with a perishable as
it approaches its expiration date, especially for
consumers with low household consumption rates or
categories for which the aging process typically
cannot be stopped (e.g., milk).
WTP is higher in situations
in which consumers plan to
stop the aging process.
Marketers should provide recipes, cooking
suggestions, reminders to freeze the product on
arriving home, and so forth, to encourage
consumers to stop the aging process. They may
also offer smaller package sizes to serve the
same purpose.GRAPH: FIGURE 1; WTP as a Percentage of List Price Throughout the Course of a Perishable's Shelf Life
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~~~~~~~~
By Michael Tsiros and Carrie M. Heilman
Michael Tsiros is Associate Professor of Marketing, University of Miami, and Tassos Papastratos Research Associate Professor of Marketing, Athens Laboratory of Business Administration, Athens, Greece.
Carrie M. Heilman is Assistant Professor of Commerce, McIntire School of Commerce, University of Virginia
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Record: 156- The Effect of Reward Structures on the Performance of Cross-Functional Product Development Teams. By: Sarin, Shikhar; Mahajan, Vijay. Journal of Marketing. Apr2001, Vol. 65 Issue 2, p35-53. 19p. 1 Diagram, 6 Charts. DOI: 10.1509/jmkg.65.2.35.18252.
- Database:
- Business Source Complete
THE EFFECT OF REWARD STRUCTURES ON THE PERFORMANCE OF
CROSS-FUNCTIONAL PRODUCT DEVELOPMENT TEAMS
This study examines the effect of reward structures on the performance of cross-functional product development teams. Results suggest that when it is easy to evaluate individual performances, position-based differential rewards lead to greater satisfaction. For long and complex projects, process-based rewards have a negative effect and outcome-based rewards have a positive effect on performance. For risky projects and highly competitive or relatively stable industries, a nonlinear and monotonically decreasing relationship exists between outcome-based rewards and product quality.
New product development (NPD) is critical for the renewal, survival, and success of organizations (Brown and Eisenhardt 1995; Wind and Mahajan 1997). To ensure success in the development of new products, marketing needs to have a significant influence; however, marketing often ends up playing a secondary role to engineering (Workman 1993). Greater coordination among functional areas is essential for successful NPD (Adler 1995; Olson, Walker, and Ruekert 1995; Wind and Mahajan 1997). Cross-functional teams have become an increasingly popular mechanism for achieving greater interfunctional integration and cooperation in the NPD process (Adler 1995; Griffin 1997; Olson, Walker, and Ruekert 1995; Pascarella 1997; Wind and Mahajan 1997).
Reward structures have been identified as one of the most important determinants of interfunctional integration among organizational employees and units (Coombs and Gomez-Mejia 1991). The use of rewards as a means of controlling, managing, and enhancing performance has been well established in marketing, especially in the areas of distribution channels (e.g., Gundlach and Cadotte 1994), sales force management (e.g., Ingram and Bellenger 1983), and organizational buying behavior (e.g., Anderson and Chambers 1985). In this article, we extend this line of inquiry to include another vital function of marketing--NPD.
We examine how reward structures affect the performance of cross-functional product development teams (CFPDTs). Performance of CFPDTs is measured in terms of speed to market, level of innovation, product quality, adherence to budget and schedule, and market performance (among others)--variables that have long been the focus of marketing literature (for some recent examples, see Olson, Walker, and Ruekert 1995; Workman 1993). In their introduction to Journal of Marketing Research' s Special Issue on Innovation and New Products, Wind and Mahajan (1997) identify speed to market, innovation, and product quality as among the most critical issues facing NPD in marketing. Our study draws attention to the unexplored area of reward structures, which can have a significant influence on these NPD outcomes.
Even small changes in the reward and evaluation structures of CFPDTs may lead to relatively large payoffs for NPD performance (Feldman 1996). Despite its obvious potential impact, examination of this topic by either academic researchers or organizations remains sparse (Griffin 1997). As such, an examination of the effect of reward structures on CFPDT performance presents an intriguing problem from both theoretical and managerial perspectives.
Organizational reward and evaluation structures have not kept pace with the changes in the work environment (Wallace 1987). Robbins and Finley (1995) contend that outdated reward structures are a common reason teams fail in organizations. They note that rewards and evaluations are still functionally determined: Teams and individual members are often rewarded for the wrong things. Many articles in the popular business press have also commented on the pervasiveness and complexity of this problem:
Building cross-functional teams can work wonders in developing new products, but only if people are rewarded as members of a team .... That's one reason why most teams fail to produce. (BusinessWeek 1995, p. 154)
[In order to succeed in streamlining and flattening their structure,] organizations must change the appraisal and pay systems to reward team results, not just individual performance. (Byrne 1993, p. 78)
When it comes to paying teams, managers still throw up their hand-held computers in despair. Pay the team as a group? Then won't your star performers feel slighted? Pay for individual performance? What does that do to encourage teamwork? (Dumaine 1994, p. 87)
Pascarella (1997) notes that two major questions confront organizations that actively use CFPDTs: how and when to reward such teams. Field interviews conducted during this study confirm that managers struggle with two issues pertaining to CFPDT rewards: how to distribute rewards among team members and on what criteria the team rewards should be based. Rewards can be distributed among team members equally or on the basis of the position/status enjoyed by the individual members in the organization. Similarly, teams can be rewarded on the basis of the outcome produced by them or for adhering to a certain process.
Existing literature suggests that instead of representing end points of two continuums, equal/position-based rewards and process-/outcome-based rewards constitute four distinct constructs (e.g., Jaworski 1988; Steers and Porter 1991). In this exploratory study, we examine the effect of these four reward structures on the performance of CFPDTs and propose a midrange theory in a new domain. To our knowledge, this is one of the first studies that uses industry data to examine empirically the effect of reward structures on CFPDT performance. Team performance is measured along multiple dimensions that vary from speed to market to team member satisfaction (broadly classified as internal and external team performance dimensions). In Figure 1, we outline the conceptual framework developed and tested in this study. This framework addresses the following research questions:
- How are the internal performance dimensions of CFPDTs (i.e., team member satisfaction and self-rated performance) affected by equal and position-based distribution of rewards among team members? Under what conditions should rewards be distributed equally among the team members? When should rewards be distributed on the basis of the position/status of the team member in the organization?
- How are the external performance dimensions of CFPDTs (i.e., speed to market, level of innovation, adherence to budget and schedule, product quality, and market performance) affected by rewards that are linked to the NPD process versus the outcome produced by the team? Under what conditions should team rewards be linked to the outcome as opposed to the process of product development?
Reviews of both academic and popular business literature reveal little to guide managerial decision making on rewarding CFPDTs. Some researchers (e.g., Deschamps and Nayak 1995; Parker 1994; Robbins and Finley 1995) have alluded to the topic; however, their treatment is based more on experience and case studies than on empirical data. Other studies (e.g., Gladstein 1984; Thamhain 1990; Wageman 1995) have addressed group rewards tangentially. Although some recent attempts (Faure and Weitz 1993; Walker, Ruekert, and Olson 1992) directly address this issue, the literature still lacks a comprehensive examination of team rewards (Griffin 1997; Olson, Walker, and Ruekert 1995).
A lack of theory in the area of team rewards is further complicated by our observations that no mechanisms of rewarding CFPDTs appear to be consistently effective in the field. Furthermore, little empirical data are available to support the strategies recommended in the popular business press. In the absence of a well-defined stream of research on a topic, a qualitative, practitioner-oriented approach to theory development is recommended (Kohli and Jaworski 1990; Zaltman, LeMasters, and Heffring 1982). Therefore, we conducted extensive qualitative interviews to develop the conceptual framework presented in Figure 1.
Over 18 months, qualitative data were collected in 37 interviews with 57 individuals. These individuals were drawn from a cross-section of functional backgrounds and hierarchical levels and were involved in the NPD process. They included representatives from 26 different CFPDTs and executives involved in product development at nine medium-sized to large organizations. The data collected during this phase were used to establish the external validity of the conceptual framework, generate preliminary hypotheses and scale items, and pretest the survey instrument used during the second phase of data collection.
Initial field interviews indicated that the reward and evaluations structures that resulted in positive outcomes in one team had a negative effect on others, suggesting the influence of contextual variables. Although discerning patterns proved difficult, three consistencies emerged from our interviews:
- When teams are rewarded, that is, the timing of rewards, had a significant impact on team performance (i.e., process- and outcome-based rewards).
- Team performance was also affected by how the rewards were distributed among the members of the team (i.e., equal and position-based rewards).
- The effects of these reward structures were contextual and depended on the characteristics of the project and industry under consideration.
The moderating influence of some project- and industry-specific variables, such as project/product complexity and industry dynamism, was suggested by previous studies as well (e.g., Gladstein 1984; Pascarella 1997). We incorporated these qualitative data into the conceptual framework before commencing the second phase of data collection using a survey instrument.
Team Performance
Performance of CFPDT is a multidimensional construct. It implies different things to different people under different contexts. Shea and Guzzo (1987) suggest that multiple measures should be used to evaluate team performance. In this study, the performance of CFPDTs is disaggregated and measured along seven underlying dimensions, broadly classified along external and internal criteria. External performance criteria are largely market based, and internal criteria are more pertinent to the team/organization.
Five external performance dimensions were considered, three of which were previously identified in the literature: (1) speed to market, a relative measure of time taken to launch a product; (2) adherence to budget and schedule; and (3) level of innovation, the degree of newness of the product (Ancona and Caldwell 199 I, 1992a; Olson, Walker, and Ruekert 1995; Walker, Ruekert, and Olson 1992). Our interviews, as well as recent studies, suggested the inclusion of two additional external performance dimensions: (4) product quality, measured in terms of customer satisfaction, product reliability, number of product-related complaints, and warranty and repair costs (Meyer 1994; Olson, Walker, and Ruekert 1995); and (5) market performance, postintroduction performance of the product relative to expectation and competition (Meyer 1994).
Teams were also evaluated along two internal performance dimensions: (1) self-rated team performance, a measure of the team's performance compared with other NPD teams in the organization (Ancona and Caldwell 1991, 1992a); and team member satisfaction, the degree to which association with the team is considered a worthwhile, productive, and satisfying experience by team members (Meyer 1994; Pinto, Pinto, and Prescott 1993; Warr, Cook, and Wall 1979). The distribution of rewards is expected to influence the internal dimensions of team performance significantly, and the basis on which teams are rewarded is expected to be more germane to the external dimensions of team performance.
Relationship Between Equal and Position-Based Rewards and Internal Dimensions of Team Performance
Deutsch (1968) examines cooperation and competition in workgroups and suggests that in cooperative situations members enjoy relatively equal standing with respect to an outcome/objective, whereas in competitive situations their standing may be different. On the basis of this argument, two dimensions of reward structure were identified as they related to the distribution of rewards among the team members: Equal rewards are defined as the degree to which rewards are distributed evenly among team members, and position-based rewards are defined as the degree to which rewards are distributed among team members on the basis of their position/status in the organization.
Some researchers argue that team members should be rewarded as a unit because differential rewards are inherently inconsistent with the team concept ( Business Week 1995, p. 154; Parker 1994). Others suggest that the decision whether to reward team members equally or differentially should be based on the degree to which the team members are dependent on one another for the performance of their tasks (Deutsch 1968; Faure and Weitz 1993; Wageman 1995). The general consensus among these researchers is that equal rewards are more appropriate when the level of task interdependence is high. However, our field interviews suggested that it is not task interdependence per se but the ease with which individual performance can be identified and evaluated that should determine the manner of reward distribution. Implicit in the task interdependence argument is the assumption that individual contributions will be difficult to evaluate in highly interdependent tasks.
Consistent with the observation of some researchers (e.g., Parker 1994; Pascarella 1997), our interviews indicated that even in highly interdependent NPD projects, the efforts, responsibilities, or contributions of one or more members stood out. When interviewed alongside other team members, these star performers expressed no desire for extra recognition or rewards. However, when interviewed separately, they expressed a yearning for some extra recognition or reward that acknowledged their above-average contributions. Such conflicting feedback suggested that the issue of differential versus equal reward distribution was a complex one, in which true desires may be concealed because of peer pressure to be perceived as a team player.
Desire for personal recognition is consistent with individualism, a fundamentally Western/U.S. cultural value (Nahavandi and Aranda 1994). Qualitative data indicate that in an inherently collective (team) situation, this tendency, though suppressed, still persists. Harder (1992) discovered that underrewarding can lead to selfish and less-cooperative behavior in an interdependent group with a common goal. Therefore, when contributions are obvious or easy to evaluate, organizations may be well advised to recognize extraordinary individual effort, or they will risk losing their star performers (Zenger 1992).
However, contributions to the team should not be determined on the basis of effort alone. Even though members of a CFPDT have joint responsibility and joint accountability, the responsibility and risk shared by different members are not always equal. The amount of risk and responsibility shouldered by each member depends on his or her seniority, position in the organization, and role on the team. Wallace (1988) argues that the distribution of rewards should be structured to reflect the different levels of risk and responsibility assumed by each member. We consider contributions to the team to include the effort, risk, and responsibility assumed by each team member.
The need to reward individuals differentially in a group/team setting can also be argued from organizational justice and fairness perspectives. Distributive justice relates to individuals' perception of whether they are receiving a fair share of the available rewards--proportionately to their contribution to the group (Baron and Byrne 1997). Equity theory pertains to fairness in social exchanges and distributive justice, whereby individuals compare the ratio of their own rewards and contributions (to a group) to those of other individuals in the group (Adams 1965; Greenberg 1993).
Cropanzano and Randall (1993) note that perceptions of injustice (rewards being low in proportion to an individual's contribution) or inequity (in the distribution of rewards among individuals) can result in dissatisfaction, lower motivation, and even dysfunctional behavior. They suggest that equity and distributive justice should be taken into consideration, especially when dealing with people from individualistic cultures or when the emphasis is on maximizing group performance. Parker (1994, p. 134) summarized this argument as follows:
[In addition to bringing the rewards down to the team level,] we still need to recognize team members who are outstanding team players--people who go beyond what is required and those who make an outstanding individual contribution to a team.
Many researchers have criticized the notion that team members need to be individually (differentially) recognized and rewarded, as doing so would be inconsistent with a team-based approach. Donnellon and Scully (1994) argue that differential rewards or individual recognition takes too narrow and dim a view of human nature. Furthermore, unequal reward distribution under interdependent conditions may undermine cooperation and increase competition within the team, lowering overall group productivity (Baron and Cook 1992; Deutsch 1968). This line of reasoning notwithstanding, support in favor of individual recognition in addition to team rewards remains strong (e.g., Deschamps and Nayak 1995; Parker 1994; Pascarella 1997; Robbins and Finley 1995).
Conflicting evidence presented by both qualitative field interviews and relevant theories compels us to examine competing viewpoints. Therefore, we propose and test alternative hypotheses in this study with the intention of reducing the contradictions in the literature. The discussion so far suggests that position-based differential rewards could be used when outstanding individual contribution is apparent to most members of the team (Pascarella 1997). This leads us to propose the following set of alternative hypotheses:
H1: When the ease of individual evaluation is high, a position-based reward structure will be positively related to internal dimensions of CFPDT performance.
H1(alt): When the ease of individual evaluation is high, a position-based reward structure will be negatively related to internal dimensions of CFPDT performance.
When the cost and effort required to monitor individual contributions in a team is high, organizations are likely to share rewards (Alchian and Demsetz 1972). Therefore, when evaluating individual performance in a group setting is difficult, an equal reward structure is expected to be more effective. Procedural justice suggests that people are also sensitive to the fairness of the procedures used to distribute rewards among group members (Cropanzano and Randall 1993). People make a clear distinction between equity (the perception that the members' rewards are proportional to their contribution to the group) and procedural justice (the perception that fair and just procedures are followed to distribute rewards within a group) (Baron and Byrne 1997). Any perception of unfairness in the procedure can affect people's satisfaction with the rewards (Cropanzano and Randall 1993). Baker, Jensen, and Murphy (1988, p. 608) note that "Biased and inaccurate performance evaluation reduces productivity by reducing the effectiveness of incentives in organizations."
Therefore, when the ease of individual evaluation is low, a perception of unfairness may persist in the distribution of rewards. Such a perception may be hard to dislodge and could lead to dissatisfaction and lower morale and performance, regardless of how the rewards are distributed within the team (Cropanzano and Randall 1993). Therefore, we propose the following set of alternative hypotheses:
H2: When the ease of individual evaluation is low, an equal reward structure will be positively related to internal dimensions of CFPDT performance.
H2(alt): When the ease of individual evaluation is low, an equal reward structure will be negatively related to internal dimensions of CFPDT performance.
Relationship Between Process- and Outcome-Based Rewards and External Dimensions of Team Performance
The dimensions of process- and outcome-based rewards are derived from the organizational control literature, which describes control as a process of monitoring and evaluating behaviors and outcomes (Eisenhardt 1985; Ouchi and Maguire 1975). Rewards and punishments are logical extensions of the control process, following monitoring and evaluation. Jaworski (1988) identifies several types of formal and informal controls. Two formal control mechanisms, process and outcome controls, are considered relevant to this study. Process controls are exercised during the execution of a task; output controls are exercised after a task is completed. Analogous to these two kinds of controls, process-based rewards are defined as the degree to which team rewards are tied to procedures, behaviors, or other means of achieving desired outcomes (i.e., completion of certain phases in the development process) (Deschamps and Nayak 1995). Outcome-based rewards are defined as the degree to which team rewards are tied to the bottom-line profitability of the project.
Although the relationship between organizational control and overall team performance has been examined (Henderson and Lee 1992; Walker, Ruekert, and Olson 1992), little is known about (1) how external dimensions of team performance are affected by linking team rewards to either process- or outcome-based measures and (2) how various product and industry characteristics affect the relationship between external dimensions of team performance and process-/outcome-based rewards. Process- and outcome-based controls can be used simultaneously (Merchant 1985), and researchers have argued that each can positively affect team performance (Henderson and Lee 1992; Walker, Ruekerr, and Olson 1992). Our field interviews suggest that the effect of process- and outcome-based rewards is more complicated than anticipated. Process- and outcome-based rewards appear to have opposite effects, and their effect on performance is moderated by several factors.
For products with long development cycles, the team must stay motivated over the course of the development process. Such teams have a greater probability of turnover in their membership. During interviews, members of these teams reported less tolerance for delaying gratification (rewards) until the market performance of the product was evident. Under such conditions, output controls may be ineffective or even counterproductive (Hopwood 1972). Consistent with Parker's (1994) work, our qualitative data suggest that for long projects a process-based reward system focused on meeting procedural milestones (such as completion of specific phases) may be more effective.
However, the organizational control literature associates several potential disadvantages with process-based criteria for evaluating performance. Process-based criteria could make people dependent on the process itself, thus making them inert and resistant to change (Merchant 1985). Emphasizing process over outcome could promote risk-averse behavior (Cardinal 1990) and lower motivation and satisfaction (Hackman and Oldham 1976). This discussion suggests the following alternative hypotheses:
H3: For products with long development cycles, a process-based reward system will be positively related to external dimensions of CFPDT performance.
H3(alt): For products with long development cycles, a process-based reward system will be negatively related to external' dimensions of CFPDT performance.
Similarly, organizations may need to exercise greater control over planning, monitoring, and scheduling the development of complex products (Benghozi 1990). This is more easily achieved if the reward and control mechanisms are linked to process-based measures, because doing so ensures that minimum acceptable standards of quality are met and satisfied. Process-based controls and rewards help ensure predictability in behaviors, activities, and procedures deemed critical to success (Cardinal 1990). However, process-based control structures also restrict opportunities for achievement by discouraging creativity, innovation, and flexibility (Merchant 1985). Therefore, process-based rewards are expected to encourage team members to focus on the procedures required to produce the desired outcome rather than on the outcome itself. In such a situation, teams are likely to have a much lower stake in the success of the product, and most of the risk associated with developing the product is transferred to the organization. This discussion suggests the following alternative hypotheses:
H4: For highly complex products, a process-based reward system will be positively related to external dimensions of CFPDT performance.
H4(alt): For highly complex products, a process-based reward system will be negatively related to external dimensions of CFPDT performance.
As a semiautonomous unit, the team acts as an agent of the organization with a mandate to develop a particular product. As such, agency theory (Bergen, Dutta, and Walker 1992; Eisenhardt 1985, 1989) can be used to design suitable reward structures for CFPDTs, as has been done for other professionals employed by organizations (Bloom and Milkovitch 1998). Agency theory makes the fundamental assumptions that both the agent and the principal are rational and self-interested and the agent is both effort- and risk-averse (Bloom and Milkovitch 1998). This creates a moral hazard, in which the agent (CFPDT) tends to maximize its compensation without exerting the effort required to maximize the principal's (organization's) goals (Baiman 1990; Bergen, Dutta, and Walker 1992; Bloom and Milkovitch 1998; Eisenhardt 1989).
Consequently, when dealing with agents, organizations prefer outcome-based reward and evaluation criteria, because they minimize the organization's risk by ensuring that the desired output is obtained (Eisenhardt 1989). Agents, in contrast, prefer process-based reward and evaluation criteria, because this minimizes the agent's risk by ensuring it of compensation regardless of the project outcome. Baiman (1990) suggests that organizations should balance their use of outcome-based rewards so that the rewards motivate the agents to act in the organization's best interest.
Bloom and Milkovitch (1998) posit that such a balance addresses the inherent conflict of interest between the agent and the organization and is likely to result in an optimal reward structure as long as an undue amount of risk is not passed on to either party. They further note that though classical agency theory places equal emphasis on both the risk-and effort-averse nature of the agent, much prior research has overlooked risk considerations, concentrating mainly on the effort aversiveness of the agent. Consideration of risk is an essential element in the selection of an appropriate reward structure, because risk and reward jointly affect performance. Therefore, balancing incentives and risk sharing may be essential in designing reward structures for achieving optimal performance (Bloom and Milkovitch 1998; Eisenhardt 1989).
Risk has been defined in terms of uncertainty about future events/outcomes and the magnitude of failure (March and Shapira 1987). For CFPDTs, risk can arise from the nature of the project itself (i.e., product/project risk) or from the larger environmental conditions existing in the industry (i.e., industry dynamism and competitive intensity). When dealing with risky products or highly dynamic industries, CFPDTs face considerable uncertainty about future outcomes. For teams in highly competitive and dynamic industries, the cost of failure is very high. Under each of these conditions, both the organization (principal) and the team (agent) tend to minimize their own risks. Sharing risk provides an optimal way to maintain autonomy and accountability without sacrificing objectives of either the team or the organization. A purely outcome-based reward structure is likely to be counterproductive in these cases, because it places an excessive amount of risk on the CFPDT. In contrast, disconnecting the rewards from project outcomes not only forces the organization to absorb a disproportionate amount of the risk but also fails to motivate the team.
When environmental, technological, or organizational factors obscure the measurement of output or hinder output performance, purely output-based controls can be ineffective (Hopwood 1972). Similar impediments and ambiguities are likely when the industry is highly competitive or turbulent. Agency theory suggests that under such conditions, linking reward structure to the outcomes to a moderate degree should distribute the risk evenly between the team and the organization, resulting in an optimal (most effective) contract. For risky products, or when teams are operating in highly competitive or dynamic industries, an inverted-U-shaped relationship is likely to exist between outcome-based rewards and the external team performance dimensions. Performance is expected to be highest for moderate levels of outcome-based reward structures and is likely to suffer if rewards are completely dependent on or independent of the outcomes. Therefore, we hypothesize the following:
H5: For highly risky products, an inverted-U-shaped relationship exists between outcome-based rewards and external dimensions of CFPDT performance.
H6: For highly competitive industries, an inverted-U-shaped relationship exists between outcome-based rewards and external dimensions of CFPDT performance.
H7: For highly dynamic industries, an inverted-U-shaped relationship exists between outcome-based rewards and external dimensions of CFPDT performance.
Study Context and Sample Selection
High-tech industries were chosen as the context for this research study because of their extensive experience in using cross-functional teams in the NPD process (e.g., Ancona and Caldwell 1992a, b; Henderson and Lee 1992). Several Fortune-lO00 and medium-sized companies (annual revenues ranging from $100 million to $1 billion) were invited to participate in this study through personal contacts and executive and faculty referrals. Nine of these organizations agreed to participate in Phase I of the study, which involved in-depth interviews with team members and managers. In Phase 2 of the study, a survey instrument was administered to a larger sample comprising respondents from six organizations. Four of these six organizations were drawn from the original nine organizations that participated in Phase 1. Five organizations dropped out of the study after Phase 1 of data collection, citing the sensitive nature of the information sought or a lack of time.
Three criteria were used to screen teams for both the interviews and the survey:
- The team should be strictly intraorganizational.
- The product being developed by the team should be intended for the open (competitive) market.
- Teams should have either introduced their product within the past 12 months or have an ongoing project at an advanced stage of development.
The first criterion was needed to avoid contamination of the data due to spurious variance caused by interorganizational factors. The second criterion was adopted because market performance would be irrelevant for products destined for inhouse use. Third, products older than one year were excluded to avoid problems with recall and take into account the typically high turnover in high-tech organizations. Each organization was asked to provide teams that varied along the following product-market dimensions: level of competitive intensity, level of product innovation, project risk, duration of product development cycle, and degree of success of the product.
Measure Development and Pretesting
Wherever possible, established scales were used to measure the constructs in this study. However, for some constructs (e.g., process-and outcome-based rewards), new measures were adapted from existing scales. For others (e.g., product quality), field interviews were used to generate a pool of items, which was then used to develop new scales. The measures were pretested in three stages. First, we presented the survey instrument to four doctoral students who were trained in psychometric theory and experienced in survey development. They were asked to fill out the preliminary survey instrument and identify any ambiguous or irrelevant items. These items were dropped or modified in the second draft of the questionnaire.
In the second stage, we solicited feedback from six academic experts. All constructs were clearly defined and their item measures identified, so that the experts could critically evaluate the scale items and their ordering. They were also asked to identify items that failed to capture the construct and to suggest additional items that would capture the entire construct domain. Third, we administered the resulting draft to 16 members representing three teams from two separate organizations. In face-to-face interviews, they were asked to point out items or instructions they found confusing, irrelevant, or repetitive.
Data Collection
Data were collected in two phases: qualitative field interviews and survey administration. As discussed previously, the purpose of the initial qualitative interviews with 57 members of 26 teams was to refine the theoretical framework and develop the survey instrument. Next, a survey was administered to a larger sample of CFPDT members, and the resulting data were used to test our hypotheses empirically. Survey data were collected from 246 members of 65 teams, drawn from 13 divisions of 6 medium-sized to large organizations. The teams varied in size from 3 to 22 members, with an average of 7.8. By definition, these teams were temporary, having worked together for anywhere from 3 to 72 months.
Respondents were instructed to return the completed questionnaires directly to the researcher or to a key liaison in each organization. Instead of being distributed through a mass mailing, questionnaires were distributed to one or two organizations at a time to ensure a high response rate. Multiple respondents were requested on each team for a cross-section of opinion (Ancona and Caldwell 1992a; Henderson and Lee 1992; Olson, Walker, and Ruekert 1995). Each team leader was asked to forward the questionnaire to at least three members of the core team. For smaller teams, responses from a single key member were considered acceptable. The number of informants from each team varied from I to 13, with an average of 3.7. Respondents were asked to identify their functional backgrounds and status (i.e., team leader or member). Responses from team members who represented different functional areas and hierarchical levels were obtained.
Data from multiple (key) respondents on the same team were pooled and averaged to obtain an aggregate (team-level) response (Ancona and Caldwell 1992a; Olson, Walker, and Ruekert 1995). This method was preferred to using responses from all team members, because some teams had 20 or more members. Such an approach is consistent with that of Henderson and Lee (1992), who demonstrated the utility of administering surveys to multiple key respondents on each team. The level of convergence among the multiple respondents appears in Appendix A, which presents the low, high, and average standard deviations on each construct across the teams in the sample.
Scale Refinement
The reliability and validity of the measures in this study were established according to standard procedures recommended by Gerbing and Anderson (1988). First, we performed an exploratory factor analysis on each construct to investigate its unidimensionality and underlying factor structure. Items with significant cross-loadings (i.e., >.35) were deleted from the analysis until a single-factor solution was obtained. Exploratory factor analysis was also used to verify whether the reward structures examined in this study constituted opposite ends of two continuums or four distinct constructs. We present the results of this principal component analysis in Appendix B. A stable four-factor solution was obtained where factors corresponded to each of the rewards constructs used in the study.
Second, we used confirmatory factor analysis to establish the psychometric properties of the scales used to measure the constructs. The size of our sample (n = 65) precluded the use of confirmatory factor analysis on aggregate-level data (Anderson and Gerbing 1995; Hu and Bentler 1995). Therefore, we disaggregated data to the individual level to perform the confirmatory factor analyses.[1] We used procedures outlined by Venkatraman (1989) to establish the unidimensionality and convergent and discriminant validity of the constructs as well as the validity of the nomological network. All constructs either met or exceeded the recommended criteria deemed acceptable for the establishment of these psychometric properties. In two cases of convergent validity, adherence to budget and schedule and product quality, the Bentler-Bonett Index values (delta) of .89 were below the recommended level of .90. However, we considered these values acceptable because they were close to the recommended threshold (Venkatraman 1989).
A more encompassing and rigorous "all item-all construct'' test of discriminant validity was also conducted using LISREL. A process analogous to the Lagrange-Multiplier test in EQS was followed (Schumacker and Lomax 1996). This test examined all underlying relationships between the 82 exogenous items measuring 16 endogenous constructs in the study.[2] Project length was excluded from this analysis because it was measured using a single objective item. The results of this test largely supported the discriminant validity of our measures. The modification indices show that of the 1230 nonhypothesized paths between the exogenous items and the endogenous constructs, 291 paths had chi-square values greater than 3.84 (degrees of freedom = 1, alpha < .05). At a 95% confidence level, 62 of these paths would likely be significant by chance alone. Schumacker and Lomax (1996) note that this method may cause meaningless parameters to be included in a subsequent model; therefore, any interpretation of the modification indices should be guided by theoretical rationale. They recommend that these indices should be used as potential indicators of misfits rather than givens for respecifying a model.
Most substantial modification indices were seen between the items measuring different performance variables and the endogenous performance constructs (i.e., product quality and innovation and self-rated performance/ adherence to budget and schedule and team member satisfaction). These results suggest that the performance dimensions may not be orthogonal and may have interrelationships that could be explored in future studies. Modification indices for items measuring project risk and complexity with endogenous performance constructs (i.e., product quality and innovation) suggest that these moderator variables may be quasi moderators as opposed to pure moderators (Sharma, Durand, and Gur-Arie 1981).
Finally, to establish the internal consistency of the measures, we computed Cronbach's alpha coefficients to 'estimate the reliability of each scale. We dropped items with low item-to-total correlation from the analysis. Operational definitions of the constructs, along with the items used to measure them and the reliability coefficient of each scale, are presented in Appendix C. Nearly all scales display high internal consistency and exceed the .70 level of acceptability laid down by Nunnally (1978). At .68, .63, and .69, respectively, the scales for process- and outcome-based rewards and equal rewards are only slightly below Nunnally's criterion.
Model Estimation
Because some of the hypothesized relationships are nonlinear, both linear and nonlinear regression analyses were used to examine the effect of reward structures (independent variables) on various dimensions of team performance (dependent variables). A separate regression model was analyzed for each dimension of team performance shown in Figure 1. The sign and significance of the standardized coefficients associated with each independent variable provide a test of our hypotheses. Wherever alternative hypotheses were specified, two-tailed t-test results are considered.
We hypothesized that several industry- and project-specific variables will moderate the relationship between team rewards and performance. These effects are estimated by dividing the sample along the mean into two subgroups of high and low levels of the moderator variable. Separate regression analyses were conducted on each subgroup to test the specific hypothesis (Sharma, Durand, and Gur-Arie 1981). Chow's (1960) F-test is performed in each case to determine whether the difference between the high and low subgroups is statistically significant.
H5 through H7 predict a nonlinear (inverted-U-shaped) relationship between the independent and dependent variables. This relationship is tested for each relevant subgroup by regressing the dependent variable (Y) on the linear (X) and square term (X2) of the independent variable simultaneously. An inverted-U-shaped relationship is supported if the coefficient of the X term is positive and the coefficient of the square term (X2) is negative (Aiken and West 1991, pp. 65-66). Chow's F-test is then performed to determine whether the difference between the high and low subgroups is statistically significant.
Relationship Between Equal and Position-Based Rewards and Internal Dimensions of Team Performance
H1 predicts that when ease of individual evaluation is high, position-based rewards will be positively related to the internal dimensions of team performance. To test this hypothesis, we split the sample into high and low ease of individual evaluation subgroups for both team member satisfaction and self-rated performance. Regression results presented in Table 1 show that position-based rewards have a significant, positive association with team member satisfaction (beta = .35, p = .05) for teams in which the ease of individual evaluation was high. No such relationship was found for the low ease of individual evaluation subgroup. Chow's F-test showed that the difference between the two subgroups is statistically significant (F2, 49 = 3.23, p < .05). We conducted a similar analysis for the relationship between position-based rewards and self-rated performance, which was not significant. Therefore, H1 is partially supported.
H2 posits that when ease of individual evaluation is low, equal rewards will be positively related to internal dimensions of team performance. The alternative hypothesis suggests that this relationship will be negative. Table 1 shows that when ease of individual evaluation is low, equal rewards are negatively related to both team member satisfaction (beta = -.36, p = .08) and self-rated performance (beta = -.47, p = .01 ). The difference between the high and low ease of individual evaluation subgroups was significant for both team member satisfaction (F2, 49 = 3.24, p < .05) and self-rated performance (F2, 49 = 3.33, p < .05). These results support H2(alt).
Relationship Between Process- and Outcome- Based Rewards and External Dimensions of Team Performance
H3 predicts that for long product development cycles, a process-based reward structure will be positively related to the external dimensions of team performance, whereas H3(alt) suggests that this relationship will be negative. The sample was split into two subgroups of long or short product development cycles. The team performance dimensions were regressed on process-based rewards for each subgroup. Table 2, Part A, shows that for long development cycles, process-based rewards had a stronger negative relationship to speed to market (beta = -.48, p = .01), adherence to budget and schedule (beta = -.57, p = .003), product quality (beta = -.51, p = .01), and market performance (beta = -.53, p = .007) than did shorter projects. Chow's F-test showed significant differences between the long and short development cycle subgroups in each case. These results support H3(alt).
In further analysis, we examined how the length of the development cycle moderated the relationship between outcome-based rewards and team performance. Table 2, Part A, shows that for long development cycles, outcome-based rewards have a marginally significant, positive relationship with speed to market (beta = .36, p = .08), innovation (beta = .36, p = .08), and product quality (beta = .38, p = .07) and a significant, positive relationship with market performance (beta = .46, p = .02). Differences between the subgroups were significant for speed to market (F2, 49 = 4.29, p < .025), innovation (F2, 49 = 3.30, p < .05), and market performance (F2, 49 = 3.25, p < .05). We did not find any difference between the long and short product development cycle subgroups; however, for the total sample, product quality (beta = .34, p = .01) showed a significant, positive relationship with outcome-based rewards. These results are consistent with our previous argument that process- and outcome-based rewards have opposite effects on team performance.
H4 explores the moderating effect of project complexity on the relationship between process-based rewards and the external dimensions of team performance. Table 2, Part B, shows that for the high-complexity subgroup, process-based rewards have a significant, negative association with speed to market (beta = -.36, p = .04) and a marginally significant, negative association with product quality (beta = -.29, p = .09). The negative relationship between process-based rewards and product quality was stronger for low-complexity products (beta = -.48, p < .05) than high-complexity products. Chow's F-test showed a significant difference between the high- and low-complexity subgroups for both speed to market (F2, 49 = 3.26, p < .05) and product quality (F2, 49 = 3.48, p < .05), in partial support of H4(alt). Additional analysis examined whether outcome-based rewards had an effect opposite to that of process-based rewards when product complexity moderated the relationship. Table 2, Part B, shows that for the low-complexity subgroup, outcome-based rewards were positively associated with speed to market (beta = .42, p = .05), product quality (beta = .36, p = .09), and market performance (beta = .52, p = .01). However, Chow's F-test was significant only for speed to market (F2, 49 = 3.86, p < .05).
H5 predicts that for high-risk products, an inverted-U-shaped relationship will exist between outcome-based rewards and the external dimensions of team performance. Such a relationship was found to exist only for product quality (see Table 3). For the high-risk subgroup, outcome-based rewards were positively related (b = 3.67, p = .01), and the square term of outcome-based rewards was negatively related (b = -3.29, p = .02) to product quality. Chow's F-test shows that the difference between the high- and low-risk subgroups is significant (F3, 47 = 4.23, p < .025).
We also hypothesized that an inverted-U-shaped relationship will exist between outcome-based rewards and external performance dimensions for highly competitive (H6) and dynamic (H7) industries. As before, a significant relationship was found to exist only between outcome-based rewards and product quality (see Table 3). For highly competitive industries, outcome-based rewards were positively related (b = 2.94, p = .04), and the square term was negatively related (b = -2.57, p = .08) to product quality; the difference between the two subgroups is significant (F3, 47 = 3.02, p < .05). However, contrary to expectations, for industries with low levels of dynamism, product quality was found to be significantly related to outcome-based rewards (b = 4.54, p = .01) and its square term (b = -4.15, p = .01). The difference between the two subgroups was statistically significant (F3, 47 = 3.51, p < .025). This finding suggests a nonlinear relationship between outcome-based rewards and product quality for less dynamic industries rather than for highly dynamic industries. Thus, H7 was not supported.
Even though the conditions for establishing the inverted-U-shaped relationships between outcome-based rewards and product quality were satisfied in the case of risky projects and competitive or dynamic industries, further analysis revealed that these relationships were nonlinear and monotonically decreasing.[3] The implications of these results are discussed in the following section.
The primary objective of this study was to find ways of enhancing NPD performance by drawing attention to the critical but overlooked area of NPD team rewards. This was done by empirically examining the following research questions:
- How are the internal performance dimensions of CFPDTs affected by equal and position-based distribution of rewards among team members?
- How are the external performance dimensions of CFPDTs affected by rewards that are linked to the development process rather than the outcome?
We find that when it is easy to evaluate individual performance in teams, a position-based differential reward structure is more effective and leads to greater team member satisfaction. Such a reward structure recognizes the different levels of contribution, responsibility, and risk shouldered by different members of the team. These findings are consistent with the thesis proposed by Nahavandi and Aranda (1994), who argue that for teams to be successful in Western cultures, the team-based approach must be adapted to Western cultural values. Similarly, Cropanzano and Randall (1993) suggest paying close attention to fairness in group reward distribution (i.e., rewards proportional to individual contribution) when dealing with members from individualistic cultures or when superior group performance is desired.
When ease of individual evaluation is low, an equal distribution of rewards was expected to be more effective. Contrary to expectations, equal rewards were negatively related to both team member satisfaction and self-rated team performance. For low ease of individual evaluation, even a position-based differential distribution of rewards showed a weak, negative association with self-rated performance; however, these differences were not statistically significant. Although surprising, these findings can be explained on the basis of procedural justice (Baron and Byrne 1997; Cropanzano and Randall 1993). When ease of individual evaluation is low, a perception of bias and inaccuracy is likely to persist in the minds of team members. Such perceptions of subjectivity/unfairness in the reward and evaluation process may be difficult to dispel and can have a negative influence on both team member satisfaction and team performance, regardless of how the rewards are distributed.
These findings imply that organizations should develop evaluation systems to monitor the performance of individual team members accurately (Pascarella 1997). In a survey of the salary and incentive practices of the NPD function, Feldman (1996) found that of the CFPDTs examined, team members were evaluated by their functional managers 57% of the time, by their team leader 7% of the time, and by a combination of the two I 1% of the time. Our qualitative data suggest that functional managers frequently had little knowledge of a member's performance on a CFPDT, which led to inaccurate and biased evaluations. If cross-functional teams are to achieve their full potential in realizing substantial improvements in the NPD process, reward and evaluation systems must be modified accordingly. Simply considering input from those who are most familiar with a team member's performance (e.g., team leaders, other members) could significantly improve the evaluation process. This would make evaluations more accurate and credible and the reward distribution more effective.
An empirical examination of process- and outcome-based rewards yielded some surprising results as well. We had expected that for long and complex products, a process-based structure would be positively related to external dimensions of team performance. However, for long development cycles, process-based rewards had a significant, negative association with speed to market, adherence to budget and schedule, product quality, and market performance, whereas outcome-based rewards had a positive association with speed to market, level of innovation, and market performance. For the total sample, outcome-based rewards showed a significant, positive relationship with product quality.
Similar results were obtained for highly complex products: Process-based rewards showed a negative relationship with speed to market and product quality. However, the negative relationship between process-based rewards and product quality was stronger for less complex products. In contrast, for the total sample, outcome-based rewards showed a positive association with product quality and market performance. The positive relationship between outcome-based rewards and speed to market was stronger for less complex products than the more complex ones. These results indicate that teams respond well to rewards that are linked to outcomes under conditions in which outcomes are more predictable (i.e., when developing less complicated products).
The pattern of results suggests that for NPD projects in general and for long and complex projects in particular, linking rewards to process-based criteria (such as procedures or behavior) is detrimental to team performance, whereas linking rewards to the output produced by the team has a positive influence on the external dimensions of CFPDT performance. These results are consistent not only with recent research on NPD (e.g., Bonner, Ruekert, and Walker 1998) but also with the organizational control literature and expectancy theory. The central tenet of expectancy theory suggests that people are motivated to greater performance when they perceive a clear link between their efforts/performance and rewards (Baron and Byrne 1997). Making rewards contingent on the outcome establishes such a clear link (Merchant 1985). Exceeding established performance criteria results in rewards, and failure to meet them could result in sanctions (Child 1984). Lawler and Rhodes (1976) note that because outcome-based criteria are objective, they are more effective in fostering the behavior required for achieving set goals.
On the basis of agency theory, we suggested that the relationship between an organization and a team is similar to that between a principal and an agent, because both sides try to minimize their respective risks. For risky projects and highly competitive or dynamic industries, we expected that sharing risk between the team and the organization would result in optimal CFPDT performance. Under these conditions, we expected an inverted-U-shaped relationship to exist between outcome-based rewards and the external dimensions of team performance. However, instead of an inverted-U-shaped relationship, a nonlinear and monotonically decreasing relationship was found to exist between outcome-based rewards and product quality for high-risk projects and highly competitive industries. Therefore, for CFPDTs involved in risky projects or highly competitive industries, a completely outcome-based reward structure is likely to be counterproductive.
Similar results were also obtained for less dynamic industries. Turbulence in an industry can lead to vague standards for evaluating performance. However, even conditions under which team outcomes and the standards used to evaluate these outcomes are relatively predictable and stable, CFPDTs are willing to assume only a negligible amount of the risk. A possible explanation may be that the perceptions of industry dynamism are relative. This study was conducted in high-tech industries, which are well known for their highly turbulent and dynamic environments. By the standards of these industries, relatively stable environments may still be turbulent enough to present excessive risk to CFPDTs. Even in relatively less dynamic environments of these industries, the rules of the game and the standards for measuring success and failure change frequently, which is a source of concern to the teams in our sample. Linking rewards to outcome under these circumstances places the teams at greater risk and has a counterproductive effect on product quality.
For risky projects and highly competitive and less dynamic industries, the relationship between product quality and outcome-based rewards is nonlinear but monotonically decreasing. Product quality decreased with increasing levels of outcome-based rewards (which denotes team risk), which led us to conclude that NPD teams make a clear distinction between the risk to themselves and the risk to the organization. These CFPDTs operate in an extremely risk-averse mode, and the amount of risk they are willing to assume remains highly skewed in their own favor.
Process- and outcome-based rewards exert a complicated influence on the external dimensions of team performance. Our results suggest that though moderating risk (by using process-based reward) makes CFPDTs complacent, high levels of risk (associated with outcome-based reward) make teams vulnerable. Teams respond to reward structures in a manner that minimizes their own risk. Such risk-minimizing behavior on the part of the teams is consistent with agency theory. As Robbins and Finley (1995, p. 131) astutely note, "teams will not carry out business objectives if doing so puts them at risk."
The seemingly contradictory effects of outcome-based reward structures on performance are similar to those seen in the goal-setting theory literature. Similar to rewards, clear and specific goals are expected to motivate higher performance (Locke et al. 1981). However, recent research in goal-setting theory suggests that goals considered ambiguous, risky, or too difficult to achieve are rejected, which leads to lower performance (Earley, Shalley, and Northcraft 1992).
We observe similar effects in our results: Outcome-based rewards have a positive influence on performance, as long as the rewards are considered low risk and achievable (i.e., long and less complex projects). However, industry dynamism can lead to vague standards, and risky projects and competitive environments can result in outcome objectives that are perceived to be too difficult. Under these conditions, teams may reject an outcome-based reward structure, which leads to lower performance. Cases of rejection are more frequent when the outcome is a function of strategy rather than effort and no clear optimal strategy is evident (Earley, Connolly, and Ekegren 1989). Because these conditions are characteristic of the NPD process, the acceptance/rejection of rewards offers another possible explanation for the complicated effect of outcome-based reward structures on CFPDT performance.
Managers can draw four implications from our findings. First, when it is easy to evaluate individual performances in the team, rewarding members differentially on the basis of their position/status in the organizations is likely to result in higher satisfaction among team members, because senior team members bear a disproportionate share of the risk and responsibility associated with the team.
Second, organizations should consider investing in more accurate and unbiased methods of evaluating individual team members. Because both position-based and equal rewards exert a negative influence on performance when ease of individual evaluation is low, better evaluation systems may minimize perceptions of unfairness and inaccuracies in the distribution of rewards. Third, for long or less complex projects, process-based rewards have a negative influence on team performance, whereas outcome-based rewards enhance performance.
Fourth, for risky projects and highly competitive or relatively stable industries, outcome-based rewards exhibit a nonlinear and monotonically decreasing relationship with product quality. Under these conditions, linking rewards to the project outcomes has a detrimental effect on product quality. Our results suggest that NPD teams clearly distinguish between risk to the organization and risk to themselves. Although teams are willing to share some risk with the organization, they tend to minimize their own risk exposure. Reward structures that are most effective in enhancing team performance are those that present minimal risk to the team. Outcome-based rewards that are perceived to be too risky, too vague, or too difficult to achieve are likely to be rejected by the team, which leads to lower product quality.
This study is one of the first to examine empirically the critical issue of how reward structures affect CFPDT performance. In a significant improvement over existing studies, we measure team performance in terms of seven underlying dimensions. Our results show that performance dimensions are not affected uniformly by the reward structures. This lends support to our argument that CFPDT performance must be disaggregated to develop a clearer picture of the NPD process. Many of our results are counterintuitive and challenge conventional wisdom. We hope that these findings will encourage both academicians and practitioners to reexamine some currently accepted theories and practices.
Readers should bear in mind some caveats when interpreting the results of this study. This study was conducted on NPD teams in high-tech industries, and therefore the results may not generalize to teams in other contexts and industries. Some measures of reward structures used here performed below expectations and could be further refined in the future. Another possible limitation is that all members were not surveyed in each team. Reliance on key respondents may bias the data; however, we sampled multiple respondents in each team to minimize such bias. Performance measures used here are largely self-reported, perceptual, and relative. Olson, Walker, and Ruekert (1995) argue that these kinds of measures are justified because organizations are hesitant to share confidential data. Prior research has used multiple respondents to minimize the problems caused by perceptual and self-reported measures and common-method bias (e.g., Ancona and Caldwell 1992a; Henderson and Lee 1992; Olson, Walker, and Ruekert 1995). It is possible that specification errors were introduced in the analysis because of the omission of firm/line-of-business effects. Finally, testing alternative hypotheses resulted in a large number of unsupported hypotheses, which, coupled with low regression coefficients, increases the probability of finding significance by chance alone. We suggest that readers exercise caution when interpreting the results of this study.
The issue of rewarding CFPDTs is ripe with opportunities for further research. Extensions of this study might examine the effect of other reward mechanisms (e.g., financial rewards, formal versus informal rewards, punitive rewards). Future studies could also examine the effect of position-based and equal rewards on the internal and external dynamics of the team. Finally, an examination of team evaluation structures and their effects on CFPDT performance offers a fruitful area of future exploration.
1 We are grateful to one of the anonymous reviewers for this suggestion.
- 2 We are grateful to one of the anonymous reviewers for this suggestion.
- 3 We are grateful to one of the anonymous reviewers for bringing this to our attention.
TABLE 1 Effect of Equal and Position-Based Rewards on the Internal Dimensions of Team Performance: Regression Results for Total and Subgroup Samples Based on High and Low Ease of Individual Evaluation
Internal Dimensions of Team Performance
Team/Member Self-Rated
Satisfaction Performance
Reward Structures Total High Low Total High Low
Position-based .17 .35** -.11 .02 .17 -.34*
rewards
R2 .03 .12** .01 .00 .03 .12*
Equal rewards -.05 .14 -.36* -.09 .06 -.47***
R2 .00 .02 .13* .00 .00 .22***
*p < .10.
**p < .05.
***p < .01.Notes: n (total) = 53, n (high) = 29, n (low) = 24. The high and low subgroups for which the Chow test indicates a statistically significant difference are presented in bold.
TABLE 2 Regression Results for the Effect of Process- and Outcome-Based Rewards on the External Dimensions of Team Performance
Legend for chart:
A = Speed to Market: Total
B = Speed to Market: Long
C = Speed to Market: Short
D = Adherence to Budget and Schedule: Total
E = Adherence to Budget and Schedule: Long
F = Adherence to Budget and Schedule: Short
G = Level of Innovation: Total
H = Level of Innovation: Long
I = Level of Innovation: Short
J = Product Quality: Total
K = Product Quality: Long
L = Product Quality: Short
M = Market Performance: Total
N = Market Performance: Long
O = Market Performance: Short
A: Total Sample and Long and Short Product
Development Cycles Subgroups
Team Performance Dimensions
A B C D E
Reward F G H I J
Structures K L M N O
Process-based -.17 -.48*** .10 -.19 -.57***
rewards .08 .00 -.21 .07 -.39***
-.51*** -.30* -.22* -.53*** .12
R2 .03 .23*** .01 .04 .33***
.00 .00 .05 .00 .15***
.26*** .09* .05* .28*** .01
Outcome-based .31** .36* .20 .17 .29
rewards .04 .18 .36* .14 .34***
.37* .28 .38*** .46** .25
R2 .09** .13* .04 .03 .08
.00 .03 .13* .02 .11***
.14* .08 .15*** .22** .06
B: Total Sample and High and Low Project
Complexity Subgroups
Team Performance Dimensions
Process-based -.17 -.36** -.04 -.19 -.28
rewards -.13 .00 .08 .10 -.39***
-.29* -.48** -.22* -.30* -.11
R2 .03 .13** .00 .04 .08
.02 .00 .00 .00 .15***
.08* .23** .05* .09* .01
Outcome-based .31** .20 .42** .17 .06
rewards .32 .18 .05 .22 .34***
.28 .36* .38*** .20 .52***
R2 .09** .04 .18** .03 .00
.10 .03 .00 .22 .11***
.08 .13* .15*** .04 .27***
*p < .10.
**p < .05.
***p < .01.Notes: In Part A, n (total) = 53, n (long) = 24, n (short) = 29; in Part B, n (total) = 53, n (high) = 31, n (low) = 22. The high and low subgroups for which the Chow test indicates a statistically significant difference are presented in bold.
TABLE 3 Effect of Outcome-Based Rewards on the External Dimensions of Team Performance: Regression Results for Total and Subgroup Samples of High and Low Levels of Product/Project Risk, Competitive Intensity, and Industry Dynamism
Legend for chart:
A = Speed to Market: Total
B = Speed to Market: High
C = Speed to Market: Low
D = Adherence to Budget and Schedule: Total
E = Adherence to Budget and Schedule: High
F = Adherence to Budget and Schedule: Low
G = Level of Innovation: Total
H = Level of Innovation: High
I = Level of Innovation: Low
J = Product Quality: Total
K = Product Quality: High
L = Product Quality: Low
M = Market Performance: Total
N = Market Performance: High
O = Market Performance: Low
External Dimensions of Team Performance
A B C D E
Reward F G H I J
Structures K L M N O
A: Product/Project Risk
Outcome-based
rewards 1.16 .98 .94 .05 .80
-.37 1.09 .47 1.75 1.49
3.67*** -.06 .84 1.60 .11
Outcome-based
rewards2 -.87 -.82 -.51 .12 -.59
.52 -.93 -.26 -1.73 -1.17
-3.29** .22 -.46 -1.29 .32
R2 .11** .04 .19* .03 .05
.03 .05 .05 .07 .14**
.32*** .03 .15** .12 .18**
B: Competitive Intensity
Outcome-based
rewards 1.16 2.16 .41 .05 1.36
-1.21 1.09 -.75 2.43* 1.49
2.94** .45 .84 2.50* -.24
Outcome-based
rewards2 -.87 -1.87 -.08 .12 -1.13
1.31 -.93 .86 -2.19* -1.17
-2.57* -.16 -.46 -2.05 .53
R2 .11** .14 .11 .03 .08
.05 .05 .02 .18* .14**
.25** .08 .15** .28** .09
C: Industry Dynamism
Outcome-based
rewards 1.16 .67 2.23 .05 -.70
2.47 1.09 .81 1.26 1.49
.02 4.54*** .84 .03 2.26
Outcome-based
rewards2 -.87 -.38 -1.90 .12 .81
-2.28 -.93 -.66 -1.00 -1.17
.24 -4.15*** -.46 .17 -1.72
R2 .11** .10 .16 .03 .03
.09 .05 .04 .08 .14**
.07 .35*** .15** .04 .33***
*p < .10.
**p < .05.
***p < .01.Notes: For product/project risk end competitive intensity, n (total) = 53, n (high) = 28, n (low) = 25; for industry dynamism, n (total) = 53, n (high) = 27, n (low) = 26. The high and low subgroups for which the Chow test indicates a statistically significant difference are presented in bold.
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Level of Convergence Among the Multiple Respondents on Teams for Each Construct
Range of Standard Deviations Across Teams
Lowest Highest Average
Standard Standard Standard
Construct Deviation Deviation Deviation Mean Scale
Outcome-based .00 1.43 .47 2.19 1-5
rewards
Process-based .14 1.50 .47 3.48 1-5
rewards
Equal rewards .14 1.06 .40 3.12 1-5
Position-based .00 1.67 .54 3.05 1-5
rewards
Ease of individual .00 1.01 .39 3.47 1-5
evaluation
Project/product .11 1.97 .58 3.51 1-5
complexity
Project/product .00 1.50 .60 3.75 1-5
risk
Length of .00 24.90 15.60 23.30 (Months)
development cycle
Competitive .00 .95 .39 3.68 1-5
intensity
Industry dynamism .06 1.01 .47 2.84 1-5
Speed to market .11 1.24 .56 2.92 1-5
Level of innovation .00 1.54 .54 3.68 1-5
Adherence to budget .07 1.13 .52 3.49 1-5
and schedule
Product quality .05 1.26 .43 3.86 1-5
Market performance .09 1.16 .59 3.21 1-5
Team/member .08 1.31 .46 3.54 1-5
satisfaction
Self-rated .00 1.66 .45 3.57 1-5
performance
Principal Component Analysis of Items Measuring Reward Structures
Principal Component Analysis of Items Measuring Reward Structures
Factor Factor Factor Factor
Items 1 2 3 4
1. The best performers on
our team receive extra
rewards. .69
2. All team members are
rewarded equally for
their work on the team,
independent of their
individual
contributions. .69
3. The rewards team
members receive for
working on this team
are proportional to
their contribution to
the team's performance. .65
4. Members who perform
well on the team are
individually rewarded/
recognized in the team
for their work. .62
5. The rewards team
members receive for
working on this team
are proportional to
their individual
salaries. .81
6. The rewards team
members receive for
working on this team
are proportional to
their position in the
organization. .90
7. The rewards received
by our team/individual
members are related
entirely to the profit
contribution attributed
to the team. .70
8. Rewards to the team/
individual members are
deferred until
bottom-line results of
the team (e.g., product
performance, market
share, profitability,
sales) are available. 89
9. The team is rewarded
for completing major
milestones/stages/
phases during the
development of the
product. .88
10. Teamwork behavior is
taken into account
when evaluating/
rewarding the team. .74
11. The team is rewarded
for meeting certain
prescribed conditions
when reviewed
periodically during
product development. .87Notes: Factor 1 corresponds to process-based rewards, Factor 2 corresponds to equal rewards (items 1, 3, and 4 are reverse coded), Factor 3 corresponds to position-based rewards, and Factor 4 corresponds to outcome-based rewards.
Construct Definitions and Measures
Legend for chart:
A = Construct
B = Definition
C = Items
D = Adapted From
A
B
C
D
Equal rewards
(4 items, alpha = .69)
The degree to which rewards are distributed uniformly among team
members.
The best performers on our team receive extra rewards. [R] All
team members are rewarded equally for their work on the team,
independent of their individual contribution. The rewards team
members receive for working on this team are proportional to
their contribution to the team's performance. [R] Members who
perform well on our team are individually rewarded/recognized in
the team for their work. [R]
New scale
Deutsch (1968); Faure and
Weitz (1993)
Position-based rewards
(2 items, alpha = .80)
The degree to which rewards are distributed among team members on
the basis of the relative position/status enjoyed by them in the
organization.
The rewards team members receive for working on this team are
proportional to their individual salaries. The rewards team
members receive for working on this team are proportional to
their position in the organization.
New scale
Deutsch (1968); Faure and
Weitz (1993)
Outcome-based rewards
(2 items, alpha = .63)
The degree to which team rewards are tied to the bottom line/
profitability of the product developed by the team.
The rewards received by our team/individual members are related
entirely to the profit contribution attributed to the team.
Rewards to the team/individual members are deferred until
bottom-line results of the team (e.g., product performance,
market share, profitability, sales) are available.
New scale
Deschamps and Nayak
(1995); Jaworski (1988)
Process-based rewards
(3 items, alpha = .68)
The degree to which team rewards are tied to procedures,
behaviors, and other means of achieving desired outcomes.
The team is rewarded for completing major milestones/stages/
phases during the development of the product. Teamwork behavior
is taken into account when evaluating/rewarding the team. The
team is rewarded for meeting certain prescribed conditions when
reviewed periodically during product development.
New scale
Deschamps and Nayak
(1995); Jaworski (1988)
Project risk
(4 items, alpha = .88)
The magnitude of failure associated with the project.
Our organization has a lot riding on this project. Poor market
performance by this product will have serious consequences for
our business. Our organization has made a significant investment
in the development of this product. The outcome of this project
has high strategic value for our organization.
New scale
March and Shapira (1987)
Project/product complexity
(5 items, alpha = .86)
The degree to which the development process was complicated and
difficult.
The product developed by our team was technically complex to
develop. Our team had to use nonroutine technology to develop
the product. The development process associated with the product
was relatively simple. [R] Development of this product required
pioneering innovation. The product developed by our team is/was
complex.
Hill (1972); Kahn and
Mentzer (1992); McCabe
(1987)
Length of the product development cycle
A measure of the duration of the project (in months).
Please indicate the number of months that elapsed (or will have
elapsed) between the time that this product was first formally
approved and the time that it was (or will be) finally
introduced/launched in the market.
Ease of individual evaluation
(4 items, alpha = .80)
The ease with which individual performances can be evaluated in
the team effort.
It is easy to identify a few individuals on this team without
whom this project would not have been possible. It is easy to
evaluate what each member contributed to this project. It is
easy to evaluate how much each member contributed to this
project. It is difficult to evaluate how much effort any member
in this team really put into this project. [R]
New scale
John and Weitz (1989)
Industry competitive intensity
(4 items, alpha = .76)
The degree to which companies experience rivalry within their
industry.
Competitive pressures have led to firms in this industry spending
more of each sales dollar on marketing. Firms in this industry
aggressively fight to hold onto their share of the market.
Competition in this industry is intense. Firms in this industry
follow a philosophy of peaceful coexistence. [R]
Lusch and Laczniak (1987)
Industry dynamism
(8 items, alpha = .89)
The perceived frequency of change and turnover in marketing
forces in the industry environment.
Frequency of changes in the mix of products/brands available.
Frequency of changes in sales strategies. Frequency of changes
in product standards. Frequency of changes in customer
preferences in product features. Frequency of changes in
customer preferences in product quality. Frequency of changes in
technology used. Frequency of major competitors entering or
leaving the industry. Frequency of changes in customer
preference in product price. (Measured on a fivepoint scale:
1 = "very infrequent," 5 = "very frequent").
Achrol and Stern (1988)
Speed to market
(5 items, alpha = .86)
A (time) measure of the pace at which the product was developed
by the team.
This product was developed much faster than other comparable
products developed by our organization. This product was
developed much faster than similar products developed by our
nearest competitors. This product could have been developed in a
shorter time. [R] The product concept formation (i.e.,
opportunity identification and product design) took longer than
expected. [R] The product commercialization (i.e., product/
market testing, production, distribution, promotion, sales) took
longer than expected. [R]
New scale
Olson, Walker, and Ruekert
(1995)
Adherence to budget and schedule
(7 items, alpha = .89)
The degree to which the team met its scheduled deadlines and
stayed within its budget.
The team made efficient use of its time. The team's project is
behind schedule. [R] The team operated in a cost-efficient
manner. The team did a good job adhering to its budget. The team
did a good job of meeting all of its schedule deadlines. The
team's project is over budget. [R] In general, this team
operated in an efficient manner.
Ancona and Caldwell
(1991, 1992a); Olson,
Walker, and Ruekert (1995)
Degree of innovation
(6 items, alpha - .85)
The degree of newness of the product under development.
Several product-related innovations were introduced during the
development of this product. High-quality technical innovations
were introduced during the development of this product. Compared
to similar products developed by our competitors, our product
will offer unique features/attributes/benefits to the customers.
Our product introduces many completely new features to this
class of products. Compared to similar products developed by our
organization, our product will offer unique features/attributes.
Check (one of the following) statement that best (or most
closely) describes the product developed by your team [R]:
(a) The product is entirely new to both our firm as well as the
customers, (b) The product is new to the customers but not
very new to our firm, (c) The product is new to our firm but not
very new to the customers, (d) The product is neither new to our
firm nor new to the customer, (e) Our product is an imitation of
an existing product.
New scale
Ancona and Caldwell
(1992a); Booz, Allen & Hamilton (1982);
Olson, Walker, and Ruekert (1995)
Product quality
(10 items, alpha = .93)
An overall measure of the degree to which the product delivers
value to the customer and meets the quality control standards
laid out for it by the team/organization.
Quality of this product compares well with similar products
offered by our competitors. The product meets the customers'
needs. Complaints have been received regarding the poor
performance of this product. [R] The product meets the
specifications outlined for it. The product is reliable. This
product is of a higher quality than competing products available
to the customer. The product's performance shows little
deviation from expected standards. Quality of this product
compares well with other products developed by our organization.
The consumers of this product perceive our product to be better
than our competitors'. This product will deliver benefits to the
customers that are not currently available to them.
New scale
Olson, Walker, and Ruekert (1995)
Market performance
(6 items, alpha = .91)
A measure of how the developed product is faring in the market,
relative to expectations.
Level of sales achieved. Customer satisfaction with the product.
Market performance of the product relative to its competition.
Chances of the product being a success in the market. Level of
initial market penetration. Projected financial returns on this
product. (Measured on a five-point scale: 1 = "far below
expectations," 5 = "far above expectations").
New scale
Meyer (1994)
Self-rated performance
(7 items, alpha = .90)
A measure of the team members' perception of their team's
performance compared with other teams in their organization.
The quality of the product developed. Team's reputation for work
excellence. Attainment of the goals set for the team. Efficiency
of the team's operations. Morale of the team. Adherence to
schedule. Adherence to budget. (Measured on a five-point scale:
1 = "far below average," 5 = "far above average").
Ancona and Caldwell
(1991, 1992a); Van De Ven and Ferry (1980)
Team/member satisfaction
(5 items, alpha = .87)
The degree to which association with the team and its project
is/was considered to be worthwhile and productive by the team
members.
Team members are satisfied with the recognition they get for
their work on this team's project. Team members are satisfied
with the amount of responsibility they were given on this team.
Team members are satisfied with the way this team was managed.
Team members are satisfied with the opportunities they were
given to use their abilities. Team members are satisfied with
the amount of job variety that was offered by this project.
Brayfield and Rothe (1951);
Pinto, Pinto, and Prescott
(1993); Warr, Cook, and Wall (1979).
Notes: Unless otherwise mentioned, all items were measured on a five-point Likert-type scale (1 = "strongly disagree," 5 = "strongly agree"). [R] denotes reverse coding.
DIAGRAM: FIGURE 1 The Effect of Equal/Position Rewards and Outcome-/Process-Based Rewards on Team Performance
~~~~~~~~
By Shikhar Sarin and Vijay Mahajan ichael D. Hutt and Peter H. Reingen
Shikhar Sarin is Robert and Irene Bozzone Assistant Professor of Management and Technology, Lally School of Management and Technology, Rensselaer Polytechnic Institute. Vijay Mahajan is John R Harbin Centennial Chair in Business, Department of Marketing, University of Texas at Austin. The authors are grateful for the access provided by the participating organizations and the financial support provided by the Bonham Fund, University of Texas at Austin. They also thank Robert Baron, Rohit Deshpande, Bob Lusch, Rob McDonald, Trina Sego, S. Venkatraman, and the three anonymous JM reviewers for their helpful comments and Stacey Barlow Hills for her research assistance.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 157- The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention. By: Gustafsson, Anders; Johnson, Michael D.; Roos, Inger. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p210-218. 9p. 3 Charts. DOI: 10.1509/jmkg.2005.69.4.210.
- Database:
- Business Source Complete
The Effects of Customer Satisfaction, Relationship
Commitment Dimensions, and Triggers on Customer Retention
In a study of telecommunications services, the authors examine the effects of customer satisfaction, affective commitment, and calculative commitment on retention. The study further examines the potential for situational and reactional trigger conditions to moderate the satisfaction-retention relationship. The results support consistent effects of customer satisfaction, calculative commitment, and prior churn on retention. Prior churn also moderates the satisfaction-retention relationship. The results have implications for both customer relationship managers and researchers who use satisfaction surveys to predict behavior.
Marketing scholars emphasize the influence of customer satisfaction on loyalty (Fornell et al. 1996; Mittal and Kamakura 2001). The relationship management literature emphasizes two different dimensions of relationship commitment that drive loyalty: affective commitment, as created through personal interaction, reciprocity, and trust, and calculative commitment, as created through switching costs (Bendapudi and Berry 1997; Fullerton 2003; Garbarino and Johnson 1999; Morgan and Hunt 1994). Loyalty is often interpreted as actual retention, which is a cornerstone of customer relationship management (CRM). Yet the vast majority of prior research has demonstrated the effects of these constructs only on behavioral intentions. Relatively few studies explain actual behavior (exceptions include Bolton 1998; Bolton and Lemon 1999; Mittal and Kamakura 2001; Verhoef 2003), and to our knowledge, no study examines the effects of all three constructs on retention.
Our goal is to provide insight into the drivers of retention using a combination of survey and longitudinal data from a telecommunications service provider. The research contributes to the CRM literature in three important ways. First, we examine the competing effects of customer satisfaction, affective commitment, and calculative commitment on customer retention. Second, we demonstrate the importance of controlling for heterogeneity (Mittal and Kamakura 2001) or prior loyalty (Guadagni and Little 1983) when predicting retention. Third, we explore the potential for different precipitating events, or "triggers," to moderate the effect of satisfaction on retention.
Effective CRM strategies vary considerably depending on which factors are driving retention. If customer satisfaction is the primary driver of retention, a firm should improve product or service quality or offer better prices. If affective or calculative commitment is more important, a firm should either build more direct relationships with customers or build switching barriers in relation to competitors. Even these strategies may be dependent on the trigger condition that customers face (Smith, Bolton, and Wagner 1999).
The Drivers of Customer Retention
To understand the complexity of customer loyalty, it is important to understand the evaluations, attitudes, and intentions that affect behavior (Oliver 1999). We focus on three prominent drivers of retention in the marketing literature: overall customer satisfaction, affective commitment, and calculative commitment.
Customer satisfaction is defined as a customer's overall evaluation of the performance of an offering to date (Johnson and Fornell 1991). This overall satisfaction has a strong positive effect on customer loyalty intentions across a wide range of product and service categories, including telecommunications services (Fornell 1992; Fornell et al. 1996). As an overall evaluation that is built up over time, satisfaction typically mediates the effects of product quality, service quality, and price or payment equity on loyalty (Bolton and Lemon 1999; Fornell et al. 1996). It also contains a significant affective component, which is created through repeated product or service usage (Oliver 1999). In a service context, overall satisfaction is similar to overall evaluations of service quality. Compared with more episode-based or transaction-specific measures of performance, overall evaluations are more likely to influence the customer behaviors that help a firm, such as positive word of mouth and repurchase (Boulding et al. 1993).
Historically, satisfaction has been used to explain loyalty as behavioral intentions (e.g., the likelihood of repurchasing and recommending). However, Verhoef (2003) argues that longitudinal data that combine survey measures with subsequent behavior should be used to establish a causal relationship between perceptions and behavior. For example, Bolton (1998) finds a positive effect of overall customer satisfaction on the duration of the relationship for cellular phone customers, and Bolton and Lemon (1999) show a positive effect of overall satisfaction on customer usage of telecommunications subscription services. In a large-scale study of automotive customers, Mittal and Kamakura (2001) show a strong, albeit nonlinear, effect of customer satisfaction on repurchase behavior, such that the functional form relating satisfaction to repurchase is marginally increasing. They also find large differences in the satisfaction-retention relationship across customer characteristics. On the basis of these studies, we expect customer satisfaction to have a significant influence on customer retention that varies across customers.
The relationship marketing literature recognizes another potential driver of customer loyalty: relationship commitment (Bendapudi and Berry 1997; Morgan and Hunt 1994). Drawing on the organizational behavior literature (Meyer and Allen 1997), marketing scholars have variously defined commitment as a desire to maintain a relationship (Moorman, Deshpandé, and Zaltman 1993; Morgan and Hunt 1994), a pledge of continuity between parties (Dwyer, Schurr, and Oh 1987), the sacrifice or potential for sacrifice if a relationship ends (Anderson and Weitz 1992), and the absence of competitive offerings (Gundlach, Achrol, and Mentzer 1995). These various sources create a "stickiness" that keeps customers loyal to a brand or company even when satisfaction may be low.
The various definitions suggest two major dimensions of relationship commitment: affective commitment and calculative, or continuance, commitment (Fullerton 2003; Hansen, Sandvik, and Selnes 2003; Johnson et al. 2001). Calculative commitment is the colder, or more rational, economic-based dependence on product benefits due to a lack of choice or switching costs (Anderson and Weitz 1992; Dwyer, Schurr, and Oh 1987; Heide and John 1992). Affective commitment is a hotter, or more emotional, factor that develops through the degree of reciprocity or personal involvement that a customer has with a company, which results in a higher level of trust and commitment (Garbarino and Johnson 1999; Morgan and Hunt 1994).
In a financial services context, Verhoef (2003) demonstrates direct effects of affective commitment on both relationship maintenance (retention) and relationship development (share of a customer's business). Although both satisfaction and payment equity were positive antecedents of affective commitment, they did not directly affect behavior. Verhoef measured satisfaction using aggregated customer beliefs about specific dimensions of service performance (e.g., satisfaction with personal attention, willingness to explain procedures, response to claims). In contrast, we measure satisfaction as an overall evaluation of performance (Bolton and Lemon 1999; Fornell et al. 1996). In addition, Verhoef did not include calculative commitment in his study.
An important conceptual difference between customer satisfaction and the commitment dimensions is that satisfaction is "backward looking," whereas the commitment dimensions are more "forward looking." Satisfaction is a function of performance to date, whereas affective and calculative commitment capture the strength of the relationship and the resultant commitment to proceed forward. In our empirical study of telecommunications services, we operationalize customer retention using the degree of churn that occurs in a customer's use of fixed-phone service, cellular phone service, modem-based Internet service, or broadband Internet service. On the basis of the literature, we predict that affective commitment and calculative commitment each has a negative effect on churn (i.e., positive effect on retention).
In our preliminary analyses, we included the effects of price and quality as latent variables on retention. When these constructs were examined on their own, they had a negative effect on churn. However, when we included customer satisfaction in our churn equation, both price and quality became nonsignificant. Because tests of mediation (Baron and Kenny 1986) showed that the effects of price and quality on churn were completely mediated by satisfaction, we excluded these factors from further analysis.
In general, a trigger is a factor or an event that changes the basis of a relationship (Roos, Edvardsson, and Gustafsson 2004). In the marketing literature, triggers are frequently cast as alarm clocks that concentrate energy for further actions (Edvardsson and Strandvik 2000; Gardial, Flint, and Woodruff 1996). As we describe in our empirical study, preliminary qualitative interviews support the use of Roos's (1999, 2002) situational and reactional triggers.
Situational triggers alter customers' evaluations of an offering based on changes in their lives or in something affecting their lives. These include demographic changes in the family (e.g., becoming "empty nesters"), changes in job situations, and changes in the economic situations. In a way, the product has expired; it no longer reflects the needs of the customer. In telecommunications, situational triggers may be represented by the need to replace or remove a type of service or subscribe to a different type of service. However, it may take considerable time before the switching path is complete (Keaveney 1995; Roos 1999).
Reactional triggers are those critical incidents of deterioration in perceived performance that are traditionally described in the literature (Gardial, Flint, and Woodruff 1996). When something out of the ordinary occurs, such as a decline in performance before purchase, during purchase, or during consumption, it redirects a customer's attention to evaluate present performance more closely, which may put customers on a switching path (Roos 1999, 2002). For example, Bolton (1998) finds that unreported service failures have a significant, negative effect on retention.
The discussion suggests that either a situational or a reactional trigger affects the relevance of pior-performance information when predicting retention. When faced with a situational trigger, customer satisfaction as an overall evaluation of prior performance may become less relevant to the prediction of retention. Similarly, because customers in a reactional trigger condition are actively problem solving, they may focus on present or future performance. Waiting to observe how the company addresses the product or service problem, these customers may put less rather than more stock in prior performance, as measured by overall customer satisfaction. On the basis of these arguments, we predict that that the satisfaction-retention link is weaker for customers in either a situational or a reactional trigger condition.
Empirical Study of Telecommunications Customers
We conducted the research using customers of a large Swedish telecommunications company that provides fixed-phone service, mobile phone service, modem-based Internet service, and broadband Internet service. Before we administered the periodic customer survey, we conducted initial qualitative interviews to better understand the triggers that occur in this context. We used a population of customers who had switched from the company either partly (a subset of services) or completely for the initial interviews. Of the 83 customers we contacted for the study, 48 participated in retelling their switching paths. We taped, transcribed, and content analyzed the interviews. Because the main purpose of these interviews was to ensure that the correct triggers were identified for the survey, we summarize the interview results here. Customers classified as having reactional triggers referred to some form of critical incident in which, for example, the customer support was poor or the service was unreliable. Customers classified as having situational triggers identified fundamental changes in their situation, including a lower need to make calls, a greater need to make long-distance calls, or the need to add an Internet supplier.
We used the results of the interviews to develop categories into which customers self-selected on the basis of which statement best described them as a customer. These statements were incorporated into the company's periodic customer survey. In addition to the trigger categories, the survey asked customers to rate the service in question (fixed phone, cellular phone, modem Internet, or broadband Internet) using the multi-item scales that appear in Table 1. Each latent variable had at least three indicators.
The satisfaction questions are the same as those used in the national customer satisfaction barometers (Fornell et al. 1996; Johnson et al. 2001). We adapted the affective and calculative commitment questions from prior studies (Johnson et al. 2001; Kumar, Hibbard, and Stern 1994; Meyer and Allen 1997). The affective commitment measures refer to the pleasure in being a customer of the company, the presence of reciprocity in the relationship, trust, and whether the company takes care of its customers. Calculative commitment refers explicitly to the economic consequences of ending the relationship (e.g., based on the company's locations). All statements were rated on a ten-point scale. We conducted a pilot study on 50 respondents to test the questions, during which we encouraged respondents to identify unclear questions. We reworded some of the questions on the basis of feedback from the respondents.
The company's periodic survey is administrated through a professional market research firm. The sample made available for our analysis consisted of 2734 respondents. The ages of the respondents in the sample varied from 18 to 65 years; the mean age was 39.5 years. In addition, 52% of the respondents were female, and 48% were male. We asked all respondents to decide whether they belonged to one or more of the trigger conditions or to state explicitly that none of the conditions applied. Two customers did not respond to the trigger questions, and 19 did not have churn data available, which resulted in a final sample size of 2715. Of these, there were 712 (26.2%) fixed phone, 1503 (55.5%) mobile phone, 303 (11.2%) modem-based Internet, and 197 (7.3%) broadband Internet customers. With respect to the trigger conditions, 2249 (82.8%) indicated that no triggers applied, 338 (12.4%) indicated a situational trigger, 197 (7.3%) indicated a reactional trigger, and 69 (2.5%) indicated both a situational and a reactional trigger.
We used principal components analyses to operationalize latent variables from the survey measures. For each set of measures, we extracted the first principal component to create each latent variable for use in subsequent regression analyses. As we show in Table 1, the loadings for the customer satisfaction and the commitment constructs are all relatively large and positive. When these loadings are squared, they indicate the communality of the measure, or the variance that the measure has in common with the latent variable. When the communality measures are standardized, the average communality of a block of indicators is referred to as average variance extracted (AVE) (Fornell and Larcker 1981). The criterion for establishing reliability is that the AVE measures should exceed .5 to ensure that, on average, the measures share at least half of their variation with the latent variable (Fornell and Larcker 1981; Hjorth 1994). As we show in Table 1, the AVE criterion is met for each of the latent variables, which supports the reliability of the measures.
Table 2 presents a correlation matrix for all of the variables used in the churn equations. Here, we focus on the correlations involving the latent constructs. To ensure the discriminant validity of the constructs, Fornell and Larcker (1981) argue that the AVEs of any two constructs should be greater than their squared correlation. When the latent variable correlations in Table 2 are squared (not shown), none exceeds the AVE of the constructs. This supports the discriminant validity of the constructs.
We include customer satisfaction (CSt), affective commitment (ACt), calculative commitment (CCt), a situational trigger condition (STt), and a reactional trigger condition (RTt), all in time t, to predict churn in time t + 1 (Churnt + 1) using ordinary least squares regression. We collected the survey variables from customers who were using fixed-phone, cellular phone, modem-based Internet, or broadband Internet service at time t. We matched the periodic survey data with customers' account data for the service through their telephone number and an identification number. The account data included a monthly churn measure for each customer beginning with the first periodic survey in April 2003 and continuing through August 2004 (17 months). Because the survey is periodic, a complete 17 months of behavioral data are available only for a small sample of customers. To retain our sample size, we explored relatively short windows for churn. We explored measures of the total amount of churn, which we defined as the total number of months over a given period that the customer was not retained, over a 3-, 6-, and 9-month period. Because there was more variation using the 9-month cumulative churn measure, we relied on this measure to test our predictions.
Thus, we surveyed our test sample from April to November of 2003, with nine months of account data available through August 2004. Because customers were using the service in question (or were retained) in the month they responded to the survey, Churnt + 1 captures the total months of churn in the nine months following the survey. The average churn is 3.52 months (standard deviation [s.d.] = 3.738). Because we surveyed respondents over different time periods, we used the month in which we surveyed the customer as a fixed factor in our preliminary analysis of churn. This time factor did not approach significance, and thus we omitted it from further analyses.
Preliminary estimation of the churn models also included a four-level fixed factor to capture differences in the type of service studied. We included all possible interaction effects involving this fixed factor and found only a main effect difference in average months of churn among the fixed-phone customers (1.93) and the other services (4.09). For parsimony, we collapsed the four-level factor into a dummy variable, Fixedt, to indicate fixed-phone service; we included this as a control variable.
It is important to control for heterogeneity across customers when predicting retention (Mittal and Kamakura 2001). Churn data were available for the four months before the month during which a customer was using a service and participated in the periodic satisfaction survey. We define Churnt - 1 as the total number of months in which customers were not using the service before the survey; the average is 1.46 months of prior churn (s.d. = 1.749). Use of this prior churn as a state-dependent variable to predict future churn helps control for heterogeneity. It explains variation in churn due to the inherent predisposition of some customers to switch and others to remain loyal. We also explore the potential for a weaker satisfaction-retention relationship among those customers who are predisposed to churn.
The analyses all assume that the relationships between the latent variables and our churn measure are linear. Recall that Mittal and Kamakura (2001) find a nonlinear (marginally increasing) relationship between vehicle satisfaction and repurchase. Although our churn measure is relatively short term (ranging from zero months of churn to nine complete months of churn), we estimated nonlinear relationships between each of our key latent variables (customer satisfaction, affective commitment, and calculative commitment) and churn. In each case, the linear component was significant, whereas neither the quadratic nor cubic terms approached significance. This suggests that the relationships are essentially linear.
Given the collinearity among the latent variables and their interaction terms, we use a series of regression equations to predict churn. We first estimate the effects of prior performance, in the form of overall customer satisfaction, on churn. We include the effects of prior churn to capture heterogeneity and type of service as a control variable:
( 1) Churnt + 1 = β0 + β1CSt + β2Churnt - 1 + β3Fixedt,
where Churnt + 1 is the total months of churn (0-9) postsurvey, CSt is customer satisfaction measured in the survey at time t, Churnt - 1 is total months of churn (0-4) presurvey, and Fixedt is a dummy variable to capture whether the service involved fixed-phone service. Table 3 reports the standardized parameters for the churn equations and their significance; it also reports the variance explained for each equation.
Equation 1 shows significant effects of customer satisfaction, prior churn, and fixed-phone service. Churn decreases with satisfaction, increases with prior churn, and decreases for fixed-phone customers. That satisfaction predicts behavior is consistent with previous studies (Bolton 1998; Bolton and Lemon 1999; Mittal and Kamakura 2001). That prior churn influences future churn is consistent with Mittal and Kamakura's (2001) findings that there is significant heterogeneity in the satisfaction-retention relationship. The standardized parameters suggest that prior churn has a relatively large effect on churn. As we describe subsequently, the relatively short time horizon involved helps explain this result.
In our second churn model, we explore whether addition of the commitment constructs adds significant predictors of churn:
( 2) Churnt + 1 = β0 + β1CSt + β2ACt + β3CCt + β4Churnt - 1 + β5Fixedt.
When we include affective and calculative commitment in the model, all previously included variables remain significant. Whereas calculative commitment becomes a significant predictor of churn, affective commitment does not.
Note from Table 2 that customer satisfaction and affective commitment are highly correlated latent variables. If we remove customer satisfaction from Equation 2, the main effect of affective commitment becomes significant. Although our top-down, or theoretical, analysis of reliability and discriminant validity supports separate satisfaction and affective commitment constructs, their correlation and our results suggest that they are capturing similar information. To understand this issue better, we conducted an exploratory principal components analysis of the survey measures (with Varimax rotation). This bottom-up, or data-driven, analysis reveals two principal components with eigenvalues greater than one; these account for 62.9% of the variation in the survey measures. The three measures of customer satisfaction and the four measures of affective commitment load highest on the first component (average loading = .759), whereas the four measures of calculative commitment load highest on the second component (average loading = .727). This analysis demonstrates that both satisfaction and affective commitment capture customers' overall evaluation of the offering. In contrast, calculative commitment captures more of the competitive nature of the offering with respect to switching costs or the availability of viable alternatives. In our subsequent churn equations, we keep customer satisfaction and remove affective commitment because satisfaction is the significant predictor.
We then estimate two churn equations to explore the potential for interactions. Customers who are predisposed to churn or switch may be less sensitive to prior-performance information. This suggests that the effect of customer satisfaction on churn is lower for customers who are prone to churn. To explore this prediction, we include the interaction between satisfaction and prior churn as follows:
( 3) Churnt + 1 = β0 + β1CSt + β2CCt β3Churnt - 1 + β4CSt x Churnt - 1 + β5Fixedt.
Equation 3 reveals a significant interaction between customer satisfaction and prior churn. The significant, positive coefficient for this interaction (.038, p = .022) demonstrates that the negative main effect of satisfaction on churn (-.065, p < .000) is indeed lower for customers with a history of prior churn. We ran other models (not reported) that represent variations on Equation 3. For example, when we substitute affective commitment for customer satisfaction, we obtain the same pattern of results. When we include other possible two-and three-way interactions, they are not significant.
Finally, we explore the potential for the situational and reactional trigger conditions to influence churn. We add to Equation 3 the main effects of the triggers on churn and their potential to moderate the satisfaction-retention relationship:
( 4) Churnt + 1 = β0 + β1CSt + β2CCt + β3STt + β4RTt + β5Churnt - 1 + β6CSt x Churnt - 1 + β7CSt x STt + β8CSt x RTt + β9Fixedt.
Although this model replicates the results from Equation 3, none of the trigger main effects or interactions are significant. Again, the addition of all possible two-and three-way interactions to this equation reveals no other significant effects. Thus, Equation 3 captures the consistent predictors of churn in our data.
Discussion and Implications
Customer relationship managers benefit from a thorough understanding of the various factors that drive retention. The customer satisfaction and relationship marketing literature suggests three predictors of retention: overall customer satisfaction, affective commitment, and calculative commitment. Customer satisfaction is an overall evaluation of performance to date, affective commitment captures the trust and reciprocity in a relationship, and calculative commitment captures the existence of switching costs or lack of viable alternatives. Prior research has used only a subset of these constructs to explain behavior.
Our study contributes to the marketing literature in several ways. First, we combine customer satisfaction, affective commitment, and calculative commitment to predict retention. Second, we control for heterogeneity in the satisfaction-retention relationship by incorporating both the main and moderating effects of prior churn. Third, we explore for the potential moderating effects of situational and reactional triggers on the satisfaction-retention relationship. Finally, by combining a telecommunications company's periodic customer survey with longitudinal account data, we provide support for causal relationships between the survey measures and subsequent behavior.
Several important findings emerge. In line with prior studies, customer satisfaction has a consistent negative effect on churn (a positive effect on retention). In contrast, affective commitment does not predict churn when it is included with customer satisfaction. Although Verhoef (2003) finds the opposite result (i.e., affective commitment rather than satisfaction predicts retention), it is important to recognize the differences in measurement variables between his study and ours. Recall that his measure of satisfaction was an aggregate of attribute performance ratings, whereas ours is a latent variable based on three overall evaluations of performance (overall satisfaction, performance versus expectations, and performance versus an ideal provider in the category). Our findings suggest that when satisfaction is measured as an overall evaluation of performance, it indeed predicts churn.
Affective commitment is also measured differently in the two studies. Verhoef (2003) operationalizes affective commitment using agreement ratings for the statements: "I am a loyal customer of XYZ," and "Because I have a strong attachment to (sense of belonging with) XYZ, I want to remain a customer of XYZ." In contrast, our measures of affective commitment are agreement ratings for statements about the pleasure or positive affect in being a customer of the company: whether the company takes care of its customers, the presence of reciprocity in the relationship, and feelings of trust toward the company (for exact wording, see Table 1). Whereas our measures focus more on the theoretical basis of affective commitment, Verhoef's measures are akin to behavioral intentions. However, an exploratory principal components analysis reveals that our satisfaction and affective commitment measures tap the same overall evaluations. Further research should explore ways to delineate satisfaction from affective commitment without relying on behavioral intentions.
Another important finding is that calculative commitment, a construct not included in previous studies of retention, has a consistent negative effect on churn. This calculative commitment is important because it captures the competitiveness of the value proposition. Whereas customer satisfaction and affective commitment focus on perceptions of an offering per se, calculative commitment reflects the viability of competitive offerings. This finding is analogous to research that supports the effects of "should" expectations on customer perceptions. Boulding and colleagues (1993) find that customer expectations about what a company should deliver decreases perceptions of performance. What customers know about competitive offerings presumably affects these "should" expectations.
Two other findings involve the effects of prior churn on future churn. Rather than rely solely on psychometric constructs to explain churn, we included prior churn as a state-dependent variable to explain subsequent churn. This is a relatively simple way for relationship managers to control for the heterogeneity across customers with respect to predisposition to churn. Although prior churn has the largest effect on churn in our equations, we expect that this is due to the relatively short time period involved. With only a nine-month window of behavior to explain, it is difficult for the survey constructs to compete with prior churn. We also find a significant interaction between prior churn and customer satisfaction. Customer satisfaction has more (less) influence on churn for those customers who are inherently prone to stay (switch).
We also explored the potential for situational and reactional trigger conditions to influence churn. The trigger literature suggests that triggers may either lower retention directly or lower the effects of customer satisfaction on retention. However, the triggers did not affect either retention or the satisfaction-retention relationship. Although our study did not detect any trigger effects, the existence of triggerlike effects in other studies (Bolton 1998; Seiders et al. 2005) suggest that they remain an important topic for further research. We expect that our triggers simply take more than nine months to create a switching path.
Our study suggests that customer relationship managers should include both overall evaluations of performance (e.g., customer satisfaction) and the viability of competitive offerings (e.g., calculative commitment) in periodic surveys used to predict retention. Whereas customer satisfaction is commonly included in such surveys, calculative commitment is not. Calculative commitment helps capture the competitive element that is often missing when predicting retention. The actions that CRM managers take depend on which of these factors has the greatest influence on churn. If customer satisfaction is the key driver, retention programs and efforts should focus on improving satisfaction whether or not competitors are doing the same things. In contrast, if calculative commitment is the key driver, the emphasis shifts to improving the aspects of the value proposition that are more unique to the offering. In other words, calculative commitment forces managers to think beyond improving satisfaction to consider specifically how to improve their competitive advantage.
Of further importance to relationship managers is the need to control for heterogeneity in the satisfaction--retention relationship. We offer a relatively simple solution: Include prior churn in the analysis. This enables relationship managers to understand the effects of customer satisfaction and relationship commitment on retention beyond inherent differences in customers' propensities to churn. Not only are some customers predisposed to stay or to churn, but they are also more or less sensitive to changes in customer satisfaction. By identifying which customers are prone to stay with a provider and likely to respond to satisfaction improvement efforts, managers stand to improve their return on marketing investment.
Although our trigger predictions were not supported, this is still relevant for relationship management. In some highly competitive and dynamic market environments, such as cellular phones, what a company does to keep customers over the next few months may be critical to its survival. Our results show that neither a situational nor a reactional trigger has a significant main effect or moderating effect when added to our churn models. This suggests that relationship managers need not worry about trigger conditions in the short run. In the long run, however, the situation may differ considerably. Our data follow the customers' behavior for only nine months, and triggers often take time to work. Although customers may be aware of a situational or reactional trigger, the effect on actual behavior may be delayed. If such triggers are shown to have an effect over a longer period, identifying them early gives a relationship manager some lead time to intervene and prevent switching.
Finally, our study has implications for researchers who use periodic surveys to explain behavior. Following traditional rules for evaluating the reliability and discriminant validity of latent variables, our analyses support the use of customer satisfaction, affective commitment, and calculative commitment as separate predictors of churn. A subsequent exploratory principal components analysis suggests that satisfaction and affective commitment tap the same customer perceptions, whereas calculative commitment captures something different. Across studies, however, there are considerable differences as to how affective commitment is measured. The challenge for researchers is to find a better way to discriminate satisfaction and affective commitment as backward-versus forward-looking evaluations of an offering.
A limitation of our study is that we explore only nine months of retention. We expect an improvement in the ability of the survey variables and trigger conditions to predict retention as this time frame increases. Another possible limitation is that customers self-selected into the various trigger conditions using the company's own survey. However, we identified the trigger categories using qualitative interviews from a separate sample of the company's customers. More in-depth interviews with the customers who actually responded to the survey would help ensure the more accurate prediction of the type of switching path each customer may be on (Roos 2002).
The authors thank Ove Jansson of Telia, Sweden, for providing the data used in the study.
Legend for Chart:
A - Trigger or Construct
B - Measure
C - Loading
D - Average Variance Extracted
A
B
C D
Situational trigger
There has been a recent change in your working
conditions, family situation, or living conditions that has
caused you to consider switching to another operator.
N.A. N.A.
Reactional trigger
There has been a recent change in your relationship with
the company that has caused you to consider switching
to another operator, such as poor service, receiving a
faulty invoice, or something similar.
N.A. N.A.
Customer satisfaction
1: Overall satisfaction (1 = "very dissatisfied,"
10 = "very satisfied")
.901 .766
2: Expectancy disconfirmation (1 = "falls short of
expectations, "10 = "exceeds expectations")
.895
3: Performance versus the customer's ideal service
provider in the category (1 = "not very close to ideal
provider, "10 = "very close to ideal provider")
.827
Affective commitment(a)
1: I take pleasure in being a customer of the company.
.798 .692
2: The company is the operator that takes the best care
of their customers.
.837
3: There is a presence of reciprocity in my relationship
with the company.
.825
4: I have feelings of trust toward the company.
.865
Calculative commitment(a)
1: It pays off economically to be a customer of the
company.
.862 .630
2: I would suffer economically if the relationship were
broken.
.833
3: The company has location advantages versus other
companies.
.674
(a) Agree-disagree scale (1 = "strongly disagree,
"10 = "strongly agree").
Notes: N.A. = not applicable. Legend for Chart:
B - CSt
C - ACt
D - CCt
E - STt
F - RTt
G - Churnt - 1
H - Churnt + 1
A B C D E
F G H
ACt .748
(.000)
CCt .519 .545
(.000) (.000)
STt -.243 -.225 -.173
(.000) (.000) (.000)
RTt -.256 -.248 -.143 -.191
(.000) (.000) (.000) (.000)
Churnt - 1 -.090 -.088 -.117 .035
(.000) (.000) (.000) (.065)
-.033
(.086)
Churnt + 1 -.130 -.119 -.150 .054
(.000) (.000) (.000) (.005)
-.010 .757
(.606) (.000)
Fixedt .050 .051 .054 .016
(.009) (.008) (.005) (.401)
-.025 -.265 -.254
(.198) (.000) (.000)
Notes: N = 2715; significance levels are in parentheses
(two-tailed test). CS = customer satisfaction, AC = affective
commitment, CC = calculative commitment, ST = situational
trigger, and RT = reactional trigger. Legend for Chart:
A - Churn Model
B - R²
A B
Model 1: Churnt+1 .579
= -.061CSt + .737Churnt - 1 - .056Fixedt
(.000) (.000) (.000)
Model 2: Churnt+1 .581
= -.040CSt + .000ACt - .040CCt
(.036) (1.000) (.008)
+ .734Churnt - 1 - .055Fixedt
(.000) (.000)
Model 3: Churnt+1 .581
= -.065CSt - .039CCt + .735Churnt - 1
(.000) (.008) (.000)
+ .038CSt x Churnt - 1 - .055Fixedt
(.022) (.000)
Model 4: Churnt + 1 .582
= -.068CSt - .038CCt + .734Churnt - 1
(.001) (.010) (.000)
+ .014STt + .001RTt + .039CSt
(.336) (.963) (.018)
x Churnt - 1 - .001CSt x STt
(.931)
+ .015CSt x RTt - .056Fixedt
(.379) (.000)
Notes: We report the standardized coefficients, with
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~~~~~~~~
By Anders Gustafsson; Michael D. Johnson and Inger Roos
Anders Gustafsson is Professor of Business Economics, and Inger Roos is Associate Professor of Business Economics, Service Research Center, Karlstad University. Michael D. Johnson is D. Maynard Phelps Professor of Business Administration and Professor of Marketing, Stephen M. Ross School of Business, University of Michigan.
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Record: 158- The Effects of Entrepreneurial Proclivity and Market Orientation on Business Performance. By: Matsuno, Ken; Mentzer, John T.; Özsomer, Aysegül. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p18-32. 15p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.66.3.18.18507.
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The Effects of Entrepreneurial Proclivity and Market Orientation on Business Performance
The recent literature suggests a potential tension between market orientation and entrepreneurial proclivity in achieving superior business performance. This is unsettling for marketers, because it could mean that being market oriented is detrimental to a firm that is also trying to be entrepreneurial and successful. To examine this unnerving potential, the authors investigate structural influences (both direct and indirect) of entrepreneurial proclivity and market orientation on business performance. The results indicate that entrepreneurial proclivity has not only a positive and direct relationship on market orientation but also an indirect and positive effect on market orientation through the reduction of departmentalization. The results also suggest that entrepreneurial proclivity's performance influence is positive when mediated by market orientation but negative or nonsignificant when not mediated by market orientation. The authors also provide a discussion and future research implications.
In his book Innovator's Dilemma, Christensen (1997) argues that well-managed companies often fail to innovate precisely because they are rightfully preoccupied with the market--existing or potential-they know. The contention is an unnerving one for managers who believe in a market orientation: Know the market, share the market information, and act on it (Jaworski and Kohli 1993; Kohli and Jaworski 1990). It is unsettling because the argument suggests that being market oriented, a good management practice and the foundation of marketing strategy formulation and execution, could be harmful for a firm that is also trying to be entrepreneurial and successful. This potential tension between market orientation and entrepreneurial proclivity (i.e., an organizational predisposition to entrepreneurial management processes, to be discussed in detail subsequently) deserves serious attention because compelling evidence exists that market orientation leads to positive business performance (e.g., Baker and Sinkula 1999; Green-ley 1995; Han, Kim, and Srivastava 1998; Jaworski and Kohli 1993; Matsuno and Mentzer 2000; Narver and Slater 1990; Selnes, Jaworski, and Kohli 1996), and entrepreneurial proclivity is also argued to contribute to superior firm performance (Barringer and Bluedorn 1999; Covin and Slevin 1989; Drucker 1998; Lumpkin and Dess 1996; Miller 1983). Although it is well argued that fundamental functions of businesses are the creation of both satisfied customers (i.e., marketing) and entrepreneurial innovation (Deshpandé, Farley, and Webster 1993; Drucker 1954), perhaps companies cannot have both.
The purpose of this study is to investigate how market orientation and entrepreneurial proclivity affect business performance. To this end, our theoretical model conceives market orientation as a firm's intelligence-related activities and responsiveness(Kohli and Jaworski 1990)and considers market orientation as both a consequent phenomenon of entrepreneurial proclivity and a systematic safeguard against undue risk-taking tendencies. Furthermore, from the purposeful enactment perspective, we consider an organization's entrepreneurial proclivity an antecedent to how an organization is designed and structured (means) to achieve the desired outcome when it faces new business opportunities (Child 1972; Lumpkin and Dess 1996; Van de Ven and Poole 1995; Weick 1979). We also argue that organizational design and structure are derivatives of the organization's predisposition toward external environments(Joyce and Slocum 1990), and the design and structure, shaped by entrepreneurial proclivity, in turn affect the level of market orientation.
By integrating the distinct and yet related bodies of literature of market orientation, strategic management, and entrepreneurship, we investigate the structural relationships among market orientation, organizational structure, entrepreneurial proclivity, and business performance. In the following section, we present the conceptual model and propose the hypotheses.
Entrepreneurship was originally studied as a market entry problem: "What business shall we enter?" (Miles and Snow 1978). A more recent conceptual domain of entrepreneur-ship involves entrepreneurial management processes, "the methods, practices, and decision-making styles managers use to act entrepreneurially" (Lumpkin and Dess 1996, p. 136). Reflecting this extension of the conceptual domain to a generalized management process, the literature reveals several different terms, such as entrepreneurial proclivity (e.g., Pellissier and Van Buer 1996), entrepreneurial orientation (e.g., Lumpkin and Dess 1996), and entrepreneurial management (e.g., Stevenson and Jarillo 1990), that are used interchangeably to describe the equivalent generalized concept. Indeed, the consensus in the strategic management and entrepreneurship literature offers three underlying dimensions of the organizational predisposition to entrepreneurial management processes: innovativeness, risk taking, and proactiveness (Barringer and Bluedorn 1999; Caruana, Morris, and Vella 1998; Covin and Slevin 1989; Jennings and Young 1990; Khandwalla 1977; Miller 1983; Miller and Friesen 1982; Morris, Avila, and Allen 1993). For this study, we use the term entrepreneurial proclivity and define it as the organization's predisposition to accept entrepreneurial processes, practices, and decision making, characterized by its preference for innovativeness, risk taking, and proactiveness.
A business can achieve market orientation's full potential when driven by an entrepreneurial proclivity, appropriate organizational design, and structure(Slater and Narver 1995). Furthermore, the recent conceptual and definitional debate on market orientation has recognized that the development of a market orientation should be understood from both organizational and behavioral perspectives, encouraging a holistic approach to the antecedent investigation of market orientation (Deshpandé and Farley 1998a, b; Narver and Slater 1998). To this end, we follow the conceptual approach of market orientation as a set of behaviors and processes related to continuous assessment of external environments (Deshpandé and Farley 1998a; Jaworski and Kohli 1993). Because entrepreneurial proclivity refers to the predisposition to accept entrepreneurial management processes, tapping part of the broader organizational culture, we conceptualize entrepreneurial proclivity and organization structure as the organizational antecedents (or why's)of market orientation, a set of activities and behaviors (Deshpandé and Farley 1998b).
Our conceptual model (Figure 1) specifies the relationships among the four building blocks of our study: entrepreneurial proclivity, organizational structural dimensions, market orientation, and business performance. We model entrepreneurial proclivity and organizational dimensions and individually relate them to market orientation to explain how and why a firm recognizes and acts on market opportunities (Deshpandé and Farley 1998b), which lead to varied levels of business performance. In the following sections, we develop the hypothetical relationships in the model.
Although two theoretically related but distinct bodies of literature--strategic management and market orientation--are replete with studies that address these concepts' relationships with organizational structure, relatively little empirical research structurally bridges all three, with one notable exception by Deshpandé, Farley, and Webster (1993).1 Collectively, the three underlying dimensions of entrepreneurial proclivity (innovativeness, risk taking, and proactiveness) constitute the rationale for firms to renew the organization, destroy the existing order of the market (Schumpeter 1934), and offer an alternative and potentially superior customer value proposition (Deshpandé, Farley, and Webster 1993; Slater and Narver 1995). Entrepreneurial proclivity is suggested to have a direct impact on how an organization is designed and structured to achieve the desired business performance (Drazin and Howard 1984; Govindarajan 1988). Specifically, an organization's three structural dimensions (formalization, centralization, and departmentalization) are of interest in this study.
Formalization is defined as "the emphasis placed within the organization on following specific rules and procedures in performing one's job" (Zaltman, Duncan, and Holbek 1973, p. 138). The degree of centralization refers to the amount of responsibility and authority delegated (Flippo 1966). More formally, Zaltman, Duncan, and Holbek (1973, p. 142) define centralization as "the locus of the authority and decision making in the organization." Greater formalization and centralization produce uniformity of policy and action, lessen risks of errors by personnel who lack either information or skill, utilize the skills of central and specialized experts, and enable closer control of operations (Flippo 1966, p. 131). Conversely, less formalization and centralization tend to lead to speedier decisions and actions at any hierarchical level, and such decisions are more likely to be adapted to individual situations (Flippo 1966, p. 131). Departmentalization or specialization generally refers to the extent to which a breadth of tasks is confined to a predetermined domain (Kohli and Jaworski 1990; Mintzberg 1979; Ruekert, Walker, and Roering 1985). Although departmentalization is sometimes operationalized as the number of departments into which organizational tasks are partitioned and compartmentalized, perhaps the sheer number of departments in organizations may not be as indicative of departmentalization as the existence (or lack) of departmental interaction-the degree of formal and informal direct contact among employees across departments. Conceptualizing in this manner, we define and operationalize departmentalization as the extent to which members of departments are isolated from interdepartmental interactions.
An argument can be made that entrepreneurs (and their businesses)are often characterized by a centralized vision and strong leadership. Therefore, centralization, formalization, and departmentalization can be instrumental for entrepreneurs to efficiently implement their vision and strategy. However, the generalized concept of entrepreneurship is not equal to "being an entrepreneur" or "driven by an entrepreneur" but rather is an organizational process characterized as entrepreneurial or even "intrapreneurial" (Pinchot 1985). Specifically, an organization's predisposition to the generalized concept of entrepreneurship (or entrepreneurial proclivity) has been argued to be an antecedent to organizational structure and design(Lumpkin and Dess 1996). Lumpkin and Dess (1996) argue that entrepreneurially predisposed organizations value autonomy and freedom to encourage creativity and champion untested but promising ideas. Autonomy, in an organizational context, refers to action taken free of structural constraints that stifle risk taking, exploration, and out-of-the-box thinking. Thus, a greater degree of formalization, centralization, and departmentalization appears to be neither consistent with the generalized concept of entrepreneurial management processes nor conducive to the pursuit of entrepreneurial opportunities. A limited number of empirical studies support this generally negative relationship (Caruana, Morris, and Vella 1998; Moon 1999).
H1: Entrepreneurial proclivity is negatively related to formalization.
H2: Entrepreneurial proclivity is negatively related to centralization.
H3: Entrepreneurial proclivity is negatively related to departmentalization.
Whereas entrepreneurial proclivity is studied as an antecedent to organizational design and structure in the strategic management literature, the structural dimensions are studied predominantly as antecedents or influencers of marketing activities and processes in the marketing literature (e.g., Dwyer, Shurr, and Oh 1987; Moorman, Deshpandé, and Zaltman 1993; Ruekert, Walker, and Roering 1985). Particularly in the past decade, a stream of research has addressed how an organization's structural dimensions influence the level of market orientation, because certain levels of organizational dimensions are thought to be more (or less) conducive to a firm becoming market oriented (e.g., Deshpandé, Farley, and Webster 1993; Jaworski and Kohli 1993; Kohli and Jaworski 1990; Narver and Slater 1991; Ruekert 1992; Selnes, Jaworski, and Kohli 1996; Slater and Narver 1995). Jaworski and Kohli (1993) hypothesize formalization as negatively related to intelligence generation and dissemination and positively correlated with responsiveness (Kohli and Jaworski 1990; Zaltman, Duncan, and Holbek 1973). Similar to the relationship between formalization and market orientation, centralization may lower intelligence generation and intelligence dissemination and increase responsiveness (Kohli and Jaworski 1990). Empirical results are mixed, however.
Jaworski and Kohli (1993) find that centralization is negatively correlated to all three dimensions of market orientation, though statistical significance varies between the two sample sets in their study. Nonetheless, in line with Deshpandé and Zaltman (1982), we expect centralization to inhibit information utilization, particularly limiting intelligence dissemination and responsiveness.
Jaworski and Kohli (1993, p. 59) operationalize departmentalization as "a count of the number of departments in the business unit." The greater number of departments involved, the more difficult it may be for organizations to communicate information and respond quickly. Conceptually, a greater degree of departmentalization seems to be antagonistic to a market orientation, a set of organization-wide intelligence activities. Thus, it appears that the degree of departmentalization reduces the magnitude of market orientation. Although Jaworski and Kohli (1993, p. 56) find that departmentalization is not significantly correlated to any of the three dimensions of market orientation, their study suggests that the sheer number of an organization's departments may not be as significant as departmental connectedness--the degree of formal and informal direct contact among employees across departments. High inter-departmental connectedness and low interdepartmental conflict are positively related to the level of market orientation (Jaworski and Kohli 1993). Implicit in the number of departments conceptualization is an expectation that a greater number of departments should lead to increased alienation, lower connectedness, and greater interdepart-mental conflict. Therefore, we expect a negative relationship between market orientation and departmentalization.
H4: Formalization is negatively related to market orientation.
H5: Centralization is negatively related to market orientation.
H6: Departmentalization is negatively related to market orientation.
In addition to the indirect relationship between entrepreneurial proclivity and market orientation, mediated by organizational structure, we believe that a direct relationship exists between them. Recall that firms with entrepreneurial proclivity are innovative, risk taking, and proactive, and a central theme of the innovation literature is that information gathering and analysis are critical to the successful development and execution of innovation strategies (Barringer and Bluedorn 1999; Covin 1991). Furthermore, according to Barringer and Bluedorn (1999) entrepreneurial firms tend to engage in a greater level of information-scanning activities (Hambrick 1982). Menon and Varadarajan (1992) also argue that a proinnovation culture promotes information sharing and utilization (a substantial part of market orientation), and if such a culture is maintained to foster the organization's predisposition toward innovativeness, a positive relationship between entrepreneurial proclivity and market orientation could be enhanced.
In addition, although entrepreneurs recognize that challenging the existing order is inherently risky, they engage in entrepreneurial endeavors. Whether a risk-taking, innovative drive is initially caused by new technology (i.e., technology driven) or by customer needs (i.e., customer driven), the ultimate goal of entrepreneurial efforts (rather than scientific or engineering efforts) lies in business success, which happens only when the offer meets the market needs. Indeed, many high technology start-up firms headed by scientists and engineers engage in risk taking without much assurance of the successful commercialization of the technology. Among them, some may be driven by the desire to prove the technology (i.e., technology for technology's sake), yet others may be motivated by the prospect of meeting market needs (i.e., commercializing the technology). Those with a purely technological obsession may be tempted to truncate or avoid altogether the process of market learning; thus, the risk taking could result in a lower market orientation. However, because challenging the existing business order is inherently risky, we believe that entrepreneurs distinguish themselves from those fixated on the technology and science by attempting to manage the risk through learning the market, executing actions quickly enough to distance themselves from the competition, and maintaining the high reward potential. Therefore, the risk-taking dimension of entrepreneurial proclivity should lead to a higher level of market orientation.
In the context of entrepreneurship, proactiveness refers to a forward-looking perspective, to the tendency of "taking initiative by anticipating and pursuing new opportunities and by participating in emerging markets" (Lumpkin and Dess 1996, p. 146). We believe that the proactiveness dimension of entrepreneurial proclivity promotes identifying new market opportunities (e.g., new product introduction ahead of the competition in an emerging market segment) and acting on those opportunities (Miller and Friesen 1982; Venkatraman 1989), which results in an increased level of both intelligence generation and responsiveness (Kohli and Jaworski 1990).
Therefore, we reasoned that the three dimensions of an organization's entrepreneurial proclivity collectively facilitate organization members' willingness and ability to engage in market learning activities, recognize the need to reduce undue uncertainty, and take a more calculated risk, thus promoting market orientation as defined by Kohli and Jaworski (1990). This position is consistent with the works of Deshpandé and colleagues (Deshpandé and Farley 1998a,b; Deshpandé, Farley, and Webster 1993; Moorman 1995)that strongly suggest the reinforcing effect of the adhocracy culture and organizational innovativeness on customer orientation.
H7: Entrepreneurship is positively and directly related to market orientation.
The generally positive performance influence of a market orientation is well documented (Baker and Sinkula 1999; Han, Kim, and Srivastava 1998; Jaworski and Kohli 1993; Narver and Slater 1990; Selnes, Jaworski, and Kohli 1996). The positive performance outcomes, however, should be viewed not only in absolute terms but also in competitive terms (i.e., compared with a firm's relevant competitors), because a market orientation has been posited as one of a firm's competitive capabilities and sources of advantage (Hunt and Morgan 1996). Therefore,
H8: Market orientation is positively and directly related to the relative measures of (a) market share growth, (b) percentage of new product sales to total sales, and (c) return on investment (ROI).
The performance impact of market orientation (e.g., Baker and Sinkula 1999; Han, Kim, and Srivastava 1998; Jaworski and Kohli 1993; Narver and Slater 1990) and that of entrepreneurial proclivity (e.g., Barringer and Bluedorn 1999; Covin and Slevin 1986, 1989; Zahra 1991, 1993a) have been studied in two separate bodies of literature, but few studies bridge the two. To our knowledge, neither the joint effect of the two constructs nor the separate, individual effect of one construct while the other is controlled has been empirically investigated. This is unfortunate because each of the research streams points to theoretical connections between entrepreneurial proclivity and market orientation.
More important, this gap in the literature points to an unsettling issue: a potential tension between market orientation and entrepreneurial proclivity. Specifically, although both theoretical and empirical literature support the direct and positive relationships between market orientation and performance measures (H8), when entrepreneurial proclivity enters the picture (Figure 1) the critical question becomes, Given both indirect (H1-H6) and direct (H7) relationships between entrepreneurial proclivity and market orientation, what is the individual contribution of each construct (i.e., entrepreneurial proclivity and market orientation) to business performance?
Although it may be argued that a market orientation is inherently entrepreneurial, we believe that subtle but important distinctions should be made between entrepreneurial proclivity, viewed as an organization's predisposition to the three entrepreneurial dimensions, and market orientation, viewed as organizational behaviors and processes related to the external market environment (Deshpandé and Farley 1998b; Slater and Narver 1995). Because of the external focus on developing information about markets, market-oriented firms are arguably well positioned to anticipate and respond to the emerging needs of their customers (Jaworski and Kohli 1993; Kohli and Jaworski 1990; Narver and Slater 1990) and may also be more likely to innovate successfully. Therefore, a market-oriented business may appear to have an inherent entrepreneurial proclivity and advantage in its speed and effectiveness in responding to opportunities and threats. However, we argue that the positive performance impact of market orientation hinges on the level of entrepreneurial proclivity. According to Slater and Narver (1995), a business can achieve market orientation's full performance impact only if the market orientation is driven by an entrepreneurial spirit and appropriate organizational structures, processes, and incentives. Therefore, we argue that entrepreneurial proclivity is an antecedent to business performance, in which the effect is sequentially mediated first by organizational structure and then by market orientation.
H9: Entrepreneurial proclivity's indirect impact, mediated by organizational structure and market orientation, on the relative measure of (a) market share growth (b) percentage of new product sales to total sales, and (c) ROI is positive.
Having hypothesized the indirect performance contribution of entrepreneurial proclivity (H9), one remaining question is, What is the direct impact of entrepreneurial proclivity on business performance? In the context of our conceptual model, what is the performance impact of entrepreneurial proclivity when that of market orientation is accounted for?
Researchers seem to agree conceptually that entrepreneurial proclivity should contribute to a firm's superior performance and survival (Barringer and Bluedorn 1999; Drucker 1954, 1998; Lumpkin and Dess 1996; Miller 1983). However, empirical results provide only mixed support (Zahra 1993b). For example, Covin and Slevin (1989) find that entrepreneurial proclivity is not significantly related to a multi-item financial performance scale (sales, sales growth, cash flow, return on equity, profit margin, net profit, ROI), but the same authors find a positive relationship between the same measures in a previous study (Covin and Slevin 1986). Zahra (1991) finds that entrepreneurial proclivity has a positive association with profitability and sales growth.
To make the matter even more complex, a potential trade-off seems to exist between market orientation and entrepreneurial proclivity. It is argued that many well-managed companies fail to become successful innovators precisely because they listen too much to their current customers, invest aggressively in technology, and provide more and better products of the sort the customers say they want (Christensen 1997). This proposition is consistent with the findings of Glazer and Weiss (1993), who report that intensive, formal intelligence-related activities--an important part of market orientation--are negatively related to performance in a fast-moving environment. For example, Procter & Gamble's recent struggles are often attributed to its meticulous attention to extensive (and excessive) research on existing customers, which has resulted only in modest, incremental product improvements and caused the firm to fall behind in developing truly new products and markets (Useem 1999). Conversely, however, Gatignon and Xuereb (1997) report that a customer orientation, also an important part of market orientation, has a positive influence on an innovation's commercial success even in high demand-uncertainty cases. Overall, the direct and independent performance effect of entrepreneurial proclivity, while market orientation is controlled, is not well established in the literature. Therefore, we offer the following hypothesis, based on a conceptual plausibility that entrepreneurial proclivity contributes to a firm's superior business performance (Barringer and Bluedorn 1999; Drucker 1954, 1998; Lumpkin and Dess 1996; Miller 1983):
H10: There is a positive and direct impact of entrepreneurial proclivity on the relative measures of (a) market share growth, (b) new product sales to total sales, and (c) ROI.
Data Collection
We collected the data for this study in conjunction with previously published studies (Matsuno and Mentzer 2000; Matsuno, Mentzer, and Rentz 2000). We obtained a master list of 1300 U.S. manufacturing companies that identified one marketing executive (vice president or director level) per company from a well-known, Midwest-based commercial vendor. We randomly chose the 1300 companies from all the listed manufacturing companies (a total of approximately 600,000) in the vendor's quarterly updated master list, which encompassed a wide range of the Standard Industrial Classification codes (2011-3999).2 The profiles (employee size and annual sales) of the 300 manufacturing companies in the pretest and the 1000 companies for the final sample are given by Matsuno and Mentzer (2000). In the pretest survey instrument, we included all the measures pertinent to this study (i.e., market orientation, entrepreneurial proclivity, three organizational structure variables, and performance measures) as well as demographic variables for both respondents and strategic business units (SBUs; e.g., number of SBUs responsible, title of respondent, SBU size, industry). Two-wave mailings produced a response rate of 31.3% for this pretest. Purification of items was conducted, on the basis of both substantive (e.g., breadth of theoretical content coverage by the item, consistency of the contents tapped by individual items under a single factor, clarity of the meaning and comprehensibility of the item) and empirical (e.g., descriptive statistics, fit statistics such as modification index and standardized residuals, reliability statistics) criteria. The detailed results of measurement validation, throughout the pretest and final data collection, are provided in the "Measures" section.
For the final data collection, a questionnaire packet, including cover letter, stamped return envelope, and questionnaire, was sent to a random sample of 1000 marketing executives of the 1300 companies in the master list. Three-wave mailings produced an effective response rate of 38.76% (or 364 usable responses) after the number of undeliverable survey packets returned to the authors was subtracted. For nonresponse bias examination, multivariate analysis of variance was applied on the three business performance variables (market share, percentage of new product sales to total sales, and ROI) on the basis of the three mailing waves. Because none of the multivariate tests of significance indicated differences in the performance variables, we concluded that nonresponse bias was not a significant problem for the analysis.
Measures
This section explains our measures and their development process. All the final scale items are provided in the Appendix.
The market orientation scale. Our focal research question calls for distinct operationalization, measurement, and modeling of the interrelationships among the four groups of theoretically related variables (entrepreneurial proclivity, market orientation, organizational structure dimensions, and business performance). Because the cultural (or broadly held organizational values and belief) market orientation scale (Narver and Slater 1990) could confound with the attitudinal construct of entrepreneurial proclivity through normative bias (Deshpandé and Farley 1998a, b), we capitalized on Kohli and Jaworski's intelligence behavioral market orientation scale (Jaworski and Kohli 1993; Kohli, Jaworski, and Kumar 1993).
Although we support the fundamental conceptual position that market orientation consists of intelligence-related activities (Jaworski and Kohli 1993; Kohli and Jaworski 1990), we believed that the MARKOR scale (Kohli, Jaworski, and Kumar 1993) could be improved in two primary areas: breadth of item sampling domain, especially the range of market stakeholders and forces (Jaworski and Kohli 1996; Kohli, Jaworski, and Kumar 1993; Slater and Narver 1995), and factorial structure and fit (Kohli, Jaworski, and Kumar 1993; Siguaw, Simpson, and Baker 1998). Building on the MARKOR scale, we decided to use a modified, 22-item version of market orientation scale (Matsuno and Mentzer 2000; Matsuno, Mentzer, and Rentz 2000) that improved both item domain breadth and psychometric properties of the MARKOR scale. The 22-item market orientation (or MO, for a notational purpose) scale is provided in the Appendix. After conducting a confirmatory factor analysis (CFA) on the measurement model to validate the internal and external consistencies among the factors, we conducted a second-order CFA (MO was the second-order factor with three intelligence-based first-order factors, or IG, ID, and RESP).
We found that the path coefficients between the higher-order construct (MO) and the three dimensions were all significant at the a = .05 level (Table 1). The fit statistics (X2 = 404.666, degrees of freedom [d.f.] = 206; goodness-of-fit index [GFI] = .913; adjusted goodness-of-fit index [AGFI] = .893; noncentrality parameter [NCP] = 157.623; Tucker-Lewis index [TLI] = .894; normed fit index [NFI] = .809; comparative fit index [CFI] = .906) demonstrate significant improvement over the three-component market orientation scale (MOD3) reported by Kohli, Jaworski, and Kumar (1993; fit statistics: X2 = 1010.05, d.f. = 464; GFI = .722; AGFI = .675; TLI = .641; NFI = .524). The correlation matrix is supportive of the convergent validity of the improved MO scale with Kohli and Jaworski's (1993) original 32-item market orientation scale and its predictive validity with regard to the performance indicators (see Matsuno and Mentzer 2000). The reliability coefficients (Table 1) were also found acceptable: .84 for the entire new MO scale (22 items). The improvement of psychometric properties (more theoretically consistent dimensionality and factorial structure, fit, and reliability) and convergent validity (MO components versus Kohli and Jaworski's 32-item scale components), all in the context of the broader item domain (market factors and market participants), demonstrate the MO scale's substantial improvement. Therefore, the revised second-order scale of market orientation was deemed adequate for the purpose of this study. For subsequent measurement model evaluation and hypothesis testing, we aggregated the MO scale to have three indicators (i.e., IG, ID, and RESP) by summing of the measurement items at the first-order construct level.3
Entrepreneurial proclivity. We are particularly interested in the relationships among an organization's entrepreneurial proclivity, degree of market orientation, and business performance. An organization's entrepreneurial proclivity, we hypothesize, partly explains its level of market orientation. Miller (1983) uses the three dimensions of innovativeness, risk taking, and proactiveness to characterize the degree of an organization's entrepreneurial proclivity. Several researchers have adopted an approach based on Miller's (1983) original conceptualization to describe the attitudinal predisposition to entrepreneurship, or entrepreneurial proclivity (e.g., Covin and Slevin 1989; Morris and Paul 1987; Naman and Slevin 1993). The literature is not explicit about the within-construct relationships among the three dimensions, given that these dimensions are argued to constitute a broader construct (i.e., entrepreneurial proclivity); however, we conceptualize a second-order factorial structure in which these dimensions represent first-order factors that are the manifestation of the higher-order factor, entrepreneurial proclivity. Each of the three dimensions is distinct, but they collectively constitute the broader, multidimensional higher-order entrepreneurial proclivity construct.
Building on Miller's (1983) and others' works, we developed eight candidate items for the pretest. In the item purification process, based on both substantive and empirical criteria, including iterative CFA, we found that one item was cross-loaded across innovativeness and proactiveness. We subsequently removed the item and obtained a seven-item, second-order scale of entrepreneurial proclivity (ENTRE in the Appendix) that measures the three first-order dimensions of entrepreneurial proclivity, namely, receptiveness to innovation (INNOV; V23, V24), risk-taking attitude (RISK; V25, V26, V27), and proactiveness toward opportunities (PROACT; V28, V29). We then subjected the seven-item, second-order ENTRE scale to CFA with the final data set. The scale's CFA fit statistics were good (X2 = 14.535, d.f. = 11; GFI = .988; AGFI = .970; NFI = .982; CFI = .995), in support of the second-order factorial structure. The reliability coefficient for the entire seven-item ENTRE construct was .83. As in the case of the MO scale, we aggregated the ENTRE scale to have three indicators (i.e., INNOV, RISK, and PROACT) by summing the measurement items at the first-order construct level for subsequent measurement model evaluation and hypothesis testing.
Organizational structure. We measured the three constructs (formalization, centralization, and departmentalization) by multiple-item scales adapted from a previous study by Jaworski and Kohli (1993). Because theory suggests that centralization, formalization, and departmentalization are significantly correlated, we suspected that the items of the three constructs might be highly correlated and cross-loaded. Therefore, we conducted a series of measurement model CFA to purify the three scales. Several rounds of measurement model CFA, after those that were severely cross-loaded were eliminated, resulted in three items for formalization, four items for centralization, and four items for departmentalization. The fit statistics for the three-scale measurement model were adequate (X2 = 157.365, d.f. = 41; GFI = .925; AGFI = .880; NFI = .900; CFI = .924), indicating the discriminant and convergent validity of the three scales. The reliabilities of these scales were .63 (formalization), .87 (centralization), and .71 (departmentalization). The scale items for the three constructs (FORM, CENT, and DEPT) are provided in the Appendix.
Business performance. Three self-reported, relative business performance indicators--market share (SOM), percentage of new product sales to total sales (PCTNP), and ROI--were developed (labeled V41-V43 in the Appendix). These performance variables were measured relative to those of the organization's relevant competition, because market orientation is considered to result in competitive (thus, relative) advantage (Hunt and Morgan 1996). Because competitors are the standard of comparison in the performance scale, each outcome item is phrased so that respondents evaluated these aspects of business performance relative to their business unit's primary competitors' (Conant, Mokwa, and Varadarajan 1990). Subjective performance measures were used because ( 1) objective (i.e., certifiable by a third-party) relative performance measures were virtually impossible to obtain at the business unit level, ( 2) subjective measures have been shown to be correlated to objective measures of performance (Dess and Robinson 1984; Slater and Narver 1994), and ( 3) subjective measures have been used in prior market orientation-performance studies (Jaworski and Kohli 1993; Narver and Slater 1990; Slater and Narver 1994).
Measurement Model and Structural Equation Model
To assess discriminant and convergent validity of all five latent constructs of interest, we examined a CFA measurement model by allowing each of the five latent constructs (i.e., ENTRE, MO, FORM, CENT, and DEPT) to correlate with the others, while constraining the measurement items and their error terms to be uncorrelated. The CFA fit statistics (X2 = 334.487, d.f. = 109; GFI = .897; AGFI = .855; NCP = 225.487; NFI = .872; CFI = .909) indicate an acceptable level of convergent and discriminant validity, leading us to fit the structural equation model (Figure 2) for hypothesis testing.
We estimated the structural equation model by LISREL, using the maximum likelihood estimation method. The overall model fit was acceptable. The fit statistics indicate a reasonable model fit (X2 = 422.127, d.f. = 156; GFI = .892; AGFI = .854; NFI = .856; CFI = .903; incremental fit index [IFI] = .904). Before accepting this model as an appropriate basis for hypothesis testing, we performed a further analytical step to test the presence of a potential interaction effect between ENTRE and MO on business performance, in addition to the mediation and direct performance effects of ENTRE.4 Although we had no a priori theoretical reason to model such an interaction effect, the step serves as an alternative, empirical model verification step because of the possibility of a predictor or antecedent variable operating as a "quasi moderator" (Sharma, Durand, and Gur-Arie 1981). We used a procedure called the indicant product analysis technique for latent variables (see Ping 1995, 1996) and found the standardized estimates of the interaction effect not statistically significant on both SOM (.036, t = .733) and PCTNP (.004, t = .086) and only marginally significant on ROI (-.108, t = -1.999). In addition, the model fit was substantially worse than that of the proposed model (Figure 2). Therefore, we concluded that the originally proposed model is an appropriate basis for hypothesis testing. The results, with selected standardized path coefficients and t-values, are provided in Table 2. H1-H3 predict negative relationships between entrepreneurial proclivity and the three organizational structure variables. The path coefficients between ENTRE and FORM, CENT, and DEPT were found to be significant and negative (Table 2). Thus, all three hypotheses (H1-H3) were supported. In contrast, H4, H5, and H6 pertain to the antecedent status of the three organizational structure variables to a market orientation. We expected all three variables to be negatively related to the leve l of market orientation. Among the three organizational structure variables, however, only the path between departmentalization and market orientation was found to be negative and significant.5 Therefore, the data were not supportive of H4 and H5 but rendered support for H6. Taking H1-H6 together, our data suggest that organizations with a high level of entrepreneurial proclivity generally avoid high levels of organizational formalization, centralization, and departmentalization (Caruana, Morris, and Vella 1998; Moon 1999) and achieve a greater degree of market orientation through a low level of departmentalization in particular.
H7 proposes that entrepreneurial proclivity is positively and directly related to the level of market orientation. We found the direct path coefficient from entrepreneurial proclivity to market orientation to be significant and positive (.468)--the greater the level of entrepreneurial proclivity, the greater is the level of market orientation. Thus, H7 was supported.
With regard to the direct performance implications of a market orientation, we predicted that market orientation is positively related to the three performance indicators (H8). The path coefficients (SOM, PCTNP, and ROI) were all found to be significant and positive (.460, .379, and .724, respectively), in support of all three subhypotheses. In addition, in our model, entrepreneurial proclivity was hypothesized to influence business performance, mediated by organizational structures and market orientation (H9). The results for these indirect-effect hypotheses (Table 2) demonstrate that entrepreneurial proclivity has a significant, indirect effect on the three business performance measures (.340 on SOM, .281 on PCTNP, and .536 on ROI) through market orientation. Therefore, H9 was supported. Notably, these indirect effects of entrepreneurial proclivity on the three performance indicators represent a substantial portion of market orientation's total effect on the same performance measures (i.e., .460 on SOM, .379 on PCTNP, and .724 on ROI; see Table 2). This suggests that entrepreneurial proclivity is a significant and positive antecedent for market orientation to have a positive performance impact.
Finally, we examined the direct effect of entrepreneurial proclivity on the performance measures (i.e., the effect of entrepreneurial proclivity without mediation by organizational structures and market orientation). We found that two of the paths (i.e., ENTRE -- SOM, ENTRE -- PCTNP) were not significant and only one path (i.e., ENTRE -- ROI) was negative and statistically significant (Table 2). Subsequently, we modified this original model by setting the two nonsignificant paths to be zero (i.e., no direct effect from ENTRE to either SOM or PCTNP) while keeping the ENTRE -- ROI path free. All the parameter estimates for the modified model remained consistent (i.e., no changes in hypothesis testing results), and the ENTRE -- ROI direct path was negative and significant. The nested-model comparison confirmed that the model fit for the modified model (X2 = 422.441, d.f. = 158; GFI = .892; AGFI = .856; NFI = .856; CFI = .904; IFI = .905) over the original model (Figure 2) was not significant at the a = .05 level (X2 = 422.441 - 422.127 = .314; d.f. = 158 - 156 = 2), which led us to retain the original model as the appropriate basis for the hypothesis testing results.
Overall, our finding of the direct performance influence of entrepreneurial proclivity is consistent with that of Covin and Slevin (1989). In summary, the results from H8-H10 suggest that ( 1) entrepreneurial proclivity's positive effect on the performance measures is not a direct one but is only achieved through a market orientation and ( 2) its direct performance effect is only negative on ROI. They indicate that entrepreneurial proclivity's impact is positive overall (i.e., positive total effect), but it is so because of the mediated path through low departmentalization and high market orientation (i.e., positive indirect effect), not because of its direct effect on business performance.
Notwithstanding the interesting results, several limitations need to be acknowledged, as the validity of the results depends on several key research design-and method-related issues. First, the study relies on single-informant, cross-sectional, and subjective measures. Using multiple informants (e.g., Deshpandé, Farley, and Webster 1993) and obtaining objective performance measures at the SBU level are desirable in further research, which should render proper qualifications to our results. Longitudinal data are particularly desirable for testing a structural model like ours, because the model conceptually assumes a sequential, temporal order of causality, whereas our cross-sectional data set does not. Second, although our market orientation measure demonstrated a modest but important improvement (in its domain specification, operationalization, and psychometric properties), the validity of the scale can be established only through retest and refinement (Churchill 1979). Particularly toward this end, using different types of samples (e.g., industry, competitive environment, nationality) and testing the discriminant validity with other market orientation scales would be particularly useful.
The purpose of our study was to investigate how market orientation and entrepreneurial proclivity are related and affect business performance. From both theoretical and empirical standpoints, we attempted to bridge the gap between the two distinct but related research streams of market orientation and entrepreneurial proclivity. Consequently, several integrative implications and future research opportunities have emerged from the study.
Managerial Implications
Taken together, the results lead to a twofold question: Does entrepreneurial proclivity require a market orientation to have a positive performance impact, and does a market orientation greatly benefit from (or even need) entrepreneurial proclivity to have a substantial performance impact? Some scholars have suggested that this is so and that a market orientation alone may not necessarily bring about sufficient willingness on the part of the organization to take risks and successfully capture market opportunities. If a market orientation is narrowly construed and practiced within the existing boundaries of market opportunity, it could merely reinforce current beliefs about existing customers, competitors, and market environments (Jaworski, Kohli, and Sahay 2000; Slater and Narver 1995). Such an emphasis on existing constituencies and market contingencies may result in the company ignoring or overlooking emerging market opportunities (Christensen 1997). Therefore, it seems reasonable that market intelligence activities and responsiveness are driven by and predicated on entrepreneurial proclivity that encourages proactiveness, innovativeness, and risk taking that takes nothing for granted even in good times. The results of this study seem consistent with this advice to be both entrepreneurial and market oriented. By itself, entrepreneurial proclivity negatively influences performance, and the idea of a non ---###### successful entrepreneur seems to be an oxymoron. For businesses that already possess a high entrepreneurial proclivity, it is highly advisable to promote a market orientation while maintaining their level of entrepreneurial proclivity.
For example, the developers and marketers of the original Palm Pilot (a personal digital assistant, or PDA) resisted the seeming attractiveness of the idea to develop an ultra-small personal computer and instead probed usage situations for potential users from scratch. They began the product design first by learning about the tasks that potential users would most likely perform with the hand-held electronic products (primarily appointment and address book tasks), translated the user tasks into a limited set of functions and attributes, and customized an entirely new system of hardware and software for the function. This model contrasts strongly with Microsoft's Windows CE (now called Pocket PC) and Apple's ill-fated Newton NotePad (an early innovator of the PDA). Microsoft essentially attempted to scale down the desktop/notebook personal computer tasks and functions without a wholesale effort to think outside the box. This less successful model may have resulted from a lack of entrepreneurial proclivity (innovativeness, risk taking, proactiveness) and a preoccupation with the existing business order and market knowledge (i.e., a myopic application of a market orientation). But it is worth noting that entrepreneurial proclivity alone is not enough; the failure to study market needs and match them with an appropriate technology (e.g., handwriting recognition technology) led to the downfall of Apple's Newton NotePad.
In addition, our results suggest that a low degree of departmentalization is related to a high degree of market orientation, which goes to the heart of market orientation implementation3/4organization-wide involvement in intelligence-related activities (Maltz and Kohli 1996). Thus, managers face an important issue in understanding how to promote entrepreneurial proclivity and reduce departmentalization. For example, top management's expressed commitment to entrepreneurial risk taking, its continuous encouragement of risk-taking initiatives across different functional departments, and appropriate reward systems to support them seem particularly helpful. These initiatives are consistent with the goal of increasing the market orientation that requires organization-wide, continuous learning about the market (Jaworski and Kohli 1993).
Future Research Issues
In terms of future direction, several fruitful research areas can be offered. First is the continued inquiry into the ways market orientation and entrepreneurial proclivity influence organizational structure and process and different measures of business performance. Our results indicate that entrepreneurial proclivity exerts a positive influence only through the path mediated by a market orientation. This finding is consistent with our theoretical argument for the sequence of events: Entrepreneurial proclivity promotes a lower degree of departmentalization, which in turn promotes market orientation, which leads to positive performance. Would this model hold for other performance measures and different environments (e.g., competitive intensity, uncertainty)? For example, consider a true pioneer with a disruptive, future technology (Christensen 1997) venturing into uncharted territory with no comparable competition. Such an entrepreneurial business might be in the innovation efforts for the long haul-say, for five years. For this sort of innovator, other criterion variables-such as the track record of "first to the market," product innovativeness, and the ability to create a new product category--might be more appropriate performance measures. Consequently, the direct and indirect performance effects of entrepreneurial proclivity and the mediating effect of market orientation are important issues to study in grasping the roles of both constructs in the context of different organizational settings and strategies. Further research therefore needs to incorporate an additional, diverse set of business performance measures and longitudinal research designs.
Second, detailed inquiry into the process by which both market orientation and entrepreneurial proclivity are implemented would be productive. Particularly promising is the investigation of the type of learning that occurs when both market orientation and entrepreneurial proclivity are present. Previous research (Baker and Sinkula 1999; Miller 1993; Slater and Narver 1995) suggests that entrepreneurial proclivity leads to more generative learning (learning from exploration and experimentation), whereas market orientation leads to more adaptive learning (learning from exploitation of expressed customer needs and existing competitor strengths). This type of research would greatly benefit from qualitative methods, such as case study, historical analysis, and participant observation. Given the high level of interest among practitioners and academics in the learning organization, research efforts that integrate the types of organizational learning, entrepreneurial proclivity, and market orientation would be extremely valuable. Because the marketing strategy literature has devoted a relatively small amount of volume to empirical research related to entrepreneurial proclivity, we hope our study has opened a window of research opportunities.
[ 1] Deshpandé, Farley, and Webster (1993) focus on the relationships among innovativeness, customer orientation, and organizational characteristics. However, they do not explicitly treat innovativeness as a part of the entrepreneurial proclivity construct.
[ 2] Included were food; tobacco; textiles; apparel; lumber and woods; furniture; paper; printing; chemical; petroleum; rubber; leather; stone, clay, glass, and concrete; metal; machinery; electronic and electrical equipment; transportation equipment; and measuring instruments, among others.
[ 3] This aggregation is justified because ( 1) the validity of the second-order MO scale with all 22 item measures has been established; ( 2) given the sample size, it enables us to maximize the degrees of freedom in estimating the path coefficients between MO and performance measures; and ( 3) it reduces levels of random error while accounting for measurement error and retaining the three-dimensional scale of market orientation.
[ 4] We are grateful to a JM reviewer for this suggestion.
[ 5]Departmentalization, operationalized differently, was found to be not significant by Jaworski and Kohli (1993).
Final LISREL Standardized Estimates and t-Values (Improved Second-Order MO Scale)
LISREL Reliability
Parameter Estimate t-Value (Cronbach's α)
MO (22 items) -- -- .84
IG (8 items): λ (IG-MO) .790 8.59 .66
λ (IG V1) .324 4.94
λ (IG V2) .312 4.78
λ (IG V3) .585 7.86
λ (IG V4) .584a --
λ (IG V5) .447 6.47
λ (IG V6) .451 6.51
λ (IG V7) .429 6.26
λ (IG V8b) .503 7.08
ID (6 items): λ (ID-MO) .967 11.04 .78
λ (ID V9) .633 9.95
λ (ID V10) .407 6.69
λ (ID V11) .669a --
λ (ID V12) .579 9.22
λ (ID V13) .674 10.48
λ (ID V14) .685 10.62
RESP (8 items): λ (RESP-MO) .701 9.91 .74
λ (RESP V15b) .583 9.85
λ (RESP V16b) .646 10.89
λ (RESP V17b) .369 6.25
λ (RESP V18) .431 7.30
λ (RESP V19) .741 12.32
λ (RESP V20b) .749a --
λ (RESP V21) .250 4.24
λ (RESP V22b) .314 5.32
[a]Indicates a fixed item.
[b]Indicates a reversed item.
Notes: For definitions of abbreviations, see the Apendix.
Selected Total and Indirect Effects (Completely Standardized)
Total Effects of On LISREL Estimates t-Value
ENTRE FORM -.595 -7.614
CENT .599 9.027
DEPT .709 9.734
MO .740 7.451
SOM .330 5.630
PCTNP .330 5.634
ROI .259 4.383
FORM MO .342 .647a
CENT MO .264 .505a
DEPT MO .449 3.698
MO SOM .460 4.187
PCTNP .379 3.559
ROI .724 5.518
Indirect Effects of On LISREL Estimates t-Value
ENTRE MO .273 3.210
SOM .340 4.168
PCTNP .281 3.560
ROI .536 5.323
Direct Effects of On LISREL Estimates t-Value
ENTRE MO .468 4.179
SOM .010 .105a
PCTNP .049 .497a
ROI .277 2.527[a]Indicates not significant at α = .05 (or t = 1.96).
Notes: Model fit: x( 2) = 422.127, d.f. = 156; GFI = .892; AGFI = .854; NFI = .856; CFI = .903; IFI = .904.
For definitions of abbreviations, see the Appendix.
DIAGRAM: FIGURE 1: The Conceptual Model and Hypothesized Relationships
DIAGRAM: FIGURE 2: The Structural Equation Model with Hypothesized Relationships
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Construct
Item No. Item
Source
Intelligence generation (IG)
V1 We poll end users at least once a year to assess the
quality of our products and services.
Jaworski and Kohli (1993)
V2 In our business unit, intelligence on our competitors
is generated independently by several departments.
Jaworski and Kohli (1993)
V3 We periodically review the likely effect of changes in
our business environment (e.g., regulation) on customers.
Jaworski and Kohli (1993)
V4 In this business unit, we frequently collect and evaluate
general macroeconomic information (e.g., interest rate,
exchange rate, gross domestic product, industry growth
rate, inflation rate).
Newly developed
V5 In this business unit, we maintain contacts with officials
of government and regulatory bodies (e.g., Department of
Agriculture, Food and Drug Administration, Federal Trade
Commission, Congress) in order to collect and evaluate
pertinent information.
Newly developed
V6 In this business unit, we collect and evaluate information
concerning general social trends (e.g., environmental
consciousness, emerging lifestyles) that might affect our
business.
Newly developed
V7 In this business unit, we spend time with our suppliers to
learn more about various aspects of their business (e.g.,
manufacturing process, industry practices, clientele).
Newly developed
V8a In our business unit, only a few people are collecting
competitor information.
Newly developed
Intelligence dissemination (ID)
V9 Marketing personnel in our business unit spend time
discussing customers' future needs with other
functional departments.
Jaworski and Kohli (1993)
V10 Our business unit periodically circulates documents
(e.g., reports, newsletters) that provide information
on our customers.
Jaworski and Kohli (1993)
V11 We have cross-functional meetings very often to discuss
market trends and developments (e.g., customers,
competition, suppliers).
Newly developed
V12 We regularly have interdepartmental meetings to update our
knowledge of regulatory requirements.
Newly developed
V13 Technical people in this business unit spend a lot of time
sharing information about technology for new products with
other departments.
Newly developed
V14 Market information spreads quickly through all levels
in this business unit.
Newly developed
Responsiveness (RESP)
V15a For one reason or another, we tend to ignore changes
in our customers' product or service needs.
Jaworski and Kohli (1993)
V16a The product lines we sell depend more on internal politics
than real market needs.
Jaworski and Kohli (1993)
V17a We are slow to start business with new suppliers even
though we think they are better than existing ones.
Newly developed
V18 If a major competitor were to launch an intensive
campaign targeted at our customers, we would implement
a response immediately.
Jaworski and Kohli (1993)
V19 The activities of the different departments in this
business unit are well coordinated.
Jaworski and Kohli (1993)
V20a Even if we came up with a great marketing plan, we
probably would not be able to implement it in a timely
fashion.
Jaworski and Kohli (1993)
V21 If a special interest group (e.g., consumer group,
environmental group) were to publicly accuse us of
harmful business practices, we would respond to the
criticism immediately.
Newly developed
V22a We tend to take longer than our competitors to respond to a
change in regulatory policy.
Newly developed
Entrepreneurial proclivity (ENTRE)- innovativeness (INNOV)
V23 When it comes to problem solving, we value creative
new solutions more than the solutions of conventional
wisdom.
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
V24 Top managers here encourage the development of innovative
marketing strategies, knowing well that some will fail.
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
ENTRE-risk taking (RISK)
V25a We value the orderly and risk-reducing management process
much more highly than leadership initiatives for change.
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
V26a Top managers in this business unit like to "play it
safe."
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
V27a Top managers around here like to implement plans only
if they are very certain that they will work.
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
ENTRE- proactiveness (PROACT)
V28 We firmly believe that a change in market creates a
positive opportunity for us.
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
V29 Members of this business unit tend to talk more about
opportunities rather than problems.
Adapted from Covin and Slevin (1989); Morris and Paul (1987); Naman
and Slevin (1993)
Formalization (FORM)
V30a I feel that I am my own boss in most matters.
Jaworski and Kohli (1993)
V31a A person can make his own decisions without checking
with anybody else.
Jaworski and Kohli (1993)
V32 The employees are constantly being checked for rule
violations.
Jaworski and Kohli (1993)
Centralization (CENT)
V33 There can be little action taken here until a supervisor
approves a decision.
Jaworski and Kohli (1993)
V34 A person who wants to make his own decision would be
quickly discouraged here.
Jaworski and Kohli (1993)
V35 Even small matters have to be referred to someone higher
up for a final answer.
Jaworski and Kohli (1993)
V36 I have to ask my boss before I do almost anything.
Jaworski and Kohli (1993)
Departmentalization (DEPT)
V37a Employees from different departments feel that the
goals of their respective departments are in harmony
with each other.
Adapted from Jaworski and Kohli (1993)
V38 Protecting one's departmental turf is considered to be a
way of life in this business unit.
Adapted from Jaworski and Kohli (1993)
V39a There is little or no interdepartmental conflict in
this business unit.
Adapted from Jaworski and Kohli (1993)
V40a There is ample opportunity for informal "hall talk"
among individuals from different departments in this
business unit.
Adapted from Jaworski and Kohli (1993)
Performance-market share growth (SOM)
V41 Our business unit's market share growth in our primary
market last year.
Newly developed
Performance-percentage of new product sales (PCTNP)
V42 Percentage of sales generated by new products last year
relative to major competitors.
Newly developed
Performance-ROI
V43 Our business unit's return on investment (ROI) relative
to major competitors last year.
Newly developed[a]Indicates a reversed item.
~~~~~~~~
By Ken Matsuno; John T. Mentzer and Ays¸egül Özsomer
Ken Matsuno is Assistant Professor of Marketing and The Irving Pike Term Chair, Marketing Division, Babson College. John T. Mentzer is The Harry J. and Vivienne R.Bruce Excellence Chair of Business, Department of Marketing, Logistics, and Transportation, University of Tennessee. Ays¸egül Özsomer is Assistant Professor of Marketing, Koç University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 159- The Effects of Ingredient Branding Strategies on Host Brand Extendibility. By: Desai, Kalpesh Kaushik; Keller, Kevin Lane. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p73-93. 21p. 2 Diagrams, 6 Charts. DOI: 10.1509/jmkg.66.1.73.18450.
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The Effects of Ingredient Branding Strategies on Host Brand Extendibility
A decision of increasing importance is how ingredient attributes that make up a product should be labeled or branded, if at all. The authors conduct a laboratory experiment to consider how ingredient branding affects consumer acceptance of a novel line extension (or one that has not been introduced before) as well as the ability of the brand to leverage that ingredient to introduce future category extensions. The authors study two particular types of novel line extensions or brand expansions: ( 1) slot-filler expansions, in which the level of one existing product attribute changes (e.g., a scent in Tide detergent that is new to the laundry detergent category) and ( 2) new attribute expansions, in which an entirely new attribute or characteristic is added to the product (e.g., cough relief liquid added to Life Savers candy). The authors examine two types of ingredient branding strategies by branding the target attribute ingredient for the brand expansion with either a new name as a self-branded ingredient (e.g., Tide with its own EverFresh scented bath soap) or an established, well-respected name as a cobranded ingredient (e.g., Tide with Irish Spring scented bath soap). The results indicate that with slot-filler expansions, a cobranded ingredient facilitates initial expansion acceptance, but a self-branded ingredient leads to more favorable subsequent category extension evaluations. With more dissimilar new attribute expansions, however, a cobranded ingredient leads to more favorable evaluations of both the initial expansion and the subsequent category extension. The authors offer interpretation, implications, and limitations of the findings, as well as directions for further research.
Ingredient branding, in which key attributes of one brand are incorporated into another brand as ingredients, is becoming increasingly popular among marketers. The strategy is an example of a broader marketing trend reflected by the increasing number of firms that are establishing brand alliances by linking themselves through their products or other aspects of their marketing program to other firms or brands (Rao, Qu, and Ruekert 1999; Shocker, Srivastava, and Ruekert 1994). Marketplace examples of ingredient branding abound, such as Beechnut baby foods with Chiquita banana, Ben and Jerry's Heath Bar Crunch ice cream, and Fat Free Cranberry Newtons with Ocean Spray cranberries. The basic motivation for using ingredient branding is that it enhances the differentiation of the host brand from competition by characterizing the ingredient attribute in the host brand more specifically (e.g., Tide with a "new scent" versus the "scent of Irish Spring bath soap"). This will improve the competitiveness of the host brand. Moreover, ingredient branding could enhance the equity of the host brand by sending a strong signal to consumers that the host product offers the combined benefits of two quality brands in one. The impact of ingredient branding, however, will depend on the inherent importance of the ingredient itself. Branding a more important ingredient (e.g., scent versus packaging in the case of Tide detergent) should facilitate the host brand's differentiation and evaluations to a greater extent.
Almost all the brands adopting an ingredient branding strategy have adopted a few common elements in their implementation. First, most use a cobranded ingredient branding strategy, in which the attribute ingredients are supplied by another firm (e.g., Intel inside Dell computers); that is, the ingredient is branded using an identified brand name or other brand element associated with another firm (Norris 1992). Even though cobranded ingredients offer the benefits of competitive differentiation and enhanced equity to the host brand, the downsides involve monetary compensation of some sort to marketers of the ingredient brand, risk arising from the possible withdrawal of the ingredient brand from the alliance in the future, and a potential lack of control over the marketing strategy of the involved ingredient brand. Do marketers of host brands interested in adopting ingredient branding have an alternative way of implementing the strategy that can be as effective as the cobranded strategy? This study investigates an alternative--a self-branded ingredient branding strategy, in which the host brand brands the ingredient with a new name, logo, symbol, and so forth that is proprietary to the company marketing the host brand; that is, the host brand owns the new self-brand.
Second, the nature of ingredient branding adopted by most brands is tactical and revolves around the host product category. Specifically, in most cases, the ingredient brand modifies an existing attribute in the host category, often to help the host brand improve perceptions of performance on that attribute. For example, Smuckers' fruit filling is used by Kellogg's Pop-Tarts to improve the perceived performance on one of its existing attributes-fruit filling. Even though improved product performance in the host category is important, one critical issue is whether the host brand has exploited the full potential of the ingredient. Can the ingredient brand play a more profound strategic role? This study examines two such possible roles. First, can the ingredient brand help the host brand introduce a completely new attribute (from the ingredient category) into the host category that can expand the usage of the host brand and thus be a source of major competitive advantage (e.g., Life Savers candy incorporating the cough relief ingredient of Dayquil cough relief liquid, an attribute that is not provided by any other hard flavored candy)? Second, after the ingredient brand is incorporated into the host brand, can the ingredient be leveraged by the latter to extend into categories in which the host brand could have difficulty extending on its own (e.g., after the incorporation of Dayquil cough liquid, can Life Savers extend into children's flu medicine)?
Prior research on this topic has more or less mirrored the ingredient branding strategies adopted by brands in the marketplace. They have also focused on cobranded ingredients and the role of the ingredient brand in the host category. Specifically, Park, Jun, and Shocker (1996) examine ingredient branding in the context of composite brand extensions. That is, they investigate how a host brand (Slim-Fast) could overcome a limitation (poor taste) when extending into a new category (Slim-Fast cake mix) through the use of an ingredient cobrand (Godiva). They show that by combining two brands with complimentary attribute levels, a composite brand extension has a better attribute profile in consumers' minds than either a direct extension of the dominant brand or an extension that consists of two highly favorable, but not complementary, brands. Simonin and Ruth's (1998) research, in contrast, focuses on the spillover effects of brand alliance, and the authors find that consumers' attitudes toward a brand alliance can influence their subsequent attitudes toward each partner's brands. Moreover, the authors examine the influence of product fit on the evaluation of alliance. ..FT-Prior research has helped marketers' understanding about this topic. However, there are gaps in the knowledge about this area. Specifically, little research has explored the implications of self-branded ingredients or considered the possible disadvantages of cobranded ingredients. Similarly, prior research has not examined the role played by the ingredient brand in the alliance-modifying a current attribute versus introducing a new attribute in the host category schema. Finally, prior research has not considered the effects of ingredient branding strategies on the introduction of subsequent category extensions.
Our research addresses two main sets of questions. First, do the brand names given to ingredients as part of a line (or category) extension affect consumer evaluations? Does the relative effectiveness of a cobranded versus a self-branded ingredient depend on how similar the line extensions are to the host brand? In particular, are cobranded ingredients more valuable for line extensions that are less similar to existing products of the host brand, for example, to establish credibility? This would provide marketers of host brands the flexibility of using an appropriate ingredient branding strategy (depending on product similarity) and yet balance the costs (of compensation, withdrawal, and lack of control) associated with such a strategy that were discussed previously.
Second, can the ingredient product (e.g., Life Savers) in the host brand (e.g., Dayquil cough liquid) be strategically leveraged to enhance the latter's extendibility, that is, enable the host brand to extend into categories that are related to the ingredient but that are difficult for it to extend into on its own (e.g., children's flu medicine)? Furthermore, does this enhancement vary between the two ingredient branding strategies? In particular, does the equity of the cobrand transfer to the host brand in a way that facilitates consumer acceptance of an ingredient-related category extension? If not, a firm may be better off adopting the self-branded ingredient strategy. For example, if Tide detergent wanted to introduce the extension of a scented hand soap, would it be better off initially branding the scented bath soap ingredient with a new name (e.g., EverFresh) than borrowing equity from another brand (e.g., Irish Spring)?
Our article is organized as follows: First, on the basis of theoretical notions from both psychology and the branding literature in marketing, we present the conceptual background and hypotheses of our research. We next describe the experimental methodology and then discuss the key results and their implications.
Some Definitions
Line extensions (e.g., Pantene baby shampoo) involve minor product changes in the host brand (Desai and Hoyer 1993; Farquhar 1989; Keller 1998). Some of these changes may have already been introduced by other brands in the category (e.g., a flavor already introduced by other toothpaste brands), whereas others may be introduced into the host category for the first time (i.e., novel line extensions). These new changes could involve either the modification of a current attribute of the host category (e.g., a new kind of scent introduced in the laundry detergent category) or the introduction of a new attribute in that category (e.g., cough relief introduced in the flavored hard candy category).
The host brand may not brand all these new changes, because either it chooses not to or they could not be done (e.g., ingredient brand's packaging that is new for the host category). However, when these new changes are branded (by either a self-branded or a cobranded ingredient), they are called brand expansions. Novel line extensions involving modification of a current attribute are called slot-filler expansions; novel line extensions involving the addition of a new attribute are called new attribute expansions. Branding the modified or new ingredient attribute makes brand expansion a type of ingredient branding, but ingredient branding also includes other types of brand leveraging strategies, such as the composite brand extensions (e.g., Slim-Fast cake mix by Godiva) examined by Park, Jun, and Shocker (1996). Figure 1 illustrates the linkages between this terminology and corresponding concepts.
Theoretical Perspectives
The distinction of expansions into slot fillers and new attributes can be related to the literature on product schema (Crocker 1984; Meyers-Levy and Tybout 1989; Sujan and Bettman 1989) and concept combinations (Murphy 1990; Wisniewski 1997). According to these streams of research, product concepts can be represented as schemata, that is, structured lists of slots (or attributes) and fillers. The slots define quite generally what properties a product concept can have; the fillers specify what their characteristics are. For example, the laundry detergent product is characterized by many slots, one of which is scent. Now, for this same slot, distinct brands of laundry detergent could assume different kinds of scent (or fillers). One brand may have a fruity scent, whereas another may be characterized by a floral scent. When these notions are applied to brand expansions, the modifying concept (e.g., the ingredient brand) modifies the head noun (e.g., the host brand, as a member of the host product category) either by filling an existing slot in the head noun's concept differently (from other brands in the host category) or by introducing a new slot (i.e., attribute) in the head noun concept. The former (latter), according to our conceptualization, are classified as slot-filler (new attribute) expansions. Both these kinds of changes are introduced into the host category for the first time. Thus, they are inconsistent with the host category schema and are likely to produce corresponding changes in it.
Note that though there could be feedback effects of the alliance on the ingredient brand, our research does not examine that issue. Moreover, although the previous discussion is not inconsistent with research in ad hoc categories (e.g., Barsalou 1983) that has shown that consumers store information around many different kinds of nodes (e.g., attributes, places, products), we assume that consumers store information in memory only around brand (or product) nodes, consistent with other research in branding (e.g., Broniarczyk and Alba 1994).
Schema incongruity models. To understand how consumers would evaluate these two types of expansions, it is important to examine how the expansions would be represented in consumers' memory. Two schema incongruity models-the schema-plus-tag model and the subtyping model-reviewed by Sujan and Bettman (1989) provide useful insight. Their research investigated the effects of conveying moderately versus strongly discrepant information about the brand on brand positioning (e.g., "differentiated" versus "subtyped") and the corresponding effects on consumers' perceptions of the brand and of the product category. Moderately discrepant information (explained by the schema-plus-tag model) resulted in a differentiated positioning of the brand within the overall market, which implied that the brand was viewed as sharing important attributes with other brands in the category but was superior on differentiating attributes. The second strategy (explained by the subtyping model), reflecting strongly incongruent information, involved an attempt to create a separate submarket or subtype for the new brand. The brand is set apart from the general category rather than positioned within the overall market (as in the first strategy). The differentiating attributes are used to create a strong perception of difference-that the brand is in a class or category by itself. Figure 2 highlights the important aspects of these two models, as applicable to the current study.
The schema-plus-tag model suggests, among other things, that attributes discrepant from the general (category) schema cannot be represented in the schematic portion (meant for consistent attributes) and instead are linked to the (brand) representation by unique tags, or functionally separate organizational units (Graesser, Gordon, and Sawyer 1979). Consistent with the general view of a differentiated brand, the model predicts that the advocated brand will be perceived as sharing many consistent attributes with other brands in the category. The brand will also be considered different from other brands in that its schema will contain a unique tag linking the differentiating attribute to the brand. The model predicts that differentiated brands will be processed categorically and memory will decline faster over time for discrepant (tagged) than for consistent attributes because discrepant attributes are not as strongly associated or integrated with the organizing schema (Schmidt and Sherman 1984). Therefore, extensive advertising is required to remind consumers of the tagged (unique) attributes.
In contrast, the subtyping model suggests, among other things, that in an attempt to reconcile inconsistent information with the schema, the discrepant information is processed deeply and well remembered. The specific process of resolution is the formation of subcategories to accommodate the discrepant cases such that the schema for the category as a whole could be maintained (Weber and Crocker 1983). The subtyping model predicts that for a subtyped brand, memory for discrepant brand attributes should be high because the discrepant ingredient brand is strongly linked to or integrated with the host brand. Furthermore, the evaluation of the brand is more likely to depend on a piecemeal approach (Fiske 1982), wherein the evaluation is based on the attribu9es of the brand and how those attributes are evaluated.
Schema incongruity models and brand expansions. In the context of this study, slot-filler and new attribute expansions could be considered cases of moderately and strongly incongruent brands, respectively. Accordingly, the schema-plus-tag and the subtyping models could be applied to interpret lot-filler and new attribute expansions (and extensions), respectively. In the case of slot-filler expansions, the overall function of the host brand changes little (if at all), because only the value of a current attribute is modified. Nevertheless, branding the modified attribute would be expected to improve the performance of the host brand on that attribute (i.e., as a schema plus tag). In contrast, the addition of a new attribute from another product category in new expansions (e.g., cough relief by LifeSavers candy) enables the host brand to provide consumers a completely new function that was delivered by another product category. Because this new function is so distinct from what other brands in the category typically provide, consumers are likely to perceive the host brand with the new ingredient attribute to be unique (i.e., as a subtype).
Next, using the two schema incongruity models and the psychology and branding literature as theoretical underpinnings, we consider how brand expansions and extensions are affected by the particular ingredient branding strategy that is adopted. In doing so, we assume that consumers will focus predominantly on the target attribute ingredient (e.g., scent for the ingredient cobrand of Irish Spring) when evaluating brand expansions and category extensions, though we examine the role of other attributes as well.
Brand Expansion Effects
Basic principles from branding theory can be used to interpret the effects of alternative ingredient branding strategies. Consumer evaluations of a brand expansion or any type of extension can be considered an inferential process by which consumers must formulate their evaluations on the basis of what they already know about the host brand and what product or other type of information is provided about the extension (Bridges, Keller, and Sood 1999). In particular, prior research has shown that various associations that characterize the host brand may transfer to the extension brand depending on their perceived fit (Aaker and Keller 1990; Boush and Loken 1991; Broniarczyk and Alba 1994; Herr, Farquhar, and Fazio 1996; Keller and Aaker 1992; Park, Milber2, and Lawson 1991; Reddy, Holak, and Bhat 1994).
Simonin and Ruth's (1998) results imply that if overall brand quality and/or brand-specific associations of two partner brands are inconsistent, brand expansion evaluations are likely to suffer. Therefore, at least as long as the cobranded ingredient is perceived as well-regarded and relevant to the host brand product and thus has sufficient equity and fit, the ingredient provides several advantages that should facilitate positive evaluations of a brand expansion. First, because the ingredient cobrand is well liked by consumers, they may choose to transfer their evaluations of it directly to the brand expansion through an affect transfer mechanism (Wright 1975). Second, a key predictor of extension evaluations is consumers' perceptions of the manufacturer's ability (or expertise) to make the extension product (Aaker and Keller 1990; Keller and Aaker 1992). Because consumers are likely to perceive the quality of an established ingredient cobrand to be higher than that of an unknown ingredient self-brand, the cobranded ingredient (by its association with the host brand) should improve the credibility of the host brand at least to "deliver" that particular ingredient. Third, because of its already strong association to the ingredient category, an ingredient cobrand should enhance the perceived fit for a brand expansion (Simonin and Ruth 1998). That is, the expansion (of host brand) into the ingredient category may not seem as much of a stretch by virtue of linking with a high-equity product (i.e., ingredient cobrand) of the expansion category. At least in these three ways, a host brand may be able to borrow some of the equity of the cobranded ingredient to improve expansion evaluations (Keller 1998).
Research in psychology also supports these conclusions. Both information integration and expectancy value theory suggest that a more highly valued attribute leads to more favorable evaluations (Andersen 1981; Fishbein and Ajzen 1975). Therefore, an ingredient attribute would be deemed more valuable with a cobrand than with a self-brand.
The extent of these effects, however, may depend on the nature of the brand expansion. For several reasons, the advantages from affect transfer, greater credibility, and higher perceived fit should be more important with a new attribute expansion than with a slot-filler expansion. First, because new attribute expansions are more discrepant from the host brand schema than lot-filler expansions are-new attribute expansions introduce a distinct new attribute as opposed to a different level of an already existing attribute-fit between the host and ingredient categories should be comparatively poorer for new attribute expansions. Greater schema discrepancy will result in new attribute expansions being processed more deeply (Fiske and Pavelchak 1984; Sujan 1985), highlighting the need for expertise on part of the ingredient brand to "pull off" the expansion (Aaker and Keller 1990). Compared with a elf-brand, the cobrand, because of its familiarity and high equity, is likely to be perceived as strongly linked to the ingredient category and thus in a better position to help the host brand overcome perceptions of poor fit. Second, greater discrepancy in new attribute expansions implies that the ingredient product will play a much larger role in the host product category when new attribute expansions rather than slot-filler expansions are introduced (i.e., adding a new product function versus modifying a current product function). This enhanced role will be better played by an ingredient cobrand because it not only is likely to be strongly linked to the ingredient category but also is a better exemplar (and better representative) of the ingredient category than a self-brand is.
On the basis of this and the previous reasoning, we formally offer the following hypotheses:
H<SUB>1</SUB>: The evaluations of expansions with a cobranded ingredient will be more favorable than the evaluations of expansions with a self-branded ingredient.
H<SUB>2</SUB>: The beneficial effect on evaluations of expansions with a cobranded versus a self-branded ingredient will be greater with new attribute expansions than with slot-filler expansions.
Subsequent Brand Extension Effects
Our other key research question involves whether the adoption of different types of ingredient branding strategies with a prior expansion differentially affects the subsequent category extendibility of host brands. We focus only on extensions that leverage the target cobranded or self-branded ingredient in some fashion. Although a host of bases of extension fit exists, marketers often leverage only one key attribute or benefit in extending into a new category.
Similar to brand expansion evaluations, category extension evaluations depend on host brand associations and perceived fit of the extension. In the context of this study, however, evaluation of a category extension will depend in part on how good a fit is perceived between the expanded or augmented host brand and the extension category. This assumption is based on Keller and Aaker's (1992) findings that successfully introducing an intervening category extension, by broadening the meaning of the parent brand and thus enhancing perceptions of extension fit (and corporate credibility), facilitates the adoption of a subsequently introduced, related category extension (see also Dacin and Smith 1994; Dawar and Anderson 1994). In our study, two principles should guide the fit perceptions between the augmented host brand and the extension (see Figure 2). First, because the extension leverages the target attribute ingredient, the more the ingredient brand is seen as part of (versus separate from) the host brand, the greater presumably would be perceptions of extension fit, which would thus produce more favorable evaluations. That is, stronger linkage between the ingredient and host brands would make consumers regard the high fit between the ingredient brand and the extension category as that between the host brand and the extension product. Second, the more the ingredient brand is perceived to have the credibility and expertise to make the extension product (Aaker and Keller 1990), the greater would be perceptions of extension fit. The rationale is that ( 1) the ingredient brand is linked to the host brand in the expansion and ( 2) the host brand (through its linkage with the ingredient brand) will be perceived to have the ability to produce a good-quality extension product.
The representation (i.e., how the ingredient brand is linked to the host brand) and processing differences (i.e., if the ingredient brand has the expertise to manufacture the extension) implied by the schema-plus-tag model (applicable for slot-filler expansions) and subtyping model (applicable for new attribute expansions) can be used to interpret the effects of subsequent category extension introductions (see Figure 2). According to the schema-plus-tag model, the discrepant ingredient brand in a slot-filler expansion will be linked to the host brand as a distinct tag, but as noted previously, this linkage may not be very strong. Furthermore, the expansion will be processed categorically. In contrast, according to the subtyping model, the extreme discrepancy in new attribute expansions is deeply processed, and the host and ingredient brands will be strongly linked to each other. The influences of these differences on category extension evaluations are discussed next.
Slot-filler extensions. Because the slot-filler extensions are processed in a categorical (versus piecemeal) manner, the second principle influencing the perceptions of extension fit-whether the ingredient brand has the expertise to make the extension product-would be largely ignored. Therefore, the evaluations of category extensions would be based more on how the ingredient brand is linked to the host brand.
In a slot-filler extension involving a cobrand (e.g., Tide hand soap with the scent of Irish Spring bath soap), the distinct tag of the ingredient cobrand to the host brand in the minds of consumers could result in their perceiving the host brand and the ingredient brand as two functionally separate organizational units versus a single integrated host brand (Graesser, Gordon, and Sawyer 1979). Thus, instead of recognizing one fit between the augmented host brand and the extension category, the separateness of the ingredient cobrand tag (from the host brand) may prompt consumers to be more likely to assess and compare, in effect, two fits-the fit between the ingredient cobrand and the extension category and the fit between the original host brand and the extension category. Because the extension category leveraged the ingredient attribute, consumers would presumably perceive the cobrand to have higher fit with the extension category compared with the fit for the original host brand. Consequently, the slot-filler extension of a host brand involving a cobrand would be expected to fare comparatively poorly-consumers would rather purchase an extension directly from the ingredient product.
Even though the literature on ingredient branding has not specifically examined the issue of two versus one extension fit, prior research in branding has implied such a process. In particular, Dacin and Smith (1994) have argued that when consumers evaluate the fit of an extension product introduced by a multiproducts parent brand, they could evaluate the fit of the extension with respect to either the combined abstract parent brand or any one or more of the individual products marketed under its name. Similarly, when an associated brand that has adopted a subbranding or an associative branding strategy (Levi's Dockers or Courtyard by Marriott) extends into another category (e.g., Levi's Dockers cologne), customers are likely to consider the fit between the extension product and each of the two brands when evaluating the extension (Keller and Sood 1997; Park, McCarthy, and Milberg 1993). Finally, for the slot-filler (composite) category extensions they examined, Park, Jun, and Shocker (1996) strongly argue that to maximize transfer of relevant information from individual constituent brands to the extension product, the two brands must have a high degree of product-level fit with the extension product.
Reverting to the hypothesis, in a slot-filler extension involving a self-brand (e.g., Tide hand soap with the scent of EverFresh bath soap), the self-brand is labeled as the host brand's own brand, and its existence is not as independent as is that of a cobrand. Therefore, the self-brand would not be expected to have as distinct a tag designation as the cobrand. In other words, the ingredient self-brand and the host brand are likely to be perceived as less separate. As a result, consumers should be less likely to ascertain two competing fits when evaluating the self-branded slot-filler extensions-instead, the single fit of the modified host brand should be more salient. This realization, coupled with the extension category's leveraging of the (pelf-branded) ingredient, suggests that fit perceptions of the modified host brand with the extension category would likely be high, resulting in more favorable evaluations of category extensions that involve prior self-brand versus cobrand ingredient expansions.
The preceding postulation of less favorable evaluation of slot-filler extensions involving a stronger (co)brand versus a weaker (self-)brand, though based on theoretical arguments, may seem counterintuitive. However, prior research has revealed similar results. In particular, Broniarczyk and Alba (1994) show that category extensions of weaker versus stronger parent brands can be more favorably evaluated if they build on and enhance their strong brand-specific associations.
New attribute extensions. Because the host and ingredient (co- and self-) brands are more strongly and more integrally linked together in new attribute extensions, consumers are likely to engage in ascertaining only one fit between the composite host brand and the extension category. Therefore, the differential impact of the ingredient branding strategy on the evaluation of category extension would be exclusively influenced by the amount of processing of the extension. The piecemeal processing induced by a new attribute expansion, however, would be likely to expose the credibility-related deficiencies of the elf-branded ingredient as part of a new attribute extension. In addition, a self-brand is likely to be perceived as possessing less favorable brand associations. In contrast, a cobranded ingredient should be viewed as a better and more representative exemplar of the ingredient category and have greater perceived familiarity, expertise, and equity. Moreover, consumers may regard the new attribute extension with an ingredient cobrand as a continuation of the partnership between the host brand and the cobrand.
H<SUB>3</SUB>: Extension evaluations will be comparatively more favorable for a cobranded than a self-branded ingredient for new attribute extensions. In contrast, extension evaluations will be comparatively more favorable for a self-branded than a cobranded ingredient for slot-filler extensions.
Overview
In exchange for extra credit, 262 students from a large public university participated in a laboratory study that purportedly designed to "explore students' opinions about some current and new brands in the marketplace." Specifically, students were told the following:
Marketers often introduce new products in selected parts of the country before extending them nationally. This strategy helps marketers ascertain consumers' reactions to those new products before any large resource commitments are made in promoting these products nationally. The questions that follow pertain to products that have been already introduced in other parts of the country. Thus, even if you have never heard of these products, please respond to questions about them using your judgment as a consumer.
Subjects then evaluated three different brand expansions and subsequent extensions in turn, in which the expansions varied in terms of type and branding strategy, as described subsequently. To test the value of different ingredient branding strategies, w chose extensions that were similar in fit to the ingredient brand that was the basis of the expansion. The remainder of this section summarizes the experimental procedure, design, manipulations, and measures.
Experimental Design
The design involved a 2 (expansion type: slot-filler or new attribute) × 2 (ingredient branding strategy: self-branded or cobranded ingredient) × 3 (category replicate) × 3 (order) mixed factorial experiment. Expansion type, ingredient branding strategy, and order were between-subject factors; category replicate was a within-subject factor. The order of presentation of the three replicates was counterbalanced such that subjects saw them in one of the three (from possible six) randomly determined sequences; information about the other three independent factors is provided subsequently.
Manipulations
We used real brands as experimental stimuli to enhance the external validity of the research. To develop the experimental manipulations, we conducted four pretests to ( 1) select appropriate host and ingredient product categories and brands, ( 2) establish equivalence between cobrands and self-brands, ( 3) select appropriate extension categories, and ( 4) control for various covariates and possible confounds. Of the four pretests, we conducted three to select appropriate brand expansions, whereas the fourth helped us select appropriate category extensions. We first generated a total of 26 possible expansions-13 new attributes and 13 slot fillers-each a combination of a host brand, an ingredient attribute, and an ingredient cobrand. The four pretests resulted in the selection of six expansions for the actual study, three each of slot-filler and new attribute types. The nine-point scales used to operationalize different constructs along with their Cronbach's αs- are listed in the Appendix.
Pretest 1. The goal of Pretest 1 was to select expansions ( 1) with cobrands that were plausible, ( 2) for which the target attribute ingredient was perceived important, ( 3) for which 50% or more subjects "transferred" the target attribute from the ingredient brand]to the expansion, ( 4) that were perceived as either slot-filler or new attribute expansions,[ 1] ( 5) for which the attitudes of host and ingredient cobrands were high, and ( 6) for which familiarity of host and ingredient categories was high. Each of the 182 subjects who participated in this pretest rated two expansions, which resulted in an average of 14 subjects rating each of the 26 initial expansions.
For variables related to each of these five criteria, we calculated simple means for each expansion and compared the differences between each pair of means using Tukey's (and the Student NK) test that controlled for Type I experiment-wise error. We used the general guideline of performance at or above the midpoint on the scale of a control factor in the retention of expansions for further pretesting. Because we were trying to control for 14 different variables, however, in isolated circumstances (only twice across 14 variables and four pretests), we chose to retain an expansion even if it did not satisfy the guideline on a variable, because the expansion fared well on the remaining 13 variables.
The results of Pretest 1 led to the selection of 12 expansions that fared well on each of the following criteria: expansion plausibility (grand mean of the selected 12 expansions was M = 6.84), transfer of ingredient attribute to the host brand (M = 10/14), attitude toward the host brand (M = 7.51), attitude toward the ingredient cobrand (M = 6.87), host category familiarity (M = 6.39), and ingredient category familiarity (M = 5.85). Furthermore, as expected (see n. 1), importance of the ingredient attribute was found to be lower for new attribute than for slot-filler expansions (M = 4.26 versus 6.65).
Pretest 2. Next, for the 12 expansions that were selected from Pretest 1, it was important to check whether subjects liked the candidate names for the ingredient self-brands and whether they would perceive the concepts of the cobrand and self-brand to be similar. Controlling for these two factors would ensure that the differential evaluations of the expansions (and extensions) involving cobranded and self-branded ingredients (H<SUB>1</SUB>-H<SUB>3</SUB>) could not be attributed to the extraneous factors of disliking the self-brand names and perceiving dissimilarity in the concepts of the two ingredient brands. In Pretest 2, each of the 85 subjects who participated responded to two expansions (n = 13-15 subjects per expansion). Subjects first provided a thought listing prompted by the ingredient brand names. For example, one set of subjects was told, "A leading marketing company in the country is considering using the brand name EverFresh for its new bath soap. What does the name suggest to you? Please list below all associations that come to your mind when you think of EverFresh bath soap." Next, subjects expressed their opinion about the new brand name. Subjects then expressed their attitude toward the cobrand and listed whatever associations came to their mind when they thought about the cobrand (e.g., Irish Spring). Finally, subjects compared the two brand names. They were told that even though they may not be aware of the specific properties of the self-brand, they may be able to imagine what type of product it might be from its name.
The results of analyses similar to the ones reported for Pretest 1 led us to drop four more expansions because either their self-brand names were not liked or the equivalence between the two brand concepts was poor. The remaining eight expansions performed satisfactorily on both these criteria (M = 5.99 and 6.20, respectively). Furthermore, the four most often mentioned attributes of the cobrands were selected to describe both the cobrands and their corresponding self-brands in the main study. Providing subjects the same relevant information about the ingredient product helped create further equivalence between the two ingredient brand types in the main study.
Pretest 3. In Pretest 3, 56 subjects each rated two expansions (n = 13-15 subjects per expansion) to help control for three variables: ( 1) the strength of association between the ingredient attribute and the ingredient cobrand, ( 2) the plausibility of the ingredient attribute in the host brand category, and ( 3) attitude toward the ingredient category. Controlling for the first factor helped ensure that subjects perceived the ingredient brand to "own" the ingredient attribute, and controlling for the latter two factors helped ensure that the ingredient made sense and was worthwhile to the host brand. Analysis of the data resulted in the dropping of two expansions that did not perform well (less than the midpoint of the scale) on one or more of the three control factors. The grand means of the six retained expansions on the three control factors are 6.82, 6.29, and 6.93, respectively.
Pretest 4. Finally, 42 subjects participated in Pretest 4 to select extension categories for the six expansions (n = 14). One of the key research objectives of this study was to explore whether the host brand, after the incorporation of the ingredient attribute (i.e., post-expansion), could extend into categories into which it would have had difficulty extending directly on its own (i.e., pre-expansion). Accordingly, it was necessary to select extension categories such that pre-expansion extensions of the host brand were rated low in fit and those of the ingredient cobrands were rated high in fit. The latter criterion was required because, as stated previously, the extension categories were intended to leverage the ingredient. Each subject rated eight extensions (two each for two different expansions and, within the same expansion, one for the host brand and one for the ingredient cobrand). For each expansion, subjects first rated the two extensions of the host brand and then the two extensions of the ingredient cobrand. Analysis of the data revealed that there was at least one extension (for each of the six expansions) that met the criteria. For the six expansions that were retained, the grand mean for the extension fit of the host brand was 4.20 and that for the ingredient cobrand was 6.88.
Final decisions. Given the high number of variables ( 14) that we needed to control for, it was difficult to find the same set of host brands for both new attribute and slot-filler expansions. Consequently, different sets of host brands were used for the two types of expansions. Nevertheless, category familiarity and attitudes for the two sets of host brands were similarly favorable, which aided comparability. Furthermore, despite the high number of control variables, the pretest results were extremely positive.[ 2] Specifically, of the 28 possible cases in which the means of the replicates could have differed (14 control factors × 2 expansion types), there were only 9 cases in which they differed. Furthermore, in 8 of those 9 cases, the means for the three replicates were either all above or all below the midpoint on the scale.[ 3] Moreover, of all cases of mean differences across replicates, familiarity with ingredient category was the only variable whose mean varied across both slot-filler and new expansion replicates (though all were above the midpoint). Therefore, it was used as a covariate. This discussion suggests that appropriate brand expansions and category extensions were selected for both sets of replicates (for a summary of manipulations and stimuli, see Table 1).
Procedure
After receiving the cover story, subjects first provided attitude measures for host brands in each of the three product categories. Next, subjects received the product set information for the first host brand. Specifically, subjects were told that in selected parts of the country, the host brand was available with the ingredient brand included. A one-sentence description of the ingredient brand followed (see Table 1). This description, containing the four most often mentioned attributes of the cobrand (from Pretest 2), ensured that subjects knew something about the self-brand (compared with the corresponding well-known cobrand). After evaluating the brand expansion, subjects were told that in the parts of the country where the host brand was already available with the ingredient brand, the manufacturer was considering introducing a new product using the host brand name. Subjects were then given the category extension description in terms of host brand name and relevant ingredient brand name and were asked to evaluate it on the basis of various measures.
Subjects proceeded to the second and third product sets and completed the same questions in the same order. After completing all three product sets, subjects provided some background measures for all product sets simultaneously and provided covariate information. A similar procedure was followed for control cells (discussed subsequently).
Measures
Subjects first provided their overall brand attitudes for each of the three host brands ("bad"/"good," "dislike"/"like," "appealing"/"unappealing"). Next, subjects provided the following measures for each expansion/extension product set, completing all the questions for a product set before proceeding to answer all the questions for the next product set:
- Overall attitude toward the brand expansion ("bad"/"good," "dislike"/"like," "unappealing"/"appealing").
- Written retrospective thought protocol of expansion attitude rating.
- Beliefs about the target attribute ingredient and the four other filler attributes for the expansion, in terms of the likelihood that they would provide corresponding benefits ("strongly disagree"/"strongly agree"). The belief statements were generated on the basis of the rationale that these attributes would be likely to be important criteria in making a choice in those categories.
- Perceived fit of the brand extension ("not at all logical"/"logical," "not at all appropriate"/"appropriate").
- Overall attitude toward the category extension ("bad"/"good," "dislike"/"like," "unappealing"/"appealing").
- Written retrospective thought protocol of the extension attitude rating.
- Similar to Question 3, beliefs about the target attribute ingredient and the four other filler attributes for the extension.
- Transferability of expertise for the host brand making the extension product: "Would the people, facilities, and skills used in making [host brand] be helpful in making an [extension product]?" ("not at all helpful"/"very helpful").
- Similar to Question 8, transferability of expertise for the host brand making the ingredient product.
- Attitude toward the ingredient brand ("bad"/"good," "dislike"/"like," "unappealing"/"appealing").
Expansions were described as, for example, "Tide laundry detergent with Irish Spring scented bath soap as an ingredient" (cobrand) or "Tide laundry detergent with its own EverFresh scented bath soap as an ingredient" self-brand). Similarly, extensions were described as "Tide hand soap with the scent of Irish Spring bath soap" (cobrand) or "Tide hand soap with the scent of its own EverFresh bath soap" (self-brand). The word "own" was used in elf-brand descriptions to convey clearly to subjects that self-brands were manufactured by the companies marketing the host brands and to ensure that consumers did not infer poor quality on seeing unfamiliar brand names.
All scaled measures were on nine-point scales. Overall scale reliability (for scales with more than two items) exceeded satisfactory coefficient alpha norms (.7). Protocol questions included the following instructions: "Please list below all product-related thoughts that occurred to you when you were evaluating [brand]. What positive and/or negative aspects of this product did you think about? Please write each separate product-related thought on a different line." Eight numbered blank lines then followed. For expansions, responses were coded into positive or negative thoughts about the ( 1) ingredient brand, ( 2) host brand, or ( 3) expansion itself. For extensions, responses were coded into positive and negative thoughts about the ( 1) host brand, ( 2) extension fit or ( 3) extension evaluation.
After subjects responded to this set of questions for each expansion/extension scenario in turn, a final section of questions (identical to those asked in the pretests) collected key background measures (same as pretest questions). Subjects first responded to questions pertaining to the importance of the target attribute ingredient in the expansion and then rated the plausibility of the target attribute ingredient in the host product category and the strength of association between the target attribute ingredient and the ingredient brand. This was followed by a rating (if applicable) of the liking of the self-brand name as well as familiarity with the three ingredient product categories.[ 4]
Because the hypotheses were framed in terms of brand evaluations, the dependent measures were overall attitude toward the expansion (for H<SUB>1</SUB> and H<SUB>2</SUB>) and the extension (for H<SUB>3</SUB>). Supporting variables included target attribute ingredient beliefs for the expansion and extension, perceived fit of the extension, and other measures that were designed to tap various perceptual measures (e.g., transferability of expertise, plausibility of ingredient) that might provide structured insights into the process that led to these consumer evaluations. The protocols were designed to provide unstructured process insights into how consumers evaluated expansions and extensions.
Control Cells
In addition, to provide greater interpretation of the experimental cells, there were two sets of control cells, as follows:
- The first set of control conditions contained four cells that involved the same ingredient-related expansions, but the extension description did not include the ingredient brand from the prior expansion (e.g., Tide's scented hand soap). The extension was described only as including the target attribute ingredient. When compared with the experimental cells, these control groups provide insight into the effects of withdrawing a previously introduced branded ingredient on evaluations of ingredient-related extensions.
- The second set of control conditions contained two control cells that involved the slot-filler-based and new attribute-based extensions but without any prior expansion or mention of any ingredient brand (e.g., Tide hand soap). When compared with the experimental cells, these control groups provide insight into the advantages of fully leveraging an ingredient brand through expansion and as part of an extension.
In other words, the first set of control cells examines a possible downside of ingredient branding, whereas the second set of control cells assesses a possible upside of ingredient branding.
Manipulation Checks
Table 2 lists the F-statistics and significance levels of all the effects for dependent and supporting measures. Table 3 contains least square cell means of these measures, and Table 4 contains the verbal protocol results for brand expansions. Verbal protocols were coded by two independent student judges, one a doctoral student and the other an MBA student. Both were blind to the study hypotheses, and they agreed 92.7% of the time. Differences between them were resolved through discussions. Thus, all thoughts verbalized by subjects were used in the study.
Several analyses were conducted first to support the integrity of the experimental stimuli, manipulations, and procedure.[ 5] First, subjects reported that they were familiar with the ingredient product categories that made up the expansion (M = 6.08).[ 6] Experimental subjects also reported that they liked both the slot-filler and the new attribute self-brand names equally (M = 6.10). In terms of the manipulation of ingredient branding strategy, the ingredient was liked better (F( 1, 131) = 9.3, p < .01) if it was a cobrand (M = 6.73) than if it was a self-brand (M = 5.99). In addition, because the ingredient cobrand was more familiar than the ingredient self-brand, the target attribute ingredient, as expected, was more strongly associated (F( 1, 131) = 14.7, p < .0002) with the former (C = 7.16, S = 6.10; t = 5.1, p < .001). These results are consistent with the inherent strengths of cobrands (versus self-brands) that were the basis, in part, of the hypothesis development.
In terms of the manipulation of expansion type, as was the case in the pretest, the target attribute was rated much more important (F( 1, 131) = 143.5, p < .001) for a slot-filler expansion (M = 7.48) than for a new attribute expansion (M = 4.50). Similarly, slot-filler expansions (SF) were more favorably evaluated than new attribute expansions (NA) in terms of overall attitudes (SF = 7.20 versus NA = 6.27; F( 1, 131) = 22.6, p < .001). Furthermore, the target attribute was perceived as more strongly associated with the host brand for a slot-filler expansion than for a new attribute expansion (SF = 7.15 versus NA = 5.74; F( 1, 131) = 40.4, p < .001), probably because the host category in slot-filler expansions is already characterized by the ingredient attribute, whereas the ingredient attribute must be "imported" from the ingredient category for new attribute expansions. Finally, the proportion of negative expansion fit thoughts (e.g., "the two products should not be mixed") to total expansion thoughts was much greater for new attribute than for slot-filler expansions (49/405 = .12 versus 8/415 = .02, respectively). Similarly, the proportion of (all) negative thoughts to total expansion thoughts was much greater for new attribute than for slot-filler expansions (180/405 = .44 versus 58/415 = .14, respectively). Thus, subjects drew distinctions, as intended, between slot-filler and new attribute expansions and perceived the latter in some sense as more of a stretch or as incongruent.[ 7]
Finally, analysis also revealed that the covariate did not interact with the independent variables for the dependent measures of expansion and extension attitudes (p > .40). Thus, the covariate satisfied the assumption of homogeneity of regression. The next section presents the results of a posttest that was conducted to confirm some of the assumptions of schema-plus-tag and subtyping models.
Posttest
A posttest was conducted among 62 students to examine ( 1) if the slot-filler (new attribute) expansions were perceived as differentiated (subtyped) products, ( 2) if the ingredient self-brand (and cobrand) was perceived to have the expertise to manufacture the extension product, ( 3) if the ingredient brand was tagged to (or integrated with) the host product, and ( 4) if the one-item measure of the plausibility of the ingredient attribute in the host category variable was valid. The survey employed four different forms corresponding to the two expansion types and two types of ingredient brands. Each form, rated by 14-16 subjects, asked questions about the corresponding three replicates used in the final study. The scale items used to operationalize different constructs along with their reliability information are listed in the Appendix.
Subjects were informed about the first expansion (in an identical way as in the main study) and were asked to list the ways in which the host brand would be different. Next, using two items, subjects rated how different the host brand would be as a result of the different changes they listed. Subjects then rated how different the expansion was in comparison with the other brands in the category. For this purpose, the four-item scale employed by Sujan and Bettman (1989) was modified to account for the low involvement and relative absence of deep category structures for packaged versus nonpackaged products (e.g., laundry detergent versus cameras). This set of items was intended to measure the degree of incongruity of the host brand with respect to host product schema. The next set of items indirectly attempted to measure whether the ingredient brand was tagged (versus integrated) to the host brand. Subjects were asked to what extent they agreed with the likely better performance of the expansion on five features and to which of the three products (the host brand, the ingredient brand, and the composite product of host and ingredient brands) they would attribute that performance. Of the five, two features were of a general nature (e.g., overall good quality), performance on which could be attributed to any of the three products. Attributing it more to the host brand (composite product) would imply tagging (integration). Next, items relevant to establishing convergent and discriminant validity of the plausibility of the ingredient attribute in the host category scale were asked. The previous set of questions was then repeated for the other two expansions. Next, subjects were informed about the category extensions of (modified) host brands and were ask(tm)d to provide evaluations of them. Finally, they were asked if they believed that the host brand had the expertise to manufacture the extension product.
Unless otherwise stated, we analyzed the data using 2 (expansion type) × 2 (ingredient brand) × 3 (replicates) analyses of variance (ANOVAs). The results of the analyses revealed that host brands were perceived to have undergone a greater modification as a result of the ingredient product (F( 1, 60) = 9.01, p < .004) in new attribute than in slot-filler expansions (M = 6.08 versus 5.10). Ratings on the modified differentiation versus subtyping scale were also consistent with the previous result. Specifically, the ingredient attribute was perceived to characterize the host category to a lesser extent in new attribute than in slot-filler expansions (F( 1, 60) = 32.40, p < .0001; M = 3.82 versus 5.85). Furthermore, on the ingredient attribute, both new attribute and slot-filler expansions were perceived to be different from other brands in the host category, but the new expansions were perceived to be more different (F( 1, 60) = 4.75, p < .03; M = é.98 versus 5.32). Similarly, even on other (than the ingredient) attributes, subjects perceived the new attribute expansions to be less similar than the slot-filler expansions to other brands in the host category (F( 1, 60) = 14.76, p < .0003; M = 4.65 versus 5.87) and perceived the modified host brand to be very different from other brands in the host category for new attribute versus slot-filler expansions (F( 1, 60) = 25.13, p < .0001; M = 6.10 versus 4.48).
These results are comparable to those obtained by Sujan and Bettman (1989). Thus, it is safe to conclude that slot-filler (new attribute) expansions were perceived to be differentiated (subtyped) products. Furthermore, analysis regarding the perceived expertise of the ingredient brand to manufacture the extension product revealed an insignificant expansion type × ingredient branding interaction effect (F( 1, 58) < 1, n.s.). As argued in H<SUB>3</SUB> cobrands were perceived to have greater expertise than self-brands for manufacturing the extension product for new expansions (t = 1.87, p < .06; M = 6.33 versus 5.38), whereas self- and cobrands were perceived to have equal expertise to manufacture the extension product for slot-filler expansions (t = 1.33, p > .19; M = 6.24 versus 6.90). The latter, though not argued in H<SUB>3</SUB> because the role of the ingredient brand in making the extension product would be ignored for slot-filler extensions-is as expected because slot-filler extensions are more similar than new attribute extensions to the host product category. Thus, an important assumption underlying H<SUB>3</SUB> was confirmed.
A chi-square analysis was conducted to examine the issue of tagging versus integration of the ingredient brand with the host brand. Subjects' attribution of the performance of the expansion on two general attributes (good value for money and overall good quality) was contrasted between new attribute and lot-filler expansions. The chi-square statistic was significant (χ² <SUB>( 2)</SUB> = 22.59, p < .0001), and as expected, the results showed that the attribution of performance to the host brand was greater in slot-filler than in new attribute expansions (36% versus 15.5%), whereas attribution to the composite product was greater in new attribute than in slot-filler expansions (76% versus 60.7%). Thus, there is some support for the greater integration of the ingredient brand with the host brand in new attribute than in slot-filler expansions. In summary, the key assumptions of the two models were adequately supported in the context of this study.
Finally, the one-item plausibility of the ingredient attribute in the host category scale was a valid measure of the variable (see the Appendix for the scale item). Convergent validity of the measure was established by positive correlation between the scale item and two other items (r = .47 and .75, p < .01 and .0001, respectively) with which it was expected to be positively related. Similarly, discriminant validity of the item was established by showing no relationship (r = -.10 and .27, p > .76 and .25, respectively) between the item and two other items with which it was supposed to have no relationships.
Brand Expansion Findings
H<SUB>1</SUB> and H<SUB>2</SUB> asserted that a cobranded ingredient would lead to more favorable brand expansion evaluations than a self-branded ingredient would, but that this advantage would be comparatively greater for a new attribute expansion than a slot-filler expansion. Tests of H<SUB>1</SUB> and H<SUB>2</SUB> were based on analysis of expansion attitudes. A 2 (expansion type) × 2 (type of ingredient brand) × 3 (order) × 3 (category replicate) mixed analysis of covariance (ANCOVA) analysis was conducted with ingredient category familiarity as a covariate.[ 8] The only within-subjects factor was the category replicate, and special error terms were constructed to calculate the significance of various effects (Keppel 1973).
Moreover, for the two dependent variables of this research-expansion and extension attitudes-the replicate category factor did not interact with either of the two key independent variables, expansion type and ingredient branding (see Table 2). Therefore, we averaged the mean scores of the replicates. For consistency and ease of interpretation, we also averaged the results of supporting variables. Finally, although there are significant higher-level effects present, the nature of these effects does not compromise the reported lower-level effects. Therefore, we do not report the significant higher-level effects in our interpretation of the results of the study.
The ANCOVA analysis of overall expansion attitudes revealed a significant main effect (F( 1, 131) = 5.2, p < .02) of ingredient branding strategy, consistent with H<SUB>1</SUB>, but no significant interaction effect of ingredient branding strategy and expansion type (F < 1), failing to support H<SUB>2</SUB>. The results of simple main effects of ingredient branding indicated that overall expansion attitudes were more favorable with a cobranded (C) than a self-branded (S) ingredient for both slot-filler expansions (C = 7.43, S = 6.97; t = 1.9, p < .05) and new attribute expansions (C = 6.53, S = 6.00; t = 2.3, p < .02). Relatedly, beliefs toward the target attribute ingredient were also significantly more favorable (F( 1, 131) = 11.5, p < .001) with a cobranded ingredient than with a self-branded ingredient for both slot-filler expansions (C = 7.57, S = 6.73; t = 4.0, p < .001) and new attribute expansions (C = 6.10, S = 5.38; t = 3.6, p < .001). Finally, similar to expansion attitude, the interaction of ingredient branding strategy and expansion type was also not significant for target attribute ingredient beliefs.
Thus, H<SUB>1</SUB> is supported, but H<SUB>2</SUB> is not. Two sets of supplementary analyses provide additional insight into these results. First, as noted previously, the ingredient brand was evaluated more favorably when identified as a cobrand rather than as a self-brand. Second, the target attribute ingredient was perceived as more strongly associated (F( 1, 131) = 14.7, p < .0002) with the cobranded than the self-branded ingredient for both slot-filler expansions (C = 7.33, S = 6.08; t = 5.8, p < .001) and new attribute expansions (C = 6.99, S = 6.12; t = 4.1, p < .001). Combined, these findings suggest that the self-brand did not as strongly or positively evoke the target attribute as the cobrand, even if a slot-filler expansion was involved. Consequently, its contribution to expansion evaluations was weaker regardless of the type of expansion involved, though this effect was not even weaker for new attribute expansions; the results thus fail to support H<SUB>2</SUB>.
As further evidence of the relative success of a cobranded expansion versus a self-branded expansion, the coded protocols indicated that subjects in the cobranded conditions expressed relatively more total positive thoughts (P) than did those in the se f-branded conditions for both the slot-filler expansions (C = 215, S = 142) and the new attribute expansions (C = 143, S = 81).
Subsequently Introduced Brand Extension Findings
In general, evaluations of extensions were substantially lower than evaluations of expansions, reinforcing the notion that extensions involved more of a stretch than did expansions.[ 9] Nevertheless, the results reveal an improvement in the post-expansion fit of these extensions of the host brand (M = 5.69) in comparison with their pre-expansion fits (M = 4.20) from the pretest, which suggests that host brands were able to leverage the ingredients to extend into the categories into which they would have found it difficult to extend on their own.
Tests of H<SUB>3</SUB> were based on ANCOVA analyses (similar to those of H<SUB>1</SUB> and H<SUB>2</SUB>) of extension attitudes as well as perceived extension fit and ingredient attribute beliefs. Unlike expansion attitudes, however, the anticipated interaction between ingredient branding strategy and expansion type on extension attitudes did emerge, supporting H<SUB>3</SUB>, as follows.
Slot-filler-based extensions. As posited, overall attitudes toward slot-filler-based extensions were moderately more favorable after a self-branded expansion than after a cobranded expansion (S = 6.01, C = 5.32; t = 1.8, p < .07). Moreover, consistent with the theoretical rationale from the schema-plus-tag model, perceptions of overall extension fit were significantly greater after a self-branded expansion than a cobranded expansion (S = 6.00, C = 5.22; t = 2.1, p < .04). In contrast, beliefs toward the target attribute ingredient were still significantly greater for a cobranded extension than a self-branded extension (C = 6.86, S = 6.22; t = 2.3, p < .02). The latter finding may reflect the inherent strength and association of the ingredient cobrand to the ingredient attribute.
Therefore, even though consumers viewed the ingredient attribute more favorably when it was cobranded, extension evaluations were lower after a cobranded expansion because subjects appeared to view the extension as fitting less well and thus as less appropriate. The coded extension protocols provide further evidence, consistent with this explanation (see Table 5). Subjects were much more likely to report negative extension fit thoughts such as "Tide is not a soap," "Gillette should stick to shaving," and "Kellogg's should make cereal" after a cobranded than after a self-branded expansion (C = 26, S = 6). Thus, as posited, the weak linkage of the ingredient cobrand to the host brand is likely to have prompted subjects, when evaluating the category extension, to be sensitive to the fit of the ingredient brand and extension categories as well as to that of host brand and extension categories.
In contrast, it is likely to have been easier for subjects to imagine after a self-branded expansion that the host brand would be able to leverage this ingredient and extend into a new category, because not only was the self-brand labeled as the host brand's "own" but also the self-brand did not have a prior independent identity, as was the case with the cobrand. Consistent with this reasoning, subjects believed that the plausibility of the target attribute ingredient in the host product (i.e., the extent to which incorporating the ingredient in any host category brand made sense) was higher after a self-branded than after a cobranded expansion (S = 7.08, C = 6.36; t = 2.19, p < .03). Furthermore, subjects believed that the host brand was better able to manufacture the ingredient product (marginally) after a self-branded than after a cobranded expansion (S = 5.85, C = 5.27; t = 1.6, p < .11).
New attribute-based extensions. With new attribute extensions, however, extension attitudes were more favorable after a cobranded than after a elf-branded expansion strategy (C = 6.14, S = 5.27; t = 2.2, p < .03). Moreover, in this case, overall extension fit ratings were higher (C = 6.35, S = 5.19; t = 3.0, p < .003) and beliefs regarding the target attribute ingredient were more favorable for a cobranded than for a self-branded extension (C = 5.95, S = 5.25; t = 2.4, p < .02).
Thus, as posited, because a new attribute expansion involved more of a stretch for the host brand, a cobranded ingredient was more beneficial than a self-branded ingredient not only for the expansion but also for subsequent extensions. The host brand appeared to lack credibility to introduce a more distinct expansion and subsequent extension by virtue of only having self-branded the ingredient. In other words, on deeper processing, subjects in this case may have believed that the expansion was really not possible without the help of another brand and consequently an ingredient-related extension might also need the same cobranded ingredient.
Consistent with this reasoning, subjects were less likely to associate the target attribute ingredient with a self-brand than with a cobrand (C = 6.99, S = 6.12; t = 4.1, p < .001), which suggests that the self-brand did not as strongly evoke the target attribute. Relatedly, subjects believed that the plausibility of the target attribute ingredient in the host product was significantly lower after a self-branded than after a cobranded expansion (C = 6.08, S = 5.30; t = 2.3, p < .02).
Control Group Comparisons
Tests of our research hypotheses provide strong supporting evidence as to the advantages and disadvantages of cobranded and self-branded ingredients. Comparing the experimental cells with the control cells helps address two relevant questions that provide further insight into the effects of alternative ingredient branding strategies. Table 6 contains the least square means of the control cells.
What happens if, after the prior expansion, the host brand is unable or unwilling to use the ingredient brand in a related extension? Answering this question required us to compare the responses of two different sets of subjects: the experimental cells subjects, who rated brand expansion and category extension (both included a reference to the ingredient brand), and control cells subjects, who rated brand expansion and category extension (but only the former included a reference to the ingredient brand). The difference in the extension evaluations and fit ratings between these two groups will provide insights into whether withdrawing the ingredient brand helps or hurts the extensions. We used a 2 (expansion type) × 2 (ingredient branding) × 2 (extension description: ingredient brand present versus absent) × 3 (order) × 3 (replicate category) mixed ANCOVA design to analyze the data. Except for replicate category, all variables were between-subjects factors. Furthermore, we used special error terms to ascertain the significance of different effects. As before, the results depended on the type of expansion and the ingredient branding strategy adopted.
For slot fillers, extension ratings were significantly lower after a cobranded expansion when the cobrand was not present (NC) than when it was present (C) for extension attitudes (C = 5.33, NC = 4.60; t = 2.0, p < .05) and target attribute beliefs (C = 6.81, NC = 5.53; t = 4.2, p < .001), though not for perceived fit (C = 5.21, NC = 4.78; t = 1.2, p > .20). Thus, although the cobrand created much more favorable expansion evaluations, its effects were not enduring and disappeared when it was not present. Withdrawal of the cobrand could have been perceived as a diminishment in the value of the host brand due to the loss of equity of the cobrand. Moreover, the prior appearance of the cobrand as part of the brand expansion could have resulted in its still being salient in subjects' minds even though it was not part of the new extension. This salience could have reinforced the logic of extending the cobrand into the extension category as opposed to extending the host brand.
Extension evaluations were not different (p > .15), however, after a self-branded expansion when the self-brand was not present versus when it was present for extension attitudes, perceived fit, and target attribute beliefs. In this case, subjects seemed to assume that even though the ingredient was no longer branded, the manufacturer still had the people, facilities, skills, and so forth necessary to make the ingredient product (M = 6.35 on a nine-point scale) and extension product (M = 6.15). This assumption may be partly a result of the labeling of self-brands as host brands' "own" brands so that they had not yet established a distinct identity for themselves, as was noted previously.
For new attributes, there were no significant differences in extension evaluations after a cobranded expansion when the ingredient was branded as a cobrand compared with when it was not (p > .20). Therefore, the credibility of the host brand was sufficiently enhanced after a cobranded new attribute expansion such that even withdrawing the ingredient did not seem to harm subsequent extensions. Subjects still perceived the extension as fitting well overall (C = 6.27, NC = 6.01; t = .71, p > .40) and believed that the manufacturer had the people, facilities, skills, and so forth necessary to make the ingredient product (C = 5.17, NC = 5.26; t = .26, p > .70) and extension product (C = 5.50, NC = 5.54; t = .12, p > .90). These results are consistent with the subtyping model analysis presented previously, which postulated that with new attribute expansions, the ingredient brand could become strongly and integrally linked to the host brand (as if there was one composite brand). Consequently, excluding the (better performing) ingredient cobrand from the extension did not seem to affect the subjects, as if they still believed that the ingredient brand was in some ways part of the host brand.
In contrast, extension evaluations were marginally lower after a self-branded expansion when the self-brand was present (S) than when it was not (NS) for extension attitudes (S = 5.25, NS = 5.79; t = 1.4, p < .15), extension fit (S = 5.18, NS = 5.79; t = 1.7, p < .10), and target attribute beliefs (S = 5.26, NS = 5.99; t = 2.4, p < .02). Thus, even though the self-brand may be perceived to be integrally linked to the host brand according to the subtyping model, after detailed processing, subjects appeared to believe that the host brand was attempting to leverage an ingredient for which it had no real expertise. As further proof of this conjecture, subjects believed more strongly that the manufacturer had the people, facilities, skills, and so forth necessary to make the extension product when the self-branded ingredient was not present than when it was (S = 5.12, NS = 5.84; t = 1.85, p < .06), even though they believed its ability to make the ingredient product was not different (S = 5.46, NS = 5.62; t = .46, p > .65).
What are the effects of no prior expansion and the ingredient brand not being included as part of the brand extension? That is, what if the host brand attempted to extend directly without leveraging the ingredient at all? A complementary set of conclusions emerges from comparing the responses of two different sets of subjects: the experimental cells subjects, who rated both the expansion and extension with the reference of ingredient brand, and the (second set of) control cells subjects, who rated the extensions without any prior expansion or mention of any ingredient brand (NEI). We used a 2 (expansion type) × 3 (ingredient branding: host brand's extension with cobrand, self-brand, and no ingredient brand) × 3 (order) × 3 (replicate category) mixed ANCOVA design to analyze the data. Except for the replicate category, all variables were between-subjects factors. Furthermore, we used special error terms to ascertain the significance of different effects.
With slot fillers, prior expansion and ingredient branding improved the attitude reported toward the extension-compared with the case in which there had been no prior expansion and no mention of the ingredient with the extension-for the self-branded expansion (S = 5.98, NEI = 5.31; t = 1.8, p < .07), but not for the cobranded expansion (C = 5.33, NEI = 5.31; p > .20). Consistent with the postulations of the schema-plus-tag model and the H<SUB>3</SUB> results, with a slot-filler extension, the host brand received benefits from self-branding but not from cobranding an ingredient. Indeed, the host brand was as successful in extending to a category when it entered directly as when it entered by using a cobrand for the expansion and then also as part of the extension.
With new attributes, however, prior expansion and ingredient branding marginally improved the extension attitudes-compared with the case in which there had been no prior expansion and no mention of the ingredient with the extension-for the cobranded expansion (C = 6.11, NEI = 5.41; t = 1.8, p < .08), but not for the self-branded expansion (S = 5.25, NEI = 5.41; t < 1). Again, consistent with the postulations of the subtyping model and the H<SUB>3</SUB> results, with a new attribute extension, the host brand needed the cobranded ingredient but could not benefit from the self-branded ingredient.
Summary
A laboratory experiment was conducted that explored how subjects reacted to alternative ingredient branding strategies for different types of brand expansions and how these ingredient branding strategies, in turn, affected evaluations of subsequent ingredient-related category extensions. Ingredient branding strategy was manipulated in terms of a well-regarded cobranded ingredient or a new self-branded ingredient. Brand expansions were manipulated to be slot-filler expansions, in which the level of an existing attribute for the host brand was changed, or new attribute expansions, in which a new attribute was added to the host product.
The experimental findings revealed the following: For slot-filler expansions, a cobranded ingredient was more useful for initial expansions, but a self-branded ingredient was more useful for subsequent extensions. Subjects appeared not to credit the hoot brand for the cobrand association in evaluating subsequent extensions, and if anything, they held it against the host brand. For new attribute expansions, a cobranded ingredient was more useful than a self-branded ingredient for both initial expansions and subsequent brand extensions. Because a self-branded ingredient did not help "broaden" the equity of the host brand, and because the host brand may have lacked credibility, an extension involving a self-branded ingredient was less favorably evaluated.
The control group results, even though comparatively weaker, provided some additional context and insights as to the effects of alternative branding strategies and reinforced these basic conclusions. For slot fillers, a prior cobranded expansion did not improve extension evaluations, compared with the host brand's direct extension. Moreover, if the host brand was expanded but the cobrand was not used as part of a subsequent extension, lower evaluations occurred. A self-branded strategy, however, was more robust, as extension evaluations benefited from prior self-branded expansion, and was not penalized if the self-brand was not used as part of the extension. For new attributes, opposite effects were observed.
Contributions
The major contributions of this study are as follows: First, this is the first study that identifies and examines elf-branding as an alternative way of branding the attribute ingredient. Prior research in ingredient branding (Park, Jun, and Shocker 1996; Simonin and Ruth 1998) has focused exclusively on the cobranding strategy. In addition, the findings from this study highlight the viability and benefits of elf-branded ingredients. Second, this research study was the first to demonstrate the downside of cobranded ingredients. Although cobranded ingredients could facilitate an initial slot-filler expansion, subsequent extension evaluations could be less favorable than if the host brand had initially expanded with self-branded ingredients.
Finally, this is the first study that explores a more strategic role for the ingredient brand, a role that goes beyond just modifying one of the attributes that currently characterizes the host product (Park, Jun, and Shocker 1996). The findings from this study reveal that the ingredient brand can help the host brand successfully introduce a completely new attribute (inherited from the ingredient category) into the host category. Besides helping improve the competitiveness of the host brand, the new attribute can, in some cases, expand the usage of the host brand (e.g., cough relief in Life Savers candy). Moreover, the ability of the host brand to successfully leverage the ingredient to extend into categories into which it would have had difficulty extending on its own is also something unique to this study. This ability of the host brand highlights a different kind of spillover effect of the ingredient brand on the host brand that has not been examined by prior ingredient branding research (e.g., Simonin and Ruth 1998). That is, the expanded meaning of the host brand after expansion helped it enhance its extendibility.
Implications
There are several theoretical and managerial implications of the current study in the nascent topic of ingredient branding. The role played by the ingredient brand in the evaluations of brand expansions and ingredient-related category extensions is different. In the former, the attitude toward the ingredient brand played a decisive role, whereas in the latter, the strength of linkage of the ingredient brand to the host brand was more important than the attitude toward the ingredient brand. This explains the more favorable evaluations of co- versus self-branded slot-filler and new attribute expansions but the relatively poor evaluations of co- versus self-branded slot-filler extensions.
The findings suggest possible advantages and disadvantages of using cobranded ingredients. On the positive side, such a strategy may be a short-term means to enhance the equity of a host brand and its value. This improvement can be valid even if the host brand stretches to add an attribute that is less similar in fit. On the negative side, a cobranded ingredient strategy can be limiting in the long run to the extent that the host brand might want to extend into a closer category that is more directly related to the ingredient. By borrowing equity, host brands are not building equity and therefore fail to reap the benefits from having done so.
The key moderator variable determining the optimal long-term ingredient branding strategy in this study was the perceived distance or fit of the new product extension from the host brand (Keller and Aaker 1992). The farther or more dissimilar the extension, the more valuable was a cobranded ingredient, regardless of the time horizon involved. With a closer and more similar extension, although a cobranded ingredient improved immediate expansion evaluations, a self-branded ingredient improved subsequent extension evaluations. This information can be useful in negotiations of the compensation (e.g., royalty) that a host brand must pay the ingredient cobrand for its use in brand expansion and/or category extension.
Furthermore, as in any kind of alliance, marketers of host brands need to take precautionary steps against the possible withdrawal of the ingredient cobrand from the alliance-control cells results, though weaker, reveal that evaluations of (only) slot-filler extensions suffer after the cobrand that was originally used in the expansion is dropped from the extension. Advertising the benefits of the ingredient in the extension without explicitly highlighting the association with the cobrand could be one possible solution to the problem. Alternatively, a longer and more expensive but surer approach of solving the problem could be to invest up front in advertising to build the equity of a self-brand that could be a substitute ingredient to the cobrand. A final managerial implication of this study is that given the subtyping (versus differentiating) perception of new attribute expansions and extensions, marketers of such products could position them as niche or unique products in the category.
Limitations and Further Research
There are several caveats and qualifications to the conclusions and interpretations of our research findings. One limitation of our study is that very specific types of expansions and category extensions were studied. A different basis for extension fit-one that goes beyond physical product attributes-could produce different results. Furthermore, even though it was hypothesized that cobrands are generally better than self-brands, it is possible that by persistent advertising and other promotions, marketers may be able to build brand equity for their self-branded ingredient products. In addition, it should be noted that favorable effects of cobranded ingredients were demonstrated only when they were perceived as well-regarded and relevant to the host brand product and thus had sufficient equity and fit. Next, the control group findings pertaining to the withdrawal of the ingredient brands in subsequent category extensions suffered from the limitation that subjects evaluated the new extension immediately after seeing the original expansion, including the ingredient brand. In the "real world," however, the interval between seeing these two products could be far greater, and thus the effects could be mitigated. Next, subjects in this study did not use the expansions and extensions; instead, they only rated profiles of those products. It is possible that evaluations could change after consumers use those products.
Several avenues of further research are suggested by this study. In a broad sense, one important priority would be to explore the design and implementation of ingredient branding strategies. Managerial guidelines that suggest when and how to brand ingredients are badly needed. One important area of further research would be to examine whether there are any other feedback effects of brand expansions and category extensions on the ingredient brand over and above those at the brand attitude level (Simonin and Ruth 1998). Specifically, can the ingredient product leverage its association with the host brand to introduce category extensions that are related to the host brand? Would this effect vary between self-branded and cobranded ingredients?
1 The importance of the ingredient attribute was used as a surrogate measure for expansion type with the expectation that subjects would rate the new versus slot-filler ingredient attributes as less important. This assumption is based on the results of Murphy (1990), who shows that subjects elaborated on the new attribute expansions much more, because they are not easy to comprehend. Thus, subjects would take much longer to realize the full implications of new versus slot-filler attribute ingredients in the host category. In addition, posttest results also support our manipulation of expansion type.
- 2 Information about the pretest means of the 14 control variables for the six selected brands is available from the first author.
- 3 The one exception was the low attitude toward the ingredient cobrand for the Godiva expansion. Subjects seem not to like the Slim-Fast brand, though their attitude toward the corresponding ingredient category was high. As it turned out, this issue was not a problem because in the main study it did not result in any variation between Godiva and the other two new attribute replicates on either expansion evaluation (p > .22) or extension evaluation (p > .18)-the two dependent variables of the study.
- 4 Regarding the validity of the 11 different measures reported in Table 2, the following 10 used items similar to those used in prior research, and thus they did not need to be validated: ( 1-3) expansion attitude, extension attitude, and ingredient brand attitudes (e.g., Simonin and Ruth 1998); ( 4-5) attribute belief measures (e.g., Loken and Roedder John 1993); ( 6) extension fit (Keller and Aaker 1992); ( 7) importance of the ingredient attribute (Sujan and Bettman 1989); ( 8-9) TRANSFER (Aaker and Keller 1990); and ( 10) strength of association of the ingredient attribute to the ingredient cobrand (based on the speed and ease of accessibility of attitude as a measure of attitude strength; Fazio et al. 1982). The only measure that needed validation is the plausibility of the ingredient attribute in the host category. The results of a posttest (see the "Method" section) confirmed the validity of this measure.
- 5 Although we did not directly check whether subjects correctly identified the host brand in the expansion as the "head noun" and the ingredient product as the "modifier," several factors strongly suggest that to be the case. First, every subject's verbal protocol for brand expansions predominantly described the changes in the host brand brought about by the ingredient brand and very few thoughts about the ingredient brand. Not a single subject described the expansion as the ingredient product modified by the host brand. Second, not a single subject expressed surprise (or inability) at being asked to evaluate the modified host brand and rate it on a couple of attributes (including the target attribute ingredient) after being exposed to the brand expansion label. Finally, the labels of all expansions and extensions used in the current study clearly mentioned the host brand first, followed by the connector "with," the ingredient brand, and the ingredient attribute (e.g., Tide laundry detergent with Irish Spring scented bath soap as an ingredient). The labels clearly suggest that it is the host brand that is being modified by the ingredient brand, and not the other way around. A similar connector, "by," was used by Park, Jun, and Shocker (1996) in their research (e.g., Slim-Fast cake mix by Godiva), and respondents in that study perceived the product as a Slim-Fast product modified by Godiva.
- 6 The reported familiarity with the ingredient product categories is higher in the cobrand than in the self-brand condition. Subjects' more (less) familiarity with the cobrand (self-brand) presumably resulted in their feeling more (less) familiar with the ingredient categories.
- 7 The means reported for various manipulation checks were generally consistent across replicates. Information about these replicate means is available from the first author.
- 8 Note that analysis of the brand expansion hypotheses used the four experimental cells as well as the first set of four control cells because the experimental methodology for the eight cells was identical through the dependent measures of expansion evaluations.
- 9 The expansions of Gillette and Kellogg's Rice Krispies seem to be available in few markets around the country. However, this did not seem to influence the results for two dependent variables of expansion attitude and extension attitude. We examined the pairwise means for each of these two replicates with their other two corresponding replicates (e.g., expansion of Gillette with Tide and Kellogg's frozen fruit expansions) and conducted this exercise for both ingredient self- and cobrands separately (i.e., 2 [replicates] 2 [ingredient brands] × 2 [expansion type]). The results for extension attitude revealed that only one of eight possible comparisons was significantly different, whereas for expansion attitude, none of the eight comparisons was significantly different.
Legend for chart:
A = Expansion Type
B = Host Brand
C = Cobrand
D = Self-Brand
E = Target Attribute
F = Brand Extension
G = Ingredient Brand Description
Slot-Filler Type
A Tide + bath soap
B Tide
C Irish Spring
D EverFresh
E Scent
F Hand soap
G Green in color and provides the benefits of pleasant
scent, feeling
fresh and clean
A Gillette + moisturizer
B Gillette
C Noxzema
D ClearSkin
E Skin softening
F Anti-acne cream
G Creamy and provides the benefits of skin softening,
cleaner skin, and
relief from dry skin
A Kellogg's frozen fruit bars + fruit
B Kellogg's frozen fruit bars
C Dole
D Rainbow
E Fruit quality
F Fruit-filled Ice cream
G Juicy and healthy and are of good quality and delicious taste
New Attribute Type
A Life Savers + cough liquid
B Life Savers
C Dayquil
D ClearCold
E Cough relief
F Children's flu medicine
G Thick and nondrowsy and provides the benefits of cough relief
And relief from stuffy head
A Kellogg's Rice Krispies cereal bar + chocolate chip cookies
B Kellogg's Rice Krispies cereal bar
C Chips Ahoy
D Choclatee
E Chocolate chip cookie bits
F Chocolate-flavored ice cream
G Crunchy, high in fat, with lots of chocolate chip bits; can be
eaten with milk
A Godiva chocolates + diet food
B Godiva
C Slim-Fast
D Slim & Trim
E Healthy
F Chocolate-flavored yogurt
G Available in a variety of product categories and are healthy meal
substitutes that help people lose weight
Legend for chart:
A = Effects
B = Expansion Attitude
F-Statistic
C = Expansion-Ingredient Attribute Belief
F-Statistic
D = Extension Attitude
F-Statistic
E = Extension Fit
F-Statistic
F = Extension-Ingredient Attribute Belief
F-Statistic
G = Ingredient Brand Attitude
F-Statistic
H = Target Attribute Importance
F-Statistic
I = Target Attribute-Ingredient Brand
F-Statistic
J = Plausibility of Target Attribute in Host Category
F-Statistic
K = Transferability to Extension Product
F-Statistic
L = Transferability to Ingredient Product
F-Statistic
A B C D
E F G
H I J
K L
Exptyp (E) 22.58[a] 40.37[a] <1
<1 12.27*** 19.09[a]
143.52[a] <1 7.57**
<1 <1
Ingbrnd (I) 5.19* 11.52*** <1
<1 5.65* 9.34**
1.40 14.67*** <1
<1 1.31
Order (O) <1 <1 <1
<1 <1 <1
<1 3.36* 1.16
<1 <1
Repcat (R) 6.24** 12.56[a] 2.58
1.35 8.24*** 6.19**
1.25 5.03** <1
9.82[a] 6.19**
E × I <1 <1 5.15*
6.97** <1 3.92*
<1 <1 4.00*
<1 <1
E × O 2.51 1.57 <1
<1 1.51 1.52
1.59 3.09* 1.71
1.32 <1
E × R 1.75 <1 <1
<1 6.21** <1
<1 <1 10.95[a]
1.68 1.90
I × O <1 1.28 <1
1.43 <1 <1
<1 <1 3.20*
<1 <1
I × R <1 <1 1.07
1.38 <1 <1
<1 1.96 2.53
<1 1.97
O × R 3.83** 4.70*** 2.16
<1 <1 3.54*
2.80* 5.61*** 1.05
<1 1.26
E × I × O 2.28 2.22 <1
<1 2.39 4.32**
<1 <1 1.20
1.75 1.74
E × I × R <1 6.60** <1
<1 1.49 <1
<1 <1 1.30
1.20 <1
E × O × R 1.64 2.14 2.23
<1 1.86 5.56***
<1 1.56 <1
<1 2.56*
I × O × R <1 <1 2.27
2.87* <1 2.30
1.60 4.77*** <1
<1 1.59
E × I × O × R 2.10 1.60 <1
1.55 1.58 7.33[a]
2.04 3.49 <1
1.53 <1
Icfam-cov 2.95 6.38** 1.21
<1 <1 <1
<1 1.98 <1
2.17[a] 3.17
*p < .05.
**p < .01.
***p < .001.
[a]p < .0001.1 Slot-Filler Expansion New Attribute Expansion
Dependent and
Supporting Measures Cobrand Self-Brand Cobrand Self-Brand
Expansion attitude 7.43 6.97 6.53 6.00
Expansion attribute 7.57 6.73 6.10 5.38
beliefs
Extension attitude 5.32 6.01 6.14 5.27
Extension fit 5.22 6.00 6.35 5.19
Extension attribute 6.86 6.22 5.95 5.25
beliefs
Ingredient brand 7.44 6.26 6.03 5.72
attitude
Target attribute 7.62 7.34 4.70 4.30
importance
Association of target 7.33 6.08 6.99 6.12
attribute and
ingredient brand
Plausibility of target 6.36 7.08 6.08 5.30
attribute in host
category
Transferability of 5.48 5.76 5.51 5.14
skills to extension
product
Transferability of 5.27 5.85 5.22 5.48
skills to ingredient
product
Familiarity with 6.80 5.72 6.34 5.47
ingredient category[a]
[a]Figures for this variable refer to cell means.
Slot-Filler Expansions
Ingredient Brand Host Brand Expansion
Thoughts Thoughts Thoughts
Ingredient Positive Negative Positive Negative Positive Negative Total
Brand
Cobrand 41 7 15 -- 159 16 175
Self-brand 2 2 8 1 132 32 164
New Attribute Expansions
Ingredient Brand Host Brand Expansion
Thoughts Thoughts Thoughts
Ingredient Positive Negative Positive Negative Positive Negative
Brand
Cobrand 7 11 15 1 121 76 197
self-brand 1 -- 11 2 69 91 160 Slot-Filler Category Extensions
Extension Extension
Host Brand Evaluation Fit
Thoughts Thoughts Thoughts
Ingredient
Brand Positive Negative Positive Negative Total Positive Nega- Total
tive
Cobrand 5 -- 39 8 47 6 26 32
Self- 4 -- 38 15 53 2 6 8
brand
New Attribute Category Extensions
Host Extension Extension
Brand Evaluation Fit
Thoughts Thoughts Thoughts
Ingredient
Brand Positive Negative Positive Negative Total Positive Nega- Total
tive
Cobrand 3 -- 39 16 55 6 16 22
Self- 3 1 27 22 49 3 13 16
brand No Prior Expansion
No Ingredient and No Ingredient
Slot Filler New Attribute
Dependent and Slot New
Supporting Cobrand Self- Cobrand Self- Filler Attribute
Variables Brand Brand
Extension attitude 4.60 5.46 5.94 5.79 5.31 5.41
Extension fit 4.78 5.83 6.01 5.79 5.53 5.11
Extension attribute 5.53 6.10 6.14 5.99 5.77 5.43
beliefs
Transferability of 5.31 6.15 5.54 5.84 6.59 4.72
skills to extension
product
Transferability of 5.15 6.35 5.26 5.62 6.52 4.80
skills to
ingredient productDIAGRAM: FIGURE 1 Terminology and Concepts
DIAGRAM: FIGURE 2 Representation and Processing Implications of Expansion Type for Extension Evaluation
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1. Expansion plausibility (Cronbach's α = .95) was measured with four nine-point scales: (a) 1 = "less believable," 9 = "more believable"; (b) 1 = "does not make any sense," 9 = "makes lot of sense"; (c) 1 = "very unreasonable," 9 = "very reasonable"; and (d) 1 = "very inappropriate," 9 = "very appropriate."
- 2. Perceived importance of the ingredient attribute (Cronbach's α = .93) was measured with three nine-point scales: (a) When evaluating laundry detergents, the scent attribute is (1 = "not at all important," 9 = "very important"); (b) When selecting one brand of laundry detergent over another, the scent attribute is (1 = "irrelevant to my choice," 9 = "very relevant to my choice"; and (c) When selecting one brand of laundry detergent over another, scent is an attribute that I would (1 = "definitely not consider," 9 = "definitely consider").
- 3. Transfer of the ingredient attribute to the expansion- This was an open-ended question: In what ways will Tide laundry detergent be different after incorporating Irish Spring bath soap as an ingredient?
- 4, 5. Attitudes toward the host and ingredient brands and attitude toward the ingredient category were measured with the standard three nine-point scales: (a) 1 = "bad," 9 = "good"; (b) 1 = "dislike," 9 = "like"; and (c) 1 = "unappealing," 9 = "appealing."
- 6. Familiarity with the host and ingredient categories gas measured using a single nine-point scale: 1 = "very low familiarity," 9 = "very high familiarity."
- 7. Liking self-brand names (Cronbach's α = .97) was measured with four nine-point scales: (a) 1 = "bad name," 9 = "good name"; b) 1 = "dislike the name," 9 = "like the name"; (c) 1 = "unappealing name," 9 = "appealing name"; and (d) 1 = "less appropriate name," 9 = "more appropriate name."
- 8. 5imilarity in the concepts of self-brand and cobrand (Cronbach's α = .92) was measured with five nine-point scales each, with endpoints "very dissimilar"/"very similar": (a) EverFresh and Irish Spring bath soaps are likely to be ...; (b) the brand images of EF and IS bath soaps are likely to be ...; (c) the attributes characterizing these two brands are likely to be ...; (d) the consumers of EF and IS bath soaps are likely to be ...; and (e) if you were to describe these two brands to someone, your descriptions of these two brands are likely to be....
- 9. Strength of association between the ingredient attribute and ingredient cobrand (correlation = .89) was operationalized with two nine-point scales: When thinking of Irish Spring brand name, the scent association comes to my mind ... (with endpoints [a] 1 = "very weakly," 9 = "very strongly" and [b] 1 = "very slowly," 9 = "very quickly").
- 10. Plausibility of the ingredient attribute in the host category was measured with a nine-point scale with endpoints 1 = "definitely disagree," 9 = "definitely agree": Incorporating the scent of Irish Spring in any laundry detergent, in general, makes sense.
- 11. Extension fit(correlation = .95) was operationalized using two nine-point scales: (a) 1 = "not at all logical," 9 = "very logical" and (b) 1 = "not at all appropriate," 9 = "very appropriate."
- 12. Extent of change in the host brand was measured using two nine-point items (correlation = .77): As a result of the different changes in Tide that you listed above, how different would the new Tide be compared to when Irish Spring was not incorporated in it as an ingredient? (a) 1 = "not at all different," 9 = "very different" and (b) 1 = "very superficially different," 9 = "very fundamentally different."
- 13. Expansion performance and attribution scale included five pairs of similar items, each pair comprising rating the expansion on an attribute and attributing that performance to one of three options: (a and b) Please indicate the extent of your agreement with the likely performance of the new Tide with the scent of ... on the below listed attributes (e.g., cleans clothes well; with endpoints 1 = "strongly disagree," 9 = "strongly agree"), and to which product would you attribute that performance: (i) mainly Tide detergent, (ii) mainly Irish Spring bath soap, or (iii) mainly to the composite (or integrated) product of Tide and Irish Spring (circle any number from 1 to 3).
- 14. Modified differentiation versus subtyping scale comprised the following four nine-point items (Cronbach's a = .77), with the first three items anchored by 1 = "strongly disagree," 9 = "strongly agree": (a) Laundry detergents, in general, are characterized by the attribute of pleasant scent; (b) On the attribute of pleasant scent, the new Tide with the scent of ... is different from other brands of laundry detergent; (c) On attributes other than the pleasant scent, the new Tide ... is generally like other brands of laundry detergent; and (d) Incorporating the scent of Irish Spring soap in Tide makes the new Tide laundry detergent (with endpoints 1 V "only a little different," 9 = "completely different from other detergent brands").
- 15. Convergent validity (to the plausibility scale) items, each with endpoints 1 = "strongly disagree," 9 = "strongly agree": (a) The scent of Irish Spring bath soap delivered through Tide laundry detergent and through any laundry detergent will be very similar, and (b) If the scent of Irish Spring bath soap were to be incorporated in any laundry detergent, your attitude toward that other laundry detergent will be positive.
- 16. Discriminant validity (to the plausibility scale) items, each with endpoints 1 = "strongly disagree," 9 = "strongly agree": (a) Price of Irish Spring bath soap will increase because of its incorporation in other laundry detergent, and (b) Incorporating the scent of Irish Spring bath soap in any (hair shampoo, in general, makes sense.
- 17. Expertise in making the extension as measured by one nine-point item with endpoints 1 = "strongly disagree," 9 = "strongly agree": In my opinion, the manufacturer of Irish Spring bath soap has the people, facilities, and skills to manufacture a good quality hand soap product.
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By Kalpesh Kaushik Desai and Kevin Lane Keller
Kalpesh Kaushik Desai is Assistant Professor of Marketing, State University of New York at Buffalo. Kevin Lane Keller is E.B. Osborn Professor of Marketing, Amos Tuck School of Business, Dartmouth College. The authors thank research seminar participants at the University of Toronto, INSEAD, University of Minnesota, Boston University, and Duke University and the three anonymous JM reviewers for constructive comments and suggestions.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 160- The Effects of Strategic Orientations on Technology- and Market-Based Breakthrough Innovations. By: Zheng Zhou, Kevin; Yim, Chi Kin (Bennett); Tse, David K. Journal of Marketing. Apr2005, Vol. 69 Issue 2, p42-60. 19p. 1 Diagram, 5 Charts. DOI: 10.1509/jmkg.69.2.42.60756.
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The Effects of Strategic Orientations on Technology- and
Market-Based Breakthrough Innovations
Does market orientation impede breakthrough innovation? To date, researchers have presented opposing arguments with respect to this important issue. To address this controversy, the authors conceptualize and empirically test a model that links different types of strategic orientations and market forces, through organizational learning, to breakthrough innovations and firm performance. The results show that a market orientation facilitates innovations that use advanced technology and offer greater benefits to mainstream customers (i.e., technology-based innovations) but inhibits innovations that target emerging market segments (i.e., market-based innovations). A technology orientation is beneficial to technology-based innovations but has no impact on market-based innovations, and an entrepreneurial orientation facilitates both types of breakthroughs. Different market forces (demand uncertainty, technology turbulence, and competitive intensity) exert significant influence on technology-and market-based innovations, and these two types of innovations affect firm performance differently. The results have significant implications for firm strategies to facilitate product innovations and achieve competitive advantages.
A market orientation endorses the classic marketing tenet that urges firms to stay close to their customers and put their customers at the top of the organizational chart. Various studies have provided empirical support for the positive link between market orientation and firm performance (e.g., Jaworski and Kohli 1993; Matsuno, Mentzer, and Özsomer 2002; Narver and Slater 1990; Slater and Narver 1994). Recent research has further emphasized the role of innovation in facilitating the market orientation-performance relationship (e.g., Han, Kim, and Srivastava 1998; Hurley and Hult 1998). With such strong conceptual and empirical support, market orientation has become a pivotal construct that affects a firm's strategy and operation.
However, some researchers have raised doubts about the unquestioning focus that firms may place on their markets (e.g., Bennett and Cooper 1979; Christensen and Bower 1996; Frosch 1996; Meredith 2002). They caution that an overemphasis on customers could lead to trivial innovations and myopic research and development (R&D), which might lower the firm's innovative competence. Because customers are inherently shortsighted, market-oriented firms may risk losing the foresight of innovating creatively in their attempt to serve customers' existing needs (Hamel and Prahalad 1994). Moreover, customers do not necessarily know what they really want, because they are not completely knowledgeable about the latest market trends or technologies (MacDonald 1995; Von Hippel 1988). Thus, being market oriented may not provide a firm with true insight into product innovation (Frosch 1996; Leonard-Barton and Doyle 1996; Workman 1993). Therefore, firms should "ignore your customers" or "don't listen to your customers" while pursuing breakthrough innovations (Martin 1995, p. 123; Meredith 2002 p. 59).
Whereas early evidence against market orientation was largely anecdotal and drew its implications from selected case studies, Christensen and Bower (1996) add to the criticism with a historical analysis of the computer disk-drive industry. They find that financially strong, rationally managed, and well-established leading firms may fail to embrace breakthrough innovations and may be surpassed by competitors because they are too customer oriented. Recent studies in Journal of Marketing also provide evidence that indirectly bears on this concern. For example, Voss and Voss (2000) find that a customer orientation has a negative impact on firm performance in professional theaters, possibly because of the lack of breakthrough innovation. Grewal and Tansuhaj (2001) find that a market orientation is detrimental to firm performance after an economic crisis, which they attribute to the lack of foresight of market-oriented firms.
The findings have provoked a series of debates. Commenting on Christensen and Bower's (1996) work, Slater and Narver (1998, p. 1001) distinguish customer-led strategies from market-oriented ones: The former focuses on satisfying customers' expressed needs, whereas the latter "goes beyond satisfying expressed needs to understanding and satisfying customers' latent needs." Conner (1999) argues that Slater and Narver's distinction may be too simplistic, and he suggests that to be successful, a firm should balance satisfying its customers' current, expressed needs (i.e., customer-led) with satisfying their future, potential needs (i.e., market-oriented). In response, Slater and Narver (1999) argue that a market orientation considers both expressed and latent needs and thus is more than simply being customer led. Hult and Ketchen (2001) show that as a component of positional advantage, market orientation positively affects firm performance, but they note that the potential value of market orientation should be considered together with other important firm capabilities, such as entrepreneurship and organizational learning. Matsuno, Mentzer, and Özsomer (2002) also find that entrepreneurship in combination with market orientation positively affects firm performance. They encourage additional research to inquire into the process by which firms implement strategic orientations, such as through organizational learning. Most recently, Im and Workman (2004) find that a customer orientation is the driving force of new product success, despite its negative effect on new product novelty. They recommend further studies to examine innovation and its performance implications directly and together with other intangible assets, such as entrepreneurship.
Despite growing interest in this debate, the central issue of whether market orientation facilitates or impedes breakthrough innovation remains unanswered. In this study, we present a model that links strategic orientation, market force, organizational learning, breakthrough innovation, and firm performance in an attempt to contribute to the literature in four ways. First, we differentiate between two types of breakthrough innovations (i.e., technology versus market based) and argue that market orientation may have both positive and negative effects, depending on the type of innovation. In this way, we provide some insights that may resolve the ongoing debate. Second, we examine two important yet less-researched types of strategic orientations, technology and entrepreneurial, and assess how they affect breakthrough innovations through organizational learning. Third, we investigate the effects of different market forces on breakthrough innovations. Fourth, we explore the differential impacts of technology-and market-based innovations on performance. To our knowledge, this study represents the first attempt to distinguish empirically between technology-and market-based innovations and to assess their performance impacts.
Breakthrough Innovation
Innovation is the generation and/or acceptance of ideas, processes, products, or services that the relevant adopting unit perceives as new (Garcia and Calantone 2002). It can be new to either the firm or the firm's customers. Depending on their "newness," innovations can be incremental (continuous) or breakthrough (discontinuous). Incremental innovations refer to minor changes in technology, simple product improvements, or line extensions that minimally improve the existing performance. In contrast, breakthrough innovations are novel, unique, or state-of-the-art technological advances in a product category that significantly alter the consumption patterns of a market (Wind and Mahajan 1997).
Recent studies further differentiate two types of breakthrough innovations on the basis of their ( 1) advances of existing technology and ( 2) departure from the existing market segment (Benner and Tushman 2003). The first type, which we define as "technology-based innovations" ( hereinafter, tech-based innovations), adopts new and advanced technologies and improves customer benefits relative to existing products for customers in existing markets. The second type, which we define as "market-based innovations," departs from serving existing, mainstream markets. Market-based innovations involve new and different technologies and create a set of fringe, and usually new, customer values for emerging markets (Benner and Tushman 2003; Christensen and Bower 1996).
Tech- and market-based innovations differ in both the technology and the market dimensions. On the technology side, though both employ new technologies, the former usually represent state-of-the-art technological advances ( Benner and Tushman 2003; Chandy and Tellis 1998). In contrast, the latter are not necessarily technologically advanced; instead, market-based innovations often use simpler new technology (e.g., off-road versus over-the-road motorcycles, personal computers [PCs] versus minicomputers) and sometimes can be new ideas about business operations (e.g., discount retailing such as Wal-Mart versus traditional retailing such as Sears, health maintenance versus conventional health insurance) (Benner and Tushman 2003; Christensen 1997). On the market side, tech-based innovations address the needs of existing markets and provide greater customer benefits than do existing products (Chandy and Tellis 1998). In contrast, market-based innovations are designed for new or emerging markets and offer benefits that the new segments value, and their performance along traditional dimensions often may be worse than that of existing products (Christensen 1997). In other words, they disrupt the existing customer-preference structure by introducing new benefit dimensions. Therefore, market-based innovations are often perceived as highly different, and they require current mainstream customers to undergo major changes in thinking and behavior (Benner and Tushman 2003). Mainstream customers may not easily recognize or appreciate the new benefits, and market-based innovations may be initially difficult for mainstream customers to adopt or use (Adner 2002).
Tech-based innovations that fundamentally change the technological trajectory and improve customer benefits are called "radical innovations" (e.g., color versus black-and-white television, diesel versus steam locomotive, jets versus turbojets) (Benner and Tushman 2003; Chandy and Tellis 1998; Tushman and Anderson 1986). Market-based innovations that improve performance through subsequent development to a level superior to existing products and that eventually overtake existing products in mainstream markets are called "disruptive innovations" (Christensen 1997). The introduction of PCs serves as an example.
Personal computers were designed to meet the needs of customers, such as small businesses and individual customers, who were not served by minicomputers. Mainstream customers of minicomputers were large businesses and organizations that mostly used minicomputers for scientific computation. When PCs were introduced, minicomputer users showed little interest, because PCs performed much worse than minicomputers along traditionally valued dimensions, such as scientific computation. At that time, PCs were a market-based innovation. However, PC technology developed much faster than that of minicomputers. Over time, PCs overtook minicomputers, even in the latter's mainstream market. As a result, PCs became a disruptive innovation (Christensen 1997; Schnaars 1994).
Both tech-and market-based innovations are highly risky to pursue. A tech-based innovation is technologically risky because developing state-of-the-art technology is extremely expensive and requires substantial investment (Sorescu, Chandy, and Prabhu 2003; Wind and Mahajan 1997). However, because it addresses the well-understood needs of mainstream customers, the perceived market risk is low. In contrast, a market-based innovation may be technologically straightforward, but it is extremely risky on the market side because the customers do not yet exist (Christensen and Bower 1996). Therefore, companies often are reluctant to invest in either type of innovation. In turn, it is important to understand what drives a firm's willingness to undertake risky activities and to introduce breakthroughs to achieve a sustainable competitive advantage.
To answer this question, we refer to the competitive force perspective and the resource-based view (RBV), two major theories about how competitive advantage is achieved. The former represents an "outside-in" perspective and argues that external market forces, such as demand uncertainty, technological turbulence, and competitive intensity, primarily drive competitive advantage (Porter 1980, 1985). In contrast, the RBV reflects an "inside-out" approach and suggests that a firm's competitive advantage stems from its unique assets and distinctive capabilities (Barney 1991; Wernerfelt 1984). Although the two views diverge, both are influential in the explanation of a firm's competitive advantage. Thus, recent studies have encouraged researchers to consider their complementary nature (e.g., Spanos and Lioukas 2001). Building on the two views, we present a framework that links a firm's strategic orientations (from the RBV) and market forces (from the competitive force perspective), through organizational learning, to breakthrough innovations and firm performance (see Figure 1).
Hypotheses Development
The RBV focuses on resource heterogeneity and immobility as potential sources of competitive advantage (Barney 1991). Firm resources can be classified as assets and capabilities (Day 1994; Hunt and Morgan 1995). Assets are the more tangible resources that the organization has accumulated, such as an economy of scale, reputation, spatial preemption, and brand equity. Capabilities are the glue that brings these assets together and enables a firm to deploy them advantageously, such as the skills underlying the innovativeness and the superior quality of a firm's offerings. Capabilities differ from assets in that they are difficult to quantify monetarily, and they encompass skills that are embedded deeply in organizational routines and practices (Barney 1991; Day 1994).
An important firm capability is its strategic orientation. Strategic orientation reflects the firm's philosophy of how to conduct business through a deeply rooted set of values and beliefs that guides the firm's attempt to achieve superior performance (Gatignon and Xuereb 1997). These values and beliefs define the resources to be used, transcend individual capabilities, and unify the resources and capabilities into a cohesive whole (Day 1994). Such capabilities are intangible and interaction based. They are difficult to trade, imitate, or duplicate, and thus they are the most likely sources of competitive advantage (Day 1994; Hunt and Morgan 1995).
Our study focuses on three important types of strategic orientations: market, technology, and entrepreneurial. Market orientation is of central interest because of the market orientation debate we identified previously. Technology orientation focuses predominantly on new technologies and thus has direct implications for product innovations (Gatignon and Xuereb 1997; Hamel and Prahalad 1994; Tushman and Anderson 1986). Entrepreneurial orientation also deserves consideration because it has long been recognized as the key for initiating innovative activities (Miller 1983; Slater and Narver 1995).
Market orientation. Market orientation places the highest priority on the profitable creation and maintenance of superior customer value (Narver and Slater 1990). It emphasizes the need for the entire organization to acquire, disseminate, and respond to market intelligence from the firm's target buyers and current and potential competitors (Jaworski and Kohli 1993). Some researchers suggest that market orientation is essentially customer orientation (Deshpandè, Farley, and Webster 1993), representing the concept of "customer pull" in a firm's strategic planning and implementation (Day 1994).
By prioritizing customers, a market-oriented firm excels in its ability to seek and use market information to create and deliver superior customer value. Unlike a customer-led firm, which simply listens to its customers, a market-oriented firm commits to understanding both the expressed and the latent needs of its customers (Slater and Narver 1999). Its ability to uncover consumers' latent needs can be enhanced further by the lead-user technique; that is, putting the most advanced technology available into the hands of the "most sophisticated and demanding users" often "leads to the discovery of new solutions to unexpressed needs" (Slater and Narver 1998, p. 1003; see also Von Hippel, Thomke, and Sonnack 1999).
Such insights are beneficial for fostering tech-based innovations, which can greatly improve customer benefits in existing markets and cater to the needs of the most sophisticated customers (Chandy and Tellis 1998). Although investment in technology is substantial and risky, signs from the market are clear and certain. With a strong commitment to serving its customers, a market-oriented firm is willing to direct the resources necessary to fulfill customers' latent needs through developing tech-based innovations (Slater and Narver 1995). Some studies also indicate that many breakthrough innovations are generated from consumers' insights. For example, Von Hippel (1988) shows that lead users contribute to a considerable percentage of breakthroughs-some as high as 70%-85%--in various product classes. Thus, market orientation is more than being customer led, and it can lead to tech-based innovations.
H1a: Market orientation has a positive effect on tech-based innovations.
The primary focus of a market orientation is to create "superior customer value, which is based on knowledge derived from customer and competitor analysis" (Slater and Narver 1995, p. 68). However, such a focus may risk overlooking the potential contributions of other sources, such as firms in different industries (Achrol 1991); threats from new, nontraditional competitors (Stalk, Evans, and Shulman 1992); or opportunities in future markets (Chandy and Tellis 1998), thus lowering the possibility of generating innovations for emerging markets. In addition, intelligence generated from existing customers or even lead users may not provide critical guidelines for introducing products that are desired by new markets with different preferences. As Von Hippel, Thomke, and Sonnack (1999) note, lead users--the most sophisticated and demanding users of current products--can offer insights into existing value systems but not into markets with different values. Im and Workman's (2004) unexpected finding that customer orientation negatively affects new product novelty provides some support for this logic.
Although market-based innovations may be straightforward in terms of technology, they are extremely risky on the demand side because managers can only guess at the size of the new market, the profitability of the new products, or the desirable product attributes (Christensen and Bower 1996; Hamel and Prahalad 1994; Tellis and Golder 2001). However, "a market orientation may not encourage a sufficient willingness to take [such] risks.... This danger is the result of narrowly focusing market intelligence efforts on current customers and competitors, thus ignoring emerging markets and/or competitors" (Slater and Narver 1995, p. 67, emphasis added). As such, a market-oriented firm may risk itself in the "tyranny of the served market": The firm aggressively pursues tech-based innovations that directly address existing customers' unsatisfied needs and that promise the best return, but it is unlikely to invest substantially in market-based innovations that have an unknown future (Christensen 1997; Hamel and Prahalad 1994). Thus:
H1b: Market orientation has a negative effect on market-based innovations.
Technology orientation. Unlike the customer-pull philosophy of market orientation, technology orientation reflects the philosophy of "technological push," which posits that consumers prefer technologically superior products and services (Gatignon and Xuereb 1997; Wind and Mahajan 1997). Accordingly, a technology-oriented firm advocates a commitment to R&D, the acquisition of new technologies, and the application of the latest technology (Gatignon and Xuereb 1997). Although both market and technology orientations promote openness to new ideas, market orientation favors ideas that better satisfy customer needs, whereas technology orientation prefers those that employ state-of-the-art technologies.
Because a technology-oriented firm champions the use of the latest technologies in its new products and heavily devotes its resources to R&D, it excels in technical proficiency and flexibility, which are critical drivers for breakthrough innovations (Ali 1994; Workman 1993). Furthermore, in a technology-oriented firm, creativity and invention are the organizational norms and values that guide its activities and strategies. A technology-oriented firm tolerates and often encourages employees with "crazy ideas" or an instinctive interest in inventing something drastically new. In such a firm, introducing breakthroughs becomes a strategic and cultural priority (Hamel and Prahalad 1994; Hurley and Hult 1998). Because tech-based innovations employ state-of-the-art technology, they should be highly valued by a technology-oriented firm. However, a technology-oriented firm may not value market-based innovations, because such innovations may be too technologically straightforward. Therefore, we hypothesize the following:
H2: Technology orientation has a positive effect on tech-based innovations.
Entrepreneurial orientation. Entrepreneurial orientation reflects a firm's propensity to engage in "the pursuit of new market opportunities and the renewal of existing areas of operation" (Hult and Ketchen 2001, p. 901). It promotes values such as being highly proactive toward market opportunities, tolerant of risk, and receptive to innovations (Lumpkin and Dess 1996; Matsuno, Mentzer, and Özsomer 2002). Accordingly, the ability to initiate change, take risks, and innovate distinguishes entrepreneurial firms (Naman and Slevin 1993).
Entrepreneurial orientation highlights the spirit of creating new business out of ongoing practices and rejuvenating stagnant companies, which is often accomplished through the introduction of breakthrough innovations (Lumpkin and Dess 1996). As Miller (1983, p. 771) notes, an entrepreneurial firm is one that "engages in product market innovations, undertakes somewhat risky ventures, and is the first to come up with 'proactive' innovations." In particular, the emphasis on being proactive toward new opportunities cultivates capacities that enable the firm to create products not only ahead of competitors but also ahead of the recognition of existing customers (Slater and Narver 1995). Often, this proactive quality requires substantial financial and managerial commitment. With its risk-taking nature, an entrepreneurial firm is willing to devote the necessary resources to opportunities that may result in costly failures (Naman and Slevin 1993). In such conditions, both tech-and market-based innovations are likely to occur. Therefore, the essential outcome of entrepreneurial orientation is the firm's entry into new or established markets through breakthroughs (Lumpkin and Dess 1996).
H3: Entrepreneurial orientation has a positive effect on both tech-and market-based innovations.
Organizational learning as a mediating process. Recently, more effort has been strongly called for to uncover the process (in particular, organizational learning) through which strategic orientations affect organizational outcomes (e.g., Matsuno, Mentzer, and Özsomer 2002; Slater and Narver 1995). Organizational learning represents the development of new knowledge or insights that facilitate performance-enhancing organizational changes (Sinkula 1994; Slater and Narver 1995). Dickson (1992) emphasizes the importance of learning in the transferring of information into knowledge, arguing that in dynamic and turbulent markets, the ability to learn more quickly than competitors may be the only source of sustainable competitive advantage. Slater and Narver (1995, p. 63) concur: "[T]he critical challenge for any business is to create the combination of culture and climate that maximizes organizational learning." They further indicate that market-oriented and entrepreneurial cultures, with their focus on market information processing and proactivity toward change, greatly enhance a firm's ability to learn. Similarly, Noble, Sinha, and Kumar (2002) suggest that a technology orientation is an important factor that leads to more knowledge-learning behaviors.
Organizational learning as a process includes information acquisition, information dissemination, shared interpretation, and organizational memory (Sinkula 1994; Slater and Narver 1995). Information, which can be acquired through the market, direct experience, or the experiences of others, is a pivotal aspect of innovation development because firms cannot generate insights for breakthroughs without it (Sinkula, Baker, and Noordewier 1997). Effective information dissemination further increases the value of information. When different organizational units view the information from various angles, they respond with feedback that often provides new insights into the process of new product development (Slater and Narver 1995). However, before an organization can use the information, it must interpret the information to determine its meaning and implications, and it must reach a consensus (Day 1994). At this stage, a relatively high level of disagreement is actually beneficial for generating truly insightful knowledge, but there must still be effective conflict resolution to reach consensus in a timely manner (Slater and Narver 1995). Finally, organizational memory, or the ability to store and access prior lessons, enables a firm to maintain a steady pace of long-term learning (Sinkula 1994). Because of its inherent flexibility, a learning organization can reconfigure its structure and reallocate its resources to foster breakthroughs, including those for emerging markets (Slater and Narver 1995). Therefore, we hypothesize the following:
H4: Organizational learning mediates the relationships between strategic orientations and breakthrough innovations.
The competitive-force perspective argues that competitive advantage lies in a firm's correct positioning in a market (Porter 1985). The sustainability of the competitive advantage that stems from such a position critically depends on the relative influence of the market forces that the firm encounters (Porter 1980). In line with the work of Voss and Voss (2000), we break market forces down into three categories: demand (e.g., demand uncertainty, market growth), competition (e.g., competitive intensity, hostility), and supply (e.g., technological turbulence, supply power) characteristics. Of these, demand uncertainty, technological turbulence, and competitive intensity are the three most fundamental characteristics because they represent the influences of customers, technology, and competition in the market (Li and Calantone 1998). Although research has noted their potential impact on product innovation, formal conceptualizations and empirical validations are scarce (Ali 1994; Gatingon and Xuereb 1997). We attempt to extend the existing research by examining how these market forces drive breakthrough innovations.
Demand uncertainty. Demand uncertainty refers to the instability of consumer preferences and expectations. In a stable market in which consumer preferences remain unchanged, there is no need for firms to modify their products drastically to satisfy customers. As a result, incremental but not breakthrough innovations are likely to occur because introducing breakthrough innovations is risky and requires substantial resources (Ali 1994; Sorescu, Chandy, and Prabhu 2003). However, if consumer preferences are unstable and change quickly, identification of consumers' changing needs becomes increasingly difficult, and incremental innovations are unlikely to satisfy them (Wind and Mahajan 1997). In such a market, companies could turn to breakthrough innovations to provide offerings that precede customer needs and create customer demand by reshaping the way customers behave (Hamel and Prahalad 1994; Porter 1985). Because consumer preferences are difficult to predict, the external guidelines by which firms judge whether a tech-or a market-based innovation is more promising are blurred. Thus, firms value both types of innovations. Therefore, we hypothesize the following:
H5: Demand uncertainty has a positive effect on both tech- and market-based innovations.
Technological turbulence. Technological turbulence refers to the rate of technological advances within an industry. In an industry in which technology is undergoing rapid change, firms must promote more breakthroughs. Fast technological advances significantly shorten the life cycle of existing products, erode the competitive advantage of even well-entrenched firms, and propel other firms to the forefront (Porter 1985; Tushman and Anderson 1986). Firms must enhance their R&D strength and seize the opportunities that new technologies create to advance next-generation products; otherwise, they will be squeezed from the market (Li and Calantone 1998). Furthermore, fast-changing technologies make breakthrough innovations more possible by changing the way the existing value chain works. For example, technological development may "raise or lower scale economies, make interrelationships possible where they were not before, create the opportunity for advantages in timing, and influence nearly any of the other drivers of cost or uniqueness" (Porter 1985, p. 171). In such conditions, firms can take advantage of technological advances to alter components of the existing value chain significantly. As a result, both tech-and market-based innovations are likely to occur.
H6: Technological turbulence has a positive effect on both tech-and market-based innovations.
Competitive intensity. Competitive intensity refers to the degree of competition that a firm faces within its industry. Intense competition is characterized by severe price wars, heavy advertising, diverse product alternatives, and added services (Porter 1980). In such conditions, two options are especially desirable. First, firms can internalize their competitors' strength simply through imitation. Porter (1985) suggests that in a highly competitive market, companies should pay special attention to costs because of the greater pressure that price wars cause. Thus, imitation becomes an obvious choice to reduce the high cost of product innovation (Day and Wensley 1988). Accordingly, firms may mimic their competitors' behavior and copy their technologies, thereby leading to fewer tech-based innovations.
Second, firms can nullify their competitors' strength by identifying a new segment and serving new customers who have a different value system (Porter 1985). Instead of competing head-to-head with their rivals by imitating or introducing more advanced products for the existing market (i.e., tech-based innovations), firms can introduce market-based innovations that target a new, unserved market (Porter 1985). Some of these market-based innovations may have the potential to outperform technological advances in the existing market in the future and thus may succeed in invading the existing market and replacing existing products. In other words, competitive intensity is conducive to market-based innovations. Therefore,
H7: Competitive intensity has (a) a negative effect on tech-based innovations and (b) a positive effect on market-based innovations.
Empirical assessments of the performance impact of breakthrough innovations are limited, partly because innovation is portrayed as a "universally useful and productive end in and of itself" (Drazin and Schoonhoven 1996, p. 1067; Sorescu, Chandy, and Prabhu 2003). In response, we explore the performance outcomes of tech-and market-based innovations.
Unlike incremental innovations, breakthroughs have the potential to create markets, shape consumers' preferences, and change consumers' basic behavior; sometimes the changes are so fundamental that soon after they are implemented, people cannot imagine living any other way (Hamel and Prahalad 1994). Thus, breakthrough innovations can contribute significantly to profitability (Wind and Mahajan 1997). Hamel and Prahalad (1994) also argue that the introduction of breakthrough innovations is the key to survival in turbulent environments. In particular, because tech-based innovations provide greater benefits to a firm's mainstream customers and market-based innovations are embraced by new or emerging markets (Benner and Tushman 2003), both should positively affect performance. Thus:
H8: Both tech-and market-based innovations have a positive effect on performance.
Methodology
As does previous research on innovation (e.g., Gatignon and Xuereb 1997), we test our hypotheses by examining brands at the strategic business unit (SBU) level in consumer product categories. We obtained the sample as follows: First, we acquired a sampling frame of 2260 brands of commonly used consumer durable and nondurable products in 48 categories from the Sino-Monitor International Company's (2000) annual China Marketing & Media Study.( n1) Second, we used a stratified random sampling to select the brands for the survey. We divided the brands in each product category into two groups on the basis of their market share. The first group included the 10 brands with the highest share; the second group consisted of the rest of the brands. We selected at least 3 brands at random from each brand group in each product category, for a total of 150 and 250 brands from the first and second groups, respectively. Third, for each brand in the sample, we called the company to identify a senior manager (e.g., marketing director, general manager, regional brand manager) as the key informant. We further screened the key informant to ensure that he or she possessed well-rounded knowledge about the brand's various functional areas and was committed to cooperating with the research project.( n2) Each respondent reported in reference to a single brand.
Hoskisson and colleagues (2000) suggest that in an emerging economy, collaboration with local researchers is a key means of obtaining reliable and valid information; in addition, face-to-face interviews are desirable because they increase response rates and generate more valid information. For these reasons, we commissioned a national marketing research firm to administer the survey through personal interviews.( n3) All respondents were informed in advance of the confidentiality of their responses, and an enclosed official university letter explained the academic purpose of the project to them. Each respondent received a valuable gift and was promised a summary report of the survey. These efforts were highly effective. We obtained a total of 350 completed surveys, 139 from the 150 brands in the first brand group (92.7%) and 211 from the 250 brands in the second brand group (84.4%). Of the participating brands, 61.1% were domestic brands, and 38.9% were foreign brands. Product categories included appliances (23.4%), beverages (13.4%), snacks (10.6%), cosmetics (10.3%), clothes and shoes (10%), cigarettes and liquors (7.1%), cleaning products (6.9%), automobiles (4%), PCs (3.7%), and others (10.6%).
We chose the Chinese market because, with its prevalence of tech-and market-based innovations, it offers an appropriate context to test our model. On the firm or supply side, companies are introducing more and more new products into the market as product innovation becomes increasingly important for business success (Finance Channel 2003; Wong and Maher 1997). As China moves toward a market economy, foreign firms have rushed into the market, many with more advanced or newer technologies. To survive the competition, local firms are updating their technologies through internal R&D, knowledge transfer in joint ventures, and/or direct imports from developed countries. To sustain their competitive advantage, foreign firms must not only exploit their existing capabilities but also develop new ones specifically for the Chinese market. For example, General Motors has invested more than $1 billion since 1994 in its Volvo project in China, focusing on the design, development, and production of a Chinese version that is "completely adapted to local buyers" (Luo 2002, p. 60). In 2003, Ford also announced a $1 billion investment to develop new Focus models for the Chinese market (BBC News 2003).
On the consumer or demand side, Chinese consumers perceive many new products as breakthrough innovations for two reasons. First, Chinese consumers have limited exposure to and knowledge of Western products (Zhou, Su, and Bao 2002).( n4) Thus, Chinese consumers may perceive many products that are developed for the West as new when the products are initially introduced into the Chinese market. Second, both local and foreign companies that are betting on China's huge market potential are constantly introducing many truly breakthrough innovations into the market (China Daily 2004; Luo 2002).
The various innovative products included in our sample (see Appendix A)( n5) also support the use of the Chinese market for our study. For example, Panasonic introduced a tech-based innovation in the color television category that employs some of the most advanced video technologies, such as movement compensation, high-resolution display tube, and gigahertz picture handling. The innovation offers clearer, more continuous, and smoother pictures than do existing products, attributes that mainstream television buyers value. Similarly, Shinco's air conditioner adopts several leading technologies, including "oxygen bar" technology, a high-efficiency air filter, and a negative ion generator. Such advanced technologies help provide an oxygen-enriched, healthier indoor environment and improved allergen and bacteria control, and mainstream consumers have welcomed these benefits.
The Royal Jelly brand of chocolate is an example of market-based innovation. It employs a different technology to preserve the efficacy of royal jelly when it is added to chocolate. The benefit of royal jelly (building up the human immune system) appeals to health-conscious customers but not to mainstream consumers (mostly young people, who focus on the taste and enjoyment benefits of chocolate). Another example is the Langchao brand of laptop computers, which employs a sturdy design and associated technologies that focus exclusively on protecting the laptop against damage, shock, water, dust, and even electromagnetic radiation. These benefits are targeted at a new segment of consumers who use their laptops in harsh and hazardous conditions.
All the measures were professionally translated with back translation to ensure conceptual equivalence (Hoskisson et al. 2000). The questionnaire items were pretested on a sample of ten senior managers. On the basis of their responses, we revised a small number of questionnaire items to enhance the clarity. Unless specifically indicated, we measured all the items using a seven-point Likert scale (1 = "strongly disagree," 7 = "strongly agree") (see Appendix B).
Strategic orientation. We adopted Narver and Slater's (1990) scale to measure market orientation, and we treated it as a second-order factor with three first-order indicators: customer orientation, competitor orientation, and interfunctional coordination. We adapted the measure of technology orientation from the work of Gatignon and Xuereb (1997) and Hurley and Hult (1998), with items assessing a firm's proactivity in using state-of-the-art technologies in new product development. We developed the measure of entrepreneurial orientation on the basis of Naman and Slevin's (1993) and Hult and Ketchen's (2001) work. The items emphasize a firm's proactivity in preparing for change because China has been experiencing extensive changes during its transition to a market economy.
Organizational learning. Consistent with previous conceptual (e.g., Day 1994; Slater and Narver 1995) and empirical (Sinkula 1994; Sinkula, Baker, and Noordewier 1997) studies, we operationalized organizational learning as a second-order factor that consists of four first-order indicators: information acquisition, dissemination, shared interpretation, and organizational memory. We assessed information acquisition as the extent to which an organization learns from various sources, information dissemination as the degree to which an organization shares learned knowledge (Sinkula, Baker, and Noordewier 1997), shared interpretation as team decision making and conflict resolution (Slater and Narver 1995, p. 65), and organizational memory as the amount of knowledge and experience an organization has in new product development (Moorman and Miner 1997).
Breakthrough innovation. We adapted the measure of tech-based innovation from Gatignon and Xuereb's (1997) work. It captures the degree of technological advances and improved benefits over existing products; this is also consistent with Chandy and Tellis's (2000) measure of radicalness, which has two items that assess the degree of technological advances and benefit improvement. However, our informants (i.e., managers) differ from those in Chandy and Tellis's study (i.e., three expert raters).
We developed the measure of market-based innovation on the basis of Benner and Tushman's (2003) and Christensen and Bower's (1996) conceptualizations. They emphasize that such innovation is characterized not by its technological difficulty but rather by its difficulty in evaluation and the lack of ready acceptance by mainstream customers (see also Adner 2002). Thus, we adapted four items from Eliashberg and Robertson's (1988) work to capture the characteristics of market-based innovation from the customer's viewpoint: ( 1) difficulty in evaluating the product concept, ( 2) higher switching cost, ( 3) extra effort needed to learn the new product, and ( 4) additional time required to understand the product's full benefits.
To examine the validity of our innovation measures across different methods, we collected relevant brand information from secondary sources and asked two trained judges (Chinese consumers) to classify the brands in our sample according to the definitions of innovations (i.e., newness of the technology and the consumer benefit) because consumers are the ultimate judges of innovativeness (Im and Workman 2004). The judges' classification results are consistent with managers' ratings of tech-and market-based innovations. This supports the convergent validity of the measures and suggests that managers' responses can represent customers' perceptions. A summary of the judging exercise and its results appears in Appendix C.
Market force. We measured both demand uncertainty and competitive intensity with a two-item scale that we adapted from Jaworski and Kohli's (1993) work. Also on the basis of Jaworski and Kohli's (1993) work, we developed a scale of technological turbulence specifically for this study to capture the characteristics of the technological environment in China.
Performance. As has been done in previous studies (e.g., Slater and Narver 1994), we included two types of indicators: firm (i.e., SBU) performance and product performance. We measured firm (SBU) performance by asking respondents to assess their firm's sales growth, return on investment, and profit level relative to that of their major competitors (1 = "much worse," 7 = "much better"). We measured product performance by assessing the product quality and value to customers relative to competing products (adapted from Gatignon and Xuereb 1997). We used relative measures because they are not subject to product category-or industry-specific effects.
Construct validity. We refined the measures and assessed their construct validity as follows: First, we ran exploratory factor analyses for each set of focal constructs (i.e., strategic orientations, organizational learning, innovations, market forces, and performance), which resulted in factor solutions as theoretically expected. Second, we ran confirmatory factor analyses for each set of focal constructs. After we dropped some items that possessed either low factor loadings or high cross-loadings, the confirmatory models fit the data satisfactorily (see Appendix B).
Furthermore, we assessed the convergent and discriminant validity of the focal constructs by estimating an 11-factor confirmatory measurement model. All 11 constructs were latent variables. Each questionnaire item loaded only on its latent construct (or first-order factor). We allowed the latent constructs to be correlated, whereas we constrained the measurement items and their error items to be uncorrelated. The model provides a satisfactory fit to the data (Χ²[505] = 1202.90, p < .001; goodness-of-fit index [GFI] = .87; confirmatory fit index [CFI] = .89; incremental fit index [IFI] = .89; and root mean square error of approximation [RMSEA] = .06), indicating the unidimensionality of the measures (Anderson and Gerbing 1988). Furthermore, all factor loadings were highly significant (p < .001), and the composite reliabilities of all constructs exceeded the usual .60 benchmark (Bagozzi and Yi 1988). Thus, the measures demonstrate adequate convergent validity and reliability.
We assessed the discriminant validity of the measures in two ways. First, we ran chi-square difference tests for all the constructs in pairs (55 tests) to determine whether the restricted model (correlation fixed as 1) was significantly worse than the freely estimated model (correlation estimated freely). All chi-square differences were highly significant (e.g., the test for product performance and firm performance: ΔΧ² = 15.24, p < .001), providing evidence of discriminant validity (Anderson and Gerbing 1988). Second, we calculated the shared variance between all possible pairs of constructs to determine whether they were lower than the average variance extracted for the individual constructs. The results show that for each construct, the average variance extracted was much higher than its highest shared variance with other constructs, providing additional support for the discriminant validity (see Appendix B) (Fornell and Larker 1981). Overall, the results show that the measures in our study possess adequate reliability and validity.
Control variables. To account for the effects of extraneous variables, we included firm size, incumbency, product category, and entry barrier as control variables. The importance of firm size and incumbency in innovation research has been well documented (Chandy and Tellis 1998, 2000). We used the logarithm of the number of an SBU's employees as an indicator of firm size. Following Ali's (1994) work, we deemed incumbency as the firm's relative position in the market (i.e., market share) and measured it as a dummy variable (1 = "brands with a higher market share," 0 = "brands with a lower market share"). We included product category as a dummy variable to control for potential variations between "durable" (coded as 1) and " nondurable" (coded as 0) products. Finally, we controlled for entry barrier because of its influence on performance (Jaworski and Kohli 1993; Slater and Narver 1994). We measured it by asking respondents to indicate the degree to which "in the industry where our brand operates, it is difficult to enter the markets far away from our home-base city" (1 = "strongly disagree," 7 = "strongly agree"). We present the basic descriptive statistics and correlations of the measures in Table 1.
To test the hypotheses, we employed structural equation modeling with the maximum likelihood estimation method, using the model illustrated in Figure 1 as the base model. Because of the complexity of our model, we treated the two second-order factors (i.e., market orientation and organizational learning) as two latent factors with summated first-order indicators. Given the measurement validity of the overall market orientation and organizational learning scales, this technique could reduce model complexity and be used for structural model analysis and hypotheses testing (Anderson and Gerbing 1988; Matsuno, Mentzer, and Özsomer 2002). To test the mediating role of organizational learning, we followed Baron and Kenny's (1986) procedures and estimated two models: Model 1 without organizational learning and Model 2 with organizational learning as the intermediate variable (i.e., the model in Figure 1). In Model 2, we also linked market forces to organizational learning to explore whether their relationships are significant (see Slater and Narver 1995). In addition, we included the four control variables in the models and linked them directly to innovation and performance. Both models fit the data adequately, and the results appear in Tables 2 and 3, respectively.
Results
As Table 2 shows, market orientation has a positive effect on tech-based innovation (β = .095, p < .05) and a negative impact on market-based innovation (β = -.175, p < .001), in support of H1a and H1b. Technology orientation is positively associated with tech-based innovation (β = .250, p < .001) but is not related to market-based innovation (β = .070, p > .10), in support of H2. Entrepreneurial orientation positively affects both tech-based (β = .339, p < .001) and marketbased (β = .179, p < .001) innovation, in support of H3.
H4 tests the mediating role of organizational learning. According to Baron and Kenny (1986), when the mediator (organizational learning) enters the model, the contribution of a previously significant independent variable (in Model 1) should drop significantly (in Model 2) for partial mediation and become insignificant for full mediation. Therefore, the results in both Tables 2 and 3 are needed to assess H4. Table 3 shows that all three types of strategic orientation significantly affect organizational learning (all p < .01) and that learning positively affects tech-based innovation (p <.01) but not market-based innovation (p > .10); thus, learning is not a mediator between market orientation and market-based innovation. A comparison of Tables 2 and 3 reveals that organizational learning fully mediates the relationships between market orientation and tech-based innovation and partially mediates the relationships between technology or entrepreneurial orientation and tech-based innovation. Thus, H4 receives mixed support.
As both Tables 2 and 3 show, demand uncertainty positively affects both tech-based (p < .05) and market-based (p < .001) innovations, in support of H5. Technological turbulence is positively related to tech-based innovation (p < .001) but has no relationship with market-based innovation (p > .10); thus, H6 is partially supported. The relationship between competitive intensity and tech-based innovation is negative (β = -.063, -.062) but not significant (p > .10). Furthermore, competitive intensity positively influences market-based innovation (p < .001), in partial support of H7.( n6) In addition, because market forces have no direct impact on learning (see Table 3), learning does not mediate the relationships between market forces and innovations.
Consistent with our predictions in H8, the results show that both tech-and market-based innovations positively affect firm and product performance (Tables 2 and 3). To test which type of innovation has a stronger impact on performance, we conducted a chi-square difference test to compare an unconstrained model that freely estimates all the coefficients with a constrained model in which the coefficients associated with tech-and market-based innovations are fixed as equal for each performance indicator, using Model 2 as the baseline model. The results of model comparisons show that tech-based innovation has a stronger effect than market-based innovation on both firm (Δχ²[ 1] = 6.36, p < .01) and product (ΔΧ²[ 1] = 10.63, p < .001) performance.( n7)
In addition, as Table 3 shows, large SBUs tend to perform better than small SBUs. Incumbents appear to be superior in both firm and product performance. Furthermore, entry barriers negatively affect both performance indicators. We also performed a series of post hoc analyses, and we report the results subsequently.
Similar to our test of H4, we examined the mediating role of breakthrough innovations in the organizational learning-performance relationship. In the model without innovations, organizational learning positively affects both firm (β = .176, p < .001) and product (β = .230, p < .001) performance. After innovations enter the model, the effects of learning on firm (β = .161, p < .01) and product (β = .217, p < .001) performance become weaker. Thus, breakthrough innovations seem to mediate the relationships between learning and performance partially.
We also explored the interaction effects between innovations and market forces on performance, using Ping's (1995) technique. The results show that none of the interaction terms is significant for firm performance and that two are marginally significant for product performance: Tech-based innovation is more effective in improving product performance when demand is highly uncertain (β = .095, p < .10), and market-based innovation is more effective when competition is highly intensive (β = .11, p < .10).
We also tested the interaction effects between strategic orientations and market forces on innovations. The results show that only one of nine interaction terms is significant for tech-based innovation. Technology orientation has a weaker impact on tech-based innovation when technology is more turbulent (β = -.125, p < .05). Three of nine interaction terms are significant for market-based innovation. When demand is highly uncertain, market orientation has a more negative effect (β = -.138, p < .05) and technology orientation has a more positive effect (β = .170, p < .01) on market-based innovation. In addition, entrepreneurial orientation has a more positive impact on market-based innovation when competition is intense (β = .110, p < .05).
Discussion
This article provides several important implications to the breakthrough innovation area. First, this study examines the relationships between strategic orientations and breakthrough innovations, thereby filling a research gap about how firm resources affect breakthroughs (Chandy and Tellis 1998). We find that a market orientation has a positive impact on tech-based innovation but a negative impact on market-based innovation. A technology orientation is positively associated with tech-based innovation but has no effect on market-based innovation. It is possible that a technology-oriented firm has less interest in market-based innovations because the innovations may not necessarily involve state-of-the-art technologies. An entrepreneurial orientation positively affects both tech-and market-based innovations, which is in line with the work of Hamel and Prahalad (1994), who place a high priority on entrepreneurial foresight in competing in the future, and with the work of Tellis and Golder (2001), who emphasize the role of vision in generating breakthroughs. Consistent with the RBV, our findings suggest that strategic orientations are culture-based, firm-specific, complex capabilities that can lead to competitive advantages (Day 1994; Hunt and Morgan 1995).
These findings help resolve the ongoing debate about the effect of market orientation on innovation. Contrary to the long-standing concern that market orientation may impede innovations (e.g., Bennett and Cooper 1979; Frosch 1996; MacDonald 1995; Meredith 2002), our results indicate that a market orientation facilitates tech-based innovations, which address the needs of mainstream customers. These findings support Slater and Narver's (1998, 1999) contention that market orientation is more than just a customer-led concept; rather, it can help identify and fulfill mainstream customers' latent or unmet needs and enable a firm to achieve a competitive advantage and superior performance.
However, our findings also reveal a limitation of market orientation; that is, it hinders market-based innovations, which initially address the needs of new and emerging markets. A market-oriented firm, with a goal of serving its best customers, is less likely to invest sufficiently in pursuing opportunities in emerging markets (Christensen and Bower 1996; Conner 1999). This result helps explain Voss and Voss's (2000) unexpected finding that a customer orientation negatively influences performance in an artistic environment. Taken together, our findings suggest that market orientation is a pivotal resource that affects a firm's strategy and operation, but its potential value should be complemented with other firm capabilities, such as entrepreneurship.
Second, whereas extant studies of strategic orientation have focused mainly on the orientation-performance relationship and left the underlying process largely untapped (see Noble, Sinha, and Kumar 2002), we explore the mediating role of organizational learning. We find that it acts as a partial mediator between strategic orientations and techbased innovation. This suggests that strategic orientations as firm capabilities do not automatically lead to better performance. Instead, they represent deeply rooted values and beliefs that bring about certain behaviors, which in turn affect firm performance.
Third, this article adds to the breakthrough innovation literature by examining the effects of different market forces. We find market forces to be significant contributors to innovations. Demand uncertainty positively affects both tech-and market-based innovations. It seems that when consumer preferences change quickly, firms tend to introduce more creative products to lead rather than follow the market. Technological turbulence leads to more tech-based innovations but has no impact on market-based innovations. This suggests that possessing new technology is not sufficient to develop market-based innovations; a favorable attitude toward change (i.e., entrepreneurial orientation) may be the key. Competitive intensity facilitates more marketbased innovations, signifying that firms tend to explore new markets in intense competitive conditions. In summary, these results are consistent with the competitive force perspective and suggest its complementary nature with the RBV in explaining competitive advantage.
Fourth, our study represents an initial effort to distinguish empirically between tech-and market-based innovations and to assess their differential effects on performance. Both types of innovations are beneficial to performance, but tech-based innovation has a greater impact on performance than does market-based innovation, possibly because the former provides improved benefits for mainstream customers in established markets. These findings echo the call for more research to assess the performance effects of different types of innovations (Drazin and Schoonhoven 1996; Sorescu, Chandy, and Prabhu 2003).
Fifth, this study adds to the innovation literature by testing a model with data from companies in an emerging economy. Most extant innovation research focuses on firms in developed countries (mostly the United States), calling into question the generalizability of the findings to other economies (Drazin and Schoonhoven 1996). In an emerging economy, such as China, the fast-changing environment poses serious challenges to the theoretical development and strategic choices of firms (Hoskission et al. 2000). Our findings indicate that though the RBV and competitive force perspective originate in developed economies, they appear applicable to emerging economies as well, given that the results support most of our hypothesized effects. The results also extend research on emerging economies by showing how firms should strategize during fundamental transitions (Peng 2003).
This study provides several managerial implications for firms to facilitate product innovations. Firms can foster tech-based innovations by following a market, a technology, or an entrepreneurial orientation. Practitioners have recognized the importance of a market orientation in achieving competitive advantage (e.g., slogans such as "putting people first" [British Airways] or "have it your way" [Burger King]). Our results reinforce this belief: Market-oriented firms are able to identify customers' latent needs and satisfy those needs by offering tech-based innovations. However, adopting a market orientation alone hinders the market-based innovations that initially address the needs of new and emerging markets. To resolve this issue, firms should couple a market orientation with entrepreneurial values, which encourages frame-breaking actions, enhances truly innovative abilities, and enables firms to escape the myopia of their served markets.
When a market becomes highly competitive, it is increasingly difficult for firms to differentiate themselves from their competitors (Gatignon and Xuereb 1997). This study suggests that a focus on market-based innovation is a feasible strategy. By targeting new and unserved segments, firms can introduce innovations that seize opportunities in emerging markets.
Some anecdotal experiences suggest that in emerging economies, because of consumers' low consumption power and limited product exposure, an imitation strategy with a low price is the key to business success (Kotler 2002). Our findings suggest that an innovation strategy is also a viable option. Although consumers in emerging economies may have limited exposure to new technologies, they are eager to learn and try new and innovative products that offer genuine benefits over existing products. Thus, introducing creative products to shape rather than respond to consumer preferences may enable a firm to become the dominant player in the market, as demonstrated by the success of innovative firms, such as Haier and Oriental Communication in China (Finance Channel 2003).
As an initial effort to address a complicated phenomenon, this study is subject to several limitations. Insights provided on the breakthrough innovation-performance relationship are limited by the cross-sectional nature of this study. As Chandy and Tellis (1998) note, it may take a long time for breakthrough innovation to demonstrate its effects on performance fully. A longitudinal study is warranted to examine this issue further.
In addition, this study focuses mainly on the link between strategic orientations and breakthrough innovations. Additional research should expand our model by considering other important firm resources and capabilities, such as physical assets and learning orientation. Furthermore, this study explores only the mediating role of organizational learning. However, strategic orientation as culture-level values and norms and organizational learning as a learning process do not automatically lead to superior performance. Instead, they accomplish this by guiding actions based on learned information and knowledge. Therefore, further research should identify the underlying action components to understand how strategic orientation works. In addition, because this is the first study to differentiate between tech-and market-based innovations, the measures need further development. We encourage further research to develop a more systematic measure of different types of breakthrough innovations.
Moreover, our empirical findings are based on data from China. Although China shares many characteristics with other emerging economies and emerging industries in developed economies in terms of technology development, consumer behavior, and market conditions (Hoskisson et al. 2000; Peng 2003), it also possesses some idiosyncrasies. This may limit the generalizability of our findings. Therefore, further research should corroborate our strategic orientation-breakthrough innovation study in other developing and developed markets. In addition, our research context is consumer products. The sources of breakthrough innovations may be different for industrial products or in service markets. Therefore, additional research using samples from industrial or service markets is needed to ascertain the generalizability of our findings.
The study was supported by research grants from the Research Grant Council, Hong Kong SAR Government, and the Chinese Management Centre, The University of Hong Kong. The authors thank the anonymous JM reviewers for their insightful and constructive comments. They also thank Kimmy Chan and Xiaoyun Chen for their help in data collection.
( n1) One of the largest surveys of its type conducted in China, the China Marketing & Media Study sample consists of 50,000 residents aged 15-64 years. The sample includes residents of 20 major cities in China: Beijing, Shanghai, Guangzhou, Chengdu, Tianjin, Jinan, Zhengzhou, Nanjing, Xian, Shengyang, Haerbin, Kunming, Fuzhou, Xiamen, Chongqing, Shenzhen, Qingdao, Hangzhou, Wuhan, and Dalian. Respondents are sampled in urban and suburban areas of the 20 cities through a stratified random sampling method according to population density.
( n2) The screening questions included whether the respondent was responsible for formulating the brand's marketing and sales strategy plan, implementing the brand's marketing plan, managing the sales of the brand, and planning the brand's image.
( n3) The firm's headquarters are in Beijing, and it has branches and affiliates across China. It has long-standing multinational clients, such as AT&T and Procter & Gamble, that have awarded the firm quality-service certificates for its marketing research services.
( n4) According to a survey that the Horizon Group (1998) conducted of 5764 consumers in 11 major cities in China, consumers' knowledge and understanding of Western products is limited; they scored only 2 on a scale for which 4 was the highest possible score.
( n5) Because of length constraints, Appendix A presents only a few examples of the tech-and market-based innovations in our sample. A table with more examples is available on request.
( n6) To explore the interaction effects of technology turbulence and competitive intensity on innovations, we ran another model in which we added an interaction term into Model 2, using Ping's (1995) technique. The results show that the interaction term has no impact on either tech-based (β = .010, p = .825) or market-based (β = .045, p = .334) innovations.
( n7) We also tested the interaction effects of tech-and market-based innovations on performance. The results show that the interaction term is not related to either firm (β = -.003, p = .946) or product (β = .021, p = .737) performance.
( n8) Details of the judging instructions and coding schemes are available from the authors on request.
( n9) We calculated and compared several widely used interrater reliability measures, including Cohen's kappa (Kn), Perreault and Leigh's (1989) index of reliability, intraclass correlations, and Krippendorff's (2004)
( n10) Standardized ratings of tech-based and market-based innovations are reported because the means and standard deviations of the two ratings in the overall sample are quite different. Means (standard deviations) are 5.20 (1.13) and 3.05 (1.24) for tech-and market-based innovation, respectively.
Legend for Chart:
A - Construct
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
M - 12
N - 13
O - 14
P - 15
A B C D
E F G H
I J K L
M N O P
1. Firm (SBU) performance 1.00
2. Product performance .51(**) 1.00
3. Tech-based innovation .31(**) .31(**) 1.00
4. Market-based innovation .12(*) .11(*) .15(**)
1.00
5. Market orientation .07 .20(**) .31(**)
-.11(*) 1.00
6. Technology orientation .10(*) .21(**) .43(**)
.08 .50(**) 1.00
7. Entrepreneurial orientation .44(**) .38(**) .45(**)
.14(**) .26(**) .28(**) 1.00
8. Organizational learning .31(**) .33(**) .37(**)
.02 .44(**) .39(**) .46(**)
1.00
9. Demand uncertainty -.08 -.05 .10(*)
.40(**) -.10 .04 .07
-.01 1.00
10. Technology turbulence .02 .07 .30(**)
.12(*) .19(**) .32(**) .07
.18(**) .13(*) 1.00
11. Competitive intensity -.30(**) -.19(**) -.08
.31(**) -.06 .00 -.19(**)
-.10 .48(**) .03 1.00
12. Firm size .25(**) .14(*) .07
-.02 -.01 -.01 .14(**)
.11(*) -.18(*) .04 -.16(**)
1.00
13. Incumbency .26(**) .21(**) .06
.06 .03 .09 .20(**)
.07 -.12(*) -.05 -.12(*)
.36(**) 1.00
14. Product category .02 .12(*) .11(*)
.11(*) .06 .07 .08
.02 .05 .14(**) -.02
.23(**) .05 1.00
15. Entry barrier -.22(**) -.16(**) -.03
.11(*) -.03 -.04 -.20(**)
-.08 .23(**) .12(*) .22(**)
-.12(*) -.20(**) -.16(**) 1.00
Mean 4.25 4.93 5.20
3.05 5.91 5.15 5.06
5.43 3.71 5.55 4.23
4.80 .38 .35 4.50
Standard deviation 1.35 .97 1.13
1.24 .64 .98 1.02
.78 1.40 .92 1.46
1.64 .49 .48 1.64
(*) p < .05 (two-tailed).
(**) p < .01. Legend for Chart:
A - Endogenous Variables
B - Tech-Based Innovation
C - Market-Based Innovation
D - Firm Performance
E - Product Performance
A
B C
D E
Independent Variables
Market orientation
.095(*) (1.859) -.175(***) (-3.169)
-- --
Technology orientation
.250(***) (4.717) .070 (1.229)
-- --
Entrepreneurial orientation
.339(***) (6.909) .179(***) (3.380)
-- --
Demand uncertainty
.082(*) (1.753) .240(***) (4.205)
-- --
Technological turbulence
.163(***) (3.470) .065 (1.287)
-- --
Competitive intensity
-.063 (-1.221) .216(***) (3.906)
-- --
Tech-based innovation
-- --
.261(***) (4.900) .351(***) (5.256)
Market-based innovation
-- --
.082(*) (1.731) .088(*) (1.816)
Controls
Firm size
.033 (.686) -.022 (-.417)
.196(***) (3.476) .041 (.607)
Incumbency
-.031 (-.649) .096 (1.860)
.142(**) (2.582) .195(**) (2.838)
Product category
.027 (.586) .094 (1.904)
-.101 (-1.916) -.065 (-1.010)
Entry barrier
.023 (.479) .065 (1.286)
-.185(***) (-3.456) -.149(*) (-2.290)
R²
.350 .245
.210 .240
Goodness-of-fit: χ²(485) = 1083.01, p < .001;
GFI = .87; CFI = .89; IFI = .89; RMSEA = .059
(*) p < .05.
(**) p < .01.
(***) p < .001.
Notes: t-tests are one-tailed for hypothesized effects and
two-tailed for controls. Legend for Chart:
A - Endogenous Variables
B - Organizational Learning
C - Tech-Based Innovation
D - Market-Based Innovation
E - Firm Performance
F - Product Performance
A B
C D
E F
Independent Variables
Market orientation .272(***) (5.333)
.052 (.989) -.177(***) (-3.084)
-- --
Technology orientation .130(**) (2.470)
.238(***) (4.473) .069 (1.200)
-- --
Entrepreneurial orientation .352(***) (7.435)
.310(***) (5.917) .176(***) (3.109)
-- --
Organizational learning --
.134(**) (2.521) .007 (.127)
-- --
Demand uncertainty -.024 (-.469)
.083(*) (1.780) .240(***) (4.206)
-- --
Technological turbulence .071 (1.534)
.158(***) (3.360) .065 (1.274)
-- --
Competitive intensity -.003 (-.054)
-.062 (-1.219) .216(***) (3.907)
-- --
Tech-based innovation --
-- --
.260(***) (4.897) .351(***) (5.254)
Market-based innovation --
-- --
.082(*) (1.731) .088(*) (1.816)
Controls
Firm size --
.025 (.509) -.023 (-.432)
.196(***) (3.477) .041 (.607)
Incumbency --
-.024 (-.510) .096 (1.872)
.142(**) (2.581) .195(**) (2.838)
Product category --
.032 (.708) .094 (1.914)
-.101 (-1.915) -.065 (-1.010)
Entry barrier --
.021 (.448) .065 (1.283)
-.184(***) (-3.456) -.149(*) (-2.290)
R² .347
.354 .245
.209 .240
Goodness-of-fit: χ²(622) = 1422.28, p < .001;
GFI = .86; CFI = .88; IFI = .88; RMSEA = .060
(*) p < .05.
(**) p < .01.
(***) p < .001.
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Tech-Based Innovations
Air conditioning: Shinco
Used the world's leading technologies, such as "oxygen bar" technology (increases indoor oxygen content from 21% to 30% by forcing outdoor air through an oxygen-enriching membrane), high-efficiency air filter, negative ion generator, and auto opening/closing of air-intake door. The oxygen-enriching function provides a healthier indoor environment, cleaner and more refreshing indoor air, and improved allergen and bacteria control.
Bottled water: Robust
Used the world's most advanced high-technology reverse osmosis process (every drop of water is passed through 27 rigorous stages) to produce high-quality, pure water that is easily absorbed by body cells.
Color television: Panasonic
Applied the world's most advanced "movement compensation" technology to produce smooth pictures of quick movement and adopted high-resolution display tube and gigahertz picture-handling technology for displaying clearer, continuous, and smoother-changing pictures.
Mobile telephone: Bird
Cooperated with French companies in technology exchange. Adopted advanced technology from France. Applied self-developed and patented high-end, intelligent mobile telephone technology and introduced the preset "information wizard" function for extensive access to information about sports, news, weather, financial, and stock markets.
Refrigerator: Aucma
Used advanced technology and developed super coolant (R134a) from own R&D center. Provides continuous control of odor-causing bacteria and other potential contaminants; intelligent, computerized temperature control; and cool-air distribution/circulation across compartments for reducing energy consumption.
Washing machine: Sanyo
Adopted the most advanced product and manufacturing technology from Japan, such as artificial intelligence, fuzzy logic control, three-dimensional jet-spray design, and so forth. Products provide automatic control of detergent, water level, and washing time based on weight, materials, and other attributes of clothes that are detected by various sensors, which improves cleaning, avoids twisting clothes, and reduces wear and tear, time, energy, and water requirements.
Market-Based Innovations
Battery: Firebird
Imported new battery-manufacturing technology. Self-developed and applied latest nanotechnology in battery making. This product emphasizes a unique benefit, namely, an extra antileaking feature that differentiates the product from others as being environmentally safe. Consumers believe this benefit appeals only to those who are environmentally aware; most customers would consider it unimportant.
Beer: Baisha
Developed the latest automatic beer production and packing technology. Uniquely produced beer without heat treatment to preserve nutrients and freshness. This product emphasizes its different feature: a 24-hour expiration and freshness guarantee. Consumers believe it is difficult to control the freshness when the beer expires in one day. Some are concerned about the inconvenience of not being able to keep a stock at home.
Chocolate: Royal Jelly
Manufactured using latest foreign-imported chocolatemaking technology and uniquely adds royal jelly to chocolate. Provides the significant benefit of building up human immune system; differentiated from other brands of chocolate by positioning itself as a healthy chocolate. Consumers consider chocolate and royal jelly two incompatible benefit-positioning concepts (enjoyment versus healthy). Some consumers are concerned about the taste of chocolate when it is mixed with royal jelly.
Grape wine: Yeluan
Used new wine-making technology and production methods. Produces a completely and uniquely different grape wine by adding traditional herbal medicine, Wu Wei Zi, which helps improve heart and lung functions. Most consumers will not focus on whether herbal medicine is added. Some are concerned about the bitter taste of Wu Wei Zi.
Laptop computer: Langchao
Developed new technology and employed a sturdy design that focuses exclusively on stronger protection against damage, shock, water, dust, electromagnetic radiation, and so forth. Consumers believe that the claimed benefits (ultraprotection) will appeal only to specific segments. The extra weight would be considered a disadvantage.
Tea drink: Topsun
Used new foreign production technology and set up own R&D center to develop a cholesterol-controlling tea that helps reduce cholesterol level in the body and encourages weight loss (positioned as a weight-loss product as opposed to a beverage). Consumers find the benefits difficult to believe and to evaluate. Some raise concerns about side effects.
Notes: At the micro level, product innovation is viewed as "new to the firm or new to the firm's customers" (Garcia and Calantone 2002, p. 119). Therefore, the examination and interpretation of tech-and market-based innovations, particularly in terms of the "newness" of technology and consumer benefits, should take place from the perspectives of firms and consumers in China because it is still a developing economy.
Legend for Chart:
C - Standardized Factor Loading
A B C
Strategic Orientation: χ²(203) = 521.30,
p < .001; GFI = .90; CFI = .91; IFI = .91;
RMSEA = .07
Market orientation: second-order factor,
CR = .82, AVE = .60, HSV = .28
Customer orientation: first-order .844
factor, CR = .73
• Customer commitment .502
• Create customer value .396
• Understand customer needs .539
• Customer satisfaction objectives .562
• Measure customer satisfaction .667
• After-sales service .656
Competitor orientation: first-order factor, .640
CR = .70
• Salespeople share competitor information .664
• Respond rapidly to competitors' actions .671
• Top managers discuss competitors' .654
strategies
• Target opportunities for competitor (*)
advantage(a)
Interfunctional coordination: .820
first-order factor, CR = .87
• Interfunctional customer calls .680
• Information shared among functions .813
• Functional integration in strategy .812
• All functions contribute to customer value .768
• Share resources with other business units .704
Technology orientation: CR = .73, AVE = .41,
HSV = .28
• We use sophisticated technologies .463
in our new product development.
• Our new products always use .552
state-of-the-art technology.
• Technological innovation based .784
on research results is readily
accepted in our organization.
• Technological innovation is readily .711
accepted in our program/project management.
Entrepreneurial orientation: CR = .81,
AVE = .52, HSV = .26
• We actively build our capacity .615
to react effectively to market changes.
• We ensure that our advantages can .706
withstand changes in the industry.
• We actively prepare for the changes .857
brought by China's entry to the World Trade
Organization.
• We are ready to face the challenges .695
brought by the e-commerce trend.
Market Force: χ²(18) = 70.45, p <.001;
GFI = .95; CFI = .90; IFI = .90; RMSEA = .09
Demand uncertainty: CR = .74, AVE = .60, HSV = .29
• It is difficult to understand .594
consumers' expectations of a brand.
• Consumers always look for novelty; .922
they are never loyal to a single brand.
Technological turbulence: CR = .73, AVE = .41,
HSV = .12
Over the last 5 years, we see that
in the industry where our brand operates,
• The diversity in production .472
technology has dramatically increased.
• The leading foreign firms have .547
introduced their state-of-the-art
products into China at the
same time as their home market.
With China entering the World Trade Organization,
the impacts on the industry where our brand
operates will include
• More new product ideas. .679
• More state-of-the-art manufacturing .809
methods.
Competitive intensity: CR = .61, AVE = .44,
HSV = .29
• There are too many similar products .710
in the market; it is very difficult
to differentiate our brand.
• This market is too competitive; .611
price wars often occur.
Learning and Innovation: χ²(83) = 207.12,
p < .001; GFI = .93; CFI = .95; IFI = .95;
RMSEA = .07
Tech-based innovation: CR = .74, AVE = .60,
HSV = .25
• Our product is highly innovative, .755
replacing an inferior alternative.
• Our product incorporates a radically .764
new technological knowledge.
• Overall, our product is similar (*)
to our main competitors' products (reversed item).
• The application of our product (*)
is totally different from that of
our main competitors' products.
Market-based innovation: CR = .83,
AVE = .56, HSV = .26
• Our product concept is difficult .791
for mainstream customers to evaluate or understand.
• Our product involves high switching .862
costs for mainstream customers.
• The use of our product requires .759
a major learning effort by mainstream customers.
• It takes a long time for mainstream .550
customers to understand our product's
full benefits.
Organizational learning: second-order
factor. CR = .81, AVE = .53, HSV = .26
Information acquisition: first-order .825
factor, CR = .79
• We often visit other companies 645
to improve our knowledge of production,
marketing, and management.
• We often attend all sorts of .833
expert presentations to improve
our knowledge of production,
marketing, and management.
• We often attend training programs .750
to improve our knowledge of
production, marketing, and management.
Information dissemination: first-order .927
factor, CR = .82
• We often exchange ideas on learned .802
knowledge to improve our knowledge of production,
marketing, and management.
• Our employees often share the .855
learned knowledge with top managers.
Shared interpretation: first-order .615
factor, CR = .88
• We encourage teamwork, team .850
decision making, and internal communication.
• We are good at resolving conflicts .919
among the staff.
Organizational memory: first-order .439
factor, CR = .83
• We have extensive knowledge of .797
and experience in developing new products.
• We have extensive experience in .878
formulating new production processes.
Performance: χ²(8) = 53.93, p < .001;
GFI = .95; CFI = .96; IFI = .96; RMSEA = .09
Firm (SBU) performance: CR = .88,
AVE = .64, HSV = .33
• Sales growth in the past two years .921
• Return on investment .697
• Profit level .620
• Market share .946
Product performance: CR = .67, AVE = .47,
HSV = .33
• Product quality .682
• Value to customer (quality/price) .692
Overall Model Fit: χ²(505) = 1202.90,
p < .001; GFI = .87; CFI = .89; IFI = .89; RMSEA = .06
(*) We deleted these items from further analysis because of low
factor loadings or high cross-loadings.
Notes: CR = composite reliability, AVE = average variance
extracted, and HSV = highest shared variance with other Collecting Brand Information
We conducted an extensive search of companies' Web sites, public announcements, and news clips in the media for information relevant to the innovation characteristics and technology/consumer benefits dimensions for each brand. We also conducted two focus groups (of 18 Chinese consumers) to obtain additional brand information. We obtained relevant information on 314 of the 350 brands (89.7%) and used the information in the brand-classification exercise.
Classification Procedure
We adopted the three-step procedure for "expert judging" recommended in content-analysis literature (Kassarjian 1977; Kolbe and Burnett 1991; Krippendorff 2004). In the first step, one author and a research assistant developed and pretested the judging instructions and coding schemes (i.e., number of classification categories and the corresponding definitions) in an iterative process.( n8) In the second step, we recruited two graduate students from China who were majoring in nonbusiness fields (geography and anthropology) and trained them as judges of the newness of the technology and the consumer benefit for each brand. The judges coded two training samples (each with 35 brands) until the interrater reliability for various indicators reached the .80 cutoff point (Kassarjian 1977; Krippendorff 2004).( n9) In the final step, the two judges independently and formally coded the remaining 244 brands. A very high level of interrater reliability (.90-.99) was reached. We deleted five cases from the final classification result because the two judges could not reach an agreement, resulting in a useful sample of 239 brands.
Results
Table C1 summarizes the results of the classification exercise. Inside each cell are the managerial (standardized mean) ratings for tech-and market-based innovations.( n10) The managerial rating for tech-based innovation is highest in Group 1, moderate in Group 2, and lowest in Group 3 (all are significantly different from each other at the .05 level). The managerial rating for market-based innovation is highest in Group 2 (significantly higher than in the other two groups), and the ratings in Groups 1 and 3 are not significantly different from each other. These results provide support for the convergent validity of the classification of tech- and market-based innovations across the two methods (i.e., managerial ratings obtained from the survey and classification by trained judges).
Legend for Chart:
A - Newness of Technology
B - Newness of Consumer Benefits Low to Medium Levels
C - Newness of Consumer Benefits High to Very High Levels
A B
C
High to very high levels Group 1 (N = 142)
Tech-based innovation = .49(a)
Market-based innovation = -.38(d)
Group 2 (N = 35)
Tech-based innovation = -.21(b)
Market-based innovation = 1.43(e)
Low to medium levels Group 3 (N = 62)
Tech-based innovation = -1.22(c)
Market-based innovation = -.44(d)
Group 4 (N = 0)
Notes: Standardized means of managerial ratings with the same
superscript are not significantly different from each other at
α = .05.~~~~~~~~
By Kevin Zheng Zhou; Chi Kin (Bennett) Yim and David K. Tse
Kevin Zheng Zhou is Assistant Professor of Marketing, School of Business, The University of Hong Kong.
Chi Kin (Bennett) Yim is Associate Professor of Marketing and Associate Director of Chinese Management Centre, School of Business, The University of Hong Kong.
David K. Tse is Chair Professor of International Marketing and Director of Chinese Management Centre, School of Business, The University of Hong Kong.
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Record: 161- The Formation of Buyer—Supplier Relationships: Detailed Contract Drafting and Close Partner Selection. By: Wuyts, Stefan; Geyskens, Inge. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p103-117. 15p. 3 Charts, 1 Graph. DOI: 10.1509/jmkg.2005.69.4.103.
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The Formation of Buyer—Supplier
Relationships: Detailed Contract Drafting and
Close Partner Selection
Firms face two strategic decisions when engaging in a new purchase transaction: the decision whether to draft a detailed contract and the decision whether to select a partner with which they share a close tie. The authors study how organizational culture affects these decisions and the effectiveness of these decisions in curtailing the partner's opportunistic behavior. The results suggest that organizational culture exerts an important but different influence on both decisions. Selecting a close partner shows a marked ability to hedge against partner opportunism, but beyond a certain point, it encourages the opportunism it is designed to discourage. Contracting becomes effective only when a nonclose partner is selected and when the focal relationship is embedded in a network of close mutual contacts.
In an era when product life cycles are short and markets become more specialized, the disadvantages of vertical integration become more acute. In response, the past 20 years have witnessed an unprecedented growth worldwide in the number of hybrid governance modes--structures that are less arm's length than the typical market relationship but less tight than the relationships between parts of the same firm (e.g., franchise arrangements, strategic alliances, joint ventures). Effectively structuring hybrid governance modes to inhibit the partner's opportunistic behavior represents a critical challenge for any firm. In this respect, firms face two strategic choices (Ryall and Sampson 2003; Williamson 1991). First, they must choose the level of detail of the contractual agreement, which can range from a simple contract that conveys only the broad lines of exchange to an explicit contract that specifies as precisely as possible both parties' responsibilities and remedies for foreseeable contingencies. Second, they must decide on the extent to which they wish to select a partner with which they have a close prior tie. Ties may vary from the atomistic and socially detached relationship to the tie with a history of close collaboration.( n1)
The research to date has generated important insights into firms' choices among basic governance forms, such as hybrid governance modes versus vertical integration or market contracting (e.g., Klein, Frazier, and Roth 1990), and into the extent to which firms draft detailed contracts during the formation phase of the relationship and rely on relational norms during the implementation phase of the relationship (e.g., Lusch and Brown 1996). Yet notable gaps remain. First, little is known about the relative effectiveness of the strategic decisions that firms make in structuring hybrid governance modes to hedge against the partner's opportunistic behavior from either an empirical or a theoretical point of view. Empirically, relational governance measures, as opposed to contractual explicitness measures, often confound the governance decision per se (i.e., choosing to interact with a close partner in the hope that it promotes the reliance on relational norms) with the quality of that decision (i.e., reliance on relational norms to govern the relationship). Theoretically, some researchers argue that detailed contract drafting offers a way to protect against the partner's opportunism through the threat of legal enforcement (Joskow 1987), whereas others argue that detailed contracts are seldom used in practice because they are costly to draft and enforce (Macaulay 1963). In a similar vein, some authors posit that close partner selection is a more effective and less costly alternative to detailed contract drafting because it is a self-enforcing safeguard. Others have made a case that close relationships have a "dark side" in that over time, ongoing business exchanges develop characteristics that serve to destabilize the relationship from within (e.g., Dyer and Singh 1998). These conflicting views indicate a need for more research on the effectiveness of detailed contract drafting and close partner selection in curtailing the partner's opportunism.
Second, there are unanswered questions about the influence patterns that exist between these two strategic choices. Literature on relational governance suggests that prior close relationships substitute for costly, detailed contracts (e.g., Dyer and Singh 1998). In contrast, others suggest that formal contracts are complementary to close relationships (e.g., Poppo and Zenger 2002).
Third, the extant literature has studied these choices in isolation of the network in which the exchange is embedded (for a recent exception with respect to close partner selection, see Wuyts et al. 2004). This neglect is notable because "individual relationships are embedded in the context of other relationships that could have governance implications" (Heide 1994, p. 81).
Finally, on the antecedent side, researchers primarily associate these two strategic choices with transaction attributes (Williamson 1991). They rely on transaction cost theory, which is a theory of the firm; it deals with the best generic governance decisions for organizing a particular transaction. However, this does not fully explain how any particular firm chooses to organize that particular activity (Madhok 2002). Because it has been shown that organizational culture influences the terms and types of trade (Kreps 1990) and because culture is often cited as a cause for the failure of interorganizational relationships (Segil 1998), we study how firms with different organizational cultures choose different ways to protect against opportunism. Although the impact of organizational culture has been acknowledged in marketing (e.g., Deshpandé, Farley, and Webster 1993), it has largely been overlooked in the governance literature.
We aim to address the preceding gaps, with a focus on the formation phase of buyer--supplier relationships, by ( 1) studying the impact of a firm's organizational culture on detailed contract drafting and close partner selection, ( 2) examining the effects of detailed contract drafting and close partner selection on partner opportunism, ( 3) testing whether detailed contract drafting and close partner selection operate as complements or substitutes, and ( 4) exploring the moderating mechanism of network embeddedness on the effectiveness of detailed contract drafting and close partner selection in reducing opportunism. From a managerial standpoint, we provide empirical evidence of the (in)effectiveness of detailed contract drafting and close partner selection as hedges against opportunism. The insight into how organizational culture steers managers toward one of these two strategic choices can help them make better choices that reduce failure.
Conceptual Framework and Hypotheses
This research takes the perspective of a buyer firm in a vertical exchange relationship, which reports on its own position and choices and on the subsequent opportunistic behavior of its exchange partner. When erecting hybrid governance structures, buyer firms try to solve potential governance problems proactively by drafting detailed contracts and/or selecting a partner with which they have a history of close collaboration (Ryall and Sampson 2003).
Detailed contract drafting. A first important strategic choice that buyers must make at the outset of a new purchase agreement pertains to the extent of detailed contract drafting. Explicit contracts detail roles and responsibilities to be performed, determine outcomes to be delivered, and specify adaptive processes for resolving unforeseeable outcomes (Lusch and Brown 1996; Poppo and Zenger 2002). Sufficiently elaborate and carefully constructed contracts serve as a form of quasi-integration and establish a vertical interfirm authority relation that can subsequently guide behavior (Stinchcombe 1985).
Close partner selection. A second important strategic choice for buyers when selecting a supplier for a new purchase agreement is the closeness of prior interaction. Although buyers may share close prior ties with some suppliers, they may share only loose or no prior ties with other suppliers. Closeness refers to the intensity and valence of prior interaction (Marsden and Campbell 1984; Mathews et al. 1998), and it varies from distant arm's-length delivery to intense cooperation that resembles real teamwork. By selecting a partner with which a firm has a history of close collaboration, the focal party hopes to create a "shadow of the past," which promotes the emergence of relational governance based on relational norms (Macneil 1980). These norms, or "principles of right action" (Macneil 1980, p. 38), are adhered to in view of continuation of the relationship, and they serve to regulate proper behavior (Stinchcombe 1985).
Organizational culture is defined as the values that senior managers in a firm share regarding appropriate business practices in the supply chain (cf. Nooteboom, Berger, and Noorderhaven 1997).( n2) Different perspectives exist as to the relationship between organizational culture and strategy (for a detailed overview, see Koen 2005). One approach suggests that the two are essentially synonymous because they are both deeply ingrained patterns of management behavior (e.g., Greiner 1983). A second approach views organizational culture as an important driving force behind all movements in the organization. Mintzberg (1979), among others, advocates this approach. The first approach is less popular than the second because it "would be true … only for emergent strategies based on the default of management" (Hofstede 2001, p. 420). In addition, the second approach has the benefit of sensitizing managers to the ways that organizational culture affects their decision making, which may help them make better choices. We take the second approach and study the effect of organizational culture on two strategic decisions: detailed contract drafting and close partner selection.
We investigate three elements of organizational culture, conceptualized as organizational values: uncertainty avoidance, collectivism, and power distance. These dimensions are firm-level equivalents of Hofstede's (2001) nation-level dimensions. According to Hofstede (1980, p. 5), the culture of a collectivity is, in general terms, characterized by values that reflect a "broad tendency to prefer certain states of affairs over others." In line with this characterization, which is not restricted to nations but pertains to any kind of social system, collectivism, uncertainty avoidance, and power distance have been applied to other social systems as well, including individual firms (for uncertainty avoidance, see, e.g., Sitkin and Pablo 1992; for collectivism and power distance, see, e.g., Bates et al. 1995).
Uncertainty avoidance. Firms vary in their tolerance of uncertainty and ambiguity. We define a firm's uncertainty avoidance as the extent to which the firm feels threatened by and tries to avoid uncertain, ambiguous, or undefined situations in the supply chain (Hofstede 2001). Firms that are high in uncertainty avoidance need predictability and uniformity (i.e., standardization; see Erramilli 1996). They have a strong preference for codification and the establishment of formal rules (Steensma et al. 2000). Conversely, firms that are low in uncertainty avoidance tend to accept uncertainty without much discomfort, take risks easily, and show greater tolerance of various opinions and behaviors. Accordingly, they do not welcome standardization but rather value flexibility (Erramilli 1996; Hofstede 2001).
Detailed contracts that use standard contractual terms and that are legally enforceable can mitigate the anxiety of decision makers and bring clarity (Pan and Tse 2000; Steensma et al. 2000). Because high-uncertainty-avoidance firms value formal rules and explicit guidelines over flexibility, they are likely to write more detailed contracts. In contrast, low-uncertainty-avoidance firms are less prone to draft detailed contracts because they experience them as limiting the opportunities that may be brought to them by new and uncertain situations. Thus:
H1: Uncertainty avoidance increases detailed contract drafting.
The collaborative character of a close tie can alleviate uncertainty because it provides the flexibility to cope with inevitable environmental uncertainties that arise in an exchange (Poppo and Zenger 2002). Moreover, the intense communication that characterizes a close tie reduces behavioral uncertainty (Noordewier, John, and Nevin 1990). Because high-uncertainty-avoidance firms try to make events more predictable and typically consider what is different as dangerous (Hofstede 2001), such firms may be more prone to select a close partner. Thus:
H2: Uncertainty avoidance increases close partner selection.
Individualism-collectivism. Individualism-collectivism refers to the extent to which a firm believes that it should focus on personal goals rather than collective goals when working with partners. Whereas collectivists value the social fabric and group norms, individualists desire independence from other firms (Steensma et al. 2000).
Collectivist firms do not require detailed contracts for governing exchange. Because the norm is for group goals to take priority over personal goals, there is little need for contracts as a mechanism for conflict resolution (Wagner 1995). Other scholars argue more strongly that collectivists not only do not need contracts but also do not like contracts; they prefer short, imprecise contracts that commit parties to work together to resolve difficulties as they emerge (Sako and Helper 1998). For collectivists, formal contractual arrangements may indicate conflict between partners, which is inconsistent with their prioritizing of group norms (Steensma et al. 2000). In summary, collectivist firms are likely to deemphasize contractual detail. In contrast, because the norm in individualist firms is for personal goals to take priority over group goals (Wagner 1995), individualist firms are more likely to expect that their potential partners also have their own objectives and goals in the forefront. Therefore, detailed contracts, which ensure that the partner's individual goals and aspirations do not interfere with the firm's own individual goals, have a greater appeal to more individualist firms (Steensma et al. 2000). Following a rationale similar to ours, albeit in a different context, Bates and colleagues (1995) show that compared with collectivists, individualists believe in more detailed formal systems that explicitly define, control, and evaluate individual contributions. Thus:
H3: Collectivism decreases detailed contract drafting.
Scholars have found that collectivists are more cooperative in general; they enjoy working together, care about their business partners, and perform better in close cooperation with others (Hofstede 2001). Wagner (1995, p. 168) asserts that actors with more collectivist orientations are more likely to form cooperative partnerships, even "within a single societal culture." In contrast, individualist firms value the independence and flexibility provided by an arm's-length relationship and may be reluctant to forgo control by pursuing relational governance (Steensma et al. 2000). Thus:
H4: Collectivism increases close partner selection.
Power distance. Power distance refers to the "practice of inequalities in the distribution of … power and authority" (Hofstede 1980, p. 72). It describes the extent to which the firm believes that more powerful firms should have more to say than their less powerful channel partners. Whereas an organizational culture that is low in power distance revolves around equality and consultative decision making, an organizational culture that is high in power distance revolves around limits of authority and explicit definition of tasks (Bates et al. 1995; Hofstede 1980).
High-power-distance firms experience a greater need for explicit definition of tasks (Bates et al. 1995) and tight control over their and their partners' behavior (Shane 1994). They believe in giving and receiving detailed instructions with little autonomy to interpret them. Therefore, such firms are more comfortable with formalized decision making and roles and responsibilities that are fixed by some clear structure and set of rules (Hofstede 2001).( n3) Contracts promote formalized decision making because they provide a hierarchical structure with legitimate authority and assign roles and responsibilities to the different parties involved (Heide 1994; Stinchcombe 1985). Thus:
H5: Power distance increases detailed contract drafting.
High-power-distance firms do not view their channel partners as equals, and therefore they dislike relationships that are characterized by consultative decision making and adherence to informal norms (Hofstede 1980). Selecting a close partner may promote the development of relational norms (Heide 1994), including restraint in the use of power (Macneil 1980), and thus it is less attractive to high-power-distance firms. In contrast, a firm that is low in power distance views its channel partner as relatively equal and engages in informal communication between entities at different hierarchical levels (Hofstede 1980). Consistent with Hofstede's (1980) finding that organizational controls in non-power distant cultures are often based on shared values or a sense of duty or obligation to others, we hypothesize that low-power-distance firms are more likely to select partners with which they have close prior ties. Thus:
H6: Power distance decreases close partner selection.
In this section, we discuss how detailed contract drafting and close partner selection may curb the partner's opportunistic behavior. Opportunism is defined as self-interest seeking with guile; it includes such behaviors as lying and cheating as well as more subtle forms of deceit, such as not fully disclosing information or violating the spirit of an agreement (Rindfleisch and Heide 1997).
Effect of detailed contract drafting on partner opportunism. The effectiveness of detailed contracts in hedging against opportunistic behavior is not without controversy. Masten (1996) contrasts two lines of thought. On the one hand, writing down binding contract terms has the obvious benefit that the court can be used to force transactors to perform to the literal terms of the contract (Klein 1996). Thus, through clearly articulated clauses, contracts narrow the domain around which parties can be opportunistic. For example, precise definition of each party's role decreases the likelihood that the partner will break promises, such as selling in an unauthorized territory, and precise statement of how each party is to perform decreases the likelihood that the partner will hide important performance-related information, such as information about capacity constraints. On the other hand, failing to specify all elements of the exchange contractually increases incentives for short-term cheating.
Alternatively, detailed contracts are difficult and costly to write, and legal redress is expensive. In addition to the extra "ink costs" of writing down responses to additional contingencies, complete contractual specification entails wasteful search and negotiation costs associated with discovering and negotiating prespecified contractual responses to all potential contingencies (Klein 1996). Moreover, writing detailed contracts may signal distrust by constraining partners in their behavior (Jap and Ganesan 2000). In turn, this may encourage opportunism in situations that are left unspecified within these contracts (Ghoshal and Moran 1996). Thus, it is possible to postulate both positive and negative effects of detailed contracts on opportunism. Nonetheless, the pervasive logic in the governance literature is that drafting detailed contracts reduces opportunism (e.g., Heide 1994; Joskow 1988). Thus:
H7: Detailed contract drafting reduces opportunism.
Effect of close partner selection on partner opportunism. A large and rapidly expanding body of literature has demonstrated the importance of close buyer--supplier relationships to facilitate cooperation and hedge against opportunism (e.g., Brown, Dev, and Lee 2000). As relationships grow closer, partners consider the relationship ongoing and beneficial and therefore are inclined to refrain from behaviors that might jeopardize it (Heide 1994). Consistent with this argument, researchers have typically hypothesized a negative effect of relational governance on opportunism (e.g., Achrol and Gundlach 1999; Brown, Dev, and Lee 2000).
In contrast, several researchers from various disciplines have recently drawn attention to the dark side of close relationships, arguing that relationships that are too close may actually become harmful. Grayson and Ambler (1999, p. 139) observe that "the sustainable competitive advantage enjoyed by long-term relationships carries the seeds of its own destruction." Jeffries and Reed (2000, p. 873) note that "too much trust is as bad as too little." Wicks, Berman, and Jones (1999, p. 99) argue that researchers need to focus on the notion of optimal trust, or the "golden mean" between excess (overinvestment in trust) and deficiency (underinvestment in trust). Some indirect evidence for these ideas has been provided by Moorman, Zaltman, and Deshpandé (1992) and Grayson and Ambler (1999), who find that long-term relationships dampen the positive influence of trust. In line with these researchers and drawing on the self-enforcement literature, we argue that the relationship between close partner selection and opportunism is unlikely to be as simple as the previously proposed linear model; rather, it is a U shape. According to the self-enforcement literature, opportunism is inhibited if the expected long-term gains of maintaining the relationship outweigh the short-term gains of cheating (Nagin et al. 2002; Telser 1980).
As closeness increases from low to moderately high, the expected long-term gains to maintaining the relationship are likely to increase accordingly. Several studies have found that selecting a close partner promotes socialization practices that align the interests of the exchange partners ex ante, which renders costly ex post monitoring unnecessary (Dyer and Singh 1998; Nooteboom, Berger, and Noorderhaven 1997). Socialization practices not only reduce costs but also lead to a wealth of benefits, including opportunities to develop organizational capabilities that are vital for the realization of firm objectives (Wicks, Berman, and Jones 1999). These increased expectations for long-term benefits reduce the attractiveness of short-term cheating. In summary, as closeness increases from low to moderately high, the self-enforcing mechanism is strengthened, and therefore opportunism is less likely to occur.
Although long-term benefits accrue to close partner selection, beyond a certain level of relational closeness, these benefits may fade. An increase in closeness from moderately high to very high may come at the expense of economic efficiency. Prior literature has offered multiple reasons for this effect, such as firms' decreased motivation to innovate (Baiman and Rajan 2002), their decreased motivation to find optimal solutions to problems of adaptation (Jeffries and Reed 2000), and their tendency to lock themselves into one particular field of knowledge at the expense of external opportunities (Poppo and Zenger 1998). Thus, as a tie becomes excessively strong, it becomes increasingly salient that change could deliver performance gains, and firms may become willing to "destroy established taken-for-granted rules if they perceive such action to be profitable" (Beckert 1999, p. 786).
Concurrently, with this decrease in long-term benefits comes the increased short-term benefit of cheating. The observation that monitoring decreases as relationships grow closer poses a paradox at high levels of closeness. Although close partner selection establishes norms and expectations about appropriate behavior, thus lowering the need for monitoring in the exchange, it also increases the temporal window for collecting cheating rents before being discovered and thus provides the partner with ample opportunity to steal from the firm with relative impunity (Dyer and Singh 1998; Wicks, Berman, and Jones 1999). Moreover, this opportunity for opportunistic exploitation goes hand in hand with increases in the payoff should the firm decide to behave opportunistically. As Granovetter (1985, p. 491) notes, "the more complete the trust, the greater the potential gain from malfeasance."( n4) In short, as closeness increases from moderately high to very high, diminishing long-term benefits and increasing payoffs from opportunistic exploitation weaken the self-enforcing mechanism, thus increasing the extent to which firms shirk or cheat (Nagin et al. 2002). Combining these arguments with those that support moderate closeness over low closeness, we suggest that there is a U-shaped relationship between close partner selection and opportunism. Formally,
H8: The effect of close partner selection on opportunism is U shaped.
Detailed contract drafting in combination with close partner selection. There are two competing views on the joint effect of detailed contract drafting and close partner selection on partner opportunism. The complement view regards close partner selection as filling in the holes of contracts, and vice versa. In contrast, the substitution view regards detailed contracts as redundant and even counter-effective when a close partner is selected.
According to the complement view, both contracts and close ties have limitations. Regardless of how explicit a contract is, certain dimensions of the exchange may prove impossible to specify contractually: "[C]ontracts are not indefinitely elastic" (Williamson 1991, p. 273), and managers are constrained in their ability to account for all future potential contingencies contractually (Macneil 1980). In addition, the utility of close partner selection has been questioned. Selecting a partner with which a firm shares a history of close collaboration creates a shadow of the past, which promotes the emergence of relational norms (Heide 1994). Relational norms tend to be broadly defined and are subject to various interpretations. As such, they can be manipulated to the advantage of one party (Achrol and Gundlach 1999). Some authors have suggested that close partner selection helps overcome the adaptive limits of detailed contracts, and vice versa (e.g., Bradach and Eccles 1989; Poppo and Zenger 2002). Selecting a close partner promotes the emergence of relational norms, which may safeguard against hazards that are poorly protected by the contract, whereas contracts complement relational closeness through the formal specification of unspoken assumptions that help establish a solid basis for the development of norms. From this perspective, the combination of selecting a close partner and drafting a detailed contract may reduce partner opportunism more than either decision in isolation.
However, others view detailed contract drafting and close partner selection as substitutes (e.g., Dyer and Singh 1998; Uzzi 1997). If a firm selects a partner with which it has developed a close tie in the past, there is little need to draft detailed contracts, and vice versa. A close tie is characterized by positive valence and collaboration, and thus it promotes goodwill trust. Because goodwill trust reduces the need for specifying and monitoring contractual clauses, it makes contractual safeguards redundant (Dyer and Singh 1998). Moreover, because drafting detailed contracts and maintaining close ties both involve considerable costs in terms of time and resources, firms will concentrate on either one or the other but not on both. Other scholars argue that drafting detailed contracts may undermine the development of relational norms and thereby encourage the opportunistic behavior they are designed to discourage. Making contracts more detailed may be interpreted as a signal of distrust (Jap and Ganesan 2000; Macaulay 1963). Because distrust begets distrust (Bradach and Eccles 1989), the partners may end up in a vicious cycle of suspicion and retaliation (Nooteboom, Berger, and Noorderhaven 1997), and opportunism surrounding actions that cannot be specified in contracts may increase. Similarly, social norms and trust conventions can undermine the effectiveness of explicit contracts because they can stand in the way of effective enforcement of contractual details (Antia and Frazier 2001).
In summary, there appear to be two different, but equally logical, arguments for the joint effect of detailed contract drafting and close partner selection on partner opportunism. Therefore, we advance competing hypotheses:
H9a: Detailed contract drafting and close partner selection are complementary in hedging against partner opportunism.
H9b: Detailed contract drafting and close partner selection are substitutes in hedging against partner opportunism.
Moderating role of network embeddedness. Network embeddedness is the extent to which the focal relationship is embedded in a network of mutual contacts (Uzzi 1997). It reflects how close a firm's relationships are with its partner's partners and thus accounts for firms' relationships not only with each other but also with the same third parties.
Network embeddedness may increase the effectiveness of detailed contract drafting and close partner selection in curtailing opportunism for two reasons. First, firms with close mutual ties develop group norms through the diffusion of common understandings across the network (Rowley, Behrens, and Krackhardt 2000). Group norms are standards against which the appropriateness of behavior can be evaluated, and they regulate behavior among group members. Attempts to violate group norms by acting out of line with the behavior specified by the norms will typically be met with sanctions within the group (Rowley, Behrens, and Krackhardt 2000). As such, group norms reduce a firm's incentive to ignore contractual and relational codes of conduct for opportunistic ends.
Second, the more closely a firm is connected with its partner's partners, the more constraints there are on the partner's behavior because of a reputational effect (Jones, Hesterly, and Borgatti 1997). In other words, a firm is less likely to be cheated on by its partner when they are tied to the same third parties because these mutual contacts are likely to become aware of the cheating partner's actions (Granovetter 1985). If a firm's exchange partner acts opportunistically, the firm that is cheated on can spread the word through the network of mutual contacts. This may damage the partner's reputation as a trustworthy exchange partner (Granovetter 1985). As a result, the transactor engaging in the opportunistic behavior will incur increased costs of conducting business in the future because potential trading partners will be less willing to rely on the transactor's promises and may demand more favorable contract terms (Klein 1996). In addition, the opportunistic exchange partner may lose future contracts (Houston and Johnson 2000). As such, network embeddedness may stimulate exchange partners to adhere to the rules of good conduct as explicitly outlined by contracts or implicitly implied by relational norms. Thus:
H10: Network embeddedness enhances the effectiveness of (a) detailed contract drafting and (b) close partner selection in reducing partner opportunism.
In addition to the effect of organizational culture on detailed contract drafting and close partner selection decisions, prior research has recognized the influence of transaction cost variables (Heide 1994). Transaction-specific assets are assets that are tailored to a particular relationship and that cannot be redeployed easily. Their idiosyncratic nature leads to a safeguarding problem in the sense that market competition no longer serves as a restraint on opportunistic exploitation. Environmental uncertainty refers to the unpredictability of relevant contingencies surrounding an exchange. The primary consequence of environmental uncertainty is an adaptation problem (i.e., difficulties with adjusting agreements cause high transaction costs). The effect of behavioral uncertainty is a performance evaluation problem (i.e., the difficulty in ascertaining ex post whether contractual compliance has taken place). In contrast to markets, the authority relationships available through detailed contract drafting and the relational norms available through close partner selection are assumed to embody greater safeguarding, adaptation, and evaluation capabilities. Therefore, we expect that detailed contract drafting and close partner selection increase in response to transaction-specific assets, environmental uncertainty, and behavioral uncertainty. Finally, transaction frequency, or the extent to which transactions recur, provides an incentive for firms to write detailed contracts or select a close partner because the overhead cost of contract writing and relationship maintenance will be easier to recover for transactions that recur. By including the transaction cost variables in our model of detailed contract drafting and close partner selection, we can better identify the unique effects of organizational culture and rule out rival explanations for our results.
Method
We test our hypotheses in the context of purchase decisions made by manufacturers in two industries. We selected a random sample of 838 small to medium-sized firms (up to 500 employees) in the two-digit Standard Industrial Classification codes 35 (Industrial and Commercial Machinery and Computer Equipment) and 36 (Electronic and Other Electrical Equipment and Components) from the database Reach, which provides detailed information on more than 200,000 officially registered companies in the Netherlands. This context is particularly relevant for our research objectives because vertical integration is typically not feasible for smaller buyer firms in these industries because such an approach is costly. As an alternative to market governance, such firms typically rely on hybrid governance modes. Interviews with purchasing managers supported our expectation that our conceptual model is relevant to this setting.
Each firm was contacted by telephone to locate an appropriate key informant (typically the purchasing manager) and to verify the firm's industry and size. Subsequently, personalized letters were mailed to each informant, explaining the purpose of the study and announcing that we would call them within two weeks to schedule an appointment for administering a questionnaire. The questionnaire took between 30 and 45 minutes. In all, we conducted fully completed interviews with 206 purchasing managers, for a response rate of 25%. This response rate compares favorably with other response rates reported in studies in business markets. We eliminated 29 questionnaires for two reasons: ( 1) Respondents indicated low levels of knowledge of purchasing agreements and partner selection, and ( 2) respondents were unable to identify an appropriate purchasing agreement. Thus, 177 questionnaires remained for further analysis, for a response rate of 21%. The 177 buyer respondents averaged 9 years of experience in their area and had been with their companies for an average of 12 years. We evaluated the quality of informants using two questions that assessed their level of involvement with the partner and knowledge of their firm's dealings with the partner. The 177 responses were uniformly high, as suggested by mean ratings of 6.18 and 6.16, respectively, on seven-point scales.
We directed informants to complete the questionnaire for their most recent purchasing agreement that met the following criteria: It should be an important (as opposed to a routine) purchase with which they were personally involved. Furthermore, it should not be a one-shot purchase. Thus, we restricted our research setting to only those kinds of purchases for which selecting a close partner was a viable option, consistent with the understanding that "occasional" frequency is the minimum threshold for departure from market-based governance (Williamson 1985, p. 79). We checked for potential nonresponse bias by comparing the final sample with a random sample of 100 nonrespondents with respect to company sales volume and number of employees. Because we did not find any significant differences (p > .10), nonresponse bias does not appear to be a concern.
We developed multi-item measures based on construct definitions and research precedents. We administered a draft of the questionnaire in face-to-face interviews with eight purchasing managers. We made some minor wording changes based on their feedback. The final measures appear in the Appendix.
Detailed contract drafting. The items for the detailed contract drafting construct describe the level of detail with which the original contract prescribes roles, responsibilities, expected performance, and how to handle unplanned events and conflicts. We instructed respondents to reflect on the original contract as it was specified at the time the purchase agreement was closed. The four items are based on those that Lusch and Brown (1996) use.
Close partner selection. Our measure of close partner selection taps the closeness of the tie at the beginning of the specified purchasing agreement. Our four items are based on the work of Marsden and Campbell (1984) and Mathews and colleagues (1998).
Partner opportunism. This scale describes the extent to which the partner pursues its self-interest with guile. For example, partners sometimes provide wrong or incomplete information, break promises, or exaggerate their needs to get what they desire. The four items are based on the work of Gundlach, Achrol, and Mentzer (1995) and Dahlstrom and Nygaard (1999).
Network embeddedness. Network embeddedness reflects how close a firm's relations are with its partner's partners (Uzzi 1997). We constructed a new four-item scale based on the conceptual definition of network embeddedness and prestudy interviews.
Organizational culture. Using related work on organizational values as a guide (e.g., Bates et al. 1995; Sitkin and Pablo 1992), we developed three three-item scales to measure uncertainty avoidance, collectivism, and power distance. The uncertainty avoidance items describe the extent to which firms try to avoid uncertain and ambiguous situations in the supply chain. The collectivism items measure the firm's inherent belief in cooperation and the notion that firms in the supply chain are jointly responsible for successes and failures. The power distance items reflect the firm's opinion that more powerful channel members should have more to say in their relationships than their less powerful partners.
Transaction cost covariates. The three items of transaction-specific assets are based on the work of Anderson and Weitz (1992) and reflect investments dedicated to the relationship that cannot be redeployed easily if the relationship were to terminate. The three-item scale for environmental uncertainty is based on the work of John and Weitz (1988) and reflects the difficulty to predict volume requirements for the supplier's component. The three-item scale for behavioral uncertainty is based on the work of Stump and Heide (1996) and measures the extent to which the buyer firm cannot accurately assess its supplier's performance by objective, readily available output measures. The single-item frequency measure is based on the work of Bucklin and Sengupta (1993) and indicates how frequently buyers expected to interact with the supplier in a typical month.
Other covariates. To ensure that differences in the recency of the purchase agreements do not bias the results, we control for the time lapse (in months) between the time the purchase agreement was closed (i.e., when detailed contract drafting and close partner selection decisions were made) and the time the questionnaire was administered.
Results
We estimated the equations in our model simultaneously using partial least squares (PLS), a structural equation modeling technique. We chose PLS because it is distribution free, and the presence of interaction effects does not satisfy the requirements of multivariate normality required by maximum likelihood estimation. In line with the work of Chin, Marcolin, and Newsted (2003), we represent latent interaction variables by creating all possible products from the two sets of indicators. We standardized the indicators before multiplying them. Because PLS makes no distributional assumptions, traditional parametric tests of significance are not appropriate. Therefore, we used bootstrapping to ascertain the significance of the parameter estimates. Although the measurement and structural parameters are estimated together, the results are interpreted in two stages: first by an assessment of the measurement model and, second, by an assessment of the structural model (Hulland 1999).
Using approaches that Fornell and Larcker (1981) developed for a PLS context, we assessed the adequacy of the measurement model through an examination of individual item reliabilities, convergent validity, and discriminant validity. To assess individual item reliability, we inspected the loadings of the items on their corresponding constructs. As Hulland (1999) recommends, we deleted items with factor loadings smaller than .70. For the remaining items, we checked convergent validity using Fornell and Larcker's (1981) internal consistency measure. The internal consistency values for all constructs appear in the Appendix and exceed the .70 guideline that Nunnally (1978) recommends.
We assessed discriminant validity in two ways. First, Fornell and Larcker (1981) suggest comparing the square root of the average variance extracted (i.e., the diagonals in Table 1) with the correlations among constructs (i.e., the off-diagonal elements in Table 1). An examination of Table 1 reveals that the diagonal elements of this matrix are significantly greater than the off-diagonal elements, indicating that each construct shares more variance with its measures than with other constructs. Second, we checked and found no statistically significant item cross-loadings. Collectively, these results support the discriminant validity of all constructs.
Table 2, Part A, presents the results for the antecedents of detailed contract drafting and close partner selection. All organizational values significantly influence the degree of detail of the drafted contract. The positive effects of uncertainty avoidance (β = .29, p < .01) and power distance (β = .15, p < .05) support H1 and H5. Although the effect of collectivism is also significant (β = .15, p < .05), its sign is not consistent with H3. None of the control variables is significant.
We find mixed support for the impact of the organizational values on close partner selection. Collectivist firms are more likely to select a close partner, as we predict in H4 (β = .24, p < .01). We do not find support for the effects of uncertainty avoidance (H2) and power distance (H6) on close partner selection (p > .10). For the transaction cost covariates, we find positive effects for behavioral uncertainty (β = .24, p < .01) and transaction frequency (β = .21, p < .01).( n5)
The variance explained increases significantly by augmenting the transaction dimensions with organizational culture. For detailed contract drafting, variance explained increases from .04 to .21 (pseudo -F( 3, 170) = 9.29, p < .01) (Mathieson, Peacock, and Chin 2001). For close partner selection, variance explained increases from .17 to .23 (pseudo-F(3, 170) = 4.22, p < .01).
As Table 2, Part B, indicates, we find no support for H7, which involves the effect of contractual detail on opportunism (p > .10). However, we find support for H8; the close partner selection squared parameter is positive and significant (β = .18, p < .05), indicating that the closeness with the selected partner has a U-shaped effect on opportunism (see Figure 1). Thus, we find evidence for the dark side of close partner selection. We find a positive and significant interaction effect between detailed contract drafting and close partner selection (β = .26, p < .01), in support of H9b (i.e., the substitute relationship) but not H9a (i.e., the complement relationship); high levels of both detailed contract drafting and close partner selection enhance opportunism. We find mixed support for the moderating role of network embeddedness. We find support for H10a that network embeddedness enhances the effectiveness of detailed contract drafting in reducing opportunism (β = -.26, p < .05), but we do not find support for H10b that network embeddedness enhances the effectiveness of close partner selection (p > .10).( n6) Collectively, the interaction effects account for a significant increase in R² from .09 to .19 (pseudo-F(3, 169) = 6.67, p < .01). The time-lapse control variable is not significant (p > .10).( n7)
To understand the exact nature of the significant interaction between detailed contract drafting and close partner selection, we calculated the regression coefficient associated with detailed contract drafting using values of close partner selection one standard deviation below and above the mean, which represents low and high levels, respectively, of a history of relational closeness with the selected partner (Jaccard, Turrisi, and Wan 1990). The analysis indicates that the null relationship between detailed contract drafting and partner opportunism at moderate levels of closeness becomes positive when closeness is high (βH = .27); however, the null relationship becomes negative when closeness is low (βL = -.18). This result suggests that efforts to draft detailed contracts to reduce opportunism are effective only when a nonclose partner is selected. A similar analysis of the significant interaction between contractual detail and network embeddedness reveals that at high levels of network embeddedness, the relationship between contractual detail and opportunism is negative (βH = -.17), and at low levels of network embeddedness, the relationship is positive (βL = .27). This result suggests that efforts to draft detailed contracts to reduce opportunism are less likely to be effective without the benefit of network embeddedness.
Discussion
Our study investigates the determinants and effects on partner opportunism of two strategic decisions that firms face when engaging in a new purchase transaction: the decision whether to draft a detailed contract and the decision whether to select a partner with which the firm shares a history of close collaboration.
Our results on the determinants of detailed contract drafting and close partner selection show that they have distinct organizational culture drivers. What drives firms to select close partners? We find that collectivist firms are more prone to select partners with which they share prior close ties. This finding is in line with collectivist firms performing better in close cooperation with their partners, as opposed to individualist firms feeling restricted by close ties. Two countervailing forces cancelling each other out could explain the nonsignificant effect of uncertainty avoidance on close partner selection. On the one hand, selecting a close partner may alleviate uncertainty by providing the flexibility to cope with inevitable uncertainties that arise in an exchange. On the other hand, it may also create a different type of uncertainty-namely, relational uncertainty, or the firm's risk of being taken advantage of by its partner (Steensma et al. 2000). Contrary to our expectations, close partner selection does not necessarily go hand in hand with low power distance (i.e., firms that favor equal power distributions in the supply chain). A possible explanation is that in case of a power imbalance, the more powerful exchange partner often uses ideology as a cooperation-inducing force (Geyskens et al. 1996).
What drives firms to draft detailed contracts? We contribute to the debate by showing that firms that are more collectivist, more uncertainty avoidant, and more tolerant of power distance in the supply chain show a greater propensity to write explicit contracts. The positive effect of collectivism on contractual detail is surprising, and it led us to consider factors that might explain this result. Ryall and Sampson's (2003) recent observation that firms frequently include contract terms that are not legally enforceable but that contribute to providing a more detailed plan for collaborative activities could be relevant. Alternatively, collectivists may not consider the terms of the contracts a threat over the partner's head but rather a goal that the partners both want to achieve. They may view contract negotiations as ways to learn about each other rather than ways to extract the maximum number of favorable terms at the negotiation table.
Our results also provide insight into the effectiveness of detailed contract drafting and close partner selection as facilitators in reducing opportunism. Although some scholars have lamented the shortcomings of contracts for governing exchange (e.g., Macaulay 1963; Macneil 1980), there has been no decrease in the use of contracts or in the length or complexity of the rather formidable contracts that circulate in many industries. Therefore, a key question is, When are contracts useful modes of governance? Drawing on network theory, we argue that the answer lies in the conjunction of contracts and network embeddedness. Network embeddedness, in the form of close mutual ties, serves an indirect social control function, which is activated by group norms and reputational concerns. Our findings indicate that detailed contracts become effective in hedging against opportunism when they are complemented with this indirect social control function. In contrast, opportunism increases when detailed contracts are used in the absence of close mutual contacts. A potential explanation is that writing something down to be enforced by the court creates rigidity. Because contract terms are necessarily imperfect, transactors without close mutual ties face fewer reputational constraints to engage in opportunism by rigidly enforcing the imperfect contract terms, even if the literal terms are contrary to the intent of the contracting parties.
Selecting a close partner shows a marked ability to control opportunism. However, close relationships also have shortcomings, in that very high levels of closeness actually increase rather than attenuate opportunism. This dark side of close relationships surfaces as relationships grow so close that beyond a certain point, the perceived benefits of behaving opportunistically exceed the perceived benefits of sustaining the relationship. To the best of our knowledge, our significant U-shaped effect provides the most direct evidence of the dark side of close relationships.
Contrary to expectations, however, we do not find an interaction effect between close partner selection and network embeddedness. This implies that there is no synergy between the two. Direct social control through dyadic norms does not require additional indirect social control through group norms that emerge at high levels of network embeddedness.
Notably, we find that detailed contract drafting and close partner selection act at cross-purposes, and their combination increases opportunism. Contracts set parameters on what the partner can legitimately do. As such, detailed contracts may signal distrust, which conflicts with the trust conventions that typify close relationships between a firm and its partner, thus encouraging rather than discouraging opportunistic behavior. Similarly, the direct social control function of relational closeness stands in the way of firms strictly enforcing the terms of the contract. In other words, selecting a close partner and drafting a detailed contract are not compatible. This positive interaction effect also indicates that contracts gain effectiveness in terms of reducing opportunism at low levels of closeness, which further highlights the complexity of the relationship between detailed contract drafting and opportunism.
Considering our findings for the interaction effects of detailed contract drafting with network embeddedness and with close partner selection in combination, we arrive at an interesting conclusion. Contracts are not compatible with direct social control and dyadic norms resulting from close partner selection, but they are compatible with indirect social control and group norms resulting from network embeddedness. These findings suggest an important difference between dyadic norms and group norms, the theoretical foundation of which dates back to Simmel's ([1908] 1950) seminal work. Whereas dyadic norms are attributed entirely to the personal interaction with the individual exchange partner, group norms are attributed to a higher-level impersonal group structure. As a result, contractual terms in combination with dyadic norms can result in a vicious circle of suspicion and retaliation (Nooteboom, Berger, and Noorderhaven 1997), but they are less likely to conflict with group norms. Additional research into the difference between dyadic norms and group norms is required to corroborate these arguments.
Our study highlights several practices that managers should consider when structuring hybrid governance modes to inhibit their partner firm's opportunistic behavior. First, managers should choose between drafting detailed contracts and selecting a close partner. The combination of both triggers rather than reduces partner opportunism. Second, using detailed contracts as a hedge against opportunism is more effective when the focal relationship is embedded in a network of close mutual contacts. When the focal relationship is not embedded in a network, close partner selection is the better option to subdue the partner's opportunistic inclinations. However, managers should be wary of selecting relationships that have become too close or of allowing relationships to become too close. Moderately close relationships are more effective in curbing the partner's opportunism than either arm's-length or very close relationships. Finally, firms that are high in uncertainty avoidance and power distance should be careful of engaging in detailed contract drafting when this is not opportune, especially given the absence of a network.
There are some limitations to consider, which highlight several worthwhile avenues for further research. First, our model is only a partial model and does not purport to represent all the possible antecedents of decisions to draft detailed contracts and to select close partners. We have contributed to governance theory by incorporating organizational culture. This is important because it moves the theory beyond a transaction focus. Further research could also account for differences in firm resources, such as superior technology and brand equity, that might have governance implications (Ghosh and John 1999). Second, our sample was limited to small and medium-sized buyers from two related industries in the Netherlands. Further research should examine the generalizability of our results in different channel contexts. Third, we studied only differences among firms in their organizational values, not among different countries. A study of cultural interactions between two groups of entities (e.g., firms and countries) may provide insight into how firms make governance decisions in a variety of cultural environments that may be similar or dissimilar to their cultural values. Fourth, in studying the role of network embeddedness, we considered only the effect of close mutual contacts (i.e., positive valence ties); however, negative valence ties could also affect the effectiveness of detailed contract drafting and close partner selection. Finally, our study uses cross-sectional data, which exclude tests for the effectiveness of detailed contract drafting and close partner selection over time. Perhaps the complement-substitute relationship is time dependent and changes over the course of the relationship life cycle. Contracts may help ensure that the early, more vulnerable stages of exchange are successful (failure to contractually specify elements of the exchange that are easily specified merely heightens incentives for short-term cheating and lowers expectations of cooperation), but their importance may decline with time as social norms emerge in a relationship (Jap and Ganesan 2000). Further research could study the theoretical mechanisms that drive a complement-substitute relationship between detailed contract drafting and close partner selection.
The authors thank Jan Heide, Jan-Benedict Steenkamp, Christophe Van den Bulte, and Peter Verhoef for valuable comments and suggestions on previous versions of this article and Wynne Chin for providing the partial least squares software. The authors also thank the Institute for the Studies of Business Markets (Penn State University), the NEVI (Dutch Association of Purchase Managers), and the Netherlands Organization for Scientific Research for financial assistance.
( n1) We refer to these two strategic choices as detailed contract drafting and close partner selection.
( n2) Organizational culture has typically been conceptualized as a molar concept that is indicative of the organization's goals and appropriate means to goal attainment. However, the culture construct has recently been expanded to include a more specific focus to a particular referent, as in the culture with respect to service or to safety (Schneider 2000). We take the latter approach in studying the firm's cultural values with respect to appropriate practices in the supply chain.
( n3) In the context of intraorganizational relationships, Hofstede (2001, p. 82) argues that power distance is related to "the value systems of both bosses [i.e., the more powerful partner] and subordinates [i.e., the less powerful partner] and not to the value of the bosses only, even though they are the more powerful partners." In line with Hofstede, we do not expect the effect of power distance to differ depending on the locus of power in the buyer-supplier relationship.
( n4) Close relationships are characterized by greater proprietary information exchange than arm's-length transactions, which in turn increases the potential for information misappropriation. According to Baiman and Rajan (2002), the value to the information recipient of misappropriating sensitive information can exceed the entire value of the contract with the information provider.
( n5) Transaction cost researchers are divided between those who posit a main effect of environmental uncertainty and those who posit that environmental uncertainty is problematic only in the presence of specific assets. We also tested for an interaction effect between environmental uncertainty and transaction-specific investments. This interaction effect was not significant for detailed contract drafting or for close partner selection (p > .10).
( n6) We also included interaction effects between detailed contract drafting and close partner selection squared and between network embeddedness and close partner selection squared. Neither of these higher-order interaction effects were significant (p > .10). For reasons of parsimony, we dropped them from the model.
( n7) We also included the transaction cost covariates in the opportunism equation. The increase in R2 from .19 to .21 due to the transaction cost covariates was not significant (pseudo-F( 4, 165) = 1.19, p > .10). Thus, the transaction dimensions affect partner opportunism only through their effect on detailed contract drafting and close partner selection. We also created interaction effects between the time-lapse variable and all of our explanatory variables to explore whether any of our effects would change depending on the time elapsed between the starting date of the agreement and the date on which the questionnaire was administered. None of these interaction effects was significant (p > .10). To further ensure that differences in the recency of the purchasing agreements did not bias our results, we performed a Tukey outlier analysis in which we removed the 10% most extreme observations from our sample (i.e., the lower 5% and the upper 5%). The results remained substantively the same. We repeated this analysis for the 20% most extreme observations (i.e., the lower 10% and the upper 10%). Again, our results did not change substantively.
Legend for Chart:
A - Measure
B - Correlation Matrix DCD
C - Correlation Matrix CPS
D - Correlation Matrix OPP
E - Correlation Matrix NET
F - Correlation Matrix UA
G - Correlation Matrix COL
H - Correlation Matrix PD
I - Correlation Matrix DCD x CPS
J - Correlation Matrix DCD x NET
K - Correlation Matrix CPS x NET
L - Correlation Matrix TSI
M - Correlation Matrix ENV
N - Correlation Matrix BEH
O - Correlation Matrix FRQ
A
B C D E F G H
I J K L M N O
Detailed contract drafting (DCD)
.78
Close partner selection (CPS)
.09 .91
Partner opportunism (OPP)
.05 .14 .72
Network embeddedness (NET)
.10 .18 .05 .86
Uncertainty avoidance (UA)
.37 .06 .02 .22 .88
Collectivism (COL)
.31 .28 -.11 .21 .45 .79
Power distance (PD)
.24 .18 .03 .00 .21 .21 .75
DCD x CPS
.01 .08 .23 .01 .11 -.01 .04
N.A.
DCD x NET
.10 .01 -.13 .25 .13 .17 .08
.12 N.A.
CPS x NET
.01 .06 .08 -.05 .10 .02 .10
.09 .01 N.A.
Transaction-specific investments (TSI)
.10 .24 .12 .07 .05 .09 .22
.16 -.03 .01 .85
Environmental uncertainty (ENV)
-.04 -.04 .08 .07 .10 -.02 -.15
.04 -.12 .00 .07 .80
Behavioral uncertainty (BEH)
-.07 .22 .17 -.10 -.02 .02 .07
.11 -.14 .03 .40 .24 .74
Frequency (FRQ)
.05 .28 -.04 .09 -.03 .12 .14
.00 -.03 .10 .28 .00 .05 1.00
Mean
4.49 3.64 1.99 3.39 5.07 4.91 3.81
16.62 15.47 12.95 2.20 3.85 2.70 7.63
Standard deviation
1.38 2.01 .95 1.63 1.34 1.25 1.28
11.34 9.96 10.35 1.32 1.67 1.17 8.37
Notes: Diagonal elements are the square roots of average
variance extracted. N.A. = not applicable. A: Drivers of Detailed Contract Drafting and Close Partner
Selection
Legend for Chart:
B - Parameter Estimate (Standard Error) Detailed Contract
Drafting
C - Parameter Estimate (Standard Error) Close Partner Selection
A B C
Organizational Culture
Uncertainty avoidance .29 (.08)(**) -.02 (.07)
Collectivism .15 (.08)(*) .24 (.07)(**)
Power distance .15 (.07)(*) .08 (.07)
Control Variables
Transaction-specific investments .10 (.07) .09 (.08)
Environmental uncertainty -.01 (.09) -.10 (.08)
Behavioral uncertainty -.15 (.09) .24 (.08)(**)
Frequency -.03 (.07) .21 (.07)(**)
R² .21 .23
B: Effect of Detailed Contract Drafting and Close Partner
Selection on Opportunism
Legend for Chart:
B - Parameter Estimate (Standard Error)
A B
Detailed contract drafting .05 (.08)
Close partner selection .08 (.08)
Close partner selection squared .18 (.09)(*)
Network embeddedness .11 (.11)
Detailed contract drafting .26 (.08)(**)
x close partner selection
Detailed contract drafting -.26 (.11)(*)
x network embeddedness
Close partner selection x .04 (.13)
network embeddedness
Time lapse .10 (.08)
R² .19
(*) p < .05 (two-tailed test).
(**) p < .01 (two-tailed test).
MEASUREMENT APPENDIX
Legend for Chart:
A - Construct
B - Items
C - Internal Consistency
A
B C
Detailed contract drafting
• In dealing with this supplier, .86
our contract precisely defines the
role of each partner.
• In dealing with this supplier,
our contract precisely defines the
responsibilities of each partner.
• In dealing with this supplier,
our contract precisely states how
each party is to perform.
• In dealing with this supplier,
our contract precisely states what
will happen in the case of events
occurring that were not planned.
Close partner selection
Before our firm selected this supplier .94
for this purchasing agreement,
• Our firm worked very intensively
with this supplier.
• Our firm had a very close
relationship with this supplier.
• Our firm's relationship with
this supplier was like an arm's-length
delivery of the components.(a)
• Our firm and this supplier had
a very collaborative relationship,
like a real team.
Partner opportunism
• This supplier often exaggerates .81
its needs to get what it desires.
• This supplier often alters the
facts to get what it wants.
• This supplier often promises
to do things, even though it actually
had no intention of following through.
• We have reason to believe that
this supplier hides important information
from us.
Network embeddedness
Before our firm selected this supplier .89
for this purchasing agreement,
• Our firm worked very intensively
with one or more partners of this
supplier.
• Our firm had a very close
relationship with one or more partners
of this supplier.
• Our firm's relationship with
the partners of this supplier was
arm's length, purely restricted to
executing transactions.(a)
• Our firm had a very collaborative
relationship with one or more partners
of this supplier, like a real team.
Uncertainty avoidance
• Uncertain situations in our .87
supply chain are a threat to our firm.
• Our firm goes to great length
to avoid uncertain situations in our
supply chain.(a)
• Our firm goes to great length
to avoid unclear and ambiguous
situations in our supply chain.
Individualism-collectivism
• Firms and their suppliers are .84
always jointly responsible for the
successes and failures of their working
relationships.
• Our firm considers it as the
most normal thing that firms in the
supply chain try to cooperate as much
as possible.
• Close cooperation with other
firms in our supply chain is to be
preferred over working independently.
Power distance
• Firms in the supply chain that .79
are in a powerful position should
have more to say in their relationships
than their partners.
• Firms in the supply chain that
are not in a powerful position should
generally follow the will of their
partners.
• In a supply chain, it is logical
that firms in powerful positions have
the last word.
Transaction-specific investments
• If we ended our relationship .89
with this supplier, we would need to
invest a lot of time and effort
redeploying those of our people who
are presently serving this supplier.
• If we ended our relationship
with this supplier, we would be wasting
a lot of knowledge that is tailored
to this relationship.
• If we switched suppliers, the
production system that incorporates
this component would need to be adapted
substantially before we could start
working with another supplier.
Environmental uncertainty
• Volume requirements for this .80
component are subject to unpredictable
fluctuations.
• Our forecasts of this component's
volume requirements are quite inaccurate.
• It is difficult to reliably
estimate the required volumes for the
supplier's component.
Behavioral uncertainty
• Evaluating this supplier's .77
performance is a highly subjective
process.
• This supplier is performing
so many different tasks that it is
difficult to ascertain whether a good
job is being done.
• It is difficult to determine
whether agreed-upon quality standards
and specifications are adhered to.
Transaction frequency
• At the beginning of your N.A.
relationship with this supplier, how
frequently did your firm believe it
would need to interact with the supplier
firm in a typical month?
(a) We dropped this item from further analysis.
Notes: We measured all items except for transaction frequency,
which is a count variable, on seven-point scales, anchored by
"strongly disagree" and "strongly agree." N.A. = not applicable.GRAPH: FIGURE 1 Effect of Close Partner Selection on Opportunism
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By Stefan Wuyts and Inge Geyskens
Stefan Wuyts is Assistant Professor of Marketing (e-mail: S.H.K.Wuyts@uvt.nl), and Inge Geyskens is Associate Professor of Marketing (e-mail: I.Geyskens@uvt.nl), Tilburg University.
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Record: 162- The Geography of Thought: How Asians and Westerners Think Differently...and Why (Book). By: Ungson, Gerardo R.; Braunstein, Daniel N. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p130-132. 3p. DOI: 10.1509/jmkg.68.3.124.34771a.
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The Geography of Thought: How Asians and Westerners Think
Differently ...and Why (Book)
The Geography of Thought: How Asians and Westerners
Think Differently ... and Why
by Richard Nisbett (New York: The Free Press,
2003, 288 pp., $24)
A casual walk down any sidewalk reveals the incredible variety of human beings. Physical differences are noted and occasionally translated into salient differences in the culture or ancestry of others. This propensity to sort people into cultural categories has led to stereotypes, some of which are intuitively plausible, but many of which are often unjustified and unwarranted. For example, in general, Asians are regarded as reserved, shy, and reticent, though Indians and Koreans can be outright and outspoken. Americans and Europeans are cast as aggressive, extroverted, and opinionated, though significant exceptions continue to be noted. The stereotypes go on and, over the course of history, have resulted in cross-cultural mishaps, snafus, and human suffering.
Notably, although popular cultural stereotypes abound, in general it is assumed that everyone uses the same tools for perception, for memory, and for reasoning. Such an assumption was once held by the University of Michigan psychologist Richard Nisbett, also the Theodore M. Newcomb Distinguished University Professor and Codirector of the Culture and Cognition Program. Nisbett observed that many cognitive scientists believed that "all human groups perceive and reason in the same way" (pp. xiii-xiv). Whatever skin color, nationality, or religion, a logically true statement should hold true. The same should apply for graphical information; that is, everyone should at least see the same picture.
In his provocative new book, The Geography of Thought , Nisbett challenges this assumption of universality or invariance across cultures. Using an arsenal of experimental studies conducted mostly by himself and his colleagues, Nisbett builds a case that East Asians and Westerners think differently, and he establishes the grounds for why this is so.
Prompted by a Chinese student who opined that the world is a circle, not a straight line, Nisbett embarked on an intensive reading of the comparative literature on the nature of thought by both Eastern and Western philosophers, historians, and anthropologists. His finding that forms the core argument of the book is that Westerners (primarily Americans, Europeans, and citizens of the British Commonwealth) tend to be categorical. They focus on particular objects in isolation from their context and believe that if they can know the rules that govern objects, they can control the objects' behavior. In contrast, East Asians (principally the people of China, Korea, and Japan) tend to be broader and contextual. They believe that events are highly complex and cannot be properly interpreted without considering their underlying context. Moreover, because many factors determine events, they are much more difficult, if not impossible, to control. The rest of the book builds on this core argument in ways to attempt to resolve current-day puzzles and contradictions pertaining to attention, perception, reasoning, causal inference, and knowledge organization.
Chapter 1 explores traditional differences between Aristotle and Confucius as exemplars of Western and Eastern schools of thought. Such differences are manifest in the ancient Greeks' sense of personal agency, individualism, curiosity, and debate that is held in contrast to the Chinese sense of harmony, collectivism, practicality, and self-control. Greek concern for matter as particulate and separate led to distinctive ways of thinking: Salient attributes of an object are identified, the attributes become the basis of categorization, and the categories become the basis for rule construction. Ensuing events are understood in terms of objects behaving in accord with these rules.
Because the Chinese orientation toward life is shaped by the influences of Taoism, Buddhism, and Confucianism, Chinese thinking emphasizes harmony, holism, and interrelatedness. That is, every event is related to every other event. The Way, or the pursuit of harmonious relationships, became the goal of philosophy. Although Greek preoccupation with the discovery of the truth paved the way for the discovery of the processes of invention and science, the Chinese focus on complex interactions led to the primacy of relationships, context, and dialecticism, or the use of contradictions to understand relations among objects or events.
Chapter 2 undergirds these differences in the context of the ecologies of Greece and China. Early agricultural developments in China compelled people to share resources to live together in a reasonably harmonious way. The mountains of Greece favored hunting, fishing, and trade, which inculcated individual skills and achievements. Although agriculture reached Greece later than it reached China, by then it was commercial rather than subsistence agriculture, which thus enhanced individual skills. Historically, the success of the Chinese economy and culture engendered a belief in Chinese superiority, which resulted in a lack of interest in outside intellectual and technological advances.
The balance of Geography of Thought expands on the possible implications of cognitive differences between East Asians and Westerners. Nisbett's evidence consists primarily of data from contrived experiments, many of which measure reaction times or identify salient cues in recall of previously shown objects and scenes. Most of the experiments are visual, requiring subjects to identify relationships among objects; other tests are verbal and structured to infer logical rules. An example is an experiment in which U.S. and Japanese students were shown an animated underwater scene and later queried as to what they saw. What emerged was that the U.S. students focused on the objects (i.e., big fish swimming with small fish), and the Japanese students made observations about the background environment. On the basis of such studies, Nisbett reports several notable findings (the subsequent six headings correspond to the topic of Nisbett's Chapters 3-8, respectively).
In Chapter 3, "Living Together Versus Going It Alone," Nisbett discusses his finding that Westerners develop values directed at individualism (e.g., "individual distinctiveness is valued," p. 47) and choice (e.g., "a selection of 40 breakfast cereals in the supermarket," p. 49), whereas East Asians favor collectivism (e.g., "a peg that stands out is pounded down," p. 48) and identify strongly with their in-groups (e.g., "achieve harmony in a network of supportive relationships," p. 51). Another finding is that Westerners exhibit greater independence and favor the freedom of individual action, whereas East Asians are more interdependent and favor collective acceptance.
When comparing how Westerners and East Asians perceive the world, in Chapter 4, Nisbett finds that Westerners perceive the world as a compilation of discrete objects and unconnected things. This form of perception leads to an emphasis on individual actions as central to Westerners' managing of their environments. East Asians are inclined to perceive the world as connected substances, with an emphasis on holistic integration. Whereas Westerners focus on objects, East Asians are more inclined to focus on the context or the background of the object's environment.
Nisbett's findings on causal attributions, which he addresses in Chapter 5, include that Westerners tend to attribute the causes of behavior to the actor or agent, and East Asians are more inclined to attribute the causes of behavior to context. Thus, Westerners emphasize personality traits. In contrast, East Asians are more likely to attribute behavior to situational factors.
In Chapter 6, Nisbett poses the question, Why do Western infants learn nouns more rapidly than verbs, when it is the other way around in East Asia? With respect to organization of knowledge, many studies suggest that East Asians are less likely than Westerners to use categorical rules. East Asians' preference for collective action is revealed by an emphasis on highly reactive verbs that connote relationships. In contrast, Westerners, because of their fondness for categories, tend to learn nouns that are more inert and independent of context.
Regarding the use of formal logic and reasoning, discussed in Chapter 7, Nisbett finds that East Asians are more likely to set aside logic in favor of the typicality and plausibility of conclusions. Moreover, they are more likely to set logic aside in favor of the desirability of conclusions. Contrariwise, Westerners are apt to arrive at conclusions by using logic that is less dependent on plausibility and desirability. For this reason, East Asians are more likely to embrace contradictions, in contrast to Westerners' tendency to avoid them in their pursuit of principles to resolve them.
If East Asians are measurably more holistic and if Westerners are more analytical, does it matter? In Chapter 8, Nisbett explores several areas in which it does seem to matter, such as medicine (the Chinese holistic, harmonious approach versus the Western proclivity toward analytic, object-oriented interventions), law (Western legal confrontation versus East Asian use of intermediaries), science (lack of confrontation and debate among Japanese scientists), rhetoric (Western linear method of refutation), contracts (Japanese view of changing context as a basis for renegotiation), international relations (Chinese consider causality ambiguous and the result of many factors), human rights (Chinese conception of rights is based on part-whole rather than Western one-many conception), religion (Western "right-wrong" mentality versus East Asian "both/and" orientation). Other areas in which different cognitive styles seem to matter include thinking per se, formal reasoning, training and testing, and intelligence tests.
In the Epilogue, Nisbett discusses two familiar and opposing views of the future: Francis Fukuyama's vision of convergence toward Western values and Samuel Huntington's disruptive vision of a "clash of civilizations." However, Nisbett proceeds to argue a third view: a convergence based on the blending of Western and Eastern social systems and values. Although Western traditions dominate, he adds (p. 226) that "the entry of East Asians into the social science is going to transform how we think about human thought and behavior across the board." On an optimistic note, Nisbett concludes (p. 229), "I believe the twain shall meet by virtue of each moving in the direction of the other."
It is easy to like this book because of its clarity and its engaging style. Because it is also written in a journalistic style, devoid of academic jargon and footnotes, it is accessible to a large audience. Even so, these strengths also constitute its weaknesses.
Nisbett uncritically leans on Geert Hofstede's oft-cited work. Although Hofstede's seminal work has advanced the study of cross-cultural differences, it has also been criticized by international business scholars for its methodological limitations. Taken broadly, this casts a precautionary note on interpreting Nisbett's own sources and methods, which more often are citations of his work and those of his colleagues only in a confirmatory way, without the presentation of contrary evidence. Although he appends a fairly comprehensive bibliography, it would have been informative (particularly for academicians) to include some data and statistical results of selected studies cited in the text.
Moreover, it would have been helpful if Nisbett had defined fundamental cognitive abilities upfront instead of having descriptions distributed across different chapters. For example, studies of intelligence depict different levels: ( 1) ordinary cognition as including perception, sensation, and attention; ( 2) cognitive processes, such as representation, abstract reasoning, problem solving, and decision making; and (3) the etic manifestation of formal, learned, declarative versus experiential, procedural knowledge (see Earley and Ang 2003). Lacking such upfront distinctions, it is difficult to distinguish between inborn or early developed thinking processes from ones that are culturally acquired and enhanced. This context is useful for scholars who might quibble with Nisbett's beginning premise that cognitive processes are invariant across cultures anyway. In addition, for acceptance of Nisbett's core premise, a theory about how innate traits are transformed by enduring cultural influences would strengthen his core arguments. The discussion of "homeostatic socio-cognitive systems" (pp. 32-33) is a promising start, but it is not nearly enough to resolve the issue.
Throughout the book, the reader is confronted with categorizations of Western versus East Asian thought processes. After dwelling on the ecological reasons for this distinction in Chapter 1, there is a profound implication that the Western versus Eastern categorization might be, of itself, far too broad. Despite Nisbett's references to particular cultures when discussing his experiments, a reader might be easily seduced into thinking in terms of the two broad, simplistic categorizations without thoughtfully examining the extensive differences within Western and East Asian cultures. Anyone teaching in the various countries of Western Europe or East Asia will become quickly sensitized to significant differences in cognitive structure and worldview among Germans, Dutch, and Danes or among Japanese, Chinese, and Koreans. Absent such discussion, it is easy to fall back on cultural stereotypes and risk overgeneralization.
Although we agree with Nisbett's directions for biculturalism in training and testing, we believe that this could have been developed further. It is true that more and more East Asians are attending Western academic institutions, but it is also true that the theories and concepts of business used and disseminated in those institutions are derived predominantly from Western traditions, beliefs, and scholarship. A recent Harvard Business School survey corroborates this, reporting that approximately 80% of its instructional cases were out of date and that the relatively few international cases were ethnocentric in nature. Scholars who have studied this concept argue that ethnocentrism is a limiting problem in training business students. In view of significant ethnocentrism, which perhaps now occurs on both sides of the Pacific, the imperative for facilitating bicultural thinking is much more compelling.
Nisbett's conclusion about East-West cultural convergence is intellectually soothing if not reassuring. Among other things, however, it does not fully resolve the issue of how cognitive thinking might change as a result of cultural socialization. Many Asians initially raised in their home countries have now received their education in the West, and could this have changed their thinking styles into more Western ones, and vice versa?
Our comments notwithstanding, we applaud Nisbett's book and wholeheartedly recommend it. The book provides a nice foundation for marketing-related research, particularly in consumer behavior (as a perusal of Nisbett's reference list suggests). Among the many exciting developments discussed are Hong and colleagues' (2000) emerging studies of bicultural behavior that examine circumstances under which behaviors are evoked and used by people who are born in one country but are educated in another.( n2)
Geography of Thought contains many provocative arguments and useful insights that are bound to influence further research in cross-cultural and international marketing. For example, advertisements focused on individual versus collective outcomes may affect East Asian and Western audiences differently. Marketing messages and cues that are presented in isolation rather than embedded in social backgrounds may help reduce or enhance the rate of recall, depending on which audience is targeted. Sports marketing promotions directed at celebrating the individual prowess and achievement of a stellar athlete might lead to misdirected results and unintended effects among East Asian audiences. For those tasked with training future business professionals, the book serves an astute reminder that MBA training, with its emphasis on specialized functional disciplines and pedagogical discourse, can run counter to many East Asians' preference for holistic development and instructional methods that lead to collective harmony.
( n2) We acknowledge the assistance in preparing this review provided by Ana Valenzuela, Assistant Professor of Marketing, College of Business, San Francisco State University.
REFERENCES Earley, P. Christopher and Soon Ang (2003), Cultural Intelligence . Stanford, CA: Stanford University Press.
Hong, Ying-yi, Michael W. Morris, Chi-yue Chiu, and Veronica Benet-Martinez (2000), "Multicultural Minds: A Dynamic Constructivist Approach to Culture and Cognition," American Psychologist, 55, 709-720.
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By Gerardo R. Ungson, San Francisco State University and Daniel N. Braunstein, Oakland University
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Record: 163- The GMS: A Broad Conceptualization of Global Marketing Strategy and Its Effect on Firm Performance. By: Zou, Shaoming; Cavusgil, S. Tamer. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p40-56. 17p. 3 Diagrams, 4 Charts. DOI: 10.1509/jmkg.66.4.40.18519.
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The GMS: A Broad Conceptualization of Global Marketing Strategy and Its Effect on Firm Performance
Despite the strong interest in global marketing, there is no consensus in the literature about what constitutes a global marketing strategy and whether it affects a firm's global market performance. The authors develop a broad conceptualization of global marketing strategy, the GMS, that integrates three major perspectives--namely, the standardization, configuration-coordination, and integration perspectives--of global marketing strategy. They also develop a conceptual model that links the GMS to a firm's global market performance. On the basis of a survey of business units competing in global industries, the authors find support for the broad GMS perspective and the fundamental relationship between the GMS and firms' global market performance. The authors also discuss theoretical and managerial implications of their findings.
A fundamental proposition of international marketing is that a firm's global marketing strategy has a positive effect on its global market performance (e.g., Craig and Douglas 2000; Hout, Porter, and Rudden 1982; Jain 1989; Levitt 1983; Ohmae 1989; Yip 1995; Zou and Cavusgil 1996). Yet there is little agreement as to what constitutes global marketing strategy. In the current literature, there are three major perspectives: standardization (e.g., Jain 1989; Laroche et al. 2001; Levitt 1983; Samiee and Roth 1992), configuration-coordination (e.g., Craig and Douglas 2000; Porter 1986; Roth, Schweiger, and Morrison 1991), and integration (e.g., Birkinshaw, Morrison, and Hulland 1995; Yip 1995; Zou and Cavusgil 1996). The lack of a generally accepted conceptualization of global marketing strategy represents a major gap in the existing literature, because when different views of global marketing strategy are adopted by different researchers, ( 1) confusion can occur regarding not only the actual relationship between global marketing strategy and performance but also the rationale for that relationship, and ( 2) findings of different studies cannot be meaningfully compared, which hinders further advancement of knowledge. To facilitate further research, a unified conceptualization of global marketing strategy must be developed.
There is also a dearth of empirical support for the fundamental proposition that global marketing strategy affects a firm's performance. For example, Samiee and Roth (1992) find that there is no relationship between global marketing standardization and a firm's performance, whereas Cavusgil and Zou (1994) find a positive relationship between product adaptation and an export venture's performance. Because the validity of much of the knowledge in global marketing is based on the supposition that global marketing strategy positively affects a firm's global market performance, this lack of empirical support for the effect of global marketing strategy represents another major gap in the literature. It is therefore important to substantiate the effect of marketing strategy on a firm's performance in the global marketing context.
The purpose of the present research is twofold: First, we develop a broad conceptualization of global marketing strategy, named the GMS, to integrate the three major existing perspectives of global marketing strategy. Second, we attempt to substantiate the fundamental relationship between global marketing strategy and a firm's performance by developing and testing a conceptual model that links the proposed GMS to a firm's global market performance. Our aim is to fill the two major gaps in the global marketing literature by addressing two fundamental questions: ( 1) What is global marketing strategy? and ( 2) Does it matter whether a firm pursues a global marketing strategy?
The remainder of this article is organized into five major sections. First, we present a brief review of the global marketing literature, with an emphasis on the various perspectives of global marketing strategy. Second, we propose a new conceptualization of global marketing strategy, the GMS, that incorporates three major perspectives. Third, we develop a conceptual model that links the GMS to its antecedents and effects and advance the related research hypotheses. Fourth, we describe the research design and methodology of the study and present the results of the empirical analysis. Fifth, we discuss major findings and their theoretical and managerial implications.
As world markets have globalized, the effect of global marketing strategy on a firm's performance has been frequently discussed in the literature. Many researchers (e.g., Birkinshaw, Morrison, and Hulland 1995; Hamel and Prahalad 1985; Jain 1989; Levitt 1983; Ohmae 1989; Porter 1986; Yip 1995; Zou and Cavusgil 1996) argue that global marketing strategy plays a critical role in determining a firm's performance in the global market. Yet there is a lack of agreement about what constitutes global marketing strategy. In the current literature, there exist three major perspectives of global marketing strategy. Table 1 summarizes the main theoretical logic, the key variables, and the causes and effects associated with these three perspectives.
Perhaps the most influential view is the standardization perspective (see Jain 1989; Levitt 1983; Ohmae 1989; Samiee and Roth 1992). It views a firm as pursuing a global marketing strategy if its marketing programs across different countries are standardized, particularly with regard to its product offering, promotional mix, price, and channel structure (Johansson 1997; Keegan 2000). The influence of this view is reflected by the large volume of publications on the standardization/adaptation topic in the literature (e.g., Cavusgil, Zou, and Naidu 1993; Jain 1989; Laroche et al. 2001; Samiee and Roth 1992; Szymanski, Bharadwaj, and Varadarajan 1993a).
Proponents of standardization believe that world markets are being homogenized by advances in communication and transportation technology (Jain 1989; Levitt 1983). Increasingly, customers in distant parts of the world tend to exhibit similar preferences and demand the same products (Jain 1989; Ohmae 1985). Therefore, a major source of competitive advantage in the global market is the ability to produce high-quality, low-price products (Levitt 1983). To attain a low-cost position, the optimum global marketing strategy is to sell standardized products using standardized marketing programs. To these proponents, major benefits of standardization include economies of scale in production and marketing (Levitt 1983), consistency in dealing with customers (Laroche et al. 2001; Zou, Andrus, and Norvell 1997), and the ability to exploit good ideas on a global scale (Ohmae 1989; Quelch and Hoff 1986). Although the standardization approach is popular, several researchers have cautioned against its unconditional adoption (e.g., Boddewyn, Soehl, and Picard 1986; Douglas and Wind 1987). These researchers contend that a standardized strategy enhances performance only in industries in which competition is global in scope.
A second major perspective of global marketing strategy focuses on configuration and coordination of a firm's value-chain activities (Craig and Douglas 2000; Porter 1986; Roth, Schweiger, and Morrison 1991). According to this view, global marketing strategy is considered the means to exploit the synergies that exist across different country markets as well as the comparative advantages associated with various host countries. To be effective in global competition, a firm must configure its value-chain activities optimally and coordinate its efforts in different markets (Craig and Douglas 2000; Porter 1986; Roth 1992). On the basis of this perspective, proper configuration enables a firm to exploit location-specific comparative advantages through specialization (Craig and Douglas 2000; Ghoshal 1987; Kogut 1989; Yip 1995), whereas cross-national coordination captures synergies derived from economies of scale, scope, and learning (Bartlett and Ghoshal 1987; Kogut 1989; Roth 1992). A major aspect of configuration is the degree of concentration (Porter 1986; Roth, Schweiger, and Morrison 1991; Zou and Cavusgil 1996). Because different countries have unique comparative advantages (Hill 1996), concentration of value-chain activities in a few country locations where they can be performed most efficiently enables a firm to maximize efficiency. For example, product development and engineering activities can be concentrated in a limited number of countries where world-class engineering skills exist, whereas labor-intensive manufacturing can be concentrated in countries where low-cost labor is abundant. In this way, a firm can benefit from the comparative advantage of respective countries.
A third perspective of global marketing strategy is the integration view. It is concerned with how a firm's competitive battles are planned and executed across country markets. According to this view, a key to global marketing success is participation in all major world markets to gain competitive leverage and effective integration of the firm's competitive campaigns across these markets (Birkinshaw, Morrison, and Hulland 1995; Yip 1989, 1995; Zou and Cavusgil 1996). In global industries, operations in different countries are interdependent, and a firm must be able to subsidize operations in some markets with resources generated in others (Birkinshaw, Morrison, and Hulland 1995; Ghoshal 1987; Hamel and Prahalad 1985) and respond to competitive attacks in one market by counterattacking in others (Yip 1989, 1995). Thus, in the integration perspective, the essence of global marketing strategy is to integrate the firm's competitive moves across the major markets in the world (Birkinshaw, Morrison, and Hulland 1995; Ghoshal 1987).
Regardless of the perspective adopted, most researchers ascribe a firm's positive performance in global industries to the pursuance of a global marketing strategy. Although this relationship is often presumed (see Jain 1989; Ohmae 1989; Porter 1986; Yip 1995), empirical evidence is limited and inconclusive. For example, Samiee and Roth (1992) find no significant relationship between global standardization and a firm's performance, whereas Szymanski, Bharadwaj, and Varadarajan (1993a) find that businesses are better off standardizing their strategic resource mix and competitive strategy across Western markets. In the exporting context, Cavusgil and Zou (1994) find a positive relationship between product adaptation and export venture performance. The lack of consistent and conclusive empirical evidence for the effect of global marketing strategy on performance is a serious issue, because if the effect cannot be substantiated, much of the knowledge in global marketing could be called into question.
Our review of the literature suggests that a major reason for the inconclusiveness in the literature is the lack of a unified conceptualization of global marketing strategy. Without a broad construct of global marketing strategy, there are at least two major problems: First, with a few exceptions (e.g., Yip [1989] and Johansson and Yip [1994], who take a broad view of global marketing strategy), previous researchers often subscribe to one perspective of global marketing strategy and use the same term to refer to different aspects of a firm's behavior, producing results that are difficult to compare and sometimes contradictory. Second, the richness of global marketing strategy cannot be exploited. Adopting only one perspective means that the full range of options for a firm to improve its global market performance may not be captured, which leads to partial explanation of a firm's performance and to incomplete theory.
Another reason for the inconclusiveness in the literature is the dearth of cross-sectional empirical studies that test the effect of global marketing strategy on performance (Szymanski, Bharadwaj, and Varadarajan 1993a). Most studies draw their conclusions from anecdotal cases of company experience. In addition, with the notable exception of Johansson and Yip (1994), the few cross-sectional empirical studies that exist have focused on only one perspective of global marketing strategy: the standardization perspective (e.g., Laroche et al. 2001; Samiee and Roth 1992; Szymanski, Bharadwaj, and Varadarajan 1993a), the configuration-coordination perspective (e.g., Roth 1992), or the integration perspective (e.g., Birkinshaw, Morrison, and Hulland 1995). Compounding the problem is the lack of a unified scheme for measuring global marketing strategy. When different measurement schemes are used, findings of different studies cannot be meaningfully compared. As a result, confusion is likely to occur when researchers attempt to generalize the findings of a study to another context, hindering further advancement of knowledge.
In light of the limitations associated with the existing literature, it is important to develop a broad conceptualization of global marketing strategy that can incorporate the existing perspectives. It is also essential to empirically substantiate the effect of global marketing strategy on a firm's performance by means of a cross-sectional study.
The prominence of the three perspectives of global marketing strategy in the literature suggests that a new conceptualization of global marketing strategy should be sufficiently broad to accommodate all three perspectives. From the preceding discussion and Table 1, it is evident that this is feasible on two grounds. First, each perspective focuses on different aspects of a firm's global marketing behavior but on the same goal: enhancing the firm's performance in the global market. Each offers a partial explanation of how to do that. Specifically, the standardization perspective emphasizes how much a firm's product, promotion, channel structure, and price are standardized across the country markets. In contrast, the configuration-coordination perspective focuses on the degree to which a firm's value-chain activities are concentrated in a few country locations and coordinated across the countries, whereas the integration perspective stresses a firm's competitive leverage through participation in the major markets and integration of competitive moves across country markets.
Second, the theoretical logic of how to enhance a firm's global market performance that underlies each perspective is different but not mutually exclusive. The pursuance of a perspective does not preclude a firm from pursuing another perspective. It should be recognized, however, that the extent to which a perspective can be applied may be constrained by the product market environments as well as by the internal organizational characteristics. The global marketing literature stresses the degree to which each perspective can be pursued, as opposed to the extreme points of the perspective (e.g., Cavusgil and Zou 1994; Jain 1989). Indeed, the three perspectives can be pursued simultaneously to the extent that they fit the external market environments and internal organizational characteristics.
Building on the literature and the rationale advanced previously, we define the GMS as the degree to which a firm globalizes its marketing behaviors in various countries through standardization of the marketing-mix variables, concentration and coordination of marketing activities, and integration of competitive moves across the markets. In contrast to a domestic marketing strategy, which is concerned with the content of the elements of a specific marketing program in a single country, the GMS is concerned with the relationship among the firm's marketing operations across countries. The aim of the GMS is to enhance the firm's over-all performance on a worldwide basis. In Figure 1, the GMS is presented as the second-order factor of eight dimensions that are derived, respectively, from the standardization, configuration-coordination, and integration perspectives of global marketing strategy.[ 1] These GMS dimensions capture diverse perspectives to examine the relationship among the firm's various country-based marketing strategies. The definitions of these eight GMS dimensions are presented in Table 2.
In the remainder of this article, "the GMS" is used specifically to denote this newly defined broad concept of global marketing strategy, whereas "global marketing strategy" is used in a generic sense. The GMS has two key characteristics: First, the GMS effectively integrates all three major existing perspectives of global marketing strategy. Thus, each perspective becomes a special case of the GMS.
Compared with previous studies, the GMS offers a more complete explanation of how a firm enhances its performance in the global market. Second, drawing from previous studies, distinctive GMS dimensions are used to represent each of the three perspectives of global marketing strategy. Specifically, consistent with Cavusgil, Zou, and Naidu (1993), Cavusgil and Zou (1994), and Cooper and Klein-schmidt (1985), the standardization perspective is captured by the standardization of four marketing-mix elements: product standardization, promotion standardization, standardized channel structure, and standardized price.
Following Craig and Douglas (2000), Porter (1986), Roth, Schweiger, and Morrison (1991), and Roth (1992) and focused on the marketing activities in a firm's value-chain, the configuration-coordination perspective is summarized by concentration and coordination of marketing activities. In the present study, marketing activities refer to the process of developing promotional campaigns, making pricing decisions, engaging in distribution activities, and implementing after-sale services. These are different from the firm's marketing program, which is the focus of the standardization perspective. In accord with Birkinshaw, Morrison, and Hulland (1995) and Johansson and Yip (1994), the integration view is represented by global market participation and integration of competitive moves. In the following discussion, the GMS is linked to its main antecedents and performance effects.
Theoretical Foundations
Two theoretical perspectives are central to explaining the relationship between the GMS and a firm's performance: the industrial organization (IO) theory and the resource-based view (RBV). The IO theory focuses on the external market to identify drivers of a firm's strategy and contends that the firm's performance is determined by its strategy. The RBV uses the firm's internal organizational resources to explain its strategy and performance (Barney 1991; Collis 1991; Deligonul and Cavusgil 1997).
According to the IO framework, external market and industry structure determines a firm's strategy (conduct), which in turn determines its performance (Porter 1980; Scherer and Ross 1990). The IO framework is best captured in the principle of co-alignment, which contends that the fit (or congruency) between a firm's strategy and its environment has significant, positive implications for performance (Venkatraman and Prescott 1990).
In the IO framework, competitive advantage is viewed as a position of superior performance that a firm attains through offering either undifferentiated products at low prices or differentiated products for which customers are willing to pay a price premium (see Day 1994; Porter 1980, 1985). Strategy is conceived as a firm's deliberate response to the external industry/market imperatives, whereas competitive advantage can be sustained by business strategy (Barney 1991; Porter 1980). The premise is that the external market or industry imposes selective pressures to which a firm must respond (Conner 1991). Firms that respond successfully to these pressures through formulating and implementing a strategy will survive and prosper, whereas those that fail to respond are doomed to failure (Collis 1991). Therefore, in the IO framework, the principal determinant of performance is a firm's strategy, and the primary drivers of the firm's strategy are external market forces. The RBV recognizes the importance of a firm's internal organizational resources as determinants of the firm's strategy and performance (Barney 1991).
The RBV theorists contend that the differential endowment of strategic resources among firms is the ultimate determinant of their performance (Barney 1991; Grant 1991; Wernerfelt 1984). The term "resources" is used in a broad sense by the RBV theorists. Barney (1991) defines internal organizational resources as all assets, capabilities, organizational processes, firm attributes, information, knowledge, and so forth that are controlled by a firm and that enable it to conceive and implement strategies to improve its efficiency and effectiveness. Porter (1991, 1996) argues that the most critical resources are those that are superior in use, hard to imitate, difficult to substitute for, and more valuable within the firm than outside. Prahalad and Hamel (1990) and Hamel and Prahalad (1994) use the term "core competence" for this type of internal organizational resources.
According to the RBV, a firm's competitive advantages reside in the inherent heterogeneity of the immobile strategic resources the firm controls (e.g., Barney 1991; Porter 1991). Strategy is viewed as a firm's conscious move to leverage its idiosyncratic endowment of strategic resources (Hamel and Prahalad 1994; Lado, Boyd, and Wright 1992; Wernerfelt 1984). Thus, the principal drivers of a firm's competitive strategy and performance may be internal to the firm. Although the RBV recognizes that a firm's physical resources are important determinants of performance, it places primary emphasis on the intangible skills and resources of the firm (Barney 1991; Collis 1991; Hamel and Prahalad 1994; Porter 1996), such as organization culture and experience.
A Structural Model of the GMS
Building on the IO framework and the recent developments in the RBV, as well as the prior studies on global marketing strategy, we present a structural model of the GMS and a firm's global market performance in Figure 2. The core tenet of this model is that when there is a good fit between the newly conceptualized GMS and a firm's external market environment and internal organizational characteristics, the GMS is a key determinant of the firm's performance in the global market. By focusing on the GMS, the model incorporates all three major perspectives of global marketing strategy in the literature. Moreover, the GMS is posited to be driven by both external globalizing conditions, as suggested by the IO framework, and internal firm characteristics such as international experience and global orientation, as suggested by the RBV in the recent strategy literature. It is also noteworthy that the proposed model is different from a typical marketing strategy-performance model in that it is focused on how the relationship among a firm's marketing strategies in various countries affects the firm's worldwide performance.
Model Components and Hypotheses
The GMS and firms' global market performance. The GMS is posited in the model as a key determinant of a firm's global market performance. Consistent with previous studies (e.g., Cavusgil and Zou 1994; Samiee and Roth 1992), a firm's global market performance is conceived as having both a strategic and a financial dimension and is assessed on a worldwide basis that includes the domestic market. Strategic performance refers to a firm's global market share and competitive position relative to major rivals, whereas financial performance involves the firm's efficiency in carrying out global marketing, including its cost position, sales growth, and profitability in the global market. Although financial performance is the ultimate goal for many firms, strategic performance is a vital intermediary gauge because it can lead to enhanced financial performance. For example, a firm's market share has been found to affect its profitability (Buzzell and Gale 1987; Szymanski, Bharadwaj, and Varadarajan 1993b).
As noted previously, all three perspectives of global marketing strategy have a common focus on enhancing a firm's performance in the global market, though each emphasizes a particular aspect of the firm's global marketing. Therefore, individual dimensions of the GMS that originate from the three perspectives of global marketing strategy are believed to fit the external forces in the global industries (Ghoshal 1987; Jain 1989; Levitt 1983; Porter 1986; Samiee and Roth 1992; Yip 1995; Zou and Cavusgil 1996). On the basis of the principle of co-alignment (Venkatraman and Prescott 1990), the GMS should have a positive effect on the firm's global market performance. Therefore, we expect that the GMS influences a firm's global strategic performance and financial performance positively:
H1: A firm's global strategic performance is positively influenced by the GMS.
H2: A firm's global financial performance is positively influenced by the GMS.
H3: A firm's global financial performance is positively related to its global strategic performance.
External globalizing conditions. External globalizing conditions are defined as external forces that are conducive to global marketing. In global industries, many external forces may prompt a firm to adopt the GMS. In the model, the five-item composite scale that Samiee and Roth (1992) develop to incorporate "five prerequisites" of global marketing is taken as the summary measure of external globalizing conditions. This scale summarizes many external causes of the GMS (see Samiee and Roth 1992, p. 8), such as the convergence of consumer demand (Levitt 1983; Yip 1995), the technological infrastructure for global marketing (Yip 1995; Zou and Cavusgil 1996), and the competitive pressure in the global market (Porter 1986; Yip 1995). When globalizing conditions exist, it is feasible and advisable for a firm to participate in all major world markets (Levitt 1983; Ohmae 1985), standardize its marketing programs (Jain 1989; Levitt 1983; Samiee and Roth 1992), concentrate and coordinate its worldwide marketing activities (Porter 1986; Roth, Schweiger, and Morrison 1991), and integrate its competitive moves across markets (Birkinshaw, Morrison, and Hulland 1995; Yip 1995). Therefore, the following relationship is expected:
H4: The GMS is positively influenced by external globalizing conditions.
Global orientation. Recent literature highlights the significance of internal organizational resources in determining a firm's strategy and performance (e.g., Bartlett and Ghoshal 1991; Kim and Mauborgne 1991; Prahalad and Hamel 1990; Wernerfelt 1984, 1995). Although many resources may play a role, two factors stand out in the proposed model of the GMS: global orientation (Ohmae 1989; Perlmutter 1969) and international experience (Cavusgil and Zou 1994; Douglas and Craig 1989). Global orientation is the organization-wide emphasis on success on a worldwide basis rather than on a country-by-country basis (Ohmae 1989; Zou and Cavusgil 1996). Global orientation is a part of a firm's organization culture because it reflects the firm's value (Kotter and Heskett 1992). In Perlmutter's (1969) framework, involving firms' ethnocentric, polycentric, regiocentric, and geocentric orientations, a firm with an ethnocentric orientation focuses on success in its domestic market, whereas a firm with a polycentric orientation responds to idiosyncratic local demands with customized marketing programs and seeks success on a country-by-country basis. A firm with a regiocentric/geocentric orientation treats the region/world as a single market and seeks success on a region-wide/world-wide basis.
Global orientation is a valuable organizational resource because it is difficult to copy in a firm's global rationalization of marketing operations (Douglas and Craig 1989) and has been linked to a firm's adoption of global marketing strategy (Ghoshal 1987; Levitt 1983). With a global orientation, the corporate headquarters would adopt an "equidistant" perspective toward various markets in the world and strive to achieve success on a worldwide basis (Ohmae 1989), and subsidiaries would be willing to contribute to and make sacrifices for the global performance of the firm. Thus, with a global orientation, subsidiaries are more likely to support the headquarters' mandate to pursue a global marketing strategy (Quelch and Hoff 1986; Yip 1995). Therefore,
H5: The GMS is positively influenced by a firm's global orientation.
International experience. The value of international experience for global marketing is well established in the literature. Douglas and Craig (1989) stress this as a driver in a firm's global expansion. Their three-stage evolutionary model of international marketing contends that the most experienced international firms are likely to seek global rationalization of their marketing operations, whereas the less experienced are unlikely to do so (Douglas and Craig 1989). International experience affects not only whether a firm pursues a global marketing strategy but also how well it positions itself in the global market. Experienced international firms are more likely to identify strategic markets to enter, respond to changing global market environment, and take advantage of the differential comparative advantages of various countries (Hill 1996). Such firms are more likely to enjoy superior competitive positions in the global market. Cavusgil and Zou (1994) offer empirical support for the positive effect of international experience on a firm's export performance. Therefore, the following effects of international experience are expected:
H6: The GMS is positively influenced by the firm's international experience.
H7: A firm's global strategic performance is positively influenced by its international experience.
Sampling Frame
A cross-industry mail survey of business units (BUs) competing in global industries was conducted. The BU, as opposed to the multinational corporation (MNC) as a whole, was selected for analysis because most MNCs are so well diversified that their various BUs may face a diverse set of globalizing conditions, have different internal characteristics, and pursue different strategies. A focus on the MNC would inevitably introduce measurement errors and possibly invalidate the research findings.
The selection of the global industries paralleled the procedures developed by Samiee and Roth (1992). First, a thorough review of the literature (e.g., Ghoshal 1987; Hamel and Prahalad 1985; Hout, Porter, and Rudden 1982; Johansson and Yip 1994; Porter 1980, 1986; Roth, Schweiger, and Morrison 1991; Samiee and Roth 1992; Yip 1995) revealed more than 40 industries that are global in scope. Second, we decided to focus only on manufacturing firms, because the literature revealed a fundamental difference in international strategy between service and manufacturing firms (e.g., Erramilli and Rao 1993). This reduced the list to 28. Third, we examined the trade ratio of each industry, because Porter (1986) and Samiee and Roth (1992) argue that a high level of intra-industry trade indicates the interdependency necessary for an industry to be global. We set a 30:70 trade ratio (i.e., 30% of total industry sales are accounted for by intra-industry trade) as the minimum qualification, and this limited the sampling frame to 23 manufacturing industries.[ 2] Fourth, we examined the 23 industries to ascertain that at least one firm competes globally (Samiee and Roth 1992). All 23 candidates met this criterion.
Within the 23 global industries, 434 BUs were identified through D&B's America's Corporate Families and The Directory of Corporate Affiliations. Four criteria were used in selecting these BUs: First, the BU needed to be based in the United States, though the parent company could be based elsewhere. This criterion was used to avoid adding extra costs to data collection (such as international postage and translation and back-translation costs) without restricting the subjects to U.S. firms only. Second, the BU needed to have operations in Japan or Europe in addition to the United States so that it was indeed competing in major foreign markets, not just in the United States. Third, the BU needed to have at least 200 employees. Fourth, annual sales of the BU needed to total at least $20 million. These criteria, which are in line with Dess, Ireland, and Hitt's (1990) recommendation for controlling industry effects, were considered necessary to enhance the relative homogeneity of the sampling frame so that any relationships discovered could not be attributed to extraneous factors.
Instrument Development
We developed a structured survey instrument in several stages. First, we screened the literature on global marketing to identify verified scale items for measuring the factors in this research. Although some had been developed by previous researchers, such as Samiee and Roth's (1992) globalizing conditions and Roth, Schweiger, and Morrison's (1991) concentration and coordination of value-chain activities, we developed others for the present study.
Second, a list of items that would be potentially useful as measures was developed on the basis of the literature. These items were then expanded into Likert-type statements anchored by a seven-point scale ranging from "strongly disagree" ( 1) to "strongly agree" ( 7). Third, personal interviews were conducted with three MNC executives responsible for international operations and with four scholars familiar with global marketing research. All were asked to evaluate whether the statements ( 1) were meaningful, understandable, "loaded," or offensive and ( 2) were valid measures of the factors proposed in the model. On the basis of these interviews, some statements were dropped, and a few were modified. For example, the item measuring the standardized price was dropped, because the executives believed that they could not provide accurate information on this: Local regulations and competitive situations were such that their BUs had little control over the final prices of their products in foreign markets. Fourth, the revised instrument was sent back to the same executives and scholars to ensure that they were satisfied with the changes. The modified instrument was then adopted with some amendments.
Fifth, as a pretest, 12 BUs were randomly chosen from the sampling frame of 434. The preliminary instrument was sent to the chief executive officer (CEO) or president of each for an evaluation of the questionnaire length, the time needed to complete it, and the content of individual items. Three "expert researchers" in the field also were asked for an evaluation. The final questionnaire reflected the feedback of three responding CEOs and the three experts. Consistent with Dillman's (1978) suggestion, the instrument was printed as a booklet. Instructions at the beginning asked respondents to refer to "the most important product (or product line)" of their business unit when completing it. This was designed to avoid the confounding problem that could occur if a BU had more than one product and a different strategy for each.
Measurement of the Constructs
The GMS. We developed scales for measuring the first-order dimensions of the GMS on the basis of prior literature. With the exception of standardized channel structure, which was measured by a single item, multiple indicators were employed to measure directly how a BU pursued each dimension. We developed measures for product standardization and promotion standardization on the basis of Cavusgil and Zou's (1994) and Yip's (1991) work. These were intended to gauge the degree to which a BU's product and promotional mix (e.g., advertising and sales promotion) are standardized across the markets. Measures for coordination and concentration of marketing activities were adapted from Roth, Schweiger, and Morrison (1991) to four important marketing activities: development of promotional campaigns, pricing decisions, distribution activities, and after-sale services. For global market participation, Yip's (1991) measures were expanded and adapted. Finally, measures for integration of competitive moves were developed on the basis of Hout, Porter, and Rudden's (1982), Hamel and Prahalad's (1985), and Yip's (1995) work. All items were registered on Likert-type scales with seven points, except concentration and coordination of marketing activities, which were measured on a seven-point bipolar scale.
Global market performance. On the basis of Cavusgil and Zou's (1994) and Porter's (1985, 1986) work, we developed multiple measures for a BU's global strategic performance. These items were designed to assess the BU's global market share, competitiveness, strategic position, and leadership position relative to major rivals. Similarly, measures for global financial performance were adapted from Cavusgil and Zou (1994), Roth, Schweiger, and Morrison (1991), and Samiee and Roth (1992). These were designed to assess the BU's global cost position, sales, profitability, and return on investment relative to major rivals. These items were formatted in Likert-type statements and registered on a seven-point scale.
Global orientation and international experience. We developed measures of global orientation on the basis of the work of Perlmutter (1969), Levitt (1983), and Ohmae (1989). These were designed to tap the BU's perceived importance of the global market, subsidiaries' willingness to make sacrifices for better performance of the BU as a whole, and the BU's equidistant perspective. We used a seven-point Likert scale for each. Similarly, items for measuring the BU's international experience were adapted from Cavusgil, Zou, and Naidu (1993). We used these items to assess the BU's length of international involvement and management's experience abroad. They were also measured on a seven-point Likert scale.
External globalizing conditions. We adopted the five-item scale developed by Samiee and Roth (1992) to assess the external globalizing conditions. They were designed to ascertain the degree to which ( 1) customer needs are standardized worldwide, ( 2) product awareness and information exist worldwide, ( 3) standardized product technology exists worldwide, ( 4) standardized purchasing practices exist worldwide, and ( 5) major competitors market standardized products worldwide (SamieeandRoth1992,p.8). These five items reflect managers' perceptions of their BUs' external environment and are described by Samiee and Roth (1992) as the prerequisites of global marketing. We used a seven-point Likert scale in this study to register the responses.
Data Collection
Data were gathered in three phases. Initially, a personalized cover letter, the survey instrument, and a postage-paid business reply envelope were sent to the CEO, president, or vice president for international operations of each of the 422 BUs remaining in the sampling frame (12 were used for the pretest). Three weeks thereafter, completed questionnaires had been returned by 72 BUs. Another 15 questionnaires were returned as undeliverable because of a wrong mailing address or because the addressee had retired or was no longer with the BU.
The second phase started three weeks after the initial mailing. A personalized cover letter, a replacement copy of the survey instrument, and a postage-paid business reply envelope were sent to those who had not responded. Four weeks later, completed questionnaires had been returned by another 40 BUs. A few months later, with new research funding, a third mailing was conducted. A personalized cover letter, a copy of the questionnaire, and a postage-paid business envelope were sent to the executives who had not responded previously. From this third mailing, 14 completed questionnaires were received but 10 other questionnaires were returned as undeliverable because the executives were no longer with the BUs.
Overall, completed questionnaires were received from 126 BUs, of which 44 were from CEOs or presidents and 82 were from vice presidents. Twenty-three BUs wrote back that they were unable to respond because the survey was inappropriate to their experience or it was the company policy not to respond to any survey. Excluding the undeliverable cases and those who believed that it was inappropriate to respond, the overall effective response rate was 33.6% (126 of 374). The BUs in the sample averaged approximately 1467 employees, $341 million in annual sales, and 11 years of international experience. Of the 126 BUs in the sample, approximately 21% marketed consumer products, 45% marketed industrial products, and the remainder marketed both.
Assessment of Nonresponse Bias
We assessed potential nonresponse bias by comparing the responding and nonresponding BUs, as well as the early and late respondents (Armstrong and Overton 1977). In the former comparison, there was no significant difference in BUs' number of employees, but the responding BUs had higher sales volume than the nonresponding ones (t = -2.537, p < .01). In the latter comparison, we compared the BUs that responded to the first mailing with those that responded to later mailings in terms of annual sales, number of employees, performance, international experience, global orientation, and globalizing conditions. Using t-tests, we found no significant difference at the .05 level in these comparisons. Overall, nonresponse bias does not seem to be a serious concern.
To assess the GMS scale and the measurement model of the factors and to test the structural model and the related research hypotheses, we adopt a two-stage data analysis approach recommended by Anderson and Gerbing (1988) and Hunter and Gerbing (1982) in the present study. First, we perform a second-order confirmatory factor analysis (CFA) to test the convergent validity and discriminant validity of the GMS dimensions, as well as the entire measurement of the factors included in the structural model. We calibrate the loadings of the first-order GMS dimensions on the GMS, the second-order factor, in this analysis. In addition, the covariance matrix of the GMS and other investigated factors are obtained for subsequent analysis. Second, we conduct a structural path analysis on the covariance matrix obtained from the first-stage analysis to test the structural model and the related research hypotheses. The advantages of separating the measurement model from the structural model for analysis over simultaneous assessment of the two models have been well articulated by Anderson and Gerbing (1988) and Hunter and Gerbing (1982). These include the ability to pinpoint model misspecification and the opportunity to minimize the potential for interpretational confounding (Anderson and Gerbing 1988, p. 418). The practice is also well accepted in the marketing literature (e.g., Li and Calantone 1998).
The Second-Order CFA
To assess the measurement model of the GMS scale and other factors, we carried out a second-order CFA. In the measurement model, the GMS is the second-order factor of seven first-order GMS dimensions. The standardized price dimension of the GMS was not included in the analysis because, as mentioned previously, the executives indicated that their BUs had little control over the final prices in the foreign markets and that they could not respond to the question accurately. As a result, we dropped the question about standardized price from the questionnaire.
The second-order CFA model was fitted by the elliptical reweighted least square (ERLS) procedure of the EQS program (see Bentler 1995). Elliptical distributions are a broad family of statistical distributions that include the normal distribution as a special case. Because the elliptical distributions are not constrained regarding kurtosis, the use of ERLS is recommended over the maximum likelihood (ML) when the assumption of normality may not be met (Browne 1984).According to Sharma, Durvasula, and Dillon (1989), the performance of ERLS is equivalent to that of ML for normal data and superior to that of other estimation techniques for nonnormal data. Given the nature of the measures of marketing constructs, the assumption of multivariate normality is seldom met in an empirical data set, and as a result, ERLS is a desirable procedure to use in marketing research (Mohr and Sohi 1995; Singh 1993). In EQS, the ML estimates are obtained by an iterative process that starts at the input parameter values supplied by the researcher or by default values, whereas the ERLS estimates are obtained by starting the iterative process with converged values based on the ML (Bentler 1995, p. 47). The results of the analysis are shown in Table 3. Consistent with Anderson and Gerbing's (1988) suggestion for purifying the measurement model, four items were dropped because of their low loadings.
To evaluate the fit of the second-order CFA measurement model of the GMS scale and other factors, we followed the procedure recommended by Bagozzi and Yi (1988). First, we screened the univariate and multivariate statistics of the input variables and detected no apparent outlier. Second, we examined the EQS outputs but found no anomalies and no special problem in the minimization process. In addition, the variance estimates were all significantly greater than zero. These findings suggest that the estimation process converged properly. Third, we examined the model fit statistics. The ERLS chi-square is 900.52 (degrees of freedom = 576), which is statistically significant at the .05 level. However, because the chi-square statistic should not be used as the sole measure of model fit, we examined other measures of model fit (Bentler and Bonett 1980). We found that the Bentler-Bonett nonnormed fit index, the Bentler-Bonett normed fit index, the comparative fit index, and the Bollen's fit index are .901, .786, .910, and .911, respectively. Given the relatively complex nature of the measurement model, which includes a second-order factor (i.e., the GMS), these fit indices suggest that the second-order CFA model fit the data adequately (Bentler 1995; Bollen 1989).
Fourth, we checked the internal structure of the model and tested the convergent validity of the GMS dimensions and other factors. We found that the normalized residuals are small, and there is no improper solution in the output. More important, the loadings of items on their respective factors are all positive, high in magnitude, and statistically significant. In addition, the loadings of the seven first-order GMS dimensions on the GMS are also positive and statistically significant. These findings indicate that the factors in the measurement model, including the GMS's first-order dimensions, have strong convergent validity (Anderson 1987; Anderson and Gerbing 1988). Finally, to assess the discriminant validity of the factors in the measurement model, we conducted two types of analysis. In the first type of analysis, we followed the procedure recommended by Bagozzi, Yi, and Phillips (1991). Specifically, we conducted a series of CFAs to test whether, for every pair of the factors in the measurement model, a two-factor model would fit significantly better than a one-factor model (Anderson 1987; Bollen 1989). If the two-factor model fits significantly better than the one-factor model, the discriminant validity of the two factors is supported (Bagozzi, Yi, and Phillips 1991). Because the one-factor model is nested within the two-factor model, we used the chi-square difference test to assess whether the two-factor model fits better than the one-factor model (Bollen 1989). In all cases, we found that the two-factor model fits significantly better than the one-factor model, which suggests the presence of discriminant validity.3
In another type of analysis based on Fornell and Larcker's (1981) suggestion, we found that the average variance extracted by the measure of each factor is larger than the squared correlation of that factor's measure with all measures of other factors in the model. Thus, the factors exhibit discriminant validity (Fornell and Larcker 1981). On the basis of the findings of these two types of analysis, we conclude that all factors in the measurement model possess strong discriminant validity (Bagozzi, Yi, and Phillips 1991; Fornell and Larcker 1981) and the seven first-order GMS dimensions are indeed distinctive dimensions of the GMS scale.
Combining all aspects of the model evaluation described previously, we conclude that all factors in the measurement model posses both convergent and discriminant validity and that the second-order CFA model fits the data adequately.
Test of the Structural Path Model
We tested the structural model of the GMS with its antecedents and effects in Figure 2 by a path analysis using the EQS program. This approach to fitting the structural path model after the measurement model has been purified is suggested by Anderson and Gerbing (1988) and Hunter and Gerbing (1982) and is consistent with the approach taken by Cavusgil and Zou (1994) and Li and Calantone (1998). Specifically, we applied the ERLS procedure within the EQS program to the variance-covariance matrix of the GMS scale and other factors that was obtained from the first-stage analysis. No anomalies or special problems were encountered, and the program converged properly. Figure 3 shows the major parameter estimates and fit statistics of the structural model.
As shown in Figure 3, the chi-square is 27.664, which, with 5 degrees of freedom, is significant at the .05 level. Because the chi-square should not be used alone to evaluate model fit (Bollen 1989), we also examined other fit indices, parameter estimates, and the internal structure of the model to evaluate the model, following the procedure recommended by Bagozzi and Yi (1988). We found that the Bentler-Bonett normed fit index is .925, the nonnormed fit index is .808, the comparative fit index is .936, and Bollen's fit index is .938. In addition, the standardized residuals are small, and all parameter estimates are in the expected direction. The high fit indices and the theoretically consistent parameter estimates suggest that the structural path model fits the data well. Thus, we conclude that the path coefficients adequately represent the relationships between the GMS scale and its antecedents and effects.
We then used the estimates of the path coefficients to test the hypothesized relationship between the GMS scale and other factors. The path coefficients in Figure 3 show that a BU's global financial performance is influenced positively and significantly by the GMS (t = 2.077, p < .05) and by its global strategic performance (t = 4.601, p < .01). Thus, H2 and H3 are supported. In addition, a BU's global strategic performance is influenced positively and significantly by the GMS (t = 4.681, p < .01) and by its international experience (t = 4.185, p < .01). Thus, support is found for H1 and H7.
When examining the antecedents of the GMS, we found that the GMS is influenced positively and significantly by the external globalizing conditions (t = 5.481, p < .01), by the BU's global orientation (t = 12.173, p < .01), and by the BU's international experience (t = 3.503, p < .01). Thus, H4, H5, and H6 are supported. Overall, the structural path model fits the data adequately, and the hypothesized relationships between the GMS and its antecedents and consequences are supported by the findings.
To shed additional light on how individual GMS dimensions are related to a BU's global market performance, we used the loadings of the first-order GMS dimensions on the second-order GMS scale in Table 3 in conjunction with the path coefficients from the GMS to a BU's global market performance in Figure 3 to estimate the effect size of the individual GMS dimensions on firm performance. We summarize the results in Table 4. On the basis of the effect size of individual GMS dimensions, we can make several observations.
First, a BU's global strategic performance is influenced, in the order of the effect size, by the BU's global market participation, integration of competitive moves, promotion standardization, product standardization, and coordination of marketing activities. The effects of standardized channel structure and concentration of marketing activities on the BU's global strategic performance are relatively small. Second, a BU's global financial performance is influenced by its global market participation, integration of competitive moves, promotion standardization, and product standardization. The effects of standardized channel structure, coordination of marketing activities, and concentration of marketing activities are relatively small. It appears, therefore, that the GMS dimensions derived from the integration and the standardization perspectives of global marketing strategy contribute most significantly to the BU's global market performance, whereas those related to the configuration- coordination perspective have a relatively small effect on the BU's global market performance.
The GMS
There is little agreement in the literature as to what constitutes global marketing strategy. Previous research often narrowly conceives it as standardization of marketing programs, configuration and coordination of value-chain activities, or integration of competitive campaigns. In the present study, we propose the GMS as a broad conceptualization of global marketing strategy that incorporates all three perspectives. Our empirical findings lend support to this broad GMS conceptualization and provide a second-order GMS scale for measuring it. Individual GMS dimensions are shown to be distinctive, yet related, global marketing behaviors of the firm. In addition, the GMS scale is shown to be related to a firm's global market performance, the external globalizing conditions, and the firm's global orientation and international experience. Thus, the GMS serves as a broad, unified view of what constitutes global marketing strategy.
The GMS perspective developed in this study has several implications for theory development in the global marketing literature. It provides a broad basis for resolving the current incongruence in the definition of global marketing strategy. Instead of using the term "global marketing strategy" narrowly to denote standardization, configuration and coordination, or integration, scholars should use the term to represent a broader perspective such as the GMS proposed here. In addition, the GMS serves as a foundation for integrating previous research findings and reconciling their differences. For example, some inconsistent findings obtained in prior works could be explained by researchers' focus on different dimensions of global marketing strategy and their adoption of various measurement schemes.
As a broad perspective, the GMS reflects an entire set of a firm's marketing actions, all within the control of management. Management can exercise much discretion as to the degree of standardization it should seek in product, promotion, or channel structure. Similarly, the firm can choose to locate its marketing activities at home, abroad, or in a combination of countries. Furthermore, it can decide how much coordination is desirable in its worldwide marketing activities between headquarters and country subsidiaries as well as among subsidiaries. Management also may assess the linkages among key regions of the world and determine whether the firm's competitive moves should be integrated across these regions. We argue that, as a whole, these considerations contribute to the firm's global marketing strategy and eventually determine its performance in world markets. These insights should enrich the knowledge of successful global marketing and stimulate further research in the area.
The Effect of the GMS on Performance
Another significant finding of this study is that the GMS has a positive and significant effect on a firm's global market performance. Specifically, the GMS is found to influence firm's strategic performance positively in the global market. Presumably, the GMS enables the firm to gain competitive advantages in the global market through its effects on efficiency (Jain 1989; Levitt 1983), synergy (Craig and Douglas 2000; Porter 1986, Roth, Schweiger, and Morrison 1991), and cross-subsidization (Hamel and Prahalad 1985; Yip 1995). The GMS is also found to affect a firm's global financial performance, both directly and indirectly through its effect on the firm's global strategic performance. In summary, our findings help substantiate the fundamental relationship between global marketing strategy and a firm's global market performance when global marketing strategy is broadly construed as the GMS.
This finding is significant because the literature is inconclusive about the effect of global marketing strategy. For example, Samiee and Roth (1992) conclude that a firm's global marketing standardization has no significant effect on its performance. Yet much of the existing knowledge in global marketing is grounded in the assumption that global marketing strategy affects firm performance positively. Should a firm's global marketing strategy have no effect on its performance, the validity of much of the global marketing literature would be called into question.
The present study offers much-needed empirical support for the fundamental strategy-performance link in the global market context. Indeed, it reaffirms the fundamental tenet of the global marketing literature and provides an empirical foundation for further research in the global marketing area.
Drivers of the GMS
We found that the GMS is driven by external globalizing conditions as well as by a firm's global orientation and international experience. These findings are consistent with both the IO framework and the RBV literature. When external forces create pressure for firms to achieve global economies of scale, deal with market interdependency, or seek cross-country synergies, firms should develop and implement the GMS. This will help attain a fit between the firm's strategy and the external environment, leading to positive performance (Scherer and Ross 1990; Venkatraman and Prescott 1990). Yet firms also must assess their internal organizational characteristics before committing to the GMS. In particular, a firm with a global orientation and/or substantial international experience is in the best position to adopt the GMS. Such a firm is more likely to understand the global market trend, and its subsidiaries are more willing to make sacrifices for the welfare of the firm as a whole (Bartlett and Ghoshal 1987; Douglas and Craig 1989).
In practice, especially over the past two decades, managers have increasingly embraced the goal of creating a global culture and organization. Ample evidence for this is found in CEOs' remarks, corporate reports, and the many cases of organizational transformation and restructuring. Benchmarking efforts attempted by global media (e.g., Fortune's World's Most Admired Companies, Financial Times' World's Most Respected Companies, Interbrand's Top 100 Global Brands) also promote the urgency of building globally focused business enterprises.
Theoretically, this finding appears to suggest that both the traditional IO model and the recently emerged RBV offer a partial explanation of the drivers of the GMS and a firm's performance. Researchers should draw on both perspectives to develop a more complete model of the determinants of the GMS and firm performance.
The Effect of International Experience on Performance
We found that a firm's global strategic performance is influenced positively by its international experience. This is consistent with the previous finding that international experience is a valuable organizational resource and an important determinant of success in international markets (Cavusgil and Zou 1994). Theoretically, then, researchers should take this factor into account when they investigate global marketing issues. The failure to model international experience explicitly or control it in the research design may lead to confounding errors. In terms of managerial practice, our findings indicate that it pays to promote constant accumulation of organization wide international experience. Decision makers should develop firsthand knowledge of different country subsidiaries and market environments through over-seas assignments and frequent international travel. Management should encourage employee training on international issues and global market trends. With international experience, the firm will be able to attain a stronger global strategic position, which eventually will lead to improved global financial performance.
Recent efforts to build knowledge-focused organizations and to implement corporate wide knowledge management systems provide further evidence for this view. Concerned about the sheer size and geographic spread of the multinational corporation and the elusive nature of knowledge, corporate leaders have moved deliberately to implement systems designed to capture, code, and disseminate knowledge and experience among their employees. Knowledge portals that reside on corporate intranets and global talent pools are just two examples of this trend. These deliberate attempts to gather and share knowledge and experience are even more critical for units of the MNC that are geographically and culturally distant.
Managerial Implications
The findings of the present study have several implications for international marketing managers in global industries. The broadened view of global marketing strategy represented by the GMS suggests that a firm competing globally can respond to external and internal challenges with several strategic levers, including standardization of marketing programs, concentration and coordination of marketing activities, and integration of competitive moves. Because the GMS is driven by external globalizing conditions and idiosyncratic internal organizational characteristics such as global orientation and international experience, the specific degree of standardization and integration that the firm should seek depends on both its external and internal environment. Given that GMS dimensions derived from the standardization and integration perspectives affect a firm's global market performance significantly, it appears advisable to have marketing operations in all major world markets, integrate the firm's competitive moves across the countries, and seek a high degree of standardization in the firm's product and promotion. More specifically, the following actions could help the internationally active firm exploit the benefits of global efficiency, effectiveness, and synergies.
First, managers should carefully assess the attractiveness of various key regions or markets and carry on marketing activities in areas deemed essential. Experience suggests that these areas tend to be those where major customers and/or competitors are located and where new ideas, products, and technologies flourish. Operating in key markets affords a firm the opportunity to closely monitor rivals and engage in timely action to counter their moves. Second, an attempt to integrate competitive moves recognizes that the key regions or markets of the world are now tightly inter-linked. Competitive pressures should dictate whether activities in certain markets (say, in home markets of chief rivals) should be subsidized for the benefit of the entire organization. Third, standardization of the promotional mix enables firms to gain worldwide efficiencies. This does not necessarily imply that advertising themes, appeals, or media choice should not be adapted to suit the conditions of the local markets. Instead, it implies that a firm should adapt its promotional efforts only when it is necessary to respond to local customer preferences, media use patterns, and advertising regulations.
Finally, a key determinant of performance in global markets lies in managers' ability to establish common needs among the customer segments worldwide so that core product features are kept intact. In practice, managers may depart from a totally standardized product to meet regulatory restrictions or channel preferences. Nevertheless, a standardized product will provide the firm with substantial efficiency in its global operations. Indeed, a standardized strategy will render scale economies, synergies, and efficiencies (Hamel and Prahalad 1985; Kogut 1989; Levitt 1983; Porter 1986; Yip 1995). In addition, it will simplify worldwide planning and afford the firm's brands a consistent image with global customers.
A paramount task in the global marketing strategy literature is to clarify what constitutes global marketing strategy and reassess the relationship between global marketing strategy and a firm's global market performance. This study provides considerable support for a broad view of global marketing strategy through the GMS. The findings also affirm the fundamental relationship between the GMS and a firm's global market performance. These insights should enrich the global marketing strategy literature by broadening the perspective of global marketing and by offering a framework for reconciling the existing controversies in the literature.
Several limitations of this study should be noted and point to the need for further research. First, the composition of the sample means that the generalizability of present findings needs further testing. Further research might direct more resources to data collection to increase the sample size and consider other types of firms or industries. Second, the research design is not longitudinal, and all information was obtained from the mail survey. Therefore, the causal attribution of relationships is relatively weak. Future work should consider adopting a longitudinal design to further test the causal order of the factors. Third, because only BUs based in the United States were surveyed, the findings may have limited generalizability to other countries. For that reason, further research should test the applicability of the GMS in other countries. Any limiting factors (cultural, social, political, and economic) should be investigated. Finally, although certain external globalizing conditions and internal organizational attributes were examined here, they are by no means exhaustive. Building on our theoretical framework, further research should explore the relevance of other external and internal factors for a firm's global marketing strategy and performance. In addition, the possibility that the globalization potential of an industry may moderate the relationship between global marketing strategy and a firm's performance should be investigated. Further research work in this direction should considerably increase the knowledge of global marketing and its fundamental tenets.
Notes:
1 We originally treated the GMS as a composite of its dimensions. A reviewer suggested that we treat the GMS as a second-order factor and use the second-order CFA to fit the measurement model. The results of the structural analysis remain the same in terms of the sign and significance, regardless of whether the GMS is treated as a composite or as the second-order factor.
2 The 23 global industries that resulted from this process were typesetting machinery (2791); pharmaceutical preparations (2834); soap and other detergents (2841); perfumes, cosmetics, and other toilet preparations (2844); pesticides and agricultural chemicals (2879); oil and gas field machinery and equipment (3533); textile machinery (3552); ball and roller bearings (3562); electronic computers (3571); computer peripheral equipment (3577); scales and balances (3596); household refrigerators and freezers (3632); household appliances (3639); household audio and video equipment (3651); semiconductors and related devices (3674); passenger automobiles (3711); automotive parts (3714); civilian aircraft (3721); aircraft parts and auxiliary equipment (3728); electromedical apparatus (3845); X-ray apparatus (3844); photographic equipment and supplies (3861); and watches, clocks, and parts (3873).
Table 1: Major Perspectives of Global Marketing Strategy
Legend for chart:
A - Perspective
B - Basic Logic
C - Key Variables
D - Antecedents
E - Effects
A
B
C
D
E
Standardization perspective
Scale Economies Low-cost Simplification
Product Standardization Promotion standardization Standardized
channel structure Standard price
Convergence of cultures Similarity of demand Low trade barriers
Technological advances Orientation of firm
Efficiency Consistency Transfer of ideas
Configuration-coordination perspective
Comparative advantage Interdependency Specialization
Concentration of value-chain activities Coordination of value-chain
activities
Low trade barriers Technological advances Orientation of firm
International experience
Efficiency Synergies
Integration perspective
Cross-subsidization Competitive dislocation Rationalization
Integration of competitive moves Global market participation
Low trade barriers Orientation of firm International experience
Integrated markets
Effectiveness in competition Competitive leverage
Table 2: Definition of the GMS Dimensions
Legend for chart:
A - The GMS Dimension
B - Definition
A
B
Product standardization
The degree to which a product is standardized across country markets.
Promotion standardization
The degree to which the same promotional mix is executed across
country markets.
Standardized channel structure
The degree to which the firm uses the same channel structure across
country markets.
Standardized price
The degree to which the firm uses the same price across country
markets.
Concentration of marketing activities
The extent to which a firm's marketing activities, including
development of promotional campaign, pricing decision, distribution
activities, and after-sale services, are deliberately performed in a
single or a few country locations.
Coordination of marketing activities
The extent to which a firm's marketing activities in different
country locations, including development of promotional campaign,
pricing decision, distribution activities, and after-sale services,
are planned and executed interdependently on a global scale.
Global market participation
The extent to which a firm pursues marketing operations in all major
markets in the world.
Integration of competitive moves
The extent to which a firm's competitive marketing moves in different
countries are interdependent.
Table 3: Results of the Second-Order CFA by ERLS
Legend for chart:
A - Factor
B - Item
C - Standardized Loading
D - t-Value
A
B C D
Global Marketing Strategy (Second-Order Factor)
GMS1: Product standardization (seven-point Likert scale)
1. We adopt a standardized core product across all major markets in
the world .830 -
2. Globally standardized components make up a significant percentage
of the total cost of our product .578 6.417
3. Main features of our product are standardized across major markets
in the world .894 10.204
4. The product designs we use in different country markets are very
similar .661 7.545
GMS2: Promotion standardization (seven-point Likert scale)
1. Execution of our advertising varies greatly from one country market
to another. (R) .984 -
2. We use very different techniques for sales promotion in different
country markets. (R) .938 28.850
GMS3: Standardized channel structure (seven-point Likert scale)
1. We develop similar channel structure for distributing any product
in different country markets .976 -
GMS4: Concentration of marketing activities (seven-point
bipolar scale)
1. Development of promotional campaigns. (1 = "dispersed,"
7 = "concentrated") .761 -
2. Pricing decisions. (1 = "dispersed,"
7 = "concentrated") .624 6.170
3. Distribution activities. (1 = "dispersed,"
7 = "concentrated") .759 7.325
4. After-sale services. (1 = "dispersed,"
7 = "concentrated") .755 7.302
GMS5: Coordination of marketing activities
(seven-point bipolar scale)
1. Development of promotional campaigns. (1 = "not coordinated
at all," 7 = "highly coordinated") .686 -
2. Pricing decisions. (1 = "not coordinated at all,"
7 = "highly coordinated") .726 6.845
3. Distribution activities. (1 = "not coordinated at all,"
7 = "highly coordinated") .834 7.553
4. After-sale services. (1 = "not coordinated at all,"
7 = "highly coordinated") .780 7.252
GMS6: Global market participation (seven-point Likert scale)
1. We have business operations in all major
markets in the world .946 -
2. The revenues from our product line are well spread across
different country markets .749 10.564
3. Our business unit competes in all major markets
in the world .893 14.732
GMS7: Integration of competitive moves (seven-point Likert scale)
1. We often subsidize our competitive campaign in a country with
resources generated from other countries .567 -
2. Our competitive moves across all major markets in the world
are highly coordinated .981 7.287
Global Orientation (seven-point Likert scale)
1. Every market in the world is important to our
business unit .587 6.033
2. Individual subsidiaries are willing to sacrifice their
profitability in order to achieve better performance for our business
unit as a whole .547 5.564
3. We place a higher priority on our domestic business than on our
foreign businesses. (R) .709 7.402
International Experience (seven-point Likert scale)
1. Our management possesses a great deal of international business
experience .884 8.889
2. We have had a long history of international business
involvement .761 7.806
Globalizing Conditions (seven-point Likert scale)
1. Customer needs are standardized
worldwide .953 9.012
2. Product awareness and information exist
worldwide .675 9.258
3. Standardized product technology exists
worldwide .713 10.192
4. Standardized purchasing practices exist
worldwide .679 9.357
5. Our major competitors market standardized products
worldwide .603 7.737
Strategic Performance (seven-point Likert scale)
1. The strategic position of our business unit in the global market is
very strong .817 10.792
2. Relative to our major competitors, our business unit is very
competitive in the global market .881 12.185
3. Our global market share is very high relative to our major
competitors .870 11.952
4. We have been able to build a global leadership position in our
industry .945 13.798
Financial Performance (seven-point Likert scale)
1. Compared to major competitors, global sales of our business unit
have been increasing rapidly .649 7.539
2. The global operations of our business unit are very profitable
relative to our major competitors .927 12.151
3. Our return on investment (ROI) is higher than that of our major
competition .776 9.490
Second-Order GMS Scale
GMS1: Product standardization .536 5.157
GMS2: Promotion standardization .579 -
GMS3: Standardized channel structure .283 2.313
GMS4: Concentration of marketing
activities .182 1.943
GMS5: Coordination of marketing
activities .429 3.842
GMS6: Global market participation .740 8.321
GMS7: Integration of competitive moves .698 5.450
Model Fit Statistics
Chi-square statistic of the model 900.515
(Degrees of freedom) (576)
Bentler-Bonett nonnormed fit index .901
Bentler-Bonett normed fit index .786
Comparative fit index .910
Bollen fit index .911
Notes: -- indicates a fixed scaling parameter.
Table 4: The Effect of the GMS Dimensions on Firm's Global Market Performance
Legend for chart:
A - The GMS Dimension
B - Global Strategic Performance
C - Global Financial Performance
A B C
GMS1: Product standardization .202 .183
GMS2: Promotion standardization .218 .197
GMS3: Standardized channel structure .088a .079a
GMS4: Concentration of marketing activities .068a .062a
GMS5: Coordination of marketing activities .161 .146a
GMS6: Global market participation .278 .252
GMS7: Integration of competitive moves .262 .238
(a) Not significant at .05.
Figure 1: The GMS: A Broad Conceptualization of Global Marketing Strategy
Figure 2: A Structural Model of the GMS
Figure 3: Fitted Structural Model of the GMS
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By Shaoming Zou and S. Tamer Cavusgil
Shaoming Zou is Assistant Professor of Marketing and International Business, Department of Marketing, College of Business, University of Missouri-Columbia. S. Tamer Cavusgil is University Distinguished Professor and The John W. Byington Endowed Chair in Global Marketing, Department of Marketing and Supply Chain Management, Michigan State University. The authors gratefully acknowledge the financial support of this research by MSU-CIBER. The authors thank the anonymous JM reviewers for their insightful and constructive comments on previous versions of this article.
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Record: 164- The Hidden Minefields in the Adoption of Sales Force Automation Technologies. By: Speier, Cheri; Venkatesh, Viswanath. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p98-111. 14p. 1 Diagram, 4 Charts. DOI: 10.1509/jmkg.66.3.98.18510.
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The Hidden Minefields in the Adoption of Sales Force Automation Technologies
Sales force automation technologies are increasingly used to support customer relationship management strategies; however, commentary in the practitioner press suggests high failure rates. The authors use identity theory as a lens to better understand salesperson perceptions associated with technology rejection. They collected survey data from 454 salespeople across two firms that had implemented sales force automation tools. The results indicate that immediately after training, salespeople had positive perceptions of the technology. However, six months after implementation, the technology had been widely rejected, and salesperson absenteeism and voluntary turnover had significantly increased. There were also significant decreases in perceptions of organizational commitment, job satisfaction, person-organization fit, and person-job fit across both firms. Finally, salespeople with stronger professional commitment indicated more negative job-related perceptions as experience with the technology increased.
There have been few times in the history of information technology where the mathematics of using [sales force automation] have been so compelling. Economic returns are immediate.
--Siebel and Malone (1996)
61% of all sales force automation projects fail to show measurable benefit.
--Block and colleagues (1996)
Sales force automation (SFA) tools are frequently implemented to facilitate customer relationship management (CRM) processes as part of a relationship marketing philosophy. By improving the speed and quality of information flow among the salesperson, customer, and organization, SFA tools support the sales process. Although it is intuitive to believe that increases in salesperson effectiveness and efficiency will result in successful SFA technology adoption, commentary in the practitioner press suggests high failure rates (Block et al. 1996; Petersen 1997). Difficulties associated with implementing information technology (IT), including technologies supporting the sales function (Sviokla 1996), have been well documented since the 1980s (e.g., Ginzberg 1981; Robey and Boudreau 1999). However, there are only a few examples of systematic research on knowledge workers' acceptance of technology in a field setting (e.g., Sviokla 1996). This limits the under-standing of the factors that might lead to successful technology adoption instead of failure.
Given the proliferation of SFA tools, the magnitude of implementation failures, and the unique job function of a salesperson, an in-depth investigation of SFA implementation failures will contribute to the science and practice in marketing and IT. By collecting data from salespeople employed in two different firms where SFA tools were rejected after implementation, this research specifically investigates salespeople's perceptions and behaviors that result in rejection of SFA tools. We collected perceptual data immediately after SFA training and three months after implementation; we also gauged subjective outcomes soon after those points in time and assessed objective measures related to the implementation for a six-month period.
In today's marketplace, researchers and practitioners emphasize the importance of relationship marketing--nur-turing an ongoing relationship between a buyer and seller by increasing trust and commitment between the parties (Morgan and Hunt 1994). This has led to the development of CRM processes and technologies, which are associated with identifying customers, creating customer knowledge, and building customer relationships that enable a firm to best create, satisfy, and sustain customer needs through customer intimacy and partnerships (Srivastava, Shervani, and Fahey 1999).
In many firms, salespeople are the primary source of information exchange within a customer-seller relationship (Morgan and Hunt 1994) and thus play a critical role in the formation and sustainability of customer relationships (Cannon and Perreault 1999). The amount and type of information exchange desired between buyers and sellers will be determined by a firm's customer orientation strategy (Lambe and Spekman 1997) and technology where SFA tools can facilitate the customer-salesperson information exchange process (Srivastava, Shervani, and Fahey 1999). Tools for SFA can support a range of sales roles; however, the focus of this study is "relational" sales roles that emphasize long-term relationships with customers. We therefore describe SFA technologies and explore the role of the salesperson as an important mechanism for creating and sustaining CRM practices.
Because of CRM's increased emphasis on information exchange, increased product and sales process complexity, and organizational downsizing, interest in SFA technologies has exploded, creating a projected $10 billion market (Siebel and Malone 1996). There is some evidence that the implementation of SFA tools leads to higher revenues due to increased closure rates and high customer retention that stems from enhanced customer satisfaction (Fisher 1998). However, other evidence suggests that these tools do little to increase revenue generation or shorten sales cycles (Petersen 1997). One difficulty with understanding these results is that little scholarly work has investigated this phenomenon (Fisher 1998).
With more than 600 companies in the SFA marketplace (Petersen 1997), there is a range of functionality associated with different SFA tools. Adoption of SFA likely results in different capabilities across different firms. Technology for SFA involves a variety of hardware and software capabilities and can support cost reduction or emphasize gains in customer relationship effectiveness. Tools for SFA may include real-time access to product/competitive information, product configurators, real-time collaboration, and information sharing, including electronic ordering and order tracking (Petersen 1997; Siebel and Malone 1996).
Tools for SFA vary in complexity and the degree to which integration into existing organizational infrastructure is needed. There is no evidence that more complex implementations are more likely to fail; similarly, there is no evidence that more simplistic SFA tools provide insufficient value to the organization, salesperson, or customer and are therefore failures. It is important to implement and integrate the appropriate SFA tools for the sales-oriented tasks a firm wishes to support with technology. The integration of SFA technologies into the sales process results in both task (e.g., decision making, productivity) and nontask (e.g., control, social interactions, job enhancement, work environment) outcomes (Kraemer and Danzinger 1990). Assuming an appropriate customer orientation-technology fit and a successful implementation process, enhanced decision making and productivity would be rational outcomes of SFA implementation. A salesperson would have more and likely better (e.g., more timely, more accurate) information to work with when using SFA tools, resulting in an increased capacity to formulate alternatives, make effective decisions, stimulate more effective customer relationships, and thereby increase productivity (Hill and Swenson 1994).
Prior research has used a variety of theories to examine user acceptance and rejection of technologies--including innovation diffusion theory (Moore and Benbasat 1991; Rogers 1995) and the theory of planned behavior (Ajzen 1991). Although these theoretical lenses are helpful, they lack sufficient insight into the effects of technology implementation on employees' job and professional expectations, thus making it difficult to explain the magnitude of SFA failure exhibited in the organizations under investigation. We first present the general theory regarding user acceptance of technology to capture many of the issues that are known to influence technology acceptance and failure. Subsequently, we present identity theory as a mechanism to better understand how a salesperson might socially construct the meaning of technology implementation in an organizational context. We embed identity theory in a broader context of individual, job, and organizational attributes to create a richer conceptualization of user acceptance and usage of technology. Various activities have been identified as critical success factors in garnering user acceptance of technology, including individual characteristics (Venkatesh 2000), role perceptions (Goodhue 1995), organizational characteristics (Robey and Boudreau 1999), professional state (Burkhardt and Brass 1990), and perceptions of technology (Agarwal and Prasad 1997). The basic framework (shown in boldface) and the underlying theoretical model (including operationalized constructs) guiding this research are presented in Figure 1.
Before presenting specific hypotheses, we draw the reader's attention to an important issue. We knew while developing this article that the SFA technology had been rejected in the organizations studied; however, we did not know why. Therefore, integrating technology diffusion and identity theory provides a theoretical lens through which to examine why this technology rejection occurred. The longitudinal perspective taken provides insight into how the implementation of SFA tools and subsequent experience with the tools affects subjective and objective outcomes.
Individual Characteristics
A person's belief structure mediates the relationship between external characteristics and outcomes (Ajzen 1991). Individual characteristics can lead to different individual perceptions about a particular technology and subsequent outcomes associated with using the technology. We divide individual characteristics into two categories: those that might influence a person's perceptions and use of technology on the basis of individual traits and dispositions toward technology.
The traits examined were age and sex, as these have been found to be important factors in prior research. Age has been found to influence both individual perceptions about technology (Morris and Venkatesh 2000), including technology outcomes such as usage and performance (Czaja and Sharit 1993), and job-related perceptions such as job satisfaction (Near, Rice, and Hunt 1978). Older workers have been shown to have more negative perceptions about a given technology and use it less frequently (Morris and Venkatesh 2000). Similar effects have been found for sex: Women exhibit greater anxiety (Rosen and Maguire 1990) and more negative perceptions toward a technology, and they use the system less than men do (Venkatesh and Morris 2000).
The dispositions toward technology examined in this research are computer self-efficacy and computer playfulness. Computer self-efficacy assesses the degree to which a person believes that he or she has the ability to use a technology to accomplish a particular task/job--for example, using the SFA to accomplish sales-oriented tasks (see Compeau and Higgins 1995). Only a small percentage of sales-people consider themselves experienced technology users, and the vast majority have little to no experience (Petersen 1997). Typically, lower scores on computer self-efficacy lead to more negative individual perceptions about the technology in question (Venkatesh 2000). Computer playful-ness--a person's tendency to act imaginatively and/or spontaneously with computers--can also affect technology acceptance. Prior research has shown that some people inherently approach technology use with higher levels of computer playfulness, which leads to increased computer acceptance (Venkatesh 2000; Webster and Martocchio 1992).
H1: Individual characteristics will influence individual perceptions of the SFA technology: (a) Younger employees will have more positive perceptions of the SFA technology, (b) men will have more positive perceptions of the SFA technology, (c) employees with higher computer self-efficacy will have more positive perceptions of the SFA technology, and (d) employees with higher computer playfulness will have more positive perceptions of the SFA technology.
Role Perceptions
Prior research in sales management has highlighted the boundary-spanning role of salespeople (Singh 1998). Boundary-spanning behaviors often lower role clarity (i.e., uncertainty regarding the type of job behavior to perform in a specific situation) and increase role conflict (i.e., experi --encing incompatible expectations that need to be satisfied simultaneously) that can influence individual perceptions of technology and work outcomes. Implementations of technologies such as SFA tools make it easier for salespeople to manage their boundary-spanning roles more effectively (Kolodny et al. 1996), as they have more accurate and timely information to share with customers and typically a more robust set of tools for communicating between parties (Yan and Louis 1999). Prior research demonstrates that higher levels of role clarity and role conflict also negatively influence job satisfaction, absenteeism, and voluntary turnover (Brown and Peterson 1993). Therefore,
H2: Role perceptions will positively influence individual perceptions of the SFA technology.
H3: Role perceptions will negatively influence subjective outcomes.
H4: Role perceptions will negatively influence objective outcomes.
Organizational Characteristics
Prior research has identified some organizational characteristics that can have significant influence on the successful implementation of technology, and these factors are also expected to affect SFA acceptance (Petersen 1997; Siebel and Malone 1996). Specific critical success factors including top management support (Jarvenpaa and Ives 1991) and early involvement and participation of the user population (Barki and Hartwick 1994; Hartwick and Barki 1994) have been demonstrated to influence individual perceptions of a technology favorably. Another characteristic is perceived voluntariness, which focuses on the extent to which the use of an innovation is perceived to be nonmandated by the organization (Agarwal and Prasad 1997; Moore and Benbasat 1991). Prior research has demonstrated that the degree to which a technology innovation is perceived as voluntary (versus mandatory) has a positive effect on perceptions of the technology and ultimately technology infusion--that is, the internalized use of a technology (Hartwick and Barki 1994). Therefore,
H5: Organizational characteristics will positively influence individual perceptions of the SFA technology.
Individual Perceptions of Technology
Prior research investigating user acceptance of technology has illustrated the importance of innovation diffusion theory (e.g., Moore and Benbasat 1991; Rogers 1995) in offering a rich perspective (Plouffe, Hulland, and Vandenbosch 2001) for understanding the degree to which a technology is likely to result in adoption and use. The perceptions tailored to technology adoption are outlined by Moore and Benbasat (1991). Relative advantage is the extent to which a system is perceived as better than its precursor. Compatibility is the extent to which a system is compatible with existing norms, beliefs, values, and past experiences. Complexity is the extent to which a person believes that using a particular system will be free of physical and mental effort. Visibility is the extent to which the system is seen in the organization. Image is the extent to which a system is perceived to enhance a person's status in the social system. Results demonstrability is the extent to which a system is amenable to demonstration and the advantages are more visible. Trial-ability is the extent to which a person can experiment with a system.
Although there is evidence that each of these perceptions can influence technology adoption and usage, it has been demonstrated that different individual perceptions of technology will be more or less salient depending on the technology (Agarwal and Prasad 1997). More specifically, the meaningfulness of a specific individual technology perception is linked to the consequences of using the technology (Thompson, Higgins, and Howell 1991). Therefore, favorable individual perceptions about the technology should lead to positive perceptions regarding the manner in which the technology can support job or professional activities.
H6: Individual perceptions of the SFA technology will positively influence perceptions of job and professional fit.
Person-Technology Fit
Identity theory provides a lens to examine the conflict among the different roles a person plays in his or her professional and personal life (e.g., Abrams and Hogg 1990). More specifically, identity theory suggests that people develop meanings and expectations associated with specific roles and act in ways to preserve the meanings and expectations inherent in each role (Thoits and Virshup 1997). A person typically views himself or herself as having multiple roles (Stets and Burke 2000). For example, salespeople are likely to identify with two different work-related roles: a professional identity ("I am a salesperson") and an organizational identity ("I handle the northeastern sales territory for ABC Corporation"). Sometimes these roles are synergistic, and activities performed in one role are consistent with expectations of the other role. At other times, there might be significant conflict between expectations inherent in two or more roles a person performs.
How might a given technology influence professional and organizational roles? Prior evidence demonstrates that a specific technology (e.g., SFA) can be perceived as competence-enhancing or competence-destroying depending on the users' socially constructed meaning (Burkhardt and Brass 1990). A technology is competence-enhancing when it preserves and enriches the value of existing skills, knowledge, and relationships. In contrast, competence-destroying technologies render existing skills, knowledge, and relationships obsolete, creating perceptions of "deskilling" (Braverman 1974; Burkhardt and Brass 1990) and resulting in negative perceptions of the job (Hackman and Oldham 1980). For example, if an SFA is implemented using embedded expert system capabilities, the salesperson may perceive that his or her competencies are no longer an integral part of the sales process. Furthermore, perceptions of deskilling may result in an employee believing that he or she is losing important competencies and skills relative to the marketplace (Braverman 1974), leading to voluntary turnover.
In addition, SFA tools often automate many of the routine information flows (e.g., between the salesperson and technical support staff) associated with the sales process through the use of e-mail, electronic data interchange, and bulletin boards. This can lead to less interaction with coworkers and customers (Hill and Swenson 1994) and/or a decrease in perceived trust among parties (Barrett and Wal-sham 1999), which can lead to negative perceptions about the technology. Thus, although salespeople can assess features of the SFA technology before use, ongoing usage of the technology results in a salesperson developing a socially constructed meaning regarding the degree to which the SFA tools are consistent with his or her job (i.e., the degree to which SFA tools enhance the job) and profession (i.e., the degree to which SFA tools enhance professional development or long-term career opportunities). A competence enhancing socially constructed meaning will result in positive perceptions of person-technology fit and will result in favorable outcomes; in contrast, deskilling and routinization will result in negative perceptions of person-technology fit that lead to negative outcomes. Therefore,
H7: Person-technology fit (job fit and professional fit) will positively influence subjective outcomes.
H8: Person-technology fit (job fit and professional fit) will positively influence objective outcomes.
Professional State
Can a technology implementation create a situation in which a salesperson experiences conflict between his or her professional and organizational identities? When a conflict occurs (e.g., a conflict between what it means to be a salesperson and the actual job of being a salesperson at ABC Company), a salesperson would first try to negotiate the expectations inherent in the conflicting roles with the hope of reducing or eliminating the conflict. One way a salesperson could change his or her expectations would involve avoiding the conflict-creating behavior--refusing to use the technology. For example, Klein and Sorra (1996) demonstrate that when technology inhibits the fulfillment of an employee's values, commitment to use a given technology is reduced. Alternatively, a salesperson could alter his or her conceptualization of fit to the job and/or organization. More specifically, a salesperson could alter his or her person-organization fit (i.e., the match between the salesperson's values, beliefs, and norms and those of the organization) or person-job fit (i.e., the degree to which the sales --person's knowledge, skills, and abilities are a match to the job requirements) to be consistent with the current job and/or organization environment (Saks and Ashforth 1997).
The manner in which a salesperson manages this conflict may have significant implications for the organization. If the conflict between expectations across roles cannot be eliminated, the salesperson would typically make a decision to eliminate one of the roles (Riley and Burke 1995). People have different levels of commitment to different roles (Stryker and Serpe 1994), and the role for which there is the least commitment is typically eliminated (Stets and Burke 2000). In the context of this research, the two identities (roles) for the salesperson are professional and organizational. Therefore, in the conflict between being a salesperson (however a person conceptualizes this role) and being a salesperson for ABC Company, it is crucial to determine which role exerts greater commitment. Professional commitment--the degree to which a person is dedicated to, cares about, and is proud to be a member of a given profession (Wallace 1995)--is a more enduring type of commitment than job or organizational commitment (Morrow and Wirth 1989). Professional commitment acts as a lens that filters an employee's response to change within the work environment (Van Maanen and Barley 1994) and can alter perceptions of work-related outcomes such as organizational commitment (Hackman and Oldham 1980). Thus, professional commitment should influence the degree to which the employee perceives job-technology fit and profession-technology fit as being consistent or inconsistent with his or her conceptu --alization of the sales profession.
Therefore, if SFA tools result in job tasks that are either competence destroying or inconsistent with a salesperson's view of his or her job or professional role, conflict between organizational and professional identities occurs. In this situation, the salesperson would likely choose the professional identity over the organizational identity (Rotondi 1975). Evidence of this phenomenon has been found in the financial trading industry since the introduction of electronic markets; traders and underwriters questioned both the design of their jobs and how electronic markets influenced their professional identities (Barrett and Walsham 1999). Thus, when a given technology is perceived as competence destroying, professional commitment will be more important than organizational commitment, leading to poor profession-technology fit and negative subjective outcome effects (Barrett and Walsham 1999):
H9: Professional commitment will negatively influence person-technology fit.
H10: Professional commitment will negatively influence subjective outcomes.
Experience with Technology
In addition to the hypotheses (H1 through H10) that helped describe the relationships shown in the research model, we are also interested in understanding how the various outcomes change over time as experience with the technology increases. Prior research investigating human behavior (Doll and Ajzen 1992) and, more specifically, technology use (Burkhart 1994; Robey and Boudreau 1999) has demonstrated the importance of actual behavioral experience in solidifying attitudes and, ultimately, behavior. Active experience leads to sensemaking and enables a person to socially construct opinions about the technology after using the technology in the intended context (Burkhart 1994; Robey and Boudreau 1999). Adaptive structuration theory has extended this examination and suggests that social structures and human interaction with the technology create a technology-in-use that may be similar to or different from the way the technology was originally conceptualized by either the designer or user (DeSanctis and Poole 1994; Orlikowski et al. 1995).
The sensemaking or technology-in-use process should generate strong salesperson attitudes (positive or negative) regarding the capabilities and value of the technology given the significant role SFA tools may have in supporting the salesperson's job. In the social construction of these attitudes, prior research has demonstrated that employees tend to value individual benefits, such as career advancement, over management goals, such as employee use of technology (Francik et al. 1991). More specifically, salespeople may reject or minimize their use of the SFA tools, reduce their commitment to the organization, have lower job satisfaction and higher absenteeism, and/or voluntarily leave the organization if they are required to use SFA tools (Saks and Ashforth 1997) that are perceived as competence destroying. Thus, as the salespeople acquire experience with the SFA system, if it is perceived as competence destroying and if its use is inconsistent with the salesperson's professional development and opportunities (e.g., career advancement), negative outcomes are likely to result.
H11: With increasing experience with the SFA technology, subjective and objective outcomes will become more negative compared with outcomes earlier in the implementation process.
We investigated two organizations where a new SFA technology was implemented and ultimately rejected. We provide a description of the two organizations, measures, and techniques used to collect the data.
Organizations and Participants
Firm 1: Telecom. Telecom sells and leases telecommunication equipment and provides repair and maintenance services. Telecom had revenues in excess of $800 million in the year the SFA technology was implemented. A significant component of its business involves postsales service and upgrades, given the frequency and magnitude of the telecommunication needs of its customers. During the year of study, Telecom operated four separate branch offices and employed nearly 4000 people, of whom 399 were salespeople; all salespeople participated in this study, and 277 provided all data at all points of measurement. As we discuss subsequently, a significant part of this loss in sample size is attributable to voluntary turnover. Salespeople had, on average, seven years of sales experience and had been with the firm for approximately five years. Salespeople were required to have sufficient technical selling expertise, because they sold three different products: ( 1) network service contracts, ( 2) network review contracts, and ( 3) network upgrade contracts to business customers.
Before acquisition and implementation of the SFA tool, the organization held focus groups with all salespeople to determine their needs. Vendors were invited to make presentations regarding their products to management and salespeople. The attendance of salespeople at these presentations was voluntary. Estimates by the organization indicated that most presentations were attended by nearly half the sales force. Six vendors were invited to submit bids. A committee that included two salespersons made the final selection. Each salesperson received a laptop computer enabling immediate access to information, such as hardware and software price checks and equipment availability, to share with the customer. The specific SFA system chosen enabled the organization to integrate internal databases and informational sources to the laptop with wireless modem capability. Salespeople in Telecom bring technology competence and network assessment capabilities to the sales process. The SFA technology was designed to augment and support the knowledge base the salespeople already had regarding the products and services available. In addition, the SFA technology contained expert system capabilities, supporting the salesperson's ability to provide contract recommendations based on changing user demands. It was believed that the use of these tools would enhance the firm's responsiveness to its customers and increase the salesperson's ability to close orders on-site. Members of top management within and outside the sales hierarchy were strong proponents of the SFA system and strongly encouraged its use. Overall, the system was also consistent with the organization's image of "being at the cutting edge" of IT.
Firm 2: Real Estate. Real Estate is a national real estate agency, and six locations spread across two major Midwest metropolitan areas participated in this study. The six locations together employed 289 sales agents, of whom 251 participated in this study, and 177 provided all data at all points of measurement.The sales agents, on average, had ten years of selling experience and had been with the firm for more than five years. Each salesperson had access to SFA technology on desktop/laptopcomputers housed at the corporate office/personal residence. The six branch offices were among several branches that were invited to participate in the early phase of the introduction to the "advanced SFA tool" (reported from the brochure of the corporate office). Sales managers at each of the branch offices introduced the tool in the branch and made it available to the agents to support sales activities. As did Telecom, Real Estate encouraged (but did not mandate) the use of the SFA technology. The SFA tools enabled agents to perform database searches of existing properties that fit a buyer's criteria and were available for sale. Furthermore, these tools enabled the sales agent to take prospective buyers on a virtual tour of some of the properties before making an in-person visit to the property. It was believed that these virtual tours would enable the buyer to quickly reject properties and/or identify properties that were of great interest, thus streamlining the over-all sales process. Furthermore, this streamlining was expected to enhance the customers' experience by enabling the customers to examine more properties in less time, potentially increasing their satisfaction with the search process and improving the purchase decision. Overall, the SFA technology was consistent with the image and vision of the branch office management and agents of "embracing cutting edge technology to better customer experiences" (reported from interviews with agents and management prior t o implementation).
Data Collection
Data were collected over a six-month period: Perceptual data were collected at two points in time (immediately after training and three months after implementation), and objective data were collected during the entire six months of the study. When available, objective data that were routinely collected (e.g., turnover, absenteeism) are provided in the time frame prior to the study for a better understanding of the firms before the SFA technology implementations.
T1: immediately after training. In each firm, the sales force underwent a two-day training program, conducted on the firms' premises, before implementation; the training program was tailored to each firm's SFA tools. All salespeople participated in the training, which had a dual emphasis. Salespeople were acclimated to the new tools and were provided substantial hands-on experience to gain comfort with the tools. The trainers focused on what the firm and sales management perceived to be appropriate use of the tools across a range of different customer interactions. Immediately after training (T1), participants completed a survey instrument on-site. Each respondent's survey used a unique identifier (bar code) to facilitate the tracking of responses over time.
Extended data collection: T1a, T2, T2a, and T3. To minimize common method bias, we measured subjective outcomes of interest six weeks after implementation. Subjective outcomes measured at T1a can therefore be predicted by posttraining individual reactions from T1. We can make a stronger case for causality and predictive validity by collecting outcome measures after salespeople had experience with the technology. Six weeks after T1, a paper-and-pencil survey was distributed to individual mailboxes and returned to dropboxes located on the premises. Three months after implementation (T2), participants completed the same survey instrument as at T1. Approximately six weeks after T2, denoted as T2a, we measured subjective outcomes. The survey distribution and collection process was identical to T1a and T2. We took a final set of objective measures at T3, six months after implementation. Thus, we could use the perceptual data gathered at T1 to predict objective outcomes measured between T1 and T2; similarly, we could use perceptual data gathered at T2 to predict objective outcomes measured between T2 and T3.
Measures
We used validated scales to measure the various constructs collected through surveys. The scales employed and their sources are shown in Table 1. We measured the objective outcomes--actual system usage, sales performance, absenteeism, and voluntary turnover--using archival data provided by the two participating firms (measures are not shown in Table 1). In both firms, we measured usage by monitoring duration of use--90 seconds or more of inactivity was no longer counted by the system while measuring use; when activity began again, the usage again counted toward the duration of use measurement. This ensured that the usage behavior measurement was not just the duration that the user was logged onto the system but rather time of active use--such measurement is consistent with previous research that measures actual usage (see Collopy 1996). Furthermore, the usage measure in the analysis was the average weekly use--this helped control for natural ebbs and flows in usage.
Absenteeism was based on recorded absences from work, and the measure is tabulated as the number of absences per month per salesperson. We recognize that this measure has limited usefulness because salespeople often work extended and unconventional hours and may necessarily formally miss much work. Voluntary turnover was measured as whether the respondent resigned from the firm. In addition, archival data on absenteeism and turnover in the time preceding the study were maintained as part of normal personnel records in both firms--we examined three six-month windows prior to the six-month study. Sales performance was measured differently in the two firms: Of the various indicators tracked, Telecom was willing to share the number of contracts and sales volume; the comparable measures in Real Estate were the number of sales closed and the volume of business. However, Real Estate was willing to share only each salesperson's number of contracts closed during the time frame of the study.
EQS 3.0 (Byrne 1994) was used to perform a confirmatory factor analysis (CFA) and test of the structural model.
Measurement Model
The overall fit of the measurement model, based on a CFA, was good at both points of measurement in both firms. Measures of overall fit at Telecom (T1 and T2) and Real Estate (T1 and T2), respectively, were as follows: comparative fit index = .94, .94, .92, .93; normed fit index = .92, .92, .91, .91; nonnormed fit index = .91, .90, .89, .89. As evidence of convergent validity, all items (with the exception of trialability) loaded on their prespecified construct and were significant as determined by the t-values. Table 1 presents the item parameter values for the factor structure matrix and Cronbach's alpha estimates for all scales in Telecom at T1. The pattern of results was highly consistent at T2 in Telecom and at T1 and T2 in Real Estate--the specific results for these are not reported here in the interest of brevity because of the high degree of consistency with Telecom at T1 and prior validation of the scales used. As is evident from the results, the scales exhibited high reliability with one exception--namely, trialability; all other Cronbach's alpha estimates were greater than .70, and convergent/discriminant validity was supported. In addition, Lagrange-multiplier tests indicated no significant cross-loadings for measurement items, further establishing discriminant validity. Trial-ability was not included in further analysis because of low reliability (.42, .57, .49, .63). Thus, a modified measurement model excluding trialability was estimated and supported.
Structural Model
After confirming the appropriateness of the measurement model, we used EQS to test the research model. We conducted two model tests for each firm: We used perceptual measures taken at T1 to predict subjective outcomes measured at T1a and objective outcomes measured between T1 and T2; similarly, we used perceptual measures taken at T2 to predict subjective outcomes measured at T2a and objective outcomes measured between T2 and T3. Results across the two firms were similar. Examination of overall fit measures indicates a good fit of the model to the data in Telecom (at T1 and T2) and Real Estate (at T1 and T2): comparative fit index = .92, .94, .91, .91; root mean square error of approximation = .04, .05, .02, .05; goodness-of-fit index = .94, .93, .91, .93; adjusted goodness-of-fit index = .90, .90, .91, .92; Akaike's information criterion = 107.2, 100.3, 89.9, 94.2; consistent Akaike's information criterion = -301.93, -289.33, -344.23, -300.01. Standardized parameters and the associated significance appear in Table 2.
H1 was supported in both firms at T1 and T2--both sex (H1a) and age (H1b) influenced relative advantage and complexity, respectively. The effect of sex and age on only those two perceptions and not other perceptions is consistent with previous research (see Morris and Venkatesh 2000; Venkatesh and Morris 2000); similarly, the effect of self-efficacy (H1c) and playfulness (H1d) on complexity is consistent with Venkatesh's (2000) findings. H2 was partially supported at T1 and T2 in both firms, in that role clarity and role conflict had an effect on compatibility but none of the other perceptions of the technology. H3 and H4 were not supported at T1 or T2 in both firms, as role perceptions did not influence subjective or objective outcomes directly; it appears that role perceptions have only indirect influences on the subjective and objective outcomes. H5 was partially supported at T1 and T2 in both firms, as all four organizational characteristics had some degree of influence on one of the perceptions about the technology. The notion that the antecedents did not influence all the individual perceptions of technology is consistent with prior research (Agarwal and Prasad 1997). H6 was partially supported at T1 and T2 in both firms, as relative advantage had an effect on both person-technology fit constructs in both firms at both points of measurement. H7 was supported at T1 and T2 in both firms; job fit had an effect on all four subjective outcomes, and professional fit had an effect only on organizational commitment. Some of the most telling effects were observed in the support for H8 in both firms: ( 1) Person-technology fit positively influenced usage; ( 2) however, lower perceptions of person-technology fit led to increased absenteeism and turnover, and ( 3) increased perceptions of person-technology fit were associated with higher sales in the T2 to T3 time frame though not in the T1 to T2 time frame. Another interesting pattern emerged in the support for H9 and H10--the effects of professional commitment on person-technology fit and subjective outcomes, respectively. In relation to both hypotheses in both firms at T1, a positive influence was observed. However, as experience with the technology increased (i.e., at T2), increasing professional commitment had a positive influence on perceptions of person-organization fit, person-job fit, and organizational commitment.
We tested H11 using repeated measures analysis of variance to examine changing subjective outcomes over time by comparing measures at T1a with measures at T2a; also, we contrasted objective outcomes in the T1-T2 time frame with measures from the T2-T3 time frame. It is apparent (Table 3) that the subjective outcomes were favorable in the short run (T1a) and were comparable or even marginally better than preimplementation measures. However, in the long run (T2a), the subjective outcomes were significantly more negative. We also examined the changes in usage behavior, absenteeism, voluntary turnover, and sales performance over time (Table 4). Usage of the SFA technology in both firms decreased over the duration of the study. In both firms, absenteeism increased significantly after implementation, which was confirmed by a Scheffe's test. The turnover data are startling when before versus after implementation are compared, as there are substantial increases after implementation. Company exit interviews indicated that the SFA tools were a primary driver in many of the voluntary turnover decisions, and many of these salespeople left the organization to join competing firms. In contrast to the other objective data, the sales performance did not increase over time--this was true when we compared the two measurements taken after implementation at Telecom and Real Estate and when we compared the preimplementation measures with the postimplementation measures at Telecom.
The results of this research highlight specific challenges associated with integrating technology into boundary-spanning relationships between salesperson and customer. Data from the longitudinal field studies demonstrate that salespeople reacted fairly positively to SFA tools immediately after training. However, this initial response turned negative after salespeople had access to and/or used the tools for six months. These negative reactions were manifested not only as a rejection of the SFA tools but also as increased absenteeism and voluntary turnover. The primary driver of this reversal stems from the growing lack of professional fit between the SFA tools and the sales force. Inter-view data corroborated this finding: Salespeople perceived that the SFA tools had a negative impact on and/or disrupted the sales process to the point that the system did not play to their strengths as salespeople.
Prior research has suggested that people can form either realistic or unrealistic expectations of new systems (Venkatesh 2000). It appears that salespeople can form fairly accurate assessments about their own future interaction with the system but cannot fully anticipate how the system will change their jobs in the future. Therefore, the lessons about technology implementation failures in the current research have far-reaching implications for IT in general and SFA tools in particular.
Limitations
Although longitudinal fieldwork enhances the relevance (real salespeople, tool, and interactions with customers) of the findings, it is important to recognize two potential limitations related to generalizability and internal validity. From a perspective of generalizability, two different SFA implementations were assessed within two different firms that had strong relationship-oriented customer strategies. Examining various SFA tools, SFA implementation processes, firms, and customer orientation strategies might have resulted in different outcomes. This issue is somewhat alleviated because the features of the SFA technologies examined in this research are included in the SFA technologies provided by the top-20 SFA vendors, which suggests that the SFA technologies studied are similar to and representative of many other SFA implementations, thus increasing the potential generalizability of the current work.
From an internal validity perspective, there are several decisions influencing outcomes that may or may not be made in a six-month period. This is of particular concerngiven the absenteeism and turnover results. There were no changes in sales force compensation, structure, quotas, and so forth over the duration of the study. Although there may have been other contributing factors to turnover decisions (e.g., better job offers, spouse relocation), some interviews (details are not reported in this article because of space constraints) suggested that the integration of SFA tools into the work environment was a primary motivator and, in some cases, "the straw that broke the camel's back "when it came to increased turnover. The preimplementation absenteeism and voluntary turnover data help allay fears about one alternative explanation--that is, that salespeople were exhibiting negative out-come effects before the SFA technology implementation.
Similarly, both firms implemented the SFA technologies consistent with well-known critical success factors of IT implementation success (e.g., SFA champion, top management support, training). Although we cannot rule out all alternative explanations for the relationship between SFA implementation and outcomes, especially because this was a field study, we believe that many of the issues that can lead to implementation problems were addressed by the manner in which the implementation took place.
Implications
The results of this study suggest that identity theory may be an important lens through which to examine sales force technology implementation. Identity theory can explain phenomena across a variety of organizational and social settings, and its underpinnings appear to capture potential conflicts that are particularly salient to salespeople. Many sales-people experience greater autonomy than peers in other organizational roles and have greater access to external firsthand information given their boundary-spanning roles, and their performance is assessed quite visibly within and even outside the firm. These attributes suggest that a salesperson is more likely to think of himself or herself primarily as a salesperson in general and secondarily as an employee of a given firm. If this perspective is valid, it suggests that identity theory has significant explanatory potential for the examination of a range of phenomena associated with sales-people and changes in the sales process, including customer orientation, compensation, and so forth. The strong effect of professional commitment on person-technology fit and subjective outcomes (positive relationship initially followed by a negative effect with growing technology experience) demonstrates the critical role that professional identity can play in work-related perceptions and outcomes.
The integration and empirical validation of the research model, which incorporates individual attributes, role perceptions, and organizational technology acceptance characteristics with identity theory concepts, provides a richer under-standing of the factors that most significantly influence perceptions of job-technology and profession-technology fit and outcomes over time. The identity theory constructs (e.g., professional commitment, person-organization fit) demonstrate salient relationships above and beyond the variance explained by individual, job, and organizational attributes, providing additional support for the meaningfulness of the results. Thus, this model provides a more complete understanding of SFA implementation in particular and employee acceptance of technology in general. Further research should examine this model in the context of other technologies and professions to determine its generalizability and/or identify possible contingencies.
The results of this research are consistent with adaptive structuration theory and sensemaking, as described previously (DeSanctis and Poole 1994), which suggests that users cannot assess the full range of effects a specific technology will have on their jobs until after ongoing use of the technology. In this research, salespeople were unable to accurately forecast their assessment of relative advantage in the context of changes the SFA technology would bring to their job (i.e., perceptions of relative advantage were high, yet the advantage provided by the technology did not seem to be desired when the SFA technology was actively used). Alternatively, it could be argued that, a priori, the model did not identify key constructs that might shed light on a technology implementation gone awry. Professional commitment and relative advantage were the only drivers of the person-technology fit. Although this is consistent with prior research findings that not all innovation characteristics drive usage (e.g., Agarwal and Prasad 1997), it leaves open the possibility that there is a broader set of factors beyond the constructs typically studied in acceptance of technology (see Rogers 1995; Venkatesh 2000) that could and should be assessed to provide an early warning of potential issues or problems in gaining salesperson buy-in. A more proactive set of measures that requires the participant to conceptualize how the technology could be used for specific activities might capture inconsistencies in perceptions regarding technology in use in advance of implementation.
The data associated with job fit indicate more negative perceptions of job fit subsequent to the SFA technology implementation. Future examination of technology implementations and/or other events that affect the sales process should examine explicitly the degree to which the event will result in enhancing or destroying perceptions of competence and the subsequent career implications for salespeople. Consistent with the findings from this research, prior research has demonstrated that attitudes and behaviors are shaped with continued access to and/or use of a technology in the user's work context (Burkhart 1994; Robey and Boudreau 1999). In hindsight, many of the frustrations experienced by salespeople in this study could very well have been identified as potential issues before design and implementation. Therefore, one important question for researchers and managers is, To what degree can a particularly challenging technology be assessed before implementation, and/or can implementation halt when it becomes apparent that these technologies will not be successful?
Rasmussen (1999) suggests that salespeople should be involved in the implementation process and then adequately trained to overcome the high SFA resistance that often exists. The results from our research suggest that this may be insufficient to ensure success. Salespeople should be actively involved with management in understanding the degree to which SFA technologies will augment the sales role and sales process well before purchase and implementation. What activities will SFA tools automate? To what degree are the tools automating activities (value-added and non-value-added) versus changing activities? If managers and salespeople can identify these issues before design and implementation, facets of the tools that might provide limited value can be eliminated. Also, facets of the tools that can provide value but might be perceived negatively (e.g., feelings of being replaced by SFA tools) should be managed appropriately.
Given the potential that salespeople will experience competence-destroying SFA technologies, how should SFA implementation be proactively managed? Although managers can alter the entire sales management system (e.g., compensation, expectations) to better fit the breadth of sales processes to the firm's customer strategy, some SFA tools may truly replace the functionality provided by the salesperson. In this case, an organization could choose not to implement the SFA technology because of the fear of alienating successful salespeople and the higher potential for the SFA technologies to be rejected. Although this might be a rational short-term decision, it may create a long-term competitive disadvantage with either higher selling costs or inefficiencies in the sales process. Alternatively, a firm could make a conscious decision to replace the "sales" skill set through voluntary turnover or downsizing (Chiesa and Manzini 1998). As Chiesa and Manzini (1998, p. 121) note, "the earlier the firm recognizes that a competence-destroying set of technologies is emerging, the earlier it can attempt to refresh its competence." For example, if the perception is that for certain customers or customer interactions, a Web site providing design recommendations is the appropriate strategy, then firms should not hire engineers as salespeople. Thus, firms need to assess proactively how SFA tools change the salesperson's role and identify the salesperson capabilities that are most appropriate.
Finally, post hoc interview data (details are not reported in this article because of space constraints) indicate that the salespeople at Real Estate expressed concern about the firm's motives in implementing SFA technology and were particularly cognizant about being disintermediated. These feelings are consistent with prior research that establishes the existence of greater internal conflict and power redistribution when competence-destroying technologies are implemented (Clemons, Thatcher, and Row 1995). Furthermore, prior research describes management's ability to garner increased control when technology has been implemented (Burkhardt and Brass 1990)--managers can quickly and easily assess the number of, frequency of, and time allocated to sales calls, which results in increased monitoring (Sviokla 1996). In addition, many salespeople may believe that their role is threatened, as managers would have access to all the information they might have about a customer (Sviokla 1996), which increases the power differential between manager and salesperson in favor of the manager. Organizations need to be aware of these perceptions to make effective technology implementation decisions.
Implementation of CRM systems has been and should continue to be an important consideration for many businesses. By no means does this research study suggest that implementing IT is somehow negative. More specifically, however, there may be other fundamental changes a firm needs to make when implementing SFA or other CRM technology when and where warranted. This research demonstrates empirically that technology, and specifically SFA tools, can generate excessive within-salesperson conflict that results in significant organizational costs--a loss of not only financial investment but also valued employees. By understanding and proactively assessing the potential for this conflict and then implementing mechanisms to manage this conflict appropriately, firms will stand a much better chance of obtaining successful SFA implementations.
Construct Measures and Reliabilities Based on CFA
Self-Efficacy (Compeau and Higgins 1995):
I could complete the job using the software package ... (ten-
point scale from "not at all confident" to "totally confident") .90
if there was no one around to tell me what to do. .82
if I had only the software manuals for reference. .91
if I had a lot of time to complete the job for which the
software was provided. .91
if I had seen someone else using it before trying it myself. .91
if someone else had helped me get started. .88
if I could call someone for help if I got stuck. .87
if I had just the built-in help facility for assistance. .84
if someone showed me how to do it first. .83
Computer Playfulness (Webster and Martocchio 1992):
Characterize yourself when using computers. Circle the number
that best matches a description of you interacting with
computers. .83
Spontaneous .81
Unimaginative (Reverse-Scored) .93
Flexible .83
Creative .82
Playful .83
Unoriginal (Reverse-Scored) .84
Uninventive (Reverse-Scored) .85
Role Clarity (Saks and Cronshaw 1990):
I have a clear idea of what someone in my job does. .88
I am well aware of the duties that will be required of me. .70
I have a very good idea of what my job entails. .70
Role Conflict (Rizzo, House, and Lirtzman 1970): .71
I have to do things that should be done differently. .70
I work under incompatible policies and guidelines. .72
I work on unnecessary things. .72
Voluntariness (Moore and Benbasat 1991): .84
My superiors expect me to use the system. .84
My use of the system is voluntary. .82
Although it might be helpful, using the system is certainly
not compulsory in my job. .81
My supervisor does not require me to use the system. .82
Complexity (Moore and Benbasat 1991): .84
Interacting with the system does not require a lot of my
mental effort. .81
My interaction with the system is clear and understandable. .88
I find the system to be easy to use. .83
I find it easy to get the system to do what I want it to do. .84
User Participation (Hartwick and Barki 1994): .80
I played an important role in the design and/or development
of the SFA system. .82
I felt my opinion was adequately considered during the
process of design and/or development of the SFA system. .81
I participated in the design and/or development of the SFA
system. .84
User Involvement (Adapted from Barki and Hartwick 1994):
I consider the new system ... .82
To be of no concern to me/to be of concern to me. .80
To be irrelevant to me/to be relevant to me. .84
To be insignificant/to be significant. .82
To mean nothing to me/to mean a lot to me. .85
Management Support (Leonard-Barton and Deschamps 1988): .80
The use of the system is encouraged by management. .79
My manager supports the use of the system. .78
My supervisor encourages the use of the system. .80
Relative Advantage (Moore and Benbasat 1991): .83
Using the system enables me to accomplish tasks more quickly. .81
Using the system enhances my effectiveness on the job. .82
Using the system makes it easier to do my job. .87
Compatibility (Moore and Benbasat 1991): .81
Using the system is compatible with all aspects of my work. .81
I think that using the system fits well with the way I like
to work. .82
Using the system fits into my work style. .83
Visibility (Moore and Benbasat 1991): .85
I have seen what others do using the system. .87
In my organization, one sees the system on many desks. .84
The system is not very visible in my organization. .83
Image (Moore and Benbasat 1991): .83
People in my organization who use the system have a high
profile. .83
Having the system is a status symbol in my organization. .83
Results Demonstrability (Moore and Benbasat 1991): .82
I have no difficulty telling others about the results of
using the system. .83
The results of using the system are apparent to me. .81
Job Fit (Thompson, Higgins, and Howell 1991): .80
The system can increase the quantity of output for the same
amount of effort. .81
Using the system has no effect on the performance of my job. .84
Using the system decreases the time needed for my important
job responsibilities. .80
Using the system significantly increases the quality of
output of my job. .84
Using the system increases the effectiveness of performing
job tasks. .86
Professional Fit (Thompson, Higgins, and Howell 1991): .76
Using the system increases the level of challenge in my
career. .81
Using the system increases the flexibility of changing jobs. .80
Using the system increases the amount of variety in my
career. .76
Using the system increases the opportunity for more
meaningful work. .80
Using the system increases the opportunity for preferred
career assignments. .74
Using the system increases the opportunity to gain job
security. .71
Person-Job Fit (Peters, Jackofsky, and Salter 1981): .78
I fit right into the job. .77
Taking everything into account, the job is a complete fit for
me. .80
The job provides a total fit for me. .80
Person-Organization Fit (Peters, Jackofsky, and Salter 1981): .80
I would fit right in to the organization. .80
The organization will be a total fit for me. .80
Taking everything into account, the organization will be a
complete fit for me. .81
Professional Commitment (Adapted from O'Reilly and Chatman 1986): .75
I am proud to tell others that I am part of this profession. .75
I talk up this profession to my friends as a great profession
to work for. .72
I feel a sense of belonging to this profession rather than it
just being a job. .77
Organizational Commitment (Adapted from O'Reilly and Chatman
1986): .75
I am proud to tell others that I am part of this organization. .80
I talk up this organization to my friends as a great organi-
zation to work for. .77
I feel a sense of "ownership" for this organization rather
than just being an employee. .70
Job Satisfaction (Adapted and Extended from O'Reilly and Caldwell
1981): .81
Overall, I am satisfied with my job. .79
I would prefer another, more ideal job. (reverse-scored) .80
I am satisfied with the important aspects of my job. .88Notes: Boldface numbers are Cronbach's alphas, and regular typeface numbers are item parameters from the structural equation measurement model.
Research Model Testing
Legend for Chart:
A -
B - Telecom, T1
C - Telecom, T2
D - Real Estate, T1
E - Real Estate, T2
A
B C D E
H1: Individual attributes to individual perceptions of technology:
H1a Age --> relative advantage
.20** .19* .22* .19*
H1a Age --> complexity
.17* .18* .19* .15*
H2a Sex --> relative advantage
.15* .15* .17* .16*
H2a Sex --> complexity
.17* .15* .20** .16*
H3 Self-efficacy --> complexity
.16* .17* .15* .19*
H4 Playfulness --> complexity
.18* .17* .16* .17*
H2: Organizational characteristics to individual perceptions of
technology of technology:
Role clarity --> compatibility
.20** .22** .22** .20*
Role conflict --> compatibility
-.18** -.17* -.17* -.16*
H3: Role perceptions to subjective outcomes:
All n.s. All n.s. All n.s. All n.s.
H4: Role perceptions to objective outcomes:
All n.s. All n.s. All n.s. All n.s.
H5: Organizational characteristics to individual perceptions
of technology:
Voluntariness --> relative advantage
.15* .15* .17* .15*
User involvement --> relative advantage
.16* .16* .18* .18*
User participation --> relative advantage
.15* .18* .17* .15*
Management support --> visibility
.22** .20** .20* .18*
Management support --> image
.23** n.s. .18* n.s.
H6: Individual perceptions of technology to person-technology
Fit:
Relative advantage --> job fit
.28*** .25*** .25*** .22***
Relative advantage --> professional fit
.28*** .26*** .29*** .27***
H7: Person-technology fit to subjective outcomes:
Job fit --> organizational commitment
.15* .25** .19* .23**
Professional fit --> organizational commitment
.20* .20** .16* .21**
Job fit --> job satisfaction
.28*** .29*** .32** .30***
Job fit --> person-organization fit
.19* .20** .19* .18*
Job fit --> person-job fit
.20** .16* .17* .20**
H8: Person-technology fit to objective outcomes:
Job fit --> usage
.35*** .33*** .32*** .30***
Professional fit --> usage
.31*** .28*** .27*** .32***
Job fit --> absenteeism
-.49*** -.43*** -41*** -.40***
Professional fit --> absenteeism
-.23** -.22** -.20*** -.25***
Job fit --> turnover
-.28*** -.26*** -.26** -.23**
Professional fit --> turnover
-.22** -.20** -.24* -.18*
Job fit --> sales performance
n.s. .15* n.s. .17*
Professional fit --> sales performance
n.s. .18* n.s. .15*
H9: Professional state to person-technology fit:
Professional commitment --> job fit
.20** -.18* .18* -.20**
Professional commitment --> professional fit
.19** -.16* .22** -.21**
H10: Professional state to subjective outcomes:
Professional commitment --> organizational commitment
.16* -.22** .17* -.20**
Professional commitment --> person-organization fit
.19* -.21** .18* -.18*
Professional commitment --> person-job fit
.20** -.20** .20** -.20**
*p < .05.
**p < .01.
***p < .001.
Notes: n.s. = not significant.
Comparison of Subjective Outcomes over Time
Telecom Real Estate
T1a T2a T1a T2a
M (S.D.) M (S.D.) M (S.D.) M (S.D.)
Organizational commitment 5.1 (1.07) 3.9 (.78) 5.3 (1.01) 4.1 (.93)
Job satisfaction 5.3 (1.20) 4.0 (.93) 5.0 (1.04) 3.8 (.81)
Person-organization fit 5.3 (1.01) 4.1 (1.00) 4.9 (.74) 3.7 (.79)
Person-job fit 4.8 (.87) 3.7 (1.10) 4.3 (.89) 3.2 (.77)Notes: All differences between T1a and T2a were found to be significant by means of a Scheffe's test.
S.D. = standard deviation.
Comparison of Objective Outcomes over Time
Legend for Chart:
A -
B - Telecom, 13-18 Months, PI, M (S.D.)
C - Telecom, 7-12 Months, PI, M (S.D.)
D - Telecom, 1-6 Months, PI, M (S.D.)
E - Telecom, T1-T2, M (S.D.)
F - Telecom, T2-T3, M (S.D.)
G - Real Estate, 13-18 Months, PI, M (S.D.)
H - Real Estate, 7-12 Months, PI, M (S.D.)
I - Real Estate, 1-6 Months, PI, M (S.D.)
J - Real Estate, T1-T2, M (S.D.)
K - Real Estate, T2-T3, M (S.D.)
A
B C D E F
G H I J K
Usage[a]
N.A. N.A. N.A. 6.2 (1.1) 2.8(.7)
N.A. N.A. N.A. 11.9 (2.7) 6.2 (1.8)
Absenteeism[b]
.9(.2) .8(.3) .8(.4) 1.8(.4) 2.3(.4)
.4(.1) .4(.1) .4(.1) 1.3(.4) 1.2(.5)
Turnover[c]
353 98[c]
93 10 52[c]
Sales performance:
Number of
contracts[d]
7.1 (1.0) 6.9(.9) 7.0 (1.1) 7.1 (1.0) 7.1 (1.4)
N.A. N.A. N.A. 3.2 (1.0) 3.3 (1.1)
Sales performance:
Sales volume[e]
222(45) 251(48) 266(55) 240(51) 246(49)
N.A. N.A. N.A. N.A. N.A.
[a]Usage measures before implementation are by definition irrelevant. Usage is reported as duration of use per week per salesperson. Usage (T1-T2) was significantly different from Usage (T2-T3) on the basis of a Scheffe*s test.
[b]Absenteeism is based on number of absences per month per salesperson. Preimplementation measures were significantly different from postimplementation measures on the basis of a Scheffe's test.
[c]Turnover data are for a six-month time frame.
[d]Number of contracts reported is the number of contracts per salesperson per month. All differences were nonsignificant.
[e]Telecom sales volume data are in thousands; all differences were nonsignificant. Real Estate sales volume is not available for publication.
Notes: PI = preimplementation, S.D. = standard deviation, N.A. = not available.
DIAGRAM: FIGURE 1: Research Model
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By Cheri Speier and Viswanath Venkatesh
Cheri Speier is Associate Professor of Information Systems, Eli Broad College of Business, Michigan State University.Viswanath Venkatesh is Assistant Professor in Decision and Information Technology, The Robert H. Smith School of Business, University of Maryland.The authors acknowledge Roger Calantone, Fred Davis, Richard Spreng, Tracy Ann Sykes, and Robert Zmud.
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Record: 165- The Identity Salience Model of Relationship Marketing Success: The Case of Nonprofit Marketing. By: Arnett, Dennis B.; German, Steve D.; Hunt, Shelby D. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p89-105. 17p. 3 Diagrams, 5 Charts. DOI: 10.1509/jmkg.67.2.89.18614.
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The Identity Salience Model of Relationship Marketing
Success: The Case of Nonprofit Marketing
Researchers suggest that developing long-term relationships with key stakeholders is an important strategy in today's intensely competitive business environment. Many organizations have embraced this concept, which is referred to as relationship marketing. Much of the research on relationship marketing success has examined relationships that ( 1) are primarily economic in nature, ( 2) involve business-to-business marketing, and ( 3) involve for-profit firms. However, the authors argue that relationship marketing is a viable strategy in such contexts as those involving high levels of social exchange, business-to-consumer marketing, and nonprofit marketing. In these contexts, relationship marketing success may require different relationship characteristics from those identified in previous research. The authors develop "the identity salience model of relationship marketing success," which they posit is useful for explaining relationship marketing success in exchange relationships that ( 1) involve individuals and ( 2) are based primarily on social exchange. The authors further develop and test the model in the context of nonprofit higher education marketing. The results provide support for the model.
My car makes me feel free, yet secure.
--Saab Owner
I needed to feel like I was doing my part. It makes me feel good. It's a great feeling.
--Red Cross Blood Donor
Consumers often receive benefits from marketing exchanges that go beyond basic economic benefits. For example, consider the two epigraphs. Although the motivation for buying a car is transportation, consumers often derive noneconomic benefits (e.g., prestige, security). Similarly, donors to nonprofit organizations also can derive considerable noneconomic benefits from their exchanges with nonprofits (e.g., feeling good, pride). As a result, competition among firms is often based considerably on communicating the noneconomic benefits from exchange relationships, and firms seek strategies that will enable them to communicate both economic and noneconomic benefits better. One strategic option that has received significant attention is relationship marketing. In this option, organizations should view ( 1) stakeholders as partners, ( 2) the process of dealing with stakeholders as a means of creating value, and ( 3) the resulting partnerships as tools for increasing the firm's ability to compete (Sheth and Parvatiyar 1995a, b). Relationship marketing is based on the premise that marketing exchanges are not of the discrete, "transactional" variety, but rather are long in duration and reflect an ongoing relationship-development process (Dwyer, Schurr, and Oh 1987). These relational exchanges, it is argued, are becoming so important that they can constitute firm resources that can lead to competitive advantage (Hunt 1997, 2000; Hunt and Morgan 1995).[ 1]
Much of the research on relationship marketing success has focused on relationships that ( 1) are primarily economic in nature, ( 2) involve business-to-business marketing, and ( 3) involve for-profit firms. However, we argue that relationship marketing is a viable strategy in such contexts as those involving high levels of social exchange, business-toconsumer marketing, and nonprofit marketing. In these contexts, relationship marketing success may require different relationship characteristics from those identified in previous research. That is, the importance of particular relationship characteristics in producing relationship marketing success may be more context specific than heretofore thought. We suggest that "identity salience," a construct not previously investigated in relationship marketing, may be an important characteristic of successful relationship marketing in particular contexts.
Identity salience is grounded in identity theory (Burke 1980; Laverie, Kleine, and Kleine 2002; McCall and Simmons 1978; Stryker 1968, 1980, 1987a, b; Turner 1978), which posits that people have several "identities," that is, self-conceptions or self-definitions in their lives. Identity theory posits that identities are arranged hierarchically and that salient identities are more likely to affect behavior than those that are less important. We propose that identity salience may play an important role in relationships that are distinguished by a minimum of two characteristics. First, though most theoretical and empirical research in relation-ship marketing focuses on characteristics of successful business-to-business relationships, such as trust and commitment (Morgan and Hunt 1994), many exchange relationships involve individuals. It is not unusual for organizations to attempt to develop long-term relationships with consumers on an individual basis. We argue that in contexts in which one partner is an individual, for example, business-toconsumer marketing, identity salience may be an important construct that mediates relationship-inducing factors, such as reciprocity and satisfaction, and relationship marketing success.
Second, though relationship marketing has long recognized the importance of social benefits in relational exchange, most empirical research (e.g., Anderson and Narus 1990; Dwyer, Schurr, and Oh 1987; Lusch and Brown 1996; Morgan and Hunt 1994; Smith and Barclay 1997) has been conducted in contexts in which the benefits to both partners are primarily economic. We argue that identity salience may play a crucial role in contexts in which one of the partners to the exchange receives substantial social benefits. For example, in the clothing industry many consumers use strong brand names as social symbols, which can affect the formation and maintenance of identities (Laverie, Kleine, and Kleine 2002; Solomon 1983). Therefore, the underlying thesis of this article is that identity salience is an important characteristic of relationship marketing success in contexts in which ( 1) one party to the exchange is an individual and ( 2) the individual receives significant social benefits from the relationship. Although many of the relationships in the for-profit sector involve individuals and extensive social benefits, we suggest that these characteristics may be more prominent in nonprofit relationships. For example, many nonprofit organizations are using relation-ship marketing as a strategy to develop and maintain relationships with individual donors (Block 1998; Remley 1996; Selladurai 1998; Squires 1997). Therefore, we propose that identity salience may be associated with nonprofit relationship marketing success.
In summary, ( 1) many exchanges involve both economic and noneconomic (i.e., social) benefits, ( 2) firms are turning to relationship marketing strategies to communicate exchange benefits, but ( 3) most research in relationship marketing has not focused on the factors key to success in contexts in which
- benefits received are substantially social,
- the exchanges are business-to-consumer, and
- the firm is a nonprofit organization.
To fill this gap in the literature, we develop and test what we label the "identity salience model of relationship marketing success." Our article is structured as follows: First, we examine the nature of exchange relationships in which social benefits to individual consumers play a primary role. Second, drawing on identity theory, we develop the identity salience model of relationship marketing success (see Figure 1). Third, we further develop our model in the specific nonprofit context of higher education marketing (see Figure 2). Fourth, we test and refine our model using self-reported data from more than 950 donors to a large southwestern university and objective donation data from their alma mater.
A transaction is typically considered an exchange of money for a product or service. However, in some exchanges one or both partners may receive benefits that are not economic in nature. For example, when donors give money to a nonprofit they do not receive any product or service in return. Similarly, when they donate products or services they do not receive monetary compensation. This type of transaction is better represented by Kotler's (1972) broader concept of transaction, which he defines as an exchange of values between two parties. By stipulating value as the criterion for exchange, Kotler allows a transaction to include exchanges that are not primarily economic in nature.
Consumers often derive benefits from products that go beyond the basic economic ones. In a for-profit exchange, for example, though Mercedes-Benz automobiles provide their owners with basic transportation, they may also symbolize personal success and worth. Such transactions have characteristics that are consistent with social exchange (e.g., Blau 1964). Unlike pure economic exchange, in which rewards from the exchange manifest themselves as money, products, or services, rewards from social exchange may be either economic or social (or both). In the case of nonprofit organizations, economic rewards may include such items as tax breaks and gifts, and social rewards include emotional satisfaction, spiritual values, and the sharing of humanitarian ideals. Cermak, File, and Prince (1994) find that donors tend to fall into one of four market categories: ( 1) affiliators: people who are motivated to donate by a combination of social ties and humanitarian factors, ( 2) pragmatists: people who are motivated by tax advantages, ( 3) dynasts: people who donate out of a sense of family tradition, and (4) repayers: people who are motivated by having benefited personally from the charity or know someone who has.
As Blau (1968, p. 455) points out, the "most important benefits involved in social exchange do not have any material value on which an exact price can be put at all, as exemplified by social approval and respect." That is, social rewards are often valued more than economic rewards. For this reason, many for-profit organizations focus on social rewards in their promotional campaigns. An example of this is Jaguar's print advertisement for its XK series of automobiles, which features the slogan, "It's why people stop and look before crossing the road," which suggests the social benefits of owning one. Social exchange theory is often used as a theoretical foundation for commitment and trust in relationship marketing (e.g., Anderson and Narus 1990; Dwyer, Schurr, and Oh 1987; Lusch and Brown 1996; Morgan and Hunt 1994; Smith and Barclay 1997). As Dwyer, Schurr, and Oh (1987, p. 12) note, "relational exchange participants can be expected to derive complex, personal, noneconomic satisfactions." The rewards that partners receive from engaging in social exchange over time aid in developing cooperation, a key relationship characteristic (Blau 1964; Dwyer, Schurr, and Oh 1987; Homans 1958).
Because organizations often rely heavily on the promise of social benefits from their products, it is important that they acquire a better understanding of the factors that affect relationships that involve primarily social exchange. Drawing on identity theory, we posit that identity salience is an important factor that influences relationships that are primarily based on social exchange.
Identity Salience and Relationship Marketing Success
Identity theory focuses on the connections among the self, personalized roles, society, and role performance. Identity theory is a microsociological theory that examines people's identity-related behaviors (Hogg, Terry, and White 1995). It views the relationship between the self and social structure as central to furthering the understanding of social behavior (Serpe 1987).
Research suggests that identity theory can be used to provide a better understanding of exchange processes. For example, Burke (1997) finds that computer simulations of network exchanges based on a model of identity processes, as suggested by identity theory, match closely the results from prior experiments. Furthermore, Burke (2000) posits, identity theory can provide insights into why people buy certain goods and services.
Research suggests that the structure of the self is relatively stable over time, and changes in the self are related directly to changes in the social structure surrounding the person (Serpe 1987; Wells and Stryker 1988). "Thus, the theory presumes both relative constancy in the structure of the self, given the absence of movement within the social structure, and relative change in the structure of the self, given such movement" (Serpe 1987, p. 44). Identity theory posits that the self should be regarded as a multifaceted, organized construct. That is, the self is a structure of multiple identities that reflect roles in differentiated networks of interaction (Stryker 1980, 1987a, b). People have an identity for each distinct network of relationships in which they occupy positions and play roles (Burke 2000). As self-conceptions or self-definitions that people apply to themselves, identities provide meaning for the self. For example, a person may, at the same time, think of himself or herself as a parent, a golfer, an American, a blood donor, a Dallas Cowboy fan, and a Southwest Airline employee.
Identity theory acknowledges that some of a person's identities have more self-relevance or salience. As a result, identities are organized hierarchically. Identities that are placed high in the hierarchy (i.e., are more salient) provide more meaning for the self and, as a result, are more likely to evoke identity-related behaviors (Burke 2000; Laverie and Arnett 2000; McCall and Simmons 1978; Stryker 1968). In addition, these identities often compete against one another. As Bhattacharya, Rao, and Glynn (1995, p. 54) suggest, "identification is not simply a bilateral relationship between a person and an organization, isolated from other organizations, but a process in a competitive arena." Identity theory seeks to understand how and why people select among role performances given the various possible alternatives (Stryker 1987b). For example, why do some people choose to stay and work late and others choose to go home to their children? Identity theory suggests that one factor that influences the decision is the salience of the person's work-related identity. That is, people whose work-related identities are stronger in salience than their parent identities would be more likely to choose to stay at work longer, whereas people whose parent identities are stronger would choose to go home.
The successful enactment of identity-related behaviors validates and confirms a person's status as a member of an identity group (e.g., fathers) and reflects positively on self-evaluation (Callero 1985). A person's perception that he or she is performing behaviors consistent with an identity can enhance his or her self-esteem. Conversely, poor performance can lead to poor self-esteem and even psychological distress (Thoits 1991). Therefore, people who have strong salience for a particular identity will try to perform successfully the behaviors that are associated with that identity. Therefore, identity theory captures the social nature of an exchange relationship. That is, it explicitly incorporates many of the social benefits that are derived from relationships (e.g., self-esteem).
Research suggests that identity salience mediates the tie between relationship-inducing factors and identity-related behaviors (Welbourne and Cable 1995). Laverie and Arnett (2000) find that identity salience (related to a specific basketball team) is a key mediating construct between three relationship-inducing factors (situational involvement, attachment, and enduring involvement) and game attendance. In addition, research on the antecedents (Kleine, Kleine, and Kernan 1993; Laverie et al. 2002) and consequences (Callero 1985; Callero, Howard, and Piliavin 1987; Charng, Piliavin, and Callero 1988; Lee, Piliavin, and Call 1999) of identity salience assume implicitly the mediating role of identity salience. Therefore, we posit that identity salience will be a key mediating construct in exchange relations that ( 1) are based primarily on social exchange and ( 2) have an individual as one of the partners (see Figure 1). Morgan and Hunt (1994) define success in channel relationships as an organization encouraging certain behaviors in its partner. Note that identity salience is posited to lead to appropriate identity-related behaviors. In the case of forprofits, desired behaviors include cooperation, acquiescence, a reduced propensity to leave the relationship, and increased functional conflict
(Morgan and Hunt 1994). For nonprofit marketing, success can be defined as a nonprofit organization generating supportive behaviors from key stakeholders (e.g., donations from large corporations, adequate volunteerism, stakeholders providing positive word of mouth for the non-profit) (Mael and Ashforth 1992). We posit that organizations will be more successful in their relationship marketing strategies when individual consumers involved in the exchange have salient identities related to the exchange relationship (Figure 1, Path B). For example, people who consider themselves "racquetball players" (i.e., they have a salient identity related to racquetball) are more likely to buy products (e.g., the newest state-of-the-art racquet or branded clothing) from the kind of manufacturers they perceive as important for their racquetball identity (e.g., Ektelon and Penn).
People seek out opportunities to enhance salient identities (Serpe and Stryker 1987). When they succeed in doing so, the related identity is reinforced. However, when such opportunities are not available, changes in the salience of the identity occur (Burke 2000). For example, Serpe and Stryker (1987) find that when students first enter a university, they try to join organizations that are consistent with prior salient identities. Sen and Bhattacharya (2001) suggest that organizations can be an important factor in developing the network of social relations. Therefore, identity salience is affected by the number and quality of social interactions related to the identity, which we label relationship-inducing factors (Figure 1, Path A). The identity will be reinforced when relationship-inducing factors support or confirm the identity. Figure 1, Path C, recognizes that there are other, non- relationship-inducing factors that may affect relationship marketing success. For example, though people may be loyal Ford customers, they may not have any strong identity related to Ford automobiles. Instead, their purchase behavior may be more strongly related to a desire not to go against a family tradition of buying Fords. Therefore, specific models and empirical works should include both kinds of factors. To test the general model represented in Figure 1, we examine it in the specific context of nonprofit higher education marketing.
Nonprofit higher education marketing provides an appropriate context in which to further develop and test the general model shown in Figure 1. The context-specific model shown in Figure 2 focuses on the exchange relationship between a university and its alumni donors because ( 1) individual consumers constitute one party in the exchange and ( 2) it is primarily based on social exchange:
The majority of nonprofits raise funds through charitable donations or foundation grants. These might be called quasi-economic transactions in that there is money exchanged but the "other side" of the transaction does not involve goods or services. This is not to say that there are not important returns to donors or funders in psychic and social satisfaction. (Andreasen 2001, p. 87)
That is, nonprofit-donor relationships involve primarily social exchanges.
Figure 2, our proposed identity salience model of non-profit relationship marketing success, stresses the importance of identity salience in explaining success. Successful relationships are ones in which organizations encourage certain cooperative behaviors in their partners (Morgan and Hunt 1994). Within the context of higher education, we define "success" as a university generating cooperative, supportive behaviors from such stakeholders as alumni. Important supportive behaviors include making financial contributions and promoting the university to others (i.e., providing positive word of mouth) (Mael and Ashforth 1992). These activities are crucial for the success of both private and public universities. Indeed, public funds are often scarce, and as a result, public institutions--not just private universities-- must rely on voluntary support from businesses, foundations, and individuals (Bruggink and Siddiqui 1995).
To be successful, a university must find ways to promote supportive behaviors among its alumni. We argue that non-profit success results from four major relationship-inducing factors: participation, reciprocity, prestige, and satisfaction. However, these factors do not promote relationship marketing success directly. Rather, we model these relationshipinducing factors as influencing success through a key mediating construct--identity salience. Figure 1 and empirical research suggest that certain non-relationship-inducing factors can also influence a person's donating behavior. Therefore, for our context, we include income and perceived need--constructs commonly found to be associated with donating--as control factors in our study (Harrison 1995; Nichols 1994; Warren and Walker 1991).
University Identity Salience
Research suggests that people form identities related to being a donor (Callero 1985; Callero, Howard, and Piliavin 1987; Lee, Piliavin, and Call 1999). For example, Lee, Piliavin, and Call (1999) find that the salience of a donationrelated identity predicts the donation of time, money, and blood. Many people form a strong identity related to their former university. For these people, being a "Trojan" or a "Gator," for example, is an important part of their lives. Heckman and Guskey (1998) suggest that relational bonds with a university are among the strongest predictors of supportive behaviors. People are more likely to enact behaviors that they believe are consistent with a salient identity (Burke 2000; Laverie and Arnett 2000). Laverie and Arnett (2000) examine women's basketball fans and find that fans whose team-related identities are more salient attend university basketball games more frequently than other fans. We suggest that the stronger a person's salience for a particular university identity (e.g., a "fighting Irish" identity), the more likely they will be to enact certain supportive behaviors (e.g., donating money to and providing positive word of mouth for the university). Therefore, we posit that
H1: University identity salience is related positively to donating to the university.
H2: University identity salience is related positively to promoting the university.
Relationship-Inducing Factors
As shown in Figure 2, we distinguish between factors that are likely to induce a relationship between donors and nonprofit organizations and factors that (though influencing donor behaviors) do not foster the relationship. Using identity theory research, we identify four major factors that influence identity salience: participation, reciprocity, prestige, and satisfaction.
Participation in university activities. Research suggests that participation in university activities (e.g., student government, sports, Greek orders) increases the likelihood of future donations (Bruggink and Siddiqui 1995; Harrison, Mitchell, and Peterson 1995). As Mael and Ashforth (1992) suggest, people who are actively involved in an organization tend to identify more with the organization. Students tend to engage in activities that are consistent with their salient identities (Serpe and Stryker 1987). Identity theory posits that participation in identity-related activities encourages the formation and maintenance of an identity (Stryker 1968, 1980). As people participate in university activities, they develop a more salient identity related to the university. That is, their university-related identities are confirmed through participation in the university activities, and as a result, the salience for that identity is reinforced (Burke 2000). As Callero (1985, p. 205) emphasizes, "it is through action that role identities are realized and validated." Identities require self-expression and positive feelings that affirm the identity (McCall and Simmons 1978). Students who are involved in university activities provide themselves with many positive experiences related to their university-related identities. For example, to promote membership, most student organizations schedule social events that are designed to be enjoyable. Although the proximate purpose of these events is to increase the likelihood that students will join and become involved in the student organization, because these organizations are part of the university experience, the events also reaffirm and strengthen participants' university-related identities. Therefore, we posit that
H3: Participation in university activities is related positively to university identity salience.
Reciprocity. The term "reciprocity" implies that a non-profit organization not only takes but also gives something in return (e.g., expressions of gratitude or recognition) (Eisenberger, Fasolo, and Davis-LaMastro 1990). Farmer and Fedor (1999) find that perceived reciprocity is associated with increased volunteerism and lower donor turnover rates because perceived reciprocity by donors is an important part of the "psychological contract" that nonprofits have with their donors. In general, donors believe that the relationship they have with the nonprofit creates a promissory contract (Rousseau and Parks 1993). In donors' minds, each party is bound by a set of beliefs regarding what each is obliged to provide. Because reciprocity tends to be pervasive in society, people expect, seek, and create psychological contracts to define relationships (Farmer and Fedor 1999). Bagozzi (1995, p. 275) maintains that reciprocity is "at the core of marketing relationships" and regards it as "a fundamental virtue" that goes beyond behavioral norms. When nonprofit organizations fulfill their end of the psychological contract (e.g., by acknowledging that the donor's contribution is contributing to the success of the nonprofit), donors form a general perception that the organization values their contributions. In turn, such acknowledgment induces positive feelings in the donor (Eisenberger, Fasolo, and Davis-LaMastro 1990). These feelings reflect positively on self-evaluation, which in turn provides a reaffirmation of the identity related to the nonprofit (Callero 1985; Hoetler 1983). Therefore, we posit that
H4: Perceived reciprocity is related positively to university identity salience.
Prestige of university. Because prestigious organizations are assumed to be successful, the prestige of an organization often serves as an indicator of organizational success. Bhattacharya, Rao, and Glynn (1995, p. 48) suggest that "the more prestigious the organization, the better the opportunity to enhance self-esteem through identification." They find that perceived organizational prestige is associated positively with organizational identification, which they define as a sense of oneness with or belongingness to an organization. They suggest that nonprofits might enhance the prestige of their organizations by eliciting the support of celebrities.
Cialdini and colleagues (1976) find that people attempt to associate themselves with a successful group to bolster their self-esteem in a process referred to as "basking in reflected glory" (BIRGing). In contrast, people may also try to maintain their self-esteem by disassociating themselves from an unsuccessful group, which is referred to as "cutting off reflected failure" (CORFing). Wann and Branscombe (1990), in the area of sports marketing, demonstrate that higher identification with an organization can lead to an increase in the likelihood of BIRGing and a decrease in the likelihood of CORFing. On the one hand, BIRGing increases the salience of the related identity by providing positive reinforcement. On the other hand, CORFing reduces the identity salience because the person believes that the behavior related to the identity should be hidden.
People who associate themselves with prestigious organizations can therefore increase their self-esteem by BIRG-ing. For example, donors may display prominently plaques and other paraphernalia associated with donating. Shenkar and Yuchtman-Yaar (1997) submit that organizational members and prospective members are more affected by organizational prestige than other stakeholders because they are in constant contact with the organization. Mael and Ashforth (1992) find that organizational prestige is related positively to organizational identification, and many educational institutions use this to their advantage. For example, Lively (1997) finds that some colleges are elevating themselves to universities to communicate more prestige to potential donors and, in turn, improve their fundraising efforts. Therefore, we posit that
H5: Perceived prestige is related positively to university identity salience.
Satisfaction. Satisfaction has become a central construct in marketing research. For example, studies have examined satisfaction's antecedents (Bitner, Booms, and Mohr 1994; Voss, Parasuraman, and Grewal 1998) and its effects on intentions (Cronin and Taylor 1992; Garbarino and Johnson 1999), economic returns (Andersen, Fornell, and Lehmann 1994), and strategic orientation (Oliva, Oliver, and MacMillan 1992). Many organizations focus on satisfaction as a means to retain current consumers and attract new ones. Satisfaction is often used as a referent by which organizations measure their performance (Fornell et al. 1996). Satisfaction is considered crucial for organizations that strive for long-term relationships with customers: "[S]atisfaction in exchange is necessary if ongoing relationships are to be maintained and future relationships are to be facilitated" (Oliver and Swan 1989, p. 21).
Satisfaction is an important factor that leads to organizational identification (Covin et al. 1996; Mael and Ashforth 1992). Welborne and Cable (1995) find that pay satisfaction influences the enactment of work-related behaviors. They suggest that the positive affect derived from satisfaction with an event results in people reevaluating the salience of different identities. The satisfaction the person feels reaffirms his or her identity, which in turn increases the salience of the identity. As McCall and Simmons (1978) maintain, positive feelings that affirm the identity are important for the development and maintenance of identities. We suggest that satisfaction influences supportive behaviors indirectly by increasing the salience of the related identity. That is, alumni who are satisfied with their university experiences are more likely to place a university identity higher in their hierarchy of identities. Therefore, we posit that
H6: Satisfaction with the university experience is related positively to university identity salience.
Non-Relationship-Inducing Factors
We include two non-relationship-inducing factors as controls in our study: the donors' income and the perception of the organization's financial need. Research suggests that people with higher levels of income are more likely to donate to nonprofit organizations (Harrison 1995). Bruggink and Siddiqui (1995) argue that income is an important factor because people with higher levels of income have excess resources available for donating. Indeed, households "earning more than $80,000 have more than $11,000 a year to spend on leisure, charitable and other nonessential purchases" (Nichols 1994, p. 14). In an effort to boost donations, some nonprofits appeal to potential donors on the grounds that their organization, its "customers," or its programs have special needs that require additional donations. Warren and Walker (1991) find that this strategy is more successful if the organization identifies the need as short-term and focuses on a single case (e.g., showing how the donation will help a specific person). Universities often stress financial need when soliciting funds for new construction or for specific scholarships. The conventional wisdom is that people enjoy contributing to "needy" causes because they empathize with them. House (1987) finds that alumni who perceive that an institution is in great need of financial support are more likely to donate. Therefore, we expect that both higher levels of income and perceived financial need will be related positively to donating. Because these are control factors in our study, we do not include them among our formal hypotheses.
Following Bollen and Long (1992), we compare our model with a rival model (see Figure 3), which we label the satisfaction model of nonprofit relationship marketing success. Based on the extensive research on satisfaction in the marketing literature, a potential alternative model would be one that provides a more central role for satisfaction. A mediating role for satisfaction is implicit in works that examine the antecedents or outcomes of satisfaction (e.g., Bitner, Booms, and Mohr 1994; Voss, Parasuraman, and Grewal 1998). Indeed, Garbarino and Johnson (1999, p. 74), referring to their model that hypothesizes satisfaction as a mediator, emphasize that "our satisfaction as a mediator model represents the basic model that long has guided consumer researchers." Therefore, we test a model in which satisfaction is the key mediating construct between the relationshipinducing factors included in our study (participation, reciprocity, prestige, and identity salience) and nonprofit relationship marketing success (donating and promoting).
Overall satisfaction with an organization is a cumulative evaluation that is composed of satisfaction with specific components of an exchange relationship (e.g., the people and the market offerings) (Garbarino and Johnson 1999; Westbrook 1981). In the rival model (Figure 3), four factors are modeled as antecedents of satisfaction (participation, reciprocity, prestige, and identity salience). Because these constructs represent different components of the relation-ship that donors have with their alma maters, each factor can affect a donor's overall satisfaction with the university. For example, the positive affect associated with participating in extracurricular activities could increase a person's overall satisfaction with the university. Research suggests that satisfaction may indeed play a central role in some nonprofit marketing relationships. For example, Garbarino and Johnson (1999) find that, for occasional customers of a nonprofit repertory theater company, satisfaction mediates the relationships between attitudes toward the theater company and future intentions. Therefore, the rival model in Figure 3 represents a realistic, theory-based alternative to our hypothesized model.
Sample
Alumni were sampled from a large southwestern state university. Questionnaires were sent to graduates from three classes (1954, 1974, and 1994). (Note that the sample frame consisted of all alumni in the university's database for these years.) A total of 4481 questionnaires were mailed, of which 953 completed questionnaires were returned, yielding a response rate of 21.3%. The sample consisted of slightly more men (n = 520) than women (n = 433). Most of the respondents provided their year of graduation (772 of 953). For those who responded to this question, 90 are from the class of 1954, 362 are from the class of 1974, and 320 are from the class of 1994. Approximately 12% of the respondents have incomes less than $25,000. Slightly more than half of the respondents (~53%) have incomes between $25,000 and $75,000, and the remaining respondents (~35%) have incomes over $75,000. In addition, the modal (and median) donation amount per year is modest (in the $1-$49 category). Of the respondents, 274 (29.5%) did not donate money to the university. However, 362 respondents (38%) donated more than the modal amount.
Measures
The study uses a combination of single indicant (for donating and income) and multi-item scales (for promoting, identity salience, perceived need, reciprocity, prestige, satisfaction, and participation) from two sources. To minimize problems associated with "same source" bias (i.e., the inflation and/or deflation of the strengths of the observed relationships due to common method variance), we measured donation behavior using objective donation data that come from university records (for a discussion of the effects of same source bias, see Cote and Buckley 1987, 1988; Podsakoff and Organ 1986). Data to measure the other constructs come from the self-reports of respondents. (The measures are included in the Appendix.)
Donating. We were able to elicit the support of the university whose alumni constituted our sample. The university supplied us with a list of alumni donors and their contact information. In addition, the university supplied the donors' donation histories, which enabled us to use the respondents' actual donation amounts. Members of the sampling frame were assigned to a level of donating based on their average donation amount per year since graduation (total amount donated since graduationnumber of years since graduation). To preserve the anonymity of respondents yet still identify their level of donating, we coded each questionnaire before mailing, using various colors and headings that indicated each respondent's level of donating.
Promoting. A scale was developed that reflects behaviors that promote the university to others. Three items were developed through exploratory interviews with alumni, colleagues, and nonprofit marketers. The three items capture the concept of providing positive word of mouth for or "talking up" the university. The items concentrate on positive information communicated in social situations (e.g., in conversations with friends and acquaintances). Research suggests that word of mouth is extremely effective in these situations because the recipient perceives the information as more credible (Berry and Parasuraman 1991; File, Judd, and Prince 1992).
We measured identity salience using a scale developed by Callero (1985). The scale consists of four items, each measured on a seven-point scale ("strongly disagree" to "strongly agree"). The original items measure identity salience as it relates to blood donating. Therefore, it was necessary to change the items to reflect the context of the present study. To measure participation, we asked respondents to list the extracurricular activities they participated in while attending the university and to rate their level of participation in each activity on a seven-point scale ("not active at all" to "very active"). Because we are interested in the level of participation (i.e., how actively they participated in the activities), not the number of activities they participated in, we use the average of the ratings to measure participation. We suggest that the level of participation (i.e., how actively they participated in the activities) is a better indicator of the social connections the person had when he or she attended the institution. For example, some students join many organizations on campus to improve their resumes. However, they may not be very involved in any of the organizations. Conversely, some students may participate in only one activity, such as an intercollegiate sport, but be highly involved in it, and thus the participation may promote identity salience.[ 2]
We measured the perceived prestige of the university using a scale developed by Mael and Ashforth (1992). The scale consists of four items, each measured on a seven-point scale ("strongly disagree" to "strongly agree"). We measured reciprocity using a scale adapted from Eisenberger and colleagues (1986), whose study examined reciprocity between private high school teachers and their schools. Therefore, it was necessary to adapt the items to the present context. The scale consists of six items and is measured on a seven-point scale ("strongly disagree" to "strongly agree").
We use an adapted version of a scale tested by West-brook and Oliver (1981) to measure satisfaction. Westbrook and Oliver's study examined consumer satisfaction with products or services. Therefore, it was necessary to alter the items to the present context. The scale consists of four items, each measured on a seven-point scale ("strongly disagree" to "strongly agree"). We measured perceived need using three questions developed for the study. The items are the result of exploratory interviews with university officials and nonprofit marketers. We measured income using a single-item scale.
Analysis
We analyze the data using structural equation modeling (LISREL 8.30; Jöreskog and Sörbom 1999). First, we use the entire sample (n = 953) to refine the measures and test their convergent and discriminant validity (see Table 1).
Second, we test the hypothesized structural model. As Hair and colleagues (1998) and Schumacker and Lomax (1996) suggest, if modifications of a structural model are made, the model should be cross-validated with a separate set of data. Therefore, to allow for model improvement and cross-validation, we randomly divide the sample into two subsamples (Group A consists of 477 respondents, and Group B consists of 476 respondents). The correlation matrix for each subsample is shown in Table 2. Following Bollen and Long's (1992) recommendations, we compare our model to a theory-based, rival model (see Figure 3).
Measurement model. All internal consistency measures are greater than .80, which is above the level set by Nunnally (1978) of .70, so the scales demonstrate internal reliability. During the measurement purification process, three items (REC1, PRE3, and SAT2) from three different constructs (reciprocity, prestige, and satisfaction, respectively) were dropped from the analysis because of high cross-loadings with other constructs. The final measurement model includes 24 items across nine constructs (see Table 1). The fit indices for the model are as follows: chi2(219) = 599.31, p < .01; root mean square error of approximation (RMSEA) = .044; comparative fit index (CFI) = .97. Given the size of the sample and the number of constructs, it is not surprising that the chi2 statistic is significant (p < .01). Therefore, the more robust RMSEA and CFI indices are used to assess model fit. Browne and Cudeck (1993) suggest that RMSEA values between .00 and .05 imply good approximate overall fit. Although the prior rule of thumb for CFI values has been .90 or above, recent evidence suggests that CFI values of .95 or above should be used to indicate adequate overall fit (Rigdon 1998). According to these guidelines, there is evidence that our measurement model fits the data.
The path estimates for all the latent constructs are statistically significant (p < .01), with parameter estimates ranging from 24 to 30 times as large as the standard errors; this pattern combined with the high variance extracted ([greater than or equal].59 for all reflective constructs) for each scale provides evidence of convergent validity (Cannon and Perreault 1999). We assess the discriminant validity of the constructs using a procedure suggested by Bagozzi and Phillips (1982). The technique entails analyzing a series of two-factor models--two for each pair of reflectively measured constructs. We analyze each two-factor model twice. First, we constrain the correlation between the two constructs to unity, and then it is allowed to be estimated. We compare the chi2 statistic for each model using a chi2-difference test. Evidence for discriminant validity exists when the chi2 statistic for the unconstrained model is significantly lower than that of the constrained model. All of the reflective scales passed this test. Therefore, all of the reflective constructs exhibit discriminant validity.
Hypothesized model. Wetest the hypothesized model (Figure 2) using the respondents from Group A. The results indicate that seven of the eight hypothesized paths (~88%) are supported (see Table 3). The model explains 17% of the variance in donating and 60% of the variance in promoting. Identity salience is related significantly to both donating and promoting (β = .11, p < .01, and β = .78, p < .01, respectively). Thus, H1 and H2 are supported. Three of the four hypotheses involving the relationship-inducing factors are supported. Specifically, participation is related significantly to identity salience (gamma = .15, p < .01). H3 is supported. However, reciprocity is not related significantly to identity salience. Thus, H4 is not supported. Prestige is related significantly to identity salience (gamma = .59, p < .01), which supports H5. Satisfaction is related significantly to identity salience (gamma = .18, p < .01), which supports H6. Finally, both of the non-relationship-inducing control factors (income and perceived need) are related significantly to donating (gamma = .18, p < .01, and gamma = .32, p < .01, respectively).
The fit indices indicate that the model fit could be improved (chi2232 = 586.17, p < .01; RMSEA = .056; CFI = .94). Specifically, the RMSEA value is slightly above the .05 value suggested by Browne and Cudeck (1993), and the CFI value is slightly below the .95 value discussed by Rigdon (1998). An examination of the modification indices indicates that the model would be improved considerably if prestige were allowed to influence promoting directly (i.e., if gamma33 was freed, the dotted path in Figure 2).
Respecified model. In the respecified model, we allow a path from prestige to promoting (oooo We test the respecified model on the holdout sample (Group B). The results are consistent with the initial test of the model. The analysis reveals that seven of the nine paths (78%) are supported, including the new path from prestige to promoting (gamma = .63, p < .01) (see Table 3). In addition, the model explains 10% of the variance in donating and 75% of the variance in promoting. The fit indexes indicate that the model fits the data (chi2(231) = 485.53, p < .01; RMSEA = .049; CFI = .96). In summary, the analysis supports H1, H2, H3, and H5.
The rival model. We follow a similar testing procedure for the rival model. It suggests, as does the hypothesized model, that a path from prestige to promoting is warranted. Therefore, we include this path in the rival model (see Figure 3). The results for the analysis using data from Group B for the rival model are shown in Table 4. We compare the respecified model with its rival on the following criteria: ( 1) overall fit of the model, as measured by the RMSEA, the CFI, and the Akaike information criterion (AIC); ( 2) percentage of the model's significant structural paths; ( 3) ability to explain variance in the outcomes of interest, as measured by squared multiple correlations (SMCs) of the outcome constructs, and (4) overall performance of the key mediating construct, as measured by significant paths leading to and from the key mediating construct (see Table 5).
The RMSEA for the rival model is slightly higher than that of the respecified model (.052 versus .049), indicating that the rival model does not fit the data as well as the respecified model. The two models have the same value for CFI (.96). However, the rival model has a higher AIC value than does the respecified model (665.27 versus 637.25). The AIC value is used to compare two or more models estimated from the same data (smaller values indicate a better fit). Therefore, the AIC indicates that the respecified model fits the data better than the rival model. In the rival model, only four of the nine structural paths (44%) are supported at the p < .01 level (at the p < .05 level, five of the nine paths are supported, 56%). In contrast, seven of the nine structural paths (78%) in the respecified model are supported at the p < .01 level. Examinations of the SMCs indicate that the rival model has a slightly lower SMC for donating (.08 versus .10). However, the rival has a slightly higher SMC for promoting (.77 versus .75).
A comparison of the performance of the two proposed key mediating constructs (satisfaction and identity salience) indicates an important difference between the two models. Each model has two antecedents that are related significantly to the key mediating construct at the p < .01 level. In the rival model, reciprocity and prestige are related positively to satisfaction. In contrast, in the respecified model, participation and prestige are related positively to identity salience. However, in the rival model, satisfaction is not related significantly to donating, nor is it related significantly to promoting. In comparison, in the respecified model, identity salience is related significantly to both donating and promoting (p < .01).
When organizations engage in exchange relationships with individuals that are based primarily on social exchange, what is the role of identity salience? Does identity salience influence relationship marketing success? If so, what is the nature of this effect? To answer these questions, we examine relationship marketing in the context of nonprofit higher education marketing. Our results provide evidence that identity salience indeed plays a key role in nonprofit relationship marketing by mediating the relationships between relationship-inducing factors (participation and prestige) and supportive behaviors (donating and promoting).
To provide a better test of our model, we compare it with a theory-based rival model. Although both models explain a significant amount of variance in donating and promoting, goodness-of-fit measures indicate that the respecified model fits the data slightly better than the rival model. In addition, the respecified model has a much higher percentage of significant paths (78% versus 44% at the p < .01 level), which indicates that it provides a better explanation of the relationships among the constructs investigated. More important, the analysis reveals that satisfaction does not perform a mediating role. In the rival model, neither of the paths from satisfaction to the terminal constructs is significant.
The analyses suggest that satisfaction plays a different role from the one hypothesized in our study. The results from the rival model (Table 4) indicate that both the prestige of the university and reciprocity are related positively to respondents' level of satisfaction. However, satisfaction is not related to higher levels of donating or promoting. The results from the respecified model (Table 3) indicate that satisfaction is not antecedent to identity salience. Nevertheless, the results should not be interpreted as showing that satisfaction has no role in social exchanges. Perhaps satisfaction plays a different role from the one specified here. For example, satisfaction may be related to other important constructs (e.g., relationship commitment) that are not included in our study.
Our study provides managers with a basis for marketing strategies. When organizations strive for long-term relationships with individuals (e.g., consumers, donors), they must take into account the effect of social structures. Our results suggest that organizations can improve relationship marketing success by strengthening the ties between their organizations and the identities people find important. Under-standing the role of identity salience enables marketers to have a better understanding of underlying mechanisms at work. As Morgan and Hunt (1994, pp. 31-32) emphasize, "to the manager, understanding the process of making relationships work is superior to developing simply a 'laundry list' of antecedents of important outcomes." Such an under-standing can aid managers in the development of marketing plans by suggesting potential strategies.
Our study suggests that managers who are trying to encourage supportive behaviors from donors should do so by encouraging them to develop salient identities related to the nonprofit organization. Laverie and Arnett (2000) maintain that activities that increase involvement and attachment, such as providing the opportunity for customers to get to know the employees on a more personal level, increase identity salience. In the case of nonprofits, marketers could provide more opportunities for contact with the organization (e.g., through social functions or speaking engagements), which would allow donors (or potential donors) the opportunity to create social ties with the organization. Our results suggest that for higher education marketers, encouraging students to be actively involved in school activities and improving or maintaining a level of university prestige will encourage the formation and strength of a university identity, which in turn will encourage students to engage in supportive behaviors in the future.
The importance of university prestige is also highlighted by our results. Our findings suggest that prestige affects alumni behavior in two ways. First, it increases the salience of a person's university identity, which in turn positively affects supportive behaviors (promoting and donating). Second, it has a direct and positive effect on the likelihood that a person will promote the university to others. Many universities attempt to improve their institutions' prestige (e.g., by improving academic programs and supporting faculty research efforts) and believe that such efforts will help them recruit students and faculty members and increase donations. Our study provides preliminary evidence as to the underlying process at work.
Our results do not provide support for a central role for satisfaction in nonprofit relationship marketing. Although preliminary results using the data from Group A provide support for the hypothesis that satisfaction positively affects identity salience, the data from Group B do not support this view (see Table 3). These results may be an indication that the relationship between satisfaction and identity salience is more complex than our model indicates. Perhaps it is moderated or mediated by factors not accounted for in our study. For example, people could be unsatisfied with their overall university experience but still feel strongly about specific aspects of their university experiences (e.g., a particular professor or counselor). Or our findings could be an indication that satisfaction may not be as stable of a predictor of identity salience as the other constructs in our study. Finally, it is possible that a person might not be satisfied with the college itself but could still develop a salient university identity because of other social connections (e.g., friendships).
Finally, the degree to which people have internalized an identity can affect how they respond to environmental cues. For example, Reed (2002) suggests that people who have newly adopted an identity may rely more heavily on feedback from others to validate their identities. In contrast, people whose identities are more deeply seated will rely more heavily on internal cues (e.g., feelings of satisfaction). Therefore, the level of satisfaction that donors have with their university experiences may be more relevant for people whose university-related identities are a deep-rooted part of their concepts of self.
Our data do not support the hypothesis that reciprocity influences the salience of a person's university identity. For our sample, at least, the level of perceived reciprocity did not affect respondents' identity salience. One possible explanation is that it is difficult for a university to communicate with individual alumni regarding each person's value to the university, and concomitantly, most alumni may not expect such communication. Reciprocity, however, may be an important contributor to identity salience in other relationship marketing contexts.
As indicated by our results, our model explains a higher percentage of the variance in promoting than in donating. This may be an indication that other economic factors affect people's donating behavior. For example, families that have more children may have less disposable income, which affects their ability to donate money. In addition, other factors may affect people's ability to donate to nonprofits (e.g., serious illnesses in the family, the health of the general economy, pessimism about the future). We did not control for these factors in our study.[ 3]
Our study benefited from two factors. First, the large sample size (n = 953) enabled us to use a holdout sample to better refine and test our model. As a result, we were able to respecify our model, which enabled us to investigate the direct relationship between prestige and promoting. Second, the use of objective data (donations) reduced the amount of same-source bias in our data.
Limitations and Further Research
As do all studies, ours has limitations. First, the cross-section design used in our study provides limited inferences regarding causality. Therefore, the model developed and tested here could benefit from being examined with a longitudinal design. In addition, such a design would enable researchers to investigate the stability of key constructs such as identity salience. Evidence suggests that identity salience is a more stable construct than constructs such as satisfaction. For example, Laverie and Arnett (2000) find that basketball fans' identity salience is a better predictor of attendance than is satisfaction. One possible explanation is that satisfaction levels may change from game to game because of external factors such as the performance of the team and the attitudes and behaviors of the people who attend games. However, fans' identity salience remains more constant because the identity is an important part of the self. Therefore, empirical evidence that supports or refutes this view would provide managers with additional information that would aid them in their decision making, for example, by suggesting which factors to focus on when implementing a relationship marketing strategy.
Second, the context of the study, nonprofit higher education marketing, may limit the generalizability of the results. As we argue, identity salience has the potential to be a key mediating construct in all exchanges in which one party is an individual and the exchange benefits are significantly social. However, the nature of the contact between universities and their alumni may be unique. Many organizations do not have the opportunity to be in direct contact with potential exchange partners for long periods of time (e.g., for four years while they are obtaining an undergraduate degree). Yet this limitation does not preclude other organizations from learning from our results. For example, factors such as participation and prestige may also be important in for-profit settings (e.g., selling products with such brands as Mercedes, Harley-Davidson, Ralph Lauren-Polo).
Third, we specifically investigate factors connected to university experiences (e.g., satisfaction with the education received from the university and the facilities at the university). However, universities can provide many opportunities for alumni to strengthen their ties to the university further after graduation. Furthur research could investigate how these factors affect identity salience and, in turn, donating and promoting. Such studies could investigate the effects of different types of events (e.g., alumni gatherings versus sporting events) on identity salience.
Fourth, many constructs have been investigated in the relationship marketing literature that might be used to expand our model. These concepts include commitment (Anderson and Weitz 1992), trust (Morgan and Hunt 1994), communication (Anderson and Narus 1990), cooperation (Anderson and Narus 1990), mutual goals (Morgan and Hunt 1994), shared values and norms (Heide and John 1992), social bonds (Wilson 1995), adaptation (Hakansson 1982), and satisfaction (Dwyer, Schurr, and Oh 1987). These constructs may affect the formation and maintenance of identities.
Fifth, our results provide support for the overall identity salience model of relationship marketing success (Figure 1). As Andreasen (2001) maintains, specific marketing concepts and tools that are useful in nonprofit (for-profit) settings may also be valuable in for-profit (nonprofit) environments, if the environments have similar characteristics. We argue that because many exchange relationships in the for-profit sector match the exchange characteristics examined in our study--that is, they ( 1) involve individuals and ( 2) are based primarily on social exchange--our identity salience model of relationship marketing success should provide useful insights to marketing researchers and marketing managers in other contexts. For example, research suggests that consumers can derive social benefits from the products they purchase (Laverie, Kleine, and Kleine 2002). Our model could be used to test the role of identity salience in these contexts.
Researchers suggest that promoting long-term relationships with key stakeholders is an important strategy, especially in today's intensely competitive business environment. Many organizations have embraced this concept, which is referred to as relationship marketing. Much of the research on relationship marketing success has examined relationships that ( 1) are primarily economic in nature, ( 2) involve business-to-business marketing, and ( 3) involve for-profit firms. However, we argue that relationship marketing is a viable strategy in contexts such as those involving high levels of social exchange, business-to-consumer marketing, and non-profit marketing. In these contexts, relationship marketing success requires different relationship characteristics from those identified in previous research.
Our study suggests that identity salience plays an important role in nonprofit relationships that are characterized by a minimum of two characteristics: ( 1) the exchange relationship involves individuals and ( 2) the exchange is based primarily on social exchange. Identifying the importance of identity salience in nonprofit relationship marketing is an important step in understanding how nonprofit organizations can successfully implement strategies based on relationship marketing. Our results suggest that managers in nonprofit organizations should focus on increasing the salience of their donors' organization-related identity and developing such identities in potential donors. In the case of nonprofit higher education marketing, this involves encouraging students to become more actively involved in university-related activities (e.g., student government, sports, Greek orders) as well as maintaining and, if possible, improving the prestige of the university. All of these factors are related to building a university-related identity and/or encouraging students to develop one, which in turn encourages them to promote and donate to the university in the future.
1 Forms of relationship marketing include selling alliances (Smith 1997), manufacturer-supplier relationships (Kalwani and Narayandas 1995), co-marketing alliances (Venkatesh, Mahajan, and Muller 2000), working partnerships (Anderson and Narus 1990), strategic alliances (Day 1995), interimistic alliances (Lambe, Spekman, and Hunt 2000), buyer partnerships (Berry 1983), and internal marketing partnerships (Arndt 1983).
2 Other operationalizations of participation are possible. For example, we could examine academic versus nonacademic activities or measure the intensity, frequency, or variety of activities.
3 An alternative explanation is that the higher variance explained in promoting is due to common methods variance (i.e., promoting is a self-reported behavior but donating is not).
Legend for the Chart
A Constructs/Indicators
B Standardized Loading lambda[a]
C Reliability
D Variance Extracted (Estimate)
A B C D
Donating
DON 1.00 -- --
Promoting .90 .75
PRO1 .87
PRO2 .86 PRO3 .87
Identity Salience .86 .62
ID1 .82
ID2 .75
ID3 .78
ID4 .79
Participation -- --
PAR .90
Reciprocity .91 .66
REC1 --[b]
REC2 .79
REC3 .77
REC4 .73
REC5 .90
REC6 .87
Prestige .81 .59
PRE1 .73
PRE2 .82
PRE3 --[b]
PRE4 .74
Satisfaction .84 .64
SAT1 .82
SAT2 --[b]
SAT3 .75
SAT4 .83
Income -- --
INC .90
Perceived Need .86 .67
PFN1 .76
PFN2 .76
PFN3 .92
a All loadings are significant at p < .01.
b These items were deleted during the measurement refinement process.
Notes: Descriptive fit statistics: chi2219 = 599.31 (p < .01); RMSEA = .044; CFI = .97.
Legend for the Chart
A Construct
B Mean[a]
C Standard Deviation[a]
D 1
E 2
F 3
G 4
H 5
I 6
J 7
K 8
L 9
A
B C D E F G H I J K L
1. Donating
-- -- 1.00 .19** .23** .07 .25** .07 .13** .19** .12**
2. Promoting
5.72 1.11 .14** 1.00 .76** -.21** .36** .14** .48** .84** .56**
3. Identity salience
5.26 1.40 .19** .78** 1.00 -.19** .33** .19** .43** .74** .48**
4. Income
-- -- .18** -.10* -.13* 1.00 -.03 .00 -.14** -.25** -.06
5. Perceived need
5.36 1.17 .36** .24** .30** .05 1.00 .20** .36** .42** .19**
6. Participation
3.81 1.54 .09 .23** .29** -.02 .18** 1.00 .14** .13** .04
7. Reciprocity
4.15 1.28 .16** .40** .51** -.04 .34** .18** 1.00 .57** .58**
8. Prestige
5.26 1.05 .17** .58** .75** -.18** .36** .17** .53** 1.00 .67**
9. Satisfaction
5.60 1.13 .12** .43** .55** -.08 .23** .16** .56** .53** 1.00
a These statistics are based on the entire sample (n = 953) and are calculated from the average of each person's responses for each construct. Donating and income are categorical in nature, and therefore their means and standard deviations are not reported (for descriptive statistics regarding these two constructs, see the "Sample" section).
*p < .05.
**p < .01.
Notes: Group A (n = 477) correlations are below the diagonal; Group B (n = 476) correlations are above.
Legend for the Chart
A Hypothesized Model: Group A (n = 477)
B Respecified Model: Group B (n = 476)
A B
Participation->identity salience (gamma11)
15* .09*
Reciprocity->identity salience (gamma12)
.07 .01
Prestige->identity salience (gamma13)
.59* .73*
Satisfaction->identity salience (gamma14)
.18* -.03
Income->donating (gamma35)
.18* .11*
Perceived need->donating (gamma36)
.32* .19*
Identity salience->donating (β21])
.11 .19*
Identity salience->promoting (gamma 33)
.78* .29*
Prestige->promoting (gamma33)
-- .63*
R2 (identity salience)
.62 .55
R2 (donating)
.17 .10
R2 (promoting)
.60 .75
chi2(d.f.)
586.17(232)* 485.53(231)*
RMSEA
.056 .049
CFI
.94 .96
*p < .01. Rival Model
Group B
(n = 476)
Participation->satisfaction (gamma11)
-.08**
Reciprocity->satisfaction (gamma12)
.28*
Prestige->satisfaction (gamma13)
.60*
Identity salience->satisfaction (gamma14)
-.09
Income->donating (gamma35)
.06
Perceived need->donating (gamma36)
.23*
Satisfaction->donating (β21)
.07
Satisfaction->promoting (β31)
-.02
Prestige->promoting (gamma33)
.89*
R2 (satisfaction)
.52
R2 (donating)
.08
R2 (promoting)
.77
chi2(d.f.)
535.68(231)*
RMSEA
.052
CFI
.96
*p < .01.
**p < .05. Respecified Model Rival Model
Group B Group B
(n = 476) (n = 476)
RMSEA
.049 .052
CFI
.96 .96
AIC
637.25 665.27
Percentage of significant paths (p < .01)
78% (7 of 9) 44% (4 of 9)
Percentage of significant paths (p < .05)
78% (7 of 9) 56% (5 of 9)
R2 (donating)
.10 .08
R2 (promoting)
.75 .77
Significant paths to mediator (p < .01)
2 2*
Significant paths from mediator (p < .01)
2 0
*Three paths are significant at the p < .05 level.DIAGRAM: FIGURE 1: The Identity Salience Model of Relationship Marketing Success
DIAGRAM: FIGURE 2: The Identity Salience Model of Nonprofit Relationship Marketing Success
DIAGRAM: FIGURE 3: The Satisfaction Model of Relationship Marketing Success
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I. Promoting (new scale; seven-point scale: "strongly disagree" to "strongly agree")
1. I "talk up" [university name] to people I know.
- 2. I bring up [university name] in a positive way in conversations I have with friends and acquaintances.
- 3. In social situations, I often speak favorably about [university name].
II. Identity salience (adapted from Callero 1985; seven-point scale: "strongly disagree" to "strongly agree") Being a [university name] graduate ...
1. is an important part of who I am.
- 2. is something about which I have no clear feeling.*
- 3. means more to me than just having a degree.
- 4. is something I rarely think about.*
III. Satisfaction (adapted from Westbrook and Oliver 1981; seven-point scale: "strongly disagree" to "strongly agree")
I am satisfied with ...
1. the education I received while at [university name].
- 2. the facilities at [university name] when I was a student.
- 3. the manner in which I was treated as a student at [university name].
- 4. how [university name] prepared me for a career.
IV. Participation (new scale; seven-point scale: "not active at all" to "very active")
Please list the different extra-curricular activities or organizations that you participated in while at [university name] (for example, student government, fraternities/sororities, music, drama, service organizations, athletics, intramurals) and how actively you participated:
(Respondents were given eight blank lines with eight corresponding seven-point scales--"not active at all" to "very active"--with which to rate their levels of participation.)
V. Reciprocity (Eisenberger et al. 1986; seven-point scale:
"strongly disagree" to "strongly agree")
[University name] ...
1. values my contribution to its well-being.
- 2. appreciates any extra effort from me.
- 3. listens to any complaints I might have concerning the university.
- 4. would notice if I did something that benefited the university.
- 5. shows concern for me.
- 6. takes pride in my accomplishments.
VI. Prestige (adapted from Mael and Ashforth 1992; seven-point scale: "strongly disagree" to "strongly agree")
1. People I know think highly of [university name].
- 2. It is prestigious to be an alumnus of [university name].
- 3. People seeking to advance their careers should downplay their association with [university name].*
- 4. Most people are proud when their children attend [university name].
VII. Income
For categorization purposes only, would you please check the box that contains your approximate annual household income?
___less than $25,000
___$25,000 to $49,999
___$50,000 to $74,999
___$75,000 to $99,999
___$100,000 to $124,999
___$125,000 to $149,999
___$150,000 to $174,999
___$175,000 to $199,999
___$200,000 to $249,999
___$250,000 to $499,999
___$500,000 or more
VIII. Perceived need (new scale; seven-point scale:
"strongly disagree" to "strongly agree")
1. [University name]'s need for financial support from its alumni will be even greater in the future.
- 2. State universities need the financial support of their alumni just as much as private universities.
- 3. [University name] presently needs strong financial support from its alumni.
*Denotes reverse-scored items.
~~~~~~~~
By Dennis B. Arnett; Steve D. German and Shelby D. Hunt
Dennis B. Arnett is Assistant Professor of Marketing, and Shelby D. Hunt is J.S. Rawls and P.W. Horn Professor of Marketing, Jerry S. Rawls College of Business Administration, Texas Tech University. Steve D. German is Associate Professor of Business, Lubbock Christian University.
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Record: 166- The Impact of Acquisitions on Innovation: Poison Pill, Placebo, or Tonic? By: Prabhu, Jaideep C.; Chandy, Rajesh K.; Ellis, Mark E. Journal of Marketing. Jan2005, Vol. 69 Issue 1, p114-130. 17p. 5 Charts, 5 Graphs. DOI: 10.1509/jmkg.69.1.114.55514.
- Database:
- Business Source Complete
The Impact of Acquisitions on Innovation: Poison Pill,
Placebo, or Tonic?
Do acquisitions increase, decrease, or have no effect on innovation? The empirical research on this question suggests that acquisitions may hurt innovation; that is, they may be a "poison pill" for innovation. The authors present an alternative view. For firms that first engage in internal knowledge development, the knowledge-based view the authors present suggests that acquisitions can help innovation; that is, they can be a tonic for innovation. Analysis of cross-sectional, time-series data on a sample of pharmaceutical firms during 1988-97 provides evidence to support the thesis.
Many firms consider the acquisition of other firms a way to co-opt and build on ideas from outside. Over the past several years, the volume of acquisitions in the United States has been measured in the trillions of dollars (Mergerstat 2003). Acquisitions are especially prevalent in high-tech contexts, in which the level of market and technological uncertainty is often so great that an exclusive reliance on knowledge created within the firm can be dangerous (see John, Weiss, and Dutta 1999; Rindfleisch and Moorman 2001; Wind and Mahajan 1997). Industry-defining ideas can arise outside the firm, even outside the industry. A strategy based solely on internally built knowledge is likely to delay or inhibit access to these ideas--sometimes with fatal consequences. Saxenian (1994), for example, notes that Digital Equipment Corporation's overriding reliance on internally built knowledge was a prime cause of its decline and subsequent takeover by the upstart Compaq Corporation. For these and other reasons, marketing scholars have highlighted the potential for innovation through acquisitions (see Wind and Mahajan 1997). As Capon and Glazer (1987, p. 6) point out: "a well-planned policy of external acquisition affords technology strategy options that a 'go-it-alone' attitude would preclude."
Given the importance of the topic, many studies have explored the link between acquisitions and innovation. The empirical evidence so far is not encouraging. Research suggests that acquisitions tend to hurt, not help, innovation (Ernst and Vitt 2000; Hitt et al. 1991a; Miller 1990; for a recent exception, see Ahuja and Katila 2001). The bulk of the research implies that firms in search of innovation may do well to steer clear of acquisitions. Some researchers argue that the many activities involved in trying to consummate and integrate acquisitions can distract managers from the task of innovation (Hitt, Hoskisson, and Ireland 1990). Others note that key employees, including scientists and champions of innovation, may leave the firm after acquisition (Ernst and Vitt 2000). Researchers also point out that firms may take on considerable debt to finance acquisitions; the interest expenses and repayments associated with this debt may choke off much-needed funds for innovation (Hitt et al. 1991b). For these reasons, acquisitions have been referred to as a "poison pill" for innovation (Hitt et al. 1991b, p. 22). At best, acquisitions are simply considered to be a placebo, having no effect on innovation (see Jensen 1988).
However, the research on acquisition and innovation has focused on firms in the aggregate, without further exploring differences among firms. Most studies have sought to answer the general question, Are acquisitions good or bad for innovation? However, firms vary widely in their ability to convert external knowledge into innovation outcomes. For example, some observers attribute Johnson & Johnson's strong innovation record to its ability to spot good targets and manage them better than other firms do (Barrett 2002). Therefore, a different and potentially more useful question is, Why are some firms better at generating innovations from acquisitions than others? This is the question we address in this research.
In this article, we aim to make three main contributions to the literature. First, we present a knowledge-based view of the firm that highlights reasons some firms are better than others at innovation through acquisition. We argue that firms that are rich in internal knowledge to begin with are likely to achieve significantly greater innovation from their acquisitions than are other firms. By doing so, we show that prior research, in failing to employ such a knowledge-based view, has arrived at premature and overly pessimistic conclusions about the impact of acquisitions on innovation. Instead of acquisitions being a poison pill or placebo, as prior research has concluded, we show how acquisitions might work as a tonic for innovation.
Second, we complement the growing literature in marketing on the development of internally built knowledge by highlighting a hitherto overlooked benefit of such knowledge. We show that a sustained process of internal knowledge building in particular technological areas can pay off by enabling firms to better identify and assimilate knowledge bought from other firms. In doing so, we show the synergies to be gained by a program that combines internal with external knowledge. Indeed, our results show that examination of internal and external knowledge in isolation leads to an underestimation of the influence of either form of knowledge on innovation.
Third, we aim to make an important methodological contribution. We believe that limitations in the data and methods used in the existing empirical research cause the research results to be less than conclusive. For example, studies thus far tend to ignore a key metric of innovation: new products. This is the innovation output that is of most relevance to marketing scholars. Existing studies examine innovation input (e.g., research and development [R&D] expenditures) or intermediate output (e.g., patents). Ours is the first study (to our knowledge) to address the impact of acquisitions on product innovation.
The concept of knowledge is central to our thesis. We argue that a firm's internal knowledge is a key predictor of its ability to use external knowledge to create innovations. We use arguments from organizational theory on absorptive capacity (Cohen and Levinthal 1990; Kogut and Zander 1992) and the knowledge-based view of the firm (Bierly and Chakrabarti 1996; Grant 1996; Nonaka 1994; Spender 1996; Winter 1987) as well as recent work in marketing (especially John, Weiss, and Dutta 1999) to develop our arguments on the role of knowledge in postacquisition innovation. These arguments highlight why innovation from acquisitions is challenging yet ultimately feasible.
In this article, we focus on a particular type of knowledge: technical knowledge. We define technical knowledge as scientific knowledge applied to useful purposes. In prior research, this construct has sometimes been called technical know-how (John, Weiss, and Dutta 1999).
Technical knowledge is not easily transferred across different fields (Henderson and Cockburn 1994; John, Weiss, and Dutta 1999; Pisano 1994). Technical knowledge across different fields is, however, potentially combinable (Kogut and Zander 1992; Reed and DeFillipi 1990). The field-specific nature of technical knowledge underscores the need to study, in the acquisitions context, the depth (within fields), the breadth (across fields), and the similarity of the acquirer's knowledge with the fields in which the target has knowledge. For the sake of simplicity in exposition, we henceforth use the term knowledge to refer to technical knowledge.
Dimensions of Knowledge: Depth, Breadth, and Similarity
We define knowledge depth, breadth, and similarity as follows. Depth refers to the amount of within-field knowledge possessed by the acquiring firm. Breadth is the range of fields over which the firm has knowledge. Similarity is the extent of overlap in the fields of knowledge of the acquirer and target firms. Prior discussions of knowledge have implicitly focused on depth of knowledge (e.g., John, Weiss, and Dutta 1999), though even this variable has not been examined in the acquisitions context. However, the difficulty of transferring knowledge across fields implies that breadth and similarity of knowledge are also critical in the acquisitions context.
Sources of Knowledge: Internal Versus External Knowledge
Knowledge differs not only in the dimensions discussed previously but also in its sources. Internal knowledge is based on learning by members of the firm; it results from the creation and distribution of knowledge within the boundaries of the firm (Chesbrough and Teece 1996). External knowledge, in contrast, originates from outside the firm. We use the terms internal and external knowledge in the spirit of Friedman, Berg, and Duncan (1979, p. 103); Bierly and Chakrabarti (1996, p. 127); Van den Bosch, Volberda, and de Boer (1999, p. 566); and Madhok and Osegowitsch (2000, p. 329) and differently from internalization as used by Nonaka (1994).
Acquisitions provide an important means to bring external knowledge into the firm (Bierly and Chakrabarti 1996). The emphasis in prior research on these two sources of knowledge has typically been on examining the trade-off between the two in a static sense. Thus, internal knowledge has been shown to enable firms to develop core competencies, especially in domains that are complex and deeply integrated with other domains of knowledge (Chesbrough and Teece 1996). In contrast, external knowledge is more likely to enable firms to keep abreast of new technical developments, thus increasing the flexibility of the firm in dynamic environments (Grant 1996).
By maintaining and replenishing their stock of knowledge on a continuous basis, firms that combine internal and external knowledge can reduce the chances of being locked out of areas of future technological and commercial importance (Cohen and Levinthal 1989; Schilling 1998). Much of the literature on combining internal with external knowledge is conceptual (see Cohen and Levinthal 1989; Zahra and George 2002). Only recently have researchers begun to test some of these ideas empirically (e.g., Cockburn and Henderson 1998; Lane and Lubatkin 1998; Moorman and Slotegraaf 1999). We are aware of no study that has applied these ideas in the area of acquisitions in general or acquisitions and innovation in particular. This is the focus of our research. A recent study (Wuyts, Dutta, and Stremersch 2004) examines the role of alliances in product innovation. However, ( 1) that study examines alliances, whereas ours examines acquisitions, and ( 2) that study examines technological diversity, whereas ours examines the depth, breadth, and similarity of technical knowledge. We next present hypotheses on the main effect of internal knowledge and then the interaction effect of internal and external knowledge in the context of acquisitions and innovation.
Depth of Knowledge and Innovation
A firm's ability to create knowledge is a key driver of its ability to innovate (see Griliches 1984). New knowledge does not arise in a vacuum; rather, it is a path-dependent outcome of building on prior knowledge. However, as noted previously, knowledge is field specific in nature. Firms are therefore likely to vary in the depth of knowledge they have in a particular field.
The effects of depth of knowledge on innovation are not straightforward. Some researchers have argued that greater depth in knowledge could, ceteris paribus, lead to the setting in of core rigidities, which in turn could decrease innovation (Leonard-Barton 1992). However, a larger and more recent stream of research indicates that inadequate knowledge in a particular field can result in firms being locked out of developing or assimilating new knowledge in that area (Zahra and George 2002). Specifically, to be able to develop new knowledge in a field, firms must already possess some knowledge in that field. Developing depth in key fields enables firms to produce new knowledge in those fields, gain competency in core product areas, and thus innovate (Bierly and Chakrabarti 1996; Hamel and Prahalad 1994). As a result, there is likely to be a main effect of depth of knowledge on the innovation activity of firms: The deeper a firm's knowledge in certain fields, the greater is its ability to create innovations in these and related fields.
In addition to this main effect, there is likely to be an interaction effect of depth of internal knowledge and external knowledge from acquisitions. Successful innovation from acquisition involves the ability to choose the target with the most promising knowledge, absorb the knowledge made available by the target, and exploit it to create new knowledge. Firms that differ in depth vary in their ability to evaluate, absorb, and build on external knowledge and therefore derive different levels of innovation from such knowledge. Firms with low depth are likely to fall prey to technological lockout (Cohen and Levinthal 1989). Such firms may not be able to assess accurately the innovation potential of targets. Compared with firms with high depth, these firms may acquire targets with less innovation potential. Furthermore, given their lack of experience in creating knowledge, they are likely to manage acquisitions (even those with high innovation potential) poorly. Key technical personnel from the target firm may leave the combined firm because of a perceived lack of prestige or appreciation for their activities.
Firms with high depth, in contrast, are better able to evaluate and manage new knowledge and use it to innovate (Cohen and Levinthal 1990). They are therefore best positioned to leverage acquisitions to create innovations. For all these reasons, we expect that
H1: Firms with high depth of knowledge produce more innovations than do firms with low depth of knowledge.
H2: Firms with high depth of knowledge produce more innovations from acquisitions than do firms with low depth of knowledge.
Breadth of Knowledge and Innovation
Just as they vary in depth, firms also vary in the breadth of knowledge they possess. Again, the effects of breadth on innovation are not obvious. Some researchers have noted that greater breadth in knowledge could, ceteris paribus, cause the firm to spread resources too thinly (e.g., Wernerfelt and Montgomery 1988). Breadth can also cause distraction within the firm, thus lowering innovation.
However, the bulk of the knowledge-based literature suggests that breadth in knowledge is helpful for innovation (Bierly and Chakrabarti 1996; Cohen and Levinthal 1990; Henderson 1994; Henderson and Cockburn 1994). Several researchers have pointed out the importance of being able to integrate knowledge from across different fields, especially in technically complex industries (Henderson and Cockburn 1994; Pisano 1994; Volberda 1996). The broader a firm's existing knowledge, the greater is its ability to combine knowledge in related fields in a more complex and creative manner (Bierly and Chakrabarti 1996; Kogut and Zander 1992; Reed and DeFillipi 1990). In addition, the potential for "happy accidents," whereby concepts from one field are applied to a different field in hitherto unexpected ways, increases with greater breadth of knowledge.
Moreover, firms with a broad base of knowledge are less likely to develop core rigidities and thus be locked out of emerging technical domains (Leonard-Barton 1995). With changes in market preferences and technological opportunities, knowledge that was once a source of competitive advantage may become irrelevant. Low breadth makes the firm especially vulnerable to such irrelevance. Broader knowledge, however, gives the firm greater flexibility and adaptability in responding to environmental change (Volberda 1996). Overall, these arguments suggest that the broader a firm's knowledge, the greater is its ability to create innovations.
As does depth, breadth is likely to have an interaction effect with acquisitions. Given the field-specific nature of knowledge, a firm with low breadth is perforce faced with a greater number of targets that lie outside its own field of knowledge. Therefore, the probability is higher that it will choose acquisition targets outside its own field, from fields that are unfamiliar to it (Chaudhuri and Tabrizi 1999; Hitt, Harrison, and Ireland 2001). When acquiring from outside its own field, a firm with narrow knowledge is likely to be handicapped by its lack of expertise in the other fields (see Cohen and Levinthal 1990; Rosenberg 1982; Zahra and George 2002). It may, for example, choose targets that others with more knowledge in the field may avoid (e.g., Morck, Shleifer, and Vishny 1988). More important, when it acquires such targets, it may be less able to manage and exploit, after the acquisition, the unfamiliar knowledge it has acquired through the target firm (Haspeslagh and Jemison 1991).
In the context of acquisitions, the successful exploitation of an acquired firm involves the ability to absorb the knowledge of the target firm and then use it to develop still newer knowledge (Jemison 1988). The greater the breadth of the acquirer's knowledge, the greater is its ability to absorb the knowledge of the target firm and the greater is its potential for discovery (and therefore innovation) after the acquisition, both by accident and through planning. Therefore,
H3: Firms with high breadth of knowledge produce more innovations than do firms with low breadth of knowledge.
H4: Firms with high breadth of knowledge produce more innovations from acquisitions than do firms with low breadth of knowledge.
Similarity of Knowledge and Innovation
The similarity of knowledge between the acquirer and the target is crucial to the acquirer's ability to absorb the target's knowledge and use it for innovation (Mowery, Oxley, and Silverman 1996). In general, prior research (though not directly focused on the acquisitions context) implies a linear relationship between similarity of knowledge among firms and innovation outcomes from joint activities (Henderson 1994; Henderson and Cockburn 1994). In an empirical study of strategic alliances, Lane and Lubatkin (1998) show that one firm's ability to exploit the knowledge of another depends on the similarity of both firms' knowledge bases. Cohen and Levinthal (1990) state that for firms to facilitate absorption of new knowledge, the prior knowledge should be closely related to it.
In contrast to the linear effects implied by prior research, we argue that a nonlinear relationship between similarity of knowledge and innovation is more likely (see also Ahuja and Katila 2001). Greater similarity between the acquirer and the target will make it easier for the acquirer to absorb the knowledge of the target firm. Similarity may also lead to easier postacquisition integration, less turnover of key inventors, and therefore greater innovation. However, in the case of highly similar acquisitions, there will also be less new knowledge to absorb. Too much relatedness will result in overlapping and redundant research (e.g., Rindfleisch and Moorman 2001). There will also be fewer knowledge synergies and therefore fewer opportunities for combining different types of knowledge in creative ways.
In contrast, if the two firms' knowledge is very dissimilar, the external knowledge will be difficult for the acquirer to absorb in the first place. In the context of acquisitions, because absorbing the knowledge of the target is a prerequisite for using it to create new knowledge, very dissimilar knowledge will make it difficult to generate innovations after the acquisition. Therefore, we hypothesize an inverted U-shaped relationship between the similarity and innovation:
H5: Firms with moderate similarity of knowledge produce more innovations from acquisitions than do firms with very high or very low similarity of knowledge.
Through our methodological approach, we attempt to avoid several pitfalls that are evident in research on acquisition and innovation. Data limitations have constrained the focus of much prior research to acquisitions in which the targets are large, publicly held companies. The Federal Trade Commission's Large Merger Series is a commonly used source of data on acquisitions, but this database includes only data on acquisitions of the largest target firms. It is possible that the distraction and debt arguments noted in the literature are particularly severe for acquisitions involving large targets. A vast majority of acquisitions involve small and privately held targets (see U.S. Small Business Administration 1998). The debt and distraction problems may be less acute for such targets. In this research, we study not only large acquisitions but also those involving small and medium-sized targets. We control for heterogeneity due to firm-specific effects by employing a unique panel data set that we specifically construct for this study.
In addition, research thus far has generally focused on the immediate effects of acquisitions on innovation (see Hitt et al. 1991a). However, there could be many lags involved in assimilating acquisitions and generating innovations from them. The true effects of acquisitions may become evident only over a longer term, perhaps over multiple years. To assess more completely the impact of acquisitions on innovation, we apply distributed-lag models to measure not only the current effects of acquisitions but also their future effects. This section describes the empirical context, data, and analysis used in this research.
Empirical Context
Knowledge manifests itself in different ways in different industries. The metrics used to measure knowledge therefore need to vary from industry to industry. The measurement of knowledge across multiple industries is likely to be prone to substantial errors because any uniform metric for knowledge may understate the true knowledge in some contexts while overstating it in others. For this reason, we focus on a single industry to test our hypotheses.
The empirical context for this study is the pharmaceutical industry. This context is especially suitable for our purpose. First, innovation is a particularly critical activity in the pharmaceutical industry (Graves and Langowitz 1993; Jensen 1987; Koberstein 2000; Scherer 1993). Innovative firms in this industry benefit from large and persistent competitive advantage (Comanor 1986; Cool and Dierickx 1993; Henderson and Cockburn 1994).
Second, this industry is characterized by a heavy reliance on knowledge that is codified in the form of patents (Cohen, Nelson, and Walsh 2000; Levin et al. 1987; Mansfield 1986). Both product and process innovations are patented at high rates in this industry, and pharmaceutical firms view patenting as an effective means to prevent competitors from copying innovations (Cohen, Nelson, and Walsh 2000; Klevorick et al. 1995). This reliance on patents allows for a relatively clean yet comprehensive metric for knowledge.
Third, the pharmaceutical industry is an important and widely studied context. A considerable volume of academic research has focused on this industry (e.g., Bierly and Chakrabarti 1996; Cockburn, Henderson, and Stern 1999; Lichtenberg 1998; Yeoh and Roth 1999; for reviews, see Comanor 1986; Scherer 1993). Although acquisitions are a salient issue in the industry, their impact on innovation has not received much academic attention.
Sampling Procedure
To identify our sample group, we randomly selected 47 companies from a population of 185 pharmaceutical companies in the Financial Times Sequencer, an international database of share price data, financial news articles, key financial ratios, and balance sheet and profit and loss data. To assess list validity, we compared a random sample of 40 companies from the Sequencer list with a similar list from the Datastream International database. This comparison indicated a high degree of overlap between the samples in the two lists.
Of the 47 companies in our Sequencer list, we filtered 12 out so that the final sample included only firms that were U.S. based, existed as independent entities during the period from 1988 through 1997, and had pharmaceuticals as the primary line of business. We collected information on the group of 35 acquirers over a ten-year sample period from 1988 through 1997. The firms in the acquirer data set collectively acquired 157 targets during the sample period. Recall that much of the existing research on acquisition and innovation uses the Federal Trade Commission's Large Merger Series database. Our data are more representative of the population of pharmaceutical firms than the Large Merger Series data for two reasons. First, our data consist of all acquisitions by a random sample of (public) firms. We therefore include all acquisitions by the firms in our sample, whereas the Large Merger Series includes only the largest acquisitions. Second, several firms in our randomly selected sample of acquirers are small, whereas the Large Merger Series data include only the largest acquirers.
Measures and Data Sources
We use archival time-series data to measure our conceptual variables. We do so because a proper accounting of the future effects of acquisitions requires data over time. Survey data on a long enough time series are difficult to obtain. Furthermore, survey data on knowledge and innovation are prone to self-report and memory biases.
We were unable to find a single database that contains cross-sectional, time-series data on all our variables of interest. We therefore put together our database on a firm-by-firm and year-by-year basis, using different sources for different variables. Table 1 provides a summary of the measures and data sources. We provide information on each of the key measures next.
Innovation. Innovation occurs at many stages in the pharmaceutical industry: from the discovery of new molecules to the introduction of drugs based on these molecules into the marketplace. In general, prior research has used patent counts as a measure of innovation (Ahuja and Katila 2001; Jensen 1987; Narin, Noma, and Perry 1987). Although patent counts provide a measure of technical knowledge, patents are at best an incomplete measure of innovation, because patents may or may not translate into actual drugs. It might therefore be more appropriate to consider more product-based measures of innovation.
An obvious metric at first glance would be the number of new drugs that firms in our sample introduce into the marketplace over time. On closer examination, however, this metric is not well suited for our purposes because of the large time lags involved in developing and testing new pharmaceutical products. To solve this problem, we use a measure of innovation that is intermediate between patents and actual drug introductions: the number of products in Phase 1 trials by each firm in each year. (For additional details on this measure of innovation, see Appendix A.) We obtain product information (Phase 1 drugs) from the
Pharmaprojects database, which identifies and monitors the progress of all significant new drug candidates as they enter pharmaceutical R&D programs around the world (www. pharmaprojects.co.uk; Snow 1993). New drug candidates are tracked through the various phases of pharmaceutical product development, up to market launch or discontinuation. All records are retained in the database, regardless of the fate of the drug. The cumulative nature of the database provides a comprehensive history of global drug R&D. We were unable to obtain information on product innovation for eight firms, because these firms were themselves acquired by other firms between 1997 and 2003. As such, we dropped them from the analyses.
Acquisitions. We measure the number of acquisitions in each year for each firm in the sample. We obtain this information from FIS Online, the electronic version of the Moody's Manual, and cross-check it with information from Financial Times Sequencer. Prior research has often relied on a dichotomous measure of acquisition, whereby a firm is judged to have either acquired one or more firms or not acquired at all (e.g., Hitt et al. 1991a). By measuring the actual number of acquisitions in each year, we obtain a more refined measure of the impact that knowledge obtained through acquisition has on innovation.( n1)
Knowledge. The field-specific nature of knowledge implies that any measure should address knowledge both within and across fields. Therefore, to measure knowledge for a firm properly, we need to identify the specific fields in which the firm possesses knowledge. In the pharmaceutical industry, as noted previously, patents are an excellent indicator of firms' technical knowledge. Patents have the added advantage that they are classified by the Patent Office as belonging to specific classes that relate to their field of use. Therefore, it is possible to identify the technical fields in which a firm has knowledge by studying the classes in which it holds patents.
Information on the nature of each patent class is available from the World Intellectual Property Organization. The number of approved patents in each patent class for each firm in each year is available from the Delphion database. Patent classifications vary in their level of specificity, and the World Intellectual Property Organization uses the following hierarchy of classifications: section (most general) → class → subclass → group → subgroup (most specific). An examination of patenting activity within each patent category indicates that the subclass level provides the appropriate level of specificity for our purposes, as there are more than 10,000 subclasses in which firms may have patents. Our sample of acquiring firms patented in more than 750 subclasses during the study period, and the targets that these firms acquired patented in 258 subclasses.
Depth of knowledge is measured as the average number of approved patents per patent subclass for each firm in each year. Breadth of knowledge is measured as the number of patent subclasses covered by each firm's approved patents in each year.( n2) Similarity in knowledge is measured as the number of patent subclasses shared by the acquirer and target firm, divided by the total number of patent classes owned by the acquirer and target combined.( n3)
To calculate similarity, we therefore need to identify all the unique patent classes in which both acquirers and targets have approved patents. The problem of dealing with a large number of patent subclasses is intensified because many firms acquire more than one target a year, and each of these targets potentially has patents approved in one or more of the full set of patent subclasses. We first collect the number of unique patent subclasses for each target for each year across the entire period of our sample. We then compute the total number of patent subclasses that each acquirer has in common with each of its targets for three years leading up to the year of acquisition. We divide each number by the total number of patent subclasses owned during this period by the parent and each target combined. Next, we average across all targets acquired by a firm in a particular year to arrive at a measure of similarity between an acquirer and its targets in each year of the data set.
Conceptually, a proper measure of similarity should account for overlap in knowledge not just in the current year but also in previous years. One option is to sum data on all patent subclasses for all years for all acquirers and targets before acquisition. However, given the potential for knowledge to decay in high-technology industries, similarity in most recent knowledge is likely to be more relevant than knowledge from the distant past. To balance practical constraints with conceptual completeness, we chose a three-year window before acquisition to measure similarity.
Control variables. To control for the effects of factors beyond those described previously, we include data on additional variables related to both the acquirer and the target. Success or failure of acquisitions is often deemed to be a function of the culture of the firms involved. But culture, like love, is a many-splendored thing. Dimensions of culture could include ( 1) national culture, ( 2) organizational culture, ( 3) market culture, and ( 4) scientific culture. To control for these dimensions of culture, we include the following variables:
• National culture: We measure whether the target firms have different countries of origin from the acquiring firm, and we sum across all targets in each year for each acquirer.
• Organizational culture: We measure the acquirer's size (measured as the number of persons employed by the firm) and the target's size (measured using the price at which it was acquired).( n4)
• Market culture: We measure whether the primary Standard Industrial Classification codes of the target firms are different from that of the parent, and we sum across all targets in each year for each acquirer.
• Scientific culture: We measure the acquirer's biotechnology focus (coded as biotechnology-only or general pharmaceutical).
In addition to these variables, we also include the acquirer's R&D intensity (R&D expenditures divided by sales) and the target's level of technical knowledge before the acquisition (measured as the number of patents received by the target in the preceding three years). We convert the dollar expenditures and sales figures for each year into constant 1982-1984 dollars by multiplying them by the appropriate inflation indices.
We seek to fulfill multiple objectives in our model specification. First, the model should enable us to estimate not only the current effects of internal and external knowledge on innovation but also their future effects in the years following the acquisition. This requirement allows for a more complete assessment of the true impact of acquisitions on innovation, because the effects of current knowledge (whether internal or external) on future innovation could last for many years.
Second, the model should account for heterogeneity due to firm-specific effects. Unobserved factors other than those explicitly addressed in our conceptual framework can also have an impact on innovation. For example, it could be that some firms tend to view innovation as the overriding goal of acquisitions, whereas innovation may be just one of many goals of other firms. Such goals are difficult to assess and are unobserved by the econometrician. Other examples of unobserved firm-specific variables include alliance-proneness, licensing arrangements, and quality of management. To parcel out the true effects of our hypothesized variables properly, we need to control for these unobserved effects.
To fulfill these objectives, our analysis combines an error-component regression model with a Koyck distributed-lag specification. As such, we assume that the effect of knowledge decays exponentially (Pakes and Schankerman 1984; Schott 1976, 1978; for an alternative viewpoint, see Madhavan and Grover 1998). The panel structure of our data enables us to use an error-component model that controls for unobserved firm-specific heterogeneity (Baltagi 2001). Our distributed-lag specification enables us to represent parsimoniously the effects of prior internal and external knowledge on innovation.
To obtain efficient and unbiased estimates of the future effects of current knowledge without a distributed-lag specification, we would need an infinite (or at least a very large) number of lags of data on the independent variables (Intriligator, Bodkin, and Hsiao 1996). Practically, such an estimation procedure creates severe problems with multicollinearity among the lagged knowledge terms. Our modeling challenge is similar to that faced by researchers attempting to model the effect of advertising on sales. There too, the key independent variables (advertising in that case, and knowledge in ours) could have effects not only in the current period but also in future periods. (For a classic exposition of this issue in marketing, see Clarke 1976; for a recent application, see Tellis, Chandy, and Thaivanich 2000.) The problem of multicollinearity is especially severe when key hypotheses revolve around the interaction effects, as is the case here.
A distributed-lag model, however, alleviates the need for and the problems caused by numerous lags on knowledge. Moreover, this specification provides an intuitive metric of the rate of decay of knowledge, thus enabling a calculation of the impact of current internal and external knowledge on innovation in future periods. An analysis of various lag specifications using our data indicates that a simple Koyck model (with its associated exponential decay pattern for knowledge) fits the data best. This specification is consistent with a long tradition of research in the advertising and investments areas (see Clarke 1976; Koyck 1954) and with other recent studies of innovation and knowledge (e.g., Dutta, Narasimhan, and Rajiv 1999).
We use the following model specification to test our hypotheses (for the model derivation, see Appendix B):
( 1) Iit* = β0 + λIi,t* - 1 + β1Depthi,t + β2Breadthi,t + β3Acquisitioni,t + β4(Acquisition x Depth)i,t + β5(Acquisition x Breadth)i,t + β6Similarityi,t + β7Similarityi,t2 + ∏Controli,t + μi + γt + νi,t.
The element Iit* refers to the innovation output of firm i in year t*, where t* = t + n (n ≥ 0), and Acquisition, Depth, Breadth, and Similarity are defined in Table 1. For the reasons noted in Appendix A, we set n = 4 when estimating Equation 1. Controli,t is a matrix of control variables, μi and γt are the firm-specific and time-specific effects, and νi,t is the remaining error term.
Our dependent variable, innovation, is a count variable, and therefore it might be argued that it is appropriate to use a nonlinear (Poisson or negative binomial) specification to test our hypotheses. However, such an approach is not straightforward in our context. Estimation techniques that simultaneously account for ( 1) count data, ( 2) a lagged dependent variable, and ( 3) panel data are still in their infancy (see Windmeijer 2002). Recently, some of the pioneers in this area of econometrics (see Blundell, Griffith, and Windmeijer 2002; Windmeijer 2002) have developed a generalized method of moments (GMM) estimator that meets all three requirements.
Accordingly, we employed a GMM, quasi-differenced, linear feedback model to estimate the effects of the variables in Equation 1. The model diagnostics indicate that our specification is appropriate; that is, the hypothesis that our instrument set is valid cannot be rejected at the p < .10 level (Sargan statistic: χ⊃ = 11.89, p = .53). In addition, the pattern of serial correlation in the first-differenced residuals is consistent with the assumption of these models that the uit disturbances are serially uncorrelated; that is, the δuits have a significant and negative first-order correlation (M1 = -2.01, p < .10) and no significant second-order correlation (M2 = 1.23, p < .10) (Bond 2002). Thus, overall, the null hypothesis that our moment conditions are valid cannot be rejected at the p < .10 level (Blundell, Bond, and Windmeijer 2000). Finally, the results from the GMM estimation are similar to the more standard generalized least squares estimation results we report subsequently. However, the interpretation of interaction effects is difficult in nonlinear GMM models because marginal effects are a function of all independent variables, not just the variables in the interaction term (see Ai and Norton 2001, 2003). This problem of interpretation is compounded by the presence of a large number of independent variables. Moreover, the error distribution associated with the interaction parameters in the nonlinear specification is unknown (see Ai and Norton 2001, 2003); thus, the standard errors and significance levels of the interaction parameters are not available. Therefore, we report results from the more traditional random-effects generalized least squares estimator (Baltagi 2001) in the next section.
Table 2 presents the descriptive statistics for the variables of interest. Table 3 presents the estimation results with the number of Phase 1 products developed by the acquirer as the dependent variable. We test three models: Model 1 tests the effect of external knowledge only, Model 2 tests the effect of internal knowledge only, and Model 3 tests the effect of both internal and external knowledge. Model 3 therefore presents the estimates for our model in Equation 1.
We run three models to show the importance, as we argued previously, of examining the effect of internal and external knowledge acting together as opposed to separately. As the results show, our hypothesized model (Model 3) outperforms Models 1 and 2. The R2 increases from .46 (Model 1) and .49 (Model 2) to .56 (Model 3), an increase of 22% and 14%, respectively (see Table 3). These increases are all statistically significant at the p < .001 level.
The λ coefficient is positive and significantly different from zero in all models, indicating that the effect of knowledge on innovation continues into future periods. The estimate of this decay parameter in the fully specified models (λ = .19, p < .01) can be used to compute the long-term impact of each component of knowledge using the following formula:
Long term effect of βi = βi/(1 - λ).
We next describe the results for each of our hypotheses and focus only on the results of the estimation of our hypothesized model (Model 3).
Depth of Knowledge and Innovation
H1 argues that firms with high depth of knowledge produce more innovations than do firms with low depth of knowledge. The coefficient of depth of knowledge is significant and positive in Model 3 (β = .11, p < .05) (see Table 3). Therefore, in support of H1, the greater the acquirer's depth of knowledge, the greater is its innovation output.
H2 suggests that firms with high depth of knowledge derive more innovations from acquisitions than do firms with low depth of knowledge. In other words, the hypothesis predicts a positive interaction effect of number of acquisitions and depth of knowledge. Our results support this hypothesis.
For the sake of exposition, we next present some simple results from a bivariate categorical analysis of the interaction effect of depth of knowledge and acquisitions on innovation. Table 4 reports the results using median splits of acquirers based on their number of acquisitions and depth of knowledge. Firms that are high in both depth of knowledge and the number of acquisitions appear to produce the most innovations. The formal analysis in Table 3 confirms this result. The coefficient of the interaction term of depth and acquisitions in Table 3 is significant and positive in Model 3 (β = .12, p < .01). This result suggests that the marginal impact of acquisitions on innovation is greater for firms with high depth of knowledge than for other firms.
Breadth of Knowledge and Innovation
H3 argues that high breadth of knowledge leads to greater innovation than low breadth of knowledge. The coefficient of breadth of knowledge is significant and positive in Model 3 (β = .09, p < .01) (see Table 3). Thus, in strong support of H3, the greater the acquirer's breadth of knowledge, the greater is its innovation output.
H4 suggests that firms with high breadth of knowledge derive more innovations from acquisitions than do firms with low breadth of knowledge. In other words, the hypothesis predicts a positive interaction effect of number of acquisitions and breadth of knowledge. Table 5 reports the results using median splits of firms based on their number of acquisitions and breadth of knowledge. Firms that are high in both breadth of knowledge and the number of acquisitions appear to produce the most innovations. However, the formal results in Table 3 do not support this hypothesis. The coefficient of the interaction term of breadth and acquisitions is not significantly different from zero in Model 3 (β = -.02, p = .13).
It is possible that the nonsignificant coefficient of the breadth x acquisitions interaction is due to multicollinearity among our breadth, acquisitions, and depth variables. To assess whether multicollinearity is the true cause of this result, we carry out five sets of analyses. First, we estimate the variance inflation factor for the breadth and depth variables and their interactions with acquisitions in our model. The variance inflation factor statistics for these variables are all well below the acceptable cutoff of 10. Second, using the condition index method (Belsley, Kuh, and Welsch 1980), we find the condition number to be 13.45, well below the cutoff of 30 (all singular values except one are below 10). Third, we reestimate Model 3 using Lance's (1988) method after we correct for multicollinearity due to interaction terms. The coefficient remains nonsignificant, indicating that multicollinearity between the main and interaction effects is not the cause of this result. Fourth, we test alternative functional specifications of breadth (e.g., by using a squared breadth term to test for curvilinear effects). The analyses do not provide support for such alternative specifications. Fifth, we check if the main and interaction effects of breadth hold when the main and interaction effects of depth are dropped, and vice versa. They do. In summary, these analyses indicate that multicollinearity does not drive our results.
Similarity of Knowledge and Innovation
H5 suggests that acquirers with knowledge that is moderately similar to that of their targets produce more innovations after acquisitions than do acquirers with knowledge that is very similar to or very different from that of the targets. In Model 3, the coefficient of similarity is significant and positive (β = 40.41, p < .05), and the square of similarity is significant and negative (β = -263.07, p < .05) (see Table 3). The results provide support for H5 and, taken together, imply that the overall effect of similarity is curvilinear. Figure 1 depicts the predicted effect of similarity based on the model coefficients reported in Table 3. All our data points except one fall within the range of similarity values depicted in Figure 1. As the figure indicates, increasing similarity first increases and then decreases the innovation activity of the parent firm.
Additional Analyses
Relative importance of depth versus breadth. The results described previously indicate that depth and breadth of knowledge have significant effects on innovation. To identify the relative effects of depth and breadth on innovation, we also compute the standardized coefficients of these variables. A comparison of the standardized coefficients shows that breadth has a greater impact on innovation than depth does (βbreadth = .45 versus βbreadth = .16). Furthermore, as Table 2 indicates, the effect size as captured by the Pearson correlation (see Fern and Monroe 1996; Sawyer and Ball 1981) between breadth and innovation is higher than that between depth and innovation (.57 versus .46). It appears that breadth, more than depth, promotes flexibility and adaptability (Volberda 1996), as well as the ability to combine diverse types of knowledge (see Henderson and Cockburn 1994; Pisano 1994), thus leading to greater innovation.
Process checks. An implicit argument in our hypotheses on the role of internal knowledge on innovation through acquisition is that firms with greater internal knowledge choose better targets. To test the validity of this claim, we conduct process checks on the relationship between acquirers' knowledge and the quality of the targets they acquire. Specifically, we conduct additional analyses to address whether firms with greater depth and breadth of knowledge acquire targets that ( 1) have greater knowledge and ( 2) have more significant knowledge.
First, to assess whether firms with greater internal knowledge choose targets that have greater knowledge, we compare the average number of patents per target for acquirers with high versus low depth and breadth of knowledge (see Figures 2 and 3). We classify acquirers as low or high in depth and breadth of knowledge using a median split on the depth and breadth of patents granted to these firms over the sample period. Figures 2 and 3 show that acquirers with high levels of depth and breadth of knowledge acquire targets with higher patents on average than do acquirers with low depth and breadth, respectively (7.15 versus .87 and 7.32 versus .70, respectively; p < .05).
Second, to assess whether firms with greater internal knowledge choose targets that have more significant knowledge, we compare the average number of forward citations per patent per target acquired by firms with high versus low depth and breadth of knowledge. As in academic articles, forward citations provide a reasonable measure of the significance of a particular patent, because they indicate how many other patents cited this patent in their reference list. (For a similar use of this measure in the patents context, see Dutta, Narasimhan, and Rajiv 1999; Hall, Jaffee, and Trajtenberg 2000.) Figures 4 and 5 show that acquirers with high levels of depth and breadth of knowledge acquire targets with higher forward citations per patent per target than do acquirers with low depth and breadth, respectively (53.74 versus 12.96 and 54.09 versus 9.47, respectively; p < .05 for both).
Endogeneity. It is possible that depth, breadth, and acquisitions are themselves endogenous with respect to acquirer variables such as R&D and size. Moreover, breadth of knowledge could itself be a function of depth, and vice versa. To check for potential effects of endogeneity, we test a three-stage least squares (3SLS) specification that treats depth, breadth, and acquisitions as endogenous, that is, dependent on one another as well as on various control variables such as R&D and firm size. The pattern of results is similar to those from the panel model we report in Table 3. More formally, the Hausman coefficient (m) for the comparison of the coefficients of the 3SLS model with the panel model is χ²( 15) = 3.95 (p = .97); thus, the test fails to reject the null hypothesis that the difference in coefficients is not systematic. In other words, endogeneity does not cause problems with the consistency of our parameter estimates: The panel data model we use in the article is consistent and efficient, whereas the 3SLS model is consistent but inefficient (see Pindyck and Rubinfeld 1991).
Heterogeneity in slopes. The model in Equation 1 corrects for unobserved heterogeneity through error decomposition. It does not, however, correct for heterogeneity in slopes. If, for example, companies that acquire large targets are less capable of leveraging acquisitions into innovation than companies that acquire small targets, such heterogeneity could be problematic. To account for potential heterogeneity in our key variables, we run random coefficients models that allow the coefficients for our hypothesized variables to vary randomly. We find some evidence for heterogeneity in the effect of depth on innovation. This suggests that though the effects of depth are positive for the average firm, some firms may experience a negative effect of depth on innovation. In general, however, the results of this analysis are consistent with those for the error decomposition model we report in the article.
This article highlights two sources of knowledge: internal and external. Internal knowledge by itself can drive innovation in future periods, but internal knowledge also has another, more subtle benefit. Deep internal knowledge provides firms with a superior ability to generate innovations from external sources such as acquisitions.
The results support our central argument that the innovation outcomes of acquisitions are driven by the preacquisition knowledge of the acquirer and its similarity with the targets' knowledge. We find a strong interaction effect of the depth of the acquirer's existing knowledge and that of its acquisitions on innovation output. We also find that moderate similarity leads to greater postacquisition innovation than does either low or a high similarity between the acquirer's and the target's preacquisition knowledge. Therefore, for firms with appropriate internal knowledge and fit, acquisitions can act as a tonic for innovation. However, when we control for firms' internal knowledge, we find that acquisitions on their own act as a placebo; that is, they have no effect on firms' postacquisition innovation output (βacquisition = -.02, p > .10; see Table 3).
Implications for Research
An important theoretical contribution of our article is that it sheds more light on the nature of knowledge and its role in innovation. We show how firms' internal knowledge interacts with external knowledge gained through acquisitions to foster innovation. Thus, our theoretical perspective enables us to reexamine the impact of acquisitions on innovation; this in turn enables us to empirically extend, clarify, and correct some of the conclusions of prior research.
First, in general, prior research on innovation has focused on how internal knowledge influences innovation within the firm. In contrast we examine how, over time, both internal and external knowledge interact to influence innovation. Specifically, we observe whether acquisitions as a source of external knowledge lead to positive, negative, or no gains in innovation to acquiring firms. The limited research that has examined the influence of acquisitions on innovation has generally suggested and found that this influence is negative (Ernst and Vitt 2000; Hitt et al. 1991a; Miller 1990). We argue that the opposite is more likely, especially in industries in which firms undertake acquisitions with the express intention to boost innovation by gaining external knowledge. By including both large and small firms in our sample, examining lagged effects, and controlling for firm-specific effects, we show that acquisitions can increase innovation in such contexts.
Second, prior research on acquisitions and innovation tends to focus on postacquisition integration activities. In contrast, we control for such firm-specific effects and focus on the knowledge that acquiring firms bring to the acquisition. We then examine how the depth, breadth, and similarity of knowledge influence postacquisition innovation. Relatedly, prior research on knowledge has mainly speculated on the processes by which knowledge is created in firms. There has been much discussion of how firms learn, their absorptive capacity, and the factors influencing learning and absorption (see Cohen and Levinthal 1989; Zahra and George 2002). Limited empirical research has tested these ideas, and to our knowledge, none has examined them in the context of acquisitions. We contribute to this literature by showing that absorptive capacity is particularly critical to the successful acquisition and use of external knowledge. Firms that have greater absorptive capacity because of their existing internal knowledge are better at choosing and integrating external knowledge and using it to create still newer knowledge.
Third, prior research has argued for a positive linear effect of similarity between the knowledge of the acquirer and that of the target (see Singh and Montgomery 1987; for an exception, see Ahuja and Katila 2001). In contrast, we argue for and show a nonlinear effect of similarity in knowledge on postacquisition innovation. Specifically, firms that have a moderate amount of similarity with targets' knowledge gain more from acquisitions than do firms whose knowledge is very similar to or very different from the knowledge of targets.
Implications for Firms
This article has several recommendations for firms involved in acquisition activity. First, the article suggests two independent routes to increased innovation: firms can either concentrate on building internal knowledge or buy it through acquisitions. Acquisitions offer acquiring firms access to knowledge that they may not otherwise have and that might combine with their internal knowledge to boost innovation. Independent of acquisition, however, building knowledge offers firms greater ability to use past knowledge to develop new knowledge and thus boost innovation. Moreover, it is not just the amount of internal knowledge that matters; rather, it is the type of knowledge. Firms with depth and breadth of internal knowledge are less likely to suffer from lockout (Leonard-Barton 1995), they have greater combinative capabilities (Kogut and Zander 1992), and they can use prior know-how to learn through both exploration and exploitation (Bierly and Chakrabarti 1996; March 1991). The article's results also suggest that breadth and depth have different magnitudes of impact on innovation: The impact of breadth is higher than that of depth. An excessive focus on deepening knowledge in an area could be counterproductive if it comes at the expense of breadth. Breadth of knowledge may promote flexibility, adaptability, and the ability to combine diverse types of knowledge (see Henderson and Cockburn 1994; Pisano 1994; Volberda 1996). Furthermore, the product scope that breadth provides also ensures the greater likelihood of serendipity and happy accidents, both of which are important drivers of product innovation (Helfat and Raubitschek 2000).
Second and more important, the article suggests that the two strategies of acquisitions and developing internal knowledge lead to even greater innovation when pursued in tandem. Despite the pessimism of many academics, some industry observers recognize the competitive advantages that can be derived from acquisitions. For example a Wall Street Journal article (O'Boyle 1988, p. 26) on the German chemical company Bayer's reluctance to engage in acquisitions quotes an industry expert, Michael Eckstut, as saying that though "Bayer has had a phenomenal record over the last few years, doing things internally can sometimes carry a higher risk than acquisitions." Eckstut also warned that "companies that preclude acquisitions as a way of obtaining new skills and resources run the risk of losing leadership because they are too introverted."
Firms that first develop deep internal knowledge have greater absorptive capacity; this enables them to choose and leverage external knowledge better. Indeed, successful firms appear to recognize this point. For example, in its annual report, Johnson & Johnson (1996, p. 4), one of the innovative firms in our sample, notes that "while internal development is our preferred source of growth, we view selective acquisitions as an appropriate mechanism for supplementing our efforts."
Third, it is not merely the amount of knowledge that the acquirer brings to the acquisition that drives its postacquisition innovation: The similarity of its knowledge with that of the target is crucial too (Richardson 1972). Differences in the nature of firms' knowledge can be considerable even within industries, and they can significantly affect the success of acquisitions. To gain the most from acquisitions, acquiring firms should ensure that their internal knowledge is neither too close nor too far removed from that of the target firm.
Finally, in contrast to prior research, which typically emphasizes the similarity between the markets in which parent and target operate (e.g., Singh and Montgomery 1987), the national origins of the two firms (e.g., Harzing 2002), and their relative size, we emphasize the importance to acquirers of assessing the fit in their knowledge with that of targets. We show that when similarity of knowledge is taken into account, other, more conventional measures of similarity have a less significant impact on postacquisition innovation. Of these, we find that only country of origin has a significant influence on innovation beyond the effects of similarity in knowledge we propose. The size of the target and its market similarity with the acquirer matter less. This suggests that in contexts in which innovation is an important strategic objective, acquiring firms should identify and choose targets on the basis of their fit in knowledge over other aspects of similarity.
Limitations and Further Research
Acquisitions are a complex phenomenon, and ours is by no means the last word on the topic. Our research has several limitations, some of which offer possibilities for further research. First, as in any early empirical endeavor, we have had to maneuver carefully between the Scylla of model misspecification and the Charybdis of model overparameterization. We cannot, within one study, exhaustively examine the many types of internal and external knowledge.
When studying internal knowledge, we examine the knowledge contained in patents. Patents are a widely used measure of knowledge in research across a range of high-tech industries: pharmaceuticals (Cockburn, Henderson, and Stern 1999), semiconductors (Dutta, Narasimhan, and Rajiv 1999), chemicals (Ahuja and Katila 2001), robotics (Katila and Ahuja 2002), and the industrial machinery industry in general (Arundel and Kabla 1998; Cockburn and Griliches 1988). Nevertheless, in some industries, important sources of knowledge may be difficult to codify, and future researchers on these industries may wish to employ other measures of such knowledge. Acquiring firms may also sometimes gain patents but not necessarily additional knowledge from their targets. Some patents may eventually manifest themselves as product innovations and thus artificially inflate the correlations between our dependent variable (innovation) and our independent variables (which are patent based). Fortunately, in the pharmaceutical industry, few patents end up becoming product innovations (DiMasi, Hansen, and Grabowski 2003); as such, this inflation is likely to be very small. Similarly, when studying external knowledge, we examine the knowledge obtained through acquisitions. Again, although acquisitions are an important source of external knowledge, other sources of such knowledge, such as alliances and interfirm cooperation, exist and are worthy of attention (see, e.g., Rindfleisch and Moorman 2001).
Second, we examine only one, albeit important, type of knowledge, namely, technical knowledge. The role of other types of knowledge--for example, knowledge related to consumers and competitors--also merits study (see Capron and Hulland 1999; Li and Calantone 1998; Moorman and Rust 1999). In this article, we attempt to capture the effects due to other sources of internal and external knowledge by accounting for firm-specific heterogeneity, as well as by using important control variables. Nevertheless, this remains an empirical solution. To gain a more complete theoretical picture, future researchers may wish to employ more explicit and fine-grained measures of other sources of internal and external knowledge than the ones we use here. Third, we use data that are at the yearly level of aggregation. As such, our model may overestimate the decay parameter (λ in Equation 1 (Clarke 1976). More disaggregate data will allow for a more accurate assessment of the decay parameter. Fourth, our model assumes that the effect of knowledge decays exponentially. Although this assumption is in line with the bulk of the literature (Dutta, Narasimhan, and Rajiv 1999; Pakes and Schankerman 1984; Schott 1976, 1978), other researchers posit that knowledge can appreciate over time (see Madhavan and Grover 1998). More disaggregate data would enable the application of other, more flexible functional forms of the lag specification.
Finally, we study only one, albeit important, industry: pharmaceuticals. The choice of a single industry is critical in ensuring internal validity because the appropriate measures of knowledge can be quite different across industries. The pharmaceutical industry is especially suited to our use of patents as a measure of knowledge. Choosing one industry, however, also raises the issue of generalizability. The pharmaceutical industry is unique in many ways, but it is possible to make at least some tentative generalizations from pharmaceuticals to other high-tech industries. Indeed, we are not alone in this belief (see also Bierly and Chakrabarti 1996; Cockburn, Henderson, and Stern 1999; Shankar, Carpenter, and Krishnamurthi 1998). Nevertheless, other industries in which patenting is less common might require other measures of knowledge, and further research would benefit from an exploration of such empirical contexts.
Conclusion
A successful innovation strategy requires a judicious combination of internal and external sources of knowledge. Acquisitions provide a means to access external knowledge that can be difficult or even impossible to create through internal sources. For this reason, firms may not want to rely solely on internal sources of knowledge, but they may not want to rely solely on external sources either. We show that in the context of acquisitions, the two sources of knowledge--internal and external--interact in a dynamic fashion to produce innovations.
Acquisitions need not be a poison pill or even merely a placebo for innovation. For firms that first engage in internal knowledge development, the knowledge-based view of innovation we present in this article suggests just the opposite. Acquisitions can be a tonic for innovation.
The authors are grateful for the valuable input of Kersi Antia, Mark Bergen, Ed Blair, Jack Chen, George John, Om Narasimhan, Akshay Rao, Alina Sorescu, Myles Shaver, Stefan Stremersch, Gerry Tellis, Frank Windmeijer, the four anonymous JM reviewers, and participants at research seminars at City University (London), Dartmouth College, Emory University, University of Houston, Maastricht University, University of Minnesota, University of Missouri, University of Pittsburgh, Texas Christian University, and the University of Washington. The authors appreciate the help of Brigitte Hopstaken, Anthony Maughan-Brown, Luis Wasserman, Raghunath Rao, and Sonia Basu-Monga in the data collection process.
( n1) We also tested an alternative metric of knowledge obtained through acquisition by measuring the number of patents granted to each target in the three years leading up to the year of acquisition. Our results are robust to this metric.
( n2) We also tested two alternative measures of breadth: entropy [E = Σn j =1pjln(1/pj)] anj = 1pjln(1/pj)] and the Herfindahl-Hirschman concentration index (HH = Σnj = 1pj2), where pj = Pj/P is the fraction of the firm's patents in patent subclass j relative to its overall patent portfolio. The results using these measures are consistent with those from the simpler and more intuitive measure of breadth we use in the article.
( n3) We also tested an alternative measure of similarity that does not divide by the total number of patent classes owned by the acquirer and target. The results using this measure are consistent with those from the percentage measure of similarity we use in the article.
( n4) We also measured target size using number of employees, sales, net income, and assets. Data on these measures were more difficult to find for our entire sample. For the subsample for which we have data on these measures, we find that acquisition price correlates highly with each of the other measures of target size (all correlations > .78, p < .001).
Summary of Measures and Data Sources
Legend for Chart:
A - Conceptual Variables
B - Measure (Annual)
C - Data Source
A B
C
Innovation Number of Phase 1 products
(four-year lag)
Pharmaprojects
Acquisitions Number of acquisitions
Moody's Manual
Financial Times Sequencer
Depth of knowledge Average number of approved
patents per patent subclass
U.S. Patent and Trademark Office
World Intellectual Property
Organization
Delphion
Breadth of knowledge Number of patent subclasses
approved
World Intellectual Property
Organization
Delphion
Similarity of knowledge Proportion of patent
subclasses shared by
acquirer and target
World Intellectual Property
Organization
Delphion
Control variables Acquirer R&D expenditures,
sales, and number of employees
Biotechnology focus
Number of target firm patents
Target size
Target value
Nonpharmaceutical targets(a)
Foreign targets
Market capitalization
Number of product approvals
Securities and Exchange
Commission's Edgar database
Hoover's Online
Trade and popular press reports
COMPUSTAT
Pharmaprojects
SDC Platinum
Factiva
(a) Standard Industrial Classification. Descriptive Statistics by Firm
Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
I - 8
J - 9
K - 10
L - 11
A B
C D E F G
H I J K L
1. Innovation: Phase 1 products 1.00
2. Acquisitions .18
1.00
3. Depth .46
.01 1.00
4. Breadth .57
.21 .66 1.00
5. Similarity .15
.21 -.01 .04 1.00
6. R&D intensity -.10
-.03 -.11 -.11 -.03 1.00
7. Parent size .59
.26 .50 .79 .04 -.11
1.00
8. Biotechnology -.46
-.28 -.63 -.85 -.06 .12
-.80 1.00
9. Target technical knowledge .15
.25 .01 .09 .81 -.02
.13 -.11 1.00
10. Target value (millions of dollars) .22
.13 .41 .46 .03 -.05
.32 -.36 .05 1.00
11. Nonpharmaceutical targets .26
.61 .19 .25 .08 -.09
.24 -.33 .07 .24 1.00
12. Foreign targets .13
.59 .08 .12 -.02 -.07
.15 -.29 .10 .01 .57
Mean 1.26
.47 2.41 6.32 .01 3.14
29441 .75 3.05 398.14 .38
Standard deviation 1.91
.95 2.63 8.75 .04 13.61
58742 .43 7.98 1124.68 .86 Effect of Internal and External Knowledge on Product
Innovation by Acquirer
Legend for Chart:
A - Independent Variable
B - Model 1: External Only
C - Model 2: Internal Only
D - Model 3: Internal and External
A B
C
D
Intercept -.23 (.41)
-1.18(***) (.50)
-2.69(***) (.62)
Innovation[subt[sup*]-1] .32(***) (.06)
.24(***) (.06)
.19(***) (.07)
Acquisitions[subt] .02 (.16)
--
-.02 (.17)
Depth[subt] --
.12(***) (.05)
.11(**) (.05)
Acquisitions[subt] x
Depth[subt] --
--
.12(***) (.05)
Breadth[subt] --
.06(***) (.02)
.09(***) (.02)
Acquisitions[subt] x
Breadth[subt] --
--
-.02 (.01)
Similarity[subt] --
--
40.41(**) (18.81)
Similarity[subt,sup2] --
--
-263.07(**) (130.67)
R&D Intensity[subt] -2.40 x 10[sup-3] (.006)
-2.27 x 10[sup-3] (.006)
-1.19 x 10[sup-3] (.006)
Parent Size[subt] 3.43 x 10[sup-5](***) (9 x 10[sup-6])
2.72 x 10[sup-5](***) (9 x 10[sup-6])
3.65 x 10[sup-5](***) (9 x 10[sup-6])
Biotechnology[subt] .54(*) (.41)
1.33(***) (.47)
2.68(***) (.57)
Target Technical
Knowledge[subt] -.01 (.02)
--
-.05(*) (.03)
Target Value[subt] 2.20 x 10[sup-4](**) (1.1 x 10[sup-4])
--
-2.54 x 10[sup-5] (1.2 x 10[sup-4])
Nonpharmaceutical
Targets[subt] .14 (.19)
--
.16 (.21)
Foreign Targets[subt] .18 (.14)
--
.29(**) (.15)
R² overall .46
.49
.56
Wald χ² 133.04
151.39
185.90
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: Standard errors are in parentheses. Dependent variable
= number of drugs in Phase 1 trials per firm per year. Number
of observations = 162. Mean Number of Products in Phase 1 Trials for
Firms with High Versus Low Acquisitions and
High Versus Low Depth of Knowledge
Legend for Chart:
B - Low Depth
C - High Depth
A B C
Low acquisitions 2.4 4.8
High acquisitions 2.5 13.9 Mean Number of Products in Phase 1 Trials for
Firms with High Versus Low Acquisitions and
High Versus Low Breadth of Knowledge
Legend for Chart:
B - Low Breadth
C - High Breadth
A B C
Low acquisitions 2.6 5.0
High acquisitions 2.0 12.8GRAPH: FIGURE 1; Predicted Effect of Similarity on Product Innovation
GRAPH: FIGURE 2; Average Patents per Target Acquired by Acquirers with High Versus Low Depth of Knowledge
GRAPH: FIGURE 3; Average Patents per Target Acquired by Acquirers with High Versus Low Breadth of Knowledge
GRAPH: FIGURE 4; Average Forward Citations per Patent per Target Acquired by Acquirers with High Versus Low Depth of Knowledge
GRAPH: FIGURE 5; Average Forward Citations per Patent per Target Acquired by Acquirers with High Versus Low Breadth of Knowledge
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Measuring Innovation
Though conceptually ideal, the number of new drugs that firms introduce into the marketplace over time is not empirically well suited for our purposes, mainly because of the large time lags involved in developing and testing new pharmaceutical products. After their synthesis and extraction, new drugs must go through preclinical and clinical trials and a process of Food and Drug Administration (FDA) approval before they are introduced into the market (Mathieu 2000). The average time in the 1990s for preclinical trials alone was 6 years (FDA 1999). The three phases of the clinical trials that follow took, on average, up to several months for Phase 1, several months to 2 years for Phase 2, and between 1 and 4 years for Phase 3. Finally, the process of FDA approval took, on average, another 1.5 years. As a consequence, the time from conception to product introduction in the pharmaceutical industry in the 1990s was frequently more than a decade. Given these large lags and the many intervening factors that come into play during this time, it is extraordinarily difficult to tie empirically the knowledge generated from individual acquisitions to all the future drug introductions that result from this knowledge.
To solve this problem, we use the number of products in Phase 1 trials by each firm in each year as our measure of innovation. This measure has several strengths. First, we cannot use approved products because of the time lags involved, so this is the next-best choice of a measure that goes beyond patents in being product based but nevertheless involves manageable lags. On the basis of information (FDA 1999; Mathieu 2000) on the average time from idea development to Phase 1 trials, we examine Phase 1 products four years after the year of acquisition.
Second, the measure correlates well with actual drug introductions. To confirm the link between Phase 1 trials and actual product approvals empirically, we calculated the correlation between the number of Phase 1 drugs of firms in our sample in 1996, 1997, and 1998 and the number of approved products in 2001. The correlations were all significant and positive (γ1996 = .76, p < .0000; γ1997 = .40, p < .05; and γ1998 = .51, p < .01, respectively).
Third, the measure is financially important because it is likely to correspond strongly with the stock market performance of the firm. Again, this is not surprising, given the relationship between drugs in Phase 1 and actual introductions. To confirm this intuition empirically, we collected data on the market valuation of the firm in each year in our sample. We find that the correlation between the number of Phase 1 drugs of firms in our sample in a particular year and market capitalization of the firms in that year is positive and significant (γ = .45, p < .0000).
Derivation of Empirical Model
Here, we derive our model specification. Let the relationship between knowledge and innovation for firm i at time t be represented as follows:
(B1) Ii,t = α0 + β0Ki,t + β1Ki,t - 1 +β2 Ki,t-2 + ... + εt,
where I = innovation, and K = knowledge. Assume that ( 1) the effect of current knowledge on future innovation declines exponentially and ( 2) a real number λ measures the decay (or appreciation) of knowledge from year to year. Then, Equation B1 reduces to
(B2) Ii,t = α0 + βλ0Ki,t + βλ1Ki,t-1 + βλ2Ki,t-2 + ... + εt.
A Koyck transformation on Equation B2, which involves lagging Equation B2 by one period, multiplying by λ and subtracting from the original Equation B2, results in
(B3) Ii,t = α + λIi,t-1 + βKi,t + vt,
where α = (1 - λ) α0, and vt = εt - λεt.
Next, we model the components of knowledge as follows:
(B4) Kt = f(Internal, External, Synergy),
where
Internal = f(Depth, Breadth), External = f(Acquisition), and Synergy = f(Depth x Acquisition, Breadth x Acquisition, Similarity).
Thus, Equation B4 reduces to
(B5) Kt = f(Depth, Breadth, Acquisition, Depth x Acquisition, Breadth x Acquisition, Similarity).
Substituting a linear specification of Equation B5 in Equation B3 and allowing for firm-specific and time-specific heterogeneity and an n-year lag between innovation and knowledge, we obtain Equation 1 (specified in the method section), which we use to test our hypotheses.
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By Jaideep C. Prabhu; Rajesh K. Chandy and Mark E. Ellis
Jaideep C. Prabhu is Professor of Marketing, Tanaka Business School, Imperial College London
Rajesh K. Chandy is Associate Professor of Marketing, Carlson School of Management, University of Minnesota
Mark E. Ellis is an MBA student, Harvard Business School
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Record: 167- The Impact of Brand Extension Introduction on Choice. By: Swaminathan, Vanitha; Fox, Richard J.; Reddy, Srinivas K. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p1-15. 15p. 2 Diagrams, 8 Charts. DOI: 10.1509/jmkg.65.4.1.18388.
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The Impact of Brand Extension Introduction on Choice
This article focuses on the impact of a new brand extension introduction on choice in a behavioral context using national household scanner data involving multiple brand extensions. Particularly, the authors investigate the reciprocal impact of trial of successful and unsuccessful brand extensions on parent brand choice. In addition, the authors examine the effects of experience with the parent brand on consumers' trial and repeat of a brand extension using household scanner data on six brand extensions from a national panel. In the case of successful brand extensions, the results show positive reciprocal effects of extension trial on parent brand choice, particularly among prior non-users of the parent brand, and consequently on market share. The authors find evidence for potential negative reciprocal effects of unsuccessful extensions. In addition, the study shows that experience with the parent brand has a significant impact on extension trial, but not on extension repeat.
As competitive pressures mount, brand marketers seek ways to achieve growth while reducing the cost of new product introduction and the risk of' new product failure. One popular brand strategy is to attach an existing brand name to a new product introduced in a different product category, that is, brand extension. Such a strategy is often seen as beneficial because of the reduced new product introduction cost and the increased chance of success (Kapferer 1994). In addition, a brand extension can produce reciprocal effects that enhance or diminish the equity of the parent brand.
Reciprocal effects research has focused on examining attitudinal changes toward the parent brand. From a managerial perspective, it is interesting to examine the impact of trial of an extension on choice and market share of the parent brand. Extension trial should strengthen consumers' propensities toward buying the parent brand unless the extension experience is negative. This effect should be most pronounced among consumers who have low levels of loyalty toward the parent brand, because parent brand sales are already maximized among highly loyal consumers. The role of category similarity in moderating reciprocal effects has also been examined in an attitudinal context, but not in an actual purchase context. In addition, the reciprocal effect of extension purchase across prior users and prior nonusers of the parent brand has not been studied.
In addition to the potential benefits associated with positive reciprocal effects, the use of brand extensions provides economies in securing trial in the marketplace, as noted previously. In the words of Allan Maccusker, president of a brand consultancy group, "A lot of marketers are going this [the brand extensions] route because an established brand name will generate, hopefully, quicker trial by consumers and then heavier repurchase" (Advertising Age, p. 12). The assumption underlying the use of the brand extension strategy is that extensions induce trial due to brand awareness among existing consumers. However, little research empirically tests the role of brand extensions in inducing trial in the marketplace. Therefore, another goal of this research is to examine the relationship between prior experience with the parent brand and extension trial and repeat.
In summary, this research has three major objectives. First, we investigate the reciprocal effects of extension trial on parent brand choice among users and nonusers of the parent brand. Second, we examine the role of category similarity as a moderator of reciprocal effects. Third, we investigate the impacts of experience with the parent brand on trial and repeat of a brand extension.
We address the objectives outlined through a series of three studies. In Study 1, we demonstrate positive reciprocal effects of extension trial among prior nonloyal users and nonusers of the parent brand. In addition, we demonstrate that parent brand experience has a significant effect on extension trial but not on repeat. In Study 2, we examine the role of category similarity as a moderator of positive reciprocal effects. In Study 3, we show the existence of negative reciprocal effects associated with an extension product failure.
Researchers in the brand extensions area have relied primarily on marketing experiments conducted in lab settings in which consumers are typically provided with descriptions of hypothetical brand extensions and are asked to provide their instantaneous reactions (e.g., Aaker and Keller 1990; Keller and Aaker 1992). One limitation of this type of research is that effects may be overstated (Dacin and Smith 1994). In addition, reciprocal effects develop over time and frequently cannot be captured using this approach. Furthermore, the extensions are hypothetical and not necessarily indicative of what a firm would really consider doing. Finally, lab experiments such as those just described cannot capture the impact of actual experience with a brand extension on future purchasing in the parent category. Thus, although previous research offers valuable insights regarding brand extension strategies from a theoretical perspective, the use of these highly controlled lab experiments has, to some extent, limited the usefulness of this research from a managerial perspective. One exception is the research by Erdem (1998), who examined household purchase data collected alter the brand extensions had been introduced and demonstrated that quality perceptions transfer between umbrella-branded products in the case of the companion categories of toothpaste and toothbrushes. Another exception is the research by Kim and Sullivan (1998), who model trial and repeat of a new brand introduction in the context of line extensions. However, neither of these studies explicitly focuses on testing a framework of positive and negative reciprocal effects.
We examine the phenomena of both direct and reciprocal effects across multiple brand extension cases by using ACNielsen scanner panel data to monitor household purchasing immediately before and after extension introduction. In addition, we provide a conceptual framework to examine factors that moderate reciprocal effects. An in-market study involving brand extension strategies and real brands provides rich insights for managers interested in evaluating the risks and merits of extension strategies.
All the brand extensions examined in this study represent national extensions of well-known brands. Although specific brand names and categories are not revealed because of the proprietary nature of the data, we provide examples and details of the categories to help interpret the results. We examine reciprocal effects by modeling parent brand choice, using extension brand experience as an independent variable. We develop logit models of extension trial and repeat using parent brand experience variables and other relevant purchase factors as independent variables to demonstrate direct effects. The article is organized as follows: First, we present a conceptual framework and hypotheses. Second, we describe the data. Third, we present an overview of the models along with the associated measures; describe Studies 1,2, and 3; and discuss the key findings. In conclusion, we discuss the implications of this research and directions for further research.
As previously noted, there has been mixed support for the existence of positive and negative reciprocal effects in the literature. Keller and Aaker (1992) find that positive reciprocal effects exist only when an average-quality parent brand introduces a successful extension. Keller and Sood (2000) posit that evaluations of parent brands that are already well regarded will not change significantly as a result of favorable extension experience. Gurhan-Canli and Maheswaran (1998) show that enhancement effects exist for brand extensions that are similar to the parent brand.
In general, previous research finds support for the moderating role played by category similarity in influencing both positive and negative reciprocal effects. Milberg, Park, and McCarthy (1997) show that negative reciprocal effects can occur when extension similarity is extremely low. Keller and Sood (2000) demonstrate that negative reciprocal effects can also occur when the extension is highly similar to the parent brand. Gurhan-Canli and Maheswaran (1998) show that dilution of a family brand name occurs in response to incongruent and negative information, particularly when the extension is similar to the parent brand. The evidence regarding the existence of negative reciprocal effects at the brand attribute level is also considerable (e.g., Loken and Roedder-John 1993; Roeddcr-John, Loken, and Joiner 1998). However, it is less clear whether negative reciprocal effects exist at the overall attitude level (Keller and Aaker 1992).
To illustrate the process of reciprocal effect formation in an actual choice setting, we present a hypothetical example. Suppose Nivea, known for its skin-care products, introduces a cosmetic product under the Nivea brand name. The new product is tried by a group of consumers who are heterogeneous in their prior experience with Nivea skin care products. Assuming that the new entry is a success, the trial of the product is generally a favorable experience. The trial of the Nivea cosmetic furnishes new information regarding the Nivea brand name to both prior users and prior nonusers. Consistent with previous work in the context of product experience, such as Hoch and Deighton's (1989) and Kempf and Smith's (1998), the learning provided by the product experience will lead to strongly held beliefs regarding the Nivea cosmetic. Previous brand literature has viewed brand knowledge as a network of beliefs and associations (Roedder-John, Loken, and Joiner 1998). Therefore, the beliefs regarding the Nivea cosmetic product are transferable to the Nivea skin care brand.
However, two conditions must be present for the transfer to occur. First, the extension information must be deemed relevant in the parent category. Previous research has identified category similarity between the extension and parent categories as necessary for the extension information to be considered relevant, a condition satisfied in our Nivea example (Keller and Aaker 1992; Loken and Roedder-John 1993). Second, for this transfer to occur, the beliefs about the parent brand must undergo a change. Roedder-John, Loken, and Joiner (1998), in the context of flagship products, suggest that the network of beliefs linked to the flagship product tends to be extreme, strongly held, and resistant to change because of the accumulated exposure and experience with the flagship product. The discussion regarding flagship products is relevant to our analysis of the core parent brand. The beliefs associated with the core parent brand are likely to be of varying strength across different segments of consumers. Among segments of consumers that already have a high level of loyalty toward the Nivea skin care brand and have a well-developed set of associations regarding this brand, the provision of new information regarding Nivea cosmetics is unlikely to produce a significant change. This is particularly true if the new information does not significantly contradict the existing knowledge structure.
The consumers with a high degree of loyalty toward the Nivea skin care brand are likely to have beliefs and associations that are resistant to change. According to RoedderJohn, Loken, and Joiner (1998), these may be viewed as brands that have a well-developed memory structure as a result of frequent exposure and direct experiences. Again, consistent with Roedder-John, Loken, and Joiner's (1998) arguments, the accumulated exposure and direct experiences among prior users of the parent brand make the parent brand beliefs of highly loyal prior users less resistant to change. Conversely, among segments of consumers with moderate to low loyalty toward the parent brand, the less frequent exposure to the parent brand suggests that these consumers' parent brand beliefs and associations are likely to be more amenable to change.( 1) Therefore, the potential for a positive reciprocal effect is strongest among segments of consumers with low to moderate loyalty toward the parent brand.
Although this scenario applies to a successful brand extension, another possibility is negative reciprocal effects due to unsuccessful brand extension. Suppose that the Nivea cosmetic is discontinued in the marketplace because of low market share. Therefore, it is likely that extension triers were generally not favorably disposed toward the new product and that trial furnishes negative or at least neutral information regarding the brand. Among prior users, the provision of new negative information regarding an extension product is likely to contradict existing knowledge structures, particularly among consumers with high levels of prior loyalty toward the parent brand. However, because the propensity to purchase the parent brand among prior nonusers is already zero, the provision of new negative information cannot result in a negative reciprocal (behavioral) effect among these consumers. Therefore, negative reciprocal effects of extension failure can be observed only among prior users, especially those with high loyalty toward the parent brand.
In summary, the introduction of a brand extension that is successful is likely to result in positive reciprocal effects. These positive reciprocal effects are likely to be moderated by category similarity; there will be stronger reciprocal effects under conditions of high category similarity. Conversely, negative reciprocal effects may be associated with a failed extension product. These negative reciprocal effects are likely to be strongest under conditions of high category similarity. We therefore propose that positive and negative reciprocal effects of extension trial on parent brand choice exist and are moderated by category similarity. Further positive (negative) reciprocal effects in the context of purchase behavior are limited to consumers with low to moderate loyalty toward the parent brand (users of the parent brand). A summary of the proposed effects is presented in Figure 1. In addition, experience with the parent brand is likely to increase the propensity to try the extension but not to repeat. In the next section, we outline the first of three studies designed to test this framework.
Reciprocal Effects of Brand Extension Introduction on the Parent Brand
As noted previously, research regarding both positive and negative reciprocal effects has been somewhat mixed. Ceiling effects associated with well-regarded brand names, difficulties associated with examining attitudinal shifts in experimental settings, and the omission of prior usage as a moderating variable may account for some of these mixed findings in past research. Consumers are likely to be heterogeneous in their purchasing of a brand. It is not possible to enhance purchase probability of a brand among highly loyal users of the brand, simply because propensities to purchase the brand are already so high. This "ceiling effect" argument is consistent with the prior research findings that point out that evaluations of well-regarded brand names generally do not change on account of exposure to favorable extension information (Keller and Aaker 1992). However, among users with moderate to low levels of loyalty, the exposure to the extension brand may induce positive reciprocal effects by enhancing brand familiarity, strengthening brand attitude, and ultimately increasing the likelihood of purchasing the parent brand.
For prior nonusers, extension trial provides new information regarding a brand. Information generated from product trial typically results in increased brand recall and stronger brand attitudes, which in turn have a powerful impact on parent brand evaluation and purchase (Kempf and Smith 1998; Smith and Swinyard 1982; Wright and Lynch 1995). Thus, increases in memory and familiarity and attitudinal shifts resulting from the extension trial experience should induce reciprocal effects for the parent brand among prior nonusers. Consistent with these arguments, we expect that positive reciprocal effects of extension trial will be observed among prior nonloyal users and nonusers.
Impact of Parent Brand Experience on Extension Trial
Consumer behavior research suggests that information or learning gathered from product usage is often granted a special status by consumers (Hoch and Deighton 1989; Kempf and Smith 1998; Smith and Swinyard 1982). Information gathered from personal experience is more vivid and therefore more memorable (Kempf and Smith 1998). Because information from product experience is self-generated, it is deemed more trustworthy than information gathered from advertising or communications, which results in strongly held belief (Smith and Swinyard 1982). Thus, consumers with parent brand experience have greater parent brand knowledge, better recall of the parent brand, and greater confidence in their belief about the parent brand than consumers with no parent brand experience. It has also been suggested that an existing brand name provides an assurance of quality, thereby reducing the risks involved in purchasing a new product (Erdem 1998; Wernerfelt 1988).
Although both the direct effects of parent brand on the extension and the reciprocal effects are cross-category effects, brand extension researchers typically examine these as distinct processes. Whereas the former refers to formation of brand evaluations for a new brand introduction, the latter reliefs to changes in the evaluations of an existing brand. On the basis of these arguments, we hypothesize that parent brand experience increases the likelihood of extension trial.
Impact of Parent Brand Experience on Repeat Purchase Behavior
Information economics theory suggests that the quality of the product is unambiguously revealed during product use (e.g., Nelson 1970, 1974). This is especially true when the product quality can be gauged accurately even with a single exposure to the product.( 2) Thus, when the extension has been tried, the repeat purchase decision should depend on the evidence furnished by the trial experience rather than parent brand experience. In addition, the familiarity with the extension among both past users and nonusers of the parent brand is likely to be similar after the extension has been tried. Therefore, we expect that parent brand experience has no impact on repeat purchase of the brand extension.
Data
The data for this study, obtained from ACNielsen, provide household purchase histories for selected product categories for a national panel in the time period 1990-94. We used purchase data for three brand extensions introduced during this time period to test the hypotheses. Household purchase histories in the parent categories are available for approximately one year preceding the extension introduction and for one year following extension introduction. For each of the extension categories, data are available for one year for lowing extension introduction. Given the nature of the categories examined, such as foods and personal care items, we believe that one year is sufficient to capture variations in purchase behavior due to the extension. The fictitious names Alpha, Gamma, and Zeta are introduced to label the three parent brands and their respective extensions. Alpha refers to a large personal care brand extended into a related personal care category. An example of this type of extension might be Nivea introducing Nivea beauty soap. Gamma refers to a food product extended into a related food category. An example of this type of extension is Hershey's introducing Hershey's chocolate milk. Zeta is a personal care brand extended into another personal care category (different from Alpha). An example of this type of extension is Dial bar soap introducing Dial deodorant. All three extensions leveraged the parent brand positioning in the extension category.
Criteria for qualifying households. Households in the panel enter and leave continuously. To ensure comparability in preextension and postextension introduction purchases, consistencies in time available for trial, and so forth, we constructed a "static" panel of participating households for each study. For each of the three cases, we included a household in the panel if it recorded at least one purchase in either of two frequently purchased categories during the first six months and during the last six months of the particular two-year study period. To eliminate households that are infrequent users, we further required households to have made at least two purchases in both the parent and the extension categories during the one-year period following extension introduction and two purchases in the parent category in the one-year period before extension introduction.
Sometimes, an extension rollout takes place over a relatively long time period, so an important consideration is the date of extension introduction in a market. We identified the date on which the first purchase of the extension brand was made in a market, and we used the week prior to this date as the introduction date for the extension brand in that market. To balance the time available for purchasing before and after extension introduction, we excluded markets where the extension introduction took place either very early or very late.( 3)
Descriptive Data
Table 1 contains information regarding the characteristics of both the parent and extension categories for the brands Alpha, Gamma, and Zeta. Also provided are data regarding market share and share of voice based on percentage of advertising expenditure for the parent brand categories before extension introduction and for the extension brands following extension introduction. The data provided in Table I give an overview of the types of product categories and the nature of competition in the categories used in this study. In addition, we use the information from Table 1 to gain insights into our findings.
Reciprocal Effects of Extension Trial on Parent Brand Choice
Model development and measures. A model of household choices in the parent category demonstrating that significant changes in the likelihood of parent brand purchasing are observed after the trial of the extension in the extension category would provide strong evidence of the existence of reciprocal effects. We examine reciprocal effects of extension trial using a binary legit model in which the dependent variable is parent brand choice.( 4) The unit of analysis is an individual choice occasion. Therefore, there are multiple observations for each household. The model includes household heterogeneity, marketing-mix effects, and the effects of competition as independent variables. We estimate the model using purchase data for the parent category for the static panel in the time period following extension introduction. We describe the variables used in the legit choice model next.
Parent brand experience (EXP). Prior experience with the parent brand is operationalized as a loyalty measure, that is, the frequency of purchasing the parent brand compared with the other purchases made in the category. This relative frequency measure is consistent with those in previous studies that measure brand loyalty (Russell and Kamakura 1994). This variable is constant for all choices made during the time period following extension introduction.( 5)
Relative price (RELPRI). We calculated the relative price per gram of the parent brand by indexing the parent brand's price per gram to a weighted average of the previous 14-day average price per gram or ounce for the major brands; weights were determined by market shares.( 6) Because data on competitors' prices are not available with every purchase, this is an approximate measure of competitive pricing.
Reciprocal effects indicator variable (IND). The reciprocal effects indicator is a dummy variable that indicates whether the extension was purchased on or before the date when a purchase in the parent category is made. This indicator variable is a "switch" that goes "on" (takes the value 1 as opposed to 0) when the extension trial takes place.
Displays (DISP) and advertisements (AD). The display and feature advertisement variables were also dummy (0/I) variables that indicated the presence of displays or feature advertisements associated with the parent brand.
Reciprocal effects logit model. Let X = 1 or 0 depending on whether or not the parent brand is chosen on a category purchase occasion. The probability that the parent brand is chosen is given by
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
which can be rewritten as follows:
(la) In[P(X=1)/P(X=0)I=α+β&sub1;(EXP)+β&sub2;(RELPRI) + β&sub3;(IND) + β&sub4;(DISP) + β&sub5;[(AD),
where α and β&sub1; through β&sub5; are parameters to be estimated.
Overall model significance and predictive validity are judged, respectively, by means of the likelihood ratio test statistic (X²) and the classification accuracy (as judged by the percentage correctly classified).( 7) Because of unequal group sizes, the percentage correctly classified is compared with a benchmark based on a proportional chance criterion (Morrison 1969).( 8)
The estimates for the reciprocal effects of extension trial for brands Alpha, Gamma, and Zeta are presented in Table 2. Reciprocal effects cannot exist at very high loyalty levels (e.g., for households with a 100% loyalty). Therefore, the analysis was restricted to households with a parent brand loyalty of less than 80% but greater than zero.( 9) Prior nonusers were analyzed separately. Among extension triers, only households that have had the opportunity to purchase in the parent category at least once following the trial of the extension were included in the analysis. Because the impact of the extension introduction was expected to vary on the basis of prior usage, the analyses for prior users and prior nonusers are presented separately.
In the case of the brand Alpha, displays and advertising information were not included, because these factors were active in less than 1% of the purchases made in this category. In the case of the brand Zeta, the display information is not included for the same reason. The reciprocal effect variable (IND) is significant at the 1% risk level for extensions Alpha and Zeta, but it is not significant for brand Gamma. The odds ratio for the reciprocal effect variable is 16 in the case of the brand Alpha and 52 in the case of the brand Zeta.( 10) This suggests that in the case of the brand Alpha, extension trial enhances the odds of purchasing the parent brand as opposed to some other brand by 16 times. Similarly, in the case of the brand Zeta, extension trial enhances the odds of purchasing the parent brand by 52 times. Across all three extensions, the parent brand experience variable is significant at the 1% risk level.( 11) In the case of Gamma and Zeta extensions, the parent brand experience variable is the most influential of all the variables. In the case of the Alpha extension, the impact of the reciprocal effects indicator variable appears to be more influential than parent experience or relative price. Across all three extensions, the overall model is significant at the 1% level. The incremental percentages correctly classified using the model over and above the proportional chance criterion for the brands Alpha, Gamma, and Zeta are 17%, 17%, and 19%, respectively.
Reciprocal effects were also examined among prior nonusers of the parent brand, (i.e., EXP = 0). The results for extension trial among prior nonusers of the parent brand are also presented in Table 2. The limitations imposed on households to be included in the reciprocal effects model, that is, at least one purchase in the parent category following extension trial, resulted in a limited sample in the case of the brand Zeta, which precluded analysis of prior nonusers in this case. The impact of extension trial on parent choice is significant in the case of brand Alpha but is not significant for brand Gamma. Reciprocal effects of brand extension introduction were examined in terms of market share for a macro perspective. The parent brand market shares were compared before and after extension introduction among extension triers (see Table 3).( 12)
Significant increases in market share were observed among prior nonusers for all three cases and among prior nonloyal users for brand Alpha. In the case of brand Zeta, there were indications of share increases among parent brand users, though this difference was not significant.
These results are consistent with the previous findings regarding the relatively weak reciprocal effects in the case of prior nonloyal users of brand Gamma.
Impact of Parent Brand Experience on Extension Trial and Repeat
Model development and measures. In this section, the impact of parent brand experience on extension trial and repeat is quantified. Dichotomous dependent variables arc introduced on the basis of classifying each household in the static panel as an extension trier or a nontrier (for the trial model) and classifying triers as either repeaters or nonrepeaters (for the repeat model). Thus, each household constitutes one observation for the trial model, each trier household constitutes one observation for the repeat model, and the information for each of the independent variables is captured at the household level, as opposed to the transaction level as in the previous analysis. The independent variables in the models are discussed next. Values for these variables were obtained through household purchase histories before extension introduction.
Parent brand experience (EXP). As discussed previously, parent brand experience is operationalized in terms of relative frequency of buying the parent brand in the parent category.
Deal proneness (DPRONE). Deal proneness is defined as the degree to which a consumer is influenced by sales promotion. We measured active deal proneness using Webster's (1965) deal proneness measure. For each household and for each brand in a given household's purchase history, we calculated the difference between the percentage of times a given household used a coupon when purchasing the particular brand and the average percentage of times a brand was bought using a coupon across all households. The overall deal proneness measure is a weighted average of this difference for the brands bought by a household:, the weights are market shares of the brands for the given household. Because information regarding deal proneness in the extension category before extension introduction was not available, we used the household's deal proneness in the parent category as a proxy for deal proneness in the extension category. This is consistent with prior research that suggests that there is a generalized deal proneness construct that results in a correlation in deal proneness across categories (Bawa and Shoemaker 1987).
Category experience (TOTCAT). Prior research suggests that the effect of brand knowledge may be different if the consumer is regarded as an expert rather than a novice in the context of the extension category (Broniarczyk and Aiba 1994; Smith and Park 1992). The frequency of purchasing in a category is an indicator of the knowledge or expertise in a category (Alba and Hutchinson 1987). The number of purchases in the extension category after extension introduction was introduced as a third variable that influenced extension trial and repeat.
Trial model Let T = 1 or 0 depending on whether or not a household purchases the extension. The equation for the probability of trial is
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
which can be rewritten as follows: (2a) In[P(T = 1)/P(T = 0] = α + &beta&sub1;(EXP) + β&sub2;(DPRONE) +β&sub3;(TOTCAT)
where the left-hand side represents the log odds ratio. If a coefficient, β&sub1; β&sub2;, or β&sub3; is significant, the corresponding variable has an impact on the log odds ratio.
Table 4 presents results of the logistic regression analyses for the trial and repeat models. The overall trial model is significant across all three extensions (as shown in Table 4). The percentage correctly classified is 77% for the brand Alpha, and the incremental classification over and above a proportional chance criterion is 12%. In the case of the brand Gamma, the percentage correctly classified is 75%, and the incremental percentage correctly classified is 13%. In the case of the brand Zeta, the percentage correctly classified is 90%, and the incremental percentage correctly classified is 8%.
As Table 4 shows, the parent brand experience variable (EXP) is significant across all three extensions at the 1% risk level, which is consistent with expectations. The odds ratio for this loyalty variable is 1.932 for the brand Alpha extension,( 13) 1.800 for the brand Gamma, and 6.752 for the brand Zeta. The other two variables, deal proneness and total extension category experience, are also significant across all three extensions at the 1% level.
Repeat model. Our second hypothesis examines the impact of parent brand experience on extension repeat. To test this hypothesis, using only extension triers, we fit a logistic regression model to the data, which used the same set of independent variables as in the trial model but had extension repeat as the dependent variable. In addition, we constrained the sample to ensure that only households that purchased at least once in the extension category after trial of the extension were included in the sample. The results of the models are also presented in Table 4. The overall model is not significant in the case of the brand Alpha, and no individual variable is significant. For the Gamma and Zeta extensions, overall category experience (TOTCAT) is significant at the 1% level, and the corresponding overall models are significant. Most important, in all three cases, the parent brand experience variable is not statistically significant. In other words, as expected, parent brand experience does not affect repeat purchasing of a brand extension.
Summary and discussion. For brand Alpha, extension trial positively affected the propensity to buy the parent brand among both prior nonloyal users and prior nonusers. For brand Zeta, the analysis was restricted to prior users and supports a positive reciprocal effect of extension trial. In the case of the brand Gamma, no support for the hypothesized reciprocal effect of extension trial is observed among either prior users or nonusers. The analyses also strongly support the positive impact of parent brand experience on extension trial but not on extension repeat across all three extensions. Therefore, from Study I, we conclude the following:
- There can be positive reciprocal effects of extension trial on parent brand choice among both prior users and prior nonusers. ú Reciprocal effects of extension trial can result in market share increases for the parent brand, especially among prior nonusers of a brand.
- Parent brand experience has a significant impact on extension trial.
- Parent brand experience does not have an impact on extension repeat.
The lack of reciprocal effects in the case of the Gamma extension indicates the existence of factors that moderate reciprocal effects. Previous research suggests that perceived fit, category similarity between the parent and extension brands (e.g., Aaker and Keller 1990), and relevance of the parent brand associations in the extension category (e.g., Broniarczyk and Alba 1994) moderate cross-category effects. To investigate the role of category similarity and brand association relevance in this study, a survey of student consumers was undertaken for the three extensions used in this study. The measures used in the survey included ( 1) perceived similarity between parent and extension categories, ( 2) the relevance of parent brand associations in the extension category, and ( 3) overall perceived fit. Subjects (n = 54) were asked to provide ratings of category similarity, overall perceived fit, and relevance of parent brand associations for the extensions used in this study.
The results reported in Table 5 show that the average ratings of overall perceived fit and extension association relevance for the three brands vary little (between 6.06 and 6.34 and between 2.32 and 2.63, respectively). However, as Table 5 shows, category similarity for the Gamma extension is far lower than the category similarity ratings for the other two extensions. The lower category similarity may account for the lack of significant reciprocal effects in the Gamma extension.
Another possible explanation for the lack of significant reciprocal effects in the case of the Gamma extension may be the nature of the parent category. This category is characterized by low interpurchase times, a relatively large number of brands (see Table 1 ), and a great deal of impulse buying. Therefore, it is possible that, in this category, market share increases are obtained by short-term promotional activities rather than by long-term investments in brandbuilding activities, so that brand extensions are unlikely to produce reciprocal effects for the parent brand. We examine each of these alternative explanations in Study 2.
Two factors may contribute to the lack of reciprocal effects in the case of the Gamma extension: relatively low category similarity between the parent and extension categories and the unique nature of the parent category. One way to rule out the category itself as the explanation for the lack of reciprocal effects would be to demonstrate that other extensions of the same parent brand generate reciprocal effects. Also, greater evidence for the role of category similarity in moderating reciprocal effects is necessary to substantiate the argument that category similarity may have contributed to the previous result regarding the Gamma extension.
The need to control for differences in parent brand and category characteristics made it important for us to focus on other extensions introduced by the same parent brand at various levels of category similarity to the parent brand. This also enables us to gain further insights into the role of category similarity as a moderator of reciprocal effects. The Gamma brand introduced two additional brand extensions within three years of introducing the first extension.( 14) Both extensions, introduced within one year of each other, were reasonably successful, in that they had gained noticeable market share in their extension categories by the end of the first year after introduction.
A second survey of student subjects (n = 55), incorporating the same measures of fit, relevance, and similarity as described in Table 5, was conducted. Subjects were asked to rate the original Gamma extension (Gamma 1) and the two later extensions (Gamma 2 and Gamma 3) on these measures. As shown in Table 6, the Gamma 2 extension category is perceived as more similar to the parent category than the original Gamma extension but does not reach the similarity levels of Alpha and Zeta. In contrast, the Gamma 3 extension category is perceived as less similar to the parent brand than the original Gamma extension. Therefore, if category similarity is a moderator of reciprocal effects, Gamma 2 is more likely than Gamma 3 to produce positive reciprocal effects. The ratings for the Gamma I extension were not significantly different from the ratings provided by subjects in the previous survey, which thus makes comparisons across surveys seem reasonable.
Scanner panel data, analogous to the data in Study 1 were obtained from ACNielsen for the two additional Gamma extensions and analyzed as in Study 1. The results for the reciprocal effects of extension trial across prior users (with a loyalty less than 8) and prior nonusers are presented in Table 7. The Gamma 2 extension (which has a relatively high level of category similarity) has a significant, positive reciprocal effect among both prior users and prior nonusers of the parent brand. A significant reciprocal effect of the Gamma 3 extension (which has a relatively low category similarity) is observed, but only among prior nonusers of the parent brand.( 15) Furthermore, the odds ratio (I.362) associated with this effect among prior nonusers is considerably smaller than the analogous odds ratio for Gamma 2 (1.771 ), suggesting that the reciprocal effect is weaker among prior nonusers for the Gamma 3 extension than for the Gamma 2 extension.
Study 2 provides evidence of the role of category similarity in moderating reciprocal effects and eliminates brand Gamma's parent category characteristics as a possible reason for the lack of reciprocal effects observed in the Gamma extension of Study I. By controlling for differences in parent brand characteristics, we show that a low category similarity between the parent and extension categories may hinder the transfer of brand equity, particularly among prior users of the parent brand.( 16) In summary, Study 2 suggests the following:
- The potential for positive reciprocal effects is enhanced by a high degree of category similarity between the parent and extension brands.
Although we observe the existence of positive reciprocal effects across various extensions in Studies I and 2, it is clear that not all brand extensions produce such positive effects. It is of interest to identify the boundaries to the existence of positive reciprocal effects. Can conditions be identified under which positive reciprocal effects do not exist? Can there be negative reciprocal effects, and what situations lead to such a scenario'? We investigate these issues in Study 3.
Loken and Roedder-John (1993) find that unsuccessful brand extensions can dilute the parent brand names by diminishing the favorable attribute beliefs that consumers have learned to associate with thc family brand name. Whereas Loken and Roedder-John's (1993) study indicates that specific attribute beliefs are diluted, Keller and Aaker (1992) show that there is no negative reciprocal effect in terms of overall attitude as a result of unsuccessful brand extensions. However, Keller and Sood (2000) find evidence of negative reciprocal effects in an experimental setting. These effects were evident when consumers had an unfavorable product experience with a similar brand extension. The suggestion that problems related to extension performance may lead to negative reciprocal effects has been echoed by Sullivan (1990). Her findings suggest that performance-related problems may result in negative reciprocal effects in umbrella-branded products. In Study 3, we investigate the hypothesis that trial of an unsuccessful extension can decrease the likelihood of purchasing the parent brand among prior users of the parent brand.
Scanner panel data similar to the data used in Studies I and 2 were obtained on a failed extension, Eta, which was withdrawn from the market approximately 18 months after its introduction.( 17) The parent brand is a well-known food brand and has been in existence for several years. The parent brand is a market leader with a 53% market share in the time period prior to the introduction of a food extension in a category that was perceived as dissimilar to the parent brand category and as low in overall fit (ratings of 1.92 and 3.11, respectively).
The data were analyzed in a manner similar to the analyses of Studies 1 and 2. The results are presented in Table 8. As can be seen, the reciprocal effects indicator (IND) has a significant, negative coefficient, and this coefficient is not significant among nonusers. This suggests that there are negative reciprocal effects of extension trial on the parent brand among prior users. This study shows that a failed extension (where failure is defined as the extreme situation when a brand is eventually withdrawn from the market) may cause negative reciprocal effects among prior users of the parent brand. In summary, Study 3 suggests the following:
- An unsuccessful extension can produce negative reciprocal effects among prior users of the parent brand, even when the extension category has relatively low similarity to the parent category.
Summary
Our findings indicate that positive reciprocal effects of extension trial exist, particularly among nonloyal users and among prior nonusers of the parent brand. These positive reciprocal effects also appear to translate into market share increases. In our research, we show that category similarity appears to moderate the existence and magnitude of positive reciprocal effects. In addition, negative reciprocal effects of unsuccessful extensions exist among prior users of the parent brand. A summary of the findings from the various studies is presented in Figure 2.
The findings from this research are generally consistent with previous findings based on experimental data (e.g., Gurhan-Canli and Maheswaran 1998; Keller and Aaker 1992; Roedder-John, Loken, and Joiner 1998). Building on previous research by Keller and Aaker (1992), our research shows the existence of positive reciprocal effects in terms of parent brand choice associated with successful extensions and examines the role of prior parent brand experience as a moderator of reciprocal effects. The previously overlooked role of parent brand experience as a moderator of reciprocal effects suggests a possible explanation for mixed findings regarding positive reciprocal effects in prior research that has used attitudinal data.
Previous studies, such as Gurhan-Canli and Maheswaran's (1998), Loken and Roedder-John's (1993), and Roedder-John, Loken, and Joiner's (1998), provide evidence of the existence of negative reciprocal effects at the attribute level. We find evidence of negative reciprocal effects of extension failure on parent brand choice among prior users of the parent brand. Among prior nonusers, no negative reciprocal effects were apparent because of these consumers' low prior probability of purchasing the parent brand. Establishing the potential for negative reciprocal effects in a behavioral setting contributes to our knowledge regarding the impact of a failed extension on consumer choice behavior.
This research contributes to the extant knowledge regarding category similarity as a moderator of positive reciprocal effects by examining its role in a real-world setting. Consistent with previous findings in lab settings (Gurhan Canli and Maheswaran 1998; Keller and Aaker 1992), we find evidence of a positive association between the magnitude of a positive reciprocal effect and the degree of similarity between the extension and parent categories. However, category similarity did not appear to matter as much in the context of negative reciprocal effects. Further research should focus on the differential role of category similarity in the case of successful versus unsuccessful extensions.
Previous research has shown that buyers who lack information regarding product quality tend to use brands as indicators of product quality (Rao, Qu, and Ruekert 1999). Therefore, our findings regarding the impact of experience with the parent brand on extension trial are not unexpected. However, scant research exists that examines the role played by parent brand experience on extension repeat purchases. Our findings provide evidence that the role of parent brand experience in the evaluation of a brand extension diminishes after trial. This finding has implications for researchers who support the existence of confirmatory biases that may operate and prevent a product from being judged on its own merit. However, our findings may be confined to frequently purchased packaged goods for which product trial may be sufficient to gain complete information regarding quality. Further research incorporating experience or credence goods will enhance the understanding of the role of parent brand experience in influencing repeat purchases.
Managerial Implications
Brand managers need to consider potential reciprocal effects in assessing the benefits of extension introduction. The role of brand extensions in enhancing the appeal of the parent brand among prior nonusers of the parent brand has been overlooked as an important added benefit of the extension strategy.
The introduction of a brand extension also has associated risks. The failure of a brand extension can harm brand equity by producing negative reciprocal effects. Contrary to what was believed previously, this appears to be the case even when the extension is introduced in a category with relatively little similarity to the parent category.
That parent brand experience had an impact on extension trial but not on repeat purchases suggests that the extension strategy may be used primarily to reduce the initial expenditures associated with product introduction. However, parent brand experience appears to have little impact on long-term repeat purchasing of an extension across a range of cases in which perceived similarity between the parent and extension categories varied considerably.
Limitations and Further Research
Reciprocal effects have been investigated in this article entirely in the context of purchase behavior. One way to strengthen the findings would be to support the purchase behavior data with attitudinal data. In addition, no data involving a highly similar extension that failed in the market were available. Cases of this nature will strengthen our framework.
Another issue that we do not address is the similarity of the target market for the parent and extension brands and the role of target market similarity versus category fit in influencing the transfer of associations from parent to extension categories. In other words, it is possible that reciprocal effects may exist, even in the absence of category similarity, if the target audience for the parent and extension products is similar. Although we have not addressed this in the current study, this is a promising avenue for further research.( 18)
The potentially moderating roles played by factors such as extension category loyalty in the direct transfer of parent brand experience to the extension category are not examined in this research. Further research should examine the factors that may moderate the impact of parent brand experience on extension trial and should assess the validity of the findings across various categories, such as services or technical products.
The first author thanks Procter & Gamble's Innovation Research Fund for providing funding for this study. The authors also acknowledge the help of AC Nielsen in providing the data used in this study and the Coca-Cola Center for Marketing Studies at University of Georgia. The authors also thank Chris Allen, Deborah Roedder John, Gary Russell, Kevin Keller, and Seenu Srinivasan for their input in the earlier stages of this research and the three anonymous JM reviewers for their insightful comments on previous drafts of this article. The third author acknowledges research support through the Terry and Sanford Fellowship, Terry College of Business, The University of Georgia.
( 1)This is not to discount the possibility that there may be negative associations among prior nonusers. Because prior nonusers are defined as people who have not tried the parent brand in a one-year time period prior to extension introduction, any negative experience with the parent brand occurred at least one year before extension introduction. Following Feldman and Lynch (1988), we note that the passage of time should weaken information accessibility, which in turn may weaken the potential negative associations among prior nonusers or highly infrequent users.
( 2)There may be conditions in which the influence of parent brand experience goes beyond the initial trial of the extension. This may be the case when products contain experience attributes whose qualities can only be gauged with repeated exposures to the product. This may also be true in the case of credence goods whose product quality is impossible to evaluate even after consumers have experienced the product. Other conditions in which a confirmatory bias may apply may include highly technical products that require a great deal of expert knowledge and image products (Hoch and Deighton 1989). This study is restricted to frequently purchased packaged goods products for which these conditions are not likely to apply.
( 3)For brand Alpha, the dates of introduction varied over a 46week period. In balancing the time period before and after extension introduction, we were able to retain 50% of the markets. For the brands Gamma and Zeta, we retained approximately 80% of the markets.
( 4)The multinomial logit model has been widely used in previous research (Guadagni and Little 1983; Kamakura and Russell 1989) to model brand choice behavior and capture price elasticities and changes in market structure. The multinomial logit model allows for the capture of competitive marketing-mix effects. However, in our context, because the focus is one brand introducing a brand extension, the binary logit approach with the choice of the parent brand as the dependent measure is a parsimonious approach to modeling the choice behavior in the parent category. The binary logit is also preferred because the loyalty coefficient in the multinomial logit does not enable us to estimate the incremental effect of the brand extension on choice of the parent brand over and above the impact of parent brand loyalty. This is because the loyalty coefficient is a general coefficient for all brands in the category and not specific to the parent brand. Because it is important to assess this incremental effect in the context of this research, the binary logit with the choice of the parent brand as the dependent variable is better suited to the purposes of this study.
( 5)Heterogeneity has received increased attention in the modeling literature (Chintagunta, Jain, and Vilcassim 1991) because estimates of parameters of discrete choice models that ignore heterogeneity are likely to be biased and inconsistent (Hsiao 1986). Heterogeneity among households is incorporated in our model through parent brand experience (EXP).
( 6)One of the limitations of using market shares of the major competitors as weights is that the heterogeneity in consideration sets across households is not captured. Given the dynamic nature of consideration sets across time and over purchase occasions (Andrews and Srinivasan 1995) and the need for information on marketing-mix variables (e.g., exposure to television advertising) to capture heterogeneity in consideration sets accurately, we did not incorporate this heterogeneity into the assessment of relative price. We acknowledge this as a limitation in the measurement of relative price. We thank the reviewer who pointed this out.
( 7)Typically, a holdout sample is used to avoid bias in estimating the predictive accuracy of a model. The logistic regression program in SAS uses a "jackknifing" approach that omits the observations one at a time and classifies them as "events" or "nonevents" on the basis of the model, which is estimated without the observations being classified. Because the SAS procedure provides an unbiased measure of predictive validity, we do not use a holdout sample to assess predictive validity (see SAS Institute inc. 1983, p. 45).
( 8)The proportional chance criterion is based on the hit rate, α² + (1 - α)2, where α is the percentage of times the parent brand was purchased.
( 9)The argument for truncating the loyal consumers at those with less than 80% loyalty is a conceptual one. Among perfectly loyal consumers, it is not possible to observe increases in the probability of purchasing the parent brand, because loyalty is already so high (a ceiling effect). An analysis of the distribution of frequencies across the various loyalty levels showed that across all categories, an 80% level of loyalty cutoff appeared to discriminate reasonably between the perfectly loyal group and the less than loyal group, because of a concentration of households around the 80% cutoff point across categories. We also did sensitivity analyses to ensure that choosing alternative cutoffs, such as 75%, 85%, and 90%, did not change the results significantly.
( 10)The odds ratio for the brand Zeta is a large number because the brand has a relatively small market share in the parent category. Therefore, the prior probabilities of purchasing this brand are low.
( 11)Note that the parent experience variable is expressed in terms of the frequency of purchasing the parent brand relative to all purchases made in the parent category. EXP is therefore a fraction that assumes a value between 0 and 1. The reason that the odds ratios are so large is that they represent changes in parent brand experience from 0 to 1, that is,1 unit of the independent variable.
( 12) We conducted a t-test to examine differences in market share before and after extension introduction. Because the shares were drawn from the same sample, a test for differences must account for correlations in the shares. This procedure is outlined by Cochran (1977).
( 13)In addition to loyalty, another aspect of parent brand experience is the time-varying nature of parent brand preference. Consistent with Bucklin and Lattin (1991), we created the last brand bought in the parent category before extension introduction as an alternative parent brand experience measure. We created a dummy variable labeled LP, which takes on a value of 1 if a household's last purchase in the parent category before extension introduction was the parent brand and a value of 0 otherwise. The trial and repeat models, which we estimated incorporating the LP (last purchase dummy variable) instead of the EXP variable, yielded similar results.
( 14)No other brand extensions of the Gamma brand were introduced in the three-year time period separating the introduction of these extensions.
( 15)A prior nonuser of the Gamma parent brand may have tried the previous brand extension (in Study I). Therefore, it was necessary to ensure that a prior nonuser of the Gamma parent brand was also a nonuser of the first extension. We checked to ensure that prior nonusers of the Gamma extension were also nonusers of the Gamma 1 extension.
( 16)In addition, to confirm the existence of forward transfer effects--that is, the impact of parent brand experience on extension trial but not on repeat--trial and repeat models similar to the ones described previously and in Table 4 were estimated for Gamma 2 and Gamma 3. The results confirm the findings from Study 1. Parent brand experience had a significant impact on extension trial but not on repeat in both the Gamma 2 and Gamma 3 cases.
( 17)The extension failure was chosen by scanning wire service reports and LEXIS-NEXIS announcements and selecting a recent example of a product that was withdrawn from the marketplace.
( 18)We thank a reviewer for this insight.
Table 1: Key Characteristics of Parent and Extension Categories: Study 1
Legend for chart:
A - Variables
B - Alpha Parent Category
C - Alpha Extension Category
D - Gamma Parent Category
E - Gamma Extension Category
F - Zeta Parent Category
G - Zeta Extension Category
A
B C D E
F G
Category description
Personal care Personal care Food Food
Personal care Personal care
Number of brands
18 25 45 7
25 22
Average interpurchase time (in weeks)
19.871 11.301 6.546 10.594
11.301 13.119
Strength of the brand(a)
Market share(b)
22% 5% 12% 4%
6% 1%
Share of voice(c)
14% 15% 3% 4%
3% 2%(a)Figures for market share and share of voice for the parent brand refer to the time period to extension introduction. For the extension brand, the figures reflect the market share and share of voice one year after the extension was introduced. (b)Data obtained from various issues of Market Share Reporter (1990-95). (c)Share of voice figures were obtained from LNA/Mediawatch Multimedia Service (1990-95).
Table 2: Reciprocal Impact of Extension Trial on Parent Brand Choice
Legend for chart:
A - Variable
B - Prior Users Alpha
C - Prior Users Gamma
D - Prior Users Zeta
E - Prior Nonusers Alpha
F - Prior Nonusers Gamma
Constant
-.111 -.743* -2.516* -2.732* -.459
(.485) (.328) (.485) (.409) (.165)
Parent brand experience (EXP)
1.749* 4.563* 4.224* -- --
5.751 95.938 88.340
(.762) (.236) (.253)
Relative price (RELPRI)
-.798* -.710* .214 -.374 -.709*
.450 .492 1.239 .688 .492
(.240) (.312) (.538) (.319) (.159)
Reciprocal effects indicator (IND)
2.795* .142 3.955* 1.451* -.139
16.359 1.152 52.180 4.269 .091
(1.054) (.085) (.399) (.532) (.869)
Advertisements (AD)
-- .730* .092 -- .424*
2.074 1.097 1.528
(.280) (.538) (.127)
Displays (DISP)
-- -.308* -- -- .032
.735 .085
(.167) (1.032)
Sample size Choice
176 4546 2840 261 19,415
97 1726 769 19 4540
-2 Log L X²
40.158 554.331 581.295 46.118 30.838
(p=.000) (p=.000) (p=.000) (p=.000) (p=.000)
Percentage correctly classified
67% 69% 79% 93% 77%
Proportional change
50% 52% 60% 87% 64%(*)Significant at the p<.01 level
Notes:Figures in boldface represent odds ratios; figures in parentheses represent standard errors.
Table 3: Percentage Change in Parent Brand Market Shares Before and After Extension Introduction: Means and Standard Deviations of Differences
Legend for chart:
A - Extension
B - Prior Nonusers and Extension Triers (% change)
C - Prior Users and Extension Triers (% change)
A B C
Alpha +8.1%** +13%*
(.040) (.090)
Gamma +9.7%** -4.5%(n.s.)
(.020) (.043)
Zeta +7%** +2.5%(n.s.)
(.023) (.028)(*)p < .01; (**)p < .05; Notes: Figures in parentheses represent standard deviations of the differences. n.s. = not significant.
Table 4: Impact of Parent Brand Experience on Extension Trial and Repeat
Legend for Chart:
Legend for Chart:
A - Variable
B - Extension Trial Model Alpha
C - Extension Trial Model Gamma
D - Extension Trial Model Zeta
E - Extension Repeat Model Alpha
F - Extension Repeat Model Gamma
G - Extension Repeat Model
A
B C D E F G
INTERCEPT
-1.650(*) -1.910(*) -3.694 0.376 -1.362 -1.029
(.141) (.094) (.113) (.263) (.204) (.169)
Parent brand experience (EXP)
.659(*) .588(*) 1.910(*) 0.524 0.395 0.491
1.932 1.800 6.752 1.688 1.484 0.612
(.294) (.205) (.267) (.555) (.204) (.818)
Deal proneness (DPRONE)
1.078(*) .420(*) 2.465(*) 0.211 0.194 0.048
2.938 1.522 11.762 0.810 1.215 1.049
(.241) (.099) (.204) (.348) (.422) (.389)
Total category experience (TOTCAT)
.034(*) .108(*) .138(*) 0.032 .092(*) .183(*)
1.034 1.114 1.148 1.033 1.097 1.201
(.017) (.010) (.011) (.030) (.017) (.065)
Sample size
Total
995 2428 4496 218 573 382
Triers
233 635 447 101 217 125
Percentage correct
77% 75% 90% 52% 65% 69%
Proportional chance
65% 62% 82% 50% 52% 66%
-2 Log L X²
27.861 149.09 338.829 2.489 36.675 8.737
(p = .000) (p= .000) (p = .000) (p = .477) (p = .000) (p = .033)(*)Significant at the p<.01 level.
Table 5: Survey of Perceived Fit: Study 1
Legend for chart:
A - Characteristic
B - Alpha
C - Gamma
D - Zeta
Category similarity
4.75(a) 2.81 4.47
(1.25)(b) (1.36) (1.64)
Relevance of key parent brand association to extension(c)
2.39 2.63 2.32
(1.81) (2.07) (2.04)
Overall perceived fit
6.34 6.06 6.06
(.98) (1.06) (.97)
Alpha versus Gamma versus Alpha versus
Gamma Zeta Zeta
Category similarity
P<.01 p<.01 p=.10
Association relevance
p>.10 p<.05 p>.10
Overall perceived fit
P<.05 p>.10 p<.10
(a)Numbers represent means. (b)Numbers represent standard deviations. (c)Subjects were asked to name the first parent brand associations that came to mind and rate the revelance of the first three associations in the extension category. The most commonly mentioned association across consumers was used in the calculation of extension association relevance. Extension association relevance is reverse-coded, where 1 = "very relevant" and 7= "very irrelevant."
Table 6: Survey of Perceived Fit: Study 2
Legend for chart:
A - Characteristic
B - Gamma 1
C - Gamma 2
D - Gamma 3
E - t -Test (Gamma 2 versus Gamma 3)
Category similarity
2.91(a) 3.77 2.08 p<.01
(1.33)(b) (1.51) (1.34)
Relevance of key parent brand association to extension (c)
2.51 3.66 4.53 p<.05
(2.18) (1.65) (2.01)
Overall perceived fit
6.14 4.92 4.38 p<.01
(.84) (1.49) (1.66)
(a)Numbers represent means. (b)Numbers represent standard deviations. (c)Subjects were asked to name the first parent brand associations that came to mind and rate the revelance of the first three associations in the extension category. The most commonly mentioned association across consumers was used in the calculation of extension association relevance. Extension association relevance is reverse-coded, where 1 = "very relevant" and 7= "very irrelevant."
Table 7: Reciprocal Impact of Extension Trail on Parent Brand Choice: Study 2
Legend for Chart:
A - Variable
B - Prior Users Gamma 2 High Similarity
C - Prior Users Gamma 3 Low Similarity
D - Prior Nonusers Gamma 2 High Similarity
E - Prior Nonusers Gamma 3 Low Similarity
A
B C D E
Constant
-1.092(*) -2.050 -1.152(*) -2.619(*)
(.051) (.096) (.099) (.153)
Parent brand experience (EXP)
2.775(*) 2.396(*) -- --
16.051 10.983
(.050) (.068)
Relative price (RELPRI)
-.883(*) 0.025 -.862(*) -.273(*)
0.439 0.975 0.422 1.314
(.034) (.066) (.068) (.108)
Reciprocal effects indicator (IND)
.150(*) 0.052 .571(*) .309(*)
1.163 1.053 1.771 1.362
(.024) (.032) (.063) (.060)
Advertisements(AD)
.082(*) 0.102 0.073 0.100
1.086 0.902 1.076 1.106
(.025) (.060) (.049) (.081)
Displays (DISP)
.097(*) .117(*) 0.009 0.036
0.907 1.124 0.991 1.036
(.024) (.046) (.046) (.074)
Sample size
120,616 45,747 52,732 29,611
Choice
19,822 9169 4541 2965
-2 Log L X²
3612.540 1231.822 249.783 33.123
(p = .000) (p = .000) (p = .000) (p = .000)
Percentage correctly classified
86% 80% 91% 90%
Proportional chance
72% 68% 84% 82%(*)Significant at the p<.01 level.
Table 8: Reciprocal Impact of Extension Failure on Parent Brand Choice: Study 3
Legend for Chart:
A - Variable
B - Eta Failed Extension Prior Users
C - Eta Failed Extension Prior Nonusers
A
B C
Constant
-1.362(*) -2.287(*)
(.338) (.775)
Parent brand experience (EXP)
2.207(*) --
9.085
(.194)
Relative price (RELPRI)
0.078 0.238
0.926 1.269
(.090) (.214)
Reciprocal effects indicator (IND)
-.453(*) 0.171
0.636 0.842
(.099) (.241)
Advertisements(AD)
.435(*) 0.444
1.546 1.560
(.152) (.377)
Displays (DISP)
.998(*) 0.608
2.713 1.836
(.217) (.589)
Sample size
3604 979
Choice
1303 189
-2 Log LX²
216.24 4.402
(p < .000) (p = .354)
Percentage correctly classified
65% 82%
Proportional chance
54% 69%(*)Significant at the p<.01 level.
DIAGRAM: Figure 1: A Framework of Positive and Negative Reciprocal Effects
DIAGRAM: Figure 2: Testing a Framework of Positive and Negative Reciprocal Effects: A Summary of Findings.
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By Vanitha Swaminathan, Assistant Professor of Marketing, Isenberg School of Management, University of Massachusetts, Amherst.; Richard J. Fox, Associate Professor of Marketing, Terry College of Business, University of Georgia. and Srinivas K. Reddy, Professor of Marketing, Terry College of Business, University of Georgia.
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Record: 168- The Impact of Category Management on Retailer Prices and Performance: Theory and Evidence. By: Basuroy, Suman; Mantrala, Murali K.; Walters, Rockney G. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p16-32. 17p. 18 Diagrams, 3 Charts. DOI: 10.1509/jmkg.65.4.16.18382.
- Database:
- Business Source Complete
The Impact of Category Management on Retailer Prices and Performance: Theory and Evidence
Category management (CM) is a recent retail management initiative that aims at improving a retailer's overall performance in a product category through more coordinated buying, merchandising, and pricing of the brands in the category than in the past. Despite tremendous retailer and manufacturer interest in the process of CM and its rapid adoption in the industry, much uncertainty exists about the consequences of CM for channel members. The present study focuses on how a shift to CM by a retailer affects its equilibrium prices, sales, and profitability in a competitive retail setting. On the basis of an analysis of a model of two competing national brand manufacturers that supply two competing common retailers, the authors find that one retailer's adoption of CM increases its average unit price of the category and reduces its sales volume and revenues. However, this retailer can still enjoy an increase in its gross margin profits as competing manufacturers' wholesale prices fall in the process. Also, the CM adopter's profits are greater than those of a symmetric competing retailer that follows the traditional brand-centered management of a product category when the interbrand competition is high but interstore competition is low. Applying the intervention analysis methodology, the authors empirically test several of these analytical findings, employing a unique data set that contains information about a supermarket chain's weekly average unit prices and sales of the laundry detergent category before and after this product category was moved to CM by the retailer. The propositions that adoption of CM will lead to higher retail prices and lower sales are upheld in this empirical study. The authors discuss the implications of these findings for practitioners and researchers, the limitations of the study, and directions for further research.
A fundamental change is taking place in the retail grocery and drug store industries as retailers and manufacturers begin to embrace a process called category management (CM). Traditionally, retailers assigned buyers to purchase brands of specific manufacturers, instead of making all purchases within a particular product category. Individual brand-oriented buyers sought to improve their economic performance by procuring large quantities of product on deals and then relying on retail pricing, promotions, and merchandising activities to deplete brand-level inventories as quickly as possible. In contrast, CM recognizes the interrelatedness of products in the category and focuses on improving the performance of whole product categories rather than the performance of individual brands. Under CM, traditional brand (vendor)-oriented buyers are replaced with category managers who are responsible for integrating procurement, pricing, and merchandising of all brands in a category and jointly developing and implementing category-based plans with manufacturers to enhance the outcomes of both parties (Pellet 1994; Progressive Grocer 1995a, b; Supermarket News 1997).
Retailer interest in CM is high. For example, according to one recent industry report, 83% of grocery retailers surveyed view CM as the most important issue facing them (Progressive Grocer 1996), and another study shows CM initiatives to be the most important reason that retailers are improving their information technology systems (Chain Drug Review 1997). Despite the interest in CM and its rapid adoption in the industry (ACNielsen 1998), however, much uncertainty exists about the consequences of CM for retailers, manufacturers, and consumers. For example, beyond anecdotal reports, few studies have rigorously investigated how a retailer shift from brand-centered management (BCM) to CM affects retail prices or retailer and manufacturer profits as its proponents maintain (Harris and McPartland 1993; Category Management Report 1995). The objective of the present research is to investigate the impact of a retailer's shift from BCM to CM on retail and wholesale prices, sales, and profits in a competitive decentralized channel setting. Adoption of CM results in many changes in the retailer's operations and management. We restrict our inquiry to pricing decisions and their outcomes, however, because one of the key benefits of CM is a more profitable pricing structure (ACNielsen 1998; Category Management Report 1995; McLaughlin and Hawkes 1994). Examining pricing under CM is important because changes in the retailer's approach to pricing can directly affect manufacturers, competing retailers, and consumers, who are believed to benefit from CM adoption.
For the purpose of this study, CM is defined as a situation in which a category manager jointly sets the prices of all brands in the category so as to maximize total category profits. Traditional BCM of a category is defined as a situation in which each brand's retail price is set independently so as to maximize its own profit contribution and the prices of competing brands in the category are taken as given. These definitions are consistent with the basic notion that CM involves more coordinated management of brands in a category, including price setting, than in the past (Category Management Report 1995; Food Marketing Institute 1995). We recognize that in practice, some level of coordinated price setting takes place under BCM. Clearly, CM calls for a high level of price coordination, which is the phenomenon examined in the present research. In this study, we first lay out the strategic approach that surrounds the CM business process. On the basis of this discussion, we analyze how a retailer's shift from BCM to CM alters equilibrium prices, sales, and profits within the context of a model of a two-level (competing national brand manufacturers/competing common retailers) channel system. More specifically, we derive and compare equilibrium retailer prices, sales, and profits as functions of demand function parameters in a category composed of two national brands sold by two competing common retailers. Comparisons of prices, sales, and profits are made under three commonly occurring scenarios: ( 1) Both retailers practice BCM, ( 2) one retailer practices CM and the other employs BCM, and ( 3) both retailers practice CM. The comparative analyses produce several propositions about how the adoption of CM by one retailer affects its prices, sales, and profitability compared with a competitor that stays with BCM or might also shift to CM. Several interesting implications of the retailer's adoption of CM for the manufacturers and consumers in the channel also emerge from this investigation.
We test the implications of the key analytical propositions using store-level scanner data in the laundry detergent category obtained from Information Resources Inc. The database contains information on average prices, sales volumes, and revenues of brands in this category collected from 21 stores that are affiliated with a large supermarket chain over a three-year period, January 1993 to December 1995. These 21 stores are all located in one major Midwestern metropolitan market. The supermarket chain switched the laundry detergent category from BCM to CM in February 1994, allowing an examination of the category before and during CM. Information about the average prices and sales for competitors to the chain who did not switch to CM are also contained in the database, providing researchers and practitioners with a rare look at CM effects for the retailer and its suppliers, competitors, and patrons.
CM Framework
In 1995, the Category Management Subcommittee of the ECR Best Practices Operating Committee and the Partnering Group Inc. published an important study: Category Management Report: Enhancing Consumer Value in the Grocery Industry. This report is basically the how-to of CM and lays out eight critical steps that are necessary for a proper implementation of CM by a retailer. The basic steps in the CM process are outlined in Figure 1. It is important to understand the strategic structure and process surrounding CM to evaluate the outcomes of its implementation effectively.
- Step 1: category definition. This is the first step in the category planning process. This step determines the products that constitute a category, subcategory, and major segmentation. The category definition should include all products that are either highly substitutable or closely related, subject to operational constraints.
- Step 2: category role. This step assigns the category role (purpose) based on a cross-category analysis that considers the consumer, distributor, supplier, and marketplace. Designating a role also helps the retailer allocate resources among various categories.
- Step 3: category assessment. This step involves gathering and analyzing historical data and relevant information and then developing insights for managing the category.
- Step 4: category scorecard. In this step, performance measures are established to evaluate program execution, including target gross margins, return on inventory goals, service levels, and so forth.
- Step 5: category strategies. Typical category strategies include cash generating, excitement creating, profit generating, traffic building, and so forth. For example, a traffic-building strategy is focused on drawing consumer traffic to the store and into the aisle, and a profit-generating strategy seeks to increase category gross margin percentage and gross profit dollar.
- Step 6: category tactics. This step involves the determination of optimal category pricing, promotion, assortment, and shelf management that are necessary to achieve the agreed-on role, scorecard, and strategies. *Pricing policies should be applied to the current prices to develop price changes and set overall price changes for the category. Promotional policies should be applied in the development of a promotional plan that includes frequency of promotions and recommended price points* (Category Management Report 1995, p. 45).
- Step 7: plan implementation. An implementation plan generally includes what specific tasks are to be done, when each task should be completed, and who is to accomplish each task. The plan should also note the start date of each task.
- Step 8: category review. This step involves the regular management of the intended results of the overall plan. Reviews should be scheduled at established intervals and listed in the implementation plan.
An inspection of the CM framework reveals that once the category definition (Step 1) and the category role (Step 2) are chosen, the bulk of the action lies in determining the category strategy (Step 5) and then executing the specific category tactics (Step 6). Although different strategies may be appropriate for different categories, retailers predominantly practice CM to increase profits and sales. As ACNielsen (1998, p. 5) notes in its Eighth Annual Survey of Trade Promotion Practices, *Retailers practice category management with several ends in mind, but increasing profitability, increasing revenue and optimizing item mix are * the most important motivators.* For example, 97% of retailers surveyed indicated that the top priority for practicing CM is increased profitability. Similarly, the retailer examined in this study employed a variety of strategies, including a profit-generating strategy, consistent with CM. For modeling purposes, we assume that the retailer sought to build profits by CM.
We use the components of the strategic framework of CM to develop and analyze a model of a decentralized distribution channel that consists of two competing retailers, Retailers A and C, each carrying two differentiated national brands that are produced by competing manufacturers M1 and M2, as shown in Figure 2.
Theoretical Analysis
Note that most trading areas are more complex than the one shown in Figure 2 and are composed of multiple retailers (e.g., Winn-Dixie, Kroger, Safeway, Albertson*s) that sell multiple brands in a product category, which are produced by multiple manufacturers. However, replacing the 2 * 1 * 2 structure (2 manufacturers, each produces 1 brand, 2 retailers) with a more complex structure (e.g., a 2 * 3 * 2 structure) would not change the substantive nature of the results from the present modeling framework. Because the benefits of greater realism in the form of more manufacturers and brands are outweighed by the costs of a more analytically complex model that does not alter our predictions about the effects of retailer adoption of CM, we opted for a simpler structure. The study focuses on the demand-side implications of a retailer's shift from BCM to CM. In keeping with this thrust, we assume that the manufacturers are symmetric with respect to their costs of production and that the manufacturers' marginal costs of production are constant; for ease of exposition, manufacturers' marginal costs of production are set equal to zero. We also assume that the manufacturers sell their brands to the retailers at a constant per-unit charge (i.e., the wholesale price) and the retailers incur no other costs of acquisition. Last, we assume that each manufacturer sells its brand at the same wholesale price to each retailer. This is in keeping with legal restrictions against discriminatory price discounts by manufacturers that exist in practice (see, e.g., Ingene and Parry 1995; Kotler and Armstrong 1996, p. 88).
Our investigation employs the traditional game-theoretic approach to analyzing problems of channel price coordination and competition (e.g., Choi 1991; Coughlan and Wernerfelt 1989; Ingene and Parry 1995; Jeuland and Shugan 1983; McGuire and Staelin 1983; Raju, Sethuraman, and Dhar 1995; Trivedi 1998; Zenor 1994). As do many previous researchers who model decentralized channels in this stream of literature, we assume, first, that each manufacturer determines the wholesale prices to maximize its profits. Given these wholesale prices, managers at the two retailers decide on the retail prices to maximize their respective objective functions. The manufacturers know each retailer's pricing decision rule and take these into account when setting their wholesale prices. That is, we assume that the interaction between the firms is such that each manufacturer acts as a Stackelberg leader in setting its wholesale price, and the retailers follow with their retail price decisions. Adopting a manufacturer*Stackelberg rather than retailer'stackelberg perspective is reasonable, because no store brands are involved in our analysis. Second, we assume that the retailers face symmetric brand-level demand functions that are linear in the brands' prices. More specifically, we denote the quantities of manufacturer*s brand, k (k = 1, 2), demanded at retailers j (j = A, C) by qjk, respectively. Then we specify the corresponding demand functions as follows:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where the demand function parameters h * [0,1) and g * [0,1), respectively, denote the interbrand cross-price sensitivity (degree of product differentiation) and the interstore cross-price sensitivity (degree of store differentiation), and pjk is the price of brand k (k = 1, 2) at retailer j (j = A, C). Thus, the quantities of brand k demanded at retailer/store j are directly affected by the brand's own price at store j, the difference between the two competing brands' prices at store j, and the difference between the brand's price at store j and its price at the competing store. A value of h close to zero implies that the two national brands are highly differentiated, whereas h (r) 1 implies that the brands are highly substitutable. Similarly, as g increases from zero to one, demand for a brand at one retailer is increasingly influenced by its price at the other retailer; that is, store competition for consumers is more intense. Last, note that aggregate demand in the product category at zero prices for the brands is scaled to 1.
Previous models of decentralized channel price competition involving a common retailer (i.e., an independent retailer carrying brands of different manufacturers) that faces a linear demand function structure include those of Choi (1991, 1996), Zenor (1994), Raju, Sethuraman, and Dhar (1995), and Trivedi (1998). Among these authors, only Choi (1996) and Trivedi (1998) analyze a duopoly common retailer model similar to ours. However, the focus of these authors is to compare the effects of varying channel power relationships on profits and prices, assuming that both retailers are natural category managers. In contrast, our focus is the competitive effects of a retailer's move from BCM to CM, and we derive and compare equilibrium prices, sales, and profits for the following three retail competitive scenarios: ( 1) The BCM-BCMscenario, in which both retailers practice BCM; ( 2) the CM-BCMscenario, in which one retailer (Retailer A) shifts to CM and the other (Retailer C) stays with BCM; and ( 3) the CM-CM scenario, in which both retailers adopt CM. None of these scenarios in a two-level duopoly channel structure has been previously analyzed in the literature. Indeed, only Zenor (1994) has focused on CM issues. However, Zenor concentrates on the pricing and profit benefits of CM by competing multibrand manufacturers marketing to a monopolistic retailer. In contrast, our analysis focuses on the effects of the adoption of CM by a retailer in a competitive retail setting. We now describe the three scenarios that will generate the propositions that we subsequently subject to empirical testing.
- Analysis of the BCM-BCMscenario. In this scenario, we assume that each retailer has separate managers for procuring and pricing the two manufacturers' brands. Considering the known wholesale prices, wi, i = 1, 2, of each brand, each of these managers sets price so as to maximize own-brand profits, taking the price of the other brand at the same store as well as the two brands' prices at the competing retailer as fixed. Thus, the objective functions of the two buyers at Retailer A are, respectively, Max(pA1BCM - w1BCM)qA1BCM w.r.t. pA1BCM and Max(pA2BCM - w2BCM)qA2BCM w.r.t. pA2BCM. Similarly, the objective functions of the two buyers at Retailer C are Max(pC1BCM - w1BCM)qC1BCM w.r.t. pC1BCM and Max(pC2BCM - w2BCM)qC2BCM w.r.t. pC2BCM. Substituting the corresponding demand functions in Equations 1-4 into these objective functions and simultaneously solving the four buyers' first-order conditions for profit maximization gives us the (Nash) equilibrium retail prices, pA1BCM, pA2BCM, pC1BCM, and pC2BCM as functions of the given wholesale prices, w1BCM and w2BCM, and parameters h and g. Substituting these retail pricing decision rules into the demand equations, Equations 1-4, we obtain the corresponding demands qA1BCM, qA2BCM, qC1BCM, and qC2BCM as functions of the wholesale prices w1BCM and w2BCM and the demand parameters. Then, considering the retailers' conditional pricing decision rules, Manufacturer i-s (i = 1, 2) problem is to determine the wholesale price, wiBCM, that maximizes its profit. We assume that Manufacturer i does so taking the other manufacturer's wholesale price as fixed; that is, the manufacturers are themselves engaged in Nash competition. Thus Manufacturer i solves Max{wiBCM[qA1BCM(wiBCM, wlBCM) + qCiBCM (wiBCM, wlBCM)]} w.r.t. wiBCM, i, l = 1, 2, and l - i . Simultaneously solving the manufacturers' first-order conditions gives the Nash equilibrium wholesale prices, w1-BCM and w2-BCM. With these solutions in hand, it is straightforward to derive the expressions for the eq uilibrium retail and wholesale prices, retail demands, each retailer's total category profits, and the manufacturers' brand profits as functions of the demand parameters. The analytical results for this scenario, the CM-BCMscenario, and the CM-CM scenario may be obtained from the authors.
- Analysis of the CM-BCMscenario. In a fairly common situation, one retailer adopts CM and a competitor in the same trading area remains with BCM (e.g., Supermarket News 1997b). More specifically, assume that Retailer A replaces its two separate national brand buyers with one category manager who jointly sets the two brands' prices so as to maximize total category profit, taking into account the announced wholesale prices and treating Retailer C's brand prices as fixed. This category manager's objective function is then Max{(pA1CMBCM - w1CMBCM)qA1CMBCM + (pA2CMBCM - w2CMBCM)qA2CMBCM} w.r.t. pA1CMBCM and pA2CMBCM. In contrast, the objective functions of the two buyers of Retailer C are the same as in the previous scenario; that is, Max(pC1CMBCM - w1CMBCM)qC1CMBCM w.r.t. pC1CMBCM and Max(pC2CMBCM - w2CMBCM)qC2CMBCM w.r.t. pC2CMBCM, respectively. Considering the change in Retailer A's objective function and following the same game solution approach as already described, the expressions for the equilibrium retail and wholesale prices, demands, and the retailers' and manufacturers' profits are derived.
- Analysis of the CM-CM scenario. In this scenario, we assume that each retailer has a category manager who jointly sets the prices of the brands in the category so as to maximize total category profit, taking into account the announced wholesale prices and treating the competing retailer's prices as fixed. Retailer A's category manager's objective function is then Max{(pA1CMCM - w1CMCM)qA1CMCM + (pA2CMCM - w2CMCM)qA2CMCM} w.r.t. pA1CMCM and pA2CMCM, and Retailer C's category manager's objective function is Max{(pC1CMCM - w1CMCM)qC1CMCM + (pC2CMCM - w2CMCM)qC2CMCM} w.r.t. pC1CMCM and pC2 CMCM. Following the same game solution approach as already described, the expressions for the equilibrium retail and wholesale prices, demands, and the retailers' and manufacturers' profits are derived.
To gain insight into the results obtained in the previous scenarios, we numerically evaluate the behavior of the retailers' equilibrium prices, sales, and profits in these scenarios and the differences between them as the values of the demand function parameters h (measuring intracategory brand competition) and g (measuring interstore brand competition) are each varied over the range [0,1). Next, we summarize our findings in the form of propositions with accompanying explanations.
Impact of CM on Category Prices
The initial set of issues examined involves the CM retailer's pricing decisions. A comparative analysis between various scenarios suggested by our model leads us to the following proposition, which deals with pricing changes within a retailer:
P1: All else being equal, the retail price of competing brands in a
product category will increase when a retailer (Retailer A) moves the
category from BCM to CM. This is true irrespective of whether the
competing retailer (Retailer C) remains with BCM (i.e., the CM-BCM
scenario) or shifts to CM (i.e., CM-CM scenario).
Figure 3 displays the computed difference between Retailer A's equilibrium prices in the CM-BCMand BCM-BCMscenarios at various values of h and g in the specified range. We see that the price difference is positive everywhere. This difference in prices increases as the value of h increases and that of g decreases. However, the retailer's prices in the two scenarios are the same when h = 0, whatever the value of g is. The rationale for the results is that in the CM regime, Retailer A engages in coordinated or cooperative pricing of brands in the category to maximize total category profits as opposed to the competitive pricing of brands that occurs under BCM. Coordinated pricing results in higher prices. There is no difference between coordinated and competitive pricing outcomes when the brands are perfectly differentiated, that is, when their demands are independent of each other's prices (h = 0). However, when the brands are substitutable (h > 0), coordinated management has the effect of dampening this natural price competition that exists between the brands, which results in higher prices. This dampening effect becomes more significant as the brands become more substitutable, leading to larger differences between coordinated and competitive pricing outcomes as h (r) 1. However, the price increase effect of within-store pricing coordination is tempered by the need to be competitive with the lower prices of the other retailer, which stays with BCM. This competitive effect becomes more pronounced as shoppers' propensity to engage in cross-store shopping increases. Thus, as interstore cross-price sensitivity g increases, the difference between Retailer A's prices in the two scenarios diminishes. The analysis of a retailer move from BCM-BCMto CM-CM is analogous. The following proposition relates to pricing changes and comparisons across retailers:
P2: All else being equal, the increase in the retail price of brands
in a product category moved to CM by a retailer (Retailer A) will be
higher than the increase in retail price of that category at the
competing retailer (Retailer C) that continues with BCM
(i.e., CM-BCMscenario).
Figure 4 shows differences between the retailers' equilibrium prices within the CM-BCMscenario when values of h and g are varied. The intuition is similar to that for P1. The retailer that sets prices so as to maximize total category profits will have a higher price level than a symmetric retailer that sets each competing brand's price so as to maximize its own profit contribution. However, although the basic economic explanation for P1 and P2 is fairly straightforward, the practical implications of these results are significant. Specifically, a higher average retail price in a category as a result of CM is hard to reconcile with the efficient consumer response (ECR) objective of providing higher consumer value to attract and keep customers. We return to this issue subsequently.
Impact of CM on Category Sales Volume and Revenues
Industry CM experts argue that adoption of CM should improve overall category sales for a retailer. For example, a leading trade journal (Progressive Grocer 1995, p. S4) states that "Category management's ultimate objective ought to be to increase total store sales." Similarly, another report (Category Management Report 1995, p. xvii) maintains that "Category Management represents a significant and results-proven opportunity to achieve substantial business improvements." However, as stated in the following proposition, the present analysis suggests a decline in category sales when a retailer adopts CM.
P3: All else being equal, the total unit sales in a product category
will decrease when a retailer (Retailer A) moves the category from BCM
to CM, irrespective of whether the competing retailer (Retailer C)
remains with BCM (i.e., a CM-BCMscenario) or shifts to CM (i.e., a
CM-CM scenario).
Figure 5 shows the decrease in Retailer A's unit sales upon moving from BCM to CM as values of h and g are varied. These outcomes are not surprising given the earlier observation that prices increase under CM. Note that the decline in sales is greatest when h is high and g is low, that is, in the conditions under which Retailer A's price increase is greatest. Furthermore, a higher interstore cross-price sensitivity will temper Retailer A's price increase somewhat but not enough to prevent a loss in sales to lower-priced BCM retailers such as Retailer C in a CM-BCMscenario. Therefore, we give the next proposition, which is illustrated in Figure 6. The analysis of a retailer move from BCM-BCMto CM-CM is analogous.
P4: All else being equal, the total unit sales of a product category
moved to CM by a retailer (Retailer A) will be lower than the total
unit sales of that category at a symmetric competing retailer
(Retailer C) that continues with BCM (i.e., a CM-BCMscenario).
These analytical results are consistent with the finding of a recent survey conducted in several large U.S. cities that significant numbers of shoppers have switched away from stores practicing CM (Cottrell 1995). Next, turning to sales revenues, the analytical results lead to the following proposition (see Figure 7):
P5: All else being equal, the sales revenues of the category will
decrease when a retailer (Retailer A) moves the category from BCM
to CM, irrespective of whether the competing retailer (Retailer C)
continues with BCM (i.e., a CM-BCMscenario) or shifts to CM (i.e.,
a CM-CM scenario).
Revenues decline because the price increase under CM by Retailer A does not compensate for the resulting reduction in consumer demand at this retailer. Next, we turn to category profits.
CM and Category Profits
So far in our analysis, the move to CM appears to offer little benefit to Retailer A. However, this conclusion changes when we examine this retailer's category profits (average gross margin times sales in dollars) after CM adoption. From its inception, CM has been touted as a mechanism for retailers (and manufacturers) to build overall category profits (see, e.g., Category Management Report 1995; Food Marketing Institute 1995). Our findings, summarized in the following proposition, support this contention and are consistent with the retailer's strategies in the category.
P6: All else being equal, the profits of a product category will
increase when a retailer (Retailer A) moves the category from BCM
to CM, irrespective of whether Retailer C remains with BCM (i.e.,
a CM-BCMscenario) or shifts to CM (i.e., a CM-CM scenario).
Figure 8 displays the positive difference between Retailer A's equilibrium profits in the CM-BCMand BCM-BCMscenarios at various values of h and g. The realization of higher profits despite a decline in Retailer A's unit sales as well as revenues implies a significant increase in this retailer's unit gross margin. A contribution to this increase in unit gross margin comes from a decline in the brands' equilibrium wholesale prices. As illustrated in Figure 9, our analytical results indicate that equilibrium wholesale prices in the CM-BCMscenario are lower than the corresponding levels in the BCM-BCMscenario.
The equilibrium wholesale price is lower in the CM-BCMscenario because of the reduction in the overall demand for the manufacturers' products caused by Retailer A's price increase. More precisely, in the face of lowered total market demand, the competing manufacturers maximize their individual profits at a lower wholesale price. Effectively, therefore, the move to CM helps Retailer A gain profits at the expense of the suppliers.
The lower wholesale price induced by Retailer A's move to CM also benefits the competing Retailer C that stays with BCM. Figure 10 displays Retailer C's increase in profits when Retailer A moves from BCM to CM. Thus, within the context of our model, both retailers gain profits at the expense of the manufacturers, even though only one retailer adopts CM. The analysis of a retailer move from BCM-BCMto CM-CM is analogous.
Adoption of CM will always increase Retailer A's profit, as stated in P6. However, because A's action also enhances Retailer C's profitability, it would be interesting to compare the relative profits. An evaluation and comparison of the equilibrium profits of Retailers A and C in the CM-BCMscenario (Figure 11) shows that Retailer A's equilibrium profits are greater than those of Retailer C only under certain circumstances. Retailer A's profits are higher if the interbrand cross-price sensitivity is high (i.e., h close to unity) and if the interstore cross-price sensitivity, g, is close to zero. Conversely, Retailer C's equilibrium profits can dominate those of Retailer A when h is large and g is small. That is, although Retailer A's profits under CM are higher than its own profits under BCM, relative to Retailer C, Retailer A can enjoy higher profits if few consumers visit the competing retailer, which has lower prices. But if cross-store shopping is significant, Retailer A's improved margin under CM does not adequately compensate for its loss in demand to Retailer C. This leads us to the following proposition:
P7: Retailer A's category profits are greater than those of Retailer
C when interbrand cross-price sensitivity, h, is high and interstore
cross-price sensitivity, g, is low, and Retailer A's category profits
are lower than Retailer C's profits when h is low and g is high.
P7 echoes some conclusions of Cottrell's (1995) study: "It is still not clear that those [retailers] who don't practice [CM] find themselves at any competitive disadvantage. In fact, the study found many [retailers] to be performing better than those competitors who were practicing it" (Progressive Grocer 1995, p. S8).
Data
Empirical tests of the propositions require time series data from a trading area where a retailer is known to have moved to CM. More precisely, time series data covering pre-CM and CM regimes for a retailer and its competitors are needed to test the propositions. However, such data on the effects of CM are difficult to obtain because CM is a fairly recent phenomenon, which limits the number of retailers that have databases comprehensive enough to capture category performance before and during CM. Fortunately, the data set used in this study overcomes this hurdle, providing researchers and practitioners with a rare opportunity to measure CM effects.
Aggregate store-level weekly scanner data from 21 stores of a national supermarket retail chain, hereafter called Retailer A, that had moved its laundry detergent category to CM were obtained from Information Resources Inc. (IRI). The stores are all located in one defined Midwestern U.S. urban market and together account for 35% share of laundry detergent sales in this market. The data set also contains aggregated information from the major competitors of Retailer A, hereafter labeled Retailer C. The data include the weekly average (weighted by stockkeeping unit sales) unit retail prices as well as the weekly unit sales in the laundry detergent category for Retailers A and C for 156 weeks: from January 1, 1993, through December 31, 1995. In general, implementation of CM in a product category by a retailer occurs over a period of time rather than during a particular week. The intervention analysis methodology employed here requires a specific date for the switchover to CM, however. The managers of the supermarket chain indicated to the authors that CM began in earnest around the 57th week of the data. Therefore, for analytical purposes we have assigned this week as a switchover date, acknowledging fully that CM implementation was spread over a period of time around that date. None of the competing retailers in Retailer C adopted CM during this three-year period; all stayed with traditional management approaches (BCM) throughout. Thus, the data set covers 56 weeks of the pre-CM regime (i.e., BCM) and 100 weeks of the CM regime for Retailer A and 156 weeks of only BCM regime for Retailer C. Unfortunately, the data set does not include information on wholesale prices or the retailers' margins. Therefore, we are not in a position to rigorously test our analytical propositions pertaining to retailers' profits.
Methodology
To test the propositions related to CM's effects on average prices and sales, a simple t-test might be used to compare the means of the dependent measures (average prices, sales, revenues) before and after adoption of CM by Retailer A. However, the use of a t-test to determine whether a significant difference exists between the means of the two regimes is not appropriate when the dependent measures come from the same time series. This is because a t-test assumes that data for each group are independently generated from samples with normal distributions and constant variances. The time series data used in the study does not meet these assumptions because of the presence of autocorrelation. Therefore, we employ intervention or interrupted time series analysis (e.g., Box and Tiao 1975; McDowall et al. 1980) to study the impact of Retailer A's adoption of CM on prices, sales, and revenues. Although applications of intervention analysis in the marketing literature are few (e.g., Krishnamurthi, Narayan, and Raj 1986; Mulhern and Leone 1990; Wichern and Jones 1977), the technique is widely used in other social sciences. Figure 12 (see McCain and McCleary 1979) summarizes the details of the four stages - autoregressive integrated moving-average method (ARIMA) identification, estimation, diagnosis, and intervention hypothesis testing of the intervention analysis procedure.
Empirical Analyses
We now investigate P1 through P6, which are derived from our analytical model, using the intervention analysis approach. However, to begin with, we perform a rough test of whether the laundry detergent category at Retailer A is characterized by conditions favoring CM-a high interbrand cross-price sensitivity at Retailer A and demand that is relatively insensitive to other retailers' prices. Following Raju, Sethuraman, and Dhar (1995), we use the category own-price sensitivity as a surrogate for national brand cross-price sensitivity. This approach is reasonable given the one-to-one relationship between own-price sensitivity h* and cross-price sensitivity h, namely, h* = (1 + h), in our specification of the demand functions (Equations 1-4). Consistent with the linear form of the demand functions assumed in the analytical model, we estimate the following equation:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where QtA = Retailer A's weekly unit sales of laundry detergent, PtA = Retailer A's average weekly unit price, h* = intrastore own-price sensitivity, PtC = Retailer C's average weekly unit price, g = interstore cross-price sensitivity, a = intercept, and eA = the error term. The regression results are reported in Table 1.
The results indicate that the own-price price sensitivity of the average item in the category, h*, is negative and significant for Retailer A. The nonsignificant but positive estimate of the interstore cross-price sensitivity (g) suggests that little price-based cross-store shopping takes place for brands in the category. Overall, the results suggest that the laundry detergent category possesses the competitive characteristics that support Retailer A's move to CM for this category.
Testing Implications of Propositions on CM and Retail Price
A testable implication of P1 is that Retailer A's average unit retail price of laundry detergents in the CM regime will be higher than the average unit retail price level in the pre-CM regime. A testable implication of P2 is that the increase in the average unit retail price of Retailer A as it moves from pre-CM to CM will be higher than the increase, if any, of the average unit retail price of Retailer C. Summary descriptive statistics with respect to the mean of the laundry detergent category's weekly average unit price series of Retailers A and C before and after Retailer A's move to CM are shown in Table 2. It appears that Retailer A's mean weekly average unit price rose by approximately $.33 after the move to CM. Furthermore, Retailer A's mean weekly average unit price was $.16 lower than that of Retailer C in the pre-CM regime but higher by approximately $.06 in the CM regime.
The time series plots of Retailer A's weekly average unit prices and the difference between Retailer A's and Retailer C's weekly average unit price series are displayed in Figures 13 and 14, respectively. Figure 13 indicates that there is a gradual upward shift in A's prices from the pre-CM period to the CM period, and Figure 14 shows that Retailer A's prices rose more and were higher on average than those of Retailer C in the CM period.
- Intervention analysis tests of P1 and P2. The results of the ARIMA model identification and estimation stages with respect to the time series data shown in Figures 13 and 14 are reported in Table 3. Diagnosis checks of the autocorrelation functions (ACFs) and the partial autocorrelation functions (PACFs) of the residuals of each of the estimated models in Table 3 revealed that they were white noise; that is, the models were found to be adequate.
Figures 13 and 14 show that the series in each case underwent a gradual and permanent change after the adoption of CM by Retailer A. Therefore, the following models for the intervention hypothesis testing stage of the analysis were specified:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Note that in these equations, dp + wI (elsewhere, simply wI) is the intervention component, or what is commonly referred to as the "transfer function." Furthermore, all the series that we consider in this article have been differenced once to make them stationary. The maximum likelihood estimates of the parameters of these models are reported in Table 3.
Diagnosis checks applied to the residuals of each of the estimated models showed the residuals to be white noise, indicating that the models fit the data well. As shown in Table 3, the w coefficient in Equation 6a is positive and statistically significant, which indicates that Retailer A's adoption of CM resulted in increased prices. For example, the first postintervention observation is Y57 = .206, which implies that the level of the series rose by approximately $.21 in the first postintervention period. Similarly, in each subsequent week t, the increment in the level of the price is dt, where d = .223. The asymptotic level of the series is .27 (or .206/[1 * .223]), which implies that adoption of CM by Retailer A gradually and permanently increased average unit prices in the category by approximately $.27. Prices under CM are approximately 7% higher than prices under BCM for Retailer A. Overall, the results support P1 and lead to the conclusion that adoption of CM by Retailer A resulted in a significant increase in the average unit retail price in the product category from its pre-CM level.
For P2, the results in Table 3 show that the w coefficient is positive and significant. Because we are working with the price difference series, the positive coefficient indicates that the difference between the changes in the price of Retailer A and the changes in the price of Retailer C was positive. The significant d coefficient means that the difference between their price changes was gradual and permanent. Thus, P2 is supported by the data.
Testing Implications of Propositions on CM and Sales
A testable implication of P3 is that Retailer A's shift to CM will lower this retailer's category sales. Summary descriptive statistics with respect to the means of the weekly category unit sales of Retailer A in the pre-CM and CM regimes are shown in Table 2, and the graph of the weekly unit sales series is displayed in Figure 15. From Table 2, it is clear that the weekly unit sales declined from the pre-CM period to the CM period. The average weekly decline was approximately 5267 units. Therefore, Figure 15 and Table 2 suggest that there was indeed a significant decline in Retailer A's weekly unit sales after adoption of CM.
- Intervention analysis for P3. Retailer A's weekly category unit sales (SAt ) data were log-transformed and then identified as an ARIMA (0, 1, 1) process. The results of the identification and estimation stages are reported in Table 3. Figure 15 shows that the category unit sales for Retailer A underwent an abrupt, permanent decline with the adoption of CM. Therefore, the intervention hypothesis test model was specified as
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Table 3 reports the maximum likelihood estimates of the parameters of Equation 7. As shown in Table 3, the w coefficient in Equation 7 was found to be negative and statistically significant, which indicates that Retailer A's adoption of CM had a significant, negative effect on category sales. More specifically, because the estimate of the w coefficient is stated in natural logarithms, the effect of w is actually ew. Because e.19 = 1.21, it can be concluded that the ratio of the pre-CM series to the CM series is 1.21, or a value that represents a 17% reduction in the average category unit sales from the pre-CM period. Given that the mean pre-CM category sales level was 38,257 units each week (Table 2), the results indicate that the intervention of CM was associated with an abrupt and permanent drop in the category unit sales by approximately 6503 units a week. Thus, the empirical results strongly support P3.
A testable implication of P4 is that Retailer A's shift to CM will lower the retailer's category sales in comparison with Retailer C's sales. Thus, this proposition aims at a relative comparison of sales between the two retailers. Given that Retailer A controls approximately 35% of the market, comparison of sales between the retailers in absolute terms is meaningless. Therefore, we resort to market share. This measure will provide a suitable relative comparison for the purposes of this proposition. Summary descriptive statistics with respect to the means of the weekly market shares of Retailer A in the pre-CM and CM regimes are shown in Table 2, and the graph of the weekly market share series is displayed in Figure 16.
- Intervention analysis for P4. The results of the ARIMA model identification and estimation stages are reported in Table 3. Figure 16 shows no detectable change in the market share with the adoption of CM. However, to test the proposition, we conjecture a gradual decline in the market share. Therefore, the intervention hypothesis test model was specified as follows:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Table 3 reports the maximum likelihood estimates of the parameters of Equation 8. As shown in Table 3, the w coefficient is found to be negative, but it is not significant. Therefore, P4 cannot be supported from the data.
Testing Implications of Propositions on CM and Revenues
A testable implication of P5 is that Retailer A's revenues (weekly average unit price times unit sales volume) will decline after adoption of CM. Table 2 presents the summary descriptive statistics on the means of Retailer A's weekly revenues in the pre-CM and CM regimes, and Figure 17 displays the graph of the revenue series. The data suggest that weekly average revenue declined for Retailer A.
- Intervention analysis for P5. Retailer A's weekly average revenues (RAt) data were log-transformed and then identified as an ARIMA (0, 1, 2) process. The results of the identification and estimation stages are reported in Table 3. Figure 17 shows that the revenues for Retailer A underwent an abrupt, permanent decline with the adoption of CM in a manner similar to the category unit sales series. Therefore, the intervention hypothesis test model was specified as follows:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Table 3 reports the maximum likelihood estimates of the parameters of Equation 9. As shown in Table 3, the w coefficient in Equation 9 was found to be negative and statistically significant, which indicates that Retailer A's adoption of CM had a significant, negative effect on the revenues. Because the estimate of the w coefficient is stated in natural logarithms, the actual effect of w is ew. Because e.09 = 1.10, it can be concluded that the ratio of the pre-CM series to the CM series is 1.10, or a value that represents a 9% reduction in the weekly revenue stream from the pre-CM period. Given that the mean pre-CM weekly revenue was $143,589 (Table 2), the results indicate that the intervention of CM was associated with an abrupt and permanent drop in the revenues by approximately $14,358 a week. Thus, P5 is strongly supported by the data. Next, we turn to the profits.
Sensitivity Analyses with Respect to Profits
P6 implies that Retailer A's profits will increase after adoption of CM. We cannot directly test this proposition because the database does not contain direct profit information. Therefore, we perform a simulation analysis to determine if P6 can be supported by the available data, despite the fall in unit sales as well as revenues. To calculate profits, we must estimate the margins. Different trade publications indicate that supermarket margins in the laundry detergent category vary between 12% and 20%. We performed several intervention analyses assuming different profit margins-12%, 20%, and 16% (the average of these two). The results were all similar. Therefore, we report the result for a 16% margin. We took the average category unit price in the pre-CM period, $3.77 (see Table 2), and subtracted 16% of it to come up with the approximate average unit wholesale price, $3.17, for Retailer A in the pre-CM period. We then calculated each week's margin by subtracting this number from the corresponding weekly average unit price. For the CM period, we assumed further that the wholesale prices did not rise, and therefore we used the same wholesale prices for the CM period as well. Thus, we multiplied each week's average unit margin (in dollars) by the corresponding unit sales to get to the profits. The last row of Table 2 displays the summary descriptive statistics for the computed weekly average profits of Retailer A, and Figure 18 shows the time series. The data suggest that weekly average profit increased for Retailer A. This took place despite declining unit sales and declining weekly revenues.
- Intervention analysis for P6. Retailer A's weekly average profits (pAt ) data were log-transformed and then identified as an ARIMA (0, 1, 1) process. The results of the identification and estimation stages are reported in Table 3. Figure 18 shows that the revenues for Retailer A underwent an abrupt, permanent increase with the adoption of CM. Therefore, the intervention hypothesis test model was specified as follows:
Table 3 reports the maximum likelihood estimates of the parameters of Equation 10. As shown in Table 3, the w coefficient in Equation 10 was found to be positive and statistically significant, which indicates that Retailer A's adoption of CM had a significant, positive effect on the category profits. Because the estimate of the w coefficient is stated in natural logarithms, the effect of w is actually ew. Because e.22 = 1.25, it can be concluded that the ratio of the CM series to the pre-CM series is 1.25, or a value that represents a 25% increase in the weekly average profits from the pre-CM period. Given that the mean pre-CM weekly average profits was $22,313 (see Table 2), the results indicate that the intervention of CM was associated with an abrupt and permanent increase in the weekly profits of Retailer A in the laundry detergent category by approximately $5578.
Basically, the sensitivity analysis suggests that if wholesale prices had stayed the same, profits would have been significantly higher for Retailer A. Because this analysis was based on the assumption of constant wholesale prices, the analysis would still be true if the wholesale prices declined as predicted by our theory.
In addition to the aggregated analysis, we also conducted a disaggregated analysis of the data. Retailer A in the market area under scrutiny faced two major competitors:one is a typical hi/lo retailer and operates 14 stores in the trading area, and the second is a discount chain with 5 stores in the trading area. The combined market share of the three chains is 85%. (The remaining competitors are small operators that would not affect Retailer A substantively.) In most neighborhoods or locales within the trade area, Retailer A battles only one of these two competitors.
An identical intervention analysis approach was conducted on the disaggregated data pertaining to retail prices of the three chains before and during Retailer A's adoption of CM. The price series were identified as ARIMA (0, 1, 1), similar to that in the aggregate analysis. The results of the disaggregate analysis were identical to the substantive results of the aggregate analysis. Therefore, the data have captured the nature of the effects of CM in the particular product category.
Category management is a widely heralded process designed to help the retailer achieve overall category performance objectives by coordinating buying, merchandising, and pricing decisions for products in the category. Although adoption of CM by retailers is rapidly increasing (ACNielsen 1998), little research on the effects of CM adoption on retailer prices and performance has been performed. In the present study, we attempt to measure how CM affects retailer prices, sales, revenues, and profits under different competitive conditions.
Several interesting propositions emerged from the analytical portion of the study that were subsequently confirmed by empirical analysis. For example, the data indeed show that a gradual and permanent increase in prices of laundry detergent brands occurred after CM was implemented. Prices in the laundry detergent category at the retailer were significantly greater than prices in the same category among competing retailers that did not move to CM. The results are consistent with previous work on manufacturer adoption of CM in a packaged goods category, which showed that interbrand price coordination produced higher prices for the brands in the manufacturer's product line (Zenor 1994).
Our model in this article offers one rationale for price increase under CM-coordinated pricing. However, the price increases produced by CM in Retailer A could be an outcome of several strategies employed by the retailer, including a profit-generating strategy, which is expressly discussed in the CM literature and applied by many retailers in practice (e.g., Category Management Report 1995; McLaughlin and Hawkes 1994; Progressive Grocer 1995). For example, retailer deletion of low-margin items (Broniarczyk, Hoyer, and McAlister 1998) and a reduced emphasis on price promotions in the category may have contributed to the price increase. One downside associated with each of these strategies is the potential for retail prices to increase and thereby negatively affect retailer performance if a substantial segment of the category's customers are price sensitive. The present study appears to capture this phenomenon, as is shown by the drop in consumer demand for Retailer A during the CM period.
Despite the higher prices under CM, the retailer's move to CM from a traditional BCM approach built the category profitability. Profits rise under CM over the traditional management approaches (i.e., BCM) because a decrease in consumer demand causes manufacturers to lower their (wholesale) prices, which, in combination with higher retail prices, produces higher gross margins for the retailer. Therefore, the present study supports the claims of CM consultants, experts, and practitioners that CM produces enhanced business results (Category Management Report 1995).
The findings also show that a retailer enjoys higher profits under CM than do competing non-CM retailers, but only under certain conditions. For example, CM adoption produces the greatest economic benefits for the retailer when interbrand competition is high and consumer store switching is low. Few economic benefits exist for the CM retailer when little brand competition and much interstore competition are present. These findings suggest that categories characterized by much interbrand competition and little cross-store shopping should be the focus of the retailer's CM efforts.
Implications for Managers
The present research holds several interesting implications for practitioners. First, the increase in retail prices under CM calls into question the consumer benefits of CM. The practitioner literature argues that CM adoption will enhance consumer welfare, as is captured in the definition of CM: "A distributor/supplier process of managing categories as strategic business units, producing enhanced business results by focusing on delivering consumer value" (Category Management Report 1995, p. vii). The price increases fostered by CM represent a diminution rather than an improvement in consumer value. These findings are not the first "red flags" that question the magnitude of consumer benefits from CM adoption. Research shows that retailer implementation of CM led some consumers to switch to non-CM stores, noting that "There is a certain naivet&eactue; on the part of retailers and wholesalers who believe [the benefit of] Category Management is transparent to the consumer" (Progressive Grocer 1995, p. S9). The selection of a profit-generating strategy for the category may have contributed to the price increases in the category, which suggests that other strategies might produce different, more consumer-beneficial results. Retailers should explore how CM adoption will benefit the consumer before implementing CM in the category.
A second major implication of the research is that the economic outcomes of CM are category specific. Retailers should implement CM in product categories in which ( 1) cross-price sensitivities among brands are high or much brand switching exists and ( 2) cross-store price sensitivities are low or little price-based cross-store shopping takes place for brands in the category. This implication is consistent with research showing that manufacturer adoption of CM has its greatest impact on manufacturer profitability when cross-price sensitivities among products in the manufacturer's product line are high (Zenor 1994). The results suggest that the common practice of grocery retailers'applying CM to categories with high sales volumes (McLaughlin and Hawkes 1994) is too simplistic. Empirical research shows that many high-volume categories (e.g., cookies, crackers), which appear to be ideal candidates for CM, actually possess low elasticities (e.g., Hoch et al. 1995) and probably low cross-price elasticities. Therefore, using high category sales volume as a major criterion for selecting product categories for CM may provide low returns for the retailer.
A third implication of the study is that retailer adoption of CM appears to offer manufacturers fewer economic benefits than it offers retailers, contradicting the popular notion that CM benefits retailers and manufacturers (e.g., Food Marketing Institute 1995; Category Management Report 1995). This implication is based on the results that show manufacturer prices and margins declining, likely producing a drop in manufacturer profits. The findings of the present study are consistent with reports of concern among manufacturers that retailer migration to CM will produce a win-lose situation in the channel (The Economist 1997; McLaughlin and Hawkes 1994; Reda 1995). One approach that might alleviate such a scenario is for retailers and manufacturers to codevelop category plans, strategies, and tactics and jointly implement them to achieve mutually satisfactory goals. Experts in CM have long advocated a high level of cooperation between manufacturers and retailers in implementing CM, though such a relationship is difficult to implement in practice (see Category Management Report 1995, pp. 107-12).
Finally, CM can support the retailer's ECR efforts. In the mid-1990s, ECR was a major thrust in the grocery industry: "ECR is a grocery industry strategy in which distributors and suppliers are working closely together to bring better value to the grocery consumer" (Efficient Consumer Response 1993, p. 1). The key strategies for ECR involve efficient product assortments, replenishment, promotion, and product introductions, and CM is directly linked to ECR primarily through category assortments and promotions. For example, CM can optimize the item mix by delisting slow-moving SKUs, an action consistent with the objectives of ECR. Ironically, the combination of fewer products and greater price coordination supports the retailer's ECR initiatives while violating a basic tenet of CM to enhance consumer value because retailer prices are higher. Also, ECR calls for a lowering of the intensity of consumer and trade promotions (see Efficient Consumer Response 1993, pp. 79-85), an activity consistent with some CM strategies that are available to retailers for their categories. Therefore, retail managers can employ CM to support some components of their ECR efforts.
Implications for Researchers
The study offers marketing researchers several interesting implications as well. First, the current study presents researchers with fresh insights on relationships among retailer and manufacturer pricing, retailer unit sales, and category profitability under CM. For example, increases in average category prices and decreases in overall category sales resulting from CM implementation are two relationships that were not predicted in the previous empirical work on CM (e.g., McLaughlin and Hawkes 1994; Zenor 1994) or in the practitioner literature. As research findings and empirical generalizations on CM grow, more sophisticated perspectives on CM effects will develop, leading to better models of the process.
A second, related implication of the study is that the drop in manufacturer prices and margins resulting from CM adoption can significantly affect the nature of the relationship between manufacturers and retailers. Experts and researchers in CM need to expand constructions of CM effects to include scenarios in which opportunism exists in channel relationships under a CM approach and offer remedies for the problems that result. For example, integrating work showing high relational exchange (i.e., role integrity, preservation of the relationship, and harmonization of relationship conflict among channel members) to limit opportunism in channels (Brown, Dev, and Lee 2000) should be undertaken to enhance the efficacy of CM for all channel members.
A third implication for researchers is that the effects of CM are complex, containing significant indirect influences. For example, retailer adoption of CM resulted in higher prices, which caused retail sales and manufacturer prices to decline. This set of effects resulted in higher retailer margins and category profits. The complexity of these effects should stimulate researchers to develop more robust models, perhaps using structural equations approaches (i.e., systems of equations) to gain information on the indirect effects of the phenomenon. Researchers also should consider multiple measures of performance when assessing CM effects, because single-measure models based on sales volume provide only a partial view of CM.
Finally, game theory in combination with intervention analysis represents a novel approach to the study of the impact of CM on retailer and manufacturer performance. Game theory generated interesting and practical propositions about CM effects, and intervention analysis was employed to test these propositions with longitudinal data. The present methodology complements the survey approaches employed by other researchers who describe retail practices and outcomes. Certainly, research using a variety of methodologies is warranted to tackle problems of interest to researchers and practitioners.
The limitations of the present research also represent opportunities for interesting further research in the area. One limitation is the informational assumptions of our analytical model. For example, manufacturers and retailers typically do not possess perfect information about one another's demand functions and costs as assumed in the model. Although rich data are available on manufacturer and retailer activities, in reality managers make decisions with imperfect knowledge about demand functions and costs. Further research might investigate how imperfect information among key decision makers influences CM outcomes. The model also assumes that the retailer seeks to maximize category profits. Retailers are often interested in maximizing other objectives, such as category sales or store traffic. Under a different category strategy-say, traffic building-the conclusions from the model might have been different. A future study might explore CM effects under a sales-maximization approach or a traffic-building approach and compare the results to add greater insights into the implications of a retailer move to CM. Also, our study assumes constant costs on the supply side and no quantity discounts offered by manufacturers. These assumptions could be relaxed in further research, especially given that lowering supply chain costs is an important aspect of the overall ECR initiative. Another limitation of the study is that the model does not account for competing explanations for why category prices increased, as described previously. Future model builders in the CM area could incorporate factors such as product deletion and reduction of price discounts in their models to explain such phenomena.
Last, only one dimension of retailer CM, price coordination, was examined in the study. Category management is a much more complex process, involving coordinated buying, pricing, promotion, and merchandising of brands in the category. Future studies should examine how a more complete set of CM activities affects retailer and manufacturer performance. Furthermore, the present study focused on how retailer adoption of CM influenced retailer performance. Additional research is needed on how manufacturers are affected by retailer implementation of CM. The research could follow two tracks: focusing on how the relationship between the retailer and the manufacturer is influenced by retailer adoption of CM and how retailer adoption of CM affects manufacturer sales and profit performance. Much interesting work remains to be done on the complex process of CM.
Suman Basuroy thanks the Office of Sponsored Research Programs, Rutgers University, for partially supporting this study through a research grant in 1998.
Murali Mantrala thanks the College of Business Administration and the Center for Retailing Education and Research at the University of Florida for supporting this research through summer research grants in 1996 and 1997.
Rockney Walters thanks the Kelley School of Business at Indiana University for supporting the study through a summer research grant in 1997.
All of the authors thank Information Resources Inc. for the data and Yusuf Khanji for his help with the research.
Legend for Chart:
A - Variables
B - Parameter Values
A B
Intercept 79,467.16*
Intrastore own-price sensitivity, η -13,762.10*
Interstore cross-price sensitivities, γ 2711.65 (n.s.)
* Significant at the p < .01 level.
Notes: R² = .310, adjusted R² = .284, F = 11.93.
n.s. = not significant. Legend for Chart:
A - Relevant Proposition
B - Variable
C - Pre-CM Regime Mean
D - Pre-CM Regime Standard Deviation
E - Pre-CM Regime Minimum
F - Pre-CM Regime Maximum
G - CM Regime Mean
H - CM Regime Standard Deviation
I - CM Regime Minimum
J - CM Regime Maximum
A B C D E
F G H
I J
P1 Weekly average
unit price of A 3.77 .21 3.21
4.13 4.11 .29
3.43 5.05
P2 Weekly average
price difference
between A and C -.16 .23 -.74
.54 .06 .26
-.73 1.16
P3 Weekly average
unit sales of A 38,257 5198 30,007
55,917 32,990 3975
27,124 49,877
P4 Weekly average
market share of A .344 .028 .280
.420 .346 .025
.280 .430
P5 Weekly average
revenues of A 143,589 16,471 115,100
206,893 135,295 16,567
106,667 195,606
P6 Computed weekly
average profit of A 22,313 7331 1731
35,438 30,716 9586
10,366 65,728 Legend for Chart:
A - Relevant Proposition
B - Series
C - Identification of Series
D - Estimation of Series
E - Maximum Likelihood Estimates of Parameters for Intervention
Hypothesis θ1
F - Maximum Likelihood Estimates of Parameters for Intervention
Hypothesis θ2
G - Maximum Likelihood Estimates of Parameters for Intervention
Hypothesis ω
H - Maximum Likelihood Estimates of Parameters for Intervention
Hypothesis δ
I - Maximum Likelihood Estimates of Parameters for Intervention
Hypothesis φ1
A
B
C
D E F
G H I
P1
Weekly average
unit price of A
ARIMA (0, 1, 1)
pA[sub t] = at - θ1at-1
θ1 = .72*** .896*** N/A
.206** .223*** N/A
P2
Weekly average
price difference
between A and C
ARIMA (0, 1, 1)
pA[sub t - Ct] =
at - θ1at - 1
θ1 = .85*** .909*** N/A
.168** .138** N/A
P3
Weekly average
unit sales of A
ARIMA (0 1, 1)
St = at - θ1at - 1
θ1 = .89*** .867*** N/A
-.190*** N/A N/A
P4
Weekly average
market share of A
ARIMA (0, 1, 2)
msA[sub t] = at - θ1
at - 1 - θ2at - 2
θ1 = .69***
θ2 = .24*** .320 n.s. .645***
-.002 n.s. -.405*** N/A
P5
Weekly average
revenues of A
ARIMA (0, 1, 2)
RA[sub t] = at - θ1
at - 1 - θ2at - 2
θ1 = .73***
θ2 = .18*** .739*** .181***
-.092** N/A N/A
P6
Computed weekly
average profit of A
ARIMA (0, 1, 1)
πA[sub t] = at - θ1at - 1
θ1 = .38*** .403*** N/A
.229* N/A N/A
* Significant at the p < .10 level.
** Significant at the p < .05 level.
*** Significant at the p < .01 level.Notes: n.s. = not significant, N/A = not applicable. The vertical line at the 57th week depicts the time of adoption of CM in the category by Retailer A. The horizontal line indicates zero price difference.
DIAGRAM: FIGURE 1: The Category Management Process
DIAGRAM: FIGURE 2: The Competitive Retail Structure
DIAGRAM: FIGURE 3: Difference Between Retailer A's Equilibrium Prices in CM-BCM and BCM-BCM Scenarios as a Function of Cross-Price
DIAGRAM: FIGURE 4: Difference Between Retailer A's and Retailer C's Equilibrium Prices in CM-BCM Scenario as a Function of Cross-Price
DIAGRAM: FIGURE 5 Difference Between Retailer A's Equilibrium Category Unit Sales in CM-BCM and BCM-BCM Scenarios as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 7: Difference Between Retailer A's Equilibrium Revenues in CM-BCM and BCM-BCM Scenarios as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 6: Difference Between Retailer A's and Retailer C's Equilibrium Category Unit Sales in CM-BCM Scenario as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 8: Difference Between Retailer A's Equilibrium Profits in CM-BCM and BCM-BCM Scenarios as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 9: Difference Between Manufacturers' Equilibrium Wholesale Prices in CM-BCM and BCM-BCM Scenarios as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 10: Difference Between Retailer C's Equilibrium Profits in CM-BCM and BCM-BCM Scenarios as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 11: Difference Between Retailer A's and Retailer C's Equilibrium Profits in CM-BCM Scenario as a Function of Cross-Price Sensitivities
DIAGRAM: FIGURE 12: Intervention Analysis: Modeling Strategy
DIAGRAM: FIGURE 13: Retailer A's Weekly Average Unit Prices
DIAGRAM: FIGURE 14: Difference Between Retailer A's and Retailer C's Weekly Average Unit Prices
DIAGRAM: FIGURE 15: Retailer A's Weekly Average Unit Sales
DIAGRAM: FIGURE 16: Retailer A's Weekly Average Market Share
DIAGRAM: FIGURE 17: Retailer A's Weekly Average Revenue
DIAGRAM: FIGURE 18: Retailer A's Computed Weekly Average Profit
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~~~~~~~~
By Suman Basuroy; Murali K. Mantrala and Rockney G. Walters
Suman Basuroy is Assistant Professor of Marketing, University at Buffalo.
Murali Mantrala is Manager, ZS Associates.
Rockney Walters is Associate Professor of Marketing, Kelley School of Business, Indiana University.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 169- The Impact of Cobranding on Customer Evaluation of Brand Counterextensions. By: Kumar, Piyush. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p1-18. 18p. 1 Diagram, 4 Charts, 4 Graphs. DOI: 10.1509/jmkg.69.3.1.66358.
- Database:
- Business Source Complete
The Impact of Cobranding on Customer Evaluation of Brand
Counterextensions
A brand that successfully extends from its parent category into a new extension category often faces a counterextension by a brand from the extension category back into its own parent category. However, there is little guidance available on how brand extension strategies should be adjusted to mitigate the risk to the parent brand from counterextensions. This research examines the differential impact of cobranded versus solo-branded extensions on customer evaluations of brand counterextensions. It demonstrates that customers evaluate a counterextension less favorably if the preceding extension by the focal brand is cobranded than if it is solo branded. The findings suggest that cobranding not only improves the attribute profile of a brand's own extension but also helps protect the brand against counterextensions.
A widely adopted strategy for firms entering new product markets is to use brand extensions and to take advantage of an existing brand's equity in a new category (Aaker 1991; Park, Jaworski, and MacInnis 1986). A consequence of the popularity of this strategy is that brand extension activity between product categories is now increasingly bidirectional. Often, a brand that successfully extends from its parent category into a new extension category faces a counterextension by a brand from the extension category back into its own parent category. Such interactions between extensions and counterextensions are routinely observed across many categories, such as pitcher-based and faucet-mounted water filters (Brita versus PUR), domestic and international package delivery (FedEx versus DHL), cable service and Internet service (Cox Communication versus SBC Communications), and, more broadly, consumer electronics and computer hardware (Sony and Toshiba versus Dell and Gateway).
Although the brand management literature correctly cautions against indiscriminate use of extensions (Gibson 1990; Loken and Roedder-John 1993), there has been almost no research on whether a brand's extension strategy influences customer response toward counterextensions. Thus, there is little guidance available on how extension strategies should be adjusted to mitigate the risk to the parent brand from counterextensions. However, as brand extension activity across category boundaries continues to increase, the interplay between extensions and counterextensions is likely to emerge as a key brand management issue. Therefore, marketers must begin to understand how to account for and manage the risk from counterextensions to ensure that the gains from the extension of their brand into a new product-market are not significantly offset by the losses suffered as a result of counterextensions that are launched into their product-market.
In this article, I investigate whether cobranding an extension with a partner brand instead of launching it solo branded has an effect on customers' responses toward a counterextension. My core thesis is that a successful solo-branded extension improves customer perception of the similarity and fit between the parent and extension categories and enhances customer evaluation of a counterextension. However, a cobranded extension merely inherits select attributes from each partner brand and leaves the perceptions of similarity between a parent category and the extension category relatively unchanged. Therefore, I predict that a counterextension is likely to be evaluated less favorably if the previous extension by the focal brand was cobranded with a partner brand than if it was solo branded.
I address this issue with a series of five experimental studies that focus on the difference between the evaluation of a counterextension following cobranded versus solo-branded extensions. I conclude the article with a discussion of the findings from these studies in terms of their theoretical and managerial implications, and I present an outline of a further research agenda in the area of brand counterextensions.
Conceptual Background and Hypotheses
In this section, I begin by drawing on categorization theory (Rosch and Mervis 1975) to argue that a successful launch of a solo-branded extension improves customer perception of the similarity between the parent and extension product categories and thus enhances customer evaluation of a counterextension. Then, I posit that cobranding the extension with an appropriate partner brand helps maintain the perceptual separation between the parent and extension categories and results in a lower customer evaluation of a counterextension. Throughout the discussion, I refer to the focal brand that launches the first or initial brand extension as Brand [A1 and, when applicable, to its cobranding partner as Brand A2. I refer to the brand that launches the counterextension as Brand B.
Customers tend to divide the products around them into categories and place products that are similar to one another into the same category and those that are dissimilar into different categories (Day, Shocker, and Srivastava 1979; Rosch 1978). Although category boundaries are often ill-defined, the perceived separation between a category and its neighboring category depends on whether its prototypes are unique or shared with the neighbor. Categories that contain unique prototypes tend to be more distinctive than those that do not (Tversky 1977). Brands often serve as these prototypes and provide useful cues that help separate neighboring categories from one another. Therefore, all else being equal, customers are likely to perceive a category as more differentiated if it contains unique brands than if it shares brands with other categories. For example, the perceptual separation between the peanut butter and the jelly categories is likely to be greater if they both contain unique brands than if they share brands between them.
A successful solo-branded extension by Brand A1 into the category of Brand B reduces the number of unique or distinctive members of each category and increases the number of members that are common between them. This change in the relative number of common versus unique members is likely to improve customer perception of the similarity and fit between the two categories (Aaker and Keller 1990; Dhar and Sherman 1996; Smith and Park 1992; Tversky 1977). In turn, a better fit facilitates the transfer of beliefs and affect that are associated with a brand in either category to its extension into the other (Cohen and Basu 1987; Meyers-Levy and Tybout 1989; Sujan 1985).( n1) Therefore, a subsequent counterextension by Brand B into the category of Brand A1 will be evaluated more favorably if Brand A1 previously launched a successful solo-branded extension into the category of Brand B than if did not launch the extension (see Figure 1, Panels A and B). In other words, the increase in similarity between the categories of Brand A1 and Brand B following a successful solo-branded extension by Brand A1 is likely to improve customer evaluation of a counterextension by Brand B. Formally, I hypothesize the following:
H1: Customer evaluation of a counterextension by Brand B into the parent category of Brand A1 is greater if Brand A1 previously launched a successful solo-branded extension into the parent category of Brand B than if it did not launch the extension.
A cobranded extension is one in which two brands are used jointly to present a product to the customer (Rao and Ruekert 1994). For example, Smucker's Dove ice-cream topping is a cobranded product launched jointly by Smucker's, a fruit preserves brand, and Dove, a chocolate brand, into a new product category. Similarly, Oral-B Rembrandt whitening strips is a cobranded product launched by a toothbrush brand and a whitening toothpaste brand into a new category. Previous research shows that cobranding is a useful extension strategy because it strengthens the attribute profile of the extension (Park, Jun, and Shocker 1996), helps the partner brands gain advertising synergies (Samu, Krishnan, and Smith 1999), and improves customer attitude toward the parent brands (Simonin and Ruth 2001).
More important, unlike a solo-branded extension, a cobranded extension is a composite brand concept that contains the characteristics of two underlying concepts (Cohen and Murphy 1984; Park, Jun, and Shocker 1996). Each of the two participating concepts is associated with a set of attributes that are combined according to a set of rules to form the composite concept (Eysenck and Keanne 1990; Hampton 1987). In other words, a cobranded extension does not involve the transfer of the entire brand concept from a parent category to an extension category (Park, Milberg, and Lawson 1991). Rather, it merely involves the transfer of a subset of attributes from each of the two parent brands, A1 and A2, and their recombination into a coherent composite concept that could become a member of the extension category to which Brand B belongs.
The subset of attributes that each partner brand contributes is likely to be less unique to the parent brands, A1 or A2, than is the entire set of attributes that characterize each of them (Hampton 1997). For example, consider the case of a cobranded extension of a fruit preserves brand and a chocolate brand into the ice-cream toppings category. One of the features that the former contributes to the cobranded extension is the packaging in a glass bottle, an attribute that, by itself, is less unique to the fruit preserves category than is the complete set of attributes that characterize the category. Because Brand A1 contributes only selective attributes to its cobranded extension with Brand A2, the increase in overall similarity and fit between the parent category of Brand A1 and the extension category is less than if the extension were solo branded by Brand A1. A relatively smaller increase in similarity results in a lower evaluation of the counterextension by Brand B into the category of Brand A1 than what it would have been if the extension by Brand A1 was solo branded (see Figure 1, Panels B and C). Therefore, I hypothesize the following:
H2: A counterextension by Brand B into the parent category of Brand A1 is evaluated more favorably if the previous brand extension by Brand A1 into the category of Brand B is solo branded than if it is cobranded with a partner brand, A2, from a complementary category.
Differential effect on header and modifying brands . Although by definition a cobranded extension consists of two partner brands, A1 and A2, in general both brands do not contribute equally to the cobranded concept. Typically, one of the two brands serves as a dominant or header brand, and the other serves as a dominated or modifier brand Murphy 1988). For example, in the case of Oral-B Rembrandt whitening strips, Oral-B serves as the header brand, and Rembrandt serves as the modifier brand. Previous research shows that a composite concept tends to resemble one constituent concept more than the other, a phenomenon referred to as a "dominance effect" (Hampton 1988; Storms et al. 1996). It tends to derive its features and attributes more from the dominant concept with which it is perceived to have a relatively greater overlap than from the dominated concept.
Park, Jun, and Shocker (1996) interpret this finding within the context of brand alliances and suggest that a cobranded extension is likely to be more closely identified with and interpreted in terms of the properties of the header brand rather than those of the modifier brand. The salience and the performance level of the attributes of the cobranded extension are therefore likely to be drawn more from the parent brand that serves as the header and less from the brand that serves as the modifier brand. Therefore, following the successful launch of a cobranded extension, the perception of similarity between the extension category and the parent category of Brand A1 is likely to be greater if A1 serves as the header brand rather than as the modifier brand. Overall, if both parent brands are reasonably strong and well liked, customer evaluation of a counterextension is likely to be superior if the parent brand is a header brand rather than a modifier brand in the previous cobranded extension.( n2) Formally, I hypothesize the following:
H3: A brand counterextension by Brand B into a parent category of Brand A1 is evaluated more favorably if Brand A1 serves as a header brand in the previously launched cobranded extension with Brand A2 than if it serves as a modifying brand.
Effect of positioning a cobranded extension . A cobranded extension derives its attributes by selectively drawing on the attributes of the two partner brands. It can be positioned through the use of a communication strategy that explicitly outlines the partner-specific contribution to its set of key attributes (hereinafter, an attribute partitioning strategy). Alternatively, it can be positioned through the use of a more holistic approach that outlines the pooled set of key attributes but does not map each subset to the respective parent brands (hereinafter, a unified positioning strategy). Previous research shows that the design of the communication strategy used to position a brand alliance influences how customers interpret the joint presentation of two brands and the relationship between their respective product categories (Samu, Krishnan, and Smith 1999). Specifically, a bottom-up communication strategy that explicitly highlights the attribute-level contribution of each partner induces customers to use an attribute-based processing approach to interpret an alliance between two brands.
Therefore, an attribute partitioning-based positioning rather than a unified or holistic positioning of the cobrand is more likely to increase the salience of the fact that each partner brand, A1 and A2, contributed only selectively to the extension. This positioning is more likely to induce customers to divide the attribute set of the cobranded extension into subsets, map each subset to the respective parent brand, and thus maintain the perceptual separation between the parent and extension categories. The maintenance of intercategory separation under an attribute partitioning strategy is likely to result in a lower evaluation of a counterextension by Brand B than it is under a unified positioning strategy. Therefore, I hypothesize the following:
H4: The evaluation of a counterextension by Brand B is lower if the positioning strategy for the previous cobranded extension by Brands A1 and A2 is attribute partitioning than if it is unified.
Study 1
Study 1 was designed to test H1-H3 regarding the impact of solo-branded versus cobranded extensions on customer evaluation of a counterextension. The purpose of the study was not to assess how the two branding strategies--solo branding versus cobranding--influence the evaluation of the first extension by Brand A1, because the issue has been addressed in prior research. Rather, the purpose was to examine how these two strategies influence the evaluation of the second extension, or counterextension, launched subsequently by Brand B into the parent category of Brand A1. The participants in the study were adults who were intercepted in a shopping area and asked to complete a questionnaire after receiving information about hypothetical brand extensions and counterextensions of real brands. I used real brands in the study because I wanted the participants to have some prior beliefs about the relationships between the brands and their respective product categories. However, I used hypothetical extensions because I wanted participants to respond to the study manipulations.
I selected the product categories and the brands used to construct the stimuli for the study on the basis of a series of pretests that were designed to address several objectives. The first objective was to find three somewhat-related product categories across which customers perceived ( 1) extensions and counterextensions to be moderately feasible but not trivial and ( 2) the skill transferability to be moderately high and reasonably symmetric.( n3) The second objective was to identify one brand within each category that was well liked and strongly associated with its parent category. These considerations helped ensure that participants cared about the brands and were responsive to their extension activity. The third objective was to find two brands, A1 and A2, that were complementary with respect to key attributes that could be pooled to constitute the key attributes of the third or extension category to which Brand B belonged. The fourth objective was to ensure that the solo-branded extension of each parent brand and the cobranded extension were considered feasible. The participants in each pretest were adult men and women who were randomly intercepted at a shopping area; they each received $2 for their participation.
Pretests 1 and 2. The objective of the first two pretests was to identify three related categories and one reasonably well-liked brand with a strong brand-to-category relationship in each category. In the first pretest, 25 shoppers were presented with a list of brand names of products available in the supermarket. The list consisted of brands that focused largely on a single product category. The participants were asked to list all the products they associated with each brand name. On the basis of this pretest, popcorn, corn crisps, and potato chips were selected as the three product categories. Furthermore, on the basis of the high levels of brand-to-category association that participants reported, Redenbacher's, Bugles, and Jays were selected as the respective brands in these categories. In the second pretest, 20 shoppers were asked to evaluate each of the three chosen brands, on a two-item ("like it a lot/don't like it at all" and "favorable/unfavorable"), seven-point scale. The average evaluation of each of the three brands was moderately high (XJays = 5.80, XRedenbacher's = 5.77, XBugles = 5.42).
Pretest 3. The objective of the third pretest was to assess the perceptions of intercategory similarity and skill transferability across the popcorn, corn crisps, and potato chips categories. A new sample of 19 shoppers completed a questionnaire. They reported their perceived pairwise similarity among the three categories on a four-item (needs satisfied, occasions used, skills required, and features; ingredients and attributes), seven-point scale (Cronbach's alpha ranged from .88 to .91). The average similarity rating between corn crisps and potato chips (X = 4.30) and between corn crisps and popcorn (X = 4.12) was moderately high and statistically indistinguishable (t18 = 11, p > .10).
Furthermore, the perceived difficulty for a popcorn manufacturer to make good corn crisps, which was measured on a seven-point scale ("not difficult at all/very difficult"), was moderately high (X = 3.72) and statistically no different from the perceived difficulty for a corn crisps manufacturer to make good popcorn (X = 3.83, t18 = -.09, p > .10). Similarly, the perceived difficulty for a potato chips manufacturer to make good corn crisps was moderately high (X = 3.26) and statistically no different from the perceived difficulty for a corn crisps manufacturer to make good potato chips (X = 3.57, t18 = .32, p > .10).
Pretest 4. The objective of the fourth pretest was to examine the strength of association between each of the three brands and a set of attributes that characterized the salted-snacks category to which the three brands belonged. An item pool of the salient attributes of the category was generated, and 30 adult shoppers rated each of the three brands on every attribute on a seven-point scale ("do not associate at all/strongly associate"). All three brands rated moderately low on two attributes: healthy and low calorie (mean ratings were between 3.63 and 4.03). Redenbacher's and Bugles were strongly associated with corn (mean ratings were 5.43 and 5.30, respectively), whereas Jays was not (mean rating was 2.14). In contrast, Jays was strongly associated with potato (mean rating was 5.03), whereas Redenbacher's and Bugles were not (mean ratings were 2.36 and 2.76, respectively). Furthermore, Redenbacher's was rated lower on crunchy (mean rating was 4.10) than were both Bugles and Jays (mean ratings were 5.76 and 5.53, respectively). The ratings for the three brands on the remaining attributes-snack, salty, delicious, for parties, inexpensive, and convenient-were similar and moderately high. The pretest provided corn and crunchy as the two attributes that were related to the popcorn and potato chips brands, respectively. Furthermore, both attributes were related to the corn crisps brand.
The participants also rated the feasibility of an extension into the corn crisps category under four alternative branding scenarios on a three-item ("not difficult at all/very difficult," "not feasible at all/feasible," and "not advisable at all/advisable"), seven-point scale. The four brand names were Jays, Redenbacher's, Jays Redenbacher's, and Redenbacher's Jays. Participants also reported their evaluation of the extensions using a four-item ("low quality/high quality," "inferior/superior," "negative/positive," and "not likely to try/very likely to try") scale. The feasibility ratings of the extension were similar (mean ratings were between 4.42 and 4.62) across the four branding scenarios. In addition, the evaluation of the extension was similar across the four scenarios (mean ratings were between 4.58 and 4.88). On the basis of the pretests, I selected popcorn and potato chips as the two parent categories and Redenbacher's and Jays, respectively, as the two parent brands. I also selected corn crisps as the extension category and Bugles as the brand that launched the counterextension.
The participants in the main study were 150 adult men and women who were intercepted in a shopping area and were given information about hypothetical brand extensions and counterextensions of real brands. Two factors were manipulated in a 5 x 2 between-subjects design. The first factor was the brand name under which the first extension was launched into the corn crisps category. This factor was manipulated at five levels: Jays, Redenbacher's, Jays Redenbacher's, Redenbacher's Jays, and control. There was no mention of a previous or first extension in the control condition (see Figure 1, Panel A). The second factor was the product category (popcorn versus potato chips) into which the counterextension was launched under the Bugles brand name.
The participants, who were each paid $5 for their cooperation, were randomly assigned to the various conditions, and they individually completed the study at their own pace. The cover story that accompanied the questionnaire contained an excerpt, supposedly from an article in a popular business magazine, about new products that were recently introduced into the marketplace. It described seven hypothetical brand extensions of real brands that were either successful and well received or unsuccessful and poorly received by the market. Six of these seven products, which were divided into two equal groups of successful and failed products, were held constant across the ten conditions. The seventh product was the focal successful extension into the corn crisps category and was manipulated across the treatments according to the experimental design. The story also listed two new products that were soon to be launched in the market. One of these was held constant across all conditions. The other was a brand extension of Bugles into either the potato chips or the popcorn category. The participants were told that the two new products were not yet available but that their responses toward one of them were of interest.
The participants provided data on several measures, including their overall evaluation of the Bugles counterextension, on a 4-item ("low quality/high quality," "inferior/superior," "negative/positive," "not likely to buy/very likely to buy"), seven-point scale (Cronbach's x = .90). They also reported their perceptions of the similarity between the corn crisps category and the category into which the Bugles counterextension was launched on a four-item (needs satisfied, occasions used, skills required, and features; ingredients and attributes), seven-point scale (Cronbach's x = .91). They reported their familiarity with the Bugles brand using a three-item (familiar, heard of, can recognize), seven-point scale (Cronbach's x = .87), and they were asked if they recalled whether the focal brand extension mentioned in the business story was a success or a failure on a seven-point ("big success/big failure") scale. The last item served as a manipulation check.
I began by examining the participants' reported ratings for whether the focal extension into the corn crisps category was a success or a failure. The mean rating across the eight groups, excluding the two control groups, was high (X = 5.60), indicating that the participants noted the focal extension to be successful.
Overall evaluation of the brand counterextension. Figure 2 displays the mean evaluation of the Bugles counterextension across the ten conditions. I analyzed the evaluation data using a two-way analysis of variance (ANOVA); the brand name of the first extension and the product category into which the counterextension was launched were the two between-subject factors, and familiarity with Bugles was the covariate. The covariate did not interact with the treatments or the treatment interactions (p > .10). The results of the ANOVA show that the treatments had an effect on the overall evaluation of the Bugles counterextension (F( 10, 139) = 8.43, p < .01). The familiarity covariate was significant (F( 1, 139) = 5.35, p < .05).
The difference between the mean evaluation of the Bugles counterextension into the popcorn and the potato chips categories was not statistically significant (F( 1, 139) = .09, p > .10). However, the brand name under which the first extension was launched (F( 4, 139) = 6.24, p < .01) and its interaction with the counterextension product category (F( 4, 139) = 13.47, p < .01) had a significant effect on the evaluation of the Bugles counterextension. Consistent with H1, the Bugles counterextension into the popcorn category was evaluated more favorably in the condition in which Redenbacher's had previously launched a solo-branded extension into the corn crisps category (X = 5.53) than in the control condition in which there was no previous extension into the corn crisps category (X = 3.70, F( 1, 27) = 20.03, p < .01). Similarly, the Bugles counterextension into the potato chips category was evaluated more favorably in the condition in which Jays had previously launched a solo-branded extension into the corn crisps category (X = 5.41) than in the control condition in which there was no previous extension (X = 3.80, F( 1, 27) = 36.43, p < .01).
Next, I tested H2 for the Bugles counterextension into the popcorn category by comparing the mean evaluation in the condition in which the previous extension by Redenbacher's was solo branded with the average of the mean evaluation across the two conditions in which the extension was cobranded. I found that the Bugles counterextension into the popcorn category was evaluated less favorably when the previous extension into the corn crisps category was cobranded (X = 4.58) than when it was solo branded by Redenbacher's (F( 1, 41) = 34.51, p < .01). I repeated the analysis to test the hypothesis for the Bugles counterextension into the potato chips category. Again, I found that the Bugles counterextension into the potato chips category was evaluated less favorably when the previous extension into the corn crisps category was cobranded (X = 4.52) than when it was solo branded by Jays (F( 1, 41) = 21.99, p < .01).
Furthermore, consistent with H3, I found that the Bugles counterextension into the popcorn category was evaluated more favorably when the preceding extension into the corn crisps category was launched under the Redenbacher's Jays brand name (X = 5.00) than when it was launched under the Jays Redenbacher's brand name (X = 4.16, (F( 1, 27) = 20.25, p < .01). In contrast, the Bugles counterextension into the potato chips category was evaluated less favorably when the preceding extension into the corn crisps category was launched under the Redenbacher's Jays brand name (X = 4.25) than when it was launched under the Jays Redenbacher's brand name (X = 4.88, (F( 1, 27) = 15.87, p < .05).
Finally, I examined whether cobranding the previous extension lowered the evaluation of the counterextension to the same level as that in the control condition in which no extension had previously been launched. Therefore, for both the popcorn and potato chips categories, I separately compared the average of the treatment means for the two cobranding conditions with the treatment mean for the respective control conditions. I found that the difference in the evaluation of the counterextension between the cobranding conditions and the control condition was significant for both the popcorn category (F( 1, 41) = 8.22, p < .01) and the potato chips category (F( 1, 41) = 20.46, p < .01).
Similarity. A two-way ANOVA of the reported similarity ratings showed significant treatment effects (F( 9, 140) = 7.70, p < .01). The main effect of the product category into which the counterextension was launched was not significant (F( 1, 140) = .65, p > .10). However, the brand name of the previous extension (F( 4, 140) = 5.97, p < .01) and its interaction with the product category into which the counterextension was launched (F( 4, 140) = 11.20, p < .01) had a significant effect on the reported similarity ratings.
The similarity between the popcorn and the corn crisps categories was rated higher when Redenbacher's had launched the previous solo-branded extension (X = 5.65) than it was in the control condition in which there was no previous extension (X = 3.45, F( 1, 28) = 36.79, p < .01). The similarity rating was lower when the previous extension was cobranded (X = 4.39) than when it was solo branded by Redenbacher's (F( 1, 42) = 28.50, p < .01). Finally, similarity was rated higher when Redenbacher's was the header brand in the previous cobranded extension (X = 4.75) than when it was the modifier brand (X = 4.03, F( 1, 28) = 6.00, p < .01).
Likewise, the reported similarity between the potato chips and the corn crisps categories was higher when Jays had previously launched a solo-branded extension (X = 5.38) than it was in the control condition in which there was no previous extension (X = 4.03, F( 1, 28) = 19.66, p < .01). Similarity was rated lower when the previous extension was cobranded (X = 4.50) than when it was solo branded by Jays (F( 1, 42) = 15.40, p < .01). Finally, similarity was rated higher when Jays was the header brand in the previous cobranded extension (X = 4.83) than when it was the modifier brand (X = 4.16, F( 1, 42) = 4.02, p < .05).
I conducted additional analyses to test whether the perceptions of similarity between the corn crisps category and the parent category into which the counterextension was launched mediated the relationship between the manipulated variables and the evaluation of the Bugles counterextension (Baron and Kenny 1986). I ran seemingly unrelated regressions with intercategory similarity and the evaluation of the Bugles counterextension as the two independent variables and 0-1 dummy variables representing each of the nine conditions, excluding the control condition for the popcorn category. The results of this analysis, which appear in the second and third columns of Table 1, show that the experimental treatments had an effect on similarity (i.e., the potential mediating variable) and on the evaluation of the counterextension (i.e., the dependent variable).
Next, I added similarity to the regression of the evaluation of the counterextension and examined the changes in the parameters of the independent variables. The results, which appear in the fourth column of Table 1, show that when similarity was added to the regression model, the parameters for the other variables weakened and were, at best, marginally significant (p < .10). However, the parameter for similarity was statistically significant (p < .01). These results provide strong evidence in support of the theoretical premise that perceptions of intercategory similarity mediate the relationship between the extension strategy pursued by Brand A1 and the evaluation of the counterextension by Brand B.( n4)
The results from Study1 support H1-H3 regarding the evaluation of brand counterextensions. These results can be summarized as follows (see Figure 2):
• The extension by Brand B into the category of Brand A1 was evaluated more favorably when Brand A1 had previously launched a successful solo-branded extension into the category of Brand B than when it had not launched an extension (H1).
• Cobranding the first extension with a partner brand, A2, resulted in a lower evaluation of a counterextension by Brand B than did solo branding the extension by Brand A1 (H2).
• The counterextension was evaluated less favorably when the focal brand, A1, was the modifier brand rather than the header brand in the previous cobranded extension (H3).
• Even with cobranding, the counterextension was evaluated more favorably than if no previous extension had been launched at all.
The results of the mediation analysis support the theoretical premise that cobranding may lead to a less favorable evaluation of a counterextension because it results in a smaller improvement in the perceptions of similarity between a brand's parent and extension categories than does a solo-branded extension.
Study 2
The premise underlying Study 1 was that a successful solo-branded extension improves the perceptions of intercategory similarity and enhances the evaluation of a counterextension. By implication, a failed solo-branded extension should preserve the distinction between the parent and extension categories and should not improve the evaluation of the counterextension. Therefore, cobranding should result in a lower evaluation of a counterextension relative to a successful solo-branded extension but not relative to a failed solo-branded extension. I address this issue in Study 2 and examine whether a counterextension that follows a cobranded extension is evaluated more or less favorably than a counterextension that follows either a successful or a failed solo-branded extension.
The overall design, cover story, and procedure for Study 2 were similar to those used in Study 1. I used a 4 x 2 between-subjects study design to manipulate the variables of interest. The first factor was the previous brand extension activity into the corn crisps category. The four levels of this factor were ( 1) failed solo-branded extension, ( 2) successful solo-branded extension, ( 3) successful cobranded extension with the parent brand as the header, and ( 4) a control condition in which there was no previous extension. The second factor was the parent category (popcorn versus potato chips) into which the counterextension was launched. A total of 160 adults who were intercepted at a shopping area participated in the study; they were randomly assigned to the experimental conditions and were paid $5 for their cooperation. The key dependent measure was the overall evaluation of the Bugles counterextension (Cronbach's α = .90).
Overall evaluation of the brand counterextension. As a manipulation check, I compared the reported ratings for the success versus failure of the previous extension into the corn crisps category. The extension was rated more successful in the success conditions (X = 5.47) than in the failure conditions (X = 1.77, t119 = 22.79, p < .01), which shows that the manipulation of the success versus failure of the previous extension was successful.
Figure 3 displays the mean evaluation of the Bugles counterextension across the eight conditions. I analyzed the evaluation data using a two-way ANOVA with the previous extension activity and the counterextension category as the two between-subject factors and familiarity with Bugles as a covariate. The covariate did not interact with the treatments or the treatment interactions (p > .10). The results of the ANOVA show that the treatments had an effect on the overall evaluation of the Bugles counterextension (F( 8, 151) = 7.80, p < .01). The effect of the familiarity covariate was significant (F( 1, 151) = 14.25, p < .01). The main effect of the product category into which the counterextension was launched was not significant (F( 1, 151) = .24, p > .10). This shows that the Bugles counterextensions into the popcorn and the potato chips categories were evaluated similarly. However, the main effect of the previous brand extension activity was significant (F( 3, 151) = 15.70, p < .01), whereas its interaction with the counterextension product category was not (F( 3, 151) = .27, p > .10).
The Bugles counterextension into the popcorn category was evaluated more favorably when the preceding solo-branded extension into the corn crisps category by Redenbacher's was a success (X = 5.35) than when it was a failure (X = 4.06, F( 1, 37) = 14.11, p < .01). Similarly, the Bugles counterextension into the potato chips category was evaluated more favorably when the solo-branded extension into the corn crisps category by Jays was a success (X = 5.38) than when it was a failure (X = 4.32, F( 1, 37) = 13.22, p < .01).( n5)
Furthermore, as in Study 1, the evaluation of the counterextension into the popcorn category was less favorable when the preceding extension into the corn crisps category was launched jointly under the Redenbacher's Jays brand name (X = 4.73) than when it was successfully launched by Redenbacher's (X = 5.35, F( 1, 37) = 10.90, p < .01). Similarly, the evaluation of the counterextension into the potato chips category was less favorable when the preceding extension was launched under the Jays Redenbacher's brand name (X = 4.79) than when it was launched as a solo-branded extension by Jays (F( 1, 37) = 4.11, p < .05).
More important, the counterextension into the popcorn category was evaluated more favorably when the previous extension by Redenbacher's was cobranded than when it was solo-branded but a failure (F( 1, 37) = 5.20, p < .05). Similarly, the Bugles counterextension into the potato chips category was evaluated more favorably when the previous extension by Jays was cobranded than when it was solo branded but a failure (F( 1, 37) = 4.79, p < .05).
Similarity. A two-way ANOVA of the similarity data shows that the main effect of the prior extension activity was significant (F( 3, 152) = 22.03, p < .01). The main effect of the product category into which the counterextension was launched (F( 1, 152) = .74, p > .10) and its interaction with the prior extension activity (F( 3, 152) = .47, p > .10) were both not significant.
The mean similarity rating was higher when the solo-branded extension by Redenbacher's was a success (X = 5.71) than when it was a failure (X = 4.01, F( 1, 38) = 39.98, p < .01). The mean similarity rating was higher when the solo-branded extension by Jays was a success (X = 5.67) than when it was a failure (X = 4.38, F( 1, 38) = 38.89, p < .01). However, the difference in similarity between the condition in which Redenbacher's solo-branded extension was a failure and the control condition (X = 4.33, F( 1, 38) = .78, p > .10) was not significant. Likewise, the difference in similarity between the condition in which the Jays solo-branded extension was a failure and the control condition (X = 4.51, F( 1, 38) = .21, p > .10) was not significant. Finally, similarity was lower when the preceding extension was launched jointly under the Redenbacher's Jays brand name (X = 4.97) than when it was successfully launched by Redenbacher's (F( 1, 38) = 10.79, p < .01). Similarity was also lower when the preceding extension was launched jointly under the Jays Redenbacher's brand name (X = 4.95) than when it was successfully launched by Jays (F( 1, 38) = 8.56, p < .01).
Next, I conducted additional analyses to test for the mediating effect of similarity, using a procedure similar to the one I adopted for Study 1. I first ran seemingly unrelated regressions with intercategory similarity and the evaluation of the Bugles counterextension as the two independent variables and 0-1 dummy variables representing each of the seven conditions, excluding the control condition for the popcorn category. The results of this analysis appear in the second and third columns of Table 2. These results show that the experimental treatments had an effect on similarity (i.e., the potential mediating variable) as well as on the evaluation of the counterextension (i.e., the dependent variable).
However, when I added similarity to the regression of the evaluation of the counterextension, the parameters for the other variables weakened and were, at best, marginally significant (p < .10). However, the parameter for similarity was statistically significant (p < .01). These results are consistent with the premise that perceptions of intercategory similarity mediate the relationship between the branding strategy and the outcome of the previous extension and the evaluation of the counterextension.( n6)
The results of Study 2 suggest that cobranding results in a lower evaluation of a counterextension relative to a successful solo-branded extension but not relative to a failed solo-branded extension. The results show that a failed solo-branded extension does not lead to an improvement in customer evaluation of a counterextension, because it is less likely to change perceptions of similarity between the parent and extension categories.
Study 3
In Study 1, I used hypothetical extensions and counterextensions of real brands to test H1-H3. Although the use of real brand names made the study realistic and gave it some degree of external validity, the results may have been driven in part by participants' brand-specific associations with the brands that I used to construct the stimuli. To rule out non-category-related associations as potential explanations for the findings, I conducted a third study with the same context as in Study 1 but without real brand names.
The stimuli and the cover story for the study were similar to those used in Study 1 except for one difference: The products in the cover story and in the questionnaire were referred to by their category names rather than their brand names. For example, I used the descriptor "a popcorn brand" instead of the brand name "Redenbacher's" to refer to one of the parent brands. I used a 4 x 1 between-subjects study design and manipulated the brand name of the previous extension into the corn crisps category at four levels: ( 1) a popcorn brand, ( 2) a potato chips brand, ( 3) a joint extension by a popcorn and a potato chips brand, and ( 4) a control. There was no mention of a previous extension into the corn crisps category in the control condition. A total of 72 adult men and women, who were intercepted at a shopping area, participated in the study, and they were each paid $5 for their cooperation. The key dependent measure was the overall evaluation of a counterextension into the popcorn category by a corn crisps brand (Cronbach's α = .93).
Overall evaluation of the brand counterextension. Figure 4 displays the mean evaluations of the counterextension into the popcorn category across the four treatments. A one-way ANOVA of the evaluation data shows a significant treatment effect (F( 3, 68) = 7.56, p > .01). Consistent with H1, the focal counterextension into the popcorn category was evaluated more favorably when a popcorn brand had launched the preceding brand extension into the corn crisps category (X = 5.23) than it was in the control condition in which there was no mention of any preceding extension (X = 3.84, F( 1, 34) = 14.37, p < .01).
Furthermore, consistent with H2, the evaluation of the counterextension was less favorable when the preceding extension was launched jointly as a cobranded extension of a popcorn brand and a potato chips brand (X = 4.61) than when it was launched as a solo-branded extension by a popcorn brand (F( 1, 34) = 6.06, p < .01). Finally, the evaluation of the counterextension in the control condition was statistically indistinguishable from that in the condition in which the preceding extension was launched by a brand from the potato chips product-market, an unrelated category (X = 4.19, F( 1, 34) = .97, p > .10). To summarize, a previous corn crisps extension by a popcorn brand, but not a potato chips brand, improved the evaluation of a subsequent popcorn extension by a corn crisps brand. However, the evaluation of the popcorn extension was lowered if the previous corn crisps extension was cobranded by a popcorn and a potato chips brand.( n7)
Similarity. The results of a one-way ANOVA of the similarity data show a significant treatment effect (F( 3, 68) = 8.78, p < .01). The mean similarity in the control condition (X = 3.84) was lower than it was in the condition in which the previous solo-branded extension was launched by a popcorn brand (X = 5.22, F( 1, 34) = 25.85, p < .01), but it was statistically indistinguishable from that in the condition in which the previous solo-branded extension was launched by a potato chips brand (X = 4.23, F( 1, 34) = 1.82, p > .10). The similarity in the condition in which the previous extension was cobranded was lower (X = 4.68) than that in the condition in which the extension was solo branded by a popcorn brand (F( 1, 34) = 3.85, p < .05).
The results from a mediation analysis, which I conducted using a procedure similar to the one I used in the previous two studies, appear in Table 3. They show that both intercategory similarity ratings and overall evaluation ratings in the conditions in which the prior extension was launched by the popcorn brand or jointly by the popcorn and the potato chips brands were higher than were those in the control condition (p < .01). However, when similarity was added as an independent variable to the regression model for overall evaluation, the parameters for the other variables weakened and were no longer statistically significant (p > .10 for each). However, the parameter for similarity was statistically significant (p < .01).( n8)
The results from Study 3 closely parallel those from Study 1 and provide further support to the hypothesis that the evaluation of a brand counterextension is lower when the preceding extension is cobranded with a partner than when it is solo branded. This effect was observed both with real brand names and without them. The results in both cases support the theoretical premise that intercategory similarity mediates the relationship between the choice of a solo-branding versus a cobranding extension strategy and the evaluation of a counterextension. Furthermore, in both studies, although cobranding lowered the evaluation of a counterextension, it was still higher than that in the condition in which the focal brand had not launched an extension at all. When this finding is interpreted in conjunction with the results of the mediation analyses, it suggests that cobranding reduces, but does not fully suppress, the increase in the intercategory similarity that results from launching a successful extension.
Study 4
Study 4 was designed to test H4 regarding the impact of alternative communication and positioning strategies for a cobranded extension on the evaluation of a counterextension. It specifically focuses on whether a positioning strategy that explicitly communicates the attribute-level contribution of the two partner brands to the cobranded extension results in a lower evaluation of the counterextension than a strategy that does not.
The stimulus material in the study was the same as that used in Study 1, except that the cover story described not only the cobranded extension into the corn crisps category but also the headline and the slogan from its advertisement. I used a 2 x 2 between-subjects design with a control group. The first factor was the brand name for the extension into the corn crisps category, which I manipulated at two levels: Jays Redenbacher's and Redenbacher's Jays. The second factor was the positioning statement, which I also manipulated at two levels: partitioned and unified. In the control group, there was no mention of a preceding extension into the corn crisps category.
The headline in each of the four conditions, excluding the control group, was "The new crunch in the bowl." In the first unified condition with the Redenbacher's Jays brand name, the slogan for the extension into the corn crisps category was "The crunchy corn from Redenbacher's Jays." In the second unified condition, the brand name in the slogan was reversed to Jays Redenbacher's. In the first partitioned condition with the Jays Redenbacher's brand name, the slogan was "The crunch of Jays and the corn of Redenbacher's." In the second partitioned condition, the two halves of the slogan were reversed. The key measure was the overall evaluation of the counterextension into the popcorn category (Cronbach's α = .92). The participants in the study were 125 adult men and women, who were intercepted in a shopping area, randomly assigned to the various conditions, and paid $5 for their cooperation.
The positioning statements were pretested with two random samples of 15 customers each to ensure that the statements were similar in terms of their overall likeability. The customers were drawn from the same population from which the sample for the main study was drawn, and they were each paid $2 for their cooperation. The first sample evaluated the two alternative positioning statements or slogans for the Jays Redenbacher's brand name, and the second sample evaluated the statements for the Redenbacher's Jays brand name; both groups used a four-item (likeable, memorable, stands out, and clear message), seven-point scale. The mean evaluation of the unified statement for the Jays Redenbacher's brand (X = 4.26) was statistically indistinguishable from the mean evaluation of the partitioned statement (X = 4.35, t14 = .25, p > .10). Similarly, the mean evaluation of the unified statement for the Redenbacher's Jays brand (X = 4.28) was statistically indistinguishable from the mean evaluation of the partitioned statement (X = 4.38, t14= .17, p > .10).
Overall evaluation of the brand counterextension. Figure 5 displays the mean evaluations of the counterextension into the popcorn category across the five conditions. In line with Broniarczyk and Gershoff's (2003) work, I computed the difference between the reported overall evaluation ratings in each of the four experimental conditions and the average evaluation in the control condition.( n9) I analyzed the data on these difference scores using a 2 x 2 ANOVA with familiarity with the counterextending brand as a covariate (F( 4, 95) = 4.75, p < .01). The familiarity covariate (F( 1, 95) = 1.95, p > .10) and the brand name (F( 1, 95) = 1.36, p > .10) did not have a significant effect. The main effect of the positioning strategy (F( 1, 95) = 13.02, p < .01) was significant, and its interaction with the brand name was marginally significant (F( 1, 95) = 2.65, p < .10).
The difference score averaged across the two partitioned conditions (X = .15) was statistically indistinguishable from zero (t49 = .86, p > .10) and lower than the difference score averaged across the two unified conditions (X = .75, F( 1, 97) = 6.72, p < .01). In the two partitioned conditions, the difference score in the condition in which Redenbacher's was the header brand (X = .12) was statistically indistinguishable from that in the condition in which it was the modifier brand (X = .20, F( 1, 47) = .08, p > .10). This finding shows that with a partitioned positioning strategy, the evaluation of the counterextension did not depend on whether the parent brand in the preceding extension was a header brand or a modifier brand. However, in the two unified conditions, the difference score in the condition in which Redenbacher's was the header brand (X = 1.09) was higher than that in the condition in which it was the modifier brand (X = .50, F( 1, 47) = 22.94, p < .01).
Similarity. For each of the four conditions, excluding the control, I computed the difference scores for similarity using a procedure that was similar to the one I used for computing the difference scores for the overall evaluation of the counterextension. The results from a two-way ANOVA of the data on the difference scores for similarity data showed a significant effect (F( 3, 96) = 4.54, p < .01). The average difference score for similarity for the two partitioned conditions (X = .29) was statistically indistinguishable from zero (t49 = 1.63, p > .10). However, the average difference score for the two unified conditions (X = 1.00) was higher than that in the control condition (t49 = 7.19, p < .01). Furthermore, in the partitioned conditions, the difference score for similarity in which Redenbacher's was the header brand (X = .23) was statistically indistinguishable from that in the condition in which it was the modifier brand (X = .35, F( 1, 48) = .11, p > .10).
I conducted a mediation analysis using the data from the four experimental conditions on the difference scores for both overall evaluation and similarity. The results, which appear in Table 4, indicate that the dummy variables that represent the three conditions, excluding the unified condition with the Redenbacher's Jays brand name, had a significant impact on the difference scores for similarity (i.e., the potential mediating variable) and the difference scores for evaluation (i.e., the dependent variable) (p < .10 or better). However, when the difference score for similarity was added to the regression model for the difference score for evaluation, the parameters for the other variables weakened. However, the parameter for the difference score for similarity was statistically significant (p < .01).( n10)
The results of Study 4 have two important implications: First, they demonstrate that a positioning and communication strategy that explicitly partitions the key attributes of a cobranded extension and relates them to the respective partner brands lowers the evaluation of a counterextension. Second, they show that under an attribute partitioning strategy, the evaluation of the counterextension may not depend on whether the focal brand serves as the header or the modifier brand in the previous cobranded extension. Taken together, the results imply that an attribute partitioning strategy benefits both partner brands, A1 and A2, and may result in a lower evaluation of a counterextension into the parent category of either one. Furthermore, such a positioning strategy might also provide some design flexibility for the crafting of the cobranded extension. It may enable the partners to choose the header and modifier brands on the basis of other considerations, such as improving the attribute profile of their cobranded extension.
Discussion
This research contributes to the brand management literature by demonstrating the differential impact of solo-branded versus cobranded extensions on customer evaluation of brand counterextensions. The key finding from a series of studies is that a counterextension into a brand's parent category is likely to be evaluated less favorably when the prior extension launched by the focal brand is cobranded than when it is solo branded. This finding suggests that the strategic choice between solo branding versus cobranding influences the evaluation not only of a brand's own extension (Park, Jun, and Shocker 1996; Rao and Ruekert 1994) but also of counterextensions into its parent category.
The studies show that the discrepancy between the evaluations of counterextensions following the two alternative branding strategies results from the differences in the levels of postextension perceptual similarity between a brand's parent and extension categories. Specifically, a successful solo-branded extension results in a greater improvement in the perceptions of intercategory similarity than does a cobranded extension. A brand in the extension category benefits from the relatively greater increase in the psychological proximity between the two categories following a solo-branded extension, because its counterextension is evaluated more favorably than if the previous extension was cobranded.
A key implication of this research is that a brand extension decision should perhaps be evaluated in a broader context of a sequence of extensions and counterextensions. The extension strategy must account for the risk to the parent brand from potential counterextensions and should be appropriately adjusted to manage this risk. Although a firm that launches the initial extension can potentially take several actions to manage the threat from counterextensions, this research shows that cobranding the extension is one strategy that helps mitigate the risk that arises from a favorable evaluation of future counterextensions.
The findings from the studies reported herein suggest that the total benefit from cobranding is greater than what has been identified in previous research. Specifically, not only does cobranding help improve the attribute profile of an extension and have a positive reciprocal effect on the equity of the partner brands (Park, Jun, and Shocker 1996; Rao and Ruekert 1994; Simonin and Ruth 1998), it may also protect each partner brand against future counterextensions. Taken together, these findings suggest that cobranding is a key strategic option that enables marketers to craft a balanced brand strategy that not only facilitates extensions-based revenue growth but also provides some protection from counterextensions.
Unlike the case of a solo-branded extension, the revenue gains from a cobranded extension are shared with a partner brand. Although this research does not delve into questions about the trade-off between the loss to a potential counterextension and the loss from revenue sharing with a partner brand, the results suggest that cobranding is particularly suited for brands that are strongly associated with their parent categories. A solo-branded extension of such brands is likely to increase the counterextension risk by improving intercategory similarity more than a brand that is only weakly associated with the parent category.
Although the studies point to some additional benefits from cobranding, it is important to note that the purpose of this research was not to explore whether cobranding is a superior extension strategy to solo branding. It was limited to exploring the potential differences in the counterextension risk faced by a brand under the two alternative branding scenarios. The overall choice between the two strategic options must be made on the basis of the expected returns from the alternatives, the levels of overall risk, and other organizational and environmental contingencies that might favor one strategy over another. However, the findings from this research better articulate the extent of overall risk with respect to the two strategies that marketers should take into account when selecting an extension strategy. They suggest that cobranding can potentially play a defensive role and contribute to mitigating the risk to the parent brand from future counterextensions.
Selecting a partner brand. A critical issue in the development of a cobranded extension is the selection of a partner brand. The findings reported herein provide a new perspective on this issue and complement previous research that suggests that a partner brand should be selected on the basis of its reputation and its ability to send quality signals (Rao and Ruekert 1994; Rao, Qu, and Ruekert 1999). The findings also suggest that it is useful to select a partner that enables customers to divide the key attributes of the cobranded extension easily into two subsets and to associate each subset with the respective partner brands. An enhancement in the customer's ability to partition the key attributes of the cobranded extension is likely to result in a less favorable evaluation of a future counterextension. Indeed, a positioning and communication strategy that explicitly partitions the attributes into these subsets might also help the partner brands by further lowering the evaluation of counterextensions.
However, it should be noted that it might not always be possible or feasible for a brand to find a reputed and willing partner brand in a complementary category for the development of a cobranded extension. To that extent, a brand might be limited in the number of product-markets it can extend into with a cobranding strategy.
Designing cobranded extensions. An important consideration in the design of a cobranded extension is the selection of the header brand versus the modifier brand. The results of the studies reported herein suggest that a cobranded extension should be designed on the basis of not only offensive considerations, such as signaling the best attribute profile for the extension, but also the defensive needs of the partner brands. Specifically, from a defensive perspective, the partner brand that is more vulnerable in its parent category should perhaps serve as the modifier brand rather than the header brand. Furthermore, revenue-sharing agreements between the partner brands should account for the counterextension risk faced by each in its respective parent category.
Although this research provides initial insights into the differential impact of cobranded versus solo-branded extensions on the evaluation of a counterextension, further research using different product categories and research methodologies is necessary to establish the robustness of the findings. In addition, several related questions must be addressed. First, even if one brand chooses cobranding as an extension strategy, other brands in its parent category may still launch solo-branded extensions. Further research is needed to address how one brand's choice between solo branding and cobranding influences the extension strategies of other brands in its category and the joint effect of their choices on the evaluation of counterextensions. Second, this research has focused only on the evaluation of a solo-branded counterextension. Potentially, the counterextension could itself be cobranded and may be better accepted than a solo-branded counterextension. The interplay between cobranded extensions is a worthwhile and useful area for further research that could provide insights into whether cobranding affects the evaluation of only solo-branded counterextensions or that of cobranded counterextensions as well.
The author thanks P. Rajan Varadarajan, who served as consulting editor, and the four anonymous JM reviewers for their comments and suggestions on a previous draft of the article.
( n1) However, the failure of an extension maintains the separation between the parent and extension categories. Therefore, I expect that customer evaluation of a counterextension following a failed solo-branded extension is likely to be no different from what it would have been had the prior extension not been launched at all.
( n2) If one of the partner brands is extremely weak, the salient attributes of the extension may be drawn more from the stronger partner.
( n3) If the categories are extremely dissimilar, the evaluation of a counterextension is likely to be unfavorable regardless of the branding strategy that the previous extension adopted. However, there are also less likely to be extensions across dissimilar categories.
( n4) The results from repeating the mediation analysis using a stepdown ANOVA also provide evidence for strong partial mediation of similarity. Specifically, when I ran a two-way ANOVA of the evaluation data with familiarity and similarity as covariates, the effects of familiarity (F( 1, 138) = 4.78, p < .01) and product category (F( 1, 138) = 2.54, p > .10) were not significant, and the effect of brand name of the previous extension was not significant (F( 4, 138) = 1.42, p > .10). The interaction between the brand name and the product category was still significant (F( 4, 138) = 2.64, p < .05), but it was much weaker. The effect of similarity was significant (F( 1, 138) = 261.97, p < .01).
( n5) There was no difference between the evaluation of the popcorn counterextension in the condition in which the preceding solo-branded extension into the corn crisps category by Redenbacher's was a failure and that in the control condition in which there was no preceding extension (X = 4.15, F( 1, 37) = .37, p > .10). Similarly, there was no difference between the evaluation of the potato chips counterextension in the condition in which the preceding solo-branded extension by Jays was a failure and that in the control condition in which there was no preceding extension (X = 4.10, F( 1, 37) = .53, p > .10).
( n6) The results from repeating the mediation analysis using a stepdown ANOVA also provide evidence for a mediating effect of similarity. Specifically, when I analyzed the evaluation data using a two-way ANOVA with familiarity and similarity as covariates, the effects of familiarity (F( 1, 150) = 1.72, p > .1) and product category (F( 1, 150) = .01, p > .10) were not significant. The effect of prior extension activity was significant but substantially weakened (F( 3, 150) = 2.91, p < .05), and its interaction with the product category was not significant (F( 3, 150) = .21, p > .10). The effect of similarity was significant (F( 1, 150) = 49.24, p < .01). However, as I reported previously, the prior extension activity had a significant effect in the ANOVAs for both evaluation (i.e., the dependent variable) and similarity (i.e., the mediating variable).
( n7) The key results of Study 3 were replicated in a follow-up study with 80 participants that involved a different set of products. The study design and manipulations were similar to those in Study 3 except that Brand A1 belonged to the face wash category, Brand A2 to the body lotion category, and Brand B to the body wash category. The evaluation of a counterextension by a body wash brand into the face wash category was higher when the previous extension by a face wash brand into the body wash category was solo branded (X = 5.38) than when it was cobranded with a body oil brand (X = 4.87, F( 1, 38) = 11.92, p < .01). The evaluation of the counterextension in the control condition in which there was no previous extension into the face wash category (X = 4.42) was lower than that in the condition in which a face wash brand had previously launched a solo-branded extension into the body wash category (F( 1, 38) = 19.75, p < .01), but it was statistically indistinguishable from the evaluation in the condition in which a body oil brand had previously launched an extension into the face wash category (X = 4.68, F( 1, 38) = .92, p > .10). Additional analyses showed that the perceptions of similarity between the face wash and body wash categories mediated the relationship between the manipulated variables and the evaluation of the counterextension.
( n8) The results from repeating the mediation analysis using a stepdown ANOVA were consistent with those using a regression-based approach. Specifically, when I analyzed the evaluation data for the four cells using a one-way ANOVA with similarity as a covariate, the effect of the treatments was no longer significant (F( 3, 67) = .92, p > .10). The effect of similarity was statistically significant (F( 1, 67) = 46.02, p < .01). As I reported previously, the treatments had a significant effect in the ANOVAs for both evaluation (i.e., the dependent variable) and similarity (i.e., the mediating variable).
( n9) The average evaluation of the counterextension in the control condition was 3.87.
( n10) The results from repeating the mediation analysis using a step-down ANOVA were consistent with those using a regression-based approach. Specifically, when I analyzed the difference scores for evaluation data for the four cells using a two-way ANOVA with familiarity and similarity as covariates, the effect of the positioning strategy was substantially smaller (F( 1, 94) = 4.51, p < .05). The effect of the brand name and its interaction with the positioning strategy were not statistically significant (p > .10). The coefficient for the difference score for similarity was statistically significant (F( 1, 94) = 30.90, p < .01). However, as I reported previously, the treatments had a significant effect in the ANOVAs for both evaluation (i.e., the dependent variable) and similarity (i.e., the mediating variable).
Legend for Chart:
A - Variables(b)
B - Similarity(a)
C - Evaluation of Bugles Extension(a) Similarity Omitted
D - Evaluation of Bugles Extension(a) Similarity Included
A B C D
Redenbacher's (popcorn) .58(**) .51(**) .05
Jays (popcorn) .14 .10 -.02
Jays Redenbacher's (popcorn) .15(*) .13 .02
Redenbacher's Jays (popcorn) .34(**) .35(**) .08
Redenbacher's (potato chips) .17(*) .07 -.06
Jays (potato chips) .51(**) .48(**) .07
Jays Redenbacher's (potato chips) .36(**) .31(**) .02
Redenbacher's Jays (potato chips) .19(**) .14(*) .00
Control (potato chips) .15(*) .02 -.09(*)
Similarity .80(**)
(*) p < .1.
(**) p < .01.
(a) The numbers reported are standardized regression
coefficients. The parameters for the first two columns
are based on seemingly unrelated regression.
(b) These are coded as 0-1 dummy variables for each treatment
that represents the combination of brand name of the previous
extension and the category into which the Bugles extension was
launched. Legend for Chart:
A - Variables(b)
B - Similarity(a)
C - Evaluation of Bugles Extension(a) Similarity Omitted
D - Evaluation of Bugles Extension(a) Similarity Included
A C B D
Redenbacher's failure (popcorn) -.03 -.10 .03
Redenbacher's success (popcorn) .38(***) .43(***) .14(*)
Redenbacher's Jays .19(**) .20(**) .08
success (popcorn)
Jays failure (potato chips) .06 .01 .05
Jays success (potato chips) .40(***) .42(***) .16(*)
Jays Redenbacher's .20(***) .19(**) .10
success (potato chips)
Control (potato chips) -.16 .06 -.04
Similarity .56(***)
(*) p < .1.
(**) p < .05.
(***) p < .01.
(a) The numbers reported are standardized regression
coefficients. The coefficients for the first two columns
are based on seemingly unrelated regression.
(b) These are coded as 0-1 dummy variables for each treatment
that represents the combination of brand name and outcome of
the previous extension and the category into which the Bugles
extension was launched. Legend for Chart:
A - Variables(b)
B - Similarity(a)
C - Evaluation of Bugles Extension(a) Similarity Omitted
D - Evaluation of Bugles Extension(a) Similarity Included
A B C D
Potato chips .20 .16 .09
Popcorn .62(*) .58(*) .20
Potato chips and popcorn .37(*) .32(*) .15
Similarity .47(*)
(*) p < .01.
(a) The numbers reported are standardized regression
coefficients. The parameters for the first two columns
are based on seemingly unrelated regression.
(b) These are coded as 0-1 dummy variables. Legend for Chart:
A - Variables(b)
B - Similarity(a)
C - Evaluation of Bugles Extension(a) Similarity Omitted
D - Evaluation of Bugles Extension(a) Similarity Included
A B C D
Jays Redenbacher's (partition) -.34(***) -.38(***) -.21(**)
Redenbacher's Jays (partition) -.39(***) -.42(***) -.23(**)
Jays Redenbacher's (unified) -.21(*) -.26(*) -.15
Similarity .50(***)
(*) p < .1.
(**) p < .05.
(***) p < .01.
(a) The numbers reported are standardized regression
coefficients. The parameters for the first two columns
are based on seemingly unrelated regression. The analysis
was conducted using difference scores for both overall
evaluation and similarity.
(b) The treatments are coded as 0-1 dummy variables.DIAGRAM: FIGURE 1 Solo-Branded Versus Cobranded Extensions and Brand Counterextensions
Legend for Chart:
A - Mean Evaluation of the Bugles Counterextension
B - Brand Name of the Previous Corn Crisps Extension
Counterextension into the popcorn category
C - Brand Name of the Previous Corn Crisps Extension
Counterextension into the potato chips category
A B C
Control 1 2
Jays 3 4
Jays Redenbacher's 5 6
Redenbacher's Jays 7 8
Redenbacher's 9 10
Notes: The evaluation of the Bugles counterextension into the
potato chips category was greater when the previous extension
by Jays into the corn crisps category was solo branded (4) than
when it was cobranded with Redenbacher's (6, 8). The evaluation
of the Bugles counterextension into the popcorn category was
greater when the previous extension by Redenbacher's into the
corn crisps category was solo branded (9) than when it was
cobranded with Jays (5, 7). The Bugles counterextension into
potato chips was evaluated more favorably when Jays was the
header brand in the previous cobranded extension (6) than when
it was the modifier brand (8). The counterextension into popcorn
was evaluated more favorably when Redenbacher was the header
brand (7) than when it was the modifier brand (5). Legend for Chart:
A - Mean Evaluation of the Bugles Counterextension
B - Previous Extension Activity in the Corn Crisps Category
Counterextension into the popcorn category
D - Previous Extension Activity in the Corn Crisps Category
Counterextension into the potato chips category
A B C
Control 1 2
Solo-Branded Success 3 4
Solo-Branded Failure 5 6
Cobranded 7 8
Notes: The evaluation of the counterextensions was greater than
that in the control conditions (1-2) when the previous
solo-branded extension by the focal brand was a success (3-4)
but not when it was a failure (5-6). The evaluation of the
counterextension in the conditions in which the focal brand's
previous extension was cobranded (7-8) was smaller than that
in the conditions in which the focal brand's previous
solo-branded extension was a success (3-4) but greater than that
in the conditions in which the previous solo-branded extension
was a failure (5-6). Legend for Chart:
A - Mean Evaluation of the Counterextension into the Popcorn
Category
B - Previous Extension Activity in the Corn Crisps Category
Counterextension into the popcorn category
A B
Control 1
Popcorn (Related Product Category)(a) 2
Potato Chips (Unrelated Product Category)(a) 3
Popcorn and Potato Chips (Cobranded) 4
(a) The terms "related" and "unrelated" are used here to mean
that a previous corn crisps extension by a popcorn brand, but
not by a potato chips brand, improved the evaluation of a
subsequent popcorn extension by a corn crisps brand.
Notes: The counterextension was evaluated less favorably when
the focal brand's previous extension into the category of the
brand that launched the counterextension was cobranded (4) than
when it was solo branded (2). The mean evaluation of the
counterextension in the condition in which a product from an
unrelated category had previously extended into the category
of the brand that launched the counterextension (3) was no
different from that in the control condition (1). Legend for Chart:
A - Mean Evaluation of the Bugles Counterextension into the
Popcorn Category
B - Previous Extension Activity in the Corn Crisps Category
Unified slogan
C - Previous Extension Activity in the Corn Crisps Category
Partitioned slogan
A B C
Control - 1
Redenbacher's Jays (Focal Brand as the Header) 2 3
Jays Redenbacher's (Focal Brand as the Modifier) 4 5
Notes: In the unified condition, the positioning statement for
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Crunchy Corn from Redenbacher's Jays." In the partitioned
condition with the same brand name, the slogan was "The Corn of
Redenbacher's and the Crunch of Jays." The slogans under the
unified versus partitioned conditions for the Jays Redenbacher's
extension were constructed analogously. The evaluation of the
counterextension was greater in the two unified conditions (2, 4)
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~~~~~~~~
By Piyush Kumar
Piyush Kumar is Assistant Professor of Management, Owen Graduate School of Management, Vanderbilt University (e-mail: piyush.kumar@owen.vanderbilt.edu).
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Record: 170- The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration. By: Reinartz, Werner J.; Kumar, V. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p77-99. 23p. 3 Diagrams, 5 Charts, 2 Graphs. DOI: 10.1509/jmkg.67.1.77.18589.
- Database:
- Business Source Complete
The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration
The authors develop a framework that incorporates projected profitability of customers in the computation of lifetime duration. Furthermore, the authors identify factors under a manager's control that explain the variation in the profitable lifetime duration. They also compare other frameworks with the traditional methods such as the recency, frequency, and monetary value framework and past customer value and illustrate the superiority of the proposed framework. Finally, the authors develop several key implications that can be of value to decision makers in managing customer relationships.
Relationship marketing--the establishment and maintenance of long-term buyer-seller relationships-- has profoundly influenced marketing theory and practice. Although the concept of relationship marketing is not new, organizations have recently started to focus on identifying and retaining long-term customers. Managers profess to do it in new and better ways every day (Fournier, Dobscha, and Mick 1998). Consider the following example: Before the 1990s, AT&T spent hundreds of millions of dollars per year trying to attract prospects to use its long-distance telephone service. Most prospects received similar offerings regardless of their specific needs. As a result, AT&T sent out millions of pieces of largely undifferentiated direct mail solicitations several times a year. In spite of this, less than 5% of the cases resulted in conversions, and even worse, many of these conversions were lost because of a high rate of churn (Grant and Schlesinger 1995). Today, AT&T analyzes its relationships with its customers and tracks in particular retention and termination characteristics. In 1994, through conscientious modeling efforts, AT&T attracted seven times as many customers as it did in 1990. Even more important, these customers have a different quality in their retention behavior. By analyzing the factors that drive retention, AT&T is much more efficient in ( 1) keeping customers who are at risk of defection and ( 2) better pinpointing the customers who are likely to be long-life customers in its acquisition campaigns (Li 1995). Thus, AT&T has embraced the core of relationship marketing: It is considerably more profitable to keep and satisfy existing customers than to renew a strongly churning customer base constantly.
To make relationship marketing work, marketers have started to adopt a customer management orientation, which emphasizes the importance of customer lifetime analysis, retention, and the dynamic nature of a person's customer- firm relationship over time (Kotler 1994). Given the discrepancies between concept and reality in relationship marketing, it is important to study the concept of customer management and customer lifetime for two reasons.
First, a better understanding is needed of the facets of a customer management orientation. For example, firms that adopt a customer management orientation need to consider how their activities impact their relationship with different customers. Anderson and Narus (1991) note that every industry is characterized by its own bandwidth of transactional and relational exchanges. Garbarino and Johnson (1999) show that short-and long-term-oriented customers differ in the factors that determine their future exchanges. Their results imply that a focus on customer satisfaction is likely to be effective for weak relational customers whereas marketing that is focused on building trust and commitment is more effective for the long-term relational group. Likewise, given the need to cater to specific customers rather than all possible customers (Dowling and Uncles 1997), an analysis of the relationship dynamics over time, such as lifetime activity patterns, becomes of paramount importance (Reichheld and Teal 1996). The results of Jap and Ganesan's (2000) study highlight the need to incorporate dynamic effects over the duration of the customer-firm relationship. Specifically, they find that the differential efficacy of various relationship management mechanisms changes over the course of a relationship.
The need for research in this domain also finds its expression in the Marketing Science Institute's research priorities. It has elevated the topic of customer management and the analysis of surrounding issues (e.g., value of loyalty, measuring lifetime value) to one of its capital research priorities.
Second, although the importance of an analysis of the dynamic customer-firm relationship is hardly disputed, empirical evidence is scarce. In particular, areas in need of research are noncontractual relationships--relationships between buyers and sellers that are not governed by a contract or membership. Specifically, how can the length of a customer's relationship with a firm be measured, given that the customer "never signs off?" How can the relationship be managed? Given that switching costs are low and customers choose to interact with firms at their own volition, this is a nontrivial question for noncontractual relationships. What is the strength and directional impact of the antecedent factors on the duration of a customer's relationship with a firm? If managers can understand the temporal dynamics involved in a customer's relationship with the firm, they can, for example, predict a customer's intention to leave the relationship. Consequently, they can spend marketing dollars more effectively either by not chasing customers "whose time has come" or by employing judicious marketing actions to save customers who are at risk. This issue gains added importance given Reinartz and Kumar's (2000) findings that both long-term and short-term customers can be profitable. Thus, it is imperative to develop a framework that incorporates customers' projected profitability in the computation of lifetime duration. Furthermore, this framework should also identify factors under managers' control that could increase the value of each customer for the firm. In other words, in the case of a noncontractual setting, it is a two-step process. First, we must measure lifetime duration that incorporates projected profits, and second, we must identify factors that can explain the variation in duration.
From a managerial standpoint, it would be desirable to know, at any given time, whether it would be profitable to mail a catalog or send a salesperson to a given customer. If it is profitable, the manager decides to mail the catalog or initiate a personal contract. On the basis of this decision framework, it is possible to compute lifetime durations for each customer. After profitable lifetime duration is obtained for each customer, managers are interested in knowing the factors or antecedents that drive the profitable lifetime duration. In response to this phenomenon, we present an integrated framework for measuring profitable customer lifetime duration and assessing antecedent factors. The key research objectives are to
- Empirically measure lifetime duration for noncontractual customer-firm relationships, incorporating projected profits;
- Demonstrate the superiority of our proposed framework by comparing it with the widely used recency, frequency, and monetary value (RFM) framework using the criterion of generated profits;
- Understand the structure of profitable relationships and test the factors that affect a customer's profitable lifetime duration; and
- Develop managerial implications for building and managing profitable relationship exchanges.
Our research takes place in the context of the direct marketing industry. This industry is important because in 1999, U.S. sales revenue attributable to direct marketing was estimated to reach close to $1.6 trillion. Approximately 15 million workers were employed throughout the U.S. economy as a result of direct marketing activity (Direct Marketing Association 1999). Specifically, we conducted our research for one of the leading general merchandise direct marketers (business-to-consumer [B-to-C] setting) in the United States. Furthermore, we validated the results with a customer sample from a high-technology firm (business-to-business [B-to-B] setting) selling computer hardware and software.
Several studies (Allenby, Leone, and Jen 1999; Bolton 1998; Dwyer 1997; Schmittlein and Peterson 1994) that are concerned with the empirical aspects of customer lifetime duration are somewhat limited because of the general lack of customer purchase history data. Given this historical lack of longitudinal customer information, researchers have predominantly focused on the retention construct (Crosby and Stephens 1987). Nevertheless, researchers have increasingly started to take a longitudinal perspective in their empirical work. Given the exploding managerial interest in how to manage the customer-firm relationship and the increasing availability of longitudinal customer databases, researchers have focused increasingly on empirically measuring and modeling a customer's relationship with a firm (Bolton 1998; Reinartz and Kumar 2000; Schmittlein and Peterson 1994). Although some researchers have analyzed lifetime behavior in contractual contexts (Allenby et al. 1999; Bolton 1998), findings from noncontractual settings (Reinartz and Kumar 2000; Schmittlein, Morrison, and Colombo 1987) require further investigation. In Table 1, we give a brief review of the academic literature concerned with customer lifetime duration as the focal construct. As can be seen from Table 1, research on customer lifetime duration and in particular profitable lifetime duration is scarce.
Specifically, the characteristics of a noncontractual setting, as explained previously, result in both long and short lifetime customers being profitable to the firm. Thus, there exists a need for conceptual and empirical exploration of the antecedent variables that can characterize profitable customers and not just the longer lifetime customers--thereby advancing our relationship management understanding.
Reinartz and Kumar (2000) provide a descriptive model of the lifetime duration-profitability relationship. In this study, we attempt to build on their results and furnish new under-standing in two key areas. First, the primary objective of our study is to show how an analysis of the antecedents of lifetime duration can help explain systematic differences in profitable customer lifetime duration. The goal is twofold: to better understand the structure of profitable relationships and to deduce implications for managers to better manage a customer's profitable tenure with the firm.
Second, our study builds on Reinartz and Kumar's (2000, p. 28) key finding that there exists "a substantial group of intrinsically short-lived customers, [and] it is necessary to identify this profitable yet short-lived group as early as possible and then stop chasing these customers" even though they may be contributing to the current sales. Their study does not tell the manager at what point the customers should not be pursued and which customers to let go. In this study, we attempt to provide managers with the framework to determine this information (based on their expected contribution margin [ECM]). Although a customer may be buying from the firm, it is in the firm's best interest to stop contacting that customer if the firm is losing money because of his or her transactions. In other words, our study offers a framework to identify the time periods beyond which customers may not be profitable. Niraj, Gupta, and Narasimhan (2001) also argue that estimating profitability at the individual level is important to distinguish the more profitable customers from the less profitable ones.
The modeling process of a customer's lifetime is contingent on a valid measurement framework that adequately describes the process of birth, purchase activity, and defection. The situation is far more difficult for noncontractual settings in which a customer purchases completely at his or her discretion. To our knowledge, no study has devised and tested a framework for measuring a customer's lifetime duration that considers projected profitability in a noncontractual context. Toward that end, we suggest a procedure for estimating the lifetime of customers and implement this procedure empirically.
The goal of this section is to conceptualize a model of profitable duration of the customer-firm relationship that is theoretically and empirically defendable. This model describes and analyzes how and why duration times differ systematically across customers. Thus, it is a customer-level analysis. An important aspect is that the customer's tendency to maintain a relationship is reflected in the evolutionary characteristics of his or her exchange with the firm over time (Ganesan 1994). Because our approach exploits longitudinal information obtained within customers, we refer to it as a dynamic model (see also Bolton 1998).
Figure 1 details the conceptual framework that centers on the focal construct of customers' profitable lifetime duration. Profitable lifetime duration is conceptualized as a function of the characteristics of the relationship. In the hypotheses section, we discuss the exact nature of these influences. Figure 1 not only illustrates how our study differs from Reinartz and Kumar's (2000) study but also shows how our study incorporates their findings in the proposed framework, through the incorporation of revenues and cost in measuring lifetime duration. In summary, Reinartz and Kumar (2000) focus on the consequences of lifetime duration, whereas our study focuses on the antecedents of profitable lifetime duration. Managers understand the important consequences of both longer and shorter lifetime duration from Reinartz and Kumar's (2000) study. However, our study tells managers how to incorporate those findings in determining when to stop contacting customers.
The focus of our inquiry is on variables that determine the nature of the customer-firm exchange. Exchange characteristics encompass the variables that define and describe relationship activities in the broadest sense. First, the basic building blocks of any exchange can probably be characterized as the timing, scope, and depth of buying (Blattberg, Getz, and Thomas 2001; Neslin, Henderson, and Quelch 1985). For example, there is ample evidence in the marketing literature that behavioral exchange characteristics, such as past purchases, are strong predictors of future customer behavior (Dwyer 1997; Rossi, McCulloch, and Allenby 1996). In managerial practice, the key exchange characteristics reflect themselves in traditional customer scoring models that primarily take into account information on purchase frequency and purchase amount (Hughes 1996). In addition to these basic behavioral exchange variables, we consider other characteristics that are relevant to the growth or decline of a relationship. For example, this includes the communication between the firm and the customer in the form of not only marketing efforts but also signaling dissatisfaction (e.g., through product returns) or signaling commitment (e.g., through participation in loyalty programs).
Second, accounting for observed customer heterogeneity is clearly warranted. Observed customer heterogeneity is the degree to which customers differ on observed characteristics, for example, demographic or psychographic descriptives. It is important to consider these differences because demographic and psychographic indicators are most commonly used for segmentation purposes. For example, demographic information has been traditionally used in modeling customer response (Rossi, McCulloch, and Allenby 1996). Schmittlein and Peterson (1994) advocate the use of demographic information, though they point out that past purchase behavior (i.e., core exchange) generally outpredicts geodemographic information.
In general, the antecedents that are included in our model have received broad support in the relationship marketing literature (Sheth and Parvatiyar 1995). Because our effort deals with a single firm, we do not include competitive information. Specifically, we postulate that the profitable duration of a customer-firm relationship depends, differentially, on the exchange characteristics at time t and customer heterogeneity. Conceptually,
Profitable lifetime durationi =
f(exchange characteristicsit,
customer heterogeneityi).We capture the dynamic nature of the customer-firm relationship through the time varying nature of the independent variables. Although the impact of these variables has been studied in a response-modeling context (e.g., brand choice, interpurchase time), we are not aware of any study that assesses their impact on profitable lifetime duration (or on lifetime duration itself) in a noncontractual lifetime context.
Development of Hypotheses
In this section, we advance expectations about the effects of exchange characteristics on profitable lifetime duration. We form these expectations from theoretical and empirical knowledge from the relationship marketing paradigm, the social exchange paradigm, and loyalty and satisfaction-related research. Given the lack of research in the lifetime duration area in general and more so with respect to profitable lifetime duration, we also draw on the evidence available in related contexts (e.g., contractual settings, B-to-B settings) for developing the hypotheses.
When customers enter into commercial relationships, they seek to maximize their expected utility from the entirety of the exchange (Oliver and Winer 1987). The over-all derived utility is a function of a multitude of factors, which varies across customers. However, in most cases, the substance of the expected utility is derived from the goods or services themselves.
Exchange Characteristics
Any given spending of a customer with the focal firm over time can be decomposed into three components: purchase frequency, purchase amount per incidence, and purchase composition (single or cross-category). This notion is also reflected in the generalized context of personal relationships that Kelley and Thibaut (1978) bring forward. They suggest that the interaction between two parties manifests itself in frequency of interaction, depth of interaction, and scope of interaction. Relationships intensify as exchange parties communicate more often, more deeply, and across a larger scope of issues. Thus, the argument brought forth from the social exchange domain suggests that as customers buy more, buy more frequently, and buy more across different categories, the relationship between them and the vendor becomes more durable. Oliver and Winer's (1987) utility framework supports the same logic and suggests that buyers who buy more, buy more frequently, and buy more across different categories have a better fit with the vendor's product and positioning and receive greater utility from it. Therefore, the relationship between the two parties is prolonged.
Purchase amount. Specifically, with respect to the purchase amount, Bendapudi and Berry (1997) argue that customers who have a higher commitment are also likely to seek greater relationship expansion and enhancement. There is empirical evidence in a contractual context that more satisfied customers have longer relationships with their service providers (Bolton 1998) and higher usage levels of services (Bolton and Lemon 1999). In a financial services context, Storbacka and Luukinen (1996) find that satisfaction is a function of relationship volume. Thus, a positive correlation can be expected between the relationship duration and the purchase volume. In other words, if a consumer devotes a larger share-of-wallet to a firm, the bond should be stronger. Consequently, we expect that long duration customers will, on average, have higher spending levels than those with a shorter duration.
H1: Profitable customer lifetime duration is positively related to the customer's spending level.
Cross-buying. Assuming that the firm offers products or services in different categories, consumers can purchase products in a focused manner or purchase across a variety of different categories. Cross-buying refers to the degree to which customers purchase products or services from a set of related or unrelated categories of the company. For example, in a general merchandise context, one customer might buy only women's shoes and formal wear, whereas another customer might purchase shoes, high-fashion, formal wear, sports wear, accessories, and textiles. In the latter case, the scope of interaction with the firm is rather broad, whereas in the former case, it is focused. With respect to scope of purchases (operationalized as cross-buying), there is little empirical evidence that links cross-buying with a customer's tenure. The few existing studies in the marketing domain have focused on the related, yet different, construct of cross selling (Chen et al. 1999; Drèze and Hoch 1998)--in particular on the direct effect of cross-selling on aggregate level outcomes, such as store sales or store choice. As compared with the cross-selling construct, far fewer studies deal with the cross-buying construct. According to Coughlan (1987), the benefits of one-stop shopping are the key drivers of customers engaging in cross-buying. What remains open in the literature is the effect of cross-buying on individual level outcomes such as customer retention or lifetime duration.
With respect to the impact of cross-buying on retention, our reasoning is as follows: In the context of contractual relations, customer retention is enhanced with cross-selling of multiple accounts or services as customer switching costs increase with multiple relationships (Srivastava and Shocker 1987). Although these switching costs do not exist in a non-contractual setting, customers benefit from knowing the retailers' product range, the quality levels, and interaction processes (Reichheld and Teal 1996). Evidence for this contention also comes from the B-to-B context in which O'Neal and Bertrand (1991) find that customers who are in long-term relationships with their suppliers are characterized by a greater scope of the relationship (measured in terms of products supplied). Likewise, Hoch, Bradlow, and Wansink (1999) state that variety in offerings is viewed as the entry fee for maintaining future customer loyalty. Thus, it is believed that the consumers who consume from a variety of product lines are less prone to terminate the relationship.
H2: Profitable customer lifetime duration is positively related to the degree of cross-buying behavior that customers exhibit.
Focus of buying. In addition to the reasoning that lifetime duration increases as the degree of cross-buying increases, we can make a qualifying argument that is specific to a focused buyer.[ 1] For example, a customer may buy a specific item, such as jeans, on a regular basis from the retailer. Even if the customer does not buy any other product from the retailer, this would suggest something about that customer's desire to be a long lifetime customer. In contrast, we could argue that the wear-off of involvement with the firm and decrease in excitement about the product are greater when there is only limited interaction. Unlike the cross-buying hypothesis, this suggests a negative effect on lifetime duration. Because of the conflicting argument, we test the specific effect of much focused buying (i.e., one single department/category) versus nonfocused buying (i.e., anything more than a single department/category). Because of the previous reasoning, we do not posit a directional hypothesis. Instead, we test empirically for the effect.
H3: Profitable customer lifetime duration is related to focused buying behavior that customers exhibit.
Average interpurchase time (AIT). The variable AIT has been used in the context of modeling purchase events--representing the frequency of interaction. The impact of a customer's AIT on lifetime duration can be argued from two perspectives. Lower AIT (i.e., more frequent purchasing given that the customer is alive) could be associated with longer lifetime because this would be an indicator of a strong relationship. Morgan and Hunt (1994) argue that to the extent that interactions are satisfactory, frequency of interactions might lead to greater trust, which should in turn lead to a longer relationship duration. This argument is in line with Kelley and Thibaut's (1978) social-exchange perspective and the person-perception literature (Neuberg and Fiske 1987), which suggests a stronger link between people when their interaction frequency increases. These arguments favor a negative relationship between AIT and lifetime (i.e., the longer a customer's interpurchase time, the shorter is his or her lifetime).
The previous argument purports that an extremely frequent buyer (low AIT) should have the longest lifetime. However, sustaining a high purchase frequency over a long life seems unreasonable, in particular for the general merchandise category. We might reasonably argue that there seems to be a lower limit for interpurchase time because purchases might occur on a regular basis. For example, it is unlikely that a customer purchases many different clothes at one time and then pauses to buy for several years. Rather, apparel is bought on a continuous, yet intermittent, cycle.
This argument suggests that a long customer life is associated with an intermediate length of interpurchase time. Furthermore, if a customer buys with a short burst of high intensity (low AIT given the customer is alive), it would seem that this customer has a rather low lifetime because the customer has stocked up on items that should last for an extended time. In addition, sustaining low AIT over the long run seems rather improbable given finite income resources.
Both of these arguments seem to have merit, and the only way to reconcile them is by way of accommodating an inverse U-shaped relationship between AIT and lifetime duration. Consequently, we propose that AIT and lifetime duration are related in an inverse U-shaped fashion, whereby intermediate AIT is associated with the longest lifetime. We note that the construct AIT is not tantamount to modeling frequency of purchases. Although these two constructs are related, frequency is an absolute measure and AIT is a relative measure. Thus,
H4: Profitable customer lifetime duration is related to AIT in an inverse U-shaped manner, whereby intermediate AIT is associated with the longest profitable lifetime.
From a managerial standpoint, we note that the variables of purchase frequency and purchase amount are key input to traditional scoring models for customer evaluation. Furthermore, these variables are also reflected in customer valuation models, such as the customer equity model by Blat-tberg, Getz, and Thomas (2001)--thus adding face validity to the conceptualization of our exchange variables.
Returns. Merchandise returns pose a significant problem for direct marketers (Hess and Mayhew 1997). It is suggested that increasing returns are associated with increasing dissatisfaction. Customers return merchandise because they are dissatisfied with one or more attributes of the product, such as quality, fit, or performance. This is particularly true in the direct marketing context, in which the evaluation of the product attributes cannot be performed directly, but only through reading the catalogs or the product descriptions over the Internet. If we accept an inverse relationship of proportion of returns and overall satisfaction, then we would deduce that high returns should also be associated with shorter lifetimes. There is conceptual and empirical evidence that cumulative satisfaction/dissatisfaction leads to higher/lower repurchase intentions (Anderson and Sullivan 1993) and that cumulative satisfaction is associated with longer lifetimes for a long-distance telephone service (Bolton 1998). However, Gruen (1995) notes that though research generally supports the link between dissatisfaction with a relationship and the propensity to leave that relation-ship, the link is weak at best. Nevertheless, if return behavior is evidence of dissatisfaction with the firm's merchandise, we would consequently argue for lower lifetime duration expectations for those customers who return proportionally more.
H5: Profitable customer lifetime duration is inversely related to the proportion of merchandise that customers return.
Loyalty instrument. Another variable that should have an impact on characteristics of the exchange is whether a customer subscribes to the loyalty instrument of the focal firm. The firm's loyalty instrument takes the form of a charge card for which customers can sign up. The charge card itself is a free service, though customers must go through a credit rating process. Furthermore, the charge card is vendor specific, thus it cannot be used at other stores. Although the charge card performs a function in itself (i.e., the payment function), from a theoretical perspective, ownership of a charge card can also be explained through the process of identification with the firm. This is because customers must spend additional efforts (e.g., application process, danger of rejection due to credit rating) to obtain the card. Given that purchase payments can be managed in multiple ways (e.g., credit card, check), the identification with the focal firm seems to be a substantive driver behind the ownership. Customers typically use the firm's loyalty instrument to receive some discount or accumulate points to redeem in the future. Given that card programs come at a significant cost, it seems imperative to assess their impact on relevant outcomes, one of which is customer tenure. The empirical evidence is quite mixed. Hartnett (1997) points out that store card programs are associated with increased sales in the form of higher average purchases and more frequent store visits. However, Sharp and Sharp (1997) and Crié, Meyer-Waarden, and Benavent (2000) find that loyalty programs in the grocery context have only a marginal effect on customer loyalty. In this study, we are not examining a classical loyalty program in which customers can redeem loyalty incentives. Consequently, there is no question whether the loyalty program creates loyalty toward the firm or program itself. We believe that it is the identification with the firm that drives adoption of the card. As such, this suggests a positive association between company-specific credit card ownership and lifetime duration. Besides the directional impact, it will also be useful to assess the size of the impact of card ownership on lifetime duration.
H6: Profitable customer lifetime duration is positively related to the customer's ownership of the company's loyalty instrument.
Mailings. In general merchandise direct marketing, the prime communication channel from firm to customer is the product catalog. In our case, the company engages in virtually no broadcast advertising, but relies almost exclusively on list rental and mail solicitation (e.g., promotions, catalogs). Therefore, the inclusion of mailing efforts into the model represents the major marketing component of the firm. An impediment to the straightforward inclusion of mailing efforts into the model is the inherent complexity of the simultaneous nature of marketing effort and customer response probability. This simultaneity in modeling response is caused through the application of customer scoring models, and the problem is widely known in the direct marketing field (Dwyer 1997). Nevertheless, we suggest that the effect of mailings should be taken into account. We do this by including the company's marketing effort as a lagged variable in the model. Therefore, we avoid causal misinterpretation of the effect of marketing efforts by removing the associated variance from the model. Similar to Bult and Wansbeek (1995), we operationalize mailings by the number of efforts/mail pieces sent to the customer. Over-all, we expect a positive impact of mailings on a customer's lifetime duration.
H7: Profitable customer lifetime duration is positively related to the number of mailing efforts of the company.
Product category. We classify general merchandise products customarily into two broad categories--soft goods and hard goods. Soft goods include all types of apparel, clothes, and fashion. Hard goods comprise all nonfashion items, such as small electronics, houseware, kitchenware, gifts, and the like. To control for potential systematic lifetime differences associated with one or the other category, we introduce a dummy variable that characterizes a buyer as either a soft good or a hard good purchaser depending on his or her majority of purchases. Because the variable is introduced for control purposes only, no directional hypothesis is advanced.
Customer Heterogeneity
Demographic variables that capture observed customer heterogeneity have been used consistently in response modeling. The main motivation to include these variables is for statistical control purposes and potential segmentation purposes. According to Zeithaml (2000), firms need to characterize attractive segments into identifiable and measurable groups of customers. There exists ample empirical evidence that demographic variables can be related significantly to the response variable (e.g., sales, choice, interpurchase time), yet the portion of explained variation is somewhat low (Rossi, McCulloch, and Allenby 1996). In this context, we specifically examine heterogeneity in terms of the customer's spatial location, age, and income.
Spatial location of consumer. The maintenance of relationships can be argued from a (customer's) economic perspective. It has been shown that the continuance of a relationship is a function of the cost and the benefits that accrue from the relation. Thus, this view emphasizes switching cost and dependence as key drivers of relationship maintenance (Dwyer, Schurr, and Oh 1987; Williamson 1975). For example, for grocery purchases, it has been consistently shown that store location is a key driver of store choice (Craig, Gosh, and McLafferty 1984). Thus, from an economic perspective, if a more expensive store is conveniently located to the buyer's location, ceteris paribus, the customer might still establish a relationship with that store because the customer minimizes overall cost (purchasing plus travel cost). Similarly, in our case, we expect that customers in rural areas, which are characterized by a lower population density, have fewer options in choosing their most preferred store. Fewer store options in the customer's local environment will translate, ceteris paribus, into a higher level of mail-order shopping. Consequently, because of the cost minimization argument, we expect a higher proportion of long lifetime customers to live in low-density areas than high-density areas (e.g., cities).
H8: Profitable customer lifetime duration is higher for customers living in areas with lower population density.
Age and income. Age and income are used as individual level control factors. We do not propose directional hypotheses for the age variable because of a lack of an appropriate theory. However, we posit a directional hypothesis for income. High-income households have high opportunity costs of time. They tend to substitute time by buying goods that will save time and are willing to pay for the added convenience. Therefore, high-income households tend to spend more money for the same bundle of products than low-income households. In general, customers with higher incomes are less susceptible to higher prices (Kumar and Karande 2000) and are expected to continue buying from the firm for the added convenience. Thus,
H9: Profitable customer lifetime duration is positively related to customers' income.
The data (B-to-C setting) for our study are the same as the data used by Reinartz and Kumar (2000). The use of the same data set is critical because we are trying to evaluate whether the findings from Reinartz and Kumar's (2000) study can be implemented successfully to determine which customers to let go of and when to let go of them. In addition to the two cohorts used in Reinartz and Kumar's (2000) study, an additional cohort of data was used from the data set provided to us. Thus, we can validate the results across three different sets of customers. Although the data are partially the same for both studies, the studies have different objectives. Furthermore, for this type of research, it is worthwhile to highlight that it is imperative to use cohort data (Parasuraman 1997; Reichheld and Teal 1996).
Database
We used data from a U.S. general merchandise catalog retailer for the empirical estimation in our article. The firm offers a broad assortment of products year round (e.g., apparel, gift items, decorative items, small electronics, kitchenware). We do not disclose the name of the company for reasons of maintaining confidentiality per the agreement. We describe the data here for the purpose of clarity and continuity. For this study, we recorded data that cover a three-year window on a daily basis. The database for the three cohorts consists of a total number of observations of 11,992 households. We tracked the customers from their first purchase with the firm. These households have not been prior customers of the company (i.e., no left-censoring). The sample of households belongs to three different cohorts; the structure is depicted in Figure 2.
The customer-firm interaction of Cohort 1 households was tracked for a 36-month time period, the behavior of Cohort 2 households for a 35-month time period, and the behavior of Cohort 3 households for a 34-month time period. We randomly sampled households from all households that started in January, February, and March 1995, respectively. The number of purchases ranges from 1 to 46 across the sample with a median number of 5 purchases; the median interpurchase time is 117 days, and the median transaction amount is $91 for each purchase.
Estimation of P(Alive)
We replicated the estimation of the negative binomial distribution (NBD)/Pareto model used by Reinartz and Kumar (2000) to obtain the necessary parameter estimates for this study. In contrast to Reinartz and Kumar's (2000) study, we obtained the distribution parameters of the NBD/Pareto model for the entire sample through the maximum likelihood estimation (MLE; see details in the Technical Appendix). An added benefit of using MLE in this study is that we can compare the results with the method-of-moment estimates that are available from Reinartz and Kumar (2000).
A key finding from the parameter estimation is that the method-of-moments and the MLE yield similar results (see Table 2). We also calculated the Pearson product moment correlation between the P(Alive) estimate from the MLE estimates and the P(Alive) estimates resulting from Reinartz and Kumar (2000). The correlation is greater than .99 for all three cohorts (Table 2), which also shows the convergent validity between the MLE and method-of-moment estimates. Given the convergent validity of the estimation method, we used the result of the NBD/Pareto model estimated by MLE. Specifically, we calculated the probability of being alive from month 4 tomonth 36 of the observation window. In Figure 3, we plot the resulting average probability for Cohorts 1 to 3. In the next section, we use P(Alive) and propose a rule for obtaining a finite lifetime estimate based on the expected future contributions of a customer. The procedure suggested subsequently is different from the one Reinartz and Kumar (2000) suggest. However, it integrates the knowledge gained from their study.
Lifetime Duration Calculation
We suggest a four-step process for obtaining individual lifetime duration estimates that integrates projected profitability:
1. Calculate net present value (NPV) of ECMit,
2. Decide relationship termination by comparing
NPV of ECMit with cost of mailing (i.e., if NPV
of ECMit < cost of next mailingi, then terminate),
3. Calculate finite lifetime estimates, and
4. Conduct back-end performance analysis of the
suggested procedure.In the noncontractual setting, measuring lifetime duration becomes a nontrivial case. Here, customers are subject to "silent" attrition and firms can only infer when a customer has left the relationship. For example, even though a customer makes no purchase for a much longer period than the AIT, he or she might still have some nonzero probability of purchasing again. This spirit is captured by the NBD/Pareto model that reflects the probability of the customer being alive, given his or her particular purchase history. However, even though a customer might have a small probability of being alive (and thus to potentially purchase), this might not justify investing in this customer, for all practical purposes. For example, in the direct mail context, firms must optimize their resource allocation across customers because mail is sent out to customers regularly. Therefore, given that customers reach a low activity level at some point, the firm must make a decision whether to let this customer go. This managerial viewpoint reflects the spirit of our suggested procedure. If we want to determine the factors that have an impact on the length of a customer's tenure with the firm, we must establish a finite lifetime estimate, which in turn critically depends on our capability to determine the "death event." Therefore, we argue that our suggested procedure of converting the continuous customer-specific P(Alive) estimate into a customer-specific finite lifetime estimate is fully compatible with the managerial decision-making process of retaining customers in the database and, at the same time, is a necessary intermediate step for our key modeling effort (i.e., hazard model).
Having established the raison d'être for our process, the next question is how to determine the lifetime estimate. In this section, we show how the expected future income stream can be used to determine the cutoff for the computation of profitable lifetime duration.
Calculate NPV of ECMit. Given the nature of our data (and the data structure in the direct marketing industry in general), managers can easily determine past purchase and spending activity for each customer. Likewise, managers can obtain an estimate of the P(alive) status using the NBD/ Pareto model for both past and future periods. This enables us to establish the following decision rule: If the sum of the expected discounted future contribution margin is smaller than a currently planned marketing intervention, we can establish the death event for the customer (e.g., a managerial consequence would be to stop mailing to that customer, even though this is not our primary concern). More formally, we compute the estimated future contribution margin as
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where ECMit is the estimated ECM for a given month t, AMCMit is the average contribution margin in month t based on all prior purchases since birth (updated dynamically), r is the discount rate (15% on a yearly basis), i is the customer, t is the month in which NPV is estimated, n is the number of months beyond t, and P(Alive)in is the probability that customer i is alive in month n.[ 2]
For example, the NPV of ECM for customer i in month 18 is calculated as follows: For each month and for each customer, we observe the total purchases made in dollars. Then, we multiply that purchase amount by .3 to reflect the gross margin. In other words, the cost of goods sold is accounted for, and what we have is gross profit. Next, we subtract the cost of actual marketing efforts (in this case, the cost of catalogs plus the mailing costs) to obtain the monthly contribution margin. If a decision is made at the end of month 18, we take the average (AMCMi) of months 1 through 18 by summing up all the 18 contribution margins and dividing it by 18. If we are at the end of time period 36, we take the average (AMCM) of the previous 36 months' contribution margins by summing up all the 36 contribution margins and dividing it by 36.
It is possible that a customer might exhibit certain upward or downward trends. Any trend becomes incorporated into the AMCMt and will lead to an upward or downward shift for a given period t. Nevertheless, the more we move through time, the more this trend becomes dampened because we divide by a growing number of periods. We believe this is a fair description of the actual process in which individual purchases represent shocks to the system but in which stationarity is achieved over time--representing that customer's purchase process. Furthermore, we would expect, on average, a general downward trend in the AMCMt over time because of the ubiquitous attrition effect.
Thus, the AMCM estimate is updated on a monthly basis--in other words, dynamically modeled and used as a baseline for future purchases (i.e., purchases between t and N). The past purchase level at time t is projected into the future and multiplied monthly with the predicted P(Alive) estimate. Thus, it contains endogenously the information about the mailing process as well. The future time horizon is limited to 18 months because the associated P(Alive) estimate becomes only marginally different from zero after 18 months. For example, according to the NBD/Pareto model, if a customer has not purchased in a long time, his or her probability of being alive is small. Because the predicted P(Alive) for the next 18 months will be even smaller, the NPV of the expected future contribution margin stream will be low. Thus, for a manager who must decide whether to invest in this customer (i.e., marketing intervention), chances are that this customer would not be deemed as a lucrative future customer, given the cost of mailing.
Is a formal model needed for our purpose in the first place? With the use of the proposed model, it is possible to predict the value of P(Alive) beyond the range of the data. The key contribution of the model is a decision rule that is based on an assessment of the future value of a customer. Based on an extrapolation of the two components, P(Alive) and AMCMit, the model allows for the construction of a score (NPV of ECM). This score can be constructed at any given t without having any knowledge of future customer behavior. Thus, the model is clearly aligned with the managerial decision-making process.
Decide relationship termination. Formally, if NPV of ECMit < cost of mailing, the firm would decide to terminate the relationship. Using this decision rule, we establish for every customer at what point he or she is subjected to the proposed termination policy. The decision rule incorporates the cost of mailings and an average flat contribution margin before mailings of 25%. We assume the discount rate to be 15%, which is in the range of what has been used by other researchers (e.g., Berger and Nasr 1998).
Calculate finite lifetime estimates. Based on the decision of relationship termination, the average lifetime across Cohort 1 is 29.3 months, across Cohort 2 is 28.6 months, and across Cohort 3 is 27.8 months (see Table 2). The consistency among the three cohorts is high. In all the cohorts, little more than 60% of the samples have a lifetime that is less than the observation window. Households clearly show variability in lifetime duration. This is evidenced through several factors such as the wide range between lowest and highest lifetime estimate, the standard deviation of the lifetime estimate, and the relatively small value of s in the NBD/Pareto model. Thus, we expect considerable scope for exploring the factors that have an impact on lifetime duration. Note that the lifetime duration estimates that incorporate projected profits are different from the estimates that do not incorporate profits (as in the Reinartz and Kumar [2000] study)
Conduct back-end performance analysis of the suggested procedure. We assessed the performance of the proposed method by performing three different tests. First, we assessed the quality of classification. For that purpose, we computed the proportion of customers who were misclassified, that is, whose relationships were declared as terminated, however, they purchased at least once after the lifetime event. We present the results in Table 2. Slightly more than 90% of the subjects were classified correctly within the three-year observation window. This result underlines the strength of the expected future contribution margin method.
Second, we compared the proposed method to the widely used RFM framework. Most direct marketing firms use the RFM method as a scoring method for customers and as a method for determining the mailing status of customers. Although classical RFM analysis is based on the customer's past purchase behavior along the three dimensions (Hughes 1996), we employed an advanced form of RFM scoring. Specifically, we used a regression analysis that contains the RFM variables as well as measures on cross-buying, depth of buying, and observed heterogeneity.[ 3] Reinartz and Kumar (2000) do not provide any explicit comparison of their method with the RFM framework. We compared for two different time periods (18 and 30 months) the prediction of the NBD/Pareto model with the prediction resulting from the advanced RFM model. To make the results as comparable as possible, we assume that the firm spends a fixed mailing budget at each of the two dates. We sorted the sample according to the score, and given the fixed budget, we selected the top 30%, 50%, and 70%, respectively, of the customers for targeting. We then compared the total actual revenues generated for those customers from months 18 and 30, respectively, onwards, until the end of the observation window for the two methods.[ 4] Table 3 shows that the proposed framework is superior to the RFM decision rule.
For example, the proposed framework selection method yields revenues of $590,452 (see Table 3), whereas the corresponding result using the RFM method yields $442,534. In every case, the proposed framework selection approach yields higher revenues as compared with the RFM selection approach. Similarly, the proposed framework selection method yields a profit of $123,076, whereas the RFM selection method yields only $78,555. Although we obtained this result for 30% of customers selected from the top, the findings hold across the percentages of customers selected and across the different points in time. This finding is a remarkable support for the performance of the suggested framework, which uses the estimates of P(Alive) from the NBD/ Pareto model in combination with the NPV of the ECM.
Third, we compared our method with another classification benchmark. We used cumulative past customer value as the variable to determine mailing status of the customer. The reason for choosing lifetime value is that managers expend significant efforts to retain high lifetime value customers. Similar to the RFM model, we compared for the same two time periods (18 and 30 months) the prediction of the NBD/ Pareto model; the prediction resulted from the past customer value benchmark. We report the results in Table 3. The results show that our model, which is based on the dynamic NBD/Pareto model, performs better than the traditional methods. Thus, including the dynamics of the customer lifetime value evolution seems to hold considerable potential in terms of customer scoring and customer selection.
The realized profits across the entire customer database could be of the order of millions of dollars, thereby enhancing the utility of our framework. Thus, although our suggested procedure is a theoretically sound procedure for the profitable lifetime calculation, it is also extremely well aligned with the actual managerial decision-making process. In and of itself, the result is useful to managers for optimizing mailing decisions.
In summary, we suggest a procedure for transforming the continuous NBD/Pareto outcome into a dichotomous "alive/dead" variable, which integrates projected profitability into a finite lifetime calculation. Recall that Reinartz and Kumar (2000) classify consumers with a greater than probability value of .5 for P(alive) as being alive and otherwise dead, without any consideration for profits. Our procedure improves on their heuristic. The NBD/Pareto framework could also be compared with a customer migration model (see, e.g., Dwyer 1997). Although a migration is based on average transition probabilities, our suggested framework allows for heterogeneity across customers. Even though the NBD/Pareto model might still be limited in capturing heterogeneity across the customer base, it appears to be an improvement over static approaches such as migration models.
We used survival analysis for the analysis of profitable customer lifetime durations. It is the method of choice in dealing with duration data because it is well suited for the handling of censoring. We used the proportional hazard model, which assumes a parametric form for the effects of the explanatory variables but allows for an unspecified form for the underlying survivor function (Cox 1972). In the proportional hazard model, the hazard rate hi(t) for individual i is assumed to take the following form:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where h0(t) is the baseline hazard rate and (xit ) is the impact of the independent variables.
We estimated the hazard model with the semiparametric partial likelihood method (Helsen and Schmittlein 1993). The partial likelihood considers the probability that a customer experiences the lifetime event, of all customers that are still considered alive. We used the PHREG procedure in SAS for the estimation; we handled ties using the exact likelihood instead of the more commonly used, yet less precise, Breslow approximation.
Variable Operationalization
The criterion variable is the household-specific estimate of profitable lifetime duration. Thus, there is only a single event associated with each household. We measured the length of the duration in months. The predictor variables in the model comprise both constant and time-varying variables. Time-varying variables may change during the course of a customer's lifetime spell, and they are measured and updated for each month. This procedure is exemplified with the returns variable. For example, a customer makes purchases worth $100 in month 1 and returns $20 worth of goods. Then, the customer does not purchase in month 2. Subsequently, the customer buys products for $60 in month 3 and returns nothing, and finally, in month 4, the customer buys products for $40 and returns 50%. Thus, the proportion of goods returned would be dynamically updated for every month such that the customer's returned proportion would be .2 for month 1 (because 20% is returned), .2 for month 2 (because nothing is bought), .125 for month 3 (because $20 is returned/$160 is purchased), and .2 for month 4 (because $40 is returned/$200 is purchased). We perform this updating for all time-varying variables, and this updating lies at the heart of the proportional hazard model. We do not provide means for the time-varying variables, because a simple mean statistic has limited value as a result of its averaging across time periods and individuals. (The detailed means report for every time period is available from the authors.) The time-varying variable purchase amount it enters the model as the monthly spending level ($).
The time-varying variable cross-buyingit is operationalized as the number of different departments shopped in a given six-month period. There is a total of 90 different merchandise departments. The focus-of-buying variable is operationalized as a dummy variable. The percentage of customers who are coded as "1" (buying consistently in one department) is .04, .05, and .04 across the three cohorts. We measure the time-varying variable AITit in number of days between purchases. The AITit2 is the square of the AITit variable. The return[it] variable is the ratio of returned goods ($ value) to purchased goods ($ value). We operationalized the loyalty instrument[i] variable as a dummy variable indicating ownership of the corporate charge card.
The proportion of customers holding a charge card is .39, .52, and .59 across the three cohorts. The effect of mailings[it] is operationalized as a lagged finite exponential decay of past marketing efforts, similar to procedures in advertising-sales relationship literature. Because the merchandise changes on a continuous basis, the use of a finite decay period is more realistic than an infinite period. We measured the variable by the number of efforts or mail pieces sent to the customer. The dummy variable product category[i] describes whether a buyer predominantly shops in hard goods or in soft goods. The proportion of customers buying predominantly hard goods is .50, .49, and .45 across the three cohorts. The variable population density enters the model as the absolute population number in a given two-digit zip code into the model. We obtained these numbers from the 2000 U.S. Census. The variable income[i] comes from the firm's database and is coded on a scale from 1 to 7 (1 = "yearly income of lesser than $10,000" and 7 = "yearly income of more than $150,000"). The mean rating is 5.19, 4.88, and 5.01 across the three cohorts. Finally, we measured the age[i] variable as the age of the individual in years, which was calculated from birth date information from the database. The mean rating is 34.4, 34.8, and 35.2 years across the three cohorts. We summarize all variables in Table 4.
The complete model specification is given in Equation 3. The hazard of a lifetime event of a household i at time t is given as follows: ( 3)
hi(t) = h0(t) EXP(β 1 purchase amountit + β2 cross-buyingit
+β3focus of buyingi + β4 AIT[-sub it] + β 5(AITit)2
+γ{sub 1] returnsit + γ2loyalty instrumentI + γ 3 mailingsit
+ γ4 product categoryi + δ 1 population densityi
δ2incomei + δ[-sub 3]agei).
We estimated the model for each cohort in three steps to judge the incremental variance explained by the three models. First, we modeled the traditional key exchange variables (β s), second, we added the additional exchange variables (γ s), and third, we entered the observed heterogeneity variables (γ s).
Results
We report the results of the profitable lifetime duration model for the three cohorts in Table 5. The table contains the final model parameters, including an interaction term (returns purchase amount), which was added post hoc.
The effective sample size for Cohort 1 is 3692 households, for Cohort 2 is 4323 households, and for Cohort 3 is 2491 households. We excluded 510 observations (12.1%), 642 observations (12.9%), and 334 observations (11.8%), respectively, because of missing values for the demographic variables. We rejected a chi-square likelihood ratio test of the hypothesis that the vector of independent variables is jointly equal to zero for all models (p < .0001).
As in all regression analyses, a measure analogous to R2 is of interest as a measure of model performance. In a detailed study, Schemper and Stare (1996) show that there is not a single, easy to estimate, and useful measure for the proportional hazard model. In this context, we use the statistic proposed by Cox and Snell (1989) and endorsed by Magee (1990): R2 = 1 - exp(-G2/n), where G2 is the likelihood ratio chi-square statistic and n is the sample size. We give the R2 estimates in Table 5 for all models. The increment in the proportion of variance explained in the more complex models is significant for all models and for all cohorts (p < .01).
Effects of Exchange Variables
Purchase amount. We hypothesized that the level of spending for merchandise (β 1) is positively related to profitable lifetime duration. We find support for this hypothesis across all three cohorts and across all three models (p < .01). Thus, H1 is supported. Because of the strong association between these two measures, it is important to take information on amount of purchases into account when profitable lifetime duration is managed.[ 5]
We analyze the risk ratio to better understand the relative impact of this variable on the hazard of relationship termination. From a managerial standpoint, the risk ratio helps in gauging the impact of the drivers of profitable lifetime duration. We can interpret the risk ratio as the percent change in the hazard for each one-unit increase in the independent variable--controlling for all other independent variables. We calculated the risk ratio as {[exp(-β ) - 1] 100}. When applied to the purchase amount variable, a change of only $10 in the monthly spending results in a decrease in the hazard of termination of between 31 and 35%, depending on cohort.
Cross-buying. We argued that the degree of buying across departments (β2) is positively related to profitable lifetime duration, because a broader scope of interaction constitutes a stronger relationship. This contention is supported for all models and for all cohorts in our model (p < .01). It appears that a long customer life is sustained by a higher degree of purchasing across departments. Given a certain income, people need a longer time to fill their needs if they purchase across the board rather than in a focused manner. When calculating the risk ratio for this variable, we found that purchases in an additional department are associated with a decreasing hazard of between 59.6 and 72.8%, depending on cohort. Thus, it seems to be desirable for the firm to induce customers to engage in cross-departmental shopping. Therefore, H2 and H3 are supported. This is an important finding because the effect of cross-buying on lifetime duration has not yet been documented.
Focus of buying. We did not advance a directional hypothesis with respect to focus of buying (β3) because of conflicting arguments. The empirical test resulted in a negative relationship between focused buying behavior and lifetime duration. Thus, the result is in line with the results of the cross-buying construct--that is, broader buying is generally associated positively with an increase in lifetime duration.
AIT. We hypothesized that the AIT (β 4) is related to profitable customer lifetime duration in an inverse U-shaped fashion. That is, the longest profitable lifetime should be associated with intermediate interpurchase times. We tested for this relationship by introducing a nonlinear term AIT2 (β 5). We find support for our hypothesis with both terms being significant at p < .01 and having the hypothesized sign (β4 positive andβ5 negative). That is, lifetime tends to be shorter when interpurchase times are either short or long, and lifetime is longest with an intermediate value of AIT. Therefore, H4 is supported.
Altogether, the impact of the core exchange variables on profitable lifetime duration is substantial. Between 65.2 and
69.7% of the variance is explained by this group of variables. Again, this demonstrates that the exchange variables dominate even in a noncontractual situation.
Returns. Regarding the proportion of returned goods (γ 1), we assumed a negative association of returns and profitable lifetime. That is, the higher the proportion of returned goods, the lower is the associated profitable lifetime duration. Our original results (not shown in Table 5) show that the effect was significant at p < .01 but had a positive sign for all three cohorts. Thus, our hypothesis that higher returns are a sign of greater dissatisfaction and lead to shorter lifetimes is not supported (H5). A possible explanation for this outcome could be that customers who returned merchandise had a positive encounter with the firm's service representatives, which then might affect their future purchase behavior (Hirschman 1970).
Managers told us (on further inquiry) that heavy buyers tend to return proportionately more. A possible reason for this could be that these buyers are accustomed to the procedures of returning merchandise and that they are able to do it efficiently. Thus, it might be that these customers perceive the return process as part of the mail-order buying process. If this effect dominates, a positive relationship would be expected. Likewise, this would probably mean that as customers spend more with the firm, the effect should be stronger.[ 6] To pursue this line of thought, we added, post hoc, an interaction between amount of purchases and the proportion of returns to the model. We present the final results including the interaction in Table 5. The interaction is significant for all three cohorts (p < .01).
Thus, we find evidence for the conjecture that the degree of returns depends on the degree of spending. Thus, the positive impact on lifetime duration is greatest when the level of purchases and level of returns are high. Figure 4 depicts this situation graphically. Clark, Kaminski, and Rink (1992) show evidence of the impact of positively disconfirming complainants' expectations to achieve (restore) satisfaction. Moreover, the impact of this response seems to be maintained over time. Therefore, we believe that proportionally higher returns might be an indication of this positive disconfirmation. For example, if the firm has a no-hassle return policy and customers have come to accept the technical return procedures, greater satisfaction with the exchange can result and therefore greater profitable lifetime duration. It would be desirable to have stated satisfaction measures at hand to add additional validity to our results. Similar empirical support for our finding comes from Kesler (1985) who states that Omaha Steaks, a mail-order supplier of high-quality meat, found higher profitability for the customers for whom it had quickly resolved complaints.
Loyalty instrument. The loyalty instrument (γ2) is significantly related to profitable lifetime duration (p < .01). According to our hypothesis, the use of the charge card as a loyalty instrument leads to a higher lifetime. Thus, in our sample, issuing a charge card appears to be successful as a loyalty instrument because it seems to be associated with longer customer lifetime. Thus, H6 is supported. Note that the findings in the literature so far are not favorable in terms of loyalty instrument efficiency. However, in this case, it seems at least successful with respect to profitable lifetime duration. Nevertheless, we cannot make a statement about the cost-effectiveness of the program. This is in line with Day (2001) who suggests that though investments in relationship building programs result in relationship advantages, the effect on profits is far from clear. Although we find support for Day's first contention, his second proposition is much more difficult to test. In terms of magnitude of effect, the risk ratio analysis indicates that adopting the loyalty instrument is associated with a 45%-52% decrease in hazard of relationship termination--a substantive amount.
Mailings. We introduced the mailing variable ( 3) as an important control variable specified as a lagged effect. Recall that mailings and sales are typically not independent in a direct marketing context. The hypothesized effect on lifetime duration is positive. We find a positive, significant effect (p < .01) for all three cohorts, thus our decision to control for the variable is correct. Therefore, H7 is supported in that the mailing effort is significantly related to profitable customer lifetime duration.
Product category. We were concerned in our modeling effort that the choice of product category (γ 4)could have a systematic effect on a customer's lifetime. For example, it could be that durable goods (i.e., hard goods) have a potentially long lifetime, and thus there is little need for replacement, leading to a potentially shorter customer lifetime. However, this concern is not substantiated because the parameter 4 for the dummy variable is not significant (p >.1) for any cohort.
Effects of Observed Heterogeneity
Spatial location of customer. We argued that the spatial location is linked to a customer's tenure with a direct marketer (δ 1) such that the population density is inversely associated with customer lifetime duration. Our results confirm this hypothesis (H8) for two of the three cohorts (p < .05), thus underlining the need ( 1) to account for observed heterogeneity in duration modeling and ( 2) to demonstrate support for the transaction-cost minimization argument.
Income and age. In terms of the two demographic variables income (δ 2) and age (δ3), we find that age is not related to profitable lifetime duration (p > .05), but income is related (p < .01). Our model indicates that higher income is associated with longer lifetime. Thus, H9 is supported. Overall, the information on observed customer heterogeneity adds explanatory power to the duration model, above and beyond the exchange variables.
The data for validating the proposed framework are obtained from a high-technology firm located in the United States. The organization sells computer-related products to both small and large businesses. The product range includes personal computers, printers, software, networking equipment, servers, storage, e-business applications, and so forth. The data cover an eight-year period from 1993 to 2000. Given the product category, the eight-year period gives multiple opportunities for the businesses to purchase from this firm repeatedly. From this database, we chose a cohort of customers such that their first purchase occurred in the first quarter of 1993. We tracked these customers from their first purchases for a period of eight years. These customers had not purchased anything from this firm before this date. This resulted in a sample size of 4128 businesses. The number of purchases ranges from 4 to 39 across the sample with a median number of 11 purchases; the median interpurchase time is 179 days, and the median transaction amount is $18,481 for each purchase. Unlike the catalog industry, in which customers are contacted through mailing the catalog, in this firm, businesses are contacted through tele-salespeople (i.e., salespeople contact the businesses over the telephone). The details on the cost of each contact and the frequency of contact are also available at the firm level. We estimated the NBD/Pareto model parameters, as before, using the MLE procedure and computed estimates for P(Alive). We obtained the profitable lifetime duration estimates using the first three of the four steps described previously. We used the lifetime estimates in the calibration of the hazard model (see Equation 5). The majority of the variables in Equation 5 are common to both the B-to-C and the B-to-B settings. A few points of differences between these two settings are worth noting. We replaced the loyalty instrument variable with a dummy variable whether a line of credit was available or not. Similarly, we replaced the mailings variable with the number of contacts and operationalized the product category variable as hardware versus software. In terms of heterogeneity variables, we replaced the age and the income variables with the age of the firm and the average annual revenue of the firm.
The results for the B-to-B case are similar to that of the B-to-C catalog industry. The most notable observations are that the variance explained by the key exchange, other exchange, and customer heterogeneity variables are 59%, 11%, and 4%, respectively. The businesses buying in only one department such as personal computer or networking equipment have shorter lifetime duration, firms with larger revenues have longer lifetime duration, and age of the firm was not significant. The AIT variable is significant but not the square of the AIT. However, the coefficient for AIT is negative, which indicates that shorter AIT is associated with longer profitable lifetime duration. It is possible that the range of the data is such that only a linear relationship is observed. Overall, there are more similarities than differences between the two settings (see Table 6). The findings indicate that the proposed theoretical framework holds across settings, providing evidence for generalizability of the results.
The research objectives of this study are fourfold. First, we wanted to empirically measure customer lifetime duration that integrates projected profitability in a noncontractual setting. Second, we demonstrated the superiority of the proposed framework over the commonly used RFM framework and customer value framework. Third, we attempted to show how an analysis of certain factors (the antecedents) could help explain systematic differences in profitable customer lifetime duration, both in a B-to-C and a B-to-B setting. Fourth, we wanted to discuss how managers can use this knowledge in their decision making.
The use of a proportional hazard framework with finite lifetime estimates obtained from the combination of NBD/ Pareto framework and expected NPV allows for a comprehensive and accurate analysis. We show that the model has a substantive explanatory power. Specifically, our analysis leads to the following implications:
The proportion of variance explained in the dependent variable rests to a large degree in the exchange variables. Thus, we find that the information that is quite easily available in the form of purchase history data is also strongly associated with customers' profitable lifetime duration. In terms of actionable results, this is good news for the manager who can draw on an existing set of well-known variables to start managing profitable customer lifetime.
The B-to-C data support our assertion that the AIT is related in an inverse U-shaped fashion to profitable customer lifetime. Both long and short interpurchase times are seemingly associated with lower overall lifetime durations, however, for different reasons. The common view is that the higher the buying frequency, the "better" is the customer. This might be true if managers take a short-term cash-flow perspective. Traditional promotion-oriented marketing has clearly focused on this short-term response. However, what becomes clear here is that the evaluation of success depends much on the short-and long-term stance. In this context, the real impact of buying frequency is revealed only by examining the impact on lifetime duration. For many reasons (e.g., data availability, methodological issues), managers could not make a valid judgment about the long-term impact of relationship characteristics simply because it could not be measured. Given the available knowledge (i.e., about the short-term behavior and response), we implemented short-term actions. Our approach shows that, with respect to lifetime duration, it might mislead managers to regard high-frequency purchasers as the most attractive. The results show that if managers take a long-term (lifetime) perspective, it is the buyers with intermediate frequency that are most likely to be long duration customers.
Building on this reasoning, we conducted additional analyses on the drivers of this phenomenon. Specifically, why is it that buyers with a low AIT are not as profitable? In other words, is their profitability score low because of fewer purchase incidences or because of low expenditures given their buying frequency? It is the fewer purchase occasions (albeit high frequency) that distinguish this segment. Although their spending level is not much different from the overall average, these buyers are characterized by a sequence of fewer closely pulsed purchases. This behavior is consistent with that of a variety-seeker who does not stop buying because of dissatisfaction, but rather leaves the relationship because of the increased utility of variety.
A key implication is that managers are most likely successful in maximizing the customer's value to the firm if they fully understand the impact of specific relationship characteristics on short-and long-term success measures. Thus, our empirical finding is an important contribution to the managerial toolkit of actively managing customer equity. In terms of actionable results, this means that managers can now optimize their marketing efforts with respect to traditional short-term as well as new long-term objectives. There is no doubt that some traditional marketing practices will be questioned in the future. Concretely, the results enable managers to compare a customer's current interpurchase times with lifetime duration maximizing interpurchase times and adjust their solicitation plans accordingly. This should result in longer lifetime customers as well as higher customer satisfaction because the benefit to the customer is a communication policy, which is more consistent with his or her real needs. The insight in lifetime maximizing interpurchase times can and should be combined with other key long-term measures such as customer profitability.
The results enable managers to reassess their perceptions about the reasons for product returns. It was shown that longer lifetime customers have proportionally higher returns than shorter lifetime customers. The implications are twofold. First, it establishes that higher returns can be a symptom of a durable relationship and, thus, that the firm can use the information about return behavior to infer lifetime affinity. Clearly, the argument is only one way. There could be many reasons customers do not return what they bought--they are not dissatisfied, they are not accustomed to the return process, or they are dissatisfied and do not want to deal with the firm anymore. In this case, all the managers know is that the likelihood of being a long-term customer is lower. This knowledge might lead a manager to conduct more in-depth investigations such as customer feedback surveys to assess their state-of-the-relationship. If the customer returns merchandise, it shows that the customer does not shy away from the procedural hassle. Also, it gives the firm a further opportunity to interact and bond with the customer. This is also empirically supported by the findings of Bowman and Narayandas (2001) who find that loyal customers value how they have been treated (in a customer-initiated contact) more highly than whether they walked away from the contact having not received something valuable from the company. We are not suggesting that the firm should promote returns (as if it would make customers stay longer).
However, managers should not necessarily be concerned about the relationship quality if returns from long duration customers are higher.
The second implication is that managing the cost component of the firm's return policy becomes highly critical. For example, a customer might order two different sizes of the same shirt with the purpose of trying on each and returning the one that fits worse. This behavior is more compatible with that of a regular, long lifetime client than that of a new client. If the client knows that the return process is hassle-free, he or she might as well use it. Thus, the managerial challenge is to satisfy the important client while containing operational cost.
The implication on the loyalty instrument policy is somewhat similar to the returns variable. From a response perspective, the loyalty instrument seems to be associated with longer lifetime customers, whichever way the causality works. By signing up to the card program, customers indicate that they are committed for the long haul. This is an important early signal for the manager, given that predictions on future lifetime duration are not easy to make. Thus, a manager can take this information and start treating this client as a likely long-term customer with all the relevant marketing-mix offers he or she deems appropriate. Besides the loyalty instrument's impact on exchange characteristics, cost management seems to be a major factor. Although an overall consensus on the effectiveness of loyalty programs is still not clear, the cost implications remain large (Day 2001).
Demographics matter not only with respect to purchase response and choice but also with respect to a long-term objective such as lifetime duration. In particular, we find that the higher the income and lower the population density, the greater is the likelihood of being a long-term customer. The overall incremental variance explained is relatively low, which is in line with other published research (Gupta 1991; Rossi, McCulloch, and Allenby 1996). However, the notion that an increasingly diversified and individualized population renders the value of demographics less useful (Sheth, Mittal, and Newman 1999) does not appear to hold. Thus, segmentation exercises based on demographic characteristics of the target customer groups still seem warranted. A significant advantage of the findings related to spatial location and demographics is regarding the acquisition of customers. If managers in the catalog industry are interested in recruiting new customers, a potential fertile group would be higher income people in lower population density areas. Besides these substantive conclusions, our model provides the manager with a comprehensive general framework to analyze the role of antecedent factors in profitable customer lifetime duration. The time-varying nature of our model captures dynamic purchase behavior and is an advancement over many static approaches. Specifically, the model enables managers to dynamically update and quantify the direction and degree of impact on lifetime in a noncontractual context--something that has not been established so far. Thus, the model can be used as a tool for making profitability-based retention decisions. For example, as new data become available, managers can reestimate the model and identify customers who are likely to be profitable in the future but who stopped purchasing from the firm. Consequently, appropriate retention actions can then be taken to induce repeat purchases from these customers. In addition, the model enables managers to identify the point when a relationship turns unprofitable--even though these relationships might generate revenues. This is certainly advancement over other methodologies that focus on revenue as the key dependent variable. Thus, the model allows for selecting customers when lower levels of investments or no investment at all are more appropriate marketing decisions.
The firm that provided the B-to-B database actually collects data on share-of-wallet from the buying firms' universe. This information can be combined with the profitable lifetime duration measure that is obtained in this study to create a 2 x 2 matrix as shown in Figure 5. The information in the figure should help managers deal with the existing customer. As indicated in Figure 5, if a customer belongs to the high share-of-wallet and high profitable lifetime duration, marketing strategies should aim at nurturing, defending, and retaining existing customers along with rewarding them for being such loyal customers. In contrast, if a customer belongs to a low-low segment in Figure 5, the focus could be on reducing marketing expense, considering divesting, or even outsourcing those customers for servicing by an outside agency that can operate on a percentage basis.
If a customer has high share-of-wallet but low profitable lifetime duration, the firm can use selective or optimal mailing/contract strategy to reduce cost, ensuring up-selling and cross-selling to increase the profit potential. Finally, if a customer's share-of-wallet is low but has high profitable lifetime duration, marketing strategies that focus on luring customer dollars from competition should be developed along with strategies for up-selling or cross-selling to encourage higher spending and lucrative loyalty programs. In summary, the findings in our study support the strategies suggested here.
In general, our analysis shows the relevance and importance of establishing customer relationship management capabilities. Our study shows that customers are heterogeneous on an important relationship characteristic--lifetime duration. We have documented how this heterogeneity can be measured and what the drivers of this heterogeneity are. Managers must take this knowledge and initiate systematic and appropriate supply-side responses. An appropriate firm response would be to develop customer management capabilities in situations in which customer behavior heterogeneity is present. Besides establishing the appropriate data recording, duration measurement, and profitability analysis capabilities, managers must develop systematic response procedures toward short-and long-life customers. For example, long-life customers can have access to more expensive but also more effective customer support. Alternatively, this can mean that short-life customers are subject to relation-ship enhancing activities. Regardless, of the specific marketing tactics, what is common to this kind of response is that firms must build their capabilities of handling an increasingly diverse customer base. In line with Day (2001), we believe that these are first and foremost strategic capabilities. Also, we believe that building these new customer management capabilities, which enable firms to establish competitive advantage, is one of the next frontiers of modern management. Given the importance of actively managing the customer base, our suggested framework provides an important tool toward this end.
It is important to point out the limitations of this study, some of which could be worthwhile topics for further research. Our empirical setting provides ways of testing previously unexplored issues in lifetime duration modeling, but it also limits the generalizability of the study. Although our data come from established companies in important industries, further empirical analyses in other noncontractual settings are necessary. Thus, although we implement a framework for analysis, other applications of this framework to cohort databases should yield fruitful insights.
Our procedure of calculating individual-level future contribution margin is based on a static extrapolation of a customer's ECM. Given the purpose of this study, and that the number of observations per household is limited, this measure seems adequate. Furthermore, we did not observe any trend in the purchase amount after the initial purchase in all of our datasets. Therefore, the use of an average measure is adequate. However, if enough purchase history observations are available, we could argue that incorporating any trend in purchase behavior is informative as well.
Another possible shortcoming could be the operationalization of the exchange construct. We use exclusively behavioral measures for operationalizing the duration antecedents. Although they provide for a strong and rich explanation, we could argue that attitudes such as satisfaction or attitudinal loyalty might interact with these behavioral constructs. However, we must leave this test to future inquiries because of the unavailability of appropriate data. Furthermore, if the correlation between the purchases amount variable and the AMCM is found to be high, the purchase amount variable can be removed from the analysis to avoid the problem of endogeneity.
In our calculation of a customer's NPV, we incorporate future marketing-mix costs implicitly by mirroring marketing-mix expenditures during the observation period. Although this is an important characteristic of the process, there are two aspects that are not fully considered. First, changing the amount of the marketing mix in the future might alter purchase behavior such that unprofitable customers become profitable (and vice versa). Second, our approach does not incorporate the quality of the marketing mix. For example, although our model might recommend letting a customer go, a different, better marketing mix for that customer would actually enable him or her to be profitable. Making a prediction conditional on marketing-mix expenditures would be an advancement over the current approach. We do not consider these conditional effects because both future marketing costs and purchase behavior are unknown. Also, Dwyer (1997) highlights the vexing difficulty of separating the two effects in a holdout scenario. Further research could focus on different patterns of expected marketing-mix costs in the holdout period to consider the consequences of varying the marketing-mix elements across customers.
From a technical perspective, the use of a two-step procedure of our model could be considered a limitation. However, the two-step modeling process is not particularly new to our problem but has been used and applied to many other marketing situations (e.g., Takada and Jain 1991). A well-known example is the new product diffusion context in which the first step consists of estimating the diffusion parameters and the second step regresses these parameters on a set of certain independent variables. Furthermore, although it has been shown that the benefit of integrating the two estimation procedures yields a gain in efficiency, this gain is typically small (Krishnamurthi and Raj 1988). The two-step approach also has an interesting precedent in a Grangerian sense in the econometrics literature. Our approach calculates and removes the effect of the recency of the last purchase in the NBD/Pareto model. Because the outcome variable P(Alive) has been prewhitened of the former variable's effect, the second step examines only the effect of what is left.
Estimation of P(Alive) As a result of the computational constraints imposed by the MLE, method-of-moment estimates have been the method of choice so far (Reinartz and Kumar 2000; Schmittlein and Peterson 1994). Nevertheless, there is nothing wrong with using method-of-moment estimates. First, method-of-moment estimation is advocated as a method of choice by the authors of the NBD/Pareto model (Schmittlein, Morrison, and Colombo 1987) and has been used by Schmittlein and Peterson (1994) in their empirical application. Second, Morrison and Schmittlein (1981) show for the NBD model that method-of-moments and MLE yield approximately the same results. Similarly, Gupta and Morrison (1991) show in a simulation study that method-of-moment estimation and MLE yield similar results when using number-of-purchases data (similar to our study). Thus, there is support in favor of the more manageable method-of-moment routine.
Using the likelihood as given by Schmittlein, Morrison, and Colombo (1987), we estimate the four parameters of the NBD/Pareto model (r,α, s, β) with a Fortran routine. The likelihood is
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
with M as a random sample of customers and customer i made XI = xi purchases in (0, Ti) with the last transaction time at ti. (The probability on the right of Equation A1 is given by Schmittlein, Morrison, and Colombo [1987], Appendix, Equations A15 and A16.) The resulting MLE parameters are r = 3.01,α = 9.65, s = .82, and β= 11.91 (estimation horizon is 30 months). The parameters are consistent with the estimates obtained by Reinartz and Kumar (2000) who used estimates of r = 4.24, α= 14.95, s =.93, and = β13.85. In particular, the critical parameters r/α = .312 and s/β = .069 are similar to those used by Reinartz and Kumar (2000) (r/α =.281 and s/β = .069), resulting in little bias in the P(Alive) estimates (see Table 3). Because the results are robust and the computational resources required for MLE are substantially larger, we can recommend the method-of-moment estimation for future endeavors.
The model parameters can be explained as follows: The variation across customers in their long-term purchase rate is reflected in the estimate of r only and is independent of . The larger the value of the shape parameter r, the more homogeneous is the population of customers in terms of purchase rate. Thus, r can be viewed as an overall inverse measure of the concentration in purchase rates across households. The larger the value of the shape parameter, the more homogeneous is the population of customers in terms of dropout rate. The concentration in dropout rates, , depends on the parameter s only. Overall, the model estimates seem reasonable and show a high degree of face validity and internal consistency. With the distribution parameters already calculated, the characteristic of interest is the probability that a customer with a particular observed transaction history is still alive at time T since trial. Schmittlein, Morrison, and Colombo (1987) show that this probability depends on the customer's past purchase history (through the number of purchases x) and the time t (since trial) at which the most recent transaction occurred. The desired probability for > is given by Schmittlein and Peterson (1994) as
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where a1 = r + x + s; b1 = s + 1; c1 = r + x + s + 1; z1(y) = (α-β )/(α + y); F(a1, b1; c1; z1) is the Gauss hypergeometric function, r, , s, = model parameters; x = number of purchases; t = time since trial at which the most recent transaction occurred; and T = time since trial. The corresponding probabilities for > and = are given by Schmittlein and Peterson (1994, p. 65).
The importance of analyzing cohort data is well exemplified in the following discussion. In the aggregate form, the curve in Figure 3 sheds light on a hidden, yet extremely important, phenomenon: What are the characteristics of the customer defection process? Although a cross-sectional time-series analysis of customers would yield an average P(Alive) for each period, it would yield no insight into the dynamic pattern of customer defection. In other words, the average P(Alive) would contain people who are early in the relationship and who are late in the relationship. This would yield a constant P(Alive) over time, whereas in our case P(Alive) is declining over time. Thus, a time-series cross-section analysis answers how many active customers the firm has, whether that number is growing or declining, and which individual customer is most likely to be active. Yet an analysis of the lifetime activity pattern is only possible by combining the NBD/Pareto model with cohort data analysis
(i.e., customers having the same starting period). Thus, to reveal the lifetime activity pattern at the aggregate level, we use only the cohort data.
The authors thank the JM reviewers, Reinhard Angelmar, Ed Blair, Scott Baggett, Rajesh Chandy, Trichy Krishnan, Rajkumar Venkatesan, and Anish Nagpal for their helpful comments. They also owe special thanks to a catalog retailer and a high-technology firm for providing the data for this study. This research was supported in part by a grant from the INSEAD Research & Development department. The authors also thank Renu for copyediting the manuscript.
NOTES [1] We thank a reviewer for suggesting this possible effect.
[2] Note that AMCMit is the unconditional expectation of the contribution margin in a future period. Because the contribution margin is not conditional on purchase incidence, it is calculated from past periods--averaging over both, purchase and nonpurchase periods. This approach is applicable when the goal is to calculate the NPV of all future contribution margins, as is the case in our situation. Another possibility would be to use the conditional contribution margin [E(CMBuy)]. This approach is particularly suitable if the goal is to predict discrete purchase incidences and expenditures per incidence (e.g., whenever decision makers want to optimize time and degree of marketing intervention). However, this is not the goal of our study.
[3] We thank a reviewer for suggesting the benchmark comparisons with the state-of-the-art RFM scoring and past customer value scoring.
[4] The dependent variable for the RFM regression is the purchase amount in season 3 (i.e., month 13-18) and season 5 (i.e., month 25-30) to reflect two different time periods. The eight independent variables are measures for R (whether customer bought in previous season), F (number of times bought in previous season), M ($ purchases in previous season), cross-buying (in previous season), depth of buying (up to focal period), spatial location (population density), income, and age. After estimating the coefficients, customers are scored on their purchase behavior in the current season (i.e., season 3 and 5). The score is used to determine the selection.
[5] Conceptually and empirically, it is important to include information on purchase amount in the duration model. Nevertheless, it could be argued that purchase amount is potentially correlated with AMCM. The reason it does not represent an endogeneity is because the purchase amount variable represents a revenue figure, whereas the AMCM variable represents a profit figure. It has been well established through previous research (Mulhern 1999; Niraj, Gupta, and Narasimhan 2001) that customer profitability varies tremendously through the simultaneous variation of revenues and cost per account. The second point of difference comes in through the measurement. Whereas purchase amount is measured as a six-month moving average, the AMCM is measured over the customer's lifetime. Although the time periods may have some over-lap, the value of purchase amount that goes into the computation of contribution margin is different from the value of purchase amount used for the purchase amount measure. Empirically, we tested for the correlation between purchase amount and AMCM and found it to be moderate (.41-.52) across data sets. Thus, the magnitude of the correlation between purchase amount and AMCM does not pose a threat to the validity of the analysis. We thank a reviewer for bringing this issue to our attention.
[6]We thank a reviewer for suggesting this interaction effect.
PHOTO (BLACK & WHITE)
DIAGRAM: FIGURE 1 Conceptual Model of Profitable Customer Lifetime
DIAGRAM: FIGURE 2 Database Structure for B-to-C Setting
DIAGRAM: FIGURE 3 Average P(Alive) for Cohorts 1-3
DIAGRAM: FIGURE 4 Interaction Between Proportion of Returns and Purchase Amount
DIAGRAM: FIGURE 5 Firm's Strategy Based on Share-of-Wallet Versus Profitable Lifetime Duration
Legend for Chart
A = Study
B = Data
C = Nature of Study
D = Highlights of Key Results/Remarks
A B
C
D
Dwyer (1997) n.a.
Description of the nature of customer lifetime
and the procedures for lifetime estimation.
Differentiation of always-a-share and lost-for-good customers.
Author proposes taxonomy for lifetime value estimation.
Crosby and Stephens (1987) Life insurance
Modeling satisfaction with service provider.
DV: satisfaction, retention.
Nonlapsing customers report higher satisfaction than
lapsed customers (customers followed for 13 months only).
Schmittlein and Peterson (1994) Office products in B-to-B context
Individual level analysis of purchase history.
DV: probability of being alive.
Model can be used to infer the likelihood of a customer of being
still active in a noncontractual context.
Model enables inferences about purchase and dropout process.
Li (1995) Long-distance telephone service
Proportional hazard model of customer tenure.
Model identifies variables (usage, marketing, and
demographic variables) that affect length of customer subscription.
Build profile of customers with high and low lifetime.
Bolton (1998) Cellular phone service
Model of the duration of customer's relationship
with firm (proportional hazard approach).
DV: customer tenure.
Customer satisfaction is related positively to subscription
duration.
Prior cumulative satisfaction is weighted more heavily
than recent satisfaction in decision to continue or not.
Because satisfaction plays a large role in explaining
subscription duration, understanding and managing satisfaction
becomes important.
Allenby, Leone, and Jen (1999) Financial brokerage services
Bayes model of customer interpurchase time.
The model allows managers to recognize when a customer
is changing his or her purchase patterns (i.e., showing signs
of defection). This prediction can be used managerially as
a signal for the firm to employ some kind of intervention.
Individual customer level prediction.
Reinartz and Kumar (2000) U.S. catalog retailer
Customer lifetime duration in a noncontractual setting.
Both, long and short lifetime duration customers can
be profitable to the firm.
"It is better to let go of some customers."
Present Study U.S. catalog retailer
High-tech B-to-B
Measuring profitable lifetime duration.
Explaining the variation in profitable lifetime duration.
Model uses time-varying variables for explaining the
impact of relationship characteristics on profitable
lifetime duration.
Tells which customers to let go and when to let go of
those customers.Notes: DV = dependent variable; n.a. = not applicable.
Legend for Chart
A = Sample Size
B = Pearson Correlation of P(Alive)*
C = Mean Average Percentage Error**
D = Mean Lifetime (Months)
E = Lifetime Standard Deviation
F = Percentage Right-Censored
G = Correct Classification
A B C D E F G
Cohort 1 4202 .9981 5.83% 29.3 7.5 42.9 92.7%
Cohort 2 4965 .9988 5.22% 28.6 7.7 45.6 91.1%
Cohort 3 2825 .9987 4.75% 27.8 7.2 47.2 92.5%*Generated from the NBD/Pareto estimates of Reinartz and Kumar (2000) and those of the current study, respectively.
**The P(Alive) of Reinartz and Kumar (2000) and P(Alive) of the current study.
Percentage of Cohort Evaluation at Evaluation at
(Selected from Top) 18 Months ($) 30 Months ($)
Customer
selection
based on
NBD/Pareto with ECM
30 (n = 1260) 590,452 318,831
(123,076) (62,991)
50 (n = 2101) 756,321 361,125
(148,922) (61,636)
70 (n = 2941) 864,114 380,855
(165,735) (60,305)
Advanced RFM
30 (n = 1260) 442,534 140,781
(78,555) (27,582)
50 (n = 2101) 599,100 186,267
(99,831) (36,380)
70 (n = 2941) 687,163 216,798
(110,244) (42,839)
Past Customer Value
30 (n = 1260) 508,997 179,665
(86,820) (35,916)
50 (n = 2101) 648,772 210,860
(112,723) (41,729)
70 (n = 2941) 789,526 225,910
(138,124) (44,738)Notes: Profits are in parentheses. Results are similar for Cohorts 2 and 3.
Dependent Variable Measured as
Profitable Lifetime[i]* Months
Hypothesized
Directional Impact
Independent Variables Measured as on Profitable Lifetime
Purchase amount[it]** Monthly spending level ($), +
moving average over six-month period.
Cross-buying[it] Number of departments shopped in (+)
Focus of buying[i] Dummy: 1 = buys consistently in single
department only Nondirectional
0 = all other hypothesis
AIT[it] Number of days (+)
Inverse U-shaped
relationship for
AIT and AIT
(AITit)[2] (Number of days)[2] (-)
Inverse U-shaped
relationship for
AIT and AIT[2]
Returns[it] Proportion of returns (-)
(of sales)
Loyalty instrument[i] Ownership of charge card. (+)
Dummy variable,
1 = owns card, 0 = no card
Mailings[it] Number of mailings sent in last (+)
six months (= 1 season) since
current t, exponential decay,
one month lag
Product category[i] 1 = more then 50 % of purchases
in soft goods, No
0 = more then 50 % of purchases directional
in hard goods hypothesis
Population density Number of people in two-digit
zip code (-)
Income[i] Scale from 1 to 9
(1 is < $10,000 and
9 is > 150,000) (+)
Age[i] Age of individual in years No
directional
hypothesis*Subscript "i" = variable value does not change over time, subscript "it" = time-varying variable.
**Time-varying variables are updated each month.
Legend for Chart
A = Independent Variables
B = Parameter
C = Cohort 1 Model 1[t]
D = Cohort 1 Model 2
E = Cohort 1 Model 3
F = Cohort 2 Model 1
G = Cohort 2 Model 2
H = Cohort 2 Model 3
I = Cohort 3 Model 1
J = Cohort 3 Model 2
K = Cohort 3 Model 3
A
B
C D E
F G H
I J K
Purchase amountit
[Beta]1
.0497* .0360* .0354*
(.00209) (.00212) (.00213)
.0486* .0373* .0364*
(.00186) (.00192) (.00192)
.0433 .0341* .0324*
(.00228) (.00240) (.00239)
Cross-buyingit
[Beta] 2
1.389* 1.293* 1.276*
(.0407) (.0417) (.0419)
1.226* 1.172* 1.154*
(.0327) (.0338) (.0340)
.970* .908* .912*
(.0346) (.0356) (.0360)
Focus of buyingI
[Beta] 3
-.315* -.257* -.270*
(.0647) (.0660) (.0662)
-.297* -.306* -.269*
(.0624) (.0630) (.0632)
-.289* -.213** -.177**
(.0841) (.0862) (.0865)
AITit
[Beta] 4
.0121 * .0133* .0127*
(.000521) (.000521) (.000521)
.0146* .0153* .0147*
(.000515) (.000517) (.000519)
.0171 * .0178* .0171*
(.000718) (.000724) (.000726)
(AITit)2
[Beta] 5
-8.994 E-6* -9.880 E-6* -9.487 E-6*
(6.276 E-7) (5.900 E-7) (5.892 E-7)
-.0000121* -.0000123* -.0000119*
(6.243 E-7) (6.013 E-7) (6.046 E-7)
-.0000151* -.0000154* -.0000147*
(8.912 E-7) (8.660 E-7) (8.747 E-7)
Returnsit
[Gamma] 1
-- -2.214* -2.050*
-- (.222) (.226)
-- -1.690* -1.557*
-- (.214) (.215)
-- 1.323* 1.323*
-- (.320) (.320)
Loyalty instrumenti
[Gamma] 2
-- .666* .685*
-- (.0577) (.0577)
-- .745* .753*
-- (.0482) (.0484)
-- .598* .614*
-- (.0618) (.0622)
Mailingsit
[Gamma] 3
-- .00552* .00686*
-- (.00153) (.00154)
-- .00628* .00712*
-- (.00148) (.00148)
-- .00610* .00898*
-- (.00224) (.00229)
Product categoryi
[Gamma] 4
-- -.0278 -.0554
-- -.0360 -.0476
-- -.0422 -.0740
-- (.0437) (.0438)
-- (.0414) (.0414)
-- (.0556) (.0558)
Returns x purchase amountit
[Gamma] 5
-- .221* .208*
-- .148* .134*
-- .105* .0985*
-- (.0188) (.0189)
-- (.0155) (.0155)
-- (.0186) (.0183)
Population densityi
[delta] 1
-- -- -3.475 E-8*
-- -- 2.23 E-8**
-- -- 5.305 E-9
-- -- (1.252 E-8)
-- -- (1.196 E-8)
-- -- (1.584 E-8)
Income i
[delta] 2
-- -- .124*
-- -- .111*
-- -- .133*
-- -- (.00863)
-- -- (.00805)
-- -- (.0104)
Age i
[delta] 3
-- -- 4.032 E-7
-- -- 3.628 E-6
-- -- 4.684 E-6
-- -- (4.668 E-6)
-- -- (4.123 E-6)
-- -- (5.446 E-6)
-2 Log-Likelihood
R 2
13728.6 13337.7 13126.8
15.678.0 15200.7 15004.6
9089.4 8807.7 8639.2
.697 .727 .743
.684 .719 .730
.652 .672 .693*Significant at p < .01.
**Significant at p < .05.
[t]Signs of coefficients have been revised to reflect effect on lifetime.
Legend for Chart
A = Hypothesis
B = Description
C = B-to-C Setting Result
D = B-to-B Setting Result
1
Profitable customer lifetime duration is positively
related to the customer's spending level.
Supported
Supported
2
Profitable customer lifetime duration is positively
related to the degree of cross-buying behavior that
customers exhibit.
Supported
Supported
3
Profitable customer lifetime duration is related to
the focused buying behavior that customers exhibit.
Supported
However, the relationship is negative, indicating
that buying in only a single department results in
shorter lifetime duration.
Supported
However, the relationship is negative, indicating
that buying in only a single department results in
shorter lifetime duration.
4
Profitable customer lifetime duration is related to
AIT in an inverse U-shaped manner, whereby intermediate
AIT is associated with the longest profitable lifetime.
Supported
Partial support
Only the linear term is significant.
5
Profitable customer lifetime duration is inversely related
to the proportion of merchandise that customers return.
Not supported
However, the interaction of returns with purchase
amount variable is significant.
Not supported
However, the interaction of returns with purchase
amount variable is significant.
6
Profitable customer lifetime duration is positively related
to the customer's ownership of the company's loyalty
instrument (B-to-C) or the availability of line of credit (B-to-B).
Supported
Supported
7
Profitable customer lifetime duration is positively
related to the number of mailing efforts of the company
(B-to-C) or the number of contacts (B-to-B).
Supported
Supported
8
Profitable customer lifetime duration is higher for customers
living in areas with lower population density (B-to-C) or
businesses existing in lower population density (B-to-B).
Supported
Not supported
9
Profitable customer lifetime duration is positively related to
the income of the customer (B-to-C) or income of the firm (B-to-B).
Supported
Not supported
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By Werner J. Reinartz and V. Kumar
Werner J. Reinartz is Assistant Professor of Marketing, INSEAD. V. Kumar is ING Aetna Chair Professor and Executive Director, ING Aetna Center for Financial Services, Department of Marketing, School of Business Administration, University of Connecticut.
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Record: 171- The Incomplete Autobiography of an Immigrant Marketing Professor. By: Mahajan, Vijay; Clark, Terry. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p169-173. 5p.
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Section: Book ReviewsThe Incomplete Autobiography of an Immigrant Marketing
Professor
At the doctoral consortium held at the Emory University in 2002, Terry Clark, editor of the Journal of Marketing book review section, asked me if I would be interested in writing my professional autobiography. I immediately agreed without fully realizing the implication of my commitment. It turned out to be one of the most difficult things I have done in my life. I did not know where to begin or where to end. My professional autobiography spans the previous 30 years and is still incomplete. I believe (and I hope) that my best years are still ahead of me. I still have miles to go.
Coincidentally, after I made the commitment to Terry, my university permitted me to accept a temporary position as dean of the newly established Indian School of Business in Hyderabad. After living for more than 30 years in the United States, I was back in India, the land of my birth that I left in January 1971. The guiding principle of my life comes from a line in the Gita, one of the holy Hindu scriptures, that was reinforced in me by my dear mother who died at the age of 88: "Work hard and leave the results to God! Have no expectations!" That line came to me while I was traveling back to Hyderabad to begin my new position. It seemed to be the perfect time to reflect on my career.
The Years in India
My parents came from the town of Udhampur (when they were born, the population was approximately 2000 people) in the state of Jammu and Kashmir. They were married when my mother was 13 years old and my father was approximately 17 (of course, they did not know their birthdates, and apparently, no records were kept at that time). My grandfather was a small entrepreneur and was uneducated. He migrated from Moud, a small hill village north of Udhampur, where his parents herded cattle and sold milk and cheese to English hunters. My people are called Dogras. Although unrelated to us, Dogra kings ruled Jammu and Kashmir at that time.
My grandfather believed that the best way to fight the Englishmen, who ruled India at the time, was to learn their language and become a lawyer. My father was supposed to fulfill that dream, but he did not. Much to the disappointment of my grandfather, he dropped out of college after he married my mother. Thanks to her parents, my mother was one of the few girls who had studied until the eighth grade. In her thinking, she was many years ahead of her time, and she was determined to fulfill my grandfather's dream and perhaps her dream as well. She bore my father 11 children (unfortunately, one child died because of an unknown sickness) and fulfilled her mission to educate all of us.
I am the eighth child. I was born in Jammu, a neighboring city to Udhampur. My grandfather had pushed my father to go to this "big" city and start a business in textiles with my uncle. Whereas my mother gave us her mission and determination, my father's hard work gave us the financial means to study. They became our mentors and role models. Their large family, consisting of their children, grandchildren, and great-grandchildren, now includes doctors, engineers, academics, businesspeople, and bureaucrats. My parents' sacrifices are worshipped by all of us. My father's biggest regret (he died at the age of 92 last year) was that none of his children joined his business. He always blamed my mother for this.
I was born a few months after Mahatma Gandhi was assassinated and India became a Republic. During that time, we had to obtain a "permit" (a kind of visa) to travel to India to study, vacation, or conduct business. It was a turbulent time; Salman Rushdie has called my generation Midnight's Children. Before I graduated from a government high school in Jammu, the education system had been redesigned at least three times. The education system in the state of Jammu and Kashmir is still very weak. Anyhow, I was first among all the high school students in Jammu and Kashmir.
After leaving high school, I took the common examination that is given to all the high school students in India, and to my great shock, I was the only student in my state to pass the entrance examination for the Indian Institute of Technology (IIT). Neither I nor my parents nor my parent's friends knew much about the IIT at the time (a high school classmate of mine had filled out my application). However, one of my brothers, who was studying engineering in Assam, insisted that I attend IIT, Kanpur. Because joining my father's business was not an option, I wanted to become a doctor. However, I was advised that there were already too many doctors in the family and that I should become an engineer. I was further advised to become a chemical engineer, but that turned out to be a bad decision because I would never be able to go back to my family in Jammu. Chemical engineers were not needed in Jammu and Kashmir. Indeed, after graduation in May 1970, I could not find any suitable job in India.
As a safety net, I had, as had many other IIT classmates, applied to two U.S. graduate programs, Texas A&M University and the University of Texas at Austin. My thesis advisor, Arvind Kudchadkar, was a graduate of both of these universities. I had a lot of fun at IIT, and I am not sure my advisor thought I was a serious candidate for graduate studies. Regardless, he wrote me a cautious letter of recommendation. After six months of trying to find a job in India, I decided to move to the United States. I promised my father that I would finish my master's degree in one year and return home. That was the fall of 1970.
The U.S. Chapter: Becoming a Marketing Professor
I landed in New York City with my certificates, some clothes, a few hundred dollars, and a new identity. I officially changed my name from "Vijay" to "Vijay Mahajan" because I could not obtain a visa without a last name. My cousin, who had come a few years earlier, bought me clothes and showed me Manhattan. I was dazzled. He told me that this country was so free that I could literally do anything I liked, and he directed my attention to some posters of X-rated movies to make his point!
I had come on the I-20 form of the Texas A&M University because I never received the University of Texas's version of the form. However, my cousin advised me to go to Austin first. It seems that the I-20 form from Austin never made it to India. Because I had not responded, I was told that I could join the Texas program, but no scholarships were available for at least a semester. Margaret Kidd, a remarkable woman at the international office, came to my rescue. She found me a job working as a receptionist at a dorm for international students. I worked four nights a week in exchange for a free room. I lived on potato chips, bread, and milk for at least six months. I threw up the first time I ate Indian food at the home of one of my classmates. Although I was mostly vegetarian at that time, I did eat chicken occasionally. I hate to describe my condition when someone told me that the chicken-fried steak that I had eaten and enjoyed at one of the functions was beef!
I began my graduate program in chemical engineering with Robert Gunn and worked on simulation models to estimate the thermodynamic properties of the high-boiling compounds. My studies were going well, and I was on schedule to finish a Master of Science in one year and go back to India. Part of my plan was to obtain a minor in business so I could have some "easy" courses. My classmates advised that I take "Introduction to Management Science" and "Introduction to Operation Management" with Milton Schoeman, a young professor in the business school who had recently joined the faculty with a doctorate from Case Western in operations research.
After I finished my first course with Milton Schoeman, he followed me like a shadow and eventually convinced me (and apparently a very stubborn associate dean) to join the doctoral program in operations management with a "big" assistantship teaching courses in operations management and business policy. He convinced me that education was always good and that three more years in the United States was not going to hurt my plans to return to India. My father was not happy, even though I was going to earn a doctorate in business. I was never able to explain to my businessman father why I wanted a doctorate in business and what good it is.
In the spring of 1972, I became the first Indian student, if not the first Asian student, to be admitted into the doctoral program in business at Texas. I did not feel challenged and finished the program in less than three years. On the advice of one of my fellow doctoral students from marketing, Dale Achabal, I asked a young associate professor of marketing, Robert Peterson, to serve on my committee. He hated my first dissertation proposal on policy capturing and advised my committee that "Vijay is capable of doing more" and "should look for another topic." I was told that I should talk to Peterson for advice because he was the most vocal person in my committee and that I had to follow his advice. Needless to say, I was not pleased. Peterson recommended that I read two articles that were receiving much attention in marketing in early 1974. One was by Frank Bass on a new product diffusion model in Management Science. The other was by Karl Jöreskog on structural equations published in Biometrika.
Coming from a chemical engineering background, the Bass article with differential equations intrigued me, but I would be lying if I said I always wanted to be a professor, much less a marketing professor. I would also be lying if I said I always wanted to teach and do research. Nevertheless, my encounter with this article was the beginning of my research career. I must admit that I have no formal training in marketing, economics, psychology, or statistics. One of the conditions for my admission into the doctoral program was that I take the MBA core courses. Thus, my introduction to marketing was the six-week core course taught by Mark Alpert in the summer of 1972.
In the fall of 1974, I gave a copy of my dissertation "Diffusion of Computers in the U.S. Hospitals" to a surprised committee. To write the dissertation, I had learned (on my own during the summer of 1974) discriminant analysis, diffusion models, survey research, and the U.S. health care system. I had also managed to get the dissertation sponsored by the Texas Hospital Association and had convinced an influential health care consultant to serve on my committee. I had no trouble defending my dissertation in the early spring of 1975. This was the day for Milton Schoeman and Robert Peterson to tell me "we told you so." I was Milton Schoeman's first and last doctoral student. A few years later, he had a heart attack and died.
I also did not take too many courses in management science, other than one with Abraham Charnes, who made me cry because he did not like my research presentation. I had no idea what he was teaching us in the class. However, I did pass the class. My committee recommended that if I passed the doctoral exam in the Industrial Engineering Department, I should not be required to take additional courses. As luck would have it, I passed the exam with very good marks and thus missed the opportunity to take additional courses from some bright faculty.
I thoroughly enjoyed my stay at Texas. I learned Latin dances, ate too many dinners at Milton Schoeman's and Robert Peterson's homes, and drained too many whisky bottles. I also made several professional friends, including Dale Achabal, Mark Alpert, Randy Batsell, Eli Cox, Bill Cunningham, Linda Golden, Bob Green, David Huff, Roger Kerin, and Bob Witt. Even after I left Texas in 1975, they continued to help me evaluate my career choices. On the advice of several of these friends, I decided to explore opportunities in the United States. By this time, my father had given up on me. He could not understand what I was doing in the United States, and he had already classified me as a "lost son."
I applied to several schools for a position in operations management without any luck. Unfortunately, there were not too many faculty of Indian origin in business schools at the time. The track record was unknown. Around that time, Mark Alpert suggested that I move to marketing, and he contacted a few schools for me. Joel Cohen, at the University of Florida, wrote to Mark and offered me a visiting position for a year. The Management Department at Texas agreed to keep me for one more year as a lecturer. Somewhat discouraged, I set my mind on going back to India. However, as luck would have it, in May 1975, Milton Schoeman received a call that the School of Management at the State University of New York-Buffalo was searching for someone in operations management to teach in the health care area. Excitedly, I flew to Buffalo for an interview in the middle of a big snow storm without winter clothes. However, my presentation and several appointments were canceled. I was desperate to leave Buffalo.
To my great surprise, I received a call the following day and was offered the job. To this day, I have not been able to figure out how I could get this job other than destiny. The school also agreed to sponsor me for a green card if I could obtain a "training visa" for one year from the immigration office in San Antonio. The immigration office declined, stating that there was no match between my education and the job. The position at the State University of New York-Buffalo was for an assistant professor of management, and my certificate from Texas stated that I had a doctoral degree in business administration. I was given a one-week notice to leave the country. To say the least, I was amazed by the action of the immigration office.
This news got to George Kozmetsky, who was then the dean of the business school. To my great surprise, I discovered that he had been tracking my performance throughout my doctoral program and had even read my dissertation. I realized that that was why he asked me about my grade point average whenever he saw me. I learned to avoid him. George was a first-generation Russian immigrant. Along with Kingsley Haines of the LBJ School for Public Affairs, he sent a strong letter to his friend Congressman Lloyd Bentsen, along with my passport. I was told to lay low for a week. The day before I was supposed to leave the country, the dean's office gave me my passport stamped with the training visa. George kept track of me until he died in 2003. I guess he loved for an immigrant like me to succeed. He had been a very special person in my life.
Moving into marketing was a pure coincidence. Although I was born in a place where it is very cold during the winter, I could not adjust to the Buffalo winters. Otherwise, my stay at Buffalo was fun. I had great times with Arun Jain and learned a lot from him and Brian Ratchford. However, I was ready to go back to India.
I called my Texas friend, Dale Achabal at Ohio State, to discuss my decision. He was keen on my exploring possibilities at Ohio State. After the Operations Management Department declined to hire me, Dale worked with Alan Sawyer, Jim Ginter, and Fred Sturdivant to hire me at Ohio State in marketing. During my interview, Jim Ginter gave me a copy of the most recent issue of Journal of Marketing Research and asked me if I could understand the articles. In his strong desire to hire me, Alan Sawyer drank with me most of the night. He was helping me "prepare" a marketing presentation for the next morning. Despite the hangover, I was hired.
Thus, I began my first job in marketing in the fall of 1978 as Assistant Professor of Marketing at Ohio State University. Next, I went to Wharton as an associate professor in the fall of 1980. In the fall of 1982, I became the Herman
W. Lay Chair Professor in Marketing at Southern Methodist University in Dallas. This was my first tenured job. These four years were the most grueling of my life. I was learning, teaching, and researching marketing simultaneously. I literally learned marketing on the job at Ohio State and Wharton, and I had to compete and survive.
Jerry Wind called me during my first week at Ohio State to explore my interest at Wharton. Although I had heard about Jerry from Arun Jain, I did not know him at the time. During my first visit to Wharton, Jerry introduced me to Eitan Muller, a Kellogg graduate in the Economics faculty at Penn State University, who had also done a dissertation on diffusion models. I developed strong professional and personal relationships with Jerry and Eitan. To this day, I do not understand why Wharton decided to hire me, though I suspect that my Texas friend, Randy Batsell, who was then on the Wharton faculty, may have had something to do with it.
My return to Texas at Southern Methodist University was prompted by another Texas friend, Roger Kerin. He was aware that I disliked winters and caught me at the right moment in the winter of 1981. He worked with Mike Harvey and Tom Barry in his usual comical style to tell me, "Come back to Texas! Come back home!" I was 33. In 10 years, an immigrant from India had become an endowed chair professor of marketing.
Dogra people from North India usually do not aspire to become academicians. We are happy to be small entrepreneurs, work for the government, or join the army. Although I never dreamed of becoming an academic, I thoroughly cherish my career as an academician. After all, what other profession would give me an opportunity ( 1) to travel to more than 20 countries to give more than 100 research presentations at major universities and be stimulated by comments of some of the brightest scholars and students on my research; ( 2) to work with and learn from more than 30 bright and energetic doctoral students in marketing, operations management, economics, strategic management, and information systems; ( 3) to be pushed to the limit of anxiety by senior executives in North America, Asia, South America, and Europe in executive development programs; ( 4) to be challenged by some of the most influential businesspeople, chief executive officers of Fortune 500 companies, and powerful heads of the government to establish a new world-class business school in India; ( 5) to provide consultancy on some of the most fascinating marketing problems to both entrepreneurial firms and large multinationals and realize that I am not that smart after all; ( 6) to be entrusted with the responsibility of editing major journals-Management Science (Planning and Forecasting Department) and Journal of Marketing Research--and realize that I cannot pretend to be an expert on every marketing issue; (7) to work with numerous coauthors, each of whom has left a permanent mark on my thinking; and (8) to serve on the editorial boards of several journals, including Journal of Marketing, Journal of Marketing Research, Journal of Consumer Research, and Marketing Science, and have the opportunity to learn from every article that I review?
All of these exciting things notwithstanding, the most exciting aspect of the academic life for me has been the opportunity to do research. Because I did not have my formal training in marketing, Frank Bass's diffusion article initiated me into diffusion research, and Philip Kotler's (1971) book, Marketing Decision Marking: A Model-Building Approach, initiated me into marketing modeling research. Given that there was hardly any marketing modeling literature in the 1970s, I found Kotler's book exhilarating, and even now, I read it before writing any modeling article. I also find Paul Green's articles to be brilliantly written; they are a real inspiration. He knows how to converse with and lead the reader.
Under the inspirational guidance of such great examples, I have written more than 100 peer-reviewed journal articles on innovation diffusion, new product development/marketing strategy, and marketing research methods, 38 of which were published in the select circle of journals that includes Journal of Marketing, Journal of Marketing Research, Marketing Science, Management Science, and Journal of Consumer Research, and I have written nine books.
It has also been my great privilege to work with more than 60 coauthors (excluding my doctoral students, such as Chris Ensingwood, R. Venkateh, and Ashutosh Prasad), including Dale Achabal, Sridhar Balasubamanian, Frank Bass, Rich Bettis (strategy), Bart Bronnenberg, Bob Buzzell, Abe Charnes (operations research), Wayne DeSarbo, Moshe Givon, Paul Green, David Huff, Dipak Jain, Arun Jain, Shlomo Kalish, Roger Kerin, Naresh Malhotra, Charlotte Mason, Bob Peterson, Brian Ratchford, Vithala Rao, David Schmittlein, Subhash Sharma, Seenu Srinivasan, Raj Srivastava, Joel Steckel, Wilfried Vonhonacker, Rajan Varadarajan, Andy Whinston (information systems), and Manjit Yadav.
I have had 44 articles published with two very special people, Eitan Muller and Jerry Wind. I am proud of my association with them. Eitan is a well-trained economist, and Jerry is simply excellent at everything. I have especially adored my relationship with Jerry and Eitan. Some people have suggested that one of the lost tribes went to my homeland in Jammu and Kashmir, and Jerry and Eitan have often kidded me for belonging to the lost tribe. I have learned so much from both of them: how to think analytically, how to think strategically, and about work ethics. Eitan and I have written 22 articles and one book on various aspects of innovation diffusion and dynamic marketing models, and Jerry and I have written 22 articles and five books on new product development and various strategic issues in marketing. It is heartening to know that our professional colleagues read and cite what we wrote.
Although I believe my best work is yet to be written, innovation diffusion is still my passion, so much so that I sometimes think I am still working on my dissertation. Even after 30 years, I am still fascinated by the power of differential equations and the dynamics of innovation growth. Among all my work on the topic, two stand as favorite children: ( 1) "Innovation Diffusion in the Presence of Supply Restrictions" (Jain, Mahajan, and Muller 1991) and ( 2) "Software Piracy: Estimation of Lost Sales and the Impact on Software Diffusion" (Givon, Mahajan, and Muller 1995). Both articles address innovation diffusion issues of developing countries in which 86% of the world population lives. Having traveled in many developing countries, I am convinced that academics have ignored the marketing issues of these countries. Living in India for the past two years has also convinced me of that. I have explored this conviction further in The 86 Percent Solution (2005).
Two other favorite articles come from my interest in new product development and marketing research methods: ( 1) "Issues and Opportunities in New Product Development" (Wind and Mahajan 1997) and ( 2) "A Conjoint Model for Measuring Self-and Cross-Price/Demand Relationships" (Mahajan, Green, and Goldberg 1982). I had great pleasure working with Jerry and Paul on these articles. I continue to learn from the best and brightest wherever I can find them, and every problem is interesting to me.
Miles to Go
My professional journey is probably not very different from many who, for one reason or another, decided to make the United States their home. I was fortunate to have met and worked with some of the brightest and kindest people in my professional life. I thank my professors, my colleagues, my doctoral students, and my coauthors. I learned something from each of them. My life has been full of coincidences, with nothing planned or crafted. Yet I believe that everything happens for a reason. I accept every coincidence, bad or good, though sometimes with a bit of anxiety. I hope that each coincidence has made me a more mature, tolerant, and grateful human being.
This is an exciting time for marketing. I hope that the field takes a lead with some of the challenges, including studying the issues of developing countries. We simply cannot afford to ignore 86% of the world's population. I remain a blessed and lucky Dogra boy from the hills of Jammu whose parents set no limits on learning. However, I do sometimes wonder what would have happened if I joined my father's textile business in Jammu.
REFERENCES Givon, Moshe, Vijay Mahajan, and Eitan Muller (1995), "Software Piracy: Estimation of Lost Sales and the Impact on Software Diffusion," Journal of Marketing, 59 (January), 29-37.
Jain, Dipak C., Vijay Mahajan, and Eitan Muller (1991), "Innovation Diffusion in the Presence of Supply Restrictions," Marketing Science, 16 (Winter), 83-90.
Kotler, Philip (1971), Marketing Decision Marking: A Model-Building Approach. New York: Holt, Rinehart and Winston.
Mahajan, Vijay and Kamini Banga (2005), The 86 Percent Solution: How to Succeed in the Biggest Market Opportunity of the Next 50 Years. Philadelphia: Wharton School Publishing.
-----, Paul Green, and S. Goldberg (1982), "A Conjoint Model for Measuring Self-and Cross-Price/Demand Relationships," Journal of Marketing Research, 19 (August), 334-42.
Wind, Jerry and Vijay Mahajan (1997), "Issues and Opportunities in New Product Development," Journal of Marketing Research, 34 (February), 1-12.
~~~~~~~~
By Vijay Mahajan and Terry Clark, Editor, Southern Illinois University
Vijay Mahajan is John P. Harbin Centennial Chair in Business, McCombs School of Business, University of Texas at Austin (e-mail:
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Record: 172- The Influence of Multiple Store Environment Cues on Perceived Merchandise Value and Patronage Intentions. By: Baker, Julie; Parasuraman, A.; Grewal, Dhruv; Voss, Glenn B. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p120-141. 22p. 2 Diagrams, 5 Charts. DOI: 10.1509/jmkg.66.2.120.18470.
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The Influence of Multiple Store Environment Cues on Perceived
Merchandise Value and Patronage Intentions
Research on how store environment cues influence consumers' store choice decision criteria, such as perceived merchandise value and shopping experience costs, is sparse. Especially absent is research on the simultaneous impact of multiple store environment cues. The authors propose a comprehensive store choice model that includes ( 1) three types of store environment cues (social, design, and ambient) as exogenous constructs, ( 2) various store choice criteria (including shopping experience costs that heretofore have not been included in store choice models) as mediating constructs, and ( 3) store patronage intentions as the endogenous construct. They then empirically examine the extent to which environmental cues influence consumers' assessments of a store on various store choice criteria and how those assessments, in turn, influence patronage intentions. The results of two different studies provide support for the model. The authors conclude by discussing the results to develop an agenda for additional research and explore managerial implications.
There was a time not so long ago that retail environments had few standards to meet. A store should be clean and organized to maximize sales per square foot. It should also be pretty . . . . Today, though, the retail environment must tie in directly to the brand, and, in fact, speak the brand's value proposition.
--Nancye Green
How does the retail environment tie in to customers' perceptions of the value of a store's merchandise? In a broader sense, in what way does the retail environment ultimately influence a customer's decision to patronize a particular store? There is a dearth of research-based answers to such questions, though conventional wisdom and the actions of many retailers suggest that store environment has a critical bearing on consumers' store choice processes. Scholarly verification of this conventional wisdom and research-based insights for guiding the design of store environments are lacking. Prior store environment research has achieved the following:
- Demonstrated that various environmental elements, taken one at a time, affect consumer responses. Elements examined include music (e.g., Areni and Kim 1993; Hui, Dube, and Chebat 1997; Milliman 1982), color (e.g., Bellizzi, Crowley, and Hasty 1983), scent (Spangenberg, Crowley, and Henderson 1996), and crowding (e.g., Eroglu and Machleit 1990; Hui and Bateson 1991);
- Examined how general constructs such as "store atmosphere" (e.g., Donovan and Rossiter 1982) or "physical attractiveness" of the store (e.g., Darden, Erdem, and Darden 1983) affect store patronage intentions; and
- Produced evidence suggesting that store environments trigger affective reactions in customers (e.g., Babin and Darden 1996; Baker, Grewal, and Levy 1992; Donovan et al. 1994; Hui and Bateson 1991; Wakefield and Blodgett 1999).
However, store environment research to date has not examined key issues such as how different store environment cues together shape consumers' merchandise value perceptions and how those perceptions, in turn, influence store patronage intentions. The extant literature also lacks empirical research on the relative impact of key antecedents of perceived merchandise value. For example, shopping experience costs, which include consumers' time and effort in obtaining products, as well as the psychological cost of shopping (e.g., irritation caused by loud music or crowding), have been suggested as potential determinants of merchandise value (Zeithaml 1988) and store choice (Bender 1964). However, a comprehensive model incorporating these constructs has not been tested in a retailing context.
To address the aforementioned research voids, we first propose a conceptual framework that incorporates the effects of three distinct store environment dimensions: design, social, and ambient.[ 1] We then describe and report results from two studies, the first designed to test our conceptual framework empirically and the second designed to verify the robustness of the results. Drawing on findings from the two studies, we offer implications for marketers and propose avenues for further research.
Our conceptual framework, shown in Figure 1, integrates theories from cognitive and environmental psychology with Zeithaml's (1988) proposal that value perceptions, which drive purchase decisions, are based on perceptions of product quality (what consumers get from an exchange) and price (the monetary and nonmonetary aspects of what consumers give up in an exchange). Figure 1 adapts the model proposed by Zeithaml (1988) to a retail setting and incorporates insights from Baker's (1998) and Bitner's (1992) conceptualizations of how the service environment can influence consumer decision making. The overall sequence of effects in our model is that store environmental dimensions influence consumers' perceptions of store choice criteria- namely, interpersonal service quality, shopping experience costs, and merchandise value (mediated through perceived quality, price, and shopping experience costs)-and these perceptions, in turn, affect store patronage intentions. Consumer perceptions in our model refer to inferences about the levels of quality, price, and value that consumers would expect in a store on the basis of store environment cues. As such, the model is especially appropriate when potential customers have limited a priori knowledge about a store's specific offerings, as well as in contexts in which a store undergoes a major remodeling, thereby exposing customers to a new set of store environment cues.
Four unique aspects of our model differentiate our study from previous studies. First, we explicitly identify two types of shopping experience costs-time/effort and psychic- and examine their influence on store patronage intentions. Our time/effort cost construct captures consumers' perceptions of the time and effort they are likely to expend shopping at a store. Economic pricing models acknowledge that time/effort costs influence consumers' perceptions of what they give up in an exchange (Becker 1965), and research anchored in Becker-based models (e.g., Marmorstein, Grewal, and Fishe 1992; Schary 1971) suggests that time spent in stores looking or waiting for goods and services has an economic value to consumers.
The psychic cost construct represents consumers' mental stress or emotional labor during the shopping experience. Environmental psychologists (e.g., Mehrabian and Russell 1974) have focused on understanding these costs, which we view as consumers' negative affective reactions to a store and/or its environment. Studies in environmental psychology and marketing that have examined the affective influence of the environment primarily have taken a positive view of affect (i.e., what increases a person's pleasure). In line with Zeithaml's (1988) notion of nonmonetary costs, we focus on the negative affect stemming from store environments. This perspective is also consistent with the argument that positive and negative affect are distinct constructs (Babin, Darden, and Babin 1998; Watson, Clark, and Tellegen 1988) and that negative affect has a stronger impact on consumers (Babin and Darden 1996).
Although time/effort costs and psychic costs are conceptually related constructs (e.g., crowding can trigger both perceptions of physical density and a negative emotional reaction to physical density), researchers in economics and marketing have treated them as distinct (e.g., Bender 1964; Zeithaml 1988). In Figure 1, we depict the two constructs as distinct to capture both the rational and the emotional aspects of consumers' nonmonetary costs, while acknowledging the possible correlation between them.
Second, most price-quality research examines consumers' value judgments of a specific product-price combination. In contrast, our study focuses on the broader concept of retail store patronage (rather than product choice per se). We are interested in how people perceive the general price levels for a group of products sold in a store on the basis of what they observe in the store's environment. We label this group "merchandise" to distinguish it from a specific product or brand. Our study posits that merchandise value is a function of perceived merchandise price, merchandise quality, and shopping experience costs.
Third, Zeithaml's (1988) value model focuses primarily on the evaluation of product quality. But in a retail context, consumers evaluate service quality as well as merchandise quality (Mazursky and Jacoby 1986). Therefore, our model incorporates the two types of quality as related but distinct components. An important aspect of shopping in a retail store is the quality of the interactions between store employees and customers, a construct we label "interpersonal service quality." Interpersonal service quality is a part of over-all service quality, as defined and measured by Parasuraman, Zeithaml, and Berry (1988). It includes customers being treated well and receiving prompt and personal attention from employees.
Fourth, our study is the first to examine empirically all the relationships in Figure 1 simultaneously. Table 1, which lists prior studies that offer conceptual or empirical support for various hypothesized relationships, shows that though each hypothesized link has conceptual support from one or more studies, 11 of the hypotheses have not been examined empirically. Moreover, only a handful of the studies have examined empirically the remaining hypotheses. Another void revealed by Table 1 is that each of these studies focuses on just a few of the hypothesized links; no study has examined all the links simultaneously.
Store Environment Determinants of Store Choice Criteria
Insights derived from three interrelated theories-inference theory, schema theory, and the theory of affordances-constitute the overall conceptual foundation for our hypotheses about store environment influences. Inference theory argues that people make judgments about the unknown on the basis of information they receive from cues that are available to them (Huber and McCann 1982; Nisbett and Ross 1980). Schemas are cognitive structures of organized prior knowledge, abstracted from experience, that guide inferences and predictions (Fiske 1982). They help shape people's expectations in new or ambiguous contexts (Fiske and Linville 1980). Similarly, the theory of affordances suggests that people perceive their physical environment as a meaningful entity and that such a perception conveys information directly to them (Gibson 1979). These theories together imply that consumers attend to design, social, and ambient environment cues when evaluating stores, because they believe that these cues offer reliable information about product-related attributes such as quality, price, and the overall shopping experience (Bitner 1992). For example, a customer entering a store with tile floors, the smell of popcorn, fluorescent lighting, and Top-40 music may access from memory a "discount store" schema and infer that the store's merchandise is low priced and of average quality and that the store has minimal service. Empirical evidence supports the idea that information from environmental cues influences consumers' perceptions of service providers (Baumgarten and Hensel 1987) and helps consumers categorize service firms (Ward, Bitner, and Barnes 1992).
Store design cues. As environmental psychology theory argues, the most important role of a space (in this case, the store) is its ability to facilitate the goals of its occupants (Canter 1983). For many shoppers, the goal is convenience, which includes getting in and out of the store quickly and finding the merchandise they seek easily. Layout is an example of a design cue that may influence customers' expectations of their efficient movement through a store (Titus and Everett 1995). On the basis of the foregoing evidence, we hypothesize that
H<SUB>1a</SUB>: As customers' perceptions of store design cues become more favorable, customers will perceive time/effort costs to be lower.
Prior studies offer empirical support for the link between the general, holistic environment and affect (e.g., Babin and Darden 1996; Donovan and Rossiter 1982; Wakefield and Baker 1998). Thus, poorly designed stores (e.g., a confusing store layout) may cause consumers to incur psychic costs. Mehrabian and Russell's (1974) stimulus-organism- response theory, which posits that the influence of physical environments is primarily affective, also suggests that poorly designed store environments may reduce shopping pleasure and lead to the deterioration of customers' moods (Spies, Hesse, and Loesch 1997). We therefore propose that
H<SUB>1b</SUB>: As customers' perceptions of store design cues become more favorable, customers will perceive psychic costs to be lower.
Nagle (1987) argues that an important determinant of consumers' responses to price is their perception of the entire purchase situation, which includes store environment. Moreover, in-store atmospherics may generate price beliefs independent of the actual prices and be used to create price differences for essentially undifferentiated products (Kotler 1973). Applying adaptation-level theory (Helson 1964), which posits that contextual factors shape a person's frame of reference for focal stimuli, to a retailing context suggests that store environment cues will influence consumers' price expectations. For example, Thaler (1985) finds that subjects infer that the price of beer is higher if the beer is purchased in an upscale store environment than if it is purchased in a run-down store. Grewal and Baker (1994) report that more favorable store environment perceptions increase the acceptability of the price of a picture frame. However, prior research has not examined how the aspects of store environment influence consumers' general price-level expectations for an entire store. If, for example, consumers had limited price knowledge about the clothing products carried by Gap, what would be their expectations of general price levels, based on store environment cues, before they even examined the price tags? To explore this issue, we formally propose that
H<SUB>1c</SUB>: As customers' perceptions of store design cues become more favorable, customers will perceive monetary prices to be higher.
Theoretical arguments suggest a direct link between retail store design and perceptions of interpersonal service quality (Baker 1987; Bitner 1992), as do a few empirical studies. For example, in comparing modern-style with traditional-style bank branches, Greenland and McGoldrick (1994) report that consumers find employees in the modern-style branches more approachable. Crane and Clarke (1988) find that consumers rely on office design to assess the scope and nature of four services (bank, doctors, dentists, and hair-stylists). Kotler (1973) notes that a store's atmosphere communicates its level of concern for its customers. Therefore, we propose that
H<SUB>1d</SUB>: As customers' perceptions of store design cues become more favorable, customers will perceive interpersonal service quality to be higher.
The design of a retail store environment can serve as an important basis for consumers' evaluations of merchandise quality (Kotler 1973; Olshavsky 1985). Mazursky and Jacoby (1986) find that pictures of a store's interior are heavily accessed as cues (even more so than price cues) that consumers use to evaluate merchandise quality. In a study by Gardner and Siomkos (1985), respondents evaluated the same brand of perfume more favorably when the store design was described as having "high-image" attributes (e.g., carpeted floors, wide aisles) than when it was depicted as having "low-image" attributes (e.g., tile floor, narrow aisles). In a restaurant setting, Heath (1995) finds that rest room cleanliness is an important factor in influencing customers' perceptions of overall food quality. The preceding evidence suggests that
H<SUB>1e</SUB>: As customers' perceptions of store design cues become more favorable, customers will perceive merchandise quality to be higher.
Store social (employee) cues. Eroglu and Machleit (1990) suggest that store social elements (e.g., too many people in too little space) can influence the perception of crowding; however, no empirical research has examined the relationship between store employee cues and consumers' perceptions of time/effort costs in a retail setting. Insights from the limited conceptual research suggest that the number of salespeople on the floor influences customers' time/ effort cost perceptions; for example, the presence of more salespeople may indicate that customers will spend less time searching for merchandise. Therefore,
H<SUB>2a</SUB>: As customers' perceptions of store employee cues become more favorable, customers will perceive time/ effort costs to be lower.
Prior research suggests that salespeople play a critical role in influencing consumers' moods and satisfaction (Grewal and Sharma 1991). According to a component of Barker's (1965) theory of behavioral ecology, when the number of people in a facility is less than the setting requires to function properly, a condition identified in sociology as "understaffing" occurs. The understaffing framework suggests that the number of employees in a store influences customers' perceptions and responses (Wicker 1973). Thus, when too few salespeople are on the floor (relative to customer density), customers can become frustrated and annoyed. Therefore,
H<SUB>2b</SUB>: As customers' perceptions of store employee cues become more favorable, customers will perceive psychic costs to be lower.
On the basis of adaptation-level theory and using the same logic we used to develop H<SUB>1c</SUB>, we also hypothesize that
H<SUB>2c</SUB>: As customers' perceptions of store employee cues become more favorable, customers will perceive monetary prices to be higher.
The understaffing framework (Wicker 1973) also suggests that store employee cues are likely to influence inter-personal service quality perceptions (Baker 1987). The number and appearance of employees in a retail setting are tangible signals of service quality (Parasuraman, Zeithaml, and Berry 1988). Recent research also suggests that employee-customer interactions affect consumers' assessments of service quality (Hartline and Ferrell 1996). Therefore, cues of positive interactions between customers and employees, such as acknowledging customers as they enter the store, also may influence interpersonal service quality perceptions. We predict that
H<SUB>2d</SUB>: As customers' perceptions of store employee cues become more favorable, customers will perceive interpersonal service quality to be higher.
Store employee cues are expected to have a positive influence on merchandise quality perceptions. Two studies that include descriptions of store employees as part of the overall store scenario find a positive influence of store environment on merchandise quality perceptions. Gardner and Siomkos (1985) depict salespeople as either sloppily dressed, nasty, and uncooperative or sophisticated, friendly, and cooperative. Akhter, Andrews, and Durvasula (1994) describe store employees in terms of their friendliness and knowledge. Therefore,
H<SUB>2e</SUB>: As customers' perceptions of store employee cues become more favorable, customers will perceive merchandise quality to be higher.
Store ambient (music) cues. Research suggests that music that is perceived as favorable may influence consumers' perceptions of the time spent waiting (e.g., Chebat, Gelinas-Chebat, and Filiatrault 1993; Hui, Dubé, and Chebat 1997) and thus should reduce consumers' perceptions of time/effort costs.[ 2] Therefore, we hypothesize that
H<SUB>3a</SUB>: As customers' perceptions of store music cues become more favorable, customers will perceive time/effort costs to be lower.
Ambient elements also have been associated with affective reactions (e.g., Donovan and Rossiter 1982; Greenland and McGoldrick 1994; Wakefield and Baker 1998), which consumers may experience as psychic costs in a retailing context. Some studies have demonstrated empirically that music influences affective responses in general (e.g., Hui, Dubé, and Chebat 1997) and can alleviate stress in subjects who are forced to wait (Stratton 1992). However, there is a lack of research on the effects of music on psychic costs in retail settings. To address this void and on the basis of the aforementioned studies, we propose that
H<SUB>3b</SUB>: As customers' perceptions of store music cues become more favorable, customers will perceive psychic costs to be lower.
Invoking adaptation-level theory and using the same logic we used to develop H<SUB>1c</SUB> and H<SUB>2c</SUB>, we further hypothesize that
H<SUB>3c</SUB>: As customers' perceptions of store music cues become more favorable, customers will perceive monetary prices to be higher.
Ambient cues also may influence customers' perceptions of interpersonal service quality. Several researchers have advanced conceptual arguments in support of a link between service quality and store environment perceptions as a whole (Baker 1987; Bitner 1992; Greenland and McGoldrick 1994; Kotler 1973). However, no empirical study has examined the specific relationship between in-store music cues and perceived interpersonal service quality. To test whether such a relationship exists, we propose that
H<SUB>3d</SUB>: As customers' perceptions of store music cues become more favorable, customers will perceive interpersonal service quality to be higher.
In an observational study, shoppers purchased more expensive (inferred higher quality) wine when classical music was played in a wine store than when Top-40 music was played (Areni and Kim 1993). Furthermore, previous research supports a link between music cues and merchandise quality. One study (Gardner and Siomkos 1985) describes the ambient environment as having either no soothing background music or soothing mood music playing in the background, and another (Akhter, Andrews, and Durvasula 1994) describes it in terms of the pleasantness of the music. On the basis of this evidence, we predict that
H<SUB>3e</SUB>: As customers' perceptions of store music cues become more favorable, customers will perceive merchandise quality to be higher.
Determinants of Merchandise Value
Based on Zeithaml's (1988) work, our model proposes that store patronage intentions are a function of merchandise value, interpersonal service quality, and shopping experience cost perceptions. Extensive prior research suggests a positive relationship between perceptions of product quality and value (Dodds, Monroe, and Grewal 1991; Grewal et al. 1998; Sirohi, McLaughlin, and Wittink 1998). Extending this finding to retail settings, we expect that
H<SUB>4</SUB>: The higher consumers' merchandise quality perceptions, the higher their perceptions of merchandise value will be.
Previous studies examining the impact of monetary price on value (e.g., Dodds, Monroe, and Grewal 1991; Grewal et al.1998; Sirohi, McLaughlin, and Wittink 1998) consistently suggest a negative linkage; that is, the higher the price perceptions, the lower are the product value perceptions. Prior research primarily has examined the effects of manipulated price levels, whereas we focus on the effects of merchandise price levels that consumers infer entirely from store environment cues (i.e., when no price information is provided). Nevertheless, we anticipate a similar negative link between perceived monetary price and value in our study. Therefore,
H<SUB>5a</SUB>: The higher consumers' monetary price perceptions, the lower their perceptions of merchandise value will be.
The relationship between shopping experience costs and merchandise value remains largely untested. Prior research suggests that consumers incur time/effort costs during the purchase process (Bender 1964; Zeithaml 1988) and that they place a premium on their time (Marmorstein, Grewal, and Fishe 1992). Moreover, "every product has a 'time price' that is implicitly included [in consumers' evaluations]" (Schary 1971, p. 54). Therefore,
H<SUB>5b</SUB>: The higher consumers' time/effort cost perceptions, the lower their perceptions of merchandise value will be.
Using similar logic and consistent with Zeithaml's (1988) model, if consumers are frustrated or annoyed with the in-store experience, they may develop a feeling of "giving up more than I am getting," which may be transferred to the merchandise itself. Thus, negative emotions in the form of psychic costs may decrease perceived merchandise value. As such, we predict that
H<SUB>5c</SUB>: The higher consumers' psychic cost perceptions, the lower their perceptions of merchandise value will be.
Determinants of Store Patronage
Although research consistently has shown that the effects of product quality on behavior are largely mediated by value perceptions (Dodds, Monroe, and Grewal 1991), previous studies have found a direct link between service quality and patronage intentions (e.g., Sirohi, McLaughlin, and Wittink 1998; Zeithaml, Berry, and Parasuraman 1996). Therefore,
H<SUB>6</SUB>: The higher consumers' interpersonal service quality perceptions, the higher their store patronage intentions will be.
Perceived product value is regarded as the primary driver of purchase intentions and behavior (Zeithaml 1988). Our research focuses on the broader concept of store patronage intentions, which includes the likelihood of both intending to shop at the store and recommending it to others (see Dodds, Monroe, and Grewal 1991; Zeithaml, Berry, and Parasuraman 1996). Consistent with prior research, we expect a positive link between perceived merchandise value and store patronage intentions.
H<SUB>7</SUB>: The higher consumers' merchandise value perceptions, the higher their store patronage intentions will be.
Although Zeithaml's (1988) model predicts that the influence of time/effort and psychic costs will operate solely through merchandise value, some prior research also suggests that there are direct effects of these costs on store patronage intentions. The poverty-of-time literature (e.g., Berry and Cooper 1992), the crowding literature (e.g., Eroglu and Harrell 1986; Hui and Bateson 1991), and studies on consumer responses to waiting (e.g., Hui, Dubé, and Chebat 1997; Taylor 1994) all suggest that if consumers believe they will spend too much time in a store, they may avoid even entering the store without first processing information about the merchandise value or interpersonal service quality. Thus,
H<SUB>8a</SUB>: The higher consumers' perceived time/effort costs, the lower their store patronage intentions will be.
Similarly, there may be a direct link between psychic costs and store patronage intentions. Such a link is consistent with the association between affective reactions and behavioral response posited by Mehrabian and Russell (1974) and supported by marketing studies(e.g., Baker, Grewal, and Levy 1992; Donovan et al. 1994; Hui and Bateson 1991; Wakefield and Baker 1998). We therefore predict that
H<SUB>8b</SUB>: The higher consumers' perceived psychic costs, the lower their store patronage intentions will be.
To test the conceptual model, we used videotapes to simulate a store environment experience. This approach has proved effective for environmental representation (e.g., Bateson and Hui 1992; Chebat, Gelinas-Chebat, and Filiatrault 1993; Voss, Parasuraman, and Grewal 1998). The store in the videotape was a card-and-gift store located in a large, southwestern U.S. city. Subjects viewed a five-minute videotape that visually "walked" them through the store environment, simulating a shopping or browsing experience. They then completed a questionnaire that contained items to measure the model constructs.
We conducted one study to test the model shown in Figure 1 and a second study to examine the robustness of the results. In Study 1, the subjects were 297 undergraduate students at a large, southwestern U.S. university. In Study 2, the subjects were 169 undergraduate students at a southeastern U.S. university. The majority of the students were business majors who ranged in age from 20 to 25 years. Shopping in a card-and-gift store is within the realm of experience for the student samples used in both studies; 98% of the subjects indicated that they had shopped in a card-and-gift store.
Experimental Design and Stimuli
To create variation in the environmental stimuli, we produced eight videotaped store scenarios representing low and high levels of design, social, and ambient components in a 2 × 2 × 2 between-subjects research design. The store we videotaped was being remodeled, which enabled us to implement the design manipulations (consisting of changes in color, display accent trim, layout, and general organization of the merchandise) within the same store space. We produced videotapes before the remodeling to represent the low design condition (beige/white color, no gold accent trim, grid layout, and messy displays) and then after the remodeling to represent the high design condition (peach/ green color, gold accent trim, free-form layout, and organized displays). We also manipulated store employee cues during the videotaping sessions. The high social level featured three salespeople wearing professional-looking aprons, one of them greeting "customers" (respondents) as they visually entered the store. The low social level featured just one salesperson who did not wear an apron and did not greet customers. Type of music, which is relatively easy and inexpensive to change from a retailer's standpoint, represented the ambient dimension in our study. We manipulated it by dubbing onto the finished videotapes either classical music (high level) or Top-40 music (low level).[ 3] Both music selections had a slow tempo to avoid any possible tempo effect. Although the ambient dimension includes elements other than music (e.g., scent, temperature), we could not vary those elements in the videotaped scenarios.
To identify specific environmental attributes to be included in the videotaped scenarios, we invoked insights from the marketing and retailing literature and conducted two focus groups (one student and one nonstudent) to elicit what consumers considered high and low levels of each dimension. Manipulation checks indicated that the treatment manipulations had the intended effect on the three measured factors (i.e., perceived store design cues, store employee cues, and store music cues).[ 4]
Measures
We used multi-item scales to measure the model constructs (Table 2 contains the scale items). Literature from environ-mental psychology (e.g., Mehrabian and Russell 1974; Russell and Pratt 1981), retailing (e.g., Donovan and Rossiter 1982), and marketing (e.g., Bitner 1990; Gardner and Siomkos 1985) provided the basis for the store environment perception and psychic cost scales. We derived scale items for the other constructs from the price, quality, and value literature. Time/effort cost items were based on Zeithaml's (1988) conceptualization of nonmonetary price and adapted from Dodds, Monroe, and Grewal's (1991) scales. We developed monetary price measures from items suggested by Dodds, Monroe, and Grewal (1991) and Zeithaml (1984). We adapted the four interpersonal service quality items from the SERVQUAL scale (Parasuraman, Zeithaml, and Berry 1988). We measured merchandise quality, merchandise value, and store patronage intentions with scales developed by Dodds, Monroe, and Grewal (1991). We pretested the questionnaire several times and refined it on the basis of the pretest results.[ 5]
Following Anderson and Gerbing (1988), we conducted confirmatory factor analysis to assess the reliability and validity of the multi-item scales for the ten model constructs (Table 2). Although the chi-square (χ²) value for the measurement model was significant for both data sets (p < .01), this statistic is sensitive to sample size and model complexity; as such, the goodness-of-fit index (GFI), nonnormed fit index (NNFI), and comparative fit index (CFI) are more appropriate for assessing model fit here (e.g., Bagozzi and Yi 1988; Bearden, Sharma, and Teel 1982).
For Study 1, the GFI (.89), NNFI (.94), and CFI (.95) indicate satisfactory model fit. Furthermore, all the individual scales exceeded the recommended minimum standards proposed by Bagozzi and Yi (1988) in terms of construct reliability (i.e., greater than .60) and percentage of variance extracted by the latent construct (greater than .50). Although the measurement model fit the Study 2 data somewhat less well, the construct reliability scores again exceeded .60, and the percentage of variance extracted by the latent construct exceeded .50 for all scales except the merchandise value perception scale.
Next, we assessed whether the measurement model satisfied three conditions that demonstrate discriminant validity: ( 1) For each pair of constructs, the squared correlation between the two constructs is less than the variance extracted for each construct; ( 2) the confidence interval for each pairwise correlation estimate (i.e., +/- two standard errors) does not include the value of 1; and ( 3) for every pair of factors, the χ² value for a measurement model that constrains their correlation to equal 1 is significantly greater than the χ² value for the model that does not impose such a constraint. Collectively, these conditions represent 360 individual tests of discriminant validity. Of these 360 tests, only 1 suggested that two of our constructs might not be distinct; namely, the squared correlation between perceived merchandise value and store patronage intentions for the Study 2 data exceeded the variance extracted for the perceived merchandise value construct. On the basis of these results, we conclude that our scales measure ten distinct constructs. Construct correlation estimates, along with standard errors for both data sets, are provided in Table 3.[ 6]
The purpose of Study 1 was to examine how well the proposed conceptual model (Figure 1) fit the data and to explore improvements to the model. The purpose of Study 2 was to evaluate the robustness of the Study 1 results by ( 1) reestimating the model suggested by the Study 1 sample to determine if the same relationships held for a new sample and ( 2) statistically comparing the parameter estimates from the two samples to ascertain whether there were significant differences.
Study 1: Evaluating the Proposed Model
We tested the hypothesized relationships using maximum-likelihood simultaneous estimation procedures (LISREL-VIII; Jöreskog and Sörbom 1996). Consistent with MacKenzie and Lutz's (1989) recommendations, we represented each latent construct with a single index that we calculated by averaging the item scores on the construct's scale. We established the scale of measurement for each construct by fixing its loading (lambda) to be the square root of its reliability, and we incorporated potential measurement error into each scale by setting the error term at one minus the construct reliability. Because there was a variety of measurement scales for the different constructs, we used a correlation matrix as the input.
We first evaluated the proposed model by estimating the standardized path coefficients for the hypothesized links in Figure 1. The column labeled "Proposed Model" in Table 4 presents these coefficients. The χ² value for this model was significant (p < .01), but the GFIs indicated satisfactory fit. Of the 23 proposed relationships, 14 were statistically significant.
We then constrained the 9 nonsignificant paths to zero and reestimated the structural model. The results are summarized in the "Revised Model" column of Table 4. The 14 remaining paths were statistically significant. Although the χ² value for the revised model was somewhat higher, any corresponding decrease in fit compared with the original model was not significant (χ² difference = 9.2, 9 degrees of freedom [d.f.], p > .10). Moreover, the other fit indices were virtually the same as those for the original model.
The results from Study 1 suggest eliminating three sets of paths: ( 1) from employee cue perceptions to time/effort cost perceptions, psychic cost perceptions, monetary price perceptions, and merchandise quality perceptions; ( 2) from music cue perceptions to time/effort cost perceptions, inter-personal service quality perceptions, and merchandise quality perceptions; and ( 3) from time/effort and psychic cost perceptions to merchandise value perceptions. Figure 2 shows the revised model after deleting these paths.
Study 2: Replicating the Revised Model
We used Study 2 to examine the robustness of the model in Figure 2. The revised model fit the data from Study 2 well. The "Replication Analysis" column of Table 4 contains the fit statistics. Of the 14 paths that were statistically significant in Study 1, 12 were also significant in Study 2. The paths from music cue perceptions to monetary price perceptions and from time/effort cost perceptions to store patronage intentions were nonsignificant.
We then assessed whether the strength of the relationships observed in the two studies was statistically different by testing the equivalence of the parameter estimates across samples using multigroup analysis (Jöreskog and Sörbom 1996). First, we estimated the revised model by constraining all parameters to equality across the two samples (see the "Multisample Analysis" column in Table 4). This analysis produced an overall χ² value of 142.0 (with 80 d.f.). Second, allowing a single parameter estimate to vary freely between the two samples, we estimated a second χ² (with 79 d.f.) and evaluated the χ² difference (with 1 d.f.). A significant χ² difference implies a significant difference in the strength of the corresponding link across the two samples. We conducted 14 such tests. Of the 14 links examined, the strength of only 1 differed significantly between the two samples; namely, the relationship between merchandise value perceptions and store patronage intentions was much stronger in the replication analysis. Thus, the relationships in the revised model appear to be robust across the two studies.[ 7]
Exploring the Predictive Validity of the Revised Model
Because our study represents one of the first attempts to test empirically a comprehensive retail patronage model, we were interested in examining the predictive validity of the revised model and exploring the relative contribution of the predictor variables in explaining variations in the two key criterion variables: perceived merchandise value and store patronage intentions. To examine these issues, we used the multisample analysis mentioned previously. We summarize the results in Table 5.
As Table 5 shows, the model explained a high percentage of the variation in perceived merchandise value (68%), and its most important predictor was monetary price perceptions (-.91). Other significant predictors of value included merchandise quality perceptions, which had a direct, positive effect (.64); design cue perceptions, which had an indirect, positive effect (.16); and music cue perceptions, which had an indirect, positive effect (.17).
The model also explained a high percentage of the variation in store patronage intentions (54%), and all predictor variables had significant direct or indirect effects. As might be expected, merchandise value perceptions had the strongest direct effect (.37), but psychic cost perceptions also had a strong direct effect (-.31), time/effort cost perceptions had a significant direct effect (-.17), and interpersonal service quality had a significant direct effect (.23). Perceptions of store environment (especially design cue perceptions), merchandise quality perceptions, and monetary price perceptions all had significant indirect effects on store patronage intentions.
Important linkages among store environment cues, store choice criteria, and store patronage intentions have been investigated on a piecemeal basis, if at all, in previous conceptual and empirical studies (see Table 1). As such, our conceptual model (Figure 1) contributes to the extant literature by offering an integrative synthesis of insights from previous studies, as well as from the theories invoked in positing the relationships in the model. In addition, to our knowledge, our research is the first attempt to examine empirically a comprehensive store patronage model. Our research is also the first to examine empirically the effects of shopping experience costs (i.e., time/effort and psychic costs) on merchandise value and patronage intentions. Although time/effort and psychic costs have been proposed as determinants of perceived value (e.g., Zeithaml 1988), they have not been operationalized, nor have their effects been assessed empirically in a retailing context.
By simultaneously varying three sets of store environment cues in videotaped scenarios and assessing their individual impacts on respondents' store choice criteria, our research provides some insight into the differential effects of the cues, something that heretofore has not been investigated. However, because the findings from our study do not support some of the hypothesized links, our inferences about the relative effects of store environment cues are necessarily preliminary. Nevertheless, the lack of support for some of the links, along with some surprising findings (e.g., the finding that the effects of shopping experience costs on patronage intentions are not mediated through perceived merchandise value perceptions), raises intriguing issues that pertain to the cognitive/behavioral processes that may underlie the empirical results and the boundary conditions for the observed effects. We identify and discuss these issues in the following sections.
Limitations
As is usually the case with studies conducted in simulated environments, our research has some shortcomings. Videotaped scenarios, though more experiential and realistic than written scenarios (the type of stimuli used in many studies), are not capable of representing the full range of environ-mental attributes, especially in the ambient dimension. Because of this technological limitation, the stimuli in our study captured a wider range of attributes in the design dimension than in the social or ambient dimensions. Therefore, a potential explanation for the strong design effects observed in our study is that the nature of the shopping experience simulated by the videotaped scenarios might have caused respondents to pay less attention to the employees and music than they would have during an actual shopping trip. However, our manipulation checks (summarized in n. 4) reveal that all three manipulations produced significant differences and that the differences produced by the employee and music manipulations are more pronounced. Therefore, the relatively strong design cue effects seem unlikely to have been triggered by an experimental artifact. Nevertheless, additional research using videotaped scenarios should incorporate more facets of the social dimension (e.g., presence of other customers, crowding, waiting lines) and ambient dimension (e.g., music tempo, noise levels) to produce stimuli that are more balanced across the three store environment dimensions. In case respondents deliberately look for cues because they know they are reacting to a simulated environment, a balanced scenario will offer similar opportunities for the various cues to be noticed.
Different store scenarios incorporating greater cue variety also will help address other issues, such as whether the number and types of customers in a store influence the respondents' (i.e., potential customers') perceptions. In addition, will the absence of social (employee) cue effects on time/effort and psychic cost perceptions (revealed in Table 4) hold when customer crowding is varied along with number of salespeople? In other words, will having many easily recognizable salespeople in a store have a more pronounced effect on shopping experience costs when the store is crowded than when it is not, as was the case in our research?
Another limitation of our research is that two of the ten constructs in our model (monetary price and merchandise quality) were measured with two-item scales. Although both scales have acceptable construct reliabilities in Studies 1 and 2 (Table 2), their reliabilities are generally lower than for the other constructs.
Theoretical and Research Implications
As the results in Table 4 and Figure 2 show, design cues have a stronger and more pervasive influence on customer perceptions of the various store choice criteria than do store employee and music cues. As we argued in the preceding section, this influence is unlikely to have been due solely to the content of the videotaped scenarios. Bettman (1979) suggests that in external search for information, consumers may allocate different amounts of processing capacity (i.e., attention) to various stimuli. Given that design cues are visual whereas ambient cues tend to affect the subconscious (Baker 1987), it is possible that subjects in our study paid more conscious attention to design cues than to music cues. Moreover, prior research on memory has found that because pictures have a superior ability to evoke mental imagery, they are more easily remembered than verbal information (e.g., Lutz and Lutz 1978; Paivio 1969). Although this stream of research focuses on pictures versus verbal stimuli (e.g., written words), it suggests that design cues in a store environment may evoke more vivid mental images than do music cues. The dominance of design cues over employee cues may have occurred because subjects experienced the latter only during the initial minute of the videotaped scenario as they entered the store and started browsing. Nevertheless, given that store environments typically contain more design cues than employee cues, consumers in such environments might experience these cues in a manner similar to the way our study subjects experienced them.
In addition to the respondents' cognitive processes in interpreting the store scenarios, contextual factors (e.g., type of store, product category) may offer alternative explanations for the findings. We explore these possibilities and offer directions for further research as we discuss the key results pertaining to each of the endogenous constructs.
Shopping experience costs. As hypothesized, design cue perceptions have a significant, negative effect on time/effort and psychic cost perceptions in both studies. Moreover, this effect is consistently stronger for the psychic cost component than for the time/effort cost component (e.g., the structural coefficients for the two components in the multisample analysis, as shown in the last column of Table 4, are -.62 and -.40, respectively). Thus, although design aspects influence perceived shopping speed and efficiency, they have an even stronger impact on the perceived stress involved in shopping, which is an important finding worthy of further research.
Employee cue perceptions have no impact on either time/effort or psychic costs. Our rationale for hypothesizing these effects (H<SUB>2a</SUB> and H<SUB>2b</SUB>) was based solely on limited conceptual work (see Table 1). Therefore, our research is an inaugural attempt to examine these hypotheses empirically. However, because the lack of support for them was consistent across two studies and because the manipulation checks showed that the employee cue manipulations produced the intended effects (n. 4), purchasing context is a plausible explanation for this finding. In other words, consumers may possess various schemas for different types of retail stores and/or product categories that moderate the strength of the hypothesized links. Questions such as the following can help structure research that attempts to examine the generalizability of this finding and boundary conditions for it: Is the apparent lack of impact of employee cues on shopping experience costs limited to stores that are typically self-service, as the store was in our study? Is the impact likely to vary across different categories of retail establishments (e.g., restaurants, supermarkets, jewelry stores, discount outlets) and different types of merchandise (e.g., food, groceries, luxury products, durable goods)?
Music cue perceptions have a consistent but modest negative effect on perceived psychic costs. This finding coincides with that of the only previous empirical study pertaining to this hypothesized effect (Stratton 1992). Music cues did not have a significant impact on perceived time/effort costs, contrary to what we posited on the basis of past studies (which, as Table 1 shows, all have been conceptual). Our conceptual rationale for suggesting relationships between music cue perceptions and the two types of shopping experience costs was basically the same; namely, favorable music perceptions would alleviate both types of costs. This rationale requires rethinking in light of the differential effects revealed by the simultaneous empirical examination of music's impact on time/effort and psychic costs. Many prior marketing studies have found that music has an affective influence on consumers (e.g., Bruner 1990), but few have examined the cognitive effects of music. In our study, psychic costs were more affective in nature than were time/ effort costs. Several time-perception studies have found cognitive effects of music in terms of time duration estimation (e.g., Kellaris and Mantel 1994). However, in these studies, respondents were asked to estimate actual time duration after being exposed to pieces of music rather than to infer in-store time/effort costs on the basis of music cues. Therefore, why music cues might have a differential impact on the two types of costs (and, in a broader sense, why music cues might have different influences on affective and cognitive responses) and whether the nature of that impact might vary across different purchasing contexts remain important issues for further research.
Monetary price. Findings from both studies offer support for the hypothesized positive effect of store design perceptions on perceived monetary price (i.e., a high image store design leads to correspondingly high expected prices). However, our results show no significant effect of employee cues in either study. The effect of music cues is significant in Study 1 but not in Study 2. As discussed in footnote 6, this difference is unlikely to have been caused by demographic differences between the two study samples. Moreover, the effect in Study 1 is negative, contrary to the hypothesized direction. Because both studies used the same study context, the presence of the unexpected negative effect in Study 1 but not Study 2 suggests that the effect observed in Study 1 may be spurious; that is, similar to the effect of employee cues, in reality the effect of music on monetary price perceptions may be negligible rather than negative.
In developing our hypotheses, we invoked adaptation-level theory (Helson 1964) to argue that customers would use the overall store environment as a frame of reference to make predictions about prices; in other words, more favorable (i.e., higher image) perceptions of all three types of environmental cues (design, employees, and music) would lead customers to expect higher monetary prices. No empirical studies pertaining to any of these posited links were available. Our study fills this empirical void and suggests a need for more theoretical work to understand the differential effects of the various cues. The findings suggest that the predicted positive relationship holds only for visual, design-related cues. Why it might not apply to other types of cues and whether and how product or store contexts might influence it require additional research.
Merchandise quality. Design cue perceptions are the only significant antecedents of merchandise quality perceptions, and their impact is consistently strong across studies. We did not find that employee and music perceptions affected merchandise quality perceptions, though two previous empirical studies find such links (Akhter, Andrews, and Durvasula 1994; Gardner and Siomkos 1985). A key methodological difference between those studies and the current research is that their stimuli included only two descriptive scenarios-high image and low image-in which employee and music cues were provided through written descriptions. In contrast, our research used eight videotaped scenarios in which all three types of cues were manipulated. Therefore, a plausible explanation for the differences in the results is that the respondents in the preceding two studies may have paid more explicit attention to the written descriptions of the employees and music, thereby accentuating their impact. Moreover, the written descriptions in some cases used wording that was extreme and/or leading (e.g., "sloppily dressed, nasty, and uncooperative" salespeople versus "sophisticated, friendly, and cooperative" salespeople). In our videotaped scenarios, the employee and music cues were part of a more realistic overall store environment. A contribution of our research, and one of its strengths compared with previous studies, is the examination of consumers' reactions to multiple store environment cues presented simultaneously in as realistic a simulated environment as was allowed by the videotaping technology we used. As such, the differential effects our results reveal augment the extant literature and call for additional research to understand the differences better.
Interpersonal service quality. Our research focused on just the interpersonal component of service quality. As hypothesized, employee and design cues significantly affect interpersonal service quality perceptions, but music cues have no significant impact on them. Whereas one previous study shows a positive link between perceptions of music and overall store service (Chebat 1997), our findings suggest that perceptions of the interpersonal component of customer service are independent of music perceptions. A plausible explanation for these findings, previously discussed, is that when customers process auditory and visual cues to predict the level of personal service they are likely to receive, the visual cues projected by a store's design and employees dominate. An area for further inquiry is the identification of circumstances or contexts in which auditory cues may convey information to customers about interpersonal service quality.
Merchandise value. Our empirical findings regarding the determinants of perceived merchandise value are consistent with the general notion that customers infer value by trading off what they give up relative to what they are likely to get (e.g., Zeithaml 1988). However, our results offer additional and somewhat surprising insights about merchandise value perception formation in a retailing context. Specifically, of the four hypothesized drivers of perceived merchandise value-time/effort costs, psychic costs, merchandise quality, and monetary price-only the last two are significant. The finding that neither time/effort nor psychic costs influences perceived merchandise value runs counter to the commonly held belief that both monetary and nonmonetary price are integral to the "give" component of perceived value. Contrary to what the extant literature (e.g., Zeithaml 1988) suggests, when customers assess merchandise value before purchase in a retailing context, they apparently do not integrate monetary price and time/effort and psychic costs in inferring what they must give up. Rather, their value assessments seem to rest solely on the trade-off between monetary price and merchandise quality.
Because this inference challenges conventional knowledge about the cognitive processes that customers use in perceiving value, researching its robustness should be a high priority. Woodruff (1997) offers theoretical arguments to propose that value is a dynamic construct whose content and evaluative criteria change as customers gain experience. Consistent with this dynamic notion of value and based on our findings, one useful avenue for further research is to examine empirically value perceptions at different stages of the purchase process. By the very nature of the scenarios and measures we used, all perceptual data collected in this research pertained to potential customers' prepurchase evaluations. Will similar findings emerge in postpurchase contexts? That is, will customers' actual purchase or use experience make psychic and time/effort costs more salient when customers take stock of the overall value they received? If so, are they more likely to integrate monetary and nonmonetary prices in postpurchase contexts? Will psychic and time/effort costs still have a direct impact on patronage intentions, or will their impact be mediated by perceived value? A related question worth investigating is how much more consumers are willing to pay to avoid the time/effort and psychic costs associated with longer waits due to insufficient staff and/or poorly designed stores. Answers to these questions will enrich our understanding of perceived value formation.
Our results also suggest that perceived monetary price, relative to merchandise quality, has a substantially stronger influence on perceived merchandise value, even though the videotaped scenarios contained no price information. Is this finding unique to gifts purchased in a card-and-gift store (i.e., small-ticket items bought from relatively small stores to be given to someone else), or does it extend to other merchandise and store types (e.g., a luxury item for personal use purchased from a large specialty store)? Evidence suggesting that perceived monetary price's dominant role transcends merchandise and store types would call into question the conventional wisdom and popular belief that superior merchandise quality can offset any erosion in perceived value caused by high prices. More research is needed to develop a clearer understanding of store environment's influence on potential customers' monetary price perceptions and the role of these perceptions on perceived value formation.
Store patronage intentions. All four hypothesized antecedents of store patronage intentions-interpersonal service quality, merchandise value, time/effort costs, and psychic costs-significantly influence patronage intentions, as was shown by the multisample analysis results; perceived merchandise value and psychic costs are particularly strong determinants of patronage intentions. However, there are a couple of notable differences between the two studies. In the replication study, the impact of time/effort costs is considerably weaker, and the impact of merchandise value is considerably stronger. As explained in footnote 6, demographic differences between the two samples probably cannot explain these results. Differences in relevant respondent attributes that are not measured in our research might account for the between-sample differences in the strengths of the effects of time/effort costs and merchandise value. This possibility calls for research aimed at identifying such attributes and examining whether customer segments defined by those attributes react differently to the same store environment cues.
Finally, the insights from this research are based on perceptual and intention measures provided by respondents after they finished viewing the videotaped scenarios. Further research could supplement these measures with more qualitative methodologies, such as having respondents generate verbal protocols as they experience the store scenarios. Additional insights from such interpretive research might provide a richer understanding of the process by which store environment cues influence customers. For example, which cues do customers notice first? Which cues are noticed most often? What interpretations do customers attach to specific cues, and do those interpretations vary across customers?
Managerial Implications
As implied by our discussion in the preceding sections, our research both offers new and significant insights and emphasizes the need for continuing research to examine the generalizability of our findings and enhance our understanding of the impact of store environment cues on store choice criteria and patronage intentions. Therefore, any recommendations for retailing practice based on our findings should be viewed more as food for thought than as a definitive prescription. With that caveat in mind, managers can benefit by considering the following practical implications that stem from our research.
The significant and consistent influence of design cues on shopping experience costs, especially psychic costs (see the first two rows of Table 4),underscores the need for retailers to give careful consideration to store design features (e.g., store layout, arrangement of merchandise). These features have great potential to influence would-be shoppers' psychic costs and therefore their shopping experience and store patronage behavior. As Table 5 shows, among the various direct and indirect determinants of patronage intentions, design cues have the strongest influence, with total effect of .43. Creating a superior in-store shopping experience is critical and could provide an effective competitive weapon for bricks-and-mortar retailers that face growing competition from Internet-based e-tailers offering similar merchandise at the same (or lower) prices.
Although our research focuses on bricks-and-mortar stores, the nature and strength of the findings suggest that we can extend some of their implications to e-stores as well. Specifically, according to our findings pertaining to design factors and because design is the dominant (if not only) environmental component e-shoppers experience, it seems reasonable to speculate that the design of e-stores (e.g., appearance and layout of home pages) may affect eshoppers' perceived psychic costs significantly and thus their propensity to shop at those stores.
Store design features also influence monetary price perceptions. However, this effect is relatively small (structural coefficient of .24 in the multisample analysis) compared with the negative effect that design cue perceptions have on time/effort (-.40) and psychic (-.62) costs or with the positive effect that design cue perceptions have on interpersonal service quality (.49) and merchandise quality (.59) perceptions. This finding implies that retailers offering a high-image design may be perceived as offering high quality and value, even though monetary prices are perceived as high.
Of the two key drivers of merchandise value-monetary price and merchandise quality-the former is consistently the dominant driver, having a structural coefficient of -.91 compared with a coefficient of .64 for merchandise quality. Moreover, as Table 5 shows, perceived monetary price has the strongest total effect on perceived merchandise value among all direct and indirect antecedents. Thus, although merchandise quality inferences triggered by store environment cues strongly influence perceived value, perceptions of monetary price stemming from those cues have an even stronger impact. This differential effect suggests that retailers attempting to attract customers by presenting a high-class image through their store environment cues should consider using explicit communication strategies to counteract the disproportionately high decrease in perceived merchandise value that might result from customers' inference of high monetary prices.
Finally, although customers' perceptions of time/effort and psychic costs apparently do not influence how they assess merchandise value, these shopping experience costs directly and strongly influence store patronage intentions. This result has an important implication for retailers: When store environment cues trigger high shopping experience costs, potential customers may avoid the store altogether without weighing those costs against the potential benefits (i.e., high merchandise quality and/or low monetary prices). As such, incorporating store design features that signal a low-stress shopping environment should be a top priority for retailers striving to attract new customers.
1 These dimensions, discussed by Baker (1987), are consistent with the ones Bitner (1992) uses in describing "servicescapes." Bitner's three dimensions are ambient; space/function (similar to design); and signs, symbols, and artifacts. Whereas marketing researchers traditionally have approached the design and ambient cues under the umbrella construct of store atmospherics, researchers in the field of environmental psychology distinguish between them for two fundamental reasons. First, ambient cues tend to affect nonvisual senses, whereas design cues are more visual in nature. Second, ambient cues tend to be processed at a more subconscious level than are design cues. There is some empirical evidence that design and ambient elements have differential effects on consumer responses (Wakefield and Baker 1998).
- 2 The perceived favorableness of music depends on both the pleasantness of the music and the extent to which the music is perceived as appropriate for the context in which it is played (MacInnis and Park 1991). Both these aspects were captured by our measure of perceived favorableness of store music.
- 3 Five types of music-classical, Top-40, country-and-western, oldies, and easy listening-were pretested. The respondents (157 upper-level undergraduate business students) listened to all five selections and used seven-point scales to rate the likelihood that each selection would be heard at high-and low-image stores. The five selections then were rank ordered from highest to lowest on the basis of the mean ratings. The classical selection ranked as the music most associated with the high-image store. The Top-40 selection was the second lowest ranked music but was chosen because this type of music was deemed more likely to be used by a card-and-gift store than was country-and-western music, which was the lowest ranked music.
- 4 To ensure that the manipulations produced the intended effects, we conducted manipulation checks in a pretest and again in the main study. In the high store employee level, the salesperson was perceived as significantly more friendly and helpful than in the low level (pretest means = 5.52 versus 4.12, p < .05 [one-sided]; main study means = 5.25 versus 4.01, p < .01). The high design level was perceived to be more attractive and pleasing than was the low level (pretest means = 5.55 versus 5.16, p < .05; main study means = 5.61 versus 5.35, p < .05). Finally, subjects perceived the classical music as creating a more positive ambience than the Top-40 music (pretest means = 5.58 versus 3.64, p < .01; main study means = 5.42 versus 3.85, p < .01). Thus, the three experimentally manipulated variables created the desired variation.
- 5 The purpose of the experimental manipulations in our study was to create sufficient variation in perceived environmental conditions. To estimate the paths in our structural model (Figure 1), we pooled the scaled responses across treatments. However, to ensure that such pooling was justifiable, one reviewer suggested that we conduct analyses of variance to examine if there were any significant interaction effects. We assessed the impact of all two-way and three-way treatment interactions on each of the seven endogenous variables across the two studies-a total of 56 interaction tests. The results indicated that only 5 of the 56 interaction effects were significant at the p < .05 level; moreover, none of the interaction effects was significant in both studies. These results suggest that any interaction effect among the endogenous variables was negligible.
- 6 The pairwise correlations presented in Table 3 indicate that the magnitude of correlation for the closely related constructs of time/ effort cost perceptions and psychic cost perceptions ranges from .39-.47, which implies that the shared variance between this pair of constructs is in the range of 15%-22%. We believe that this is a relatively low degree of overlap, likely due to the perceptions sharing a "common cause": They are all triggered by the same set of store environment cues.
- 7 We also explored the possibility that demographic differences across the two samples might explain the different findings across samples. Sample 2 was significantly older and contained a higher percentage of women. Because previous research has suggested that women perceive environmental cues differently than men, we split the combined samples on the basis of sex and reexamined the structural relationships for both groups. This analysis did not indicate that men and women reacted in a significantly different manner.
Legend for chart:
A = Hypothesis H<SUB>1a</SUB>: Design Perceptions-Time/Effort
Costs (-)
B = Hypothesis H<SUB>1b</SUB>: Design Perceptions-Psychic Cost (-)
C = Hypothesis H<SUB>1c</SUB>: Design Perceptions-Monetary Price (+)
D = Hypothesis H<SUB>1d</SUB>: Design Perceptions-Interpersonal
Service Quality (+)
E = Hypothesis H<SUB>1e</SUB>: Design Perceptions-Merchandise
Quality (+)
F = Hypothesis H<SUB>2a</SUB>: Employee Perceptions-Time/Effort
Cost (-)
G = Hypothesis H<SUB>2b</SUB>: Employee Perceptions-Psychic Cost (-)
H = Hypothesis H<SUB>2c</SUB>: Employee Perceptions-Monetary Price
(+)
I = Hypothesis H<SUB>2d</SUB>: Employee Perceptions-Interpersonal
Service Quality (+)
J = Hypothesis H<SUB>2e</SUB>: Employee Perceptions-Merchandise
Quality (+)
K = Hypothesis H<SUB>3a</SUB>: Music Perceptions-Time/Effort Costs
(-)
L = Hypothesis H<SUB>3b</SUB>: Music Perceptions-Psychic Cost (-)
M = Hypothesis H<SUB>3c</SUB>: Music Perceptions-Monetary Cost (+)
N = Hypothesis H<SUB>3d</SUB>: Music Perceptions-Interpersonal
Service Quality (+)
O = Hypothesis H<SUB>3e</SUB>: Music Perceptions-Merchandise
Quality (+)
P = Hypothesis H<SUB>4</SUB>: Merchandise Quality-Merchandise
Value (+)
Q = Hypothesis H<SUB>5a</SUB>: Monetary Price-Merchandise Value (-)
R = Hypothesis H<SUB>5b</SUB>: Time/Effort Cost-Merchandise Value
(-)
S = Hypothesis H<SUB>5c</SUB>: Psychic Cost-Merchandise Value (-)
T = Hypothesis H<SUB>6</SUB>: Interpersonal Service
Quality-Patronage Intentions (+)
U = Hypothesis H<SUB>7</SUB>: Merchandise Value-Patronage
Intentions (+)
V = Hypothesis H<SUB>8a</SUB>: Time/Effort Cost-Patronage
Intentions (-)
W = Hypothesis H<SUB>8b</SUB>: Psychic Cost-Patronage Intentions (-)
A B C D E F G H I J K L
Studies M N O P Q R S T U V W
Akhter, Andrews, e
and Durvasula e
(1994)
Alford and
Sherrell (1996)* C
Areni and Kim
(1993) C
Babin and Darden C C
(1996) C
Baker (1986) C C
C
Baker, Grewal,
and Levy C
(1992)
Barker (1965) C
Bellizzi, Crowley, C
and Hasty (1983)
Berry and Cooper
(1992) C
Bitner (1990) C
e C
Bitner (1992) C C C C C C
C C C C
Boulding et al.
(1993)* e
Canter (1983) C C C
e
Chebat (1997)
e C
Chebat et al. C
(1998)* C
Chebat, Gelinas- C
Chebat, and
Filiatrault
(1993)
Crane and Clarke C
(1988)
Darden and Babin C
(1994)*
Darden and C
Schwinghammer
(1985)*
Darley and Gilbert C C C
(1985)
Dodds, Monroe,
and Grewal e e e
(1991)
Donovan and C C
Rossiter (1982) C
Donovan et al. C
(1994) C
Eroglu and Harrell
(1986) C
Gardner and e e
Siomkos (1985) e C
Greenland and e C
McGoldrick C
(1994)
Grewal and Baker C
(1994)
Grewal et al.
(1998) e e e
Grewal, Monroe,
and Krishnan e e e
(1998)*
Grewal and C
Sharma (1991)
Hartline and C
Ferrell (1996)
Heath (1995) e
Helson (1964) C C
C
Hui and Bateson
(1991) C C
Hui, Dub, and C C
Chebat (1997) C
Kellaris and C
Mantel (1994)
Kerin, Jain, and
Howard (1992)* e
Kotler (1973) C C C
Mazursky and e e
Jacoby (1986)
Mehrabian and C C C
Russell (1974) C
Nagle (1987) C
C
Olshavsky (1985) C C
C
Parasuraman, C C
Zeithaml, and C
Berry (1988)
Schary (1971)
C
Sirohi, McLaughlin,
and Wittink e e e e
(1998)
Spies, Hesse, and e
Loesch (1997)
Stratton (1992) e
Taylor (1994)
C
Taylor (1995)*
C
Thaler (1985) C
Titus and Everett C
(1995)
Van Kenhove and
Desumaux C
(1997)*
Wakefield and C
Baker (1998) C
Wakefield and C
Blodgett (1999) C
Weisman (1983)* C
Wheatley and C
Chiu (1977)*
Wicker (1973) C
Zeithaml (1988)
C C C C C
Zeithaml, Berry,
and e
Parasuraman
(1996)*Studies that are not cited in the body of the paper are not listed in the references because of space constraints. A complete set of references is available from the first author. Notes: C = conceptual; e = empirical.
Legend for chart:
A = Items
B = Study 1 Lambda Loadings
C = Study 1 Construct Reliability
D = Study 1 Variance Extracted
E = Study 1 Mean (Standard Deviation)
F = Study 2 Lambda Loadings
G = Study 2 Construct Reliability
H = Study 2 Variance Extracted
I = Study 2 Mean (Standard Deviation)
A B C D E F G H I
Design Perceptions .76 .52 5.53 .82 .61 5.77
Pleasing color scheme .75 (1.04) .84 (1.08)
Attractive facilities .74 .81
Organized merchandise .66 .68
Employee Perceptions .89 .73 4.62 .92 .80 4.13
Well-dressed employees .73 (1.44) .81 (1.83)
Friendly employees .94 .97
Helpful employees .88 .91
Music Perceptions .90 .75 4.63 .87 .70 4.99
Pleasant music .95 (1.77) .96 (1.65)
Appropriate music .84 .80
Bothersome music .79 .73
Time/Effort Cost
Perceptions .76 .52 3.25 .78 .55 3.17
Shopping effort .65 (1.23) .67 (1.28)
Time sacrifice .77 .78
Search effort .73 .77
Psychic Cost Perceptions .79 .56 1.66 .86 .67 1.68
Unpleasant atmosphere .76 (.77) .81 (.90)
Displeasing atmosphere .79 .85
Uncomfortable atmospher .69 .79
Monetary Price Perceptions .70 .54 4.37 .68 .52 4.73
Expensive gifts .84 (1.19) .78 (1.29)
Too much money .61 .66
Interpersonal Service
Quality Perceptions .85 .58 4.98 .80 .51 5.31
Treated well .71 (1.10) .66 (1.03)
Personal attention .77 .75
High-quality service .78 .81
Prompt service .78 .62
Merchandise Quality
Perceptions .73 .58 4.89 .77 .63 5.21
High-quality gifts .78 (1.01) .75 (1.13)
High workmanship .74 .83
Merchandise Value
Perceptions .75 .50 3.82 .64 .38 4.13
Fair gift prices .74 (.94) .66 (.93)
Good value .67 .49
Economical gifts .71 .67
Store Patronage Intentions .88 .71 4.94 .84 .64 5.09
Willing to recommend .81 (1.22) .81 (1.15)
Willing to buy .87 .83
Shopping likelihood .84 .76
Fit Statistics
x2 with 332 d.f. 549.5 627.4
GFI .89 .81
NNFI .94 .86
CFI .95 .89
Standardized root mean
square residual .05 .07Notes: In the questionnaire, the ordering of items was randomized. The psychic cost perceptions items were measured on a six-point scale that indicated how accurately each adjective described the environment ("extremely accurate" to "extremely inaccurate"). All other items were measured on seven-point scales anchored by "strongly agree" and "strongly disagree."
Legend for chart:
A = 1
B = 2
C = 3
D = 4
E = 5
F = 6
G = 7
H = 8
I = 9
J = 10
A B C D E F G H I J
1. Design perceptions
.39 .28 -.31 -.63 .32 .66 .58 .15 .55
(.07) (.08) (.09) (.06) (.09) (.06) (.07) (.10) (.07)
2. Employee perceptions
.40 .14 -.05 -.18 -.10 .46 .03 .21 .27
(.06) (.08) (.09) (.08) (.09) (.07) (.09) (.09) (.08)
3. Music perceptions
.30 .22 -.13 -.41 .00 .24 .18 .09 .29
(.06) (.06) (.09) (.07) (.10) (.08) (.09) (.10) (.08)
4. Time/effort cost perceptions
-.43 -.15 -.16 .39 .10 -.24 -.14 -.13 -.32
(.07) (.07) (.07) (.08) (.10) (.09) (.10) (.11) (.09)
5. Psychic cost perceptions
-.68 -.23 -.31 .47 -.06 -.38 -.29 -.17 -.54
(.05) (.06) (.06) (.06) (.10) (.08) (.09) (.10) (.07)
6. Monetary price perceptions
.22 .08 -.10 .25 -.04 .25 .53 -.56 -.09
(.07) (.07) (.07) (.07) (.08) (.10) (.09) (.09) (.10)
7. Interpersonal service quality perceptions
.57 .55 .28 -.20 -.40 .16 .62 .31 .56
(.05) (.05) (.06) (.07) (.06) (.07) (.07) (.10) (.07)
8. Merchandise quality perceptions
.63 .29 .24 -.14 -.39 .50 .58 .07 .63
(.06) (.07) (.07) (.08) (.07) (.07) (.06) (.11) (.07)
9. Merchandise value perceptions
.18 .22 .27 -.23 -.23 -.64 .33 .13 .71
(.07) (.07) (.07) (.07) (.07) (.06) (.07) (.08) (.07)
10. Store patronage intentions
.57 .24 .30 -.49 -.54 -.02 .47 .42 .47
(.05) (.06) (.06) (.06) (.05) (.07) (.05) (.06) (.06)Notes: Study 1 construct correlations (and standard errors) appear below the diagonal; Study 2 construct correlations (and standard errors) appear above the diagonal.
Standardized Coefficients and Fit Statistics for the Proposed Model, the Revised Model, the Replication Analysis, and the Multisample Analysis
Legend for chart:
A = Hypothesized Paths
B = Expected Sign
C = Proposed Model
D = Revised Model
E = Replication Analysis
F = Multisample Analysis
A
B C D E F
H<SUB>1a</SUB> Design perceptions → time/effort cost
perceptions
- -.44** -.43** -.35** -.40**
H<SUB>1b</SUB> Design perceptions → psychic cost perceptions
- -.71** -.67** -.56** -.62**
H<SUB>1c</SUB> Design perceptions → monetary price perceptions
+ .22** .22** .28** .24**
H<SUB>1d</SUB> Design perceptions → interpersonal service
quality perceptions
+ .41** .45** .56** .49**
H<SUB>1e</SUB> Design perceptions → merchandise quality
perceptions
+ .54** .61** .57** .59**
H<SUB>2a</SUB> Employee perceptions → time/effort cost
perceptions
- .07
H<SUB>2b</SUB> Employee perceptions → psychic cost perceptions
- .10
H<SUB>2c</SUB> Employee perceptions → monetary price
perceptions
+ .00
H<SUB>2d</SUB> Employee perceptions → interpersonal service
quality perceptions
+ .35** .34** .29** .31**
H<SUB>2e</SUB> Employee perceptions → merchandise quality
perceptions
+ .11
H<SUB>3a</SUB> Music perceptions → time/effort cost perceptions
- -.08
H<SUB>3b</SUB> Music perceptions → psychic cost perceptions
- -.14* -.10* -.25** -.16**
H<SUB>3c</SUB> Music perceptions → monetary price perceptions
+ -.21** -.23** -.11 -.19**
H<SUB>3d</SUB> Music perceptions → interpersonal service
quality perceptions
+ .08
H<SUB>3e</SUB> Music perceptions → merchandise quality
perceptions
+ .05
H<SUB>4</SUB> Merchandise quality perceptions → merchandise
value perceptions
+ .57** .62** .70** .64**
H<SUB>5a</SUB> Monetary price perceptions → merchandise value
perceptions
- -.88** -.92** -.86** -.91**
H<SUB>5b</SUB> Time/effort cost perceptions → merchandise value
perceptions
- .03
H<SUB>5c</SUB> Psychic cost perceptions → merchandise value
perceptions
- -.06
H<SUB>6</SUB> Interpersonal service quality perceptions → store
patronage intentions
+ .21* .21** .27** .23**
H<SUB>7</SUB><SUP>a</SUP> Merchandise value perceptions → store
patronage intentions
+ .27** .27** .60** .37**
H<SUB>8a</SUB> Time/effort cost perceptions → store patronage
intentions
- -.24** -.24** -.05 -.17**
H<SUB>8b</SUB> Psychic cost perceptions → store patronage
intentions
- -.30** -.30** -.32** -.31**
Fit Statistics
d.f.
16 25 25 80
χ²
47.7 56.9 52.5 142.0
GFI
.97 .97 .94 .93
NNFI
.88 .92 .89 .94
CFI
.96 .96 .94 .95
Standardized root mean square residual
.04 .05 .05 .06*Coefficient is significant at p < .05. **Coefficient is significant at p < .01. [a]Coefficients are significantly different across data sets (p < .05).
Examining Indirect, Direct, and Total Effects of Predictor Variables on Merchandise Value Perceptions and Store Patronage Intentions
Legend for chart:
A = Predictor Variables
B = Merchandise Value Perceptions Indirect Effect
C = Merchandise Value Perceptions Direct Effect
D = Merchandise Value Perceptions Total Effect
E = Store Patronage Intentions Indirect Effect
F = Store Patronage Intentions Direct Effect
G = Store Patronage Intentions Total Effect
A B C D E F G
Design .16 .16 .43 .43
perceptions (2.80) (2.80) (10.46) (10.46)
Employee .07 .07
perceptions (3.82) (3.82)
Music .17 .17 .11 .11
perceptions (3.37) (3.37) (4.34) (4.34)
Monetary price -.91 -.91 -.34 -.34
perceptions (-11.26) (-11.26) (-6.77) (-6.77)
Merchandise .64 .64 .24 .24
quality (8.69) (8.69) (5.99) (5.99)
perceptions
Interpersonal
service .23 .23
quality (4.58) (4.58)
perceptions
Time/effort -.17 -.17
cost (-3.21) (-3.21)
perceptions
Psychic cost -.31 -.31
perceptions (-5.68) (-5.68)
Merchandise .37 .37
value (7.85) (7.85)
perceptions
Squared .68 .54
multiple
correlationNotes: Standardized path estimates are reported with t-values in parentheses. All path estimates are significant at p < .01.
DIAGRAM: FIGURE 1 A Conceptual Model of the Prepurchase Process of Assessing a Retail Outlet on the Basis of Environmental Perceptions
DIAGRAM: FIGURE 2 A Revised Model of the Prepurchase Process of Assessing a Retail Outlet on the Basis of Environmental Perceptions
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~~~~~~~~
By Julie Baker; A. Parasuraman; Dhruv Grewal and Glenn B. Voss
Julie Baker is an associate professor, Department of Marketing, University of Texas at Arlington. A. Parasuraman is Professor and Holder of The James W. McLamore Chair in Marketing, University of Miami, Coral Gables. Dhruv Grewal is Toyota Chair of e-Commerce and Electronic Business and Professor of Marketing, Babson College. Glenn B. Voss is Associate Professor of Marketing, North Carolina State University. The authors extend thanks to Diana Grewal, Chuck Lamb, Michael Levy, Michael Tsiros, and R. Krishnan, as well as to the four anonymous JM reviewers, for their constructive comments on previous versions of this article. Dhruv Grewal acknowledges the research support of Babson College and University of Miami.
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Record: 173- The Influence of Philosophy, Philosophies, and Philosophers on a Marketer's Scholarship. By: Hunt, Shelby D.; Clark, Terry. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p117-122. 6p.
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Section: Book ReviewsThe Influence of Philosophy, Philosophies, and Philosophers on a Marketer's Scholarship
Many, if not most, marketing scholars are strongly influenced by a nonmarketing discipline, such as economics, social psychology, sociology, statistics, or mathematics. This essay recounts how my own work has been so strongly influenced by the discipline of philosophy and by the tenets of specific philosophies and the works of specific philosophers. As an engineering major at Ohio University in the late 1950s and early 1960s, I took courses that focused on the sciences, mathematics, and engineering. Nonetheless, being required to take at least one course in the humanities, I took an introductory course in philosophy. The course was the typical (and much maligned) survey course. We read and discussed snippets of the works of such philosophers as Plato, Aristotle, David Hume, Immanuel Kant, and John Stuart Mill. Especially interesting, I found, was the "classical" realism of G.E. Moore and Bertrand Russell.1
In retrospect, several values that have guided my scholarship were significantly informed by that single, introductory philosophy course (the only philosophy course I ever took). First, I internalized the view that Plato's "critical discussion" was essential for knowledge development. In this view, the pursuit of truth is furthered by proposing penetrating, highly critical questions, which are to be followed by equally insightful, thoughtful answers. Second, civility in critical discussion is a virtue. For example, the use of ad hominem discourse is proscribed: Discussion should always be directed at the ideas of people, not the people themselves. Third, the use of sophistry is prohibited: It is impermissible to employ disingenuous argumentation. Fourth, reason and evidence should be respected: The fallibility of method implies being open to alternative views that provide well-reasoned arguments and evidence. Fifth, clarity in scholarship is a virtue: To be obscure should never be confused with being profound.
Upon graduating from Ohio University in 1962, I accepted a sales position with Hercules Inc., a large chemical company. Although I found selling plastics to the automotive and appliance industries to be an interesting, challenging, and valuable experience, I had always been interested in education. After deciding on a major career shift, in March of 1966 I entered the Ph.D. program in marketing at Michigan State University. There I found my most interesting course to be a seminar in marketing theory taught by B.J. "Bud" LaLonde in the summer of 1967. The course focused on theory development and critical evaluation of the works of Wroe Alderson and other marketing theorists. Although class discussion was vigorous, I noticed that students often seemed to be "talking past" one another rather than engaging in truly productive interaction. The reasons for the frequently unproductive discussions were not apparent to me.
My wife Suzanne, our daughter Michelle, and I lived in married student housing. One evening, a new neighbor, a doctoral student in philosophy, invited us to play bridge. Most curiously, throughout the evening (and in discussions thereafter), he was able to analyze critically the issues debated in our marketing theory class even though he knew nothing about marketing. He showed me that a major reason our class discussion was often unproductive was that we were failing to separate our substantive disagreements from those of a purely semantic nature. That is, as long as participants were using such terms as "science," "theory," "explanation," "hypotheses," "axioms," and "laws" in radically different ways, our semantic differences would impede us from resolving substantive disagreements. My philosophy neighbor introduced me to analytical philosophy, as exemplified by the works of Carl Hempel, Richard Rudner, and Ernest Nagel. The critical discussions I found in the works of these philosophers of science impressed me with their clarity of exposition and logical structure. As a consequence, it seemed to me that students taking marketing theory courses could benefit greatly from being exposed to the "tool kit" of the philosophy of science.
After graduating from Michigan State University in December 1968, I joined the faculty at the University of Wisconsin, Madison, and was assigned the marketing theory course. Relying heavily on LaLonde's syllabus, I added works from the philosophy of science in an effort to provide students a framework within which vigorous, rigorous, and productive discussion might take place. Although the course was well received, each time I taught it students complained that they had difficulty applying philosophy of science concepts and theories to the works of contemporary marketing theorists. Class discussion still suffered.
In an attempt to respond to students' complaints, in the summer of 1973 I began working on a monograph with the aim of integrating the philosophy of science with marketing theory and research. The book was not to be on the philosophy of science, or about it, but rather to use philosophy of science to illuminate issues in marketing theory. At that time, philosophers of science were debating the merits of three rival philosophical "isms": logical empiricism, scientific realism, and historical ("Kuhnian") relativism. Believing that historical relativism offered little to marketing theory and research, I adopted an eclectic blend of logical empiricism and realism, which I referred to as "contemporary empiricism." Two years' labor resulted in "The Nature and Scope of Marketing" (Hunt 1976b) and the first edition of Marketing Theory (Hunt 1976a). Throughout the 150-page monograph, often referred to as the "little green book," I endeavored to make the critical discussions civil, present faithful characterizations of others' views (I used quotations liberally), respect reason and evidence, and seek clarity. As I stated in the preface:
Most readers will disagree with some parts of the conceptual
framework herein presented. Some readers will disagree with
most of it. Such disagreement is welcomed, since one
objective of the monograph is to stimulate discussion of the
fundamental issues underlying marketing theory and research.
I only hope that I have made my positions clear enough to
assist critics in their efforts to show where I am in error.
I would rather be found wrong than obtuse. (Hunt 1976a)Grid Inc., a small, relatively new publisher of academic books in Columbus, Ohio, published the book. I am frequently asked, "Why did you choose Grid?" The decision was actually quite simple. I approached two dozen publishers with the book proposal, and each politely informed me that, though their reviewers believed the proposed monograph had merit, the size of the market (i.e., doctoral theory seminars) was too small. Thus, all publishers, save one, rejected it. The exception was Grid, headed by James Wilson and Nils Anderson. Therefore, I "chose" Grid (and will always be indebted to Jim and Nils). Although the first edition of Marketing Theory, spurred by the "Nature and Scope of Marketing" article, was favorably reviewed and quickly became the text of choice for marketing theory courses, the publishers were correct: Even with the 1979 Japanese translation, the book's annual sales never exceeded a few hundred copies.
In November of 1979, I received an offer to join the faculty at Texas Tech University. Though we had "put down roots" in Madison, my wife and I decided to move to Lubbock, Texas, in the Fall of 1980. Shortly thereafter, three events significantly influenced my philosophy of science research program. First, Gil Churchill called and asked if I would consider doing a revision of Marketing Theory for Richard D. Irwin Inc. After much discussion, we agreed on a version that would ( 1) revise the six chapters in the little green book, ( 2) add a chapter titled "Theory: Issues and Aspects," and ( 3) add several readings to the book to make it a more complete package for marketing theory courses. The second event was a call from Ron Bush asking me to cochair, with him, a special American Marketing Association conference on marketing theory to be held in San Antonio, Texas, in February 1982. I accepted his invitation. The third event was reading an article in the Philosophy of Science by Harvey Siegel (1980), a philosopher of education, on the "contexts" of discovery and justification. Admiring the logical approach of the article, I decided to add it as a reading in my theory seminar.
Throughout 1981, I worked on revising Marketing Theory and helped Ron Bush develop the program for the theory conference. We sought a prominent philosopher of social science to make a major presentation on current issues in the philosophy of science. Ron suggested May Brodbeck, I agreed she was a good choice, and she graciously accepted our invitation. We scheduled her talk for the first session of Monday morning, February 8. The following two sessions were to be on the same topic, with a panel of philosophically oriented marketing academics: Paul Anderson, Richard Lutz, Jerry Olson, Michael Ryan, and Gerald Zaltman. J. Paul Peter agreed to chair the session, and I participated as well.
We underestimated the interest in the three sessions, for the small room was packed and many attendees had to stand. May's presentation summarized the historical relativists' views on science: ( 1) There is no theory-independent observation language, ( 2) science cannot be objective, ( 3) all theories are equally viable, ( 4) theories and paradigms are incommensurable, ( 5) truth is an illusion, and ( 6) the practice of science is but a game. She then defended the practice of science, the pursuit of truth, and the possibility of objectivity using the tools of logical empiricism. In the discussion that followed, several panel members criticized logical empiricism as not giving an adequate or accurate account of the practice of science and offered spirited defenses of the historical relativists' views of science.
The spirited, but always civil, discussions in the three sessions prompted several "hallway debates" throughout the rest of the conference. The consensus seemed to be that the relativist approach to science had much more to offer marketing than did May's logical empiricism or my own eclectic contemporary empiricism. Accordingly, I felt an obligation to include a transcript of the sessions in the Irwin edition of Marketing Theory and invite J. Paul Peter and Paul Anderson to contribute additional position papers. J. Paul accepted my invitation; Paul cordially declined. The final version of the Irwin edition (Hunt 1983), often referred to as the "big red book," contained the revised text and 23 articles, including four that favored relativism and the Siegel (1980) article previously mentioned. Similar to the little green book, the red version became the text of choice for theory courses but sold only a few hundred copies per year.
Throughout the 1980s, relativist writings in marketing proliferated. Five major forms of relativism were advocated: cultural, ethical, rationality, reality, and conceptual framework. "Relativism," it was noted, is a term of art from philosophy. All genuine forms of relativism have two theses: ( 1) the relativity thesis that something is relative to something else and ( 2) the nonevaluation thesis that there are no objective standards for evaluating across the various kinds of "something else." For example, "reality relativism" (i.e., "social constructionism") holds that ( 1) what comes to be known as reality in science is constructed by individuals relative to their language (or group, social class, theory, paradigm, culture, worldview, or Weltanschauung), and ( 2) what comes to count as reality cannot be evaluated objectively, impartially, or nonarbitrarily across different languages (or groups, etc.).
Also throughout the 1980s, many writers were advocating "alternative ways of knowing," including such qualitative approaches as naturalistic inquiry, humanistic inquiry, ethnographic methods, historical methods, critical theory, literary explication, interpretivism, and postmodernism. The debate over these alternative ways merged with the relativism debate, because many advocates of qualitative methods adopted relativism as a philosophical foundation. (Hereafter, both debates are referred to as simply "the philosophy debates.")
After the 1983 Irwin edition, I believed I would never again revise Marketing Theory. Indeed, most of my efforts were devoted to my ethics research program.2 However, it became clear that since 1982, the philosophy debates were becoming increasingly unproductive: Discussions of ideas degenerated into ad hominem debates, epistemology morphed into "epistobabble" (Coyne 1982), honest mischaracterizations became "nastiness and purposeful distortions" (Hirschmann 1989, p. 209), and a concern for civility reverted to "ridicule" (Pechmann 1990, p. 7). Furthermore, the nihilistic implications of relativism were becoming clear. For example, advocates of reality relativism were arguing that the Holocaust was a socially constructed reality, only one of many multiple realities (Lincoln and Guba 1985, p. 84). Because such a view would imply the nihilistic conclusion that the Holocaust's occurrence or nonoccurrence could not be objectively appraised independently of the worldview of a person's social grouping--that is, there is no truth to the matter--I found this view disturbing. Therefore, by 1988 I was actively doing research on a third version of Marketing Theory.
I believed that a major factor contributing to the muddled status of the philosophy debates was a lack of understanding--on both sides--of logical positivism and logical empiricism. If participants had an accurate understanding of ( 1) what positions the logical positivists and empiricists actually espoused and rejected and ( 2) how positivism differs from other philosophical "isms," the debates could be raised to a more informed level. Both Kenneth Ketner, an advocate of Peirce's "pragmaticism," and Harvey Siegel, whose work I had included in the red book, suggested that the historical approach might be an effective way of explicating positivism. As an advocate of qualitative methods (Hunt 1989a, 1994b), I was receptive to their suggestion.
Ken's counsel led me to the work of Peter Manicas (1987) and Harvey's to that of Denis Phillips (1987). These philosophers' historical approach revealed that the philosophy debates throughout the social sciences were as muddled as those in marketing. Following their lead, I decided to trace the historical development of the fundamental tenets of logical positivism, logical empiricism, historical relativism, historical empiricism, and scientific realism. However, I found it difficult to explain these "isms" without at least briefly discussing classical realism and Hegelian idealism. Unfortunately, I could find no way to enable readers to comprehend Hegelian idealism without discussing classical rationalism and classical empiricism. At last, I recognized that I might as well start at "the beginning" of Western philosophy--Platonism.
My historical research culminated in Modern Marketing Theory (Hunt 1991a), called "the blue book," which was published by South-Western on the favorable recommendation of James R. Sitlington Jr. The blue book contained ( 1) revisions of the seven chapters in the red book, ( 2) new readings, and ( 3) four new chapters in a section titled "Philosophy of Science: Historical Perspectives and Current Status." Consistent with the values internalized early in my career, the preface's epigraph quoted the Marquis de Vauvenargues: "For the philosopher, clarity is a matter of good faith." Like its predecessors, Modern Marketing Theory sold several hundred copies per year.
In addition to the blue book, my historical research spawned a series of articles (Hunt 1989a, b, 1990, 1991b, 1992a, b, 1993a, b, 1994a, b, c, 1995a) that critically evaluated issues in the philosophy debates. Here, I would like to briefly discuss three questions and identify the philosophical works that grounded my analyses: ( 1) Does positivism dominate marketing? ( 2) Is objectivity in science possible? ( 3) Is truth an appropriate goal?
Does positivism dominate marketing and other social sciences? The philosophy debates answered yes to this question, which was then often followed by the "positivism is dead" argument:
1. Positivist research (i.e., research guided by the tenets
of the logical positivists and/or the logical empiricists)
dominates marketing and social science inquiry.
2. Positivist research reifies unobservables and is
quantitative, deterministic, causality seeking, realist,
functionalist, and/or objectivist.
3. Positivism has been shown to be dead (or thoroughly
discredited) in philosophy of science.
4. Therefore, all research that is quantitative,
deterministic, causality seeking, and so on is also
discredited.
5. Therefore, researchers should adopt some form of
relativism and qualitative methodology.My research on this issue drew heavily on Ayer (1959), Bergmann (1967), Joergensen (1970), Manicas (1987), Phillips (1987), and Suppe (1977). The sources enabled me to show in the blue book and several articles (Hunt 1989b, 1991b, 1994c, 1995a) that research guided by positivism ( 1) would not necessarily be either quantitative or deterministic, ( 2) would avoid metaphysical concepts such as "cause," ( 3) would reject the scientific realist ontology, ( 4) would be leery of functionalist explanations, and ( 5) could not possibly engage in reification.3 Therefore, I showed that the philosophy debates were muddled, at least in part, because they started from the false premise that marketing and social science are dominated by positivism.4
Is objectivity in science possible? The debates answered no to this question on five grounds: ( 1) The language of a culture determines the reality that members of that culture will see, that is, "linguistic relativism." ( 2) The paradigms that researchers hold are incommensurable. ( 3) Theories are underdetermined by facts, that is, "Humean skepticism." ( 4) The psychology of perception informs us that a theory-free observation language is impossible. ( 5) All epistemically significant observations are theory laden. I developed a paper targeted for Journal of Marketing Research that responded to each of the five arguments and then affirmed the goal of objectivity in marketing research. The journal rejected the paper because, reviewers maintained, "most people in marketing regard this 'debate' as silly," and the "reason that the bulk of the ... [debate] has been published somewhere other than JMR is because ... [it] tends not to tell a reader much new." I then submitted the paper to Journal of Marketing, and (ultimately, after much back-and-forth) it was accepted (Hunt 1993b). The ideas in the JM article, "Objectivity," then formed the foundation for a realist theory of empirical testing that was later published in the philosophy of science literature (Hunt 1994a).
Most of the arguments in "Objectivity" were developed during my research for the blue book. A major exception was the analysis of number 5, the "theory-laden" claim. In 1991 I read an article in Philosophy of Science on the objectivity of empirical research by John Greenwood (1990). He argued that advocates of the argument that theory-laden data doom objectivity make two critical mistakes. First, they fail to distinguish between the explanatory theories to be tested by data and the measurement theories informing the percepts that become the data that are then used for testing the explanatory theories. Second, they fail to note that it is not a theory-free observation language that is necessary for objectivity, but a theory-neutral observation language.5 That is, measurement theories must not "beg the question": They must not presume the truth of the explanatory theories being tested. Greenwood's (1990) arguments enabled me, in "Objectivity," to refute the last of the five claims against objectivity. When John's considerate review of the final draft concurred, it was ready for the tortuous path through JMR and JM.
Is the development of true theories and the rejection of false ones an appropriate goal? The answer of no to this question, common in the philosophy debates, was based on several closely related arguments. In the blue book and an article, "Truth" (Hunt 1990), I critically examined the major ones. One of the most prominent arguments was the "falsity of convergent realism":
1. (a) Truly referential theories will be "successful," and
conversely, (b) "successful" theories will contain
central terms that genuinely refer.
2. However, it is easy to find historical examples of
referential theories that were unsuccessful and successful
theories that were not referential.
3. Therefore, the theory of convergent realism is false.
4. Thus, because the cognitive aim of truth is linked
ineluctably with realism, truth is an inappropriate aim
for science and should be abandoned.No other philosopher has influenced my scholarship as much as the aforementioned Harvey Siegel. In a series of articles that culminated in his impeccably argued book (1987), he showed that all varieties of epistemological relativism yet advanced (including those of Protagoras, Thomas Kuhn, Jack Meiland, Gerald Doppelt, Harold Brown, Stephen Toulmin, and Larry Laudon), are self-refuting and thus incoherent. That is, all arguments for all forms of philosophical relativism contain their own refutation. Siegel's work pointed the way toward evaluating marketing's own relativism.
Consider closely the "falsity of convergent realism" argument. Note that it claims that truth should be abandoned as a goal because a particular theory of science (convergent realism) is false. But the claim that the assertions of realism are false is unintelligible without the presumption that under different circumstances, they could have been true. Thus, marketing's relativism uses the concepts truth and falsity in the very argument that purportedly demonstrates that truth is inappropriate for science. Such an argument fails minimum standards for coherence--it makes no sense. And making sense, I argued (then and now), should be the minimum desideratum for marketing science.
In conclusion, marketing scholars are often influenced strongly by some nonmarketing discipline. This essay has attempted to reconstruct how my own scholarship benefited greatly from an early exposure to philosophy, from the later study of specific philosophies, and from the even later interactions with and kind assistance of particular philosophers. No one but I am responsible for the errors in my works. But many, including scores of scholars whose contributions the space limitations of this brief essay prevent me from acknowledging, share credit for whatever illumination my works have provided on issues in the philosophy of marketing in general and marketing science in particular.6 Starting in the mid-1990s, my research program shifted toward developing the resource-advantage (R-A) theory of competition.7 As of this writing, there are tentative plans for a (final?) revision of Marketing Theory. If the plans materialize, it will no doubt sell several hundred copies per year.8
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By Shelby D. Hunt and Terry Clark, Southern Illinois University Book Reviews
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Record: 174- The Integrated Networks Model: Explaining Resource Allocations in Network Markets. By: Frels, Judy K.; Shervani, Tasadduq; Srivastava, Rajendra K. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p29-45. 17p. 1 Diagram, 5 Charts. DOI: 10.1509/jmkg.67.1.29.18586.
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The Integrated Networks Model: Explaining Resource Allocations in Network Markets
The last decade has witnessed a shift from a focus on the value created by a single firm and product to an examination of the value created by networks of firms (or product ecosystems) in which assets are comingled with external entities. The authors examine these market-based assets in the context of network markets and propose an Integrated Networks model in which three types of networks--user, complements, and producer--add value or enhance the attractiveness of the associated focal product. The authors empirically test the proposed model by surveying information technology professionals on their resource allocation decisions regarding the Unix and Windows NT operating systems. The findings suggest that the value added by these three networks is significantly and positively associated with resources allocated by business customers to competing products. The results also show that the three networks mediate the relationship between stand-alone product performance and resource allocation.
The breakdown of vertical integration due to forces unleashed by globalization, technology, and the Inter-net has led to a dramatic shift in strategy toward virtual integration of businesses and formation of horizontal alliances to better serve customer requirements. Marketing and strategy researchers have begun to examine competitive advantages and value created by assets that arise from "the commingling of the firm with entities in its external environment" (Srivastava, Shervani, and Fahey 1998, p. 2). These intangible assets, called "market-based assets," meet the definition of asset but exist outside of the firm. In a similar vein, Brandenburger and Nalebuff (1996) describe market relationships as a "value net." Here, firms (competitors, distributors, complementors, and suppliers) and customers compose a game-theoretic co-opetitive[ 1] environment of interdependencies within a market. Rindfleisch and Moor-man (2001) call such interdependencies between two competitors for the purpose of new product development "new product alliances;" Sivadas and Dwyer (2000) refer to it as "cooperative competency." Others have labeled these market-based assets or networks "value webs" (Cartwright and Oliver 2000). Under these frameworks, a firm, its customer base, the makers of products and services complementary to its own product, and even the offerings of its competitors are critical to assessing the strategic position of that firm.
A central notion of market-based assets is that a customer's decision to adopt a product is often influenced by factors other than just the value inherent in the product itself. Chief among these market-based assets are networks of customers, channel members, and competitive suppliers. The firm's ability to leverage these networks can have a significant influence on the revenue and ultimate success of the firm because in many markets--called "network markets"-- a significant portion of the utility of a product is created by the existence or expectations of networks surrounding the product (Besen and Farrell 1994). In this article, we further explicate the concept of market-based assets by combining it with work from diffusion (e.g., Bass 1969; Valente 1995), adoption (e.g., Heide and Weiss 1995), strategic alliances (e.g., Sivadas and Dwyer 2000), value nets and value webs (e.g., Brandenburger and Nalebuff 1996; Cartwright and Oliver 2000), whole product concepts (e.g., Lambkin and Day 1989; McIntyre 1988; Moore 1999), and network externalities (e.g., John, Weiss, and Dutta 1999; Katz and Shapiro 1985). We propose and empirically test a model of a buyer's resource allocation decision on the basis of perceptions of these market-based assets or networks.
A wide variety of markets meets the criteria for being classified as network markets. A network market exists if users derive benefits from the following:
- The user network, or the extent to which the product is and is likely to be used pervasively within and outside the organization. Buyers like to be assured that there is, or is likely to be, a significant pool of product users in addition to themselves.
- The complements network, or the number and variety of complementary products and services available. Buyers like to be assured that a variety of complements (e.g., hardware, software, services) are, or are likely to be, available in the future.
- The producer network, or the number and degree of competition among product vendors. Buyers do not like to be "single sourced" and prefer procurement situations with multiple qualified vendors.
Information technology (IT) examples abound, and computer hardware/software and the fax machine are the most common. However, examples extend far into other contexts. The consumer entertainment market is replete with network markets such as those created by entertainment players and content (e.g., video home system [VHS], digital video disc [DVD], compact disc [CD]). Consumer video games are another example of a consumer network market in which the user network creates a large portion of the value of owning a box (keeping up with the Jones's children), and the quality of the complements network (games) drives the sale of the focal product. Other examples range from financial services (in which a strong complements network makes the provider more attractive), to automobiles (which rely heavily on the complements network of service and fuel stations), to telephones (for which users only receive value when the user network develops), to diamond engagement rings (for which the value of such rings has grown largely because other users have validated the style; Conner 1995).
Network markets are often described as "tippy," that is, that "the existence of incompatible products may be unstable, with a single winning standard dominating the market" (Besen and Farrell 1994, p. 188; Arthur 1989; Valente 1995). Indeed, it has been argued in the literature that the value created by networks can be so great that inferior technologies (based purely on features, functionality, and technical performance characteristics) are able to push aside or hold off superior technologies. Cases such as the internal combustion engine versus the steam engine, VHS versus Beta, and Windows 95 versus OS/2 or Mac OS 7 are often cited as examples in which inferior technologies won despite arriving on the scene later than a technologically superior incumbent.
The purpose of this article is to enhance the understanding of market dynamics, value creation, and competitive advantage in network markets. Our thesis is that buyers allocate resources for the procurement of business assets (e.g., automobile fleets, IT, video systems) on the basis of a consideration of the stand-alone product performance as well as the user, complements, and producer networks.
We have two key objectives in this research and two primary areas of contribution to the marketing literature. First, we develop the Integrated Networks model, a conceptual framework of network markets, and define the three types of networks that are crucial to understanding how consumers allocate resources in network markets. Of the three networks in our model, previous marketing research focuses on the user network (e.g., Givon, Mahajan, and Muller 1995), the complements network (e.g., Bucklin and Sengupta 1993), or the producer network (e.g., Kotabe, Sahay, and Aulakh 1996). By including networks beyond the user network or installed base, we extend the current conceptualization of innovation diffusion, co-diffusion, adoption, and intraorganizational adoption (cf. Bass 1969; Bucklin and Sengupta 1993; Kim and Srivastava 1998; Mahajan, Muller, and Bass 1990), showing that the complements and producer networks also play an important role. Furthermore, we propose that intraorganizational adoption is driven by the strength of each network, as measured by five characteristics: current size, expectations of future size, compatibility, accessibility, and quality. Previous research on user networks focuses on the size of the network and expectations of the future size of the network (e.g., Economides and Himmelberg 1995). Our model's richer conceptualization of how networks create value extends previous work on networks (Martilla 1971; Valente 1995) and network externalities (Katz and Shapiro 1985) by providing more detail on the types of networks and the characteristics of networks that create utility and drive adoption in network markets.
Our framework focuses not on the initial trial or first-time adoption of a product but on the extent to which a product is adopted by a customer and the ensuing commitment of new resources to buy additional units of the product over time. Many organizations have a policy of trying emerging technologies simply to be aware of options, but it is the extent to which a technology is embraced by an organization, the adoption intensity, and the corresponding commitment of new resources or buying additional units during a finite time period that ultimately lead to the success of the supplier firm. Therefore, our emphasis is on understanding how product performance and networks of users, complements, and producers influence the intraorganizational diffusion of the innovation (Kim and Srivastava 1998).
Our second objective and area of contribution is in our empirical test of the Integrated Networks model. Our empirical study provides an early test of the market-based assets theory recently proposed by Srivastava, Shervani, and Fahey (1998, 1999) by measuring consumers' perceptions of these assets and examining their influence on the buyer's decision to purchase a firm's product. Furthermore, our study provides empirical evidence of the power of networks to influence purchase decisions and increases our confidence in research that draws on the concept of network externalities (e.g., Kotabe, Sahay, and Aulakh 1996; Srivastava, Shervani, and Fahey 1998; Valente 1995) by operationalizing, measuring, and empirically testing the Integrated Networks model as well as the network externalities framework nested within it. A dearth of empirical research in this area has prompted calls for empirical verification of network phenomena (John, Weiss, and Dutta 1999). By demonstrating that elements external to the focal product can drive its purchase, the framework provides additional support for the concept of the "whole product" (Lambkin and Day 1989; Moore 1999) and the possibility of market dominance by an inferior technology (Arthur 1994; David 1985; Liebowitz and Margolis 1995).
Although the concept of networks and network externalities is well integrated into "street knowledge" and has been examined conceptually and analytically, little empirical work exists in this area (David and Greenstein 1990; John, Weiss, and Dutta 1999). Our article differs from the empirical work that exists in four ways.
First, in one study our research incorporates all three networks reported in marketing and economics literature to play a role in driving product selection. Prior empirical research has examined either the user network (e.g., Economides and Himmelberg 1995) or the complements network (e.g., Bayus 1987; Bucklin and Sengupta 1993; Gandal, Kende, and Rob 2000). We measure and test three networks simultaneously: user, complements, and producer. We find that each network plays a significant role in determining resource allocation, even in the presence of the other two networks. Furthermore, we expand the conceptualization of network characteristics that compose the relative strength of the network from two (size and expectations of future size) to five (size, expectations of future size, compatibility, accessibility, and quality).
Second, we increase the validity of the findings in this area. Our research is based on actual perceptions of the purchaser, not on aggregate sales data. We survey purchasers regarding their perceptions of the three networks associated with a focal product and with a competing product as well as their perceptions of both products' technical capabilities and attributes. Previous research using aggregate data (e.g., Bayus 1987; Economides and Himmelberg 1995; Gandal, Kende, and Rob 2000) assumes that network effects are the "black box" between antecedent variables (such as complements or user network size) and consequence variables (such as focal product diffusion or hedonic price) (Brynjolfsson and Kemerer 1996). Although previous research indicates, for example, that CD players' diffusion is positively associated with the number of CD titles available, it does not indicate whether consumers perceive that a greater number of titles are available or whether the perception is tied to their adoption or resource allocation. No such assumption or leap is necessary in our study. We believe that by surveying consumers' perceptions directly, we greatly enhance the internal validity of the previous work that uses aggregate data and the external validity of the analytical work in this area.
Third, we examine network effects in the context of a standards battle: two products competing in a tippy market. Previous empirical works (e.g., Bayus 1987; Economides and Himmelberg 1995) do not consider a competitive situation and the "winner takes all" nature of these markets (Arthur 1989; Hill 1997). We not only examine perceived value of the focal product and its networks but also measure perceptions of the networks of the primary competing product to determine how the relative strength of the networks drives adoption, providing insight into the competitive dynamics of a network market. This provides an empirical extension of previous analytical efforts of network markets (e.g., Arthur 1989).
Fourth, our dependent variable is not a price index that must then be interpreted as a proxy for utility or adoption (e.g., Brynjolfsson and Kemerer 1996; Gandal 1994, 1995). Thus, there is no need to make a leap from hedonic price to purchase. However, we do not examine only initial or first-time adoption. Instead, we measure intraorganizational adoption, or the amount of resources allocated by the purchaser to the focal and competing products on an ongoing basis. Continued purchase by an organization represents a larger portion of overall sales than does initial trial or adoption (Kim and Srivastava 1998). Therefore, it is more likely to be indicative of product success in a tippy market.
Despite the large amount of analytical work in this area, there has been little empirical work, and none that we were able to find, that operationalizes all of the key variables and specifically measures consumers' perceptions of the networks. Our study empirically validates the street knowledge in this area and significantly expands the existing scope of empirical study.
In summary, we make several key contributions to the marketing literature. We offer a more comprehensive model of adoption in network markets, focusing on three networks that have not been examined previously in a single model. We provide a richer characterization of these networks than does preceding work in economics or marketing. We focus not on initial adoption but on intraorganization adoption, or the continued commitment of additional resources to a technology--the source of much technology spending. We contribute empirical evidence on the power of networks by not only testing the Integrated Networks model but also providing empirical support for market-based assets theory and network externality theory.
The rest of this article is divided into four sections. The next section examines the role of stand-alone product performance and user, complements, and producer networks in influencing the extent to which technologies competing for organizational resources (share of purchases, budgets) are embraced by organizations. The second section details methodological issues. In the third section, we discuss our results, and in the fourth section, we examine the contributions and limitations of the findings.
Economists and marketers both make extensive use of the term "network." In marketing research, the term has come to have many meanings such as business or social networks (Iacobucci 1996; Valente 1995), but put most simply, marketers consider networks phenomena that describe interconnections among people or organizations.[ 2]
Economists arrive at a similar meaning, albeit by a different path. Although they originally used the term "net-work" in "network externalities" to refer to benefits that accrue from connections of physical networks such as telephones or railway lines, the term was extended to include value created by networks of users sharing compatible products or standards.
Figure 1 shows the four key constructs in the Integrated Networks model that drive resource allocation: stand-alone product performance, the user network, the complements network, and the producer network. We describe these subsequently.
Stand-Alone Product Performance
Fundamental to the notion of selecting one product over another is the utility delivered by the product itself, independent of the value delivered by any network. Product performance[ 3] is based on the features and attributes of the technology as it stands alone, not utility delivered by the prod-uct's rate of diffusion, the complements, or other market-based assets that add value to the product. It is this element of utility that is considered when discussion of inferior products arises--the core technological value of the product itself without external factors considered.
The User Network
In network externality theory, the size of the user network is the key driving factor behind adoption decisions (Katz and Shapiro 1985). Both in individual consumer settings and in organizational adoption decisions, a network of previous adopters is believed to encourage adoption among nonadopters by making the product more useful, providing opportunities for word of mouth and observation, or sending a quality signal (Gatignon and Robertson 1985; Hellofs and Jacobson 1999; Martilla 1971; Rogers 1995; Valente 1995).
The influence of the user network is incorporated in most technology diffusion models through the coefficient of imitation or internal influence in marketing's diffusion research (Bass 1969). Early research on networks of innovators describes the ways that technical information and knowhow are transferred among social networks of buyers and potential buyers (e.g., Czepiel 1975; Martilla 1971). Czepiel (1975) finds that communication channels link technical decision makers in rival firms for the purpose of information acquisition, validation, and verification. These networks of innovators can include not only those making resource allocation decisions but also the firms producing the product, exchanging information with buyers to enhance the innovation and thus increase adoption of that innovation (Håkansson 1987; Von Hippel 1988).
Research on market-based assets suggests that the utility delivered by an established installed base can lead to faster market acceptance of a product, not only through word-of-mouth effects but also by lending an air of credibility to the organization (Srivastava, Shervani, and Fahey 1998). This accelerates cash flows, thus increasing shareholder value and strengthening the competitive position of the innovating firm. The positive effect of a user network can be so strong and thus important to a product's ultimate success that it may be worthwhile to tolerate some degree of piracy to grow the user base and develop the network externality benefits (Conner and Rumelt 1991; Givon, Mahajan, and Muller 1995).
What is it about the user network that influences adoption? In network externalities theory, economists state that both current size and expectations about future size enhance the strength of a user network (Besen and Farrell 1994; Katz and Shapiro 1985). In turn, a strong user network increases a product's value and, therefore, the resources it attracts and its likelihood of purchase, which creates a positive feedback loop. On the basis of previous research in marketing and strategy, we consider the following additional characteristics that add to the strength of a network: compatibility, accessibility, and quality.
- Compatibility in the user network refers to users either inside or outside the firm who are important to the potential buyer or user for reasons such as opinion leadership or compatibility (Gatignon and Robertson 1985; Rogers 1995).
- The degree to which the user network is accessible to a potential adopter (verbally, visually, or electronically) can determine the influence the user network can have on that adopter (Gatignon and Robertson 1985; Valente 1995). Accessibility is similar to Rogers's (1995) concept of the observability of the adoption and its influence on other members of the social system.
- Although the quality of the technology itself is captured in the product performance, the quality of a network refers to the technological expertise, innovativeness, soundness, reliability, and reputation of users who have adopted the technology. The quality of the members of the user network can influence potential adopters and future resource allocation by exacting a normative influence on the potential adopter through opinion leadership (Gatignon and Robertson 1985; Valente 1995), by signaling identification (Conner 1995; Solomon 1983), or in a business setting, through mimetic isomorphism (DiMaggio and Powell 1983).[ 4]
Together with current size and expectations of future size, these three characteristics compose a second-order or higher-order factor that we call the strength of the network. When these characteristics are measured in comparison with a competing set of networks, we call it the relative strength of the network. These characteristics help enhance the utility a user derives from the networks and thus influence the choice of product or technology in network markets.
H1: The greater the strength of the user network, the greater are the resources allocated to that product.
Complements Network
The complements network is composed of products and services that are needed to make the focal product more productive or complete as part of a whole solution. The whole product (also referred to as a product ecosystem or customer solution) includes not only the focal product but also additional hardware and software, training, support, or other elements needed to create a "compelling reason to buy" (Moore 1999, p. 115). In technological innovations, the whole product is essential to convince users other than technology experts to purchase, or to "cross the chasm" (Moore 1999, p. 7). Marketing researchers have called elements that compose this whole product the industry or product infrastructure (Lambkin and Day 1989; McIntyre 1988).
Just as members of the user network are linked by their purchase of a common standard for a focal product, members of the complements network are linked by their compatibility with the focal product. Complement makers such as game developers for Nintendo are connected in that they compete for a limited number of game licenses allocated each year for a platform that is attractive because it is so widely diffused (Brandenburger and Nalebuff 1996). The greater the number of complementary products (e.g., inter-active games), the greater is the usefulness of the focal product (e.g., Nintendo or Sega game console). In addition, distributors of the focal product (providing the complementary service of distribution) are linked by the interdependencies between inventories of focal and complementary products.
Similar to the user network, we propose that the strength of the complements network drives resource allocation and that the strength of the complements network can similarly be characterized by its current size, expectations of future size, compatibility, accessibility, and quality. The complements network is more compatible with the user when it contains elements that are needed to provide backward compatibility with previous systems or interoperability with other users that are critical to the buyer's intended use of the focal product. Complements (such as Universal Product Code [UPC] labels and CDs) must also be accessible before the focal product (UPC scanners and CD players, respectively) can successfully diffuse (Bayus 1987; Bucklin and Sengupta 1993; Sengupta 1998). The quality of the complements network can play a large role in resources allocated to the focal product. Lotus 1-2-3, faster and more powerful than its then-competitors, VisiCalc or Multiplan, became the "killer app" for the original IBM personal computer (PC), significantly influencing IBM's emergence as the desktop computing standard in the business segment (Carlton 1997).
The diffusion and intraorganizational adoption of the focal product may be directly tied to the diffusion rate of complementary products. Researchers point out the importance of thinking beyond the firm's own borders. Bucklin and Sengupta (1993, p. 159) posit that "Product strategies based solely upon the expansion of 'own' demand where complementarities exist may be suboptimal." If developed properly (Rindfleisch and Moorman 2001; Sivadas and Dwyer 2000), relationships with these complement providers can become an attractive market-based asset and instrumental in the success of the focal product (Srivastava, Shervani, and Fahey 1998). Thus, we include the complements network as a key element of our model.
H2: The greater the strength of the complements network, the greater are the resources allocated to that product.
Producer Network
The producer network is composed of manufacturers that produce products that are functionally equivalent to and compatible with the focal product. Thus, this network includes the original product producer and any other competitive manufacturers that, through licensing or other means, have been able to produce functionally equivalent, compatible products. These products may be imitations, clones, or generics. The co-opetitive role of additional product producers can be critical to the ultimate success of the focal firm's product (Kotabe, Sahay, and Aulakh 1996).
Similar to the complements network, the growth of the producer network can have a positive effect on product adoption, relative to that of competing product ecosystems. As more entrants compete within a single standard, price reductions may result, increasing the size of the potential market. Increased competition may lead to higher levels of distribution and promotional activity, which in turn can accelerate diffusion of the product (Kim, Bridges, and Srivastava 1999). The addition of clones to the IBM-compatible PC camp not only drove prices and margins down but also drew customers away from Apple's Macintosh. Cloners may have more experience with, knowledge of, and capabilities for serving different markets (geo-graphic or otherwise), bringing expertise and access to markets that the innovator cannot serve well (Conner 1995; Robertson 1993). Furthermore, the existence of multiple producers provides a second source to the customer, which prevents the innovating firm from price gouging the user at a later time, and therefore reduces the user's risk in committing to a product (Farrell and Gallini 1988).
The network characteristics that make the user and complements network valuable also describe the strength of the producer network. As discussed previously, current size and future size of the network influence utility. The producer network is compatible with the user if the user has an established, ongoing relationship with its members. A producer network that is not compatible with the user can lead to vendor-related switching costs (Heide and Weiss 1995) as well as losses of utility associated with the termination of existing vendor relationships (Morgan and Hunt 1994). Research in channels and distribution emphasizes the accessibility of producers (Magrath and Hardy 1991; Stern and El-Ansary 1992). Different product producers may be invited into the producer network specifically because they are accessible to a particular group of users (Conner 1995). Quality and reputation of the members of the producer network contribute to the firm's performance by influencing the likelihood of focal product adoption (Fombrun 1996; Rao 1994).
Developing the producer network is not without risk to the innovator. Under different appropriability regimes,[ 5] the innovator may or may not be able to control the development of the producer network. Also, depending on the appropriability regime, the innovator may or may not profit from the producer network's growth. In addition, the types of competitors that enter and are successful at different points in the product evolution will vary (Lambkin and Day 1989). However, it is expected that in most cases, the diffusion of the focal product will increase with a more developed producer network (Lambkin and Day 1989). Consequently, we hypothesize the following:
H3: The greater the strength of the producer network, the greater are the resources allocated to that product.
The Relationship Between Networks and Stand-Alone Product Performance
The Integrated Networks model provides insight not only in understanding adoption and intraorganizational penetration in network markets but also into the relationship between the networks and stand-alone product performance in creating value for the user. Networks are unlikely to develop around a product that is deemed unsatisfactory and unlikely to provide some degree of value for the consumer. Therefore, it is reasonable to conclude that stand-alone product performance--value delivered by the technology, independent of the networks--will influence the development of the networks. However, it has also been proposed that after the networks have begun to develop and deliver value, that value can overwhelm the value uniquely created by the product itself. Technological standards battles studied in previous research include the QWERTY keyboard versus the Dvorak keyboard (David 1985; Liebowitz and Margolis 1990, 1999), the VHS and Betamax contest (Arthur 1994; Liebowitz and Margolis 1995), and the competition between internal combustion and steam engines (Arthur 1989). The relationship between product performance and the networks suggests a mediation scenario, with the networks mediating between the stand-alone product performance and resource allocation. Thus, strongly networked products that are based on lesser technological solutions are often adopted over superior but weakly networked products because, we propose, networks mediate the relationship between product performance and resource allocation decisions.
H4: The user network mediates the relationship between stand-alone product performance and the resources allocated to the product.
H5: The complements network mediates the relationship between stand-alone product performance and the resources allocated to the product.
H6: The producer network mediates the relationship between stand-alone product performance and the resources allocated to the product.
We first describe our instrument and data collection procedures. Next, we assess the reliability of the measures and the discriminant and convergent validity of our constructs. We then examine the association of the networks with resource allocation and the ability of networks to mediate the relationship between product performance and resource allocation.
Context
The context for this study is a network market. We chose to examine a purchase decision made by IT professionals regarding a high-technology product. We surveyed IT professionals at major U.S. firms choosing between the Windows NT and Unix operating systems. We chose this context because operating system choice is typically a sufficiently significant purchase that IT professionals are likely to consider multiple attributes of the available choices. Also, IT professionals have detailed knowledge of the competing products and are able to assess the stand-alone technical product characteristics and performance (i.e., the value delivered by the technology, separate from the networks). In addition, research in technology markets has focused on similar key informants at the organization level (Gatignon and Robertson 1989; Heide and Weiss 1995; Weiss and Heide 1993). Because we examine a competitive standards battle, we focus on the relative strength of the networks and collect a comparative assessment of the stand-alone product performance. Network markets are tippy markets, and competition in such markets is appropriately analyzed by methodology that recognizes the interdependencies between competing products' diffusion processes (e.g., Arthur 1989).
Instrument
We developed a questionnaire that was targeted at IT professionals and pertained to their resource allocation decisions in situations in which Windows NT and Unix were both technically feasible options. The survey was initially pretested by three IT professionals at a large university. We modified the survey on the basis of their feedback and submitted it to a sample consisting of 25 IT professionals enrolled in an executive education class. We checked initial scale reliability and modified the survey again, on the basis of reliability measures and comments (written and verbal) from this group. The revised survey was then pretested by three IT professionals at a Fortune-500 company, and we made changes on the basis of their in-depth feedback. We then administered the survey to the sample described next.
Sample and Data Collection
The sample consisted of 3000 senior computing executives at large firms in the United States. The names were randomly selected from a list of 5000 top computing executives, provided by Phoenix-based Applied Computer Research. These firms belong to the Fortune 1000, Forbes 500, or InformationWeek 500 or they met at least one of the following criteria:
- They owned a mid-size or mainframe computer (an IBM 308x or larger, an Amdahl, or Hitachi);
- They had 50 or more IT employees;
- They had an IT budget of $4 million or more;
- They owned 200 or more PCs; or
- They owned one of the following types of systems: CDC, Tandem, Cray, Unisys A series, or DEC VAX 7000.
These constraints reduced the likelihood of surveying IT professionals who manage only desktop PCs in which Unix is less likely to be a feasible or realistic choice.
The instrument, a letter requesting the user's assistance, and an offer for a summary of the results were included, along with a business-reply return envelope. Approximately four weeks after the initial survey was mailed, a reminder postcard was sent. Of the 265 completed surveys, 237 were usable. A total of 65 other surveys were returned as undeliverable or because the addressee was no longer employed at the firm. This represented a response rate of 9%. Although this represents below average survey response rates of top management (Menon, Bharadwaj, and Howell 1996), this is not inconsistent with studies of similar target samples (Peet 1998; Vedder et al. 1999). Adequately powered t-tests (Cohen 1988) of means of key variables (network characteristics, product performance, and resource allocation) show no significant differences between those who responded before (n = 194) versus those who responded after (n = 43) the reminder postcard.
Measures
One goal of this research is to significantly extend the empirical effort in network market research by providing the first empirical measurement of consumers' perceptions of network externalities as well as of our proposed Integrated Networks model. Our measures are not proxies for network externalities, nor is our model based on aggregate data. We survey consumers on the current size of the user and complements networks and the expectations of the future size of the user and complements networks, as well as the newly introduced characteristics of each of the three networks: compatibility, accessibility, and quality. Although the measures we develop are specific to our context, operating systems, we believe they can provide guidance for future researchers in network markets.
Specific Decision Area
The first question of the survey was highlighted in a section titled "Your Specific Decision: What Decision Are You Making Today?" and asked the respondent to consider a recent or upcoming decision. The respondent was asked to select from a list or to write in the functional area of that decision (e.g., CAD/CAM operations, accounting, engineering, design use). Respondents were told that this was their specific decision area and were asked to answer all questions with respect to that decision context. Included in nearly every item are the words "specific decision area" to help ensure that the respondent reported on the networks affiliated with the operating system and the attributes of the operating system itself in that specific context. We did this to encourage a consistent perspective by the user as he or she responded to the survey and to avoid an aggregation bias across decisions made in a particular year.
Resource Allocation
We asked the user to estimate the percentage of the operating system/application/workstation budget that was to be spent on Windows NT-based services and goods and Unix-based services and goods in 1998. Again, we instructed the user to focus on the specific decision area he or she had indicated at the beginning of the survey. We used the percentage of this budget to be spent on Unix as the dependent variable. Because it is a percentage bound by 0 and 1, we replaced responses of 0 or 1 with near approximations (Cohen and Cohen 1983), and we performed a logit transformation. The transformed variable ranges from -5.29 to 5.29 with a mean of -.85 and a standard deviation of 2.26. Skewness is -.15, and kurtosis is -.01.
Assessment of Stand-Alone Product Performance
Stand-alone product performance measures the user's perception of the Unix and Windows NT operating systems as independent technological products, separate from the networks surrounding these products. We asked the users to assess the importance within their specific decision area of ten technical operating system attributes using a five-point response scale ranging from "not at all important" to "very important." The attributes can be grouped into three general categories: complex computing capabilities of the operating system (e.g., multiprocessor support, scalability, clustering, high performance features), manageability of the operating system (e.g., ease of recovery from crashes, security, ease of manageability, networking), and robustness of the operating system (e.g., robustness/stability, maturity). The list of attributes was developed from an extensive search of technical publications pertaining to operating systems (e.g., Byte 1996; Edge: Work-Group Computing Report 1996; Information Week 1997) and was pretested by IT professionals. After users rated the importance of each attribute, we asked them to rate each operating system's performance on a scale of 1 to 5, ranging from "does not provide this capability at all" to "provides this capability very well."
For eight of the ten attributes, Unix was rated significantly higher than NT (NT was rated higher on "ease of manageability" and "networking"). To avoid multicollinearity problems in subsequent analyses, we collapsed ratings into one item that represented stand-alone product performance. We computed this item by taking the Unix rating, subtracting the NT rating, and multiplying the result by the importance score.[ 6] Thus, this is a relative scale in which a large positive number indicates a belief that Unix is the superior operating system and a large negative number indicates a belief that NT is the superior operating system. This value has a mean of 19.13, standard error of 3.75, skewness of -1.15, and kurtosis of 2.93.
Perceptions of the Three Networks
We measured each characteristic of each network using a multi-item scale. The items consist of statements such as "This operating system has a sizable market share today in my specific decision area" and are followed by two five-point scales, one for NT and one for Unix, anchored by "strongly disagree" to "strongly agree." We did not include the current size of the producer network and the expectations about the future size of the producer network in the instrument. Pretests showed that questions related to size of the producer network cause confusion among users. Idiosyncratic to this industry, each operating system, regardless of its standard interfaces and alliances, is developed by a single firm; therefore, we did not measure current size and expectations of future size of the producer network in the survey. We subsequently discuss scale refinement procedures (final scales are provided in Appendix A).
Control Variables
Previous research in adoption has found other variables to be associated with resource allocation decisions. Because they are not the central focus of our framework, they are included in our analysis as control variables. The first category of control variables is associated with cost and includes cost of heterogeneity (owning more than one operating system), total cost of ownership of the operating system (Rogers 1995; Weiss 1994), concern for compatibility (Weiss and Heide 1993), and the financial switching costs associated with switching from one operating system to another (Burnham, Frels, and Mahajan 2003; Weiss 1994). The second category is associated with the firm's decisionmaking process. These include the firm's risk aversion (Puto, Patton, and King 1985; Tellis and Gaeth 1990) and innovativeness, measured as organizational centrality (Gatignon and Robertson 1989; Mansfield 1968). Final scale items are provided in Appendix B.
Scale Refinement and Analysis
To obtain a measure of the relative strength of one network over the competing network, we followed the subsequent procedure. For each item that measured a network characteristic, we subtracted the response given for NT from the response given for Unix, creating a relative scale in which a positive number indicates a belief that the Unix operating system scores higher on that particular item and a negative number indicates a belief that NT scores higher on that particular item. Peter, Churchill, and Brown (1993) warn against potential problems with the use of such difference scores: understated or overstated reliability, spurious correlations, and discriminant validity. In Appendix A, we show the reliabilities of the component measures (measures for each individual operating system) as well as the combined scores (calculated as proscribed by Peter, Churchill, and Brown 1993), all but one of which are at or above the .70 recommendation provided by Nunnally and Bernstein (1996). The exception is accessibility of the user network in which reliability is .66. Thus, we do not face the reliability issues Peter, Churchill, and Brown caution against. Because the individual measures that make up the difference score (the component measures) are not included in further analysis, Peter, Churchill, and Brown's concerns regarding spurious correlations or discriminant validity are also not applicable. Peter, Churchill, and Brown also discuss a variance restriction problem that is not applicable in these measures because the measures do not have a "more is always better" connotation. The survey contained 71 items that measured the network constructs. We submitted these items to the iterative purification process recommended by Churchill (1979), Gerbing and Anderson (1988), and Bollen (1989), consisting of exploratory factor analysis, reliability analysis, and confirmatory factor analysis. This process led us to retain a total of 42 items, which are provided in Appendix A. Confirmatory factor analyses (Anderson and Gerbing 1988) show that the measurement models fit the data well (normed fit index [NFI] and comparative fit index [CFI] > .90; see details in Appendix C). We checked the characteristics of each network for discriminant validity to assess the uniqueness of each characteristic by setting the correlation of each pair of two measures to 1.0 within the measurement model and checking the degradation of the 2 measure (Anderson and Gerbing 1988). We also tested unidimensionality and convergent validity of each construct according to procedures recommended by Anderson and Gerbing (1988). For the control variable measures, we used scale purification processes similar to those used for the network characteristics. Fit indices are provided in Appendix B.
For 8 of the 13 network characteristics measured, NT was rated as having a stronger network than Unix, whereas Unix's networks were rated stronger in only two cases: quality of the producer network and quality of the complements network. There was no significant difference between the products in the size of their user networks, the quality of their user networks, or the compatibility of their complements network.
Finally, we explored the existence of second-order factors of "relative strength of network" following methods outlined by Gerbing and Anderson (1984), Bollen (1989), Marsh (1987), and Rindskopf and Rose (1988). We found that three second-order factors fit the data better than the first-order factors on many measures and only marginally worse than the first-order factors on a few measures. On the basis of recommendations by Marsh (1987) and Rindskopf and Rose (1988), we use the second-order factors on the basis of their fit with the data, their theoretical attractiveness, and their parsimony (see Appendix C). Discriminant validity tests (Anderson and Gerbing 1988) conducted on the second-order factors show that the three second-order strength constructs are distinct from one another. Correlation coefficients among all variables are shown in Table 1.
We tested five structural models using AMOS 4.01 (Arbuckle and Wothke 1999). Model 1 is the full model as is shown conceptually in Figure 1 (for clarity, Figure 1 does not include the first-order factors, indicators, error terms, or control variables).[ 7] To test for mediation, we followed procedures described by Baron and Kenny (1986). Model 2 regresses resource allocation on stand-alone product performance, excluding the mediators from the model (Path C in Barron and Kenny [1986]). Models 3, 4, and 5 each regress one of the mediating networks on the stand-alone product performance (Path A in Barron and Kenny [1986]). In Models 1 and 2, in which resource allocation is the dependent variable, the six control variables are included in the model with single-item indicators and the error variances set to (1 - variance) reliability (Jöreskog and Sörbom 1989, p. 153).
We present the results in Table 2. Model 1, the full model, shows support for H1, H2, and H3. In testing H1, we show that the relative strength of the user network is positively associated with the resources allocated to Unix (β = .32; p < .001). In support of H2, we find that the complements network is significantly and positively associated with resource allocation ( β = .21, p < .01). In support of H3, we find that the producer network is significantly and positively associated with resource allocation (β = .34, p = .001). No control variables are significant in Model 1. Thus, we find support for H1, H2, and H3.
Testing H4-H6 requires information from Models 1-5. First, we examine Model 2 to determine if the stand-alone product performance is indeed positively associated with resource allocation when the mediators (networks) are not present. Model 2 shows that this is indeed the case (β = .47; p < .001.) Second, we examine Models 3-5 and find that the networks (the mediators) are indeed positively associated with the stand-alone product performance: Product performance → user network, β = .63, p < .001; product performance → complements network, β = .57, p < .001; product performance → producer network, β = .62, p < .001. Finally, we examine the size and significance of the relationship between product performance and resource allocation in Models 1 and 2. Although this relationship is significant when the mediators are absent (Model 2), it is not significant in the model that includes the mediators (Model 1): Product performance → resource allocation, β = -.04, p = .74. Thus, the three requirements for mediation are met, and we find support for H4-H6.
The only control variable that is significant in either Model 1 or Model 2 is "cost of heterogeneity," which is significantly and negatively associated with resource allocation (β = -.18, p < .05) in Model 2, indicating that as the perceived cost of owning multiple operating systems increases, the resources to be allocated to Unix decrease. This is reasonable given the encroachment of NT into Unix markets during the 1998 time frame and resource constraints related to IT infrastructure.
Findings
In recent years, new theory regarding network markets has been advanced (Srivastava, Shervani, and Fahey 1998, 1999) simultaneously with a call for more empirical research in this area (John, Weiss, and Dutta 1999). Our research extends the theoretical development of market-base assets and networks and provides empirical evidence on the influence of such networks on intraorganizational adoption decisions. In this study, we operationalize and test our conceptual framework, the Integrated Networks model, that explains intraorganizational diffusion and continued resource allocation in network markets. Although many markets are network markets, in this study we test our model in the operating system marketplace. The results show that networks can be characterized by five dimensions--size, expectations of future size, compatibility, accessibility, and quality--that can be represented by a higher-order factor of network strength. We find that each of the three networks--user, complements, and producer--is positively and significantly associated with resource allocation.
Furthermore, we find that the networks mediate the relationship between product performance and resource allocation. Thus, in a network market, the direct effect of stand-alone product performance on resource allocation may be insignificant. We posit that this is due to mediation by the strength of the networks, and our findings support this. Of the ten stand-alone product performance attributes we measure, Unix outperforms NT on eight, suggesting that respondents believe it to be the superior operating system. If product performance were the driving determinant of product adoption, we would expect Unix to be the favored choice. However, on average in 1998, 61% of the budget of each respondent was spent on NT. In addition, those who rated Unix's product performance superior, but perceived NT's networks stronger, were more than twice as likely to allocate resources to NT, compared with Unix. Why would this be the case? Our model suggests that it is due to the relative strength of the networks associated with NT and the networks' mediation of the effect of product performance on resource allocation. Of the 13 network characteristics we measure, NT outperforms Unix on eight of these measures, and Unix's networks rate stronger than NT's in only two cases. Thus, we present this as empirical evidence that in a market in which networks matter, the relative strength of the networks presents an important influence on purchase decisions and thus presents a mechanism by which a less preferred technology can gain market share.
Theoretical Contributions
This research makes several key contributions to the marketing literature. First, this article contributes a model of adoption in a network market that builds on our understanding of diffusion, adoption, and resource allocation. It extends our knowledge on what networks exist and influence adoption decisions as well as how those networks can be characterized. We contribute to the work in market-based assets and the "whole product" concept (Lambkin and Day 1989; McIntyre 1988; Moore 1999; Srivastava, Shervani, and Fahey 1998, 1999) by explicating the assets that exist in the marketplace and complete a technological product offering. We extend research in diffusion (e.g., Bass 1969; Mahajan, Muller, and Bass 1990) by suggesting that the coefficient of imitation, appropriate for aggregate models, can be augmented with other network characteristics when data from consumers can be gathered, which provides a richer characterization of the installed base (user network) and the way it influences consumer decisions. Furthermore, we show that both the complements and the producer networks can be as relevant as the user network in determining resource allocation. Most important, we show that when considered in one model, all three networks play a significant role in determining how consumers allocate their resources. Each of these three networks can be characterized by its current size, expectations of its future size, its compatibility and accessibility to the potential adopter, and the quality of its members. These characteristics determine the strength of the network.
We examine not first-time adoption or trial but rather intraorganizational adoption or the continued commitment of resources to a product or platform. The degree of intraorganizational adoption is more critical in determining the success of a product, because most sales of technological products are additional purchases by firms that have already tried the product (Kim and Srivastava 1998). Therefore, we examine continued resource allocation of two competing products.
Second, we advance the field's understanding beyond that which was previously analytically modeled to that which is empirically validated, providing future researchers with an expanded model of how purchase decisions are made in network markets (Arthur 1989; John, Weiss, and Dutta 1999). Our study further defines and provides an empirical examination of market-base assets (Srivastava, Shervani, and Fahey 1998, 1999) and demonstrates the influence of these firm-external assets on purchase decisions. In addition, this article contributes an empirical validation of the Integrated Networks model as well as the preceding model of network externalities. Prior to this study, a direct measure of consumers' perceptions of network externalities has not been undertaken, and analytical models have dominated (John, Weiss, and Dutta 1999). The empirical research previously undertaken relies primarily on aggregate data (e.g., Economides and Himmelberg 1995; Gandal, Kende, and Rob 2000); however, we directly measure consumers' perceptions of the characteristics of the three networks and their relationship with resource allocation decisions. This complements prior analytical and aggregate work and also extends that work significantly.
Third, in demonstrating that the networks mediate the relationship between the product performance and resource allocation, we provide evidence that with strong networks, a less-preferred technology may gain increased market share through the value delivered by its networks (Arthur 1989; Liebowitz and Margolis 1995; Valente 1995). Again, this further validates the criticality of managing the market-based assets in a network market.
Managerial Implications
Managers in network markets can draw many lessons from the Integrated Networks model. First, the framework encourages managers to develop not just the user network (as would network externalities or diffusion), but the complements and producer networks as well. All three types of market-based assets aid in creating a whole product, allowing the firm to "cross the chasm" by attracting early majority adopters crucial to product success. The Sega Dreamcast game system, introduced in 1998 and widely regarded as superior to other gaming consoles at the time, failed to reach expected sales levels because game developers (Sega included) were unable to introduce enough games (complements) simultaneously with the console. The market quickly tipped away from Sega Dreamcast, and Sega ultimately exited the market. One reason often cited for the VHS's triumph over the Sony Betamax was Matsushita's willingness to grow the producer network by licensing its design, whereas Sony chose to remain a sole provider. Again, the market tipped toward the product with the stronger network.
Second, the recognition of network strength as the underlying construct composed of multiple first-order constructs expands the strategic levers available to managers in network markets. Instead of solely increasing the size of the networks or influencing expectations regarding the future size of the networks as might be suggested by the original network externalities theory, the Integrated Networks model encourages managers to develop the networks on several dimensions, seeking users, complements, and producers that are compatible with the adopter, accessible to the adopter, and of the appropriate quality to provide utility to the adopter. For example, the diffusion of high-definition television has largely depended on the complements network, not only broadcast programming as is commonly cited but also other forms of digital input such as DVD players (Heller 2001).
Such implications apply to both entrant and incumbent firms. In our study, Windows NT (the workstation operating system entrant) had not surpassed the incumbent (Unix) on stand-alone technological performance. Nevertheless, our findings show that IT professionals intended to allocate a larger portion of their resources to NT rather than Unix, because of the strong networks Microsoft developed around NT. Thus, when an entrant's product is able to perform adequately (we do not mean to suggest that an unusable product can succeed solely through its networks), its managers should quickly address the market-based assets or networks associated with the product.
Likewise, an incumbent must protect its networks from encroachment by entrants. Our study reinforces the notion that having a superior product is not enough. Long-dominant game console makers Sony and Nintendo have worked diligently to strengthen their networks in the face of Microsoft's entrance to their network market with the X-Box. Microsoft's action in the desktop market demonstrates its own belief that its networks are crucial. In 1995, Microsoft was the incumbent in the desktop operating systems market, and it fought fiercely to protect its networks against Netscape's browser, which threatened to break the applications barrier to entry Microsoft constructed. Microsoft ensured a growing user network (consumers) by wielding its power with one of its key complements, computer manufacturers. By encouraging the manufacturers to put Internet Explorer (and only Internet Explorer) on the desktop and by bundling Internet Explorer with Windows, Microsoft significantly degraded consumers' accessibility to desktop-entrant Netscape. Microsoft's willingness to use legally risky tactics shows the criticality it attached to maintaining its strong networks.
Limitations
The results of the study must be considered together with its limitations. We conducted the study using single respondents from a sample of large firms regarding resource allocation in a single product market. For generalizable conclusions, we would need to establish the effects across a broader cross-section of goods and services. To assess the generalizability of the scales, we would need to establish their reliability as well as their convergent and discriminant validity in multiple contexts. Thus, we do not have evidence that our findings are not context specific.
Our empirical setting, operating systems, has strong network effects relative to other markets. Operating systems alone provide critical functions but rely heavily on complements such as application software, hardware, and user skills to be truly useful. Furthermore, one of the main functions of computing today is connectivity, and therefore the importance of users owning compatible systems may be exaggerated in this context when compared with markets that are further removed from the concept of a physical network providing the infrastructure links between network elements. When applying the results of this study to other contexts, the importance of networks in each context should be considered.
It is idiosyncratic that there is only one producer of one of the technologies (Microsoft's Windows NT) and multiple product producers of the other (e.g., IBM's AIX, Sun's Solaris, Hewlett-Packard's HP-UX) in this market. Therefore, we did not measure the current size or the expectations of future size of the producer network. This may also limit the generalizability of the results.
Further Research
Economists have assumed that the growth of the user network precedes that of the complements and producer networks, but strategic maneuvering by firms to attract complements (such as electronic games) prior to focal product availability suggests otherwise. Future studies might investigate the interdependencies among the networks and the influence each network has on the development of the other two.
Contingency variables are likely to delineate various competitive scenarios within network markets. What competitive environments make particular aspects of the Integrated Networks model more critical for success, more amenable to favorable strategic manipulation, or more open to threats by other firms? The extensions the Integrated Networks model makes to network externality research will shed light on the strategic levers that can be used by managers of firms sponsoring a particular technology. The use of the Integrated Networks model in other empirical contexts in which networks are likely to be more and less important will validate the axes along which the model provides meaningful insights.
Finally, research conducted from the perspective of the technology-sponsoring firm would also provide insight into network markets and standards battles. Combined with the Integrated Networks model, investigations of technological bandwagons, standard-setting alliances, and standards committees provide a starting point for such research. The shareholder value created by such market-based assets could then be more accurately assessed. Likewise, research on complementary product strategies (Bucklin and Sengupta 1993; Sengupta 1998; Teece 1986) and licensing activities (Conner 1995; Farrell and Gallini 1988; Kotabe, Sahay, and Aulakh 1996) would provide insight into how a firm might proceed in attempting to develop networks, increase adoption and resource allocation, and make its product the de facto standard.
As researchers continue to examine network business markets, and as the question of dominant inferior technologies continues to be raised, the Integrated Networks model should aid in future empirical work and in future theory building. Our work complements research on market-based assets (Srivastava, Shervani, and Fahey 1998), the value net (Brandenburger and Nalebuff 1996), and the value web (Cartwright and Oliver 2000), emphasizing a whole product or product ecosystem perspective to providing the customer with a complete solution (Lambkin and Day 1989; Moore 1999). Network markets represent a significant portion of the world's economy, and understanding such markets becomes more critical to managing that segment of the economy. As firms continue to address adoption issues in network markets, this expanded view of the forces behind these markets should aid in the decision-making process.
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
Relative strength of
user network (X1)
1.00
Relative strength of
complements
network (X2)
.80* 1.00
Relative strength of
producer network (X3)
.67* .71* 1.00
Stand-alone product
performance (X4)
.58* .61* .55* 1.00
Concern for
compatibility (X5)
-.05 -.03 -.03 -.12 1.00
Risk aversion (X6)
.06 .07 .05 .08 .00 1.00
Innovativeness (X7)
-.12 -.12 -.07 -.08 .04 -.12 1.00
Cost of
heterogeneity (X8)
-.35* -.37* -.30* -.37* .23* -.12 .12 1.00
Relative total cost
of ownership (X9)
-.08 -.08 -.07 .09 .00 .05 .07 .11 1.00
Financial switching
costs (X10)
.04 .05 .00 .05 -.15* .12 .01 -.21* -.08 1.00
Percentage of
resources allocated
to Unix (X11)
.65* .60* .57* .49* -.02 -.02 -.07 -.32* -.03 .02 1.00*Significant at least at the level of p < .05.
Legend for Chart
A = Hypothesis
B = Model 1
Full Model
C = Model 2
Product Performance →
Resource Allocation
D = Models 3, 4, and 5
Product Performance →
Network Mediator
A B C D
Structural Path
Relative strength of user network → resource allocation
H[1] .32***
Relative strength of complements network → resource allocation
H[2] .21**
Relative strength of producer network → resource allocation
H[3] .34***
Product performance → resource allocation
H[4], H[5], H[6] -.04 .47***
Product performance → relative strength of user network
H[4] .64*** .63***
Product performance → relative strength of complements network
H[5] .58*** .57***
Product performance → relative strength of producer network
H[6] .62*** .62***
Legend for Chart
A = Model 1
Full Model
B = Model 2
Product Performance →
Resource Allocation
A B
Control Variables
Concern for compatibility → resource allocation
.04 .08
Risk aversion → resource allocation
-.05 -.02
Innovativeness → resource allocation
.05 .03
Cost of heterogeneity → resource allocation
-.09 -.18*
Relative total cost of ownership → resource allocation
.09 .03
Financial switching costs → resource allocation
.02 .01
Squared multiple correlation of resource allocation
.43 .26
&chi2
2603.42 85.25
degrees of freedom (d.f.)
1157 21
CFI .89 .98
NFI .82 .98
Root mean square error of approximation
.07 .11*p < = .05
**p < = .01
***p < = .001
[T] Standardized path coefficients.
User Network
Current Size: Difference Score = α. = 80,
Unix = α = .80, NT α = .77
.79** Today, this operating system has the largest
installed base of users in my specific decision area.
.82 This operating system has a sizable market share today
in my specific decision area.
.73 The larger market share worldwide in my specific decision
area is currently held by this operating system.
Expectations of Future Size: Difference Score
α = .93, Unix α = .93, NT = α = .93
.87 In the future, I expect this operating system to have
the most users in my specific decision area.
.84 In the future, this operating system will probably have
a larger market share than any of its competitors in my
specific decision area.
.89 Over the next few years, I think more and more IT
professionals in my specific decision area will choose
this operating system for their use.
.84 In the future, this operating system is likely to attract
many more users in my specific decision area.
.82 Over the next few years, I expect the installed base for
this operating system to grow rapidly in my specific
decision area.
Compatibility: Difference Score α = .89,
Unix = α = .91, NT = α = .89
.90 People in our firm currently use this operating system.
.92 People in our firm, within my specific decision area,
currently use this operating system.
.85 Outside of my specific decision area, there are many
people in our firm with whom I need to be compatible,
who use this operating system.
Accessibility: Difference Score α = .66,
Unix α = .87, NT α = .87
.81 Currently, it is easy to find members of this operating
system's installed base to help me make decisions
regarding this operating system.
.85 It is easy to contact members of the installed base who
have adopted this operating system.
.87 If I need information about this operating system,
I can readily find a member of the installed base to
provide that information.
Quality: Difference Score α = .88,
Unix α = .92, NT α = .89
.89 Today, IT professionals in my specific decision area
who are "in the know" about technology use this
operating system.
.78 IT professionals who currently know a lot about operating
systems have chosen this operating system.
.85 IT professionals in my specific decision area whose
opinions I respect use this operating system.
.81 IT professionals in firms that lead our industry today
have adopted this operating system. Complements Network
Current Size: Difference Score α = .85,
Unix α = .89, NT α = .87
.88 Today, there is a great deal of hardware, software,
skills, and support in my decision area available for
this operating system.
.79 At this time, this operating system has the largest
amount of hardware, software, skills, and support
available for my specific decision.
.83 Today, most hardware, software, skills, and support
for my specific decision area are compatible with
this operating system.
Expectations of Future Size: Difference Score
α = .86, Unix α = .90, NT α = .90
.85 Over the next few years, more and more hardware,
software, skills, and support for my specific decision
area will be compatible with this operating system.
.89 In the future, I believe that this operating system
will have more hardware, software, skills and support
than its competitors.
.86 Over the next few years, I expect the amount of hardware,
software, skills, and support to grow very rapidly for
this operating system.
Compatibility: Difference Score α = .71,
Unix α = .83, NT α = .78
.73 The hardware, software, skills, and support needed
for backward compatibility in my specific decision
are compatible with this operating system today.
.83 Most hardware, software, skills, and support that I
need for my specific decision today are available for
this operating system.
.71 Hardware, software, skills, and support that my area
currently needs in order to interact with other units
in my firm are available for this operating system.
Accessibility: Difference Score α = .77,
Unix α = .86, NT α = .85
.78 The hardware, software, skills, and support for this
operating system are well distributed or widely available.
.71 I have seen many ads for hardware, software, skills,
and support for this operating system related to my
specific decision.
.85 Today, it is easy to get help with hardware, software,
skills, and support for this operating system.
Quality: Difference Score α = .78,
Unix α = .86, NT α = .82
.79 The hardware, software, skills, and support for
this operating system in my specific decision area
are generally of very high quality.
.84 The hardware, software, skills, and support available
for this operating system are highly reliable.
.76 The hardware, software, skills, and support available
for this operating system are the most technologically
advanced for my specific decision area. Producer Network
Compatibility: Difference Score α = .77,
Unix α = .90, NT α = .77
.80 I already have a good working relationship with
the firm(s) that develop this operating system.
.76 I have service contracts with the firm(s) that
develop this operating system.
.78 I already have procedures established for purchasing
from the firm(s) that develop this operating system.
Accessibility: Difference Score α = .71,
Unix α = .84, NT α = .86
.86 I am currently familiar with the firm(s) that develop
this operating system.
.54 I have seen many ads by the firm(s) that develop this
operating system.
.58 Today, this operating system is widely distributed and
is easy to obtain.
Quality: Difference Score α = .84,
Unix α = .91, NT α = .86
.88 Firms whose quality I respect develop this
operating system.
.77 The firm(s) that develop this operating system
has/have a reputation for knowing a great deal
about operating systems.
.85 Firms that I trust develop this operating system.*"Difference Score" signifies the coefficient alpha of the difference scores (computed as recommended by Peter, Churchill, and Brown 1993), "Unix" signifies the coefficient alpha of the Unix measures, "NT" signifies the coefficient alpha of the NT measures.
**This column contains CFA loadings, all significant at the p < .05 level.
Decision-Making Variables: χ2[sub13] = 19.53; NFI = .99; CFI = .99
Risk Aversion: α = .70
.37* My firm is the type of firm that often tries new
IT products at least once.
.65 When my firm buys IT products, it buys only well-
established brands. (R)
.76 My firm is cautious in trying new/different
IT products. (R)
.68 My firm does not like to buy something unknown
where there is the risk of making a mistake. (R)
Innovativeness: = .82
.69 When existing rules and procedures are not adequate
to make an IT decision, instructions are requested
from senior IT management.
.81 When problems arise in the technology selection
process, the decision maker goes to senior IT
management for an answer.
.84 When an unusual situation is encountered in the
IT decision-making process, senior IT management is
consulted before moving forward. Cost Variables: χ224 = 86.39; NFI = .92; CFI = .94
Concern for Compatibility:α = .68
.56 When my firm considers which operating system to
purchase, compatibility with our existing systems
is not an issue.
.55 Technically speaking, we are concerned about how
compatible this operating system will be with the other
computer-based systems in our firm.
.87 System compatibility is not an issue as we consider
adopting an operating system.
Cost of Heterogeneity: = .89
.77 It is much less expensive for us to use only one
operating system, either Windows NT or Unix, than to
use both.
.93 Our installation costs are much lower if we standardize
on a single operating system.
.88 Our training costs are much lower if we have only one
operating system in our decision environment.
Financial Switching Costs:α = .66
.55 Switching to a new operating system would require
us to spend a great deal of money on new hardware.
.69 Switching to a new operating system would require us
to spend a great deal of money on new application software.
.65 Switching to a new operating system would be very expensive
in terms of restructuring our system maintenance and our
help desk facilities. Relative Total Cost of Ownership--Formative Scale
End points of "not at all expensive" and "very expensive"
- The street price of the operating system itself and the hardware on which it runs, per seat.
- The cost of installing the operating system and its associated hardware in your decision environment.
- The cost of maintaining the operating system and its associated hardware (e.g., system administration, system back-ups, operating system upgrades, new application installation, and upgrades).
- The cost of providing a "help desk" to the users in your decision environment.
- The cost of training users in your decision environment.
*This column contains CFA loadings, all significant at the p < .05 level. Notes: (R) = reverse scored.
User Complements Producer
Network Network Network
1st 2nd 1st 2nd 1st 2nd
CFI .96 .95 .95 .95 .97 .97
NFI .92 .92 .92 .93 .95 .95
Incremental
fit index .96 .95 .95 .95 .97 .97
Goodness-of-fit
index (GFI) .89 .88 .90 .89 .96 .96
Adjusted GFI .85 .85 .86 .84 .92 .92
Parsimony GFI .65 .67 .60 .63 .51 .51
Parsimony NFI .76 .78 .70 .73 .63 .63
Root mean
square residual .12 .15 .09 .08 .08 .08
d.f. 125 130 80 85 24 24
[T][2] 266.78 293.08 191.79 230.32 51.81 51.81
Change in
[T][2] 26.30* 38.53* 0
Change in d.f. 5 5 0*p < .05.
[T][ 2] = χ2
GRAPH: Figure 1 The Integrated Networks Model
The authors thank the Center for Customer Insight at the University of Texas at Austin for funding the data collection in this study and Phoenix-based Applied Computer Research for supplying a mailing list. The authors gratefully acknowledge Thomas Burnham, Janet Wagner, and Brian Ratchford for their helpful comments on previous drafts.
NOTES [1] Ray Noorda, former chief executive officer of Novell, is credited with the term "co-opetition." Brandenburger and Nalebuff (1996) have taken it as the title of their book. The term describes environments in business that require firms to compete and cooperate at the same time. Journal of Marketing Vol. 67 (January 2003), 29-45
[2] We thank an anonymous reviewer for this concise definition.
[3] For the purpose of brevity and readability, the term "product performance" is interchanged for "stand-alone product performance."
[4] DiMaggio and Powell (1983, p. 149) describe isomorphism as a "constraining process that forces one unit in a population to resemble other units that face the same set of environmental conditions." Mimetic isomorphism occurs when firms become more similar through imitation, typically under conditions of uncertainty.
[5] Teece (1986, p. 287) defines the appropriability regime as the "environmental factors, excluding firm and market structure, that govern an innovator's ability to capture the profits generated by an innovation." These include factors such as intellectual property, whether the innovation is incorporated in a product or in a process, and whether the innovation involves tacit versus codified knowledge.
[6] We conducted regression analyses that included the operating system ratings without this arithmetic manipulation. We compared these with similar analyses that included the collapsed measure. The two yielded similar adjusted R2 figures and similar significance for other variables in the equation, but we could not interpret the signs and significance of the operating system attribute ratings because of high multicollinearity among these items.
[7] Hess's (2000) paper on unidentified recursive models is not a factor in our model, as we have no correlated error terms.
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By Judy K. Frels; Tasadduq Shervani and Rajendra K. Srivastava
Judy K. Frels is an assistant professor, R.H. Smith School of Business, University of Maryland. Tasadduq Shervani is a business consultant in Fort Worth, Texas. Rajendra K. Srivastava is Jack R. Crosby Regent's Chair in Business Administration and Professor of Marketing and Management Science & Information Systems, Department of Marketing, McCombs School of Business, University of Texas at Austin.
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Record: 175- The Market Valuation of Internet Channel Additions. By: Geyskens, Inge; Gielens, Katrijn; Dekimpe, Marnik G. Journal of Marketing. Apr2002, Vol. 66 Issue 2, p102-119. 18p. 1 Diagram, 3 Charts, 1 Graph. DOI: 10.1509/jmkg.66.2.102.18478.
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The Market Valuation of Internet Channel Additions
The emergence of the Internet has pushed many established companies to explore this radically new distribution channel. Like all market discontinuities, the Internet creates opportunities as well as threats-it can be performance-enhancing as readily as it can be performance-destroying. Making use of event-study methodology, the authors assess the net impact of adding an Internet channel on a firm's stock market return, a measure of the change in expected future cash flows. The authors find that, on average, Internet channel investments are positive net-present-value investments. The authors then identify firm, introduction strategy, and marketplace characteristics that influence the direction and magnitude of the stock market reaction. The results indicate that powerful firms with a few direct channels are expected to achieve greater gains in financial performance than are less powerful firms with a broader direct channel offering. In terms of order of entry, early followers have a competitive advantage over both innovators and later followers, even when time of entry is controlled for. The authors also find that Internet channel additions that are supported by more publicity are perceived as having a higher performance potential.
The design and management of marketing channels are powerful weapons in an increasingly competitive battle for consumers. An important way in which companies use these weapons is by adding new channels to existing ones-for example, by adding a direct to an indirect channel. As Frazier (1999, p. 232) recently observed, "the use of multiple channels of distribution is now becoming the rule rather than the exception." The most recent and radically new channel firms are expanding into is the Internet. As they add Internet channels to their existing channels, companies hope to increase their performance. However, although expansion into the Internet may increase firms' penetration levels and decrease their distribution costs, increased consumer price sensitivity and lowered levels of support in the entrenched channels may become liabilities. The net effect of these opposing forces is yet unclear, as is reflected in the following quotations:
One aspect of e-commerce that has yet to be addressed in detail is the ... performance of the new medium.... The expectations of profitability of Internet trading vary greatly, from it being perceived as a more profitable medium to the converse. (Booth 2000, p. 21)
It is difficult for executives at most companies ... to estimate accurately the returns on any Internet investment they may make. (Ghosh 1998, p. 126)
Despite the uncertainty surrounding the performance implications of adding an Internet channel to their channel portfolio, many firms, attracted by the potential access to millions of customers and the relatively low costs of setting up the channel, have rushed to establish an Internet channel. Others, daunted by the fear of a continuing price squeeze and/or an alienation of their entrenched channels, wait for more evidence to accumulate.
In this context, one of the main conclusions of the eBusiness workshop organized by Penn State's eBusiness Research Center is that academic contributions on the subject are needed, because "without sound research, eBusiness managers are sailing rudderless" (Donath 1999, p. 2). A similar call for more scholarly research is raised by Hoffman (2000), who deplores the lack of a solid base on which to make Internet-related investment decisions. We address these calls in three ways. First, we develop a conceptual framework for the various performance-enhancing and performance-destroying forces at work when a company adds an Internet channel. As is apparent from the literature we review, academic research has been characterized by a focus on a single, conventional channel, and the combined use of multiple channels, including the use of an Internet channel, has not yet received its due attention. Second, we quantify the performance potential of an extra Internet channel through its impact on the firm's stock return, that is, investors' expectations of the change in future cash flows. We thus assess whether stock market participants recognize predominantly opportunities or threats from incumbent firms' expansion into Internet channels. Third, building on our conceptual framework, we examine several firm, introduction strategy, and marketplace characteristics that may influence the direction and magnitude of the change in performance potential associated with an Internet channel addition. We test our hypotheses on a data set of Internet channel entries in the newspaper industry. Not only are several of the performance-enhancing and performance-destroying forces present in this industry, but also electronic publishing is expected to act as "a pacesetter for the Information Society" (European Commission 1996, p. 1) and to foreshadow trends that may occur more slowly in other industries.
Channel research has long emphasized the importance of recognizing the performance implications of channel decisions. This has fueled a significant, multifaceted literature. An important stream of literature, starting with Jeuland and Shugan (1983) and McGuire and Staelin (1983) and including the more recent contributions of Gerstner and Hess (1995) and Lal, Little, and Villas-Boas (1996), has extensively analyzed the performance implications of a wide variety of channel decisions (e.g., coordination of channel efforts, use of exclusive resellers) game theoretically. These studies focus on the performance implications of a supplier's channel decisions in terms of their effect on a single channel only, abstracting from their potential effect on other channels. More recently, several game-theoretical studies have started to examine the performance implications of a firm's channel decisions, taking into account their effect on the firm's entire channel system (e.g., Purohit 1997; Purohit and Staelin 1994). Of particular interest is Zettelmeyer's (2000) work, in which the profit implications of the decision to add an Internet channel to a conventional channel are analytically derived.
In addition to the extensive game-theoretical literature, many studies have tested the performance implications of channel decisions empirically. These studies can be described along two dimensions: ( 1) the nature of the performance measure being used (perceived versus factual) and ( 2) the scope of the study (single-channel versus multiple-channel). Most studies have used perceptual (e.g., Jap 1999; Kumar, Stern, and Achrol 1992) as opposed to factual (e.g., Ambler, Styles, and Xiucun 1999; Buchanan 1992) performance measures. Although it would seem that factual measures such as sales (growth) or gross margins are the preferred way to measure channel performance, several studies have questioned their use (e.g., Siguaw, Simpson, and Baker 1998). These studies argue that often respondents are unwilling to provide factual performance data, or they provide it in a way that is either not representative of true performance or not consistent with that provided by other firms. In this article, we propose an alternative factual measure that is less susceptible to these problems, stock-price returns. In terms of the scope of the study, an extensive literature review of International Journal of Research in Marketing, Journal of Marketing, Journal of Marketing Research, and Marketing Science identified only one empirical study on the performance implications of a firm's channel decisions that takes into account the decisions' effect on the firm's entire channel system. Lehmann and Weinberg (2000) focus on product sales across channels that are added sequentially. The presence of just one empirical multichannel performance study demonstrates the room for more research in this area. The current study develops a framework for conceptualizing the performance effects on the entire channel system of adding an Internet channel and provides empirical tests on the impact of a variety of moderating factors.
The addition of an Internet channel poses opportunities as well as threats-it can be performance-enhancing as readily as it can be performance-destroying. Supplementing existing channels with an Internet channel can enhance a firm's expected performance when demand-and/or supply-side advantages are bestowed on the firm. A demand-side advantage enables firms to charge a higher price at a given level of demand or generate a higher demand at a given price. Supply-side advantages occur when a lower cost structure is incurred. Adding an Internet channel can also harm expected performance, however, through demand-(reduced revenues) and/or supply-side (increased costs) disadvantages. We elaborate on each of these factors.
Demand-Side Advantages
Demand expansion. The Internet can increase sales in three ways: market expansion, brand switching, and relationship deepening. Market expansion occurs when new (segments of) customers are reached who did not yet buy in the category. Estee Lauder, for example, hopes that Clinique.com will attract customers who avoid buying at a cosmetics counter because they find the experience intimidating. Demand may also expand through brand switching, that is, by winning customers from competitors. One specific way in which new segments can be tapped or customers won from competitors is through expansion of the current market to the global market (Quelch and Klein 1996). Finally, demand may expand through relationship deepening, that is, selling more to existing customers. Barnes and Noble, for example, experienced record sales in its real-world stores upon launching its online store, because this increased its customers' interest in books.
Higher prices. Lal and Sarvary (1999) show that when the proportion of Internet shoppers is sufficiently high and the product's nondigital attributes (i.e., attributes for which a physical inspection of the product is necessary) are not overwhelming, the Internet may represent an opportunity for firms to increase their prices. Also, because the Internet enables consumers to save shopping time and effort, it makes it costly for them to try new products for which sensory attributes need to be physically evaluated. Instead of going to the store, consumers may decide to infer the missing attributes on the basis of their overall evaluation of the brand. Consequently, in some cases, consumers may become more brand loyal when purchasing through the Internet. Because loyal customers are less price sensitive, firms may be able to raise their prices and enjoy higher revenues. Finally, during the emerging stages of the Internet market, consumers tend to be more affluent and therefore less price sensitive (Degeratu, Rangaswamy, and Wu 2000).
Supply-Side Advantages
The Internet can offer supply-side advantages through reduced production and transaction costs. In a distribution context, the former refer to the costs of completing the physical distribution activity (Klein, Frazier, and Roth 1990). Transaction costs are the costs incurred as a result of the firm's efforts to coordinate and control the entities performing the physical activities. They include such ex ante costs as drafting and negotiating agreements with these entities and such ex post costs as monitoring and enforcing agreements (Rindfleisch and Heide 1997).
Lower physical distribution costs. Internet distribution can help companies dramatically cut physical distribution costs. For intangible goods that can be delivered digitally, distribution costs are often reduced by 50% to 90%. For tangible goods, Internet channels are estimated to reduce distribution costs by more than 25% (Organisation for Economic Co-operation and Development 1999). These savings can be attributed to a variety of factors: Transaction processing is eased, thereby reducing paperwork, human errors, and customer disputes; inventory costs may be reduced as intermediaries are bypassed; and some marketing functions are shifted to the customer (Hoffman, Novak, and Chatterjee 1995).
Lower transaction costs. Organizational innovations often have the purpose of economizing on transaction costs. By setting up an Internet channel, companies can reduce ex ante transaction costs by bypassing intermediaries (thereby reducing commission costs) and dealing directly with their customers (Benjamin and Wigand 1995). Airlines, for example, are making headway selling tickets online because their direct sales model eliminates the commission paid to travel agents.
Demand-Side Disadvantages
Demand reduction. Adding an Internet channel to an entrenched channel system may involve channel "shift" (customers moving from one channel to another) without channel "lift" (new sales) (Alba et al. 1997). Adding an Internet channel may even lead to a decrease in total sales when consumers buy less through the new channel than through their old channel-for example, when there are fewer impulse purchases through the Internet or when disenchanted distributors offer less support to the firm's products, resulting in more brand switching toward the firm's competitors.
Lower prices. For many firms, a major threat posed by the Internet is that profits could be eroded through the intensified price competition that might ensue as consumers' search costs are lowered (Alba et al. 1997). The Internet can increase the power of the consumer, because price comparisons across suppliers can be performed quickly and easily. Therefore, prices and margins are expected to be pushed down (Degeratu, Rangaswamy, and Wu 2000).
Supply-Side Disadvantages
Higher physical distribution costs. The cost of an Inter-net channel has two components: fixed start-up costs, such as the purchase of computer hardware and software, and the costs of Internet hosting services. Also, higher advertising expenditures may be needed to create awareness for the new channel. Even though Internet channels can vary dramatically in cost, some incremental expenditures are always involved.
Higher transaction costs. Existing channels may view the new Internet channel as unwelcome competition. They may fear their sales will be reduced if firms reach out directly to their consumers. In addition, the low physical distribution costs and easily obtainable economies of scale of Internet channels may lead firms to reduce their prices and may put pressure on the existing channels' profit margins (Alba et al. 1997). When this happens, interchannel friction becomes likely. The firm's entrenched channels may lose motivation and reduce their support for the firm's products (a passive response), retaliate, or even discontinue their distribution (active responses) (Coughlan et al. 2001, p. 252). To prevent entrenched channels from shirking, firms need to monitor them more extensively to check whether they live up to their agreements and, if necessary, enforce these agreements. This is likely to increase ex post transaction costs (Stump and Heide 1996). In a recent survey of 50 consumer goods manufacturers by Forrester Research, 66% indicated that channel conflict, with its potentially costly result, was the biggest issue they faced in their online strategies (Gilbert and Bacheldor 2000).
Net Effect: Performance-Enhancing or Performance-Destroying?
Even though it would be of interest to quantify the impact of each of the preceding factors separately, it is first and fore-most important to understand the overall net performance impact of establishing an Internet channel. Apart from quantifying this net effect, we use our conceptual framework as a guiding tool to develop hypotheses on the moderating impact of several firm, introduction strategy, and marketplace characteristics, as summarized in Table 1.
The discussion to this point has focused on the performance-enhancing versus performance-destroying capacity of an Internet channel addition. The extent to which performance is enhanced or destroyed is likely to be contingent on several factors. The marketing strategy literature suggests that the performance of a new entry depends on firm characteristics, the introduction strategy, and the marketplace or environment (see Figure 1). 1
Firm Characteristics
Firms are distinctive because they have accumulated different physical and intangible assets, such as financial reserves, equipment, brand equity, channel equity, employee skills, and marketing expertise. These firm-specific resources and capabilities may influence the effectiveness of the firm's new channel introduction. We consider three dimensions of a firm's resources and capabilities: its channel power, direct channel experience, and size. Channel power. Power is a crucial concept in marketing channel research. Channel researchers have often derived their definitions of power from Emerson's (1962) power- dependence theory: A firm's power over a distributor is determined by the latter's motivational investment in the relationship and its availability of alternatives. Motivational investment refers to the value of the resources or outcomes mediated by the firm and has often been operationalized through the "sales and profits" approach: The greater the sales and profits a firm accounts for, the greater is its power (Frazier, Gill, and Kale 1989). The availability-of alternatives component refers to the difficulty of replacing the outcomes mediated by the firm because of the lack of alternative partners: The lower the number of available alternatives, the more difficult it is to replace the sales and profits accounted for by the firm, and the greater is the firm's power over the distributor (Buchanan 1992).
When a firm establishes an Internet channel, this is likely to lead to a loss of goodwill on the part of the established channels, regardless of whether the firm is low or high in channel power. However, whether the entrenched channel will act on this loss of goodwill depends on channel power. When a firm has little channel power, opportunistic behavior may arise on the part of distributors; for example, distributors may provide less support for the firm's products while pushing competitors' products instead (Frazier 1999). This may cause some of the firm's customers to switch. To limit this unfavorable demand evolution, higher ex post transaction costs are required. In contrast, when a firm is powerful in its entrenched channels, the latter's continued cooperation is more easy to obtain because of the dormant potential to invoke sanctions. We therefore hypothesize the following:
H<SUB>1</SUB>: The performance potential of an Internet channel addition is positively related to the firm's channel power.
Direct channel experience. According to Erramilli(1991), experience has two facets-intensity and scope-that may influence firms in two distinct and possibly opposing ways. In an Internet channel context, the intensity of a firm's experience is the time span the firm has already been engaged in direct channel operations before the current entry. The scope of a firm's experience is the number of direct channels established by the firm before the current Internet channel addition.
Intensity of experience. Firms with longer experience have more and better information, face less uncertainty, and can more easily transfer technology and managerial resources (Ansoff 1965), which leads us to propose that firms with longer experience have a significant advantage in physical distribution costs. In contrast, some authors (e.g., Singh and Lumsden 1990) claim that firms may get stuck in routines that are no longer appropriate in the current environment and that bureaucratic inertia may set in. Therefore, inexperienced firms can be argued to be less committed to old (outdated) routines, which may provide them with a differential advantage when technological change is rapid (Grant 1991), as is the case in the rapidly changing Internet environment. On balance, however, the empirical evidence appears to support a positive effect for intensity of experience. We therefore hypothesize the following:
H<SUB>2</SUB>: The performance potential of an Internet channel addition is positively related to the intensity of direct channel experience.
Scope of experience. The scope of direct channel experience, or the number of direct channels a firm already operates when it sets up the Internet channel, is expected to have negative demand effects. The more direct channels a firm already offers, the lower is the probability that the new Inter-net channel will be viewed as significantly different from existing channels. A new channel that is perceived as only marginally different is less likely to attract new category demand and more likely to cause channel shift or cannibalization (Friedman and Furey 1999).
As for the supply-side effects, the learnings achieved in one channel can be translated to the other, thereby reducing the inherent risk of new ventures and allowing the firm to exploit potential economies of scope. However, adopting the Internet as an additional distribution channel may place considerable stress on the existing distribution network. The more direct channels a company establishes, the more wary the incumbent distribution network becomes: It may increasingly view this as the prelude to a conversion to direct channels only (Dutta et al. 1995). In response, distributors may provide lower levels of support for the firm's products, pushing competitors' products instead (Frazier 1999). This may cause some of the firm's customers to switch to one of these competitors. To limit this unfavorable demand evolution, higher transaction costs are required. When we total up these effects, our net prediction is as follows:
H<SUB>3</SUB>: The performance potential of an Internet channel addition is negatively related to the scope of direct channel experience.
Firm size. On the demand side, small firms typically have more to gain from an Internet channel addition than large firms do (Alba etal. 1997). Because the Internet greatly extends the geographic reach of small companies, it enables them to secure new customers from around the world in ways formerly restricted to much larger firms (Organisation for Economic Co-operation and Development 1999). Therefore, the smaller the firm, the more it can benefit from the geographic market-expansion and brand-switching opportunities offered by the addition of an Internet channel. In contrast, large firms may be better able to command a higher price/margin. To feel more secure when dealing over the Internet, consumers may be willing to pay a price premium to purchase a product from a large, well-known firm, because its reputation may signal reliability of delivery, security of information, dependability of return policy, and so forth (Smith, Bailey, and Brynjolfsson 2001).
On the supply side, it could be argued that large firms can enjoy economies of scale. The larger the firm, the more efficiently it can fulfill marketing functions in general and distribution functions in particular, and therefore the lower are its physical distribution costs (Anderson 1985). However, in the context of market discontinuities such as the introduction of Internet channels, costly investments and general marketing expertise built up over the years may become useless, and new skills and assets need to be acquired (Mitchell 1989). As a result, the superior resources and capabilities of larger organizations may no longer give them the same cost advantages as in the "old economy." Because good arguments are available to support higher performance potential for larger (price premium potential) and smaller (demand expansion potential) firms, we do not advance a hypothesis for the relationship between firm size and performance potential.
Introduction Strategy
The introduction strategy for a new channel sets the platform from which competitive advantages can be gained. We consider two introduction decisions: the order of entry and the level of publicity surrounding (media attention given to) the introduction.
Order of entry. On the demand side, order of entry may influence the Internet channel's impact on market expansion, brand switching, relationship deepening, and price. First, the opportunity to benefit from market-expansion effects declines as firms fall further behind in entering the market (Kalyanaram, Robinson, and Urban 1995). Changes in the environment, such as changes in technology, create windows of opportunity. Firms that enter soon after this window has opened are able to "skim off" new category demand, leaving fewer opportunities for firms that enter later (Kerin, Varadarajan, and Peterson 1992). Second, brand-switching advantages are also believed to accrue to early entrants. Early entrants may be able to attract customers from competitors that do not yet have an Internet offering and to avoid some of their own customers switching away to more proactive competitors. Moreover, early movers may shape customer preferences, in that customers come to view the pioneering Internet channel as a prototype against which later entries are judged (Carpenter and Nakamoto 1989). Given a favorable experience, consumers may be reluctant to switch upon later entry of other Internet channels to minimize the risks involved. Third, postponing the introduction of an Internet channel may project an image of not being a dynamic, up-to-date company. This may cause a loss of goodwill among current customers and affect their decisions to buy other products from the firm, that is, the relationship-deepening opportunities (Hendricks and Singhal 1997).Finally, early movers may be able to earn a higher price/ margin if switching costs to competing products and channels are sufficiently high (Lieberman and Montgomery 1988).
On the supply side, early entry may have positive effects on distribution costs. In addition to experience curve effects, marketing cost advantages may accrue to early movers. Later entrants may require more marketing support to over-come the barriers of entry erected by earlier firms in terms of consumer awareness and preference (Kerin, Varadarajan, and Peterson 1992).
Other researchers have advocate dearly imitation as a profitable alternative (Lee et al. 2000; Teece 1986). Specifically, technological discontinuities may create advantages in physical distribution costs to later entrants. When superior technologies are expected to become available, it may be beneficial to postpone the Internet channel introduction and to immediately incorporate the new technologies when they become available. This may enable later entrants to leapfrog early movers if these stay committed to older technologies (Dos Santos and Peffers 1995). Also, early firms may make costly mistakes, because there is little precedent from which to learn about the idiosyncrasies of the new channel. In contrast, firms that wait until some competitors have made the move can learn from the latter's experience and do better at a lower cost.
In conclusion, the previous argumentation suggests that it may be beneficial to wait and learn from the first mover's experience but still be fast enough to exploit the various demand advantages related to early entry. As such, early followers may reap the greatest benefits and outperform both pioneers and late movers. We therefore propose the following:
H<SUB>4</SUB>: The relationship between the performance potential of an Internet channel addition and order of entry takes the form of an inverted U.
This hypothesis focuses on the mere order of entry and abstracts from the time lag among the respective entrants (for an extensive discussion on this issue, see Brown and Lattin 1994).However, the longer the time in market, the longer consumer learning may take place, and therefore the more consumer preferences may be shaped. To account for this consumer learning, we follow the recommendation of Brown and Lattin(1994)and Huff and Robinson (1994)and test the order-of-entry hypothesis while controlling for the time of entry.
Publicity. A second aspect of the introduction strategy involves the level of publicity surrounding (or media attention given to) the introduction, which may have positive demand effects through its impact on market expansion, brand switching, and pricing. Publicity may assist in building awareness and lead to customer trial. It may serve as a credible source of information that helps reduce consumers' insecurities toward the new channel and thus build primary demand (Assael 1998). Publicity can also help a company build selective demand by encouraging brand switching toward its own channel. Moreover, publicity may affect price sensitivity. The "market-power" school of thought contends that publicity may increase brand loyalty and thus reduce price elasticity (Comanor and Wilson 1979). As such, more publicity may enable a firm to charge higher prices for the products/services offered through its Internet channel. On the supply side, publicity is inexpensive or even free of charge-there are few costs other than maintaining a public relations department (Assael 1998). We therefore hypothesize the following:
H<SUB>5</SUB>: The performance potential of an Internet channel addition is positively related to the level of publicity.
Marketplace Characteristics
We distinguish between two types of marketplace characteristics: the growth in demand for the product sold through the Internet channel and the growth in demand for the new channel per se.
Product-demand growth. The evolution in product demand may affect the performance potential of a new Inter-net channel through three demand-side mechanisms. First, a high product-demand growth rate implies a greater incentive for all firms to increase the breadth of their channel system to satisfy various growing consumer segments. This combined effort may cause further market expansion (cf. Bayus and Putsis 1999). Second, because of some untapped demand or need, growth markets provide both existing channels and the new Internet channel with sales opportunities (Dwyer and Oh 1987), making cannibalization less likely because firms do not need to engage in a zero-sum game. Third, in growth markets, consumers' price sensitivity tends to be lower (Aaker and Day 1986).
The supply-side mechanism for the effect of product-demand growth pivots on channel conflict and the corresponding transaction costs. Specifically, "channels in declining markets [are] often associated with intense inter-channel rivalry" (Dwyer and Oh 1987, p. 348). In contrast, in rapidly growing markets, the friction between the firm and its entrenched channels should decrease, because losses in share need not reduce the latter's absolute sales levels. Therefore, we hypothesize the following:
H<SUB>6</SUB>: The performance potential of an Internet channel addition is positively related to product-demand growth.
Channel-demand growth. Many scholars have employed a demand-pull perspective toward innovation and change. In this view, the adoption of an important organizational innovation such as the addition of an Internet channel is driven by its revenue-generating potential, which is likely to increase as the Internet community grows (Peterson, Balasubramanian, and Bronnenberg 1997). This growth may come from new customers to the category or may involve a switching from traditional channels (company-or competitor-owned). As for the prices charged, Zettelmeyer (2000) has recently shown analytically that the prices firms set are linked to the reach of the Internet. Specifically, it is shown that as the Internet's reach increases, firms tend to refrain from competitive price discounting over the Internet. Therefore, as the Internet grows, average prices on the Internet increase and need no longer be lower than prices in conventional channels. Still, it has also been argued (e.g., Erevelles, Rolland, and Srinivasan 2000) that current cases in which Internet prices are higher than prices in conventional channels are due to initial market imperfections that will disappear as the market grows and matures. Moreover, as the Internet market grows, the dominance of affluent, time-constrained customers, who are known to be less price sensitive (Degeratu, Rangaswamy, and Wu 2000), is likely to be attenuated.
On the supply side, distributors face higher competition for ownership of customers when channel-demand growth is high as opposed to low (Moriarty and Moran 1990), in which case they become more likely to neglect the firm's products in favor of its competitors' products. This may result in increased switching toward competitors unless the firm takes measures to monitor its distributors, which results in higher transaction costs. Because it is not clear how the opposite effects of the demand and supply forces total up, we do not advance a hypothesis for how channel-demand growth affects the performance potential of an Internet channel addition.
Evaluating Internet Investments
Performance appraisal of Internet-related investments is quite difficult. Commonly used performance measures such as return on sales, return on assets, and return on equity are found to be less appropriate indicators of an Internet channel's value, because ( 1) they have a historical orientation as opposed to a forward-looking focus and ( 2) their temporal aggregation level makes the link to specific events questionable. To deal with these issues, we quantify the performance potential of an Internet channel addition through its impact on the firm's stock return, that is, investors' expectations on the change in future cash flows.
First, Internet channels operate in a setting in which current accounting results are almost bound to suggest poor performance. Indeed, accounting numbers immediately reflect the costs of the investments made, but revenues are only recognized (i.e., put on the books) in the periods they materialize. Because accounting measures only evaluate "historical" performance indicators, they are not well suited to capture anticipated future revenue streams (Kalyanaram, Robinson, and Urban 1995). This is unfortunate, because Internet investments are known to take several years before they fully translate into bottom-line performance effects (Bharadwaj, Bharadwaj, and Konsynski 1999). The stock market reaction, in contrast, compares investment costs with the expected revenues. Although we do not refute the notion that, at some later point in time, realized cash flow data will eventually become available (and that these data are likely to be better than any expectation at the time of the event),we argue that the market expectations as reflected in the stock market reactions are the best option currently available to assess the performance potential of a given Internet investment.
Second, end-of-the-year accounting numbers may be influenced by various factors that took place during the year, of which the Internet channel introduction is just one. The event study methodology advocated in this study (see infra) has the advantage that it enables us to measure the impact of a specific event on daily (i.e., temporally disaggregated) stock returns. For an extensive discussion of these critiques, see Bharadwaj, Bharadwaj, and Konsynski (1999),and for a recent translation to a marketing context, see Doyle (2000, Ch. 1).
Event-Study Methodology
Cash flows are increasingly viewed as less susceptible to the two problems mentioned in the preceding section (Srivastava, Shervani, and Fahey 1998).According to financial theory, a company's stock price reflects the market's expectations of the discounted value of all future cash flows expected to accrue to the firm (Rappaport 1987). Market efficiency implies that the stock price accurately reflects all available information (including information on future expected outcomes) related to the performance of the firm. As new information becomes public, investors update their expectations about long-term future cash flows, reacting immediately by buying or selling stock. As such, information resulting in a positive (negative) change in expected future cash flows will have a positive (negative)effect on stock price. There lease of information, or event, we investigate in this study is the announcement of an Internet channel addition.
The percentage change in the stock price is the stock return:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where P<SUB>it</SUB> is the stock price of asset i at time t. This stock return reflects market expectations of the long-term financial impact of information arriving between t - 1 and t. When this information deals with the event, an "important and relatively objective indication" (Kalyanaram, Robinson, and Urban 1995, p. G219) of the event's anticipated financial consequences is obtained.
We make the link between the event and the firm's stock returns using the well-established event-study methodology.[ 2] We compare the stock return R<SUB>it</SUB> at the event day with E(R<SUB>it</SUB>), that is, the return that would be expected if the event had not taken place. Following Brown and Warner (1985), we make use of the market model to obtain estimates of expected returns. According to the market model, the expected return E(R<SUB>it</SUB>) to asset i at time t can be expressed as a linear function of the returns from a benchmark portfolio of marketable assets R<SUB>mt</SUB>:
( 2) E(R<SUB>it</SUB>) = α<SUB>i</SUB> + β<SUB>i</SUB>R<SUB>mt</SUB>,
where α<SUB>i</SUB> and β<SUB>i</SUB> are the ordinary least squares parameter estimates obtained from the regression of R<SUB>it</SUB> on R<SUB>mt</SUB> over an estimation period preceding the event, for example, 250 to 30 days prior to the event. The difference between the actual return and the estimated expected return provides a measure of "abnormal" return e it for the shares of firm i at time t:
( 3) e<SUB>it</SUB> = R<SUB>it</SUB> - E(R<SUB>it</SUB>) = R<SUB>it</SUB> - (ˆα<SUB>i</SUB> + ˆβ<SUB>i</SUB>R<SUB>mt</SUB>).
This abnormal return, or prediction error, is the unexpected change in the stock price, which is then attributed to the event that took place at time t. Because of market efficiency, the abnormal return e<SUB>it</SUB> it provides an unbiased estimate of the future earnings generated by the event and is a random variable with mean equal to 0.
We conducted an event study across several firms for which the event of interest could have taken place on different calendar dates. We tested the average effect of a particular type of event by first computing the average of the abnormal returns over all announcements:
( 4) Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where N is the number of announcements being studied. To test whether the average abnormal return is different from 0 on the event day t = 0 (which falls on different calendar days for different announcements), we use the test statistic that is distributed unit normal for large N:
( 5) Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where &epsilon<SUB>i0</SUB> = e<SUB>i0</SUB>/S<SUB>i</SUB>, and S<SUB>i</SUB> is the standard deviation of the regression residuals that were obtained before the event announcement. This test statistic enables us to determine whether, on average, investors perceive that the potential performance-enhancing factors outweigh the performance-destroying factors.
Thus far, we considered the ideal situation that there is no information leakage prior to the event day and that all information is completely disseminated during the event day. In practice, these assumptions may be violated (McWilliams and Siegel 1997). As soon as information leaks (e.g., a newspaper article speculating about a potential Internet channel introduction prior to the official announcement), the event period should include one or more days prior to the announcement of the event so that abnormal returns associated with the leakage are also captured. In a similar vein, when information becomes only gradually available to the broad public, an allowance should be made for dissemination effects on the days following the announcement. When leakage (for t<SUB>1</SUB> time periods before the event) and/or dissemination over time (for t<SUB>2</SUB> time periods after the event) occur, we can use a similar test statistic as in Equation 5 to compute the significance of the average abnormal return on these days. We can also aggregate the abnormal returns over the event period [-t<SUB>1</SUB>,t<SUB>2</SUB>] into a cumulative abnormal return (CAR) to draw overall inferences for the event of interest:
( 6) Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Because the event study is conducted over multiple events, this CAR can be averaged across events into a cumulative average abnormal return (CAAR):
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where N is the number of announcements being studied. The extent of information leakage and dissemination, and thus the length of the event period [-t<SUB>1</SUB>,t<SUB>2</SUB>], is addressed empirically. More specifically, the CAARs for various windows surrounding the event day are calculated, and the most significant one is chosen (for a similar procedure, see, e.g., Agrawal and Kamakura 1995; Chaney, Devinney, and Winer 1991).[ 3]
Cross-Sectional Variation in Stock-Price Reactions
When the event period and the CARs over that period have been established, we examine the cross-sectional variation in the stock-price reactions in more detail. Specifically, we quantify the moderating impact of firm-specific [channel POWER, INTENSity and SCOPE of direct channel experience, and firm SIZE], introduction strategy [ORDER of entry, TIME of entry, and PUBLicity], and marketplace characteristics [PRODuct-demand growth and CHANNEL-demand growth] by regressing the (standardized) cumulative abnormal return against the different covariates:
( 8) CAR<SUB>i</SUB>[-t<SUB>1</SUB>,t<SUB>2</SUB>] = a + b<SUB>1</SUB> × POWER
+ b<SUB>2</SUB> × INTENS + b<SUB>3</SUB> × SCOPE
+ b<SUB>4</SUB> × SIZE + b<SUB>5</SUB> × ORDER
+ b<SUB>6</SUB> × (ORDER)<SUP>2</SUP> + b<SUB>7</SUB> × TIME + b<SUB>8</SUB> × PUBL
+ b<SUB>9</SUB> × PROD + b<SUB>10</SUB> × CHANNEL + μ<SUB>i</SUB>,
where μ<SUB>i</SUB> is the error term for event i. The standardized CAR<SUB>i</SUB> are the CAR<SUB>i</SUB> of Equation 6 divided by the standard deviation of the regression residuals that were obtained before the event announcement (cf. Equation 5).[ 4]
Assumptions Underlying Event-Study Methodology
As with any methodology, event studies rely on some key assumptions. Specifically, we assume that ( 1) shareholders are the only relevant group of stakeholders, ( 2) researchers can isolate the stock-price reaction of the event of interest, ( 3) an appropriate benchmark is used to compute the (ab)normal returns, and most critically, ( 4) the financial markets are efficient (Bromiley, Govekar, and Marcus 1988; McWilliams and Siegel 1997). We discuss Assumption 1 in more detail in the "Discussion" section. To avoid confounding effects (Assumption 2), we must explicitly check whether no other events are announced at or around the time of the Internet channel introduction (for details, see the "Data" section). We extensively test the robustness of our findings to the specific choice of performance benchmark (Assumption 3) in the "Robustness Checks" section.
As for key Assumption 4, researchers must always keep in mind that event studies test a joint hypothesis: whether the event has an impact and the efficiency of the market. In this respect, the issue is to what extent individual investors can integrate all information that becomes publicly available on the different components of our conceptual framework.5 Friedman (1953) eloquently argues that even when people do not make all the necessary calculations reflected in an economic model, they may still act as if they could do so. Moreover, even if people make mistakes and occasionally act irrational, this still is no problem in explaining aggregate behavior, as long as these mistakes tend to cancel out. In that case, the trading of the irrational investors would not affect market prices, which would be left in the hands of the more rational investors (Rubinstein 2001). Such (often larger) investors, as well as stock analysts, tend to be well informed and indeed use valuation methods based on discounted cash flows, as discussed by Fabozzi (1995), among others. In addition, even if these more informed individual investors do not have all the relevant information, markets tend to exert an information aggregation function, through which they become more rational than the individual investors that constitute them (see Ball 1995; Hayek 1945).
These arguments all rely on the assumption of either rational or nonsystematic irrational behavior. Systematic irrational behavior, in contrast, would cause the errors to go in the same direction (in which case there would be no cancellation at the aggregate level) and result in certain anomalies. This is especially important in our context, as the recent market interest in buying and selling high-tech and estocks has led some authors (e.g., Higson and Briginshaw 2000) to argue that financial markets are no longer efficient. Recent evidence, however, suggests that extremely high share prices are mostly paid for "pure" e-firms (which do not have a bricks-and-mortar counterpart), whereas firms that complement their traditional business with Web-based operations are still judged by normal earnings criteria (The Economist 2000). We nevertheless conduct extensive checks on the robustness and validity of our substantive findings.
Sample and Data-Collection Procedure
Our empirical application is situated in the newspaper industry, which offers an interesting setting in which to apply our framework. First, it represents a mature, old-economy industry that faces rising costs, falling revenues, and increasing retail power (Nicholas et al. 1996). As a result, many publishers have examined more closely the opportunities offered by direct distribution and have wondered whether the Internet may become a profitable option. Although they share these characteristics with many other industries, newspapers have the natural advantage that they can be "delivered" online fairly easily. As a consequence, publishers have taken the lead in exploiting the Internet as a new distribution channel. By the end of 1999, more than 2700 newspapers around the world had online businesses (U.S. Department of Commerce 1998). As such, the publishing industry tends to "act as the pacesetter for the Information Society" (European Commission 1996, p. 1) and is expected to foreshadow trends that will occur more slowly in other industries.
In addition, newspaper executives are confronted with many of the performance-enhancing and performance-destroying forces identified previously, leading them to call the Internet both their prime concern and their most promising source of new revenues (Casale 2000). On the demand side, most online newspapers do not yet generate adequate revenues. Newspaper revenues come from two sources: circulation and advertising. In terms of circulation, most publishers are still reluctant to charge for their online editions. It is unclear, however, whether this situation will persist in the future, and some publishers already experiment with subscription schemes (The Economist 1998). Second, it is unclear to what extent cannibalization threats will materialize. Many Internet newsreaders still consume both mainstream and online news sources (www.poynter.org/eye-track2000). In addition, online newspapers may be able to attract readers who live abroad and/or to gain access to segments (e.g., young, with average to high social standing) in which traditional readership is declining (Picard and Brody 1997). Traditionally, advertising and circulation revenue streams are positively related. In the new economy, advertisers may well decide, even if the newspaper's total audience stays the same, to shift advertising spending from the print to the Internet edition (cannibalization) or other Inter-net hosts (brand switching) if they believe that this provides a more effective means to reach their audience. Other analysts expect advertising revenues through the Internet editions "to become an important driver of revenue growth" (as stated by the 1998 Newsquest Annual Report). On the supply side, newspaper executives do not yet have enough experience to draw firm conclusions on cost implications. On the one hand, online editions require a lower capital investment, and the marginal cost of distributing extra copies is negligible. On the other hand, costs may simply shift from physical printing and distribution to acquiring and maintaining technology while incurring higher marketing costs (U.S. Department of Commerce 1998). There is also uncertainty about the transaction costs involved. Some experts argue that online newspapers will not replace the print versions. Others fear that their distributors may interpret online editions as a declaration of war (Noack 1993). In summary, considerable uncertainty prevails on both the demand and the supply side, making the newspaper industry a good test case.
We identified all daily newspapers from four European countries (France, Germany, the Netherlands, and the United Kingdom) that have ventured on the Internet. Our search led to the identification of 7 French, 5 German, 23 Dutch, and 63 English newspapers that have embraced the Internet as an additional channel of distribution and whose parent firms are listed on the stock exchange. These 98 newspapers represent 22 different firms. We considered the event date to be the day the announcement was mentioned in the media.[ 6] We gathered information on the announcement date by contacting each newspaper, and we extensively validated it through both newspaper archive searches and the Dow Jones Interactive Publication Library. According to the theory of efficient markets, all new information is incorporated in the stock price as soon as the information becomes available. Therefore, to assess the impact of an event, we examined the change in stock price on and surrounding the date of the announcement.[ 7]
Operationalization of Measures
Financial measures. We obtained daily stock prices of the firms included in our sample and daily market indices of the Amsterdam, Frankfurt, London, and Paris Stock Exchanges (i.e., AEX-24, DAX-30, FTSE-100, and CAC-40) from the Datastream database. We used these data to calculate the firms' daily returns, R<SUB>it</SUB>, and the market returns, R<SUB>mt</SUB>.
Channel power. Following Emerson (1962), we included a measure for both motivational investment and availability of alternatives to capture channel power. We measured the former as the percentage of sales the newspaper accounts for in the total sales of an average distributor in the sales region in which the newspaper is being sold (median = 4.6%, range = .1%-55%).[ 8] We measured availability of alternatives through the number of titles distributors in a sales region can use to replace the sales accounted for by the focal newspaper (median = 3, range = 1-19). We measured both motivational investment and availability of alternatives on a per-sales-region basis, because distributors in the same region are highly similar in terms of these two constructs- distributors in the same region can use the same number of titles to replace the focal newspaper and derive roughly the same proportion of sales from these newspapers.[ 9] Because the power of a supplier over a distributor is ( 1) directly proportional to the supplier's contribution to the distributor's sales and ( 2) inversely proportional to the number of alternatives available to the distributor, we measured channel power by the ratio of contribution-to-sales to number-of alternatives, after standardization of both measures. We obtained all relevant data from the International Federation of Audit Bureaux of Circulations (IFABC).[ 10]
Intensity and scope of experience. Following Erramilli (1991), we operationalized intensity of direct channel experience as the number of days the firm was engaged in direct channel operations prior to the current Internet channel addition (median = 342, range = 0-1710). We operationalized scope of direct channel experience as the number of direct channels established by the firm before the current Internet channel addition (median = 3, range = 1-18).[ 11] We obtained data on intensity and scope of experience by contacting each newspaper, and we extensively validated these data by searching annual reports and newspaper archives.
Firm size. We compiled three measures of firm size from Wright Investors' Service: number of employees (median = 6477, range = 400-49,285), sales (median = €700 million, range = €56 million-€12,284 million), and the market value of the firm (median = €2,307 million, range = €76 million- €38,550 million). After standardization, we averaged the three items into a single scale of firm size. We log transformed firm size to account for potential diminishing returns to scale.[ 12] Through this transformation, we also reduced the skewness in this variable, thereby avoiding having a few extreme observations drive our results (see, e.g., Dekimpe et al. 1997).
Order and time of entry. Order of entry is the temporal rank order position, compared with other Internet entries in a given country (median = 28, range = 1-87). It is important to realize that in operationalizing this variable, we also account for entries made by firms not listed on the stock exchange. Time of entry is measured as the number of days the newspaper went online after the first release of Netscape Navigator on December 15, 1994 (median = 1060, range = 16-2204).
Publicity. Publicity measures whether media attention was given in the printed press to the Internet channel addition. It is a binary variable coded 1 if the Internet channel was announced in other newspapers than in the own newspaper (15% of the cases) and 0 otherwise (85% of the cases). We compiled our measure of publicity on the basis of searches of newspaper archives and the Dow Jones Interactive Publication Library.
Product-and channel-demand growth. Product-demand growth is the percent change in the industry's sales from the previous month's sales for each month of the analysis (median = 0%, range = -3.6%-3.4%). To reflect that news --papers may be competing internationally, sales include domestic and foreign sales. To measure channel-demand growth, we used the monthly growth rate in the total number of Internet users per language (median = 3.5%, range = 2.3%-18.5%). We obtained product-demand growth and channel-demand growth data from the IFABC and Global Reach (www.glreach.com), respectively.[ 13], [ 14]
The Main Effect of an Internet Channel Addition
For each firm i, we estimated the parameters (αi and βi) of the market model in Equation 2 using an estimation period of 219 days (t = -250 to t = -30 relative to the event day, t = 0). We then used the estimated market model parameters to calculate the firms' abnormal returns (e<SUB>it</SUB>). Table 2 presents the average abnormal returns for the 93 announcements on the event day, as well as for a window of ±5 days around the event day. Results show that, on average, firms establishing an Internet channel experienced .35% abnormal returns on t = 0 (p < .01) and .36% abnormal returns on t = +1 (p < .01). Of all windows surrounding the event day, the one from 0 to +1 shows the most significant CAAR, with a value of .71%. This positive value is driven by two factors: Positive evaluations occur more frequently (58% of the cases on the event day t = 0, and 64% of the cases on t = +1), and they are, on average, larger than the negative ones (the average positive CAAR over the event window [0,+1] is 1.83%, versus an average negative CAAR value of -1.36%). Our short event window of [0,+1] implies an almost instantaneous adjustment in stock prices to the arrival of the new Internet channel information, which is a necessary condition for market efficiency (McWilliams and Siegel 1997). Our estimate on the size of the stock market reaction to Internet channel announcements has the same order of magnitude as CAARs reported in other marketing-related event studies. Horsky and Swyngedouw (1987), for example, report a CAAR[0,0] of .61% for company name changes; Chaney, Devinney, and Winer (1991) find a CAAR[-1,+1] of .75% for new product announcements; and Agrawal and Kamakura (1995) report a CAAR[-1,0] of .54% in the context of celebrity endorsement contracts. Apart from the statistical significance of the CAAR values obtained, we consider their economic significance. To that extent, we calculated the average change in the market value of a median-sized firm in our sample.[ 15] A .71% cumulative abnormal return for such a company with a market value of €2,307 million results in an increase in market value (adjusted for overall market movements) of 16.38 million in two days.
Despite this statistical and economic significance, questions remain as to whether this positive evaluation is just a temporary reaction that is quickly corrected afterward. We found in this respect that the CAARs (see Figure 2) stayed at a higher level after the event, indicating that the positive evaluation is not just a short-term lift that evaporates in the days following the announcement. For newspapers that introduced their Internet version before November 8, 2000 (resulting in a sample size of 87), we subsequently computed the abnormal returns for up to 100 (trading) days after their announcement. Also, in this longer postannouncement period, no significant negative drift is observed, as is confirmed in a pooled regression of the CARs against the time since announcement (b<SUB>time</SUB> = .00, p > .10).
Identification of Successful Internet Channel Additions
The addition of an Internet channel to a firm's channel portfolio is, on average, evaluated positively by the financial markets. Still, we cannot ignore the finding that in more than 30% of the cases, negative stock returns are found (Table 2, last column), which indicates that the market at times expects the negative consequences to outweigh the positive ones. Therefore, a final aim of this article is to cross-sectionally explain the variation in observed stock-price reactions. For this purpose, we estimate Equation 8.[ 16], [ 17] The results are presented in Table 3.
Channel power has the anticipated positive effect (b = .650, p < .05). Therefore, H<SUB>1</SUB> is supported. The effect of intensity of direct channel experience is positive, as expected, but not significant (H<SUB>2</SUB>; p > .05). Scope of direct channel experience has the hypothesized negative effect (b = -.938, p < .05). Therefore, H<SUB>3</SUB> is supported. Firm size does not have a significant effect on the performance potential of an Internet channel addition (p > .05). The results for order of entry support H<SUB>4</SUB>. The positive linear (b = .133, p < .01) and negative quadratic (b = -.001, p < .05) effects imply more favorable stock market reactions for early followers than for both pioneers and later entrants.[ 18] As hypothesized (H<SUB>5</SUB>), additional publicity positively affects the stock market reaction (b = 1.175, p < .05). Finally, the performance potential of an Internet channel addition is not significantly affected by either product-demand growth (H<SUB>6</SUB>; p > .05) or channel-demand growth (p > .05). Therefore, powerful firms with fewer direct channels achieve greater gains in financial performance than do less powerful firms with a broader direct channel offering. Small firms should not recoil from adding an Internet channel to their entrenched channels; firms of any size can successfully play the game. Early followers have an advantage over both innovators and later followers, even when we control for time of entry. We also find that firms that provide additional publicity to their Internet channel introduction achieve greater gains.
We evaluate our results in four ways. We first calculate the CARs using three alternative stock portfolios. Next, we assess the stability of the results, the forecasting performance of the model, and the extent to which our data support some alternative (competing) explanations.
Alternative CARs
We use three alternative benchmark portfolios to determine the market and abnormal returns: ( 1) a market portfolio of stocks (used in Tables 2 and 3), which is the daily market index of the exchange the stock is trading on; ( 2) a broad portfolio of stocks;[ 19] and ( 3) a portfolio consisting only of printing and publishing companies that trade on the same exchange as the stock. Our results remain substantively the same, with CAARs[0,+1] of .71% (standard portfolio), .70% (broad portfolio), and .65% (publishing portfolio). We then reestimate Equation 8 using these alternative CARs as dependent variables. With one exception (the coefficient for publicity becomes insignificant when we use the portfolio of printing and publishing stocks), all results remain substantively the same. Therefore, our results are robust to the choice of market portfolio.
Stability of the Results
We use a jackknife procedure to test the stability of our parameter estimates. We calculate the jackknifed coefficients as described by Ang (1998). Our results are stable given that the t-values of the jackknifed coefficients for our significant coefficients range from 1.95 to 3.09.
Forecasting Performance
We assess the forecasting performance of our model using a procedure similar to the one by Dekimpe and colleagues (1997). Specifically, we omit the first 10% of the observations of the randomized sample and estimate the model on the basis of the remaining data points. We then use the resulting parameter estimates to forecast the omitted observations and compute the mean squared prediction error. Next, we repeat this procedure for the next 10%, until we have rotated the entire data set. Each time, we compute the mean squared prediction error, which is subsequently averaged across the different iterations. The resulting mean squared prediction error turns out to be only 8.6% higher than the mean squared estimation error, which is comparable to the results reported in previous studies. The average correlation between the holdout observations and their fore-casts is .50 when calibrated on the subsamples, as opposed to .59 when these forecasts are derived from a full-sample estimation. Because the latter figure offers an upper bound (as it uses all information in the sample), the drop in correlation is limited.
Ruling Out Alternative Explanations
It could be argued that our results are consistent with two alternative explanations. First, there is the possibility that the addition of an Internet channel does not offer real value to the firm but merely acts as a signal that the firm is innovative and responsive to changes in the marketplace and in technology. Second, questions remain as to what extent our findings are merely an artifact of the general hype surrounding high-tech and e-related stocks.
A signal of innovativeness. We followed the approach advocated by Horsky and Swyngedouw (1987) to empirically rule out the signaling hypothesis. Specifically, two of our hypotheses predict a particular directional effect that would not be found under the signaling hypothesis. First, our framework predicts a positive effect of channel power, whereas no link with channel power would be expected if Internet additions merely acted as a signal of innovativeness (i.e., distributors would prefer all their channel partners to be innovative, regardless of whether they are low or high in channel power). Second, our framework predicts a nonmonotonic relationship for order of entry, whereas a monotonically decreasing effect would be expected under the signaling hypothesis (i.e., the innovativeness content of the signal decreases as a firm lags other players in the market). Our empirical results, with a positive effect for channel power and a nonmonotonic effect for order of entry, enable us to reject the signaling hypothesis in both cases.[ 20]
The hype surrounding Internet stocks. It could be argued that the positive stock market evaluation we observed reflects a general hype surrounding all technology-related stocks. We therefore added four tests of this alternative explanation on the basis of an approach recently advocated by Cooper, Dimitrov, and Rau (2001). First, we used the Datastream European Internet index as benchmark to calculate abnormal returns, in which case the benchmark would already capture the hype effect. Even when we corrected for the general overvaluation that might affect Internet-related investments (through the use of an Internet portfolio), the market, on average, still reacted positively to companies announcing that they are expanding into Internet channels, as was evidenced by a CAAR[0,+1] of .67%. We also estimated Equation 8 using the Datastream European Internet index as benchmark. All results remained substantively the same. Second, we compared the size of the announcement effect in the sample across up and down periods by calculating the monthly index return for the Datastream European Internet index for each of the 74 months from December 1994 to January 2001 and ranked the months according to the average return on the index. We subsequently computed CARs for all firms with announcement dates in the top 37 months (for which the index returns ranged from 0% to 137.5%). We repeated this for firms with announcement dates in the bottom 37 months (for which the index returns ranged from -55.5% to -5%). Forty-two firms announced Internet channel additions in up markets, and 51 firms announced Internet channel additions in down months. A t-test fails to reject the hypothesis that the CARs are significantly different across up and down months (p = .90). Third, we distinguished between periods with much versus little Internet activity in the industry we study. If firms attempt to take advantage of a hype effect, Internet channel launches would be clustered in "hot" market periods. To test this, we computed the number of launches per quarter. We then computed average abnormal returns earned by firms that launched an Internet channel in quarters with six or more launches (N = 55) and compared these with the returns earned by firms in nonclustering quarters (N = 38). Again, the difference was not significant (p = .55). Fourth, we examined returns both before and after March 27, 2000, which is often referred to as the date the presumed high-tech bubble burst. Eighty-seven firms announced Internet channel launches before the "plunge" in e-commerce stocks on March 27, 2000. Again, CAARs were not significantly different (p = .19). Finally, we included three dummies in Equation 8 to control simultaneously for the three previous effects. All effects were nonsignificant (p = .15, .86, and .18), and results remained substantively the same. In summary, no empirical evidence was found that our results are driven by an assumed hype effect.
Adding an Internet channel to an entrenched channel system is a double-edged strategy: Although it costs money in the short run, it is as yet unclear whether the optimistic fore-sights about the long-term profit and growth potential will ever materialize. Yet managers of established firms feel pressured to decide now how to best respond to this market discontinuity. We show that, on average, stock market investors perceive that the expected gains of adding an Internet channel outweigh the present and expected costs. As such, managers and shareholders of established companies need not worry unduly about the stock market reaction as investments into Internet channels are announced. However, they cannot take for granted that the stock market will always react positively either; the market recognizes not only the potential gains but also the possible deleterious effects of adding an Internet channel, as is reflected by more than 30% of the cases resulting in negative stock returns. Therefore, it is imperative for managers to know what drives the success of an Internet channel addition strategy.
Substantive Implications
Major managerial guidelines emerging from our study are as follows:
- Do powerful firms fare better when adding an Internet channel? Powerful firms can get away with far more when supplementing their entrenched channel system with an Internet channel. Although any firm that sets up an Internet channel should expect to lose at least some of the goodwill of its entrenched channels, powerful firms can use their market clout to ensure that these distributors continue to live up to their agreements.
- Does more direct channel experience offer an advantage? Marketers generally view prior experience as an important driver for the success of new entries. We find that established firms that already have many other direct channels are financially hurt when adding a new Internet channel to their entrenched channel system. This supports our contention that adding an Internet channel is not likely to bring along substantial new category demand but instead may cause cannibalization and/or brand-damaging interchannel conflict.
- Is size an important driver of Internet channel success? Small firms should not recoil from adding an Internet channel to their entrenched channel system: Firms of any size can successfully enter the playing field. Apparently, the geographic demand expansion opportunities flowing disproportionately to smaller firms compensate for the price premiums larger firms may enjoy. In addition, the superior resources and management skills of large firms no longer appear to give them the same physical distribution cost advantages as in the old economy.
- Should firms strive to be first when adopting an Internet channel? Our results indicate that firms should indeed be fast. However, we also find that it may be beneficial to let a few other players enter first. Although firms should be fast enough to exploit various demand-side advantages, there is value in letting others experiment with different technical approaches and designs, thereby improving the new channel. We therefore recommend firms to be early followers rather than pioneers with respect to Internet channels.
- Does publicity help make an Internet channel addition successful? Publicity substantially contributes to the success of an Internet channel addition. This result suggests strong, positive effects of publicity on market expansion, brand switching, and/or profit margins.
- Does Internet channel success hinge on marketplace characteristics? Companies in declining product markets should not worry more than companies in growth markets about tarnishing channel equity when adding an Internet channel to their entrenched channel system. Also, growth in channel demand does not affect Internet channel success. Apparently, the possibility that new customers may be drawn to the category through the new channel does not outweigh concerns about potential losses of revenue due to competition for the "ownership" of customers.
One question is to what extent these findings are merely of historical interest, as the extent of press coverage on Internet introductions may well create the impression that all firms have already implemented the decision to add an Internet channel to their channel portfolios. Although this may be the case in some industries, note that many firms have not yet established an Internet presence and many others use their Web sites only for promotional purposes and not yet as distribution channels. In a recent large-scale survey of Belgian firms, Konings and Roodhooft (2000) find that of all firms that have access to the Internet, only 57% have their own Web sites, and an even smaller fraction (15%) uses those sites as additional channels to sell products online. In the United States, recent estimates indicate that more than 40% of all businesses do not yet sell online (www.nua.com/surveys;www.ecommercecommission.org), a number that increases to more than 70% when the largest businesses are excluded (The Washington Post 2001).
Limitations and Further Research
This research represents an early inquiry into a complex phenomenon. As such, the study has several limitations that offer immediate avenues for further research. First, we used stock-price data as performance information. Stock prices, however, do not measure realized operating performance but rather capture investors' anticipations. Furthermore, the underlying assumption that stockholders are the only stake-holders that matter may be too restrictive .A s pointed out by Chakravarthy (1986, p. 448), "a necessary condition for business excellence is the cooperation of the firm's multiple stakeholders," such as shareholders, employees, managers, customers, and suppliers. Whereas stock-price data provide good present estimates of future performance, on the basis of the information available at this point in time, further research should assess the performance effects of Internet channel additions through their impact on realized cash flows. It would also be of interest to quantify the effect of each performance-enhancing and performance-destroying factor separately, that is, to provide insight to the relative extent of cannibalization losses, reduced support from traditional channels due to interchannel conflict, and so forth.
Second, we considered the performance potential of Internet channel announcements at and around the time of the announcement. However, intended strategies may be modified during implementation, and also postentry implementation decisions will determine the ultimate success of the new channel. Further research could track a set of announced decisions, determine the outcome of those decisions, and attempt to assess when and by how much firm performance changed in response to the aforementioned modifications and postentry actions. Such research would measure the effectiveness of strategy formulation as well as implementation.
A third limitation may be the use of secondary data. An attractive feature of secondary data in our case is the possibility of gaining access to the past, which may be subject to problems of recall, or may even be infeasible, when using primary data-collection methods. This attractive feature carries with it a penalty, though, in that secondary data may only map approximately on concepts, which may lead to potential construct validity problems. For example, because there are no direct secondary measures of channel power, we searched for externally observable proxies. In addition, secondary data do not usually permit access to the deeper relational factors that form an important element of channel research. For example, researchers could study how the quality of a firm's relationships with its entrenched distributors affects an Internet channel's success. More comprehensive specifications, including channel constructs such as trust and commitment, could be developed and tested by linking survey data (aggregated distributor judgments) to stock-price information, in the spirit of Lane and Jacobson (1995).
Finally, we study Internet channel additions in only one industry within Europe. Because of potential idiosyncratic industry-and country-related properties of our data, the generalizability of the results needs to be assessed.
1 In case conclusive prior research/evidence in a "new economy" setting is not yet available, we use "old economy" evidence as a logical and useful starting point when developing our hypotheses. This approach is in line with the parsimony principle in developing science and empirical generalizations.
- 2 Event-study methodology, which has been developed and is most popular in the finance literature, has also been applied to assess the impact on a firm's value of marketing-related events such as new product introductions (Chaney, Devinney, and Winer 1991), company name changes (Horsky and Swyngedouw 1987), celebrity endorsements (Agrawal and Kamakura 1995), and brand extensions (Lane and Jacobson 1995).
- 3 We used the t-statistic described by Brown and Warner (1985) for testing the significance of the CAARs for various event windows. Note that the event window should be long enough to capture the significant effect of the event but short enough to exclude confounding effects.
- 4 The standardized CAR<SUB>i</SUB> are used as dependent variables to reduce heteroskedasticity problems that might arise when the estimated variances of the market model residuals vary across firms and/or events. For a formal motivation, see Jain (1982), and for a similar practice, see Agrawal and Kamakura (1995) and Horsky and Swyngedouw (1987).
- 5 It is worth noting that field interviews were conducted with fund managers from three different banks in both Luxembourg and Belgium to validate the general structure of our developed conceptual framework, which is shown in Equation 8 and Figure 1. All three fund managers reported that the suggested model of moderator effects "is plausible and covers most of the factors that [they] would take into account."
- 6 To avoid confounding effects, we checked whether no other events were announced at or around the time of the Internet channel introduction. To this extent, we systematically searched the Dow Jones Interactive Publication Library, Wright Investors' Service, Hoover's Online, and the major financial newspapers of the countries included in our sample. As a result, we deleted two events from our sample, because we found that company results were announced simultaneously. In addition, two newspapers that had followed a strategy of gradual turnover and one outlier (with a standardized residual greater than 3) were removed from the sample. Our final sample therefore consists of 93 announcements.
- 7 This announcement may occur before the date of the actual Internet channel introduction. For the majority of newspapers, they turned out to be the same, and in our subsequent analyses, we found no significant impact when we controlled for this joint occurrence.
- 8 Marketing channel studies have often combined a contribution-to-sales measure with a contribution-to-profits measure to construct an index of motivational investment. Unfortunately, we were not able to include contribution to profits because of data limitations. However, we can expect a high correlation between contribution to profits and contribution to sales in our specific newspaper setting. Indeed, prices of different newspapers show little variation, agency commissions barely vary, and we were informed by the IFABC that there is no reason to assume that a distributor's cost structure would be different for different newspapers. Therefore, restricting the measurement of motivational investment to contribution to sales is not likely to affect our substantive results.
- 9 Note that a manufacturer's channel power is distributor specific; that is, it can vary across its distributors. Because our study takes the firm as the unit of analysis (and not the individual manufacturer-distributor relationship), we were unable to take into account these differences across distributors. We addressed this issue by measuring motivational investment and availability of alternatives per sales region, because distributors in the same sales region are highly similar in terms of these two constructs. More specifically, sales regions are regions where the same set of newspapers is being sold. As a consequence, the number of titles distributors in a specific sales region can use to replace the focal newspaper (i.e., availability of alternatives) is the same across all distributors within the same sales region. The percentage of sales the newspaper accounts for in the total sales of a distributor (i.e., motivational investment) by definition is not equal across all distributors within a specific sales region. However, we were informed by the IFABC that, because of the idiosyncrasies of the newspaper industry, the percentage of sales that distributors within a region derive from various newspapers is approximately the same (even though they can vary widely in terms of sales levels).
- 10 Regional newspapers are typically sold in a single sales region (personal communication with IFABC). National newspapers, by definition, are available in multiple regions. To make the measures comparable across both types of newspapers, we computed a population-weighted average across regions for the national newspapers.
- 11 Given that each firm, through the nature of its business, tends to have a nonvirtual direct channel, the variation in this figure captures the number of virtual channels established by the firm before the current Internet channel.
- 12 Because the three items were standardized before being averaged, negative values for firm size may result. A small, positive value was therefore added to ensure the nonnegativity before taking the logarithm.
- 13 In approximately 30% of the cases, monthly data were not available. In these cases, we intrapolated using the estimates obtained through an auxiliary regression model. For example, in case of the Netherlands, where no annual circulation data are collected, we fitted a quadratic model (several specifications were tested, and the best fitting alternative was retained) with an R2 amounting to .91.
- 14 Because events are recorded daily whereas firm and market data are available only on a yearly and monthly basis, respectively, and to avoid endogeneity problems, we consistently use the values for the firm and market variables in the year and month, respectively, prior to the event. In addition, we deflated all monetary values.
- 15 The market value of the firm on any trading day is the number of common shares outstanding times the share price at the end of that trading day.
- 16 It might be argued that the level of aggregation differs between dependent (corporate-level stock returns) and independent (information on a specific newspaper) variables. Ideally, we would want to run the regression SR<SUP>NP</SUP> = βX + μ, where SR<SUP>NP</SUP> is the stock returns associated exclusively with a particular newspaper. In reality, however, we ran the following regression: SR = bX + μ′, where SR is the corporate stock return, which is composed of SR<SUP>NP</SUP> and SR<SUP>not-NP</SUP>, the stock return of the publishing company not associated with the specific newspaper. Because SR = (SR<SUP>NP</SUP> + SR<SUP>not-NP</SUP>), it can be shown that E(ˆb) = E[(X′X)<SUP>-1</SUP>X′(SR<SUP>NP</SUP> + SR<SUP>not-NP</SUP>)] = β (see Lane and Jacobson 1995); that is, an unbiased estimate of the effect of the newspaper covariates is still obtained (because E[X′SR<SUP>not-NP</SUP>] = 0).
- 17 To correct for a potential violation of the statistical-independence assumption (the 93 newspapers represent 22 different firms from four different countries), we implemented a fixed-effects correction for both dimensions in the regression equation. Two firm dummies turned out to be significant and were added to Equation 8. None of the country dummies was significant. White's test for heteroskedasticity turned out to be insignificant (p > .75), justifying the use of ordinary least squares as an estimation procedure.
- 18 We mean-centered the order-of-entry variable (before forming the quadratic term) to reduce multicollinearity (Jaccard, Turrisi, and Wan 1991).
- 19 The broad portfolio of stocks, which was obtained from the Datastream database, includes the most important companies by market value. The precise number of constituents varies from market to market. The number of stocks included in the French, German, Dutch, and U.K. broad-based portfolios amounts to 200, 200, 130, and 550, respectively.
- 20 It is interesting to note that not every moderating factor allows for a formal test against the signaling hypothesis. For example, following Horsky and Swyngedouw (1987, p. 329) and according to the signaling hypothesis, we would expect a larger impact for Inter-net additions of smaller firms, because organizational inertia hampering innovative change will be lower for smaller than for larger firms. Our framework does not predict a directional effect for firm size, however. As such, our empirical result with a positive non-significant effect for firm size does not allow discriminating among both theories.
Legend for chart:
A = Demand Issues Demand
B = Demand Issues Price Level
C = Supply Issues Physical Distribution Costs
D = Supply Issues Transaction Costs
E = Net Effect Performance Potential
A B C D E
Firm
Channel power +[a] + +
Intensity of experience + +
Scope of experience - + - -
Firm size - + ?
Introduction Strategy
Order of entry - - +/- ^
Publicity + + (-)[b] +
Marketplace
Product-demand growth + + + +
Channel-demand growth +/- +/- - ?[a]To be read as follows: When adding an Internet channel to their entrenched channel systems, more powerful firms are subject to more demand advantages and/or less demand disadvantages than less powerful firms.
[b]This effect is put in parentheses because we expect it to be of marginal magnitude. Notes: A "+" means a positive impact on the performance potential of a new Internet channel, implying more value-enhancing capacity (e.g., more demand, higher prices/margins) and/or less value destruction (e.g., lower costs). Similarly, a "-" means a negative impact on the performance potential. A "+/-" means that there are good arguments for both a positive and a negative relationship. A "^" indicates an inverted-U relationship.
Average Abnormal Percentage of Positive
Event Day Return (%) Z-Statistic Abnormal Returns[a]
-5 -.02 .28 47
-4 -.05 -.36 48
-3 -.16 -1.42 36
-2 .12 .74 47
-1 -.27 -1.59 42
0 .35* 2.89 58
+1 .36* 3.23 64
+2 -.12 -.62 43
+3 -.14 -.74 46
+4 .01 .78 40
+5 -.02 1.00 45[a]This column presents the percentage of the 93 abnormal returns that are positive for each day. For example, 58% of all cases had a positive abnormal return on the event day. *p < .01.
Hypothesized Sign b t-Value
-------------------------------------------------------------------
Intercept -.400 -.33
Firm Characteristics
Channel power + .650 2.28*
Intensity of experience + .545 1.28
Scope of experience - -.938 -2.22*
Firm size ? .949 .85
Channel Introduction Strategy
Order of entry + .133 5.25**
Order of entry squared - -.001 -2.24*
Time of entry -.006 -4.95***
Publicity + 1.175 1.91*
Marketplace Characteristics
Product-demand growth + -.043 -.71
Channel-demand growth ? .025 1.33
F(12, 80) = 4.07.
R² = .38.
R² (adjusted) = .29.
*p < .05 (one-sided).
**p < .01 (one-sided).
***p < .01 (two-sided).
DIAGRAM: FIGURE 1 The Effect of Internet Channel Additions on Performance Potential Moderated by Firm, Introduction Strategy, and Marketplace Characteristics
GRAPH: FIGURE 2 CAARs over Time
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By Inge Geyskens; Katrijn Gielens and Marnik G. Dekimpe
Inge Geyskens is Assistant Professor of Marketing, and Katrijn Gielens is Assistant Professor of Marketing, Tilburg University. Marnik G. Dekimpe is Professor of Marketing, Catholic University of Leuven, and Professor of Marketing Management, Erasmus University Rotterdam. The authors thank Jan-Benedict Steenkamp, Piet Vanden Abeele, Linda Van de Gucht, and the three anonymous JM reviewers for their constructive comments on previous versions of the article. The authors also greatly appreciate the research support of Barbara Deleersnyder. The first author gratefully acknowledges support from the European Commission under a Marie Curie Individual Fellowship (contract no. HPMF-CT-1999-00313) and the Dutch National Science Foundation (NWO) under grant no. PPS-490-00-245.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 176- The Marketplace of Revolution: How Consumer Politics Shaped American Independence. By: Cahill, Dennis J.; Clark, Terry. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p173-174. 2p. DOI: 10.1509/jmkg.69.3.169.66366a.
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Section: Book ReviewsThe Marketplace of Revolution: How Consumer Politics
Shaped American Independence
The Marketplace of Revolution: How Consumer
Politics Shaped American Independence
by T.H. Breen (New York: Oxford University Press,
2004, 370 pp., $30)
When I was a graduate student in U.S. history 35 years ago, one of the burning questions was the following: Given that the colonies were highly independent of one another and would not cooperate even in the face of an invasion by the French and their allies during the French and Indian War, how were they able to agree to mutual defense and independence from England in 1776? After all, a generation before, Benjamin Franklin and others tried in vain to create a loose confederation embodied in the so-called Albany Plan of Union. Answering this question would also require the explanation of why these particular 13 colonies, and not any of the other British colonial settlements in North America and the Caribbean, became the United States. Theories abound, mostly along various political lines, that suggest that similar cultures and political histories made ties that bound the 13 colonies despite their vast differences.
T.H. Breen of Northwestern University attacks the problem from a new and illuminating perspective: the world of goods and what they meant to the people who possessed and used them. Breen states that between the Albany Congress of 1754 at the beginning of the French and Indian War and December 1773 (the date of the Boston Tea Party) and the following two years, mainland American colonists had gone through a sea change. Before 1754, many people were still living on the frontier of the British world, and survival was their primary concern. However, by 1773, a large percentage of the American populace had undergone what Breen calls (p. xv) the "transformation of the Anglo-American consumer marketplace." This transformation began sometime during the middle of the century, and "as modestly wealthy families acquired ever larger quantities of British manufactures--for the most part everyday goods that made life warmer, more comfortable, more sanitary, or perhaps simply more enjoyable--the face of material culture changed dramatically. Suddenly, buyers voiced concerns about color and texture, about fashion and etiquette, and about making the right choices from among an expanding number of possibilities " (p. xv, emphasis added). In the generation from midcentury to the Revolution, colonists from Georgia to Maine tried to bring comfort and beauty into their lives through the use of imported consumer goods. This all sounds very twentieth century and is not completely consistent with traditional views of eighteenth-century America.
Breen's insight came to him at Colonial Williamsburg in Virginia as he walked through the Wallace Gallery, an out-of-the-way museum that showcases an array of manufactured goods that were imported to the colonies from Britain. This prompted Breen to dig into scholarship on the material culture of the eighteenth-century Anglo-American world rather than the more conventional route of examining the intellectual roots of the revolution (for a good entrée into the vast scholarship on the material culture of the time, see Brewer and Porter 1993; McKendrick, Brewer, and Plumb 1982). His point is that without the transformation of the consumer marketplace, the intellectual climate of revolution would not have been able to strike the spark that it did. The colonists were drawn together as "Americans," not by their hatred of the tyranny of Parliament but by their love for "English fripperies," which were still being decried from the pulpits almost at the eve of the Revolution.
Breen also makes the point that perhaps by the time of the Stamp Act Crisis of 1763 and certainly by the time of the Boston Tea Party and the continuing crisis that the act caused, colonists along the Atlantic seaboard learned to trust one another. Because they shared similar goods and services from one end of the seaboard to the other, they found that they were similar. This similarity and the mutual confidence that it engendered made possible the popular upswelling of the Nonimportation Agreements. These agreements were truly the precursor of the Second Continental Congress (that which in 1776 passed the Declaration of Independence).
Breen's analysis runs from a discussion of the myth of the hospitable colonial consumer causing the demand for British fripperies; to the change of merchant advertising in the mid-eighteenth century from mere lists of goods for sale to more descriptive lists in the newspapers; to the quintessential frippery and true hospitality product of the era, tea. Tea required a matrix of associated products to prepare and serve: pots, cups, spoons, sugar, and tea itself. The latter two were imported and thus bore a duty to the Crown. Tea had been drunk in large quantities in the colonies; however, it was foresworn by patriots after the Tea Party and in the face of the Nonimportation Agreements. Many historians claim that the Tea Party turned America into a country of coffee drinkers almost overnight.
So why review this book in the Journal of Marketing ? One reason is that Breen is taking consumer behavior and giving it a front-and-center seat at the "nation-building" table. The political revolution was preceded by a consumer revolution, without which it would have been more difficult to accomplish. However, this is not a marketing book, nor even a book on consumer history, though there is a lot of consumption history between the covers. Breen is a historian, not a marketing or consumer scientist. He has not quoted any of the works that those who labor in such areas are used to seeing. Some of his arguments would have been stronger had he cited Csikszentmihalyi and Rochberg-Halton (1981) on what goods mean to consumers, McCracken (1988) on the symbolic meanings of goods, and Rudmin (1991) on the ownership of possession. The essence of his argument is that people became patriots partly because of what they wore, ate, and drank and that such things marked them as patriots or loyalists. People who were willing to sign their names to the Nonimportation Agreements were perhaps more outspoken in their Whiggish proclivities, but if they shifted to homespun and returned to small beer from tea for breakfast and withdrew from the newly introduced custom of sharing tea with friends, their Whiggishness was just as evident. However, considering the rigid boundaries of academe, it is unlikely that Breen is familiar with any of this literature. However, those who know the literature will nod knowingly when Breen makes his points in the mid-eighteenth-century context.
Scholars working in consumer behavior and the history of marketing will find this book interesting and suggestive. Without exactly meaning to, Breen makes a strong argument for the importance of mundane marketing activities in turning the wheels of history. In 370 dense pages, he takes late-twentieth- and early-twenty-first-century marketing minds back 200-plus years and shows some familiar things in an unexpected context.
REFERENCES Brewer, John and Roy Porter, eds. (1993), Consumption and the World of Goods. London: Routledge.
Csikszentmihalyi, Mihalyi and Eugene Rochberg-Halton (1981), The Meaning of Things: Domestic Symbols and the Self. New York: Cambridge University Press.
McCracken, Grant (1988), Culture and Consumption: New Approaches to the Symbolic Character of Consumer Goods and Activities. Bloomington: Indiana University Press.
McKendrick, Neil, John Brewer, and J.H. Plumb (1982), The Birth of a Consumer Society: The Commercialization of Eighteenth-Century England. Bloomington: Indiana University Press.
Rudmin, Floyd W., ed. (1991), To Have Possessions: A Handbook on Ownership and Property. Corte Medara, CA: Select Press.
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By Dennis J. Cahill, President, North Union Associates Inc. and Terry Clark, Editor, Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 177- The Performance Implications of Fit Among Business Strategy, Marketing Organization Structure, and Strategic Behavior. By: Olson, Eric M.; Slater, Stanley F.; Hult, G. Tomas M. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p49-65. 17p. 1 Diagram, 6 Charts. DOI: 10.1509/jmkg.69.3.49.66362.
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The Performance Implications of Fit Among Business
Strategy, Marketing Organization Structure, and Strategic
Behavior
Adopting a contingency perspective, the authors present and test a fit-as-moderation model that posits that overall firm performance is influenced by how well the marketing organization's structural characteristics (i.e., formalization, centralization, and specialization) and strategic behavioral emphases (i.e., customer, competitor, innovation, and cost control) complement alternative business strategies (i.e., prospector, analyzer, low-cost defender, and differentiated defender). Responses from 228 senior marketing managers provide support for the model and demonstrate that each strategy type requires different combinations of marketing organization structures and strategic behaviors for success.
Firm performance is determined, at least in part, by how effectively and efficiently the firm's business strategy is implemented (Galbraith and Kazanjian 1986; Walker and Ruekert 1987). The process of implementing business strategies addresses how marketing activities are accomplished (Slater and Olson 2001; Walker and Ruekert 1987). How well these activities are accomplished is influenced by how they are organized (Mintzberg 1979; Vorhies and Morgan 2003; Weitz and Anderson 1981) and the specific behaviors the organization undertakes regarding customer orientation, competitor analysis, innovation, and cost management (e.g., Chen 1996; Day and Nedungadi 1984; Deshpandé, Farley, and Webster 1993; Gatignon and Xuereb 1997; Porter 1980).
Although these influences on performance have been examined in isolation, they have not been studied in an integrated model of strategy implementation. This study represents an important contribution to the understanding of strategy implementation in three regards: First, we use an expanded business strategy framework as the foundation for this research on strategy implementation. Second, we build on prior research (i.e., Vorhies and Morgan 2003) that examines the impact of marketing organization structure and business strategy on firm performance within the confines of a specific industry (i.e., trucking) by considering a much broader range of industries. Third, we assess whether the explanatory power of the model is enhanced by the inclusion of four types of strategic behavior. This approach provides insight into the specific characteristics of organization and behavior that are important to the implementation of each strategy type. This should guide both general managers and marketing executives as they attempt to construct high performance organizations.
The model we depict in Figure 1 illustrates the thesis of this study; that is, the relationship between marketing organization structure and strategic behavior (predictor variables) and firm performance (criterion variable) is moderated by the business strategy a firm adopts (moderator variable). Venkatraman (1989, p. 424; see also Van de Ven and Drazin 1985; Venkatraman 1990; Venkatraman and Camillus 1984) describes this as the "fit as moderation" perspective. Porter (1996, p. 73) notes, "Strategic fit among many activities is fundamental not only to competitive advantage but also to the sustainability of that advantage. It is harder for a rival to match an array of interlocked activities than it is merely to imitate a particular sales-force approach, match a process technology, or replicate a set of product features."
This contingency approach to strategy is rooted in general systems and open systems perspectives (Zeithaml, Varadarajan, and Zeithaml 1988). These perspectives view the organization as a social system composed of interdependent subsystems. Coordination within these subsystems is accomplished through management policies and practices, which in turn interact with the environment to help achieve a set of goals or objectives (Luthans and Stewart 1977). Interactions within the organization and between the organization and the environment result in two complementary open system characteristics that are central to the contingency approach: adaptation and equifinality.
The principle of adaptation holds that managers may adapt the organization's strategy to cope with changes in the external environment or the organization's structure and behavior to address the requirements of its strategy (Chakravarthy 1982). In the context of this study, the principle of adaptation suggests that marketing managers and personnel adopt specific structures and behaviors that best satisfy the unique demands of the firm as dictated by its overarching characteristic, its business strategy.
The concept of equifinality holds that superior organizational performance can be achieved through a variety of different strategies (e.g., Gresov and Drazin 1997; Hrebiniak and Joyce 1985; Katz and Kahn 1978; Venkatraman 1990) and that overall firm performance is less dependent on a specific strategy than on how well the firm implements the chosen strategy. Thus, equifinality implies that strategy choice (Child 1972), or flexibility, is available to organization designers when they are creating organizations to achieve high performance. Because organizational structure and organizational behavior are critical components of strategy implementation, it stands to reason that superior performance is contingent on how well the structure and behavior are aligned with the requirements of a specific strategy.
We follow the lead of Vorhies and Morgan (2003) and Matsuno and Mentzer (2000) by examining the impact of marketing organization structure and strategic behavior on firm performance in the context of a specific business strategy. Specifically, we build on these studies by simultaneously examining marketing organization structures and strategic behaviors. By considering these two sets of independent variables at the same time, we attempt to develop a better understanding of both their individual and relative influences on overall firm performance while keeping with the basic model of strategy implementation (Galbraith and Kazanjian 1986).
We begin this study by reviewing the literature on business strategy, marketing organization structure, and strategic behavior. We then propose a set of hypotheses that pertain to the impact of marketing organization structure and strategic behavior on overall firm performance among firms that have adopted a specific business strategy. Next, we report the results of an empirical study that tests these hypotheses. We conclude by discussing our findings and offering suggestions for managers and marketing scholars.
Business Strategy
Business strategy refers to how firms compete in an industry or market (Varadarajan and Clark 1994; Walker and Ruekert 1987). The two dominant frameworks of business strategy (Hambrick 2003) are the Miles and Snow (1978) typology, which focuses on intended rate of product-market change, and the Porter (1980) typology, which focuses on customers and competitors.
Miles and Snow (1978) develop a comprehensive framework that addresses the alternative ways that organizations define and approach their product-market domains (the entrepreneurial problem) and construct structures and processes (the administrative and technical problems) to achieve competitive advantage in those domains. Miles and Snow identify four archetypes of how firms address these issues: ( 1) "Prospectors" continuously attempt to locate and exploit new product and market opportunities, ( 2) " defenders" attempt to seal off a portion of the total market to create a stable set of products and customers, ( 3) "analyzers" occupy an intermediate position by cautiously following prospectors into new product-market domains while protecting a stable set of products and customers, and ( 4) "reactors" do not have a consistent response to the entrepreneurial problem. In contrast, Porter (1980) proposes that business strategy should be viewed as a product of how the firm creates customer value compared with its competitors (i.e., differentiation or low cost) and how it defines its scope of market coverage (i.e., focused or marketwide).
Walker and Ruekert (1987) observe that though each of these strategy typologies has inherent strengths (i.e., Porter's external focus and Miles and Snow's internal focus), each is also limited. To address this, Walker and Ruekert propose a hybrid model that synthesizes the two foci in a typology that consists of prospectors, low-cost defenders, and differentiated defenders. Although Walker and Ruekert's article has been frequently cited in the marketing and management literature, the distinctions between low-cost defenders and differentiated defenders have only recently been supported in empirical analysis (e.g., Slater and Olson 2000, 2001). In this study, we build on this work by retaining Walker and Ruekert's three strategy types and adding the analyzer strategy type because many studies have demonstrated the validity of this strategy. Because of low numbers and a lack of a proactive approach, we do not consider reactors.
Organizational Structure
Organizational structures are established to coordinate work that has been divided into smaller tasks. Mintzberg (1981, p. 104) notes, "How that coordination is achieved--by whom and with what--dictates what the organization will look like." Although Mintzberg's focus is on the organization as a whole, his observations are appropriate for the marketing organization as well. Indeed, the marketing discipline continues to increase in complexity and to create more sharply focused subdisciplines. This expansion of marketing responsibilities has created greater divisions of labor within the modern marketing unit and greater coordination challenges.
Walker and Ruekert (1987) hypothesize that firms that follow different generic business strategies adopt different structural designs. Vorhies and Morgan (2003) study the relationships among marketing organization structure, business strategy, and performance in the trucking industry. Both of these studies demonstrate that different marketing organization characteristics are more or less appropriate for different business strategies.
Alternative forms of structures are typically defined by three structural constructs, which as Walker and Ruekert (1987 p. 27) note "seem particularly important in shaping an organization's or department's performance." These three constructs are formalization, centralization, and specialization, and they are central to Mintzberg's (1979) analysis of organizational structures.
Formalization is the degree to which formal rules and procedures govern decisions and working relationships. Rules and procedures provide a means for prescribing appropriate behaviors and addressing routine aspects of a problem. Rules enable people to organize their activities to both their and the organization's benefit (Ullrich and Wieland 1980). Formal rules and procedures can lead to increased efficiency and lower administrative costs (Ruekert, Walker, and Roering 1985; Walker and Ruekert 1987), particularly in stable environments or those in which tasks are comparatively simple and/or repetitive (Olson, Walker, and Ruekert 1995). Burns and Stalker (1961) refer to firms with highly formal procedures as "mechanistic" and those with fewer formal procedures as "organic." Organic firms encourage horizontal and vertical communication and flexible roles. Benefits of the organic form include rapid awareness of and response to competitive and market change, more effective information sharing, and reduced lag time between decision and action (Miles and Snow 1992).
Centralization refers to whether decision authority is closely held by top managers or is delegated to middle-and lower-level managers. Lines of communication and responsibilities are relatively clear in centralized organizations, and the route for final approval can be traveled quickly (Hage and Aiken 1970). Although fewer innovative ideas tend to be put forth in centralized organizations, implementation tends to be straightforward after the decision is made (Ullrich and Wieland 1980). This benefit is primarily realized in stable, noncomplex environments (Olson, Walker, and Ruekert 1995; Ruekert, Walker, and Roering 1985). In contrast, in a decentralized organization, a variety of views and ideas may emerge from different groups (e.g., product management, sales). Because decision making is dispersed, it may take longer to make and implement a decision (Olson, Walker, and Ruekert 1995). In the long run, it is likely that the decentralized organization will produce more new ideas and more actual program changes than will a centralized organization (Ullrich and Wieland 1980). In addition, when a task is nonroutine and takes place in a complex environment, decentralization is likely to be more effective because it empowers managers who are close to the issue to make decisions and implement them rapidly (Ruekert, Walker, and Roering 1985). In the management literature, this construct is typically referred to as centralization, but we find that our statistical results are more readily interpretable when we consider it from the perspective of how decentralized a marketing organization is. Ultimately, the two terms simply represent opposite ends of a single spectrum.
Specialization refers to the degree to which tasks and activities are divided in the organization and the degree to which workers have control in conducting those tasks. Highly specialized organizations have a higher proportion of " specialists" who direct their efforts to a well-defined set of activities (Ruekert, Walker, and Roering 1985). Specialists are experts in their respective areas and, in complex environments, are typically given substantial authority to determine the best approach to complete their tasks (Mintzberg 1979). This expertise enables the organization to respond rapidly to changes in its environment (Walker and Ruekert 1987). Organizations that have a high proportion of marketing generalists typically are low in expertise in specific areas, such as marketing research or e-marketing. By necessity, generalists must do additional "homework" before responding to change. These organizations may be able to hold marketing costs down by reducing the expense of hiring specialists (Walker and Ruekert 1987).
Strategic Organizational Behavior
Organizational behavior refers to organizational members' work-related activities (e.g., Ouchi 1977; Robbins 2002). According to Snell (1992), management attempts to influence organizational behavior through the use of control systems. Control is any process that helps align employees' actions with the firm's interests (Snell 1992; Tannenbaum 1968). Control theory (Snell 1992) identifies three major categories of control mechanisms: behavioral control (e.g., establishing and monitoring of sets of actions), output control (e.g., goal attainment measures), and input control (e.g., training). When applied within an organizational context, control theory posits that management attempts to direct employee behavior to enhance the probability of desired outcomes. As Snell notes (p. 292), "Advocates of the behavioral perspective posit that different strategies require different behaviors." Snell also notes that this view of the link between strategy and behavior is useful because it provides a clear explanation of why behavior should be linked to strategy and because it posits a testable mediating construct (required behaviors).
In this study, we examine strategic behaviors that have the potential to create superior performance through enhancing the execution of business strategy (Gatignon and Xuereb 1997; Slater and Narver 1995). These are customer-oriented behaviors (Deshpandé, Farley, and Webster 1993), competitor-oriented behaviors (Armstrong and Collopy 1996; Chen 1996), innovation-oriented behaviors (Hurley and Hult 1998), and internal/cost-oriented behaviors (Porter 1980). It is important to understand that these strategic behaviors are not mutually exclusive and that it is common for firms to engage in multiple sets of behaviors simultaneously (e.g., Day and Nedungadi 1994; Gatignon and Xuereb 1997; Slater and Narver 1994). Furthermore, we anticipate that different combinations of emphases will prove more or less beneficial for firms that adopt different business strategies.
Firms with a strong customer orientation pursue competitive advantage by placing the highest priority on the creation and maintenance of customer value. As such, these firms engage in the organizationwide development of and responsiveness to information about the expressed and unexpressed needs of both current and potential customers (Deshpandé, Farley, and Webster 1993; Kohli and Jaworski 1990; Narver and Slater 1990). Because of the constantly refined market-sensing and customer-relating capabilities of the customer-oriented firm, it should be well positioned to anticipate customer need evolution and to respond through the development of new customer value-focused capabilities and the addition of valuable products and services (Day 1994a, b).
A different perspective on competitive advantage is simply to beat the competition (Day 1990). This orientation places a priority on the in-depth assessment of a set of targeted competitors. This assessment focuses on targeted competitors' goals, strategies, offerings, resources, and capabilities (Day and Nedungadi 1994; Porter 1980) and on the organizationwide dissemination of the information generated from this assessment (Kohli and Jaworski 1990). The result is that managers develop competitor-oriented objectives rather than economic or customer-oriented objectives (Armstrong and Collopy 1996). The behavioral goal of the firm is to match, if not exceed, competitors' strengths.
Another perspective is that firms build and renew competitive advantage through radical or discontinuous innovations (Christensen and Bower 1996; Lynn, Morone, and Paulson 1996). An innovation orientation indicates that the firm not only is open to new ideas but also proactively pursues these ideas (Hurley and Hult 1998) in both its technical and administrative domains (Han, Kim, and Srivastava 1998). An innovation orientation encourages risk taking and enhances the likelihood of developing radically new products. March (1991) argues that firms must be aware of the possibility that an innovation orientation may not allow for the follow-through that is necessary to reap the benefits of earlier innovations fully.
Porter (1980) argues that there are two basic sources of competitive advantage. The first is the differentiation advantage that a firm derives from the customer-, competitor-, or innovation-oriented behaviors that we have discussed. The second is the cost advantage that a firm derives from internal orientation. Internally oriented firms pursue efficiency in all parts of their value chain (Porter 1985). They attempt to reduce costs in primary activities, such as logistics, operations, and sales and marketing. They also attempt to reduce costs in support activities, such as procurement, research and development (R&D), and administrative functions. These firms pursue operational excellence that they can translate into higher sales through lower prices or higher margins (Treacy and Wiersema 1993). Whereas experimentation is the hallmark of firms with an innovation orientation, exploitation is the hallmark of internally oriented firms (March 1991). Exploitation enables the firm to realize improvements as it drives down the learning curve (Alberts 1989). However, overreliance on exploitation may stifle adaptation as market conditions change.
Hypotheses
We now describe the structural and behavioral characteristics that should be most critical to the achievement of superior performance for each of the four business-level strategies.
Many studies (e.g., Conant, Mokwa, and Varadarajan 1990; McDaniel and Kolari 1987; McKee, Varadarajan, and Pride 1989; Slater and Olson 2001) have shown that, on average, prospectors are the most marketing oriented of the strategy types. The key to success for prospectors is the development of innovative new products and entry into new markets. Development of new products is a complex and frequently disorderly process that requires the input of numerous specialists, both technical and marketing. These specialists require significant latitude in their decisionmaking authority to enhance creativity and hasten the development process. Thus, we follow Walker and Ruekert's (1987, p. 27) proposals that performance within prospector firms is enhanced when "( 1) decision-making authority is extended down to or at least shared with lower-level managers within the department, ( 2) rigid rules and policies are supplanted by discretion and informal coordination mechanisms, and ( 3) more specialists with more detailed knowledge about particular techniques, products, or customers are incorporated within the department."
Discontinuous innovation may result from "outside-in" processes (i.e., customer-oriented behaviors) or from "inside-out" processes (i.e., purely R&D driven innovation). Thus, to develop new products, prospectors must observe customers' use of products or services in normal routines. They must also work closely with lead users (i.e., customers who recognize a need before the majority of the market). The lead user methodology motivates product developers to search outside the firm for insight, inspiration, models, and expertise. Prospectors may conduct market experiments, learn from the results of those experiments, and modify their efforts on the basis of the new knowledge and insights (Lynn, Morone, and Paulson 1996).
In contrast, Olson, Walker, and Ruekert (1995) observe that R&D is frequently the dominant functional group associated with new product development, which suggests that an innovation orientation focused on technological breakthroughs is critical to the successful implementation of this business strategy. In addition, when prospectors attempt to transform cutting-edge technologies into products or services, they may not recognize their (potential) competitors. Thus, prospectors should be more attentive to customers and to innovation that continuously pushes product and market boundaries than to competitors. Thus, we predict the following:
H1: The highest-performing prospector firms have marketing organizations that are characterized by a high number of specialists who operate in a decentralized, informal organization and who place a greater emphasis on customer and innovation orientations.
The key to success for analyzers is to bring out either improved or less expensive versions of products that prospectors introduced while defending core markets and products. These dual demands create a structural conflict, and Vorhies and Morgan (2003) note that analyzer firms require sufficient marketing capabilities to perform complex tasks while minimizing resource commitments. As fast followers, analyzers may require informal and decentralized structures that are staffed by marketing specialists to expedite the process of bringing their "new and improved" products to market and to avoid falling too far behind. However, as territorial defenders, analyzers must also control product development and delivery costs while focusing on a stable base of existing customers. This requires a more formal and centralized structure with fewer marketing specialists. Ultimately, these conflicts appear to offset the pull toward structural extremes.
Although Walker and Ruekert (1987) do not address the challenges that analyzers face, Miles and Snow (1978) provide considerable support for our position of balance. To accommodate both dynamic and stable areas of operation, Miles and Snow state (pp. 78-79) that analyzers will develop "[m]oderately centralized control systems." In addition, they note that to address the entrepreneurial problem, analyzers will create a "[h]ybrid domain that is both stable and changing," and to control costs and reap benefits, their "domain must be optimally balanced at all times between stability and flexibility."
Miles and Snow (1978, p. 78) conclude that marketers in analyzer firms "must not only locate new product or market opportunities but also promote the sale of the organization's traditional products or services." In addition, "[t]he dual nature of the Analyzer's technology allows the organization to produce familiar products or services efficiently while keeping pace with developments engendered by Prospectors" (p. 78). With respect to performance and structure, Miles and Snow observe (p. 80), "The Analyzer's dual technological core means that the organization can never be completely efficient nor completely effective." The inherent tension in the analyzer's entrepreneurial, administrative, and technological challenges suggests that there is no clear structural solution for these firms. Consistent with Miles and Snow's assessments, we anticipate that balance and adaptation will be the mode of successful analyzers. However, we do not specifically predict that balance in marketing organization structure will be associated with superior performance, because at any given time, an individual analyzer may be operating in a way that is closer to that of either a defender or a prospector.
Golder and Tellis (1993) suggest that followers can be as successful as early entrants if they learn about the structure and dynamics of markets from early entrants' efforts and limit their new product introductions to categories that have already shown promise in the marketplace. To identify opportunities either in unattended market segments or in potential product improvements, analyzers must closely monitor customer reactions and competitors' activities, successes, and failures. In other words, although customers are certainly important to analyzers, competitors' actions are equally, if not more, important. Thus, we predict the following:
H2: The highest-performing analyzer firms have marketing organizations whose behaviors are focused on customers and competitors.
The key to success for low-cost defenders is to provide quality products or services at the lowest overall cost. The emphasis for low-cost defenders is on efficiency through standardized practices rather than on effectiveness that stems from flexibility. As such, low-cost defenders are expected to rely on centralized decision making and formal marketing organization structures to ensure that risk and administrative expenses are held to a minimum (Ruekert, Walker, and Roering 1985; Walker and Ruekert 1987).
Slater and Olson (2001) find that successful low-cost defenders engage in comparatively low levels of marketing activities. Walker and Ruekert (1987) predict that process engineering, production, distribution, and finance (rather than marketing) constitute the dominant functions in low-cost defender firms. Nevertheless, low-cost defenders still require a base level of marketing, and the management of these actions is critical to the overall success of the firm. Walker and Ruekert (1987) further predict that low-cost defenders have narrow and less technically sophisticated product lines than do firms pursuing other business strategies as well as low levels of advertising and sales promotions. As such, Walker and Ruekert hypothesize (p. 27) that "high levels of formalization and centralization together with low levels of specialization are likely to be associated with relatively efficient performance."
With costs as the driving focus behind this business strategy, it is logical that the most successful low-cost defenders emphasize cost control. We expect that given the comparatively low levels of influence that the marketing organization has in low-cost defender firms, the internal focus on costs will overshadow the three external behavioral orientations. However, if there is one external behavioral on which low-cost defenders focus, we believe that it will be on competitor orientation because competitors serve as a benchmark against which prices, costs, and performance can be compared. Thus, we predict the following:
H3: The highest-performing low-cost defender firms have marketing organizations that are characterized by few marketing specialists; formal and centralized decisionmaking processes; and a behavioral focus on internal operations, cost control, and competitors.
The key to success for differentiated defenders is to provide premium service and/or high-quality products to select sets of customers who value and are willing to pay for them. Because customer contact personnel are the employees who ultimately deliver service, it is imperative that these employees are able to make decisions about customer relations without needing to check with higher-level managers on every decision. Thus, the best service is provided in decentralized organizations in which frontline employees have substantial discretion (cf. Hartline and Ferrell 1996). However, a decentralized organization does not necessarily imply an informal organization. We anticipate that a set of formal policies and rules will be established in differentiated defender firms to guide frontline marketers with respect to how to react to and address potential customer relation issues.
Unlike their low-cost counterparts, differentiated defender firms focus on retaining customers through attention to superior service, product quality, or image. Thus, the most successful differentiated defender firms will place a greater emphasis on customer-oriented behaviors. However, this does not mean that differentiated defenders are oblivious to controlling costs or that they never engage in product or service innovation. Rather, these are not typically the most critical elements of a differentiated defender strategy, and they may well run counter to the demands of their customer base. Slater and Olson (2001) find that the highest-performing differentiated defender firms adopt a "value marketer" strategy (i.e., high-quality, moderately innovative products that are sold through selective distribution channels but at lower price than highly innovative products). Thus, we predict the following:
H4: The highest-performing differentiated defender firms have marketing departments that are characterized by a formal environment in which employees have considerable discretionary authority (i.e., decentralized) and are primarily focused on customers.
We summarize the hypothesized and observed relationships in Table 1.
Research Design
We focused this study on manufacturing and service firms operating in 20 different two-digit Standard Industrial Classification code industries (classification categories 20, 30, and 40) not only to provide a reasonably similar context for respondents but also to be broad enough for the results to be generalizable. We purchased a commercial mailing list of 1200 senior marketing managers in firms with 500 or more employees operating in these industries. In collecting the data, we followed Huber and Power's (1985) guidelines on how to obtain high-quality data from key informants. A key informant design is common in studies of marketing organization (e.g., Moorman and Rust 1999) and of market-oriented behavior (e.g., Day and Nedungadi 1994; Gatignon and Xuereb 1997). We selected senior marketing managers as key informants because they should be knowledgeable about marketing organization structure, strategic behavior, business strategy, and overall firm performance.
We sent questionnaires to the 1200 senior marketing managers along with a personal letter that provided a brief introduction and a general explanation of the study's intent and a postage-paid return envelope. The questionnaire defined the meaning of business unit and asked each respondent to refer either to the largest strategic business unit in the organization or to the one they were most familiar with when answering the questions. Three weeks after the initial mailing, we sent a follow-up mailing with a duplicate copy of the questionnaire and a return envelope. We received 228 responses (prospectors: 63; analyzers: 45; low-cost defenders: 44; differentiated defenders: 63; reactors: 12); after accounting for undeliverables, this constituted a 20% response rate. Two-thirds of the respondents who reported their positions indicated that they held positions of director or vice president of marketing within their respective firms.
Although nonresponse bias is always a concern in survey research, the response rate is within the range of typical response rates for this type of study (e.g., Cannon and Perreault 1999; Gatignon and Xuereb 1997; Homburg, Workman, and Krohmer 1999; Menon, Bharadwaj, and Howell 1996). Furthermore, Armstrong and Overton (1977) find that late respondents more closely resemble nonrespondents than do early responders. Significant differences between late responders and early responders indicate the presence of nonresponse bias. We found no significant differences between early and late responders on key measures.
We developed a questionnaire to assess dimensions of market structure, marketing organization, strategic behavior, business strategy, and firm performance. We measured all constructs on seven-point Likert scales. We pretested the instrument with ten people who are involved with marketing strategy development or implementation for their firms. We asked them specifically to comment on the clarity of the items and their relevance. On the basis of their assessments, we modified the wording of some statements to improve their clarity.
We modified the following scales to address customer-oriented behavior (Narver, Slater, and MacLachlan 2004), competitor-oriented behavior (Narver and Slater 1990), innovation-oriented behavior (Hurley and Hult 1998), and internal/cost control-oriented behavior (Homburg Workman, and Krohmer 1999; Kotha and Vadlamani 1995).
To control for the potential impact of market conditions on the relationships of interest, we also collected data on market structure. We measured two dimensions--market and technological turbulence--based on Jaworski and Kohli's (1993) work. For the marketing organization dimensions, we developed the scales used to assess formalization and specialization on the basis of Walker and Ruekert's (1987) discussion. We adapted the scale used to assess centralization from Menon and colleagues' (1999) work.
We assessed strategy type using the self-typing paragraph approach that is commonly used in strategic marketing research (e.g., McDaniel and Kolari 1987; McKee, Varadarajan, and Pride 1989; Vorhies and Morgan 2003). Several studies (Conant, Mokwa, and Varadarajan 1990; James and Hatten 1995; Shortell and Zajac 1990) have demonstrated that this is a valid measurement approach. We used the descriptions from Slater and Olson's (2000) work.
Performance should be viewed in the context of the firm's objectives, strategy, and market structure. We follow the lead of other marketing strategy researchers (e.g., Jaworski and Kohli 1993; Olson, Walker, and Ruekert 1995) and use a global measure of firm performance. We use overall firm performance (i.e., level to which a firm met expectations, exceeded major competitors, and satisfied top management) because of its relevance despite the nature of the contextual influences. As Ittner and Larcker (1997, p. 17) note, "overall perceived performance should encompass not only the organization's performance on the preceding dimensions (return on assets, return on sales, and sales growth), but also any other financial and nonfinancial goals that may be important to the organization." In a recent study, Morgan, Kaleka, and Katsikeas (2004) found a strong correlation between objective performance data and subjective assessments of performance by key informants, which supports the validity of key informant data. The scales that we used in the survey and their sources appear in the Appendix.
The results of the measurement analysis appear in Table 2, including means, standard deviations, average variances extracted, composite reliabilities, parameter estimates, and fit indexes. Table 3 reports the correlations and shared variances for the constructs included in the study. Overall, we found the ten reflective scales and their 41 purified items to be reliable and valid within the setting of this study.
Following the data collection, we tested the scales for dimensionality, reliability, and validity. We evaluated the psychometric properties in one confirmatory factor analysis (CFA) using LISREL 8.71 (Jöreskog, Toit, and Toit 2000). We evaluated the model fits using a series of indexes that Gerbing and Anderson (1992) and Hu and Bentler (1999) suggest, including DELTA2 (Bollen 1989), relative noncentrality index (RNI; McDonald and Marsh 1990), comparative fit index (CFI; Bentler 1990), Tucker-Lewis index (Tucker and Lewis 1973), and root mean square error of approximation (RMSEA; Steiger and Lind 1980). Through the use of these fit indexes, the initial 49-item measurement model resulted in a reasonably good fit to the data (DELTA2 = .91, RNI = .91, CFI = .91, Tucker-Lewis = .91, and RMSEA = .08), but several items exhibited low R2. Following Anderson and Gerbing's (1988) suggestions regarding content and statistical considerations, we removed eight items that performed poorly (R2 < .38). The refined set of 41 items resulted in an improved fit (CFI = .93; for complete results, see Table 2). In addition, we found the 41 items to be reliable and valid when evaluated on the basis of each item's error variance, modification index, and residual covariation (e.g., Fornell and Larcker 1981; Jöreskog, Toit, and Toit 2000).
Within the CFA setting, we calculated composite reliability using the procedures that Fornell and Larcker (1981) outline on the basis of the work of Werts, Lin, and Jöreskog (1974). The composite reliabilities for the ten scales ranged from .69 to 90, with factor loadings ranging from .48 to .97 (p < .01) (for measurement statistics, see Tables 2 and 3). We assessed discriminant validity using two different methods. First, we assessed the average variance extracted for each construct and verified that it was higher than the corresponding shared variance for all possible pairs of constructs (Anderson and Gerbing 1988; Bagozzi and Yi 1988). The average variances extracted ranged from .48 to .71, and the shared variances ranged from .00 to .46 (see Tables 2 and 3).
Second, we tested discriminant validity with the test that Anderson (1987) and Bagozzi and Phillips (1982) suggest. This test entailed analyzing all possible pairs of constructs in a series of two-factor CFA models. We ran each model twice, once constraining the π coefficient to unity and once freeing this parameter. We performed a chi-square difference test on the nested models to verify that the chi-square was lower for the unconstrained models (Anderson and Gerbing 1988). The critical value (Δχ²Δdegree of freedom [d.f. = 1)] > 3.84) was exceeded in all cases (the lowest Δχ²[sub Δd.f. = 1) = 59.12 was found between customer and innovation orientations).
Before testing the hypotheses, we used a CFA approach to Harmon's one-factor test (McFarlin and Sweeney 1992; Sanchez and Brock 1996) to assess whether common method variance (CMV) constituted a problem in the testing. The rationale for this test is that if CMV poses a serious threat to the analysis, a single latent factor would account for all manifest variables (Podsakoff and Organ 1986). A worse fit for the one-factor model would suggest that CMV does not pose a serious threat (Sanchez, Korbin, and Viscarra 1995). The one-factor model yielded a χ² = 3877.56, d.f. = 779 (compared with χ² = 1567.65, d.f. = 734 for the measurement model). The fit is considerably worse for the unidimensional model than for the measurement model, suggesting that common method bias is not a serious threat in the study.
In addition, as Cohen and colleagues (2003) suggest, we conducted a power analysis to determine the probability of finding the sample R2 to be greater than zero with α = .01 for each strategy type. We achieved good to excellent statistical power in each subgroup. Thus, overall, we found the measures to be reliable and valid, and the sample sizes for each strategy type were sufficient to conduct the hypotheses testing.
Results
We tested the hypotheses using ordinary least squares regression within subgroups. Subgroup analysis is an appropriate technique to test for moderation when the moderator variable is categorical (Sharma, Durand, and Gur-Arie 1981). The method of hierarchical regression analysis enabled us to determine the relative impact of the structure and behavior variables on performance with respect to strategy type after controlling for market structure. This approach builds on previous research (Vorhies and Morgan 2003) on strategy and marketing organization structure to identify the incremental contribution of the behavioral orientations to overall firm performance. In the hierarchical regression analysis, we entered the control variables of market turbulence and technological turbulence in Step 1; formalization, centralization, and specialization in Step 2; and customer-, competitor-, innovation-, and internal/cost orientations in Step 3. We also conducted an overall analysis with strategy type as a control variable and size in revenues when available. Size was not a significant predictor of performance, which suggests that the subgroup analyses are not underspecified as a result of the exclusion of size as a predictor variable. Table 4 summarizes the results. For all models, the variance inflation factors were lower than 7.27, indicating that multicollinearity does not affect the weights of the explanatory variables in the model (Mason and Perreault 1991).
We found an adjusted R² = .53 and, as predicted, a positive effect of decentralization (β = .28, p < .05), specialization (β = .29, p < .01), customer orientation (β = .27, p < .05), and innovation orientation (β = .27, p < .05) on performance. The results also show a negative effect of competitor orientation (β = -.30, p < .01) on performance that we did not predict. Further analysis shows that the relationships among performance (Y), customer orientation (X1), and competitor orientation (X2) are an example of a net suppressor effect in which X2 is positively correlated with Y but has a negative regression coefficient. The primary purpose of X2 is to suppress the error variance in X1 rather than explain much about Y (Cohen and Cohen 1975, p. 91). The appropriate conclusion is that competitor orientation has a positive but nonsignificant influence on prospector performance.
Formalization and internal/cost orientation were not significant. Thus, we conclude that H1 is supported. The ΔR² = .32 (p < .01) when we entered the control variables in Step 1 and the structure variables in Step 2. The ΔR² = .15 (p < .01) when we entered the structure variables in Step 2 and the behavior variables in Step 3.
We found an adjusted R² = .69 and, as predicted, a positive effect of customer orientation (β = .32, p < .05) and competitor orientation (β = .52, p < .01) on performance. The results also show a negative effect of innovation orientation (β = -.19, p < .10) on performance that we did not predict. Further analysis shows that the relationships among performance (Y), customer orientation (X1), and innovation orientation (X2) are also the result of a net suppressor effect.
Formalization, decentralization, specialization, and internal/cost orientation were not significant. Thus, we conclude that H2 is supported. The ΔR² = .19 (p < .01) when we entered the control variables in Step 1 and the structure variables in Step 2. The ΔR² = .31 (p < .01) when we entered the structure variables in Step 2 and the behavior variables in Step 3.
We found an adjusted R² = .78 and, as predicted, a positive effect of internal/cost orientation (β = .37, p < .01) and competitor orientation (β = .42, p < .05) on performance. Counter to our prediction, the impact of decentralization was positive (β = .33, p < .05), and specialization, formalization, and competitor orientation had no significant effect on performance. The results also show a negative effect of innovation orientation (β = -.33, p < .05) on performance, which is due to a net suppressor effect. Customer orientation was not significant in the analysis. Thus, H3 is partially supported. The ΔR² = .46 (p < .01) when we entered the control variables in Step 1 and the structure variables in Step 2. The ΔR² = .33 (p < .01) when we entered the structure variables in Step 2 and the behavior variables in Step 3.
We found an adjusted R² = .27 and, as predicted, a positive effect of customer orientation (β = .45, p < .01) and formalization (β = .42, p < .05) on performance. Contrary to our prediction, decentralization, specialization, competitor orientation, innovation orientation, internal/cost orientation, and the control variables were not significant. Thus, H4 was partially supported. The ΔR² was not significant from Step 1 to Step 2, but it was significant (ΔR² = .24, p < .01) when we entered the structure variables in Step 2 and the behavior variables in Step 3.
Discussion and Implications
Vorhies and Morgan (2003) demonstrate that firm performance within the trucking industry is influenced in part by how well the marketing organization structure variables are aligned with business strategy. Our study provides additional support for this finding and suggests that it is generalizable across a broader spectrum of industries. More important, our findings demonstrate that when behavioral orientations are considered in addition to marketing organizational structural elements, the explanatory power of the model increases significantly across all strategy types. We also find that behavioral orientation is contingent on strategy type.
Although the regression results are largely in the predicted direction and are significant, they do not always provide clear direction to managers on how to implement the most effective structures or behavioral orientations. For example, in the course of our analysis, we found (as predicted) that the only significant relationship between internal/cost orientation and firm performance was for low-cost defenders. However, a post hoc analysis of variance revealed that internal/cost orientation is the behavioral variable with the highest overall mean score (5.92) when all responding firms are considered. This finding is consistent with Porter's (1985) position that firms pursuing noncost-based strategies must be able to maintain relative parity in their costs to achieve superior business performance. Thus, we would neither expect nor recommend that managers in firms following one of the other business strategies ignore issues of cost. Similarly, although the post hoc analysis demonstrated that customer orientation is the behavioral orientation with the lowest overall mean score (4.51), the regression results proved significant in the predicted direction for three of the four strategy types. Nevertheless, the highest-performing low-cost defenders also demonstrated a comparatively high mean score of 5.1 for customer orientation. Thus, the message to managers is not to ignore any of the orientations but rather to prioritize them and then place the greatest emphases and resources on those most aligned with the successful implementation of a firm's business strategy. We now consider this issue.
The principal managerial question that motivated this study is, What should a marketing organization look like with regard to structure and behavior? To address this issue more fully, we followed Vorhies and Morgan's (2003) lead by identifying the top third of firms (on the basis of performance) in each of the four categories of strategies and then determining mean scores for each of the three structural variables and the four behavioral variables (see Table 5). We note that because of suppressor effects, the profiles for three of the four strategy types represent a modestly different picture of the independent variables that are associated with successful performance than that which is represented by the regression results.
We characterize the structures of top-performing prospectors as highly informal and decentralized organizations. In other words, these organizations are flexible and adaptive; there are few formal procedures, and important decisions are made at relatively low levels. Decentralized decision making is possible largely because these firms employ a significantly higher proportion of professionals who have specialized knowledge than do any of the other strategic types.
Prospector marketing organizations have the highest levels of innovation orientation and customer orientation and the lowest levels of internal/cost orientation of any of the strategic groups, though it would be incorrect to characterize their internal/cost orientation as anything less than moderately high. Narver, Slater, and MacLachlan (2004) distinguish conceptually and empirically between the responsive and proactive dimensions of customer orientation. A responsive orientation focuses on current customers and their expressed needs. In contrast, a proactive orientation also focuses on discovering and satisfying the latent, unarticulated needs of customers through observation of customers' behavior in context to uncover new market opportunities; on working closely with lead users; on undertaking market experiments to discover future needs; and on cannibalizing sales of existing products. To achieve the highest level of customer orientation, prospectors should practice the behaviors that are inherent in a proactive orientation. Whereas the mean score for competitor orientation is relatively high, the regression and correlation analyses demonstrate that this is a suppressor variable. Thus, we caution prospectors against allocating resources to competitor analysis at the expense of customer analysis and linking.
We characterize the structures of high-performing analyzers as moderately informal and highly decentralized with a moderate number of marketing specialists. Because of the tension between the need for exploration and exploitation (March 1991) in analyzer organizations, a more formal set of policies or guidelines is necessary than it is in prospector organizations. However, as fast followers, rapid decision making requires delegation to the experts in the organization.
Members of the marketing organization demonstrated moderately high levels of customer, competitor, and innovation orientation. We believe that compared with prospectors, analyzers should place a greater emphasis on the responsive dimension of customer orientation, carefully evaluating customers' likes and dislikes regarding prospectors' offerings and introducing improved versions. The regression analysis demonstrates that innovation orientation is a suppressor variable. Thus, we suggest that analyzers place greater emphasis on imitation or incremental innovation than on radical innovation. The mean score for internal/ cost orientation indicates that a high level is appropriate even though the regression coefficient is not significant. Consistent with our previous argument, this suggests that balance should be a goal for analyzer firms.
We characterize the structures of top-performing low-cost defenders as moderately informal and highly decentralized with the large majority of generalists workers. Members of the marketing organization demonstrated the highest levels of internal/cost orientation and competitor orientation of the four strategic groups. However, these firms demonstrated only moderately high levels of customer orientation, suggesting that the responsive form is most appropriate. In contrast to the regression results, the profile of high-performing low-cost defenders suggests that a strong innovation orientation is appropriate. We suggest that efforts at process innovation should dominate efforts at product innovation. The highest-performing low-cost defenders appear to help control costs through their reliance on marketing generalists that are able to address multiple marketing tasks. The data suggest that these generalists are given considerable latitude in determining the best ways to address these tasks. It is also apparent that these firms expend considerable energy monitoring the competition.
The structures of top-performing differentiated defenders are moderately informal with decentralized decision making and a moderate number of marketing specialists. The significant difference between low-cost defenders and differentiated defenders with respect to marketing organizational structure is the differentiated defenders' significantly greater reliance on marketing specialists. The most surprising finding in the examination of the profiles is that members of the marketing organization demonstrated the lowest levels (though still moderately high) of customer orientation and competitor orientation. On the basis of the regression analysis, we anticipated that the highest-performing differentiated defenders would have high levels of reported customer orientation. In contrast, these firms place the greatest emphasis on internal/cost orientation; only low-cost defenders are rated higher. Nevertheless, the regression results indicate that higher levels of customer orientation are strongly associated with superior firm performance among this group.
Although this study uses a standard research design, it is not without limitations. Having collected data from only companies with at least 500 or more employees, our ability to generalize the reported results to smaller companies is restricted. However, it may be that within small companies, structural issues of formalization, decentralization, and specialization are simply not relevant when attempting to explain marketing effectiveness and/or firm performance. Indeed, Mintzberg (1979) suggests that simple structures are characterized by centralization and informality because direct supervision is the key coordination mechanism in this type of organization. In addition, we recognize that there are two other limitations inherent in studies of this type. First, this study uses a cross-sectional design; thus, inferences about causality should not be made. Second, we use a single respondent from each organization. Although we find no evidence of common respondent bias, use of multiple raters may enhance the reliability of our measures (Huber and Power 1985).
Despite the preceding limitations, we believe that our findings demonstrate that firms should tailor their marketing organization and strategic behaviors to complement the requirements of their business strategy. However, it is also clear that the marketing discipline has much to do before it can claim to have thoroughly assessed its role in strategy implementation to the extent called for by Walker and Ruekert (1987, pp. 18-19). They suggest examining marketing's role in the implementation of strategy at four levels within the corporation: ( 1) corporate-business unit relationships, ( 2) interfunctional structure and processes, ( 3) marketing policies and processes, and ( 4) personal characteristics of marketing personnel.
Of the four levels of analysis related to strategy implementation, only the third (i.e., the role of marketing policies and processes) has received significant attention in the strategic marketing literature (e.g., Conant, Mokwa, and Varadarajan 1990; Matsuno and Mentzer 2000; McDaniel and Kolari 1987; McKee, Varadarajan, and Pride 1989; Slater and Narver 1993; Slater and Olson 2000, 2001; Vorhies and Morgan 2003). Despite this body of research, there are many marketing-business strategy relationships that have not been adequately examined. For example, the relative importance of identifying and targeting alternative market segments during the process of formulating alternative business strategies has yet to be examined. Even within the context of this study, we cannot state which actions a marketing executive should take to create specific structures (e.g., a high versus a moderately informal organization) or behaviors (e.g., a high customer versus moderate customer orientation). Thus, we suggest that researchers should do more to understand the antecedents to the marketing practices and structures that predict performance. This would provide useful guidance to managers and executives.
Marketing's contribution to strategy at the corporate level is underresearched. Marketing is often the tie that binds different business units in the corporate portfolio. Yet the identification of which marketing assets or competencies should be leveraged and which should be maintained as unique to a specific business unit or well-defined group of business units remains largely unexplored in the marketing literature (Varadarajan and Clark 1994).
Interfunctional structures and processes address how work is coordinated among functions in the firm. Although these structures and processes are studied extensively in the product development context (e.g., Griffin and Hauser 1996; Olson, Walker, and Ruekert 1995; Olson et al. 2001), interfunctional coordination has received scant attention in strategy implementation research. This is surprising considering that both Miles and Snow (1978) and Walker and Ruekert (1987) point out the different roles that separate functions play in different business strategies. The identification of how coordination is achieved and how conflict is resolved between groups is central to understanding this.
At the individual level, marketing managers typically play a key role in responding to opportunities and threats that a changing environment poses (Day 1984). Thus, how managers interpret a market situation directly affects the solutions they consider in their respective organizations, the resources they commit to particular projects, and the changes they make in products offered or markets served (White, Varadarajan, and Dacin 2003). A fruitful area for further research is how managers interpret and respond to information that pertains to a strategy type; there is a growing body of evidence that suggests that there are significant differences in the ways individual managers interpret and respond to a situation (e.g., Mullins and Walker 1996). Ultimately, these all are important issues for marketing practitioners and academics to consider.
Conclusion
There is now a substantial body of research that suggests that ( 1) successful business strategy implementation is required for superior performance, ( 2) marketing plays a crucial role in strategy implementation, and ( 3) the role of marketing in implementation is contingent on the specific strategy in use. Thus, we are quite comfortable recommending that managers take the steps described in this and related articles to implement their business strategy. At the same time, we encourage other scholars to continue the important work being done in this area.
The authors gratefully acknowledge the support of the Marketing Science Institute and the helpful comments of Don Lehmann and the three anonymous JM reviewers.
Legend for Chart:
A - Organizational Variable
B - Prospectors
C - Analyzers
D - Low-Cost Defenders
E - Differentiated Defenders
A
B C
D E
Marketing Organization
Structure
Formalization
Negative (none) --
Positive (none) Positive (positive)
Decentralization
Positive (positive) --
Negative (positive) Positive (none)
Specialization
Positive (positive) --
Negative (none) --
Strategic Behavior
Customer orientation
Positive (positive) Positive (positive)
-- Positive (positive)
Competitor orientation
n.m. Positive (positive)
Positive (positive) --
Innovation orientation
Positive (positive) (n.m.)
(n.m.) --
Internal/cost orientation
-- --
Positive (positive) --
Notes: n.m. = not meaningful because of net suppressor effect. Legend for Chart:
B - Mean
C - Standard Deviation
D - Variance Extracted
E - Composite Reliability
F - Factor Loadings(a)
A B C D E F
Market turbulence 4.49 1.27 .53 .69 .59-.84
Technological turbulence 4.63 1.29 .56 .83 .68-.83
Formalization 2.99 1.04 .49 .73 .52-.78
Decentralization 5.25 1.01 .50 .80 .65-.80
Specialization 3.23 1.21 .53 .77 .67-.78
Customer orientation 4.51 1.17 .48 .82 .63-.76
Competitor orientation 4.98 1.05 .51 .90 .61-.84
Innovation orientation 5.11 1.08 .54 .85 .64-.85
Internal/cost orientation 5.92 .91 .71 .87 .48-.97
Performance 4.80 1.49 .71 .88 .70-.94
Fit Statistics
χ² = 1567.65
d.f.= 734
DELTA2 = .94
RNI = .93
CFI = .93
RMSEA = .07
(a) All factor loadings are significant at the p < .01 level. Legend for Chart:
B - MT
C - TT
D - FORM
E - DECENT
F - SPEC
G - CUST
H - COMP
I - INNOV
J - ICO
K - PERF
L - SIZE
A
B C D E
F G H I
J K L
Market turbulence (MT)
1.00 .31 .00 .00
.01 .05 .02 .05
.01 .01 .00
Technological turbulence (TT)
.56(**) 1.00 .00 .00
.02 .07 .03 .04
.00 .07 .03
Formalization (FORM)
.07 .04 1.00 .25
.00 .02 .03 .03
.00 .01 .00
Decentralization (DECENT)
.07 .01 -.50(**) 1.00
.01 .11 .18 .21
.02 .14 .04
Specialization (SPEC)
.10 .13(*) .02 -.09
1.00 .03 .01 .05
.00 .07 .03
Customer orientation (CUST)
.22(**) .26(**) -.13(*) .33(**)
.18(**) 1.00 .31 .46
.02 .38 .00
Competitor orientation (COMP)
.14(*) .16(*) -.18(**) .43(**)
.08 .56(**) 1.00 .19
.11 .25 .00
Innovation orientation (INNOV)
.22(**) .21(**) -.28(*) .46(**)
.22(**) .68(**) .44(**) 1.00
.04 .20 .04
Internal/cost orientation (ICO)
.08 -.01 .06 .14(*)
-.06 .13 .33(**) .21(**)
1.00 .02 .01
Performance (PERF)
.09 .26(**) -.11 .37(**)
.26(**) .62(**) .50(**) .45(**)
.15(*) 1.00 .00
Strategic business unit size (SIZE)
-.00 .17 -.03 -.21
-.16 .02 -.04 -.19
-.10 -.06 1.00
(*) p < .05.
(**) p < .01.
Notes: We report correlations below the diagonal and shared
variances above the diagonal. The sample size is n = 228 for all
correlations except for strategic business unit size, for which
the sample size is n = 45. Legend for Chart:
A - Predictor Variables
B - Prospectors (n = 63)
C - Analyzers (n = 45)
D - Low-Cost Defenders (n = 44)
E - Differentiated Defenders (n = 64)
A B C
D E
Business Strategies
Step 1
Market turbulence .27(**) -.18
-.24 .09
Technological turbulence .16 .52(*)
.13 .18
R² .13 .25
.04 .07
Adjusted R² .11 .22
.00 .04
F-value 4.64(***) 7.01(*)
.90 2.16
Step 2
Market turbulence .20(**) -.29(***)
-.35(***) .06
Technological turbulence .05 .57(*)
.20 .15
Formalization .02 -.02
.34(***) .17
Decentralization .35(*) .42(*)
.70(*) .30(**)
Specialization .48(*) .24(**)
-.17 .15
R² .45 .44
.50 .13
Adjusted R² .40 .37
.43 .06
F-value 9.34(*) 6.19(*)
7.52(*) 1.80
ΔR² (from Step 2 to 3) .32(*) .19(*)
.46(*) .06
Step 3
Market turbulence .22(**) -.03
-.21(***) -.17
Technological turbulence -.00 .21(**)
.31(*) .09
Formalization .04 -.13
.12 .42(***)
Decentralization .28(***) .15
.33(***) .26
Specialization .29(*) .05
-.01 -.01
Customer orientation .27(***) .32(***)
.23 .45(*)
Competitor orientation -.30(*) .52(*)
.42(***) .09
Innovation orientation .27(***) -.19(**)
-.33(***) .18
Internal/cost orientation .00 -.01
.37(*) .06
R² .60 .75
.83 .38
Adjusted R² .53 .69
.78 .27
F-value 8.86(*) 11.77(*)
17.96(*) 3.62(*)
ΔR² (from Step 2 to 3) .15(*) .31(*)
.33(*) .24(*)
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: Because we collected the data for this study using a
cross-sectional design with key informant's self-report
measures, CMV has the potential to affect relationships among
the constructs. We used the CMV testing technique that Netemeyer
and colleagues (1997) suggest, and the results show the presence
of some method bias, using a χ2difference test
for each of the four strategy groups (p < .05) (cf. Hewett and
Bearden 2001; Netemeyer et al. 1997). However, regarding the
specific relationships, the results show only three significant
differences when we estimate the "same-source" factor loadings
freely versus when we set them to zero: (1) The control variable
of technological turbulence-to-performance path among low-cost
defenders was attenuated to nonsignificance when we accounted
for CMV, (2) the path between competitor orientation and
performance among prospectors was stronger when we accounted for
CMV, and (3) the path between innovation orientation and
performance among analyzers was stronger when we accounted for
CMV. The strengths of the remaining significant hypothesized
paths were consistent with our regression findings, even in the
presence of CMV. Legend for Chart:
A - Organizational Variable
B - Prospectors (n = 20)
C - Analyzers (n = 14)
D - Low-Cost Defenders (n = 16)
E - Differentiated Defenders (n = 20)
A B C
D E
Marketing Organization Structure Variables
Formalization 2.5 (1.01) 3.1 (.91)
3.1 (.83) 2.9 (.65)
Decentralization 5.4 (.68) 5.8 (.64)
5.8 (.78) 5.5 (.49)
Specialization 4.8 (.95) 3.3 (1.71)
2.1 (.59) 3.5 (1.05)
Marketing Organization Behavior Variables
Customer orientation 5.7 (.57) 5.4 (.63)
5.1 (1.02) 4.9 (.66)
Competitor orientation 5.5 (.80) 5.6 (.57)
6.0 (.84) 5.2 (.81)
Innovation orientation 6.1 (.55) 5.2 (.53)
5.5 (1.06) 5.4 (.60)
Internal/cost orientation 5.8 (1.01) 6.0 (.69)
6.7 (.34) 6.2 (.93)DIAGRAM: FIGURE 1 A Model of the Performance Implications of Fit Among Business Strategy, Marketing Organization Structure, and Strategic Behavior
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MEASUREMENT APPENDIX
Market Turbulence (based on Jaworski and Kohli 1993)
•In our business, customers' product preferences change quite a bit over time.
•Our customers tend to look for new products or services to satisfy their needs.
Technological Turbulence (based on Jaworski and Kohli 1993)
•The technological sophistication of products in this industry is changing rapidly.
•Process technology in this industry is relatively stable.(a)
•Technological change provides big opportunities in our industry.
•Many new product ideas have been made possible by technological advances in our industry.
•Technological developments in our industry are relatively minor.
Formalization (based on Walker and Ruekert 1987)
•There is little action taken unless the decision fits with standard operating procedure.
•Individuals in the marketing organization frequently refer to it as a "bureaucracy."
•If employees wish to make their own decisions, they are quickly referred to a policy manual.
Decentralization (based on Menon et al. 1999)
•In this marketing organization, decisions tend to be made at a high level. (R)(a)
•The individual decision maker has wide latitude in the choice of means to accomplish goals.(a)
•Managers are allowed flexibility in getting work done.
•A person who wants to make his own decision would quickly be discouraged. (R)
•Even small matters are referred to someone higher in the marketing organization for a decision. (R)
•Many important decisions are made locally rather than centrally.(a)
•Middle-and lower-level managers have substantial autonomy.
Specialization (based on Walker and Ruekert 1987)
•Our organization has a large number of "specialist" marketing employees who direct their efforts to a relatively narrowly defined set of activities.
•Most of our employees are generalists who perform a wide variety of marketing tasks. (R)
•We expect our marketing employees to be experts in their areas of responsibility.
Customer Orientation (based on Narver, Slater, and MacLachlan 2004)
•We continuously try to discover additional needs of our customers of which they are unaware.
•We incorporate solutions to unarticulated customer needs in our new products and services.
•We brainstorm on how customers use our products and services.
•We innovate even at the risk of making our own products obsolete.
•We work closely with lead users who try to recognize customer needs months or even years before the majority of the market may recognize them.
Competitor Orientation (based on Narver and Slater 1990; Porter 1980)
•Employees throughout the organization share information concerning competitor's activities.
•We rapidly respond to competitive actions that threaten us.
•Top management regularly discusses competitor's strengths and weaknesses.
•We target customers where we have an opportunity for competitive advantage.
•Our salespeople regularly collect information concerning competitor's activities.
•We diagnose competitor's goals.
•We track the performance of key competitors.
•We identify the areas where the key competitors have succeeded or failed.
•We evaluate the strengths and weaknesses of key competitors.
•We look for market opportunities that do not threaten competitors.(a)
•We attempt to identify competitors' assumptions about themselves or about our industry.(a)
Innovation Orientation (based on Hurley and Hult 1998)
•Technical innovation based on research results is readily accepted.
•Management actively seeks innovative ideas.
•Innovation is readily accepted in program/project management.
•Innovation in our organization is perceived as too risky and is resisted. (R)
Internal Operations/Cost Orientation (based on Homburg Workman, and Krohmer 1999; Kotha and Vadlamani 1995)
•Improving the operating efficiency of the business is a top priority.
•We have a continuing overriding concern for operating cost reduction.
•We continuously seek to improve production processes so that we can lower costs.
•Achievement of economies of scale or scope are important elements of our strategy.(a)
•We closely monitor the effectiveness of key business processes.(a)
Overall Firm Performance (based on Jaworski and Kohli 1993)
•The overall performance of the business met expectations last year.
•The overall performance of the business last year exceeded that of our major competitors.
•Top management was very satisfied with the overall performance of the business last year.
Strategy Types (based on Slater and Olson 2000)
•Prospectors: These firms are frequently the first-to-market with new product or service concepts. They do not hesitate to enter new market segments in which there appears to be an opportunity. These firms concentrate on offering products that push performance boundaries. Their proposition is an offer of the most innovative product, whether it is based on substantial performance improvement or cost reduction.
•Analyzers: These firms are seldom first-in with new products or services or first to enter emerging market segments. However, by monitoring market activity, they can be early followers with a better targeting strategy, increased customer benefits, or lower total costs.
•Low-cost defenders: These firms attempt to maintain a relatively stable domain by aggressively protecting their product market position. They rarely are at the forefront of product or service development; instead, they focus on producing goods or services as efficiently as possible. In general, these firms focus on increasing share in existing markets by providing products at the best prices.
•Differentiated defenders: These firms attempt to maintain a relatively stable domain by aggressively protecting their product-market position. They rarely are at the forefront of product or service development; instead, they focus on providing superior service and/or product quality. Their prices are typically higher than the industry average.
•Reactors: These firms do not seem to have a consistent product-market strategy. They primarily act in response to competitive or other market pressures in the short term.
(a)We deleted the indicator in the measurement purification process. Notes: (R) = reverse-coded items.
~~~~~~~~
By Eric M. Olson; Stanley F. Slater and G. Tomas M. Hult
Eric M. Olson is Associate Dean and Professor of Marketing and Strategic Management, College of Business and Administration, University of Colorado-Colorado Springs.
Stanley F. Slater is Charles and Gwen Lillis Professor of Business Administration, College of Business, Colorado State University.
G. Tomas M. Hult is Director of the Center for International Business Education and Research and Professor of Marketing and Supply Chain Management, Eli Broad Graduate School of Management, Michigan State University.
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Record: 178- The Price Is Unfair! A Conceptual Framework of Price Fairness Perceptions. By: Xia, Lan; Monroe, Kent B.; Cox, Jennifer L. Journal of Marketing. Oct2004, Vol. 68 Issue 4, p1-15. 15p. 1 Diagram, 1 Chart. DOI: 10.1509/jmkg.68.4.1.42733.
- Database:
- Business Source Complete
The Price Is Unfair! A Conceptual Framework of Price
Fairness Perceptions
Recent news coverage on pricing portrays the importance of price fairness. This article conceptually integrates the theoretical foundations of fairness perceptions and summarizes empirical findings on price fairness. The authors identify research issues and gaps in existing knowledge on buyers' perceptions of price fairness. The article concludes with guidelines for managerial practice.
The issue of price fairness has become newsworthy as concerns about gasoline prices, prescription drug prices, physicians' retainer fees, smart vending machines, hidden fees and charges, or Amazon.com's dynamic pricing test have become public knowledge. The uproar that occurred when an Amazon.com customer discovered that the price of same-title DVDs differed across purchase occasions was a public relations nightmare for the firm (Adamy 2000). This example shows that both the price offered and the rationale for offering a certain price may lead to perceptions of price unfairness. Perceptions of price unfairness may lead to negative consequences for the seller, including buyers leaving the exchange relationship, spreading negative information, or engaging in other behaviors that damage the seller (e.g., Campbell 1999).
Why do consumers at times believe that they are being treated unfairly? Given increasing public concern, it seems appropriate to explore further the theoretical bases and empirical findings to clarify what is known about the causes of perceived price unfairness and how the perceptions influence customers' behaviors. Various conceptualizations have been developed and adapted to explain the phenomenon of fairness. However, each approach tends to address a specific reason for price fairness. For example, the dual entitlement principle emphasizes the influence of supply and demand changes and the sellers' profit orientation (Kahneman, Knetsch, and Thaler 1986b). Equity theory and distributive justice emphasize the importance of equality of outcomes between two parties in an exchange (Adams 1965; Homans 1961). In contrast, procedural justice focuses on the influence of the underlying procedures used to determine the outcomes on fairness perceptions (Thibaut and Walker 1975). In this article, we present a conceptual framework for price fairness that integrates the conceptualizations and organizes existing price fairness research. We then use the framework to identify gaps in existing research and to offer guidance for further research. As we proceed, we develop a set of propositions for new research. We conclude with some practical prescriptions for pricing managers.
Over the years, researchers have developed and adapted various theories to obtain an understanding of when and how buyers form price fairness judgments (see the Appendix). Figure 1 illustrates our conceptual framework, the rationale for which we develop next. We begin by discussing the concept of price fairness. Then, we discuss various factors that influence price fairness perceptions at the transaction level. Finally, we discuss buyers' behavioral reactions to sellers when unfair price perceptions occur.
The Concept of Price Fairness
Previously, fairness has been defined as a judgment of whether an outcome and/or the process to reach an outcome are reasonable, acceptable, or just (e.g., Bolton, Warlop, and Alba 2003). The cognitive aspect of this definition indicates that price fairness judgments involve a comparison of a price or procedure with a pertinent standard, reference, or norm. Nevertheless, to develop the conceptual meaning of fairness, we need to make several clarifications about this construct. First, fairness and unfairness may be conceptually different constructs. It is possible to be clear about one without having clarity about the other (Finkel 2001). Notions of unfairness are typically clearer, sharper, and more concrete than notions of fairness. People know what is unfair when they see or experience it, but it is difficult to articulate what is fair.
Second, all price evaluations, including fairness assessments, are comparative. Both equity theory and the theory of distributive justice suggest that perceptions of fairness are induced when a person compares an outcome (e.g., input and output ratio) with a comparative other's outcome. The principle of distributive justice maintains that people, in an exchange relationship with others, are entitled to receive a reward that is proportional to what they have invested in the relationship (Homans 1961). Equity theory broadens this perspective to include various comparative others that may influence the perceived fairness of an exchange relationship (Adams 1965). A reference other may be "another person, a class of people, an organization, or the individual himself relative to his experiences from an earlier point in time" (Jacoby 1976, p. 1053). Indeed, social comparison processes are central to most theories of justice and outcome satisfaction (Major and Testa 1989). In the context of price fairness, the outcomes to be compared are prices. When the price being judged differs from the price in the reference transaction, the price difference may induce an unfairness perception. Such a price comparison is a necessary but not sufficient condition for price unfairness perceptions to occur.
It should be noted that price comparisons can be explicit as well as implicit. In explicit comparisons, people compare one price with another price or with a range of prices. For example, a consumer may claim, "I paid more than another customer did," which is a comparison between two price points, or "I paid more than I used to," which is a comparison between a price point and a price range. However, the comparison may not necessarily be explicitly stated. For example, senior citizens may claim that a price is unfair. Although this judgment seems to be based on a single price, it nevertheless is an implicit comparison to an unspecified but expected lower price that they believe they are entitled to because of their limited fixed income.
Price comparisons lead consumers to one of three types of judgments: equality, advantaged inequality, or disadvantaged inequality. A perception of price equality normally does not trigger a fairness perception, or if one is triggered, it may lead to perceived fairness. A perception of price inequality may lead to a judgment either that the price is less fair than the equal prices situation or that it is unfair.
Third, a price fairness judgment is subjective and usually is studied from the buyer's perspective. Therefore, the judgment tends to be biased by the buyer's self-interest; that is, the buyer tries to maximize his or her own outcome (i.e., tries to pay a lower price) compared with that of the other party (Oliver and Swan 1989a). Thus, the judgment and feelings associated with advantaged and disadvantaged price inequality are different. Consequently, perceived unfairness is less severe when the inequality is to the buyer's advantage than when it is to the buyer's disadvantage. That is, for an equivalent magnitude of price inequality, we expect to observe a smaller degree of perceived unfairness when the inequality is to the buyer's advantage than when it is to the buyer's disadvantage (Ordóñez, Connolly, and Coughlan 2000). Indeed, Martins (1995) finds that the perceived fairness effect of a comparable other buyer paying less is stronger than when the comparable other pays more.
Fourth, previous research has concentrated on the cognitive aspect of unfairness perceptions. We propose that affect is an important element that accompanies the cognition of price equality or inequality. A buyer may have feelings of unease or guilt when the inequality is to his or her advantage but feelings of anger or outrage when the inequality is to his or her disadvantage. These emotions may occur concurrently with the unfair cognitions, or arguably they may even precede such cognitions (Campbell 2004). Severe unfairness perceptions "typically come with heat and passion, anger, and outrage; and they insistently press for action or redress" (Finkel 2001, p. 57). This strong negative emotion is an element that distinguishes unfairness either from fairness or from less fairness. In this article, we add affect as an important element of price fairness perceptions.
Fifth, an unfairness perception and potential negative emotions usually are directed toward the party that is perceived as having caused the "unfair" situation. For price unfairness, the target of the perception and the emotions is usually the seller. Thus, the actions that buyers take when they perceive that prices are unfair are usually directed toward the seller rather than toward a comparative other buyer or the product involved in the transaction. Finally, fairness is different from satisfaction, though research has shown that the two concepts are highly correlated and are sometimes used interchangeably (Ordóñez, Connolly, and Couglan 2000). In this article, we define price fairness as a consumer's assessment and associated emotions of whether the difference (or lack of difference) between a seller's price and the price of a comparative other party is reasonable, acceptable, or justifiable.
Factors That Influence Unfairness Price Perceptions
Various factors may influence unfairness price perceptions. In this section, we summarize the potential factors into four groups. The factors vary in terms of relevancy and immediacy to a specific comparative transaction. The first group of factors includes the variables that specify the context of the comparative transactions. We have indicated that price comparisons, whether explicit or implicit, are a necessary but not sufficient condition for price fairness perceptions to occur. Although both distributive justice and equity theory use buyer and seller input and output ratios as comparatives, consumers usually do not know either the seller's cost structure or other pertinent information to determine the seller's input accurately (Bolton, Warlop, and Alba 2003). Thus, a price fairness judgment most likely is based on comparative transactions that involve different parties. When perceived price discrepancies occur, the degree of similarity between the transactions is an important element of price fairness judgments. Moreover, a fairness judgment also depends on the comparative parties involved in the transactions.
Second, in addition to information that establishes the relevant context for price fairness judgments, procedure justice theory, equity theory, and the principle of dual entitlement all indicate that information that provides reasons why a certain price is set may influence perceptions of price fairness. Previous research has shown that such information may include procedures or processes that lead to the observed prices. For example, a price increase may be caused by an increase in costs. In addition, the type of cost and whether sellers have control over the costs may influence the degree of perceived unfairness. Third, consumers may consider more than a particular transaction and make inferences based on their previous experiences. For example, a consumer who has had a good experience with a seller during repeated transactions may assume that a price increase occurs for legitimate reasons when the reason for the price increase is actually unknown. Fourth, consumers may also rely on their general knowledge or beliefs about sellers' practices to adjust their judgments of price fairness.
These four groups of influencing factors vary in their relative scope. Transaction similarity and choice of comparative party set an immediate context for the comparative transactions. Cost-profit distributions and consumer attributions are specific to a transaction (i.e., reasons for a specific price). Then, such a transaction can be considered in a broader context of buyer-seller relationships that are based on repeated transactions. Trust is the major concept in buyer-seller relationships, and we propose that it influences fairness perceptions. Finally, we place price fairness judgments in a still broader social context and suggest that social norms and consumers' metaknowledge of the marketplace also influence price fairness judgments. Most previous research has concentrated on cost-profit distributions or attributions, and we summarize previous research in that area. We further offer new propositions in the other areas.
Transaction similarity and choice of comparative other parties. Although social comparison research has focused on the similarity between comparative parties (Major 1994), we extend the concept to include all aspects of the two transactions. An economic transaction involves the exchange of a given product at a certain location for an agreed-on amount of money with specified terms between at least two parties. That is, transactions may vary in several ways. Transactions may occur at different times. Products may be the same type but with different brand names or with the same brand name but different models. The same product may be sold in a department store rather than a discount store or in two different department stores. Different terms might accompany the transactions, such as a seller's price promotion or a buyer's coupon redemption. Finally, characteristics of the parties involved also contribute to the degree of similarity between the two transactions. When another customer is the other party involved in the comparison, a person similar in age to the buyer is more comparable than a person who belongs to a different age group (e.g., child, student, senior citizen), which might be entitled to different prices (Martins 1995). Such characteristics are an integral part of the comparison, and differences in the characteristics decrease transaction similarity.
Social comparison research has reported a similarity bias, demonstrating that people tend to pay attention to the similarity between the two parties or entities being compared. Observable similarities between the two comparison entities induce people to access information that supports the similarities selectively, which leads to an assimilation effect (Mussweiler 2003). Such an assimilation effect with respect to the involved comparative parties enhances the saliency of the outcome differences that lead to a strong feeling of entitlement (Major 1994; Major and Testa 1989). However, when the dissimilarity between the two entities is obvious, people selectively access information that supports the dissimilarities, which leads to a contrast effect (Mussweiler 2003). Such a contrast effect leads to judgments that the comparative transactions or parties are not similar, which offers a natural explanation for the perceived price differences.
Although this similarity bias has been discovered in person-to-person comparisons, we argue that the same principle applies to comparisons between two transactions. For price comparisons, when the degree of similarity between the comparative transactions is relatively high, buyers have little differential information to explain a price discrepancy. Thus, the assimilation effect leads consumers to expect or believe that they are entitled to equal prices, and they are likely to judge the price discrepancy as unfair. However, when the degree of similarity between the transactions is low, the contrast between the two transactions explains the price difference. As a result, consumers will judge the price discrepancy as fair or less unfair. Indeed, a fairness judgment may not even occur if consumers consider the two transactions incomparable.
P[sub1]: Given a perceived price discrepancy between two transactions, a high degree of transaction similarity leads to a high perception of price unfairness.
Many aspects of a transaction influence the similarity between two transactions and consumers' consequent price fairness perceptions. Whether and how one element (e.g., product differences) has a greater effect than another element (e.g., store differences) is an empirical issue that needs research. It has been shown that observable product differences naturally lead to quality inferences and cost attributions (Bolton, Warlop, and Alba 2003). Such inferences are likely to decrease the degree of similarity. Because the product or service is the focus of a transaction and has a direct effect on consumers' perceived value, we expect that product differences have the greatest effect on the degree of similarity and thus on price fairness perceptions.
In an application of equity theory to price comparisons, there are three types of comparative reference parties that consumers may use: self, other customers, or different organizations (e.g., stores). Indeed, each type of references has been shown to influence price fairness perceptions (Bolton, Warlop, and Alba 2003). Here, we single out the difference between self/self and self/other-customer comparisons and suggest that price fairness judgments also depend on the source of comparison as well as transaction similarity. Although self is more similar to the individual customer than is another customer, a self/self comparison may not necessarily have a greater effect on price fairness judgments than a comparison with another customer. Therefore, we focus on the relative effects of a self-comparison compared with those of other-customer comparison on perceptions of price fairness. Which reference has a greater effect on price fairness perceptions? If multiple references are available, how do customers choose one reference over another, or what is the combined effect?
Social comparison theory has identified "similar others" as the most important comparison target because of its salience (Major 1994; Wood 1989). When people estimate their own entitlement, they are most likely to choose others who are similar to themselves as the comparative other party (Wood 1989). Only when external comparison others are unavailable or not salient in the environment, or when people regard them as too dissimilar, will they make estimates of entitlement on the basis of intrapersonal (self/self) comparisons (Major 1994). In addition, comparisons with others produce a greater effect on feelings of entitlement than do self-comparisons. Research shows that social comparisons (i.e., comparisons with others) explain more variance in satisfaction than do people's individual expected outcomes (Major and Testa 1989). Moreover, only social comparisons have a significant relationship with fairness judgments (Austin, McGinn, and Susmilch 1980).
For price comparisons, we propose that given the same transaction characteristics, the other-customer comparison has the greatest effect on perceived price unfairness because of the salience of such a comparison (Major and Testa 1989). Our early research shows that given a price discrepancy, a comparison with a similar other customer leads to higher unfair perceptions. Moreover, when there is no price discrepancy, a comparison with a similar other customer leads to higher fair perceptions than does the self/self comparison (Xia and Monroe 2004).
However, similar others are not always available as comparative references, and self/self comparisons are also common. In self/self comparisons, people typically believe that they deserve the same treatment or outcomes that they have previously received. Overall, the choice of a comparative other party depends on both immediate availability and salience (Major 1994).
P[sub2]: Given a perceived price discrepancy and two transactions with similar characteristics, the other-(similar) customer comparison, when available, has a greater effect on price unfairness judgments than does the buyer's self-reference.
Furthermore, little research has examined the effect of multiple comparative parties. Ordóñez, Connolly, and Coughlan (2000) examine the effect of multiple external references and suggest that instead of integrating all the references, people tend to compare with each reference independently. Consistent with prospect theory, Ordóñez, Connolly, and Coughlan find that the pain of a disadvantaged inequality relative to one reference is greater than the pleasure of an advantaged inequality relative to another reference. Thus, we suggest that when both self and other customers are available as references, the reference that produces a disadvantaged inequality for the buyer has a greater impact as a result of the "loss-looms-larger" effect. However, when the two references both are advantaged or disadvantaged, the similar other-customer comparative reference has a greater effect on price unfairness perceptions than do consumers' self-comparisons.
The cost-profit distribution and attributions for the inequality. A perception that a price is unfair results not only from a perceived higher price but also from consumers' understanding of why the higher price was set. The seller's cost plays an important role in buyers' assessing of whether a price or a price increase is acceptable or fair (Bolton, Warlop, and Alba 2003). When buyers believe that sellers have increased prices to take advantage of an increase in demand or a scarcity of supply, without a corresponding increase in costs, they will perceive the new higher prices as unfair (Frey and Pommerehne 1993; Kahneman, Knetsch, and Thaler 1986a, b; Urbany, Madden, and Dickson 1989). However, an unavoidable increase in a firm's costs may make the price increase acceptable (Kahneman, Knetsch, and Thaler 1986a). Buyers will perceive a disadvantaged price inequality as more unfair if they perceive that the seller profits from the buyer's loss. For example, consumers consider a price increase for snow shovels the morning after a snowstorm unfair, but they consider an increase in grocery prices after an equivalent increase in wholesale prices not unfair (Frey and Pommerehne 1993; Kahneman, Knetsch, and Thaler 1986b).
Making the seller's costs salient reduces people's estimate of a firm's profit margin, thereby decreasing their perceptions of price unfairness (Bolton, Warlop, and Alba 2003). However, not all costs are equally legitimate (Bolton, Warlop, and Alba 2003). Price increases that result from managerially influenced cost increases are perceived as less fair than are externally caused cost increases (Vaidyanathan and Aggarwal 2003). Therefore, in addition to considering the seller's cost-price (profits) relationship, consumers may make attributions as to who is responsible for such an outcome, especially when there is no clear information on the seller's actual costs and profits.
Although attribution theory is not a theory of fairness per se, it provides a basis for how people rationalize an ambiguous situation (Weiner 1985). When it is ambiguous as to why an unexpected price occurred and who is responsible for it, an explanation provides people with feelings of control over their environment and serves as an adaptive function (Folkes 1990). In general, people are less motivated to seek attributions when they perceive the inequality as to their advantage than when they perceive it as to their disadvantage (Weiner 1985).
As we discussed previously, a perception of price unfairness, especially the emotional aspect of it, typically is targeted toward the seller. Therefore, buyers seek information to determine whether the seller is responsible for the situation of inequality. It has been shown that consumers respond more unfavorably if a perceived price inequality is due to a firm's volitional intentions or actions (internal locus of causality and controllability) (Bolton, Warlop, and Alba 2003; Vaidyanathan and Aggarwal 2003). Thus, we argue that when buyers seek attributions to determine whether the seller is responsible for the price inequality, they are strict with the seller out of their self-interest. That is, the seller is responsible for the perceived inequitable price unless there is evidence that shows otherwise. Therefore, if buyers perceive the seller as having control over the situation, or if the cause of the price differential is internal to the seller, then the seller is responsible. However, buyers may accept a firm's goodwill motive even when the higher price is not due to cost-related factors and is controlled by the company (Campbell 1999).
Buyer-seller relationship and trust. Moving beyond transaction-specific information of cost-profit distributions and attributions, we now examine whether a buyer-seller relationship that is built on repeated transactions over time influences fairness perceptions. A construct that is important for understanding the status of a buyer-seller relationship is trust (Morgan and Hunt 1994; Sirdeshmukh, Singh, and Sabol 2002). Trust is a multidimensional construct, defined as "the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party" (Mayer, Davis, and Schoorman 1995, p. 712). Trust can be conceptualized as consisting of three dimensions: ability (i.e., skills and competencies of the trustee), benevolence (i.e., the extent to which a trustee is believed to want to do good to the truster), and integrity (i.e., the truster's perception that the trustee is honest and fulfills its promises) (Mayer, Davis, and Schoorman 1995). These dimensions are closely related and are necessary for the formation of overall trust.
We suggest that buyers' perceptions of price fairness are influenced by different dimensions of trust associated with the relationship. In the context of buyer-seller relationships, it is possible that buyers emphasize different dimensions of trust at different relationship stages. Lewicki and Bunker (1995) suggest that at an early stage of a buyer-seller relationship, the two parties are regulated by the potential benefits of their promises and/or by the costs of cheating (i.e., calculus-based trust). Over repeated interactions, the relationship develops, and the two parties begin to know each other (i.e., knowledge-based trust). At this stage, predictability is key to the relationship, and each party anticipates the actions of the other party. When the relationship is fully developed, trust is based on a full internalization of the other party's desires and intentions (i.e., identification-based trust). At this stage, the two parties "effectively understand, agree with, and endorse each other's wants" (Lewicki and Bunker 1995, p. 151). A party can be confident that its interests are fully protected by the other party. We propose that trust has a different meaning (i.e., emphasis on different dimensions) at different stages of a buyerseller relationship. Thus, the nature of the influence of trust on price fairness perceptions may depend on the specific stage of a buyer-seller relationship.
On initial contact with the seller, buyers have no previous transaction experience with the seller. As a result, they may base their trust on the seller's reputation and contextual cues, such as store display and product assortment, or the seller's publicized goodwill to assess the cost-benefit of transacting with this seller (i.e., calculus-based). The initial trust may not necessarily be low because a buyer may choose to trust a seller until something goes wrong (McKnight, Cummings, and Chervany 1998). At this initial stage of the relationship, the important dimension of trust may be competence, because buyers may be more concerned about various aspects of a transaction, such as product quality, delivery, and return policy. For example, the seller's reputation may serve as a cue for the buyer to form initial trust. A good reputation signals competence of the seller or the seller's goodwill and serves as a buffer to buyers' potential negative attributions for a price discrepancy (Campbell 1999). A seller's good reputation may make an equal or advantaged unequal price situation seem more fair and decrease buyers' price unfairness perceptions when a disadvantaged price inequality occurs.
As repeated transactions between buyers and the seller occur, buyers gain more information about the seller's trustworthiness. Previous transaction experiences play an important role in determining trust. Thus, trust becomes more "interpersonal" and is more knowledge-based. Moreover, buyers begin to consider themselves loyal customers, and the relationship becomes an important basis for continued transactions with the seller. At this stage, the buyer knows the competence of the seller, so there is more emphasis on the benevolence dimension than on the competence dimension. The buyer is more likely to take the seller's actions "personally." Thus, for customers who believe that they have a close relationship with the seller, when the price is as expected or lower, they may perceive it as a benefit of the relationship. However, when loyal buyers pay a price that is higher than their comparative standard, they may judge the seller as having betrayed their good relationship (Sirdeshmukh, Singh, and Sabol 2002), leading to a more unfair price perception. To illustrate, Huppertz, Arenson, and Evans (1978) find that when perceived price and service inequity are high, buyers judge the situation as less fair when they have a close and frequent exchange relationship with the seller than when the exchanges are infrequent. In the context of online dynamic pricing, Garbarino and Lee (2003) find that a comparatively higher price decreases the benevolence dimension of trust but has no significant effect on the competence dimension.
Finally, when buyers and sellers enjoy a close relationship that is truly based on identification, they share each other's values, desires, and intentions. In this case, level of trust may be high on all dimensions, and "faith" is an important element in such a relationship (Rempel, Holmes, and Zanna 1985). Because of the strong attachment between the two parties, the relationship may sustain rather strong challenges (Lewicki and Bunker 1995). Therefore, the buyer's overall trust in the seller serves as a buffer to decrease the negative effect of a comparatively disadvantaged price on price unfairness perceptions. However, most business relationships remain at the calculus-or knowledge-based level without developing into an identification-based relationship (Lewicki and Bunker 1995). In summary, the influence of trust on price fairness perceptions depends on the direction of the inequality and the nature of the trust, which varies depending on the stages of the buyer-seller relationship.
P[sub3]: When the comparative outcome is positive or neutral (i.e., advantaged inequitable or equitable prices), trust in the seller has a positive effect on price fairness perceptions.
P[sub4]: When the comparative outcome is negative (i.e., disadvantaged inequitable price), trust in the seller has a U-shaped effect on price fairness perceptions.
Social norms and metaknowledge of the marketplace. Beyond the buyer-seller relationship, consumers may draw on their general knowledge about the marketplace. As Bolton, Warlop, and Alba (2003) suggest, buyers may judge fairness at an aggregate level across a transaction space that consists of multiple dimensions. In addition, buyers' perceptions of price fairness stem both from economic comparisons and from social norm comparisons. Social norms of economic exchange are the understood rules of behavior for both buyers and sellers, and they serve as guides to behaviors of parties in exchanges (Maxwell 1999). Maxwell (1995) demonstrates that, indeed, many price fairness judgments stem from buyers' considerations of how the seller determines price and whether the price is affordable to everyone, particularly in reference to necessities such as pharmaceuticals. Therefore, consumers may also rely on their beliefs about the exchange norms to refine their price fairness judgments.
In addition, because information is more readily available in publications such as Consumer Reports and consumers are able to gain more information from their buying experiences, they develop knowledge of marketers' pricing tactics and of the relative cost-profit composition of a product's price. This metaknowledge, whether accurate or not, guides consumers' fairness judgments (Bolton, Warlop, and Alba 2003). However, the beliefs and metaknowledge may evolve over time (Wright 2002). A norm develops when many people engage in the same behavior regardless of the reason for the initial action (Opp 1982). Similarly, as the dual entitlement principle suggests, stability is the norm. A practice that is initially perceived as unfair may slowly spread and evolve into a new norm that is accepted by most people and is less likely to be perceived as unfair (Kahneman, Knetsch, and Thaler 1986b). For example, as the airlines' practice of dynamic pricing with yield management technology becomes accepted by most consumers, the practice is more likely to be perceived as fair (Kimes 1994). Therefore, perceived unfairness of a price or procedure may decline over time (Kachelmeier, Limberg, and Schadewald 1991). Overall, when and how social norms and consumers' general knowledge influence price fairness should be investigated in further research.
Effects of Buyers' Unfairness Perceptions
Previous research has shown that unfair price perceptions influence customer satisfaction, purchase intentions, and complaints (Campbell 1999; Huppertz, Arenson, and Evans 1978; Martins 1995). We suggest that price fairness perceptions influence assessments of product value and customer satisfaction. In addition, the perceptions generate negative discrete emotions that may vary in intensity and type. These value assessments and negative emotions are mediating variables that influence different behavioral actions, including purchase intentions, complaints, and negative word-of-mouth communications.
Perceived value. An important mediating variable of buyers' purchase intentions is their perceptions of the value of the seller's offering. Buyers' perceptions of value are mental trade-offs of what they believe they gain from a purchase with what they sacrifice by paying the price (Monroe 2003). Research has shown that buyers believe that a perceived unfair price represents a lower value than a financially equivalent fair price (Martins and Monroe 1994). Assuming that there is no perceived difference in quality or benefits received from the product or service, this reduction in perceived value must result from an increase in perceptions of monetary sacrifice (Monroe 2003). Similarly, Sinha and Batra (1999) find that perceived price unfairness increases buyers' price consciousness. Because price-conscious buyers tend to focus on the monetary sacrifice of a price, higher perceived price unfairness increases perceptions of monetary sacrifice. However, it should be recognized that the same asymmetry between advantaged and disadvantaged inequality exists here. Although a disadvantaged price inequality may lower the perceived value of an offer, an advantaged inequality may have no effect or may even decrease perceptions of monetary sacrifice (Martins 1995).
P[sub5]: A perceived disadvantaged price inequality increases perceptions of monetary sacrifice, thereby lowering perceived value of an offer compared with situations of equal prices or advantaged price inequality.
Negative emotions. Research shows that unfair price perceptions lead to dissatisfaction (Oliver and Swan 1989a, b). Dissatisfaction is a negative experience that is correlated with anger (Folkes, Koletsky, and Graham 1987; Storm and Storm 1987). Research also suggests that specific emotions that arise from purchase situations may be more relevant to buyers' complaint behaviors, word-of-mouth communication, switching, and repurchase than are satisfaction or dissatisfaction (Bagozzi, Gopinath, and Nyer 1999). Thus, to examine the affect dimension of price unfairness perceptions, we use a discrete emotions approach rather than dissatisfaction, and we suggest that perceived price unfairness is accompanied by various negative emotions.
An advantaged inequality may lead to feelings of uneasiness or guilt, whereas a disadvantaged inequality may induce disappointment, anger, or outrage (Austin, McGinn, and Susmilch 1980). We suggest that similar feelings occur in the context of price fairness. Emotions that accompany unfairness perceptions may vary in intensity as well as type. Although some emotions, such as uneasiness, may not lead to specific actions, some strong negative emotions, such as anger, may require a person to use coping mechanisms (Bougie, Pieters, and Zeelenberg 2003).
P[sub6]: An advantaged price inequality is associated with feelings of uneasiness or guilt, whereas a disadvantaged price inequality is associated with feelings of disappointment or anger.
These emotions may occur concurrently with or after the cognition of a price inequality, which leads to immediate reactions, or they may occur (or be modified) during value assessments, which lead to more deliberate actions. Aiming to reinstate a price equality condition and coping with the psychological discomfort of perceived unfairness, buyers may initiate actions to compensate themselves for the monetary sacrifices and/or to "vent" their emotions in ways that help them return to a normal emotional state. In the next section, we discuss buyers' reactions when perceived price unfairness occurs. Because the target of buyers' perceptions and emotions is the seller, buyers' reactions have consequences for the seller.
Buyers' Behavioral Reactions
Early price fairness research was motivated by the belief that perceived price unfairness constrains firms' attempts to maximize profitability (Kahneman, Knetsch, and Thaler 1986b). That is, buyers react in ways that produce negative consequences for firms, including lower purchase intentions, complaints, and negative word of mouth (Campbell 1999; Huppertz, Arenson, and Evans 1978; Martins 1995). We believe that when perceptions of unfair prices occur, buyers act to address the two elements of the outcome of their assessments. Therefore, an objective of buyers is to protect themselves financially and to seek monetary compensation. Another objective is to cope with the negative emotions that may have occurred. A perceived large price inequality motivates consumers to seek monetary compensation. In addition, the different types of emotions that arise with perceptions of unfairness induce different actions (Bougie, Pieters, and Zeelenberg 2003; Raghunathan and Pham 1999; Zeelenberg and Pieters 2004). Such responses to perceived unfairness may be viewed as coping mechanisms to restore the desired equitable situation both financially and psychologically. We now outline a set of actions that buyers may take when they perceive prices as fair or unfair. Some actions are taken mainly to address the financial issue, whereas others address the psychological issue.
It is not costless when buyers take actions to cope with a perceived inequitable situation. If they decide to leave the relationship, they may incur switching costs that include time, effort, and even money (Urbany, Madden, and Dickson 1989). In addition, when considering the actions to take, buyers may also estimate their relative power and the likelihood that they will succeed in executing the potential actions. Thus, the cost of action and relative powers between the buyer and seller moderate buyers' potential actions when they face a perceived unfair situation.
No action. In a "no-action" situation, perceived unfairness has no significant influence on buyers' planned transactions with the seller. When buyers are advantaged, the situation does not lead either to lower perceptions of value or to strong negative emotions, though buyers may have feelings of unease or guilt. Research has shown that feelings of guilt may promote a desire to "redistribute" or a "giving" behavior (Walster, Walster, and Berscheid 1978). For example, in the context of price (un)fairness, the target for the potential giving activity, if there is any, may be a charity rather than the seller, because the seller is not the disadvantaged party that suffers. Because the giving action is directed outside the buyer-seller transactions, feelings of guilt lead to no particular action in the buyer-seller relationship.
When buyers are slightly disadvantaged, there may be some decrease in perceived value and feelings of disappointment. If so, buyers either are not motivated to take action or believe it is not worthwhile to take action because of the cost of complaining or switching to another seller (Urbany, Madden, and Dickson 1989). However, although consumers may take no action to change the current or future transaction relationships with the seller, they may still spread negative word of mouth to vent their discomfort or disappointment with the seller (Zeelenberg and Pieters 2004).
Self-protection. When buyers believe that an inequality in an exchange is unacceptable and are upset, disappointed, or regretful (if they believe that there is a better option), they may choose to complain, ask for a refund, spread negative word of mouth, and/or leave the relationship, depending on their assessment of which action is most likely to restore equity with the least cost. In addition, they may search for additional information to assess the potential switching costs or to assess their power to renegotiate with the seller. For example, Bougie, Pieters, and Zeelenberg (2003) find that the experience of dissatisfaction usually evokes thoughts about what buyers missed out on and the need to search for more information to find out who or what is responsible for the event. In terms of actions, buyers tend to make a deliberate judgment about how to act, or they try to devote attention to something else. Therefore, when consumers perceive a price as less fair, they may choose actions to enhance their own benefits and to reduce their perceived monetary sacrifice. When the actions are less obtainable or too costly, they may choose to leave the relationship (Huppertz, Arenson, and Evans 1978). The objective of these actions is essentially for consumers to protect themselves from being taken advantage of in the future. At the same time, spreading negative word of mouth is a low-cost action that helps buyers cope with their negative feelings of disappointment or regret and prevents other customers in their social network from being exploited.
Revenge. When a strong negative emotion, such as anger or outrage, occurs with a perception of price unfairness, customers' leaving the relationship or complaining may not be sufficient to address the perceived inequity. The feeling of anger, which is a distinct emotion from dissatisfaction or disappointment (Bougie, Pieters, and Zeelenberg 2003), typically is associated with perceived unfairness and leads to a tendency toward aggressive behavior. Anger evokes immediate actions with no deliberation of how to act. Studying consumers' reactions to product failures, Folkes (1990) suggests that anger mediates the relationship between the attributions regarding the seller's responsibility and the desire to engage in conflict with the seller. Thus, to cope with anger or outrage, customers may seek revenge. Angry customers want to "get back" at the organizations (Bougie, Pieters, and Zeelenberg 2003). Such actions can even occur at the customer's expense, rather than compensating them for their perceived loss. It has been demonstrated that customers seek revenge for a company's wrongdoing by switching to the company's direct competitor, even when switching is a less-than-optimal choice (Bechwati and Morrin 2003). Although the choice itself seems to be irrational, the psychological benefit of switching helps customers cope with the situation. In addition, when customers become more angry, they are more likely to complain and engage in negative word of mouth and less likely to repatronize the seller (Folkes, Koletsky, and Graham 1987). Although negative word of mouth in no action and self-protection is a mechanism for customers to comfort themselves psychologically, negative word of mouth driven by anger transcends customers' social network and has the objective of damaging the seller. Therefore, additional actions such as reports to the media or legal and regulatory agencies are possible. In summary, we argue that the severity of the perceived inequality and the differences in emotions experienced further induce different actions that customers may take, the objectives of these actions, and the degree of damage inflicted on the seller.
P[sub7]: When buyers perceive a price as less fair as a result of an advantaged inequality, they take no particular actions to change the transactions or relationships with the seller.
P[sub8]: When buyers perceive a price as less fair as a result of a disadvantaged inequality, the value for money is the major driver of their actions. They evaluate the costs of action and inaction and are likely to respond to the situation by either no action or actions that seek mainly monetary compensation.
P[sub9]: When buyers perceive a price as unfair, negative emotions are the major driver of their actions. They are more likely to cope with the negative emotion by spreading negative word of mouth or even by seeking revenge with the goal of harming the seller to "get even" psychologically.
Overall, we have examined the various influencers and consequences of price fairness perceptions. Our framework is not completely inclusive; we consider price fairness in a buyer-seller transaction context and examine it from the buyer's perspective. This perspective does not mean that price fairness cannot be studied from the seller's or third party's perspective or at the group or organizational level. In addition, factors such as buyers' individual characteristics may influence whether customers evoke perceptions of price unfairness under certain circumstances and how they may react to those perceptions.
The importance of price unfairness perceptions and their impact on firms' profitability has long been recognized (Kahneman, Knetsch, and Thaler 1986a, b). Although buyers' perceptions of price unfairness are based on perceived price differences, a goal of fair pricing does not mean a one-price policy for everyone, nor does it mean that customers do not accept price changes or price differences. Indeed, a survey of retail businesses found 12 different customer groups to which price discounts can be offered (Martins 1995). A key question is how to make price differences more acceptable and less likely to evoke unfairness perceptions. We now offer some guidelines for achieving and maintaining perceived fair prices in the context of differential pricing.
Decrease Transaction Similarity
As we conceptualize, when customers perceive two transactions as similar, the effect of observed price differences on perceptions of price unfairness is greater than for other situations. Therefore, perceptions of price unfairness can be mitigated by a decrease in the similarity of the transactions. The practice of yield management sets different prices for seemingly similar products or services, such as a hotel room or an airplane seat, but additional benefits or restrictions are attached to each offer, which makes the products or services less comparable. These restrictions decrease the similarity of the transactions and the attention that customers place on perceived price differences, thereby reducing the likelihood of price unfairness perceptions. Contrary to this principle, Amazon.com charged the same customer a higher price for the same product on the basis of his purchasing history. There was no differentiation between the products or service in the two transactions. As a result, Amazon.com received negative customer and media response when the practice was discovered (Adamy 2000).
We suggest that product differentiation is a dominant factor in decreasing transaction similarity. Consumers infer quality differences when products differ, which helps them attribute the price differences to sellers' cost, thereby reducing perceptions of price unfairness (Bolton, Warlop, and Alba 2003). Therefore, product customization and differentiation help decrease transaction similarity and the likelihood of price unfairness perceptions. Information technology offers firms opportunities to customize their price and products. Price differentiation without corresponding product customization may evoke price unfairness perceptions among consumers.
Anticipate Reactions to Price Differences and Provide Relevant Information
Our review indicates that additional information is helpful for buyers to sort out whether the seller is responsible for the price differences and whether the seller benefits from such differences. Anticipating that buyers will find price discrepancies based on the sellers' pricing strategies and tactics, marketers should proactively provide relevant information to influence buyers' attributions for the price discrepancies.
When buyers are uncertain about product quality, price, and the seller's costs in an exchange relationship, sellers can communicate their costs or inputs to the exchange relationship in several ways. Buyers perceive cost-based pricing rules as fairer than market-based ones (Maxwell 1999); however, consumers have little knowledge of a seller's actual costs and profit margins (Bolton, Warlop, and Alba 2003). Therefore, sellers' making the relevant cost and quality information transparent helps. Considerable amounts of such information are available on various Web sites. For example, marketing communications campaigns that explain the firm's commitment to using top-of-the-line raw materials for its products signal to consumers that the seller's quality and costs are relatively high (Kirmani and Rao 2000).
In addition, although sellers may be unwilling to make their cost structures and margins known to customers, they can switch buyers' attention away from prices to focus on the value that they provide. For example, sellers' emphasis on flexible travel dates, the ability to seek a refund, and friendly cancellation policies communicate the relative value of a comparatively higher airfare. Furthermore, such benefits help reduce the tension of a comparatively higher price when the buyers value the benefits. Buyers are more likely to seek information when price discrepancies occur. Thus, sellers' offering relevant information in advance may decrease the likelihood of severe perceptions of price unfairness (Collie, Bradley, and Sparks 2002).
Manage Customer Relationships
We have conceptualized price fairness in the context of buyer-seller transactions, and we have argued that trust is an important factor that influences perceptions of price fairness. As we argue, trust may have different meanings in different stages of the buyer-seller relationship. First, a seller's building of a good reputation may help build initial trust and attract new customers, and this trust may serve as a buffer that helps decrease negative attributions when price discrepancies occur. For example, as demand increases for products such as building materials after the occurrence of a natural disaster, local retailers often maintain prices for necessity items (Haddock and McChesney 1994). Second, repeat transactions with a seller help build benevolence trust. Therefore, loyal customers focus more on whether sellers care about them. When customers perceive an unfair price, they are likely to perceive it as exploitation and are more likely to punish the seller. To show appreciation to loyal customers, sellers offer various reward mechanisms, such as loyalty programs. Finally, the benefit to sellers of continuously building such good relationships is higher overall trust, which can survive a strong challenge. Although it is important for marketers to attract new customers while maintaining existing profitable customers, we recommend that marketers focus on different needs in trust building and use different communication programs or offer differentiated products to different segments to minimize potential unfavorable price comparisons across groups of customers.
Damage Control When Perceptions of Unfairness Arise
It is important not only to prevent unfair price perceptions but also to control the damage when perceptions of unfairness occur. Our framework suggests that buyers believe that they have made monetary sacrifices and/or have negative emotions when they perceive a price as unfair. Although increased perceived monetary sacrifice induces switching or complaint behaviors, the effect of negative emotions due to price unfairness perceptions has not been studied. We argue that negative emotions accompany a perceived unfair price and that buyers use different repair mechanisms to cope with the increased perceived monetary sacrifice and their negative emotions.
When buyers' major concern is the actual price difference, the seller may control the potential damage by offering a refund, an additional reward (monetary or gift), or another form of compensation. However, when unfairness perceptions are accompanied with strong negative emotions, financial compensation may not be sufficient. The seller needs to offer a venue that allows buyers to "vent" their negative emotions. Negative word of mouth is a common behavior that consumers use to release their disappointment with a transaction. Instead of having consumers spread such negative word of mouth to their social network or beyond, marketers can set up a forum, such as an online discussion board monitored by the firm, to redirect such feelings and to give the firm an opportunity to explain and offer compensation.
In addition, the interaction between the buyers and the seller's representatives is the key to managing angry customers. The desire for vengeance after a dissatisfying experience is influenced by how well consumers are treated during the redress process (Bechwati and Morrin 2003). When treated appropriately (e.g., politely, respectfully), buyers may reinstate their normal emotional state (Bowman and Narayandas 2001; Smith, Bolton, and Wagner 1999). Although buyers may still choose to leave the relationship, they may be less likely to seek revenge, an action that is most damaging to the seller. Thus, sellers need to reach their customers proactively when severe unfair price perceptions arise and to minimize the damages by redressing the situation appropriately. Honest and fair prices and practices can prevent detrimental buyer behavior and harm to the buyer-seller relationship.
Research in the area of price fairness has been sparse until recently. Our framework integrates existing theories of fairness and provides potential directions for further research. First, existing research has used the concept of price fairness without explicitly defining it. We argue that price fairness is a different concept from that of price unfairness. Consumers are clearer and more articulate about what they perceive as unfair prices than they are about fair prices. Indeed, price fairness may not even be an issue until consumers perceive a price as unfair. We have added affect as an important element of the price fairness concept, and we suggest that there are different types of negative emotions associated with price unfairness perceptions. A truly unfair perception is accompanied by strong negative emotions, such as anger and outrage, which may lead to severe actions toward the seller. Using qualitatively different emotions as anchors and the cognition of price inequality, we argue that much of existing empirical research on price unfairness can be labeled "less fairness." Although consumers may believe that it is less fair that a department store sells a similar product at a higher price than a discount store, this "less price fairness" perception may not prevent them from shopping at the department store. Conceptually distinguishing between less fair and unfair and different types of emotions associated with price inequality helps us focus on the real unfair price situations, which have not received much research attention in marketing.
Second, we use similarity and the source of comparison (parties involved in the transactions being compared) as the key concepts of the price comparison process. Although previous price fairness research has recognized that fairness judgments are comparative, both the specifics of what is compared (other than price) and how customers choose a comparison source among various available references have not been studied. Empirical identification of the characteristics of the comparative transactions that lead to greater similarity and thus greater unfairness perceptions will provide ideas on how to control or reduce such perceptions.
Third, we suggest that researchers examine factors that influence price fairness judgments across the spectrum: transaction contextual information, procedure information (e.g., specific attributions), buyer-seller relationships (e.g., different types and dimensions of trust along relationship development), and more generic influences (e.g., social norms, consumer knowledge, individual characteristics). Recent research has focused on the influence of cost-profit distributions and buyers' attributions of the causes of price discrepancies (e.g., Bolton, Warlop, and Alba 2003; Campbell 1999; Vaidyanathan and Aggarwal 2003). We suggest that the influence of the buyer-seller relationship and the different types and dimensions of trust in the different stages of the relationship are worthy and testable factors. In addition, consumers may have different degrees of sensitivity to fairness or equity issues, which provides a potentially interesting covariate for further empirical research (Oliver 1997).
Finally, we have identified potential consequences of perceived fairness or unfairness on both buyers and sellers. For buyers, a perceived less fair or unfair price may lead to lower perceived value and/or negative emotions. These two consequences may require different coping actions, thus leading to different behaviors. It would be informative to test whether and how perceptions of value and negative emotions mediate the relationship between perceived unfairness and the various types of actions that consumers may take. In addition, recent research has pointed out that different types of emotions can be qualitatively different and associated with different thoughts and behavioral reactions. An examination of different emotions that accompany different degrees of perceived price unfairness may enhance the understanding of consumers' potential responses and consequences to the sellers. These consequences and responses provide a basis for broadening the previous research focus beyond buyers' purchase intentions and make important substantive contributions to marketing knowledge.
The authors gratefully acknowledge the support of the anonymous JM reviewers for their helpful suggestions and for their support during the development of this article.
FIGURE 1; A Conceptual Framework of Price Fairness
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Legend for Chart:
A - Author(s)
B - Proposed Theory
C - Study
D - Variables Tested
E - Key Results
A B
C
D
E
Bolton, Warlop, and Fairness judgments
Alba (2003) may be based on
previous prices,
competitor prices, and
profits; attributions
depend on the
difference between
reference point and
price.
Tests reactions to
perceived differences
of historical prices,
relation between
store-price levels,
expected profits,
perceived firm costs,
and profit sources.
Historical prices;
store-price image;
store strategies, risks,
and costs; and
perceived price
fairness.
• People do not have
accurate mental cost
or profit models for
firms.
• Increases in some
firm (fair) costs lead
to increased
perceived fairness;
some costs are unfair
for price increases.
• Price differences are
fairest when
attributed to quality
differences.
Campbell (1999) Inferred motive and a
firm's reputation affect
perceptions of price
fairness and future
shopping intentions.
Tests consumer
reactions to retail
purchasing scenarios;
presents variations in
the seller's intent and
reputation.
Firm's reputation,
inferred motive,
inferred profit,
perceived fairness,
and shopping
intentions.
• Relative profit and
inferred motive
influence fairness
perceptions, which in
turn affect shopping
intentions.
• A firm's reputation
moderates inferences
of motive.
Collie, Bradley, and When outcomes of
Sparks (2002) others are unknown,
judgments vary with
procedural fairness,
but not when others'
outcomes are known.
Tests scenarios in
which subjects paid
more, less, or equal to
comparable others
and did or did not
know others' prices.
Knowledge of others'
outcomes, outcome
fairness, and
satisfaction with
interaction.
• Subjects who did not
know others'
outcomes rated their
outcomes as more
fair.
• It is difficult to judge
distributive fairness
because of ambiguity
of why the outcomes
occurred.
Darke and Dahl Greater satisfaction
(2003) occurs when the
outcome/input ratio of
a comparative other is
equivalent.
Tests scenarios in
which subjects
received smaller or
equal discounts
Bargain size, loyalty
status of comparative
other, satisfaction, and
perceived fairness.
• Perceived fairness
mediates the bargain
size-satisfaction
relationship.
• Perceptions of
fairness enhance the
value of a bargain.
Dickson and Perceived fairness of
Kalapurakal (1994) a price depends on
the rule used to set
price.
Surveys traders of
bulk electricity to
determine use of and
perceived fairness of
four cost-based
pricing rules and four
market-based rules.
Frequency of rule use,
fairness of rules, and
response to perceived
unfair prices.
• Rules that treat cost
increases and
decreases
symmetrically are fair.
• Price increases due
to demand increases
are unfair. The more
frequently a rule
occurs, the fairer the
rule is perceived.
Frey and Consumers evaluate
Pommerehne (1993) fairness by starting
from a fair or just
price.
Surveys consumers to
determine
acceptability of
rationing excess
demand.
Fairness judgments
and acceptability of
allocation alternatives.
• Perceived price
fairness for a price
increase with excess
demand is higher
when supply may
expand.
• Increasing price to
profit from demand is
unfair.
Huppertz, Arenson, When consumers
and Evans (1978) perceive certain
factors in a
relationship as
inequitable, they seek
inequity reduction.
Tests consumer
judgments of fairness
of hypothetical retail
exchange situations.
Price inequity, service
inequity, shopping
frequency, item cost,
and behavioral
response.
• Price inequity may
dominate service
inequity in consumer
buying situations.
• Buyers are more apt
to complain when
price inequity is high.
• Frequent buyers are
more likely to
perceive inequity in a
relationship.
Kahneman, Knetsch, Dual entitlement:
and Thaler (1986a) Fairness
considerations
constrain profit-maximizing
firms.
Surveys consumers to
determine standards
of fairness applicable
to price setting and to
understand the effects
of fairness rules on
market outcomes.
Fairness judgments
when presented with
reference transaction,
outcomes of the seller
and buyer and the
reason behind the
changes.
• It is fair for a firm to
raise prices when
faced with increasing
costs.
• It is fair for a firm to
maintain prices as
costs decline.
• It is unfair for a firm
to benefit from shifts
in demand by raising
prices.
Kalapurakal, Dickson, Fairness of the dual
and Urbany (1991) entitlement principle is
subject to context
effects and is not as
general as previously
believed.
Conducts experiment
with students using
three pricing rules
over four context
scenarios.
Perceived fairness of
the pricing rule.
• Absorbing cost
increases and
decreases and using
cost-plus pricing is
more fair than the
dual entitlement rule.
• Fairness perceptions
are influenced by
information about the
seller's costs,
margins, profits, and
pricing behavior.
Kimes (1994) Yield management
practices often
encounter perceptions
of unfairness.
Surveys hotel visitors
to gauge their
reactions to and
perceptions of fairness
when presented with
different scenarios.
Fairness judgments,
role of information,
role of restrictions and
benefits, and
perceived differences.
Yield management
practices would be
perceived fair if:
• Information on
varying pricing
options is available;
• Substantial discounts
are given along with
reasonable
restrictions; and
• Products perceived
as different have
different prices.
Martins (1995) Buyers may compare
prices with
comparable other
buyers; perceptions of
price fairness are
affected by
discrepancies.
Manipulates price paid
by reference other,
reference other
income, and product
type.
Perceived monetary
sacrifice and
perceived price
fairness.
• Presence of a price
discrepancy is
perceived as unfair.
• Perceived monetary
sacrifice is
significantly less
when reference
others pay more and
significantly more
when reference
others pay less.
Maxwell (1995) Fairness judgments
depend on economic
and social variables.
Asks consumers to
cite cases of fair and
unfair pricing.
Price fairness.
• Both economical and
social components
affect determinations
of price fairness.
Maxwell (1999) Social norms are
important in long- and
short-term exchange
relationships.
Tests consumers'
reactions to questions
conveying selected
firms' pricing.
Social norms and
personal and societal
approval.
• A classification
system and proposed
method of quantifying
social norms enables
further study of the
effects of social
norms on consumer
transactions.
Maxwell (2002) If price equals
reference price,
buyers infer
procedural price
fairness; if not,
consumers have less
intention to buy.
Tests fairness
judgments using two
levels of reference
price, seller power,
levels of justification,
and three levels of
price procedures.
Perceived fair price,
attitude toward seller,
and willingness to
purchase.
• Adherence to social
norms for pricing
procedures forms a
basis for fairness
judgments.
• Judged fairness of
pricing practices
influences attitudes
toward the seller and
willingness to buy.
Maxwell, Nye, and Self-interest and
Maxwell (1999) social utility can exist
simultaneously when
buyers have been
primed for fairness
considerations.
Examines the fairness
and acceptability of
prices before
negotiation by priming
fairness in bargaining
scenarios.
Fair prices, acceptable
prices, and effects of
priming.
• Priming buyers to
consider fairness
enables sellers to
increase buyer
satisfaction without
sacrificing profit.
• Fairness-primed
buyers demonstrate
more cooperative
behavior.
Oliver and Swan Fairness perceptions
(1989a) in an exchange result
from not only equity
dimensions but also
satisfaction.
Surveys automobile
purchasers'
perceptions of fairness
and satisfaction in an
exchange situation.
Buyers' and seller's
inputs and outcomes,
fairness, intention,
satisfaction, and
disconfirmation.
• An exchange is fair if
the buyer's outcomes
and seller's inputs
are high.
• Intention is influenced
by satisfaction, and
satisfaction is
explained by fairness
perceptions.
Oliver and Swan Consumers compare
(1989b) inputs and outcomes
of other parties with
their own on the basis
of role expectations.
Fair price is implicit in
this comparison.
Surveys automobile
purchasers, dealers,
and salespeople's
perceptions of
fairness, satisfaction,
preference, and
disconfirmation.
Buyers',
salespeople's, and
dealer's inputs;
outcomes; fairness;
disconfirmation; and
satisfaction.
• Consumers'
perceptions of
fairness are stronger
when their
outcome-input
scores exceed the
merchant's.
• Fairness is highly
related to satisfaction.
Ordóñez, When making fairness
Connolly, and Coughlan and satisfaction
(2000) judgments, consumers
use multiple reference
points.
Tests satisfaction and
fairness judgments
when individuals
compared hypothetical
salaries offered to
MBA graduates.
Satisfaction and
fairness.
• Both advantageous
and disadvantageous
inequity is unfair; the
latter is judged as
more unfair.
• Satisfaction and
fairness are distinct
from each other.
Sinha and Batra Consumers are more
(1999) price conscious when
they perceive price
unfairness by national
brands; such price
unfairness leads to
purchases of private
brands.
Surveys 404 shoppers
about eight grocery
products and uses
rating scales.
Perceived risk, price
versus perceived
quality, price
consciousness, and
perceived price
unfairness.
• Strong positive effect
of perceived price
unfairness of national
brands on price
consciousness.
• Perceived price
unfairness has
indirect effect on
choice through price
consciousness.
• Nonsignificant
relationship between
price quality and
price unfairness.
Urbany, Madden, and ATM fee with cost
Dickson (1989) justification is more
fair than without
justification; switching
costs inhibit intent to
leave bank.
Surveys 40 adults with
scenario that depicts a
bank implementing a
new ATM fee.
Perceived fairness,
behavioral intentions,
and switching costs.
• Confirm dual
entitlement:
Cost-justified fee is
perceived as more
fair.
• Fairness perceptions
may not predict
behavioral intentions.
Vaidyanathan and Inferred causes of
Aggrarwal (2003) price increases affect
perceptions of price
fairness.
Tests fairness
judgments with
scenarios that provide
reasons for price
increases.
Internal versus
external causes of
price change,
controllability, and
perceived fairness.
• A price increase
caused by external
factors and not under
the control of the
seller is perceived as
fair.
• A cost-justified price
increase is not
necessarily judged as
fair.~~~~~~~~
By Lan Xia; Kent B. Monroe and Jennifer L. Cox
Lan Xia is Assistant Professor of Marketing, Bentley College (e-mail: lxia@bentley.edu). Kent B. Monroe is J.M. Jones Professor of Marketing, Department of Business Administration, University of Illinois, Champaign (e-mail: kbmonroe@uiuc.edu). Jennifer L. Cox is Associate Brand Manager, John Deere Worldwide Commercial & Consumer Equipment (e-mail: CoxJenniferL@JohnDeere.com).
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Record: 179- The Role of Relational Information Processes and Technology Use in Customer Relationship Management. By: Jayachandran, Satish; Sharma, Subhash; Kaufman, Peter; Raman, Pushkala. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p177-192. 16p. 1 Diagram, 6 Charts, 1 Graph. DOI: 10.1509/jmkg.2005.69.4.177.
- Database:
- Business Source Complete
The Role of Relational Information Processes and
Technology Use in Customer Relationship Management
Drawing on the relationship marketing and market information processing literature streams, the authors conceptualize and measure relational information processes, or organizational routines that are critical for customer relationship management (CRM). The authors examine the key drivers and outcome of relational information processes and the role of technology in implementing CRM using data collected from a diverse sample of firms. The results show that relational information processes play a vital role in enhancing an organization's customer relationship performance. By moderating the influence of relational information processes on customer relationship performance, technology used for CRM performs an important and supportive role. The study provides insights into why the use of CRM technology might not always deliver the expected customer relationship performance outcome.
Relationship marketing scholars have long advocated that pursuing long-tem relationships with customers instead of a transaction-oriented approach is more profitable for firms (e.g., Morgan and Hunt 1994). Customer relationship management (CRM) is a core organizational process that focuses on establishing, maintaining, and enhancing long-term associations with customers (Srivastava, Shervani, and Fahey 1999). The rapid advance in information technology (IT) has presented firms with new technology-based solutions--namely, CRM technology--to manage customer relationships. Such technology is a suite of IT solutions designed to support the CRM process (Rigby, Reichheld, and Schefter 2002). Many firms have invested in CRM technology (Day 2000), hoping to discriminate between profitable and unprofitable customers, provide customized service, and obtain greater customer retention (Peppers, Rogers, and Dorf 1999). However, the results of using CRM technology have been mixed (e.g., Reinartz, Krafft, and Hoyer 2004), and this has created substantial concern about its viability and effectiveness (Rigby, Reichheld, and Schefter 2002). The business press also gives conflicting accounts about the efficacy of CRM technology (e.g., Whiting 2001), and research on this issue has been limited (Winer 2001).
The unease with CRM technology use is similar to the disillusionment that firms encountered in the late 1980s with the use of IT to automate business activities. The frustration with IT systems led to a focus on information process redesign in organizations to take advantage of the technology (see El-Sawy 2001). Akin to the situation with the use of IT systems in organizations, disappointing outcomes from CRM technology use could be the result of inappropriate information processes. Therefore, research exploring organizational information processes relevant to CRM (hereafter, relational information processes) could help shed light on the role of CRM technology in firms.
To address this need, the objectives of this study are to conceptualize and examine the roles of relational information processes and CRM technology in customer relationship management. We define relational information processes as encompassing the specific routines that a firm uses to manage customer information to establish long-term relationships with customers. The academic research on market information use (e.g., Menon and Varadarajan 1992; Moorman 1995), market orientation (e.g., Kohli and Jaworski 1990; Narver and Slater 1990), and organizational learning (e.g., Sinkula 1994; Slater and Narver 1995) has long emphasized the important role of organizational information processes (e.g., information acquisition, dissemination, use) in shaping how firms respond to their market environment. Our study follows this tradition. To conceptualize relational information processes, we draw on previous research and managers feedback. Then, using data collected from a diverse sample of firms, we empirically examine the key drivers and outcome of relational information processes. We evaluate the role of CRM technology use in customer relationship management by testing its moderating influence on the association between relational information processes and customer relationship performance (i.e., the performance of the organization on customer satisfaction and retention).
The contributions of the manuscript are the following: First, we conceptualize and measure relational information processes. Second, we demonstrate how relational information processes mediate the influence of organizational culture and management system on customer relationship performance. Third, we draw a distinction between relational information processes, which are grounded in relationship marketing theory, and the use of technology for CRM. Fourth, we measure CRM technology use and show that it interacts with relational information processes to influence customer relationship performance. The latter finding implies that CRM technology enables a more effective implementation of relational information processes. Thus, this article addresses the role of CRM technology in organizations, an issue of vital importance to managers, by building on the theoretical foundations of relationship marketing and organizational information--processing research.
In the following section, we identify relational information processes. Then, we develop hypotheses that detail how organizational culture and management systems drive the relational information processes and how relational information processes and CRM technology use influence customer relationship performance. Thereafter, we explain the research methodology. Last, we discuss the results, implications for research and practice, and limitations and future research directions.
Relational Information Processes
Relationship marketing is based on the generation of a foundation of shared interest, in which firms and customers are committed to each other. Firms strive to use interactions with customers to generate commitment, a lasting desire in customers to maintain a valued relationship, and trust, a readiness to rely on the exchange partner. Trust is considered especially critical for relational exchanges because it is a crucial determinant of commitment. An important antecedent of trust is communication (Morgan and Hunt 1994). Communication in the CRM context involves the sharing of information between a firm and its customers (De Wulf, Odeken-Schröder, and Iacobucci 2001). To establish and maintain relationships, it is also imperative that organizations use the information to shape appropriate responses to customer needs. In effect, information plays a key role in building and maintaining customer relationships.
Relationship marketing follows different precepts from those of transactional marketing in the firm--customer interaction. Compared with transactional marketing, relationship marketing requires a much greater degree of firm--customer information sharing and differs in terms of the type of learning involved (Selnes and Sallis 2003) and in how customer information is used. Therefore, although general marketing information processes have been discussed in prior research (e.g., Menon and Varadarajan 1992; Moorman 1995), customer information processes for relationship marketing require specific attention (see also Zahay and Griffin 2004). As we previously noted, we conceptualize these as relational information processes. These information processes systematize the capture and use of customer information so that a firm's effort to build relationships is not rendered ineffective by poor communication, information loss and overload, and inappropriate information use.
Our approach to understanding relational information processes involved a review of extant academic and business literature on CRM. In addition, we interviewed 15 managers (in eight companies that employ customer relationship managers and seven CRM technology vendors) and conducted a preliminary survey on a CRM-focused Web site to glean insights into relational information processes. On the basis of the literature review, interviews, and the preliminary Web-based survey, we suggest that the relational information processes construct consists of five dimensions: information reciprocity, information capture, information integration, information access, and information use. Information reciprocity ensures effective communication, information capture and integration prevent information loss, information access limits information overload, and information use routines ensure that customer information is used consistently with the needs of CRM. We describe these processes next.
Information reciprocity. Reciprocity occurs when actions taken by one exchange partner are matched by the other; it is a key defining characteristic of CRM (De Wulf, Odeken-Schröder, and Iacobucci 2001). Therefore, emphasizing processes for such interactive firm--customer information exchange is important for a firm to execute its relationship marketing strategy effectively (Day 2000). Information reciprocity refers to the processes that enable customers to interact and share information with the firm and that enable the firm to respond to customers. Information reciprocity is an integral part of relational information processes because trust and commitment, the pillars of a strong relationship, are unlikely to develop in the absence of collaborative or mutual interactive communication (Mohr, Fisher, and Nevin 1996).
Information capture. Research in market orientation (e.g., Kohli and Jaworski 1990; Narver and Slater 1990), market information use (e.g., Menon and Varadarajan 1992), and organizational learning (e.g., Sinkula 1994) has emphasized the importance of information acquisition. Building customer relationships requires detailed and up-to-date information about customer interactions with an organization. Customers often have multiple channels to communicate with a firm and could interact with numerous departments, such as sales, customer service, and marketing. The information from these interactions serves as the basis for future interactions in the context of CRM (Peppers and Rogers 1997). Thus, information capture processes that acquire information from customer interactions with various sources and channels are a critical aspect of relational information processes.
Information integration. All interactions between a firm and its customers through different departments and contact points are sources of customer information. However, if this information exists in disparate form with the sources that interact with the customer, it can impede consistent and efficient communication. The development of trust is contingent on customers obtaining consistent and effective responses when they interact with the firm. Such responses are possible only when the history of a customer's relationship with the firm is available to support customer interactions. This requires information integration processes to ensure the assimilation of customer information from all firm--customer interactions to develop a detailed history of customer relationships and prevent loss of customer information.
Information access. The market orientation literature (e.g., Kohli and Jaworski 1990; Narver and Slater 1990) considers information dissemination a crucial component of the information processes that enhance the responsiveness of the firm. Customers may interact with various functional areas in the firm, such as sales, marketing, and customer service. Thus, providing relevant employees with access to updated and integrated customer information should be a priority for firms practicing CRM. Although the market orientation literature focuses on information dissemination, the preliminary research we conducted suggests that employees who are responsible for managing customer relationships viewed the issue more from the perspective of information access than information dissemination on a continuous basis. Mere dissemination, which implies distribution, was perceived as likely to result in information overload as a result of the vast numbers of customer interactions with an organization. Thus, we consider the term information access more accurately descriptive of the information process required to sustain customer relationships.
Information use. Market information use has been classified into action-oriented use, knowledge-enhancing use, and affective use (see Menon and Varadarajan 1992). To build and sustain customer relationships, firms should deploy the acquired customer information in a manner that is consistent with the philosophy of relationship management. Doing so would imply that firms use the information to understand the needs and behaviors of their customer (knowledge-enhancing use) and develop and offer customer-specific products and services (action-oriented use). Relationship marketing also suggests that customers should be treated in accordance with the value they offer to the firm, which in turn enhances customer lifetime value (Venkatesan and Kumar 2004). Therefore, customer information is also used to identify high-value customers.
Antecedents to Relational Information Processes
Traditionally, it was assumed that firms in the business-to-business sector and those involved in marketing services had greater motivation to build relationships with their customers. However, Coviello and colleagues (2002) find that firms compete using transactional, relational, or hybrid approaches regardless of whether they supply services or goods in the consumer or business-to-business arenas. These results imply that researchers need to examine factors that are more specific than the broad services/goods and business-to-consumer/business-to-business classifications as antecedents to relational information processes.
Organizational learning theory provides theoretical guidance to assess the antecedents to relational information processes. The marketing literature on organizational learning (e.g., Sinkula 1994) suggests that four types of factors could be antecedents to information processes: organizational culture, organizational systems, task-related factors, and environmental factors. We address two types of antecedents to relational information processes: customer relationship orientation (organizational culture) and customer-centric management system (organizational systems). Environmental factors (i.e., competitive intensity and environmental dynamism) form the background against which the relationships are tested and used as covariates. The conceptual model appears in Figure 1.
Previous marketing literature supports the view that organizational culture influences information processes (Menon and Varadarajan 1992; Sinkula 1994). An organization's culture is the deeply embedded values and beliefs that establish the norms for appropriate behavior (Deshpandé, Farley, and Webster 1993). Organizational culture affects a firm's choice of outcomes and the means to accomplish those outcomes (Moorman 1995). Therefore, customer relationship orientation, which is rooted in the firm's overall culture, guides the organization's attitude toward both CRM and the implementation of the necessary processes (Day 2000). Essentially, customer relationship orientation establishes a "collective mind" (Weick and Roberts 1993) or a belief system for the organization that considers customer relationship an asset and drives the choice of means (processes) to accomplish this outcome (Day 2000). Because relational information processes are the means to establishing effective relationships, customer relationship orientation motivates their implementation.
H1: Customer relationship orientation has a positive association with relational information processes.
Information processes are likely to be influenced by an organization's management system (Menon and Varadarajan 1992). The management system represents the organizational climate, which comprises the structure and incentives that motivate behaviors consistent with a culture (Slater and Narver 1995). As such, a management system or configuration (Day 2000) that is consistent with a customer relationship orientation and reflects the design of the organization's structure and incentives is likely to influence the implementation of CRM. A customer-centric management system should consist of structural aspects that ensure that organizational actions are driven by customer needs and not by the internal concerns of functional areas. In addition, employee evaluation schemes and incentives should be designed to encourage behaviors consistent with a customer relationship oriented culture by augmenting the organization's ability to focus on customer interactions and by ensuring that expertise from different functional areas is deployed to promote the quality of customer experience (Day 2000). A customer-centric management system helps organizations initiate relational information processes by breaking down functional barriers to customer-centered actions and ensuring adequate focus on customer interactions.
H2: Customer-centric management system has a positive association with relational information processes.
Performance Outcome of Relational Information
Processes and CRM Technology Use
In this study, customer relationship performance focuses on two key aspects of relationships: customer retention and customer satisfaction. By providing quick and effective responses to customers, relational information processes are likely to enhance customer satisfaction by providing consumption-related fulfillment (Oliver 1996). Apart from shaping responses to customers, by enabling customers to communicate easily with the organization, relational information processes help register customers' complaints and provide them feedback. In addition, the integration of customer information and the sharing of it with key customer contact employees enable customers to communicate with firms more effectively. Cannon and Homburg (2001) find that frequent and open communication between a supplier and a customer boosts the customer's efficiency in using the firm's products or services, thereby improving customer satisfaction and loyalty. Relational information processes may also boost customer relationship learning (Selnes and Sallis 2003) by providing customers with a greater understanding of organizations' attempts to respond to their demands and enhancing customer satisfaction and loyalty. In summary,
H3: Relational information processes have a positive association with customer relationship performance.
Customer relationship management technology entails IT designed for CRM. In this study, we consider CRM technology use distinct from the relational information processes that drive CRM. This approach is consistent with that advocated by prior research in technology use in organizations that regard technology as a resource that supports the implementation of information processes (e.g., Brynjolfsson and Hitt 2000; Hitt and Snir 1999; Reinartz, Krafft, and Hoyer 2004). The use of CRM technology is expected to boost the ability of an organization to sustain profitable customer relationships by enabling information to be integrated and shared smoothly, thus facilitating more efficient and effective firm--customer interaction, analysis of customer data, and customization of responses (Day 2003). Technology components of CRM include front office applications that support sales, marketing, and service; a data depository; and back office applications that help integrate and analyze the data (Greenberg 2001).( n1) Sales support is designed to help the sales force acquire and retain customers, reduce administrative time, and enable the efficient management of accounts (Speier and Venkatesh 2002). Therefore, sales support permits the management of sales leads and supplies competitor and customer information to the sales force. In addition, sales support helps manage sales through multiple channels by tracking product availability and delivery. Marketing support includes market planning, campaign execution, and campaign performance measurement (Greenberg 2001). As such, marketing support comprises the generation of customized offers and communications and the assessment of product profitability. Service support coordinates the request and delivery of service and helps customers serve themselves by providing ready access to a knowledge base of solutions (Meuter et al. 2000).
These front office or customer interaction solutions are supported by a customer data depository and software that helps integrate and analyze the data. Firms develop a central data bank in which all customer-related information is stored. Creating a database that is guided by market intelligence is a critical component of a firm's attempts to create customer assets through long-term relationships (Berger et al. 2002). The database should be accessible to relevant functions, such as sales, customer service, and marketing. The data are integrated and analyzed by means of software to understand customer preferences and estimate customer lifetime value, retention, and loyalty (Greenberg 2001).
Prior research suggests that IT plays a complementary role by enhancing the effectiveness of organizational processes (Hitt and Snir 1999; Melville, Kraemer, and Gurbaxani 2004). Two factors are considered complementary if an increase in the level of one factor enhances the marginal value of the other factor (Milgrom and Roberts 1995). Although IT does not substitute for organizational processes, it increases their marginal value by enabling effective implementation (Hitt and Snir 1999). Therefore, firms adopt IT solutions to complement organizational processes by enhancing their marginal value (Brynjolfsson and Hitt 2000).
Relational information processes are implemented so that the information required to establish trust and commitment between a firm and its customers is developed, provided to decision makers, and used effectively. Relational information processes concretize the implementation of customer relationship orientation by laying out the way that a firm should use customer information to develop strong and enduring relationships with valuable customers. In the absence of a clear delineation of these processes, the implementation of CRM technology might not be consistent with employees' expectations of customer information management. The mismatch between the customer information management practices in the organization and the information-handling and -processing capability of the CRM technology system could prevent the organization from taking advantage of the capabilities of the CRM technology system. Using IT solutions without designing appropriate processes may create "significant productivity losses as any benefits of computerization are more than outweighed by negative interactions with existing organizational practices" (Brynjolfsson and Hitt 2000, p. 25). If relational information processes are delineated, CRM technology ensures that their implementation is rendered efficient by enabling a smoother reciprocal flow of information and by limiting information loss and overload by capturing, integrating, and providing information access to decision makers. Reinartz, Krafft, and Hoyer (2004) note that CRM technology is a facilitator of CRM activities. By playing a complementary role, CRM technology enhances the marginal value of relational information processes, thereby improving customer relationship performance.
H4: CRM technology use has a positive moderating influence on the association between relational information processes and customer relationship performance.
Institutional theory (DiMaggio and Powell 1983) suggests that environmental variables (e.g., competitive intensity; the extent of interfirm rivalry; environmental dynamism, the variability of customer needs, and technology) influence organizational actions. Competitive intensity might compel firms to institute relational information processes by emphasizing the need to retain customers and thus hurt customer relationship performance by reducing customer retention. Environmental dynamism might motivate firms to institute relational information processes because relationship learning might be more critical in rapidly changing environments. Customer relationship performance might be lower in dynamic environments because the rapid changes in customer needs and technology opportunity might hurt customer retention. In this study, we use competitive intensity and environmental dynamism as covariates.
Methodology
Firms pursue customer relationship programs in both services and goods firms and in business-to-business and business-to-consumer markets (Coviello et al. 2002). Therefore, in the interest of generalizability of the results, we decided not to constrain our sample to specific industries. On the basis of the interviews and pretesting, we identified a competent key informant as a marketing, sales, or customer service executive, typically at the level of vice president or general manager in a strategic business unit (SBU).
Furthermore, because we found in our preliminary research that implementation of CRM and relational information processes is feasible without CRM technology (see also Rigby, Reichheld, and Schefter 2002), it was not essential that our sample include only firms that had implemented CRM technology. Using two commercial lists, we developed a contact list of senior marketing, sales, and customer service managers in 1105 SBUs of top firms in the United States (in terms of sales revenue).
The first list was vetted using telephone calls; it provided key informant names and/or e-mail addresses in 542 organizations. We mailed these informants the print questionnaire two times, and when the e-mail address was available, we sent e-mails requesting participation. Informants were also given an option to fill out the questionnaire on a Web site. The format of the online questionnaire was similar to that of the print questionnaire. We had e-mail addresses for all 563 contacts on the second list, and we emailed them a maximum of three times, requesting them to respond using the questionnaire on the Web site.
A total of 172 key informants responded to the mail and Web survey, for a response rate of 15.56%. We used data for 21 respondents only for measurement analysis because of missing information on several questions. The questionnaire was complex and long, and senior managers were targeted as key informants. Given these considerations, the response rate is consistent with that reported in previous organizational research (e.g., Homburg and Pflesser 2000). Of the 172 respondents, 48 answered the mail questionnaire, and the remaining 124 responded on the Web. The two commercial lists that provided the mailing sample generated 45.5% and 55.5% of the respondents, respectively. We compared respondents from the two lists and those who responded online and by mail on key variables, such as implementation of CRM system, annual revenue, and how long the key informant had been with the firm. On the basis of chi-square and F tests, mail and Web-based respondents and respondents from both lists did not significantly differ on any of these factors. Therefore, we pooled the data for further analysis.
Of the firms that provided data, 28% had implemented CRM technology, and another 28.2% were planning to do so. On average, the key informant had been with the company for approximately eight years. The median annual revenue for the firms that responded was $140 million. Business-to-business SBUs constituted 69.5% of the respondents, and the other 30.5% were predominantly business-to-consumer SBUs (approximately 50% of which also had some business-to-business transactions). Of the respondents, 49.7% were goods firms, and 50.3% were service firms. A comparison of early and late responders to the survey indicated no significant differences in the characteristics of these SBUs on the means of constructs such as CRM technology use and relational information processes, leading us to conclude that the likelihood of nonresponse bias is minimal.
We developed measurement scales for customer relationship orientation, customer-centric management system, and five dimensions of relational information processes by following procedures observed in the marketing literature (see Churchill 1979). On the basis of a review of the literature on relationship marketing and information use, managerial interviews, and the preliminary survey, we developed a list of indicators to measure the constructs. We pretested these measures over two stages with samples of academics and managers. Three academics checked the scale indicators for face validity and provided comments that we used to revise the scales. Using e-mail, we collected data from 46 managers engaged in CRM activities. We conducted exploratory factor analysis, revised the scales, and developed the questionnaire. The scales consisted of seven-point Likert-type indicators. We describe the measures next (see Table 1).
We measured customer relationship orientation using a scale that reflects the cultural propensity of the organization to undertake CRM (Day 2000). In developing this scale, we focused on shared values of an organization that are consistent with CRM (e.g., considering customer relationships a valuable asset and emphasizing customer retention) and senior management support for CRM. Customer-centric management system refers to the structure and incentives that provide an organization with the ability to build and sustain customer relationships (Day 2000). Therefore, this measure assessed the organization and coordination of the firm around customers and their needs and specific incentives that enable the firm to focus on CRM.
The information reciprocity scale used indicators that focused on reciprocal communication between the firm and the customer. The information capture measure emphasized the acquisition of customer information on an ongoing basis from various sources. The information integration scale reflected the efforts of the organization to bring together the information collected from various sources and functions on a customer basis. The information access measure focused on the degree to which relevant employees could gain access to integrated customer data in a timely manner. The information use scale assessed the extent to which the firm used customer information to undertake actions that are consistent with CRM.
The customer relationship performance scale assessed customer satisfaction and customer retention. Firms use relational information processes to gain a competitive advantage over their rivals. The performance of an organizational action designed to obtain competitive advantage is more meaningful when it is assessed in relation to competition (Matsuno, Mentzer, and Ozsomer 2002). Therefore, we measured customer relationship performance relative to competition; we derived the measure from Rust, Moorman, and Dickson's (2002) measure. We measured environmental dynamism and competitive intensity by adapting scales from the work of Jaworski and Kohli (1993).
We developed an index for the CRM technology use measure that was similar to the measure of innovation in Han, Kim, and Srivastava's (1998) work and was based on Greenberg' s (2001) conceptualization. The CRM technology use measure has six aspects: sales support, marketing support, customer service support, data analysis support, data integration and access support, and customer database. In the questionnaire, we asked the respondents to mark from a list of CRM technology applications the items that their organization was using. We aggregated the marked items to measure CRM technology use.
Results
We ran a confirmatory factor analysis (CFA) to assess the measurement properties of the reflective latent constructs.( n2) Because there were a large number of indicators for the latent constructs ( 46), we ran a CFA on each construct.( n3) Table 2 presents the CFA results. The chi-square statistics were significant. However, because of its sensitivity to sample size, we used other recommended goodness-of-fit statistics to evaluate the fit of various models and suggest acceptable fit for all the constructs. The construct reliabilities ranged from .80 to .94 and were well above the recommended values. As we show in Table 1, the loadings range from .50 to greater than .90 (with most exceeding .70), suggesting that the indicators of the construct are acceptable.
We conceptualized relational information processes as a second-order construct with five subfactors, or dimensions. We examined the second-order factor structure by conducting a one-factor CFA on the summed scores of the respective five first-order constructs. The model fit was good, lending support to the second-order factor conceptualization for relational information processes (χ² = 17.12, degrees of freedom [d.f.] = 5; goodness-of-fit index [GFI] = .96, adjusted goodness-of-fit index [AGFI] = .88, Bentler and Bonett's normed index = .96, Bollen's normed index = .91, and Tucker Lewis index = .97).
We assessed discriminant validity using the procedures that Bagozzi (1980) and Fornell and Larcker (1981) suggest. We formed scores for each of the reflective measures by summing the respective indicators, and we fit a six-factor correlated model. We fixed the loadings of the single indicator factor models at the square root of the factor's reliability. We used the summed scores of each of the five information factors as indicators of the relational information processes construct. These results appear in Table 3. The goodness-of-fit indexes suggest an acceptable fit for the correlated model. For Bagozzi's procedure, we fixed correlations between each pair of constructs at one, and we used the differences in chi-square degrees of freedom to determine whether these correlations were different from one. The chi-square difference tests for all pairs of constructs except one were significant at p < .05 (the customer relationship orientation--customer-centric management system pair was significant at p < .08). To implement Fornell and Larcker's procedure, we computed the shared variance between the indicators of a construct and the construct. We also computed the shared variance between two constructs. As evident from Table 4, the shared variances of all constructs and their indicators are greater than the shared variances between all pairs of constructs. Overall, the results offer support for discriminant validity among the constructs.
Common method variance could bias the findings when both independent and dependent measures are obtained from the same source, as is the case in this study. We assessed method bias using the procedure that Lindell and Whitney (2001) recommend.( n4) According to their procedure, a marker variable or a scale that is theoretically unrelated to other scales should be included in the questionnaire so that there is a priori rationale for this scale to have zero correlation with other scales. If this is not done (as is the case in our study), the best alternative is to identify a scale that has a small correlation with the dependent construct. The correlation of this scale with the endogenous construct scale is considered indicative of method variance. Therefore, after this scale is identified, its correlation with the endogenous construct is used to partial out its effect from other correlations to assess the extent of method variance. In addition, Lindell and Whitney suggest a sensitivity analysis in which 95% and 99% confidence intervals are constructed for the correlations of the marker scale, and the procedure is repeated.
As the marker scale, we used competitive intensity, which had a nonsignificant correlation of .081 with customer relationship performance. Table 5 gives the results of the procedure and shows that the partial correlations between the dependent and the independent variables are high and significant, suggesting that these correlations are not merely due to common method bias. Note also that method variance is unlikely to influence correlations involving the CRM technology use measure because respondents simply indicate the current functions of their CRM technology system. Furthermore, the interaction of CRM technology use and relational information processes should have minimal method bias.
We estimated the following equations using least squares regression to test hypotheses H1-H4.
( 1) RIP = β0 + β1CRO + β2CCM + β3CI + β4ED + ε1;
( 2) CP = β01 + β11RIP + β21CTU + β41CI + β51ED + ε11; and
(3) CP = β01 + β11RIP + β21CTU +
β31RIP x CTU + β41CI
+ β51 ED + ε11,
where
RIP = relational information processes,
CRO = customer relationship orientation,
CCM = customer-centric management system,
CI = competitive intensity,
ED = environmental dynamism,
CP = customer relationship performance, and
CTU = CRM technology use.
We used a stepwise regression approach to test the interaction hypothesis in Equation 3. We created the interaction term after mean centering the data. When we included the interaction term in Equation 2 to form Equation 3, the adjusted R² for the estimation increased from .21 to .24, and the partial F statistic (6.141, 146) was significant at p < .05. Tests of multicollinearity provided no evidence of the same, because none of the variance inflation factors exceeded 10. The results from the estimation appear in Table 6.
In H1 and H2, we hypothesized positive associations for customer relationship orientation and customer-centric management system, respectively, with relational information processes; these were supported (.36, t-value = 3.97; .31, t-value = 3.47). In addition, we found support for H3 (.46, t-value = 5.85), confirming that relational information processes are positively associated with customer relationship performance. We found support for the mediating role of relational information processes on the association between the antecedents (i.e., customer relationship orientation and customer-centric management system) and customer relationship performance using the Sobel (1982) test, which is in line with the procedures that Baron and Kenny (1986) recommend. The Sobel test showed that relational information processes mediate the relationship between customer relationship orientation and customer relationship performance (t-value = 2.21, p < .03) and between customer-centric management system and customer relationship performance (t-value = 2.96, p < .003).( n5) Although not hypothesized, we examined and found no significant difference between business-to-business and business-to-consumer firms in their usage of relational information processes (p = .18). In addition, we did not observe any significant difference between goods and services firms in the extent of their use of relational information processes (p = .25).
In H4, we predicted that CRM technology use enhances the influence of relational information processes on customer relationship performance; we found support for this (.19, t-value = 2.52). We conducted simple slope analysis (Aiken and West 1991) to clarify the nature of this interaction. As we show in Figure 2, relational information processes enhance customer relationship performance when CRM technology use is both low and high. However, as relational information processes go from low to high, customer relationship performance improves more rapidly for a high level of CRM technology use than for a low level of CRM technology use. The slope of the association between relational information processes and customer relationship performance was .03 (t-value = 2.94) when CRM technology use was low. The slope for the same association was .06 (t-value = 6.03) when CRM technology use was high. Customer relationship performance at low values of relational information processes was inferior when CRM technology use was higher than when it was lower (see Figure 2; M = 9.40 versus M = 10.41).( n6)
We also examined whether the use of CRM technology provided differential customer relationship performance advantage for business-to-consumer and business-to-business SBUs and found no significant difference (p = .86). In addition, we found no significant difference in the influence of CRM technology use on the customer relationship performance of goods and services firms (p = .14). Thus, our results suggest that business-to-business and services SBUs do not enjoy any advantage over their business-to-consumer and goods counterparts, respectively, in terms of the influence of CRM technology use on customer relationship performance. Finally, the covariates (i.e., environmental dynamism and competitive intensity) did not have any significant effects on relational information processes and customer relationship performance.
Discussion
Although extant marketing literature has emphasized the importance of information processes (e.g., Menon and Varadarajan 1992; Moorman 1995), information processes relevant to CRM have not received adequate attention. Thus, an important contribution of this article is the conceptualization and measurement of relational information processes and the demonstration of its antecedents. We also developed a measure for CRM technology use and showed that CRM technology use moderates the influence of relational information processes on customer relationship performance. Overall, our findings support the contention that relational information processes provide guidelines to help firms manage customer information and interact with customers in ways that are consistent with the demands of CRM. These processes are necessary to enhance customer relationship performance while CRM technology performs a supportive role. Reinartz, Krafft, and Hoyer (2004) speculate that CRM technology use may even have a negative effect on performance, and our study implies that this could occur when appropriate relational information processes are not implemented.
To clarify our results further for relational information processes and CRM technology use, we conducted e-mail or telephone follow-up interviews with respondents in 19 of the 48 firms in the sample that had implemented CRM technology. Several respondents that mentioned that their firm was successful with its use of CRM technology indicated that the user groups played important roles in planning for the implementation of CRM technology, thus ensuring that their information needs and processes received dominant consideration. The respondents in firms that expressed frustration with CRM technology use had their implementation effort driven by technology and not by user needs. In addition, in some firms, even if the planning was done collaboratively, the user groups found it difficult to adapt to a new way of working. In many cases, the respondents cited that the learning curve was steep and that they needed to retrace their steps and redesign processes and software to ensure that the relationship marketing effort became more effective. Some organizations tried unsuccessfully to implement many aspects of the technology on the basis of the tools that were available. Subsequently, they scaled back the technology implementation, prioritized a few specific applications, and had better success.
In the interviews, respondents also stated that implementing CRM technology enabled them to communicate much better with their customers and to help customers manage their own needs (information reciprocity), helped capture data more effectively when there were large numbers of customers (information capture), enabled customer service employees to access consolidated customer information (information integration and access), and enhanced senior management's decision-making ability by providing a "dashboard" of customer information and by identifying critical problem areas (information integration, access, and use). Some of these firms were sophisticated users of CRM technology; this was apparent in the comments from one respondent: "It will track all of the interactions--you call to complain, you called the help desk, you don't like this, you don't like that. … It allows me to do analysis on what is [the] average length a prospect is in our system before they re revenue producing, how much revenue by customer, it does all these cool pie charts and graphs. … It's knowledge capital." However,, several of the respondents revealed that their firm was using CRM technology in a limited way, focusing largely on information capture and access.( n7)
Importance of relational information processes. This study identifies the key relational information processes that should be implemented by firms that opt to pursue CRM. Delineation of relational information processes enables managers to track and evaluate the information routines that are relevant for CRM. Furthermore, this article explores key antecedents of relational information processes, helping firms assess whether their customer relationship orientation and customer-centric management system, both of which managers can control, are consistent with the demands of relationship management. Designing effective relational information processes and enhancing them using CRM technology could help a firm develop customer-relating capability (see Day 2000).
Implementation of CRM technology. Firms should deploy CRM technology to enhance the effectiveness of relational information processes. Although CRM technology use by itself is not a panacea to CRM problems (see also Rigby, Reichheld, and Schefter 2002), in the presence of properly designed relational information processes, the technology promotes customer relationship performance. Customer relationship management technology is a complex suite of applications. Implementing this technology successfully to improve customer relationship performance requires a thorough understanding of relational information processes within the organization. Therefore, the key decision that managers who are deliberating the use of CRM face is not whether to implement CRM technology but whether their organization could benefit from relational information processes. In addition, the interview data suggest that organizations benefit from adopting a multistage approach to CRM technology implementation to enable employee learning.
We delineate the information processes that help organizations develop sustained bonds with their customers. In doing so, this study extends and links the relationship marketing and market information--processing literature streams. In addition, we draw a distinction between CRM, a process long advocated by marketing academics, and CRM technology, its narrower connotation, which has been widely deployed in organizations. The illumination of the distinctive roles of relational information processes and CRM technology in the pursuit of CRM strategy helps advance the relationship marketing research stream.
This study is based on self-reported data and could be constrained by common method bias, though Lindell and Whitney's (2001) procedure shows that this influence is likely to be minimal. Obtaining objective performance data could have further ameliorated this potential problem. However, because our focus is on whether relational information processes and CRM technology use provide a differential advantage, we require relative performance data for the customer relationship performance construct rather than absolute performance data. However, relative customer relationship performance data are not easily available from public sources.( n8)
Our findings should be evaluated against the background that several of the CRM technology users among the respondents were in the early stages of adoption and, thus, possibly still learning to use the complex technology. Despite this, we found support for the ability of CRM technology to enhance customer relationship performance in conjunction with relational information processes. However, to confirm our findings further, additional research with firms at later stages of CRM technology adoption would be beneficial, as would be research with a larger sample of firms that have deployed CRM technology. Notably, Day and Van den Bulte (2002) find that CRM deployment is unlikely to contribute to customer-relating capability after a minimum competency level is reached. We tested this and found no support for the diminishing positive influence of CRM technology use on customer relationship performance. As we noted, however, it is possible that firms using CRM technology in our sample were still in the learning stage and, therefore, had not reached the minimum competency level. Our study provides only a snapshot of ongoing processes; a longitudinal study to assess the role of experience with CRM technology use would help clarify this issue.
Reinartz, Krafft, and Hoyer (2004) do not find support for the moderating influence of CRM technology use on the relationship between the CRM process and financial performance, though we found that CRM technology moderates the association between relational information processes and customer relationship performance. The difference in the results from these studies could be attributed to the effect of CRM technology use possibly materializing more easily and earlier on intermediate process measures, such as customer relationship performance, than on financial performance. As such, and as Reinartz, Krafft, and Hoyer note, the result might change over time after firms become more competent in their use of CRM technology. In addition, unlike financial performance measures, customer relationship performance does not consider the cost implications of implementing CRM technology. Thus, the return on investment of CRM technology use deserves further research attention, with an assessment of the costs of implementing the technology using more comprehensive measures of financial performance.
Because of data limitations, we could not evaluate the differential influence of aspects of CRM technology use, such as sales support, marketing support, and service support, on customer relationship performance. Thus, further research is required to examine this. Other opportunities for research are provided by our conceptualization and measurement of relational information processes. For example, assessment of the role of relational information processes on relationship learning (Selnes and Sallis 2003) and customer-relating capability (Day 2000) could potentially enrich the relationship marketing literature.
The authors thank the Teradata Center for Customer Relationship Management at Duke University and the Center for International Business Research at the University of South Carolina for financial support. They also thank the consulting editors, Richard Staelin and William Boulding, and the two anonymous JM reviewers for their helpful suggestions.
( n1) Although there are different conceptualizations of CRM technology components, on the basis of the interviews we conducted with CRM users, we decided to adapt Greenberg's (2001, pp. 40-42) conceptualization.
( n2) Because the technology use measure is an index, it is not subjected to tests of reliability and CFA.
( n3) We ran a ten-factor correlated model (with all the 44 indicators) and a ten-factor model with relational information processes as a second-order factor (again, with all the 44 indictors). The goodness-of-fit indexes for both of these models were acceptable, and indicator loadings were similar to those we obtained from a separate CFA of each construct. However, given the size of the sample compared with the number of indicators, we chose the analysis we report in the article. That is, we report the CFA of each construct.
( n4) As a reviewer recommended, we also used Harmon's onefactor test (in accordance with Podsakoff and Organ's [1986] article) to assess common method bias. Ten factors had eigenvalues greater than one, and together they accounted for 74% of the total variance; the first factor accounted for 34% of the total variance. A limitation of Harmon's one-factor test is that there are no guidelines on how high the variance of the first factor should be for common method bias to be detected. In addition, the first factor would contain variance that is due to methods bias and to the traits, and it is not possible to isolate the variance attributable to the method in this test. Velicer (1976) shows that the minimum value of the mean absolute squared value of the partial correlations (after the effect of the common factors is removed) suggests the number of factors to retain. Therefore, we used the mean absolute squared error of the partial correlations to assess which factor structure is better, and its value was the lowest for ten factors (.0187). We also computed the root mean square of the off-diagonals using an exploratory factor analysis. For the one-factor model, the root mean square of the off-diagonals is .1142, whereas for the ten-factor model, it is .0264. In other words, the variance remaining after the removal of the variance attributable to common method bias is substantial and is explained better using a ten-factor solution.
( n5) We also informally examined the mediation effect using the regression tests that Baron and Kenny (1986) recommend. These tests involve regressing ( 1) the antecedents on the mediating variable, ( 2) the mediating variable on the outcome variable, ( 3) the antecedents on the outcome variable, and ( 4) the antecedents and mediating variable on the outcome variable. For mediation to be established, the antecedents should be related to the mediating variable, the mediating variable should be related to the outcome variable, and the effect of the antecedents on the outcome variable should be diminished by the mediating variable. In our analysis, all the conditions were met, and the tests found that the influence of customer-centric management system on customer relationship performance was completely mediated through relational information processes, and the influence of customer relationship orientation was partially mediated. In the presence of relational information processes, the influence of customer-centric management system on customer relationship performance was rendered insignificant, and that of customer relationship orientation was diminished (β = .39 versus .46 without relational information processes as the mediating variable). We used all the variables, including the covariates in the regression analysis.
( n6) Because only a part of our sample used CRM technology, as a reviewer suggested, we tested whether this result was driven by outlying values of CRM technology use. Using a stem-and-leaf plot, we identified seven outlying observations of CRM technology use. We estimated the model after removing these observations from the sample and obtained results consistent with those of the full sample. We also reestimated the equation after removing one multivariate influential observation with a standardized residual greater than 3. The results in this case were also consistent with Table 6.
( n7) As a reviewer recommended, we empirically examined the moderating influence of CRM technology use on the association between individual dimensions of relational information processes and customer relationship performance. We ran five separate regressions for this purpose and observed that CRM technology use enhanced the influence of information reciprocity (t-value = 3.71), information access (t-value = 2.32), and information use (t-value = 2.77), but it did not enhance the influence of information capture (t-value = 1.187) and information integration (t-value = 1.549) on customer relationship performance. All five dimensions of relational information processes had significant main effects on customer relationship performance (p < .05). The differential influence of CRM technology use could be a reflection of the relative skill of firms to leverage CRM technology to enhance the influence of various dimensions of relational information processes on customer relationship performance.
( n8) We thank a reviewer for this suggestion.
Legend for Chart:
B - Loadings
A B
Customer Relationship Orientation
• In our organization, retaining customers is .837
considered to be a top priority.
• Our employees are encouraged to focus on .915
customer relationships.
• In our organization, customer relationships .912
are considered to be a valuable asset.
• Our senior management emphasizes the importance .914
of customer relationships.
Customer-Centric Management System
• We focus on customer needs while designing .820
business processes.
• In our organization, employees receive incentives .578
based on customer satisfaction measures.
• A key criterion used to evaluate our customer .695
contact employees is the quality of their customer
relationships.
• In our organization, business processes are .859
designed to enhance the quality of customer interactions.
• We organize our company around customer-based .503
groups rather than product or function-based groups.
• In our organization, various functional areas .801
coordinate their activities to enhance the quality
of customer experience.
Relational Information Processes
Information Reciprocity
• We enable our customers to have interactive .695
communications with us.
• We provide our customers with multiple ways .990
to contact the organization.
• We focus on communicating periodically with .887
our customers.
• We maintain regular contact with our customers. .803
Information Capture
• We collect customer information on an ongoing .921
basis.
• We capture customer information from internal .768
sources within the organization.
• We collect customer information using external .502
sources (such as market research agencies, syndicated
data sources, and consultants).
• The information collected from customers is .717
updated in a timely fashion.
• We use customer interactions to collect .635
information.
Information Integration
• We integrate customer information from the .820
various functions that interact with customers (such
as marketing, sales, and customer service).
• We integrate internal customer information .711
with customer information from external sources.
• We integrate customer information from different .851
communication channels (such as telephone, mail,
e-mail, the Internet, fax, and personal contact).
• We merge information collected from various .864
sources for each customer.
Information Access
• In our organization, relevant employees find .884
it easy to access required customer information.
• In our organization, relevant employees can .874
access required customer information even when other
departments/functional areas have collected it.
• In our organization, relevant employees always .876
have access to up-to-date customer information.
• In our organization, relevant employees are .829
provided the information required to manage customer
relationships.
Information Use
• We use customer information to develop customer .693
profiles.
• We use customer information to segment markets. .710
• We use customer information to assess customer .666
retention behavior.
• We use customer information to identify .776
appropriate channels to reach customers.
• We use customer information to customize our .739
offers.
• We use customer information to identify our .797
best customers.
• We use customer information to assess the .620
lifetime value of our customers.
Customer Relationship Performance
In the most recent year, relative to your competitors,
how has your business unit performed with respect
to
• Achieving customer satisfaction? 1.000
• Keeping current customers? .590
Environmental Dynamism
• In our business, customers' product preferences .528
change substantially over time.
• We are witnessing demand for our products and .520
services from customers who never bought them before.
• The technology in our industry is changing .851
rapidly.
• Technological changes provide big opportunities .910
in our industry.
• A large number of new product ideas have been .831
made possible through technological breakthroughs
in our industry.
Competitive Intensity
• Competition in our business is cut throat. .902
• We are in a business with very aggressive .970
competitors.
• Price competition in this business is severe. .825 Legend for Chart:
A - Construct
B - Number of Indicators
C - Construct Reliability
D - Chi-Square (d.f.)
E - GFI (AGFI)
F - Bentler's Normed Fit Index
G - Bollen's Normed Index
H - Rescaled Normed Index
I - Tucker-Lewis Index
A B C D E F
G H I
Customer relationship 4 .941 2.580 .992 .997
orientation (2) (.959)
.999 1.000 .993
Customer-centric 6 .863 35.906 .929 .919
management system (9) (.834)
.938 .937 .895
Information reciprocity 4 .912 .927 .997 .997
(1) (.973)
.979 1.000 1.000
Information capture 5 .840 14.023 .968 .960
(5) (.904)
.974 .973 .973
Information integration 4 .886 6.178 .982 .983
(2) (.908)
.948 .988 .964
Information access 4 .923 8.215 .975 .983
(2) (.872)
.983 .981 .940
Information use 7 .803 49.869 .919 .906
(14) (.837)
.931 .930 .895
Customer relationship
performance(a) 2 .795 -- -- --
-- -- --
Competitive intensity(a) 3 .928 -- -- --
-- -- --
Environmental dynamism 5 .889 13.653 .962
(5) (.885) .964
.977 .977 .953
(a) We do not report goodness-of-fit indexes for constructs with
three or fewer indicators, because they have a perfect fit. Legend for Chart:
A - Constructs
B - Loadings
A B
Relational Information Processes (.875)(a)
• Information reciprocity .700
• Information capture .777
• Information integration .835
• Information access .699
• Information usage .803
Customer Relationship Orientation .970(b)
Customer-Centric Management System .929(b)
Environmental Dynamism .943(b)
Competitive Intensity .963(b)
Customer Relationship Performance .892(b)
(a) The value is construct reliability.
(b) Loadings are fixed to square roots of respective
reliabilities.
Notes: Goodness-of-fit indexes: χ² = 61.344, d.f. = 25
(p = .000); GFI = .928, AGFI = .841, Bentler and Bonett's normed
fit index = .912, Bollen's normed index = .841, rescaled normed
index = .944, and Tucker-Lewis index = .900. Legend for Chart:
A - Constructs
B - Mean
C - Standard Deviation
D - Constructs RIP
E - Constructs CRO
F - Constructs CCM
G - Constructs ED
H - Constructs CI
I - Constructs CP
A B C D E F G H I
RIP 112.17 25.69 .585 .434 .441 .077 .101 .294
CRO 28.23 6.32 .612 .941 .607 .064 .136 .423
CCM 25.19 7.41 .596 .706 .863 .076 .117 .349
ED 22.74 7.98 .249 .246 .253 .889 .050 .063
CI 16.51 6.86 .301 .352 .315 .214 .928 .008
CP 10.80 4.29 .461 .564 .492 .216 .081 .796
CTU 4.77 7.98 .126 .084 .110 .058 .032 .052
Notes: Diagonal entries are shared variances between the
indicators and their respective constructs, entries below the
diagonal are correlations, and entries above the diagonal are
shared variance among the respective constructs obtained from
CFA. RIP = relational information processes, CRO = customer
relationship orientation, CCM = customer-centric management
system, ED = environmental dynamism, CI = competitive intensity,
CP = customer relationship performance, and CTU = CRM technology
use.
Legend for Chart:
A - Constructs
B - Constructs RIP
C - Constructs CRO
D - Constructs CCM
E - Constructs ED
F - Constructs CI
G - Constructs CP
A B C D E F G
RIP --
CRO .612(*)
.578(*)
.534(*) --
.504(*)
CCM .596(*) .706(*)
.560(*) .680(*)
.514(*) .647(*) --
.484(*) .624(*)
ED .249(*) .246(*) .253(*)
.183(*) .180(*) .187(*)
.097 .094 .102(*) --
.040 .037 .046(*)
CI .301(*) .352(*) .315(*) .214(*)
.239(*) .296(*) .255(*) .145(*)
.160(*) .221(*) .177(*) .055 --
.107 .172(*) .125(*) -.004
CP .461(*) .564(*) .492(*) .216(*) .081(a)
.414(*) .526(*) .447(*) .147(*)
.352(*) .476(*) .389(*) .058
.312(*) .443(*) .351(*) -.002
(*) p < .05 (one-tailed test).
(a) This is a marker correlation.
Notes: The first value in the cell is the correlation, the second
value is the correlation corrected for method bias, the third
value is 95% sensitivity analysis, and the fourth value is 99%
sensitivity analysis. RIP = relational information processes, CRO
= customer relationship orientation, CCM = customer-centric
management system, ED = environmental dynamism, CI = competitive
intensity, and CP = customer relationship performance. Legend for Chart:
A - Predictor Variables
B - Hypothesis
C - Equation 1 Dependent Variable: RIP Standardized Coefficient
(t-Value)
D - Equation 3 Dependent Variable: CP Standardized Coefficient
(t-Value)
A B C
D
E
CRO H1 .36(*) (3.97)
--
--
CCM H2 .31(*) (3.47)
--
--
RIP H3 --
.46(*) (5.85)
.48(*) (6.18)
CTU --
-.01 (.134)
-.04 (.60)
RIP x CTU H4 --
--
.19(*) (2.52)
CI -- .07 (1.08)
-.08 (1.06)
-.11 (1.43)
ED -- .07 (.96)
.12 (1.59)
.10 (1.31)
Adjusted R² .42
.21
.24
F statistic(d.f.) 28.48(4, 146)(*)
10.92(4, 146)(*)
10.32(5, 145)(*)
(*) p < .05.
Notes: RIP = relational information processes, CRO = customer
relationship orientation, CCM = customer-centric management
system, ED = environmental dynamism, CI = competitive intensity,
CP = customer relationship performance, and CTU = CRM technology
use.DIAGRAM: FIGURE 1 Conceptual Framework
GRAPH: FIGURE 2 Slope Analysis: The Moderating Effect of CRM Technology Use on the Association Between Relational Information Process and Customer Relationship Performance
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By Satish Jayachandran; Subhash Sharma; Peter Kaufman and Pushkala Raman
Satish Jayachandran is an associate professor Moore School of Business, University of South Carolina.
Subhash Sharma is James F. Kane Professor of Business Moore School of Business, University of South Carolina.
Peter Kaufman is an assistant professor, College of Business, Illinois State University.
Pushkala Raman is Assistant Professor of Marketing, Texas Women's University.
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Record: 180- The Role of Relational Knowledge Stores in Interfirm Partnering. By: Johnson, Jean L.; Sohi, Ravipreet S.; Grewal, Rajdeep. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p21-36. 16p. 1 Diagram, 4 Charts, 1 Graph. DOI: 10.1509/jmkg.68.3.21.34765.
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The Role of Relational Knowledge Stores in Interfirm
Partnering
Drawing on the notions of relational capabilities and absorptive capacity, the authors examine the effects of interactional, functional, and environmental knowledge stores on relationship quality and relationship portfolio effectiveness. The results suggest that the knowledge stores affect the outcome variables differently and that the effects vary by levels of industry turbulence.
Over the past decade, concepts pertaining to knowledge acquisition and development by firms (i.e., organizational learning) have assumed an important role in the understanding of how firms succeed. A reason for the enthusiastic interest in knowledge acquisition and management could be the close relationship between a firm's knowledge stores and its capabilities or competences (e.g., Eisenhardt and Martin 2000; Leonard-Barton 1992; Winter 1987, 2000). Given the widely acclaimed strategic significance of capabilities that derive from a firm's knowledge stores, particularly complex stores that are deeply embedded and difficult to observe or imitate, knowledge can result in competitive advantage and superior performance (e.g., Eisenhardt and Martin 2000; Teece, Pisano, and Shuen 1997). Indeed, many firms allocate substantial resources to knowledge development and management. For example, General Motors has implemented a companywide learning system that enables adaptation to market conditions. The mutual insurance company USAA has designed and implemented a knowledge system that enables the company to manage its strategic initiatives. Kraft Foods has created a knowledge system that enables expenditures to be redeployed to high-value activities. Ocean Spray has implemented a marketing-knowledge repository that provides marketplace information to managers. Finally, Toyota has created a knowledge-sharing network with suppliers to facilitate learning (Dyer and Nobeoka 2000; Slotegraaf 1999). Beyond these, other examples of firm-level learning efforts abound.
Recognizing that knowledge is fundamental to building capabilities, scholars have emphasized the role of organizational learning and knowledge management in marketing (e.g., Day 1994, 2000; Glazer 1991; Sinkula 1994; Slater and Narver 1995). In terms of empirical work, some researchers have investigated the role of knowledge and learning in new product development processes (Li and Calantone 1998; Madhavan and Grover 1998; Moorman and Miner 1997). Others have examined learning in the context of market information processing (Hult and Ferrell 1997) and indirectly in market and learning orientations (Baker and Sinkula 1999; Hurley and Hult 1998; Sinkula, Baker, and Noordewier 1997). Although learning and knowledge are believed to play a significant role in interfirm relationships (Dyer and Singh 1998), work in this area is limited to a few conceptual articles (e.g., Lukas, Hult, and Ferrell 1996; Mohr and Sengupta 2002). The broad objective of this article is to extend this stream of research by empirically examining the role of knowledge stores in buyer-seller interfirm relationships (IRs).
Scholars have suggested that IRs often serve as strategic assets and, as such, firms should try to develop the ability to manage them accordingly (Day 1994, 2000; Dyer and Singh 1998; Jap 1999, 2001; Johnson 1999; Srivastava, Shervani, and Fahey 1998). This vital ability, referred to as a firm's "relational capability" (Dyer and Singh 1998), is based on the creation of knowledge stores (Winter 1987, 2000). Although relational capability is strategically crucial, little is known about how it is developed and used, particularly in the turbulent conditions that typify the business environment that many firms face today. Realization of any potential advantage of relational capability requires an understanding of the processes involved in developing the relevant knowledge that underpins it and the use of that knowledge across different environmental conditions.
In this article, we suggest that IR knowledge stores develop from a firm's absorptive capacity (Cohen and Levinthal 1990). Furthermore, because the garnering of superior outcomes is a primary impetus for learning and capabilities development, we examine the effects of IR knowledge on relationship outcomes. We focus on two such outcomes. First, we examine the effects of IR knowledge on relationship quality (e.g., Hibbard, Kumar, and Stern 2001). Second, we investigate the impact of knowledge stores on relationship effectiveness across the firm's IR portfolio (Cannon and Perreault 1999). Given that firms often operate in an ever-changing environment, we also investigate the moderating effect of environmental turbulence.
Relational capability involves a firm's learned ways of behaving in its IRs, including procedures and policies in IR management. The development and manipulation of knowledge stores is integral to the creation of relational capability (e.g., Day 1994; Dyer and Singh 1998; Leonard-Barton 1992). To develop knowledge stores for the socially complex and deeply embedded routines that constitute relational capability, firms actively engage in learning at the organizational level (Collis 1994; Nonaka 1994; Winter 2000). Over the past decade, various perspectives have been presented on organizational learning, including ones that conceptualize learning as the process of gathering, disseminating, and interpreting information (e.g., Sinkula 1994; Slater and Narver 1995). These works, which focused on information and its use and integration in the firm, have been particularly useful in learning about markets and market responses (e.g., Li and Calantone 1998). A complementary perspective on learning that has received considerable attention in the organizational literature (Lane, Koka, and Pathak 2002), and one that readily lends itself to the explanation of capabilities development, is that of absorptive capacity (Cohen and Levinthal 1990).
Absorptive capacity depicts learning as the processes by which a firm derives and absorbs knowledge from its experiences and actions (e.g., Cohen and Levinthal 1990; Lane and Lubatkin 1998; Zahra and George 2002). At an abstract level, absorptive capacity has been defined as a firm's ability or capability to build and upgrade knowledge stores. An important implication of the absorptive capacity perspective is that it assumes that there is some preexisting level of knowledge. Absorptive capacity describes how knowledge develops cumulatively and builds on prior knowledge stores (Cohen and Levinthal 1990). Firms that have prior relevant knowledge are better able to acquire and assimilate new knowledge. The three central components of absorptive capacity are ( 1) an understanding of new information based on the accumulation of observations and cues from experiences, ( 2) assimilation and integration of the information into knowledge stores, and ( 3) application of the knowledge (Cohen and Levinthal 1990; Lane, Salk, and Lyles 2001).
Absorptive capacity provides the theoretical framework for our conceptual model, which is shown in Figure 1. Firms come to terms with new information and assimilate it into knowledge stores through sensemaking, wherein meaning is assigned to observations and cues in situations, events, and occurrences (Weick 1995). Knowledge emerges from offline or retrospective processing that is involved in sensemaking because it reorganizes and creates order from discrepant observations and cues. Here, we focus particularly on the knowledge-application phase of absorptive capacity. We expect that when IR knowledge is developed, it can be applied in interfirm relationships to enhance their individual quality and effectiveness as a group.
IR Knowledge Stores
Knowledge stores in IRs are a firm's reservoirs of collective insights, beliefs, behavioral routines, procedures, and policies in IR management (Day 1994; Walsh and Ungson 1991). When firms form strategic relationships with a few suppliers instead of arm's-length transactions with many suppliers (e.g., Anderson and Weitz 1992; Celly, Spekman, and Kamauff 1999; Ganesan 1994; Watts, Kim, and Hahn 1992), the role of knowledge related to interactions and relational dynamics becomes extremely important. Firms also must manage the functional components in the IR, such as costs, quality, delivery, and inventory aspects in the supply chain. Indeed, relationship management and supply chain management have been proposed as core business processes (Lehmann and Jocz 1997; Srivastava, Shervani, and Fahey 1999), which makes knowledge about the processes crucial to company success. Prior research also points to the need to study environmental conditions (e.g., Achrol, Reve, and Stern 1983). A preponderance of the strategy literature suggests that to be successful, firms need to identify and adapt to the environmental conditions (Moorman and Slotegraaf 1999). Firms' IRs serve as antennas that scan their environments and act as sources of information on various aspects, including the competitive environment (e.g., Celly, Spekman, and Kamauff 1999). Firms' IRs also serve as mechanisms for coping with the environment (e.g., Achrol and Stern 1988; Dwyer and Welsh 1985), which makes knowledge about the environment another crucial component of IR knowledge stores. On the basis of this and exploratory interviews with managers, we consider three important subdomains of IR knowledge in this study: ( 1) interactional knowledge stores, ( 2) functional knowledge stores, and ( 3) environmental knowledge stores.
Interactional knowledge stores. Interactional knowledge stores consist of knowledge about issues related to interactions in partner relationships. Interactional knowledge includes aspects such as communication, negotiation, conflict management, and development and implementation of cooperative programs (e.g., Ganesan 1993; Heide and John 1990; Mohr, Fisher, and Nevin 1996; Murry and Heide 1998). Interactional knowledge stores reflect the sociopsychological components of IRs.
Functional knowledge stores. Functional knowledge stores consist of a firm's knowledge about issues related to the management of supply chain functions. Functional knowledge includes working with suppliers in areas such as cost reduction, quality control, operations and production, logistics and delivery, inventory management, and product development (e.g., Frazier, Spekman, and O'Neal 1988; Klein, Frazier, and Roth 1990; Srivastava, Shervani, and Fahey 1999).
Environmental knowledge stores . Environmental knowledge stores consist of a firm's knowledge about its external operating environment. Environmental knowledge stores include factors in the secondary and macro task environments, such as competitive behavior, market conditions, and variations in laws and regulations (e.g., Achrol and Stern 1988; Dwyer and Welsh 1985; Grewal and Dharwadkar 2002).
In this study, we examine the effects of each IR knowledge store on two forms of strategically desirable relational outcomes. First, we consider the effect of IR knowledge on relationship quality, which is a higher-order construct composed of three dimensions: trust, commitment, and stability (e.g., Hibbard, Kumar, and Stern 2001; Jap 1999). Second, we consider the influence of IR knowledge on the effectiveness of a firm's IR portfolio. Despite their advantages, not all IRs can or should be close and collaborative (e.g., Cannon and Perreault 1999; Frazier 1999); certain relationships simply may not merit the resources required to maintain them, "partner" firms may prefer arm's-length arrangements, or transaction characteristics may make market governance more effective (e.g., Williamson 1996). Thus, outcomes of IR knowledge extend beyond individual IRs to include portfolio perspectives in which IR effectiveness is considered as a group. We expect that the various dimensions of IR knowledge (interactional, functional, and environmental) influence the two outcomes in complex ways.
Effects of Interactional Knowledge Stores
A firm's portfolio of relationships may vary from arm's-length, transaction-based arrangements to close, collaborative partnerships (Cannon and Perreault 1999; Frazier 1999). Given the extent to which interactional issues (e.g., planning and managing partnering activities, negotiating, managing conflict) pervade and extend over the range of a firm's IRs, knowledge gained in any one IR may be applied to others. Because of these transferability and knowledge spillover effects (Uzzi and Gillespie 2002), interactional stores should increase the effectiveness of a firm's relationship portfolio. Furthermore, we expect that this increase is nonlinear because interactional knowledge stores can potentially generate positive feedback effects with respect to portfoliowide effectiveness, which should manifest in the accrual of returns at an increasing rate. Positive feedback effects are self-reinforcing mechanisms that enhance outcomes through learning, coordination, and/or scale effects (Arthur 1994; Dickson, Farris, and Verbeke 2001). For example, a firm with strong negotiation skills can apply the skills in all negotiating activities across its relationship portfolio for significant payoffs in multiple relationships. Thus, we suggest the following:
H[sub1]: The effect of interactional knowledge stores on the general effectiveness of IRs is positive and nonlinear; greater levels of interactional knowledge stores increase the effectiveness of the IR portfolio at an increasing rate.
Interactional knowledge stores are instrumental in the identification and facilitation of the development of behaviors and properties that are desirable in close, partner-style IRs (e.g., Ford and McDowell 1999; Morgan and Hunt 1994). Interactional knowledge--such as that involved in negotiating with suppliers, planning and managing IR activities, implementing cooperative programs, and managing conflict--can be key in building trust and commitment in a relationship (Crosby, Evans, and Cowles 1990; Morgan and Hunt 1994; Stern, Sternthal, and Craig 1973; Sullivan et al. 1981). Firms can also use effective interactional knowledge build strong bonds with a partner, thereby providing stability to a relationship (e.g., Anderson and Weitz 1992). However, because trust, commitment, and stability are idiosyncratic to each relationship, the transference and feedback effects of interactional knowledge are limited and nonmultiplicative. Therefore:
H[sub2]: The level of interactional knowledge stores positively influences relationship quality.
Effects of Functional Knowledge Stores
Firms garner functional knowledge stores by investing in a relationship through programs and activities such as total quality management (TQM), just-in-time (JIT) systems, or product codevelopment. In the process, a firm develops a deep understanding of its partner's (e.g., its supplier's) way of doing business, of its resources and objectives, and of the supplier firm in general. In this respect, it is not practical or even desirable to invest in and develop such deep understanding for all suppliers. Even when TQM, JIT, or other similar programs are desirable for a supplier, some adaptation of the program is needed to accommodate the uniqueness from supplier to supplier. Thus, because of the unique properties of the functional knowledge stores, and because of the type of relationships in which these stores develop and come into play, they are somewhat more limited in their generalizability than are the other IR knowledge stores.
Despite this, some expertise and skill can be reasonably applied across the firm's portfolio of relationships, because in developing rich functional knowledge stores, a firm has a greater understanding of what can and should be accomplished throughout its IR portfolio in terms of functions and cost reduction. With rich functional knowledge stores in place, the firm has a platform from which it can effectively adapt and extend its knowledge. For example, developing effective interfirm TQM programs with a supplier provides the firm with a point from which to develop TQM programs with others, even though the programs may not be the same. Thus, we expect the following:
H[sub3]: The level of functional knowledge stores positively influences the overall effectiveness of the IR portfolio.
Functional knowledge stores imply that a buyer builds knowledge from working closely with individual suppliers in areas such as cost reduction, new product development, and quality improvements and from integrating a supplier in the buyer's JIT and extranet systems. A firm's building of this knowledge store mandates resource utilization in order to understand its partner's objectives, needs, and ways of doing business. As a result, the knowledge accumulated is often proprietary and, relative to the other components of IR knowledge, specific to that partner firm. However, when functional knowledge stores have been acquired, they enable a firm to leverage facets of the supply chain and to provide increased returns for the partner (Srivastava, Shervani, and Fahey 1999), thereby enhancing the quality of the relationship between the firms. For example, a firm may invest in developing a TQM program with a partner firm. When the program is in place, the benefits accrue continually, which further strengthens the relationship between the firms (e.g., Douglas and Judge 2001; Flynn, Sakakibara, and Schroeder 1995) and enhances relationship quality at an increasing rate. Thus, positive feedback effects due to localized learning are set in motion (Dickson, Farris, and Verbeke 2001). Therefore, we hypothesize the following:
H[sub4]: The effect of functional knowledge stores on relationship quality is positive and nonlinear; greater levels of functional knowledge stores increase relationship quality at an increasing rate.
Effects of Environmental Knowledge Stores
It is important for firms to manage the uncertainty that stems from the environment (Achrol, Reve, and Stern 1983; Achrol and Stern 1988; Dwyer and Welsh 1985). Environmental knowledge stores help a firm recognize environmental contingencies and develop its IR portfolio to manage them (e.g., Cannon and Perreault 1999). Based on the environmental conditions, these knowledge stores enable a firm to recognize and assemble an array of relationships, some of which provide flexibility and ease of dissolution when needed and others that provide the advantages of close, partner-style IRs. Consistent with the open-systems perspective (e.g., Achrol, Reve, and Stern 1983), these knowledge stores are geared toward helping firms manage their environment by configuring the optimal IR portfolio. However, the effectiveness of these knowledge stores depends on the degree of environmental turbulence. In stable environments, organizations are less likely to change their patterns of behavior (Mintzberg and Waters 1985). However, in turbulent environments, previous patterns of behavior are less informative, and firms must draw on their environmental knowledge stores to guide their actions with respect to reconfiguring and managing the IR portfolio. Thus, an increase in environmental turbulence enhances the positive effect of environmental knowledge stores on IR portfolio effectiveness, which suggests an interaction effect:
H[sub5]: Environmental turbulence moderates the effect of environmental knowledge stores on the overall effectiveness of the IR portfolio; the positive effect strengthens as environmental turbulence increases.
Scholars have argued that individual IRs provide buffers and are an effective means for firms to cope with environmental uncertainty (e.g., Achrol 1991). In the context of buyer-seller interactions, when environmental conditions vary, firms tend to develop closer relationships with partner firms because close relationships create flexibility, which enables the dyadic partners to adapt and negotiate adjustments mutually (Noordewier, John, and Nevin 1990). Furthermore, when environmental demands and practices are constantly changing, close relationships facilitate communication and coordination across the dyad (Jap 1999). Sometimes, firms also establish close relations with supplier firms, especially in international markets, to create supply stability and to have access to the resources they need (Keister 1999). Environmental knowledge stores facilitate the creation of close relationships because they enable a firm to recognize and communicate environmental contingencies to partners. Thus, we expect that environmental knowledge stores positively affect relationship quality, but the effect depends on the degree of environmental turbulence. When the environment is stable, firms have little need to draw on these knowledge stores. However, in turbulent environments, the need and value of the knowledge stores increase. Thus, we posit the following:
[[sub6]: Environmental turbulence moderates the effect of environmental knowledge stores on relationship quality; the positive effect strengthens as environmental turbulence increases.
Questionnaire Development and Pretesting
We based the measures developed for this study on the academic and practitioner literature and on field interviews. These sources provided the foundation for construct item pools, which were subjected to several iterations of peer review by experts in the field. After a satisfactory conclusion on the item pools, the completed research instrument was again peer reviewed with format, appearance, and flow as the major focus. Next, we pretested the questionnaire through in-depth interviews with executives from four firms. In the interviews, we discussed the study objectives in general terms; after the pretest, subjects completed the questionnaire, and we debriefed them extensively. This pretesting approach was appropriate first because concerns regarding the sample size that, in general, are assessed through broader-based pretesting were addressed in our study by the extensive key-informant prescreening procedure we used. Second, given that several constructs in our study are new, we believed that we could isolate measurement and questionnaire format problems more effectively with in-depth interviews.
An important third concern in pretesting involved the transition between reporting tasks. Some elements of the questionnaire focused on a firm's supplier relationships in general, and other elements focused on a specific supplier relationship. To address this, we separated the two reporting tasks in the questionnaire. As a task-transition mechanism, a distinct section of the questionnaire focused attention on an individual relationship by asking respondents to identify a specific relationship and to report demographic and descriptive information on that relationship. Pretesting ascertained that the respondents indeed made the transition from general interfirm partnering tendencies to a specific supplier relationship without confusion. Our transition mechanism to change respondent focus was effective. In addition, all respondents completed the questionnaire in the expected amount of time and understood the instructions, reporting tasks, items, and language we used.
Sample and Data Collection Procedures
We collected data from a multi-industry mail survey that included the chemical and allied products, rubber and plastic products, metal fabrications and products, industrial machinery and equipment, electronic and electric equipment, and automotive and transportation equipment industries. As a preliminary step in the research process, we conducted several in-depth telephone and personal interviews with materials-acquisition executives in the industries. The results of the interviews implied that the research topic was relevant and compelling for the incumbent firms. In addition, the interviews suggested that the construct variance would likely be sufficient for testing the posited relationships.
We procured a list of firms from a commercial list source. After removing duplicate and incomplete addresses from the list, we had a sample of 781 firms for the project. The first step in data collection was a mail prescreening of potential respondents to assess their appropriateness and to determine whether they met the criteria of involvement and knowledgeability, as indicated by Campbell (1955). Using a short prescreening questionnaire, we determined their position with the firm, number of years in that position, and percentage of time spent on supplier-related activities. In addition, using seven-point scales, we assessed ( 1) the extent to which respondents were personally involved in supplier relationships and ( 2) how knowledgeable they were about their firm's dealings with suppliers. Of the 781 firms, we received prescreening responses from 330. Of these, we eliminated 11 because they were low on the knowledgeability and involvement criteria (i.e., scored lower than six on a seven-point scale), spent less than 70% of their time on supplier-related activities, were in their position less than two years, or were low-level purchasing personnel. The 319 remaining managers identified as appropriate key informants held titles including vice president or directors of operations, procurement, manufacturing, materials management, or supply processing.
Each of the 319 qualified potential respondents was mailed a packet that included a cover letter, the survey instrument, and a prepaid return envelope. As an incentive for participation in the study, we offered the respondents an executive summary. To overcome a possible selection bias of managers automatically reporting on the largest supplier, we used a 2 x 2 design and randomly assigned respondents to report on the following aspects of a supplier relationship: relationship duration of less than two years versus greater than two years and average versus crucially important components or products supplied in the relationship. Assignment to one of the four conditions resulted in reports on a diverse range of relationships for study (Ganesan 1994). As a cross-check for key informant validity, we also included the items we used for prescreening in the survey instrument. The initial mailing and one follow-up mailing generated 176 responses (23% of the original sample and 55% of the qualified informants), of which 169 were usable. To check for nonresponse bias, we compared the respondents with nonrespondents on the prescreening questions, Standard Industrial Classification (SIC) codes, company sales volume, and number of employees. We then compared the early and the late respondents on demographic and model variables (Armstrong and Overton 1977). The t-tests showed no statistically significant differences, which suggests that response bias was not an issue in this study.
Measures
We used formative scales to assess the interfirm relational knowledge stores. The measures focused on the managers' perceptions of the content and level of IR partnering knowledge retained in the firm's memory (e.g., Moorman and Miner 1997). The responding executives rated the extent of knowledge that they believed their firms held, using a seven-point Likert scale anchored by 1 = "very little knowledge" and 7 = "extensive knowledge." The measure of interactional knowledge stores consisted of five items that pertained to negotiating practices, planning and management of partnering activities, development and use of cooperative activities, computer networking with suppliers, and conflict management. We assessed functional knowledge stores with six items that consisted of cost reduction, product development, delivery time, quality management, inventory management, and production efficiency. For environmental knowledge stores, four items pertaining to laws and regulations, market conditions, labor conditions, and competitors' behaviors constituted the measure. The Appendix provides full detail on the measures in the study.
Relationship quality consisted of three dimensions: trust, commitment, and stability. The measure of trust consisted of six items that focused on, for example, partner honesty, reliability, and partner concern for the firm's welfare (Doney and Cannon 1997). We assessed commitment with a five-item scale that included issues such as loyalty, willingness to invest, and expectations of a long-term association (Anderson and Weitz 1992). For the trust and commitment measures, the responses ranged from 1 = "strongly disagree" to 7 = "strongly agree." We created a four-item bipolar adjective scale to assess relationship stability. For example, executives rated the extent to which their supplier relationship was insecure or secure on a seven-point scale.
The measure for relationship portfolio effectiveness, which we also created for this study, focused on perceptions about the firm's IRs as a group. Four statements constituted the scale. An example of the statements is, "Across the board, our supplier relationships operate well for us." Other items assessed productivity, efficiency and effectiveness, and the extent to which the IRs met the firm's needs as a group or portfolio. Executives responded in terms of whether they agreed or disagreed with the statements on a seven-point scale.
We based the environmental turbulence measure on secondary data. We used trade literature, government documents, and popular business press (e.g., The Wall Street Journal, BusinessWeek) to gather information on technological change, innovation, significant new product introduction, growth or decline in markets, competitive and industry structure changes (e.g., mergers, acquisitions, alliances, number of firms leaving or entering, changes in market shares or concentration ratios), legal or regulatory changes, and other factors that we judged as part of the relevant environment. On the basis of the information, we used a Delphilike iterating approach to develop the scale using the following procedure: ( 1) A team of MBA students compiled environmental turbulence reports for each industry; ( 2) the reports were shared across the six industry teams; ( 3) using a scale that ranged from one (least turbulent) to six (most turbulent), each team evaluated the turbulence level for each industry; ( 4) the teams paired up to reevaluate and reconcile their ratings; ( 5) the pairs of teams were then joined by another pair of teams, and they reconciled the turbulence ratings again; and ( 6) the remaining two teams joined and again reconciled the ratings to reach a final consensus for the scale. Table 1 provides detail on the environmental turbulence scale.
Although we did not formally hypothesize a role for the sensemaking construct, we expect that it acts as a covariate with regard to knowledge application. Thus, we operationalized it on the basis of Weick's (1995) conceptualization. The measure consisted of five items that focused on sensemaking by boundary-spanning materials-acquisition executives in IRs (e.g., "In our supplier relationship, we constantly assess and analyze the effects of our decisions so that we know what adjustments to make"). Other items addressed firms' identifying and understanding mistakes, understanding successful activities and programs, and understanding the effects of actions and decisions and various adjustments to them in the relationship. Respondents indicated the extent of their disagreement or agreement on a seven-point scale (1 = "disagree," 7= "agree").
Measure Validation
We used confirmatory factor analysis (CFA) for measure validation. The χ² for the model of all the first-order reflective constructs was 447.86, with 242 degrees of freedom. The comparative fit index (CFI), normed fit index (NFI), and nonnormed fit index (NNFI) were .97, .94, and .97, respectively. The ranges of loadings for each measure as well as construct reliabilities and average variance extracted (AVE) are shown in the Appendix. Construct reliabilities were .91 or greater, exceeding the benchmarks that are suggested as acceptable (Nunnally and Bernstein 1994); AVEs were greater than .72. All the indicators loaded significantly and substantively on their hypothesized factors (p < .001). To test for discriminant validity, we ran a series of nested CFA model comparisons in which we constrained the covariance between each pair of reflective constructs to one (Anderson and Gerbing 1988). For all pairs, when we compared the constrained model with a free model, the difference was significant, which indicates discriminant validity. We also examined the variance extracted by each construct relative to the squared correlation between construct pairs (Fornell and Larcker 1981). In all cases, the variance extracted by each factor exceeded the squared correlation between the factor pair, thus indicating discriminant validity.
We developed the measures of the three knowledge stores as formative (Diamantopoulos and Winklhofer 2001; Edwards and Bagozzi 2000). Thus, the precision and thoroughness with which we established and tapped (content validity) the construct domains provided the major validation tool (Howell 1987). Our procedures in the preliminary stages of research, interviews, and pretesting, along with visual inspection of the scale items, provided evidence of content validity. This procedure, along with the CFA results for the reflective measures, suggests that all the measures in this study are adequately reliable and valid.
We estimated a second-order CFA for relationship quality, because we conceptualized it as a higher-order construct that comprised trust, commitment, and stability. The target coefficient, which compares the parsimonious second-order factor model with a measurement model that contains the three subconstructs, was at the acceptable level of .90 (Marsh and Hocevar 1985). As we expected, the three relationship-quality subconstructs loaded positively on overall relationship quality (i.e., trust: γ[sub1] = .94, t = 7.80, p < .001; commitment: γ[sub2] = .82, t = 8.49, p < .001; stability: γ[sub3] = .69, t = 6.15, p < .001). The χ² for this model was 242.94 (p < .01), with 88 degrees of freedom. The fit indexes were CFI = .96, NFI = .94, and NNFI = .96. Reliability for the higher-order construct was .94. We provide descriptive statistics for latent constructs in Table 2.
Hypothesis Testing
We used ordinary least squares regression to test our hypotheses. We used the product term between the concerned latent constructs to test the interaction effects; we used the square of the latent constructs to test the hypothesized nonlinear effects. Because product terms and the square terms can induce collinearity, we mean-centered the variables before we constructed the terms (Aiken and West 1996; Cronbach 1987). The reported results are based on mean-centered latent explanatory variables.
The results show that the independent variables explain significant variance in relationship portfolio effectiveness (R2 = .428, p < .01) and relationship quality (R² = .121, p < .01). For the hypotheses, the results indicate support for H[sub1]; interactional knowledge stores enhance relationship portfolio effectiveness (b = .320, p < .01) at an increasing rate (b = .122, p < .01). We graph this nonlinear effect in Figure 2. We also find support for H2, which proposes that rich interactional knowledge stores enhance relationship quality (b = .343, p < .01).
The results do not indicate support for H[sub3], which suggests that functional knowledge stores positively affect relationship portfolio effectiveness (b = .049, p > .33). In H[sub4], we expected that greater levels of functional knowledge stores would increase relationship quality at an increasing rate. Although the linear term for functional knowledge stores is not statistically significant (b = .029, p > .40), the nonlinear term is positive and significant (b = .069, p < .05) for its effect on relationship quality, which provides support for H[sub4]. According to our data, as the level of functional knowledge stores increases, relationship quality is enhanced at an increasing rate (see Figure 2).
In H[sub5] and H[sub6], we expected that the influence of environmental knowledge stores on relationship portfolio effectiveness and individual relationship quality would be greater in turbulent environments. The results do not show support for H[sub5]. Neither the main effect of environmental knowledge store on relationship portfolio effectiveness (b = -.039, p > .62) nor its interaction with environmental turbulence is statistically significant (b = .013, p > .33). Likewise, the main effect of environmental knowledge stores on relationship quality is not statistically significant (b = -.050, p > .56). However, we find limited support for H[sub6]; the effect of environmental knowledge stores on relationship quality is moderated by environmental turbulence (b = .039, p < .10).
Post Hoc Analysis of the Role of Turbulence
Although preliminary hypotheses testing did not reveal a substantive pattern of moderation by environmental turbulence, powerful theoretical arguments indicate that there might be important effects that did not emerge in our analysis. Thus, we probed the moderating effects of environmental turbulence more comprehensively through a post hoc dummy-variable analysis. Specifically, for the environmental knowledge stores' impact on relationship portfolio effectiveness and relationship quality models, we replaced the turbulence index with six dummy variables, one for each level of environmental turbulence. Elimination of the constant term in the equation accommodated for the six dummy variables. Using the dummy variables and environmental knowledge stores, we created six product terms to reflect the influence of the knowledge store on the outcome for each of the six levels of turbulence (again, we deleted the main effect of environmental knowledge stores to accommodate the six interactions).
The middle section of Table 3 shows results for the dummy-variable turbulence model (although we included all original explanatory variables in the post hoc analysis, for parsimony and clarity, we report only the moderation results in Table 3). As is shown, environmental knowledge stores positively influence relationship portfolio effectiveness for Turbulence Level 3 but negatively for Level 4. For relationship quality, environmental knowledge stores' effects are positive for Turbulence Levels 3-5. To further examine differences in the influence of the environmental knowledge stores across environmental conditions, we used Wald tests to compare the coefficients reported in the middle of Table 3 (for each knowledge store, there are 15 such comparisons, i.e., [sup6]C[sub2] = 15). The comparisons shown in the lower section of Table 3 indicate that in our data, the influence of environmental knowledge stores on relationship portfolio effectiveness and relationship quality varies from one level of turbulence to another. For example, the effects of the environmental knowledge store on relationship portfolio effectiveness differ significantly between Turbulence Level 3 and the other five levels (Levels 3 and 2: b = .809, p < .01; Levels 3 and 1: b = .796, p < .01; Levels 4 and 3: b =-1.053, p < .01; Levels 5 and 3: b = -.861, p < .05; and Levels 6 and 3: b = -.733, p < .01). Consistent with H[sub5] and H[sub6], the post hoc analyses reveal that environmental knowledge stores influence IR outcomes differently in various environmental conditions.
The Role of Sensemaking in IR Knowledge Store Development
Although we did not formally specify hypotheses regarding the role of sensemaking, we treat it as a covariate and test its effect on relationship portfolio effectiveness because, at a broader level, sensemaking can facilitate how IRs work together as a group. Furthermore, sensemaking can determine how various relationships complement one another, and how each works in conjunction with others (e.g., Bensaou and Venkatraman 1995). The results show that sensemaking has a positive, direct influence on relationship portfolio effectiveness (b = .386, p < .01).( n1)
With regard to the role of sensemaking in knowledge store development, the correlations reported in Table 2 show a strong pattern of association between sensemaking and the IR knowledge stores. The coefficients of .57 (p < .01), .56 (p < .01), and .47 (p < .01) for interactional, functional, and environmental knowledge stores, respectively, provide preliminary evidence of a strong role for sensemaking in knowledge development. To explore the question of environmental moderation in knowledge store development, we performed the same post hoc analysis as we did with the outcomes of knowledge stores. As the results in Table 4 show, 17 of the 18 coefficients for sensemaking are positive and statistically significant (p < .05), which indicates that in most environmental conditions, sensemaking positively influences IR knowledge stores. The Wald test results in Table 4 show that for interactional and environmental knowledge stores, the influence of sensemaking differs across turbulence. However, this difference is not a simple linear difference that can be captured by one interaction term. For example, in the case of interactional knowledge stores, sensemaking has a greater impact for Turbulence Level 6 than for Level 2 (b = .272, p < .05) and Level 4 (b = .276, p < .01). However, the effect of sensemaking on interactional knowledge stores is lower for Turbulence Level 6 than for Level 3 (b = -.209, p < .05). Nonetheless, our data indicate that sensemaking is important in developing knowledge stores, and its effects vary depending on the level of environmental turbulence.
We drew on the absorptive capacity perspective to extend our understanding of relational capability. We advanced the notion that relational capability derives from the development, and particularly the leveraging, of knowledge stores specific to the domain of IR making and management. An important issue in our research involves the differential effects of the three knowledge subdomains (relational, functional, and environmental) that underpin interfirm relational capability. As we expected, our results imply that the three relational knowledge stores indeed generate different outcomes in individual IRs and in terms of a firm's IR portfolio.
Interactional knowledge stores seem to be an important factor in terms of both the magnitude and the consistency of their effects. The knowledge stores strongly influence individual IR quality and have broader-based implications for the IR portfolio. The influence on portfolio effectiveness not only is strong but also gains momentum through positive feedback effects. Evidently, investment in this particular knowledge store generates returns at an increasing rate for the firm. Our results indicate that the benefits of this knowledge domain are not confined to one or a few limited relationships. Rather, when this knowledge domain is developed, it is transferable and generalizable and, more important, can be parlayed repeatedly into positive outcomes for the firm.
For functional knowledge stores, we expected that the benefits realized in an individual IR would multiply at an increasing rate. In general, the data indicate that with respect to the individual relationship, investment in functional knowledge stores yields large payoffs, and these pay-offs gain momentum and amplify. We also believed that whereas functional knowledge would have some broaderbased application over a firm's portfolio, it would be less generalizable and transferable. Rather than leveraging the functional knowledge stores readily from one IR to another, only limited pockets of knowledge would transfer across IRs. According to our data, functional knowledge does not have significant portfoliowide benefits. This implies that learning about, for example, JIT or other inventory management systems in a relationship may not necessarily result in skills or subroutines that the firm can leverage to enhance the effectiveness of its relationships with other partners.
Another focus of our research involves the moderating effects of environmental turbulence on the use of the environmental knowledge stores. Although environmental knowledge does not affect outcomes in all conditions, post hoc investigations show a pattern of moderation by environmental turbulence. In industries characterized by moderate levels of turbulence, environmental knowledge seems to enable portfolio effectiveness greatly. It facilitates adjustments and reorientations in the portfolio to accommodate for some reasonable level of change in the environment. However, at the highest level of turbulence, this knowledge may not be sufficient to cope with changes. With respect to relationship quality, the effects of environmental knowledge stores are similar, though they come into play at somewhat higher levels of turbulence. The magnitude of the differences in effects of environmental knowledge stores at various levels of industry turbulence suggests that marketing scientists must do more to understand how the management of IRs should differ in various environmental conditions. Given our treatment of the environment at an industry level, examination of turbulence in terms of industry conditions may be useful.
For the role of sensemaking, evidence indicates that it is strongly associated with the development of knowledge stores. Furthermore, it seems that sensemaking enhances knowledge stores regardless of environmental conditions. However, we must temper our conclusions somewhat with regard to two of the knowledge stores. We have found what appears to be a nonlinear moderation effect between sensemaking and environmental turbulence for interactional and environmental knowledge stores (i.e., the moderation occurs only at certain levels of turbulence and in certain forms, as opposed to a monotonic bilinear moderation). Although implications of this pattern of moderation are not entirely clear, in general, sensemaking seems to be most productive at medium to high levels of turbulence.
Contributions and Managerial Implications
Scholars note that knowledge is crucial on several fronts, yet there is a scarcity of empirical research that investigates the role of knowledge, especially in IRs. We advance the emergent literature on relational capabilities by showing that domain-specific IR knowledge stores play a crucial and complex role in developing and managing relationships. Related to this, we advance the buyer-seller relationship literature by offering an expanded perspective on IR outcomes. We augment traditional treatments of relationship quality (e.g., Hibbard, Kumar, and Stern 2001; Jap 1999) by including a relationship portfolio perspective in accordance with recent assertions that close, partner-type relationships may not be universally desirable (e.g., Buvik and John 2000).
In addition to the theoretical contributions, our research has several implications for managers. Several leading companies (e.g., BP, Ford, Coca-Cola, General Electric, Monsanto, IBM) have made knowledge management a top priority, on the basis of the premise that knowledge is crucial for competing in today's economy (Hackett 2000). On a broad level, our study validates the action of these companies and underscores the role of knowledge as a strategic resource.
Furthermore, our study has implications for a firm's relationship-marketing strategy. Examples of companies using relational knowledge stores are well documented in the customer relationship management literature. In addition, firms can leverage their functional knowledge stores to enhance relationship quality in several ways. For example, Intel has created personalized Web sites for 10,000 buyers in 400 companies to order its microprocessors, check their order status, and collaborate with engineers. Although the personalized Web sites shorten the order-fulfillment cycles to some extent, their real value is in building and strengthening relationships with partners. Cisco Systems' knowledge-exchange system connects its employees with its customers and suppliers. By sharing information about sensitive issues such as sales forecasts, Cisco has been able to build trust with its channel partners (Knowledge Management 2001). Dell's Virtual Integration System enables it to share information and knowledge freely with its external suppliers on a real-time basis (Magretta 1998). Although the information sharing permits tighter coordination of the supply chain, it also helps develop higher-quality relationships.
However, the results indicate that functional knowledge does not enhance overall relationship portfolio effectiveness. This has implications for companies such as Toyota that have created a knowledge-sharing network with suppliers (Ahmadjian and Lincoln 2001; Dyer and Nobeoka 2000). According to our data, the leveraging of functional knowledge across a portfolio of IRs may not be productive; functional knowledge does not seem to transfer readily or to generalize across a range of relationships. To capitalize on this knowledge, firms may be better off developing and leveraging functional knowledge stores that focus on individual relationships.
Addressing an uncertain environment is an issue of importance to most managers. Our research suggests that by developing knowledge bases about trends and changes in the external environment, firms are in a better position to manage relationships. Consider an example from the food industry: Manufacturers of food and consumer packaged goods own vast amounts of information on market trends, demographics, and consumer behavior. Supermarkets have large databases of information on individual and household purchasing activity. Seven manufacturers (Anheuser-Busch, Coca-Cola, Kraft Foods, Pillsbury, Procter & Gamble, Nabisco, and Warner-Lambert) and one supermarket chain (Wegmans Food Markets) combined the two sets of information to better manage their relationships with customers (Goldberg 2000). However, the usefulness and applicability of environmental knowledge stores is a function of the turbulence level in the industry environment. Apparently, such knowledge stores can be leveraged most effectively in moderately turbulent industries.
Another issue of importance for managers intent on capabilities development is the content of knowledge: What type of knowledge results in strong capabilities and superior performance? Our research implies that there is no standard mix of knowledge. Rather, the content domain of knowledge depends on the specific capability involved. Firms should focus on knowledge that underpins capabilities in specific strategically crucial functional areas (e.g., IRs). Even further, within these functional areas, the firm can and should build various relevant subdomains such that strong capabilities emerge. Our research shows that the various knowledge subdomains have differential and sometimes nonlinear impacts, which implies that, as with other resources, firms must understand how to combine and leverage the subdomains for optimal outcomes.
Our research suggests that the prudent manager should devote efforts and resources to sensemaking because it is critical in capabilities development. Managerial focus on sensemaking may be even more vital than our data indicate because it can be broken down into specific actions and activities that can be trained and rewarded in the firm. The literature is replete with admonitions that firms must be learning organizations and must "learn how to learn." With a focus on sensemaking, managers can isolate activities and skills necessary to the perpetual adjustment and reframing of events and cues. Managers can be trained in the search for critical questions that need to be asked, even as they search for answers to the questions. Personnel assessments and reviews could directly address sensemaking activities, linking them directly to performance evaluations and financial rewards for relevant managers and thereby reinforcing training efforts.
Limitations and Further Research
The results of this study must be viewed in conjunction with its limitations. A measurement concern involves the scope and domain of our formative measures for the IR knowledge stores. The validity of formative measures hinges on extensive tapping of the construct domain, if not exhaustion of it. Although our measures are well grounded in theory and fieldwork with managers, they are new. Therefore, construct domains need to be further verified and established. In addition, our results may be strengthened by the inclusion of additional informants for each firm or perhaps by the development of objective measures. Although we took precautions in questionnaire development and pretesting, and the results do not seem to indicate it, common methods variance can be problematic in single-informant survey data.
Our research is the first we know of that has empirically verified the existence of environmental moderation in learning. Furthermore, this moderation is quite complex in form and warrants further investigation before any detailed conclusions can be drawn. Our measure for environmental variance has the important advantage of being derived from objective secondary data. However, it is ordinal in nature and is based on assessments of industry conditions. Given that reasonably strong findings emerged with this objective but somewhat limited environmental turbulence measure, it is critical to managers and researchers that this issue be extensively addressed in further research. A multifaceted measure of environmental turbulence may reveal more about this complex and subtle phenomenon.
Although this study offers important groundwork for understanding IR knowledge stores, more research is needed on how the knowledge is developed. Related to this, tracking the evolution of sensemaking skills and their implications for knowledge creation would be useful. Another issue in this research and all capabilities and learning research is the connection to firm-level performance outcomes. The firm is necessarily driven by the need to "take it to the bank." Performance implications of learning and capabilities have strong and compelling theoretical grounding, but how and when do they play out in firm performance? These issues are difficult ones, but it is critical to address them in the IR context.
The authors thank Christine Moorman for her helpful suggestions during the development phase of the manuscript. Funding for this project was provided by the University of Nebraska's Layman Research Grant Fund.
( n1) An interesting question that arises is whether the three knowledge stores mediate the influence of sensemaking on relationship portfolio effectiveness. To test this, we estimated a model in which sensemaking affected relationship portfolio effectiveness in the absence of the influence of the knowledge stores (b = .513, p < .01). Because this coefficient is substantially greater than the coefficient estimated in the presence of the knowledge stores (i.e., b = .386, p < .01) in absolute magnitude, we have evidence for partial mediation (Baron and Kenny 1986).
Legend for Chart:
A - Industry and SIC Range
B - Turbulence Level
C - Nature of Turbulence
D - Average Dollar Sales/ Revenues (in Millions)
E - Average Relationship Length
F - Average Number of Other Suppliers for Product Category
G - Average Percentage of Product Category Purchased from This
Supplier
H - Average Percentage of Total Purchases from This Supplier
I - Average Percentage of Supplier's Sales Accounted for by This
Relationship
A B
C
D E F G H I
Metal fabrications and 1 (n = 6)
products, 3411-3499
In general, stable on most
dimensions; moderate technology
change; relative stability in
competitive activity, composition,
and demand
$229.0 14.2 4.5 65% 39% 13%
Industrial machinery 2 (n = 41)
and equipment,
3511-3599
Reasonable new product
introduction; innovation incremental
in most sectors; relative stability in
industry composition, competitive
activity, and demand
33.6 14.1 6.2 66 40 18
Chemical and allied 3 (n = 26)
products, 2812-2899
New product introduction and
innovation significant in some
sectors, reasonably intense
competition, some alliances,
relatively stable demand
26.6 11.0 3.0 78 58 13
Rubber and plastics, 4 (n = 22)
3011-3069
Reasonable rates of innovation and
new products, relatively few new
entrants, some alliance activity,
somewhat dynamic and growing
markets
173.0 11.7 2.7 64 35 15
Automotive 5 (n = 26)
transportation and
equipment,
3711-3799
Frequent new product introduction
and incremental innovation, intense
competition, heavy alliance activity,
dynamic demand
232.0 8.1 4.6 68 39 22
Electronic and electric 6 (n = 48)
equipment,
3624-3647
Strong activity on multiple fronts:
radical innovation, technology, new
entrants, alliances, dynamic
markets and demand, intense
competition
177.0 12.0 3.7 62 40 21 Legend for Chart:
B - 1
C - 2
D - 3
E - 4
F - 5
G - 6
H - 7
A
B C D E
F G H
1. Interactional knowledge stores
1.00
2. Functional knowledge stores
.78(**) 1.00
3. Environmental knowledge stores
.66(**) .70(**) 1.00
4. Sensemaking
.57(**) .56(**) .47(**) 1.00
5. Relationship quality
.32(**) .23(**) .19(*) .22(**)
1.00
6. Relationship portfolio effectiveness
.53(**) .47(**) .37(**) .58(**)
.28(**) 1.00
7. Environmental turbulence
-.06 -.01 -.08 .01
-.02 .03 1.00
Mean
4.86 4.59 4.17 5.18
5.73 5.10 2.83
Standard deviation
1.03 1.08 1.21 1.19
1.06 .98 1.90
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Explanatory Variable
B - Relationship Portfolio Effectiveness Unstandardized
Parameter Estimate
C - Relationship Portfolio Effectiveness Standard Error
D - Relationship Quality Unstandardized Parameter Estimate
E - Relationship Quality Standard Error
A B C
D E
Interactional knowledge stores .320(***) .109
.343(***) .111
Functional knowledge stores .049 .115
.029 .121
Environmental knowledge stores -.039 .079
-.050 .088
Environmental turbulence .028 .031
.006 .034
Sensemaking .386(***) .079
-- --
Interactional knowledge stores(2) .122(***) .037
-- --
Functional knowledge stores(2) -- --
.069(**) .034
Environmental knowledge stores x turbulence .013 .030
.039(*) .029
R² = .428
R² = .121
Environmental Knowledge Stores at Environmental
Turbulence Levels(a)
Lowest(1) -.059 .117
-.096 .123
Somewhat low(2) -.072 .094
-.168 .134
Moderate(3) .737(***) .267
.348(*) .213
Fair(4) -.316(**) .175
.521(***) .155
Somewhat high(5) -.124 .319
.684(***) .155
Highest(6) .005 .130
-.018 .113
Wald Tests Comparing Moderation of Environmental
Turbulence on Outcomes(b)
(6)-(5) .129 .336
-.702(***) .174
(6)-(4) .320(*) .219
-.539(***) .192
(6)-(3) -.733(***) .291
-.366(*) .234
(6)-(2) .076 .144
.151 .151
(6)-(1) .063 .156
.078 .145
(5)-(4) .192 .365
.162 .217
(5)-(3) -.861(**) .405
.336(*) .243
(5)-(2) -.053 .324
.852(***) .186
(5)-(1) -.066 .330
.780(***) .176
(4)-(3) -1.053(***) .313
.173 .252
(4)-(2) -.244 .191
.690(***) .199
(4)-(1) -.257 .201
.617(***) .191
(3)-(2) .809(***) .263
.516(**) .232
(3)-(1) .796(***) .270
.444(**) .215
(2)-(1) -.013 .118
-.073 .153
(*) p≤.10.
(**) p≤.05.
(***) p≤.01.
(a) For each regression model, we created six dummy variables
(Variable 1 represents the lowest level of turbulence, and
Variable 6 represents the highest level). For each model, we
created six interaction terms for environmental knowledge stores.
In all models, we included the other remaining explanatory
variables. So a significant effect in the original consequences
model implies a significant effect on the outcome at that level
of turbulence.
(b) We calculate the coefficient for the Wald test as
b = b[sub1] - b[sub1], where for the first test (i.e.,
[6]-[5]), for interaction knowledge stores, b[sub1] = .669
and b[sub2]= .704 (see Table 3). We calculate the standard
error for the coefficient b as SE(b) = [Var(b)][sup½]
= [Var(b[sub1]) + Var(b[sub2]) - 2Cov(b[sub1],
b[sub2])][sup½], where SE = standard error,
Var = variance, and Cov = covariance (we report one-tailed
tests). We show the difference in effects of environ mental
knowledge stores on outcomes between turbulence levels (e.g.,
[6]-[5] implies the difference in the effects of environmental
knowledge stores on relationship outcomes for SIC codes coded
as 6 and 5, respectively, for turbulence).
Notes: All the equations had a constant term, which we do not
report for parsimony reasons; we report one-tailed tests. Legend for Chart:
A - Levels of Turbulence(a)
B - Interactional Knowledge Stores
C - Functional Knowledge Stores
D - Environmental Knowledge Stores
A B C D
Lowest (1) .556(***) .664(***) .473(***)
Somewhat low (2) .379(***) .454(***) .768(***)
Moderate (3) .878(**) .939(**) 1.047(***)
Fair (4) .393(***) .571(***) -.218(**)
Somewhat high (5) .704(***) .686(***) .819(***)
Highest (6) .669(***) .455(**) .617(***)
Wald Tests Comparing Moderation of Environmental Turbulence on
Outcomes(b)
(6)-(5) -.035 -.232 -.202
(6)-(4) .276(***) -.116 .843(***)
(6)-(3) -.209(**) -.484 -.431(***)
(6)-(2) .272(**) .001 .151
(6)-(1) .113 -.209 .144
(5)-(4) .311(**) .115 1.037(***)
(5)-(3) -.174 -.253 -.228(*)
(5)-(2) .307(*) .233 .353(**)
(5)-(1) .148 .022 .347(**)
(4)-(3) -.485(***) -.368 -1.265(***)
(4)-(2) -.004 .117 -.683(***)
(4)-(1) -.163 -.093 -.690(***)
(3)-(2) .481(***) .485 .581(***)
(3)-(1) .321(***) .275 .574(***)
(2)-(1) -.159 -.210(*) -.007
(*) p≤.10.
(**) p≤.05.
(***) p≤.01.
(a) For each regression model, we created six dummy variables
(Variable 1 represents the lowest level of turbulence, and
Variable 6 represents the highest level). For each model, we
created six interaction terms for sensemaking. Interactional
knowledge stores R² = .39, functional knowledge stores
R² = .36, and environmental knowledge stores R² = .29.
(b) We calculate the coefficient for the Wald test as
b = b[sub1] - b[sub2], where for the first test (i.e.,
[6]-[5]), for interaction knowledge stores, b[sub1] = .669
and b[sub2] = .704 (see Table 3). We calculate the standard
error for the coefficient b as SE(b) = [Var(b)][sup½])
= [Var(b[sub1]) + Var(b[sub2]) - 2Cov(b[sub1],
b[sub2])][sup½, where SE = standard error, Var = variance,
and Cov = covariance (we report one-tailed tests). We show the
difference in effects of sensemaking on the three knowledge
stores between turbulence levels (e.g., [6]-[5] implies the
difference in the effects of sensemaking on each knowledge store
for SIC codes coded as 6 and 5, respectively, for turbulence.DIAGRAM: FIGURE 1 Model Depicting Conceptual Framework and Hypothesized Relationships
GRAPH: FIGURE 2 Interpretation of Nonlinear Effects
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Knowledge Stores (New Scale; Formative)
(Scale items anchored by 1 = "very little knowledge" and 7 = "lots of knowledge") Interactional
1. Negotiating with suppliers
- 2. Planning and management of partnering activities
- 3. Initiating and implementing cooperative programs with suppliers
- 4. Using computers to network and communicate with suppliers
- 5. Managing conflict with suppliers
Functional
1. Cost-reduction strategies involving suppliers
- 2. Working with supplier to develop products
- 3. Working with suppliers to reduce delivery times
- 4. Working with suppliers on quality management
- 5. Integrating suppliers into the firm's JIT system
- 6. Enhancing suppliers' production capabilities and capacities
Environmental
1. Laws and regulations relevant to supplier relationships
- 2. Market conditions affecting buying
- 3. Labor conditions in supplier firms
- 4. Competitors' purchasing behaviors
Relationship Quality (Conceptualized as a Higher Order Construct That Consists of ...)
Trust (scale adapted from Doney and Cannon 1997)
(Construct reliability = .95; AVE = .75; range of loadings .85-.91; scale items anchored by 1 = "strongly disagree" and 7 = "strongly agree")
1. This supplier keeps promises made to our firm.
- 2. This supplier is always frank and truthful with us.
- 3. We believe the information this supplier provides us.
- 4. This supplier is genuinely concerned that our business succeeds.
- 5. When making decisions, this supplier considers our welfare as well as their own.
- 6. This supplier is trustworthy.
Commitment (scale adapted from Anderson and Weitz 1992)
(Construct reliability = .94; AVE = .77; range of loadings .78-.95; scale items anchored by 1 = "strongly disagree" and 7 = "strongly agree")
1. We have a strong sense of loyalty to this supplier.
- 2. We expect this supplier to be working with us a long time.
- 3. We are willing to make long-term investments to help this supplier.
- 4. We are really committed to developing a working relationship with this supplier.
- 5. We see this relationship as a long-term alliance.
Relationship stability (new scale)
(Construct reliability = .91; AVE = .73; range of loadings .74-.95; responses ranged from 1 to 7 on bipolar adjectives)
1. Stable/unstable (reverse coded)
- 2. Long-term/short-term (reverse coded)
- 3. Insecure/secure
- 4. Unsteady/steady
IR Portfolio Effectiveness (New Scale)
(Construct reliability = .91; AVE = .72; range of loadings .80-.90; scale items anchored by 1 = "strongly disagree" and 7 = "strongly agree")
1. For the most part, our supplier relationships are very effective.
- 2. Across the board, our supplier relationships operate well for us.
- 3. Our supplier relationships do everything we need them to do and more.
- 4. In general, we find our supplier relationships to be very productive and efficient.
Sensemaking (New Scale)
(Construct reliability = .91; AVE = .73; range of loadings .76-.93; scale items anchored by 1 = "strongly disagree" and 7 = "strongly agree")
In our supplier relationships...
1. If something seems to be going wrong, we try hard to figure out why.
- 2. We quickly try to identify our mistakes so that they are not repeated.
- 3. If a program is successful, we try to understand what makes it work well.
- 4. If we see a mistake has been made, we retrace our actions to understand what happened.
- 5. We constantly assess and analyze the effects of our decisions so that we know what adjustments to make.
~~~~~~~~
By Jean L. Johnson; Ravipreet S. Sohi and Rajdeep Grewal
Jean L. Johnson is Professor of Marketing, Washington State University (e-mail: johnsonjl@wsu.edu). Ravipreet S. Sohi is Associate Professor of Marketing, University of Nebraska (rsohi1@unl.edu). Rajdeep Grewal is Assistant Professor of Marketing, Smeal College of Business Administration, Pennsylvania State University (rug2@psu.edu).
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Record: 181- The Role of Spokescharacters as Advertisement and Package Cues in Integrated Marketing Communications. By: Garretson, Judith A.; Burton, Scot. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p118-132. 15p. 3 Color Photographs, 4 Charts, 1 Graph. DOI: 10.1509/jmkg.2005.69.4.118.
- Database:
- Business Source Complete
The Role of Spokescharacters as Advertisement and
Package Cues in Integrated Marketing Communications
Marketers often use verbal claims to highlight brand benefits in marketing communication campaigns. However, spokescharacters may also be incorporated into campaigns and are often featured both in advertisements and on product packages. The authors use three studies to examine various integrated marketing communications (IMC) strategic combinations, including the effects related to the use of spokescharacters versus verbal attributes; advertisement-package coordination; character relevancy; and the presence of new, favorable brand information that may compete for cognitive resources on packages. Based on conceptual rationales drawn from encoding specificity, network associations, and the elaboration likelihood model, the findings offer empirical evidence that pertains to the potential benefits of including spokescharacters in IMC campaigns. Specifically, the use of spokescharacters results in more favorable brand attitudes, even when brand attribute recall is affected adversely by deviations from the primary message conveyed in the IMC campaigns. The authors offer some implications of these and other findings for marketers attempting to affect consumer evaluations favorably with spokescharacters in IMC campaigns.
Spokescharacters have been integrated into various historical and modern advertising campaigns. Originally created in the late nineteenth century as brand identifiers (Callcott 1993; Dotz, Morton, and Lund 1996), these characters, which are sometimes referred to as character icons, have become a type of brand sign and can symbolically convey a brand attribute or benefit. Examples of spokescharacters include the Pillsbury Doughboy, Chester the Cheetah, and the Snuggle Bear. Hundreds of such characters exist today, and many more are being created for marketing purposes each year. Practitioners and scholars alike recognize the potential benefits of these popular visual cues (Fournier 1998; Shimp 2003; Thompson 2000). Initial investigations have suggested that these unique icons appearing in advertisements can benefit brands beyond merely their identification of the product (Callcott 1993; Mizerski 1995; Phillips 1996), though research pertaining to such benefits remains limited.
The Energizer Bunny case illustrates some supplemental benefits of placing characters on product packages and in advertisements. For some time after its introduction, many consumers were unsure whether the bunny character shown in advertisements was associated with the Duracell or Energizer brand batteries (Lipman 1990). Energizer responded by including the character on its product packages to remind consumers which brand "kept going and going." The brand response in the Energizer Bunny example demonstrates a successful integrated marketing communication (IMC) effort, in which marketers convey more consistent messages across different points of consumer contact (Shimp 2003), such as advertisements, in-store promotions, and packaging. Marketing practitioners believe that the use of consistent messages across multiple points of contact has important implications for brand equity and sales (Duncan and Everett 1993; Low 2000; McArthur and Griffin 1997).
In considering such implications, this research examines the strategic combinations of advertising cues, including spokescharacters and verbal attributes (i.e., written product attribute claims), that IMC campaigns use that might yield both memory and attitudinal benefits. As Low (2000) notes in his study of marketing managers, IMC represents an underresearched philosophy even though it has been embraced by many organizations. Furthermore, although even cosmetic inconsistencies in the use of spokescharacters and verbal attributes can invite potential problems (Goodstein 1993), few studies empirically examine strategic message coordination across marketing communication tools. In addition, further research regarding characters is necessary because despite their prolific use in IMC campaigns, their potential impact is relatively unexplored. A common assumption is that spokescharacters convey relevant attributes about their respective products, which is true in some cases; for example, the Pillsbury Doughboy conveys freshness, and the Snuggle Bear suggests softness.
Arguably, however, other characters may appear further removed from their product categories. For example, Tony the Tiger appears to have little connection to the relevant attributes of cereal.
In this research, we address the benefits of featuring more versus less relevant spokescharacters, both in advertisements and on product packages. By also examining verbal attribute claims, which represent the status quo communication format for most marketers, we consider the potential incremental and conditional effects of using characters. In three separate studies, we examine how strategies used at more than one point of consumer contact can either enhance or undermine favorable spokescharacter campaign outcomes. Drawing on a variety of theories, we offer predictions and findings that address situations in which IMC coordination can generate superior brand attribute recall and more favorable brand attitudes (Ab) through the use of spokescharacters.
Experiment 1
Evidence suggests that both more and less relevant information can facilitate learning (Heckler and Childers 1992; Lee and Mason 1999). We define relevancy as the degree of fit between the spokescharacter and the advertised product (see, e.g., Miniard et al. 1991; Sengupta, Goodstein, and Boninger 1997) and the degree to which the character communicates brand-relevant information, such as an attribute of the product with which it is paired. According to some empirical findings, more relevant stimuli may promote learning and memory (Heckler and Childers 1992; Keller, Heckler, and Houston 1998; Lee and Mason 1999) because they are encoded and integrated with brand-related material more easily. Therefore, and in accordance with the associative network theory (Anderson 1983), stronger associations form in long-term memory. However, other work has found that less relevant information can also enhance learning and memory at times (MacInnis, Moorman, and Jaworski 1991). According to this view, the less relevant stimuli provide schema-inconsistent information that elicits curiosity and motivation to process the information, and this leads to stronger links among the encoded associations.
In response to these divergent views, the elaboration likelihood model (ELM) (Petty, Cacioppo, and Schumann 1983) may offer some explanations about more and less relevant stimuli and their role in memory. According to the ELM, the way consumers view and process advertisements may determine the degree to which particular cues receive elaboration and become encoded. In turn, it may also affect the degree to which particular package cues effectively provide access to encoded cues, such as brand claim information that was initially presented with the package cues. Consumers who are motivated to process the details that are pertinent to brand attributes (i.e., brand processors) will be more sensitive to the relevancy of brand information in advertisements than will those who are less motivated to do so (i.e., ad processors). That is, brand processors focus more on attribute-pertinent details and therefore elaborate and integrate the relevant brand information (MacInnis, Moorman, and Jaworski 1991). Consistent with the encoding specificity principle, which suggests that cues used during both encoding and retrieval enhance memory (Higham 2002; Thomson and Tulving 1970), we believe that the presentation of relevant cues on packages can prime brand associations (i.e., brand claims in advertisements) that ultimately influence consumer brand evaluations (Alba, Hutchinson, and Lynch 1991).
The contention that consumers who process brand information may be more sensitive to the relevancy of presented material does not suggest that ad processors' encoding and retrieval of brand claims will be totally unaffected by character relevancy. Although ad processors primarily focus on the peripheral cues rather than on specific brand information when browsing advertisements, they can attend to and process some brand information, but this processing is rather shallow (Goodstein 1993; Hastak and Olson 1989). Therefore, ad processors' retrieval of information with the less relevant character will have a less detrimental effect on brand outcomes.
In summary, given the tenets of the ELM and encoding specificity, we predict a three-way interaction among processing goals, relevancy, and package cues. For brand processors, more relevant spokescharacters will be associated with the various brand claims made by the advertisements, which will make such spokescharacters superior package cues that enhance consumers' access to the brand claims at the point of purchase. In contrast, the effects of relevancy will be less pronounced for ad processors. Moreover, verbal attribute cues that consist of short phrases about product attributes and appear written on product packages may aid brand processors in retrieving brand claims at the point of purchase, but their usefulness should not depend on character relevancy. In addition, as the framing literature (Grewal, Gotlieb, and Marmorstein 1994; Maheswaran and Meyers-Levy 1990) and the relevancy/congruency literature (Fiske 1982; Goodstein 1993) note, the valence of retrieved material (i.e., positive brand claims) may frame subsequent consumer evaluations. Therefore, we offer the following hypotheses for both memory and Ab:
H1: The effects of package cues are moderated by relevancy and processing goals. More specifically,
(a) For brand processors, the use of more brand-relevant spokescharacters as package cues results in improved memory for brand claims and more favorable Ab than does the use of less brand-relevant spokescharacters. For ad processors, the effects of character relevancy on memory and Ab is less pronounced.
(b) In contrast, with the use of a verbal attribute package cue, relevancy and processing goals have weaker effects on brand claim recognition and Ab.
We tested H1 using a 2 (relevancy: high versus low) x 2 (processing goals: brand versus advertisement) x 2 (package cue: spokescharacter versus verbal attribute) between-subjects design that we replicated within subjects for two product categories (cheese and laundry detergent). For the three-stage experiment, we randomly assigned participants to the experimental conditions. In the first stage, we manipulated the participants' processing goals (brand versus advertisement) and character relevancy by exposing them to ad stimuli (three filler and two counterbalanced target advertisements). In the second stage, we provided a distraction task to clear participants' short-term memory. Finally, in the third stage, we manipulated the package cue by showing participants a package mock-up for the advertised products (two counterbalanced target and one filler advertisement) and then asking them to respond with regard to the dependent measures, manipulation checks, covariates, demand characteristic checks, and demographic variables.
Relevancy. We followed procedures and used measures similar to those used in previous research to manipulate the relevancy of the spokescharacters for advertised products (e.g., Miniard et al. 1991; Sengupta, Goodstein, and Boninger 1997). In the initial pretest (n = 54), we attempted to identify ( 1) two spokescharacters that evoked similar attitudes and ( 2) a nondurable product category relevant to each spokescharacter. According to the results, the relevant conditions included a mouse with muscles matched with cheese and a sheep with thick wool matched with laundry detergent. For the less relevant condition, the characters and categories were reversed (i.e., the mouse paired with laundry detergent and the sheep paired with cheese). In the second pretest (n = 68), we assessed the relevancy manipulations for each product category. Measures of the spokescharacter's relevancy to an advertised product included the following: "The idea of the [character] pictured with [product category] represents a very good fit," and "I think that the picture of the [character] is relevant for [product category]." The coefficient alpha was .96 for both products, and the differences in the check means were significant (p < .001) and in the appropriate direction, in support of the relevancy manipulation.
Processing goals. To simulate consumer processing types, we manipulated the processing goal factor through the instructions the participants received immediately before their ad exposure (e.g., Goodstein 1993). We instructed participants assigned to the ad-processing goal to browse through the advertisements and assess their various execution elements (e.g., layout); we instructed those assigned to the brand-processing goal to examine the brand-related information contained in the advertisements. To assess the processing goal manipulation, we used four seven-point items that measured the description of the participants' processing (e.g., "As I viewed the advertisements, I was mainly thinking about the …," with the endpoints "layout and creative elements in the advertisement" versus "attributes and benefits of the brand" [α = .86]). The findings from the second pretest support the processing goal manipulation (F = 41.10, p < .001) with means in the appropriate direction.
Package cue. To operationalize the package cue manipulation, we placed different types of cues in the lower right-hand corner of the product packages for the target brands. For the spokescharacter condition, the manipulated area of the package included a smaller color reproduction of the same character we used in the advertisement in the first stage (either more or less relevant). In the verbal attribute condition, the manipulated area contained the verbal meaning that the character shown in the advertisement in the first stage was supposed to convey (i.e., cheese: "makes you strong"; laundry detergent: "makes clothes fluffy"). Pretests indicated that the spokescharacters and verbal attributes did not differ in terms of meaning.( n1)
Target advertisement and mock package stimuli. For each of the target brands, participants viewed a one-page color advertisement that contained a headline with a fictitious brand name and product category, both of which were bolded and centered at the top of the page.( n2) (For ad stimuli, refer to Appendixes A and B.) A picture of the product, a spokescharacter (either more or less relevant), and a verbal cue appeared below the heading in the center portion of each advertisement. In the lower portion of each page, there were two paragraphs, one on each side of the page, that explained a different brand claim. Directly above each of these paragraphs, there was a bolded heading that was related in general to the claim. The package stimuli were printed in color and contained a capitalized, bolded brand name (for an example, see Appendix C). The package markings were identical across all conditions, except for the package cue manipulation located in the bottom right-hand corner of each package. Other than the described manipulations, all aspects of the advertisement and package stimuli were invariant.
We gathered two brand claim recognition measures for each of the two target brands, which resulted in four total measures. These seven-point scaled items, anchored by "definitely true" and "definitely not true," measured the degree of recognition that participants experienced for the particular attributes that were featured as the two main claims shown in the bottom portion of each target advertisement. To measure Ab, we used four seven-point bipolar adjective scales (e.g., positive/negative, good/bad). The coefficient alphas exceeded .95 for each of the product categories.
Manipulation checks. The manipulation check measures for relevancy (cheese: α = .95; laundry detergent: α = .94) and processing goals (coefficient α = .82) are identical to those we used in the second pretest. In the relevancy manipulation, there are significant effects for the relevancy of the appropriate character for both the cheese (F = 69.19, p < .001) and the laundry detergent (F = 35.53, p < .001). There is also a significant effect of the processing goal manipulation (F = 27.84, p < .001). The means for the processing goals and relevancy manipulations are all in the appropriate direction.
Brand claim recognition . In H1, we predicted a three-way interaction among processing goals, relevancy, and package cue for brand claim recognition. Because we were interested primarily in the overall effect of these factors on brand claim recognition, we performed an analysis of variance (ANOVA) on the mean brand claim recognition scores. As we predicted in H1 and show in Table 1, the three-way interaction is significant (p < .01). The cell means appear in Table 2.
We performed planned contrasts to assess the specific predictions in H1a and H1b about brand claim recognition. As we predicted in H1a, brand processors experience stronger recognition of brand claims when more (mean [M] = 5.50) rather than less (M = 4.08) relevant characters are used as cues on product packages (F( 1, 97) = 11.31, p < .01). In contrast and as we expected, for ad processors, the effects are less pronounced (F( 1, 97) = 3.31, p < .10). Consistent with H1b, the planned contrasts for verbal attribute package cues do not differ across the character relevancy conditions for either brand (F( 1, 97) = .16, p > .10) or ad (F( 1, 97) = 1.56, p > .10) processors.
Brand attitude. In H1, we also predicted that the effects of the package cue on Ab would be moderated by both relevancy and processing goals (i.e., a three-way interaction), which we tested with a repeated-measures ANOVA for the two product categories. As we show in Table 1 and consistent with our predictions, there is a significant (p < .025) three-way interaction. In Figure 1, Panel A, we depict the plotted means for the eight cells, and in Table 2, we provide the cell means.
We also used planned contrasts to assess H1a and H1b with regard to their Ab predictions. For brand processors, the relatively large differences that H1a predicted are between the more (M = 5.09) and less (M = 3.16) relevant conditions when spokescharacters are used as cues on packages (F( 1, 97) = 17.73, p < .001). As we expected for ad processors, Ab is not significantly different when cues are less relevant (M = 4.22) than when they are more relevant (M = 4.68; F( 1, 97) = .99, p > .10). (Plotted means are in Figure 1, Panel B.) Similar to the findings for the brand claim measure, planned contrasts that test H1b for the verbal attribute package cue indicate no differences across the spokescharacter relevancy conditions for either brand (F( 1, 97) = 1.24, p > .10) or ad (F( 1, 97) = .08, p > .10) processors.
Although both more and less relevant spokescharacters appear in various marketing communication campaigns, our results show that more relevant spokescharacters as package cues may help promote brand claim recognition among brand processors. Spokescharacters appear to offer more than mere brand identification, which has served as the impetus for the creation of many historical characters. For consumers who are initially motivated to process brand-related information, more relevant characters can improve both memory and brand evaluations, a finding that has not been identified specifically in prior retrieval cue work (e.g., Keller 1991).
We predicted that ad processors' memory for brand claims and Ab would be less affected by the relevancy of the character. Although marginal, ad processors' memory for brand claims improved with less relevant spokescharacters. One possible explanation for this finding lies in the incongruency literature (Fiske 1982; Goodstein 1993; MacInnis, Moorman, and Jaworski 1991). For ad processors, the presence of the less relevant spokescharacter during encoding might enhance their motivation to process the brand claim information, but this may also cause them to generate and encode less favorable reactions. Whereas less relevant character package cues might have primed brand claims, thus resulting in somewhat enhanced memory, they also may have primed unfavorable associations that negatively influenced participants' evaluations. On average, the marginally superior memory that results from the less relevant cue does not render superior Ab.
In general, this study suggests that more relevant spokescharacters, which consumers perceive as conveying a particular brand attribute, used at different points of IMC contact appear to offer brands the benefits of both increased memory strength and improved evaluations among consumers who actively process brand information. This finding is consistent with Kardes's (1988) work, which indicates that inferred conclusions (i.e., attributes conveyed by pictorial spokescharacters) in higher involvement conditions generate thoughts and attitudes that subsequently are relatively accessible. When these cues are used both in advertisements and on product packages, they can offer brands benefits beyond attention-getting visuals.
Finally, although some evidence suggests that recognition and recall measures produce similar patterns of results (Stapel 1998), other work indicates that recall represents a stronger association measure that may offer a more compelling test of our theory (Krishnan and Chakravarti 2003). Therefore, we conducted another study to provide additional evidence for the potential effects of relevant spokescharacters on memory using recall measures and to address the boundary conditions for these effects in coordinated media campaigns.
Experiment 2
As Low (2000) notes, one area for potential improvement in IMC campaigns pertains to the coordination of campaigns by a single source. That is, whereas creative advertising strategies typically are outsourced, other communication strategies often are devised in-house (McArthur and Griffin 1997). Using multiple agencies affords brands a strongly diversified set of creative ideas and specific competencies, but it also can result in inconsistencies; when one agency develops advertisements and another prepares in-store promotion and packaging, there may be deviations from a single-message IMC strategy.
Such deviations, especially if they deviate from information that is initially presented to consumers, might result in less effective marketing communications. Consider again the Energizer Bunny example. Could the brand have gained similar benefits from placing the verbal claim of "keeps going and going" rather than the actual spokescharacter on its packaging? Although the difference between these messages may seem minor, theory suggests that presenting an attribute in one form (i.e., visual) during encoding and then in a different form (i.e., verbal) during retrieval can compromise potential memory-related benefits (Higham 2002; Thomson and Tulving 1970). Similarly, a new attribute that is not included in the information initially conveyed to consumers but that is presented on in-store packages represents another type of deviation. Effective IMC campaigns require consistency at all points of contact (Reid 2003), and such simple variations can impede a campaign's success (Goodstein 1993). In Experiment 2, we further examine the effects of relevant spokescharacters and cosmetic variations in IMC to aid marketers as they consider integrated campaigns.
The encoding specificity principle suggests that "matched" cues improve the accessibility of information from memory; therefore, to be most effective as package cues, relevant spokescharacters on packages should be presented first in advertisements as spokescharacters (pictures) rather than as verbal attributes (words), and any verbal attribute cues should follow the same pattern. For example, Energizer could improve both consumer recall of brand information from advertisements and consumer evaluations by placing the Energizer Bunny character from a previous advertisement on packages rather than a verbal attribute with a comparable meaning (i.e., "keeps going and going"). As are spokescharacters, verbal attributes initially shown in advertisements are encoded in memory and can be linked to brand associations, which means that showing them again on product packages should prime these established associations and enhance consumer recall of other closely linked brand information.
In addition, according to prior work on picture superiority (e.g., McBride and Dosher 2002; Shepard 1967), there may be somewhat stronger effects from matching visual characters than from matching verbal attributes, and the use of both visual and verbal cues during encoding and retrieval offers advantages over a single format. Given the dual-coding theory, which states that visuals are encoded more automatically into memory if presented in two modes (image and verbal codes; Goolkasian 2000; Paivio 1971), it is also plausible that a combined character and verbal attribute package cue presented after a spokescharacter advertisement (rather than after a verbal attribute advertisement) might produce more favorable outcomes because of the visual match.
The presence of new, competing information may also influence the effectiveness of the package cues, which would result in a response-set suppression effect (Postman and Underwood 1973). In this research, we use learning about a new brand attribute that differs from the attributes initially identified in a brand advertisement and that takes place during the consumer's opportunity to retrieve his or her previously learned information (e.g., viewing a package at the point of purchase) as a form of competing information. Consistent with prior work (Mann and Brenner 1996), information presented at the point of purchase may distract consumers from the task of recalling information they learned previously by competing for the cognitive resources they require for the retrieval process. For example, Costley and Brucks (1992) find that cues originally presented in advertisements for target brands and then used in advertisements by competing brands unfavorably affected recall of the target brand's attributes.
The presence of new, competing attribute information on package stimuli should moderate the effects of the ad and package cues on recall. That is, the new information will distract consumers from using the package cue to prime the advertised brand attributes. In addition, the new information might consume processing capacity, which diminishes the resources available for the retrieval task (Krishnan and Chakravarti 2003). This distracting effect may be contingent on the conditions that existed during encoding. Again, the dual-coding theory (Goolkasian 2000; Paivio 1971) suggests the superiority of visual cues; furthermore, visuals can facilitate learning about semantic information and improve the meaning and organization of the text with which they are presented (Macklin 1996). Considering these enhanced effects of visuals, we expect that target visuals serve as anchors for "to-be-remembered" brand attributes, thereby making spokescharacters that are used both in advertisements and on product packages less susceptible to the distraction effects of competing information on packages. In contrast, we believe that the verbal attributes used in both the ad and the package conditions will be more susceptible to unfavorable competing cue effects. When combined spokescharacter and verbal attribute cues appear both in advertisements and on packages, the effect of competing information should be less influential because of dual encoding and the benefits of the spokescharacters as visuals during encoding. This overall pattern of predictions suggests a three-way interaction among ad, package, and competing cues.
H2: The ad cue and competing information moderate the effects of the package cue on brand attribute recall and Ab. Specifically, we expect the following:
(a) When competing information is absent, brand attribute recall and Ab are superior for spokescharacter package cues in the spokescharacter ad condition (ad-package cue match) than in the verbal attribute ad condition (ad-package cue mismatch).
(b) When competing information is absent, brand attribute recall and Ab are superior for verbal attribute package cues in the verbal attribute ad condition (ad-package cue match) than in the spokescharacter ad condition (ad-package cue mismatch).
(c) When competing information is absent, brand attribute recall and Ab are similar for the combined spokescharacter and verbal attribute package condition across all the ad cue conditions.
(d) The effects of the competing information on brand attribute recall and Ab is less pronounced for the spokescharacter ad-package cue condition (visual match) and the combined spokescharacter and verbal attribute ad-package cue condition (combined visual and verbal match) than for the verbal attribute ad-package cue condition (verbal match).
The experiment involved a 3 (ad cue: spokescharacter, verbal attribute, or combination) x 4 (package cue: spokescharacter, verbal attribute, combination, or no cue) x 2 (competing information: present versus absent) between-subjects mixed design, which we repeated within subjects across two product categories. We provide the specific design and conditions in Appendix D. As in Experiment 1, the procedures consisted of three stages.( n3) We also used the same product categories, spokescharacters, and verbal attribute cues that we used in Experiment 1. Consistent with the first experiment, we manipulated the ad cue in the center portion of the advertisement and placed both the package cue and the competing information manipulations in the lower righthand corner of the package stimuli. The competing information manipulation (when present) appeared to the right of the package cue manipulation. As in Experiment 1, we used filler ad and package stimuli and counterbalanced the target stimuli order.
Brand attribute recall. We calculated the brand attribute recall as the percentage of correct brand attributes recalled relative to the total number of brand attributes mentioned in the advertisement, which included the two target brand claims, information in the two paragraphs of the advertisement, and the attributes mentioned in the primary headline. Participants' responses were coded into the appropriate brand attribute category by two judges, whose interjudge reliability was high (>85%) and who resolved any differences.
Brand attitude measure and covariates. We used the same multi-item Ab measure that we used in Experiment 1, and we used the importance-of-brand-selection multi-item measure for each product category as a potential covariate. Coefficient alphas exceeded .95 for the multi-item dependent variable and covariate measures.( n4)
Manipulation checks. We assessed the cue manipulations used both in advertisements and on product packages and the competing information manipulation on the product packages. As we noted for one of the pretests in Experiment 1, although the format for the cue manipulations differed, the meaning conveyed in the advertisements was consistent across the three ad and package cue conditions.( n5) In addition, we found that more than 99% of participants who were shown a competing attribute on the product package stimuli identified it as new information that was not in the advertisement. These findings demonstrate the successful manipulations of all three factors.
Brand attribute recall . In H2, we proposed three-way interactions in which the effects of package cue on recall and Ab are moderated by both the ad cue and competing information. Table 3 contains the results of a 3 x 4 x 2 repeated-measures analysis of covariance for attribute recall and Ab. To assess the specific predictions, we performed planned contrasts.
The results of the planned comparisons for the match hypotheses when no competing cue appears support our predictions. For the spokescharacter package cue condition, brand attribute recall is greater for the spokescharacter advertisement (M = .25) than for the verbal attribute advertisement (M = .17; F( 1, 339) = 3.77, p = .05). Recall is also marginally greater for the verbal attribute ad-package match; the verbal attribute package cue results in greater recall for the verbal attribute ad condition (M = .27) than for the spokescharacter ad condition (M = .19; F( 1, 339) = 3.38, p = .067). As we expected for the combined spokescharacter and verbal attribute package cue condition, recall is no better for the combined advertisement (M = .24) than for the spokescharacter (M = .23; F( 1, 339) = .13, p > .10) or verbal attribute (M = .21; F( 1, 339) = .41, p > .10) advertisements. In general, these recall results support our predictions in H2a-c for the match/mismatch of ad and package cues.
A planned contrast for the presence of a competing attribute does not support our predictions about the spokescharacter ad-package cue condition. Recall is lower for the presence (M = .15) than for the absence (M = .25) of the competing attribute (p < .05). Although they are not significant, the mean values for the verbal attribute ad-package cue match condition are in the predicted direction. Recall is lower in the presence (M = .23) than in the absence (M = .27) of the competing attribute condition (F( 1, 339) = 1.01, p > .10), and as we expected, the combined spokescharacter and verbal attribute ad-package condition is not adversely affected by the presence of competing information. Overall, the findings that pertain to recall demonstrate minimal support of H2d.
Brand attitude. Our predicted three-way interaction for Ab (see Table 3) is significant (p < .05), and the planned contrasts we used to assess our specific predictions when no competing cue is present indicate a significantly more favorable Ab for the spokescharacter package cue condition when the advertisement contains a character (M = 5.10) rather than a verbal attribute (M = 4.11; F( 1, 339) = 8.83, p < .01). In contrast to our predictions about the verbal attribute package cue condition, Ab is relatively similar for the verbal attribute (M = 4.69) and the spokescharacter (M = 5.00) ad conditions (F( 1, 339) = .94, p > .10). The results for the combined spokescharacter and verbal attribute package cue condition indicate that Ab is just as favorable for the spokescharacter (M = 4.97; F( 1, 339) = .70, p > .10) and verbal attribute (M = 4.84; F( 1, 339) = .19, p > .10) advertisements as it is for the combined spokescharacter and verbal attribute ad condition (M = 4.69), in support of our predictions.
Regarding the presence of a competing attribute in the spokescharacter ad-package cue condition, the planned contrast supports our predictions and shows a similar Ab when the competing attribute is present (M = 5.07) and when it is absent (M = 5.10; F( 1, 339) = .01, p > .10). Although it is not significantly different, the Ab for the verbal attribute ad-package cue condition appears to be lower with the presence (M = 4.24) than with the absence (M = 4.69) of the competing attribute (F( 1, 339) = 1.97, p > .10). Despite means in the predicted direction, the anticipated effect of this condition is not supported. As we predicted, however, Ab is not adversely affected when the competing attribute is present (M = 5.05) than when it is absent (M = 4.69) for the combined spokescharacter and verbal attribute ad-package condition (F( 1, 339) = 1.15, p > .10). In summary, for Ab, H2a and H2c are supported, H2d is partially supported, and H2b is not supported. That is, the three-way interactions and planned contrasts for recall and Ab offer partial support for our predictions in H2.
Main effects. Whereas the findings for the hypothesized interactions are of the greatest theoretical interest, the main effect results in Table 3 suggest managerial implications for firms that are considering IMC-related issues. The main effect of the ad cue on both brand attribute recall (F = 5.97, p < .01) and Ab (F = 7.67, p = .001) suggests some of the advantages that can be reaped by featuring spokescharacters in advertisements. These mean values also reveal superior brand attribute recall scores for advertisements with combined spokescharacter and verbal attribute cues (M = .23) than for only verbal attribute cues (M = .19, p < .01). Advertisements that contain either the spokescharacter (M = 4.84) or the combined spokescharacter and verbal attribute cues (M = 4.74) rather than the verbal attribute cue alone (M = 4.41) also led to more favorable Ab (p < .001 and p < .01, respectively).
The main effect of package cues was also significant for both recall (F = 8.43, p < .001) and Ab (F = 3.94, p < .01). Again, various types of package cues produced beneficial effects. For example, with the combined spokescharacter and verbal attribute package cue, as opposed to no package cue, both recall and Ab improved significantly. Collectively, the significant interactions and main effects found in Experiment 2 suggest that marketers can benefit from using spokescharacters either alone or with verbal attributes.
Although the results for H2 are mixed, these findings provide general support for the IMC-related predictions in the absence of competing information, especially for spokescharacters. Both attribute recall and Ab are significantly greater for spokescharacter package cues when the spokescharacter rather than a verbal attribute is used in the ad condition. Early pioneers already have established the role of pictorial icons relative to verbal cues during initial exposure (encoding) (e.g., Childers and Houston 1984; Lutz and Lutz 1977); our work also supports the influence of icons at a subsequent contact point. In addition, although recall benefits emerge for matched verbal attributes, Ab does not become significantly more favorable. Although they are not significant, greater Ab mean values occur for the verbal attribute package cue with the spokescharacter rather than with the verbal attribute ad condition. This finding suggests that some favorable brand information initially associated with spokescharacters during advertisement exposure can be accessed through verbal attribute cues on product packages and, in turn, influence Ab.
Our results also demonstrate the effects of competing information on product packages. Presenting a new attribute on product packages impairs recall for the spokescharacter ad-package cue condition but does not affect Ab adversely. Collectively, previous results of the verbal match and this finding suggest that processes beyond cognition can account for Ab. Other nonclaim, favorable brand associations appear to be generated by the spokescharacter and later accessed through the character. Furthermore, as for the combined package condition, Ab is favorable for both the spokescharacter and the combined spokescharacter and verbal attribute ad conditions. In summary, although our findings are not fully consistent with our predictions, they suggest that as long as advertisements contain a spokescharacter, Ab tends to be stronger.
Experiment 3
To confirm and extend the findings about whether advantages gained by coordinated IMC campaigns are affected by the introduction of new, favorable brand information, we conducted a third study.( n6) Specifically, we wanted to assess the pattern of results further for any single modality conditions in which either the spokescharacter or the verbal attribute was used instead of the combined condition. In Experiment 2, when the competing information was present, the character match condition did not yield the predicted memory advantage, but it resulted in more favorable Ab. By separating ad and package exposures temporally and spatially in Experiment 3, we hope to increase the external validity and reassess the finding that the cosmetic coordination of spokescharacters at two points of contact promotes favorable attitudes, regardless of any new, competing information that may compromise recall.
In terms of the method, this study differs from Experiment 2 in two specific ways. First, we assess consumer responses to product package stimuli in a different environment than that for the ad exposure conditions. Second, we gather the recall and Ab measures 24 hours after ad exposure. Because the ad and package exposures are temporally and spatially distinct, we have created conditions that are more similar to the actual marketplace.
Experiment 3 involved a 2 x 2 x 2 design with three between-subjects factors: ad cue (spokescharacter versus verbal attribute), package cue (spokescharacter versus verbal attribute), and competing information (present versus absent). Consistent with Experiments 1 and 2, there were two different exposure stages. We manipulated the ad cue with the advertisement and the package cue and competing information with the product package stimuli. In the first stage, participants were exposed to an advertisement in a lab environment; in the second stage, they viewed the product package stimuli at their homes 24 hours later.( n7) At this later time, participants completed all measures.
In Experiment 3, we used a single product category; namely, we used the ad and package stimuli for the cheese product and used the same placements of the ad and package manipulations as those in Experiment 2. We also gathered the same two dependent measures of brand attribute recall and Ab (α = .96).
As a manipulation check, we performed an ANOVA to ensure that though the ad and package cues differed in terms of form, they did not differ in the meaning they conveyed. As with the first two studies, we did not find any differences between the spokescharacter and the verbal attribute in terms of meaning or persuasiveness (p s > .10).( n8)
Brand attribute recall. The results of the specific planned contrasts when no competing attribute is present reveal effects similar to those we found in Experiment 2 for brand attribute recall. In H2a, we predicted that the matched ad-package condition for the spokescharacter would enhance recall, and it is supported marginally by greater recall for the spokescharacter package cue with the spokescharacter ad condition (M = .23) than with the verbal attribute ad condition (M = .16; F( 1, 155) = 3.48, p = .06). Although it is not significant for the verbal attribute ad-package condition (H2b), the pattern of means is in the predicted direction (Ms = .19 and .16; F( 1, 155) = .43, p > .10).
The planned contrasts for the presence of a competing cue for these matched conditions (H2d) are consistent with the findings from Experiment 2 as well. For the spokescharacter ad-package condition, recall is marginally lower when the competing information is present (M = .16) than when it is absent (M = .23; F( 1, 155) = 3.62, p = .06). The presence (M = .13) versus absence (M = .19) of the competing cue also results in lower mean values for the verbal attribute ad-package condition, though the difference is not significant (F( 1, 155) = 2.55, p > .10). In summary, for the spokescharacter, recall appears to be slightly greater for matched than for mismatched conditions, and the spokescharacter ad-package condition results in the highest recall value (M = .23) of all ad-package conditions. However, the presence of a new attribute appears to interfere with the retrieval of stored information, even in conditions in which the ad and package cues match.
Brand attitude. The planned contrast results also indicate consistency with the Experiment 2 findings for Ab. As we expected for the ad-package match, the results show that Ab is better when the spokescharacter package cue condition is paired with the spokescharacter advertisement (M = 4.32) than when it is paired with the verbal attribute advertisement (M = 3.57; F( 1, 155) = 3.92, p < .05), despite the spatial and temporally distinct ad and package exposure conditions. For the verbal attribute package cue, Ab is similar for both the verbal attribute (M = 3.57) and the spokescharacter (M = 3.78) advertisements (F( 1, 155) = .26, p > .10). We also found support for H2d, which proposes that A[sub b is less susceptible to the unfavorable effects of competing information in the spokescharacter ad-package condition; the results show that the presence (M = 4.08) versus the absence (M = 4.32) of a competing cue does not compromise Ab (F( 1, 155) = .31, p > .10). In addition, Ab is not significantly affected when the competing attribute is present (M = 3.23) than when it is absent (M = 3.57; F( 1, 155) = .83, p > .10) in the verbal attribute ad-package condition. The results also show that a match in the spokescharacter leads to a stronger Ab than does a match in the verbal cue, regardless of the presence or absence of competing information. In summary, these findings show that the spokescharacter ad-package match results in a more favorable Ab, and seven of the eight comparisons made in Experiment 3 are consistent with those in Experiment 2.
General Discussion
Although IMC campaigns have been strongly endorsed by both practitioners and academic researchers (Duncan and Everett 1993; Low 2000), relatively few controlled studies focus on consumers' responses to integrated campaigns, and to our knowledge, none directly addresses the use of spokescharacters. In this article, we use a series of studies to examine various IMC-related strategic combinations, including the use of spokescharacters versus verbal attribute cues; advertisement-package coordination; character relevancy; and the placement of new, favorable brand information on packages. Drawing from encoding specificity, network associations, and ELM theory, we find empirical evidence for the potential benefits of using spokescharacters in IMC campaigns. However, the higher-order interactions also suggest distinct conditions that must be considered if marketers are to realize both memory and attitudinal benefits.
Specifically, across the various manipulations, the pattern of findings suggests only one option that provides both memory and attitudinal benefits: More relevant spokescharacters rather than verbal attributes should be integrated into IMC campaigns in which there are no deviations from the primary message. These findings support the argument that even seemingly minor cosmetic inconsistencies can be influential (Goodstein 1993).
For consumers who focus on brand information, Experiment 1 demonstrates that marketers can obtain considerable benefits through the introduction and consistent use of characters that are perceived as more relevant to products. Related work in the spokesperson (e.g., Kamins 1990; Till and Busler 2000), visual processing (e.g., Heckler and Childers 1992; Lee and Mason 1999), and branding (e.g., Herr, Farquhar, and Fazio 1996; Keller, Heckler, and Houston 1998) literature streams also indicates the relevancy advantage.
However, consistent with research on incongruency (see, e.g., Fiske 1982; Goodstein 1993), we find that less relevant characters may motivate some consumers (ad processors) to elaborate on their processing of brand information. Although in Experiment 1 this incongruency marginally enhanced brand recall, there is a major potential disadvantage of using less relevant characters; namely, such characters could generate affective, non-message-related associations that adversely affect brand evaluations when characters are presented at subsequent points of consumer contact (i.e., point of purchase). Thus, in terms of overall Ab, our results indicate that marketers will reap greater benefits by designing symbols that convey key attributes that are relevant to advertised brands.
We also note that a spokescharacter ad-package IMC match in the absence of competing information yields both greater recall of brand attributes and more favorable Ab in Experiments 2 and 3, which suggests the superiority of this specific mode of attribute presentation. This option requires that everybody involved with the IMC campaign understands its primary message and consistently presents that message along with the spokescharacter across different points of consumer contact. As Low (2000) notes, such coordination is often more difficult than might be anticipated. If marketers prefer the flexibility of introducing new (favorable) information in IMC campaigns that feature spokescharacters, some benefits still can be realized because spokescharacters featured both in advertisements and on packages yield favorable Ab even when competing information is introduced in the campaign. However, this new information represents a distraction that appears to compete for consumers' cognitive resources and minimize the effectiveness of the IMC match, which in turn reduces the ability of the package character to initiate memory traces that enhance the retrieval of brand attributes.
However, IMC campaigns that use verbal information to describe attributes are more common in the marketplace. Unfortunately, our findings suggest that only minor benefits appear to be associated with verbal attribute coordination. In coordinated IMC campaigns in which there are no inconsistencies, verbal attributes might offer memory-related benefits; as we show in Experiment 2, verbal attributes that are used in advertisements and then are repeated on product packages appear to prime memory traces that are beneficial for decision making. However, this condition does not offer memory advantages in Experiment 3, and it fails to offer marketers the key outcome (i.e., more favorable Ab) compared with spokescharacters. Even without competing information, the verbal attribute match does not yield the same favorable results as do spokescharacters. Furthermore, if the IMC campaign becomes inconsistent at any point, all memory-related benefits are lost. In summary, IMC campaigns that use verbal information to communicate attributes may be common and practical, but they appear to offer fewer advantages than the use of coordinated campaigns that involve spokescharacters.
This research offers several contributions to applications of encoding specificity, network associations, and ELM conceptual frameworks and suggests some opportunities for additional studies that involve spokescharacters and IMC coordination. The three-way interactions demonstrate the complexity of the issues related to initial encoding and the types of cues and conditions during subsequent IMC exposures. Across studies, our findings suggest that more relevant characters appear to possess persuasive benefits that are accrued regardless of whether they are processed peripherally, centrally, or both, which is consistent with prior work on attitude persistence (Sengupta, Goodstein, and Boninger 1997). Despite the relatively small research stream, marketing researchers recognize the importance of IMC campaign studies. For researchers who are interested in examining the influence of temporally and spatially distinct exposure IMC contexts, the methodology in Experiment 3 offers a practical means.
We acknowledge several limitations that are common to experimental marketing research. First, in all three experiments, study participants received only one opportunity to examine the stimuli. In practice, consumers may be exposed to spokescharacter/product pairings on many occasions over the course of months or years. However, the findings from this research illustrate that the favorable effects of these characters even may be gained initially when characters featured in advertisements also appear on product packages. Second, the distraction task we used between the advertisement and package exposure in Experiments 1 and 2 is shorter than the intervals usually experienced in the marketplace. However, this distraction procedure is consistent with prior memory-related research, which shows that the procedure effectively clears short-term memory (Hunt and Ellis 1999; Keller 1991). Furthermore, the third study offers a more externally valid procedure to test the effects. Finally, to limit effects of differences in prior knowledge, our experiments focused on fictitious brands and characters. Because results for more familiar brands and characters potentially vary (Campbell and Keller 2003), we recommend that further research address effects across familiar brands and characters.
Given the paucity of controlled studies involving spokescharacters, there are several directions for further research. Because we gathered measures during only the package exposure stage, further research might extend our findings by collecting cognitive responses and evaluations immediately after ad exposures to various ad treatments. This extension might clarify why the combined spokescharacter/verbal attribute package cue performed equally well across the three ad conditions in Experiment 2 or why in Experiments 2 and 3 brand attribute recall was impaired but favorable attitudes were not affected when competing information was presented for the spokescharacter ad-package condition. Perhaps the competing information consumed cognitive resources (Burke and Srull 1988; Eagly and Chaiken 1993; Krishnan and Chakravarti 2003), and the spokescharacter provided during retrieval activated the more favorable Ab previously developed through peripheral processing. It would also be worthwhile to examine whether spokescharacters suppress claims made in advertisements by competing brands and to assess differences in package claims that were supplemental rather than competing with the original ad claims. Such extensions would provide a more complete explanation of the role of spokescharacters and verbal attributes in the memory process.
The continuous endorsements of IMC and the prolific use of spokescharacters in advertisements also warrant further inquiries that identify the boundary conditions for the effect of characters on recall and evaluations. One such issue related to the ELM is whether the strength of verbal claims can dominate spokescharacters and make them less powerful marketing communication cues. In addition, if researchers were to consider the effect of the package retrieval cues across brands with varying association and consideration set sizes, they might also identify the conditions in which characters have greater potential to benefit brands in terms of both memory and consumer choice.
Finally, spokescharacters might offer benefits beyond pure visual superiority. For example, in addition to depicting brand-relevant attributes, spokescharacters might embody certain perceived personality traits (Aaker 1997; Fournier 1998). Research that addresses these superiority effects of spokescharacters compared with other visual stimuli could complement our work by further examining the breadth of spokescharacters' value in campaigns.
In summary, although there is considerable opportunity for additional research, our findings contribute to the knowledge about the value of spokescharacters and empirical findings about IMC campaigns. The findings go beyond highlighting the general benefits of IMC (Naik and Raman 2003); they also demonstrate that even minor cosmetic deviations from coordinated campaigns can compromise measurable outcomes such as recall. In this day and age of fierce competition, brand parity, reduced loyalty, and brand accountability, marketers must understand the potential benefits and avoidable pitfalls of their efforts to enhance marketing communication effectiveness.
This research was supported by a grant from the Council on Research at Louisiana State University. The authors thank the four anonymous JM reviewers for their constructive comments and helpful suggestions during the development of this article. The authors provided external funds for the use of color in the appendixes.
( n1) We conducted an additional pretest to ensure that these meanings did not differ, in which we assigned 51 participants randomly to either a spokescharacter or a verbal attribute advertisement. While participants were exposed to the advertisement, they responded on a seven-point, single-item ("The information that is featured with the picture of the product is communicating that [the product] will [claim]") measure, anchored by "strongly agree" and "strongly disagree." The pretest findings verify that both conditions are similar in terms of meaning. For laundry detergent, both conditions similarly communicated that the product would "make clothes fluffy" (t = .68, p > .30). For cheese, the mean values for the belief that the product would "make you strong" were similar for both conditions (t = .90, p > .30).
( n2) Similar to the procedure in previous work (Desai and Keller 2002; Lane 2000), we exposed participants to typical consumer product goods and recruited them on a volunteer basis from business courses. There were 105 participants in Experiment 1; they ranged from 19 to 43 years of age. In Experiment 2, 364 participants between 19 and 44 years of age completed the study. Experiment 3 comprised 156 participants who were between 20 and 28 years of age.
( n3) We conducted a pretest to assess the degree to which the new, competing information on product packages was consistent with the primary message conveyed in the advertisement. Drawing from prior work (Heckler and Childers 1992; Peck and Childers 2003), we asked participants, "How consistent is the claim with the main advertisement message?" (anchored by "very inconsistent" and "very consistent"). We also asked them to rate their confidence in their answer to the question on a seven-point ("not confident" to "very confident"), single-item scale. The new information was perceived as low in consistency (M = 1.93), and participants were confident about that inconsistency (M = 5.81).
( n4) We used brand selection importance as a covariate in the analyses for two reasons. First, the use of the measure is consistent with prior research involving multiple product categories (Keller 1991). Second, it eliminated a small but significant product main effect in Experiment 2 (i.e., there were no significant product interactions). We asked participants to respond to the question, "How important is it to you to buy just the right brand of [product category]?" We measured their responses on a seven-point scale, anchored by "very important/very unimportant," "very essential/very nonessential," and "means a lot to me/means nothing to me."
( n5) In addition, we conducted two pretests to ensure that the package cue conditions did not differ in terms of the amount of information conveyed or their importance. The results from one pretest (n = 51) show that the amount of information conveyed about the main attribute across ad conditions did not differ, and the second pretest (n = 47) indicates that the primary ad attributes and competing package cue attributes were similar in terms of importance (ps > .10).
( n6) We appreciate the specific suggestions about the design of Experiment 3 provided by two anonymous JM reviewers.
( n7) We took several distinct steps to ensure that all study participants adhered to the specific study procedures. Participants received a sealed package that included the delay condition materials, and they were instructed to open the package 24 hours later; on the seal of the back of the package, there was a label indicating that they should open it only after a specific hour. In the space below this label, participants were told to write down the time they opened the package and sign their name. They also were required to provide their name, the date, the time they began the study, and where they were located while completing the study on the front page of the materials inside the package. All of the returned materials were checked; all participants provided the required information.
( n8) To assess ad cue strength, we asked participants to respond to a two-item, seven-point scale that we adapted from the work of Petty, Cacioppo, and Schumann (1983). The scales were anchored by "unpersuasive/persuasive" and "a weak reason/a strong reason" (a = .72).
Legend for Chart:
A - Independent Variables
B - Brand Claim Recognition F Value
C - Brand Claim Recognition Degrees of Freedom
D - Brand Claim Recognition eta²
E - Brand Attitude F Value
F - Brand Attitude Degrees of Freedom
G - Brand Attitude eta²
A B C D
E F G
Processing goals (PG) 3.16(†) (1, 97) .03
.44 (1, 97) .01
Relevancy (R) 2.54 (1, 97) .03
4.58(*) (1, 97) .05
Package cue (PC) .05 (1, 97) .00
.69 (1, 97) .01
PG x R 4.53(*) (1, 97) .05
.78 (1, 97) .01
PG x PC 2.89(†) (1, 97) .03
.53 (1, 97) .01
R x PC .01 (1, 97) .00
9.00(***) (1, 97) .09
PG x R x PC 8.94(***) (1, 97) .08
5.27(**) (1, 97) .05
(†) p ≤ .10.
(*) p ≤ .05.
(**) p ≤ .025.
(***) p ≤ .01. Legend for Chart:
A - Independent Variables
B - Brand Claim Recognition
C - Brand Attitude
A B C
Spokescharacter Package Cue
Brand Processing Goal
More relevant spokescharacter in advertisement 5.50 5.09
Less relevant spokescharacter in advertisement 4.08 3.16
Ad Processing Goal
More relevant spokescharacter in advertisement 5.15 4.68
Less relevant spokescharacter in advertisement 5.92 4.22
Verbal Attribute Package Cue
Brand Processing Goal
More relevant spokescharacter in advertisement 5.19 4.22
Less relevant spokescharacter in advertisement 5.02 4.75
Ad Processing Goal
More relevant spokescharacter in advertisement 5.39 4.54
Less relevant spokescharacter in advertisement 4.85 4.41 Legend for Chart:
A - Sources of Variation
B - Brand Attribute Recall F Value
C - Brand Attribute Recall Degrees of Freedom
D - Brand Attribute Recall eta²
E - Brand Attitude F Value
F - Brand Attitude Degrees of Freedom
G - Brand Attitude eta²
A B C D
E F G
Independent Variables
Ad cue (AC) 5.97(**) (2, 339) .03
7.67(***) (2, 339) .04
Package cue (PC) 8.43(***) (3, 339) .07
3.94(**) (3, 339) .03
Competing information (CI) .44 (1, 339) .00
.32 (1, 339) .00
Two-Way Interactions
AC x PC 1.68 (6, 339) .03
2.39(*) (6, 339) .04
AC x CI 1.59 (2, 339) .01
.62 (2, 339) .00
PC x CI .32 (3, 339) .00
1.05 (3, 339) .01
Three-Way Interaction
AC x PC x CI 2.02(†) (6, 339) .04
2.33(*) (6, 339) .04
(†) p ≤ .10.
(*) p ≤ .05.
(**) p ≤ .01.
(***) p ≤ .001.GRAPH: FIGURE 1 Experiment 1: Effects of Processing Goal, Relevancy, and Package Cue on Brand Attitude
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PHOTO (COLOR): APPENDIX A: Advertisement with the Relevancy Manipulation (More Relevant Spokescharacter)
PHOTO (COLOR): APPENDIX B: Advertisement with the Relevancy Manipulation (More Relevant Spokescharacter)
PHOTO (COLOR): APPENDIX C: Package with the Package Cue Manipulation (Spokescharacter)
Legend for Chart:
A - Ad Cue
B - Package Cue Spokescharacter Competing Information Absent
C - Package Cue Spokescharacter Competing Information Present
D - Package Cue Verbal Attribute Competing Information Absent
E - Package Cue Verbal Attribute Competing Information Present
F - Package Cue Spokescharacter and Verbal Attribute Competing
Information Absent
G - Package Cue Spokescharacter and Verbal Attribute Competing
Information Present
H - Package Cue Neither Cue (Control) Competing Information
Absent
I - Package Cue Neither Cue (Control) Competing Information
Present
A B D C E F G H I
Spokescharacter
Verbal attribute
Spokescharacter and
verbal attribute~~~~~~~~
By Judith A. Garretson and Scot Burton
Judith A. Garretson is an assistant professor and the Janet I. and E. Robert Theriot Professor, Department of Marketing, E.J. Ourso College of Business Administration, Louisiana State University.
Scot Burton is a professor and Wal-Mart Chair in Marketing, Department of Marketing and Logistics, Sam M. Walton College of Business, University of Arkansas.
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Record: 182- The Role of the Institutional Environment in Marketing Channels. By: Grewal, Rajdeep; Dharwadkar, Ravi. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p82-97. 16p. 1 Diagram, 1 Chart. DOI: 10.1509/jmkg.66.3.82.18504.
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The Role of the Institutional Environment in Marketing Channels
Set within the political economy framework, marketing channels literature predominantly has used an efficiency based task environment perspective and largely overlooked a legitimacy-based institutional environment approach in studying channel attitudes, behaviors, processes, and structures. The purpose of this article is to highlight the importance of the institutional environment and develop a comprehensive conceptual framework that incorporates the institutional environment into current marketing channels research. The institutional environment perspective relies on the primacy of ( 1) regulatory institutions (e.g., laws), ( 2) normative institutions (e.g., professions), and ( 3) cognitive institutions (e.g., habitual actions) in influencing the legitimacy of channel members. Using institutional theory, the authors augment the current task environment approach by developing three institutional processes and their underlying mechanisms and elaborating on how these institutions might influence channel relationships. The article ends by laying out a research agenda and highlighting managerial implications.
In the past two decades, many scholars have assessed the implications of the macroenvironment for microdyadic channel structures and processes using the political economy framework (Achrol, Reve, and Stern 1983; Frazier 1999; Hutt, Mokwa, and Shapiro 1986; Stern and Reve 1980). In most of these studies, researchers implicitly have ascribed active choice behavior to channel constituents while stressing efficiency in managing channel relationships. Specifically, scholars have considered the implications of environmental uncertainty and/or dependence on environmental resources for dyadic sentiments (such as conflict and cooperation; Dwyer, Schurr, and Oh 1987), power balances (such as power-dependence relationships; Frazier 1983b), and relationships with extradyadic entities (such as regulators and other actors; Dutta, Heide, and Bergen 1999) from a traditional economic efficiency perspective. In the process, researchers have largely overlooked the ubiquitous influence of the institutional environment and how inter-organizational relationships such as marketing channels are embedded in the larger social context (Granovetter 1985). Recent advances in organization theory suggest that organizations strive for both economic fitness, which emphasizes the competition for scarce resources and underscores the importance of the task environment, and social fitness, which stresses the pursuit of legitimacy in the eyes of important societal stakeholders and accentuates the significance of the institutional environment (DiMaggio and Powell 1983; Oliver 1991; Scott 1987). However, limited research in the political economy domain has explicitly addressed the implications of the social context for channel behaviors.
The purpose of this article is to develop a conceptual framework that incorporates the institutional environment into current marketing channels research. The institutional environment perspective relies on the primacy of ( 1) regulatory institutions (e.g., laws), ( 2) normative institutions (e.g., professions), and ( 3) cognitive institutions (e.g., habitual actions) in influencing the legitimacy of channel members in the larger societal context.[ 1] We augment the political economy framework by developing three institutional processes (regulating, validating, and habitualizing) that lead to the formation of the these institutions (regulatory, normative, and cognitive, respectively). We suggest that the institutional processes result in institutional pressures (imposition and inducements for regulating, authorizing and acquisition for validating, and imprinting and bypassing for habitualizing) and that these institutional pressures influence channel structures and processes. To illustrate the nature of the influence of institutional pressures on channel structures and processes, we develop illustrative propositions. Moreover, throughout this article, we refer to various examples of institutionally driven channel characteristics with respect to the United States (e.g., automobiles), Japan (e.g., keiretsu systems), France (e.g., agricultural products), China (e.g., manufacturing), and other countries that demonstrate the utility and applicability of the institutional perspective developed herein.
Our research makes three contributions that advance understanding of the influence of the institutional environment on marketing channels: First, we identify the various institutions in the institutional environment and highlight the processes by which they influence channel dyads. This process framework, when combined with the task environment perspective, provides a more holistic conceptualization of the macroenvironment in the political economy framework. Second, we elucidate the influences of the mechanisms used by the institutions on marketing channels. We thereby demonstrate the utility of the institutional perspective in explaining internal economy and polity issues that heretofore may have been considered only from an economic efficiency standpoint. Third, we contribute to institutional theory literature by developing a process-based typology of institutional environments. Such a typology adds to the existing outcome based typologies by providing richer insights into the methods by which features of institutional environments are formed.
To position the institutional environment perspective with respect to the political economy framework, we briefly review extant research that highlights the dominant efficiency-based research streams (for a detailed review, see Frazier 1999) and then develop a conceptual framework that could account for social context by considering all pervasive institutional environments in and around marketing channels. Most research on marketing channels builds on the political economy frame-work, in which the channel dyad is a social system influenced by economic and sociopolitical forces (Stern and Reve 1980). Researchers have focused explicitly on internal economic structures and processes, internal sociopolitical structures and processes, and the external economic environment (Achrol, Reve, and Stern 1983). They also have extended this framework to develop the concept of parallel political marketplaces operating in the environment of a channel dyad (Hutt, Mokwa, and Shapiro 1986). However, we believe that marketing channels research has overlooked the ubiquitous influence of the institutional environment.
Internal economy researchers have examined economic structures ranging from spot transactions to vertically integrated distribution channels. Set within the transaction cost economics framework (Rindfleisch and Heide 1997), this research has found that asset specificity (e.g., Anderson and Coughlan 1987), environmental uncertainty (e.g., John and Weitz 1988), and scale economies (e.g., Klein, Frazier, and Roth 1990) influence the level of vertical integration. In examining economic processes (i.e., the nature of decision-making mechanisms used by channel constituents; Stern and Reve 1980, pp. 55-56), researchers have found that centralization, formalization, and participation influence the functioning of channel relationships (e.g., Dwyer and Oh 1987; John 1984).
Internal polity research (i.e., the nature of the power-dependence relationships among channel constituents; Stern and Reve 1980, p. 57) has studied the possession, use, and effects of the power of one channel member over another (e.g., Anderson and Weitz 1992; Frazier 1983b), the implications of the nature of dependence on channel member attitudes (e.g., Kumar, Scheer, and Steenkamp 1995; Lusch and Brown 1996), and the performance consequences of control mechanisms (e.g., Bello and Gilliland 1997; Celly and Frazier 1996). In understanding sociopolitical processes (i.e., the dominant channel sentiments), researchers have concluded that conflict (e.g., Frazier and Rody 1991), commitment (e.g., Morgan and Hunt 1994), and social norms (e.g., Heide and John 1992) influence channel attitudes.
Beyond the microdyadic issues, channel researchers have scrutinized the implications of the external economic environment for dyadic structures and processes. Using the task environment approach, two separate domains of research that mirror developments in organization theory have evolved simultaneously (Aldrich 1979; Pfeffer and Salancik 1978). The first domain considers the environment as an information source, which results in the problem of uncertainty about external conditions (Aldrich 1979). Research in this tradition has studied the impact of environmental heterogeneity and variability on channel structures and processes (e.g., Achrol and Stern 1988; Dwyer and Welsh 1985). The second domain, based on resource dependence theory, considers the task environment a stock of resources and raises the issue of channel member dependence on the environment for critical resources (Pfeffer and Salancik 1978; see also Achrol and Stern 1988; Dwyer and Oh 1987).
Although the external polity of marketing channels has been recognized in the political economy framework, the institutional aspects of the external polity have not been accorded their due importance in research. Even though some scholars have addressed a few facets of the external polity in terms of the regulatory environment, parallel economy, or institutional environments in isolation, they have ignored the implications of the broad array of these institutional influences on the internal polity and economy of marketing channels (Hutt, Mokwa, and Shapiro 1986; McCammon 1971; Stern and Brown 1969). For example, scholars have recognized that social norms develop within a broader institutional context, but they have not examined the institutional processes through which such norms develop (Achrol 1997; Anderson and Narus 1990; Heide and John 1992). Also, scholars have recognized that channels adopt prescribed behavior on the basis of regulatory forces and/or normative reference organizations, but no research has delved into the institutional specifics of such adoptions (Bridges 1971; Gill and Stern 1969). We maintain that institutional environments have received limited theoretical and empirical attention because of the lack of a comprehensive framework that can enable researchers to assess the implications of the institutional environment in an orderly manner.
Institutional Environment
Many organizational theorists have argued that traditional environmental approaches ignore not only the institutional influences on actors in an organizational system but also the manner in which institutional bases are imported into organizations as underlying invisible assumptions. We rely on research in institutional theory (DiMaggio and Powell 1983; Meyer and Rowan 1977; Scott 1987) to redefine the notion of environment and complement environmental research on the political economy framework.
A conceptual framework depicting the relationship between the various facets of the institutional environment and the internal political economy of marketing channels appears in Figure 1. Institutional environments refer to the processes of institutionalization and corresponding institutions (external as well as invasive) and mechanisms of influence that pertain to legitimacy in a particular societal context. Legitimacy, the key demand factor in assessing social fitness, "is a generalized perception or assumption that the actions of an entity [channel member] are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions" (Suchman 1995, p. 574). Unlike the task environment approach, with its emphasis on uncertainty and dependence reduction, the institutional approach focuses on the necessity of organizational legitimacy. This concern with social fitness leads to the development of processes that result in the formation of institutions and the emergence of corresponding institutional mechanisms that influence the internal polity and economy of marketing channels. The political process of making public policy (Kelman 1987), the communal process of the emergence of societal norms (Selznick 1984), and the cognitive process of habit formation (Berger and Luckmann 1967) all represent examples of institutionalization processes.
The institutions that result from institutional processes (such as formation of rules, laws, certification, accreditation, prevalence, and precedence) have two underlying characteristics: ( 1) They can be identified in terms of patterns (Jep-person 1991), and ( 2) they have the ability to recur by reproducing themselves (Friedland and Alford 1991). Most institutions arise in response to pressures and conditions of a particular era. Some of the pressures and conditions may be temporary, whereas others may be permanent. However, when the institutions (such as regulatory agencies, professional associations, or habits) are in place, they tend to change slowly and incrementally. In the long run, such adaptations may be far from ideal, and the societal players may need to adjust their behaviors to institutions, rather than vice versa.
The institutions use various mechanisms at their disposal to describe the rules, expectations, and habitualized actions to which organizations must conform to receive legitimacy (Scott and Meyer 1983). In doing so, they simultaneously empower and constrain; they provide organizations with avenues for pursuing activities consistent with predefined patterns of conduct while stifling innovation and creativity by precluding some activities (Fararo and Skvoretz 1986). When the institutions and processes of institutionalization that influence channel behaviors, structures, and processes are taken together, they form the institutional environment. As a caveat, in highlighting the differing emphases (task environment and efficiency versus institutional environment and legitimacy), it is tempting to categorize the two types of environments in a dichotomous manner. Such a distinction would be rather superficial, because institutional and task factors are likely to influence each other. This interaction, however, is beyond the scope of this article. Therefore, we focus on the implications of the institutional environment for channel relationships and acknowledge that the interplay between the two types of environments should be addressed.
Underlying Processes, Institutions, and Mechanisms
Most institutional theorists recognize three broad sets of institutions and corresponding legitimacy concerns (DiMaggio and Powell 1983; Suchman 1995). First, regulative institutions include regulatory bodies that can influence channels to behave in certain ways (patterns) again and again (regeneration). Regulative institutions focus on the pragmatic legitimacy concerns of channel members in managing the demands of regulators and governments (Kelman 1987; Figure 1). In most cases, the basis of compliance is expedience, and noncompliance can result in either direct or indirect regulatory sanctions. Second, normative institutions include trade associations, professional associations, accreditation agencies, or professions themselves that can use social obligation requirements to induce and regenerate patterns within channels. Normative institutions are concerned with procedural legitimacy and require channel members to embrace socially accepted norms and behaviors (Selznick 1984; Figure 1). The basis of compliance in this case derives from social obligations, and nonadherence can result in societal and professional sanctions. Third, cognitive institutions represent culturally supported habits and exert subtle influences on channel behaviors, which then tend to be repeated. In most cases, they are associated with cognitive legitimacy concerns that are based on some taken-for-granted cultural account of channel management (Berger and Luckmann 1967; Figure 1). Compliance in the case of cognitive legitimacy concerns is due to habits; channel members may not even be aware that they are complying.
To study how the institutional environment influences the internal polity and economy of marketing channels, we turn our attention to the processes of institutionalization that result in the formation of the previously mentioned institutions and their subsequent impact on channels (Bakke 1955; Selznick 1984). On the basis of the differing nature of institutions and their legitimacy concerns, we conceptualize the corresponding institutional processes in terms of the ( 1) creation of obvious forces stemming from regulatory bodies (processes of regulating), ( 2)development of midrange pressures to comply with societal expectations of voluntary associations and professions (processes of validating), and ( 3) importation of the invisible and habitual aspects of social reality (processes of habitualizing). In each of the three cases, the processes use different mechanisms to govern channel actions by establishing predefined patterns of conduct and guide channel behavior in specific directions. Taken together, the three processes and their associated mechanisms elucidate the institutional environment that governs channel attitudes, behaviors, processes, and structures.
Processes of Regulating
Regulating processes result in the evident interactions between channel members and regulatory institutions. Some regulatory institutions (e.g., governments at federal, state, and local levels; the legal system) exist to ensure stability, order, and continuity of societies (Arndt 1979; Kelman 1987). Others (such as the Federal Communication Commission, the Federal Trade Commission, and the Bureau of Alcohol, Tobacco, and Firearms) are created specifically to ensure social welfare, promote fair competition, or protect the weaker elements of society. However, the definition and conceptualization of social welfare, competition, or weak elements vary across societies (Kostova and Zaheer 1999; Krapfel 1982). These institutions arise in the context of their time and are influenced by the structure of the political marketplace (Benson 1975; Cook 1977). The structure of the political marketplace is manifest in ( 1) the relative power of the various societal constituents; ( 2) the extent of linkages among these constituents, including the interconnectedness and multiplicity of demands made by them; and ( 3) the societal support garnered by these constituents at large (Hutt, Mokwa, and Shapiro 1986).
Over time, these institutions begin to act as interpreters and enforcers of laws; in that capacity, they interact with various channel members or their representatives. Two primary mechanisms used by regulatory institutions to influence the structures and processes of channels are imposition and inducements. Although channel structures and processes can evolve by the volition of channel members, regulatory institutions often are sufficiently powerful to impose direct constraints, in the form of authoritative orders, or indirect constraints through rigorous rules and regulations. Alternatively, when regulatory institutions do not possess the ability to institute legal constraints, they may be inclined to provide valued inducements and influence the internal economy and polity of marketing channels.
Imposition. Regulative institutions use coercive power when they perceive that channel members' efforts are in conflict with the larger societal good. When this occurs, the societal response is to create institutional elements with the coercive ability to manage the socially illicit aspects of channel functioning. These institutional elements use their authority to interpret societal standards and impose constraints on channel structures and processes. Although such constraints may impinge on economic efficiency, neoclassical economic theory shows that they are beneficial to society at large (Oliver 1991). Cases in point include the regulation and subsequent deregulation of the telephone industry due to changing perceptions about the monopoly power of AT&T (Hall and Clark 1984; Yang 1997) and the Food and Drug Administration's regulations governing the sale, distribution, advertising, and promotion of cigarettes (Dwyer 1996; Rosenfield 1996).
Channel members can respond to such constraints by either resisting or accepting them. Frequently, channel constituents meet such impositions with resistance and institute cosmetic changes to ward off the unwanted consequences of noncompliance (Stern, El-Ansary, and Coughlan 1996). However, when enacted as laws, these constraints are likely to force channels to make necessary changes in their structure and processes (DiMaggio and Powell 1983). For example, cigarette manufacturers initially opposed the regulation of their distribution channels. Nonetheless, with changing times and new information about possible health risks to smokers becoming widely available, regulatory bodies have been able to influence the distribution arrangements in the cigarette industry significantly, and cigarette makers no longer can overlook issues related to addiction in minors. These institutional forces, which originate in regulatory bodies and consumer forums, are responsible for the imposition of certain channel structures (e.g., limits on selling tobacco products through vending machines).
Inducements. Regulatory constituents often do not possess the power or authority to impose structural definitions on channels. However, these institutional bodies might be in the position to provide strong inducements for channel constituents to conform to their wishes (Meyer and Scott 1992). Inducement mechanisms create structural changes by providing incentives (or disincentives) to channel members for conforming (or not conforming) to the demands of the agency that is offering the inducement. Funding decisions by government and other institutional agents fall within this purview. Government funds come with the added requirement that the recipient organization provide detailed evidence with respect to structural and/or procedural conformity (Scott 1987).
Incentives provided in the form of subsidies by federal and state governments to multinational corporations (MNCs) and disincentives in the form of tariff or administrative trade barriers are examples of some common inducement mechanisms. Typically, such subsidies and barriers influence the physical flow of products and the formation of channel structures of multinational subsidiaries in the host country. These inducement mechanisms emerge when regulatory institutions decide that the costs of inducements are offset by the benefits to the society that emanate from economic growth or the prosperity of local businesses. As an illustration, the U.S. Department of Agriculture provided $1.7 billion of inducements (over a ten year period) to manufacturers, cooperatives, and trade associations to promote the selling of U.S. products in foreign markets and thereby influenced channel arrangements in foreign countries (Hill 2000). Although specific subsidies may be given to a particular organization, often they are codified and accessible to a multitude of organizations. The many special economic zones(SEZs) along China's coast explicitly provide tax incentives, lower foreign exchange restrictions, and lower regulatory pressures for potential overseas investors (Spar 1994). Consequently, various MNCs have used their Chinese subsidiaries or joint ventures to make inroads into the potentially large Chinese local market or have used the SEZs as low-cost manufacturing bases to export products to developed countries.
Alternatively, inducements can work indirectly. For example, Palamountain (1955) documents the processes by which chain taxes developed as inducements for small, family-owned stores. Small businesses grouped together to fight the "common enemy," that is, the chain stores, and chain taxes were levied on each store, in excess of a certain number, owned by a single organization. The magnitude of these taxes often increased with the number of stores. According to Palamountain (1955), external factors, such as chain ownership outside the region, increased the cohesiveness of the movement. Eventually, the lobbying and other political activities of these groups of small businesses resulted in chain taxes (Stern and Brown 1969). Thus, taxation policies were used effectively to provide some form of inducement in the distribution channels of prescription drugs and groceries.
Processes of Validating
Processes of validating subsume the midrange processes that represent the interactions between normative institutions (such as trade associations, professions and professional associations, and mimicking behaviors) and channel members and give rise to standards for socially acceptable behaviors (Baum and Oliver 1991; Meyer and Zucker 1989). Normative institutions, such as trade associations, may arise to protect economically weaker segments within a channel (e.g., weak, small retailers versus large, multi-outlet retailers) and lead to channel arrangements that are economically suboptimal. Other institutions, such as professional associations, may emerge to promote the interests of their members or create accreditation standards for their profession. Normative institutions also may evolve out of mimetic behavior on the part of channel members. In conditions of high environmental uncertainty, channel dyads may copy the structures of other channels that are not necessarily efficient but nevertheless are considered legitimate from the societal perspective. Again, over time, channel structures that allay the concerns of normative elements by conforming to the norms of the institutional constituents are rewarded with institutional support, despite being suboptimal (Elsbach 1994).
We refer to the two mechanisms associated with normative institutions as authorization and acquisition. Authorization involves the development of rules or codes of conduct that are deemed appropriate and require channel members to seek voluntarily the approval of the authorizing agents, such as trade associations or professionalization agencies. Acquisition refers to channel members' attainment of structures and processes by mimicking other channels to achieve legitimacy without being fully cognizant of the means-ends relationships that reside within the structures and processes.
Authorization. Many societies support the formation of organized bodies, such as trade associations or labor unions, to further the interests of their members. These institutions typically lack regulatory authority but can use their standing in society to influence codes of conduct and behavior. Such codes may be detrimental from a purely economic efficiency perspective but benefit the organizations that adopt them by increasing their procedural legitimacy. In the late 1980s, for example, there was tremendous pressure within the textile industry to surmount global challenges from low-cost manufacturing organizations located in developing countries (Dreyfack 1986; Levi 1989). The industry trade association initiated the "Crafted with Pride in the USA" program to legitimize buying from high-cost local manufacturers by emphasizing consumers' positive perceptions of locally produced goods and to increase awareness of the country of origin through advertising. In response to such mobilization, Wal-Mart was one of the first organizations to institute a "buy American" program, which was adopted subsequently by many retailers such as Sears, Roebuck and Co.; JC Penney; and Kmart (Buzzell and Ortmeyer 1995). Recently, authorization mechanisms have been used to protect local channel members from competition in the global environment. For example, the major French agricultural trade association Federation National des Syndicates d'Exploitantes Agricoles, which emerged after World War II to protect the interests of French farmers, uses a "national self-sufficiency" justification for channels of agricultural products that make no sense from an economic perspective for many French consumers (The Economist 2000).
In addition to trade associations, professions and professional associations can promote procedural legitimacy to establish a working environment, control output, and create bases for occupational autonomy (DiMaggio and Powell 1983; Larson 1977; Scott 1987). There are two main sources for the pressures of professionalization: First, formal education systems provide legitimacy for the intellectual resources in a society. Second, professional networks that span organizations propagate similar standards and models. Both these sources result in a pool of somewhat exchangeable people who occupy similar positions across organizations and possess similar dispositions and skills (DiMaggio and Powell 1983; Perrow 1974). Empirical research provides support for the emergence of socially accepted behaviors due to the pressures of professionalization (March and March 1977). Professional standardization or certification of organizations results in similar structural arrangements that have normative acceptance (e.g., financial reporting in successful firms; Mezias 1990). At the same time, professionalization can further the interests of channel members. For example, the higher degree of cohesion and organization in the movement against chain stores by small drug stores in comparison with the grocery stores' movement was attributable to greater reliance on professionals in the distribution of pharmaceuticals than in the distribution of groceries (McCammon 1971; Strasser 1998).
Acquisition. Not all pressures emanate from distinctive institutions that are easily identifiable. When cause-effect relationships are poorly understood in the organizational context, for example, structures and processes that may have suboptimal economic outcomes often are adopted (Haunschild and Miner 1997; Haveman 1993). The recent adoption of total quality management (TQM) programs by many firms is an example of poorly understood organizational processes. Day (1994) points out that two-thirds of TQM programs are terminated in less than two years, and in many of the failures, firms have a poor understanding of the implementation requirements of such programs. Because the goals of these programs are ambiguous and the environment is uncertain, firms simply mimic the structures, processes, and behaviors of other firms they deem legitimate.
Marketing scholars (e.g., Dickson 1992) also have studied mimicking behaviors in competitive strategy. The main thesis of this research stream is that a firm mimics the strategies and behaviors of its successful competitors. Dickson (1992, p. 79) suggests that the most effective method of strategic planning is to "study how admired companies in other markets make decisions and to imitate their planning procedures." Competitive strategy literature views imitation as an uncertainty reduction mechanism and recommends copying successful strategies (Jacobson 1992), whereas institutional theory literature views this uncertainty reduction mechanism as a means to attain legitimacy (Scott 1987). Institutional theorists, however, acknowledge that legitimacy frequently goes hand-in-hand with success (DiMaggio and Powell 1983). As the link between effort and performance becomes more ambiguous, it is often difficult to separate success mimicking from legitimacy mimicking (Scott 1995). We believe that legitimacy mimicking may offer "partial explanations of social phenomena that are not explainable in purely economic terms" (Gill and Stern 1969, p. 35).
Therefore, it is important that channels acquire structures and processes by mimicking the actions of other legitimate channels, irrespective of efficiency considerations (Haunschild 1993; Haveman 1993; Kraatz 1995). Because the primary motivation behind mimicking behaviors is to acquire the structures and processes of the channels that are perceived to be legitimate, legitimizing processes play a critical role in developing the pressures of acquisition that manifest themselves in mimicking behaviors. The procedure suggested here develops as follows: A channel adopts a structural or behavioral change and is perceived as having enhanced its legitimacy. Other channels imitate the change, believing that by adopting the structural transformation, they also will be perceived as legitimate. Over time, these structural transformations obtain normative sanctions from the community of practice or, in some cases, the entire society. In addition to mimicking success, researchers attribute the acquisition to the bounded rationality of the adoptee organization; it may not be the best solution, but it is good enough (Scott 1987).
There are many cases of acquisition of channel structures and processes. In the early 1990s, U.S. automobile manufacturers mimicked their Japanese counterparts' supplier management strategies by treating all suppliers as first-tier suppliers (close partners). In reality, most Japanese automobile manufacturers had a four-tiered supplier system, ranging from full-service providers and integrated partners at one end to contractual commodity suppliers at the other. Under pressure to replicate successful Japanese automobile manufacturing strategies, U.S. auto manufacturers mimicked poorly understood channel structures and processes. Similarly, the initial adoption of the Internet by many firms as a communication and distribution channel was more a legitimacy concern (to portray an image of being technologically savvy) than an economic motive (Grewal, Comer, and Mehta 2001).
Processes of Habitualizing
Habitualizing refers to the invisible, base-level institutional processes in which actions that are repeated become cast into a particular pattern that then can be reproduced with minimal effort and is recognized by its performer as the particular pattern (Berger and Luckmann 1967). All human, organizational, and channel behaviors are susceptible to habitualizing. Habitualizing institutions emerge for two primary reasons: First, the creation of social order requires the adoption of routines that can be easily reproduced, which leads to habitualizing. Second, psychological economies that result from the organizational ability to manage repetitive situations also require habitualizing (Berger and Luckmann 1967). Habitualizing makes it possible for channel constituents to develop informal psychological contracts that are based on common understandings and decreases the need for channel members to articulate structures and processes explicitly and regularly (Zucker 1983). Thus, habitualizing is both a cognitive and a phenomenological process that causes social relationships and behaviors to be taken for granted (see also Alderson 1957).
The two primary mechanisms that facilitate the processes of habitualizing are imprinting and bypassing. Both these mechanisms result in "programmed actions" (Berger and Luckmann 1967, p.75) or "common responses to similar situations" (Mead 1934, p. 263). Imprinting refers to the maintenance of structures and processes that were codified in the early years of the organization's existence and have become sacrosanct because of reification and symbolism. Bypassing captures the process by which institutional expectations are defined in the larger cultural context, which reduces the need for well-articulated structures and processes.
Imprinting. Imprinting represents the preservation of channel structures and processes over time. Research on organizational founding suggests that organizations acquire features at the time of their inception, and subsequent inertia preserves these characteristics (Baum and Oliver 1992; Lomi 1995). Historic considerations often can explain many issues related to the internal polity and economy of marketing channels (Corey, Cespedes, and Rangan 1989). Palamountain (1955, p. 107), for example, documents how the dominance of the "big three" automobile manufacturers over their dealers has historical roots:
From 1900 to 1920 the agent gradually assumed more responsibilities and performed more functions, finally becoming an independent dealer. But his [the dealer's] independence has never become as full as that of most merchants, and in some respects, he [the dealer] is still primarily a manufacturer's agent.
Frequently, organizational inertia precludes rational adaptations, and therefore even if the structures are inefficient, they remain unchanged(Baum and Mezias 1992; Kimberly 1975).As the channel participants mature among these structures, it is difficult for them to understand the necessity of change, which results in the imprinting of these structures. In his study of the evolution of multiple industry prototypes, including automobile repairs, air transport, construction, hotels, and railroads, Stinchcombe(1965) recognizes that the organizational structures and processes of firms across industries retain remnants of their foundation. His analysis shows that habitualized practices operate as powerful myths and are ceremonially reproduced (see also Meyer and Rowan 1977). As Stinchcombe (1965, p. 143) states, a strong correlation exists "between the time in history that a particular type of organization was invented and the social structure of the organization of that type which exists at the present time."
Bypassing. Often, cultural control substitutes for structural control, which results in the bypassing of formal organizational structures and processes (Zucker 1977). Such a phenomenon occurs in highly institutionalized environments (e.g., educational institutions), where participants (e.g., principal, teachers, students, parents) are aware of their role expectations, irrespective of their organizational affiliation (Meyer, Scott, and Deal 1981). In such environments, role expectations and definitions (that is, students come to learn and socialize) are the same across organizations (educational institutions) with widely shared beliefs (students respecting and listening to teachers).As a result, organizational actors and role players are highly socialized into their role expectations, acting habitually in response to environmental signals and thereby bypassing formal structural controls (Gill and Stern 1969).
In the context of channels, the process of bypassing involves the mapping of shared symbols and beliefs onto channel structures and processes by the channel constituents, in line with cultural expectations. Consequently, it is less essential to encode channel members' expectations into channel structures and processes, because cultural infrastructure already embeds the expectations of the channel constituents. Perhaps the best example of cultural control is the keiretsu system used by Japanese firms. As Kenneth Courtis, Bank Economist and Historian of Japan, notes about keiretsu systems, "These groups operate as communities of shared interest, shared capital, and shared risk.... They can look at problems and deal with it from A to Z, from manufacturing, sourcing, production, financing, to distribution, to after sales. They can cut the whole network" (Smith 1995, p. 293).
The success of the keiretsu system in the U.S. market and the challenges faced by U.S. MNCs in establishing themselves in Japan hint at the power of this cultural control mechanism. Similar examples of traditional cultural control mechanisms exist in other countries, including guanxi in China, blat in Russia, pratik in Haiti, and chaebol in South Korea (Nee 1989, 1992; Walder 1986; Xin and Pearce 1996).
Regulatory, normative, and cognitive institutions define the social context of channels and bring to the forefront the legitimacy concerns of various societal stakeholders. We now consider how these varied legitimacy concerns can sway the internal sentiments of dyads or encourage the adoption of particular economic structures in channel arrangements. In other words, how does the social context facilitate or hinder transactions that occur within channel dyads? To answer this question, we explore the implications of the mechanisms used by different institutions for aspects of the internal polity and economy of marketing channels. Thus far, we have established that regulatory institutions can either coerce or provide inducements to channel members. Similarly, normative institutions can use authorization and acquisition mechanisms to influence channel members' behaviors, and cognitive institutions can lay the foundations for channel members' behavior on the basis of well-established cultural mores and habits. We believe that in their quest for social fitness, channel members often behave in a manner dictated by the institutions and consequently adopt structures, practices, and sentiments favored by these institutions.
As illustrated in Figure 1, the three institutionalizing processes (regulating, validating, and habitualizing) and their underlying mechanisms coexist in real-world settings (Suchman 1995). Consequently, channel members are likely to focus on all three legitimacy concerns with varying emphasis; moreover, the importance of particular institutions or legitimacy concerns may vary depending on the social context of the channel. Because each channel may be affected simultaneously to a varying degree by the underlying mechanisms of the three processes of institutionalizing, it is imperative that researchers identify the relevant processes, along with the appropriate influence mechanisms, with respect to a set of channel dyads.
We also believe that each institutional process may influence all facets of the channel dyad. It might behoove researchers to consider the simultaneous implications of the processes for both internal political and economic structures and processes. For example, regulatory institutions may bring pragmatic legitimacy concerns to the fore, which would influence the level of integration, formation of norms, and the nature of control mechanisms used, among other things. Consequently, researchers must be aware that the processes of institutionalization may affect the entire social system of the dyad. It would also be inappropriate to consider the institutions from a stagnant perspective. The institutional theory framework favors a dynamic approach in which institutions gradually evolve over time (Meyer and Scott 1992). In particular, regulatory or normative institutions formed at one point in time eventually can codify channel structures and processes that, down the road, may appear to be products of cognitive institutions. The French normative institutions that originated in the post-World War II era have taken on cognitive overtones in recent years (The Economist 2000). Similarly, regulatory institution scan evolve normative overtones. For example, in the context of the Chinese market, regulatory mechanisms that provide inducements in the form of subsidies may have driven initial market entry, and subsequent market entry could be ascribed to the mimicking mechanisms that result from normative institutions (Spar 1994). Therefore, it is essential to recognize the evolutionary nature of the institutions, whereby visible institutions can result in less visible invasive institutions (shown by the different shades of arrowsin Figure 1).
To demonstrate how this adoption might occur, we develop propositions that link facets of the institutional environment with dimensions of the internal political economy of the channel. Consistent with Achrol, Reve, and Stern (1983), we use the focal dyad (representing interactions between two adjacent channel members) as the unit of analysis, and we appropriate four sectors (input, output, competitive, and regulatory) from the political economy framework into the institutional domain. In doing so, we build on the political economy approach and, at the same time, highlight the importance and implications of the institutional environment for channel management.
This vast domain of channel research precludes us from developing an exhaustive proposition inventory that links all mechanisms in the institutional environment to all variables studied by channel research. However, to demonstrate the utility of our framework and with the hope that empirical researchers will be able to develop parallel propositions and study the vast possibilities of linkages between institutional environment and channel political economy, we develop a limited propositional inventory that links each mechanism (imposition, inducement, authorization, acquisition, imprinting, and bypassing) to at least one of the four aspects (economic structures, economic processes, sociopolitical structures, and sociopolitical processes; Figure 1) of the internal political economy of channels(Stern and Reve 1980).Using Frazier's (1999) exhaustive review, we select variables to represent all four aspects of the internal political economy.
We examine ( 1) vertical integration (economic structures); ( 2) centralization, formalization, and participation in channel decision making (economic processes); ( 3) control mechanisms and the use of power (sociopolitical structures); and ( 4) relational norms of solidarity and opportunism (sociopolitical processes). Thus, we develop propositions for the influence of ( 1) imposition mechanisms on channel norms of solidarity; ( 2) inducement mechanisms on channel integration; ( 3) authorization mechanisms on centralization, formalization, and participation; ( 4) acquisition mechanisms on the use of process/output control; ( 5) imprinting mechanisms on opportunism; and ( 6) bypassing mechanisms on the use of power within a channel (Figure 1).
Processes of Regulating
Imposition. Imposition could influence both the internal economy (e.g., by dictating the level of channel integration) and the internal polity (e.g., by influencing decision making in channels). To demonstrate how imposition could influence the dyadic social system, we use one aspect of internal polity, that is, social norms. Marketing channels researchers suggest that social norms that reflect the shared expectations within dyads (such as solidarity, flexibility, mutuality, role integrity, and harmonization of conflict) influence channel outcomes (Boyle et al. 1992; Cannon and Perreault 1999; Heide and John 1992; Jap and Ganesan 2000). Although research in this domain suggests that higher levels of shared expectations will result in improved channel performance, little attention has been paid to the source of channel norm formation in the societal context. We believe that imposition mechanisms that raise pragmatic legitimacy concerns in the input, output, and regulatory sectors of the channel environment can influence norms in a channel dyad. We illustrate the implications thereof using the norm of solidarity. This particular norm assesses the degree to which channel partners believe that their success depends on cooperating with one another (Cannon, Achrol, and Gundlach 2000).
For a given channel dyad, the imposition pressures and ensuing concerns for pragmatic legitimacy from the regulatory environment can be either symmetric or asymmetric. Imposition pressures are symmetric when both firms of the focal channel dyad face the same pressures. In contrast, asymmetries in pragmatic legitimacy concerns develop when imposition pressures are greater for one channel member than for the other. Symmetric imposition mechanisms are evidenced, for example, in the dyad of a gun manufacturer and its distributor (which only deals in guns and related products), in that the legitimacy of both parties is threatened by growing concerns about private ownership of guns. However, the dyad of a gun manufacturer and a nonspecialist retailer, such as Wal-Mart, would exemplify asymmetric imposition pressures. In this case, the pragmatic legitimacy concerns are greater for the manufacturer. Norms of solidarity are likely to be nurtured when a channel is characterized by bilateral convergence (Dwyer, Schurr, and Oh 1987; Kumar, Scheer, and Steenkamp 1995). In our case, bilateral convergence occurs for symmetric pragmatic legitimacy concerns, because both channel partners face similar imposition pressures. Therefore, we propose that symmetric imposition pressures will foster greater norms of solidarity in a channel dyad.
P1: Symmetric imposition mechanisms in the input and output sector of a channel dyad will foster greater norms of solidarity in a channel dyad compared with asymmetric imposition mechanisms.
Inducement. Inducement mechanisms also can influence both the internal economy(e.g., by providing financial support for the appropriate level of channel integration) and the internal polity (e.g., by providing tax breaks for the use of the right bureaucratic structures). Economic perspectives have rarely considered the role of inducement mechanisms in influencing internal economic structures. The level of channel integration varies from fully integrated channels (i.e., direct channels) to channels with multiple intermediaries (e.g., Klein, Frazier, and Roth 1990). By relying on transaction cost economics (Williamson 1975), this research stream suggests that the level of channel integration depends on the extent of reliance on specialized investments, performance ambiguity, and environmental uncertainty (Anderson and Coughlan 1987; John and Weitz 1988). We contend that regulatory institutions can provide inducements, which, when taken into account, will add substantially to the understanding of channel arrangements in the MNC context beyond the channel integration efforts explained through the transaction cost perspective.
For a given MNC arrangement (e.g., exporting, licensing, wholly owned subsidiaries), inducement pressures will occur when the regulatory institutions in the host country decide that the benefits of subsidies provided to change channel arrangements far outweigh the cost of the subsidies. In response to such pressures, MNCs will attempt to alter their channel arrangements in accord with the inducements provided by various governmental agencies in a host country. For example, many MNCs have entered into joint venture agreements in SEZs in China because of the tax breaks and other incentives provided by the Chinese government, and they now use these zones to manufacture and export their products to locations with high manufacturing costs, often in their home countries (Spar 1994). In turn, MNCs that benefited from the inducements of the Chinese government accepted governmental recommendations regarding the choice of joint venture partners, the extent of equity investments, and other aspects of channel organization when designing their distribution channel. Thus, inducements by the Chinese government, to an extent, made the MNCs dependent on the Chinese government, which resulted in the MNCs taking the recommendation of the Chinese government into consideration when deciding on governance structures.
P2: The greater the attractiveness of inducements offered by the institutional environment of a marketing channel dyad, the higher is the likelihood of the dyad adopting the level of channel integration recommended by the institutional environment.
Processes of Validation
Authorization. Authorization mechanisms develop out of codes of conduct that organized bodies, such as trade associations, institute to further the interests of their members. Although these codes could influence various aspects of the internal economy and polity, we illustrate the influence of authorization mechanisms by examining their impact on the bureaucratic functioning (in terms of centralization, formalization, and participation in decision making) of channel dyads. Centralization refers to the extent to which decisionmaking authority is concentrated, formalization is defined as the extent to which decision making is regulated by explicit rules and procedures, and participation is theorized as the degree of cooperation and partaking among channel members in decision making (e.g., Dwyer and Oh 1987; John 1984). Empirical research in marketing channels has studied the antecedents (e.g., environmental uncertainty) and consequences (e.g., relationship quality) of centralization, formalization, and participation but has rarely considered the importance of societal influences (e.g., Dwyer and Welsh 1985).
We argue that authorization mechanisms result in increased interactions among members, which aids in the diffusion of thinking of key association members. The increased interactions lead to more homogenized standards and model building within an industry (DiMaggio and Powell 1983; Scott 1987). With these common standards and models, dyad members are more likely to adopt similar attitudinal, behavioral, and structural postures to manage their channel partners. Note that the influence of authorization pressures ( 1) varies from one industry to another because the power and nature of societal bodies is industry specific and ( 2) changes from one firm to another within an industry depending on the extent to which the firm embraces trade associations and other relevant bodies. The extent of the influence of authorizational so depends on whether the firm is single industry or multi-industry. Higher levels of authorization pressures exist in single-industry firms than in multi-industry firms. In industries with a greater presence of authorization mechanisms, such as powerful trade associations, managers are more likely to exchange ideas about channel management practices. Such exchange of ideas usually emphasizes the importance of consensus building and the involvement of all concerned parties when decisions are made (Meyer and Scott 1992). In the channel domain, consensus building would involve increased participation and lowered centralization and formalization in channel decision making (Dwyer, Schurr, and Oh 1987).
P3: The greater the influence of authorization processes in the competitive sector of a marketing channel dyad, the lower is the level of centralization and formalization and the higher is the level of participation in decision making in the dyad.
Acquisition. Acquisition mechanisms are most influential in conditions of high uncertainty. Whereas researchers have explored the implications of task environment (e.g., environmental uncertainty) with respect to the choice of control mechanisms used in channels, we suggest that examining the influence of acquisition mechanisms could shed more light on the matter (Bello and Gilliland 1997; Celly and Frazier 1996). Marketing channels researchers have relied on control theory (Anderson and Oliver 1987; Jaworski 1988) to suggest two types of unilateral control mechanisms that a channel member can use to manage its channel partner (Bello and Gilliland 1997; Bergen, Dutta, and Walker 1992; Celly and Frazier 1996). A manufacturer might use process control to influence the behavior of its distribution partner or simply rely on output control by measuring the final outcomes of the process. Research in this area suggests that channel members rely on process control, as opposed to output control, in conditions of high uncertainty and performance ambiguity (Bello and Gilliland 1997).
Our institutional arguments suggest that acquisition mechanisms will help channel members cope with uncertainty by facilitating the mimicking of competing dyads that are perceived to be legitimate. Acquisition pressures are likely to be higher when there are a few large competitors as opposed to many small competitors (Scott 1987). For example, when a new automobile manufacturer enters the U.S. market, it is instantly aware that the dealer network is the dominant channel. However, the dominant channel is not as obvious in the relatively more fragmented jewelry industry. Thus, in the case of high industry concentration, channel members can observe the legitimate (dominant) leaders easily and mimic their structures and behaviors. In contrast, when industry concentration is low, no legitimate channel management model is available. Higher acquisition pressures (i.e., the presence of a dominant channel) reduce uncertainty for channel members, which in turn decrease their use of process control and increase their use of output control mechanisms to manage their partners (Celly and Frazier 1996).
P4: The greater the influence of acquisition processes in the competitive sector of a marketing channel dyad, the lower is the stress on process control and the higher is the emphasis on output control to manage channel partners.
Processes of Habitualizing
Imprinting. Imprinting mechanisms are most likely to influence channels in which cognitive legitimacy concerns remain stable over time (Oliver 1990). When imprinting is dominant, internal economic, as well as sociopolitical, structures and processes will retain the characteristics of the time of their founding. Imprinting is likely to foster positive channel sentiments while decreasing negative behaviors, such as opportunism, that lead to increased transaction costs. However, researchers rarely have examined the social context of opportunism and mainly have focused on the monitoring and transaction cost implications of opportunism, based on Williamson's (1975) framework (Brown, Dev, and Lee 2000; John 1984; Wathne and Heide 2000). We believe that cognitive legitimacy concerns emanating from imprinting processes will affect opportunism in a channel dyad.
Imprinting processes result in the preservation of channel structures and processes over time and are manifested in channel partners with historically embedded perceptions regarding their position and role in the channel relationship. The Japanese distribution system, for example, still retains features of its time of inception during the Tokugawa period (Fahy and Taguchi 1995).As a result of the stability in cognitive legitimacy, retailers, wholesalers, and manufacturers exhibit behaviors congruent with their historically defined roles. The institutional embeddedness of such distribution systems results in channel members understanding one another's roles. These shared cognitive norms homogenize expectations, which then result in habitualized behaviors. In other words, habitualizing due to imprinting causes sets of behaviors that have societal approval. Because opportunism is unlikely to be habitualized, any opportunistic acts that result in short-term gains are likely to be met with censure from the community with the imprinted expectations (Scott 1987), a heavy cost that can threaten firm survival. Thus, the benefits of opportunism in the short run are outweighed by the costs of loss of reputation and wariness on the part of members in future channel interactions in imprinted settings.
P5: The greater the influence of imprinting processes in the input or output sector of a marketing channel dyad, the lower are the levels of opportunism within the dyad.
Bypassing. Finally, we turn our attention to bypassing mechanisms that result in the creation of shared symbols and beliefs, such that there is a high level of cognitive congruence between channel partners. We believe that bypassing mechanisms facilitate the evolution of channel structures and processes on the basis of cognitive congruence over time. Specifically, we assess the influence of bypassing on the use of power within a channel relationship, for which power is considered in terms of its potential to influence channel partners' beliefs, attitudes, and behaviors (Frazier 1983b; Gaski 1984). Marketing scholars recognize that the possession, use, and effect of power are distinct constructs (Frazier 1983a; Frazier and Antia 1995). Power and dependence go hand-in-hand, such that the increased power of a firm over its partner implies a higher level of dependence of the partner on the firm (Frazier and Summers 1986; Ganesan 1993). Researchers have studied how symmetry (or asymmetry) of power (or dependence) affects channel relationships (Dwyer and Walker 1981; Lusch and Brown 1996).
We believe that the value congruence that results from bypassing mechanisms has important implications for the power-dependence relationship in a channel dyad. This congruence can substitute for formal control and coordination mechanisms (Zucker 1977). Bypassing mechanisms differ from imprinting mechanisms. Specifically, imprinting results in the maintenance of the status quo in terms of channel dyads, whereas bypassing results in the evolution of channel structures and processes through informal cultural means. Ultimately, the evolving informal mechanisms lead to cognitive congruence, which facilitates long-term bonding and cooperation between channel partners, a clan-like relational exchange (Ouchi 1980). As a result, channel members within a dyad do not need to resort to using or applying power to resolve channel problems. Therefore, the existence of bypassing mechanisms should be manifested in low levels of the use of power (Gundlach and Cadotte 1994; Kumar, Scheer, and Steenkamp 1995).
P6: The greater the influence of bypassing mechanisms within a marketing channel dyad, the lower is the likelihood of a channel member using power.
The focus of this article has been to render a comprehensive conceptualization of environments in the political economy framework by supplementing the task environment approach with the institutional environment perspective. We emphasize the influence of institutions, institutional processes, and their underlying mechanisms on channel attitudes, behaviors, processes, and structures. There are many empirical issues and managerial implications that flow from our conceptualization. Therefore, we present a research agenda and discuss managerial implications.
A Research Agenda
Our primary objective is to stimulate research on the evolution and influences of the institutional environment. We elaborate on the three challenges that an empiricist is likely to face, namely, ( 1) operationalizing aspects of the institutional environment, ( 2) developing nomological networks to incorporate institutional variables, and ( 3) creating typologies that will assist in the management of the institutional environments.
Operationalization. The first empirical challenge is to develop appropriate sets of measures for assessing legitimacy concerns, as well as the extent of the influence of the various institutional mechanisms. This would assist greatly in building a comprehensive description of the impact of the macroenvironment on channels. Consider the implications of validating processes that work through the authorization mechanisms. A researcher could measure the impact of trade or professional associations as authorizing agents directly, with the primary objective of establishing legitimacy in channels. The researcher might consider the homogeneity of membership or the extent of active membership in trade associations. Note that such measures could be at the industry and/or firm level. An industry-level measure of authorizing pressures could be the number of firms involved in the trade association, whereas a firm-level measure could be the level of participation of the firm in the trade association.
Similarly, researchers could develop measures that tap into institutional pressures. For example, to operationalize imposition pressures, an empiricist could use dependence and multiplicity, where dependence represents the extent to which a firm relies on regulatory agencies and multiplicity is a measure of the complexity and contradictory nature of the demand made by the various regulatory bodies of the firm. (In Table 1, we elaborate on some more operationalizations.) In using such an approach, it is critical to ensure that these subconstructs (dependence and multiplicity in our example) capture the domain of the latent constructs (imposition) appropriately.
Nomological networks. The second empirical challenge lies in assessing the nomological validity of the institutional constructs. Researchers would need to place these constructs within a theoretical net with other established constructs. Empiricists could adopt two different approaches: First, they could identify the various antecedents and consequences that would be associated with particular institutional processes and pressures. On the basis of this identification, researchers could study the evolution of institutional processes and pressures according to the changing nature of antecedents and consequences. We deliberately have adopted a process view of the environment to suggest that the environment is neither static nor a given. Insights can be gained by studying the processes by which institutional environments evolve. For example, a critical competitive advantage for the Saturn automobile company stems from the ethical practices of its dealer network. By habitualizing these ethical practices, Saturn is able to expand its dealer network easily while maintaining high ethical standards. Competitors' inability to duplicate the ethical practices of the Saturn dealer network highlights the sustainability of this competitive advantage and the need to study the processes by which Saturn habitualized ethical practices.
Second, researchers could study the institutional constructs in current marketing channel nomological networks. For example, as suggested by transaction cost economics, scholars have studied the incidence and management of opportunism in channel relationships (e.g., John 1984). We believe that facets of the institutional environment are important antecedents of the extent of opportunism in channel relationships and can provide information for managing opportunism. In our framework, we suggest that imprinting can lower the levels of opportunism in channel dyads. Similarly, authorization pressures could unleash socialization processes (Wathne and Heide 2000), whereby the greater a channel dyad's embeddedness in the industry networks (e.g., trade associations, professional bodies), the lower is the opportunistic behavior of channel members. Thus, additional insights can be gained by studying relevant institutional pressures in models of not only opportunism but also other attitudes, behaviors, and structures in channel relationships.
Typologies. The third empirical challenge involves developing typologies that articulate the key constructs and relationships among these constructs in particular industrial or national contexts. Typologies would assist in classifying institutional environments and developing strategies that work in a particular environment. In the context of globalization, in which MNCs must manage operations scattered across countries, understanding the typologies of the institutional environment assumes even greater importance. For example, German regulators are more likely to accept the efficiency argument for consolidation in channels than are their Italian counterparts in the food industry. Germany has four retail chains that control 65% of the market for food products, whereas in neighboring Italy, no retail chain controls more than 2% of the market (Hill 2000). The typological approach would not only facilitate the understanding of these differences but also help explain the implications of the institutional environments across different national contexts.
Managerial Implications
It is important for managers to ( 1) identify institutional processes and their mechanisms of influence, ( 2) understand the implications of the institutional environment in terms of opportunities and constraints, and ( 3) strategically manage the institutional environment. Managers may identify obvious regulating processes easily, but they might overlook the implications of the institutional environment with respect to validating and habitualizing processes. If managers fail to identify institutional processes and pressures, they might be affected adversely by them. For example, high environmental uncertainty about the new channel arrangements that arose with the advent of the Internet (e.g., pets.com) may have resulted in excessive mimicking of unstable forms. Channel members failed to recognize that they were falling prey to the institutional pressures of acquisition. The recognition of validating and habitualizing processes would enlarge organizational cognitive capacity, thereby reducing the bounds on rationality. To recognize societal norms and habits, managers must focus on observing the obvious and studying the mundane. To do so, managers could use the previously mentioned operationalizations to track institutional changes over time and across national contexts. When identified as important, an understanding of the influences of institutional processes on channel structures and processes should help managers ( 1) better manage their channel within the confines of the institutional environment and ( 2) devise strategies to think and move beyond the confines of the environment. Such an understanding would rest on managerial ability to grasp the antecedents and consequences of the various processes and their underlying mechanisms. For example, inducements may provide the enabling conditions for entering the Chinese market, but they may reduce the long-term flexibility of channel arrangements. Managers must understand that long-term implications arising from path dependence may outweigh the short-term benefits of inducements, thereby moving beyond the confines of the institutional environment.
It is also critical for managers to manage the channel strategically within the confines of the institutional environment. Such strategic management is likely to be facilitated by the use of typologies of the institutional environments. For example, a typology could be based on attitudes of channel members, ranging from compliance to defiance, and approaches adopted by the channel members, ranging from active management to passive submission, to adopt one of the four possible strategies (Oliver 1991). Managers could either deliberately adopt institutional requirements or routinely adhere to institutional demands. Alternatively, managers could attempt to change and exert power over institutional expectations actively or dismiss and ignore the institutional demands. Managers should recognize the trade-offs involved in adopting a particular strategy. Likewise, managers should view the institutional environment as a resource, similar to the task environment, and frame the managerial question in terms of managing this resource. Institutional environment is one of the factors of production for a channel's value-adding activities--and similar to other value-adding activities, it must be managed effectively.
[ 1] To illustrate the broad influences of the institutional environment on the political economy of channels, we briefly consider distribution channels in Japan (Batzer and Laumer 1989; Czinkota and Woronoff 1991; Herbig 1995). The well-publicized complexities in the Japanese distribution system and the emergence of cognitive institutions, such as the powerful keiretsu systems, can be traced to the Tokugawa era in the seventeenth century (Fahy and Taguchi 1995). The evolutionary nature of channel interactions during the past four centuries has caused these cognitive institutions to over-shadow market factors (such as efficiency and price) by creating socially accepted cultural accounts of channel relationships (Helweg 2000). In addition to the cognitive bases of inefficiencies in the Japanese distribution system, the normative institutional forces of social welfare also affect channel management in Japan. The retail trade serves as a proxy for social welfare because it employs excess labor and provides income for retirees. As a result, many small, sub-optimal retail organizations have the societal approval not only of the general public but also of Japanese wholesalers and manufacturers (Czinkota and Woronoff 1986). These cognitive and normative institutions are supplemented by regulative institutions (such as governmental regulations) that protect small retailers with laws such as the Large-Scale Retail Store Law, according to which retail stores larger than a certain prespecified size must obtain govern-mental approval (Cateora 1995). The current state of historical evolution, societal needs, and regulatory processes in Japanese distribution channels must be considered in the larger institutional context, in which both efficiency and legitimacy concerns arising from societal attitudes and expectations influence the nature of the relationships among various channel constituents. Such institutional influences are not exclusive to Japan and help define the co ntext in which channel relationships evolve.
Institutional Processes: Descriptions, Examples, and Operationalizations
Institutional Explanation: Imposition:
Description: Use of legal or regulatory mechanisms on the part of one or more institutional constituents to force structural and/or procedural changes in distribution channels.
Example: Cigarette manufacturers are not allowed to distribute free samples or sell their products through vending machines.
Operationalization[a]: Imposition: extent to which channel members believe that the regulatory environment reduces their ability to operate efficiently or the number of regulations and regulators with which channel members comply.
Institutional Explanation: Inducement
Description: Use of incentives on the part of one or more institutional constituents to influence channel members to make specific structural and/or procedural changes.
Example: The U.S. Department of Agriculture provides $1.7 billion of inducements (over a ten-year period) to manufacturers, cooperatives, and trade associations to promote the selling of U.S. products in foreign markets (Hill 2000).
Operationalization[a]: Attractiveness: types and volumes of incentives offered by institutional bodies to channel members.
Institutional Explanation: Authorization
Description: Channel members voluntarily seek approval of authorizing agents with the primary objective of establishing legitimacy.
Example: Wal-Mart responds to issues raised by the trade association (Crafted with Pride in the USA) representing the textile manufacturing industry.
Operationalization[a]: Power of trade associations: homogeneity and extent of membership or extent of professionalization.
Institutional Explanation: Acquisition
Description: Channel members mimic structures and processes of particular benchmarked distribution channels that are deemed legitimate.
Example: U.S. automobile manufacturers (in the early 1990s) try to implement their Japanese counterparts' supplier management strategies and treat all suppliers as first-tier suppliers (the close partners). In reality, most Japanese automobile manufacturers had a four-tiered supplier system with full-service providers and integrated partners at one end and contractual commodity suppliers at the other.
Operationalization[a]: Acquisition: degree of ambiguity in the goals of a benchmarked organization that is deemed legitimate.
Institutional Explanation: Imprinting
Description: Retaining channel characteristics that originated at the time of channel inception.
Example: The roots of Japanese wholesalers' domination over Japanese retailers and manufacturers can be traced to the seventeenth-century Tokugawa regime.
Operationalization[a]: Time of founding: the period at which the channel was created and shaped.
Institutional Explanation: Bypassing
Description: Channel members using cultural norms and shared beliefs developed collectively as a substitute for formal control and coordination mechanisms.
Example: Japanese keiretsu systems, in which communities practice trust, shared interest, shared capital, and shared risk; informal control mechanisms are preferred over formal procedures.
Operationalization[a]: Shared cultural norms: the extent of use of informal mechanisms for channel management.
[a]To aid in further empirical research, we present a possible manner to operationalize facets of the institutional environment. In no way are we precluding the possibility of other operationalizations. We acknowledge that there are many ways to operationalize these constructs.
DIAGRAM: FIGURE 1: Institutional Environment and Marketing Channels
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By Rajdeep Grewal and Ravi Dharwadkar
Rajdeep Grewal is Assistant Professor of Marketing, Smeal College of Business Administration, Pennsylvania State University. Ravi Dharwadkar is Assistant Professor of Strategy and Human Resources, School of Management, Syracuse University. Both authors contributed equally to this research.The authors appreciate the constructive feedback of the three anonymous JM reviewers. The authors also thank Pamela Brandes, Bob Dwyer, John Grabner, Jean Johnson, and Suprateek Sarker for their comments on this research and acknowledge financial support from the Institute for the Study of Business Markets, Smeal College of Business Administration, Pennsylvania State University.
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Record: 183- The Roles of Channel-Category Associations and Geodemographics in Channel Patronage. By: Inman, J. Jeffrey; Shankar, Venkatesh; Ferraro, Rosellina. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p51-71. 21p. 1 Diagram, 8 Charts, 4 Graphs. DOI: 10.1509/jmkg.68.2.51.27789.
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The Roles of Channel-Category Associations and
Geodemographics in Channel Patronage
Consumers purchase goods from various channels or retail formats, such as grocery stores, drugstores, mass merchandisers, club stores, and convenience stores. To identify the most appropriate channels and to allocate the distribution of products among channels efficiently, managers need a better understanding of consumer behavior with respect to these channels. The authors examine the moderating role of channel-category associations in consumer channel patronage by extending the literature on brand associations to the context of channels, and they estimate a model that links channel-category associations with consumer geodemographics and channel share of volume. The authors first identify the product categories associated with particular channels through a correspondence analysis of a field-intercept survey. They then use the channel-category associations and geodemographic factors to estimate their direct and interactive effects on channel share of volume. The channel-category associations have significant main effects and interaction effects with channel type and geodemographic factors on channel share of volume, and they account for the majority of the explained variance (72%) in channel share of volume. Overall, the findings provide several conceptual and managerial insights into consumer channel perceptions and patronage behavior.
Consumer goods are typically available through several channels or retail formats. For example, consumers can purchase dishwashing liquids from grocery stores, mass merchandisers, club stores, and drugstores. Because consumers can select channels on the basis of factors such as price, convenience, assortment, and service, channels must position themselves and undertake initiatives that enable them to compete with one another successfully (Webster 2000). For example, evidence that mass merchandisers are gaining ground at the expense of other channels (e.g., Levy 2000) has led to initiatives such as efficient consumer response by grocery stores (Kahn and McAlister 1997). Determinants of brand choice, such as promotion (e.g., Guadagni and Little 1983), consideration sets (Roberts and Lattin 1991), variety seeking (e.g., Inman 2001; Kahn and Raju 1991), and drivers of store choice (Kumar and Leone 1988; Solgaard and Hansen 2003), have been extensively studied. However, little is known about choice behavior at the highest level in the purchase decision tree: the selection of the channel.
In this research, we explore the influence of two factors that are relevant to channel patronage decisions: geodemographics and associations between the channel and particular product categories. Geodemographics is the classification of people by the neighborhood in which they live, combined with demographic variables, to form an overall consumer profile (Johnson 1989). It is based on the notion of social clustering; that is, people tend to congregate with people like themselves according to the same factors that determine consumption: social rank, household composition, ethnicity, urbanicity, and mobility (Goss 1995). Geodemographic targeting is a two-step process: Households or block groups (depending on data availability) are combined into similar groups by means of cluster analysis of various geographic and demographic factors, and then similarities in purchasing behavior (i.e., based on panel data or customer databases) are examined to identify the most viable targets for the product or service in question.( n1) If consumption of the focal product or service skews toward certain geodemographic groups, a "geotargeting" strategy can be cost effective. A market research subindustry has developed around geodemographic targeting (e.g., Curry 1993), including firms such as VNU (PRIZM, Spectra) and National Decision Systems (MicroVision).
Although the notion of channel associations offers conceptual appeal, it has received little attention from researchers. Several scholars have argued that brand associations in memory are a central component in brand evaluation and choice (e.g., Keller 1993; van Osselaer and Janiszewski 2001). Brand associations are defined as the "informational nodes linked to the brand node in memory and contain[ing] the meaning of the brand for consumers" (Keller 1993, p. 3). Research reveals that brand associations influence persuasion (Greenwald and Leavitt 1984), evaluations (Broniarczyk and Alba 1994; Janiszewski and van Osselaer 2000), and usage intentions (Lane 2000). Similarly, channel-category associations can be viewed as consumer perceptions from the perspectives of both channel and category; that is, a given channel can be strongly associated with some product categories. Likewise, a given product category may be more closely associated with one channel than with others. Conceptually, the association of a given channel with a given category can be viewed as a measure of consumer perceptual similarity between the specific channel and the specific category.
Our goal is to identify the categories of consumer goods that are most closely associated with particular channels (i.e., mass merchandisers, grocery stores, club stores, and drugstores) and to examine the interplay between the channel-category associations and geodemographics in the explanation of variation in channel share of volume across product categories. Our study aims to provide a descriptive behavior of channel patronage that will facilitate the development of a theory of channel purchasing behavior. An understanding of these phenomena is key to addressing several managerially important questions that arise in a multichannel environment. More specifically, we address three key questions in this research.
Research Question 1 is, What types of product categories are associated with a given channel? That is, what signature product categories tend to come to mind when consumers think of that channel? If the basket of goods drives channel selection (e.g., "if we need detergent, soaps, and other cleaning items, we go to a mass merchandiser"), an understanding of the products most closely associated with particular channels becomes key to the development of relationship-marketing programs with retailers. Brand associations with product features and benefits are a central component of Keller's (1993) brand-equity framework, because product aspects that are strongly associated with certain brands represent points of differentiation between brands (e.g., Dillon et al. 2001). Similarly, retailers need to know if they are well positioned on the factors that differentiate between channels and thus serve as the important drivers of channel patronage decisions.
Research Question 2 is, What channel-category associations and geodemographic clienteles influence channel share of volume? Conceptually, stronger associations between a category and specific channels should lead to an increased share of volume for those channels. In terms of practice, the answer to this question can guide retailers in determining their store layout and merchandising strategies. Category consumption typically varies widely across geodemographic groups, thus forming the basis for geotargeting strategies. However, to our knowledge, no one has examined the role of geodemographics in channel share of volume across product categories. For example, are certain geodemographic clienteles more likely to shop at mass merchandisers?
Research Question 3 is, How do channel-category associations moderate the relationship between geodemographics and channel share of volume? Keller (1993) argues that different types of brand associations can interact, but heretofore no research has examined interaction effects of channel-category associations. Besides the conceptual importance of this interaction, it is critical to managers who wish to coordinate their promotional efforts across channels. If channel-category associations interact with user geodemographics, marketers in categories that are strongly associated with a certain channel need to tailor their integrated marketing communications strategy accordingly by allocating a larger portion of their trade efforts to that channel and by targeting consumers from the appropriate geodemographic groups (e.g., a soft drink manufacturer may find it more profitable to promote differently to mass merchandiser buyers and club store buyers). This issue is important to public policy officials as well. If lower sociodemographic groups tend toward higher-priced channels (e.g., drugstores, convenience stores) for the categories associated with those channels, they experience a "double whammy" of lower incomes and higher prices.
The rest of the article is organized as follows. We begin by reviewing the channel patronage, brand association, and geodemographics literature. We address the research questions in an empirical study by using correspondence analysis to determine the channel-category associations and by merging the results with data from a leading geodemographics data supplier. We then analyze the effects of channel-category associations and geodemographics and their interactions on channel share of volume. We conclude with a discussion of the findings' conceptual implications, the ways the findings might alter marketing practice, and directions for further research.
Channel Patronage
The store patronage literature is relevant to an understanding of channel patronage. Researchers have examined both the impact of store or shopping center attributes (e.g., price level, convenience, quality, ambience) on patronage (e.g., Arnold, Oum, and Tigert 1983; Louviere and Gaeth 1987; Nevin and Houston 1980) and temporal variations in store patronage behavior (e.g., Kahn and Schmittlein 1989; Popkowski-Leszcyc and Timmermans 1997). Although this research has increased the understanding of the general factors that motivate store switching, it has neither explored differences in the evoked attributes or product categories across channels nor examined the geodemographic and behavioral drivers of channel patronage.
Researchers (e.g., Bell, Ho, and Tang 1998; Bell and Lattin 1998; Lal and Rao 1997; Popkowski-Leszcyc and Timmermans 1997) have examined the factors that influence consumer choice between grocery stores with different price formats (i.e., everyday low price [EDLP] versus "high-lo" stores). In general, the results suggest that consumers with larger shopping lists (and concomitantly larger basket sizes) prefer EDLP stores. Furthermore, consistent with retail location theory (e.g., Huff 1962), consumers' affinity for a store tends to be inversely related to the distance thereto. Bell, Ho, and Tang (1998) segment consumers in terms of the relative importance of fixed costs (e.g., store loyalty, distance) and variable costs (e.g., basket cost, category-specific store loyalty). They find that both fixed and variable costs influence consumers' choice of supermarket. Zettelmeyer (2000) uses a game-theoretic approach of multichannel competition to conclude that firms can leverage multiple channels to achieve a more refined consumer segmentation. However, he does not empirically examine whether different types of consumers shop at different channels. Messinger and Narasimhan (1997) conclude that consumers seeking time-saving convenience have contributed to the growth in the one-stop shopping retail format over time. Although these studies provide valuable insights, they have not addressed the issue of channel-category associations and how they might be related to channel share of volume.
Channel-Category Associations
Researchers (e.g., Aaker 1991; Keller 1993) have conceptualized associations using associative network models of memory (Anderson 1983; Collins and Loftus 1975). In this view, brand information stored in memory is represented as a network of interlinking nodes. Specific bits of information are stored in each node, and the strength of the linkage between the bits is proportionate to the strength of the association between the nodes. That is, the semantic relatedness between two nodes is represented by the strength of the association, which can be affected by factors such as advertising frequency and product experience. For example, for most consumers, the linkage between McDonald's and fast food is probably stronger than the linkage between Arby's and fast food.
In this view, likelihood of recall is driven by the "spreading activation" (e.g., Collins and Loftus 1975) induced by either an external stimulus or some internal prompt. Activation spreads from the activated node to other nodes as a function of the strength of the linkages between them. That is, when a consumer considers the choice of a fast-food restaurant, McDonald's is more likely to come to mind than Arby's because of McDonald's greater association with fast food. Other associations linked to McDonald's should then be evoked, such as hamburgers, efficient service, and so on. Keller and Aaker (1992) show that core brand associations influence evaluations of brand extensions (see also Aaker and Keller 1990). They argue that the strength of this effect is driven by the accessibility of the associations from memory. As we already discussed, this is a function of the association between the activated node and the brand.
By extension to the context of channels, when the consumer is considering purchasing a given set of goods, the likelihood of a particular channel coming to the fore should be a function of the sum of its associations with each product category being considered and the specific features/ benefits offered by each channel. If the set of needs consists primarily of food products, the consumer arguably would evoke grocery store or club store. Similarly, if time is of the essence, convenience or quick in and out (i.e., features and benefits) may be the activated nodes, so convenience stores may come to the fore. A unique aspect of the associative network model in this context is that the associations between the channel and entire product categories are important. In the brand-level association models, the product category is often the activated node, and brands follow. The reverse is the case here; channels follow from the activation.
Although researchers have tended to focus on the conceptualization per se (e.g., Keller 1993) or the process by which the associations are learned (e.g., Janiszewski and van Osselaer 2000), our focus is on the nature of channel associations (Research Question 1) and the role of the associations in channel share of volume (Research Question 2). Specifically, although Keller (1993) describes user imagery as a type of association, we focus on empirically examining the direct effects of channel-category associations on channel share of volume and the interaction effects of the associations with shopper geodemographics (Research Question 3).
Geodemographics
Insights into the potential impact of geodemographics on channel choice can be gained from the research in structural sociology and marketing on behavioral differences along social class and other demographic lines. People in different social classes differ not only in terms of the products they buy but also in terms of the type of store they frequent to buy products. That is, shopping sites tend to take on fixed class identities (Miller et al. 1998). For example, Martineau (1958) finds that the social status of a store often becomes the primary basis for its definition by the shopper. Each store, even if it is a grocery store, acquires status identification. Martineau's findings suggest that when making a store choice, the shopper goes to where he or she will "fit in." Although an entire market research subindustry has developed around the notion of geodemographic targeting (e.g., Curry 1993), to our knowledge, no one has examined the role of geodemographics (i.e., shopper characteristics) in channel share of volume across product categories, nor have researchers applied the conceptual associative network model as a moderator of this relationship.
Although we agree that geodemographic effects on channel share of volume merit examination, we argue that it is also important to consider the potential moderating role of channel-category associations on this relationship. Janiszewski and van Osselaer (2000, p. 333) apply the connectionist model of memory (e.g., Smith 1996) in the context of associations between brand name (i.e., cue) and quality (i.e., outcome), describing the connectionist model as having the desirable property that "the association strengths between each cue and an outcome depend on the association strengths between other cues and the same outcome." We extend this idea to the context of channel share of volume. A household may tend to shop more heavily at one type of channel, perhaps because of proximity (e.g., Huff 1962) or feelings of fitting in (Miller et al. 1998). A systematic tendency for households with particular characteristics to shop at one channel more heavily than another results in a relationship between geodemographics and channel share of volume. However, if channel-category associations are strong, a particular channel may be evoked when the household sets out to purchase a given set of goods. This association should result in the attenuation of the geodemographic-channel share of volume relationship in favor of the evoked channel. We argue that the strength of the channel-category association also results in a main effect of associations in channel share of volume. Figure 1 provides an overview of the three research questions and the nature of the expected relationships.
In summary, we argue that channels are associated with certain types of product categories (e.g., food items, household items, cosmetics) and a particular geodemographic customer base (e.g., presence of children, affluence). Furthermore, category-channel associations and geodemographics should act in conjunction to drive channel share of volume. More specifically, channels that are strongly associated with a product category should exhibit a weaker geodemographic-channel share of volume relationship.
Our purpose is to examine the drivers of consumers' tendency to shop at one type of channel, which we operationalize using channel share of volume as the dependent variable (which we subsequently define in detail). Our main independent variables are channel-category associations and geodemographic consumer-level factors. Although researchers have consistently reported strong effects of consumer characteristics on category volume (e.g., Dardis and Sandler 1971; Rich 1963) and reactions to marketing-mix variables (e.g., Hoch et al. 1995; Shankar and Bolton 2004), the literature offers little guidance on geodemographic factors that might influence channel share of volume. Thus, our selection of geodemographic variables is admittedly exploratory and based on industry practice. We employ a geodemographic segmentation database that segments consumers into a 54-cell lifestyle/life-stage grid and by channel (Figure 2). Specifically, we use Spectra software, which is used by many consumer goods firms that wish to target consumers on a geodemographic basis. We use Nielsen wand-panel data to populate the grid. If firms find a skew such that certain lifestyle or life-stage groups are heavier consumers, they define this as the geodemographic target.
The grid is composed of nine lifestyle rows, defined in terms of affluence and urbanicity, and six life-stage columns, defined in terms of age of the head of household (HOH) and the presence or absence of children. As we mentioned previously, the grid can be populated with any variable, such as consumption (i.e., annual volume per household), penetration (i.e., percentage of households purchasing), or buying rate (i.e., annual volume per purchasing household). A Spectra geodemographic grid for ground coffee purchased at grocery stores (annual household volume) is shown in Table 1.
The rows in Table 1 are sorted in order of declining affluence. Furthermore, the rows titled "Metro elite," "Midurban melting pot," and "Downscale urban" are more urban than the others. The columns are in order of increasing HOH age, and households with HOH ages of 18-34 and 35-54 are broken out in terms of presence or absence of children. In the example grid shown in Table 1, households with HOH ages of 55-64 tend to be heavier consumers of ground coffee through the grocery channel; their annual volume (122.0 oz. per household) is much greater than the overall average (91.2 oz. per household). Similarly, traditional families (104.9 oz. per household) tend to be somewhat heavier purchasers of ground coffee through grocery stores.
To prepare the data for this analysis, we computed channel share of volume for each cell in each grid by dividing the annual volume for that cell for each particular channel by the total annual volume for that cell across all channels. For example, upscale suburban households with HOH ages of 18-34 with children (the upper-leftmost cell in the grid) purchased 77.7 ounces of coffee per household in the year 2000 across the four channels (grocery, mass, drug, and club). The channel share of volume of this geodemographic group was 70% for grocery, 9% for mass, 1% for drug, and 20% for club. We now describe the geodemographic variables used in the analysis and explain how we derived the channel-category association dimensions.
Operationalizing Geodemographics and Channel-Category Associations
Geodemographics. The geodemographic variables that we examine are affluence, urbanicity, presence of children, and HOH age.( n2) The variables for urbanicity and presence of children were binary, and those for affluence and HOH age were continuous. Specifically, we used information about the median annual income and urbanicity of the households in the nine Spectra lifestyle rows to define the affluence and urbanicity variables. We used the median household income reported by Spectra for each row in the grid shown in Table 1 to specify the affluence variable. Furthermore, we coded the rows in the grid as a one if the Spectra description in the database definition indicated an urban neighborhood (these are the rows titled "Mid/upscale suburbs," "Metro elite," "Mid-urban melting pot," and "Downscale urban").( n3) We used the grid columns to define the presence-of-children indicator variable (i.e., one if children were present and zero otherwise). Finally, we created the HOH age variable using the midpoint of the range in each column (i.e., 26 for HOH ages 18-34, 44.5 for HOH ages 35-54, 59.5 for HOH ages 55-64, and 70 for HOH ages of 65 and over).( n4)
Channel-category associations. Geodemographics were available directly from the Spectra database, but channel-category associations were not. Thus, we were forced to adopt a "data-fusion" approach, estimating channel-category associations from a different sample and then merging the results with the Spectra-supplied data. To do this, we used data provided by Meyers Research Center in New York. In the fall of 1999, 1698 consumers from five cities participated in a field-intercept survey. They were asked to indicate at which of the following channels they shop in a typical month (number of affirmative responses is shown in parentheses): supermarkets (1698), drugstores (1165), megastores such as Kmart or Wal-Mart supercenters (729), regular discount department stores such as Kmart, Wal-Mart, or Target (966), and warehouse club stores such as Costco or Sam's Club (351). It was important to select channels that carry a relatively common assortment of product categories; otherwise the construct of channel-category associations has little meaning.
Fazio, Williams, and Powell (2000) examine three general approaches for measuring association strength: naming methods, latency methods, and facilitation methods. They demonstrate that the methods converge in terms of assessing association strength. Because shoppers were interviewed in the field, the naming approach was more practical because the other two methods require a computer. In the naming method, respondents are presented with the stimulus (channel, in this case) and asked to recall items that they associate with that channel. Specifically, respondents were first asked to recall up to three product categories that they associate with each channel, followed by the first three words or thoughts other than product categories that come to mind. To mitigate the influence of previously recalled items, subjects were given a maximum number of three items to list (e.g., Farquhar and Herr 1993). As would be expected from an open-ended format, respondents mentioned a wide variety of categories: 113 for supermarkets, 100 for drugstores, 105 for superstores, 98 for regular mass merchandisers, and 107 for club stores.
The naming approach and the resulting perceptual-distance measure have both strengths and weaknesses. The strengths are that the measures enable us to capture memory and salience-based associations between channels and categories, reflect consumers' cognitive mapping of channels, and take into account consumers' attribute-based similarity judgments. The weakness is that some of the evoked associations from a consumer probably reflect the consumer's purchases at the different channels, so we must be cautious in interpreting their role in channel patronage decisions.
To identify the strength of the association between product categories and channels, we performed a correspondence analysis (see Hoffman and Franke 1986). Correspondence analysis is a mapping technique that uses cross-tabulation data as input (i.e., the number of product mentions for each channel) and converts the data into a joint space map by using the chi-square value for each cell. It is quite useful in this regard for several reasons. First, its ability to consider multiple categorical variables simultaneously enables us to map jointly the product categories elicited by the open-ended responses. Second, as with other graphical algorithms (e.g., multidimensional scaling, factor analysis), correspondence analysis aids in uncovering the structural relationship among the variables. Third, the only data requirement for correspondence analysis is a rectangular data matrix with nonnegative entries. Thus, it is perfectly suited for the open-ended elicitations that were the source of the data. Finally, correspondence analysis generates a dual display of both the columns (channels, in this case) and the rows (the products). The displays have similar interpretations, which facilitates the detection of relationships.
The primary caveat of correspondence analysis is that the specific distances between the row variables and column variables cannot be interpreted, because the distances do not represent a defined metric (Hoffman and Franke 1986). That is, the columns and rows are scaled independently, so (in our case) the channel scaling can be put through any monotonic transformation. Although the between-set distances cannot be strictly interpreted, a channel tends toward a position in its space that corresponds to the products that are the most prominent in its profile. In other words, the specific distances between the channels and the products should not be interpreted, but the ordinal proximity of particular products to certain channels has meaning. This is because any monotonic transformation of the channel scaling on the map maintains the ordinal distance from a particular category to the channels. For example, it is inappropriate to report that soft drinks are 1.5 units from the club channel and .9 units away from grocery, but any monotonic transformation of the channel scaling will leave soft drinks closer to grocery than to club in a relative sense.( n5)
Both channels and products are plotted in Figure 3 (Figures 4 and 5 show expanded views of the two areas in Figure 3 in which several categories cluster). The analysis suggests that two dimensions (hereafter referred to as "association dimensions") adequately capture the variation in product mentions across channels. The vertical dimension captures 55% of the variance, and the horizontal association dimension captures 36%, for a total of 91% of the variance. Notably, a channel triangle is revealed, with the drug channel in the upper-left corner, the grocery channel at the bottom, and the superstores and regular mass merchandiser channels in the upper-right corner. The proximity of the superstores to regular mass merchandisers implies that consumers perceive them as quite similar in terms of the products that each evokes. The club channel is located roughly equidistant between grocery and superstore/regular mass merchandisers.
For the product categories (see Figures 3-5), the food and beverage items tend toward the bottom of the plot. The pet food and supplies are near the middle of the plot, as are the vice products (e.g., liquor, cigarettes). Less differentiated, or "commodity," products such as general household items (e.g., toys, clothing, lawn and garden supplies, domestics) and cleaning supplies tend toward the upper-right quadrant, whereas personal care products (e.g., oral care, feminine hygiene, deodorants) and health-related categories (e.g., cold and flu medication, analgesics, vitamins) are in the upper-left quadrant. Grocery tends to be more closely associated with food items, drug is associated with personal care and health-related categories, club tends to bring food items and pet supplies to mind, and the two mass merchandiser channels are associated with less differentiated, infrequently purchased general household items. Thus, we labeled the vertical dimension "category purchase frequency," with less frequently purchased items in the upper portion, and the horizontal dimension "category differentiation," with less differentiated products on the right-hand side. This analysis identifies which categories are associated with which channel, thus addressing Research Question 1. Table 2 shows the product category associations along the two dimensions. The results are relevant to the positioning of channels with respect to the products they carry.
Several channel-category associations uncovered from correspondence analysis are novel and somewhat counterintuitive. Table 3 provides a summary of the associations and the relevant rationale. The drug channel is closely associated with categories such as alcohol, tobacco, candy, magazines, and soap, which are not intuitively obvious. Alcohol and cigarettes are associated more with the drug channel than with the other channels, though the drug channel is associated with health-related products. The likely reason is that consumers perceive the attributes that are key to the purchase of these categories (convenience, selection, and service) as superior at the drug channel than at the other channels (Inman, Shankar, and Ferraro 2002). Candy is associated with the drug channel more than with other channels, particularly the grocery channel, with which food items are typically associated. Candy is bought on impulse, and it may be more convenient to buy candy at or close to the checkout at the drug channel because the basket sizes are typically smaller in this channel than in the grocery channel. The rationale for associations of categories such as magazines, photo supplies, and soap are provided in Table 3.
Consumers perceive the mass merchandiser channel as the closest channel for categories such as automotive, beauty care, cleaning products, gifts, miscellaneous household items, and paper goods. Cleaning products are more closely linked to the mass merchandiser channel than to the grocery or the club channel. Because these items are storable and frequently consumed, prices and selection are important. Although club stores typically offer good prices, the selection is not as wide as it is in mass merchandiser stores, and whereas grocery stores offer wide selection, they do not typically offer lower prices than mass merchandiser stores. Table 3 offers a rationale for the associations of other categories to mass merchandiser stores.
Club stores are strongly associated with bulk foods, frozen foods, pet foods, and snacks. Shoppers typically buy the same brands of pet foods for their pets, because they do not like to change the brands to which their pets have grown accustomed. Therefore, repeat purchases are important for these foods. Because club stores offer some of the best prices for a given brand or set of brands, they are more closely associated with pet foods than are other channels. Bulk foods, frozen foods, and snacks are also purchased by affluent and, often times, stressed-out shoppers (the geodemographic clientele of club stores), who may buy large quantities at low prices so that they do not need to make multiple shopping trips.
Analytical Approach
We merged the measures of channel-category dimensions from the correspondence analysis with the geodemographics database and performed a regression analysis to study the roles of channel-category associations and geodemographics on channel patronage. An aspect of the Spectra system that is suitable for our purpose is that the consumption grids (annual volume per 100 households) are available by channel (e.g., Table 1 is for grocery). That is, we were able to generate separate grids for grocery (73), mass merchandiser (74), drug (63), and club (59) stores.
We tried to generate consumption grids for each of the categories identified in the field study that yielded the channel-category dimensions. Unfortunately, there was not a one-to-one mapping of all the categories from the field study in the Spectra database. For example, the household cleaners category is not in the database, so we used two less abstract categories: bathroom cleaners and floor cleaners. Furthermore, superstore mass merchandisers and regular mass merchandisers are not broken out separately in the Spectra database. We did not view this as a serious limitation, because the consumers questioned in the field study perceived the two channels as similar. Thus, we attempted to extract a separate category-channel grid for each category across four channels (club, drug, grocery, and mass). The final database consists of 269 category-channel grids across 74 product categories (see Table 2) and yields 14,526 observations (269 grids x 54 cells per grid).
We performed the analysis in two ways: ( 1) a pooled analysis of all the observations from the channel-category Spectra grids and ( 2) a hierarchical regression analysis, consistent with previous research (e.g., Bolton 1989; Bolton and Shankar 2003). The pooled regression enabled us to examine the relative effects of each group of factors-namely, type of channel, channel-category associations, geodemographics, and all the possible interactions (three two-way interactions and one three-way interaction)-in one model and to draw insights from them. The hierarchical regression serves as a robustness check. Because the hierarchical regressions are similar to the pooled regression, we report only the pooled regression results.( n6) The general equation that we estimate is the following:
( 1) SOV = f(channel effects, geodemographic effects, association
effects, two-way interactions, three-way interactions),
where SOV is the channel share of volume. The channel effects include those of the mass merchandiser, club store, and drug channels relative to the grocery channel; the association effects include the effects of the channel-category association dimensions (purchase frequency and differentiation); and the geodemographic effects include the effects of affluence, urbanicity, HOH age, and presence of children. The specific equation is
( 2) [Multiple line equation(s) cannot be represented in ASCII text]
where i, j, and k represent category, channel, and geodemographic group, respectively; MASS, DRUG, and CLUB are dummy variables that denote whether the observation is from mass merchandiser, drug, or club channels, respectively; AFF represents affluence; URB denotes urbanicity; KIDS represents presence of children in the household, and AGE denotes age of HOH. The variables PFREQ and DIFF measure purchase frequency and differentiation, the channel-category association dimensions as obtained from the correspondence analysis. DGINT is an interaction variable involving channel-category dimensions and geodemographics, CGINT is an interaction variable involving channel and geodemographics, CDINT is an interaction variable involving channel and channel-category dimensions, and CDGINT is an interaction variable involving channel, channel-category dimensions, and geodemographics. The variable ε is an error term, and α Β γ Λ φ ψ and λ are parameters associated with the variables.( n7)
Results
We now discuss the significant effects in the pooled regression model. As is shown in Equation 1, the interpretation of the channel-interaction coefficients is relative to the grocery channel (i.e., grocery is the baseline channel). Furthermore, Dimension 1 is the vertical channel-category association dimension from the correspondence analysis (see Figure 3) that we named "category purchase frequency." Dimension 2 is the horizontal dimension, which we interpreted as "category differentiation." As is shown in Figure 3, each of the dimensions ranges between approximately -1.2 and 1.2. Table 2 lists the categories that scored relatively high and low in the two dimensions.
Table 4 shows the individual parameter estimates. Our second research question seeks to examine the direct effects of channel-category associations and geodemographics on channel share of volume. Specifically, we argue that the channel-category associations should have a significant, direct impact on channel share of volume. This thesis is strongly supported, because the base effects (with the channel indicator variables all set to zero) are substantive and significant. Furthermore, the effects of the channel-category associations on volume share vary widely across the channels (i.e., all six channel x association parameters are significant). The effect of purchase frequency ranges from -.357 for grocery to .334 for mass (Table 5 breaks out the results by channel), and the effect of differentiation ranges from -.138 for grocery to .160 for mass. The higher the purchase frequency at a channel, the higher is the channel share of volume; the more a category is differentiated from other categories in a channel, the higher is the channel share of volume.
Our results also suggest that geodemographic effects vary quite a bit across channels (i.e., geodemographic x channel interactions); 8 of the 12 geodemographic x channel parameters are significant. The implications are that in terms of affluence, presence of children, and HOH age, the grocery and club channels are equivalent, but club stores attract fewer urban shoppers (i.e., the urbanicity x club interaction is negative). Mass merchandisers draw a younger, less affluent, and more rural clientele with children (all four geodemographics x mass parameters are significant). On average, drugstores tend to attract less affluent, older shoppers without children.
In the discussion of Research Question 3, we argue that the channel-category associations moderate the geodemographic-channel volume share relationship (i.e., the two-way geodemographic x category-channel associations interactions and the three-way geodemographic x category-channel associations x channel interactions). This thesis is supported; 5 of the 8 possible two-way interactions and 9 of the 24 possible three-way interactions are statistically significant (at p < .01). The 14 significant effects are split relatively evenly across the two association dimensions (8 for purchase frequency and 6 for differentiation). Moreover, 4 of the significant three-way interactions involve the mass merchandiser channel, and the other 5 involve the drug channel. As is evident in the two-way geodemographics and channel results, mass merchandiser and drugstore clienteles are different from those of grocery, but club and grocery clienteles are similar.
We also argue that the channel-category associations with channel such that channels enjoy a greater time share for categories with which they are more associated. This is strongly supported; all six of the association x channel interactions are statistically significant. As we predicted, mass merchandisers' volume share is buoyed for the categories that are more closely associated with mass merchandisers (i.e., as is shown in Figure 4, low category-purchase frequency/low category differentiation). Club's volume share is relatively unaffected by the associations (see Table 4), which makes sense because its position near the center of Figure 3 indicates relatively weak category associations. Finally, drug's volume share is greater for the categories with which it enjoys a closer association (i.e., per Figure 3, low category-purchase frequency/high category differentiation).
Across the four channels there are 60 parameters, which makes interpretation of the results somewhat arduous. In an effort to simplify the exposition, we compiled Table 5, which shows the net channel share equation for each channel (i.e., setting the channel-indicator variables appropriately in Equation 1). Because grocery is the base channel, the model intercept, the geodemographics main effects, the channel-category association main effects, and the two-way interaction between geodemographics and the channel-category associations represent the estimate of grocery channel share of volume (i.e., the channel dummy variables are all set to zero so that all the nongrocery terms drop out). Because the dependent variable is channel volume share, the magnitude and direction of a coefficient may indicate that the impact of the channel and the channel-category association interactions are ( 1) in the same direction as grocery but weaker/stronger or ( 2) in the opposite direction as grocery. With the mass merchandiser equation as an example, the share sensitivity intercept (.452) represents the sum of the model intercept (i.e., .508) and the mass merchandiser indicator parameter (-.056). Similarly, the urbanicity x purchase-frequency effect (-.045) is the sum of the urbanicity x purchase-frequency interaction (.012) and the threeway mass merchandiser x urbanicity x purchase-frequency interaction (-.057).
Grocery. Grocery enjoys the greatest base share (i.e., the largest intercept). However, the dimensions affect grocery's average volume share substantially. Because the channel-category association dimensions range between -1.2 and 1.2, the purchase-frequency parameter of -.357 indicates that the grocery channel's share of volume is approximately 86% (i.e., .508 + .357) for categories with a purchase-frequency value of -1.0 and only 15% for categories with a purchase-frequency value of 1.0 (when we control for the other variables). Similarly, differentiation affects grocery's share of volume, albeit to a much lower degree (b = -.138). Grocery's share for a category with a differentiation value of -1.0 is approximately 65%, but it is only 37% for a category with a differentiation value of 1.0. For the geodemographic variables, only affluence and urbanicity exert a significant effect on grocery's volume share. The positive values of the parameters indicate that more-affluent shoppers and more urban shoppers tend to satisfy their needs in the grocery channel. Furthermore, the interactions between affluence and urbanicity with purchase frequency indicate that this attraction is even stronger for the categories that are more closely associated with grocery (higher purchase-frequency categories). The effects of presence of children and HOH age are weak; only two of the four parameters achieved significance. Households with children tend to shop at grocery stores for frequently purchased categories (i.e., the kids x purchase-frequency interaction is positive and significant), and older consumers tend to shop at grocery for categories that are more differentiated (i.e., the age x differentiation interaction is positive and significant).
Mass merchandisers. Mass merchandisers present a much different picture than grocery does. It is not much of an exaggeration to note that mass merchandisers attract the opposite clientele of grocery. Both channel-category association dimension parameters are positive and significant. The net effect is that mass merchandisers would enjoy a 79% share (i.e., .452 + .334) for categories with a purchase-frequency value of 1.0 and only a 12% share for categories with a purchase-frequency value of -1.0. The effect of differentiation is less but still substantial; mass merchandiser share ranges from 61% for categories with a differentiation value of 1.0 to 29% for categories with a differentiation value of -1.0.
Notably, all the geodemographic effects are statistically different from grocery, and three are of the opposite sign. Mass merchandisers attract less affluent, more rural households than does grocery, and the households tend to be younger and to have children. Furthermore, three of the four interactions with purchase frequency are significant and negative. This implies that younger, less affluent, rural households are attracted to mass merchandisers for infrequently purchased categories. These tend to be nonfood categories, a traditional strength of mass merchandisers. Only one interaction with differentiation is significant: that with age. This means that younger households are attracted to mass merchandisers for categories that are less differentiated.
Club stores. The geodemographic profile of club stores is quite similar to that of grocery, but the role of the channel-category association dimensions is attenuated. The base share of club is only 3% on average (i.e., the intercept is .032), and the channel-category association dimensions serve to shift this by a relatively small amount. Club realizes a 4% share for categories with a purchase-frequency value of 1.0 and a 2% share for categories with a purchase-frequency value of -1.0. The effect of differentiation on club share is practically nil (i.e., the differentiation parameter of -.138 is negated by the club x differentiation interaction of .139). This makes intuitive sense, because the club channel is located near the center of Figure 3, which suggests that it is not particularly associated with many product categories. Furthermore, only 1 of the 12 geodemographic parameters is significant: urbanicity. Compared with grocery stores, club stores attract a slightly less urban shopper. This is probably the result of club stores being located in suburbs, which are not quite urban and not quite rural. In summary, club's share of channel volume is small but relatively stable across product categories.
Drugstores. The drug channel's base share (the intercept) is small, at approximately 2%. Given the size of the base share, the effects of the channel-category association dimensions and the geodemographics are rather limited. The effects of the two channel-category association dimensions are almost the same magnitude but in opposite directions (.025 for purchase frequency and -.023 for differentiation). This implies that drug benefits the most from the categories that are relatively more closely associated with drug, that is, products that are relatively more differentiated and purchased infrequently (e.g., health-related products). For example, drug has a predicted 4% share for a category with a purchase-frequency value of 1.0 (.019 + .025) and for a category with a differentiation value of -1.0 (.019 + .023).
Regarding geodemographics, drugstore shoppers are less likely to have children, and they tend to be older than shoppers in other channels. That is, households without children exhibit a drug-channel share that, on average, is 1.7% (parameter value of -.017) greater than households with children, whereas a household with HOH age of 65 has a predicted drug-channel share that is 3.1% greater than a household with HOH age of 26 (i.e., 65 x .001 - 26 x .001). The affluence parameters are the smallest of all channels; the significance of the two-way affluence x drug interaction deflates the affluence effect for drugstores (i.e., drugstore shoppers tend to be less affluent in general). In contrast, urban shoppers are more likely to patronize a drugstore, which increases the expected share by more than 3%. The geodemographic x association dimensions' interactions mirror those of the association dimensions' main effects, and the signs reverse across the dimensions. This implies that drug's volume share benefits from categories with which it is more closely associated (more differentiated and infrequently purchased) among more urban, older households without children.
Taken together, the results of the empirical study provide insight into the three research questions we posed in the introduction. First, for channel-category associations, the channels form a channel triangle in which drug, mass merchandisers, and grocery are near the vertices and club stores are in the middle. Each vertex is associated with certain product categories: the grocery channel with food products, the drug channel with medications and health-related products, and the mass merchandiser channel with household items. In contrast, the club channel exhibits more heterogeneity in terms of product categories that readily come to consumers' minds. In a relative sense, frozen foods, pet foods, and snack items are most closely associated with the club channel; cleaning supplies, automotive, gifts, beauty care, miscellaneous household items, and paper goods are most closely related to the mass merchandiser channel; and tobacco, alcohol, candy, magazines, and soaps are perceived as closest to the drug channel.
The correspondence analysis reveals important new insights into the channel-category associations. For example, animal or pet products map closer to the club channel than to other channels, though a substantial portion of pet food is sold in grocery and mass merchandiser channels (e.g., according to the Nielsen wand panel in 2000, 32% of dry cat and dog food was sold through mass merchandisers, and 54% was sold through grocery stores). Furthermore, miscellaneous items such as hosiery and beauty aids are more closely associated with the mass merchandiser channel, though the drug channel carries many of these items. Cleaning supply items are more closely associated with the mass merchandiser channel than with the club channel, though club stores sell a sizable amount of cleaning supplies. Notably, vice products, such as cigarettes and alcohol, are closest to the drug channel, despite the fact that the drug channel's signature products are health care and medical items and that other channels sell a high amount of vice products (e.g., drug's share of cigarette and liquor sales was 7% and 22%, respectively).
Second, channel share of volume depends directly on the geodemographic factors of affluence and urbanicity and indirectly on all four geodemographic factors (i.e., affluence, urbanicity, presence of children, and HOH age). Drug channel share of volume tends to be driven by older, more urban households without children, and mass merchandiser share of volume is driven by younger, less affluent, nonurban households with children. The drivers of club share of volume factors tend to be quite similar to those of grocery. Notably, the geodemographic groups exhibit the "polygamous loyalty" that Dowling and Uncles (1997) identify; that is, they split their loyalty across multiple channels. Furthermore, the relative variation in the channel splitting varies across categories, depending on the associations of product categories with the channels.
Third, our findings suggest that the channel-category associations influence channel share of volume both directly and indirectly. Figure 6 is a Venn diagram that depicts the percentage of total variation in channel volume share that is attributable to the three factors examined here (channel-category associations, channel, and geodemographics). The strong role of channel-category associations is clear; the associations explain 72% of variation in channel share of volume (when we sum across the main effects and all the interactions). Notably, the lion's share of this explanatory effect derives from the interaction with channel, which accounts for 43% of the total explained variance. In sharp contrast to their role in a specific category (Curry 1993), geodemographics explain only a small proportion of variation in volume share across categories and channels, contributing to only approximately 2% of explained variance. This illustrates the importance of channel-category associations in driving channel volume and emphasizes that it is imperative to consider these associations when mapping out a channel strategy.
Illustration of Channel Share of Volume
To illustrate the sensitivity of channel share of volume to the variables, we examined the range in channel volume share across the four channels for four different product categories. Referring to Figure 3, we selected a hypothetical category at the middle of the map (where the two association dimensions are zero) and one near each corner of the channel triangle. These are health care/over-the-counter (OTC) medications near the upper-left-hand vertex (purchase frequency = .90, differentiation = -.63), juices near the lower vertex (purchase frequency = -.95, differentiation = -.07), and cleaning supplies near the upper-right-hand vertex (purchase frequency = .43, differentiation = .93). The estimation of the range of share for each channel for each category was a two-step process. We began by inserting the appropriate values for the association dimensions and channels into the regression results. We then used the solver function in Excel to determine the maximum possible share for each channel given the range of possible geodemographic values (e.g., urbanicity is 0 or 1, age ranges between 26 and 65). The results of the illustration are shown in Table 6.
The wide variation in channel share as a function of the channel-category associations is readily apparent. The maximum expected share of grocery ranges from 43% for the health care/OTC category to 91% for juices (the category with which it enjoys the strongest association), and mass merchandisers' maximum expected share ranges from a low of only 12% for juices to a high of 68% for cleaning supplies (the category with which it enjoys the closest association). Similarly, the maximum expected share of drug varies from a low of 1% for juices to a high of 31% for health care/OTC (with which it is most closely associated), whereas club's maximum expected share ranges from 9% for juices to 19% for cleaning supplies. Club's share does not exhibit as much variability across categories, which is a direct outcome of its central location on the map.
Notably, the geodemographic factors create a rather wide range of expected shares across the channels and categories. For example, based on the level of the geodemographic factors, the maximum share of grocery for a category in the center of Figure 3 is ten share points greater than the minimum expected share (62% versus 52%). This difference between the maximum and minimum expected shares is replicated across categories and channels. Thus, although the geodemographics explain a relatively small percentage of the variation, they play a substantial role in driving channel volume and deserve managerial attention.
Managerial Implications
Although the results of our study are not normative, several insights emerge from our analysis. A summary of the linkages among the channels, channel-category associations, geodemographic clientele, interaction effects of the associations and geodemographics, and managerial implications appears in Table 7. The overarching takeaway is that though each channel has its own associated product categories and clienteles, the interaction of the associations and geodemographics offers valuable managerial guidelines for channel-category targeting and promotion decisions.
In addition to the implications highlighted in Table 7, the findings have several managerial implications. First, our results suggest that the geodemographics-channel relationship should not be generalized across product categories without taking category-channel perceptions into account. That is, managers should assess the importance of this relationship in light of the strength of the associations between their product category and the channels through which it is sold. Therefore, retailers' marketing to geodemographic groups should be specific to product categories and dependent on the extent of category association with that channel.
Second, it may be the case that the tailoring of integrated marketing communications efforts across channels would increase profits. For channels in which there is a strong product association, manufacturers and retailers can leverage that association for competitive advantage. For example, household items are associated more with mass merchandisers. Mass merchandisers can use this knowledge to promote the items in order to reinforce their association with the products, and they can locate the items strategically to try to spur in-store need recognition (Inman and Winer 1998) and leverage interproduct complementarities (Shocker, Bayus, and Kim 2003). Similarly, club stores can promote snack items to capitalize on their association with these categories. In contrast, because mass merchandisers know that the association between their channel and household products is secure, they can move toward securing an association between their channel and other products.
From a public policy standpoint, the households patronizing lower-priced channels such as club stores tend to be more affluent, which may partially be a result of access and knowledge of prices in different channels. Less affluent households are less likely to have transportation to club stores and thereby suffer a double whammy of less affluence and higher prices. This is compounded by the tendency of club and grocery stores to be located in suburbs. It is noteworthy that less affluent rural households do not suffer from this price disadvantage; they have more ready access to mass merchandisers, particularly Wal-Mart, which tends to locate stores in small towns. Older, more-urban people tend to shop at drugstores, possibly because they dislike travel or are less price sensitive.
Further Research
Our findings should be viewed as a first step toward a better understanding of channel shopping behavior, because they suggest several directions for further research. First, our analysis was at the category and channel levels. It may be that certain brands in a category fare better in some channels than in others, perhaps as a function of the match between the target consumer and the channel's shopper base. For example, Target may be associated with more-upscale brands than Wal-Mart. This hypothesis remains unexplored. This type of association may be related to a deeper investigation of channel-category associations from a consumer behavior standpoint. Experimental investigation of consumer perceptions of channels and their associations with categories, and vice versa, would be useful in this regard.
Second, the role of urbanicity in channel share of volume can be explored in greater depth by examining recent channel-purchase data. As more mass merchandiser and club stores locate near urban areas, the negative relationship between urbanicity and share of volume of these channels may be weakened. This hypothesis can be tested with data from urban neighborhoods that have witnessed an increase in the number of mass merchandisers and club stores.
Third, the role of retail branding in channel-category associations and their effects on channel patronage deserve greater attention. For example, Wal-Mart now has different channels: mass merchandiser (Wal-Mart), hypermarket (Wal-Mart Supercenter), and grocery (Wal-Mart Neighborhood). The associations of a product category may extend from one channel to other channels if Wal-Mart branding is common across the channels. These changes in channel-category associations may have noteworthy effects on channel patronage. This possibility can be tested with the help of data from areas in which Wal-Mart operates in multiple channels.
Finally, our analysis has examined channel associations and their role in channel share of volume at a single point in time. If longitudinal data were available, it would be useful to explore the evolution of channel-category and channel-attribute associations over time. For example, mass merchandisers are devoting increasing amounts of shelf space to food items. This may lead to a "drift" of these items toward mass merchandisers and would help address the issue of the direction of the relationship between channels and particular product categories. It would also be worthwhile to determine how channel-service associations evolve as services become more common (e.g., banking, dry cleaning) in these channels.
The authors thank the anonymous JM reviewers, Meyers Research Center, and Spectra for providing the data used in this research and Nitika Garg, Adwait Khare, and Vikas Mittal for their assistance and comments.
(n1) In some cases, such as trade area analysis, the analysis focuses on profiling heterogeneity in geodemographics across trade areas (e.g., Faulds and Gohmann 2001). Our focus is on the variation in consumption across geodemographic groups.
(n2) We tested for potential multicollinearity among independent variables. The correlation matrix and variance inflation factors indicated that multicollinearity is not a problem.
(n3) It can be argued that some shoppers choose to live in urban or suburban areas on the basis of proximity to their desired channels, suggesting that urbanicity may be endogenous. Although some people may locate close to channel locations because of channel characteristics, we believe that this is not a serious issue, because our dependent variable is channel volume share, not price or another channel attribute. It seems unlikely that people would locate in urban or rural neighborhoods on the basis of channel share of volume in the neighborhood. Moreover, geographic location is the only variable under consumers' real control (unlike demographic variables such as age or income) and is just one component of geodemographics. Therefore, we do not believe that endogeneity is a major concern.
(n4) To test the robustness of our results to the operationalization of the geodemographic variables, we repeated the analyses for different categorical scale operationalizations of affluence, urbanicity, kids, and age. For example, because a linear coding may be unduly restrictive, we reestimated the model using three age dummy variables (one for 35-54, one for 54-65, and one for 65-and-over categories) for the main and nonlinear effects of age and all of the interactions. The adjusted R2 for the analyses were almost identical, suggesting that our results are robust (e.g., the linear coding adequately captures the age effect).
(n5) Thus, it is important that we use the category positions from the correspondence analysis rather than the distances from particular channels.
(n6) The hierarchical results are available from the authors.
(n7) We acknowledge that a limitation of our approach is that the individual channel-share estimates may not be logically consistent i.e., strictly between zero and one) and that the sum of the channel shares is not constrained to sum to one. However, Inman (1990) reports that parameters estimated with a set of constrained share equations were substantively identical to parameters estimated with an unconstrained set of equations. Thus, we do not believe that this represents a serious concern.
Legend for Chart:
A - Lifestyle
B - Ages 18-34 With Children
C - Ages 18-34 No Children
D - Ages 35-54 With Children
E - Ages 35-54 No Children
F - Ages 55-64
G - Ages 65 and over
H - Total
A B C D E
F G H
Upscale suburbs 54.2 44.4 95.6 86.0
111.0 130.3 93.8
Traditional families 75.8 40.3 105.0 106.6
135.9 122.3 104.9
Mid/upscale suburbs 74.8 29.1 101.8 86.0
128.8 115.0 99.1
Metro elite 45.3 28.5 85.1 69.9
103.4 117.6 74.6
Working-class towns 59.2 40.6 96.4 95.3
129.2 126.0 94.8
Rural towns and farms 49.3 34.4 86.6 97.4
131.8 122.9 92.8
Mid-urban melting pot 51.8 37.9 91.4 80.1
113.0 121.7 87.5
Downscale rural 64.0 31.2 101.8 100.0
130.8 121.9 101.0
Downscale urban 47.0 42.9 72.7 79.5
106.9 93.2 75.0
Total 56.8 36.2 93.3 87.9
122.0 118.4 91.2
Source: Spectra (www.spectramarketing.com). Used with permission. Legend for Chart:
A - Purchase Frequency
B - Differentiation Low
C - Differentiation High
A B
C
Low Automotive [(oil, engine treatments)]
Beauty care [(lipstick, nail polish)]
Cleaning products [(bathroom, floor cleaners)]
Clothing
Cooking utensils [(kitchen utensils)]
Domestics
Electronics [(computer software)]
Footwear
Fresh flowers/plants
Furniture
Gifts
Groceries
Health and beauty aids
Hair care [(shampoo)]
Hosiery
Household cleaners
Hardware/electric/plumbing [(light bulbs)]
Jewelry and watches
[Lawn and garden]
[Office/school supplies]
[Prerecorded videos/tapes]
[Seasonal items]
[Small appliances]
[Sporting goods/toys]
[Analgesics]
[Batteries]
Candy [(chocolate)]
Cards/wraps/party [(egg coloring, candles)]
[Cold and flu medications]
Cosmetics [(cosmetic kits)]
[Deodorants]
Feminine hygiene [(sanitary napkins)]
First aid supplies [(bandages, treatments)]
Gastrointestinal products [(antacids)]
Health care/OTC medications [(arthritis)]
Hobby and crafts
[Liquor]
Magazines/newspapers
Oral care [(toothpaste)]
Photo finishing
Photo supplies [(film)]
Prescription drugs
Shaving supplies [(razors)]
[Skin care]
[Soap]
[Vitamins/supplements]
High Baby products [(baby bath)]
Canned food [(vegetables)]
[Cat food]
[Crackers]
[Diapers]
Dishwashing detergent]
[Dog food]
Food
[Frozen dinners]
[Frozen foods]
[Frozen meat]
[Frozen pizza]
Household products
Laundry supplies [(detergent)]
[Oil/shortening]
[Paper goods]
Pet food
Pet supplies
[Salad dressing]
Snacks
[Baby food]
[Beer]/wine
Beverages/drinks
[Cereal]
Chips [(potato chips)]
[Cigarettes]
[Coffee]
[Cookies]
Dairy [(butter)]
Deli [(deli meat)]
[Eggs]
Fresh baked goods
[Fresh meat]
[Ice cream]
[Juice/juice drinks]
[Milk]
Packaged baked goods [(bread)]
[Packaged cheese]
Packaged cold cuts
Produce [(lettuce)]
Side-dish items [(frozen potatoes, bagged rice)]
Soft drinks
Notes: [] Categories used in estimation are in italics. Legend for Chart:
A - Category
B - Closest Associated Channel(s)
C - Rationale
A B
C
Alcohol Drug
Although the drug channel is perceived as
closest to health-related products, it is
also perceived as an ideal channel for
alcohol, likely because of superior
convenience, selection, and service
attributes compared with those of other
channels (Inman et al. 2002).
Automotive Mass
Although club and drugstores carry auto
supplies (which are infrequently purchased
search goods), auto products are perceived
as closer to mass merchandisers, likely
because of broader selection.
Beauty care Mass, drug
Purchases of beauty care items are generally
hedonic, and the mass merchandiser channel
offers greater selection and convenience
than the other channels.
Candy Drug
Candy is an impulse purchase item, and
purchases at drugstores are of smaller
basket sizes than are those at other
channels. Therefore, it is easier to buy more
candy at or close to the checkout in the drug
channel without substantially increasing the
weight of purchases.
Cigarettes Drug
Although the drug channel is perceived as
closest to health-related products, it is
also perceived as an ideal channel for
cigarettes, likely because of superior
convenience, selection, and less crowded
ambience (and thus reduced visibility)
compared with those of other channels.
Cleaning products Mass
Cleaning products are storable but frequently
consumed. Mass merchandisers are likely to
offer both favorable prices and wider
selection than are other types of retailers.
Cosmetics Drug
Cosmetics are more price inelastic but
display elastic: A drug channel is better
positioned than other retailers in these
aspects.
Diapers Mass, grocery
Diapers are storable, frequently purchased
items. Although club stores can offer lower
prices, the mass and grocery channels
attract more buyers with children and offer
wider selection and convenience for frequent
use than the club channel.
Feminine hygiene Drug
The drug channel is most attractive for these
items because of the superior convenience,
selection, and greater privacy (less crowded)
than other channels.
Bulk foods Mass, club
Prices are critical determinants of foods
purchased in bulk. Mass and club are
perceived as better than the other channels
in price.
Gifts Mass
Gifts are planned purchases, so consumers
search for the best selection and prices.
The mass merchandiser channel is best
positioned in these attributes relative to
the other channels.
Magazines/ Drug
newspapers
Magazines and newspapers are bought on
either a frequent basis or impulse.
Drugstores are perceived as ideal for
both attributes.
Miscellaneous Mass
household items
Miscellaneous household items are
typically storable but infrequently
purchased. Mass merchandisers are likely
to offer more favorable prices than other
types of stores and more location
convenience than club stores.
Paper goods Mass
Paper products such as paper and bath
towels and tissues are highly storable
and consumed items. Prices and selection
are important. Mass merchandisers
are perceived as ideal on these attributes.
Pet foods Club
Repeat brand purchase and prices are
important for pet foods. Club stores are
perceived to be ideal on these attributes.
Photo supplies Drug
Although mass merchandisers and club stores
can offer better prices on super-sized packs,
drugstores deliver faster on photo processing
and promote these items, so they are
perceived as more synonymous with photo
supplies than other channels.
Snacks Grocery, club
Grocery stores are the logical choice for
food-related items. However, frequently
consumed snacks such as potato chips,
cookies, and crackers are also storable
and used for party occasions. Prices are
most critical for such purchases, so the
club store is an ideal channel.
Soap Drug
Soaps are frequently purchased and consumed
items. Although the grocery channel has
lower prices on these than the drug channel,
the drug channel offers more convenience and
can evoke soaps in consumers' baskets better
because of the drug channel's and soap's
association with cosmetics.
Notes: The data should be interpreted such that a given category
(Column 1) provides the channel with the strongest association
(Column 2) and explains why the channel is associated most
strongly with the category (Column 3). Legend for Chart:
B - Parameter
C - Standard Error
D - RSSCP
E - Group RSSCP
A B C
D E
Intercept .508(**) .007
Geodemographics
.32
Affluence .001(**) .000
.19
Urbanicity .042(**) .003
.13
Children -.002 .004
.00
Age .000 .000
.00
Channel Type
28.33
Mass -.056(**) .010
.96
Drug -.489(**) .011
14.58
Club -.476(**) .011
12.79
Association Dimensions
27.19
Purchase frequency(a) -.357(**) .009
25.00
Differentiation(a) -.138(**) .011
2.19
Dimension-Geodemographic Interaction
.27
Affluence x purchase frequency .001(**) .000
.05
Affluence x differentiation .000 .000
.00
Urbanicity x purchase frequency .012(*) .004
.01
Urbanicity x differentiation .016(*) .005
.02
Kids x purchase frequency .012(*) .005
.01
Kids x differentiation -.010 .006
.00
Age x purchase frequency .000 .000
.02
Age x differentiation .001(**) .000
.15
Channel-Geodemographic Interactions
1.34
Affluence x mass -.003(**) .000
.62
Affluence x drug -.001(**) .000
.04
Affluence x club -.000 .000
.01
Urbanicity x mass -.124(**) .005
.52
Urbanicity x drug -.007(**) .005
.00
Urbanicity x club -.030(**) .005
.03
Kids x mass -.014(*) .006
.01
Kids x drug -.015(*) .006
.00
Kids x club .009 .006
.00
Age x mass -.001(**) .000
.05
Age x drug .001(**) .000
.06
Age x club .000 .000
.01
Dimensions-Channel Interactions
41.96
Club x purchase frequency .369(**) .013
6.19
Drug x purchase frequency .382(**) .014
7.18
Mass x purchase frequency .691(**) .013
24.73
Club x differentiation .139(**) .017
.48
Drug x differentiation .115(**) .016
.30
Mass x differentiation .298(**) .015
3.08
Three-Way Interactions
.60
Affluence x club x purchase frequency -.000 .000
.00
Affluence x drug x purchase frequency -.000 .000
.00
Affluence x mass x purchase frequency -.001(**) .000
.11
Affluence x club x differentiation .000 .000
.00
Affluence x drug x differentiation -.000 .000
.00
Affluence x mass x differentiation -.000 .000
.00
Urbanicity x club x purchase frequency -.009 .006
.00
Urbanicity x drug x purchase frequency .023(**) .007
.01
Urbanicity x mass x purchase frequency -.057(**) .006
.08
Urbanicity x club x differentiation -.009 .008
.00
Urbanicity x drug x differentiation -.040(**) .008
.03
Urbanicity x mass x differentiation -.011 .007
.00
Kids x club x purchase frequency -.016 .007
.00
Kids x drug x purchase frequency -.032(**) .008
.02
Kids x mass x purchase frequency -.002 .007
.00
Kids x club x differentiation .013 .009
.00
Kids x drug x differentiation .027(*) .009
.01
Kids x mass x differentiation .002 .009
.00
Age x club x purchase frequency -.000 .000
.00
Age x drug x purchase frequency .000 .000
.01
Age x mass x purchase frequency -.001(**) .000
.11
Age x club x differentiation -.001 .000
.01
Age x drug x differentiation -.002(**) .000
.10
Age x mass x differentiation -.001(**) .000
.09
(*) p < .01.
(**) p < .0001.
(a) We measured Association Dimensions 1 and 2 such that the
higher the value of the dimension, the lower are the purchase
frequency and differentiation, respectively.
Notes: Adjusted R² = .86, n = 14,526, RSSCP = relative
squared standardized coefficient percentage. Legend for Chart:
B - Grocery Main Effect
C - Grocery Purchase-Frequency Interaction
D - Grocery Differentiation Interaction
E - Mass Merchandisers Main Effect
F - Mass Merchandisers Purchase-Frequency Interaction
G - Mass Merchandisers Differentiation Interaction
H - Club Main Effect
I - Club Purchase-Frequency Interaction
J - Club Differentiation Interaction
K - Drug Main Effect
L - Drug Purchase-Frequency Interaction
M - Drug Differentiation Interaction
A B C D E F G
H I J K L M
Intercept .508 .452
.032 .019
Purchase frequency -.357 .334
.012 .025
Differentiation -.138 .160
.001 -.023
Affluence .001 .001 .000 -.002 -.001 .000
.001 .000 .000 .000 .000 -.000
Urbanicity .042 .012 .016 -.082 -.045 .005
.012 .003 .008 .034 .035 -.023
Children -.002 .012 -.011 .012 .011 -.008
.007 -.003 .003 -.017 -.019 .016
Age .000 .000 .001 -.001 -.001 -.001
.000 .000 .000 .001 .001 .001 Legend for Chart:
B - Grocery
C - Mass
D - Drug
E - Club
A B C D E
Map Midpoint(a)
Maximum share .62 .41 .13 .12
Minimum share .52 .17 .03 .05
Health care/OTC
Maximum share .43 .60 .31 .12
Minimum share .27 .23 .07 .06
Juices
Maximum share .91 .12 .01 .09
Minimum share .82 .01 0 .04
Cleaning Supplies
Maximum share .44 .68 .09 .19
Minimum share .26 .37 .02 .07
(a) We estimated maximum share and minimum share on the basis of
the category's position in the correspondence analysis map
(Figure 3) as well as the most favorable and least favorable
geodemographics. Legend for Chart:
A - Channel
B - Some Closely Associated Categories
C - Typical Geodemographic Profile of Shoppers
D - Interaction Effects of Associations and Geodemographics on
Channel Share of Volume
E - Managerial Implications
A B C
D
E
Grocery Food, diapers, Affluent, urban,
frozen foods, middle-aged
snacks (high shoppers with
frequency, average family
high/low size
differentiation)
Positive effects of urbanicity
and affluence are attenuated
for high-purchase-frequency
and high-differentiation
categories. Households with
(without) children tend to shop
at grocery for categories
purchased less (more)
frequently. Older (younger)
customers shop at grocery
channel for less (more)
differentiated categories.
Rather than geotargeting the
average grocery consumer,
managers should market to
customer groups for
categories that exhibit low
differentiation and high
frequency differently from
those for other categories.
Drug Health care/OTC, Less affluent,
alcohol, candy, urban, older
beauty care, shoppers without
cigarettes, children
cosmetics,
feminine hygiene,
magazines/newspapers,
soap (low
frequency, high
differentiation)
Drug's volume share benefits
from categories with which it is
more closely associated (more
differentiated and infrequently
purchased) among more
urban, older households
without children.
Because differentiated and
infrequently purchased
categories such as candy,
cosmetics, and magazines
provide more share of volume,
drug-channel managers can
promote these items to their
geodemographic clientele.
Mass Cleaning products, Less affluent,
automotive, rural, younger
beauty care, shoppers with
diapers, bulk food, children
gifts, household
items, paper
goods (low
frequency, low
differentiation
Younger, less affluent, rural
households are attracted to
mass merchandisers for
infrequently purchased
categories. Younger
households are attracted to
mass merchandisers for
categories that are less
differentiated.
Mass channel can gain
significant share by focusing
on low-frequency, low-differentiation
categories such
as cleaning products,
household items, and gifts
aimed at rural, less wealthy,
and younger shoppers.
Club Pet foods, bulk Affluent, less
food, frozen foods, urban, middle-aged
snacks (high shoppers
frequency, low with average
differentiation) family size
The interaction effects are
similar to those of the grocery
channel, but club's share of
volume is higher for rural
customers for infrequently
purchased and highly
differentiated categories.
It is easier for club-channel
managers to increase traffic
by reinforcing the associated
categories, such as pet foods
and snacks, and by targeting
rural shoppers rather than
typical destination categories
to avoid direct competition
with the grocery channel.DIAGRAM: FIGURE 1; The Role of Channel-Category Associations in Channel Volume Share
Lifestyle Descriptions
Upscale Suburbs: 12.05%
- Major metro suburbs and urban fringe neighborhoods
- Top-end incomes, educations, and occupations
Traditional Families: 9.56%
- Suburbs and outlying towns
- Mixed white collar/well-paid blue collar
- Upper-middle incomes and educations
- Typically dual-income households
Mid/Upscale Suburbs: 9.88%
- Metro urban fringe locations
- Mixed single-unit and apartment neighborhoods
- Upper incomes and educations
Metro Elite: 9.30%
- Urban and urban fringe
- Townhouse and high-rise apartment areas
- Above-average incomes and occupations, very high educations
- Younger, professional population
Working-Class Towns: 13.69%
- Towns and outlying suburbs
- Mixed lower-level white collar, upper-level blue collar
- Middle-class incomes and educations
Lifestyle Descriptions
Rural Towns and Farms: 13.27%
- Mill, factory, and mining towns with rural farm areas
- Middle to lower-middle incomes
- Predominately blue-collar occupations with farming
- Rust Belt mill towns and midwestern farmers
Mid-Urban Melting Pot: 8.29%
- Major metro urban and urban fringe
- Lower-level white collar and service occupations
- Mid to lower-middle incomes and strong ethnic presence
Downscale Rural: 12.19%
- Rural towns, hamlets, villages, and farming areas
- Very low incomes and educations
- Light industry, textiles, and agriculture
- Strongly skewed to southeastern United States
Downscale Urban: 11.77%
- Densely populated urban areas, most common in
northeastern United States
- Very low incomes and educations
- Lower-level blue-collar and service occupations
- Strong ethnic presence
Life-Stage Descriptions
18-34 with kids 14.30%
18-34 without kids 12.75%
35-54 with kids 24.15%
35-54 without kids 14.44%
55-64 13.34%
65+ 21.02%
Source: Spectra (www.spectramarketing.com). Used with permission.
Notes: All percentages refer to U.S. population.
GRAPH: FIGURE 3; Correspondence Analysis Map of Categories and Channel Data
GRAPH: FIGURE 4; Correspondence Analysis Map of Categories and Channel Data: Upper-Right Quadrant
GRAPH: FIGURE 5; Correspondence Analysis Map of Categories and Channel Data: Lower Quadrants
DIAGRAM: FIGURE 6; Venn Diagram of Relative Squared Standardized Coefficient Percentages of Channel Type, Geodemographics, and Channel-Category Associations on Channel Volume Share
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~~~~~~~~
By J. Jeffrey Inman; Venkatesh Shankar and Rosellina Ferraro
J. Jeffrey Inman is Albert Wesley Frey Professor of Marketing, University of Pittsburgh (e-mail: jinman@katz.pitt.edu).
Venkatesh Shankar is Ralph J. Tyser Fellow and Associate Professor of Marketing, University of Maryland (e-mail: vshankar@rhsmith.umd.edu).
Rosellina Ferraro is a doctoral student in marketing at Duke University (e-mail: rf@mail.duke.edu).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 184- The Severity of Contract Enforcement in Interfirm Channel Relationships. By: Antia, Kersi D.; Frazier, Gary L. Journal of Marketing. Oct2001, Vol. 65 Issue 4, p67-81. 15p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.65.4.67.18385.
- Database:
- Business Source Complete
The Severity of Contract Enforcement in Interfirm Channel Relationships
Little is known about how channel members react to violations of explicit contracts. The authors develop and test an integrative conceptual framework that focuses on the severity of the enforcement response in channel relationships. The empirical results provide evidence of discerning enforcement practices by channel members, reflecting channel system, network, and dyadic concerns.
The importance of explicit contracts in the management of channel relationships has been recognized for quite some time (see Frazier 1983). Accordingly, many marketing scholars have conducted research on the deployment (Jeuland and Shugan 1983; Lal 1990; Moorthy 1988) and use (Lusch and Brown 1996) of explicit contracts. No matter how well-designed, however, contracts still may be violated (Hennart 1993; Williamson 1996). As Rousseau and Parks (1993, p. 2) indicate, "Contracts are fundamental to the " actions of organizations. They imply cooperation and consensus, but often engender dispute and disagreement."
The integrity of firms' explicit contracts and the effectiveness of their coordination efforts depend to a large extent on sound enforcement practices (see Stern, El-Ansary, and Coughlan 1996). Yet few studies in the marketing literature have addressed this important issue. Dutta, Bergen, and John"s (1994) analytical findings suggest that enforcement is more severe when the importance of agent services is high, agents are offered higher margins, and the principal's commitment to the channel is high. Bergen, Heide, and DuttA's (1998) empirical results lend further support to these findings.
The purpose of this study is to promote the understanding of the contract enforcement practices of channel members. On the basis of an integration of transaction cost economics (TCE), the relational exchange paradigm, and network theory, we develop a conceptual framework that explains the severity of the principal's disciplinary response to an agent's violation of a contractual obligation.1 Prestudy interviews with personnel in more than 20 supplier and franchisor organizations also contributed to the conceptualization. The framework was designed to apply to any channel context in which explicit contracts exist between channel members. Our test of the conceptual framework is based on a field survey of franchisors in six industries.
Prior studies on contract enforcement in distribution channels, though they provide valuable insights, have limited their focus to a single type of violation (i.e., resale restrictions) and a single enforcement response by the principal-termination (Bergen, Heide, and Dutta 1998; Dutta, Bergen, and John 1994). Our field study is the first to examine a range of actual contract violations by agents and a continuum of enforcement severity by principals in response. Thus, we significantly extend the empirical domain of enforcement. An additional limitation of extant channels research arises from its overwhelming focus on the characteristics of the focal dyad as an explanation for principal-agent actions. This almost exclusive attention to the focal relationship fails to acknowledge that interfirm relationships are affected not only by direct dyadic considerations but also by channel system and network issues (see Heide and John 1992). Accordingly, we also consider contract enforcement drivers at the channel system and network levels of analysis. Our examination of network effects appears especially valuable considering their likely importance in interfirm channel relationships and the sparse attention devoted to them in empirical channels research (see Anderson, H'kansson, and Johanson 1994).
Development of the Conceptual Framework
- Contract enforcement refers to the severity of a principal's disciplinary response to an agent's violation of a contractual obligation.2 The disciplinary response can range from lenient actions, such as ignoring the violation entirely or making only mild and informal attempts to gain compliance, to tough and punitive actions, such as strict cease-and-desist orders or termination proceedings. A thorough understanding of this complex decision requires a multilevel approach informed by complementary theories.
We take the perspective of the principal making the contract enforcement decision. From the outset, we were influenced by research that stresses the need to examine characteristics outside the bounds of the channel dyad. As Heide (1994, p. 81) emphasizes, "individual relationships are embedded in a context of other relationships that could have governance implications." Factors influencing relationships among the principal and agents that constitute the general channel system, relationships among the agents themselves, and the relationship between the principal and the focal agent that violated the contract all could affect the principal's contract enforcement decision (see Macaulay 1963).
Using this foundation, we developed the conceptual framework, displayed in Figure 1, through an integration of TCE, the relational exchange paradigm, and network theory. Individual contract violations and subsequent enforcement actions may influence governance costs across all agents, not just with the focal agent (see Williamson 1996, p. 64). Accordingly, we examine transaction-specific investments (TSIs), environmental volatility, and the criticality of the violated obligation at the channel system level of analysis, combining a TCE-based perspective with a "task-characteristic" variant of the relational exchange paradigm (Thibaut and Kelley 1959). We include the relationalism and interdependence constructs at the dyadic level. In addition, on the basis of network theory, we examine two elements of the informal network among agents'network density and network centrality"that may affect the contract enforcement decision. Where appropriate, we also consider moderator effects among the constructs. Each of the theoretical perspectives we apply has a cost'benefit orientation as its basis. Specifically, the principal is hypothesized to consider the relative costs incurred in and benefits attributable to undertaking particular enforcement responses.
Research Hypotheses
- Channel system level of analysis. The principal's channel system comprises the organized collection of channel members (agents) performing activities required to link producers with end customers (see Berman 1996). How violations of contracts affect relationships the principal has with associated agents is of central interest at this level of analysis. Principals may react severely to contract violations that jeopardize the integrity of their channel policies and system performance and/or raise systemic transaction costs (see Stern, El-Ansary, and Coughlan 1996).
- Transaction-specific investments are investments that cannot be redeployed from existing uses and users except at a significant loss of productive value (Williamson 1996). They can be specific to both individual agents and channel systems (see Erramilli and Rao 1993; Jones 1987).3 As Fein and Anderson (1997, p. 23) indicate, "Transaction specific investments " can be made in multiple relationships simultaneously, though when made, they are difficult to redeploy to other relationships." Our focus is TSIs made by the principal that are specific to the channel system, such as training programs and time spent with agents in developing strategic plans. The immobile nature of these investments and the serious ramifications of the systemwide asset devaluation that could arise from contract violations by individual agents are likely to make systemic TSIs highly salient to the principal's enforcement response.
The principal's TSIs in its channel system should build capabilities among associated agents, which involve the knowledge and skills necessary to serve end customers effectively (see Day 1994; Nelson and Winter 1982). Principals that have committed significant systemic TSIs should be particularly sensitive about contract violations. If an individual agent's shirking goes unabated, the incentive for other agents in the channel system to follow suit may increase. "Free-riding" tendencies also may be heightened in such cases (see Mathewson and Winter 1984). Without the full commitment of agents to perform all their responsibilities, systemwide capabilities and the TSIs underlying them could be eroded, which would thereby increase transaction costs, or the costs of governing the channel system (see Day 1994). Thus, when systemic TSIs are high, the cost of an individual agent's violation may be borne by the entire system (Klein 1996). In such conditions, in the interest of general deterrence (Gibbs 1975) and to safeguard distinctive capabilities and the specific investments that led to them, the principal is expected to take a relatively severe enforcement response (Williamson 1996).4
H1: The principal's TSIs in the channel system are related
positively to contract enforcement.
- Environmental volatility is the extent to which the rate of change in the external environment is rapid and unpredictable (see Achrol and Stern 1988). We consider the systemwide volatility the principal faces within its overall business. Sudden changes in customer preferences, unexpected new product development by competitors, and similar systemwide shocks are expected to influence relationships within the channel system more than relatively localized conditions in a focal agent's trading area (see Pfeffer and Salancik 1978).
When rapid and unpredictable changes occur with high frequency in an industry, a greater likelihood exists that some changes will be needed to the originally specified contract (Williamson 1996). Some agents, lacking the broader perspective of the principal, may attempt to react to systemwide volatility in a manner best suited to their local needs. These locally optimal responses, however, may adversely affect the performance of the channel system while heightening transaction costs. As a result, when volatility is high, the principal likely will want to control any changes made to the contract and implement such changes in a uniform manner (Anderson 1985). It is not the principal's opposition to change but rather the need to bring about the change in a coordinated manner that may drive the principal to maintain control.
H2: Environmental volatility within the channel system is related
positively to contract enforcement.
The risks and costs of improper realignment by independent agent actions in volatile environments are likely to become even more pronounced when TSIs are high in the channel system. The potential for improper realignment, if realized, would likely result in significant dilution and possible loss of the principal's TSIs. To preclude, or at least minimize, the possibility of such losses, the principal's imperative for control may increase when TSIs are significant (Anderson 1985). Therefore, the presence of high TSIs may strengthen the positive relationship between volatility and the severity of the enforcement response. In such conditions, a stricter response may help maintain the principal's control over its agents, thereby helping the principal safeguard specialized investments, as well as effectively and efficiently adapt to new and shifting conditions (Anderson 1985; see also Williamson 1985, p. 60).
H3: The greater the principal's TSIs, the stronger is the positive
relationship between environmental volatility and contract enforcement.
- Obligation criticality is the importance attached by the principal to the contractual obligation violated by the agent. Certain obligations are more critical to the principal than others are, especially when functions vital to the very survival of the principal's channel system are involved (e.g., cleanliness standards for restaurants) (see Rubin 1990). The principal is likely to take strong disciplinary action against an agent that violates a highly critical contractual obligation. Maintaining full compliance with vital routines is essential to the performance of the channel system (see Nelson and Winter 1982). A severe enforcement response is likely to reinforce to the focal agent and other members of the channel system the importance of effectively carrying out the obligation, thereby reducing the likelihood of future violations and the corresponding costs of governing relationships (Hunt and Vasquez-Parraga 1993).
H4: Obligation criticality is related positively to contract
enforcement.
Network level of analysis. The network level of analysis addresses the impact of the relationships among agents on the principal's enforcement decision. Formal networks among agents (e.g., the collection of franchisees within a franchise) comprise consciously planned and designed sets of relations (see Krackhardt and Hansen 1993). Such networks help identify the relevant participants but are gross indicators, at best, of the actual interaction shared by them. In contrast, our study focuses on the spontaneous, as opposed to consciously planned, relationships that exist among agents, namely, the informal network ties that "shadow formally prescribed work flow and authority relationships" (Brass and Burkhardt 1993, p. 444). We focus on the density of the informal network and the centrality of the focal agent in that network. Network density reflects the average strength of relations in a network (see Burt 1992). High levels of information sharing among members of a dense network result in shared beliefs (Friedkin 1984) and high levels of consensus among network participants (Galaskiewicz and Wasserman 1989; Meyer and Rowan 1977). Strongly interconnected agent networks can mount a concerted action against a principal (Fombrun 1986).The principal is likely to be concerned about the way severe enforcement responses will be interpreted in a dense network. The principal's enforcement response and the reasons behind it are not completely observable to all agents in the channel system, who instead must rely on "constructed and shared interpretations" of the enforcement response (see Burt 1987). If the principal's enforcement response is viewed as too severe and unfair, a negative backlash against the principal could occur (Galaskiewicz 1985). The principal's goodwill and support among the collective group of agents could be reduced in such cases, which would hinder the principal's general coordination efforts (see Rowley 1997). Costs of governing relationships could significantly increase. As a result, the principal may decide to take a somewhat less severe enforcement response with a focal agent that is embedded in a dense network. The potential costs of retaliation are likely to mitigate potential benefits of a more severe enforcement response.5
H5: Network density is inversely related to contract enforcement.
- Network centrality refers to the strength of an individual agent's position in an agent network (Benson 1975). Whereas network density is concerned with interconnections among all agents, network centrality reflects the extent to which the focal agent that violates a contractual obligation has connections with other network participants. A central agent occupies a position of prominence for at least a portion of an agent network (Benson 1975), influencing information flows and behavioral expectations among other agents (Rowley 1997).
A principal is expected to weigh carefully how to react to a central agent's violation (Benson 1975). Because of its prominence within the network, a central agent could bias the views of other agents regarding the fairness of the principal's enforcement response. An enforcement response viewed as too severe by a central agent could influence not only its goodwill and support but also the way other agents aligned with the central agent react to the principal (see Ford, McDowell, and Tomkins 1996). Transaction costs attributable to coordinating relationships could increase significantly as a result. Such risks are likely to lead the principal to a more tempered enforcement response.
H6: The network centrality of an agent is inversely related to contract
enforcement.
We are not arguing that members of a dense agent network or a central agent will revolt against a principal that severely enforces its contracts. Rather, our point is simply that principals will take possible network effects into account when shaping their enforcement actions. Moreover, our comments regarding the principal's concerns about potential retaliation are not to suggest that agent violations will be ignored. The severity of enforcement still may be high but will be tempered compared with a loosely connected agent network or an agent with low centrality.
- Channel system and network interactions. To maintain the integrity of channel policies and systemwide performance, the principal is likely to take a severe disciplinary response against an agent that has violated a critical obligation. However, the presence of high network density may curtail the severity of this response. Because of high levels of information sharing, members of a dense network tend to develop shared beliefs that do not always correspond with those of the principal (Sherif and Sherif 1969). Accordingly, agents in a dense network may not agree with the principal that the violated obligation is critical. Should the principal's response be viewed as unfair, a dense network may take a unified stance against the principal, which heightens transaction costs (see Oliver 1991). Thus, the potential risks of agent retaliation may counterbalance the benefits of a severe enforcement response.
H7: The greater the level of network density, the weaker is
the positive relationship between obligation criticality and
contract enforcement.
Members of a dense network will not support a focal agent's violation of a critical obligation blindly, especially when the agent's actions may compromise the economic outcomes of everyone in the network (see Rubin 1990). However, even in cases in which there is general agreement within the network about the criticality of the violated obligation, the principal is likely to be wary about how its response will be perceived among members of a close-knit group (Galaskiewicz 1985). We are not arguing that violations of critical obligations will go unpunished in dense networks; rather, the severity of the principal's response to the violation of a critical obligation is likely to be relatively low in a dense network.
Network centrality also is expected to moderate the relationship between obligation criticality and contract enforcement but in a different direction than network density does. Although the principal generally may treat central agents with care, this may change when a central agent violates a critical obligation. Central agents serve as role models to associated agents (Wasserman and Galaskiewicz 1994), and by taking a severe enforcement response with a central agent, the principal would be sending a strong signal to all of its agents about the importance of fulfilling critical obligations (Benson 1975). The message would be that the obligation is so critical that even prominent agents in the network are severely disciplined if they violate it (Williamson 1983). The potential benefits of a severe enforcement response to maintain the integrity of channel policies and systemwide performance, including a possible reduction in future transaction costs, are likely to overcome the principal's concerns about retaliation.
H8: The greater the level of an agent's network centrality, the
stronger is the positive relationship between obligation
criticality and contract enforcement.
The common underlying factor in both network density and network centrality is the principal's acknowledgment of the constraints imposed by informal network relations on its response strategies (Wasserman and Galaskiewicz 1994). What differs in these proposed moderator effects, however, is the focus of the principal's concern and the corresponding net benefits of alternative enforcement responses. In a highly dense network, the principal fears a concerted response by connected agents with strong ties. In contrast, the high-centrality situation poses the threat of alienating the single prominent agent and the subset of network participants influenced by it (Oliver 1991). Therefore, the risk of agent retaliation due to a highly severe enforcement response for a central agent appears to be lower. Furthermore, the signaling effects associated with taking a severe approach with a central agent that violates a critical obligation may produce important benefits for the principal, benefits that appear to be lacking in the case of a dense network.
- Dyadic level of analysis. The focus at this level of analysis is the dyadic relationship between the principal and focal agent as opposed to the relationships within the agent network. The principal must protect the strength and value of its relationship with the focal agent while carefully considering its ability to take a severe enforcement approach. A firm's dependence in a channel relationship reflects its need to maintain the relationship to achieve its desired goals (Frazier 1983). Interdependence magnitude is the total extent to which each firm is dependent in a dyadic channel relationship (Gundlach and Cadotte 1994). When interdependence magnitude is high, firms rely heavily on each other for the performance of channel functions and access to scarce resources (Buchanan 1992; Lusch and Brown 1996).
Reciprocally interdependent parties can affect each other's outcomes by performing or failing to perform required actions (Cheng 1983). An agent's failure to fulfill contractual obligations can hinder the effective operation of the exchange, increase transaction costs, and result in a significant drop in the principal's ability to achieve its own goals. Therefore, an incentive may exist for the principal to take a severe enforcement response when interdependence magnitude is high in order to closely coordinate activities in and protect the value of the relationship (Gundlach and Cadotte 1994; Kumar, Scheer, and Steenkamp 1995). At the same time, the principal may be concerned about how the focal agent in a highly interdependent relationship will react to a relatively severe enforcement response. The agent may take reciprocal actions that could lead to a conflict spiral (see Kumar, Scheer, and Steenkamp 1998). In our judgment, the principal's need for due performance from an important focal agent will dominate any possible retaliation effect. After all, it was the focal agent's violation of an obligation that necessitated an enforcement response, not the principal's desire to influence the agent's autonomous decision making. The focal agent may acknowledge this and react accordingly.
H9: Interdependence magnitude is related positively to contract
enforcement.
- Interdependence asymmetry is the comparative level of each firm's dependence in a channel relationship (Gundlach and Cadotte 1994; Lusch and Brown 1996). A power advantage exists for a firm when it has lower relative dependence (Frazier 1983). Firms with a power advantage may not be as bound by the constraints of maintaining dyadic relationships, because they have less incentive to continue such relationships should they fail to meet expectations (Lusch and Brown 1996). Moreover, firms with relatively low dependence compared with associated channel members may not be as concerned about the likelihood and consequences of retaliations to its actions (Kumar, Scheer, and Steenkamp 1995). Accordingly, the principal should take a more severe enforcement response when it enjoys a power advantage in a relationship. In such cases, the principal is likely to have the leverage to make a severe enforcement response work effectively with few associated risks (Buchanan 1992).
H10: Interdependence asymmetry favoring the principal
is related positively to contract enforcement.
Relationalism reflects the degree to which relational norms are established in a channel relationship (see Brown, Dev, and Lee 2000; Heide and John 1992). Three partially overlapping norm types have been used commonly to reflect relationalism's extent (see Lusch and Brown 1996; Noordeweir, John, and Nevin 1990). Solidarity is the willingness of the firms to strive for joint solutions and benefits, flexibility reflects the willingness of the firms to make alterations as circumstances change, and information exchange represents the willingness of the firms to provide information proactively that is useful to the other.
When norms of solidarity, flexibility, and information exchange are solidly entrenched in a relationship, more cooperative interaction among the firms is likely to result (Dwyer, Schurr, and Oh 1987; Jap and Ganesan 2000). This atmosphere of cooperation may lead the principal to take a less severe enforcement response.6 The principal is likely to consider the strong ties that have been built up with the agent and the potential damage a severe enforcement action could have on those ties, including increased transaction costs. As Sitkin and Bies (1994) indicate, a severe enforcement response could lead to a more formalized relationship, with less give and take between the firms and less emphasis on joint outcomes. To avoid driving a wedge into a cooperative interaction and harming relational norms, the principal may take a less severe enforcement approach when relationalism is high.7
H11: Relationalism in the principal-agent dyadic relationship is
inversely related to contract enforcement.
- Channel system and dyadic interaction. Principals can be expected to safeguard specialized investments and the systemwide capabilities that arise from them in most circumstances (Anderson 1985). Therefore, when significant specialized investments are at risk in a channel system, the principal's tendency to take a milder enforcement response when relationalism is high may be weakened. Although the spirit of cooperation inherent in exchanges with high relationalism still will be present (see Ben-Porath 1980; Treas 1993), the principal's imperative to maintain the value of its specific investments may reduce the level of tolerance it displays (see Heide and John 1992). The potential downside for the principal in harming the level of relationalism may be outweighed by the benefits to the principal in protecting its specialized investments. Any increase in transaction costs resulting from lowered cooperation may be more than offset by savings in transaction costs from safeguarding TSIs.
H12: The greater the principal's TSIs in a channel system,
the weaker is the inverse relationship between relationalism and
contract enforcement.
Research Context
Although we could have selected any channel context in which explicit contracts exist to perform our study, we chose to focus on franchising. Because important and elaborate contracts exist within franchise systems, we expect this setting to display a relatively high fit between the deployment and enforcement of contracts. If we find variation in enforcement severity here, such variation also may exist in other channel settings.
We selected six franchising industries (automobile, business-to-business, food, cleaning, personal care, and personnel recruitment services) for inclusion. Sampling multiple industries provides more variance on the study variables and promotes the generalizability of our results. Initially, we conducted a series of ten unstructured interviews, five with manufacturers and five with franchisors, with the objective of gaining general insight into contract enforcement. We then conducted 16 further in-depth interviews with franchisors to enhance the foundation of our study.
Contract Obligations and Violations
Among the five archetypal rules identified by Ellickson (1987, p. 77), we focus on the violation of substantive rules, those "that determine what conduct ... is to be punished, rewarded, or left alone." Within this class, we selected four areas of obligations commonly found in franchise contracts: ( 1) restriction on sources of products/services; ( 2) maintenance, appearance, and remodeling requirements; ( 3) payment of advertising/royalty contribution; and ( 4) compliance with standards/operating manuals. Prestudy interviews indicated that these obligations commonly were violated. In the survey, respondents were provided with these areas of obligations and asked to select one that had been violated by an agent in the past year. Respondents also had the opportunity to indicate a fifth category, "other," if the violation they recalled did not fit into any of the areas. The respondents were asked to focus on this obligation and the specific agent that violated it in responding to many of the items.
Data Collection
On the basis of a review of the literature and prestudy interviews, we developed a survey instrument. A panel of three academic experts was asked to evaluate the items, after which managers from six different franchisor organizations were asked to fill out the questionnaire in our presence and raise questions as problems or ambiguities arose. We then conducted a pretest on a random sample of 100 franchisors. We received 22 returned surveys and used them to improve several of the scales further.
We drew a national sample of top managers in franchisor organizations at random from the Franchise Opportunities Handbook published by the U.S. Department of Commerce (1995). We made telephone calls to firms in the sample to check mailing addresses and verify that the managers were directly involved in making enforcement decisions. After we excluded firms we could not contact and those that did not qualify (e.g., licensing firms, no longer franchising, no longer in business), our sampling frame consisted of 500 franchisors.
Letters were mailed to the identified representatives to prenotify them of the study and solicit their participation. A week later, the survey was mailed. Follow-up attempts consisted of two further rounds of telephone calls, interspersed with a reminder letter. Finally, another set of surveys was mailed by priority mail to those who had not yet responded. Our iterative data collection effort yielded 229 responses, of which 16 were incomplete or unusable. The effective response rate of 43% compares favorably with those obtained in prior channels research. Of the 213 usable responses, 160 were received in the first wave. Our respondents had considerable experience within the firm (mean = 9.1 years, standard deviation [S.D.] = 7.9), as well as in their positions (mean = 5.7 years, S.D. = 6.38). Table 1 provides additional details on the sample.
A multivariate analysis of variance contrasting first- and second-wave respondents on the focal constructs of our study indicated no significant differences. Furthermore, a comparison of respondents versus nonrespondents on the year of establishment and number of outlets yielded no significant differences. Therefore, nonresponse bias does not appear to be a serious problem in our study.
Construct Measures
Whenever possible, we used measurement scales from previous research that we modified to our setting. If preexisting scales were not available, we developed measures on the basis of the conceptual definitions of the constructs and prestudy interviews. We first examined the intercorrelations among the items designed for each scale, removing items that exhibited low correlations. We then conducted principal components factor analyses to determine the scales' unidimensionality and discriminant validity and further refined the scales when necessary. Finally, we conducted confirmatory factor analyses on the scale items (Anderson and Gerbing 1988). Because of the number of constructs and our sample size, we conducted confirmatory factor analyses on groups of maximally similar constructs (see Moorman and Miner 1997). The results suggest a satisfactory fit across the four models tested: ( 1) the task variables and enforcement (c2 = 227.53, degrees of freedom [d.f.] = 74, p < .001, Tucker-Lewis index [TLI] = .91, comparative fit index [CFI] = .92, root mean square error of approximation [RMSEA] = .10); ( 2) the TCE-related constructs (c2 = 51.74, d.f. = 32, p < .02, TLI = .93, CFI = .95, RMSEA = .05); ( 3) the network variables (c2 = 151.4, d.f. = 43, p < .001, TLI = .89, CFI = .92, RMSEA = .10); and ( 4) relational norms, conceptualized as a second-order factor (c2 = 106.04, d.f. = 42, p < .001, TLI = .89, CFI = .92, RMSEA = .08). A series of chi-square difference tests on the respective factor correlations provides further evidence of discriminant validity, because the chi-square of the constrained model exceeds that of the unconstrained model in all cases (see Bagozzi 1988).8
In Table 2, we report the correlation matrix and descriptive statistics for the finalized measures, including alpha coefficients if appropriate. All alphas are greater than .70, with the exception of those for the measures of environmental volatility and flexibility.
For contract enforcement, the severity of the principal's disciplinary response to an agent's violation of a contractual obligation, we found no suitable existing measure. Seven items constitute its final scale, and alpha = .95. We used both Likert items and semantic differentials to reduce potential method bias. The survey included an alternative measure of enforcement; respondents were asked to list, from a menu of options, the actions they took in response to the specific violation (see the Appendix). The two measures are correlated at .56 (p < .001), thus offering some evidence of convergent validity.
The TCE constructs were measured at the channel system level. We relied on items in Anderson (1985) and John and Weitz (1988) in developing the scale for TSIs; the final four-item scale has an alpha of .77. The measure of environmental volatility was adapted from Achrol and Stern (1988); the final three-item scale has an alpha of .61.
We built on prior dependence measures developed by Gundlach and Cadotte (1994) and Kumar, Scheer, and Steenkamp (1995).9 We used semantic differentials to measure franchisor dependence, whereas we measured franchisee dependence with a combination of semantic differential items and a single Likert item. Each of the final measures is composed of six items. They are considered formative or composite indicators (Bollen and Lennox 1991) because dependence is determined by two components that may not be that highly correlated, motivational investment in goals and replaceability. We measured interdependence magnitude as the sum of the dependence scores, whereas we measured asymmetry as the difference between dependence scores. (A positive value indicates a power advantage for the franchisor.)
We measured relationalism as a higher-order construct. We summed the scales of the three facets to arrive at an underlying syndrome of relationalism (see Lusch and Brown 1996; Noordeweir, John, and Nevin 1990). The underlying scales had alphas of .73, .66, and .78.
We found no suitable existing measures for the remaining three constructs. Obligation criticality is the importance attached by the principal to the contractual obligation violated by the agent. Its final measure is a four-item scale, with an alpha of .85. Network density reflects the average strength of relations in a network. The six-item scale is composed of three Likert items and three semantic differentials, with an alpha of .91. Network centrality is the strength of an agent's position in an agent network. The final five-item scale has an alpha of .82 and is composed of two Likert items and three semantic differentials.
Control Variables
The principal's ability to enforce could be compromised by inadequate information regarding agent performance and prohibitively high costs of enforcement. We therefore controlled for the performance ambiguity across the channel system and the cost of enforcement. Furthermore, prestudy interviews suggested that enforcement severity may vary by industry, as well as by firm size. Accordingly, we included a set of five dummy variables to represent the industry sectors of the sample with one holdout and a firm size measure based on the logarithm of the number of employees.10 Furthermore, although our focus is the informal agent network, we also controlled for an element of the formal network: whether the focal agent was a master franchisee (i.e., owned and operated more than one franchised outlet). Refer to the Appendix for the specific multi-item measures of performance ambiguity and cost of enforcement.
Model Specification
We regressed enforcement on the hypothesized explanatory variables, including the moderators and control variables. We standardized all variables to reduce multicollinearity between the multiplicative terms and their constituent variables (see Aiken and West 1991; Freidrich 1982). The results of the full model estimation are displayed in Table 3.
An incremental F-Test (Aiken and West 1991) was used to compare the full model with a main effects'only model. Inclusion of the moderator effects increases the variance explained by almost 15% compared with the base model, which indicates that the inclusion of the interactions is warranted (F = 21.62, p < .001). When a significant moderator effect is found, Aiken and West (1991) recommend assessing the relationship between the independent variable and the dependent variable for "1 standard deviation of the moderator to help determine the exact nature of the effect. Such an analysis was performed in this study and is reported for each significant moderator effect. The tests conducted at different levels of the moderator variables indicate monotonic moderator effects (see Schoonhoven 1981). It should be noted that in the presence of significant moderator effects, the main effects are conditional in that they indicate their impact on enforcement at mean levels of the moderator variables (Aiken and West 1991).
Hypothesis Tests
We find significant support for H1 (b = .13, p < .05). Principals are likely to take a relatively strict enforcement response when systemic TSIs are at risk.11 However, we do not find support for H2, which involves the main effect of volatility (b = .05), or for H3, which focuses on the moderator effect of TSIs on the connection between volatility and contract enforcement (b = '.02). H4 is strongly supported (b = .43, p < .01), indicating that when a critical obligation is violated, adverse consequences to the principal and its channel system are likely to be substantial and contract enforcement is likely to be severe.
Network density is inversely related to contract enforcement, in support of H5, though marginally (b = '.08, p < .10). Furthermore, H6, which involves the agent's network centrality and the severity of the principal's enforcement response, is supported (b = '.15, p < .05). The possibility of retaliation from a dense network and from a central agent and its supporters appears to dampen the principal's enthusiasm for a severe enforcement response to some degree, compared with the enforcement response in loosely connected networks and with low-status agents.
Network density significantly moderates the relationship between obligation criticality and contract enforcement (b = '.12, p < .05; bunder low density = .55, p < .01; bunder high density = .31, p < .01), so H7 is supported. In dense networks, principals may temper their enforcement response to some degree when critical obligations are violated to avoid a possible negative backlash. Furthermore, consistent with H8, we find that when network centrality is high, the positive relationship between obligation criticality and contract enforcement is heightened (b = .12, p < .05; bunder low centrality = .30, p < .01; bunder high centrality = .54, p < .01). By responding more strongly to a central agent's violation of a critical obligation, the principal is sending a signal of its commitment to maintain important standards and routines to other agents. Both network moderators in question are strength moderators, in that they change the extent to which criticality has a positive relationship with enforcement but not the nature or form of the relationship.
We find strong support for H9, which posits a positive relationship between interdependence magnitude and the severity of the principal's enforcement response (b = .14, p < .05). A relatively severe enforcement response may be used in high-magnitude relationships to ensure that agent operations and the principal's goal attainment in such important exchanges are not put in jeopardy. H10, involving interdependence asymmetry and enforcement, is supported, albeit marginally (b = .12, p < .10). Principals with a power advantage in their channel relationships may be in a position to make a severe response work effectively.
Strong support is offered for H11, which involves relationalism and enforcement severity (b = '.25, p < .01). However, this relationship is significantly altered by the presence of high TSIs (b = .18, p < .01). Analyzing the impact of relationalism on enforcement, we find bunder low TSI = '.43, p < .01, whereas bunder high TSI = '.08, p > .10. The nonsignificant t-value of b in the latter case suggests that the tolerance displayed in conditions of relationalism disappears when high levels of TSIs exist. Although these results are consistent with H12, they suggest a stronger moderating influence of TSIs than hypothesized.
Performance ambiguity has a significant tempering effect on enforcement (b = '.12, p < .05). Furthermore, we find a significant, positive relationship between firm size and enforcement (b = .10, p < .05) and between the focal agent being a master franchisee and enforcement (b = .35, p < .05). Cost of enforcement and industry type appear to have no influence. Of the total variance explained (.37), the control variables together account for .03.
Post Hoc Analyses
We also analyzed whether enforcement severity varied by the type of violated obligation. Because the frequency distribution across the obligation areas is quite dispersed (see Table 1), we did not have enough degrees of freedom to estimate the full regression model. Instead, we conducted a series of Chow tests at the subsample level, iteratively dichotomizing the sample by the type of obligation violated (e.g., Violation 1 compared with the rest; see Table 1). We used the original regression model in each case, excluding the control variables. The F-value in each case was nonsignificant, indicating that the coefficients are stable across the type of violated obligation.
Research Implications
The need for behavioral channels research to expand beyond its traditional focus on the dyadic relationship has been recognized for quite some time (see Anderson, H'kansson, and Johanson 1994; Heide 1994). The results of our study underscore the importance of such a broadening. We show that the severity of the principal's enforcement response is influenced significantly by constructs that operate at the channel system, network, and dyadic levels of analysis. Ignoring constructs at any of these three levels would lead to an incomplete and poorly specified conceptual framework. In addition, we find that significant moderator effects exist when the levels of analysis overlap with one another.
Our study also integrates three theoretical perspectives'TCE, the relational exchange paradigm, and network theory'in an attempt to build a sound conceptual foundation for the research hypotheses. Specifically, we complement TCE's emphasis on transaction cost minimization with benefit-based arguments consistent with the relational exchange approach. In a similar manner, we also incorporate transaction cost issues within the cost- and benefit-based analysis of relational exchange and network theory.
At the channel system level, the principal appears primarily concerned about maintaining the integrity of its channel policies, enhancing system performance, and minimizing systemic transaction costs. Relatively severe enforcement responses are likely to result when contract violations occur in channel systems with high levels of the principal's systemic TSIs. Such an enforcement response is likely undertaken to protect the systemic TSIs and agent capabilities arising from them. Moreover, principals are likely to react rather severely to the violation of critical obligations. Allowing critical obligations to be violated without a severe response is likely to undermine the integrity of channel policies and system-level performance.
We find no evidence of the impact of environmental volatility on enforcement. It is possible that extreme levels of volatility may outstrip the principal's ability to enforce incomplete contracts or that volatility may have its primary effect on the occurrence of violations rather than on the enforcement responses that transpire thereafter. Another possible explanation is that volatility in the focal agent's trading area may have more impact on enforcement than volatility at the channel system level. Whatever the case, the nature of the relation between environmental volatility and enforcement requires further exploration.
Our study provides empirical evidence that network characteristics matter in channel relationships, both directly and in combination with other constructs. The principal's judgments about how agents in informal networks will react to enforcement actions and how these reactions will affect system performance, including transaction costs, appear to influence the nature of the principal's enforcement response. The principal's fear of retaliation seems to explain the tempering of enforcement severity when the principal faces a densely connected network of agents or a highly central agent. This also may explain why high network density appears to weaken the positive connection between obligation criticality and contract enforcement. However, fear of retaliation appears to be outweighed by positive signaling effects when a central agent violates a critical obligation. In such cases, the principal may seize the opportunity to make an example of such a prominent agent, sending the message that the obligation is so critical to system success that even a highly influential agent will face a severe enforcement response. Although a central agent and the subset of agents affiliated with it could react negatively to such an approach, the potential benefits of signaling the principal's resolve to others in the network appear to outweigh such risks.
In providing evidence of differential moderator effects on obligation criticality across network density and centrality, this study aids the understanding of the subtleties of network effects in channel relationships. Network characteristics are likely to influence most aspects of the interfirm coordination process and deserve heightened attention in further channels research.
At the dyadic level, the principal's enforcement response appears to be shaped by its need to protect the strength and value of the exchange relationship and its ability to take a severe enforcement approach. When interdependence magnitude is high, contract violations are likely to be met with a relatively severe enforcement response. By judiciously reacting to the violation of a previously agreed-on contractual obligation, the principal may be cementing the integrity and long-term stability of an important exchange relationship. In such cases, the focal agent appears unlikely to retaliate because its violation of an obligation is what necessitated the enforcement response. In terms of interdependence asymmetry, the principal's ability to take a severe enforcement response seems enhanced when it enjoys a power advantage in the exchange. Note that our sample reports only moderate levels of interdependence magnitude and asymmetry. Additional research is required to determine if our interdependence results are generalizable to channel settings with greater levels of magnitude and asymmetry.
When relationalism is high, interfirm interactions should be more cooperative in nature (Dwyer, Schurr, and Oh 1987). Accordingly, channel members are likely to take less severe enforcement responses to avoid ruining the underlying foundation of such exchanges. However, we find that the tempering effect of relationalism on contract enforcement vanishes in the presence of significant TSIs. Although protecting relational norms in a single dyadic relationship may have economic benefits in terms of more cooperative interaction, the need to safeguard systemic TSIs appears to take precedence, at least when the contract enforcement decision is the focus.
Managerial Implications
Three managerial implications of our study appear to be most critical. First, practitioners can use our study and its results as a check on the adequacy of their existing enforcement models. The entire set of significant channel system-, network-, and dyadic-level constructs in our study should be weighed in making the enforcement decision. Furthermore, the trade-offs inherent in making a more or less severe enforcement response based on frequently conflicting channel system-, network-, and dyadic-level constructs need explicit attention. The mere process of examining and evaluating our results may lead to better enforcement decisions, especially for managers and firms with relatively little experience in making such decisions.
Second, practitioners are advised to consider the objectives underlying their enforcement responses and their relative priority. Objectives could include maintaining the integrity of channel policies, enhancing system performance, minimizing transaction costs, avoiding retaliation, signaling resolve, or protecting the strength and value of an important exchange relationship. For a particular violation, understanding each objective and its relative priority could help in making the inherent trade-offs among the channel system-, network-, and dyadic-level constructs. It may make sense for channel members to establish policy guidelines regarding how they will tend to react to certain contract violations, thereby establishing a priority sequence among the objectives underlying enforcement decisions.
Third, it is important for practitioners to have accurate perceptions of the informal networks among agents and the agents' likely reaction to enforcement actions. Without this, any evaluation of the costs and benefits of alternative enforcement responses based on network density and centrality will be error ridden. More specifically, if practitioners either under- or overestimate the potential negative backlash by associated agents, their enforcement responses will be misguided. Firms can use the network measures developed in our study to grasp the nature of informal agent networks better. They then can track responses of informal networks to enforcement responses over time, perhaps supplementing them with information gathering from certain agents or groups (e.g., agent advisory boards), to grasp likely reactions to enforcement responses.
Limitations
Although our results appear useful, three main limitations of the study must be considered. First, the generalizability of our results is in question. Although we gathered data from six different types of franchise systems, whether our findings hold in other franchise settings and channel contexts other than franchising must be explored. For example, although we found environmental volatility to have no bearing on the level of contract enforcement in our study, it remains to be seen whether this finding is robust across different channel contexts.
Second, we relied on data from a single member of the dyad, the franchisor. Although data collection with the upstream channel member is fairly uncommon, obtaining corresponding viewpoints from downstream agents would have made our study stronger. Additional research based on data collected from both sides of the dyad would yield insight into areas of divergence.
Third, specification error could be a problem, even given the strong explanatory power of our model. Because our study is an early attempt to build and test a conceptual framework of contract enforcement, important factors may have been omitted. Attributed opportunism, the frequency of the particular violation across the system, and the principal's response to past occurrences all could have a significant bearing on current enforcement practices. Extended conceptual frameworks should be developed and examined empirically in the future.
Future Research Directions
In addition to the research needs discussed, a variety of others exist. We focused on the severity of the enforcement response in our study, but contract enforcement is a matter of both degree and kind. Therefore, more specific attention to the exact type or types of enforcement actions being used, such as cure letters and cease-and-desist warnings, is needed. Additional studies of the nuances of enforcement are warranted.
We also focused on contractual violations by agents. An additional issue worth considering is the possibility of contractual violations by principals. An implicit assumption is often made that the principal's concern for its own reputation will preclude, or at least minimize, the possibility of opportunistic behavior (Klein 1996). This assumption is unlikely to hold in many situations.
Additional research is needed to explore network effects more deeply. Specifically, a greater understanding of the motives attributed by the agent network to the principal's actions could prove beneficial. For example, in channel systems in which strong common goals exist between the principal and agents (e.g., enhanced brand equity), a severe enforcement response by the principal may not be viewed by densely connected agents as unfair but rather as a necessary and justifiable action to safeguard system interests. Self-policing efforts by agents also would be interesting to explore in such a line of research. Moreover, although we controlled for one type of formal network (i.e., master franchisees), the impact of other types of formal networks (e.g., industry trade associations) should be examined in the future.
Finally, the deployment and enforcement of contracts are separable issues but likely are interrelated. That is, the exact nature of the explicit contract, including aspects of the governance structure (e.g., formalization, exclusive territories, exclusive dealing), is likely to have a bearing on both the frequency of contract violations by agents and the severity of the enforcement responses by principals. Research examining such interconnections could make important contributions to channels research.
This research was supported by grants from the Center for Research on Contracts and the Structure of Enterprise at University of Pittsburgh and the Marshall School of Business at University of Southern California. Both authors gratefully acknowledge the assistance of Prokriti Mukherjee in collecting the data and the thoughtful suggestions of Rajesh Chandy, Shantanu Dutta, Bernard Jaworski, Robert Lusch, and Allen Weiss on previous drafts of the article. They also thank the three anonymous JM reviewers for their comments on prior versions of the article.
Footnotes [1] Henceforth, we use the term "principal" to denote the party offering the contract and the term "agent" to identify the party accepting it.
[2] Contract violations may result from an agent's lack of awareness of the obligation, insufficient agent resources to fulfill the obligation, or honest attempts to react to unforeseen circumstances; violations need not imply opportunism.
[3] Just as the principal is concerned that its investments in an individual agent may not be transferable to other relationships, the principal's systemwide investments are specific to the system if they cannot be redeployed to another system without significant loss of value.
[4] It is possible that principals making significant nonspecific investments in their channel systems also will take a relatively severe enforcement response. However, the relative ease of redeploying generalized investments to other applications and relationships may lessen their impact on contract enforcement, at least relative to TSIs, because the need for protection and coordination may not be as great (see Williamson 1985).
[5] Some scholars might argue that the principal, knowing that information about its enforcement action is likely to become public knowledge within a densely connected network, will adopt a severe enforcement approach (Rubin 1990). In our judgment, the principal's fear of retaliation from a dense network will dominate any potential signaling benefits. Our prestudy interviews helped us arrive at this judgment.
[6] Work on psychological contracts suggests that when relationalism is high and a psychological contract is violated, a perception of betrayal may prompt the aggrieved principal to retaliate with a severe enforcement response (Parks 1997; Robinson 1996). In channel settings, however, attempts may be made to maintain strong channel relationships even when firms are dealing with contract violations, and termination is an extreme last resort (Dwyer, Schurr, and Oh 1987). As Parks (1997, p. 136) cautions in discussing psychological contracts and their impact, "We have not always differentiated between retribution at the interpersonal or the organizational level."
[7] When relationalism is high, opportunistic agents could violate both relational norms and contractual obligations while relying on the principal to take relatively mild enforcement responses. However, if the principal detects such opportunism, the norms on which relationalism rests would be seriously damaged. Therefore, the risks of such opportunism appear greater than the short-term benefits to agents.
[8] Iteratively constraining pairs of variables to r = 1.0 resulted in chi-squared values as follows: task variables (c2 = 460.55, 380.73, 378.41), TCE variables (c2 = 159.89, 152.95, 156.71), and network variables (c2 = 189.45). 1
[9] We also tested our framework with the multiplicative operationalization of interdependence used by Heide (1994) and Lusch and Brown (1996). The results and their interpretation are, for the most part, the same as for the additive operationalization. Our use of the additive operationalization enables us to decompose the effects of magnitude and asymmetry rather than consider them simultaneously, as is required by the multiplicative approach.
[10] We used the log of firm size because of high variation in the number of employees across the firms in our sample.
Legend for Chart:
A - Nature of Violations
B - Violation Code
C - Frequency of Violation
A B C
Restriction on sources of products/services 1 25
Maintenance, appearance, and remodeling requirements 2 43
Payment of advertising/royalty contribution 3 58
Compliance with standards/operating manuals 4 67
Other 5 20
Legend for Chart:
A - Industry
B - Questionnaires Sent
C - Questionnaires Received
D - Response Rate
E - Modal Violation(a)
F - Mean Number of Outlets
G - Mean Number of Employees
(a) Refer to violation code above.
A B C D E F G
Automobile services 44 20 45.4% 4 106 1647
Business-to-business
services 79 35 44.3% 3 120 98
Fast food and restaurants 252 97 38.5% 3 129 248
Cleaning services 40 19 47.5% 3 659 84
Personal care 39 23 58.9% 4 128 149
Personnel recruitment
agencies 46 19 41.3% 2 197 407
Full sample 500 213 42.6% 4 178 344 Legend for Chart:
A - Correlation Matrix Number of Items
B - Correlation Matrix Mean
C - Correlation Matrix S.D.
D - Correlation Matrix ENF
E - Correlation Matrix TSIs
F - Correlation Matrix VOL
G - Correlation Matrix CRIT
H - Correlation Matrix DENS
I - Correlation Matrix CENTR
J - Correlation Matrix MAG
K - Correlation Matrix ASYM
L - Correlation Matrix REL
M - Correlation Matrix PA
N - Correlation Matrix COST
O - Correlation Matrix Mast Fr
P - Correlation Matrix SIZE
Q - Correlation Matrix TSIs x VOL
R - Correlation Matrix CRIT x DENS
S - Correlation Matrix CRIT x CENTR
A B C D E
F G H I J
K L M N O
P Q R S
Enforcement (ENF)
7 27.38 11.88 .95
TSIs
4 21.35 4.6 .23** .77
Volatility (VOL)
3 12.41 3.79 .00 .11
.61
Obligation criticality (CRIT)
4 21.86 5.51 .49* .13
-.03 .85
Network density (DENS)
6 27.81 7.44 -.04 .26*
.14 .07 .91
Network centrality (CENTR)
5 15.31 5.3 -.20* .05
.05 -.06 .26* .82
Interdependence magnitude (MAG)
12 35.28 7.99 -.04 .18*
-.01 -.01 .05 .47* --
Interdependence asymmetry (ASYM)
12 4.55 7.63 .17 .16**
-.07 -.05 .05 -.34** -.09
--
Relational norms (REL)
11 53.28 11.22 -.32** .14**
.10 -.18* .12 .36 .38*
-.06 --
Performance ambiguity (PA)
3 10.03 3.55 -.13 -.35**
.04 -.03 -.13* -.02 -.05
-.10 -.19* .73
Cost of enforcement (COST)
3 10.85 4.38 -.02 .00
.03 .01 -.02 -.08 -.06
-.07 -.15* .17* .72
Master franchisee (MastFr)
1 1.08 0.29 .00 -.05
.03 -.10 .16* .19* .09
.06 .06 .10 .02 --
Log (Number of employees) (SIZE)
1 4.09 2.09 .21* .16
-.02 .09 .01 -.09 -.08
.03 -.13 -.02 -.05 .02
--
TSIs x VOL
-- .11 .97 -.13* -.11
.13 -.18* .00 .00 -.11
.06 .19** -.07 -.17* .09
.01 --
CRIT x DENS
-- .06 .99 -.19* -.05
.05 -.10 .17* .00 -.03
.03 .11 .00 -.05 -.03
.00 .16* --
CRIT x CENTR
-- -.06 1.03 -.04 -.03
-.09 -.12 .00 .15* -.02
-.01 .07 .00 .05 -.05
-.06 .04 .22* --
TSIs x REL
-- .14 1.12 .17* -.02
.17 .02 .06 .06 -.15**
-.07 -.04 .10 .00 .04
.10 .07 -.08 -.11Notes: Figures on the diagonal represent a values, where appropriate.
Legend for Chart:
A - Independent Variables
B - Parameter Estimates (It-Value)
C - Hypothesis
D - Supported?
* p < .10, one-tailed test.
** p < .05, one-tailed test.
*** p < .01, one-tailed test.
A B C D
Intercept -.39 (1.81)
TSIs .13 (1.98)** H1 Yes
Volatility .05 (.90) H2 No
TSIs x volatility -.02 (.29) H3 No
Obligation criticality .43 (7.73)*** H4 Yes
Network density -.08 (1.36)* H5 Yes
Network centrality -.15 (2.01)** H6 Yes
Network density x obligation
criticality -.12 (1.98)** H7 Yes
Network centrality x
obligation criticality .12 (2.14)** H8 Yes
Interdependence magnitude .14 (2.10)** H9 Yes
Interdependence asymmetry .12 (1.95)* H10 Yes
Relationalism -.25 (3.96)*** H11 Yes
TSIs x relationalism .18 (3.45)*** H12 Yes
Control Variables
Performance ambiguity -.12 (2.07)**
Cost of enforcement -.05 (.99)
Master franchisee .35 (1.83)**
Firm size .10 (1.84)**
D1 .03 (.43)
D2 -.05 (.61)
D3 -.03 (.31)
D4 -.04 (.52)
D5 -.02 (.31)
Adjusted R² = .41
F21,191 = 7.92DIAGRAM: Figure 1: Conceptual Framework
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Construct Measures
All items are measured on seven-point "strongly agree" to "strongly disagree" Likert scales unless otherwise mentioned.
Contract Enforcement: ENF (7 items, a = .95)a
1. We took tough measures when this particular clause was violated.
- 2. Our response to this violation was firm.
- 3. We took strict disciplinary action against this franchisee.
- 4. Stern punitive action was taken against this franchisee.
- 5. Lenient-Uncompromising.
- 6. No sanctions imposed-Highest levels of sanctions imposed.
- 7. Minimal action-Severe action.
The alternative measure asked the respondents to check which enforcement action (or actions) was taken with the agent: informal attempts to persuade, telephone call, site visit, cure letter, cease-and-desist warning, or termination proceedings. The actions were in increasing order of severity. We took the most severe response checked as the value for this measure.
TSIs at the Channel System Level (4 items, a = .77)
1. Our franchise has invested heavily in personnel dedicated to our franchisees.
- 2. Our franchise has made significant investments in displays, training, etc. dedicated to our franchise relationships.
- 3. We have developed very specialized procedures and systems for our franchisees to follow.
- 4. Training and qualifying our franchisees has involved considerable commitment of time and money.
TSIs at the Dyadic Relationship Level (4 items, a =.76)
1. If this relationship were to terminate, it would be difficult for us to recoup investments made in this franchisee.
- 2. Training and qualifying this franchisee has involved considerable commitments of time and money.
- 3. We have invested a great deal in building up this franchisee's business.
- 4. If we were to terminate this franchisee, we would lose a lot of our investment in him/her.
Environmental Volatility: VOL (3 items, a = .61)
1. In our business, customer tastes change rapidly.
- 2. Production/service technology changes are few and far between in our business. (R)
- 3. New developments evolve very rapidly in our industry.
Relationalism: REL (11-item summation of 3 facets)
Solidarity (3 items, a = .73 )
1. Problems that arise in the course of this relationship are treated by the parties as joint rather than individual responsibilities.
- 2. The parties are committed to improvements that may benefit the relationship as a whole and not only the individual parties.
- 3. The responsibility for making sure that the relationship works for both of us is shared jointly. Flexibility (4 items, a = .66 )
- 4. Flexibility in response to requests for changes is a characteristic of this relationship.
- 5. We expect to make adjustments in the ongoing relationship to cope with changing circumstances.
- 6. When some unexpected situation arises, we would rather work out a new deal together than hold each other to the original terms.
- 7. Changes in terms are not ruled out by the parties if considered necessary. Information Exchange (4 items, a = .78 )
- 8. In this relationship, it is expected that any information that might help the other party will be provided to them.
- 9. Exchange of information in this relationship takes place frequently and informally.
- 10. It is expected that the parties will provide proprietary information if it can help the other party.
- 11. It is expected that we keep each other informed about events or changes that may affect the other party.
Interdependence Structure: Magnitude and Asymmetry (formative scales) Franchisor Dependence on Franchisee (semantic differentials)
1. Easy to replace this franchisee-Very difficult to replace this franchisee.
- 2. Does not account for a high percentage of our sales in that market-Accounts for a high percentage of our sales in that market.
- 3. Does not account for a high percentage of our profits in that market-Accounts for a high percentage of our profits in that market.
- 4. We are not dependent on him/her-We are very dependent on him/her.
- 5. There are plenty of alternatives to him/her-We do not have a good alternative to him/her.
- 6. We do not need to maintain this relationship-We need to maintain this relationship.
Franchisee Dependence on Franchisora
1. Easy to replace us-Very difficult to replace us.
- 2. S/he is not dependent on us-S/he is very dependent on us.
- 3. There are plenty of alternatives to us-S/he does not have a good alternative to us.
- 4. S/he does not need to maintain this relationship-S/he needs to maintain this relationship.
- 5. We are not important to this franchisee-We are important to this franchisee.
- 6. If this franchisee were to stop representing us, s/he would suffer a significant loss in income.
Obligation Criticality: CRIT (4 items, a = .85)
1. Compliance with this clause is vital to the very survival of our franchise.
- 2. The performance of this function is crucial to our organizational objectives.
- 3. Failure to perform this function has serious consequences for our franchise.
- 4. Inadequate performance of this task will not affect us too seriously. (R)
Network Density: DENS (6 items, a = .91)a
1. Franchisees of our system share close ties amongst themselves.
- 2. There is very little interaction among our franchisees. (R)
- 3. Relations among our franchisees are very close.
- 4. Share frequent communications-Rarely communicate with each other.
- 5. Frequently discuss common problems-Rarely discuss common problems.
- 6. Extremely close ties-Not very cohesive.
Network Centrality: CENTR (5 items, a = .81)a
1. This franchisee is a crucial cog in the franchisee network.
- 2. This franchisee maintains few relations with other franchisees. (R)
- 3. Not at all active in franchise network-Very active in franchise network.
- 4. Has few links with other franchisees-Has extensive links with other franchisees.
- 5. Not at all central to our system-Very central to our franchise system.
Performance Ambiguity: PA (semantic differentials; 3 items, a = .73; a seven-point semantic differential scale was used)
1. Not possible to supervise closely-Easy to supervise closely.
- 2. Difficult to evaluate level of franchisee effort-Easy to evaluate level of franchisee effort.
- 3. Our evaluation is based on very "fuzzy" information-Our evaluation is based on very accurate information.
Cost of Enforcement: COST (semantic differentials; 3 items, a = .7)
1. Very difficult to enforce this clause-Easy to enforce this clause.
- 2. Very expensive to enforce-Not at all expensive to enforce.
- 3. Action would require a lot of time-Action would not consume much time. aConstruct was measured using a combination of Likert scale items and semantic differentials.
Notes: (R) indicates a reverse-scored item.
~~~~~~~~
By Kersi D. Antia and Gary L. Frazier
Kersi D. Antia is Assistant Professor, Richard Ivey School of Business, University of Western Ontario.
Gary L. Frazier is Richard and Jarda Hurd Professor of Distribution Management, Marshall School of Business, University of Southern California.
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Record: 185- The Social Influence of Brand Community: Evidence from European Car Clubs. By: Algesheimer, René; Dholakia, Utpal M.; Herrmann, Andreas. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p19-34. 16p. 3 Diagrams, 5 Charts. DOI: 10.1509/jmkg.69.3.19.66363.
- Database:
- Business Source Complete
The Social Influence of Brand Community: Evidence from
European Car Clubs
The authors develop and estimate a conceptual model of how different aspects of customers' relationships with the brand community influence their intentions and behaviors. The authors describe how identification with the brand community leads to positive consequences, such as greater community engagement, and negative consequences, such as normative community pressure and (ultimately) reactance. They examine the moderating effects of customers' brand knowledge and the brand community's size and test their hypotheses by estimating a structural equation model with survey data from a sample of European car club members.
Marketers have become more interested in learning about, organizing, and facilitating brand communities (e.g., McAlexander, Schouten, and Koenig 2002), which are "based on a structured set of relationships among admirers of a brand" (Muniz and O'Guinn 2001, p. 412). Many reasons underlie this interest, including the ability of brand communities to influence members' perceptions and actions, often in persistent and broad-based fashions (e.g., Muniz and Schau 2005); to rapidly disseminate information (e.g., Brown, Kozinets, and Sherry 2003); to learn consumer evaluations of new offerings, competitive actions, and so forth; and to maximize opportunities to engage and collaborate with highly loyal customers (e.g., Franke and Shah 2003). In the present-day cluttered and often hostile marketing environment, many marketers believe that the facilitation of brand communities is both cost effective and powerful.
Harley-Davidson's Harley Owners Group (HOG) is perhaps the prototypical example of a brand community cited in published studies (e.g., Fournier et al. 2001; Schouten and McAlexander 1995). This brand community, as are many others, is actively nurtured by the company. In particular, when customers buy a Harley-Davidson motorcycle, they are courted by the firm to join the local HOG chapter, attend its meetings, and participate in various events. These HOG chapters are usually managed by and meet at local dealerships, and they receive funding from Harley-Davidson.
These overtures are made effective by the ongoing sentiments and satisfaction that community members experience through frequent social interactions in HOGs. Such interactions provide not only utilitarian support in the form of riding and maintenance tips to members but also intellectual and social support through experiences of learning, social activism, and fellowship. Participation in HOGs has been found to increase members' affection for the Harley brand, making them committed, dependable, and, in many cases, even evangelical consumers (e.g., Fournier et al. 2001).
Furthermore, the HOGs example and other success stories, such as Macintosh user groups (e.g., Belk and Tumbat 2002), Star Wars fans (Brown et al. 2003), and Sun's Java center community (Williams and Cothrel 2000), have buttressed the positive aspects of brand communities in marketing managers' minds, leading many firms to make (or to consider making) significant investments in building and facilitating brand communities. At the same time, marketers embarking on such initiatives want to better understand how brand communities create value for their firms. A salient issue is how to measure and evaluate the success of a brand community program in terms that are comparable to other, more established marketing programs, such as image advertising or customer relationship management (Balasubramanian and Mahajan 2001). To accomplish this, it is important to understand how brand communities influence consumers and the conditions that increase this influence.
The purpose of our research is to develop and estimate a conceptual model of how different aspects of customers' relationships with the brand community influence their intentions and behaviors. We describe how the customer's relationship with the brand precedes and contributes to his or her identification with the brand community and loyalty intentions. We also describe how identification with the brand community leads to positive consequences, such as greater community engagement, and negative consequences, such as normative community pressure and (ultimately) reactance. Moreover, we consider the interplay among these three constructs and their effects on customers' intentions and behaviors. We also examine the moderating effects of customers' brand knowledge and the brand community's size. For example, we find that the brand community's social influence is greater for knowledgeable customers than for novice customers.
We test our hypotheses by estimating a structural equation model with survey data from a sample of European car club members. We also develop and validate new scales to measure several community-related constructs in the model, such as identification, engagement, and normative pressure, that may be useful in conducting further survey-based brand community research.
Research Setting
The brand communities we studied are car clubs in German-speaking Europe (Germany, Austria, and Switzerland). We chose cars because they are known to elicit high levels of emotion and involvement in many consumers (e.g., Brown et al. 2003; McAlexander and Schouten 1998), and this is conducive to brand community participation. Indeed, our preliminary research revealed that clubs are associated with virtually every major automobile brand in German-speaking Europe.
The clubs that we studied span various car brands and, in a majority of cases, are individual car clubs that are organized around a particular corporate brand (e.g., Ford, Volkswagen) rather than a specific product family or make. These clubs range from about a dozen members to hundreds of members. Moreover, as do HOGs, most car clubs receive significant financial support from the respective car company and its dealers, which enables them to organize members-only events throughout the year.
Car clubs are social organizations. Within most clubs, members meet face-to-face on a regular basis, often at monthly meetings. They also communicate extensively with one another online (e.g., through e-mail lists, bulletin boards) and engage in various social activities and events (e.g., boat trips; parties; barbecues; drives to distant events, such as concerts) throughout the year. In this respect, car clubs differ from the "Brandfests"--concentrated (approximately) week-long, brand-centered, corporate-sponsored events that are held infrequently--that McAlexander and colleagues study (McAlexander and Schouten 1998; McAlexander, Schouten, and Koenig 2002). As in any other voluntary social organization, car club members vary in their level of interest and participation as well as in their knowledge of and attachment to the car brand.
Furthermore, a majority of members join the car club after having purchased and owned their car, often for a long time. This aspect of car club membership is similar to Brandfest participation, because Brandfests also require prior ownership of the brand's products for participation (McAlexander and Schouten 1998). As a result, in our model, the consumer's relationship with his or her car brand precedes and may serve as a basis for joining and participating in the car club. However, we acknowledge that over time, the consumer's relationship with the car brand can be influenced by brand community participation. We provide further details about our respondent sample in the "Method" section.
Theoretical Framework and Hypotheses
Our conceptual framework explicates the bases and consequences of the brand community's influence on consumers. The framework draws on recent marketing studies of brand communities (e.g., McAlexander, Schouten, and Koenig 2002; Muniz and O'Guinn 2001), social identification (e.g., Bhattacharya and Sen 2003), and group-based consumer interactions (e.g., Dholakia, Bagozzi, and Pearo 2004), and it adds to these ideas by explicitly including the consumer's relationship with the brand as an antecedent, the negative aspects of community influence, and the conditions that accentuate community influence.
As we show in Figure 1, the model depicts brand community identification as the strength of the consumer's relationship with the community. Identification is influenced by the consumer's relationship with the brand, and it leads to engagement and perceptions of normative pressure and reactance. These consequences influence various community- and brand-related behaviors of managerial relevance. Next, we develop the model in detail.
We begin by considering the strength of the consumer's relationship with the brand community, which we characterize through "brand community identification," whereby the person construes himself or herself to be a member--that is, as "belonging" to the brand community. In contrast to other identities, which may render a person unique and separate, this is a shared or collective identity (Bhattacharya, Rao, and Glynn 1995; Tajfel and Turner 1985). Several studies suggest that social identity, defined in terms of a valued group, such as a brand community, involves both cognitive and affective components (e.g., Bergami and Bagozzi 2000; Bhattacharya and Sen 2003).
Regarding the cognitive component, identification with the brand community involves categorization processes, whereby the consumer formulates and maintains a self-awareness of his or her membership within the community (e.g., "I see myself as part of the community"), emphasizing the perceived similarities with other community members and dissimilarities with nonmembers. This captures the consciousness-of-kind aspect of brand communities (Muniz and O'Guinn 2001). Regarding the affective component, identification implies a sense of emotional involvement with the group, which social psychologists have characterized as an "affective commitment" to the group (Ellemers, Kortekaas, and Ouwerkerk 1999) and which brand community research has characterized as "kinship between members" (McAlexander, Schouten, and Koenig 2002). Therefore, identification means that the consumer agrees (or strives to agree) with the community's norms, traditions, rituals, and objectives (Bhattacharya, Rao, and Glynn 1995) and promotes its well-being (Wiswede 1998).
Brand community identification is posited to have both positive and negative consequences for consumers. Recent studies document many of its positive consequences. For example, McAlexander, Schouten, and Koenig (2002) find that participation in a Jeep Brandfest event increases consumers' attachment to their vehicles and to the Jeep brand significantly. Muniz and O'Guinn (2001) report that Macintosh computer community members help other members by sharing information about ways to enhance their computers' functioning. Muniz and Schau (2005) find that even six years after Apple Computer Inc. had discontinued the Newton product, its brand community members still continued to support one another and advocate the product's use to outsiders.
In our model, "community engagement" refers to the positive influences of identifying with the brand community, which are defined as the consumer's intrinsic motivation to interact and cooperate with community members. Community engagement suggests that members are interested in helping other members, participating in joint activities, and otherwise acting volitionally in ways that the community endorses and that enhance its value for themselves and others.
Community engagement results from the overlaps that members perceive between their own unique self-identity and their group-based identity; group participation is viewed as congruent to and as an expression of personal values (Bhattacharya and Sen 2003). It is also consistent with the notion of "citizenship" as formulated in the organizational behavior and marketing literature. Therefore, we hypothesize the following:
H1: Stronger brand community identification leads to greater community engagement.
Furthermore, belonging to any community entails restrictions to act in certain ways. In our framework, "normative community pressure" defines the consumer's perceptions of the brand community's extrinsic demands on a person to interact and cooperate within the community. These demands are attended by sometimes overt but often implicit coercion to conform to the community's norms, rituals, and objectives (e.g., Wellman et al. 1996). Social psychologists have noted that the need for consensual validation by others within the community is perhaps the primary reason normative pressure is effective in regulating members' actions (McMillan and Chavis 1986).
Normative pressure may influence members' actions about recruitment, initiation, and ongoing interactions as well as the representation of the brand community to outsiders. For example, a Saab car owner may believe that he or she needs to wave to other Saab drivers as a token of fellowship in the Saab brand community (Muniz and O'Guinn 2001). Similarly, a HOG member may believe that he or she is obligated to disparage other, particularly Japanese, motorcycle brands and mock their riders (Schouten and McAlexander 1995).
The influence of normative pressure from group members is an important element of attitude-theoretic formulations of behavior as well (e.g., Ajzen 1991). This research suggests that the group's influence on the individual, which is activated through subjective norms, has two distinct and not necessarily coincident aspects: ( 1) publicly visible compliance with the group's norms and ( 2) the private acceptance of those norms (e.g., Cialdini and Goldstein 2004). When publicly visible compliance is not accompanied by complete private acceptance, the person experiences normative pressure, which influences behavior significantly (Eagly and Chaiken 1993).
We hypothesize that greater identification with the brand community reduces the consumer's perceptions of normative pressure by increasing the overlap between the community's and the individual's norms, values, and goals. With greater identification, members tend to internalize these norms and view their actions as stemming from this overlap rather than from expectations or requirements of the brand community. Thus:
H2: Stronger brand community identification leads to reduced normative community pressure.
In addition, community engagement and normative pressure, two consequences of community identification, are not mutually exclusive for the following reason: Higher levels of engagement, though stemming from positive self-endorsed motives, are likely to be accompanied by greater degrees of conspicuous participation within the community (e.g., Langerak et al. 2003). Highly engaged members may take on leadership roles, become active and vocal recruiters and/or defenders of the community, be more adversarial toward competing brand communities, and so on. In turn, such actions are likely to increase not only the expectations of other members for behavior but also the member's own perceptions of community expectations. This should raise normative pressure. Thus, we hypothesize the following:
H3: Greater community engagement leads to greater normative community pressure.
Thus, the consumer experiences constraints and perceives less freedom to act with volition. Psychologists have called such a motivational state in which a person attempts to regain the lost freedom "reactance" (e.g., Brehm 1966). For example, during our preliminary qualitative research, a Fiat car club member told us the following:
On the one hand, the club is like a second family to me,... finding friends there that trust and support me and with whom I have lots of things in common. On the other hand, I often feel that it is expected of me to participate in all club events, to take on many responsibilities and organizational tasks. Seeing everything through the "Fiat" lens makes me feel sick so that I sometimes feel like I want to leave the club in order to realize more personal freedom.
Perhaps I would then be able to drive an old Austin Healey.
To the extent that belonging to the brand community and participating in it is perceived as entailing compliance and an obligation to think and act in certain ways, the consumer may experience reactance. This is consistent with Brehm's (1966) theory that reactance increases as perceptions of constraints increase. Therefore, we hypothesize the following:
H4: Higher levels of normative community pressure lead to stronger perceptions of reactance.
We measured reactance using an item from Dowd, Milne, and Wise's (1991) scale.
Next, we consider how normative pressure and community engagement influence three behavioral intentions of members. The first is "membership continuance intentions," which is the member's intentions to maintain membership and ties to the brand community in the future. Such an intention implies willingness to stay committed to the community and to meet any conditions, such as fees, that are required for membership. The second is the person's intentions to recommend the brand community to nonmembers, and the third pertains to the person's level of participation. From a marketing standpoint, these intentions are crucial to perpetuate the brand community, to attain goals, and to create an effective marketing program. For example, management writers have noted that higher participation levels lead to higher levels of involvement with marketer-sponsored communities, "turning visitors into members, members into contributors, and contributors into evangelists" (Langerak et al. 2003, p. 10). These three behavioral intentions likely help marketing managers frame and communicate the brand community's influence on their customers in familiar terms.
Consider the impact of normative community pressure on the behavioral intentions. The greater the pressure on members to conform to the brand community's norms and objectives, the more burdensome is the association with and participation in the brand community. As a result, consumers are less inclined to engage in community-related activities, which leads to the following hypothesis:
H5: Stronger normative community pressure leads to weaker (a) membership continuance intentions, (b) community recommendation intentions, and (c) community participation intentions.
In contrast to normative pressure (experienced as a punishment by participants), community engagement, which represents the positive and self-instigated aspects of the brand community's influence, is likely to be experienced positively. Members should be eager to repeat behaviors that lead to such positive rewards, and they should have higher levels of behavioral intentions as a result. Thus:
H6: Stronger community engagement leads to stronger (a) membership continuance intentions, (b) community recommendation intentions, and (c) community participation intentions.
Finally, we consider the influence of reactance on behavioral intentions. For example, the aforementioned Saab driver and Harley-Davidson rider may behave as their respective norms dictate--waving to other Saab drivers and disparaging riders of other motorcycle brands--but these actions may be accompanied with a conscious awareness that they are dictated by the group and that it is not proper to behave in such a way. The primary effect of such reactance is that people who experience it try to reassert their freedom (Brehm 1966), and they are motivated "to move in the direction opposite from the influence effort" (Clee and Wicklund 1980, p. 390). As the previously quoted car club member suggests, one way to reassert freedom is to discontinue membership in the brand community. Thus:
H7: Higher levels of reactance lead to weaker membership continuance intentions.
Reactance is also likely to have a negative effect on the person's future use of the brand. Because brand communities, by definition, advocate the brand's use and often strongly dissuade members from trying or using competing brands, another upshot of reactance should be to motivate the consumer to disobey this directive to regain his or her lost freedom. Thus, we hypothesize the following:
H8: Higher levels of reactance lead to weaker brand loyalty intentions.
Brand loyalty offers a useful way to examine the interplay between the consumer's relationship with the brand and the brand community. Muniz and O'Guinn (2001) emphasize these two relationships implicitly by positioning their conceptualization of brand community as involving a triadic consumer-brand-consumer relationship. McAlexander, Schouten, and Koenig (2002) go a step further and show that the consumer's integration within a brand community is a function of his or her perceived relationships not only with the brand and other community members but also with the product and the company.
A key construct in our conceptual framework is the consumer's relationship with the brand, which we characterize as "brand relationship quality" (see Figure 1). Consistent with existing consumer-centric views of brand relationships (e.g., Fournier 1998), we define brand relationship quality as the degree to which the consumer views the brand as a satisfactory partner in an ongoing relationship. In our model, it is the consumer's overall assessment of the strength of his or her relationship with the brand (e.g., DeWulf, Odekerken-Schröeder, and Iacobucci 2001). This meaning is also consistent with research showing that consumers frequently view brands in human terms, often assigning animate characteristics to them (e.g., Aaker 1997), and they often take the perspective of a brand (as if it were a person) to articulate their own perceptions of their relationships with it (Fournier 1998). Such a view is also informed by recent brand community research (McAlexander, Schouten, and Koenig 2002), which indicates that the consumer's ties to the brand can encompass the entire firm (e.g., Ford and its specific products that the consumer owns, such as a Ford Explorer).
In our framework, brand relationship quality is based on the extent to which the consumer identifies with his or her car brand or views his or her self-image as close to and overlapping with the car's image (Hogg and Abrams 1988). As a result, our definition seems more consistent with McAlexander, Schouten, and Koenig's (2002) view of consumer ties to specific products. In addition, and in line with existing research (e.g., Fournier 1994), we include cognitive (a self-awareness of the closeness of the relationship) and evaluative (the positive evaluation of self-worth that stems from a relationship with the brand) aspects of the consumer's perceptions in the operationalization of brand relationship quality. It is consistent with the conceptualization of consumer-brand identification (Bhattacharya and Sen 2003); we measure this using items from Fournier's (1994) and Aaker's (1997) studies.
From our perspective, the consumer's relationship with the brand precedes and contributes to his or her relationship with the brand community. Many consumers first discover and value the brand for the functional and symbolic benefits it provides. A harmonious relationship with the brand can lead consumers to seek out and interact with like-minded consumers who share their enthusiasm. Moreover, an existing identification with the brand is likely to facilitate integration and identification with the brand community. For example, even when traditions, such as greeting other brand users, appear peculiar to the consumer, a strong relationship with the brand may help the person accept them and intrinsically endorse these practices. Thus:
H9: Higher levels of brand relationship quality lead to a stronger brand community identification.
Membership and participation in the brand community should also have an impact on the consumer's brand-related behaviors. In particular, we expect that members' intentions to remain engaged with the brand community have a positive impact on their loyalty toward the brand because a key marker of community membership is ongoing purchase and use of the brand. Thus:
H10: Higher levels of membership continuance intentions lead to stronger brand loyalty intentions.
Finally, consistent with attitude-theoretic formulations of goal-directed behavior (e.g., Ajzen 1991; Eagly and Chaiken 1993), we also expect that ( 1) higher levels of brand relationship quality lead to greater brand loyalty intentions and ( 2) behavioral intentions lead to corresponding behaviors. These paths are not stated formally as hypotheses, because they have been well documented in the literature, but they are included in our model for the sake of completeness (see Figure 1).
From a managerial standpoint, it is important to consider what consumer and community characteristics accentuate the brand community's influence on its members. We consider one consumer characteristic, the person's brand knowledge, and one community characteristic, its size (as defined by membership count). Both these characteristics are not only managerially significant in the sense of being observable but also actionable in the sense that they provide specific guidance to managers on actions they can take (e.g., organize communities of a certain size).
First, consider the consumer's brand knowledge. Broadly speaking, we predict that the brand community exerts more influence on its knowledgeable members because brand knowledge captures both the aspects of interest in the brand and the consumer's previous experience level with it, suggesting that knowledgeable consumers are more engaged with the brand and the community. Such consumers are also more likely to take on leadership roles in brand community activities (Schouten and McAlexander 1995). Conversely, novice consumers are more likely to be the brand community's newer members and may still be in the process of learning about the brand and the community as well as forming relationships within it. Furthermore, the more participants know about the brand, the more confident they are when expressing their (positive or negative) opinions within the community, which leads to greater opportunities both for engagement and for experiencing normative pressure. Thus, we hypothesize that the average levels of key constructs in our conceptual model (i.e., community identification, engagement, normative pressure, and brand relationship quality) are significantly higher, and the strengths of the paths between them are much greater for knowledgeable consumers than for novice ones. We articulate these predictions subsequently in H11-H15.
Second, consider the brand community's size. In larger brand communities (defined in our empirical study as those that have 50 active members or more), members are more likely to identify with the community as a whole rather than with specific people in it (Dholakia, Bagozzi, and Pearo 2004). Sociological research has also shown that membership in larger communities often serves functional purposes--for example, to find an expert who is capable of solving a particular problem with a car (e.g., Wellman et al. 1996). In contrast, people join smaller communities more often for friendship and socialization motives. Therefore, we expect that members are less connected to larger brand communities because of the relatively tenuous and functional relationships therein.
In smaller communities (those with fewer than 50 members), "everybody knows everybody else," which results in stronger and multifaceted interpersonal relationships between consumers and a greater interest in engaging in social activities (Dholakia, Bagozzi, and Pearo 2004). Thus, members of small communities are likely to be more connected to the brand community, which results in significantly higher levels of community identification, engagement, normative pressure, and brand relationship quality perceptions.
Furthermore, recent research on virtual communities (those not organized around particular brands) also suggests that members of larger communities participate for more specific, well-defined reasons that are closely related to the community's primary purpose (Dholakia, Bagozzi, and Pearo 2004). For example, in a chatroom devoted to the online game Everquest, most of the conversation centers on aspects of playing the game successfully. In contrast, members of smaller communities participate to accomplish broader, more abstract goals, such as catching up with friends or having a good time. In certain cases, it is also possible that small communities are small simply because the topic is of interest to few people, which results in a greater variance in engagement in the brand. Such possibilities imply that in larger communities, brand relationship quality is likely to drive community identification, identification is likely to influence engagement and normative pressure, and, in turn, these are likely to influence behavioral intentions to a greater extent than in smaller brand communities. In general, we expect that though the levels of these key constructs in the model are lower, the strengths of the paths between constructs are greater for members of large brand communities than for members of small brand communities.
The following five hypotheses summarize this discussion regarding the moderating roles of the consumer's brand knowledge and the brand community's size in our proposed conceptual model:
H11: Community identification, community engagement, normative community pressure, and brand relationship quality are greater (a) for knowledgeable than for novice consumers and (b) for members of small brand communities than for members of large brand communities.
H12: The positive impact of brand relationship quality on community identification is stronger (a) for knowledgeable consumers than for novice consumers and (b) for members of large brand communities than for members of small brand communities.
H13: The impacts of community identification on community engagement (positive) and on normative community pressure (negative) are stronger (a) for knowledgeable consumers than for novice consumers and (b) for members of large brand communities than for members of small brand communities.
H14: The negative impact of normative community pressure on community-oriented behavioral intentions is stronger (a) for knowledgeable consumers than for novice consumers and (b) for members of large brand communities than for members of small brand communities.
H15: The negative impact of reactance on membership continuance intentions and brand loyalty intentions is stronger (a) for knowledgeable consumers than for novice consumers and (b) for members of large brand communities than for members of small brand communities.
Method
We derived measures for several constructs in our framework from existing scales or studies in the literature (as we described previously), and we adapted them to suit the context of our study. For community identification, engagement, and normative pressure, we developed new scales. Briefly, we used the following procedure that Churchill (1979) advocates. We conducted in-depth interviews with four car club presidents in Germany and held a focus group with 13 car club members in Switzerland to better understand how these experts perceived and described the constructs. We generated an initial set of items from this exploratory research.
Next, to enhance the constructs' face validity, we had 13 other experts evaluate this initial item set. We provided construct definitions and asked the experts to evaluate each item with respect to wording, fit with construct, completeness, and uniqueness. We rephrased improperly worded items and deleted those that did not fit the construct definition. In the final step, 46 graduate marketing students who belonged to one or more brand communities participated in a quantitative pretest of the modified items. They responded to the items, described their understanding of each one, provided an explanation for their responses, and indicated any problems they encountered while responding. We made several minor changes in wording based on this feedback and finalized the items to be used for the main study (provided in the Appendix). We note that some of our items for community identification are similar in content to Mael and Ashforth's (1992) identification scale.
In recruiting participants for our study, we identified and targeted all German-speaking car clubs located in Germany and the German-speaking regions of Switzerland and Austria; there were a total of 282 such clubs.( n1) For each car club, we contacted its president or organizer and requested him or her either to provide us with a list of the club's members or to forward our survey to the club's membership. We reached approximately 2440 existing car club members through this procedure.( n2)
We completed the study in two waves. We invited the car club members to participate in a Web-based survey in exchange for entering them into a raffle for valuable coupons. We made the first wave of the survey available online for four weeks in early 2003; we introduced it to participants as an "opinion survey regarding car clubs in Germany, Switzerland, and Austria." Participants completed the survey in approximately 15 minutes. After approximately ten weeks, we contacted all participants again by e-mail and asked them to respond to five additional questions pertaining to their brand-and brand community-related behaviors during the intervening period. These responses constitute the terminal dependent variables in our structural model. Of the participants who completed both waves of the survey, we randomly chose five to receive automotive services coupons worth €100 each (approximately $125 at the time we collected the data).
Of the 2440 car club members that we contacted, a total of 824 completed the first wave of the survey (a response rate of 33.8%), and a total of 529 completed both waves, resulting in a usable response rate of 21.7%. The analysis that follows is based on these 529 respondents, who belong to a total of 101 different car clubs.
The sample's demographics are as follows: 86.9% were male and 13.1% were female. Respondents ranged in age from 16 to 59 years, with a mean age of 32 years (median = 35, standard deviation = .71). By nationality, 87.4% were German, 5.3% were Swiss, and 5.8% were Austrian; 1.3% did not disclose their nationality. By duration of membership, 108 (or 20.6%) had belonged to their respective car club for less than a year, 219 (or 41.7%) had belonged between one and three years, and 198 (or 37.7%) had belonged for more than three years. The most represented car brands in the sample were Ford (n = 137), Volkswagen (n = 88), Mercedes (n = 53), Opel (n = 52), and BMW (n = 41). Other brands that were represented by fewer respondents included Porsche, Smart, Audi, Mini, Volvo, Renault, and Citroen.
Brand knowledge. We used the following multivariate normal mixture model to classify participants on the basis of their brand knowledge: Let y1, y2, ..., yn denote an observed three-dimensional sample of size n. The three dimensions correspond to the three measures of brand knowledge from the survey (see the Appendix). Therefore, each data point is assumed to be a realization of the random three-dimensional vector Y with the g-segment mixture probability density function: f(y; Ω) = Σi=1, sub g πiφi( (y; μi, Σi), where the mixing proportions πi are nonnegative, Σi=1g πi = 1, and Ω = (π1, ..., πg-1, mu;i, Σi). Furthermore, the multivariate normal probability density function has a mean (vector) μi and covariance matrix Σi.
We estimated the number of segments (g) and the mixing proportions πi with the EMMIX software program (McLachlan et al. 1999), which uses the E-M algorithm (Dempster, Laird, and Rubin 1977). This analysis revealed two segments of participants, which we labeled as "novice" and "knowledgeable." The novice segment constituted 21% of the sample (n = 111) and had a mean score of 4.27 on the three brand knowledge measures (on ten-point scales). The knowledgeable segment constituted 79% of the sample (n = 418) and had a mean score of 8.45 on the three measures (which was significantly higher than the novice group, p < .001).
Brand community size. We classified respondents as belonging to either small or large car clubs. We defined small car clubs as those with fewer than 50 members and large clubs as those with 50 or more members. On the basis of responses to our survey question about car club size (see the Appendix), we classified 288 participants (54.3%) as belonging to small car clubs and 236 (44.5%) as belonging to larger car clubs; 5 members did not indicate their car club's size.
Our full-sample structural equation model included all survey respondents, and we used it to test H1-10; we used the knowledgeable/novice and small/large subsamples to test the moderation hypotheses (H11-15). We ran all the models that we describe subsequently using the LISREL 8.54 program (Jöreskog and Sörbom 1999). We assessed the goodness-of-fit of the models with chi-square tests, the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), the nonnormed fit index (NNFI), and the comparative fit index (CFI). Discussions of these indexes can be found in the work of Bentler (1990), Marsh and Hocevar (1985), and Marsh, Balla, and Hau (1996). Satisfactory model fits are indicated by nonsignificant chi-square tests, SRMR and RMSEA values ≤ .08, and NNFI and CFI values ≥ .90.
When they were available, we used two indicators to operationalize each latent construct. Using the partial disaggregation model (Bagozzi and Edwards 1998), we combined latent constructs for which more than two items were available (i.e., community identification, community engagement, membership continuance intentions, brand relationship quality, and brand loyalty intentions) to produce two indicators. Compared with models in which each item is a separate indicator, such an approach results in models with fewer parameters to estimate and reasonable ratios of cases to parameters, and it smoothes out measurement error to a certain extent. We performed all analyses using covariance matrices (Cudeck 1989).
Results
Internal consistency. We used two measures to evaluate the internal consistency of constructs. The composite reliability (ρε) is a measure analogous to coefficient α (Fornell and Larcker 1981, Eq. 10), whereas the average variance extracted (ρ[sub VC(ξ)) estimates the amount of variance captured by a construct's measure relative to random measurement error (Fornell and Larcker 1981, Eq. 11). Estimates of ρε greater than .60 and ρVC(ε) greater than .50 are usually considered to support internal consistency (Bagozzi and Yi 1988). As Table 1 shows, all values are significantly greater than these stipulated criteria and therefore are indicative of good internal consistency.
Discriminant validity. We evaluated discriminant validity of the model constructs using two different approaches. We built a confirmatory factor analysis model with 12 latent constructs and a total of 20 measures. The results show that the model fit the data well. The goodness-of-fit statistics for the model are as follows: χ²(108) = 417.49, p ≈ .00, RMSEA = .07, SRMR = .03, NNFI = .95, and CFI = .97. The φ matrix (correlations between constructs, corrected for attenuation) appears in Table 2. As a first test of discriminant validity, we checked whether the correlations among the latent constructs were significantly less than one. Because none of the confidence intervals of the φ values (± two standard errors) included the value of one (Bagozzi and Yi 1988), this test provides evidence of discriminant validity.( n3)
In addition, for each pair of factors, we compared the chi-square value for a measurement model and constrained the correlation to equal one to a baseline model without this constraint. We performed a chi-square difference test for each pair of factors (a total of 66 tests in all), and every case resulted in a significant difference, again suggesting that all the measures of constructs in the measurement model achieve discriminant validity.
With respect to the fit statistics for the full model (χ²[147] = 747.7, p ≈ .00, RMSEA = .08, NNFI = .93, and CFI = .94), the chi-square is significant (p < .05), which is usually the case for large sample sizes. All the other statistics are within the acceptable ranges, which indicates a good model fit. We found that the impact of brand community identification on community engagement is strong and positive (β = .99, standard error [s.e.] = .06), in support of H1, and its impact on normative community pressure is significant and negative (β = -1.99, s.e. = .32), in support of H2. Figure 2 summarizes these and other results.
Furthermore, as we predicted, community engagement influences normative pressure positively (β = 2.06, s.e. = .31), in support of H3. Antecedents explain 81% of the variance in community engagement and 31% of the variance in normative pressure. We found that the impact of normative pressure on reactance is positive and significant (β = .38, s.e. = .05), as we expected. In addition, H4 receives support, and 34% of the variance in reactance is explained.
The impact of community pressure on a member's behavioral intentions is addressed in H5. As we expected, community pressure has significant, negative effects on both recommendation (β = -.13, s.e. = .03) and participation (β = -.14, s.e. = .02) intentions, but its effect on membership continuance intentions is not significant. Thus, H5b and H5c are supported, but H5a is not. For H6, we found support for all three (i.e., H6a-c) because the effects of community engagement on all three behavioral intentions--membership continuance (β = 1.12, s.e. = .07), recommendation (β = .91, s.e. = .07), and participation (β = .87, s.e. = .06)--are significant. Moreover, reactance has a negative impact on membership continuance intentions (β = -.26, s.e. = .08), in support of H7. These results suggest that the negative effects of normative community pressure on membership continuance intentions are fully mediated by reactance. The percentages of variance in membership continuance intentions, community recommendation intentions, and community participation intentions, as explained by their respective antecedents, were 76%, 62%, and 86%, respectively.
The remaining three main effect hypotheses address the interplay between brand- and brand community-related constructs. In H8, we predicted a negative impact of reactance on brand loyalty intentions; the results support this prediction (β = -.55, s.e. = .09). With respect to H9, we found that brand relationship quality influences brand community identification significantly (γ = .23, s.e. = .04), in support of H9. Brand relationship quality explains 10% of the variance in community identification. Finally, membership continuance intentions influence brand loyalty intentions significantly (β =.26, s.e. = .05), in support of H10. The antecedents of brand loyalty intentions explain 67% of the variance. This path is consistent with McAlexander, Schouten, and Koenig's (2002) finding that integration with the brand community can have a positive impact on the consumer's perceptions of the brand. Furthermore, we also found support for the other expected relationships, such as a positive impact of brand relationship quality on brand loyalty intentions (for details, see Figure 2).
One important criterion of a model's success is its performance compared with that of rival models (Bagozzi and Yi 1988). Our proposed model is based on an elaborate theory that hypothesizes a specific nomological network of constructs. For example, our model allows no direct paths from antecedents, such as brand relationship quality and community identification, to community-related behavioral intentions, and therefore it assumes that engagement and normative pressure mediate all the effects. A nonparsimonious rival model would hypothesize direct paths from these antecedent constructs to reactance and the four behavioral intentions constructs (see Figure 3). Such a model imposes relatively little nomological structure on the constructs.
We compared our hypothesized model with the rival model using the following criteria: overall fit, percentage of the model's statistically significant parameters, theoretical interpretation of the paths, and explained variance of the endogenous constructs. The overall fit for the rival model was about equal to that of our proposed model (χ²][94] = 564.53, p ≈ .00, RMSEA = .09, NNFI = .93, and CFI = .95), but it was accompanied by reduced parsimony. In our proposed model, 94.7% (or 18 of 19) of the paths were significant, whereas only 75% (15 of 20) of the paths were significant in the rival model. Even more problematic, many of the paths in the rival model did not make theoretical sense. For example, the path from community identification to membership continuance intentions (γ = -.69, s.e. = .24) and to recommendation intentions (γ = -.65, s.e. = .22) were both significant and negative.( n4) Finally, except for participation intentions (R2rival = .91 versus R2proposed = .86), the explained variances for all other endogenous constructs were much lower in the rival model (brand loyalty intentions: R2rival = .49 versus R2proposed = .67; membership continuance intentions: R2rival = .75 versus R2proposed = .76; recommendation intentions: R2rival = .52 versus R2proposed = .62; and reactance: R2rival = .27 versus R2proposed = .34). On the basis of these findings, we acknowledge that this comparison provided added confidence to the nomological network in our conceptual model.
We conducted multiple sample analyses (Jöreskog and Sörbom 1999) for the knowledgeable/novice and small/large brand community subsamples to test our hypotheses regarding the role of moderating variables. In H11, we posited that the community identification, engagement, pressure, and brand relationship quality would be greater for knowledgeable and small community subsamples than for novice and large community subsamples. To test this hypothesis, we conducted a structured means analysis in LISREL, using the following model of means structures (Jöreskog and Sörbom 1999): x(g) = τx + Πxξ(g) + δ(g), where g refers to the respective subsample, x(g) is a vector of input variables, τx is a vector of constant intercept terms, Πx is a matrix of coefficients of the regression of x on ξ, ξ is a vector of latent independent variables, δ is a vector of measurement errors in x, and the means of the ξ(g) equal κ(g).
For the two moderating variables, we set the κKnowlegeable and κSmall equal to zero to define the origin and units of measurement of the ξ factors; then, we computed κNovice and κLarge and determined whether the differences in the factor means between the groups were significantly different from each other. Table 3 provides the results.
The top panel of Table 3 shows that when novice consumers are compared with knowledgeable consumers, the factor means for all four constructs are significantly lower for novice consumers, as we hypothesized. In the lower panel of Table 3, we find mixed results. Furthermore, as we predicted, when large communities are compared with small communities, factor means of community identification and normative pressure are significantly lower for large communities. However, factor means of community engagement and brand relationship quality are not significantly different for the two subsamples. On the whole, these results support H11.
To test H12-H15, we built separate structural models for the knowledgeable/novice consumer and the small/large community subsamples, and we conducted tests of moderation to determine whether the respective path coefficients differed. Table 4 summarizes the analyses and results. The procedure that we used was as follows for each test: We constructed two multiple-sample models. In the first model, all paths were unconstrained between the two groups. This is the "no constraints" or baseline model in Table 4. In the second model, we constrained the relevant path (e.g., brand relationship quality to community identification for H12) to be equal for both subsamples. This is the "equal paths" model. The difference in chi-square values between the two models provides a test for the equality of the path for the two groups.
Consider the first row of Table 4 in light of this procedure. For knowledgeable versus novice consumers, the results indicate that the path from brand relationship quality to community identification is stronger for the knowledgeable group (γ = .19, s.e. = .04) than for the novice group (γ = .13, s.e. = .23, p < .001), in support of H12. Similarly, the path is stronger for the large community subsample (γ = .26, s.e. = .07) than for the small community subsample (γ = .11, s.e. = .04, p ≈ .06). Thus, H12 is supported for both moderator variables.
With respect to the remaining hypotheses, the paths from community integration to engagement and normative pressure are stronger for knowledgeable consumers than for novice consumers (p < .10), but the paths are not different for large and small communities. Thus, H13 is partly supported. In H14, we predicted stronger paths from normative pressure to the three intentions, but this is not supported for either moderator. Finally, in H15, we predicted that the path from reactance to membership continuance intentions and brand loyalty intentions would be more negative; this is supported for knowledgeable consumers but not for novice consumers (p < .10), and it is supported for large communities but not for small communities. On the whole, we found evidence that brand community's social influence is accentuated more for knowledgeable consumers than for novice consumers and more for large communities than for small communities.
Discussion and Implications
In the current research, we studied the social influence of brand community on consumers. We found support for our conceptual framework in a large sample of German-speaking car club members. Broadly speaking, this example points to the importance of purposely selecting, initiating, managing, and controlling interactions among customers when facilitating brand communities. In particular, our study contributes to existing brand community research in several ways.
First, our model found that the consumer's relationship with the car brand was an influential antecedent to his or her identification with the brand community. This finding provides useful insights into current practice. Specifically, when soliciting members for their brand communities, many firms tend to target new or potential customers. For example, the Audi club of North America promotes driving schools for new drivers who have not yet purchased a car in the hope that they will buy an Audi. National Instruments targets membership in its LabVIEW Zone community toward customers who have recently purchased the LabVIEW software. Their goal is for the community to help new users install and learn to use the software. Our finding suggests that such approaches toward new member acquisition do not work well if the firm's goals are to enlist engaged, active community members and to create a vibrant, self-sustaining brand community. Instead, the impact of brand relationship quality on community identification suggests that it is more effective for a firm to solicit and enroll its existing, long-tenured customers who already have an affirmative relationship with the brand. In managerial parlance, brand community membership is more useful as a customer retention device than as a customer acquisition tool.
Second, it is noteworthy that a majority of the car clubs we studied were "corporate" brand communities (i.e., organized around brands such as Ford or Volkswagen). However, as we noted previously, most consumers have a relationship with a specific car brand, such as the Ford Explorer or the Volkswagen Passat. This difference raises the possibility that though identification with the car club is influenced by the customer's relationship with his or her car brand, it may also help foster a connection between the customer and the car company as a whole (e.g., Bhattacharya and Sen 2003), thus strengthening the customer's relationship at this level and keeping him or her in the folds of the firm (see also McAlexander, Schouten, and Koenig 2002).
Third, by using a second-wave survey that elicited respondents' self-reported behaviors, we show that members' various behavioral intentions, including membership continuance, recommendation, active participation, and loyalty to the brand, all translate into corresponding subsequent behaviors. This finding has considerable managerial value because it links the brand community's influence to customer behaviors that affect profitability and provides grist to the mill of marketing managers who advocate building brand communities for their customers.
Fourth, we found that brand communities can influence their members in negative ways; this finding contributes to existing studies that have focused on the positive aspects of brand communities. Normative pressure, an extrinsic obligation to abide by the community's norms, results in reactance, a motivational state of resistance, and both are found to influence the consumer's behavioral intentions negatively. Notably, the source of normative pressure and reactance in our model was the consumer's engagement in the community, indicating that the community's positive influences give birth to its negative influences. We suggest that such a finding should not be taken as a final conclusion but rather as a preliminary finding that provokes further thought.
One question that arises from this finding is the composition of community engagement. It is possible that engagement comprises multiple components, with some leading to wholly positive outcomes and others to negative states. For example, it could be that private aspects of community engagement lead to positive outcomes, whereas perceptions of pressure and reactance stem mainly from the conspicuous public behaviors that highly engaged consumers frequently perform (e.g., Cialdini and Goldstein 2004). Further research is necessary to study this issue.
Another question is understanding how the firm's role in sponsoring the brand community leads to negative community influences. In the current study, the car companies visibly supported most of the respective car clubs. However, many other successful brand communities are organized and facilitated entirely by enthusiastic customers with little or no firm involvement. For example, National Instruments' LabVIEW product has several active customer communities, such as OpenG (www.openg.org) and LAVA (www.lavausergroup.org), that were begun and are operated by LabVIEW enthusiasts. Although this issue remains to be studied, we conjecture that participation in enthusiast-organized communities may be less susceptible to the negative influences that our study reveals. If this is the case, firms may be well advised to adopt a passive, "behind-the-scenes" approach when facilitating brand communities.
Fifth, we found that both the consumer's brand knowledge and the community size moderate the brand community's influence on its members (see Tables 3 and 4). Consumers who are knowledgeable about the brand not only experience higher levels of identification, engagement, and pressure but also reveal stronger paths in our model than do novices. This further reinforces the importance of firms' recruiting seasoned customers rather than novices into brand communities if their goal is to influence customers.
Furthermore, we found that the moderating impact of brand community size was more nuanced. Smaller car clubs engendered higher levels of identification and normative pressure, perhaps because of the richer and multifaceted nature of interpersonal relationships therein (Dholakia, Bagozzi, and Pearo 2004). However, we found that the strengths of the paths in the conceptual model were greater for larger car clubs, suggesting that when firms plan venues for enabling consumer interactions, larger sizes are more appropriate if their goal is to have greater community influence on key behaviors.
Despite these contributions, we acknowledge the limitations of this research. The survey method that we used collected all the measures except self-reported behaviors only once. As a result, although we took various precautions during analysis, such as establishing discriminant validity of constructs and comparing the performance of our proposed model against that of rival models, the results must still be interpreted with caution. In addition, care must also be taken when extrapolating our findings to other types of brand communities, such as those that predominate virtual, enthusiast-run communities (e.g., Dholakia, Bagozzi, and Pearo 2004) or those that consist of brand-centered events that are organized infrequently by the firm, such as Brandfests (McAlexander, Schouten, and Koenig 2002).
In conclusion, it seems appropriate to echo the optimism of brand community researchers such as McAlexander, Schouten, and Koenig (2002) and Muniz and O'Guinn (2001) and to suggest that brand communities offer a fresh, effective, and viral approach to building brands in the present-day, unresponsive marketing environment. Marketers may do well to take advantage of the opportunities that brand communities present.
The authors thank Rick Bagozzi for helpful comments, and they are grateful to the three JM reviewers for constructive and insightful comments on previous drafts of this article.
( n1) We used two Web sites (in March 2003) to identify these car clubs: http://auto.degui.de and http://www.allesklar.de/s. php?jump=100-30609-31175.
( n2) This calculation is based on the total number of members that we individually contacted in addition to counts provided by car club organizers who forwarded our request to their club's members.
( n3) Note that because the φ values in Table 2 have been corrected for attenuation, the corresponding product-moment correlations between constructs are actually lower than these values.
( n4) Detailed results for the rival model are available on request. We also tested two other rival models that were more parsimonious and reflected current conventional wisdom about the value of brand communities. Our hypothesized model out-performed both models. The detailed comparisons for these two additional models are also available on request.
Legend for Chart:
A - Construct
B - Number of Measures
C - Mean
D - Standard Deviation
E - ρε
F - ρ VC(ξ)
A B C D E F
Community identification 5 8.13 1.85 .92 .70
Community engagement 4 7.49 2.02 .88 .64
Normative community pressure 2 3.47 2.31 .81 .68
Reactance 1 4.46 2.85 -- --
Membership continuance intentions 3 8.05 1.97 .84 .64
Community recommendation intentions 2 7.12 2.39 .78 .64
Community participation intentions 1 8.53 1.90 -- --
Community membership behavior 1 2.35 .85 -- --
Community recommendation behavior 1 2.39 .91 -- --
Community participation behavior 1 2.30 .70 -- --
Brand relationship quality 3 6.54 2.54 .81 .66
Brand recommendation intentions 3 7.36 2.35 .90 .75
Brand-related purchase behavior 1 1.28 .54 -- --
Brand knowledge 3 7.57 2.08 .89 .75 Legend for Chart:
B - CI
C - CP
D - CE
E - RE
F - MCI
G - CRI
H - CPI
I - BLI
J - BP
K - CRB
L - CPB
M - BRQ
A B C D E F G
H I J K L M
CI 1
CP .03 1
CE .77(*) .23(*) 1
RE .09(*) .33(*) .05 1
MCI .81(*) .02(*) .72(*) -.15(*) 1
CRI .62(*) .02 .64(*) -.09(*) .75(*) 1
CPI .33(*) .02 .35(*) -.15(*) .34(*) .68(*)
1
BLI .35(*) -.26(*) .28(*) -.11(*) .46(*) .41(*)
.17(*) 1
BP .19(*) .06 .12(*) .06 .32(*) .32(*)
.14(*) .24(*) 1
CRB .42(*) .03 .28 .14(*) .34(*) .25(*)
.40(*) .18(*) .20(*) 1
CPB .40(*) .05 .31(*) .02 .33(*) .20(*)
.42(*) .09 .37(*) .37(*) 1
BRQ .28(*) .07 .28(*) .20(*) .36(*) .34(*)
.33(*) .59(*) .26(*) .32(*) .18(*) 1
(*) Significant at α = .05; all correlations are
significantly less than 1.00.
Notes: CI = community identification, CP = normative community
pressure, CE = community engagement, RE = reactance,
MCI = membership continuance intentions, CRI = community
recommendation intentions, CPI = community participation
intentions, BLI = brand loyalty intentions, BP = brand-related
purchase behavior, CRB = community recommendation behavior,
CPB = community participation behavior, and BRQ = brand
relationship quality. Construct
Legend for Chart:
A - Knowledgeable/Novice Consumer Subsamples
B - Knowledgeable Consumer Subsample Factor Mean
(κKnowledgeable)
C - Novice Consumer Subsample Factor Mean (κNovice)
D - t-Value, p-Value
A B C D
Community identification 0 -.96 -5.17, p < .001
Community engagement 0 -.52 -1.94, p ≈ .05
Normative community pressure 0 -.63 -2.77, p < .01
Brand relationship quality 0 -2.01 -8.09, p < .001
Legend for Chart:
A - Small/Large Community Subsamples
B - Small Community Subsample Factor Mean (κSmall)
C - Large Community Subsample Factor Mean (κLarge)
D - t-Value, p-Value
A B C D
Community identification 0 -.76 -5.50, p < .001
Community engagement 0 -.14 -.56, not significant
Normative community pressure 0 -.86 -4.78, p < .001
Brand relationship quality 0 -.31 -1.45, not significant Legend for Chart:
A - Hypothesis
B - Knowledgeable Versus Novice Consumer Subsamples Path
Coefficients in Unconstrained Model
C - Knowledgeable Versus Novice Consumer Subsamples χ²
Test Results
D - Small Versus Large Community Subsamples Path Coefficients
in Unconstrained Model
E - Small Versus Large Community Subsamples χ² Test
Results
A
B
C
D
E
Baseline model
No constraints model:
χ²(210) = 447.7
No constraints model:
χ²(210) = 455.3
H12
BRQ → CI is greater
for knowledgeable/
large than for
novice/small
subsamples,
respectively.
γ(K)(a) = .19(***)(b) (.04)(c)
γ(N) = .13 (.23)
Equal paths model:
χ²(211) = 468.84
Test of H1:
χd2(1) = 21.14,
p < .001
H12 is supported.
γ(S)(d) = .11(**) (.04)
γ(L) = .26(***) (.07)
Equal paths model:
χ²(211) = 458.90
Test of H1:
χd2(1) = 3.6, p ≈ .06
H12 is supported.
H13
CI → CE and CI →
CP are greater for
knowledgeable/
large than for
novice/small
subsamples,
respectively.
BCI → CE
β(K) = 1.07(***) (.07)
β(N) = .88(***) (.10)
BCI → NCP
β(K) = -2.17(***) (.30)
β(N) = -1.11(**) (.33)
Equal paths model:
χ²(212) = 453.16
Test of H1:
χd2(2) = 5.46,
p ≈ .06
H13 is supported.
BCI → CE
β(S) = 1.03(***) (.08)
β(L) = .94(***) (.08)
BCI → NCP
β(S) = -1.38(***) (.31)
β(L) = -2.04(**) (.34)
Equal paths model:
χ²(212) = 458.89
Test of H1:
χd2(2) = 3.59,
p > .16
H13 is not supported.
H14
CP → MCI, CP →
CRI, and CP → CPI
are greater for
knowledgeable/
large than for
novice/small
subsamples,
respectively.
NCP → MCI
β(K) = -.23(***) (.05)
β(N) = -.26 (.14)
NCP → CRI
β(K) = -.13(**) (.04)
β(N) = .07 (.10)
NCP → CPI
β(K) = -.22(***) (.04)
β(N) = -.16 (.08)
Equal paths model:
χ²(213) = 451.54
Test of H1:
χd2(3) = 3.8, p > .28
H14 is not supported.
NCP → MCI
β(S) = -.18(**) (.06)
β(L) = -.25(***) (.07)
NCP → CRI
β(S) = -.03 (.05)
β(L) = -.14(*) (.07)
NCP → CPI
β(S) = -.13(***) (.04)
β(L) = -.24(***) (.06)
Equal paths model:
χ²(213) = 458.39
Test of H1:
χd2(3) = 3.09,
p > .37
H14 is not supported.
H15
RE → MCI and RE →
BLI are greater for
knowledgeable/
large than for
novice/small
subsamples,
respectively.
R → MCI
β(K) = -.04(*) (.02)
β(N) = .04 (.07)
R → BLI
β(K) = -.05(*) (.02)
β(N) = .10 (.09)
Equal paths model:
χ²(212) = 452.90
Test of H1:
χd2(2) = 5.2, p ≈ .07
H15 is supported.
R → MCI
β(S) = -.00 (.03)
β(L) = -.14(***) (.03)
R → BLI
β(S) = -.00 (.04)
β(L) = -.19(***) (.04)
Equal paths model:
χ²(212) = 461.11
Test of H1:
χd2(2) = 5.2,
p ≈ .054
H15 is supported.
(*) p < .05.
(**) p < .01.
(***) p < .001.
(a) The subscript "K" refers to the knowledgeable subsample, and
"N" refers to the novice subsample.
(b) Unstandardized coefficient.
(c) Standard error.
(d) The subscript "S" refers to small community subsample, and
"L" refers to the large community subsample.
Notes: BRQ = brand relationship quality, CI = community
identification, CE = community engagement, CP = normative
community pressure, RE= reactance, MCI = membership continuance
intentions, CRI = community recommendation intentions,
CPI = community participation intentions, and BLI = brand
loyalty intentions.DIAGRAM: FIGURE 1 Hypothesized Model
DIAGRAM: FIGURE 2 Estimated Model
DIAGRAM: FIGURE 3 Rival Model
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Legend for Chart:
A - Construct
B - Measures(a) (Item Loading)(b)
A B
Constructs in Conceptual Model
Community
identification 1. I am very attached to the community.
(.80(*))
2. Other brand community members and I
share the same objectives. (.67(*))
3. The friendships I have with other brand
community members mean a lot to me. (.83(*))
4. If brand community members planned
something, I'd think of it as something
"we" would do rather than something "they"
would do. (.80(*))
5. I see myself as a part of the brand
community. (.82(*))
Community
engagement 1. I benefit from following the brand
community's rules. (.61(*))
2. I am motivated to participate in the
brand community's activities because I feel
better afterwards. (.81(*))
3. I am motivated to participate in the
brand community's activities because I am
able to support other members. (.82(*))
4. I am motivated to participate in the
brand community's activities because I am
able to reach personal goals. (.70(*))
Normative
community
pressure 1. In order to be accepted, I feel like I
must behave as other brand community members
expect me to behave. (.74(*))
2. My actions are often influenced by how
other brand community members want me to
behave. (.73(*))
Reactance 1. Since I joined the brand community, I
have felt a desire to preserve my personal
freedom. (1.0(*))
Membership
continuance
intentions 1. It would be very difficult for me to
leave this brand community. (.77(*))
2. I am willing to pay more money to be a
member of this brand community than I would
for membership in other brand communities.
(.64(*))
3. I intend to stay on as a brand community
member. (.72(*))
Community
recommendation
intentions 1. I never miss an opportunity to recommend
this brand community to others. (.65(*))
2. If friends or relatives were to search
for an automobile brand community, I would
definitely recommend this one. (.70(*))
Community
participation
intentions 1. I intend to actively participate in the
brand community's activities. (1.0(*))
Community
membership
behavior(d) 1. How often did you think about leaving
this brand community within the last ten
weeks? (c)(1.0(*))
Community
recommendation
behavior(d) 1. How often did you recommend this brand
community within the last ten weeks? (1.0(*))
Community
participation
behavior(d) 1. How often did you participate in
activities of this brand community within
the last ten weeks? (1.0(*))
Brand relationship
quality 1. This brand says a lot about the kind of
person I am. (.50(*))
2. This brand's image and my self-image are
similar in many respects. (.72(*))
3. This brand plays an important role in my
life. (.73(*))
Brand loyalty
intentions 1. I intend to buy this brand in the near
future. (.89(*))
2. I would actively search for this brand in
order to buy it. (.84(*))
3. I intend to buy other products of this
brand. (.64(*))
Brand-related
purchase
behavior(d) 1. How often did you buy products of this
brand within the last ten weeks? (1.0(*))
Moderating Variables
Brand
knowledge 1. When compared to other people, I know a
lot about this brand. (.82(*))
2. My friends consider me an expert regarding
this brand. (.82(*))
3. I consider myself very experienced with
this brand. (.82(*))
Brand
community
size 1. How many members does your car club
have?(e)
(*) p < .001.
(a) Unless indicated otherwise, we obtained responses using
ten-point Likert scales, anchored by 1 = "strongly disagree"
and 10 = "strongly agree."
(b) We report standardized item loadings.
(c) We reverse coded these items.
(d) This construct contains a single item and was elicited as a
frequency through an open-ended question; we then recoded it into
the following four categories: never, one to five times, six to
ten times, and more than ten times.
(e) The choices for this question were (1) less than 50 members,
(2) 50-199 members, (3) 200-499 members, (4) 500-999 members,
and (5) 1000 members or more.~~~~~~~~
By René Algesheimer; Utpal M. Dholakia and Andreas Herrmann
René Algesheimer is Assistant Professor of Marketing, Institute for Strategy and Business Economics, University of Zurich, Switzerland
Utpal M. Dholakia is Assistant Professor of Management, Jesse H. Jones Graduate School of Management, Rice University
Andreas Herrmann is Professor of Business Metrics, Center for Business Metrics, University of St. Gallen, Switzerland
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 186- The Social Life of Information. By: O'Malley, Lisa; Clark, Terry. Journal of Marketing. Oct2002, Vol. 66 Issue 4, p124-127. 4p. DOI: 10.1509/jmkg.66.4.118.18521a.
- Database:
- Business Source Complete
Section: Book ReviewsThe Social Life of Information (Book)
The Social Life of Information by John Seely Brown and
Paul Duguid (Boston: Harvard Business School Press, 2000,
317 pp., $25.95 hardcover, $16.95 paperback)
"Info-enthusiasts" like to describe a glamorous, digitized future in which technology is capable of acquiring, storing, and transmitting information, as well as creating the information, independently of human agency. In this brave new world, intelligent agents (bots) will (it is presumed) free us from the drudgery of routine tasks, while we work from high-tech offices in our high-tech homes, constantly topping our supplies of lifelong learning, without the necessity of traditional universities. Technology will remove the mundane from our lives, leaving only the exciting, the interesting, the relevant. However, there is an alternative view.
The opposing view is that technology is somehow dangerous and is to be feared. It replaces human effort with machine power: It propels people along a trajectory that leads inexorably to the fragmentation of society. Technophobes view the problems of modern life as the inevitable result of technological growth. Moreover, they experience exquisite nostalgia for the past and grope for more authentic life, in which communities are physical rather than virtual.
Against the backdrop of these opposing worldviews, Brown and Duguid position The Social Life of Information as a caution against both perspectives. While acknowledging the problems of technology and information, they highlight that few people would really wish to return to a world without telephones, faxes, photocopiers, or e-mail. They describe a world in which society's fundamental need for information has been satisfied but where info-enthusiastic "tunnel vision" begets its own, often unacknowledged, problems. In essence, this book carries a simple message: Information does not and cannot exist in a vacuum but is socially, spatially, and historically situated.
Perhaps the most beguiling of the many tales the authors tell is of Paul Duguid's work in the archive of a 250-year-old business. As he trawled through correspondence dating back to the American Revolution, he was joined by a historian.
Because of the accumulation of dust and mites in the archive, Duguid was uncomfortable in the extreme, coughing and spluttering and contemplating how much easier it would be if the letters had been digitized. The historian seemed to be uninterested in the contents of the letters, reading barely a word. To Duguid's disgust, the historian concerned himself primarily with smelling each of the dust-laden documents. As a medical historian, he was interested in documenting outbreaks of cholera and explained that he was able to trace the spread of the disease because all letters from an infected town had been treated with vinegar. The faint traces of vinegar remained after 250 years, making it possible for the historian to locate what he sought. As Duguid explains:
His research threw new light on the letters I was reading. Now cheery letters telling customers and creditors that all was well, business thriving, and the future rosy, read a little differently if a whiff of vinegar came off the page. Then the correspondent's cheeriness might be an act to prevent the collapse of business confidence--unaware that he or she might be betrayed by a hint of vinegar. (p. 174)
The point is, information cannot always be easily or usefully divorced from context. My once pristine copy of The Social Life of Information has taken something of a battering. Many pages are dog-eared, margins annotated, and passages underlined. Coffee stains celebrate several excerpts that demanded close attention. I wonder if other readers who happen upon my copy of the book will be distracted or intrigued by its current condition. Those who believe that books should remain immaculate will be irritated, whereas those who believe that books are principally social vehicles will be intrigued. Indeed, because future readers are exposed both to Brown and Duguid's thoughts and ideas as represented in this text and to my own thoughts and ideas as a result of my reading of it, the original text remains unchanged, yet in many subtle ways I have added to it. If (in the unlikely event) I become famous, this particular copy of the book would command a significantly higher social (and probably market) value than one hot off the printing press. This is one of the central arguments of Brown and Duguid's work. That is, the context in which information is situated is fundamental to its understanding and use. Marketers are already aware of this phenomenon in terms of the market value of sought-after memorabilia and the profitability associated with merchandising for a movie, an event, or an individual. What is particularly interesting for me is that marketers implicitly recognize the importance of the social context of products and services and their worth in communicating shared values among interested communities (see Belk, Wallendorf, and Sherry 1989; Cova 1997). Yet at the same time, we seem to ignore that, in the same ways, the value of information depends on context.
Like the info-enthusiasts Brown and Duguid hope to challenge, many marketers adhere to the notion that contemporary marketing problems can be solved by access to more information. In particular, changes in the social landscape and greater competition among organizations demand that individual marketers develop closer relationships with their customers and other stakeholders. Such attempts focus inevitably on database marketing (Copulsky and Wolf 1990; Goldberg 1988) to enhance customer information, often without customers' knowledge or consent (see Shultz 1993). Despite this operational focus, it is widely acknowledged that good relationships rely more on issues of sociability such as the development of trust, commitment, mutual interest, respect, and shared values (see Dwyer, Schurr, and Oh 1987; Gundlach and Murphy 1993; Wilson 1995) than they do on the garnering of information. Thus, attempts to generate something akin to close interpersonal relationships between buyers and sellers through a technological interface seem destined to fail. As Brown and Duguid highlight, "generations of confident videophones, conferencing tools, and technologies for tele-presence are still far from capturing the essence of a firm handshake or a straight look in the eye" (p. 5).
The focus on both information and technology in contemporary marketing is understandable. Many marketers view consumers as information-processing problem solvers. Perceiving the world as information oriented leads marketers to contemplate problems in terms of the need for more information. In this regard, the database, sophisticated customer profiling, geodemographics, and the like are regarded as important solutions in the competitive war marketers wage for the hearts and minds of consumers. More recently, the Internet seems (to some) to offer possibilities for relationship building in dazzlingly innovative ways. According to Hoffman, Novak, and Chatterjee (1995), "the popularity of WWW as a commercial medium ... is due to its ability to facilitate global sharing of information and resources, and its potential to provide an efficient channel for advertising, marketing, and even direct distribution of certain goods and information services."
The Internet is therefore conceptualized as an entirely new channel of distribution and a revolutionary communication system. Interaction takes place in the "marketspace" (Rayport and Sviokla 1994) rather than the marketplace, and as such, traditional interpersonal interactions between buyers and sellers are eliminated in favor of virtual ones. The true effects of such a shift have yet to be observed.
Brown and Duguid's thesis counteracts theories of a future consisting of bytes rather than atoms, a future in which communication is digital rather than face-to-face, and a future in which the only information worth considering is online. Contemporary conceptualizations of communication rely almost exclusively on the conduit metaphor (Reddy 1993). That is, language about language is metaphorically structured. Ideas (or meanings) are viewed as objects and linguistic expressions as containers. Communication involves putting ideas (objects) into words (containers) and "sending" them (along a conduit) to a receiver who takes the idea-objects out of the word-containers. Within this model of communication, the conduit itself is less important than the word-containers employed. That is, there is no distinction among face-to-face communication, telephone, paper, and digitized documents. What is regarded as important is the sender's ability to encode and the receiver's ability to decode. Although most will readily accept the limitations of this conceptualization, there is no doubt that the conduit metaphor is one of the most influential in the practice of our everyday lives. However, as McLuhan (1962) reminds us, the means of communication and the context in which it takes place are themselves of great importance in the under-standing of that communication.
One of the most compelling arguments in Brown and Duguid's book is to "look ... to things that lie beyond information" (p. 15) and to move beyond the tunnel vision of the information-orientated lens. Brown and Duguid counsel the reader to look more to the ways in which society and information interact and intertwine, because, at the end of the day, "it is people, in their communities, organizations, and institutions, who ultimately decide what it all means and why it matters" (p. 18). Indeed, despite being positioned as the functional department that understands consumers, modern marketing seems particularly susceptible to falling into the trap of believing that data equal customer knowledge, in an unproblematic fashion: that data collected on individual consumers is isomorphic with those consumers. However, collecting data on people is a poor substitute for meeting them. In many cases, the profile falls short because of the quality (or lack of it) of the original data.
The Social Life of Information presses its case further by considering how developments, such as digitized and personalized newspapers, may undermine the social fabric of society by inhibiting the emergence of shared values and a sense of community that arise as a result of reading the same text. Direct marketers might also consider how personalized communication undermines the importance of shared meanings in the creation and maintenance of brand value (Patterson 1998).
Brown and Duguid also launch an intelligent attack on those who peddle distance education. The distance learning myth is predicated on the notion that universities are primarily information providers and that in the future there will be a more limited role for traditional universities. The authors defuse such notions by arguing that universities are much more than mere information spouts. Indeed, the traditional university campus facilitates the mixing of teaching and learning communities and the sharing of ideas from which creative tensions emerge. In contrast, the plug-and-pay modules delivered by specialists in a virtual world inhibit social interaction. Moreover, in the world of virtual education, credentials amount to "little more than an intellectual bill of lading, a receipt for knowledge on board much like any other receipt for freight-on-board" (p. 219). In short, the potential for "knowledge markets" is limited, because the interactions that are not easily valued in the market remain socially valuable experiences of the traditional campus:
The technological reach that conquers distance doesn't necessarily provide the reciprocity that allows people to form, join in, or participate in worthwhile learning communities. Yet it can seem to. Certainly, the word community crops up all over the Web sites of distance courses. But it refers to groups that are communities in little more than the sense that eBay is a community. More generally, the 'Net can give the appearance of membership or access that it does not provide in a meaningful way." (pp. 225-26)
Parenthetically, the authors point out that the ability to send a message to president@whitehouse.gov infers greater access, participation, and social proximity than is actually the case. Many contemporary consumers of politics, education, and other products and services have less access than ever before but are encouraged to maintain the illusion that they are members of an inclusive society.
Brown and Duguid doubt that the much-touted information-oriented future will ever be realized. Taking a historical perspective, they draw attention to the failure of the prophecies of years gone by to materialize. Fewer people work from home than even the most cautious prophets foretold. Hot-desking, epitomized by the much-publicized changes to Chiat Day's office structure, has failed to become the norm.[ 1] Mass customization has yet to fulfill its promise and remains likely to favor large organizations rather than a new generation of niche marketers. As Brown and Duguid note, "The Henry Ford of the new economy would tell us that we can all have jeans made to measure, so long as they are Levis" (p. 27).
Technological "progress" has not engendered greater representation of individuals or, indeed, of governments. Organizations have not become flatter and employees more empowered. Indeed, the converse may be true as information technologies centralize authority, perhaps having the opposite effect of disempowering the individual. As the authors point out, the U.S. Navy resisted the introduction of ship-to-shore radio, because it would lose independence of action if higher commanders could communicate and intervene. Undoubtedly, many of marketing's foot soldiers understand this as they deliver their scripted dialogues while working on the telephones and conduct their day-to-day activities under the camera's gaze. In an age when customer relationships are viewed as critical, technology demands that traditional human interaction and conversation are eschewed in favor of order, data collection, and control. Yet how many consumers are frustrated when forced to interact with marketers who cannot and will not deviate from their carefully scripted spiel? Brown and Duguid point out that the legendary computer program, Eliza (which was intended to masquerade as a therapist and was developed in 1966), is the forerunner of those currently used in customer service and that, even today, the legacy remains: "Irate customers often resemble the deranged and customer service agents, automatons" (p. 36).
The Social Life of Information is a good antidote to the mindless prophesies of a bright info-technological future. However, its deeper message is more compelling: Technological progress is impossible and useless without human interaction. Just ask any father who has to call on his eight-year-old son or daughter to help him program the videocassette recorder.
1 In the early 1990s, the global advertiser Chiat Day attempted to change the way its employees worked. Its new building in Los Angeles was unconventional, eschewing dedicated offices and desks for its personnel. Each day, employees were required to check out a laptop computer and a cellular phone and find a place to work. Within a few years, the experiment was deemed to have failed. Rather than encouraging creativity, it had resulted in chaos. Within five years, Chiat Day returned to a more familiar office structure and more conventional ways of working.
REFERENCES Belk, R.W., M. Wallendorf, and J.F. Sherry (1989), "The Sacred and the Profane in Consumer Behavior: Theodicy on the Odyssey," Journal of Consumer Research, 16 (June), 1-38.
Copulsky, J.R. and M.J. Wolf (1990), "Relationship Marketing: Positioning for the Future," Journal of Business Strategy, (July/August), 16-26.
Cova, B. (1997), "Community and Consumption: Towards a Definition of the 'Linking Value' of Product and Services," European Journal of Marketing, 31 (3/4), 297-316.
Dwyer, F. Robert, Paul H. Schurr, and Sejo Oh (1987), "Developing Buyer-Seller Relationships," Journal of Marketing, 51 (April), 11-27.
Goldberg, B. (1988), "Relationship Marketing," Direct Marketing, 51 (6), 103-105.
Gore, J.P. (1998), "Who Has the Hook-Up?" Bank Marketing, (November), 24-30.
Gundlach, Gregory T. and Patrick E. Murphy (1993), "Ethical and Legal Foundations of Relational Marketing Exchanges," Journal of Marketing, 57 (October), 35-46.
Hoffman, D., T. Novak, and D. Chatterjee (1995), "Commercial Scenarios for the Web: Opportunities and Challenges," Journal of Computer-Mediated Communications, 1 (3), (accessed January 17, 2001), [available at http://www.asusc.org/jcme/vol1/ issue3/hoffman.html.bak].
McLuhan, Marshall (1962), The Guttenberg Galaxy: The Making of Typographic Man. Toronto: University of Toronto Press.
Patterson, M. (1998), "Direct Marketing in Postmodernity: From Individualism to Neo-Tribes," Marketing Intelligence and Planning, 16 (1), 68-74.
Rayport, J. and J. Sviokla (1994), "Managing in the Marketspace," Harvard Business Review, 72 (6), 141-50.
Reddy, M.J. (1993), "The Conduit Metaphor: A Case of Frame Conflict in Our Language About Language," in Metaphor and Thought, 2d ed., A. Ortony, ed. New York: Cambridge University Press, 164-201.
Shultz, D. (1993), "Marketing from the Outside In," Journal of Business Strategy, 14 (4), 25-29.
Wilson, D.T. (1995), "An Integrated Model of Buyer-Seller Relationships," Journal of the Academy of Marketing Science, 23 (4), 335-45.
~~~~~~~~
By Lisa O'Malley, University of Limerick and Terry Clark, Editor. Southern Illinois University
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 187- The Structural Influence of Marketing Journals: A Citation Analysis of the Discipline and Its Subareas Over Time. By: Baumgartner, Hans; Pieters, Rik. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p123-139. 17p. 4 Charts, 2 Graphs. DOI: 10.1509/jmkg.67.2.123.18610.
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- Business Source Complete
The Structural Influence of Marketing Journals: A Citation
Analysis of the Discipline and Its Subareas Over Time
The authors investigate the overall and subarea influence of a comprehensive set of marketing and marketingrelated journals at three points in time during a 30-year period using a citation-based measure of structural influence. The results show that a few journals wield a disproportionate amount of influence in the marketing journal network as a whole and that influential journals tend to derive their influence from many different journals. Different journals are most influential in different subareas of marketing; general business and managerially oriented journals have lost influence, whereas more specialized marketing journals have gained in influence over time. The Journal of Marketing emerges as the most influential marketing journal in the final period (1996-97) and as the journal with the broadest span of influence across all subareas. Yet the Journal of Marketing is notably influential among applied marketing journals, which themselves are of lesser influence. The index of structural influence is significantly correlated with other objective and subjective measures of influence but least so with the impact factors reported in the Social Sciences Citation Index. Overall, the findings demonstrate the rapid maturation of the marketing discipline and the changing role of key journals in the process.
Journals have become the primary medium to communicate scholarly knowledge in marketing, and the number of marketing-related journals has increased rapidly in recent years. Only a handful of journals covered marketing issues before the 1960s, the foremost being the Harvard Business Review (established in 1920), Journal of Retailing (1925), Journal of Business (1928), and Journal of Marketing (1936). Since then, the number of journals in which research relevant to marketing is published has mushroomed. Currently, there are 551 journals listed in Cabell's Directory of Publishing Opportunities in Management and Marketing (Cabell 1997-98). Of these, 59 have the word "marketing" in the title, and an additional 41 cover topics such as advertising, brand management, consumer behavior, consumer policy, purchasing, and retailing. Many other, more general journals frequently contain marketing-related research as well (e.g., Journal of Business Research, Management Science).
The rapid growth of the journal market and the proliferation of outlets in which research relevant to marketing is published make it increasingly important to gain insights into the relative influence of marketing-related journals (Doreian 1988; Garfield 1972; Kerin 1996; Singleton 1976). Journal influence affects many important decisions and is of interest to a variety of constituents (Borokhovich et al. 1995; Corrado and Ferris 1997; Fry, Walters, and Scheuermann 1985; Myers, Greyser, and Massy 1979; Tahai and Meyer 1999; Trieschmann et al. 2000). First, researchers, educators, practitioners, and other students of marketing, all with limited time budgets, need to know which journals are most likely to contain useful information based on content and quality criteria. Similarly, university and corporate libraries with limited financial budgets must decide which journals to subscribe to on the basis of patrons' interest in different publications and journals' contribution to scholarly discourse and practical impact. Second, authors seeking publishing opportunities want to know which journals are most apt to enhance the visibility and impact of their research. Although the premier journals of a discipline are usually well established, there is generally less consensus about journals' influence in particular subareas or niches of the discipline. Third, promotion and tenure decisions in research-oriented universities depend almost exclusively on publications in well-respected journals, and salary levels, author reputation, and the ability to obtain research grants are closely tied to the number of publications in prestigious journals. Journal rankings are particularly important when a scholar's research is evaluated by people who are not specialists in the discipline and who thus must rely on a journal's reputation as a proxy for article and research quality. Fourth, rankings of the quality of universities, schools, and academic departments are strongly influenced by evaluations of research productivity, and productivity is usually assessed by publications in a limited set of high-quality journals. Fifth, journal editors want to know about the relative standing of their journals in the discipline and the effects of editorial policies on the journal's influence. The rapid growth of the journal market makes this information increasingly difficult to discern.
Studying the structure of influence in a discipline, both cross-sectionally and longitudinally, is also important because it provides valuable insights into the development and current status of a discipline (Franke, Edlund, and Oster 1990; Lukka and Kasanen 1996; Zinkhan, Roth, and Saxton 1992). Such an analysis shows which journals contribute significantly to the exchange of ideas in a field of inquiry and how concentrated or dispersed the diffusion of knowledge is. It also indicates whether there are important differences in the influence of journals in various subareas of the discipline and how journal influence has evolved over time.
Unfortunately, at present little is known about the relative influence of the huge volume of journals that contain marketing-related research. Most published work is relatively old or has examined a restricted set of journals, often in narrowly defined areas (for an exception, see Hult, Neese, and Bashaw 1997, which we describe subsequently). In addition, there are alternative measurement approaches and specific indices of journal influence, all with their own strengths and weaknesses, and it is not obvious which influence measure is most appropriate. Moreover, no study in the marketing discipline has systematically examined the evolution of journal influence over time, either overall or in specific subareas of marketing. This has led to conflicting assessments of the development of journal influence over time. For example, some time ago Grether (1976) surmised that the establishment of specialized journals with distinctive positioning and homogeneous constituencies, such as the Journal of Consumer Research, might reduce the influence of broader journals such as the Journal of Marketing. Day (1996, p. 14) expressed concern about the "gradual erosion of the Journal of Marketing's traditional role as a thought-leader within the academic discipline of marketing." In contrast, Kerin (1996) argued that the reputation of the Journal of Marketing among marketing academicians had grown over the years and that it was one of the premier repositories of marketing literature. Yet the important questions whether and how the influence of specific journals in marketing has changed over time have not been investigated empirically.
To address these gaps in the literature, we rely on citation analysis to investigate the structure of influence in a comprehensive set of marketing and marketing-related journals over time. We assess the influence of each journal in the marketing discipline as a whole and in five specific subareas of the marketing discipline, and we ascertain how concentrated or dispersed each journal's influence is. To track journal influence over time, we consider citation exchanges among 11 journals in 1966-67, 25 journals in 1981-82, and 49 journals in 1996-97. We use the index of structural influence proposed by Salancik (1986) to assess journal influence, which is based on a substantive theory of influence, has desirable properties, and has rarely been used in marketing. To illustrate its validity, we compare the index with previously published objective and subjective measures of journal influence in marketing.
Our research aims to make several contributions to the marketing literature. First, it is the only study to provide a comprehensive ranking of the influence of marketing journals based on objective, citation-based data. Of the 49 journals for which we collected citation data for the final time period, 26 are not contained in the Social Sciences Citation Index (SSCI), which provides information about journal influence based on citation counts. Second, we employ a theory-based measure of structural influence that has been proposed in management and apply it in a citation analysis of the influence of marketing journals. We illustrate the validity of the measure and show its advantages over popular alternative subjective and objective measures of journal influence. Third, this study is the first to examine both the level and span of journal influence. It shows not only how influential journals are in the marketing discipline as a whole but also how narrow or broad their influence is and how influential they are in specific subareas of marketing. This provides new insights into the role that journals play in the creation and dissemination of knowledge in the marketing discipline and indicates whether a journal is a generalist or a niche player. Fourth, the analysis during a 30-year time period establishes which journals have gained or lost influence and how the marketing discipline has matured.
In the next section, we describe the strengths and weaknesses of objective and subjective measures of journal influence. Next, we introduce the measure of structural influence and compare it with other citation-based measures of journal influence. Then, we present the methodology and findings of our study.
A scholarly journal is influential to the extent that it publishes articles that contribute significantly to the exchange of ideas in some field of inquiry. This is variously referred to as influence, importance, impact, or quality. To identify a journal's influence, subjective and objective approaches have been proposed.
Key Informants' Judgments of Journal Influence
The subjective approach to assessing journal influence is based on key informant opinion surveys. Key informants in previous research have been deans, department heads, faculty members, and academic and practitioner members of professional organizations (e.g., the American Marketing Association). Typically, informants are asked to rank or rate different journals according to quality or to list a certain number of important or influential journals. Representative works of this approach in marketing are Browne and Becker (1979, 1985, 1991), Coe and Weinstock (1983), Fry, Walters, and Scheuermann (1985), Gordon and Heischmidt (1992), Hult, Neese, and Bashaw (1997), and Luke and Doke (1987). In the most recent study of this kind, Hult, Neese, and Bashaw (1997) surveyed 309 marketing faculty members (assistant, associate, and full professors) and asked them to indicate their top-10 most important journals from a list of 63 marketing-related journals. Respondents could also add to the list if a journal was not listed. The results show that the Journal of Marketing was ranked in the top-10 most often, followed by the Journal of Marketing Research, Journal of Consumer Research, Journal of Retailing, and Journal of the Academy of Marketing Science. Hult, Neese, and Bashaw (1997) also computed separate rankings for American Association of Collegiate Schools of Business (AACSB)-accredited and non-AACSB-accredited, as well as doctorate-granting and non-doctorate-granting, institutions. Although the overall correlation among the different rankings was quite high, some differences emerged. For example, Marketing Science was ranked fourth among doctorate-granting institutions but only tenth among non- doctorate-granting institutions.
The primary advantage of key informant surveys is that, in principle, they can capture the multifaceted construct of the perceived status of journals in a discipline. Perceived status encompasses various aspects of journal influence that objective measures cannot easily condense into a single judgment, such as the publication and editorial history of the journal, the quality of its review process, and the size and characteristics of its user base. However, key informant surveys have several serious shortcomings. One issue is that the ranking of journals depends on the quality of the survey (i.e., whether the population of respondents was defined appropriately, whether respondents were sampled correctly, and whether nonresponse and measurement error distorted the findings). Another problem is that expert ratings might be influenced by strategic responding and self-serving biases. For example, respondents may exaggerate the influence of journals in which they have published or for which they review, and they may overstate the role of journals in their own area of expertise. In addition, informants may not be familiar with all the journals they are asked to rate, and they may systematically underrate unfamiliar and overrate familiar journals. The latter problem can be addressed by taking into account respondents' familiarity with journals, but such judgments may be prone to similar biases and strategic responding. These mechanisms may systematically distort the resulting assessments of journal influence, such that some journals are overrated and others are underrated. This threatens the construct validity of subjective influence measures. Finally, if rankings or ratings for a comprehensive sample of journals are required, the burden on key informants may quickly become excessive, which promotes measurement unreliability. These problems have stimulated researchers to consider objective measures of journal influence.
Citation-Based Measures of Journal Influence
Objective measures of journal influence are based mostly on citation counts. The basic idea is that influential journals are the recipients of many citations from other journals. If a journal publishes an article that is cited by articles in other journals, it contributes to the exchange of ideas in a field of inquiry and is thus considered influential. Several objective measures of journal influence based on citation counts are available, such as the volume of citations received, the number of citations received per average article published, and the ratio of citations received to citations made (Doreian 1988). Representative studies in marketing using this approach are Leong (1989), Pieters and colleagues (1999), and Zinkhan, Roth, and Saxton (1992).
Citation-based methods of assessing journal influence also have several limitations (see Brown and Gardner 1985; Pierce 1990). One important issue is that articles may be cited for a variety of reasons, not all of which reflect a transfer of knowledge or true acknowledgment of intellectual indebtedness. Although it is usually assumed that citing others' work signifies that the cited document served as a relevant source of information, other motives are possible. Small (1982) reviews seven studies that examine the functions that citations serve on the basis of an analysis of the context in which they appear. Although the schemes to classify the functions of citations vary, they usually contain functions such as use/application, affirmation/support, review, perfunctory mention, and negation. The various functions of references reflect the differential influence of the cited document, and some references, for example, perfunctory mention (Kotler 1972), may not be good indicators of influence. Perfunctory mentions were found to account for, on average, 20% to 60% of references. Related to this, authors may cite an article without using it, for example, when a cited source has not been consulted or is irrelevant to the argument (Wertsch 1995). In addition, authors may cite articles for strategic reasons, for example, because the authors of the cited articles are potential reviewers of the research (Tellis, Chandy, and Ackerman 1999). To the extent that these mechanisms affect the journals in a discipline similarly, they lower the validity and reliability of citation-based measures of journal influence.
Although these limitations are important, citation-based measures appear less prone to systematic biases than subjective measures and are more readily available. Thus, they are becoming the preferred measures of journal influence in many disciplines (e.g., Doreian 1988; Johnson and Podsakoff 1994; Laband and Piette 1994; Pieters and Baumgartner 2002). The specific citation-based measure used in this study and its conceptual background are described next.
In social networks, members exchange valued resources. Journals that cite one another's articles form a social network in which knowledge is the valued resource and references are the medium of exchange. On the basis of theories of organizational influence, Salancik (1986) formulates three general requirements that a measure of influence in social networks should possess.
First, influence in a network should be based on dependency. That is, a member's influence in a network is proportional to other participants' dependency on that member for their resources. A citation indicates that the citing journal depends on the cited journal for its knowledge. Therefore, Journal A is more influential than Journal B if A depends less on B than B depends on A. In that case, the proportion of citations that Journal A sends to Journal B is lower than the proportion of citations that Journal B sends to Journal A.
Second, dependencies require different weights. That is, a member's influence in a network depends on the influence of the members that are dependent on it. When multiple others are dependent on a member of the network, the dependence of influential members contributes more to influence Structural Influence of Marketing Journals / 125 than does the dependence of less influential members. In other words, a citation from a journal that is influential should count more heavily than a citation from a relatively minor journal.
Third, indirect dependencies should be accounted for. That is, a member's influence in the network should be a function of both the influence that it directly exerts on other network members and the influence that it indirectly exerts through other members. In other words, if Journal A is strongly influenced by Journal B, which in turn is strongly influenced by Journal C, C should receive credit for its indirect influence on A through B, even though it may not influence A directly.
On the basis of work by Katz (1953) and Hubbell (1965), Salancik (1986) proposes a measure of structural influence that meets all three requirements. Assume for simplicity that a citation network consists of only three journals, A, B, and C. The influence of the three journals can be expressed in matrix notation, as follows:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Thus, the influence of Journal A (InfluenceA) is the sum of ( 1) the dependency of Journal B on Journal A (DependenceAB) weighted by the influence of Journal B (InfluenceB), ( 2) the dependency of Journal C on Journal A (DependenceAC) weighted by the influence of Journal C (InfluenceC), and ( 3) the intrinsic influence of Journal A (IntrinsicA). Operationally, dependencies are defined as the proportion of a journal's citations that go to another journal. For example, if Journal B made 1000 citations to other sources (including itself) during a given time period and 100 of these went to Journal A, then DependenceAB is .1.
The general solution to the system of simultaneous linear equations in Equation 1 is given by
( 2) Influence = [I - D]-1 Intrinsic.
Influence is an N x 1 vector of overall influence scores for a network of N journals, I is an N x N identity matrix, D is an NN dependency matrix, and Intrinsic is a vector containing the intrinsic influences of each journal. The intrinsic influences are usually fixed at 1 for computational purposes (Salancik 1986). Then, the minimum influence of any journal is 1, but the index has no upper bound (in practice, the influence scores are much smaller than the number of journals).
The index of structural influence is based on dependencies (requirement 1). Because the dependencies are weighted by the dependent journal's influence, citations are not treated equally in calculating this index (requirement 2). Furthermore, by solving the system of equations in Equation 1 algebraically, we can show that a journal's influence does not only depend on direct dependencies but also incorporates indirect dependencies (requirement 3). The measure has the additional advantage of allowing an analysis of the influence of journals in the discipline as a whole, as well as in specific subareas. This is an attractive feature that makes it possible to examine the span (or breadth) of journal influence. Journals that exert an influence in multiple subareas of marketing have a broader influence base than do journals that exert their influence in one or a few subareas. To analyze journals' span of influence, we partition the total set of journals into nonoverlapping subareas, and we calculate separate influence scores for each subarea, as follows:
( 3) InfluenceSub = [I - D]-1 DM,
where InfluenceSub is an N x K matrix of subarea influence scores (K is the number of subareas), D is as defined previously, and M is an N x K matrix of zeros and ones (with one nonzero entry per row) representing a journal's membership in one of the K subareas. The sum of a journal's influence scores in each of the K subareas yields the journal's total influence in the network minus 1 (its intrinsic influence). In the empirical section, we specify how subareas in marketing are identified in this study.
The most popular citation-based measure of journal influence is the impact factor reported in the SSCI (e.g., 1997 Social Sciences Citation Index 1998). The SSCI impact factor measures the number of citations received by the average article in a journal two years after publication. A journal's impact factor in year t is the number of times articles published in the journal during (t - 1) and (t - 2) were cited during t by other journals included in the SSCI, divided by the total number of articles that the target journal published in (t - 1) and (t - 2). The index of structural influence differs from the SSCI impact factors in several ways.
First, structural influence takes into account citations received by all the articles published in a journal, not only articles published during the previous two years. Therefore, the index of structural influence captures total journal influence, whereas impact factors capture recent influence.
Second, the index of structural influence measures over-all journal influence, whereas the impact factors assess the influence of the average article in a journal (see Harter and Nisonger 1997). Thus, journals with the same structural influence score may differ in impact if they publish different numbers of articles.
Third, the index is based on the notion of dependency, which refers to the number of citations sent to another journal as a proportion of the total number of citations made. The impact factors are based on the raw number of citations made. That is, of two journals that receive the same number of citations from other journals, one is more influential than the other if it receives a higher proportion of the citations made by the citing journals.
Fourth, the structural influence index takes into account the influence of the dependent journal and incorporates indirect dependencies. In contrast, impact factors do not consider the influence of the source of a citation and ignore indirect effects of citations.
Fifth, the structural influence index does not use self-citations (citations of a journal's own articles), whereas impact factors are based on all citations that journals receive, including self-citations. Theoretically, a journal that is not cited by other journals may still have a high impact factor if it cites itself frequently, which is an undesirable factor if the objective is to establish the influence of a journal in a network.
Sixth, in practical applications, the structural influence index is always based on a smaller network of journals than the impact factors. For example, the impact factors for 1996 are derived from citation exchanges among more than 1500 journals covered by the SSCI, whereas the citation network considered in this study consists of only 49 marketingrelated journals. Although this appears to be a limitation, most of the journals listed in the SSCI are not relevant to marketing, and many journals that are members of the marketing network are not included in the SSCI. Specifically, 26 of the 49 journals studied in this article are not covered by the SSCI. When the goal is to assess the influence of marketing and marketing-related journals in the marketing discipline, the journal network considered in this study seems more relevant than the journal network on which the impact factors are based.
Although our discussion indicates important conceptual differences between the structural influence index and impact factors and between objective and subjective influence measures, the important question is whether it really matters how journal influence is measured. Research in a related field shows that it does. Johnson and Podsakoff (1994) compare the structural influence index with various objective and subjective influence measures for a large set of journals in management. They find that the SSCI impact factors correlated quite poorly with other objective influence measures, including the structural influence index. Furthermore, the structural influence index correlated more highly with the subjective influence measures than with the SSCI impact factors.
In summary, the index of structural influence captures the total weighted influence of a journal in a specific network of journals, whereas SSCI impact factors capture the recent, direct influence of the average article published, including self-citations. Therefore, and in view of Johnson and Podsakoff's (1994) results, we chose the index of structural influence as a starting point for our research on journal influence in marketing. We use the index to document journal influence in the marketing discipline as a whole, as well as in specific subareas of marketing, and examine journal influence over time. To identify the subareas in marketing, we build on recent developments in citation research (e.g., Pieters and Baumgartner 2002), as explained subsequently. In addition, we assess the correspondence among the structural influence index, impact factors, and a recently reported subjective measure of journal influence to provide evidence of the degree of convergence among alternative influence measures.
To document journal influence in the discipline across a 30-year period, a total of 49 marketing-related journals were included in the citation analysis. Citation exchanges among each of the 49 journals were collected for the period 1996-97. Of the 49 journals, only 11 existed in 1966-67 and 25 in 1981-82. For these, we collected citations for the earlier time periods.
The journal selection procedure was as follows: In the first stage, the top-40 marketing journals from the study by Hult, Neese, and Bashaw (1997) were sampled. As mentioned previously, these authors conducted a survey of 309 marketing faculty members who were asked to name their top 10 journals. Respondents were provided with a list of 63 journals, which were selected on the basis of frequency of citations in the marketing literature, appearance in previous marketing journal hierarchies, popularity, and readership. Respondents could also include journals that were not on the list. Because 2 journals were tied for 40th place in Hult, Neese, and Bashaw's study, we included 41 journals in our sample. In the second stage, we added journals that met the following criteria: First, journals were included that appeared on the original list of 63 journals in Hult, Neese, and Bashaw's article and were listed in the SSCI (Journal of Consumer Affairs, Journal of Economic Psychology, Journal of the Market Research Society). Second, Hult, Neese, and Bashaw presented rankings for various subgroups of respondents (e.g., respondents from doctorate-granting and non-doctorate-granting institutions). If a journal was listed in the top 40 of one of the subgroups, the journal was included (Journal of Business to Business Marketing, Journal of Direct Marketing, Journal of Nonprofit and Public Sector Marketing, Journal of Professional Services Marketing). Third, the Journal of Consumer Policy was added because it was included in the citation study by Zinkhan, Roth, and Saxton (1992). Following this procedure, the final list contained 49 marketing and marketing-related journals. The final list includes some bibliometric sources that are not journals in the narrow sense, such as the proceedings of the American Marketing Association and Advances in Consumer Research. They were included in keeping with previous research in marketing (Hult, Neese, and Bashaw 1997; Phillips, Baumgartner, and Pieters 1999; Zinkhan, Roth, and Saxton 1992) and because they are published periodically, which the SSCI honors by including them in its list of periodicals.
To avoid instability of citation patterns due to short-term fluctuations, data were collected and summed across two years (1996-97, 1981-82, 1966-67). If a journal was listed in the SSCI, the relevant citation counts were compiled from data provided in the Journal Citation Reports. However, this was only the case for 23 of the 49 journals in our sample for 1996-97. The citation data for the 26 remaining journals were collected manually. To this end, we counted, for all articles that were published in the journals in 1996 and 1997, how often the articles cited the 49 journals in the sample. A similar procedure was used for the earlier time periods. Our findings are based on the 42,023 citations that the sampled journals made to one another in the three time periods we study.
The presentation of the findings proceeds as follows: First, we provide an initial description of citation patterns during the three time periods. Second, we calculate the index of structural influence to establish the overall level of journal influence in each of the three time periods. Third, we identify five subareas in marketing on the basis of the citation patterns, and we investigate the influence of journals within each subarea. Fourth and finally, we examine the convergence of citation-based and subjective measures of journal influence and assess their association with two correlates of journal influence (the journal's age and the number of articles published per year).
Frequency of Citing and Being Cited
Table 1 reports descriptive statistics about the frequency of citations that the journals in the network made and received in each of the three two-year periods within the network of journals. The number of citations made and received has increased steadily over time. In 1966-67, the average per journal was 117; in 1981-82, it was 301; and in 1996-97, it was 678.
The number of citations received from other journals (including self-citations) provides a rough measure of how important a journal is in the network. Management Science, Harvard Business Review, and Journal of Marketing received the greatest number of citations in the first period. The Journal of Marketing Research, Journal of Marketing, and Journal of Consumer Research received the most citations in the second period. The Journal of Marketing was by far the most popular recipient of citations in the third period, followed by the Journal of Marketing Research and the Journal of Consumer Research.
Overall Influence of Marketing Journals Over Time
Table 2 reports the influence of journals for those time periods during which they were in existence. We emphasize journal influence shares (i.e., relative influence) rather than absolute journal influence scores to facilitate interpretation of the results and to make comparisons over time meaningful.[ 1] To determine the influence shares, the intrinsic importance of each journal (which equals 1) was subtracted from the structural influence index (so that it has a minimum value of 0), and these influence scores were divided by the sum of the influence scores across journals in a particular time period, then multiplied by 100. The resulting index shows the percentage of the total influence in the network accounted for by a journal in each time period during which the journal existed. The rank of a journal's influence share in a time period is also reported for ease of interpretation.
During the first time period (1966-67), the Harvard Business Review was the most influential journal, accounting for 27% of the total influence available in the network. Other influential members were the Journal of Marketing, Journal of Marketing Research, Management Science, and, to a somewhat lesser degree, the Journal of Advertising Research and Journal of Business.
During the second period (1981-82), the Journal of Marketing Research was the most influential journal, accounting for 28% of the total influence. Other influential journals were the Journal of Marketing, Journal of Consumer Research, Harvard Business Review, Advances in Consumer Research, Management Science, and Journal of Advertising Research.
During the third period (1996-97), the Journal of Marketing was the most influential journal, followed by the Journal of Marketing Research and the Journal of Consumer Research. Other influential journals were Harvard Business Review, Management Science, Advances in Consumer Research, Marketing Science, Journal of the Academy of Marketing Science, Journal of Retailing, and Industrial Marketing Management. Figure 1 depicts the evolution of influence shares over time for the ten journals that had the highest influence share in the third time period.
We can draw several conclusions from Table 2 and Figure 1. First, the overall ranking of journals in terms of influence shows remarkable stability over time. The Spearman rank-order correlation between the 1966-67 influence scores of the 11 journals that were already in existence during that time period and the influence scores of these same journals in 1981-82 and 1996-97 is .89 and .81, respectively; the corresponding correlation between the 1981-82 and 1996-97 scores is .90. In particular, the journals that were most influential in 1966-67 were also among the most influential journals in 1981-82 and 1996-97.
Second, despite this overall stability, some journals substantially lost or gained influence. Although the Journal of Consumer Research and Marketing Science are relatively young journals, they quickly acquired an influential position in the field. In contrast, journals such as the Harvard Business Review, Journal of Business, and Management Science, as well as Advances in Consumer Research and Journal of Advertising Research, suffered considerable influence loss over time. The influence of the Journal of Marketing Research has fluctuated noticeably across the three periods; its share of influence more than doubled from 1966-67 to 1981-82, but then it almost dropped to its 1966-67 level in 1996-97. The influence share of the Journal of Marketing has held steady at approximately 19% across the 30-year period. This finding is inconsistent with the claims of some authors that the establishment of increasingly specialized journals has eroded the influence of general interest marketing journals. Rather, it demonstrates the maturation of the discipline, with several general business journals losing influence, more specialized marketing journals gaining influence, and a general interest marketing journal such as the Journal of Marketing maintaining a dominant position.
Third, influence in this network of marketing and marketing-related journals is very concentrated. Whereas the number of journals more than quadrupled over the time period studied (from 11 in 1966-67 to 25 in 1981-82 to 49 in 1996-97), a small set of journals accounts for a disproportionate share of total influence and received most of the citations in the network. In contrast, many of the secondary journals exert no significant structural influence on other network members. For example, during the first time period, the first four journals accounted for 74% of the total influence and the first six for 94%. During the second period, the first four journals accounted for 70% of the total influence and the first six for 82%. During the third period, the first four journals accounted for 56% of the total influence and the first six for 63%. The concentration of influence has remained rather high despite the dramatic increase in journal volume.
One question that arises is whether a journal's total influence depends on the number of journals from which it receives citations. We call this the span of influence of a journal. A journal's influence is narrow if relatively few journals are dependent on it; it is broad if many other journals are dependent on it. Specialized journals have a narrow span of influence, and general interest journals have a broad span. The matrix term [I - D]-1 in Equation 2 indicates how much influence each journal in the network derives from other journals. It is thus possible to compute the share of a journal's influence obtained from other network members and investigate the breadth of its influence. A convenient overall measure of a journal's span of influence can be defined with the Herfindahl index as proposed in economics (for a recent application in a related context, see Tellis, Chandy, and Ackerman 1999). The Herfindahl index is calculated as Hi = Sigmajalphaij2 where alphaij (i not equal to j) is the percentage share of journal i's total influence derived from journal j. Thus, the index ranges from 0 to 1. We define influence span as 1 - Hi, so values close to 0 indicate narrow influence, and values close to 1 indicate broad influence.
The span of influence of each journal was calculated and correlated with its influence level. Although in principle the level and span of a journal's influence need not be highly correlated (i.e., specialized, narrow journals could have a high or low level of influence, as could general interest, broad journals), we find that there is a strong relationship between the two variables. The rank-order correlation between the level and span of influence in each of the three periods is .90 (n = 11, p < .05), .89 (n = 25, p < .0001), and .87 (n = 49, p < .0001), respectively. In other words, influential marketing journals tend to have a broad span of influence (i.e., derive their influence from many different journals), and specialized journals tend not to be very influential in marketing.
The journals with the broadest span of influence were the Harvard Business Review, Journal of Marketing, and Journal of Business in 1966-67 and the Journal of Marketing, Journal of Marketing Research, and Harvard Business Review in 1981-82 and 1996-97. Only a few journals show a marked deviation from the general pattern that level and span are strongly related, such that their level of influence is higher than would be expected from their span of influence (e.g., the Journal of Consumer Affairs in the second period and the Journal of International Business Studies, Journal of Marketing Education, and Industrial Marketing Management in the third period). However, in absolute terms, the influence of these journals is relatively small.[ 2]
Journal Influence in Subareas of Marketing
So far, the analysis of journal influence has dealt with the marketing discipline as a whole, represented by the 49 journals. Perhaps the influence of some journals differs systematically across various subareas in the marketing discipline. Such journals may be influential in one area but less influential in others. An overall analysis of the span of influence is an important first step, but only an analysis of subarea influence can show in which areas narrow journals exert most of their influence. To establish journal influence in subareas of marketing, the subareas must be established first. Then, influence scores can be calculated for each of the subareas.
Subareas of the marketing discipline. Following previous work in citation analysis (e.g., Pieters and Baumgartner 2002; Pieters et al. 1999), we identified subareas in marketing on the basis of the volume of citations that journals send to and receive from other journals. The idea is that journals with strong mutual citation relationships are likely to be similar in substantive content or theoretical and/or methodological approach. For example, a journal that covers advertising is likely to cite journals that deal with advertising issues relatively frequently and journals devoted to, say, marketing education less frequently. Likewise, a marketing education journal will cite other marketing education journals more frequently than it cites advertising journals. Which subareas in marketing actually emerge depends on the extent to which specific journals cite one another.
To identify subareas in marketing on the basis of journal citation patterns, we estimated the log-multiplicative model recently proposed by Pieters and colleagues (1999) for citation analysis, which is based on the work of Goodman (1991) and other researchers in sociology (Clogg and Shihadeh 1994). The model, described in the Appendix, represents the journals in a low-dimensional space similar to multidimensional scaling and can be used to identify groups of journals with strong mutual citation relationships. Although we estimated a log-multiplicative model for each of the three two-year periods, meaningful subareas of marketing emerged only for the time period 1996-97. In the first period, which included only 11 journals, the onedimensional solution had a more parsimonious fit than the two-dimensional solution, and in the second period, it was difficult to identify discrete clusters of journals, even though the two-dimensional solution yielded an acceptable fit and was similar to the solution for the final period.[ 3] The twodimensional solution for the third period, which was optimal, is shown in Figure 2. Journals that are close together in Figure 2 entertain strong mutual citation relationships, and journals that are distant entertain weak or no mutual citation relationships.
The two dimensions in the citation map are readily interpretable. The horizontal dimension distinguishes journals with a managerial orientation (right) from those with a consumer orientation (left). On the right-hand side of the map are journals with a managerial perspective, such as California Management Review, Sloan Management Review, and Harvard Business Review. On the left-hand side of the map are consumer journals (and journal-like publications) such as Advances in Consumer Research, Journal of Consumer Psychology, and Journal of Consumer Research. In the middle of the citation plot, where the firm meets the consumer, typical marketing journals are located, such as the Journal of Marketing, Journal of Marketing Research, and European Journal of Marketing.
The vertical dimension distinguishes journals with a formal, quantitative, or more theoretical orientation (top) from journals with an application, qualitative, or more descriptive orientation (bottom). At the top are modeling-oriented and methodological journals such as Decision Sciences, Marketing Science, and Management Science. At the bottom are application-oriented, descriptive journals such as Journal of Marketing Education, Journal of Global Marketing, and Journal of Health Care Marketing. A cluster analysis (using Ward's method based on the coordinates of the journals in the map) identified five groups of cohesive journals in the citation map, which constitute our subareas in marketing.[ 4] In Figure 2, we have drawn ellipses around the subareas.
Subarea 1 comprises the core marketing journals (n = 8). This cluster consists of the general interest marketing journals such as Journal of Marketing, Journal of Retailing, and International Journal of Research in Marketing and several more quantitative marketing journals, such as Marketing Science, Journal of Marketing Research, and Marketing Letters.
Subarea 2 represents the consumer behavior journals (n = 9). It consists of journals such as Journal of Consumer Research, Journal of Consumer Psychology, and Journal of Economic Psychology and consumer policy journals such as Journal of Consumer Affairs, Journal of Consumer Policy, and Journal of Public Policy & Marketing.
Subarea 3 consists of the managerial marketing journals (n = 9). It includes managerial journals such as California Management Review, Sloan Management Review, and Harvard Business Review and inter-and multidisciplinary academic journals that cover marketing issues, such as Management Science, Journal of Business, and Journal of Product Innovation Management.
Subarea 4 consists of journals oriented toward marketing applications (n = 21). Included in this subarea are general marketing-related journals (Journal of Business Research), industrial marketing journals (e.g., Industrial Marketing Management, Journal of Business and Industrial Marketing), international marketing journals (e.g., Journal of International Business Studies, Journal of Global Marketing), and service marketing journals (Journal of Services Marketing, Journal of Professional Services Marketing). These journals deal with specific marketing tactics, target groups, or application areas, and they tend to have less influence. Their location in the middle to lower part of the citation map indicates that they cover general interest marketing issues with a focus on application.
Finally, subarea 5 consists of the two journals specializing in marketing education issues, the Journal of Marketing Education and Marketing Education Review.
Subarea influence analysis. We can now determine how the influence of journals varies by subareas in marketing using Equation 3. The subarea influence shares and ranks of journals are shown in Table 3.
Several findings stand out. Note that in each of the five subareas in marketing, a different journal attains the top influence rank. In the core marketing area, the Journal of Marketing Research is most influential. In the consumer behavior area, the Journal of Consumer Research is most influential. In the managerial marketing area, the Harvard Business Review is most influential. In the marketing applications area, the Journal of Marketing is most influential. Finally, in the marketing education area, the Journal of Marketing Education is most influential. However, the Journal of Marketing and Journal of Marketing Research have particularly broad spans of influence, attaining a top-five position in each of the subareas.
Also note that influence is concentrated most heavily in the consumer behavior area, in which the Journal of Consumer Research itself accounts for 32% of the total influence. The Harvard Business Review is almost as dominant in the managerial marketing area (29%).
The Journal of Marketing Research is the most influential journal in the subgroup of core marketing journals (23%), followed by the Journal of Consumer Research and the Journal of Marketing. Marketing Science is fourth, and Management Science is fifth in this cluster. The journals that are typically considered "A journals" in research-oriented universities are ranked as the top-five influential journals in the core marketing subarea. Together, these five journals account for 69% of the total influence in this area.
The Journal of Marketing is the dominant journal in the marketing application area (23%). Other influential journals in this area are the Journal of Marketing Research, Journal of Consumer Research, Harvard Business Review, and Journal of the Academy of Marketing Science. These five journals account for 58% of the total influence.
Now that we have described the evolution of influence in the disciple as a whole and identified specific subareas in marketing, we can analyze how the subareas have changed over time. Despite the small numbers of journals in the first two periods, which calls for caution in interpreting the findings, some fascinating trends can be discerned. First, the largest growth in journals during the 30-year period has taken place in the marketing applications subarea. This sub-area went from a single representative in 1966-67 (Business Horizons) to 21 journals in 1996-97 and became the largest subarea in the final period. Similarly, though less spectacular, there were no journals in the subarea of consumer behavior in 1966-67 but 9 in 1996-97. In contrast, the core marketing and managerial subareas only grew from 5 to 8 and from 5 to 9 journals, respectively, during the 30-year period. This development in marketing is similar to the general tendency of maturing markets to become more differentiated.
Second, although the core marketing journals have retained their influence shares during the 30-year time span, the managerial marketing-related journals have lost influence in marketing. The five core marketing journals that existed in 1966-67 jointly had a 47% influence share in the first period. The same five journals had an influence share of 55% in 1981-82. The eight that existed in 1996-97 had an influence share of 46% in the final period. In sharp contrast, the five managerial marketing-related journals that existed in 1966-67 had an influence share of 52% during the first period, but the seven journals that existed in 1981-82 had an influence share of only 22% during that period, and the nine journals that existed in 1996-97 had an influence share of 16% in the final period. This downward trend for the managerial marketingrelated journals is particularly noteworthy because the influence of the core marketing journals remained quite stable.
Relationships Among Measures of Journal Influence
On the basis of the work of Salancik (1986), we argue that structural influence is the preferred theory-based measure of journal influence. Yet to the extent that various measures of journal influence capture the same underlying construct, we still expect sizable correlations among alternative measures. To examine the convergence of journal influence measures, we correlated the structural influence index with alternative measures of journal influence for the most recent time period (1996-97).
The impact factor reported in the SSCI was included in the analysis as an additional citation-based measure. We collected the impact factors for the 23 journals listed in the SSCI for the years 1996 and 1997 and averaged the two scores to obtain a single impact factor for the time period under consideration.
In addition, we included a subjective measure of journal influence derived by Hult, Neese, and Bashaw (1997). These authors asked 309 marketing faculty members at U.S. universities to list their top-ten marketing-related journals in order of decreasing importance. From this information, they computed the popularity/familiarity index (PFI). The PFI is the number of top-ten votes divided by the number of topten votes received by the most popular journal. Scores are available for 41 of our 49 journals.
Zero-order correlations between the two citation-based measures and the subjective influence measure appear in the lower part of the correlation matrix in Table 4. We report Spearman rank-order correlations because most of the measures are skewed.
As we expected, the index of structural influence is significantly and substantially correlated with the impact factors and the subjective measure of journal influence. The SSCI impact factor, however, is not significantly correlated with the subjective measure of journal influence (r = .37, not significant [n.s.]), and it has a lower correlation (r = .54, p < .01) with the index of structural influence than does the subjective measure (r = .80, p < .001).
One possible explanation for the lower correlation of the impact factor with structural influence and subjective influence is that the former captures recent influence of the average article in a journal, whereas the latter capture the influence of a journal, which may be based on a longer publication history and a larger article base. That is, a journal's age and the number of articles it publishes annually should be positively correlated with structural influence and subjective influence, such that older journals and journals with a higher annual article production are more influential. The impact factor should not be correlated with these variables, because it is based on recent, average article influence.
If the correlation of structural influence and subjective influence with journal age and annual article production accounts for the lower correlations of the impact factor with structural influence and subjective influence, then controlling for these variables should increase the correlations among the influence measures. If, however, the lower correlation of the impact factor with the other influence measures is due to other variables, the correlations among influence measures should remain largely unchanged. In that case, structural influence and impact factors are, as we have argued, fundamentally different and cannot be readily converted from one to the other.
To examine this issue, we calculated partial correlations among the three influence measures while controlling for journal age and annual article production. We used rank- order correlations again because of the skewed distributions of the influence measures. Zero-order correlations of journal age and annual article production with the three influence measures appear in the last two rows of Table 4. Partial correlations among the three influence measures (controlling for journal age and annual article production) are in the upper half of the correlation matrix in Table 4.
As we predicted, structural influence is positively correlated with a journal's age (r = .67, p < .001) and a journal's annual article production (r = .41, p < .001). Also as expected, the impact factor is uncorrelated with these two variables. Furthermore, subjective influence is positively correlated with a journal's age (r = .60, p < .001), but the correlation with annual article production is not significant (r = .04, n.s.).
The partial correlations in the upper half of Table 4 show that controlling for a journal's age and annual article production does not significantly change the pattern of correlations among the three influence measures. That is, none of the correlations increases, the correlation between structural influence and subjective influence remains highest (r = .70, p < .001), and the correlation between the impact factor and subjective influence remains insignificant (r = .26, n.s.). On the basis of theoretical considerations, previous findings in related disciplines (Johnson and Podsakoff 1994), and these results, we suggest that structural influence is the preferred citation-based measure of overall journal influence in a discipline.
This study demonstrates the new insights that citation analysis can provide about the structure of journal influence and, more broadly, the creation and diffusion of scholarly knowledge in a discipline. A clear portrait of a maturing marketing discipline emerges when the various findings are integrated. The sheer volume of journals that currently exist, their specific content areas and theoretical/methodological perspectives, and the extent to which the number of journals has grown over the years are indicative of a rapidly evolving field. Marketing is not a homogeneous field of inquiry with a single broad group of tightly knit journals, but rather a diverse discipline consisting of specific subareas. In addition to the core marketing area, specific areas of consumer behavior, managerial marketing, marketing applications, and marketing education can be distinguished. These distinct subareas illustrate the level of specialization that has taken place in the discipline. Whereas in the 1960s, there were only a handful of journals that dealt with marketing issues and journal space was scarce, the number of marketing and marketing-related journals has since grown considerably. It has even become a challenge to be aware of all the journals and assess how influential they are in generating and disseminating marketing knowledge.
There have been distinct shifts in the influence of specific journals over time. On the one hand, the influence share of the more general business and management-oriented journals, such as the Harvard Business Review, Journal of Business, California Management Review, and Management Science, has declined systematically in marketing. On the other hand, there has been a simultaneous increase in the influence of specialized marketing journals such as the Journal of Consumer Research, Marketing Science, and Journal of the Academy of Marketing Science. The concentration of influence in marketing in a select set of leading journals is high and, despite the increasing number of journals, has remained quite stable during the 30-year time period studied. In the third time period, 1996-97, the top-5 journals accounted for more than 60% of the total influence available in the network of 49 marketing journals. That is, a small group of journals dominates the scientific discourse, and most other journals exert no noticeable structural influence in the marketing network. Jointly, these trends reveal a rapid specialization in marketing and a loosening of ties with the broader discipline of management and business.
The increasing number of specialized marketing journals, the fragmentation of the discipline into subareas, the stronger interdependencies among journals, the greater influence of specialized marketing journals, and the smaller influence of general business and management-oriented journals are evidence of the maturation of marketing into an independent, segmented academic discipline, which is indebted to, but separate from, related fields such as management.
The findings also clarify the role of the Journal of Marketing in the marketing discipline. Our bibliometric analysis confirms Kerin's (1996) view of its large and pervasive influence. It is the most influential marketing journal over-all, and true to its editorial policy and focus, it spans the entire discipline. Moreover, it is the only journal to serve this role. The Journal of Marketing is unique in occupying a top-three position in each of the five subareas in marketing. In addition, the influence share of the journal is high and has remained stable during the 30-year period studied. This high and steady influence share is remarkable in view of the dynamics of the marketing journal market and the increases and decreases in influence of various other journals over time. These findings go against Day's (1996) speculation that the emergence of new, specialized marketing journals has reduced the influence of the Journal of Marketing as an overall thought leader. Rather, the introduction of several new marketing journals appears to have added to its influence in the discipline. However, there is evidence for a change in its role in the marketing discipline. Whereas previously, it exerted a dominant influence on the core marketing journals, which presumably produce the most fundamental marketing knowledge, the journal now ranks only third in the core marketing subarea. It currently receives most of its influence from journals in the marketing applications field, many of which are of recent origin. From a thought leader at the forefront of generating specific marketing knowledge, the Journal of Marketing appears to have grown into an integrator, with the more global role of piecing individual parts of the marketing puzzle together and balancing academic rigor with managerial relevance. Some time ago, Lazer (1976, p. 78) argued that its weakness was that it had tried to be "an everything publication for everyone in marketing." Rather than a weakness, this is perhaps the responsibility and strength of a journal that tries to integrate the multifaceted discipline that marketing has become.
Our research supports the usefulness of the index of structural influence to assess journal influence in a discipline. The index is based on a substantive theory of influence in exchange networks and identifies both the level and span of journal influence. It also shows convergence with alternative influence measures. The ability to decompose journal influence into various subareas provides insights that cannot easily be obtained from alternative influence measures. The joint application of the log-multiplicative model to identify subareas in marketing and the index of structural influence to assess journal influence have proved fruitful. The log-multiplicative model captures reciprocity in journal citations (i.e., citation symmetry), whereas the index of structural influence captures dependence in journal citations (i.e., citation asymmetry). Used in combination, these techniques reveal which journals are influential, both longitudinally and in different subareas of marketing.
Further Research and Implications
This study examines the influence of marketing journals in the diffusion of scholarly knowledge in a network of marketing journals rather than other types of journal influence. Journals may be influential in other domains, for example, by offering a forum for discussion within a professional or academic organization, transferring academic knowledge to marketing professionals, being included in the marketing curricula of universities, being a source of knowledge for marketing textbooks, and so forth. The influence of journals may vary across domains, and to the extent that this occurs, our analysis under-estimates the influence of journals that serve these other functions. For example, some journals may be influential in inspiring other marketing journals, whereas others may inspire marketing curricula or have a large readership among marketing practitioners. Building on the experience of other business administration disciplines, such as finance (Corrado and Ferris 1997), it seems worthwhile to examine marketing journals' influence across multiple domains to gain greater insight into the diverse roles that specific journals play in the development and propagation of scientific knowledge in the discipline.
The current research examines journal influence in the marketing discipline only. Some journals may have an influence in other disciplines as well. Thus, the current analysis underestimates the total structural influence of interdisciplinary journals. This is particularly true for managementoriented journals, such as the Harvard Business Review, and broad journals, such as the Journal of Business and Management Science. It raises the more general issues of the interdisciplinary influence of journals and the cross-fertilization of related disciplines, which was recently explored by Pieters and Baumgartner (2002). They examine citation patterns among ten social science and business administration disciplines, each represented by five key journals. Marketing was represented by the Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research, Journal of Retailing, and Marketing Science. It appears that the other disciplines build only to a small extent on knowledge developed in marketing journals, with the exception of management information systems/operations research (which included Management Science, a journal that frequently contains marketing-oriented articles). The five top marketing journals were cited only 53 times by the five top journals in psychology between 1995 and 1997 and not at all by top journals in economics, sociology, or anthropology. In other words, marketing knowledge does not yet have much influence on its sister disciplines, at least as reflected in citation patterns. It is therefore unlikely that including nonmarketing journals in the citation network would have much of an effect on the ranking of marketing journals.
Pieters and Baumgartner (2002) find that marketing journals rely significantly on knowledge from several other disciplines, notably management, psychology, management information systems/operations research, and economics, though there were few citations from marketing to finance, accounting, political science, sociology, and anthropology. In their analysis, citation patterns were examined after aggregation across the five journals that represented each discipline. A more fine-grained investigation of interdisciplinary citation patterns among specific marketing journals and specific journals in other disciplines would be promising. It might identify marketing journals that are more or less mono-or multidisciplinary or those that serve as strong or weak ties in knowledge development and dissemination across disciplines.
It would also be worthwhile to explore in greater detail why some journals are more influential than others. Our findings indicate that structural influence is correlated with a journal's age and the number of articles it publishes per year. The relationship with age, which also held for a subjective measure of influence, may indicate a first-mover advantage, by which journals that launch a discipline or a subarea of a discipline are able to establish a position of leadership that is difficult to challenge in the future. This might also be the reason for the rapid ascent of the Journal of Consumer Research and Marketing Science, which succeeded, during a time when the journal market grew fairly rapidly, in positioning themselves as the thought leaders in areas that were of central concern to the discipline (i.e., consumer behavior, analytical modeling) but were not covered adequately by existing journals. The findings also show that, in marketing at least, a journal's overall level of influence is strongly related to its breadth of influence. That is, a journal is influential to the extent that many other journals cite it. At present, we do not know why some journals succeed in attracting citations from many other journals, and further research will need to show whether the correlation between level and span of influence is a general phenomenon that is typical of other fields. It is likely that methods other than citation analysis will need to be used to uncover the whys of journal influence, because there are probably intricate social processes at work that require in-depth longitudinal analyses of individual journal histories.
Our findings may be employed in several ways, two of which we discuss in more detail. Marketing researchers, educators, professionals, students, and libraries can use rankings of journals by structural influence when deciding which journals to read or subscribe to. Although structural influence should not be the only factor on which such decisions are based, it is an important indicator of the likelihood that a journal will contain information that may affect the discipline or specific subareas. This should be helpful to prospective consumers of scientific marketing knowledge. Rankings of journals by structural influence can also be useful to potential producers of knowledge, such as authors considering to which journals to submit their work. Authors want to have their manuscripts published in journals that are likely to enhance the visibility and impact of their research. The journal ranking reported here is based on a theory-based measure of structural influence that has good convergence with a recent expert rating of journal reputation, and it is more complete than other influence rankings. Furthermore, rankings are available by subareas in marketing. This may help prospective authors who work in particular areas of marketing, because there are important differences in journal rankings by area.
The results of this study might also be useful for hiring and tenure decisions. Although articles in the "big 3" (Journal of Marketing, Journal of Marketing Research, and Journal of Consumer Research) are universally regarded as top publications, articles in other journals may not be properly recognized, particularly if the candidate is working in a specific area. Consider, for example, a researcher in the managerial marketing area. In this field, the Harvard Business Review is the most influential journal, Management Science is third, and Sloan Management Review is fifth. For departments emphasizing managerial marketing and for professors with such a research focus, these journals should be among the premier publication outlets, and articles in these journals should be given appropriate weight in tenure decisions.
The authors thank Bill Ross and the four anonymous JM reviewers for helpful comments on previous versions of this article.
1 Increases in the size of the network (period 1 = 1285 citations; period 2 = 7525; period 3 = 33,213) and the average number of citations made and received require relative rather than absolute influence scores when influence over time is compared. This is consistent with the notion that status is a positional construct (Katz 1953).
2 In the current application, structural influence is highly correlated with the number of citations received (Spearman rank-order correlations of .92, .95, and .94 for periods 1 to 3), but for other disciplines and networks, this need not be the case. There are several reasons for this substantive finding. First, because influence is extremely concentrated in marketing (a few journals account for most of the influence, and the remaining journals vary relatively little in influence), weighting citations by influence has little effect. Second, even direct dependencies are relatively small in this journal network, so indirect dependencies (which are based on the products of direct dependencies) contribute little. Third, the high correlation between level and span of influence in marketing means that journals that are cited by many other journals have a large influence as well.
3 A Procrustes analysis (Peay 1988), which assesses how well two sets of solutions coincide, between the dimension coefficients for the common journals in 1981-82 and 1996-97 accounted for 92% of the variance (n = 25, p < .001).
4 Visual inspection of the dendrogram clearly indicates five clusters, the R2 of .78 is acceptable, and interpretation of the five clusters is straightforward. To validate the solution, we calculated citation exchanges among journals both within and between clusters. Journals that belong to the same cluster on average cite one another four times more frequently than they cite journals that belong to a different cluster. The only exception occurs for the applied marketing journals, which form a relatively diffuse cluster of journals dealing with specific marketing topics.
Legend for the Chart
A 1966-67: Citations Sent to Other Journals
B 1966-67: Citations Received from Other Journals
C 1981-82: Citations Sent to Other Journals
D 1981-82: Citations Received from Other Journals
E 1996-97: Citations Sent to Other Journals
F 1996-97: Citations Received from Other Journals
A B C D E F
ACR -- -- 1237 664 1625 1108
AMA -- -- -- -- 1611 163
BH 65 19 99 74 288 333
CMR 55 25 130 40 156 378
DS -- -- 39 26 113 117
EJM -- -- 130 20 1492 540
HBR 129 266 288 704 3 1765
IJRM -- -- -- -- 1092 258
IMM -- -- 20 46 1302 1029
JA -- -- 55 63 517 486
JAMS -- -- 371 18 1232 932
JAR 93 129 360 417 487 809
JB 89 108 84 248 74 184
JBBM -- -- -- -- 399 18
JBE -- -- -- -- 1583 1146
JBIM -- -- -- -- 837 110
JBL -- -- 33 3 415 248
JBR -- -- 387 37 1954 723
JCA -- -- 103 63 225 142
JCM -- -- -- -- 431 114
JCPO -- -- 6 0 138 102
JCPS -- -- -- -- 470 78
JCR -- -- 830 899 598 4119
JDM -- -- -- -- 360 185
JEP -- -- -- -- 304 196
JGM -- -- -- -- 620 59
JHCM -- -- -- -- 209 173
JIBS -- -- 103 41 662 910
JIM -- -- -- -- 352 50
JM 66 198 765 1036 1470 6043
JME -- -- 57 8 213 251
JMM -- -- -- -- 1249 192
JMR 157 124 974 2000 1292 4461
JMRS 24 0 81 41 239 128
JMTP -- -- -- -- 1145 17
JNPSM -- -- -- -- 337 14
JPIM -- -- -- -- 812 656
JPPM -- -- -- -- 579 324
JPSM -- -- -- -- 355 64
JPSSM -- -- 154 4 821 527
JR 37 27 326 276 766 895
JSM -- -- -- -- 699 135
MER -- -- -- -- 290 65
MKS -- -- -- -- 379 857
ML -- -- -- -- 648 148
MM -- -- -- -- 47 110
MNS 533 386 825 756 830 1208
PM -- -- -- -- 1237 169
SMR 37 3 68 41 256 474Notes: Numbers include self-citations. Journal abbreviations are shown at the bottom of Figure 2.
Legend for Chart:
A - 1966-67 Share(%)
B - 1966-67 Rank
C - 1981-82 Share(%)
D - 1981-82 Rank
E - 1996-97 Share(%)
F - 1996-97 Rank
A B C D E F
ACR -- -- 6.9 5 3.5 6
AMA -- -- -- -- .5 27
BH 1.7 9 1.7 11 .8 20
CMR 2.4 7 .7 15 1.0 19
DS -- -- .2 18 .3 37
EJM -- -- .2 17 1.5 17
HBR 27.4 1 10.7 4 6.9 4
IJRM -- -- -- -- .8 22
IMM -- -- 1.0 12 2.6 10
JA -- -- .9 13 1.5 15
JAMS -- -- .1 21 2.9 8
JAR 11.7 5 4.7 7 2.5 11
JB 8.2 6 2.5 9 .6 26
JBBM -- -- -- -- .0 47
JBE -- -- -- -- .7 23
JBIM -- -- -- -- .2 39
JBL -- -- .1 20 .1 44
JBR -- -- .4 16 2.2 12
JCA -- -- 2.1 10 .4 30
JCM -- -- -- -- .3 35
JCPO -- -- .0 22 .1 43
JCPS -- -- -- -- .2 40
JCR -- -- 12.0 3 13.7 3
JDM -- -- -- -- .3 36
JEP -- -- -- -- .4 32
JGM -- -- -- -- .1 45
JHCM -- -- -- -- .3 33
JIBS -- -- .0 22 1.9 13
JIM -- -- -- -- .2 42
JM 19.2 2 19.4 2 19.1 1
JME -- -- .0 22 .6 24
JMM -- -- -- -- .3 34
JMR 13.8 3 27.9 1 16.4 2
JMRS .0 11 .2 18 .3 38
JMTP -- -- -- -- .0 48
JNPSM -- -- -- -- .0 49
JPIM -- -- -- -- 1.5 16
JPPM -- -- -- -- .8 21
JPSM -- -- -- -- .2 41
JPSSM -- -- .0 22 1.4 18
JR 2.1 8 2.6 8 2.6 9
JSM -- -- -- -- .4 31
MER -- -- -- -- .1 46
MKS -- -- -- -- 3.3 7
ML -- -- -- -- .6 25
MM -- -- -- -- .4 28
MNS 13.4 4 4.9 6 3.6 5
PM -- -- -- -- .4 29
SMR .2 10 .8 14 1.8 14
Total influence 1.151 4.690 15.141Notes: Journal abbreviations are shown at the bottom of Figure 2.
Legend for Chart:
A - Overall Influence Share (%)
B - Overall Influence Rank
C - Subarea1:Core Marketing Share (%)
D - Subarea1:Core Marketing Rank
E - Subarea2: Consumer Behavior Share (%)
F - Subarea2: Consumer Behavior Rank
G - Subarea 3: Managerial Marketing Share (%)
H - Subarea 3: Managerial Marketing Rank
I - Subarea 4: Marketing Applications Share (%)
J - Subarea 4: Marketing Applications Rank
K - Subarea 5: Marketing Education Share (%)
L - Subarea 5: Marketing Education Rank
A B C D E F
G H I J K L
JM 19.1 1 15.3 3 14.5 3
15.1 2 22.8 1 12.0 2
JMR 16.4 2 23.2 1 16.5 2
10.8 4 15.1 2 6.9 3
JCR 13.7 3 16.2 2 32.0 1
4.2 6 9.1 3 4.6 6
HBR 6.9 4 3.7 6 1.6 11
28.5 1 6.7 4 6.1 4
MNS 3.6 5 5.9 5 1.5 12
11.6 3 2.4 11 1.3 19
ACR 3.5 6 3.0 9 8.3 4
.4 22 2.8 10 .8 25
MKS 3.3 7 7.9 4 2.5 6
2.6 9 2.1 13 1.3 19
JAMS 2.9 8 1.6 13 1.3 13
.7 15 4.0 5 5.9 5
JR 2.6 9 2.3 10 2.0 7
1.0 13 3.2 7 1.0 22
IMM 2.6 10 .8 18 .3 24
3.0 7 3.8 6 4.3 8
JAR 2.5 11 3.4 7 4.6 5
.5 18 1.9 15 .8 25
JBR 2.2 12 1.7 12 1.6 11
.2 25 2.8 9 2.0 15
JIBS 1.9 13 3.0 27 .5 21
.7 15 2.9 8 4.1 10
SMR 1.8 14 1.0 17 .3 29
9.1 5 1.5 17 2.0 15
JA 1.5 15 3.0 8 1.9 9
.2 25 1.1 20 .8 25
JPIM 1.5 16 1.9 11 .5 21
2.2 10 1.6 16 .5 29
EJM 1.5 17 .5 21 .3 24
.4 20 2.2 12 4.1 10
JPSSM 1.4 18 .6 19 .2 31
1.2 12 2.0 14 4.3 8
CMR 1.0 19 .2 32 .2 33
2.9 8 1.2 19 2.0 15
BH .8 20 .1 37 .1 36
.4 22 1.4 18 .3 35
JPPM .8 21 .5 21 2.0 8
.1 29 .6 23 .3 35
IJRM .8 22 1.1 16 .3 24
.1 29 .8 21 .5 29
JBE .7 23 .2 32 .9 15
.2 25 .8 22 1.8 17
JME .6 24 .2 34 .0 45
.0 40 .2 39 17.1 1
ML .6 25 1.2 14 .6 18
.2 25 .4 33 .3 35
JB .6 26 1.1 16 .3 29
.5 18 .4 30 1.0 22
AMA .5 27 .3 29 .3 27
.0 40 .6 24 3.6 11
MM .4 28 .4 23 .1 36
.6 16 .5 28 1.3 19
PM .4 29 .2 34 .7 17
.0 40 .5 27 .0 44
JCA .4 30 .3 29 1.1 14
.0 40 .3 37 .0 44
JSM .4 31 .3 25 .2 31
.0 40 .5 26 .3 35
JEP .4 32 .4 24 .8 16
.0 40 .3 35 .0 44
JHCM .3 33 .1 37 .2 33
.0 40 .5 26 .0 44
JMM .3 34 .2 34 .0 39
.4 20 .4 30 .5 29
JCM .3 35 .0 42 .3 27
.0 40 .4 31 .0 44
JDM .3 36 .1 37 .1 36
.0 40 .4 33 .3 35
DS .3 37 .2 30 .0 39
1.9 11 .1 43 .5 29
JMRS .3 38 .4 23 .2 33
.0 40 .3 38 .0 44
JBIM .2 39 .0 42 .0 39
.1 29 .3 34 1.0 22
JCPS .2 40 .3 27 .5 21
.0 40 .1 44 .0 44
JPSM .2 41 .0 47 .0 45
.0 40 .3 37 .5 29
JIM .2 42 .0 47 .0 45
.0 40 .2 42 2.8 13
JCPO .1 43 .1 39 .5 21
.0 40 .1 45 .3 35
JBL .1 44 .0 47 .0 45
.0 40 .2 40 .0 44
JGM .1 45 .0 47 .0 45
.0 40 .2 41 .0 44
MER .1 46 .0 42 .0 45
.0 40 .0 49 2.8 13
JBBM .0 47 .0 47 .0 45
.2 25 .1 46 .0 44
JMTP .0 48 .0 47 .0 45
.0 40 .0 47 .3 35
JNPSM .0 49 .0 42 .0 45
.0 40 .0 48 .0 44
Total
influence 15.141 3.031 2.387
1.116 8.210 .392Notes: Journal abbreviations are shown at the bottom of Figure 2.
Index of SSCI
Structural Impact PFI
Influence Factor Index
Index of structural influence 1.00 .55[c] .70[a]
(49) (23) (41)
SSCI impact factor .54[b] 1.00 .26
(23) (23) (20)
PFI index .80[a] .37 1.00
(41) (20) (41)
Age of journal .67[a] .35 .60[a]
(49) (23) (41)
Number of articles published during 1996-97 .41[a] -.02 .04
(49) (23) (41)
a p < .001.
bp < .01.
c p < .05.Notes: Zero-order correlations among journal influence measures are shown below the diagonal, and partial correlations (adjusted for age of journal and number of articles published) are above the diagonal (for first three variables). Numbers in parentheses are sample sizes.
GRAPH: FIGURE 1: Influence Shares of the Top Ten Journals in 1996-97 for Three Time Periods
GRAPH: FIGURE 2: Subareas in Marketing Based on Journal Citation Patterns
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The following symmetric log-multiplicative citation model was estimated:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
The expected number of citations from journal i to journal j is denoted by Fij, and the u's are standard log-linear parameters. The u parameter is a constant, the uS parameters control for differences among journals in the overall volume of citing other journals in the network, and the uR parameters account for differences among journals in the overall volume of being cited by other journals in the network. The deltaij parameter represents the effects of self-citations in the diagonal of the citation matrix (i.e., deltaij = 0 for i not equal to j and free otherwise), and the last term is a symmetric log-multiplicative effect. Specifically, 0 upsihim and upsihjm are the scores of journals i and j on the mth dimension, and psim is a scaling factor. Details are provided by Clogg and Shihadeh (1994), Goodman (1991), Pieters and colleagues (1999), and Pieters and Baumgartner (2002).
We estimated the citation model in Equation A1 for 1 to 7 dimensions (M = 1 to 7) using routines available in the LEM program (Vermunt 1998). The following benchmark models were estimated: an independence or main-effects model (containing the first three terms in Equation A1) and a model of modified independence accounting for self-citations (containing the first four terms in Equation A1). Model selection was based on fit (Bayesian information criterion and percentage inertia accounted) and interpretability of the solution. We selected the two-dimensional solution because it fit the data well and yielded the most meaningful interpretation of the data. It decreased the L2 statistic of the independence model by 79% and that of the modified independence model by 55% (Bayesian information criterion = -12479.40, L2 = 9997.30 with 2159 degrees of freedom).
~~~~~~~~
By Hans Baumgartner and Rik Pieters
Hans Baumgartner is Professor of Marketing, Smeal College of Business, The Pennsylvania State University. Rik Pieters is Professor of Marketing, Department of Marketing, Tilburg University.
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Record: 188- The Vagaries of Becoming (and Remaining) a Marketing Research Methodologist. By: Green, Paul E.; Clark, Terry. Journal of Marketing. Jul2001, Vol. 65 Issue 3, p104-108. 5p.
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Section: Book ReviewsTHE VAGARIES OF BECOMING (AND REMAINING) A
MARKETING RESEARCH METHODOLOGIST
Recently, I received an e-mail from Terry Clark, editor of the Journal of Marketing's Book Review section, asking me to "talk about your development over the years as a marketing scholar. Why did Paul Green get into marketing? Who/what influenced him? What twists and turns has his career taken? How does he go about doing research? How does he pick his topics? What are his idiosyncrasies?" This essay represents an attempt to respond to Terry's questions and, in the process, comment on some recent trends in research. (However, I'll go easy on the idiosyncrasies.)
random walk: the path taken by a point or quantity that moves by steps, where the direction of each step is determined randomly
In the fall of 1946, armed with a university scholarship and the GI Bill, I began my freshman year at the University of Pennsylvania's College of Arts and Sciences. Starting in the fifth grade, I had maintained an almost single-minded interest in chemistry. Most of my spending money went for home laboratory equipment. I fortunately managed to avoid blowing up the lab (with me in it), though I had a few close calls along the way. Once, while running some experiments with a Tesla high voltage coil, I accidentally brushed against a standing water paper in our attic; the results were not pretty.
When I entered Penn, I fully intended to major in chemistry and eventually become a research chemist. To my dismay, all the chemistry courses I wanted to take were being reserved for GIs who were majoring in premed, so I decided to major in mathematics and, being ever mindful of eventually needing a job, economics as well. I put my background and interest in music to good use by performing a few nights a week with some other part-time musicians. We worked in a variety of local bars and bistros, all of dubious reputation.
After the first year of college, I found that I quite liked my dual economics/mathematics major and decided to stick with it. Meanwhile, the music gigs provided ample income for a full-time student. I resisted an offer to go on the road with a traveling combo; summer jobs at the New Jersey shore provided sufficient fun and thrills.
Graduating from Penn in January 1950, I landed a job at the Sun Oil Company's home office in Philadelphia. After a one-month mandatory stint in the mail room, I was given my first real job as a statistical research analyst. Because Penn offered graduate courses in statistics, I decided to enroll part-time in the university's master's degree program and earn an A.M. degree. I was extremely lucky to have Simon Kuznets (later a Nobel Prize winner in economics) as my master's thesis advisor.
Three years later (armed with the master's degree), I was approached by the Lukens Steel Company in Coatesville, Penn., to work in its marketing research department. This relatively new department was engaged in carrying out business-to-business market studies for its major steel plate and weldments customers. While at Lukens, I became engrossed in the then-fledgling field of operations research (OR). The company sent me to several OR short courses taught by West Churchman and Russ Ackoff at the Case Institute of Technology. My manager asked me to start a small OR group at Lukens, and I jumped at the chance. Our three-person group quickly got involved in work scheduling, inventory control, and simulation problems. The work was sufficiently interesting to management for it to give me the opportunity to enroll (part-time) in Penn's doctoral program in statistics. In 1958, while still at Lukens, I was approached by a recruiting firm to explore a job opening in market planning at DuPont's Wilmington headquarters. I took this job and, with my new manager's approval, continued (part-time) the doctoral program in statistics at Penn.
In 1961, eight years after receiving the A.M. degree, I received a Ph.D. in statistics. My dissertation was in the then-arcane fields of Bayesian statistics and Von Neumann-Morgenstern utility theory. I was able to use these new concepts in some real-world research at DuPont. My doctoral dissertation advisor and longtime friend, Morris Hamburg, introduced me to Bayesian inference and expected utility concepts. I became an ardent proponent of these ideas, particularly as they related to the modeling of the cost versus value of marketing research information (Green 1963).
During my last couple of years in Penn's doctoral program, I had the pleasure of sitting in on some of Wroe Alderson's lectures in the marketing department. Wroe was a major figure in the world of marketing research. Earlier, he had headed his own research firm, Alderson and Sessions. After selling his interest in the firm, he focused his considerable energies on teaching and academic research.
Wroe's first book, Marketing Behavior and Executive Action (Alderson 1957), was a landmark publication; many of its concepts are as fresh today as when they first appeared. During the last two years of my doctoral studies, I had the opportunity to spend a lot of time with Wroe. When he asked me if I would be interested in an academic appointment, I was overjoyed.
In the summer of 1962 I left DuPont and started, full-time, in Wharton's marketing department. I also joined Wroe, as a consultant, in his new research firm, Behavior Systems. I was pleasantly surprised to learn that DuPont was interested in my consulting services. I spent a year and a half in this capacity while still teaching at Wharton.
During this same time, Wroe and I completed a book together, called Planning and Problem Solving in Marketing (Alderson and Green 1964). Here, I had the opportunity to show how some relatively new tools, including Bayesian analysis, could be used to address marketing problems. A couple of years later, Don Tull and I published the text Research for Marketing Decisions (Green and Tull 1966). This book was completed through the mails, because Don was then at California State University, Fullerton. (The book was published and on the market before we had the opportunity to meet in person.)
Wroe's influence on marketing science was broad and deep. In the mid-1950s, he initiated the marketing theory seminar (held in alternate years at the University of Vermont and the University of Colorado). We Young Turks were treated to the experiences and wisdom of the early marketing scholars, including Leo Aspinwall, Lyndon Brown, Richard Clewett, Paul Converse, Ewald Grether, Edmund McGarry, and Hugh Wales, to name a few. The seminars continued until Wroe's death in 1965 at the age of 68.
In the early 1960s, Wroe was also instrumental in persuading Thomas McCabe, president of Scott Paper Company, to found the Marketing Science Institute (MSI), Wendell Smith being selected as its first director. Patrick Robinson and Michael Halbert were hired as principal investigators. The institute's quarters were only a stone's throw from Wharton.
Meanwhile, Wharton's marketing department was acquiring an impressive portfolio of new talent-Ron Frank, Jerry Wind, Len Lodish, Scott Armstrong, Peter FitzRoy, and Irv Gross all arrived in the mid- to late 1960s. Several of us were asked to consult on MSI's research projects.
During one such project, involving experimental gaming on how managers process and act on "noisy" information, Peter FitzRoy and I became interested in psychometric techniques as related to correlates of risk aversion (Green, FitzRoy, and Robinson 1967). One thing led to another, and before I knew it, I was deeply involved in multidimensional scaling (MDS) and clustering methods; this epiphany occurred in the mid-1960s. I have been interested in these areas ever since.
After the experimental gaming project was completed, Frank Carmone (then at MSI) and I were able to meet and work with some of the major psychometricians of the 1960s, including Clyde Coombs, Warren Torgerson, Roger Shepard, Joe Kruskal, and Doug Carroll. Doug, in particular, became my MDS mentor and has continued to keep me up to date ever since. Out of that beginning came the Workshop on Multidimensional Scaling, sponsored by Bell Labs and the University of Pennsylvania and held at Penn in June 1972. Scholars came from around the world to present papers.
Between 1968 and 1972 I was involved in producing three monographs-two on MDS (Green and Carmone 1970; Green and Rao 1972) and one on conjoint analysis (Green and Wind 1973). Continued research in MDS, clustering, conjoint analysis, discrete choice, latent class analysis, and general multivariate data analysis still proceeds apace. Major contributors to this area include Wayne DeSarbo, Michel Wedel, Wagner Kamakura, Jordan Louviere, Moshe Ben Aikiva, Greg Allenby, Eric Bradlow, and Roland Rust, to name just a few.
Starting in the late 1960s (and continuing to the present day), I learned much about scaling techniques from Doug Carroll, my friend and collaborator for many years and currently the Board of Governors Professor of Management and Psychology at Rutgers University. He and I wrote two books together (Green and Carroll 1976, 1978) and have another on the way (coauthored with Jim Lattin). Other researchers who have influenced my thinking include Jerry Wind, with whom I have worked the longest-since 1966 when he joined Wharton. Frank Carmone, now at Wayne State University, Vithala Rao at Cornell, and Wayne DeSarbo at Pennsylvania State University have all collaborated with me on various articles over the years. (I have, indeed, been fortunate in the company I have kept.)
My interest in marketing research methodology started in the 1960s and continues to this day. My principal collaborator during the last two decades has been Abba Krieger, the Robert Steinberg Professor of Statistics at Wharton. Abba's technical background in theoretical mathematics, OR, and statistics (with degrees from Massachusetts Institute of Technology and Harvard) is outstanding. Helped by Abba's creativity and diverse skills, my interest in data analysis and descriptive modeling has taken a turn toward optimization modeling. The research that Abba and I have done over the past two decades reflects an emphasis on normative modeling, such as conjoint simulators and optimizers, competitive pricing models, customer satisfaction optimization, optimal reach analysis, and a variety of other efforts that have led to decision support systems.
Meanwhile, we continue to work on clustering methods, conjoint modeling, market segmentation, and various kinds of hybrid models for preference analysis. In summary, my old areas of research have not disappeared-they have been revisited and extended. Most important, I am still interested in models and techniques that have (actual or potential) relevance for managerial decision making.
One of the fringe benefits of working with cluster analysis, MDS, conjoint analysis, and other multivariate techniques is that their application often leads to practical (and sometimes high-profile) results:
- Cluster analysis has been used in IBM's change from an industry-based to a needs-based segmentation strategy (entailing personal computers). Other examples include the development of AT&T's "Reach out and touch someone" advertising campaign and a psychographic-based cluster analysis for General Motors' Chevrolet division.
- A variety of problems has been approached by means of MDS methods, including, for example, tracing the results of Life cereal's advertising campaign (involving moving earlier perceptions of the cereal as a strictly nutritional product to one that also appeals to children). Also, MDS was used in developing Coca-Cola's slogan, "It's the real thing!"
- Conjoint analysis has been applied to a host of applications, including hotel design (Marriott's Courtyard and Ritz Carlton's offering of deluxe services), ATT's first cellular telephone, the E-Z Pass toll collection system (now used in New York, New Jersey, and parts of Pennsylvania), and IBM's Risc 6000 and AS400 computer design and pricing.
- Thousands of conjoint studies have been carried out over the past 30 years. Commercial software is readily available for most of these techniques, and all the larger marketing firms provide consulting help on their application.
Now that I have discussed my own research and the sizable impact of my colleagues on that research, a few more general comments on research practice may be of interest.
Over the past 40 years it seems to me that research in marketing has moved rapidly away from the single-author articles and more toward collaboration among scholars. This is also true in marketing research methodology. The inducements are many: Friendships, complementary and compatible skills, similarity of interests, and ticking tenure clocks are just a few reasons.
On examining my curriculum vitae recently, I noted that over the past 40 years, I have collaborated with 55 different coauthors, 23 of whom were academics, 10 are former doctoral students of mine, and 22 are research practitioners. I have thoroughly enjoyed working with every one of them. Rare is the case that I have not learned something from collaborations with my colleagues, whether they were peers, students, or practitioners.
For the fun of it, I took my top seven collaborators (using a cutoff of at least 12 joint publications with me.) Their rank order in terms of number of coauthored publications is as follows: Krieger, Carroll, Carmone, Wind, Rao, Schaffer, and DeSarbo. These scholars, representing 13% of my coauthors, accounted for more than half of my joint publications. Although I have not attempted to replicate the venerable "20/80 rule," my publication history seems to reflect the tendency (at least in my case) to stay with scholars with whom common interests and respect remain over extended periods of time.
Big ideas in market research methodology are rare, and any researcher is fortunate to be associated with one, let alone several, over the course of a career. I have tried to develop the methodological skills needed to tackle various substantive problems, be they research questions on segmentation, pricing, competition, product positioning, or whatever. (This is not the same as learning a technique and going around solely looking for cases in which it can be applied.)
For me, the research process generally starts with a problem. Then I draw on the set of skills and models that promise (or do not) to provide useful solutions. Other researchable problems stem from critiquing others' research, accepting consulting assignments, trying to answer students' or colleagues' questions, and even self-critiquing my own prior research.
As I see it, there are two kinds of researchers in marketing research methodology. The first has a broad range of interests and becomes attracted to interesting and often offbeat problems that appear to be amenable to solutions. Typically, the solutions are interesting and creative but often lack transfer value to other classes of problems. The second type of researcher starts out with a related set of topics (e.g., interest in clustering methods) and spends time researching the literature, looking for unsolved aspects of the methodology, and tackling one or more of these unsolved problems. The second approach has the added value of leading to cumulative research contributions that relate to a specific domain, such as conjoint analysis or hierarchical clustering. Although the second approach tends to be more time consuming, it also prompts the researcher to look for incompletely solved subproblems or new problems that arise with new findings. Over time, this type of researcher usually has the better chance of making more interconnected contributions to the field.
With few exceptions, my research has focused on methodologies that promise to help practitioners cope with certain classes of problems, such as market segmentation, product positioning, price/demand relationships, distribution, product design, customer satisfaction, and so on. Researchers who take this path do so on the expectation that the client/decision maker will make better decisions if these tools and techniques are correctly used.
Of course, this is not the only type of research strategy that methodologists need follow. Quite the contrary: Several new research niches have emerged in recent years. Examples include causal modeling and covariance structure analysis, economic modeling of marketing phenomena, and game theory. Because each has attained a critical mass of researchers whose work now appears in top marketing journals, I say a few words about each.
Causal Modeling
With the introduction of sophisticated statistical techniques (Bentler 1985; Joreskog and Sorbom 1978; Wold 1985), causal models began appearing in marketing (Bagozzi 1980) during the late 1980s, and their growth accelerated during the 1990s. Although causal modeling is not without its critics (e.g., Cliff 1983; Freedman 1987), it has attracted considerable attention, particularly among consumer behavior researchers. Causal modeling is concerned more with the descriptive aspects of behavior and ways to test theories of how buyers (and managers) make decisions than with normative prescription. The presence of such studies in the journals and research textbooks is a testimony to their growing importance. An area of emerging applicability for causal modeling seems to be customer satisfaction, in which industry practitioners are beginning to investigate the potential value of covariance structure analysis and partial least squares models in tracking and analyzing customer satisfaction problems.
Economic Modeling
Another area of intense research activity is economic modeling. Moorthy (1993) provides a clear and nontechnical description of this research stream. He views economic modeling as consisting of the following components:
- The researcher observes a phenomenon.
- The researcher constructs an economic model to explain the phenomenon.
- The researcher develops a set of assumptions to help define the model.
- The researcher explores the logical consequences of the model: (a) The consequences should be at least consistent with the observed phenomenon; (b) some of these consequences should be empirically testable; and (c) other consequences might describe the phenomenon if the model were perturbed, that is, if the decision maker's environment were different.
These guidelines do not rule out other (possibly better) models. Note also that the approach is basically descriptive. The theoretical model builder does not have to make normative recommendations or tell the business person how to make better decisions. This is in contrast with decision support models that are designed to provide a mathematical description of a managerial problem with the intent of finding an improved (possibly optimal) solution to some aspect of current business practice. Economic modeling has become a fast-growing enterprise. Applications of the approach routinely appear in Marketing Science, and articles are also starting to appear in Journal of Marketing Research (Hauser and Wernerfelt 1989; Lal 1990) and Marketing Letters (Ingene and Parry 1998).
Game Theory
Although game theory also relies heavily on economic modeling, its principal feature entails two or more players whose fates are intertwined in various ways. Examples include Moorthy's (1984) work on buyer self-selection. Green and Krieger (1997) describe the application of Nash equilibrium in the context of new product design, in which researchers' interest is in long-term return optimization, given the responses of rational opponents. Choi and DeSarbo (1993) and Choi, DeSarbo, and Harker (1990) also discuss related competitive issues in the context of choice modeling.
My limited experience with game theory leads me to believe that game-theoretic notions-even under highly simplifying assumptions-can still offer useful insights into product design decisions, such as identifying product attribute levels that are reasonably resistant to rational competitive retaliation. Although game-theoretic model formulations employ highly simplifying assumptions, they can provide help in tracing out the possible consequences of short-term actions on long-term payoffs. Although marketing researchers' acceptance of causal modeling approaches has been more or less universal, I believe that all three research niches-causal modeling, economic modeling, and game theory-will continue to attract young scholars with theoretical interests and methodological training. The main problem seems to be in constructing interesting marketing theories in which the methodology can be fruitfully used.
As I look back over my research career of over 40 years (my first publication was in 1957 when I was at the Lukens Steel Company), I like to think that my many collaborators and I have made some useful contributions to marketing research methodology. In turn, I have had the pleasure and honor of working with extremely talented colleagues, ranging from those in the early years (Jerry Wind, Ron Frank, Frank Carmone, Doug Carroll, Arun Jain, Vithala Rao, and Wayne DeSarbo) to those later on (including Cathy Schaffer and, particularly, Abba Krieger, my latest coauthor). Abba and I have coauthored 45 articles in the approximately two decades that we have worked together.
If my thoughts are at least close to the mark, researchers in marketing are entering the age of research "pluralism," in which methodologists, economic modelers, and consumer behaviorists will live side by side and learn from one another. They will need at least four skill sets: teaching basic material (possibly remote from their own specialty), tolerating others' specialties, being mindful of the needs of business practitioners, and engaging in self-criticism of their research. Hopefully, the expected fragmentation will nonetheless continue to produce useful scholarly output-at least in the long run. Because marketing academics work for business schools, it would be a decided plus if the output were eventually important to somebody, say, the business practitioner or the MBA student.
As for future scholarly research, I still see a need for prescriptive research methodology that can help managers make more informed decisions. I also find it useful and refreshing to open the door to researchers who are less interested in prescriptive modeling than in understanding how the marketing world works, be it first-mover advantage, competitive duopoly, vertical structure payoffs, or what have you. Prescriptive and descriptive researchers can easily live side by side. Often both talents exist in the same researcher.
As for me, it is comforting to know that not everything is brand new. As I write this essay, I note that empirical and hierarchical Bayesian techniques are now quite the rage in choice modeling and related areas. They are more sophisticated than the Bayesian concepts I learned in the early 1960s; still, it is nice to know that "new wine in old bottles" is something more than a catchphrase.
As far as the vagaries of remaining a marketing research methodologist are concerned, it now seems inevitable that I have stayed a marketing researcher. With no talent for or interest in administration, I successfully avoided making the mistake of believing I had either.
References
Alderson, Wroe (1957), Marketing Behavior and Executive Action. Homewood, IL: Richard D. Irwin.
--- and Paul E. Green (1964), Planning and Problem Solving in Marketing. Homewood, IL: Richard D. Irwin.
Bagozzi, Richard P. (1980), Causal Models in Marketing. New York: John Wiley & Sons.
Bentler, Peter M. (1985), Theory and Implementation of EQS: A Structural Equations Program. Los Angeles: BMDP Statistical Software.
Choi, S.C. and Wayne S. DeSarbo (1993), "Game Theoretic Derivations of Competitive Strategies in Conjoint Analysis," Marketing Letters, 4 (4), 337-48.
---, ---, and Patrick T. Harker (1990), "Product Positioning Under Price Competition," Management Science, 36 (2), 175-99.
Cliff, N. (1983), "Some Cautions Concerning the Application of Causal Modeling Methods," Multivariate Behavioral Research, 18 (1), 115-26.
Freedman, David A. (1987), "As Others See Us: A Case Study in Path Analysis," Journal of Educational Statistics, 12 (Summer), 101-28.
Green, Paul E. (1963), "Bayesian Decision Theory in Pricing Strategy," Journal of Marketing, 27 (January), 5-14.
--- and Frank Carmone Jr. (1970), Multidimensional Scaling and Related Techniques in Marketing Analysis. Boston: Allyn and Bacon.
--- and J. Douglas Carroll (1976), Mathematical Tools for Applied Multivariate Analyses. New York: Academic Press.
--- and --- (1978), Analyzing Multivariate Data. Hinsdale, IL: The Dryden Press.
---, Peter T. FitzRoy, and Patrick J. Robinson (1967), Experiments on the Value of Information in Simulated Marketing Experiments. Boston: Allyn & Bacon.
--- and Abba M. Krieger (1997), "Using Conjoint Analysis to View Competitive Interaction Through the Consumer's Eyes," in Wharton on Dynamic Competitive Strategy, George Day and David Reibstein, eds. New York: John Wiley & Sons, 343-67.
--- and Vithala R. Rao (1972), Applied Multidimensional Scaling. Hinsdale, IL: Holt, Rinehart & Winston.
--- and Donald S. Tull (1966), Research for Marketing Decisions. Englewood Cliffs, NJ: Prentice Hall.
--- and Yoram Wind (1973), Multiattribute Decisions in Marketing. Hinsdale, IL: Holt, Rinehart & Winston.
Hauser, John and B. Wernerfelt (1989), "The Competitive Implications of Relevant Set/Response Analysis," Journal of Marketing Research, 26 (November), 391-405.
Ingene, Charles A. and M.E. Parry (1998), "Manufacturer-Optimal Wholesale Pricing When Retailers Compete," Marketing Letters, 9 (February), 65-78.
Joreskog, Carl and D. Sorbom (1978), LISREL: Analysis of Linear Structural Relationships by the Method of Maximum Likelihood. Chicago: International Educational Services.
Lal, R. (1990), "Manufacturer Trade Deals and Retail Price Promotions," Journal of Marketing Research, 2 (November), 428-44.
Lattin, James, Paul E. Green, and J. Douglas Carroll (2002), Analyzing Multivariate Data. Belmont, CA: Duxbury Press.
Moorthy, K. Sridhar (1984), "Market Segmentation, Self-Selection, and Product Line Design," Marketing Science, 3 (Fall), 288-305.
--- (1993), "Theoretic Modeling in Marketing," Journal of Marketing, 57 (April), 92-106.
Wold, Herman (1985), "Partial Least Squares," in Encyclopedia of Statistical Sciences, Vol. 6, S. Katz and N.L. Johnson, eds. New York: John Wiley &
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By Paul E. Green and Terry Clark, Editor, Southern Illinois University
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Record: 189- To Have and To Hold: The Influence of Haptic Information on Product Judgments. By: Peck, Joann; Childers, Terry L. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p35-48. 14p. 2 Black and White Photographs, 3 Charts, 4 Graphs. DOI: 10.1509/jmkg.67.2.35.18612.
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To Have and To Hold: The Influence of Haptic
Information on Product Judgments
Haptic information, or information attained through touch by the hands, is important for the evaluation of products that vary in terms of material properties related to texture, hardness, temperature, and weight. The authors develop and propose a conceptual framework to illustrate that salience of haptic information differs significantly across products, consumers, and situations. The authors use two experiments to assess how these factors interact to impair or enhance the acquisition and use of haptic information. Barriers to touch, such as a retail display case, can inhibit the use of haptic information and consequently decrease confidence in product evaluations and increase the frustration level of consumers who are more motivated to touch products. In addition, written descriptions and visual depictions of products can partially enhance acquisition of certain types of touch information. The authors synthesize the results of these studies and discuss implications for the effect of haptic information for Internet and other nonstore retailing as well as for traditional retailers.
The lesson here is simple: I still want to see and touch a product before I buy it. Web sites are pretty good for selling books and airplane tickets. But they don't do feel.
--Gleckman (2000, emphasis added)
Remember "Mr. Whipple, please don't squeeze the Charmin!"? How often when shopping do you hear parents telling their children, "Please don't touch"? The historical prominence of touch is reflected in language (Ackerman 1990; Katz 1925; Montagu 1986) with the use of various touch-terms prevalent in English. Some terms convey affect, such as "a touching story" or "how do you feel?" whereas others are more concerned with cognition, as in "Do you grasp the meaning?" "Did you catch that mistake?" and "Can you handle the problem?" The importance of communication is stressed in such phrases as "keep in touch," "on the other hand," and even AT&T's "Reach out and touch someone." Touch is an almost irresistible urge for children and adults alike, yet despite its importance, the fascinating sense related to product touch has not been incorporated into the marketing literature.
In marketing, Hornik (1992) examines interpersonal touch and finds that it affects both attitudes and behavior. However, to date, no research has focused on how touch is used for product purchase decisions. Study of the sense of touch is timely given the growth of "nontouch" retailers that provide shopping through catalogs and over the Internet. In an article involving interactive home shopping (Alba et al. 1997, p. 39), one of the critical attributes affecting the adoption of interactive home shopping is the "faithful reproduction of descriptive and experiential product information." Currently, information obtained by the sense of touch cannot be veridically reproduced in the nontouch media, which indicates that perhaps for some consumers and for some products, traditional retail shopping may not be easily replaced. Similarly, Paco Underhill (1999, p. 158, emphasis added), a retail anthropologist, has noted that "we live in a tactile deprived society, and shopping is one of our few chances to freely experience the material world firsthand." Underhill argues that various forms of nontouch shopping, such as over the Internet or through catalogs, will never seriously challenge traditional retail stores. Although this is not our claim, we recognize the importance of understanding the sense of touch as it relates to marketing. Casual observation reveals that touch is an important source of information to consumers, but little is known about the role of touch in product judgments and decision making.
In this article, we conceptualize touch as a form of direct experience with a product and examine product, individual, and situational factors that enhance or impair the use of touch information during product evaluation. We then examine whether, in the absence of direct product experience, mechanisms such as a written description or pictures may compensate for the information derived through touch. Our research reveals that products and consumers differ in their need for touch and that this difference has critical implications for marketing strategies.
Hand as Touch Organ: Haptics Defined
Although many studies of touch involve different areas of the human body, the primary studies of interest involve using the hands as the principal source of input to the touch perceptual system. The hand was proposed as the organ of touch that was as unitary as the organs of the other senses (Katz 1925) and has been called a person's outer brain. The term "haptics," from Greek, means "able to lay hold of" and was first introduced in 1931 by Révész (cited in Gibson 1966; Révész 1950). The term "haptics" or "the haptic system" is used throughout this article to refer to the active seeking and pickup of information by the hands.
Haptic Information Framework
Italian clothes designer Ottavio Missoni stated, "Because of the products we are offering and the clients we have throughout the world, we don't see a real need for this kind of strategy [online, Web-based]. Our clients want to touch the fabrics."
--Romano (2000, emphasis added)
Product factors. Products differ in the extent to which they possess salient material properties. The haptic system is particularly adept at encoding the object's material properties that correspond to texture, hardness, temperature, and weight information (Klatzky and Lederman 1992, 1993; Lederman and Klatzky 1987). For example, consumers may assess a sweater's texture by touching the fabric to determine its softness or squeeze a tomato to assess firmness (ripeness). Likewise, consumers may test a cellular telephone in the palm of their hands to assess its weight. Although other senses may be used to extract this information (e.g., visually observing steam rising from a hot drink), Lederman and Klatzky (1987) demonstrate that the haptic system is more efficient at assessing these four attributes of an object, which they term "material properties." Product categories in which the material properties of texture, hardness, temperature, or weight information vary in a diagnostic manner are more likely to encourage touch.[ 1]
Instrumental and autotelic material properties. A further distinction can be made with regard to the type of haptic information extracted from products. One type, instrumental information, is more intrinsic to the product and more specific to the goal-directed evaluation of a product's performance or its purchase (Holbrook and Hirschman 1982). These instrumental properties are related less to the sensory enjoyment of the product than to its structural properties. In contrast, autotelic forms of information are related to the sensory experience and hedonic appreciation of the product (Holbrook and Hirschman 1982). When touch is unavailable, we conjecture that it may be more difficult to compensate for this sensory enjoyment of autotelic information than for instrumental information. We elaborate on this distinction subsequently.
Some pundits say Webvan and other online delivery systems failed largely because shoppers wanted to squeeze Charmin and jab tomatoes instead of ordering through an online catalog.
--Konrad (2001)
Individual consumer factors. Although this statement may not be true for all shoppers, it may be true for some shoppers. In addition to product-based sources of salience for haptic information, we maintain that the salience of haptic properties is likely to depend on the person. In consumer behavior, evidence has been found for individual differences in terms of preference for sensory information (for a discussion of visual versus verbal information processing, see Childers, Houston, and Heckler 1985). Similarly, for certain people, haptic information is chronically more salient, and these haptically oriented people are more likely to use this information for product evaluation. An individual difference in touch involves an ability component, or a person's sensitivity to touch, and a motivation or preference component. Although the sensitivity to tactile stimuli, or the ability to feel, varies among individuals, the variance is quite small (Spreen and Strauss 1991). Rather than any inherent sensitivity of the hand region or fingers, a more important factor seems to be a person's motivation or preference to touch, termed the "need for touch," or NFT.
For example, more haptically oriented consumers, those high in NFT, consider material properties earlier during product evaluation and have greater chronic accessibility to haptic information (Peck and Childers 2002). Because of this chronic accessibility and preference for haptic information, haptically motivated consumers are likely to be more frustrated when shopping if they do not have the opportunity to experience products directly. In contrast, consumers who are less motivated to assess products through touch (those low in NFT) may still assess haptically oriented attributes, but they do so by visually examining a product (Klatzky, Lederman, and Matula 1993). For less haptically motivated consumers, a picture of a sweater in a catalog or on a Web page may be sufficient in satisfying their need to assess the texture of the sweater before making a purchase decision.
Situational factors. In addition to attributes of the product and characteristics of the person that may affect the salience of material properties, characteristics of the situation may also increase the salience of material properties. The situation (Bloch and Richins 1983; Houston and Rothschild 1978) may increase interest in different aspects of the environment and thus capture the consumer's attention. In certain retail scenarios, such as shopping through the Inter-net, catalogs, or television channels such as the Home Shopping Channel, a consumer has no or an impaired opportunity to touch the product before purchase. In addition, in-store obstacles, such as packaging or retail display cases, preclude or diminish consumers' opportunity to experience a product through touch directly. In contrast, tables displaying products with haptically salient attributes are often placed at the entry point of retail stores to invite consumers to pick them up and experience their material properties. For example, The Limited characteristically places texture-laden products such as sweaters and shirts on tables at the store's entry to entice direct experience and facilitate enjoyment of the clothing's pleasurable haptic attributes (Underhill 1999).
Consistent with prior segmentation models (Dickson 1982; Srivastava, Alpert, and Shocker 1984), we maintain that product, individual consumer, and situational factors act in concert to affect the extent to which consumers will assess a product's material properties by directly experiencing the product through touch. In the following section, we elaborate on the interrelationships among the three factors and hypothesize about their effects under conditions in which the opportunity to touch a product is impaired (e.g., evaluating a product behind a retail display case). We also consider whether, when touch is unavailable, compensation for touch information is possible in the form of either pictures or written descriptions of the product and how this might differ across consumers and by type of touch information (instrumental or autotelic).
Situational Characteristics and Extracting Haptic Information
Prior research has found that direct experience with an attitude object (product) can have a variety of effects, including increasing the confidence with which an attitude is held (Fazio and Zanna 1978; Smith and Swinyard 1983). However, not all retail environments enable the consumer to engage in direct haptic experiences. For example, when shopping through catalogs or online, a shopper has no opportunity to touch the product before purchase. Similarly, in a retail store, packaging or retail display cases preclude or diminish consumers' opportunity to experience a product directly through touch. Impairment to touch is expected to interact with a person's need for haptic information. As discussed, some consumers consider haptic information more important to their decision process, and we expect them to be more confident in their attitude toward a product when they can versus cannot directly touch the product. In contrast, those less oriented toward haptic information will not be as dependent on haptic product evaluation; therefore, confidence in their judgments should be relatively unaffected by the lack of opportunity to touch a product.[ 2]
H1: For more haptically oriented consumers, the confidence with which an attitude toward a product is held will be greater (lower) when they can (cannot) touch. For those less motivated to obtain haptic information, confidence in their attitude judgment will be unaffected by an opportunity to touch.
Opportunity to Touch, NFT, and Frustration
For people who are more motivated to obtain haptic information, barriers to direct experience will also lead to a more frustrating outcome. Mikulincer (1988) finds that frustration is related to failure in a problem-solving task. In addition, Weiner (1985) proposes that feelings of frustration are aroused by nonattainment of a desired goal and are heightened by involvement with a task. For people who vary in their motivation to touch products, to both assess haptic properties and experience their pleasurable sensations, barriers to touch should affect product evaluation frustration, as follows:
H2: For more haptically motivated consumers, frustration with the evaluation task will be less (more) when then can (cannot) touch, whereas for those less motivated to obtain haptic information, frustration with the evaluation task will be unaffected.
Barriers to Touch, Types of Haptic Information, and Compensation for Touch
For more haptically motivated consumers, it may be possible under certain conditions to provide additional information that would compensate for their inability to experience a product directly through touch (Petty, Cacioppo, and Schumann 1983). Additional information regarding a prod-uct's instrumental haptic properties may compensate for an inability to examine a product directly by stimulating retrieval of information about the category or the product's haptic properties stored in memory. In particular, a written description for an instrumental material property, such as weight, would be more likely to trigger past experiences that would compensate for the lack of actual touch. In contrast, for the more autotelic aspect of touching for pleasure, a written description would not adequately represent this rich sensory experience and would not be expected to compensate for a lack of touch for more haptically motivated consumers. In essence, we are asserting that for haptically motivated consumers, there is no substitute for touch in order to enjoy the experiential aspects of a product. This is in contrast to less haptically motivated consumers, for whom a clear visual examination of a product would be expected to satisfy their needs for both instrumental and autotelic haptic information when there is a barrier to touch (Klatzky, Lederman, and Matula 1993).
A similar prediction can be made for frustration with the evaluation task. A written description of instrumental material properties, but not autotelic properties, may help consumers who are more motivated to obtain haptic information compensate for actual touch. However, we expect that those less motivated to obtain haptic information will not be more frustrated when touch is unavailable; thus, compensation for a lack of touch is not as important.
H3: When touch is unavailable, for more haptically motivated consumers, a written description of instrumental but not autotelic material properties will increase confidence (decrease frustration) in their judgment compared with when no haptic description is provided. For less haptically motivated consumers, under the same conditions, a written haptic description will not have a compensatory effect on either confidence or frustration for either instrumental or autotelic material properties of a product.
Visual Compensation for Haptic Information
In cognitive psychology, evidence has been found for a "visual preview model," which states that vision provides a quick "glance" that results in broad but coarse information about the haptic properties of an object (Klatzky, Lederman, and Matula 1993). For less haptically motivated consumers, a visual assessment may be sufficient to trigger the retrieval of information from memory about the product's instrumental material properties, thus reducing the need for direct product experience. Therefore, we expect that when touch is unavailable, those less motivated to obtain haptic information will use visual information, provided through a picture, for example, to assess instrumental haptic information. In contrast, a visual representation will not substitute for the pleasurable experience obtained directly through autotelic touch. Neither more nor less haptically motivated consumers will consider the coarse haptic information provided by a visual presentation a substitute for autotelic direct experience, and neither will be compensated if a picture of the product is provided.
H4: When touch is unavailable, for less haptically motivated consumers, confidence in judgment will be increased (unchanged) when a picture of the product is provided with an instrumental (autotelic) haptic written description versus when no picture is present. For more highly haptically motivated consumers, under the same conditions, a picture will not have this compensatory effect for either instrumental or autotelic haptic information.
We investigated these hypotheses in two experiments. These studies investigate the effects pertaining to situational salience of haptic information and mechanisms for compensating for the inability to directly experience a product through touch.
Overview
The purpose of this study was to test H1-H4, pertaining to the interaction between individual differences in the NFT, the opportunity to obtain haptic product information, and compensation for touch. Experiment 1 consisted of four factors: 2 (levels of NFT) x 2 (opportunity to touch) x 2 (type of product information, haptic/nonhaptic) x 2 (type of product); the first factor was measured, the next two between subjects, and the last factor within subjects.
Procedure
A total of 199 subjects participated in the study. Subjects were recruited for a study on product evaluation. Participants evaluated a sweater and a cellular telephone individually in a lab, and they either had the opportunity to touch the product or saw the product presented under Plexiglas, which provided no opportunity to touch the product. After each product examination, subjects filled out measures of attitude, attitude confidence, frustration, and the NFT scale.
Independent Variables
Opportunity to touch. For participants in the touch condition, the products were provided on the table, and the subjects were able to touch the products if they desired. In the no-touch condition, the sweater and the cellular telephone were placed under Plexiglas, simulating a retail display case. The order of product presentation was counterbalanced with order effects that were nonsignificant in all cases (p > .05).
NFT. The individual difference in motivation to acquire and use haptic information, or NFT, was measured with a 12-item scale (reliability = .95).[ 3] The potential range of the 12-item NFT scale was represented in this sample. Low and high NFT were determined by a median split: Subjects scoring above the median (a score of 9) were classified as high in NFT (n = 100), and those scoring at or below the median were classified as low in NFT (n = 99).
Pretesting of Stimuli of Haptic and Nonhaptic Written Descriptions
In this study, the type of haptic information to be described in written form was manipulated through two products, a sweater and cellular telephone. For haptic descriptions, we provided autotelic compensatory information for the sweater and instrumental information for the cellular telephone. We provided nonhaptic descriptions by communicating the overall design qualities of both products (Table 1). Because this embeds the type of compensation information within the type of product, we conducted pretests to equate the manipulations along several dimensions. In addition, all statistical tests for compensation effects were conducted within each product. First, the overall importance of the material properties used as compensation information was rated as comparable for the two products. In the first pretest, 23 undergraduate subjects rated 15 products. Subjects rated each product in terms of whether touch played an important role in their decision process on a seven-point scale (1 = "touch is not important at all" to 7 = "touch is extremely important"). The mean importance of touch when evaluating was 6.0 for the sweater and 5.9 for the cellular telephone, p > .05. We administered a second pretest with 32 under-graduate students to determine the importance of the attributes to an evaluation of each product. For a sweater, softness (haptic) and overall design (nonhaptic) were rated equally important (on a five-point scale, means of 4.0 and 4.2, p > .05). Similarly, the same 32 subjects evaluated a cellular telephone on important attributes: Weight (haptic) and over-all design (nonhaptic) were rated equally important to product evaluation (on a five-point scale, means of 4.1 each, p > .05).
On the basis of these results, the haptic autotelic written version of the sweater description focused on softness, and the haptic instrumental version conveyed weight for the cellular telephone. For both products, the nonhaptic written descriptions focused on overall design (Table 1). Identification of two fictitious brand names was based on a third pretest in which it was determined that participants had neutral associations with each name.
For the cellular telephone, the product description consisted of three paragraphs (for each description, see Appendix A). The instrumental haptic versus nonhaptic manipulation was contained in the second (middle) paragraph, while the other information was held constant. The nonhaptic manipulation was taken from Web page descriptions of similar cellular telephones in terms of their overall design. For the instrumental haptic manipulation, this middle paragraph contained words related to the weight attribute (e.g., six ounces, slim, sleek). The autotelic information for the sweater was contained in a single paragraph of seven sentences. The autotelic haptic description for softness was manipulated in the second and third sentences, while the remaining four sentences were held constant. The haptic autotelic property (softness) was described with terms such as "richly textured," "plush feel," and "feels wonderful," and the nonhaptic description (overall design) was based on catalog descriptions related to fabric strength, construction, and stitching.
Given that the wording and the attributes differed across the haptic and nonhaptic product descriptions for the autotelic and instrumental properties, one issue is whether the argument strength is equally persuasive across the conditions. To assess this, we conducted a pretest following the guidelines provided by Petty, Cacioppo, and Schumann (1983). Forty-three undergraduate students rated either the haptic or the nonhaptic written description using scales that assessed argument strength (two scales ranging from 1 to 11, with end points anchored "unpersuasive" to "persuasive" and "weak reasons" to "strong reasons"). For both the instrumental and the autotelic descriptions, the haptic and the nonhaptic conditions were equally persuasive. Specifically, for the cellular telephone, both the persuasiveness and the strength of the reasons measures were not significantly different across versions (M = 7.8 and 7.4 for persuasiveness for instrumental haptic and nonhaptic versions, respectively; t(41) = .6, p > .05; M = 7.6 and 7.7 for strength of reasons for instrumental haptic and nonhaptic versions, respectively; t(41) = .05, p > .05). We obtained similar results for the written description of the sweater (M = 7.4 and 7.4 for persuasiveness for autotelic haptic and nonhaptic versions, respectively; t(41) = .1, p > .05; M = 7.5 and 7.4 for strength of reasons for autotelic haptic and nonhaptic versions, respectively; t(41) = .1, p > .05).
Dependent Measures
We measured attitude confidence by asking subjects how confident they were in their attitude judgments. The endpoints of the two seven-point scales were "not very confident" to "very confident" and "not very sure" to "very sure." We calculated an overall confidence measure by taking the mean of the two items (r = .98). We measured frustration with the task using a seven-point scale that asked, "How frustrated were you with this evaluation task?" with endpoints "not frustrated at all" and "very frustrated."
Results
Opportunity to touch and attitude confidence. Results of the multivariate analysis of variance with confidence and frustration as dependent variables indicated a significant two-way interaction for NFT and opportunity to touch (Wilks' lambda F( 2, 190) = 133.7, p < .05). For the univariate analysis on confidence in the evaluation judgment, H1 was supported by an interaction (F( 1, 191) = 147.4, p < .05) between NFT and opportunity to touch. Consistent with H1, for low-NFT subjects, confidence in the evaluation of the product was not dependent on whether subjects were able to touch the products (M = 5.0 and 5.4 in the touch and no-touch conditions, respectively; F( 1, 191) = 1.4, p > .05). Also as we predicted in H2, high-NFT subjects had more confidence in their judgments when they touched the product when evaluating (M = 6.0 and 3.2 for touch and no-touch conditions, respectively; F( 1, 191) = 224.0, p < .05).
Opportunity to touch and frustration with the evaluation task. H2 states that people high in NFT will be more frustrated when they are unable to touch than when they can touch during evaluation, whereas for low-NFT subjects, frustration with the evaluation task will be unaffected by the opportunity to touch. The interaction (F( 1, 191) = 139.5, p < .05) between NFT and opportunity to touch on frustration with the task was significant, in support of H2. Specifically, there was no difference in the level of frustration with the evaluation task for low-NFT subjects depending on whether touch was available or prohibited (M = 2.6 and 2.4 for touch and no-touch, respectively; F( 1, 191) = .9, p > .05). However, high-NFT subjects were significantly less frustrated with the evaluation task when touch was available (M = 2.0 and 5.2 for touch and no-touch conditions, respectively; F( 1, 191) = 308.9, p < .05).
Subjects were also asked an open-ended question, "What additional information would have helped in your evaluation of the product?" Significantly more high-NFT subjects in the no-touch condition mentioned that the opportunity to touch the product would have aided their evaluation. Specifically, 35 of the 50 high-NFT subjects (70%) in the no-touch condition mentioned that touch would have been helpful, whereas only 9 of the 48 low-NFT subjects (19%) in the no-touch condition mentioned touch (chi2 = 26.0, p < .05).
Compensation for an inability to touch: confidence in evaluation. H3 predicted that when touch was unavailable, for high-NFT subjects, a written description of an 5.0 mental but not an autotelic material property would increase confidence in their judgment compared with the nonhaptic written information. The results of the multivariate analysis of variance with confidence and frustration as dependent variables indicated a significant three-way interaction for type of product, NFT, and haptic/nonhaptic written description on confidence in judgment (Wilks' lambda F( 2, 190) = 4.2, p < .05). For the univariate analysis on confidence in the evaluation judgment, H3 was initially supported by the three-way interaction of type of product, NFT, and haptic/ nonhaptic written description on confidence in judgment (F( 1, 191) = 7.64, p < .05). As expected, when high-NFT subjects could not touch, confidence in their evaluation was significantly greater when instrumental haptic information (weight) was provided in written form than when nonhaptic information (overall design) was available (M = 4.1 for instrumental haptic [cellular telephone weight] versus 2.8 for instrumental nonhaptic [cellular telephone overall design]; F( 1, 191) = 17.1, p < .05; see Figure 1, Panel A). Also, as expected, a compensatory written description of autotelic haptic information did not increase confidence for high-NFT subjects (M = 3.0 for autotelic haptic [sweater softness] versus 3.0 for nonhaptic [sweater overall design]; p > .05).
Inconsistent with H3 , however, was the finding that a written description of an instrumental haptic property significantly increased the confidence of low-NFT subjects when they could not touch (M = 5.9 and 5.2 for instrumental haptic description [cellular telephone weight] versus nonhaptic description [cellular telephone overall design]; F( 1, 191) = 7.0, p < .05; see Figure 1, Panel A). Consistent with H3, subjects low in NFT were not affected by a written description of an autotelic haptic property (M = 5.4 and 5.0 for autotelic haptic description [sweater softness] versus nonhaptic description [sweater overall design]; F( 1, 191) = 1.3, p > .05).
Compensation for an inability to touch: frustration with evaluation task. H3 also predicts that when touch is unavailable for high-NFT subjects, a written description of instrumental (weight) but not autotelic (softness) material properties will decrease their frustration with the evaluation task compared with when nonhaptic written information (overall design) is provided. This was supported by the univariate three-way interaction (F( 1, 191) = 3.4, p < .05; Figure 1, Panel B). The frustration of high-NFT subjects decreased from 5.7 to 4.7 when instrumental haptic versus nonhaptic information was provided in written form (F( 1, 191) = 9.2, p < .05). However, providing autotelic written information did not decrease the frustration of those high in NFT (M = 5.0 and 5.4 for autotelic haptic and nonhaptic written information, respectively; F( 1, 191) = 1.7, p > .05). Again, for high-NFT subjects, there was no substitute for the direct haptic exploration of autotelic attributes. The open-ended frustration measure also supports this explanation. Of the 50 high-NFT subjects, 35 mentioned that touch would have helped in their evaluation.[ 4]
No change in frustration was expected for low-NFT subjects, because they were not expected to be frustrated by an inability to touch. However, an unexpected finding (but consistent with the confidence judgment result) was that frustration for low-NFT subjects also decreased when written instrumental haptic information was provided (M = 1.6 and
3.0 for instrumental haptic and nonhaptic written information, respectively; F( 1, 191) = 28.3, p < .05). Consistent with H3, written autotelic information did not affect low-NFT subjects' frustration (M = 2.8 and 2.2 for autotelic haptic and nonhaptic written information, respectively; F( 1, 191) = 1.9, p > .05).
Discussion of Study 1
Study 1 tested hypotheses pertaining to the interaction between the individual-based motivation for consumers to obtain haptic product information and their opportunity to do so, while investigating a compensatory mechanism for an inability to touch. Overall, high-NFT subjects were more confident and less frustrated when they could touch to evaluate products, whereas low-NFT subjects' confidence in their attitude judgments did not change on the basis of whether they could touch products.
In addition, written instrumental haptic information acted as a compensation mechanism for high-NFT subjects' inability to evaluate products through touch, but written autotelic haptic information did not serve this compensatory function. An unexpected finding was that written instrumental haptic information increased confidence and decreased frustration with the evaluation task for those low in NFT when they could not touch, but a written description did not compensate low-NFT subjects for autotelic haptic information.
For those high in NFT, the results indicate that a written description is less likely to compensate for touching a product when these consumers assess pleasurable haptic information, but a written instrumental haptic description did increase confidence and lower frustration. Similar results were obtained for low-NFT subjects for the compensatory effects of instrumental and noncompensatory effects of autotelic written haptic descriptions. One alternative explanation for the effects of the written description is the possibility that subjects were also able to examine the products visually. A visual assessment may provide a quick glance, which may result in the extraction of broad but coarse information about the haptic properties of an object (Klatzky, Lederman, and Matula 1993). Particularly for consumers who are less haptically motivated (low-NFT), a clear visual examination of the product (in the simulated retail display case) may have compensated for a lack of touch, thus increasing their confidence and lowering their frustration level. For low-NFT subjects, a visual examination may have been sufficient to assess the product's haptic properties, thus reducing the need for direct perceptual encoding by haptic exploration.
Overview
We conducted Study 2 to investigate whether a visual examination of the product through a picture would compensate subjects low in NFT when they could not touch, as predicted by the visual preview model (Klatzky, Lederman, and Matula 1993). In this study, pictures of the products and/or written descriptions were used, rather than actual products. Study 2 was a 2 (levels of NFT)2 (visual cue/yes or no)2 (type of product description, haptic/nonhaptic)2 (type of product) design in which the first factor was measured, the next two were between subjects, and the last factor was within subjects.
Procedure
A total of 171 subjects participated individually in the study. Subjects were informed that they were needed for a study on product evaluation and were provided with stimulus booklets. Some subjects evaluated a sweater and a cellular telephone with a visual cue (a picture) and a written description, but for other subjects, only written descriptions of the products were provided. Each product was on a separate page, and subjects were allowed to self-pace through the product descriptions. The order of product presentation was counterbalanced with nonsignificant order effects (p > .05). When they finished reading the descriptions, subjects were instructed to complete the dependent measures and the NFT scale without reviewing the stimuli.
Independent Variables
Visual cue. Black-and-white pictures were selected to present a full view of the product, but to be neutral with respect to the attributes expressed in the product descriptions (for examples of the stimuli, see Appendix B). The picture of the cellular telephone was a front view without any background and was obtained from a manufacturer's Web site. Similarly, the picture of the sweater was captured from a retailer's Web site and also provided a full view of the product. All identifying brand information was digitally removed.
Haptic or nonhaptic product descriptions. The same four product descriptions from the first study were used in Study 2 (see Appendix A).
NFT. We measured NFT with the 12-item scale (reliability = .88). The entire range was represented in this sample. Low and high NFT were determined by a median split: Subjects scoring above the median (a score of 8) were classified as high NFT (n = 83), and those scoring at or below the median were classified as low NFT (n = 87).
Measures
We measured attitude confidence (r = .98) and frustration with the task as in Study 1. Postexposure ratings of product beliefs were also included at the end of the questionnaire. Subjects rated the cellular telephone in terms of weight (1 = "very heavy," 5 = "very light") and quality (1 = "bad quality," 5 = "good quality"). For the sweater, ratings were provided for softness (1 = "rough texture," 5 = "soft texture") and quality (1 = "bad quality," 5 = "good quality").
Results
Visual compensation on confidence and frustration. If a visual preview can compensate for touch for low-NFT subjects, we expected that those low in NFT would be significantly more confident in their judgment when a picture was provided with an instrumental haptic description versus when no picture was available (H4). The four-way univariate interactions for confidence in the judgment task (F( 1, 162) = 22.4, p < .05) and frustration with the task (F( 1, 162) = 13.9, p < .05) were both significant. (Given the number of tests that we conducted, to conserve space we report the results only for confidence in judgment; the results for frustration parallel the findings reported for confidence.)
When instrumental written haptic information was provided (a written description of cellular telephone weight), there was a positive effect of visual information on confidence in the judgment task. More specifically, when a picture supplemented a written instrumental haptic description, confidence was increased for both low-and high-NFT subjects (low-NFT: M = 6.4 and 3.8 for picture versus no picture; F( 1, 162) = 95.3, p < .05; high-NFT: M = 4.5 and 3.7; F( 1, 162) = 8.4, p < .05; Figure 2, Panel A). In contrast, when a written instrumental haptic product description was not available (no weight information, just overall design information), there was an incremental picture compensatory effect on confidence, but only for low-NFT and not for high-NFT subjects (low-NFT: M = 5.1 and 2.2 for picture versus no picture; F( 1, 162) = 118.3, p < .05; high-NFT: M = 3.2 and 3.3; F( 1, 162) = .4, p > .05). These results for instrumental haptic information provide support for the compensatory effects of a picture, as H4 predicts, for low-NFT subjects, but we find that a picture provides a compensatory effect for high-NFT subjects as well (inconsistent with H4).
When autotelic written information was provided (a description of sweater softness), there was a positive effect on confidence in the judgment task when a picture was provided for low-NFT but not (as expected) for high-NFT subjects (low-NFT: M = 6.2 and 2.2 for picture versus no picture; F( 1, 162) = 450.1, p < .05; high-NFT: M = 3.7 and 3.5; F( 1, 162) = 2.0, p > .05; Figure 2, Panel B). In addition, this effect also occurred when nonhaptic written information was provided (a description of overall design). Low-NFT but not high-NFT subjects' confidence was increased when a picture was added (low-NFT: M = 5.9 and 2.1 for picture versus no picture; F( 1, 162) = 204.1, p < .05; high-NFT: M = 4.1 and 3.5; F( 1, 162) = 2.4, p > .05; Figure 2, Panel B). In summarizing results for the visual haptic compensation mechanism, consistent with H4, if a picture was provided with or without an instrumental haptic written description, we found that low-NFT subjects were more confident in their judgment. In contrast, the picture increased confidence as well for high-NFT subjects, but only if a written instrumental haptic description accompanied the picture. In addition, there was a compensatory effect of the picture for autotelic information as well, but only for low-NFT subjects. A picture increased confidence for low-NFT subjects whether or not a written autotelic haptic description was provided. These results imply that adding a picture of the product provides a unique contribution to confidence, but it is not clear how this occurs. Some additional results bear insight on this process and contribute to a better understanding of the differential effects observed between low-and high-NFT subjects.
Postexposure ratings indicated that a written haptic description increased confidence by increasing beliefs for the haptically described instrumental property. When a picture and an instrumental haptic description were provided, compared with when a picture and a nonhaptic description were provided, both low-and high-NFT subjects rated the cellular telephone as lighter (low-NFT: M = 4.5 and 3.4 for instrumental haptic [weight] versus nonhaptic [overall design] descriptions; F( 1, 162) = 14.6, p < .05; high-NFT: M = 4.1 and 3.2; F( 1, 162) = 8.9, p < .05, see Table 2). In comparison, when no picture was provided, a written haptic instrumental description increased beliefs only for low-NFT but not for high-NFT subjects (low-NFT: M = 4.2 and 3.5 for instrumental haptic versus nonhaptic description; F( 1, 162) = 7.4, p < .05; high-NFT: M = 4.3 and 4.4; F( 1, 162) = .01, p > .05, Table 2). That leaves the question whether the picture incrementally increased instrumental beliefs to reflect the pattern of picture effects reported for confidence in judgment. The results do not support this explanation. All comparison tests (as were conducted for confidence) for the picture versus no-picture conditions were insignificant (p > .05). Therefore, although the addition of the picture incrementally increased confidence beyond the effect of the instrumental written description, the picture's effect did not occur through increasing beliefs about the degree to which the product possessed the instrumental haptic property. Rather, the effects appear to occur through inducing additional inferential processing about the product, but only for low-NFT subjects.
For low-NFT subjects and the provision of an instrumental haptic description, postexposure ratings of product quality were increased through a picture (M = 4.6 and 3.3 for picture versus no picture; F( 1, 162) = 60.1, p < .05; Table 3), and this effect also occurred if no instrumental haptic description was provided (M = 4.7 and 3.4 for picture versus no picture; F( 1, 162) = 57.1, p < .05; Table 3). Thus, the use of the picture increased inferences of product quality, regardless of whether there was an instrumental written haptic description, which mirrors the pattern of results obtained for confidence in judgment.
In comparison, for high-NFT subjects and the provision of an instrumental haptic description, a picture increased product quality beliefs only when a haptic description of an instrumental property was provided (M = 3.7 and 2.7 for picture versus no picture with an instrumental haptic description; F( 1, 162) = 31.5, p < .05; M = 3.8 and 3.9 for picture versus no picture without an instrumental haptic description; F ( 1, 162) = .4, p > .05; Table 3). These results imply that it was not the picture that generated increased product quality beliefs for high-NFT subjects; rather, these subjects inferred product quality from the written description (or the pattern of effects would be the same whether a written instrumental haptic description was present or not, as was found for low-NFT subjects). However, these results also indicate that product quality judgments are enhanced for high-NFT subjects when a picture is present. These conclusions are further supported by the pattern of effects when we make the same comparisons for autotelic beliefs and product quality when an autotelic haptic description was provided.
Postexposure ratings indicated that a written haptic description increased confidence by increasing beliefs for the haptically described autotelic property but mirrored the confidence results only for low-NFT subjects. When a picture and an autotelic haptic description were provided, compared with when a picture and a nonhaptic description were provided, low-NFT but not high-NFT subjects rated the sweater as softer (low-NFT: M = 3.6 and 2.8 for autotelic haptic [softness] versus nonhaptic [overall design] descriptions; F( 1, 162) = 7.6, p < .05; high-NFT: M = 3.8 and 3.6; F( 1, 162) = .6, p > .05; see Table 2). Also, when no picture was provided, a written haptic autotelic description increased beliefs again only for low-NFT but not for high-NFT subjects (low-NFT: M = 4.0 and 3.2 for autotelic haptic versus nonhaptic descriptions; F( 1, 162) = 8.2, p < .05; high-NFT: M= 4.3 and 4.4; F ( 1, 162) = .4, p > .05; Table 2).
We next examine the pattern of results for the product quality ratings. Low-NFT subjects were more likely to rate product quality higher when a picture was provided (M = 4.6 and 2.9 for picture versus no picture with a written autotelic haptic description; F( 1, 162) = 93.8, p < .05; M = 4.9 and 3.2 for picture versus no picture without an autotelic haptic description; F( 1, 162) = 88.1, p > .05; Table 3). In contrast, high-NFT subjects were not affected by the presence or absence of a picture when judging product quality for an autotelically described product (M = 2.7 and 2.6 for picture versus no picture with an autotelic haptic description; F( 1, 162) = .3, p > .05; M = 3.8 and 3.8 for picture versus no picture without an autotelic haptic description; F( 1, 162) = .03, p > .05; Table 3). These results help explain the only compensatory increase in confidence that resulted for an autotelic product-based judgment. As in the results for confidence in judgment, we found that a picture compensated for lack of touch by encouraging low-NFT subjects to make inferences about product quality. Consistent with the visual preview model (Klatzky, Lederman, and Matula 1993), for low-NFT subjects, a visual examination of the product through a picture can support both the extraction of haptic beliefs and the inference of higher product quality.
Studies 1 and 2 revealed that when touch was unavailable, high-NFT subjects were less confident in their evaluations and more frustrated with the task than when touch was available. We also found that a written description of instrumental haptic information compensated high-NFT subjects for an inability to touch, but a written description did not compensate high-NFT subjects for autotelic haptic exploration. Also, low-NFT subjects were not necessarily less confident or more frustrated when touch was unavailable, but they required a visual cue or picture of the product. If the picture was not present, low-NFT subjects were significantly more frustrated and less confident in their evaluations. We conclude that consistent with the premise underlying the visual preview model, for low-NFT consumers, visual information compensates for actual haptic exploration. The nature of this visual compensation mechanism differs significantly in the manner in which it is processed by high-versus low-NFT consumers. For those more haptically motivated (high-NFT), when effective, the visual cue is secondary to the written description that emphasizes haptic instrumental information, but the picture incrementally reinforces the description. For less haptically motivated consumers (low-NFT) and for both instrumental and autotelic haptic attributes, the picture not only reinforces the written description but also becomes a primary compensation mechanism when no written haptic information is provided. Under these conditions, the picture increases inferential processing by low-NFT consumers, which leads to higher overall product quality beliefs.
Limitations of our research are directly related to the experimental methodology we used in conducting our studies and to several future research opportunities. Our studies used a more homogeneous set of undergraduate college students. Although we pretested products for their appropriate use by this sample, differences in the degree to which a more general population may embrace or eschew haptic information are yet to be assessed. Our sampling of products was also limited in that we used only products that were high in haptic attribute salience; further research should extend this by examining other products, particularly those more moderate or potentially more ambiguous in their salience of haptic information. This also applies to incorporating the study of more multisensory-oriented leisure consumption environ-ments--for example, museum and theme park patronage, where more hedonic motivations may dominate the haptic experience.
The issue of extending the study of autotelic and instrumental touch is also germane to our experiments. In our studies, we manipulated this distinction between products and used different product attributes across these product categories. We tested our product categories along several dimensions and judged them equal in the importance of haptic information for product evaluation. The attributes were rated equivalent in importance both within and across the haptic and nonhaptic descriptions we used. The haptic descriptions were also rated to be equivalent in their persuasiveness across these product categories. However, as Lynch (1982) has noted, potential background variables differentially associated with haptic information across these product categories might have confounded our results. We know of no alternative explanation for our findings, but further research should consider manipulating the presentation of instrumental and autotelic haptic information within product categories to better control for any potential background treatment interactions. The controlled setting we used in our laboratory environment was also designed to isolate the influence of haptic information on product evaluations more effectively, but further research should also extend the situation by observing consumers in a natural setting, such as in a grocery store as they examine such haptically salient products as fruits, vegetables, breads, and pastries.
Is touch important in marketing, and if so, for which products and for which consumers and under what conditions? This research found evidence that product characteristics, individual difference characteristics, and situational characteristics all affect the motivation to obtain haptic product information.
Situational Characteristics, NFT, and Haptic Behavior
High-versus low-NFT subjects were consistently more frustrated and less confident in their attitude toward a product if they could not touch a product during evaluation. We also found evidence for the visual preview model. A visual cue or picture of the product compensated low-NFT subjects for instrumental and, on a more limited basis, autotelic haptic touch when they could not touch products. In contrast, high-NFT subjects were only partially compensated for actual touch. A written description of instrumental haptic information serves a compensatory role, but a written description does not compensate those high in NFT for autotelic touch information.
Theoretical Implications
Motivation to touch products and types of haptic information. These differential effects for the provision of instrumental and autotelic haptic information have implications for expanding the understanding of the role of inferential processing in persuasion. The process underlying the effect of the visual compensatory mechanism varies on the basis of the motivation of the consumer to obtain haptic information and the availability of a written haptic product description. Low-NFT subjects were more likely to increase confidence in their judgment by using both haptic and nonhaptic information to infer product quality, whereas high-NFT subjects were more likely to form their beliefs through written haptic descriptions. However, this effect was dependent on whether a picture of the product was available. In the absence of a picture, low-NFT subjects increased their confidence through a written description of an instrumental haptic property. In comparison, when a picture was present, low-NFT subjects were induced by the picture to engage in inferential processing, which led to an increased assessment of product quality. The text in the nonhaptic description conveyed information about overall product design and likely contributed to the product quality inference. However, beyond this, the picture induced even greater inferential processing, and this extended to autotelic products, an effect not found for high-NFT subjects.
These findings expand our current understanding of the conditions in which consumers may spontaneously make inferences about product quality. Research has demonstrated (Kardes 1988; Stayman and Kardes 1992) that implicit implications are inferred only by high-but not by low-involvement consumers, and the former also hold more accessible brand attitudes. In contrast, low-NFT consumers, though less haptically motivated, spontaneously made inferences about product quality. In addition to the internal motivation of a highly involved processor of information, our research suggests that an external compensatory aid can induce less motivated consumers to engage in inferential processing, form an implicit conclusion regarding product quality, and be more confident in their judgments. Our research falls short of determining whether this more confident judgment leads to more accessible judgments. Further research is needed to assess whether the explicit versus implicit beliefs formed for haptic versus nonmaterial properties vary in their accessibility. In addition, further research should explore other conditions (e.g., mental imagery, repetition) under which both highly motivated and less motivated consumers may be externally induced to engage in spontaneous inferences.
Compensatory mechanisms for haptic information. If a consumer is aware that products in a category differ with respect to a haptic attribute, but there is no opportunity for direct experience, it is possible that a meaningful extrinsic nonhaptic cue (Olson and Jacoby 1972) could moderate the motivation to obtain haptic information or touch the product. A nonhaptic cue, such as a brand name (Erdem and Swait 1998), a return policy (Kirmani and Rao 2000), a warranty (Boulding and Kirmani 1993), or a low price (Dawar and Sarvary 1997), may also serve as a compensation mechanism that lessens or eliminates a consumer's motivation to obtain haptic information. Research on the role of these mechanisms would prove useful to the understanding of how these obstacles can be mitigated in nontouch media.
Relationship between visual and haptic processing. When high-and low-NFT subjects examined the sweater under the clear retail display case, subjects low in NFT were less frustrated at the inability to touch. The visual glance may have retrieved suitable information for those low in NFT yet revealed to those high in NFT that additional haptic information was available and desired. It is possible that for high-NFT consumers, a higher-resolution color picture or a three-dimensional representation, such as could be found on a Web site or in a catalog, would result in more frustration at an inability to touch the product than would a picture or a sketch that is not quite as clear or detailed. It would be useful to expose high-and low-NFT subjects to catalog-type sketches compared with referents of products that vary in their resolution and subsequently determine their effects on subjects' confidence in judgment, frustration with the task, and intention to purchase.
Managerial Implications
Internet implications and other nonstore retailing. In the increasing presence of nontouch media such as the Internet and catalog shopping, this research suggests that sales of product categories that consumers perceive to not differ on material properties are more likely to flourish through non-touch media. There is some evidence that this has occurred for sales over the Internet. Amazon.com, one of the most successful Internet retailers, offers books, music, and videos, which are all product categories for which haptic attributes are not diagnostic among choices. Similarly, BusinessWeek has detailed products that sell well over the Internet and estimated their expected sales (Hof, McWilliams, and Saveri 1998). Products selling better than others over the Internet include financial services, entertainment, travel, personal computer hardware and software, books and music, tickets for events, and clothing and apparel. Of all the categories, only clothing and apparel vary appreciably on material properties. It has been predicted that clothing sales on the Internet would be one of the slowest growing categories (Hof, McWilliams, and Saveri 1998) and that current sales skew toward unfitted clothing items.
In addition to specific product categories in which consumers would be less willing to forgo prepurchase touch, our research demonstrates that certain consumers would also be less willing to forgo prepurchase touch. High-NFT shoppers may be difficult to convert to users of nontouch media; therefore, an integrated "bricks and clicks" strategy may be necessary. There is some anecdotal evidence of this, as direct marketer Gateway Computers has opened retail stores and the television-based retailer Home Shopping Network has announced the opening of a store at Mall of America (Bloomington, Minn.). Both of these additions were made, ostensibly, to provide consumers with the opportunity to experience products directly before purchasing them.
Product packaging and print advertising implications. This research also has implications for product packaging and advertising. Because barriers to touch are especially frustrating to shoppers who are high in NFT, product packaging that enables some haptic exploration may be worthwhile. This is currently used in packaging for some pens. The package for Paper Mate Dyna-Grip pens has a portion of the package cut out, which enables consumers to feel the grip of the pen. This type of packaging would enable high-NFT shoppers to feel less frustrated and more confident regarding their attitude toward the product.
Insight from this research could also be applied to print advertisements. A one-page advertisement in Fortune magazine (December 29, 1997) stated, "Go ahead: tear this page in half." Attempting to tear the page conveys the product benefit of the strength of the paper (the page is impossible to tear), which may improve the consumer's attitude toward the product. The page in the advertisement is composed of a material used to make overnight courier envelopes. Even if no haptic product attribute information is conveyed, an advertisement using pleasurable haptic feedback may influence the attitude toward the ad and the product.
Conclusion
This research reveals insights into the nature of product touch in marketing. We found that three primary factors interact to influence whether a consumer will be motivated to touch a product to obtain haptic information before purchase. Specifically, products differ in the extent to which they possess salient material properties, consumers differ in their motivation to obtain haptic information (NFT), and elements in the situation may affect the salience of haptic properties of a product. With the growth of nontouch media, the sense of touch is especially important to investigate. Although eventually there may be a substitute for the sense of touch in the nontouch world, the sense of touch may be the most complicated sense to replicate. "Estimates are that it will be fifteen years before a good replication of touch is perfected" (CNN: Moneyline 2000). In the meantime, it is important to understand the effects of this sense on consumers. Much is left to study, but our hope is that this research will provide a foundation for subsequent research on the role of haptic information in marketing.
This research was funded in part by a grant to the first author from the Carlson School of Management, University of Minnesota.
The authors thank Jennifer Gregan-Paxton, University of Delaware; Akshay Rao, University of Minnesota; and the anonymous JM reviewers for their useful insights and suggestions.
1 In an initial study, we found that products possessing more salient material properties led to greater use of haptic information during product evaluation. Using three levels of salience of material properties and two replicates (high material property salience: sweater, tennis racket; medium material property salience: calculator, cordless telephone; low material property salience: cereal, toothpaste), we found that as the material property salience increased, consumers touched the products longer when evaluating and verbalized more haptic attributes. To conserve space, we eliminated this study from the article. To obtain more detailed results for this study, contact the first author.
2 Note that the valence of the attitude is not our focus in this research. Consistent with the availability valence model (Kisielius and Sternthal 1986), attitude toward the product could increase or decrease when touching is available versus not available depending on the valence of the accessed information during haptic exploration.
3 We define NFT as a preference for the extraction and use of information obtained through the haptic system. We operationalized the construct through a 12-item scale that was tested in seven studies. Construct validity of the scale was supported through its convergent validity with a measure of the use of touch for product evaluation. Discriminant validity was supported through the absence of a relationship between the scale and measures of social desirability, need for cognition, and need for evaluation. Nomological validity was supported though a series of tests, including a negative correlation between the scale and shopping through catalogs and the Internet and a positive correlation with a measure of experiential shopping behavior. The 12 items of the NFT scale, with endpoints of "strongly agree" (+3) to "strongly disagree" (-3), include the following:
Touching products can be fun.
I place more trust in products that can be touched before purchase.
I like to touch products even if I have no intention of buy ing them.
4. I feel more comfortable purchasing a product after physically examining it.
5. When browsing in stores, I like to touch lots of products.
6. When walking through stores, I can't help touching all kinds of products.
7. I feel more confident making a purchase after touching a product.
8. If I can't touch a product in the store, I am reluctant to purchase the product.
9. The only way to make sure a product is worth buying is to actually touch it.
10. When browsing in stores, it is important for me to handle all kinds of products.
11. I find myself touching all kinds of products in stores.
12. There are many products that I would only buy if I could handle them before purchase.
For further details on these results, see Peck and Childers (2002). Note that these 12 items are copyrighted with all rights reserved. Permission to use the NFT scale must be obtained in writing from the authors.
Of the 35 subjects, 21 were in the nonhaptic description condition, and 14 were in the haptic description condition. Although not a significant difference, this suggests that some compensation was provided by the written description.
Information Type
Product Haptic Nonhaptic
Sweater Softness (autotelic) Overall design
Cellular telephone Weight (instrumental) Overall design
Picture
Haptic (Cellular
Telephone: Weight: Nonhaptic
Sweater: Softness) (Overall Design)
Weight of Cellular Telephone (1 = "heavy," 5 = "light")
Low NFT 4.5[a] 3.4[a]
High NFT 4.1[b] 3.2[b]
Softness of Sweater (1 = "rough," 5 = "soft")
Low NFT 3.6[d] 2.8[d]
High NFT 3.8 3.6
No Picture
Haptic (Cellular
Telephone: Weight: Nonhaptic
Sweater: Softness) (Overall Design)
Weight of Cellular Telephone (1 = "heavy," 5 = "light")
Low NFT 4.2[c] 3.5[c]
High NFT 4.3 4.4
Softness of Sweater (1 = "rough," 5 = "soft")
Low NFT 4.0[e] 3.2[e]
High NFT 3.6 3.2Notes: Numbers with the same superscript [ ] are significantly different at p <
Haptic Information (Instrumental,
Cellular Telephone: Weight; Nonhaptic Information
Autotelic, Sweater: Softness) (Overall Design)
Picture No Picture Picture No Picture
Quality of Cellular Telephone
Low NFT 4.6[a] 3.3[a] 4.7[b] 3.4[b]
High NFT 3.7[c] 2.7[c] 3.8 3.9
Quality of Sweater
Low NFT 4.6[d] 2.9[d] 4.9[e] 3.2[e]
High NFT 2.7 2.6 3.8 3.8Notes: Numbers with the same superscript [ ] are significantly different at p < .05.
GRAPHS: FIGURE 1: No-Touch Condition: Confidence in Evaluation and Frustration by NFT, Type of Haptic Information, and Type of Written Description: A: Confidence, B: Frustration
GRAPHS: FIGURE 2: Four-Way Interaction for Confidence in Evaluation by NFT, Type of Haptic Information, Visual Cue, and Type of Written Description: A: Cellular Telephone (Instrumental Haptic: Weight; Nonhaptic: Overall Design); B: Sweater (Autotelic Haptic: Softness; Nonhaptic: Overall Design)
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Logicall Cell-Phone (Haptic Version--Weight Attribute)
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Our many features include voice-activated dialing allowing you hands-free recognition of up to 20 names. Two number dialing capability for separating business and personal use. The illuminated semi-symbol keypad protects against accidental power-off and call initiating. The easy to ready display has auto-word wrapping and full screen cursor editing. Colors include: Urban Grey, Metropolitan Blue and Crustacean Red. Available for overnight delivery, just call 1-800-436-CALL or visit our web site at: www.Logicall.com today!
Logicall Cell-Phone (Nonhaptic Version--Overall Design Attribute)
In a downtown office, cruising down the freeway, the lightweight Logicall Phone gives you access to digital and analog wireless networks nationwide, so your calls, voice mail and text messages always make it through.
The phone's exterior is based upon the popular arched design and its interior contains the most up-to-date CDMA technology. Dynamic display layout adjusts automatically for best viewing with full graphics for animation and web menus. Our user interface is easily and quickly navigated and has automatic time zone updating.
Our many features include voice-activated dialing allowing you hands-free recognition of up to 20 names. Two number dialing capability for separating business and personal use. The illuminated semi-symbol keypad protects against accidental power-off and call initiating. The easy to ready display has auto-word wrapping and full screen cursor editing. Colors include: Urban Grey, Metropolitan Blue and Crustacean Red. Available for overnight delivery, just call 1-800-436-CALL or visit our web site at: www.Logicall.com today!
Baxter Sweater (Haptic Version--Softness Attribute)
This classic sweater is a blend of the finest 82% Merino wool imported from Italy and 18% nylon, which adds strength to the blend. The fabric is richly textured with a delicious multi-ply weave. Our sweaters go through an extra combing process that aligns the fibers for a plush feel. The lofty texture feels wonderful against your skin and prevents piling. The collar is a 22 rib-knit that's doubled over for extra comfort and durability. Rib knit cuffs and bottom include elastomer for good shape retention. Colors are Sunset Coral, True Blue and Silver Heather. Machine washable. Made in USA.
Baxter Sweater (Nonhaptic Version--Overall Design Attribute)
This classic sweater is a blend of the finest 82% Merino wool imported from Italy and 18% nylon, which adds strength to the blend. Our design is fully fashioned for a naturally comfortable fit. The breathe-easy stitching has airy pores knit right into the body structure. Refined set-in armholes add to this sweater's neat profile. This classic sweater has a collar which is a 22 rib-knit that's doubled over for extra comfort and durability. Rib knit cuffs and bottom include elastomer for good shape retention. Colors are Sunset Coral, True Blue and Silver Heather. Machine washable. Made in USA.
In a downtown office, out on the farm, cruising down the freeway, the Logicall Phone gives you access to digital and analog wireless networks nationwide, so your calls, voice mail and text messages always make it through.
The phone's exterior is based upon the popular arched design and its interior contains the most up-to-date CDMA technology. Dynamic display layout adjustments automatically for best viewing with full graphics for animation and web menus. Our best interface is easily and quickly navigated and has automatic time zone updating.
Our many features include voice-activated dialing allowing you hands-free recognition of up to 20 names. Two numbering dialing capability for separating business and personal use. The illuminated semi-symbol keypad protects against accidental power-off and call initiating. The easy to read display has auto-word wrapping and full screen cursor editing. Colors include: Urban Grey, Metropolitan Blue, and Crustacean Red. Available for overnight delivery, just call 1-800-436-CALL or visit our web site at: www. Logicall.com today!
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Baxter Classic Sweater for Men and WomenThis classic sweater is a blend of the finest 82% Merino wool imported from Italy and 18% nylon, which adds strength to the blend. Our design is fully fashioned for a naturally comfortable fit. The breathe-easy stitching has airy pores knit right into the body structure. Refined set-in armholes add to this sweater's neat profile. This classic sweater has a collar which is a 22 rib-knit that's doubled over for extra comfort and durability. Rib knit cuffs and bottom include elastomer for good shape retention. Colors are Sunset Coral, True Blue and Silver Heather. Machine washable. Made in USA.
Men's M 38-40 L 42-44 XL 46-48
4101-2G14
Women's S 6-8 M 10-12 L 14-16 XL 18-20
4101-3G 1XPHOTO (BLACK & WHITE)
PHOTO (BLACK & WHITE)
~~~~~~~~
By Joann Peck and Terry L. Childers
Joann Peck is Assistant Professor of Marketing, University of Wisconsin-Madison. Terry L. Childers is the Gatton Endowed Chair in Interactive Marketing, Gatton College of Business and Economics, University of Kentucky.
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Record: 190- Trading Off Between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis. By: Mizik, Natalie; Jacobson, Robert. Journal of Marketing. Jan2003, Vol. 67 Issue 1, p63-76. 14p. 1 Diagram, 4 Charts, 3 Graphs. DOI: 10.1509/jmkg.67.1.63.18595.
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Trading Off Between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis
Firms allocate their limited resources between two fundamental processes of creating value (i.e., innovating, producing, and delivering products to the market) and appropriating value (i.e., extracting profits in the marketplace). Although both value creation and value appropriation are required for achieving sustained competitive advantage, a firm has significant latitude in deciding the extent to which it emphasizes one over the other. What effect does strategic emphasis (i.e., emphasis on value creation versus value appropriation) have on firm's financial performance? The authors address this issue by examining the effect that shifts in strategic emphasis have on stock return. They find that the stock market reacts favorably when a firm increases its emphasis on value appropriation relative to value creation. This effect, however, is moderated by firm and industry characteristics, in particular, financial performance, the past level of strategic emphasis of the firm, and the technological environment in which the firm operates. These results do not negate the importance of value creation capabilities, but rather highlight the importance of isolating mechanisms that enable the firm to appropriate some of the value it has created.
Marketing strategy is concerned with creating sustained competitive advantage, which in turn leads to superior financial performance. Two processes, which combine and interact, are fundamental to achieving this outcome. The first process involves the creation of customer value (i.e., innovating, producing, and delivering products to the market); the other focuses on appropriating value in the marketplace (i.e., extracting profits). Value creation is a cornerstone of marketing. The marketing concept identifies the customer as the primary focus and the force that defines the scope and the purpose of a business enterprise. It postulates that for an organization to achieve an advantage, it must create superior value for its customers (Drucker 1954).
Value creation alone, however, is insufficient to achieve financial success. A second necessary process involves a firm's ability to restrict competitive forces (e.g., erect barriers to imitation) so as to be able to appropriate some of the value that it has created in the form of profit. Indeed, firms have little incentive to engage in value creation in the absence of "isolating mechanisms" that prevent the immediate dissipation of profits associated with a value creating initiative (e.g., an innovation). Firms that do not have the capabilities to restrict competitive forces are unable to appropriate the value they have created. Instead, competitors and customers will claim it (Ghemawat 1991). Factors as varied as reputation and brand effects, customer switching costs, advertising, and network externalities, for example, are isolating mechanisms that are central considerations to marketing managers.
Firms are faced with the strategic task of balancing the two processes in their marketing strategies and determining an adequate amount of support for each. Firms need to simultaneously develop or acquire value creation capabilities and capabilities that facilitate value appropriation. These two sets of capabilities require substantial resource commitments and management attention. The task of allocating limited organizational resources between value creation and value appropriation capabilities necessitates strategic prioritizations and trade-offs. As such, we define strategic emphasis as the relative emphasis a firm places on value appropriation relative to value creation. A fundamental issue facing managers is deciding how a firm chooses to compete (Day 1994). Strategic emphasis is a central aspect of this choice.
Research in marketing has extensively explored how acquiring resources and skills and developing different capabilities affects financial performance (see, e.g., the meta-analysis by Capon, Farley, and Hoenig [1990]). Although less study has been directed toward assessing the relative benefits of emphasizing one capability over another, prior research has highlighted various types of strategic and tactical trade-offs that firms make. For example, Porter (1996) considers the trade-offs involved in positioning strategies, Miles and Snow (1978) propose alternative strategic archetypes, Boulding and Lee (1992) address the issue of marketing mix specialization versus diversity, and Ettlie and Johnson (1994) note the trade-off between focusing on customers and processes. Although the inherent trade-off between value appropriation and value creation capabilities has been acknowledged (e.g., March 1991), research to date has not explored what effect strategic emphasis has on financial performance. Our study addresses this issue by examining the effect shifts in strategic emphasis (i.e., the emphasis on value appropriation versus value creation capabilities) have on stock return.
Our analysis makes use of movements in the {[advertising expenditures - research and development (R&D) expenditures]/assets} ratio as an indicator of shifts in strategic emphasis. Although other factors also influence value appropriation and value creation, movements in this measure can be expected to provide information about shifts in strategic emphasis related to value appropriation versus value creation. That is, increases in the ratio will tend to be associated with increased emphasis on value appropriation, and decreases in the ratio will tend to be associated with increased emphasis on value creation. Empirically, we find that the stock market reacts favorably when a firm increases its emphasis on value appropriation rather than on value creation. However, this effect is moderated by firm and industry characteristics, in particular, financial performance, the past level of strategic emphasis of the firm, and the technological environment in which the firm operates. These results do not negate the importance of value creation capabilities, but rather highlight the importance of isolating mechanisms that enable the firm to appropriate some of the value it has created.
Firms engage in innovative activities that lead to creation of societal value, that is, the total social surplus arising from the difference between the utility that consumers derive from the product and the costs of producing it. The societal value will end up being captured by three major players in the market: The innovating firm will appropriate some of the societal value it has created in the form of economic profit, the customers will claim a portion of it in the form of consumer surplus, and other firms (competitors and noncompetitors) will get a portion of it through profits stemming from imitation and development cost savings (Mansfield et al. 1977).
Considerable variation exists across innovations as to the proportion of the surplus captured by each of the major players. The polio vaccine is perhaps the most extreme example of an innovation that created tremendous societal value, but where the innovator did not appropriate any surplus. Jonas Salk did not patent the vaccine (stating a desire not to personally profit from it) but rather wished the vaccine to be disseminated as widely as possible. As such, consumers claimed the entire surplus from the innovation.
Even firms with a desire for profit often do not profit from their innovations. For example, the CT scanner was invented by EMI Ltd., but the firm's inability to profit from the innovation led to its takeover around the same time the inventors were receiving the Nobel Prize in Medicine. Competitors and consumers claimed the surplus generated by the innovation. However, it is the hope of realizing profits that motivates firms to innovate. Indeed, countless examples exist in which a firm captured considerable surplus from its innovation. Dupont with Teflon, G.D. Searle with NutraSweet, Microsoft with Windows, and Pfizer with Viagra, for example, were all able to appropriate a substantial proportion of the societal value created by their innovations.
As such, both value creation and value appropriation capabilities are required for achieving sustained competitive advantage (Figure 1). A firm, however, has significant latitude in deciding the extent to which it emphasizes one set of capabilities as opposed to the other. They both shape the firm's competitive advantage (Ghemawat 1991; Rumelt 1987). Value creation influences the potential magnitude of the advantage; value appropriation influences the amount of the advantage the firm is able to capture and the length of time the advantage persists. Because firm value depends on both the magnitude and the persistence of advantage, both processes influence financial performance. As such, they both complement and serve as imperfect substitutes for each other.
Strategic Emphasis: Trading Off Between Value Creation and Value Appropriation Capabilities
Firms divide their limited resources and attention between the two fundamental processes of creating and appropriating value. As a result, trade-offs occur between developing customer-value creation capabilities and developing value appropriation capabilities. A firm is forced to prioritize its resources between these alternative uses according to the way it has chosen to compete.
At one end of the spectrum, a firm may choose to compete primarily on the basis of value creation. It constantly moves ahead and innovates as competition erodes the profits from its previous initiatives. Alternatively, a firm can choose to fiercely defend its position in the market against competition by erecting barriers to imitation through, for example, brand-based advertising. In this case, a firm attempts to lengthen the time its advantage persists.
Most companies avoid the extremes and strive to choose a strategy that balances sufficient support for value creation efforts with adequate investments in capabilities that facilitate the appropriation of value. Yet differences in strategic emphasis exist among firms. Although industry characteristics shape the options available to the firm, even within the same industry, firms will take different courses of action reflected in different levels of strategic emphasis.
Consider, for example, ethical drug companies. Value creation, such as the development of new drugs, is central to success for firms in the industry. However, companies vary in the degree to which they emphasize value creation relative to value appropriation. For example, as the patent protection for a drug ends and generic clones enter the market, many firms discontinue support for the drug and focus on new innovation and the remaining patent-protected products. Alternatively, other drug companies place more emphasis on value appropriation. For example, Johnson & Johnson uses an umbrella brand for its products and successfully competes with generic drug manufacturers on the basis of superior brand image after the patent protection expires.
Various organizational resources and capabilities (i.e., technological, financial, physical, legal, human, organizational, informational, and relational) influence value creation and value appropriation. Most resources cannot be exclusively classified as pertaining just to value creation or to value appropriation: They influence both.
Yet two elements have been consistently highlighted in prior research as central to the value creation and value appropriation processes. That is, a firm's technology capabilities driven by R&D expenditures have been linked to value creation, whereas a firm's ability to differentiate its offering through advertising has been linked to value appropriation.
Technology in the Value Creation Process
Schumpeter (1942, p. 132) discusses value creation activities as "to reform or revolutionize the pattern of production by exploiting an invention, or more generally, an untried technological possibility for producing a new commodity or producing an old one in a new way, by opening up a new source of supply of materials or a new outlet for products, by reorganizing an industry." As such, value creation uses various organizational resources and encompasses a wide range of activities. Yet it is the innovations resulting from R&D that have received the most attention as a cornerstone of value creation. Firms engage in R&D and build technological capabilities to generate superior products and improvements in the production and distribution processes. A firm uses its technological capability to build a new solution and to answer and meet new needs of the users (Gatignon and Xuereb 1997).
Value is created both through product innovations used by firms and/or households and through process innovation (Mansfield et al. 1977). An extensive literature in economics, stimulated by the work of Solow (1957), has documented a significant positive effect of R&D on economic growth and productivity. Some of the estimates from initial research in the area serve as useful benchmarks. For example, Denison (1962) reports that approximately 40% of the total increase in per capita national income was attributable to technological change and conjectures that about one-fifth of this amount stemmed from "organized R&D." Mansfield and colleagues (1977) estimate the median social return to R&D at 56%. Although estimates vary, Griliches (1995) notes that all recent studies of R&D continue to report significant social returns from it.
A great deal of interest has been devoted to the gap between the societal value created and the profits appropriated by the innovating firm. At issue is that the returns realized by the innovating firm may bear little relation to the commercial success of the product or process it introduces. In theory, patents provide a solution to the problem of imperfect appropriability. However, in practice, patent protection has proved to offer only limited effectiveness. Competitors can "invent around" the patent. Levin and colleagues (1987) report that managers view other mechanisms as much more effective than patents in appropriating the returns from innovation (e.g., in only 4% of the industries surveyed did managers view patent protection as highly effective). In particular, marketing activities, such as advertising, were viewed as central isolating mechanisms and far more effective than patents in capturing advantages generated by R&D activities.
Advertising in the Value Appropriation Process
Just as there does not exist a single organizational factor that uniquely defines value creation, no single capability or activity determines a firm's ability to appropriate value. Several different capabilities give rise to isolating mechanisms and influence the length of time a firm is able to earn economic profits. Accumulated assets, as varied as a loyal customer base and network externalities, serve as isolating mechanisms and influence the ability of competitors to dissipate a firm's advantage. One key component of value appropriation capability that is of particular concern to marketing managers relates to the effects of advertising.
Two polar views exist with respect to the role of advertising as an isolating mechanism. One view argues that advertising is anticompetitive (i.e., erects barriers to imitation by differentiating the firm's offering). The opposing view regards advertising as procompetitive, in that it provides information that serves to dissipate competitors' isolating mechanisms. Although the debate of the aggregate competitive effect of advertising is likely to be never-ending, both views suggest that a firm's advertising will improve its position by either lengthening its value appropriation opportunities or reducing the value appropriation opportunities of its competitors.
Of these two, the first--the ability of advertising to differentiate a firm's offering from that of competitors--has received the most attention (Chamberlin 1933). This ability is one of the central features governing brand strategy. For example, Aaker (1996) notes that a brand can serve as the foundation for meaningful differentiation, especially in contexts in which brands are similar with respect to product attributes. A brand can be a formidable barrier to imitation, making it difficult for competitors to copy and dissipate a firm's advantage. As such, brand-based differentiation serves to prolong a firm's advantage and is frequently used as an entry deterrence strategy (Bunch and Smiley 1992).
Indeed, the often-cited Advertising Age (1983) study reports that of 25 leading consumer brands of 1923, 19 still remained leaders 50 years later. Although the length of time this stability actually lasts is subject to question (Golder 2000), few question the durability of advantage enjoyed by well-established brands. This durability stems not from the product attributes, which are typically readily imitable, but from the differentiation sustained by advertising. Indeed, Golder (2000) highlights advertising as one of the key factors that separates market share leaders that maintain their advantage from those that do not. For example, in contrast to American Chicle, which attempted to maximize short-term profits by minimizing marketing expenditures, Wrigley invested in building its brand through a commitment to advertising.[ 1]
Empirical evidence regarding the effect of advertising on value appropriation capabilities (e.g., the persistence of profits) is sparse but consistent. The empirical results suggest a significant positive effect of advertising on persistence of profits (e.g., Kessides 1990; Mueller 1990). These findings reinforce the view that excess returns erode more slowly for firms advertising heavily. Thus, firm advertising facilitates value appropriation because it extends the duration of competitive advantage.
This is not to suggest that no advertising creates value. Rather, our contention is that the association of advertising with value creation is substantially weaker than the association between R&D and value creation. Indeed, in contrast to the substantial empirical literature highlighting the effect of R&D on economic activity, advertising expenditures have not been systematically linked to value creation. For example, Ashley, Granger, and Schmalensee (1980) conclude that advertising does not lead to increased economic activity, but rather follows it. This lack of association is consistent with the premise that a substantial amount of advertising is not directed at creating value, but rather toward other goals, in particular, value appropriation.
An Indicator of Strategic Emphasis
Within organizations, different projects and applications compete for the same scarce resources. In this internal competition for resources, the most essential and strategically appropriate applications win. Resources end up concentrated in the areas of the greatest perceived importance. Consequently, the strategy of a company is revealed in the allocation choices and the trade-offs it makes between the different possible applications of its resources. Indeed, past research (e.g., Harrison et al. 1991, 1993; Ittner, Larcker, and Rajan 1997; Ramaswamy 1997) has used resource allocation patterns to depict the underlying strategies of the organization.
Because advertising tends to have a greater association with value appropriation efforts and R&D has greater association with value creation, we expect the following indicator of strategic emphasis, which we label S9 to be correlated with strategic emphasis:
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Positive scores represent companies that have relatively stronger commitment to value appropriation-based marketing strategies and negative scores represent companies that have relatively stronger commitment to value creation- based strategies. Intertemporal increases in the S9 indicator will tend to depict an increasing emphasis on value appropriation, whereas decreases in the indicator will depict shifts toward greater emphasis on value creation. Because factors other than R&D and advertising affect strategic emphasis, it is possible that the S9 measure is only a weak indicator (i.e., the signal-to-noise ratio will be low). If this is the case, analysis based on S9 will be biased toward zero, and tests will have low power in uncovering a statistically significant effect. However, given the prominent role played by R&D and advertising in influencing strategic emphasis, we have reason to believe that shifts in S9 will indeed be indicative of shifts in strategic emphasis. Analysis of S9 characteristics appears to support this view.
We observe that the S9 indicator exhibits significant variation across different industries, among firms in the same industry, and over time for the same firm. For example, for the period 1980-99 the mean S9 for publicly traded firms in food industries (i.e., Standard Industrial Classification [SIC] codes 2000-2099) is .091. This indicates greater relative reliance on value appropriation capabilities. For instruments (i.e., SIC codes 3800-3841), the S9 mean of -.099 indicates greater relative emphasis on value creation capabilities. The difference in the indicator is consistent with the importance technology plays in these industries. In high-technology industries, such as instruments, the success of a company depends crucially on its ability to constantly innovate and stay ahead of the competition in developing new technologies or introducing new products. For firms in low-technology industries, such as food, the importance of research and technology is not as pertinent.
However, even within a given industry, firms will choose different ways of competing, and this will manifest itself in a different level of strategic emphasis. For example, considerable variation in the S9 measure exists for firms in the pharmaceutical industry (SIC code 2834). The historical mean of S9 for Johnson & Johnson (-.026) lies close to the industry mean of -.036. This can be compared with .083 for Bristol-Myers Squibb. At the other end of the spectrum is Genentech with a mean S9 of -.136. These differences reflect the different emphases firms place on value creation compared with value appropriation in their strategies.
Strategic emphasis will also change for a given firm over time to reflect a change in strategy. Consider Figure 2, which plots the S9 measure for Intel for the period 1982-98. Late in 1991, in response to increased competitive pressures from Advanced Micro Devices and C&T, Intel launched its "Intel Inside" campaign. The campaign marked a shift in strategy for Intel to bolster its brand attributes. We show in Figure 2 that the S9 measure captures Intel's shift to enhancing value appropriation capabilities. Although Intel maintained an emphasis on value creation capabilities (i.e., S9 is negative for the entire period), the measure shows a definite positive drift associated with the execution of the "Intel Inside" campaign and the shift in emphasis toward the development of value appropriation assets.
Our research goal is to assess the financial effect generated by shifts in emphasis between value creation and value appropriation and to address the conditions under which these shifts might have differential performance implications. To capture the long-term financial impact (i.e., the total expected value), our analysis focuses on the effect of strategic emphasis on the stock market valuation of the firm.
Stock Return as a Measure of the Long-Term Financial Performance
The economic return to a marketing strategy is not attained typically in a single reporting period, but rather is realized over a long-term time horizon. Yet most of the research in marketing assessing strategic decision has involved measures such as sales, accounting return on investment, or market share, whose current value provides, at best, an incomplete picture of the value of a strategy. An alternative is to make use of stock market data, which provide the financial markets' estimate of the total expected value of the strategy.
A firm's marketing strategy can be viewed as an intangible asset that influences future returns (Srivastava, Shervani, and Fahey 1998). The value of the strategy can be represented as the excess future returns generated by the firm when this particular strategy is employed. As such, the value of a marketing strategy to the firm can be depicted as a discounted present value of the future cash flows generated through the use of this marketing strategy:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
where Vi is the present value of marketing strategy (i), CFt is cash flow at time period t generated as a result of the use of marketing strategy (i), and r is the cost of capital.
In practice, it is virtually impossible to estimate the value of a marketing strategy with this formula. Although the measure of Vi is not available, under the efficient markets hypothesis, abnormal stock return (the difference between the actual and expected return, given the market and firm risk characteristics) will provide an unbiased estimate of the change in Vi. Given efficient markets, all available information about future cash flows is incorporated into the current stock price. When an unanticipated change in strategy occurs, the markets react, and the new stock price reflects the long-term implications such change is expected to have on future cash flows. As such, abnormal stock return provides an estimate of the difference in market value of the firm before and after the change in marketing strategy occurs. Therefore, it can be used as an estimate of the long-term financial value that results from a shift in marketing strategy.[ 2]
Testing for the "Information Content" of Strategic Emphasis
We seek to assess the extent to which changes in strategic emphasis are associated with long-term financial performance. We do so by examining the information content of strategic emphasis (i.e., whether changes in the S9 series are associated with stock return). A significant relation would indicate that investors view these changes as signaling changes in the discounted future cash flow of the firm. That is, stock prices move because investors change their expectations of the future cash flows because of factors associated with information contained in the measure.
Early work in the area of assessing information content, for example, in accounting, focuses on the role of changes in accounting variables such as size-adjusted earnings. More recent work (e.g., Aaker and Jacobson 1994, 2001; Barth et al. 1998) has begun to investigate the role of nonfinancial variables, such as brand attributes. These studies seek to test for incremental information content, that is, the degree to which a series provides added explanatory power to current earnings information in explaining stock price movements.
Assessing the incremental information content of strategic emphasis can take place by regressing stock returns on changes in accounting business performance and changes in strategic emphasis. That is, estimating the following model:[ 3]
( 2) StkRit = α 0 + α 1 ΔROAit + α2 SEit + εit,
where StkRit is the stock return for firm i at time t ΔROAit is the change in accounting business performance, ΔSEit it is the change in our indicator measure of strategic emphasis, and it is the error term. Because stock market efficiency implies that investors react only to unanticipated changes, we define changes in the measures as deviations of the series from what could have been predicted on the basis of past information. These deviations are typically operationalized as the residual from a time series forecast model.
Equation 2 reflects that accounting measures supply information about current and future-term financial performance. This effect is captured by α1 (commonly known as the "earnings response coefficient"), which depicts the stock market response to unanticipated changes in accounting information. However, accounting indicators are limited in their ability to capture completely the expected net cash flow from the future opportunities facing the firm. Do investors view shifts in a firm's strategic emphasis as providing additional information about these opportunities and their impact on the firm's future cash flows?
The null hypothesis is that 2 = 0, which would imply that the indicator of strategic emphasis has no incremental information content. That is, the financial markets perceive the measure to provide no information about future earnings beyond that reflected in current-term earnings. The alternate hypothesis is that 2 0, which implies that stock market participants perceive the change in the strategic emphasis indicator to contain information (incremental to that reflected in current-term accounting business performance) about future cash flows.
In this study, we are particularly concerned with investigating the opposing elements of the alternative hypotheses of α2 > 0 and α2 < 0, that is, whether a shift in strategic emphasis toward value appropriation versus value creation has a positive or a negative effect on expectations of future cash flows. Although increasing either value creation or value appropriation capabilities should enhance firm performance, the effects of shifts in emphasis between the two have not been examined previously.[ 4]
The effect of strategic emphasis on market value may not be constant across firms. Rather, investors may have a differential response to shifts in strategic emphasis under different conditions. In particular, the response may vary systematically with ( 1) situational factors regarding the firm and ( 2) the type of environment in which the firm is operating.
The Situation of the Firm
Market response can vary depending on the situation of the firm. One key difference among firms is profitability. Competing hypotheses about the moderating effect of profitability exist. One view emphasizes exploiting opportunities when they arise. Under this view, firms with positive unexpected earnings should focus on locking in their advantage through a shift to greater emphasis on value appropriation. By the same logic, firms in a weaker than expected financial position would be better served by emphasizing value creation capabilities (i.e., they are not creating sufficient value to justify increased investments in value appropriation). However, an alternative view emphasizes the dissipation of profits. Under this view, firms cannot rest on their past success. Firms should not focus on sustaining existing advantages, which is often futile, but rather on creating new advantages at a faster rate than the old advantages are being eroded by competition (Grant 1991). As such, a firm in a superior financial position needs to prepare for the eventual dilution of its existing advantages by focusing more on value creation projects.
The firm's existing strategic emphasis may also moderate the stock market response to shifts in strategic emphasis. The concept of path dependency advanced in evolutionary economics postulates that the strategic choices a firm made in the past shape its current strategic position and, as such, the viability of future choices. That is, the stock market response to an unanticipated strategy change (ΔSEit) may be moderated by the past strategic choice (SEit - 1). However, the theory is not clear regarding the direction of the effect. Diminishing marginal returns hypothesis suggests that firms with high levels of value creation capability would receive less gain from expanding their value creation capabilities and firms with high levels of value appropriation capability would receive less gain from expanding their value appropriation capabilities. Conversely, Lei, Hitt, and Bettis (1996) argue that as a firm's skills become more specialized, they may produce expertise that is difficult for the competitors to imitate and, therefore, may become a source of competitive advantage. Thus, firms with high levels of value creation emphasis should further enhance their value creation capabilities, and firms with high levels of value appropriation emphasis should continue to enhance their value appropriation capabilities.
Allowing for this type of differential response can be achieved by modifying Equation 2 to allow for systematic variation in 2 depending on unanticipated ROA and the past level of strategic emphasis. That is, estimating a model of the form
( 3) StkRit =α0 + α1 δ ROAit + α2 δ SEit + εit
with
α2 = α20 + α21 δ ROAit + α22 SEit - 1
yields the estimating equation
StkRit =α0 + α1 δ ROAit + α20 δ SEit + α21 δ ROAit δSEit + α22 SEit - 1 δSEit + εit
The coefficient α21 depicts the extent to which unanticipated ROA moderates the effect of strategic emphasis on stock return. A value of α21 >0 would indicate that firms in a weak (strong) financial position are better suited by emphasizing value creation (value appropriation). A value of α21 <0 would indicate that firms in a weak (strong) financial position are better served by emphasizing value appropriation (value creation). The coefficient 22 depicts the moderating effect of past strategic emphasis on the stock market response to an unanticipated shift in strategic emphasis. Values of α22 <0 would support the diminishing marginal returns hypothesis; values of α22 >0 would support the specialization hypothesis.
The Role of the Technological Environment
Differences in market response can be posited to stem not only from firm-specific factors but also from industrywide characteristics. Chandler (1994) highlights the role of technology as a key characteristic differentiating industries. He defines high-technology industries as those in which new product development is the critical element of interfirm competition. These industries tend to be characterized by high R&D intensity, changing products, and long-term horizons for achieving a payback. He contrasts this with stable-and low-technology industries in which the final product has historically remained much the same. Competition is more functional and strategic than in high-technology industries. That is, firm performance, for example, is based more on the improvement of the existing product and processes and on enhanced marketing efforts. Research and development is still important, but it is likely to be less intensive and focuses more on product improvement and cost reductions than on new product development.
One hypothesis is that value creation capability is more important in environments in which technology is changing (i.e., in high-technology industries). A firm cannot stem the tide of innovation and constantly must adopt new technologies and create new products to be successful. Conversely, value appropriation capability is more important in stable-and low-technology industries. Here, there is less opportunity for value creation, and firms must work to sustain their advantages. This suggests that increasing emphasis on value creation capability is more important in high-technology markets than in stable-and low-technology markets. This suggests differences in magnitude (or even in sign) for the estimates of 20 among the technology environments.
An alternative view stemming from the literature on imitation suggests that, even for the high-technology firms, the ability to capitalize on innovations is at least as important as the ability to create new value. Levin and colleagues (1987) find that it is relatively easy and at least 35% cheaper for competitors to replicate an innovation than to develop it. The majority of typical unpatented innovations can be imitated within a year, and major patented innovations within three years. However, it is not easy and cheaper to imitate superior reputation or brand image. Because patents do not provide adequate protection in many high-technology industries, firms are forced to seek other ways to restrict competitors from dissipating their profits. Thus, even in high-technology industries, firms should engage in development of value appropriation capabilities. Testing for differential effects of technological environment can be achieved by estimating separate regressions for the different technological environments and then testing whether the coefficients differ among them.
The data set used in our analysis comes from the Standard & Poor's 1999 COMPUSTAT database. This database provides annual accounting and stock market information for publicly traded firms on the New York, American, and Nasdaq stock exchanges. The sample of companies used in the analysis is restricted to manufacturing companies reporting their market value, R&D expenditures, advertising expenditures, assets, and net income. Table 1 provides a list of the industries included in our study and classifies them into the high-, stable-, and low-technology subsamples. To ensure correspondence between the stock price and accounting information, an additional requirement that companies have a December fiscal year is used. Our data sample consists of observations from 566 different firms reporting for all or some of the period 1980-98. We have a total of 3480 observations available for analysis. In Table 2, we provide descriptive statistics for and the definitions of the variables that form the basis of our analysis.
We first estimate first-order autoregressive time series models for ROA and strategic emphasis. In Table 3, we present the estimated models. Following the convention (e.g., Kormendi and Lipe 1987), we use the residuals from these models as the measures of the unanticipated changes in ROA and strategic emphasis of a firm.5 In Table 4, we present the results of estimating Equation 4 for our entire sample and for the high-, stable-, and low-technology subsamples.[ 6]
The Full Sample Results
In Table 4, the results for the full sample estimation indicate that unanticipated ROA has a positive (1.58) and significant effect on stock return. The coefficient estimate greater than 1.0 does not indicate that investors are short-term oriented in that they overvalue current-term results. Rather, consistent with the time series models showing that ROA exhibits persistence, a shock to ROA will not dissipate immediately but is likely to persist over several years. The greater the persistence of a ROA shock, the larger is the earnings response coefficient in the stock return equation (Miller and Rock 1985). As such, the market reaction to unanticipated ROA reflects that it provides information not only about the current-term results but also about the future-term profits.
Table 4 also shows that changes in strategic emphasis are significantly related to stock return. The positive coefficient (1.18) means that, on average, investors view increases in emphasis on value appropriation coming at the expense of value creation as being positively related to future-term performance.7 Because the model accounts for the direct influence of unanticipated ROA, this effect is incremental to information contained in accounting returns. Investors perceive strategic emphasis as providing incremental information about the future-term prospects of the firm above and beyond that contained in current accounting returns.
However, the total effect of strategic emphasis is not constant, but rather is evidenced to vary systematically. Although the interactive effect with lagged strategic emphasis (i.e., -.61) is statistically insignificant, the interactive effect with unanticipated ROA is positive and highly significant. The positive interactive effect (3.73) indicates that investors view a shift toward value appropriation capability as amplifying firm value when a firm is experiencing a positive shock to ROA.8 Conversely, when firms experience a negative shock to profits, investors view a shift toward value appropriation capabilities less positively. Indeed, depending on the magnitude of the earnings shock, conditions exist in which investors view a shift toward value creation capability as more preferable. This condition exists when unanticipated ROA is less than -.32 (i.e., 1.18/3.73).
The Role of the Technological Environment
Analysis of the high-, stable-, and low-technology subsamples reveals both similarities and differences across the three environments. All three samples exhibit positive effects of unanticipated ROA on stock return. One difference to note among the samples relates to the magnitude of the earnings response coefficient estimates. The estimated effect is lowest for the high-technology sample (1.36), increases for the stable-technology sample (1.80), and is highest for the low-technology sample (3.09). Theoretical valuation models (e.g., Miller and Rock 1985) depict the magnitude of the earnings response coefficient to increase the greater the persistence of profits and decrease the larger the discount rate. The observed differential effect is consistent with differences across the three environments. Shocks to ROA are more likely to persist and future-period returns are discounted less, the less dynamic the environment is.
The estimated direct effects of strategic emphasis are positive and significant for both high-and stable-technology markets. Although the estimated coefficients decrease in magnitude, moving from high-(2.01) to stable-(1.5) to low-(.91) technology markets, a Chow test is unable to reject the hypothesis that the direct effect of strategic emphasis is the same across technological environments. Thus, we find no evidence to suggest that value appropriation is any less important in high-technology markets than in stable-technology markets.
Moderating effects of profitability. The moderating effect of unanticipated ROA on strategic emphasis is positive for both the high-technology and stable-technology environments. This positive effect indicates that investors value a shift toward emphasizing value appropriation capability when earnings are greater than anticipated. In other words, when a firm is doing well, the market wants the firm to increase emphasis on value appropriation. The moderating effect is larger in stable-technology markets than in the high-technology sector (5.37 versus 2.79). This is consistent with the relative role that Chandler (1994) notes innovation plays in these two markets. In stable-technology markets, where innovation is less central, firms need to place greater emphasis on appropriation when the firm has an advantage. Locking in an advantage is still important in high-technology markets, but less important than in the stable-technology markets. The estimated effect is negative for the low-technology firms. However, the size of the standard error makes it difficult to isolate the effect or draw conclusions.
Moderating effects of the past strategy. The most dramatic difference among industry groupings is for the inter-active effect of unanticipated strategic emphasis with the lagged level of strategic emphasis. The estimated effect is positive and significant for high-technology firms (6.00), negative and significant for stable-technology firms (-3.44), and negative (though insignificant) for low-technology firms (-5.80).
The negative effect is consistent with the proposition of diminishing marginal returns to a high value creation or value appropriation capability emphasis. This finding suggests that in the stable-technology sample, the higher the past level of strategic emphasis, the less positive the market reacts to increases in this emphasis. Indeed, for high levels of strategic emphasis, the effect turns negative. Figure 3 graphically depicts the estimated relation between stock return and strategic emphasis, depending on the previous level of strategic emphasis.[ 9] For the majority of the stable-technology firms, the market reacts positively to emphasizing value appropriation capability. However, there exists a threshold value S, such that for S9 greater than S, there is no need to increase emphasis on value appropriation capability. For those firms, which already have a high emphasis on value appropriation capability, its further development has a negative effect on stock return. Our results suggest that for stable-technology firms, there is a single converging equilibrium. An optimum point S exists (estimated at advertising intensity - R&D intensity = .44, i.e., 1.50/3.44, the numbers coming from estimating Equation 4). Deviating from point S has a negative effect on return. Movement toward S (represented by arrows in Figure 3) has a positive effect on stock return.
Conversely, for high-technology firms, the positive coefficient for the interactive effect of unanticipated strategic emphasis with the lagged strategic emphasis is reflective of positive reenforcing or specialization effects. Figure 4 graphically represents our findings for the high-technology sample. Here, a single optimum solution does not exist. Firms with high orientation on value creation are rewarded for further emphasis on value creation capabilities. All other firms are rewarded for further investments into their value appropriation capability. A separating point H is estimated at advertising intensity - R&D intensity = -.33 (i.e., -2.01/ 6.00). Movement toward H is viewed negatively by the market. Movement away from H to the extremes of value creation or value appropriation emphasis (represented by arrows in Figure 4) is rewarded by the stock market. This result suggests two possible sources of competitive advantage for the high-technology manufacturing firms: either a high value creation emphasis or a high value appropriation emphasis in their marketing strategy.
We undertook several tests to assess the sensitivity of our analysis. We found that alternative specifications and expanded models did not perform as well or added little to the analysis. We tested whether some alternative means of scaling the advertising-R&D differential for firm size, for example, dividing by the sum of advertising and R&D expenditures, sales, or lagged market capitalization, instead of assets, would enhance the information content of the S9 measure or lead to different conclusions. Indeed, these alternatives generated similar implications (e.g., all showed that the financial markets react favorably to shifts in emphasis toward value appropriation) and displayed similar or lower information content than the asset-scaled size adjustment.[ 10]
We also assessed the presence of feedback effects from stock return to strategic emphasis. That is, our results could stem not from S9 having information content but from firms shifting their strategic emphasis in the wake of changes in stock market value. The presence of this type of feedback would induce the correlation between the error in Equation 4 and S9it and lead to biased coefficient estimates. We found no evidence of such an effect. Although we found that ΔROAit influences S9it, we observed no feedback effects from stock return to strategic emphasis that would lead to biased estimates in Equation 4.
Because the market response to shifts in strategic emphasis may also depend on factors other than those we modeled, we tested some additional possible moderating factors. For example, the anticipated component of ROAit (i.e., the predicted value from the univariate return on investment model), rather than just unanticipated ROA (i.e., ROAit), might moderate the effect of shifts in strategic emphasis on stock return. However, the tests revealed that only the unanticipated component of ROAit had a statistically significant moderating effect. In addition, we hypothesized that the market response might differ depending on the change in the intensity of combined R&D and advertising expenditures (i.e., the extent to which the firm is expanding or contracting its combined value creation and value appropriation activities). However, we found this moderating effect to be small and statistically insignificant, -.12 with a t-statistic of -.1. Similarly, we found no significant difference in response when we estimated separate effects for shifts in strategic emphasis for those firms increasing R&D and advertising expenditures versus those decreasing expenditures. The estimated effect of .127 for those increasing spending was not significantly different from the estimate of .097 for those decreasing spending (i.e., the t-statistic for the difference in effects was .5). We also tested whether the response to shifts in strategic emphasis varied by the size of the firm and the total amount spent on R&D and advertising. Here, we also found no significant differential.
Although these sensitivity tests did not uncover results that challenged our findings, this is not to suggest that further work is not needed. Indeed, many directions for additional research are warranted. One would be to improve the measure of strategic emphasis. For example, we used resource allocation patterns to discern firm strategic emphasis. An alternative would be to survey experts or use statements in the annual reports to operationalize strategy. In addition, because our study examined firms across different industries, the S9 indicator we employed had merit as an aggregate indicator of strategic emphasis. Future work focused at the business-unit level or on analyzing a particular industry could seek to develop better industry-specific measures of strategic emphasis. In other words, Figure 1 would be best operationalized at the business-unit level. These measures should seek to incorporate factors other than R&D and advertising that facilitate the processes of value creation and value appropriation.
Another avenue might focus not on trying to measure the extent of shifts in emphasis, but rather on isolating events when a shift occurred and determining whether the event reflected increased emphasis on value appropriation or on value creation. An event study (i.e., assessing how the stock market reacted to these shifts) could then be undertaken.
Future work in the area could also explore the potential role of other moderating factors (e.g., economic conditions, cross-cultural differences, stage of the company life cycle, the effectiveness of patent protection). Indeed, a host of factors other than those included in our model can generate a nonlinear or even nonmonotonic stock market response to shifts in strategic emphasis. One approach would be to undertake threshold analysis to isolate different regimes in which the effect of strategic emphasis on financial performance differs.
Further research aimed at better understanding strategic emphasis is also in order. A potential research avenue would be to investigate the factors that influence strategic emphasis and that motivate a firm to shift its emphasis.
Our study shows that the relative emphasis that firms place on value appropriation relative to value creation contains information relevant to investors in the valuation of the firm. In general, we find that increases in emphasis toward value appropriation capability and away from value creation capability are associated with increases in stock return. This result serves to reinforce the view of Teece (1987) and others who note that many firms, particularly in the high-technology sector, labor under the illusion that developing new, superior products ensures success not only for the product but also for the firm. These firms do not pay sufficient attention to restricting competition from imitating innovation and dissipating a firm's returns from it. Our results show that even in the high-technology markets, where innovation and R&D are central to firm success, investors view favorably a shift toward value appropriation capability.
The positive response to enhancing value appropriation is particularly strong when a firm has better than expected earnings. In other words, when a firm is doing well, the market wants it to increase emphasis on value appropriation. When a positive shock to earnings occurs, this provides a signal to existing and potential competitors as to the direction resources should flow. The inflow of resources into areas with positive shocks will tend to bring returns back toward the competitive rate of return. If management wants to insulate itself from this process, it needs to place greater emphasis on value appropriation and restricting imitation.
However, conditions exist in which the financial markets view increases in value appropriation capability negatively. For example, for firms experiencing a negative shock to ROA, increased focus on value appropriation capability would in some cases lead to a drop in market value. If a firm is not doing well financially, the financial markets respond positively to efforts designed to generate value creation capabilities. The same is true for firms operating in stable-technology markets that are already highly emphasizing their value appropriation capability. For these firms, further increases in the value appropriation capability can decrease market value. If a firm already has placed considerable focus on value appropriation, the markets realize that there may be limits to the firm's ability to extract surplus. In this case, efforts to expand surplus through enhanced emphasis on value creation are rewarded.
Nonetheless, our results serve to highlight the importance stock market participants place on value appropriation. Why is this so, and why have firms not already acted on this information? Should firms shift their emphasis toward value appropriation? Two phenomena that are not mutually exclusive, namely, signaling and managerial inefficiency, provide some answers to these questions. First, changes in strategic emphasis may provide a signal to the marketplace. Firms shifting to strategy with greater emphasis on value appropriation may be signaling that they now possess sufficient value creation capability and are seeking to lock in their value creation advantage. Indeed, this can describe the Intel experience in which Intel possessed great value creation capabilities and sought to exploit this advantage by creating brand loyalty with the "Intel Inside" campaign. This reasoning indicates that not all firms should shift to value appropriation. Rather, it suggests that our results are driven by those firms having the necessary value creation capabilities that decided to shift their strategic emphasis.
Second, our results may be indicating that firms are inefficient in allocating resources in that they may be consistently underinvesting in value appropriation (e.g., marketing) relative to value creation (e.g., R&D) activities. This can be explained by the difficulty managers have in justifying marketing expenditures. Many commentators have noted that because of a lack of reliable measures in documenting the effect of marketing, fewer resources than should be are devoted to marketing. The Marketing Science Institute, for example, has noted this problem and has recently called for proposals to help address this issue.
Value creation investment decisions cannot be divorced from issues of appropriability. Countless examples exist of innovations that created enormous value, but where the innovating firm was unable to capture the surplus. For example, although exceptions exist, Xerox's Palo Alto Research Center is best known as a breeding ground for innovations from which Xerox was unable to achieve strategic or commercial success (e.g., the personal computer, Ethernet, graphical user interface, page-description language). Firms that fail to pay sufficient attention to value appropriation cannot be expected to achieve sustained competitive advantage and reap the rewards from their value creation capabilities.
Footnotes [1] Golder (2000) also notes Underwood's inability to sustain its place in the typewriter market as a result of a failure to innovate. Again, this highlights the need for firms to invest in both value creation and value appropriation capabilities.
[2] Although market anomalies exist, they tend to be rare and short-lived. As such, particularly for analysis based on a large number of firms across a long time period, the efficient markets hypothesis appears to be a good approximation for the functioning of the financial markets. Even those who question the overreliance on the efficient markets hypothesis (e.g., De Bondt and Thaler 1985) agree that it is a good starting point.
[3] Differences in firm return stem not only from differential changes in expected cash flows but also from differences in risk. That is, riskier firms earn higher returns. Historically, differences in risk have been controlled by modeling the systematic risk of the firm, as reflected by its beta. More recent work (Fama and French 1992, 1996) has expanded on this single-factor capital asset pricing model by allowing risk to depend not only on beta but also on size and "book-to-market" factors. Fama and French (1992) find that after the role of size (as modeled by log[market value]) at the start of the period and book-to-market equity (as modeled by the log[book value/market value]) at the start of the period are accounted for, estimates of beta are unrelated to firm stock return. To control for these risk factors, our model also includes log(book valuet - 1/market valuet - 1) and log(market valuet - 1). Because the effect of these factors may vary depending on economic conditions, we allow their effect to differ over time (i.e., we allow for differential effects by year). By including these factors in the model, we control for the different types of risk, and as such, our analysis is based on abnormal (i.e., risk adjusted) return. The model also includes (1) annual dummy variables so as to capture the effects of economywide factors and (2) industry dummy variables to capture industry-specific effects.
[4] Other studies (e.g., Erickson and Jacobson 1992) have examined the separate effects of advertising and R&D on stock return. Comparing response coefficients from this type of model would result in our testing different effects than what we are trying to assess in our analysis. In particular, this approach would not only capture shifts in emphasis (as does our S9 measure) but also depict market reaction to changes in total expenditures. Consider a simple example to see how the analyses differ. A firm spends equally on R&D and advertising. It doubles both activities, which results in a substantial change in both variables. In contrast, the S9 measure would exhibit no change, because the firm's strategic emphasis has remained the same despite the doubling of expenditures. By being a difference, (advertising - R&D)/assets relates exclusively to a firm's shift in emphasis; separate analysis of advertising and R&D does not.
[5] The use of a residual, as opposed to the series itself, in a stock return model follows directly from the efficient markets hypothesis. Stock return should exhibit a higher correlation with the residual series because the raw series includes an anticipated component that will be unrelated to stock return. As evidenced by the following correlation matrix, our results are consistent with this efficient markets implication:
Correlation Matrix
Legend for chart:
A - StkRetit
B - &Stilde;&Etild;it
C - ROAit
D - &Stilde;&Etild;it
E - ROAit
StkRetit 1.0
&Stilde;&Etild;it .005 1.0
ROAit .120 .411 1.0
&Stilde;&Etild;it .080 .490 .228 1.0
ROAit .230 .192 .657 .321 1.0
[ 6]Although not reported in Table 4, consistent with previous research, we find significant coefficients for the yearly log (book value t - 1/market value t - 1) and log(market value t - 1) measures. Controversy exists regarding how to interpret the coefficients. One view is that the estimated effects reflect the mispricing of stocks. An alternate view (e.g., that raised by Fama and French [1992]) is that the factors adjust for risk considerations. The overall negative effect of size reflects reduced risk associated with larger firms. The positive effect of book-to-market reflects risk associated with relative distress. The key for our analysis is not so much interpreting the rationale for the significance of the factors, which is an ongoing debate in finance, but rather controlling for these factors so that we are able to conclude that shifts in strategic emphasis are not associated with stock return merely through risk.
[ 7] An additional effect of S9it, an indirect effect that works through ROA, may also exist. We estimate a simultaneous equations model and find evidence of a positive effect of S9it on ROAit. As such, 20 provides a conservative estimate of the impact of strategic emphasis on stock return because it only assesses the direct path.
[ 8] Another interpretation of this interactive effect, which is observationally equivalent, is that S9it moderates the effect of ROAit. Firms experiencing increased emphasis on value appropriation have higher earnings response coefficients. This interpretation has merit in that value appropriation capabilities enhance the persistence of ROA and the magnitude of the earnings response coefficient.
[ 9] The result follows directly from taking a first-order derivative of the estimated Equation 4. That is, we use the coefficient values from Table 4 as the estimated parameters in Equation 4 and take a partial derivative of the model with respect to S9, while holding ROA = 0. From this, we find a range of S9it - 1, where -- S9it is positive (i.e., StkRetit increases with increasing S9it) and negative (i.e., StkRetit decreases with increasing S9it).
[ 10] See, for example, Fisher (1984) for a discussion of issues relating to alternative size deflators.
DIAGRAM: FIGURE 1 Marketing Strategy and the Sustainable Competitive Advantage Framework
GRAPH: FIGURE 2 Strategic Emphasis Indicator for the Intel Corporation 1982-98
DIAGRAM: FIGURE 4 Effects of the Directional Change in the Strategic Emphasis on Stock Return Given the Past Level of Strategic Emphasis: The High-Technology Sample
DIAGRAM: FIGURE 3 Effects of the Directional Change in the Strategic Emphasis on Stock Return Given the Past Level of Strategic Emphasis: The Stable-Technology Sample
Legend for Chart
A = High-Technology Group
B = Stable-Technology Group
C = Low-Technology Group
A B C
Pharmaceuticals Chemicals Food and tobacco
Computers Rubber and plastic Textile
Electronics Fuel Apparel
Instruments Industrial machinery Paper and forest
Semiconductors Aircraft Furniture and fixture
Telecommunications Automotive Building materials
Electrical equipment
Metal
Miscellaneous manufacturingSource: Chandler (1994) and Chan, Martin, and Kensinger (1990).
Legend for Chart
A = Full Sample
B = High-Technology Group
C = Stable-Technology Group
D = Low-Technology Group
A B C D
Stock return
Mean .27 .28 .26 .24
S.D. (.87) (.91) (.88) (.69)
ROA
Mean .087 .052 .099 .145
S.D. (.22) (.28) (.19) (.11)
SE
Mean -.024 -.07 -.007 .049
S.D. (.11) (.11) (.10) (.08)
Number of
observations 3480 1288 1770 422Variable definitions:
Multiple line equation(s) cannot be represented in ASCII text; please see PDF of this article if available.
Notes: S.D. = standard deviation.
Legend for Chart
A = Full Sample
B = High-Technology Group
C = Stable-Technology Group
D = Low-Technology Group
A B C D
Model: ROA[it] = φ[sub10] + 11 ROAit - 1 + h it
ROAit - 1 .77** .78** .73** .83**
(64.35) (40.84) (41.99) (28.39)
R2 .57 .59 .51 .71
Number of
observations 3563 1324 1808 431
Model: SEit = φ[sub20] + φ[sub21] SEit - 1 + µit
SEit - 1 .87** .84** .89** .94**
(88.71) (46.78) (71.06) (47.57)
R2 .76 .64 .78 .88
Number of
observations 3563 1324 1808 431*t-statistics are in parentheses.
**p < .001. Notes: Each equation also includes ( 1) annual dummy variables to capture the effects of economywide factors and ( 2) industry dummy variables to capture industry-specific effects. TABLE 4 Stock Market Reaction to Changes in Strategic Emphasis Dependent Variable: Stock Return*
Legend for Chart
A = Full Sample
B = High-Technology Group
C = Stable-Technology Group
D = Low-Technology Group
A B C DModel: StkRetit = &alpha0 + &alpha1δROAit + α20 δSEit + &alpha21δROAitδSEit + &alpha22 SEit - 1δSEit + εiti
Unanticipated
ROA 1.58*** 1.36*** 1.80*** 3.09***
(15.26) (9.49) (10.85) (6.45)
Unanticipated
strategic 1.18*** 2.01*** 1.50** .91
emphasis (3.54) (3.61) (2.83) (.55)
Unanticipated
ROA* 3.73*** 2.79*** 5.37*** -14.77
Unanticipated
strategic (6.93) (3.91) (5.93) (-1.42)
emphasis
Strategic
emphasis[t] - 1* -.61 6.00** -3.44** -5.80
Unanticipated
strategic (-.59) (2.91) (-2.59) (-.91)
emphasis
R2 .20 .26 .22 .43
Number of
observations 3480 1288 1770 422*t-statistics are in parentheses.
**p < .01.
***p < .001. Notes: Each equation also includes ( 1) annual dummy variables to capture the effects of economywide factors, ( 2) industry dummy variables to capture industry-specific effects, and ( 3) annual effects for log(market value[t - 1]) and log(book value/market value)[t - 1] to capture firm-specific risk factors.
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~~~~~~~~
By Natalie Mizik and Robert Jacobson
Natalie Mizik is assistant professor, Graduate School of Business, Columbia University. Robert Jacobson is Evert McCabe Distinguished Professor of Marketing and Transportation, School of Business, University of Washington, Seattle.
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Record: 191- Understanding Adolescent Intentions to Smoke: An Examination of Relationships Among Social Influence, Prior Trial Behavior, and Antitobacco Campaign Advertising. By: Andrews, J. Craig; Netemeyer, Richard G.; Burton, Scot; Moberg, D. Paul; Christiansen, Ann. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p110-123. 14p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.68.3.110.34767.
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- Business Source Complete
Understanding Adolescent Intentions to Smoke: An
Examination of Relationships Among Social Influence, Prior
Trial Behavior, and Antitobacco Campaign Advertising
Telephone interviews were conducted with more than 900 adolescents aged 12 to 18 as part of a multimillion dollar, statewide, antitobacco advertising campaign. The interviews addressed two primary questions: ( 1) Do counter-advertising campaign attitudes directly affect antismoking beliefs and intent in a manner similar to those of conventional advertisements? and ( 2) Can advertising campaign attitudes have a stronger effect on beliefs and intent for adolescents with prior smoking behavior and for adolescents exposed to social influence (i.e., friends, siblings, or adult smoker in the home)? The authors' findings show that advertising campaign attitudes, prior trial behavior, and social influence all directly affect antismoking beliefs and that advertising campaign attitudes interact with prior trial behavior to strengthen antismoking beliefs. The results indicate that attitudes related to the campaign, prior trial behavior, and social influence directly influence intent, and advertising campaign attitudes interact with social influence and prior trial behavior to attenuate adolescent intent to smoke. In addition, the effect of advertising campaign attitudes in attenuating social influence and prior trial behavior effects on adolescent intent to smoke persists even when the authors account for strongly held beliefs about smoking. The authors discuss implications for counter-marketing communications and the design and understanding of future antismoking campaigns.
An understanding of why adolescents decide to smoke and the development of successful countermeasures are important issues in the public health and social marketing fields today. The statistics on smoking morbidity are compelling. For example, tobacco use is the leading preventable cause of death in the United States, contributing to more than 440,000 deaths each year and resulting in $75 billion in direct medical costs (Centers for Disease Control and Prevention [CDC] 2002). Furthermore, the U.S. Food and Drug Administration has characterized smoking as a pediatric disease; 80% of current adult smokers began before age 18, and more than 5000 youths try their first cigarette each day (CDC 2002). Adolescents are estimated to have three times the sensitivity to cigarette advertising than adults (Pollay et al. 1996), and recent documents have shown youths to be an important target market for the tobacco industry (Cohen 2000; Pollay and Lavack 1993).
What, then, leads adolescents to smoke? Demographic analyses suggest that there is a greater tendency for older, Caucasian, male youths who are not in school to smoke than there is for other adolescent groups (Jamieson and Romer 2001). Other potential predictors of adolescent smoking include social influence (e.g., friends or family effects), prior smoking behavior, and imagery portrayed in tobacco advertising (Aloise-Young, Graham, and Hansen 1994; Pechmann and Knight 2002). For example, tobacco advertising has been shown to have both direct and indirect effects on adolescent smoking behavior. Cohen (2000) suggests that tobacco advertising conveys symbolic and/or physiological benefits to adolescents that directly affect their decision to smoke. Alternatively, Romer and Jamieson (2001) present models that show that cigarette advertising encourages adolescents' attraction to peers who smoke and that peers' approval of smoking helps initiate smoking trial in peer groups (an indirect effect). Romer and Jamieson also find that cigarette advertising directly influences peer approval separate from its impact on imagery and feelings smoking. In summary, evidence suggests that social influence and tobacco advertising can affect the decision to begin smoking.
In principle, Romer and Jamieson (2001) argue that antitobacco advertising may operate in a similar and opposite manner of tobacco advertising. That is, antitobacco advertisements are likely to counteract the approval and attraction process through the use of negative images of smokers and favorable images of nonsmokers. Thus, the models of Romer and Jamieson (2001) may be applicable to the study of social influence and antismoking ad campaigns. Given that state antitobacco programs have shown that counter-advertising can reduce the positive perceptions of smoking in peer networks and overall views of cigarette advertising (cf. Siegel and Biener 2000), antismoking advertisements may negatively affect intent to smoke. Still, how antismoking ad campaigns interact with social influence (and with other variables) has largely been unexplained, and the potential mediating role that beliefs about smoking may have in affecting intent to smoke has not been explored. Furthermore, it has been suggested that knowledge of teen smoking can be advanced by field studies that examine the combined effects of social influence, beliefs, and counter-advertising campaigns (Romer and Jamieson 2001). As such, our study addresses two primary questions: ( 1) Do counter-advertising campaign attitudes directly affect antismoking beliefs and intent in a manner similar to that of conventional advertisements? and ( 2) Can ad campaign attitudes have a stronger effect on beliefs and intent for adolescents with prior smoking behavior and for adolescents exposed to social influence (i.e., friends, siblings, or adult smoker in the home)? We use data from a major state antitobacco campaign to address these questions.
Campaign Overview
Our study examines predicted relationships that are tested in conjunction with Wisconsin's first major antitobacco advertising campaign, for which some $6.5 million was allocated. Specific antismoking advertisements used in the campaign targeted youths of middle school and high school age, for whom smoking incidence rates were higher than national averages. The specific advertisements used had been successfully tested and run in other states, and the advertisements were placed in youth television and radio spots in seven major markets in the state running the antismoking campaign. A primary theme of the campaign was that of industry deception and anti-imagery, and other message themes focused on addiction and harmful effects of secondhand smoke. The campaign ran for approximately six months.
Model Overview
Figure 1 presents direct, interaction, and mediating relationships for two key dependent variables: antismoking beliefs about the campaign (beliefs) and intent to smoke. The interaction and mediating effects are of particular interest in contributing to the literature on counter-marketing campaign advertising. We predict that prior smoking trial (prior trial behavior) as well as having siblings, an adult in the home, or friends who smoke (social influence) will negatively affect antismoking beliefs about the campaign (beliefs). A favorable attitude toward specific antitobacco campaign advertisements (ad campaign attitude) will have a positive effect on beliefs, and the negative effects of prior trial behavior and social influence on beliefs will be attenuated when they interact with ad campaign attitude. For intent to smoke, we predict that beliefs will have a negative effect, prior trial behavior and social influence positive effects, and ad campaign attitude a negative effect. In addition, we predict interactions such that the effects of prior trial behavior and social influence on intent to smoke will be attenuated by ad campaign attitude. Furthermore, we predict that beliefs will partially mediate the effects of social influence, ad campaign attitude, and the ad campaign attitude x social influence and ad campaign attitude x prior trial behavior interactions on intent to smoke. We subsequently discuss the rationale for these relationships.
Predicted Effects on Beliefs
Direct effects of prior trial behavior and social influence. Antismoking beliefs related to the campaign (beliefs) include that tobacco companies use deceptive practices in advertising, that smoking is addictive, and that secondhand smoke is harmful. We expect that prior trial behavior and social influence directly affect these beliefs for the following reasons. It has been shown that consumers ignore or counter-argue messages that contradict their beliefs and behaviors (Petty and Cacioppo 1981), and research indicates that smokers tend to discount the negative consequences of smoking (Romer and Jamieson 2001). Such a process can occur even though the basis for such beliefs may be more emotional than has previously been believed (Slovic 2001). Thus, we expect that prior trial behavior negatively affects antismoking beliefs associated with the campaign.
We operationalize social influence as a behavioral construct that reflects the influence of family and peer smoking behavior on adolescents. The socialization process suggests that family and friends strongly influence the beliefs of adolescents (John 1999), and this effect is evident in the smoking literature. For example, Romer and Jamieson (2001) show that having friends or siblings who smoke weakens adolescents' beliefs about the risks of smoking, and Simons-Morten and colleagues (2001) suggest that parental guidance on not smoking lessens the positive beliefs that adolescents might have about smoking. Thus, we expect that social influence negatively affects antismoking beliefs (beliefs).
H[sub1]: Antismoking beliefs (beliefs) are negatively affected by (a) trying cigarettes in the past (prior trial behavior) and
(b) social influence (i.e., a sibling or friends who smoke, an adult smoker in the household).
Direct effects of ad campaign attitude. We expect that ad campaign attitude affects antismoking beliefs (beliefs) for several reasons. Hierarchy-of-effects models suggest a positive and direct relationship between attitude toward the ad and beliefs about the advertisement's message. For example, Brown and Stayman (1992) report a link between attitude and consumer cognitions, and studies that involve dual-mode processes support a positive effect of attitude toward the ad on brand-related beliefs (Lutz 1985). Evidence from the diffusion model of smoking also suggests that with the creation of favorable imagery and affect, a subsequent decline in the negative risk beliefs about smoking follows (Romer and Jamieson 2001). If the diffusion model principle that antismoking advertisements can have the opposite effects holds, ad campaign attitude should positively and directly influence antismoking beliefs. Thus, ad campaign attitude should explain incremental variance in beliefs related to the campaign that prior trial behavior and social influence do not explain.
H[sub2]: Favorable attitudes toward specific campaign advertisements (ad campaign attitude) positively affect antismoking beliefs (beliefs).
Interaction effects with ad campaign attitude. Although H[sub1] and H[sub2] are of interest to the smoking and advertising literature, they are direct-effects hypotheses. However, an understanding of the interactions among ad campaign attitude, prior trial behavior, and social influence may enhance knowledge of how these factors act in tandem to affect antismoking beliefs and intent to smoke. Although such interactions are consistent with conceptualizations that combine prior trial behavior with effects related to advertising (Vakratsas and Ambler 1999), we are not aware of any work that has addressed counter-advertising's interactions with prior trial behavior and social influence in their effects on adolescent antismoking beliefs.
We propose that ad campaign attitude serves to weaken the negative effects of prior trial behavior and social influence on antismoking beliefs. Antismoking beliefs are typical among youths (Barton et al. 1982; Chassin et al. 1981), but as we hypothesize, these beliefs can be weakened through direct experience with smoking or contact with peers who smoke (Pechmann and Knight 2002; Pechmann et al. 2003). Given such weaker antismoking beliefs, favorable campaign attitudes should have significant opportunity to affect beliefs. We expect this effect because there is greater room for change when prior antismoking beliefs are less robust. However, for adolescents with little smoking exposure, ad campaign attitude will have less opportunity to affect antismoking beliefs because they are already quite strong (for these adolescents, ad campaign attitude may tend to reinforce their existing antismoking beliefs). This rationale suggests that interactions of prior trial behavior and social influence with ad campaign attitude are positive and significant predictors of antismoking beliefs (beliefs). Specifically, we expect that ad campaign attitude has a stronger (and more positive) influence on antismoking beliefs for adolescents who have tried smoking or who have been exposed to social influence through friends or family.
H[sub3]: The negative effects of (a) prior trial behavior and (b) social influence on antismoking beliefs (beliefs) are attenuated by favorable attitudes toward campaign advertisements (ad campaign attitude).
Predicted Effects on Intent to Smoke
Direct effects of prior trial behavior and social influence. As Bem (1967) suggests, attitudes and intent are often inferred from prior behavior. As applied to adolescent smoking, prior trial behavior (i.e., smoking trial in which at least one cigarette was partially smoked) should affect future behavior (and intent). Research suggests that when intent is measured for a specific context that involves observable and easily initiated behavior, it is likely that intent predicts onset (Sheppard, Hartwick, and Warshaw 1988). Research also indicates that prior smoking behavior is a strong predictor of future smoking among adolescents (Stacy, Bentler, and Flay 1994). Similarly, the advertising literature shows that prior product usage dominates the effects of advertising influences on intent and behavior (Vakratsas and Ambler 1999). Thus, we expect that prior trial behavior positively affects intent to smoke.
Prior research also shows a link between social influence and intent to smoke. For example, teens whose parents smoke are more likely to smoke because cigarettes are more readily available at home, and through the socialization process, teens model adult behavior (Smith and Stutts 1999). Studies also show pronounced linkages between smoking or intent to smoke and having friends or siblings who smoke (Kaufman et al. 2002; Romer and Jamieson 2001). As such, we expect that social influence positively affects intent to smoke.
H[sub4]: Intent to smoke is positively affected by (a) trying cigarettes in the past (prior trial behavior) and (b) social influence (i.e., a sibling or friends who smoke, an adult smoker in the household).
Direct effects of ad campaign attitude on intent to smoke. In addition to the effects of prior trial behavior and social influence on intent to smoke, we anticipate that ad campaign attitude negatively affects intent to smoke. In the work of Armstrong and colleagues (1990), the perceived influence of cigarette advertisements outweighs parental, sibling, and peer smoking as a predictor of smoking behavior, and Smith and Stutts (1999) find that antismoking advertisements have a significant, negative effect on intent to smoke among adolescents. Such findings are consistent with the advertising literature that indicates that advertisements work on both cognitive and affective levels (Vakratsas and Ambler 1999) and that both cognitive (beliefs) and affective (attitude toward the ad) responses can have separate effects on intent (Burke and Edell 1989). Thus, if the antitobacco campaign is to be viewed as effective, ad campaign attitude should explain variance in intent to smoke in addition to that explained by prior trial behavior and social influence.
H[sub5]: Favorable attitudes toward specific campaign advertisements (ad campaign attitude) negatively affect intent to smoke.
Interaction effects with ad campaign attitude. We predict that ad campaign attitude will interact with prior trial behavior and social influence to weaken their relationships with intent to smoke. We offer two reasons for this prediction. First, Vakratsas and Ambler (1999) stress that the interaction of affect and personal experience is needed to appreciate the processing and effectiveness of advertising fully. Smith and Swinyard (1982) also note that under normal ad-processing conditions, advertising may not result in higher levels of affective impact until a person considers his or her own trial and experience. In the context of smoking, passive learning of smoking imagery over time may reinforce perceptions of others' decision to smoke within peer groups or a person's own smoking trial behavior. As we previously indicated, Romer and Jamieson (2001, p. 132) predict that antitobacco advertising works by counteracting the diffusion of favorable images by disseminating either unfavorable images of smokers (or industry practices) or favorable images of nonsmokers.
Second, adolescents with prior usage of and exposure to social influence are likely to express greater intent to smoke. Intent for such adolescents also differs from that of adolescents who have antismoking beliefs and express little intent to smoke (Pechmann and Knight 2002). Affect (attitudes toward campaign advertisements) related to counter-advertising messages tends to cue and reinforce negative smoking perceptions among adolescents with prior smoking behavior or social influence. In turn, this should reduce the positive effects of the social and behavioral influences on intent to smoke. We expect relatively weaker effects of such ad-related outcomes for adolescents who have little direct exposure to smoking, a segment for which intent to smoke is already low and thus is more difficult to influence through antismoking campaigns (Pechmann et al. 2003). (In addition, it is likely that the campaign will reinforce nonsmoking adolescents' resolve not to smoke.) Overall, this rationale suggests that ad campaign attitude should attenuate the effects of prior trial behavior and social influence on intent. This suggests that the ad campaign attitude x prior trial behavior and ad campaign attitude x social influence interaction terms are negative and significant predictors of intent to smoke.
H[sub6]: The positive effects of (a) prior trial behavior and (b) social influence on intent to smoke are attenuated by favorable attitudes toward campaign advertisements (ad campaign attitude).
Direct and mediating effects of beliefs. Empirical research indicates that beliefs about the consequences of a behavior are related to the intent to perform that behavior (Ajzen and Fishbein 1980) and that negative beliefs about the consequences of smoking are predictors of intent (Rindfleisch and Crockett 1999). It has been shown that when intent is specific to a behavior in terms of target, action, context, and time, the ability of intent to predict behavior is enhanced (Ajzen and Fishbein 1980; Fishbein and Ajzen 1975). Thus, we predict that antismoking beliefs related to the campaign will negatively affect intent to smoke.
In Figure 1, antismoking beliefs (beliefs) also are positioned as a partial mediator of the effects that social influence, ad campaign attitude, and their relevant interactions have on intent to smoke. As we noted previously, adolescents are likely to have strongly held beliefs about smoking that serve as a primary predictor of intent (Slovic, Fischhoff, and Lichtenstein 1980). Thus, given this strong effect on intent to smoke, we expect that inclusion of antismoking beliefs in the model reduces the effects of social influence, ad campaign attitude, and the ad campaign attitude x prior trial behavior and ad campaign attitude x social influence interactions on intent to smoke. However, given the important role of affect in the evaluation of adolescents' smoking (Slovic 2001), the role of affect in judgment formation (Schwarz 1990), and the enduring impact of social influence on adolescents (John 1999), we do not expect that beliefs fully account for the effect of these independent variables on intent to smoke. Thus, our predictions for the direct and partial mediation effects of beliefs are as follows:( n1)
H[sub7]: Antismoking beliefs (beliefs) (a) negatively affect intent to smoke and (b) partially mediate the effects of social influence, ad campaign attitude, and the ad campaign attitude x prior trial behavior and ad campaign attitude x social influence interactions on intent to smoke.
The Wisconsin Antitobacco Media Campaign
As a result of the 1998 Master Settlement Agreement of the tobacco industry with the states, the Wisconsin Tobacco Control Board (WTCB; 2002) was created in 1999. An important objective of the board was to target antismoking programs and messages toward youths of middle and high school age. According to the CDC, 32.9% of high school students in Wisconsin were smokers, a rate higher than the national average (Campaign for Tobacco-Free Kids 2002). More than $6 million ($6.5 million total, or $1.21 per capita) was allocated for the state's first major antitobacco advertising campaign (WTCB 2000). Because of the CDC's (1999) suggested minimum per capita allocations of $1.00 and ideal per capita allocations of $3.00 (Pechmann and Reibling 2000), the Wisconsin media campaign for 2001 provided an appropriate level of funding to help achieve exposure. A main objective of the youth campaign was to convey a message of industry deception and anti-imagery. This objective is consistent with recent successful programs that aimed to reduce and prevent adolescent smoking (cf. Bauer et al. 2000; Pechmann and Knight 2002; Perrachio and Luna 1998; Romer and Jamieson 2001) and with theory on attempts to persuade (Friestad and Wright 1994). Other important message themes focused on addiction and the harmful nature of secondhand smoke.
Four specific advertisements ("Janet," "Patrick," Mohammed," and "FACT") were placed in youth television and radio spots in seven major Wisconsin markets from July 2001 to December 2001. The advertisements had been successfully tested and run in other states (e.g., Massachusetts) and were recommended as part of CDC's best-practices list. "Janet" features a former cigarette model with a coarse voice talking about how she used to try to convince people to smoke and now tells people to stop smoking. "Patrick" shows a man talking about being part of a family of cigarette manufacturers and wanting people to know that they should not smoke. "Mohammed" is about a young African American man reading about ways the tobacco companies have tried to target kids to start smoking. Finally, in "FACT" (i.e., "Fighting Against Corporate Tobacco"), a cigarette company executive is dreaming about kids tracking him down and yelling at him about lies he has told about smoking.( n2)
Interviewing Procedure and Sample
Telephone interviews were conducted with adolescents ranging in age from 12 to 18 years. The sample was based on a list purchased from a major list vendor, and telephone numbers of potential respondents were randomly selected from the list. Up to eight callbacks were made, for a response rate of 31% for adolescents who were known to be eligible for inclusion in the sample. The survey took between 10 and 15 minutes to complete. The introduction noted that the firm conducting the telephone interviews was doing "a survey of Wisconsin youth about their attitudes and opinions toward tobacco and other health issues." Parental permission to participate in the survey was obtained at the time of the interview for all respondents, and they were assured that their responses would remain confidential. The average age of respondents was 14.7 years, 97% were in grades 7 to 12, 47% of the respondents were female, and 16% were African American. As such, the sample was quite similar to that of Wisconsin census figures. All data were collected in the latter portion of 2001, approximately six months after the antismoking campaign first began airing. Across all analyses, the sample sizes used ranged from n = 924 to n = 943.
Measures
Measures were drawn from previous state surveys, including the University of Massachusetts Tobacco Study Youth Instrument, Florida's Anti-Tobacco Media Evaluation Survey, the California Adult Tobacco Survey, and the CDC's Youth Tobacco Survey and Youth Risk Behavior Survey. All measures and procedures were pretested with 75 respondents before the survey was conducted.
Independent and control variables. We assessed prior trial behavior by asking respondents whether they had ever tried smoking cigarettes, even one or two puffs (no = 0, yes = 1). Three items measured social influence. The first item asked respondents whether they had a sibling who smoked (no = 0, yes = 1), the second asked whether there was an adult smoker in the household (no = 0, yes = 1), and the third asked whether they had any friends (among their four closest friends) who smoked (no = 0, yes = 1). We summed the scores of these three items (sibling, adult smoker, and friends) to form one overall social influence composite that ranged from 0 to 3, such that a higher score suggested a stronger influence to smoke. Respondents were screened such that they had to recall at least one campaign advertisement to ensure a basis for a campaign attitude. For advertisements recalled, respondents rated their attitude toward the specific advertisements by indicating how much they liked each campaign advertisement on a ten-point scale (0 = "did not like the ad at all," 10 = "liked it very much"). We summed these ratings and averaged them to form the ad campaign attitude measure. We also included measures of age, gender (0 = female, 1 = male), and race (African American = 0, Caucasian = 1) as control variables in all analyses.
Dependent variables. We measured both dependent variables in the model with multiple items. Antismoking beliefs related to the campaign pertained to three message themes: ( 1) deceptiveness of the tobacco companies in their marketing practices, ( 2) the harmfulness of secondhand smoke, and ( 3) the addictiveness of smoking cigarettes. Within each of these themes, we summed the scores on four items rated on four-point "strongly disagree/agree" scales and then averaged them to form an overall theme composite. We summed and averaged the three composite scores to form indicators for one overall antismoking beliefs construct (beliefs). We measured intent to smoke with three items on four-point "definitely no/definitely yes" scales. For the three indicators of the beliefs construct and the three items of the intent construct, standardized loadings from confirmatory factor models ranged from .65 to .95, and average variance extracted estimates for each of these multi-indicator/item measures exceeded .50 (Fornell and Larcker 1981). The Appendix shows all measures, and Table 1 reports all correlations and reliability estimates.
Measurement checks. We conducted analyses to determine whether antismoking beliefs should be represented as indicators of one overall construct. First, in a confirmatory factor analysis, we specified the beliefs construct as a higher-order factor with the three themes (i.e., deceptiveness of the tobacco companies in their marketing practices, harmfulness of secondhand smoke, and addictiveness of smoking cigarettes) as first-order factors. We then specified the individual items used to assess each theme as manifest indicators of their respective first-order factors. We used three indexes to examine the fit of this model: ( 1) the comparative fit index (CFI), ( 2) the nonnormed fit index (NNFI), and ( 3) the root mean square error of approximation (RMSEA). Values in the mid-to high-.90 range indicate good fit for the CFI and NNFI, and values of .08 and less indicate good fit for the RMSEA (Hu and Bentler 1999). The higher-order model fit the data well (χ² = 73.97, degrees of freedom [d.f.] = 51; CFI = .99; NNFI = .98; RMSEA = .02) (Hu and Bentler 1999), and the standardized loadings of the first-order factors to the higher-order factor ranged from .74 to .95 (p < .01), indicating a high degree of convergence among the first-order factors (Bagozzi and Heatherton 1994). Furthermore, the composite reliability estimates (estimates analogous to coefficient alpha) for the manifest indicators to their first-order factors were .78 for the deceptiveness of the tobacco companies in their marketing practices, .72 for the harmfulness of secondhand smoke, and .80 for the addictiveness of smoking cigarettes.
Second, we conducted a series of t-tests between dependent correlations to determine whether the three beliefs individually had different correlations with ad campaign attitude (an antecedent of beliefs) and intent to smoke (a consequence of beliefs). For example, to test whether the correlation between the belief of addictiveness of smoking cigarettes and intent to smoke differed from the correlation between the belief of harmfulness of secondhand smoke and intent to smoke, we used the procedure that Cohen and Cohen (1983, pp. 56-57) recommend. Across all correlational comparisons (six in total), we found no significant differences (t-values ranged from .92 to 1.23, p > .10). Thus, the relationships between the beliefs about the deceptiveness of the tobacco companies, the harmfulness of secondhand smoke, and the addictiveness of smoking cigarettes and ad campaign attitude and intent to smoke did not differ. In summary, we found evidence for a single antismoking beliefs construct.
Analysis and Results
Consistent with the procedures of Holmbeck (1997), we used structural equation modeling (SEM) as our analytical approach. This approach allows for an assessment of the incremental effects of the campaign-related constructs and their interactions after the effects of prior trial behavior and social influence have been taken into account. This approach also incorporates the potentially biasing impact of measurement error on path estimates. For beliefs and intent to smoke, we accounted for error in measurement by allowing the SEM package (LISREL 8) to estimate item/indicator loadings and measurement error terms freely. For ad campaign attitude, we used its summed item score and set its measurement loading to the square root of its internal consistency estimate and its error term to 1 - coefficient alpha x construct variance to account for measurement error (Jöreskog and Sörbom 1982). For all other single indicator constructs (i.e., prior trial behavior, social influence, and the interaction terms), we set item loadings to 1 and error terms to 0. Consistent with prior smoking research, we accounted for any demographic effects by entering age, gender, and race as control variables for all models estimated.
Effects on Antismoking Beliefs
H[sub1]-H[sub2]: Direct effects. Panel A of Table 2 presents the results that pertain to beliefs. H[sub1] predicts that prior trial behavior (H[sub1a]) and social influence (H[sub1b]) negatively affect antismoking beliefs related to the campaign (beliefs). Model 1 examines the two direct effect predictions, and the evidence shows that H[sub1a] and H[sub1b] are supported. Across fit indexes (CFI, NNFI, and RMSEA), the model fits the data well, and both prior trial behavior and social influence negatively affect antismoking beliefs. H2 predicts that ad campaign attitude positively affects beliefs, and Model 2 tests this prediction by adding ad campaign attitude to the model. Model 2, which includes the direct effects of all three predictors, also fits the data well, and the difference in fit between Model 1 and Model 2 is significant (χ²[subdiff] = 64.21, d.f.[subdiff] = 1, p < .01). This difference between the models indicates that the inclusion of ad campaign attitude explains an additional 8% of the variance in beliefs that was not previously explained by prior trial behavior and social influence. In total, prior trial behavior, social influence, and ad campaign attitude account for 13% of the variance in beliefs.
H[sub3]: Interaction effects of ad campaign attitude on beliefs. We predict that ad campaign attitude attenuates the negative effects of prior trial behavior (H[sub3a]) and social influence (H[sub3b]) on antismoking beliefs. Thus, we expect that the interactions of prior trial behavior and social influence with ad campaign attitude are positive and significant predictors of beliefs. We mean-centered ad campaign attitude and social influence before creating the interaction terms (Aiken and West 1991), and then we added the interaction terms hierarchically to the predictors already in Model 2 to form Model 3. As is shown in Panel A of Table 2, Model 3 fits the data well and is significantly better fitted than Model 2 (χ²][subdiff] = 10.74, d.f.[subdiff] = 2, p < .01). This significant difference between models indicates that the predicted interactions explain additional variance in beliefs. Thus, H[sub3a] is supported; the ad campaign attitude x prior trial behavior interaction term positively affects and incrementally explains 2% of the variance in beliefs. However, the coefficient for the interaction of social influence and ad campaign attitude is not significant and thus does not support H[sub3b].
Effects on Intent to Smoke
H[sub4]-H[sub5]: Direct effects. We predict that prior trial behavior (H[sub4a] and social influence (H[sub4b]) have positive effects on intent to smoke, and tests of the predictions are presented in Model 1, Panel B, in Table 2. Model 1 fits the data well and supports these predictions, as both prior trial behavior and social influence are significant predictors and explain 41% of the variance in intent to smoke. We also predict that ad campaign attitude (H[sub5]) negatively affects intent and explains variance in addition to the effects of prior trial behavior and social influence. Model 2 in Panel B of Table 2 supports this hypothesis. Model 2 has a better fit than Model 1 (χ² = 39.12, d.f.[subdiff] = 1, p < .01), and all three constructs (prior trial behavior, social influence, and ad campaign attitude) affect intent in the predicted direction. The addition of ad campaign attitude has a negative effect on intent and explains an additional 3% of the variance in this construct.
H[sub6]: Interaction effects of ad campaign attitude on intent. H[sub6] predicts that ad campaign attitude interacts with prior trial behavior and social influence such that ad campaign attitude has a stronger (and more negative) influence on intent to smoke for adolescents with prior trial behavior (H[sub6a]) and with social influence (H[sub6b]). Thus, we anticipate that the interaction terms ad campaign attitude x prior trial behavior and ad campaign attitude x social influence are negative and significant predictors of intent to smoke. We added the mean-centered interaction terms hierarchically to the predictors already in Model 2 to create Model 3. Model 3 fit better than Model 2 (χ²[subdiff] = 71.80, d.f.[subdiff] = 2, p < .01), which suggests that the addition of the interaction terms explains incremental variance in intent. Both the ad campaign attitude x prior trial behavior and the ad campaign attitude x social influence interaction terms are significant (p < .01) and negative, as we predicted, in support of H[sub6]. The interaction terms explain an additional 4% of the variance in intent to smoke.( n3)
To explore the specific nature and implications of these interactions further, we examined correlational results for ( 1) adolescents with and without prior smoking trial behavior and ( 2) adolescents with and without social influence. For adolescents with prior trial behavior, the correlation of ad campaign attitude with intent to smoke is -.38 (p < .01), and for adolescents without prior trial behavior, the correlation is -.07 (p < .05, one-tailed test). The difference in the correlations between ad campaign attitude and intent for adolescents with and without prior trial behavior is statistically significant (t = 4.52, p < .01). For adolescents with social influence, the correlation of ad campaign attitude with intent is -.35, and for adolescents without social influence, the correlation is -.06 (t = 4.63, p < .01). These correlational differences are consistent with the significant interaction terms between prior trial behavior and ad campaign attitude and between social influence and ad campaign attitude, shown in Panel B of Table 2. As we hypothesized, they suggest that a favorable campaign attitude has a stronger (and more negative) impact on intent to smoke for adolescents with prior trial behavior and for adolescents exposed to social influence.( n4)
H[sub7]: Effects of beliefs on intent and mediation analyses. To determine whether the beliefs construct mediates the effects of prior trial behavior, social influence, ad campaign attitude, ad campaign attitude x prior trial behavior, and ad campaign attitude x social influence on intent, four conditions must hold: ( 1) The predictor variables (prior trial behavior, social influence, ad campaign attitude, ad campaign attitude x prior trial behavior, and ad campaign attitude x social influence) must affect the mediator (beliefs) in the predicted direction, ( 2) the mediator (beliefs) must affect the dependent variable (intent to smoke) in the predicted direction, ( 3) the predictor variables must affect the dependent variable (intent to smoke) in the predicted direction, and ( 4) the impact of the predictors on the dependent variable (intent to smoke) must be not significant (full mediation) or reduced (partial mediation) after controlling for the mediator (beliefs) (Baron and Kenny 1986; Holmbeck 1997).
The first two conditions are largely satisfied by Model 1 (the fully mediated model) in Table 3. Consistent with the previous analyses, all predictor variables (with the exception of ad campaign attitude x social influence) affect beliefs, and beliefs affect intent. The effect of beliefs on intent supports H[sub7a], and this model also shows adequate fit indexes. The third condition is satisfied by Model 2, in which the predictor variables affect the dependent variable. As is shown in Table 3, this "predictor variable affects dependent variable" model estimates only the effects of the predictor variables on the dependent variable of intent. This model fits well, and with the exception of the ad campaign attitude → intent path, all predictor variables affect intent. The fourth condition is satisfied if the effects of the predictor variables (prior trial behavior, social influence, ad campaign attitude, ad campaign attitude x prior trial behavior, and ad campaign attitude x social influence) on the dependent variable (intent) become nonsignificant when controlling for the effects of the mediator. Model 3 (no-mediation model) in Table 3 accounts for the effects of the predictor variables on the proposed mediator (beliefs) and for the effect of the mediator on the dependent variable. If full mediation exists, the fit of Model 3 (no mediation) should not be significantly better than the fit of Model 1 (fully mediated), and the path estimates of Model 3 for the predictor variables to the dependent variable should not be significant. As is shown in Table 3, this is not the case. The fit of Model 3 is significantly better than the fit of Model 1 (χ²[subdiff] = 363.29, d.f.[subdiff] = 5, p < .01), and four of the five Model 3 paths from the predictor variables to the dependent variable are significant. Furthermore, the paths are virtually identical in magnitude to the paths of Model 2 (in which the mediator is not included), which suggests that the paths also are not partially mediated. In summary, the effects of prior trial behavior, social influence, ad campaign attitude, ad campaign attitude x prior trial behavior, and ad campaign attitude x social influence on intent are not mediated by beliefs, and H[sub7b] is not supported. Therefore, the moderating effects of ad campaign attitude on prior trial behavior and social influence on intent remain after we introduced beliefs as a mediator in the model.( n5)
Given estimates that 6.4 million of today's adolescents are likely to die prematurely because of their decision to smoke cigarettes, and given the continuing promotional activities of tobacco companies (CDC 2002), furthering the understanding of the effectiveness of counter-marketing campaigns in reinforcing antismoking beliefs and in reducing adolescent smoking intent is important. Our research used a field study for a $6.5 million antismoking campaign to address two primary questions: ( 1) Do counter-advertising campaign attitudes directly affect antismoking beliefs and intent in a manner similar to that of conventional advertisements? and ( 2) Can ad campaign attitudes have a stronger effect on beliefs and intent for adolescents with prior smoking trial behavior and for adolescents exposed to social influence (i.e., friends or siblings who smoke or an adult smoker in the home)? Our results suggest that the answer to both questions is yes, because ad campaign attitudes, prior trial behavior, and social influence all directly affected antismoking beliefs and intent to smoke. Furthermore, ad campaign attitude interacted with prior trial behavior to strengthen antismoking beliefs; in turn, ad campaign attitude also interacted with social influence and prior trial behavior to attenuate adolescent intent to smoke. On the basis of our results confirming eight of ten hypotheses, we found considerable support for the proposed relationships. We discuss implications of these results next.
Implications for Counteradvertising Programs and Public Policy
Direct and indirect effects. Our study offers implications for advertisers, social marketers, the public health community, and public policy officials on the use of counter-advertising to affect message-related beliefs and intent. These implications stem from the direct, indirect, interaction, and mediating effects that we found in the study. Given the direct and strong effect of prior smoking trial behavior on intent, our study suggests that similar antismoking campaigns can be used to help reduce continued smoking experimentation by adolescents. Creative ways of reducing continued trial may affect intent for adolescents both directly and indirectly through the networks of friends who smoke. For example, although many antismoking campaigns have focused primarily on cognitive-based reasons not to smoke, recent results suggest that affective approaches may be beneficial in counteracting favorable images for peers evoked in cigarette advertising (Pechmann et al. 2003; Perrachio and Luna 1998). Such an approach was used in our campaign and is consistent with work on understanding persuasion influence (Friestad and Wright 1994). We also found that adolescents' antismoking beliefs can be directly influenced by attitudes toward campaign advertisements. The incremental variance in beliefs explained by this campaign-related affective construct (8%) again demonstrates that well-designed antismoking campaigns can affect adolescents' smoking-related beliefs (Siegel and Biener 2000).
Interaction effects. Our study extends findings from other field studies by examining how ad campaign attitude interacts with prior trial behavior and social influence. Our results show that ad campaign attitude attenuates the influence of prior trial behavior on antismoking beliefs and intent to smoke. There also is partial support for the interaction effect of the ad campaign attitude construct for social influence in the case of intent, but not for antismoking beliefs; only the ad campaign attitude x friends interaction term had a significant and positive effect on antismoking beliefs. However, this latter finding indicates that counter-advertising campaigns, such as the one used in the present study, can be successful in lessening the influence of smoking peers on adolescent smoking beliefs held. The ad campaign attitude x friends interaction also helps extend prior research on the direct impact of antismoking advertisements on both adolescent health-risk beliefs (Pechmann et al. 2003) and adolescent antismoking beliefs (Pechmann and Knight 2002). However, overall (i.e., across all social influence types), it should be noted that the strength of ad campaign attitude is similar in our study for adolescents with and without social influence.
For smoking intent, results from Table 2 suggest that a favorable ad campaign attitude operates primarily through a negative impact on smoking intent for adolescents with prior smoking experience and for adolescents with social influence. Because these at-risk segments have developed a stronger intent to smoke, a greater opportunity exists to reduce smoking intent through antismoking ad campaigns, such as the one used in the present study. In total, our results show that the interaction effects with antismoking campaign attitude explained an additional 4% of the variance in intent to smoke.
Our interaction results are also consistent with recent experimental work by Pechmann and colleagues (2003, p. 9), who use advertisements with social disapproval messages. Their results suggest that special strategies and executions may be needed for adolescents who are susceptible to becoming smokers on a more permanent basis or who consider themselves invulnerable to long-term risks. In our case, we found success for executions that stressed industry deception and harmful effects through the use of advertising spokespeople. In general, we view our results that favorable campaign attitudes have stronger negative effects on smoking intent for segments with prior experience and close exposure to smokers as supportive for the potential of positive outcomes for carefully designed counter-marketing campaigns. However, such positive outcomes have not always been found (cf. Romer and Jamieson 2001), and the direct measurement of important influencing factors, such as prior smoking trial behavior and social influence, should be considered so that the potential effectiveness of campaign efforts is not diluted.
Mediation effects. We also hypothesized that antismoking beliefs would partially mediate the effects of social influence, ad campaign attitude, and the interactions of campaign attitude with prior trial behavior and social influence on intent. Although the results show that beliefs failed to mediate the ad campaign attitude and interaction effects even partially, they suggest a potentially important implication. That is, ad campaign affect can have a significant and negative impact on personal smoking intent that is not accounted for by antismoking beliefs, especially for adolescents who could benefit most from the campaign (i.e., ones with prior smoking trial or exposed to social influence). The effect of the ad campaign attitude interactions coupled with the significant, direct (but not mediating) effect of beliefs on intent also suggests that the affective and cognitive aspects related to a counter-marketing campaign can operate independently of each other in terms of their effects on consequences (Schwarz 1990). Moreover, our results are consistent with prior adolescent smoking research that demonstrates the importance of affective reactions in the evaluation of smoking and antismoking stimuli (cf. Pechmann and Knight 2002; Romer and Jamieson 2001).
Contributions to Marketing Knowledge
It has been proposed that affective responses to advertising can interact with product usage experience, and the relative influence of each is dependent on the product category and other contextual factors (Vakratsas and Ambler 1999, p. 36). Consistent with this view, our direct effects show that for a highly visible, mature product category such as cigarettes, prior behavior and social influence strongly influence intent. However, our results also indicate that cognitive ad campaign outcomes (e.g., antismoking beliefs related to the campaign) can have direct effects on intent to smoke, whereas affective ad campaign outcomes (e.g., ad campaign attitude) can have important interaction effects on intent to smoke. Such separate processing routes are consistent with tenets of the elaboration likelihood model, which shows that one's elaboration on affect can be as persuasive at times as that of cognitive beliefs (Petty and Cacioppo 1986, pp. 213-14). Thus, although a counter-advertising campaign attempts to convince target consumers not to engage in a particular behavior, the results support the hypothesized interactions between affective campaign responses and both prior trial behavior and social influence effects. As such, our study extends prior research on advertising effects (Vakratsas and Ambler 1999) to ( 1) a counter-advertising campaign and ( 2) prior trial behavior and social influences that encourage intent. In addition, an examination of our model of counter-advertising, prior trial behavior, and social influence indicates that it is capable of accounting for a substantial portion (i.e., 48%) of variance in smoking intent.
Further Research
As reviewed by Pechmann (1997, p. 198), a variety of approaches have been used to study the effects of antismoking ad campaigns, and now include cross-sectional (Romer and Jamieson 2001) and longitudinal (Popham et al. 1994) field studies, controlled lab experiments (Pechmann and Knight 2002; Pechmann et al. 2003), and qualitative research (Peracchio and Luna 1998). In our study, we used cross-sectional field data for a six-month counter-advertising campaign. Although our results are encouraging, the cross-sectional design prohibits us from drawing causal inferences. Longer-running field studies with longitudinal data that use the same set of respondents are desirable and could provide insight into long-term cessation rates and overall levels of smoking among adolescents (Schar and Gutierrez 2001). (For example, the CDC [1999] recommends media campaigns of several years to achieve behavioral outcomes for actual smoking rates.) Other field studies could have respondents keep ad diaries, or "metered" exposure data could be obtained for some time period, and effects on subsequent cessation and initiation rates could be assessed. In addition, in the case of actual antismoking field campaigns, researchers (with input from state tobacco control boards) must weigh the appropriate mix of counter-advertising campaign messages with other beneficial programs (e.g., school-based programs, community outreach efforts) to determine the most efficient use of the funds available for adolescent smoking prevention and cessation. Experimental research, varying exposure levels over different time frames and regions, may also provide insight into the relationships tested in this study by measuring attitudes and intent across groups with and without prior behavior. In general, research has shown that negative information has stronger effects than positive information because it may be viewed as more diagnostic in decision making (Kahneman and Tversky 1979). Experiments designed to address the impact of antismoking information presented in the context of pro-tobacco company promotions across adolescents with and without prior trial behavior and social influence would be of interest (cf. Pechmann and Ratneshwar 1994).
Other research might consider variables not measured in our study. For example, increases in price are believed to reduce adolescent intent and behavior with respect to smoking (Chaloupka et al. 2002). Thus, the strength of a pricing effect relative to long-term advertising effects and the ability of marketing-mix variables to complement one another would be of interest. On a broader level, the marketing and public health literature on how outcomes of counter-marketing media campaigns may moderate the positive effects of prior experience and social and family influences on intent and long-term behavior is in need of greater study. Comprehensive prevention programs (including counter-marketing campaigns) might examine other factors such as school misbehavior and academic failure (Bryant et al. 2002), positive parental involvement (Simons-Morton 2002), and related problem behaviors in an effort to help reduce adolescent smoking intent and use. In addition, further research might address similar direct, interacting, and mediating effects for other adolescent counter-marketing campaigns that involve drug and alcohol consumption (Block et al. 2002; Rose, Bearden, and Manning 1996). Finally, our study findings point to the importance of accounting for key underlying characteristics of the target population in the assessment of counter-marketing campaigns. Studies that examine general adolescent populations while ignoring the measurement of important factors such as prior trial behavior and social influence may mask significant effects of the campaign. In summary, we encourage the consideration of these issues to help improve the understanding of the problem consumption behaviors of adolescents while enhancing future consumer and societal welfare.
The authors gratefully acknowledge the helpful comments from Joel Cohen, Connie Pechmann, Terry Shimp, and the anonymous JM reviewers on a previous version of this article. They also appreciate the help of the Wisconsin Tobacco Control Board, BVK-McDonald, Market Strategies, David Ahrens, and Amanda Riemer in the data collection process.
( n1) Because prior behavior has been shown to be the strongest predictor of future smoking behavior (Stacy, Bentler, and Flay 1994), we do not hypothesize that the antismoking beliefs construct mediates the effect of prior behavior on intent to smoke.
( n2) A media campaign aimed at young adults directly focused on the following message themes: ( 1) secondhand smoke kills and ( 2) nicotine is addictive and tobacco is deadly. These messages served as additional objectives for adolescents; there were spillover effects of the young adult campaign for the adolescents. Given the results of the confirmatory factor analyses we describe subsequently, we decided that one beliefs construct related to the three message themes was relevant and appropriate for tests of hypotheses. Because we assessed hypotheses using a field study, we acknowledge that the industry deception, addiction, and secondhand smoke beliefs may be potentially influenced by other sources of information at the time of the campaign.
( n3) Given the four advertisement themes, three belief themes, and three types of social influence (friends, siblings, or adults in the home), we conducted several analyses that disaggregated the ad campaign attitude, beliefs, and social influence measures. First, in general, attitudes toward each of the four campaign advertisements were consistent and favorable; mean scores on a zero-to-ten scale ranging from 7.04 to 7.78. Correlations of the attitude measure for each of the four specific advertisements with the beliefs measure ranged from .20 to .29 (p < .01) and from -.23 to -.27 (p < .01) for the intent measure.
Second, because specific advertisements of the campaigns were more closely related to tobacco company deceptive practices, we also estimated all models shown in Tables 2 and 3 with only deceptiveness of the tobacco companies as the beliefs measure. In terms of statistical significance and coefficient magnitude, all independent variables that predicted beliefs (using only the deceptiveness measure) were similar to the ones in Tables 2 and 3. Use of the deceptiveness measure as a predictor of smoking intent also yielded similar results to the ones in Tables 2 and 3. Third, we analyzed each social influence separately. The results are similar to the ones in Table 2 for beliefs and intent, with the exception that the ad campaign attitude x friends interaction term affected
beliefs, and the ad campaign attitude x sibling and the ad campaign attitude x adult interaction terms did not. The ad campaign attitude x prior trial behavior term remained significant for intent across all disaggregated social influence models.
Finally, it is important to know whether there are any differences in the relative abilities of the four advertisement themes to predict smoking intent for adolescents who had and had not tried smoking in the past. For adolescents who had not tried smoking, the correlations of each advertisement with intent ranged from -.03 ("Mohammed") to -.08 ("Janet"), without any significant differences across the correlations (Cohen and Cohen 1983). For adolescents who had tried smoking, the correlations of each advertisement with intent ranged from -.24 ("FACT") to -.46 ("Mohammed"), again without any significant differences across the correlations.
( n4) We also performed similar tests for the beliefs measure. For adolescents with prior trial behavior, the correlation of ad campaign attitude with beliefs is .47; for adolescents without prior trial behavior the correlation is .20 (t = 4.05, p < .01). For adolescents with social influence, the correlation of ad campaign attitudes with beliefs is .34; for adolescents without social influence the correlation is .24 (t = 1.67, p < .05, one-tailed test). As with intent, the results suggest that a favorable ad campaign attitude has a greater positive impact on antismoking beliefs for adolescents with prior trial behavior and social influence.
( n5) Partial mediation is supported if the effects of the predictors on the dependent variable are significantly diminished after accounting for the mediator. Thus, we tested whether the regression coefficients for social influence, ad campaign attitude, ad campaign attitude x prior trial behavior, and the ad campaign attitude x social influence were lower when we controlled for the effect of beliefs. Using linear regression and equations suggested by Kenny, Kashay, and Bolger (1998), we found that partial mediation was not statistically supported for any predictor variable. That is, there were no significant differences between the predictor variable coefficients with or without the effects of beliefs being controlled. We attempted to test the extent to which common methods variance affected our results using Netemeyer and colleagues' (1997) procedures. Tests for common method effects using SEM required direct (linear) effects and multiple-item measures for the constructs. Given these requirements, we estimated the potential biasing impact of common methods for the beliefs → intent path. With this test, we found no evidence that common methods variance attenuated the beliefs → intent path.
We also conducted analyses to assess the effects of some key variables that we did not include in our model estimation (e.g., price of cigarettes, parental vigilance, academic performance, parents' education level). Our analyses revealed that the omitted variables were not likely to account for the effects we report in our model estimation. The analyses are also available on request.
Legend for Chart:
B - Mean
C - Standard Deviation
D - 1
E - 2
F - 3
G - 4
H - 5
I - 6
J - 7
A
B C D E F
G H I J
1. Prior trial behavior
.27 .44 --
2. Social influence
.72 .86 .48 --
3. Ad campaign attitude
7.31 2.11 -.19 -.20 .76(a)
4. Ad campaign attitude x prior trial behavior
-- -- -.23 -.22 .60
--
5. Ad campaign attitude x social influence
-- -- -.14 -.20 .27
.56 --
6. Beliefs
3.29 .38 -.16 -.16 .30
.29 .12 .79(a)
7. Intent
1.34 .62 .54 .44 -.29
-.40 -.30 -.30 .85(a)
Notes: (a) Italicized entries on the diagonal for Variables 3,
6, and 7 are coefficient alpha estimates of internal consistency.
All correlations are statistically significant (p < .01). A. Results with Antismoking Beliefs as Dependent Variable
(n = 943)
Legend for Chart:
B - Fit Estimates χ²
C - Fit Estimates d.f.
D - Fit Estimates χ² diff
E - Fit Estimates d.f.[subdiff]
F - Fit Estimates CFI
G - Fit Estimates NNFI
H - Fit Estimates RMSEA
A B C D E F G H
Model 1 104.30 19 -- -- .96 .95 .07
Model 2 40.09 18 64.21(***) 1 .99 .98 .04
Model 3 29.35 16 10.74(***) 2 .99 .98 .03
Legend for Chart:
B - Direct Effects Only Model 1
C - Direct Effects Only Model 2
D - Direct and Interaction Effects Model 3
A B C
D
PB → beliefs -.10 (-.14)(***) -.07 (-.10)(***)
-.06 (-.08)(***)
SI → beliefs -.03 (-.09)(**) -.02 (-.05)(*)
-.01 (-.05)
ACA → beliefs .04 (.30)(***)
.03 (.21)(***)
ACA x PB → beliefs
.04 (.16)(***)
ACA x SI → beliefs
-.01 (-.04)
R² .05 .13
.15
B. Results with Intent to Smoke as Dependent Variable (n = 924)
Legend for Chart:
B - Fit Estimates χ²
C - Fit Estimates d.f.
D - Fit Estimates χ² diff
E - Fit Estimates d.f.[subdiff]
F - Fit Estimates CFI
G - Fit Estimates NNFI
H - Fit Estimates RMSEA
A B C D E F G H
Model 1 346.44 49 -- -- .93 .92 .08
Model 2 307.32 48 39.12(***) 1 .94 .93 .08
Model 3 235.52 46 71.80(***) 2 .95 .91 .06
Legend for Chart:
B - Direct Effects Only Model 1
C - Direct Effects Only Model 2
D - Direct and Interaction Effects Model 3
A B C
D
PB → intent .70 (.49)(***) .67 (.47)(***)
.63 (.44)(***)
SI → intent .15 (.21)(***) .14 (.19)(***)
.12 (.16)(***)
ACA → intent -.04 (-.18)(***)
-.01 (-.03)
ACA x PB → intent
-.10 (-.22)(***)
ACA x SI → intent
-.03 (-.09)(***)
Beliefs → intent
R² .41 .44
.48
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: Paths not in parentheses are unstandardized, and paths
in parentheses are standardized. PB = prior trial behavior,
SI = social influence, and ACA = ad campaign attitude. Legend for Chart:
B - Fit Estimates χ²
C - Fit Estimates d.f.
D - Fit Estimates χ² diff
E - Fit Estimates d.f.[subdiff]
F - Fit Estimates CFI
G - Fit Estimates NNFI
H - Fit Estimates RMSEA
A B C D E F G H
Model 1 462.88 45 -- -- .90 .80 .09
Model 2 235.52 46 -- .95 .91 .06
Model 3 99.59 40 363.29(***) 5 .99 .97 .04
Legend for Chart:
B - Model 1 Fully Mediated
C - Model 2 PV Affects DV
D - Model 3 No Mediation
A B C
D
PB → beliefs -.08 (-.12)(***)
-.06 (-.08)(***)
SI → beliefs -.02 (-.05)(*)
-.02 (-.04)
ACA → beliefs .03 (.21)(***)
.03 (.21)(***)
ACA x PB → beliefs .05 (.20)(***)
.05 (.18)(***)
ACA x SI → beliefs -.01 (-.03)
-.01 (-.04)
PB → intent .63 (.44)(***)
.61 (.43)(***)
SI → intent .12 (.16)(***)
.11 (.15)(**)
ACA → intent -.01 (-.03)
.00 (-.01)
ACA x PB → intent -.10 (-.22)(***)
-.09 (-.19)(***)
ACA x SI → intent -.03 (-.09)(***)
-.03 (-.10)(***)
Beliefs → intent -.71 (-.36)(***)
-.31 (-.16)(***)
R²
Beliefs .16
.15
Intent .21 .48
.51
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes: Paths not in parentheses are unstandardized, and paths
in parentheses are standardized. PV = predictor variable,
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Social Influence
Do you have any brothers or sisters who currently smoke cigarettes? (Sibling)
Is there an adult in your household who is a regular smoker? (Adult smoker)
How many of your four closest friends smoke cigarettes? (Friends)
Prior Trial Behavior
Have you ever tried cigarette smoking, even one or two puffs?
Ad Campaign Attitude
For each of the four specific advertisements in the campaign, respondents were asked, "How much did you like the advertisement?"
Antismoking Beliefs
Tobacco Company Deception Beliefs
Tobacco companies specifically try to get young people to start smoking.
Tobacco companies fool young people into believing smoking is okay.
Tobacco companies encourage people to start smoking.
Tobacco companies use deceptive practices to get people hooked on smoking.
Secondhand Smoke Beliefs
Breathing smoke from someone else's cigarette is harmful.
Secondhand smoke is dangerous to nonsmokers.
Secondhand smoke is not as dangerous as people make it out to be. (Reverse coded)
Secondhand smoke kills people.
Smoking Addictiveness Beliefs
Smoking is addictive.
Nicotine is physically addictive.
Tobacco is a deadly product in any form.
Tobacco is a dangerous product.
Intent to Smoke
If one of your best friends offered you a cigarette, would you smoke it?
Do you think you will smoke a cigarette at anytime during the next year?
Do you think you will be smoking cigarettes five years from now?
~~~~~~~~
By J. Craig Andrews; Richard G. Netemeyer; Scot Burton; D. Paul Moberg and Ann Christiansen
J. Craig Andrews is Professor and Charles H. Kellstadt Chair in Marketing, College of Business Administration, Marquette University (e-mail: craig.andrews@marquette.edu). Richard G. Netemeyer is Professor of Commerce, McIntire School of Commerce, University of Virginia (e-mail: rgn3p@forbes2.comm.virginia.edu). Scot Burton is Professor and WalMart Chairholder in Marketing, Sam M. Walton College of Business, University of Arkansas (e-mail: sburton@walton.uark.edu). D. Paul Moberg is Director, Center for Health Policy & Program Evaluation, and Senior Scientist, Department of Population Health Sciences, University of Wisconsin Medical School (e-mail: dpmoberg@wisc.edu). Ann Christiansen is Associate Researcher, Comprehensive Cancer Center, University of Wisconsin (e-mail: christia@uwccc.wisc.edu).
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 192- Understanding Firms' Customer Satisfaction Information Usage. By: Morgan, Neil A.; Anderson, Eugene W.; Mittal, Vikas. Journal of Marketing. Jul2005, Vol. 69 Issue 3, p131-151. 21p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.69.3.131.66359.
- Database:
- Business Source Complete
Understanding Firms' Customer Satisfaction Information
Usage
Despite theoretical and empirical research linking a firm's business performance to the satisfaction of its customers, knowledge of how firms collect and use customer satisfaction information is limited. The authors investigate firms' customer satisfaction information usage (CSIU) by drawing on in-depth interviews, a focus group of managers, and the existing literature. They identify key characteristics of the major processes involved in firms' CSIU and compare the CSIU practices revealed in their fieldwork with widely held normative theory prescriptions. They also identify variations in CSIU among the firms in the fieldwork and uncover factors that may help explain the observed differences.
There is increasing evidence linking a firm's financial performance to the level of satisfaction reported by its customers (Anderson, Fornell, and Lehmann 1994; Anderson, Fornell, and Rust 1997; Bolton 1998). Therefore, managers are keen to discover how to improve customer satisfaction (CS) and thus business performance (Piercy and Morgan 1995; Westbrook 2000). The literature posits that the accomplishment of this goal requires formal systems that are designed to understand and monitor CS (Sharma, Niedrich, and Dobbins 1999; Westbrook 2000; Woodruff 1997). Such CS systems are prescribed to provide managers with practical insights into how the firm's resources should be deployed to improve satisfaction (Hayes 1992; Heskett et al. 1994; Mittal, Ross, and Baldasare 1998) and with timely and accurate leading indicators of future financial performance (Fornell 1992; Fornell et al. 1996; Ittner and Larcker 1998).
Despite this strongly advocated normative prescription, little is known about the processes by which firms actually collect and use customer satisfaction information (CSI). Many important questions remain unanswered: What are the key processes that should constitute firms' customer satisfaction information usage (CSIU)? What do firms actually do in practice? Are there areas in which CSIU practice is at variance with normative prescriptions, and if so, why? and Does CSIU enable firms to gain significant customer-based insights and thus gain competitive advantage?
The purpose of this article is to illuminate the internal processes by which firms monitor and use CSI. We begin by drawing on insights from field research and the extant literature to develop a model of CSIU, identifying the key components of CSIU and how these are linked together. Next, we use our model to compare and contrast CSIU in practice with widely held normative prescriptions from the literature. On the basis of our findings, we then identify contingency factors that affect how firms implement CSIU in practice. Finally, we bring together our model and the identified contingency factors in a fully explicated model of CSIU processes, influencing factors, and the association between CSIU and firm performance. We conclude with a discussion of the implications of our work for both theory and practice.
We believe that this investigation is timely and important for both improving practice and developing theory. Customer satisfaction data collection is typically the single largest item of firms' annual expenditure on market intelligence and is often the only systematic market intelligence that a firm generates (Wilson 2002). Yet there is little or no guidance for managers on how exactly to design and implement CS systems successfully (Piercy and Morgan 1995; Powaga 2002). It is not well understood what the components of such a system should be or how they should be managed to yield maximum benefit to the firm (Griffin et al. 1995; Hauser, Simester, and Wernerfelt 1994; Westbrook 2000). As a result, many CSIU initiatives fail to reach their potential in terms of providing the hoped-for benefits of either increased CS or improved financial performance; this has resulted in a growing frustration among managers with their firms' CS programs (Reichheld 1996; Rigby 1999). A better understanding of CSIU and how best to implement such systems in practice is necessary, or managerial skepticism is likely to grow, and the resources required to support such efforts will be allocated elsewhere (Rust, Zahorik, and Keiningham 1994).
Achieving a better understanding of CSIU is also important with respect to the broader landscape of organizational theory and systems theory in marketing as a whole. Customer satisfaction information usage lies at the heart of firm's market orientation (i.e., the ability of a firm to understand and respond to its environment) and should be a major contributing factor to any link between a firm's degree of market orientation and its financial performance (Hunt and Morgan 1995; Jaworski and Kohli 1993; Narver and Slater 1990). Developing an improved understanding of firms' CSIU should therefore contribute to the further explication of the underlying processes of market orientation and the theoretical mechanisms by which it is associated with firm performance. Moreover, there is growing interest in research that links marketing activities to firms' business performance. Customer satisfaction has been identified as a key outcome of marketing activities associated with subsequent business performance (Fornell et al. 1996) and therefore is widely viewed as a useful metric in implementing marketing strategy and monitoring marketing and firm performance (Hauser, Simester, and Wernerfelt 1994; Rust et al. 2004). Understanding how best to create and manage marketing control systems using appropriate performance metrics such as CS is a potentially important contribution (Morgan, Clark, and Gooner 2002; Srivastava, Shervani, and Fahey 1998).
Theory Framework
Customer satisfaction information usage refers to the processes that a firm uses to monitor, diagnose, and take action to optimize CS. We posit four distinct CSIU processes by synthesizing insights from models of information use in marketing information systems, organizational learning in management, market research utilization, and market information processing in marketing. First, CS data scanning refers to the generation of CS data. This is consistent with the intelligence generation process in market information-processing models (Jaworski and Kohli 1993) and the information acquisition process in organizational learning (Huber 1991). Second, CS data analysis refers to the examination and organization of CS to imbue it with meaning. This process is consistent with aspects of the interpretation stage of organizational learning (Crossan, Lane, and White 1999) and of sensemaking in models of organizations as information processing systems (Glazer 1991; Thomas, Clark, and Gioia 1993). Third, CSI dissemination refers to the exchange of CSI within the firm. This process is consistent with the dissemination stage of market information-processing (Maltz and Kohli 1996) and organizational learning (Slater and Narver 1995) models and the communication stage of market research utilization models (Menon and Varadarajan 1992). Fourth, CSI utilization refers to how a firm uses CSI to understand the environment, make decisions, and deploy resources. This process is consistent with conceptualizations of knowledge utilization in models of market information processing (Moorman 1995) and market research use (Deshpandé and Zaltman 1987).
Three main bodies of literature indicate the importance of firms' CSIU and provide a theoretical foundation linking it to business performance. First, the strategic management literature posits that business performance is a function of a firm's ability to process information in ways that enable it to adapt to its environment (Boisot and Child 1999; Cockburn, Henderson, and Stern 2000). Indeed, the systems perspective in management views information processing as the fundamental task of any organization (Daft and Weick 1984; Thomas, Clark, and Gioia 1993). More recent theory developments in this vein focus on models of organizational learning, delineating the processes by which firms create and use knowledge through information processing (Huber 1991) and the benefits of doing so for a firm's ability to adapt to its environment successfully (Crossan and Bedrow 2003). To the extent that CSIU affects a firm's processing of relevant environmental information, and therefore the firm's ability to adapt to its environment, CSIU should contribute to business performance.
Second, drawing on the organizational learning perspective, market orientation theory focuses explicitly on learning about markets and posits that firms that are engaged in more extensive market information processing develop superior knowledge about customers, competitors, and channel members (Day 1994b; Sinkula 1994). Firms with superior market orientation develop a "know-what" advantage over rivals that enables them to deploy their available resources in ways that more closely match target customer requirements and thus deliver superior customer value (Hunt and Morgan 1995; Slater and Narver 1998). As a result, market orientation theory indicates that firms with superior CSIU should have superior customer knowledge and be able to develop offerings that better satisfy the needs and wants of target customers (Day 1994a; Jaworski and Kohli 1993; Slater and Narver 1995).
Third, control systems theory indicates that CSIU may provide an important mechanism for directing the firm's resource deployments and the behavior of its personnel. Control systems are formalized routines and procedures that use information to maintain or alter patterns in organizational activity (Jaworski 1988; Simons 1995). Control systems theory identifies four core steps in management control systems: ( 1) setting a desired performance standard, ( 2) collecting and communicating information related to actual performance, ( 3) comparing this information with the performance standard, and ( 4) taking corrective action when necessary (Anthony 1988; Green and Welsh 1988). Customers have been identified as an important relational resource for firms (Srivastava, Shervani, and Fahey 1998), and CS has been identified as a leading predictor of firms' financial performance (Fornell 1992). Therefore, CSIU may be an important component of a firm's management control system that aids in monitoring performance (Ittner and Larcker 2003), implementing strategy (Kaplan and Norton 1996), and directing attention and resources toward satisfying target customer needs to develop and protect this relational source of competitive advantage (Griffin et al. 1995).
Having identified four CSIU subprocesses suggested in the literature and noting three streams of literature that indicate the importance of CSIU in firm performance, we now turn our attention to the methodology we used in our study to deepen the understanding of firms' CSIU.
Research Approach
Researchers have noted that given the undeveloped state of knowledge in this important domain, understanding firms' CSIU requires significant conceptual development (Piercy and Morgan 1995; Westbrook 2000). Inductive field research has been identified as the most appropriate research approach to enhance understanding of phenomena in relatively undeveloped areas of knowledge (Bonoma 1985; Flint, Woodruff, and Gardial 2002; Zaltman, LeMasters, and Heffring 1982). Whereas inductive research in marketing has been most closely equated with the "interpretive" perspective in consumer research, it has also been successfully used to enhance the understanding of key organizational issues (Kohli and Jaworski 1990; Narayandas and Rangan 2004; Workman, Homburg, and Gruner 1998).
The nature of CSIU requires a balance between a "coarse-grained" field research approach to capture the essence of the wide-ranging domain of CSIU and a "fine-grained" approach to identify important variables and relationships that provide both a basis for future empirical research and more specific insights for managers (Eisenhardt 1989; Harrigan 1983). We bridge these conflicting requirements by adopting a discovery-oriented approach (Kohli and Jaworski 1990; Menon et al. 1999). Rather than rely on either fieldwork observations (Glaser and Strauss 1967) or existing theory and literature (Srivastava, Shervani, and Fahey 1998), we iteratively synthesize literature and field-based insights to develop a comprehensive conceptual framework that identifies key factors and relationships that enhance the understanding of firms' CSIU (Burawoy 1991; Gioia and Pitre 1990; Workman, Homburg, and Gruner 1998).
The first stage in our research involved using insights from the literature to establish initial boundaries to focus our inquiry and to guide the selection of an appropriate field research sample (Bonoma 1985; Eisenhardt 1989). We reviewed the available literature on CSIU and the broader literature on marketing information systems, organizational learning, management control systems, market information processing, and market research utilization to identify factors that may be important to the understanding of firms' CSIU. We used these literature-based insights to develop an initial conceptual framework from which we constructed a semistructured interview protocol for use in open-ended, in-depth interviews (see the Appendix). The interview protocol enabled us to focus our fieldwork investigation while providing the flexibility to incorporate fieldwork observations of issues outside the domain of our initial framework.
We then considered the field research sample that may be appropriate. Because previous research has not identified important contingencies that may affect CSIU, we adopted a purposive sampling plan to ensure the representation of a wide variety of firms in terms of areas of business activity, geographic scope, and size (Workman, Homburg, and Gruner 1998). We also included a specialist consulting firm in our sample because each consultant's exposure to CSIU in many different firms and industries provides the potential for unique insights.
Next, we conducted face-to-face in-depth interviews with individual managers. Each interview lasted between 60 and 90 minutes. We conducted multiple interviews at each firm in our sample to triangulate and build on information regarding CSIU in each firm (Eisenhardt 1989). In each firm, we attempted to identify and interview designers of CS systems, personnel who were intimately involved in the ongoing operation of CS systems, and managers who we expected to be users of CSI.
As we show in Table 1, we interviewed a total of 142 managers in 38 different firms.( n1) Excluding the specialist consulting firm (for which we focused our interviews on the CSIU of the firm's clients), the 37 firms we examined ranged widely in terms of size and industry type. Of the 37, 6 are based outside the United States. For 3 firms (AirCo, FilterCo, and ScienceCo), we conducted the interviews at a time when the firms were redesigning their CS systems. Given the logistical issues involved in interviewing such a large number of managers in different locations and the desirability of conducting interviews in the same general time period, it was not possible for us to conduct all the interviews personally. To balance the competing demands of logistical and time constraints with the need to maintain as much standardization as possible in the field research to ensure comparability (Bonoma 1985; Eisenhardt 1989), we adopted a two-stage approach. First, we conducted 47 interviews across 14 firms. Even though we guaranteed confidentiality, when research access was granted, most firms identified their CS systems as "commercially sensitive" and would not allow the interviews to be taped. Therefore, following established approaches (Workman, Homburg, and Gruner 1998), we took handwritten notes during each interview and elaborated on and transcribed them within 24 hours of the conclusion of each interview.
Second, using the experience and insight gained from this first round of interviews, we developed a detailed protocol that we used to train two different groups of graduate students to aid in subsequent data collection.( n2) The first group of students were participants in an executive MBA program, and the second group were participants in a fulltime MBA program who were enrolled in a marketing strategy class. For the executive MBA students, we selected employer firms of individual students that were appropriate research sites, and we contacted an identified manager at the firm. For the second group, we selected appropriate research sites and again negotiated access. We then gathered the graduate students into small teams and trained them to use the interview protocol to collect the required data. At least two students conducted each interview to ensure that all required questions and appropriate follow-up prompts were used (Bonoma 1985; Eisenhardt 1989). All the students involved in the interview jointly transcribed it within 24 hours. Subsequently, we debriefed the students to clarify any points that arose from the transcribed notes. In addition, we required each student group to pool the insights from all the interviews in a particular firm and to produce a written report outlining CSIU in that firm.
Although interviewer bias is always a possibility in qualitative data collection, the use of open-ended questions, the involvement of more than one interviewer in the vast majority of interviews, and multiple interviews in each research site helped reduce the likely presence and impact of such bias in our study (Narayandas and Rangan 2004; Strauss and Corbin 1998). We then reviewed all 142 individual interview transcripts and the reports on each firm that the graduate students produced. We used the insights generated from this review to modify and refine the initial literature-based framework. We then used the revised conceptual framework to guide a second, more focused review of the literature. Our aim was to gain theoretical support (or a lack thereof) for the insights that had emerged thus far. In addition, during this stage of the research, we used the emerging conceptualization of CSIU as the basis of interview discussions with three academics (all of whom have studied various aspects of CSIU) to provide insights that may not have been available from the fieldwork or the existing literature.
In the final stage of our research, we discussed the conceptual framework and fieldwork observations regarding CSIU practice in a focus group setting with 12 managers from seven different firms. The managers in our focus group were intimately connected with some aspect of CSIU and worked in a wide range of industries, none of which had been a part of our fieldwork interview sample. This stage of the research provided an additional form of "triangulation" (Deshpandé 1982; Menon et al. 1999), which enabled us to assess the face validity of our conceptual framework and our fieldwork insights. It also enabled us to refine key CSIU constructs, explicate expected relationships, and achieve a balance among theoretical rigor, domain coverage, model parsimony, and managerial relevance (Zaltman 1997).
CSIU
We now turn to our fieldwork findings about CSIU, drawing particular attention to aspects of CSIU in which we observed substantial departures from extant theory prescriptions. We summarize the most salient characteristics of each of the four CSIU subprocesses in Figure 1 and the pertinent numerical data from our fieldwork observations in Table 2.
CS data scanning. We identified four salient aspects of CS data scanning: formalization, frequency, measures, and sampling. "Formalization" refers to the degree to which standardized rules and procedures are used to gather CS data (Menon et al. 1999). Our fieldwork indicates that managers view formalization as enabling CS scanning efficiencies through specialization and routinization. Consistent with institutional theory (Feldman and March 1981; Zeitz, Mittal, and McCauly 1999), formalization is also sometimes viewed as having a symbolic value in signaling the importance of CSI within the firm. In addition, our fieldwork suggests that formalization is viewed as useful in minimizing potential risks associated with data collection. For example, at CellCo, the CS program manager, who championed a more formalized data collection system, noted, "We want to avoid the risk that different parts of our organization approach the same customers at different times with almost the same questions, which would certainly annoy customers." Despite these benefits, we observed that 10 of the 37 companies in our sample did not have formal CS data collection processes. The literature also advocates complementing formalized CS data collection processes with informal customer feedback to achieve richer insights (Chakrapani 1998; Day 1994b). In the 27 firms with formalized systems, our fieldwork revealed some support for normative propositions that supplementing formalized CS data-scanning processes with more informal CS data collection can enhance the customer knowledge generated. For example, a manager at CruiseCo commented, "I also sometimes undertake some informal qualitative research work. It's the only way to really understand what drives customers scoring a '4' versus those scoring a '5' in our surveys."
"Frequency" of CS data scanning refers to the number of times various scanning activities are performed in a given time frame. In our fieldwork, we observed that firms often invest in several different CS scanning activities. The number of different CS data collections (i.e., separate data collection exercises that generate CS data) ranged from one to six for each of the 37 firms in our sample (mean = 2.11, mode = 1). Normative prescriptions suggest that CS data scanning should be a "continuous process" (Chakrapani 1998; Day 2000). However, in our fieldwork, we found large variations in CS scanning frequency with firms that collected CS data--daily: 15 firms; weekly: 2 firms; monthly: 6 firms; quarterly: 2 firms; biannually: 3 firms; annually: 5 firms; and less frequently: 4 firms.
"Measures used" refers to the specific indicators of CS and related constructs with which firms collect data. The consumer behavior (Spreng, MacKenzie, and Olshavsky 1996; Yi 1990) and managerial (Naumann and Giel 1995) literature suggest a wide range of different CS measures. Normative prescriptions advocate capturing standardized CS data on attribute-level and overall satisfaction and on important postpurchase intentions to enable tracking over time and offer diagnostic insights; normative prescriptions also advocate open-ended questions to facilitate a richer understanding (Gale 1994; Hanan and Karp 1989). In the 78 different CS data collections across the 37 firms in our fieldwork, we observed that attribute-specific CS questions were the most common and were used in 64 data collections; overall CS questions were used in 58 data collections, likelihood-to-recommend questions were used in 40 data collections, future purchase intention questions were used in 38 data collections, and open-ended questions of various types were used in 28 data collections. In addition, as we show in Table 2, our fieldwork revealed that firms use several different data collection mechanisms to collect CS data on these measures.
"Sampling" refers to the ways that firms identify and target customers from whom they collect CS data. We observed significant sampling differences among firms. Our fieldwork suggests that there are particularly important differences in the identification of strategic versus other types of existing customers and the inclusion of "lost" and competitors' customers in the sample. The literature suggests the collection of CS data from representative samples that include all current customers (with the ability to identify key accounts or strategic customers separately), lost customers, and competitors' customers (Griffin and Hauser 1993; Reichheld 1996; Rust, Zeithaml, and Lemon 2000). In our fieldwork, all 37 firms collected CS data from a sample of all of their current customers. However, only 7 firms separately identify and collect CS data from strategic customers, only 4 firms collect data from competitors' customers, and only 2 firms collect data from lost customers.
CS data analysis. Our research revealed three particularly important characteristics of the CS data analysis process: data integration, analytical sophistication, and relationships examined. "Data integration" refers to the degree to which CS-related data from different sources are combined and considered holistically as a single data set (Zahay and Griffin 2002). We found that many firms collect CS-related data of multiple kinds and through multiple scanning activities. For example, many of the firms we studied track CS scores and have customer complaint monitoring systems. The literature prescribes integrating such diverse CS-related data for analysis to provide data synergy benefits and enable richer interpretation (Davenport and Klahr 1998; Powaga 2002). The value of such CS-related data integration was also emphasized in our interviews. For example, a research manager at ImageCo commented, "Pulling all the formal and informal CS data from different sources together provides real opportunities for insight." However, the firms in our sample varied in their efforts to integrate diverse sets of available CS-related data, and only 8 firms considered CS-related data from different sources simultaneously in analyzing CS. Many of the 29 firms that undertook no data integration viewed this as a weakness in their CSIU.
"Analytical sophistication" refers to the complexity of the statistical analysis approaches used to interpret and derive meaning from CS data. The literature advocates using sophisticated multivariate data analyses in analyzing CS data (Anderson and Mittal 2000). However, our fieldwork indicates wide variation in analytical sophistication among firms. For example, of the 37 firms in our fieldwork sample, 3 undertake no quantitative analysis at all, and 34 undertake univariate analyses, primarily calculating means, frequencies, and trend lines for attribute-level and overall CS scores. However, only 14 of these firms also use more sophisticated multivariate analyses, primarily multiple regression analyses of the relationships between CS and individual attributes and in constructing overall CS "index" scores. Despite this, our fieldwork indicates that managers in firms that use more sophisticated multivariate analyses believe that deeper and more actionable insights are realized through the firm's CS data analysis.
"Relationships examined" refers to the linkages among variables that are studied in CS data analysis. Our fieldwork suggests that analyzing relationships between CS drivers and CS and between CS and other internal metrics and performance outcomes is an important characteristic of a firm's CS data analysis. The literature suggests examining relationships between overall CS and ( 1) attribute-level performance to identify CS drivers (Chakrapani 1998; Sharma, Niedrich, and Dobbins 1999), ( 2) postpurchase phenomenon to identify drivers of buying and recommendation behavior (Gale 1994; Perkins 1993), and ( 3) other internal performance metrics to understand the firm's "service-profit" chain and validate the firm's performance-monitoring system (Anderson and Mittal 2000; Rust, Zahorik, and Keiningham 1994). In our fieldwork sample, 34 firms relate current attribute-level and overall CS to prior scores in their CS data analysis. However, only 14 firms relate attribute-level satisfaction to overall CS, only 7 firms relate overall CS to future purchase intentions, and only 2 firms relate CS to likelihood-to-recommend. We observed regular CS data analyses that examined linkages between CS and customers' subsequent purchase behavior in only 1 firm (CarCo). Furthermore, we encountered efforts (single, isolated experimental projects) to relate CS with other internal performance metrics, such as employee satisfaction and sales, in only 1 firm (AirwayCo). None of the firms in our sample had ever tried linking CS data to their financial performance.
CSI dissemination. A firm often generates CS data in one particular department in the firm (e.g., marketing research, customer service department), whereas employees acting on that information reside in various other departments of the organization. Therefore, it is crucial that CSI is disseminated to these internal audiences (Day 2000). Our fieldwork indicated three important aspects of CSI dissemination: frequency, vertical and horizontal dissemination, and recipient perceptions. "Frequency" refers to the number of CSI dissemination events during a given period of time (Fisher, Maltz, and Jaworski 1997). The literature prescribes frequent dissemination to emphasize the value of CSI and to provide personnel with timely information for decision making (Dutka 1993; Maltz and Kohli 1996). Our fieldwork indicates that managers believe that frequent dissemination leads to CSI being more routinely used. For example, FinanceCo senior managers view their monthly dissemination of CSI to all branches as key in maintaining a customer focus. One respondent indicated, "The results are widely anticipated and a great source of excitement for these [front-line service] employees." Conversely, at DrugCo, one manager commented, "Communicating satisfaction research here is so rare that it never becomes a part of what our product and sales managers routinely think about." Table 2 reveals wide variance in CSI dissemination frequency among the 37 companies in our sample; more than half the firms disseminate CSI quarterly or less frequently. Notably, CSI is often not disseminated as frequently as it is collected and analyzed in our fieldwork sample.
The extent of "vertical and horizontal dissemination" refers to the degree to which CSI is disseminated up and down the firm's hierarchy and across functional areas. Our fieldwork suggests that managers view such dissemination as an important determinant of CSI use in decision making. As one ConsultCo manager commented, "Measurement doesn't change anything, people change things.… So you have to make sure you get the satisfaction data in the hands of whoever may be able to use it." The literature advocates horizontal CSI dissemination to focus attention on the CS outcomes of all activities within the firm (Griffin et al. 1995; Hanan and Karp 1989) and vertical dissemination to provide important control information to senior managers and information that is useful in guiding the behavior of frontline employees (Davenport and Klahr 1998; Gale 1994). However, we observed that 12 of the 37 firms in our sample do not undertake any horizontal dissemination of CSI. In an extreme example at NetworkCo, a design vice president was amazed to discover from the researcher conducting the interview that the firm had an extensive system for collecting satisfaction data from its largest customers. Furthermore, 5 of the 37 firms in our sample do not engage in upward vertical dissemination of CSI, and 14 do not routinely engage in any downward vertical dissemination to frontline employees.
"Recipient perceptions" refer to the perceived characteristics of CSI among those to whom it is disseminated. Both fieldwork observations and the literature suggest that such user perceptions are critical determinants of the utilization of CSI (Moorman, Deshpandé, and Zaltman 1993). Our fieldwork suggests that three types of recipient perceptions are of particular importance: ( 1) Accuracy is the degree to which recipients view the CSI as valid and reliable (Moenaert and Souder 1990; Piercy and Morgan 1995), ( 2) usability is the degree to which recipients perceive CSI to be relevant and timely (Maltz and Kohli 1996; Menon and Varadarajan 1992), and ( 3) diagnosticity is the degree to which CSI enables the recipient to understand the drivers of the level of CS (Sharma, Niedrich, and Dobbins 1999; Woodruff 1997). For example, CellCo's account managers suggested that they ignore the CSI they receive because they believe that the CS questions are flawed, thus producing invalid responses. One manager commented, "The people [collecting CSI data] have no clue about our industry or our customers." In another example, managers at MedicCo reported being reluctant to attach much importance to CSI in making decisions because the information was typically five-to six-months old when they received it. Furthermore, supporting the importance of diagnosticity, a manager at InsCo commented, "People here say 'don't give me numbers, just tell me what I got to do!'"
CSI utilization. Our fieldwork indicates that a firm's CSI does not have value unless it is translated into appropriate strategy and tactics. For example, one vice president stated, "We know which of our clients are happy with us and which aren't. We know that before we call them. The real question is what do we do with the [CS survey] information once we get it?" The literature suggests that firms should use CSI as an important input in almost all significant decisions across all functional areas (Hanan and Karp 1989; Jaworski and Kohli 1993). However, our fieldwork reveals that most of the firms in our sample use CSI as an input in only a limited number of decisions, most of which are in the domain of customer service and account management. The degree to which CSI is used in decisions outside these domains appears to be an important feature that distinguishes CSI utilization differences among firms. For example, a PowerCo manager commented, "It's amazing the number of decisions that impact customers that get taken here without considering our satisfaction data." Conversely, at SportCo, senior managers indicated that CSI is an important input in significant product resource allocation decisions with respect to roster changes and free-agent hires.
Information utilization has been theoretically conceptualized in terms of instrumental and conceptual use (Menon and Varadarajan 1992), and the literature suggests that firms should benefit from both (Day 2000; Slater and Narver 1998). "Instrumental" use refers to using information directly to solve a specific problem or make a particular decision (Moorman 1995). Our fieldwork suggests that in most firms, CSI utilization is typified by instrumental use, such as the identification of key drivers of overall satisfaction and the execution of decisions designed to manage the firm's performance on these attributes (Sharma, Niedrich, and Dobbins 1999). Of the 37 firms in our sample, 31 engage in some form of instrumental CSI utilization of this kind. For example, at FinanceCo, CSI indicated that customers were dissatisfied with the reliability of the bank's automated teller machines. Therefore, senior managers invested $10 million to upgrade metro location machines, and with this aspect of the bank's service, CS subsequently increased significantly.
"Conceptual" use refers to using information to enhance thinking processes that do not lead to short-term actions (Menon and Varadarajan 1992). For example, conceptual use of CSI may entail learning about customer preferences for existing products. Therefore, it is more forward looking and enables managers to identify opportunities for developing new offerings (Moorman 1995). However, we identified only eight firms in our sample that exhibit conceptual utilization of CSI. For example, at MedicCo, CS data trends led managers to conclude that customers had pastoral care needs that were not being met. This resulted in the development of a new set of services that was radically different from those previously available. More typically, however, our fieldwork suggests that managers often consider the CSI they receive as tactical rather than strategic in terms of the revealed insights.
In our fieldwork, many managers voiced a strong belief that effective CSIU is associated with superior business performance. Although we were not able to verify this empirically--and notably, none of the firms in our sample had ever tried to do so--these beliefs are consistent with the theoretical (Anderson and Mittal 2000; Rust, Zahorik, and Keiningham 1994) and managerial (Flanagan and Fredericks 1993; Naumann and Giel 1995) literature. Our fieldwork suggests that CSIU is related to three different types of performance outcomes. First, both the literature and the fieldwork suggest a relationship between CSIU and employee outcomes (Heskett et al. 1994). For example, LightCo managers reported an increase in team spirit among employees following the introduction of a new CS system. In general, managers in our fieldwork believe that by signaling a clear and believable customer focus, firms that invest greater time and effort in CSIU have more satisfied employees. Most managers also believe that increases in employee satisfaction associated with CSIU subsequently lead to increased CS. Notably, however, at AirwaysCo, an attempt to link CS to employee satisfaction indexes revealed a negative correlation, and the trends in these metrics moved in opposite directions over time. A similar experience was recounted in our focus group, suggesting that, in general, though managers believe that there is a positive monotonic relationship between employee satisfaction and CS, the relationship is more complex.
Second, our fieldwork suggests that there is a relationship between CSIU and customer perceptions and behaviors. Consistent with the literature (Anderson and Mittal 2000; Kamakura et al. 2002), managers believe that effective CSIU is directly related to CS, behavioral loyalty (retention, price sensitivity, and share of business), and resulting sales revenue (sales growth and market share). For example, managers at ShieldCo directly attributed its recent return to growth (after four years of steady decline) to its investment in CSIU. In addition to these "effectiveness" market performance dimensions, the literature indicates that CSIU may also be related to adaptive performance in terms of the firm's ability to develop new products (Griffin and Hauser 1993). This was supported in our interviews, in which several managers suggested that insights from CSI provided a source of ideas for product development and refinement, as well as information relevant to the positioning and launching of new products.
Third, our fieldwork indicates that CSIU is related to firms' financial performance. For example, WireCo managers stated that the firm's customer service reputation, which they attributed in large part to its CSIU, enabled them to charge higher prices than competitors and achieve higher margins while maintaining customer retention and enjoying above-industry sales growth. Our fieldwork indicates that managers believe that CSIU contributes to financial performance by prioritizing resource allocation to the areas that are most likely to maximize CS. For example, managers at CarCo, which is widely viewed as one of the most efficient auto manufacturers, indicated that the firm's CSI drives almost all product and even component redesign decisions. Analogous to the efficient market hypothesis in capital markets, our fieldwork suggests that improvements in CS-related information flows within firms improve the efficiency of resource allocations in their "internal capital markets" (Anderson and Mittal 2000; Rust, Moorman, and Dickson 2002). For example, CSI led ShieldCo managers to halt a new service development project and, instead, to allocate resources to advertise more heavily two existing services that seemed to link directly to observed weaknesses in two particular drivers of CS. A manager commented, "The market now perceives considerable value from these service 'enhancements,' yet the cost to the company is minimal."
CSIU Variance Among Firms
In addition to the divergence between CSIU theory prescriptions and the fieldwork reality we previously described, we also observed wide CSIU variance among the firms in our sample. Assuming that managers are acting rationally, such variance indicates that there are contingency factors that lead to interfirm CSIU differences. Our fieldwork suggests several factors that affect firms' CSIU. We identify particular contingencies observed in our fieldwork and how these factors may affect individual CSIU subprocesses; we also examine a firm's cultural orientation, which our fieldwork indicates is a contingency that affects a firm's entire CSIU.
Our fieldwork indicates that customer concentration (i.e., the amount of a firm's output purchased by a small number of customers) affects firms' CS data scanning. High customer concentration implies a larger risk to firms in losing such important customers, providing a greater incentive to understand and monitor their satisfaction (Li and Calantone 1998). For example, NetworkCo established a separate unit and a formal system to track the CS of its 12 largest customers; because these customers accounted for the majority of the firm's revenue, dissatisfaction among any one of them could risk significant revenue loss. Customer concentration also implies more powerful customers that may mandate that their suppliers use particular CS systems, as was the case at TechCo (cf. Zeitz, Mittal, and McCauly 1999).
The cultural orientation of a firm also appears to affect CS data scanning. The literature identifies three major cultural orientations among firms: customer orientation, competitor orientation, and technology orientation (Day and Nedungadi 1994; Kohli and Jaworski 1990; Narver and Slater 1990). We observed that customer- and competitor-oriented firms in our sample engaged in more frequent CS data collection, were more likely to capture data on customers' postpurchase intentions, and more often included key accounts and competitors' customers in their CS sampling frame than did firms with a strong technology orientation. Our fieldwork also suggests that customer-oriented firms are particularly likely to measure CS relative to customer expectations, whereas competitor-oriented firms are most likely to measure CS relative to competitors. We also observed that technology-oriented firms appear to be the most likely to sample the firm's newest customers in CS data collection.
An important contingency that our fieldwork revealed is firms' human and technology resources. For example, ShieldCo managers identified their inability to make the different systems used to collect and store customer account, inquiry, complaint, and satisfaction data to "talk to each other" as a major weakness in their CS data analysis. They expected that the firm's investment in a new enterprise resource-planning system would enable them to link these diverse sets of CS-related data in the future. From an analytical sophistication perspective, managers in several firms identified the statistical knowledge and skills of personnel involved in analyzing and managing CS data as key. Even when firms outsource data analysis to specialist vendors, managers still need sufficient statistical knowledge to ask for and understand sophisticated CS data analyses. As one vendor in our focus group commented, "If they [the client's vendor manager] cannot fully comprehend and explain the dynamics of satisfaction--interaction terms, nonlinearities, causality directions, [and so forth]--to their bosses and peers, then they don't want to hear the realities of what their satisfaction data are saying."
Our fieldwork indicates that the cultural orientation of the firm is also an important determinant of CS data analysis. Surprisingly, given our preceding observation, we found that customer- and competitor-oriented firms in our sample were the most likely to integrate data from multiple different sources in analyzing CS data. Because firms with a strong technology orientation may be expected to have greater technology resources, this suggests that access to technology provides the ability but not necessarily the motivation to integrate diverse sets of CS-related data. We also observed that firms with a strong technology orientation were less likely to examine relationships between attribute-level and overall satisfaction and between satisfaction and loyalty in their CS data analysis than were more customer- and competitor-oriented firms.
Our fieldwork suggests that the positional advantage pursued by a firm affects its CSI dissemination. We observed that firms emphasizing revenue enhancement exhibited more extensive dissemination of CSI than did firms pursuing a cost-based competitive advantage (Rust, Moorman, and Dickson 2002). For example, one of the most extensive CSI disseminators in our sample was AutoCo. A manager reported, "It's all about quality and satisfaction here.… We never compete on price." Although the literature suggests that firms pursuing cost-based strategies must maintain acceptable CS levels to achieve competitive advantage (Porter 1980), our fieldwork indicates that such firms are much less likely to disseminate CSI widely. For example, at PhotoCo, CS data dissemination is limited. A manager explained, "We want to keep our peoples' focus on enhancing our profitability by driving productivity."
Our fieldwork indicates that the cultural orientation of the firm also affects CSI dissemination. We observed that customer- and competitor-oriented firms in our sample engaged in much greater dissemination of CSI. For example, at WireCo, which views itself as customer focused, managers indicated that after CSI is collected and analyzed, it is quickly and widely shared in the firm to maximize the ability of as many decision makers as possible to use it. Similarly, ImageCo, which views itself as being strongly competitor focused, expends considerable resources in the blind tracking of customer and prospect satisfaction with both the firm's own products and those of its major competitors. This CSI is quickly and widely disseminated in the firm and is viewed by most recipients as both valid and useful.
Our fieldwork suggests that the competitive intensity of a firm's marketplace is an important factor affecting CSI utilization. Firms facing intense competition appear to use CSI in more decisions and across more diverse areas than do firms facing less competitive pressure. For example, at HopsitalCo and PowerCo, both of which enjoy a quasi-monopoly position in their geographic marketplaces, managers reported using CSI in a limited number of tactical decisions, primarily in the area of customer service. Conversely, firms facing intense competition, such as InsCo, CruiseCo, and CarCo, reported a greater utilization of CSI in making a wide range of strategic and tactical decisions across a wide range of business activities.
The cultural orientation of the firm also appears to affect CSI utilization. We observed that firms in our sample with strong customer or competitor orientation were more likely to make greater conceptual and instrumental use of CSI and to use CSI as an important input across a broader range of decisions than were firms with a technology orientation. For example, TechCo, a firm described as "heavily technology focused," has a CS data collection system in place for only one of its customers, and this was at the insistence of that customer. This CSI is accessed only by the program director in the firm, who views it as a "[public relations] exercise to keep this particular customer on board" and who reported using this CSI for no other purpose. Conversely, managers at CruiseCo, one of the most customer-oriented firms in our sample, report using CSI in almost every decision made within the firm.
The normative literature posits that CSIU affects performance outcomes under all conditions (Dutka 1993; Hanan and Karp 1989; Sharma, Niedrich, and Dobbins 1999). However, our fieldwork indicates that the link between CSIU and firm performance may be moderated by factors in the firm's environment. Two such moderating factors that were suggested are "customer homogeneity" and "market dynamism." When customers are heterogeneous in their preferences, their key satisfaction drivers and the nature of the satisfaction-retention-profitability linkages should differ markedly (Anderson and Mittal 2000). Our fieldwork suggests that in highly heterogeneous customer markets, CSIU distinguishes a firm's ability to understand and effectively segment its markets and deliver higher satisfaction levels to different groups of customers. For example, ShieldCo managers indicated that CSIU helped them segment their markets and match their service offerings better with each segment than their rivals. However, when customers are homogeneous, satisfaction drivers may become well known in the industry, thus reducing the ability of a firm's CSIU to deliver competitive advantage. For example, a TelCo manager indicated that CSIU benefits were primarily "defensive" because residential telephone customer preferences were relatively homogeneous and well known.
Market dynamism refers to the rate of change in the composition of customers and their preferences (Jaworski and Kohli 1993). When customers and their preferences change slowly over time, all firms may achieve a similar level of customer knowledge, and the CSIU process may add differentially less competitive advantage. For example, FilterCo managers indicated that their customers' preferences changed relatively rarely, and this made it difficult to have a know-what advantage over rivals through CSIU. However, when customer preferences change rapidly, CSIU processes may be unable to keep pace and, again, may not deliver significant competitive advantage (Flint, Woodruff, and Gardial 2002). For example, NetworkCo's manager of the key account CS program indicated that customers' technology demands change so quickly that it is difficult for the CS program to do anything more than address customer service issues. However, when customers and their preferences change but do so at a speed that can be detected and acted on by a firm's CSIU, our fieldwork suggests that the potential performance benefits of firms' CSIU are enhanced.
Overall Role of CSIU in the Firm
Although our fieldwork indicates many differences among firms on specific CSIU characteristics, we also observed that the firms in our sample could be categorized into one of four groups with respect to their overall CSIU. First, six firms in our sample exhibited limited CSIU. These firms were characterized by less formal and more infrequent CS data collections, typically ad hoc data collections occurring less than once a year. These ad hoc data collections often seem to be associated with some precipitating event or problem, such as the development of a new offering or a loss of market share. As a result of the relatively infrequent CS data collections, most of the subsequent stages of the CSIU process were necessarily more limited than were those exhibited in the rest of our fieldwork sample.
A second group of 17 firms exhibited more extensive CSIU that was primarily connected with the firm's management control system. In these firms, CSIU is used along with other monitoring systems, typically those that focus on financial and sales indicators, to track CS as one of several key performance indicators. Of the 37 firms in our sample, 29 regularly use CS data to help monitor firm performance. In these 17 firms, however, CSIU is viewed as primarily a performance-monitoring issue. In this group, our field research indicates that CSIU tends to be characterized by ( 1) more formalized CS data collection systems that are particularly likely to use CS measures that include specific referents (e.g., customer expectations, perceptions of competitors' products); ( 2) relatively simple univariate analyses of overall CS; ( 3) a more frequent dissemination of CS data and recipients that view the CSI as having little diagnostic capacity; and ( 4) a stronger instrumental CSI utilization that is focused on strategy implementation and control, including the greatest use of explicit CS goals and systems that link employee rewards to their achievement.
In a third group of five firms, we observed that CSIU primarily provides a mechanism for learning about customers, and the managers in these firms believe that it provides customer knowledge that can be leveraged to provide a strategic advantage (Barabba and Zaltman 1991; Sinkula 1994). In these firms, CSIU appears to be congruent with theoretical conceptualizations of organizational learning (Crossan and Bedrow 2003; Sinkula 1994). Consistent with this literature, the majority of the learning about customers that firms in our field research derive from CSIU appears to be adaptive (Slater and Narver 1995). However, the five firms for which CSIU predominantly fills an organizational learning role also often engage in more open-ended ad hoc CS inquiries that are consistent with generative organizational learning (Day 1994b). We observed that CSIU in these firms ( 1) is more likely to involve both informal and formal CS data collection, ( 2) is likely to use more sophisticated multivariate analyses that focus on understanding the attribute drivers of overall CS and the relationship between CS and customer loyalty, ( 3) expends more time and effort uncovering diagnostic and actionable CS insights, and ( 4) makes greater conceptual use of CSI and uses CSI in formulating strategy.
Fourth, we identified a group of nine firms that appeared to use CSIU as both a key component of the firm's management control system and an important customer-focused organizational learning mechanism. Notably, each of these nine firms operated in consumer rather than business-to-business markets. Compared with the other three groups identified, these firms ( 1) are the most likely to include lost and strategic customers in their CS data collection samples; ( 2) are the most likely to integrate CS-related data from different sources in CS data analysis; and ( 3) have both the greatest dissemination of CS data within the firm and some of the most positive user perceptions of the accuracy, relevance, and diagnosticity of CSI. However, there appears to be less generative learning from CSIU in these firms than in those in which CSIU is predominantly a learning mechanism. This may be a result of the lower use of supplemental ad hoc and informal CS-related data collections to complement formalized CS systems. This is consistent with our observation that the firms in this final group tend to have more extensive formal CS data collection systems and make greater use of these formal systems to derive primarily adaptive learning benefits.
Overall, this grouping of firms' CSIU in our fieldwork sample suggests that there are tensions between using CSIU for control purposes and for generative organizational learning. Comparisons between current and prior CS data are important contributors to both management control system success and adaptive learning within the firm (Moorman 1995; Slater and Narver 1995). These comparisons contribute to a shared mental model of what managers believe is important to customers, and they provide actionable benchmarks against which managers can monitor the firm's performance (Day 1994a, 2000). However, our fieldwork indicates that these same characteristics can also be restrictive (Moorman and Miner 1997). They can make open-ended inquiry that challenges a firm's assumptions about its customers difficult, even if a firm's CS systems identify triggers that suggest the need for such an inquiry. For example, several managers in our fieldwork admitted that their firms continue to use CS systems they believe to be outdated--even in terms of measuring performance on attributes that may no longer be relevant to their customers' satisfaction--to continue to provide the same CS data for performance monitoring. Managers indicated that in these situations, it can be difficult to instigate research projects to verify whether changes in CS drivers have occurred because of the pressure to maintain consistency with historical CS data for benchmarking purposes.
Implications
We summarize a selection of the most important findings from our fieldwork in Table 3. The table presents typical practices we found with respect to the four subprocesses of CSIU, and it classifies each practice along a continuum from encouraging to discouraging.
Encouraging practices are those we find to be consistent with normative prescriptions and appropriate given the context in which the firm operates; they include widespread use of formalized data collection, multivariate driver analysis, regular dissemination of findings, and an impact on decision making in customer service and account management functions. To a large extent, these are the fundamental components of any successful CSIU system.
Normative departures are practices that do not fit with normative theory but may be appropriate given certain situational factors. Such practices include the lack of formal in-depth inquiry into underlying causes of satisfaction and dissatisfaction, the use of single-item scales, little integration of CS data with other relevant data within the firm (only one firm in our sample linked CS to purchase behavior and none to financial consequences), failure to inform frontline employees, and CSI not making its way into functional areas other than customer service and account management. For example, in many industries, it is difficult to link CS data with purchase behavior or customer profitability. Managers whose CSIU systems exhibit such departures should ensure that these are appropriate given the nature of the company and the competitive environment it faces.
Discouraging practices are those that both depart from normative theory and would benefit from correction regardless of the situation. The major practices that fall into this category include the following: Few firms sample lost customers or customers who work with competitors, many firms do not conduct driver analyses, few firms link CS data to postpurchase intentions, firms often disseminate data without interpretation or guidance to help recipients respond appropriately, recipients often have not "bought in" to the use of CSI, and firms use CS data more often for tactical adjustments than for strategic decision making. We believe that corrective action should be taken in such cases, beginning with conducting appropriate driver analysis and disseminating CSI in a way that is immediately useful and diagnostic. Finally, we highlight three general implications for theory development and managerial practice that we believe are particularly important.
Instead of attempting to emulate a normative CSIU ideal, CSIU implementation should vary across firms to the extent that each firm faces different situational factors. Our findings indicate that managers should focus on identifying the key contingencies the firm faces and should design a CSIU system that is appropriate for these specific conditions. Investment in further CSIU improvements that move the firm closer to a classical theory-based norm would likely not produce a reasonable return. Thus, for example, it may not always make sense for a firm to build competitive superiority in its CSIU. In markets with either slow or rapidly changing customer preferences and in firms with a strong orientation toward technological innovation or low costs, investments in building extensive CSIU processes seem unlikely to be the most efficient use of resources.
Many of the firms in our sample do not appear to gain significant customer-focused learning benefits from their CS systems, because they are designed to act primarily as a control mechanism. Consistent with conceptualizations of learning traps in organizational learning theory (Levinthal and March 1993), we observed that using CSI primarily for control purposes can actually limit managers' ability to learn about customers. Although firms may gain adaptive learning benefits by investing in more extensive CSIU, our study suggests that generative learning about customers also requires less-formalized ad hoc research projects. This has important implications for the further development of conceptualizations and operationalizations of market orientation and for management practice.
For firms that operate in contexts in which CSIU can deliver a competitive advantage, our study indicates that managers may be well served to reevaluate how they deploy their existing CSIU resources. The majority of CSIU resources in the firms in our sample are consumed in CS data collection. This often leads to too few resources being allocated to the analysis, dissemination, and utilization of this information to realize fully the potential payback from the investment in data collection. For example, our fieldwork suggests that investments to improve the sophistication of CS analyses and to make CSI more frequently and widely available to managers have a significant impact on recipient perceptions of its accuracy, usability, and diagnosticity. In turn, this may lead to a greater utilization of CSI in decision making and boost the learning outcomes of firms' CSIU, which may benefit firms even if their major CSIU objective is to enhance their management control systems.
Conclusion
The ability to acquire and use CSI is central to marketing theory and practice. Our fieldwork identifies important characteristics of the scanning, analysis, dissemination, and utilization subprocesses in firms' CSIU. We highlight numerous areas in which CSIU practice diverges from normative theory prescriptions and identify a wide variance in CSIU among firms. We bring these insights together to propose a new contingency-based model of CSIU and its relationship with firm performance.
The proposed model provides new theoretical insights into CSIU and enriches our understanding of CSIU, of how CSIU is influenced by contextual factors, and of the role of CSIU in organizational learning and making marketing metrics work within an organization. We believe that the model provides a foundation for further theoretical and empirical work. Important next steps might include examining the link between CSIU and financial performance, understanding the relative payoff of investing in different components and subprocesses of CSIU, and learning to balance CSIU initiatives with those aimed at generative learning and developing new customer insights.
The model is also useful to managers who want guidance in creating, managing, and improving their CSIU systems. In particular, the model highlights the components and processes of CSIU that must be managed successfully for the full benefits of an organization's investment in CSIU to be realized. By identifying key contingency factors that influence how CSIU should be implemented in different firms, the framework also provides a roadmap for managers to prioritize their investments in CSIU.
In general, we recommend that managers do the following:
• Collect data from current customers, former customers, and competitors' customers;
• Conduct multivariate analyses that link attribute perceptions to overall satisfaction and intentions and, when possible, to customer behaviors and profitability;
• Ensure that CSI is viewed by recipients as accurate, usable, and diagnostic;
• Use CSI as an input for strategic decision making and for day-to-day tactics;
• Augment CSIU systems with market research geared toward the generative learning of new customer insights;
• Design CSIU systems that are dependent on contingency factors (i.e., desired positional advantage, ability to match CS data to actual purchase behavior, and competitive intensity); and
• Examine the allocation of CSIU budgets to determine whether sufficient resources are being devoted to the analysis, interpretation, and dissemination of CS data after it is collected.
Customer satisfaction is a central concept in marketing and a core strategic objective for any firm. Customers are ultimately the primary source of all positive cash flows. Therefore, attracting and retaining profitable customers must be one of the firm's most fundamental tasks. Thus, the creation and successful management of CSIU systems that enable the firm to achieve a superior understanding of customer needs and respond more effectively and efficiently than competitors are important ways that marketing makes significant contributions to the success of the firm. It is our hope that this study furthers the theoretical understanding of such initiatives and enhances their implementation in practice.
The authors gratefully acknowledge insightful comments and suggestions in the development of this article from Bruce Clark, Rohit Deshpandé, Claes Fornell, John Hulland, Don Lehmann, Nigel Piercy, Paul Root, and Valarie Zeithaml, as well as the financial support of the Marketing Science Institute.
( n1) To preserve anonymity, we use industry- or market-related pseudonyms when we refer to individual firms.
( n2) This involved carefully explaining the detailed protocol prepared on the basis of our interviews, role playing of interviewees by the primary researchers with feedback to students, and question-and-answer sessions with feedback to students following their initial interviews.
Legend for Chart:
A - Firm and CSIU Type
B - Firm and Market Characteristics
C - Number of Interviews and Interviewee Position Titles
A
B
C
AirwayCo: control
Large publicly traded Europe-based airline with a
strong market share on most routes but facing
both consolidation and new low-cost, short-haul
entrants
1: Head of market research
ITC: control
One of the largest global information technology
firms with a strong market share in most strategic
business units in a dynamic and highly
competitive market
3: Director of marketing, managers of CS and
product marketing
TelCo: control
Large European telecommunications firm with a
dominant share of domestic markets and a
smaller share of European markets in a dynamic
and somewhat regulated market
1: Market research manager
PowerCo: control
Large regional investor-owned utility (electricity
and gas) supplier in a heavily regulated industry
with quasi monopoly
3: Quality consultant, senior corporate planner,
vice president customer service
InsCo: control and
learning
One of the largest national life insurance and
investment services providers in a somewhat
dynamic and regulated market environment
3: Market research manager, directors of voice of
the customer and service quality
ImageCo: control
One of the largest global imaging supplies and
services provider in a declining and highly
competitive core market with dynamic new
technologies emerging
2: Director of business research, director of CS
GolfCo: limited
Regional medium-sized leisure and hospitality
firm in an increasingly saturated and fragmented
market that is highly competitive
2: Directors of business development and
associate development
ConsultCo: not
applicable
Medium-sized global marketing research and
consulting with a high share of the specialist
satisfaction-related niche
3: Managing consultant, senior consultant,
program director
FilterCo: limited
One of the largest national industrial filter
manufacturers and suppliers in a competitive and
maturing market that is consolidating
9: Operations director; managers of marketing,
sales, customer service, channels, engineering
development, quality, planning, and channel
development
AirCo: control
Medium-sized regional industrial air services
supplier with a relatively small share of a
fragmented market in a relatively simple
environment
5: Chief executive officer, account representative,
customer service representative, sales manager,
operations manager
PhotoCo: control
Largest firm in global photo processing equipment
and services in a highly competitive and mature
market that is increasingly driven by new
technology
2: Market analyst, marketing program manager
DrugCo: limited
Large global pharmaceutical company with a high
market share in several different therapeutic
markets that are regulated and competitive
1: Product manager
ScienceCo: limited
Large European scientific equipment supplies and
services with a large global share of several
relatively uncompetitive and stable niche markets
9: General manager; directors of quality
assurance and operations; managers of service,
sales operations, channels, quality assurance,
customer service, and operations
ResortCo: control
and learning
Small local hotel and golf club with a relatively
large market share in a particular local niche
market that is stable and relatively uncompetitive
6: Director of sales and marketing, reservations
manager, director of catering, operations
manager, operations supervisor, reception
manager
InnCo: control and
learning
Small local hotel and restaurant with a relatively
large market share in a particular local niche
market that is stable and relatively uncompetitive
5: General manager, assistant manager, front
office manager, restaurant manager,
housekeeping manager
GymCo: limited
Small local consumer fitness facility with a small
share of a growing but competitive and
fragmented local market
4: General manager, sales manager, front desk
manager, fitness coordinator
BankCo: control
Large regional consumer and business banking
services with a relatively large share of a
competitive and consolidating regional banking
market
4: Vice president CS, market research manager,
regional president, regional president
HospitalCo: control
Large regional teaching hospital with a high
market share in several specialties and a quasi
monopoly for these in a local, regulated market
3: Managers of marketing and customer quality
information, vice president of marketing and
public affairs
SportCo: control
and learning
Medium regional professional sports franchise in
the national league with a local monopoly for this
sport and a large share of the total regional
sports market
3: Assistant director marketing, promotions
manager, vice president marketing
AutoCo: control
and learning
One of the largest global luxury automobile
manufacturers with a relatively large share of a
competitive and consolidating market
3: CS supervisor, managers of customer
knowledge and CS research
MarketCo: learning
Small local gourmet and specialty food retailer
with a large share of a relatively uncompetitive
local niche market
2: General manager, floor manager
HotelCo: control
Small local historic hotel with a large share of a
niche market that is becoming increasingly
competitive
4: Director of operations; managers of front office,
housekeeping, and concierge services
WireCo: learning
One of the largest global providers of financial
market information services in a competitive,
maturing, and consolidating market
3: Managers of sales, service development, and
customer service
CellCo: control
One of the largest global mobile telephone
infrastructure manufacturers with a large share of
a highly competitive but rapidly growing market
6: Directors of global accounts, quality, and sales;
managers of brand research and analysis,
operations development, and research
HotelGroupCo:
control and
learning
Large Latin American hotel group with a large
domestic market share and a smaller share in
other Latin American countries in a competitive
market
4: General manager, manager of food and
beverage, manager of reception, guest
satisfaction system coordinator
DevelopCo:
learning
Small slow-growth regional technology transfer
services with a quasi monopoly in a particular
local niche market
4: Director, associate director, chair of technology
review panel, technology development associate
PCHelpCo: control
Medium personal computer customer service
center serving customers of one of the largest
global personal computer manufacturers in a
competitive and maturing market
6: Managers of customer support, help center,
operational service, staffing and planning, quality
assurance, and development projects
FinanceCo: control
and learning
Large regional consumer and business banking
services with a relatively large share of a
competitive and consolidating regional banking
market
4: Chief executive officer, head of customer
service, managers of customer service and group
retail banking
CivilCo: learning
Medium regional civil engineering services with a
small share of fragmented and competitive market
and a larger share in some specialist niches
3: Vice president of marketing, division vice
president, office manager
TechCo: limited
Medium national technology research, consulting,
and services organization with a small share of a
fragmented and dynamic market
3: Director of center, assistant director, program
area manager
HealthCo: control
Medium regional health maintenance organization
with relatively small share of regional market in a
competitive, consolidating, and regulated market
4: Chief executive officer, vice president service
excellence, director of medical economics,
medical director
MedicCo: control
and learning
Large regional hospital with significant and stable
share of increasingly competitive local market
3: Strategic planning director, market analyst,
case manager
ShieldCo: control
and learning
Large regional health insurance provider with
largest share of regional market in competitive,
consolidating, and regulated market
3: Vice presidents of CS and market research,
market research manager
LightCo: control
Large regional investor-owned electric utility in a
regulated industry with a quasi monopoly in the
geographic markets in which it operates
3: Vice president CS, managers of market
research and customer service
ChemicalCo:
control
Large global industrial hygiene services with a
relatively large share of a fragmented,
competitive, and increasingly dynamic market
environment
8: Vice presidents of national accounts, sales, and
marketing; managers of marketing, sales
information, customer reporting, technical
services, and customer service
NetworkCo: control
Large global telecommunications technology
provider with a subordinate share in a
consolidating, highly competitive, and dynamic
market
4: Vice president of development, managers of
customer value and customer accounts, market
research services director
CruiseCo: learning
Large national cruise line with a growing share of
a niche market in a growing, consolidating, and
dynamic market
3: Vice president of marketing, accommodation
director, consumer insight manager
CarCo: control
One of the largest global automobile
manufacturers with a significant share of all of the
global market in a consolidating and highly
competitive industry
3: Vice president customer knowledge, managers
of customer loyalty and group CS Legend for Chart:
A - CSI Elements
B - Literature Prescription
C - Fieldwork
D - Possible Contingencies
A
B
C
D
Scanning: formalization
Formalized systems
complemented by informal
customer feedback
In total, 27 of the 37 (73%) firms have
formal CS data collection systems. Few
of these 27 integrate or use any additional
informal customer feedback.
Control versus learning focus of
CSIU
Customer concentration
Scanning: frequency
Continuous CS data collection to
provide "real-time" feedback
The 37 firms engage in 78 different CS
data collections (range = 1-6,
mean = 2.11, and mode = 1).
Frequency range for the 78
different CS data collections
across our sample:
• Daily: 24 (31%)
• Weekly: 3 (4%)
• Monthly: 10 (13%)
• Quarterly: 11 (14%)
• Biannually: 10 (13%)
• Annually: 14 (18%)
• Less frequently: 6 (8%)
Frequency range for CS
collection among the 37 firms:
• Daily: 15 (41%)
• Weekly: 3 (8%)
• Monthly: 8 (22%)
• Quarterly: 9 (24%)
• Biannually: 6 (16%)
• Annually: 11 (30%)
• Less frequently: 4 (11%)
Control versus learning focus of
CSIU
Cultural orientation
Scanning: CS measures
Multi-item scales, relative to
expectations, importance
weights, price, nonattribute cues,
postpurchase attitudes and
behaviors
In the 78 CS data collections and
in 37 firms, frequency of
measures used:
• Attribute-level satisfaction: 64
(82%) and 34 (92%)
• Overall satisfaction: 58 (74%)
and 32 (86%)
• Likelihood-to-recommend: 40
(52%) and 20 (54%)
• Purchase intentions: 38 (49%)
and 18 (49%)
• Open-ended questions: 28
(36%) and 18 (49%)
Data collection mechanisms used
in the 37 firms:
• Mail surveys: 18 (49%)
• Telephone surveys: 18 (49%)
• "In-venue" surveys: 9 (24%)
• Online surveys: 5 (14%)
• Focus groups: 4 (11%)
• Depth interviews: 2 (5%)
• Mystery shoppers: 2 (5%)
Control versus learning focus of
CSIU
Cultural orientation
Scanning: sampling
approach
"Strategic" and other existing
customers, lost customers
(defectors), and competitors'
customers
Sampling approaches used in the 37 firms:
• All existing customers:
37 (100%)
• Separate strategic customers:
7 (19%)
• Competitors customers: 4
(11%)
• Lost customers: 2 (5%)
Control versus learning focus of
CSIU
Cultural orientation
Analysis: CS data
integration
Customer insight database with
CS monitoring data, complaint
data, customer service data,
customer behavior data, and so
forth
No integration of CS data with any other
related customer data in 29 of the 37 firms.
Availability of human and
technology resources
Control versus learning focus of
CSIU
Cultural orientation
Analysis: relationships
examined
Drivers of CS and other
postpurchase phenomenon, CS
relationships with purchase
behavior, internal metrics, and
business performance outcomes
Relationships examined in the 37 firms:
• Current attribute-level and overall
satisfaction and past scores: 34 (92%)
• Attribute-level satisfaction and
overall satisfaction: 14 (38%)
• Overall satisfaction and future
purchase intentions: 7 (19%)
• Overall satisfaction and
likelihood-to-recommend: 2 (5%)
• CS and subsequent customer behavior: 1 (3%)
• CS and internal performance metrics: 1 (3%)
Availability of human and
technology resources
Control versus learning focus of
CSIU
Cultural orientation
Analysis: analytical
sophistication
Multivariate assessment of
measurement properties and
relationships, time-series
analyses to establish causality
Analyses performed in the 37 firms:
• Univariate analyses: 34 (92%)
• Multivariate analyses: 14 (38%)
• No quantitative analyses: 3(8%)
Availability of human and
technology resources
Control versus learning focus of
CSIU
Cultural orientation
Dissemination:
frequency
Frequent, if not continuous,
dissemination of CS scores and
data
Frequency of CS dissemination in the 37 firms:
• Daily: 2 (5%)
• Quarterly: 9 (24%)
• Weekly: 1 (3%)
• Monthly: 10 (27%)
• Annually: 1 (3%)
• Biannually: 2 (5%)
• Less frequently: 8 (22%)
Desired positional advantage
Control versus learning focus of
CSIU
Cultural orientation
Dissemination: vertical
and horizontal
CS monitoring data made
accessible to everyone in the
firm
CS dissemination targets in the 37 firms:
• Upward to senior managers: 32 (86%)
• Downward to frontline employees: 23 (62%)
• Horizontal to other departments: 25 (68%)
Desired positional advantage
Control versus learning focus of
CSIU
Cultural orientation
Dissemination:
perceived CSI
characteristics
Users perceive CSI as valid and
reliable, timely, relevant, and
actionable
Variation in user reliability and validity
assessments, CSI disseminated often viewed
by potential users as "too late" to be considered
timely even if relevant, no "causes" or "fixes"
for CS-related issues or problems identified.
Desired positional advantage
Control versus learning focus of
CSIU
Cultural orientation
User awareness of CSI
properties
Utilization: number of
decisions
CSI widely used in most
decisions that may affect
customers
CSI use in the 37 firms:
• Instrumental use: 31 (84%)
• Conceptual use: 9 (24%)
Dissemination of CSI
Desired positional advantage
Competitive intensity
Control versus learning focus of
CSIU
Cultural orientation
Utilization: decision
domains
CSI should be an important input
in decisions across all functional
areas
CSI mainly used in customer service decision
making.
Dissemination of CSI
Desired positional advantage
Competitive intensity
Control versus learning focus of
CSIU
Cultural orientation
Utilization: instrumental
use
CSI as a key input in any
decisions that affect the value
delivered to customers, CS data
as an important component of
reward and evaluation systems
CSI important in customer service decisions,
few formal links to reward and evaluation system.
Dissemination of CSI
Desired positional advantage
Competitive intensity
Control versus learning focus of
CSIU
Cultural orientation
Utilization: conceptual
use
CS monitoring as a mechanism
for organizational learning, CSI
as a key input into strategic
planning
CSI use in the 37 firms:
• Primarily as a performance-monitoring
system component: 22 (59%)
• Primarily as a tool for
learning about customers: 15 (41%)
Dissemination of CSI
Desired positional advantage
Competitive intensity
Control versus learning focus of
CSIU
Cultural orientation Legend for Chart:
B - Encouraging Practices
C - Normative Departures
D - Discouraging Practices
A B
C
D
Scanning • Formalized systems are
common.
• Data collection is usually
frequent.
• There are few formal inquiries to
understand the causes of
(dis)satisfaction.
• Most firms use single-item
scales.
• Data are usually collected only
from existing customers(few
sample former customers or
competitors).
• Few firms distinguish strategic or
valuable customers from others.
Analysis • Multivariate analysis is used by
nearly half the firms in our
sample to examine the drivers of
satisfaction.
• There is little integration of CS
data with other customer data or
relevant data from elsewhere in
the firm.
• Only one firm links CS to
purchase behavior.
• None of the firms in our sample
attempts to link CS to
profitability.
• More than half the firms do not
conduct driver analyses linking
attributes to overall satisfaction.
• Only two firms routinely link CS
to postpurchase intentions.
Dissemination • Most firms disseminate CS data
internally at least once a quarter.
• Approximately 40% of firms do
not routinely disseminate
satisfaction data to frontline
employees.
• Data are often disseminated
without identifying root causes or
fixes to guide recipients.
• Many users are skeptical of the
CS data they receive.
Utilization • Satisfaction information is an
important input into many
decisions in the customer
service and account
management domain.
• CSI is not a key input to
decisions in many key functional
areas in which it would be
useful.
• Satisfaction data tend to be used
in decision making at a tactical
rather than a strategic level.
Overall • CSIU systems are widespread.
• Basic subprocesses are often
well executed within specific
functional domains.
• CSIU implementation often
reflects the purpose of the
system in a particular firm and
key contingency factors.
• Using CSIU for control purposes
(adaptive learning) can lead to
myopia with respect to gaining
new insights about customers
(generative learning).DIAGRAM: FIGURE 1 A Framework for Understanding Firms' CSIU
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1. How is CS measured here?
• Measures: complaints, overall CS, CS with different aspects of product and exchange, outcomes or perceptions/ expectations, importance weights, attribute drivers versus cues.
• Frequency: time frames, systematic or around key events (e.g., test marketing new product).
• Sampling: all customers versus subset, current versus previous customers/defectors, customers versus prospects, competitors' customers, end users versus channel.
• Data collection method: qualitative versus quantitative, complaints versus CS, written survey versus telephone versus face-to-face, internal versus third party, primary versus secondary.
• Processes: formalized versus informal.
2. How is CS data analyzed and interpreted?
• Data analysis tools: averages, correlations, multiple regressions, and so forth.
• Relationships examined: purchase behavior, market performance, financial performance, employee satisfaction, other internal metrics.
3. How is CSI disseminated within the organization?
• Frequency.
• Channels and media used.
• User targets: vertical dissemination, horizontal dissemination.
• How information is viewed by recipient managers (e.g., valid and reliable, useful, timely).
4. How is CSI used in decision making?
• In what types of decisions are CS data a routine input?
• How important is CS data in each of these decisions?
• In what types of decisions are CS data an infrequent input?
• Orientation of decisions? Time scale of decision? Speed of utilization?
5. What resources and capabilities are required for effective CS usage?
• Financial, human, technological, research skills, customer knowledge.
6. What aspects of the organizational context seem to help and hinder the effective generation and use of CSI?
• Culture: information sharing norms, customer versus competitor orientation, internal versus external orientation.
• Structure: formalization, specialization, centralization, interdepartmental connectedness.
• Strategic: CS goals, competitive strategy, reward system link.
• Political: management commitment, cross-functional buy-in.
7. What are the outcomes of CSIU in this company?
• Internal: employee satisfaction, team spirit.
• Market: satisfaction, price sensitivity, loyalty, retention, new product success, sales growth, market share.
• Financial: margin, return on investment, cash flow.
8. What internal and/or external factors may affect the relationship between CSIU and performance outcome?
• Market issues, competitive conditions, customer characteristics.
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By Neil A. Morgan; Eugene W. Anderson and Vikas Mittal
Neil A. Morgan is Associate Professor of Marketing, Kelley School of Business, Indiana University
Eugene W. Anderson is Associate Dean for Degree Programs and Professor of Marketing, National Quality Research Center, Stephen M. Ross School of Business, University of Michigan
Vikas Mittal is Professor of Marketing, Katz Graduate School of Business, and Associate Professor of Psychiatry, School of Medicine, University of Pittsburgh
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 193- Understanding Service Convenience. By: Berry, Leonard L.; Seiders, Kathleen; Grewal, Dhruv. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p1-17. 17p. 1 Diagram. DOI: 10.1509/jmkg.66.3.1.18505.
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Understanding Service Convenience
The subject of service convenience is important in service economies, yet little is known about this topic. The consumer convenience literature--strong in certain respects, underdeveloped in other respects--gives insufficient attention to service convenience. The prevailing pattern is either to treat service convenience generally or to lump services and goods together into an overall convenience construct. The authors seek to stimulate a higher level of research activity and dialogue by proposing a more comprehensive and multidimensional conceptualization of service convenience and a model delineating its antecedents and consequences. The authors build their case by systematically examining the convenience literature, explicating the dimensions and types of service convenience, developing the overall model and related research propositions, and presenting directions for further research.
Consumer convenience in buying and using services is not well understood. Convenience is acknowledged to be increasingly important to consumers, yet no known research has defined the service convenience construct or examined how it is evaluated. Although most researchers and managers consider service convenience to involve more than locational proximity or hours of operation, the specific types of service convenience have not been established, and no comprehensive analytical framework has been presented in the literature.
Observers have long noted consumers' interest in conserving time and effort (see, e.g., Anderson 1972; Gross and Sheth 1989; Kelley 1958; Nickols and Fox 1983). This phenomenon has encouraged the development of convenience goods and services, increased advertisers' promotion of the time-oriented benefits of their products, and motivated consumers to use convenience as a basis for making purchase decisions (Anderson and Shugan 1991; Gross and Sheth 1989; Jacoby, Szybillo, and Berning 1976). The continuous rise in consumer demand for convenience has been attributed to socioeconomic change, technological progress, more competitive business environments, and opportunity costs that have risen with incomes (Berry 1979; Etgar 1978; Gross 1987; Seiders, Berry, and Gresham 2000).
Because the demand for convenience has become so strong, marketers must develop a more precise and complete understanding of the concept. Convenience is integral to the marketing of both goods and services and merits deeper examination in both cases. Our focus in this article is service convenience, which we conceptualize as consumers' time and effort perceptions related to buying or using a service. We propose the different types of service convenience and consider how time and effort costs influence consumers' convenience perceptions.
In some convenience studies, the distinction between service and goods convenience is clear. For example, consumers' convenience orientation has been related to all products that save consumers time and effort - "laborsaving" goods (e.g., frozen dinners) and services (e.g., child care). Some proposed aspects of the convenience construct are specific to manufactured goods. These include product size, preservability, packaging, and design, which can reduce consumers' time and effort in purchasing, storage, and use (Anderson and Shugan 1991; Kelley 1958). However, many discussions of goods-related convenience are distribution oriented, focusing on convenience related to the distribution of goods through retailers, which falls in the realm of service convenience. All types of convenience that reduce consumers'time or effort in shopping, such as operating hours or credit availability, belong to the domain of service convenience.
Service organizations create value for consumers through performances. All businesses are service businesses to some degree. Computer manufacturers and food retailers create consumer value through a goods--service mix. Commercial banks and hospitals create consumer value largely through services. Service convenience facilitates the sale of goods as well as the sale of services. Fast checkout in a retail store is service convenience, as are available, competent salespeople who help consumers find the right garment to buy. Because virtually all organizations create value for consumers through performances and because convenience is an important consideration for most consumers, it follows that understanding service convenience better is useful. The extant convenience literature offers little explicit discussion of service convenience. Much of this literature is relevant to service convenience but lacks the specificity and comprehensiveness that more focused efforts could bring. We seek to provide such focus in this article.
The concept of convenience first appeared in the marketing literature in relation to categories of products. Copeland's (1923) classification of consumer products included convenience goods: intensively distributed products that require minimal time and physical and mental effort to purchase. Later product classification schemas also incorporated the convenience goods category (e.g., Bucklin 1963; Murphy and Enis 1986). Thus, in early marketing usage, "convenience" denoted the time and effort consumers used in purchasing a product rather than a characteristic or attribute of a product (Brown 1990). Focusing on resources such as time, opportunity, and energy that consumers give up to buy goods and services, some researchers began to view convenience as an attribute that reduces the nonmonetary price of a product (Etgar 1978; Kelley 1958; Kotler and Zaltman 1971).
Because the issue of nonmonetary cost is central to the convenience concept, literature related to time and energy expenditure (effort) is particularly relevant to our research. The literature on time is substantial and multi-disciplinary in nature; the literature on effort is smaller and limited primarily to cognitive effort. Two specific marketing literature streams also are salient to our study. The first and most extensive stream is the consumer waiting literature, which examines how consumers respond to waiting and how firms manage the waiting process. The second stream focuses on consumer convenience orientation, examining why some consumers are more likely than others to purchase convenience-related goods and services.
Researchers characterize time as a limited and scarce resource (Jacoby, Szybillo, and Berning 1976); the term saving time actually means reallocating time across activities to achieve greater efficiency (Feldman and Hornik 1981). Time, unlike money, cannot be expanded; it is finite (Berry 1979; Gross 1987). Although time usage in consumption can be perceived as either an investment or a cost, it is more common to view it as a cost (Anderson and Shugan 1991). Becker (1965) incorporated time into the classic economic choice model, recognizing that time, like income and price, constrains choice. Economic household production models such as Becker's acknowledge that time is used in production (work) and consumption (leisure): Consumers sell time in the labor market and buy it with time-saving goodsand services (Feldman and Hornik1981). Researchers following a time budget allocation approach view the cost of time as an opportunity cost of forgone income or participation in other activities (Bivens and Volker 1986). Consistent with economic theory, the marketing literature has assumed a relationship between time scarcity and consumers' desire for goods and services that offer convenience.
Time-related consumer research includes studies of time allocation, temporal orientation and perception, and cultural influences (Gross and Sheth 1989; Voli 1998). Time allocation, an outcome of demographic, socioeconomic, and psychographic determinants, influences lifestyle and consumption behavior (Holbrook and Lehmann 1981). Consumer researchers have focused on time expenditures associated with information acquisition and choice behavior (Jacoby, Szybillo, and Berning 1976). Most studies have modeled and analyzed activities as if people performed them one at a time (monochronic time use), but respondents have reported combining activities (polychronic time use) (Kaufman, Lane, and Lindquist 1991).
Studies indicate that people differ in their temporal orientation, including perceived time scarcity, the degree to which they value time, and their sensitivity to time-related issues (Bergadaa 1990; Durrande-Moreau and Usunier 1999; Graham 1981; Hornik 1984; Murphy and Enis 1986; Shimp 1982). Noting that cultural factors can affect attitudes toward time, Gagliano and Hathcote (1994) examine how cultural differences affect the evaluation of convenience. Luqmani, Yavas, and Quraeshi (1994) use convenience orientation as an international market segmentation variable.
Time has been classified according to work and non-work roles; nonwork includes activities of necessary self-maintenance, household maintenance, and leisure (Hol-brook and Lehmann 1981). Classifying activities allows an understanding of why noneconomic variables are significant --why consumers sometimes seek to prolong rather than minimize time expenditures (Jacoby, Szybillo, and Berning 1976). For example, consumers may choose a mode of travel that is more expensive and time-consuming than alternatives (Feldman and Hornik 1981).
Consumers' energy expenditures, or effort, are acknowledged to be a distinct type of nonmonetary cost that, like time, influences perceived convenience (Seiders, Berry, and Gresham 2000) and satisfaction (Lovelock 1994). Downs (1961) cites the basic costs of consumption as money, time, and effort, and Mabry (1970) notes that stamina constraints, in addition to time and money constraints, influence choices among activities (Jacoby, Szybillo, and Berning 1976). In consumer convenience research, however, the role of energy expenditures has received far less attention than the role of time expenditures. Because the convenience literature has concentrated almost exclusively on saving time, attributes that save work are perceived instead as saving time (Brown 1990). For example, O'Shaughnessy (1987) explains performance-based product choice by noting that consumers buy time by using brands that are more labor saving (Voli 1998).
Effort has been viewed as a relevant and positive input to an exchange: In an equitable exchange, the more effort one party exerts, the more outcome he or she expects in return (see Oliver and Swan 1989). Youngdahl and Kellogg (1997) relate effort to time, thought (intellectual effort), and emotion. Mohr and Bitner (1995), in the context of employee behavior, suggest the dimensions of physical, cognitive, and emotional effort. These dimensions are likely to apply equally well to consumers of services.
The dimension of physical effort has received little attention in consumer research, and emotional effort has been explored only slightly more (in relation to the psychological costs of waiting). However, cognitive (or mental) effort has been the focus of many studies in psychology, decision theory, economics, and marketing (Bettman, Johnson, and Payne 1990). A consistent finding is that people have limited cognitive resources and, as cognitive misers, conserve these resources during decision making (Fennema and Kleinmuntz 1995; Fiske and Taylor 1984). Studies suggest that people have only limited ability to estimate or predict how much effort will be required by a task (Fennema and Kleinmuntz 1995). Moreover, Bettman, Johnson, and Payne (1990) find significant individual differences in consumers' perceptions of required effort.
Several marketing studies have focused on the management of consumer waiting time (Durrande-Moreau and Usunier 1999; Katz, Larson, and Larson 1991). Researchers have defined two aspects of waiting time that influence consumers' evaluation of convenience (Davis and Vollmann 1990). Objective time is continuous and metric and can be measured by clocks. Subjective time is based on perceptions and influenced by psychological factors (Durrande-Moreau and Usunier 1999). Research suggests that consumers, on average, significantly overestimate time spent waiting (Hornik 1984).
Although waiting for service delivery traditionally has been treated as an economic (or time)cost, the psychological cost of waiting also has been documented by consumer researchers (Carmon, Shanthikumar, and Carmon 1995; Osuna 1985; Pruyn and Smidts 1998). The stress, boredom, anxiety, and annoyance often triggered by waiting influence consumers' service evaluations and satisfaction with the firm (Dube-Rioux, Schmitt, and Leclerc 1989; Kumar, Kalwani, and Dada 1997; Taylor 1994).Recent marketing studies have examined the factors that influence consumers' reactions to waiting and the methods firms can use to manage satisfaction with waiting (Durrande-Moreau and Usunier 1999; Pruyn and Smidts 1998; Taylor 1994). Among the factors widely cited as influencing consumers' perceptions of waiting are service, facility, and customer characteristics; perceived fairness of the wait; and information provided by the firm.
Aspects of a service that are believed to affect consumers include its value and importance and whether it can be obtained elsewhere or at another time (Katz, Larson, and Larson 1991; Maister 1985). In necessary services, consumers have limited control and cannot "balk" (Carmon, Shanthikumar, and Carmon 1995). The stage of a service encounter (preprocess, in-process, postprocess) during which the delay occurs also can influence affective response. Service stage is argued to be influential relative to its distance to the consumer's goal for the service encounter (Dube-Rioux,Schmitt, and Leclerc 1989; Hui, Thakor, and Gill 1998). Preprocess waits are theorized to feel longer and be more unpleasant for consumers than in-process waits(Larson 1987; Maister 1985; Taylor 1994). Facility characteristics such as location, attractiveness, and the presence of distractions to occupy customer time are proposed to affect consumers' perceptions, though empirical results have been mixed(Baker and Cameron 1996; Davis and Vollmann 1990; Pruyn and Smidts 1998).
Consumers' individual differences also influence waiting perceptions. Consumers' expectations for the length of a wait are an internal reference that affects the consumers' willingness to accept the wait (Hui and Tse 1996; Leclerc, Schmitt, and Dube 1995). Expectations vary according to a person's prior experiences with the service firm and its competitors (Kumar, Kalwani, and Dada 1997). Other individual difference factors that influence perceptions of waiting include consumers' time orientation and sense of time urgency (Katz, Larson, and Larson 1991; Taylor 1994). The perceived fairness of a wait is believed to be a major influence on consumers' satisfaction (Katz, Larson, and Larson 1991; Maister 1985). Fairness perceptions are influenced by attributions of controllability: When consumers believe that a service provider has control over a delay, affect and judgments of fairness and service quality are adversely affected (Folkes, Koletsky, and Graham 1987; Seiders and Berry 1998; Taylor 1994). Seeking to under-stand the links among attribution, fairness, and satisfaction, researchers have examined the effect of offering consumers various types of information about waiting (Folkes, Koletsky, and Graham 1987; Taylor 1994).
Convenience orientation refers to a person's general preference for convenient goods and services. Anderson (1972) was among the first to examine convenience-oriented consumption, focusing on the use of convenience-oriented food products and appliances. Yale and Venkatesh (1986) identify convenience preference as a distinct consumption strategy, and Morganosky (1986, p. 37) defines a convenience oriented consumer as one who seeks to "accomplish a task in the shortest time with the least expenditure of human energy." More recent research defines convenience orientation as the value consumers place on goods and services with inherent time-or effort-saving characteristics (Brown 1990; Voli 1998). Researchers agree that convenience orientation has a major impact on consumers' buying decisions.
Several studies have sought to determine the factors that influence consumers' use of convenient goods and services. Convenience consumption has been operationalized by the use of convenience foods(e.g., frozen expensive entrees, ready-to eat cold cereal), timesaving durables (e.g., microwave oven, dishwasher, freezer), and paid services (e.g., domestic services, child care). Total household income has consistently been found to correlate with convenience consumption. Other demographic variables proposed to relate to convenience orientation include age, occupation, wife's employment, hours worked per year by husband, residence, family size, stage in family life cycle, education, and socioeconomic status(Anderson 1971, 1972; Bellante and Foster 1984; Morganosky 1986; Nickols and Fox 1983; Reilly 1982; Soberon-Ferrer and Dardis 1991; Strober and Weinberg 1980). Lifestyle variables considered relevant include time pressure, role overload, emphasis on leisure, hedonism, attention to mental and physical self-improvement, and devotion to work (Berry 1979; Etgar 1978; Fram and DuBrin 1988; Reilly 1982).
Many convenience orientation studies have reported inconclusive findings. Demographics believed to be related to time constraints have shown relatively weak and inconsistent relationships with convenience-oriented behavior (Voli 1998). In addition, problems in operationalizing the dependent variable have been noted (Bellante and Foster 1984; Reilly 1982). Although consumers' willingness to pay for convenience or to sacrifice convenience for a lower price is commonly acknowledged and cost-oriented and convenience-oriented consumers have been found to be significantly different (Morganosky 1986), researchers have yet to understand the price--convenience trade-off process.
In summary, the research streams most related to service convenience are those focused on consumers' time and effort expenditures, consumer waiting, and convenience orientation. Although convenience orientation has been examined relative to services (e.g., Nickols and Fox 1983), no known studies offer an in-depth, explicit focus on service convenience. Thus, the extant literature is helpful only to a point. Much work needs to be done to further the under-standing of service convenience. Toward this end, we propose an overall model of service convenience and related propositions in the next section.
A conceptual model of service convenience is presented in Figure 1. Certain service characteristics, including some traditionally used to classify services, are important influences of consumer-perceived convenience. Specifically, convenience perceptions vary on the basis of whether a service is consequential, inseparable, supply constrained, labor intensive, or hedonic. Central to our model is the service convenience construct, conceptualized as consumers' time and effort perceptions related to buying or using a service. These perceived time and effort expenditures encompass five defining types of convenience--decision access, transaction, benefit, and postbenefit--which mirror the activities consumers undergo to purchase or use a service. The dimensions of time and effort can be viewed as the benefits of convenience (saving time and/or effort) or the burdens of inconvenience (wasting time and/or effort). Service convenience is affected by a variety of firm-related factors, including the physical service environment, information provided consumers, company branding, and service system design. Individual consumer differences, such as a person's overall time orientation, time pressure, empathy toward the service provider, and prior experience, also affect convenience perceptions.
Perceptions of service convenience affect consumers' overall evaluation of the service, including satisfaction with the service and perceived service quality and fairness. The relationship between service convenience and service evaluation is moderated by consumers' attributions of firm controllability. In the sections that follow, we address the various constructs in our model and the key relationships among those constructs.
Consumers perceive convenience differently according to the type of service they are buying or using. Researchers have proposed several classifications that group services according to relevant marketing characteristics (Lovelock 1983). These classifications consider whether the service is tangible or intangible dominant (Shostack 1977), supply constrained (Lovelock 1983), equipment-or people-based (Kotler 1980), performed for people or their possessions (Hill 1977), or remote or face-to-face (Shostack 1985). Other frameworks consider the extent to which consumers participate in or co-produce the service (Chase 1978; Hub-bert 1995).
Service characteristics most germane to convenience include consequentiality (Katz, Larson, and Larson 1991), inseparability (Chase 1978; Hubbert 1995; Shostack 1985), supply constraints (Berry, Parasuraman, and Zeithaml 1984; Lovelock 1983), labor intensiveness (Berry 1995), and hedonic value (Holbrook and Lehmann 1981).
Consequential services include those that are highly valued by consumers and/or involving (see, e.g., Murphy and Enis 1986). When waiting to purchase a service with a highly valued outcome, for example, consumers would likely be more tolerant of inconvenience. Most high-involvement purchases include relatively high levels of perceived risk, and consumers typically exert more cognitive effort when making high-involvement purchase decisions (Celsi and Olson 1988; Hawkins and Hoch 1992; Richins and Bloch 1986).
Service inseparability refers to the simultaneity and interconnectedness of service performance and use. Because inseparable services involve consumer participation (Kelley, Donnelly, and Skinner 1990), consumers' time and effort costs are heightened. If a service's availability is constrained, consumers will expect to spend more time and effort, and their convenience demands will lessen. Unless they are willing to forgo the service, consumers have no choice but to accept the added time and effort burden associated with supply constraints (e.g., waiting for a table at a popular restaurant).
Labor-intensive services introduce a degree of variability that is not usually found in equipment-intensive services or in goods. Expected differences in the skills and attitudes of service personnel encourage consumers to be careful in selecting a service provider. Consumers perceive time and effort costs differently for hedonic services that are pursued for pleasure (Bellante and Foster 1984). More time and effort can increase the value of a hedonic service.
The literature indicates that the nature and type of service influence consumers' sensitivity to time and effort expenditures and affect service convenience. Some types of convenience are likely to be affected more strongly than others. We further examine and formally propose these relationships in our discussion of the service convenience construct.
Intrinsic to consumers' perceptions of service convenience are the time and effort required to buy or use a service. Time and effort are nonmonetary costs consumers must bear to receive the service. The degree of cost varies, but the presence of some amount of time and effort cost is inherent. Time and effort are opportunity costs that prevent consumers from participating in other activities (see Bivens and Volker 1986).
Consumer assessment of time expenditures is both objective and subjective (Davis and Vollmann 1990; Hornik 1984, 1993). Time spent waiting often involves significant psychological costs (Carmon, Shanthikumar, and Carmon 1995; Osuna 1985; Pruyn and Smidts 1998) and affective reactions (Dube-Rioux, Schmitt, and Leclerc 1989; Hui and Tse 1996; Taylor 1994). Cognitive and affective judgments about waiting time affect each other reciprocally (Durrande-Moreau and Usunier 1999; Hornik 1993), though the influence of cognition on affect appears to be smaller than that of affect on cognition (Pruyn and Smidts 1998).
The marketing literature has emphasized the importance of consumers' desire for convenience and the value of time. In general, the greater the time costs associated with a service, the lower are consumers' perceptions of service convenience. An exception would be time-investment services, in which a service's duration, to a degree, increases its value, such as a cruise. Time-investment services often have hedonic value, which is especially relevant to discretionary activities pursued for their own sake rather than as a means to another goal(Hol-brook and Lehmann 1981).Most services, however, are time-cost services rather than time-investment services.
Some elements of time evaluation--for example, that subjectivity is involved and that required expenditures are estimated in advance-can be applied to effort-related judgments. The physical and emotional dimensions of effort are likely to underlie the affective component of response to delay and other types of inconvenience, but these relationships are not explicitly stated in the literature. In many service exchanges, especially those requiring a consumer's participation, physical, emotional, and cognitive effort are all likely to be relevant.
Researchers argue that consumers seek to conserve cognitive effort (Fennema and Kleinmuntz 1995; Fiske and Taylor 1984). When people exert more cognitive effort in processing an alternative, they are likely to experience more negative affect (Garbarino and Edell 1997). Kahneman (1973) observes that though two mental tasks may take a similar amount of time, one might be perceived as requiring more effort than the other.
Whereas cognitive effort associated with purchase decisions is expended for both goods and services, physical and emotional effort may be greater for services in which consumers participate in the production process (Kelley, Don --nelly, and Skinner 1990). Interacting with service providers may require significant effort from consumers (Surprenant and Solomon 1987). The more effort spent by a services consumer, the stronger is that consumer's commitment to the service outcome and the higher is the potential for frustration (Hui, Thakor, and Gill 1998). We suggest that physical and emotional efforts, similar to cognitive effort, are treated by consumers as scarce resources (Bettman, Johnson, and Payne 1990; Kahneman 1973). In the aggregate, consumers' perceptions of convenience are negatively influenced by their perceptions of the cognitive, physical, and emotional effort associated with the service.
Consumers' perceived expenditure of time and effort interacts to influence their perceptions of service convenience. Research that relates stress and other psychological costs of waiting to perceived time duration provides insights into the interactive effect of effort and time costs (Kumar, Kalwani, and Dada 1997). The perception of high effort costs may inflate the perception of time costs, which occurs when a consumer who is supposed to be at work is waiting at home for a late-arriving plumber and expending involuntary mental and emotional effort during the wait. Alternatively, when consumers self-scan their grocery purchases, the voluntary effort they expend may reduce their perceived waiting time. Whereas consumers'voluntary effort to reduce time is likely to increase their perceptions of service convenience, involuntary effort is likely to make time costs more salient and decrease perceptions of service convenience.
Time and effort saving are the two aspects of convenience most often cited in the literature (Anderson 1971, 1972; Anderson and Shugan 1991; Bellante and Foster 1984; Brown 1989, 1990; Gehrt, Yale, and Lawson 1996; O'Shaughnessy 1987; Reilly 1982; Strober and Weinberg 1980; Yale and Venkatesh 1986). Whereas some researchers (e.g., Luqmani, Yavas, and Quraeshi 1994) have labeled the convenience-related costs of time and effort as dimensions, others have defined distinct types or categories of convenience as dimensions.
Yale and Venkatesh (1986) divided product convenience into six types (e.g., accessibility, portability); later, it was found that these overlap, however, and do not represent discrete categories (Gehrt and Yale 1993). Drawing on economic utility theory, Brown(1989,1990)proposed five types of convenience: time, place, acquisition, use, and execution. The execution dimension refers to the contracting out of previously performed tasks. Similar to Brown (1990),Anderson and Shugan (1991) used a convenience continuum to show that products with the highest levels of time-and effort-reducing attributes are those that represent an alternative to the consumer's own time and effort (see also Lovelock 1994). Shopping convenience has been examined by Seiders, Berry, and Gresham (2000), who developed a convenience framework related to consumer shopping speed and ease.
We propose five types of service convenience: decision convenience, access convenience, transaction convenience, benefit convenience, and postbenefit convenience. These convenience types reflect stages of consumers' activities related to buying or using a service. Consumers' perceived time and effort costs related to each type of service convenience affect the consumers' overall convenience evaluations. An activities-based approach to defining service convenience is consistent with the services literature. The study of service encounters and service design has evolved in response to the process and experience-oriented nature of services and service delivery (Shostack 1987). Service maps or blueprints, for example, define the steps in a service encounter by noting the sequence of consumers' activities (Heskett 1992; Zeithaml and Bitner 2000).
Decision convenience. Consumers who desire a particular performance devote time and effort to deciding how to obtain it. The first decision is whether to self-perform or purchase the service. A decision to purchase requires decisions on which supplier to use and what specific service to buy. Decision convenience involves consumers' perceived time and effort expenditures to make service purchase or use decisions.
Consumers confront the "make-or-buy" decision more commonly for services than for goods. Whereas many services lend themselves to self-performance, few goods lend themselves to self-manufacture. The decision to self-perform or buy can be complex. A service that is designed to save consumers time may be perceived as not worth the effort of finding a reliable supplier or monitoring that supplier's performance. One form of convenience may trigger another form of inconvenience. Consumers who self-perform services that are readily available for purchase often do so to conserve effort. For example, using an online bill-paying service may create a trade-off between time-saving convenience (contracting out bill payment) and effort-consuming inconvenience (worrying if the right payments are being made at the right time). One study reports that the main attraction of electronic bill-paying was "convenience" but that 37% of respondents using online bill-paying services said they disliked losing control and not knowing when a bill would be paid (Lloyd 2000).
Many services require special training or equipment, making purchase the only realistic option for most consumers. Service intangibility means that consumers inform their buying decisions without the benefit of prepurchase product inspection. Instead, they use surrogate evidence such as word-of-mouth communications, the company brand, and the appearance of service facilities and personnel. Unlike manufactured goods consumers, who also use surrogate evidence in making buying decisions, service consumers are totally reliant on such evidence.
Consumers have learned to expect variability in labor intensive services and devote time and effort to finding services in which they can be confident-especially services that are consequential, involving, complex, and recurring. Labor-intensive services with some or all of these characteristics are common, such as financial, professional, transportation, and health care services. Consumers of these services often seek enduring relationships with a supplier they can trust, in part because of the time and effort economies in repurchasing. Gwinner, Gremler, andBitner (1998) find that consumer confidence--reduced anxiety and faith in the trustworthiness of the service provider--is the most important benefit to consumers of maintaining a relationship with a service firm.
Informing service-buying decisions requires time and effort and is a facet of service convenience. Prior research has addressed how consumers reduce time and effort costs by enlisting the help of others such as opinion leaders (Montgomery and Silk 1971), surrogate shoppers (Solomon 1986), and market mavens (Feick and Price 1987). Demand for third-party support has spurred the growth of concierge agencies and personal shoppers as well as the creation of new online services. Some consumers use shopping bots (e.g., mysimon.com, bizrate.com) to locate the lowest prices in the market. Other Internet applications enable consumers to obtain the opinions of others, for example, restaurant rankings (e.g., zagat.com).
P1: Consumers consider decision convenience more important when selecting a labor-intensive service than a service perceived as less labor intensive.
Access convenience. Access convenience involves consumers' perceived time and effort expenditures to initiate service delivery. It involves consumers' required actions to request service and, if necessary, be available to receive it. Consumers may initiate service in person (going to a restaurant), remotely (telephoning a take-out order), or through both means (telephoning for a reservation and then going to the restaurant). Service facility location, operating hours, parking availability, and remote contact options figure prominently in the access convenience of firms that rely on consumers' physical presence (Seiders, Berry, and Gresham 2000). Receiving the service, which may be separated by space and time from requesting it, can be affected by service delivery capacity and flexibility and the option to make appointments or reservations (Bitner, Brown, and Meuter 2000). Regarding access, convenience in buying a good falls in the realm of service convenience, such as the convenience of a store's location or a product's location in the store.
Access convenience typically plays a more complex role for inseparable services. Services performed directly for the consumer (such as a taxi service) rather than for the consumer's property (such as product repair) are usually inseparable. Inseparability means that consumers must synchronize their availability with the availability of the service. They shop when stores are open, fly according to an airline's schedule, and make appointments to see doctors. Users of manufactured goods need not be present at the factories where the goods are produced, but users of inseparable services must be present at a site where the services can be performed. Service inseparability heightens the importance of accessibility.
One reason for the growing use of self-service technologies, as discussed by Meuter and colleagues (2000), is that many of them reduce time and effort costs for inseparable services. Access convenience is a primary reason for consumers to self-perform certain services. Self-service reduces consumers' dependence on service providers whose accessibility may be inconvenient. Automatic teller machines are popular in part because they are available when financial institution offices are closed.
Nothing happens until consumers gain access to the service. Ultimately, services marketing success may rest on whether a convenience-minded consumer is willing to make a left turn into traffic to reach the service facility. The speed and ease with which consumers can access the service may powerfully influence the choices they make.
P2: Consumers using inseparable services will perceive access convenience as more important than will consumers using separable services.
Transaction convenience. Transaction convenience involves consumers' perceived expenditures of time and effort to effect a transaction. Transaction convenience focuses strictly on the actions consumers must take to secure the right to use the service. When consumers have decided to buy a service and have reached the service site, they still must participate in a transaction. An exchange must occur--usually money for the promise of service performance. Transaction convenience inherently falls within the domain of service convenience. Completing transactions requires firms to render performances (services) such as the checkout service. The waiting time literature reveals the negative consequences for companies that make consumers wait too long to pay (Larson 1987; Tom and Lucey 1997).
Waiting to pay can be the least rewarding act required of consumers. Consumers normally pay for (or agree to pay for) services before they experience them. The implication of transaction inconvenience is converging nonmonetary cost (time and effort) and monetary cost before consumers experience any benefits.
According to a Forrester Research report, two-thirds of Internet shoppers abandoned their "shopping carts" before actually buying something (Tedeschi 2000). Another study found that most Internet shoppers abandon their shopping carts in slow sites in as little as eight seconds (Cimino 2000). Transaction inconvenience (including required completion of detailed registration forms), though not the only cause of high abandonment rates, is a contributing factor. The e-commerce case illustrates a consumer convenience maxim that holds regardless of transaction format: Paying for services or goods is an unwanted chore.
Transaction inconvenience is an opportunity cost. Concurrent time usage generally is not practical for consumers whose presence is required in a queue. Moreover, consumers are inclined to perceive wait times to be longer than they actually are (Hornik 1984). Transaction inconvenience also can exact an emotional toll on consumers who incorrectly guess which of several queues to enter and become trapped in the slower line or who question the fairness of the service system (Larson 1987).
P3: Consumers are more likely to perceive higher time and effort costs related to transaction convenience than to decision or access convenience.
Benefit convenience. Benefit convenience is consumers' perceived time and effort expenditures to experience the service's core benefits, such as being transported in a taxi or watching a movie. Moving consumers efficiently and effectively to the benefit stage of the service process only to inconvenience them at this point can have a powerfully negative effect because the perception of burden interferes with the perception of benefit.
Benefit convenience is illustrated by the example of an airline passenger who begins a connecting-flight trip with a scheduled 30-minute span between the arrival of the first flight and the departure of the second. The first flight arrives at the airport on time; however, the designated arrival gate is occupied by another aircraft. The passenger needs at least 10 minutes when inside the terminal to reach the departure gate of the connecting flight. Meanwhile, the arriving aircraft waits near the occupied gate for what turns out to be 27 minutes, and the passenger misses the connection. Officially, the plane is only about a half hour late, but the passenger experiences considerable benefit inconvenience. The extra time cost causes the passenger to be late for an important meeting. The effort cost, which includes sitting on the first plane with mounting anxiety and running through the airport to the connecting gate, also is high. Benefit inconvenience diminished the core benefit of the service.
Consumers do not normally seek to minimize time and effort costs in the benefit stage of a hedonic service experience (see Bellante and Foster 1984; Feldman and Hornik 1981; Holbrook and Lehmann 1981; Jacoby, Szybillo, and Berning 1976). Because time and effort are more often viewed as investments, benefit convenience does not play a prominent role in consumers' evaluation of these services. Decision, access, and transaction convenience remain salient, however.
P4: Consumers' negative perceptions of benefit convenience will have a more adverse effect on overall service convenience than will negative perceptions of decision, access, or transaction convenience.
P5: Consumers using services with high hedonic value will perceive benefit convenience as less important than will consumers using services with low hedonic value.
Postbenefit convenience. Postbenefit convenience involves the consumer's perceived time and effort expenditures when reinitiating contact with a firm after the benefit stage of the service. Postbenefit convenience can be related to a consumer's need for product repair, maintenance, or exchange. Sometimes consumers reinitiate contact because of a service failure that is not recognized or resolved during the service encounter--for example, when a consumer calls a house painter back for touch-ups. (Service failure and recovery also may occur during the service encounter and be subsumed into decision, access, transaction, or benefit convenience, as is discussed subsequently.) Some activities related to postbenefit convenience are initiated by service firms, as when a patient returns to a surgeon for a postoperative evaluation. Postbenefit convenience might be experienced as timely, nonintrusive reminders from a dentist to schedule routine appointments.
Research supports the importance of the postpurchase experience to overall consumer satisfaction (Berry and Parasuraman 1991; Bitner, Booms, and Tetreault 1990). Tax, Brown, and Chandrashekaran (1998), using a justice theory framework, find that perceived convenience of complaint handling increases consumers' satisfaction with that process. The importance of postbenefit convenience has been underscored in recent years because of difficulties encountered by consumers in returning products purchased over the Internet.
It stands to reason that consumers will perceive not having a postbenefit encounter to be more convenient than having such an encounter unless they receive additional benefit. Consumers spend their time and effort resources to receive benefits. They have no incentive to spend more of these resources without the expectation of additional benefit. The postsurgery patient is likely to be willing to return to the surgeon for a follow-up appointment, because the surgeon can reassure the patient, offer advice, or determine a new course of recuperative treatment.
P6: Consumers'perceptions of postbenefit convenience will be positively correlated with their perceptions of the benefit received from the follow-up service.
Service failure and recovery. Consumer recovery efforts related to service failure can characterize postbenefit inconvenience, as mentioned previously. Service failure and recovery efforts also can affect decision, access, transaction, and benefit convenience, according to the stage at which the failure occurred and the stage at which it was recognized by the consumer. Service failure can affect decision convenience if a consumer is given incorrect information, access convenience if an online connection fails or a parking area has no vacancies, transaction convenience if an incorrect price is charged and its correction delays a consumer, and benefit convenience if a dining experience is flawed by unresponsive service.
In general, the less time and effort required of cons umers to effectively deal with a failed service, the better is the recovery service. Research conducted by Federal Express showed that 77% of complaining consumers were satisfied with the recovery service if they could resolve their complaint through only one contact. Only 61% were satisfied if they were sent to a second company representative (The Service Edge 1991). An extensive literature exists on effective service recovery. Several frequently mentioned guidelines involve service convenience, including making it easy for consumers to complain, responding quickly, and keeping consumers informed (Berry and Parasuraman 1991; Hart, Heskett, and Sasser 1990; Rust, Subramanian, and Wells 1992; Tax and Brown 1998; Zemke and Bell 1990).
A firm's marketing and operations can dramatically influence consumers' perceptions of service convenience. Studies have examined initiatives designed to reduce the actual length of a wait and improve consumers' response to waiting (Kumar, Kalwani, and Dada 1997). Firm-related factors that affect consumers' perceived convenience include service facility distractions and enhancements (Baker and Cameron 1996; Bitner 1992), information that clarifies required time and effort costs (Whitt 1999), the company brand (Berry 2000), and the design of the service system (Katz, Larson, and Larson 1991; Meuter et al. 2000).
Service environment. Research suggests that consumers typically overestimate time spent waiting when they are in a passive mode (Davis and Vollmann 1990; Hornik 1984). Maister (1985), in his theory of queue psychology, argues that because unoccupied time feels longer, time perception is influenced by the degree to which waiting time is filled up. Environments that offer engaging activities (distractions) and enhancements increase satisfaction (Katz, Larson, and Larson 1991) and moderate perceived waiting time and affective responses (Hui, Thakor, and Gill 1998). For example, televisions in airports for travelers and free appetizers served to restaurant patrons who are not yet seated are welcome distractions intended to offset perceived waiting costs. Although some studies examining the use of distractions have produced inconclusive results (Pruyn and Smidts 1998), considerable evidence suggests the effectiveness of this approach (Houston, Bettencourt, and Wenger 1998; Katz, Larson, and Larson 1991; Taylor 1995).
Elements that enhance the service environment have been shown to positively influence consumers' affective responses in general (Bitner 1990, 1992) and reactions toward waiting in particular (Baker, Grewal, and Parasuraman 1994). Music has been found to reduce both the perceived length of a wait and emotional effort costs related to waiting (Hui, Dube, and Chebat 1997; Kellaris and Kent 1992). The presence of an appealing scent in a service environment also can create positive affect and reduce the perceptions of time spent (Mitchell, Kahn, and Knasko 1995).
P7: Consumers' perceptions of service convenience will be higher for service firms whose environments provide engaging distractions and enhancements.
Consumer information. Several researchers have examined the effects of providing consumers information about the potential waiting time (Folkes, Koletsky, and Graham 1987; Taylor 1994). Osuna (1985) argues that it is appropriate to inform consumers about wait times, and the failure to provide this information adds psychological cost. When people are uncertain about the length of a wait and have limited information, stress typically will increase(Hui, Thakor, and Gill 1998; Leclerc, Schmitt, and Dube 1995; Maister 1985; Osuna 1985). This psychological stress may be reduced when consumers are informed about the expected length of the wait and/or the reasons for a delay(Hui and Tse 1996; Larson 1987; Whitt 1999).
Because anxiety makes a wait seem longer, uncertain and unexplained waits are perceived to require more time and effort costs than waits that are defined or explained(Houston, Bettencourt, and Wenger 1998; Maister 1985). Providing information is particularly effective in situations in which consumers endure long waits for service (Hui and Tse 1996). Although information can reduce consumer uncertainty and minimize negative attributions of firm controllability, its effectiveness varies according to the type of information offered and the type of wait (Hui and Tse 1996; Osuna 1985).
P8: Consumers' perceptions of service convenience will be higher when they receive information that reduces their uncertainty about required time and effort costs.
Company brand. Branding plays a special role in service companies, because strong brands increase consumers' trust of the invisible, enabling them to better visualize and under-stand the service and reduce their perceived risk. This positive response to a brand, related to brand meaning and awareness, is considered brand equity(Keller1993). Whereas the product is the primary brand in packaged goods, the company is the primary brand for services. This is due in part to service intangibility: An automobile insurance firm, such as USAA, cannot package and display its service the way Kraft packages and displays food. Even more important is the source of consumer value creation. Brand impact shifts from product to firm as service plays a greater role in determining value(Berry 2000).
Consistent with cue utilization theory (Jacoby and Olson 1977), research suggests that consumers use brand names to assess the quality of goods and services (Dodds, Monroe, and Grewal 1991; Rao and Monroe 1989). Consumers can reduce time costs through brand loyalty; buyers under time pressure are less likely to adopt a new brand (Hafstrom, Chae, and Chung 1992; Howard and Sheth 1969; Jacoby, Szybillo, and Berning 1976). A strong service brand offers consumers decision convenience by functioning as a time-and effort-saving heuristic. Because brand equity offsets the perceived risk in selecting a service or service provider, the choice process may be simplified and decision convenience enhanced. Buying an invisible service from a respected organization is an appealing option for many consumers.
P9: Consumers' perceptions of service convenience will be positively correlated with a firm's brand equity.
Service system design. Service system design is instrumental in managing the time and effort costs required for consumers to use a service. Environmental psychology suggests that the most important role of space in a facility is promoting the goals of its occupants (Canter 1983; Darley and Gilbert 1985). Spatial layout and functionality are especially important in limited-or self-service environments, where the availability of employee assistance is minimal (Zeithaml and Bitner 2000). For example, store layout and design influence consumers' efficient movement through a store (Baker, Grewal, and Parasuraman 1994; Titus and Everett 1995) and affect their goals of getting in and out quickly and finding the desired merchandise easily (Seiders, Berry, and Gresham 2000).
Probably the most significant convenience research related to service system design is studies of queue management. Whereas early research involved company cost minimization, more recent studies have incorporated consumers' service expectations, justice perceptions, and psychological costs (Carmon, Shanthikumar, and Carmon 1995). For example, consumers' aversion to unfairness has been found to create a preference for queues that are guaranteed first-come, first-served even when the queue and the wait are longer (Larson 1987; Pruyn and Smidts 1998).
Technology is a key adjunct to service system design (see Meuter et al. 2000). Technologies specifically designed to improve consumer convenience can affect each type of service convenience. For example, toll-road users who dis-play E-Z Pass computer-chip tags on their automobile's front window derive transaction convenience. Patients served by doctors using electronic medical records may receive more benefit convenience. Intelligence embedded in an organization's information systems and available to service providers can improve not only information content but also speed of delivery (Bitner, Brown, and Meuter 2000). Technology can streamline service performance by auto-mating manual processes that are slower and more error prone. Well-designed technologies can give consumers more control and more options, including the option to be their own service providers. Online technology enables consumers to be their own stockbrokers or travel agents. Pay-at-the-pump technology allows gasoline purchasers to be their own cashiers and save the time and effort of walking to a facility (and possibly entering a queue) to pay. Not all consumers will prefer a self-service option, even if one is provided. Self-service technologies are most likely to improve consumers' convenience perceptions when consumers can choose the mode of service--full-service or self-service.
P10: Consumers' perceptions of service convenience will be influenced by their perceived fairness of a firm's queue design. Under standing
P11: Consumers' perceptions of service convenience will be higher for a firm that offers a choice between full-service and self-service when self-service technology is available.
Several individual consumer characteristics may influence convenience perceptions. A consumer's tolerance for inconvenience may be partially explained by demographic characteristics (e.g., gender, income) or shopping style (Bergadaa 1990; Goldman 1977; Jacoby, Szybillo, and Berning 1976). However, in this article we explicitly focus on the role of time orientation (Kaufman, Lane, and Lindquist 1991; Shimp 1982), perceived time pressure (Katz, Larson, and Larson 1991; Taylor 1994), empathetic feelings (Bagozzi and Moore 1994; Thompson 1997), and consumer's experience with service providers (Hui and Tse 1996; Kumar, Kalwani, and Dada 1997; Leclerc, Schmitt, and Dube 1995).
Time orientation. Consumers differ in their time orientation and approaches to allocating time (Bergadaa 1990; Durrande-Moreau and Usunier 1999). Researchers have studied cultural differences related to monochronic and polychronic time use, present and future orientations, linear and cyclic time concepts, and beliefs on whether time is finite (Graham 1981; Hall and Hall 1987; Kluckhohn and Strodtbeck 1961). Studies examining cultural differences in attitudes toward time indicate that time and energy conservation influences buying behavior (Hafstrom, Chae, and Chung 1992; Luqmani, Yavas, and Quraeshi 1994).
Polychronic (concurrent) time use, which enables people to accomplish several goals at the same time, is preferred by consumers who view time as a scarce resource and plan its use carefully (Jacoby, Szybillo, and Berning 1976; Kaufman, Lane, and Lindquist 1991). Research suggests the need for service providers to offer consumers more opportunities to be polychronic--to combine activities - reducing their perceived time costs (Kaufman, Lane, and Lindquist 1991). For example, some mall parking garages offer car detailing and servicing for customers while they are shopping. Consumers who are culturally influenced to view time as a finite resource are likely to be particularly sensitive to the time costs of activities (Shimp 1982). Accordingly, cultural differences in time orientation have been found to have an effect on perceptions of convenience (Gagliano and Hath-cote 1994).
P12: Consumers who are given the opportunity to engage in polychronic time use will have more favorable perceptions of service convenience than other consumers.
P13: Consumers who view time as finite will have less favorable perceptions of service convenience than consumers who view time as nonfinite.
Perceived time pressure. The situational variable of time pressure, which occurs when people perceive their available time to be insufficient (see Landy et al. 1991), also has been found to affect people's time allocation strategies (Bergadaa 1990; Durrande-Moreau and Usunier 1999; Hornik 1982, 1984). For example, situational time pressure will affect a consumer who must complete a task quickly to meet a deadline (e.g., shopping for a birthday gift on the way to a birthday party). Time pressure is considered a lifestyle variable by convenience orientation researchers, who relate it to role overload (Reilly 1982).
People who are more concerned about time than others may be susceptible to the physical and psychological symptoms associated with strain when time demands are high (Landy et al. 1991). When influenced by required expenditures of time such as waiting, time pressure may trigger strong, negative emotions such as impatience and helplessness and result in particularly negative convenience perceptions (Hui and Tse 1996; Maister 1985).
P14: Consumers influenced by situational time pressure will perceive lower service convenience than will those who are not time pressured.
Empathy. Empathy, which is identified as an other-focused emotion (as opposed to an ego-focused emotion), involves feeling compassion for others in a social or inter-personal context. Whereas ego-focused emotions (e.g., pride, anger) are exclusive of others and reflect the need for individual expression, empathy satisfies the need for unity and harmony by fostering feelings of affiliation and connectedness (Aaker and Williams 1998). Empathy has been related to altruism in that it is an emotional response, driven by personalized norms and internalized values, motivating one person to help another (Thompson 1997). Aspects of empathy include perspective taking, compassion/pity, and protection motivation (Bagozzi and Moore 1994).
Empathetic responses vary across individuals, and those most likely to experience empathy possess either high empathetic ability (prior experience with the need faced by someone else) or an emotional attachment to a particular issue (Bendapudi, Singh, and Bendapudi 1996). Consumers may demonstrate empathy toward a service provider by taking his or her perspective in a service experience. This has been defined as cognitive role taking(Stephens and Gwinner 1999). Such responses may cause consumers to exhibit self-control andrefrain from voicing dissatisfaction in a service encounter. Feelings of empathy with a service provider are likely to affect consumers' convenience perceptions: With greater empathy, perceived time and energy costs will be lower.
P15: Consumers who are empathetic toward a service provider will perceive higher service convenience than will consumers who are not empathetic.
Experience. Prior research has consistently demonstrated that consumers' experience or familiarity influences how they use information to make decisions and assess goods and services (Brucks 1985; Rao and Monroe 1988; Sujan 1985). As consumers gain experience with service providers, decision convenience costs decline as provider choice sets become smaller and relationships solidify. However, when consumers are inexperienced--making their first overseas trip, for example--decision convenience costs will rise. When people relocate to a new town and move from an experienced to an inexperienced status, they will invest significant time and energy resources to rebuild supplier networks. The work of Solomon (1986) and others suggests that a consumer's perceived self-expertise is inversely related to the probability of seeking help with purchase decisions; for example, low confidence may mediate the likelihood of enlisting the services of an interior decorator, wardrobe consultant, or stockbroker.
Prior research has demonstrated that consumers have scripts and schemas for specific situations and transactions, and the more developed these schemas, the more easily evaluations are formed (Goodstein 1993; Sujan 1985; Wansink and Ray 1996). When new information is consistent with past schemas (and experiences), evaluations are more favorable (Wansink and Ray 1996). Consumers who know where to go and what to do as participants in a service operation minimize wasted time and energy. Experience influences service expectations and affects convenience perceptions. For example, satisfaction with waiting is related to expectations for the length of a wait, which is determined in part by a consumer's experience with a firm (Davis and Vollmann 1990; Hui and Tse 1996; Leclerc, Schmitt, and Dube 1995). Therefore, consumers' familiarity with a service provider is likely to improve their perceptions of convenience (Kumar, Kalwani, and Dada 1997).
P16: Consumers who are familiar with a service provider's systems will perceive higher service convenience than will consumers who are unfamiliar with them.
Researchers have consistently found that consumers' evaluation of waiting time affects their satisfaction with the service. Several waiting time studies report a strong relation-ship between consumers' evaluation of the wait and overall service satisfaction. For example, Carmon, Shanthikumar, and Carmon (1995) find dissatisfaction with waiting for services to be highly correlated with overall satisfaction judgments. Kumar, Kalwani, and Dada (1997) and Pruyn and Smidts (1998) find that consumer satisfaction increases when waiting time proves to be shorter than expected. Houston, Bettencourt, and Wenger (1998) find that perceived waiting time affects overall service quality. Their results suggest that waits perceived to be unacceptable negatively affect service quality perceptions, even for relatively unimportant transactions. Keaveney (1995) finds that service inconvenience contributes to consumer switching behavior.
Researchers also have considered the impact of consumer-perceived fairness on service satisfaction and quality (see, e.g., Seiders and Berry 1998; Tax, Brown, and Chandrashekaran 1998). Convenience perceptions in general are likely to affect consumers' evaluation of service fairness. Equity theory (see Adams 1965), which focuses on distributive justice, relates fairness to the equitable balance of input (such as time and effort) and output among exchange partners. The relationship between consumers' justice perceptions and their attitudes toward waiting has long been noted in the literature (Katz, Larson, and Larson 1991; Larson 1987).
Consumers' convenience perceptions and their effects on service evaluation are likely to be influenced by attributions of blame for unexpectedly high time and energy costs (Bitner 1990). Whether the inconvenience is deemed within or beyond the control of the firm has been found to play a central role in consumers' emotional responses and cognitive assessments (Katz, Larson, and Larson 1991; Maister 1985; Taylor 1994). We expect the relationships between consumers' perceptions of service inconvenience and their evaluations of quality, satisfaction, and fairness to be moderated by their attributions of control to the service provider. More specifically, when consumers believe that a service provider has control over service inconvenience, their judgments of quality, satisfaction, and fairness are likely to be more negative (Folkes, Koletsky, and Graham 1987; Seiders and Berry 1998; Taylor 1994; Weiner 1986). Airline passengers are less likely to blame an airline for a weather-related delay than a delay believed to be caused by management--union tensions.
P17: Consumers' perceptions of convenience will have a positive influence on their (a) satisfaction with the service, (b) assessments of service quality, and (c) perceptions of fairness.
P18: Consumers' perceptions of inconvenience will more adversely affect their evaluation of a service when they believe the inconvenience was controllable.
This article provides a conceptual framework designed to guide further research in the domain of service convenience. Developing scales to assess the five types of service convenience and empirically testing the propositions presented offer avenues for further research. A crucial early step is to develop an instrument to measure the types of service convenience. We have identified some items for illustrative purposes and present them in this section. The items could be assessed using a Likert format.
Decision convenience is consumers' perceived time and effort expenditures to make service purchase or use decisions:
- It took minimal time to get the information needed to choose a service provider.
- Making up my mind about what I wanted to buy was easy.
- It was easy to get the information I needed to decide which service provider to use.
Access convenience is consumers' perceived time and effort expenditures to initiate service delivery:
- It was easy to contact the service provider.
- It did not take much time to reach the service provider.
- I was able to get to the service provider's location quickly.
Transaction convenience is consumers' perceived time and effort expenditures to effect a transaction:
- I did not have to make much of an effort to pay for the service.
- They made it easy for me to conclude my purchase.
- I was able to complete my purchase quickly.
Benefit convenience is consumers' perceived time and effort expenditures to experience the service's core benefits:
- I was able to get the benefits of the service with minimal effort.
- The service was easy to use.
- The time required to receive the benefits of the service was appropriate.
Postbenefit convenience is consumers' perceived time and effort expenditures to reinitiate contact with the service provider after the benefit stage of the service:
- The service provider resolved my problem quickly.
- It took little effort to arrange follow-up service.
- The service provider made it easy for me to resolve my problem.
When researchers have developed psychometrically valid scales for consumer perceptions of service convenience, the propositions advanced in this article could be tested by means of experimental and survey methods. Ostrom and Iacobucci (1995) provide a relevant discussion of the experimental methodology needed to manipulate various kinds of services. In their research, they explicitly examine the role of experience versus credence services. A similar procedure could be used to manipulate various service characteristics, such as labor intensiveness, inseparability, and hedonic value. Such procedures would involve manipulating these factors and asking subjects to assess their perceptions and the relative importance of the five types of service convenience. For example, researchers could test P1 by manipulating labor intensity of the service (high, low) and asking subjects to assess the aforementioned measures.
Experimental research also could provide important insights on the convenience effects of the firm-related variables, such as service facility enhancements and the availability of information. The four firm-related factors could be individually manipulated (i.e., four studies with a between-subjects design having two levels). The service environment could be manipulated using the presence versus the absence of engaging activities (e.g., music videos) at the checkout counter, similar to the approach used by Pruyn and Smidts (1998). Providing consumers with information about potential wait times could be manipulated as either the presence versus absence of the information (see Folkes, Koletsky, and Graham 1987; Taylor 1994) or the duration of the wait time. The service provider brand could be manipulated at high versus low levels of brand reputation or equity (see the research on manipulating brand names by Dodds, Monroe, and Grewal [1991]). Service system design could be manipulated using the presence versus absence of a time-saving option (e.g., self-scanners) (see the research by Meuter et al. [2000] and Bitner, Brown, and Meuter [2000]. The critical dependent variable in these studies would be the service convenience construct, which could be operationalized as the sum of each type of service convenience (decision, access, transaction, benefit, and postbenefit) weighted by its importance. Postbenefit convenience would enter the summation only when applicable. Such a research design would enable testing of P7, P8, P9, and P11.
Alternatively, a 2 X 2 X 2 research design could be used to test the effects of any three firm-related factors. Such a design would enable the researcher to test the complicated interactions between the firm-related factors and service convenience. For example, by examining the effects of time-saving options, brand equity, and wait time information, researchers could investigate the effects of the three twoway interactions. It might be expected that the effects of time-saving technology options on consumer perceptions of convenience would be more pronounced for a well-regarded service provider (i.e., an interaction between service delivery technology and the service brand). Another possible interaction is between the wait time information factor and the brand reputation factor. The effect of providing wait time information is likely to be more pronounced for well-known than for less-known providers because the information probably will be viewed as more credible and accurate. Finally, the presence of time-saving options will particularly enhance perceived convenience when consumers are aware of the potential wait (i.e., consumers will have a greater opportunity to employ the time-saving options). Studies such as these would benefit from developing interaction hypotheses and testing both main and interaction effect hypotheses.
Survey methods could be used to assess individual consumer differences, such as time orientation (P12 and P13), time pressure (P14), empathetic feelings (P15), and level of experience (P16). The effects of these factors on service convenience and, in turn, its effect on satisfaction (P17a), quality (P 17b), and fairness (P17c) could then be assessed using causal modeling procedures. In our article, we do not explicate the moderating effects that these individual consumer differences may have on the effects of firm-related factors on perceived service convenience. Further research needs to specifically examine and test these relationships. For example, the effects of self-service delivery options on service convenience (P11) are likely to be more pronounced for consumers who view time as a scarce resource or are operating under time pressure.
Service convenience is consumers' time and effort perceptions related to buying or using a service. Service convenience is a pervasive construct and an important issue. It is pervasive because all marketing performances that require consumer time and effort fall within its domain. It is important because time and effort are resources people must give up to become consumers. Time is nonrenewable and effort depletable. Societal trends such as the participation of women and mothers in the labor force and technological advances that create more communications, information, and entertainment options have placed added pressure on people's time and effort resources. Frequently, marketing effectiveness is more a function of saving consumers time and effort than saving them money.
It is useful to make a distinction between service convenience and goods convenience. Services performed directly for consumers require their presence where and when the service is available--on the plane at departure time, in the classroom during the lecture. Buying intangibles also requires consumers to make purchase decisions without inspecting the product. For some services, this may be of little practical significance. However, consumers may invest considerable time and effort to select nonstandardized, labor-intensive services that are personally important. Understanding service convenience better will help marketers improve the value of their market offers. Because goods marketing depends on support services such as personal selling, credit, and checkout, service convenience facilitates the marketing of goods, not just the marketing of services.
Service convenience is more instrumental to consumers in some situations than in others, for both determining the choice of a service firm and evaluating a firm's performance. The three sets of antecedents in our model address the influence of convenience on both choice and evaluation. Service characteristics identify conditions in which convenience may be particularly valuable to consumers, whereas firm-related factors identify company actions or traits that affect how favorably consumers rate convenience. Individual consumer differences identify characteristics that affect both the perceived importance of convenience (e.g., the degree to which a consumer is time pressured) and how favorably it will be rated (e.g., a consumer's level of empathy with service firm employees).
We propose that service convenience has two dimensions--time and effort. Consumers spend time and effort deciding on, accessing, transacting for, and benefiting from a service. They may also need to spend more time and effort after the service encounter. The relative importance of these convenience types varies across situations, services, and consumers. For example, waiting in an automobile queue to pay a bridge toll (transaction convenience) is likely to be more inconvenient to a driver who is late for an appointment than to many others in the same queue. Access convenience is particularly important for inseparable services, whereas decision convenience is central to consequential and labor-intensive services. All forms of service convenience are likely to be more salient to convenience-oriented consumers (Morganosky 1986; Yale and Venkatesh 1986).
A service can be convenient in some ways, inconvenient in other ways. One type of inconvenience may cancel the positive effects of other types of convenience. Consumers' perceptions of service convenience directly affect their perceptions of a firm's service quality and their satisfaction with a specific encounter or experience. Because time and effort are personal resources consumers must give up to buy or use a service, fairness issues also may surface when consumer convenience expectations are violated.
Consumers' service convenience perceptions are influenced not only by the characteristics of the service and individual consumer differences but also by firm-related factors. Marketers can do much to improve consumers' convenience perceptions. They can lower consumers' actual time and effort costs in many cases and can almost always improve the quality of consumers' waits for service. Information is an essential tool. Of particular importance is information that ( 1) reduces consumers' uncertainty and anxiety about delays ("The doctor is running about 20 minutes late."), ( 2) helps consumers use the service system properly("Please use this line for mailing packages outside of the United States."), and ( 3) explains the reasons for delays("Because of the inclement weather, air traffic control is slowing the arrival of aircraft to the airport.").
Gathering information about consumers and using it to anticipate their requirements also can lead to improved convenience. Ritz-Carlton is among several hotel chains that use information technology to predict consumers' preferences and customize the service experience accordingly, such as assigning repeat guests to a preferred room prestocked with their favorite beverages and snacks. Walgreens' satellite-based information system, known as Intercom Plus, reduces consumer waiting time through an automated queuing process that has prescriptions ready when consumers want to pick them up.
Understanding the core issue underlying each convenience type is critical to improving service convenience. Decision convenience is important because making decisions about intangible and variable services can be difficult for consumers. Firms can reduce the difficulty through clear, accessible information and brand-strengthening efforts that include reliable service performance. Not only does convenience affect service quality, but service quality also affects convenience. Consumers who are confident about a firm's service quality because of their past experiences have an easier service-supplier decision to make than consumers who lack confidence.
Access convenience is important because so many services require consumers' participation. Consumers must be present at the right time and place. Firms can improve access convenience by ( 1) offering consumers multiple ways to initiate service, including the use of self-service technologies; ( 2) separating required front-end administrative tasks in time and place from the benefit-producing part of a service, such as allowing consumers to reserve a rental car online; ( 3) bringing the service to the consumer rather than bringing the consumer to the service; and ( 4) reducing consumers' time and effort in moving from the core service (such as buying a home) to functionally related services (such as mortgage financing and homeowners' insurance).
Transaction convenience is important because waiting to pay is especially unrewarding for consumers. The Wall Street Journal reports studies in which 83% of women and 91% of men indicate that long checkout lines have prompted them to stop patronizing a particular store (see Nelson 2000). McDonald's has determined that sales increase by 1% for every six seconds consumers save in using the drive-through window (Ordonez 2000).
Benefit convenience is important because a service's benefit is what consumers invest resources (including time and effort) to receive. Benefit inconvenience is common. Viewers complain about having to sit through too many commercials when watching telecasts of major sporting events such as the Olympics. Restaurant consumers complain about entrees that arrive at the table too late--or too soon. Benefit inconvenience can reduce the benefit.
Postbenefit convenience is important because consumers must allocate additional time and effort resources to reinitiate contact with a firm after a service encounter. In the case of a service failure, consumers'time and effort expenditures are not only additive but also unanticipated. Postbenefit inconvenience is exacerbated by recency effects; it comes at the end of the consumer's service experience. Unde rstanding
Much useful research has been done on one aspect or another of consumer convenience. The waiting time literature is particularly robust, yet the convenience literature in marketing is neither cohesive nor mature. Questions dominate answers. Marketers know convenience is important to consumers even if they are not always sure how to deliver it.
Service convenience is uncharted territory. It tends to be either treated generally in the services literature or lumped into a broader convenience construct for which distinctions between goods and services are not made. Yet a distinction between goods and service convenience is necessary. Man --ufactured goods convenience includes issues such as product form, size, packaging, and preservability. Service convenience leads in some other directions. Our quest in this article was to integrate the consumer convenience and services literature to propose a comprehensive model of service convenience. We know of no other such model and hope our model will stimulate needed research in this subject area. In service economies that include so many time-and energy-impoverished consumers, learning more about service convenience should be a priority.
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~~~~~~~~
By Leonard L. Berry; Kathleen Seiders and Dhruv Grewal
Leonard L. Berry is Distinguished Professor of Marketing and M.B. Zale Chair in Retailing and Marketing Leadership, Texas A&M University. Kathleen Seiders is Associate Professor of Marketing and Constantine Simonides Term Chair, and Dhruv Grewal is Toyota Chair of e-Commerce and Electronic Business and Professor of Marketing, Babson College. The authors thank the four anonymous JM reviewers, Rajan Varadarajan, Michael Tsiros, and Gopalkrishnan Iyer for their helpful comments on previous drafts of this article.
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Record: 194- Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development. By: Verhoef, Peter C. Journal of Marketing. Oct2003, Vol. 67 Issue 4, p30-45. 16p. 2 Diagrams, 6 Charts, 1 Graph. DOI: 10.1509/jmkg.67.4.30.18685.
- Database:
- Business Source Complete
Understanding the Effect of Customer Relationship
Management Efforts on Customer Retention and Customer
Share Development
Scholars have questioned the effectiveness of several customer relationship management strategies. The author investigates the differential effects of customer relationship perceptions and relationship marketing instruments on customer retention and customer share development over time. Customer relationship perceptions are considered evaluations of relationship strength and a supplier's offerings, and customer share development is the change in customer share between two periods. The results show that affective commitment and loyalty programs that provide economic incentives positively affect both customer retention and customer share development, whereas direct mailings influence customer share development. However, the effect of these variables is rather small. The results also indicate that firms can use the same strategies to affect both customer retention and customer share development.
Customer relationships have been increasingly studied in the academic marketing literature (Berry 1995; Dwyer, Schurr, and Oh 1987; Morgan and Hunt 1994; Sheth and Parvatiyar 1995). An intense interest in customer relationships is also apparent in marketing practice and is most evident in firms' significant investments in customer relationship management (CRM) systems (Kerstetter 2001; Reinartz and Kumar 2002; Winer 2001). Customer retention rates and customer share are important metrics in CRM (Hoekstra, Leeflang, and Wittink 1999; Reichheld 1996). Customer share is defined as the ratio of a customer's purchases of a particular category of products or services from supplier X to the customer's total purchases of that category of products or services from all suppliers (Peppers and Rogers 1999).
To maximize these metrics, firms use relationship marketing instruments (RMIs), such as loyalty programs and direct mailings (Hart et al. 1999; Roberts and Berger 1999). Firms also aim to build close relationships with customers to enhance customers' relationship perceptions (CRPs). Although the impact of these tactics on customer retention has been reported (e.g., Bolton 1998; Bolton, Kannan, and Bramlett 2000), there is skepticism about whether such tactics can succeed in developing customer share in consumer markets (Dowling 2002; Dowling and Uncles 1997).
Several studies have considered the impact of CRP on either customer retention or customer share, but not on both (e.g., Anderson and Sullivan 1993; Bolton 1998; Bowman and Narayandas 2001; De Wulf, Odekerken-Schröder, and Iacobucci 2001). A few studies have considered the effect of RMIs on customer retention (e.g., Bolton, Kannan, and Bramlett 2000). In contrast, the effect of RMIs on customer share has been overlooked. Furthermore, most studies focus on customer share in a particular product category (e.g., Bowman and Narayandas 2001). Higher sales of more of the same product or brand can increase this share; however, firms that sell multiple products or services achieve share increases by cross-selling other products. Moreover, no study has considered the effect of CRPs and RMIs on both customer retention and customer share. It is often assumed in the literature that the same strategies used for maximizing customer share can be used to retain customers; however, recent studies indicate that increasing customer share might require different strategies than retaining customers (Blattberg, Getz, and Thomas 2001; Bolton, Lemon, and Verhoef 2002; Reinartz and Kumar 2003).
Prior studies have used self-reported, cross-sectional data that describe both CRPs and customer share (e.g., De Wulf, Odekerken-Schröder, and Iacobucci 2001). The use of such data may have led to overestimation of the considered associations because of methodological problems such as carryover and backfire effects and common method variance (Bickart 1993). Such data cannot establish a causal relationship; indeed, the argument could be made that causality works the other way (i.e., I am loyal, therefore I like the company) (Ehrenberg 1997). Longitudinal data rather than cross-sectional data should be used to establish the causal relationship between customer share and its antecedents.
I have the following research objectives: First, I aim to understand the effect of CRPs and RMIs on customer retention and customer share development over time. Second, I examine whether the effect of CRPs and RMIs on customer retention and customer share development is different. My study analyzes questionnaire data on CRPs, operational data on the applied RMIs, and longitudinal data on customer retention and customer share of a (multiservice) financial service provider.
CRPs and Customer Behavior
Table 1 provides an overview of studies that report the effect of CRPs on customer behavior, and it describes the dependent variables, the design and context of the study, the CRPs studied, and the effect of CRPs on behavioral customer loyalty measures (which can be self-reported or actual observed loyalty measures). Table 1 shows that the results of studies that relate CRPs to actual customer behavior are mixed.
RMIs and Customer Behavior
Table 2 provides an overview of the limited number of academic studies that consider the effect of RMIs. The majority of the studies have focused on loyalty or preferential treatment programs, and the results show mixed effects of these programs on customer loyalty. Despite the intensive use of direct mailings in practice, their effect on customer loyalty has almost been ignored. More important, the effect of RMIs on customer share development over time has not been investigated.
Figure 1 shows the conceptual model. In this model, I consider customer retention and customer share development between two periods (T1; and T[sub 0;) as the dependent variables, which are affected by CRPs and RMIs. Because I consider customer retention and customer share development as two separate processes, relationship maintenance and relationship development, the underlying hypotheses of the model explicitly predict that different constructs of CRPs, and different RMIs influence customer retention and customer share development. The rationale for this distinction is that a customer's decision to stay in a relationship with a firm may be different from his or her incremental decision to add or drop existing products. Consistent with this notion, Blattberg, Getz, and Thomas (2001) argue that customer retention is not the same as customer share, because two firms could retain the same customer. Reinartz and Kumar (2003) suggest that relationship duration and customer share should be considered as two separate dimensions of the customer relationship. Bolton, Lemon, and Verhoef (2002) propose that the antecedents of customer retention might be different from the antecedents of cross-buying behavior. I explicitly address these differences in the hypotheses.
The inclusion of CRPs as antecedents of retention and customer share development is based on relationship marketing theory, which suggests that CRPs affect behavioral customer loyalty. I included RMIs because a successful customer relationship largely depends on the applied RMIs (Bhattacharya and Bolton 2000; Christy, Oliver, and Penn 1996; De Wulf, Odekerken-Schröder, and Iacobucci 2001). Moreover, because of the increasing popularity of CRM among businesses, an increasing number of firms are using RMIs.
In the model, I also include customers' past behavior in the relationship as control variables, which might capture inertia effects that are considered important determinants of customer loyalty in business-to-consumer markets (Dowling and Uncles 1997; Rust, Zeithaml, and Lemon 2000). Past customer behavioral variables (e.g., relationship age, prior customer share) can also be indicators of past behavioral loyalty, which often translates into future loyalty. Prior research suggests that the type of product purchased in the past is an indicator of future cross-selling potential (e.g., Kamakura, Ramaswami, and Srivastava 1991).
CRPs
Relationship marketing theory and customer equity theory posit that customers' perceptions of the intrinsic quality of the relationship (i.e., strength of the relationship) and customers' evaluations of a supplier's offerings shape customers' behavior in the relationship (Garbarino and Johnson 1999; Rust, Zeithaml, and Lemon 2000; Woodruff 1997). The most prominent perception representing the strength of the relationship is (affective) commitment (Moorman, Zaltman, and Desphandé 1992; Morgan and Hunt 1994). Because satisfaction and payment equity are important constructs with respect to the evaluation of a supplier's offerings (Bolton and Lemon 1999), I included these three constructs in the model. The two categories of constructs differ in terms of both content and time orientation: Affective commitment is forward looking, whereas satisfaction and payment equity are retrospective evaluations.
In the customer equity and relationship marketing literature, other CRPs that are not included in my model are often studied. Trust and brand perceptions are the most prominent of these variables (Morgan and Hunt 1994; Rust, Zeithaml, and Lemon 2000). I did not include brand perceptions because the focus is on current customers. My contention is that the brand is especially significant in attracting new customers. During the relationship, the brand probably influences affective commitment (Bolton, Lemon, and Verhoef 2002). I did not include trust, because trust should be considered merely an antecedent of satisfaction and commitment (Geyskens, Steenkamp, and Kumar 1998). No direct effect on customer behavior should be expected.
Affective Commitment
Commitment is usually defined as the extent to which an exchange partner desires to continue a valued relationship (Moorman, Zaltman, and Desphandeacute; 1992). I focus on the affective component of commitment, that is, the psychological attachment, based on loyalty and affiliation, of one exchange partner to the other (Bhattacharya, Rao, and Glynn 1995; Gundlach, Achrol, and Mentzer 1995).
Effect on customer retention. Given the previous definition of affective commitment, it might be expected that this type of commitment affects customer retention positively. In line with this, researchers who relate commitment to self-reported behavior, such as purchase intentions, usually find that commitment positively affects customer loyalty (e.g., Garbarino and Johnson 1999; Morgan and Hunt 1994). However, the appearance of such an effect has recently been questioned (Gruen, Summers, and Acito 2000; MacKenzie, Podsakoff, and Ahearne 1998). Despite this, I hypothesize the following:
H1;: Affective commitment positively affects customer retention.
Effect on customer share development. Relationship marketing theory posits that because affectively committed customers believe they are connected to the firm, they display positive behavior toward the firm. As a consequence, affectively committed customers are less likely to patronize other firms (Dick and Basu 1994; Morgan and Hunt 1994; Sheth and Parvatiyar 1995). In other words, committed customers are more (less) likely to increase (decrease) their customer share for the focal supplier over a period of time.
H2;: Affective commitment positively affects customer share development over time.
Satisfaction
I define satisfaction in this study as the emotional state that occurs as a result of a customer's interactions with the firm over time (Anderson, Fornell, and Lehmann 1994; Crosby, Evans, and Cowles 1990). Szymanski and Henard's (2001) meta-analysis shows that satisfaction has a positive impact on self-reported customer loyalty.
Despite such positive results in the literature, the link between satisfaction and actual customer loyalty has been questioned (e.g., Jones and Sasser 1995). Researchers have searched for a better understanding of this link and have proposed a nonlinear relationship between satisfaction and customer behavior (e.g., Anderson and Mittal 2000; Bowman and Narayandas 2001). Other studies have shown that relationship age, product usage, variety seeking, switching costs, consumer knowledge, and sociodemographics (e.g., age, income, gender) moderate the link between satisfaction and customer loyalty (Bolton 1998; Bowman and Narayandas 2001; Capraro, Broniarczyck, and Srivastava 2003; Homburg and Giering 2001; Jones, Mothersbaugh, and Beatty 2001; Mittal and Kamakura 2001). Finally, dynamics during the relationship may also affect this link. Customers update their satisfaction levels using information gathered during new interaction experiences with the firm, and this new information may diminish the effect of prior satisfaction levels (Mazursky and Geva 1989; Mittal, Kumar, and Tsiros 1999).
Effect on customer retention. Despite the apparent absence of an empirical link between satisfaction and behavioral customer loyalty, several studies show that satisfaction affects customer retention (Bolton 1998; Bolton, Kannan, and Bramlett 2000). The underlying rationale is that customers aim to maximize the subjective utility they obtain from a particular supplier (Oliver and Winer 1987). This depends on, among other things, the customer's satisfaction level. As a consequence, customers who are more satisfied are more likely to remain customers. Thus:
H3;: Satisfaction positively affects customer retention.
Effect on customer share development. Although a positive relationship between satisfaction and customer share has been demonstrated in a single product category (Bowman and Narayandas 2001), this does not necessarily imply that satisfaction also positively affects customer share development for a multiservice provider. A theoretical explanation for the absence of such an effect could be that positive evaluations of currently consumed products or services do not necessarily transfer to other offered products or services. In other words, satisfied customers are not necessarily more likely to purchase additional products or services (Verhoef, Franses, and Hoekstra 2001). Another explanation is that though customer retention relates to the focal supplier alone, customer share development also involves competing suppliers. As a result, development of a customer's share might be affected more by the actions of competing suppliers than by the focal firm's prior performance. Thus, I do not expect satisfaction to have a positive effect on customer share development.
Payment Equity
Payment equity is defined as a customer's perceived fairness of the price paid for the firm's products or services (Bolton and Lemon 1999, p. 173) and is closely related to the customer's price perceptions. Payment equity is mainly affected by the firm's pricing policy. As a result of its grounding in fairness, a firm's payment equity also depends on competitors' pricing policies and the relative quality of the offered services or products.
Effect on customer retention. Higher payment equity (i.e., price perceptions) leads to greater perceived utility of the purchased products or services (Bolton and Lemon 1999). As a result of this greater perceived utility, customers should be more likely to remain with the firm. Consequently, payment equity should have a positive effect on customer retention. This is consistent with empirical studies that show that payment equity positively affects customer retention (Bolton, Kannan, and Bramlett 2000; Varuki and Colgate 2001). Thus:
H4;: Payment equity positively affects customer retention.
Effect on customer share development. Although I expect payment equity to have a positive effect on customer retention, I do not necessarily expect this to be true for customer share development. There are two reasons payment equity may have no effect on customer share development. First, literature on price perceptions suggests that customers with higher price perceptions are more likely to search for better prices (Lichtenstein, Ridgway, and Netemeyer 1993). Intuitively, the suggestion that such customers are less loyal makes sense. For example, customers of discounters (with high scores on price perceptions) are known to visit the greatest number of stores in their search for the best bargain.
According to this reasoning, customers with better price perceptions are more likely to decrease customer share over time. Second, as is satisfaction, a customer's payment equity is based on the customer's awareness of the prices of services or products purchased from the focal firm in the past (Bolton, Lemon, and Verhoef 2002). However, the prices of additional services or products from the focal supplier might be different from the currently purchased services or products. Therefore, a high payment equity score may not indicate that the customer will purchase other products or services from the same supplier. As a consequence, I do not expect payment equity to affect customer share development.
RMIs
Bhattacharya and Bolton (2000) suggest that RMIs are a subset of other marketing instruments that are specifically aimed at facilitating the relationship, and they distinguish between loyalty or reward programs and tailored promotions. In addition, RMIs can be classified according to Berry's (1995) first two levels of relationship marketing. At the first level (Type I), firms use economic incentives, such as rewards and pricing discounts, to develop the relationship. At the second level (Type II), instruments include more social attributes. By using Type II instruments, firms attempt to give the customer relationship a personal touch.
In this study, I focus on two specific Type I RMIs: direct mailings and loyalty programs. Direct mailings usually are personally customized offers on products or services that the customer currently does not purchase. In most cases, price discounts or other sales promotions (e.g., gadgets) are used to entice the customer to buy. I focus on direct mailings that are a "call to action" rather than only a reinforcing mechanism for the relationship (e.g., thank-you letters). The loyalty program I include in the study is a reward program that provides price discounts based on the number of products or services purchased and the length of the relationship.
Direct Mailings
Direct mailings have some unique characteristics: enablement of personalized offers, no direct competition for the attention of the customer from other advertisements, and a capacity to involve the respondent (Roberts and Berger 1999). Because direct mailings focus on creating additional sales, I do not expect them to influence customer retention. Moreover, the data do not enable me to relate direct mailings to customer retention.
Effect on customer share development. There are several theoretical reasons direct mailings should positively influence customer share development. First, direct mailings can create interest in a (new) service and thereby lead to a final purchase (Roberts and Berger 1999). Second, the personalization afforded by direct mailings may increase perceived relationship quality, because customers are approached with individualized communications that appeal to their specific needs and desired manner of fulfilling them (De Wulf, Odekerken-Schröder, and Iacobucci 2001; Hoekstra, Leeflang, and Wittink 1999). Third, according to the sales promotions literature, the short-term rewards (i.e., price discounts) offered by direct mailings may motivate customers to purchase additional services and thus increase customer share. In support of this claim, Bawa and Shoemaker (1987) report short-term gains in redemption rates of direct mail coupons. I hypothesize the following:
H5;: Direct mailings positively affect customer share development over time.
Loyalty Programs
Effect on customer retention and customer share development. There are several theoretical reasons the reward-based loyalty program being studied should positively affect both customer retention and customer share development. First, psychological investigations show that rewards can be highly motivating (Latham and Locke 1991). Research also shows that people possess a strong drive to behave in whatever manner necessary to achieve future rewards (Nicholls 1989). According to Roehm, Pullins, and Roehm (2002, p. 203), it is reasonable to assume that during participation in a loyalty program, a customer might be motivated by program incentives to purchase the program sponsor's brand repeatedly.
Second, because the program's reward structure usually depends on prior customer behavior, loyalty programs can provide barriers to customers' switching to another supplier. For example, when the reward structure depends on the length of the relationship, customers are less likely to switch (because of a time lag before the same level of rewards can be received from another supplier). It is well known that switching costs are an important antecedent of customer loyalty (Dick and Basu 1994; Klemperer 1995).
Despite the theoretical arguments in favor of the positive effect of loyalty programs on customer retention and customer share development, several researchers have questioned this effect (e.g., Dowling and Uncles 1997; Sharp and Sharp 1997). In contrast, Bolton, Kannan, and Bramlett (2000) and Rust, Zeithaml, and Lemon (2000) show that loyalty programs have a significant, positive effect on customer retention and/or service usage. In this study, I build on the theoretical argument in favor of the positive effect that loyalty programs have on customer retention and customer share development.
H6;: Loyalty program membership positively affects (a) customer retention and (b) customer share development.
Research Design
I combined survey data from customers of a Dutch financial services company with data from that company's customer database. I used a panel design, displayed in Figure 2, to collect the data. I collected the survey data at two points in time: T0; and T1. I used the first (T0;) survey to measure CRPs of the company, customer ownership of various insurance products, and customer characteristics. In the second (T1;) survey, I collected data on customer ownership of various insurance products.
Although the company whose data I used offers other products, such as loans, I limited the study to the category of insurance products. The rationale for this limitation is that customers usually buy each type of insurance product from a single insurance carrier (i.e., insurance type X [life insurance] from insurance carrier Y [i.e., Allianz Life Company]), but this does not necessarily hold for other financial products or services. For example, it is well known that many customers have savings accounts at several financial institutions. Moreover, the insurance market is the most important market for this company in terms of the number of customers and customer turnover (approximately 90%). As a result of this choice, the sample is restricted to those customers who purchase insurance products only from the company. This resulted in a usable sample size of 1677 customers for the first measurement (T0;) and 918 for the second measurement (T1;).
Contents of the Company Customer Database
The company's customer database provided data on the past behavior of individual customers and the company RMIs directed at individual customers. The past customer behavior data cover two periods. The first period starts at the beginning of a relationship between the company and the customer and ends at T0; (this period differs among customers). The data on past customer behavior included variables such as number of insurance policies purchased, type of insurance policies purchased, and relationship length. The second period covers the interval between T0; and T1;. For this period, the database provided data about which customers left the company and the number of company insurance policies a customer owned at T1;.
The company's customer database contains the following information on RMIs: loyalty program membership at T0; and the number of direct mailings sent between T0; and T1;. Every customer who purchases one or more financial services from the company can become a member of the loyalty program (an opt-in program). At the end of each year, the program gives customers a monetary reward based on the number of services purchased and the age of the relationship. Because the company uses regression-type models to select the customers with the highest probability of responding to direct mailings, the number of direct mailings sent differs among customers.
Customer Survey Data Collection
At T0;, customer survey data were collected by telephone from a random sample of 6525 customers of the company. A quota sampling approach was used to obtain a representative sample. I received data from 2300 customers (35% response rate). After those responses with too many missing values were deleted, a sample size of 1986 customers remained. At T1;, I again collected data from those customers, except for those who left the company between T0; and T1;. In the second data collection effort, 1128 customers were willing to cooperate (65% response rate). To assess nonresponse bias at T1;, I tested whether respondents and nonrespondents differed significantly with respect to customer share at T0;. A t-test does not reveal a significant difference (p =.36). Thus, I conclude that there is no nonresponse bias.
Measurement of CRPs
For the measurement of CRPs (i.e., affective commitment, satisfaction, and payment equity), I adapted existing scales to fit the context of financial services. For the affective commitment scale, I adapted items from the studies of Anderson and Weitz (1992), Garbarino and Johnson (1999), and Kumar, Scheer, and Steenkamp (1995). To measure satisfaction, I adapted Singh's (1990) scale and added four new items. Finally, I based the payment equity scale on items adapted from Bolton and Lemon's (1999) and Singh's (1990) studies.
To assess construct validity and clarify wording, the original scales were tested by a group of 12 marketing academics and 3 marketing practitioners familiar with customer relationships. Subsequently, the scales were tested by a random sample of 200 customers of the company. On the basis of interitem correlations, item-to-total correlations, coefficient alpha, and exploratory and confirmatory factor analysis, I reduced the set of items in each scale.( n1)
Validation of CRPs
The final measures are reported in the Appendix. The scales for commitment and satisfaction have reasonable coefficient alphas. For payment equity, I report a correlation coefficient of .49, which is not considerably high.( n2) However, note that the reported composite reliabilities of all scales are sufficient (Bagozzi and Yi 1988). I applied confirmatory factor analysis in Lisrel 83 to further assess the quality of the measures (Jöreskog and Souml;rbom 1993), and I achieved the following model fit: χ2 = 217.4 (degrees of freedom [d.f.]) = 51, p < .01), χ2/d.f. = 4.26 (d.f. = 1, p < .05), goodness-of-fit index = .98, adjusted goodness-of-fit index = .97, comparative fit index =.98, and root mean square error of approximation =.04. These fit indexes satisfy the criteria for a good model fit (Bagozzi and Yi 1988; Baumgartner and Homburg 1996). A series of χ2 difference tests on the respective factor correlations provided further evidence for discriminant validity (Anderson and Gerbing 1988). On the basis of these results, I summed the scores on the items of each construct. The means, standard deviations, and correlation matrix are shown in Table 3.
Measurement of Dependent Variable
An often-used method of measuring customer share is asking customers to report the number of purchases of the focal brand they normally make (Bowman and Narayandas 2001; De Wulf, Odekerken-Schröder, and Iacobucci 2001). In this study, I sought a more objective measure. In line with the conceptualization of customer share, I define customer share of customer i for supplier j in category k at time t as
(1) Customer sharei, j, k, t;
Number of services
purchased in category k
at supplier j at time t
/
Number of services
purchased in category k
from all suppliers at time t
Data for the numerator were available from the company customer database; however, data for the denominator were generally not stored in the company customer database. Therefore, I asked customers in the survey which insurance products (of both the company and competitors) they owned at T0 and at T1.
Analysis
The theoretical distinction between customer retention and customer share development has implications for my analysis. As a result of this distinction, I use a dual approach. I first estimate a probit model to explain customer retention or defection for the remaining sample after T0 (N = 1677). Second, I use a regression model to explain customer share development over time for the customers who remain with the company. A serious issue with this type of approach is that the explanatory variables explaining customer retention also explain customer share development. As a consequence, the regression parameters may be biased (Franses and Paap 2001). I apply the Heckman (1976) two-step procedure to correct for this bias. Using this procedure, I include the so-called Heckman correction term (or inverse Mills ratio) in the regression model for customer share development. This correction term is calculated by means of outcomes of the probit model for customer retention. This modeling approach is also known as the Tobit2 model (Franses and Paap 2001). Because the inclusion of this correction term may cause heteroskedasticity, I apply White's (1980) method to adjust for heteroskedasticity. Another issue with the approach is that restricting the sample in the customer share development regression model to remaining customers might restrict the potential variance in the dependent variable, thus affecting the estimation results. To assess whether this is true, I calculated the standard deviations for the restricted and unrestricted sample. The differences between standard deviations in customer share development are small: .10 for the unrestricted sample, including defectors, and .09 for the restricted sample. In the empirical modeling, I further assess this issue by estimating the customer share development model for the unrestricted sample and comparing the results with those of the restricted sample.
Because I am interested in the changes in customer share over time, I use a difference model to test the hypotheses (Bowman and Narayandas 2001). In line with the literature on market share models, the difference between the logs of customer share at T1 and T0 (CS0, CS1) is the dependent variable in the regression model. This variable can be interpreted as the percentage change in customer share over the measured period.
In both the probit model for customer retention and the regression model for customer share development of the customers who remain with the company, I use a hierarchical modeling approach. I include the past customer behavior covariates (past behavior) as independent variables and the mean-centered composites of the items in the relationship perception scales (perceptions; e.g., affective commitment, satisfaction, payment equity). Finally, I include RMIs. For the loyalty program, I constructed a dummy variable that indicated whether the customer was a member of the loyalty program at T0;. I dealt with the number of direct mailings sent to a customer as follows: Because the company stops direct mailing customers when they defect, the number of direct mailings was not included in the probit model for customer retention. Because customers leave during the period covered in the study, the number of mailings could be correlated with defection. However, this correlation is not due to the positive effect of direct mailings on customer retention; rather, it is the result of the company's mailing policy. The foregoing results in the following two equations:
( 2) P(retention = 1) = α0; + α1; pastbehavior0; + α2; perceptions0; + α3;RMIs0 - 1;, and
( 3) Log(CS1;) - log(CS0;) = β0; + β1;past behavior0; + β2;perceptions0; + β3;RMIS0- 1; + β4;Heckman correction.
In Equations 2 and 3, I provide the formulation of the model in the form of matrices in which each α or β may comprise several separate parameters. For example, in the case of β2; there are three different parameters for the effect of commitment, satisfaction, and payment equity.
Customer Retention
Approximately 6.4% of the 1677 customers in the sample defected during the period of the study.( n3) I report the estimation results of Equation 3 in Table 4. The first model (which only includes control variables with respect to past customer behavior) explains approximately 17% of the variance and is significant (p < .01). The coefficients of the included control variables intuitively have the expected signs. Customers with high prior customer shares and lengthy relationships are less likely to defect. Furthermore, the ownership of a coinsurance, damage insurance, car insurance, and/or life insurance product has a positive effect (p < .05). In the second model, including CRPs, McFadden R² increases by approximately 1% (p = .06). Only affective commitment has a significant, positive effect (p < .01) on customer retention, in support of H1;. I found no effect for either satisfaction or payment equity. These results do not support H3; or H4;. Following Bolton (1998), I also explored whether relationship age moderates the effect of satisfaction. The estimation results indicate that the interaction term between satisfaction and relationship age is significant (α = .28; p = .01), in support of the idea that relationship age enhances the effect of satisfaction. In the third model, with the loyalty program included, McFadden R² increases by approximately 1% (p < .05). I found the loyalty program to have a significant, positive effect (p < .05), in support of H6a;.
Customer Share Development for Remaining Customers
Figure 3 shows the changes in customer share for the customers who did not defect. Although on average changes in customer share are almost zero, I observed changes in customer share for approximately 68% of the customers in the sample (N = 918). The distribution in Figure 3 is symmetrical. For 34% of customers in the sample, I observed negative changes, and for approximately 34%, their customer shares increased. As a logical consequence, the average for changes in customer share is zero (i.e., the mean values for customer share at T0; and T1; have approximately the same value of .285).
The regression results of Equation 3 are reported in Table 5. The first model (including past customer behavior) explains approximately 10% of the variance in customer share changes. The log of customer share at T0 has a negative effect on changes in customer share (p < .01). Thus, customers with large (small) customer shares are more likely to decrease (increase) their customer share in the next period. Customers who own damage insurance, car insurance, or coinsurance are more likely to increase their customer share (p < .01). The estimation results of the second model (which includes CRPs) show that affective commitment has a significant, positive effect on customer share development (p < .05). Thus, I find support for H2;. However, I found no significant effect for either satisfaction or payment equity. These results are in line with my expectations that such CRPs do not directly affect customer share development. In the third model (which includes RMIs), the loyalty program has a significant, positive effect on customer share development (p < .05). Direct mailings also positively affect customer share development (p < .05).( n4) Thus, both H5 and H6b; are supported.
The Heckman (1976) correction term is not significant, which implies that selecting only the remaining customers does not affect the estimation results (Franses and Paap 2001). It might be argued that leaving out defectors would reduce variance in the customer share development measure, which in turn might affect the estimation results. To assess this issue further, I also estimated a model that included the defectors.( n5) However, there are two problems with the model.
First, I cannot include direct mailings as an explanatory variable because, as I noted previously, no mailings are sent to defectors. Second, because the log of 0 does not exist, the differences in logs of customer share between T1; and T0; for defectors cannot be calculated. A solution to this problem is to impute a share value that is close to 0 (e.g., .001). I used this approach and imputed several different values to assess the stability of the results, and the results remained the same for the different imputations. The estimation results for an imputed value for customer share at T1; for defectors of .001 show that the coefficients of affective commitment and the loyalty program remain significant, but there is no effect of satisfaction or payment equity. The R² of the model is .09, which is lower than the R² of .12 of the model that includes only the remaining customers reported in Table 5. Given these results, I conclude that restricting the sample to remaining customers does not affect the hypotheses-testing results.
Mediating Effect of Commitment(n6)
In the relationship marketing literature, there has been a debate about the mediating role of commitment (Garbarino and Johnson 1999; Morgan and Hunt 1994). In this study, commitment may mediate the effect of payment equity and satisfaction on customer share development, which in turn may explain the nonsignificant effects of both satisfaction and payment equity. To test for this mediating effect, I used Baron and Kenny's (1986) proposed mediation test. I reestimated Model 2 (Column 3, Tables 4 and 5) in both the customer retention and the customer share development applications, but I left out commitment. The parameter estimates for satisfaction and payment equity remain insignificant in both models (customer retention: α = -.10, p > .10; α = .03, p > .10; customer share development: β =.01, p > .10; β = - .01, p > .10). In addition, I reestimated both models, leaving out satisfaction and payment equity. The parameter estimates for commitment were significant in both models (customer retention: α = .17, p < .05; customer share development: β = .02, p < .01). Finally, I estimated a regression model in which I related satisfaction and payment equity to commitment. The parameters of both satisfaction and payment equity were positive and significant (γ = .61, p < .01; γ =.09, p < .05). These results show that satisfaction and payment equity should be considered antecedents of affective commitment; however, affective commitment does not function as a mediating variable.
Summary of Findings
In this article, I contributed to the marketing literature by studying the effect of CRPs and RMIs on both customer retention and customer share development in a single study. The objectives of this article were twofold. First, I aimed to understand the effect of CRPs and RMIs on customer retention and customer share development. Second, I examined whether different variables of CRPs and RMIs influence customer retention and customer share development. Using a longitudinal research design, I related CRPs and RMIs to actual customer retention and customer share development. An overview of the hypotheses, those that were supported and those that were not supported, is provided in Table 6. For the remainder of this discussion, I focus on the notable findings.
Effect of CRPs and RMIs on Customer Retention and Customer Share Development
The first notable finding of this research is that affective commitment is an antecedent of both customer retention and customer share development. This result is not in line with recent findings that commitment does not influence customer retention (e.g., Gruen, Summers, and Acito 2000). However, it confirms previous claims in the relationship marketing literature that commitment is a significant variable in customer relationships (Morgan and Hunt 1994; Sheth and Parvatiyar 1995); more precisely, it affects both relationship maintenance and relationship development. At the same time, the absence of an effect of satisfaction and payment equity raises some notable issues. This result contradicts previous findings in the literature (e.g., Bowman and Narayandas 2001; Szymanski and Henard 2001); several reasons may explain this. First, prior research has typically relied on survey measures for which self-reported dependent variables are correlated as a result of common method of measures. This study uses behavioral data based (partially) on internal company data. Second, unlike prior studies on customer share (e.g., Bowman and Narayandas 2001; De Wulf, Odekerken-Schröder, and Iacobucci 2000) in which causality is problematic, this study focuses on the change in customer share. An understanding of customer share development may require a deeper understanding of the role of CRPs and RMIs. Third, prior studies focus on customer share of a single brand in a single product category (Bowman and Narayandas 2001), but this study focuses on customer share across multiple different services.
Customer share changes occur over time when customers add (or drop) new (current) products or services to (from) their portfolio of purchased products or services at the focal supplier or at competing suppliers. In this underlying decision process, satisfaction and payment equity play only a marginal role for several reasons. First, satisfaction and payment equity are based on one's current experiences with the focal supplier. These experiences do not necessarily transfer to other products or services of that supplier: New events may occur during the relationship that could change these perceptions (e.g., Mazursky and Geva 1989; Mittal, Kumar, and Tsiros 1999), thereby limiting the explanatory power current perceptions. Second, in a competitive environment, firms attempt to maximize customer share. Although customers may be satisfied with the focal firm's offering, they may be equally satisfied with competing offerings from other suppliers. This again limits the explanatory power of satisfaction and payment equity. In contrast, affective commitment seems less vulnerable to new experiences in the relationship; it is also unlikely that customers will consider themselves committed to multiple suppliers. Instead of satisfaction and payment equity being considered direct antecedents of customer retention and customer share development, they should be considered variables that shape commitment (e.g., Morgan and Hunt 1994).
A second notable finding is that RMIs can influence customer retention and customer share development. Direct mailings with a "call to action" are suitable to enhance customer share over time. Loyalty programs that provide economic rewards are useful both to lengthen customer relationships and to enhance customer share. Bolton, Kannan, and Bramlett (2000) report that loyalty programs for credit card customers have a strong, positive effect on customer retention; however, no studies have yet considered the effect of loyalty programs and direct mailings on customer share development. The repeatedly reported positive effect of the loyalty program counters the contention of Dowling and Uncles (1997, p. 75) that "it is difficult to increase brand loyalty above the market norms with an easy-to-replicate 'add on' customer loyalty program."
The third relevant finding pertains to the explanatory power of both CRPs and RMIs. For both customer retention and customer share development, past customer behavior explains the largest part of the variance (CRPs and RMIs are responsible only for approximately 10% of the total explained variances in both the customer retention and the customer share development models). This finding seems to support the claims of skeptics of CRM that there is not much a firm can do to affect customer loyalty in consumer markets (Dowling 2002). During reflection on the results of the customer share development model, it might also be perceived that Ehrenberg's (1997, p. 19) remarks on the antecedents of market share also hold for the antecedents of customer share development; in particular, his claim "that most markets are near stationary and that everybody has to run hard to stand still" might also be applicable to customer share development. In the short run, my results point to the effect of RMIs as only marginal. For example, stopping direct mailings for one year may not necessarily severely harm customer share development in that year. In a long-term perspective, the effects might be different. The effect of both CRPs and RMIs on customer purchase behavior could result in increased relationship age, increased customer shares, and purchases of certain additional products or services (e.g., car insurance, life insurance). Some of these variables positively affect customer retention and customer share development in later stages of the customer relationship.
Differences Between the Antecedents of Customer Retention and Customer Share Development
Another research objective was to examine whether the antecedents of customer retention and customer share development are different. Theoretically, there is a clear distinction between relationship maintenance and relationship development; however, this has not been empirically investigated. Unfortunately, a statistical comparison of the coefficients in the customer retention model and customer share development model is not possible (Franses and Paap 2001, Ch. 4). Thus, the only possible comparison is whether the significant predictors are different. The results show that the significant variables (see Table 6) are remarkably consistent across the two models (i.e., affective commitment and loyalty programs are significant predictors of both customer retention and customer share development). The only exception is the interaction effect between satisfaction and relationship age.
However, with consideration of the effect of the past customer behavior control variables, there are some differences. For example, whereas high prior customer share has a positive effect on customer retention, it has a negative effect on customer share development. Likewise, relationship age has a positive effect on customer retention but no effect on customer share development. The latter results confirm that different variables affect customer retention and customer share development. However, from a CRM perspective, this difference is not as important as it seems, because the same CRM variables affect both customer retention and customer share development.
Management Implications
This research provides implications for effective management of customer relationships. First, if managers strive to affect customer retention, they should focus on creating committed customers. In addition, a loyalty program with economic incentives leads to greater customer retention. These results contrast with recent recommendations that creating close ties with customers is a better strategy for enhancing customer loyalty than using economically oriented programs (Braum 2002); firms should do both. Both affective commitment and economically oriented RMI programs (direct mailings and loyalty programs) enhance customer retention and customer share development. Enhancing satisfaction and using attractive pricing policies can also increase affective commitment. Other Type II RMIs, such as affinity programs and other socially oriented programs, may help as well (Rust, Zeithaml, and Lemon 2000). If firms strive for immediate results, economically based loyalty programs and direct mailings are preferable.
Second, if firms strive to maximize customer share, creating affectively committed customers using a loyalty program and sending direct mailings that provide economic incentives are recommended. However, the short-term positive effects of such approaches are rather small. This might support the claim of experts of CRM that trying to maximize customer retention and customer share development is difficult. However, this does not mean that firms should not use such strategies. In the long run, the positive effects of such strategies may be larger. The short-term small positive effects of these strategies on customer retention and customer share development could result in larger positive effects in the long run as a result of the positive effects of past customer behavioral variables, such as relationship age and prior customer share.
Third, my analysis suggests that, in general, firms can use the same strategies to affect customer retention and customer share development. Fourth, a principle of CRM is to focus efforts on the most loyal customers. However, improving share for loyal customers is much more difficult, because they have a greater tendency to reduce their shares in the future.
Research Limitations
This study has the following limitations: First, it is conducted for one company in the financial services market. I chose the financial services market because it is an important segment of the economy and because there is a long tradition of customer data storage in this market, which makes it relatively easy to collect behavioral customer loyalty data. However, the financial services market has some unique characteristics. Customers purchase insurance products infrequently, and as a result changes in customer share are not observed as frequently as in other industries. Because of relatively high switching costs, switching behavior is not common. These characteristics may have limited the variance in the customer share development measure. These characteristics may also explain some of the results and may, to some extent, threaten the generalizability of the results. Thus, there is a need to extend this study to other markets, especially markets in which more switching is observed.
Second, although the study applied a longitudinal research design, the causality question remains difficult. Because of the dynamic nature of customer relationships, multiple measurements in time (including changes in CRPs) are needed in the model.
Third, modeling the effect of RMIs is rather difficult, particularly if the RMIs are self-selected or based on customers' purchase behavior. In the loyalty program I studied, customers can choose whether to become a member. It could be argued that customers who expect to purchase new services are more inclined to join. I chose not to correct for this in the analysis at this time. Further research could develop models to correct for possible endogeneity of the RMIs.
The last research limitation pertains to the measurement of payment equity. In this research, I used only two items (see the Appendix), which could have undermined the reliability of the measurement. Further research could develop more extensive scales.
Further Research
Further research should focus on the following issues: First, the results show that the effect of CRPs and RMIs on customer retention and customer share is not large. Perhaps other variables, such as service calls or sales visits, are important antecedents. In addition, competing marketing variables, such as competitive loyalty programs and direct mailings, have not been included here. Further research could investigate the effect of these variables. A second avenue for further research is the effect of RMIs on CRPs and in turn on customer behavior. A simultaneous equation approach, with an appropriate test for mediating effects, would be necessary to address this issue. In this respect, the interactions between CRPs and RMIs could also be investigated. Finally, further research could develop decision support type models (using data available in customer databases and data from questionnaires) that would demonstrate the impact of various CRM strategies.
The author gratefully acknowledges the financial and data support of a Dutch financial services company. The author thanks Bas Donkers, Fred Langerak, Peter Leeflang, Loren Lemon, Peeter Verlegh, Dick Wittink, and the four anonymous JM reviewers for their helpful suggestions. The author also acknowledges the comments of research seminar participants at the University of Groningen, Yale School of Management, Tilburg University, and the University of Maryland. Finally, he acknowledges his two dissertation advisers, Philip Hans Franses and Janny Hoekstra, for their enduring support.
(n1) I follow Steenkamp and van Trijp's (1991) proposed method, using exploratory factor analysis and then confirmatory factor analysis to validate marketing constructs.
(n2) I report correlation coefficient rather than Cronbach's alpha because I used only two items. Cronbach's alpha is designed to test the interitem reliability of a scale by comparing every combination of each item with all other items in the scale as a group. Because there is no group with which each item can be compared in a two-item scale (only the other item), Cronbach's alpha is meaningless for two-item scales. It might also be argued that one of the single items would be better suited for measuring the construct from a content validity perspective. To check this, I also estimated the models (see Tables 4 and 5) with a single item as an antecedent. For both items, the effect of payment equity remained insignificant in the two models. Because in general multiple-item measurement is preferred over single-item measurement, I report the model results of the summated two-item scores.
(n3) The sample of 1677 for the analysis of the antecedents of customer retention is much larger than the sample used in the customer share development model, because behavioral data about customers' past purchase behavior were unnecessary in the customer retention analysis. Consequently, customers who did not respond in the second survey can be included in this analysis.
(n4) An issue in estimating the effect of direct mailings is that the company whose data are used does not randomly select customers to receive such mailings; the company uses models to target the most receptive customers. These models are not known. The company's use of such models might lead to an endogeneity problem, which could result in (upwardly biased) inconsistent parameter estimates for direct mailings. To test for possible endogeneity, I used the Hausman test that Davidson and MacKinnon (1989) propose. This test does not reveal any evidence for endogeneity (p = .88).
(n5) Notwithstanding this result, I also used two approaches to correct for possible endogeneity. The first approach applied instrumental variables using two-stage least squares in the estimation of a system of two equations (Pindyck and Rubinfeld 1998). I used two sociodemographic variables as instrumental variables: income and age. I selected these variables because they are often included in CRM models (Verhoef et al. 2003). The estimation of this model results in the same parameter estimate for direct mailings (.04); however, this parameter is only marginally significant (p =.10). The second approach estimated a system of equations in which two separate equations are estimated: one with customer share development as a dependent variable and the other with the number of direct mailings as a dependent variable. With this approach, the effect of direct mailings remained significant (p <.05); however, the parameter estimate decreased from .04 to .013. On the basis of these analyses, I conclude that endogeneity of direct mailings does not affect the hypothesis testing.
(n6) A reviewer suggested this analysis.
Legend for Chart:
A - Behavioral Loyalty Measurement
B - Examples of Studies
C - Study Design
D - Study Context
E - Included Perceptions (Effect)
F - Additional Results/Comments
A B C
D E
F
Self-Reported
Purchase Anderson and Experiment
intentions Sullivan (1993)
Morgan and Cross-sectional
Hunt (1994)
Zeithaml, Berry, and Cross-sectional
Parasuraman (1996)
Garbarino and Cross-sectional
Johnson (1999)
Mittal, Kumar, and Longitudinal
Tsiros (1999)
Various industries Satisfaction(+)
Channels Benefits(+),
commitment(+)
Various industries Service quality(+)
Theater visitors Satisfaction(+),
commitment(-)
Car market Satisfaction(+)
Effect depends on relationship
orientation of customer
Customer share Macintosch and Cross-sectional
Lockshin (1997)
De Wulf, Odekerken- Cross-sectional
Schroder, and
Iacobucci (2001)
Bowman and Cross-sectional
Narayandas (2001)
Retailing Commitment(+)
Retailing Relationship
quality(+)
Grocery brands Satisfaction(+)
Quadratic effect of satisfaction
Observed
Customer Gruen, Summers, and Cross-sectional
retention and/or Acito (2000)
relationship
duration
Bolton (1998) Longitudinal
Bolton, Kannan, and Longitudinal
Bramlett (2000)
Mittal and Kamakura Longitudinal
(2001)
Lemon, White, and Longitudinal
Winer (2002)
Professional Commitment(0)
association
Telecommunications Satisfaction(+)
Credit card Satisfaction(+),
payment equity(+)
Car market Satisfaction(+)
Entertainment Satisfaction(0)
Effect of satisfaction enhanced
by relationship age
Performance differences with
other firms are important
Effect of satisfaction
moderated by consumer
characteristics
Effect of satisfaction mediated
by future expected service
usage
Service usage Bolton and Lemon Longitudinal
(1999)
Bolton, Kannan, and Longitudinal
Bramlett (2000)
Telecommunications, Satisfaction(+)
entertainment
Credit card Satisfaction(+),
payment equity(+)
Payment equity positively
affects satisfaction
Performance differences with
other firms are important
Cross-buying Verhoef, Franses, Longitudinal
and Hoekstra (2001)
Financial services Satisfaction(0),
payment equity(0)
Effect of satisfaction and
payment equity enhanced by
relationship age Legend for Chart:
A - RMI
B - Study
C - Loyalty Measure
D - Study Design
E - Study Context
F - Results
A B C
D E
F
Direct mail Bawa and Aggregated purchase
Shoemaker (1987) shares
De Wulf, Customer share
Odekerken-Schröder,
and Iacobucci (2001)
Panel design Grocery
brands
Cross-sectional survey, Retailing
perceptions on direct
mail use
Short-term positive effect
on purchase rates
No effect
Loyalty Dowling and No empirical data
programs Uncles (1997)
Sharp and Sharp (1997) Aggregated
penetration,
average purchase
frequency, customer
share, sole buyers
Rust, Zeithaml, Purchase intentions
and Lemon (2000)
Bolton, Kannan, Customer retention,
and Bramlett (2000) service usage
De Wulf, Customer share
Odekerken-Schröder,
and Iacobucci (2001)
-- --
Aggregated panel data Retailing
Cross-sectional survey Airlines
data, perceptions on
loyalty program use
Longitudinal Credit card
Cross-sectional survey Retailing
data, perceptions on
preferential treatment
programs
--
No convincing effect of
loyalty programs
Positive effect
Positive effect on retention
and service usage
No effect Legend for Chart:
B - Mean
C - X1
D - X2
E - X3
F - X4
G - X5
H - X6
A B C D
E F G
H
X1 Commitment 2.96 (.77) 1.00
X2 Satisfaction 3.75 (.44) .37(**) 1.00
X3 Payment equity 3.41 (.56) .14(**) .21(**)
1.00
X4 Direct mail 3.51 (2.12) .01 .02
.01 1.00
X5 Loyalty program .30 (.46) .09(**) .14(**)
.03 .56(**) 1.00
X6 Log customer
share T0 -.152 (.66) .12(**) .09(**)
.06(*) .48(**) .53(**)
1.00
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Variable
B - Hypothesis (Sign)
C - Model 1 (z-Value)
D - Model 2 (z-Value)
E - Model 3 (z-Value)
A B C
D E
Constant 1.66(5.10)(**)
1.68(5.03)(**) 1.58 (4.58)(**)
Log customer
share T0 .34(2.23)(*)
.33(2.13)(**) .30 (1.89)
Log relationship age .11(2.32)(*)
.11(2.14)(*) .09 (1.79)
Coinsurance .12(1.21)
.12(1.22) .11 (1.04)
Damage insurance .78(4.13)(**)
.78(4.06)(**) .74 (3.92)(**)
Car insurance .36(2.97)(**)
.33(2.70)(**) .33 (2.72)(**)
Life insurance 1.02(4.00)(**)
1.01(3.99)(**) 1.00 (3.95)(**)
Perceptions
Commitment H1(+)
.21(2.66)(**) .20 (2.58)(**)
Satisfaction H3(+)
-.21(1.52) -.22 (1.63)
Payment equity H4(+)
-.03(.26) -.03 (.30)
RMIs
Loyalty program H6a(+)
.38 (2.02)(*)
McFadden R² .168
.178 .184
Adjusted McFadden
R² .165
.173 .179
Likelihood ratio
statistic 127.64(**)
135.20(**) 139.68(**)
(d.f.) (6)
(9) (10)
Akaike information
criterion .384
.383 .382
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Variable
B - Hypothesis (Sign)
C - Model 1 (t-Value)
D - Model 2 (t-Value)
E - Model 3 (t-Value)
A B C
D E
Constant -.44(6.84)(**)
-.46 (7.09)(**) -.52 (7.80)(**)
Heckman correction .06(.90)
.07 (1.27) .10 (1.48)
Log customer
share T0 -.17(9.97)(**)
-.19 (10.3)(**) -.20(11.0)(**)
Coinsurance .02(3.83)(**)
.02 (3.92)(**) .02 (3.26)(**)
Damage insurance .14(6.35)(**)
.15 (6.52)(**) .14 (6.09)(**)
Car insurance .04(2.69)(**)
.01 (2.29)(*) .04 (2.52)(**)
Legal insurance .03(1.15)
.03 (1.16) .03 (1.16)
Perceptions
Commitment H2(+)
.03 (2.55)(*) .03 (2.58)(**)
Satisfaction H3(+)
.00 (.01) -.00 (.21)
Payment equity H4(+)
-.01 (.85) -.01 (.66)
RMIs
Loyalty program H5(+)
.04 (2.22)(*)
Direct mailing H6(+)
.01 (2.31)(*)
R² .10
.11 .13
Adjusted R² .10
.10 .12
F-value 16.95(**)
12.21(**) 11.72(**)
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Antecedents
B - Customer Retention Hypothesis (Sign)
C - Customer Retention Effect
D - Customer Retention Support
E - Customer Share Development Hypothesis (Sign)
F - Customer Share Development Effect
G - Customer Share Development Support
A B C D
E F G
Affective H1(+) + Yes
commitment
H2(+) + Yes
Satisfaction H3(+) 0; positively No
moderated by
relationship age
No effect 0 Yes
Payment H4(+) 0 No
equity
No effect 0 Yes
Direct No effect N.A. N.A.
mailings
H5(+) + Yes
Loyalty H6a(+) + Yes
program
H6b(+) + Yes
Notes: N.A. = not available; this effect could not be estimated
because of data limitations.DIAGRAM: FIGURE 1 Conceptual model
DIAGRAM: FIGURE 2 Panel Design
GRAPH: FIGURE 3 Customer Share Development (N = 918)
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Commitment (Cronbach's Alpha [CA] =.77; Composite Reliability [CR] =.78)
I am a loyal customer of XYZ.
Because I feel a strong attachment to XYZ, I remain a customer of XYZ.
Because I feel a strong sense of belonging with XYZ, I want to remain a customer of XYZ.
Satisfaction (CA =.83; CR =.83)
How satisfied (1 = "very dissatisfied" and 5 = "very satisfied") are you about
• personal attention of XYZ.
• willingness of XYZ to explain procedures.
• service quality of XYZ.
• responding by XYZ to claims.
• expertise of the personnel of XYZ.
• relationship with XYZ.
• alertness of XYZ.
Payment Equity (r =.49; CR =.88)
How satisfied (1 = "very dissatisfied" and 5 = "very satisfied") are you about the insurance premium?
Do you think the insurance premium of your insurance is too high, high, normal, low, or too low?
~~~~~~~~
By Peter C. Verhoef
Peter C. Verhoef is Assistant Professor of Marketing, Department of Marketing and Organization, Rotterdam School of Economics, Erasmus University, Rotterdam.
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Record: 195- Unpacking My Library: The Marketing Professor in the Age of Electronic Reproduction. By: Bekl, Russell W. Journal of Marketing. Jan2002, Vol. 66 Issue 1, p120-126. 7p.
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Section: Book ReviewsUnpacking My Library (Book)
Unpacking My Library:
The Marketing
Professor in the Age of
Electronic
Reproduction
Russell W. Belk
My title steals from Walter Benjamin twice. The main title is taken from his "Unpacking My Library: A Talk About Book Collecting" (1968b). In that somewhat obscure paper, Benjamin wistfully conveys some of the rich memories conjured up by the books in his library. Each volume evokes a detailed recollection of how it was ardently stalked, carefully acquired, and lovingly placed on his shelves, even though Benjamin, like most book collectors, never sullied his treasures by reading them. Likewise, he did not play with the toys he collected. Strange as this may seem, it is characteristic, if not definitive, that objects in a collection are taken out of their ordinary uses and given special revered status as part of a sacred set (Belk 1995). This part of the title is meant to announce that in this essay, I intend to say something about books, collecting, and the mnemonic power of objects.
My subtitle is a transmogrification of another Benjamin (1968a) paper title, "The Work of Art in the Age of Mechanical Reproduction." In this more famous paper, Benjamin worries about the loss of mystical "aura" when visual artistic images are no longer produced as totally unique hand-wrought works of art but are instead duplicated in mass quantities through photography, film, and printing (this was before television, e-mail, faxes, videocassette recorders, digital versatile disks [DVDs], compact disks [CDs], and the Internet and before Andy Warhol began mass producing pop art in a loft he called The Factory). My transliteration of this title is meant to suggest that marketing professors might be thought of as works of art but that their value may be subject to rapid decline in an age in which access to scholarship is democratized through new electronic media. But this is only one of the senses in which my subtitle is intended. I also explore the more positive opportunities that the emerging electronic age provides for professionals who study and teach about marketing and consumption. Among other things, these changes mean that professors may not all die of white lung disease from scratching calcium carbonate on blackboards. There are some addition)l benefits of new technologies as well.
To preface these inquiries and provide a thin thread that bastes them together, I include a short narrative about my own encounters with books and alternative electronic media. Lest I be thought to be presumptuously comparing myself to Walter Benjamin, I hasten to emphasize that the only partial similarities are some shared German Jewish heritage (though I lack a Jewish upbringing) and an abiding interest in consumption phenomena and art. Furthermore, whereas Benjamin's material trajectory from his family of birth was decidedly downward from affluence, mine has been modestly upward, thanks to being the first college graduate in my family. Whereas Benjamin was born before the dawn of the twentieth century and felt the turmoil of Hitler's rise to power in Germany, I was born just after World War II and felt the rush of consumer culture's rise to power in the United States. Whereas Benjamin regarded it as a virtue never to use the first person singular in his writings (even his "autobiographical" writings), I do not, as is already evident. And, most significantly, whereas Benjamin has been described as the last of the great intellectuals, I would hardly describe myself as such, even in my fondest imagination.
Like many middle-class, suburban American, first-wave baby boom children, my brother and I were proud preadolescent possessors of a small library that included the ten-volume Junior Classics, the Encyclopedia Britannica, World Book Encyclopedia, Ripley's Believe It or Not, a Bible, assorted Little Golden Books, a world atlas, and a big book about zoo animals. But this was also the age of Sputnik and the space race, which meant that we also had a spate of science books. We possessed, no less proudly, books about minerals, fossils, dinosaurs, electronics, home repair, camping, trees, flowers, insects, scientific discovery, space, stamps, coins, and woodcraft. In our home, regular periodicals included Life, Saturday Evening Post, Sports Illustrated, Sunset Magazine, Reader's Digest, and occasional comic books featuring Donald Duck, Archie, and Fox and Crow, as well as every issue of Mad Magazine he could find.
I must have been affected by books at quite a young age, because surviving volumes show considerable evidence of my preliterate scribbles. In truth, my handwriting has not improved much, and my present margin annotations do not really differ that much from my early attempts at writing. Although my scribbles may have presaged a career of writing, I was also a would-be child entrepreneur. In addition to attempts at a sidewalk lemonade stand, a basement zoo, a neighborhood newspaper, and an outdoor carnival, I decided to put my books to work by opening a library. In the back of each volume, I carefully pasted a typed due date slip, though I cannot recall anyone ever checking out a volume, much less my assessing and collecting any late fees.
By the time I was in sixth grade, two momentous media events changed my home environment: Our family bought a television set, and I obtained a copy of Vance Packard's (1957) Hidden Persuaders. The television began to attract my attention away from books. Given the quality of television programming at the time, my intellectual development is no doubt the poorer for this acquisition. But Packard's book had the opposite effect. My father was then an advertising agency production manager, and the book precipitated extended critical discussions with him of what advertisements (like those on our new television) do and how they affect people. I did not realize it for some years, but I now have no doubt that those discussions were instrumental in my becoming interested in marketing and consumer behavior. I did not realize it when I enrolled in college in geophysics or when I switched to English literature. But eventually I found myself taking business courses, and as I approached graduating with a master's degree in business, I considered taking an offer to work for General Mills in marketing research. Fortunately the master's program at University of Minnesota at the time required either a thesis or three long "Plan B" papers, and by pursuing the latter papers, I had learned the joy of doing original research. This led me to go on for a doctorate so that my career could be devoted to doing my own research rather than others'.
When I wrote my doctoral thesis in 1972 (multiple experiments and a three-mode factor analysis of situational effects on consumer behavior), we were still using mainframe computers and carrying around boxes of computer punch cards. That was also how I input the three-mode factor analysis program I wrote. There was also a computer lab full of mechanical Friedan calculators that we would set up to do a complex calculation we called "The Freddy Friedan March" at the end of the day. Shortly before I graduated, the marketing department invested in two four-function, two-memory electronic calculators. One was manufactured by Wang and the other by Singer, so it seems appropriate that each machine was the size of a portable sewing machine, but not that each cost about $1,200. Because my department was not sure how reliable these new devices would be, it also invested a similar amount in yearly service contracts. Today, such calculators are somewhat anachronistic, are the size of a credit card, and are either give-aways or $5 purchases at Wal-Mart or OfficeMax.
In writing my thesis, I used an electric typewriter and lots of correction fluid. Although I used some drawings for mock advertisements in both my thesis and one Plan B paper, it was several years before I began to explore 35-millimeter photography and more years before I began to incorporate it in my research. Electronic copying machines were available, but most classroom materials were still mimeographed after being typed on those blue film sheets that required another messy liquid to make corrections. Like other instructors, I made the breakthrough to black-and-white overhead transparencies for use in class and eventually began to incorporate color slides as well. But the blackboard still loomed large in any college class. Come to think of it, for many marketing professors, including those with access to classroom computers, white boards, liquid crystal display (LCD) projectors, Elmo imaging machines, and a host of other electronic classroom technology, it still does.
My big breakthrough into multimedia research did not come until 1984, when I proposed a project that came to be known as the Consumer Behavior Odyssey (see Belk 1989). Shortly before this, I had acquired an early consumer camcorder and had taken a videography class. The possibility of studying consumer behavior using video intrigued me. Although I had been reading anthropology and sociology literature for more than ten years in pursuing my research on gift giving, I had yet to do any qualitative research. Fortunately, two of those who joined the Odyssey project early on were John Sherry, an anthropologist, and Melanie Wallendorf, who, unlike me, had some training in sociology and who was at that time toying with nonpositivist research. Together, they joined me in a demonstration project at a swap meet in Arizona, where I began to learn to conduct depth interviews, take field notes, and use still and video photography in research (see Belk, Wallendorf, and Sherry 1988). This was in November 1985, and by the following summer, we were joined by nearly two dozen other researchers in the cross-country Odyssey from Los Angeles to Boston. We bought a couple of early laptop computers to assist in taking field notes, borrowed a big three-quarter-inch video camera from University of Illinois, and bought 35-millimeter bulk film to load for photography. We were fortunate to be joined by Tom O'Guinn, who had some professional video camera experience from the television series Austin City Limits. A major sponsor, the Marketing Science Institute, suggested that we do our final report for it as a video, and after much trial and error learning, the result was a video distributed by Marketing Science Institute (Wallendorf and Belk 1987). I describe the details of this project elsewhere (Belk 1989), but for me and others who participated, it opened up the worlds of both qualitative research and visual methods. There is no necessary relationship between qualitative and visual m ethods, but both were largely unknown in academic marketing and consumer research at the time.
After the Odyssey project, I began to require my students to use video and still photography in class research projects as well as to prepare field notes and transcriptions from depth interviews they conducted for these projects. I began to use more videos and slides in class and bought an LCD panel to connect to my laptop computer in order to demonstrate software for managing qualitative data. Students then used this software to find and organize the data transcriptions pooled from student data collection. I began attending meetings of the Society for Visual Sociology and the Society for Visual Anthropology, and following one meeting in Rochester, N.Y., I took a week-long course in photography from visual sociologist Howie Becker. By the time I did a sabbatical year in Romania in 1991-92, I was committed to qualitative research but still used some questionnaires as well. In addition to an audio recorder, I took along a still camera and film, and when I returned I set up a dark room and began to develop and print black-and-white photos as well as color slides. Seven years later, when I did my sabbatical year in Zimbabwe, I had along two digital video cameras, three tripods, assorted microphones and lights, a digital still camera, an audio recorder, a laptop computer, and an LCD projector for classroom use. I taught my students in Africa to use the cameras and assist me in my research as well as carry out their own (see Belk 2000).
Between these two sabbaticals, Ron Groves and I hooked up (sometimes with other researchers, including Per Østergaard and Ron Hill) for a series of visual and qualitative research projects in Australia and Thailand and over Antarctica. Although it is not necessary to go to another country to benefit from visual methods, such media help make the strange familiar to people elsewhere. In the Australia work, we initially had the help of a three-person professional camera crew and subsequent professional editing for these videos (e.g., Belk and Groves 1994, 1997). This is a wonderful luxury, but one of the costs is a partial loss of control. So when digital camcorder and editing equipment became affordable (complete costs dropped from more than $100,000 to less than $10,000 during the 1990s), we began to do our own visual work throughout the process (e.g., Belk and Groves 1999). We received research grants for some of the equipment and bought the rest ourselves-in my case, partly by picking up used equipment through eBay and online classified advertisements.
Meanwhile, back in the classroom, I moved from requiring my students to videotape some of their depth interviews to also giving them the option of editing a videotape rather than turning in a written paper. Thus far, students who have chosen this option have spent two to three times as long as those who write papers, but they report enjoying it far more. In classroom teaching, I have long tried to incorporate multimedia. In addition to videos, slides, films, overhead transparencies, and PowerPoint presentations (interactive when feasible), I use CD-ROMs (advertising clips, music such as that by Marketing Mike and the Suits, interactive learning tools, and old promotional films), DVDs, and the Internet. All have proved useful and popular with students.
With my multimedia adventures as backdrop, I return to the more theoretical and practical issues raised by Benjamin's essays. Having held appointments at three U.S. schools and having spent three sabbaticals outside of the United States, I have had the experience of unpacking my library (or at least the portions I brought along) several times. My first sabbatical was in Vancouver, and I am pleased to report that the University of British Columbia has a first-rate library. By the time I got to Zimbabwe, I could obtain some of what I needed online and on CD-ROMs I had brought along. This was not the case in Romania, however, and I was pleased to have a few books with me. I was at this time (the start of the 1990s) even more dependent on the laptop computer I brought. There was no e-mail in Romania at the time, and the university to which I was attached had four personal computers for 40,000 students. Until the 1989 Christmas revolution, typewriters in Romania had to be registered as instruments of propaganda. As a result, even students who gained access to computers had no typing skills. Nevertheless, the newly released flood of advertising on Romanian television and in the streets made students anxious to acquire computers so they could keep track of the flood of new consumer information. Western cynicism toward advertising had yet to set in, and there was instead incredulity that both Pepsi-Cola and Coca-Cola could claim that they were the choice of the young generation. But by the time I made a return visit four years later, Romania had become a part of global consumer culture. This was entirely predictable. During the revolution, the first thing the rebels broadcast on the commandeered national television station was a bootlegged copy of the movie E.T.
During the year my wife and I spent in Romania, I was more like these Romanians craving the new than I was like Walter Benjamin reveling in the memories evoked by old books. But visual material was another matter, and the photographs my daughter sent of her graduation from Rutger's University were especially treasured. In receiving her degree, she carried out a family in-joke dating from her fourth birthday by wearing a Groucho nose, moustache, and glasses. I cannot remember enjoying a photograph more than this. Other than a few such photos, I was attached to very little in Romania, and it was an interesting experience to learn how little I really needed. Nevertheless, my wife and I craved a few basic services, such as reliable food sources, 24-hour access to water, hot water, reliable electrical power, and copying machines. The desire for copying machines may be linked to a time when I needed to reproduce some questionnaires to measure materialism in Romania. I brought a ream of paper (from the United States) and the obligatory quart of Scotch whiskey to the senior professor to see if he would obtain the copies from the university copying machine. When I received copies two weeks later, they were on poor-quality newsprint paper, and still the pro5essor demanded money for his facilitating services.
I wish I could say that living in Romania and my newfound appreciation of how little we really need gave me an urge to live more simply. Instead, after my return I rewarded myself with a new (well, two-year-old) Audi. Never mind that the price was $12,000 because of a false story about sticking accelerators on 60 Minutes or that I needed the four-wheel drive where I live in the winter; it was a luxury compared with my previous Subarus. Since that time, I have heard a few anthropologists talk about how they too "reward" themselves with an extravagant purchase after living in some remote third-world society. My self-reward helped me understand why Romanians at the time rated highest of 12 countries in materialism scores (Ger and Belk 1996). The explanation I heard over and over from Romanians was that they had suffered long enough during a decade in which Ceauçescu exported all desirable consumer goods in order to repay a massive foreign debt. They too offered a deprivation-based justification of deservingness for their insistent consumer desires now that the window on the world was opened. So if I did not carry much of the United States with me when I went to Romania, I may have inadvertently carried more of Romania back with me on my return to Utah. And like Benjamin, my reflections (not on my book collection, but on my joys in being back in a land where finding potatoes is not an all-day search and students do not need to wear mittens and stocking hats to keep warm in class in the winter) prompted a desire in me for more, not less.
I took many photographs in Romania, but as foreigners we were still under the surveillance of the secret police, Securitate, which had occasion to stop me, take my camera, and ask why I was taking photographs. Fresh from the days of suspicion of everyone under Ceauçescu, Romanians also were understandably wary of being photographed. So I have not been able to convey those experiences as visually as I might wish to students and colleagues. Words can do a good job of conveying what has been called "propositional knowledge," but visual images are generally superior for conveying "experiential knowledge" of what something feels like. Whereas propositional knowledge is knowledge about something and produces cognitive understanding, experiential knowledge is knowledge of something and produces emotional understanding that ideally enables the recipient to gain a shared sense of what it might be like to be another person embedded in another culture. I do have a few photos of Romanian consumption on my Web page, but other than that, my reported research from these experiences is primarily written.
As suggested previously, my sabbatical in Zimbabwe was much better documented visually, primarily through videotaped observations and interviews (55 hours of videotape before editing). I also recorded approximately 6 hours of television commercials that are used in one of the resulting videos. One of the other two videos I have made on the basis of fieldwork in Zimbabwe is a collaborative effort with my MBA students there. Because the focus of this video is the consumption patterns of the new black elite in Zimbabwe and because these students were virtually all a part of this elite (with the exception of those from other African countries), the project lent itself to such joint representation quite well. The issue of who represents whom is a critical one in postmodern ethnography (e.g., Clifford 1988; Clifford and Marcus 1986), though because I did the editing after my return to the United States, I still retained interpretive authority. Although I can hardly regard my interpretations as definitive and although they are not necessarily the emic interpretations shared by my informants, I believe that to abrogate this interpretative role in favor of someone else's does not make the best use of my meager talents. Unlike in Romania, during my year in Zimbabwe, I had access to e-mail and the Internet, which allowed me to stay in much closer touch with friends, family, and colleagues around the world. When my daughter became pregnant, my wife and I were able to see a sonar-gram image sent by e-mail. I save perhaps never appreciated visual images more, at least until I received pictures of my granddaughter Zoe by e-mail as well. And sure enough, one of these photos showed weeks-old Zoe sporting a Groucho nose and glasses (Belk 2000).
But suppose researchers return from the field or laboratory ready to share their videotapes, photographs, and other recordings with others through either local access digital formats such as videotape, CD-ROMs, and DVD or distributed access digital media such as the Internet. Are these researchers obsolescing themselves at the same time that they are democratizating access to their data and interpretations by sharing such experiential knowledge? Isn't this just a case of inflating available knowledge with the predictable result that it is cheapened? That is, as more knowledge is put into circulation and made more accessible, the market becomes subject to the forces of inflation, and as with Gresham's Law, the bad money of cheap electronic information drives out the good money of flesh-and-blood marketing professors. Contrary to Gresham's Law, however, professors are subject to decay and cannot be readily stockpiled, whereas electronic media can remain fresh on the shelf and in cyberspace indefinitely until they are brought to life. Perhaps these fears have some merit, but I have no fear that flesh-and-blood marketing professors will be rendered obsolete any time soon. Furthermore, there are benefits to both the professor and others from sharing knowledge in these new ways. In addition to providing potentially more vivid experiential knowledge, such images may reach broader audiences than academic journal articles and books ever will. Although I have not seeded my Web page at all, it has stimulated comments from consumers and researchers interested in Fiji, collecting, gift-giving, Zimbabwe, Romania, and French literature. It has provided a way for me to share some of my research conclusions with my former collaborators in Zimbabwe. And it provides a ready resource for students, both mine and others', who can access it with increasing ease. Furthermore, purely visual media transcend language and literacy barriers, increasing not only the potential audience fo r such images but the potential supply of images as well. No doubt because of international and global marketing, the field of marketing has not been as ethnocentric as psychology, but marketers primarily draw on the United States for their concepts, research subjects, and research outlets. Other than language (and translation programs help transcend this barrier), the Internet knows no such national boundaries. As bandwidth increases, streaming video will become as accessible as photographs on the Web.
Given such benefits from making use of a broader library of materials (both academics' and others') than print media alone, why has the academy continued to resist visual media and clung to the tacit belief that text is superior? Although part of the answer is no doubt resistance to change and innovation, Heisley (2001) suggests some additional answers. Today, we regard the gaze at others as somewhat voyeuristic. Promotion and tenure committees have a difficult time evaluating visual material in the behavioral sciences. There is generally no peer review of such material before "publication." Because it provides the audience a greater opportunity for interpretation, scholars may feel a heightened vulnerability or loss of control of their material. It is hard to use yellow highlighters or adhesive notes with visual material. And it takes a lot of work and new skills to produce good visual material. Although each of these explanations has some merit for the time being, all are likely to be decreasingly persuasive in the future, as more professors acquire visual skills and as the academy begins to feel more comfortable with such media. Furthermore, students raised in a television/Internet/video/CD/DVD/MP-3-saturated environment respond favorably to such media in the context of university education. Instructors who produce and provide such materials are likely to have an advantage in teaching and research over those who do not. And with time, I have faith that academic curmudgeons will begin to appreciate such materials more and more. If the audience at the 2000 and 2001 Association for Consumer Research sessions at which Rob Kozinets and I showed our videos is any indication, there is rapidly budding curiosity and enthusiasm about videography.
One further thing might dismay Walter Benjamin about the electronic revolution that is going on in research and teaching media: The materials made available within these new media are generally more ephemeral experiential objects of consumption than are books. It is possible to hold and even caress a book. The reader can feel the pages and leave his or her marks on them. Readers can curl up with a book and get lost in revelry or contemplation of ideas. Books may even acquire a slightly musty smell that no doubt aided the recollections that prompted Benjamin's nostalgic reflections on his library. None of this applies as readily to visual materials. Although some of the same things can be done with a book of photographs, videotapes, CD-ROMs, DVDs, and Internet pages are not very cuddly, and some experiences with these objects (especially the Internet) cannot be put on a shelf and stored in some possessive manner as can be done with books. Given what I have said about possessions and the extended self (Belk 1988), it might well seem that the fleeting nature of these visual experiences will keep people from incorporating and assimilating them. But students are ahead of professors here, and I find it necessary to try to convince students that there is more to the sum total of human knowledge than that which can be found on the Internet. In addition, my studies with collectors suggest that people can also be collectors of experiences and regard them no less dearly than the more tangible objects in others' collections of books or other objects. Indeed, as Walter Benjamin's reflections on his books suggest, he was collecting not just the objects themselves but the experiences of hunting, discovering, and acquiring them. However, unlike Benjamin and other collectors' extra-utilitarian fascination with objects taken out of their original uses, I hope that users of visual media will continue to benefit from the content of the visual image. Despite changing content and disappearing sites, some Internet sites are likely to prove as permanent as the printed word-and much more quickly locatable and accessible. Unlike books, which use a linear approach, verbal or visual images can be acquired in a nonlinear way and can be considered more collages than fixed narratives directed solely by the author. This means they can be used more creatively as well. Such advantages are likely to more than outweigh the lack of tangibility in some visual media.
I see new electronic media supplementing rather than replacing the printed word. I encourage students to read promiscuously-anything and everything they find interesting, without regard to whether the sources are high or low (a distinction that is also disappearing in a postmodern age; see Seabrook 2000). The same advice applies to visual material. Thanks to university human subjects regulations, people need not feel they are guilty voyeurs for being fascinated with images of the Other. In an undeniably global world, people need to expand their scope and become more attuned to cultural differences and similarities. People need to gain critical visual literacy and become more actively involved in both scrutinizing images they see and creating images for others to use. This is the threshold of an era in which access to still and moving visual images and accompanying sounds will be quick, easy, and randomly accessible. Although the printed word will continue to be useful, it will increasingly seem inadequate to fully capture the meanings of interesting activities, people, and events.
I have referred to several videotapes about consumer behavior and marketing in this discussion. Although these are in the documentary genre, many researchers have discovered how parts of feature films and other visual and oral media can also convey "truths" about human behavior. As an invitation to delve more deeply into this new world of images, I close with a brief suggestive bibliography of potentially useful materials. I hope readers will find some of them they may not have encountered as fascinating as I do. And I hope that use of the insights and examples provided by such works will help professors themselves become works of art in an age of electronic reproduction.
Feature Films
American Psycho (I think the film is better than Bret Easton Ellis's book, especially the scenes of comparing business cards; 2000, directed by Mary Harron).
Babbette's Feast--(based on a novel by Isak Dinesen [Karen Blixen], the joys of food versus the pleasure-denying austerity of the Protestants in this case; 1987, directed by Gabriel Axel).
Bonfire of the Vanities (a bomb-Tom Wolfe's book is much better-but still a telling portrait of the 1980s as the decade of greed, much more consumption oriented than Wall Street; 1990, directed by Brian De Palma).
Chocolat (Joanne Harris' book is a bit deeper, but the film is also quite effective in depicting consumption joys versus religious austerity in a French village; 2000, directed by Lasse Hallström).
Clerks (the antithesis of relationship marketing; 1994, directed by Kevin Smith).
Fanny and Alexander (a seemingly strange choice perhaps, but some wonderful scenes redeeming consumption as a source of fantasy that opposes the everyday world of somber adulthood; 1983, directed by Ingmar Bergman).
The Gods Must Be Crazy (steeped in postcolonial racism, a Western fairy tale about the power of Western goods to beguile the hapless native; 1981, directed by Jaime Uys).
The Great Gatsby (it is hard to do justice to F. Scott Fitzgerald's classic novel and Daisy Buchanan's awe at the beauty of Gatsby's shirts, but an instructive look at consumption nevertheless; 1974, directed by Jack Clayton).
The Jerk (materialism, especially the scene in which Steve Martin has gone bankrupt and leaves his mansion, saying he needs nothing of his former possessions, except...; 1979, directed by Carl Reiner).
Like Water for Chocolate (based on a novel by Laura Esquivel and a logical companion piece to Chocolate but Mexican, and based on the emotional content of food; 1992, directed by Alphonso Arau who was then married to Laura Esquivel).
Pretty Woman (Cinderella tale in which poor prostitute Julia Roberts is transformed into the fiancé of Richard Gere with the help of Rodeo Drive; 1990, directed by Garry Marshall).
Pulp Fiction (early scene in which European and America fast food are compared; 1994, directed by Quentin Tarantino).
Trading Places (a "there but for the grace of God go I" look at wealth, poverty, and consumption; street smarts win out over Wall Street, prep schools, and the Ivy League; 1983, directed by John Landis).
Scenes from a Mall Woody Allen and Bette Midler show how to be a postmodern consumer in the shopping mall [see Belk and Bryce 1993]; 1991, directed by Paul Mazursky).
Wayne's World (the scene in which Wayne decries product placements and endorsements while blatantly displaying and promoting a long series of brands; 1992, directed by Penelope Spheeris).
Videos
The Ad and the Ego (much more effective than Affluenza in appreciating media influences on consumption; San Francisco: California Newsreel, 57 minutes).
Affluenza (a PBS dramatized comedy about the affliction of affluence and consumption; KCTS, Seattle and Oregon Public Broadcasting, 1997, 58 minutes).
Photograph Books
Aaland, Mikkel (1981), County Fair Portraits. Santa Barbara, CA: Capra Press.
Agee, James and Walker Evans (1941), Let Us Now Praise Famous Men. New York: Riverside Press.
Aperture (1996), Everything that Lives, Eats. New York: Aperture.
Conkelton, Sheryl and Anne Lamott (1993), Home and Other Stories: Photographs by Catherine Wagner1 Los Angeles: Los Angeles County Museum of Art.
Evans, Christopher (1990), In The Money. London: Blue Window Books.
Goldberg, Jim (1985), Rich and Poor. New York: Random House.
Hansen, Ursala and Karin Blüher (1993), Handel und Konsumkultur. Hannover, Germany: Fackelträger-Verlag.
Hoover, Dwight W. (1986), Magic Middletown- Bloomington, IN: Indiana University Press.
Kugelmass, Jack (1994), Masked Culture: The Greenwich Village Halloween Parade' New York: Columbia University Press.
Lesy, Michael (1973), Wisconsin Death Trip. New York: Random House.
Levinson, Joel D. (1983), Flea Markets. Berlin: Braus.
Lewandowska, Marysia and Neil Cummings (1995), Lost Property. London: Chance Books.
Mahardige, Dale and Michael Williamson (1989), And Their Children After Them. New York: Random House.
Owens, Bill (1999), Suburbia, 2d ed. New York: Fotofolio.
Pratt, Gretta (1994), In Search of the Corn Queen. Washington, DC: National Museum of American Art.
Putnam, Tim and Charles Newton, eds. (1990), Household Choices London: Futures Publications.
Rousseau, Ann Marie (1981), Shopping Bag Ladies. New York: Pilgrim Press.
Rutkovsky, Paul (1982), Commodity Character. Rochester, NY: Visual Studies Workshop Press.
Salinger, Adrienne (1995), In My Room. an Francisco: Chronicle Books.
Seale, William (1981), Tasteful Interlude: American Interiors Through the Camera's Eye, 1860-1917, 2d ed. Nashville, TN: American Association for State and Local History.
Secretan, Thierry (1995), Going into Darkness: Fantastic Coffins from Africa. London: Thames and Hudson.
Tuchman, Mitch (1994), Magnificent Obsessions: Twenty Remarkable Collectors in Pursuit of Their Dreams. San Francisco: Chronicle Books.
Verra, Yvonne et al. (1996), Images of the West. Harare, Zimbabwe: Baobab Books; Copenhagen, Denmark: Images of Africa.
Williams, Heathcote (1991), Autogeddon New York: Arcade Publishing.
CD-ROMs
Material World Scholarship Fund (1994), Material World: A Global Family Portrait. CD-ROM. Napa, CA: Ignite.
Prelinger, Rick (1996a), Our Secret Century, Archival Films from the Dark Side of the American Dream, Vol. 1: The Rainbow Is Yours. CD-ROM. New York: Learn Technologies Interactive.
----- (1996b), Our Secret Century, Archival Films from the Dark Side of the American Dream, Vol. 2: Capitalist Realism4 CD-ROM. New York: Learn Technologies Interactive.
Queensland University of Technology (1999), Consumer Behaviour Version 1.1, Interactive CD-ROM. Brisbane: School of Marketing and International Business, Queensland University of Technology, Teaching and Learning Support Services.
Wardlow, Dan (1998), Principles of Marketing: An Interactive Approach, CD-ROM. Cincinnati, OH: South-Western College Publishing.
Web Sites
Center for Consumer Culture [available at http://c3.business.utah.edu].
Rick Wilk's Museum of Weird Consumer Culture [available at http://www.indiana.edu/˜wanthro/museum.html].
Rob Kozinet's Web page [available at http://www.kellogg.nwu.edu/faculty/Kozinets/htm/].
Russ Belk's Web page [available at http://www.business.utah.edu/˜mktrwb/].
Society for Visual Sociology [available at http://www.sjmc.umn.edu/faculty/schwartz/ivsa/].
Music
Marketing Mike and the Suits (1996a), Business Blues, CD. Mountain View, CA: Biz Blues Records.
----- (1996b), Marketing Blues, CD. Mountain View, CA: Biz Blues Records.
References Belk, Russell W. (1988), "Possessions and the Extended Self," Journal of Consumer Research, 15 (September), 139-68.
-----, ed. (1989), Highways and Buyways: Naturalistic Research from the Consumer Behavior Odyssey. Provo, UT: Association for Consumer Research.
----- (1995), Collecting in a Consumer Society. London: Routledge.
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Record: 196- What to Convey in Antismoking Advertisements for Adolescents: The Use of Protection Motivation Theory to Identify Effective Message Themes. By: Pechmann, Cornelia; Zhao, Guangzhi; Goldberg, Marvin E.; Reibling, Ellen Thomas. Journal of Marketing. Apr2003, Vol. 67 Issue 2, p1-18. 18p. 1 Diagram, 4 Charts, 2 Graphs. DOI: 10.1509/jmkg.67.2.1.18607.
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What to Convey in Antismoking Advertisements for Adolescents:
The Use of Protection Motivation Theory to
Identify Effective Message Themes
Antismoking advertising is increasingly used, but its message content is controversial. In an initial study in which adolescents coded 194 advertisements, the authors identified seven common message themes. Using protection motivation theory, the authors develop hypotheses regarding the message theme effects on cognitions and intentions and test them in an experiment involving 1667 adolescents. Three of the seven message themes increased adolescents' nonsmoking intentions compared with a control; all did so by enhancing adolescents' perceptions that smoking poses severe social disapproval risks. Other message themes increased health risk severity perceptions but were undermined by low perceived vulnerability.
There is considerable agreement that programs should be undertaken to prevent minors from smoking cigarettes (Centers for Disease Control and Prevention [CDC] 1999). The number of U.S. states that use paid anti-smoking advertising targeted at youths has increased from 1 in 1986 (Minnesota Department of Health 1991) to more than 21 in 2002 (Campaign for Tobacco-Free Kids 2002). Also, the American Legacy Foundation (2002) runs anti-smoking television advertisements nationwide. Funding primarily comes from the 1997 settlement between tobacco firms and the U.S. attorneys general (National Association of Attorneys General 2000). The sponsors of antismoking advertising use diverse message themes, and though there is widespread agreement that choice of theme matters, there is considerable disagreement as to what choice to make. As Teinowitz (1998, p. C1) explains,
Do you warn teens, many of whom think they are invincible, about death and disfigurement? Or do you suggest that Big Tobacco is the new evil empire ... or that not smoking is much cooler than engaging in it? If you think the answer is obvious, you haven't seen the distinctly different approaches taken by the four states that have recently run anti-smoking ad campaigns.... History shows you get less smoking but how much less will depend, to a large degree, on the message used.
Evidence of the efficacy of different antismoking message themes is limited and conflicting. A report by Teenage Research Unlimited (1999) concludes that health messages are efficacious, whereas Goldman and Glantz (1998) advocate messages attacking the tobacco industry and Worden, Flynn, and Secker-Walker (1998) recommend social norm messages. Many of these conclusions are based on focus group research, which can be unreliable (Blankenship and Breen 1993), as can uncontrolled field studies. Florida has reported that its "Truth" advertisements attacking tobacco firms are effective, on the basis of surveys showing 40% and 16% declines in smoking among middle and high school students in the state, respectively (Bauer et al. 2000; see also Farrelly et al. 2002). However, Monitoring the Future (Johnston, O'Malley, and Bach-man 2001) shows nearly comparable declines (30% and 14%) in the southern region of the United States as a whole, where no antismoking advertisements were running. Apparently, most of the decline was due to a macro trend, rather than to an advertisement-specific effect. Therefore, it is unclear whether anti-tobacco industry advertisements work.
The most fundamental question that must be addressed is whether using any of the common antismoking messages makes sense from a public health perspective, compared with doing nothing at all (Pechmann 2002). That is, will any of these message themes dissuade youths from smoking? To address this question, we examined 194 antismoking advertisements and created a typology of commonly used message themes. Then, we conducted an experiment to investigate the effects of each message theme on adolescents' smoking-related cognitions and intentions compared with a no-message control. We employed protection motivation theory (Rogers 1983) to help us predict why certain message themes might work or not work, because it is a highly comprehensive theory of health communication (Boer and Seydel 1996). Moreover, the antismoking advertisement sponsors sought to influence many of the cognitions that are the focus of this theory (Parpis 1997). Although our research primarily addresses social marketing, it also explores the broader issue of how youths make decisions about risky behaviors (Benthin, Slovic, and Severson 1993; Fischhoff et al. 2000). In particular, we examine the weight placed on health versus social risks (Ho 1998) and the integration of data about risk severity versus vulnerability (Weinstein 2000).
Protection motivation theory (Rogers 1983) posits that people's motivations or intentions to protect themselves from harm are enhanced by four critical cognitions or perceptions, regarding the severity of the risks, vulnerability to the risks, self-efficacy at performing the advocated risk-reducing behavior, and the response efficacy of the advocated behavior. In addition, the theory posits that people's intentions to protect themselves are weakened by the perceived costs of the advocated risk-reducing behavior and the perceived benefits of the opposing risk-enhancing behavior. These cognitive processes are divided into two subprocesses: threat appraisal (severity, vulnerability, and benefits) and coping appraisal (self-efficacy, response efficacy, and costs). In general, the factors underlying each appraisal process have been studied separately, though occasionally threat or coping appraisal has been studied as a whole (Sturges and Rogers 1996; Tanner, Hunt, and Eppright 1991). According to the theory, people can be motivated to engage in desirable health behaviors not only to avoid health risks but also to avoid social or interpersonal risks (Rogers 1983). Of late, researchers have increasingly focused on messages that stress social risks (Dijkstra, De Vries, and Roijackers 1998; Mahler et al. 1997; Schoenbachler and Whittler 1996). Furthermore, protection motivation theory has recently been extended formally to include social risks (Ho 1998). Some researchers have argued that cognitive mediators are insufficient for explaining people's intentions to avoid risks and that fear should be included as an added affective mediator (Tanner, Hunt, and Eppright 1991; Witte 1992). Rogers (1983, p. 165) disagrees, however, and cites his results showing that "fear arousal does not facilitate attitude change unless this arousal directly affects ... cognitive appraisal."
Protection motivation theory (Rogers 1975, 1983) posits that, in most cases, cognitions will affect intentions directly and additively, though at times, certain cognitions will function interactively or synergistically. The 1975 version of the theory posits two-and three-way interactions among severity, vulnerability, and efficacy. The 1983 version of the theory excludes all three-way interactions, as well as the twoway interactions of severity with vulnerability and self-efficacy with response efficacy. However, a recent meta-analysis (Floyd, Prentice-Dunn, and Rogers 2000) suggests that these two-way interactions may be important after all (see also Weinstein 2000).
Researchers have sometimes tested protection motivation theory using surveys. They have measured all the cognitive variables and intentions and examined the cognition-intentions links (e.g., Flynn, Lyman, and Prentice-Dunn 1995; Ho 1998). More commonly, though, researchers have conducted experiments in which they have manipulated a subset of the cognitive factors through social marketing messages, frequently using real messages from practitioners, as we do here (Burgess and Wurtele 1998; Castle, Skin-ner, and Hampson 1999; Steffen 1990; Weinstein, Sandman, and Roberts 1991). They have examined the effects of these messages on the target cognitions and intentions, often compared with a no-message control group (Mahler et al. 1997; Tanner, Hunt, and Eppright 1991; Witte 1992). This is the approach we adopt. To our knowledge, no experiment has sought to manipulate all six protection motivation theory cognitions because doing so would be too unwieldy, particularly given a standard full-factorial design. The perceived costs of the risk-reducing behavior and the perceived benefits of the risk-enhancing behavior have been studied the least (Floyd, Prentice-Dunn, and Rogers 2000; Milne, Sheeran, and Orbell 2000), perhaps because these factors weaken protection motivation intentions, and researchers have pragmatically focused on factors that strengthen intentions.
In this research, we use protection motivation theory to formulate hypotheses regarding the likely impact of seven common antismoking message themes on the cognitions that they attempt to influence, namely, health and social risk severity and self-efficacy at refusing cigarette offers and resisting tobacco marketing. In formulating these hypotheses, we review prior experiments to assess "cognitive malleability," or the ease with which severity and efficacy perceptions can be influenced. Furthermore, we assess the likelihood that if a message theme affects a cognition, it will also affect intentions. Here, we refer to meta-analysis results regarding the average effect size of each cognition on intentions (Floyd, Prentice-Dunn, and Rogers 2000; Milne, Sheeran, and Orbell 2000). In considering effects on intentions, we also examine possible two-way interactions between cognitions, because a cognitive variable could have a weak effect on intentions as a result of a moderator that reduces, nullifies, or even at times reverses its impact (Rogers 1975, 1983). We studied all of the protection motivation theory cognitions except response efficacy, which we presumed to be irrelevant in this context, because refraining from smoking is 100% effective for avoiding the risks incurred by becoming a smoker.
Disease and Death Message Theme
Disease and Death messages discuss how smokers suffer from serious diseases, such as emphysema and lung cancer, and often die prematurely. The goal of these advertisements is to convey the "harsh medical realities of the effects of smoking" (Parpis 1997, p. 35). In one stimulus advertisement used in our study, a camera follows smoke going down the throat of an adult smoker, which reveals fleshy lumps starting to grow; a voice-over states, "One damaged cell is all it takes to start lung cancer growing." Another advertisement talks about how smokers inhale poisons such as "arsenic, carbon monoxide, and formaldehyde" that "immediately affect their hearts, lungs, and brains." A third advertisement shows an adolescent male smoking, who slowly turns into a skeleton; it states, "Smoking: it's only a matter of time."
From the perspective of protection motivation theory (Rogers 1983), the intent is to increase perceptions of health risk severity. Prior studies have used similar manipulations to increase the perceived severity of unhealthy behaviors such as smoking (Maddux and Rogers 1983), unprotected sex (Block and Keller 1998), illicit drug use (Schoenbachler and Whittler 1996), and alcohol abuse (Kleinot and Rogers 1982). Manipulating health risk severity seems fairly easy to do through brief text or graphics, as in a brochure stating that unprotected sex can cause AIDS or syphilis (Block and Keller 1998) or a graphic print advertisement showing a person in a hospital who has overdosed on a drug (Schoenbachler and Whittler 1996).
We did not expect the Disease and Death messages to affect health risk vulnerability perceptions, however. These messages included none of the information that is known to enhance vulnerability perceptions, such as personal or genetic risk factors (Rippetoe and Rogers 1987; Weinstein 1983; Wurtele and Maddux 1987), probabilities of occur-rence (Maddux and Rogers 1983; Mulilis and Lippa 1990), or familiar symptoms (DePalma, McCall, and English 1996). Instead, we predicted a single effect for Disease and Death messages on cognitions.
H1: The Disease and Death (versus control) antismoking message theme will enhance adolescents' perceptions of the severity of the health risks of smoking.
Endangers Others Message Theme
Endangers Others messages stress how secondhand smoke, and smoking in general, can seriously harm smokers' family members, coworkers, and peers. The primary intent of these advertisements is "raising individuals' awareness of environmental tobacco smoke (ETS), with advertisements that portray the risks of breathing someone else's smoke" (California Department of Health Services [CA DHS] 2001, p. 84). Some advertisements also stress that when smokers die prematurely, family members suffer emotionally and financially. In one stimulus advertisement, an uncaring father's cigarette smoke envelops his frightened toddler who, in a plea for help, spells out "sudden infant death syndrome" in alphabet blocks. Another advertisement shows smoke entering rooms in a home with children and states, "Your children don't smoke and they don't want to; but when your home fills with second hand smoke, they don't have a choice; instead, every innocent breath they take eats away at them, causing asthma...." In yet another advertisement, a teenager sadly explains that her mother has died of a smoking-related disease and will never attend her graduation or wedding: "All the important stuff, she won't be there."
Endangers Others advertisements are similar to Disease and Death advertisements in terms of depicting severe health risks. What is unique about Endangers Others advertisements is that they also convey that smokers may encounter strong social disapproval from nonsmokers. The advertisements suggest that many nonsmokers are disappointed in or angry at smokers for their lack of consideration of others. Some advertisements also subtly chastise smokers for hurting others. Surveys have found that Endangers Others advertising often prompts nonsmokers to voice their dis-approval of smoking by asking the smokers in their midst-- for example, family members or friends--not to smoke around them or even to stop smoking altogether (CA DHS 2001; Connolly and Robbins 1998).
According to protection motivation theory, Endangers Others messages seek to increase the perceived severity of the health and social disapproval risks of smoking. On the basis of prior studies, it appears to be fairly easy to manipulate social risk severity perceptions (Jones and Leary 1994; Mahler et al. 1997), just as it is with health risk severity perceptions. Schoenbachler and Whittler (1996) used a print advertisement showing young people rejecting a teenage drug user. Dijkstra, De Vries, and Roijackers (1998) sent letters to smokers stating that their family members would appreciate it if they quit. Therefore, we predict that
H2: The Endangers Others (versus control) antismoking message theme will enhance adolescents' perceptions of (a) the severity of the health risks and (b) the severity of the social disapproval risks of smoking.
Cosmetics Message Theme
Cosmetics messages stress that smokers must cope with highly unattractive and annoying side effects that are cosmetic in nature, such as smelliness. The messages attempt to convey that "smoking has many unpleasant consequences that can lead to social disapproval, such as bad breath, yellow teeth, smelling bad" (Minnesota Department of Health 1991, p. 52). In one stimulus advertisement, a teen compares a smoker's breath to a dog's breath and concludes that the latter "is slightly less putrid." In another advertisement, a teen offers strategies to enhance guys' attractiveness to girls and warns, "Nix the smoking; that yellow teeth and cigarette stench thing; it's not working." In a third advertisement, youths brush their teeth after smoking but find that their mouths are full of ashes; the advertisement warns, "You can brush, you can gargle, but you can't get rid of cigarette mouth."
From the perspective of protection motivation theory, Cosmetics messages attempt to enhance perceptions that smoking poses severe social disapproval risks because of its unattractive side effects. However, it is possible that adolescents might not be too concerned about such problems, which cosmetic products such as breath sprays and gums can easily remedy. In most prior studies that enhanced perceptions of social risk severity, the messages stressed appearance-related risks that cosmetics products could not remedy, such as curvature of the spine from osteoporosis (Klohn and Rogers 1991) or wrinkles from excessive sun exposure (Jones and Leary 1994). The Endangers Others messages seem to convey more serious social concerns as well, by stressing that many nonsmokers believe that smoking is inconsiderate and violates their right to breathe clean air. However, given adolescents' hypersensitivity to being evaluated by others (Graham, Marks, and Hansen 1991), we predict that even Cosmetics messages will be effective.
H3: The Cosmetics (versus control) antismoking message theme will enhance adolescents' perceptions of the severity of the social disapproval risks of smoking.
Smokers' Negative Life Circumstances Message Theme
Most adolescents want to appear mature, independent, savvy, attractive, and cool, and many think that smoking will help them realize these goals (CA DHS 1990, p. xi; see also Miller 1998). Smokers' Negative Life Circumstances messages suggest that smoking "is a barrier to achieving [these] goals" (Miller 1998, p. 2743). Specifically, the advertisements use graphic, gross, and antisocial images to convey that smoking is a hindrance, rather than a pathway, to achieving higher-order aspirational goals (Parpis 1997; Pechmann and Shih 1999). Smokers are depicted as disheveled "losers" in a variety of unattractive life circumstances, who have quite obviously taken the wrong path in life.
In one stimulus advertisement, an attractive young male demonstrates to a disheveled and befuddled smoker that smoking is as ill-conceived as sticking one's head in a toilet; "Smoke away," the advertisement jeers at the end. Another advertisement pokes fun at a sophomore who unwisely "started smoking in junior high," showing him as a scrawny old man with a whiney voice and a cigarette poking out of his mouth. One more advertisement shows a young female smoker who tries to beautify herself for a date; instead, she turns into an ugly witch sitting in a bathtub, giggling inanely. The graphic, negative imagery in Smokers' Negative Life Circumstances advertising is designed to suggest that smokers are viewed as losers and will experience severe social disapproval from peers. Translating this idea into protection motivation theory terms, we predict that
H4: The Smokers' Negative Life Circumstances (versus control) antismoking message theme will enhance adolescents' perceptions of the severity of the social disapproval risks of smoking.
Refusal Skills Role Model Message Theme
Refusal Skills Role Model messages explain why many attractive role models view smoking as unappealing and demonstrate refusals of cigarette offers (Worden et al. 1988). In one advertisement, a girl confides to a friend, "I don't want to go out with him; he was smoking and he thought it was cool"; instead, she is impressed with another boy who says "no thanks" when offered a cigarette. A different advertisement shows kids being stalked by a cigarette, and one strong, brave boy knocks the cigarette out with boxing gloves. In yet another advertisement, a famous football player symbolically kicks a cigarette away like a football, stating, "No way was I going to lose to some tiny little cigarette."
Turning to protection motivation theory, one goal of the advertising is to increase perceptions that smoking poses social disapproval risks. The attractive role models clearly indicate that they disapprove of smoking and smokers. These role models could make quite an impression because, as was mentioned previously, social risk perceptions generally appear to be malleable (Dijkstra, De Vries, and Roijackers 1998; Jones and Leary 1994; Mahler et al. 1997; Schoenbachler and Whittler 1996). Refusal Skills Role Model advertising also attempts to enhance adolescents' perceptions of self-efficacy at refusing cigarette offers (Worden et al. 1988). The advertising shows role models successfully refusing cigarettes, which may teach skills and raise viewers' expectations that they too are capable of refusing (Bandura 1997).
However, self-efficacy perceptions have proved to be quite rigid and often cannot be changed unless intense interventions are used that permit practice and mastery of focal skills (Bandura 1997). Rohrbach and colleagues (1987) increased adolescents' feelings of self-efficacy at refusing alcohol offers with a three-hour intervention involving demonstrations and practice that progressed from simple rehearsals to extended role plays. Bryan, Aiken, and West (1996) boosted female subjects' self-efficacy regarding condom use with a multifaceted intervention including a video of condom purchases, role-playing of asking a partner to wear a condom, and demonstrations of how to put a condom on a partner. Refusal Skills Role Model advertising relies on passive observation, so we were uncertain whether it would influence self-efficacy perceptions. However, we expected the advertising to influence social risk perceptions.
H5: The Refusal Skills Role Model (versus control) antismoking message theme (a) will enhance adolescents' perceptions of the severity of the social disapproval risks of smoking and (b) may enhance their perceptions of self-efficacy at refusing cigarette offers.
Marketing Tactics Message Theme
Marketing Tactics messages stress that tobacco firms use powerful marketing tactics such as image advertising and target marketing and that children, women, and minorities are prime targets. The advertising sponsors believe that "the strategy makes [children] stop and consider that smoking may not be an act of their own free will" (CA DHS 1990, p. 26). In one stimulus advertisement, cigarettes rain down on a schoolyard while a tobacco executive explains, "We have to sell cigarettes to your kids; we need half a million new smokers a year just to stay in business, so we advertise near schools, at candy counters.... We have to." Another advertisement features a former tobacco lobbyist who says, "Maybe they'll get to your little brother or sister, or maybe they'll get to the kid down the block, but one thing is perfectly clear to me: the tobacco companies are after children." One more advertisement shows a cigarette billboard claiming that women want "rich flavor." The billboard peels away to reveal the company's true motive: "Women are making us rich."
Marketing Tactics messages attempt to increase adolescents' knowledge about cigarette marketing tactics, including the perpetrators, target audiences, effects, and ethics. This multidimensional knowledge base has been labeled "persuasion knowledge" (Friestad and Wright 1994). Ideally, such knowledge should enhance youths' perceptions of control over tobacco marketers' persuasion attempts (Camp-bell and Kirmani 2000). As Friestad and Wright (1994) explain, when a person understands that an agent's action is a persuasion attempt, a "change of meaning" occurs, wherein the person can exert control over the persuasion attempt.
In protection motivation theory terms, Marketing Tactics advertising seeks to boost adolescents' knowledge regarding tobacco marketing tactics and, ultimately, their self-efficacy at resisting such tactics. The advertising may increase knowledge, as many media literacy programs have been shown to do (Banspach, Lefebvre, and Carleton 1989; Brucks, Armstrong, and Goldberg 1988). However, it is less clear whether the advertising will enhance skills and self-efficacy, because it relies on passive observation (Bandura 1997). Media literacy programs that have improved skills typically have enabled students to practice and master those skills (Dorr, Graves, and Phelps 1980; Feshbach, Feshbach, and Cohen 1982). Consider, for example, Peterson and Lewis's (1988, p. 167) successful program:
The individual learning module for that day was defined ... and modeled by the experimenter who gave several examples.... Then, an advertisement that included the item relevant to that learning module was shown.... The rest of the session was spent with the children viewing advertisements and identifying items relevant to the present learning module, and helping the children make up their own examples.
Because watching advertising is fundamentally different from this type of program, we predict that
H6: The Marketing Tactics (versus control) antismoking message theme may enhance adolescents' perceptions of self-efficacy at resisting tobacco marketing.
Selling Disease and Death Message Theme
Selling Disease and Death messages claim that tobacco firms use manipulation and deception to pressure consumers into purchasing a product that causes serious diseases and even death. The advertising seeks to persuade adolescents to resist tobacco marketers' tactics. As one advertising sponsor explains, youths "are quick to excuse the tobacco executives as simply doing their jobs," and so it is important to "expose the tobacco industry as different from other industries" (Miller 1998, pp. 2743-44). One advertisement features a former cigarette model who pleads with viewers in a grossly distorted voice due to throat cancer. She says, "I was a model in cigarette ads, and I convinced many young people to smoke; I hope I can convince you not to." A second advertisement shows the brother of a Marlboro Man who has died from lung cancer; he explains, "The tobacco industry used my brother ... to create an image that smoking makes you independent; don't believe it; lying there with all those tubes in you, how independent can you really be?" A third advertisement shows a woman who has lost her trachea because of smoking; she smokes from a hole in her throat and states, "They say nicotine isn't addictive; how can they say that?" These advertisements stress smoking's severe health effects. They also seek to enhance persuasion knowledge, so youths will be less influenced by tobacco marketing and feel a greater sense of control over it, which should translate into enhanced self-efficacy. On the basis of our previous assump-tion that severity perceptions are more malleable than self-efficacy perceptions, we posit that
H7: The Selling Disease and Death (versus control) antismoking message theme (a) will enhance adolescents' perceptions of the severity of the health risks of smoking and (b) may enhance their perceptions of self-efficacy at resisting tobacco marketing.
Substantive Variation Message Condition
In our experiment, each subject saw just one of the previously discussed message themes, which was represented by eight stimulus advertisements. We also tested a heterogeneous, or Substantive Variation, condition (Schumann, Petty, and Clemons 1990), in which subjects saw all themes, one advertisement per theme. The Disease and Death, Selling Disease and Death, and Endangers Others advertisements dealt with health risk severity. The Endangers Others, Cosmetics, Smokers' Negative Life Circumstances, and Refusal Skills Role Model advertisements dealt with social risk severity. The Marketing Tactics and Selling Disease and Death advertisements addressed self-efficacy at resisting tobacco marketing. The Refusal Skills Role Model advertisement addressed self-efficacy at refusing cigarette offers.
In the Substantive Variation condition, just one or at most two advertisements conveyed each message theme, so that the total number of stimulus advertisements could be held constant, at eight advertisements, across message conditions. Ideally, the one or two advertisements on each theme would influence the focal cognitions almost as effectively as the set of eight similar advertisements in each other message condition. Prior protection motivation studies have included substantively varied or heterogeneous message conditions and have found them to be highly effective at influencing cognitions (Sturges and Rogers 1996). For example, Maddux and Rogers (1983) find that essays that discuss health risk severity and vulnerability and self-and response efficacy enhance all four types of cognitions. On the basis of this rationale, we predict that
H8: The Substantive Variation (versus control) antismoking message condition (a) will enhance adolescents' perceptions of the severity of the health and social disapproval risks of smoking and (b) may enhance their perceptions of self-efficacy at resisting tobacco marketing and refusing cigarette offers.
Next, we turn to the issue of whether antismoking message themes that induce changes in adolescents' risk severity or self-efficacy cognitions will produce corresponding changes in their intentions. Meta-analyses indicate that all of the protection motivation theory cognitions significantly affect youths' and adults' intentions and behaviors (Floyd, Prentice-Dunn, and Rogers 2000; Milne, Sheeran, and Orbell 2000). However, self-efficacy perceptions seem to have at least twice as much influence as risk severity perceptions. Milne, Sheeran, and Orbell (2000) report mean effect sizes of .10 for severity and .33 for self-efficacy. Floyd, Prentice-Dunn, and Rogers's (2000) estimates are .39 for severity and .88 for self-efficacy. Therefore, although we posited previously that severity perceptions are more malleable and more likely to be affected by antismoking advertising, self-efficacy perceptions seem to be more important in terms of influencing intentions.
It should be noted, though, that these meta-analysis results are based primarily on messages that stress health risks. The effect sizes for social disapproval risks are unknown. Recent studies suggest that young people may be more influenced by social risks than health risks (Ho 1998; Jones and Leary 1994; Schoenbachler and Whittler 1996). With regard to smoking, youths' perceptions of social norms have been found to be among the strongest predictors of their smoking intentions (Chassin et al. 1984; Collins et al. 1987; Conrad, Flay, and Hill 1991). For parsimony, though, we base our formal hypothesis on protection motivation theory (Rogers 1975, 1983), which makes no predictions regarding the relative impact of different cognitions on intentions, thus implying roughly equivalent effects for each cognition.
H9: Adolescents' intentions not to smoke will be a positive function of perceived (a) health risk severity and vulnerability, (b) social disapproval risk severity and vulnerability, (c) self-efficacy at refusing peers' cigarette offers, and (d) self-efficacy at resisting tobacco marketing; these intentions will be a negative function of perceived (e) benefits of smoking and (f) costs of not smoking. Therefore, if anti-smoking advertising influences risk severity or self-efficacy perceptions (H1-H8), it should influence intentions too.
Meta-analyses have examined only two potential inter-active effects (Floyd, Prentice-Dunn, and Rogers 2000). The joint effect of self-efficacy and response efficacy was found to have a .41 effect size, but as discussed previously, response efficacy does not seem to be relevant in the context of smoking prevention. (Not smoking is clearly an effective response for avoiding the risks of being a smoker.) Of greater interest here is that the joint effect of health risk severity and vulnerability had an effect size of .54. What is most notable is that when these variables were manipulated separately, their effect sizes were .39 and .41, respectively. The variables' combined effect might be expected to be .80 (.39 + .41), yet it was only .54, which suggests a negative synergistic effect. For example, the combined manipulation might have increased severity perceptions a great deal and vulnerability perceptions much less so. This result could be problematic, because increases in severity given low vulnerability could have null or even counterproductive effects on intentions (Mulilis and Lippa 1990).
Considerable research shows that stressing the severe health risks a behavior poses can enhance its allure by making it more thrilling or positively arousing, if perceived vulnerability is low (Benthin, Slovic, and Severson 1993; Klein 1993; Wood et al. 1995). This phenomenon has been referred to as a "forbidden fruit" reaction (Pechmann and Shih 1999). For example, the most extreme roller coaster ride often has the greatest appeal because riders can experience an intense thrill and feel brave and macho with no apparent risk to themselves. Adolescents in particular seem to be attracted to forbidden fruit, because many believe they are invulnerable to physical harm (Arnett 2000; Cohen et al. 1995; Pechmann and Shih 1999; Urberg and Robbins 1984). However, youths do not feel immune to social disapproval risks; on the contrary, most youths are hypersensitive to how peers evaluate them (Graham, Marks, and Hansen 1991; McNeal and Hansen 1999). Therefore, any forbidden fruit reaction should be restricted to health risk severity messages and should not be evoked by social risk severity messages.
H10: If an antismoking (versus control) message theme enhances adolescents' perceptions of health risk severity but perceived health risk vulnerability is low, nonsmoking intentions could be weakened.
Subjects and Procedure
We obtained 194 antismoking television advertisements that had aired between 1986 and 1997. Most came from Arizona, California, Canada, Massachusetts, Minnesota, or the University of Vermont, but some came from the American Cancer Society, Australia, Michigan, New Hampshire, or the U.S. CDC. We used real television advertisements because we wanted to generalize our results to such advertisements. To the best of our knowledge, only the Vermont advertisements had been pretested for message content (Worden et al. 1988). Therefore, it seemed important to conduct a preliminary study to identify advertisements that contained the focal message themes. The study involved 1129 seventh and tenth graders, representing middle school and high school, respectively.
The 194 antismoking advertisements were copied onto 24 videotapes, so that each videotape contained eight or nine randomly selected advertisements. Groups of seventh and tenth graders watched each videotape. After each advertisement was viewed twice, the videotape was paused and subjects answered a series of "yes"/"no" questions regarding its message content (see Table 1). Perceived ad effectiveness was also measured with the question, "Overall, I think this ad is effective for kids my age" (1 = "strongly disagree," 5 = "strongly agree"; Biener 2000). The other procedures were similar to those used in the main experiment.
Results
The criterion for determining if an advertisement contained a message theme was 80% or higher agreement among the roughly 45 subjects who viewed that advertisement. A total of 129 advertisements fell into one of the seven thematic message categories shown in Table 1.[ 1] The largest category was Selling Disease and Death, with 27 advertisements; the smallest was Marketing Tactics, with 9 advertisements. We randomly selected 8 advertisements from each message category (56 advertisements total) to be used in the main experiment. For the selected advertisements, the intersubject agreement on message content averaged 91%. We also created a Substantive Variation condition, with 2 Selling Dis-ease and Death advertisements and 1 advertisement from each other condition (8 advertisements total). The advertisements were chosen at random so that any differences in sponsor, year, quality, or style would be randomly distributed across conditions. Each condition contained advertisements from approximately four sponsors and spanned roughly nine years of advertising. The advertisement selection procedure seems to have controlled for any major quality differences, in that subjects perceived the advertisements in each condition to be similar in terms of their effectiveness (p > .30), except the Marketing Tactics advertisements, which were rated as slightly weaker than the others (p < .05). For the selected advertisements, the average effectiveness rating was 3.5, slightly above the midpoint of 3.0.
Subjects and Design
Subjects were 1667 students (46% male), consisting of 788 seventh graders (47%) and 879 tenth graders. Subjects were recruited from four middle schools and four high schools; each school contributed roughly 200 students. Schools were paid $1,000 honorariums. Student assent and parental consent were obtained, and participation rates exceeded 90%. The schools were publicly funded and ethnically diverse and were located in middle-to lower-middle-class neighborhoods. Of the subjects, 44% were Hispanic, 35% were White, and 21% were some other ethnicity. Only 4% of the subjects were regular smokers.
The design was a between-subjects factorial with one factor, antismoking message theme, and nine manipulated levels (eight treatment, one control). We randomly assigned approximately 185 subjects to each condition. Each treatment condition consisted of eight advertisements selected randomly from among the set identified in the advertisement coding study. Using eight advertisements enabled us to assess thematic message effects rather than individual ad effects and minimized the influence of extraneous executional factors, in that each message theme was represented by several ad executions. The control condition consisted of eight randomly selected advertisements from the Ad Council on the health and social risks of drunk driving. We copied the advertisements onto videotapes in random order. To ensure a strong manipulation, we showed each advertisement twice in succession, and there was no filler material.
Data Collection Procedures
At each school, two classrooms were equipped with rented televisions and videocassette recorders. Subjects were released from class to participate in the study and were randomly assigned to one of these classrooms. The videotape to be shown in each classroom was determined in advance through a random-number algorithm. Data collection at each school was completed in one day to minimize subject contamination. Each data collection session lasted 50 minutes. Subjects were told they would view a videotape of advertisements and then complete an anonymous survey. Subjects viewed the videotapes in groups of 25-40, and no talking was permitted. Immediately after watching the entire videotape of advertisements, subjects completed a written survey with the dependent measures. Subjects placed their completed surveys in sealed envelopes and were instructed not to discuss the study with others. Subjects in the control condition reported no problems completing a survey about smoking, perhaps because the anti-drunk driving advertisements appropriately primed them by addressing a drug-related issue.
Measures
Behavioral intentions. We derived the dependent measures from prior protection motivation studies on smoking (Maddux and Rogers 1983; Sturges and Rogers 1996) as well as surveys of adolescent tobacco and alcohol use (Bauman 1997; Grube 1997; Rose 1997). Five-point ( 1-5) scales were used unless otherwise stated. We assessed behavioral intentions with a previously validated three-item scale (Pierce et al. 1996): "In the future, you might smoke one puff or more of a cigarette," "You might try out cigarette smoking for a while," and "If one of your best friends were to offer you a cigarette, you would smoke it" ("definitely yes" to "definitely no").
Health risk perceptions. We assessed perceived severity of and vulnerability to the health risks of smoking using nine items pertaining to dying early; contracting diseases; becoming addicted; breathing poisons; premature aging; causing others to die, get diseases, or breathe poisons; and harming babies. The health severity question asked subjects to mark each outcome they viewed as very serious. The health vulnerability question asked subjects the likelihood that they would personally experience each outcome if they smoked regularly ("sure it would not happen" to "sure it would happen").
Social risk perceptions. To assess the perceived severity of social disapproval risks, we used five semantic differentials: "How acceptable is smoking cigarettes to your close friends?" "How do you think your close friends feel, or would feel, about you smoking?" "How attractive would you look to others if you smoked?" "How attractive would you look to dates, or potential dates, if you smoked?" and "How well would you fit in with kids your age if you smoked?" To assess the perceived vulnerability to social dis-approval risks, we asked subjects how important it was for them to look attractive to others, look attractive to dates, fit in with kids their age, and fit in at parties ("not important" to "very important").
Efficacy, cost, and benefit perceptions. We assessed perceived self-efficacy at refusing cigarette offers with three items: "If others pressure you to smoke, you can say no, walk away, or change the subject" ("sure you cannot" to "sure you can"). We measured self-efficacy at resisting tobacco marketing with two items (same scale): "You can resist being fooled by cigarette advertisements and by cigarette promotions." For completeness, we also assessed perceptions of the costs of not smoking on a two-item "disagree"/"agree" scale ("being made fun of," "being looked down upon") and the benefits of smoking on a similar four-item scale ("feel less stressed," "feel in a good mood," "concentrate better," "look confident").
Analysis of Variance Results on How Message Themes Affected Cognitions
Analysis plan and control variables. We used fixed effects analyses of variance to assess whether the antismoking (versus control) message themes affected cognitions. If there was a significant message theme effect, we conducted follow-up t-tests in which each antismoking message mean was compared with the control mean. Because we used the control mean multiple times, we used Dunn-Sidak critical tstatistics to avoid an inflated Type I error rate. Initially, sex, ethnicity, and perceived ad effectiveness were included as covariates but were dropped because they had no effect on the results. The sex and ethnicity covariates were nonsignificant, indicating that randomly assigning subjects to message conditions had ensured that the conditions were closely matched on these variables. Perceived ad effectiveness was a significant covariate, indicating that the message conditions were slightly imbalanced on this factor. Here, the Refusal Skills Role Model message theme was perceived as somewhat less effective than the other message themes (p < .05).[ 2] However, when we conducted the pairwise comparisons of means, we obtained the same pattern of results regardless of whether we used covariate adjusted or unadjusted means; for parsimony, we report unadjusted means.
A final control variable, grade in school, was included as a blocking factor because of concerns about possible ceiling effects among seventh graders. We included seventh graders in the research because most smoking prevention campaigns target middle school as well as high school students (e.g., Worden et al. 1988). It is believed that the opportunity to forewarn and inoculate youths against smoking is present in middle school, before significant numbers of them have even tried a cigarette (CDC 1994; Glynn 1989). However, because the prevalence of current (i.e., past month) smoking among seventh graders is only about 4% (U.S. Department of Health and Human Services 1999), we were concerned that our seventh graders might report strong antismoking intentions or cognitions, leaving little room for improvement after exposure to antismoking advertisements. By including seventh graders, we left our options open. If it was possible to detect ad effects among this group, we would be able to do so and determine which message themes work best for them. If effects among seventh graders were masked because of ceiling effects, we could use grade as a block to detect effects among tenth graders. Among tenth graders, the prevalence of current smoking is much higher, approximately 26% (U.S. Department of Health and Human Services 1999), so ceiling effects were much less likely.
Results. Table 2 shows the omnibus F-statistics and cell means; t-tests follow. Among all subjects, four message themes enhanced health risk severity perceptions: Disease and Death (t = 2.79, p < .05), Endangers Others (t = 3.33, p < .01), Selling Disease and Death (t = 4.11, p < .01), and Substantive Variation (t = 3.40, p < .01). Three message themes enhanced the perceived severity of the social disapproval risks of smoking: Endangers Others (t = 2.71, p < .06), Smokers' Negative Life Circumstances (t = 2.82, p < .05), and Refusal Skills Role Model (t = 2.73, p < .05). Intentions not to smoke were bolstered by the same three message themes: Endangers Others (t = 3.96, p < .01), Smokers' Negative Life Circumstances (t = 3.51, p < .01), and Refusal Skills Role Model (t = 2.81, p < .05). However, the effects on social risk severity perceptions and intentions were confined to tenth graders, so the preceding t-tests pertain to this group. Seventh graders' social risk perceptions and intentions were unaffected. (For social risk severity: message effect F(8, 1649) = 1.34, p = .22; message by grade F(8, 1649) = 2.33, p < .05. For intentions: message effect F(8, 1643) = 2.23, p < .05, but among seventh graders, there were no effects for antismoking versus control messages; message by grade F(8, 1643) = 3.08, p < .01.)
None of the message themes affected self-efficacy at refusing cigarette offers, self-efficacy at resisting tobacco marketing, health risk vulnerability, the benefits of smoking, or the costs of not smoking (p > .10), but null results were expected in the last three cases because the message themes did not address these topics. Finally, the Marketing Tactics message theme unexpectedly bolstered tenth graders' perceived vulnerability to social disapproval risks (t = 3.37, p < .01). This theme apparently implied that if marketers make such a concerted effort to influence societal opinions, those opinions must be important. To reiterate, the Disease and Death, Selling Disease and Death, Marketing Tactics, Cosmetics, and Substantive Variation message themes did not significantly affect intentions. None of the antismoking (versus control) message themes had any effects beyond those reported previously (p > .10).[ 3, 4]
LISREL Results on How Cognitions Affected Intentions
Analysis plan. We predicted that the eight measured protection motivation theory cognitions should directly, and possibly also interactively, influence intentions. To test these interrelationships, we pooled the data from all experimental conditions.[ 5] We then used LISREL analyses, because all focal variables were measured and LISREL models errors in measurement and estimates path coefficients with less bias than analysis of variance or regression (Jöreskog and Sörbom 1993). We used 24 indicators to measure our nine latent constructs (eight cognitions plus intentions), with 2 to 4 indicators per construct. If a construct was measured by several items, the items were randomly divided into 2 or 3 indicator variables to enhance parsimony and facilitate model estimation (Jöreskog and Sörbom 1996a). We restricted all indicators to load onto their respective latent constructs. We allowed the eight latent cognitive variables to covary freely. We assumed error terms to be independent. Because the data were ordinal and skewed, we used weighted least square estimation (Jöreskog and Sörbom 1996a). We used PRELIS 2 to generate input matrices and LISREL 8 to estimate the models (Jöreskog and Sörbom 1996a, b).
To test for two-way interactions among cognitions, we applied multiple-group structural equation modeling (Bollen 1989; Jöreskog and Sörbom 1993). For each variable that theoretically could be involved in an interaction (severity, vulnerability, and efficacy; Rogers 1975), we divided subjects into two levels on the basis of whether they scored above the variable's mean. Then, for each theorized two-way interaction, we estimated two models of effects on intentions. In the constrained model, we restricted the effect of the first variable in the two-way interaction to be equal across both levels of the second variable. In the unconstrained model, we allowed the effect of this first variable to vary freely. If the unconstrained (versus constrained) model produced a significant oo reduction (p < .05), we concluded that there was an interaction effect.[ 6]
Measurement properties. We first examined the psycho-metric properties of our measurement model by conducting a confirmatory factor analysis using LISREL 8, and the results were favorable. The reliability estimates for the indicators ranged from .87 to .99. Furthermore, the indicators had large and significant (p < .001) factor loadings on their respective latent constructs, and the variance extracted by each latent construct was greater than the recommended level of .50 (Fornell and Larcker 1981). The discriminant validity results were also favorable. Using a series of nested confirmatory factor analysis models, we found that whenever the correlation between two latent constructs was restricted to one rather than being allowed to vary, the fit of the model worsened, as indicated by a significant increase in chi2 In addition, the variance extracted by each latent construct was substantially larger than its shared variance with other latent constructs (Fornell and Larcker 1981). For details, see Table 3.
Main effects. The main effects structural model predicting a direct relationship between each cognition and intentions fits the data well. The model chi2 is 539.79 (216 degrees of freedom [d.f.], p < .01). The chi2 divided by the degrees of freedom (2.50), root mean square error of approximation (.031), goodness-of-fit index (.998), adjusted goodness-offit index (.997), normed fit index (.997), nonnormed fit index (.998), and comparative fit index (.998) all indicate an adequate fit of the model. The following cognitions, listed from most to least influential, enhanced intentions not to smoke: severity of social disapproval risks, self-efficacy at refusing cigarette offers, vulnerability to social disapproval risks, and vulnerability to health risks (which is qualified by an interaction; see the subsequent discussion). Also, the perceived benefits of smoking lowered nonsmoking intentions. Severity of health risks, self-efficacy at resisting tobacco marketing, and costs of not smoking were not associated with intentions. For coefficients and t-values, see Table 4.
Interaction effects. We examined all theoretically possible two-way interactions among severity, vulnerability, and efficacy (Rogers 1975). There were ten such interactions (see Table 5). All theorized three-way interactions were also tested, but no meaningful patterns emerged, so these analyses are not discussed further. Two two-way interactions were significant (see Figure 1). Health risk severity and health risk vulnerability interactively influenced intentions. Among subjects who were at or below the mean on health risk vulnerability (n = 511), higher perceived health risk severity was associated with weaker nonsmoking intentions. Among subjects who were above the mean (n = 1069, a larger group due to a skewed distribution), health risk severity perceptions and intentions were unassociated. It appears that these subjects felt only moderately vulnerable because, had they felt highly vulnerable, there would have been a positive association between severity and intentions (Block and Keller 1998; Kleinot and Rogers 1982).[ 7] Health risk vulnerability and self-efficacy at refusing cigarette offers also functioned synergistically. Higher perceived health risk vulnerability was associated with stronger nonsmoking intentions among subjects who were above the mean on self-efficacy at refusing offers (n = 1097), whereas health risk vulnerability and intentions were unassociated among subjects who were at or below the mean (n = 483).
Summary of Main Results
Of the seven antismoking message themes we tested, only three (Endangers Others, Refusal Skills Role Model, and Smokers' Negative Life Circumstances) bolstered adolescents' intentions not to smoke, and all did so by conveying that smoking cigarettes poses severe social disapproval risks (see Figure 2). Cosmetics messages sought to influence social risk severity perceptions but failed, apparently because the problems stressed (e.g., bad breath) could be minimized by using cosmetic products. Refusal Skills Role Model messages had a secondary aim: to boost adolescents' perceived self-efficacy at refusing cigarette offers. However, such perceptions seemed to be relatively unmalleable, as they were unaltered by ad exposure, though they were predictive of behavioral intentions. To boost self-efficacy perceptions, it may be necessary to implement media literacy programs that enable practice and mastery of focal skills.
Two message themes (Disease and Death and Selling Disease and Death) increased health, rather than social, risk severity perceptions. However, it seems that few adolescents felt vulnerable to the health risks, which undercut the efficacy of health severity messages. Among youths who felt immune to health risks, higher perceived health risk severity was associated with stronger intentions to smoke. In other words, in the context of low perceived vulnerability, stressing health risks could increase smoking's symbolic value as a risk-seeking, rebellious, and thus attractive behavior. Two message themes (Marketing Tactics and Selling Disease and Death) discussed tobacco marketing tactics. However, these message themes failed to influence adolescents' perceived self-efficacy at resisting tobacco marketing tactics, and in any event, such perceptions were not predictive of behavioral intentions. Finally, we tested a heterogeneous, or Substantive Variation, message condition, with essentially one advertisement per message theme. This message condition boosted health risk severity perceptions, but not social risk severity or self-efficacy perceptions, and had no effect on intentions. We speculate as to why in the next section.
Substantive Contributions and Implications
On the basis of our findings, when policy officials and advertising agencies design antismoking campaigns for adolescents, they should seriously consider using norm-based appeals--specifically, appeals that convey that smoking poses severe social disapproval risks (see also Pechmann and Shih 1999). This strategy would be consistent with considerable prior research that suggests a strong link between adolescents' perceptions of smoking norms and their intentions and behaviors (Chassin et al. 1984; Collins et al. 1987; Conrad, Flay, and Hill 1991; Pechmann and Knight 2002; Pechmann and Ratneshwar 1994). Our latest research indicates that norm-based appeals are declining in prevalence, which would appear to be an undesirable trend. Although many of the recent Philip Morris antismoking advertisements seem to contain social norm messages, they do not appear to be effective (Farrelly et al. 2002), perhaps because their messages are mixed. In our view, many of the Philip Morris advertisements seem to imply that both nonsmoking and smoking are socially acceptable behaviors, which does not constitute a clear antismoking message. Furthermore, the Philip Morris advertisements tend to show nonsmokers who are clean-cut and stereotypically "good" and might imply that adolescents should smoke if they want to demonstrate that they are not "goody two shoes" (Amos et al. 1998).
When youths are targeted, stressing the severity of long-term health risks does not appear to be an effective strategy; indeed, doing so could enhance smoking's forbidden fruit allure. However, our recent findings indicate that advertisements that stress health risk vulnerability, not severity, seem to work. Therefore, if policy officials want to use health-based appeals, we recommend that the appeals convey that adolescents are highly vulnerable to smoking's health risks. The advertisements might, for example, tell true-life stories of younger victims, stress how quickly these victims became addicted to smoking, and show how much they have suffered (Biener 2000; Teenage Research Unlimited 1999). When health risk vulnerability advertisements are developed, it may be useful to keep in mind the vulnerabilityefficacy interaction that we observed. This interaction suggests that advertisements about health risk vulnerability bolster nonsmoking intentions among youths who feel capable of refusing cigarette offers, but not among youths who feel incapable of refusing (Rogers 1975, 1983). On the basis of this finding, it may be beneficial to supplement vulnerability-focused advertisements with school programs that teach refusal skills (CDC 1994; Glynn 1989).
Our findings to date suggest that tobacco marketing (anti-industry) advertisements may not be especially effective with adolescents, though such advertisements are popular, in part because of the apparent success of the Florida Truth campaign (Bauer et al. 2000; Farrelly et al. 2002). The advertisements we studied did not alter adolescents' behavioral intentions. It is possible that advertisements of this type may work if they elicit stronger reactance or rebellion against tobacco firms. According to reactance theory (Brehm 1972), it should be possible to intensify reactance by, for example, showing tobacco firms using heavy-handed tactics to persuade youths to smoke or stressing the number and importance of the threatened freedoms (Clee and Wicklund 1980). Alternatively, what may be needed are advertisements that address youths' primary misconception about why they smoke. Most youths naively believe they smoke not because of tobacco marketing but because their friends look cool doing it (Pechmann and Knight 2002). According to Pechmann and Knight's (2002) research, youths perceive that smokers "look cool" in large part because the attractive, cool models in cigarette advertisements prime or make salient positive smoker stereotypes and bias social perceptions (see also Romer and Jamieson 2001). Therefore, tobacco marketing advertisements may be needed that educate youth about this priming phenomenon. We recommend further research on these issues.
In the current research, the Substantive Variation (heterogeneous) message condition did not perform as well as expected, having no impact on intentions. However, this may be due to the way we set up the Substantive Variation condition. We used only one advertisement (at most two) to convey each theme, so the total number of advertisements was eight, the same as in the other message conditions. Furthermore, we selected the advertisements in the Substantive Variation condition at random from those used in the other conditions. We did this to equate the message conditions as much as possible and thus minimize confounds. As it turns out, though, the advertisements that were used to convey social risk severity in the Substantive Variation condition may have been too few in number or too weak to do much good. Because the advertisements did not affect this critical social cognition, the Substantive Variation condition as a whole had no impact on intentions. On the basis of these findings, we do not recommend that sponsors of antismoking advertising campaigns use all seven of the message themes studied here concurrently, particularly if they are on a limited budget. However, they may want to use the three most effective themes identified here (Endangers Others, Refusal Skills Role Model, and Smokers' Negative Life Circumstances) if they have an adequate budget. Substantively varied advertising campaigns have been shown to forestall tedium and wearout, particularly when the advertising topic is highly relevant to viewers (Schumann, Petty, and Clemons 1990), as the antismoking issue may well be.
Because it is a challenge to create good advertising, quantitative copy testing should be conducted to ensure that any advertisement that is included in a campaign actually bolsters antismoking beliefs and intentions (Pechmann and Reibling 2000). Furthermore, we recommend copy testing advertisements among ninth or tenth graders, not among middle school students. Our findings indicate that middle school youths' survey responses may be so strongly anti-smoking that no ad effects can be discerned because of ceiling effects. Finally, we do not recommend that advertisements be evaluated on the basis of viewers' ratings of perceived ad effectiveness (Biener 2000; Teenage Research Unlimited 1999). In our research, all sets of advertisements were virtually equivalent on perceived ad effectiveness, yet they were found to differ in their effects on both beliefs and intentions.
Theoretical Contributions
This research supports recent efforts by Ho (1998) to extend protection motivation theory formally to include social risks. We find that social risk severity and vulnerability are distinguishable from their health risk counterparts and that social risk severity perceptions are especially predictive of adolescents' behavioral intentions. Our results further indicate that Rogers's (1983) decision to drop the health risk severityvulnerability interaction from protection motivation theory and to focus on threatcoping appraisal inter-actions may have been ill advised; his original formulation seems preferable.
We also contribute to the literature on decision making and risk, in which severityvulnerability interactions have been theorized but rarely observed in health contexts (Wein-stein 1993, 2000). Weinstein (2000) argues that important interactions between health risk severity and vulnerability have not been documented because prior studies have examined mid-level variable values, and interactive effects occur at more extreme levels. Here, we document an interaction that has rarely been observed. Given low perceived vulnerability, higher perceived health risk severity was associated with increased intentions to engage in a risky behavior. Given moderate vulnerability, severity and intentions were unassociated. If found to be prevalent, this interaction would appear to have important implications. Most theories posit that health risk severity messages will discourage risky behaviors (Rogers 1975, 1983; Weinstein 1993), yet they could have the opposite effect among people who view themselves as invincible.
Research Limitations
We studied antismoking advertising's impact on intentions, not behavior, because a field experiment of seven message themes would have been too costly. Also, to differentiate our study from prior work on multifaceted tobacco control efforts, we focused strictly on advertising. It is conceivable that advertising that is ineffective on its own becomes effective when combined with other efforts. We used a forced-exposure copy test method; therefore, if a certain message theme was more attention getting than others, this effect would have been masked. We did not embed the advertisements in television programming, because a cluttered copy test environment "adds complexity and is possibly confounding" (Aaker, Batra, and Myers 1992, p. 425). Because subjects were told that the study was about advertisements, they could conceivably have sought to provide socially desirable, proadvertisement responses (Aaker, Batra, and Myers 1992). If this bias was operative, though, all of the message themes should have been effective, and many were found to be ineffective. Nevertheless, it would be beneficial to conduct a follow-up study that uses a more naturalistic exposure environment.
There was a weak correspondence between message theme and execution. To examine this issue post hoc, three trained research assistants coded the stimulus advertisements on several executional variables, including spokesperson age, emotional tone and intensity, and sensation value (Schoenbachler and Whittler 1996). The findings indicate that these variables alone cannot explain the results. For example, compared with other message themes, the Refusal Skills Role Model and Cosmetics message themes used somewhat more youthful spokespeople, yet only the former bolstered nonsmoking intentions. Also, both the Selling Disease and Death and Endangers Others message themes scored relatively highly on emotional intensity, but only the latter influenced intentions. However, researchers may want to address the possible moderating effects of these and other executional variables. The funding that is available for antismoking advertising is unprecedented, and sound marketing research should play a major role in helping ensure that the money is wisely spent.
This research was funded by a grant to the first author from the California Tobacco-Related Disease Research Program.
The authors sincerely thank University of California, Irvine MBA students Marion McHugh, Deborah Thompson, and George Chen for their assistance with data collection and Gerald Gorn and the anonymous JM reviewers for their feedback on previous drafts of this article.
1 Our initial categorization scheme included two additional message themes: smoking's effect on athletic performance and youths' involvement in antismoking political activities. The athletics category yielded too few advertisements (n = 7) to be included. The activism category was slightly larger (n = 10), but it appeared that the primary goal was to encourage adamantly antismoking youths to become antismoking activists, not to deter the average youth from smoking, so we did not study this message theme. Twenty-two advertisements fell into miscellaneous combination categories, such as Selling Disease and Death plus Endangers Others, none of which were prevalent enough to study. Twenty-six advertisements did not seem to contain any clear-cut message, in that fewer than 80% of subjects responded "yes" to any of the message content questions, so these advertisements were excluded from further analyses as well.
2 Refusal Skills Role Model advertisements were rated lowest on perceived ad effectiveness in the main experiment, and Marketing Tactics advertisements were rated lowest in the advertisement coding study (Table 1). We suspect that the difference may arise because advertisements were rated in sets of eight in the main experiment and individually in the advertisement coding study. Apparently, for Refusal Skills Role Model advertisements, the whole was perceived to be less than the sum of the parts. For Marketing Tactics advertisements, the whole was perceived to be greater than the sum of the parts.
3 Additional analyses revealed that message, past smoking behavior, and grade interactively influenced intentions (F(8, 1581) = 2.72, p < .01). The Endangers Others, Smokers' Negative Life Circumstances, and Refusal Skills Role Model (versus control) message themes boosted intentions not to smoke among tenth graders who had tried smoking (t = 2.79, 3.40, and 2.74; p < .05). The absence of effects among tenth graders who had never smoked seems to be attributable to ceiling effects on intentions (mean = 4.52, maximum = 5); likewise for seventh graders who had never smoked (mean = 4.42). Too few seventh graders had tried smoking to permit meaningful tests of our hypotheses.
4 We also assessed subjects' knowledge of the sources, tactics, effects, and ethics of pro-tobacco messages, which are the key dimensions of persuasion knowledge (Friestad and Wright 1994). The Marketing Tactics, Selling Disease and Death, and Substantive Variation (versus control) message themes enhanced subjects' persuasion knowledge (p < .05), but this knowledge failed to bolster subjects' feelings of self-efficacy at being able to resist tobacco marketing.
5 The data were also analyzed separately within each message condition, but the pattern of findings was unchanged. In other words, the antismoking message theme affected the mean levels of cognitions and intentions, but not the relationships between cognitions and intentions. These findings are consistent with protection motivation theory, which assumes that the cognition-intention relationships are relatively stable and predictable.
6 In these model pairs, cost effects were constrained to be equal, as were benefit effects, because neither variable has been theorized to be involved in any interaction. The remaining four cognitive variables were allowed to vary freely.
7 We split the sample into three vulnerability groups in an attempt to find high-vulnerability subjects. However, the pattern of results was unchanged, which indicates that few subjects felt highly vulnerable to smoking's health risks.
Legend for the Chart
A Message Theme Labels
B Message Content
C Adolescents' Agreement That Advertisements Contained
Content: % (Standard Deviation)
D Adolescents' Perceptions of Ad Effectiveness:
Mean (Standard Deviation)
A B C D
Disease and Death Smokers suffer from health effects 92 3.63
such as cancer, lung disease, and (6.4) (.064)
premature death.
Endangers Others Smokers endanger the health and 91 3.63
well-being of their families and (6.3) (.064)
others, primarily because of
secondhand smoke.
Cosmetics Smokers must cope with unattractive 91 3.42
side effects, such as bad breath (7.8) (.065)
and smelly clothes, hair, and
ashtrays.
Smokers' Negative Smokers have adopted a grotesque, 91 3.64
Life Circumstances loser lifestyle and have therefore (5.3) (.061)
chosen the wrong life path.
Refusal Skills Attractive role models do not 89 3.51
Role Model smoke because they view it as (4.5) (.066)
highly unappealing, and they
refuse others' cigarette offers.
Marketing Tactics Tobacco firms use powerful tactics, 91 3.22*
such as target marketing and image (5.2) (.068)
advertising, to reach youths and
others.
Selling Disease Tobacco firms use manipulation and 90 3.62
and Death deception to sell a product that (4.3) (.065)
causes serious diseases and even
death.
Substantive Included advertisements from each 92 3.65
Variation condition to test the efficacy of a (4.4) (.065)
heterogeneous message approach.* Indicates that the designated antismoking message theme differed from the others (p < .05).
Notes: These results are from the advertisement coding study, when eight advertisements representing each message theme were selected at random for the main experiment.
Legend for the Chart
A Message Effect: F (d. f.)
B Antismoking Message Theme: Disease and Death
C Antismoking Message Theme: Endangers Others
D Antismoking Message Theme: Cosmetics
E Antismoking Message Theme: Smokers' Negative Life Circumstances
F Antismoking Message Theme: Refusal Skills Role Model
G Antismoking Message Theme: Marketing Tactics
H Antismoking Message Theme: Selling Disease and Death
I Antismoking Message Theme: Substantive Variation (All Messages)
J Antismoking Message Theme: Messages Unrelated to Smoking (Control)
A B C D E F G H I J
Dependent Measure
Severity of health risks
3.84*** 7.68** 7.91*** 7.46 7.10 7.54 7.14 8.15*** 7.90*** 6.68
(8, 1649)
Vulnerability to health
1.16*** 4.43 4.42 4.40 4.55 4.35 4.32 4.39 4.42 4.34
risks (8, 1631)
Severity of social disapproval risks
2.10*** 3.98 4.09* 3.98 4.09** 4.10** 3.89 3.96 3.82 3.77
(8, 870)
Vulnerability to social disapproval risks
2.90*** 2.30 1.96 2.02 2.48 2.17 2.76*** 2.29 2.22 2.10
(8, 870)
Self-efficacy at refusing cigarette offers
.39*** 4.25 4.25 4.32 4.35 4.33 4.29 4.31 4.27 4.23
(8, 1639)
Self-efficacy at resisting tobacco marketing
1.68*** 4.16 4.25 4.35 4.04 4.38 4.12 4.31 4.29 4.22
(8, 1640)
Benefits of smoking
1.20*** 2.06 1.83 1.88 2.01 1.82 1.97 1.98 1.99 2.04
(8, 1632)
Costs of not smoking
1.23*** 2.37 2.36 2.28 2.22 .48 2.50 2.32 2.39 2.50
(8, 1589)
Intentions not to smoke
3.56*** 3.95 4.22*** 3.87 4.13*** 4.03** 3.68 3.88 3.64 3.53
(8, 866)
Notes: Higher numbers indicate higher scores on the indicated variables. All scales are 1-5 except severity of health risks (0- 9). F-statistics and means for social disapproval risks and intentions not to smoke are based on the tenth grade sample, because message theme effects were confined to these subjects. Asterisks indicate either an omnibus message theme effect or an anti-smoking message theme (versus control group) effect: *p < .06, ** p < .05, *** p < .01.
Legend for the Chart
A Dependent Measures
B 1
C 2
D 3
E 4
F 5
G 6
H 7
I 8
j 9
K Variance Extracted
L Factor Loadings
A
B C D E F G H I J K L
1. Intentions not to smoke
.94* .01 .06 .34 .00 .10 .02 .22 .01 .85 .907, .928, .925
2. Severity of health risks
.09 .99* .03 .02 .01 .01 .03 .02 .00 .96 .960, .998, .980
3. Vulnerability to health risks
.25 .16 .96* .07 .01 .08 .04 .06 .04 .89 .964, .924, .935
4. Severity of social disapproval risks
.58 .13 .27 .89* .00 .09 .03 .45 .01 .79 .915, .867
5. Vulnerability to social disapproval risks
.01 -.09 -.08 -.02 .96* .00 .04 .00 .00 .85 .940,.964,.858,.916
6. Self- efficacy at refusing cigarette offers
.32 .10 .29 .30 -.06 .87* .14 .07 .01 .70 .856, .908, .739
7. Self- efficacy at resisting tobacco marketing
.14 .17 .20 .16 -.19 .38 .95* .03 .00 .91 .931, .980
8. Benefits of smoking
-.47 -.15 -.25 -.67 .06 -.27 -.17 .93* .01 .87 .904, .959
9. Costs of not smoking
-.10 -.05 -.21 -.09 .05 -.08 -.03 .08 .93* .87 .881, .983Notes: For columns 1-9, the numbers below the diagonal are correlations, the numbers above the diagonal are shared variances, and the boldface[*] numbers on the diagonal are construct reliabilities. Factor loadings are standardized; all p < .001. Factor loadings for the specific scale items can be obtained from the first author.
Variables Posited to Affect Intentions Coefficient t-Value
Not to Smoke, Standardized Based on
Protection Motivation Theory
(Expected Signs)
Severity of health risks (+) -.028 -1.13
Vulnerability to health risks (+) .062 2.23*
Severity of social disapproval risks (+) .502 11.42**
Vulnerability to social disapproval risks (+) .079 3.29**
Self-efficacy at refusing cigarette offers (+) .213 6.09**
Self-efficacy at resisting tobacco marketing (+) .007 .21
Benefits of smoking (-) -.093 -2.04*
Costs of not smoking (-) -.019 -.83
*p < .05.
**p < .01.
Legend for the Chart
A chi2
B d.f.
C chi2 Difference
D d.f.
Possible Interaction Effects Posited by
Protection Motivation Theory A B C D
Health risk severity x health risk vulnerability
Unconstrained (interaction) model 689.58 337
Constrained model 699.51 338 9.93** 1
Health risk severity x self-efficacy at refusing offers
Unconstrained (interaction) model 689.29 337
Constrained model 689.62 338 .33 1
Health risk vulnerability x self-efficacy at refusing offers
Unconstrained (interaction) model 689.29 337
Constrained model 694.70 338 5.41* 1
Health risk severity x self-efficacy at resisting marketing
Unconstrained (interaction) model 800.07 379
Constrained model 800.33 380 .26 1
Health risk vulnerability x self-efficacy at resisting marketing
Unconstrained (interaction) model 800.07 379
Constrained model 800.39 380 .32 1
Social risk severity x social risk vulnerability
Unconstrained (interaction) model 475.71 298
Constrained model 478.80 299 3.09 1
Social risk severity x self-efficacy at refusing offers
Unconstrained (interaction) model 689.29 337
Constrained model 691.82 338 2.53 1
Social risk vulnerability x self-efficacy at refusing offers
Unconstrained (interaction) model 689.29 337
Constrained model 689.51 338 .22 1
Social risk severity x self-efficacy at resisting marketing
Unconstrained (interaction) model 800.07 379
Constrained model 800.16 380 .09 1
Social risk vulnerability x self-efficacy at resisting marketing
Unconstrained (interaction) model 800.07 379
Constrained model 800.08 380 .01 1
*p < .05.
**p < .01.
Notes: A smaller chi2 indicates a better fit between the observed and estimated covariation matrices.
GRAPH: FIGURE 1: Illustrations of Interactions Detected in LISREL
DIAGRAM: FIGURE 2: Summary of Key Findings
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~~~~~~~~
By Cornelia Pechmann; Guangzhi Zhao; Marvin E. Goldberg and Ellen Thomas Reibling
Cornelia Pechmann is Associate Professor of Marketing, and Guangzhi Zhao is a doctoral student in marketing, Graduate School of Management, University of California, Irvine. Marvin E. Goldberg is Irving and Irene Bard Professor of Marketing, Smeal College of Business Administration, Pennsylvania State University. Ellen Thomas Reibling is Director of the Health Education Center and a doctoral student, School of Social Ecology, University of California, Irvine.
Copyright of Journal of Marketing is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Record: 197- What Will the Future Bring? Dominance, Technology Expectations, and Radical Innovation. By: Chandy, Rajesh K.; Prabhu, Jaideep C.; Antia, Kersi D. Journal of Marketing. Jul2003, Vol. 67 Issue 3, p1-18. 18p. 8 Charts. DOI: 10.1509/jmkg.67.3.1.18652.
- Database:
- Business Source Complete
What Will the Future Bring? Dominance, Technology
Expectations, and Radical Innovation
Are dominant firms laggards or leaders at innovation? The answers to this question are conflicting and controversial. In an attempt to resolve conflicting answers to this question, the authors argue that dominance is a multifaceted construct in which individual facets result in differing (and countervailing) propensities to innovate. To identify the overall effects of dominance, it is necessary to consider the effects of these facets taken together. The authors also study a hitherto ignored yet important driver of innovation, technology expectations, and show that managers have widely divergent expectations of the same new technology. Furthermore, even when their expectations are the same, managers of dominant firms display investment behavior at odds with their counterparts at nondominant firms. The authors use a triangulation of research methods and combine insights from lab studies with those from field interviews, archival data, and a survey of bricks-and-mortar banks' responses to Internet banking.
The relationship between dominance and innovation is one of enduring (and renewed) interest to scholars in marketing, corporate strategy, economics, and sociology, among other fields (Cooper and Schendel 1976; Henderson 1993; Miller 1990; Scherer 1992; Schumpeter 1942). The prognosis from this research has mostly been gloomy, albeit with a hint of hope. First, the gloomy part: Many scholars note that as firms become more dominant, they become more wedded to the status quo and reluctant to embrace radically new products (e.g., Cooper and Schendel 1976; Henderson 1993; Schumpeter 1942). Incremental improvements become firms' preferred mode of action, and dominant firms either spurn radical innovations or, at best, leave them to collect dust on laboratory shelves (e.g., Utterback 1994). As the technological environment turns on the dominant firms, their reluctance to pursue radically new products eventually leads to their weakening and downfall. Dominant firms' very success sows the seeds of their failure. For this reason, some scholars have compared dominant firms to Icarus, the tragic figure from Greek mythology whose success at flying to great heights led to his death when the sun melted his wings and he plunged into the sea (Miller 1990).
However, reality does not always adhere to the plot of a Greek tragedy; there are some reasons for hope. As Cohen and Levin (t989, p. 1Q78) state in an extensive review of the literature, the results linking dominance and innovation "are perhaps most accurately described as fragile." Followers of a more recent school of thought note that dominant firms do enjoy some important advantages. For example, dominant firms have greater access to resources, which us a key advantage in trying to build and sustain radically new technologies and markets. Some recent research suggests that large and incumbent firms are often some of the most aggressive radical innovators (e.g. , Chandy and Tellis 2000; Zucker and Darby 1997). A casual glance at business periodicals reveals that many dominant firms actively pursue such new technologies and are relatively successful in doing so. What explains this performance? Little is known about why some dominant firms pursue radical innovations aggressively and others do not.
We attempt to reconcile the opposing views on the relationship between dominance and radical innovation. We consider dominance a composite of several facets, each with different and countervailing behavioral effects on firms' propensity to innovate. This viewpoint is in contrast to existing research, which ( 1) has typically equated dominance with related though conceptually distinct proxies, such as firm size and incumbency, and ( 2) has rarely integrated the different facets of dominance to assess its overall effect on radical innovation. By examining the behavioral consequences of each facet of dominance and the combined effects of these facets taken together, we attempt to provide a clearer understanding of the relationship between dominance and innovation, something that researchers in the held have repeatedly called for (e.g., Scherer 1992).
We argue that there is another, hitherto overlooked, reason some dominant firms invest aggressively in radical innovation and others do not: managerial expectations. When a radically new technology is nascent, managers confronting the same technology may hold differing expectations about the technology's likely effect on existing products. Specifically, managers may hold at least three differing expectations about the technology's likely effect on existing products:
- The new technology will enhance the effectiveness of existing products, just as electric motors made dishwashers and laundry machines more powerful.
- The new technology will make existing products obsolete, just as integrated circuit technology made slide rules obsolete.
- The new technology will have little or no effect on existing products, just as microwave heating technology hardly affected conventional oven sales.
We argue that these expectations result in significantly different levels of investment in radical innovation. Moreover, managers who have the same technology expectations may exhibit different investment behavior, depending on their level of dominance in the existing product generation. Studying expectations and their interaction with firms' overall dominance provides a more complete explanation for the empirical disconnect between the pessimistic predictions of much of the literature on dominance and radical innovation and the aggressively innovative behavior of some dominant firms.
In addition, studying expectations helps us understand the dynamics of investment in radical new technology before the actual effects of the technology are evident. Although emerging research focuses on the effects of radically new technologies on existing products (e.g., Anderson and Tushman 1990; Cooper and Schendel 1976), most of this research examines the impact of new technologies in a historical context, after the impact is already evident. It is possible to categorize specific technologies post hoc as haying helped, hindered, or had no effect on the existing product category (Utterback 1994), but managers make investment decisions before the effects have taken place. Key decisions are made while the technology is still nascent, when its eventual effect on existing products is far from certain. Yet little research has examined decision making by managers in this "pre-paradigmatic" stage of radical innovation (Dosi 1982).
Moreover, many authors note the importance of a "vision" for the future in promoting radical innovation (see Ohmae 1984). By introducing managers' expectations into the analysis, we present a view of managers as active agents who employ their imaginations in making decisions and who, to a certain extent at least, are instrumental in creating their own futures. We show that "paranoid" firms (e.g., Grove 1996) are the most aggressive innovators.
Finally, we use experimental techniques to investigate the causal relationships among dominance, expectations, and radical innovation, and we use field studies to provide real-world context and insight. Few studies of innovation employ time-series experimentation to examine causality (Poole et al. 2000; Weick 1967). Our field study enables us to study real-world firms in an industry facing the effects of a radically new technology; specifically, we study how managers of bricks-and-mortar banks responded to the advent of Internet banking. We employ multiple methods--in-depth interviews, survey data, and archival data--to study the impact of dominance and expectations at a unique point in the evolution of Internet banking. The triangulation of research methods yields a rich payoff in terms of empirical insight, a balance of internal and external validity, and robust findings.
Definitions
The term dominance refers to the extent of market power that firms enjoy (Bain 1968; Scherer 1980). A radical innovation is a product that requires substantially different technology and marketing skills compared with existing products in the industry (Chandy and Tellis 1998; see also Garcia and Calantone 2002). The greater a firm's emphasis on a radically new product, the more aggressive it is in radical innovation. We define technology expectations as managers' beliefs about the likely impact (obsolescence, enhancement, or no effect) of the new technology on existing products.
Conceptual Overview
Investment in a radical innovation is a function of a firm's motivation and ability to do so. Firms with the motivation and ability to invest are likely most aggressive in pursuing the radical innovation. Firms' dominance affects their motivation and ability to invest. Dominant firms are prone to inertia and escalation of commitment, both of which reduce motivation to invest. As a result, dominant firms may show a preference for the status quo; that is, they may continue with the existing product generation. However, dominant firms are also wealthier than nondominant firms and therefore have greater ability to invest in the radical innovation.
Technology expectations have a critical role in driving investment in radical innovation. Specifically, they alter the manner in which managers frame an investment and, by doing so, amplify (or diminish) managers' motivation to pursue radical innovation. The effect of expectations on the motivation to pursue radical innovation results in a corresponding change in firms' investment aggressiveness.
Study Scope and Assumptions
For conceptual and empirical clarity, we restrict our scope to incumbent firms. Thus, we do not attempt to explain the behavior of firms that have no presence in the existing product generation. This approach is in line with previous research, which also focuses on incumbent firms (e.g., Chandy and Tellis 1998; Hannan and Freeman 1989; Scherer 1992). All incumbent firms have a stake in the status quo because they have some investments in the current product generation.
We assume the impact of the new technology on existing products to be an exogenous shock: Individual firms, even powerful ones, have little control (at least in the long run) over whether the new technology enhances, makes obsolete, or has no effect on current products (see Anderson and Tushman 1990; Solow 1956). Although some dominant firms might appear all-powerful and invincible at one point, over the long run few firms control the fates of technologies and industries.
We also assume that managers (even those of wealthy firms) have capital constraints. One consequence of capital constraints is that investing in a new product implies less investment in existing products; that is, there is a trade-off between existing products and new products. Investing in the new product likely makes a firm less competitive in the existing product generation (e.g., Blundell, Griffith, and Van Reenen 1999). The default course of action is therefore to continue investing in the existing product generation; the alternate course of action is to invest in the new product.
Are dominant firms more or less likely than nondominant firms to invest aggressively in a radical innovation? Schumpeter (1942) first highlighted the role of market power in innovation, arguing that dominance favors radical innovation. Many researchers have since steadily attempted to test Schumpeter's hypothesis empirically (see Cohen 1995; Scherer 1992), yet few researchers provide a behavioral rationale for dominant firms' radical innovation behavior (Scherer 1992). Indeed, prominent researchers have criticized the atheoretical nature of work in the field (Cohen 1995). We highlight the multifaceted nature of dominance and provide behavioral explanations of how each facet affects dominant firms' investment in innovation. We also consider how these facets taken together influence the overall impact of dominance on investment in radical innovation.
The Many Faces of Dominance
Consider Microsoft or Intel today. Both firms are well entrenched and thus have larger investments in their current markets than do other firms. They also have greater market shares than do other firms. Finally, both firms are wealthier and have greater access to resources than do other firms. These three facets--greater investments, greater market shares, and greater resources--define dominance (see Bain 1968; Borenstein 1990, 1991). These three facets may also have different impacts on dominant firms' motivation and ability to pursue radical innovation. Although there is a substantial literature on some behavioral effects, such as escalation of commitment and inertia, previous research has not linked these effects to the three facets of dominance or brought together these effects to understand the overall influence of dominance on radical innovation (see Cohen 1995; Scherer 1992). By doing so, we hope to clarify the conflicting views in the literature on dominance and radical innovation.
Escalation of commitment: The effect of investments. The theory of escalation of commitment attempts to explain why people continue to pursue courses of action even after it is irrational to do so (Boulding, Morgan, and Staelin 1997; Staw 1981). According to this theory, managers frame the decision to invest in a new product relative to continuing with the initial commitment to the old product. The more committed managers are to the old course of action, the greater the loss they perceive in the decision to switch to the new course of action (Bazerman 1994). Loss aversion (Kahneman and Tversky 1979) therefore causes managers to be unlikely to switch from the old course of action (Brockner and Rubin I 985) and to place less emphasis on the new compared with the old course of action. By definition, all incumbents have some investment in (and therefore some commitment to) the existing product generation (see Brockner and Rubin 1985; Staw 1981). However, because dominant firms have more investments in the existing product than do other firms, they are especially prone to escalate their commitment to the existing product compared with the radical innovation. Thus,
H1a: The larger a firm's investments in the existing product generation, the less aggressively its managers invest in the radical innovation relative to the existing product generation.
Inertia: The effect of market success. Incumbent managers' susceptibility to inertia, and their resulting preference for the status quo, is well documented in prior research (Hannan and Freeman 1989; Nelson and Winter 1982). All incumbents are prone to inertia, but as with escalation of commitment, dominant incumbents may be especially susceptible to it. A major source of inertia in a firm is its perceived success in its current course of action (see Leonard-Barton 1992; Nelson and Winter 1982). The more successful the firm perceives its current course of action, the more it reinforces its commitment to that course of action. A strong market position signals the validity of the firm's decision-making procedures; it legitimizes precedents and causes them to become normative standards for the future (Hannan and Freeman 1989; Nelson and Winter 1982). The firm subsequently makes decisions about the future simply based on inertia from the past. According to this argument, the stronger a firm's market position, the greater are the inertial constraints it faces. Dominant firms therefore are less motivated to switch from the status quo, and they likely invest less aggressively in radical innovation than do nondominant firms. Therefore,
H1b: The stronger a firm's market position in the existing product generation, the less aggressively its managers invest in the radical innovation relative to the existing product generation.
The wealth effect. The escalation of commitment and inertia arguments do not, however, account for dominant firms' having more resources than other firms. The greater wealth of dominant firms provides them with greater ability to invest in radical innovation. Greater wealth also cushions dominant firms from the risk of failure inherent in radical innovation (Nohria and Gulati 1996); thus, dominant firms have the means to experiment extensively in research and development, which could result in dominant firms investing more in a new product. Managers of dominant firms may also invest heavily in radically new products rather than existing products because they might stand a greater chance of making the new idea a marketplace success than would firms with few financial and marketing resources. For example, dominant firms likely have larger sales forces, which enables them to ensure greater distribution of a fledgling product (Chandy and Tellis 2000). Thus,
H1c: The greater a firm's wealth, the more aggressively its managers invest in the radical innovation relative to the existing product generation.
Taken together, what are the overall effects of dominance on managers' investment aggressiveness in radical product innovation? Recent evidence suggests that, overall, dominant firms are likely more aggressive in their investments in a radically new product than are other firms. Radical innovations are resource intensive and could become increasingly so over time (e.g., Chandy and Tellis 2000; Jelinek and Schoonhoven 1990). In addition, the innovation ethic is now more widespread among managers, including those of dominant firms. This awareness of the need for innovation is partly a result of a significant recent literature on the (beneficial and destructive) effects of innovation (e.g., Christensen 1997; Hamel 1999), combined with the many consulting and education activities by the authors and followers of this literature (e.g., Hamel 2001; Mack 1999). The implication of these arguments is that any increased inertia and escalation of commitment that comes with dominance might be outweighed by the benefits of greater wealth. In light of these findings, we propose the following:
H1d: Overall, managers of dominant firms invest more aggressively in the radical innovation relative to the existing product generation than do managers of nondominant firms.
Expectations and Radical Innovation
In the subsequent paragraphs, we develop hypotheses on the role of technology expectations in radical innovation decisions in general. We then consider how these expectations influence dominant and nondominant firms. Throughout the section, we compare the condition in which managers expect the new technology to enhance the existing technology or to make it obsolete with the case in which they expect the new technology to have no impact on existing technology. Thus, the no-effects expectation is the benchmark against which we compare the other two types of expectations: obsolescence and enhancement.
Obsolescence versus no-effect expectations. Expectations of obsolescence cause managers to be less secure about their current course of action (e.g., Jassawala and Shashittal 1998). In this case, the new technology has a negative effect on the success of the current course of action, based as it is on the old, soon-to-be-obsolete technology. Managers who expect obsolescence therefore perceive that continuing with the existing technology will lead to a major loss in market position. Conversely, managers who expect the new technology to have no effect on existing products perceive no such loss (and, therefore, no effect on the success of the current course of action) (see Clark and Montgomery 1996; Grove 1996). Thus,
H2: Managers who expect the radical innovation to make existing products obsolete invest more aggressively in the radical innovation relative to the existing product generation than do those who expect the new technology to have no effect on existing products.
Enhancement versus no-effect expectations. What if managers expect that investing in the new technology is likely to enhance the performance of existing products? We argue that these managers invest less aggressively in the new technology than do managers who expect the technology to have no effect. The rationale for this hypothesis rests on the absence of a compelling incentive to switch emphasis from an existing technological base that is expected to be only enhanced by the new technology. Specifically, managers who expect enhancement do not frame investing in the new technology and continuing with the old technology as competing courses of action. Moreover, they perceive that the existing technology plays a significant, enhanced role in the market (e.g., Jassawala and Shashittal 1998). They therefore expect the new technology to have a positive effect on the success of the current course of action. Because the new technology is an exogenous shock, this positive outcome occurs regardless of a firm's own investments in the new technology (Solow 1956). The managers' perceptions of greater success by maintaining the current course of action feeds their inertia (Henderson 1993; Nelson and Winter 1982) and reinforces their commitment to the existing technology. Managers who expect no effect, however, experience less inertia and escalation of commitment, because they receive no such reinforcement. Thus,
H3: Managers who expect the radical innovation to enhance existing products invest less aggressively in the radical innovation relative to the existing product generation than do those who expect the new technology to have no effects on existing products.
Interaction of Dominance and Expectations
As we noted previously, there is considerable empirical evidence that some dominant firms invest aggressively in radical new technologies and others do not. What explains this variation in dominant firms' investment in radical innovation? In an attempt to address this question, we examine the interaction effects of firm dominance and managers' technology expectations on the level of investment in radical innovation.
Under expectations of obsolescence, managers of all firms, dominant and nondominant, perceive that maintaining the current course of action will cause a loss in market position. However, dominant firms have more to lose from obsolescence than do their nondominant competitors. Specifically, dominant firms risk losing their strong market position because their success is based on the old technology. Thus, managers of dominant firms perceive the new technology to be a greaten threat to their market position than do managers of nondominant firms. Therefore, managers of dominant firms are even more motivated than are those of nondominant firms to break out of their inertia, reduce their commitment to the existing product generation, and invest aggressively in radical innovation. Thus,
H4: Dominant firm managers who expect the radical innovation (0 make existing products obsolete invest more aggressively in the radical innovation relative to the existing product generation than do nondominant firm managers with the same expectations.
We noted previously that when managers expect the new technology to enhance the performance of the existing technology, managers of both dominant and nondominant firms might invest less aggressively than they would otherwise. However, dominant firms expect to gain more than nondominant firms would from the positive influence of the new technology. Specifically, given dominant firms' stronger market position, any positive influence from the new technology on existing products is magnified. Managers of dominant firms therefore expect to be even more successful by maintaining the existing course of action. This perception of renewed (enhanced) success causes dominant firms to be less motivated and more wedded to the status quo when they expect enhancement. Therefore, they invest even less aggressively in radical innovation under this condition. Thus,
H5: Dominant firm managers who expect the radical innovation to enhance the performance of existing products invest less aggressively in the radical innovation relative to the existing product generation than do nondominant firm managers with the same expectations.
We used two empirical approaches: ( 1) time-series, cross-sectional analysis in a controlled setting and ( 2) structured interview--informed survey research combined with archival data in a field setting. The time-series, cross-sectional analysis tests causal links among the key variables being studied. In-depth interviews enabled us to obtain direct, firsthand insights into the actual dynamics of technology expectations and radical innovation. Archival data, together with our survey of managers in an industry confronting radical innovation (i.e., retail banking and the Internet), provide evidence of the applicability of our arguments to a real-world context. By employing multiple methodologies to investigate radical product innovation in a programmatic fashion, we can better ensure the internal and external validity of the research (e.g., Winer 1999). As Jick (1979) notes, multiple and independent methods, such as the ones proposed here, do not share the same weaknesses or potential for bias. Triangulation is particularly appropriate for initial research in an area, because it provides "thick descriptions" of phenomena and facilitates their interpretation.
Research Context
We used the MARKSTRAT2 simulation (Larreche and Gatignon 1990) to test our hypotheses in a controlled setting. MARKSTRAT provides an excellent environment for this research for several reasons, which we outline in Appendix A. We tested our hypotheses over two separate studies. Study 1 tests H1, which describes competing arguments on the role of dominance in decisions on radical innovation. Study 2 tests hypotheses H2-H5, which incorporate the effects of technology expectations on radical innovation. The subsequent sections provide the details of each study and descriptions of the results.
Subjects and Procedure
In Study 1, we used data from eight MARKSTRAT2 runs (each run involved the creation of one industry), conducted with MBA students at a large public university in California. For each run, we randomly assigned participants to teams of three to four members each and then randomly assigned the teams to 1 of 5 possible firms per industry (in MARKSTRAT there are 5 firms per industry). All participants played the run over seven periods in six of the runs and over ten periods in the other two. Overall, therefore, we gathered data from 40 firms competing across eight runs (industries) over seven to ten periods for a total of 310 observations.
We collected data on each firm's expenditures, market shares, and budgets in each period in the Sonite (existing technology) and Vodite (new technology) markets.( n1) We used these variables to test for the relative strength of escalation of commitment, inertia, and wealth, respectively, and the overall effect of dominance on firms' relative expenditure on new technology (H1a-H1d).
Measures
Consistent with our definition, we measured investment aggressiveness in a relative sense: each firm's expenditure in the Vodite market divided by its combined expenditures in the Sonite and Vodite markets. These expenditures include research and development and advertising expenses that are specific to the Sonite and Vodite products.( n2) This measure of investment in radical innovation thus measures the firm's emphasis on Vodite investments relative to its overall product investments.( n3) We also measured investment in absolute terms: the firm's total investments in the Vodite market.
Recall that the escalation of commitment effect is based on the firm's level of past investments. To test the escalation of commitment effect, we calculated the average cumulative expenditures by the firm in the existing (Sonite) technology until the previous period. The inertia effect is based on the firm's market position. To test the inertia effect, we used the firm's average market share (in MARKSTRAT dollar sales) in the existing technology until the previous period. The wealth effect is based on the firm's financial resources. To test the wealth effect, we used the average cumulative budget available to the firm until the current period.( n4) (In MARKSTRAT, a firm's budget is a linear function of its net marketing contribution or profit.) We obtained all this data from the output that MARKSTRAT2 provides to the game administrator. MARKSTRAT2 also provides each team with information on its market share, profits, and several other variables each period.
We also tested the overall effect of dominance on investment in the radically new technology. To do so, we first conducted a principal component factor analysis of the previous three variables (past investment, market share, and budget). We used the factor score from this factor analysis as a consolidated measure of firm dominance (Bollen and Lennox 1991).
Model Formulation
To test our hypotheses, we use a fixed-effects model with a Prais-Winsten regression estimator that accounts for AR(l) serial correlation and computes panel-corrected standard errors (Greene 2000). The fixed-effects specification listed subsequently also enables us to account for unobserved heterogeneity due to team-, firm-, and industry-specific effects. We estimate the following two equations to test hypotheses H1a-H1d, which pertain to the effect of dominance on investment in radically new technology. Equation 1 decomposes the effects of dominance into the escalation of commitment, inertia, and wealth effects. Equation 2 represents the overall effects of dominance (measured with the factor score from the factor analysis described previously) on radical innovation.
( 1) Investmentit = α0 + α1 (average cumulative expenditures in existing technology)i, t - 1
+ α2 (average market share in existing technology)i, t - 1
+ α3 (average cumulative budget)1, t
+ Φ (industry average expenditure)
+ κ (firm) + Vi + εit,
( 2) Investmentit = β0 + β1 (dominance)1, t - 1 + λ (industry average expenditure) + γ (firm) + v1 + εit,
where
investment = (new technology expenditure)/(total expenditure in new and existing technology) for relative measure of investment and new technology expenditure for absolute measure of investment,
Industry average expenditure = a variable that controls for industry-specific effects,
firm = a matrix of dummies that control for firm-specific (fixed effects,
εit = ρεi, t - 1 ηit, |ρ| < 1, ηit ∼ IIN (0, σ2, sub η), and
vi = team-specific errors.
Results
Table 1 presents the estimation results for Study 1 . All reported coefficients reflect standardized values (Kim and Ferree 1981). For this and all subsequent analyses, we also computed the White (1980) general test statistic; the tests indicate that heteroskedasticity is not a problem. We use the terms αiR and βiR to refer to the coefficients based on the relative measure, and we use αiA and βiA to refer to the coefficients based on the absolute measure of investment in radical innovation. We account for industry-specific effects by including an industry-level variable that measures the average total expenditure in each period across all firms in the industry. The firm variable controls for heterogeneity due to firm assignment (e.g., differences in starting positions for Firms 1-5). We only include statistically significant fixed effects in the final regression equation.
The escalation of commitment effect (H1a) implies that a firm with many investments in an existing product generation invests less aggressively in the radical innovation. We found a significant, negative effect of past Sonite expenditures on the aggressiveness with which firms invest in the radical innovation (α1R = -.08, p < .10; α1A = -.18, p < .01). The inertia effect (H1b) argues that, other things being equal, managers with strong market positions likely continue with the existing product generation at the expense of the radical innovation. We found that firms with high lagged market shares invest less aggressively in new Vodite products than do other firms (α2R = -.20, p < .05; α2A -.17, p < .05). The wealth effect (H1c) suggests that high profits endow dominant firms with resources that enable them to be more aggressive in their investments in radical innovation than are other firms. The results indicate a positive, significant effect of firms' budgets on investment in radical innovation (α3R = .40, p < .01; α3A = .24, p < .01).
We further test the overall effect of dominance (H1d) by estimating Equation 2. The factor score from the factor analysis of the past investment, market share, and wealth variables has a positive coefficient that is significantly different from zero(β1R = .40, p < .01; β1A = .15, p <. 10).
Discussion
The Study 1 results suggest that the three facets of dominance--market share, investments, and wealth--affect innovation behavior differently; therefore it is important to account for these differing effects. Overall, dominance has a positive effect on the aggressiveness with which managers pursue radical innovation, but managers might hold different expectations about the likely effects of the new technology on existing products. We manipulate participants' expectations about the effects of the new technology in Study 2.
In Study 2, we attempt to answer the question, How do expectations about new technology influence managers' product development decisions in dominant and nondominant firms? We used time-series, cross-sectional data to test our causal relationships in a controlled setting.
Subjects and Procedure
Similar to Study I, we used the MARKSTRAT2 simulation to test the hypotheses.( n5) Participants in the simulation were graduate students in business at a public university in Europe. We conducted the study over one semester and used data from six concurrent runs (industries) of the simulation. We randomly assigned participants to teams of three to four members each. We then randomly assigned these teams to firms in one of the six industries. All participants played the game over eight periods. Therefore, we gathered data from 30 firms competing in six industries over eight periods for a total of 240 observations.
We experimentally manipulated (at the industry Level) participants' expectations about the radically new technology.( n6) We assigned ten teams each (two industries each consisting of five firms) to the enhancement and obsolescence conditions. We assigned the teams in the remaining two industries to the no-effect and control conditions, respectively.
H2-H5, which we test in this study, pertain to the role of technology expectations and their interaction with dominance. Because our interaction hypotheses apply only to overall dominance, we did not decompose the overall measure of dominance in this analysis. We did, however, replicate our test of hypotheses H1a-H1c by reestimating Equation I with Study 2 data. We also surveyed each team in each period on its perceived dominance (see Appendix B). The correlation between this measure and our archival measure of dominance is high (r = .84, p < .01), which indicates that our measure of dominance reflects participants' own views of their relative market position.
We introduced the technology expectation manipulations at the end of the fourth period, by which point clear patterns of dominance had emerged in each industry. Specifically, by the end of the fourth period, the cumulative marketing contribution of firms across industries ranged from $26 million to $486 million. None of the participants had made any investments in the new product generation before this time, and they did not have any market research data on the new product generation for much of the time until we introduced the manipulations. Thus, participants made decisions on the new product after we introduced the technology expectation manipulations.
Manipulations
At the end of the fourth decision period, we provided participants with a memo that contained information on prospects for the radically new technology (see Appendix C). We told firms in the enhancement (obsolescence) conditions that the new technology was likely to make products based on the existing technology more effective (obsolete). We instructed firms in the no-effect (no specific expectations) conditions that the new technology was likely to have no effect (unclear effects) on products based on the existing technology.
As we noted previously, the MARKSTRAT student manual actually suggests that there are no interactions between the existing and the new technologies. To allow for varying expectations about the effects of the new technology, the simulation administrator instructed participants at the start of the simulation to ignore this sentence in the student manual. As part of the cover story for the experiment, the administrator told participants that the game parameters had been modified at the start and that the effects of the new technology were unclear. The administrator also noted that a memo with information about the likely effects of the new technology was forthcoming. Manipulation checks indicate that the cover story worked as intended.
Manipulation Checks
To further understand the process underlying participants' investment decisions in each condition, we also surveyed each team on its perceptions of the potential for gains or losses in the industry in the next period (the two items for this perceived loss scale are provided in Appendix B). We collected this perceptual data for each period after the fourth period, when we distributed the memo. The differences in covariance-adjusted means of perceived loss across conditions are as expected. Specifically, the difference between obsolescence and no effect (1.82, p < .05) and enhancement and no effect (-6.38, p < .05) is statistically significant and in the right direction. The difference between the no-effect and no-specific-expectations conditions is not statistically significant at p < .05. These data provided additional evidence for our manipulations.
Model Specification
To test hypotheses H2-H5, we again used the Prais-Winsten regression estimator to estimate the following fixed-effects model with AR(l) errors.
( 3) Investmentit = β0 + β1 (dominance)i, t - 1 + β2 (enhancement)i
+ β3 (obsolescence)
+ β4 (dominancei, t - 1 x enhancement)i
+ β5 (dominancei, t - 1 x obsolescence)i
+ γ (loan)i, t - 1
+ λ (industry average expenditure)
+ τ (firm) + vi + εit.
Enhancement and obsolescence are represented as dummy variables. Participants in the no-specific-expectations and no-effect conditions behaved similarly on key variables of interest. Therefore, we pooled these two groups into one no-effects condition. The coefficients for the enhancement and obsolescence conditions are therefore estimated relative to this control condition. The loan amount (if any) is represented by the loan variable; other variables are as defined previously. Because the objective of this study is to test the effects of technology expectations on investment behavior, we only used data collected after the period in which the memo with the experimental manipulation had been administered (n = 120).
Results
Replication tests of H1a - H1c. Because hypotheses Ha - H1c apply to investment behavior in the absence of obsolescence or enhancement expectations, we estimated Equation 1 only for those teams that fell into the control condition. This analysis is a conceptual replication of the corresponding analysis in Study 1. The results in Table 2 are consistent with our hypotheses (with the exception of the escalation of commitment effect on absolute investment) and provide further support for hypotheses H1a - H1c.
Main effects of expectations. H2 suggests that managers who expect a new technology to make existing products obsolete invest more aggressively in radical innovation than do managers who expect the new technology to have no effect on existing products. The results support this hypothesis (see Table 3). Specifically, obsolescence has a positive, statistically significant main effect on investment in radical innovation (β3R = .24, p < .01; β3A = .24, p < .05).
For the enhancement versus no-expectation condition, H3 proposes that managers invest less aggressively in a radical innovation than do managers who expect no effect. In support of H3, the coefficient of enhancement is negative and statistically significant (β2R = -.52, p < .01; β2A = -.41, p < .05).
Interactions of dominance and expectations. H4 predicts that, given expectations of obsolescence, managers of dominant firms likely invest more aggressively in radical innovation than do managers of nondominant firms. As predicted, the coefficient for the interaction of dominance and obsolescence is positive and significant (β5R = .21, p < .05; β5A = .34. p < .05), in support of H3.
H5 predicts that, given expectations of enhancement, managers of dominant firms likely invest less aggressively in the new technology than do managers of nondominant firms. This hypothesis is not supported: The coefficient for the interaction of dominance and enhancement is not significantly different from zero (β4R = .06, p = .26; β4A = .01 , p = .28).
Main effect of dominance. The results in Table 3 indicate that the main effect of dominance is positive and significant (β1R = .18, p < .01; β1A = .55. p < .01). The results support H1d: Managers of dominant firms tend to invest more aggressively in radical innovation than do managers of nondominant firms.( n7)
Discussion
Overall, the results indicate that technology expectations play a complex role in driving investments in radical innovations. An expectation of obsolescence causes both dominant and nondominant firms to invest significantly greater proportions of their resources toward radical innovations than do firms in industries in which expectations of no effect are prevalent The situation is different in an industry in which the enhancement expectation is prevalent. Both dominant and nondominant firms invest significantly lower proportions toward radical innovation in such industries, compared with industries facing expectations of obsolescence or no effect. Moreover, regardless of whether the expectation is one of obsolescence or enhancement, expectations have a greater effect on investment behavior for dominant firms than for nondominant firms.
Given its longitudinal and experimental design, the MARKSTRAT-based study helps ensure internal validity. Study 3 presents insights from a real industry and practicing managers involved in making actual financial decisions.
The U.S. retail banking industry during 1999 and 2000 proved an excellent setting for our field study (see Schotema 2001). We provide details in Appendix D. The following sections describe the full-scale field study, in which we attempt to quantify the effects of expectations and dominance on bricks-and-mortar banks' investments in Internet banking. (Additional details on the methodological aspects to reflect their beliefs about the likely impact of the Internet of the study are available in Chandy, Prabhu, and Antia's [2003] work.)
Unit of Analysis and Sampling
Our unit of analysis is the U.S. retail banking division for each bank (the key informant was the officer in charge of U.S. retail banking or the equivalent). We used a frequently updated and detailed database published by Thomson/Polk to construct our sample frame, which consisted of 550 U.S. retail banks, chosen randomly from the population of U.S. retail banks. Our data collection efforts yielded a total of 189 usable questionnaires, representing a 39.4% response rate. The mean number of employees at responding institutions was 428 (standard deviation = 2933) and the mean number of bricks-and-mortar branches was 27 (standard deviation = 147). Of the 189 usable questionnaire responses, 129 were from publicly held retail banks. We also checked for nonresponse bias; results indicate that such bias is unlikely.( n8)
Measures
The final measures for each construct appear In Appendix B. Table 4 reports the correlation matrix and descriptive statistics for these measures.( n9) The scale of relative investment comprises three items with an a of .88, and the scale of absolute investment comprises four items with an a of .86. To further test the convergent validity of our measure, we also included a third, nonperceptual measure of investment in the survey (see Appendix B). The correlations between this measure and our dependent measures of relative and absolute investment are .67 (p < .01) and .63 (p < .01), respectively.
We measured expectations (obsolescence, enhancement, and no effect) by asking respondents to allocate 100 points on bricks-and-mortar banking, both in the short term (next two years) and in the long term (next ten years). Recall that our hypotheses compare the behavior of firms that expect obsolescence and enhancement with that of firms that expect no effect. To ensure consistency with our hypotheses and comparability between the experimental and the field studies, we averaged the short- and long-term variables and created two dummy variables (enhancement and obsolescence) to represent the three conditions. We categorize a firm as expecting enhancement (or obsolescence) if it allocates more points to that condition relative to the median number of points allocated to that condition across all firms.( n10)
Consistent with the in-depth interviews we conducted and the measure of dominance adopted in Studies 1 and 2, we measured dominance as a composite of three accounting variables. We used the average dollar value of bricks-and-mortar assets (net of depreciation) as a measure of investment in the existing product, average dollar value of deposits as a measure of market share,( n11) and average net equity (total equity capital net of preferred and common stock, surplus, and undivided profits from bricks-and-mortar operations) as a measure of cumulative earnings. These averages are over a six-year period before the survey (using five- and four-year averages produces consistent results). To minimize common method bias, we collected archival data on the preceding variables from the Federal Deposit Insurance Corporation. We controlled for individual firms' willingness to cannibalize with a three-item, seven-point scale adapted from Chandy and Tellis (1998). In addition, we controlled for banks' ownership with a dummy variable coded as 1 for publicly owned banks and as 0 otherwise.
Analysis
We regressed firms' investments on the hypothesized explanatory variables, including the moderators and control variables, as depicted in Equation 4. We used Lance's (1988) residual centering approach to reduce multicollinearity in the interaction terms.
( 4) Investment = β0 + β1 (dominance) + β2 (obsolescence)
+ β3 (enhancement)
+ β4 (dominance x obsolescence)
+ β5 (dominance x enhancement)
+ β6 (willingness to cannibalize)
+ β7 (public ownership) + ε.
Results
Table 5 presents regression coefficients for Equation 4. The models are statistically significant (F = 11.74, p < .01; F = 26.62, p < .01, for relative and absolute measures, respectively) and explain a significant percentage of variation in Internet banking investments (R² = .14 and .12, respectively).
In support of H1d, the results suggest that, in general, managers of dominant firms invest more aggressively in radical innovation than do managers of nondominant firms (β1R = .12, p < .01; β1A = .11 P < .01). We also find significant support for H2 (β2R = .12, p < .05; β2A = .13, p < .05). However, we do not find support for H3, which involves the main effect of expectations of enhancement (β3R = -00; β3A = .02).
H4 is supported (β4R .07. p < .01 β4A = .04, p < .05), indicating that dominant firm managers who expect the new technology to make existing products obsolete invest more aggressively in radical innovation than do managers of nondominant firms with the same expectations. We also find support for H5, which posits that dominant firm managers who expect the new technology to enhance the performance of existing products invest less aggressively in radical innovation than do managers of nondominant firms with the same expectations (β5R = -.09, p < .05; β5A = -.07, p < .05). As we expected, banks with greater willingness to cannibalize (β6R = .24, p < .01; β6A = .21, p < .01) invest more in radical innovation, though public banks invest less in Internet banking (β7R = -.19, p < .01; β7A = -.19, p < .01).
Finally, Table 6 presents the parameter estimates for the replication tests of hypotheses H1a - H1c using Equation 1. These results suffer from multicollinearity and should be interpreted with caution. The results in Table 6 are mostly consistent with our hypotheses (with the exception of the effect of deposits on investment, which is positive instead of negative). Thus, we find some additional support for H1a and H1c in the Internet banking context.
Contributions to Research
This article makes three main contributions to the research on radical innovation (for a summary of results across measures and contexts, see Tables 7 and 8). First, we reconcile the opposing views in the literature on the relationship between dominance and radical innovation. Existing research typically equates dominance with related though conceptually distinct proxies, such as firm size, and rarely integrates the three facets of dominance to assess its overall effects on radical innovation. We show that relying solely on individual proxies leads only to an incomplete picture and, more significant, to misleading conclusions. Dominance is a rich composite of all three facets. Only when these facets are examined in a composite manner can the overall effects of dominance on radical innovation be properly identified.
Second, we help explain why some dominant firms invest aggressively in radical innovation and others do not. We examine the role of expectations; in particular, we examine how different expectations increase or decrease managers' motivation to maintain the status quo rather than invest in radical innovation. Research so far has not accounted for the effect of expectations on investment in radical innovation. Most research has instead focused on evaluating the impact of the new technology in hindsight, that is, after it has been introduced. Yet, as we argue and show, managers form expectations and make investments in radical innovation before the eventual effects are evident. Managers' a priori expectations strongly affect their investment decisions. To the best of our knowledge, our study is the first to incorporate the important role such expectations play. Our findings suggest that the fear of obsolescence is a greater incentive to invest in new technologies than is the lure of enhancement. Our findings also suggest that current research is overly pessimistic in portraying dominant firms as laggards in pursuing radically new technologies.
Contributions to Practice
Our results have implications for managers of both dominant and nondominant firms. For dominant firms, the results suggest that they have less to worry about than some of the existing research might lead them to believe. Although some aspects of dominance--greater investments and stronger market position in the existing product generation--reduce dominant firms' motivation to invest in radical innovation, dominant firms' greater wealth compensates for this reduction. Across three studies--two in the lab and one in the real-world context of Internet banking--dominance, as an overall composite of its various facets, has a positive impact on investment in radical innovation.
Our findings also point to an important way that dominant firms can overcome the negative effects of inertia and escalation of commitment. When managers of dominant firms believe that the new technology is likely to make the existing products obsolete, their behavior hardly suggests sloth or inertia. This finding may partly explain the energetically innovative behavior of firms such as Intel and Microsoft, where such fear of obsolescence is a strong part of the corporate mind-set (Gates, Myrvhold, and Rinearson 1995; Grove 1996). The results suggest that such "paranoia" causes firms to pursue investments aggressively in radically new technologies.
Our results also show that dominant firm managers who believe that the new technology is likely to increase sales of their existing products actually invest less aggressively in the new technology than do managers who believe otherwise. Consequently, the fear of loss as a result of obsolescence appears to be a much stronger motivator of investments in radical innovation among such firms than is the lure of gains from enhancement. This result has important implications for product champions and change agents trying to steer a dominant firm toward a new technology. Such persons should use obsolescence rather than enhancement as the rallying cry for their troops.
(n1) The MARKSTRAT manual instructs participants that the existing and new technologies are independent of each other; that is, the growth of the new technology has no effect on the existing technology. As a result, all participants in this study have the same expectation of no effects. We thus control for the effect of expectations on investment behavior.
(n2) In MARKSTRAT2, expenses related to sales force and distribution are not specific to a particular technology. Consequently, we do not expect these expenses to have a systematic impact on the firm's expenses in the new technology relative to the existing technology. Although firms spend money to purchase Sonite and Vodite specific market research, the costs of the market research are low compared with the other expenses. They are also relatively constant across all teams (see, e.g., Glazer and Weiss 1993, p. 516). Consequently, we do not include sales force and market research expenses in calculating technology expenditures. On average, market research expenditures are 5% of total expenditures (standard deviation = 3, range = 0%-18%). To check for robustness, we also estimated Equation 3 using a measure of investment that included market research expenditures. The effects remain robust to this change.
(n3) Use of the relative measure results in a dependent variable that lies between zero and one. To avoid the problem of predictions outside this range, we used a logistic transformation, y = ln[p/(1 - p)]; this also provides a unit of measurement that is related more linearly to the independent variables (Neter, Wasserman, and Kutner 1985). Our use of generalized least squares estimation for each operationalization of the dependent variable enables us to report R² measures. To facilitate the logistic transformation, we replaced data points with zero values with a small fraction (.01) and those with values of 1 with .99.
(n4) In this study, and in Studies 2 and 3, we also used single-period measures of each of these components of dominance. The effects remain robust to these alternate formulations.
(n5) All aspects of the game were identical to those in Study 1, with one exception. In Study 2, we made interest-free loans available, subject to a formal application process. We did so because the positive overall effect of dominance in Study I could potentially have been due to a MARKSTRAT-specific bias in favor of initially wealthy firms. We wished to rule out this possibility in Study 2. We made the availability of interest-free loans known to all participants in the first period and reminded them of it in every period. Some firms sought and received loans, and others did not. We control for the loan amount in our models.
(n6) We chose not to manipulate dominance because Study 1 suggests that this factor varies naturally within the simulation from period to period. Even if we had ensured starting positions that place some firms in a better position than others, this superiority would have washed out because of subjects' use of individual strategies. Therefore, we measured dominance as a continuous function of firms' average cumulative budgets, past investments, and market share by using a factor analysis procedure identical to Study 1.
(n7) Some scholars (e.g., Ettlie and Rubenstein 1987) have suggested a nonlinear (U-shaped or inverted U-shaped) relationship between dominance and innovation. To test for possible nonlinearity in the effects of dominance, we also tested an alternate model that included a squared dominance term in Equation 1. The coefficient for this term was not significantly different from zero: therefore, we do not include the results in Table 3.
(n8) The first wave of responses included 139 of the 189 usable responses. We first tested for differences between early and late respondents (Armstrong and Overton 1977), using the focal variables of the study as dependent variables. The analysis of variance yielded no significant differences on any of the variables (F = 04; p = .52). We further compared the two groups on the mean number of employees, assets, deposits, net equity, and ownership pattern. We did not find any significant differences between the two groups on any of these measures.
(n9) The overall fit of the mixed-measurement model consisting of the three reflective scales and the composite index of dominance is high (χ2, sub 70 = 104.7, p = .005; Cmin/degrees of freedom = 1.49; root mean square error of approximation = .05; Akaike information criterion = 202.71; comparative fit index = .99; nonmed fit index = .98; and Tucker-Lewis index = .99), suggesting unidimensionality of the reflective scales. All items loaded on their prespecified constructs and had t-values significant at .05, which provides evidence of convergent validity. Appendix B presents the item parameter values for the factor structure matrix and Cronbach's alpha estimates for all reflective scales. All reliability estimates exceed .70. An alternate model with cross-loadings specified failed to converge, which supports the discriminant validity of the constructs. Discriminant validity of the scales is further supported by the Lagrange-multiplier tests: None of the possible cross-loadings exceeds the critical value of the χ² with one degree of freedom (Speier and Venkatesh 2002).
(n10) In 14 cases, the previous procedure assigns firms to more than one condition. In these cases, to maintain the mutually exclusive nature of the dummy variables, we assigned the firm to the condition with the higher average score. The parameter estimates remain robust to dropping these 14 cases.
(n11) The market share for any firm is simply that firm's sales divided by industry sales. In our case, the denominator term (industry sales) is constant across all firms because our data comes from a single industry. As such, a firm sales measure is equivalent to a market share measure.
Legend for Chart:
A - Independent Variables
B - Process
C - Hypothesized Effect
D - Relative Vodite Investment Model 1
E - Relative Vodite Investment Model 2
F - Absolute Vodite Investment Model 1
G - Absolute Vodite Investment Model 2
A
B C D E
F G
Expenditures in existing technology
Escalation of - .08(*)
commitment
-.18(***)
Market share in existing technology
Inertia - -.20(**)
.17(*)
Budget
Wealth + .40(**)
.24(***)
Dominance
+ .40(***)
.15(*)
Industry average expenditure
.24(***) .22(***)
.68(**) .57(***)
Firm 2
.32(**) .26(**)
Firm 3
-.27(**)
-.79(***)
R²
.38 .28
.42 .31
(*) p < .10.
(**) p < .05.
(***) p < .01.
Notes. Models 1 and 2 present the estimation results
of Equations 1 and 2, respectively. Legend for Chart:
A - Independent Variables
B - Hypothesized Effect
C - Relative Vodite Investment
D - Absolute Vodite Investment
A B C D
Expenditures in existing technology - -.17(*) .06
Market share in existing technology - -1.04(**) -.75(**)
Budget + 1.24(**) .96(***)
Industry average expenditure 1.13(*) .56
Firm 3 .76(***)
Loan .45(**) .38(*)
R² .41 .60
(*) p < .01.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Independent Variables
B - Hypothesized Effect
C - Relative Vodite Investment
D - Absolute Vodite Investment
A B C D
Dominance + .18(**) .55(**)
Obsolescence: + .24(**) .24(*)
Enhancement - -.52(**) -.41(*)
Dominance x obsolescence + .21(*) .34(*)
Dominance < enhancement - .06 .01
industry average expenditure .62(**) .43(*)
Firm 3 .35(**) .37(**)
Firm 5 .37(**) .22(*)
Loan -.04 .36(**)
R² .57 .42
(*) p < .05.
(**) p < .01. Legend for Chart:
B - Mean
C - Standard Deviation
D - Relative Internet Investment
E - Absolute Internet Investment
F - Dominance
G - Enhancement
H - Obsolescence
I - No Effect
J - Willingness to Cannibalize
A B C D
E F G
H I J
Relative Internet investment 12.09 5.84
Absolute Internet investment 13.49 5.85 .84(***)
Dominance 0 1 .12(*)
.11(*)
Enhancement .58 .49 -.01
.00 .06
Obsolescence .10 .29 .07
.08 -.03 -.38(***)
No effect .32 .46 -.04
-.06 .04 -.81(***)
-.22(**)
Willingness to cannibalize 12.42 3.41 24(***)
.22(**) -.08 .00
-.04 .02
Public ownership 1.66 .47 -.23(***)
-.22(***) -.15(**) -.11(*)
.10(*) .07 -.08
(*) p < .10.
(**) p < .05.
(***) p < .01. Legend for Chart:
A - Independent Variables
B - Hypothesized Effect
C - Relative Internet Investment
D - Absolute Internet Investment
A B C D
Dominance + .12(**) .11(**)
Obsolescence + .12(*) .13(*)
Enhancement - -.00 .02
Dominance x obsolescence + .07(**) .04(*)
Dominance x enhancement - -.09(*) -.07(*)
Willingness to cannibalize .24(**) .21(**)
Public ownership -.19(**) -.19(**)
R² .14 .12
(*) p < .05.
(*) p < .01 Legend for Chart:
A - Independent Variables
B - Hypothesized Effect
C - Relative Investment
D - Absolute Investment
A B C D
Assets in existing technology - -3.37(**) -3.01(**)
Deposits in existing technology - 3.38(**) 2.95(**)
Net equity + .22(*) .26(*)
Willingness to cannibalize .29(*) .27(*)
Public ownership -.13 -.10
R² .19 .16
(*) p < .05.
(**) p < .01. Legend for Chart:
A - Independent Variables
B - Hypothesis
C - Predicted Effect
D - Studies and Measures of Radical innovation Study 1
Relative Investment
E - Studies and Measures of Radical innovation Study 1
Absolute Investment
F - Studies and Measures of Radical innovation Study 2
Relative Investment
G - Studies and Measures of Radical innovation Study 2
Absolute Investment
H - Studies and Measures of Radical innovation Study 3
Relative Investment
I - Studies and Measures of Radical innovation Study 3
Absolute Investment
A B C D
E F G
H I
Expenditures in
existing technology H1a - Supported
Supported Supported Not supported
Supported Supported
Market snare in
existing technology H1b - Supported
Supported Supported Supported
Not supported Not supported
Wealth H1c + Supported
Supported Supported Supported
Supported Supported
Dominance H1d + Supported
Supported Supported Supported
Supported Supported Legend for Chart:
A - Independent Variables
B - Hypothesis
C - Predicted Effect
D - Studies and Measures of Radical Innovation Study 2
Relative Investment
E - Studies and Measures of Radical Innovation Study 2
Absolute Investment
F - Studies and Measures of Radical Innovation Study 3
Relative Investment
G - Studies and Measures of Radical Innovation Study 3
Absolute Investment
A B C
D E
F G
Obsolescence H2 +
Supported Supported
Supported Supported
Enhancement H3 -
Supported Supported
Not supported Not supported
Dominance x obsolescence H4 +
Supported Supported
Supported Supported
Dominance x enhancement H5 -
Not supported Not supported
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First, decisions on new technology are intrinsic in the MARKSTRAT decision environment. Participants make decisions about the adoption of a new technology and develop radically new products (Vodite) even as they manage portfolios of products based on an existing technology (Sonite). More specifically, the Vodite fits our definition of a radical innovation as a product that involves technology and marketing skills that are new to the industry (see Garcia and Calantone 2002). For example, the MARKSTRAT2 student's manual describes Vodites as products that come from "a basic technological breakthrough" and that "satisfy an entirely different need than that of the Sonites" (Larreche and Gatignon 1990). Second, managers and academics alike consider MARKSTRAT a realistic simulation of the real world (Glazer and Weiss 1993; Kinnear and Klammer 1987). Third, researchers have frequently used the simulation to study how managers make decisions (e.g., Glazer, Steckel, and Winer 1992; Glazer and Weiss 1993). Therefore, MARKSTRAT provides a well-tested research environment. Fourth, participants make decisions on various business issues, including targeting and positioning, advertising, sales force, pricing, and distribution (Larreche and Gatignon 1990) in addition to technology investment decisions. Because decision makers' attention is not focused on technology and new product decisions, MARKSTRAT provides a relatively conservative means of testing our research hypotheses. Fifth, the MARKSTRAT context enables us to collect data on (I) the decision-making processes used by participants over time and ( 2) the actual decisions they made during this period. This longitudinal information is extremely difficult to obtain in the field.
Items marked with an asterisk are reverse coded. All Likert-type items are seven-item, "strongly agree" to "strongly disagree," and have Cronbach's alpha and item parameter values for the factor structure matrix reported.
Measures of Constructs Used in Study 2
Perceived Dominance
1. Our performance so far has been better than that of everyone else in our industry.
- 2. We have had few serious threats to our position as industry leaders so far.
- 3. We have led the market from the start.
Perceived Loss
How would you characterize the situation
MARKSTRAT industry in the next period?
a) 1 2 3 4 5 6 7
Potential Potential
for loss for gain(*)
a) 1 2 3 4 5 6 7
Positive Negative
Situation situation
Measures of Constructs Used in Study 3
Investment in Internet Banking
Listed below are statements regarding your Internet-related investments:
(A) in general
(B) relative to bricks-and-mortar operations
(C) relative to total development expenditures
Please indicate the extent to which you agree or disagree with the following statements.
Measure of Absolute investments in internet Banking (a = .86) (A) Our Internet related investments in general:
- We have done very little with respect to Internet banking at our bank.(*) .92
- Our bank has only a token Web presence.(*) .74
- We haven't done much yet to develop our Internet banking capabilities.(*) .93
- Most of our development expenditures are targeted toward Internet banking efforts. .57
Measure of Relative investments in Internet Banking (a = .88)
(B) Relative to our bricks-and-mortar operations:
- We have not invested aggressively in Internet banking.(*) .96
- Our bank is yet to make significant investments in Internet banking.(*) .94
- We have earmarked few managerial resources to Internet banking in the short term.(*) .71
Please indicate the percentage of your bank's development expenditures on Internet banking in the last year, relative to total development expenditures: ( )%
Willingness to Cannibalize (α = .70)
1. Our bank's investments in bricks-and-mortar branches make switching to Internet banking difficult.(*) .74
- 2. We rely too much on our bricks-and-mortar branches to switch focus to Internet banking. .78
- 3. We are reluctant to cannibalize our investments in bricks-and-mortar branches.(*) .63
Technology Expectations
Please indicate your expectations about the likely effects of the Internet on bricks-and-mortar banking IN GENERAL (i.e., across all retail banks), by allocating 100 points across the following three alternative scenarios.
For example, if you strongly believe that Internet banking is very likely to have no effect on bricks-and-mortar banking in the next two years, you could allocate the 100 points above as follows: (a) 0 points, (b) 0 points, and (c) 100 points. If you believe all three scenarios are equally likely, you could allocate the 100 points above as follows: (a) 33.3 points, (b) 33.3 points, and (c) 33.3 points.
Legend for Chart:
A - Scenario
B - Points Awarded In the Next Two Years
C - Points Awarded In the Next Ten Years
A B C
1. Internet banking is
likely to make bricks-and-mortar
banking obsolete.
2. Internet banking is
likely to enhance
bricks-and- mortar
banking.
3. Internet banking is
likely to have no
effect on bricks-and-mortar
banking. To: XXX Industry Participants
From: Technology Marketing Consultants, Inc.
CC: MARKTSTRAT Administrator
Date: XX/XX/XX
Subject: How will Vodite technology affect the Sonite
industry?Per your request, we conducted an extensive study of the likely effects of the Vodite technology on the Sonite industry. This study involved analysis of multiple sources of data, including the following:
• In-depth interviews with 78 leading technology and market The Vodite technology may make Sonite products obsolete, experts
• A survey of 2132 likely Vodite buyers
• An observational study of product usage patterns in 165 selected households in a representative test market
• Historical data on sales and adoption patterns of other (comparable) consumer durable goods
[Obsolescence Manipulation, emphases in original]
Based on the results of this analysis, it is our opinion that products based on the Vodite technology are quite likely to make Sonite products obsolete. Vodites fulfill similar needs relative to Sonites and serve similar customers. Yet the performance of Vodite-based products is likely to be superior to Sonite products. For example, the introduction of tape recorders decreased the sales of gramophones. The Vodite technology is also projected to offer greater opportunities for performance improvement relative to the Sonite product category. Thus, our analysis indicates that Sonite sales will probably drop substantially as the Vodite technology is developed and introduced to the market.
[Enhancement Manipulation, emphases in original]
Based on the results of this analysis, it is our opinion that products based on the Vodite technology are quite likely to make Sonite products more effective than before. Vodites fulfill similar needs relative to Sonites and serve similar customers. Moreover, their performance characteristics are likely to complement those of the Sonite products. For example, the introduction of camcorders led to an increase in the sale of videocassette recorders. The Vodite technology is also projected to offer greater opportunities for performance improvement in the Sonite product category. Thus, our analysis indicates that Sonite sales will probably increase substantially as the Vodite technology is developed and introduced to the market.
[No-Effect Manipulation, emphases in original]
Based on the results of this analysis, it is our opinion that products based on the Vodite technology are quite likely to have no effect on Sonite products. Vodites fulfill somewhat different needs relative to Sonites. The performance characteristics of Vodite-based products are likely to be different from Sonite products. For example, the introduction of microwave ovens had no effect on the sales of conventional ovens. Performance improvement in the Vodite technology is also projected to be independent of any improvements in the Sonite product category. Thus, our analysis indicates that Sonite sales will probably be unaffected as the Vodite technology is developed and introduced to the market.
[Control Condition]
Our analysis indicates little consensus among experts and consumers on how the Vodite technology will affect Sonite products. Three different scenarios are possible.
• The Vodite technology may make Sonite products obsolete, leading to a decrease in Sonite sales. For example the introduction of tape recorders decreased the sates of gramophones.
• The Vodite technology may make Sonite products more effective, leading to an increase in Sonite sales. For example, the introduction of camcorders led to an increase in the sale of videocassette recorders.
• The Vodite technology may have no effect on Sonite products. For example, the introduction of microwave ovens had no effect on the sales of conventional ovens.
Given the uncertainty in the market at the present time, we are unable to provide any definitive forecasts on which of these three scenarios is most likely to come true.
First, Internet banking fits our definition of radical innovation. In the banking context, the World Wide Web is widely considered an innovation that caused discontinuities both in the technology embedded in new products that employed it and in the marketing skills needed to market the products (Schotema 2001; for a more general discussion of the World Wide Web and radical innovation, see also Garcia and Calantone 2002). Internet banking was, especially at the time of the study, salient in the minds of banking executives (Fraser 1996). Yet only a handful of banks had achieved the ability to conduct transactions over the Internet during I 999 and 2000. Specifically, according to data from the Online Banking Report, only 319 (3.12%) of the 10,239 banks operating in the United States in 1999 had Internet transaction capability by the end of that year, and only 462(4.62%) of the 10,006 banks operating in the United States in 2000 had Internet transaction capability by the end of that year. The banks' actions with respect to Internet banking were considered likely to have considerable impact on their competitive positions going forward. Second, our research also revealed considerable variance in opinions about the likely effects of the Internet on bricks-and-mortar banking. Third, U.S. banking firms vary considerably in market positions, assets, and resources, which thereby enabled us to test the effects of dominance on innovation.
Structured interviews with 14 industry executives with diverse designations (chief information officer, chief technology officer, e-commerce director, head of retail banking, president) provided further confirmation of the suitability of the Internet banking context for our research on radical innovation. From the interviews, it became clear that some managers expected Internet banking to make bricks-and-mortar banking obsolete in the not-too-distant future, but others expected Internet banking to enhance bricks-and-mortar banking. These two expectations closely fit the two key conditions that are of theoretical interest to us: obsolescence and enhancement.
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By Rajesh K. Chandy; Jaideep C. Prabhu and Kersi D. Antia
Rajesh K. Chandy is Assistant Professor of Marketing, Carlson School of Management, University of Minnesota.
Jaideep C. Prabhu is Assistant Professor of Marketing, Judge Institute of Management, University of Cambridge.
Kersi D. Antia is Assistant Professor of Marketing and Earl H. Orser/London Life Faculty Fellow, Richard Ivey School of Business, University of Western Ontario. This research was supported by a grant from the Marketing Science Institute. The authors thank Raul Rivadeneyra, Bharat Sud, and Pratik Sharma for help with data collection; Kathy Jocz for help in securing access to managers; and Don Barclay, Mark Bergen, Ed Blair, Niraj Dawar, Raj Echambadi, Robert Fisher, Yany Gregoire, Brigitte Hopstaken, Mike Houston, George John, Eli Jones, Akshay Rao, Gerry Tellis, Mark Vandenbosch, Eden Yin, and participants at seminars at University of Houston, University of Central Florida, and University of Minnesota for their valuable input.
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Record: 198- When Does Trust Matter? Antecedents and Contingent Effects of Supervisee Trust on Performance in Selling New Products in China and the United States. By: Atuahene-Gima, Kwaku; Li, Haiyang. Journal of Marketing. Jul2002, Vol. 66 Issue 3, p61-81. 21p. 1 Diagram, 2 Charts. DOI: 10.1509/jmkg.66.3.61.18501.
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When Does Trust Matter? Antecedents and Contingent Effects of Supervisee Trust on Performance in Selling New Products in China and the United States
There is a strong normative bias toward the inherent value of trust among both marketing researchers and practitioners. Yet there is little empirical evidence of a positive impact of trust on performance. Indeed, scholars suggest that the sources of trust may provide opportunities for its abuse. Following this line of thinking, the authors investigate the dual roles of sales controls and supervisor behaviors as antecedents of salespeople's belief in the benevolence of the supervisor (i.e., supervisee trust). The authors then examine these antecedents as moderators of the relationship between supervisee trust and sales performance in the context of selling new products. Data on field salespeople from high-technology firms in China and the United States suggest that factors such as supervisor accessibility engender supervisee trust but do not necessarily enhance its impact on sales performance. In the Chinese sample, supervisee trust enhances sales performance when output control is adopted, when the supervisor has a higher level of achievement orientation style, and when the salesperson has higher role ambiguity. Furthermore, the results suggest that the supervisee trust-sales performance relationship is negative when supervisor accessibility is high. With the exception of achievement orientation and supervisor accessibility, these effects are negative or nonexistent in the U.S. sample. The authors discuss theoretical and practical implications of the study's findings.
Trust is given the pride of place among the factors that foster productive relationships in marketing and other contexts. In extant research, there is a strong normative bias toward the inherent value of trust-that is, trust is good for performance (e.g., Anderson and Weitz 1989; McAllister 1995; Morgan and Hunt 1994; Wicks, Berman, and Jones 1999). Consistent with the scholarly literature, the popular press also lauds trust as the hallmark of effective organizational relationships and performance and perceives the effectiveness of managers as dependent on their ability to gain the trust of their subordinates (Culbert and McDonough 1985). For example, in the sales context, the supervisor trusts the salesperson to exert maximum effort and commitment to achieve organizational goals. Reciprocally, the salesperson trusts the supervisor to be benevolent-to provide support, show fairness and objectivity in work assignments and performance appraisals, and show consideration for the salesperson's welfare. The salesperson's trust in the supervisor's benevolence is therefore believed to enhance sales performance (see Rich 1997).
Where is the evidence for this strong normative bias toward the inherent value of trust in the various contexts? Our review of the diverse literature suggests that there is little empirical evidence to support the validity of this viewpoint. Rich(1997) reports a positive relationship between supervisee trust and overall job performance rather than sales performance. Indeed, Dirks's (1999) recent review of the management literature concludes that there is little support for a positive relationship between trust and performance. Studies of trust in several marketing contexts tell a similar story. Smith and Barclay (1997) observe that trust among sales teams is only a modest predictor of task performance. Dahlstrom and Nygaard (1995) show that the effect of interpersonal trust on performance in marketing franchises could be positive or negative depending on the country sample. Crosby, Evans, and Cowles (1990) report that customers' trust in the salesperson is unrelated to sales performance. Similarly, Doney and Cannon (1997) report that trust in the buyer and trust in the salesperson are unrelated to purchase choice. Their results suggest that trust is only an order qualifier rather than an order winner. Finally, Aulakh, Kotabe, and Sahay(1996) observe that trust is unrelated to performance in interfirm marketing relationships.
Given this state of empirical findings on the trust-performance relationship in the various contexts, we argue that trust may be in danger of being "oversold" and inappropriately used in practice if its moderating conditions are not critically assessed (see Dirks 1999). Trust connotes risk and vulnerability, a willingness to open oneself to harm, and the likelihood of being taken advantage of (Mayer, Davis, and Schoorman 1995; Soule 1998). Thus, trust carries a risk of betrayal (Elangovan and Shapiro 1998; Noteboom 1996, p. 989), may lead to illegal and immoral pursuits (Brenkert 1998), and may stifle creativity (Wicks, Berman, and Jones 1999). The risks inherent in trust arise because people's integrity and their propensity to cheat vary across relationships and because there is incomplete information about other people's intentions (Mayer, Davis, and Schoorman 1995).
Consequently, researchers argue that the conditions that are conducive for the emergence of trust may also allow for its abuse (Elangovan and Shapiro 1998; Granovetter 1985). For example, Shapiro (1987, p. 625) asserts that the sources of trust "may provide the opportunity and means for its abuse." Similarly, Kramer, Brewer, and Hanna (1996, p. 380) observe that "the very properties of identity-based trust that contribute to its resilience might sometimes render individuals more vulnerable to misplaced trust." The language of these authors implies that examining the antecedents of trust tells only half the story. It must also be determined whether the antecedents of trust affect its impact on performance. Such a dual role for antecedent factors has been observed in the marketing literature on improvisation and innovation (Moorman and Miner 1998). To our knowledge, no study in the marketing literature has examined the moderating impact of antecedents of trust on performance.
Trust occurs in different contexts, such as between individuals (e.g., employees and management; McAllister 1995; Rich 1997; Soule 1998), between firm employees (e.g., salespeople) and customers (Doney and Cannon 1997), within groups (such as sales teams; Smith and Barclay 1997), between organizations (Aulakh, Kotabe, and Sahay 1996; Ganesan 1994), and at the societal level (Fukuyama 1995). In this study, we focus on the salesperson --### dyad in an attempt to contribute to the literature in three ways: First, with the exception of Rich's (1997) examination of supervisor coaching, there is almost total silence on antecedents of the salesperson's trust in the supervisor's benevolence (hereafter supervisee trust) in the marketing literature. We believe that our understanding of supervisee trust will not be complete without a systematic examination of other supervisory behaviors (Kohli 1985, 1989) and supervisor-initiated control systems (Jaworski 1988). Second, we explore the extent to which the effect of supervisee trust on sales performance is moderated by these antecedents.[ 1] The moderating approach not only is consistent with the literature (e.g., Grayson and Ambler 1999; Moorman, Zaltman, and Deshpandé 1992) but also responds to recent calls for research of this kind. For example, Geyskens, Steenkamp, and Kumar (1998, p. 245) encourage researchers to "explore more complex interactive patterns relating to trust." Similarly, Moorman, Deshpandé, and Zaltman (1993, p. 94) urge that "how trust operates in conjunction with other relationship factors such as ... controls warrants greater research attention." Third, we test these relationships in both China and the United States. Compared with the West, China is a low-trust society, and therefore trust is of the highest importance in organizations (Fukuyama 1995; Redding 1993). Yet comparative research on trust in these two countries has rarely been conducted in marketing scholarship. The rest of the article is organized as follows: We first present the theoretical background and hypotheses. Next, we present the study methods and tests of the hypotheses. We conclude with a discussion of the results and their implications.
Supervisee Trust
The vast literature on trust provides different definitions for the term. Some scholars refer to trust as a person's belief and expectation about the likelihood of having a desirable action performed by another, as a person's assessment of another's goodwill and reliability (e.g., Mayer, Davis, and Schoorman 1995; Soule 1998; Wicks, Berman, and Jones 1999), or behavior reflecting a person's vulnerability to another in an exchange relationship involving risk (e.g., Das and Teng 1998; Moorman, Zaltman, and Deshpandé 1992; Morgan and Hunt 1994). Marketing scholars working in the domain of interfirm and group relationships distinguish between two main forms of trust: honesty (the belief that the party is reliable, stands by his or her word, and fulfills promised role obligations) and benevolence (the belief that one party is genuinely interested in the welfare of the other party and is motivated to seek mutually beneficial gains) (see Doney and Cannon 1997; Ganesan 1994; Geyskens, Steenkamp, and Kumar 1998). Scholars who work in the interpersonal domain (e.g., manager-subordinate relationship) define trust as the employee's attachment or bond with the manager and his or her belief in the manager's benevolence (Lewis and Weigert 1985, p. 970; McAllister 1995; Soule 1998; Tyler and Degoey 1996; Wicks, Berman, and Jones 1999, pp. 100 -### Theorists propose that this form of trust represents a higher stage or "deeper" level of trust and is highly stable (McAllister 1995, p. 30). Rempel, Holmes, and Zanna (1985, p. 97) assert that this deeper level of trust, which they refer to as "faith," requires emotional investments of caring responses and a foundation of affective attachments.
We adopt this view of trust and note that it reflects the benevolence dimension of trust identified by marketing scholars in the interfirm and group contexts. In both these contexts, benevolence trust involves showing consideration and sensitivity for the needs and interests of the other party in the relationship, acting in a way that protects these interests, and refraining from exploiting the other party for the benefit of one's own interests (see Mayer, Davis, and Schoorman 1995; Rich 1997; Whitener et al. 1998, p. 518). Formally, in the current study, we define supervisee trust as the degree to which the salesperson perceives the supervisor as benevolent and believes that the supervisor is genuinely interested in the salesperson's welfare and provides due care for his or her needs. Supervisee trust implies an emotional bond or attachment of the supervisor to the salesperson (see Rempel, Holmes, and Zanna 1985; Rich 1997).[ 2]
In extant research, considerable attention has been focused on identifying the factors that engender trust in marketing exchange relationships. In the interorganizational context, research has shown that factors such as transaction-specific investments (Ganesan 1994); controls (John 1984); mutual values (Morgan and Hunt 1994); support, cultural similarity, goal congruence, and communication (Anderson and Weitz 1989); reputation (Anderson and Weitz 1989; Ganesan 1994); and the organizational structure and culture, along with the characteristics of the exchange object (Moorman, Deshpandé, and Zaltman 1993), influence the formation of trust. In the customer-organization context, a salesperson's expertise, frequency of interactions with customers, similarity, and communication have been identified as antecedents of trust (Crosby, Evans, and Cowles 1990; Doney and Cannon 1997; Smith and Barclay 1997). We extend the literature on trust by investigating some of the factors uncovered at the interorganizational level in the salesperson-supervisor context. We consult transaction cost analysis (TCA) and social exchange theory to isolate the potential antecedents of supervisee trust.
TCA and Trust
According to TCA, opportunistic behaviors occur in exchange relationships largely because of bounded rationality and information uncertainty. Opportunistic behavior involves withholding or distorting information, shirking responsibilities, cheating, and other subtle forms of dishonest behaviors (Williamson 1985, p. 47). Although TCA is ambivalent about trust, it acknowledges that exchange relationships that feature personal trust will survive greater stress and will display greater adaptability (Williamson 1985, pp. 62 - Two tenets of TCA inform our study: First, it argues that to forestall opportunism and ensure trustworthy behavior, the parties in an exchange must resort to control mechanisms. The notion of controls forestalling opportunism has led to the suggestion that controls influence trust formation because trust reflects abstinence from opportunistic behavior. For example, Smith and Barclay (1997, p. 6) contend that forbearance from opportunistic behavior is a trusting behavior. This notion of controls affecting trust is also reflected in Ouchi's (1979, p. 846) view that "people must either be able to trust each other or to closely monitor each other if they are to engage in cooperative enterprises." Similarly, informed by TCA, Doney and colleagues (Doney and Cannon 1997; Doney, Cannon, and Mullen 1998) suggest that trust building is a calculative process involving one party calculating the costs and rewards of another party cheating or cooperating in the relationship. Viewed from another angle, trust is "about believing that others will perform whatever serves the trustor's interests, even in the absence of control" (Das and Teng 1998, p. 498). The implication of this literature is that if the sales supervisor trusts that the salesperson will do the best job possible, there is no need for controls. It follows that from the salesperson's perspective, sales controls have implications about the supervisor's intentions in the working relationship. W e deduce that because controls imply the sharing of performance risk between the organization and the employee (Oliver and Anderson 1994, p. 54; Whitener et al. 1998), a salesperson infers the positive or negative support and caring intentions of the supervisor from the types of sales controls instituted, thereby affecting his or her trust in the supervisor.
The second tenet of TCA is that opportunism is more likely when the environment of the exchange permits. This notion suggests that parties to the exchange weigh the costs and benefits of opportunistic behavior (see Hill 1990). For example, Noteboom (1996, p. 989) argues that the temptation of a party to abuse trust in an interorganizational relationship depends on the likelihood of detection and the ensuing risk of loss of reputation. Consistently, Elangovan and Shapiro (1998, pp. 555-56) postulate that managers weigh the costs and benefits of violations of trust. Hill (1990, p. 510) also claims that opportunism is a viable strategy when the benefits outweigh the costs. Consequently, we argue that sales controls may influence the salesperson's evaluations of the costs and risks of untrustworthy behavior, thereby affecting the efficacy of supervisee trust on sales performance. In support of our position, researchers (e.g., Ghoshal and Moran 1996, p. 27; Hill 1990; John 1984) contend that bureaucratic controls may increase opportunistic behavior or dissipate the positive returns from an exchange. This could occur for two reasons: First, controls may be perceived by the controlled party as depriving him or her of self-control and autonomy, thereby implying a lack of trust. For this reason, John (1984, p. 284) reports a positive relationship between bureaucratic controls and opportunistic behavior in the interorganizational marketing context. Second, controls may be perceived to increase or lower the performance risk of the employee, thereby affecting his or her evaluation of the costs and benefits of violations of trust. Therefore, we believe that though sales controls may affect supervisee trust, they also moderate the effect of supervisee trust on sales performance.
This idea of the potential direct and moderating effects of sales controls provides an extension of TCA, which tends to focus on only the behaviors of the person being monitored rather than on his or her attitudes toward controls. It provides a fresh perspective of sales controls, which seems to have begun with Anderson and Oliver (1987) but has not yet been systematically investigated. In this study, we examine the differential impact of output and process controls because of their salience in prior research as the supervisor's initiated control mechanisms (Jaworski 1988) that are likely to affect trust (see Anderson and Oliver 1987; Das and Teng 1998). Furthermore, these controls have been singled out as potential moderators of the effect of trust on performance (see Moorman, Deshpandé, and Zaltman 1993, p. 94).
Social Exchange Theory and Trust
Critics argue that TCA undersocializes exchange relationships by underestimating the role of social interactions between the parties in a relationship (Ghoshal and Moran 1996; Granovetter 1985, p. 490; Hill 1990). Social exchange theory (Blau 1964; Thibaut and Kelley 1959) suggests that the behaviors of parties in an exchange relationship cannot be explained only through economic exchange. They can also be explained through social interactions entailing repeated exchanges, future obligations, and the belief that each party will discharge his or her obligations in the long run. From this theoretical perspective, the main sources of trust are shared values and length of attachment, which ensure effective communication and understanding between the parties. For example, Doney, Cannon, and Mullen (1998) suggest that people build trust in others through an intentionality process based on repeated inter-actions and common values and goals. Therefore, as we argue previously, in the supervisor-subordinate relationship, trust implies affective attachments, the feelings of being connected and joined, that emerge through relational exchanges (McAllister 1995, p. 30; Whitener et al. 1998, p. 518).
The social exchange perspective has informed several previous studies of trust in various relationship contexts in the marketing literature (e.g., Anderson and Weitz 1989; Doney and Cannon 1997). Partner behaviors such as communication and interaction frequency (Doney and Cannon 1997; Smith and Barclay 1997), goal congruency, and shared values (Anderson and Weitz 1989) have received particular attention as antecedents of trust. In this study, we extend the literature by investigating three of these behaviors at the supervisor-salesperson level: supervisor accessibility, achievement orientation, and role ambiguity. Supervisor accessibility is a similar concept to interaction frequency (Doney and Cannon 1997; McAllister 1995). Achievement orientation captures the convergence of the salesperson's and supervisor's goals (Kohli 1989), thereby reflecting shared values and agreement on goals (Tyler and Degoey 1996). Achievement orientation is particularly important in China's emerging market economy because it overcomes the "iron rice bowl" mentality of workers (i.e., expectation of rewards for merely being an employee rather than for achieving organizational goals) developed during the planned economy. Role ambiguity is a salient construct in this study because it captures the lack of communication or mutual understanding about supervisee roles (see Das and Teng 1998, pp. 504-505).
Social exchange theory also argues that trust prevails even when opportunism might be rationally expected. For example, Granovetter (1985) proposes that the interactions in an exchange relationship encourage trustworthy behavior even if controls against opportunistic behavior are not in place. This view has led to the contention that in interpersonal relationships, trust is characterized by a "leap of faith" beyond the expectations that reason and experience alone would warrant (Lewis and Weigert 1985, p. 970; Wicks, Berman, and Jones 1999, pp. 100-101). The logic for this viewpoint is that the relational context itself acts as a "moral control" on the behavior of exchange partners (Ghoshal and Moran 1996; Granovetter 1985; Hill 1990). Consequently, we argue that to the extent that the supervisor's behaviors forestall the abuse of trust, they would enhance the impact of supervisee trust on sales performance. In consonance with this argument, Shapiro (1987, p. 631) suggests that managers forge "agency relationships based on familiarity, interdependence, and continuity that provide strong incentives for trustworthy performance and a potent array of informal social control options to punish abuse." In other words, managers can deal with the potential abuse of trust by "personalizing the agency relationship by embedding it in structures of social relations" (Shapiro 1987, p. 631). Similarly, Tyler and Degoey (1996) observe that supervisee trust has a greater influence on the employee's acceptance of the supervisor's decisions when the supervisor is considered a friend and when they share similar goals.
In summary, trust is a common element in bothTCA and social exchange theories. This is because, as Ouchi (1979) and others (e.g., Ghoshal and Moran 1996) argue, even the purest form of economic exchange involves social and emotional requirements. Whereas TCA assumes that untrustworthy behavior in the form of opportunism can be prevented by controls, social exchange theory intimates that opportunism can be prevented by the social relations between the parties to an exchange. Both therefore highlight the essential feature of exchange relationships between supervisors and subordinates. That is, the relationship is based on an expectation that each party offers something the other party perceives as valuable, that each party must perceive the exchange as reasonably equitable and fair, and that trustworthy behavior (i.e., lack of opportunism) is required to ensure desirable outcomes. We believe that the integration of these two theoretical perspectives yields a model with better predictive validity.
Does National Culture Matter?
National culture influences individual and organizational behavior such that it has implications for trust development and efficacy (Doney, Cannon, and Mullen 1998). Two cultural dimensions are relevant to our study: ( 1) individualism/ collectivism (or the degree to which people look after their own interests as opposed to the interest of in-groups) and ( 2) uncertainty avoidance (or the degree to which people in a society tolerate ambiguity and uncertainty and feel threatened by uncertain situations). Compared with the United States, China is a collectivist and high--uncertainty avoidance society (Hofstede 1980, 1997). Relationships between managers and subordinates have more far-reaching implications for the nature of trust in collectivist/high-uncertainty avoidance societies than in individualist/low-uncertainty avoidance societies. For example, in the West, managers have the power to control some behaviors, but employees retain control over other behaviors. As Shenkar and von Glinow (1994, p. 62) argue, the situation in China is markedly different:
Unlike his/her Western counterpart, the Chinese manager impacts not only the work domain, but also all other spheres of life of his/her subordinates, including even such matters as birth control. There are no time, place or role restrictions on his power, and the total nature of enterprise allows for constant supervision.
Furthermore, the level and the importance of trust are distinct but related constructs. Compared with Western societies such as the United States, Chinese societies are low-trust societies (Fukuyama 1995). Chinese societies are strongly familistic such that there is a lack of trust outside the family (Redding 1993, p. 67). As an indication of low trust, Chinese societies tend to be high-power distance societies, where inequality of power is accepted and managers tend to centralize decision making, share little information, and expect and receive compliance from subordinates (Hofstede 1980; Shane 1994). In addition, the recent political and social history of China, involving a great deal of suspicion and betrayal (e.g., in the Cultural Revolution), means that people view others as a threat and are less inclined to trust them.[ 3] Finally, Dahlstrom and Nygaard (1995) suggest that in transitional economies such as China, institutional underdevelopment creates an uncertain and risky environment that generates low trust among people. Given that trust is low in the society, we argue that a higher level of importance is given to interpersonal trust in China than in the United States (see Fukuyama 1995; Reeder 1987). The preceding discussion suggests that culture matters and that China and the United States provide ideal sites to test the potential conditional impact of supervisee trust on sales performance. Indeed, Reeder (1987) observes that many U.S. businesses failed in China because their managers tended to ignore the development of interpersonal trust.
Figure 1 presents the theoretical model. Because trust is context specific and is more operative in situations of risk and uncertainty (see Das and Teng 1998, p. 494; Doney and Cannon 1997, p. 36), we contend that in the sales context, supervisee trust could be more operative when the situation involves selling a new product. Akin to the difference between a new buy and a straight rebuy in industrial marketing (see Doney and Cannon 1997), a new product involves greater selling risk and uncertainty than an old product (see Atuahene-Gima 1997; Hultink and Atuahene-Gima 2000). Therefore, we test our model in the context of the most recent new product introduced by the firm. In the next section, we develop our hypotheses by first presenting the logic for a general effect of each antecedent variable on supervisee trust. We then suggest how culture influences the strength of the relationship posited.
Antecedents of Supervisee Trust
The effects of output and process controls. Output control refers to the extent to which a supervisor places emphasis on results when monitoring, evaluating, and rewarding salespeople. In contrast, process control reflects the extent to which a supervisor emphasizes procedures and behavioral activities in monitoring, evaluating, and rewarding salespeople (Anderson and Oliver 1987). As mentioned previously, TCA suggests that trust is influenced by control mechanisms. However, the nature of the influence appears to depend on the inferences the salesperson makes about the motives of the supervisor from the sales controls used (see Anderson and Oliver 1987). Output control represents a "hands-off" approach to managing salespeople, in that they are given a great deal of autonomy and independence to perform their duties and are compensated for the output they achieve. Thus, output control shifts substantial performance risk to the salesperson because output may be affected by environmental and company factors beyond his or her control (Oliver and Anderson 1994, p. 54). As Whitener and colleagues (1998, p. 515) argue, to the extent that the employee is compensated on the basis of outcomes beyond his or her control, performance risk to him or her is greater. We believe that by increasing the salesperson's performance risk, output control sends a negative signal of the supervisor's lack of concern or support for the salesperson. As Hopewood (1972) suggests, when managers rely heavily on financial and quantitative measures, employees tend to show increased tension and perceive poorer relations with them. In contrast to output control, process control ensures that the salesperson receives rewards as long as process requirements are met, irrespective of the output achieved. It therefore reduces the pressure to produce output, because the organization rather than the salesperson assumes much of the performance risk (Anderson and Oliver 1987; Cravens et al. 1993). Consequ ently, although process control may limit autonomy and self-control, unlike output control, it sends a positive signal of the supervisor's concern, care, and support for the salesperson. As Oliver and Anderson (1994, p. 54) argue, employees feel committed and grateful to supervisors who use process control, because it provides them a nurturing climate and reduced performance risk.
Prior research suggests that the effects of sales controls may depend on the risk preferences of the salesperson (Anderson and Oliver 1987; Basu et al. 1985). Risk preferences differ across cultures. For example, in individualistic and low-uncertainty avoidance cultures, people relish risk and challenging work (Hofstede 1980). This suggests that these cultural dimensions will moderate the impact of output and process controls on supervisee trust. The negative implications of output control mentioned previously are more likely to be felt by the Chinese because of their collectivist and high-uncertainty avoidance cultural orientation. Being individualists and being relatively comfortable with uncertainty, U.S. salespeople are more likely to cherish the autonomy and opportunities afforded by output control to achieve high individual performance. Therefore, they may be less concerned with the performance risk implications of output control. Compared with their U.S. counterparts, Chinese salespeople are likely to value the supervisor's care and support, which are implied by process control, and be less concerned with the lack of autonomy and self-control. Consistent with this argument, Dahlstrom and Nygaard (1995) report a positive relationship between process formalization and interpersonal trust in the former East Germany, a collectivist and high-uncertainty avoidance society. In contrast to their Chinese counterparts, U.S. salespeople may be more concerned with the lack of autonomy and loss of personal discretion from process control and interpret these as non-benevolent behaviors of the supervisor. Therefore, we posit that
H1a:. Output control is related negatively to supervisee trust.
H1b: The negative effect of output control on supervisee trust is stronger in China than in the United States.
H2a: Process control is related positively to supervisee trust.
H2b: The positive effect of process control on supervisee trust is stronger in China than in the United States.
The effects of supervisor behaviors. In addition to sales controls, we consider three supervisor behaviors as antecedents of supervisee trust: supervisor accessibility, achievement orientation, and role ambiguity. Supervisor accessibility refers to the extent to which the supervisor is available to meet and interact with the salesperson (e.g., making joint sales calls with the salesperson). It reflects the degree of personal communication and interaction frequency between them. Frequent communication between partners in an exchange relationship fosters trust because it provides opportunities to resolve disputes, thereby aligning the partners' perceptions and expectations (Anderson and Weitz 1989; Doney and Cannon 1997). Such common perspectives, according to social exchange theory, facilitate a sense of understanding and even similarity, which increases the partners' confidence in the relationship (Anderson and Weitz 1989, p. 314). Thus, by enhancing communication, supervisor accessibility reduces the salesperson's performance risk because it allows for due consideration of the salesperson's problems and views in selling (see Oliver and Anderson 1994). In other words, the performance problems of the salesperson are likely to be considered in appraisals of the salesperson's performance. Given that the Chinese are relatively more collectivist and uncomfortable with uncertainty, we argue that they would attach greater importance to the support and caring benefits inherent in supervisor accessibility than their individualist U.S. counterparts would. Indeed, the latter may view supervisor accessibility as interference in their work. Therefore,
H3a: Supervisor accessibility is related positively to supervisee trust.
H3b: The positive effect of supervisor accessibility on supervisee trust is stronger in China than in the United States.
Achievement orientation refers to the degree to which the supervisor sets challenging goals, expects high levels of performance, and expresses confidence in the salesperson's ability to meet the goals and expectations (Kohli 1985). From a TCA perspective, an emphasis on achievement of goals fosters trustworthy behavior. The logic is that achievement orientation reflects a mutual hostage situation because of the close linkage between the successful performance of the salesperson and the performance of the supervisor. From a social exchange viewpoint, achievement orientation is a supervisor's demonstration of the importance of joint gain. Reflecting the logic of goal congruence (see Anderson and Weitz 1989), achievement orientation leads to supervisee trust because the salesperson's and supervisor's goals are linked, which implies mutual vulnerability. Given the individualist U.S. culture, salespeople will cherish achievement orientation, because it is designed to motivate high individual performance (see Kohli 1989). In contrast, the collectivist Chinese place greater weight on the in-group's interests than on personal interests. Thus, achievement orientation style may be perceived as undue pressure to achieve individual goals rather than the group's goals (see Earley 1989). Therefore,
H4a: Achievement orientation is related positively to super-visee trust.
H4b: The positive effect of achievement orientation on supervisee trust is weaker in China than in the United States.
Role ambiguity refers to the degree of discrepancy between the information available to the salesperson and the information he or she requires to perform the job adequately (Singh and Rhoads 1991). In other words, role ambiguity reflects the degree of uncertainty and lack of clarity the salesperson perceives in his or her job. There are several dimensions of role ambiguity (Singh and Rhoads 1991). However, we focus on customer role ambiguity because it has been contended that the customer interface is perhaps the most important dimension affecting sales performance (Weitz, Sujan, and Sujan 1986). Customer role ambiguity (e.g., lack of clarity about what services to provide to customers, what company strengths to emphasize to customers, and how to handle customer objections) hinders supervisee trust because it reflects a lack of communication and understanding between the salesperson and the supervisor about the role requirements of the salesperson. It undermines the supervisor-salesperson relationship by creating conflicts over goals and the tactics to achieve them, because the perceptions and expectations of the salesperson and the super-visor are not aligned. The strength of the negative effect of role ambiguity on supervisee trust may depend on the level of uncertainty avoidance in the culture. In high-uncertainty avoidance cultures, people place great emphasis on clarity of roles because of the fear of the unknown (Hofstede 1997). In this sense, the level of uncertainty avoidance magnifies the negative impact of role ambiguity on supervisee trust. Because China has a higher uncertainty avoidance orientation than the United States, we propose that
H5a: Role ambiguity is related negatively to supervisee trust.
H5b: The negative effect of role ambiguity on supervisee trust is stronger in China than in the United States.
Direct and Contingency Effects of Supervisee Trust on Sales Performance
In the preceding subsection, we examine the antecedents of supervisee trust. The effectiveness of such trust, when it occurs, is another matter because the factors that breed trust may also stifle its effectiveness on desired outcomes (Granovetter 1985; Kramer, Brewer, and Hanna 1996; Shapiro 1987). Therefore, in the next subsection, we first examine the main effect of supervisee trust on sales performance and then investigate how the antecedents may moderate the relationship between supervisee trust and sales performance.
The effect of supervisee trust on sales performance. The performance outcomes of trust have been conceptualized differently across contexts. For example, in an interorganizational context, performance is conceptualized and measured as commitment, satisfaction, or long-term orientation in the relation-ship (see Ganesan 1994; Geyskens, Steenkamp, and Kumar 1998). In the business-to-customer context, performance is measured as a customer's decision to purchase a product (e.g., Doney and Cannon 1997) or as customer satisfaction (e.g., Moorman, Zaltman, and Deshpandé 1992). In theory, goal achievement reflects the salespeople's efficiency in processing and using task-related information because of their different work and decision-making styles. Thus, from a methodological standpoint, salespeople produce measurable output as a result of task performance and can be reliably compared on the basis of such production. Therefore, in this study, we define sales performance as the extent of achievement of sales objectives for a specific new product that recently has been introduced by the firm (see Sujan, Weitz, and Kumar 1994).
Supervisee trust is related positively to sales performance for several reasons. First, a salesperson who trusts the supervisor believes that he or she will receive fair treatment and equitable rewards. Thus, supervisee trust enhances the salesperson's commitment in performing the job. Second, to increase sales performance, the supervisor must provide advice to the salesperson and must set performance goals for him or her to achieve. Supervisee trust is likely to increase the salesperson's acceptance of the advice and goals of the supervisor, thereby energizing him or her to work harder. Supervisee trust may play a more significant role in influencing sales performance in China than in the United States. This is because, being collectivists, the Chinese are not only more likely to value the supervisor's benevolence but also less likely to abuse trust. Unlike people from high-uncertainty avoidance cultures, those from low-uncertainty avoidance cultures (the United States) do not fear the future and can tolerate risk easily. It follows that they may abuse trust even if it damages the exchange relationship (Doney, Cannon, and Mullen 1998, pp. 610, 614). This proposition is buttressed by the argument that in low-trust societies such as China, trust is rare outside the family (Fukuyama 1995) and therefore is of utmost importance and is highly valued in organizations (see Redding 1993). We hypothesize that
H6a: Supervisee trust is related positively to sales performance.
H6b: The positive effect of supervisee trust on sales performance is stronger in China than in the United States.
The moderating role of output and process controls. Although H1a predicts that output control will hinder supervisee trust, we contend that it will enhance the effectiveness of supervisee trust on sales performance. Recall that output control shifts substantial performance risk to the salesperson because he or she is compensated on the basis of outcomes that may be beyond his or her control (see Oliver and Anderson 1994; Whitener et al. 1998). Thus, from a TCA perspective, output control suggests to salespeople that opportunistic behavior (e.g., shirking of responsibility) is unrewarding. Consistent with this view, several scholars contend that trust is psychologically more important for performance in situations in which its abuse would lead to unfavorable consequences for the abuser (see Brockner et al. 1997; Shapiro 1987). For example, Noteboom, Berger, and Noorderhaven (1997) argue that abuse of trust is self-defeating for a partner whose benefits depend on actual contribution to the outcomes of the partnership. This discussion suggests that output controls enhance the effectiveness of supervisee trust on sales performance.
H2a predicts that process control engenders supervisee trust. However, we argue that it could simultaneously reduce the value of supervisee trust because it creates conditions for opportunistic behavior (see Whitener et al. 1998). There are three reasons for this: First, as we argue previously, a potential disadvantage of process control is that it could be perceived by the salesperson as limiting his or her self-control and autonomy. It thereby raises questions about the supervisor's benevolence in the relationship. In other words, under process control, a salesperson must live up to whatever process requirements are demanded by the supervisor (e.g., making a specified number of sales calls, working a specified number of hours per week). This usually means more monitoring that could be construed as implying lack of trust and limiting self-control, thereby interfering with sales performance. Second, process control lowers the perceived performance risk of the salesperson (Anderson and Oliver 1987; Cravens et al. 1993), because under process control, rewards are dependent on the salesperson's activities over which he or she has maximum control. To the extent that the salesperson performs all the stipulated process requirements, he or she receives rewards irrespective of the actual performance output achieved. However, a process control could be subjective, because the supervisor does not know for certain which behaviors to emphasize (Anderson and Oliver 1987) or may find it difficult to implement them (Oliver and Anderson 1994). This means that to ensure greater output, salespeople need to perform significant extra-role discretionary activities that are not stipulated by the control system, such as building long-term customer relationships, adopting creative selling methods, and engaging in other organizational citizenship behaviors (Netemeyer et al. 1997). Yet on the basis of TCA logic, in contrast to output control, under process control the salespers on can calculate that there is relatively less to lose from failing to perform such discretionary activities because they are not stipulated by the control system. Third, a process control must be detailed to guide salespeople regarding the "correct" way to carry out selling tasks (Cravens et al. 1993; Oliver and Anderson 1994, p. 54). We argue that such detailed guidance reduces performance risk by making it easier for the salesperson to perform the process requirements and meet the supervisor's expectations. Formally, we posit that
H7a: The greater the level of output control, the greater is the likelihood that supervisee trust will lead to higher sales performance.
H7b: The greater the level of process control, the greater is the likelihood that supervisee trust will lead to lower sales performance.
The moderating role of supervisory behaviors. According to social exchange theory, supervisor accessibility enhances mutual communication, thereby increasing the supervisor's knowledge of the conditions surrounding the salesperson's task performance. We argue that, with increased supervisor accessibility, problems encountered by the salesperson in selling and his or her views about them are likely to be factored into performance appraisal decisions. This is likely to reduce the salesperson's perceived performance risk, thereby increasing the likelihood of opportunistic behavior. Consistent with this argument, Moorman, Zaltman, and Deshpandé (1992, p. 323) argue that opportunistic behavior could follow from increased communication and deeper exchanges. Similarly, other scholars suggest that trust has greater relevance for performance when there is little consideration of the employee's views in decision making (see Kohli 1989; Tyler and Degoey 1996). Furthermore, Grayson and Ambler (1999, p. 139) report that trust is more effective in short-term than in long-term relationships. A plausible reason for this finding is that the increased communication and similarity that develop between parties in long-term relationships reduce performance risk, thereby dampening the impact of trust by increasing its potential abuse. In brief, we argue that to the extent that supervisor accessibility reduces the performance risk of the salesperson, supervisee trust becomes less important for sales performance.
We mentioned previously that salespeople infer mutual vulnerability from the supervisor's achievement orientation style, because such a style reflects the parties' mutual commitment to organizational goals and performance risk (see Williamson 1985). It follows that achievement orientation strengthens the impact of supervisee trust on sales performance; it makes an abuse of trust unrewarding for the salesperson because his or her goals are closely linked with those of the organization. As support for this argument, Tyler and Degoey (1996) show that agreement on goals and mutual concern for achievement between the employee and the manager strengthen the impact of trust on the employee's acceptance of managerial decisions. Similarly, Shapiro (1987) argues that one means of curtailing the abuse of trust is to ensure agreement and commitment to mutual goals.
By its very nature, role ambiguity increases the salesperson's performance risk. It therefore enhances the impact of supervisee trust on sales performance for two reasons: First, role ambiguity suggests a high need for clarity on the part of the salesperson because of the inadequate information about his or her role. Social exchange theory suggests that supervisee trust becomes critical in creating an environment in which the salesperson is comfortable raising issues with the supervisor in an attempt to clarify his or her role. This line of reasoning is in keeping with the argument that the supervisor's considerateness (which is defined to include trust) has a stronger positive effect on sales performance when the salesperson's need for clarity is high (Kohli 1989). Second, role ambiguity threatens goal achievement because it increases the probability of the salesperson making incorrect decisions, thereby increasing the potential for performance failure and unfavorable assessment of performance. Consequently, there is little incentive for the salesperson to behave opportunistically when role ambiguity is higher. Therefore,
H8a: The greater the level of supervisor accessibility, the greater is the likelihood that supervisee trust will lead to lower sales performance.
H8b: The greater the level of achievement orientation, the greater is the likelihood that supervisee trust will lead to higher sales performance.
H8c: The greater the level of role ambiguity, the greater is the likelihood that supervisee trust will lead to higher sales performance.
Cross-national differences in moderating effects. People in collectivist and high-uncertainty avoidance cultures are less likely than their counterparts in individualist and low-uncertainty avoidance cultures to abuse trust, because they perceive higher costs of such behavior (see Doney, Cannon and Mullen 1998, pp. 610-14). This notion of differential propensity to abuse trust suggests that the moderating effects presented previously differ between China and the United States. For example, output control has the potential to encourage opportunistic behavior depending on the risk preferences of the employee (see Anderson and Oliver 1987, p. 78). Given the differences in the level of uncertainty avoidance between China and the United States, this idea suggests that the Chinese are less likely to take advantage of trust under output control because they perceive higher performance risk than their U.S. counterparts do. In other words, the positive effect of the interaction of supervisee trust with output control on sales performance would be stronger in China than in the United States. However, we do not offer specific hypotheses about the relative effects of the interactions between supervisee trust and the moderating variables on sales performance other than the implicit notion that each may differ between the two samples. This is because it is not clear from the current literature whether the different propensities to abuse trust by people from the two different cultures will be maintained in each of the contingencies examined here. Therefore, the issue of the differential moderating effects between China and the United States is an exploratory aspect of our study that we address in our empirical analysis.
Sample and Data Collection
The Chinese sample for the study consisted of sales employees of firms in the electronics, information technology, software development, biotechnology, and other high-technology sectors. We randomly selected the sample from a sample frame of firms located in Beijing's High Technology Experimental Zone. We contacted chief executive officers of 250 firms to introduce the study and encourage participation. A total of 150 firms agreed to participate in the study. Using a list of sales employees, we randomly selected 3 from each firm for the study, which resulted in a sample of 450 salespersons. We collected the data through on-site interviews. We assured confidentiality to all respondents to encourage candid responses. Our data collection efforts yielded 215 completed questionnaires (i.e., those who agreed to participate and did participate in the interviews), for a participation rate of 48% (215 of 450). Missing data and list wise deletion reduced the current analytic sample to 157, for an effective participation rate of 34.9%.4 To test whether our respondents were different from nonrespondents, we obtained by telephone demographic data from 30 nonrespondents (salespeople who agreed initially to participate but then refused participation at the time of the study) to compare with those of the study participants. We found no statistically significant differences in age, tenure, education, and sales experience. The U.S. sample frame, supplied by a commercial list provider, consisted of 3000 salespeople from the same types of industries. We selected at random 1000 salespeople but were left with 750 after deleting those who worked in nonmanufacturing firms. We collected the data through a mail survey and obtained 190 usable responses after two follow-ups. This response rate of 25% is similar to that of other studies that use a similar methodology (e.g., Siguaw, Brown, and Widing 1994). We found no significant differences between early and late respondents in the U.S . sample, which indicated that nonresponse bias was not a major problem (Armstrong and Overton 1977).
Regarding salespeople's characteristics, 61% and 64% were less than 34 years of age in the Chinese and U.S. samples, respectively. Other characteristics were as follows: sex (male: China = 74%, U.S. = 79%); education below university level (China = 60%, U.S. = 58%), average company tenure (China = 3.1 years, U.S. = 6.2 years), sales experience (China = 4.2 years, U.S. = 12.9 years), and average number of hours worked per week (China = 44.6, U.S. = 48.8). The t-test results showed that the U.S. salespeople were more likely to have higher company tenure and higher sales experience and tended to work more hours per week than their Chinese counterparts.
Measurement Development and Validation
The Appendix provides the measures for all the constructs in the study. All variables were measured on a five-point Likert-type scale that focused on the firm's most recent new product. All the scales, except the one for sales performance (anchored at 1 = "no extent" to 5 = "to a great extent"), were anchored at 1 ("strongly disagree") and 5 ("strongly agree"). The research instrument was developed on the basis of prior studies in the West. For the Chinese sample, the English questionnaire was translated into Chinese by a Chinese marketing professor educated in the West who had significant knowledge of marketing issues in China. Two doctoral students then independently back-translated it into English to verify its accuracy. We conducted 20 in-depth interviews with sales supervisors and salespeople to ensure the face validity of the measures. At these interviews, each respondent was probed regarding the relevance and completeness of the measures. As an example, supervisee trust needed precise translation. It could refer to integrity, credibility, or reputation and character of a person, as in credit rating in business circles or reliance on personal integrity in trading relationships (xinyong). Our measures of supervisee trust capture "personal trust" (xinren), reflecting the degree of the supervisor's personal attachment and emotional bond with the salesperson and genuine care and concern for the salesperson's welfare (McAllister 1995). These items are similar to Rich's (1997) measures, which reflect the salesperson's faith in the supervisor's benevolence and fairness.
In light of TCA's assumption that opportunistic behavior results from information and behavioral uncertainty, other variables that may influence supervisee trust are the complexity of the exchange object and the market environment (Williamson 1985). For example, environmental uncertainty influences trust in marketing exchange because it affects the level of conflict and understanding between the parties in the relationship (Ganesan 1994; Geyskens, Steenkamp, and Kumar 1998; Moorman, Deshpandé, and Zaltman 1993). Therefore, we controlled statistically for product complexity (the extent to which the new product being sold is technically complex and sophisticated; see Bello and Gilliland 1997), competitive intensity (the perceived intensity of market competition), and market volatility (the degree of market and demand changes).
We examined the validity of the measures in a two-step approach recommended by Anderson and Gerbing (1988). First, we conducted exploratory factor analysis to assess the underlying factor structure of the items. This analysis also helped us assess the potential problem of common method variance with Harman's one-factor method (Podsakoff and Organ 1986). The results indicated that the first factor did not account for a majority of the variance and there was no general factor in the unrotated factor structure in both samples, which suggested that common method variance was not a problem. Second, we performed confirmatory factor analysis (CFA) to assess the validity of the measures. Because the inclusion of a large number of measures would result in too complex a measurement model for LISREL, Bentler and Chou (1987) recommend that submodels should be analyzed. This approach is well established in the marketing literature (e.g., Doney and Cannon 1997). We ran three separate measurement models, grouping related constructs. The first CFA grouped items measuring sales performance, output, and process controls. The second CFA analyzed measures of supervisee trust, supervisor accessibility, achievement orientation, and role ambiguity. The third CFA model included product complexity, competitive intensity, and market volatility. The fit indices presented in the Appendix indicate that the models fit the data well in both samples. All item standardized loadings for each construct were significant (p < .01), which supports the dimensionality of the constructs.
As reported in the Appendix, the Cronbach's alpha and composite reliabilities exceed the recommended minimum level in both samples. The average variance extracted, which assesses the amount of variance captured by the construct's measures relative to measurement error and the correlations (Φ estimates) among the latent constructs in the model, is also reported. Estimates of .50 or higher indicate validity for a construct's measure. All but two of our constructs achieved this criterion in the Chinese sample (process control and market volatility) and in the U.S. sample (process control and achievement orientation). We assessed discriminant validity of the measures in two ways: First, we conducted a chi-square difference test for all the constructs in pairs to examine whether they were distinct from each other. The process involved collapsing each pair of constructs into a single model and comparing its fit with that of a two-construct model, as suggested by Anderson and Gerbing (1988). In each case, a two-factor model had a better fit than a single-factor model in both the Chinese and the U.S. sample. Second, the constructs evidenced discriminant validity by meeting Fornell and Larcker's (1981) criterion, which requires that the square of the parameter estimate between two constructs (Φ 2). be less than the average variance extracted estimates of the two constructs. Table 1 presents the correlation matrices and descriptive statistics of the measures.
Equality of factor structure and loadings is needed to make comparisons of the inferences about relationships between variables across national cultures. We tested for measurement equivalence in two steps. First, using CFA procedures, we assessed separately whether the number of factors and items that load on each factor are similar across the two samples (Ryan et al. 1999). As shown in the Appendix, although the factor loading weights varied slightly across the samples, each CFA yielded the same number of factors with similar item loading patterns, providing evidence of measurement equivalence. Second, we ran a multiple group analysis for each construct separately, in which each sample served as a separate group (Netemeyer, Durvasula, and Lichtenstein 1991). We estimated two stacked models, one in which the factor loadings across the Chinese and U.S. samples were constrained to be equal and one in which the factor pattern was specified as invariant across the two samples. We compared the constrained and unconstrained models for each construct in terms of the difference in X2 and other model fit indices such as goodness-of-fit index (GFI), comparative fit index (CFI), nonnormed fit index (NNFI), and root mean square error of approximation (RMSEA). The X2 difference between the two models in each case was not significant, suggesting that the pattern of factor loadings is invariant across the two samples.[ 5]
Antecedents of Supervisee Trust
We ran a regression analysis for each sample to test the hypotheses. Following previous studies (e.g., Yang et al. 2000), we then used a t-test to assess differences in the impact of each significant factor between the two samples. We controlled for the number of hours worked per week, market duration of the new product, sales experience, level of education, sex, type of market (consumer [0] versus industrial [ 1]), size of the firm, product complexity, competitive intensity, and market volatility. Table 2 presents the results.
The results indicate an R2 of .54 in the Chinese sample and .46 in the U.S. sample, suggesting that we explain a fair portion of the variance in supervisee trust. H1a, positing a negative relationship between output control and supervisee trust, is not supported in both samples. H2a, predicting a positive effect of process control on supervisee trust, is supported in the Chinese sample (B = .16, p < .05) but not in the U.S. sample (B = .04, n.s.) (t = 3.09, p < .01). Two variables are positively related to supervisee trust in both samples: supervisor accessibility (H3a; Chinese sample: B = .33, p < .001; U.S. sample: B = .26, p < .001) (t = 2.87, p < .01) and achievement orientation (H4a; Chinese sample: B = .34, p < .001; U.S. sample: B = .38, p < .001) (t = -.87, n.s.). Role ambiguity is unrelated to supervisee trust in either sample. The t-test results suggest support for H2b and H3b, indicating that the positive effects of process control and supervisor accessibility on supervisee trust are stronger in the Chinese sample than in the U.S. sample. In our study, the control variable, product complexity, had a statistically significant, positive effect on supervisee trust in both samples: Chinese sample (B = .12, p < .05) versus U.S. sample (B = .10, p < .10) (t = .60, n.s.), though the later effect is marginal. Market volatility had a significant, positive effect on supervisee trust only in the U.S. sample (B = .15, p < .01). These findings suggest that the complexity of the new product and market volatility influence supervisee trust.
Contingent Effects of Antecedents of Supervisee Trust on Sales Performance
We tested the moderating hypotheses with hierarchical moderated regression analysis (Aiken and West 1991). We ran an initial regression with the control variables, supervisee trust, and the moderator variables to determine their main effects. We added the hypothesized interactions in the second model. We mean-centered the constituent variables before creating the interaction terms to eliminate multicollinearity (Aiken and West 1991). We found that the variance inflation factors were well below the cutoff of 10, which suggests that multicollinearity is not a problem.
The results presented in Table 3 indicate that the addition of the interaction terms to the main effects model increases R2 by 11% (ΔF = 4.86, p < .001) in the Chinese sample and by 6% (ΔF = 2.80, p < .01) in the U.S. sample. We do not find support for H6a and H6b, which involve the main effect of supervisee trust on sales performance. H7a, which posited that supervisee trust is likely to enhance sales performance when output control is high, is supported in the Chinese sample (B = .22, p < .01) but rejected in the U.S. sample (B = -.18, p < .05) (t = 2.01, p < .05). H7b is not supported, because the interaction between supervisee trust and process control is not significantly related to sales performance in either sample. H8a, positing that supervisee trust will lead to lower sales performance when supervisor accessibility is high, is supported strongly in the Chinese sample (B = -.34, p < .001) and marginally in the U.S. sample (B = -.15, p < .10) (t = 4.55, p < .01). H8b is supported in both samples (Chinese sample: B = .34, p < .001; U.S. sample: B = .25, p < .01) (t = 3.22, p < .05), which indicates that supervisee trust enhances sales performance when achievement orientation is high. Finally, H8c, positing that the inter-action of supervisee trust and role ambiguity is positively related to sales performance, is supported in the Chinese sample (B = .29, p < .001) but marginally rejected in the U.S. sample (B = -.11, p < .10) (t = 3.11, p < .01). As the results indicate, t-tests for the standardized coefficients within each sample showed that the effects of the interaction of supervisee trust with output control, supervisor accessibility, achievement orientation, and role ambiguity are stronger in the Chinese sample than in the U.S. sample. An interesting pattern of results in Table 3 is that the control variables, particularly sales experience, level of education, sex, and competitive intensity had a statistically significant influence on sales performance in the U.S. sample but not in the Chinese sample.
Despite limited empirical evidence, the growing body of work on trust in marketing and other contexts provides a normatively positive view of trust. Drawing on previous research (e.g., Granovetter 1985; Shapiro 1987), we argue that trust carries a risk of betrayal and therefore the positive view of trust needs reassessment and extension. Consistent with this view, the results of the current study highlight the message that supervisee trust may not always enhance sales performance. We show that in some situations, supervisee trust may provide the conditions for opportunistic behavior that hinders sales performance. This occurs largely because of the salesperson's interpretation of the supervisor's motives from sales controls and relational behaviors, which in turn affects his or her perception of performance risk. Overall, our results suggest that, theoretically, the potential antecedents of supervisee trust may be categorized into four groups: ( 1) those that engender supervisee trust and enhance its effect on sales performance (e.g., achievement orientation in both the Chinese and the U.S. samples), ( 2) those that engender supervisee trust but have no effect on and/or hinder its impact on sales performance (e.g., process control in the Chinese sample, supervisor accessibility in both samples), ( 3) those that do not engender supervisee trust but enhance its effect on sales performance (e.g., output control and role ambiguity in the Chinese sample), and ( 4) those that do not engender supervisee trust but hinder its impact on sales performance (e.g., output control and role ambiguity in the U.S. sample). Theoretically, our study provides insight into why the antecedents of supervisee trust might provide the conditions under which supervisee trust enhances sales performance but also under which behaviors detrimental to sales performance are likely to occur.
This study suggests that there may be few differences between Chinese and U.S. salespeople regarding the antecedents of supervisee trust. For example, the evidence suggests that supervisor accessibility and achievement orientation influence supervisee trust in both samples. The only exception is that process control is related positively to supervisee trust in the Chinese sample but not in the U.S. sample. This finding supports the assertion that given their relatively high-uncertainty avoidance culture, Chinese salespeople may perceive process control as more supportive and nurturing than their U.S. counterparts do. Note that some researchers have found that process control leads to opportunistic behavior in the interfirm context (John 1984) and that it may reduce trust (see Das and Teng 1998). Our findings in the Chinese sample contrast with this viewpoint, suggesting that the relationship may be culturally specific.
Regarding the moderating hypotheses, the results suggest that output control ensures a positive impact of super-visee trust on sales performance in the Chinese sample. This finding provides support for arguments made in marketing (e.g., Aulakh, Kotabe, and Sahay 1996), management, and social psychology (e.g., Brockner et al. 1997; Shapiro 1987) literature that trust is more important for performance in situations of high performance risk, because opportunistic behavior becomes unprofitable. However, contradicting this argument, the opposite effect was found in the U.S. sample. Several reasons may account for this differential finding. First, the high uncertainty avoidance in China suggests that the disadvantages of output control, particularly the shifting of performance risk to the employee, may be perceived more by the Chinese than their U.S. counterparts. Therefore, we argue that supervisee trust becomes more important for sales performance in the former sample than in the latter sample. Second, there is a higher expectation of equity and fairness in risk sharing between the organization and the employee in high-trust cultures (the United States). Consequently, by transferring a disproportionate amount of performance risk to salespeople, output control may damage the basis of supervisee trust in the United States. It may also be that because of the lower uncertainty avoidance among U.S. salespeople, they are more accepting of the risk of abusing trust when performance risk is shifted to them (see Doney, Cannon, and Mullen 1998). Finally, given their high level of individualism, U.S. salespeople under output control may pursue short-term goals through activities that could harm customer relationships and overall sales performance in the long run. These explanations reflect the finding that output control might lead to opportunistic behavior depending on the salesperson's acceptance of risk (see Anderson and Oliver 1987, p. 78) and that output control is n ot always optimal (Basu et al. 1985). Note that unlike output control, process control is related positively to sales performance in both samples. Comparatively, it seems that output control has a more limited role in sales performance than process control, as has been found in other research (see Cravens et al. 1993).
Consistent with our hypothesis, supervisor accessibility ensures a negative impact of supervisee trust on sales performance in both samples. However, this effect is stronger in the Chinese sample than in the U.S. sample. It is possible that in collectivist/low-trust China, supervisor accessibility enhances potential abuse of trust because it reduces performance risk. In addition to reducing performance risk, in the United States, supervisor accessibility may also be perceived as interference and "looking over my shoulder" behavior. Such a behavior contradicts the expectations of salespeople in such a high-trust and individualist society. The evidence suggests that achievement orientation ensures a positive impact of supervisee trust on sales performance in both samples. This finding implies that achievement orientation engenders mutual performance risk and concern for the goals of the organization, both of which restrain opportunistic behavior (see Granovetter 1985; Tyler and Degoey 1996). Our findings therefore support the view that achievement orientation is a vital construct in motivating salespeople to refrain from effort aversion in a trusting relationship (see Kohli 1989).
Role ambiguity implies that the salesperson faces high performance risk and uncertainty. Our finding that role ambiguity interacts with supervisee trust to enhance sales performance in the Chinese sample supports this view. Low-trust and high-uncertainty avoidance cultures exhibit limited information flow between the parties to an exchange. It therefore appears that role ambiguity enhances the impact of supervisee trust on sales performance because it increases the salesperson's perceived risk from opportunistic behavior. In contrast, our results suggest a negative interaction effect in the high-trust and low-uncertainty avoidance context of the United States, where exchange partners are not afraid to share information. Because role ambiguity reflects a lack of communication about responsibilities, it may be that when U.S. salespeople trust their supervisors, they perceive role ambiguity as a sign of the supervisor's bad faith in the relationship. Given their high tolerance of risk, our finding suggests that they may reciprocate with lower sales performance.
In summary, we believe that considering antecedents of supervisee trust as moderators of its impact on sales performance ensures a more insightful understanding of the relationship between supervisee trust and sales performance. The different findings in the two samples suggest that antecedents of supervisee trust may have positive or negative implications for sales performance when matched with supervisee trust, depending on the national context. Theoretically, our study broadens current conceptualizations of the impact of supervisee trust on sales performance into low-and high-trust societal contexts. It offers new avenues for critical study of trust, its antecedents, and its linkage with performance in the current context as well as in other marketing contexts by both managers and researchers.
Managerial implications
Reeder (1987) suggests that to ensure the success of their businesses in China, Western managers need to take the time to build trust with their workers. Our theoretical framework and results have implications for these and other managers. First, our framework is a challenge to the normative view that trust is good for performance: Supervisee trust in this study and various forms of trust in other studies did not exhibit a direct, positive relationship with performance. Our results caution managers that an unquestionable positive view of trust may be too simplistic. As our findings show, supervisee trust may be good, but it is only conditionally good, because its antecedents may offer potential conditions for it to hurt sales performance (see Granovetter 1985; Shapiro 1987).
Second, our findings have some implications for super-vising the sales force in low-and high-trust societies. In low-trust societies, trust is of utmost importance in exchange relationships not only because of its rarity but also because transaction costs are high and time and energy are required to monitor and check for opportunistic behavior. Because people in these societies tend to have high uncertainty avoidance, they are likely to protect themselves by communicating less information and taking conservative actions. The results from the Chinese sample inform managers that by using controls and behaviors that reduce the salesperson's perceived performance risk, supervisors may unwittingly reduce the potential for their benevolence (supervisee trust) to enhance sales performance. This is because salespeople may perceive greater gains than losses from opportunistic behavior under such conditions. In contrast, when managers use controls and behaviors that shift performance risk to the salesperson, they encourage more trustworthy behavior that enhances sales performance. This is a radical suggestion, but it is consistent with the theoretical logic that only under conditions in which opportunistic behavior is self-defeating will an exchange partner attach greater importance to trust and refrain from abusing trust (Noteboom, Berger, and Noorderhaven 1997). However, our findings from the United States suggest caution in shifting disproportionate performance risk to the salesperson. Given that U.S. salespeople are individualists in a high-trust environment, have better coping strategies, and can tolerate risk, they are less willing to shoulder disproportionate performance risk for reasons of fairness and equity. Thus, unlike in China, the results suggest that output control and role ambiguity may combine with supervisee trust to hinder sales performance in the United States. It appears that in the U.S. context, output control and role ambiguity may violate the b asis of supervisee trust.
The preceding discussion implies that in both low-and high-trust societies, managers should view more critically the implications of sales controls and relational behaviors for the development and efficacy of supervisee trust. As we have shown, social exchange theory suggests that trust evolves over time on the basis of a series of observations, experience, and repeated interactions (see Mayer, Davis, and Schoorman 1995). Because it takes time to build supervisee trust and make it effective, the situations described in this study involve potential trade-offs and should be carefully crafted. For example, our findings imply that in both China and the United States, managers must enhance their accessibility to salespeople when they want to build trust (perhaps at the initial stage of the relationship). However, they must reduce such accessibility after trust has been built. They must also use an achievement orientation style to build and enhance the effect of supervisee trust. However, unlike in the high-trust context of the United States, in the low-trust context of China, output control may need to be emphasized when supervisee trust has already been built. This is because it is likely to prevent opportunism and thus enhance the impact of supervisee trust on sales performance. To build supervisee trust in the low-trust and collectivist Chinese context, managers may need to emphasize process control. The logic is that the Chinese attach greater importance to the care, support, and nurturing benefits of process control than to the loss of autonomy and self-control implied by this control method (see Oliver and Anderson 1994).
In summary, accepting that China is a low-trust society and the United States is a high-trust society, our study sheds some light on what is likely to happen more generally in supervisor-subordinate relationships in low-and high-trust environments (e.g., low-/high-trust sectors and organizations in high-/low-trust cultures). In both low-and high-trust contexts, managers may need to give attention to factors that engender supervisee trust and enhance its effectiveness on sales performance (e.g., achievement orientation) when the objective is to build trust and at the same time enhance its impact on sales performance. In a low-trust context, if trust has already been built, managers may need to focus on factors that do not engender supervisee trust but enhance its effect on sales performance (e.g., output control and role ambiguity). In a high-trust context, however, these factors may be detrimental to performance because they suggest a lack of equity and fairness in the exchange relationship. If managers are interested only in building trust, our results suggest that they may need to focus on factors that engender supervisee trust irrespective of their impact on its effectiveness (e.g., process control in China, supervisor accessibility and achievement orientation in both samples). These implications suggest that supervisee trust (and other forms of trust) may involve a dilemma and that managers must perform a delicate balancing act in building and in enhancing its effectiveness in different cultures. As Shapiro (1987, p. 651) comments,
The paradox of trust is akin to the choice between Type I and Type II errors. Should procedural constraints of trust be set so narrowly that desirable agency behavior is deterred or so flexibly that inappropriate behavior is tolerated? Most often, principals equivocate: they really hope that trustees do not take their instructions too literally yet simultaneously fear that they will not.
We hope that the results of our study make this dilemma more manageable.
Limitations and Further Research
Even though our research extends and enriches the marketing literature, it has several limitations that must be taken into account in the interpretations of the findings. First, previous research has shown that honesty is an important dimension of trust in marketing exchanges, but our study does not address this line of inquiry. Our focus on the benevolence dimension of trust and the omission of the honesty dimension of trust represent a missed opportunity. We believe that a comparison of the antecedent and interactive effects of the two dimensions of trust would have yielded useful insights. This is an important issue for further research. Despite this limitation and future research directions, our omission of the honesty dimension of trust does not invalidate the empirical results, because previous research suggests a strong correlation between the two dimensions. Furthermore, it could be argued that a trust scale comprising both benevolence and honesty items has a higher content validity than do scales measuring only a single dimension (see Geyskens, Steenkamp, and Kumar 1998, p. 234). Therefore, we acknowledge that we may have obtained stronger relationships if our trust scale comprised both benevolence and honesty items. However, we believe that the strong association between the two dimensions found in previous studies provides a good foundation for arguing that our empirical results still appropriately illustrate actual associations.[ 6]
Second, the study focused on the supervisor-salesperson relationship in the context of selling the most recent new product introduced by the firm. Therefore, we make no claims about the generalizability of the findings beyond this context. Third, the study identified relationships, not causes, because it is cross-sectional. We therefore cannot rule out the possibility that the hypothesized relationships could be reversed. For example, it could reasonably be argued that low supervisee trust could lead to higher role ambiguity. Fourth, factors other than those examined here may influence the formation and efficacy of supervisee trust. Of particular importance is opportunistic behavior that underlies our theoretical arguments, which we did not measure. We found that supervisee trust is unrelated to sales performance in both the Chinese and U.S. samples. Perhaps the relationship is not direct but rather indirect through other variables. Another limitation of our study therefore is that we did not examine potential mediators, such as salespeople's effort, commitment, and perceptions of fairness and equity. Further research should expand our understanding by investigating these and other specific mechanisms through which supervisee trust affects sales performance. We also note that in each of the two samples, two constructs displayed average variance extracted estimates below the recommended level of .50. Although this is not uncommon with marketing constructs (see Netemeyer et al. 1997), it suggests that the domains of these constructs require further development and refinement. Finally, we administered the questionnaire on-site in China. In contrast, we collected the U.S. data through a mail survey. Although we do not believe this influenced the results, we cannot completely discount the possibility.
Beyond these limitations, our results point to two other directions for further research. First, our study argues for more critical study of supervisee trust and its antecedents in further research. Our categorization of the antecedents of supervisee trust suggests that a more promising avenue of research is the investigation of factors that are positive antecedents of supervisee trust and at the same time are positive moderators of its impact on sales performance. Similarly, in the future, attention should be focused on factors that may not engender supervisee trust but nevertheless enhance its impact on sales performance. Research that uncovers more of these two groups of factors in the inter-personal and organizational contexts will provide value to practitioners. A second promising line of research is to extend the conceptual model investigated here to the marketing channel relationships and other interorganizational contexts. In comparison with the supervisor-salesperson dyad, research on trust in the interorganizational context is far more advanced in the marketing literature. Yet as we mentioned previously, this stream of research has been criticized for focusing too much on antecedents of trust and paying little attention to the interactions between trust and other relationship factors for their performance implications (Geyskens, Steenkamp, and Kumar 1998).
This study appears to be one of the few investigating the effects of sales controls and supervisor behaviors on supervisee trust and their contingent effects on the linkage between supervisee trust and sales performance. Our results indicate that supervisee trust does not translate into sales performance in all conditions. Rather, they suggest that supervisee trust must be managed with a careful attention to the potential trade-off effects of its antecedents. We believe that this study opens up new lines of research and hope it inspires more scholars to undertake studies on the contingent value of trust.
[ 1] This line of inquiry and the resulting insights and implications became possible because of an anonymous reviewer's suggestion that we explore the moderating variables as antecedents of super-visee trust.
[ 2] Our focus on benevolence trust is not meant to downplay the importance of the honesty dimension of trust in the salesperson-supervisor context. We address this point subsequently.
[ 3] We owe this explanation to an anonymous reviewer.
[ 4] Several authors have raised concerns about social and Western desirability biases with respect to surveys in the Chinese context, because they create substantial pressure for respondents to appear knowledgeable (e.g., Adler, Campbell, and Laurent 1989). Scholars recommend that to reduce this pressure and allow for candid responses, respondents should be offered a "don't know" option. Consequently, during our interviews, we encouraged respondents to skip questions or specific items they did not want to answer or could not recall.
[ 5] Detailed results of the multiple group analysis are available on request.
[ 6] We owe these ideas to an anonymous reviewer.
Correlation Matrices and Descriptive Statistics of the Chinese and U.S. Samples
Legend for Chart:
A - Variables
B - SP
C - TR
D - OC
E - PC
F - SA
G - AO
H - RA
I - PK
J - CI
K - MV
L - SE
M - ED
N - GD
O - PM
P - HR
Q - FS
R - MS
A
B C D E F
G H I J K
L M N O P
Q R
Mean
3.20 3.84 3.65 3.01 3.30
4.03 1.98 3.98 3.38 3.07
12.99 13.77 48.82
3861
Standard deviation
1.00 .88 .92 .84 1.09
.70 .91 .87 1.03 .88
9.60 13.38 9.95
1825
Sales performance(SP)
1.00 .20** .28** .19* .06
.18* -.23** .13 -.08 -.01
.34** -.25** -.00 .10 .12
.04 .11
Supervisee trust(TR)
.37** 1.00 .31** .30** .47**
.56** -.05 .20** .12 .17*
.15* -.19** -.11 .03 .14*
-.01 -.09
Output control(OC)
.22** .29** 1.00 .39** .14*
.45** .02 .01 -.08 -.14*
.15* -.30** -.13 .06 .20**
.12 .02
Process control(PC)
.47** .48** .40** 1.00 .45**
.35** -.06 .00 .11 -.07
.06 .00 .09 -.20** -.04
-.03 .04
Supervisor accessibility(SA)
.38** .64** .40** .45** 1.00
.44** .02 .05 .18* -.04
.12 -.01 -.05 .02 .09
-.07 .04
Achievement orientation(AO)
.44** .61** .29** .46** .61**
1.00 -.04 .08 .09 .07
-.01 -.18* -.00 .02 .15*
.07 .07
Role ambiguity(RA)
-.02 -.01 -.11 .08 -.04
-.06 1.00 -.06 -.00 -.05
-.19** .06 .03 .06 -.09
.02 .03
Product complexity(PK)
.09 .24* .26** .24** .17*
.24* -.09 1.00 .15* .19*
.20* -.28** .06 -.06 .01
-.04 -.12
Competitive intensity(CI)
.11 .09 .09 .10 .16*
.12 .06 -.00 1.00 .24**
.11 -.08 .13 -.05 -.01
-.06 .01
Market volatility(MV)
-.04 .04 -.01 -.00 .04
-.00 .12 .18* .32** 1.00
-.00 -.09 -.04 -.11 -.02
-.04 -.19**
Sales experience(SE)
.22 .16* .13 .29** .21**
.20* .06 -.06 .03 -.03
1.00 -.11 .14** -.00 .18**
.10 .04
Level of education(ED)
-.18* -.14 -.01 .17* -.20*
-.12 -.01 .12 -.09 .08
-.14 1.00 .18** -.00 -.38**
-.07 .00
Sex(GD)
-.04 -.11 -.01 -.00 -.07
-.08 -.18* -.09 .06 -.00
-.09 -.02 1.00 -.07 -.27**
-.04 .10
Product market duration(PM)
.12 .00 .14 .11 .15
.00 -.22** .11 -.04 -.14
.20* .17* -.12 1.00 -.01
-.00 -.05
Hours worked per week(HR)
.14 .02 .06 .10 .11
.06 -.10 .18 -.02 -.02
.05 -.01 -.10 .03 1.00
.06 .06
Firm size(FS)
.04 .08 -.05 -.11 -.13
.01 .07 .02 -.05 -.06
.00 .13 -.07 .14 .02
1.00 .18**
Type of market served(MS)
.12 .00 .05 -.04 .16
.05 -.05 -.10 .12 -.12
-.06 -.06 -.00 .03 .08
.14 1.00
Mean
3.49 3.69 4.17 4.07 3.40
3.99 2.36 3.67 3.87 307
4.29 17.81 45.22
5792
Standard deviation
.80 .92 .67 .94 1.05
.81 1.16 .91 1.07 .88
3.29 14.65 11.97
2646
*p < .01.
**p < .001.
Notes: The Chinese sample is below and the U.S. sample is above the diagonal.
Regression Analysis of Antecedents of Supervisee Trust (Standardized Coefficients)
Chinese Sample U.S. Sample
# t-Value # t-Value
Control Variables
Number of hours worked per week -.02 -.38 .01 .23
Product market duration .02 .40 -.07 -1.10
Sales experience -.03 -.54 .08 1.20
Level of education -.01 -.25 -.03 -.44
Sex .06 .97 -.04 -.69
Type of market served -.10 -1.61* -.08 -1.38
Size of firm -.04 -.68 -.01 -.31
Product complexity .12 1.84* .10 1.47[t]
Competitive intensity .01 .08 -.01 -.25
Market volatility[a] .02 .24 .15 2.25**
Independent Variables
Output control -.01 -.27 .07 .98
Process control[a] .16 1.87* .04 .58
Supervisor accessibility[a] .33 3.88*** .26 3.48***
Achievement orientation .34 3.84*** .38 4.96***
Role ambiguity -.03 -.51 -.01 -.18
R2 .54 .46
Adjusted R2 .48 .41
F-value 9.70*** 8.97***
N 134 175[t]p < .10 (one-tailed).
*p < .05 (one-tailed).
**p < .01 (one-tailed).
***p < .001 (one-tailed).
[a]Significant difference in effects between the two samples (p < .05).
Moderated Regression Analysis of the Effect of Supervisee Trust on Sales Performance (Standardized Coefficients)
Legend for Chart:
A -
B - Chinese Sample, Model 1, beta
C - Chinese Sample, Model 1, t-Value
D - Chinese Sample, Model 2, beta
E - Chinese Sample, Model 2, t-Value
F - U.S. Sample, Model 1, beta
G - U.S. Sample, Model 1, t-Value
H - U.S. Sample, Model 2, beta
I - U.S. Sample, Model 2, t-Value
A
B C D E
F G H I
Control Variables:
Hours worked per week
.05 .66 .08 1.19
-.02 -.26 -.04 -.74
Product market duration
-.08 -1.08 -.03 -.43
.12 1.76* .16 2.40**
Sales experience
.11 1.47[t] .06 .88
.31 4.09*** .31 4.22***
Level of education
.02 .34 -.02 -.29
-.20 -2.47*** -.21 -2.66**
Sex
.05 .69 .05 .76
.13 1.81* .14 1.99*
Type of market served
-.01 -.16 -.06 -.78
.12 1.76* .09 1.38
Size of firm
.08 1.11 .12 1.71*
-.03 -.52 -.04 -.67
Product complexity
.05 .64 .06 .89
.02 .29 .03 .52
Competitive intensity
.03 .41 .05 .64
-.17 -2.41*** -.19 -2.68**
Market volatility
-.11 -1.27 -.17 -2.05*
.05 .78 .03 .43
Independent Variables:
Supervisee trust
-.03 -.29 .03 .36
.05 .65 -.06 -.64
Output control
-.03 -.36 -.01 -.11
.10 1.13 .07 .83
Process control
.19 1.88* .14 1.44*
.15 1.74* .16 1.85*
Supervisor accessibility
.15 1.40* .17 1.66*
-.07 -.81 -.05 -.60
Achievement orientation
.29 2.66*** .25 2.21**
.04 .48 .14 1.40*
Role ambiguity
-.03 -.45 -.13 -1.69*
-.16 -2.40** -.19 -2.73***
Relevant Interactions:
Supervisee trust:
x Output control(a)
.22 2.62**
-.18 -1.77*
x Process control
.05 .52
-.02 -.20
x Supervisor accessibility(a)
-.34 -3.03***
-.15 -1.54*
x Achievement orientation
.34 3.07***
.25 2.46**
x Role ambiguity(a)
.29 3.40***
-.11 -1.47*
R(2)
.36 .47
.31 .37
Adjusted R(2)
.28 .38
.24 .28
F-value
4.46*** 5.09***
4.42*** 4.23***
Incremental R(2)
.11
.06
F-value for Incremental R(2)
4.86***
2.80**
N
138 138
170 170[t]p < .10 (one-tailed).
*p < .05 (one-tailed).
**p < .01 (one-tailed).
***p < .001 (one-tailed).
[a]Significant difference between the two samples (p < .05).
DIAGRAM: FIGURE 1: Conceptual Framework
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CFA Results of Measures
Legend for Chart:
A - Constructs, Sources, and Measurement Items
B - Chinese Sample, Standardized Factor Loading
C - Chinese Sample, t-Value
D - U.S. Sample, Standardized Factor Loading
E - U.S. Sample, t-Value
A
B C D E
Sales Performance (Sujan, Weitz, and Kumar 1994)
(Chinese sample: beta = .88, CR = .75, AVE = .50;
U.S. sample: beta = .92, CR = .89, AVE = .59):
1. Contributing to your company's gaining significant
market share.
.71 7.82 .84 11.64
2. Generating a high level of sales.
.63 6.03 .92 13.38
3. Quickly generating sales from the new product.
.88 9.71 .75 9.99
4. Identifying major accounts and selling to them.
.81 8.72 .73 9.56
5. Exceeding sales targets.
.73 8.01 .85 11.86
6. Assisting your sales supervisor in achieving his/her
objectives.
.51 5.90 .77 10.26
Output Control (Adapted from Jaworski and MacInnis
1989)
(Chinese sample: beta = .76, CR = .87, AVE = .56;
U.S. sample: beta = .87, CR = .84, AVE = .53):
1. My pay increases and other tangible rewards depend
on how my performance compares with goals.
.80 11.52 .66 8.32
2. If my performance goals are not met, I will be asked
to explain why.
.71 9.66 .66 8.22
3. Performance evaluations of salespeople place primary
weight on results.
.75 10.47 .71 9.09
4. My pay increases and other tangible rewards depend
on the degree to which I achieve specific goals set.
.80 11.52 .88 12.22
5. My immediate supervisor monitors the extent to which
I achieve my performance goals.
.68 9.12 .88 12.17
6. I receive feedback from my immediate supervisor on
the extent to which I have achieved my goals.[a]
7. Specific performance goals are established for my job.[a]
Process Control (Adapted from Jaworski and MacInnis
1989)
(Chinese sample: beta = .79, CR = .79, AVE = .40;
U.S. sample: beta = .81, CR = .84, AVE = .46):
1. My pay increases and other tangible rewards depend
on how well I follow sales procedures.
.76 10.29 .71 8.59
2. My pay increases and other tangible rewards depend
on my knowledge of selling procedures.
.70 9.23 .59 6.86
3. My immediate supervisor monitors the extent to which I
follow established procedures.
.59 7.36 .77 9.21
4. My immediate supervisor evaluates procedures we use
to accomplish the task of selling.
.67 8.75 .80 9.68
5. My immediate supervisor modifies the procedures if
desired results are not obtained.
.52 6.38 .68 8.08
6. Primary weight in evaluating salespersons' performance
is placed on sales behavior.
.49 5.98 .74 9.01
7. Salespeople are accountable for their actions in selling
regardless of the results they achieve.[a]
8. I receive feedback on how I accomplish my goals.[a]
Model Fit Indices:
China: x(2)= 189.07 (p = .0), x(2)/d.f. = 1.68, RMSEA = .05, GFI = .89,
CFI = .92.
U.S.: x(2)= 268.34 (p = .0), x(2)/d.f. = 2.39, RMSEA = .07, GFI = .87,
CFI = .89.
Model 2:
Supervisee Trust (McAllister 1995)
(Chinese sample: beta = .88, CR = .89, AVE = .61;
U.S. sample: beta = .87, CR = .86, AVE = .57):
1. My supervisor and I have a sharing relationship; we
freely share our ideas, feelings, and hopes about the
work we do.
.86 11.63 .82 10.68
2. I can freely talk to him/her about difficulties I am
having at work and know that he/she wants to listen.
.87 11.97 .86 12.87
3. If I share my problems with my supervisor, I know
he/she would respond constructively and caringly.
.77 10.90 .94 14.84
4. We both would feel a sense of loss if we could no
longer work together.
.76 10.71 .55 7.15
5. I would have to say, my supervisor and I have made
considerable emotional investments in our working
relationship.
.77 10.84 .52 6.82
Supervisor Accessibility (Adapted from Oliver and
Anderson 1994)
(Chinese sample: beta = .84, CR = .88, AVE = .65;
U.S. sample: beta = .89, CR = .90, AVE = .70):
1. My supervisor is available to meet with me.
.75 10.61 .54 7.11
2. My supervisor spends time with me.
.79 11.34 .93 15.26
3. My supervisor makes joint sales calls with me.
.83 12.27 .95 15.89
4. My supervisor observes my performance in the field.
.85 12.62 .86 13.30
Achievement Orientation (Adapted from Oliver and
Anderson 1994)
(Chinese sample: beta = .84, CR = .91, AVE = .71;
U.S. sample: beta = .78, CR = .75, AVE = .43):
1. My supervisor shows that he/she has confidence in
my ability to meet most objectives.
.91 13.78 .74 9.48
2. My supervisor lets me know he/she expects me to
perform at my highest level.
.81 11.74 .74 9.64
3. My supervisor consistently sets challenging goals for
me to attain.
.78 11.19 .52 6.30
4. My supervisor encourages continual improvement in
my performance.
.87 12.79 .61 7.27
Role Ambiguity (Singh and Rhoads 1991)
(Chinese sample: beta = .93, CR = .91, AVE = .61;
U.S. sample: beta = .87, CR = .86, AVE = .59)
1. I am not sure how much service I should provide to
customers.
.76 10.85 .84 12.30
2. I am not sure which specific company strengths I
should present.
.78 11.27 .80 11.40
3. I am not sure which product benefits I should highlight
to customers.
.84 12.68 .68 9.12
4. I am not sure how I am expected to handle customer
objections.
.78 11.37 .69 9.29
5. I am not sure how I am expected to handle unusual
customer problems and situations.
.80 11.59 .69 9.21
6. I am not sure how I am expected to interact with
customers.
.74 10.39 .70 9.44
Model Fit Indices:
China: x(2)= 248.32 (p = .00),x(2)/d.f. = 1.73, RMSEA = .05, GFI = .87,
CFI = .94.
U.S.: x(2)= 270.31 (p = .00), x(2)/d.f. = 1.94, RMSEA = .07, GFI = .85,
CFI = .93.
Model 3:
Product Complexity (Bello and Gilliland 1997)
(Chinese sample: beta = .81, CR = .77, AVE = .54;
U.S. sample: beta = .82, CR = .69, AVE = .63):
1. Simple/unsophisticated-sophisticated
.64 7.90 .69 10.04
2. Nontechnical-highly technical
.78 9.49 .91 13.92
3. Low engineering content-high engineering content
.77 9.41 .77 11.30
Competitive Intensity (New Scale)
(Chinese sample: beta = .88, CR = .87, AVE = .71;
U.S. sample: beta = .78, CR = .77, AVE = .53):
1. Few-many competitors
.90 13.49 .72 9.81
2. Weak-strong competition
.84 12.19 .78 10.67
3. Few-many competing products
.78 11.10 .70 9.59
Market Volatility (Adapted from Bello and Gilliland 1997)
(Chinese sample: beta = .73, CR = .72, AVE = .40;
U.S. sample: beta = .82, CR = .82, AVE = .53):
1. Stable-unstable
.65 7.72 .70 9.89
2. Certain-uncertain
.70 8.38 .65 8.93
3. Changes slowly-changes rapidly
.61 7.16 .79 11.37
4. Predictable-unpredictable
.56 6.49 .78 11.21
Model Fit Indices:
China: x(2)= 51.75 (p = .01), x(2)/d.f. = 1.61, RMSEA = .05, GFI = .94,
CFI = .96.
U.S.: x(2)= 50.82 (p = .01), x(2)/d.f. = 1.58, RMSEA = .05, GFI = .95,
CFI = .97.
[a]Items deleted because of high cross-loadings.
Notes: CR = composite reliability, AVE = average variance extracted.
~~~~~~~~
By Kwaku Atuahene-Gima and Haiyang Li
Kwaku Atuahene-Gima is Professor of Innovation Management and Marketing, Department of Management, City University of Hong Kong. Haiyang Li is Assistant Professor of Strategic Management and Innovation, Department of Management, Lowry Mays College & Graduate School of Business, Texas A&M University. The authors thank Dean Tjosvold, Peter Walters, and the four anonymous JM reviewers for the thoughtful comments and suggestions that helped improve this article immeasurably. The authors gratefully acknowledge the assistance of Ashok Gupta and Charles Blankson in data collection.The work reported in this article was supported by a g rant from City University of Hong Kong (project number 7000918).
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Record: 199- When Is a Preannounced New Product Likely to Be Delayed? By: Wu, Yuhong; Balasubramanian, Sridhar; Mahajan, Vijay. Journal of Marketing. Apr2004, Vol. 68 Issue 2, p101-113. 13p. 1 Diagram, 3 Charts. DOI: 10.1509/jmkg.68.2.101.27792.
- Database:
- Business Source Complete
When Is a Preannounced New Product Likely to Be Delayed?
Consider that a firm announces a deadline for a new product introduction. Conditional on such a preannouncement, how must an external observer evaluate whether the product will be delayed beyond that deadline? Using data collected from managers in the computer hardware, software, and telecommunications industries, the authors present an analysis that demonstrates that delays in new product introductions beyond preannounced deadlines can be jointly explained by factors related to ( 1) the firm's motivations to delay the product, ( 2) the presence of constraints that prevent delay (or the availability of opportunities to delay the product), and ( 3) the firm's abilities pertaining to product development.
A new product preannouncement (NPPA) is a formal, deliberate communication that releases information about a product well in advance of the product's actual introduction (Eliashberg and Robertson 1988; Koku 1998). Preannounced products are often delayed, particularly in technology-intensive industries. For example, as many as 47% of 123 key software products that were announced before introduction during 1985-1995 were delayed by more than three months (Bayus, Jain, and Rao 2001). According to the Standish Group's 1995 survey of 8000 U.S.-based software projects, 84% did not finish on time, on budget, or with all features installed (Hoch et al. 2000). Furthermore, more than 30% of the projects were cancelled before completion.
When a product is preannounced, delays in introduction can cause a slew of problems for partner firms and customers. Delays can also hurt the announcing firm. Hendricks and Singhal (1997) study 101 firms that did not meet product introduction deadlines and find that delay announcements, on average, decreased the firms' market value by 5.25% (or, equivalently, by a substantial $119.3 million, measured in 1991 dollars).
However, NPPAs can also be beneficial: They can advertise a firm's presence at the cutting edge of technology, enable customers and partners to prepare for the product, and provide information to the stock market. Preannouncements can also promote social welfare and increase competition (Fisher, McGowan, and Greenwood 1983; Landis and Rolfe 1985).
The phenomenon of product introduction delays beyond preannounced deadlines has attracted attention across managerial, research, and policy arenas. The following question is of particular interest: From the perspective of an observer outside the preannouncing firm (e.g., a customer, a manager in a firm that is a competitor or a partner/complementor), how should a preannounced product introduction deadline be evaluated? After all, talk is cheap.
The literature lacks a systematic empirical analysis of the factors that lead to delays in the introduction of preannounced products; this article addresses the corresponding knowledge gap. Adopting the perspective of an outside observer who has the task of evaluating an NPPA, we theoretically motivate and empirically validate a framework that explains delays in NPPA fulfillment. Ex ante, such an accounting can help the outside observer arrive at more informed, better-reasoned conclusions about specific NPPAs. Ex post, such an accounting can facilitate a richer interpretation of a delay that has already occurred.
In constructing the framework, we draw from the motivation-opportunity-ability (MOA) paradigm that has been employed in other contexts (e.g., Heer and Poiesz 1998; MacInnis, Moorman, and Jaworski 1991). We demonstrate that a robust accounting for product delays beyond the preannounced introduction date must incorporate explanatory variables related to motivation (i.e., whether the managers in the firm want to introduce the product on time), opportunity (i.e., whether certain forces constrain managers from delaying the product), and ability (i.e., whether the firm and its managers are capable of introducing the product on time).
We do not focus here on either the motivations for preannouncements or the decisions regarding the lead time to product introduction (i.e., the length of time between the date of the preannouncement and the promised introduction date). Rather, our focus is on explaining product-launch delay beyond the preannounced deadline, conditional on the preannouncement being made. This approach is particularly appropriate from our adopted perspective of the outside observer.
We first synthesize existing perspectives about NPPAs and then present a conceptual framework and describe the survey methodology. We subsequently discuss the empirical findings and the limitations of our study.
Researchers across disciplines have studied issues related to NPPAs; for example, the issue of whether a firm should preannounce has been considered from both analytical (Farrell and Saloner 1986) and empirical (Eliashberg and Robertson 1988) perspectives. Farrell and Saloner (1986) argue that NPPAs can halt competitive momentum in network-based markets. Eliashberg and Robertson (1988) enrich this perspective by establishing detailed conditions that guide the NPPA decision. For example, they demonstrate that large firms may hold back from NPPAs on account of antitrust concerns and potentially significant cannibalization of existing products. Strategic motivations may drive the NPPAs of yet other firms; the credibility of the preannouncements may depend on the presence or absence of a credible threat to entry (Desai and Srinivasan 1994), the existing reputation of the announcing firm (Levy 1995), and the development costs of the announcing firm (Bayus, Jain, and Rao 2001).
If a firm preannounces, when should it do so? Lilly and Walters (1997) and Kohli (1999) examine this issue of NPPA timing by providing a propositional inventory that describes how factors related to competitors, products, buyers, and the preannouncing firm can influence such timing. Kohli empirically illustrates how purchase patterns, customer learning needs, and expected competitive reactions influence such timing.
Finally, what are the effects of NPPAs and the implications of not living up to them? In this context, Robertson, Eliashberg, and Rymon (1995) examine the likelihood, aggressiveness, and path of a firm's reaction to a competitor's NPPA. In addition to demarcating a range of industry-and signal-related factors that guide such reaction (e.g., the firm's commitment to the product category, available patent protection), Robertson, Eliashberg, and Rymon find that firms may respond to NPPAs that use marketing-mix instruments other than the product itself. Koku (1998) examines stock market reactions to NPPAs and finds that the stock market reacts positively to new product announcements accompanied by detailed information releases rather than NPPAs. Hendricks and Singhal (1997) determine that not living up to NPPAs can erode market capital.[ 1]
The existing literature provides insights into the existence, nature, and effects of NPPAs. However, from the perspective of an outside observer, a systematic examination of factors that may cause a new product to be delayed beyond the preannounced deadline is missing in the literature. Research on this issue represents a logical extension of existing work in the area.
Multiple factors can cause a product to be delayed beyond a preannounced deadline. First, research on new product development has focused on how the lack of firm-level abilities can delay product introductions. These studies have highlighted the role of technical problems related to design and development, poor management of the development process, and the lack of resources and senior management support. Suggested remedial measures include the implementation of cross-functional teams and concurrent engineering and the allocation of adequate managerial resources toward development (e.g., Cooper 1995; Gupta and Wilemon 1990; Hendricks and Singhal 1997). The firm's internal capabilities play an important role in determining whether a firm conforms to a preannounced deadline; however, they provide only a partial accounting of delays, particularly when the introduction has been preannounced.
Irrespective of a firm's ability to introduce a product on time, it may be motivated to delay a preannounced product. For example, the NPPA may be designed to preempt or respond quickly to a competitor's initiative. In such cases, the NPPA may aim more to prevent customer defection in the short run by promising an upgraded product in the near future. When the threat from the competitor has been successfully eliminated, the incentive to introduce the product on time is diluted.
Finally, even when a firm is motivated to delay product introduction beyond a preannounced deadline, the firm's key customers and partners, who may be adversely affected by such delays, may constrain the firm to introduce the product on time (i.e., pressure from these entities may shrink the opportunity for delay). These entities often schedule their own activities and initiatives on the basis of the expectations set up by the preannouncement. Consequently, from their perspective, delays beyond preannounced deadlines can be inconvenient and expensive.
Taken together, these arguments suggest that any explanation of delays in the introduction of preannounced products must accommodate the announcing firm's ability to introduce the product on time, its motivations to delay the product, and the presence or absence of opportunities to delay the product. Such MOA triads have been applied in the consumer behavior literature to study how consumers process information (Andrews 1988; Batra and Ray 1986; Heer and Poiesz 1998; MacInnis and Jaworski 1989; MacInnis, Moorman, and Jaworski 1991). The motivation-ability framework has also been applied in the marketing and strategy literature to study organizational behaviors (e.g., Chen and Hambrick 1995; Grewal 2001).
We selected specific constructs related to the framework as follows: We began with a large set of variables based on the existing literature on product delays in general, and we then winnowed down the list to focus on a smaller set of salient variables. We achieved this refinement in two ways. First, we examined the literature thoroughly to identify and include constructs that were particularly salient for preannounced products. Second, we conducted in-depth, in-person interviews with eight managers from firms across the computer hardware, software, and telecommunications industries. The interviews provided a practical perspective on product delays in the context of NPPAs and helped us demarcate key constructs related to the announcing firm's motivations, opportunities, and abilities. Figure 1 describes the resulting conceptual framework.
Note that some of the antecedent variables in Figure 1 may influence ( 1) the time to actual launch from the date of the preannouncement (e.g., t1) and ( 2) the preannouncement lead time (i.e., the gap between the date the preannouncement is made and the promised product introduction date; e.g., t<sub>2</sub>). The difference between the two time durations (t<sub>1</sub> - t<sub>2</sub>) constitutes the product introduction delay, which may be negative if the product is launched before the preannounced deadline. Figure 1 thus presents what may be considered a reduced-form model that is particularly relevant from an outside observer's perspective, because such an observer can evaluate an NPPA only after the introduction deadline is announced. Stated differently, we treat the preannouncement date as exogenously determined.[ 2]
Factors Related to Motivation
Competitive objectives. Firms frequently employ NPPAs to communicate plans for a retaliatory move against a competitor and to preempt competitive entry (Rabino and Moore 1989). Kohli (1999) notes that approximately 25% of the preannouncements in his study were made in response to a competitor's announcement or product introduction. Such NPPAs can deliver significant competitive advantages (e.g., IBM's NPPA about its disk-drive system for storing data on mainframe computers flattened the sales of the industry leader EMC) (Lohr 1994).
A new product is likely to be delayed when it is preannounced with competitive objectives. For an NPPA to hinder the momentum of a competitor's product, it must be communicated shortly before or after the competitor's own announcement or introduction. In a firm's haste to act, it may communicate either a new product concept that is at an early stage of development or plans for a product that has not yet been conceived in sufficient detail.[ 3] In each case, there is tension between high levels of uncertainty and the need to convince the market immediately that the firm will have a competitive offering available in the near future. Furthermore, announcements made in reaction to those of competitors are more likely to be "smoke," that is, designed expressly to forestall any deleterious defection of customers and partners while the firm considers a more deliberate response.
Even if a new product preannounced in a preemptive or reactive context has been under sustained development, it may need major changes to match or exceed the competitor's announced or introduced offering. Consequently, market introduction may be delayed. On the basis of these arguments, we propose the following:
H<sub>1</sub>: As the degree to which a firm uses an NPPA as a competitive tool to preempt or react to a competitor's move increases, the delay in introducing the preannounced product increases.
Controlling cannibalization. New product sales can come from market expansion (i.e., demand stimulation), sales of competing products, or cannibalization of sales of the firm's existing products (Kohli 1999). When a new product is expected to cannibalize the sales of existing products, customers may wait for the preannounced product rather than purchase existing offerings. This can reduce cash inflow, thereby placing the firm in a dire financial situation.
The willingness to cannibalize existing products or organizational routines has sometimes been viewed as a desirable trait that promotes radical product innovation and long-term corporate success (e.g., Chandy and Tellis 1998; Copulsky 1976; Kerin, Harvey, and Rothe 1978). In such cases, firms must carefully plan for cannibalization after taking multiple factors into account, including the difference in the profit margins of the old and new products.
In general, though, the fear of cannibalization can discipline firm behavior with respect to NPPAs. The fear of cannibalization can discourage a firm from even undertaking NPPAs (Eliashberg and Robertson 1988). Furthermore, in designing NPPAs, firms that fear cannibalization may ensure that the time between the NPPA and the proposed product introduction deadline is not substantial (Kohli 1999; Lilly and Walters 1997). Finally, when a firm has preannounced, it may believe that it is pressured to meet the preannounced deadline to avoid a situation in which consumers waiting for the product continue to postpone purchases, thereby constricting the firm's cash flow. For example, Osborne Computer released a premature NPPA for its next-generation computer; the NPPA dried out cash inflow from existing computer lines and led Osborne to file for bankruptcy (Casselman 1991). On the basis of these arguments, we propose the following:
H<sub>2</sub>: As the potential for cannibalization of the announcing firm's existing products increases, the delay in introducing the preannounced product decreases.
Factors Related to Opportunity
Market dominance. Dominant firms are held to higher corporate standards, and they value positive public relations and their reputation as important assets. A dominant firm's delayed product introduction is likely to have strong impacts on customers and partners/complementors. The resulting criticism can damage the reputation of the dominant firm.
Dominant firms with significantly delayed products are also more likely to attract antitrust attention from the Federal Trade Commission and other regulators.[ 4] For firms such as IBM and Microsoft, such legal entanglements can absorb precious resources, including managerial attention, and can adversely affect their leadership and innovativeness. Consider this description of IBM's predicament during the early 1970s, when government litigation against the firm was at its height: "At IBM, people got afraid to move. They were afraid to take risks" (BBC News 1999). Likewise, Nicholas Katzenbach, the former U.S. attorney general who led IBM's defense, noted that during the 1970s, senior executives at IBM carried two briefcases, "one for company business and one for the lawsuit" (BBC News 1999).
The immediate effects of NPPAs may well be stronger for dominant firms. A dominant firm's NPPAs can effectively evoke consumers' fears, uncertainties, and doubts and can slow the momentum of a popular rival product (Johnston 1995). However, significant delays in product introduction can hurt the dominant firm's strategic position in the long run, because the only way a firm can trick the marketplace and get away with delayed products in the short run is by cashing in on some of its existing (good) reputation (Levy 1995).
Finally, there is empirical support for the notion that dominant firms tend to be conservative and to conform to the rules in the context of product introduction. For example, large firms are less likely to broadcast NPPAs because they are sensitive to potential antitrust action on account of "market overhanging" (i.e., preannouncing a product far in advance, with the deliberate intent of injuring competitors' sales) (Eliashberg and Robertson 1988). Likewise, it has been established that significant product introduction delays (three months and more) are negatively and significantly related to firm size (Bayus, Jain, and Rao 2001). On the basis of these arguments, we propose the following:
H<sub>3</sub>: As the market dominance of an announcing firm increases, the delay in introducing the preannounced product decreases.
Partner power. Partner power refers to the degree to which partners can penalize the offender when an NPPA deadline is not fulfilled. Such punishment can include negative word of mouth, reduced cooperation, and even termination of the relationship.
Delayed product introductions are particularly harmful for the firm's partners in high-technology industries. When changes in technology are rapid and product life cycles are short, complementary products from partners are often developed in parallel with the focal product and are based on the projected timing and features of the focal product. The NPPAs can encourage partners to undertake the risks involved with such concurrent development. When the preannounced product is delayed, the expected cash flows of partner firms may not materialize, because their products may not have independent markets. For example, when an operating system is late, application software developers may need to stretch budgets for 6 to 18 months or even abandon projects altogether (Scannell and Johnston 1994). The systems integrator and Sun Microsystems reseller DigiNet ceased operations partly because of the inability of Sun's Java programming language to deliver on its promises in a timely manner (Gage and Foley 1998).
Complementary products' added value to the focal product can endow partners with the potential ability to punish the offending focal-product producer; however, that ability is likely to be moderated by the differential distribution of power and mutual dependence of the parties. This ability to punish is curtailed when ( 1) the market for complementary products is competitive, ( 2) the partners are relatively small, ( 3) there are few other ways to get to market apart from associating with the focal product, and/or ( 4) the partners have incurred substantial fixed costs specific to the focal-product producer. In such cases, the focal-product producers are less likely to live up to their commitments; however, in other cases, the partners may wield significant influence over the announcing firm. For example, the influence of partners may be enhanced when the relationship between the announcing firm and its partners is guided by strong relational norms that curtail opportunistic behavior (Heide and John 1992). In other cases, the announcing firm may itself be tightly linked to its partners through either idiosyncratic investments that are difficult to deploy in other relationships or contractual terms that constrain activities related to that relationship (Anderson and Weitz 1992). In these conditions, the adverse consequences of delaying the product are likely to be significant, and the announcing firm will try to meet preannounced deadlines.[ 5] On the basis of these arguments, we propose the following:
H<sub>4</sub>: As partner power increases, the delay in introducing the preannounced product decreases.
Factors Related to Ability
Product innovativeness. Product innovativeness reflects the degree to which new technology and advanced features are embedded in a product. Two innovation dimensions are relevant here: the technology dimension and the market/customer dimension (Abernathy and Clark 1985; Chandy and Tellis 1998).
Product innovativeness increases uncertainty in the product development and marketing contexts and can delay product introduction. First, in the development context, innovative products are associated with increased technological risk (i.e., the firm may not be able to effectively design and efficiently manufacture the product). To control technological risk, firms need to sequence and carefully manage complex development processes, but this can be a challenging task (Krishnan, Eppinger, and Whitney 1997).
In addition, firms may reduce technological risk by designing and rigorously testing new product platforms, seeking new technology partners, and building new knowledge related to the product. These initiatives can take considerable and unpredictable amounts of time. Therefore, increased technology uncertainty can prolong development and delay the product beyond the promised deadline.
Second, innovative products are associated with increased market risk (the risk that the product may not find market acceptance even after successful design and manufacture). Firms typically preannounce innovative products early to advance consumer learning (Eliashberg and Robertson 1988) and to build a sense of familiarity that diminishes resistance to the unknown and unfamiliar (Lilly and Walters 1997). Although preannouncement of an innovative product may help prepare the marketplace for change, it is frequently difficult to control the pace of the development process and, accordingly, to predict the product's introduction date with confidence. Given that uncertainty is gradually clarified as the firm progresses through the development process, deadlines promised in NPPAs for innovative products are more likely not to be fulfilled. On the basis of these arguments, we propose the following:
H<sub>5</sub>: As the innovativeness of the preannounced product increases, the delay in introducing the preannounced product increases.
Interfunctional coordination. Interfunctional coordination refers to the extent to which the work activities of different functions are logically consistent, coherent, and mutually coordinated with respect to preestablished performance objectives (Cheng 1983; Wood and Tandon 1994). Interfunctional coordination has been associated with greater new product success and a stronger market orientation (Cooper 1984; Kohli and Jaworski 1990).
New products are frequently delayed by a lack of coordination both among members of the core product development team and between the product team and functional areas such as marketing and manufacturing (Jenkins 1988). Product teams often accuse functional areas of being overoptimistic about development outcomes and timelines and of disseminating news without careful consultation with developers (Kay 1992). Kay (1992) cites the example of Monarch Computer, which promised its textile industry customers a six-month delivery time frame for a new knitting machine. The director of software development noted, "they were only concerned with selling the machine itself, and it didn't even occur to them that it may be difficult to write the (software) system that drives the machine within the six months" (Kay 1992, p. 81). However, management and marketing had their sides of the story as well. They complained that developers kept working on the product until they were completely satisfied, without realizing the urgency of introducing a product on time to beat competition.
Cross-functional teams are increasingly used in new product development. The members of such teams have collective knowledge that cannot be effectively held by individual members; therefore, coordination is crucial if the embedded knowledge possessed by team members is to be translated into embodied knowledge that is resident in the product (Madhavan and Grover 1998). Cross-functional product development teams can support timely product introductions (Cooper 1995); however, for such teams to work well, effective interfunctional coordination is required. On the basis of these arguments, we propose the following:
H<sub>6</sub>: As the interfunctional coordination in the firm increases, the delay in introducing the preannounced product decreases.
Top management emphasis. Top management shapes an organization's values (Kohli and Jaworski 1990), and its emphasis on timely product introduction can influence the attitudes of lower-level managers toward delay in product launch. If top management believes that a delay in introducing a preannounced product is a mistake that the firm does not want and cannot afford to make, that message would influence the entire product development process, including decisions related to NPPAs. Living up to public commitments would then be a priority for the firm.
Senior managers at many firms work to define and improve the product development process. At EDS, Dick Brown, the chief executive officer, who inculcated a culture of living up to internal and external commitments, noted: "It's easier to miss a budget than a commitment, because a budget is just an accumulation of numbers. A commitment is your personal pledge to get the job done. And that's how we strive to behave as a team" (Fast Company 2001, p. 106). At EDS, the company's leaders know exactly when a client's expectations are not being met. At another consumer product firm, top management established an informal but widely known two-year deadline for bringing new products to market. Managers who consistently met these deadlines were promoted.[ 6]
When top management sets an appropriate tone, lower-level managers strive to introduce the preannounced product in a timely manner. On the basis of these arguments, we propose the following:
H<sub>7</sub>: As top management's emphasis on delivering a preannounced product on time increases, the delay in introducing the preannounced product decreases.
Data Collection
The unit of analysis is an NPPA in the computer hardware, software, and telecommunications industries. These industries preannounce new products more often than other industries. Furthermore, the focus on a limited set of industries ensures sufficient within-sample consistency. Product development processes and environmental factors vary widely across industries; thus, if we studied disparate industries, we would need to control for the substantial variances. Our focus on a set of allied industries reduces the generality of the results to an extent; however, we are reasonably assured that the broad parameters related to development processes and the competitive environment are consistent across the studied firms. This enhances our confidence in the results.
As a first step, we conducted in-depth, in-person interviews with eight managers from firms across the chosen industries. The interviews provided a practical perspective on NPPAs and product delays and helped segregate the antecedents of greatest importance. We acquired a list of potential respondents, including senior marketing executives in the industries, from CorpTech. We restricted participants to strategic business units (SBUs) with 250 or more employees. This ensured, by design, that we did not include small divisions in the surveys, which further enhances the comparability of results. The sampling frame covered 1252 potential respondents.
Following previous work in the area (e.g., Eliashberg and Robertson 1988; Kohli 1999; Robertson, Eliashberg, and Rymon 1995), we used key informant reports as the source for data. Typically, the head of marketing was the key informant. Most potential respondents (more than 80%) were at the level of director or higher. The questionnaire also requested a self-report of the respondent's level of knowledge about the NPPA activities of their SBU. The sample reported a high average score of 6.4 on a seven-point scale. Thus, we are reasonably confident that the respondents were able to comment authoritatively on the issues of interest.
The survey and a personalized cover letter were mailed to potential respondents. We assured potential respondents that their identities would remain anonymous and that we would only report aggregated results. Approximately ten days later, the first reminder letter was mailed. Approximately five weeks after the first copy of the survey was mailed, the second reminder letter and a new copy of the survey were mailed to nonrespondents. The projected incentive for completing the survey was that the research project would help advance managerial knowledge and guide managerial initiatives related to NPPAs; we also promised respondents a copy of the tabulated results, aggregated to ensure that respondents could not be identified. From 1252 mailings, 26 were returned because of inaccurate addresses or absence of the addressees, which reduced the frame to 1226. A total of 201 surveys were returned, for an overall response rate of 16.4%. Among the 201 returned surveys, 82 had no NPPAs in the past two years, and 6 were unusable because of missing input. Thus, we used data from 113 respondents for the final analysis.
This response rate is reasonable, and even encouraging, for the following reasons: First, the survey covered questions about the SBUs' NPPAs and product-delay times. In general, answers to these questions are confidential and/or sensitive. Second, the targeted respondents were typically heads of the marketing function (marketing vice presidents or marketing directors), who usually operate under significant time constraints (Calantone and Schatzel 2000).
To check for response bias, we classified the data into two groups on the basis of survey-return dates. We classified responses received before six weeks from the first mailing date as early responses (n = 80) and those we received later as late responses (n = 33). We compared response means for the dependent and the explanatory variables across early and late respondents; with the exception of the partner-power construct, the means were statistically indistinguishable (p < .1), suggesting the absence of any significant response bias. Consequently, we pooled the data for further analysis.
Measurement
We asked respondents to provide extensive data for the most recent NPPA in the previous two years. The dependent variable measures the length of delay in the introduction of a preannounced product, which is the difference between the product introduction date specified in the preannouncement and the actual introduction date. To measure the dependent variable, we asked respondents to provide information about whether the product was delayed beyond the preannounced introduction date. If a delay did occur, we asked respondents to report the length of delay beyond the preannounced deadline (in weeks).
We generated items for measuring the independent variables on the basis of the existing literature, field interviews with eight managers, and the theoretical antecedents to the proposed hypotheses. Whenever possible, we used scales from the existing literature; however, we specifically constructed some scales for this study. We pretested the items in several steps. First, several doctoral students reviewed the items for content and readability. Second, six academic experts critically evaluated the items. Finally, eight managers who were knowledgeable in NPPA activities provided feedback. We modified items on the basis of feedback from the different sources.
We established the reliability and validity of the measures using the standard procedure that Gerbing and Anderson (1988) recommend. We performed a Varimax-rotated principle component analysis to investigate the unidimensionality of each construct. We deleted items with significant cross-loadings. The results are displayed in Table 1: Note that despite the relatively large number of constructs, there is a clean separation between the resulting seven factors (we did not prespecify the number of factors). Because of the size of the sample (n = 113), we did not conduct confirmatory factor analysis. Nonconvergence and improper solutions are likely to occur when sample sizes are smaller than 150 (Gerbing and Anderson 1988).
The specific items used, their sources, and the Cronbach alphas associated with their respective scales are detailed in Appendix A. All the included items demonstrated acceptable item-to-total correlations. The alphas for all scales compare favorably with the .7-and-greater criterion that Nunnally (1978) suggests, except for the scale that measures partner power. At .67, Cronbach's alpha for this scale is close to Nunnally's criterion. There are some limitations to our scales, particularly the new ones we developed for this study: We expect that they will be expanded and refined in further research.
Approximately 70% of respondents encountered some delay in introducing preannounced products. For delayed products, the average delay was 9.3 weeks (minimum 1 week and maximum 52 weeks). These findings are broadly consistent with those of Kohli (1999).
For a firm that introduces the product on time, the dependent variable (i.e., number of weeks the product is delayed beyond the preannounced deadline) is zero. Therefore, the dependent variable is left-censored (i.e., there is a mass point at zero representing firms that introduced the product on or before the preannounced deadline). In such cases, ordinary least squares regression yields estimates that are inconsistent and biased toward zero (Judge et al. 1985); instead, a Tobit model provides the appropriate estimation approach (Tobin 1958). We represented independent variables with their average item score.
Before estimation, we tested the data for multicollinearity. Diagnostics indicated that the variance inflation factors associated with each variable were much less than 2. Furthermore, none of the condition indexes associated with the eigenvalues of the variable matrix was greater than 20. In conjunction with the separation of constructs demonstrated in Table 1, these tests imply the absence of significant multicollinearity problems (Johnston 1991, p. 250).
An initial estimation that included dummy variables to capture potential industry-specific effects revealed no significant variation by industry; thus, we pooled the data for the final estimation. For firms with positive delay in product introduction, both a visual inspection of the residuals and a formal Glesjer test did not signal the presence of significant heteroskedasticity (Johnston 1991). We then obtained maximum-likelihood estimates using TSP 4.5. We initially included two variables (technology uncertainty and market uncertainty) to control for the possible influence of the dynamic nature of the technological environment and of rapid changes in product requirements and/or definition on product introduction times. Neither of the variables was significant; thus, we dropped them during the final estimation.
Results
Table 2 presents the results for the Tobit estimation. In the context of motivation, as we hypothesized, objectives related to competitive reaction or preemption are positively related to delays in product introduction (p = .016). Contrary to the proposed hypothesis, motivations related to the control of cannibalization are also positively related to delay (p = .035). We advance explanations for this unexpected finding in the "Discussion" section. In the context of opportunity, we find that delays are reduced when the firm is dominant in its market (p = .084) and when partner power is high (p = .083). Finally, in the context of ability, as we hypothesized, greater product innovativeness is positively associated with delay (p = .04), whereas greater interfunctional coordination and a higher level of top management emphasis on timely introduction are negatively associated with delay (p = .000 and p = .016, respectively).
This article addresses the question, Conditional on a preannouncement being made, how must an outside observer evaluate a firm's preannounced product introduction deadline? We demonstrate that to answer this question, factors related to ( 1) the firm's motivations to delay the product, ( 2) the presence or absence of opportunities to delay the product, and ( 3) the firm's ability to deliver the product on time must be considered. We discuss our findings next, beginning with the unexpected finding that increased cannibalization potential is associated with increased delay in product introduction.
Findings
The positive link between cannibalization potential and increased delay . When a new product that is expected to replace an existing product is preannounced, customers anticipating the former will postpone purchases of the latter. Ostensibly, managers who seek to avoid such delayed sales will strive to introduce the new product on time. Contrary to our hypothesis, though, we find that increased potential for cannibalization is associated with increased delay in product introduction beyond preannounced deadlines.
The following explanations are consistent with our finding. First, when existing products are performing well, managers may seek to postpone the uncertainty caused by their replacement with new, relatively untested products. Such an aversion to the potential downsides of replacement may be more pronounced as the product introduction deadline approaches. Increased aversion of this kind to potential losses in the short run is consistent with the behavioral phenomenon of myopic loss aversion (Benartzi and Thaler 1999).
Second, the new product may present a less attractive profit proposition because it may ( 1) involve higher variable costs than anticipated and thus cannot quickly reap economies of scale in production; ( 2) involve substantial unforeseen expenditures related to advertising, consumer education, and other aspects of information dissemination; and ( 3) need to be priced lower than expected to penetrate the market sufficiently even while embedding more sophisticated and expensive materials and technologies. Managers and design engineers who supervise early stages of product development often focus more on issues related to innovation and technology and do not pay adequate attention to the overall economic case for product development. Furthermore, while focusing on the product under development, managers may tend to ignore firm-level costs and implications related to the product line and customer mix (Kaplan 1990).
The economic disadvantages, both perceived and real, associated with switching to the new product are clarified and magnified as the product introduction deadline approaches. Under these conditions, the existing product increasingly takes on the characteristics of a relatively safe bet. In response, managers tend to delay the new product and treat it as an option, to be exercised at an appropriate time in the future. On the basis of these arguments, and consistent with the argument of Christensen (1997), established firms might fear cannibalization and thus delay the introduction of new technologies.
These arguments are supported by firms' real-world experiences. For example, fearing cannibalization, IBM often delayed or otherwise stymied innovative technologies and products that were ready for market entry (McGrath 2001). Although IBM developed reduced instruction set computing technology, it delayed implementing it and lost ground to competing computer makers. Likewise, IBM intentionally reduced the capabilities of PCjr to avoid cannibalizing the personal computer market. Similarly, in the early 1980s, IBM hesitated to push into desktop computers and workstations because it feared losing business on its high-margin mainframe computers. This was a lapse of great consequence that opened up the markets to new and aggressive competitors, a lapse that hurts IBM to this day.
Among the considered antecedents to product delay, note that such aversion to risk associated with the replacement of the tested and familiar with the new and unfamiliar is in a direction opposite to the stated hypothesis and works in favor of increased delay in the case of fear of cannibalization. Among other antecedents, consistent with the corresponding hypotheses, such aversion to risk, in general, works in favor of a timely introduction of the preannounced product. For example, managers who are averse to risk would work toward a timely introduction when they fear scrutiny on account of market dominance, are apprehensive of backlash from partner firms, or seek to keep top management satisfied.[ 7]
Findings related to the MOA framework. The results suggest that to obtain a relatively complete accounting of factors that delay preannounced products, factors related to each element of the MOA framework must be considered. First, in the context of motivation, not every preannouncement is backed up with honest intentions. For example, firms may not intend to fulfill NPPAs when they seek to protect the substantial cash inflow from existing products or when they announce a new product to react to or preempt a competitor. Second, constraints from the strategic partners and the environment can discipline the announcing firm's product introduction plans and increase the likelihood of timely introduction. Third, some delays in product introduction may derive from the true inability of the announcing firm to uphold its promises made in good faith. For example, the challenges encountered in the development of innovative products are difficult to anticipate ex ante and can be underestimated in the excitement and can-do spirit associated with new product and NPPA activity.
Managerial and Policy Implications
The findings offer guidelines for outside observers who seek to evaluate NPPAs, for managers who design NPPAs, and for policy planners who seek to reduce the anticompetitive effects of product-introduction delays beyond preannounced deadlines.
NPAA evaluation. From the perspective of an outside observer, a blind acceptance of product introduction deadlines in an NPPA can prove a costly mistake; many companies have suffered substantial financial losses while waiting for preannounced products, and some have gone out of business while doing so. In response, though, observers of NPPAs in the field have tended to smother all NPPAs in a blanket of suspicion and to develop rather rough heuristics to deal with the problem. For example, consider the reaction of a senior information technology manager (Brandel 1994, p. 14): "Always wait until Version 1.1 or 1.2[;]... the software version that ends with .0 is always deadly."
When outside observers integrate information about the factors related to all components of the MOA triad as demarcated in this study, they are in a better position to interpret and evaluate NPPAs and to arrive at more informed conclusions about how they should react.
However, before evaluating the NPPA, outside observers must develop a keen understanding of the conditions that lead to the predictors themselves. For example, consider the ability of partners to punish the announcing firm. This ability might depend on the level of mutually embedded assets that the firm and the partners possess (the greater such embeddedness, the lesser is the ability of a partner to walk away and thus the lower is its ability to punish), the availability of other partners to switch to, and the degree to which product standards compel the partners to stick with the announcing firm. After considering such conditions that relate to the predictors, observers can meaningfully judge the salience of each predictor in a specific context.
When a high probability of delay is associated with an NPPA, observers can take steps in response. As we subsequently describe, these steps vary according to whether the observers are managers in partner or complementor firms, managers in competing firms, or customers.
Managers in partner or complementor firms are primarily concerned with reducing their exposure to delay. First, these managers can avoid overcommitment by defensively scheduling projects that embed the preannounced product. Second, they can seek more credible commitments for skeletal versions of the product from the announcing firm: These versions may deliver lower functionality but can be introduced in a shorter time frame. Third, they can coordinate with other partner firms to emphasize collectively the importance of meeting promised deadlines to the announcing firm. Finally, in the absence of other recourse, they can plan around the preannounced product or switch to competing products.
Managers in competing firms are primarily concerned with designing their business strategies to accommodate the moves of the announcing firm. When these managers estimate that the focal firm is not likely to adhere to a promised product introduction deadline, they must avoid the tendency to overreact. This does not imply that they must take no actions in response to the NPPA; rather, the nature and degree of their response must be carefully calibrated to accommodate the likelihood that the preannounced product either may be significantly delayed or may not even materialize. For example, these managers may proceed with plans for developing a competing product to an extent, thus leaving further development and launch as options to be potentially invoked as the announcing firm clarifies its plans and intentions.
A customer evaluating an NPPA can choose between adopting an existing product of the announcing firm (or of a competitor) and postponing the adoption decision (in which case either the preannounced product or any other product can be adopted at some future point). Much as complementor and partner firms do, customers must develop contingency plans in case the preannounced product is delayed and/or an appropriate substitute is unavailable. Furthermore, customers must be particularly careful to avoid the sunk-cost fallacy (i.e., a long wait for a preannounced product must not be used to justify a decision to wait even longer). Instead, customers must dispassionately evaluate the costs and benefits of waiting longer at periodic intervals.
Designing an NPPA. From the perspective of managers in the announcing firm, the results draw attention to conditions that can hinder the implementation of NPPAs according to preannounced deadlines. For example, managers must think twice before instinctively reacting to competitors' NPPAs or aggressively preempting their forthcoming initiatives. It may be tempting to forestall customer migration to the competitor in the short run, but such a move may have significant negative consequences in the long run, including dilution of the firm's marketplace credibility. Likewise, even at an early stage of product development, managers must carefully evaluate whether they are ready to cannibalize a profitable, well-established product with a new product that may be technologically advanced but offers risky market prospects and is less profitable on a per-unit basis. In addition, management should act more conservatively when deciding on introduction times for highly innovative products.
Policy implications. Although there may exist some policy benefits of ensuring that all new products are introduced according to preannounced deadlines, the delays that raise the greatest antitrust and anticompetitive concerns are those that are intentional or strategic (these broadly correspond to the motivation aspect of our analysis). However, as is shown in Appendix B, it is exceedingly difficult to distinguish between strategic and innocent delays. There is an intense debate in the policy arena about whether all substantial delays in preannounced products should be treated equally under antitrust law, regardless of the announcer's intent (much like deceptive advertising under the Lanham Act). Our findings serve as a reminder that to arrive at a fair and balanced evaluation of delays in preannounced products, factors related to opportunity and ability must be considered in parallel with those related to motivation.
Limitations and Further Research
First, although we believe that we targeted the correct key informants and that the informants provided relatively candid responses, extension of the analysis with independently collected information might lead to even greater confidence in the results. Second, our set of key explanatory variables is not exhaustive. For example, researchers might consider variables that represent customer power, development team motivation, and team leadership quality. Whereas problems with measurement inhibited the consideration of customer power in the current analysis, this variable must figure in further research. Third, the scales used must be refined and expanded in further research. This is particularly true for the scale that measures partner power, which currently comprises only two items. Fourth, we focused on a subset of high-technology industries to reduce interindustry confounds. The study can be replicated in other settings.
The following three themes present particularly worthwhile research opportunities in the area: First is an explanation of the entire timeline of NPPA-related activities. In communicating with the marketplace in the context of new product development, managers must decide ( 1) when the NPPA should be made (i.e., the preannouncement date), ( 2) the gap between the preannouncement date and the promised product introduction date (i.e., the preannounced lead time), and ( 3) the actual time of product introduction (this may partly be driven by factors outside of the managers' control). A single model that considers the drivers of each of these decisions and the relationships between these decisions appears to be an appropriate next step.
Second is management of the release of information related to new product development. Little is known about how a firm should manage the entire process of information exchange with the marketplace during the product development process. For example, when and how must management inform the marketplace about delays on an ongoing basis? Such an incremental approach might help the firm better manage the expectations of various constituencies and, in turn, help it manage its own plans to accommodate such delays. Furthermore, the design of innovative NPPAs deserves attention. For example, NPPAs could provide a distribution of probabilities over a range of potential introduction times, as opposed to a single deadline.
Third is the establishment of the timeliness-quality trade-off. Because product introductions tend to be well-defined public acts, research has focused on issues related to the timing of new product initiatives. However, firms can often trade off timeliness against quality (e.g., it may be possible to introduce a product of marginal quality on time). When the focus is solely on introduction timing, the issue of what is being introduced into the marketplace is overlooked. The relationships among NPPA deadlines, product introduction timing, and product quality require closer examination; a focus on timing alone tells but part of the story.
Yuhong Wu and Sridhar Balasubramanian acknowledge support from the Center for Customer Insight at the McCombs School of Business. Yuhong Wu acknowledges support from the Dora Bonham Memorial Fund at the University of Texas. The authors thank Barry Bayus, Richard Briesch, Eli Cox, Robert Prentice, Phillip Zerrillo, and other faculty and doctoral students at the McCombs School of Business and at the Kenan-Flagler Business School for their useful suggestions.
[1] Heil and Walters (1993) use a signaling framework (e.g., Heil and Robertson 1991; Spence 1974) to examine incumbent reactions to new product introductions (rather than to NPPAs).
[2] We thank an anonymous reviewer for this interpretation.
[3] For example, consider Airbus's NPPA about its double-decker 555-seat A3XX jetliner. The A3XX was to compete with Boeing's 747-400 Jumbo, which dominates the long-range, large-carrier market. Boeing responded by preannouncing a 747X Stretch version with a lengthened upper-deck hump, sleeping berths over coach class, and high-quality in-seat entertainment. Boeing then withdrew the plans for the 747X in favor of a smaller, near supersonic passenger aircraft: the Sonic Cruiser. Plans for this plane, too, are now on hold.
[4] The ruling of Judge Stanley Sporkin in Civil Action No. 94-1564 (United States of America v. Microsoft Corporation 1995, § V.B.3) offers evidence of a dominant firm being scrutinized for the practice of aggressive preannouncement: "Microsoft has a dominant position in the operating systems market, from which the [g]overnment's expert concedes it would be very hard to dislodge it. Given this fact, Microsoft could unfairly hold onto this position with aggressive preannouncements of new products in the face of the introduction of possibly superior competitive products."
[5] Although we initially included customer power as an explanatory variable in the model, we dropped it on account of measurement problems.
[6] We thank Abbie Griffin for suggesting this example.
[7] Dominant firms may be less willing to delay preannounced products to avoid cannibalizing existing offerings; that is, the degree of dominance may moderate the estimated positive relationship between fear of cannibalization and introduction delay. Ex post, we estimated a Tobit model with an interaction term to explore for such a moderating effect. Although the interaction term was not significant, this moderating effect deserves closer attention in further research.
Legend for Chart:
A - Construct
B - Items
C - Components 1
D - Components 2
E - Components 3
F - Components 4
G - Components 5
H - Components 6
I - Components 7
A B C D E
F G H
I
Competitive objectives OBJ1 .813 -.052 -.017
-.072 .061 .090
.000
OBJ2 .764 .204 .152
.002 -.084 .043
-.025
OBJ3 .900 .050 -.050
-.015 -.041 -.044
.070
Controlling cannibalization CANN1 .039 .766 .124
-.066 -.122 -.098
.097
CANN2 .034 .778 .067
.104 .023 -.091
.060
CANN3 .122 .751 -.227
-.008 .042 .050
-.131
Market dominance DOMIN1 .050 .002 .732
.085 .311 .281
.025
DOMIN2 .149 .037 .834
-.094 .126 .051
.192
DOMIN3 -.101 -.030 .822
-.214 .131 -.038
-.099
Partner power POWER1 .053 .022 -.075
.874 .071 -.124
-.043
POWER2 -.154 .017 -.134
.788 -.149 -.124
-.095
Product innovativeness INNOV1 .080 -.329 .035
-.076 .593 .191
.123
INNOV2 .075 .244 .192
.057 .694 .189
.190
INNOV3 -.124 -.048 .180
-.033 .665 .137
.225
INNOV4 -.042 .016 -.009
-.019 .783 .019
.008
INNOV5 -.016 -.057 .272
-.015 .713 .011
.138
Interfunctional coordination COORD1 .220 -.060 -.032
-.219 .214 .674
.294
COORD2 .041 .107 .048
-.057 .178 .655
.443
COORD3 .012 -.096 .093
-.040 -.010 .688
.240
COORD4 -.057 -.098 .093
-.076 .157 .733
-.073
Top management emphasis EMPH1 .001 -.038 .004
-.137 .071 .110
.866
EMPH2 .047 .186 .117
.193 .099 .238
.777
EMPH3 -.086 .137 .040
-.101 .283 .041
.709
EMPH4 .083 -.227 .023
-.010 .057 .117
.827
EMPH5 .035 .026 -.044
-.202 .448 .262
.585
Notes: Extraction method is principal component analysis.
Rotation method is Varimax with Kaiser normalization. We did not
prespecify the number of factors. For items, see Appendix A. Legend for Chart:
A - Predictors
B - Estimate
C - Standard Error
D - p-Value
E - Support for Hypothesis
A
B C D E
(Constant)
21.102 7.849 .007 --
Competitive objectives (H<sub>1</sub>)
.455 .189 .016 Yes
Controlling cannibalization (H<sub>2</sub>)
.468 .223 .035 Significant, but in
opposite direction
Market dominance (H<sub>3</sub>)
-.442 .256 .084 Yes
Partner power (H<sub>4</sub>)
-.615 .355 .083 Yes
Product innovativeness (H<sub>5</sub>)
.618 .212 .040 Yes
Interfunctional coordination (H<sub>6</sub>)
-1.116 .247 .000 Yes
Top management emphasis (H<sub>7</sub>)
-.413 .172 .016 Yes
Notes: N = 113. Positive delay: N = 80 (70.8%), Schwarz Bayesian
information criterion = 334.96, and log-likelihood = -316.05.DIAGRAM: FIGURE 1; Explaining Product Introduction Delays Beyond Preannounced Deadlines: An MOA Framework
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Legend for Chart:
A - Construct
B - Scale Items
C - Source
A B
C
Competitive
objectives
α = .79 1. One objective of our last new product
preannouncement was to respond to or
to preempt a competitor's announcement/
introduction of a new product.
2. One objective of our last new product
preannouncement was to discourage customers
from switching to a competitor's product.
3. Our last new product preannouncement was
not related to competitors' new product
introduction activity or plans. (reverse
coded)
New scale.
Controlling
cannibalization
α = .70 1. The sale of the preannounced new product
may come from three sources: (1) sales
replacing our existing products, (2) sales
displacing our competitors' products, and
(3) sales from new customers. Please
estimate the percentage of the new product
sales that was expected to come from a
replacement of the existing product sales.
2. The preannounced product was expected to
greatly reduce the cash inflow from our
existing products.
3. For the preannounced product, the
possibility that customers might delay
purchases of existing products was a
serious concern.
Item 1 is adopted from
Kohli (1999).
Market dominance
α = .78 1. Perceived dominance within the product
category.
2. Perceived leadership within the product
category.
3. Product category market share relative to
that of the top four players.
Items 1 and 2 are
adopted and Item 3 is
adapted from Eliashberg
and Robertson (1988).
Partner power
α = .67 1. Our complementors will need to rely on us
in the future even if our relationship is
not on the best terms. (reverse coded)
2. Our complementors are very dependent on our
product to serve their customers. (reverse
coded)
New scale.
Product
innovativeness
α = .78 1. The preannounced product included innovative
product features.
2. High-quality technological innovations were
embedded in the preannounced product.
3. Compared to similar products developed by
our competitors, the preannounced product
offered unique features/attributes/benefits
to customers.
4. In terms of the embedded technology, the
preannounced product was substantially
more innovative compared to existing
products available in the market.
5. The preannounced product was only a minor
product improvement/modification over
existing products available in the
market. (reverse coded)
Items 1, 2, 3, and 5 are
adapted from Sarin and
Mahajan (2001).
Interfunctional
coordination
α = .75 1. The activities of the different functions
are well coordinated.
2. Management teams from different functions
feel that the goals of their respective
functional groups are in harmony with one
another.
3. We share resources with other functional
units within the organization.
4. Functional units in our organization often
blame each other when products fail.
(reverse coded)
Item 2 is adopted from
Jaworski and Kohli
(1993). Item 3 is
adopted from Narver
and Slater (1990).
Top management
emphasis
α = .86 1. Top managers emphasize that delivering a
new product on time is key to product
success.
2. Top managers clearly communicate the
message that failure to deliver
preannounced product on time causes great
harm to the company's reputation and image.
3. Top managers emphasize that we will not
announce a product unless we are confident
that it will be launched on time.
4. Top managers here generally tolerate
product delays well. (reverse coded)
5. Delivering a product within the preannounced
time frame is a top priority for our senior
managers.
New scale.
Technology
uncertainty
(control)
α = .75 1. In this product category, technology is
changing rapidly.
2. In this product category, technological
changes provide big opportunities.
3. In this product category, a large number
of product ideas have been made possible
through technological breakthroughs.
4. In this product category, technological
developments are rather minor. (reverse
coded)
Adopted from Jaworski
and Kohli (1993)
Market
uncertainty
(control)
α = .73 Indicate how quickly the following factors
change:
1. Customers' preferences for product features
2. Competitors' selling strategies
3. Competitors' products and models
4. Competitors' product pricing
5. Competitors' promotion/advertising
strategies
Items 1, 2, 3, and 5 are
adopted from Maltz and
Kohli (1996).
Notes: Except for Item 1 in the product cannibalization scale,
we measured all items on a seven-point Likert-type scale
(1 = "strongly disagree" and 7 = "strongly agree"). Vaporware has been variously defined as the occurrence of substantial product introduction delays, the absence of promised features, or even the belief that a preannounced product will never ship. However, the most common interpretation of vaporware pertains to the intent to deceive on the part of the announcing firm. Vaporware is controversial precisely because of this; in the absence of a smoking gun (e.g., a transcript of internal meetings or an exchange of email) that captures such intent, the occurrence of vaporware is difficult to prove. As Prentice (1996, p. 1175) argues, delays in NPPA fulfillment may be perfectly innocent and are to be expected:
Of course, not all of the products will meet projected introduction dates. Others may not carry all the promised features. Others may not see the light of day. After all, the design, testing, and manufacturing of high-tech products is a very complicated business. Many projects are so very complex that it is nearly impossible to know when all the glitches will be worked out. Not all delays are foreseeable. Not all broken promises are broken intentionally.
In the proposed MOA framework, the variables related to motivation are the ones that are most likely to be associated with vaporware. However, even when variables related to motivation are relevant and must be accommodated by an outside manager in evaluating an NPPA, it is difficult to prove intent to mislead in a court of law. To prove such intent, it is not sufficient to demonstrate, for example, that the NPPA was made in reaction to an announcement from a competitor. Instead, it must be demonstrated that the information in the NPPA was not merely a reflection of the honest, if overambitious, intentions of the announcing firm but that the NPPA was explicitly designed for anticompetitive purposes.
~~~~~~~~
By Yuhong Wu; Sridhar Balasubramanian and Vijay Mahajan
Yuhong Wu is Assistant Professor of Marketing, Christos M. Cotsakos College of Business, William Paterson University (e-mail: wuy@wpunj.edu).
Sridhar Balasubramanian is Assistant Professor of Marketing, Kenan-Flagler Business School, University of North Carolina, Chapel Hill (e-mail: balasubs@bschool.unc.edu).
Vijay Mahajan is John P. Harbin Centennial Chair in Business, Department of Marketing, McCombs School of Business, University of Texas at Austin, and Dean, Indian School of Business, Hyderabad (e-mail: vijay.mahajan@bus.utexas.edu).
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Record: 200- Why Do Customer Relationship Management Applications Affect Customer Satisfaction? By: Mithas, Sunil; Krishnan, M. S.; Fornell, Claes. Journal of Marketing. Oct2005, Vol. 69 Issue 4, p201-209. 9p. 1 Chart. DOI: 10.1509/jmkg.2005.69.4.201.
- Database:
- Business Source Complete
Why Do Customer Relationship Management Applications
Affect Customer Satisfaction?
This research evaluates the effect of customer relationship management (CRM) on customer knowledge and customer satisfaction. An analysis of archival data for a cross-section of U.S. firms shows that the use of CRM applications is positively associated with improved customer knowledge and improved customer satisfaction. This article also shows that gains in customer knowledge are enhanced when firms share their customer-related information with their supply chain partners. Despite substantial investments in customer relationship management (CRM) applications, there is a lack of research demonstrating the benefits of such investments. In particular, there has been limited research on the role and contribution of CRM applications in managing customer encounters (Bitner, Brown, and Meuter 2000; Meuter et al. 2000). Although marketing and information systems researchers have developed theories about the effect of CRM applications, with some progress toward empirical validation (Jayachandran et al. 2005; Reinartz, Krafft, and Hoyer 2004; Romano and Fjermestad 2003; Srinivasan and Moorman 2005), there is limited knowledge about the effect of CRM applications on a firm's customer knowledge and customer satisfaction. Furthermore, prior research does not shed light on why CRM applications affect customer satisfaction or the role of complementary investments in supply chain management systems.
Against the backdrop of significant investment in CRM applications and limited empirical work on the effect of CRM applications on customer relationships, this article poses the research question, What is the effect of CRM applications on customer knowledge and customer satisfaction? We performed an empirical study using a cross-section of large U.S. firms. This study includes the development of a theoretical model and the collection of archival data from the National Quality Research Center at the University of Michigan and an InformationWeek survey of senior information technology (IT) managers. We examine the role of customer knowledge as a mediating mechanism to explain the effect of CRM applications on customer satisfaction. We also study the moderating effect of supply chain integration in leveraging CRM applications.
We structure the rest of the article as follows: In the next section, we review the literature and develop the hypotheses. Then, we discuss the methodology and present the results. We conclude with a discussion of the implications of the study.
Research Model and Theory
The customer equity literature provides the basic rationale for investing in customer relationships. There is increasing recognition of the importance of managing customer relationships and customer assets. Marketing has moved from a brand-centered focus to a customer-centered approach. Hogan, Lemon, and Rust (2002) argue that the ability to acquire, manage, and model customer information is key to sustaining a competitive advantage. Berger and colleagues (2002) develop a framework to assess how customer database creation, market segmentation, customer purchase forecasting, and marketing resource allocations affect customers' lifetime value to the firm. Hogan and colleagues (2002) extend this work and provide conceptual support for linking customer assets (in terms of customer lifetime value) and financial performance.
Next, we develop the hypotheses for the effect of CRM applications on customer knowledge and customer satisfaction. We also discuss the moderating role of supply chain integration to understand the effect of CRM applications on customer knowledge.
A primary motivation for a firm to implement CRM applications is to track customer behavior to gain insight into customer tastes and evolving needs. By organizing and using this information, firms can design and develop better products and services (Davenport, Harris, and Kohli 2001; Nambisan 2002). Davenport and Klahr (1998) argue that customer knowledge has certain attributes that make it one of the most complex types of knowledge. For example, customer knowledge may be derived from multiple sources and media and may have many contextual meanings. Customer knowledge is also dynamic, and it changes rapidly.
Customer relationship management applications facilitate organizational learning about customers by enabling firms to analyze purchase behavior across transactions through different channels and customer touchpoints. Glazer (1991) provides examples of how FedEx and American Airlines used their investments in IT systems at the customer interface to gain valuable customer knowledge. More recently, firms have invested in an integrated set of tools and functionalities offered by leading software vendors to gather and store customer knowledge. Firms with greater deployment of CRM applications are in a better position to leverage their stock of accumulated knowledge and experience into customer support processes. In addition, firms with a greater deployment of CRM applications are likely to be more familiar with the data management issues involved in initiating, maintaining, and terminating a customer relationship. This familiarity gives firms a competitive advantage in leveraging their collection of customer data to customize offerings and respond to customer needs.
Customer relationship management applications help firms gather and use customer knowledge through two mechanisms. First, CRM applications enable customer contact employees to record relevant information about each customer transaction. After this information is captured, it can be processed and converted into customer knowledge on the basis of information-processing rules and organizational policies. Customer knowledge captured across service encounters can then be made available for all future transactions, enabling employees to respond to any customer need in a contextual manner. Firms can also use customer knowledge to profile customers and identify their latent needs on the basis of similarities between their purchase behaviors and those of other customers. Second, firms can share their accumulated customer knowledge with customers to enable those customers to serve themselves by defining the service and its delivery to suit their needs (Prahalad, Ramaswamy, and Krishnan 2000). The process of customer self-selection of service features provides additional opportunities for firms to learn about their customers' evolving needs and to deepen their customer knowledge.
H1: The use of CRM applications is associated with an improvement in the customer knowledge that firms gain.
Supply chain integration refers to the extent to which a firm shares relevant information about its customers with its supply chain partners. Supply chain integration ensures that products and services offered by various organizational units and suppliers are coordinated to provide a better customer experience. Previous research suggests that integration of IT systems in a firm's value chain is essential to the realization of the full benefits of seamless information sharing and data completeness (Brohman et al. 2003; Gosain, Malhotra, and El Sawy 2005; Rai, Patnayakuni, and Patnayakuni 2005). For example, Fisher, Raman, and McClelland (2000) note that IT-enabled data accuracy is critical for efficient forecasting and to design agile supply chain management processes. Anderson, Banker, and Ravindran (2003, p. 94) argue that "interweaving of IT links throughout the supply chain create[s] value by enabling each member of the supply chain to identify and respond to dynamic customer needs." Creating an integrated IT infrastructure enables organizational units to leverage their resources effectively to address customers' evolving needs (Sambamurthy, Bharadwaj, and Grover 2003). For example, superior customer ratings and the success of customer relationship programs at Saturn, Dell, and Southwest have been attributed to their excellent supply chain management integration (Harvard Business Review 2003). Conversely, industry observers have noted that the failure of many CRM efforts is due to "the propensity of firms to avoid the important 'data transformation and convergence' processes including all transactions, interactions, and networked touch points" (Swift 2002, p. 95). Thus, we expect that firms with greater supply chain integration benefit more from their CRM applications in terms of improved customer knowledge.
H2: Firms with greater supply chain integration are more likely to benefit from their CRM applications and achieve improved customer knowledge.
Customer satisfaction has significant implications for the economic performance of firms (Bolton, Lemon, and Verhoef 2004). For example, customer satisfaction has been found to have a negative impact on customer complaints and a positive impact on customer loyalty and usage behavior (Bolton 1998; Fornell 1992). Increased customer loyalty may increase usage levels (Bolton, Kannan, and Bramlett 2000), secure future revenues (Rust, Moorman, and Dickson 2002), and minimize the likelihood of customer defection (Anderson and Sullivan 1993; Mithas, Jones, and Mitchell 2002). Customer satisfaction may also reduce costs related to warranties, complaints, defective goods, and field service costs (Fornell 1992). Finally, in a recent study, Anderson, Fornell, and Mazvancheryl (2004) find a strong relationship between customer satisfaction and Tobin's q (as a measure of shareholder value) after controlling for fixed, random, and unobservable factors.
Customer relationship management applications are likely to have an effect on customer satisfaction for at least three reasons. First, CRM applications enable firms to customize their offerings for each customer. By accumulating information across customer interactions and processing this information to discover hidden patterns, CRM applications help firms customize their offerings to suit the individual tastes of their customers. Customized offerings enhance the perceived quality of products and services from a customer's viewpoint. Because perceived quality is a determinant of customer satisfaction, it follows that CRM applications indirectly affect customer satisfaction through their effect on perceived quality. Second, in addition to enhancing the perceived quality of the offering, CRM applications also enable firms to improve the reliability of consumption experiences by facilitating the timely, accurate processing of customer orders and requests and the ongoing management of customer accounts. For example, Piccoli and Applegate (2003) discuss how Wyndham uses IT tools to deliver a consistent service experience across its various properties to a customer. Both an improved ability to customize and a reduced variability of the consumption experience enhance perceived quality, which in turn positively affects customer satisfaction. Third, CRM applications also help firms manage customer relationships more effectively across the stages of relationship initiation, maintenance, and termination (Reinartz, Krafft, and Hoyer 2004). In turn, effective management of the customer relationship is the key to managing customer satisfaction and customer loyalty.
H3: The use of CRM applications is associated with greater customer satisfaction.
Although customer knowledge and customer satisfaction by themselves are important metrics for tracking the success of CRM applications, from a theoretical perspective, it is important to consider whether the association of CRM applications with improvement in customer satisfaction is mediated by an improvement in customer knowledge. From a managerial perspective, an understanding of causal mechanisms will shed light on the conditions that facilitate CRM success in terms of customer satisfaction. We posit that the real value of CRM applications lies in the collection and dissemination of customer knowledge gained through repeated interactions. This customer knowledge subsequently drives customer satisfaction because firms can tailor their offerings to suit their customers' requirements. Previous research provides support for this view. For example, Bharadwaj (2000) notes the advantages of gathering customer knowledge from customer encounters and disseminating this knowledge to employees for cross-selling and forecasting product demand. Bolton, Kannan, and Bramlett (2000) provide empirical evidence that IT-enabled loyalty programs enable firms to gain valuable customer knowledge about customers' purchase behavior. Jayachandran, Hewett, and Kaufman (2004) show that customer knowledge processes enhance the speed and effectiveness of a firm's customer response. Better knowledge of customer behavior enables firms to manage and target customers on the basis of evolving service experiences rather than stable demographic criteria, which increases the perceived value of the firm's offering and decreases the chance of loyal customers defecting to the competition. Firms also derive a competitive advantage by making cumulative customer knowledge available to their customers to help those customers manage their internal operations using information from the firm (Glazer 1991). As the preceding discussion suggests, better customer knowledge facilitated by CRM should enable a firm to improve its customer satisfaction. Therefore, we posit that the effect of CRM applications on customer satisfaction is mediated through customer knowledge.
H4: Customer knowledge mediates the effect of CRM applications on customer satisfaction.
Because this research studies the effect of CRM applications on customer satisfaction, we control for other relevant variables to account for alternative and complementary explanations. We control for firms' aggregate IT investments because such investments influence perceived quality, perceived value, and customer satisfaction (Prahalad, Krishnan, and Mithas 2002). We control for firm size, which may influence a firm's ability to benefit from CRM investments as a result of organizational inertia and a greater potential in large organizations for leveraging slack resources. Finally, consistent with previous research, we control for sector differences (manufacturing versus services), which may affect gains in customer knowledge and customer satisfaction.
Research Design and Methodology
A major strength of this study is its use of data on key independent and dependent variables from separate sources to avoid common method bias. We obtained the CRM and IT-related data from InformationWeek, a leading, widely circulated IT publication in the United States. InformationWeek collected this data by surveying the top IT managers at more than 300 large U.S. firms during the 2001-2002 period. InformationWeek is considered a reliable source of information, and previous academic studies have used data from InformationWeek surveys (Santhanam and Hartono 2003). We collected customer satisfaction data (American Customer Satisfaction Index [ACSI]) that was tracked by the National Quality Research Center (NQRC) at the University of Michigan to obtain an archival measure of customer satisfaction for the firms common in the InformationWeek data and the NQRC database.
ACSI. The ACSI is considered a reliable indicator of a firm's customer satisfaction, and the data have been used in several academic studies in the accounting and marketing literature (e.g., Anderson, Fornell, and Mazvancheryl 2004; Fornell et al. 1996).
Customer knowledge (CUSTKNOW). Customer knowledge is a binary variable for which 1 indicates that a firm has gained significant knowledge about its customers from its customer-related IT systems, and 0 indicates that a firm does not perceive any gains in customer knowledge from its customer-related systems.
CRM applications (CRMAPLC). This variable encompasses both the legacy IT applications (i.e., the applications that firms developed before modern CRM applications were introduced) and newer IT applications (i.e., the integrated suite of marketing and sales applications developed by CRM and enterprise resource-planning vendors). We measured the first component of CRM applications (legacy customer-related IT applications) using a 12-item summative index that indicates the deployment of IT systems to support business processes associated with customer acquisition and disposal of a firm's products and services. The specific IT systems covered by this scale are related to product marketing information, multilingual communication, personalized marketing offerings, dealer locator, product configuration, price negotiation, personalization, transaction system, online distribution and fulfillment system, customer service, and customer satisfaction tracking. We measured the second component of CRM applications (modern CRM systems) using a binary variable (1 = the firm has deployed modern CRM systems, 0 = the firm has not deployed modern CRM systems). We added these two components of CRM systems after standardizing the legacy CRM component (mean = 0, standard deviation = 1). Thus, the variable (i.e., CRM applications) provides greater weight to modern CRM systems but also captures the deployment of legacy customer-related IT applications. Overall, this variable measures a firm's sophistication in managing customer-related information.
Supply chain integration (SCMINTGR). This variable refers to the extent to which a firm's suppliers and partners are included in its electronic supply chain and how much access they have to the firm's customer-related data or applications. It consists of a five-item summative index that describes whether a firm provides its suppliers with electronic access to the following types of application or data: sales forecasts, marketing plans, sales or campaign results, customer demographics, customer loyalty, and satisfaction metrics. We used the standardized (after standardization, mean = 0, standard deviation = 1) value of this variable in our estimation for easier interpretation of the results, particularly because we also investigate the interaction of this variable with CRM applications.
IT intensity (ITINVPC). This variable refers to the level of IT investment as a percentage of the firm's sales revenue.
Industry sector (MFG). This indicator variable represents whether the firm's offering is primarily a good or a service (1 = good, 0 = service).
Firm size (SIZE). This variable is the natural log of the firm's sales revenue.
Because the dependent variable (i.e., customer knowledge) appears as a binary choice, the ordinary least squares (OLS) approach for modeling the binary dependent variable is not appropriate because of heteroskedastic error distribution. A linear model may result in predicted probabilities less zero or greater than one. In addition, a linear model does not allow us to consider the nonlinear nature of the effect of independent variables on the binary dependent variable. To overcome these estimation problems inherent in the OLS approach, we conducted our analysis for this model using the probit approach with the following specification:
( 1) Probability(CUSTKNOW = 1) = Φ(β10 + β11CRMAPLC + β12ITINVPC + β13MFG + β14SIZE + β15SCMINTGR + β16CRMAPLC x SCMINTGR + ε),
where βs are the parameters for the respective variables, and Φ denotes the normal cumulative distribution function (the area under the normal curve).
We used the OLS approach to estimate the customer satisfaction model because the ACSI is a continuous measure of customer satisfaction.
( 2) ACSI = (β20 + β21CRMAPLC + β22ITINVPC + β23MFG + β24SIZE + β25SCMINTGR + β26CUSTKNOW + ε).
The sample size for Equation 1 is 360, and the sample size for Equation 2 is 40. Table 1 shows the results of empirical estimation of the models in Equations 1 and 2.
Results
Consistent with H1, we find that CRM applications are positively associated with an improvement in customer knowledge (Column 1 of Table 1; β11 = .280, p < .001). Because the probit model is inherently nonlinear, we interpret the effect of each individual variable, holding all other variables at their mean values.
In H2, we posit a moderating effect of supply chain integration on the relationship between CRM applications and customer knowledge. We find support for this hypothesis because the joint hypothesis test for the terms involving CRM applications and the interaction of CRM applications with supply chain integration is statistically significant. This result suggests that CRM applications are likely to have a greater association with customer knowledge when firms are electronically integrated in their supply chain and share customer-related data with their supply chain partners.
We also find support for H3, which posits a positive association between CRM applications and customer satisfaction (Column 2 of Table 1; β21 = 1.266, p < .069). In H4, we suggest that the association between CRM applications and customer satisfaction is mediated by the effect of CRM applications on customer knowledge. We used the Sobel test to assess this mediation effect (Baron and Kenny 1986). We find evidence for the indirect effect of CRM applications on customer satisfaction mediated through customer knowledge (β26 = 4.307, p < .028). This result implies that, holding other factors constant, firms that report an improvement in customer knowledge due to their customer-related IT systems have ACSI scores 4.307 points greater than firms that report no gains in customer knowledge following investments in CRM applications. Because the coefficient of the CRM applications variable is statistically significant in Column 2 of Table 1, our results suggest partial mediation and imply that CRM applications may also have a direct effect on customer satisfaction.
The results showing the effect of control variables on customer satisfaction also provide useful insights. Note that when we control for the presence of CRM applications, the effect of IT investments on customer satisfaction is negative and statistically significant (Column 2 of Table 1; β22 = -.437, p < .011). This result is consistent with the observation that specific IT applications, such as CRM, that are directly involved in business processes affecting the customer experience may be much more effective in improving customer satisfaction than are aggregate IT investments (Mithas, Krishnan, and Fornell 2002). Focusing on CRM applications also avoids aggregation across several IT applications, in which applications may be relevant for customer satisfaction and others may have a negative or zero impact (Banker et al. 2005; Kauffman and Weill 1989; Mukhopadhyay, Kekre, and Kalathur 1995). Column 2 of Table 1 also shows that, on average, manufacturing firms have greater customer satisfaction than services firms, a finding that is consistent with previous research (Fornell et al. 1996).
We conducted additional sensitivity analyses to check the robustness of our results. Because Equation 1 uses data from InformationWeek sources on dependent and independent variables, we tested for common method bias using Harman's one-factor test. Because no single factor emerged as a dominant factor accounting for most of the variance, common method bias is unlikely to be a serious problem in the data.
As we previously noted, the variable (i.e., CRM applications) represents a combination of legacy CRM systems and modern CRM applications. We considered whether the use of modern CRM applications (captured by a binary variable in our data set) "causes" customer knowledge. Because a treatment such as CRM is not exogenously assigned to firms, we investigated the sensitivity of our results due to the potential correlation of CRM with unobservable variables that may have affected our findings (Boulding and Staelin 1995; Wierenga, Van Bruggen, and Staelin 1999). We used a matching estimator based on propensity scores to calculate the treatment effect of CRM implementation on improvement in customer knowledge (Heckman, Ichimura, and Todd 1997; Rubin 2003). Using a procedure that Rosenbaum (2002) suggests, we bound the matching estimator to evaluate the uncertainty of the estimated treatment effect due to selection on unobservables.
After matching the propensity scores and thus adjusting for the observed characteristics, we find that the average CRM effect for improvement in customer knowledge is positive and statistically significant. This calculation is based on the assumption that treatment and control groups are different because they differ on the observed variables in the data set. However, if treatment and control groups differ on unobserved measures, a positive association between treatment status and performance outcome would not necessarily represent a causal effect (Boulding and Staelin 1995). Given that we already accounted for selection bias due to observed characteristics, sensitivity analysis provides an assessment of the robustness of treatment effects due to factors not observed in the data. Because it is not possible to estimate the magnitude of selection bias due to unobservables with nonexperimental (i.e., observational) data, we calculated the upper and lower bounds on the test statistics used to test the null hypothesis of the no-treatment effect for different values of unobserved selection bias.
For firm i, assume that ui is an unobserved variable and that γ is the effect of ui on the probability of participating in a treatment. Under the assumption that the unobserved variable u is a binary variable, the following expression can be derived (Rosenbaum 2002):
- Lambda; ≤ [π/(1 - πi]/[πj/(1 - πj)] ≤ Λ,
where Λ = exp(γ), i and j are two different firms within a stratum, and π is the conditional probability (propensity score) that a firm with given observed characteristics will be in the treatment group. If unobserved variables have no effect on the probability of getting into the treatment group (i.e., γ = 0), or if there are no differences in unobserved variables (i.e., [ui - uj] = 0), there is no unobserved selection bias, and the odds ratio will be exp(0) = 1. In the sensitivity analysis, we evaluate how inferences about the treatment effect will be altered by changing the values of γ and (ui - uj). If changes in the neighborhood of exp(γ) = 1 change the inference about the treatment effect, the estimated treatment effects are posited to be sensitive to unobserved selection bias.
We find that improvement in customer knowledge is not sensitive to unobserved selection bias even if the binary unobserved variable makes it twice as likely for a firm to be in the treatment group than in the control group (after we control for several observed characteristics). Overall, these results provide evidence for the robustness of our findings, showing the positive effect of CRM applications on customer knowledge and, in turn, on customer satisfaction.
Discussion and Conclusion
Our goal in this research was to study the effect of CRM applications on customer knowledge and customer satisfaction. We developed a theoretical model that posits a mediating role of customer knowledge and a moderating role of supply chain integration in explaining the effect of CRM applications on customer satisfaction. We used archival data on CRM applications and a perceptual measure of customer knowledge on a cross-section of large U.S. firms during the 2001-2002 period. The study's time frame encompasses a period when firms made significant investments in IT, particularly Internet-based and integrated suites of CRM systems. We matched part of this data set with the sample of firms common to the ACSI to study the effect of CRM applications on customer satisfaction.
Consistent with our expectations, we find that CRM applications are associated with a greater improvement in customer knowledge when firms are willing to share more information with their supply chain partners. Our results suggest that the effect of CRM applications on customer satisfaction is mediated by an improvement in firms' customer knowledge. These results lend support to our previously developed theory and conceptual framework.
This study makes three contributions: First, it builds on previous research that links IT systems and customer satisfaction to contribute to the cumulative knowledge in this stream of literature (Balasubramaniam, Konana, and Menon 2003; Brynjolfsson and Hitt 1998; Chabrow 2002; Devaraj and Kohli 2000; Susarala, Barua, and Whinston 2003). More specifically, given the paucity of research on the benefits gained from CRM technology investments, this study augments the understanding of the beneficial effects of CRM applications by relating them to customer knowledge and customer satisfaction at the firm level. We extend previous research on the effect of CRM processes at the customer-facing level in European countries (Reinartz, Krafft, and Hoyer 2004) and at the strategic business unit level in the United States (Jayachandran et al. 2005) by providing answers to strategic questions about the effect of CRM technology investments on customer knowledge and customer satisfaction. By emphasizing the strategic benefits of CRM applications in terms of customer knowledge and customer satisfaction, we provide a complementary view of judging returns from CRM applications by considering nontangible aspects that are critical for the creation of shareholder wealth (Anderson, Fornell, and Mazvancheryl 2004).
Second, our study points to the importance of customer knowledge as one of the mediating mechanisms that explains the association between CRM applications and customer satisfaction. Although several studies provide conceptual support for the effect of CRM applications on customer knowledge, our study empirically establishes this association. More broadly, our study provides support for the emerging view that IT applications affect firm performance by enabling other business processes and capabilities, which in turn may affect firm performance (Mithas et al. 2005; Pavlou et al. 2004).
Third, the results of this study suggest that it is important to account for the effect of factors such as supply chain integration in the evaluation of returns from CRM applications. We find that though CRM applications are associated with customer knowledge and customer satisfaction, they are even more beneficial if firms share their customer-related information with supply chain partners. This result provides empirical support for the importance of supply chain integration for firm performance (Chopra and Meindl 2003; Fisher, Raman, and McClelland 2000; Gosain, Malhotra, and El Sawy 2005; Narasimhan and Jayaram 1998). This finding is also consistent with studies by Barua and colleagues (2001) and Rai, Patnayakuni, and Patnayakuni (2005), emphasizing the importance of flexibility in the supply chain and sharing information with supply chain partners.
We identify several promising areas for further research in this stream of literature. First, firm strategies for treating customers may differ depending on a business-to-consumer (B2C) or business-to-business (B2B) context. These differences arise because of the differential ability of firms to negotiate Pareto-optimal contracts across these contexts, the large but infrequent purchases in the B2B context (compared with small but frequent purchases in the B2C context), and the presence of multiple stakeholders in the buying center in the B2B context (compared with individual decision making in the B2C context). Future studies could explore whether CRM initiatives in the B2B context have an effect on customer satisfaction and other customer-based metrics.
Second, although our study suggests that the association between CRM applications and customer satisfaction is mediated by customer knowledge, note that CRM applications merely enable firms to collect customer knowledge. Only when firms act on this knowledge by modifying service delivery or by introducing new services will they truly benefit from their CRM applications.( n1) Furthermore, firms may need to make changes in their incentive systems and institute complementary business processes to leverage CRM investments. There is a need for further research to trace the causal chain linking CRM applications and customer satisfaction at a finer level of granularity by specifically accounting for such complementary actions. The studies by Barua and colleagues (2004), Barua and colleagues (2001), and Wu, Mahajan, and Balasubramaniam (2003) provide good instrumentation and research design for undertaking such research.
Finally, although many firms justify the implementation of CRM applications based on expected gains in customer satisfaction, there is a need for further research to examine the benefits in terms of increased revenue, profitability, and market value compared with the costs of implementing CRM applications. Equally, there is a need to consider and quantify the potential risks of not implementing CRM applications in a competitive environment. An attractive area for further research may be to evaluate the extent to which the implementation of CRM applications helps firms enhance the net present value of their customer base and improve the effectiveness of cross-selling and one-to-one marketing programs (Peppers and Rogers 2004).
This study also has several managerial implications. First, for firms evaluating CRM applications, it is important to understand the conditions under which deployment of those applications contribute to improved customer knowledge and customer satisfaction. Our results showing the importance of supply chain integration in realizing the benefits from CRM applications could be useful to managers who are currently evaluating or implementing CRM applications. Our analysis shows that firms with greater supply chain integration are more likely to benefit from CRM applications in terms of customer knowledge and customer satisfaction. The results imply that firms need to be willing to share their customer-related information with supply chain partners to benefit from the implementation of CRM applications.
Second, the importance of customer knowledge as a mediator for customer satisfaction suggests that in addition to implementing CRM, managers should also ensure that customer knowledge is disseminated across customer touchpoints in order to benefit in terms of customer satisfaction. An implication of this finding is that managers need to institute measurement systems to capture the gains in customer knowledge following the implementation of CRM applications because gains in customer knowledge are a precursor to gains in customer satisfaction.
This research empirically tested the effect of CRM applications on customer knowledge and customer satisfaction. Using archival data on a broad cross-section of U.S. firms, we found that CRM applications are likely to affect customer knowledge when they are well integrated into the supply chain. Our findings provide empirical support for the conjectures that CRM applications help firms gain customer knowledge and that this knowledge helps firms improve their customer satisfaction. Our research contributes to empirically valid theory by synthesizing insights from the marketing and information systems literature and by investigating the effect of organizational variables that leverage CRM investments. Overall, our results suggest that firms that make investments in CRM applications reap significant intangible benefits, such as improved customer knowledge and customer satisfaction. Achieving such customer-focused business objectives is a critical ingredient for success in increasingly competitive markets.
The authors thank William Boulding, Richard Staelin, and the three anonymous JM reviewers for their guidance and helpful comments in improving this manuscript. They thank InformationWeek and the National Quality Research Center at the University of Michigan for providing the data for this research. They thank Rusty Weston, Bob Evans, Brian Gillooly, Stephanie Stahl, Lisa Smith, and Helen D'Antoni for their help in data and for sharing their insights. They also acknowledge helpful comments from Jonathan Whitaker and participants at the 2005 AMA Winter Educators' Conference in San Antonio. They thank Eli Dragolov for her excellent research assistance. Financial support for this study was provided in part by a research grant from A.T. Kearney and the Michael R. and Mary Kay Hallman fellowship at the Ross School of Business.
( n1) We thank an anonymous reviewer for this insightful observation.
Legend for Chart:
A - Dependent Variable
B - Model 1: Improvement in Customer Knowledge (Probit)
C - Model 2: ACSI (OLS)(a)
A B C
D E
CRM applications β11 .280(***)
(.001)
β21 1.266(*)
(.069)
IT investments as percentage β12 .042(*)
of revenue (.056)
β22 -.437(**)
(.011)
Manufacturing(b) β13 .011
(.474)
β23 7.420(***)
(.000)
Firm size β14 .254(***)
(.009)
β24 -.987(**)
(.046)
Supply chain integration β15 .011
(.453)
β25 -.555
(.166)
Interaction term (CRM x supply β16 .120(*)
chain integration) (.084)
Improvement in customer
knowledge
β26 4.307(**)
(.028)
Constant β10 .488(**)
(.018)
β20 73.333(***)
(.000)
Observations 360
40
Overall fit χ² 28.06
χ² .661
(*) p < .10 (one-tailed test).
(**) p < .05 (one-tailed test).
(***) p < .01 (one-tailed test).
(a) We also conducted an additional analysis that controlled for
the ACSI score before CRM implementation, and we obtained
broadly similar results.(b) We also estimated models with more detailed industry classification, and our primary results remain unchanged. Notes: p values are shown in parentheses.
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~~~~~~~~
By Sunil Mithas; M. S. Krishnan and Claes Fornell
Sunil Mithas is an assistant professor, Robert H. Smith School of Business, University of Maryland
M.S. Krishnan is a Professor of Business Information Technology and Area Chair and Michael R. and Mary Kay Hallman e-Business Fellow, University of Michigan.
Claes Fornell is Donald C. Cook Professor of Business Administration and Director of the National Quality Research Center, Ross School of Business, University of Michigan.
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Record: 201- Why We Boycott: Consumer Motivations for Boycott Participation. By: Klein, Jill Gabrielle; Smith, N. Craig; John, Andrew. Journal of Marketing. Jul2004, Vol. 68 Issue 3, p92-109. 18p. 2 Diagrams, 6 Charts. DOI: 10.1509/jmkg.68.3.92.34770.
- Database:
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Why We Boycott: Consumer Motivations for Boycott
Participation
Although boycotts are increasingly relevant for management decision making, there has been little research of an individual consumer's motivation to boycott. Drawing on the helping behavior and boycott literature, the authors take a cost-benefit approach to the decision to boycott and present a conceptualization of motivations for boycott participation. The authors tested their framework during an actual boycott of a multinational firm that was prompted by factory closings. Consumers who viewed the closures as egregious were more likely to boycott the firm, though only a minority did so. Four factors are found to predict boycott participation: the desire to make a difference, the scope for self-enhancement, counterarguments that inhibit boycotting, and the cost to the boycotter of constrained consumption. Furthermore, self-enhancement and constrained consumption are significant moderators of the relationship between the perceived egregiousness of the firm's actions and boycott participation. The authors also explore the role of perceptions of others' participation and discuss implications for marketers, nongovernmental organizations, policymakers, and researchers.
The boycott is the way we take our cause to the public. For surely if we cannot find justice in the courts of rural California, we will find support with our brothers and sisters throughout the nation.
--Cesar Chavez (qtd. in Why We Boycott [United
Farm Workers of America 1973])
We've taken significant actions to improve the lives, opportunities, and working conditions of the people who make our product around the world, and [we] regularly invest in the communities where we do business. And we do this so that consumers can buy Nike products with the knowledge that these products have been manufactured under safe and fair working conditions.
--Nike's response to criticism in
Naomi Klein's No Logo (Nike 2000)
Boycotts are an intriguing form of consumer behavior. They are unwelcome to marketers yet consistent with the marketing concept, because firms targeted by a well-supported consumer boycott have apparently failed to sustain a sufficient customer focus. As a result of greater public attention to corporate social responsibility (CSR) and the increased vulnerability of brands and corporate reputations, boycotts have become ever more relevant for management decision making. Furthermore, given that they represent a source of consumer power and a mechanism for the social control of business, boycotts also have public policy implications. Boycotters deliberately use their "purchase votes" to favor firms with preferred societal impacts, consistent with the idea of consumer choice as a rationale for capitalism (Dickinson and Hollander 1991; Smith 1990). Yet there has been little research into the factors that influence a consumer's motivation to boycott, despite the need for marketers, boycott organizers, and policymakers to better understand these factors.
Friedman (1985, p. 97) defines a consumer boycott as "an attempt by one or more parties to achieve certain objectives by urging individual consumers to refrain from making selected purchases in the marketplace." The "urging" of a boycott typically comes from a nongovernmental organization (NGO) that is protesting corporate practices. Thus, boycotts are an extreme case of a broader category of consumer behavior in which social and ethical issues, such as environmentalism, influence purchase decisions. Therefore, a better understanding of boycott participation not only is useful in its own right but also is likely to inform the understanding of ethical influences on buyer behavior in general.
Consumer boycotts date back at least as far as the fourteenth century and have contributed to some spectacular successes for relatively powerless groups. In the United States, boycotts were the key to unionization (Wolman 1916), and the 1955 Montgomery bus boycott marks the beginning of the modern civil rights movement (Friedman 1999). Elsewhere, examples include Gandhi's boycotts of British salt and cloth before Indian independence and the British boycott of Barclays Bank before its withdrawal from apartheid South Africa (Smith 1990). In the 1990s, the business press agreed that boycotts were often successful and were occurring more frequently (e.g., The Economist 1990).( n1) Recent prominent consumer boycotts include the European boycott of Shell because of its plan to sink the Brent Spar oil platform at sea and the multicountry boycott of Nike over alleged sweatshop conditions at Asian suppliers. As these examples suggest, boycotts today are more typically focused on corporate practices rather than on broader sociopolitical goals such as civil rights. This shift in boycott focus reflects both the increased power of the modern transnational corporation and, paradoxically, the heightened vulnerability of corporate reputation and brand image, and it is consistent with recent findings that a firm's CSR record affects consumer perceptions of the firm's brands and products (Brown and Dacin 1997; Sen and Bhattacharya 2001). It is with such a context in mind that we develop and test a conceptualization of motivations for boycott participation.
Table 1 summarizes prior research on consumer boycotts. Most boycott studies have been conceptual or descriptive (case studies), with a focus on boycott organizers and targets rather than on the consumer. Only two studies have reported empirical research that focuses directly on variables that influence an individual consumer's boycott decision. Kozinets and Handelman's (1998) netnographic study suggests that boycott participation represents a complex emotional expression of individuality and a vehicle for moral self-realization. Sen, Gürhan-Canli, and Morwitz (2001) test a theoretical framework that proposes that a fundamental question underlies a consumer's decision to boycott: Will the boycott be successful? They find that consumers' participation decisions are influenced by their perception of the likelihood of the boycott's success, their susceptibility to normative social influences (social pressure), and the costs associated with boycotting.
Sen, Gürhan-Canli, and Morwitz (2001) conceptualize boycotts as social dilemmas, wherein a consumer chooses between the individual benefit of consumption and the wish of a collective to refrain from consumption so that all receive the shared benefits of a successful boycott. Similarly, a theoretical economic model of boycotting by John and Klein (2003) treats boycott participation as a collective action problem, in which individual consumers' incentives to participate are limited by the knowledge that they are small relative to the market and by their opportunity to free ride on the boycotting of others.
Consistent with the articles by Sen, Gürhan-Canli, and Morwitz (2001) and John and Klein (2003), we view boycotting as a form of prosocial behavior by which "actions [are] intended to benefit one or more people other than oneself-behaviors such as helping, comforting, sharing, and cooperation" (Batson 1998, p. 282). This is broadly referred to as "helping behavior." Over the years, a substantial body of literature in social psychology has grown from initial analyses of emergency helping to a broad set of studies of helping in many different contexts, including non-emergency helping (Piliavin et al. 1981, 1982); voting, volunteering, and charitable donations (e.g., Chambre 1987; Piliavin and Charng 1990); blood donations (Piliavin and Callero 1999); and corporate philanthropy (Piliavin and Charng 1990). At its core, this research aims to understand when and why people apparently act against selfish interests for the good of others.
Boycotting is a collective act similar to voting, which is a prosocial behavior in which the individual benefit appears to be limited; nonetheless, people go to the polls in large numbers (for a discussion of the collective action problem in voting, see, e.g., Blais 2001; Downs 1957). In the early helping literature, helping typically was not viewed as a collective action problem, but as the literature has broadened in scope, it has incorporated cases (e.g., voting) that involve collective action. Likewise, some charitable contributions (e.g., to local public goods, such as National Public Radio in the United States) are examples of collective action.( n2) Boycotting is also related to customer complaining behavior, though complaining typically is neither prosocial in intent nor collective. In most cases, a complaint is a purely individual act that is completely independent of the behavior of others (Blodgett and Granbois 1992; Boote 1998). Nonetheless, the literature identifies one form of complaint as simply exit (i.e., the consumer decides to shun the firm's product offerings in the future), which is akin to an individual act of boycott (Boote 1998; Hirschmann 1970). In addition, as with boycotting, there is a trigger event that prompts a dissatisfied customer to evaluate the relative costs and benefits of lodging a complaint (Blodgett and Granbois 1991; Singh and Wilkes 1991).
An explanation of helping that has received extensive empirical support over the past three decades is the arousal: cost-reward model (see Dovidio et al. 1991). According to this approach, when a potential helper encounters another person in distress, the helper interprets the seriousness of the situation and experiences arousal based on this interpretation. In response, the helper assesses the potential costs and benefits of helping. The higher the net benefit of helping (rewards minus costs), the more likely it is that help will be given. Our approach to boycotting is similar: Consumers encounter an initial trigger event (which we refer to as a firm's "egregious act") that engenders negative arousal. In response, each consumer evaluates the expected costs and benefits of boycotting.
Table 1 indicates that several costs and benefits of boycotting have previously been identified, but most have not been tested empirically. Thus, drawing on the helping literature and the prior boycott literature, we conceptualize the decision to participate in a boycott as akin to the decision to help others in distress, to contribute to a charity, or to donate blood. More specifically, we take a cost-benefit approach to our investigation of boycott participation. Figure 1 depicts our model.
Perceived Egregiousness
Consistent with the boycott literature (e.g., Friedman 1999; Garrett 1987; Smith 1990; Smith and Cooper-Martin 1997), our starting point is the observation that, in general, boycott participation is prompted by the belief that a firm has engaged in conduct that is strikingly wrong and that has negative and possibly harmful consequences for various parties (e.g., workers, consumers, society at large). Typically, this perception varies across consumers: Some will consider the firm's actions seriously wrong, whereas others will be less likely to do so, just as people in helping situations often have different interpretations of whether the scenario they witness is serious enough to precipitate intervention (Latane and Darley 1968; Schwartz 1977). To test this idea, we conducted a preliminary study using materials that promote the long-standing boycott of Nestlé over its marketing of infant formula in developing countries. Perceived egregiousness differed across consumers and predicted both boycott participation and a more negative brand image (Klein, Smith, and John 2003). Accordingly, we propose that the level of perceived egregiousness has a direct impact on boycott participation.
H[sub1]: Consumers who find the firm's actions to be more egregious are more likely to boycott.
However, not all consumers who view the firm's actions as egregious will participate in the boycott. In our preliminary study, 70% of participants rated the problematic company practice at or above the midpoint on a composite seven-point scale measure of egregiousness, but only a minority (45%) said that they would definitely or probably boycott Nestlé. Thus, we ask, Why do people not participate in boycotts in response to perceived egregious conduct? From this perspective, our goal is as much to explain why some people do not boycott as it is to explain why others do.
Answers to open-ended questions suggested that boycotters often had multiple and different motivations for participation, which reflected perceived costs or benefits of participation. Drawing from economic and psychological theory, especially the cost-reward model of helping, and from our preliminary study and the boycott literature, we propose four different categories of motivations: make a difference, self-enhancement, counterarguments, and constrained consumption (Figure 1).
Benefits and Costs
Make a difference. The motivation to bring about societal change by participating in a boycott reflects perceived benefits of boycotting. Boycotters may have an instrumental motivation to change the target firm's behavior and/or to signal to the firm and others the necessity of appropriate conduct (Friedman 1999; Kozinets and Handelman 1998). Such motivation is typically tempered by a general "willingness to boycott," influenced by perceived consumer effectiveness, that affects a consumer's participation in any specific boycott (Smith 1990). Likewise, Sen, Gürhan-Canli, and Morwitz (2001) refer to "perceived efficacy" as the extent to which a consumer believes that each boycott participant can contribute to the achievement of collective goals, and John and Klein (2003) discuss how an exaggerated view of effectiveness might explain why people boycott when the target is unlikely to notice. All this research is consistent with findings that people are more cooperative in social dilemmas if they expect that the group will attain its goals (Wiener and Doescher 1991). It is also consistent with research that shows that helping is more likely when potential helpers believe themselves competent to help and have confidence that their actions will result in positive outcomes (e.g., Midlarsky 1984). Thus:
H[sub2a]: Beliefs in boycotting to make a difference predict boycott participation. Consumers who believe that boycotting is appropriate and that it can be effective are most likely to participate in the boycott.
As is shown in Figure 1, we suggest that this and our other cost-benefit motivations directly affect boycott participation. For example, if a consumer believes that by boycotting he or she can change the firm's behavior, the consumer is more likely to boycott beyond the direct effect of egregiousness. We also propose that such motivations moderate the relationship between perceived egregiousness and the boycott decision: The effects of perceived egregiousness may be enhanced or diminished through interactions with the cost-benefit motivations. For make a difference, we expect that the relationship between egregiousness and boycotting is stronger for consumers who believe that boycotts can bring about change than for consumers who do not. This interaction can be inferred from John and Klein's (2003) theoretical boycott analysis and is consistent with helping studies in which perceptions of the seriousness of a situation have been shown to interact with perceived competence to help (Cramer et al. 1988). Furthermore, research on the relationship between environmental concern and related prosocial behaviors, such as recycling, has identified a moderating role for perceived consumer effectiveness (Berger and Corbin 1992; Ellen, Wiener, and Cobb-Walgren 1991).
H[sub2b]: Beliefs in boycotting to make a difference moderate the relationship between egregiousness and the boycott decision. When these beliefs are strongly held, the relationship between egregiousness and boycotting is greater than when the beliefs are less strongly held.
Self-enhancement. Although H[sub2a] is consistent with Sen, Gürhan-Canli, and Morwitz's (2001) focus on the utility gained from boycott success, we suggest that in addition to such instrumental rewards, there are also intrinsic benefits from boycott participation, potentially regardless of boycott outcome. There is substantial evidence from the helping behavior literature that people's feeling good about themselves and being admired by others are key benefits of helping, whereas self-blame and public censure are consequences of not helping (Dovidio et al. 1991). Thus, our second category of cost-benefit motivations incorporates psychosocial variables that are associated with self-enhancement: Participation enables the boycotter to boost social and personal self-esteem either by associating with a cause or group of people or simply by viewing him-or herself as a moral person. Kozinets and Handelman (1998, p. 477) observe that boycotting seems to allow for a bettering that is "akin to a hygienically cleansing process." This comparison is reminiscent of Smith's (1990) notion that potential boycotters may believe that they are under a moral obligation to keep away from the company's products in order to have "clean hands."
Socially embedded expectations or social pressures are also likely to affect the guilt or positive feelings associated with boycotting. The relevance of social pressure for boycott participation is widely acknowledged in the boycott literature (Friedman 1999; Garrett 1987; Rea 1974; Sen, Gürhan-Canli, and Morwitz 2001) and in the helping literature (Dovidio et al. 1991).( n3) Thus, self-enhancement through boycott participation includes the avoidance of feelings of guilt or the negative perceptions of others.
H[sub3a]: Self-enhancement factors predict boycott participation. The greater the perceived scope for self-enhancement (and avoidance of guilt or social censure), the more likely is a consumer to boycott.
We also expect that self-esteem moderates the relationship between egregiousness and boycotting, such that consumers who perceive an opportunity for self-enhancement are more likely to translate perceived egregiousness into boycott participation. Although to the best of our knowledge this interaction has not been investigated directly in the helping literature, there are helping studies that suggest that perceived egregiousness and self-enhancement motivations interact. For example, participants who are made to feel guilty are more likely to offer help in an unrelated situation to reduce their negative feelings (Carlsmith and Gross 1969), and participants' experience of a boost or threat to self-esteem affects perceptions of egregiousness (McMillen, Sanders, and Solomon 1977). Boycotting when egregiousness is high (equivalently, giving aid when and where it is most needed) presumably allows for the greatest degree of self-enhancement. Thus:
H[sub3b]: Self-enhancement factors moderate the relationship between egregiousness and the boycott decision. The greater the scope for self-enhancement, the greater is the relationship between perceived egregiousness and boycotting.
Counterarguments. Although there are benefits of boycotting, there are also costs. Helping studies show that as costs for helping increase, helping decreases. For example, Schwartz (1977) asserts that in the process of deciding to help another person in need, there is a "defensive step" of assessing potential negative outcomes of helping (e.g., injuring or embarrassing the person in need). Thus, a potential boycotter, even one who perceives the firm's actions as highly egregious, might refrain from participation if he or she believes that boycotting could lead to unintended harm. For example, consumers might not boycott sweatshop suppliers because the protest could hurt those it was intended to help.
Another type of counterargument pertains to the consumer's perception of whether his or her individual contribution will play any role in achieving the collective action goal. In this case, the counterargument pertains to the probability that the consumer's boycott decision will influence the firm's decision. There are two variations. First, boycotters might believe that their actions will have no impact because they are too small to be noticed (John and Klein 2003). This is analogous to the argument that there is no point in voting because any individual vote will almost surely not affect the outcome of an election. Similarly, helping often fails to occur because the potential helper believes that he or she is unable to intervene effectively; there is a sense of a powerlessness to change the victim's predicament. Second, boycotters might believe that their actions are unnecessary because they can free ride on the boycott decisions of others. In the helping literature, free-riding tendencies are examined in studies of "diffusion of responsibility," which find that the probability that a person will help someone in need is drastically reduced when others are also available to help (e.g., Latane and Nida 1981). The cost-benefit model of helping suggests that this occurs because the costs for not helping (e.g., guilt, worry about the victim) are reduced as a result of the expectation that the victim will receive help from others. Similarly, although boycotts require widespread participation to be effective (i.e., to reduce sales substantially), if the boycott is successful all will receive the benefits regardless of whether they participated. Thus, some would-be participants might free ride.( n4)
H[sub4a]: Counterarguments about boycotting are negatively related to boycott participation. The more a consumer engages in counterarguments, the less likely is the consumer to boycott.
We propose that counterarguments also moderate the relationship between perceived egregiousness and boycotting. Conflict models of choice (Ajzen 1996) suggest that a decision (in this case, boycotting) will not be reached if a person feels powerless (small agent), shifts responsibility to others (free rides), or becomes concerned about potential consequences. Decision making can be impeded even when the stakes (in this case, egregiousness) are high and can result in inaction even when the situation calls for action (Hogarth 1980). Analogously, increased egregiousness may fail to translate into boycotting if counterarguments loom large in the minds of consumers. Thus:
H[sub4b]: Counterarguments about boycotting moderate the relationship between egregiousness and the boycott decision. The stronger the counterarguments, the weaker is the relationship between perceived egregiousness and boycotting.
Constrained consumption. The direct cost of boycotting (i.e., forgoing a preferred good) also factors into the consumer's boycott decision. Boycotting is likely to be most costly for heavy users of the targeted company's products before the boycott because such consumers face the greatest constraint on their consumption if they participate in the boycott. Thus, we anticipate that constrained consumption has a direct effect on boycott participation.
H[sub5a]: The degree to which consumption is constrained predicts boycott participation. Consumers whose consumption is most constrained by boycotting are less likely to boycott.
If the sacrifice required to help is sufficiently large, increased seriousness of the situation will not necessarily predict helping (Dovidio et al. 1991). Similarly, egregiousness is more likely to be related to boycotting at lower levels of constrained consumption. We predict that this relationship is weaker at high levels of constrained consumption because even high-egregiousness consumers may find it too costly to boycott. Thus, the degree to which boycotting constrains consumers' consumption influences the effect of egregiousness on their boycott decisions:
H[sub5b]: The degree to which consumption is constrained moderates the relationship between egregiousness and the boycott decision. There is a weaker relationship between egregiousness and boycotting for consumers who suffer the greatest constraint in their consumption.
Estimated Participation of Others
Sen, Gürhan-Canli, and Morwitz (2001) and John and Klein (2003) suggest that perceptions of how many others are boycotting (estimated participation) also affect individual boycott participation. Empirical work on social dilemmas (e.g., Wiener and Doescher 1994) suggests that an increase in estimated participation is likely to lead to an increase in actual participation. In the boycott context, there are many ways such an effect can operate. For example, more people taking part may generate increased social pressure or may affect perceptions of boycott efficacy; when more people participate, an individual consumer may believe that his or her own boycotting is more likely to affect the outcome (because the consumer believes that the boycott is close to a "tipping point" at which the firm might well capitulate). Thus, in addition to a direct effect on boycotting, we might find that estimated participation moderates the effect of self-enhancement or make a difference.
Another possibility is that estimated participation moderates constrained consumption. When the direct cost of boycotting is high, people may be particularly averse to being exploited by others' free-riding (Sen, Gürhan-Canli, and Morwitz 2001; Wiener and Doescher 1991) and thus may be highly attuned to whether others are taking part. Finally, higher estimated participation may decrease boycotting because it affects the incentive to free ride. When more people participate, the boycott is more likely to be successful, and so the temptation to free ride increases (John and Klein 2003); this suggests that estimated participation could moderate our free-rider variable. Thus, there are theoretical reasons that estimated participation might moderate all four factors. However, the links are complex; thus, our analysis of this variable is more exploratory and lacks explicit hypotheses.( n5)
Brand Image
Consistent with our preliminary study, we expect that egregiousness affects brand image: Consumers who believe that a firm has erred will have a more negative image of it than will consumers who do not judge its actions as egregious (see Dawar and Pillutla 2000; Smith and Cooper-Martin 1997).
H[sub6a]: There is a direct relationship between egregiousness and brand image; the greater the perceived egregiousness, the more negative is the brand image.
We also expect that boycotting damages brand image beyond the direct effects of egregiousness. It is well established in social psychology that actions can intensify attitudes in the direction of the behavior. Both cognitive dissonance theory (e.g., Festinger 1957) and self-perception theory (e.g., Bem 1972) predict that undertaking an action leads to behavior-consistent attitudes. Thus, independent of egregiousness perceptions, consumers who boycott are likely to devalue their perception of the brand, simply because they boycotted.
H[sub6b]: The boycott decision mediates the relationship between egregiousness and brand image.
Finally, the management of targeted firms often communicates with consumers to discourage boycott participation. Thus, we also examine responses to messages intended to counter the boycott.
The Bremmer Boycott
We tested our hypotheses in an empirical study of an actual, ongoing boycott. This contrasts with the work of both Sen. Gürhan-Canli, and Morwitz (2001), who conducted a laboratory study (they informed subjects about an ongoing boycott and manipulated factors predicted to affect participation), and Kozinets and Handelman (1998), who examined Internet chat-room data (they selected sites for the prevalence of boycott discussions). Thus, ours is the first quantitative, consumer-focused study of an ongoing boycott in a natural setting.( n6) The advantage of this approach is that we capture real-time reactions to a boycott in the social milieu in which it occurred. Although we lack the experimental control of the laboratory, we gain access to a real boycott, with all its inherent emotion and public controversy, without needing to elicit or simulate moral outrage in an artificial setting.
The boycott in question was called against Bremmer (name disguised), a European-based multinational firm that sells consumer food products, primarily through grocery outlets. The announcement of two factory closings occurred approximately one month before the start of data collection. The closings were a major event that received extensive media coverage, and some consumers began to boycott.( n7) Two weeks after the announcement, Bremmer's chief executive officer was interviewed in the media about the closings. A large demonstration at a factory closing was staged a week later, and a group of Bremmer employees and various pressure groups, including an NGO known for its campaigns against globalization, called for an official boycott. Consumers were asked to boycott all the firm's products, but two product brands received the most media attention: the brand made in the factories to be closed (Brand A) and Bremmer brand-name products made in other factories (Brand B). Bremmer had other brands, but only some consumers were aware of this.
We had access to sales-tracking data for Brand A, which showed an 11% decrease in market share (i.e., 11% of share in the three previous months) in the first two weeks following the announcement of the closings (before the boycott was "officially" called). Sales then recovered somewhat, but for the four months following the call to boycott, market share was down an average of approximately 4%. No significant events occurred during the data collection period (the third to tenth weeks following the call to boycott), and market share remained lower and steady. Sales did not recover to near their preboycott levels until five months after the boycott was announced.
The Study
Our study was conducted by Bremmer's research agency, which used questions that we added to its corporate tracking surveys. We did not have control over the exact wording or the format of the response scales (because of the need to maintain consistency with previous Bremmer tracking studies).
Subjects. A nationally representative stratified random sample of 1216 adult consumers participated in the study through a telephone survey (response rate was 40%). Respondents were asked to participate in "a survey on some companies," and there was no reference during recruitment to Bremmer or to the boycott. Interviews were conducted in two phases: during the third and tenth weeks after the boycott was called. Women constituted 52.6% of the sample, and the average age was 46.( n8)
Measurement. Respondents were asked to indicate their opinion of the firm on a three-point scale (from "poor" to "very good"). They were then asked whether they had heard about the factory closings ("There have been recent reports in the press about planned factory closings and job losses in the Bremmer group. Have you heard about them?"). They were also asked three questions that measured the perceived egregiousness of the closings. Respondents were asked to rate their confidence (on a four-point scale of "no confidence at all" to "complete confidence") in the managers of Bremmer "to not close factories except when necessary" and "to ensure that the factory closings take place in the best possible way for the workers." A third question asked for agreement on a four-point scale to the statement, "Bremmer must close certain unprofitable factories to avoid putting its entire [product] line in danger."( n9) Respondents were also asked whether they disapproved of Bremmer's actions.
The next question asked about boycott participation, stating that there had been appeals to boycott Bremmer in reaction to the factory closings. Respondents were given three possible responses: "I am boycotting the products of Bremmer"; "I am tempted to boycott, but I don't know if I will"; and "I am not boycotting the products of Bremmer."( n10) Respondents were also asked to estimate the percentage of Bremmer customers who were boycotting Bremmer products.
We measured the hypothesized benefit and cost motivators on a ten-point scale (1 = "strongly disagree" and 10 = "strongly agree"). Table 2 lists the questions and descriptive statistics for the make-a-difference, self-enhancement, and counterargument items. We measured constrained consumption by previous purchase frequency, because heavy purchasers of Bremmer products would pay a higher price for boycotting than would light purchasers. Respondents were asked how often, in general, they had bought the two focal brands (A and B), on a four-point scale (1 = "never or almost never," and 4 = "very often").
Bremmer management communicated two messages about the factory closings: Three alternative jobs had been offered to each worker in the closing factories, and Bremmer was seeking other companies that would be willing to create jobs at the sites to be closed. In the survey, respondents were asked whether they had heard these messages ("yes" or "no") and whether they were more sympathetic to the firm as a result of these initiatives, on a four-point scale (1 = "not at all" and 4 = "very").
The Bremmer controversy was well publicized: 95% of the sample had heard about the factory closings. Overall, 67% of the sample were not boycotting, 17% were tempted to boycott, and 16% were boycotting. Although respondents overwhelmingly disapproved (81%) of Bremmer's factory closings, most consumers who disapproved were nonboycotters (61%). Only 19% of disapprovers were currently boycotting, and 20% were tempted to boycott but had not yet done so. For most consumers, disapproval of the firm's actions did not lead to boycotting.
We averaged the three measures of egregiousness described previously (Cronbach's α = .73). The mean egregiousness score was 2.79 (recall that responses to these questions were on a four-point scale, and thus higher numbers indicate greater egregiousness). Although 60% of the sample averaged three or higher on the scale, only 22% of this group boycotted (21% were tempted), which again indicates that not all consumers who view a firm's actions as wrong participate in a boycott.( n11)
A principal components analysis (PCA) with Varimax rotation of the cost-benefit (motivation) variables found four factors, as we predicted (Table 3). The PCAs conducted factor by factor revealed each factor to be unidimensional. The most strongly endorsed items were the counterarguments, particularly the idea that boycotting would hurt other jobs and would lead consumers to buy foreign products. The least strongly endorsed were self-enhancement items, particularly ones associated with social pressure. As Table 2 shows, all motivations showed the expected differences across boycott groups (all p s < .001).( n12)
Regression Analyses
We first indexed our items according to our predicted factors. Make a difference has a Cronbach's α of .78, and self-enhancement has a Cronbach's α of .73. We averaged the two purchase history questions as a measure of constrained consumption (r = .485, p < .01).( n13) The counterargument items, while forming a clear factor in the PCA, had a Cronbach's α of only .61; we return to this issue subsequently.
As Table 2 shows, the mean of each of the motivation variables is consistent with an equal interval dependent variable: In each case, the mean for "tempteds" falls between the means for the nonboycotting and boycotting groups, equidistant or approximately equidistant from the two extreme means. (This was the case across both phases of the data collection.) We thus report simple ordinary least squares regression (i.e., a linear probability model) in our analysis. We also conducted analyses that do not require an interval-scaled dependent variable-specifically, discriminant analysis and ordinal (logit) regression-and we obtained similar results. We centered predictor variables (raw score minus mean; Cohen and Cohen 1983).
Model A in Table 4 is our benchmark model. It includes egregiousness and all the cost-benefit motivation factors as well as the interaction terms of each factor with egregiousness. We included respondents' sex in the model (see the subsequent discussion) and added a dummy variable for the phase of data collection. As H[sub1] predicts, egregiousness has a direct effect on boycotting, as do the four sets of cost-benefit factors. There were significant interactions between egregiousness and self-enhancement and between egregiousness and constrained consumption. The make a difference x egregiousness and the counterargument x egregiousness interactions are in the predicted direction but are not significant.
Table 5 indicates the magnitude of the findings. We use our data to predict how many people in our sample would have boycotted had egregiousness and our four factors been counterfactually lower or higher.( n14) For example, had our sample had higher egregiousness (such that mean egregiousness for the entire sample equaled the true mean level for the top third of the sample), we predict that 261 participants would have boycotted, which is a 45% increase from the original 180. Had constrained consumption also been low, we predict that 339 participants would have boycotted.( n15) Table 5 also illustrates the interaction effect: Of this increase of 159 (= 339 - 180), we can attribute 50 (= 230 - 180) to the partial effect of the change in constrained consumption and 81 (= 261 - 180) to the partial effect of the change in egregiousness. The remaining 28 (18% of the total change) are attributable to the interaction effect. Our ordinal regression and discriminant analysis approaches yielded similar predictions and interactions.
In Table 4, Models B-E, we examine the counterarguments in more detail. Although the four counterarguments loaded on a single factor, they measure different objections to boycotting: free-riding is not the same as refraining from boycotting because other jobs will be threatened. Furthermore, Cronbach's α of .61 is less than the generally accepted cutoff of .70, which suggests that the items should perhaps be represented individually in the regression equation. At the same time, the items are correlated with one another (as indicated by the factor analysis and by the r's ranging from .18 to .45), so the inclusion of all four items in a single equation presents collinearity problems. We thus ran separate regressions (Models B-E) that included each counterargument in turn as an individual variable. The counterargument variables all have significant direct effects on boycotting, and "too small" and "hurts jobs" show significant interactions with egregiousness. All interaction terms are in the predicted direction.( n16)
Thus, from Models A-E in Table 4, we find support for H[sub1]-H[sub5b] (support for H[sub2b] was directional but not significant, and support for H[sub4b] was directional but not significant, though we found significant differences for the counterargument variables too small and hurts jobs). We find the strongest direct effects for make a difference and counterarguments. The interactions were less powerful but were often significant, and all were in the predicted direction; the strongest moderators were constrained consumption and self-enhancement.
Estimated participation. On average, respondents believed that 27% of consumers were boycotting, which is a substantial overestimate of the actual number that reported participation (16%). Boycotters estimated that 40% were participating. Nonboycotters also overestimated participation (at 23%). When we include estimated participation (see Table 6), the basic model (egregiousness and cost-benefit motivations) remains substantially unchanged, and the explanatory power increases only slightly. Still, perception of others' boycott activity plays a noteworthy role in the model. Estimated participation has a significant direct effect on boycotting: The greater the number of people a consumer believed were participating, the more likely was the consumer to join in. Estimated participation also interacts significantly with make a difference and counterarguments.( n17) All the signs of the coefficients are consistent with our previous conjectures about the effects of estimated participation.
Brand image. Tracking data indicated that before public knowledge of the intended factory closings, Bremmer's image was extremely positive: 96% had a "very good" or "good" opinion of the firm, and 2% had a "poor" opinion. This positive rating had dropped to 68% by the start of our data collection, and the percentage of respondents with a negative opinion rose to 30% during the same period. Thus, the controversy damaged an image that had previously been almost universally positive.
Higher egregiousness was inversely related to brand image (β = -.33, p < .001; we report standardized coefficients here for ease of comparison), in support of H[sub6a] (see Figure 1). Egregiousness also predicted the boycott decision (β = .31, p < .001). When both egregiousness and the boycott decision predict brand image, both are significant (β = -.24, p < .001, and β = -.27, p < .001, respectively), and the direct path from egregiousness to boycotting drops significantly (from -.33 to -.24, t = 2.19, p < .05).
Thus, we find support for the partial mediation of boycotting on the relationship between egregiousness and brand image (Baron and Kenny 1986). This finding suggests that boycotting itself (beyond the effects of perceived egregiousness) predicts brand image, in support of H[sub6b]. The effect of boycotting on brand image remains significant even if all direct effects and interactions (from Model F) are included in the regression (β = -.21, p < .001).( n18) From a different angle, among participants who were high in egregiousness (scores of three or greater on the four-point egregiousness scale), 72% of boycotters assigned a "poor" rating to Bremmer, whereas only 24.2% of nonboycotters gave this rating. These figures are much higher than the preboycott, full sample "poor" rating of 2%.
Other Results
Corporate messages and egregiousness. Respondents who had heard that Bremmer had offered alternative jobs to dismissed workers gave significantly lower egregiousness ratings than did respondents who had not heard this communication (m = 2.58 and 2.86, respectively, t[1203] = 5.04, p < .001). We found similar results for the message that Bremmer had tried to find other firms to employ the workers (m = 2.65 and 2.86, respectively, t[1204] = 4.29, p < .001). For respondents who had heard either message, a positive reaction to the message was inversely related to egregiousness (r = -.32, p < .001 and r = -.19, p < .001 for other jobs and other firms, respectively). However, responsiveness to corporate messages did not moderate the relationship between egregiousness and the boycott decision (interaction terms were not significant). Thus, corporate messages were effective in reducing egregiousness, but they did not disrupt the relationship between egregiousness and boycotting.
Demographics. Age was not a predictor of egregiousness or boycotting (r = -.04, not significant, and r = -.06, not significant, respectively). Respondents' sex predicted boycotting (χ² = 11.01, p < .01); 19% of women boycotted compared with 13% of men. Furthermore, in the models presented in Tables 4 and 6, sex was significant. An examination of mean differences between men and women shows that women tend to be stronger on the motivations for boycotting and that there are significant differences in perceived egregiousness, make a difference, self-enhancement, and free-riding.
Our study investigated a high-profile social-issue boycott. The boycott target has been widely condemned and has received extensive negative coverage in national print and broadcast media. Respondents were well aware of the issue leading to the boycott and overwhelmingly disapproved of the company's actions.( n19) It was a situation that marketers prefer to avoid.
The perceived egregiousness of the firm's actions was a powerful predictor of boycott participation. The more egregious a consumer perceived the firm's behavior, the more likely the consumer was to boycott. However, egregiousness notwithstanding, most of the sample was not participating in the boycott. To help understand this, we drew on theories of prosocial behavior and the boycott literature to test a cost-benefit approach to boycott participation. We found that in addition to perceived egregiousness, consumers need to believe that boycotting is an appropriate and effective response (make a difference). Consumers also need to realize intrinsic rewards of boycott participation, potentially boosting or maintaining self-esteem by, for example, avoiding guilt and responding to social pressure (self-enhancement). This has a direct effect and moderates the effect of egregiousness on boycott participation. Furthermore, consumers also take into account the cost of a preferred product forgone (constrained consumption) and the costs of boycott-induced harms and doubts about whether participation is necessary (counterarguments). Many of these costs also moderate the effect of egregiousness on boycotting.
Consumers' estimates of support for a boycott also appear to influence participation directly, but this effect is moderated by counterarguments (people who counter-argued were less likely to boycott when they believed that many others were boycotting) and by make a difference (people who were high in make a difference were more likely to boycott if they believed that many others were boycotting). The (not significant) directional finding for the interaction between estimated participation and constrained consumption supports Sen, Gürhan-Canli, and Morwitz's (2001) contention that when boycotting costs are high, people are particularly averse to being exploited by others' free-riding and are less likely to boycott. Furthermore, there may be individual differences with regard to boycott propensity, including that women are more likely to boycott than men. We also found that the act of boycotting harmed brand image beyond the effects of perceived egregiousness. Finally, although firm communications did not lessen the effect of egregiousness on boycotting, they appeared to lower levels of perceived egregiousness.
Managerial Implications
At first glance, our findings might appear to be encouraging for managers of targeted firms and disappointing for managers of NGOs that call for boycotts, given the boycott's high profile and the relatively low level of participation (16%). Keeping consumers from becoming boycotters is a key consideration for firms. Asking why consumers do not boycott is likewise critical for NGOs. Our findings suggest that even high levels of perceived egregiousness are insufficient to motivate boycott participation because cost-benefit motivations for participation play a key role.
On closer examination, however, the boycott appears to have inflicted damage on Bremmer. First, the boycott represented substantial lost sales in a highly competitive market (retail audit evidence of sales declines during the boycott corresponded to the level of participation reported in the study); Table 5 reveals that the loss could have been much worse. Second, the boycott provided shoppers with a reason to try competitors' products. Third, participants' attitudes were probably hardened against Bremmer through boycott participation. Perhaps the most consequential finding for the firm is that brand image was harmed among both nonboycotters and boycotters. This indirect effect may be far more significant in the long run than the direct loss of sales. Managers of targeted firms must pay attention to nonboycotters as well as boycotters.
More fundamentally, there is empirical support for the argument that boycotts can serve as a mechanism by which consumers can hold firms accountable for perceived CSR failings. As well as being of practical significance to NGOs, this finding is of theoretical significance because it demonstrates enhanced consumer sovereignty (Smith 1990). Changes in company policy following boycotts of Shell and Nike, for example, are consistent with the idea of firms being held accountable in consumer markets and with this pressure translating into a "business case" for CSR (Smith 2003).
We propose a variation of the well-known awareness, trial, repurchase model of buyer behavior as a framework for boycott management. Our awareness, egregiousness, boycott (AEB) model incorporates our findings on motivations for boycott participation and offers propositions for managers of both firms and NGOs. Although we offer propositions to each party separately, each should be aware of the other party's propositions as a source of insight on opposing strategies and tactics. Figure 2 depicts the model with data from the Bremmer boycott.
The first step for the firm is the measurement of awareness. For many boycotts, awareness is extremely low (Friedman 1999). Firms might nonetheless choose to treat a boycott call as a warning signal and investigate the allegedly egregious conduct. Even a small number of protestors might have a valid criticism of the firm's behavior. However, if awareness levels are high or growing, the next step is research to measure the level of perceived egregiousness and to gain a better understanding of why consumers find the firm's actions objectionable. The firm can then formulate its boycott strategy and decide whether to change current practice, engage in mitigating actions, or communicate the reasons behind its actions (Smith 1990). Bremmer was unwilling to reconsider its factory-closure decision, but many firms are not so committed to a policy that prompts a boycott, and this is likely to have a significant impact on the firm's strategy and on boycott success (Garrett 1987). The increasing power of NGOs emphasizes the need for a firm to be particularly careful in assessing its policy commitment (Spar and La Mure 2003).
Our findings illuminate the firm's key strategy decision of whether to fight or to acquiesce to NGO demands. For example, in the Nestlé boycott, the company initially chose to ignore its critics, then fought them, and eventually acceded to their demands in an embarrassing climb-down (Smith 1990). Our results suggest that this is a highly inefficient strategy because Nestlé's initial nonresponse enraged activists and probably harmed brand image for the long run, whereas its eventual backdown probably increased perceived efficacy. However, the company's establishing the Nestlé Infant Formula Audit Commission, an impartial outside group of social auditors, was widely credited with lessening support for the boycott, in part by reducing widespread perceptions of egregious conduct by Nestlé (Pagan 1986). Nonetheless, a firm's early and easy capitulation to NGO demands might signal weakness and enhance boycotters' sense that they can influence the firm and make a difference. This could prompt further demands as the NGO ups the ante. For example, after being threatened with a global boycott, Starbucks quickly agreed to meet the demand by Global Exchange that 1% of its coffee sales be certified as "fair trade"; Global Exchange is now seeking to increase that percentage to 5% (Sullivan 2003).
Good customer relationships facilitate communication, and prior investments in branding and a socially responsible image can serve as a form of "insurance" to counteract information about egregious conduct (Dawar and Pillutla 2000; Klein and Dawar 2003). Communications should be directed at both nonboycotters and boycotters to reduce perceptions of egregiousness and thereby to protect brand image and to lower boycott participation. For the NGO's part, it must be ready to respond. This leads to our first propositions for firms (P[sub1f]) and NGOs (P[sub1n]), which are shown in Figure 2.
The next step is to examine whether perceived egregiousness translates into boycott participation. Here, our findings on costs and benefits come into play. If consumers believe that the boycott will influence the firm, they are more likely to participate (make a difference). As P[sub2f] suggests (Figure 2), firms can communicate that the boycott is unlikely to be successful. This message must be communicated carefully, for showing that consumer objections have been heard is a key component of P[sub2f]. Corresponding strategies for NGOs are suggested in P[sub2n]. Consistent with building a sense of consumer efficacy and power, the Ethical Consumer Research Association lists recent boycott successes on its Web site. Another effective strategy for NGOs is probably to start small and build on successes more easily achieved. University apparel was an initial focus of campaigns in the United States against sweatshop labor, and several university administrations quickly acceded to student demands in the face of campus demonstrations (Brixey 2000).
Our findings suggest that people boycott to feel good about themselves (self-enhancement), but they are less likely to participate if they believe that the boycott has negative outcomes (counterarguments). Although managers should refrain from directly challenging the self-enhancement value of social action, their communication of harmful effects of boycotting may lessen the feel-good aspects of participation and reduce the link between perceived egregiousness and boycotting. For example, a firm boycotted because of its low wages (by Western standards) to overseas workers may counter that a "successful" boycott would result in the closure of factories, leaving former employees in poverty. Thus, we propose P[sub3f] (Figure 2).
As for NGOs, they should prepare rebuttals to company arguments about boycott-induced harms (P3n in Figure 2). In response to the claim that blacks were hurt most by boycotts of firms associated with apartheid South Africa, NGOs responded that the goal of freedom from apartheid was more important (Smith 1990). Furthermore, a key insight for an NGO that calls for a boycott is that its motivations need not be identical to those of individual consumers. An NGO with a strongly instrumental goal may best achieve its aims by encouraging self-enhancement through boycotting. Thus, with subtlety, the NGO could imply that failure to participate would lead to feelings of guilt. Equally, the use of celebrity endorsements by NGOs can be expected to help boost consumers' self-esteem through boycott participation. For example, Bianca Jagger has lent her support to the Exxon boycott called over its environmental policies (Whitney 2001). More generally, NGOs should seek media attention to the boycott because this can be expected to increase both awareness and social pressure for participation.
Boycotting entails a sacrifice by the consumer (constrained consumption). For NGOs, the task is to justify and minimize this sacrifice. Firms often cut advertising during a boycott (as Bremmer did) because they do not wish to draw attention to the brand or because they believe that the advertising spend would be wasted. The opposite strategy may be warranted. Well-crafted advertising could strengthen brand commitment and reinforce positive associations to the brand in memory, thus increasing the cost of boycotting and helping counteract the effects of negative information (Ahluwalia, Burnkrant, and Unnava 2000; Tybout, Calder, and Sternthal 1981). Furthermore, advertising that taps into values such as health and safety might be beneficial, because forgoing a product with these attributes may reduce the possible self-enhancement of boycott participation. Thus, we propose P[sub4f] and P[sub4n] (Figure 2).
Study Limitations and Directions for Further Research
Our research faces some limitations that stem from the exigencies of researching an actual boycott in progress. Although our measures were informed by our prior study, we had neither the luxury of piloting some measures nor absolute discretion over survey content, administration, and the precise wording of questions. However, we believe that these limitations do not materially influence our results and, on balance, are minor compared with the opportunity to study a real, ongoing boycott.
There are issues of generalizability. Our study is of a particular boycott on a specific issue. The egregious act in this case (i.e., factory closings) may be associated with greater self-interest than is the case for boycotts prompted by, for example, animal rights. The boycott was particularly high profile (which is all the more useful to the extent that we were interested in explaining why people do not boycott), but it is possible, or even probable, that our results are not relevant to the many calls to boycott that consumers largely ignore and that management can also safely ignore. In addition, although our data are from a nationally representative sample, they are from a specific country. We doubt that the trigger for this boycott would have had the same resonance in the United States, for example. We have sound theoretical reasons to believe that the motivations we uncovered are quite general, but this remains to be confirmed by research on other boycotts in different countries, prompted by different issues.
It is unlikely that we have uncovered all potential motivations for boycott participation, and further research could investigate other costs and rewards of boycotting that we may have failed to capture in our model. We did not investigate the boycotter's decision-making process, and particularly whether egregiousness leads to a firm's product being excluded from the consumer's consideration set or whether the firm's egregious conduct is traded off against product attributes. The answer may lie in the level of perceived egregiousness: Perhaps at moderate levels of egregiousness, the consumer trades off the firm's conduct, but at high levels, the consumer excludes the product from consideration.
The study also has implications for research on corporate associations and CSR-related issues. Our findings with respect to boycotts may well extend to the broader category of ethical influences on consumer behavior, which implies scope for exploring the role of a similar set of moderators in the models of, for example, Brown and Dacin (1997) and Sen and Bhattacharya (2001). Our work might also contribute to research on complaining behavior, thus suggesting motivations that may underlie complaining (e.g., self-enhancement). Furthermore, our framework could be used to study other collective action situations, such as voting.
Finally, in light of our claims on self-enhancement, we believe that it is appropriate to identify boycotting as part of a broader form of "symbolic nonconsumption." Writing on possessions and symbolic consumption, Belk (1988, p. 139) states, "That we are what we have ... is perhaps the most basic and powerful fact of consumer behavior." What does it mean when consumers choose not to consume a product because of a social issue associated with the producer? Perhaps we boycott because we are also what we do not have.
The authors thank Richard P. Bagozzi, Paula Bone, Pierre Chandon, Pam Scholder Ellen, Minette E. Drumwright, Randall Heeb, John G. Lynch Jr., Naufel Vilcassim, and the Behavioral Reading Group at London Business School for helpful comments on previous drafts of this article. They also gratefully acknowledge the collaboration of their anonymous corporate sponsor and its research agency as well as financial and other assistance provided by London Business School, INSEAD, and University of New South Wales.
( n1) The incidence of boycotts and their success are inherently hard to quantify because of difficulties in identifying calls for boycotts by NGOs and the understandable reluctance of firms to report sales declines due to boycotts or to publicize concessions to boycott organizers.
( n2) As a technical matter, the case of a single helper can often be reformulated as a collective action problem in which there is a set of potential helpers who decide to help with some probability (Lynch and Cohen 1978). In the language of game theory, potential helpers might employ a "mixed strategy."
( n3) People can learn that helping is a good and desirable behavior that is independent of social approval (or direct reward) and thus engage in self-reward. This has been shown to be important to the maintenance of long-term helping, such as repeated and regular blood donation (Piliavin et al. 1982).
( n4) Although some of the counterarguments may appear to be simply the reverse of the make-a-difference motivation, analyses reveal that they are two distinct constructs. This is consistent with findings in psychology that the positive and negative sides of the same attitude are often distinct (e.g., Cacioppo and Berntson 1994).
( n5) These arguments also imply the possibility of three-way interactions between estimated participation, egregiousness, and the variables that we discuss. We do not search for such interactions here because our model is already complex. This discussion also does not exhaust the possible ways that, as a matter of theory, estimated participation might affect boycotting. For example, consumers might infer egregiousness from estimated participation. Our data are not rich enough to distinguish among these potential effects.
( n6) Miller and Sturdivant (1977) study an actual boycott due to worker mistreatment, but they focus on the effect of one firm's actions on perceptions of an affiliated firm and do not examine consumer motivations for participation.
( n7) Our confidentiality agreement precludes direct identification of the boycott target, and thus we provide no citations here (although, in accordance with our agreement, we did identify the target to the reviewers of this article). The plant closures and resultant boycott were highly significant events that resulted in front-page newspaper articles and major coverage in other media. The negative media coverage included the company's plant closures being described in the press as "brutal" and the company being depicted as a "symbol of corporate greed" because it was making closures even though it was profitable. A government minister even decried the closures as "unacceptable."
( n8) Professionals represented 12% of the sample; mid-to low-level managers and technicians, 15%; clerical workers, 11%; unskilled workers, 22%; and unemployed or retired, 32%. A stratified sampling approach was taken to ensure that sample demographics matched those of the population.
( n9) In the media, much of the anger associated with the boycott was attributed to reports that Bremmer was closing factories despite being profitable overall.
( n10) This question was modified in a subsequent phase of the survey. Interviewers first asked if the respondent was boycotting; if the answer was negative, they asked whether he or she was tempted to boycott.
( n11) Of participants in the sample who were high in egregiousness (scored three or higher) and who disapproved, 25.5% were boycotting, 20.2% were tempted, and 54.3% were not boycotting.
( n12) There were no substantial changes over time in the measures reported in Table 2, with the exception that the last wave of respondents gave higher ratings to the belief that "boycotts are an effective means to make a company change its actions" (p < .05), and there were fewer respondents who were tempted to boycott because they were more decided on boycott participation. As a precaution, we included the time of interview as a control in our regression analyses.
( n13) If respondents answered the questionnaire appropriately, the purchase history questions should capture constrained consumption. However, there are two mismeasurement issues that can arise: First, consumers who never purchased Bremmer's products might decide to report themselves as boycotters; second, respondents might have misinterpreted the purchase history question as a question about their current purchases. As a check, we also ran our analyses with the omission of consumers who reported never purchasing Bremmer's products; our results were essentially unchanged.
( n14) The low (high) values in Table 5 scale each score such that the mean score for the scaled variable equals the true mean for the lower (upper) third of the actual sample. For example, consider egregiousness. In our sample, the mean value of egregiousness is 2.77. The mean value for the lower third of the sample is 1.85. To construct low egregiousness, we scaled down the egregiousness score for our entire sample such that the mean for the constructed data is 1.85. Thus, we multiply everyone's score on egregiousness by a factor equal to approximately 1.85/2.77 = .668 (our actual scaling factor is slightly lower [.663], because we truncated the data such that the minimum score is still 1). Likewise, to construct high egregiousness, we multiply all scores by a factor of 1.67, which ensures that the mean in our new constructed data equals the actual mean for the top one-third of the true data.
We generated the predictions using the ordinary least squares regression as an indicator function for the respondents in our sample, using the true data for all variables except those noted. Specifically, we used our original regression to identify a cutoff value between boycotting and being tempted to boycott. (We could not simply sum the predicted probabilities because of the tempteds.) Thus, the values in Table 5 should be interpreted as follows: If consumers in our sample had, counterfactually, had proportionately higher egregiousness (such that mean egregiousness was equal to that of the top third of the sample), we predict that 261 would have been above the cutoff and would have boycotted. Had they also had a higher score on make a difference (such that the mean was equal to that of the top third of the sample), our model predicts that there would have been more than 500 boycotters rather than 180.
( n15) Although the interpretation of a change in egregiousness is straightforward (the firm's actions could have been perceived as more egregious), a counterfactual change in a motivation is more complex. One interpretation is that the strength of the motivations is indeed mutable for each participant. Another interpretation is that different values of the motivations correspond to different subsamples of the population.
( n16) Although our analysis raises the possibility of misspecification bias, this appears to be limited given that the results for the rest of the model are similar in all five models in Table 4. In effect, the individual regressions provide an upper bound on the effect of each individual counterargument.
( n17) When we include the argument variables separately in this model (as in Models B-E), the interactions between estimated participation and hurt jobs and between estimated participation and country are significant (p < .05), and the interactions with the other two counterargument variables are in the expected direction (p = .24 for too small, and p = .18 for free-riding, which is consistent with the prediction that the incentive to free ride is stronger when more people participate).
( n18) An alternative model, in which brand image mediated the relationship between egregiousness and the boycott decision, was not supported.
( n19) As further (indirect) evidence of perceived egregiousness, a national poll at the time of the boycott found that nearly nine of ten people judged it "unacceptable" for profitable companies to make employees redundant.
Legend for Chart:
A - Author(s)
B - Orientation
C - Methodological Approach
D - Variables Influencing Boycott Participation(a)
A
B
C
D
John and Klein
(2003)
Consumer behavior
Dynamic modeling
• Free-riding and small agent issues
• False consensus
• Expressive and instrumental motivations
Sen, Gürhan-Canli,
and Morwitz
(2001)
Consumer behavior
Experiments (laboratory studies)
• Perception of boycott success likelihood
(as a function of expectations of overall
participation, perceived efficacy of
participation, message frame of
pro-boycott communications)
• Susceptibility to normative social influences
(internal and external social pressure from
boycotting reference group)
• Costs of boycotting (preference for boycotted
product and access to substitutes)
Friedman (1985,
1991, 1995, 1999)
Consumer policy and activism
Multiple methods: historical
research (price increase boycotts,
1900-1970), secondary sources
(media reports of U.S. boycotts,
1970-1980), and survey research
(with boycott organizers)
• "Valence" (consumers care about the boycott
issues and objectives, issue is
exciting/engaging, consumer anger and desire
for justice or to punish target)
• Ease of participation (target easy to identify,
few brand names, few competing boycotts)
• No adverse consequences (extent of sacrifice,
substitutes readily available and acceptable)
• Social pressure
Kozinets and Handelman (1998)
Consumer behavior
Ethnographic (netnographic data
collection from active boycotters)
• Seeking widespread social change
• Moral self-expression
• "Individuation" (coming to selfhood
and self-realization)
• Express uniqueness (differentiate from the crowd)
• "Cleansing" (free from guilt)
Smith (1990)
Consumer policy and activism
Case studies (of ongoing boycotts
based on interviews with boycott
organizers and targets as well as
secondary sources)
Consumers must be concerned, willing, and able
to boycott. More specifically:
• Consumer characteristics (aware of boycott,
"moral outrage" over issue, perceived
consumer effectiveness)
• Issue characteristics (right issue at right
time, understanding of and sympathy for cause)
• Product characteristics (connection with
issue, low cost, frequently purchased,
visibility of purchase/consumption)
• Product substitutability (availability of
alternatives, consumer preferences)
Witkowski (1989)
Consumer behavior
Historical research (colonial
nonimportation movement)
Political and moral values (force repeal of tax laws,
patriotism, rejection of materialism)
• Availability of substitutes
• Social pressure
• Guilt
• Sacrifice (inferior alternatives or abstention)
Garrett (1987)
Marketing management
Survey research (boycott targets
and organizers) and secondary
sources (media reports)
• Potential participants' awareness of the
boycott
• Whether participant attitudes are consistent
with boycott goals
• Participant values
• Cost of participation
• Social pressure
• Credibility of boycott leader
Miller and Sturdivant
(1977)
Consumer behavior
Survey research (during boycott)
• Potential participants' awareness
of the boycott
• Attitudes toward consumers' social
responsibility
Mahoney (1976)
Consumer behavior
Survey research of full versus
partial boycott supporters(in
advance of boycott start)
• Expectations of success
• Participant alienation (less perceived
powerlessness)
• Participant values ("future world" orientation)
(a) Only John and Klein (2003), Sen, Gurhan-Canli, and Morwitz
(2001), and Kozinets and Handelman (1998) focus directly on
variables that influence a consumer's boycott decision. Other
research cited does not focus directly on variables that
influence boycott participation, but such variables can be
reasonably inferred from the investigation reported. Legend for Chart:
A - Variable
B - Mean (s.d.)
C - Means Nonboycotters
D - Means Tempteds
E - Means Boycotters
A
B C D
E
Egregiousness
2.77 (.81) 2.60 (.81) 3.00 (.66)
3.23 (.75)
Make a Difference
4.51 (2.67)(a) 3.54 (2.23) 5.55 (2.36)
7.44 (2.06)
Boycotts are an effective means to make a company
change its actions.
4.76 (3.14) 3.96 (2.90) 5.48 (2.98)
7.17 (2.84)
Everyone should take part in the boycott because
every contribution, no matter how small, is important.
4.65 (3.21) 3.57 (2.80) 5.91 (2.68)
7.83 (2.68)
By boycotting, I can help change Bremmer's decision.
4.14 (3.29) 3.10 (2.76) 5.20 (3.13)
7.34 (3.03)
Self-Enhancement
2.82 (2.15)(a) 2.21 (1.76) 3.41 (2.03)
4.75 (2.41)
I would feel guilty if I bought Bremmer products.
3.27 (3.10) 2.48 (2.63) 4.07 (2.94)
5.76 (3.49)
I would feel uncomfortable if other people who are
boycotting saw me purchasing or consuming
Bremmer products.
2.85 (2.91) 2.52 (2.71) 3.07 (2.75)
4.01 (3.49)
My friends/my family are encouraging me to boycott
Bremmer.
2.04 (2.31) 1.76 (1.94) 2.37 (2.58)
2.89 (3.09)
I will feel better about myself if I boycott Bremmer.
3.08 (3.01) 2.04 (2.16) 4.14 (2.89)
6.24 (3.54)
Counterarguments
5.67 (2.23)(a) 6.25 (2.00) 5.10 (2.00)
3.84 (2.24)
I do not need to boycott Bremmer; enough other
people are doing so.
4.52 (3.32) 4.83 (3.37) 4.34 (2.95)
3.43 (3.25)
I do not buy enough Bremmer products for it to be
worthwhile boycotting; it would not even be noticed.
5.22 (3.35) 5.53 (3.37) 5.25 (3.07)
3.89 (3.21)
One shouldn't boycott because it will put other
Bremmer jobs in danger.
7.02 (3.05) 7.92 (2.63) 5.89 (2.82)
4.46 (3.09)
I don't boycott Bremmer because it is a (country)
company and boycotting would lead me to buy
foreign products.
5.89 (3.47) 6.69 (3.29) 4.91 (3.11)
3.59 (3.28)
Constrained Consumption (Purchase History)
2.77 (.75)(a) 2.88 (.66) 2.80 (.68)
2.27 (.95)
Brand A (central)
2.61 (.89) 2.70 (.85) 2.60 (.89)
2.27 (.99)
Brand B (central)
2.93 (.85) 3.06 (.75) 3.00 (.72)
2.28 (1.06)
(a) Descriptive statistics are for the average of the items
within each factor.
Notes: s.d. = standard deviation. Legend for Chart:
B - Component Make a Difference
C - Component Self-Enhancement
D - Component Counterarguments
E - Component Constrained Consumption
A
B C D E
Eigenvalue
(% of Variance)
3.64 2.03 1.37 1.01
(28.01) (15.59) (10.57) (7.78)
Boycotts are an effective means to make a
company change its actions.
.851(a) .047 .026 -.037
Everyone should take part in the boycott because
every contribution, no matter how small, is important.
.753(a) .224 -.171 -.088
By boycotting, I can help change Bremmer's decision.
.762(a) .361 -.093 -.018
I would feel guilty if I bought Bremmer products.
.286 .724(a) -.071 -.114
I would feel uncomfortable if other people who are
boycotting saw me purchasing or consuming
Bremmer products.
.137 .712(a) .172 -.027
My friends/my family are encouraging me to boycott
Bremmer.
.007 .756(a) .059 .042
I will feel better about myself if I boycott Bremmer.
.466 .648(a) -.129 -.128
I do not need to boycott Bremmer; enough other
people are doing so.
.105 .070 .731(a) .043
I do not buy enough Bremmer products for it to be
worthwhile boycotting; it would not even be noticed.
-.009 .003 .631(a) -.240
One shouldn't boycott because it will put other
Bremmer jobs in danger.
-.310 -.089 .632(a) .223
I don't boycott Bremmer because it is a (country)
company and boycotting would lead me to buy
foreign products.
-.206 .099 .659(a) .264
Purchase history Brand A
-.030 -.013 .049 .831(a)
Purchase history Brand B
-.084 -.099 .065 .833(a)
Notes: Numbers in (a) boldface indicate variables included
in component. Legend for Chart:
B - Model A: Four Factors b
C - Model A: Four Factors t
D - Model A: Four Factors p
E - Model B: Three Factors + Free Ride b
F - Model B: Three Factors + Free Ride t
G - Model B: Three Factors + Free Ride p
H - Model C: Three Factors + Tool Small b
I - Model C: Three Factors + Tool Small t
J - Model C: Three Factors + Tool Small p
K - Model D: Three Factors + Hurt Jobs b
L - Model D: Three Factors + Hurt Jobs t
M - Model D: Three Factors + Hurt Jobs p
N - Model E: Three Factors + Country b
O - Model E: Three Factors + Country t
P - Model E: Three Factors + Country p
A
B C D E F G
H I J K L M
N O P
Egregiousness
.10(a) 4.62(a) .00(a) .13(a) 5.86(a) .00(a)
.14(a) 6.11(a) .00(a) .11(a) 4.81(a) .00(a)
.11(a) 4.82(a) .00(a)
Make a difference
.09(a) 12.13(a) .00(a) .11(a) 13.34(a) .00(a)
.11(a) 13.28(a) .00(a) .10(a) 12.06(a) .00(a)
.10(a) 12.79 .00
Difference x egregiousness
.01 1.08 .28 .01 1.24 .22
.01 1.24 .22 .01 1.15 .25
.01 1.35 .18
Self-enhancement
.07(a) 7.71(a) .00(a) .07(a) 6.78(a) .00(a)
.07(a) 6.61(a) .00(a) .06(a) 6.26(a) .00(a)
.07(a) 7.19(a) .00(a)
Self-enhancement x egregiousness
.02(a) 1.98(a) .05(a) .03(a) 2.18(a) .03(a)
.02(a) 1.97(a) .05(a) .03(a) 2.12(a) .03(a)
.03(a) 2.34(a) .02(a)
Counterarguments
-.09(a) -11.25(a) .00(a) -- -- --
-- -- -- -- -- --
-- -- --
Counterarguments x egregiousness
-.01 -1.07 .28 -- -- --
-- -- -- -- -- --
-- -- --
Constrained consumption
-.10(a) -4.37(a) .00(a) -.12(a) -5.03(a) .00(a)
-.13(a) -5.43(a) .00(a) -.10(a) -4.12(a) .00(a)
-.10(a) -4.20(a) .00(a)
Constrained consumption x egregiousness
-.07(a) -2.58(a) .01(a) -.10(a) -3.22(a) .00(a)
-.08(a) -2.82(a) .01(a) -.08(a) -2.65(a) .01(a)
-.09(a) -3.02(a) .00(a)
Free ride
-- -- -- -.03(a) -5.64(a) .00(a)
-- -- -- -- -- --
-- -- --
Free ride x egregiousness
-- -- -- -.00 -.17 .87
-- -- -- -- -- --
-- -- --
Too small
-- -- -- -- -- --
-.03(a) -5.81(a) .00(a) -- -- --
-- -- --
Too small x egregiousness
-- -- -- -- -- --
-.01(a) -2.12(a) .03(a) -- -- --
-- -- --
Hurt jobs
-- -- -- -- -- --
-- -- -- -.06(a) -9.27(a) .00(a)
-- -- --
Hurt jobs x egregiousness
-- -- -- -- -- --
-- -- -- -.02(a) -2.16(a) .03(a)
-- -- --
Country products
-- -- -- -- -- --
-- -- -- -- -- --
-.05(a) -8.74(a) .00(a)
Country x egregiousness
-- -- -- -- -- --
-- -- -- -- -- --
-.01 -.75 .45
Sex
.07(a) 2.12(a) .04(a) .07(a) 1.97(a) .05(a)
.06(a) 1.60(a) .11(a) .06(a) 1.79(a) .07(a)
.06(a) 1.85(a) .07(a)
Phase
.10(a) 2.96(a) .00(a) .11(a) 3.22(a) .00(a)
.11(a) 3.25(a) .00(a) .11(a) 3.18(a) .00(a)
.10(a) 3.03(a) .00(a)
Adjusted R² = .47 Adjusted R² = .42
Adjusted R² = .42 Adjusted R² = .46
Adjusted R² = .46
Notes: Numbers in (a) boldface indicate variables that
are significant at p < .05. Legend for Chart:
C - Egregiousness Low
D - Egregiousness Medium
E - Egregiousness High
A B C D
E
Make a difference Low 12 (1%) 52 (5%)
75 (7%)
Medium 110 (10%) 180 (16%)
261 (24%)
High 254 (23%) 387 (35%)
504 (45%)
Self-enhancement Low 52 (5%) 96 (9%)
124 (11%)
Medium 110 (10%) 180 (16%)
261 (24%)
High 190 (17%) 309 (28%)
409 (37%)
Counterarguments Low 177 (16%) 281 (25%)
394 (36%)
Medium 110 (10%) 180 (16%)
261 (24%)
High 74 (7%) 122 (11%)
166 (15%)
Constrained consumption Low 123 (11%) 230 (21%)
339 (31%)
Medium 110 (10%) 180 (16%)
261 (24%)
High 95 (9%) 154 (14%)
198 (18%)
Notes: The numbers are based on the linear probability model.
In our sample, there were 180 actual boycotters out of 1108
observations (less than our total sample of 1216 because of
missing data). For an explanation of how we calculated the
predictions, see Note 14. Legend for Chart:
B - b
C - t
D - p
A B C D
Egregiousness .106(a) 4.52(a) .00(a)
Make a difference .089(a) 10.62(a) .00(a)
Difference x egregiousness .009 .85 .40
Self-enhancement .056(a) 5.27(a) .00(a)
Self-enhancement x egregiousness .022(a) 1.71(a) .09(a)
Counterarguments -.090(a) -10.55(a) .00(a)
Counterarguments x egregiousness -.008 -.71 .48
Constrained consumption -.120(a) -4.89(a) .00(a)
Constrained consumption(*)
egregiousness -.073(a) -2.36(a) .02(a)
Percentage others .004(a) 4.14(a) .00(a)
Others x egregiousness -.001 -.51 .61
Others x make a difference .001(a) 2.11(a) .04(a)
Others x self-enhancement .000 .95 .34
Others x counterarguments -.001(a) -2.39(a) .02(a)
Others x constrained consumption .002 1.37 .17
Sex .064(a) 1.77(a) .08(a)
Phase .087(a) 2.49(a) .01(a)
Adjusted R² = .49.
Notes: Numbers in boldface indicate variables that are
significant at p < .05. Mean estimate of the percentage
of other people boycotting = 27% (standard deviation = 19.12).DIAGRAM: FIGURE 1 Motivators of Boycott Decisions
DIAGRAM: FIGURE 2 AEB Model
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~~~~~~~~
By Jill Gabrielle Klein; N. Craig Smith and Andrew John
Jill Gabrielle Klein is Associate Professor of Marketing, INSEAD (e-mail: jill.klein@insead.edu). N. Craig Smith is Associate Professor of Marketing and Ethics, London Business School (e-mail: ncsmith@london.edu). Andrew John is Chief Executive Officer, AJK Executive Consulting (e-mail: andrew.john@aya.yale.edu).
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